pax_global_header00006660000000000000000000000064145217704600014520gustar00rootroot0000000000000052 comment=44204b599f9e6d04540f76dd7abcfd3c187f93ec libpysal-4.9.2/000077500000000000000000000000001452177046000133535ustar00rootroot00000000000000libpysal-4.9.2/.gitattributes000066400000000000000000000001031452177046000162400ustar00rootroot00000000000000*.ipynb linguist-language=Python libpysal/_version.py export-subst libpysal-4.9.2/.github/000077500000000000000000000000001452177046000147135ustar00rootroot00000000000000libpysal-4.9.2/.github/CONTRIBUTING.md000066400000000000000000000015071452177046000171470ustar00rootroot00000000000000Thank you for your interest in contributing! We work primarily on Github. Please review the [contributing procedures](https://github.com/pysal/pysal/wiki/GitHub-Standard-Operating-Procedures) so that we can accept your contributions! Alternatively, contact someone in the [development chat channel](https://gitter.im/pysal.pysal). ## Style and format 1. Python 3.6, 3.7, and 3.8 are the officially supported versions. 2. This project follows the formatting conventions of [`black`](https://black.readthedocs.io/en/stable/) and utilizes [`pre-commit`](https://pre-commit.com) to format commits prior to pull requests being made. * LJ Miranda provides an [excellent, concise guide](https://ljvmiranda921.github.io/notebook/2018/06/21/precommits-using-black-and-flake8/) on setting up and implementing a `pre-commit` hook for `black`. libpysal-4.9.2/.github/ISSUE_TEMPLATE.md000066400000000000000000000025751452177046000174310ustar00rootroot00000000000000Thank you for filing this issue! To help troubleshoot this issue, please follow the following directions to the best of your ability before submitting an issue. Feel free to delete this text once you've filled out the relevant requests. Please include the output of the following in your issue submission. If you don't know how to provide the information, commands to get the relevant information from the Python interpreter will follow each bullet point. Feel free to delete the commands after you've filled out each bullet. - Platform information: ```python >>> import os; print(os.name, os.sys.platform);print(os.uname()) ``` - Python version: ```python >>> import sys; print(sys.version) ``` - SciPy version: ```python >>> import scipy; print(scipy.__version__) ``` - NumPy version: ```python >>> import numpy; print(numpy.__version__) ``` Also, please upload any relevant data as [a file attachment](https://help.github.com/articles/file-attachments-on-issues-and-pull-requests/). Please **do not** upload pickled objects, since it's nearly impossible to troubleshoot them without replicating your exact namespace. Instead, provide the minimal subset of the data required to replicate the problem. If it makes you more comfortable submitting the issue, feel free to: 1. remove personally identifying information from data or code 2. provide only the required subset of the full data or code libpysal-4.9.2/.github/PULL_REQUEST_TEMPLATE.md000066400000000000000000000020121452177046000205070ustar00rootroot00000000000000Hello! Please make sure to check all these boxes before submitting a Pull Request (PR). Once you have checked the boxes, feel free to remove all text except the justification in point 5. 1. [ ] You have run tests on this submission locally using `pytest` on your changes. Continuous integration will be run on all PRs with [GitHub Actions](https://github.com/pysal/libpysal/blob/master/.github/workflows/unittests.yml), but it is good practice to test changes locally prior to a making a PR. 2. [ ] This pull request is directed to the `pysal/master` branch. 3. [ ] This pull introduces new functionality covered by [docstrings](https://en.wikipedia.org/wiki/Docstring#Python) and [unittests](https://docs.python.org/2/library/unittest.html)? 4. [ ] You have [assigned a reviewer](https://help.github.com/articles/assigning-issues-and-pull-requests-to-other-github-users/) and added relevant [labels](https://help.github.com/articles/applying-labels-to-issues-and-pull-requests/) 5. [ ] The justification for this PR is: libpysal-4.9.2/.github/dependabot.yml000066400000000000000000000011671452177046000175500ustar00rootroot00000000000000# To get started with Dependabot version updates, you'll need to specify which # package ecosystems to update and where the package manifests are located. # Please see the documentation for all configuration options: # https://help.github.com/github/administering-a-repository/configuration-options-for-dependency-updates version: 2 updates: - package-ecosystem: "github-actions" directory: "/" schedule: interval: "daily" reviewers: - "sjsrey" - "jGaboardi" - package-ecosystem: "pip" directory: "/" schedule: interval: "daily" reviewers: - "sjsrey" - "jGaboardi" libpysal-4.9.2/.github/release.yml000066400000000000000000000005171452177046000170610ustar00rootroot00000000000000changelog: 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: - "*"libpysal-4.9.2/.github/workflows/000077500000000000000000000000001452177046000167505ustar00rootroot00000000000000libpysal-4.9.2/.github/workflows/build_docs.yml000066400000000000000000000035171452177046000216100ustar00rootroot00000000000000 name: Build Docs on: push: # Sequence of patterns matched against refs/tags tags: - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10 workflow_dispatch: inputs: version: description: Manual Doc Build Reason default: test required: false jobs: docs: name: Build & Push Docs runs-on: ${{ matrix.os }} timeout-minutes: 90 strategy: matrix: os: ['ubuntu-latest'] environment-file: [ci/312.yaml] experimental: [false] defaults: run: shell: bash -l {0} steps: - name: Checkout repo uses: actions/checkout@v4 with: fetch-depth: 0 # Fetch all history for all branches and tags. - name: Setup micromamba uses: mamba-org/setup-micromamba@v1 with: environment-file: ${{ matrix.environment-file }} micromamba-version: 'latest' - name: Install run: pip install -e . --no-deps --force-reinstall - name: Make Docs run: cd docs; make html - name: Commit Docs run: | git clone https://github.com/ammaraskar/sphinx-action-test.git --branch gh-pages --single-branch gh-pages cp -r docs/_build/html/* gh-pages/ cd gh-pages git config --local user.email "action@github.com" git config --local user.name "GitHub Action" git add . git commit -m "Update documentation" -a || true # The above command will fail if no changes were present, # so we ignore the return code. - name: Push to gh-pages uses: ad-m/github-push-action@master with: branch: gh-pages directory: gh-pages github_token: ${{ secrets.GITHUB_TOKEN }} force: true libpysal-4.9.2/.github/workflows/release_and_publish.yml000066400000000000000000000034641452177046000234720ustar00rootroot00000000000000 # Release package on GitHub and publish to PyPI # Important: In order to trigger this workflow for the organization # repo (organzation-name/repo-name vs. user-name/repo-name), a tagged # commit must be made to *organzation-name/repo-name*. If the tagged # commit is made to *user-name/repo-name*, a release will be published # under the user's name, not the organzation. #-------------------------------------------------- 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 steps: - name: Checkout repo uses: actions/checkout@v4 with: fetch-depth: 0 # Fetch all history for all branches and tags. - name: Set up python uses: actions/setup-python@v4 with: python-version: "3.x" - name: Install Dependencies run: | python -m pip install --upgrade pip build twine python -m build twine check --strict dist/* - name: Create Release Notes uses: actions/github-script@v6 with: github-token: ${{secrets.GITHUB_TOKEN}} script: | await github.request(`POST /repos/${{ github.repository }}/releases`, { tag_name: "${{ github.ref }}", generate_release_notes: true }); - name: Publish distribution 📦 to PyPI uses: pypa/gh-action-pypi-publish@release/v1 with: user: __token__ password: ${{ secrets.PYPI_PASSWORD }} libpysal-4.9.2/.github/workflows/unittests.yml000066400000000000000000000036751452177046000215500ustar00rootroot00000000000000name: Tests on: push: branches: [main] pull_request: branches: - "*" schedule: - cron: "0 0 * * 1,4" workflow_dispatch: inputs: version: description: Manual Unittest 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/310-oldest.yaml - ci/310.yaml - ci/311.yaml - ci/312.yaml - ci/312-dev.yaml - ci/312-no-optional.yaml include: - environment-file: ci/312.yaml os: macos-latest - environment-file: ci/312.yaml os: windows-latest defaults: run: shell: bash -l {0} steps: - name: Checkout repo uses: actions/checkout@v4 with: fetch-depth: 0 # Fetch all history for all branches and tags. - name: Setup micromamba uses: mamba-org/setup-micromamba@v1 with: environment-file: ${{ matrix.environment-file }} micromamba-version: "latest" - name: Install libpysal run: | pip install .; python -c 'import libpysal; libpysal.examples.fetch_all()' - name: Spatial versions run: | python -c 'import geopandas; geopandas.show_versions();' - name: Test libpysal run: | pytest -v --color yes --cov libpysal --cov-append --cov-report term-missing --cov-report xml . - name: Codecov uses: codecov/codecov-action@v3 - name: Generate and publish the report if: | failure() && steps.status.outcome == 'failure' && github.event_name == 'schedule' && github.repository_owner == 'pysal' uses: xarray-contrib/issue-from-pytest-log@v1 with: log-path: pytest-log.jsonl libpysal-4.9.2/.gitignore000066400000000000000000000037001452177046000153430ustar00rootroot00000000000000*.py[cod] *.bak .ipynb_checkpoints/ # C extensions *.so .idea/ # Packages *.egg *.egg-info dist build eggs parts bin var sdist develop-eggs .installed.cfg lib lib64 __pycache__ # virtual environment venv/ # Installer logs pip-log.txt # Unit test / coverage reports .coverage .tox nosetests.xml # Translations *.mo # Mr Developer .mr.developer.cfg .project .pydevproject # OS generated files # ###################### .DS_Store .DS_Store? ._* .Spotlight-V100 .Trashes Icon? ehthumbs.db Thumbs.db # pysal # lattice.* .vagrant/ pysal/contrib/viz/.ipynb_checkpoints/ pysal/contrib/viz/bp.png pysal/contrib/viz/fj.png pysal/contrib/viz/fj_classless.png pysal/contrib/viz/lmet.tex pysal/contrib/viz/lmp.tex pysal/contrib/viz/lmplot.png pysal/contrib/viz/lmss.tex pysal/contrib/viz/lmt.tex pysal/contrib/viz/out.png pysal/contrib/viz/p.tex pysal/contrib/viz/quantiles.png pysal/contrib/viz/quantiles_HR60.png pysal/contrib/viz/quantiles_HR70.png pysal/contrib/viz/quantiles_HR80.png pysal/contrib/viz/quantiles_HR90.png pysal/contrib/viz/quatiles.png pysal/contrib/viz/region.ipynb pysal/contrib/viz/south_base.html pysal/contrib/viz/sp.tex pysal/contrib/viz/sss.tex pysal/examples/south.prj #Vi *.swp .ropeproject/ .eggs/ pysal/contrib/planar/ pysal/esda/.ropeproject/ pysal/esda/jenks_nb.ipynb pysal/examples/snow_maps/fake.dbf pysal/examples/snow_maps/fake.prj pysal/examples/snow_maps/fake.qpj pysal/examples/snow_maps/fake.shp pysal/examples/snow_maps/fake.shx pysal/examples/snow_maps/fixed.dbf pysal/examples/snow_maps/fixed.prj pysal/examples/snow_maps/fixed.qgs pysal/examples/snow_maps/fixed.qgs~ pysal/examples/snow_maps/fixed.qpj pysal/examples/snow_maps/fixed.shp pysal/examples/snow_maps/fixed.shx pysal/examples/snow_maps/snow.qgs pysal/examples/snow_maps/snow.qgs~ pysal/examples/snow_maps/soho_graph.dbf pysal/examples/snow_maps/soho_graph.prj pysal/examples/snow_maps/soho_graph.qpj pysal/examples/snow_maps/soho_graph.shp pysal/examples/snow_maps/soho_graph.shx libpysal-4.9.2/.pre-commit-config.yaml000066400000000000000000000001631452177046000176340ustar00rootroot00000000000000repos: - repo: https://github.com/psf/black rev: "23.10.0" hooks: - id: black language_version: python3 libpysal-4.9.2/CHANGELOG.md000066400000000000000000006520051452177046000151740ustar00rootroot00000000000000# Version 4.3.0 (2020-06-28) We closed a total of 85 issues (enhancements and bug fixes) through 27 pull requests, since our last release on 2020-02-01. ## Issues Closed - Standardize libpysal/examples/*.py docstrings (#294) - Fetch (#295) - Mac builds seem to take longer — bump up timeout (#273) - Voronoi_frames function causes jupyter notebook kernel to die (#281) - ENH: allow specific buffer in fuzzy_contiguity (#280) - Return alpha option & use pygeos for alphashaping if available (#278) - add weights writing as a method on weights. (#276) - Docs ci badge (#277) - [rough edge] libpysal.examples w/o internet? (#259) - removing six from ci (#275) - Handle connection errors for remote datasets (#274) - examples directory prevents installing with pyInstaller (#263) - GH-263: Don't implicitly import examples when importing base library (#264) - Error in the internal hack for the Arc_KDTree class inheritance and the KDTree function (#254) - GitHub Actions failures (#271) - Bugfix (#255) - dropping nose in ci/36.yml (#270) - Follow-up To Do for GH Actions (#268) - Polish up GitHub Action residuals (#269) - TEST: turning off 3.6 on github actions (#266) - Initializing complete Github Actions CI (#267) - fix for issue #153 (#256) - DOC: Udpdating citations, minor description editing (#265) - Cleaning up weights/weights.py docs (#262) - Unused code in weights.from_networkx()? (#261) - redirect pysal/#934 to libpysal (#9) - defaulting to using the dataframe index as the id set (#35) - Handling coincident points in KNN (#23) - MGWR_Georgia_example.ipynb fails due to different sample data shapes (#67) - Kernel docstring does not mention unique Gaussian kernel behavior (#47) - MGWR_Georgia_example.ipynb missing pickle import statement (#69) - weights.Voronoi is a function, not a class. (#99) - some weights util functions are lost in __ini__.py (#121) - Current weight plot method is time consuming for a large data set (#123) - [ENH][WIP] Adding a `rasterW` to extract `W` from raster and align values (#150) - network kernel weights (#151) - Add `from_sparse` and `from_numpy` methods, to match the other `from_` methods (#173) - Weight Object Question (#208) - ENH: setting up github actions (#258) - deprecate or test shapely_ext (#114) - Tests failures under Python 3.8 (#177) - Update reqs for tests (#250) - Nbdocs (#253) - test_fiiter fails on 3.8 but passes on < 3.8 (#249) - 3.8 (#251) - rebuild docs; (#235) - DOC: Fix invalid section headings. (#243) - Fix syntax errors (#242) - Remove calls to deprecated/removed time.clock. (#240) - Fix and simplify filter_adjlist. (#244) - set up appveyor or circle ci for multiplatform testing (#219) - Nose is unmaintained (#241) - Add appveyor badge (#248) - Appveyor (#247) - correct name for beautifulsoup4 (#239) - REL: version bump for bug fix release (#238) - test_map breakage due to pandas 1.0 deprecation of ufunc.outer (#236) - BUG: ufunc.outer deprecated (#237) ## Pull Requests - Standardize libpysal/examples/*.py docstrings (#294) - Fetch (#295) - Mac builds seem to take longer — bump up timeout (#273) - ENH: allow specific buffer in fuzzy_contiguity (#280) - Return alpha option & use pygeos for alphashaping if available (#278) - add weights writing as a method on weights. (#276) - Docs ci badge (#277) - removing six from ci (#275) - Handle connection errors for remote datasets (#274) - GH-263: Don't implicitly import examples when importing base library (#264) - Bugfix (#255) - dropping nose in ci/36.yml (#270) - Polish up GitHub Action residuals (#269) - Initializing complete Github Actions CI (#267) - DOC: Udpdating citations, minor description editing (#265) - Cleaning up weights/weights.py docs (#262) - ENH: setting up github actions (#258) - Update reqs for tests (#250) - Nbdocs (#253) - 3.8 (#251) - DOC: Fix invalid section headings. (#243) - Fix syntax errors (#242) - Fix and simplify filter_adjlist. (#244) - Appveyor (#247) - correct name for beautifulsoup4 (#239) - REL: version bump for bug fix release (#238) - BUG: ufunc.outer deprecated (#237) The following individuals contributed to this release: - Serge Rey - James Gaboardi - Martin Fleischmann - Dani Arribas-Bel - Levi John Wolf - Bryan Bennett - Jeffery Sauer - Elliott Sales De Andrade - Joshua Wagner # Version 4.2.2 (2020-02-01) This is a bug fix release. We closed a total of 32 issues (enhancements and bug fixes) through 12 pull requests, since our last release on 2020-01-04. ## Issues Closed - test_map breakage due to pandas 1.0 deprecation of ufunc.outer (#236) - BUG: ufunc.outer deprecated (#237) - raise warning when islands are used in to_adjlist (#230) - Some example datasets are missing documentation (#113) - DOC: Cleaning up docs and docsr for tutorial (#229) - `to_adjlist(remove_symmetric=True)` fails on string-indexed weights. (#165) - AttributeError: 'Queen' object has no attribute 'silent_island_warning' (#204) - 4.2.1 (#226) - Revert "4.2.1" (#228) - 4.2.1 (#227) - DOC: images for notebooks (#225) - 4.2.1 (#224) - 4.2.1 (#223) - duplicate pypi package badge (#221) - 4.2.1 (#222) - REL: 4.2.1 (#220) - libpysal 4.2.0 won't import on Windows (#214) - libpysal 4.2.0 Windows import issue (#215) - Constructing contiguity spatial weights using from_dataframe and from_shapefile could give different results (#212) - fix bug 212 (#213) ## Pull Requests - BUG: ufunc.outer deprecated (#237) - raise warning when islands are used in to_adjlist (#230) - DOC: Cleaning up docs and docsr for tutorial (#229) - Revert "4.2.1" (#228) - 4.2.1 (#227) - DOC: images for notebooks (#225) - 4.2.1 (#224) - 4.2.1 (#223) - 4.2.1 (#222) - REL: 4.2.1 (#220) - libpysal 4.2.0 Windows import issue (#215) - fix bug 212 (#213) The following individuals contributed to this release: - Serge Rey - Levi John Wolf # Version 4.2.1 (2020-01-04) This is a bug fix release. We closed a total of 14 issues (enhancements and bug fixes) through 5 pull requests, since our last release on 2019-12-14. ## Issues Closed - libpysal 4.2.0 won't import on Windows (#214) - libpysal 4.2.0 Windows import issue (#215) - Constructing contiguity spatial weights using from_dataframe and from_shapefile could give different results (#212) - fix bug 212 (#213) - alpha_shapes docs not rendering (#216) - corrected docstrings in cg.alpha_shapes.py (#217) - Updating requirements (#211) - Big tarball (#174) - Fetch (#176) ## Pull Requests - libpysal 4.2.0 Windows import issue (#215) - fix bug 212 (#213) - corrected docstrings in cg.alpha_shapes.py (#217) - Updating requirements (#211) - Fetch (#176) The following individuals contributed to this release: - Serge Rey - James Gaboardi - Levi John Wolf # Version 4.2.0 (2019-12-14) We closed a total of 57 issues (enhancements and bug fixes) through 21 pull requests, since our last release on 2019-09-01. ## Issues Closed - Updating requirements (#211) - Big tarball (#174) - Fetch (#176) - metadata for examples (#125) - DOC: math rendering in sphinx, and members included for W (#209) - (docs) automatically generate docstrings for class members (#210) - (docs) keep file .nojekyll in docs when syncing between docs/ and docsrc/_build/html/ (#207) - (bug) replace silent_island_warning with silence_warnings for weights (#206) - Documentation does not work (#205) - updating cg.standalone.distance_matrix docs (#203) - error message in cg.standalone.distance_matrix() (#195) - improved docs in io.util.shapefile (#202) - [ENH] moving jit import to common.py / improve documentation (#201) - rearrange shapely import in cg.alpha_shapes (#199) - fix quasi-redundant import of shapely (#200) - Remove more relics (from pre-reorg PySAL) (#196) - [BUG] alpha_shapes/shapely import error (#197) - [BUG] correcting shapely import bug (#198) - README.txt refers to pre-reorg PySAL (#194) - remove `distribute_setup.py`? (#147) - requires() decorator for libpysal.cg.alpha_shapes (#128) - decorating functions with requires() (#129) - [WIP] removing unused relics (#193) - necessity of libpysal.common.iteritems()? (#191) - removing iteritems decorator (#192) - Voronoi results in weights of different shape than input points (#189) - BUG: alpha_shape_auto can fail to contain all points in the set. (#190) - WSP(sparse).to_W() has `array`s in weights,neighbors dictionaries, rather than lists. (#185) - Cast arrays as lists (Issue 185) (#186) - BUG: Update for geopandas use of GeometryArray (#188) - Updated documentation error (link incorrectly specified) in README.rst (#187) - Docs: badges for pypi (#182) - development guidelines link failure (#178) - DOCS: moving off rtd (#181) - REL 4.1.1 bf release (#180) - BUG: Updating manifest for additional requirements files (#179) ## Pull Requests - Updating requirements (#211) - Fetch (#176) - (docs) automatically generate docstrings for class members (#210) - (docs) keep file .nojekyll in docs when syncing between docs/ and docsrc/_build/html/ (#207) - (bug) replace silent_island_warning with silence_warnings for weights (#206) - updating cg.standalone.distance_matrix docs (#203) - improved docs in io.util.shapefile (#202) - [ENH] moving jit import to common.py / improve documentation (#201) - fix quasi-redundant import of shapely (#200) - Remove more relics (from pre-reorg PySAL) (#196) - decorating functions with requires() (#129) - [WIP] removing unused relics (#193) - removing iteritems decorator (#192) - BUG: alpha_shape_auto can fail to contain all points in the set. (#190) - Cast arrays as lists (Issue 185) (#186) - BUG: Update for geopandas use of GeometryArray (#188) - Updated documentation error (link incorrectly specified) in README.rst (#187) - Docs: badges for pypi (#182) - DOCS: moving off rtd (#181) - REL 4.1.1 bf release (#180) - BUG: Updating manifest for additional requirements files (#179) The following individuals contributed to this release: - Serge Rey - Wei Kang - James Gaboardi - Levi John Wolf - Siddharths8212376 # Version 4.1.1 (2019-09-01) This is a bug fix release. We closed a total of 32 issues (enhancements and bug fixes) through 13 pull requests, since our last release on 2019-07-01. ## Issues Closed - BUG: Updating manifest for additional requirements files (#179) - libpysal 4.1.0 is not released on pypi or conda-forge (#169) - addressing DeprecationWarning: fromstring() (#131) - ENH: fromstring has been deprecated (#175) - addressing DeprecationWarning: fromstring() (#132) - Ci (#172) - minor change to W's silence_warnings workflow (#171) - Automatically voronoi input point dataframes to Queen/Rook (#135) - (docs, bug) silence warning for disconnected components and islands (#170) - BUG: add zstd as a dependency to work around conda glitch (#168) - unable to updata libpysal in Anaconda (#133) - Modernize the travis builds (#167) - make id_order propagate through symmetrize(inplace=False) (#137) - W.symmetrize(inplace=False) resets id order (#136) - swap to masking instead of querying in adjlist (#166) - REL: 4.1.0 changelog (#160) - removing alumni devs from travis notifications (#161) (#162) - remove alumni from travis (#161) - update setup.py to accommodate the transition to python3.6 and 3.7 (#163) ## Pull Requests - BUG: Updating manifest for additional requirements files (#179) - addressing DeprecationWarning: fromstring() (#132) - Ci (#172) - minor change to W's silence_warnings workflow (#171) - Automatically voronoi input point dataframes to Queen/Rook (#135) - (docs, bug) silence warning for disconnected components and islands (#170) - BUG: add zstd as a dependency to work around conda glitch (#168) - Modernize the travis builds (#167) - make id_order propagate through symmetrize(inplace=False) (#137) - swap to masking instead of querying in adjlist (#166) - REL: 4.1.0 changelog (#160) - removing alumni devs from travis notifications (#161) (#162) - update setup.py to accommodate the transition to python3.6 and 3.7 (#163) The following individuals contributed to this release: - Serge Rey - James Gaboardi - Wei Kang - Levi John Wolf # Version 4.1.0 (2019-07-01) We closed a total of 45 issues (enhancements and bug fixes) through 15 pull requests, since our last release on 2018-10-27. ## Issues Closed - Allow for **kwargs any time there's a weights construction (#158) - Some functions do not support silence_warnings=True (#134) - REL: update changelog (#159) - MAINT: bumping version for a release (#157) - update interactive examples in inline docstrings (#122) - BUG: fix for scipy bump #154 (#156) - Revert "bump supported Python versions and correct lat2SW doctest" (#155) - bump supported Python versions and correct lat2SW doctest (#154) - WIP debugging travis failure (#141) - replace deprecated "fromstring" with "frombytes" (#152) - doctests on weights are failing across the board (#48) - Use Unix line-endings for all files. (#149) - Remove unnecessary executable bits. (#148) - `import pysal` in libpysal/io/iohandlers/dat.py (#144) - enforce strict channel in .travis.yml (#143) - continued failing doctests in libpysal.io (#145) - sphinxcontrib-napoleon is no longer necessary (#146) - pysal --> libpysal docs conv & modernizing .travis.yml (#142) - fix README for pypi (#7) - build_lattice_shapefile swapped arguments (#138) - Accidental create of branch (#124) - Travis errors on Python3.6 PYTHON_PLUS=True (#127) - [WIP] solution for Travis CI failures (#140) - Conda travis (#139) - alphashapes & n<4 (#111) - [WIP] ensure safe returns for small n alphashapes (#115) - swapping ncols <-> nrows in the build_lattice_shapefile function (#130) - docstring for min_threshold_dist_from_shapefile is wrong (#120) - doc: requirements (#119) - REL: version bump for v4.0.1 (#118) ## Pull Requests - Allow for **kwargs any time there's a weights construction (#158) - REL: update changelog (#159) - MAINT: bumping version for a release (#157) - update interactive examples in inline docstrings (#122) - BUG: fix for scipy bump #154 (#156) - bump supported Python versions and correct lat2SW doctest (#154) - Use Unix line-endings for all files. (#149) - Remove unnecessary executable bits. (#148) - pysal --> libpysal docs conv & modernizing .travis.yml (#142) - [WIP] solution for Travis CI failures (#140) - [WIP] ensure safe returns for small n alphashapes (#115) - swapping ncols <-> nrows in the build_lattice_shapefile function (#130) - docstring for min_threshold_dist_from_shapefile is wrong (#120) - doc: requirements (#119) - REL: version bump for v4.0.1 (#118) The following individuals contributed to this release: - Serge Rey - Wei Kang - Martin Fleischmann - James Gaboardi - Elliott Sales De Andrade - Levi John Wolf - Renanxcortes # Version 4.0.1 (2018-10-27) We closed a total of 21 issues (enhancements and bug fixes) through 8 pull requests, since our last release on 2018-08-22. ## Issues Closed - weights.distance.KNN.from_dataframe ignoring radius (#116) - Always make spherical KDTrees if radius is passed (#117) - [ENH] should `weights.util.get_ids()` also accept a geodataframe? (#97) - enh: add doctests to travis (#2) (#112) - sphinx docs need updating (#49) - Add notebooks for subpackage contract (#108) - Api docs complete (#110) - Doctests and start of documentation for libpysal (#109) - Add dependencies to requirements_plus.txt for test_db (#107) - Weights/util/get ids gdf (#101) - missing adjustments to lower case module names (#106) - Rel.4.0.0 (#105) - REL: 3.0.8 (#104) ## Pull Requests - Always make spherical KDTrees if radius is passed (#117) - enh: add doctests to travis (#2) (#112) - Api docs complete (#110) - Doctests and start of documentation for libpysal (#109) - Add dependencies to requirements_plus.txt for test_db (#107) - Weights/util/get ids gdf (#101) - missing adjustments to lower case module names (#106) The following individuals contributed to this release: - Serge Rey - Levi John Wolf - Wei Kang # Version 4.0.0 (2018-08-22) We closed a total of 52 issues (enhancements and bug fixes) through 18 pull requests, since our last release on 2018-07-15. ## Issues Closed - REL: 3.0.8 (#104) - error importing v3.0.7 (#100) - Lower case module names (#98) - remove function regime_weights (#96) - depreciating regime_weights in the new release? (#94) - inconsistency in api? (#93) - Ensure consistency in `from .module import *` in components of libpysal (#95) - [WIP] cleanup (#88) - docstrings for attributes are defined in properties (#87) - docstrings in W class need editing (#64) - version name as __version__ (#92) - remove `del` statements and modify alphashape __all__ (#89) - libpysal/libpysal/cg/__init__.py not importing `rtree` (#90) - including rtree in imports (#91) - fix hardcoded swm test (#86) - BUG: test_weights_IO.py is using pysal and hard-coded paths (#85) - check for spatial index if nonplanar neighbors (#84) - nonplanar_neighbors fails when sindex is not constructed. (#63) - increment version number and add bugfixes, api changes (#79) - Spherebug (#82) - only warn once for islands/disconnected components (#83) - only warn on disconnected components if there are no islands (#81) - LEP: Stuff/use pysal/network stuff to provide queen weights on linestring dataframes (#59) - swm fix not ported forward from pysal. (#66) - import scipy syntax typo in the new issue template (#68) - deletion of extra spaces in warning message (#78) - Nightli.es build permissions (#77) - name of geometry column is hardcoded in nonplanar_neighbors (#75) - changed geometry column name from a str to an attribute (#76) - Missing example file (#71) - if numba isn't present, libpysal warns every time imported (#73) - add check for disconnected components (#65) - clean up for release (#74) - update for new examples (#72) ## Pull Requests - Lower case module names (#98) - remove function regime_weights (#96) - Ensure consistency in `from .module import *` in components of libpysal (#95) - [WIP] cleanup (#88) - docstrings for attributes are defined in properties (#87) - version name as __version__ (#92) - remove `del` statements and modify alphashape __all__ (#89) - including rtree in imports (#91) - fix hardcoded swm test (#86) - check for spatial index if nonplanar neighbors (#84) - increment version number and add bugfixes, api changes (#79) - Spherebug (#82) - only warn once for islands/disconnected components (#83) - deletion of extra spaces in warning message (#78) - changed geometry column name from a str to an attribute (#76) - add check for disconnected components (#65) - clean up for release (#74) - update for new examples (#72) The following individuals contributed to this release: - Serge Rey - Levi John Wolf - Wei Kang - James Gaboardi - Eli Knaap # v<1.13.0>, 2016-11-24 We closed a total of 38 issues, 7 pull requests and 31 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (7): * :ghpull:`844`: Geotable plot * :ghpull:`875`: Spint constant * :ghpull:`874`: Use standard python facilites for warning * :ghpull:`873`: updating release schedule * :ghpull:`871`: Put requirements into setup.py so they are installed if missing * :ghpull:`870`: Doc/release * :ghpull:`869`: Dev Issues (31): * :ghissue:`844`: Geotable plot * :ghissue:`877`: documentation links to numpy and scipy are broken * :ghissue:`875`: Spint constant * :ghissue:`874`: Use standard python facilites for warning * :ghissue:`873`: updating release schedule * :ghissue:`871`: Put requirements into setup.py so they are installed if missing * :ghissue:`591`: check pysal version and report if a more recent stable version is available * :ghissue:`410`: prototype LISA cluster map * :ghissue:`333`: Add k functions * :ghissue:`274`: Implement LISA in network module * :ghissue:`746`: Network data structures * :ghissue:`751`: A method to get a list of example-files by type * :ghissue:`219`: inconsistent treatment of centroids in arc distance calculations in weights/user.py * :ghissue:`173`: implement cross sectional and space-time scan statistics * :ghissue:`170`: centralize all kernel based calculations * :ghissue:`134`: Complete cg.locators * :ghissue:`94`: Smoothing: add another module based on model-based smoothing * :ghissue:`91`: Smoothing: Develop simulations for comparing different smoothers * :ghissue:`90`: Overhaul Polygon class * :ghissue:`89`: Optimize shapefile reader * :ghissue:`88`: Optimize Clockwise test * :ghissue:`86`: Spatial_Dynamics: LISA Time paths * :ghissue:`85`: Spatial_Dynamics: modified knox statistic * :ghissue:`84`: Spatial_Dynamics: Optimize Theta * :ghissue:`652`: Use cKDtree for Arc_KDTree * :ghissue:`697`: Update Release Management to Support Rolling Releases * :ghissue:`761`: Object-oriented design for viz module * :ghissue:`767`: ZeroDivisonError when calculating certain centroids * :ghissue:`849`: dbf2df can not read dbf files within which there are Chinese characters * :ghissue:`870`: Doc/release * :ghissue:`869`: Dev # v<1.12.0>, 2016-09-21 We closed a total of 100 issues, 33 pull requests and 67 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (33): * :ghpull:`864`: addressing issue #845 and adding tests * :ghpull:`862`: Memory efficient Gini and tests * :ghpull:`865`: fix space/tab inconsistency * :ghpull:`861`: GSOC -SpInt * :ghpull:`847`: spatial interaction weights * :ghpull:`863`: B859 * :ghpull:`860`: Incoprate updates to db driver and unittests * :ghpull:`858`: Dev mltest * :ghpull:`857`: Fix TabErrors, replace tabs with spaces * :ghpull:`856`: Make the output and build reproducible * :ghpull:`851`: fixed typo in test_network.py * :ghpull:`850`: [REBASE] Distance band speed ups * :ghpull:`843`: update and clean aesthetic of Network_Usage.ipynb * :ghpull:`842`: typo correction in network.py * :ghpull:`841`: [REBASE & REDIRECT] Conditional Database Imports & Docos, #692 * :ghpull:`840`: minor bugfix to #816 * :ghpull:`839`: documentation cleanup on network.analysis.py * :ghpull:`838`: network.util.py documentation cleanup * :ghpull:`836`: re: network.py documentation cleanup * :ghpull:`768`: Modified the way area of a ring is calculated to allow for more precision * :ghpull:`829`: numba autojit _fisher_jenks_means if numba is available * :ghpull:`832`: Handling a deprecation warning, and latex errors corrected. * :ghpull:`834`: Travis testing matrix * :ghpull:`831`: Refactoring Markov classes for efficiency * :ghpull:`827`: ESDA Tabular Functions * :ghpull:`823`: typo and format of docstring of user.py in weights module * :ghpull:`821`: Pdio * :ghpull:`817`: D/sur * :ghpull:`818`: Documentation fix + some PEP8 standardization * :ghpull:`811`: DistanceBand should correctly handle named weights * :ghpull:`808`: Dev * :ghpull:`807`: Updating contrib docs and bumping version for dev * :ghpull:`797`: working moran plot func Issues (67): * :ghissue:`855`: Inefficient Gini Coefficient calculation? * :ghissue:`864`: addressing issue #845 and adding tests * :ghissue:`862`: Memory efficient Gini and tests * :ghissue:`865`: fix space/tab inconsistency * :ghissue:`861`: GSOC -SpInt * :ghissue:`847`: spatial interaction weights * :ghissue:`859`: Wrong there is one disconnected observation (no neighbors) * :ghissue:`863`: B859 * :ghissue:`860`: Incoprate updates to db driver and unittests * :ghissue:`858`: Dev mltest * :ghissue:`857`: Fix TabErrors, replace tabs with spaces * :ghissue:`854`: handle verication context for githubstats * :ghissue:`856`: Make the output and build reproducible * :ghissue:`851`: fixed typo in test_network.py * :ghissue:`850`: [REBASE] Distance band speed ups * :ghissue:`846`: DistanceBand speed ups * :ghissue:`843`: update and clean aesthetic of Network_Usage.ipynb * :ghissue:`842`: typo correction in network.py * :ghissue:`692`: Conditional Database Import / Docos * :ghissue:`841`: [REBASE & REDIRECT] Conditional Database Imports & Docos, #692 * :ghissue:`769`: Windows 7, 64 bit installation issue with visual C++ for python * :ghissue:`816`: Exception TypeError in geoda_txt.py * :ghissue:`840`: minor bugfix to #816 * :ghissue:`839`: documentation cleanup on network.analysis.py * :ghissue:`397`: integrate optimized contiguity builder * :ghissue:`531`: add user space function to generate numpy arrays * :ghissue:`654`: meta update for 2-3 conversion * :ghissue:`676`: Meta not importable from pysal * :ghissue:`838`: network.util.py documentation cleanup * :ghissue:`753`: Fix the network ring bug * :ghissue:`836`: re: network.py documentation cleanup * :ghissue:`768`: Modified the way area of a ring is calculated to allow for more precision * :ghissue:`837`: re: network.allneighbordistances() diagonal fill * :ghissue:`822`: two issues about function choropleth_map in viz module * :ghissue:`835`: fix deprecation warnings noted in #822 * :ghissue:`829`: numba autojit _fisher_jenks_means if numba is available * :ghissue:`832`: Handling a deprecation warning, and latex errors corrected. * :ghissue:`834`: Travis testing matrix * :ghissue:`825`: Headbanging Median Rate ignores edge correction * :ghissue:`826`: Spatial Filtering grid definition * :ghissue:`824`: Direct Age Standardization fails for empty regions * :ghissue:`833`: PySAL+ optional testing matrix * :ghissue:`830`: [REBASED] PySAL+ optional testing matrix * :ghissue:`831`: Refactoring Markov classes for efficiency * :ghissue:`827`: ESDA Tabular Functions * :ghissue:`815`: rook case not working in ContiguityWeightsPolygons * :ghissue:`828`: Fisher_Jenks pure python implementation is too slow * :ghissue:`814`: Explore Classmethods for alternative constructors * :ghissue:`795`: switch to scipy.linalg instead of numpy.linalg * :ghissue:`799`: w_subset(weights:W, ids:np.ndarray) constructs faulty weights object * :ghissue:`823`: typo and format of docstring of user.py in weights module * :ghissue:`821`: Pdio * :ghissue:`794`: spreg ML_lag doesn't always set W in __init__ * :ghissue:`754`: Update README * :ghissue:`819`: add LIMAs * :ghissue:`817`: D/sur * :ghissue:`818`: Documentation fix + some PEP8 standardization * :ghissue:`809`: Fixed documentation * :ghissue:`813`: w.remap_ids(ids) never sets w.id_order_set * :ghissue:`775`: Added a prototype for constructing weights from a list of shapely Polygons * :ghissue:`810`: DistanceBand fails to accept custom ids * :ghissue:`811`: DistanceBand should correctly handle named weights * :ghissue:`780`: Doctests failing on travis * :ghissue:`801`: ImportError: No module named scipy.spatial * :ghissue:`808`: Dev * :ghissue:`807`: Updating contrib docs and bumping version for dev * :ghissue:`797`: working moran plot func # v<1.11.2>, 2016-05-18 We closed a total of 20 issues, 6 pull requests and 14 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (6): * :ghpull:`805`: pre Rel1.11.2 and #840 * :ghpull:`802`: fixed issues with model handler failing to correctly discover models * :ghpull:`798`: fix for css problem on rtd #790 * :ghpull:`793`: Getting weights doctests to pass * :ghpull:`791`: Doc/rolling * :ghpull:`792`: Local Moran was using the incorrect moments in z_sim and p_z_sim Issues (14): * :ghissue:`805`: pre Rel1.11.2 and #840 * :ghissue:`803`: check_contiguity error.. * :ghissue:`802`: fixed issues with model handler failing to correctly discover models * :ghissue:`800`: `ps.threshold_continuousW_from_shapefile` returning inf along diagonal * :ghissue:`771`: KDtree type mismatch in knnW * :ghissue:`798`: fix for css problem on rtd #790 * :ghissue:`796`: working moran plot func * :ghissue:`787`: Update docs to reflect Python-3 compatibility * :ghissue:`587`: ML Lag indexing error on optimization result * :ghissue:`793`: Getting weights doctests to pass * :ghissue:`791`: Doc/rolling * :ghissue:`792`: Local Moran was using the incorrect moments in z_sim and p_z_sim * :ghissue:`674`: Have PySAL included on OSGeo Live 9 * :ghissue:`779`: DistanceBand include the point itself as neighbor # v<1.11.1>, 2016-04-01 We closed a total of 62 issues, 20 pull requests and 42 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (20): * :ghpull:`777`: fix minor issues with using stdlib warnings in mapclassify.py * :ghpull:`766`: Constant check * :ghpull:`781`: Dev * :ghpull:`778`: Wkb * :ghpull:`776`: Updating & find-bin-as-call for Map_Classifiers * :ghpull:`770`: adding github chrome to make project contributions easier to handle * :ghpull:`764`: add folium changes needed for notebook to run * :ghpull:`760`: Update docs for #697 * :ghpull:`763`: docs fix: incorrect array dimensions listed for spatial interaction SpaceTimeEvents * :ghpull:`756`: B726 * :ghpull:`749`: remove cruft in git root and add gitter badge to the readme * :ghpull:`748`: Replace deprecated np.rank with np.ndim * :ghpull:`745`: Lag Categorical & Find Bins * :ghpull:`741`: fix for #740 * :ghpull:`739`: Dev * :ghpull:`738`: fixing master version * :ghpull:`737`: Bumping dev * :ghpull:`736`: Merge pull request #734 from sjsrey/master * :ghpull:`735`: Dev in sync with master for 1.11 * :ghpull:`734`: Release 1.11 Issues (42): * :ghissue:`773`: add isKDTree typecomparison to handle divergent cKDTree and KDTree types * :ghissue:`777`: fix minor issues with using stdlib warnings in mapclassify.py * :ghissue:`766`: Constant check * :ghissue:`782`: Contrib docs * :ghissue:`781`: Dev * :ghissue:`762`: viz: folium_mapping.ipynb AttributeError: 'Map' object has no attribute '_build_map' * :ghissue:`778`: Wkb * :ghissue:`776`: Updating & find-bin-as-call for Map_Classifiers * :ghissue:`770`: adding github chrome to make project contributions easier to handle * :ghissue:`774`: Added a prototype for constructing weights from a list of shapely Polygons * :ghissue:`772`: knnW user guide doc error * :ghissue:`765`: potential constant_check bug * :ghissue:`759`: Fixed code in ipython notebooks * :ghissue:`752`: [WIP] Add J function to network submodule * :ghissue:`764`: add folium changes needed for notebook to run * :ghissue:`760`: Update docs for #697 * :ghissue:`763`: docs fix: incorrect array dimensions listed for spatial interaction SpaceTimeEvents * :ghissue:`750`: Add gitter badge to README on master branch * :ghissue:`758`: Fixed code in ipython notebooks * :ghissue:`755`: add speedup of conditional randomization * :ghissue:`726`: Compatibility for Scipy 16.1 * :ghissue:`756`: B726 * :ghissue:`749`: remove cruft in git root and add gitter badge to the readme * :ghissue:`587`: ML Lag * :ghissue:`748`: Replace deprecated np.rank with np.ndim * :ghissue:`747`: Replace deprecated np.rank with np.ndim * :ghissue:`653`: network is returning NAN's on diagonal of distance matrix * :ghissue:`660`: insert zeros on symmetric matrix diagonal * :ghissue:`745`: Lag Categorical & Find Bins * :ghissue:`744`: [REBASED] Update moran.py with much faster iterations * :ghissue:`732`: Update moran.py with much faster iterations * :ghissue:`743`: [REBASED]: Update moran.py with much faster iterations * :ghissue:`742`: Links not working * :ghissue:`740`: Moran_Local's EI is returned as an array instead of a float * :ghissue:`741`: fix for #740 * :ghissue:`739`: Dev * :ghissue:`738`: fixing master version * :ghissue:`737`: Bumping dev * :ghissue:`151`: Port pysal to python3 * :ghissue:`736`: Merge pull request #734 from sjsrey/master * :ghissue:`735`: Dev in sync with master for 1.11 * :ghissue:`734`: Release 1.11 # v<1.11.0>, 2016-01-27 GitHub stats for 2015/07/29 - 2016/01/27 These lists are automatically generated, and may be incomplete or contain duplicates. The following 13 authors contributed 216 commits. * Dani Arribas-Bel * David Folch * Levi John Wolf * Levi Wolf * Philip Stephens * Serge Rey * Sergio Rey * Wei Kang * jlaura * levi.john.wolf@gmail.com * ljw * ljwolf * pedrovma We closed a total of 86 issues, 33 pull requests and 53 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (33): * :ghpull:`724`: add synchronization tool * :ghpull:`733`: Fb/bump * :ghpull:`731`: Small docfixes * :ghpull:`730`: Contrib docs * :ghpull:`728`: B179 * :ghpull:`727`: Geodf io * :ghpull:`725`: try pinning scipy,numpy * :ghpull:`723`: make sure to test all moran classes * :ghpull:`720`: Moving natural breaks to a cleaner kmeans implementation * :ghpull:`718`: force counts to be same length as bins * :ghpull:`714`: Dev * :ghpull:`715`: Heads * :ghpull:`713`: Enh712 * :ghpull:`710`: Patsy/Pandas wrapper * :ghpull:`711`: Travis fixes * :ghpull:`706`: precommit hook * :ghpull:`707`: Keep dev updated with any bugfixes into master * :ghpull:`702`: fix for chi2 test 0 denominator and invocation of chi2 test in LISA_Markov * :ghpull:`704`: Allcloser * :ghpull:`703`: Swapping to Allclose and RTOL=.00005 in spreg * :ghpull:`701`: By col array * :ghpull:`700`: small optimization of bivariate moran motivated by #695 * :ghpull:`696`: Pypi * :ghpull:`691`: Update doctest for one-off bug that was fixed with #690 * :ghpull:`690`: fix for lisa markov one off for significance indicator * :ghpull:`689`: Clpy flex w * :ghpull:`688`: pep 8 edits * :ghpull:`687`: Change array assertions into allclose * :ghpull:`686`: Moran local bivariate * :ghpull:`684`: 591 * :ghpull:`682`: release instructions updated * :ghpull:`681`: version bump for next dev cycle * :ghpull:`680`: Rel1.10 Issues (53): * :ghissue:`705`: spreg check valve * :ghissue:`344`: Explore new dependency on ogr * :ghissue:`459`: Problem with bandwidth * :ghissue:`552`: Viz organization * :ghissue:`491`: Test np.allclose() for unit tests * :ghissue:`529`: Clarity needed on proper reference formatting in sphinx docs * :ghissue:`699`: Trouble importing pysal - ImportError: DLL load failed * :ghissue:`716`: `min_threshold_dist_from_shapefile` creating an island in some cases * :ghissue:`724`: add synchronization tool * :ghissue:`733`: Fb/bump * :ghissue:`731`: Small docfixes * :ghissue:`730`: Contrib docs * :ghissue:`719`: pysal not working with matplotlib v1.5 for plot_lisa_cluster, plot_choropleth, etc. * :ghissue:`728`: B179 * :ghissue:`727`: Geodf io * :ghissue:`725`: try pinning scipy,numpy * :ghissue:`723`: make sure to test all moran classes * :ghissue:`720`: Moving natural breaks to a cleaner kmeans implementation * :ghissue:`717`: esda.mapclassify return problematic counts when there is 0 occurrence in the last class * :ghissue:`718`: force counts to be same length as bins * :ghissue:`714`: Dev * :ghissue:`712`: `block_weights` does not take argument `idVariable` * :ghissue:`715`: Heads * :ghissue:`713`: Enh712 * :ghissue:`710`: Patsy/Pandas wrapper * :ghissue:`711`: Travis fixes * :ghissue:`706`: precommit hook * :ghissue:`708`: 2-3: is six a dependency or do we ship it? * :ghissue:`707`: Keep dev updated with any bugfixes into master * :ghissue:`702`: fix for chi2 test 0 denominator and invocation of chi2 test in LISA_Markov * :ghissue:`704`: Allcloser * :ghissue:`703`: Swapping to Allclose and RTOL=.00005 in spreg * :ghissue:`698`: Py3merge * :ghissue:`701`: By col array * :ghissue:`700`: small optimization of bivariate moran motivated by #695 * :ghissue:`695`: Bivariate global moran's I formula * :ghissue:`683`: Py3 Conversion Project * :ghissue:`694`: Allclose in SPREG * :ghissue:`696`: Pypi * :ghissue:`691`: Update doctest for one-off bug that was fixed with #690 * :ghissue:`693`: Trouble installation: No module named 'shapes' * :ghissue:`690`: fix for lisa markov one off for significance indicator * :ghissue:`689`: Clpy flex w * :ghissue:`688`: pep 8 edits * :ghissue:`685`: BV Lisa * :ghissue:`687`: Change array assertions into allclose * :ghissue:`686`: Moran local bivariate * :ghissue:`677`: Make meta importable from base * :ghissue:`684`: 591 * :ghissue:`682`: release instructions updated * :ghissue:`679`: pysal.cg.sphere.fast_knn bug * :ghissue:`681`: version bump for next dev cycle * :ghissue:`680`: Rel1.10 # v<1.10.0>, 2015-07-29 GitHub stats for 2015/01/31 - 2015/07/29 These lists are automatically generated, and may be incomplete or contain duplicates. The following 20 authors contributed 334 commits. * Charlie Schmidt * Dani Arribas-Bel * Daniel Arribas-Bel * David C. Folch * David Folch * Jay * Levi John Wolf * Marynia * Philip Stephens * Serge Rey * Sergio Rey * Taylor Oshan * The Gitter Badger * Wei Kang * jay * jlaura * ljw * ljwolf * luc * pedrovma We closed a total of 156 issues, 58 pull requests and 98 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (58): * :ghpull:`675`: Update README.md * :ghpull:`673`: Adding init at pdutilities so they are importable * :ghpull:`672`: ENH: option to locate legend * :ghpull:`669`: add nonsudo travis * :ghpull:`666`: Cleaned up conflicts in ref branch * :ghpull:`664`: Lisa map * :ghpull:`663`: Examples * :ghpull:`661`: Reorganization of examples * :ghpull:`657`: Assuncao test division errors * :ghpull:`649`: Add a Gitter chat badge to README.md * :ghpull:`647`: Addresses 646 * :ghpull:`645`: Update to weights module documentation for PySAL-REST * :ghpull:`644`: removed test print statements from df2dbf * :ghpull:`643`: using dtypes.name in df2dbf to avoid gotcha in type * :ghpull:`642`: Updating copyright year * :ghpull:`634`: allows non-symmetric distance matrices * :ghpull:`641`: turning off generatetree * :ghpull:`592`: adding check for version #591 * :ghpull:`636`: vertical line point simulation * :ghpull:`639`: Snapping * :ghpull:`640`: Add users to travis * :ghpull:`627`: Networkrb * :ghpull:`631`: Fixing typoes in analysis.py * :ghpull:`626`: cast arrays over inconsistent kdtree return types * :ghpull:`620`: adding explicit check for random region contiguity * :ghpull:`619`: Fixing spreg's warnings * :ghpull:`618`: initial folder with dbf utilities using pandas * :ghpull:`616`: Adding isolation and theil indices to inequality._indices.py * :ghpull:`615`: Network docs * :ghpull:`614`: cleaning up pr testing * :ghpull:`613`: test coverage to 98% on network * :ghpull:`612`: small change for testing PR * :ghpull:`611`: stubbed in minimal tests * :ghpull:`607`: B603 * :ghpull:`602`: Documentation Extraction Notebook * :ghpull:`606`: pct_nonzero was reporting a ratio not a percentage * :ghpull:`604`: Contribpush * :ghpull:`601`: Documentation Cleanup * :ghpull:`599`: Casting bugfix from #598 * :ghpull:`600`: Updates for coveralls * :ghpull:`598`: IO in Python 3 * :ghpull:`597`: Decoupling bbox from map_XXX_poly * :ghpull:`595`: Removed testing line in travis.yml and added a .coveragerc file to manag... * :ghpull:`590`: using numpy sum method * :ghpull:`589`: Wconstructor * :ghpull:`588`: Coveralls * :ghpull:`585`: Fisher Jenks bug in `plot_choropleth` * :ghpull:`584`: Alpha in plot chor * :ghpull:`583`: Fixed 576 * :ghpull:`580`: working on #576 * :ghpull:`578`: Fixes #577 * :ghpull:`574`: Handle case where a region has a 0 share. * :ghpull:`571`: Dict to unique value mapper * :ghpull:`570`: numpy doc cleanup for weights module * :ghpull:`569`: folium viz scripts * :ghpull:`568`: inline with numpy doc spec (spatial_dynamics module) * :ghpull:`567`: New/masterbump * :ghpull:`566`: Fix for 1.9.0 missing file in setup.py Issues (98): * :ghissue:`675`: Update README.md * :ghissue:`658`: Travis.CI """Legacy""" architecture * :ghissue:`667`: Examples Not Found * :ghissue:`673`: Adding init at pdutilities so they are importable * :ghissue:`672`: ENH: option to locate legend * :ghissue:`669`: add nonsudo travis * :ghissue:`671`: Shapefile Read - PolygonM Attribute Error * :ghissue:`670`: examples README markdown files reformatting * :ghissue:`668`: Wconstructor * :ghissue:`666`: Cleaned up conflicts in ref branch * :ghissue:`664`: Lisa map * :ghissue:`662`: Pep8 * :ghissue:`665`: Refs * :ghissue:`663`: Examples * :ghissue:`573`: Examples * :ghissue:`661`: Reorganization of examples * :ghissue:`656`: Assuncao rate improper division * :ghissue:`657`: Assuncao test division errors * :ghissue:`280`: handle multi-segment links in net_shp_io.py * :ghissue:`649`: Add a Gitter chat badge to README.md * :ghissue:`647`: Addresses 646 * :ghissue:`646`: arc distance in knnW * :ghissue:`645`: Update to weights module documentation for PySAL-REST * :ghissue:`644`: removed test print statements from df2dbf * :ghissue:`643`: using dtypes.name in df2dbf to avoid gotcha in type * :ghissue:`603`: Polygon.contains_point does not correctly process multipart polygons. * :ghissue:`642`: Updating copyright year * :ghissue:`623`: reading road shapfiles into network * :ghissue:`608`: Scipy Sparse Graph * :ghissue:`621`: network distance speedup * :ghissue:`632`: network point snapping * :ghissue:`633`: point to point distances on network * :ghissue:`635`: simulating points on vertical lines * :ghissue:`634`: allows non-symmetric distance matrices * :ghissue:`641`: turning off generatetree * :ghissue:`637`: speedup distance computations * :ghissue:`592`: adding check for version #591 * :ghissue:`628`: Re-enable doctests * :ghissue:`636`: vertical line point simulation * :ghissue:`639`: Snapping * :ghissue:`640`: Add users to travis * :ghissue:`638`: Add users to Travis * :ghissue:`627`: Networkrb * :ghissue:`622`: New network branch from clean master * :ghissue:`630`: NetworkG api is broken * :ghissue:`631`: Fixing typoes in analysis.py * :ghissue:`625`: Installation - Binstar and Anaconda * :ghissue:`624`: Network topology * :ghissue:`629`: changes to spreg tests for travis * :ghissue:`166`: pysal.esda.mapclassify.Fisher_Jenks - local variable 'best' referenced before assignment * :ghissue:`626`: cast arrays over inconsistent kdtree return types * :ghissue:`596`: [question] unsupervised classification * :ghissue:`620`: adding explicit check for random region contiguity * :ghissue:`617`: Random_Region not respecting contiguity constraint * :ghissue:`619`: Fixing spreg's warnings * :ghissue:`618`: initial folder with dbf utilities using pandas * :ghissue:`616`: Adding isolation and theil indices to inequality._indices.py * :ghissue:`615`: Network docs * :ghissue:`614`: cleaning up pr testing * :ghissue:`613`: test coverage to 98% on network * :ghissue:`612`: small change for testing PR * :ghissue:`611`: stubbed in minimal tests * :ghissue:`607`: B603 * :ghissue:`602`: Documentation Extraction Notebook * :ghissue:`606`: pct_nonzero was reporting a ratio not a percentage * :ghissue:`605`: RTree Weights * :ghissue:`604`: Contribpush * :ghissue:`601`: Documentation Cleanup * :ghissue:`554`: Beginning documentation cleanup * :ghissue:`599`: Casting bugfix from #598 * :ghissue:`600`: Updates for coveralls * :ghissue:`598`: IO in Python 3 * :ghissue:`597`: Decoupling bbox from map_XXX_poly * :ghissue:`595`: Removed testing line in travis.yml and added a .coveragerc file to manag... * :ghissue:`586`: Look at using Coveralls * :ghissue:`590`: using numpy sum method * :ghissue:`589`: Wconstructor * :ghissue:`588`: Coveralls * :ghissue:`576`: Predecessor lists inconsistencies * :ghissue:`585`: Fisher Jenks bug in `plot_choropleth` * :ghissue:`584`: Alpha in plot chor * :ghissue:`583`: Fixed 576 * :ghissue:`582`: Fixes #576 * :ghissue:`581`: Network * :ghissue:`580`: working on #576 * :ghissue:`575`: Network from Lattice * :ghissue:`578`: Fixes #577 * :ghissue:`577`: bug in FileIO.cast * :ghissue:`574`: Handle case where a region has a 0 share. * :ghissue:`343`: Edge Segmentation * :ghissue:`571`: Dict to unique value mapper * :ghissue:`570`: numpy doc cleanup for weights module * :ghissue:`569`: folium viz scripts * :ghissue:`568`: inline with numpy doc spec (spatial_dynamics module) * :ghissue:`567`: New/masterbump * :ghissue:`564`: Bug in setup.py * :ghissue:`566`: Fix for 1.9.0 missing file in setup.py * :ghissue:`565`: Bsetup # v<1.9.1>, 2015-01-31 GitHub stats for 2015/01/30 - 2015/01/31 These lists are automatically generated, and may be incomplete or contain duplicates. The following 4 authors contributed 14 commits. * Dani Arribas-Bel * Serge Rey * Sergio Rey * jlaura We closed a total of 8 issues, 3 pull requests and 5 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (3): * :ghpull:`566`: Fix for 1.9.0 missing file in setup.py * :ghpull:`563`: Updating release instructions * :ghpull:`561`: Rolling over to 1.10 Issues (5): * :ghissue:`566`: Fix for 1.9.0 missing file in setup.py * :ghissue:`565`: Bsetup * :ghissue:`563`: Updating release instructions * :ghissue:`562`: adjustments to release management * :ghissue:`561`: Rolling over to 1.10 # v<1.9.0>, 2015-01-30 GitHub stats for 2014/07/25 - 2015/01/30 These lists are automatically generated, and may be incomplete or contain duplicates. The following 12 authors contributed 131 commits. * Andy Reagan * Dani Arribas-Bel * Jay * Levi John Wolf * Philip Stephens * Qunshan * Serge Rey * jlaura * ljwolf * luc We closed a total of 113 issues, 44 pull requests and 69 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (44): * :ghpull:`560`: modifying import scheme for network module * :ghpull:`559`: Network2 * :ghpull:`558`: Network2 * :ghpull:`557`: Network2 * :ghpull:`556`: Added analytical functions and edge segmentation * :ghpull:`550`: Network2 * :ghpull:`553`: correction in denominator of spatial tau. * :ghpull:`547`: Updates to get network integrated * :ghpull:`544`: update .gitignore * :ghpull:`543`: k nearest neighbor gwt example file for baltimore points (with k=4) added to examples directory * :ghpull:`542`: new format nat_queen.gal file added to examples directory * :ghpull:`541`: Update tutorial docs for new book * :ghpull:`540`: doc: updating instructions for anaconda and enthought * :ghpull:`539`: doc: pysal is now on sagemathcloud * :ghpull:`538`: Clean up of cg and fixes of other doctests/formats * :ghpull:`536`: adding entry for getis ord module * :ghpull:`537`: new opendata module for contrib * :ghpull:`535`: Add method for extracting data columns as Numpy array rather than list * :ghpull:`534`: added geogrid to __all__ in sphere.py * :ghpull:`533`: added geogrid function to create a grid of points on a sphere * :ghpull:`532`: new functions to deal with spherical geometry: lat-lon conversion, degre... * :ghpull:`530`: I390 * :ghpull:`528`: Replacing 0 by min value in choropleths * :ghpull:`526`: B166 * :ghpull:`525`: copyright update * :ghpull:`524`: New homogeneity tests for general case and spatial markov as a special case * :ghpull:`523`: pointing to github.io pages * :ghpull:`520`: Same typo. Toolkit. * :ghpull:`518`: Update util.py * :ghpull:`519`: Typo * :ghpull:`517`: Documentation correction for Prais Conditional Mobility Index * :ghpull:`516`: ENH for https://github.com/PySAL/PySAL.github.io/issues/17 * :ghpull:`515`: BUG: conditional check for extension of lower bound of colorbar to conta... * :ghpull:`514`: ENH: adding the user_defined classification * :ghpull:`513`: rewriting to not use ipython notebook --pylab=line * :ghpull:`512`: Viz * :ghpull:`508`: Adding barebones pysal2matplotlib options in viz * :ghpull:`511`: DOC updating news * :ghpull:`507`: Sched * :ghpull:`510`: BUG: fix for #509 * :ghpull:`506`: 1.9dev * :ghpull:`505`: REL bumping master to 1.9.0dev * :ghpull:`504`: Release prep 1.8 * :ghpull:`503`: Grid for landing page Issues (69): * :ghissue:`560`: modifying import scheme for network module * :ghissue:`559`: Network2 * :ghissue:`558`: Network2 * :ghissue:`557`: Network2 * :ghissue:`556`: Added analytical functions and edge segmentation * :ghissue:`555`: Added edge segmentation by distance * :ghissue:`550`: Network2 * :ghissue:`553`: correction in denominator of spatial tau. * :ghissue:`549`: Network2 * :ghissue:`547`: Updates to get network integrated * :ghissue:`548`: Installation Issues * :ghissue:`546`: Network2 * :ghissue:`545`: Network * :ghissue:`544`: update .gitignore * :ghissue:`543`: k nearest neighbor gwt example file for baltimore points (with k=4) added to examples directory * :ghissue:`542`: new format nat_queen.gal file added to examples directory * :ghissue:`541`: Update tutorial docs for new book * :ghissue:`540`: doc: updating instructions for anaconda and enthought * :ghissue:`539`: doc: pysal is now on sagemathcloud * :ghissue:`538`: Clean up of cg and fixes of other doctests/formats * :ghissue:`536`: adding entry for getis ord module * :ghissue:`537`: new opendata module for contrib * :ghissue:`535`: Add method for extracting data columns as Numpy array rather than list * :ghissue:`534`: added geogrid to __all__ in sphere.py * :ghissue:`533`: added geogrid function to create a grid of points on a sphere * :ghissue:`532`: new functions to deal with spherical geometry: lat-lon conversion, degre... * :ghissue:`390`: add option to have local moran quadrant codes align with geoda * :ghissue:`530`: I390 * :ghissue:`528`: Replacing 0 by min value in choropleths * :ghissue:`526`: B166 * :ghissue:`176`: contrib module for proj 4 * :ghissue:`178`: contrib module for gdal/org * :ghissue:`203`: implement network class in spatialnet * :ghissue:`204`: pysal-networkx util functions * :ghissue:`209`: csv reader enhancement * :ghissue:`215`: Add a tutorial for the spreg module * :ghissue:`244`: ps.knnW_from_shapefile returns wrong W ids when idVariable specified * :ghissue:`246`: Only use idVariable in W when writing out to file * :ghissue:`283`: Create new nodes at intersections of edges * :ghissue:`291`: Enum links around regions hangs * :ghissue:`292`: Handle multiple filaments within a region in the Wed construction * :ghissue:`302`: Handle hole polygons when constructing wed * :ghissue:`309`: Develop consistent solution for precision induced errors in doctests across platforms * :ghissue:`350`: reading/writing weights file with spaces in the ids * :ghissue:`450`: x_name and summary method not consistent in ols * :ghissue:`521`: Nosetests don't accept setup.cfg * :ghissue:`509`: ESDA bin type inconsistency * :ghissue:`525`: copyright update * :ghissue:`524`: New homogeneity tests for general case and spatial markov as a special case * :ghissue:`523`: pointing to github.io pages * :ghissue:`520`: Same typo. Toolkit. * :ghissue:`522`: Nosetests for python3 porting * :ghissue:`518`: Update util.py * :ghissue:`519`: Typo * :ghissue:`517`: Documentation correction for Prais Conditional Mobility Index * :ghissue:`516`: ENH for https://github.com/PySAL/PySAL.github.io/issues/17 * :ghissue:`515`: BUG: conditional check for extension of lower bound of colorbar to conta... * :ghissue:`514`: ENH: adding the user_defined classification * :ghissue:`513`: rewriting to not use ipython notebook --pylab=line * :ghissue:`512`: Viz * :ghissue:`508`: Adding barebones pysal2matplotlib options in viz * :ghissue:`511`: DOC updating news * :ghissue:`507`: Sched * :ghissue:`510`: BUG: fix for #509 * :ghissue:`502`: spreg.ml_lag.ML_Lag is very very very time-consuming? * :ghissue:`506`: 1.9dev * :ghissue:`505`: REL bumping master to 1.9.0dev * :ghissue:`504`: Release prep 1.8 * :ghissue:`503`: Grid for landing page # v<1.8.0>, 2014-07-25 GitHub stats for 2014/01/29 - 2014/07/25 These lists are automatically generated, and may be incomplete or contain duplicates. The following 8 authors contributed 281 commits. * Dani Arribas-Bel * Jay * Philip Stephens * Serge Rey * Sergio Rey * jlaura * pedrovma * sjsrey We closed a total of 160 issues, 60 pull requests and 100 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (60): * :ghpull:`503`: Grid for landing page * :ghpull:`501`: Two figs rather than three * :ghpull:`500`: More efficient higher order operations * :ghpull:`499`: renamed nat_queen.gal for #452 * :ghpull:`497`: ENH Deprecation warning for regime_weights #486 * :ghpull:`494`: Enables testing against two versions of SciPy shipped with the last two Ubuntu LTS versions. * :ghpull:`490`: Fix for #487 * :ghpull:`492`: BUG cleaning up temporary files for #398 * :ghpull:`493`: Phil: Skipping several tests that fail due to precision under older scipy * :ghpull:`489`: test suite fixes * :ghpull:`488`: More tests to skip if scipy less than 11 * :ghpull:`484`: ENH: cleaning up more test generated files * :ghpull:`483`: Forwarding Phil's commit: skipping doctests, conditional skip of unit tests * :ghpull:`482`: DOC cleaning up files after running doctests #398 * :ghpull:`481`: DOC contrib updates and links * :ghpull:`480`: DOC cleaning up doctests * :ghpull:`479`: ENH Changing regime_weights to block_weights for #455 * :ghpull:`478`: DOC: link fixes * :ghpull:`477`: cKDTree for #460 * :ghpull:`476`: redefining w.remap_ids to take only a single arg * :ghpull:`475`: Adding docstrings and error check to fix #471 * :ghpull:`470`: fixing order of args for api consistency. * :ghpull:`469`: Idfix for #449 * :ghpull:`463`: updating gitignore * :ghpull:`462`: ENH: handle the case of an ergodic distribution where one state has 0 probability * :ghpull:`458`: ENH: Vagrantfile for PySAL devs and workshops * :ghpull:`447`: Clusterpy * :ghpull:`456`: BUG: fix for #451 handling W or WSP in higher_order_sp * :ghpull:`454`: Foobar * :ghpull:`443`: Updating spreg: several minor bug and documentation fixes. * :ghpull:`453`: Resolving conflicts * :ghpull:`448`: Wsp * :ghpull:`445`: ENH: unique qualitative color ramp. Also refactoring for future ipython deprecation of --pylab=inline * :ghpull:`446`: Wmd * :ghpull:`444`: Scipy dependency * :ghpull:`442`: Wmd * :ghpull:`441`: fixed kernel wmd for updated wmd structure * :ghpull:`440`: ENH: sidebar for Releases and installation doc update * :ghpull:`439`: - events * :ghpull:`438`: ENH: pruning to respect flake8 * :ghpull:`437`: BUG: fix for removal of scipy.stat._support #436 * :ghpull:`433`: Rank markov * :ghpull:`424`: testing * :ghpull:`431`: FOSS4G * :ghpull:`430`: Network * :ghpull:`429`: moving analytics out of wed class and into their own module * :ghpull:`428`: Network * :ghpull:`427`: devel docs * :ghpull:`425`: Viz2contrib * :ghpull:`423`: Update news.rst * :ghpull:`422`: ENH: Update doc instructions for napoleon dependency * :ghpull:`421`: Adding files used in some examples as per Luc's request. * :ghpull:`419`: Doc fixes 1.7 * :ghpull:`393`: Doc fixes 1.7 * :ghpull:`417`: ENH hex lattice W for #416 * :ghpull:`415`: Temporarily commenting out tests that are blocking Travis. * :ghpull:`407`: Viz: Moving into contrib/viz in master * :ghpull:`404`: version change * :ghpull:`401`: fixes #388 * :ghpull:`402`: release changes Issues (100): * :ghissue:`503`: Grid for landing page * :ghissue:`501`: Two figs rather than three * :ghissue:`500`: More efficient higher order operations * :ghissue:`452`: nat_queen.gal example file * :ghissue:`499`: renamed nat_queen.gal for #452 * :ghissue:`486`: add a deprecation warning on regime_weights * :ghissue:`497`: ENH Deprecation warning for regime_weights #486 * :ghissue:`449`: Lower order neighbor included in higher order * :ghissue:`487`: Issue with w.weights when row-standardizing * :ghissue:`398`: running test suite generates files * :ghissue:`358`: Graph weights * :ghissue:`338`: ENH: Move Geary's C calculations to Cython. * :ghissue:`494`: Enables testing against two versions of SciPy shipped with the last two Ubuntu LTS versions. * :ghissue:`490`: Fix for #487 * :ghissue:`492`: BUG cleaning up temporary files for #398 * :ghissue:`493`: Phil: Skipping several tests that fail due to precision under older scipy * :ghissue:`489`: test suite fixes * :ghissue:`485`: Revert "ENH: cleaning up more test generated files" * :ghissue:`488`: More tests to skip if scipy less than 11 * :ghissue:`484`: ENH: cleaning up more test generated files * :ghissue:`483`: Forwarding Phil's commit: skipping doctests, conditional skip of unit tests * :ghissue:`482`: DOC cleaning up files after running doctests #398 * :ghissue:`481`: DOC contrib updates and links * :ghissue:`480`: DOC cleaning up doctests * :ghissue:`455`: regime weights vs block weights * :ghissue:`479`: ENH Changing regime_weights to block_weights for #455 * :ghissue:`478`: DOC: link fixes * :ghissue:`460`: Optimize KDTree * :ghissue:`477`: cKDTree for #460 * :ghissue:`472`: Check for any side effects from new id remapping in w.sparse * :ghissue:`473`: update all user space functions for new w.remap_ids * :ghissue:`476`: redefining w.remap_ids to take only a single arg * :ghissue:`263`: Transition to scipy.spatial.cKDTree from scipy.spatial.KDTree * :ghissue:`414`: Travis build is killing nosetests * :ghissue:`335`: Weights transformation docs * :ghissue:`471`: add docstring example for w.remap_ids * :ghissue:`475`: Adding docstrings and error check to fix #471 * :ghissue:`405`: ENH: Handling ids in W (Leave open for discussion) * :ghissue:`470`: fixing order of args for api consistency. * :ghissue:`469`: Idfix for #449 * :ghissue:`467`: redirect pysal.org to new dynamic landing page * :ghissue:`466`: design the grid for the notebooks * :ghissue:`464`: design new dynamic landing page for github.io * :ghissue:`465`: move news out of docs and into dynamic landing page * :ghissue:`468`: Move dynamic items out of sphinx docs and into dynamic landing page * :ghissue:`463`: updating gitignore * :ghissue:`451`: docs for higher_order_sp have wrong argument types * :ghissue:`462`: ENH: handle the case of an ergodic distribution where one state has 0 probability * :ghissue:`458`: ENH: Vagrantfile for PySAL devs and workshops * :ghissue:`447`: Clusterpy * :ghissue:`456`: BUG: fix for #451 handling W or WSP in higher_order_sp * :ghissue:`457`: This is a test to see if pull request notifications get sent out to the list * :ghissue:`454`: Foobar * :ghissue:`443`: Updating spreg: several minor bug and documentation fixes. * :ghissue:`453`: Resolving conflicts * :ghissue:`412`: On travis and darwin test_ml_error_regimes.py hangs * :ghissue:`448`: Wsp * :ghissue:`435`: Will spatial durbin model be added in the near future? * :ghissue:`445`: ENH: unique qualitative color ramp. Also refactoring for future ipython deprecation of --pylab=inline * :ghissue:`446`: Wmd * :ghissue:`444`: Scipy dependency * :ghissue:`442`: Wmd * :ghissue:`441`: fixed kernel wmd for updated wmd structure * :ghissue:`440`: ENH: sidebar for Releases and installation doc update * :ghissue:`439`: - events * :ghissue:`438`: ENH: pruning to respect flake8 * :ghissue:`436`: Scipy 0.14 induced breakage * :ghissue:`437`: BUG: fix for removal of scipy.stat._support #436 * :ghissue:`408`: Use of `platform.system()` to determine platform * :ghissue:`403`: Scipy dependency * :ghissue:`434`: W Object Metadata Attribute * :ghissue:`433`: Rank markov * :ghissue:`424`: testing * :ghissue:`432`: Implementation of rank Markov classes * :ghissue:`431`: FOSS4G * :ghissue:`430`: Network * :ghissue:`429`: moving analytics out of wed class and into their own module * :ghissue:`420`: Local Moran's I, I Attribute Undefined * :ghissue:`418`: Extended pysal.weights.user.build_lattice_shapefile * :ghissue:`428`: Network * :ghissue:`427`: devel docs * :ghissue:`426`: dev docs * :ghissue:`425`: Viz2contrib * :ghissue:`423`: Update news.rst * :ghissue:`422`: ENH: Update doc instructions for napoleon dependency * :ghissue:`421`: Adding files used in some examples as per Luc's request. * :ghissue:`419`: Doc fixes 1.7 * :ghissue:`393`: Doc fixes 1.7 * :ghissue:`416`: Add hexagonal lattice option for lat2W * :ghissue:`417`: ENH hex lattice W for #416 * :ghissue:`409`: add wiki page on viz module design * :ghissue:`413`: Temporary fix for https://github.com/pysal/pysal/issues/412 * :ghissue:`415`: Temporarily commenting out tests that are blocking Travis. * :ghissue:`407`: Viz: Moving into contrib/viz in master * :ghissue:`406`: Viz: pruning old code and adding more examples for TAZ paper * :ghissue:`380`: Pep 8 and Line Length * :ghissue:`404`: version change * :ghissue:`401`: fixes #388 * :ghissue:`388`: update testing procedures docs * :ghissue:`402`: release changes # v<1.7.0>, 2014-01-29 36d268f Philip Stephens -Merge pull request #400 from sjsrey/mldoc c2c4741 Serge Rey -Formatting ml docs 685f5e3 Sergio Rey -Merge pull request #399 from sjsrey/master 481ccb4 Serge Rey -correct thanks 4a5cce3 Sergio Rey -Update index.txt 1fe7aeb Philip Stephens -Merge pull request #396 from sjsrey/mldoc e731278 Serge Rey -EHN: fixing link to bleeding edge docs. e4e9930 Serge Rey -ENH: adding ml docs to api 9b3c77e Serge Rey -Merge branch 'master' of github.com:pysal/pysal dda3c01 Philip Stephens -Merge pull request #389 from dfolch/master 74b26d5 Philip Stephens -Merge pull request #392 from pedrovma/spreg17 b47ba84 pedrovma -Bump. 3d8504c Sergio Rey -Merge pull request #386 from pastephens/master f9b59ea Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal 429e19e pedrovma -Upgrading to spreg 1.7. c698747 David Folch -removing legacy speedup hack that is no longer relevant 88177d0 Sergio Rey -Merge pull request #387 from sjsrey/scipy13 64a4089 Serge Rey -BUG: sorting ijs for asymmetries 5539ef5 Sergio Rey -Merge pull request #1 from sjsrey/scipy13 8a86951 Serge Rey -BUG: fixes for scipy .0.9.0 to 0.13.0 induced errors fe02796 Philip Stephens -tweaking travis to only run master commits 8c1fbe8 jlaura -Merge pull request #385 from sjsrey/docupdate b71aedc Serge Rey -ENH: update date 4f237e4 Sergio Rey -Merge pull request #384 from sjsrey/moran 01da3be Serge Rey -ENH: Analytical p-values for Moran are two-tailed by default #337 918fe60 Philip Stephens -further travis tweaks 3920d73 Sergio Rey -Merge pull request #382 from sjsrey/st_docs d90bc70 Serge Rey -DOC: updating refs for concordance algorithm 0db2790 Philip Stephens -tweaks to travis 063e057 Philip Stephens -upgrading scipy on travis f90e742 Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal edc9c07 Dani Arribas-Bel -Merge pull request #379 from sjsrey/b244 82479bb Serge Rey -BUG: fix for the comment https://github.com/pysal/pysal/issues/244#issuecomment-30055558 57ba485 jlaura -Update README.md 981ed31 Sergio Rey -Merge pull request #377 from darribas/master 3320c39 darribas -Changing cmap default in plot_choropleth so every type defaults to its own adecuate colormap e063bee darribas -Fixing ignorance of argument cmap in base_choropleth_unique 1f10906 Dani Arribas-Bel -Merge pull request #375 from sjsrey/viz 94aa3e7 Dani Arribas-Bel -Merge pull request #376 from pedrovma/baltim_data 7568b0b pedrovma -Adding Baltimore example dataset for use with LM models. 5b23f89 Serge Rey -greys for classless map d4eae1e Dani Arribas-Bel -Merge pull request #374 from sjsrey/viz 652440d Serge Rey -shrinking colorbar c17bf67 Sergio Rey -Merge pull request #373 from darribas/master a71c3cb darribas -Fixing minor conflict to merge darribas viz branch into darribas master ec27e30 Dani Arribas-Bel -Merge pull request #372 from sjsrey/viz 8c03170 Serge Rey -option for resolution of output figs 3fc5bd4 Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal 2b5cb23 jlaura -Merge pull request #371 from sjsrey/geopandas 469afa7 Serge Rey -fix for #370 59cdafc jlaura -Merge pull request #369 from pedrovma/south_data 6b88e13 jlaura -Merge pull request #368 from schmidtc/issue367 40fe928 pedrovma -Adding south data to be used in ML doctests. bcc257e schmidtc -fixes #367 87e057f jlaura -Merge pull request #366 from sjsrey/ml_lag a64eb27 Serge Rey -queen contiguity for nat.shp 77add5c Sergio Rey -Merge pull request #365 from sjsrey/news 82464ef Serge Rey -narsc workshop fd79424 Sergio Rey -Merge pull request #364 from sjsrey/news bc7f25a Serge Rey -Merge branch 'master' of https://github.com/sjsrey/pysal d669913 David Folch -Merge pull request #363 from sjsrey/maxp 22f9e36 Serge Rey -update example for bug fix #362 fac3b8a Serge Rey -- update tests for bug fix #362 44b4b06 Sergio Rey -Merge pull request #1 from sjsrey/maxp 1e6f1e5 Serge Rey -- fix for #362 68ab3e9 Sergio Rey -Merge pull request #361 from sjsrey/components aa27c7e Serge Rey -doc test fix 7c08208 Serge Rey -putting Graph class back in for component checking 003b519 Serge Rey -alternative efficient component checker 2080e62 Serge Rey -- fixing doc 4fda442 Serge Rey -Merge branch 'components' of github.com:sjsrey/pysal into components e9e613b Serge Rey -reverting back to old component check 83d855e Serge Rey -updating example 9defd86 jlaura -Merge pull request #360 from sjsrey/components 6f92335 Serge Rey -more efficient connectivity test ebde3d1 Dani Arribas-Bel -Adding try/except for ogr since it's only used to reprojection methods but not on the plotting toolkit 5b170eb Sergio Rey -Merge pull request #356 from sjsrey/classification c9dac41 Serge Rey -- update unit tests for reshaping jenks caspal d9b06e2 Sergio Rey -Merge pull request #355 from sjsrey/cleanup/moran dc589e8 darribas -Adding caution note when plotting points to the notebook. Ideally, we wanna be able to build a PathCollection out of the XYs, but for now we rely on plt.scatter, which gets the job done but has some problems. 2224b95 darribas -Including support for points in base_choropleth_unique and base_choropleth_classless ac2d08a darribas -Modifying example to show how to do choropleth mapping on points 270786e darribas -Adding support for choropleth plotting on point map objects (this may come from map_point_shp or from a simple matplotlib scatter e56697c Sergio Rey -Merge pull request #357 from jlaura/newstyle_wed 4c67c2f Jay -errors in segmentation fixed 512cc76 Serge Rey -have Jenks-Caspal bins be a one dimensional array - to be consistent with all other classifiers 5254859 Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal 788ecab Serge -pruning 5b6b7b6 Serge -pruning eb7e9a1 Jay -bug fix and all pointers filled for external edges e47aa7a Jay -Node insertion, precursor to segmentation. 18a44d1 darribas -*Replacing shp by map_obj in medium layer functionality. *Bringing everything else in line with it *Adding example for line colorig and mixing overlaying of points. bd041b1 darribas -Replacing shp_link by shp as input for medium and low-level layers. This brings much more flexibility and opens the door to plot formats other than shapefiles (e.g. geojson) c74a361 darribas -Adding IP notebook to exemplify and keep track of development of mapping module d23c882 darribas -Minor fixes 4b82a76 darribas -New commit message* Replacing map_poly_shp_lonlat for map_poly_shp in base_choropleth_classif/unique/classless * removed 'projection' from base_choropleth_classif/unique/classless * Allow base_choropleth_classif/unique/classless to plot multi-part polygons properly * changes streamlined to generic plot_choropleth * Added dependency on pandas for rapid reindexing (this is done externally on the method _expand_values to it is easy to drop the dependency when neccesary/time available) 7a0eaec darribas -Merge branch 'viz' of github.com:darribas/pysal into viz 5536424 darribas -Merge branch 'master' of github.com:darribas/pysal e54ce16 Sergio Rey -Merge pull request #353 from darribas/master 819ee60 darribas -Adding immediate todo on head of the file 946772d darribas -Passing k to base_choropleth_classif from plot_choropleth. This should fix Issue #352 f299b45 darribas -Merge branch 'master' of https://github.com/pysal/pysal f044f43 Jay -Added W generation 5f48446 jlaura -Merge pull request #348 from sjsrey/master 938a1ae Serge Rey -- adding nn stats to point based methods a86a051 Philip Stephens -removing dependency tracking service, it was ruby only 1e24fde Philip Stephens -testing dependency tracking service 3aa410c Philip Stephens -Merge pull request #347 from pedrovma/w_silence_island 03990f6 pedrovma -Extending PR #310 (silence island warnings) to include w.transform. 160001a Sergio Rey -Merge pull request #346 from jlaura/newstyle_wed 44989f9 Sergio Rey -Merge pull request #345 from sjsrey/master 2fd99b8 Sergio Rey -Update README.md bdcc6a8 Jay -NCSR with uniform distribution 769aa03 Jay -Fixed snapping 2561071 Jay -saved notebook and updated readme 3784783 Jay -ReadMe for Changes 019e16b Sergio Rey -Merge pull request #334 from jseabold/fix-build-example-dirs 1889885 Skipper Seabold -BLD: Correctly install package_data dirs. ff4e355 Serge Rey -- assignments c5b0cc0 Serge Rey -- reorg a4f5642 Serge Rey -Merge branch 'network' of github.com:pysal/pysal into network a95fec8 jlaura -Update README.md 1713145 Serge Rey -Merge branch 'master' of github.com:pysal/pysal into network ede75c0 Sergio Rey -Merge pull request #329 from jlaura/wed_polar 7399cf2 Jay -Single-source shortest path notebook 9eb3fc1 Philip Stephens -Merge pull request #331 from sjsrey/docfix ef9c82a Serge Rey -- sphinx doctest markup fix 1e2b6b3 jlaura -Update README.md e19bffa jlaura -Merge pull request #330 from pysal/b328 6afc30b Serge Rey -- tutorial doc fixes for #328 c7239f1 Serge Rey -- b328 fix d5fec13 Serge Rey -- fix for #328 making all p-values one-tailed 16b5e6e Jay -enumeration working with filaments 9507bbc jlaura -Update README.md eef8eec Serge Rey -- stub for design of module 2707d60 Jay -Filaments in polar coordinates b64f9e2 Serge Rey -Documentation for the development of network module b90876e Serge Rey -Merge branch 'network' of github.com:pysal/pysal into network ddad2a5 Philip Stephens -Merge pull request #326 from sjsrey/doc 6b0cd08 Serge Rey -- update release schedule 4cc7bca Jay -bisecting for single point working 79c77d9 jlaura -Merge pull request #324 from pysal/bf_id 9f4c7c9 Serge Rey -id is a keyword 72b1f85 Sergio Rey -Merge pull request #323 from jlaura/network b5cdae0 Jay -fix to shp2graph 846dce2 Jay -Brute force for point outside network d6c2ef4 Jay -Added length computation, alter global morans b7e1465 Jay -Added new pointer to reader/writer 616d62d Jay -LISA and Global Morans on the network 16f84d6 Jay -Added explicit point external to network warning 34f4d8e Jay -update to the ipython notebook e359e59 Jay -JSON and cPickle Bianry WED Reader/Writer 5373c82 Sergio Rey -Merge pull request #322 from jlaura/network 059d99c Jay -wed into class, tests added aa5969d Sergio Rey -Merge pull request #320 from pastephens/master a18000b Philip Stephens -version added info 5b8d490 Philip Stephens -typo d31a22a Philip Stephens -stubs for cg docs 4dbdfe3 schmidtc -fixes #318 35a0317 Jay -Merge branch 'master' of https://github.com/pysal/pysal into network 77e8387 Jay -Merge branch 'geojson' of https://github.com/pysal/pysal into network ad670c5 Sergio Rey -Merge pull request #317 from pastephens/master 628f27e Philip Stephens -merging local changes f9dcb3e Philip Stephens -simplified install instructions f2fab4c Serge Rey -- notebook on w construction for geojson 830826b Serge Rey -prototyping W from geojson b10240d Serge Rey -created with "ogr2ogr -lco WRITE_BBOX=YES -f "GeoJSON" columbus.json columbus.shp" d546926 Philip Stephens -merging with pull d711011 darribas -Merge branch 'rod' 8bef782 darribas -Merge branch 'rod' of https://github.com/pysal/pysal into rod 03c1003 pedrovma -Merge pull request #315 from sjsrey/rod 950fe8b Serge Rey -Replacing ROD with regular dictionary b1f009f Philip Stephens -Changes to release docs. 028364a Sergio Rey -Update THANKS.txt 94f5916 Sergio Rey -Update INSTALL.txt # v<1.6.0>, 2013-07-31 5fa9d09 darribas -silent_island_warning implemented for w_union 6526c62 Sergio Rey -Update README.md ea826c1 darribas -silent_island_warning implemented for w_intersection 335540a darribas -silent_island_warning implemented for w_difference 0a156cb darribas -silent_island_warning implemented for w_symmetric_difference. Previous commit included support of silent_island_warning for WSP2W as well 34d20d7 darribas -silent_island_warning implemented for w_clip 499815d pedrovma -Test fixing... 8778f75 pedrovma -Test fixing... a799a13 pedrovma -Test fixing... 6482d81 pedrovma -Test fixing... 2752b1b pedrovma -Test fixing... 0c0a5bf pedrovma -Test fixing... bbf9dcb pedrovma -Test fixing... 05c34ff pedrovma -Test fixing... 8a3986a Serge Rey -- preparing for release, version updates 9106cfe pedrovma -Matching travis results reg. precision issues. 3cd0ce1 Serge Rey -- updating changelog 74dadd6 pedrovma -Bump. c7774fb Serge Rey -- update THANKS.txt - testing travis for timing out cd98057 Serge Rey -- travis fix for multiprocessing permission error 86702f8 Serge Rey -- start of changelog for 1.6 3ee686d pedrovma -Reloading to check new results from Travis. 2de1d21 Serge Rey -- docs ef72edc Serge Rey -- update docs 0716581 Serge Rey -- deal with multiprocessing on travis b508c88 Serge Rey -- excluding network from 1.6 release ff13e31 pedrovma -Matching Travis results. Multiprocessing errors still an issue. 5b916ba pedrovma -Adding Chow test on lambda and updating dynamics of regime_err_sep and regime_lag_sep in combom models. b6e687f darribas -Patch to include switch for island warning as proposed in #295. The method is modified as well to include the switch 7ea5f35 pedrovma -Fixing defaults 62ca76b pedrovma -Updating documentation and checking if there are more than 2 regimes when regimes methods are used. 3212249 pedrovma -Fixing documentation on 'name_regimes' a782d50 pedrovma -Updating tests for integration with pysal 1.6 14f9181 pedrovma -Merging spreg_1.6 with my pysal fork. 817f2c2 Serge Rey -- having build_lattice_shapefile also create the associated dbf file - useful for testing our contiguity builders against geoda since dbf is required by the latter 41d59a4 Serge Rey -- adding diagonal option to kernel weights in user.py 506d808 Serge Rey -update when added b2ec3d4 Serge Rey -- updating api docs 9d45496 Serge Rey -- example and doctests for spatial gini 95635bb Serge Rey -updating release docs bd2f924 darribas -Fixing doctest of towsp method by including isinstance(wsp, ps.weights.weights.WSP) 76183d7 darribas -Fixing doctest of towsp method by including type(wsp) 0c54181 darribas -Adding method in W that calls WSP class for convenience and elegance. Related to issue #226 f3b23e8 Philip Stephens -adding source build to travis-ci 60930e7 Philip Stephens -adding new url for downloads 9bf7f5b Philip Stephens -modified release docs. f98d4a9 Philip Stephens -interim ci aa19028 Philip Stephens -Adding docs about installing in develop mode. 674112f Philip Stephens -starting rewrite of install docs af0d9b3 Philip Stephens -working on doc tickets 200e77e Serge Rey -handle ties in knnW in doctest d0d2dd2 Serge Rey -resetting README for pysal/pysal 6afb6ac Serge Rey -- updating docs for new api in interation.py 4c5572f Serge Rey -- updating tests for new api fabd16a Serge Rey -- refactored signatures to use numpy arrays rather than event class 6367947 Serge Rey -- refactor knox for large samples 5fad3b2 Serge Rey -- updating travis test 06894d8 Serge Rey -- updated README 8b06e63 Serge Rey -- so only i get email when i commit locally efbb7ff Serge Rey -- removing google pysal-dev circle 9859bda Serge Rey -- turning off gmail circle 51f6d3e Serge Rey -- fixing 46b1084 Serge Rey --docos 4e2c27a Philip Stephens -missing if statement added d1a83fd Serge Rey -- fixing docs 8275d76 Serge Rey -- fix precision 87ea5cc Philip Stephens -adding to authors and quick test fix for linux 1cfb67f Serge Rey -cant easily remove idVariable, reverting 5933d1e Serge Rey -removing idvariable from Distance - causes too many issues 05f2573 Philip Stephens -removing coverage tests fcb8c6f Philip Stephens -Knox using KDTree. 2237173 Serge Rey -with tests against previous implementation removed 233e59a Serge Rey -speed comparison for change to query_pairs in kdtree fb78ea9 Serge Rey -removing test file 4d04575 Philip Stephens -testing 357a184 Serge Rey -second great idea 1fafc2b Serge Rey -on a plane commit 1 fef6eae Philip Stephens -fix 86c17ac Serge Rey -- test file a619f62 Philip Stephens -interim ci 1a9d881 Serge Rey -- knox test using kdtrees 7459c44 Serge Rey -Fixing reference to missing shapefile Fixing one rounding error induced test 5616b12 Serge Rey -refactored to avoid second loop in explicit queen or rook check d3d2f71 Philip Stephens -Revert "Changed doctest path calls to account for modified shapefile." da1d8a1 Philip Stephens -Changed doctest path calls to account for modified shapefile. f591c99 Philip Stephens -progress on permutations of knox for larger datasets 8d31cde Serge Rey -Testing integration of spatialnet creation and reading into wed 11de6f3 Jay -Fixed wed_modular.py 077658a Serge Rey -adding new test case for wed extraction from a spatialnet shapefile bbb10b4 Philip Stephens -saving state of development 44076b7 Serge Rey -- update doc test 6fdd94d Serge Rey -- moved regions_from_graph into wed_modular - documented all functions and cleaned up 5bd27c3 Serge Rey -- wrapping in functions 3ad162f Serge Rey -- working version of wed_modular module - starting point for clean up 2380f15 Philip Stephens -Copy of sphinx install docs. Closes #251 5687700 Philip Stephens -tweaks to install instructions 9ffd432 Serge Rey -- updating for switch from svn to git fdaf521 Philip Stephens -Fixing 250 5ba4fdf Serge Rey -Fixes #249 Closes #249 d89944d Pedro -Adding docs for each regimes estimator f03bb63 Serge Rey -- updating docs for spatial regimes in spreg a49d0f7 Philip Stephens -Adding info to setup script. 1f27605 Philip Stephens -mainly docs 04f8a31 Philip Stephens -Adding test coverage with nose, data collected and presented on coveralls.io 6db978b Philip Stephens -last changes 137e088 Philip Stephens -added bigdata parameter 7ca81c2 Philip Stephens -got Knox stat working in alt form 24c1fcc Philip Stephens -workign on refactoring the space-time matrices for the Knox test [ci-skip] 28013f0 Serge Rey -- enumeration of cw edges for faces baa8f60 Serge Rey -- hole is now included and enumeration of links (cw) around nodes works for all nodes. - isolated nodes also handled in enumeration of links around nodes. 33741c8 Serge Rey -- filaments inserted and pointers updated - have to add hole polygon and isolated nodes, but almost there!!!!!!!!! 416d3db Serge Rey -- pointers updated for edges of connected components c34e274 Serge Rey -- convex/between edge test as start of testing for insertion of multiple internal filaments in one region. 78d96b1 Serge Rey -- filament insertion and pointer updates ced2c5b Serge Rey -- filament insertion (inc) ba4263f Jay -Logic roughed in for filaments [ci skip] cf3b0bc Jay -updated wed ipynb [ci skip] 33ce81e Serge Rey -- refactoring of wed construction (incomplete) 0fc16fc Jay -modular WED Pulled Apart 2 funcs in 1 cell bf73b90 Jay -modular WED 3163377 Serge Rey -- new modular wed construction e50b31d Jay -added test_wed additions to test_wed2 1cbc941 Serge Rey -- isolated nodes handled d28b97f Serge Rey -- isolated filament handled 6188fd5 Serge Rey -- hole component handled a96040b Serge Rey -- getting connected components (current 14,15,16 and 25,26,27 are not included) 3aa31a5 Jay -Added boolean arg to include or exclude holes [ci skip] d07876d Jay -Filament identification [ci skip] 0139ea5 Philip Stephens -Slight speed improvement getting rid of append calls in reading shapefile and building x,y lists. 43010b5 Serge Rey -- fixed logic problem with enum for v1, starting on components 8737918 Pedro -Adding more meaningful error message to inverse distance weights 01f52f6 Serge Rey -- replacing code that got deleted previously 7c4c6e1 Philip Stephens -Replacing deleted files. a8da725 Philip Stephens -added date support to spacetimeevents class, a date column to example dbf. 90c4730 Philip Stephens -logic works, numeric test still failing b8e43e1 Philip Stephens -saving progress on interaction 81f2408 Serge Rey -- handling external end-node-filament 7de6253 Serge Rey -- adding end node filament handling - edge enumeration around node working f542b9a Serge -- adding end node filament handling - edge enumeration around node working d7e3a57 Philip Stephens -[ci skip] disabling nose-progressive so travis output looks best fe03013 Dani Arribas-Bel -Adding set of diversity indices to inequality module under _indices.py for now. Still lacks doctests, unittests, and a few others will be added 951b6f5 Dani Arribas-Bel -Adding try/except to the import of Basemap to allow the use of the module when there is no Basemap installation 89003eb Serge Rey -- adding wed for eberly example 665ef22 Serge Rey -- fixed 7,2 failure 71fc9ad Serge Rey -start of adding gini and other inequality measures f7b7bcc Phil Stephens -Adding nose-progressive plugin to test suite. Devs can run test suite with 'make test'. f5db7bf Serge Rey -- updating copyright 07574b5 Serge Rey -- docs 478d2cb Philip Stephens -Adding requirement. Removing redundancy. 916a6ca Serge Rey -- more island check updates edd9960 Serge Rey -- more island check doctest changes ad1a91c Serge Rey -- updating doctests for island check ce77772 Serge Rey -- fixing doctests to incorporate new island warning 554a30b Serge Rey -- silencing floating point warning 4f76862 Serge Rey -- moving default contiguity builder back to binning from rtree b99665b Jay -Eberly d911344 Jay -mp removed, passing nosetests on my machine serial f005675 Serge Rey -improved binning algorithm for contiguity builder 4a69557 Serge Rey -- double checking threshold in Distance Band - new example to show functionality 7256f13 Serge Rey -- fix handling of idVariable for knnW 31bb36e Jay -bug fixes [ci skip] a2d2dd4 Jay -WEberly - WED Building [ci skip] 3abc55e Serge Rey -- fixing doctests for new check/reporting for islands 756ac05 Serge Rey -- adding warning if islands exist upon W instantiation db097a6 Jay -Weberly, bug fix, c and cc link remaining d5cc6f9 Jay -All but start / end working 033963d Jay -Integration to WEberly error fixed [ci skip] 22b931a Serge Rey -- removing main for doc tests which can be run from nosetests. - updating testing docs bf753e9 Jay -Integration to WEberly started [ci skip] 6506e07 Serge Rey -- typo aede375 Serge Rey -- replacing double quotes around multi word ids with strings joined with underscores cf029e8 Serge Rey -- changes to wrap string ids in gwt writer - see https://github.com/pysal/pysal/issues/244#issuecomment-16707353 626ac08 Serge Rey -- adding shapefile and variable name to gwt objects created in user space 3c84bb0 Jay -Working version 4.19 [ci skip] 7d77da9 darribas -Include warning in sp_att when rho is outside (-1, 1), ammends #243 although the true problem (pearsonr in diagnostics_tsls) will still raise an error 3719d21 Jay -working WED [ci skip] b4ce294 Serge Rey -checking edges f4bb412 Jay -excessive print statements removed. ci skip 9f7dee6 Jay -SUCCESS! ci skip 9077615 Phil Stephens -Note, [ci skip] anywhere in your commit message causes Travis to NOT build a test run. cb072c4 Jay -getting there d3b36bc Serge Rey -correcting typo user told me about 19ea051 Jay -trivial working b9ea577 Jay -eberly cycles - edge issue still d5153e3 Serge Rey -more refinement of wed from plannar graph edff44b Philip Stephens -adding git ignore file 8093f21 Serge Rey -wed from minimum cycle basis b5bcead Serge Rey -handle filaments 9a8927a Serge Rey -face extraction using horton algorithm 10d66c1 Serge Rey -updating readme formatting 59f3750 schmidtc -adding Universal newline support to csvReader, fixes #235 09e813f Serge Rey -- updating notifications f8b0a26 Serge Rey -- fixing Distance.py and testing travis message d1ec0f2 Phil Stephens -quieting pip output and fix one doctest 927e799 Phil Stephens -adding networkx, tweaks to travis config 5971bb1 Serge Rey -neighbors from wed 28f0e55 Serge Rey -adding robust segment intersection tests 3bcac73 Serge Rey -adding doubly connected edge list to network module 86f0fea darribas -Adding methods to read line and point shapefiles and improving the method to append different collections to one axes. Still in progress b61cb55 Serge Rey -- fixing introduced bug in knnW_arc 801e78d Serge Rey -Handle point sets with large percentage of duplicate points dbafbc4 serge -update pointer to github 427a620 Serge Rey -dealing with filaments 23216ef Serge Rey -Fixed cw enumeration of links incident to a node 0a51a53 Serge Rey -- readme 5f4cab4 sjsrey -cw enumeration not working for all nodes f2e65d3 Serge Rey -- cw traversal of edges incident with a node 90d150c sjsrey -- version debug for travis 24598a8 sjsrey -- noting move to org 9fb8a17 sjsrey -- fixing tutorial tests 5a14f9e serge -- cleaning up weights tests 6265b3b Serge Rey -- fixing doc tests 7e8c4fe Serge Rey -- testing after move to org 37fc8d4 Serge Rey -- testing post commit emails bed7f6e Phil Stephens -removed files eab2895 Phil Stephens -removed virginia_queen files bcef010 Serge Rey -- adding diagonal argument to Kernel weights - adding doctest evaluation to Distance.py 02d27e9 Phil Stephens -adding libgeos-dev 1126d71 Phil Stephens -pipe build output to null 37dbb35 Phil Stephens -adding -y flag to pip uninstall 06d56e9 Phil Stephens -adding libgeos_c install, pysal from pip 4c53277 Phil Stephens -trying to quiet output, using Makefile 74448e8 Phil Stephens -find setup.py 4634fb1 Phil Stephens -test install in venv and build 5d58723 Phil Stephens -working out travis-ci doctest configuration 5e905d3 Phil Stephens -adding numpydoc 33a5298 Phil Stephens -tweaks travis config 5c85f50 Phil Stephens -tweaking service configs 4ed1201 Josh Kalderimis -use the correct syntax for sysytem_site_packages 954b6d2 Phil Stephens -stop! 311eca8 Phil Stephens -ssp=true c601bca Phil Stephens -numpy first 54b0afe Phil Stephens -ok, so travis is serious about not using system site packages. 2b912cc Phil Stephens -doh 28994df Phil Stephens -better yaml ce1d89e Phil Stephens -testing b535d3e Phil Stephens -testing 440a772 Phil Stephens -tweaking pip requirements file 34a74e2 Phil Stephens -tweaking travis file 33b13aa Serge Rey -- new links 8e09d7b Serge Rey -- setting up travis d33001e Sergio Rey -Update CHANGELOG.txt 9d4de66 Serge Rey -- added authors ab672c9 Serge Rey -- modified knnW to speed up dict construction 4edd2ab Serge Rey -- update cr 39e6564 Phil Stephens -syncing install instructions with docs 9e98db9 Phil Stephens -adding website favicon; chrome does not empty cache properly!! * migration to github from svn svn2git http://pysal.googlecode.com/svn --authors ~/Dropbox/pysal/src/pysal/authors.txt --verbose # v<1.5.0>, 2013-01-31 2013-01-29 20:36 phil.stphns * doc/source/users/installation.txt: updating and simplifying user install instructions. 2013-01-18 16:17 sjsrey * Adding regime classes for all GM methods and OLS available in pysal.spreg, i.e. OLS, TSLS, spatial lag models, spatial error models and SARAR models. All tests and heteroskedasticity corrections/estimators currently available in pysal.spreg apply to regime models (e.g. White, HAC and KP-HET). With the regimes, it is possible to estimate models that have: -- Common or regime-specific error variance; -- Common or regime-specific coefficients for all variables or for a selection of variables; -- Common or regime-specific constant term; - Various refactoring to streamline code base and improve long term maintainability - Contributions from Luc Anselin, Pedro Amaral, Daniel Arribas-Bel and David Folch 2013-01-18 14:08 schmidtc * pysal/common.py: implemented deepcopy for ROD, see #237 2013-01-08 12:28 dreamessence * pysal/contrib/spatialnet/__init__.py: Adding __init__.py to make it importable 2012-12-31 22:53 schmidtc * pysal/core/IOHandlers/gwt.py: adding kwt support, see #232 2012-12-21 20:53 sjsrey@gmail.com * pysal/__init__.py, pysal/cg/rtree.py, pysal/contrib/weights_viewer/weights_viewer.py, pysal/weights/weights.py: - turning off randomization in rtree 2012-12-06 16:34 dfolch * pysal/contrib/shapely_ext.py: adding unary_union() to shapely contrib; note this only works with shapely version 1.2.16 or higher 2012-11-29 13:39 dreamessence * pysal/contrib/viz/mapping.py: Added option in setup_ax to pass pre-existing axes object to append. It is optional and it enables, for instance, to embed several different maps in one single figure 2012-11-20 00:23 dfolch * pysal/contrib/shapely_ext.py: adding shapely's cascaded_union function to contrib 2012-11-12 18:08 dreamessence * pysal/contrib/viz/mapping.py: -Adding transCRS method to convert points from one prj to another arbitrary one -Adding map_poly_shp to be able to plot shapefiles in arbitrary projections, not needing to be in lonlat and not depending on Basemap 2012-11-09 15:40 sjsrey@gmail.com * pysal/weights/weights.py: - distinguish between intrinsic symmetry and general symmetry 2012-11-02 17:48 schmidtc * pysal/weights/user.py, pysal/weights/util.py: Adding Minkowski p-norm to min_threshold_dist_from_shapefile, see issue #221 2012-10-19 22:35 sjsrey@gmail.com * pysal/weights/weights.py: explicitly prohibit chaining of transformations - all transformations are only applied to the original weights at instantiation 2012-10-19 17:38 sjsrey@gmail.com * pysal/spatial_dynamics/markov.py: - fixing bug in permutation matrix to reorder kronecker product in the join test 2012-10-17 17:55 sjsrey@gmail.com * pysal/weights/util.py: - higher order contiguity for WSP objects 2012-10-17 15:43 sjsrey@gmail.com * pysal/weights/user.py: - id_order attribute was always NONE for wsp created from queen/rook_from_shapefile with sparse=True 2012-10-16 19:25 schmidtc * pysal/weights/util.py: improving memory usage of get_points_array_from_shapefile, no need to read entire shapefile into memory. 2012-10-15 00:44 dreamessence * pysal/contrib/viz/mapping.py: First attempt to refactor Serge's code for choropleth mapping. It now offers a more general and flexible architecture. Still lots of work and extensions left. The module is explained in a notebook available as a gist at https://gist.github.com/3890284 and viewable at http://nbviewer.ipython.org/3890284/ 2012-10-12 18:34 schmidtc * pysal/contrib/spatialnet/spatialnet.py: modified SpatialNetwork.snap to calculate and return the snapped point 2012-10-12 17:05 dfolch * pysal/contrib/viz/mapping.py: made edits to unique_values_map to allow for unlimited number of categories; I commented out the previous code so these changes can easily be rolled back if it breaks something somewhere else 2012-10-12 15:03 schmidtc * pysal/cg/segmentLocator.py: Fixing issue with segmentLocator, when query point is extreamly far from the grid boundary, overflow errors were causing the KDTree to not return any results. Changed both KDtree's to use Float64 and share the same data. Previously, cKDTree was using float64 and KDtree was using int32. 2012-10-11 08:12 dreamessence * pysal/contrib/viz/__init__.py: Adding __init__.py to viz module to make it importable 2012-08-31 02:57 phil.stphns * pysal/spreg/tests/test_diagnostics.py, pysal/spreg/tests/test_diagnostics_sp.py, pysal/spreg/tests/test_diagnostics_tsls.py, pysal/spreg/tests/test_error_sp.py, pysal/spreg/tests/test_error_sp_het.py, pysal/spreg/tests/test_error_sp_het_sparse.py, pysal/spreg/tests/test_error_sp_hom.py, pysal/spreg/tests/test_error_sp_hom_sparse.py, pysal/spreg/tests/test_error_sp_sparse.py, pysal/spreg/tests/test_ols.py, pysal/spreg/tests/test_ols_sparse.py, pysal/spreg/tests/test_probit.py, pysal/spreg/tests/test_twosls.py, pysal/spreg/tests/test_twosls_sp.py, pysal/spreg/tests/test_twosls_sp_sparse.py, pysal/spreg/tests/test_twosls_sparse.py: - autopep8 -iv spreg/tests/*.py - nosetests pysal - no fixes needed 2012-08-31 01:16 phil.stphns * pysal/spreg/diagnostics.py, pysal/spreg/diagnostics_sp.py, pysal/spreg/diagnostics_tsls.py, pysal/spreg/error_sp.py, pysal/spreg/error_sp_het.py, pysal/spreg/error_sp_hom.py, pysal/spreg/ols.py, pysal/spreg/probit.py, pysal/spreg/robust.py, pysal/spreg/summary_output.py, pysal/spreg/twosls.py, pysal/spreg/twosls_sp.py, pysal/spreg/user_output.py, pysal/spreg/utils.py: - autopep8 -iv spreg/*.py - fixed autopep8-introduced doctest failures - fixed lingering scientific notation test failures 2012-08-31 00:26 phil.stphns * pysal/esda/gamma.py, pysal/esda/join_counts.py, pysal/esda/mapclassify.py, pysal/esda/mixture_smoothing.py, pysal/esda/moran.py, pysal/esda/smoothing.py: - autopep8 fixes - make sure to run unit and doc tests before committing - one autofix breaks long lines, and thus breaks some doctests; must be fixed manually 2012-08-31 00:10 phil.stphns * pysal/esda/getisord.py: - using autopep8 module - call: autopep8 -vi getisord.py 2012-08-30 23:18 phil.stphns * pysal/esda/geary.py: - pep8 clear - removed wildcard import 2012-08-26 22:53 phil.stphns * pysal/spatial_dynamics/directional.py, pysal/spatial_dynamics/ergodic.py, pysal/spatial_dynamics/interaction.py, pysal/spatial_dynamics/markov.py, pysal/spatial_dynamics/rank.py, pysal/spatial_dynamics/util.py: -pep8 and pylint fixes -clean wildcard imports 2012-08-26 21:03 phil.stphns * pysal/region/maxp.py, pysal/region/randomregion.py: - cleaning up imports 2012-08-26 18:16 phil.stphns * pysal/region/maxp.py: - style fixes with pep8 - cmd line call: pep8 --show-source --ignore=E128,E302,E501,E502,W293,W291 region/maxp.py 2012-08-26 17:47 phil.stphns * pysal/common.py, pysal/examples/README.txt, pysal/region/components.py, pysal/region/randomregion.py: - using pep8 module 2012-08-24 20:47 schmidtc * pysal/network, pysal/network/__init__.py: adding network module 2012-08-21 22:53 phil.stphns * doc/source/_templates/ganalytics_layout.html: - updating analytics tracker 2012-08-17 17:11 sjsrey@gmail.com * pysal/contrib/spatialnet/util.py: - more utility functions for pysal - networkx interop 2012-08-16 23:44 phil.stphns * setup.py: - tweak for build names 2012-08-12 13:15 dreamessence * doc/source/index.txt: Adding announcement links to landing page 2012-08-11 17:38 sjsrey * LICENSE.txt: - update 2012-08-09 17:19 phil.stphns * doc/source/developers/pep/pep-0008.txt: updating spatial db pep 2012-08-08 17:22 schmidtc * pysal/weights/Distance.py: Fixing bug in Kernel weights that causes erroneous results when using ArcDistances. See issue #218. 2012-08-04 21:14 sjsrey * doc/source/developers/docs/index.txt: - fixed links 2012-08-04 21:03 sjsrey * doc/source/developers/docs/index.txt: - hints on editing docs 2012-08-04 20:14 phil.stphns * doc/source/developers/pep/pep-0011.txt: note about travis-ci and github 2012-08-04 16:24 sjsrey * doc/source/developers/pep/pep-0011.txt: PEP-0011 2012-08-04 16:22 sjsrey * doc/source/developers/pep/index.txt: - PEP 0011 Move from Google Code to Github 2012-08-04 04:42 sjsrey * doc/source/index.txt: - broken link 2012-08-04 04:35 sjsrey * doc/source/index.txt: - news updates 2012-08-04 04:24 sjsrey * doc/source/index.txt: - reorg 2012-08-02 02:32 sjsrey * pysal/examples/__init__.py: - moving back to r1049 but leaving r1310 in history for ideas on moving forward - we need to distinguish between using examples in the doctests (which the users see) and for the developers since we are no longer distributing examples with the source 2012-08-02 01:49 sjsrey * pysal/examples/__init__.py: - correct conditional this time (i hope) 2012-08-02 01:36 sjsrey * pysal/examples/__init__.py: - compromise - returns pth rather than None if file does not exist 2012-08-02 00:58 sjsrey * pysal/examples/__init__.py: - link to examples download 2012-08-02 00:42 sjsrey * pysal/examples/__init__.py: - explicit check if examples are actually present # v<1.4.0>, 2012-07-31 2013-01-31 2012-07-31 21:30 sjsrey@gmail.com * pysal/spatial_dynamics/ergodic.py, pysal/spatial_dynamics/rank.py: - docs/example 2012-07-31 20:47 sjsrey@gmail.com * pysal/spreg/tests/test_error_sp_hom.py: - rounding/precision issue 2012-07-31 20:27 sjsrey@gmail.com * pysal/spatial_dynamics/directional.py, pysal/spatial_dynamics/tests/test_directional.py: - fixing pvalue bug 2012-07-31 20:24 sjsrey@gmail.com * doc/source/users/tutorials/dynamics.txt: - fixed rounding problem 2012-07-31 19:58 sjsrey@gmail.com * doc/source/index.txt, doc/source/users/tutorials/autocorrelation.txt, doc/source/users/tutorials/dynamics.txt, doc/source/users/tutorials/econometrics.txt, doc/source/users/tutorials/fileio.txt, doc/source/users/tutorials/index.txt, doc/source/users/tutorials/intro.txt, doc/source/users/tutorials/region.txt, doc/source/users/tutorials/smoothing.txt, doc/source/users/tutorials/weights.txt: - adding links to API for more details 2012-07-31 19:05 sjsrey@gmail.com * pysal/spatial_dynamics/directional.py: - consistency on pvalues for randomization 2012-07-31 19:02 sjsrey@gmail.com * pysal/weights/Distance.py: - docs 2012-07-31 18:58 sjsrey@gmail.com * doc/source/users/tutorials/dynamics.txt: - seed issue 2012-07-31 18:36 sjsrey@gmail.com * doc/source/users/tutorials/autocorrelation.txt: - closing issue 214 2012-07-31 18:19 sjsrey@gmail.com * doc/source/users/tutorials/autocorrelation.txt: - fixing random.seed issues in doctests 2012-07-31 17:31 schmidtc * pysal/cg/shapes.py, pysal/cg/tests/test_shapes.py: Fixing small bugs with VerticleLines and testing 2012-07-31 16:26 sjsrey@gmail.com * doc/source/developers/guidelines.txt, doc/source/users/installation.txt: - updating docs 2012-07-26 15:24 schmidtc * pysal/core/FileIO.py, pysal/core/Tables.py: Fixing issue #190 2012-07-24 16:32 schmidtc * pysal/cg/sphere.py: Allowing linear2arcdist function to maintin 'inf', this allows compatability with Scipy's KDTree and addresses issue 208. 2012-07-24 16:07 schmidtc * pysal/cg/locators.py, pysal/core/FileIO.py, pysal/core/Tables.py: Addressing issue 212, renaming nested and private classes to begin with an underscore. By default sphinx does not try to document private object, which avoids what appears to be a a bug in Sphinx. 2012-07-17 22:06 sjsrey@gmail.com * pysal/spreg/probit.py: pedro doc fixes 2012-07-17 15:07 schmidtc * pysal/cg/segmentLocator.py, pysal/cg/tests/test_segmentLocator.py: Cleaned up fix for Issue 211 2012-07-13 22:50 sjsrey@gmail.com * doc/source/users/tutorials/autocorrelation.txt: fixing sphinx weirdness in footnotes 2012-07-13 22:37 sjsrey@gmail.com * doc/source/users/tutorials/autocorrelation.txt: update for new default parameter values 2012-07-13 22:13 sjsrey@gmail.com * pysal/esda/geary.py, pysal/esda/tests/test_geary.py: consistency on transformation and permutation args 2012-07-13 19:59 sjsrey@gmail.com * doc/source/users/tutorials/dynamics.txt, pysal/__init__.py, pysal/spatial_dynamics/rank.py: - update user tutorial and __init__ 2012-07-13 19:33 sjsrey@gmail.com * pysal/spatial_dynamics/rank.py, pysal/spatial_dynamics/tests/test_rank.py: - O(n log n) algorithm for spatial tau (old one was O(n^2)) - closing ticket http://code.google.com/p/pysal/issues/detail?id=83 2012-07-13 17:57 schmidtc * pysal/core/IOHandlers/pyDbfIO.py, pysal/core/IOHandlers/tests/test_pyDbfIO.py: Adding better support for writing Null values to DBF. See issue #193 2012-07-13 15:55 schmidtc * pysal/core/util/shapefile.py, pysal/core/util/tests/test_shapefile.py: Cleaning up support for ZM points, polylines and polygons in the shapefile reader. Added unit tests for same. 2012-07-13 15:42 sjsrey@gmail.com * doc/source/library/esda/gamma.txt: - update version info 2012-07-13 15:37 sjsrey@gmail.com * doc/source/library/esda/gamma.txt, doc/source/library/esda/index.txt: - adding gamma to api docs 2012-07-13 00:21 sjsrey@gmail.com * pysal/esda/gamma.py: optimizations 2012-07-12 21:28 schmidtc * pysal/core/IOHandlers/pyDbfIO.py: Disabling mising value warning for DBF files. See issue #185 2012-07-12 21:07 schmidtc * pysal/cg/segmentLocator.py, pysal/cg/shapes.py, pysal/cg/tests/test_segmentLocator.py, pysal/contrib/spatialnet/spatialnet.py: Adding unittests for segmentLocator (including one that fails see #211). Added VerticalLine class to represent verticle LineSegments. Updated __all__ in segmentLocator. Minor comment formatting in spatialnet. 2012-07-12 19:41 lanselin@gmail.com * doc/source/users/tutorials/autocorrelation.txt: tutorial for gamma index 2012-07-12 19:40 lanselin@gmail.com * pysal/esda/gamma.py, pysal/esda/tests/test_gamma.py: gamma with generic function 2012-07-12 14:17 sjsrey@gmail.com * pysal/__init__.py: - gamma index added 2012-07-12 03:14 lanselin@gmail.com * pysal/esda/tests/test_gamma.py: tests for gamma 2012-07-12 03:13 lanselin@gmail.com * pysal/esda/gamma.py: gamma index of spatial autocorrelation 2012-07-12 03:11 lanselin@gmail.com * pysal/esda/__init__.py: gamma index 2012-07-11 21:32 lanselin@gmail.com * pysal/esda/join_counts.py, pysal/esda/tests/test_join_counts.py: join counts without analytical results, new permutation 2012-07-11 21:32 lanselin@gmail.com * doc/source/users/tutorials/autocorrelation.txt: updated docs for join counts 2012-07-10 21:13 lanselin@gmail.com * doc/source/users/tutorials/autocorrelation.txt: docs for join count in autocorrelation 2012-07-10 21:12 lanselin@gmail.com * pysal/esda/join_counts.py, pysal/esda/tests/test_join_counts.py: additional test in join counts, docs added 2012-07-10 19:24 lanselin@gmail.com * pysal/esda/join_counts.py, pysal/esda/tests/test_join_counts.py: join counts with permutations for BB, updated tests to include permutations 2012-07-09 04:22 sjsrey * pysal/weights/weights.py: - fixing bug luc identified with regard to mean_neighbor property. wrong key name was used in cache dictionary. 2012-07-07 17:00 sjsrey * pysal/__init__.py: update for spreg and contrib inclusion 2012-07-07 16:51 sjsrey * pysal/spatial_dynamics/markov.py: - updating doc strings 2012-07-07 16:17 sjsrey * pysal/spreg/probit.py: - fixing doc string and refs 2012-07-06 21:58 dfolch * doc/source/library/spreg/probit.txt: txt file to include probit in the HTML docs 2012-07-06 21:11 dfolch * pysal/spreg/tests/test_ols_sparse.py: fixing unittest error; still no solution to scientific notation formatting in doctests 2012-07-06 20:24 dfolch * pysal/spreg/__init__.py, pysal/spreg/diagnostics.py, pysal/spreg/diagnostics_sp.py, pysal/spreg/diagnostics_tsls.py, pysal/spreg/error_sp.py, pysal/spreg/error_sp_het.py, pysal/spreg/error_sp_hom.py, pysal/spreg/ols.py, pysal/spreg/probit.py, pysal/spreg/robust.py, pysal/spreg/summary_output.py, pysal/spreg/tests/test_diagnostics.py, pysal/spreg/tests/test_diagnostics_sp.py, pysal/spreg/tests/test_diagnostics_tsls.py, pysal/spreg/tests/test_error_sp.py, pysal/spreg/tests/test_error_sp_het.py, pysal/spreg/tests/test_error_sp_het_sparse.py, pysal/spreg/tests/test_error_sp_hom.py, pysal/spreg/tests/test_error_sp_hom_sparse.py, pysal/spreg/tests/test_error_sp_sparse.py, pysal/spreg/tests/test_ols.py, pysal/spreg/tests/test_ols_sparse.py, pysal/spreg/tests/test_probit.py, pysal/spreg/tests/test_twosls.py, pysal/spreg/tests/test_twosls_sp.py, pysal/spreg/tests/test_twosls_sp_sparse.py, pysal/spreg/tests/test_twosls_sparse.py, pysal/spreg/twosls.py, pysal/spreg/twosls_sp.py, pysal/spreg/user_output.py, pysal/spreg/utils.py: -Adding classic probit regression class -Adding spatial diagnostics for probit -Allowing x parameter to be either a numpy array or scipy sparse matrix in all regression classes -Adding additional unit tests -Various refactoring to streamline code base and improve long term maintainability -Contributions from Luc Anselin, Pedro Amaral, Daniel Arribas-Bel, David Folch and Nicholas Malizia 2012-07-03 18:59 sjsrey * pysal/spatial_dynamics/markov.py, pysal/spatial_dynamics/tests/test_markov.py: - refactor significant move_types for clarity and fixing a logic bug 2012-06-20 04:50 sjsrey@gmail.com * doc/source/developers/docs/index.txt: - added section for how to write a tutorial for new modules 2012-06-20 02:45 sjsrey * doc/source/developers/docs/index.txt: - updating doc building instructions 2012-06-06 18:58 phil.stphns * .build-osx10.6-py26.sh, .build-osx10.6-py27.sh: - local modifications for Frameworks builds 2012-06-05 20:56 phil.stphns * .build-osx10.6-py26.sh, .build-osx10.6-py27.sh, .build-osx10.7-py27.sh, .runTests.sh: - adding experimental build and test scripts. 2012-06-05 16:43 schmidtc * pysal/cg/segmentLocator.py, pysal/cg/shapes.py, pysal/contrib/spatialnet/spatialnet.py: initial snap function for spatialnet 2012-06-05 16:38 schmidtc * pysal/core/IOHandlers/pyShpIO.py, pysal/core/util/shapefile.py, pysal/core/util/tests/test_shapefile.py: Adding PolygonZ support to Shapefile IO 2012-05-24 21:57 sjsrey * pysal/esda/mapclassify.py: - truncate option for fisher_jenks sampling 2012-05-15 20:08 schmidtc * pysal/cg/segmentLocator.py, pysal/cg/shapes.py: Added query to SegmentLocator 2012-05-11 22:17 sjsrey * pysal/esda/mapclassify.py: - added Fisher_Jenks_Sampled 2012-05-11 00:45 mhwang4 * pysal/contrib/network/distances.csv, pysal/contrib/network/simulator.py, pysal/contrib/network/test_lincs.py, pysal/contrib/network/test_weights.py, pysal/contrib/network/weights.py: adding test code for distance-file-based weight generator; updates on simulator 2012-05-10 22:37 mhwang4 * pysal/contrib/network/klincs.py, pysal/contrib/network/lincs.py, pysal/contrib/network/test_klincs.py, pysal/contrib/network/test_lincs.py: adding test code for network-constrained lisa 2012-05-10 21:11 mhwang4 * pysal/contrib/network/crimes.dbf, pysal/contrib/network/crimes.shp, pysal/contrib/network/crimes.shx, pysal/contrib/network/test_klincs.py: test code for local K function 2012-05-08 18:05 mhwang4 * pysal/contrib/network/streets.dbf, pysal/contrib/network/streets.shp, pysal/contrib/network/streets.shx, pysal/contrib/network/test_network.py: adding a test data set 2012-05-08 16:34 schmidtc * pysal/cg/segmentLocator.py, pysal/cg/shapes.py, pysal/core/FileIO.py: Adding start of segmentLocator, adding minimal slicing support to FileIO 2012-05-03 17:03 schmidtc * pysal/cg/shapes.py, pysal/cg/tests/test_shapes.py: Adding solve for x support to Line. Cleaning up LineSegment's Line method. 2012-04-20 17:48 schmidtc * pysal/cg/shapes.py: adding arclen method to Chain object. 2012-04-19 16:37 dfolch * pysal/weights/Distance.py: reducing number of distance queries in Kernel from n^2 to n 2012-04-17 21:20 schmidtc * pysal/contrib/spatialnet/spatialnet.py: adding distance 2012-04-17 19:46 schmidtc * pysal/contrib/spatialnet/cleanNetShp.py, pysal/contrib/spatialnet/spatialnet.py: Adding FNODE/TNODE to dbf when cleaning shapefiles. Added util function createSpatialNetworkShapefile Added SpatialNetwork class 2012-04-17 15:32 schmidtc * pysal/contrib/weights_viewer/weights_viewer.py: "revert back to the background when the point is outside of any unit" - request from serge 2012-04-11 02:50 schmidtc * pysal/cg/kdtree.py: Fixing user submitted bug,issue #206. 2012-04-10 22:00 dreamessence * pysal/weights/Wsets.py: Including w_clip in __all__ 2012-04-10 21:58 dreamessence * pysal/weights/Wsets.py: Adding w_clip method to clip W matrices (sparse and/or pysal.W) with a second (binary) matrix 2012-04-10 21:57 schmidtc * pysal/contrib/spatialnet/beth_roads.shp, pysal/contrib/spatialnet/beth_roads.shx, pysal/contrib/spatialnet/cleanNetShp.py: Adding network shapefile cleaning tools and temporary sample data. 2012-04-10 21:48 sjsrey * pysal/contrib/spatialnet/util.py: - more stubs for util mod 2012-04-10 19:58 sjsrey * pysal/contrib/spatialnet/util.py: - start of util module 2012-04-03 20:43 sjsrey * pysal/contrib/spatialnet: - new contrib module - integrate geodanet functional (move over from network) - wrap networkx 2012-04-03 01:21 schmidtc * pysal/cg/rtree.py: Adding pickle support to RTree 2012-03-28 23:27 mhwang4 * pysal/contrib/network/kernel.py, pysal/contrib/network/kfuncs.py, pysal/contrib/network/test_access.py, pysal/contrib/network/test_kernel.py, pysal/contrib/network/test_kfuncs.py, pysal/contrib/network/test_network.py: adding examples for network-related modules 2012-03-19 15:33 schmidtc * pysal/core/IOHandlers/pyDbfIO.py: Adding support for writing Null dates 2012-03-14 21:04 phil.stphns * doc/source/developers/testing.txt, doc/source/users/installation.txt: Small changes to user install instructions to highlight the ease with which pysal can be installed ;-> And, developer instructions for running the test suite from within a session if desired. 2012-03-03 00:00 phil.stphns * pysal/spatial_dynamics/markov.py: Potential source of dev docs pngmath latex fail. 2012-02-24 23:29 mhwang4 * pysal/contrib/network/network.py: fixing bug in network.py 2012-02-20 19:50 phil.stphns * doc/source/developers/py3k.txt: Developer doc to explain setting up PySAL for Python3. 2012-02-20 16:18 schmidtc * pysal/esda/__init__.py: removing invalid __all__ from esda's init. See #194 2012-02-16 23:15 phil.stphns * pysal/__init__.py, pysal/core/util/shapefile.py: Minor changes to imports that cause py3tool to stumble. 2012-02-15 23:16 phil.stphns * doc/source/developers/py3k.txt, doc/source/users/installation.txt: Modified links in user installation instructions. Added more steps for developers setting up Python3 dev environments on OSX. 2012-02-14 21:55 schmidtc * pysal/esda/getisord.py: fixing side effect caused when changing the shape of y, creating a new view with reshape instead. 2012-02-14 21:21 schmidtc * pysal/esda/getisord.py: optimizing G_Local 2012-02-14 20:37 schmidtc * pysal/esda/getisord.py: optimizing G 2012-02-14 00:21 phil.stphns * doc/source/developers/index.txt, doc/source/developers/py3k.txt, doc/source/developers/release.txt: Adding early docs on Python 3 support. Modifying release instructions. # v<1.3.0>, 2012-01-31 * core/IOHandlers/pyDbfIO.py: Addressing issue #186 * cg/shapes.py: fixing small bug in polygon constructor that causes an exception when an empty list is passed in for the holes. * cg/standalone.py: removing standalone centroid method. see issue #138. * esda/mapclassify.py, esda/tests/test_mapclassify.py: - new implementation of fisher jenks * spreg/__init__.py, spreg/diagnostics_sp.py, spreg/diagnostics_tsls.py, spreg/error_sp.py, spreg/error_sp_het.py, spreg/error_sp_hom.py, spreg/ols.py, spreg/robust.py, spreg/tests, spreg/twosls.py, spreg/twosls_sp.py, spreg/user_output.py, spreg/utils.py: Adding the following non-spatial/spatial regression modules: * Two Stage Least Squares * Spatial Two Stage Least Squares * GM Error (KP 98-99) * GM Error Homoskedasticity (Drukker et. al, 2010) * GM Error Heteroskedasticity (Arraiz et. al, 2010) * Anselin-Kelejian test for residual spatial autocorrelation of residuals from IV regression Adding also utility functions and other helper classes. * cg/standalone.py: slight improvment to get_shared_segments, in part to make it more readable. * cg/shapes.py, cg/tests/test_standalone.py: adding <,<=,>,>= tests to Point, this fixes a bug in the get_shared_segments function that was causing some LineSegments to be incorectly ordered because the default memory address was being used instead of the points location. * core/IOHandlers/tests/test_wkt.py, core/IOHandlers/wkt.py, core/util/tests/test_wkt.py, core/util/wkt.py, weights/tests/test_Distance.py, weights/tests/test_user.py, weights/user.py: Fixing small numerical errors n testing that resulted from changing the centroid algorithm. * esda/moran.py: another optimization for __crand see issue #188 * weights/util.py: Added option for row-standardized SW in lat2SW. Implementing suggestion from Charlie in Issue 181 from StackOverflow * esda/moran.py: another optimization to __crand, see issue #188 for details. * esda/moran.py: Optimized __crand in Local_Moran * cg/shapes.py, cg/standalone.py, contrib/shapely_ext.py: Adddressing issue #138, centroids for polygons with holes Fixing some issues with the shapely wrapper and out implemenation of __geo_interface__ * weights/Distance.py: previous 'fix' to uniform kernel did not have correct dimensions * core/IOHandlers/arcgis_txt.py, core/IOHandlers/dat.py, weights/user.py: fixing rounding errors with docstrings * contrib/README, contrib/shared_perimeter_weights.py: Adding shared perimeter weights, see Issue #46 * contrib/README, contrib/shapely_ext.py: moving shapely_ext into contrib * core/IOHandlers/pyDbfIO.py: Fixing issue with scientific notation is DBF files. #182 * core/IOHandlers/pyShpIO.py: clockwise testing should only be performed on Polygons. #183 * spreg/diagnostics_sp.py: Switching ints to floats in variance of Morans I for residuals to get correct results * core/util/shapefile.py, examples/__init__.py: Add a "get_path" function to examples module. pysal.examples.get_path('stl_hom.shp') will always return the correct system path to stl_hom.shp, no matter where it's run from. This is useful for testing. Modified shapefile tests to use the new function. * spreg/diagnostics.py: Adding check on condition_index to pick OLS (xtx) or IV (hth) model * core/IOHandlers/template.py: Updating template to pass unit testing. * core/util/shapefile.py: Fixing issue #180. Making shapefile opener case insensitive. * spatial_dynamics/interaction.py, spatial_dynamics/tests/test_interaction.py: Adding modified Knox and changes to existing tests in spatial_dynamics. * core/IOHandlers/arcgis_txt.py, core/IOHandlers/tests/test_arcgis_txt.py: fixing arcgis_txt.py so that it ignores self-neighbors with zero weights * core/FileIO.py: Updating library README. Removing docstrings from FileIO module. * contrib/README: adding contrib to installer and adding initial README * core/IOHandlers/gwt.py: rewrote GWT reader to avoid list appends. resulted in speed up of about 12x. * core/IOHandlers/pyDbfIO.py: implementing _get_col for dbf files. * core/IOHandlers/gwt.py: Adding a small fix to gwt reader, if the ids cannot be found in the associated DBF, they will be read in order from the GWT file. * contrib/weights_viewer/weights_viewer.py: Small change to identify polygons that are their own neighbor. * weights/Distance.py: removing incorrect kernel functions and fixing bug in uniform kernel * weights/util.py: refactoring insert_diagonal so that it can add or overwrite the diagonal weights * contrib, contrib/README, contrib/__init__.py, contrib/weights_viewer, contrib/weights_viewer/__init__.py, contrib/weights_viewer/transforms.py, contrib/weights_viewer/weights_viewer.py: Adding 1st contrib, a wxPython based Weights file viewer. * spatial_dynamics/markov.py: - handle case of zero transitions in spatial markov, consistent with treatment in classic markov * core/FileIO.py, core/IOHandlers/pyShpIO.py: Changes to allow reading of null polygons. * core/util/shapefile.py, core/util/tests/test_shapefile.py: refactoring shapefile reader, see issue #89 * core/FileIO.py: small change to FileIO to allow FileFormat argument to be passed through * esda/getisord.py: fixing bug in local Z values for integer data * cg/__init__.py, weights/user.py, weights/util.py: adding radius option to user weights methods * cg/kdtree.py, common.py, weights/Distance.py, weights/tests/test_Distance.py: Distance weights can not be passed an instnace of KDTree instead of an array. If the KDTree is of type ArcKDTree, the weights returns will be based on ArcDistances. Adding tests for Arc cases off KNN and DistanceBand. * weights/util.py: - added function for local clustering coefficient - summary for W as a graph * cg/kdtree.py, cg/sphere.py: finishing up Arc_KDTree * weights/Distance.py: More doctest fixes. * region/maxp.py, spreg/diagnostics.py, weights/Distance.py, weights/user.py: Fixing the doctests for dusty python setup. * cg/kdtree.py, cg/sphere.py: adding spherical wrapper around scipy kdtree * cg/__init__.py, cg/sphere.py: Adding spherical distance tools to cg. Related to issue #168 * core/IOHandlers/gwt.py, core/IOHandlers/tests/test_gwt.py: re-enabled gwt writing. 'o' transform is used on all GWTs for writing (w is returned to existing transform on exit) Also, setting '_shpName' and '_varName' attributes on W's which are read in through gwt. the writer will check if these vars exist and use them for the header, this prevents metadata loss on simple copies * esda/join_counts.py: - fix for handling int array type * spreg/diagnostics.py: Adding more efficient constant check for spreg. * cg/shapes.py: adding __geo_interface__ and asShape adapter for Point, LineString and Polygon * spreg/diagnostics.py: minor change to t-stat function to accommodate future regression models * esda/mapclassify.py: - more general fix for #166 # v<1.2.0>, 2011-07-31 * pysal/spreg/user_output.py: Fix for bug 162 * pysal/spatial_dynamics/markov.py: Added markov mobility measures; addresses issue 137 * pysal/weights/weights.py: Partially addressed issue 160 by removing the shimbel, order, and higher_order methods from W. * doc/source/users/installation.txt: Adding known issue regarding GNU/Linux testing and random seeds; see ticket 52. * pysal/esda/geary.py: Adding sparse implementation of Geary's C; substantial gains on larger datasets. * pysal/core/IOHandlers/mtx.py: Adding WSP2W function for fast conversion of sparse weights object (WSP) to pysal W. * pysal/esda/getisord.py: Adding Getis-Ord G test module * pysal/weights/util.py: Added function that inserts values along the main diagonal of a weights object * doc/source/users/tutorials: Fixed issue 76. * pysal/core/IOHandlers/mtx.py: Added an IOHandler for MatrixMarket MTX files * pysal/esda/moran.py: Optimized conditional randomization * pysal/weights/util.py: Re-adding full2W() method to convert full arrays into W objects; related to issue #136. * pysal/core/IOHandlers/gal.py: Added sparse WSP (thin W); gal reader can return W or WSP * pysal/core/IOHandlers/pyDbfIO.py: Bug Fix, DBF files are not properly closed when opened in 'r' mode. See issue #155. * pysal/core/IOHandlers/stata_txt.py: Adding FileIO handlers for STATA text files * pysal/weights/user.py: Fixed issue #154, adding k option to User Kernel weights functions. * pysal/core/IOHandlers/mat.py: Adding an IOHandler for MATLAB mat file * pysal/core/IOHandlers/wk1.py: Adding an IO handler for wk1 file * pysal/core/IOHandlers/geobugs_txt.py: Adding an IO handler for geobugs text file. * pysal/core/IOHandlers/arcgis_swm.py: Added ArcGIS SWM file handler * pysal/core/IOHandlers/arcgis_dbf.py: Adding a spatial weights file in the (ArcGIS-style) DBF format. * pysal/core/IOHandlers/arcgis_txt.py: Added ArcGIS ASCII file IO handler. * pysal/core/IOHandlers/dat.py: Added DAT file handler. * pysal/cg/locators.py: Added point in polygon method for Polygon and PolygonLocator * pysal/weights/Distance.py: Optimized Kernel() method to run much faster for the case of adaptive bandwidths * pysal/weights/user.py: Added helper function in user.py to create scipy sparse matrix from a gal file * pysal/common.py: Added shallow copy method to Read-Only Dict to support multiprocessing. * pysal/spatial_dynamics/rank.py: More efficient regime weights * pysal/weights/Distance.py: Adding epanechnikov and bisquare kernel funtions * pysal/core/IOHandlers/pyDbfIO.py: Adding NULL support to numerical DBF fields; modifying PointLocator API to match PolygonLocator API * pysal/cg/locators.py: Handles case when query rectangle is completely inside a polygon * pysal/cg/locators.py: Explicit polygon overlap hit test * pysal/cg/standalone.py: Adding point-polygon intersection support for polygons with holes. * pysal/spatial_dynamics/markov.py: Added homogeneity test. * pysal/spatial_dynamics/markov.py: Added spillover test in LISA_Markov. * pysal/cg/locators.py: Added Rtree based spatial index for polygonlocator. * pysal/cg/rtree.py: Added pure python Rtree module. * doc/source/developers/pep/pep-0010.txt: Added PEP 0010: Rtree module in pure python. * pysal/esda/geary.py: Fixed bug 144. * pysal/spatial_dynamics/markov.py: Added significance filtering of LISA markov. * doc/source/developers/pep/pep-0009.txt: Added new PEP, "PEP 0009: Add Python 3.x Support." * doc/source/developers/guidelines.txt: New release cycle schedules for 1.2 and 1.3. * doc/source/developers/release.txt: Updated pypi instructions; PySAL available on the Python Package Index via download, easy_install, and pip. # v<1.1.0>, 2011-01-31 * pysal/core/FileIO.py, pysal/core/IOHandlers/pyDbfIO.py: Added missing value support to FileIO. Warnings will be issued when missing values are found and the value will be set to pysal.MISSINGVALUE, currently None, but the user can change it as needed. * pysal/spreg/: Added Spatial Regression module, spreg, and tests. Added non-spatial diagnostic tests for OLS regression. * pysal/core/IOHandlers/gwt.py: Fixing bottle neck in gwt reader, adding support for GeoDa Style ID's and DBF id_order. * pysal/cg/standalone.py: adding, distance_matrix, full distance matrix calculation using sparse matrices * pysal/core/util: Moved "converters" into core.util, allows them to be used independently of FileIO. * pysal/weights/Distance.py: Adding work around for bug in scipy spatial, see pysal issue #126 * pysal/weights/user.py: Added build_lattice_shapefile in weights.user, which writes an ncol by nrow grid to a shapefile. * pysal/weights/Distance.py: fixed coincident point problem in knnW and made sure it returns k neighbors * pysal/spatial_dynamics/interaction.py: Added a suite of spatio-temporal interaction tests including the Knox, Mantel, and Jacquez tests. * pysal/weights/util.py: Added lat2SW, allows to create a sparse W matrix for a regular lattice. * pysal/tests/tests.py: - new 1.1 integration testing scheme. * pysal/esda/interaction.py: added standardized Mantel test and improved readability. * pysal/spatial_dynamics/directional.py: - adding directional LISA analytics * pysal/esda/mapclassify.py: Natural_Breaks will lower k for data with fewer than k unique values, prints warning. * pysal/region/randomregion.py: improvements to spatially constrained random region algorithm * pysal/esda/smoothing.py: Adding choynowski probabilities and SMR to smoothing.py * doc/source/developers/release.txt: - updating release cycle - release management # v<1.0.0>, 2010-07-31 -- Initial release. The following 13 authors contributed 216 commits. * Dani Arribas-Bel * David Folch * Levi John Wolf * Levi Wolf * Philip Stephens * Serge Rey * Sergio Rey * Wei Kang * jlaura * levi.john.wolf@gmail.com * ljw * ljwolf * pedrovma We closed a total of 86 issues, 33 pull requests and 53 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (33): * :ghpull:`724`: add synchronization tool * :ghpull:`733`: Fb/bump * :ghpull:`731`: Small docfixes * :ghpull:`730`: Contrib docs * :ghpull:`728`: B179 * :ghpull:`727`: Geodf io * :ghpull:`725`: try pinning scipy,numpy * :ghpull:`723`: make sure to test all moran classes * :ghpull:`720`: Moving natural breaks to a cleaner kmeans implementation * :ghpull:`718`: force counts to be same length as bins * :ghpull:`714`: Dev * :ghpull:`715`: Heads * :ghpull:`713`: Enh712 * :ghpull:`710`: Patsy/Pandas wrapper * :ghpull:`711`: Travis fixes * :ghpull:`706`: precommit hook * :ghpull:`707`: Keep dev updated with any bugfixes into master * :ghpull:`702`: fix for chi2 test 0 denominator and invocation of chi2 test in LISA_Markov * :ghpull:`704`: Allcloser * :ghpull:`703`: Swapping to Allclose and RTOL=.00005 in spreg * :ghpull:`701`: By col array * :ghpull:`700`: small optimization of bivariate moran motivated by #695 * :ghpull:`696`: Pypi * :ghpull:`691`: Update doctest for one-off bug that was fixed with #690 * :ghpull:`690`: fix for lisa markov one off for significance indicator * :ghpull:`689`: Clpy flex w * :ghpull:`688`: pep 8 edits * :ghpull:`687`: Change array assertions into allclose * :ghpull:`686`: Moran local bivariate * :ghpull:`684`: 591 * :ghpull:`682`: release instructions updated * :ghpull:`681`: version bump for next dev cycle * :ghpull:`680`: Rel1.10 Issues (53): * :ghissue:`705`: spreg check valve * :ghissue:`344`: Explore new dependency on ogr * :ghissue:`459`: Problem with bandwidth * :ghissue:`552`: Viz organization * :ghissue:`491`: Test np.allclose() for unit tests * :ghissue:`529`: Clarity needed on proper reference formatting in sphinx docs * :ghissue:`699`: Trouble importing pysal - ImportError: DLL load failed * :ghissue:`716`: `min_threshold_dist_from_shapefile` creating an island in some cases * :ghissue:`724`: add synchronization tool * :ghissue:`733`: Fb/bump * :ghissue:`731`: Small docfixes * :ghissue:`730`: Contrib docs * :ghissue:`719`: pysal not working with matplotlib v1.5 for plot_lisa_cluster, plot_choropleth, etc. * :ghissue:`728`: B179 * :ghissue:`727`: Geodf io * :ghissue:`725`: try pinning scipy,numpy * :ghissue:`723`: make sure to test all moran classes * :ghissue:`720`: Moving natural breaks to a cleaner kmeans implementation * :ghissue:`717`: esda.mapclassify return problematic counts when there is 0 occurrence in the last class * :ghissue:`718`: force counts to be same length as bins * :ghissue:`714`: Dev * :ghissue:`712`: `block_weights` does not take argument `idVariable` * :ghissue:`715`: Heads * :ghissue:`713`: Enh712 * :ghissue:`710`: Patsy/Pandas wrapper * :ghissue:`711`: Travis fixes * :ghissue:`706`: precommit hook * :ghissue:`708`: 2-3: is six a dependency or do we ship it? * :ghissue:`707`: Keep dev updated with any bugfixes into master * :ghissue:`702`: fix for chi2 test 0 denominator and invocation of chi2 test in LISA_Markov * :ghissue:`704`: Allcloser * :ghissue:`703`: Swapping to Allclose and RTOL=.00005 in spreg * :ghissue:`698`: Py3merge * :ghissue:`701`: By col array * :ghissue:`700`: small optimization of bivariate moran motivated by #695 * :ghissue:`695`: Bivariate global moran's I formula * :ghissue:`683`: Py3 Conversion Project * :ghissue:`694`: Allclose in SPREG * :ghissue:`696`: Pypi * :ghissue:`691`: Update doctest for one-off bug that was fixed with #690 * :ghissue:`693`: Trouble installation: No module named 'shapes' * :ghissue:`690`: fix for lisa markov one off for significance indicator * :ghissue:`689`: Clpy flex w * :ghissue:`688`: pep 8 edits * :ghissue:`685`: BV Lisa * :ghissue:`687`: Change array assertions into allclose * :ghissue:`686`: Moran local bivariate * :ghissue:`677`: Make meta importable from base * :ghissue:`684`: 591 * :ghissue:`682`: release instructions updated * :ghissue:`679`: pysal.cg.sphere.fast_knn bug * :ghissue:`681`: version bump for next dev cycle * :ghissue:`680`: Rel1.10 # v<1.10.0>, 2015-07-29 GitHub stats for 2015/01/31 - 2015/07/29 These lists are automatically generated, and may be incomplete or contain duplicates. The following 20 authors contributed 334 commits. * Charlie Schmidt * Dani Arribas-Bel * Daniel Arribas-Bel * David C. Folch * David Folch * Jay * Levi John Wolf * Marynia * Philip Stephens * Serge Rey * Sergio Rey * Taylor Oshan * The Gitter Badger * Wei Kang * jay * jlaura * ljw * ljwolf * luc * pedrovma We closed a total of 156 issues, 58 pull requests and 98 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (58): * :ghpull:`675`: Update README.md * :ghpull:`673`: Adding init at pdutilities so they are importable * :ghpull:`672`: ENH: option to locate legend * :ghpull:`669`: add nonsudo travis * :ghpull:`666`: Cleaned up conflicts in ref branch * :ghpull:`664`: Lisa map * :ghpull:`663`: Examples * :ghpull:`661`: Reorganization of examples * :ghpull:`657`: Assuncao test division errors * :ghpull:`649`: Add a Gitter chat badge to README.md * :ghpull:`647`: Addresses 646 * :ghpull:`645`: Update to weights module documentation for PySAL-REST * :ghpull:`644`: removed test print statements from df2dbf * :ghpull:`643`: using dtypes.name in df2dbf to avoid gotcha in type * :ghpull:`642`: Updating copyright year * :ghpull:`634`: allows non-symmetric distance matrices * :ghpull:`641`: turning off generatetree * :ghpull:`592`: adding check for version #591 * :ghpull:`636`: vertical line point simulation * :ghpull:`639`: Snapping * :ghpull:`640`: Add users to travis * :ghpull:`627`: Networkrb * :ghpull:`631`: Fixing typoes in analysis.py * :ghpull:`626`: cast arrays over inconsistent kdtree return types * :ghpull:`620`: adding explicit check for random region contiguity * :ghpull:`619`: Fixing spreg's warnings * :ghpull:`618`: initial folder with dbf utilities using pandas * :ghpull:`616`: Adding isolation and theil indices to inequality._indices.py * :ghpull:`615`: Network docs * :ghpull:`614`: cleaning up pr testing * :ghpull:`613`: test coverage to 98% on network * :ghpull:`612`: small change for testing PR * :ghpull:`611`: stubbed in minimal tests * :ghpull:`607`: B603 * :ghpull:`602`: Documentation Extraction Notebook * :ghpull:`606`: pct_nonzero was reporting a ratio not a percentage * :ghpull:`604`: Contribpush * :ghpull:`601`: Documentation Cleanup * :ghpull:`599`: Casting bugfix from #598 * :ghpull:`600`: Updates for coveralls * :ghpull:`598`: IO in Python 3 * :ghpull:`597`: Decoupling bbox from map_XXX_poly * :ghpull:`595`: Removed testing line in travis.yml and added a .coveragerc file to manag... * :ghpull:`590`: using numpy sum method * :ghpull:`589`: Wconstructor * :ghpull:`588`: Coveralls * :ghpull:`585`: Fisher Jenks bug in `plot_choropleth` * :ghpull:`584`: Alpha in plot chor * :ghpull:`583`: Fixed 576 * :ghpull:`580`: working on #576 * :ghpull:`578`: Fixes #577 * :ghpull:`574`: Handle case where a region has a 0 share. * :ghpull:`571`: Dict to unique value mapper * :ghpull:`570`: numpy doc cleanup for weights module * :ghpull:`569`: folium viz scripts * :ghpull:`568`: inline with numpy doc spec (spatial_dynamics module) * :ghpull:`567`: New/masterbump * :ghpull:`566`: Fix for 1.9.0 missing file in setup.py Issues (98): * :ghissue:`675`: Update README.md * :ghissue:`658`: Travis.CI """Legacy""" architecture * :ghissue:`667`: Examples Not Found * :ghissue:`673`: Adding init at pdutilities so they are importable * :ghissue:`672`: ENH: option to locate legend * :ghissue:`669`: add nonsudo travis * :ghissue:`671`: Shapefile Read - PolygonM Attribute Error * :ghissue:`670`: examples README markdown files reformatting * :ghissue:`668`: Wconstructor * :ghissue:`666`: Cleaned up conflicts in ref branch * :ghissue:`664`: Lisa map * :ghissue:`662`: Pep8 * :ghissue:`665`: Refs * :ghissue:`663`: Examples * :ghissue:`573`: Examples * :ghissue:`661`: Reorganization of examples * :ghissue:`656`: Assuncao rate improper division * :ghissue:`657`: Assuncao test division errors * :ghissue:`280`: handle multi-segment links in net_shp_io.py * :ghissue:`649`: Add a Gitter chat badge to README.md * :ghissue:`647`: Addresses 646 * :ghissue:`646`: arc distance in knnW * :ghissue:`645`: Update to weights module documentation for PySAL-REST * :ghissue:`644`: removed test print statements from df2dbf * :ghissue:`643`: using dtypes.name in df2dbf to avoid gotcha in type * :ghissue:`603`: Polygon.contains_point does not correctly process multipart polygons. * :ghissue:`642`: Updating copyright year * :ghissue:`623`: reading road shapfiles into network * :ghissue:`608`: Scipy Sparse Graph * :ghissue:`621`: network distance speedup * :ghissue:`632`: network point snapping * :ghissue:`633`: point to point distances on network * :ghissue:`635`: simulating points on vertical lines * :ghissue:`634`: allows non-symmetric distance matrices * :ghissue:`641`: turning off generatetree * :ghissue:`637`: speedup distance computations * :ghissue:`592`: adding check for version #591 * :ghissue:`628`: Re-enable doctests * :ghissue:`636`: vertical line point simulation * :ghissue:`639`: Snapping * :ghissue:`640`: Add users to travis * :ghissue:`638`: Add users to Travis * :ghissue:`627`: Networkrb * :ghissue:`622`: New network branch from clean master * :ghissue:`630`: NetworkG api is broken * :ghissue:`631`: Fixing typoes in analysis.py * :ghissue:`625`: Installation - Binstar and Anaconda * :ghissue:`624`: Network topology * :ghissue:`629`: changes to spreg tests for travis * :ghissue:`166`: pysal.esda.mapclassify.Fisher_Jenks - local variable 'best' referenced before assignment * :ghissue:`626`: cast arrays over inconsistent kdtree return types * :ghissue:`596`: [question] unsupervised classification * :ghissue:`620`: adding explicit check for random region contiguity * :ghissue:`617`: Random_Region not respecting contiguity constraint * :ghissue:`619`: Fixing spreg's warnings * :ghissue:`618`: initial folder with dbf utilities using pandas * :ghissue:`616`: Adding isolation and theil indices to inequality._indices.py * :ghissue:`615`: Network docs * :ghissue:`614`: cleaning up pr testing * :ghissue:`613`: test coverage to 98% on network * :ghissue:`612`: small change for testing PR * :ghissue:`611`: stubbed in minimal tests * :ghissue:`607`: B603 * :ghissue:`602`: Documentation Extraction Notebook * :ghissue:`606`: pct_nonzero was reporting a ratio not a percentage * :ghissue:`605`: RTree Weights * :ghissue:`604`: Contribpush * :ghissue:`601`: Documentation Cleanup * :ghissue:`554`: Beginning documentation cleanup * :ghissue:`599`: Casting bugfix from #598 * :ghissue:`600`: Updates for coveralls * :ghissue:`598`: IO in Python 3 * :ghissue:`597`: Decoupling bbox from map_XXX_poly * :ghissue:`595`: Removed testing line in travis.yml and added a .coveragerc file to manag... * :ghissue:`586`: Look at using Coveralls * :ghissue:`590`: using numpy sum method * :ghissue:`589`: Wconstructor * :ghissue:`588`: Coveralls * :ghissue:`576`: Predecessor lists inconsistencies * :ghissue:`585`: Fisher Jenks bug in `plot_choropleth` * :ghissue:`584`: Alpha in plot chor * :ghissue:`583`: Fixed 576 * :ghissue:`582`: Fixes #576 * :ghissue:`581`: Network * :ghissue:`580`: working on #576 * :ghissue:`575`: Network from Lattice * :ghissue:`578`: Fixes #577 * :ghissue:`577`: bug in FileIO.cast * :ghissue:`574`: Handle case where a region has a 0 share. * :ghissue:`343`: Edge Segmentation * :ghissue:`571`: Dict to unique value mapper * :ghissue:`570`: numpy doc cleanup for weights module * :ghissue:`569`: folium viz scripts * :ghissue:`568`: inline with numpy doc spec (spatial_dynamics module) * :ghissue:`567`: New/masterbump * :ghissue:`564`: Bug in setup.py * :ghissue:`566`: Fix for 1.9.0 missing file in setup.py * :ghissue:`565`: Bsetup # v<1.9.1>, 2015-01-31 GitHub stats for 2015/01/30 - 2015/01/31 These lists are automatically generated, and may be incomplete or contain duplicates. The following 4 authors contributed 14 commits. * Dani Arribas-Bel * Serge Rey * Sergio Rey * jlaura We closed a total of 8 issues, 3 pull requests and 5 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (3): * :ghpull:`566`: Fix for 1.9.0 missing file in setup.py * :ghpull:`563`: Updating release instructions * :ghpull:`561`: Rolling over to 1.10 Issues (5): * :ghissue:`566`: Fix for 1.9.0 missing file in setup.py * :ghissue:`565`: Bsetup * :ghissue:`563`: Updating release instructions * :ghissue:`562`: adjustments to release management * :ghissue:`561`: Rolling over to 1.10 # v<1.9.0>, 2015-01-30 GitHub stats for 2014/07/25 - 2015/01/30 These lists are automatically generated, and may be incomplete or contain duplicates. The following 12 authors contributed 131 commits. * Andy Reagan * Dani Arribas-Bel * Jay * Levi John Wolf * Philip Stephens * Qunshan * Serge Rey * jlaura * ljwolf * luc We closed a total of 113 issues, 44 pull requests and 69 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (44): * :ghpull:`560`: modifying import scheme for network module * :ghpull:`559`: Network2 * :ghpull:`558`: Network2 * :ghpull:`557`: Network2 * :ghpull:`556`: Added analytical functions and edge segmentation * :ghpull:`550`: Network2 * :ghpull:`553`: correction in denominator of spatial tau. * :ghpull:`547`: Updates to get network integrated * :ghpull:`544`: update .gitignore * :ghpull:`543`: k nearest neighbor gwt example file for baltimore points (with k=4) added to examples directory * :ghpull:`542`: new format nat_queen.gal file added to examples directory * :ghpull:`541`: Update tutorial docs for new book * :ghpull:`540`: doc: updating instructions for anaconda and enthought * :ghpull:`539`: doc: pysal is now on sagemathcloud * :ghpull:`538`: Clean up of cg and fixes of other doctests/formats * :ghpull:`536`: adding entry for getis ord module * :ghpull:`537`: new opendata module for contrib * :ghpull:`535`: Add method for extracting data columns as Numpy array rather than list * :ghpull:`534`: added geogrid to __all__ in sphere.py * :ghpull:`533`: added geogrid function to create a grid of points on a sphere * :ghpull:`532`: new functions to deal with spherical geometry: lat-lon conversion, degre... * :ghpull:`530`: I390 * :ghpull:`528`: Replacing 0 by min value in choropleths * :ghpull:`526`: B166 * :ghpull:`525`: copyright update * :ghpull:`524`: New homogeneity tests for general case and spatial markov as a special case * :ghpull:`523`: pointing to github.io pages * :ghpull:`520`: Same typo. Toolkit. * :ghpull:`518`: Update util.py * :ghpull:`519`: Typo * :ghpull:`517`: Documentation correction for Prais Conditional Mobility Index * :ghpull:`516`: ENH for https://github.com/PySAL/PySAL.github.io/issues/17 * :ghpull:`515`: BUG: conditional check for extension of lower bound of colorbar to conta... * :ghpull:`514`: ENH: adding the user_defined classification * :ghpull:`513`: rewriting to not use ipython notebook --pylab=line * :ghpull:`512`: Viz * :ghpull:`508`: Adding barebones pysal2matplotlib options in viz * :ghpull:`511`: DOC updating news * :ghpull:`507`: Sched * :ghpull:`510`: BUG: fix for #509 * :ghpull:`506`: 1.9dev * :ghpull:`505`: REL bumping master to 1.9.0dev * :ghpull:`504`: Release prep 1.8 * :ghpull:`503`: Grid for landing page Issues (69): * :ghissue:`560`: modifying import scheme for network module * :ghissue:`559`: Network2 * :ghissue:`558`: Network2 * :ghissue:`557`: Network2 * :ghissue:`556`: Added analytical functions and edge segmentation * :ghissue:`555`: Added edge segmentation by distance * :ghissue:`550`: Network2 * :ghissue:`553`: correction in denominator of spatial tau. * :ghissue:`549`: Network2 * :ghissue:`547`: Updates to get network integrated * :ghissue:`548`: Installation Issues * :ghissue:`546`: Network2 * :ghissue:`545`: Network * :ghissue:`544`: update .gitignore * :ghissue:`543`: k nearest neighbor gwt example file for baltimore points (with k=4) added to examples directory * :ghissue:`542`: new format nat_queen.gal file added to examples directory * :ghissue:`541`: Update tutorial docs for new book * :ghissue:`540`: doc: updating instructions for anaconda and enthought * :ghissue:`539`: doc: pysal is now on sagemathcloud * :ghissue:`538`: Clean up of cg and fixes of other doctests/formats * :ghissue:`536`: adding entry for getis ord module * :ghissue:`537`: new opendata module for contrib * :ghissue:`535`: Add method for extracting data columns as Numpy array rather than list * :ghissue:`534`: added geogrid to __all__ in sphere.py * :ghissue:`533`: added geogrid function to create a grid of points on a sphere * :ghissue:`532`: new functions to deal with spherical geometry: lat-lon conversion, degre... * :ghissue:`390`: add option to have local moran quadrant codes align with geoda * :ghissue:`530`: I390 * :ghissue:`528`: Replacing 0 by min value in choropleths * :ghissue:`526`: B166 * :ghissue:`176`: contrib module for proj 4 * :ghissue:`178`: contrib module for gdal/org * :ghissue:`203`: implement network class in spatialnet * :ghissue:`204`: pysal-networkx util functions * :ghissue:`209`: csv reader enhancement * :ghissue:`215`: Add a tutorial for the spreg module * :ghissue:`244`: ps.knnW_from_shapefile returns wrong W ids when idVariable specified * :ghissue:`246`: Only use idVariable in W when writing out to file * :ghissue:`283`: Create new nodes at intersections of edges * :ghissue:`291`: Enum links around regions hangs * :ghissue:`292`: Handle multiple filaments within a region in the Wed construction * :ghissue:`302`: Handle hole polygons when constructing wed * :ghissue:`309`: Develop consistent solution for precision induced errors in doctests across platforms * :ghissue:`350`: reading/writing weights file with spaces in the ids * :ghissue:`450`: x_name and summary method not consistent in ols * :ghissue:`521`: Nosetests don't accept setup.cfg * :ghissue:`509`: ESDA bin type inconsistency * :ghissue:`525`: copyright update * :ghissue:`524`: New homogeneity tests for general case and spatial markov as a special case * :ghissue:`523`: pointing to github.io pages * :ghissue:`520`: Same typo. Toolkit. * :ghissue:`522`: Nosetests for python3 porting * :ghissue:`518`: Update util.py * :ghissue:`519`: Typo * :ghissue:`517`: Documentation correction for Prais Conditional Mobility Index * :ghissue:`516`: ENH for https://github.com/PySAL/PySAL.github.io/issues/17 * :ghissue:`515`: BUG: conditional check for extension of lower bound of colorbar to conta... * :ghissue:`514`: ENH: adding the user_defined classification * :ghissue:`513`: rewriting to not use ipython notebook --pylab=line * :ghissue:`512`: Viz * :ghissue:`508`: Adding barebones pysal2matplotlib options in viz * :ghissue:`511`: DOC updating news * :ghissue:`507`: Sched * :ghissue:`510`: BUG: fix for #509 * :ghissue:`502`: spreg.ml_lag.ML_Lag is very very very time-consuming? * :ghissue:`506`: 1.9dev * :ghissue:`505`: REL bumping master to 1.9.0dev * :ghissue:`504`: Release prep 1.8 * :ghissue:`503`: Grid for landing page # v<1.8.0>, 2014-07-25 GitHub stats for 2014/01/29 - 2014/07/25 These lists are automatically generated, and may be incomplete or contain duplicates. The following 8 authors contributed 281 commits. * Dani Arribas-Bel * Jay * Philip Stephens * Serge Rey * Sergio Rey * jlaura * pedrovma * sjsrey We closed a total of 160 issues, 60 pull requests and 100 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (60): * :ghpull:`503`: Grid for landing page * :ghpull:`501`: Two figs rather than three * :ghpull:`500`: More efficient higher order operations * :ghpull:`499`: renamed nat_queen.gal for #452 * :ghpull:`497`: ENH Deprecation warning for regime_weights #486 * :ghpull:`494`: Enables testing against two versions of SciPy shipped with the last two Ubuntu LTS versions. * :ghpull:`490`: Fix for #487 * :ghpull:`492`: BUG cleaning up temporary files for #398 * :ghpull:`493`: Phil: Skipping several tests that fail due to precision under older scipy * :ghpull:`489`: test suite fixes * :ghpull:`488`: More tests to skip if scipy less than 11 * :ghpull:`484`: ENH: cleaning up more test generated files * :ghpull:`483`: Forwarding Phil's commit: skipping doctests, conditional skip of unit tests * :ghpull:`482`: DOC cleaning up files after running doctests #398 * :ghpull:`481`: DOC contrib updates and links * :ghpull:`480`: DOC cleaning up doctests * :ghpull:`479`: ENH Changing regime_weights to block_weights for #455 * :ghpull:`478`: DOC: link fixes * :ghpull:`477`: cKDTree for #460 * :ghpull:`476`: redefining w.remap_ids to take only a single arg * :ghpull:`475`: Adding docstrings and error check to fix #471 * :ghpull:`470`: fixing order of args for api consistency. * :ghpull:`469`: Idfix for #449 * :ghpull:`463`: updating gitignore * :ghpull:`462`: ENH: handle the case of an ergodic distribution where one state has 0 probability * :ghpull:`458`: ENH: Vagrantfile for PySAL devs and workshops * :ghpull:`447`: Clusterpy * :ghpull:`456`: BUG: fix for #451 handling W or WSP in higher_order_sp * :ghpull:`454`: Foobar * :ghpull:`443`: Updating spreg: several minor bug and documentation fixes. * :ghpull:`453`: Resolving conflicts * :ghpull:`448`: Wsp * :ghpull:`445`: ENH: unique qualitative color ramp. Also refactoring for future ipython deprecation of --pylab=inline * :ghpull:`446`: Wmd * :ghpull:`444`: Scipy dependency * :ghpull:`442`: Wmd * :ghpull:`441`: fixed kernel wmd for updated wmd structure * :ghpull:`440`: ENH: sidebar for Releases and installation doc update * :ghpull:`439`: - events * :ghpull:`438`: ENH: pruning to respect flake8 * :ghpull:`437`: BUG: fix for removal of scipy.stat._support #436 * :ghpull:`433`: Rank markov * :ghpull:`424`: testing * :ghpull:`431`: FOSS4G * :ghpull:`430`: Network * :ghpull:`429`: moving analytics out of wed class and into their own module * :ghpull:`428`: Network * :ghpull:`427`: devel docs * :ghpull:`425`: Viz2contrib * :ghpull:`423`: Update news.rst * :ghpull:`422`: ENH: Update doc instructions for napoleon dependency * :ghpull:`421`: Adding files used in some examples as per Luc's request. * :ghpull:`419`: Doc fixes 1.7 * :ghpull:`393`: Doc fixes 1.7 * :ghpull:`417`: ENH hex lattice W for #416 * :ghpull:`415`: Temporarily commenting out tests that are blocking Travis. * :ghpull:`407`: Viz: Moving into contrib/viz in master * :ghpull:`404`: version change * :ghpull:`401`: fixes #388 * :ghpull:`402`: release changes Issues (100): * :ghissue:`503`: Grid for landing page * :ghissue:`501`: Two figs rather than three * :ghissue:`500`: More efficient higher order operations * :ghissue:`452`: nat_queen.gal example file * :ghissue:`499`: renamed nat_queen.gal for #452 * :ghissue:`486`: add a deprecation warning on regime_weights * :ghissue:`497`: ENH Deprecation warning for regime_weights #486 * :ghissue:`449`: Lower order neighbor included in higher order * :ghissue:`487`: Issue with w.weights when row-standardizing * :ghissue:`398`: running test suite generates files * :ghissue:`358`: Graph weights * :ghissue:`338`: ENH: Move Geary's C calculations to Cython. * :ghissue:`494`: Enables testing against two versions of SciPy shipped with the last two Ubuntu LTS versions. * :ghissue:`490`: Fix for #487 * :ghissue:`492`: BUG cleaning up temporary files for #398 * :ghissue:`493`: Phil: Skipping several tests that fail due to precision under older scipy * :ghissue:`489`: test suite fixes * :ghissue:`485`: Revert "ENH: cleaning up more test generated files" * :ghissue:`488`: More tests to skip if scipy less than 11 * :ghissue:`484`: ENH: cleaning up more test generated files * :ghissue:`483`: Forwarding Phil's commit: skipping doctests, conditional skip of unit tests * :ghissue:`482`: DOC cleaning up files after running doctests #398 * :ghissue:`481`: DOC contrib updates and links * :ghissue:`480`: DOC cleaning up doctests * :ghissue:`455`: regime weights vs block weights * :ghissue:`479`: ENH Changing regime_weights to block_weights for #455 * :ghissue:`478`: DOC: link fixes * :ghissue:`460`: Optimize KDTree * :ghissue:`477`: cKDTree for #460 * :ghissue:`472`: Check for any side effects from new id remapping in w.sparse * :ghissue:`473`: update all user space functions for new w.remap_ids * :ghissue:`476`: redefining w.remap_ids to take only a single arg * :ghissue:`263`: Transition to scipy.spatial.cKDTree from scipy.spatial.KDTree * :ghissue:`414`: Travis build is killing nosetests * :ghissue:`335`: Weights transformation docs * :ghissue:`471`: add docstring example for w.remap_ids * :ghissue:`475`: Adding docstrings and error check to fix #471 * :ghissue:`405`: ENH: Handling ids in W (Leave open for discussion) * :ghissue:`470`: fixing order of args for api consistency. * :ghissue:`469`: Idfix for #449 * :ghissue:`467`: redirect pysal.org to new dynamic landing page * :ghissue:`466`: design the grid for the notebooks * :ghissue:`464`: design new dynamic landing page for github.io * :ghissue:`465`: move news out of docs and into dynamic landing page * :ghissue:`468`: Move dynamic items out of sphinx docs and into dynamic landing page * :ghissue:`463`: updating gitignore * :ghissue:`451`: docs for higher_order_sp have wrong argument types * :ghissue:`462`: ENH: handle the case of an ergodic distribution where one state has 0 probability * :ghissue:`458`: ENH: Vagrantfile for PySAL devs and workshops * :ghissue:`447`: Clusterpy * :ghissue:`456`: BUG: fix for #451 handling W or WSP in higher_order_sp * :ghissue:`457`: This is a test to see if pull request notifications get sent out to the list * :ghissue:`454`: Foobar * :ghissue:`443`: Updating spreg: several minor bug and documentation fixes. * :ghissue:`453`: Resolving conflicts * :ghissue:`412`: On travis and darwin test_ml_error_regimes.py hangs * :ghissue:`448`: Wsp * :ghissue:`435`: Will spatial durbin model be added in the near future? * :ghissue:`445`: ENH: unique qualitative color ramp. Also refactoring for future ipython deprecation of --pylab=inline * :ghissue:`446`: Wmd * :ghissue:`444`: Scipy dependency * :ghissue:`442`: Wmd * :ghissue:`441`: fixed kernel wmd for updated wmd structure * :ghissue:`440`: ENH: sidebar for Releases and installation doc update * :ghissue:`439`: - events * :ghissue:`438`: ENH: pruning to respect flake8 * :ghissue:`436`: Scipy 0.14 induced breakage * :ghissue:`437`: BUG: fix for removal of scipy.stat._support #436 * :ghissue:`408`: Use of `platform.system()` to determine platform * :ghissue:`403`: Scipy dependency * :ghissue:`434`: W Object Metadata Attribute * :ghissue:`433`: Rank markov * :ghissue:`424`: testing * :ghissue:`432`: Implementation of rank Markov classes * :ghissue:`431`: FOSS4G * :ghissue:`430`: Network * :ghissue:`429`: moving analytics out of wed class and into their own module * :ghissue:`420`: Local Moran's I, I Attribute Undefined * :ghissue:`418`: Extended pysal.weights.user.build_lattice_shapefile * :ghissue:`428`: Network * :ghissue:`427`: devel docs * :ghissue:`426`: dev docs * :ghissue:`425`: Viz2contrib * :ghissue:`423`: Update news.rst * :ghissue:`422`: ENH: Update doc instructions for napoleon dependency * :ghissue:`421`: Adding files used in some examples as per Luc's request. * :ghissue:`419`: Doc fixes 1.7 * :ghissue:`393`: Doc fixes 1.7 * :ghissue:`416`: Add hexagonal lattice option for lat2W * :ghissue:`417`: ENH hex lattice W for #416 * :ghissue:`409`: add wiki page on viz module design * :ghissue:`413`: Temporary fix for https://github.com/pysal/pysal/issues/412 * :ghissue:`415`: Temporarily commenting out tests that are blocking Travis. * :ghissue:`407`: Viz: Moving into contrib/viz in master * :ghissue:`406`: Viz: pruning old code and adding more examples for TAZ paper * :ghissue:`380`: Pep 8 and Line Length * :ghissue:`404`: version change * :ghissue:`401`: fixes #388 * :ghissue:`388`: update testing procedures docs * :ghissue:`402`: release changes # v<1.7.0>, 2014-01-29 36d268f Philip Stephens -Merge pull request #400 from sjsrey/mldoc c2c4741 Serge Rey -Formatting ml docs 685f5e3 Sergio Rey -Merge pull request #399 from sjsrey/master 481ccb4 Serge Rey -correct thanks 4a5cce3 Sergio Rey -Update index.txt 1fe7aeb Philip Stephens -Merge pull request #396 from sjsrey/mldoc e731278 Serge Rey -EHN: fixing link to bleeding edge docs. e4e9930 Serge Rey -ENH: adding ml docs to api 9b3c77e Serge Rey -Merge branch 'master' of github.com:pysal/pysal dda3c01 Philip Stephens -Merge pull request #389 from dfolch/master 74b26d5 Philip Stephens -Merge pull request #392 from pedrovma/spreg17 b47ba84 pedrovma -Bump. 3d8504c Sergio Rey -Merge pull request #386 from pastephens/master f9b59ea Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal 429e19e pedrovma -Upgrading to spreg 1.7. c698747 David Folch -removing legacy speedup hack that is no longer relevant 88177d0 Sergio Rey -Merge pull request #387 from sjsrey/scipy13 64a4089 Serge Rey -BUG: sorting ijs for asymmetries 5539ef5 Sergio Rey -Merge pull request #1 from sjsrey/scipy13 8a86951 Serge Rey -BUG: fixes for scipy .0.9.0 to 0.13.0 induced errors fe02796 Philip Stephens -tweaking travis to only run master commits 8c1fbe8 jlaura -Merge pull request #385 from sjsrey/docupdate b71aedc Serge Rey -ENH: update date 4f237e4 Sergio Rey -Merge pull request #384 from sjsrey/moran 01da3be Serge Rey -ENH: Analytical p-values for Moran are two-tailed by default #337 918fe60 Philip Stephens -further travis tweaks 3920d73 Sergio Rey -Merge pull request #382 from sjsrey/st_docs d90bc70 Serge Rey -DOC: updating refs for concordance algorithm 0db2790 Philip Stephens -tweaks to travis 063e057 Philip Stephens -upgrading scipy on travis f90e742 Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal edc9c07 Dani Arribas-Bel -Merge pull request #379 from sjsrey/b244 82479bb Serge Rey -BUG: fix for the comment https://github.com/pysal/pysal/issues/244#issuecomment-30055558 57ba485 jlaura -Update README.md 981ed31 Sergio Rey -Merge pull request #377 from darribas/master 3320c39 darribas -Changing cmap default in plot_choropleth so every type defaults to its own adecuate colormap e063bee darribas -Fixing ignorance of argument cmap in base_choropleth_unique 1f10906 Dani Arribas-Bel -Merge pull request #375 from sjsrey/viz 94aa3e7 Dani Arribas-Bel -Merge pull request #376 from pedrovma/baltim_data 7568b0b pedrovma -Adding Baltimore example dataset for use with LM models. 5b23f89 Serge Rey -greys for classless map d4eae1e Dani Arribas-Bel -Merge pull request #374 from sjsrey/viz 652440d Serge Rey -shrinking colorbar c17bf67 Sergio Rey -Merge pull request #373 from darribas/master a71c3cb darribas -Fixing minor conflict to merge darribas viz branch into darribas master ec27e30 Dani Arribas-Bel -Merge pull request #372 from sjsrey/viz 8c03170 Serge Rey -option for resolution of output figs 3fc5bd4 Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal 2b5cb23 jlaura -Merge pull request #371 from sjsrey/geopandas 469afa7 Serge Rey -fix for #370 59cdafc jlaura -Merge pull request #369 from pedrovma/south_data 6b88e13 jlaura -Merge pull request #368 from schmidtc/issue367 40fe928 pedrovma -Adding south data to be used in ML doctests. bcc257e schmidtc -fixes #367 87e057f jlaura -Merge pull request #366 from sjsrey/ml_lag a64eb27 Serge Rey -queen contiguity for nat.shp 77add5c Sergio Rey -Merge pull request #365 from sjsrey/news 82464ef Serge Rey -narsc workshop fd79424 Sergio Rey -Merge pull request #364 from sjsrey/news bc7f25a Serge Rey -Merge branch 'master' of https://github.com/sjsrey/pysal d669913 David Folch -Merge pull request #363 from sjsrey/maxp 22f9e36 Serge Rey -update example for bug fix #362 fac3b8a Serge Rey -- update tests for bug fix #362 44b4b06 Sergio Rey -Merge pull request #1 from sjsrey/maxp 1e6f1e5 Serge Rey -- fix for #362 68ab3e9 Sergio Rey -Merge pull request #361 from sjsrey/components aa27c7e Serge Rey -doc test fix 7c08208 Serge Rey -putting Graph class back in for component checking 003b519 Serge Rey -alternative efficient component checker 2080e62 Serge Rey -- fixing doc 4fda442 Serge Rey -Merge branch 'components' of github.com:sjsrey/pysal into components e9e613b Serge Rey -reverting back to old component check 83d855e Serge Rey -updating example 9defd86 jlaura -Merge pull request #360 from sjsrey/components 6f92335 Serge Rey -more efficient connectivity test ebde3d1 Dani Arribas-Bel -Adding try/except for ogr since it's only used to reprojection methods but not on the plotting toolkit 5b170eb Sergio Rey -Merge pull request #356 from sjsrey/classification c9dac41 Serge Rey -- update unit tests for reshaping jenks caspal d9b06e2 Sergio Rey -Merge pull request #355 from sjsrey/cleanup/moran dc589e8 darribas -Adding caution note when plotting points to the notebook. Ideally, we wanna be able to build a PathCollection out of the XYs, but for now we rely on plt.scatter, which gets the job done but has some problems. 2224b95 darribas -Including support for points in base_choropleth_unique and base_choropleth_classless ac2d08a darribas -Modifying example to show how to do choropleth mapping on points 270786e darribas -Adding support for choropleth plotting on point map objects (this may come from map_point_shp or from a simple matplotlib scatter e56697c Sergio Rey -Merge pull request #357 from jlaura/newstyle_wed 4c67c2f Jay -errors in segmentation fixed 512cc76 Serge Rey -have Jenks-Caspal bins be a one dimensional array - to be consistent with all other classifiers 5254859 Philip Stephens -Merge branch 'master' of https://github.com/pysal/pysal 788ecab Serge -pruning 5b6b7b6 Serge -pruning eb7e9a1 Jay -bug fix and all pointers filled for external edges e47aa7a Jay -Node insertion, precursor to segmentation. 18a44d1 darribas -*Replacing shp by map_obj in medium layer functionality. *Bringing everything else in line with it *Adding example for line colorig and mixing overlaying of points. bd041b1 darribas -Replacing shp_link by shp as input for medium and low-level layers. This brings much more flexibility and opens the door to plot formats other than shapefiles (e.g. geojson) c74a361 darribas -Adding IP notebook to exemplify and keep track of development of mapping module d23c882 darribas -Minor fixes 4b82a76 darribas -New commit message* Replacing map_poly_shp_lonlat for map_poly_shp in base_choropleth_classif/unique/classless * removed 'projection' from base_choropleth_classif/unique/classless * Allow base_choropleth_classif/unique/classless to plot multi-part polygons properly * changes streamlined to generic plot_choropleth * Added dependency on pandas for rapid reindexing (this is done externally on the method _expand_values to it is easy to drop the dependency when neccesary/time available) 7a0eaec darribas -Merge branch 'viz' of github.com:darribas/pysal into viz 5536424 darribas -Merge branch 'master' of github.com:darribas/pysal e54ce16 Sergio Rey -Merge pull request #353 from darribas/master 819ee60 darribas -Adding immediate todo on head of the file 946772d darribas -Passing k to base_choropleth_classif from plot_choropleth. This should fix Issue #352 f299b45 darribas -Merge branch 'master' of https://github.com/pysal/pysal f044f43 Jay -Added W generation 5f48446 jlaura -Merge pull request #348 from sjsrey/master 938a1ae Serge Rey -- adding nn stats to point based methods a86a051 Philip Stephens -removing dependency tracking service, it was ruby only 1e24fde Philip Stephens -testing dependency tracking service 3aa410c Philip Stephens -Merge pull request #347 from pedrovma/w_silence_island 03990f6 pedrovma -Extending PR #310 (silence island warnings) to include w.transform. 160001a Sergio Rey -Merge pull request #346 from jlaura/newstyle_wed 44989f9 Sergio Rey -Merge pull request #345 from sjsrey/master 2fd99b8 Sergio Rey -Update README.md bdcc6a8 Jay -NCSR with uniform distribution 769aa03 Jay -Fixed snapping 2561071 Jay -saved notebook and updated readme 3784783 Jay -ReadMe for Changes 019e16b Sergio Rey -Merge pull request #334 from jseabold/fix-build-example-dirs 1889885 Skipper Seabold -BLD: Correctly install package_data dirs. ff4e355 Serge Rey -- assignments c5b0cc0 Serge Rey -- reorg a4f5642 Serge Rey -Merge branch 'network' of github.com:pysal/pysal into network a95fec8 jlaura -Update README.md 1713145 Serge Rey -Merge branch 'master' of github.com:pysal/pysal into network ede75c0 Sergio Rey -Merge pull request #329 from jlaura/wed_polar 7399cf2 Jay -Single-source shortest path notebook 9eb3fc1 Philip Stephens -Merge pull request #331 from sjsrey/docfix ef9c82a Serge Rey -- sphinx doctest markup fix 1e2b6b3 jlaura -Update README.md e19bffa jlaura -Merge pull request #330 from pysal/b328 6afc30b Serge Rey -- tutorial doc fixes for #328 c7239f1 Serge Rey -- b328 fix d5fec13 Serge Rey -- fix for #328 making all p-values one-tailed 16b5e6e Jay -enumeration working with filaments 9507bbc jlaura -Update README.md eef8eec Serge Rey -- stub for design of module 2707d60 Jay -Filaments in polar coordinates b64f9e2 Serge Rey -Documentation for the development of network module b90876e Serge Rey -Merge branch 'network' of github.com:pysal/pysal into network ddad2a5 Philip Stephens -Merge pull request #326 from sjsrey/doc 6b0cd08 Serge Rey -- update release schedule 4cc7bca Jay -bisecting for single point working 79c77d9 jlaura -Merge pull request #324 from pysal/bf_id 9f4c7c9 Serge Rey -id is a keyword 72b1f85 Sergio Rey -Merge pull request #323 from jlaura/network b5cdae0 Jay -fix to shp2graph 846dce2 Jay -Brute force for point outside network d6c2ef4 Jay -Added length computation, alter global morans b7e1465 Jay -Added new pointer to reader/writer 616d62d Jay -LISA and Global Morans on the network 16f84d6 Jay -Added explicit point external to network warning 34f4d8e Jay -update to the ipython notebook e359e59 Jay -JSON and cPickle Bianry WED Reader/Writer 5373c82 Sergio Rey -Merge pull request #322 from jlaura/network 059d99c Jay -wed into class, tests added aa5969d Sergio Rey -Merge pull request #320 from pastephens/master a18000b Philip Stephens -version added info 5b8d490 Philip Stephens -typo d31a22a Philip Stephens -stubs for cg docs 4dbdfe3 schmidtc -fixes #318 35a0317 Jay -Merge branch 'master' of https://github.com/pysal/pysal into network 77e8387 Jay -Merge branch 'geojson' of https://github.com/pysal/pysal into network ad670c5 Sergio Rey -Merge pull request #317 from pastephens/master 628f27e Philip Stephens -merging local changes f9dcb3e Philip Stephens -simplified install instructions f2fab4c Serge Rey -- notebook on w construction for geojson 830826b Serge Rey -prototyping W from geojson b10240d Serge Rey -created with "ogr2ogr -lco WRITE_BBOX=YES -f "GeoJSON" columbus.json columbus.shp" d546926 Philip Stephens -merging with pull d711011 darribas -Merge branch 'rod' 8bef782 darribas -Merge branch 'rod' of https://github.com/pysal/pysal into rod 03c1003 pedrovma -Merge pull request #315 from sjsrey/rod 950fe8b Serge Rey -Replacing ROD with regular dictionary b1f009f Philip Stephens -Changes to release docs. 028364a Sergio Rey -Update THANKS.txt 94f5916 Sergio Rey -Update INSTALL.txt # v<1.6.0>, 2013-07-31 5fa9d09 darribas -silent_island_warning implemented for w_union 6526c62 Sergio Rey -Update README.md ea826c1 darribas -silent_island_warning implemented for w_intersection 335540a darribas -silent_island_warning implemented for w_difference 0a156cb darribas -silent_island_warning implemented for w_symmetric_difference. Previous commit included support of silent_island_warning for WSP2W as well 34d20d7 darribas -silent_island_warning implemented for w_clip 499815d pedrovma -Test fixing... 8778f75 pedrovma -Test fixing... a799a13 pedrovma -Test fixing... 6482d81 pedrovma -Test fixing... 2752b1b pedrovma -Test fixing... 0c0a5bf pedrovma -Test fixing... bbf9dcb pedrovma -Test fixing... 05c34ff pedrovma -Test fixing... 8a3986a Serge Rey -- preparing for release, version updates 9106cfe pedrovma -Matching travis results reg. precision issues. 3cd0ce1 Serge Rey -- updating changelog 74dadd6 pedrovma -Bump. c7774fb Serge Rey -- update THANKS.txt - testing travis for timing out cd98057 Serge Rey -- travis fix for multiprocessing permission error 86702f8 Serge Rey -- start of changelog for 1.6 3ee686d pedrovma -Reloading to check new results from Travis. 2de1d21 Serge Rey -- docs ef72edc Serge Rey -- update docs 0716581 Serge Rey -- deal with multiprocessing on travis b508c88 Serge Rey -- excluding network from 1.6 release ff13e31 pedrovma -Matching Travis results. Multiprocessing errors still an issue. 5b916ba pedrovma -Adding Chow test on lambda and updating dynamics of regime_err_sep and regime_lag_sep in combom models. b6e687f darribas -Patch to include switch for island warning as proposed in #295. The method is modified as well to include the switch 7ea5f35 pedrovma -Fixing defaults 62ca76b pedrovma -Updating documentation and checking if there are more than 2 regimes when regimes methods are used. 3212249 pedrovma -Fixing documentation on 'name_regimes' a782d50 pedrovma -Updating tests for integration with pysal 1.6 14f9181 pedrovma -Merging spreg_1.6 with my pysal fork. 817f2c2 Serge Rey -- having build_lattice_shapefile also create the associated dbf file - useful for testing our contiguity builders against geoda since dbf is required by the latter 41d59a4 Serge Rey -- adding diagonal option to kernel weights in user.py 506d808 Serge Rey -update when added b2ec3d4 Serge Rey -- updating api docs 9d45496 Serge Rey -- example and doctests for spatial gini 95635bb Serge Rey -updating release docs bd2f924 darribas -Fixing doctest of towsp method by including isinstance(wsp, ps.weights.weights.WSP) 76183d7 darribas -Fixing doctest of towsp method by including type(wsp) 0c54181 darribas -Adding method in W that calls WSP class for convenience and elegance. Related to issue #226 f3b23e8 Philip Stephens -adding source build to travis-ci 60930e7 Philip Stephens -adding new url for downloads 9bf7f5b Philip Stephens -modified release docs. f98d4a9 Philip Stephens -interim ci aa19028 Philip Stephens -Adding docs about installing in develop mode. 674112f Philip Stephens -starting rewrite of install docs af0d9b3 Philip Stephens -working on doc tickets 200e77e Serge Rey -handle ties in knnW in doctest d0d2dd2 Serge Rey -resetting README for pysal/pysal 6afb6ac Serge Rey -- updating docs for new api in interation.py 4c5572f Serge Rey -- updating tests for new api fabd16a Serge Rey -- refactored signatures to use numpy arrays rather than event class 6367947 Serge Rey -- refactor knox for large samples 5fad3b2 Serge Rey -- updating travis test 06894d8 Serge Rey -- updated README 8b06e63 Serge Rey -- so only i get email when i commit locally efbb7ff Serge Rey -- removing google pysal-dev circle 9859bda Serge Rey -- turning off gmail circle 51f6d3e Serge Rey -- fixing 46b1084 Serge Rey --docos 4e2c27a Philip Stephens -missing if statement added d1a83fd Serge Rey -- fixing docs 8275d76 Serge Rey -- fix precision 87ea5cc Philip Stephens -adding to authors and quick test fix for linux 1cfb67f Serge Rey -cant easily remove idVariable, reverting 5933d1e Serge Rey -removing idvariable from Distance - causes too many issues 05f2573 Philip Stephens -removing coverage tests fcb8c6f Philip Stephens -Knox using KDTree. 2237173 Serge Rey -with tests against previous implementation removed 233e59a Serge Rey -speed comparison for change to query_pairs in kdtree fb78ea9 Serge Rey -removing test file 4d04575 Philip Stephens -testing 357a184 Serge Rey -second great idea 1fafc2b Serge Rey -on a plane commit 1 fef6eae Philip Stephens -fix 86c17ac Serge Rey -- test file a619f62 Philip Stephens -interim ci 1a9d881 Serge Rey -- knox test using kdtrees 7459c44 Serge Rey -Fixing reference to missing shapefile Fixing one rounding error induced test 5616b12 Serge Rey -refactored to avoid second loop in explicit queen or rook check d3d2f71 Philip Stephens -Revert "Changed doctest path calls to account for modified shapefile." da1d8a1 Philip Stephens -Changed doctest path calls to account for modified shapefile. f591c99 Philip Stephens -progress on permutations of knox for larger datasets 8d31cde Serge Rey -Testing integration of spatialnet creation and reading into wed 11de6f3 Jay -Fixed wed_modular.py 077658a Serge Rey -adding new test case for wed extraction from a spatialnet shapefile bbb10b4 Philip Stephens -saving state of development 44076b7 Serge Rey -- update doc test 6fdd94d Serge Rey -- moved regions_from_graph into wed_modular - documented all functions and cleaned up 5bd27c3 Serge Rey -- wrapping in functions 3ad162f Serge Rey -- working version of wed_modular module - starting point for clean up 2380f15 Philip Stephens -Copy of sphinx install docs. Closes #251 5687700 Philip Stephens -tweaks to install instructions 9ffd432 Serge Rey -- updating for switch from svn to git fdaf521 Philip Stephens -Fixing 250 5ba4fdf Serge Rey -Fixes #249 Closes #249 d89944d Pedro -Adding docs for each regimes estimator f03bb63 Serge Rey -- updating docs for spatial regimes in spreg a49d0f7 Philip Stephens -Adding info to setup script. 1f27605 Philip Stephens -mainly docs 04f8a31 Philip Stephens -Adding test coverage with nose, data collected and presented on coveralls.io 6db978b Philip Stephens -last changes 137e088 Philip Stephens -added bigdata parameter 7ca81c2 Philip Stephens -got Knox stat working in alt form 24c1fcc Philip Stephens -workign on refactoring the space-time matrices for the Knox test [ci-skip] 28013f0 Serge Rey -- enumeration of cw edges for faces baa8f60 Serge Rey -- hole is now included and enumeration of links (cw) around nodes works for all nodes. - isolated nodes also handled in enumeration of links around nodes. 33741c8 Serge Rey -- filaments inserted and pointers updated - have to add hole polygon and isolated nodes, but almost there!!!!!!!!! 416d3db Serge Rey -- pointers updated for edges of connected components c34e274 Serge Rey -- convex/between edge test as start of testing for insertion of multiple internal filaments in one region. 78d96b1 Serge Rey -- filament insertion and pointer updates ced2c5b Serge Rey -- filament insertion (inc) ba4263f Jay -Logic roughed in for filaments [ci skip] cf3b0bc Jay -updated wed ipynb [ci skip] 33ce81e Serge Rey -- refactoring of wed construction (incomplete) 0fc16fc Jay -modular WED Pulled Apart 2 funcs in 1 cell bf73b90 Jay -modular WED 3163377 Serge Rey -- new modular wed construction e50b31d Jay -added test_wed additions to test_wed2 1cbc941 Serge Rey -- isolated nodes handled d28b97f Serge Rey -- isolated filament handled 6188fd5 Serge Rey -- hole component handled a96040b Serge Rey -- getting connected components (current 14,15,16 and 25,26,27 are not included) 3aa31a5 Jay -Added boolean arg to include or exclude holes [ci skip] d07876d Jay -Filament identification [ci skip] 0139ea5 Philip Stephens -Slight speed improvement getting rid of append calls in reading shapefile and building x,y lists. 43010b5 Serge Rey -- fixed logic problem with enum for v1, starting on components 8737918 Pedro -Adding more meaningful error message to inverse distance weights 01f52f6 Serge Rey -- replacing code that got deleted previously 7c4c6e1 Philip Stephens -Replacing deleted files. a8da725 Philip Stephens -added date support to spacetimeevents class, a date column to example dbf. 90c4730 Philip Stephens -logic works, numeric test still failing b8e43e1 Philip Stephens -saving progress on interaction 81f2408 Serge Rey -- handling external end-node-filament 7de6253 Serge Rey -- adding end node filament handling - edge enumeration around node working f542b9a Serge -- adding end node filament handling - edge enumeration around node working d7e3a57 Philip Stephens -[ci skip] disabling nose-progressive so travis output looks best fe03013 Dani Arribas-Bel -Adding set of diversity indices to inequality module under _indices.py for now. Still lacks doctests, unittests, and a few others will be added 951b6f5 Dani Arribas-Bel -Adding try/except to the import of Basemap to allow the use of the module when there is no Basemap installation 89003eb Serge Rey -- adding wed for eberly example 665ef22 Serge Rey -- fixed 7,2 failure 71fc9ad Serge Rey -start of adding gini and other inequality measures f7b7bcc Phil Stephens -Adding nose-progressive plugin to test suite. Devs can run test suite with 'make test'. f5db7bf Serge Rey -- updating copyright 07574b5 Serge Rey -- docs 478d2cb Philip Stephens -Adding requirement. Removing redundancy. 916a6ca Serge Rey -- more island check updates edd9960 Serge Rey -- more island check doctest changes ad1a91c Serge Rey -- updating doctests for island check ce77772 Serge Rey -- fixing doctests to incorporate new island warning 554a30b Serge Rey -- silencing floating point warning 4f76862 Serge Rey -- moving default contiguity builder back to binning from rtree b99665b Jay -Eberly d911344 Jay -mp removed, passing nosetests on my machine serial f005675 Serge Rey -improved binning algorithm for contiguity builder 4a69557 Serge Rey -- double checking threshold in Distance Band - new example to show functionality 7256f13 Serge Rey -- fix handling of idVariable for knnW 31bb36e Jay -bug fixes [ci skip] a2d2dd4 Jay -WEberly - WED Building [ci skip] 3abc55e Serge Rey -- fixing doctests for new check/reporting for islands 756ac05 Serge Rey -- adding warning if islands exist upon W instantiation db097a6 Jay -Weberly, bug fix, c and cc link remaining d5cc6f9 Jay -All but start / end working 033963d Jay -Integration to WEberly error fixed [ci skip] 22b931a Serge Rey -- removing main for doc tests which can be run from nosetests. - updating testing docs bf753e9 Jay -Integration to WEberly started [ci skip] 6506e07 Serge Rey -- typo aede375 Serge Rey -- replacing double quotes around multi word ids with strings joined with underscores cf029e8 Serge Rey -- changes to wrap string ids in gwt writer - see https://github.com/pysal/pysal/issues/244#issuecomment-16707353 626ac08 Serge Rey -- adding shapefile and variable name to gwt objects created in user space 3c84bb0 Jay -Working version 4.19 [ci skip] 7d77da9 darribas -Include warning in sp_att when rho is outside (-1, 1), ammends #243 although the true problem (pearsonr in diagnostics_tsls) will still raise an error 3719d21 Jay -working WED [ci skip] b4ce294 Serge Rey -checking edges f4bb412 Jay -excessive print statements removed. ci skip 9f7dee6 Jay -SUCCESS! ci skip 9077615 Phil Stephens -Note, [ci skip] anywhere in your commit message causes Travis to NOT build a test run. cb072c4 Jay -getting there d3b36bc Serge Rey -correcting typo user told me about 19ea051 Jay -trivial working b9ea577 Jay -eberly cycles - edge issue still d5153e3 Serge Rey -more refinement of wed from plannar graph edff44b Philip Stephens -adding git ignore file 8093f21 Serge Rey -wed from minimum cycle basis b5bcead Serge Rey -handle filaments 9a8927a Serge Rey -face extraction using horton algorithm 10d66c1 Serge Rey -updating readme formatting 59f3750 schmidtc -adding Universal newline support to csvReader, fixes #235 09e813f Serge Rey -- updating notifications f8b0a26 Serge Rey -- fixing Distance.py and testing travis message d1ec0f2 Phil Stephens -quieting pip output and fix one doctest 927e799 Phil Stephens -adding networkx, tweaks to travis config 5971bb1 Serge Rey -neighbors from wed 28f0e55 Serge Rey -adding robust segment intersection tests 3bcac73 Serge Rey -adding doubly connected edge list to network module 86f0fea darribas -Adding methods to read line and point shapefiles and improving the method to append different collections to one axes. Still in progress b61cb55 Serge Rey -- fixing introduced bug in knnW_arc 801e78d Serge Rey -Handle point sets with large percentage of duplicate points dbafbc4 serge -update pointer to github 427a620 Serge Rey -dealing with filaments 23216ef Serge Rey -Fixed cw enumeration of links incident to a node 0a51a53 Serge Rey -- readme 5f4cab4 sjsrey -cw enumeration not working for all nodes f2e65d3 Serge Rey -- cw traversal of edges incident with a node 90d150c sjsrey -- version debug for travis 24598a8 sjsrey -- noting move to org 9fb8a17 sjsrey -- fixing tutorial tests 5a14f9e serge -- cleaning up weights tests 6265b3b Serge Rey -- fixing doc tests 7e8c4fe Serge Rey -- testing after move to org 37fc8d4 Serge Rey -- testing post commit emails bed7f6e Phil Stephens -removed files eab2895 Phil Stephens -removed virginia_queen files bcef010 Serge Rey -- adding diagonal argument to Kernel weights - adding doctest evaluation to Distance.py 02d27e9 Phil Stephens -adding libgeos-dev 1126d71 Phil Stephens -pipe build output to null 37dbb35 Phil Stephens -adding -y flag to pip uninstall 06d56e9 Phil Stephens -adding libgeos_c install, pysal from pip 4c53277 Phil Stephens -trying to quiet output, using Makefile 74448e8 Phil Stephens -find setup.py 4634fb1 Phil Stephens -test install in venv and build 5d58723 Phil Stephens -working out travis-ci doctest configuration 5e905d3 Phil Stephens -adding numpydoc 33a5298 Phil Stephens -tweaks travis config 5c85f50 Phil Stephens -tweaking service configs 4ed1201 Josh Kalderimis -use the correct syntax for sysytem_site_packages 954b6d2 Phil Stephens -stop! 311eca8 Phil Stephens -ssp=true c601bca Phil Stephens -numpy first 54b0afe Phil Stephens -ok, so travis is serious about not using system site packages. 2b912cc Phil Stephens -doh 28994df Phil Stephens -better yaml ce1d89e Phil Stephens -testing b535d3e Phil Stephens -testing 440a772 Phil Stephens -tweaking pip requirements file 34a74e2 Phil Stephens -tweaking travis file 33b13aa Serge Rey -- new links 8e09d7b Serge Rey -- setting up travis d33001e Sergio Rey -Update CHANGELOG.txt 9d4de66 Serge Rey -- added authors ab672c9 Serge Rey -- modified knnW to speed up dict construction 4edd2ab Serge Rey -- update cr 39e6564 Phil Stephens -syncing install instructions with docs 9e98db9 Phil Stephens -adding website favicon; chrome does not empty cache properly!! * migration to github from svn svn2git http://pysal.googlecode.com/svn --authors ~/Dropbox/pysal/src/pysal/authors.txt --verbose # v<1.5.0>, 2013-01-31 2013-01-29 20:36 phil.stphns * doc/source/users/installation.txt: updating and simplifying user install instructions. 2013-01-18 16:17 sjsrey * Adding regime classes for all GM methods and OLS available in pysal.spreg, i.e. OLS, TSLS, spatial lag models, spatial error models and SARAR models. All tests and heteroskedasticity corrections/estimators currently available in pysal.spreg apply to regime models (e.g. White, HAC and KP-HET). With the regimes, it is possible to estimate models that have: -- Common or regime-specific error variance; -- Common or regime-specific coefficients for all variables or for a selection of variables; -- Common or regime-specific constant term; - Various refactoring to streamline code base and improve long term maintainability - Contributions from Luc Anselin, Pedro Amaral, Daniel Arribas-Bel and David Folch 2013-01-18 14:08 schmidtc * pysal/common.py: implemented deepcopy for ROD, see #237 2013-01-08 12:28 dreamessence * pysal/contrib/spatialnet/__init__.py: Adding __init__.py to make it importable 2012-12-31 22:53 schmidtc * pysal/core/IOHandlers/gwt.py: adding kwt support, see #232 2012-12-21 20:53 sjsrey@gmail.com * pysal/__init__.py, pysal/cg/rtree.py, pysal/contrib/weights_viewer/weights_viewer.py, pysal/weights/weights.py: - turning off randomization in rtree 2012-12-06 16:34 dfolch * pysal/contrib/shapely_ext.py: adding unary_union() to shapely contrib; note this only works with shapely version 1.2.16 or higher 2012-11-29 13:39 dreamessence * pysal/contrib/viz/mapping.py: Added option in setup_ax to pass pre-existing axes object to append. It is optional and it enables, for instance, to embed several different maps in one single figure 2012-11-20 00:23 dfolch * pysal/contrib/shapely_ext.py: adding shapely's cascaded_union function to contrib 2012-11-12 18:08 dreamessence * pysal/contrib/viz/mapping.py: -Adding transCRS method to convert points from one prj to another arbitrary one -Adding map_poly_shp to be able to plot shapefiles in arbitrary projections, not needing to be in lonlat and not depending on Basemap 2012-11-09 15:40 sjsrey@gmail.com * pysal/weights/weights.py: - distinguish between intrinsic symmetry and general symmetry 2012-11-02 17:48 schmidtc * pysal/weights/user.py, pysal/weights/util.py: Adding Minkowski p-norm to min_threshold_dist_from_shapefile, see issue #221 2012-10-19 22:35 sjsrey@gmail.com * pysal/weights/weights.py: explicitly prohibit chaining of transformations - all transformations are only applied to the original weights at instantiation 2012-10-19 17:38 sjsrey@gmail.com * pysal/spatial_dynamics/markov.py: - fixing bug in permutation matrix to reorder kronecker product in the join test 2012-10-17 17:55 sjsrey@gmail.com * pysal/weights/util.py: - higher order contiguity for WSP objects 2012-10-17 15:43 sjsrey@gmail.com * pysal/weights/user.py: - id_order attribute was always NONE for wsp created from queen/rook_from_shapefile with sparse=True 2012-10-16 19:25 schmidtc * pysal/weights/util.py: improving memory usage of get_points_array_from_shapefile, no need to read entire shapefile into memory. 2012-10-15 00:44 dreamessence * pysal/contrib/viz/mapping.py: First attempt to refactor Serge's code for choropleth mapping. It now offers a more general and flexible architecture. Still lots of work and extensions left. The module is explained in a notebook available as a gist at https://gist.github.com/3890284 and viewable at http://nbviewer.ipython.org/3890284/ 2012-10-12 18:34 schmidtc * pysal/contrib/spatialnet/spatialnet.py: modified SpatialNetwork.snap to calculate and return the snapped point 2012-10-12 17:05 dfolch * pysal/contrib/viz/mapping.py: made edits to unique_values_map to allow for unlimited number of categories; I commented out the previous code so these changes can easily be rolled back if it breaks something somewhere else 2012-10-12 15:03 schmidtc * pysal/cg/segmentLocator.py: Fixing issue with segmentLocator, when query point is extreamly far from the grid boundary, overflow errors were causing the KDTree to not return any results. Changed both KDtree's to use Float64 and share the same data. Previously, cKDTree was using float64 and KDtree was using int32. 2012-10-11 08:12 dreamessence * pysal/contrib/viz/__init__.py: Adding __init__.py to viz module to make it importable 2012-08-31 02:57 phil.stphns * pysal/spreg/tests/test_diagnostics.py, pysal/spreg/tests/test_diagnostics_sp.py, pysal/spreg/tests/test_diagnostics_tsls.py, pysal/spreg/tests/test_error_sp.py, pysal/spreg/tests/test_error_sp_het.py, pysal/spreg/tests/test_error_sp_het_sparse.py, pysal/spreg/tests/test_error_sp_hom.py, pysal/spreg/tests/test_error_sp_hom_sparse.py, pysal/spreg/tests/test_error_sp_sparse.py, pysal/spreg/tests/test_ols.py, pysal/spreg/tests/test_ols_sparse.py, pysal/spreg/tests/test_probit.py, pysal/spreg/tests/test_twosls.py, pysal/spreg/tests/test_twosls_sp.py, pysal/spreg/tests/test_twosls_sp_sparse.py, pysal/spreg/tests/test_twosls_sparse.py: - autopep8 -iv spreg/tests/*.py - nosetests pysal - no fixes needed 2012-08-31 01:16 phil.stphns * pysal/spreg/diagnostics.py, pysal/spreg/diagnostics_sp.py, pysal/spreg/diagnostics_tsls.py, pysal/spreg/error_sp.py, pysal/spreg/error_sp_het.py, pysal/spreg/error_sp_hom.py, pysal/spreg/ols.py, pysal/spreg/probit.py, pysal/spreg/robust.py, pysal/spreg/summary_output.py, pysal/spreg/twosls.py, pysal/spreg/twosls_sp.py, pysal/spreg/user_output.py, pysal/spreg/utils.py: - autopep8 -iv spreg/*.py - fixed autopep8-introduced doctest failures - fixed lingering scientific notation test failures 2012-08-31 00:26 phil.stphns * pysal/esda/gamma.py, pysal/esda/join_counts.py, pysal/esda/mapclassify.py, pysal/esda/mixture_smoothing.py, pysal/esda/moran.py, pysal/esda/smoothing.py: - autopep8 fixes - make sure to run unit and doc tests before committing - one autofix breaks long lines, and thus breaks some doctests; must be fixed manually 2012-08-31 00:10 phil.stphns * pysal/esda/getisord.py: - using autopep8 module - call: autopep8 -vi getisord.py 2012-08-30 23:18 phil.stphns * pysal/esda/geary.py: - pep8 clear - removed wildcard import 2012-08-26 22:53 phil.stphns * pysal/spatial_dynamics/directional.py, pysal/spatial_dynamics/ergodic.py, pysal/spatial_dynamics/interaction.py, pysal/spatial_dynamics/markov.py, pysal/spatial_dynamics/rank.py, pysal/spatial_dynamics/util.py: -pep8 and pylint fixes -clean wildcard imports 2012-08-26 21:03 phil.stphns * pysal/region/maxp.py, pysal/region/randomregion.py: - cleaning up imports 2012-08-26 18:16 phil.stphns * pysal/region/maxp.py: - style fixes with pep8 - cmd line call: pep8 --show-source --ignore=E128,E302,E501,E502,W293,W291 region/maxp.py 2012-08-26 17:47 phil.stphns * pysal/common.py, pysal/examples/README.txt, pysal/region/components.py, pysal/region/randomregion.py: - using pep8 module 2012-08-24 20:47 schmidtc * pysal/network, pysal/network/__init__.py: adding network module 2012-08-21 22:53 phil.stphns * doc/source/_templates/ganalytics_layout.html: - updating analytics tracker 2012-08-17 17:11 sjsrey@gmail.com * pysal/contrib/spatialnet/util.py: - more utility functions for pysal - networkx interop 2012-08-16 23:44 phil.stphns * setup.py: - tweak for build names 2012-08-12 13:15 dreamessence * doc/source/index.txt: Adding announcement links to landing page 2012-08-11 17:38 sjsrey * LICENSE.txt: - update 2012-08-09 17:19 phil.stphns * doc/source/developers/pep/pep-0008.txt: updating spatial db pep 2012-08-08 17:22 schmidtc * pysal/weights/Distance.py: Fixing bug in Kernel weights that causes erroneous results when using ArcDistances. See issue #218. 2012-08-04 21:14 sjsrey * doc/source/developers/docs/index.txt: - fixed links 2012-08-04 21:03 sjsrey * doc/source/developers/docs/index.txt: - hints on editing docs 2012-08-04 20:14 phil.stphns * doc/source/developers/pep/pep-0011.txt: note about travis-ci and github 2012-08-04 16:24 sjsrey * doc/source/developers/pep/pep-0011.txt: PEP-0011 2012-08-04 16:22 sjsrey * doc/source/developers/pep/index.txt: - PEP 0011 Move from Google Code to Github 2012-08-04 04:42 sjsrey * doc/source/index.txt: - broken link 2012-08-04 04:35 sjsrey * doc/source/index.txt: - news updates 2012-08-04 04:24 sjsrey * doc/source/index.txt: - reorg 2012-08-02 02:32 sjsrey * pysal/examples/__init__.py: - moving back to r1049 but leaving r1310 in history for ideas on moving forward - we need to distinguish between using examples in the doctests (which the users see) and for the developers since we are no longer distributing examples with the source 2012-08-02 01:49 sjsrey * pysal/examples/__init__.py: - correct conditional this time (i hope) 2012-08-02 01:36 sjsrey * pysal/examples/__init__.py: - compromise - returns pth rather than None if file does not exist 2012-08-02 00:58 sjsrey * pysal/examples/__init__.py: - link to examples download 2012-08-02 00:42 sjsrey * pysal/examples/__init__.py: - explicit check if examples are actually present # v<1.4.0>, 2012-07-31 2013-01-31 2012-07-31 21:30 sjsrey@gmail.com * pysal/spatial_dynamics/ergodic.py, pysal/spatial_dynamics/rank.py: - docs/example 2012-07-31 20:47 sjsrey@gmail.com * pysal/spreg/tests/test_error_sp_hom.py: - rounding/precision issue 2012-07-31 20:27 sjsrey@gmail.com * pysal/spatial_dynamics/directional.py, pysal/spatial_dynamics/tests/test_directional.py: - fixing pvalue bug 2012-07-31 20:24 sjsrey@gmail.com * doc/source/users/tutorials/dynamics.txt: - fixed rounding problem 2012-07-31 19:58 sjsrey@gmail.com * doc/source/index.txt, doc/source/users/tutorials/autocorrelation.txt, doc/source/users/tutorials/dynamics.txt, doc/source/users/tutorials/econometrics.txt, doc/source/users/tutorials/fileio.txt, doc/source/users/tutorials/index.txt, doc/source/users/tutorials/intro.txt, doc/source/users/tutorials/region.txt, doc/source/users/tutorials/smoothing.txt, doc/source/users/tutorials/weights.txt: - adding links to API for more details 2012-07-31 19:05 sjsrey@gmail.com * pysal/spatial_dynamics/directional.py: - consistency on pvalues for randomization 2012-07-31 19:02 sjsrey@gmail.com * pysal/weights/Distance.py: - docs 2012-07-31 18:58 sjsrey@gmail.com * doc/source/users/tutorials/dynamics.txt: - seed issue 2012-07-31 18:36 sjsrey@gmail.com * doc/source/users/tutorials/autocorrelation.txt: - closing issue 214 2012-07-31 18:19 sjsrey@gmail.com * doc/source/users/tutorials/autocorrelation.txt: - fixing random.seed issues in doctests 2012-07-31 17:31 schmidtc * pysal/cg/shapes.py, pysal/cg/tests/test_shapes.py: Fixing small bugs with VerticleLines and testing 2012-07-31 16:26 sjsrey@gmail.com * doc/source/developers/guidelines.txt, doc/source/users/installation.txt: - updating docs 2012-07-26 15:24 schmidtc * pysal/core/FileIO.py, pysal/core/Tables.py: Fixing issue #190 2012-07-24 16:32 schmidtc * pysal/cg/sphere.py: Allowing linear2arcdist function to maintin 'inf', this allows compatability with Scipy's KDTree and addresses issue 208. 2012-07-24 16:07 schmidtc * pysal/cg/locators.py, pysal/core/FileIO.py, pysal/core/Tables.py: Addressing issue 212, renaming nested and private classes to begin with an underscore. By default sphinx does not try to document private object, which avoids what appears to be a a bug in Sphinx. 2012-07-17 22:06 sjsrey@gmail.com * pysal/spreg/probit.py: pedro doc fixes 2012-07-17 15:07 schmidtc * pysal/cg/segmentLocator.py, pysal/cg/tests/test_segmentLocator.py: Cleaned up fix for Issue 211 2012-07-13 22:50 sjsrey@gmail.com * doc/source/users/tutorials/autocorrelation.txt: fixing sphinx weirdness in footnotes 2012-07-13 22:37 sjsrey@gmail.com * doc/source/users/tutorials/autocorrelation.txt: update for new default parameter values 2012-07-13 22:13 sjsrey@gmail.com * pysal/esda/geary.py, pysal/esda/tests/test_geary.py: consistency on transformation and permutation args 2012-07-13 19:59 sjsrey@gmail.com * doc/source/users/tutorials/dynamics.txt, pysal/__init__.py, pysal/spatial_dynamics/rank.py: - update user tutorial and __init__ 2012-07-13 19:33 sjsrey@gmail.com * pysal/spatial_dynamics/rank.py, pysal/spatial_dynamics/tests/test_rank.py: - O(n log n) algorithm for spatial tau (old one was O(n^2)) - closing ticket http://code.google.com/p/pysal/issues/detail?id=83 2012-07-13 17:57 schmidtc * pysal/core/IOHandlers/pyDbfIO.py, pysal/core/IOHandlers/tests/test_pyDbfIO.py: Adding better support for writing Null values to DBF. See issue #193 2012-07-13 15:55 schmidtc * pysal/core/util/shapefile.py, pysal/core/util/tests/test_shapefile.py: Cleaning up support for ZM points, polylines and polygons in the shapefile reader. Added unit tests for same. 2012-07-13 15:42 sjsrey@gmail.com * doc/source/library/esda/gamma.txt: - update version info 2012-07-13 15:37 sjsrey@gmail.com * doc/source/library/esda/gamma.txt, doc/source/library/esda/index.txt: - adding gamma to api docs 2012-07-13 00:21 sjsrey@gmail.com * pysal/esda/gamma.py: optimizations 2012-07-12 21:28 schmidtc * pysal/core/IOHandlers/pyDbfIO.py: Disabling mising value warning for DBF files. See issue #185 2012-07-12 21:07 schmidtc * pysal/cg/segmentLocator.py, pysal/cg/shapes.py, pysal/cg/tests/test_segmentLocator.py, pysal/contrib/spatialnet/spatialnet.py: Adding unittests for segmentLocator (including one that fails see #211). Added VerticalLine class to represent verticle LineSegments. Updated __all__ in segmentLocator. Minor comment formatting in spatialnet. 2012-07-12 19:41 lanselin@gmail.com * doc/source/users/tutorials/autocorrelation.txt: tutorial for gamma index 2012-07-12 19:40 lanselin@gmail.com * pysal/esda/gamma.py, pysal/esda/tests/test_gamma.py: gamma with generic function 2012-07-12 14:17 sjsrey@gmail.com * pysal/__init__.py: - gamma index added 2012-07-12 03:14 lanselin@gmail.com * pysal/esda/tests/test_gamma.py: tests for gamma 2012-07-12 03:13 lanselin@gmail.com * pysal/esda/gamma.py: gamma index of spatial autocorrelation 2012-07-12 03:11 lanselin@gmail.com * pysal/esda/__init__.py: gamma index 2012-07-11 21:32 lanselin@gmail.com * pysal/esda/join_counts.py, pysal/esda/tests/test_join_counts.py: join counts without analytical results, new permutation 2012-07-11 21:32 lanselin@gmail.com * doc/source/users/tutorials/autocorrelation.txt: updated docs for join counts 2012-07-10 21:13 lanselin@gmail.com * doc/source/users/tutorials/autocorrelation.txt: docs for join count in autocorrelation 2012-07-10 21:12 lanselin@gmail.com * pysal/esda/join_counts.py, pysal/esda/tests/test_join_counts.py: additional test in join counts, docs added 2012-07-10 19:24 lanselin@gmail.com * pysal/esda/join_counts.py, pysal/esda/tests/test_join_counts.py: join counts with permutations for BB, updated tests to include permutations 2012-07-09 04:22 sjsrey * pysal/weights/weights.py: - fixing bug luc identified with regard to mean_neighbor property. wrong key name was used in cache dictionary. 2012-07-07 17:00 sjsrey * pysal/__init__.py: update for spreg and contrib inclusion 2012-07-07 16:51 sjsrey * pysal/spatial_dynamics/markov.py: - updating doc strings 2012-07-07 16:17 sjsrey * pysal/spreg/probit.py: - fixing doc string and refs 2012-07-06 21:58 dfolch * doc/source/library/spreg/probit.txt: txt file to include probit in the HTML docs 2012-07-06 21:11 dfolch * pysal/spreg/tests/test_ols_sparse.py: fixing unittest error; still no solution to scientific notation formatting in doctests 2012-07-06 20:24 dfolch * pysal/spreg/__init__.py, pysal/spreg/diagnostics.py, pysal/spreg/diagnostics_sp.py, pysal/spreg/diagnostics_tsls.py, pysal/spreg/error_sp.py, pysal/spreg/error_sp_het.py, pysal/spreg/error_sp_hom.py, pysal/spreg/ols.py, pysal/spreg/probit.py, pysal/spreg/robust.py, pysal/spreg/summary_output.py, pysal/spreg/tests/test_diagnostics.py, pysal/spreg/tests/test_diagnostics_sp.py, pysal/spreg/tests/test_diagnostics_tsls.py, pysal/spreg/tests/test_error_sp.py, pysal/spreg/tests/test_error_sp_het.py, pysal/spreg/tests/test_error_sp_het_sparse.py, pysal/spreg/tests/test_error_sp_hom.py, pysal/spreg/tests/test_error_sp_hom_sparse.py, pysal/spreg/tests/test_error_sp_sparse.py, pysal/spreg/tests/test_ols.py, pysal/spreg/tests/test_ols_sparse.py, pysal/spreg/tests/test_probit.py, pysal/spreg/tests/test_twosls.py, pysal/spreg/tests/test_twosls_sp.py, pysal/spreg/tests/test_twosls_sp_sparse.py, pysal/spreg/tests/test_twosls_sparse.py, pysal/spreg/twosls.py, pysal/spreg/twosls_sp.py, pysal/spreg/user_output.py, pysal/spreg/utils.py: -Adding classic probit regression class -Adding spatial diagnostics for probit -Allowing x parameter to be either a numpy array or scipy sparse matrix in all regression classes -Adding additional unit tests -Various refactoring to streamline code base and improve long term maintainability -Contributions from Luc Anselin, Pedro Amaral, Daniel Arribas-Bel, David Folch and Nicholas Malizia 2012-07-03 18:59 sjsrey * pysal/spatial_dynamics/markov.py, pysal/spatial_dynamics/tests/test_markov.py: - refactor significant move_types for clarity and fixing a logic bug 2012-06-20 04:50 sjsrey@gmail.com * doc/source/developers/docs/index.txt: - added section for how to write a tutorial for new modules 2012-06-20 02:45 sjsrey * doc/source/developers/docs/index.txt: - updating doc building instructions 2012-06-06 18:58 phil.stphns * .build-osx10.6-py26.sh, .build-osx10.6-py27.sh: - local modifications for Frameworks builds 2012-06-05 20:56 phil.stphns * .build-osx10.6-py26.sh, .build-osx10.6-py27.sh, .build-osx10.7-py27.sh, .runTests.sh: - adding experimental build and test scripts. 2012-06-05 16:43 schmidtc * pysal/cg/segmentLocator.py, pysal/cg/shapes.py, pysal/contrib/spatialnet/spatialnet.py: initial snap function for spatialnet 2012-06-05 16:38 schmidtc * pysal/core/IOHandlers/pyShpIO.py, pysal/core/util/shapefile.py, pysal/core/util/tests/test_shapefile.py: Adding PolygonZ support to Shapefile IO 2012-05-24 21:57 sjsrey * pysal/esda/mapclassify.py: - truncate option for fisher_jenks sampling 2012-05-15 20:08 schmidtc * pysal/cg/segmentLocator.py, pysal/cg/shapes.py: Added query to SegmentLocator 2012-05-11 22:17 sjsrey * pysal/esda/mapclassify.py: - added Fisher_Jenks_Sampled 2012-05-11 00:45 mhwang4 * pysal/contrib/network/distances.csv, pysal/contrib/network/simulator.py, pysal/contrib/network/test_lincs.py, pysal/contrib/network/test_weights.py, pysal/contrib/network/weights.py: adding test code for distance-file-based weight generator; updates on simulator 2012-05-10 22:37 mhwang4 * pysal/contrib/network/klincs.py, pysal/contrib/network/lincs.py, pysal/contrib/network/test_klincs.py, pysal/contrib/network/test_lincs.py: adding test code for network-constrained lisa 2012-05-10 21:11 mhwang4 * pysal/contrib/network/crimes.dbf, pysal/contrib/network/crimes.shp, pysal/contrib/network/crimes.shx, pysal/contrib/network/test_klincs.py: test code for local K function 2012-05-08 18:05 mhwang4 * pysal/contrib/network/streets.dbf, pysal/contrib/network/streets.shp, pysal/contrib/network/streets.shx, pysal/contrib/network/test_network.py: adding a test data set 2012-05-08 16:34 schmidtc * pysal/cg/segmentLocator.py, pysal/cg/shapes.py, pysal/core/FileIO.py: Adding start of segmentLocator, adding minimal slicing support to FileIO 2012-05-03 17:03 schmidtc * pysal/cg/shapes.py, pysal/cg/tests/test_shapes.py: Adding solve for x support to Line. Cleaning up LineSegment's Line method. 2012-04-20 17:48 schmidtc * pysal/cg/shapes.py: adding arclen method to Chain object. 2012-04-19 16:37 dfolch * pysal/weights/Distance.py: reducing number of distance queries in Kernel from n^2 to n 2012-04-17 21:20 schmidtc * pysal/contrib/spatialnet/spatialnet.py: adding distance 2012-04-17 19:46 schmidtc * pysal/contrib/spatialnet/cleanNetShp.py, pysal/contrib/spatialnet/spatialnet.py: Adding FNODE/TNODE to dbf when cleaning shapefiles. Added util function createSpatialNetworkShapefile Added SpatialNetwork class 2012-04-17 15:32 schmidtc * pysal/contrib/weights_viewer/weights_viewer.py: "revert back to the background when the point is outside of any unit" - request from serge 2012-04-11 02:50 schmidtc * pysal/cg/kdtree.py: Fixing user submitted bug,issue #206. 2012-04-10 22:00 dreamessence * pysal/weights/Wsets.py: Including w_clip in __all__ 2012-04-10 21:58 dreamessence * pysal/weights/Wsets.py: Adding w_clip method to clip W matrices (sparse and/or pysal.W) with a second (binary) matrix 2012-04-10 21:57 schmidtc * pysal/contrib/spatialnet/beth_roads.shp, pysal/contrib/spatialnet/beth_roads.shx, pysal/contrib/spatialnet/cleanNetShp.py: Adding network shapefile cleaning tools and temporary sample data. 2012-04-10 21:48 sjsrey * pysal/contrib/spatialnet/util.py: - more stubs for util mod 2012-04-10 19:58 sjsrey * pysal/contrib/spatialnet/util.py: - start of util module 2012-04-03 20:43 sjsrey * pysal/contrib/spatialnet: - new contrib module - integrate geodanet functional (move over from network) - wrap networkx 2012-04-03 01:21 schmidtc * pysal/cg/rtree.py: Adding pickle support to RTree 2012-03-28 23:27 mhwang4 * pysal/contrib/network/kernel.py, pysal/contrib/network/kfuncs.py, pysal/contrib/network/test_access.py, pysal/contrib/network/test_kernel.py, pysal/contrib/network/test_kfuncs.py, pysal/contrib/network/test_network.py: adding examples for network-related modules 2012-03-19 15:33 schmidtc * pysal/core/IOHandlers/pyDbfIO.py: Adding support for writing Null dates 2012-03-14 21:04 phil.stphns * doc/source/developers/testing.txt, doc/source/users/installation.txt: Small changes to user install instructions to highlight the ease with which pysal can be installed ;-> And, developer instructions for running the test suite from within a session if desired. 2012-03-03 00:00 phil.stphns * pysal/spatial_dynamics/markov.py: Potential source of dev docs pngmath latex fail. 2012-02-24 23:29 mhwang4 * pysal/contrib/network/network.py: fixing bug in network.py 2012-02-20 19:50 phil.stphns * doc/source/developers/py3k.txt: Developer doc to explain setting up PySAL for Python3. 2012-02-20 16:18 schmidtc * pysal/esda/__init__.py: removing invalid __all__ from esda's init. See #194 2012-02-16 23:15 phil.stphns * pysal/__init__.py, pysal/core/util/shapefile.py: Minor changes to imports that cause py3tool to stumble. 2012-02-15 23:16 phil.stphns * doc/source/developers/py3k.txt, doc/source/users/installation.txt: Modified links in user installation instructions. Added more steps for developers setting up Python3 dev environments on OSX. 2012-02-14 21:55 schmidtc * pysal/esda/getisord.py: fixing side effect caused when changing the shape of y, creating a new view with reshape instead. 2012-02-14 21:21 schmidtc * pysal/esda/getisord.py: optimizing G_Local 2012-02-14 20:37 schmidtc * pysal/esda/getisord.py: optimizing G 2012-02-14 00:21 phil.stphns * doc/source/developers/index.txt, doc/source/developers/py3k.txt, doc/source/developers/release.txt: Adding early docs on Python 3 support. Modifying release instructions. # v<1.3.0>, 2012-01-31 * core/IOHandlers/pyDbfIO.py: Addressing issue #186 * cg/shapes.py: fixing small bug in polygon constructor that causes an exception when an empty list is passed in for the holes. * cg/standalone.py: removing standalone centroid method. see issue #138. * esda/mapclassify.py, esda/tests/test_mapclassify.py: - new implementation of fisher jenks * spreg/__init__.py, spreg/diagnostics_sp.py, spreg/diagnostics_tsls.py, spreg/error_sp.py, spreg/error_sp_het.py, spreg/error_sp_hom.py, spreg/ols.py, spreg/robust.py, spreg/tests, spreg/twosls.py, spreg/twosls_sp.py, spreg/user_output.py, spreg/utils.py: Adding the following non-spatial/spatial regression modules: * Two Stage Least Squares * Spatial Two Stage Least Squares * GM Error (KP 98-99) * GM Error Homoskedasticity (Drukker et. al, 2010) * GM Error Heteroskedasticity (Arraiz et. al, 2010) * Anselin-Kelejian test for residual spatial autocorrelation of residuals from IV regression Adding also utility functions and other helper classes. * cg/standalone.py: slight improvment to get_shared_segments, in part to make it more readable. * cg/shapes.py, cg/tests/test_standalone.py: adding <,<=,>,>= tests to Point, this fixes a bug in the get_shared_segments function that was causing some LineSegments to be incorectly ordered because the default memory address was being used instead of the points location. * core/IOHandlers/tests/test_wkt.py, core/IOHandlers/wkt.py, core/util/tests/test_wkt.py, core/util/wkt.py, weights/tests/test_Distance.py, weights/tests/test_user.py, weights/user.py: Fixing small numerical errors n testing that resulted from changing the centroid algorithm. * esda/moran.py: another optimization for __crand see issue #188 * weights/util.py: Added option for row-standardized SW in lat2SW. Implementing suggestion from Charlie in Issue 181 from StackOverflow * esda/moran.py: another optimization to __crand, see issue #188 for details. * esda/moran.py: Optimized __crand in Local_Moran * cg/shapes.py, cg/standalone.py, contrib/shapely_ext.py: Adddressing issue #138, centroids for polygons with holes Fixing some issues with the shapely wrapper and out implemenation of __geo_interface__ * weights/Distance.py: previous 'fix' to uniform kernel did not have correct dimensions * core/IOHandlers/arcgis_txt.py, core/IOHandlers/dat.py, weights/user.py: fixing rounding errors with docstrings * contrib/README, contrib/shared_perimeter_weights.py: Adding shared perimeter weights, see Issue #46 * contrib/README, contrib/shapely_ext.py: moving shapely_ext into contrib * core/IOHandlers/pyDbfIO.py: Fixing issue with scientific notation is DBF files. #182 * core/IOHandlers/pyShpIO.py: clockwise testing should only be performed on Polygons. #183 * spreg/diagnostics_sp.py: Switching ints to floats in variance of Morans I for residuals to get correct results * core/util/shapefile.py, examples/__init__.py: Add a "get_path" function to examples module. pysal.examples.get_path('stl_hom.shp') will always return the correct system path to stl_hom.shp, no matter where it's run from. This is useful for testing. Modified shapefile tests to use the new function. * spreg/diagnostics.py: Adding check on condition_index to pick OLS (xtx) or IV (hth) model * core/IOHandlers/template.py: Updating template to pass unit testing. * core/util/shapefile.py: Fixing issue #180. Making shapefile opener case insensitive. * spatial_dynamics/interaction.py, spatial_dynamics/tests/test_interaction.py: Adding modified Knox and changes to existing tests in spatial_dynamics. * core/IOHandlers/arcgis_txt.py, core/IOHandlers/tests/test_arcgis_txt.py: fixing arcgis_txt.py so that it ignores self-neighbors with zero weights * core/FileIO.py: Updating library README. Removing docstrings from FileIO module. * contrib/README: adding contrib to installer and adding initial README * core/IOHandlers/gwt.py: rewrote GWT reader to avoid list appends. resulted in speed up of about 12x. * core/IOHandlers/pyDbfIO.py: implementing _get_col for dbf files. * core/IOHandlers/gwt.py: Adding a small fix to gwt reader, if the ids cannot be found in the associated DBF, they will be read in order from the GWT file. * contrib/weights_viewer/weights_viewer.py: Small change to identify polygons that are their own neighbor. * weights/Distance.py: removing incorrect kernel functions and fixing bug in uniform kernel * weights/util.py: refactoring insert_diagonal so that it can add or overwrite the diagonal weights * contrib, contrib/README, contrib/__init__.py, contrib/weights_viewer, contrib/weights_viewer/__init__.py, contrib/weights_viewer/transforms.py, contrib/weights_viewer/weights_viewer.py: Adding 1st contrib, a wxPython based Weights file viewer. * spatial_dynamics/markov.py: - handle case of zero transitions in spatial markov, consistent with treatment in classic markov * core/FileIO.py, core/IOHandlers/pyShpIO.py: Changes to allow reading of null polygons. * core/util/shapefile.py, core/util/tests/test_shapefile.py: refactoring shapefile reader, see issue #89 * core/FileIO.py: small change to FileIO to allow FileFormat argument to be passed through * esda/getisord.py: fixing bug in local Z values for integer data * cg/__init__.py, weights/user.py, weights/util.py: adding radius option to user weights methods * cg/kdtree.py, common.py, weights/Distance.py, weights/tests/test_Distance.py: Distance weights can not be passed an instnace of KDTree instead of an array. If the KDTree is of type ArcKDTree, the weights returns will be based on ArcDistances. Adding tests for Arc cases off KNN and DistanceBand. * weights/util.py: - added function for local clustering coefficient - summary for W as a graph * cg/kdtree.py, cg/sphere.py: finishing up Arc_KDTree * weights/Distance.py: More doctest fixes. * region/maxp.py, spreg/diagnostics.py, weights/Distance.py, weights/user.py: Fixing the doctests for dusty python setup. * cg/kdtree.py, cg/sphere.py: adding spherical wrapper around scipy kdtree * cg/__init__.py, cg/sphere.py: Adding spherical distance tools to cg. Related to issue #168 * core/IOHandlers/gwt.py, core/IOHandlers/tests/test_gwt.py: re-enabled gwt writing. 'o' transform is used on all GWTs for writing (w is returned to existing transform on exit) Also, setting '_shpName' and '_varName' attributes on W's which are read in through gwt. the writer will check if these vars exist and use them for the header, this prevents metadata loss on simple copies * esda/join_counts.py: - fix for handling int array type * spreg/diagnostics.py: Adding more efficient constant check for spreg. * cg/shapes.py: adding __geo_interface__ and asShape adapter for Point, LineString and Polygon * spreg/diagnostics.py: minor change to t-stat function to accommodate future regression models * esda/mapclassify.py: - more general fix for #166 # v<1.2.0>, 2011-07-31 * pysal/spreg/user_output.py: Fix for bug 162 * pysal/spatial_dynamics/markov.py: Added markov mobility measures; addresses issue 137 * pysal/weights/weights.py: Partially addressed issue 160 by removing the shimbel, order, and higher_order methods from W. * doc/source/users/installation.txt: Adding known issue regarding GNU/Linux testing and random seeds; see ticket 52. * pysal/esda/geary.py: Adding sparse implementation of Geary's C; substantial gains on larger datasets. * pysal/core/IOHandlers/mtx.py: Adding WSP2W function for fast conversion of sparse weights object (WSP) to pysal W. * pysal/esda/getisord.py: Adding Getis-Ord G test module * pysal/weights/util.py: Added function that inserts values along the main diagonal of a weights object * doc/source/users/tutorials: Fixed issue 76. * pysal/core/IOHandlers/mtx.py: Added an IOHandler for MatrixMarket MTX files * pysal/esda/moran.py: Optimized conditional randomization * pysal/weights/util.py: Re-adding full2W() method to convert full arrays into W objects; related to issue #136. * pysal/core/IOHandlers/gal.py: Added sparse WSP (thin W); gal reader can return W or WSP * pysal/core/IOHandlers/pyDbfIO.py: Bug Fix, DBF files are not properly closed when opened in 'r' mode. See issue #155. * pysal/core/IOHandlers/stata_txt.py: Adding FileIO handlers for STATA text files * pysal/weights/user.py: Fixed issue #154, adding k option to User Kernel weights functions. * pysal/core/IOHandlers/mat.py: Adding an IOHandler for MATLAB mat file * pysal/core/IOHandlers/wk1.py: Adding an IO handler for wk1 file * pysal/core/IOHandlers/geobugs_txt.py: Adding an IO handler for geobugs text file. * pysal/core/IOHandlers/arcgis_swm.py: Added ArcGIS SWM file handler * pysal/core/IOHandlers/arcgis_dbf.py: Adding a spatial weights file in the (ArcGIS-style) DBF format. * pysal/core/IOHandlers/arcgis_txt.py: Added ArcGIS ASCII file IO handler. * pysal/core/IOHandlers/dat.py: Added DAT file handler. * pysal/cg/locators.py: Added point in polygon method for Polygon and PolygonLocator * pysal/weights/Distance.py: Optimized Kernel() method to run much faster for the case of adaptive bandwidths * pysal/weights/user.py: Added helper function in user.py to create scipy sparse matrix from a gal file * pysal/common.py: Added shallow copy method to Read-Only Dict to support multiprocessing. * pysal/spatial_dynamics/rank.py: More efficient regime weights * pysal/weights/Distance.py: Adding epanechnikov and bisquare kernel funtions * pysal/core/IOHandlers/pyDbfIO.py: Adding NULL support to numerical DBF fields; modifying PointLocator API to match PolygonLocator API * pysal/cg/locators.py: Handles case when query rectangle is completely inside a polygon * pysal/cg/locators.py: Explicit polygon overlap hit test * pysal/cg/standalone.py: Adding point-polygon intersection support for polygons with holes. * pysal/spatial_dynamics/markov.py: Added homogeneity test. * pysal/spatial_dynamics/markov.py: Added spillover test in LISA_Markov. * pysal/cg/locators.py: Added Rtree based spatial index for polygonlocator. * pysal/cg/rtree.py: Added pure python Rtree module. * doc/source/developers/pep/pep-0010.txt: Added PEP 0010: Rtree module in pure python. * pysal/esda/geary.py: Fixed bug 144. * pysal/spatial_dynamics/markov.py: Added significance filtering of LISA markov. * doc/source/developers/pep/pep-0009.txt: Added new PEP, "PEP 0009: Add Python 3.x Support." * doc/source/developers/guidelines.txt: New release cycle schedules for 1.2 and 1.3. * doc/source/developers/release.txt: Updated pypi instructions; PySAL available on the Python Package Index via download, easy_install, and pip. # v<1.1.0>, 2011-01-31 * pysal/core/FileIO.py, pysal/core/IOHandlers/pyDbfIO.py: Added missing value support to FileIO. Warnings will be issued when missing values are found and the value will be set to pysal.MISSINGVALUE, currently None, but the user can change it as needed. * pysal/spreg/: Added Spatial Regression module, spreg, and tests. Added non-spatial diagnostic tests for OLS regression. * pysal/core/IOHandlers/gwt.py: Fixing bottle neck in gwt reader, adding support for GeoDa Style ID's and DBF id_order. * pysal/cg/standalone.py: adding, distance_matrix, full distance matrix calculation using sparse matrices * pysal/core/util: Moved "converters" into core.util, allows them to be used independently of FileIO. * pysal/weights/Distance.py: Adding work around for bug in scipy spatial, see pysal issue #126 * pysal/weights/user.py: Added build_lattice_shapefile in weights.user, which writes an ncol by nrow grid to a shapefile. * pysal/weights/Distance.py: fixed coincident point problem in knnW and made sure it returns k neighbors * pysal/spatial_dynamics/interaction.py: Added a suite of spatio-temporal interaction tests including the Knox, Mantel, and Jacquez tests. * pysal/weights/util.py: Added lat2SW, allows to create a sparse W matrix for a regular lattice. * pysal/tests/tests.py: - new 1.1 integration testing scheme. * pysal/esda/interaction.py: added standardized Mantel test and improved readability. * pysal/spatial_dynamics/directional.py: - adding directional LISA analytics * pysal/esda/mapclassify.py: Natural_Breaks will lower k for data with fewer than k unique values, prints warning. * pysal/region/randomregion.py: improvements to spatially constrained random region algorithm * pysal/esda/smoothing.py: Adding choynowski probabilities and SMR to smoothing.py * doc/source/developers/release.txt: - updating release cycle - release management # v<1.0.0>, 2010-07-31 -- Initial release. libpysal-4.9.2/LICENSE.txt000066400000000000000000000027771452177046000152130ustar00rootroot00000000000000Copyright (c) 2007-2015, PySAL Developers All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the GeoDa Center for Geospatial Analysis and Computation 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. libpysal-4.9.2/README.md000066400000000000000000000044031452177046000146330ustar00rootroot00000000000000# Python Spatial Analysis Library Core [![Continuous Integration](https://github.com/pysal/libpysal/actions/workflows/unittests.yml/badge.svg)](https://github.com/pysal/libpysal/actions/workflows/unittests.yml) [![codecov](https://codecov.io/gh/pysal/libpysal/branch/main/graph/badge.svg)](https://codecov.io/gh/pysal/libpysal) [![PyPI version](https://badge.fury.io/py/libpysal.svg)](https://badge.fury.io/py/libpysal) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/libpysal.svg)](https://anaconda.org/conda-forge/libpysal) [![DOI](https://zenodo.org/badge/81501824.svg)](https://zenodo.org/badge/latestdoi/81501824) ## libpysal modules - `libpysal.cg` – Computational geometry - `libpysal.io` – Input and output - `libpysal.weights` – Spatial weights - `libpysal.examples` – Built-in example datasets ## Example Notebooks - [Spatial Weights](notebooks/weights.ipynb) - [Voronoi](notebooks/voronoi.ipynb) - [Input and Output](notebooks/io.ipynb) ## Development libpysal development is hosted on [github](https://github.com/pysal/libpysal). Discussions of development occurs on the [developer list](http://groups.google.com/group/pysal-dev) as well as [gitter](https://gitter.im/pysal/pysal?). ## Contributing PySAL-libpysal is under active development and contributors are welcome. If you have any suggestions, feature requests, or bug reports, please open new [issues](https://github.com/pysal/libpysal/issues) on GitHub. To submit patches, please review [PySAL's documentation for developers](https://pysal.org/docs/devs/), the PySAL [development guidelines](https://github.com/pysal/pysal/wiki), and the [libpysal contributing guidelines](https://github.com/pysal/libpysal/blob/main/.github/CONTRIBUTING.md) before opening a [pull request](https://github.com/pysal/libpysal/pulls). Once your changes get merged, you’ll automatically be added to the [Contributors List](https://github.com/pysal/libpysal/graphs/contributors). ## Bug reports To search for or report bugs, please see [libpysal's issues](https://github.com/pysal/libpysal/issues). ## License information See [LICENSE.txt](https://github.com/pysal/libpysal/blob/main/LICENSE.txt) for information on the history of this software, terms & conditions for usage, and a DISCLAIMER OF ALL WARRANTIES. libpysal-4.9.2/THANKS.txt000066400000000000000000000032121452177046000151020ustar00rootroot00000000000000PySAL is an open source library of routines for exploratory spatial data analysis using Python. It is a community project sponsored by the GeoDa Center for Geospatial Analysis and Computation at Arizona State University. PySAL originated with code contributions by Luc Anselin and Serge Rey. Since then many people have contributed to PySAL, in code development, suggestions, and financial support. Below is a partial list. If you've been left off, please email the "PySAL Developers List" Pedro Amaral Luc Anselin Jotham Apaloo Daniel Arribas-Bel Martin Fleischmann David C. Folch James Gaboardi Forest Gregg Myunghwa Hwang Wei Kang Eli Knaap Marynia Kolak Julia Koschinsky Jason Laura Xun Li Nicholas Malizia Mark McCann Taylor Oshan Serge Rey Charles R. Schmidt Skipper Seabold Alessandra Sozzi Philip Stephens Bohumil Svoma Ran Wei Andrew Winslow Levi Wolf Jing Yao Xinyue Ye Funding from the following sources has supported PySAL development: Google Summer of Code 2016 National Science Foundation New Approaches for Spatial Distribution Dynamics National Science Foundation CyberGIS Software Integration for Sustained Geospatial Innovation National Institute of Justice Flexible Geospatial Visual Analytics and Simulation Technologies to Enhance Criminal Justice Decision Support Systems National Institutes of Health Geospatial Factors and Impacts: Measurement and Use (R01CA126858-02) National Science Foundation An Exploratory Space-Time Data Analysis Toolkit for Spatial Social Science Research (0433132) National Science Foundation Hedonic Models of Location Decisions with Applications to Geospatial Microdata (0852261) libpysal-4.9.2/ci/000077500000000000000000000000001452177046000137465ustar00rootroot00000000000000libpysal-4.9.2/ci/310-oldest.yaml000066400000000000000000000010351452177046000164240ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.10 - beautifulsoup4=4.10 - geopandas=0.10.0 - jinja2=3.0 - numpy=1.22 - packaging=22 - pandas=1.4 - requests=2.27 - scipy=1.8 - shapely=2.0.1 # testing - codecov - matplotlib>=3.6 - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional - geodatasets=2023.3.0 - joblib>=1.2 - networkx=2.7 - numba=0.55 - pyarrow>=7.0 - scikit-learn=1.1 - sqlalchemy=2.0 - zstd - xarray=2022.3 - pip - pip: - platformdirs==2.0.2 libpysal-4.9.2/ci/310.yaml000066400000000000000000000006341452177046000151400ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.10 - beautifulsoup4 - geopandas - jinja2 - packaging - pandas - platformdirs - requests - scipy - shapely # testing - codecov - matplotlib - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional - geodatasets - joblib - networkx - numba - pyarrow - scikit-learn - sqlalchemy - xarray - zstd libpysal-4.9.2/ci/311.yaml000066400000000000000000000006361452177046000151430ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 - beautifulsoup4 - geopandas - jinja2 - packaging - pandas - platformdirs - requests - scipy - shapely # testing - codecov - matplotlib - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional - geodatasets - joblib - networkx - numba - pyarrow - scikit-learn - sqlalchemy - xarray - zstd libpysal-4.9.2/ci/312-dev.yaml000066400000000000000000000014221452177046000157120ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 - beautifulsoup4 - jinja2 - platformdirs - requests # testing - codecov - matplotlib - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional - Cython - fiona - geodatasets - geos - joblib - networkx # - numba # follow up when numba is ready for 3.12 - packaging - pyarrow - pyproj - sqlalchemy - zstd - pip - pip: # dev versions of packages - --pre --index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple --extra-index-url https://pypi.org/simple - pandas - scikit-learn - scipy - xarray - git+https://github.com/geopandas/geopandas.git@main - git+https://github.com/shapely/shapely.git@main libpysal-4.9.2/ci/312-no-optional.yaml000066400000000000000000000005431452177046000173760ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 - beautifulsoup4 - fiona - geopandas-base>=0.12.0 # base to avoid pulling sklearn - jinja2 - pandas - platformdirs - requests - scipy # testing - codecov - matplotlib - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional used in ci - geodatasets libpysal-4.9.2/ci/312.yaml000066400000000000000000000011701452177046000151360ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 - beautifulsoup4 - geopandas - jinja2 - packaging - pandas - platformdirs - requests - scipy - shapely # testing - codecov - matplotlib - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional - geodatasets - joblib - networkx # - numba # follow up when numba is ready for 3.12 - pyarrow - scikit-learn - sqlalchemy - zstd - xarray # for docs build action (this env only) - mkdocs-jupyter - myst-parser - nbsphinx - numpydoc - pandoc - sphinx - sphinxcontrib-bibtex - sphinx_bootstrap_theme libpysal-4.9.2/codecov.yml000066400000000000000000000005431452177046000155220ustar00rootroot00000000000000codecov: notify: after_n_builds: 6 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: 6 require_changes: true libpysal-4.9.2/docs/000077500000000000000000000000001452177046000143035ustar00rootroot00000000000000libpysal-4.9.2/docs/.buildinfo000066400000000000000000000003461452177046000162620ustar00rootroot00000000000000# Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. config: 52ba2b6e743b06fefe15e50660339930 tags: 645f666f9bcd5a90fca523b33c5a78b7 libpysal-4.9.2/docs/.nojekyll000066400000000000000000000000011452177046000161220ustar00rootroot00000000000000 libpysal-4.9.2/docs/Makefile000066400000000000000000000015221452177046000157430ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build SPHINXPROJ = libpysal SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @rsync -r --exclude '.ipynb_checkpoints/' ../notebooks/ ./notebooks/ @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) github: @make html sync: @rsync -avh _build/html/ ../docs/ --delete @make clean touch .nojekyll clean: rm -rf $(BUILDDIR)/* rm -rf auto_examples/ rm -rf generated/ 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This paper extends the work of Blommestein and Koper (1992)–BK–on the construction of higher-order spatial lag operators without redundant and circular paths. For the case most relevant in spatial econometrics and spatial statistics, i.e., when contiguity between two observations (locations) is defined in a simple binary fashion, some deficiencies of the BK algorithms are outlined, corrected and an improvement suggested. In addition, three new algorithms are introduced and compared in terms of performance for a number of empirical contiguity structures. Particular attention is paid to a graph theoretic perspective on spatial lag operators and to the most efficient data structures for the storage and manipulation of spatial lags. The new forward iterative algorithm which uses a list form rather than a matrix to store the spatial lag information is shown to be several orders of magnitude faster than the BK solution. This allows the computation of proper higher-order spatial lags “on the fly†for even moderately large data sets such as 3,111 contiguous U. S. counties, which is not practical with the other algorithms.}, year = {1996} } @Article{pysal2007, author={Rey, Sergio J. and Anselin, Luc}, title={{PySAL: A Python Library of Spatial Analytical Methods}}, journal={The Review of Regional Studies}, year=2007, volume={37}, number={1}, pages={5-27}, keywords={Open Source; Software; Spatial} } @Article{Watts1998, author={Watts, D.J. and S.H. Strogatz}, year={1998}, title={Collective dynamics of 'small-world' networks}, journal={Nature}, volume={393}, pages={440-442}, keywords={networks} } libpysal-4.9.2/docs/api.rst000066400000000000000000000114511452177046000156100ustar00rootroot00000000000000.. _api_ref: .. currentmodule:: libpysal libpysal API reference ====================== Spatial Weights --------------- .. autosummary:: :toctree: generated/ libpysal.weights.W Distance Weights ++++++++++++++++ .. autosummary:: :toctree: generated/ libpysal.weights.DistanceBand libpysal.weights.Kernel libpysal.weights.KNN libpysal.weights.Gabriel libpysal.weights.Delaunay libpysal.weights.Relative_Neighborhood Contiguity Weights ++++++++++++++++++ .. autosummary:: :toctree: generated/ libpysal.weights.Queen libpysal.weights.Rook libpysal.weights.Voronoi libpysal.weights.W spint Weights +++++++++++++ .. autosummary:: :toctree: generated/ libpysal.weights.WSP libpysal.weights.netW libpysal.weights.mat2L libpysal.weights.ODW libpysal.weights.vecW Weights tools to interface with rasters +++++++++++++++++++++++++++++++++++++++ .. autosummary:: :toctree: generated/ libpysal.weights.da2W libpysal.weights.da2WSP libpysal.weights.w2da libpysal.weights.wsp2da libpysal.weights.testDataArray Weights Util Classes and Functions ++++++++++++++++++++++++++++++++++ .. autosummary:: :toctree: generated/ libpysal.weights.block_weights libpysal.weights.lat2W libpysal.weights.comb libpysal.weights.order libpysal.weights.higher_order libpysal.weights.shimbel libpysal.weights.remap_ids libpysal.weights.full2W libpysal.weights.full libpysal.weights.WSP2W libpysal.weights.get_ids libpysal.weights.get_points_array_from_shapefile libpysal.weights.fill_diagonal Weights user Classes and Functions ++++++++++++++++++++++++++++++++++ .. autosummary:: :toctree: generated/ libpysal.weights.min_threshold_distance libpysal.weights.lat2SW libpysal.weights.w_local_cluster libpysal.weights.higher_order_sp libpysal.weights.hexLat2W libpysal.weights.attach_islands libpysal.weights.nonplanar_neighbors libpysal.weights.fuzzy_contiguity libpysal.weights.min_threshold_dist_from_shapefile libpysal.weights.build_lattice_shapefile libpysal.weights.spw_from_gal libpysal.weights.neighbor_equality Set Theoretic Weights +++++++++++++++++++++ .. autosummary:: :toctree: generated/ libpysal.weights.w_union libpysal.weights.w_intersection libpysal.weights.w_difference libpysal.weights.w_symmetric_difference libpysal.weights.w_subset libpysal.weights.w_clip Spatial Lag +++++++++++ .. autosummary:: :toctree: generated/ libpysal.weights.lag_spatial libpysal.weights.lag_categorical cg: Computational Geometry -------------------------- alpha_shapes ++++++++++++ .. autosummary:: :toctree: generated/ libpysal.cg.alpha_shape libpysal.cg.alpha_shape_auto voronoi +++++++ .. autosummary:: :toctree: generated/ libpysal.cg.voronoi_frames sphere ++++++ .. autosummary:: :toctree: generated/ libpysal.cg.RADIUS_EARTH_KM libpysal.cg.RADIUS_EARTH_MILES libpysal.cg.arcdist libpysal.cg.arcdist2linear libpysal.cg.brute_knn libpysal.cg.fast_knn libpysal.cg.fast_threshold libpysal.cg.linear2arcdist libpysal.cg.toLngLat libpysal.cg.toXYZ libpysal.cg.lonlat libpysal.cg.harcdist libpysal.cg.geointerpolate libpysal.cg.geogrid shapes ++++++ .. autosummary:: :toctree: generated/ libpysal.cg.Point libpysal.cg.LineSegment libpysal.cg.Line libpysal.cg.Ray libpysal.cg.Chain libpysal.cg.Polygon libpysal.cg.Rectangle libpysal.cg.asShape standalone ++++++++++ .. autosummary:: :toctree: generated/ libpysal.cg.bbcommon libpysal.cg.get_bounding_box libpysal.cg.get_angle_between libpysal.cg.is_collinear libpysal.cg.get_segments_intersect libpysal.cg.get_segment_point_intersect libpysal.cg.get_polygon_point_intersect libpysal.cg.get_rectangle_point_intersect libpysal.cg.get_ray_segment_intersect libpysal.cg.get_rectangle_rectangle_intersection libpysal.cg.get_polygon_point_dist libpysal.cg.get_points_dist libpysal.cg.get_segment_point_dist libpysal.cg.get_point_at_angle_and_dist libpysal.cg.convex_hull libpysal.cg.is_clockwise libpysal.cg.point_touches_rectangle libpysal.cg.get_shared_segments libpysal.cg.distance_matrix locators ++++++++ .. autosummary:: :toctree: generated/ libpysal.cg.Grid libpysal.cg.PointLocator libpysal.cg.PolygonLocator kdtree ++++++ .. autosummary:: :toctree: generated/ libpysal.cg.KDTree io -- .. autosummary:: :toctree: generated/ libpysal.io.open libpysal.io.fileio.FileIO examples -------- .. autosummary:: :toctree: generated/ libpysal.examples.available libpysal.examples.explain libpysal.examples.get_path Experimental ------------ Experimental modules with unstable API. graph ----- .. autosummary:: :toctree: generated/ libpysal.graph.Graphlibpysal-4.9.2/docs/conf.py000066400000000000000000000251621452177046000156100ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # libpysal documentation build configuration file, created by # sphinx-quickstart on Wed Jun 6 15:54:22 2018. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # 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 sphinx_bootstrap_theme import libpysal # -- 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", #'sphinx.ext.napoleon', "matplotlib.sphinxext.plot_directive", "nbsphinx", ] bibtex_bibfiles = ["_static/references.bib"] # 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 = "libpysal" 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 = libpysal.__version__ release = libpysal.__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 # -- 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": project, # 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": [ # ("Gallery", "auto_examples/index"), ("Installation", "installation"), ("Tutorial", "tutorial"), ("API", "api"), ("References", "references"), ], } # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # html_sidebars = {'sidebar': ['localtoc.html', 'sourcelink.html', 'searchbox.html']} # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = "%sdoc" % project # -- 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, "%s.tex" % project, "%s Documentation" % project, "pysal developers", "manual", ), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, project, "%s Documentation" % project, [author], 1)] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, project, "%s Documentation" % project, author, project, "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 class_members_toctree = True # numpydoc_show_inherited_class_members = True numpydoc_xref_param_type = True # automatically document class members autodoc_default_options = {"members": True, "undoc-members": True} # display the source code for Plot directive plot_include_source = True def setup(app): app.add_css_file("pysal-styles.css") # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { "geopandas": ("https://geopandas.org/en/latest/", None), "libpysal": ("https://pysal.org/libpysal/", None), "matplotlib": ("https://matplotlib.org/stable/", None), "networkx": ("https://networkx.org/documentation/stable/", None), "numpy": ("https://numpy.org/doc/stable/", None), "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), "python": ("https://docs.python.org/3.12/", None), "scipy": ("https://docs.scipy.org/doc/scipy/", None), } # This is processed by Jinja2 and inserted before each notebook nbsphinx_prolog = r""" {% set docname = env.doc2path(env.docname, base=None) %} .. only:: html .. role:: raw-html(raw) :format: html .. nbinfo:: This page was generated from `{{ docname }}`__. Interactive online version: :raw-html:`Binder badge` __ https://github.com/pysal/libpysal/blob/master/{{ docname }} .. raw:: latex \nbsphinxstartnotebook{\scriptsize\noindent\strut \textcolor{gray}{The following section was generated from \sphinxcode{\sphinxupquote{\strut {{ docname | escape_latex }}}} \dotfill}} """ # This is processed by Jinja2 and inserted after each notebook nbsphinx_epilog = r""" .. raw:: latex \nbsphinxstopnotebook{\scriptsize\noindent\strut \textcolor{gray}{\dotfill\ \sphinxcode{\sphinxupquote{\strut {{ env.doc2path(env.docname, base='doc') | escape_latex }}}} ends here.}} """ # List of arguments to be passed to the kernel that executes the notebooks: nbsphinx_execute_arguments = [ "--InlineBackend.figure_formats={'svg', 'pdf'}", "--InlineBackend.rc={'figure.dpi': 96}", ] mathjax3_config = { "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}}, } libpysal-4.9.2/docs/index.rst000066400000000000000000000067721452177046000161600ustar00rootroot00000000000000.. libpysal documentation master file libpysal: Python Spatial Analysis Library Core ============================================== .. image:: https://github.com/pysal/libpysal/workflows/.github/workflows/unittests.yml/badge.svg :target: https://github.com/pysal/libpysal/actions?query=workflow%3A.github%2Fworkflows%2Funittests.yml .. image:: https://badges.gitter.im/pysal/pysal.svg :target: https://gitter.im/pysal/pysal .. image:: https://badge.fury.io/py/libpysal.svg :target: https://badge.fury.io/py/libpysal .. raw:: html ************ Introduction ************ **libpysal** offers four modules that form the building blocks in many upstream packages in the `PySAL family `_: - Spatial Weights: libpysal.weights - Input-and output: libpysal.io - Computational geometry: libpysal.cg - Built-in example datasets libpysal.examples Examples demonstrating some of **libpysal** functionality are available in the `tutorial `_. Details are available in the `libpysal api `_. For background information see :cite:`pysal2007`. *********** Development *********** libpysal development is hosted on github_. .. _github : https://github.com/pysal/libpysal Discussions of development occurs on the `developer list `_ as well as gitter_. .. _gitter : https://gitter.im/pysal/pysal? **************** Getting Involved **************** If you are interested in contributing to PySAL please see our `development guidelines `_. *********** Bug reports *********** To search for or report bugs, please see libpysal's issues_. .. _issues : http://github.com/pysal/libpysal/issues *************** Citing libpysal *************** If you use PySAL in a scientific publication, we would appreciate citations to the following paper: `PySAL: A Python Library of Spatial Analytical Methods `_, *Rey, S.J. and L. Anselin*, Review of Regional Studies 37, 5-27 2007. Bibtex entry:: @Article{pysal2007, author={Rey, Sergio J. and Anselin, Luc}, title={{PySAL: A Python Library of Spatial Analytical Methods}}, journal={The Review of Regional Studies}, year=2007, volume={37}, number={1}, pages={5-27}, keywords={Open Source; Software; Spatial} } ******************* License information ******************* See the file "LICENSE.txt" for information on the history of this software, terms & conditions for usage, and a DISCLAIMER OF ALL WARRANTIES. libpysal ======== Core components of the Python Spatial Analysis Library (`PySAL`_) .. toctree:: :hidden: :maxdepth: 4 :caption: Contents: Installation Tutorial API References .. _PySAL: https://github.com/pysal/pysal libpysal-4.9.2/docs/installation.rst000066400000000000000000000027351452177046000175450ustar00rootroot00000000000000.. Installation Installation ============ libpysal supports python >= `3.8`_ only. Please make sure that you are operating in a python 3 environment. Installing released version --------------------------- conda +++++ libpysal is available through conda:: conda install -c conda-forge libpysal pypi ++++ libpysal is available on the `Python Package Index`_. Therefore, you can either install directly with `pip` from the command line:: pip install -U libpysal or download the source distribution (.tar.gz) and decompress it to your selected destination. Open a command shell and navigate to the decompressed folder. Type:: pip install . Installing development version ------------------------------ Potentially, you might want to use the newest features in the development version of libpysal on github - `pysal/libpysal`_ while have not been incorporated in the Pypi released version. You can achieve that by installing `pysal/libpysal`_ by running the following from a command shell:: pip install git+https://github.com/pysal/libpysal.git You can also `fork`_ the `pysal/libpysal`_ repo and create a local clone of your fork. By making changes to your local clone and submitting a pull request to `pysal/libpysal`_, you can contribute to libpysal development. .. _3.8: https://docs.python.org/3.8/ .. _Python Package Index: https://pypi.org/project/libpysal/ .. _pysal/libpysal: https://github.com/pysal/libpysal .. _fork: https://help.github.com/articles/fork-a-repo/ libpysal-4.9.2/docs/references.rst000066400000000000000000000001461452177046000171570ustar00rootroot00000000000000.. reference for the docs References ========== .. bibliography:: _static/references.bib :cited: libpysal-4.9.2/docs/tutorial.rst000066400000000000000000000004351452177046000167020ustar00rootroot00000000000000libpysal Tutorial ================= Spatial Weights --------------- .. toctree:: :glob: Spatial Weights Voronoi Example Datasets ---------------- .. toctree:: :glob: Example Data libpysal-4.9.2/environment.yml000066400000000000000000000006171452177046000164460ustar00rootroot00000000000000name: libpysal channels: - conda-forge dependencies: # core env deps - python - jupyterlab # core libpysal deps - beautifulsoup4 - geopandas - jinja2 - packaging - pandas - platformdirs - requests - scipy - shapely # optional libpysal deps - geodatasets - joblib - matplotlib - networkx - numba - pyarrow - scikit-learn - sqlalchemy - xarray - zstd libpysal-4.9.2/libpysal/000077500000000000000000000000001452177046000151725ustar00rootroot00000000000000libpysal-4.9.2/libpysal/.gitignore000066400000000000000000000003351452177046000171630ustar00rootroot00000000000000contrib/points/ contrib/viz/Untitled.ipynb contrib/viz/color.ipynb contrib/viz/dispatch.ipynb contrib/viz/geotable_plot.html contrib/viz/rect5.png contrib/viz/selected.png inequality/gini_decomp.ipynb ../tools/gh_api.py libpysal-4.9.2/libpysal/__init__.py000066400000000000000000000013471452177046000173100ustar00rootroot00000000000000""" libpysal: Python Spatial Analysis Library (core) ================================================ Documentation ------------- PySAL documentation is available in two forms: python docstrings and an html \ webpage at http://pysal.org/ Available sub-packages ---------------------- cg Basic data structures and tools for Computational Geometry examples Example data sets for testing and documentation io Basic functions used by several sub-packages weights Tools for creating and manipulating weights """ import contextlib from importlib.metadata import PackageNotFoundError, version from . import cg, examples, io, weights with contextlib.suppress(PackageNotFoundError): __version__ = version("libpysal") libpysal-4.9.2/libpysal/cg/000077500000000000000000000000001452177046000155635ustar00rootroot00000000000000libpysal-4.9.2/libpysal/cg/__init__.py000066400000000000000000000003511452177046000176730ustar00rootroot00000000000000""" A module for computational geometry. """ from .alpha_shapes import * from .kdtree import * from .locators import * from .rtree import * from .shapes import * from .sphere import * from .standalone import * from .voronoi import * libpysal-4.9.2/libpysal/cg/alpha_shapes.py000066400000000000000000000525661452177046000206030ustar00rootroot00000000000000""" Computation of alpha shape algorithm in 2-D based on original implementation by Tim Kittel (@timkittel) available at: https://github.com/timkittel/alpha-shapes Author(s): Dani Arribas-Bel daniel.arribas.bel@gmail.com Levi John Wolf levi.john.wolf@gmail.com """ import numpy as np import scipy.spatial as spat from packaging.version import Version from scipy import sparse from ..common import HAS_JIT, jit, requires if not HAS_JIT: from warnings import warn NUMBA_WARN = ( "Numba not imported, so alpha shape construction may be slower than expected." ) try: import shapely assert Version(shapely.__version__) >= Version("2") HAS_SHAPELY = True except (ModuleNotFoundError, AssertionError): HAS_SHAPELY = False EPS = np.finfo(float).eps __all__ = ["alpha_shape", "alpha_shape_auto"] @jit(nopython=True) def nb_dist(x, y): """numba implementation of distance between points `x` and `y` Parameters ---------- x : ndarray Coordinates of point `x` y : ndarray Coordinates of point `y` Returns ------- dist : float Distance between `x` and `y` Examples -------- >>> x = np.array([0, 0]) >>> y = np.array([1, 1]) >>> dist = nb_dist(x, y) >>> dist 1.4142135623730951 """ sum_ = 0 for x_i, y_i in zip(x, y): # noqa B905 sum_ += (x_i - y_i) ** 2 dist = np.sqrt(sum_) return dist @jit(nopython=True) def r_circumcircle_triangle_single(a, b, c): """Computation of the circumcircle of a single triangle Parameters ---------- a : ndarray (2,) Array with coordinates of vertex `a` of the triangle b : ndarray (2,) Array with coordinates of vertex `b` of the triangle c : ndarray (2,) Array with coordinates of vertex `c` of the triangle Returns ------- r : float Circumcircle of the triangle Notes ----- Source for equations: > https://www.mathopenref.com/trianglecircumcircle.html [Last accessed July 11th. 2018] Examples -------- >>> a = np.array([0, 0]) >>> b = np.array([0.5, 0]) >>> c = np.array([0.25, 0.25]) >>> r = r_circumcircle_triangle_single(a, b, c) >>> r 0.2500000000000001 """ ab = nb_dist(a, b) bc = nb_dist(b, c) ca = nb_dist(c, a) num = ab * bc * ca den = np.sqrt((ab + bc + ca) * (bc + ca - ab) * (ca + ab - bc) * (ab + bc - ca)) if den == 0: return np.array([ab, bc, ca]).max() / 2.0 else: return num / den @jit(nopython=True) def r_circumcircle_triangle(a_s, b_s, c_s): """Computation of circumcircles for a series of triangles Parameters ---------- a_s : ndarray (N, 2) array with coordinates of vertices `a` of the triangles b_s : ndarray (N, 2) array with coordinates of vertices `b` of the triangles c_s : ndarray (N, 2) array with coordinates of vertices `c` of the triangles Returns ------- radii : ndarray (N,) array with circumcircles for every triangle Examples -------- >>> a_s = np.array([[0, 0], [2, 1], [3, 2]]) >>> b_s = np.array([[1, 0], [5, 1], [2, 4]]) >>> c_s = np.array([[0, 7], [1, 3], [4, 2]]) >>> rs = r_circumcircle_triangle(a_s, b_s, c_s) >>> rs array([3.53553391, 2.5 , 1.58113883]) """ len_a = len(a_s) r2 = np.zeros((len_a,)) for i in range(len_a): r2[i] = r_circumcircle_triangle_single(a_s[i], b_s[i], c_s[i]) return r2 @jit(nopython=True) def get_faces(triangle): """Extract faces from a single triangle Parameters ---------- triangles : ndarray (3,) array with the vertex indices for a triangle Returns ------- faces : ndarray (3, 2) array with a row for each face containing the indices of the two points that make up the face Examples -------- >>> triangle = np.array([3, 1, 4], dtype=np.int32) >>> faces = get_faces(triangle) >>> faces array([[3., 1.], [1., 4.], [4., 3.]]) """ faces = np.zeros((3, 2)) for i, (i0, i1) in enumerate([(0, 1), (1, 2), (2, 0)]): faces[i] = triangle[i0], triangle[i1] return faces @jit(nopython=True) def build_faces(faces, triangles_is, num_triangles, num_faces_single): """Build facing triangles Parameters ---------- faces : ndarray (num_triangles * num_faces_single, 2) array of zeroes in int form triangles_is : ndarray (D, 3) array, where D is the number of Delaunay triangles, with the vertex indices for each triangle num_triangles : int Number of triangles num_faces_single : int Number of faces a triangle has (i.e. 3) Returns ------- faces : ndarray Two dimensional array with a row for every facing segment containing the indices of the coordinate points Examples -------- >>> import scipy.spatial as spat >>> pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]]) >>> triangulation = spat.Delaunay(pts) >>> triangulation.simplices array([[3, 1, 4], [1, 2, 4], [2, 1, 0]], dtype=int32) >>> num_faces_single = 3 >>> num_triangles = triangulation.simplices.shape[0] >>> num_faces = num_triangles * num_faces_single >>> faces = np.zeros((num_faces, 2), dtype=np.int_) >>> mask = np.ones((num_faces,), dtype=np.bool_) >>> faces = build_faces( ... faces, triangulation.simplices, num_triangles, num_faces_single ... ) >>> faces array([[3, 1], [1, 4], [4, 3], [1, 2], [2, 4], [4, 1], [2, 1], [1, 0], [0, 2]]) """ for i in range(num_triangles): from_i = num_faces_single * i to_i = num_faces_single * (i + 1) faces[from_i:to_i] = get_faces(triangles_is[i]) return faces @jit(nopython=True) def nb_mask_faces(mask, faces): """Run over each row in `faces`, if the face in the following row is the same, then mark both as False on `mask` Parameters ---------- mask : ndarray One-dimensional boolean array set to True with as many observations as rows in `faces` faces : ndarray Sorted sequence of faces for all triangles (ie. triangles split by each segment) Returns ------- masked : ndarray Sequence of outward-facing faces Examples -------- >>> import numpy as np >>> faces = np.array( ... [ ... [0, 1], [0, 2], [1, 2], [1, 2], [1, 3], [1, 4], [1, 4], [2, 4], [3, 4] ... ] ... ) >>> mask = np.ones((faces.shape[0], ), dtype=np.bool_) >>> masked = nb_mask_faces(mask, faces) >>> masked array([[0, 1], [0, 2], [1, 3], [2, 4], [3, 4]]) """ for k in range(faces.shape[0] - 1): if mask[k] and np.all(faces[k] == faces[k + 1]): mask[k] = False mask[k + 1] = False return faces[mask] def get_single_faces(triangles_is): """Extract outward facing edges from collection of triangles Parameters ---------- triangles_is : ndarray (D, 3) array, where D is the number of Delaunay triangles, with the vertex indices for each triangle Returns ------- single_faces : ndarray Examples -------- >>> import scipy.spatial as spat >>> pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]]) >>> alpha = 0.33 >>> triangulation = spat.Delaunay(pts) >>> triangulation.simplices array([[3, 1, 4], [1, 2, 4], [2, 1, 0]], dtype=int32) >>> get_single_faces(triangulation.simplices) array([[0, 1], [0, 2], [1, 3], [2, 4], [3, 4]]) """ num_faces_single = 3 num_triangles = triangles_is.shape[0] num_faces = num_triangles * num_faces_single faces = np.zeros((num_faces, 2), dtype=np.int_) mask = np.ones((num_faces,), dtype=np.bool_) faces = build_faces(faces, triangles_is, num_triangles, num_faces_single) orderlist = [f"x{i}" for i in range(faces.shape[1])] dtype_list = [(el, faces.dtype.str) for el in orderlist] # Arranging each face so smallest vertex is first faces.sort(axis=1) # Arranging faces in ascending way faces.view(dtype_list).sort(axis=0) # Masking single_faces = nb_mask_faces(mask, faces) return single_faces @requires("geopandas", "shapely") def _alpha_geoms(alpha, triangles, radii, xys): """Generate alpha-shape polygon(s) from `alpha` value, vertices of `triangles`, the `radii` for all points, and the points themselves Parameters ---------- alpha : float Alpha value to delineate the alpha-shape triangles : ndarray (D, 3) array, where D is the number of Delaunay triangles, with the vertex indices for each triangle radii : ndarray (N,) array with circumcircles for every triangle xys : ndarray (N, 2) array with one point per row and coordinates structured as X and Y Returns ------- geoms : GeoSeries Polygon(s) resulting from the alpha shape algorithm, in a GeoSeries. The output is a GeoSeries even if only a single polygon is returned. There is no CRS included in the returned GeoSeries. Examples -------- >>> import scipy.spatial as spat >>> pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]]) >>> alpha = 0.33 >>> triangulation = spat.Delaunay(pts) >>> triangles = pts[triangulation.simplices] >>> triangles array([[[6, 7], [3, 5], [9, 3]], [[3, 5], [4, 1], [9, 3]], [[4, 1], [3, 5], [0, 1]]]) >>> a_pts = triangles[:, 0, :] >>> b_pts = triangles[:, 1, :] >>> c_pts = triangles[:, 2, :] >>> radii = r_circumcircle_triangle(a_pts, b_pts, c_pts) >>> geoms = alpha_geoms(alpha, triangulation.simplices, radii, pts) >>> geoms 0 POLYGON ((0.00000 1.00000, 3.00000 5.00000, 4.... dtype: geometry """ from geopandas import GeoSeries from shapely.geometry import LineString from shapely.ops import polygonize triangles_reduced = triangles[radii < 1 / alpha] outer_triangulation = get_single_faces(triangles_reduced) face_pts = xys[outer_triangulation] geoms = GeoSeries(list(polygonize(list(map(LineString, face_pts))))) return geoms @requires("geopandas", "shapely") def alpha_shape(xys, alpha): """Alpha-shape delineation (Edelsbrunner, Kirkpatrick & Seidel, 1983) from a collection of points Parameters ---------- xys : ndarray (N, 2) array with one point per row and coordinates structured as X and Y alpha : float Alpha value to delineate the alpha-shape Returns ------- shapes : GeoSeries Polygon(s) resulting from the alpha shape algorithm. The GeoSeries object remains so even if only a single polygon is returned. There is no CRS included in the object. Note that the returned shape(s) may have holes, as per the definition of the shape in Edselbrunner et al. (1983) Examples -------- >>> pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]]) >>> alpha = 0.1 >>> poly = alpha_shape(pts, alpha) >>> poly 0 POLYGON ((0.00000 1.00000, 3.00000 5.00000, 6.... dtype: geometry >>> poly.centroid 0 POINT (4.69048 3.45238) dtype: geometry References ---------- Edelsbrunner, H., Kirkpatrick, D., & Seidel, R. (1983). On the shape of a set of points in the plane. IEEE Transactions on information theory, 29(4), 551-559. """ if not HAS_JIT: warn(NUMBA_WARN, stacklevel=2) if xys.shape[0] < 4: from shapely import geometry as geom from shapely import ops return ops.unary_union([geom.Point(xy) for xy in xys]).convex_hull.buffer(0) triangulation = spat.Delaunay(xys) triangles = xys[triangulation.simplices] a_pts = triangles[:, 0, :] b_pts = triangles[:, 1, :] c_pts = triangles[:, 2, :] radii = r_circumcircle_triangle(a_pts, b_pts, c_pts) del triangles, a_pts, b_pts, c_pts geoms = _alpha_geoms(alpha, triangulation.simplices, radii, xys) geoms = _filter_holes(geoms, xys) return geoms def _valid_hull(geoms, points): """Sanity check within ``alpha_shape_auto()`` to verify the generated alpha shape actually contains the original set of points (xys). Parameters ---------- geoms : GeoSeries See alpha_geoms() points : list xys parameter cast as shapely.geometry.Point objects Returns ------- flag : bool Valid hull for alpha shape [True] or not [False] """ # if there is not exactly one polygon if geoms.shape[0] != 1: return False # if any (xys) points do not intersect the polygon if HAS_SHAPELY: return shapely.intersects(geoms[0], points).all() else: return all(point.intersects(geoms[0]) for point in points) @requires("geopandas", "shapely") def alpha_shape_auto( xys, step=1, verbose=False, return_radius=False, return_circles=False ): """Computation of alpha-shape delineation with automated selection of alpha. This method uses the algorithm proposed by Edelsbrunner, Kirkpatrick & Seidel (1983) to return the tightest polygon that contains all points in `xys`. The algorithm ranks every point based on its radius and iterates over each point, checking whether the maximum alpha that would keep the point and all the other ones in the set with smaller radii results in a single polygon. If that is the case, it moves to the next point; otherwise, it retains the previous alpha value and returns the polygon as `shapely` geometry. Note that this geometry may have holes. Parameters ---------- xys : ndarray Nx2 array with one point per row and coordinates structured as X and Y step : int [Optional. Default=1] Number of points in `xys` to jump ahead after checking whether the largest possible alpha that includes the point and all the other ones with smaller radii verbose : Boolean [Optional. Default=False] If True, it prints alpha values being tried at every step. Returns ------- poly : shapely.Polygon Tightest alpha-shape polygon containing all points in `xys` Examples -------- >>> pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]]) >>> poly = alpha_shape_auto(pts) >>> poly.bounds (0.0, 1.0, 9.0, 7.0) >>> poly.centroid.x, poly.centroid.y (4.690476190476191, 3.4523809523809526) References ---------- Edelsbrunner, H., Kirkpatrick, D., & Seidel, R. (1983). On the shape of a set of points in the plane. IEEE Transactions on information theory, 29(4), 551-559. """ if not HAS_JIT: warn(NUMBA_WARN, stacklevel=2) from shapely import geometry as geom if return_circles: return_radius = True if xys.shape[0] < 4: from shapely import ops if xys.shape[0] == 3: multipoint = ops.cascaded_union([geom.Point(xy) for xy in xys]) alpha_shape = multipoint.convex_hull.buffer(0) else: alpha_shape = geom.Polygon([]) if xys.shape[0] == 1: if return_radius: if return_circles: out = [alpha_shape, 0, alpha_shape] return alpha_shape, 0 return alpha_shape elif xys.shape[0] == 2: if return_radius: r = spat.distance.euclidean(xys[0], xys[1]) / 2 if return_circles: circle = _construct_centers(xys[0], xys[1], r) return [alpha_shape, r, circle] return [alpha_shape, r] return alpha_shape elif return_radius: # this handles xys.shape[0] == 3 radius = r_circumcircle_triangle_single(xys[0], xys[1], xys[2]) if return_circles: circles = construct_bounding_circles(alpha_shape, radius) return [alpha_shape, radius, circles] return [alpha_shape, radius] return alpha_shape triangulation = spat.Delaunay(xys) triangles = xys[triangulation.simplices] a_pts = triangles[:, 0, :] b_pts = triangles[:, 1, :] c_pts = triangles[:, 2, :] radii = r_circumcircle_triangle(a_pts, b_pts, c_pts) radii[np.isnan(radii)] = 0 # "Line" triangles to be kept for sure del triangles, a_pts, b_pts, c_pts radii_sorted_i = radii.argsort() triangles = triangulation.simplices[radii_sorted_i][::-1] radii = radii[radii_sorted_i][::-1] geoms_prev = _alpha_geoms((1 / radii.max()) - EPS, triangles, radii, xys) points = shapely.points(xys) if HAS_SHAPELY else [geom.Point(pnt) for pnt in xys] if verbose: print("Step set to %i" % step) for i in range(0, len(radii), step): radi = radii[i] alpha = (1 / radi) - EPS if verbose: print(f"{(i + 1) / radii.shape[0]:.2f}% | Trying a = {alpha:f}") geoms = _alpha_geoms(alpha, triangles, radii, xys) if _valid_hull(geoms, points): geoms_prev = geoms radi_prev = radi else: break if verbose: print(geoms_prev.shape) if return_radius: out = [geoms_prev[0], radi_prev] if return_circles: out.append(construct_bounding_circles(out[0], radi_prev)) return out # Return a shapely polygon return geoms_prev[0] def construct_bounding_circles(alpha_shape, radius): """Construct the bounding circles for an alpha shape, given the radius computed from the `alpha_shape_auto` method. Parameters ---------- alpha_shape : shapely.Polygon An alpha-hull with the input radius. radius : float The radius of the input alpha_shape. Returns ------- center : numpy.ndarray of shape (n,2) The centers of the circles defining the alpha_shape. """ coordinates = list(alpha_shape.boundary.coords) n_coordinates = len(coordinates) centers = [] for i in range(n_coordinates - 1): a, b = coordinates[i], coordinates[i + 1] centers.append(_construct_centers(a, b, radius)) return centers @jit(nopython=True) def _construct_centers(a, b, radius): midpoint_x = (a[0] + b[0]) * 0.5 midpoint_y = (a[1] + b[1]) * 0.5 d = ((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2) ** 0.5 if b[0] - a[0] == 0: m = np.inf axis_rotation = np.pi / 2 else: m = (b[1] - a[1]) / (b[0] - a[0]) axis_rotation = np.arctan(m) # altitude is perpendicular bisector of AB interior_angle = np.arccos(0.5 * d / radius) chord = np.sin(interior_angle) * radius dx = chord * np.sin(axis_rotation) dy = chord * np.cos(axis_rotation) up_x = midpoint_x - dx up_y = midpoint_y + dy down_x = midpoint_x + dx down_y = midpoint_y - dy # sign gives us direction of point, since # shapely shapes are clockwise-defined sign = np.sign((b[0] - a[0]) * (up_y - a[1]) - (b[1] - a[1]) * (up_x - a[0])) if sign == 1: return up_x, up_y else: return down_x, down_y def _filter_holes(geoms, points): # noqa ARG001 """ Filter hole polygons using a computational geometry solution """ import geopandas if (geoms.interiors.apply(len) > 0).any(): from shapely.geometry import Polygon # Extract the "shell", or outer ring of the polygon. shells = geoms.exterior.apply(Polygon) # Compute which original geometries are within each shell, self-inclusive if Version(geopandas.__version__) >= Version("0.13"): inside, outside = shells.sindex.query(geoms, predicate="within") else: inside, outside = shells.sindex.query_bulk(geoms, predicate="within") # Now, create the sparse matrix relating the inner geom (rows) # to the outer shell (cols) and take the sum. # A z-order of 1 means the polygon is only inside if its own exterior. # This means it's not a hole. # A z-order of 2 means the polygon is inside of exactly one other exterior. # Because the hull generation method is restricted to be planar, this # means the polygon is a hole. # In general, an even z-order means that the polygon is always exactly # matched to one exterior, plus some number of intermediate # exterior-hole pairs. Therefore, the polygon is a hole. # In general, an odd z-order means that there is an uneven number of exteriors. # This means the polygon is not a hole. zorder = sparse.csc_matrix((np.ones_like(inside), (inside, outside))).sum( axis=1 ) zorder = np.asarray(zorder).flatten() # Keep only the odd z-orders to_include = (zorder % 2).astype(bool) geoms = geoms[to_include] return geoms if __name__ == "__main__": import time import geopandas as gpd import matplotlib.pyplot as plt plt.close("all") xys = np.random.random((1000, 2)) t0 = time.time() geoms = alpha_shape_auto(xys, 1) t1 = time.time() print("%.2f Seconds to run algorithm" % (t1 - t0)) f, ax = plt.subplots(1) gpd.GeoDataFrame({"geometry": [geoms]}).plot(ax=ax, color="orange", alpha=0.5) ax.scatter(xys[:, 0], xys[:, 1], s=0.1) plt.show() libpysal-4.9.2/libpysal/cg/kdtree.py000066400000000000000000000300411452177046000174110ustar00rootroot00000000000000""" KDTree for PySAL: Python Spatial Analysis Library. Adds support for Arc Distance to scipy.spatial.KDTree. """ # ruff: noqa: ARG002, N801, N802, N816 import math import numpy import scipy.spatial from numpy import inf from . import sphere from .sphere import RADIUS_EARTH_KM __author__ = "Charles R Schmidt " __all__ = ["DISTANCE_METRICS", "FLOAT_EPS", "KDTree"] DISTANCE_METRICS = ["Euclidean", "Arc"] FLOAT_EPS = numpy.finfo(float).eps def KDTree(data, leafsize=10, distance_metric="Euclidean", radius=RADIUS_EARTH_KM): """kd-tree built on top of kd-tree functionality in scipy. If using scipy 0.12 or greater uses the scipy.spatial.cKDTree, otherwise uses scipy.spatial.KDTree. Offers both Arc distance and Euclidean distance. Note that Arc distance is only appropriate when points in latitude and longitude, and the radius set to meaningful value (see docs below). Parameters ---------- data : array The data points to be indexed. This array is not copied, and so modifying this data will result in bogus results. Typically nx2. leafsize : int The number of points at which the algorithm switches over to brute-force. Has to be positive. Optional, default is 10. distance_metric : string Options: "Euclidean" (default) and "Arc". radius : float Radius of the sphere on which to compute distances. Assumes data in latitude and longitude. Ignored if distance_metric="Euclidean". Typical values: pysal.cg.RADIUS_EARTH_KM (default) pysal.cg.RADIUS_EARTH_MILES """ if distance_metric.lower() == "euclidean": if ( int(scipy.version.version.split(".")[1]) < 12 and int(scipy.version.version.split(".")[0]) == 0 ): return scipy.spatial.KDTree(data, leafsize) else: return scipy.spatial.cKDTree(data, leafsize) elif distance_metric.lower() == "arc": return Arc_KDTree(data, leafsize, radius) # internal hack for the Arc_KDTree class inheritance if ( int(scipy.version.version.split(".")[1]) < 12 and int(scipy.version.version.split(".")[0]) == 0 ): temp_KDTree = scipy.spatial.KDTree else: temp_KDTree = scipy.spatial.cKDTree class Arc_KDTree(temp_KDTree): def __init__(self, data, leafsize=10, radius=1.0): """KDTree using Arc Distance instead of Euclidean Distance. Returned distances are based on radius. For Example, pass in the radius of earth in miles to get back miles. Assumes data are Lng/Lat, does not account for geoids. For more information see docs for scipy.spatial.KDTree Examples -------- >>> pts = [(0,90), (0,0), (180,0), (0,-90)] >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM) >>> d,i = kd.query((90,0), k=4) >>> d array([10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801]) >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM >>> round(d[0],5) == round(circumference/4.0,5) True """ self.radius = radius self.circumference = 2 * math.pi * radius temp_KDTree.__init__(self, list(map(sphere.toXYZ, data)), leafsize) def _toXYZ(self, x): if not issubclass(type(x), numpy.ndarray): x = numpy.array(x) if len(x.shape) == 2 and x.shape[1] == 3: # assume point is already in XYZ return x if len(x.shape) == 1 and x.shape[0] == 3: # assume point is already in XYZ return x elif len(x.shape) == 1: x = numpy.array(sphere.toXYZ(x)) else: x = list(map(sphere.toXYZ, x)) return x def count_neighbors(self, other, r, p=2): """See scipy.spatial.KDTree.count_neighbors Parameters ---------- p: ignored, kept to maintain compatibility with scipy.spatial.KDTree Examples -------- >>> pts = [(0,90), (0,0), (180,0), (0,-90)] >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM) >>> kd.count_neighbors(kd,0) 4 >>> circumference = 2.0*math.pi*sphere.RADIUS_EARTH_KM >>> kd.count_neighbors(kd,circumference/2.0) 16 """ if r > 0.5 * self.circumference: raise ValueError( "r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5 ) r = sphere.arcdist2linear(r, self.radius) return temp_KDTree.count_neighbors(self, other, r) def query(self, x, k=1, eps=0, p=2, distance_upper_bound=inf): """See scipy.spatial.KDTree.query Parameters ---------- x : array-like, last dimension self.m query points are lng/lat. p: ignored kept to maintain compatibility with scipy.spatial.KDTree Examples -------- >>> import numpy as np >>> pts = [(0,90), (0,0), (180,0), (0,-90)] >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM) >>> d,i = kd.query((90,0), k=4) >>> d array([10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801]) >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM >>> round(d[0],5) == round(circumference/4.0,5) True >>> d,i = kd.query(kd.data, k=3) >>> d2,i2 = kd.query(pts, k=3) >>> (d == d2).all() True >>> (i == i2).all() True """ eps = sphere.arcdist2linear(eps, self.radius) if distance_upper_bound != inf: distance_upper_bound = sphere.arcdist2linear( distance_upper_bound, self.radius ) d, i = temp_KDTree.query( self, self._toXYZ(x), k, eps=eps, distance_upper_bound=distance_upper_bound ) dims = len(d.shape) r = self.radius if dims == 0: return sphere.linear2arcdist(d, r), i if dims == 1: # TODO: implement linear2arcdist on numpy arrays d = [sphere.linear2arcdist(x, r) for x in d] elif dims == 2: d = [[sphere.linear2arcdist(x, r) for x in row] for row in d] return numpy.array(d), i def query_ball_point(self, x, r, p=2, eps=0): """See scipy.spatial.KDTree.query_ball_point Parameters ---------- p : ignored kept to maintain compatibility with scipy.spatial.KDTree Examples -------- >>> import numpy as np >>> pts = [(0,90), (0,0), (180,0), (0,-90)] >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM) >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM >>> kd.query_ball_point(pts, circumference/4.) array([list([0, 1, 2]), list([0, 1, 3]), list([0, 2, 3]), list([1, 2, 3])], dtype=object) >>> kd.query_ball_point(pts, circumference/2.) array([list([0, 1, 2, 3]), list([0, 1, 2, 3]), list([0, 1, 2, 3]), list([0, 1, 2, 3])], dtype=object) """ eps = sphere.arcdist2linear(eps, self.radius) # scipy.sphere.KDTree.query_ball_point appears to ignore # the eps argument. we have some floating point errors moving # back and forth between cordinate systems, so we'll account # for that be adding some to our radius, 3*float's eps value. if r > 0.5 * self.circumference: raise ValueError( "r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5 ) r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3 return temp_KDTree.query_ball_point(self, self._toXYZ(x), r, eps=eps) def query_ball_tree(self, other, r, p=2, eps=0): """See scipy.spatial.KDTree.query_ball_tree Parameters ---------- p : ignored kept to maintain compatibility with scipy.spatial.KDTree Examples -------- >>> pts = [(0,90), (0,0), (180,0), (0,-90)] >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM) >>> ( ... kd.query_ball_tree(kd, kd.circumference/4.) ... == [[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]] ... ) True >>> ( ... kd.query_ball_tree(kd, kd.circumference/2.) ... == [[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]] ... ) True """ eps = sphere.arcdist2linear(eps, self.radius) # scipy.sphere.KDTree.query_ball_point appears to ignore the eps argument. # we have some floating point errors moving back and forth between # coordinate systems, so we'll account for that be adding some # to our radius, 3*float's eps value. if self.radius != other.radius: raise ValueError("Both trees must have the same radius.") if r > 0.5 * self.circumference: raise ValueError( "r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5 ) r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3 return temp_KDTree.query_ball_tree(self, other, r, eps=eps) def query_pairs(self, r, p=2, eps=0): """See scipy.spatial.KDTree.query_pairs Parameters ---------- p : ignored kept to maintain compatibility with scipy.spatial.KDTree Examples -------- >>> pts = [(0,90), (0,0), (180,0), (0,-90)] >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM) >>> kd.query_pairs(kd.circumference/4.) == set([(0, 1), (1, 3), (2, 3), (0, 2)]) True >>> ( ... kd.query_pairs(kd.circumference/2.) ... == set([(0, 1), (1, 2), (1, 3), (2, 3), (0, 3), (0, 2)]) ... ) True """ if r > 0.5 * self.circumference: raise ValueError( "r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5 ) r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3 return temp_KDTree.query_pairs(self, r, eps=eps) def sparse_distance_matrix(self, other, max_distance, p=2): """See scipy.spatial.KDTree.sparse_distance_matrix Parameters ---------- p : ignored kept to maintain compatibility with scipy.spatial.KDTree Examples -------- >>> pts = [(0,90), (0,0), (180,0), (0,-90)] >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM) >>> kd.sparse_distance_matrix(kd, kd.circumference/4.).todense() matrix([[ 0. , 10007.54339801, 10007.54339801, 0. ], [10007.54339801, 0. , 0. , 10007.54339801], [10007.54339801, 0. , 0. , 10007.54339801], [ 0. , 10007.54339801, 10007.54339801, 0. ]]) >>> kd.sparse_distance_matrix(kd, kd.circumference/2.).todense() matrix([[ 0. , 10007.54339801, 10007.54339801, 20015.08679602], [10007.54339801, 0. , 20015.08679602, 10007.54339801], [10007.54339801, 20015.08679602, 0. , 10007.54339801], [20015.08679602, 10007.54339801, 10007.54339801, 0. ]]) """ if self.radius != other.radius: raise ValueError("Both trees must have the same radius.") if max_distance > 0.5 * self.circumference: raise ValueError( "max_distance, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5 ) max_distance = sphere.arcdist2linear(max_distance, self.radius) + FLOAT_EPS * 3 D = temp_KDTree.sparse_distance_matrix(self, other, max_distance) D = D.tocoo() # print D.data a2l = lambda x: sphere.linear2arcdist(x, self.radius) # print map(a2l,D.data) return scipy.sparse.coo_matrix((list(map(a2l, D.data)), (D.row, D.col))).todok() libpysal-4.9.2/libpysal/cg/locators.py000066400000000000000000000624171452177046000177750ustar00rootroot00000000000000""" Computational geometry code for PySAL: Python Spatial Analysis Library. """ __author__ = "Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow" __credits__ = "Copyright (c) 2005-2011 Sergio J. Rey" # ruff: noqa: B028, F403, F405 import copy import math import warnings from .rtree import * from .shapes import * from .standalone import * __all__ = ["Grid", "BruteForcePointLocator", "PointLocator", "PolygonLocator"] dep_msg = "is deprecated and will be removed in a future version of libpysal" class Grid: """ Representation of a binning data structure. """ def __init__(self, bounds, resolution): """ Returns a grid with specified properties. __init__(Rectangle, number) -> Grid Parameters ---------- bounds : the area for the grid to encompass resolution : the diameter of each bin Examples -------- TODO: complete this doctest >>> g = Grid(Rectangle(0, 0, 10, 10), 1) """ warnings.warn("Grid " + dep_msg, FutureWarning) if resolution == 0: raise Exception("Cannot create grid with resolution 0") self.res = resolution self.hash = {} self.x_range = (bounds.left, bounds.right) self.y_range = (bounds.lower, bounds.upper) try: self.i_range = int( math.ceil((self.x_range[1] - self.x_range[0]) / self.res) ) self.j_range = int( math.ceil((self.y_range[1] - self.y_range[0]) / self.res) ) except Exception as e: raise Exception( "Invalid arguments for Grid(): (" + str(x_range) + ", " + str(y_range) + ", " + str(res) + ")" ) from e def in_grid(self, loc): """ Returns whether a 2-tuple location _loc_ lies inside the grid bounds. Test tag: #is#Grid.in_grid """ return ( self.x_range[0] <= loc[0] <= self.x_range[1] and self.y_range[0] <= loc[1] <= self.y_range[1] ) def __grid_loc(self, loc): i = min(self.i_range, max(int((loc[0] - self.x_range[0]) / self.res), 0)) j = min(self.j_range, max(int((loc[1] - self.y_range[0]) / self.res), 0)) return (i, j) def add(self, item, pt): """ Adds an item to the grid at a specified location. add(x, Point) -> x Parameters ---------- item : the item to insert into the grid pt : the location to insert the item at Examples -------- >>> g = Grid(Rectangle(0, 0, 10, 10), 1) >>> g.add('A', Point((4.2, 8.7))) 'A' """ if not self.in_grid(pt): raise Exception( "Attempt to insert item at location outside grid bounds: " + str(pt) ) grid_loc = self.__grid_loc(pt) if grid_loc in self.hash: self.hash[grid_loc].append((pt, item)) else: self.hash[grid_loc] = [(pt, item)] return item def remove(self, item, pt): """ Removes an item from the grid at a specified location. remove(x, Point) -> x Parameters ---------- item : the item to remove from the grid pt : the location the item was added at Examples -------- >>> g = Grid(Rectangle(0, 0, 10, 10), 1) >>> g.add('A', Point((4.2, 8.7))) 'A' >>> g.remove('A', Point((4.2, 8.7))) 'A' """ if not self.in_grid(pt): raise Exception( "Attempt to remove item at location outside grid bounds: " + str(pt) ) grid_loc = self.__grid_loc(pt) self.hash[grid_loc].remove((pt, item)) if self.hash[grid_loc] == []: del self.hash[grid_loc] return item def bounds(self, bounds): """ Returns a list of items found in the grid within the bounds specified. bounds(Rectangle) -> x list Parameters ---------- item : the item to remove from the grid pt : the location the item was added at Examples -------- >>> g = Grid(Rectangle(0, 0, 10, 10), 1) >>> g.add('A', Point((1.0, 1.0))) 'A' >>> g.add('B', Point((4.0, 4.0))) 'B' >>> g.bounds(Rectangle(0, 0, 3, 3)) ['A'] >>> g.bounds(Rectangle(2, 2, 5, 5)) ['B'] >>> sorted(g.bounds(Rectangle(0, 0, 5, 5))) ['A', 'B'] """ x_range = (bounds.left, bounds.right) y_range = (bounds.lower, bounds.upper) items = [] lower_left = self.__grid_loc((x_range[0], y_range[0])) upper_right = self.__grid_loc((x_range[1], y_range[1])) for i in range(lower_left[0], upper_right[0] + 1): for j in range(lower_left[1], upper_right[1] + 1): if (i, j) in self.hash: items.extend( [ item[1] for item in [ item for item in self.hash[(i, j)] if x_range[0] <= item[0][0] <= x_range[1] and y_range[0] <= item[0][1] <= y_range[1] ] ] ) return items def proximity(self, pt, r): """ Returns a list of items found in the grid within a specified distance of a point. proximity(Point, number) -> x list Parameters ---------- pt : the location to search around r : the distance to search around the point Examples -------- >>> g = Grid(Rectangle(0, 0, 10, 10), 1) >>> g.add('A', Point((1.0, 1.0))) 'A' >>> g.add('B', Point((4.0, 4.0))) 'B' >>> g.proximity(Point((2.0, 1.0)), 2) ['A'] >>> g.proximity(Point((6.0, 5.0)), 3.0) ['B'] >>> sorted(g.proximity(Point((4.0, 1.0)), 4.0)) ['A', 'B'] """ items = [] lower_left = self.__grid_loc((pt[0] - r, pt[1] - r)) upper_right = self.__grid_loc((pt[0] + r, pt[1] + r)) for i in range(lower_left[0], upper_right[0] + 1): for j in range(lower_left[1], upper_right[1] + 1): if (i, j) in self.hash: items.extend( [ item[1] for item in [ item for item in self.hash[(i, j)] if get_points_dist(pt, item[0]) <= r ] ] ) return items def nearest(self, pt): """ Returns the nearest item to a point. nearest(Point) -> x Parameters ---------- pt : the location to search near Examples -------- >>> g = Grid(Rectangle(0, 0, 10, 10), 1) >>> g.add('A', Point((1.0, 1.0))) 'A' >>> g.add('B', Point((4.0, 4.0))) 'B' >>> g.nearest(Point((2.0, 1.0))) 'A' >>> g.nearest(Point((7.0, 5.0))) 'B' """ search_size = self.res while self.proximity(pt, search_size) == [] and ( get_points_dist((self.x_range[0], self.y_range[0]), pt) > search_size or get_points_dist((self.x_range[1], self.y_range[0]), pt) > search_size or get_points_dist((self.x_range[0], self.y_range[1]), pt) > search_size or get_points_dist((self.x_range[1], self.y_range[1]), pt) > search_size ): search_size = 2 * search_size items = [] lower_left = self.__grid_loc((pt[0] - search_size, pt[1] - search_size)) upper_right = self.__grid_loc((pt[0] + search_size, pt[1] + search_size)) for i in range(lower_left[0], upper_right[0] + 1): for j in range(lower_left[1], upper_right[1] + 1): if (i, j) in self.hash: items.extend( [ (get_points_dist(pt, item[0]), item[1]) for item in self.hash[(i, j)] ] ) if items == []: return None return min(items)[1] class BruteForcePointLocator: """ A class which does naive linear search on a set of Point objects. """ def __init__(self, points): """ Creates a naive index of the points specified. __init__(Point list) -> BruteForcePointLocator Parameters ---------- points : a list of points to index (Point list) Examples -------- >>> pl = BruteForcePointLocator([Point((0, 0)), Point((5, 0)), Point((0, 10))]) """ warnings.warn("BruteForcePointLocator " + dep_msg, FutureWarning) self._points = points def nearest(self, query_point): """ Returns the nearest point indexed to a query point. nearest(Point) -> Point Parameters ---------- query_point : a point to find the nearest indexed point to Examples -------- >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))] >>> pl = BruteForcePointLocator(points) >>> n = pl.nearest(Point((1, 1))) >>> str(n) '(0.0, 0.0)' """ return min(self._points, key=lambda p: get_points_dist(p, query_point)) def region(self, region_rect): """ Returns the indexed points located inside a rectangular query region. region(Rectangle) -> Point list Parameters ---------- region_rect : the rectangular range to find indexed points in Examples -------- >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))] >>> pl = BruteForcePointLocator(points) >>> pts = pl.region(Rectangle(-1, -1, 10, 10)) >>> len(pts) 3 """ return [ p for p in self._points if get_rectangle_point_intersect(region_rect, p) is not None ] def proximity(self, origin, r): """ Returns the indexed points located within some distance of an origin point. proximity(Point, number) -> Point list Parameters ---------- origin : the point to find indexed points near r : the maximum distance to find indexed point from the origin point Examples -------- >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))] >>> pl = BruteForcePointLocator(points) >>> neighs = pl.proximity(Point((1, 0)), 2) >>> len(neighs) 1 >>> p = neighs[0] >>> isinstance(p, Point) True >>> str(p) '(0.0, 0.0)' """ return [p for p in self._points if get_points_dist(p, origin) <= r] class PointLocator: """ An abstract representation of a point indexing data structure. """ def __init__(self, points): """ Returns a point locator object. __init__(Point list) -> PointLocator Parameters ---------- points : a list of points to index Examples -------- >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))] >>> pl = PointLocator(points) """ warnings.warn("PointLocator " + dep_msg, FutureWarning) self._locator = BruteForcePointLocator(points) def nearest(self, query_point): """ Returns the nearest point indexed to a query point. nearest(Point) -> Point Parameters ---------- query_point : a point to find the nearest indexed point to Examples -------- >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))] >>> pl = PointLocator(points) >>> n = pl.nearest(Point((1, 1))) >>> str(n) '(0.0, 0.0)' """ return self._locator.nearest(query_point) def region(self, region_rect): """ Returns the indexed points located inside a rectangular query region. region(Rectangle) -> Point list Parameters ---------- region_rect : the rectangular range to find indexed points in Examples -------- >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))] >>> pl = PointLocator(points) >>> pts = pl.region(Rectangle(-1, -1, 10, 10)) >>> len(pts) 3 """ return self._locator.region(region_rect) overlapping = region def polygon(self, polygon): """ Returns the indexed points located inside a polygon """ # get points in polygon bounding box # for points in bounding box, check for inclusion in polygon def proximity(self, origin, r): """ Returns the indexed points located within some distance of an origin point. proximity(Point, number) -> Point list Parameters ---------- origin : the point to find indexed points near r : the maximum distance to find indexed point from the origin point Examples -------- >>> points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))] >>> pl = PointLocator(points) >>> len(pl.proximity(Point((1, 0)), 2)) 1 """ return self._locator.proximity(origin, r) class PolygonLocator: """ An abstract representation of a polygon indexing data structure. """ def __init__(self, polygons): """ Returns a polygon locator object. __init__(Polygon list) -> PolygonLocator Parameters ---------- polygons : a list of polygons to index Examples -------- >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))]) >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))]) >>> pl = PolygonLocator([p1, p2]) >>> isinstance(pl, PolygonLocator) True """ warnings.warn("PolygonLocator " + dep_msg, FutureWarning) self._locator = polygons # create and rtree self._rtree = RTree() for polygon in polygons: x = polygon.bounding_box.left y = polygon.bounding_box.lower X = polygon.bounding_box.right Y = polygon.bounding_box.upper self._rtree.insert(polygon, Rect(x, y, X, Y)) def inside(self, query_rectangle): """ Returns polygons that are inside query_rectangle Examples -------- >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))]) >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))]) >>> p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))]) >>> pl = PolygonLocator([p1, p2, p3]) >>> qr = Rectangle(0, 0, 5, 5) >>> res = pl.inside( qr ) >>> len(res) 1 >>> qr = Rectangle(3, 7, 5, 8) >>> res = pl.inside( qr ) >>> len(res) 0 >>> qr = Rectangle(10, 10, 12, 12) >>> res = pl.inside( qr ) >>> len(res) 0 >>> qr = Rectangle(0, 0, 12, 12) >>> res = pl.inside( qr ) >>> len(res) 3 Notes ----- inside means the intersection of the query rectangle and a polygon is not empty and is equal to the area of the polygon """ left = query_rectangle.left right = query_rectangle.right upper = query_rectangle.upper lower = query_rectangle.lower # rtree rect qr = Rect(left, lower, right, upper) # bb overlaps res = [r.leaf_obj() for r in self._rtree.query_rect(qr) if r.is_leaf()] qp = Polygon( [ Point((left, lower)), Point((right, lower)), Point((right, upper)), Point((left, upper)), ] ) ip = [] GPPI = get_polygon_point_intersect for poly in res: lower = poly.bounding_box.lower right = poly.bounding_box.right upper = poly.bounding_box.upper left = poly.bounding_box.left p1 = Point((left, lower)) p2 = Point((right, upper)) if GPPI(qp, p1) and GPPI(qp, p2): ip.append(poly) return ip def overlapping(self, query_rectangle): """ Returns list of polygons that overlap query_rectangle Examples -------- >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))]) >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))]) >>> p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))]) >>> pl = PolygonLocator([p1, p2, p3]) >>> qr = Rectangle(0, 0, 5, 5) >>> res = pl.overlapping( qr ) >>> len(res) 2 >>> qr = Rectangle(3, 7, 5, 8) >>> res = pl.overlapping( qr ) >>> len(res) 1 >>> qr = Rectangle(10, 10, 12, 12) >>> res = pl.overlapping( qr ) >>> len(res) 0 >>> qr = Rectangle(0, 0, 12, 12) >>> res = pl.overlapping( qr ) >>> len(res) 3 >>> qr = Rectangle(8, 3, 9, 4) >>> p1 = Polygon([Point((2, 1)), Point((2, 3)), Point((4, 3)), Point((4,1))]) >>> p2 = Polygon([Point((7, 1)), Point((7, 5)), Point((10, 5)), Point((10, 1))]) >>> pl = PolygonLocator([p1, p2]) >>> res = pl.overlapping(qr) >>> len(res) 1 Notes ----- overlapping means the intersection of the query rectangle and a polygon is not empty and is no larger than the area of the polygon """ left = query_rectangle.left right = query_rectangle.right upper = query_rectangle.upper lower = query_rectangle.lower # rtree rect qr = Rect(left, lower, right, upper) # bb overlaps res = [r.leaf_obj() for r in self._rtree.query_rect(qr) if r.is_leaf()] # have to check for polygon overlap using segment intersection # add polys whose bb contains at least one of the corners of the query # rectangle sw = (left, lower) se = (right, lower) ne = (right, upper) nw = (left, upper) pnts = [sw, se, ne, nw] cs = [] for pnt in pnts: c = [r.leaf_obj() for r in self._rtree.query_point(pnt) if r.is_leaf()] cs.extend(c) cs = list(set(cs)) overlapping = [] # first find polygons with at least one vertex inside query rectangle remaining = copy.copy(res) for polygon in res: vertices = polygon.vertices for vertex in vertices: xb = vertex[0] >= left xb *= vertex[0] < right yb = vertex[1] >= lower yb *= vertex[1] < upper if xb * yb: overlapping.append(polygon) remaining.remove(polygon) break # for remaining polys in bb overlap check if vertex chains intersect # segments of the query rectangle left_edge = LineSegment(Point((left, lower)), Point((left, upper))) right_edge = LineSegment(Point((right, lower)), Point((right, upper))) lower_edge = LineSegment(Point((left, lower)), Point((right, lower))) upper_edge = LineSegment(Point((left, upper)), Point((right, upper))) for polygon in remaining: vertices = copy.copy(polygon.vertices) if vertices[-1] != vertices[0]: vertices.append(vertices[0]) # put on closed cartographic form nv = len(vertices) for i in range(nv - 1): head = vertices[i] tail = vertices[i + 1] edge = LineSegment(head, tail) li = get_segments_intersect(edge, left_edge) if ( li or get_segments_intersect(edge, right_edge) or get_segments_intersect(edge, lower_edge) or get_segments_intersect(edge, upper_edge) ): overlapping.append(polygon) break # check remaining for explicit containment of the bounding rectangle # cs has candidates for this check sw = Point(sw) se = Point(se) ne = Point(ne) nw = Point(nw) for polygon in cs: if ( get_polygon_point_intersect(polygon, sw) or get_polygon_point_intersect(polygon, se) or get_polygon_point_intersect(polygon, ne) or get_polygon_point_intersect(polygon, nw) ): overlapping.append(polygon) break return list(set(overlapping)) def nearest(self, query_point, rule="vertex"): """ Returns the nearest polygon indexed to a query point based on various rules. nearest(Polygon) -> Polygon Parameters ---------- query_point : a point to find the nearest indexed polygon to rule : representative point for polygon in nearest query. vertex -- measures distance between vertices and query_point centroid -- measures distance between centroid and query_point edge -- measures the distance between edges and query_point Examples -------- >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))]) >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))]) >>> pl = PolygonLocator([p1, p2]) >>> try: n = pl.nearest(Point((-1, 1))) ... except NotImplementedError: print("future test: str(min(n.vertices())) == (0.0, 1.0)") future test: str(min(n.vertices())) == (0.0, 1.0) """ # noqa E501 raise NotImplementedError def region(self, region_rect): """ Returns the indexed polygons located inside a rectangular query region. region(Rectangle) -> Polygon list Parameters ---------- region_rect : the rectangular range to find indexed polygons in Examples -------- >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))]) >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))]) >>> pl = PolygonLocator([p1, p2]) >>> n = pl.region(Rectangle(0, 0, 4, 10)) >>> len(n) 2 """ n = self._locator for polygon in n: points = polygon.vertices pl = BruteForcePointLocator(points) pts = pl.region(region_rect) if len(pts) == 0: n.remove(polygon) return n def contains_point(self, point): """ Returns polygons that contain point Parameters ---------- point: point (x,y) Returns ------- list of polygons containing point Examples -------- >>> p1 = Polygon([Point((0,0)), Point((6,0)), Point((4,4))]) >>> p2 = Polygon([Point((1,2)), Point((4,0)), Point((4,4))]) >>> p1.contains_point((2,2)) 1 >>> p2.contains_point((2,2)) 1 >>> pl = PolygonLocator([p1, p2]) >>> len(pl.contains_point((2,2))) 2 >>> p2.contains_point((1,1)) 0 >>> p1.contains_point((1,1)) 1 >>> len(pl.contains_point((1,1))) 1 >>> p1.centroid (3.3333333333333335, 1.3333333333333333) >>> pl.contains_point((1,1))[0].centroid (3.3333333333333335, 1.3333333333333333) """ # bbounding box containment res = [r.leaf_obj() for r in self._rtree.query_point(point) if r.is_leaf()] # explicit containment check for candidate polygons needed return [poly for poly in res if poly.contains_point(point)] def proximity(self, origin, r, rule="vertex"): """ Returns the indexed polygons located within some distance of an origin point based on various rules. proximity(Polygon, number) -> Polygon list Parameters ---------- origin : the point to find indexed polygons near r : the maximum distance to find indexed polygon from the origin point rule : representative point for polygon in nearest query. vertex -- measures distance between vertices and query_point centroid -- measures distance between centroid and query_point edge -- measures the distance between edges and query_point Examples -------- >>> p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))]) >>> p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))]) >>> pl = PolygonLocator([p1, p2]) >>> try: ... len(pl.proximity(Point((0, 0)), 2)) ... except NotImplementedError: ... print("future test: len(pl.proximity(Point((0, 0)), 2)) == 2") future test: len(pl.proximity(Point((0, 0)), 2)) == 2 """ raise NotImplementedError libpysal-4.9.2/libpysal/cg/ops/000077500000000000000000000000001452177046000163645ustar00rootroot00000000000000libpysal-4.9.2/libpysal/cg/ops/__init__.py000066400000000000000000000000361452177046000204740ustar00rootroot00000000000000from . import atomic, tabular libpysal-4.9.2/libpysal/cg/ops/_accessors.py000066400000000000000000000025721452177046000210700ustar00rootroot00000000000000# ruff: noqa: F822 import functools as _f __all__ = [ "area", "bbox", "bounding_box", "centroid", "holes", "len", "parts", "perimeter", "segments", "vertices", ] def get_attr(df, geom_col="geometry", inplace=False, attr=None): outval = df[geom_col].apply(lambda x: x.__getattribute__(attr)) if inplace: outcol = f"shape_{func.__name__}" # noqa F821 df[outcol] = outval return None return outval _doc_template = """ Tabular accessor to grab a geometric object's {n} attribute. Parameters ---------- df : pandas.DataFrame A pandas.Dataframe with a geometry column. geom_col : str The name of the column in ``df`` containing the geometry. inplace : bool A boolean denoting whether to operate on ``df`` inplace or to return a pandas.Series contaning the results of the computation. If operating inplace, the derived column will be under 'shape_{n}'. Returns ------- ``None`` if inplace is set to ``True`` and operation is conducted on ``df`` in memory. Otherwise, returns a pandas.Series. See Also -------- For further documentation about the attributes of the object in question, refer to shape classes in ``pysal.cg.shapes``. """ _accessors = {} for k in __all__: _accessors[k] = _f.partial(get_attr, attr=k) _accessors[k].__doc__ = _doc_template.format(n=k) globals().update(_accessors) libpysal-4.9.2/libpysal/cg/ops/_shapely.py000066400000000000000000000133331452177046000205450ustar00rootroot00000000000000# ruff: noqa: F822 import functools as _f from warnings import warn from ...common import requires as _requires __all__ = [ "to_wkb", "to_wkt", "area", "distance", "length", "boundary", "bounds", "centroid", "representative_point", "convex_hull", "envelope", "buffer", "simplify", "difference", "intersection", "symmetric_difference", "union", "has_z", "is_empty", "is_ring", "is_simple", "is_valid", "relate", "contains", "crosses", "disjoint", "equals", "intersects", "overlaps", "touches", "within", "equals_exact", "almost_equals", "project", "interpolate", ] def _atomic_op(df, geom_col="geometry", inplace=False, _func=None, **kwargs): """Atomic operation internal function.""" outval = df[geom_col].apply(lambda x: _func(x, **kwargs)) outcol = f"shape_{_func.__name__}" if not inplace: new = df.copy() new[outcol] = outval return new df[outcol] = outval _doc_template = """ Tabular version of pysal.contrib.shapely_ext.{n} Parameters ---------- df : pandas.DataFrame A pandas dataframe with a geometry column. geom_col : str The name of the column in ``df`` containing the geometry. inplace : bool A boolean denoting whether to operate on the dataframe inplace or to return a series contaning the results of the computation. If operating inplace, the derived column will be under 'shape_{n}'. **kwargs : dict Keyword arguments to be passed to the elementwise functions. Returns ------- If inplace, None, and operation is conducted on dataframe in memory. Otherwise, returns a series. Notes ----- Some atomic operations require an 'other' argument. See Also -------- pysal.contrib.shapely_ext.{n} """ # ensure that the construction of atomics is done only if we can use shapely _shapely_atomics = {} try: from .. import shapely_ext as _s for k in __all__: _shapely_atomics.update({k: _f.partial(_atomic_op, _func=_s.__dict__[k])}) _shapely_atomics[k].__doc__ = _doc_template.format(n=_s.__dict__[k].__name__) except ImportError: pass globals().update(_shapely_atomics) ############## # Reductions # ############## @_requires("shapely") def cascaded_union(df, geom_col="geometry", **groupby_kws): """Returns the cascaded union of a possibly-grouped dataframe. Parameters ---------- df : pandas.DataFrame A dataframe containing geometry objects which are being unioned. geom_col : string The name of the column in ``df`` containing the geometry. **groupby_kws : dict Keyword arguments to pass transparently to the groupby function in ``df``. Returns ------- out : {libpysal.cg.{Point, Chain, Polygon}, pandas.DataFrame} A PySAL shape or a dataframe of shapes resulting from the union operation. See Also -------- pysal.shapely_ext.cascaded_union pandas.DataFrame.groupby """ by = groupby_kws.pop("by", None) level = groupby_kws.pop("level", None) if by is not None or level is not None: df = df.groupby(by=by, level=level, **groupby_kws) out = df[geom_col].apply(_s.cascaded_union) else: out = _s.cascaded_union(df[geom_col].tolist()) return out @_requires("shapely") def unary_union(df, geom_col="geometry", **groupby_kws): """Returns the unary union of a possibly-grouped dataframe. Parameters ---------- df : pandas.DataFrame A dataframe containing geometry objects which are being unioned. geom_col : string The name of the column in ``df`` containing the geometry. **groupby_kws : dict Keyword arguments to pass transparently to the groupby function in ``df``. Returns ------- out : {libpysal.cg.{Point, Chain, Polygon}, pandas.DataFrame} A PySAL shape or a dataframe of shapes resulting from the union operation. See Also -------- pysal.shapely_ext.unary_union pandas.DataFrame.groupby """ by = groupby_kws.pop("by", None) level = groupby_kws.pop("level", None) if by is not None or level is not None: df = df.groupby(**groupby_kws) out = df[geom_col].apply(_cascaded_union) # noqa F821 else: out = _cascaded_union(df[geom_col].tolist()) # noqa F821 return out @_requires("shapely") def _cascaded_intersection(shapes): it = iter(shapes) outshape = next(it) for _i, shape in enumerate(it): try: outshape = _s.intersection(shape, outshape) except NotImplementedError: warn("An intersection is empty!", stacklevel=2) return None return outshape @_requires("shapely") def cascaded_intersection(df, geom_col="geometry", **groupby_kws): """Returns the cascaded intersection of a possibly-grouped dataframe Parameters ---------- df : pandas.DataFrame A dataframe containing geometry objects which are being intersectioned. geom_col : string The name of the column in ``df`` containing the geometry. **groupby_kws : dict Keyword arguments to pass transparently to the groupby function in ``df``. Returns ------- out : {libpysal.cg.{Point, Chain, Polygon}, pandas.DataFrame} A PySAL shape or a dataframe of shapes resulting from the intersection operation. See Also -------- pysal.shapely_ext.cascaded_intersection pandas.DataFrame.groupby """ by = groupby_kws.pop("by", None) level = groupby_kws.pop("level", None) if by is not None or level is not None: df = df.groupby(**groupby_kws) out = df[geom_col].apply(_cascaded_intersection) else: out = _cascaded_intersection(df[geom_col].tolist()) return out libpysal-4.9.2/libpysal/cg/ops/atomic.py000066400000000000000000000004071452177046000202130ustar00rootroot00000000000000from . import _accessors as _a from . import _shapely as _s # prefer access to shapely computation _all = {} _all.update(_s.__dict__) _all.update(_a.__dict__) globals().update({_k: _v for _k, _v in list(_all.items()) if not _k.startswith("_")}) _preferred = _a libpysal-4.9.2/libpysal/cg/ops/tabular.py000066400000000000000000000121031452177046000203650ustar00rootroot00000000000000import functools as _f from warnings import warn from ...common import requires as _requires from ...io.geotable.utils import to_df, to_gdf try: import pandas as _pd @_requires("pandas") @_f.wraps(_pd.merge) def join(*args, **kwargs): return _pd.merge(*args, **kwargs) except ImportError: pass @_requires("geopandas") def spatial_join( df1, df2, left_geom_col="geometry", right_geom_col="geometry", **kwargs ): """Perform a spatial join between two ``pandas.DataFrames`` datasets by calling out to ``geopandas``. Parameters ---------- df1 : pandas.DataFrame The first dataset. It must have a 'MultiPolygon' or 'Polygon' geometry column. df2 : pandas.DataFrame The second dataset. It must have a 'MultiPolygon' or 'Polygon' geometry column. left_geom_col : str The left (``df1``) dataset's geometry column name. Default is ``'geometry'``. right_geom_col : str The right (``df2``) dataset's geometry column name. Default is ``'geometry'``. **kwargs : dict Optional keyword arguments passed in ``geopandas.tools.sjoin``. These may include (1) ``'how'`` (the method of spatial join), with valid values including ``'left'`` (use keys from ``df1`` and retain only the ``df1`` geometry column), ``'right'`` (use keys from ``df2`` and retain only the ``df2`` geometry column, and ``'inner'`` (use the intersection of keys from both ``df1`` & ``df2`` and retain only the `df2`` geometry column.; (2) ``'op'`` (defaults to ``'intersects'``), with other valid values including ``'contains'`` and ``'within'``. See the `Shapely docs `_ for more information.; (3) ``'lsuffix'`` (defaults to ``left'``), the suffix to apply to overlapping column names from ``df1``.; and (4) ``'rsuffix'`` defaults to ``right'``), the suffix to apply to overlapping column names from ``df2``. Returns ------- df : pandas.DataFrame A pandas.DataFrame with a new set of polygons and attributes resulting from the overlay. """ import geopandas as gpd gdf1 = to_gdf(df1, geom_col=left_geom_col) gdf2 = to_gdf(df2, geom_col=right_geom_col) out = gpd.tools.sjoin(gdf1, gdf2, **kwargs) df = to_df(out) return df @_requires("geopandas") def spatial_overlay( df1, df2, how, left_geom_col="geometry", right_geom_col="geometry", **kwargs ): """Perform a spatial overlay between two polygonal datasets by calling out to ``geopandas``. It currently only supports data (``pandas.DataFrames``) with polygons and implements several methods that are all effectively subsets of the union. Parameters ---------- df1 : pandas.DataFrame The first dataset. It must have a 'MultiPolygon' or 'Polygon' geometry column. df2 : pandas.DataFrame The second dataset. It must have a 'MultiPolygon' or 'Polygon' geometry column. how : str The method of spatial overlay. Options are inculde ``'intersection'``, ``'union', ``'identity'``, ``'symmetric_difference'`` or ``'difference'``. left_geom_col : str The left (``df1``) dataset's geometry column name. Default is ``'geometry'``. right_geom_col : str The right (``df2``) dataset's geometry column name. Default is ``'geometry'``. **kwargs : dict Optional keyword arguments passed in ``geopandas.tools.overlay``. Returns ------- df : pandas.DataFrame A pandas.DataFrame with a new set of polygons and attributes resulting from the overlay. """ import geopandas as gpd gdf1 = to_gdf(df1, geom_col=left_geom_col) gdf2 = to_gdf(df2, geom_col=right_geom_col) out = gpd.tools.overlay(gdf1, gdf2, how, **kwargs) df = to_df(out) return df @_requires("shapely") def dissolve(df, by="", **groupby_kws): from ._shapely import cascaded_union as union return union(df, by=by, **groupby_kws) def clip(return_exterior=False): # noqa ARG001 # return modified entries of the df that are within an envelope # provide an option to null out the geometries instead of not returning raise NotImplementedError def erase(return_interior=True): # noqa ARG001 # return modified entries of the df that are outside of an envelope # provide an option to null out the geometries instead of not returning raise NotImplementedError @_requires("shapely") def union(df, **kws): if "by" in kws: warn( "When a 'by' argument is provided, you should be using 'dissolve'.", stacklevel=2, ) return dissolve(df, **kws) from ._shapely import cascaded_union as union return union(df) @_requires("shapely") def intersection(df, **kws): from ._shapely import cascaded_intersection as intersection return intersection(df, **kws) def symmetric_difference(): raise NotImplementedError def difference(): raise NotImplementedError libpysal-4.9.2/libpysal/cg/ops/tests/000077500000000000000000000000001452177046000175265ustar00rootroot00000000000000libpysal-4.9.2/libpysal/cg/ops/tests/__init__.py000066400000000000000000000000001452177046000216250ustar00rootroot00000000000000libpysal-4.9.2/libpysal/cg/ops/tests/test_accessors.py000066400000000000000000000501021452177046000231220ustar00rootroot00000000000000import numpy as np import pytest from ....common import ATOL, RTOL from ....examples import get_path from ....io.geotable.file import read_files as rf from ...shapes import LineSegment, Rectangle from .. import _accessors as to_test class TestAccessors: def setup_method(self): self.polygons = rf(get_path("Polygon.shp")) self.points = rf(get_path("Point.shp")) self.lines = rf(get_path("Line.shp")) def test_area(self): with pytest.raises(AttributeError): to_test.area(self.points) with pytest.raises(AttributeError): to_test.area(self.lines) areas = to_test.area(self.polygons).values answer = [0.000284, 0.000263, 0.001536] np.testing.assert_allclose(answer, areas, rtol=RTOL, atol=ATOL * 10) def test_bbox(self): with pytest.raises(AttributeError): to_test.bbox(self.points) with pytest.raises(AttributeError): to_test.bbox(self.lines) answer = [ [ -0.010809397704086565, -0.26282711761789435, 0.12787295484449185, -0.250785835510383, ], [ 0.0469057130870883, -0.35957259110238166, 0.06309916143856897, -0.3126531125455273, ], [ -0.04527237752903268, -0.4646223970748078, 0.1432359699471787, -0.40150947016647276, ], ] bboxes = to_test.bbox(self.polygons).tolist() for ans, bbox in zip(answer, bboxes, strict=True): np.testing.assert_allclose(ans, bbox, rtol=RTOL, atol=ATOL) def test_bounding_box(self): with pytest.raises(AttributeError): to_test.bounding_box(self.points) line_rects = to_test.bounding_box(self.lines).tolist() line_bboxes = [[(a.left, a.lower), (a.right, a.upper)] for a in line_rects] pgon_rects = to_test.bounding_box(self.polygons).tolist() pgon_bboxes = [[(a.left, a.lower), (a.right, a.upper)] for a in pgon_rects] line_answers = [ [ (-0.009053924887015952, -0.2589587703323735), (0.007481157395930582, -0.25832280562918325), ], [ (0.10923550990637088, -0.2564149115196125), (0.12895041570526866, -0.2564149115196125), ], [ (0.050726757212867735, -0.356261369920482), (0.06153815716710198, -0.3130157701035449), ], [ (-0.0414881247497188, -0.46055958124368335), (0.1391258509563127, -0.4058666167693217), ], ] pgon_answers = [ [ (-0.010809397704086565, -0.26282711761789435), (0.12787295484449185, -0.250785835510383), ], [ (0.0469057130870883, -0.35957259110238166), (0.06309916143856897, -0.3126531125455273), ], [ (-0.04527237752903268, -0.4646223970748078), (0.1432359699471787, -0.40150947016647276), ], ] for bbox, answer in zip(line_bboxes, line_answers, strict=True): np.testing.assert_allclose(bbox, answer, atol=ATOL, rtol=RTOL) for bbox, answer in zip(pgon_bboxes, pgon_answers, strict=True): np.testing.assert_allclose(bbox, answer, atol=ATOL, rtol=RTOL) for rectangle in line_rects + pgon_rects: assert isinstance(rectangle, Rectangle) def test_centroid(self): with pytest.raises(AttributeError): to_test.centroid(self.points) with pytest.raises(AttributeError): to_test.centroid(self.lines) centroids = to_test.centroid(self.polygons).tolist() centroid_answers = [ (0.06466214975239247, -0.257330080795802), (0.05151163524856857, -0.33495102150875505), (0.04759584610455384, -0.44147205133285744), ] for ct, answer in zip(centroids, centroid_answers, strict=True): np.testing.assert_allclose(ct, answer, rtol=RTOL, atol=ATOL) def test_holes(self): holed_polygons = rf(get_path("Polygon_Holes.shp")) with pytest.raises(AttributeError): to_test.centroid(self.points) with pytest.raises(AttributeError): to_test.centroid(self.lines) no_holes = to_test.holes(self.polygons).tolist() holes = to_test.holes(holed_polygons).tolist() for elist in no_holes: assert elist == [[]] answers = [ [ [ (-0.002557818613137461, -0.25599115990199145), (0.0012028146993492903, -0.25561239107915107), (0.004909338180001697, -0.2596435735508095), (-0.0019896653788768724, -0.2616726922445973), (-0.007021879739470651, -0.25834493758678534), (-0.002557818613137461, -0.25599115990199145), ], [ (0.11456291239229519, -0.2534750527216944), (0.11878347927537383, -0.2540973157877893), (0.11878347927537383, -0.2540973157877893), (0.12335576006537571, -0.25596410498607414), (0.11605093276773958, -0.258155553175365), (0.11020707092963067, -0.2579391138480276), (0.11456291239229519, -0.2534750527216944), ], ], [ [ (0.04818367618951632, -0.31403748200228154), (0.052755956979518195, -0.31384809759086135), (0.04975286131271223, -0.3566219196559085), (0.04818367618951632, -0.31403748200228154), ] ], [ [ (-0.039609525961703126, -0.4112999047245106), (-0.013745026344887779, -0.43770550265966934), (-0.015260101636249357, -0.4393287976146996), (-0.04242323721708889, -0.4140053963162277), (-0.039609525961703126, -0.4112999047245106), ], [ (0.027838379419803827, -0.4597823140480808), (0.07350707748798824, -0.45859189774772524), (0.07469749378834376, -0.46064807135743024), (0.028487697401815927, -0.46270424496713525), (0.027838379419803827, -0.4597823140480808), ], [ (0.11192505809037084, -0.43467535207694624), (0.13962929198955382, -0.4037245282677028), (0.14092792795357803, -0.405023164231727), (0.11463054968208794, -0.4370561846776573), (0.11192505809037084, -0.43467535207694624), ], ], ] for hole, answer in zip(holes, answers, strict=True): for sub_hole, sub_answer in zip(hole, answer, strict=True): np.testing.assert_allclose(sub_hole, sub_answer, rtol=RTOL, atol=ATOL) def test_len(self): with pytest.raises(AttributeError): to_test.len(self.points) line_len = to_test.len(self.lines) pgon_len = to_test.len(self.polygons) pgon_answers = [24, 7, 14] line_answers = [ 0.016547307853772356, 0.019714905798897786, 0.058991346117778738, 0.21634275419393173, ] np.testing.assert_allclose(line_len, line_answers, rtol=RTOL, atol=ATOL) np.testing.assert_allclose(pgon_len, pgon_answers, rtol=RTOL, atol=ATOL) def test_parts(self): with pytest.raises(AttributeError): to_test.parts(self.points) line_parts = to_test.parts(self.lines) line_answers = [ [ [ (-0.009053924887015952, -0.25832280562918325), (0.007481157395930582, -0.2589587703323735), (0.007481157395930582, -0.2589587703323735), ] ], [ [ (0.10923550990637088, -0.2564149115196125), (0.12895041570526866, -0.2564149115196125), ] ], [ [ (0.050726757212867735, -0.3130157701035449), (0.050726757212867735, -0.356261369920482), (0.06153815716710198, -0.3448140052630575), (0.06153815716710198, -0.3448140052630575), ] ], [ [ (-0.0414881247497188, -0.41286222850441445), (-0.012233748402967204, -0.4402087107415953), (0.027196063194828424, -0.46055958124368335), (0.07489341593409732, -0.4586516871341126), (0.11241533342232213, -0.43639292252245376), (0.1391258509563127, -0.4058666167693217), ] ], ] for part, answer in zip(line_parts, line_answers, strict=True): for piece, sub_answer in zip(part, answer, strict=True): np.testing.assert_allclose(piece, sub_answer, rtol=RTOL, atol=ATOL) pgon_parts = to_test.parts(self.polygons) pgon_answers = [ [ [ (-0.010809397704086565, -0.25825973474952796), (-0.007487664708911018, -0.25493800175435244), (-0.0016746319673538457, -0.2532771352567647), (0.003307967525409461, -0.2545227851299555), (0.006214483896188033, -0.25701408487633715), (0.007044917144981927, -0.26033581787151266), (0.003307967525409461, -0.26241190099349737), (-0.0029202818405446584, -0.26282711761789435), (-0.008318097957704912, -0.26199668436910045), (-0.009978964455292672, -0.26075103449590964), (-0.010809397704086565, -0.25825973474952796), ], [ (0.10711212362464478, -0.25618365162754325), (0.1112642898686142, -0.25203148538357384), (0.11583167273698053, -0.250785835510383), (0.12164470547853773, -0.25203148538357384), (0.12538165509811022, -0.25410756850555855), (0.12787295484449185, -0.2574293015007341), (0.12579687172250714, -0.26033581787151266), (0.1191534057321561, -0.26116625112030656), (0.1141708062393928, -0.26158146774470353), (0.11084907324421728, -0.25992060124711575), (0.10794255687343868, -0.25909016799832185), (0.10794255687343868, -0.25909016799832185), (0.10711212362464478, -0.25618365162754325), ], ], [ [ (0.05396439570183631, -0.3126531125455273), (0.05147309595545463, -0.35251390848763364), (0.059777428443393454, -0.34254870950210703), (0.06309916143856897, -0.34462479262409174), (0.048981796209073, -0.35957259110238166), (0.0469057130870883, -0.3126531125455273), (0.05396439570183631, -0.3126531125455273), ] ], [ [ (-0.04527237752903268, -0.413550752273984), (-0.039874561411872456, -0.4077377195324269), (-0.039874561411872456, -0.4077377195324269), (-0.010809397704086565, -0.43680288324021277), (0.02656009849163815, -0.45756371446005983), (0.07181871055090477, -0.45673328121126594), (0.1104338566198203, -0.4338963668694341), (0.1394990203276062, -0.40150947016647276), (0.1432359699471787, -0.4052464197860452), (0.1162468893613775, -0.4380485331134035), (0.07763174329246192, -0.4625463139528231), (0.02780574836482899, -0.4646223970748078), (-0.013715914074865138, -0.4442767824793577), (-0.04527237752903268, -0.413550752273984), ] ], ] for part, answer in zip(pgon_parts, pgon_answers, strict=True): for piece, sub_answer in zip(part, answer, strict=True): np.testing.assert_allclose(piece, sub_answer, rtol=RTOL, atol=ATOL) def test_perimeter(self): with pytest.raises(AttributeError): to_test.perimeter(self.points) with pytest.raises(AttributeError): to_test.perimeter(self.lines) pgon_perim = to_test.perimeter(self.polygons) pgon_answers = np.array([0.09381641, 0.13141213, 0.45907697]) np.testing.assert_allclose( pgon_perim.values, pgon_answers, rtol=RTOL, atol=ATOL ) def test_segments(self): with pytest.raises(AttributeError): to_test.segments(self.points) with pytest.raises(AttributeError): to_test.segments(self.polygons) line_segments = to_test.segments(self.lines) flattened = [l_[0] for l_ in line_segments] answers = [ [ ( (-0.009053924887015952, -0.25832280562918325), (0.007481157395930582, -0.2589587703323735), ), ( (0.007481157395930582, -0.2589587703323735), (0.007481157395930582, -0.2589587703323735), ), ], [ ( (0.10923550990637088, -0.2564149115196125), (0.12895041570526866, -0.2564149115196125), ) ], [ ( (0.050726757212867735, -0.3130157701035449), (0.050726757212867735, -0.356261369920482), ), ( (0.050726757212867735, -0.356261369920482), (0.06153815716710198, -0.3448140052630575), ), ( (0.06153815716710198, -0.3448140052630575), (0.06153815716710198, -0.3448140052630575), ), ], [ ( (-0.0414881247497188, -0.41286222850441445), (-0.012233748402967204, -0.4402087107415953), ), ( (-0.012233748402967204, -0.4402087107415953), (0.027196063194828424, -0.46055958124368335), ), ( (0.027196063194828424, -0.46055958124368335), (0.07489341593409732, -0.4586516871341126), ), ( (0.07489341593409732, -0.4586516871341126), (0.11241533342232213, -0.43639292252245376), ), ( (0.11241533342232213, -0.43639292252245376), (0.1391258509563127, -0.4058666167693217), ), ], ] for parts, points in zip(flattened, answers, strict=True): for piece, answer in zip(parts, points, strict=True): assert isinstance(piece, LineSegment) p1, p2 = piece.p1, piece.p2 np.testing.assert_allclose([p1, p2], answer) def test_vertices(self): with pytest.raises(AttributeError): to_test.vertices(self.points) line_verts = to_test.vertices(self.lines).tolist() pgon_verts = to_test.vertices(self.polygons).tolist() line_answers = [ [ (-0.009053924887015952, -0.25832280562918325), (0.007481157395930582, -0.2589587703323735), (0.007481157395930582, -0.2589587703323735), ], [ (0.10923550990637088, -0.2564149115196125), (0.12895041570526866, -0.2564149115196125), ], [ (0.050726757212867735, -0.3130157701035449), (0.050726757212867735, -0.356261369920482), (0.06153815716710198, -0.3448140052630575), (0.06153815716710198, -0.3448140052630575), ], [ (-0.0414881247497188, -0.41286222850441445), (-0.012233748402967204, -0.4402087107415953), (0.027196063194828424, -0.46055958124368335), (0.07489341593409732, -0.4586516871341126), (0.11241533342232213, -0.43639292252245376), (0.1391258509563127, -0.4058666167693217), ], ] pgon_answers = [ [ (-0.010809397704086565, -0.25825973474952796), (-0.007487664708911018, -0.25493800175435244), (-0.0016746319673538457, -0.2532771352567647), (0.003307967525409461, -0.2545227851299555), (0.006214483896188033, -0.25701408487633715), (0.007044917144981927, -0.26033581787151266), (0.003307967525409461, -0.26241190099349737), (-0.0029202818405446584, -0.26282711761789435), (-0.008318097957704912, -0.26199668436910045), (-0.009978964455292672, -0.26075103449590964), (-0.010809397704086565, -0.25825973474952796), (0.10711212362464478, -0.25618365162754325), (0.1112642898686142, -0.25203148538357384), (0.11583167273698053, -0.250785835510383), (0.12164470547853773, -0.25203148538357384), (0.12538165509811022, -0.25410756850555855), (0.12787295484449185, -0.2574293015007341), (0.12579687172250714, -0.26033581787151266), (0.1191534057321561, -0.26116625112030656), (0.1141708062393928, -0.26158146774470353), (0.11084907324421728, -0.25992060124711575), (0.10794255687343868, -0.25909016799832185), (0.10794255687343868, -0.25909016799832185), (0.10711212362464478, -0.25618365162754325), ], [ (0.05396439570183631, -0.3126531125455273), (0.05147309595545463, -0.35251390848763364), (0.059777428443393454, -0.34254870950210703), (0.06309916143856897, -0.34462479262409174), (0.048981796209073, -0.35957259110238166), (0.0469057130870883, -0.3126531125455273), (0.05396439570183631, -0.3126531125455273), ], [ (-0.04527237752903268, -0.413550752273984), (-0.039874561411872456, -0.4077377195324269), (-0.039874561411872456, -0.4077377195324269), (-0.010809397704086565, -0.43680288324021277), (0.02656009849163815, -0.45756371446005983), (0.07181871055090477, -0.45673328121126594), (0.1104338566198203, -0.4338963668694341), (0.1394990203276062, -0.40150947016647276), (0.1432359699471787, -0.4052464197860452), (0.1162468893613775, -0.4380485331134035), (0.07763174329246192, -0.4625463139528231), (0.02780574836482899, -0.4646223970748078), (-0.013715914074865138, -0.4442767824793577), (-0.04527237752903268, -0.413550752273984), ], ] for part, answer in zip(line_verts, line_answers, strict=True): np.testing.assert_allclose(part, answer, atol=ATOL, rtol=RTOL) for part, answer in zip(pgon_verts, pgon_answers, strict=True): np.testing.assert_allclose(part, answer, atol=ATOL, rtol=RTOL) libpysal-4.9.2/libpysal/cg/ops/tests/test_shapely.py000066400000000000000000000155011452177046000226060ustar00rootroot00000000000000from warnings import warn import numpy as np import pytest from ....examples import get_path from ....io.geotable import read_files as rf # from ... import comparators as comp # from ... import shapely as she from ...shapes import Chain from .. import _shapely as sht @pytest.mark.skip("Skipping shapely during reorg.") class TestShapely: def setup_method(self): self.polygons = rf(get_path("Polygon.shp")) self.points = rf(get_path("Point.shp")) self.lines = rf(get_path("Line.shp")) self.target_poly = self.polygons.geometry[2] self.target_point = self.points.geometry[1] self.target_line = self.lines.geometry[0] self.dframes = [self.polygons, self.points, self.lines] self.targets = [self.target_poly, self.target_point, self.target_line] def compare(self, func_name, df, **kwargs): geom_list = df.geometry.tolist() shefunc = she.__dict__[func_name] # noqa F821 shtfunc = sht.__dict__[func_name] # noqa F821 try: she_vals = (shefunc(geom, **kwargs) for geom in geom_list) sht_vals = shtfunc(df, inplace=False, **kwargs) sht_list = sht_vals[f"shape_{func_name}"].tolist() for tabular, shapely in zip(sht_list, she_vals, strict=True): if comp.is_shape(tabular) and comp.is_shape(shapely): # noqa F821 comp.equal(tabular, shapely) # noqa F821 else: assert tabular == shapely except NotImplementedError as e: warn(f"The shapely/PySAL bridge is not implemented: {e}.", stacklevel=2) return True def test_to_wkb(self): for df in self.dframes: self.compare("to_wkb", df) def test_to_wkt(self): for df in self.dframes: self.compare("to_wkt", df) def test_area(self): for df in self.dframes: self.compare("area", df) def test_distance(self): for df in self.dframes: for other in self.targets: self.compare("distance", df, other=other) def test_length(self): for df in self.dframes: self.compare("length", df) def test_boundary(self): for df in self.dframes: self.compare("boundary", df) def test_bounds(self): for df in self.dframes: self.compare("bounds", df) def test_centroid(self): for df in self.dframes: self.compare("centroid", df) def test_representative_point(self): for df in self.dframes: self.compare("representative_point", df) def test_convex_hull(self): for df in self.dframes: self.compare("convex_hull", df) def test_envelope(self): for df in self.dframes: self.compare("envelope", df) def test_buffer(self): np.random.seed(555) for df in self.dframes: self.compare("buffer", df, radius=np.random.randint(10)) def test_simplify(self): tol = 0.001 for df in self.dframes: self.compare("simplify", df, tolerance=tol) def test_difference(self): for df in self.dframes: for target in self.targets: self.compare("difference", df, other=target) def test_intersection(self): for df in self.dframes: for target in self.targets: self.compare("intersection", df, other=target) def test_symmetric_difference(self): for df in self.dframes: for target in self.targets: self.compare("symmetric_difference", df, other=target) def test_union(self): for df in self.dframes: for target in self.targets: self.compare("union", df, other=target) def test_has_z(self): for df in self.dframes: self.compare("has_z", df) def test_is_empty(self): """ PySAL doesn't really support empty shapes. Like, the following errors out: ``` ps.cg.Polygon([[]]) ``` and you can make it work by: ``` ps.cg.Polygon([[()]]) ``` but that won't convert over to shapely. So, we're only testing the negative here. """ for df in self.dframes: self.compare("is_empty", df) def test_is_ring(self): for df in self.dframes: self.compare("is_ring", df) def test_is_simple(self): for df in self.dframes: self.compare("is_simple", df) def test_is_valid(self): for df in self.dframes: self.compare("is_valid", df) def test_relate(self): for df in self.dframes: for target in self.targets: self.compare("relate", df, other=target) def test_contains(self): for df in self.dframes: for target in self.targets: self.compare("contains", df, other=target) def test_crosses(self): for df in self.dframes: for target in self.targets: self.compare("crosses", df, other=target) def test_disjoint(self): for df in self.dframes: for target in self.targets: self.compare("disjoint", df, other=target) def test_equals(self): for df in self.dframes: for target in self.targets: self.compare("equals", df, other=target) def test_intersects(self): for df in self.dframes: for target in self.targets: self.compare("intersects", df, other=target) def test_overlaps(self): for df in self.dframes: for target in self.targets: self.compare("overlaps", df, other=target) def test_touches(self): for df in self.dframes: for target in self.targets: self.compare("touches", df, other=target) def test_within(self): for df in self.dframes: for target in self.targets: self.compare("within", df, other=target) def test_equals_exact(self): for df in self.dframes: for target in self.targets: self.compare("equals_exact", df, other=target, tolerance=0.1) def test_almost_equals(self): for df in self.dframes: for target in self.targets: self.compare("almost_equals", df, other=target) def test_project(self): np.random.seed(555) self.compare("project", self.lines, other=self.targets[2]) def test_interpolate(self): np.random.seed(555) for df in self.dframes: if isinstance(df.geometry[0], Chain): self.compare("interpolate", df, distance=np.random.randint(10)) else: with pytest.raises(TypeError): self.compare("interpolate", df, distance=np.random.randint(10)) libpysal-4.9.2/libpysal/cg/ops/tests/test_tabular.py000066400000000000000000000042021452177046000225670ustar00rootroot00000000000000import numpy as np from ....common import requires as _requires from ....examples import get_path from ....io import geotable as pdio from ... import ops as GIS # noqa N812 from ...shapes import Polygon class TestTabular: def setup_method(self): import pandas as pd self.columbus = pdio.read_files(get_path("columbus.shp")) grid = [ Polygon([(0, 0), (0, 1), (1, 1), (1, 0)]), Polygon([(0, 1), (0, 2), (1, 2), (1, 1)]), Polygon([(1, 2), (2, 2), (2, 1), (1, 1)]), Polygon([(1, 1), (2, 1), (2, 0), (1, 0)]), ] regime = [0, 0, 1, 1] ids = list(range(4)) data = np.array((regime, ids)).T self.exdf = pd.DataFrame(data, columns=["regime", "ids"]) self.exdf["geometry"] = grid @_requires("geopandas") def test_round_trip(self): import geopandas as gpd import pandas as pd geodf = GIS.tabular.to_gdf(self.columbus) assert isinstance(geodf, gpd.GeoDataFrame) new_df = GIS.tabular.to_df(geodf) assert isinstance(new_df, pd.DataFrame) for new, old in zip(new_df.geometry, self.columbus.geometry, strict=True): assert new == old def test_spatial_join(self): pass def test_spatial_overlay(self): pass def test_dissolve(self): out = GIS.tabular.dissolve(self.exdf, by="regime") assert out[0].area == 2.0 assert out[1].area == 2.0 answer_vertices0 = {(0, 0), (0, 1), (0, 2), (1, 2), (1, 1), (1, 0), (0, 0)} answer_vertices1 = {(2, 1), (2, 0), (1, 0), (1, 1), (1, 2), (2, 2), (2, 1)} s0 = {tuple(map(int, t)) for t in out[0].vertices} s1 = {tuple(map(int, t)) for t in out[1].vertices} assert s0 == answer_vertices0 assert s1 == answer_vertices1 def test_clip(self): pass def test_erase(self): pass def test_union(self): new_geom = GIS.tabular.union(self.exdf) assert new_geom.area == 4 def test_intersection(self): pass def test_symmetric_difference(self): pass def test_difference(self): pass libpysal-4.9.2/libpysal/cg/polygonQuadTreeStructure.py000066400000000000000000001530671452177046000232140ustar00rootroot00000000000000""" Quad Tree Structure Class for PySAL_core/cg/shapes.Ring, PySAL_core/cg/shapes.Polygon This structure could speed up the determining of point in polygon process """ __author__ = "Hu Shao" import math from .shapes import Ring def cwt(a, b, tolerance=1e-9): """compare_with_tolerance For the float value comparing, there are some situlation that two values are actually the same but been shown differently, e.g 1.230 and 1.2300000000000001. Especially after some calculation. This function is used to compare two float value with some tolerance Parameters ---------- a : float The first value b : float The second value tolerance : float Tolerance for the comparing Returns ------- if_bigger_than : int if a is bigger than b 1: a > b 0: a == b -1: a < b """ tolerance = math.fabs(tolerance) if a - b > tolerance: return 1 elif a - b < -tolerance: return -1 else: return 0 class Cell: """Basic rectangle geometry used for dividing research area (polygon) into quadtree structure. Attributes ---------- level : int Quadtree level this cell belongs to. Begins with 0 min_x : float min x coordinate of this cell min_y : float min y coordinate of this cell length_x : float width of this cell length_y : float height of this cell arcs : list detected arc list which are within this cell status : str enum status of this cell, indicating this cell's spatial relationship with the research area "in" : this cell lies totally inside of the research area "out" : this cell lies totally outside of the research area "maybe" : this cell intersects with the research area's boundary children_l_b : Cell children of current cell, left-bottom children_l_t : Cell children of current cell, left-top children_r_b : Cell children of current cell, right-bottom children_t_t : Cell children of current cell, right-top """ def __init__(self, level, min_x, min_y, length_x, length_y, arcs, status): """ Parameters ---------- level : int on which quadtree level this cell belongs to. Begins with 0 min_x : float min x coordinate of this cell min_y : float min y coordinate of this cell length_x : float width of this cell length_y : float height of this cell arcs : list detected arc list which are within this cell status : str enum status of this cell, indicating this cell's spatial relationship with the research area "in" : this cell lies totally inside of the research area "out" : this cell lies totally outside of the research area "maybe" : this cell intersects with the research area's boundary """ self.level = level self.min_x = min_x self.min_y = min_y self.length_x = length_x self.length_y = length_y self.arcs = arcs self.status = status self.zero_tolerance = 1e-9 self._rings = None self.children_l_b = None self.children_l_t = None self.children_r_b = None self.children_r_t = None @property def rings(self): """the list of rings which are formed by the intersection of this cell and the arcs pass them Returns ------- rings : list """ if self._rings is None: if self.status == "in" or self.status == "out": self._rings = [] else: self._rings = [] extract_result = extract_segments_from_cell_with_arcs( [self.min_x, self.min_y], self.length_x, self.length_y, self.arcs, self.zero_tolerance, ) for point_list in extract_result[0]: self._rings.append(Ring(point_list)) return self._rings def split(self): """Equally split current cell into 4 sub cells. If this cell in needed to be splitted into four parts, add the result cells as children to current cell. """ if self.status == "in" or self.status == "out": # no need to conduct the splitting return level = self.level + 1 length_x = self.length_x / 2 length_y = self.length_y / 2 middle_x = self.min_x + length_x middle_y = self.min_y + length_y """ Do the splitting work here. Some properties of the arc: - Point order of the arcs MUST be clockwise - The two end-points of each arc MUST lie on the borders of the cell - When a arc goes in a cell, it MUST goes out from the same one - The intersection points MUST be lying on the inner-boundaries which are used to divide the cell into 4 sub-cells - Use the intersection points to split the arcs into small ones - No need to store cell boundaries as arcs, store the intersection points, points' relative location from """ if self.level == 0: if len(self.arcs) != 1: raise LookupError( "Unexpected arc number! Only single ring " "can be assigned to the root cell" ) return # Do some initialize work, find one point lies on # the border of the rectangle(cell) and begin with this point arc = self.arcs[0] if ( arc[0] == arc[len(arc) - 1] ): # remove the duplicated points the the end of the arc arc = arc[0 : len(arc) - 1] min_x = arc[0][0] min_x_index = 0 for index in range(0, len(arc)): if arc[index][0] < min_x: min_x = arc[index][0] min_x_index = index arc = arc[min_x_index : len(arc)] + arc[0 : min_x_index + 1] self.arcs[0] = arc # l: left, r: right, b: bottom, t: top cell_arcs_l_b = [] cell_arcs_r_b = [] cell_arcs_l_t = [] cell_arcs_r_t = [] for arc in self.arcs: temp_arc = [] temp_arc_belonging = None for index in range(0, len(arc) - 1): x0 = arc[index][0] y0 = arc[index][1] x1 = arc[index + 1][0] y1 = arc[index + 1][1] if temp_arc_belonging is None: """ In this section, determine which sub-cell does the current temp_arc belong to. Although every single arc must begin and end on the cell's outer boundaries, when the split process begin, there might be some sub-arcs begin at the split-line. So here we should consider all possible situations See the image cell_boundary_category_rule to better understand the process """ if cwt(x0, self.min_x, self.zero_tolerance) == 0: # left border if cwt(y0, middle_y, self.zero_tolerance) == -1: # position 1 temp_arc_belonging = "l_b" elif cwt(y0, middle_y, self.zero_tolerance) == 1: # position 2 temp_arc_belonging = "l_t" else: # just by chance at the middle point if cwt(y1, y0, self.zero_tolerance) == -1: # going down temp_arc_belonging = "l_b" elif cwt(y1, y0, self.zero_tolerance) == 1: # going up temp_arc_belonging = "l_t" else: # just by chance this segment lies on # split_line_h, throw it continue elif ( cwt(x0, self.min_x + self.length_x, self.zero_tolerance) == 0 ): # right border if cwt(y0, middle_y, self.zero_tolerance) == -1: # position 6 temp_arc_belonging = "r_b" elif cwt(y0, middle_y, self.zero_tolerance) == 1: # position 5 temp_arc_belonging = "r_t" else: # just by chance at the middle point if cwt(y1, y0, self.zero_tolerance) == -1: # going down temp_arc_belonging = "r_b" elif cwt(y1, y0, self.zero_tolerance) == 1: # going up temp_arc_belonging = "r_t" else: # just by chance this segment lies on # split_line_h, throw it continue elif cwt(y0, self.min_y, self.zero_tolerance) == 0: # bottom border if cwt(x0, middle_x, self.zero_tolerance) == -1: # position 8 temp_arc_belonging = "l_b" elif cwt(x0, middle_x, self.zero_tolerance) == 1: # position 7 temp_arc_belonging = "r_b" else: # just by chance at the middle point if cwt(x1, x0, self.zero_tolerance) == -1: # going left temp_arc_belonging = "l_b" elif cwt(x1, x0, self.zero_tolerance) == 1: # going right temp_arc_belonging = "r_b" else: # just by chance this segment lies on # split_line_v, throw it continue elif ( cwt(y0, self.min_y + self.length_y, self.zero_tolerance) == 0 ): # top border if cwt(x0, middle_x, self.zero_tolerance) == -1: # position 3 temp_arc_belonging = "l_t" elif cwt(x0, middle_x, self.zero_tolerance) == 1: # position 4 temp_arc_belonging = "r_t" else: # just by chance at the middle point if cwt(x1, x0, self.zero_tolerance) == -1: # going left temp_arc_belonging = "l_t" elif cwt(x1, x0, self.zero_tolerance) == 1: # going right temp_arc_belonging = "r_t" else: # just by chance this segment lies # on split_line_v, throw it continue elif cwt(x0, middle_x, self.zero_tolerance) == 0: # split_line_v if cwt(y0, middle_y, self.zero_tolerance) == 1: # position c if cwt(x1, x0, self.zero_tolerance) == 1: temp_arc_belonging = "r_t" elif cwt(x1, x0, self.zero_tolerance) == -1: temp_arc_belonging = "l_t" else: # x1==x0, just by chance on the split_line_v continue elif cwt(y0, middle_y, self.zero_tolerance) == -1: # position d if cwt(x1, x0, self.zero_tolerance) == 1: temp_arc_belonging = "r_b" elif cwt(x1, x0, self.zero_tolerance) == -1: temp_arc_belonging = "l_b" else: # x1==x0, just by chance on the split_line_v continue else: # in condition that p0 lies at the # cross point of two split lines if ( cwt(x1, x0, self.zero_tolerance) == 0 or cwt(y1, y0, self.zero_tolerance) == 0 ): # on one of the split_line continue elif cwt(x1, x0, self.zero_tolerance) == 1: if cwt(y1, y0, self.zero_tolerance) == 1: temp_arc_belonging = "r_t" else: temp_arc_belonging = "r_b" else: # on condition that x1 < x0 if cwt(y1, y0, self.zero_tolerance) == 1: temp_arc_belonging = "l_t" else: temp_arc_belonging = "l_b" elif cwt(y0, middle_y, self.zero_tolerance) == 0: # split_line_h if cwt(x0, middle_x, self.zero_tolerance) == 1: # position r if cwt(y1, y0, self.zero_tolerance) == 1: temp_arc_belonging = "r_t" elif cwt(y1, y0, self.zero_tolerance) == -1: temp_arc_belonging = "r_b" else: # y1==y0, just by chance on the split_line_h continue else: # on condition that x0 < middle_x, position a if cwt(y1, y0, self.zero_tolerance) == 1: temp_arc_belonging = "l_t" elif cwt(y1, y0, self.zero_tolerance) == -1: temp_arc_belonging = "l_b" else: # y1==y0, just by chance on the split_line_h continue if temp_arc_belonging is None: raise Exception("Error on cell split!!!") # At this point, the belonging sub-cell # of current segment is already known. # Let's begin the splitting! """ Firstly determine if the segment totally lies on the split_lines. p1 (x1, y1) could lie on the split_lines. This situation is not the same with previous ones which "both points lie on the same split_line". In previous situation, the p1 is the begin point of a sub arc, we can just throw that segment if it totally lie on split_line. However, in current situation, the segment is in the middle of the sub-arc. So if the segment is detected totally lie on the split_line, we should split the sub_arc here. """ if ( cwt(x0, x1, self.zero_tolerance) == cwt(x0, middle_x, self.zero_tolerance) == 0 or cwt(y0, y1, self.zero_tolerance) == cwt(y0, middle_y, self.zero_tolerance) == 0 ): # split the arc here, throw current segment if len(temp_arc) != 0: if temp_arc_belonging == "l_b": cell_arcs_l_b.append(temp_arc) elif temp_arc_belonging == "l_t": cell_arcs_l_t.append(temp_arc) elif temp_arc_belonging == "r_b": cell_arcs_r_b.append(temp_arc) elif temp_arc_belonging == "r_t": cell_arcs_r_t.append(temp_arc) temp_arc = [] temp_arc_belonging = None continue intersect_point_h = None intersect_point_v = None # Check if the segment intersects with split_line_h if ( cwt(y0, middle_y, self.zero_tolerance) == -1 and cwt(middle_y, y1, self.zero_tolerance) <= 0 ) or ( cwt(y0, middle_y, self.zero_tolerance) == 1 and cwt(middle_y, y1, self.zero_tolerance) >= 0 ): if ( cwt(x0, x1, self.zero_tolerance) == 0 ): # the segments is vertical intersect_point_h = [x0, middle_y] else: a = (y1 - y0) / (x1 - x0) b = y0 - a * x0 x_new = (middle_y - b) / a intersect_point_h = [x_new, middle_y] # Check if the segment intersects with split_line_v if ( cwt(x0, middle_x, self.zero_tolerance) == -1 and cwt(middle_x, x1, self.zero_tolerance) <= 0 ) or ( cwt(x0, middle_x, self.zero_tolerance) == 1 and cwt(middle_x, x1, self.zero_tolerance) >= 0 ): if ( cwt(y0, y1, self.zero_tolerance) == 0 ): # the segments is horizontal intersect_point_v = [middle_x, y0] else: a = (y1 - y0) / (x1 - x0) b = y0 - a * x0 y_new = a * middle_x + b intersect_point_v = [middle_x, y_new] # check if the intersect point(s) exist intersect_point = None intersect_point_mark = None if (intersect_point_h is not None) and (intersect_point_v is not None): # In this situation, the current segment # cannot be vertical nor horizontal. # Find the closer intersection point to p0 if math.fabs(intersect_point_h[0] - x0) < math.fabs( intersect_point_v[0] - x0 ): intersect_point = intersect_point_h intersect_point_mark = "h" else: intersect_point = intersect_point_v intersect_point_mark = "v" elif intersect_point_h is not None: intersect_point = intersect_point_h intersect_point_mark = "h" elif intersect_point_v is not None: intersect_point = intersect_point_v intersect_point_mark = "v" if intersect_point is not None: # split the arc here if len(temp_arc) == 0: temp_arc.append([x0, y0]) temp_arc.append(intersect_point) if temp_arc_belonging == "l_b": cell_arcs_l_b.append(temp_arc) elif temp_arc_belonging == "l_t": cell_arcs_l_t.append(temp_arc) elif temp_arc_belonging == "r_b": cell_arcs_r_b.append(temp_arc) elif temp_arc_belonging == "r_t": cell_arcs_r_t.append(temp_arc) if temp_arc_belonging == "l_b": if intersect_point_mark == "h": if (intersect_point_h is not None) and ( intersect_point_v is not None ): # under the situation that a single segment # intersects with both split lines, # here need carefully process small_arc = [intersect_point_h, intersect_point_v] if ( cwt( intersect_point_h[0], intersect_point_v[0], self.zero_tolerance, ) != 0 ): # deal with the situation that the segment # just goes through center point cell_arcs_l_t.append(small_arc) temp_arc = [intersect_point_v, [x1, y1]] temp_arc_belonging = "r_t" else: temp_arc = [intersect_point, [x1, y1]] temp_arc_belonging = "l_t" else: if (intersect_point_h is not None) and ( intersect_point_v is not None ): # under the situation that a single segment # intersects with both split lines, # here need carefully process small_arc = [intersect_point_v, intersect_point_h] if ( cwt( intersect_point_h[0], intersect_point_v[0], self.zero_tolerance, ) != 0 ): # deal with the situation that # the segment just goes through center point cell_arcs_r_b.append(small_arc) temp_arc = [intersect_point_h, [x1, y1]] temp_arc_belonging = "r_t" else: temp_arc = [intersect_point, [x1, y1]] temp_arc_belonging = "r_b" elif temp_arc_belonging == "l_t": if intersect_point_mark == "h": if (intersect_point_h is not None) and ( intersect_point_v is not None ): # under the situation that a single segment # intersects with both split lines, # here need carefully process small_arc = [intersect_point_h, intersect_point_v] if ( cwt( intersect_point_h[0], intersect_point_v[0], self.zero_tolerance, ) != 0 ): # deal with the situation that the segment # just goes through center point cell_arcs_l_b.append(small_arc) temp_arc = [intersect_point_v, [x1, y1]] temp_arc_belonging = "r_b" else: temp_arc = [intersect_point, [x1, y1]] temp_arc_belonging = "l_b" else: if (intersect_point_h is not None) and ( intersect_point_v is not None ): # under the situation that a single segment # intersects with both split lines, # here need carefully process small_arc = [intersect_point_v, intersect_point_h] if ( cwt( intersect_point_h[0], intersect_point_v[0], self.zero_tolerance, ) != 0 ): # deal with the situation that # the segment just goes through center point cell_arcs_r_t.append(small_arc) temp_arc = [intersect_point_h, [x1, y1]] temp_arc_belonging = "r_b" else: temp_arc = [intersect_point, [x1, y1]] temp_arc_belonging = "r_t" elif temp_arc_belonging == "r_b": if intersect_point_mark == "h": if (intersect_point_h is not None) and ( intersect_point_v is not None ): # under the situation that a single segment # intersects with both split lines, # here need carefully process small_arc = [intersect_point_h, intersect_point_v] if ( cwt( intersect_point_h[0], intersect_point_v[0], self.zero_tolerance, ) != 0 ): # deal with the situation that # the segment just goes through center point cell_arcs_r_t.append(small_arc) temp_arc = [intersect_point_v, [x1, y1]] temp_arc_belonging = "l_t" else: temp_arc = [intersect_point, [x1, y1]] temp_arc_belonging = "r_t" else: if (intersect_point_h is not None) and ( intersect_point_v is not None ): # under the situation that a single segment # intersects with both split lines, # here need carefully process small_arc = [intersect_point_v, intersect_point_h] if ( cwt( intersect_point_h[0], intersect_point_v[0], self.zero_tolerance, ) != 0 ): # deal with the situation that # the segment just goes through center point cell_arcs_l_b.append(small_arc) temp_arc = [intersect_point_h, [x1, y1]] temp_arc_belonging = "l_t" else: temp_arc = [intersect_point, [x1, y1]] temp_arc_belonging = "l_b" elif temp_arc_belonging == "r_t": if intersect_point_mark == "h": if (intersect_point_h is not None) and ( intersect_point_v is not None ): # under the situation that a single segment # intersects with both split lines, # here need carefully process small_arc = [intersect_point_h, intersect_point_v] if ( cwt( intersect_point_h[0], intersect_point_v[0], self.zero_tolerance, ) != 0 ): # deal with the situation that # the segment just goes through center point cell_arcs_r_b.append(small_arc) temp_arc = [intersect_point_v, [x1, y1]] temp_arc_belonging = "l_b" else: temp_arc = [intersect_point, [x1, y1]] temp_arc_belonging = "r_b" else: if (intersect_point_h is not None) and ( intersect_point_v is not None ): # under the situation that a single segment # intersects with both split lines, # here need carefully process small_arc = [intersect_point_v, intersect_point_h] if ( cwt( intersect_point_h[0], intersect_point_v[0], self.zero_tolerance, ) != 0 ): # deal with the situation that # the segment just goes through center point cell_arcs_l_t.append(small_arc) temp_arc = [intersect_point_h, [x1, y1]] temp_arc_belonging = "l_b" else: temp_arc = [intersect_point, [x1, y1]] temp_arc_belonging = "l_t" if ( cwt(temp_arc[0][0], temp_arc[1][0], self.zero_tolerance) == 0 and cwt(temp_arc[0][1], temp_arc[1][1], self.zero_tolerance) == 0 ): # to deal with the situation that p1 # just lied on one of the split-lines temp_arc = [] temp_arc_belonging = None else: # simply append the point to current arc if len(temp_arc) == 0: temp_arc.append([x0, y0]) temp_arc.append([x1, y1]) # Allocate the last left arc to a sub-cell if len(temp_arc) > 0: if temp_arc_belonging == "l_b": cell_arcs_l_b.append(temp_arc) elif temp_arc_belonging == "l_t": cell_arcs_l_t.append(temp_arc) elif temp_arc_belonging == "r_b": cell_arcs_r_b.append(temp_arc) elif temp_arc_belonging == "r_t": cell_arcs_r_t.append(temp_arc) status_l_b = "maybe" status_l_t = "maybe" status_r_b = "maybe" status_r_t = "maybe" """ At this point, all the arcs in this cell have been split into sub-arcs and allocated to 4 sub-cells. So, we can try to create the cells on left-bottom, right-bottom, left-top and right-top. Before doing that, we need to determine the status of each sub-cell, especially those who are totally within or out of the study area. These two kind of sub-cell have the same property: they don't have arc allocated. So, If here exists cell(s) who don't have arcs allocated, we need to begin the check """ if ( len(cell_arcs_l_b) * len(cell_arcs_l_t) * len(cell_arcs_r_b) * len(cell_arcs_r_t) == 0 ): extract_result = extract_segments_from_cell_with_arcs( [self.min_x, self.min_y], self.length_x, self.length_y, self.arcs, self.zero_tolerance, ) construct_rings = [] for arc in extract_result[0]: construct_rings.append(Ring(arc)) # determine the totally within and out-of sub cells if len(cell_arcs_l_b) == 0: center = [self.min_x + length_x / 2, self.min_y + length_y / 2] is_in = False for ring in construct_rings: if ring.contains_point(center): is_in = True status_l_b = "in" if is_in else "out" if len(cell_arcs_l_t) == 0: center = [self.min_x + length_x / 2, middle_y + length_y / 2] is_in = False for ring in construct_rings: if ring.contains_point(center): is_in = True status_l_t = "in" if is_in else "out" if len(cell_arcs_r_b) == 0: center = [middle_x + length_x / 2, self.min_y + length_y / 2] is_in = False for ring in construct_rings: if ring.contains_point(center): is_in = True status_r_b = "in" if is_in else "out" if len(cell_arcs_r_t) == 0: center = [middle_x + length_x / 2, middle_y + length_y / 2] is_in = False for ring in construct_rings: if ring.contains_point(center): is_in = True status_r_t = "in" if is_in else "out" cells_l_b = Cell( level, self.min_x, self.min_y, length_x, length_y, cell_arcs_l_b, status_l_b ) cells_l_t = Cell( level, self.min_x, middle_y, length_x, length_y, cell_arcs_l_t, status_l_t ) cells_r_b = Cell( level, middle_x, self.min_y, length_x, length_y, cell_arcs_r_b, status_r_b ) cells_r_t = Cell( level, middle_x, middle_y, length_x, length_y, cell_arcs_r_t, status_r_t ) self.children_l_b = cells_l_b self.children_l_t = cells_l_t self.children_r_b = cells_r_b self.children_r_t = cells_r_t # =================== def contains_point(self, point): """Decide if this cell (rectangle) contains a given point Parameters ---------- point : list Point structure, like [x, y] Returns ------- if_contains : bool """ if self.status == "out": return False if ( point[0] < self.min_x or point[0] > self.min_x + self.length_x or point[1] < self.min_y or point[1] > self.min_y + self.length_y ): return False if self.status == "in": return True else: is_in = False for ring in self.rings: if ring.contains_point(point): is_in = True return is_in def extract_connecting_borders_between_points( cell_min_point, cell_length_x, cell_length_y, point_begin, point_end, zero_tolerance ): """There is an rectangle and two points on the border, this function is used to extract the borders connecting these two points. The segments must be clockwise Parameters ---------- cell_min_point : list the bottom-left point of the cell, like [x0, y0] cell_length_x : float width of the cell cell_length_y : float height of the cell point_begin : list the first point on the cell's border. like [xa, ya] point_end : list the second point on the cell's border. like [xb, yb] result_type : str MUST be one of ["segments", "border_ids"]. Indicts which kind of result will return. "segments": return the segments list which connecting these two points "border_ids" return a list of ids of the orders of the cell connecting these two points zero_tolerance : float value of zero_tolerance for determining if two float values are equal Returns ------- segments_and_ids : tuple like (segments, involved_border_ids) 1. list of points, including the start and end points 2. list of border ids being involved in the segments, not necessary to be in the original order """ if point_begin == point_end: return ([], []) # Determine which borders do the point_begin and point_end belong border_id_p_begin = -1 border_id_p_end = -1 if cwt(point_begin[0], cell_min_point[0], zero_tolerance) == 0: border_id_p_begin = 0 elif cwt(point_begin[1], cell_min_point[1] + cell_length_y, zero_tolerance) == 0: border_id_p_begin = 1 elif cwt(point_begin[0], cell_min_point[0] + cell_length_x, zero_tolerance) == 0: border_id_p_begin = 2 elif cwt(point_begin[1], cell_min_point[1], zero_tolerance) == 0: border_id_p_begin = 3 if cwt(point_end[0], cell_min_point[0], zero_tolerance) == 0: border_id_p_end = 0 elif cwt(point_end[1], cell_min_point[1] + cell_length_y, zero_tolerance) == 0: border_id_p_end = 1 elif cwt(point_end[0], cell_min_point[0] + cell_length_x, zero_tolerance) == 0: border_id_p_end = 2 elif cwt(point_end[1], cell_min_point[1], zero_tolerance) == 0: border_id_p_end = 3 if border_id_p_begin == -1 or border_id_p_end == -1: print( ( cell_min_point, cell_min_point[0] + cell_length_x, cell_min_point[1] + cell_length_y, point_begin, point_end, cell_length_x, cell_length_y, ) ) raise Exception("Error! begin/end point doesn't lie on the cell border!!!") # Now, move forward from point_begin to point_end segments = [point_begin] involved_border_ids = [border_id_p_begin] border_id_p_search = border_id_p_begin if ( border_id_p_search == border_id_p_end ): # first check if they lie on the same border at the beginning if border_id_p_search == 0: if cwt(point_begin[1], point_end[1], zero_tolerance) == -1: segments.append(point_end) return (segments, involved_border_ids) else: segments.append([cell_min_point[0], cell_min_point[1] + cell_length_y]) border_id_p_search = (border_id_p_search + 1) % 4 elif border_id_p_search == 1: if cwt(point_begin[0], point_end[0], zero_tolerance) == -1: segments.append(point_end) return (segments, involved_border_ids) else: segments.append( [ cell_min_point[0] + cell_length_x, cell_min_point[1] + cell_length_y, ] ) border_id_p_search = (border_id_p_search + 1) % 4 elif border_id_p_search == 2: if cwt(point_begin[1], point_end[1], zero_tolerance) == 1: segments.append(point_end) return (segments, involved_border_ids) else: segments.append([cell_min_point[0] + cell_length_x, cell_min_point[1]]) border_id_p_search = (border_id_p_search + 1) % 4 elif border_id_p_search == 3: if cwt(point_begin[0], point_end[0], zero_tolerance) == 1: segments.append(point_end) return (segments, involved_border_ids) else: segments.append([cell_min_point[0], cell_min_point[1]]) border_id_p_search = (border_id_p_search + 1) % 4 while True: involved_border_ids.append(border_id_p_search) if border_id_p_search != border_id_p_end: # add a whole border if border_id_p_search == 0: segments.append([cell_min_point[0], cell_min_point[1] + cell_length_y]) elif border_id_p_search == 1: segments.append( [ cell_min_point[0] + cell_length_x, cell_min_point[1] + cell_length_y, ] ) elif border_id_p_search == 2: segments.append([cell_min_point[0] + cell_length_x, cell_min_point[1]]) elif border_id_p_search == 3: segments.append([cell_min_point[0], cell_min_point[1]]) border_id_p_search = (border_id_p_search + 1) % 4 else: # add the border segment according to the enc point segments.append(point_end) return (segments, list(set(involved_border_ids))) def get_relative_location_on_cell_border( cell_min_point, cell_length_x, cell_length_y, point, zero_tolerance ): """When a point is on the border of a cell, this function can be used to calculate the relative location of the point to cell's left-bottom corner. Parameters ---------- cell_min_point : list the bottom-left point of the cell, like [x0, y0] cell_length_x : float width of the cell cell_length_y : float height of the cell point : list the point on the cell's border. like [x, y] zero_tolerance : float value of zero_tolerance for determining if two float values are equal Returns ------- distance : float range from 0 to 4 """ border_id_p = -1 if cwt(point[0], cell_min_point[0], zero_tolerance) == 0: border_id_p = 0 border_id_p += (point[1] - cell_min_point[1]) / cell_length_y elif cwt(point[1], cell_min_point[1] + cell_length_y, zero_tolerance) == 0: border_id_p = 1 border_id_p += (point[0] - cell_min_point[0]) / cell_length_x elif cwt(point[0], cell_min_point[0] + cell_length_x, zero_tolerance) == 0: border_id_p = 2 border_id_p += 1 - (point[1] - cell_min_point[1]) / cell_length_y elif cwt(point[1], cell_min_point[1], zero_tolerance) == 0: border_id_p = 3 border_id_p += 1 - (point[0] - cell_min_point[0]) / cell_length_x return border_id_p def extract_segments_from_cell_with_arcs( cell_min_point, cell_length_x, cell_length_y, arcs, zero_tolerance ): """At the end of study area quadtree dividing, there will be some node cells intersect with arcs. The arcs are segments of original study border and the begin and end points of the arcs MUST lie on node cell border. This function can intersect the node cell and Parameters ---------- cell_min_point : array the bottom-left point of the cell, like [x0, y0] cell_length_x : float width of the cell cell_length_y : float height of the cell arcs : array array of point lists zero_tolerance : float value of zero_tolerance for determining if two float values are equal Returns ------- rings_and_border_ids : tuple like (rings, involved_border_ids) 1. the list of rings extracted, each ring contains a sequence of points - the begin and end points are the same. Note that there might be multiple rings extracted in a cell. 2. the ids of borders of the cell who are involved in the ring. Duplicated ids are removed and they may not be in the original order """ arc_begin_points = [] # beginning points of each arc arc_begin_points_location = [] # location of beginning points of each arc arc_end_points = [] # ending points of each arc arc_end_points_location = [] # location of ending points of each arc for single_arc in arcs: arc_begin_points.append(single_arc[0]) arc_end_points.append(single_arc[len(single_arc) - 1]) arc_begin_points_location.append( get_relative_location_on_cell_border( cell_min_point, cell_length_x, cell_length_y, single_arc[0], zero_tolerance, ) ) arc_end_points_location.append( get_relative_location_on_cell_border( cell_min_point, cell_length_x, cell_length_y, single_arc[len(single_arc) - 1], zero_tolerance, ) ) rings = [] involved_border_ids = [] used_arc_ids = [] # every time find an unused arc with minimum begin-point-location, # and begin the track form here new_ring = [] # the point list new_ring_end_point = None new_ring_end_point_location = -1 selected_arc_ids = [] while len(used_arc_ids) < len(arcs): if len(selected_arc_ids) == 0: # init the process of constructing a new ring # find the unused arc with min begin point location arc_id_with_min_begin_location = -1 for i in range(0, len(arcs)): if i in used_arc_ids: continue if ( arc_id_with_min_begin_location == -1 or cwt( arc_begin_points_location[arc_id_with_min_begin_location], arc_begin_points_location[i], zero_tolerance, ) == 1 ): arc_id_with_min_begin_location = i new_ring_end_point = arc_end_points[arc_id_with_min_begin_location] new_ring_end_point_location = arc_end_points_location[ arc_id_with_min_begin_location ] selected_arc_ids.append(arc_id_with_min_begin_location) for point in arcs[arc_id_with_min_begin_location]: new_ring.append(point) else: # there is already a selected arc, find the next # available arc(maybe itself) and add the borders between # these two arcs arc_id_with_relatively_min_begin_location = -1 for i in range(0, len(arcs)): if i in used_arc_ids: continue if arc_id_with_relatively_min_begin_location == -1: arc_id_with_relatively_min_begin_location = i else: distance_to_end_point_min = ( arc_begin_points_location[ arc_id_with_relatively_min_begin_location ] - new_ring_end_point_location ) if distance_to_end_point_min < 0: distance_to_end_point_min += 4 distance_to_end_point_now = ( arc_begin_points_location[i] - new_ring_end_point_location ) if distance_to_end_point_now < 0: distance_to_end_point_now += 4 if ( cwt( distance_to_end_point_min, distance_to_end_point_now, zero_tolerance, ) == 1 ): arc_id_with_relatively_min_begin_location = i extract_result = extract_connecting_borders_between_points( cell_min_point, cell_length_x, cell_length_y, new_ring_end_point, arc_begin_points[arc_id_with_relatively_min_begin_location], zero_tolerance, ) point_list = extract_result[0] border_id_list = extract_result[1] for i in range(0, len(point_list)): if i == 0 and point_list[i] == new_ring[len(new_ring) - 1]: continue new_ring.append(point_list[i]) for border_id in border_id_list: if border_id not in involved_border_ids: involved_border_ids.append(border_id) new_ring_end_point = arc_end_points[ arc_id_with_relatively_min_begin_location ] new_ring_end_point_location = arc_end_points_location[ arc_id_with_relatively_min_begin_location ] if arc_id_with_relatively_min_begin_location not in selected_arc_ids: # find a new arc, add the point sequence # in this arc to the ring, and continue the searching further selected_arc_ids.append(arc_id_with_relatively_min_begin_location) single_arc = arcs[arc_id_with_relatively_min_begin_location] for i in range(0, len(single_arc)): if i == 0 and single_arc[i] == new_ring[len(new_ring) - 1]: continue new_ring.append(single_arc[i]) else: # the newly found arc is exactly the beginning one, # a whole closed ring is formed. stop here rings.append(new_ring) new_ring = [] new_ring_end_point = None new_ring_end_point_location = -1 for arc_id in selected_arc_ids: used_arc_ids.append(arc_id) selected_arc_ids = [] if len(new_ring) > 0: raise Exception("Error in extract_segments_from_cell_with_arcs!!!") return (rings, involved_border_ids) class QuadTreeStructureSingleRing: """This class is the main manager of cells. By giving a study area. This class can construct a cell list depicting the study area. When given a new point. This class could rapidly determine whether the point lies in the study area Attributes __________ root_cell : Cell The Cell structure for storing the quad-tree for this ring """ def __init__(self, ring): """ Parameters ---------- ring : Ring the point list of study area. But in the class of Ring in PySAL Example: Ring([[0.0, 0.0], [3.0, 2.0], [5.0, 1.0]]) """ self.ring = ring self.root_cell = Cell( 0, ring.bounding_box.left, ring.bounding_box.lower, ring.bounding_box.width, ring.bounding_box.height, [ring.vertices], "maybe", ) # here build the quad tree structure # The criterion of stopping splitting the tree: # 1. The status is "in" or "out" # 2. The level >= 5 and the the number of current # cell only contains one ring and all segments # of the ring is no more than 4 # 3. The level >= 8 cells_for_processing = [self.root_cell] total_cell_count = 1 for _i in range(0, 8): # 10 result_cell_list = [] while len(cells_for_processing) > 0: cell = cells_for_processing.pop() cell.split() total_cell_count += 4 children_cells = [ cell.children_l_b, cell.children_l_t, cell.children_r_b, cell.children_r_t, ] for child in children_cells: if child.status == "out" or child.status == "in": continue if child.level >= 5 and ( # 6 len(child.rings) == 1 and child.rings[0].len <= 5 ): continue result_cell_list.append(child) cells_for_processing = result_cell_list def contains_point(self, point): """Quickly determine if the study area contains a point Parameters ---------- point : list the point structure, like [x, y] Returns ------- if_contains : bool """ # bbox check if ( point[0] < self.min_x or point[0] > self.min_x + self.region_width or point[1] < self.min_y or point[1] > self.min_y + self.region_height ): return False # find the leaf cell for checking cell_to_check = self.root_cell while True: if cell_to_check.children_l_b is None: break middle_x = cell_to_check.min_x + cell_to_check.length_x / 2 middle_y = cell_to_check.min_y + cell_to_check.length_y / 2 if point[0] <= middle_x and point[1] <= middle_y: cell_to_check = cell_to_check.children_l_b elif point[0] <= middle_x and point[1] > middle_y: cell_to_check = cell_to_check.children_l_t elif point[0] > middle_x and point[1] <= middle_y: cell_to_check = cell_to_check.children_r_b else: cell_to_check = cell_to_check.children_r_t return cell_to_check.contains_point(point) @property def region_width(self): return self.ring.bounding_box.width @property def region_height(self): return self.ring.bounding_box.height @property def min_x(self): return self.ring.bounding_box.left @property def min_y(self): return self.ring.bounding_box.lower libpysal-4.9.2/libpysal/cg/rtree.py000066400000000000000000000774301452177046000172710ustar00rootroot00000000000000# pylint: disable-msg=C0103, C0301 """ Pure Python implementation of RTree spatial index. Adaptation of http://code.google.com/p/pyrtree/ R-tree. see doc/ref/r-tree-clustering-split-algo.pdf """ __author__ = "Sergio J. Rey" __all__ = ["RTree", "Rect", "Rtree"] import array import time import numpy MAXCHILDREN = 10 MAX_KMEANS = 5 BUFFER = numpy.finfo(float).eps class Rect: """A rectangle class that stores an axis aligned rectangle and two flags (swapped_x and swapped_y). The flags are stored implicitly via swaps in the order of minx/y and maxx/y. """ __slots__ = ("x", "y", "xx", "yy", "swapped_x", "swapped_y") def __getstate__(self) -> tuple: return (self.x, self.y, self.xx, self.yy, self.swapped_x, self.swapped_y) def __setstate__(self, state: tuple): self.x, self.y, self.xx, self.yy, self.swapped_x, self.swapped_y = state def __init__(self, minx: float, miny: float, maxx: float, maxy: float): self.swapped_x = maxx < minx self.swapped_y = maxy < miny self.x = minx self.y = miny self.xx = maxx self.yy = maxy if self.swapped_x: self.x, self.xx = maxx, minx if self.swapped_y: self.y, self.yy = maxy, miny def coords(self) -> tuple: """Return the coordinates of the rectangle.""" return self.x, self.y, self.xx, self.yy def overlap(self, orect): """Return the overlapping area of two rectangles. Parameters ---------- orect : libpysal.cg.Rect Another rectangle. Returns ------- overlapping_area : float The area of the overlap between ``orect`` and ``self``. """ overlapping_area = self.intersect(orect).area() return overlapping_area def write_raw_coords(self, toarray, idx: int): """Write the raw coordinates of the rectangle.""" toarray[idx] = self.x toarray[idx + 1] = self.y toarray[idx + 2] = self.xx toarray[idx + 3] = self.yy if self.swapped_x: toarray[idx] = self.xx toarray[idx + 2] = self.x if self.swapped_y: toarray[idx + 1] = self.yy toarray[idx + 3] = self.y def area(self) -> float: """Calculate the area of the rectangle.""" w = self.xx - self.x h = self.yy - self.y return w * h def extent(self) -> tuple: """Return the extent of the rectangle in the form: (minx, minx, width, height). """ x = self.x y = self.y return (x, y, self.xx - x, self.yy - y) def grow(self, amt=None, sf=0.5): """Grow the bounds of a rectangle. Parameters ---------- amt : float The amount to grow the rectangle. Default is ``None``, which triggers the value of ``BUFFER``. sf : float The scale factor for ``amt``. Default is ``0.5``. Returns ------- rect : libpysal.cg.Rect A new rectangle grown by ``amt`` and scaled by ``sf``. """ if not amt: amt = BUFFER a = amt * sf rect = Rect(self.x - a, self.y - a, self.xx + a, self.yy + a) return rect def intersect(self, o): """Find the intersection of two rectangles. Parameters ---------- o : libpysal.cg.Rect Another rectangle. Returns ------- intersection : {libpysal.cg.NullRect, libpysal.cg.Rect} The intersecting part of ``o`` and ``self``. """ intersection = None if self is NullRect or o is NullRect: intersection = NullRect if not intersection: nx, ny = max(self.x, o.x), max(self.y, o.y) nx2, ny2 = min(self.xx, o.xx), min(self.yy, o.yy) w, h = nx2 - nx, ny2 - ny intersection = NullRect if w <= 0 or h <= 0 else Rect(nx, ny, nx2, ny2) return intersection def does_contain(self, o): """Check whether the rectangle contains the other rectangle. Parameters ---------- o : libpysal.cg.Rect Another rectangle. Returns ------- dc : bool ``True`` if ``self`` contains ``o`` otherwise ``False``. """ dc = self.does_containpoint((o.x, o.y)) and self.does_containpoint((o.xx, o.yy)) return dc def does_intersect(self, o): """Check whether the rectangles interect. Parameters ---------- o : libpysal.cg.Rect Another rectangle. Returns ------- dcp : bool ``True`` if ``self`` intersects ``o`` otherwise ``False``. """ di = self.intersect(o).area() > 0 return di def does_containpoint(self, p): """Check whether the rectangle contains a point or not. Parameters ---------- p : libpysal.cg.Point A point. Returns ------- dcp : bool ``True`` if ``self`` contains ``p`` otherwise ``False``. """ x, y = p dcp = x >= self.x and x <= self.xx and y >= self.y and y <= self.yy return dcp def union(self, o): """Union two rectangles. Parameters ---------- o : libpysal.cg.Rect Another rectangle. Returns ------- res : libpysal.cg.Rect The union of ``o`` and ``self``. """ if o is NullRect: res = Rect(self.x, self.y, self.xx, self.yy) elif self is NullRect: res = Rect(o.x, o.y, o.xx, o.yy) else: x = self.x y = self.y xx = self.xx yy = self.yy ox = o.x oy = o.y oxx = o.xx oyy = o.yy nx = x if x < ox else ox ny = y if y < oy else oy nx2 = xx if xx > oxx else oxx ny2 = yy if yy > oyy else oyy res = Rect(nx, ny, nx2, ny2) return res def union_point(self, o): """Union the rectangle and a point Parameters ---------- o : libpysal.cg.Point A point. Returns ------- res : libpysal.cg.Rect The union of ``o`` and ``self``. """ x, y = o res = self.union(Rect(x, y, x, y)) return res def diagonal_sq(self) -> float: """Calculate the squared diagonal of the rectangle.""" if self is NullRect: diag_sq = 0.0 else: w = self.xx - self.x h = self.yy - self.y diag_sq = w * w + h * h return diag_sq def diagonal(self) -> float: """Calculate the diagonal of the rectangle.""" return numpy.sqrt(self.diagonal_sq()) NullRect = Rect(0.0, 0.0, 0.0, 0.0) NullRect.swapped_x = False NullRect.swapped_y = False def union_all(kids): """Create union of all child rectangles. Parameters ---------- kids : list A list of ``libpysal.cg._NodeCursor`` objects. Returns ------- cur : {libpysal.cg.Rect, libpysal.cg.NullRect} The unioned result of all child rectangles. """ cur = NullRect for k in kids: cur = cur.union(k.rect) assert False is cur.swapped_x return cur def Rtree(): # noqa N802 return RTree() class RTree: """An RTree for efficiently querying space based on intersecting rectangles. Attributes ---------- count : int The number of nodes in the tree. stats : dict Tree generation statistics. leaf_count : int The number of leaves (objects) in the tree. rect_pool : array.array The pool of rectangles in the tree in the form :math:`[ax1, ay1, ax2, ay2, bx1, by1, bx2, by2, ..., nx1, ny1, nx2, ny2]` where the first set of 4 coordinates is the bounding box of the root node and each successive set of 4 coordinates is the bounding box of a leaf node. node_pool : array.array The pool of node IDs in the tree. leaf_pool : list The pool of leaf objects in the tree. cursor : libpysal.cg._NodeCursor The non-root node and all its children. Examples -------- Instantiate an ``RTree``. >>> from libpysal.cg import RTree, Chain >>> segments = [ ... [(0.0, 1.5), (1.5, 1.5)], ... [(1.5, 1.5), (3.0, 1.5)], ... [(1.5, 1.5), (1.5, 0.0)], ... [(1.5, 1.5), (1.5, 3.0)] ... ] >>> segments = [Chain([p1, p2]) for p1, p2 in segments] >>> rt = RTree() >>> for segment in segments: ... rt.insert(segment, Rect(*segment.bounding_box).grow(sf=10.)) Examine the tree generation statistics. The statistics here are all 0 due to the simple structure of the tree in this example. >>> rt.stats {'overflow_f': 0, 'avg_overflow_t_f': 0.0, 'longest_overflow': 0.0, 'longest_kmeans': 0.0, 'sum_kmeans_iter_f': 0, 'count_kmeans_iter_f': 0, 'avg_kmeans_iter_f': 0.0} Examine the number of nodes and leaves. There five nodes and four leaves (the root plus its four children). >>> rt.count, rt.leaf_count (5, 4) The pool of nodes are the node IDs in the tree. >>> rt.node_pool array('L', [0, 4, 0, 0, 1, 1, 2, 2, 3, 3]) The pool of leaves are the geometric objects that were inserted into the tree. >>> rt.leaf_pool[0].vertices [(0.0, 1.5), (1.5, 1.5)] The pool of rectangles are the bounds of partitioned space in the tree. Examine the first one. >>> rt.rect_pool[:4] array('d', [-2.220446049250313e-15, -2.220446049250313e-15, 3.000000000000002, 3.000000000000002]) Add the bounding box of a leaf to the tree manually. >>> rt.add(Chain(((2,2), (4,4))), (2,2,4,4)) >>> rt.count, rt.leaf_count (6, 5) Query the tree for an intersection. One object is contained in this query. >>> rt.intersection([.4, 2.1, .9, 2.6])[0].vertices [(0.5, 2), (1, 2.5)] Query the tree with a much larger box. All objects are contained in this query. >>> len(rt.intersection([-1, -1, 4, 4])) == rt.leaf_count True Query the tree with box outside the tree objects. No objects are contained in this query. >>> rt.intersection([5, 5, 6, 6]) [] """ # noqa E501 def __init__(self): self.count = 0 self.stats = { "overflow_f": 0, "avg_overflow_t_f": 0.0, "longest_overflow": 0.0, "longest_kmeans": 0.0, "sum_kmeans_iter_f": 0, "count_kmeans_iter_f": 0, "avg_kmeans_iter_f": 0.0, } # This round: not using objects directly -- they take up too much memory, and # efficiency goes down the toilet (obviously) if things start to page. Less # obviously: using object graph directly leads to really long GC pause times, # too. Instead, it uses pools of arrays: self.count = 0 self.leaf_count = 0 self.rect_pool = array.array("d") self.node_pool = array.array("L") # leaf objects. self.leaf_pool = [] self.cursor = _NodeCursor.create(self, NullRect) def _ensure_pool(self, idx: int): """Ensure sufficient slots in rectangle and node pools.""" bb_len, pool_slot = 4, [0] node_len = int(bb_len / 2) if len(self.rect_pool) < (bb_len * idx): self.rect_pool.extend(pool_slot * bb_len) self.node_pool.extend(pool_slot * node_len) def insert(self, o, orect): """Insert an object and its bounding box into the tree. Parameters ---------- o : libpysal.cg.{Point, Chain, Rectangle, Polygon} The object to insert into the tree. orect : ibpysal.cg.Rect The object's bounding box. """ self.cursor.insert(o, orect) assert self.cursor.index == 0 def query_rect(self, r): """Query a rectangle. Parameters ---------- r : {tuple, libpysal.cg.Point} The bounding box of the rectangle in question; a :math:`(minx,miny,maxx,maxy)` set of coordinates. Yields ------ x : generator ``libpysal.cg._NodeCursor`` objects. """ yield from self.cursor.query_rect(r) def query_point(self, p): """Query a point. Parameters ---------- p : {tuple, libpysal.cg.Point} The point in question; an :math:`(x,y)` coordinate. Yields ------ x : generator ``libpysal.cg._NodeCursor`` objects. """ yield from self.cursor.query_point(p) def walk(self, pred): """Walk the tree structure with ``pred`` (a function).""" return self.cursor.walk(pred) def intersection(self, boundingbox): """Query for an intersection between leaves in the ``RTree`` and the bounding box of an object. Parameters ---------- boundingbox : list The bounding box: ``[minx, miny, maxx, maxy]``. Returns ------- objs : list A list of objects whose bounding boxes intersect with the query bounding box. """ # grow the bounding box slightly to handle coincident edges qr = Rect(*boundingbox).grow(sf=10.0) objs = [r.leaf_obj() for r in self.query_rect(qr) if r.is_leaf()] return objs def add(self, id_, boundingbox): """Add the bounding box of a leaf to the ``RTree`` manually with a specified ID. Parameters ---------- id_ : int An object id. boundingbox : list The bounding box: ``[minx, miny, maxx, maxy]``. """ self.cursor.insert(id_, Rect(*boundingbox)) class _NodeCursor: """An internal class for keeping track of, and reorganizing, the structure and composition of the ``RTree``. Parameters ---------- rooto : libpysal.cg.{Point, Chain, Rectangle, Polygon} The object from which the node will be generated. index : int The ID of the node. rect : libpysal.cg.Rect The bounding rectangle of the leaf object. first_child : int The ID of the first child of the node. next_sibling : int The ID of the sibling of the node. Attributes ---------- root : libpysal.cg.RTree The root node of the tree. npool : array.array See ``RTree.node_pool``. rpool : array.array See ``RTree.rect_pool``. """ @classmethod def create(cls, rooto, rect): """Create a node in the tree structure. Parameters ---------- rooto : libpysal.cg.{Point, Chain, Rectangle, Polygon} The object from which the node will be generated. index : int The ID of the node. rect : libpysal.cg.Rect The bounding rectangle of the leaf object. Returns ------- retv : libpysal.cg._NodeCursor The generated node. """ idx = rooto.count rooto.count += 1 rooto._ensure_pool(idx + 1) retv = _NodeCursor(rooto, idx, rect, 0, 0) retv._save_back() return retv @classmethod def create_with_children(cls, children, rooto): """Create a non-leaf node in the tree structure. Parameters ---------- children : list The child nodes of the node to be generated rooto : libpysal.cg.{Point, Chain, Rectangle, Polygon} The object from which the node will be generated. Returns ------- nc : libpysal.cg._NodeCursor The generated node with children. """ rect = union_all(list(children)) Rect(rect.x, rect.y, rect.xx, rect.yy) assert not rect.swapped_x nc = _NodeCursor.create(rooto, rect) nc._set_children(children) assert not nc.is_leaf() return nc @classmethod def create_leaf(cls, rooto, leaf_obj, leaf_rect): """Create a leaf node in the tree structure. Parameters ---------- rooto : libpysal.cg.{Point, Chain, Rectangle, Polygon} The object from which the node will be generated. leaf_obj : libpysal.cg.{Point, Chain, Rectangle, Polygon} The leaf object. leaf_rect : libpysal.cg.Rect The bounding rectangle of the leaf object. Returns ------- res : libpysal.cg._NodeCursor The generated leaf node. """ rect = Rect(leaf_rect.x, leaf_rect.y, leaf_rect.xx, leaf_rect.yy) # Mark as leaf by setting the xswap flag. rect.swapped_x = True res = _NodeCursor.create(rooto, rect) idx = res.index res.first_child = rooto.leaf_count rooto.leaf_count += 1 res.next_sibling = 0 rooto.leaf_pool.append(leaf_obj) res._save_back() res._become(idx) assert res.is_leaf() return res __slots__ = ( "root", "npool", "rpool", "index", "rect", "next_sibling", "first_child", ) def __getstate__(self) -> tuple: return ( self.root, self.npool, self.rpool, self.index, self.rect, self.next_sibling, self.first_child, ) def __setstate__(self, state: tuple): ( self.root, self.npool, self.rpool, self.index, self.rect, self.next_sibling, self.first_child, ) = state def __init__(self, rooto, index, rect, first_child, next_sibling): self.root = rooto self.rpool = rooto.rect_pool self.npool = rooto.node_pool self.index = index self.rect = rect self.next_sibling = next_sibling self.first_child = first_child def walk(self, predicate): """Walk the tree structure with ``predicate`` (a function).""" if predicate(self, self.leaf_obj()): yield self if not self.is_leaf(): for c in self.children(): yield from c.walk(predicate) def query_rect(self, r): """Yield objects that intersect with the rectangle (``r``).""" def p(o, x): # noqa ARG001 return r.does_intersect(o.rect) yield from self.walk(p) def query_point(self, point): """Yield objects that intersect with the point (``point``).""" def p(o, x): # noqa ARG001 return o.rect.does_containpoint(point) yield from self.walk(p) def lift(self): """Promote a node to (potentially) rearrange the tree structure for optimal clustering. Called from ``_NodeCursor._balance()``. Returns ------- lifted : libpysal.cg._NodeCursor The lifted node. """ lifted = _NodeCursor( self.root, self.index, self.rect, self.first_child, self.next_sibling ) return lifted def _become(self, index: int): """Have ``self`` become node ``index``.""" recti = index * 4 nodei = index * 2 rp = self.rpool x = rp[recti] y = rp[recti + 1] xx = rp[recti + 2] yy = rp[recti + 3] if x == 0.0 and y == 0.0 and xx == 0.0 and yy == 0.0: self.rect = NullRect else: self.rect = Rect(x, y, xx, yy) self.next_sibling = self.npool[nodei] self.first_child = self.npool[nodei + 1] self.index = index def is_leaf(self) -> bool: """Return ``True`` if the node is a leaf, otherwise ``False``.""" return self.rect.swapped_x def has_children(self) -> bool: """Return ``True`` if the node has children, otherwise ``False``.""" return not self.is_leaf() and self.first_child != 0 def holds_leaves(self) -> bool: """Return ``True`` if the node holds leaves, otherwise ``False``.""" if self.first_child == 0: return True else: return self.has_children() and self.get_first_child().is_leaf() def get_first_child(self): """Get the first child of a node. Returns ------- c : libpysal.cg._NodeCursor The first child of the specified node. """ c = _NodeCursor(self.root, 0, NullRect, 0, 0) c._become(self.first_child) return c def leaf_obj(self): """Return the leaf object if the node is a leaf, other return ``None``.""" if self.is_leaf(): return self.root.leaf_pool[self.first_child] else: return None def _save_back(self): """Save a node back into the tree structure.""" rp = self.rpool recti = self.index * 4 nodei = self.index * 2 if self.rect is not NullRect: self.rect.write_raw_coords(rp, recti) else: rp[recti] = 0 rp[recti + 1] = 0 rp[recti + 2] = 0 rp[recti + 3] = 0 self.npool[nodei] = self.next_sibling self.npool[nodei + 1] = self.first_child def nchildren(self) -> int: """The number of children nodes.""" c = 0 for _x in self.children(): c += 1 return c def insert(self, leafo, leafrect): """Insert a leaf object into the tree. See ``RTree.insert(o, orect)`` for parameter description. """ index = self.index # tail recursion, made into loop: while True: if self.holds_leaves(): self.rect = self.rect.union(leafrect) self._insert_child(_NodeCursor.create_leaf(self.root, leafo, leafrect)) self._balance() # done: become the original again self._become(index) return else: # Not holding leaves, move down a level in the tree: # ---------------------- # Micro-optimization: # inlining union() calls -- logic is: # ignored, child = min( # [ # ((c.rect.union(leafrect)).area() - c.rect.area(),c.index) # for c in self.children() # ] # ) child = None minarea = -1.0 for c in self.children(): x, y, xx, yy = c.rect.coords() lx, ly, lxx, lyy = leafrect.coords() nx = x if x < lx else lx nxx = xx if xx > lxx else lxx ny = y if y < ly else ly nyy = yy if yy > lyy else lyy a = (nxx - nx) * (nyy - ny) if minarea < 0 or a < minarea: minarea = a child = c.index # End micro-optimization # ---------------------- self.rect = self.rect.union(leafrect) self._save_back() # recurse. self._become(child) def _balance(self): """Balance the leaf layout where possible through ``k_means_cluster()`` and ``silhouette_coeff()`` for (heuristically) optimal clusterings of nodes in the tree structure after the child count of a node has grown past the maximum allowed number (see ``MAXCHILDREN``). Called from ``_NodeCursor.insert()``. """ if self.nchildren() <= MAXCHILDREN: return t = time.process_time() s_children = [c.lift() for c in self.children()] clusterings = [ k_means_cluster(self.root, k, s_children) for k in range(2, MAX_KMEANS) ] score, bestcluster = max([(silhouette_coeff(c), c) for c in clusterings]) # generate the (heuristically) optimally-balanced cluster of nodes nodes = [ _NodeCursor.create_with_children(c, self.root) for c in bestcluster if len(c) > 0 ] self._set_children(nodes) dur = time.process_time() - t c = float(self.root.stats["overflow_f"]) oa = self.root.stats["avg_overflow_t_f"] self.root.stats["avg_overflow_t_f"] = (dur / (c + 1.0)) + (c * oa / (c + 1.0)) self.root.stats["overflow_f"] += 1 self.root.stats["longest_overflow"] = max( self.root.stats["longest_overflow"], dur ) def _set_children(self, cs: list): """Set up the (new/altered) leaf tree structure. Called from ``_NodeCursor.create_with_children()`` and ``_NodeCursor._balance()``. """ self.first_child = 0 if len(cs) == 0: return pred = None for c in cs: if pred is not None: pred.next_sibling = c.index pred._save_back() if self.first_child == 0: self.first_child = c.index pred = c pred.next_sibling = 0 pred._save_back() self._save_back() def _insert_child(self, c): """Internal function for child node insertion. Called from ``_NodeCursor.insert()``. Parameters ---------- c : libpysal.cg._NodeCursor A child ``libpysal.cg._NodeCursor`` object. """ c.next_sibling = self.first_child self.first_child = c.index c._save_back() self._save_back() def children(self): """Yield the children of a node.""" if self.first_child == 0: return idx = self.index fc = self.first_child ns = self.next_sibling r = self.rect self._become(self.first_child) while True: yield self if self.next_sibling == 0: break else: self._become(self.next_sibling) # Go back to becoming the same node we were. # self._become(idx) self.index = idx self.first_child = fc self.next_sibling = ns self.rect = r def avg_diagonals(node, onodes): """Calculate the mean diagonals. Parameters ---------- node : libpysal.cg._NodeCursor The target node in question. onodes : ist A list of ``libpysal.cg._NodeCursor`` objects. Returns ------- diag_avg : float The mean diagonal distance of ``node`` and ``onodes``. """ nidx = node.index sv = 0.0 diag = 0.0 memo_tab = {} for onode in onodes: k1 = (nidx, onode.index) k2 = (onode.index, nidx) if k1 in memo_tab: diag = memo_tab[k1] elif k2 in memo_tab: diag = memo_tab[k2] else: diag = node.rect.union(onode.rect).diagonal() memo_tab[k1] = diag sv += diag diag_avg = sv / len(onodes) return diag_avg def silhouette_w(node, cluster, next_closest_cluster): """Calculate a silhouette score between a certain node and 2 clusters: Parameters ---------- node : libpysal.cg._NodeCursor The target node in question. cluster : list A list of ``libpysal.cg._NodeCursor`` objects. next_closest_cluster : list Another list of ``libpysal.cg._NodeCursor`` objects. Returns ------- silw : float The silhouette score between ``{node, cluster}`` and ``{node, next_closest_cluster}``. """ ndist = avg_diagonals(node, cluster) sdist = avg_diagonals(node, next_closest_cluster) silw = (sdist - ndist) / max(sdist, ndist) return silw def silhouette_coeff(clustering): """Calculate how well defined the clusters are. A score of ``1`` indicates the clusters are well defined, a score of ``0`` indicates the clusters are undefined, and a score of ``-1`` indicates the clusters are defined incorrectly. Parameters ---------- clustering : list A list of ``libpysal.cg._NodeCursor`` objects. Returns ------- silcoeff : float Score for how well defined the clusters are. """ # special case for a clustering of 1.0 if len(clustering) == 1: silcoeff = 1.0 else: coeffs = [] for cluster in clustering: others = [c for c in clustering if c is not cluster] others_cntr = [center_of_gravity(c) for c in others] ws = [ silhouette_w(node, cluster, others[closest(others_cntr, node)]) for node in cluster ] cluster_coeff = sum(ws) / len(ws) coeffs.append(cluster_coeff) silcoeff = sum(coeffs) / len(coeffs) return silcoeff def center_of_gravity(nodes): """Find the center of gravity of multiple nodes. Parameters ---------- nodes : list A list of ``libpysal.cg.RTree`` and ``libpysal.cg._NodeCursor`` objects. Returns ------- cog : float The center of gravity of multiple nodes. """ totarea = 0.0 xs, ys = 0, 0 for n in nodes: if n.rect is not NullRect: x, y, w, h = n.rect.extent() a = w * h xs = xs + (a * (x + (0.5 * w))) ys = ys + (a * (y + (0.5 * h))) totarea = totarea + a cog = (xs / totarea), (ys / totarea) return cog def closest(centroids, node): """Find the closest controid to the node's center of gravity. Parameters ---------- centroids : list A list of (x, y) coordinates for the center of other clusters. node : libpysal.cg_NodeCursor A ``libpysal.cg._NodeCursor`` instance. Returns ------- ridx : int The index of the nearest centroid of other cluster. """ x, y = center_of_gravity([node]) dist = -1 ridx = -1 for i, (xx, yy) in enumerate(centroids): dsq = ((xx - x) ** 2) + ((yy - y) ** 2) if dist == -1 or dsq < dist: dist = dsq ridx = i return ridx def k_means_cluster(root, k, nodes): """Find ``k`` clusters. Parameters ---------- root : libpysal.cg.RTree An ``libpysal.cg.RTree`` instance. k : int The number clusters to find. nodes : list A list of ``libpysal.cg.RTree`` and ``libpysal.cg._NodeCursor`` objects. Returns ------- clusters : list Updated versions of ``nodes`` defining new clusters. """ t = time.process_time() if len(nodes) <= k: clusters = [[n] for n in nodes] return clusters ns = list(nodes) root.stats["count_kmeans_iter_f"] += 1 # Initialize: take n random nodes. # random.shuffle(ns) cluster_starts = ns[:k] cluster_centers = [center_of_gravity([n]) for n in cluster_starts] # Loop until stable: while True: root.stats["sum_kmeans_iter_f"] += 1 clusters = [[] for c in cluster_centers] for n in ns: idx = closest(cluster_centers, n) clusters[idx].append(n) # FIXME HACK TODO: is it okay for there to be empty clusters? clusters = [c for c in clusters if len(c) > 0] for c in clusters: if len(c) == 0: print("Error....") print("Nodes: %d, centers: %s." % (len(ns), repr(cluster_centers))) assert len(c) > 0 new_cluster_centers = [center_of_gravity(c) for c in clusters] if new_cluster_centers == cluster_centers: root.stats["avg_kmeans_iter_f"] = float( root.stats["sum_kmeans_iter_f"] / root.stats["count_kmeans_iter_f"] ) root.stats["longest_kmeans"] = max( root.stats["longest_kmeans"], (time.process_time() - t) ) return clusters else: cluster_centers = new_cluster_centers libpysal-4.9.2/libpysal/cg/segmentLocator.py000066400000000000000000000341421452177046000211270ustar00rootroot00000000000000# ruff: noqa: A001, A002, B028, N801, N802 import math import random import time import warnings import numpy import scipy from .shapes import LineSegment, Point, Rectangle from .standalone import get_bounding_box, get_segment_point_dist dep_msg = "is deprecated and will be removed in a future version of libpysal" __all__ = ["SegmentGrid", "SegmentLocator", "Polyline_Shapefile_SegmentLocator"] DEBUG = False class BruteSegmentLocator: def __init__(self, segments): self.data = segments self.n = len(segments) def nearest(self, pt): d = self.data distances = [get_segment_point_dist(d[i], pt)[0] for i in range(self.n)] return numpy.argmin(distances) class SegmentLocator: def __init__(self, segments, nbins=500): warnings.warn("SegmentLocator " + dep_msg, FutureWarning) self.data = segments if hasattr(segments, "bounding_box"): bbox = segments.bounding_box else: bbox = get_bounding_box(segments) self.bbox = bbox res = max((bbox.right - bbox.left), (bbox.upper - bbox.lower)) / float(nbins) self.grid = SegmentGrid(bbox, res) for i, seg in enumerate(segments): self.grid.add(seg, i) def nearest(self, pt): d = self.data possibles = self.grid.nearest(pt) distances = [get_segment_point_dist(d[i], pt)[0] for i in possibles] # print "possibles",possibles # print "distances",distances # print "argmin", numpy.argmin(distances) return possibles[numpy.argmin(distances)] class Polyline_Shapefile_SegmentLocator: def __init__(self, shpfile, nbins=500): warnings.warn("Polyline_Shapefile_SegmentLocator " + dep_msg, FutureWarning) self.data = shpfile bbox = Rectangle(*shpfile.bbox) res = max((bbox.right - bbox.left), (bbox.upper - bbox.lower)) / float(nbins) self.grid = SegmentGrid(bbox, res) for i, polyline in enumerate(shpfile): for p, part in enumerate(polyline.segments): for j, seg in enumerate(part): self.grid.add(seg, (i, p, j)) def nearest(self, pt): d = self.data possibles = self.grid.nearest(pt) distances = [ get_segment_point_dist(d[i].segments[p][j], pt)[0] for (i, p, j) in possibles ] # print "possibles",possibles # print "distances",distances # print "argmin", numpy.argmin(distances) return possibles[numpy.argmin(distances)] class SegmentGrid: """ Notes: SegmentGrid is a low level Grid class. This class does not maintain a copy of the geometry in the grid. It returns only approx. Solutions. This Grid should be wrapped by a locator. """ def __init__(self, bounds, resolution): """ Returns a grid with specified properties. __init__(Rectangle, number) -> SegmentGrid Parameters ---------- bounds : the area for the grid to encompass resolution : the diameter of each bin Examples -------- TODO: complete this doctest >>> g = SegmentGrid(Rectangle(0, 0, 10, 10), 1) """ warnings.warn("SegmentGrid " + dep_msg, FutureWarning) if resolution == 0: raise Exception("Cannot create grid with resolution 0") self.res = resolution self.hash = {} self._kd = None self._kd2 = None self._hashKeys = None self.x_range = (bounds.left, bounds.right) self.y_range = (bounds.lower, bounds.upper) try: self.i_range = ( int(math.ceil((self.x_range[1] - self.x_range[0]) / self.res)) + 1 ) self.j_range = ( int(math.ceil((self.y_range[1] - self.y_range[0]) / self.res)) + 1 ) self.mask = numpy.zeros((self.i_range, self.j_range), bool) self.endMask = numpy.zeros((self.i_range, self.j_range), bool) except Exception as e: raise Exception( "Invalid arguments for SegmentGrid(): (" + str(self.x_range) + ", " + str(self.y_range) + ", " + str(self.res) + ")" ) from e @property def hashKeys(self): if self._hashKeys is None: self._hashKeys = numpy.array(list(self.hash.keys()), dtype=float) return self._hashKeys @property def kd(self): if self._kd is None: self._kd = scipy.spatial.cKDTree(self.hashKeys) return self._kd @property def kd2(self): if self._kd2 is None: self._kd2 = scipy.spatial.KDTree(self.hashKeys) return self._kd2 def in_grid(self, loc): """ Returns whether a 2-tuple location _loc_ lies inside the grid bounds. """ return ( self.x_range[0] <= loc[0] <= self.x_range[1] and self.y_range[0] <= loc[1] <= self.y_range[1] ) def _grid_loc(self, loc): i = int((loc[0] - self.x_range[0]) / self.res) # floored j = int((loc[1] - self.y_range[0]) / self.res) # floored # i = min(self.i_range-1, max(int((loc[0] - self.x_range[0])/self.res), 0)) # j = min(self.j_range-1, max(int((loc[1] - self.y_range[0])/self.res), 0)) # print "bin:", loc, " -> ", (i,j) return (i, j) def _real_loc(self, grid_loc): x = (grid_loc[0] * self.res) + self.x_range[0] y = (grid_loc[1] * self.res) + self.y_range[0] return x, y def bin_loc(self, loc, id): grid_loc = self._grid_loc(loc) if grid_loc not in self.hash: self.hash[grid_loc] = set() self.mask[grid_loc] = True self.hash[grid_loc].add(id) return grid_loc def add(self, segment, id): """ Adds segment to the grid. add(segment, id) -> bool Parameters ---------- id -- id to be stored int he grid. segment -- the segment which identifies where to store 'id' in the grid. Examples -------- >>> g = SegmentGrid(Rectangle(0, 0, 10, 10), 1) >>> g.add(LineSegment(Point((0.2, 0.7)), Point((4.2, 8.7))), 0) True """ if not (self.in_grid(segment.p1) and self.in_grid(segment.p2)): raise Exception( "Attempt to insert item at location outside grid bounds: " + str(segment) ) i, j = self.bin_loc(segment.p1, id) I_, J_ = self.bin_loc(segment.p2, id) self.endMask[i, j] = True self.endMask[I_, J_] = True res = self.res line = segment.line tiny = res / 1000.0 for i in range(1 + min(i, I_), max(i, I_)): # noqa B020 # print 'i',i x = self.x_range[0] + (i * res) y = line.y(x) self.bin_loc((x - tiny, y), id) self.bin_loc((x + tiny, y), id) for j in range(1 + min(j, J_), max(j, J_)): # noqa B020 # print 'j',j y = self.y_range[0] + (j * res) x = line.x(y) self.bin_loc((x, y - tiny), id) self.bin_loc((x, y + tiny), id) self._kd = None self._kd2 = None return True def remove(self, segment): # noqa ARG002 self._kd = None self._kd2 = None pass def nearest(self, pt): """ Return a set of ids. The ids identify line segments within a radius of the query point. The true nearest segment is guaranteed to be within the set. Filtering possibles is the responsibility of the locator not the grid. This means the Grid doesn't need to keep a reference to the underlying segments, which in turn means the Locator can keep the segments on disk. Locators can be customized to different data stores (shape files, SQL, etc.) """ grid_loc = numpy.array(self._grid_loc(pt)) possibles = set() if DEBUG: print("in_grid:", self.in_grid(pt)) i = pylab.matshow( self.mask, origin="lower", extent=self.x_range + self.y_range, fignum=1 ) # Use KD tree to search out the nearest filled bin. # it may be faster to not use kdtree, or at least check grid_loc first # The KD tree is build on the keys of self.hash, a dictionary of stored bins. dist, i = self.kd.query(grid_loc, 1) ### Find non-empty bins within a radius of the query point. # Location of Q point row, col = grid_loc # distance to nearest filled cell +2. # +1 returns inconsistent results (compared to BruteSegmentLocator) # +2 seems to do the trick. radius = int(math.ceil(dist)) + 2 if radius < 30: a, b = numpy.ogrid[ -radius : radius + 1, -radius : radius + 1 ] # build square index arrays centered at 0,0 index = ( a**2 + b**2 <= radius**2 ) # create a boolean mask to filter indicies outside radius a, b = index.nonzero() # grad the (i,j)'s of the elements within radius. rows, cols = ( row + a - radius, col + b - radius, ) # recenter the (i,j)'s over the Q point #### Filter indicies by bounds of the grid. ### filters must be applied one at a time ### I havn't figure out a way to group these filter = rows >= 0 rows = rows[filter] cols = cols[filter] # i >= 0 filter = rows < self.i_range rows = rows[filter] cols = cols[filter] # i < i_range filter = cols >= 0 rows = rows[filter] cols = cols[filter] # j >= 0 filter = cols < self.j_range rows = rows[filter] cols = cols[filter] # j < j_range if DEBUG: maskCopy = self.mask.copy().astype(float) maskCopy += self.endMask.astype(float) maskCopy[rows, cols] += 1 maskCopy[row, col] += 3 i = pylab.matshow( maskCopy, origin="lower", extent=self.x_range + self.y_range, fignum=1, ) # raw_input('pause') ### All that was just setup for this one line... idx = self.mask[rows, cols].nonzero()[0] # Filter out empty bins. rows, cols = ( rows[idx], cols[idx], ) # (i,j)'s of the filled grid cells within radius. for t in zip(rows, cols): # noqa B905 possibles.update(self.hash[t]) if DEBUG: print("possibles", possibles) else: ### The old way... ### previously I was using kd.query_ball_point on, ### but the performance was terrible. I_ = self.kd2.query_ball_point(grid_loc, radius) for i in I_: t = tuple(self.kd.data[i]) possibles.update(self.hash[t]) return list(possibles) def random_segments(n): segs = [] for _i in range(n): a, b, c, d = (random.random() for x in [1, 2, 3, 4]) seg = LineSegment(Point((a, b)), Point((c, d))) segs.append(seg) return segs def random_points(n): return [Point((random.random(), random.random())) for x in range(n)] def combo_check(bins, segments, qpoints): G = SegmentLocator(segments, bins) G2 = BruteSegmentLocator(segs) for pt in qpoints: a = G.nearest(pt) b = G2.nearest(pt) if a != b: print(a, b, a == b) global DEBUG DEBUG = True a = G.nearest(pt) print(a) a = segments[a] b = segments[b] print("pt to a (grid)", get_segment_point_dist(a, pt)) print("pt to b (brut)", get_segment_point_dist(b, pt)) input() pylab.clf() DEBUG = False def brute_check(segments, qpoints): # noqa ARG001 t0 = time.time() G2 = BruteSegmentLocator(segs) t1 = time.time() print("Created Brute in %0.4f seconds" % (t1 - t0)) t2 = time.time() q = list(map(G2.nearest, qpoints)) t3 = time.time() print("Brute Found %d matches in %0.4f seconds" % (len(qpoints), t3 - t2)) print("Total Brute Time:", t3 - t0) print() return q def grid_check(bins, segments, qpoints, visualize=False): t0 = time.time() G = SegmentLocator(segments, bins) t1 = time.time() G.grid.kd # noqa B018 t2 = time.time() print("Created Grid in %0.4f seconds" % (t1 - t0)) print("Created KDTree in %0.4f seconds" % (t2 - t1)) if visualize: pylab.matshow( G.grid.mask, origin="lower", extent=G.grid.x_range + G.grid.y_range ) t2 = time.time() list(map(G.nearest, qpoints)) t3 = time.time() print("Grid Found %d matches in %0.4f seconds" % (len(qpoints), t3 - t2)) print("Total Grid Time:", t3 - t0) qps = len(qpoints) / (t3 - t2) print("q/s:", qps) # print return qps def binSizeTest(): q = 100 minN = 1000 maxN = 10000 stepN = 1000 minB = 250 maxB = 2000 stepB = 250 sizes = list(range(minN, maxN, stepN)) binSizes = list(range(minB, maxB, stepB)) results = numpy.zeros((len(sizes), len(binSizes))) for row, n in enumerate(sizes): segs = random_segments(n) qpts = random_points(q) for col, bins in enumerate(binSizes): print("N, Bins:", n, bins) qps = test_grid(bins, segs, qpts) # noqa F821 results[row, col] = qps return results if __name__ == "__main__": import pylab pylab.ion() n = 100 q = 1000 t0 = time.time() segs = random_segments(n) t1 = time.time() qpts = random_points(q) t2 = time.time() print("segments:", t1 - t0) print("points:", t2 - t1) # test_brute(segs,qpts) # test_grid(50, segs, qpts) SG = SegmentLocator(segs) grid = SG.grid libpysal-4.9.2/libpysal/cg/shapely_ext.py000066400000000000000000000250321452177046000204640ustar00rootroot00000000000000from shapely import __version__ as shapely_version from shapely import geometry as geom from shapely import ops as shops from .shapes import asShape _basegeom = geom.base.BaseGeometry __all__ = [ "to_wkb", "to_wkt", "area", "distance", "length", "boundary", "bounds", "centroid", "representative_point", "convex_hull", "envelope", "buffer", "simplify", "difference", "intersection", "symmetric_difference", "union", "unary_union", "cascaded_union", "has_z", "is_empty", "is_ring", "is_simple", "is_valid", "relate", "contains", "crosses", "disjoint", "equals", "intersects", "overlaps", "touches", "within", "equals_exact", "almost_equals", "project", "interpolate", ] GEO_INTERFACE_ATTR = "__geo_interface__" SHAPE_TYPE_ERR = "%r does not appear to be a shape." def to_wkb(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) return o.to_wkb() def to_wkt(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) return o.to_wkt() # Real-valued properties and methods # ---------------------------------- def area(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) return o.area def distance(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.distance(o2) def length(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) return o.length # Topological properties # ---------------------- def boundary(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) res = o.boundary return asShape(res) def bounds(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) return o.bounds def centroid(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) res = o.centroid return asShape(res) def representative_point(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) res = o.representative_point() return asShape(res) def convex_hull(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) res = o.convex_hull return asShape(res) def envelope(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) res = o.envelope return asShape(res) def buffer(shape, radius, resolution=16): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) res = o.buffer(radius, resolution) return asShape(res) def simplify(shape, tolerance, preserve_topology=True): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) res = o.simplify(tolerance, preserve_topology) return asShape(res) # Binary operations # ----------------- def difference(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) res = o.difference(o2) return asShape(res) def intersection(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) res = o.intersection(o2) return asShape(res) def symmetric_difference(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) res = o.symmetric_difference(o2) return asShape(res) def union(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) res = o.union(o2) return asShape(res) def cascaded_union(shapes): o = [] for shape in shapes: if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o.append(geom.shape(shape)) res = shops.unary_union(o) return asShape(res) def unary_union(shapes): # seems to be the same as cascade_union except that it handles multipart polygons if shapely_version < "1.2.16": raise Exception( "shapely 1.2.16 or higher needed for unary_union; " "upgrade shapely or try cascade_union instead" ) o = [] for shape in shapes: if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o.append(geom.shape(shape)) res = shops.unary_union(o) return asShape(res) # Unary predicates # ---------------- def has_z(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) return o.has_z def is_empty(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) return o.is_empty def is_ring(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) return o.is_ring def is_simple(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) return o.is_simple def is_valid(shape): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) return o.is_valid # Binary predicates # ----------------- def relate(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.relate(o2) def contains(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.contains(o2) def crosses(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.crosses(o2) def disjoint(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.disjoint(o2) def equals(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.equals(o2) def intersects(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.intersects(o2) def overlaps(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.overlaps(o2) def touches(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.touches(o2) def within(shape, other): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.within(o2) def equals_exact(shape, other, tolerance): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.equals_exact(o2, tolerance) def almost_equals(shape, other, decimal=6): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.almost_equals(o2, decimal) # Linear referencing # ------------------ def project(shape, other, normalized=False): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) if not hasattr(other, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) o2 = geom.shape(other) return o.project(o2, normalized) def interpolate(shape, distance, normalized=False): if not hasattr(shape, GEO_INTERFACE_ATTR): raise TypeError(SHAPE_TYPE_ERR % shape) o = geom.shape(shape) res = o.interpolate(distance, normalized) return asShape(res) # Copy doc strings from shapely for method in __all__: if hasattr(_basegeom, method): locals()[method].__doc__ = getattr(_basegeom, method).__doc__ libpysal-4.9.2/libpysal/cg/shapes.py000066400000000000000000001453021452177046000174250ustar00rootroot00000000000000""" Computational geometry code for PySAL: Python Spatial Analysis Library. """ __author__ = "Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow, Hu Shao" # ruff: noqa: A003, B028, N802, E402 import math import warnings from typing import Union from .sphere import arcdist __all__ = [ "Point", "LineSegment", "Line", "Ray", "Chain", "Polygon", "Rectangle", "asShape", ] dep_msg = ( "Objects based on the `Geometry` class will deprecated " "and removed in a future version of libpysal." ) def asShape(obj): """Returns a PySAL shape object from ``obj``, which must support the ``__geo_interface__``. Parameters ---------- obj : {libpysal.cg.{Point, LineSegment, Line, Ray, Chain, Polygon} A geometric representation of an object. Raises ------ TypeError Raised when ``obj`` is not a supported shape. NotImplementedError Raised when ``geo_type`` is not a supported type. Returns ------- obj : {libpysal.cg.{Point, LineSegment, Line, Ray, Chain, Polygon} A new geometric representation of the object. """ if isinstance(obj, Point | LineSegment | Line | Ray | Chain | Polygon): pass else: geo = obj.__geo_interface__ if hasattr(obj, "__geo_interface__") else obj if hasattr(geo, "type"): raise TypeError("%r does not appear to be a shape object." % (obj)) geo_type = geo["type"].lower() # if geo_type.startswith('multi'): # raise NotImplementedError, "%s are not supported at this time."%geo_type if geo_type in _geoJSON_type_to_Pysal_type: obj = _geoJSON_type_to_Pysal_type[geo_type].__from_geo_interface__(geo) else: raise NotImplementedError("%s is not supported at this time." % geo_type) return obj class Geometry: """A base class to help implement ``is_geometry`` and make geometric types extendable. """ def __init__(self): pass class Point(Geometry): """Geometric class for point objects. Parameters ---------- loc : tuple The point's location (number :math:`x`-tuple, :math:`x` > 1). Examples -------- >>> p = Point((1, 3)) """ def __init__(self, loc): warnings.warn(dep_msg, FutureWarning) self.__loc = tuple(map(float, loc)) @classmethod def __from_geo_interface__(cls, geo): return cls(geo["coordinates"]) @property def __geo_interface__(self): return {"type": "Point", "coordinates": self.__loc} def __lt__(self, other) -> bool: """Tests if the point is less than another object. Parameters ---------- other : libpysal.cg.Point An object to test equality against. Examples -------- >>> Point((0, 1)) < Point((0, 1)) False >>> Point((0, 1)) < Point((1, 1)) True """ return (self.__loc) < (other.__loc) def __le__(self, other) -> bool: """Tests if the point is less than or equal to another object. Parameters ---------- other : libpysal.cg.Point An object to test equality against. Examples -------- >>> Point((0, 1)) <= Point((0, 1)) True >>> Point((0, 1)) <= Point((1, 1)) True """ return (self.__loc) <= (other.__loc) def __eq__(self, other) -> bool: """Tests if the point is equal to another object. Parameters ---------- other : libpysal.cg.Point An object to test equality against. Examples -------- >>> Point((0, 1)) == Point((0, 1)) True >>> Point((0, 1)) == Point((1, 1)) False """ try: return (self.__loc) == (other.__loc) except AttributeError: return False def __ne__(self, other) -> bool: """Tests if the point is not equal to another object. Parameters ---------- other : libpysal.cg.Point An object to test equality against. Examples -------- >>> Point((0, 1)) != Point((0, 1)) False >>> Point((0, 1)) != Point((1, 1)) True """ try: return (self.__loc) != (other.__loc) except AttributeError: return True def __gt__(self, other) -> bool: """Tests if the point is greater than another object. Parameters ---------- other : libpysal.cg.Point An object to test equality against. Examples -------- >>> Point((0, 1)) > Point((0, 1)) False >>> Point((0, 1)) > Point((1, 1)) False """ return (self.__loc) > (other.__loc) def __ge__(self, other) -> bool: """Tests if the point is greater than or equal to another object. Parameters ---------- other : libpysal.cg.Point An object to test equality against. Examples -------- >>> Point((0, 1)) >= Point((0, 1)) True >>> Point((0, 1)) >= Point((1, 1)) False """ return (self.__loc) >= (other.__loc) def __hash__(self) -> int: """Returns the hash of the point's location. Examples -------- >>> hash(Point((0, 1))) == hash(Point((0, 1))) True >>> hash(Point((0, 1))) == hash(Point((1, 1))) False """ return hash(self.__loc) def __getitem__(self, *args) -> Union[int, float]: """Return the coordinate for the given dimension. Parameters ---------- *args : tuple A singleton tuple of :math:`(i)` with :math:`i` as the index of the desired dimension. Examples -------- >>> p = Point((5.5, 4.3)) >>> p[0] == 5.5 True >>> p[1] == 4.3 True """ return self.__loc.__getitem__(*args) def __getslice__(self, *args) -> slice: """Return the coordinates for the given dimensions. Parameters ---------- *args : tuple A tuple of :math:`(i,j)` with :math:`i` as the index to the start slice and :math:`j` as the index to end the slice (excluded). Examples -------- >>> p = Point((3, 6, 2)) >>> p[:2] == (3, 6) True >>> p[1:2] == (6,) True """ return self.__loc.__getslice__(*args) def __len__(self) -> int: """Returns the dimensions of the point. Examples -------- >>> len(Point((1, 2))) 2 """ return len(self.__loc) def __repr__(self) -> str: """Returns the string representation of the ``Point``. Examples -------- >>> Point((0, 1)) (0.0, 1.0) """ return str(self) def __str__(self) -> str: """Returns a string representation of a ``Point`` object. Examples -------- >>> p = Point((1, 3)) >>> str(p) '(1.0, 3.0)' """ return str(self.__loc) # return "POINT ({} {})".format(*self.__loc) class LineSegment(Geometry): """Geometric representation of line segment objects. Parameters ---------- start_pt : libpysal.cg.Point The point where the segment begins. end_pt : libpysal.cg.Point The point where the segment ends. Attributes ---------- p1 : libpysal.cg.Point The starting point of the line segment. p2 : Point The ending point of the line segment. bounding_box : libpysal.cg.Rectangle The bounding box of the segment. len : float The length of the segment. line : libpysal.cg.Line The line on which the segment lies. Examples -------- >>> ls = LineSegment(Point((1, 2)), Point((5, 6))) """ def __init__(self, start_pt, end_pt): warnings.warn(dep_msg, FutureWarning) self._p1 = start_pt self._p2 = end_pt self._reset_props() def __str__(self): return "LineSegment(" + str(self._p1) + ", " + str(self._p2) + ")" # return "LINESTRING ({} {}, {} {})".format( # self._p1[0], self._p1[1], self._p2[0], self._p2[1] # ) def __eq__(self, other) -> bool: """Returns ``True`` if ``self`` and ``other`` are the same line segment. Examples -------- >>> l1 = LineSegment(Point((1, 2)), Point((5, 6))) >>> l2 = LineSegment(Point((5, 6)), Point((1, 2))) >>> l1 == l2 True >>> l2 == l1 True """ eq = False if not isinstance(other, self.__class__): pass else: if (other.p1 == self._p1 and other.p2 == self._p2) or ( other.p2 == self._p1 and other.p1 == self._p2 ): eq = True return eq def intersect(self, other) -> bool: """Test whether segment intersects with other segment (``True``) or not (``False``). Handles endpoints of segments being on other segment. Parameters ---------- other : libpysal.cg.LineSegment Another line segment to check against. Examples -------- >>> ls = LineSegment(Point((5, 0)), Point((10, 0))) >>> ls1 = LineSegment(Point((5, 0)), Point((10, 1))) >>> ls.intersect(ls1) True >>> ls2 = LineSegment(Point((5, 1)), Point((10, 1))) >>> ls.intersect(ls2) False >>> ls2 = LineSegment(Point((7, -1)), Point((7, 2))) >>> ls.intersect(ls2) True """ ccw1 = self.sw_ccw(other.p2) ccw2 = self.sw_ccw(other.p1) ccw3 = other.sw_ccw(self.p1) ccw4 = other.sw_ccw(self.p2) intersects = ccw1 * ccw2 <= 0 and ccw3 * ccw4 <= 0 return intersects def _reset_props(self): """**HELPER METHOD. DO NOT CALL.** Resets attributes which are functions of other attributes. The getters for these attributes (implemented as properties) then recompute their values if they have been reset since the last call to the getter. Examples -------- >>> ls = LineSegment(Point((1, 2)), Point((5, 6))) >>> ls._reset_props() """ self._bounding_box = None self._len = None self._line = False def _get_p1(self): """**HELPER METHOD. DO NOT CALL.** Returns the ``p1`` attribute of the line segment. Returns ------- self._p1 : libpysal.cg.Point The ``_p1`` attribute. Examples -------- >>> ls = LineSegment(Point((1, 2)), Point((5, 6))) >>> r = ls._get_p1() >>> r == Point((1, 2)) True """ return self._p1 def _set_p1(self, p1): """**HELPER METHOD. DO NOT CALL.** Sets the ``p1`` attribute of the line segment. Parameters ---------- p1 : libpysal.cg.Point A point. Returns ------- self._p1 : libpysal.cg.Point The reset ``p1`` attribute. Examples -------- >>> ls = LineSegment(Point((1, 2)), Point((5, 6))) >>> r = ls._set_p1(Point((3, -1))) >>> r == Point((3.0, -1.0)) True """ self._p1 = p1 self._reset_props() return self._p1 p1 = property(_get_p1, _set_p1) def _get_p2(self): """**HELPER METHOD. DO NOT CALL.** Returns the ``p2`` attribute of the line segment. Returns ------- self._p2 : libpysal.cg.Point The ``_p2`` attribute. Examples -------- >>> ls = LineSegment(Point((1, 2)), Point((5, 6))) >>> r = ls._get_p2() >>> r == Point((5, 6)) True """ return self._p2 def _set_p2(self, p2): """**HELPER METHOD. DO NOT CALL.** Sets the ``p2`` attribute of the line segment. Parameters ---------- p2 : libpysal.cg.Point A point. Returns ------- self._p2 : libpysal.cg.Point The reset ``p2`` attribute. Examples -------- >>> ls = LineSegment(Point((1, 2)), Point((5, 6))) >>> r = ls._set_p2(Point((3, -1))) >>> r == Point((3.0, -1.0)) True """ self._p2 = p2 self._reset_props() return self._p2 p2 = property(_get_p2, _set_p2) def is_ccw(self, pt) -> bool: """Returns whether a point is counterclockwise of the segment (``True``) or not (``False``). Exclusive. Parameters ---------- pt : libpysal.cg.Point A point lying ccw or cw of a segment. Examples -------- >>> ls = LineSegment(Point((0, 0)), Point((5, 0))) >>> ls.is_ccw(Point((2, 2))) True >>> ls.is_ccw(Point((2, -2))) False """ v1 = (self._p2[0] - self._p1[0], self._p2[1] - self._p1[1]) v2 = (pt[0] - self._p1[0], pt[1] - self._p1[1]) return v1[0] * v2[1] - v1[1] * v2[0] > 0 def is_cw(self, pt) -> bool: """Returns whether a point is clockwise of the segment (``True``) or not (``False``). Exclusive. Parameters ---------- pt : libpysal.cg.Point A point lying ccw or cw of a segment. Examples -------- >>> ls = LineSegment(Point((0, 0)), Point((5, 0))) >>> ls.is_cw(Point((2, 2))) False >>> ls.is_cw(Point((2, -2))) True """ v1 = (self._p2[0] - self._p1[0], self._p2[1] - self._p1[1]) v2 = (pt[0] - self._p1[0], pt[1] - self._p1[1]) return v1[0] * v2[1] - v1[1] * v2[0] < 0 def sw_ccw(self, pt): """Sedgewick test for ``pt`` being ccw of segment. Returns ------- is_ccw : bool ``1`` if turn from ``self.p1`` to ``self.p2`` to ``pt`` is ccw. ``-1`` if turn from ``self.p1`` to ``self.p2`` to ``pt`` is cw. ``-1`` if the points are collinear and ``self.p1`` is in the middle. ``1`` if the points are collinear and ``self.p2`` is in the middle. ``0`` if the points are collinear and ``pt`` is in the middle. """ p0 = self.p1 p1 = self.p2 p2 = pt dx1 = p1[0] - p0[0] dy1 = p1[1] - p0[1] dx2 = p2[0] - p0[0] dy2 = p2[1] - p0[1] if dy1 * dx2 < dy2 * dx1: is_ccw = 1 elif (dy1 * dx2 > dy2 * dx1) or (dx1 * dx2 < 0 or dy1 * dy2 < 0): is_ccw = -1 elif dx1 * dx1 + dy1 * dy1 >= dx2 * dx2 + dy2 * dy2: is_ccw = 0 else: is_ccw = 1 return is_ccw def get_swap(self): """Returns a ``LineSegment`` object which has its endpoints swapped. Returns ------- line_seg : libpysal.cg.LineSegment The ``LineSegment`` object which has its endpoints swapped. Examples -------- >>> ls = LineSegment(Point((1, 2)), Point((5, 6))) >>> swap = ls.get_swap() >>> swap.p1[0] 5.0 >>> swap.p1[1] 6.0 >>> swap.p2[0] 1.0 >>> swap.p2[1] 2.0 """ line_seg = LineSegment(self._p2, self._p1) return line_seg @property def bounding_box(self): """Returns the minimum bounding box of a ``LineSegment`` object. Returns ------- self._bounding_box : libpysal.cg.Rectangle The bounding box of the line segment. Examples -------- >>> ls = LineSegment(Point((1, 2)), Point((5, 6))) >>> ls.bounding_box.left 1.0 >>> ls.bounding_box.lower 2.0 >>> ls.bounding_box.right 5.0 >>> ls.bounding_box.upper 6.0 """ # If LineSegment attributes p1, p2 changed, recompute if self._bounding_box is None: self._bounding_box = Rectangle( min([self._p1[0], self._p2[0]]), min([self._p1[1], self._p2[1]]), max([self._p1[0], self._p2[0]]), max([self._p1[1], self._p2[1]]), ) return Rectangle( self._bounding_box.left, self._bounding_box.lower, self._bounding_box.right, self._bounding_box.upper, ) @property def len(self) -> float: """Returns the length of a ``LineSegment`` object. Examples -------- >>> ls = LineSegment(Point((2, 2)), Point((5, 2))) >>> ls.len 3.0 """ # If LineSegment attributes p1, p2 changed, recompute if self._len is None: self._len = math.hypot(self._p1[0] - self._p2[0], self._p1[1] - self._p2[1]) return self._len @property def line(self): """Returns a ``Line`` object of the line on which the segment lies. Returns ------- self._line : libpysal.cg.Line The ``Line`` object of the line on which the segment lies. Examples -------- >>> ls = LineSegment(Point((2, 2)), Point((3, 3))) >>> l = ls.line >>> l.m 1.0 >>> l.b 0.0 """ if self._line is False: dx = self._p1[0] - self._p2[0] dy = self._p1[1] - self._p2[1] if dx == 0 and dy == 0: self._line = None elif dx == 0: self._line = VerticalLine(self._p1[0]) else: m = dy / float(dx) # y - mx b = self._p1[1] - m * self._p1[0] self._line = Line(m, b) return self._line class VerticalLine(Geometry): """Geometric representation of verticle line objects. Parameters ---------- x : {int, float} The :math:`x`-intercept of the line. ``x`` is also an attribute. Examples -------- >>> ls = VerticalLine(0) >>> ls.m inf >>> ls.b nan """ def __init__(self, x): warnings.warn(dep_msg, FutureWarning) self._x = float(x) self.m = float("inf") self.b = float("nan") def x(self, y) -> float: # noqa ARG002 """Returns the :math:`x`-value of the line at a particular :math:`y`-value. Parameters ---------- y : {int, float} The :math:`y`-value at which to compute :math:`x`. Examples -------- >>> l = VerticalLine(0) >>> l.x(0.25) 0.0 """ return self._x def y(self, x) -> float: # noqa ARG002 """Returns the :math:`y`-value of the line at a particular :math:`x`-value. Parameters ---------- x : {int, float} The :math:`x`-value at which to compute :math:`y`. Examples -------- >>> l = VerticalLine(1) >>> l.y(1) nan """ return float("nan") class Line(Geometry): """Geometric representation of line objects. Parameters ---------- m : {int, float} The slope of the line. ``m`` is also an attribute. b : {int, float} The :math:`y`-intercept of the line. ``b`` is also an attribute. Raises ------ ArithmeticError Raised when infinity is passed in as the slope. Examples -------- >>> ls = Line(1, 0) >>> ls.m 1.0 >>> ls.b 0.0 """ def __init__(self, m, b): warnings.warn(dep_msg, FutureWarning) if m == float("inf"): raise ArithmeticError("Slope cannot be infinite.") self.m = float(m) self.b = float(b) def x(self, y: Union[int, float]) -> float: """Returns the :math:`x`-value of the line at a particular :math:`y`-value. Parameters ---------- y : {int, float} The :math:`y`-value at which to compute :math:`x`. Raises ------ ArithmeticError Raised when ``0.`` is passed in as the slope. Examples -------- >>> l = Line(0.5, 0) >>> l.x(0.25) 0.5 """ if self.m == 0: raise ArithmeticError("Cannot solve for 'x' when slope is zero.") return (y - self.b) / self.m def y(self, x: Union[int, float]) -> float: """Returns the :math:`y`-value of the line at a particular :math:`x`-value. Parameters ---------- x : {int, float} The :math:`x`-value at which to compute :math:`y`. Examples -------- >>> l = Line(1, 0) >>> l.y(1) 1.0 """ if self.m == 0: return self.b return self.m * x + self.b class Ray: """Geometric representation of ray objects. Parameters ---------- origin : libpysal.cg.Point The point where the ray originates. second_p : The second point specifying the ray (not ``origin``.) Attributes ---------- o : libpysal.cg.Point The origin (point where ray originates). See ``origin``. p : libpysal.cg.Point The second point on the ray (not the point where the ray originates). See ``second_p``. Examples -------- >>> l = Ray(Point((0, 0)), Point((1, 0))) >>> str(l.o) '(0.0, 0.0)' >>> str(l.p) '(1.0, 0.0)' """ def __init__(self, origin, second_p): warnings.warn(dep_msg, FutureWarning) self.o = origin self.p = second_p class Chain(Geometry): """Geometric representation of a chain, also known as a polyline. Parameters ---------- vertices : list A point list or list of point lists. Attributes ---------- vertices : list The list of points of the vertices of the chain in order. len : float The geometric length of the chain. Examples -------- >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))]) """ def __init__(self, vertices: list): warnings.warn(dep_msg, FutureWarning) if isinstance(vertices[0], list): self._vertices = list(vertices) else: self._vertices = [vertices] self._reset_props() @classmethod def __from_geo_interface__(cls, geo: dict): if geo["type"].lower() == "linestring": verts = [Point(pt) for pt in geo["coordinates"]] elif geo["type"].lower() == "multilinestring": verts = [list(map(Point, part)) for part in geo["coordinates"]] else: raise TypeError("%r is not a Chain." % geo) return cls(verts) @property def __geo_interface__(self) -> dict: if len(self.parts) == 1: return {"type": "LineString", "coordinates": self.vertices} else: return {"type": "MultiLineString", "coordinates": self.parts} def _reset_props(self): """**HELPER METHOD. DO NOT CALL.** Resets attributes which are functions of other attributes. The ``getter``s for these attributes (implemented as ``properties``) then recompute their values if they have been reset since the last call to the ``getter``. """ self._len = None self._arclen = None self._bounding_box = None @property def vertices(self) -> list: """Returns the vertices of the chain in clockwise order. Examples -------- >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))]) >>> verts = c.vertices >>> len(verts) 4 """ return sum(list(self._vertices), []) @property def parts(self) -> list: """Returns the parts (lists of ``libpysal.cg.Point`` objects) of the chain. Examples -------- >>> c = Chain( ... [ ... [Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))], ... [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))] ... ] ... ) >>> len(c.parts) 2 """ return [list(part) for part in self._vertices] @property def bounding_box(self): """Returns the bounding box of the chain. Returns ------- self._bounding_box : libpysal.cg.Rectangle The bounding box of the chain. Examples -------- >>> c = Chain([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))]) >>> c.bounding_box.left 0.0 >>> c.bounding_box.lower 0.0 >>> c.bounding_box.right 2.0 >>> c.bounding_box.upper 1.0 """ if self._bounding_box is None: vertices = self.vertices self._bounding_box = Rectangle( min([v[0] for v in vertices]), min([v[1] for v in vertices]), max([v[0] for v in vertices]), max([v[1] for v in vertices]), ) return self._bounding_box @property def len(self) -> int: """Returns the geometric length of the chain. Examples -------- >>> c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))]) >>> c.len 3.0 >>> c = Chain( ... [ ... [Point((0, 0)), Point((1, 0)), Point((1, 1))], ... [Point((10, 10)), Point((11, 10)), Point((11, 11))] ... ] ... ) >>> c.len 4.0 """ def dist(v1: tuple, v2: tuple) -> Union[int, float]: return math.hypot(v1[0] - v2[0], v1[1] - v2[1]) def part_perimeter(p: list) -> Union[int, float]: return sum([dist(p[i], p[i + 1]) for i in range(len(p) - 1)]) if self._len is None: self._len = sum([part_perimeter(part) for part in self._vertices]) return self._len @property def arclen(self) -> Union[int, float]: """Returns the geometric length of the chain computed using 'arcdistance' (meters). """ def part_perimeter(p: list) -> Union[int, float]: return sum([arcdist(p[i], p[i + 1]) * 1000.0 for i in range(len(p) - 1)]) if self._arclen is None: self._arclen = sum([part_perimeter(part) for part in self._vertices]) return self._arclen @property def segments(self) -> list: """Returns the segments that compose the chain.""" return [ [LineSegment(a, b) for (a, b) in zip(part[:-1], part[1:], strict=True)] for part in self._vertices ] class Ring(Geometry): """Geometric representation of a linear ring. Linear rings must be closed, the first and last point must be the same. Open rings will be closed. This class exists primarily as a geometric primitive to form complex polygons with multiple rings and holes. The ordering of the vertices is ignored and will not be altered. Parameters ---------- vertices : list A list of vertices. Attributes ---------- vertices : list A list of points with the vertices of the ring. len : int The number of vertices. perimeter : float The geometric length of the perimeter of the ring. bounding_box : libpysal.cg.Rectangle The bounding box of the ring. area : float The area enclosed by the ring. centroid : {tuple, libpysal.cg.Point} The centroid of the ring defined by the 'center of gravity' or 'center or mass'. _quad_tree_structure : libpysal.cg.QuadTreeStructureSingleRing The quad tree structure for the ring. This structure helps test if a point is inside the ring. """ def __init__(self, vertices): warnings.warn(dep_msg, FutureWarning) if vertices[0] != vertices[-1]: vertices = vertices[:] + vertices[0:1] # msg = "Supplied vertices do not form a closed ring, " # msg += "the first and last vertices are not the same." # raise ValueError(msg) self.vertices = tuple(vertices) self._perimeter = None self._bounding_box = None self._area = None self._centroid = None self._quad_tree_structure = None def __len__(self) -> int: return len(self.vertices) @property def len(self) -> int: return len(self) @staticmethod def dist(v1, v2) -> Union[int, float]: return math.hypot(v1[0] - v2[0], v1[1] - v2[1]) @property def perimeter(self) -> Union[int, float]: if self._perimeter is None: dist = self.dist v = self.vertices self._perimeter = sum( [dist(v[i], v[i + 1]) for i in range(-1, len(self) - 1)] ) return self._perimeter @property def bounding_box(self): """Returns the bounding box of the ring. Returns ------- self._bounding_box : libpysal.cg.Rectangle The bounding box of the ring. Examples -------- >>> r = Ring( ... [ ... Point((0, 0)), ... Point((2, 0)), ... Point((2, 1)), ... Point((0, 1)), ... Point((0, 0)) ... ] ... ) >>> r.bounding_box.left 0.0 >>> r.bounding_box.lower 0.0 >>> r.bounding_box.right 2.0 >>> r.bounding_box.upper 1.0 """ if self._bounding_box is None: vertices = self.vertices x = [v[0] for v in vertices] y = [v[1] for v in vertices] self._bounding_box = Rectangle(min(x), min(y), max(x), max(y)) return self._bounding_box @property def area(self) -> Union[int, float]: """Returns the area of the ring. Examples -------- >>> r = Ring( ... [ ... Point((0, 0)), ... Point((2, 0)), ... Point((2, 1)), ... Point((0, 1)), ... Point((0, 0)) ... ] ... ) >>> r.area 2.0 """ return abs(self.signed_area) @property def signed_area(self) -> Union[int, float]: if self._area is None: vertices = self.vertices x = [v[0] for v in vertices] y = [v[1] for v in vertices] N = len(self) A = 0.0 for i in range(N - 1): A += (x[i] + x[i + 1]) * (y[i] - y[i + 1]) A = A * 0.5 self._area = -A return self._area @property def centroid(self): """Returns the centroid of the ring. Returns ------- self._centroid : libpysal.cg.Point The ring's centroid. Notes ----- The centroid returned by this method is the geometric centroid. Also known as the 'center of gravity' or 'center of mass'. Examples -------- >>> r = Ring( ... [ ... Point((0, 0)), ... Point((2, 0)), ... Point((2, 1)), ... Point((0, 1)), ... Point((0, 0)) ... ] ... ) >>> str(r.centroid) '(1.0, 0.5)' """ if self._centroid is None: vertices = self.vertices x = [v[0] for v in vertices] y = [v[1] for v in vertices] A = self.signed_area N = len(self) cx = 0 cy = 0 for i in range(N - 1): f = x[i] * y[i + 1] - x[i + 1] * y[i] cx += (x[i] + x[i + 1]) * f cy += (y[i] + y[i + 1]) * f cx = 1.0 / (6 * A) * cx cy = 1.0 / (6 * A) * cy self._centroid = Point((cx, cy)) return self._centroid def build_quad_tree_structure(self): """Build the quad tree structure for this polygon. Once the structure is built, speed for testing if a point is inside the ring will be increased significantly. """ self._quad_tree_structure = QuadTreeStructureSingleRing(self) def contains_point(self, point): """Point containment using winding number. The implementation is based on `this `_. Parameters ---------- point : libpysal.cg.Point The point to test for containment. Returns ------- point_contained : bool ``True`` if ``point`` is contained within the polygon, otherwise ``False``. """ point_contained = False if self._quad_tree_structure is None: x, y = point # bbox checks bbleft = x < self.bounding_box.left bbright = x > self.bounding_box.right bblower = y < self.bounding_box.lower bbupper = y > self.bounding_box.upper if bbleft or bbright or bblower or bbupper: pass else: rn = len(self.vertices) xs = [self.vertices[i][0] - point[0] for i in range(rn)] ys = [self.vertices[i][1] - point[1] for i in range(rn)] w = 0 for i in range(len(self.vertices) - 1): yi = ys[i] yj = ys[i + 1] xi = xs[i] xj = xs[i + 1] if yi * yj < 0: r = xi + yi * (xj - xi) / (yi - yj) if r > 0: if yi < 0: w += 1 else: w -= 1 elif yi == 0 and xi > 0: if yj > 0: w += 0.5 else: w -= 0.5 elif yj == 0 and xj > 0: if yi < 0: w += 0.5 else: w -= 0.5 if w == 0: pass else: point_contained = True else: point_contained = self._quad_tree_structure.contains_point(point) return point_contained class Polygon(Geometry): """Geometric representation of polygon objects. Returns a polygon created from the objects specified. Parameters ---------- vertices : list A list of vertices or a list of lists of vertices. holes : list A list of sub-polygons to be considered as holes. Default is ``None``. Attributes ---------- vertices : list A list of points with the vertices of the polygon in clockwise order. len : int The number of vertices including holes. perimeter : float The geometric length of the perimeter of the polygon. bounding_box : libpysal.cg.Rectangle The bounding box of the polygon. bbox : list A list representation of the bounding box in the form ``[left, lower, right, upper]``. area : float The area enclosed by the polygon. centroid : tuple The 'center of gravity', i.e. the mean point of the polygon. Examples -------- >>> p1 = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) """ def __init__(self, vertices, holes=None): warnings.warn(dep_msg, FutureWarning) self._part_rings = [] self._hole_rings = [] def clockwise(part: list) -> list: if standalone.is_clockwise(part): return part[:] else: return part[::-1] vl = list(vertices) if isinstance(vl[0], list): self._part_rings = list(map(Ring, vertices)) self._vertices = [clockwise(part) for part in vertices] else: self._part_rings = [Ring(vertices)] self._vertices = [clockwise(vertices)] if holes is not None and holes != []: if isinstance(holes[0], list): self._hole_rings = list(map(Ring, holes)) self._holes = [clockwise(hole) for hole in holes] else: self._hole_rings = [Ring(holes)] self._holes = [clockwise(holes)] else: self._holes = [[]] self._reset_props() @classmethod def __from_geo_interface__(cls, geo: dict): """While PySAL does not differentiate polygons and multipolygons GEOS, Shapely, and geoJSON do. In GEOS, etc, polygons may only have a single exterior ring, all other parts are holes. MultiPolygons are simply a list of polygons. """ geo_type = geo["type"].lower() if geo_type == "multipolygon": parts = [] holes = [] for polygon in geo["coordinates"]: verts = [[Point(pt) for pt in part] for part in polygon] parts += verts[0:1] holes += verts[1:] if not holes: holes = None return cls(parts, holes) else: verts = [[Point(pt) for pt in part] for part in geo["coordinates"]] return cls(verts[0:1], verts[1:]) @property def __geo_interface__(self) -> dict: """Return ``__geo_interface__`` information lookup.""" if len(self.parts) > 1: geo = { "type": "MultiPolygon", "coordinates": [[part] for part in self.parts], } if self._holes[0]: geo["coordinates"][0] += self._holes return geo if self._holes[0]: return {"type": "Polygon", "coordinates": self._vertices + self._holes} else: return {"type": "Polygon", "coordinates": self._vertices} def _reset_props(self): """Resets the geometric properties of the polygon.""" self._perimeter = None self._bounding_box = None self._bbox = None self._area = None self._centroid = None self._len = None def __len__(self) -> int: return self.len @property def len(self) -> int: """Returns the number of vertices in the polygon. Examples -------- >>> p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))]) >>> p1.len 4 >>> len(p1) 4 """ if self._len is None: self._len = len(self.vertices) return self._len @property def vertices(self) -> list: """Returns the vertices of the polygon in clockwise order. Examples -------- >>> p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))]) >>> len(p1.vertices) 4 """ return sum(list(self._vertices), []) + sum(list(self._holes), []) @property def holes(self) -> list: """Returns the holes of the polygon in clockwise order. Examples -------- >>> p = Polygon( ... [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], ... [Point((1, 2)), Point((2, 2)), Point((2, 1)), Point((1, 1))] ... ) >>> len(p.holes) 1 """ return [list(part) for part in self._holes] @property def parts(self) -> list: """Returns the parts of the polygon in clockwise order. Examples -------- >>> p = Polygon( ... [ ... [Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))], ... [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))] ... ] ... ) >>> len(p.parts) 2 """ return [list(part) for part in self._vertices] @property def perimeter(self) -> Union[int, float]: """Returns the perimeter of the polygon. Examples -------- >>> p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) >>> p.perimeter 4.0 """ def dist(v1: Union[int, float], v2: Union[int, float]) -> float: return math.hypot(v1[0] - v2[0], v1[1] - v2[1]) def part_perimeter(part) -> Union[int, float]: return sum([dist(part[i], part[i + 1]) for i in range(-1, len(part) - 1)]) sum_perim = lambda part_type: sum([part_perimeter(part) for part in part_type]) if self._perimeter is None: self._perimeter = sum_perim(self._vertices) + sum_perim(self._holes) return self._perimeter @property def bbox(self): """Returns the bounding box of the polygon as a list. Returns ------- self._bbox : list The bounding box of the polygon as a list. See Also -------- libpysal.cg.bounding_box """ if self._bbox is None: self._bbox = [ self.bounding_box.left, self.bounding_box.lower, self.bounding_box.right, self.bounding_box.upper, ] return self._bbox @property def bounding_box(self): """Returns the bounding box of the polygon. Returns ------- self._bounding_box : libpysal.cg.Rectangle The bounding box of the polygon. Examples -------- >>> p = Polygon([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))]) >>> p.bounding_box.left 0.0 >>> p.bounding_box.lower 0.0 >>> p.bounding_box.right 2.0 >>> p.bounding_box.upper 1.0 """ if self._bounding_box is None: vertices = self.vertices self._bounding_box = Rectangle( min([v[0] for v in vertices]), min([v[1] for v in vertices]), max([v[0] for v in vertices]), max([v[1] for v in vertices]), ) return self._bounding_box @property def area(self) -> float: """Returns the area of the polygon. Examples -------- >>> p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) >>> p.area 1.0 >>> p = Polygon( ... [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], ... [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))] ... ) >>> p.area 99.0 """ def part_area(pv: list) -> float: __area = 0 for i in range(-1, len(pv) - 1): __area += (pv[i][0] + pv[i + 1][0]) * (pv[i][1] - pv[i + 1][1]) __area = __area * 0.5 if __area < 0: __area = -area # noqa F821 return __area sum_area = lambda part_type: sum([part_area(part) for part in part_type]) _area = sum_area(self._vertices) - sum_area(self._holes) return _area @property def centroid(self) -> tuple: """Returns the centroid of the polygon. Notes ----- The centroid returned by this method is the geometric centroid and respects multipart polygons with holes. Also known as the 'center of gravity' or 'center of mass'. Examples -------- >>> p = Polygon( ... [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], ... [Point((1, 1)), Point((1, 2)), Point((2, 2)), Point((2, 1))] ... ) >>> p.centroid (5.0353535353535355, 5.0353535353535355) """ CP = [ring.centroid for ring in self._part_rings] AP = [ring.area for ring in self._part_rings] CH = [ring.centroid for ring in self._hole_rings] AH = [-ring.area for ring in self._hole_rings] A = AP + AH cx = sum([pt[0] * area for pt, area in zip(CP + CH, A, strict=True)]) / sum(A) cy = sum([pt[1] * area for pt, area in zip(CP + CH, A, strict=True)]) / sum(A) return cx, cy def build_quad_tree_structure(self): """Build the quad tree structure for this polygon. Once the structure is built, speed for testing if a point is inside the ring will be increased significantly. """ for ring in self._part_rings: ring.build_quad_tree_structure() for ring in self._hole_rings: ring.build_quad_tree_structure() self.is_quad_tree_structure_built = True def contains_point(self, point): """Test if a polygon contains a point. Parameters ---------- point : libpysal.cg.Point A point to test for containment. Returns ------- contains : bool ``True`` if the polygon contains ``point`` otherwise ``False``. Examples -------- >>> p = Polygon( ... [Point((0,0)), Point((4,0)), Point((4,5)), Point((2,3)), Point((0,5))] ... ) >>> p.contains_point((3,3)) 1 >>> p.contains_point((0,6)) 0 >>> p.contains_point((2,2.9)) 1 >>> p.contains_point((4,5)) 0 >>> p.contains_point((4,0)) 0 Handles holes. >>> p = Polygon( ... [Point((0, 0)), Point((0, 10)), Point((10, 10)), Point((10, 0))], ... [Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))] ... ) >>> p.contains_point((3.0, 3.0)) False >>> p.contains_point((1.0, 1.0)) True Notes ----- Points falling exactly on polygon edges may yield unpredictable results. """ searching = True for ring in self._hole_rings: if ring.contains_point(point): contains = False searching = False break if searching: for ring in self._part_rings: if ring.contains_point(point): contains = True searching = False break if searching: contains = False return contains class Rectangle(Geometry): """Geometric representation of rectangle objects. Attributes ---------- left : float Minimum x-value of the rectangle. lower : float Minimum y-value of the rectangle. right : float Maximum x-value of the rectangle. upper : float Maximum y-value of the rectangle. Examples -------- >>> r = Rectangle(-4, 3, 10, 17) >>> r.left #minx -4.0 >>> r.lower #miny 3.0 >>> r.right #maxx 10.0 >>> r.upper #maxy 17.0 """ def __init__(self, left, lower, right, upper): warnings.warn(dep_msg, FutureWarning) if right < left or upper < lower: raise ArithmeticError("Rectangle must have positive area.") self.left = float(left) self.lower = float(lower) self.right = float(right) self.upper = float(upper) def __bool__(self): """Rectangles will evaluate to False if they have zero area. ``___nonzero__`` is used "to implement truth value testing and the built-in operation ``bool()``" ``-- http://docs.python.org/reference/datamodel.html Examples -------- >>> r = Rectangle(0, 0, 0, 0) >>> bool(r) False >>> r = Rectangle(0, 0, 1, 1) >>> bool(r) True """ return bool(self.area) def __eq__(self, other): if other: return self[:] == other[:] return False def __add__(self, other): x, y, X, Y = self[:] x1, y2, X1, Y1 = other[:] return Rectangle( min(self.left, other.left), min(self.lower, other.lower), max(self.right, other.right), max(self.upper, other.upper), ) def __getitem__(self, key): """ Examples -------- >>> r = Rectangle(-4, 3, 10, 17) >>> r[:] [-4.0, 3.0, 10.0, 17.0] """ l_ = [self.left, self.lower, self.right, self.upper] return l_.__getitem__(key) def set_centroid(self, new_center): """Moves the rectangle center to a new specified point. Parameters ---------- new_center : libpysal.cg.Point The new location of the centroid of the polygon. Examples -------- >>> r = Rectangle(0, 0, 4, 4) >>> r.set_centroid(Point((4, 4))) >>> r.left 2.0 >>> r.right 6.0 >>> r.lower 2.0 >>> r.upper 6.0 """ shift = ( new_center[0] - (self.left + self.right) / 2, new_center[1] - (self.lower + self.upper) / 2, ) self.left = self.left + shift[0] self.right = self.right + shift[0] self.lower = self.lower + shift[1] self.upper = self.upper + shift[1] def set_scale(self, scale): """Rescales the rectangle around its center. Parameters ---------- scale : int, float The ratio of the new scale to the old scale (e.g. 1.0 is current size). Examples -------- >>> r = Rectangle(0, 0, 4, 4) >>> r.set_scale(2) >>> r.left -2.0 >>> r.right 6.0 >>> r.lower -2.0 >>> r.upper 6.0 """ center = ((self.left + self.right) / 2, (self.lower + self.upper) / 2) self.left = center[0] + scale * (self.left - center[0]) self.right = center[0] + scale * (self.right - center[0]) self.lower = center[1] + scale * (self.lower - center[1]) self.upper = center[1] + scale * (self.upper - center[1]) @property def area(self) -> Union[int, float]: """Returns the area of the Rectangle. Examples -------- >>> r = Rectangle(0, 0, 4, 4) >>> r.area 16.0 """ return (self.right - self.left) * (self.upper - self.lower) @property def width(self) -> Union[int, float]: """Returns the width of the Rectangle. Examples -------- >>> r = Rectangle(0, 0, 4, 4) >>> r.width 4.0 """ return self.right - self.left @property def height(self) -> Union[int, float]: """Returns the height of the Rectangle. Examples -------- >>> r = Rectangle(0, 0, 4, 4) >>> r.height 4.0 """ return self.upper - self.lower _geoJSON_type_to_Pysal_type = { # noqa N816 "point": Point, "linestring": Chain, "multilinestring": Chain, "polygon": Polygon, "multipolygon": Polygon, } # moving this to top breaks unit tests ! from . import standalone from .polygonQuadTreeStructure import QuadTreeStructureSingleRing libpysal-4.9.2/libpysal/cg/sphere.py000066400000000000000000000407121452177046000174270ustar00rootroot00000000000000""" sphere: Tools for working with spherical geometry. Author(s): Charles R Schmidt schmidtc@gmail.com Luc Anselin luc.anselin@asu.edu Xun Li xun.li@asu.edu """ __author__ = ( "Charles R Schmidt ," "Luc Anselin >> pt0 = (0, 0) >>> pt1 = (180, 0) >>> d = arcdist(pt0, pt1, RADIUS_EARTH_MILES) >>> d == math.pi * RADIUS_EARTH_MILES True """ dist = linear2arcdist(euclidean(toXYZ(pt0), toXYZ(pt1)), radius) return dist def arcdist2linear(arc_dist, radius=RADIUS_EARTH_KM): """Convert an arc distance (spherical earth) to a linear distance (R3) in the unit sphere. Parameters ---------- arc_dist : float The arc distance to convert. radius : float The radius of a sphere. Default is Earth's radius in kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``) is also an option. Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html Returns ------- linear_dist : float The linear distance conversion of ``arc_dist``. Examples -------- >>> pt0 = (0, 0) >>> pt1 = (180, 0) >>> d = arcdist(pt0, pt1, RADIUS_EARTH_MILES) >>> d == math.pi * RADIUS_EARTH_MILES True >>> arcdist2linear(d, RADIUS_EARTH_MILES) 2.0 """ circumference = 2 * math.pi * radius linear_dist = ( 2 - (2 * math.cos(math.radians((arc_dist * 360.0) / circumference))) ) ** (0.5) return linear_dist def linear2arcdist(linear_dist, radius=RADIUS_EARTH_KM): """Convert a linear distance in the unit sphere (R3) to an arc distance based on supplied radius. Parameters ---------- linear_dist : float The linear distance to convert. radius : float The radius of a sphere. Default is Earth's radius in kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``) is also an option. Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html Returns ------- arc_dist : float The arc distance conversion of ``linear_dist``. Raises ------ ValueError Raised when ``linear_dist`` exceeds the diameter of the unit sphere. Examples -------- >>> pt0 = (0, 0) >>> pt1 = (180, 0) >>> d = arcdist(pt0, pt1, RADIUS_EARTH_MILES) >>> d == linear2arcdist(2.0, radius=RADIUS_EARTH_MILES) True """ if linear_dist == float("inf"): arc_dist = linear_dist elif linear_dist > 2.0: msg = "'linear_dist', must not exceed the diameter of the unit sphere, 2.0." raise ValueError(msg) else: circumference = 2 * math.pi * radius a2 = linear_dist**2 theta = math.degrees(math.acos((2 - a2) / (2.0))) arc_dist = (theta * circumference) / 360.0 return arc_dist def toXYZ(pt): # noqa N802 """Convert a point's latitude and longitude to x,y,z. Parameters ---------- pt : tuple A point assumed to be in form (lng,lat). Returns ------- x, y, z : tuple A point in form (x, y, z). """ phi, theta = list(map(math.radians, pt)) phi, theta = phi + pi, theta + (pi / 2) x = 1 * sin(theta) * cos(phi) y = 1 * sin(theta) * sin(phi) z = 1 * cos(theta) return x, y, z def toLngLat(xyz): # noqa N802 """Convert a point's x,y,z to latitude and longitude. Parameters ---------- xyz : tuple A point assumed to be in form (x,y,z). Returns ------- phi, theta : tuple A point in form (phi, theta) [y,x]. """ x, y, z = xyz if z == -1 or z == 1: phi = 0 else: phi = math.atan2(y, x) if phi > 0: phi = phi - math.pi elif phi < 0: phi = phi + math.pi theta = math.acos(z) - (math.pi / 2) return phi, theta def brute_knn(pts, k, mode="arc", radius=RADIUS_EARTH_KM): """Computes a brute-force :math:`k` nearest neighbors. Parameters ---------- pts : list A list of :math:`x,y` pairs. k : int The number of points to query. mode : str The mode of distance. Valid modes are ``'arc'`` and ``'xyz'``. Default is ``'arc'``. radius : float The radius of a sphere. Default is Earth's radius in kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``) is also an option. Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html Returns ------- w : dict A neighbor ID lookup. """ n = len(pts) full = numpy.zeros((n, n)) for i in range(n): for j in range(i + 1, n): if mode == "arc": lng0, lat0 = pts[i] lng1, lat1 = pts[j] dist = arcdist(pts[i], pts[j], radius=radius) elif mode == "xyz": dist = euclidean(pts[i], pts[j]) full[i, j] = dist full[j, i] = dist w = {} for i in range(n): w[i] = full[i].argsort()[1 : k + 1].tolist() return w def fast_knn(pts, k, return_dist=False, radius=RADIUS_EARTH_KM): """Computes :math:`k` nearest neighbors on a sphere. Parameters ---------- pts : list A list of :math:`x,y` pairs. k : int The number of points to query. return_dist : bool Return distances in the ``wd`` container object (``True``). Default is ``False``. radius : float The radius of a sphere. Default is Earth's radius in kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``) is also an option. Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html Returns ------- wn : dict A neighbor ID lookup. wd : dict A neighbor distance lookup (optional). """ pts = numpy.array(pts) kd = scipy.spatial.KDTree(pts) d, w = kd.query(pts, k + 1) w = w[:, 1:] wn = {} for i in range(len(pts)): wn[i] = w[i].tolist() if return_dist: d = d[:, 1:] wd = {} for i in range(len(pts)): wd[i] = [linear2arcdist(x, radius=radius) for x in d[i].tolist()] return wn, wd return wn def fast_threshold(pts, dist, radius=RADIUS_EARTH_KM): """Find all neighbors on a sphere within a threshold distance. Parameters ---------- pointslist : list A list of lat-lon tuples. This **must** be a list, even for one point. dist: float The threshold distance. radius : float The radius of a sphere. Default is Earth's radius in kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``) is also an option. Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html Returns ------- wd : dict A neighbor distance lookup where the key is the ID of a point and the value is a list of IDs for other points within ``dist`` of the key point, """ d = arcdist2linear(dist, radius) kd = scipy.spatial.KDTree(pts) r = kd.query_ball_tree(kd, d) wd = {} for i in range(len(pts)): l_ = r[i] l_.remove(i) wd[i] = l_ return wd def lonlat(pointslist): """Converts point order from lat-lon tuples to lon-lat (x,y) tuples. Parameters ---------- pointslist : list A list of lat-lon tuples. This **must** be a list, even for one point. Returns ------- newpts : list A list with tuples of points in lon-lat order. Examples -------- >>> points = [ ... (41.981417, -87.893517), (41.980396, -87.776787), (41.980906, -87.696450) ... ] >>> newpoints = lonlat(points) >>> newpoints [(-87.893517, 41.981417), (-87.776787, 41.980396), (-87.69645, 41.980906)] """ newpts = [(i[1], i[0]) for i in pointslist] return newpts def haversine(x): """Computes the haversine formula. Parameters ---------- x : float The angle in radians. Returns ------- haversine_dist : float The square of sine of half the radian (the haversine formula). Examples -------- >>> haversine(math.pi) # is 180 in radians, hence sin of 90 = 1 1.0 """ x = math.sin(x / 2) haversine_dist = x * x return haversine_dist # Lambda functions # degree to radian conversion d2r = lambda x: x * math.pi / 180.0 # radian to degree conversion r2d = lambda x: x * 180.0 / math.pi def radangle(p0, p1): """Radian angle between two points on a sphere in lon-lat (x,y). Parameters ---------- p0 : tuple The first point in (lon,lat) format. p1 : tuple The second point in (lon,lat) format. Returns ------- d : float Radian angle in radians. Examples -------- >>> p0 = (-87.893517, 41.981417) >>> p1 = (-87.519295, 41.657498) >>> radangle(p0, p1) 0.007460167953189258 Notes ----- Uses haversine formula, function haversine and degree to radian conversion lambda function ``d2r``. """ x0, y0 = d2r(p0[0]), d2r(p0[1]) x1, y1 = d2r(p1[0]), d2r(p1[1]) d = 2.0 * math.asin( math.sqrt(haversine(y1 - y0) + math.cos(y0) * math.cos(y1) * haversine(x1 - x0)) ) return d def harcdist(p0, p1, lonx=True, radius=RADIUS_EARTH_KM): """Alternative the arc distance function, uses the haversine formula. Parameters ---------- p0 : tuple The first point decimal degrees. p1 : tuple The second point decimal degrees. lonx : bool The method to assess the order of the coordinates. ``True`` for (lon,lat); ``False`` for (lat,lon). Default is ``True``. radius : float The radius of a sphere. Default is Earth's radius in kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth's radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``) is also an option. Set to ``None`` for radians. Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html Returns ------- harc_dist : harc_dist The distance in units specified, km, miles or radians. Examples -------- >>> p0 = (-87.893517, 41.981417) >>> p1 = (-87.519295, 41.657498) >>> harcdist(p0, p1) 47.52873002976876 >>> harcdist(p0, p1, radius=None) 0.007460167953189258 Notes ----- Uses the ``radangle`` function to compute radian angle. """ if not (lonx): p = lonlat([p0, p1]) p0 = p[0] p1 = p[1] harc_dist = radangle(p0, p1) if radius is not None: harc_dist = harc_dist * radius return harc_dist def geointerpolate(p0, p1, t, lonx=True): r"""Finds a point on a sphere along the great circle distance between two points on a sphere also known as a way point in great circle navigation. Parameters ---------- p0 : tuple The first point decimal degrees. p1 : tuple The second point decimal degrees. t : float The proportion along great circle distance between ``p0`` and ``p1`` (e.g., :math:`\mathtt{t}=0.5` would find the mid-point). lonx : bool The method to assess the order of the coordinates. ``True`` for (lon,lat); ``False`` for (lat,lon). Default is ``True``. Returns ------- newpx, newpy : tuple The new point in decimal degrees of (lon-lat) by default or (lat-lon) if ``lonx`` is set to ``False``. Examples -------- >>> p0 = (-87.893517, 41.981417) >>> p1 = (-87.519295, 41.657498) >>> geointerpolate(p0, p1, 0.1) # using lon-lat (-87.85592403438788, 41.949079912574796) >>> p3 = (41.981417, -87.893517) >>> p4 = (41.657498, -87.519295) >>> geointerpolate(p3, p4, 0.1, lonx=False) # using lat-lon (41.949079912574796, -87.85592403438788) """ if not (lonx): p = lonlat([p0, p1]) p0 = p[0] p1 = p[1] d = radangle(p0, p1) k = 1.0 / math.sin(d) t = t * d A = math.sin(d - t) * k B = math.sin(t) * k x0, y0 = d2r(p0[0]), d2r(p0[1]) x1, y1 = d2r(p1[0]), d2r(p1[1]) x = A * math.cos(y0) * math.cos(x0) + B * math.cos(y1) * math.cos(x1) y = A * math.cos(y0) * math.sin(x0) + B * math.cos(y1) * math.sin(x1) z = A * math.sin(y0) + B * math.sin(y1) newpx = r2d(math.atan2(y, x)) newpy = r2d(math.atan2(z, math.sqrt(x * x + y * y))) if not lonx: return newpy, newpx return newpx, newpy def geogrid(pup, pdown, k, lonx=True): """Computes a :math:`k+1` by :math:`k+1` set of grid points for a bounding box in lat-lon. Uses ``geointerpolate``. Parameters ---------- pup : tuple The lat-lon or lon-lat for the upper left corner of the bounding box. pdown : tuple The lat-lon or lon-lat for The lower right corner of The bounding box. k : int The number of grid cells (grid points will be one more). lonx : bool The method to assess the order of the coordinates. ``True`` for (lon,lat); ``False`` for (lat,lon). Default is ``True``. Returns ------- grid : list A list of tuples with (lat-lon) or (lon-lat) for grid points, row by row, starting with the top row and moving to the bottom; coordinate tuples are returned in same order as input. Examples -------- >>> pup = (42.023768, -87.946389) # Arlington Heights, IL >>> pdown = (41.644415, -87.524102) # Hammond, IN >>> geogrid(pup,pdown, 3, lonx=False) [(42.023768, -87.946389), (42.02393997819538, -87.80562679358316), (42.02393997819538, -87.66486420641684), (42.023768, -87.524102), (41.897317, -87.94638900000001), (41.8974888973743, -87.80562679296166), (41.8974888973743, -87.66486420703835), (41.897317, -87.524102), (41.770866000000005, -87.94638900000001), (41.77103781320412, -87.80562679234043), (41.77103781320412, -87.66486420765956), (41.770866000000005, -87.524102), (41.644415, -87.946389), (41.64458672568646, -87.80562679171955), (41.64458672568646, -87.66486420828045), (41.644415, -87.524102)] """ corners = [pup, pdown] if lonx else lonlat([pup, pdown]) tpoints = [float(i) / k for i in range(k)[1:]] leftcorners = [corners[0], (corners[0][0], corners[1][1])] rightcorners = [(corners[1][0], corners[0][1]), corners[1]] leftside = [leftcorners[0]] rightside = [rightcorners[0]] for t in tpoints: newpl = geointerpolate(leftcorners[0], leftcorners[1], t) leftside.append(newpl) newpr = geointerpolate(rightcorners[0], rightcorners[1], t) rightside.append(newpr) leftside.append(leftcorners[1]) rightside.append(rightcorners[1]) grid = [] for i in range(len(leftside)): grid.append(leftside[i]) for t in tpoints: newp = geointerpolate(leftside[i], rightside[i], t) grid.append(newp) grid.append(rightside[i]) if not (lonx): grid = lonlat(grid) return grid libpysal-4.9.2/libpysal/cg/standalone.py000066400000000000000000001055311452177046000202720ustar00rootroot00000000000000""" Helper functions for computational geometry in PySAL. """ __author__ = "Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow" __credits__ = "Copyright (c) 2005-2009 Sergio J. Rey" # ruff: noqa: F403, F405 import copy import math import random from itertools import islice import numpy as np import scipy.spatial from .shapes import * EPSILON_SCALER = 3 __all__ = [ "bbcommon", "get_bounding_box", "get_angle_between", "is_collinear", "get_segments_intersect", "get_segment_point_intersect", "get_polygon_point_intersect", "get_rectangle_point_intersect", "get_ray_segment_intersect", "get_rectangle_rectangle_intersection", "get_polygon_point_dist", "get_points_dist", "get_segment_point_dist", "get_point_at_angle_and_dist", "convex_hull", "is_clockwise", "point_touches_rectangle", "get_shared_segments", "distance_matrix", ] def bbcommon(bb, bbother): """Old Stars method for bounding box overlap testing. Also defined in ``pysal.weights._cont_binning``. Parameters ---------- bb : list A bounding box. bbother : list The bounding box to test against. Returns ------- chflag : int ``1`` if ``bb`` overlaps ``bbother``, otherwise ``0``. Examples -------- >>> b0 = [0, 0, 10, 10] >>> b1 = [10, 0, 20, 10] >>> bbcommon(b0, b1) 1 """ chflag = 0 if (not ((bbother[2] < bb[0]) or (bbother[0] > bb[2]))) and ( not ((bbother[3] < bb[1]) or (bbother[1] > bb[3])) ): chflag = 1 return chflag def get_bounding_box(items): """Find bounding box for a list of geometries. Parameters ---------- items : list PySAL shapes. Returns ------- rect = libpysal.cg.Rectangle The bounding box for a list of geometries. Examples -------- >>> bb = get_bounding_box([Point((-1, 5)), Rectangle(0, 6, 11, 12)]) >>> bb.left -1.0 >>> bb.lower 5.0 >>> bb.right 11.0 >>> bb.upper 12.0 """ def left(o): # Polygon, Ellipse if hasattr(o, "bounding_box"): return o.bounding_box.left # Rectangle elif hasattr(o, "left"): return o.left # Point else: return o[0] def right(o): # Polygon, Ellipse if hasattr(o, "bounding_box"): return o.bounding_box.right # Rectangle elif hasattr(o, "right"): return o.right # Point else: return o[0] def lower(o): # Polygon, Ellipse if hasattr(o, "bounding_box"): return o.bounding_box.lower # Rectangle elif hasattr(o, "lower"): return o.lower # Point else: return o[1] def upper(o): # Polygon, Ellipse if hasattr(o, "bounding_box"): return o.bounding_box.upper # Rectangle elif hasattr(o, "upper"): return o.upper # Point else: return o[1] rect = Rectangle( min(list(map(left, items))), min(list(map(lower, items))), max(list(map(right, items))), max(list(map(upper, items))), ) return rect def get_angle_between(ray1, ray2): """Returns the angle formed between a pair of rays which share an origin. Parameters ---------- ray1 : libpysal.cg.Ray A ray forming the beginning of the angle measured. ray2 : libpysal.cg.Ray A ray forming the end of the angle measured. Returns ------- angle : float The angle between ``ray1`` and ``ray2``. Raises ------ ValueError Raised when rays do not have the same origin. Examples -------- >>> get_angle_between( ... Ray(Point((0, 0)), Point((1, 0))), ... Ray(Point((0, 0)), Point((1, 0))) ... ) 0.0 """ if ray1.o != ray2.o: raise ValueError("Rays must have the same origin.") vec1 = (ray1.p[0] - ray1.o[0], ray1.p[1] - ray1.o[1]) vec2 = (ray2.p[0] - ray2.o[0], ray2.p[1] - ray2.o[1]) rot_theta = -math.atan2(vec1[1], vec1[0]) rot_matrix = [ [math.cos(rot_theta), -math.sin(rot_theta)], [math.sin(rot_theta), math.cos(rot_theta)], ] rot_vec2 = ( rot_matrix[0][0] * vec2[0] + rot_matrix[0][1] * vec2[1], rot_matrix[1][0] * vec2[0] + rot_matrix[1][1] * vec2[1], ) angle = math.atan2(rot_vec2[1], rot_vec2[0]) return angle def is_collinear(p1, p2, p3): """Returns whether a triplet of points is collinear. Parameters ---------- p1 : libpysal.cg.Point A point. p2 : libpysal.cg.Point A point. p3 : libpysal.cg.Point A point. Returns ------- collinear : bool ``True`` if ``{p1, p2, p3}`` are collinear, otherwise ``False``. Examples -------- >>> is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 5))) True >>> is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 0))) False """ eps = np.finfo(type(p1[0])).eps slope_diff = abs( (p2[0] - p1[0]) * (p3[1] - p1[1]) - (p2[1] - p1[1]) * (p3[0] - p1[0]) ) very_small_dist = EPSILON_SCALER * eps collinear = slope_diff < very_small_dist return collinear def get_segments_intersect(seg1, seg2): """Returns the intersection of two segments if one exists. Parameters ---------- seg1 : libpysal.cg.LineSegment A segment to check for an intersection. seg2 : libpysal.cg.LineSegment The segment to check against ``seg1`` for an intersection. Returns ------- intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, None} The intersecting point or line between ``seg1`` and ``seg2`` if an intersection exists or ``None`` if ``seg1`` and ``seg2`` do not intersect. Examples -------- >>> seg1 = LineSegment(Point((0, 0)), Point((0, 10))) >>> seg2 = LineSegment(Point((-5, 5)), Point((5, 5))) >>> i = get_segments_intersect(seg1, seg2) >>> isinstance(i, Point) True >>> str(i) '(0.0, 5.0)' >>> seg3 = LineSegment(Point((100, 100)), Point((100, 101))) >>> i = get_segments_intersect(seg2, seg3) """ p1 = seg1.p1 p2 = seg1.p2 p3 = seg2.p1 p4 = seg2.p2 a = p2[0] - p1[0] b = p3[0] - p4[0] c = p2[1] - p1[1] d = p3[1] - p4[1] det = float(a * d - b * c) intersection = None if det == 0: if seg1 == seg2: intersection = LineSegment(seg1.p1, seg1.p2) else: a = get_segment_point_intersect(seg2, seg1.p1) b = get_segment_point_intersect(seg2, seg1.p2) c = get_segment_point_intersect(seg1, seg2.p1) d = get_segment_point_intersect(seg1, seg2.p2) if a and b: # seg1 in seg2 intersection = LineSegment(seg1.p1, seg1.p2) if c and d: # seg2 in seg1 intersection = LineSegment(seg2.p1, seg2.p2) if (a or b) and (c or d): p1 = a if a else b p2 = c if c else d intersection = LineSegment(p1, p2) else: a_inv = d / det b_inv = -b / det c_inv = -c / det d_inv = a / det m = p3[0] - p1[0] n = p3[1] - p1[1] x = a_inv * m + b_inv * n y = c_inv * m + d_inv * n intersect_exists = 0 <= x <= 1 and 0 <= y <= 1 if intersect_exists: intersection = Point( (p1[0] + x * (p2[0] - p1[0]), p1[1] + x * (p2[1] - p1[1])) ) return intersection def get_segment_point_intersect(seg, pt): """Returns the intersection of a segment and point. Parameters ---------- seg : libpysal.cg.LineSegment A segment to check for an intersection. pt : libpysal.cg.Point A point to check ``seg`` for an intersection. Returns ------- pt : {libpysal.cg.Point, None} The intersection of a ``seg`` and ``pt`` if one exists, otherwise ``None``. Examples -------- >>> seg = LineSegment(Point((0, 0)), Point((0, 10))) >>> pt = Point((0, 5)) >>> i = get_segment_point_intersect(seg, pt) >>> str(i) '(0.0, 5.0)' >>> pt2 = Point((5, 5)) >>> get_segment_point_intersect(seg, pt2) """ eps = np.finfo(type(pt[0])).eps if is_collinear(pt, seg.p1, seg.p2): if get_segment_point_dist(seg, pt)[0] < EPSILON_SCALER * eps: pass else: pt = None else: vec1 = (pt[0] - seg.p1[0], pt[1] - seg.p1[1]) vec2 = (seg.p2[0] - seg.p1[0], seg.p2[1] - seg.p1[1]) if abs(vec1[0] * vec2[1] - vec1[1] * vec2[0]) < eps: pass else: pt = None return pt def get_polygon_point_intersect(poly, pt): """Returns the intersection of a polygon and point. Parameters ---------- poly : libpysal.cg.Polygon A polygon to check for an intersection. pt : libpysal.cg.Point A point to check ``poly`` for an intersection. Returns ------- ret : {libpysal.cg.Point, None} The intersection of a ``poly`` and ``pt`` if one exists, otherwise ``None``. Examples -------- >>> poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) >>> pt = Point((0.5, 0.5)) >>> i = get_polygon_point_intersect(poly, pt) >>> str(i) '(0.5, 0.5)' >>> pt2 = Point((2, 2)) >>> get_polygon_point_intersect(poly, pt2) """ def pt_lies_on_part_boundary(p, vx): vx_range = range(-1, len(vx) - 1) seg = lambda i: LineSegment(vx[i], vx[i + 1]) return [i for i in vx_range if get_segment_point_dist(seg(i), p)[0] == 0] != [] ret = None # Weed out points that aren't even close if get_rectangle_point_intersect(poly.bounding_box, pt) is None: pass else: if [ vxs for vxs in poly._vertices if pt_lies_on_part_boundary(pt, vxs) ] != [] or [vxs for vxs in poly._vertices if _point_in_vertices(pt, vxs)] != []: ret = pt if poly._holes != [[]]: if [vxs for vxs in poly.holes if pt_lies_on_part_boundary(pt, vxs)] != []: # pt lies on boundary of hole. pass if [vxs for vxs in poly.holes if _point_in_vertices(pt, vxs)] != []: # pt lines inside a hole. ret = None # raise NotImplementedError, # 'Cannot compute containment for polygon with holes' return ret def get_rectangle_point_intersect(rect, pt): """Returns the intersection of a rectangle and point. Parameters ---------- rect : libpysal.cg.Rectangle A rectangle to check for an intersection. pt : libpysal.cg.Point A point to check ``rect`` for an intersection. Returns ------- pt : {libpysal.cg.Point, None} The intersection of a ``rect`` and ``pt`` if one exists, otherwise ``None``. Examples -------- >>> rect = Rectangle(0, 0, 5, 5) >>> pt = Point((1, 1)) >>> i = get_rectangle_point_intersect(rect, pt) >>> str(i) '(1.0, 1.0)' >>> pt2 = Point((10, 10)) >>> get_rectangle_point_intersect(rect, pt2) """ if rect.left <= pt[0] <= rect.right and rect.lower <= pt[1] <= rect.upper: pass else: pt = None return pt def get_ray_segment_intersect(ray, seg): """Returns the intersection of a ray and line segment. Parameters ---------- ray : libpysal.cg.Ray A ray to check for an intersection. seg : libpysal.cg.LineSegment A segment to check for an intersection against ``ray``. Returns ------- intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, None} The intersecting point or line between ``ray`` and ``seg`` if an intersection exists or ``None`` if ``ray`` and ``seg`` do not intersect. See Also -------- libpysal.cg.get_segments_intersect Examples -------- >>> ray = Ray(Point((0, 0)), Point((0, 1))) >>> seg = LineSegment(Point((-1, 10)), Point((1, 10))) >>> i = get_ray_segment_intersect(ray, seg) >>> isinstance(i, Point) True >>> str(i) '(0.0, 10.0)' >>> seg2 = LineSegment(Point((10, 10)), Point((10, 11))) >>> get_ray_segment_intersect(ray, seg2) """ # Upper bound on origin to segment dist (+1) d = ( max( math.hypot(seg.p1[0] - ray.o[0], seg.p1[1] - ray.o[1]), math.hypot(seg.p2[0] - ray.o[0], seg.p2[1] - ray.o[1]), ) + 1 ) ratio = d / math.hypot(ray.o[0] - ray.p[0], ray.o[1] - ray.p[1]) ray_seg = LineSegment( ray.o, Point( ( ray.o[0] + ratio * (ray.p[0] - ray.o[0]), ray.o[1] + ratio * (ray.p[1] - ray.o[1]), ) ), ) intersection = get_segments_intersect(seg, ray_seg) return intersection def get_rectangle_rectangle_intersection(r0, r1, checkOverlap=True): """Returns the intersection between two rectangles. Parameters ---------- r0 : libpysal.cg.Rectangle A rectangle to check for an intersection. r1 : libpysal.cg.Rectangle A rectangle to check for an intersection against ``r0``. checkOverlap : bool Call ``bbcommon(r0, r1)`` prior to complex geometry checking. Default is ``True``. Prior to setting as ``False`` see the Notes section. Returns ------- intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, libpysal.cg.Rectangle, None} The intersecting point, line, or rectangle between ``r0`` and ``r1`` if an intersection exists or ``None`` if ``r0`` and ``r1`` do not intersect. Notes ----- The algorithm assumes the rectangles overlap. The keyword ``checkOverlap=False`` should be used with extreme caution. Examples -------- >>> r0 = Rectangle(0,4,6,9) >>> r1 = Rectangle(4,0,9,7) >>> ri = get_rectangle_rectangle_intersection(r0,r1) >>> ri[:] [4.0, 4.0, 6.0, 7.0] >>> r0 = Rectangle(0,0,4,4) >>> r1 = Rectangle(2,1,6,3) >>> ri = get_rectangle_rectangle_intersection(r0,r1) >>> ri[:] [2.0, 1.0, 4.0, 3.0] >>> r0 = Rectangle(0,0,4,4) >>> r1 = Rectangle(2,1,3,2) >>> ri = get_rectangle_rectangle_intersection(r0,r1) >>> ri[:] == r1[:] True """ # noqa E501 intersection = None common_bb = True if checkOverlap and not bbcommon(r0, r1): # raise ValueError, "Rectangles do not intersect" common_bb = False if common_bb: left = max(r0.left, r1.left) lower = max(r0.lower, r1.lower) right = min(r0.right, r1.right) upper = min(r0.upper, r1.upper) if upper == lower and left == right: intersection = Point((left, lower)) elif upper == lower: intersection = LineSegment(Point((left, lower)), Point((right, lower))) elif left == right: intersection = LineSegment(Point((left, lower)), Point((left, upper))) else: intersection = Rectangle(left, lower, right, upper) return intersection def get_polygon_point_dist(poly, pt): """Returns the distance between a polygon and point. Parameters ---------- poly : libpysal.cg.Polygon A polygon to compute distance from. pt : libpysal.cg.Point a point to compute distance from Returns ------- dist : float The distance between ``poly`` and ``point``. Examples -------- >>> poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) >>> pt = Point((2, 0.5)) >>> get_polygon_point_dist(poly, pt) 1.0 >>> pt2 = Point((0.5, 0.5)) >>> get_polygon_point_dist(poly, pt2) 0.0 """ if get_polygon_point_intersect(poly, pt) is not None: dist = 0.0 else: part_prox = [] for vertices in poly._vertices: vx_range = range(-1, len(vertices) - 1) seg = lambda i: LineSegment(vertices[i], vertices[i + 1]) # noqa B023 _min_dist = min([get_segment_point_dist(seg(i), pt)[0] for i in vx_range]) part_prox.append(_min_dist) dist = min(part_prox) return dist def get_points_dist(pt1, pt2): """Returns the distance between a pair of points. Parameters ---------- pt1 : libpysal.cg.Point A point. pt2 : libpysal.cg.Point The other point. Returns ------- dist : float The distance between ``pt1`` and ``pt2``. Examples -------- >>> get_points_dist(Point((4, 4)), Point((4, 8))) 4.0 >>> get_points_dist(Point((0, 0)), Point((0, 0))) 0.0 """ dist = math.hypot(pt1[0] - pt2[0], pt1[1] - pt2[1]) return dist def get_segment_point_dist(seg, pt): """Returns (1) the distance between a line segment and point and (2) the distance along the segment to the closest location on the segment from the point as a ratio of the length of the segment. Parameters ---------- seg : libpysal.cg.LineSegment A line segment to compute distance from. pt : libpysal.cg.Point A point to compute distance from. Returns ------- dist : float The distance between ``seg`` and ``pt``. ratio : float The distance along ``seg`` to the closest location on ``seg`` from ``pt`` as a ratio of the length of ``seg``. Examples -------- >>> seg = LineSegment(Point((0, 0)), Point((10, 0))) >>> pt = Point((5, 5)) >>> get_segment_point_dist(seg, pt) (5.0, 0.5) >>> pt2 = Point((0, 0)) >>> get_segment_point_dist(seg, pt2) (0.0, 0.0) """ src_p = seg.p1 dest_p = seg.p2 # Shift line to go through origin points_0 = pt[0] - src_p[0] points_1 = pt[1] - src_p[1] points_2 = 0 points_3 = 0 points_4 = dest_p[0] - src_p[0] points_5 = dest_p[1] - src_p[1] segment_length = get_points_dist(src_p, dest_p) # Meh, robustness... # maybe should incorporate this into a more general approach later if segment_length == 0: dist, ratio = get_points_dist(pt, src_p), 0 else: u_x = points_4 / segment_length u_y = points_5 / segment_length inter_x = u_x * u_x * points_0 + u_x * u_y * points_1 inter_y = u_x * u_y * points_0 + u_y * u_y * points_1 src_proj_dist = get_points_dist((0, 0), (inter_x, inter_y)) dest_proj_dist = get_points_dist((inter_x, inter_y), (points_4, points_5)) if src_proj_dist > segment_length or dest_proj_dist > segment_length: src_pt_dist = get_points_dist((points_2, points_3), (points_0, points_1)) dest_pt_dist = get_points_dist((points_4, points_5), (points_0, points_1)) if src_pt_dist < dest_pt_dist: dist, ratio = src_pt_dist, 0 else: dist, ratio = dest_pt_dist, 1 else: dist = get_points_dist((inter_x, inter_y), (points_0, points_1)) ratio = src_proj_dist / segment_length return dist, ratio def get_point_at_angle_and_dist(ray, angle, dist): """Returns the point at a distance and angle relative to the origin of a ray. Parameters ---------- ray : libpysal.cg.Ray The ray to which ``angle`` and ``dist`` are relative. angle : float The angle relative to ``ray`` at which ``point`` is located. dist : float The distance from the origin of ``ray`` at which ``point`` is located. Returns ------- point : libpysal.cg.Point The point at ``dist`` and ``angle`` relative to the origin of ``ray``. Examples -------- >>> ray = Ray(Point((0, 0)), Point((1, 0))) >>> pt = get_point_at_angle_and_dist(ray, math.pi, 1.0) >>> isinstance(pt, Point) True >>> round(pt[0], 8) -1.0 >>> round(pt[1], 8) 0.0 """ v = (ray.p[0] - ray.o[0], ray.p[1] - ray.o[1]) cur_angle = math.atan2(v[1], v[0]) dest_angle = cur_angle + angle point = Point( (ray.o[0] + dist * math.cos(dest_angle), ray.o[1] + dist * math.sin(dest_angle)) ) return point def convex_hull(points): """Returns the convex hull of a set of points. Parameters ---------- points : list A list of points for computing the convex hull. Returns ------- stack : list A list of points representing the convex hull. Examples -------- >>> points = [Point((0, 0)), Point((4, 4)), Point((4, 0)), Point((3, 1))] >>> convex_hull(points) [(0.0, 0.0), (4.0, 0.0), (4.0, 4.0)] """ def right_turn(p1, p2, p3) -> bool: """Returns if ``p1`` -> ``p2`` -> ``p3`` forms a 'right turn'.""" vec1 = (p2[0] - p1[0], p2[1] - p1[1]) vec2 = (p3[0] - p2[0], p3[1] - p2[1]) _rt = vec2[0] * vec1[1] - vec2[1] * vec1[0] >= 0 return _rt points = copy.copy(points) lowest = min(points, key=lambda p: (p[1], p[0])) points.remove(lowest) points.sort(key=lambda p: math.atan2(p[1] - lowest[1], p[0] - lowest[0])) stack = [lowest] for p in points: stack.append(p) while len(stack) > 3 and right_turn(stack[-3], stack[-2], stack[-1]): stack.pop(-2) return stack def is_clockwise(vertices): """Returns whether a list of points describing a polygon are clockwise or counterclockwise. Parameters ---------- vertices : list A list of points that form a single ring. Returns ------- clockwise : bool ``True`` if ``vertices`` are clockwise, otherwise ``False``. See Also -------- libpysal.cg.ccw Examples -------- >>> is_clockwise([Point((0, 0)), Point((10, 0)), Point((0, 10))]) False >>> is_clockwise([Point((0, 0)), Point((0, 10)), Point((10, 0))]) True >>> v = [ ... (-106.57798, 35.174143999999998), ... (-106.583412, 35.174141999999996), ... (-106.58417999999999, 35.174143000000001), ... (-106.58377999999999, 35.175542999999998), ... (-106.58287999999999, 35.180543), ... (-106.58263099999999, 35.181455), ... (-106.58257999999999, 35.181643000000001), ... (-106.58198299999999, 35.184615000000001), ... (-106.58148, 35.187242999999995), ... (-106.58127999999999, 35.188243), ... (-106.58138, 35.188243), ... (-106.58108, 35.189442999999997), ... (-106.58104, 35.189644000000001), ... (-106.58028, 35.193442999999995), ... (-106.580029, 35.194541000000001), ... (-106.57974399999999, 35.195785999999998), ... (-106.579475, 35.196961999999999), ... (-106.57922699999999, 35.198042999999998), ... (-106.578397, 35.201665999999996), ... (-106.57827999999999, 35.201642999999997), ... (-106.57737999999999, 35.201642999999997), ... (-106.57697999999999, 35.201543000000001), ... (-106.56436599999999, 35.200311999999997), ... (-106.56058, 35.199942999999998), ... (-106.56048, 35.197342999999996), ... (-106.56048, 35.195842999999996), ... (-106.56048, 35.194342999999996), ... (-106.56048, 35.193142999999999), ... (-106.56048, 35.191873999999999), ... (-106.56048, 35.191742999999995), ... (-106.56048, 35.190242999999995), ... (-106.56037999999999, 35.188642999999999), ... (-106.56037999999999, 35.187242999999995), ... (-106.56037999999999, 35.186842999999996), ... (-106.56037999999999, 35.186552999999996), ... (-106.56037999999999, 35.185842999999998), ... (-106.56037999999999, 35.184443000000002), ... (-106.56037999999999, 35.182943000000002), ... (-106.56037999999999, 35.181342999999998), ... (-106.56037999999999, 35.180433000000001), ... (-106.56037999999999, 35.179943000000002), ... (-106.56037999999999, 35.178542999999998), ... (-106.56037999999999, 35.177790999999999), ... (-106.56037999999999, 35.177143999999998), ... (-106.56037999999999, 35.175643999999998), ... (-106.56037999999999, 35.174444000000001), ... (-106.56037999999999, 35.174043999999995), ... (-106.560526, 35.174043999999995), ... (-106.56478, 35.174043999999995), ... (-106.56627999999999, 35.174143999999998), ... (-106.566541, 35.174144999999996), ... (-106.569023, 35.174157000000001), ... (-106.56917199999999, 35.174157999999998), ... (-106.56938, 35.174143999999998), ... (-106.57061499999999, 35.174143999999998), ... (-106.57097999999999, 35.174143999999998), ... (-106.57679999999999, 35.174143999999998), ... (-106.57798, 35.174143999999998) ... ] >>> is_clockwise(v) True """ clockwise = True if not len(vertices) < 3: area = 0.0 ax, ay = vertices[0] for bx, by in vertices[1:]: area += ax * by - ay * bx ax, ay = bx, by bx, by = vertices[0] area += ax * by - ay * bx clockwise = area < 0.0 return clockwise def ccw(vertices): """Returns whether a list of points is counterclockwise. Parameters ---------- vertices : list A list of points that form a single ring. Returns ------- counter_clockwise : bool ``True`` if ``vertices`` are counter clockwise, otherwise ``False``. See Also -------- libpysal.cg.is_clockwise Examples -------- >>> ccw([Point((0, 0)), Point((10, 0)), Point((0, 10))]) True >>> ccw([Point((0, 0)), Point((0, 10)), Point((10, 0))]) False """ counter_clockwise = True if is_clockwise(vertices): counter_clockwise = False return counter_clockwise def seg_intersect(a, b, c, d): """Tests if two segments (a,b) and (c,d) intersect. Parameters ---------- a : libpysal.cg.Point The first vertex for the first segment. b : libpysal.cg.Point The second vertex for the first segment. c : libpysal.cg.Point The first vertex for the second segment. d : libpysal.cg.Point The second vertex for the second segment. Returns ------- segments_intersect : bool ``True`` if segments ``(a,b)`` and ``(c,d)``, otherwise ``False``. Examples -------- >>> a = Point((0,1)) >>> b = Point((0,10)) >>> c = Point((-2,5)) >>> d = Point((2,5)) >>> e = Point((-3,5)) >>> seg_intersect(a, b, c, d) True >>> seg_intersect(a, b, c, e) False """ segments_intersect = True acd_bcd = ccw([a, c, d]) == ccw([b, c, d]) abc_abd = ccw([a, b, c]) == ccw([a, b, d]) if acd_bcd or abc_abd: segments_intersect = False return segments_intersect def _point_in_vertices(pt, vertices): """**HELPER METHOD. DO NOT CALL.** Returns whether a point is contained in a polygon specified by a sequence of vertices. Parameters ---------- pt : libpysal.cg.Point A point. vertices : list A list of vertices representing as polygon. Returns ------- pt_in_poly : bool ``True`` if ``pt`` is contained in ``vertices``, otherwise ``False``. Examples -------- >>> _point_in_vertices( ... Point((1, 1)), ... [Point((0, 0)), Point((10, 0)), Point((0, 10))] ... ) True """ def neg_ray_intersect(p1, p2, p3) -> bool: """Returns whether a ray in the negative-x direction from ``p3`` intersects the segment between. """ if not min(p1[1], p2[1]) <= p3[1] <= max(p1[1], p2[1]): nr_inters = False else: if p1[1] > p2[1]: vec1 = (p2[0] - p1[0], p2[1] - p1[1]) else: vec1 = (p1[0] - p2[0], p1[1] - p2[1]) vec2 = (p3[0] - p1[0], p3[1] - p1[1]) nr_inters = vec1[0] * vec2[1] - vec2[0] * vec1[1] >= 0 return nr_inters vert_y_set = {v[1] for v in vertices} while pt[1] in vert_y_set: # Perturb the location very slightly pt = pt[0], pt[1] + -1e-14 + random.random() * 2e-14 inters = 0 for i in range(-1, len(vertices) - 1): v1 = vertices[i] v2 = vertices[i + 1] if neg_ray_intersect(v1, v2, pt): inters += 1 pt_in_poly = inters % 2 == 1 return pt_in_poly def point_touches_rectangle(point, rect): """Returns ``True`` (``1``) if the point is in the rectangle or touches it's boundary, otherwise ``False`` (``0``). Parameters ---------- point : {libpysal.cg.Point, tuple} A point or point coordinates. rect : libpysal.cg.Rectangle A rectangle. Returns ------- chflag : int ``1`` if ``point`` is in (or touches boundary of) ``rect``, otherwise ``0``. Examples -------- >>> rect = Rectangle(0, 0, 10, 10) >>> a = Point((5, 5)) >>> b = Point((10, 5)) >>> c = Point((11, 11)) >>> point_touches_rectangle(a, rect) 1 >>> point_touches_rectangle(b, rect) 1 >>> point_touches_rectangle(c, rect) 0 """ chflag = 0 if (point[0] >= rect.left and point[0] <= rect.right) and ( point[1] >= rect.lower and point[1] <= rect.upper ): chflag = 1 return chflag def get_shared_segments(poly1, poly2, bool_ret=False): """Returns the line segments in common to both polygons. Parameters ---------- poly1 : libpysal.cg.Polygon A Polygon. poly2 : libpysal.cg.Polygon A Polygon. bool_ret : bool Return only a ``bool``. Default is ``False``. Returns ------- common : list The shared line segments between ``poly1`` and ``poly2``. _ret_bool : bool Whether ``poly1`` and ``poly2`` share a segment (``True``) or not (``False``). Examples -------- >>> from libpysal.cg.shapes import Polygon >>> x = [0, 0, 1, 1] >>> y = [0, 1, 1, 0] >>> poly1 = Polygon(list(map(Point, zip(x, y))) ) >>> x = [a+1 for a in x] >>> poly2 = Polygon(list(map(Point, zip(x, y))) ) >>> get_shared_segments(poly1, poly2, bool_ret=True) True """ # get_rectangle_rectangle_intersection inlined for speed. r0 = poly1.bounding_box r1 = poly2.bounding_box wLeft = max(r0.left, r1.left) wLower = max(r0.lower, r1.lower) wRight = min(r0.right, r1.right) wUpper = min(r0.upper, r1.upper) segmentsA = set() common = list() for part in poly1.parts + [p for p in poly1.holes if p]: if part[0] != part[-1]: # not closed part = part[:] + part[0:1] a = part[0] for b in islice(part, 1, None): # inlining point_touches_rectangle for speed x, y = a # check if point a is in the bounding box intersection if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper: x, y = b # check if point b is in the bounding box intersection if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper: if a > b: segmentsA.add((b, a)) else: segmentsA.add((a, b)) a = b _ret_bool = False for part in poly2.parts + [p for p in poly2.holes if p]: if part[0] != part[-1]: # not closed part = part[:] + part[0:1] a = part[0] for b in islice(part, 1, None): # inlining point_touches_rectangle for speed x, y = a if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper: x, y = b if x >= wLeft and x <= wRight and y >= wLower and y <= wUpper: seg = (b, a) if a > b else (a, b) if seg in segmentsA: common.append(LineSegment(*seg)) if bool_ret: _ret_bool = True return _ret_bool a = b if bool_ret: if len(common) > 0: _ret_bool = True return _ret_bool return common def distance_matrix(X, p=2.0, threshold=5e7): r"""Calculate a distance matrix. Parameters ---------- X : numpy.ndarray An :math:`n \\times k` array where :math:`n` is the number of observations and :math:`k` is the number of dimensions (2 for :math:`x,y`). p : float Minkowski `p`-norm distance metric parameter where :math:`1<=\mathtt{p}<=\infty`. ``2`` is Euclidean distance and ``1`` is Manhattan distance. Default is ``2.0``. threshold : int If :math:`(\mathtt{n}**2)*32 > \mathtt{threshold}` use ``scipy.spatial.distance_matrix`` instead of working in RAM, this is roughly the amount of RAM (in bytes) that will be used. Must be positive. Default is ``5e7``. Returns ------- D : numpy.ndarray An n by :math:`m` :math:`p`-norm distance matrix. Raises ------ TypeError Raised when an invalid dimensional array is passed in. Notes ----- Needs optimization/integration with other weights in PySAL. Examples -------- >>> x, y = [r.flatten() for r in np.indices((3, 3))] >>> data = np.array([x, y]).T >>> d = distance_matrix(data) >>> np.array(d) array([[0. , 1. , 2. , 1. , 1.41421356, 2.23606798, 2. , 2.23606798, 2.82842712], [1. , 0. , 1. , 1.41421356, 1. , 1.41421356, 2.23606798, 2. , 2.23606798], [2. , 1. , 0. , 2.23606798, 1.41421356, 1. , 2.82842712, 2.23606798, 2. ], [1. , 1.41421356, 2.23606798, 0. , 1. , 2. , 1. , 1.41421356, 2.23606798], [1.41421356, 1. , 1.41421356, 1. , 0. , 1. , 1.41421356, 1. , 1.41421356], [2.23606798, 1.41421356, 1. , 2. , 1. , 0. , 2.23606798, 1.41421356, 1. ], [2. , 2.23606798, 2.82842712, 1. , 1.41421356, 2.23606798, 0. , 1. , 2. ], [2.23606798, 2. , 2.23606798, 1.41421356, 1. , 1.41421356, 1. , 0. , 1. ], [2.82842712, 2.23606798, 2. , 2.23606798, 1.41421356, 1. , 2. , 1. , 0. ]]) """ if X.ndim == 1: X.shape = (X.shape[0], 1) if X.ndim > 2: msg = "Should be 2D point coordinates: %s dimensions present." % X.ndim raise TypeError(msg) n, k = X.shape if (n**2) * 32 > threshold: D = scipy.spatial.distance_matrix(X, X, p) else: M = np.ones((n, n)) D = np.zeros((n, n)) for col in range(k): x = X[:, col] xM = x * M dx = xM - xM.T if p % 2 != 0: dx = np.abs(dx) dx2 = dx**p D += dx2 D = D ** (1.0 / p) return D libpysal-4.9.2/libpysal/cg/tests/000077500000000000000000000000001452177046000167255ustar00rootroot00000000000000libpysal-4.9.2/libpysal/cg/tests/__init__.py000066400000000000000000000000001452177046000210240ustar00rootroot00000000000000libpysal-4.9.2/libpysal/cg/tests/data/000077500000000000000000000000001452177046000176365ustar00rootroot00000000000000libpysal-4.9.2/libpysal/cg/tests/data/alpha_05.gpkg000066400000000000000000003000001452177046000220720ustar00rootroot00000000000000SQLite format 3@ 'ØGPKG .;ñöûö  Ó1 Ó–\=mUndefined geographic SRSNONEundefinedundefined geographic coordinate reference system[ÿÿÿÿÿÿÿÿÿ;kUndefined cartesian SRSNONEÿundefinedundefined cartesian coordinate reference system‚f¡f +„ WGS 84 geodeticEPSGæGEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]longitude/latitude coordinates in decimal degrees on the WGS 84 spheroid ÁÁ=  = alpha_05featuresalpha_052020-04-09T10:53:41.478Z ôô  alpha_05 ôô  alpha_05 óó  alpha_05 ôô  alpha_05 ääalpha_05geomPOLYGON ïï alpha_05geom ôô  alpha_05     òëú Ý·|  š h ¶ k~ ¸w8ë¤Mòž‚JQ-„!triggergpkg_tile_matrix_zoom_level_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_update' BEFORE UPDATE of zoom_level ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END‚<Q-„triggergpkg_tile_matrix_zoom_level_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END? S-indexsqlite_autoindex_gpkg_tile_matrix_1gpkg_tile_matrixƒC --†9tablegpkg_tile_matrixgpkg_tile_matrix CREATE TABLE gpkg_tile_matrix (table_name TEXT NOT NULL,zoom_level INTEGER NOT NULL,matrix_width INTEGER NOT NULL,matrix_height INTEGER NOT NULL,tile_width INTEGER NOT NULL,tile_height INTEGER NOT NULL,pixel_x_size DOUBLE NOT NULL,pixel_y_size DOUBLE NOT NULL,CONSTRAINT pk_ttm PRIMARY KEY (table_name, zoom_level),CONSTRAINT fk_tmm_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name))ƒ 55…_tablegpkg_tile_matrix_setgpkg_tile_matrix_set CREATE TABLE gpkg_tile_matrix_set (table_name TEXT NOT NULL PRIMARY KEY,srs_id INTEGER NOT NULL,min_x DOUBLE NOT NULL,min_y DOUBLE NOT NULL,max_x DOUBLE NOT NULL,max_y DOUBLE NOT NULL,CONSTRAINT fk_gtms_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gtms_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))G [5indexsqlite_autoindex_gpkg_tile_matrix_set_1gpkg_tile_matrix_set „77‡tablegpkg_geometry_columnsgpkg_geometry_columnsCREATE TABLE gpkg_geometry_columns (table_name TEXT NOT NULL,column_name TEXT NOT NULL,geometry_type_name TEXT NOT NULL,srs_id INTEGER NOT NULL,z TINYINT NOT NULL,m TINYINT NOT NULL,CONSTRAINT pk_geom_cols PRIMARY KEY (table_name, column_name),CONSTRAINT uk_gc_table_name UNIQUE (table_name),CONSTRAINT fk_gc_tn FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gc_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))I ]7indexsqlite_autoindex_gpkg_geometry_columns_2gpkg_geometry_columns I]7indexsqlite_autoindex_gpkg_geometry_columns_1gpkg_geometry_columns //[tablegpkg_ogr_contentsgpkg_ogr_contentsCREATE TABLE gpkg_ogr_contents(table_name TEXT NOT NULL PRIMARY KEY,feature_count INTEGER DEFAULT NULL)AU/indexsqlite_autoindex_gpkg_ogr_contents_1gpkg_ogr_contentsƒ''…wtablegpkg_contentsgpkg_contentsCREATE TABLE gpkg_contents (table_name TEXT NOT NULL PRIMARY KEY,data_type TEXT NOT NULL,identifier TEXT UNIQUE,description TEXT DEFAULT '',last_change DATETIME NOT NULL DEFAULT (strftime('%Y-%m-%dT%H:%M:%fZ','now')),min_x DOUBLE, min_y DOUBLE,max_x DOUBLE, max_y DOUBLE,srs_id INTEGER,CONSTRAINT fk_gc_r_srs_id FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys(srs_id))9M'indexsqlite_autoindex_gpkg_contents_2gpkg_contents9M'indexsqlite_autoindex_gpkg_contents_1gpkg_contents‚55ƒ)tablegpkg_spatial_ref_sysgpkg_spatial_ref_sysCREATE TABLE gpkg_spatial_ref_sys (srs_name TEXT NOT NULL,srs_id INTEGER NOT NULL PRIMARY KEY,organization TEXT NOT NULL,organization_coordsys_id INTEGER NOT NULL,definition TEXT NOT NULL,description TEXT) F Æg ûŸS÷«OÆ® k e ä: \ È x FŒ_tablealpha_05alpha_05CREATE TABLE "alpha_05" ( "fid" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, "geom" POLYGON, "id" INTEGER)‚YU-„;triggergpkg_tile_matrix_pixel_y_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_update' BEFORE UPDATE OF pixel_y_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_y_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚YU-„;triggergpkg_tile_matrix_pixel_x_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_update' BEFORE UPDATE OF pixel_x_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_x_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚YW-„9triggergpkg_tile_matrix_matrix_height_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_update' BEFORE UPDATE OF matrix_height ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); END‚HW-„triggergpkg_tile_matrix_matrix_height_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); ENDÊ‚TU-„1triggergpkg_tile_matrix_matrix_width_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_update' BEFORE UPDATE OF matrix_width ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); END‚DU-„triggergpkg_tile_matrix_matrix_width_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); ENDP++Ytablesqlite_sequencesqlite_sequenceCREATE TABLE sqlite_sequence(name,seq) 33JƒGP5@ð?"@ @ð?@@"@2@!@5@@5@ð?0@ð?"@ð?@ óó  alpha_05   ^-q!alpha_05geomgpkg_rtree_indexhttp://www.geopackage.org/spec120/#extension_rtreewrite-only ÞÞ!- alpha_05geomgpkg_rtree_index ûû  - -‰P“$A¨?€A  $û ö ü ½ 6 ª " Ž >ɵø$Q'A‚Otriggerrtree_alpha_05_geom_deletealpha_05CREATE TRIGGER "rtree_alpha_05_geom_delete" AFTER DELETE ON "alpha_05" WHEN old."geom" NOT NULL BEGIN DELETE FROM "rtree_alpha_05_geom" WHERE id = OLD."fid"; END‚&CƒWtriggerrtree_alpha_05_geom_update4alpha_05CREATE TRIGGER "rtree_alpha_05_geom_update4" AFTER UPDATE ON "alpha_05" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_05_geom" WHERE id IN (OLD."fid", NEW."fid"); ENDƒ!%C…mtriggerrtree_alpha_05_geom_update3alpha_05CREATE TRIGGER "rtree_alpha_05_geom_update3" AFTER UPDATE ON "alpha_05" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_05_geom" WHERE id = OLD."fid"; INSERT OR REPLACE INTO "rtree_alpha_05_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚$CƒMtriggerrtree_alpha_05_geom_update2alpha_05CREATE TRIGGER "rtree_alpha_05_geom_update2" AFTER UPDATE OF "geom" ON "alpha_05" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_05_geom" WHERE id = OLD."fid"; END‚r#C…triggerrtree_alpha_05_geom_update1alpha_05CREATE TRIGGER "rtree_alpha_05_geom_update1" AFTER UPDATE OF "geom" ON "alpha_05" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_alpha_05_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚M"A„Gtriggerrtree_alpha_05_geom_insertalpha_05CREATE TRIGGER "rtree_alpha_05_geom_insert" AFTER INSERT ON "alpha_05" WHEN (new."geom" NOT NULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_alpha_05_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END!AA-tablertree_alpha_05_geom_parentrtree_alpha_05_geom_parentCREATE TABLE "rtree_alpha_05_geom_parent"(nodeno INTEGER PRIMARY KEY,parentnode) ==tablertree_alpha_05_geom_nodertree_alpha_05_geom_nodeCREATE TABLE "rtree_alpha_05_geom_node"(nodeno INTEGER PRIMARY KEY,data) ??!tablertree_alpha_05_geom_rowidrtree_alpha_05_geom_rowidCREATE TABLE "rtree_alpha_05_geom_rowid"(rowid INTEGER PRIMARY KEY,nodeno)331tablertree_alpha_05_geomrtree_alpha_05_geomCREATE VIRTUAL TABLE "rtree_alpha_05_geom" USING rtree(id, minx, maxx, miny, maxy)=Q+indexsqlite_autoindex_gpkg_extensions_1gpkg_extensionsw++ƒ%tablegpkg_extensionsgpkg_extensionsCREATE TABLE gpkg_extensions (table_name TEXT,column_name TEXT,extension_name TEXT NOT NULL,definition TEXT NOT NULL,scope TEXT NOT NULL,CONSTRAINT ge_tce UNIQUE (table_name, column_name, extension_name))‚Wƒtriggertrigger_delete_feature_count_alpha_05alpha_05CREATE TRIGGER "trigger_delete_feature_count_alpha_05" AFTER DELETE ON "alpha_05" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count - 1 WHERE lower(table_name) = lower('alpha_05'); END‚Wƒtriggertrigger_insert_feature_count_alpha_05alpha_05CREATE TRIGGER "trigger_insert_feature_count_alpha_05" AFTER INSERT ON "alpha_05" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count + 1 WHERE lower(table_name) = lower('alpha_05'); ENDlibpysal-4.9.2/libpysal/cg/tests/data/alpha_auto.gpkg000066400000000000000000003000001452177046000226160ustar00rootroot00000000000000SQLite format 3@ 'ØGPKG .;ñöûö  Ó1 Ó–\=mUndefined geographic SRSNONEundefinedundefined geographic coordinate reference system[ÿÿÿÿÿÿÿÿÿ;kUndefined cartesian SRSNONEÿundefinedundefined cartesian coordinate reference system‚f¡f +„ WGS 84 geodeticEPSGæGEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]longitude/latitude coordinates in decimal degrees on the WGS 84 spheroid ½½A !! = alpha_autofeaturesalpha_auto2020-04-09T10:53:41.530Z òò ! alpha_auto òò ! alpha_auto ññ ! alpha_auto òò ! alpha_auto ââ!alpha_autogeomPOLYGON íí! alpha_autogeom òò ! alpha_auto     òëú Ý·|  š h ¶ k~ ¸w8ë¤Mòž‚JQ-„!triggergpkg_tile_matrix_zoom_level_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_update' BEFORE UPDATE of zoom_level ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END‚<Q-„triggergpkg_tile_matrix_zoom_level_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END? S-indexsqlite_autoindex_gpkg_tile_matrix_1gpkg_tile_matrixƒC --†9tablegpkg_tile_matrixgpkg_tile_matrix CREATE TABLE gpkg_tile_matrix (table_name TEXT NOT NULL,zoom_level INTEGER NOT NULL,matrix_width INTEGER NOT NULL,matrix_height INTEGER NOT NULL,tile_width INTEGER NOT NULL,tile_height INTEGER NOT NULL,pixel_x_size DOUBLE NOT NULL,pixel_y_size DOUBLE NOT NULL,CONSTRAINT pk_ttm PRIMARY KEY (table_name, zoom_level),CONSTRAINT fk_tmm_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name))ƒ 55…_tablegpkg_tile_matrix_setgpkg_tile_matrix_set CREATE TABLE gpkg_tile_matrix_set (table_name TEXT NOT NULL PRIMARY KEY,srs_id INTEGER NOT NULL,min_x DOUBLE NOT NULL,min_y DOUBLE NOT NULL,max_x DOUBLE NOT NULL,max_y DOUBLE NOT NULL,CONSTRAINT fk_gtms_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gtms_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))G [5indexsqlite_autoindex_gpkg_tile_matrix_set_1gpkg_tile_matrix_set „77‡tablegpkg_geometry_columnsgpkg_geometry_columnsCREATE TABLE gpkg_geometry_columns (table_name TEXT NOT NULL,column_name TEXT NOT NULL,geometry_type_name TEXT NOT NULL,srs_id INTEGER NOT NULL,z TINYINT NOT NULL,m TINYINT NOT NULL,CONSTRAINT pk_geom_cols PRIMARY KEY (table_name, column_name),CONSTRAINT uk_gc_table_name UNIQUE (table_name),CONSTRAINT fk_gc_tn FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gc_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))I ]7indexsqlite_autoindex_gpkg_geometry_columns_2gpkg_geometry_columns I]7indexsqlite_autoindex_gpkg_geometry_columns_1gpkg_geometry_columns //[tablegpkg_ogr_contentsgpkg_ogr_contentsCREATE TABLE gpkg_ogr_contents(table_name TEXT NOT NULL PRIMARY KEY,feature_count INTEGER DEFAULT NULL)AU/indexsqlite_autoindex_gpkg_ogr_contents_1gpkg_ogr_contentsƒ''…wtablegpkg_contentsgpkg_contentsCREATE TABLE gpkg_contents (table_name TEXT NOT NULL PRIMARY KEY,data_type TEXT NOT NULL,identifier TEXT UNIQUE,description TEXT DEFAULT '',last_change DATETIME NOT NULL DEFAULT (strftime('%Y-%m-%dT%H:%M:%fZ','now')),min_x DOUBLE, min_y DOUBLE,max_x DOUBLE, max_y DOUBLE,srs_id INTEGER,CONSTRAINT fk_gc_r_srs_id FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys(srs_id))9M'indexsqlite_autoindex_gpkg_contents_2gpkg_contents9M'indexsqlite_autoindex_gpkg_contents_1gpkg_contents‚55ƒ)tablegpkg_spatial_ref_sysgpkg_spatial_ref_sysCREATE TABLE gpkg_spatial_ref_sys (srs_name TEXT NOT NULL,srs_id INTEGER NOT NULL PRIMARY KEY,organization TEXT NOT NULL,organization_coordsys_id INTEGER NOT NULL,definition TEXT NOT NULL,description TEXT)  ™g ÅiÁu™® W Q Ê 8 ª ? Z~!!Gtablealpha_autoalpha_autoCREATE TABLE "alpha_auto" ( "fid" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, "geom" POLYGON)‚YU-„;triggergpkg_tile_matrix_pixel_y_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_update' BEFORE UPDATE OF pixel_y_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_y_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚YU-„;triggergpkg_tile_matrix_pixel_x_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_update' BEFORE UPDATE OF pixel_x_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_x_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚YW-„9triggergpkg_tile_matrix_matrix_height_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_update' BEFORE UPDATE OF matrix_height ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); END‚HW-„triggergpkg_tile_matrix_matrix_height_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); END‚TU-„1triggergpkg_tile_matrix_matrix_width_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_update' BEFORE UPDATE OF matrix_width ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); END‚DU-„triggergpkg_tile_matrix_matrix_width_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); ENDP++Ytablesqlite_sequencesqlite_sequenceCREATE TABLE sqlite_sequence(name,seq) ””‚i…VGP5@ð?"@@ð?@@@@"@@@'@@,@@2@!@2@ @5@@5@ð?3@@0@ð?(@@"@ð?@@@@@@@ ññ !alpha_auto žž`!-q!alpha_autogeomgpkg_rtree_indexhttp://www.geopackage.org/spec120/#extension_rtreewrite-only ÜÜ#!- alpha_autogeomgpkg_rtree_index ûû  - -‰P“$A¨?€A  ºñ â è ©  Š ü b ‰k»˜º['E!‚[triggerrtree_alpha_auto_geom_deletealpha_autoCREATE TRIGGER "rtree_alpha_auto_geom_delete" AFTER DELETE ON "alpha_auto" WHEN old."geom" NOT NULL BEGIN DELETE FROM "rtree_alpha_auto_geom" WHERE id = OLD."fid"; END‚ &G!ƒctriggerrtree_alpha_auto_geom_update4alpha_autoCREATE TRIGGER "rtree_alpha_auto_geom_update4" AFTER UPDATE ON "alpha_auto" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_auto_geom" WHERE id IN (OLD."fid", NEW."fid"); ENDƒ-%G!…}triggerrtree_alpha_auto_geom_update3alpha_autoCREATE TRIGGER "rtree_alpha_auto_geom_update3" AFTER UPDATE ON "alpha_auto" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_auto_geom" WHERE id = OLD."fid"; INSERT OR REPLACE INTO "rtree_alpha_auto_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚$G!ƒYtriggerrtree_alpha_auto_geom_update2alpha_autoCREATE TRIGGER "rtree_alpha_auto_geom_update2" AFTER UPDATE OF "geom" ON "alpha_auto" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_auto_geom" WHERE id = OLD."fid"; END‚|#G!…triggerrtree_alpha_auto_geom_update1alpha_autoCREATE TRIGGER "rtree_alpha_auto_geom_update1" AFTER UPDATE OF "geom" ON "alpha_auto" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_alpha_auto_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚W"E!„Striggerrtree_alpha_auto_geom_insertalpha_autoCREATE TRIGGER "rtree_alpha_auto_geom_insert" AFTER INSERT ON "alpha_auto" WHEN (new."geom" NOT NULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_alpha_auto_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END!EE1tablertree_alpha_auto_geom_parentrtree_alpha_auto_geom_parentCREATE TABLE "rtree_alpha_auto_geom_parent"(nodeno INTEGER PRIMARY KEY,parentnode) AA!tablertree_alpha_auto_geom_nodertree_alpha_auto_geom_nodeCREATE TABLE "rtree_alpha_auto_geom_node"(nodeno INTEGER PRIMARY KEY,data)CC%tablertree_alpha_auto_geom_rowidrtree_alpha_auto_geom_rowidCREATE TABLE "rtree_alpha_auto_geom_rowid"(rowid INTEGER PRIMARY KEY,nodeno) 775tablertree_alpha_auto_geomrtree_alpha_auto_geomCREATE VIRTUAL TABLE "rtree_alpha_auto_geom" USING rtree(id, minx, maxx, miny, maxy)=Q+indexsqlite_autoindex_gpkg_extensions_1gpkg_extensionsw++ƒ%tablegpkg_extensionsgpkg_extensionsCREATE TABLE gpkg_extensions (table_name TEXT,column_name TEXT,extension_name TEXT NOT NULL,definition TEXT NOT NULL,scope TEXT NOT NULL,CONSTRAINT ge_tce UNIQUE (table_name, column_name, extension_name))‚ [!ƒ'triggertrigger_delete_feature_count_alpha_autoalpha_autoCREATE TRIGGER "trigger_delete_feature_count_alpha_auto" AFTER DELETE ON "alpha_auto" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count - 1 WHERE lower(table_name) = lower('alpha_auto'); END‚ [!ƒ'triggertrigger_insert_feature_count_alpha_autoalpha_autoCREATE TRIGGER "trigger_insert_feature_count_alpha_auto" AFTER INSERT ON "alpha_auto" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count + 1 WHERE lower(table_name) = lower('alpha_auto'); ENDlibpysal-4.9.2/libpysal/cg/tests/data/alpha_fifth.gpkg000066400000000000000000003000001452177046000227460ustar00rootroot00000000000000SQLite format 3@ 'ØGPKG .;ñöûö  Ó1 Ó–\=mUndefined geographic SRSNONEundefinedundefined geographic coordinate reference system[ÿÿÿÿÿÿÿÿÿ;kUndefined cartesian SRSNONEÿundefinedundefined cartesian coordinate reference system‚f¡f +„ WGS 84 geodeticEPSGæGEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]longitude/latitude coordinates in decimal degrees on the WGS 84 spheroid »»C ## = alpha_fifthfeaturesalpha_fifth2020-04-09T10:53:41.520Z ññ# alpha_fifth ññ# alpha_fifth ðð# alpha_fifth ññ# alpha_fifth áá#alpha_fifthgeomPOLYGON ìì# alpha_fifthgeom ññ# alpha_fifth     òëú Ý·|  š h ¶ k~ ¸w8ë¤Mòž‚JQ-„!triggergpkg_tile_matrix_zoom_level_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_update' BEFORE UPDATE of zoom_level ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END‚<Q-„triggergpkg_tile_matrix_zoom_level_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END? S-indexsqlite_autoindex_gpkg_tile_matrix_1gpkg_tile_matrixƒC --†9tablegpkg_tile_matrixgpkg_tile_matrix CREATE TABLE gpkg_tile_matrix (table_name TEXT NOT NULL,zoom_level INTEGER NOT NULL,matrix_width INTEGER NOT NULL,matrix_height INTEGER NOT NULL,tile_width INTEGER NOT NULL,tile_height INTEGER NOT NULL,pixel_x_size DOUBLE NOT NULL,pixel_y_size DOUBLE NOT NULL,CONSTRAINT pk_ttm PRIMARY KEY (table_name, zoom_level),CONSTRAINT fk_tmm_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name))ƒ 55…_tablegpkg_tile_matrix_setgpkg_tile_matrix_set CREATE TABLE gpkg_tile_matrix_set (table_name TEXT NOT NULL PRIMARY KEY,srs_id INTEGER NOT NULL,min_x DOUBLE NOT NULL,min_y DOUBLE NOT NULL,max_x DOUBLE NOT NULL,max_y DOUBLE NOT NULL,CONSTRAINT fk_gtms_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gtms_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))G [5indexsqlite_autoindex_gpkg_tile_matrix_set_1gpkg_tile_matrix_set „77‡tablegpkg_geometry_columnsgpkg_geometry_columnsCREATE TABLE gpkg_geometry_columns (table_name TEXT NOT NULL,column_name TEXT NOT NULL,geometry_type_name TEXT NOT NULL,srs_id INTEGER NOT NULL,z TINYINT NOT NULL,m TINYINT NOT NULL,CONSTRAINT pk_geom_cols PRIMARY KEY (table_name, column_name),CONSTRAINT uk_gc_table_name UNIQUE (table_name),CONSTRAINT fk_gc_tn FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gc_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))I ]7indexsqlite_autoindex_gpkg_geometry_columns_2gpkg_geometry_columns I]7indexsqlite_autoindex_gpkg_geometry_columns_1gpkg_geometry_columns //[tablegpkg_ogr_contentsgpkg_ogr_contentsCREATE TABLE gpkg_ogr_contents(table_name TEXT NOT NULL PRIMARY KEY,feature_count INTEGER DEFAULT NULL)AU/indexsqlite_autoindex_gpkg_ogr_contents_1gpkg_ogr_contentsƒ''…wtablegpkg_contentsgpkg_contentsCREATE TABLE gpkg_contents (table_name TEXT NOT NULL PRIMARY KEY,data_type TEXT NOT NULL,identifier TEXT UNIQUE,description TEXT DEFAULT '',last_change DATETIME NOT NULL DEFAULT (strftime('%Y-%m-%dT%H:%M:%fZ','now')),min_x DOUBLE, min_y DOUBLE,max_x DOUBLE, max_y DOUBLE,srs_id INTEGER,CONSTRAINT fk_gc_r_srs_id FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys(srs_id))9M'indexsqlite_autoindex_gpkg_contents_2gpkg_contents9M'indexsqlite_autoindex_gpkg_contents_1gpkg_contents‚55ƒ)tablegpkg_spatial_ref_sysgpkg_spatial_ref_sysCREATE TABLE gpkg_spatial_ref_sys (srs_name TEXT NOT NULL,srs_id INTEGER NOT NULL PRIMARY KEY,organization TEXT NOT NULL,organization_coordsys_id INTEGER NOT NULL,definition TEXT NOT NULL,description TEXT) Ï F¹ b„(Ü€4ØF † E ? µ ò##etablealpha_fifthalpha_fifthCREATE TABLE "alpha_fifth" ( "fid" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, "geom" POLYGON, "id" INTEGER)‚YU-„;triggergpkg_tile_matrix_pixel_y_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_update' BEFORE UPDATE OF pixel_y_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_y_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚YU-„;triggergpkg_tile_matrix_pixel_x_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_update' BEFORE UPDATE OF pixel_x_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_x_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚YW-„9triggergpkg_tile_matrix_matrix_height_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_update' BEFORE UPDATE OF matrix_height ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); END‚HW-„triggergpkg_tile_matrix_matrix_height_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); END“‚TU-„1triggergpkg_tile_matrix_matrix_width_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_update' BEFORE UPDATE OF matrix_width ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); END‚DU-„triggergpkg_tile_matrix_matrix_width_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); END óó‚ „GP5@ð?"@ @ð?@@"@'@@,@@2@!@5@@5@ð?0@ð?(@@"@ð?@@@ ðð# alpha_fifth a#-q!alpha_fifthgeomgpkg_rtree_indexhttp://www.geopackage.org/spec120/#extension_rtreewrite-only ÛÛ$#- alpha_fifthgeomgpkg_rtree_index ûû  - -‰P“$A¨?€A  3®š † Œ M ½ ( — ú›ô>3`'G#‚atriggerrtree_alpha_fifth_geom_deletealpha_fifthCREATE TRIGGER "rtree_alpha_fifth_geom_delete" AFTER DELETE ON "alpha_fifth" WHEN old."geom" NOT NULL BEGIN DELETE FROM "rtree_alpha_fifth_geom" WHERE id = OLD."fid"; END‚%&I#ƒitriggerrtree_alpha_fifth_geom_update4alpha_fifthCREATE TRIGGER "rtree_alpha_fifth_geom_update4" AFTER UPDATE ON "alpha_fifth" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_fifth_geom" WHERE id IN (OLD."fid", NEW."fid"); ENDƒ3%I#†triggerrtree_alpha_fifth_geom_update3alpha_fifthCREATE TRIGGER "rtree_alpha_fifth_geom_update3" AFTER UPDATE ON "alpha_fifth" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_fifth_geom" WHERE id = OLD."fid"; INSERT OR REPLACE INTO "rtree_alpha_fifth_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚ $I#ƒ_triggerrtree_alpha_fifth_geom_update2alpha_fifthCREATE TRIGGER "rtree_alpha_fifth_geom_update2" AFTER UPDATE OF "geom" ON "alpha_fifth" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_fifth_geom" WHERE id = OLD."fid"; ENDƒ#I#…!triggerrtree_alpha_fifth_geom_update1alpha_fifthCREATE TRIGGER "rtree_alpha_fifth_geom_update1" AFTER UPDATE OF "geom" ON "alpha_fifth" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_alpha_fifth_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚\"G#„Ytriggerrtree_alpha_fifth_geom_insertalpha_fifthCREATE TRIGGER "rtree_alpha_fifth_geom_insert" AFTER INSERT ON "alpha_fifth" WHEN (new."geom" NOT NULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_alpha_fifth_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END!GG3tablertree_alpha_fifth_geom_parentrtree_alpha_fifth_geom_parentCREATE TABLE "rtree_alpha_fifth_geom_parent"(nodeno INTEGER PRIMARY KEY,parentnode) CC#tablertree_alpha_fifth_geom_nodertree_alpha_fifth_geom_nodeCREATE TABLE "rtree_alpha_fifth_geom_node"(nodeno INTEGER PRIMARY KEY,data)EE'tablertree_alpha_fifth_geom_rowidrtree_alpha_fifth_geom_rowidCREATE TABLE "rtree_alpha_fifth_geom_rowid"(rowid INTEGER PRIMARY KEY,nodeno) 997tablertree_alpha_fifth_geomrtree_alpha_fifth_geomCREATE VIRTUAL TABLE "rtree_alpha_fifth_geom" USING rtree(id, minx, maxx, miny, maxy)=Q+indexsqlite_autoindex_gpkg_extensions_1gpkg_extensionsw++ƒ%tablegpkg_extensionsgpkg_extensionsCREATE TABLE gpkg_extensions (table_name TEXT,column_name TEXT,extension_name TEXT NOT NULL,definition TEXT NOT NULL,scope TEXT NOT NULL,CONSTRAINT ge_tce UNIQUE (table_name, column_name, extension_name))‚]#ƒ-triggertrigger_delete_feature_count_alpha_fifthalpha_fifthCREATE TRIGGER "trigger_delete_feature_count_alpha_fifth" AFTER DELETE ON "alpha_fifth" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count - 1 WHERE lower(table_name) = lower('alpha_fifth'); END‚]#ƒ-triggertrigger_insert_feature_count_alpha_fifthalpha_fifthCREATE TRIGGER "trigger_insert_feature_count_alpha_fifth" AFTER INSERT ON "alpha_fifth" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count + 1 WHERE lower(table_name) = lower('alpha_fifth'); ENDP++Ytablesqlite_sequencesqlite_sequenceCREATE TABLE sqlite_sequence(name,seq)libpysal-4.9.2/libpysal/cg/tests/data/alpha_fourth.gpkg000066400000000000000000003000001452177046000231550ustar00rootroot00000000000000SQLite format 3@ 'ØGPKG .;ñöûö  Ó1 Ó–\=mUndefined geographic SRSNONEundefinedundefined geographic coordinate reference system[ÿÿÿÿÿÿÿÿÿ;kUndefined cartesian SRSNONEÿundefinedundefined cartesian coordinate reference system‚f¡f +„ WGS 84 geodeticEPSGæGEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]longitude/latitude coordinates in decimal degrees on the WGS 84 spheroid ¹¹E %% = alpha_fourthfeaturesalpha_fourth2020-04-09T10:53:41.541Z ðð% alpha_fourth ðð% alpha_fourth ïï% alpha_fourth ðð% alpha_fourth àà%alpha_fourthgeomPOLYGON ëë% alpha_fourthgeom ðð% alpha_fourth     òëú Ý·|  š h ¶ k~ ¸w8ë¤Mòž‚JQ-„!triggergpkg_tile_matrix_zoom_level_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_update' BEFORE UPDATE of zoom_level ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END‚<Q-„triggergpkg_tile_matrix_zoom_level_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END? S-indexsqlite_autoindex_gpkg_tile_matrix_1gpkg_tile_matrixƒC --†9tablegpkg_tile_matrixgpkg_tile_matrix CREATE TABLE gpkg_tile_matrix (table_name TEXT NOT NULL,zoom_level INTEGER NOT NULL,matrix_width INTEGER NOT NULL,matrix_height INTEGER NOT NULL,tile_width INTEGER NOT NULL,tile_height INTEGER NOT NULL,pixel_x_size DOUBLE NOT NULL,pixel_y_size DOUBLE NOT NULL,CONSTRAINT pk_ttm PRIMARY KEY (table_name, zoom_level),CONSTRAINT fk_tmm_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name))ƒ 55…_tablegpkg_tile_matrix_setgpkg_tile_matrix_set CREATE TABLE gpkg_tile_matrix_set (table_name TEXT NOT NULL PRIMARY KEY,srs_id INTEGER NOT NULL,min_x DOUBLE NOT NULL,min_y DOUBLE NOT NULL,max_x DOUBLE NOT NULL,max_y DOUBLE NOT NULL,CONSTRAINT fk_gtms_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gtms_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))G [5indexsqlite_autoindex_gpkg_tile_matrix_set_1gpkg_tile_matrix_set „77‡tablegpkg_geometry_columnsgpkg_geometry_columnsCREATE TABLE gpkg_geometry_columns (table_name TEXT NOT NULL,column_name TEXT NOT NULL,geometry_type_name TEXT NOT NULL,srs_id INTEGER NOT NULL,z TINYINT NOT NULL,m TINYINT NOT NULL,CONSTRAINT pk_geom_cols PRIMARY KEY (table_name, column_name),CONSTRAINT uk_gc_table_name UNIQUE (table_name),CONSTRAINT fk_gc_tn FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gc_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))I ]7indexsqlite_autoindex_gpkg_geometry_columns_2gpkg_geometry_columns I]7indexsqlite_autoindex_gpkg_geometry_columns_1gpkg_geometry_columns //[tablegpkg_ogr_contentsgpkg_ogr_contentsCREATE TABLE gpkg_ogr_contents(table_name TEXT NOT NULL PRIMARY KEY,feature_count INTEGER DEFAULT NULL)AU/indexsqlite_autoindex_gpkg_ogr_contents_1gpkg_ogr_contentsƒ''…wtablegpkg_contentsgpkg_contentsCREATE TABLE gpkg_contents (table_name TEXT NOT NULL PRIMARY KEY,data_type TEXT NOT NULL,identifier TEXT UNIQUE,description TEXT DEFAULT '',last_change DATETIME NOT NULL DEFAULT (strftime('%Y-%m-%dT%H:%M:%fZ','now')),min_x DOUBLE, min_y DOUBLE,max_x DOUBLE, max_y DOUBLE,srs_id INTEGER,CONSTRAINT fk_gc_r_srs_id FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys(srs_id))9M'indexsqlite_autoindex_gpkg_contents_2gpkg_contents9M'indexsqlite_autoindex_gpkg_contents_1gpkg_contents‚55ƒ)tablegpkg_spatial_ref_sysgpkg_spatial_ref_sysCREATE TABLE gpkg_spatial_ref_sys (srs_name TEXT NOT NULL,srs_id INTEGER NOT NULL PRIMARY KEY,organization TEXT NOT NULL,organization_coordsys_id INTEGER NOT NULL,definition TEXT NOT NULL,description TEXT) Ï C¹ b„(Ü€4ØC | ; 5 ¨  | Ü%%gtablealpha_fourthalpha_fourthCREATE TABLE "alpha_fourth" ( "fid" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, "geom" POLYGON, "id" INTEGER)‚YU-„;triggergpkg_tile_matrix_pixel_y_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_update' BEFORE UPDATE OF pixel_y_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_y_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚YU-„;triggergpkg_tile_matrix_pixel_x_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_update' BEFORE UPDATE OF pixel_x_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_x_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚YW-„9triggergpkg_tile_matrix_matrix_height_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_update' BEFORE UPDATE OF matrix_height ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); END‚HW-„triggergpkg_tile_matrix_matrix_height_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); END“‚TU-„1triggergpkg_tile_matrix_matrix_width_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_update' BEFORE UPDATE OF matrix_width ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); END‚DU-„triggergpkg_tile_matrix_matrix_width_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); END ÓÓ‚*„VGP5@ð?"@@ð?@@@@"@'@@,@@2@!@5@@5@ð?0@ð?(@@"@ð?@@@@@ ïï% alpha_fourth œœb%-q!alpha_fourthgeomgpkg_rtree_indexhttp://www.geopackage.org/spec120/#extension_rtreewrite-only ÚÚ%%- alpha_fourthgeomgpkg_rtree_index ûû  - -‰P“$A¨?€A  þ®• | ‚ C °  „ ä€÷Ïæþe'I%‚gtriggerrtree_alpha_fourth_geom_deletealpha_fourthCREATE TRIGGER "rtree_alpha_fourth_geom_delete" AFTER DELETE ON "alpha_fourth" WHEN old."geom" NOT NULL BEGIN DELETE FROM "rtree_alpha_fourth_geom" WHERE id = OLD."fid"; END‚*&K%ƒotriggerrtree_alpha_fourth_geom_update4alpha_fourthCREATE TRIGGER "rtree_alpha_fourth_geom_update4" AFTER UPDATE ON "alpha_fourth" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_fourth_geom" WHERE id IN (OLD."fid", NEW."fid"); ENDƒ9%K%† triggerrtree_alpha_fourth_geom_update3alpha_fourthCREATE TRIGGER "rtree_alpha_fourth_geom_update3" AFTER UPDATE ON "alpha_fourth" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_fourth_geom" WHERE id = OLD."fid"; INSERT OR REPLACE INTO "rtree_alpha_fourth_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚%$K%ƒetriggerrtree_alpha_fourth_geom_update2alpha_fourthCREATE TRIGGER "rtree_alpha_fourth_geom_update2" AFTER UPDATE OF "geom" ON "alpha_fourth" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_fourth_geom" WHERE id = OLD."fid"; ENDƒ#K%…'triggerrtree_alpha_fourth_geom_update1alpha_fourthCREATE TRIGGER "rtree_alpha_fourth_geom_update1" AFTER UPDATE OF "geom" ON "alpha_fourth" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_alpha_fourth_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚a"I%„_triggerrtree_alpha_fourth_geom_insertalpha_fourthCREATE TRIGGER "rtree_alpha_fourth_geom_insert" AFTER INSERT ON "alpha_fourth" WHEN (new."geom" NOT NULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_alpha_fourth_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END!II5tablertree_alpha_fourth_geom_parentrtree_alpha_fourth_geom_parentCREATE TABLE "rtree_alpha_fourth_geom_parent"(nodeno INTEGER PRIMARY KEY,parentnode) EE%tablertree_alpha_fourth_geom_nodertree_alpha_fourth_geom_nodeCREATE TABLE "rtree_alpha_fourth_geom_node"(nodeno INTEGER PRIMARY KEY,data)GG)tablertree_alpha_fourth_geom_rowidrtree_alpha_fourth_geom_rowidCREATE TABLE "rtree_alpha_fourth_geom_rowid"(rowid INTEGER PRIMARY KEY,nodeno);;9tablertree_alpha_fourth_geomrtree_alpha_fourth_geomCREATE VIRTUAL TABLE "rtree_alpha_fourth_geom" USING rtree(id, minx, maxx, miny, maxy)=Q+indexsqlite_autoindex_gpkg_extensions_1gpkg_extensionsw++ƒ%tablegpkg_extensionsgpkg_extensionsCREATE TABLE gpkg_extensions (table_name TEXT,column_name TEXT,extension_name TEXT NOT NULL,definition TEXT NOT NULL,scope TEXT NOT NULL,CONSTRAINT ge_tce UNIQUE (table_name, column_name, extension_name))‚_%ƒ3triggertrigger_delete_feature_count_alpha_fourthalpha_fourthCREATE TRIGGER "trigger_delete_feature_count_alpha_fourth" AFTER DELETE ON "alpha_fourth" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count - 1 WHERE lower(table_name) = lower('alpha_fourth'); END‚_%ƒ3triggertrigger_insert_feature_count_alpha_fourthalpha_fourthCREATE TRIGGER "trigger_insert_feature_count_alpha_fourth" AFTER INSERT ON "alpha_fourth" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count + 1 WHERE lower(table_name) = lower('alpha_fourth'); ENDP++Ytablesqlite_sequencesqlite_sequenceCREATE TABLE sqlite_sequence(name,seq)libpysal-4.9.2/libpysal/cg/tests/data/alpha_tenth.gpkg000066400000000000000000003000001452177046000227700ustar00rootroot00000000000000SQLite format 3@ 'ØGPKG .;ñöûö  Ó1 Ó–\=mUndefined geographic SRSNONEundefinedundefined geographic coordinate reference system[ÿÿÿÿÿÿÿÿÿ;kUndefined cartesian SRSNONEÿundefinedundefined cartesian coordinate reference system‚f¡f +„ WGS 84 geodeticEPSGæGEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]longitude/latitude coordinates in decimal degrees on the WGS 84 spheroid »»C ## = alpha_tenthfeaturesalpha_tenth2020-04-09T10:53:41.497Z ññ# alpha_tenth ññ# alpha_tenth ðð# alpha_tenth ññ# alpha_tenth áá#alpha_tenthgeomPOLYGON ìì# alpha_tenthgeom ññ# alpha_tenth     òëú Ý·|  š h ¶ k~ ¸w8ë¤Mòž‚JQ-„!triggergpkg_tile_matrix_zoom_level_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_update' BEFORE UPDATE of zoom_level ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END‚<Q-„triggergpkg_tile_matrix_zoom_level_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END? S-indexsqlite_autoindex_gpkg_tile_matrix_1gpkg_tile_matrixƒC --†9tablegpkg_tile_matrixgpkg_tile_matrix CREATE TABLE gpkg_tile_matrix (table_name TEXT NOT NULL,zoom_level INTEGER NOT NULL,matrix_width INTEGER NOT NULL,matrix_height INTEGER NOT NULL,tile_width INTEGER NOT NULL,tile_height INTEGER NOT NULL,pixel_x_size DOUBLE NOT NULL,pixel_y_size DOUBLE NOT NULL,CONSTRAINT pk_ttm PRIMARY KEY (table_name, zoom_level),CONSTRAINT fk_tmm_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name))ƒ 55…_tablegpkg_tile_matrix_setgpkg_tile_matrix_set CREATE TABLE gpkg_tile_matrix_set (table_name TEXT NOT NULL PRIMARY KEY,srs_id INTEGER NOT NULL,min_x DOUBLE NOT NULL,min_y DOUBLE NOT NULL,max_x DOUBLE NOT NULL,max_y DOUBLE NOT NULL,CONSTRAINT fk_gtms_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gtms_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))G [5indexsqlite_autoindex_gpkg_tile_matrix_set_1gpkg_tile_matrix_set „77‡tablegpkg_geometry_columnsgpkg_geometry_columnsCREATE TABLE gpkg_geometry_columns (table_name TEXT NOT NULL,column_name TEXT NOT NULL,geometry_type_name TEXT NOT NULL,srs_id INTEGER NOT NULL,z TINYINT NOT NULL,m TINYINT NOT NULL,CONSTRAINT pk_geom_cols PRIMARY KEY (table_name, column_name),CONSTRAINT uk_gc_table_name UNIQUE (table_name),CONSTRAINT fk_gc_tn FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gc_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))I ]7indexsqlite_autoindex_gpkg_geometry_columns_2gpkg_geometry_columns I]7indexsqlite_autoindex_gpkg_geometry_columns_1gpkg_geometry_columns //[tablegpkg_ogr_contentsgpkg_ogr_contentsCREATE TABLE gpkg_ogr_contents(table_name TEXT NOT NULL PRIMARY KEY,feature_count INTEGER DEFAULT NULL)AU/indexsqlite_autoindex_gpkg_ogr_contents_1gpkg_ogr_contentsƒ''…wtablegpkg_contentsgpkg_contentsCREATE TABLE gpkg_contents (table_name TEXT NOT NULL PRIMARY KEY,data_type TEXT NOT NULL,identifier TEXT UNIQUE,description TEXT DEFAULT '',last_change DATETIME NOT NULL DEFAULT (strftime('%Y-%m-%dT%H:%M:%fZ','now')),min_x DOUBLE, min_y DOUBLE,max_x DOUBLE, max_y DOUBLE,srs_id INTEGER,CONSTRAINT fk_gc_r_srs_id FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys(srs_id))9M'indexsqlite_autoindex_gpkg_contents_2gpkg_contents9M'indexsqlite_autoindex_gpkg_contents_1gpkg_contents‚55ƒ)tablegpkg_spatial_ref_sysgpkg_spatial_ref_sysCREATE TABLE gpkg_spatial_ref_sys (srs_name TEXT NOT NULL,srs_id INTEGER NOT NULL PRIMARY KEY,organization TEXT NOT NULL,organization_coordsys_id INTEGER NOT NULL,definition TEXT NOT NULL,description TEXT) Ï F¹ b„(Ü€4ØF † E ? µ ò##etablealpha_tenthalpha_tenthCREATE TABLE "alpha_tenth" ( "fid" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, "geom" POLYGON, "id" INTEGER)‚YU-„;triggergpkg_tile_matrix_pixel_y_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_update' BEFORE UPDATE OF pixel_y_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_y_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚YU-„;triggergpkg_tile_matrix_pixel_x_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_update' BEFORE UPDATE OF pixel_x_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_x_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚YW-„9triggergpkg_tile_matrix_matrix_height_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_update' BEFORE UPDATE OF matrix_height ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); END‚HW-„triggergpkg_tile_matrix_matrix_height_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); END“‚TU-„1triggergpkg_tile_matrix_matrix_width_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_update' BEFORE UPDATE OF matrix_width ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); END‚DU-„triggergpkg_tile_matrix_matrix_width_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); END zƒvGP5@ð?"@ @ð?@@"@'@@,@@2@!@5@@5@ð?0@ð?"@ð?@@@ ðð# alpha_tenth a#-q!alpha_tenthgeomgpkg_rtree_indexhttp://www.geopackage.org/spec120/#extension_rtreewrite-only ÛÛ$#- alpha_tenthgeomgpkg_rtree_index ûû  - -‰P“$A¨?€A  3®š † Œ M ½ ( — ú›ô>3`'G#‚atriggerrtree_alpha_tenth_geom_deletealpha_tenthCREATE TRIGGER "rtree_alpha_tenth_geom_delete" AFTER DELETE ON "alpha_tenth" WHEN old."geom" NOT NULL BEGIN DELETE FROM "rtree_alpha_tenth_geom" WHERE id = OLD."fid"; END‚%&I#ƒitriggerrtree_alpha_tenth_geom_update4alpha_tenthCREATE TRIGGER "rtree_alpha_tenth_geom_update4" AFTER UPDATE ON "alpha_tenth" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_tenth_geom" WHERE id IN (OLD."fid", NEW."fid"); ENDƒ3%I#†triggerrtree_alpha_tenth_geom_update3alpha_tenthCREATE TRIGGER "rtree_alpha_tenth_geom_update3" AFTER UPDATE ON "alpha_tenth" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_tenth_geom" WHERE id = OLD."fid"; INSERT OR REPLACE INTO "rtree_alpha_tenth_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚ $I#ƒ_triggerrtree_alpha_tenth_geom_update2alpha_tenthCREATE TRIGGER "rtree_alpha_tenth_geom_update2" AFTER UPDATE OF "geom" ON "alpha_tenth" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_alpha_tenth_geom" WHERE id = OLD."fid"; ENDƒ#I#…!triggerrtree_alpha_tenth_geom_update1alpha_tenthCREATE TRIGGER "rtree_alpha_tenth_geom_update1" AFTER UPDATE OF "geom" ON "alpha_tenth" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_alpha_tenth_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚\"G#„Ytriggerrtree_alpha_tenth_geom_insertalpha_tenthCREATE TRIGGER "rtree_alpha_tenth_geom_insert" AFTER INSERT ON "alpha_tenth" WHEN (new."geom" NOT NULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_alpha_tenth_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END!GG3tablertree_alpha_tenth_geom_parentrtree_alpha_tenth_geom_parentCREATE TABLE "rtree_alpha_tenth_geom_parent"(nodeno INTEGER PRIMARY KEY,parentnode) CC#tablertree_alpha_tenth_geom_nodertree_alpha_tenth_geom_nodeCREATE TABLE "rtree_alpha_tenth_geom_node"(nodeno INTEGER PRIMARY KEY,data)EE'tablertree_alpha_tenth_geom_rowidrtree_alpha_tenth_geom_rowidCREATE TABLE "rtree_alpha_tenth_geom_rowid"(rowid INTEGER PRIMARY KEY,nodeno) 997tablertree_alpha_tenth_geomrtree_alpha_tenth_geomCREATE VIRTUAL TABLE "rtree_alpha_tenth_geom" USING rtree(id, minx, maxx, miny, maxy)=Q+indexsqlite_autoindex_gpkg_extensions_1gpkg_extensionsw++ƒ%tablegpkg_extensionsgpkg_extensionsCREATE TABLE gpkg_extensions (table_name TEXT,column_name TEXT,extension_name TEXT NOT NULL,definition TEXT NOT NULL,scope TEXT NOT NULL,CONSTRAINT ge_tce UNIQUE (table_name, column_name, extension_name))‚]#ƒ-triggertrigger_delete_feature_count_alpha_tenthalpha_tenthCREATE TRIGGER "trigger_delete_feature_count_alpha_tenth" AFTER DELETE ON "alpha_tenth" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count - 1 WHERE lower(table_name) = lower('alpha_tenth'); END‚]#ƒ-triggertrigger_insert_feature_count_alpha_tenthalpha_tenthCREATE TRIGGER "trigger_insert_feature_count_alpha_tenth" AFTER INSERT ON "alpha_tenth" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count + 1 WHERE lower(table_name) = lower('alpha_tenth'); ENDP++Ytablesqlite_sequencesqlite_sequenceCREATE TABLE sqlite_sequence(name,seq)libpysal-4.9.2/libpysal/cg/tests/data/eberly_bounding_circles.gpkg000066400000000000000000003000001452177046000253540ustar00rootroot00000000000000SQLite format 3@ 'ØGPKG .;ñöûö  Ó1 Ó–\=mUndefined geographic SRSNONEundefinedundefined geographic coordinate reference system[ÿÿÿÿÿÿÿÿÿ;kUndefined cartesian SRSNONEÿundefinedundefined cartesian coordinate reference system‚f¡f +„ WGS 84 geodeticEPSGæGEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]longitude/latitude coordinates in decimal degrees on the WGS 84 spheroid ……y ;; =eberly_bounding_circlesfeatureseberly_bounding_circles2020-04-09T10:53:41.508Z¿ö"{Šâ‚ã¿á6§¤¸,Ó@7 ["ê@#iÆ+š åå; eberly_bounding_circles åå; eberly_bounding_circles ãã;eberly_bounding_circles åå; eberly_bounding_circles ××';eberly_bounding_circlesgeomPOINT àà; eberly_bounding_circlesgeom åå; eberly_bounding_circles     ò¤ú Ý·|  š h ¶ k~ ¸w8ë¤Mòž‚DU-„triggergpkg_tile_matrix_matrix_width_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); END‚JQ-„!triggergpkg_tile_matrix_zoom_level_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_update' BEFORE UPDATE of zoom_level ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END‚<Q-„triggergpkg_tile_matrix_zoom_level_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_zoom_level_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: zoom_level cannot be less than 0') WHERE (NEW.zoom_level < 0); END? S-indexsqlite_autoindex_gpkg_tile_matrix_1gpkg_tile_matrixƒC --†9tablegpkg_tile_matrixgpkg_tile_matrix CREATE TABLE gpkg_tile_matrix (table_name TEXT NOT NULL,zoom_level INTEGER NOT NULL,matrix_width INTEGER NOT NULL,matrix_height INTEGER NOT NULL,tile_width INTEGER NOT NULL,tile_height INTEGER NOT NULL,pixel_x_size DOUBLE NOT NULL,pixel_y_size DOUBLE NOT NULL,CONSTRAINT pk_ttm PRIMARY KEY (table_name, zoom_level),CONSTRAINT fk_tmm_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name))ƒ 55…_tablegpkg_tile_matrix_setgpkg_tile_matrix_set CREATE TABLE gpkg_tile_matrix_set (table_name TEXT NOT NULL PRIMARY KEY,srs_id INTEGER NOT NULL,min_x DOUBLE NOT NULL,min_y DOUBLE NOT NULL,max_x DOUBLE NOT NULL,max_y DOUBLE NOT NULL,CONSTRAINT fk_gtms_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gtms_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))G [5indexsqlite_autoindex_gpkg_tile_matrix_set_1gpkg_tile_matrix_set „77‡tablegpkg_geometry_columnsgpkg_geometry_columnsCREATE TABLE gpkg_geometry_columns (table_name TEXT NOT NULL,column_name TEXT NOT NULL,geometry_type_name TEXT NOT NULL,srs_id INTEGER NOT NULL,z TINYINT NOT NULL,m TINYINT NOT NULL,CONSTRAINT pk_geom_cols PRIMARY KEY (table_name, column_name),CONSTRAINT uk_gc_table_name UNIQUE (table_name),CONSTRAINT fk_gc_tn FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gc_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))I ]7indexsqlite_autoindex_gpkg_geometry_columns_2gpkg_geometry_columns I]7indexsqlite_autoindex_gpkg_geometry_columns_1gpkg_geometry_columns //[tablegpkg_ogr_contentsgpkg_ogr_contentsCREATE TABLE gpkg_ogr_contents(table_name TEXT NOT NULL PRIMARY KEY,feature_count INTEGER DEFAULT NULL)AU/indexsqlite_autoindex_gpkg_ogr_contents_1gpkg_ogr_contentsƒ''…wtablegpkg_contentsgpkg_contentsCREATE TABLE gpkg_contents (table_name TEXT NOT NULL PRIMARY KEY,data_type TEXT NOT NULL,identifier TEXT UNIQUE,description TEXT DEFAULT '',last_change DATETIME NOT NULL DEFAULT (strftime('%Y-%m-%dT%H:%M:%fZ','now')),min_x DOUBLE, min_y DOUBLE,max_x DOUBLE, max_y DOUBLE,srs_id INTEGER,CONSTRAINT fk_gc_r_srs_id FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys(srs_id))9M'indexsqlite_autoindex_gpkg_contents_2gpkg_contents9M'indexsqlite_autoindex_gpkg_contents_1gpkg_contents‚55ƒ)tablegpkg_spatial_ref_sysgpkg_spatial_ref_sysCREATE TABLE gpkg_spatial_ref_sys (srs_name TEXT NOT NULL,srs_id INTEGER NOT NULL PRIMARY KEY,organization TEXT NOT NULL,organization_coordsys_id INTEGER NOT NULL,definition TEXT NOT NULL,description TEXT) ì ð  ¡EùQõ@® Àð´ Z ¡ ìF ìú‚Mu;ƒutriggertrigger_delete_feature_count_eberly_bounding_circleseberly_bounding_circlesCREATE TRIGGER "trigger_delete_feature_count_eberly_bounding_circles" AFTER DELETE ON "eberly_bounding_circles" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count - 1 WHERE lower(table_name) = lower('eberly_bounding_circles'); END2;;{tableeberly_bounding_circleseberly_bounding_circlesCREATE TABLE "eberly_bounding_circles" ( "fid" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, "geom" POINT, "radius" REAL)‚YU-„;triggergpkg_tile_matrix_pixel_y_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_update' BEFORE UPDATE OF pixel_y_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_y_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_y_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_y_size must be greater than 0') WHERE NOT (NEW.pixel_y_size > 0); END‚YU-„;triggergpkg_tile_matrix_pixel_x_size_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_update' BEFORE UPDATE OF pixel_x_size ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚IU-„triggergpkg_tile_matrix_pixel_x_size_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_pixel_x_size_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: pixel_x_size must be greater than 0') WHERE NOT (NEW.pixel_x_size > 0); END‚YW-„9triggergpkg_tile_matrix_matrix_height_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_update' BEFORE UPDATE OF matrix_height ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); END‚HW-„triggergpkg_tile_matrix_matrix_height_insertgpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_height_insert' BEFORE INSERT ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'insert on table ''gpkg_tile_matrix'' violates constraint: matrix_height cannot be less than 1') WHERE (NEW.matrix_height < 1); ENDgÔ‚Mu;ƒutriggertrigger_insert_feature_count_eberly_bounding_circleseberly_bounding_circlesCREATE TRIGGER "trigger_insert_feature_count_eberly_bounding_circles" AFTER INSERT ON "eberly_bounding_circles" BEGIN UPDATE gpkg_ogr_contents SET feature_count = feature_count + 1 WHERE lower(table_name) = lower('eberly_bounding_circles'); END‚TU-„1triggergpkg_tile_matrix_matrix_width_updategpkg_tile_matrixCREATE TRIGGER 'gpkg_tile_matrix_matrix_width_update' BEFORE UPDATE OF matrix_width ON 'gpkg_tile_matrix' FOR EACH ROW BEGIN SELECT RAISE(ABORT, 'update on table ''gpkg_tile_matrix'' violates constraint: matrix_width cannot be less than 1') WHERE (NEW.matrix_width < 1); ENDMw++ƒ%tablegpkg_extensionsgpkg_extensionsCREATE TABLE gpkg_extensions (table_name TEXT,column_name TEXT,extension_name TEXT NOT NULL,definition TEXT NOT NULL,scope TEXT NOT NULL,CONSTRAINT ge_tce UNIQUE (table_name, column_name, extension_name))P++Ytablesqlite_sequencesqlite_sequenceCREATE TABLE sqlite_sequence(name,seq)  ÏÕªT)þÓ¨}R' ü Ñ ¦ { P % ú Ï)FGPÿÿÿÿÿÿø?@@MÏ }« )FGP!v³ú¶@øhMžé¨@@MÏ }« )FGP@Xxd¥Miâ?@MÏ }« )FGP&Vc @,¹°CPà?@MÏ }« )FGPÊtÐoA&@Œ Š+î‰Ø¿@MÏ }« )FGPyéôŠ,H+@@ñÒéY°?@MÏ }« ) FGPe :è· 2@Œ Š+î‰Ø¿@MÏ }« ) FGPMma%û2@Ô,¸¤§6á¿@MÏ }« ) FGPê"[ 7@@@MÏ }« ) FGPö“=p{*3@ø :zm@@MÏ }« ) FGPÿÿÿÿÿo2@@@MÏ }« )FGPp0@ÿÿÿÿÿÿ@@MÏ }« )FGP;¥|.àl0@à äÔ'@@MÏ }« )FGP€)@~àëê"@@MÏ }« )FGP#@ !@@MÏ }« )FGP"°"V{ "@¦d81´"@@MÏ }« )FGP£ þ#@š+Æi#@@MÏ }« )FGP@Ó9D¶6"@@MÏ }« )FGPã‚âŠ{"ö¿“5è ß‚@@MÏ }«  ãã;eberly_bounding_circles ‘‘m;-q!eberly_bounding_circlesgeomgpkg_rtree_indexhttp://www.geopackage.org/spec120/#extension_rtreewrite-only ÏÏ0;- eberly_bounding_circlesgeomgpkg_rtree_index ¡ûöñìçâÝØÓÎÉÄ¿ºµ°«¦¡                         - -‰P“$¿±Þ¿±Ü@Äý@Äÿ@ @ AÁµAÁ¶@€¡@€¡A›NA›OA[ÚA[ÛA} A}¢AAA A ALALAWXAWZAƒgAƒg@Ð=@Ð?Aƒ€Aƒ€@Ïÿþ@Ð A“ÿA“€@À@À A™SÚA™SÜ@¸#j@¸#l A¸`ÙA¸`Û@ @ A—Ù+A—Ù,¿ µ>¿ µ= A‘¿A‘À¾ÄOs¾ÄOqAZAdAZAf=‚ȯ=‚ȰA2 }A2 ¾ÄOs¾ÄOq@ÐPC@ÐPE?‚?‚@È@È?Jm?Jn@E°Ö@E°Ø@ GL@ GM?Çÿþ?È@@  f ( ' s º  D©éŠŒ(á‚a&a;„1triggerrtree_eberly_bounding_circles_geom_update4eberly_bounding_circlesCREATE TRIGGER "rtree_eberly_bounding_circles_geom_update4" AFTER UPDATE ON "eberly_bounding_circles" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_eberly_bounding_circles_geom" WHERE id IN (OLD."fid", NEW."fid"); ENDƒ{%a;†etriggerrtree_eberly_bounding_circles_geom_update3eberly_bounding_circlesCREATE TRIGGER "rtree_eberly_bounding_circles_geom_update3" AFTER UPDATE ON "eberly_bounding_circles" WHEN OLD."fid" != NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_eberly_bounding_circles_geom" WHERE id = OLD."fid"; INSERT OR REPLACE INTO "rtree_eberly_bounding_circles_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END‚\$a;„'triggerrtree_eberly_bounding_circles_geom_update2eberly_bounding_circlesCREATE TRIGGER "rtree_eberly_bounding_circles_geom_update2" AFTER UPDATE OF "geom" ON "eberly_bounding_circles" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" ISNULL OR ST_IsEmpty(NEW."geom")) BEGIN DELETE FROM "rtree_eberly_bounding_circles_geom" WHERE id = OLD."fid"; ENDƒ=#a;…itriggerrtree_eberly_bounding_circles_geom_update1eberly_bounding_circlesCREATE TRIGGER "rtree_eberly_bounding_circles_geom_update1" AFTER UPDATE OF "geom" ON "eberly_bounding_circles" WHEN OLD."fid" = NEW."fid" AND (NEW."geom" NOTNULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_eberly_bounding_circles_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); ENDƒ"_;…!triggerrtree_eberly_bounding_circles_geom_inserteberly_bounding_circlesCREATE TRIGGER "rtree_eberly_bounding_circles_geom_insert" AFTER INSERT ON "eberly_bounding_circles" WHEN (new."geom" NOT NULL AND NOT ST_IsEmpty(NEW."geom")) BEGIN INSERT OR REPLACE INTO "rtree_eberly_bounding_circles_geom" VALUES (NEW."fid",ST_MinX(NEW."geom"), ST_MaxX(NEW."geom"),ST_MinY(NEW."geom"), ST_MaxY(NEW."geom")); END>!__Ktablertree_eberly_bounding_circles_geom_parentrtree_eberly_bounding_circles_geom_parentCREATE TABLE "rtree_eberly_bounding_circles_geom_parent"(nodeno INTEGER PRIMARY KEY,parentnode)2 [[;tablertree_eberly_bounding_circles_geom_nodertree_eberly_bounding_circles_geom_nodeCREATE TABLE "rtree_eberly_bounding_circles_geom_node"(nodeno INTEGER PRIMARY KEY,data)6]]?tablertree_eberly_bounding_circles_geom_rowidrtree_eberly_bounding_circles_geom_rowidCREATE TABLE "rtree_eberly_bounding_circles_geom_rowid"(rowid INTEGER PRIMARY KEY,nodeno)1QQOtablertree_eberly_bounding_circles_geomrtree_eberly_bounding_circles_geomCREATE VIRTUAL TABLE "rtree_eberly_bounding_circles_geom" USING rtree(id, minx, maxx, miny, maxy)=Q+indexsqlite_autoindex_gpkg_extensions_1gpkg_extensions{‚'_;ƒ)triggerrtree_eberly_bounding_circles_geom_deleteeberly_bounding_circlesCREATE TRIGGER "rtree_eberly_bounding_circles_geom_delete" AFTER DELETE ON "eberly_bounding_circles" WHEN old."geom" NOT NULL BEGIN DELETE FROM "rtree_eberly_bounding_circles_geom" WHERE id = OLD."fid"; ENDlibpysal-4.9.2/libpysal/cg/tests/data/pecos_points.txt000066400000000000000000000033721452177046000231110ustar00rootroot00000000000000-101.758293152, 30.664484024 -102.144584656, 30.6617736816 -102.146247864, 30.6036663055 -102.373565674, 30.6040420532 -102.370162964, 30.2847862244 -102.573104858, 30.2779312134 -102.576850891, 30.0599365234 -103.442886353, 30.6678009033 -103.585655212, 30.7707481384 -103.017219543, 31.3781299591 -102.991844177, 31.3591747284 -102.929168701, 31.3457660675 -102.878448486, 31.3215103149 -102.867454529, 31.298500061 -102.842445374, 31.2881774902 -102.826713562, 31.2671108246 -102.768844604, 31.2973403931 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31.3938179016libpysal-4.9.2/libpysal/cg/tests/fast_point_in_polygon_algorithm.ipynb000066400000000000000000010041661452177046000264510ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fast Point in Polygon Testing Method\n", "---------------- \n", "**Author: Hu Shao **\n", "\n", "## Content\n", "* [Introduction](#Introduction) \n", "* [How to Use](#How-to-Use) \n", "* [The process of building quadtree](#The-process-of-building-quadtree) \n", "* [Visualizing the result of \"Point in Polygon\" test](#Visualizing-the-result-of-\"Point-in-Polygon\"-test)\n", "* [Test the performance of this quad-tree-structure](#Test-the-performance-of-this-quad-tree-structure)\n", "* [Validate the correctness of this quad-tree-structure](#Validate-the-correctness-of-this-quad-tree-structure)\n", "* [Algorithm of building quadtree cells for study area](#Algorithm-of-building-quadtree-cells-for-study-area)\n", "* [Reference](#Reference)\n", "\n", "## Introduction\n", "Testing whether a point locates inside a polygon is a time consuming work. Especially when the point number is huge and the boundary of study area is complex. \n", "Here we implement a \"Fast Point in Polygon Testing Method\" to help users determine whether a point is inside a specific polygon rapidly. \n", "\n", "The Quadtree structure is employed to divide a polygon into plenty of lattices. During the dividing, each lattices is marked as **in**, **out** or **maybe**, which means \"totally lies inside the polygon\", \"totally lies out side the polygon\" and \"intersect with the boundary if the polygon\". \n", "For each point, we firstly allocate it into a lattice. If the lattice is marked as \"in\" or \"out\", we will immediately know the result; if the lattice is marked as \"maybe\", we then need to do some further calculation, which won't be complex. \n", "Once the quadtree structure is constructed, the \"point in polygon testing\" will be very fast.\n", "\n", "This method is suitable for the situations where point numbers are huge, polygon numbers are small and polygon boundary is complex. E.g., simulation in point pattern analysis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "import finished!\n" ] } ], "source": [ "# Import essential libraries for following calculation\n", "import libpysal as ps\n", "import numpy as np\n", "\n", "from libpysal.cg.shapes import Ring, Polygon\n", "from libpysal.cg.segmentLocator import BruteSegmentLocator\n", "from libpysal.cg.polygonQuadTreeStructure import QuadTreeStructureSingleRing\n", "import libpysal.examples as pysal_examples\n", "%matplotlib inline\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import time\n", "# import codecs\n", "# import shapely\n", "# from pysal.cg.shapes import Polygon, Point\n", "# from shapely.geometry import Polygon as spPolygon\n", "# from shapely.geometry import Point as spPoint\n", "# import time\n", "import random\n", "print(\"import finished!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How to Use\n", "### Data preparing" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_ring_from_file(path):\n", " vertices = []\n", " file = open(path, \"r\")\n", " for line in file:\n", " if len(line)<2:\n", " continue\n", " coordinates = line.split(\",\")\n", " x = (float)(coordinates[0])\n", " y = (float)(coordinates[1])\n", " vertices.append((x, y))\n", " file.close()\n", " return vertices\n", "\n", "# Prepare the polygons for future use.\n", "Texas = Polygon(get_ring_from_file(\"data/texas_points.txt\"))\n", "Pecos = Polygon(get_ring_from_file(\"data/pecos_points.txt\"))\n", "San_Saba = Polygon(get_ring_from_file(\"data/san_saba_points.txt\"))\n", "Texas_with_holes = Polygon(Texas.vertices, \n", " [Pecos.vertices, San_Saba.vertices])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "quad tree build finished\n", "(-101.88926984960028, 33.54910494748045, True)\n", "(-101.9324657625388, 30.99562838608417, True)\n", "(-96.66509999476706, 31.769415333990857, True)\n", "(-106.04482267119155, 29.30860980945098, False)\n", "(-102.05124458612478, 28.377630545869593, False)\n", "(-95.7952998307517, 33.2606998037955, True)\n", "(-105.3043106118911, 29.64211319980175, False)\n", "(-96.07086614456198, 36.43497839388729, False)\n", "(-93.8102383176018, 35.720624066002756, False)\n", "(-101.27867134062416, 31.762914510273433, True)\n" ] } ], "source": [ "# construct the quadtree structure by explicitly calling the function \"build_quad_tree_structure\" in polygon\n", "Texas.build_quad_tree_structure()\n", "print \"quad tree build finished\"\n", "# create some random point and test if these points locate in the polygon\n", "for i in range(0, 10):\n", " x = random.uniform(Texas.bbox[0], Texas.bbox[2])\n", " y = random.uniform(Texas.bbox[1], Texas.bbox[3])\n", " print(x, y, Texas.contains_point([x, y]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The process of building quadtree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quadtree building process - visualize the building result" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ljaYunKqp06XwVU0a/NDeeu+zbfTKmHv14UO/1Y0/3l9nflKnwYHP5QyqVlnk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\n2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The region of Texas, to make the steps more clear, here we only use the main region\n", "texas_main_vertices = Texas.parts[0]\n", "fig, ax = plt.subplots(figsize=(16,11))\n", "qts_singlering = QuadTreeStructureSingleRing(Ring(texas_main_vertices))\n", "patches = []\n", "color_array = []\n", "cells_to_draw = [qts_singlering.root_cell]\n", "while len(cells_to_draw) > 0:\n", " cell = cells_to_draw.pop()\n", " \n", " if cell.children_l_b is None:\n", " # this is a leaf in the quad tree structure, draw it\n", " verts = [\n", " [cell.min_x, cell.min_y],\n", " [cell.min_x, cell.min_y + cell.length_y],\n", " [cell.min_x + cell.length_x, cell.min_y + cell.length_y],\n", " [cell.min_x + cell.length_x, cell.min_y],\n", " [cell.min_x, cell.min_y]\n", " ]\n", " patches.append(verts)\n", " if cell.status == \"in\":\n", " color_array.append(\"#c8e6c9\") # in color green\n", " elif cell.status == \"out\":\n", " color_array.append(\"#b0bec5\") # in color grey\n", " else: # means \"maybe\"\n", " color_array.append(\"#ffa726\") # in color orange\n", " else:\n", " cells_to_draw.append(cell.children_l_b)\n", " cells_to_draw.append(cell.children_l_t)\n", " cells_to_draw.append(cell.children_r_b)\n", " cells_to_draw.append(cell.children_r_t)\n", "coll = matplotlib.collections.PolyCollection(np.array(patches), facecolors=color_array, edgecolors='#eceff1')\n", "ax.add_collection(coll)\n", "point_x_list = []\n", "point_y_list = []\n", "for point in texas_main_vertices:\n", " point_x_list.append(point[0])\n", " point_y_list.append(point[1]) \n", "plt.plot(point_x_list, point_y_list)\n", "ax.autoscale_view()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the result of \"Point in Polygon\" test" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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c+Q888EDw8caNG9m4ceMEuy2EmKpUFVnKOwx/V1cwKAXA50Pr6ZHANMp0LbAP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D33HAxux+tsaiLh7rs9dudvslJGRYFSCQ4HAJJWiyI83GPHj7rpJgzvv4+jt5fgefPQZGbS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\nNuIjo/jGmTMoa2rgP9aubPecZy66asj9/Yv0t/4IphrrJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,12))\n", "\n", "for vertices in Texas_with_holes.parts:\n", " line_x_list = []\n", " line_y_list = []\n", " for point in vertices:\n", " line_x_list.append(point[0])\n", " line_y_list.append(point[1]) \n", " plt.plot(line_x_list, line_y_list, c=\"#6a1b9a\")\n", "for vertices in Texas_with_holes.holes:\n", " line_x_list = []\n", " line_y_list = []\n", " for point in vertices:\n", " line_x_list.append(point[0])\n", " line_y_list.append(point[1]) \n", " plt.plot(line_x_list, line_y_list, c=\"#1565c0\")\n", " \n", "point_x_list = []\n", "point_y_list = []\n", "point_colors = []\n", "bbox = Texas_with_holes.bbox\n", "for i in range(0, 1000):\n", " x = random.uniform(bbox[0], bbox[2]) \n", " y = random.uniform(bbox[1], bbox[3])\n", " point_x_list.append(x)\n", " point_y_list.append(y) \n", " if Texas_with_holes.contains_point([x, y]):\n", " point_colors.append(\"#e57373\") # inside, red \n", " else:\n", " point_colors.append(\"#4db6ac\") # outside, green\n", "\n", "plt.scatter(point_x_list, point_y_list, c = point_colors, linewidth = 0)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Test the performance of this quad-tree-structure" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 random points generated\n", "------------------------------\n", "Begin test without quad-tree-structure\n", "Test without quad-tree-structure finished, time used = 23.204s\n", "------------------------------\n", "Begin test with quad-tree-structure\n", "Test with quad-tree-structure finished, time used = 0.451s\n" ] } ], "source": [ "# construct a study area with 3000+ vertices\n", "Huangshan = Polygon(get_ring_from_file(\"data/study_region_huangshan_point.txt\"))\n", "points = []\n", "bbox = Huangshan.bounding_box\n", "for i in range(0, 10000):\n", " x = random.uniform(bbox[0], bbox[2]) \n", " y = random.uniform(bbox[1], bbox[3])\n", " points.append((x, y)) \n", " \n", "print str(len(points)) + \" random points generated\" \n", "\n", "print \"------------------------------\"\n", "print \"Begin test without quad-tree-structure\"\n", "time_begin = int(round(time.time() * 1000))\n", "for point in points:\n", " Huangshan.contains_point(point)\n", "time_end = int(round(time.time() * 1000))\n", "print \"Test without quad-tree-structure finished, time used = \" + str((time_end-time_begin)/1000.0) + \"s\"\n", "\n", "print \"------------------------------\"\n", "print \"Begin test with quad-tree-structure\"\n", "time_begin = int(round(time.time() * 1000))\n", "Huangshan.build_quad_tree_structure()\n", "count_error = 0\n", "for point in points:\n", " Huangshan.contains_point(point)\n", "time_end = int(round(time.time() * 1000))\n", "print \"Test with quad-tree-structure finished, time used = \" + str((time_end-time_begin)/1000.0) + \"s\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Validate the correctness of this quad-tree-structure" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Study region read finished, with vertices of 3891\n", "1000 random points generated\n", "finished ==================== no error found\n" ] } ], "source": [ "# polygons = ps.open(\"data/Huangshan_region.shp\") # read the research region shape file\n", "# research_region = polygons[0] # set the first polygon as research polygon\n", "# len(research_region.vertices)\n", "vertices = get_ring_from_file(\"data/study_region_huangshan_point.txt\")\n", "print \"Study region read finished, with vertices of \" + str(len(vertices))\n", "huangshan = Ring(vertices)\n", "\n", "points = []\n", "bbox = huangshan.bounding_box\n", "for i in range(0, 1000):\n", " x = random.uniform(bbox[0], bbox[2]) \n", " y = random.uniform(bbox[1], bbox[3])\n", " points.append([x, y, True]) \n", "print str(len(points)) + \" random points generated\" \n", "\n", "# First, test if these points are inside of the polygon by using the conventional method, record the result\n", "for point in points:\n", " is_in = huangshan.contains_point((point[0], point[1]))\n", " point[2] = is_in\n", "\n", "# Then, build the quad-tree and do the test again. Compare the results of two methods.\n", "count_error = 0\n", "huangshan.build_quad_tree_structure() \n", "for point in points:\n", " is_in = huangshan.contains_point((point[0], point[1]))\n", " if point[2] != is_in:\n", " print \"Error found!!!\"\n", " count_error += 1\n", "\n", " \n", "if count_error == 0:\n", " print \"finished ==================== no error found\"\n", "else:\n", " print \"finished ==================== ERROR FOUND\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algorithm for building quadtree cells for study area\n", "A huge number of points will be simulated and test if falls in the study area in the real case. This calculation process is very computing intensive. Especially when the boundary of study area is complex and contains a lot of segments, or the simulation time is also large. \n", "In order to fast decide whether a point is contained in the study area, we can prepare an grid structure which divide the study area into quadtree based regular rectangles. Each rectangles will have a specific status from ['in', 'out', 'maybe']. After we prepared this kind of grid structure, deciding whether a points falls in the study area will be very easy: first, allocate the point into a specific cell. For the cell with different status: \n", "- in: the point must be in the study area \n", "- out: the point must not be in the study area \n", "- maybe: decide if the points falls into the study area by some following-up calculation. However, the small polygon contains much less boundary segments, the calculation will be much easier. What's more, this kind of grids only take over a very small part of the whole grids \n", "![quadtree_example](img/quadtree_example.png)\n", "\n", "### Process of duadtree dividing of the study area:\n", "Treat the boundary of the study area as arc. Each time of dividing the study area means use two straight lines (on horizontal and one vertical) to split a big rectangle (cell) into 4 smaller ones. During this process, the arc should also be used to intersect with the straight lines and break into small segments. Different segments should belong to different cells and can be used to determine the status of the cell (as we mentioned: in, out or maybe inside of the study area.) Repeat this process until the cell's size is small enough. \n", "During the dividing, there are some special properties of the arcs we need to know: \n", "- Point order of the arcs **MUST** be clockwise \n", "- The two end-points of each arc **MUST** lie on the borders of the cell \n", "- When a arc goes in a cell, it **MUST** goes out from the same one \n", "- The intersection points **MUST** be lying on the inner-boundaries which are used to divide the cell into 4 sub-cells\n", "- Use the intersection points to split the arcs into small ones \n", "- No need to store cell boundaries as arcs, store the intersection points, points' relative location from \n", "\n", "\n", "The following image depicts the categorize rule of cell boundary when being divided into sub cells: \n", "cell_boundary_category_rule \n", "![cell_boundary_category_rule](img/cell_boundary_category_rule.png)\n", "\n", "segment_sequence_search_rule \n", "![segment_sequence_search_rule](img/segment_sequence_search_rule.png)\n", "\n", "In the situations that there are some arcs intersect with a cell and we need to extract the segment squence, here is the rule: \n", "1. Start on the bottom-left point of the cell, go clockwise to search points. \n", "2. Find the first arc-begin-point on the cell's border. The actual segment sequence begin from here. \n", "3. Go alone the arc until the end point on the border. Then go alone the cell's border until find next arc-begin-point. \n", "4. Repeat **Step.3** until reach the first arc-begin-point at **Step.2**. Search stop.\n", "\n", "From the image we can see that the red border lines also belong to the segment sequence. \n", "During the quadtree dividing, when there is a cell who doesn't intersect with any arc, we need to determine whether this cell is totally within the study area or not. we Can use the method above to determine: If this cell share a border which belongs to other cells' segment sequence, then this cell is totally within the study area; vice versa\n", "\n", "\n", "extract_connecting_boders_between_points \n", "![extract_connecting_boders_between_points](img/extract_connecting_boders_between_points.png)\n", "\n", "situation_segment_intersect_with_two_split_line\n", "![situation_segment_intersect_with_two_split_line](img/situation_segment_intersect_with_two_split_line.png)\n", "\n", "Under the sitiation that a single segment intersects with both split-lines. This kind of situation should be carefully treated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Reference\n", "Point in Polygon Strategies http://erich.realtimerendering.com/ptinpoly/ \n", "Samet, Hanan. Foundations of multidimensional and metric data structures. Morgan Kaufmann, 2006. \n", "Jiménez, Juan J., Francisco R. Feito, and Rafael J. Segura. \"A new hierarchical triangle-based point-in-polygon data structure.\" Computers & Geosciences 35, no. 9 (2009): 1843-1853. \n", "http://stackoverflow.com/questions/12881848/draw-polygons-more-efficiently-with-matplotlib \n", "http://matplotlib.org/api/collections_api.html \n", "http://materializecss.com/color.html " ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 } libpysal-4.9.2/libpysal/cg/tests/img/000077500000000000000000000000001452177046000175015ustar00rootroot00000000000000libpysal-4.9.2/libpysal/cg/tests/img/cell_boundary_category_rule.png000066400000000000000000000513151452177046000257620ustar00rootroot00000000000000‰PNG  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;Ad'H€ì ²$@v‚ÈNÙý?A Fñ·+”ÇIEND®B`‚libpysal-4.9.2/libpysal/cg/tests/test_ashapes.py000066400000000000000000000111111452177046000217550ustar00rootroot00000000000000import os import geopandas import numpy as np from packaging.version import Version from shapely import geometry from ...examples import get_path from ..alpha_shapes import alpha_shape, alpha_shape_auto GPD_013 = Version(geopandas.__version__) >= Version("0.13") this_directory = os.path.dirname(__file__) class TestAlphaShapes: def setup_method(self): eberly = geopandas.read_file(get_path("eberly_net.shp")) eberly_vertices = eberly.geometry.apply( lambda x: np.hstack(x.xy).reshape(2, 2).T ).values eberly_vertices = np.vstack(eberly_vertices) self.vertices = eberly_vertices self.a05 = ( geopandas.read_file(os.path.join(this_directory, "data/alpha_05.gpkg")) .geometry.to_numpy() .item() ) self.a10 = ( geopandas.read_file(os.path.join(this_directory, "data/alpha_tenth.gpkg")) .geometry.to_numpy() .item() ) self.a2 = ( geopandas.read_file(os.path.join(this_directory, "data/alpha_fifth.gpkg")) .geometry.to_numpy() .item() ) self.a25 = ( geopandas.read_file(os.path.join(this_directory, "data/alpha_fourth.gpkg")) .geometry.to_numpy() .item() ) self.a25 = ( geopandas.read_file(os.path.join(this_directory, "data/alpha_fourth.gpkg")) .geometry.to_numpy() .item() ) circles = geopandas.read_file( os.path.join(this_directory, "data/eberly_bounding_circles.gpkg") ) self.circle_radii = circles.radius.iloc[0] self.circle_verts = np.column_stack( (circles.geometry.x.values, circles.geometry.y.values) ) self.autoalpha = geopandas.read_file( os.path.join(this_directory, "data/alpha_auto.gpkg") ).geometry[0] def test_alpha_shapes(self): new_a05 = alpha_shape(self.vertices, 0.05).to_numpy().item() new_a10 = alpha_shape(self.vertices, 0.10).to_numpy().item() new_a2 = alpha_shape(self.vertices, 0.2).to_numpy().item() new_a25 = alpha_shape(self.vertices, 0.25).to_numpy().item() assert new_a05.equals(self.a05) assert new_a10.equals(self.a10) assert new_a2.equals(self.a2) assert new_a25.equals(self.a25) def test_auto(self): auto_alpha = alpha_shape_auto(self.vertices, 5) assert self.autoalpha.equals(auto_alpha) def test_small_n(self): new_singleton = alpha_shape(self.vertices[0].reshape(1, -1), 0.5) assert isinstance(new_singleton, geometry.Polygon) new_duo = alpha_shape(self.vertices[:1], 0.5) assert isinstance(new_duo, geometry.Polygon) new_triple = alpha_shape(self.vertices[:2], 0.5) assert isinstance(new_triple, geometry.Polygon) new_triple = alpha_shape_auto( self.vertices[0].reshape(1, -1), return_circles=True ) assert isinstance(new_triple[0], geometry.Polygon) new_triple = alpha_shape_auto(self.vertices[:1], return_circles=True) assert isinstance(new_triple[0], geometry.Polygon) new_triple = alpha_shape_auto(self.vertices[:2], return_circles=True) assert isinstance(new_triple[0], geometry.Polygon) def test_circles(self): ashape, radius, centers = alpha_shape_auto(self.vertices, return_circles=True) np.testing.assert_allclose(radius, self.circle_radii) np.testing.assert_allclose(centers, self.circle_verts) def test_holes(self): np.random.seed(seed=100) points = np.random.rand(1000, 2) * 100 inv_alpha = 3.5 geoms = alpha_shape(points, 1 / inv_alpha) assert len(geoms) == 1 holes = geopandas.GeoSeries(geoms.interiors.explode()).reset_index(drop=True) assert len(holes) == 30 # No holes are within the shape (shape has holes already) if GPD_013: result = geoms.sindex.query(holes.centroid, predicate="within") else: result = geoms.sindex.query_bulk(holes.centroid, predicate="within") assert result.shape == (2, 0) # All holes are within the exterior shell = geopandas.GeoSeries(geoms.exterior.apply(geometry.Polygon)) if GPD_013: within, outside = shell.sindex.query(holes.centroid, predicate="within") else: within, outside = shell.sindex.query_bulk( holes.centroid, predicate="within" ) assert (outside == 0).all() np.testing.assert_array_equal(within, np.arange(30)) libpysal-4.9.2/libpysal/cg/tests/test_geoJSON.py000066400000000000000000000030401452177046000215770ustar00rootroot00000000000000from ... import examples as pysal_examples from ...io.fileio import FileIO as psopen # noqa N813 from ..shapes import Chain, Point, asShape class TesttestMultiPloygon: def test___init__1(self): """Tests conversion of polygons with multiple shells to geoJSON multipolygons and back. """ shp = psopen(pysal_examples.get_path("NAT.shp"), "r") multipolygons = [p for p in shp if len(p.parts) > 1] for poly in multipolygons: json = poly.__geo_interface__ shape = asShape(json) assert json["type"] == "MultiPolygon" assert str(shape.holes) == str(poly.holes) assert str(shape.parts) == str(poly.parts) class TesttestMultiLineString: def test_multipart_chain(self): vertices = [ [Point((0, 0)), Point((1, 0)), Point((1, 5))], [Point((-5, -5)), Point((-5, 0)), Point((0, 0))], ] # part A chain0 = Chain(vertices[0]) # part B chain1 = Chain(vertices[1]) # part A and B chain2 = Chain(vertices) json = chain0.__geo_interface__ assert json["type"] == "LineString" assert len(json["coordinates"]) == 3 json = chain1.__geo_interface__ assert json["type"] == "LineString" assert len(json["coordinates"]) == 3 json = chain2.__geo_interface__ assert json["type"] == "MultiLineString" assert len(json["coordinates"]) == 2 chain3 = asShape(json) assert chain2.parts == chain3.parts libpysal-4.9.2/libpysal/cg/tests/test_locators.py000066400000000000000000000027611452177046000221720ustar00rootroot00000000000000"""locators Unittest.""" from ..locators import * # ruff: noqa: F403, F405 from ..shapes import * class TestPolygonLocator: def setup_method(self): p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))]) p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))]) p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))]) self.polygons = [p1, p2, p3] self.pl = PolygonLocator(self.polygons) pt = Point pg = Polygon polys = [] for i in range(5): l_ = i * 10 r = l_ + 10 b = 10 t = 20 sw = pt((l_, b)) se = pt((r, b)) ne = pt((r, t)) nw = pt((l_, t)) polys.append(pg([sw, se, ne, nw])) self.pl2 = PolygonLocator(polys) def test_polygon_locator(self): qr = Rectangle(3, 7, 5, 8) res = self.pl.inside(qr) assert len(res) == 0 def test_inside(self): qr = Rectangle(3, 3, 5, 5) res = self.pl.inside(qr) assert len(res) == 0 qr = Rectangle(0, 0, 5, 5) res = self.pl.inside(qr) assert len(res) == 1 def test_overlapping(self): qr = Rectangle(3, 3, 5, 5) res = self.pl.overlapping(qr) assert len(res) == 2 qr = Rectangle(8, 3, 10, 10) res = self.pl.overlapping(qr) assert len(res) == 1 qr = Rectangle(2, 12, 35, 15) res = self.pl2.overlapping(qr) assert len(res) == 4 libpysal-4.9.2/libpysal/cg/tests/test_polygonQuadTreeStructure.py000066400000000000000000003021351452177046000254050ustar00rootroot00000000000000"""locators Unittest.""" from ..polygonQuadTreeStructure import QuadTreeStructureSingleRing from ..shapes import Ring class TestQuadTreeStructureSingleRing: def test_quad_tree_structure_single_ring(self): """Tests if the class could successfully determine if a point is inside of a polygon. """ ring_texas = Ring( [ (-105.99835968, 31.3938179016), (-106.212753296, 31.4781284332), (-106.383041382, 31.7337627411), (-106.538970947, 31.7861976624), (-106.614440918, 31.8177280426), (-106.615577698, 31.8446350098), (-106.643531799, 31.8951015472), (-106.633201599, 31.9139976501), (-106.63205719, 31.9721183777), (-106.649513245, 31.9802284241), (-106.623077393, 32.0009880066), (-106.377845764, 32.0006446838), (-106.002708435, 32.0015525818), (-104.921798706, 32.0042686462), (-104.850563049, 32.0031509399), (-104.018814087, 32.0072784424), (-103.980895996, 32.0058898926), (-103.728973389, 32.0061035156), (-103.332092285, 32.0041542053), (-103.05796814, 32.0018997192), (-103.05519104, 32.0849952698), (-103.059547424, 32.5154304504), (-103.048835754, 32.9535331726), (-103.042602539, 33.3777275085), (-103.038238525, 33.5657424927), (-103.03276062, 33.8260879517), (-103.029144287, 34.3077430725), (-103.022155762, 34.7452659607), (-103.024749756, 34.964717865), (-103.025650024, 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(-99.5852203369, 34.3848571777), (-99.5778503418, 34.4089126587), (-99.5538635254, 34.4151802063), (-99.5021362305, 34.4040679932), (-99.4794387817, 34.3835220337), (-99.4383773804, 34.3647041321), (-99.4099578857, 34.3691062927), (-99.3941574097, 34.3967437744), (-99.392791748, 34.4289932251), (-99.3642044067, 34.4501953125), (-99.3232955933, 34.4127082825), (-99.2671737671, 34.3982849121), (-99.2541046143, 34.3682136536), (-99.2054901123, 34.331993103), (-99.1963043213, 34.3051223755), (-99.2045974731, 34.255645752), (-99.1904830933, 34.2237358093), (-99.1761550903, 34.2127304077), (-99.1279449463, 34.2014694214), (-99.0784301758, 34.2083587646), (-99.0352172852, 34.1989212036), (-98.9961929321, 34.2094955444), (-98.952507019, 34.1945648193), (-98.8913421631, 34.1608200073), (-98.8110656738, 34.1459350586), (-98.7785339355, 34.1319618225), (-98.705291748, 34.1307144165), (-98.6822128296, 34.1499977112), (-98.6617202759, 34.1470375061), (-98.6259918213, 34.1584358215), 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(-103.375450134, 29.0321083069), (-103.474075317, 29.0721340179), (-103.526237488, 29.1466464996), (-103.720314026, 29.1906318665), (-103.739852905, 29.230348587), (-103.782157898, 29.2297954559), (-103.76776123, 29.2812404633), (-103.786994934, 29.2672595978), (-104.045631409, 29.328119278), (-104.164382935, 29.4007148743), (-104.204734802, 29.484041214), (-104.377593994, 29.550611496), (-104.535247803, 29.6794662476), (-104.577560425, 29.8079357147), (-104.674369812, 29.9092826843), (-104.696495056, 30.057302475), (-104.674758911, 30.1489639282), (-104.702613831, 30.238489151), (-104.813957214, 30.3504695892), (-104.806472778, 30.3764476776), (-104.852996826, 30.3922634125), (-104.890678406, 30.5705566406), (-104.986930847, 30.6413249969), (-104.997543335, 30.6843338013), (-105.060562134, 30.6878700256), (-105.21434021, 30.8120861053), (-105.25818634, 30.7976531982), (-105.287597656, 30.831949234), (-105.313781738, 30.8165073395), (-105.390312195, 30.8530807495), (-105.409065247, 30.9025096893), (-105.554382324, 30.9982852936), (-105.603218079, 31.0864276886), (-105.769729614, 31.1707801819), (-105.99835968, 31.3938179016), ] ) qtssr_texas = QuadTreeStructureSingleRing(ring_texas) points = [ [-96.83201838665211, 30.43633054583931, True], [-94.97179271816618, 30.46609834459278, True], [-96.87911915799319, 29.002783392404453, True], [-104.03817768478906, 26.080885130466065, False], [-101.05107033713017, 26.110092672074, False], [-101.90984946051287, 30.2673660562615, True], [-104.2666097673043, 26.638892954348243, False], [-99.00209313228278, 29.312408039204637, True], [-96.76821760997925, 26.53415999513266, False], [-103.60734801725461, 25.989562175464958, False], [-105.52663947372852, 35.59616356954213, False], [-99.73190928401823, 36.34065757158246, False], [-94.28455658519579, 30.510396248421543, True], [-98.287902523108, 32.42446924695663, True], [-98.36319041112684, 26.836620743656326, True], [-100.58823300352246, 28.287422480123738, False], [-106.09575378559609, 29.647073739534953, False], [-96.03197805726944, 34.67673925196511, False], [-106.13189503506499, 28.293328347459486, False], [-105.6163099497765, 34.642876249285806, False], [-102.1699840394609, 32.64792829807019, True], [-102.19213396201079, 31.137813785847744, True], [-96.8832056156457, 33.584067733298724, True], [-105.47157574361026, 33.89108923934286, False], [-101.53889206881809, 27.73095057942401, False], [-95.0730796759137, 26.09464277833887, False], [-96.45795032815394, 30.22238046821291, True], [-99.47479689046119, 27.519862201810025, True], [-104.95530088407097, 34.46894379454478, False], [-99.64935392553515, 27.166719715093752, False], [-101.8740013838435, 35.11537675606412, True], [-99.07039344683601, 33.64951303142618, True], [-106.60595918307385, 29.64471250102584, False], [-97.20030616424087, 32.31820772186446, True], [-96.23784236099425, 27.638993220475054, False], [-105.93765675735541, 35.89412147712274, False], [-97.17726496423283, 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False], [-96.65811064697854, 29.033441999230575, True], [-94.79391199397416, 32.26686772352405, True], [-94.05970729814709, 27.578098867203245, False], [-101.34732336260434, 31.621990539913554, True], [-104.3518957862908, 29.119881150805877, False], [-94.62935978446168, 30.4280012647268, True], [-94.4545353979626, 35.54695331314244, False], [-95.54556621919373, 36.13412073236616, False], [-95.35828131810126, 29.196029446685102, True], [-94.03367199299583, 29.998249298603696, True], [-106.36092437901962, 35.88480338683122, False], [-104.36739535175568, 34.44871919970392, False], [-101.23847775114616, 33.38670941589996, True], [-99.14984412490651, 33.42882053133658, True], [-103.57375420847947, 36.10822666498764, False], [-105.16720721276594, 35.376479016301516, False], [-97.83947903342955, 27.52109204805256, True], [-98.71405542652877, 35.7689674767476, False], [-103.06998061485356, 34.82880997682268, False], [-105.28204362921868, 28.400435333576176, False], [-94.15911792478674, 26.17595690168265, False], [-96.8089922559183, 28.576299745236, True], [-94.55012730211439, 35.965323310709934, False], [-102.35052435497836, 35.54979117991739, True], [-104.37005549234762, 27.1898216329847, False], [-95.68880266269171, 28.848538713723595, True], [-99.48796021732129, 33.879870610429656, True], [-101.00413095653694, 34.33763822793282, True], [-106.07730385091368, 29.134515491377414, False], [-103.45299952582074, 27.571405818685125, False], [-102.68769661036903, 35.0508397444369, True], [-102.12226125487027, 31.490531154286373, True], [-99.20157845594305, 31.551754104915446, True], [-102.55860273418811, 34.074770794654526, True], [-106.13595097934105, 30.66643783693412, False], [-106.62769073938416, 30.65343353159766, False], [-106.40940133512453, 29.82237466871204, False], [-102.1529566504516, 35.06622589749327, True], [-102.80724491463563, 34.7087445437067, True], [-97.64939497688633, 30.67928132241753, True], [-96.36801227272899, 27.90369756441311, False], [-94.09540454906752, 26.5340311979023, False], [-100.95868651879503, 29.489244579528528, True], [-99.00791521500773, 33.15713091296997, True], [-94.56949551831565, 29.097678237118387, False], [-104.30210679432342, 27.480997154392185, False], [-102.89216000521263, 28.618705789328487, False], [-96.97440058322412, 33.50865616454956, True], [-98.99276448264528, 33.16482908208919, True], [-104.83376414397748, 30.866597558655094, True], [-105.87523535009888, 26.94560046391572, False], [-99.29766049432588, 32.83368387034709, True], [-102.51690788569256, 33.11843291048262, True], [-95.8185042576338, 29.933635250495907, True], [-95.64639798097132, 29.81408328213472, True], [-95.27363238555648, 26.04686490238641, False], [-97.49469880046678, 28.7839351111114, True], [-102.55955572766781, 32.24579142286663, True], [-102.3259956134002, 29.68040823171017, False], [-97.73863157435005, 29.996562088019196, True], [-104.68631893564948, 32.01946765618769, False], [-95.04016163980175, 33.10637289096444, True], [-104.60749147021224, 34.68835575652551, False], [-103.65636297920052, 30.591031777106217, True], [-102.77020591146633, 28.84679685398533, False], [-93.68494562344958, 30.545382533660963, False], [-105.28442207266875, 35.066432824429576, False], [-96.25390521489425, 26.494829177985668, False], [-98.82250217338702, 27.932953025264148, True], [-101.74188578040746, 34.64776869635973, True], [-106.04404882060925, 28.594522513035013, False], [-93.67216964673989, 29.251042306696004, False], [-99.78759245690895, 33.06425606417031, True], [-98.40506092257863, 30.372595091544586, True], [-102.29051625332913, 32.71012257838102, True], [-103.71880332727147, 32.69104459346947, False], [-97.0545647773379, 30.76611952136734, True], [-96.38483633115239, 27.604532811233028, False], [-106.26518681815887, 30.011599887072457, False], [-96.29869292442771, 28.668280098851437, False], [-101.81643591481892, 26.171067127237418, False], [-99.84342847150265, 26.017698064471553, False], [-104.11331752582073, 29.654316762816297, True], [-99.97301004853021, 27.730105527140825, False], [-106.31647836713242, 32.17703231405249, False], [-105.53511133815337, 26.702440097120153, False], [-96.73532103327318, 35.78642691945332, False], [-99.1113903696459, 34.40215160301135, False], [-105.57490309093876, 36.00074957169259, False], [-96.91408056656418, 32.50309840505817, True], [-97.12004011524724, 26.947775539446376, False], [-104.31177621433217, 28.977811712282257, False], [-94.2821165561003, 30.71331882514141, True], [-98.29146997158631, 26.800066299911094, True], [-103.116290122766, 31.46742167424961, True], [-98.56811800705276, 29.888220960970425, True], [-98.08987891135445, 27.80577602559695, True], [-95.34430060625397, 35.0872381937164, False], [-97.29977470332764, 28.838558342329026, True], [-99.29340339460651, 32.1659999533891, True], [-98.45946176172794, 35.06767544568288, False], [-97.28077383253662, 30.77329811793486, True], [-103.2046478070315, 33.95113909648831, False], [-97.41533999785908, 32.08647437970406, True], [-106.24449926545937, 26.923081786350345, False], [-97.29470564456045, 36.18937223503928, False], [-97.66122972103732, 34.56870670469234, False], [-102.35020419742744, 27.349690061784784, False], [-93.77154189004054, 33.43617694637923, False], [-101.35103101345337, 29.463692272605073, False], [-101.62926979404627, 32.803732493055875, True], [-94.90780742661717, 27.023519182303016, False], [-106.20392742378257, 32.97382450322062, False], [-100.31113315995306, 29.431049347619815, True], [-101.26240055732195, 36.148817757576396, True], [-98.42727511780691, 27.460837740553018, True], [-98.57534771820664, 35.599414672044254, False], [-97.62020154014516, 26.369709082738453, True], [-103.2986841163323, 32.6157912851791, False], [-93.93578337162835, 34.486048529590185, False], [-93.64615854059947, 28.5736836659404, False], [-96.99391123102941, 28.896993703684423, True], [-102.45723986918175, 35.7010832684699, True], [-105.25314374574465, 26.38581255615052, False], [-100.2188547460063, 28.291673320576088, True], [-100.7877537462725, 34.49341196472571, True], [-106.45021897380575, 26.72924721086809, False], [-105.02103129126913, 30.757125052138996, True], [-98.53860937298697, 32.45578723025558, True], [-99.57879415297293, 33.96386507864504, True], [-93.7378357236408, 27.9626758279968, False], [-100.63489750669079, 35.38304941928768, True], [-106.59421404071085, 30.052955341162452, False], [-106.59172119923385, 35.689363691993364, False], [-97.34149801358657, 32.67880291850629, True], [-94.73963101360403, 27.850819649646564, False], [-100.94403277172583, 33.474538412919046, True], [-99.27226009251935, 27.468315184289615, True], [-100.4539129843546, 34.591160165118865, True], [-106.44494557869739, 28.035999053683224, False], [-94.23984184184754, 29.557288201626776, False], [-99.03587934877508, 26.59598428059316, True], [-99.74353348891233, 32.82386415542986, True], [-101.86634664448142, 35.898545381710065, True], [-104.82904170889573, 35.68593461490256, False], [-102.883660082079, 25.927764876566876, False], [-95.10167954830852, 29.132555432953012, False], [-104.16716765381528, 32.54432440506069, False], [-95.8001698490917, 30.554039020076594, True], [-98.25774265085879, 30.67468293710671, True], [-104.14549182637904, 36.13448211420454, False], [-100.79273106998009, 27.687205570475957, False], [-97.9031540972447, 34.04156602854073, False], [-99.48655188258273, 36.01928708820145, False], [-103.78885086741725, 26.649573815350735, False], [-97.54897028080592, 26.611495630090587, True], [-106.18533271033974, 26.521041809084632, False], [-102.04736124316179, 35.80143440960038, True], [-103.53014416761859, 35.94251011771925, False], [-106.37072741181773, 33.56020830971726, False], [-100.5087744739241, 33.68691572355519, True], [-103.14684873740707, 30.33159482347187, True], [-101.04498614381122, 34.01935100090506, True], [-99.23406810345234, 36.47780260248638, False], [-96.38309061733807, 28.10204238295031, False], [-106.04814322602003, 30.009165824440764, False], [-99.09937261770088, 36.27541704541078, False], [-94.9236280439113, 26.908216725432283, False], [-96.03138446605873, 29.48956628516225, True], [-101.79748242792357, 36.2396677016486, True], [-96.7954152001291, 28.981691254375008, True], [-95.95383982488345, 33.80047001504878, True], [-103.89568070095685, 26.027352965725004, False], [-104.33069038138268, 36.46092136221017, False], [-99.40487331382435, 33.362496703874385, True], [-98.0390467871466, 27.286158481267943, True], [-103.93051493329094, 36.21025032666974, False], [-100.89036166271175, 29.431712359982495, True], [-103.01093811284001, 28.951789475547162, False], [-105.6925789844033, 26.67611273491844, False], [-100.63232815476351, 35.18180560295863, True], [-95.84068243438212, 26.566440681951057, False], [-101.69166851142406, 27.58570691504354, False], [-96.39192036000713, 30.76580153991847, True], [-99.60575587904657, 32.844854917935244, True], [-99.88359268023875, 26.427305650516256, False], [-102.49870407406512, 35.670387195714135, True], [-99.02838249064023, 34.35433748751913, False], [-93.69511330973809, 28.964334494491123, False], [-102.77438414412866, 34.77587690396507, True], [-97.20637129262126, 28.62351031644859, True], [-97.20760493091282, 29.66509727888832, True], [-97.78279263302204, 28.650053248724216, True], [-105.10373645902229, 30.10605688088612, False], [-102.29320671455936, 34.913227698065626, True], [-102.08456452797166, 35.04888378920539, True], [-97.684013792577, 35.91610190976283, False], [-94.66766997135741, 28.388037123223707, False], [-93.51699445904997, 30.745955788402362, False], [-100.56415276925733, 27.122598143422042, False], [-105.51909002313077, 30.081775705006855, False], [-95.57290373716107, 26.724672719886662, False], [-103.50352867348445, 32.004206585428776, True], [-102.8747549124684, 32.8979077719816, True], [-94.54178731632837, 28.121556170094824, False], [-105.1852492618114, 34.57852818651511, False], [-101.56833897599279, 32.394936636984475, True], [-102.06612007055514, 30.0654558718264, True], [-104.27037065814437, 26.539037110206124, False], [-96.84449118726283, 34.303252013399494, False], [-100.21448448808589, 31.640102623719255, True], [-105.11541407363484, 32.59032999242083, False], [-102.47865996013053, 36.106193753181586, True], [-102.1982854302976, 30.87389402879236, True], [-103.75935920410515, 34.164405398206384, False], [-100.26058874460439, 29.97152621245525, True], [-99.98584687760041, 29.138422331875738, True], [-98.8136163575852, 31.471193466135066, True], [-98.2634703260477, 34.441857423457066, False], [-101.31389165577758, 26.67722696068761, False], [-105.25939274478021, 33.42397682936403, False], [-95.42401575533701, 36.38213160460099, False], [-93.7911156924766, 26.16919479726154, False], [-106.0793837632488, 32.0426040667038, False], [-101.67576371230456, 26.55813999428948, False], [-105.29328020590933, 33.317961711071874, False], [-106.6282733323151, 26.640651252579072, False], [-100.97693002993205, 29.778473204159543, True], [-99.81270323613123, 28.521657399940562, True], [-106.35993676923691, 32.47765856951956, False], [-98.6318807925877, 27.313103487591995, True], [-105.60269402006043, 33.9266565877851, False], [-96.50522425362368, 33.17862810486471, True], [-99.36585657273703, 27.918462376378407, True], [-102.71165541572611, 27.26977019467567, False], [-105.26203795210641, 33.50921690053216, False], [-105.45340320249285, 28.841949098625847, False], [-97.67986158008269, 31.45298937828147, True], [-95.42927279657806, 30.012853799790946, True], [-94.61186352922475, 30.990817003676423, True], [-98.75882027588506, 33.21933322666259, True], [-104.55525321655045, 36.35184775125859, False], [-97.39087680042095, 29.262712771446328, True], [-95.32479451597851, 32.558534263953774, True], [-96.17652314621081, 34.39156129414083, False], [-99.95318514227759, 29.206707699022903, True], [-105.32756527254323, 29.177591819396113, False], [-104.63110828519505, 35.14036164405956, False], [-101.75109732251778, 30.720543376919306, True], [-98.43013838260205, 28.14394272715192, True], [-97.0734335255207, 28.750007474412804, True], [-103.21154248047685, 32.22703974346086, False], [-102.39810543632244, 32.819942719538474, True], [-103.36259451330584, 27.176657526494928, False], [-103.66650593230085, 36.025520559005244, False], [-101.63539174258722, 27.803279475131447, False], [-96.34811589893562, 31.14578417358513, True], [-98.94976448687677, 31.767797366700666, True], [-97.38029827656692, 36.27728736453143, False], [-95.93103949592769, 28.74450380455483, True], [-98.8151117128684, 34.70259233670797, False], [-96.14623746963383, 30.48841309287015, True], [-101.46340647601903, 30.19511307273737, True], [-100.63602533061145, 31.749219881531914, True], [-101.63505098125265, 33.69021849156649, True], [-94.71406829659445, 31.90061330594235, True], ] for p in points: assert p[2] == qtssr_texas.contains_point((p[0], p[1])) libpysal-4.9.2/libpysal/cg/tests/test_rtree.py000066400000000000000000000030401452177046000214540ustar00rootroot00000000000000"""pyrtree Unittest.""" from ..rtree import Rect, RTree class TestPyrtree: def setup_method(self): k = 10 w = 20 objects = {} id_ = 0 for i in range(k): mn_y = i * w mx_y = mn_y + w for j in range(k): mn_x = j * w mx_x = mn_x + w objects[id_] = Rect(mn_x, mn_y, mx_x, mx_y) id_ += 1 self.objects = objects def test_rtree(self): t = RTree() for object_ in self.objects: t.insert(object_, self.objects[object_]) assert len(self.objects) == 100 qr = Rect(5, 5, 25, 25) # find objects with mbrs intersecting with qr res = [r.leaf_obj() for r in t.query_rect(qr) if r.is_leaf()] assert len(res) == 4 res.sort() assert res == [0, 1, 10, 11] # vertices are shared by all coincident rectangles res = [r.leaf_obj() for r in t.query_point((20.0, 20.0)) if r.is_leaf()] assert len(res) == 4 res = [r.leaf_obj() for r in t.query_point((21, 20)) if r.is_leaf()] assert len(res) == 2 # single internal point res = [r.leaf_obj() for r in t.query_point((21, 21)) if r.is_leaf()] assert len(res) == 1 # single external point res = [r.leaf_obj() for r in t.query_point((-12, 21)) if r.is_leaf()] assert len(res) == 0 qr = Rect(5, 6, 65, 7) res = [r.leaf_obj() for r in t.query_rect(qr) if r.is_leaf()] assert len(res) == 4 libpysal-4.9.2/libpysal/cg/tests/test_segmentLocator.py000066400000000000000000000026461452177046000233340ustar00rootroot00000000000000"""Segment Locator Unittest.""" # ruff: noqa: F403, F405 from ..segmentLocator import * from ..shapes import * class TestSegmentGrid: def setup_method(self): # 10x10 grid with four line segments, one for each edge of the grid. self.grid = SegmentGrid(Rectangle(0, 0, 10, 10), 1) self.grid.add(LineSegment(Point((0.0, 0.0)), Point((0.0, 10.0))), 0) self.grid.add(LineSegment(Point((0.0, 10.0)), Point((10.0, 10.0))), 1) self.grid.add(LineSegment(Point((10.0, 10.0)), Point((10.0, 0.0))), 2) self.grid.add(LineSegment(Point((10.0, 0.0)), Point((0.0, 0.0))), 3) def test_nearest_1(self): # Center assert [0, 1, 2, 3] == self.grid.nearest(Point((5.0, 5.0))) # Left Edge assert [0] == self.grid.nearest(Point((0.0, 5.0))) # Top Edge assert [1] == self.grid.nearest(Point((5.0, 10.0))) # Right Edge assert [2] == self.grid.nearest(Point((10.0, 5.0))) # Bottom Edge assert [3] == self.grid.nearest(Point((5.0, 0.0))) def test_nearest_2(self): # Left Edge assert [0, 1, 3] == self.grid.nearest(Point((-100000.0, 5.0))) # Right Edge assert [1, 2, 3] == self.grid.nearest(Point((100000.0, 5.0))) # Bottom Edge assert [0, 2, 3] == self.grid.nearest(Point((5.0, -100000.0))) # Top Edge assert [0, 1, 2] == self.grid.nearest(Point((5.0, 100000.0))) libpysal-4.9.2/libpysal/cg/tests/test_shapes.py000066400000000000000000000533301452177046000216250ustar00rootroot00000000000000import pytest from ..shapes import Chain, Line, LineSegment, Point, Polygon, Ray, Rectangle class TesttestPoint: def test___init__1(self): """Tests whether points are created without issue.""" for l_ in [(-5.0, 10.0), (0.0, -6.0), (1e300, float(-1e300))]: Point(l_) def test___str__1(self): """Tests whether the string produced is valid for corner cases.""" for l_ in [(-5, 10), (0, -6.0), (1e300, -1e300)]: p = Point(l_) # Recast to floats like point does assert str(p) == str((float(l_[0]), float(l_[1]))) class TesttestLineSegment: def test_is_ccw1(self): """Test corner cases for horizontal segment starting at origin.""" ls = LineSegment(Point((0, 0)), Point((5, 0))) # At positive boundary beyond segment assert not ls.is_ccw(Point((10, 0))) # On segment assert not ls.is_ccw(Point((3, 0))) # At negative boundary beyond segment assert not ls.is_ccw(Point((-10, 0))) # Endpoint of segment assert not ls.is_ccw(Point((0, 0))) # Endpoint of segment assert not ls.is_ccw(Point((5, 0))) def test_is_ccw2(self): """Test corner cases for vertical segment ending at origin.""" ls = LineSegment(Point((0, -5)), Point((0, 0))) # At positive boundary beyond segment assert not ls.is_ccw(Point((0, 10))) # On segment assert not ls.is_ccw(Point((0, -3))) # At negative boundary beyond segment assert not ls.is_ccw(Point((0, -10))) # Endpoint of segment assert not ls.is_ccw(Point((0, -5))) # Endpoint of segment assert not ls.is_ccw(Point((0, 0))) def test_is_ccw3(self): """Test corner cases for non-axis-aligned segment not through origin.""" ls = LineSegment(Point((0, 1)), Point((5, 6))) # At positive boundary beyond segment assert not ls.is_ccw(Point((10, 11))) # On segment assert not ls.is_ccw(Point((3, 4))) # At negative boundary beyond segment assert not ls.is_ccw(Point((-10, -9))) # Endpoint of segment assert not ls.is_ccw(Point((0, 1))) # Endpoint of segment assert not ls.is_ccw(Point((5, 6))) def test_is_cw1(self): """Test corner cases for horizontal segment starting at origin.""" ls = LineSegment(Point((0, 0)), Point((5, 0))) # At positive boundary beyond segment assert not ls.is_cw(Point((10, 0))) # On segment assert not ls.is_cw(Point((3, 0))) # At negative boundary beyond segment assert not ls.is_cw(Point((-10, 0))) # Endpoint of segment assert not ls.is_cw(Point((0, 0))) # Endpoint of segment assert not ls.is_cw(Point((5, 0))) def test_is_cw2(self): """Test corner cases for vertical segment ending at origin.""" ls = LineSegment(Point((0, -5)), Point((0, 0))) # At positive boundary beyond segment assert not ls.is_cw(Point((0, 10))) # On segment assert not ls.is_cw(Point((0, -3))) # At negative boundary beyond segment assert not ls.is_cw(Point((0, -10))) # Endpoint of segment assert not ls.is_cw(Point((0, -5))) # Endpoint of segment assert not ls.is_cw(Point((0, 0))) def test_is_cw3(self): """Test corner cases for non-axis-aligned segment not through origin.""" ls = LineSegment(Point((0, 1)), Point((5, 6))) # At positive boundary beyond segment assert not ls.is_cw(Point((10, 11))) # On segment assert not ls.is_cw(Point((3, 4))) # At negative boundary beyond segment assert not ls.is_cw(Point((-10, -9))) # Endpoint of segment assert not ls.is_cw(Point((0, 1))) # Endpoint of segment assert not ls.is_cw(Point((5, 6))) def test_get_swap1(self): """Tests corner cases.""" ls = LineSegment(Point((0, 0)), Point((10, 0))) swap = ls.get_swap() assert ls.p1 == swap.p2 assert ls.p2 == swap.p1 ls = LineSegment(Point((-5, 0)), Point((5, 0))) swap = ls.get_swap() assert ls.p1 == swap.p2 assert ls.p2 == swap.p1 ls = LineSegment(Point((0, 0)), Point((0, 0))) swap = ls.get_swap() assert ls.p1 == swap.p2 assert ls.p2 == swap.p1 ls = LineSegment(Point((5, 5)), Point((5, 5))) swap = ls.get_swap() assert ls.p1 == swap.p2 assert ls.p2 == swap.p1 def test_bounding_box(self): """Tests corner cases.""" ls = LineSegment(Point((0, 0)), Point((0, 10))) assert ls.bounding_box.left == 0 assert ls.bounding_box.lower == 0 assert ls.bounding_box.right == 0 assert ls.bounding_box.upper == 10 ls = LineSegment(Point((0, 0)), Point((-3, -4))) assert ls.bounding_box.left == -3 assert ls.bounding_box.lower == -4 assert ls.bounding_box.right == 0 assert ls.bounding_box.upper == 0 ls = LineSegment(Point((-5, 0)), Point((3, 0))) assert ls.bounding_box.left == -5 assert ls.bounding_box.lower == 0 assert ls.bounding_box.right == 3 assert ls.bounding_box.upper == 0 def test_len1(self): """Tests corner cases.""" ls = LineSegment(Point((0, 0)), Point((0, 0))) assert ls.len == 0 ls = LineSegment(Point((0, 0)), Point((-3, 0))) assert ls.len == 3 def test_line1(self): """Tests corner cases.""" import math ls = LineSegment(Point((0, 0)), Point((1, 0))) assert ls.line.m == 0 assert ls.line.b == 0 ls = LineSegment(Point((0, 0)), Point((0, 1))) assert ls.line.m == float("inf") assert math.isnan(ls.line.b) ls = LineSegment(Point((0, 0)), Point((0, -1))) assert ls.line.m == float("inf") assert math.isnan(ls.line.b) ls = LineSegment(Point((0, 0)), Point((0, 0))) assert ls.line is None ls = LineSegment(Point((5, 0)), Point((10, 0))) ls1 = LineSegment(Point((5, 0)), Point((10, 1))) assert ls.intersect(ls1) ls2 = LineSegment(Point((5, 1)), Point((10, 1))) assert not ls.intersect(ls2) ls2 = LineSegment(Point((7, -1)), Point((7, 2))) assert ls.intersect(ls2) class TesttestLine: def test___init__1(self): """Tests a variety of generic cases.""" for m, b in [(4, 0.0), (-140, 5), (0, 0)]: _ = Line(m, b) def test_y1(self): """Tests a variety of generic and special cases (+-infinity).""" l_ = Line(0, 0) assert l_.y(0) == 0 assert l_.y(-1e600) == 0 assert l_.y(1e600) == 0 l_ = Line(1, 1) assert l_.y(2) == 3 assert l_.y(-1e600) == -1e600 assert l_.y(1e600) == 1e600 l_ = Line(-1, 1) assert l_.y(2) == -1 assert l_.y(-1e600) == 1e600 assert l_.y(1e600) == -1e600 def test_x1(self): """Tests a variety of generic and special cases (+-infinity).""" l_ = Line(0, 0) # self.assertEquals(l.x(0), 0) with pytest.raises(ArithmeticError): l_.x(0) with pytest.raises(ArithmeticError): l_.x(-1e600) with pytest.raises(ArithmeticError): l_.x(1e600) l_ = Line(1, 1) assert l_.x(3) == 2 assert l_.x(-1e600) == -1e600 assert l_.x(1e600) == 1e600 l_ = Line(-1, 1) assert l_.x(2) == -1 assert l_.x(-1e600) == 1e600 assert l_.x(1e600) == -1e600 class TesttestRay: def test___init__1(self): """Tests generic cases.""" _ = Ray(Point((0, 0)), Point((1, 1))) _ = Ray(Point((8, -3)), Point((-5, 9))) class TesttestChain: def test___init__1(self): """Generic testing that no exception is thrown.""" _ = Chain([Point((0, 0))]) _ = Chain([[Point((0, 0)), Point((1, 1))], [Point((2, 5))]]) def test_vertices1(self): """Testing for repeated vertices and multiple parts.""" vertices = [ Point((0, 0)), Point((1, 1)), Point((2, 5)), Point((0, 0)), Point((1, 1)), Point((2, 5)), ] assert Chain(vertices).vertices == vertices vertices = [ [Point((0, 0)), Point((1, 1)), Point((2, 5))], [Point((0, 0)), Point((1, 1)), Point((2, 5))], ] assert Chain(vertices).vertices == vertices[0] + vertices[1] def test_parts1(self): """Generic testing of parts functionality.""" vertices = [ Point((0, 0)), Point((1, 1)), Point((2, 5)), Point((0, 0)), Point((1, 1)), Point((2, 5)), ] assert Chain(vertices).parts == [vertices] vertices = [ [Point((0, 0)), Point((1, 1)), Point((2, 5))], [Point((0, 0)), Point((1, 1)), Point((2, 5))], ] assert Chain(vertices).parts == vertices def test_bounding_box1(self): """Test correctness with multiple parts.""" vertices = [ [Point((0, 0)), Point((1, 1)), Point((2, 6))], [Point((-5, -5)), Point((0, 0)), Point((2, 5))], ] bb = Chain(vertices).bounding_box assert bb.left == -5 assert bb.lower == -5 assert bb.right == 2 assert bb.upper == 6 def test_len1(self): """Test correctness with multiple parts and zero-length point-to-point distances. """ vertices = [ [Point((0, 0)), Point((1, 0)), Point((1, 5))], [Point((-5, -5)), Point((-5, 0)), Point((0, 0)), Point((0, 0))], ] assert Chain(vertices).len == 6 + 10 class TesttestPolygon: def test___init__1(self): """Test various input configurations (list vs. lists of lists, holes).""" # Input configurations tested (in order of test): # one part, no holes # multi parts, no holes # one part, one hole # multi part, one hole # one part, multi holes # multi part, multi holes _ = Polygon([Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))]) _ = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))], ] ) _ = Polygon( [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], holes=[Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))], ) _ = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))], ], holes=[Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))], ) _ = Polygon( [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], holes=[ [Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))], [Point((6, 6)), Point((6, 8)), Point((8, 8)), Point((8, 6))], ], ) _ = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))], ], holes=[ [Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))], [Point((6, 6)), Point((6, 8)), Point((8, 8)), Point((8, 6))], ], ) def test_area1(self): """Test multiple parts.""" p = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))], ] ) assert p.area == 200 def test_area2(self): """Test holes.""" p = Polygon( [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], holes=[Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))], ) assert p.area == 100 - 4 p = Polygon( [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], holes=[ [Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))], [Point((6, 6)), Point((6, 8)), Point((8, 8)), Point((8, 6))], ], ) assert p.area == 100 - (4 + 4) p = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))], ], holes=[ [Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))], [Point((36, 36)), Point((36, 38)), Point((38, 38)), Point((38, 36))], ], ) assert p.area == 200 - (4 + 4) def test_area4(self): """Test polygons with vertices in both orders (cw, ccw).""" p = Polygon([Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))]) assert p.area == 100 p = Polygon([Point((0, 0)), Point((0, 10)), Point((10, 10)), Point((10, 0))]) assert p.area == 100 def test_bounding_box1(self): """Test polygons with multiple parts.""" p = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))], ] ) bb = p.bounding_box assert bb.left == 0 assert bb.lower == 0 assert bb.right == 40 assert bb.upper == 40 def test_centroid1(self): """Test polygons with multiple parts of the same size.""" p = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))], ] ) c = p.centroid assert c[0] == 20 assert c[1] == 20 def test_centroid2(self): """Test polygons with multiple parts of different size.""" p = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((35, 30)), Point((35, 35)), Point((30, 35))], ] ) c = p.centroid assert c[0] == 10.5 assert c[1] == 10.5 def test_holes1(self): """Test for correct vertex values/order.""" p = Polygon( [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], holes=[Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))], ) assert len(p.holes) == 1 e_holes = [Point((2, 2)), Point((2, 4)), Point((4, 4)), Point((4, 2))] assert p.holes[0] in [ e_holes, [e_holes[-1]] + e_holes[:3], e_holes[-2:] + e_holes[:2], e_holes[-3:] + [e_holes[0]], ] def test_holes2(self): """Test for multiple holes.""" p = Polygon( [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], holes=[ [Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))], [Point((6, 6)), Point((6, 8)), Point((8, 8)), Point((8, 6))], ], ) holes = p.holes assert len(holes) == 2 def test_parts1(self): """Test for correct vertex values/order.""" p = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((30, 40))], ] ) assert len(p.parts) == 2 part1 = [Point((0, 0)), Point((0, 10)), Point((10, 10)), Point((10, 0))] part2 = [Point((30, 30)), Point((30, 40)), Point((40, 30))] if len(p.parts[0]) == 4: assert p.parts[0] in [ part1, part1[-1:] + part1[:3], part1[-2:] + part1[:2], part1[-3:] + part1[:1], ] assert p.parts[1] in [part2, part2[-1:] + part2[:2], part2[-2:] + part2[:1]] elif len(p.parts[0]) == 3: assert p.parts[0] in [part2, part2[-1:] + part2[:2], part2[-2:] + part2[:1]] assert p.parts[1] in [ part1, part1[-1:] + part1[:3], part1[-2:] + part1[:2], part1[-3:] + part1[:1], ] else: pytest.fail() def test_perimeter1(self): """Test with multiple parts.""" p = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))], ] ) assert p.perimeter == 80 def test_perimeter2(self): """Test with holes.""" p = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))], ], holes=[ [Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))], [Point((6, 6)), Point((6, 8)), Point((8, 8)), Point((8, 6))], ], ) assert p.perimeter == 80 + 16 def test_vertices1(self): """Test for correct values/order of vertices.""" p = Polygon([Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))]) assert len(p.vertices) == 4 e_verts = [Point((0, 0)), Point((0, 10)), Point((10, 10)), Point((10, 0))] assert p.vertices in [ e_verts, e_verts[-1:] + e_verts[:3], e_verts[-2:] + e_verts[:2], e_verts[-3:] + e_verts[:1], ] def test_vertices2(self): """Test for multiple parts.""" p = Polygon( [ [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((30, 30)), Point((40, 30)), Point((40, 40)), Point((30, 40))], ] ) assert len(p.vertices) == 8 def test_contains_point(self): p = Polygon( [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))], [Point((1, 2)), Point((2, 2)), Point((2, 1)), Point((1, 1))], ) assert p.contains_point((0, 0)) == 0 assert p.contains_point((1, 1)) == 0 assert p.contains_point((5, 5)) == 1 assert p.contains_point((10, 10)) == 0 class TesttestRectangle: def test___init__1(self): """Test exceptions are thrown correctly.""" try: # right < left _ = Rectangle(1, 1, -1, 5) except ArithmeticError: pass else: pytest.fail() try: # upper < lower _ = Rectangle(1, 1, 5, -1) except ArithmeticError: pass else: pytest.fail() def test_set_centroid1(self): """Test with rectangles of zero width or height.""" # Zero width r = Rectangle(5, 5, 5, 10) r.set_centroid(Point((0, 0))) assert r.left == 0 assert r.lower == -2.5 assert r.right == 0 assert r.upper == 2.5 # Zero height r = Rectangle(10, 5, 20, 5) r.set_centroid(Point((40, 40))) assert r.left == 35 assert r.lower == 40 assert r.right == 45 assert r.upper == 40 # Zero width and height r = Rectangle(0, 0, 0, 0) r.set_centroid(Point((-4, -4))) assert r.left == -4 assert r.lower == -4 assert r.right == -4 assert r.upper == -4 def test_set_scale1(self): """Test repeated scaling.""" r = Rectangle(2, 2, 4, 4) r.set_scale(0.5) assert r.left == 2.5 assert r.lower == 2.5 assert r.right == 3.5 assert r.upper == 3.5 r.set_scale(2) assert r.left == 2 assert r.lower == 2 assert r.right == 4 assert r.upper == 4 def test_set_scale2(self): """Test scaling of rectangles with zero width/height.""" # Zero width r = Rectangle(5, 5, 5, 10) r.set_scale(2) assert r.left == 5 assert r.lower == 2.5 assert r.right == 5 assert r.upper == 12.5 # Zero height r = Rectangle(10, 5, 20, 5) r.set_scale(2) assert r.left == 5 assert r.lower == 5 assert r.right == 25 assert r.upper == 5 # Zero width and height r = Rectangle(0, 0, 0, 0) r.set_scale(100) assert r.left == 0 assert r.lower == 0 assert r.right == 0 assert r.upper == 0 # Zero width and height r = Rectangle(0, 0, 0, 0) r.set_scale(0.01) assert r.left == 0 assert r.lower == 0 assert r.right == 0 assert r.upper == 0 def test_area1(self): """Test rectangles with zero width/height.""" # Zero width r = Rectangle(5, 5, 5, 10) assert r.area == 0 # Zero height r = Rectangle(10, 5, 20, 5) assert r.area == 0 # Zero width and height r = Rectangle(0, 0, 0, 0) assert r.area == 0 def test_height1(self): """Test rectangles with zero height.""" # Zero height r = Rectangle(10, 5, 20, 5) assert r.height == 0 def test_width1(self): """Test rectangles with zero width.""" # Zero width r = Rectangle(5, 5, 5, 10) assert r.width == 0 libpysal-4.9.2/libpysal/cg/tests/test_sphere.py000066400000000000000000000132351452177046000216300ustar00rootroot00000000000000import math import numpy as np import pytest from ... import examples as pysal_examples from ...io.fileio import FileIO as psopen # noqa N813 from .. import sphere class TestSphere: def setup_method(self): self.pt0 = (0, 0) self.pt1 = (180, 0) f = psopen(pysal_examples.get_path("stl_hom.shp"), "r") self.shapes = f.read() self.p0 = (-87.893517, 41.981417) self.p1 = (-87.519295, 41.657498) self.p3 = (41.981417, -87.893517) self.p4 = (41.657498, -87.519295) def test_arcdist(self): d = sphere.arcdist(self.pt0, self.pt1, sphere.RADIUS_EARTH_MILES) assert d == math.pi * sphere.RADIUS_EARTH_MILES def test_arcdist2linear(self): d = sphere.arcdist(self.pt0, self.pt1, sphere.RADIUS_EARTH_MILES) ld = sphere.arcdist2linear(d, sphere.RADIUS_EARTH_MILES) assert ld == 2.0 def test_radangle(self): p0 = (-87.893517, 41.981417) p1 = (-87.519295, 41.657498) assert sphere.radangle(p0, p1) == pytest.approx(0.007460167953189258) def test_linear2arcdist(self): d = sphere.arcdist(self.pt0, self.pt1, sphere.RADIUS_EARTH_MILES) ad = sphere.linear2arcdist(2.0, radius=sphere.RADIUS_EARTH_MILES) assert d == ad def test_harcdist(self): d1 = sphere.harcdist(self.p0, self.p1, radius=sphere.RADIUS_EARTH_MILES) assert d1 == pytest.approx(29.532983644123796) d1 = sphere.harcdist(self.p3, self.p4, radius=sphere.RADIUS_EARTH_MILES) assert d1 == pytest.approx(25.871647470233675) def test_geointerpolate(self): pn1 = sphere.geointerpolate(self.p0, self.p1, 0.1) assert pn1 == pytest.approx((-87.85592403438788, 41.949079912574796)) pn2 = sphere.geointerpolate(self.p3, self.p4, 0.1, lonx=False) assert pn2 == pytest.approx((41.949079912574796, -87.85592403438788)) def test_geogrid(self): grid = [ (42.023768, -87.946389), (42.02393997819538, -87.80562679358316), (42.02393997819538, -87.66486420641684), (42.023768, -87.524102), (41.897317, -87.94638900000001), (41.8974888973743, -87.80562679296166), (41.8974888973743, -87.66486420703835), (41.897317, -87.524102), (41.770866000000005, -87.94638900000001), (41.77103781320412, -87.80562679234043), (41.77103781320412, -87.66486420765956), (41.770866000000005, -87.524102), (41.644415, -87.946389), (41.64458672568646, -87.80562679171955), (41.64458672568646, -87.66486420828045), (41.644415, -87.524102), ] # Arlington Heights IL pup = (42.023768, -87.946389) # Hammond, IN pdown = (41.644415, -87.524102) grid1 = sphere.geogrid(pup, pdown, 3, lonx=False) np.testing.assert_array_almost_equal(grid, grid1) def test_to_xyz(self): w2 = { 0: [2, 5, 6, 10], 1: [4, 7, 9, 14], 2: [6, 0, 3, 8], 3: [8, 2, 12, 4], 4: [1, 9, 12, 3], 5: [11, 10, 0, 15], 6: [2, 10, 8, 0], 7: [14, 1, 16, 9], 8: [12, 3, 19, 6], 9: [12, 16, 4, 1], 10: [17, 6, 15, 5], 11: [15, 13, 5, 21], 12: [8, 19, 9, 3], 13: [21, 11, 15, 28], 14: [7, 16, 22, 9], 15: [11, 27, 10, 26], 16: [14, 25, 9, 20], 17: [31, 18, 10, 26], 18: [17, 19, 23, 32], 19: [23, 20, 12, 18], 20: [23, 25, 19, 34], 21: [13, 28, 27, 15], 22: [30, 14, 29, 24], 23: [20, 19, 18, 34], 24: [30, 22, 41, 43], 25: [20, 16, 33, 34], 26: [31, 27, 38, 17], 27: [35, 28, 26, 21], 28: [21, 37, 27, 35], 29: [33, 30, 22, 25], 30: [24, 29, 43, 22], 31: [40, 26, 17, 32], 32: [39, 45, 31, 18], 33: [29, 25, 44, 34], 34: [36, 25, 39, 33], 35: [27, 37, 46, 38], 36: [39, 34, 50, 48], 37: [47, 28, 35, 46], 38: [51, 35, 26, 40], 39: [36, 45, 32, 34], 40: [49, 31, 38, 45], 41: [52, 43, 30, 53], 42: [43, 44, 33, 53], 43: [42, 53, 41, 30], 44: [42, 33, 50, 58], 45: [48, 39, 32, 40], 46: [47, 35, 55, 37], 47: [46, 37, 54, 35], 48: [45, 50, 56, 39], 49: [40, 57, 51, 45], 50: [48, 36, 59, 44], 51: [61, 38, 55, 49], 52: [41, 53, 64, 43], 53: [60, 43, 52, 64], 54: [55, 47, 46, 61], 55: [54, 61, 46, 51], 56: [62, 66, 48, 57], 57: [49, 65, 61, 56], 58: [59, 60, 68, 44], 59: [58, 63, 50, 69], 60: [53, 64, 68, 58], 61: [67, 51, 55, 57], 62: [63, 56, 66, 48], 63: [62, 70, 69, 59], 64: [60, 53, 52, 71], 65: [57, 72, 66, 67], 66: [62, 56, 75, 65], 67: [61, 65, 72, 55], 68: [60, 58, 76, 71], 69: [73, 70, 63, 59], 70: [74, 63, 69, 77], 71: [68, 76, 64, 60], 72: [65, 75, 67, 66], 73: [69, 76, 77, 68], 74: [75, 70, 77, 66], 75: [74, 66, 72, 65], 76: [73, 68, 71, 69], 77: [70, 74, 73, 69], } pts = [shape.centroid for shape in self.shapes] pts = list(map(sphere.toXYZ, pts)) assert sphere.brute_knn(pts, 4, "xyz") == pytest.approx(w2) libpysal-4.9.2/libpysal/cg/tests/test_standalone.py000066400000000000000000000556431452177046000225030ustar00rootroot00000000000000# ruff: noqa: F403, F405 import math import numpy as np import pytest from ..shapes import * from ..standalone import * class TestBbcommon: def test_bbcommon(self): b0 = [0, 0, 10, 10] b1 = [5, 5, 15, 15] assert bbcommon(b0, b1) == 1 def test_bbcommon_same(self): b0 = [0, 0, 10, 10] b1 = [0, 0, 10, 10] assert bbcommon(b0, b1) == 1 def test_bbcommon_nested(self): b0 = [0, 0, 10, 10] b1 = [1, 1, 9, 9] assert bbcommon(b0, b1) == 1 def test_bbcommon_top(self): b0 = [0, 0, 10, 10] b1 = [3, 5, 6, 15] assert bbcommon(b0, b1) == 1 def test_bbcommon_shared_edge(self): b0 = [0, 0, 10, 10] b1 = [0, 10, 10, 20] assert bbcommon(b0, b1) == 1 def test_bbcommon_shared_corner(self): b0 = [0, 0, 10, 10] b1 = [10, 10, 20, 20] assert bbcommon(b0, b1) == 1 def test_bbcommon_floats(self): b0 = [0.0, 0.0, 0.1, 0.1] b1 = [0.05, 0.05, 0.15, 0.15] assert bbcommon(b0, b1) == 1 class TestGetBoundingBox: def test_get_bounding_box(self): items = [Point((-1, 5)), Rectangle(0, 6, 11, 12)] expected = [-1, 5, 11, 12] assert expected == get_bounding_box(items)[:] class TestGetAngleBetween: def test_get_angle_between(self): ray1 = Ray(Point((0, 0)), Point((1, 0))) ray2 = Ray(Point((0, 0)), Point((1, 0))) assert get_angle_between(ray1, ray2) == 0.0 def test_get_angle_between_expect45(self): ray1 = Ray(Point((0, 0)), Point((1, 0))) ray2 = Ray(Point((0, 0)), Point((1, 1))) assert math.degrees(get_angle_between(ray1, ray2)) == 45.0 def test_get_angle_between_expect90(self): ray1 = Ray(Point((0, 0)), Point((1, 0))) ray2 = Ray(Point((0, 0)), Point((0, 1))) assert math.degrees(get_angle_between(ray1, ray2)) == 90.0 class TestIsCollinear: def test_is_collinear(self): assert is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 5))) def test_is_collinear_expect_false(self): assert not is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 0))) def test_is_collinear_along_x(self): assert is_collinear(Point((0, 0)), Point((1, 0)), Point((5, 0))) def test_is_collinear_along_y(self): assert is_collinear(Point((0, 0)), Point((0, 1)), Point((0, -1))) def test_is_collinear_small_float(self): """ Given: p1 = (0.1, 0.2), p2 = (0.2, 0.3), p3 = (0.3, 0.4) Line(p1, p2): y = mx + b m = (0.3-0.2) / (0.2-0.1) = .1/.1 = 1 y - mx = b b = 0.3 - 1*0.2 = 0.1 b = 0.2 - 1*0.1 = 0.1 y = 1*x + 0.1 Line(p2, p3): y = mx + b m = (0.4-0.3) / (0.3-0.2) = .1/.1 = 1 y - mx = b b = 0.4 - 1*0.3 = 0.1 b = 0.4 - 1*0.2 = 0.1 y = 1*x + 0.1 Line(p1, p2) == Line(p2 ,p3) Therefore ``p1, p2, p3`` are collinear. Due to floating point rounding areas the standard test, ((p2[0]-p1[0])*(p3[1]-p1[1]) - (p2[1]-p1[1])*(p3[0]-p1[0])) == 0 will fail. To get around this we use an epsilon. The ``numpy.finfo`` function return an smallest epsilon for the given data types such that, (numpy.finfo(float).eps + 1.0) != 1.0 Therefore if abs( (p2[0]-p1[0]) * (p3[1]-p1[1]) - (p2[1]-p1[1]) * (p3[0]-p1[0]) ) < numpy.finfo(p1[0]).eps The points are collinear. """ assert is_collinear(Point((0.1, 0.2)), Point((0.2, 0.3)), Point((0.3, 0.4))) def test_is_collinear_random(self): for i in range(10): a, b, c = np.random.random(3) * 10 ** (i) assert is_collinear(Point((a, a)), Point((b, b)), Point((c, c))) def test_is_collinear_random2(self): for _i in range(1000): a, b, c = np.random.random(3) assert is_collinear(Point((a, a)), Point((b, b)), Point((c, c))) class TestGetSegmentsIntersect: def test_get_segments_intersect(self): seg1 = LineSegment(Point((0, 0)), Point((0, 10))) seg2 = LineSegment(Point((-5, 5)), Point((5, 5))) assert get_segments_intersect(seg1, seg2)[:] == (0.0, 5.0) def test_get_segments_intersect_shared_vert(self): seg1 = LineSegment(Point((0, 0)), Point((0, 10))) seg2 = LineSegment(Point((-5, 5)), Point((0, 10))) assert get_segments_intersect(seg1, seg2)[:] == (0.0, 10.0) def test_get_segments_intersect_floats(self): seg1 = LineSegment(Point((0, 0)), Point((0, 0.10))) seg2 = LineSegment(Point((-0.5, 0.05)), Point((0.5, 0.05))) assert get_segments_intersect(seg1, seg2)[:] == (0.0, 0.05) def test_get_segments_intersect_angles(self): seg1 = LineSegment(Point((0, 0)), Point((1, 1))) seg2 = LineSegment(Point((1, 0)), Point((0, 1))) assert get_segments_intersect(seg1, seg2)[:] == (0.5, 0.5) def test_get_segments_intersect_no_intersect(self): seg1 = LineSegment(Point((-5, 5)), Point((5, 5))) seg2 = LineSegment(Point((100, 100)), Point((100, 101))) assert None is get_segments_intersect(seg1, seg2) def test_get_segments_intersect_overlap(self): seg1 = LineSegment(Point((0.1, 0.1)), Point((0.6, 0.6))) seg2 = LineSegment(Point((0.3, 0.3)), Point((0.9, 0.9))) expected = LineSegment(Point((0.3, 0.3)), Point((0.6, 0.6))) assert expected == get_segments_intersect(seg1, seg2) def test_get_segments_intersect_same(self): seg1 = LineSegment(Point((-5, 5)), Point((5, 5))) assert seg1 == get_segments_intersect(seg1, seg1) def test_get_segments_intersect_nested(self): seg1 = LineSegment(Point((0.1, 0.1)), Point((0.9, 0.9))) seg2 = LineSegment(Point((0.3, 0.3)), Point((0.6, 0.6))) assert seg2 == get_segments_intersect(seg1, seg2) class TestGetSegmentPointIntersect: def test_get_segment_point_intersect(self): seg = LineSegment(Point((0, 0)), Point((0, 10))) pt = Point((0, 5)) assert pt == get_segment_point_intersect(seg, pt) def test_get_segment_point_intersect_left_end(self): seg = LineSegment(Point((0, 0)), Point((0, 10))) pt = seg.p1 assert pt == get_segment_point_intersect(seg, pt) def test_get_segment_point_intersect_right_end(self): seg = LineSegment(Point((0, 0)), Point((0, 10))) pt = seg.p2 assert pt == get_segment_point_intersect(seg, pt) def test_get_segment_point_intersect_angle(self): seg = LineSegment(Point((0, 0)), Point((1, 1))) pt = Point((0.1, 0.1)) assert pt == get_segment_point_intersect(seg, pt) def test_get_segment_point_intersect_no_intersect(self): seg = LineSegment(Point((0, 0)), Point((0, 10))) pt = Point((5, 5)) assert None is get_segment_point_intersect(seg, pt) def test_get_segment_point_intersect_no_intersect_collinear(self): seg = LineSegment(Point((0, 0)), Point((0, 10))) pt = Point((0, 20)) assert None is get_segment_point_intersect(seg, pt) def test_get_segment_point_intersect_floats_1(self): seg = LineSegment(Point((0.3, 0.3)), Point((0.9, 0.9))) pt = Point((0.5, 0.5)) assert pt == get_segment_point_intersect(seg, pt) def test_get_segment_point_intersect_floats_2(self): seg = LineSegment( Point((0.0, 0.0)), Point((2.7071067811865475, 2.7071067811865475)) ) pt = Point((1.0, 1.0)) assert pt == get_segment_point_intersect(seg, pt) def test_get_segment_point_intersect_floats_no_intersect(self): seg = LineSegment(Point((0.3, 0.3)), Point((0.9, 0.9))) pt = Point((0.1, 0.1)) assert None is get_segment_point_intersect(seg, pt) class TestGetPolygonPointIntersect: def test_get_polygon_point_intersect(self): poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) pt = Point((0.5, 0.5)) assert pt == get_polygon_point_intersect(poly, pt) def test_get_polygon_point_intersect_on_edge(self): poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) pt = Point((1.0, 0.5)) assert pt == get_polygon_point_intersect(poly, pt) def test_get_polygon_point_intersect_on_vertex(self): poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) pt = Point((1.0, 1.0)) assert pt == get_polygon_point_intersect(poly, pt) def test_get_polygon_point_intersect_outside(self): poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) pt = Point((2.0, 2.0)) assert None is get_polygon_point_intersect(poly, pt) class TestGetRectanglePointIntersect: def test_get_rectangle_point_intersect(self): rect = Rectangle(0, 0, 5, 5) pt = Point((1, 1)) assert pt == get_rectangle_point_intersect(rect, pt) def test_get_rectangle_point_intersect_on_edge(self): rect = Rectangle(0, 0, 5, 5) pt = Point((2.5, 5)) assert pt == get_rectangle_point_intersect(rect, pt) def test_get_rectangle_point_intersect_on_vertex(self): rect = Rectangle(0, 0, 5, 5) pt = Point((5, 5)) assert pt == get_rectangle_point_intersect(rect, pt) def test_get_rectangle_point_intersect_outside(self): rect = Rectangle(0, 0, 5, 5) pt = Point((10, 10)) assert None is get_rectangle_point_intersect(rect, pt) class TestGetRaySegmentIntersect: def test_get_ray_segment_intersect(self): ray = Ray(Point((0, 0)), Point((0, 1))) seg = LineSegment(Point((-1, 10)), Point((1, 10))) assert get_ray_segment_intersect(ray, seg)[:] == (0.0, 10.0) def test_get_ray_segment_intersect_orgin(self): ray = Ray(Point((0, 0)), Point((0, 1))) seg = LineSegment(Point((-1, 0)), Point((1, 0))) assert get_ray_segment_intersect(ray, seg)[:] == (0.0, 0.0) def test_get_ray_segment_intersect_edge(self): ray = Ray(Point((0, 0)), Point((0, 1))) seg = LineSegment(Point((0, 2)), Point((2, 2))) assert get_ray_segment_intersect(ray, seg)[:] == (0.0, 2.0) def test_get_ray_segment_intersect_no_intersect(self): ray = Ray(Point((0, 0)), Point((0, 1))) seg = LineSegment(Point((10, 10)), Point((10, 11))) assert None is get_ray_segment_intersect(ray, seg) def test_get_ray_segment_intersect_segment(self): ray = Ray(Point((0, 0)), Point((5, 5))) seg = LineSegment(Point((1, 1)), Point((2, 2))) assert seg == get_ray_segment_intersect(ray, seg) class TestGetRectangleRectangleIntersection: def test_get_rectangle_rectangle_intersection_leftright(self): r0 = Rectangle(0, 4, 6, 9) r1 = Rectangle(4, 0, 9, 7) expected = [4.0, 4.0, 6.0, 7.0] assert expected == get_rectangle_rectangle_intersection(r0, r1)[:] def test_get_rectangle_rectangle_intersection_topbottom(self): r0 = Rectangle(0, 0, 4, 4) r1 = Rectangle(2, 1, 6, 3) expected = [2.0, 1.0, 4.0, 3.0] assert expected == get_rectangle_rectangle_intersection(r0, r1)[:] def test_get_rectangle_rectangle_intersection_nested(self): r0 = Rectangle(0, 0, 4, 4) r1 = Rectangle(2, 1, 3, 2) assert r1 == get_rectangle_rectangle_intersection(r0, r1) def test_get_rectangle_rectangle_intersection_shared_corner(self): r0 = Rectangle(0, 0, 4, 4) r1 = Rectangle(4, 4, 8, 8) assert Point((4, 4)) == get_rectangle_rectangle_intersection(r0, r1) def test_get_rectangle_rectangle_intersection_shared_edge(self): r0 = Rectangle(0, 0, 4, 4) r1 = Rectangle(0, 4, 4, 8) assert LineSegment( Point((0, 4)), Point((4, 4)) ) == get_rectangle_rectangle_intersection(r0, r1) def test_get_rectangle_rectangle_intersection_shifted_edge(self): r0 = Rectangle(0, 0, 4, 4) r1 = Rectangle(2, 4, 6, 8) assert LineSegment( Point((2, 4)), Point((4, 4)) ) == get_rectangle_rectangle_intersection(r0, r1) def test_get_rectangle_rectangle_intersection_no_intersect(self): r0 = Rectangle(0, 0, 4, 4) r1 = Rectangle(5, 5, 8, 8) assert None is get_rectangle_rectangle_intersection(r0, r1) class TestGetPolygonPointDist: def test_get_polygon_point_dist(self): poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) pt = Point((2, 0.5)) expected = 1.0 assert expected == get_polygon_point_dist(poly, pt) def test_get_polygon_point_dist_inside(self): poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) pt = Point((0.5, 0.5)) expected = 0.0 assert expected == get_polygon_point_dist(poly, pt) def test_get_polygon_point_dist_on_vertex(self): poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) pt = Point((1.0, 1.0)) expected = 0.0 assert expected == get_polygon_point_dist(poly, pt) def test_get_polygon_point_dist_on_edge(self): poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))]) pt = Point((0.5, 1.0)) expected = 0.0 assert expected == get_polygon_point_dist(poly, pt) class TestGetPointsDist: def test_get_points_dist(self): pt1 = Point((0.5, 0.5)) pt2 = Point((0.5, 0.5)) assert get_points_dist(pt1, pt2) == 0 def test_get_points_dist_diag(self): pt1 = Point((0, 0)) pt2 = Point((1, 1)) assert 2 ** (0.5) == get_points_dist(pt1, pt2) def test_get_points_dist_along_x(self): pt1 = Point((-1000, 1 / 3.0)) pt2 = Point((1000, 1 / 3.0)) assert get_points_dist(pt1, pt2) == 2000 def test_get_points_dist_along_y(self): pt1 = Point((1 / 3.0, -500)) pt2 = Point((1 / 3.0, 500)) assert get_points_dist(pt1, pt2) == 1000 class TestGetSegmentPointDist: def test_get_segment_point_dist(self): seg = LineSegment(Point((0, 0)), Point((10, 0))) pt = Point((5, 5)) assert get_segment_point_dist(seg, pt) == (5.0, 0.5) def test_get_segment_point_dist_on_end_point(self): seg = LineSegment(Point((0, 0)), Point((10, 0))) pt = Point((0, 0)) assert get_segment_point_dist(seg, pt) == (0.0, 0.0) def test_get_segment_point_dist_on_middle(self): seg = LineSegment(Point((0, 0)), Point((10, 0))) pt = Point((5, 0)) assert get_segment_point_dist(seg, pt) == (0.0, 0.5) def test_get_segment_point_diag(self): seg = LineSegment(Point((0, 0)), Point((10, 10))) pt = Point((5, 5)) assert pytest.approx(get_segment_point_dist(seg, pt)[0]) == 0.0 assert pytest.approx(get_segment_point_dist(seg, pt)[1]) == 0.5 def test_get_segment_point_diag_with_dist(self): seg = LineSegment(Point((0, 0)), Point((10, 10))) pt = Point((0, 10)) assert 50 ** (0.5) == pytest.approx(get_segment_point_dist(seg, pt)[0]) assert pytest.approx(get_segment_point_dist(seg, pt)[1]) == 0.5 class TestGetPointAtAngleAndDist: def test_get_point_at_angle_and_dist(self): ray = Ray(Point((0, 0)), Point((1, 0))) pt = get_point_at_angle_and_dist(ray, math.pi, 1.0) assert pytest.approx(pt[0]) == -1.0 assert pytest.approx(pt[1]) == 0.0 def test_get_point_at_angle_and_dist_diag(self): ray = Ray(Point((0, 0)), Point((1, 1))) pt = get_point_at_angle_and_dist(ray, math.pi, 2 ** (0.5)) assert pytest.approx(pt[0]) == -1.0 assert pytest.approx(pt[1]) == -1.0 def test_get_point_at_angle_and_dist_diag_90(self): ray = Ray(Point((0, 0)), Point((1, 1))) pt = get_point_at_angle_and_dist(ray, -math.pi / 2.0, 2 ** (0.5)) assert pytest.approx(pt[0]) == 1.0 assert pytest.approx(pt[1]) == -1.0 def test_get_point_at_angle_and_dist_diag_45(self): ray = Ray(Point((0, 0)), Point((1, 1))) pt = get_point_at_angle_and_dist(ray, -math.pi / 4.0, 1) assert pytest.approx(pt[0]) == 1.0 assert pytest.approx(pt[1]) == 0.0 class TestConvexHull: def test_convex_hull(self): points = [Point((0, 0)), Point((4, 4)), Point((4, 0)), Point((3, 1))] assert [Point((0.0, 0.0)), Point((4.0, 0.0)), Point((4.0, 4.0))] == convex_hull( points ) class TestIsClockwise: def test_is_clockwise(self): vertices = [Point((0, 0)), Point((0, 10)), Point((10, 0))] assert True is is_clockwise(vertices) def test_is_clockwise_expect_false(self): vertices = [Point((0, 0)), Point((10, 0)), Point((0, 10))] assert False is is_clockwise(vertices) def test_is_clockwise_big(self): vertices = [ (-106.57798, 35.174143999999998), (-106.583412, 35.174141999999996), (-106.58417999999999, 35.174143000000001), (-106.58377999999999, 35.175542999999998), (-106.58287999999999, 35.180543), (-106.58263099999999, 35.181455), (-106.58257999999999, 35.181643000000001), (-106.58198299999999, 35.184615000000001), (-106.58148, 35.187242999999995), (-106.58127999999999, 35.188243), (-106.58138, 35.188243), (-106.58108, 35.189442999999997), (-106.58104, 35.189644000000001), (-106.58028, 35.193442999999995), (-106.580029, 35.194541000000001), (-106.57974399999999, 35.195785999999998), (-106.579475, 35.196961999999999), (-106.57922699999999, 35.198042999999998), (-106.578397, 35.201665999999996), (-106.57827999999999, 35.201642999999997), (-106.57737999999999, 35.201642999999997), (-106.57697999999999, 35.201543000000001), (-106.56436599999999, 35.200311999999997), (-106.56058, 35.199942999999998), (-106.56048, 35.197342999999996), (-106.56048, 35.195842999999996), (-106.56048, 35.194342999999996), (-106.56048, 35.193142999999999), (-106.56048, 35.191873999999999), (-106.56048, 35.191742999999995), (-106.56048, 35.190242999999995), (-106.56037999999999, 35.188642999999999), (-106.56037999999999, 35.187242999999995), (-106.56037999999999, 35.186842999999996), (-106.56037999999999, 35.186552999999996), (-106.56037999999999, 35.185842999999998), (-106.56037999999999, 35.184443000000002), (-106.56037999999999, 35.182943000000002), (-106.56037999999999, 35.181342999999998), (-106.56037999999999, 35.180433000000001), (-106.56037999999999, 35.179943000000002), (-106.56037999999999, 35.178542999999998), (-106.56037999999999, 35.177790999999999), (-106.56037999999999, 35.177143999999998), (-106.56037999999999, 35.175643999999998), (-106.56037999999999, 35.174444000000001), (-106.56037999999999, 35.174043999999995), (-106.560526, 35.174043999999995), (-106.56478, 35.174043999999995), (-106.56627999999999, 35.174143999999998), (-106.566541, 35.174144999999996), (-106.569023, 35.174157000000001), (-106.56917199999999, 35.174157999999998), (-106.56938, 35.174143999999998), (-106.57061499999999, 35.174143999999998), (-106.57097999999999, 35.174143999999998), (-106.57679999999999, 35.174143999999998), (-106.57798, 35.174143999999998), ] assert True is is_clockwise(vertices) class TestPointTouchesRectangle: def test_point_touches_rectangle_inside(self): rect = Rectangle(0, 0, 10, 10) point = Point((5, 5)) assert point_touches_rectangle(point, rect) def test_point_touches_rectangle_on_edge(self): rect = Rectangle(0, 0, 10, 10) point = Point((10, 5)) assert point_touches_rectangle(point, rect) def test_point_touches_rectangle_on_corner(self): rect = Rectangle(0, 0, 10, 10) point = Point((10, 10)) assert point_touches_rectangle(point, rect) def test_point_touches_rectangle_outside(self): rect = Rectangle(0, 0, 10, 10) point = Point((11, 11)) assert not point_touches_rectangle(point, rect) class TestGetSharedSegments: def test_get_shared_segments(self): poly1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))]) poly2 = Polygon([Point((1, 0)), Point((1, 1)), Point((2, 1)), Point((2, 0))]) poly3 = Polygon([Point((0, 1)), Point((0, 2)), Point((1, 2)), Point((1, 1))]) poly4 = Polygon([Point((1, 1)), Point((1, 2)), Point((2, 2)), Point((2, 1))]) assert True is get_shared_segments(poly1, poly2, bool_ret=True) assert True is get_shared_segments(poly1, poly3, bool_ret=True) assert True is get_shared_segments(poly3, poly4, bool_ret=True) assert True is get_shared_segments(poly4, poly2, bool_ret=True) assert False is get_shared_segments(poly1, poly4, bool_ret=True) assert False is get_shared_segments(poly3, poly2, bool_ret=True) def test_get_shared_segments_non_bool(self): poly1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))]) poly2 = Polygon([Point((1, 0)), Point((1, 1)), Point((2, 1)), Point((2, 0))]) poly3 = Polygon([Point((0, 1)), Point((0, 2)), Point((1, 2)), Point((1, 1))]) poly4 = Polygon([Point((1, 1)), Point((1, 2)), Point((2, 2)), Point((2, 1))]) assert ( LineSegment(Point((1, 0)), Point((1, 1))) == get_shared_segments(poly1, poly2)[0] ) assert ( LineSegment(Point((0, 1)), Point((1, 1))) == get_shared_segments(poly1, poly3)[0] ) assert ( LineSegment(Point((1, 2)), Point((1, 1))) == get_shared_segments(poly3, poly4)[0] ) assert ( LineSegment(Point((2, 1)), Point((1, 1))) == get_shared_segments(poly4, poly2)[0] ) # expected = [LineSegment(Point((1, 1)), Point((1, 0)))] # assert expected == get_shared_segments(poly1, poly3) # expected = [LineSegment(Point((1, 1)), Point((1, 0)))] # assert expected == get_shared_segments(poly3, poly4) # expected = [LineSegment(Point((1, 1)), Point((1, 0)))] # assert expected == get_shared_segments(poly4, poly2) class TestDistanceMatrix: def test_distance_matrix(self): points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)] dist = distance_matrix(np.array(points), 2) for i in range(0, len(points)): for j in range(i, len(points)): x, y = points[i] X, Y = points[j] d = ((x - X) ** 2 + (y - Y) ** 2) ** (0.5) assert dist[i, j] == d libpysal-4.9.2/libpysal/cg/tests/test_voronoi.py000066400000000000000000000022561452177046000220360ustar00rootroot00000000000000import numpy as np from ..shapes import Polygon, asShape from ..voronoi import voronoi, voronoi_frames class TestVoronoi: def setup_method(self): self.points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)] self.vertices = [ [4.21783295711061, 4.084085778781038], [7.519560251284979, 3.518075385494004], [9.464219298524961, 19.399457604620512], [14.982106844470032, -10.63503022227075], [-9.226913414477298, -4.58994413837245], [14.982106844470032, -10.63503022227075], [1.7849180090475505, 19.898032941190912], [9.464219298524961, 19.399457604620512], [1.7849180090475505, 19.898032941190912], [-9.226913414477298, -4.58994413837245], ] def test_voronoi(self): regions, vertices = voronoi(self.points) assert regions == [[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]] np.testing.assert_array_almost_equal(vertices, self.vertices) def test_voronoi_frames(self): r_df, p_df = voronoi_frames(self.points) region = r_df.iloc[0]["geometry"] assert isinstance(asShape(region), Polygon) libpysal-4.9.2/libpysal/cg/voronoi.py000066400000000000000000000254721452177046000176420ustar00rootroot00000000000000""" Voronoi tesslation of 2-d point sets. Adapted from https://gist.github.com/pv/8036995 """ import numpy as np from scipy.spatial import Voronoi __author__ = "Serge Rey " __all__ = ["voronoi_frames"] def voronoi(points, radius=None): """Determine finite Voronoi diagram for a 2-d point set. See also ``voronoi_regions()``. Parameters ---------- points : array_like An nx2 array of points. radius : float (optional) The distance to 'points at infinity'. Default is ``None.`` Returns ------- vor : tuple A two-element tuple consisting of a list and an array. Each element of the list contains the sequence of the indices of Voronoi vertices composing a Voronoi polygon (region), whereas the array contains the Voronoi vertex coordinates. Examples -------- >>> points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)] >>> regions, coordinates = voronoi(points) >>> regions [[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]] >>> coordinates array([[ 4.21783296, 4.08408578], [ 7.51956025, 3.51807539], [ 9.4642193 , 19.3994576 ], [ 14.98210684, -10.63503022], [ -9.22691341, -4.58994414], [ 14.98210684, -10.63503022], [ 1.78491801, 19.89803294], [ 9.4642193 , 19.3994576 ], [ 1.78491801, 19.89803294], [ -9.22691341, -4.58994414]]) """ vor = voronoi_regions(Voronoi(points), radius=radius) return vor def voronoi_regions(vor, radius=None): """Finite voronoi regions for a 2-d point set. See also ``voronoi()``. Parameters ---------- vor : scipy.spatial.Voronoi A planar Voronoi diagram. radius : float (optional) Distance to 'points at infinity'. Default is ``None.`` Returns ------- regions_vertices : tuple A two-element tuple consisting of a list of finite voronoi regions and an array Voronoi vertex coordinates. """ new_regions = [] new_vertices = vor.vertices.tolist() center = vor.points.mean(axis=0) if radius is None: radius = vor.points.ptp().max() * 2 all_ridges = {} for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices, strict=True): all_ridges.setdefault(p1, []).append((p2, v1, v2)) all_ridges.setdefault(p2, []).append((p1, v1, v2)) for p1, region in enumerate(vor.point_region): vertices = vor.regions[region] if all(v >= 0 for v in vertices): new_regions.append(vertices) continue ridges = all_ridges[p1] new_region = [v for v in vertices if v >= 0] for p2, v1, v2 in ridges: if v2 < 0: v1, v2 = v2, v1 if v1 >= 0: continue t = vor.points[p2] - vor.points[p1] t /= np.linalg.norm(t) n = np.array([-t[1], t[0]]) midpoint = vor.points[[p1, p2]].mean(axis=0) direction = np.sign(np.dot(midpoint - center, n)) * n far_point = vor.vertices[v2] + direction * radius new_region.append(len(new_vertices)) new_vertices.append(far_point.tolist()) vs = np.asarray([new_vertices[v] for v in new_region]) c = vs.mean(axis=0) angles = np.arctan2(vs[:, 1] - c[1], vs[:, 0] - c[0]) new_region = np.array(new_region)[np.argsort(angles)] new_regions.append(new_region.tolist()) regions_vertices = new_regions, np.asarray(new_vertices) return regions_vertices def as_dataframes(regions, vertices, points): """Helper function to store finite Voronoi regions and originator points as ``geopandas`` (or ``pandas``) dataframes. Parameters ---------- regions : list Each element of the list contains sequence of the indexes of voronoi vertices composing a vornoi polygon (region). vertices : array_like The coordinates of the vornoi vertices. points : array_like The originator points. Returns ------- region_df : geopandas.GeoDataFrame Finite Voronoi polygons as geometries. points_df : geopandas.GeoDataFrame Originator points as geometries. Raises ------ ImportError Raised when ``geopandas`` is not available. ImportError Raised when ``shapely`` is not available. """ try: import geopandas as gpd except ImportError: gpd = None try: from shapely.geometry import Point, Polygon except ImportError: from .shapes import Point, Polygon if gpd is not None: region_df = gpd.GeoDataFrame( geometry=gpd.GeoSeries(Polygon(vertices[region]) for region in regions) ) point_df = gpd.GeoDataFrame( geometry=gpd.GeoSeries(Point(pnt) for pnt in points) ) else: import pandas as pd region_df = pd.DataFrame() region_df["geometry"] = [ Polygon(vertices[region].tolist()) for region in regions ] point_df = pd.DataFrame() point_df["geometry"] = [Point(pnt) for pnt in points] return region_df, point_df def voronoi_frames(points, radius=None, clip="extent"): """Composite helper to return Voronoi regions and generator points as individual dataframes. Parameters ---------- points : array_like The originator points. radius : float The distance to 'points at infinity' used in building voronoi cells. Default is ``None``. clip : {str, shapely.geometry.Polygon} An overloaded option about how to clip the voronoi cells. Default is ``'extent'``. Options are as follows. * ``'none'``/``None`` -- No clip is applied. Voronoi cells may be arbitrarily larger that the source map. Note that this may lead to cells that are many orders of magnitude larger in extent than the original map. Not recommended. * ``'bbox'``/``'extent'``/``'bounding box'`` -- Clip the voronoi cells to the bounding box of the input points. * ``'chull``/``'convex hull'`` -- Clip the voronoi cells to the convex hull of the input points. * ``'ashape'``/``'ahull'`` -- Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alphashape, using ``libpysal.cg.alpha_shape_auto``). * Polygon -- Clip to an arbitrary Polygon. Returns ------- reg_vtx : tuple Two ``geopandas.GeoDataFrame`` (or ``pandas.DataFrame`` if ``geopandas`` is unavailable) objects--``(region_df, points_df)``--of finite Voronoi polygons and the originator points as geometries. Notes ----- If ``geopandas`` is not available the return types will be ``pandas.DataFrame`` objects, each with a geometry column populated with PySAL shapes. If ``geopandas`` is available, return types are ``pandas.GeoDataFrame`` objects with a geometry column populated with shapely geometry types. Examples -------- >>> points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)] >>> regions_df, points_df = voronoi_frames(points) >>> regions_df.shape (4, 1) >>> regions_df.shape == points_df.shape True """ # noqa E501 regions, vertices = voronoi(points, radius=radius) regions, vertices = as_dataframes(regions, vertices, points) if clip: regions = clip_voronoi_frames_to_extent(regions, vertices, clip=clip) reg_vtx = regions, vertices return reg_vtx def clip_voronoi_frames_to_extent(regions, vertices, clip="extent"): """Generate a geopandas.GeoDataFrame of Voronoi cells clipped to a specified extent. Parameters ---------- regions : geopandas.GeoDataFrame A (geo)dataframe containing voronoi cells to clip. vertices : geopandas.GeoDataFrame A (geo)dataframe containing vertices used to build voronoi cells. clip : str, shapely.geometry.Polygon An overloaded option about how to clip the voronoi cells. The options are: - 'none'/None: No clip is applied. Voronoi cells may be arbitrarily larger that the source map. Note that this may lead to cells that are many orders of magnitude larger in extent than the original map. Not recommended. - 'bbox'/'extent'/'bounding box': Clip the voronoi cells to the bounding box of the input points. - 'chull'/'convex hull': Clip the voronoi cells to the convex hull of the input points. - 'ashape'/'ahull': Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alphashape, using ``libpysal.cg.alpha_shape_auto``). - Polygon: Clip to an arbitrary Polygon. Returns ------- clipped_regions : geopandas.GeoDataFrame A ``geopandas.GeoDataFrame`` of clipped voronoi regions. Raises ------ ImportError Raised when ``shapely`` is not available. ImportError Raised when ``geopandas`` is not available. ValueError Raised when in invalid value for ``clip`` is passed in. """ try: from shapely.geometry import Polygon except ImportError: raise ImportError("Shapely is required to clip voronoi regions.") from None try: import geopandas except ImportError: raise ImportError("Geopandas is required to clip voronoi regions.") from None if isinstance(clip, Polygon): clipper = geopandas.GeoDataFrame(geometry=[clip]) elif clip is None or clip.lower() == "none": return regions elif clip.lower() in ("bounds", "bounding box", "bbox", "extent"): min_x, min_y, max_x, max_y = vertices.total_bounds bounding_poly = Polygon( [ (min_x, min_y), (min_x, max_y), (max_x, max_y), (max_x, min_y), (min_x, min_y), ] ) clipper = geopandas.GeoDataFrame(geometry=[bounding_poly]) elif clip.lower() in ("chull", "convex hull", "convex_hull"): clipper = geopandas.GeoDataFrame( geometry=[vertices.geometry.unary_union.convex_hull] ) elif clip.lower() in ( "ahull", "alpha hull", "alpha_hull", "ashape", "alpha shape", "alpha_shape", ): from ..weights.distance import get_points_array from .alpha_shapes import alpha_shape_auto coordinates = get_points_array(vertices.geometry) clipper = geopandas.GeoDataFrame(geometry=[alpha_shape_auto(coordinates)]) else: raise ValueError( f"Clip type '{clip}' not understood. Try one of the supported options: " "[None, 'extent', 'chull', 'ahull']." ) clipped_regions = geopandas.overlay(regions, clipper, how="intersection") return clipped_regions libpysal-4.9.2/libpysal/common.py000066400000000000000000000062721452177046000170430ustar00rootroot00000000000000import copy import pandas # noqa F401 try: from patsy import PatsyError except ImportError: PatsyError = Exception RTOL = 0.00001 ATOL = 1e-7 MISSINGVALUE = None #################### # Decorators/Utils # #################### # import numba.jit OR create mimic decorator and set existence flag try: from numba import jit HAS_JIT = True except ImportError: def jit(function=None, **kwargs): # noqa ARG001 """Mimic numba.jit() with synthetic wrapper.""" if function is not None: def wrapped(*original_args, **original_kw): """Case 1 - structure of a standard decorator i.e., jit(function)(*args, **kwargs). """ return function(*original_args, **original_kw) return wrapped else: def partial_inner(func): """Case 2 - returns Case 1 i.e., jit()(function)(*args, **kwargs). """ return jit(func) return partial_inner HAS_JIT = False def simport(modname): """Safely import a module without raising an error. Parameters ---------- modname : str Module name needed to import. Returns ------- _simport : tuple Either (True, Module) or (False, None) depending on whether the import succeeded. Notes ----- Wrapping this function around an iterative context or a with context would allow the module to be used without necessarily attaching it permanently in the global namespace: for t,mod in simport('pandas'): if t: mod.DataFrame() else: #do alternative behavior here del mod #or don't del, your call instead of: t, mod = simport('pandas') if t: mod.DataFrame() else: #do alternative behavior here The first idiom makes it work kind of a like a with statement. """ try: exec(f"import {modname}") _simport = True, eval(modname) except (ImportError, ModuleNotFoundError): _simport = False, None return _simport def requires(*args, **kwargs): """Decorator to wrap functions with extra dependencies. Parameters ---------- args : list Modules names as strings to import. verbose : bool Set as ``True`` to print a warning message on import failure. Returns ------- inner : func The original function if all arg in args are importable. passer : func A function that passes if ``inner`` fails. """ v = kwargs.pop("verbose", True) wanted = copy.deepcopy(args) def inner(function): available = [simport(arg)[0] for arg in args] if all(available): return function else: def passer(*args, **kwargs): # noqa ARG001 if v: missing = [arg for i, arg in enumerate(wanted) if not available[i]] print(f"missing dependencies: {missing}") print(f"not running {function.__name__}") else: pass return passer return inner libpysal-4.9.2/libpysal/examples/000077500000000000000000000000001452177046000170105ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/10740/000077500000000000000000000000001452177046000174635ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/10740/10740.dbf000066400000000000000000000177741452177046000206330ustar00rootroot00000000000000_ÃÁ)WGIST_IDNFIPSSTCOCTRT2000CSTFIDC TRACTIDC 135001000107350010001071.07 235001000108350010001081.08 335001000109350010001091.09 435001000110350010001101.10 535001000111350010001111.11 635001000112350010001121.12 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libpysal-4.9.2/libpysal/examples/10740/README.md000066400000000000000000000006251452177046000207450ustar00rootroot0000000000000010740 ===== Albuquerque, New Mexico, Census 2000 Tract Data. 10740 is the Core Based Statistical Area (CBSA) code for Albuquerque, New Mexico. ----------------------------------------------- * 10740.dbf: attribute data. (k=5) * 10740.shp: shapefile. (n=195) * 10740.shx: spatial index. * 10740_queen.gal: queen contiguity weights in GAL format. * 10740_rook.gal: rook contiguity weights in GAL format. libpysal-4.9.2/libpysal/examples/Line/000077500000000000000000000000001452177046000176775ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/Line/Line.dbf000066400000000000000000000011551452177046000212450ustar00rootroot00000000000000_{WNameCPIValueN FValueN  Eye1 1 1.100 Eye2 2 2.200 Nose 3 3.300 Mouth 4 4.400libpysal-4.9.2/libpysal/examples/Line/Line.prj000066400000000000000000000002171452177046000213030ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]libpysal-4.9.2/libpysal/examples/Line/Line.shp000066400000000000000000000010641452177046000213030ustar00rootroot00000000000000' èÈßñuî=¥¿ÐDÓäÎyÝ¿(Yñ9àÎÁ?ö½Ëiп0ÀFR:ÝŠ‚¿=ÁeÎǒпŠå¤~?k@`\ˆÐ¿ÀFR:ÝŠ‚¿k@`\ˆÐ¿Å å¤~?=ÁeÎǒпŠå¤~?=ÁeÎǒп(`œ¾Ûö»?ö½Ëiпô>!}rÀ?ö½Ëiп`œ¾Ûö»?ö½Ëiпô>!}rÀ?ö½Ëiп8Ö…†Ûø©?…¾(}üÌÖ¿pEúèí¯?׆îKsÔ¿Ö…†Ûø©?׆îKsÔ¿Ö…†Ûø©?…¾(}üÌÖ¿pEúèí¯?ȯóÂnÖ¿pEúèí¯?ȯóÂnÖ¿HÈßñuî=¥¿ÐDÓäÎyÝ¿(Yñ9àÎÁ?dþcù·ùÙ¿Èßñuî=¥¿f‡K²UlÚ¿ÀÉbꉿœ*(a,Ü¿pÇèHÙ›?ÐDÓäÎyÝ¿„g7,³?[›ŒZÝ¿À(žT@Ǽ?²%œ”ÜíÛ¿(Yñ9àÎÁ?dþcù·ùÙ¿libpysal-4.9.2/libpysal/examples/Line/Line.shx000066400000000000000000000002041452177046000213060ustar00rootroot00000000000000' BèÈßñuî=¥¿ÐDÓäÎyÝ¿(Yñ9àÎÁ?ö½Ëiп20f(’8ÎHlibpysal-4.9.2/libpysal/examples/Line/README.md000066400000000000000000000003011452177046000211500ustar00rootroot00000000000000Line ===== Line Shapefile --------------- * Line.dbf: attribute data. (k=3) * Line.prj: ESRI projection file. * Line.shp: Line shapefile. (n=4) * Line.shx: spatial index. Used for testing. libpysal-4.9.2/libpysal/examples/Point/000077500000000000000000000000001452177046000201015ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/Point/Point.dbf000066400000000000000000000023241452177046000216500ustar00rootroot00000000000000_ {WNameCPIValueN FValueN  One 1 1.100 Two 2 2.200 Three 3 3.300 Four 4 4.400 Five 5 5.500 Six 6 6.600 Seven 7 7.700 Eight 8 8.800 Nine 9 9.900libpysal-4.9.2/libpysal/examples/Point/Point.prj000066400000000000000000000002171452177046000217070ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]libpysal-4.9.2/libpysal/examples/Point/Point.shp000066400000000000000000000005401452177046000217050ustar00rootroot00000000000000' °è€«ÉÊÝ ¥¿ *d@à}Ý¿`ø±ƒæÁ? ÛâåEgп M˜¶WF¿”y?E8”п  ìW×ñ½? ÛâåEgп  ˜½0ÖÒ©?")ˆ×·Õ¿ €«ÉÊÝ ¥¿Z|=iÚ¿ €¡ÃýÙy‡¿P·cÒ+Ü¿ @·cÒ+œ? *d@à}Ý¿ p2ޤ ^³?žŠ4}PXÝ¿ ð vp¼?|PXݲàÛ¿ `ø±ƒæÁ?8íóeøÙ¿libpysal-4.9.2/libpysal/examples/Point/Point.shx000066400000000000000000000002541452177046000217170ustar00rootroot00000000000000' V耫ÉÊÝ ¥¿ *d@à}Ý¿`ø±ƒæÁ? ÛâåEgп2 @ N \ j x † ” ¢ libpysal-4.9.2/libpysal/examples/Point/README.md000066400000000000000000000003061452177046000213570ustar00rootroot00000000000000Point ===== Point Shapefile --------------- * Point.dbf: attribute data. (k=3) * Point.prj: ESRI projection file. * Point.shp: Point shapefile. (n=9) * Point.shx: spatial index. Used for testing libpysal-4.9.2/libpysal/examples/Polygon/000077500000000000000000000000001452177046000204375ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/Polygon/Polygon.dbf000066400000000000000000000007621452177046000225500ustar00rootroot00000000000000_{WNameCPIValueN FValueN  Eyes 1 1.100 Nose 2 2.200 Mouth 3 3.300libpysal-4.9.2/libpysal/examples/Polygon/Polygon.prj000066400000000000000000000002171452177046000226030ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]libpysal-4.9.2/libpysal/examples/Polygon/Polygon.shp000066400000000000000000000017401452177046000226040ustar00rootroot00000000000000' ðèyÌéð-§¿,:”_¼Ý¿,YDgŽUÂ?ÞKtà пÚà/Í<#†¿jîªÔ(ÒпÌŒ$^À?ÞKtà п à/Í<#†¿åܧÖS‡Ð¿à^Űb«~¿â¸{çPпvbêo[¿á&0M±5п€ÇÏŠOk?bŽïJп`ćÑ[ty?dïI4ërп Ú‹!Û|?gïW©Ð¿€ÇÏŠOk?éI6I[Ëп€û`Fìg¿jîªÔ(Òпϋ5 ¿i¥Á½ÄпÀÞïÙo„¿è·c%°Ð¿à/Í<#†¿åܧÖS‡Ð¿Ìù;³k»?c¦`PeпÜÇ2Ñ{¼?_9ÒªH!пì ?þ$§½?ÞKtà п Ã$¿?_9ÒªH!п Ž À?áodLCпÌŒ$^À?哾¿¸yп _þ¤À?gïW©Ð¿üÓlÖ€¾?h\ئò¶Ð¿èÅôFL:½?éM2À½Ð¿Ü5`Øš`¼?çnzŠ¢Ð¿Ð7ž—¢»?æ%‘íî”пÐ7ž—¢»?æ%‘íî”пÌù;³k»?c¦`PeпPÈÁh¨?G§ÔÁ<׿ ŸÈBD'°?š X3‚Ô¿&`¥8¡«?š X3‚Ô¿èM®Zª?Àº–Ö¿(h¨%›®?¶N&lQìÕ¿ ŸÈBD'°?8…m%UÖ¿Øu¢Y$©?G§ÔÁ<׿ÈÁh¨?š X3‚Ô¿&`¥8¡«?š X3‚Ô¿ˆyÌéð-§¿,:”_¼Ý¿,YDgŽUÂ?ðÙÆT²Ù¿yÌéð-§¿{0“wڿؤiBpj¤¿v1¯ò_ڿؤiBpj¤¿v1¯ò_Ú¿à/Í<#†¿’,””ôÛ¿Pº™ ’2›?¦M\Q¹HÝ¿D™¶b²?¥s:;ݿأªdE¼?­cDõÄÛ¿$È™ÛÁ?ðÙÆT²Ù¿,YDgŽUÂ?sVó­ŽïÙ¿ðŸ,[½?ò¶üÜ¿X•’†¬ß³?ªÔÚ[šÝ¿h’x/yœ?,:”_¼Ý¿0 ÓŒ¿™½ÇâoÜ¿yÌéð-§¿{0“wÚ¿libpysal-4.9.2/libpysal/examples/Polygon/Polygon.shx000066400000000000000000000001741452177046000226140ustar00rootroot00000000000000' >èyÌéð-§¿,:”_¼Ý¿,YDgŽUÂ?ÞKtà п2ÚPdˆlibpysal-4.9.2/libpysal/examples/Polygon/README.md000066400000000000000000000003311452177046000217130ustar00rootroot00000000000000Polygon ======= Polygon Shapefile ----------------- * Polygon.dbf: attribute data. (k=3) * Polygon.prj: ESRI projection file. * Polygon.shp: Polygon shapefile. (n=3) * Polygon.shx: spatial index. Used for testing. libpysal-4.9.2/libpysal/examples/Polygon_Holes/000077500000000000000000000000001452177046000215715ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/Polygon_Holes/Polygon_Holes.cpg000066400000000000000000000000051452177046000250400ustar00rootroot00000000000000UTF-8libpysal-4.9.2/libpysal/examples/Polygon_Holes/Polygon_Holes.dbf000066400000000000000000000007621452177046000250340ustar00rootroot00000000000000t{NameCPIValueN FValueN  Eyes 1 1.100 Nose 2 2.200 Mouth 3 3.300libpysal-4.9.2/libpysal/examples/Polygon_Holes/Polygon_Holes.prj000066400000000000000000000002171452177046000250670ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]libpysal-4.9.2/libpysal/examples/Polygon_Holes/Polygon_Holes.qpj000066400000000000000000000004011452177046000250610ustar00rootroot00000000000000GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]] libpysal-4.9.2/libpysal/examples/Polygon_Holes/Polygon_Holes.shp000066400000000000000000000027701452177046000250740ustar00rootroot00000000000000' üèyÌéð-§¿,:”_¼Ý¿,YDgŽUÂ?ÞKtà пFà/Í<#†¿jîªÔ(ÒпÌŒ$^À?ÞKtà п% à/Í<#†¿åܧÖS‡Ð¿à^Űb«~¿â¸{çPпvbêo[¿á&0M±5п€ÇÏŠOk?bŽïJп`ćÑ[ty?dïI4ërп Ú‹!Û|?gïW©Ð¿€ÇÏŠOk?éI6I[Ëп€û`Fìg¿jîªÔ(Òпϋ5 ¿i¥Á½ÄпÀÞïÙo„¿è·c%°Ð¿à/Í<#†¿åܧÖS‡Ð¿`^\i"ôd¿·ö¾(bпvHf}ùÂ|¿o4¹ˆÐ¿¸«nw¡L`¿¬˜ÜÑ>¿Ð¿d‹ÙnÐt?gAžÐ¿à¤ÿrø´S?ÉÉô[п`^\i"ôd¿·ö¾(bпÌù;³k»?c¦`PeпÜÇ2Ñ{¼?_9ÒªH!пì ?þ$§½?ÞKtà п Ã$¿?_9ÒªH!п Ž À?áodLCпÌŒ$^À?哾¿¸yп _þ¤À?gïW©Ð¿üÓlÖ€¾?h\ئò¶Ð¿èÅôFL:½?éM2À½Ð¿Ü5`Øš`¼?çnzŠ¢Ð¿Ð7ž—¢»?æ%‘íî”пÐ7ž—¢»?æ%‘íî”пÌù;³k»?c¦`Peп66ºþS½?Ϩrmï8пhCnÕ‡6¼?4‰•‚пga胵½?˜ÐŠÞž…пæA;>”¿?È]÷D·aпŠöʘh¾? ÖSc!CпŠöʘh¾? ÖSc!Cп66ºþS½?Ϩrmï8пrÈÁh¨?G§ÔÁ<׿ ŸÈBD'°?š X3‚Ô¿ &`¥8¡«?š X3‚Ô¿èM®Zª?Àº–Ö¿(h¨%›®?¶N&lQìÕ¿ ŸÈBD'°?8…m%UÖ¿Øu¢Y$©?G§ÔÁ<׿ÈÁh¨?š X3‚Ô¿&`¥8¡«?š X3‚Ô¿Ë»Þ⇫¨?PÀºª0Ô¿Xèp5y©?^}¾äÒÖ¿‚ÜÊ+Ô«?Ù!ÄTÔ¿Ë»Þ⇫¨?PÀºª0Ô¿yÌéð-§¿,:”_¼Ý¿,YDgŽUÂ?ðÙÆT²Ù¿yÌéð-§¿{0“wڿؤiBpj¤¿v1¯ò_ڿؤiBpj¤¿v1¯ò_Ú¿à/Í<#†¿’,””ôÛ¿Pº™ ’2›?¦M\Q¹HÝ¿D™¶b²?¥s:;ݿأªdE¼?­cDõÄÛ¿$È™ÛÁ?ðÙÆT²Ù¿,YDgŽUÂ?sVó­ŽïÙ¿ðŸ,[½?ò¶üÜ¿X•’†¬ß³?ªÔÚ[šÝ¿h’x/yœ?,:”_¼Ý¿0 ÓŒ¿™½ÇâoÜ¿yÌéð-§¿{0“wÚ¿-;%³G¤¿ªðèÕ¼RÚ¿v-à ¸¥¿íb}Ú¿@̰+°@¿…|ˆöÜ¿ U5Z&Œ¿öÌð]Ü¿-;%³G¤¿ªðèÕ¼RÚ¿Àºªœ?Äg‡ÌmÝ¿jq·á+?„«öCòœÝ¿nêü_³?R…\ B{Ý¿àßÕ\Ѳ? _ÂÔ‘YÝ¿Àºªœ?Äg‡ÌmÝ¿ôÝà§¼?©c‘¸ÑÛ¿fÏ~mX½?ð퀺øÛ¿1«Ó$í Â?dÚGæëÙ¿†RTe_ßÁ?.`gŸÖÙ¿ôÝà§¼?©c‘¸ÑÛ¿libpysal-4.9.2/libpysal/examples/Polygon_Holes/Polygon_Holes.shx000066400000000000000000000001741452177046000251000ustar00rootroot00000000000000' >èyÌéð-§¿,:”_¼Ý¿,YDgŽUÂ?ÞKtà п2F|ròlibpysal-4.9.2/libpysal/examples/Polygon_Holes/README.md000066400000000000000000000003171452177046000230510ustar00rootroot00000000000000Polygon_Holes ================= Example to test treatment of holes ------------------------------------- * Polygon_Holes.dbf * Polygon_Holes.prj * Polygon_Holes.qpj * Polygon_Holes.shp * Polygon_Holes.shx libpysal-4.9.2/libpysal/examples/__init__.py000066400000000000000000000041601452177046000211220ustar00rootroot00000000000000"""The :mod:`libpysal.examples` module provides example datasets. The datasets consist of two sets, built-ins which are installed with this module and remotes that can be downloaded. This module provides functionality for working with these example datasets. """ from typing import Union import pandas as pd from .base import Example, example_manager from .builtin import LocalExample from .builtin import datasets as builtin_datasets from .remotes import datasets as remote_datasets available_datasets = builtin_datasets.copy() available_datasets.update(remote_datasets.datasets) __all__ = [ "get_path", "available", "explain", "fetch_all", "get_url", "load_example", "summary", ] example_manager.add_examples(available_datasets) def fetch_all(): """Fetch and install all remote datasets.""" datasets = remote_datasets.datasets names = list(datasets.keys()) names.sort() for name in names: example = datasets[name] try: example.download() except: # noqa E722 print(f"Example not downloaded: {name}") example_manager.add_examples(datasets) def available() -> pd.DataFrame: """Return a dataframe with available datasets.""" return example_manager.available() def explain(name: str) -> str: """Explain a dataset by name.""" return example_manager.explain(name) def get_url(name: str) -> str: """Get url for remote dataset.""" return example_manager.get_remote_url(name) def load_example(example_name: str) -> Example | LocalExample: """Load example dataset instance.""" example = example_manager.load(example_name) return example def get_path(file_name: str) -> str: """Get the path for a file by searching installed datasets.""" installed = example_manager.get_installed_names() for name in installed: example = example_manager.datasets[name] pth = example.get_path(file_name, verbose=False) if pth: return pth print(f"{file_name} is not a file in any installed dataset.") def summary(): """Summary of datasets.""" example_manager.summary() libpysal-4.9.2/libpysal/examples/arcgis/000077500000000000000000000000001452177046000202605ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/arcgis/README.md000066400000000000000000000003151452177046000215360ustar00rootroot00000000000000arcgis ====== arcgis testing files -------------------- * arcgis_ohio.dbf: spatial weights in ArcGIS DBF format. * arcgis_txt.txt: spatial weights in ArcGIS TXT format. Files used for internal testing. libpysal-4.9.2/libpysal/examples/arcgis/arcgis_ohio.dbf000066400000000000000000000525641452177046000232370ustar00rootroot00000000000000oΡ/WField1N RECORD_IDN NIDN WEIGHTF 72 76 1.00000000000e+000 72 79 1.00000000000e+000 72 78 1.00000000000e+000 72 70 1.00000000000e+000 72 67 1.00000000000e+000 72 64 1.00000000000e+000 85 86 1.00000000000e+000 85 83 1.00000000000e+000 85 80 1.00000000000e+000 85 82 1.00000000000e+000 74 80 1.00000000000e+000 74 82 1.00000000000e+000 74 75 1.00000000000e+000 74 76 1.00000000000e+000 74 68 1.00000000000e+000 74 67 1.00000000000e+000 74 65 1.00000000000e+000 83 79 1.00000000000e+000 83 85 1.00000000000e+000 83 82 1.00000000000e+000 83 76 1.00000000000e+000 82 80 1.00000000000e+000 82 85 1.00000000000e+000 82 83 1.00000000000e+000 82 76 1.00000000000e+000 82 74 1.00000000000e+000 76 79 1.00000000000e+000 76 83 1.00000000000e+000 76 82 1.00000000000e+000 76 74 1.00000000000e+000 76 72 1.00000000000e+000 76 67 1.00000000000e+000 77 78 1.00000000000e+000 77 70 1.00000000000e+000 77 71 1.00000000000e+000 79 83 1.00000000000e+000 79 78 1.00000000000e+000 79 76 1.00000000000e+000 79 72 1.00000000000e+000 78 79 1.00000000000e+000 78 77 1.00000000000e+000 78 72 1.00000000000e+000 78 70 1.00000000000e+000 57 56 1.00000000000e+000 57 64 1.00000000000e+000 57 61 1.00000000000e+000 57 52 1.00000000000e+000 57 50 1.00000000000e+000 56 65 1.00000000000e+000 56 67 1.00000000000e+000 56 64 1.00000000000e+000 56 57 1.00000000000e+000 56 55 1.00000000000e+000 56 50 1.00000000000e+000 56 43 1.00000000000e+000 61 64 1.00000000000e+000 61 70 1.00000000000e+000 61 71 1.00000000000e+000 61 62 1.00000000000e+000 61 57 1.00000000000e+000 61 52 1.00000000000e+000 61 48 1.00000000000e+000 55 59 1.00000000000e+000 55 65 1.00000000000e+000 55 56 1.00000000000e+000 55 49 1.00000000000e+000 55 43 1.00000000000e+000 55 46 1.00000000000e+000 62 71 1.00000000000e+000 62 61 1.00000000000e+000 62 48 1.00000000000e+000 52 57 1.00000000000e+000 52 61 1.00000000000e+000 52 48 1.00000000000e+000 52 50 1.00000000000e+000 52 44 1.00000000000e+000 71 70 1.00000000000e+000 71 77 1.00000000000e+000 71 62 1.00000000000e+000 71 61 1.00000000000e+000 70 72 1.00000000000e+000 70 78 1.00000000000e+000 70 77 1.00000000000e+000 70 71 1.00000000000e+000 70 64 1.00000000000e+000 70 61 1.00000000000e+000 64 67 1.00000000000e+000 64 72 1.00000000000e+000 64 70 1.00000000000e+000 64 61 1.00000000000e+000 64 56 1.00000000000e+000 64 57 1.00000000000e+000 65 68 1.00000000000e+000 65 74 1.00000000000e+000 65 67 1.00000000000e+000 65 59 1.00000000000e+000 65 56 1.00000000000e+000 65 55 1.00000000000e+000 67 74 1.00000000000e+000 67 76 1.00000000000e+000 67 72 1.00000000000e+000 67 65 1.00000000000e+000 67 64 1.00000000000e+000 67 56 1.00000000000e+000 86 85 1.00000000000e+000 86 84 1.00000000000e+000 86 80 1.00000000000e+000 84 86 1.00000000000e+000 84 80 1.00000000000e+000 84 81 1.00000000000e+000 84 75 1.00000000000e+000 81 84 1.00000000000e+000 81 75 1.00000000000e+000 81 73 1.00000000000e+000 80 84 1.00000000000e+000 80 86 1.00000000000e+000 80 85 1.00000000000e+000 80 82 1.00000000000e+000 80 75 1.00000000000e+000 80 74 1.00000000000e+000 73 81 1.00000000000e+000 73 75 1.00000000000e+000 73 69 1.00000000000e+000 73 68 1.00000000000e+000 73 66 1.00000000000e+000 73 60 1.00000000000e+000 75 81 1.00000000000e+000 75 84 1.00000000000e+000 75 80 1.00000000000e+000 75 73 1.00000000000e+000 75 74 1.00000000000e+000 75 68 1.00000000000e+000 60 66 1.00000000000e+000 60 73 1.00000000000e+000 60 68 1.00000000000e+000 60 59 1.00000000000e+000 60 54 1.00000000000e+000 60 49 1.00000000000e+000 59 65 1.00000000000e+000 59 68 1.00000000000e+000 59 60 1.00000000000e+000 59 55 1.00000000000e+000 59 49 1.00000000000e+000 58 66 1.00000000000e+000 58 69 1.00000000000e+000 58 63 1.00000000000e+000 58 54 1.00000000000e+000 58 51 1.00000000000e+000 58 53 1.00000000000e+000 63 69 1.00000000000e+000 63 58 1.00000000000e+000 63 53 1.00000000000e+000 69 73 1.00000000000e+000 69 66 1.00000000000e+000 69 63 1.00000000000e+000 69 58 1.00000000000e+000 68 74 1.00000000000e+000 68 75 1.00000000000e+000 68 73 1.00000000000e+000 68 65 1.00000000000e+000 68 60 1.00000000000e+000 68 59 1.00000000000e+000 54 58 1.00000000000e+000 54 66 1.00000000000e+000 54 60 1.00000000000e+000 54 51 1.00000000000e+000 54 49 1.00000000000e+000 54 45 1.00000000000e+000 53 63 1.00000000000e+000 53 58 1.00000000000e+000 53 51 1.00000000000e+000 53 40 1.00000000000e+000 53 47 1.00000000000e+000 66 69 1.00000000000e+000 66 73 1.00000000000e+000 66 60 1.00000000000e+000 66 58 1.00000000000e+000 66 54 1.00000000000e+000 51 54 1.00000000000e+000 51 58 1.00000000000e+000 51 53 1.00000000000e+000 51 45 1.00000000000e+000 51 47 1.00000000000e+000 51 39 1.00000000000e+000 49 54 1.00000000000e+000 49 60 1.00000000000e+000 49 59 1.00000000000e+000 49 55 1.00000000000e+000 49 46 1.00000000000e+000 49 45 1.00000000000e+000 49 41 1.00000000000e+000 24 35 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0.0libpysal-4.9.2/libpysal/examples/baltim/000077500000000000000000000000001452177046000202605ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/baltim/README.md000066400000000000000000000012121452177046000215330ustar00rootroot00000000000000baltim ====== Baltimore house sales prices and hedonics, 1978. ---------------------------------------------------------------- * baltim.dbf: attribute data. (k=17) * baltim.shp: Point shapefile. (n=211) * baltim.shx: spatial index. * baltim.tri.k12.kwt: kernel weights using a triangular kernel with 12 nearest neighbors in KWT format. * baltim_k4.gwt: nearest neighbor weights (4nn) in GWT format. * baltim_q.gal: queen contiguity weights in GAL format. * baltimore.geojson: spatial weights in geojson format. Source: Dubin, Robin A. (1992). Spatial autocorrelation and neighborhood quality. 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"FIREPL": 0.000000, "AC": 0.000000, "BMENT": 2.000000, "NSTOR": 2.000000, "GAR": 0.000000, "AGE": 22.000000, "CITCOU": 0.000000, "LOTSZ": 35.840000, "SQFT": 10.640000, "X": 914.000000, "Y": 558.000000 }, "geometry": { "type": "Point", "coordinates": [ 914.0, 558.0 ] } } ] } libpysal-4.9.2/libpysal/examples/base.py000066400000000000000000000213001452177046000202700ustar00rootroot00000000000000""" Base class for managing example datasets. """ # Authors: Serge Rey # License: BSD 3 Clause import io import os import tempfile import webbrowser import zipfile import pandas import requests from bs4 import BeautifulSoup from platformdirs import user_data_dir from ..io import open as ps_open def get_data_home(): """Return the path of the ``libpysal`` data directory. This folder is platform specific. If the folder does not already exist, it is automatically created. Returns ------- data_home : str The system path where the data is/will be stored. """ appname = "pysal" appauthor = "pysal" data_home = user_data_dir(appname, appauthor) try: if not os.path.exists(data_home): os.makedirs(data_home, exist_ok=True) except OSError: # Try to fall back to a tmp directory data_home = os.path.join(tempfile.gettempdir(), "pysal") os.makedirs(data_home, exist_ok=True) return data_home def get_list_of_files(dir_name): """Create a list of files and sub-directories in ``dir_name``. Parameters ---------- dir_name : str The path to the directory or examples. Returns ------- all_files : list All file and directory paths. Raises ------ FileNotFoundError If the file or directory is not found. """ # names in the given directory all_files = [] try: file_list = os.listdir(dir_name) # Iterate over all the entries for entry in file_list: # Create full path full_path = os.path.join(dir_name, entry) # If entry is a directory then get the list of files in this directory if os.path.isdir(full_path): all_files = all_files + get_list_of_files(full_path) else: all_files.append(full_path) except FileNotFoundError: pass return all_files def type_of_script() -> str: """Helper function to determine run context.""" try: ipy_str = str(type(get_ipython())) # noqa F821 if "zmqshell" in ipy_str: return "jupyter" if "terminal" in ipy_str: return "ipython" except NameError: return "terminal" class Example: """An example dataset. Parameters ---------- name : str The example dataset name. description : str The example dataset description. n : int The number of records in the dataset. k : int The number of fields in the dataset. download_url : str The URL to download the dataset. explain_url : str The URL to the dataset's READEME file. Attributes ---------- root : str The ``name`` parameter with filled spaces (_). installed : bool ``True`` if the example is installed, otherwise ``False``. zipfile : zipfile.ZipFile The archived dataset. """ def __init__(self, name, description, n, k, download_url, explain_url): """Initialze Example.""" self.name = name self.description = description self.n = n self.k = k self.download_url = download_url self.explain_url = explain_url self.root = name.replace(" ", "_") self.installed = self.downloaded() def get_local_path(self, path=None) -> str: """Get the local path for example.""" path = path or get_data_home() return os.path.join(path, self.root) def get_path(self, file_name, verbose=True) -> str | None: """Get the path for local file.""" file_list = self.get_file_list() for file_path in file_list: base_name = os.path.basename(file_path) if file_name == base_name: return file_path if verbose: print(f"{file_name} is not a file in this example.") return None def downloaded(self) -> bool: """Check if the example has already been installed.""" path = self.get_local_path() if os.path.isdir(path): self.installed = True return True return False def explain(self) -> None: """Provide a description of the example.""" file_name = self.explain_url.split("/")[-1] if file_name == "README.md": explain_page = requests.get(self.explain_url) crawled = BeautifulSoup(explain_page.text, "html.parser") print(crawled.text) return None if type_of_script() == "terminal": webbrowser.open(self.explain_url) return None from IPython.display import IFrame return IFrame(self.explain_url, width=700, height=350) def download(self, path=None): """Download the files for the example.""" path = path or get_data_home() if not self.downloaded(): try: request = requests.get(self.download_url) archive = zipfile.ZipFile(io.BytesIO(request.content)) target = os.path.join(path, self.root) print(f"Downloading {self.name} to {target}") archive.extractall(path=target) self.zipfile = archive self.installed = True except requests.exceptions.RequestException as e: raise SystemExit(e) from e def get_file_list(self) -> list | None: """Get the list of local files for the example.""" path = self.get_local_path() if os.path.isdir(path): return get_list_of_files(path) return None def json_dict(self) -> dict: """Container for example meta data.""" meta = {} meta["name"] = self.name meta["description"] = self.description meta["download_url"] = self.download_url meta["explain_url"] = self.explain_url meta["root"] = self.root return meta def load(self, file_name) -> io.FileIO: """Dispatch to libpysal.io to open file.""" pth = self.get_path(file_name) if pth: return ps_open(pth) class Examples: """Manager for pysal example datasets.""" def __init__(self, datasets={}): # noqa B006 self.datasets = datasets def add_examples(self, examples): """Add examples to the set of datasets available.""" self.datasets.update(examples) def explain(self, example_name) -> str: if example_name in self.datasets: return self.datasets[example_name].explain() else: print("not available") def available(self): """Return df of available datasets.""" datasets = self.datasets names = list(datasets.keys()) names.sort() rows = [] for name in names: description = datasets[name].description installed = datasets[name].installed rows.append([name, description, installed]) datasets = pandas.DataFrame( data=rows, columns=["Name", "Description", "Installed"] ) datasets.style.set_properties(subset=["text"], **{"width": "300px"}) return datasets def load(self, example_name: str) -> Example: """Load example dataset, download if not locally available.""" if example_name in self.datasets: example = self.datasets[example_name] if example.installed: return example else: example.download() return example else: print(f"Example not available: {example_name}") return None def download_remotes(self): """Download all remotes.""" names = list(self.remotes.keys()) names.sort() for name in names: print(name) example = self.remotes[name] try: example.download() except: # noqa E722 print(f"Example not downloaded: {name}.") def get_installed_names(self) -> list: """Return names of all currently installed datasets.""" ds = self.datasets return [name for name in ds if ds[name].installed] def get_remote_url(self, name): if name in self.datasets: try: return self.datasets[name].download_url except: # noqa E722 print(f"{name} is a built-in dataset, no url.") else: print(f"{name} is not an available dataset.") def summary(self): """Report on datasets.""" available = self.available() n = available.shape[0] n_installed = available.Installed.sum() n_remote = n - n_installed print(f"{n} datasets available, {n_installed} installed, {n_remote} remote.") example_manager = Examples() libpysal-4.9.2/libpysal/examples/berlin/000077500000000000000000000000001452177046000202635ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/berlin/README.md000066400000000000000000000010561452177046000215440ustar00rootroot00000000000000Berlin ======== Prenzlauer Berg neighborhood AirBnB data from Berlin ----------------------------------------------------- * prenzlauer.zip: attribute and goemetry data for rental point data (n=2203, k=9) * prenz_bound.zip: Polygon of Prenzlauer Berg boundary Data used in Oshan, Taylor, Ziqi Li, Wei Kang, Levi J. Wolf, and Alexander S. Fotheringham. 2018. “Mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale.†OSF Preprints. 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base_name: return file_path if verbose: print(f"{file_name} is not a file in this example") return None def explain(self): """Provide a printed description of the example.""" description = [f for f in self.get_file_list() if "README.md" in f][0] with open(description, encoding="utf8") as f: print(f.read()) def get_description(self) -> str: """Dataset description.""" description = [f for f in self.get_file_list() if "README.md" in f][0] with open(description, encoding="utf8") as f: lines = f.readlines() return lines[3].strip() builtin_root = os.path.dirname(__file__) paths = [os.path.join(builtin_root, local) for local in dirs] paths = zip(dirs, paths, strict=True) datasets = {} for name, pth in paths: files = get_list_of_files(pth) file_names = [os.path.basename(file) for file in files] if "README.md" in file_names: example = LocalExample(name, pth) datasets[name] = example libpysal-4.9.2/libpysal/examples/burkitt/000077500000000000000000000000001452177046000204745ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/burkitt/README.md000066400000000000000000000007741452177046000217630ustar00rootroot00000000000000burkitt ======= Burkitt's lymphoma in the Western Nile district of Uganda --------------------------------------------------------- * burkitt.dbf: attribute data. (k=6) * burkitt.shp: Point shapefile. (n=188) * burkitt.shx: spatial index. Source: Williams, E. H., Smith, P. G., Day, N. E., Geser, A., Ellice, J., & Tukei, P. (1978). Space-time clustering of Burkitt's lymphoma in the West Nile district of Uganda: 1961-1975. British journal of cancer, 37(1), 109–122. https://doi.org/10.1038/bjc.1978.16libpysal-4.9.2/libpysal/examples/burkitt/burkitt.dbf000066400000000000000000000166061452177046000226460ustar00rootroot00000000000000 ¼á'IDNXNYNTNAGENDATED 1.00300.00302.00 413.0022.0019010216 2.00291.00270.00 472.00 5.0019010416 3.00326.00263.00 511.0012.0019010525 4.00299.00376.00 689.00 6.0019011119 5.00267.00327.00 730.00 4.0019011230 6.00266.00356.00 847.00 6.0019020426 7.00267.00345.00 871.0010.0019020520 8.00262.00338.00 899.0010.0019020617 9.00268.00335.00 921.00 8.0019020709 10.00335.00275.001134.00 6.0019030207 11.00302.00272.001190.00 6.0019030404 12.00301.00304.001214.00 5.0019030428 13.00302.00272.001224.00 8.0019030508 14.00324.00337.001322.00 4.0019030814 15.00260.00338.001399.00 5.0019031030 16.00306.00385.001480.00 9.0019040119 17.00284.00362.001503.00 7.0019040211 18.00293.00332.001549.00 4.0019040328 19.00274.00353.001567.0011.0019040415 20.00263.00333.001607.00 5.0019040525 21.00307.00386.001615.0012.0019040602 22.00274.00357.001657.00 6.0019040714 23.00297.00282.001688.00 6.0019040814 24.00288.00365.001695.00 5.0019040821 25.00283.00366.001714.00 8.0019040909 26.00290.00352.001811.00 8.0019041215 27.00284.00341.001813.00 9.0019041217 28.00286.00341.001910.00 5.0019050324 29.00282.00350.001986.00 2.0019050608 30.00292.00278.001996.00 9.0019050618 31.00305.00381.002049.0025.0019050810 32.00298.00378.002053.00 8.0019050814 33.00299.00381.002075.00 9.0019050905 34.00287.00342.002081.0011.0019050911 35.00276.00380.002103.00 5.0019051003 36.00261.00345.002204.00 7.0019060112 37.00273.00347.002209.00 5.0019060117 38.00284.00357.002215.00 4.0019060123 39.00266.00361.002239.00 5.0019060216 40.00285.00360.002272.00 5.0019060321 41.00278.00350.002281.00 5.0019060330 42.00305.00321.002349.00 7.0019060606 43.00293.00332.002351.0010.0019060608 44.00297.00375.002373.00 9.0019060630 45.00332.00273.002377.00 5.0019060704 46.00256.00345.002388.00 6.0019060715 47.00275.00357.002411.00 8.0019060807 48.00267.00350.002412.00 4.0019060808 49.00267.00301.002419.00 6.0019060815 50.00286.00379.002424.00 9.0019060820 51.00322.00332.002488.00 6.0019061023 52.00278.00349.002510.00 5.0019061114 53.00280.00356.002512.00 7.0019061116 54.00276.00354.002533.00 5.0019061207 55.00261.00343.002545.00 5.0019061219 56.00323.00298.002588.00 7.0019070131 57.00260.00346.002616.00 4.0019070228 58.00283.00352.002617.00 6.0019070301 59.00282.00361.002629.00 5.0019070313 60.00308.00390.002658.0010.0019070411 61.00293.00275.002695.00 8.0019070518 62.00331.00272.002715.00 4.0019070607 63.00266.00308.002766.00 3.0019070728 64.00280.00354.002772.00 5.0019070803 65.00301.00271.002800.00 5.0019070831 66.00279.00361.002888.00 9.0019071127 67.00321.00280.002996.00 8.0019080314 68.00292.00391.003027.00 6.0019080414 69.00315.00307.003118.00 6.0019080714 70.00323.00333.003153.00 5.0019080818 71.00282.00302.003149.00 6.0019080814 72.00269.00348.003149.00 8.0019080814 73.00330.00349.003173.00 8.0019080907 74.00281.00302.003237.00 4.0019081110 75.00330.00396.003245.00 3.0019081118 76.00328.00341.003266.00 5.0019081209 77.00264.00323.003275.00 3.0019081218 78.00303.00382.003286.00 5.0019081229 79.00264.00285.003302.0017.0019090114 80.00259.00337.003308.0011.0019090120 81.00329.00350.003323.00 7.0019090204 82.00328.00351.003342.00 7.0019090223 83.00284.00351.003346.00 5.0019090227 84.00309.00370.003422.00 8.0019090514 85.00329.00270.003453.00 6.0019090614 86.00273.00347.003453.00 3.0019090614 87.00295.00267.003500.00 7.0019090731 88.00262.00353.003530.00 5.0019090830 89.00281.00352.003570.00 5.0019091009 90.00325.00335.003576.00 5.0019091015 91.00288.00341.003614.00 9.0019091122 92.00302.00248.003636.00 6.0019091214 93.00315.00273.003661.00 8.0019100108 94.00295.00377.003693.00 6.0019100209 95.00284.00358.003716.00 7.0019100304 96.00272.00376.003773.00 3.0019100430 97.00305.00369.003790.00 9.0019100517 98.00279.00318.003806.00 8.0019100602 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4.0019110916 125.00260.00359.004290.00 7.0019110929 126.00279.00355.004336.00 7.0019111114 127.00284.00399.004339.00 4.0019111117 128.00270.00337.004352.00 5.0019111130 129.00267.00363.004383.00 3.0019111231 130.00320.00282.004385.00 5.0019120102 131.00282.00315.004441.00 7.0019120227 132.00303.00377.004492.00 8.0019120418 133.00303.00247.004518.00 5.0019120514 134.00264.00297.004554.00 4.0019120619 135.00303.00337.004565.00 6.0019120630 136.00289.00380.004610.00 7.0019120814 137.00270.00330.004628.0011.0019120901 138.00262.00335.004637.0010.0019120910 139.00274.00342.004638.00 7.0019120911 140.00262.00333.004700.00 3.0019121112 141.00262.00333.004700.00 7.0019121112 142.00267.00369.004701.00 7.0019121113 143.00255.00290.004708.00 4.0019121120 144.00267.00336.004750.0014.0019130101 145.00273.00309.004751.00 3.0019130102 146.00270.00328.004764.0011.0019130115 147.00270.00339.004780.00 6.0019130131 148.00265.00334.004806.00 7.0019130226 149.00276.00360.004822.00 5.0019130314 150.00276.00360.004848.00 5.0019130409 151.00283.00363.004858.00 9.0019130419 152.00305.00374.004861.00 3.0019130422 153.00264.00326.004862.00 7.0019130423 154.00286.00361.004888.00 5.0019130519 155.00278.00333.004891.00 7.0019130522 156.00257.00344.004914.00 9.0019130614 157.00302.00395.004918.00 6.0019130618 158.00275.00378.004932.00 4.0019130702 159.00290.00361.004944.0012.0019130714 160.00279.00362.004948.00 7.0019130718 161.00300.00255.004964.00 6.0019130803 162.00270.00357.004975.00 7.0019130814 163.00290.00356.005001.00 7.0019130909 164.00291.00368.005002.00 3.0019130910 165.00283.00361.005072.00 6.0019131119 166.00270.00325.005135.00 5.0019140121 167.00266.00332.005199.00 5.0019140326 168.00267.00367.005353.00 6.0019140827 169.00300.00364.005358.00 4.0019140901 170.00268.00390.005361.00 8.0019140904 171.00276.00337.005489.00 3.0019150110 172.00293.00396.005495.0016.0019150116 173.00273.00345.005505.0010.0019150126 174.00267.00338.005530.00 5.0019150220 175.00278.00367.005578.0010.0019150409 176.00310.00387.005583.00 6.0019150414 177.00298.00268.005588.0013.0019150419 178.00266.00332.005599.00 6.0019150430 179.00279.00340.005641.0010.0019150611 180.00266.00361.005643.00 8.0019150613 181.00267.00344.005650.00 5.0019150620 182.00272.00370.005661.00 6.0019150701 183.00327.00383.005702.00 6.0019150811 184.00265.00335.005728.0012.0019150906 185.00310.00387.005752.00 4.0019150930 186.00279.00339.005753.00 5.0019151001 187.00277.00379.005755.00 7.0019151003 188.00258.00350.005775.00 5.0019151023libpysal-4.9.2/libpysal/examples/burkitt/burkitt.shp000066400000000000000000000123641452177046000227020ustar00rootroot00000000000000' zèào@àn@ðt@ðx@ Àr@àr@ 0r@àp@ `t@pp@ °r@€w@ °p@pt@  p@@v@ °p@u@ `p@ u@ Àp@ðt@ ðt@0q@ àr@q@ Ðr@s@ àr@q@ @t@u@ @p@ u@  s@x@ Àq@ v@ Pr@Àt@  q@v@ pp@Ðt@ 0s@ x@  q@Pv@ r@ q@ r@Ðv@ °q@àv@  r@v@ Àq@Pu@ àq@Pu@  q@àu@ @r@`q@ s@Ðw@  r@ w@! °r@Ðw@" ðq@`u@# @q@Àw@$ Pp@u@% q@°u@& Àq@Pv@'  p@v@( Ðq@€v@) `q@àu@* s@t@+ Pr@Àt@, r@pw@- Àt@q@. p@u@/ 0q@Pv@0 °p@àu@1 °p@Ðr@2 àq@°w@3  t@Àt@4 `q@Ðu@5 €q@@v@6 @q@ v@7 Pp@pu@8 0t@ r@9 @p@ u@: °q@v@;  q@v@< @s@`x@= Pr@0q@> °t@q@?  p@@s@@ €q@ v@A Ðr@ðp@B pq@v@C t@€q@D @r@px@E °s@0s@F 0t@Ðt@G  q@àr@H Ðp@Àu@I  t@Ðu@J q@àr@K  t@Àx@L €t@Pu@M €p@0t@N ðr@àw@O €p@Ðq@P 0p@u@Q t@àu@R €t@ðu@S Àq@ðu@T Ps@ w@U t@àp@V q@°u@W pr@°p@X `p@v@Y q@v@Z Pt@ðt@[ r@Pu@\ àr@o@] °s@q@^ pr@w@_ Àq@`v@` q@€w@a s@w@b pq@às@c  r@0u@d ps@€w@e r@p@f @t@Àq@g ðq@v@h °q@Àv@i °p@u@j €q@ r@k 0q@`t@l q@°r@m s@Ðv@n q@Àt@o °q@°v@p s@àv@q @s@0x@r  t@0q@s @p@0u@t r@ðt@u Àp@ s@v t@Àp@w €p@ r@x  q@`u@y àq@Pw@z pp@ u@{ p@ðt@| Ðp@@u@} @p@pv@~ pq@0v@ Àq@ðx@€ àp@u@ °p@°v@‚ t@ q@ƒ  q@°s@„ ðr@w@… ðr@àn@† €p@r@‡ ðr@u@ˆ r@Àw@‰ àp@ t@Š `p@ðt@‹  q@`u@Œ `p@Ðt@ `p@Ðt@Ž °p@w@ ào@ r@ °p@u@‘ q@Ps@’ àp@€t@“ àp@0u@” p@àt@• @q@€v@– @q@€v@— °q@°v@˜ s@`w@™ €p@`t@š àq@v@› `q@Ðt@œ p@€u@ àr@°x@ž 0q@ w@Ÿ  r@v@  pq@ v@¡ Àr@ào@¢ àp@Pv@£  r@@v@¤ 0r@w@¥ °q@v@¦ àp@Pt@§  p@Àt@¨ °p@ðv@© Àr@Àv@ª Àp@`x@« @q@u@¬ Pr@Àx@­ q@u@® °p@ u@¯ `q@ðv@° `s@0x@±  r@Àp@²  p@Àt@³ pq@@u@´  p@v@µ °p@€u@¶ q@ w@· pt@ðw@¸ p@ðt@¹ `s@0x@º pq@0u@» Pq@°w@¼  p@àu@libpysal-4.9.2/libpysal/examples/burkitt/burkitt.shx000066400000000000000000000031041452177046000227020ustar00rootroot00000000000000' "èào@àn@ðt@ðx@2 @ N \ j x † ” ¢ ° ¾ Ì Ú è ö    . < J X f t ‚  ž ¬ º È Ö ä ò    * 8 F T b p ~ Œ š ¨ ¶ Ä Ò à î ü   & 4 B P ^ l z ˆ – ¤ ² À Î Ü ê ø   " 0 > L Z h v „ ’   ® ¼ Ê Ø æ ô    , : H V d r € Ž œ ª ¸ Æ Ô â ð þ   ( 6 D R ` n | Š ˜ ¦ ´  Ð Þ ì ú   $ 2 @ N \ j x † ” ¢ ° ¾ Ì Ú è ö    . < J X f t ‚  ž ¬ º È Ö ä ò   * 8 F T b p ~ Œ š ¨ ¶ Ä Ò à î ü  & 4 B P ^ l libpysal-4.9.2/libpysal/examples/calemp/000077500000000000000000000000001452177046000202515ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/calemp/README.md000066400000000000000000000004421452177046000215300ustar00rootroot00000000000000calemp ====== Employment density for California counties ------------------------------------------ * calempdensity.csv: data on employment and employment density in California counties. (n=58, k=11) Source: Anselin, L. and S.J. Rey (in progress) Spatial Econometrics: Foundations. libpysal-4.9.2/libpysal/examples/calemp/calempdensity.csv000066400000000000000000000156271452177046000236420ustar00rootroot00000000000000"Geographic Area","Geographic Area","Geographic Name","GEONAME","GEOCOMP","STATE","Number of Employees for All Sectors","Number of employees","Class Number","sq. km","emp/sq km" "05000US06001","06001","Alameda County, California","Alameda County, California","00","06",630171,630171,5,1910.1,329.92 "05000US06003","06003","Alpine County, California","Alpine County, California","00","06",813,813,1,1913.1,0.42 "05000US06005","06005","Amador County, California","Amador County, California","00","06",9061,9061,2,1534.7,5.9 "05000US06007","06007","Butte County, California","Butte County, California","00","06",59578,59578,3,4246.6,14.03 "05000US06009","06009","Calaveras County, California","Calaveras County, California","00","06",7344,7344,2,2642.3,2.78 "05000US06011","06011","Colusa County, California","Colusa County, California","00","06",4000,4000,1,2980.5,1.34 "05000US06013","06013","Contra Costa County, California","Contra Costa County, California","00","06",338156,338156,5,1865.5,181.27 "05000US06015","06015","Del Norte County, California","Del Norte County, California","00","06",4303,4303,1,2610.4,1.65 "05000US06017","06017","El Dorado County, California","El Dorado County, California","00","06",44477,44477,3,4432.8,10.03 "05000US06019","06019","Fresno County, California","Fresno County, California","00","06",257975,257975,4,15444.7,16.7 "05000US06021","06021","Glenn County, California","Glenn County, California","00","06",4487,4487,1,3405.5,1.32 "05000US06023","06023","Humboldt County, California","Humboldt County, California","00","06",36962,36962,3,9253.5,3.99 "05000US06025","06025","Imperial County, California","Imperial County, California","00","06",34156,34156,3,10813.4,3.16 "05000US06027","06027","Inyo County, California","Inyo County, California","00","06",5820,5820,1,26397.5,0.22 "05000US06029","06029","Kern County, California","Kern County, California","00","06",183412,183412,4,21086.8,8.7 "05000US06031","06031","Kings County, California","Kings County, California","00","06",23610,23610,2,3598.8,6.56 "05000US06033","06033","Lake County, California","Lake County, California","00","06",10648,10648,2,3259.4,3.27 "05000US06035","06035","Lassen County, California","Lassen County, California","00","06",3860,3860,1,11803.9,0.33 "05000US06037","06037","Los Angeles County, California","Los Angeles County, California","00","06",3895886,3895886,5,10515.3,370.5 "05000US06039","06039","Madera County, California","Madera County, California","00","06",24957,24957,2,5538.5,4.51 "05000US06041","06041","Marin County, California","Marin County, California","00","06",101358,101358,4,1346.2,75.29 "05000US06043","06043","Mariposa County, California","Mariposa County, California","00","06",3739,3739,1,3758.6,0.99 "05000US06045","06045","Mendocino County, California","Mendocino County, California","00","06",24898,24898,2,9089,2.74 "05000US06047","06047","Merced County, California","Merced County, California","00","06",43369,43369,3,4995.8,8.68 "05000US06049","06049","Modoc County, California","Modoc County, California","00","06",1467,1467,1,10215.9,0.14 "05000US06051","06051","Mono County, California","Mono County, California","00","06",7289,7289,1,7885.2,0.92 "05000US06053","06053","Monterey County, California","Monterey County, California","00","06",108660,108660,4,8603.8,12.63 "05000US06055","06055","Napa County, California","Napa County, California","00","06",56029,56029,3,1952.5,28.7 "05000US06057","06057","Nevada County, California","Nevada County, California","00","06",29805,29805,3,2480.3,12.02 "05000US06059","06059","Orange County, California","Orange County, California","00","06",1478452,1478452,5,2045.3,722.85 "05000US06061","06061","Placer County, California","Placer County, California","00","06",133427,133427,4,3637.4,36.68 "05000US06063","06063","Plumas County, California","Plumas County, California","00","06",4863,4863,1,6614.8,0.74 "05000US06065","06065","Riverside County, California","Riverside County, California","00","06",556789,556789,5,18669.1,29.82 "05000US06067","06067","Sacramento County, California","Sacramento County, California","00","06",480346,480346,5,2501.1,192.05 "05000US06069","06069","San Benito County, California","San Benito County, California","00","06",12163,12163,2,3597.9,3.38 "05000US06071","06071","San Bernardino County, California","San Bernardino County, California","00","06",579135,579135,5,51961.2,11.15 "05000US06073","06073","San Diego County, California","San Diego County, California","00","06",1205862,1205862,5,10889.6,110.74 "05000US06075","06075","San Francisco County, California","San Francisco County, California","00","06",497485,497485,5,121,4111.45 "05000US06077","06077","San Joaquin County, California","San Joaquin County, California","00","06",179276,179276,4,3624.1,49.47 "05000US06079","06079","San Luis Obispo County, California","San Luis Obispo County, California","00","06",88413,88413,3,8558.7,10.33 "05000US06081","06081","San Mateo County, California","San Mateo County, California","00","06",368859,368859,5,1163.2,317.11 "05000US06083","06083","Santa Barbara County, California","Santa Barbara County, California","00","06",145202,145202,4,7092.6,20.47 "05000US06085","06085","Santa Clara County, California","Santa Clara County, California","00","06",886011,886011,5,3344.3,264.93 "05000US06087","06087","Santa Cruz County, California","Santa Cruz County, California","00","06",76488,76488,3,1154.3,66.26 "05000US06089","06089","Shasta County, California","Shasta County, California","00","06",52804,52804,3,9804.8,5.39 "05000US06091","06091","Sierra County, California","Sierra County, California","00","06",324,324,1,2469.4,0.13 "05000US06093","06093","Siskiyou County, California","Siskiyou County, California","00","06",9992,9992,2,16284,0.61 "05000US06095","06095","Solano County, California","Solano County, California","00","06",108653,108653,4,2145,50.65 "05000US06097","06097","Sonoma County, California","Sonoma County, California","00","06",165261,165261,4,4082.4,40.48 "05000US06099","06099","Stanislaus County, California","Stanislaus County, California","00","06",141928,141928,4,3870.9,36.67 "05000US06101","06101","Sutter County, California","Sutter County, California","00","06",20430,20430,2,1561,13.09 "05000US06103","06103","Tehama County, California","Tehama County, California","00","06",13809,13809,2,7643.2,1.81 "05000US06105","06105","Trinity County, California","Trinity County, California","00","06",1668,1668,1,8233.3,0.2 "05000US06107","06107","Tulare County, California","Tulare County, California","00","06",94949,94949,4,12495,7.6 "05000US06109","06109","Tuolumne County, California","Tuolumne County, California","00","06",14519,14519,2,5790.3,2.51 "05000US06111","06111","Ventura County, California","Ventura County, California","00","06",273745,273745,5,4781,57.26 "05000US06113","06113","Yolo County, California","Yolo County, California","00","06",63769,63769,3,2622.2,24.32 "05000US06115","06115","Yuba County, California","Yuba County, California","00","06",11374,11374,2,1632.9,6.97 libpysal-4.9.2/libpysal/examples/chicago/000077500000000000000000000000001452177046000204055ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/chicago/Chicago77.dbf000066400000000000000000000406231452177046000226020ustar00rootroot00000000000000j MÕOBJECTIDN AREANPERIMETERNCOMAREA_N COMAREA_IDN AREA_NUMBECCOMMUNITYCPAREA_NUM_1C SHAPE_AREAN SHAPE_LENN AREANON 1 51283072.18370140 33771.99810256 2 11 ROGERS PARK 1 51283072.03090000000 33771.99797640000 1 2 97696644.11361000 42710.45303344 3 22 WEST RIDGE 2 97696644.15990000000 42710.45301140000 2 3 31645755.35544680 25931.05575151 4 39 EDISON PARK 9 31645755.30940000000 25931.05584520000 9 4 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(k=11) * Chicago77.shp: Polygon shapefile. (n=77) * Chicago77.shx: spatial index. Source: Anselin, L. and S.J. Rey (in progress) Spatial Econometrics: Foundations.libpysal-4.9.2/libpysal/examples/columbus/000077500000000000000000000000001452177046000206415ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/columbus/README.md000066400000000000000000000006671452177046000221310ustar00rootroot00000000000000columbus ======== Columbus neighborhood crime data 1980 ------------------------------------- * columbus.dbf: attribute data. (k=20) * columbus.gal: queen contiguity file in GAL format. * columbus.html: metadata. * columbus.json: shape and attribute data file in JSON format. * columbus.shp: Polygon shapefile. (n=49) * columbus.shx: spatial index. Anselin, Luc (1988). Spatial Econometrics. 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28 22 9 25 15 27 4 33 28 20 22 28 9 38 37 29 27 33 35 22 26 25 29 7 37 30 28 38 24 25 26 30 5 37 29 24 25 21 31 3 39 36 34 32 4 41 40 23 20 33 4 35 20 27 28 34 4 42 36 21 31 35 7 44 43 38 40 20 33 28 36 5 46 39 34 42 31 37 6 45 38 43 28 29 30 38 6 43 35 44 28 37 29 39 3 46 36 31 40 5 47 41 32 35 20 41 3 47 32 40 42 2 34 36 43 6 48 45 35 44 38 37 44 5 49 48 35 43 38 45 4 48 49 37 43 46 2 36 39 47 2 40 41 48 4 49 44 43 45 49 3 44 48 45 libpysal-4.9.2/libpysal/examples/columbus/columbus.html000066400000000000000000000056201452177046000233630ustar00rootroot00000000000000 SAL Data Sets - Columbus

Columbus

Data provided "as is," no warranties

Description

Crime data for 49 neighborhoods in Columbus, OH, 1980

Type = polygon shape file, projected, arbitrary units

Observations = 49

Variables = 20

Source

Anselin, Luc (1988). Spatial Econometrics. Boston, Kluwer Academic, Table 12.1, p. 189.

Variables

Variable Description
AREA neighborhood area (computed by ArcView)
PERIMETER neighborhood perimeter (computed by ArcView)
COLUMBUS_ internal polygon ID (generated by ArcView)
COLUMBUS_I internal polygon ID (geneated by ArcView)
POLYID neighborhood ID, used in GeoDa User's Guide and tutorials
NEIG neighborhood ID, used in Spatial Econometrics examples
HOVAL housing value (in $1,000)
INC household income (in $1,000)
CRIME residential burglaries and vehicle thefts per 1000 households
OPEN open space (area)
PLUMB percent housing units without plumbing
DISCBD distance to CBD
X centroid x coordinate (in arbitrary digitizing units)
Y centroid y coordinate (in arbitrary digitizing units)
NSA north-south indicator variable (North = 1)
NSB other north-south indicator variable (North = 1)
EW east-west indicator variable (East = 1)
CP core-periphery indicator variable (Core = 1)
THOUS constant (= 1000)
NEIGNO another neighborhood ID variable (NEIG + 1000)


Prepared by Luc Anselin

UIUC-ACE Spatial Analysis Laboratory

Last updated June 16, 2003

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(n=10, k=2) Figure 5-31 of de Smith, Goodchild and Longley (2015) Source: Used with permission. libpysal-4.9.2/libpysal/examples/desmith/desmith.gal000066400000000000000000000001421452177046000225640ustar00rootroot0000000000000010 1 2 2 4 2 2 1 5 3 2 4 7 4 4 1 3 5 8 5 4 2 4 6 9 6 2 5 10 7 2 3 8 8 3 4 7 9 9 3 5 8 10 10 2 6 9 libpysal-4.9.2/libpysal/examples/desmith/desmith.txt000066400000000000000000000001311452177046000226360ustar00rootroot0000000000000010,2 "id","z" 1,2.24 2,3.10 3,4.55 4,-5.15 5,-4.39 6,0.46 7,5.54 8,9.02 9,-2.09 10,-3.06 libpysal-4.9.2/libpysal/examples/geodanet/000077500000000000000000000000001452177046000205765ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/geodanet/README.md000066400000000000000000000015671452177046000220660ustar00rootroot00000000000000geodanet ======== Datasets from GeoDaNet for network analysis ------------------------------------------- * crimes.dbf: attribute data for crime point set. (k=2) * crimes.prj: ESRI projection file. * crimes.sbn: spatial index. * crimes.sbx: spatial index. * crimes.shp: Point shapefile for crime data. (n=287) * crimes.shp.xml: metadata. * crimes.shx: spatial index. * schools.dbf: attribute data for schools point set. (k=1) * schools.prj: ESRI projection file. * schools.sbn: spatial index. * schools.sbx: spatial index. * schools.shp: Point shapefile for schools data. (n=8) * schools.shp.xml: metadata. * schools.shx: spatial index. * streets.dbf: attribute data for street polyline set. (k=2) * streets.prj: ESRI projection file. * streets.sbn: spatial index. * streets.sbx: spatial index. * streets.shp: Line shapefile data for street data. (n=293) * streets.shx: spatial index. libpysal-4.9.2/libpysal/examples/geodanet/crimes.dbf000066400000000000000000000126571452177046000225500ustar00rootroot00000000000000paPOLYID2N POLYIDN 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 59 59 60 60 61 61 62 62 63 63 64 64 65 65 66 66 67 67 68 68 69 69 70 70 71 71 72 72 73 73 74 74 75 75 76 76 77 77 78 78 79 79 80 80 81 81 82 82 83 83 84 84 85 85 86 86 87 87 88 88 89 89 90 90 91 91 92 92 93 93 94 94 95 95 96 96 97 97 98 98 99 99 100 100 101 101 102 102 103 103 104 104 105 105 106 106 107 107 108 108 109 109 110 110 111 111 112 112 113 113 114 114 115 115 116 116 117 117 118 118 119 119 120 120 121 121 122 122 123 123 124 124 125 125 126 126 127 127 128 128 129 129 130 130 131 131 132 132 133 133 134 134 135 135 136 136 137 137 138 138 139 139 140 140 141 141 142 142 143 143 144 144 145 145 146 146 147 147 148 148 149 149 150 150 151 151 152 152 153 153 154 154 155 155 156 156 157 157 158 158 159 159 160 160 161 161 162 162 163 163 164 164 165 165 166 166 167 167 168 168 169 169 170 170 171 171 172 172 173 173 174 174 175 175 176 176 177 177 178 178 179 179 180 180 181 181 182 182 183 183 184 184 185 185 186 186 187 187 188 188 189 189 190 190 191 191 192 192 193 193 194 194 195 195 196 196 197 197 198 198 199 199 200 200 201 201 202 202 203 203 204 204 205 205 206 206 207 207 208 208 209 209 210 210 211 211 212 212 213 213 214 214 215 215 216 216 217 217 218 218 219 219 220 220 221 221 222 222 223 223 224 224 225 225 226 226 227 227 228 228 229 229 230 230 231 231 232 232 233 233 234 234 235 235 236 236 237 237 238 238 239 239 240 240 241 241 242 242 243 243 244 244 245 245 246 246 247 247 248 248 249 249 250 250 251 251 252 252 253 253 254 254 255 255 256 256 257 257 258 258 259 259 260 260 261 261 262 262 263 263 264 264 265 265 266 266 267 267 268 268 269 269 270 270 271 271 272 272 273 273 274 274 275 275 276 276 277 277 278 278 279 279 280 280 281 281 282 282 283 283 284 284 285 285 286 286 287 287libpysal-4.9.2/libpysal/examples/geodanet/crimes.prj000066400000000000000000000007441452177046000226020ustar00rootroot00000000000000PROJCS["NAD_1983_StatePlane_Arizona_Central_FIPS_0202_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",699998.6],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-111.9166666666667],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",31.0],UNIT["Foot_US",0.3048006096012192]]libpysal-4.9.2/libpysal/examples/geodanet/crimes.sbn000066400000000000000000000062341452177046000225710ustar00rootroot00000000000000' ÿÿþpNA&*A*¹‘ÿÿÿìA&=gÿÿÿòA*åéÿÿÿúüÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ ÿÿÿÿÿÿÿÿ ÿÿÿÿÿÿÿÿ ÿÿÿÿ ÿÿÿÿÿÿÿÿ  /! 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ESRI ArcCatalog 9.3.0.1770enREQUIRED: A brief narrative summary of the data set.REQUIRED: A summary of the intentions with which the data set was developed.REQUIRED: The name of an organization or individual that developed the data set.REQUIRED: The date when the data set is published or otherwise made available for release.Mesa_Crime_ClippedMesa_Crime_Clippedvector digital dataREQUIRED: The basis on which the time period of content information is determined.REQUIRED: The year (and optionally month, or month and day) for which the data set corresponds to the ground.REQUIRED: The state of the data set.REQUIRED: The frequency with which changes and additions are made to the data set after the initial data set is completed.REQUIRED: Western-most coordinate of the limit of coverage expressed in longitude.REQUIRED: Eastern-most coordinate of the limit of coverage expressed in longitude.REQUIRED: Northern-most coordinate of the limit of coverage expressed in latitude.REQUIRED: Southern-most coordinate of the limit of coverage expressed in latitude.REQUIRED: Reference to a formally registered thesaurus or a similar authoritative source of theme keywords.REQUIRED: Common-use word or phrase used to describe the subject of the data set.REQUIRED: Restrictions and legal prerequisites for accessing the data set.REQUIRED: Restrictions and legal prerequisites for using the data set after access is granted.ShapefileMicrosoft Windows XP Version 5.1 (Build 2600) Service Pack 3; ESRI ArcCatalog 9.3.0.1770Mesa_Crime_ClippedenFGDC Content Standards for Digital Geospatial MetadataFGDC-STD-001-1998local timehttp://www.esri.com/metadata/esriprof80.htmlESRI Metadata ProfileREQUIRED: The person responsible for the metadata information.REQUIRED: The organization responsible for the metadata information.REQUIRED: The mailing and/or physical address for the organization or individual.REQUIRED: The city of the address.REQUIRED: The state or province of the address.REQUIRED: The ZIP or other postal code of the address.REQUIRED: The telephone number by which individuals can speak to the organization or individual.20100708ISO 19115 Geographic Information - MetadataDIS_ESRI1.0datasetDownloadable Data0.0000.000002file://Local Area Network0.000ShapefileVectorSimplePointFALSE0FALSEFALSEEntity point0GCS_North_American_1983NAD_1983_StatePlane_Arizona_Central_FIPS_0202_FeetState Plane Coordinate System 19832020.999900-111.91666731.000000699998.6000000.000000coordinate pairsurvey feet0.0000000.000000North American Datum of 1983Geodetic Reference System 806378137.000000298.257222NAD_1983_StatePlane_Arizona_Central_FIPS_0202_Feet0Mesa_Crime_ClippedFeature Class0FIDFIDOID400Internal feature number.ESRISequential unique whole numbers that are automatically generated.ShapeShapeGeometry000Feature geometry.ESRICoordinates defining the features.CASE_IDCASE_IDFloat1911REPORT_DATREPORT_DATDate8REPORT_TIMREPORT_TIMNumber10ADDRESSADDRESSString254APT_FLRAPT_FLRNumber10CITYCITYString254STATESTATEString254GEOXGEOXNumber10GEOYGEOYNumber10DISTRICTDISTRICTString254GRIDGRIDString254DATE1DATE1Date8TIME1TIME1Number10DAY1DAY1String254DATE2DATE2Date8TIME2TIME2Number10DAY2DAY2String254UCR_GROUPUCR_GROUPString254detail1detail1String254detail2detail2String254PrimaryLasPrimaryLasNumber1020100708Dataset copied.2010111016054300Dataset copied.2010111016154900Dataset copied.2012021009543000 libpysal-4.9.2/libpysal/examples/geodanet/crimes.shx000066400000000000000000000045341452177046000226120ustar00rootroot00000000000000' ®è*&Aìÿÿÿ‘¹*Aòÿÿÿg=&Aúÿÿÿéå*A2 @ N \ j x † ” ¢ ° ¾ Ì Ú è ö    . < J X f t ‚  ž ¬ º È Ö ä ò    * 8 F T b p ~ Œ š ¨ ¶ Ä Ò à î ü   & 4 B P ^ l z ˆ – ¤ ² À Î Ü ê ø   " 0 > L Z h v „ ’   ® ¼ Ê Ø æ ô    , : H V d r € Ž œ ª ¸ Æ Ô â ð þ   ( 6 D R ` n | Š ˜ ¦ ´  Ð Þ ì ú   $ 2 @ N \ j x † ” ¢ ° ¾ Ì Ú è ö    . < J X f t ‚  ž ¬ º È Ö ä ò   * 8 F T b p ~ Œ š ¨ ¶ Ä Ò à î ü  & 4 B P ^ l z ˆ – ¤ ² À Î Ü ê ø   " 0 > L Z h v „ ’   ® ¼ Ê Ø æ ô    , : H V d r € Ž œ ª ¸ Æ Ô â ð þ  ( 6 D R ` n | Š ˜ ¦ ´ Â Ð Þ ì ú   $ 2 @ N \ j x † ” ¢ ° ¾ Ì Ú è ö    . < J X f t ‚  ž ¬ º È Ö libpysal-4.9.2/libpysal/examples/geodanet/schools.dbf000066400000000000000000000002221452177046000227210ustar00rootroot00000000000000pA WPOLYIDN 1 2 3 4 5 6 7 8libpysal-4.9.2/libpysal/examples/geodanet/schools.prj000066400000000000000000000007441452177046000227720ustar00rootroot00000000000000PROJCS["NAD_1983_StatePlane_Arizona_Central_FIPS_0202_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",699998.6],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-111.9166666666667],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",31.0],UNIT["Foot_US",0.3048006096012192]]libpysal-4.9.2/libpysal/examples/geodanet/schools.sbn000066400000000000000000000003241452177046000227530ustar00rootroot00000000000000' ÿÿþpjA&‹T[DÇA*ÀpJÞ‹A&6K(.ŒA*Ùûj,AT ÿÿÿÿÔþÕÿº»€ÇÿÈÿÿËÿ̮ͯÎzñ{òæçlibpysal-4.9.2/libpysal/examples/geodanet/schools.sbx000066400000000000000000000001741452177046000227700ustar00rootroot00000000000000' ÿÿþp>A&‹T[DÇA*ÀpJÞ‹A&6K(.ŒA*Ùûj,AT2 B^libpysal-4.9.2/libpysal/examples/geodanet/schools.shp000066400000000000000000000005041452177046000227630ustar00rootroot00000000000000' ¢èÇD[T‹&A‹ÞJpÀ*AŒ.(K6&ATA,jûÙ*A O©T0&A÷9{…îÙ*A Nûï±#&Ar !÷›Ø*A ¸˜hBº,&A‹ÞJpÀ*A ÇD[T‹&AqÜ”{×*A çL4›&&A¸úK—*Í*A ›U+°x.&ATA,jûÙ*A Œ.(K6&AóOHKÏÔ*A žR{,ý*&A\v‰öÕ*Alibpysal-4.9.2/libpysal/examples/geodanet/schools.shp.xml000066400000000000000000002563271452177046000236020ustar00rootroot00000000000000 Arizona Department of Environmental Quality, Arizona Department of Health ServicesAugust 14, 2007SCHOOLS_EVERYTHING_8_7_08Schools 8-14-2007vector digital data\\adeq.lcl\gisprod\data\adeq\schools-everything-8-14-07.shpSCHOOLS_EVERYTHING_8_7_08This data set is a general reference for schools or "learning sites" in Arizona. It represents schools from the AZ Department of Education (CTDS numbers, charter and public schools), AZ School Facilities Board, private schools, some technical schools, colleges and universities.The intention with which the data set was developed is for general reference only. It is representative only presenting a single point in time for the topic "learning sites." It is not the final or authoritative legal documentation for the learning sites data or locations.This data set does not contain locations for all cosmetology or beauty colleges, horseshoeing or welding technical schools, or other trade schools.enREQUIRED: The year (and optionally month, or month and day) for which the data set corresponds to the ground.publication dateIn workAs needed-114.993109-108.98598337.01237531.281734144373.429325679220.6875003466849.8122804096245.182700REQUIRED: Reference to a formally registered thesaurus or a similar authoritative source of theme keywords.ADEQenvironmentArizonaEnvironmental QualityDepartment of Environmental Qualityschoolslearning sitescollegesuniversitiesgrade schoolelementary schoolhigh schoolmiddle schoolkindergartenprivate schoolparochial schoolmontessoricommunity collegejunior collegeuniversityArizona Department of EducationCharter SchoolArizona2008Access to these data are allowed for non-commercial applications without charge. Commercial uses require payment.The Arizona Department of Environmental Quality and others have compiled this data as a service to our customers using information from various sources. ADEQ and its collaborators cannot ensure that the information is accurate, current or complete. Neither the information presented nor maps derived from them are official documents. All data are provided "as is" and may contain errors. The data are for reference and illustration purposes only and are not suitable for site-specific decision making. Information found here should not be used for making financial or any other commitments. Conclusions drawn from such information are the responsibility of the user. ADEQ assumes no responsibility for errors arising from misuse of the data or maps derived from the data. ADEQ disclaims any liability for injury, damage or loss that might result from the use of this information. In no event shall ADEQ become liable to users of these data and maps, or any other party, arising from the use or modification of the data.Arizona Department of Environmental Qualitymailing and physical address
1110 W Washington St
PhoenixArizona85007USA
This data set has been created in collaboration with the Arizona Department of Education, Arizona Department of Health Services, Arizona State Land Department and the Arizona State Cartographers Office.UnclassifiedMicrosoft Windows XP Version 5.1 (Build 2600) Service Pack 3; ESRI ArcCatalog 9.3.1.1850Shapefile
Dataset copied.Metadata imported.D:\DOCUME~1\VMG~1.ADE\LOCALS~1\Temp\xmlCA.tmpMetadata imported.S:\common\vmg\schoolsmetadata.xmlMetadata imported.S:\common\vmg\schools_everything_5_19_08.xml20080708Metadata imported.T:\data\adeq\cross_media\schools_everything_7_8_08.shp.xml20080811Dataset copied.2012021009541600Dataset copied.2012021009575300Dataset copied.2012021009582900VectorSimple1FALSE2772TRUEFALSEcoordinate pair0.0000000.000000metersNorth American Datum of 1983Geodetic Reference System 806378137.000000298.257222GCS_North_American_1983NAD_1983_UTM_Zone_12NADEQ_AZ_SCHOOLS_2008Feature Class2772FIDInternal feature number.ESRISequential unique whole numbers that are automatically generated.FIDOID400ShapeFeature geometry.ESRICoordinates defining the features.ShapeGeometry000NAMESchool or learning site nameVariesGoodNames verified to mulitple sourcesAs neededString69NAME00ADDRESSPhysical address of school or learning siteVariesGoodVerified to multiple sourcesAs neededString44ADDRESS00ZIPZIP Code of physical locationUSPS ZIP CodeGoodVerified to outside sourceAs neededString7ZIP00CTDSAZ Dept of Education Identification Number, (County Code, Type Code, District Code & Site NumberAZ Dept. of EducationMediumMissing leading zeros in string fieldAs neededString10CTDS00CTDS_NUMDouble11CTDS_NUM110STATUSOperating Status (open , closed, proposed)VariesGoodValidated to multiple sourcesAs neededString8STATUS00LOCATIONLocation check methodADEQDIGDigitally verified against raster data or other data set (parcels)ADEQNONNon-specific, multiple methods of verification (digital, geocoding, GPS, etc.)ADEQGPSGlobal Positioning System - field collectedADEQGEOOriginally geocoded - address matched [location verified by other methods]ADEQGoodVerifiedAs neededString10LOCATION00QA_QCQuality Assurance / Quality Control CodeADEQ0Used as identifier for version additions to data set - GPS], location quality is "ok"ADEQ1Very high confidence of location accuracy, matched to at least two independent sourcesADEQ2Low confidence of locational accuracy, unable to match to other sourcesADEQ3Used as identifier for version additions to data set - GPS or NON, location quality is "ok"ADEQ4Used as identifier for version additions to data set - GPS or NON, location quality is "ok"ADEQ5Very high confidence of location accuracy, matched digitally, etc. to at least two independent sourcesADEQGoodInteger7QA_QC70CITYPhysical location city or townDigitalGoodVerifiedAs neededString22CITY00COUNTYCountyDigitalGoodVerifiedAs neededString12COUNTY00PHONEPhone NumberVariesMediumNot verifiedAs neededString12PHONE00FAXFAX NumberVariesMediumNot verifiedAs neededString12FAX00LOWGRADELowest class levelVariesMediumNot VerifiedAs neededString12LOWGRADE00HIGHGRADEHighest class level taughtVariesMediumNot verifiedAs neededString12HIGHGRADE00COMMENTComments FieldVariesAs neededString49COMMENT00DISTRICTSchool District or Charter HolderAZ Department of EducationMediumNot fully verifiedAs neededString55DISTRICT00GRADEGRADEString700NURSENURSEString1100RN_PHNRN_PHNString1600JUV_POPJUV_POPString900DISTNUMDISTNUMString1000MAILTOMAILTOString4400MAILCITYMAILCITYString2000MAILSTATMAILSTATString1000MAILZIPMAILZIPString800CLASSCLASSString1700TYPE_1TYPE_1String900KINDERKINDERString800FIRSTFIRSTString600SECONDSECONDString900THIRDTHIRDString600FOURTHFOURTHString800FIFTHFIFTHString600SIXTHSIXTHString600SEVENTHSEVENTHString900EIGHTHEIGHTHString700NINTHNINTHString600TENTHTENTHString700ELEVENTHELEVENTHString1000TWELFTHTWELFTHString900PRESCHLPRESCHLString900ACCURACYACCURACYString1200BOARDINGBOARDINGString1000REGIONREGIONString900WEB_PAGEWEB_PAGEString6900GRADERange of classes taughtVariesAs neededDouble11PLAC_IDNO110NURSESchool Nurse present?VariesUnknownString49COMMENT00RN_PHNRegister Nurse Phone NumberVariesUnknownString12HIGHGRADE00JUV_POPJuvenile Population (number of students)VariesUnknownString12LOWGRADE00DISTNUMSchool District Number or Charter Holder NumberAZ Department of EducationMediumPartially verifiedString12FAX00MAILTOMailing AddressVariesMediumNot verifiedAs neededString12PHONE00MAILCITYMailing Address CityVariesMediumNot verifiedString12COUNTY00MAILSTATMailing Address StateVariesMediumNot verifiedString22CITY00MAILZIPMailing Address ZIPVariesMediumNot verifiedInteger7QA_QC70CLASSClass - grade levelsVariesUniversityUniversity levelVariesComm. CollegeCommunity College - part of community college networkVariesCollegeCollege - non-university or community collegeVariesTechTechnical SchoolsVariesRel. CollegeReligious CollegeVariesSpecial NeedsSpecial needs schoolsADEQAll GradesAll grade levelsADEQHighHigh SchoolVariesJR/SR HighSeventh through twelfth gradeADEQMiddleMiddle SchoolVariesPrimaryPrimary or elementary schoolVaries(Blank)Unknown grade levelsGoodVerifiedAs neededString10LOCATION00TYPE_1Type of SchoolVariesCharterArizona Charter SchoolArizona Board of Charter SchoolsPublicPublic SchoolAz Departement of EducationBIABureau of Indian Affairs operated schoolUS BIAClosedClosed SchoolADEQPrivatePrivate or Parochial operated schoolVariesTribalTribe operated schoolVariesGoodverifiedAs neededString69NAME00KINDERKindergarden Taught?VariesMediumNot VerifiedGeometry0Shape00Coordinates defining the features.FIRSTFirst Grade Taught?VariesMediumNot VerifiedOID4FID00Sequential unique whole numbers that are automatically generated.SECONDSecond Grade Taught?VariesMediumNot VerifiedString9SECOND00THIRDThird Grade Taught?VariesMediumNot VerifiedString6THIRD00FOURTHFourth Grade Taught?VariesMediumNot VerifiedString8FOURTH00FIFTHFifth Grade Taught?VariesMediumNot VerifiedString6FIFTH00SIXTHSixth Grade Taught?VariesMediumNot VerifiedString6SIXTH00SEVENTHSeventh Grade Taught?VariesMediumNot VerifiedString9SEVENTH00EIGHTHEighth Grade Taught?VariesMediumNot VerifiedString7EIGHTH00NINTHNinth Grade TaughtVariesMediumNot VerifiedString6NINTH00TENTHTenth Grade Taught?VariesMediumNot VerifiedString7TENTH00ELEVENTHEleventh Grade Taught?VariesMediumNot VerifiedString10ELEVENTH00TWELFTHTwelfth Grade Taught?VariesMediumNot VerifiedString9TWELFTH00PRESCHLPreschool Level Taught?VariesMediumNot VerifiedString9PRESCHL00ACCURACYOriginal AccuracyADHSString12ACCURACY00BOARDINGBoarding School?VariesMediumNot VerifiedString10BOARDING00REGIONRegion of StateVariesMediumNot VerifiedString9REGION00WEB_PAGESchool Web Page AddressVariesMediumParitially VerifiedString69WEB_PAGE00PLAC_IDNODouble11PLAC_IDNO110Downloadable Data3.0170.07420090730REQUIRED: The organization responsible for the metadata information.REQUIRED: The person responsible for the metadata information.REQUIRED: The mailing and/or physical address for the organization or individual.REQUIRED: The city of the address.REQUIRED: The state or province of the address.REQUIRED: The ZIP or other postal code of the address.REQUIRED: The telephone number by which individuals can speak to the organization or individual.FGDC Content Standards for Digital Geospatial MetadataFGDC-STD-001-1998local timehttp://www.esri.com/metadata/esriprof80.htmlESRI Metadata Profilehttp://www.esri.com/metadata/esriprof80.htmlESRI Metadata Profilehttp://www.esri.com/metadata/esriprof80.htmlESRI Metadata Profileen2012021009582900FALSE20110620143029002011062014302900ADEQ_AZ_SCHOOLS_2008002144373.429325679220.6875003466849.8122804096245.18270010.074file://\\itfs1.asurite.ad.asu.edu\gisshare1$\spatial_data\AZ_Data_by_Topic\Education\AZ_Schools\ADEQ_AZ_SCHOOLS_2008.shpLocal Area NetworkProjectedGCS_North_American_1983NAD_1983_UTM_Zone_12N<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.0'><WKT>PROJCS[&quot;NAD_1983_UTM_Zone_12N&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,500000.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-111.0],PARAMETER[&quot;Scale_Factor&quot;,0.9996],PARAMETER[&quot;Latitude_Of_Origin&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,26912]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>450445547.3910538</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>26912</WKID></ProjectedCoordinateSystem>Project schools Z:\Desktop\example_data\schools_Project.shp PROJCS['NAD_1983_StatePlane_Arizona_Central_FIPS_0202_Feet',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',699998.6],PARAMETER['False_Northing',0.0],PARAMETER['Central_Meridian',-111.9166666666667],PARAMETER['Scale_Factor',0.9999],PARAMETER['Latitude_Of_Origin',31.0],UNIT['Foot_US',0.3048006096012192]] # PROJCS['NAD_1983_UTM_Zone_12N',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['False_Northing',0.0],PARAMETER['Central_Meridian',-111.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin',0.0],UNIT['Meter',1.0]]1.0{171D5199-0709-4DCB-AFC4-AD87037D006B}ArcGIS Metadata1.03.017\\adeq.lcl\gisprod\data\adeq\schools-everything-8-14-07.shpShapefilefile://Local Area NetworkSchools 8-14-2007Arizona Department of Environmental Quality, Arizona Department of Health ServicesADEQ_AZ_SCHOOLS_2008This data set is a general reference for schools or "learning sites" in Arizona. It represents schools from the AZ Department of Education (CTDS numbers, charter and public schools), AZ School Facilities Board, private schools, some technical schools, colleges and universities.The intention with which the data set was developed is for general reference only. It is representative only presenting a single point in time for the topic "learning sites." It is not the final or authoritative legal documentation for the learning sites data or locations.This data set has been created in collaboration with the Arizona Department of Education, Arizona Department of Health Services, Arizona State Land Department and the Arizona State Cartographers Office.Arizona Department of Environmental Quality1110 W Washington StPhoenixArizona85007USArizona2008montessorienvironmentjunior collegemiddle schoolelementary schoolgrade schoolcommunity collegeArizonakindergartenschoolscollegesDepartment of Environmental Qualityprivate schoolADEQArizona Department of Educationparochial schoollearning sitesCharter Schoolhigh schoolEnvironmental Qualityuniversitiesuniversitymontessorienvironmentjunior collegemiddle schoolelementary schoolgrade schoolcommunity collegeArizonakindergartenschoolscollegesDepartment of Environmental Qualityprivate schoolADEQArizona Department of Educationparochial schoollearning sitesArizona2008Charter Schoolhigh schoolEnvironmental QualityuniversitiesuniversityAccess constraints: Access to these data are allowed for non-commercial applications without charge. Commercial uses require payment.Use constraints: The Arizona Department of Environmental Quality and others have compiled this data as a service to our customers using information from various sources. ADEQ and its collaborators cannot ensure that the information is accurate, current or complete. Neither the information presented nor maps derived from them are official documents. All data are provided "as is" and may contain errors. The data are for reference and illustration purposes only and are not suitable for site-specific decision making. Information found here should not be used for making financial or any other commitments. Conclusions drawn from such information are the responsibility of the user. ADEQ assumes no responsibility for errors arising from misuse of the data or maps derived from the data. ADEQ disclaims any liability for injury, damage or loss that might result from the use of this information. 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cW(A KçJAÊW(A`ÔãJA {_(AÀâãJA 7_(AeåJAlibpysal-4.9.2/libpysal/examples/georgia/G_utm.shx000066400000000000000000000025341452177046000222300ustar00rootroot00000000000000' ®èÀÓ$#Aà;²IA Lƒ0A ¾™MA26Ð ÀÎð °vèbÈ.˜Ê@¨ºH!ø"Ø&Þ²+”À4Xà:<0>px@ìCððJä0LØMôOPVT¨\_` a´(cà8fXhxj”ðlˆ o˜q´Ðtˆ€x H{X ~ü`€`ˆ€ì؂Ȩ„tÀ†8è‰$0‹X˜ŒôxŽpØ‘Lè“8j™¦ðš(¡ÆØ£¢`¦p©zH«ÆP®Ò¯ðÈ´¼ ·à¹üX¾XhÀÄàÁ¨XÅhÇpË|ΈÒŒ Ó0@ÕtØ×PÀØÒÛêˆàvøâråvPçÊ0êþxîzðïnàñRhò¾ óbÀõ&P÷zhù渢0ÖÀšH æhRÀh‚¢( <h"¨¨%TØ&0)L°+.¸0Àh5,H8x:;¶`?C.àDFXGzˆMO"ØOþ˜QšøT–ÀVZ0[Ž]ªºahôc`0d”ºkR mö pšˆr&°tÚÈw¦h{0~F¨ò’†ˆ” Pt( ’4ð•( —LØš(›DØ À ä¥Ð§Ôð¨ÈøªÄð­¸ð±¬Ð·€`¼ä˜¾€@ÃÄ ÇhàËL¨Îø`Ó\°×0libpysal-4.9.2/libpysal/examples/georgia/README.md000066400000000000000000000030701452177046000217040ustar00rootroot00000000000000georgia ======= Various socio-economic variables for counties within the state of Georgia (1990) ------------------------------------------------------------------------------- * G_utm.shp: attribute and geometry data. (n=159, k=17) For testing against GWR4 GUI software ------------------------------------- * georgia_BS_NN_listwise.csv: bisquare nearest neighbor kernel model output * georgia_BS_NN_summary.txt: bisquare nearest neighbor kernel model summary * georgia_BS_NN.ctl: bisquare nearest neighbor kernel control file * georgia_GS_NN_listwise.csv: Gaussian nearest neighbor kernel model output * georgia_GS_NN_summary.txt: Gaussian nearest neighbor kernel model summary * georgia_GS_NN.ctl: Gaussian nearest neighbor kernel control file * georgia_BS_F_listwise.csv: bisquare fixed kernel model output * georgia_BS_F_summary.txt: bisquare fixed kernel model summary * georgia_BS_F.ctl: bisquare fixed kernel control file * georgia_GS_F_listwise.csv: Gaussian fixed kernel model output * georgia_GS_F_summary.txt: Gaussian fixed kernel model summary * georgia_GS_F.ctl: Gaussian fixed kernel control file Data used in: Fotheringham, A. Stewart, Chris Brunsdon, and Martin Charlton. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons. Oshan, Taylor, Ziqi Li, Wei Kang, Levi J. Wolf, and Alexander S. Fotheringham. 2018. “Mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale.†OSF Preprints. 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\xee?\xdf8E\x84\x84\x1d\xf0?:\xf2\xa5EYZ\x14@\xce\'/T\xd18\xf9?@\x16H\xa3Q\xf1\xe6?\x05\x19\x10\x980\xfd\x12@\xc3\xd0\xa0)AH\x08@~\xa6{\x00\x16\xc9\xf0?\x97\xc7\xa5\x83\xc6T\x03@g\xcb\x1d:\x10\xed\x17@?g\x9a4\x18\xc3\xf3?\x9aP\xfdlx\xdd\x17@\xbf\xdb\xf6\xaf\x87\x00\x08@\xa0\xa0\x06\xaa\xa5\x10\xfd?\x9b\x08\xd0\xdf\x95\x9d\x1e@l\xc4I\x13N\xdf\xed?G\xc5\x8f~\x87\x17\xf3?S\xa79)\xffO\x18@\xbb"\xfe\x87\xcfl\x00@\xcd\xf9\xca\x03\xe2+\xf2?R\x93o\x94"\xa6\x12@\xef;T\xde\xdeO\x11@qr3\x8a6\x7f\xf1?a\x8e\x83\r\x04\xb2 @\x02\xeaY\xc8\xb4;\xf3?r\x19-\xbb\xe4v\xf7?\xfc\x9c\xe8~Z\xb2\n@\xbee\xa1=h&\x06@\x98\xe7\x89\xe2\x00\x94\xe7?\x12\xb8t\x0eF}\x0c@\x80\xc5\xa28E\xd7\x10@' p13 tp14 b.libpysal-4.9.2/libpysal/examples/georgia/err.p000066400000000000000000000074401452177046000214030ustar00rootroot00000000000000cnumpy.core.multiarray _reconstruct p0 (cnumpy ndarray p1 (I0 tp2 S'b' p3 tp4 Rp5 (I1 (I159 I1 tp6 cnumpy dtype p7 (S'f8' p8 I0 I1 tp9 Rp10 (I3 S'<' p11 NNNI-1 I-1 I0 tp12 bI00 S'x\xd4\xe0?\x96\xba\x92\xd4\x0f\x00#@I`9\x18\xa7$\xfd\xbf8\x92\\\xc2\x8e|\x0c@B}\xb3\x0e1\xe6\x07\xc0y\xb6\x14\x92T\x0f\xf2\xbf\xaaYq\xe2\xa6\r(@\x08\xc0\x90\x9e\x96U\xc6\xbf\x80d\xadV\xebZ\xb5?\xe5]\x99\xc2\x9d\xea\r\xc0\x88z\xd2\xde\xcfn8@\xc8W\xc2\xa1\xd3%\x02@e|n\x13\xb5\xbf\x11@D[\x80\xde\x85\x86\x16\xc0bK\x85\xb7\x7f\xf4\xfd\xbf\x08\x87a[\x01\x16\x1a@h\xb2(\x10\x91/\xd8\xbfr\x80%\x0c!\xae\x11\xc0\xb5\xde\xb4\x06\xec\xb4\x11@\xfa\\#\xa2$\x1f&@\xf8-\xad\xfc\x0fl\xf1\xbf\x18+\x80Yw\xb9\x03\xc0~YF\xffaC\x02\xc0\xe2\x97\x16\xf3Ox\x01@\x872j\x07,*\x12\xc0X3\x7f\xbe\xf7\xba\x00\xc0\xda}a\x85\xee\xda\x07\xc0D\x1a|\xbc\x9cs\x15@\xf0\xe5\xe0\xd8\n\n\xee\xbf\xa1\x1c\xf4\x99\\\n\x0f\xc0\x89mIp{\x97\x11@I\x80\x9d\xc5\x01\xef!@\x1e\xf4]P\xaeE\n@\x18\x81e\x97"w\xe0\xbfJt\x9aV{\x94\x13\xc0c\'\x88\x9b\xb1\x08\x1f@\xb2\x89WzTz\xda\xbf0\xe7\x13f\xe4\xb6\x00@\xaas\x9d\x0e%\x0c\x13\xc04G\xaa\xea1h\x02\xc00}D\xf9\xe7\xe6\xdf\xbf\xa40\x0e\xc3\xfbM\x14\xc0\xb3\x92QD\x93\x1d \xc0,\xa4`\xdd\xcb\x89\xf4?\xce-7\x97\xed\x05\x05\xc0z\xf0\xca\xe1\xc4^\r@\xa6z\xc3\x869n\xf2?pW\x06K\xef*\xc7?\xb4Ht\x11\xde2\x04\xc0\x97OH5V\xea"@\xc6C\xc9\xd7\x9fG\x10\xc0\xb3W:Yb+6@\xbd\xf2\xd8XBK\t@\x00\xd7\xa4\x9d\xeb\xad\x8d\xbfMrn6\x90\x04\x10@/\xa3x\xca\xff\x84\xf1\xbf4b\xb8H\xd8\xf0\xe5\xbf\xc8\x99\xce\x05\x9a\x8c\xe4?G9P9\x8f}\x1e\xc0\xb4\xbf\tUr(\xf9?\x0f\xcb\x9dbd\'\n@\xb9W\xf0\xca\x7f\x15\x08\xc0[fFo\x07\x11\xfd?\xcb\x07n\xa9\xd8\xb3\x14\xc0,\xac\x80\xc7\xc2<&@\x87\xe3\xe1\x930\x07&@\x10\xd2\x00m\x0b\xcb\xd2?T\xb20#\x19\x85\xf6\xbf\xa84P\xde\xa6\xac\xd6\xbfx0K\x8b\xa1d\xf3\xbfp\x1b\xc1\xc6\x07\xd0\xf2?x\xb8\xe8\xea\x94\xcd\x0e\xc0\xfb%\x97\xd8\x8b\xec\x1a@\xd0\xccw\xfc\x11\xb1\x1b\xc0{\xb4\x96\x18\xdc\x9e\x1f\xc0\x86\x0f\x99\xdd*\x1e\x06\xc0\xf3q\xdfd\xe7\xd3\x10\xc0`\x14D\xfdlf\xf2\xbf\x8c\xe7\nD>g\xfc\xbf\xf2l\xa36\x04\x8e\x11@^\xd1\xcdx\xa1k\x18@M\xde,\xfc\x8d\x8d\x0b@0\x01\xff\x8d{.\xf4?\xf8\xbdTU\'B\x04\xc0d?ay\xc4\xa4\x0c@F\x0e\x8cx\x8c\xa5\x0c\xc0\xbd/\xe7y\xf8 \x1b\xc0\xc7\x11:\x18n=\x06\xc0\xe8\xdb\x15\xbf{\xdf\xf0?p\xb7\xbe\xdc\xfd\xfc\xe9\xbf\xea\xc2Q\x1d\xa9\xeb\t\xc0\xf2\xbb\xdd\x90\xd4\xfe\xfc? \xc9\x94k\x1b\xb3\x1f\xc0\xa1Q\xe0\xee\x88\x06\x01@\xe6\x88s\x14\xac\x9b\xf1?\xa61\x1f\xf8q\x93\x0f\xc0\x8c\xc7\xe3\xaeo\xa0\xf9\xbf5\xfaV\xb3z\x91\t@\xfb\xabFfng\x05@\x9bN\xa0\x1a\'5\x04\xc0\xb0H\x1b\xc2*%\xcf\xbf\xec\xad\x8b\x98b\xa4\xf3?\x91V\xf6Qj\xaa\x01\xc0' p13 tp14 b.libpysal-4.9.2/libpysal/examples/georgia/georgia_BS_F.ctl000066400000000000000000000013711452177046000234010ustar00rootroot00000000000000 C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv FORMAT/DELIMITER: 1 Number_of_fields: 13 Number_of_areas: 159 Fields AreaKey 001 AreaKey X 012 X Y 013 Y Gmetric 0 Dependent 006 PctBach Offset Independent_geo 4 000 Intercept 005 PctRural 009 PctPov 010 PctBlack Independent_fix 0 Unused_fields 6 002 Latitude 003 Longitud 004 TotPop90 007 PctEld 008 PctFB 011 ID MODELTYPE: 0 STANDARDISATION: 0 GTEST: 0 VSL2G: 0 VSG2L: 0 KERNELTYPE: 1 BANDSELECTIONMETHOD: 1 Goldrangeflag: 0 Goldenmax: Goldenmin: Fixedbandsize: IntervalMax: IntervalMin: IntervalStep: Criteria: summary_output: C:\Users\IEUser\Desktop\georgia_BS_F_summary.txt listwise_output: C:\Users\IEUser\Desktop\georgia_BS_F_listwise.csv predictflag: 0 prediction_def: prediction_output: libpysal-4.9.2/libpysal/examples/georgia/georgia_BS_F_listwise.csv000066400000000000000000001644601452177046000253460ustar00rootroot00000000000000Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD 0, 13001, 941396.6, 3521764, 17.773084, 2.613925, 6.799385, -0.084447, 0.022534, -3.747519, -0.206895, 0.123152, -1.680000, 0.072218, 0.053829, 1.341633, 8.200000, 8.770904, -0.570904, -0.152628, 0.534242, 0.046417, 0.000068 1, 13003, 895553, 3471916, 17.909007, 2.851004, 6.281650, -0.075912, 0.023937, -3.171273, -0.300513, 0.134796, -2.229398, 0.116286, 0.057385, 2.026436, 6.400000, 5.627943, 0.772057, 0.212853, 0.559361, 0.103315, 0.000312 2, 13005, 930946.4, 3502787, 17.741664, 2.738235, 6.479233, -0.081922, 0.023223, -3.527632, -0.235500, 0.126826, -1.856878, 0.088548, 0.057898, 1.529380, 6.600000, 8.376916, -1.776916, -0.496464, 0.547517, 0.126915, 0.002142 3, 13007, 745398.6, 3474765, 18.862030, 2.594357, 7.270406, -0.068144, 0.023305, -2.924023, -0.355610, 0.133412, -2.665500, 0.115038, 0.060624, 1.897563, 9.400000, 9.172561, 0.227439, 0.064599, 0.566464, 0.155149, 0.000046 4, 13009, 849431.3, 3665553, 25.837943, 1.440975, 17.930872, -0.132573, 0.016150, -8.208601, -0.238408, 0.086974, -2.741124, -0.014170, 0.039810, -0.355929, 13.300000, 15.404309, -2.104309, -0.567856, 0.575601, 0.064075, 0.001320 5, 13011, 819317.3, 3807616, 29.452459, 1.621412, 18.164695, -0.188534, 0.020954, -8.997310, -0.113731, 0.134442, -0.845946, -0.041081, 0.043226, -0.950360, 6.400000, 8.738397, -2.338397, -0.630888, 0.541123, 0.063666, 0.001618 6, 13013, 803747.1, 3769623, 28.770962, 1.518404, 18.948166, -0.180582, 0.019082, -9.463370, -0.138754, 0.123232, -1.125951, -0.032265, 0.039831, -0.810044, 9.200000, 14.696568, -5.496568, -1.463365, 0.558627, 0.038440, 0.005119 7, 13015, 699011.5, 3793408, 26.813145, 1.908661, 14.048145, -0.139906, 0.022388, -6.249145, -0.445255, 0.144918, -3.072471, 0.110329, 0.048948, 2.253984, 9.000000, 12.544111, -3.544111, -0.944225, 0.621363, 0.039798, 0.002210 8, 13017, 863020.8, 3520432, 17.817773, 2.293042, 7.770366, -0.078207, 0.019218, -4.069523, -0.250181, 0.102862, -2.432205, 0.085012, 0.041759, 2.035768, 7.600000, 11.301525, -3.701525, -0.989175, 0.522976, 0.045637, 0.002798 9, 13019, 859915.8, 3466377, 17.952365, 2.742204, 6.546692, -0.071777, 0.022797, -3.148468, -0.326830, 0.132602, -2.464739, 0.123942, 0.052832, 2.345960, 7.500000, 8.333094, -0.833094, -0.230898, 0.543268, 0.112750, 0.000405 10, 13021, 809736.9, 3636468, 24.680587, 1.375496, 17.943044, -0.122250, 0.015524, -7.874790, -0.284150, 0.084326, -3.369660, 0.019054, 0.038985, 0.488766, 17.000000, 18.050870, -1.050870, -0.289512, 0.598558, 0.102024, 0.000569 11, 13023, 844270.1, 3595691, 21.243540, 1.662502, 12.778051, -0.094528, 0.017022, -5.553267, -0.272811, 0.083588, -3.263751, 0.040740, 0.038950, 1.045975, 10.300000, 11.688884, -1.388884, -0.370597, 0.551615, 0.042746, 0.000367 12, 13025, 979288.9, 3463849, 18.584251, 3.536748, 5.254615, -0.091560, 0.028990, -3.158336, -0.270595, 0.166519, -1.625009, 0.117098, 0.085873, 1.363625, 5.800000, 5.039679, 0.760321, 0.233490, 0.672030, 0.277304, 0.001251 13, 13027, 827822, 3421638, 18.406127, 3.426653, 5.371458, -0.068457, 0.027443, -2.494527, -0.388811, 0.167604, -2.319822, 0.148034, 0.064784, 2.285027, 9.100000, 9.984075, -0.884075, -0.238305, 0.527200, 0.061981, 0.000224 14, 13029, 1023145, 3554982, 18.757796, 3.265023, 5.745072, -0.099750, 0.028264, -3.529264, -0.142460, 0.167059, -0.852750, 0.041247, 0.068797, 0.599546, 11.800000, 9.449995, 2.350005, 0.693671, 0.568427, 0.217779, 0.008011 15, 13031, 994903.4, 3600493, 19.977058, 2.564114, 7.791019, -0.100464, 0.024590, -4.085577, -0.160074, 0.141596, -1.130500, 0.014152, 0.050914, 0.277958, 19.900000, 9.592924, 10.307076, 2.911120, 0.560800, 0.145624, 0.086376 16, 13033, 971593.8, 3671394, 23.381373, 2.522239, 9.270086, -0.107465, 0.023837, -4.508294, -0.205462, 0.127239, -1.614777, -0.024744, 0.051951, -0.476296, 9.600000, 8.094745, 1.505255, 0.417192, 0.564821, 0.112748, 0.001323 17, 13035, 782448.2, 3684504, 26.883788, 1.356073, 19.824738, -0.150977, 0.016149, -9.349208, -0.241435, 0.097165, -2.484778, 0.000226, 0.038312, 0.005889, 7.200000, 12.043680, -4.843680, -1.288907, 0.594877, 0.037487, 0.003869 18, 13037, 724741.2, 3492653, 19.002626, 2.539986, 7.481390, -0.069926, 0.022830, -3.062963, -0.338172, 0.129720, -2.606949, 0.103909, 0.062809, 1.654362, 10.100000, 7.375325, 2.724675, 0.761889, 0.585250, 0.128345, 0.005111 19, 13039, 1008480, 3437933, 19.613265, 4.084949, 4.801349, -0.100208, 0.032522, -3.081243, -0.296955, 0.192167, -1.545297, 0.124032, 0.094322, 1.314988, 13.500000, 13.982697, -0.482697, -0.151688, 0.773166, 0.309848, 0.000618 20, 13043, 964264.9, 3598842, 19.841663, 2.269700, 8.741978, -0.094625, 0.022214, -4.259627, -0.190744, 0.119946, -1.590251, 0.023944, 0.045757, 0.523293, 9.900000, 11.055578, -1.155578, -0.311170, 0.556492, 0.060056, 0.000370 21, 13045, 678778.6, 3713250, 26.696030, 1.597285, 16.713379, -0.134240, 0.020277, -6.620453, -0.564322, 0.139328, -4.050317, 0.135815, 0.048051, 2.826501, 12.000000, 11.474032, 0.525968, 0.140163, 0.636072, 0.040273, 0.000049 22, 13047, 670055.9, 3862318, 22.743482, 2.793802, 8.140691, -0.063164, 0.037651, -1.677638, -0.681787, 0.219722, -3.102956, 0.294595, 0.100431, 2.933325, 8.100000, 12.076153, -3.976153, -1.217296, 0.657638, 0.272836, 0.033247 23, 13049, 962612.3, 3432769, 19.075145, 3.741817, 5.097830, -0.092524, 0.031391, 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-0.194247, 0.110649, -1.755519, 0.043521, 0.044402, 0.980154, 11.400000, 11.915166, -0.515166, -0.143572, 0.534286, 0.122489, 0.000172 138, 13281, 797981.7, 3872640, 28.260718, 2.095470, 13.486574, -0.171439, 0.024762, -6.923414, -0.174347, 0.161938, -1.076625, 0.016586, 0.056799, 0.292013, 11.400000, 8.675961, 2.724039, 0.740343, 0.540726, 0.077301, 0.002746 139, 13283, 919077.6, 3595170, 19.761579, 2.049165, 9.643722, -0.089037, 0.019740, -4.510423, -0.217276, 0.098109, -2.214632, 0.031289, 0.041849, 0.747657, 6.300000, 10.163391, -3.863391, -1.046088, 0.520944, 0.070392, 0.004955 140, 13285, 682616.8, 3660254, 24.727805, 1.478388, 16.726189, -0.123533, 0.019522, -6.327979, -0.492505, 0.132013, -3.730727, 0.134251, 0.056853, 2.361360, 13.600000, 15.296080, -1.696080, -0.454088, 0.648887, 0.049153, 0.000637 141, 13287, 819399.6, 3514927, 18.152342, 2.202584, 8.241384, -0.076526, 0.019204, -3.984893, -0.283253, 0.101129, -2.800916, 0.096137, 0.044487, 2.161018, 7.200000, 9.790044, -2.590044, -0.716137, 0.555042, 0.108503, 0.003733 142, 13289, 832935, 3623868, 23.617244, 1.469354, 16.073219, -0.111666, 0.015971, -6.991625, -0.280926, 0.082566, -3.402428, 0.021304, 0.039205, 0.543390, 4.800000, 6.125084, -1.325084, -0.355689, 0.581536, 0.054102, 0.000433 143, 13291, 777040.1, 3858779, 27.355973, 1.992492, 13.729526, -0.160435, 0.023575, -6.805350, -0.215422, 0.153188, -1.406258, 0.047016, 0.053449, 0.879646, 10.100000, 7.374916, 2.725084, 0.765841, 0.564511, 0.137059, 0.005570 144, 13293, 752165.2, 3639192, 24.694933, 1.374212, 17.970253, -0.126240, 0.016353, -7.719480, -0.318947, 0.095073, -3.354770, 0.048502, 0.043181, 1.123237, 9.000000, 13.110363, -4.110363, -1.087819, 0.621716, 0.026927, 0.001958 145, 13295, 658870.4, 3842167, 23.797346, 2.574103, 9.244909, -0.073567, 0.035603, -2.066340, -0.718646, 0.210264, -3.417834, 0.275347, 0.089879, 3.063513, 8.400000, 12.329924, -3.929924, -1.145116, 0.665567, 0.197272, 0.019270 146, 13297, 800384.3, 3742691, 28.359668, 1.454704, 19.495145, -0.173781, 0.017973, -9.668790, -0.158902, 0.114986, -1.381919, -0.028886, 0.038847, -0.743582, 9.400000, 15.096119, -5.696119, -1.504976, 0.568110, 0.023669, 0.003283 147, 13299, 938349.6, 3446675, 18.490102, 3.417095, 5.411059, -0.084783, 0.029304, -2.893239, -0.312518, 0.168008, -1.860136, 0.130292, 0.083147, 1.567015, 10.400000, 10.672681, -0.272681, -0.073377, 0.643221, 0.058794, 0.000020 148, 13301, 902471.1, 3699878, 26.263211, 1.793029, 14.647396, -0.135919, 0.019314, -7.037350, -0.185363, 0.101791, -1.821017, -0.044920, 0.044065, -1.019424, 4.200000, 3.922913, 0.277087, 0.077494, 0.546399, 0.128636, 0.000053 149, 13303, 894704.3, 3648583, 23.561458, 1.780554, 13.232656, -0.110584, 0.018676, -5.921070, -0.228893, 0.090454, -2.530499, -0.009672, 0.042947, -0.225197, 9.800000, 10.695591, -0.895591, -0.243482, 0.537175, 0.077885, 0.000299 150, 13305, 986832.8, 3494323, 18.297991, 3.326648, 5.500429, -0.091330, 0.027475, -3.324157, -0.227218, 0.156553, -1.451380, 0.093881, 0.077548, 1.210625, 9.600000, 9.836261, -0.236261, -0.064049, 0.621505, 0.072615, 0.000019 151, 13307, 731576.3, 3544716, 19.661101, 2.010838, 9.777565, -0.079654, 0.019877, -4.007338, -0.303385, 0.104883, -2.892595, 0.080652, 0.056418, 1.429528, 5.500000, 8.918201, -3.418201, -0.950050, 0.599234, 0.117732, 0.007202 152, 13309, 898776.3, 3563384, 18.501153, 2.113110, 8.755415, -0.085034, 0.019080, -4.456837, -0.212335, 0.097675, -2.173892, 0.052857, 0.040097, 1.318242, 8.600000, 5.152843, 3.447157, 0.958650, 0.511966, 0.118747, 0.007405 153, 13311, 796905.6, 3841086, 28.733091, 1.787983, 16.070111, -0.179033, 0.022232, -8.052889, -0.160660, 0.144555, -1.111412, -0.003425, 0.046862, -0.073091, 13.600000, 8.812706, 4.787294, 1.289709, 0.547887, 0.060936, 0.006454 154, 13313, 686891.4, 3855274, 23.394471, 2.554012, 9.159890, -0.083855, 0.032799, -2.556635, -0.572061, 0.189459, -3.019437, 0.255288, 0.086126, 2.964126, 12.000000, 12.211185, -0.211185, -0.056625, 0.650040, 0.052012, 0.000011 155, 13315, 838551.5, 3538547, 18.192778, 2.093412, 8.690492, -0.079700, 0.018426, -4.325377, -0.252670, 0.094685, -2.668518, 0.078927, 0.040585, 1.944708, 7.600000, 5.503119, 2.096881, 0.573156, 0.532468, 0.087779, 0.001890 156, 13317, 891228.5, 3749769, 28.509057, 1.735328, 16.428630, -0.164395, 0.019996, -8.221384, -0.132861, 0.119490, -1.111898, -0.066138, 0.044269, -1.494004, 10.400000, 12.670101, -2.270101, -0.611560, 0.541756, 0.060902, 0.001450 157, 13319, 858796.9, 3637891, 24.021985, 1.546444, 15.533688, -0.114677, 0.016624, -6.898087, -0.258557, 0.084212, -3.070304, 0.004043, 0.040248, 0.100446, 8.800000, 8.768099, 0.031901, 0.008848, 0.564146, 0.113973, 0.000001 158, 13321, 801018.1, 3487328, 18.349546, 2.358175, 7.781248, -0.072543, 0.020883, -3.473737, -0.323199, 0.114810, -2.815070, 0.112093, 0.049993, 2.242190, 6.300000, 8.166285, -1.866285, -0.499215, 0.558495, 0.047469, 0.000743 libpysal-4.9.2/libpysal/examples/georgia/georgia_BS_F_summary.txt000066400000000000000000000212451452177046000252150ustar00rootroot00000000000000***************************************************************************** * Semiparametric Geographically Weighted Regression * * Release 1.0.90 (GWR 4.0.90) * * 12 May 2015 * * (Originally coded by T. Nakaya: 1 Nov 2009) * * * * Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * * Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * * (c) GWR4 development team * * (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * * (3) University of Glasgow, (4) Arizona State University * ***************************************************************************** Program began at 7/25/2016 2:04:40 AM ***************************************************************************** Session: Session control file: C:\Users\IEUser\Desktop\georgia_BS_F.ctl ***************************************************************************** Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv Number of areas/points: 159 Model settings--------------------------------- Model type: Gaussian Geographic kernel: fixed bi-square Method for optimal bandwidth search: Golden section search Criterion for optimal bandwidth: AICc Number of varying coefficients: 4 Number of fixed coefficients: 0 Modelling options--------------------------------- Standardisation of independent variables: OFF Testing geographical variability of local coefficients: OFF Local to Global Variable selection: OFF Global to Local Variable selection: OFF Prediction at non-regression points: OFF Variable settings--------------------------------- Area key: field1: AreaKey Easting (x-coord): field12 : X Northing (y-coord): field13: Y Cartesian coordinates: Euclidean distance Dependent variable: field6: PctBach Offset variable is not specified Intercept: varying (Local) intercept Independent variable with varying (Local) coefficient: field5: PctRural Independent variable with varying (Local) coefficient: field9: PctPov Independent variable with varying (Local) coefficient: field10: PctBlack ***************************************************************************** ***************************************************************************** Global regression result ***************************************************************************** < Diagnostic information > Residual sum of squares: 2639.559476 Number of parameters: 4 (Note: this num does not include an error variance term for a Gaussian model) ML based global sigma estimate: 4.074433 Unbiased global sigma estimate: 4.126671 -2 log-likelihood: 897.927089 Classic AIC: 907.927089 AICc: 908.319245 BIC/MDL: 923.271610 CV: 18.100197 R square: 0.485273 Adjusted R square: 0.471903 Variable Estimate Standard Error t(Est/SE) -------------------- --------------- --------------- --------------- Intercept 23.854615 1.173043 20.335661 PctRural -0.111395 0.012878 -8.649661 PctPov -0.345778 0.070863 -4.879540 PctBlack 0.058331 0.029187 1.998499 ***************************************************************************** GWR (Geographically weighted regression) bandwidth selection ***************************************************************************** Bandwidth search Limits: 108972.626308445, 558903.094487309 Golden section search begins... Initial values pL Bandwidth: 108972.626 Criterion: 948.618 p1 Bandwidth: 280830.773 Criterion: 898.912 p2 Bandwidth: 387044.948 Criterion: 903.831 pU Bandwidth: 558903.094 Criterion: 906.526 iter 1 (p1) Bandwidth: 280830.773 Criterion: 898.912 Diff: 106214.176 iter 2 (p1) Bandwidth: 215186.802 Criterion: 895.030 Diff: 65643.971 iter 3 (p2) Bandwidth: 215186.802 Criterion: 895.030 Diff: 40570.205 iter 4 (p1) Bandwidth: 215186.802 Criterion: 895.030 Diff: 25073.766 iter 5 (p2) Bandwidth: 215186.802 Criterion: 895.030 Diff: 15496.439 iter 6 (p1) Bandwidth: 215186.802 Criterion: 895.030 Diff: 9577.326 iter 7 (p1) Bandwidth: 209267.689 Criterion: 894.983 Diff: 5919.113 iter 8 (p2) Bandwidth: 209267.689 Criterion: 894.983 Diff: 3658.213 Best bandwidth size 209267.689 Minimum AICc 894.983 ***************************************************************************** GWR (Geographically weighted regression) result ***************************************************************************** Bandwidth and geographic ranges Bandwidth size: 209267.688808 Coordinate Min Max Range --------------- --------------- --------------- --------------- X-coord 635964.300000 1059706.000000 423741.700000 Y-coord 3401148.000000 3872640.000000 471492.000000 Diagnostic information Residual sum of squares: 2012.563924 Effective number of parameters (model: trace(S)): 16.722876 Effective number of parameters (variance: trace(S'S)): 11.612295 Degree of freedom (model: n - trace(S)): 142.277124 Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 137.166544 ML based sigma estimate: 3.557757 Unbiased sigma estimate: 3.830458 -2 log-likelihood: 854.805884 Classic AIC: 890.251635 AICc: 894.982602 BIC/MDL: 944.641443 CV: 18.254062 R square: 0.607540 Adjusted R square: 0.544612 *********************************************************** << Geographically varying (Local) coefficients >> *********************************************************** Estimates of varying coefficients have been saved in the following file. Listwise output file: C:\Users\IEUser\Desktop\georgia_BS_F_listwise.csv Summary statistics for varying (Local) coefficients Variable Mean STD -------------------- --------------- --------------- Intercept 23.100425 4.102437 PctRural -0.115556 0.038728 PctPov -0.284599 0.131956 PctBlack 0.058275 0.075808 Variable Min Max Range -------------------- --------------- --------------- --------------- Intercept 17.723650 30.349333 12.625683 PctRural -0.197305 -0.054520 0.142786 PctPov -0.772356 -0.072209 0.700147 PctBlack -0.075941 0.308952 0.384893 Variable Lwr Quartile Median Upr Quartile -------------------- --------------- --------------- --------------- Intercept 18.856279 23.103493 27.099926 PctRural -0.150482 -0.106458 -0.082746 PctPov -0.339158 -0.271117 -0.198485 PctBlack 0.000226 0.057840 0.110329 Variable Interquartile R Robust STD -------------------- --------------- --------------- Intercept 8.243647 6.110932 PctRural 0.067736 0.050212 PctPov 0.140673 0.104280 PctBlack 0.110103 0.081618 (Note: Robust STD is given by (interquartile range / 1.349) ) ***************************************************************************** GWR ANOVA Table ***************************************************************************** Source SS DF MS F ----------------- ------------------- ---------- --------------- ---------- Global Residuals 2639.559 155.000 GWR Improvement 626.996 17.833 35.158 GWR Residuals 2012.564 137.167 14.672 2.396224 ***************************************************************************** Program terminated at 7/25/2016 2:04:40 AM libpysal-4.9.2/libpysal/examples/georgia/georgia_BS_NN.ctl000066400000000000000000000013731452177046000235310ustar00rootroot00000000000000 C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv FORMAT/DELIMITER: 1 Number_of_fields: 13 Number_of_areas: 159 Fields AreaKey 001 AreaKey X 012 X Y 013 Y Gmetric 0 Dependent 006 PctBach Offset Independent_geo 4 000 Intercept 005 PctRural 009 PctPov 010 PctBlack Independent_fix 0 Unused_fields 6 002 Latitude 003 Longitud 004 TotPop90 007 PctEld 008 PctFB 011 ID MODELTYPE: 0 STANDARDISATION: 0 GTEST: 0 VSL2G: 0 VSG2L: 0 KERNELTYPE: 2 BANDSELECTIONMETHOD: 1 Goldrangeflag: 0 Goldenmax: Goldenmin: Fixedbandsize: IntervalMax: IntervalMin: IntervalStep: Criteria: summary_output: C:\Users\IEUser\Desktop\georgia_BS_NN_summary.txt listwise_output: C:\Users\IEUser\Desktop\georgia_BS_NN_listwise.csv predictflag: 0 prediction_def: prediction_output: libpysal-4.9.2/libpysal/examples/georgia/georgia_BS_NN_listwise.csv000066400000000000000000001644601452177046000254740ustar00rootroot00000000000000Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD 0, 13001, 941396.6, 3521764, 18.375924, 2.414905, 7.609379, -0.087919, 0.021113, -4.164093, -0.218522, 0.115485, -1.892203, 0.069101, 0.048422, 1.427054, 8.200000, 8.815245, -0.615245, -0.162278, 0.551117, 0.041718, 0.000077 1, 13003, 895553, 3471916, 18.039692, 2.693495, 6.697503, -0.077996, 0.022571, -3.455642, -0.291285, 0.126975, -2.294038, 0.109652, 0.053382, 2.054089, 6.400000, 5.611921, 0.788079, 0.213714, 0.557455, 0.093454, 0.000315 2, 13005, 930946.4, 3502787, 18.173904, 2.525672, 7.195672, -0.085464, 0.021449, -3.984466, -0.235007, 0.118227, -1.987757, 0.081621, 0.050307, 1.622470, 6.600000, 8.495724, -1.895724, -0.518796, 0.553851, 0.109830, 0.002225 3, 13007, 745398.6, 3474765, 18.612431, 2.254469, 8.255795, -0.072676, 0.020748, -3.502894, -0.325567, 0.109910, -2.962119, 0.107978, 0.052233, 2.067223, 9.400000, 8.849965, 0.550035, 0.151238, 0.571077, 0.118198, 0.000205 4, 13009, 849431.3, 3665553, 25.027931, 1.767947, 14.156497, -0.128431, 0.019962, -6.433839, -0.188146, 0.106755, -1.762416, -0.028756, 0.050361, -0.571001, 13.300000, 15.032420, -1.732420, -0.470868, 0.559486, 0.097548, 0.001606 5, 13011, 819317.3, 3807616, 28.868732, 1.568279, 18.407904, -0.180965, 0.020098, -9.004150, -0.137907, 0.130001, -1.060813, -0.031319, 0.041694, -0.751165, 6.400000, 8.580509, -2.180509, -0.580528, 0.551175, 0.059443, 0.001427 6, 13013, 803747.1, 3769623, 29.126594, 1.578247, 18.455024, -0.185670, 0.019899, -9.330583, -0.119547, 0.128153, -0.932843, -0.039784, 0.041354, -0.962027, 9.200000, 14.919817, -5.719817, -1.508125, 0.558752, 0.041031, 0.006520 7, 13015, 699011.5, 3793408, 26.738740, 1.663068, 16.077957, -0.143921, 0.019787, -7.273569, -0.379195, 0.129687, -2.923921, 0.074043, 0.041423, 1.787476, 9.000000, 12.540446, -3.540446, -0.929355, 0.571809, 0.032462, 0.001942 8, 13017, 863020.8, 3520432, 17.332852, 2.862878, 6.054346, -0.072048, 0.022398, -3.216792, -0.259626, 0.122903, -2.112442, 0.093798, 0.048378, 1.938852, 7.600000, 11.173490, -3.573490, -0.950910, 0.513439, 0.058498, 0.003764 9, 13019, 859915.8, 3466377, 18.009999, 2.499664, 7.204967, -0.074505, 0.021286, -3.500257, -0.310736, 0.120344, -2.582057, 0.116161, 0.049213, 2.360389, 7.500000, 8.430319, -0.930319, -0.253303, 0.550571, 0.100714, 0.000481 10, 13021, 809736.9, 3636468, 23.331917, 1.754697, 13.296838, -0.117008, 0.019905, -5.878384, -0.244202, 0.105854, -2.306955, 0.017538, 0.051088, 0.343293, 17.000000, 17.490415, -0.490415, -0.139052, 0.578390, 0.170747, 0.000267 11, 13023, 844270.1, 3595691, 18.575691, 2.534320, 7.329656, -0.087278, 0.023528, -3.709493, -0.206076, 0.108188, -1.904795, 0.051458, 0.050376, 1.021487, 10.300000, 10.901700, -0.601700, -0.162155, 0.545373, 0.082058, 0.000157 12, 13025, 979288.9, 3463849, 18.853338, 2.745800, 6.866246, -0.091904, 0.023206, -3.960341, -0.248679, 0.131652, -1.888905, 0.086563, 0.057140, 1.514925, 5.800000, 5.533408, 0.266592, 0.076204, 0.604611, 0.184081, 0.000088 13, 13027, 827822, 3421638, 18.212539, 2.372760, 7.675677, -0.073817, 0.021056, -3.505663, -0.334842, 0.119039, -2.812877, 0.126093, 0.049579, 2.543305, 9.100000, 9.926865, -0.826865, 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9.255965, -0.075752, 0.020517, -3.692142, -0.317680, 0.108586, -2.925619, 0.089892, 0.056893, 1.580033, 6.000000, 9.172055, -3.172055, -0.881768, 0.589691, 0.137244, 0.008287 120, 13245, 954272.3, 3697862, 24.815945, 2.300383, 10.787743, -0.120551, 0.022525, -5.351946, -0.180967, 0.116635, -1.551575, -0.044908, 0.048682, -0.922473, 17.300000, 18.444544, -1.144544, -0.346488, 0.545224, 0.272550, 0.003014 121, 13247, 777759, 3729605, 28.996670, 1.635103, 17.733848, -0.184296, 0.020004, -9.213078, -0.141807, 0.130817, -1.084007, -0.032054, 0.044234, -0.724635, 18.100000, 16.949769, 1.150231, 0.308225, 0.568126, 0.071571, 0.000491 122, 13249, 752973.1, 3570222, 20.143981, 1.923033, 10.475110, -0.086448, 0.019432, -4.448672, -0.290041, 0.102402, -2.832382, 0.070816, 0.055933, 1.266086, 8.000000, 8.141504, -0.141504, -0.037905, 0.608463, 0.070927, 0.000007 123, 13251, 1004028, 3641918, 22.000899, 2.283359, 9.635323, -0.103888, 0.021736, -4.779433, -0.211583, 0.118168, -1.790533, 0.002808, 0.046173, 0.060804, 8.600000, 9.042787, -0.442787, -0.118149, 0.570086, 0.063635, 0.000064 124, 13253, 704495.6, 3422002, 18.552729, 2.331305, 7.958089, -0.070842, 0.021334, -3.320568, -0.340959, 0.116027, -2.938625, 0.116789, 0.053802, 2.170711, 7.800000, 7.538033, 0.261967, 0.071239, 0.566278, 0.098482, 0.000037 125, 13255, 754916.2, 3685029, 27.046720, 1.537997, 17.585678, -0.154438, 0.018841, -8.197081, -0.303840, 0.129284, -2.350179, 0.032794, 0.049959, 0.656430, 11.100000, 14.982577, -3.882577, -1.020007, 0.601112, 0.034063, 0.002458 126, 13257, 842085.9, 3827075, 28.950415, 1.568949, 18.452111, -0.180631, 0.020403, -8.853278, -0.131568, 0.131295, -1.002078, -0.037893, 0.042715, -0.887132, 13.100000, 14.615563, -1.515563, -0.404594, 0.546778, 0.064541, 0.000757 127, 13259, 703256.8, 3552857, 20.171000, 1.923869, 10.484603, -0.082675, 0.019898, -4.155026, -0.324660, 0.108254, -2.999061, 0.087485, 0.057894, 1.511118, 8.000000, 7.260976, 0.739024, 0.206144, 0.611534, 0.143175, 0.000476 128, 13261, 763457.1, 3551752, 19.436785, 2.046619, 9.497019, -0.081094, 0.019795, -4.096627, -0.283650, 0.101675, -2.789759, 0.074573, 0.054198, 1.375946, 15.900000, 12.190501, 3.709499, 0.987719, 0.594733, 0.059673, 0.004148 129, 13263, 734217.9, 3623162, 23.078763, 1.586403, 14.547858, -0.112567, 0.018725, -6.011687, -0.360965, 0.115852, -3.115739, 0.084650, 0.057540, 1.471151, 7.100000, 8.347484, -1.247484, -0.364614, 0.640115, 0.219599, 0.002506 130, 13265, 884376.9, 3717493, 27.520861, 1.772296, 15.528370, -0.151714, 0.019767, -7.675126, -0.151989, 0.114644, -1.325746, -0.058766, 0.046272, -1.270003, 5.600000, 3.895132, 1.704868, 0.474766, 0.559500, 0.140317, 0.002465 131, 13267, 963427.8, 3560039, 19.256298, 2.233654, 8.620986, -0.093360, 0.020979, -4.450236, -0.206422, 0.113088, -1.825317, 0.046954, 0.045435, 1.033424, 6.500000, 8.702775, -2.202775, -0.577719, 0.564123, 0.030782, 0.000710 132, 13269, 759410.8, 3608179, 22.034483, 1.698501, 12.972900, -0.103733, 0.018699, -5.547404, -0.312460, 0.106211, -2.941886, 0.066035, 0.055455, 1.190796, 7.100000, 5.296954, 1.803046, 0.491319, 0.626729, 0.102154, 0.001840 133, 13271, 882069.4, 3534470, 16.895516, 2.852481, 5.923095, -0.072526, 0.022747, -3.188337, -0.218596, 0.119920, -1.822841, 0.078357, 0.048073, 1.629970, 8.600000, 8.361885, 0.238115, 0.063130, 0.473791, 0.051550, 0.000015 134, 13273, 743031.8, 3522636, 19.123574, 2.168708, 8.817958, -0.075826, 0.020568, -3.686576, -0.305719, 0.107638, -2.840247, 0.088406, 0.055670, 1.588052, 9.200000, 11.708678, -2.508678, -0.689780, 0.587694, 0.118173, 0.004272 135, 13275, 795506.2, 3421725, 18.208195, 2.418624, 7.528327, -0.071792, 0.021418, -3.351909, -0.341804, 0.120813, -2.829192, 0.127193, 0.050858, 2.500930, 13.400000, 11.344938, 2.055062, 0.543157, 0.557015, 0.045636, 0.000945 136, 13277, 831682.3, 3487715, 18.006901, 2.486725, 7.241212, -0.072662, 0.021087, -3.445811, -0.311581, 0.118432, -2.630888, 0.112785, 0.048796, 2.311368, 14.000000, 10.167793, 3.832207, 1.016772, 0.548050, 0.052966, 0.003874 137, 13279, 941734.4, 3567586, 18.743279, 2.227902, 8.412973, -0.088969, 0.021102, -4.216177, -0.194575, 0.111740, -1.741319, 0.043583, 0.044856, 0.971615, 11.400000, 11.916248, -0.516248, -0.142276, 0.527237, 0.122250, 0.000189 138, 13281, 797981.7, 3872640, 27.490506, 1.586879, 17.323629, -0.161521, 0.019954, -8.094660, -0.219397, 0.130834, -1.676917, 0.011283, 0.041348, 0.272884, 11.400000, 8.266897, 3.133103, 0.834189, 0.543414, 0.059551, 0.002952 139, 13283, 919077.6, 3595170, 18.866189, 2.283136, 8.263278, -0.085392, 0.022390, -3.813858, -0.189617, 0.110938, -1.709216, 0.029085, 0.045268, 0.642506, 6.300000, 10.138875, -3.838875, -1.035981, 0.493954, 0.084581, 0.006644 140, 13285, 682616.8, 3660254, 24.730344, 1.492810, 16.566306, -0.123629, 0.019693, -6.277743, -0.491018, 0.132968, -3.692752, 0.133664, 0.057257, 2.334434, 13.600000, 15.300990, -1.700990, -0.450355, 0.643286, 0.048937, 0.000699 141, 13287, 819399.6, 3514927, 18.225195, 2.349879, 7.755803, -0.075361, 0.020383, -3.697309, -0.292708, 0.109450, -2.674345, 0.098907, 0.047627, 2.076718, 7.200000, 9.731427, -2.531427, -0.696808, 0.563722, 0.120127, 0.004442 142, 13289, 832935, 3623868, 20.965009, 2.112019, 9.926525, -0.098113, 0.022476, -4.365304, -0.229657, 0.105169, -2.183700, 0.027869, 0.052411, 0.531731, 4.800000, 6.462592, -1.662592, -0.449433, 0.537914, 0.087659, 0.001300 143, 13291, 777040.1, 3858779, 27.320591, 1.609147, 16.978309, -0.158970, 0.019948, -7.969116, -0.239476, 0.130640, -1.833093, 0.022034, 0.041008, 0.537312, 10.100000, 7.043390, 3.056610, 0.834905, 0.546488, 0.106444, 0.005564 144, 13293, 752165.2, 3639192, 24.200809, 1.564276, 15.470937, -0.123605, 0.018710, -6.606297, -0.344539, 0.119565, -2.881611, 0.065065, 0.056060, 1.160625, 9.000000, 12.872171, -3.872171, -1.017570, 0.628564, 0.034626, 0.002488 145, 13295, 658870.4, 3842167, 25.991901, 1.717976, 15.129371, -0.131963, 0.020741, -6.362486, -0.411469, 0.135161, -3.044283, 0.092313, 0.043488, 2.122713, 8.400000, 15.157496, -6.757496, -1.835909, 0.559385, 0.096799, 0.024203 146, 13297, 800384.3, 3742691, 29.279015, 1.595726, 18.348400, -0.188348, 0.019983, -9.425633, -0.091658, 0.129666, -0.706881, -0.053861, 0.043092, -1.249899, 9.400000, 15.552791, -6.152791, -1.612155, 0.560098, 0.028940, 0.005190 147, 13299, 938349.6, 3446675, 18.656967, 2.676737, 6.970040, -0.086523, 0.022520, -3.841971, -0.278733, 0.128515, -2.168882, 0.101281, 0.054819, 1.847558, 10.400000, 10.707326, -0.307326, -0.080997, 0.595942, 0.040209, 0.000018 148, 13301, 902471.1, 3699878, 26.151850, 1.941109, 13.472636, -0.134341, 0.020585, -6.526246, -0.177206, 0.108749, -1.629500, -0.049077, 0.047066, -1.042728, 4.200000, 3.984952, 0.215048, 0.059813, 0.553755, 0.138238, 0.000038 149, 13303, 894704.3, 3648583, 22.653152, 2.139217, 10.589460, -0.101212, 0.021973, -4.606206, -0.216008, 0.105576, -2.045982, -0.013325, 0.050849, -0.262048, 9.800000, 10.505011, -0.705011, -0.191521, 0.534857, 0.096610, 0.000263 150, 13305, 986832.8, 3494323, 19.058441, 2.563838, 7.433559, -0.094024, 0.022310, -4.214534, -0.231460, 0.125065, -1.850712, 0.072389, 0.052717, 1.373168, 9.600000, 9.927395, -0.327395, -0.086895, 0.598634, 0.053615, 0.000029 151, 13307, 731576.3, 3544716, 19.682721, 2.013145, 9.777100, -0.079862, 0.019978, -3.997518, -0.304909, 0.104974, -2.904624, 0.081356, 0.056410, 1.442209, 5.500000, 8.920127, -3.420127, -0.939373, 0.599113, 0.116263, 0.007778 152, 13309, 898776.3, 3563384, 16.844017, 2.682408, 6.279439, -0.074241, 0.023436, -3.167862, -0.172899, 0.117933, -1.466082, 0.050370, 0.046776, 1.076834, 8.600000, 5.695227, 2.904773, 0.818087, 0.423182, 0.159493, 0.008509 153, 13311, 796905.6, 3841086, 27.838898, 1.574679, 17.679091, -0.166676, 0.019761, -8.434538, -0.203986, 0.129239, -1.578360, 0.003179, 0.040768, 0.077966, 13.600000, 8.629694, 4.970306, 1.317270, 0.548624, 0.050856, 0.006229 154, 13313, 686891.4, 3855274, 26.164642, 1.710298, 15.298295, -0.137364, 0.020596, -6.669445, -0.367377, 0.134044, -2.740722, 0.077726, 0.042667, 1.821677, 12.000000, 12.786831, -0.786831, -0.206834, 0.553793, 0.035205, 0.000105 155, 13315, 838551.5, 3538547, 18.025600, 2.660378, 6.775579, -0.076497, 0.021987, -3.479159, -0.261962, 0.115963, -2.259014, 0.084694, 0.048373, 1.750865, 7.600000, 5.573625, 2.026375, 0.558896, 0.557453, 0.123618, 0.002952 156, 13317, 891228.5, 3749769, 28.516555, 1.762358, 16.180906, -0.164552, 0.020298, -8.106824, -0.131495, 0.121461, -1.082611, -0.066626, 0.044950, -1.482242, 10.400000, 12.676648, -2.276648, -0.606735, 0.555125, 0.061337, 0.001612 157, 13319, 858796.9, 3637891, 22.169471, 1.996974, 11.101535, -0.101861, 0.021349, -4.771271, -0.224866, 0.101262, -2.220636, 0.003954, 0.050613, 0.078119, 8.800000, 8.708923, 0.091077, 0.025605, 0.538754, 0.156479, 0.000008 158, 13321, 801018.1, 3487328, 18.263625, 2.314566, 7.890733, -0.073520, 0.020449, -3.595321, -0.314540, 0.110659, -2.842413, 0.109955, 0.048770, 2.254575, 6.300000, 8.172088, -1.872088, -0.494558, 0.561673, 0.044714, 0.000767 libpysal-4.9.2/libpysal/examples/georgia/georgia_BS_NN_summary.txt000066400000000000000000000211771452177046000253470ustar00rootroot00000000000000***************************************************************************** * Semiparametric Geographically Weighted Regression * * Release 1.0.90 (GWR 4.0.90) * * 12 May 2015 * * (Originally coded by T. Nakaya: 1 Nov 2009) * * * * Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * * Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * * (c) GWR4 development team * * (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * * (3) University of Glasgow, (4) Arizona State University * ***************************************************************************** Program began at 7/25/2016 2:04:17 AM ***************************************************************************** Session: Session control file: C:\Users\IEUser\Desktop\georgia_BS_NN.ctl ***************************************************************************** Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv Number of areas/points: 159 Model settings--------------------------------- Model type: Gaussian Geographic kernel: adaptive bi-square Method for optimal bandwidth search: Golden section search Criterion for optimal bandwidth: AICc Number of varying coefficients: 4 Number of fixed coefficients: 0 Modelling options--------------------------------- Standardisation of independent variables: OFF Testing geographical variability of local coefficients: OFF Local to Global Variable selection: OFF Global to Local Variable selection: OFF Prediction at non-regression points: OFF Variable settings--------------------------------- Area key: field1: AreaKey Easting (x-coord): field12 : X Northing (y-coord): field13: Y Cartesian coordinates: Euclidean distance Dependent variable: field6: PctBach Offset variable is not specified Intercept: varying (Local) intercept Independent variable with varying (Local) coefficient: field5: PctRural Independent variable with varying (Local) coefficient: field9: PctPov Independent variable with varying (Local) coefficient: field10: PctBlack ***************************************************************************** ***************************************************************************** Global regression result ***************************************************************************** < Diagnostic information > Residual sum of squares: 2639.559476 Number of parameters: 4 (Note: this num does not include an error variance term for a Gaussian model) ML based global sigma estimate: 4.074433 Unbiased global sigma estimate: 4.126671 -2 log-likelihood: 897.927089 Classic AIC: 907.927089 AICc: 908.319245 BIC/MDL: 923.271610 CV: 18.100197 R square: 0.485273 Adjusted R square: 0.471903 Variable Estimate Standard Error t(Est/SE) -------------------- --------------- --------------- --------------- Intercept 23.854615 1.173043 20.335661 PctRural -0.111395 0.012878 -8.649661 PctPov -0.345778 0.070863 -4.879540 PctBlack 0.058331 0.029187 1.998499 ***************************************************************************** GWR (Geographically weighted regression) bandwidth selection ***************************************************************************** Bandwidth search Limits: 48, 159 Golden section search begins... Initial values pL Bandwidth: 48.000 Criterion: 909.256 p1 Bandwidth: 90.398 Criterion: 896.463 p2 Bandwidth: 116.602 Criterion: 898.615 pU Bandwidth: 159.000 Criterion: 903.072 iter 1 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 26.204 iter 2 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 16.195 iter 3 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 10.009 iter 4 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 6.186 iter 5 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 3.823 iter 6 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 2.363 iter 7 (p1) Bandwidth: 90.398 Criterion: 896.463 Diff: 1.460 iter 8 (p2) Bandwidth: 90.398 Criterion: 896.463 Diff: 0.902 Best bandwidth size 90.000 Minimum AICc 896.463 ***************************************************************************** GWR (Geographically weighted regression) result ***************************************************************************** Bandwidth and geographic ranges Bandwidth size: 90.398227 Coordinate Min Max Range --------------- --------------- --------------- --------------- X-coord 635964.300000 1059706.000000 423741.700000 Y-coord 3401148.000000 3872640.000000 471492.000000 Diagnostic information Residual sum of squares: 2090.125305 Effective number of parameters (model: trace(S)): 14.925095 Effective number of parameters (variance: trace(S'S)): 10.193958 Degree of freedom (model: n - trace(S)): 144.074905 Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 139.343769 ML based sigma estimate: 3.625664 Unbiased sigma estimate: 3.872954 -2 log-likelihood: 860.818394 Classic AIC: 892.668583 AICc: 896.462831 BIC/MDL: 941.541173 CV: 19.186726 R square: 0.592415 Adjusted R square: 0.534505 *********************************************************** << Geographically varying (Local) coefficients >> *********************************************************** Estimates of varying coefficients have been saved in the following file. Listwise output file: C:\Users\IEUser\Desktop\georgia_BS_NN_listwise.csv Summary statistics for varying (Local) coefficients Variable Mean STD -------------------- --------------- --------------- Intercept 23.067890 4.184766 PctRural -0.118169 0.038043 PctPov -0.261744 0.097335 PctBlack 0.044847 0.059488 Variable Min Max Range -------------------- --------------- --------------- --------------- Intercept 16.844017 29.625733 12.781716 PctRural -0.190065 -0.070745 0.119320 PctPov -0.534973 -0.064893 0.470080 PctBlack -0.073499 0.133808 0.207307 Variable Lwr Quartile Median Upr Quartile -------------------- --------------- --------------- --------------- Intercept 18.853338 22.653152 27.237578 PctRural -0.153281 -0.103888 -0.082675 PctPov -0.326117 -0.248679 -0.199793 PctBlack 0.002808 0.057636 0.093798 Variable Interquartile R Robust STD -------------------- --------------- --------------- Intercept 8.384240 6.215152 PctRural 0.070606 0.052340 PctPov 0.126324 0.093643 PctBlack 0.090990 0.067450 (Note: Robust STD is given by (interquartile range / 1.349) ) ***************************************************************************** GWR ANOVA Table ***************************************************************************** Source SS DF MS F ----------------- ------------------- ---------- --------------- ---------- Global Residuals 2639.559 155.000 GWR Improvement 549.434 15.656 35.094 GWR Residuals 2090.125 139.344 15.000 2.339611 ***************************************************************************** Program terminated at 7/25/2016 2:04:17 AM libpysal-4.9.2/libpysal/examples/georgia/georgia_GS_F.ctl000066400000000000000000000013711452177046000234060ustar00rootroot00000000000000 C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv FORMAT/DELIMITER: 1 Number_of_fields: 13 Number_of_areas: 159 Fields AreaKey 001 AreaKey X 012 X Y 013 Y Gmetric 0 Dependent 006 PctBach Offset Independent_geo 4 000 Intercept 005 PctRural 009 PctPov 010 PctBlack Independent_fix 0 Unused_fields 6 002 Latitude 003 Longitud 004 TotPop90 007 PctEld 008 PctFB 011 ID MODELTYPE: 0 STANDARDISATION: 0 GTEST: 0 VSL2G: 0 VSG2L: 0 KERNELTYPE: 0 BANDSELECTIONMETHOD: 1 Goldrangeflag: 0 Goldenmax: Goldenmin: Fixedbandsize: IntervalMax: IntervalMin: IntervalStep: Criteria: summary_output: C:\Users\IEUser\Desktop\goergia_GS_F_summary.txt listwise_output: C:\Users\IEUser\Desktop\goergia_GS_F_listwise.csv predictflag: 0 prediction_def: prediction_output: libpysal-4.9.2/libpysal/examples/georgia/georgia_GS_F_listwise.csv000066400000000000000000001644601452177046000253530ustar00rootroot00000000000000Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD 0, 13001, 941396.6, 3521764, 18.497787, 2.275693, 8.128420, -0.085666, 0.020579, -4.162817, -0.232021, 0.108742, -2.133681, 0.070628, 0.046608, 1.515356, 8.200000, 8.870416, -0.670416, -0.178093, 0.544113, 0.046918, 0.000096 1, 13003, 895553, 3471916, 18.243737, 2.412516, 7.562122, -0.080182, 0.021233, -3.776309, -0.288793, 0.114221, -2.528381, 0.104956, 0.048012, 2.186025, 6.400000, 5.536022, 0.863978, 0.236274, 0.560546, 0.100691, 0.000383 2, 13005, 930946.4, 3502787, 18.330304, 2.348055, 7.806591, -0.083870, 0.020823, -4.027677, -0.249953, 0.111167, -2.248448, 0.083855, 0.048057, 1.744900, 6.600000, 8.424698, -1.824698, -0.504834, 0.556554, 0.121336, 0.002159 3, 13007, 745398.6, 3474765, 18.917715, 2.311541, 8.184027, -0.069859, 0.022128, -3.157073, -0.349929, 0.116454, -3.004877, 0.112187, 0.055260, 2.030171, 9.400000, 9.050272, 0.349728, 0.098353, 0.564771, 0.149601, 0.000104 4, 13009, 849431.3, 3665553, 25.415820, 1.399180, 18.164795, -0.128816, 0.015566, -8.275723, -0.253965, 0.083198, -3.052547, -0.002954, 0.036691, -0.080518, 13.300000, 15.345744, -2.045744, -0.549028, 0.575322, 0.066205, 0.001311 5, 13011, 819317.3, 3807616, 29.172419, 1.564461, 18.646951, -0.182481, 0.020052, -9.100191, -0.136824, 0.126886, -1.078321, -0.037705, 0.041646, -0.905356, 6.400000, 8.726694, -2.326694, -0.624508, 0.537646, 0.066445, 0.001703 6, 13013, 803747.1, 3769623, 28.726583, 1.482489, 19.377264, -0.176199, 0.018371, -9.590984, -0.167093, 0.114390, -1.460727, -0.026643, 0.038925, -0.684461, 9.200000, 14.583064, -5.383064, -1.423997, 0.556481, 0.038878, 0.005031 7, 13015, 699011.5, 3793408, 26.843339, 1.697240, 15.815879, -0.139364, 0.021008, -6.633810, -0.453352, 0.134668, -3.366454, 0.105048, 0.043602, 2.409215, 9.000000, 12.479809, -3.479809, -0.922019, 0.621765, 0.041994, 0.002286 8, 13017, 863020.8, 3520432, 18.813192, 1.947256, 9.661387, -0.080531, 0.017818, -4.519606, -0.278134, 0.091946, -3.024985, 0.081732, 0.039561, 2.066004, 7.600000, 11.469966, -3.869966, -1.027285, 0.538207, 0.045513, 0.003086 9, 13019, 859915.8, 3466377, 18.377663, 2.336748, 7.864631, -0.077065, 0.020629, -3.735820, -0.312784, 0.110420, -2.832689, 0.111258, 0.046568, 2.389130, 7.500000, 8.532026, -1.032026, -0.284282, 0.549111, 0.113621, 0.000635 10, 13021, 809736.9, 3636468, 24.324926, 1.351949, 17.992494, -0.120392, 0.015293, -7.872225, -0.283487, 0.080945, -3.502229, 0.023673, 0.035491, 0.667014, 17.000000, 17.930349, -0.930349, -0.255398, 0.595805, 0.107532, 0.000482 11, 13023, 844270.1, 3595691, 21.908296, 1.530577, 14.313753, -0.099822, 0.016021, -6.230772, -0.283128, 0.081375, -3.479289, 0.041593, 0.036618, 1.135857, 10.300000, 11.877407, -1.577407, -0.417684, 0.578132, 0.040754, 0.000455 12, 13025, 979288.9, 3463849, 19.204402, 3.142145, 6.111876, -0.093571, 0.026682, -3.506869, -0.279525, 0.151492, -1.845151, 0.103327, 0.069309, 1.490811, 5.800000, 5.233168, 0.566832, 0.170879, 0.676554, 0.259938, 0.000629 13, 13027, 827822, 3421638, 18.695340, 2.764511, 6.762622, -0.075022, 0.023684, -3.167601, -0.365136, 0.133306, -2.739074, 0.134828, 0.055677, 2.421609, 9.100000, 9.908206, -0.808206, -0.216174, 0.554388, 0.059899, 0.000183 14, 13029, 1023145, 3554982, 19.456316, 2.881836, 6.751361, -0.101365, 0.025916, -3.911217, -0.162463, 0.147809, -1.099146, 0.036312, 0.058356, 0.622260, 11.800000, 9.681023, 2.118977, 0.615241, 0.581168, 0.202190, 0.005884 15, 13031, 994903.4, 3600493, 20.392912, 2.315553, 8.806931, -0.100602, 0.022436, -4.483909, -0.169488, 0.123467, -1.372734, 0.010719, 0.047725, 0.224606, 19.900000, 9.652136, 10.247864, 2.869181, 0.532630, 0.141998, 0.083560 16, 13033, 971593.8, 3671394, 23.793175, 2.175345, 10.937657, -0.112552, 0.021486, -5.238425, -0.224380, 0.110952, -2.022324, -0.014762, 0.045841, -0.322025, 9.600000, 8.086492, 1.513508, 0.416114, 0.576048, 0.110220, 0.001316 17, 13035, 782448.2, 3684504, 26.510713, 1.334747, 19.861972, -0.145234, 0.015639, -9.286381, -0.268294, 0.088566, -3.029294, 0.011992, 0.035456, 0.338239, 7.200000, 12.090632, -4.890632, -1.294273, 0.592581, 0.039681, 0.004245 18, 13037, 724741.2, 3492653, 19.220671, 2.224523, 8.640357, -0.071804, 0.021675, -3.312706, -0.342620, 0.114613, -2.989362, 0.104370, 0.056241, 1.855755, 10.100000, 7.291232, 2.808768, 0.780655, 0.585776, 0.129334, 0.005552 19, 13039, 1008480, 3437933, 19.942606, 3.729350, 5.347475, -0.100203, 0.030602, -3.274342, -0.298317, 0.176298, -1.692119, 0.110085, 0.082565, 1.333317, 13.500000, 14.015029, -0.515029, -0.159990, 0.747834, 0.303028, 0.000683 20, 13043, 964264.9, 3598842, 20.511683, 2.032624, 10.091234, -0.096423, 0.020386, -4.729889, -0.202809, 0.106214, -1.909443, 0.018980, 0.043427, 0.437050, 9.900000, 11.187587, -1.287587, -0.344390, 0.527058, 0.059865, 0.000463 21, 13045, 678778.6, 3713250, 26.239766, 1.504491, 17.440955, -0.134549, 0.018900, -7.119080, -0.496961, 0.119399, -4.162187, 0.119626, 0.043809, 2.730609, 12.000000, 11.716358, 0.283642, 0.075030, 0.630415, 0.038813, 0.000014 22, 13047, 670055.9, 3862318, 23.647585, 2.263072, 10.449332, -0.089870, 0.030008, -2.994923, -0.560872, 0.182879, -3.066908, 0.189068, 0.064193, 2.945311, 8.100000, 13.170829, -5.070829, -1.489836, 0.601489, 0.220855, 0.038588 23, 13049, 962612.3, 3432769, 19.336749, 3.310029, 5.841868, -0.093840, 0.028496, -3.293056, -0.308039, 0.164476, -1.872853, 0.118219, 0.072447, 1.631803, 6.400000, 7.513486, -1.113486, -0.326995, 0.693452, 0.220126, 0.001851 24, 13051, 1059706, 3556747, 19.698051, 3.278939, 6.007447, -0.107620, 0.029300, -3.673092, -0.132112, 0.168471, -0.784182, 0.026855, 0.067535, 0.397651, 18.600000, 17.897897, 0.702103, 0.233366, 0.612520, 0.391215, 0.002146 25, 13053, 704959.2, 3577608, 21.624188, 1.660805, 13.020305, -0.094448, 0.018655, -5.063010, -0.335364, 0.100033, -3.352521, 0.079174, 0.048997, 1.615889, 20.200000, 19.292100, 0.907900, 0.260443, 0.642381, 0.182687, 0.000930 26, 13055, 653026.6, 3813760, 25.401577, 1.938885, 13.101126, -0.108359, 0.026201, -4.135739, -0.618126, 0.166692, -3.708196, 0.177630, 0.054586, 3.254110, 5.900000, 9.519327, -3.619327, -0.966887, 0.626307, 0.057587, 0.003504 27, 13057, 734240.9, 3794110, 27.599499, 1.625034, 16.983947, -0.156416, 0.019579, -7.988854, -0.329309, 0.125216, -2.629923, 0.053979, 0.040478, 1.333543, 18.400000, 16.645390, 1.754610, 0.473082, 0.602584, 0.074822, 0.001110 28, 13059, 832508.6, 3762905, 28.753571, 1.467127, 19.598557, -0.175090, 0.018313, -9.561152, -0.142205, 0.112222, -1.267174, -0.043887, 0.039524, -1.110395, 37.500000, 20.681305, 16.818695, 5.472825, 0.542848, 0.364817, 1.055090 29, 13061, 695793.9, 3495219, 19.254482, 2.309800, 8.335995, -0.070318, 0.022391, -3.140472, -0.342070, 0.123484, -2.770161, 0.102447, 0.061260, 1.672320, 11.200000, 6.235507, 4.964493, 1.407817, 0.593446, 0.163637, 0.023783 30, 13063, 745538.8, 3711726, 27.103317, 1.411025, 19.208249, -0.152504, 0.016793, -9.081578, -0.316086, 0.099671, -3.171284, 0.038024, 0.037150, 1.023518, 14.700000, 24.619697, -9.919697, -2.744662, 0.602380, 0.121470, 0.063882 31, 13065, 908046.1, 3428340, 18.735086, 2.946431, 6.358569, -0.085801, 0.026143, -3.281963, -0.320518, 0.147958, -2.166278, 0.125137, 0.060214, 2.078186, 6.700000, 8.660437, -1.960437, -0.539915, 0.617158, 0.113267, 0.002284 32, 13067, 724646.8, 3757187, 27.527424, 1.542368, 17.847508, -0.154918, 0.018575, -8.340206, -0.362392, 0.115587, -3.135221, 0.061247, 0.039111, 1.565992, 33.000000, 25.202176, 7.797824, 2.192155, 0.608938, 0.148976, 0.051595 33, 13069, 894463.9, 3492465, 18.226997, 2.233308, 8.161434, -0.079779, 0.019761, -4.037223, -0.273235, 0.104230, -2.621463, 0.093472, 0.044260, 2.111898, 11.100000, 9.305261, 1.794739, 0.473620, 0.542610, 0.034216, 0.000487 34, 13071, 808691.8, 3455994, 18.668308, 2.359061, 7.913448, -0.072743, 0.021379, -3.402565, -0.348435, 0.114424, -3.045123, 0.120240, 0.050504, 2.380815, 10.000000, 9.308080, 0.691920, 0.185506, 0.547550, 0.064302, 0.000145 35, 13073, 942527.9, 3722100, 26.246306, 2.079087, 12.623955, -0.132908, 0.021092, -6.301333, -0.208429, 0.108399, -1.922794, -0.037766, 0.043401, -0.870158, 23.900000, 20.390912, 3.509088, 1.029660, 0.569798, 0.218843, 0.018217 36, 13075, 839816.1, 3449007, 18.535261, 2.461574, 7.529840, -0.075505, 0.021651, -3.487343, -0.338287, 0.117703, -2.874075, 0.121617, 0.049920, 2.436253, 6.500000, 9.917521, -3.417521, -0.903073, 0.551130, 0.036806, 0.001911 37, 13077, 705457.9, 3694344, 26.117450, 1.428154, 18.287561, -0.138099, 0.017474, -7.902936, -0.415058, 0.105668, -3.927956, 0.086203, 0.040912, 2.107005, 13.300000, 12.823769, 0.476231, 0.127155, 0.623329, 0.056577, 0.000059 38, 13079, 783416.5, 3623343, 23.761320, 1.363547, 17.426111, -0.115945, 0.015559, -7.451790, -0.299411, 0.082233, -3.641021, 0.039274, 0.036432, 1.078014, 5.700000, 9.179222, -3.479222, -0.941519, 0.609455, 0.081576, 0.004829 39, 13081, 805648.4, 3537103, 19.919377, 1.762030, 11.304789, -0.084754, 0.017478, -4.849073, -0.305697, 0.088024, -3.472867, 0.080816, 0.041001, 1.971074, 10.000000, 10.238029, -0.238029, -0.063935, 0.582459, 0.067784, 0.000018 40, 13083, 635964.3, 3854592, 23.306235, 2.392725, 9.740458, -0.077786, 0.033441, -2.326110, -0.661246, 0.199674, -3.311632, 0.222182, 0.071887, 3.090705, 8.000000, 6.223420, 1.776580, 0.497158, 0.592442, 0.141150, 0.002491 41, 13085, 764386.1, 3812502, 28.104502, 1.628458, 17.258350, -0.166640, 0.019792, -8.419391, -0.241500, 0.128331, -1.881850, 0.018613, 0.040904, 0.455046, 8.600000, 8.354698, 0.245302, 0.065617, 0.579816, 0.060036, 0.000017 42, 13087, 732628.4, 3421800, 18.243729, 2.914084, 6.260537, -0.061647, 0.026182, -2.354582, -0.374035, 0.151023, -2.476672, 0.131483, 0.066322, 1.982507, 11.700000, 11.142799, 0.557201, 0.150000, 0.517041, 0.071931, 0.000107 43, 13089, 759231.9, 3735253, 27.769476, 1.445364, 19.212792, -0.161861, 0.017255, -9.380455, -0.271692, 0.104433, -2.601591, 0.018944, 0.037124, 0.510274, 32.700000, 25.475056, 7.224944, 2.074364, 0.590537, 0.184101, 0.059550 44, 13091, 860451.4, 3569933, 20.469253, 1.677580, 12.201654, -0.090444, 0.016612, -5.444365, -0.274347, 0.084017, -3.265377, 0.054057, 0.037097, 1.457158, 8.000000, 9.588201, -1.588201, -0.417816, 0.557046, 0.028199, 0.000311 45, 13093, 800031.3, 3564188, 20.929931, 1.608036, 13.015838, -0.092427, 0.016693, -5.536708, -0.302410, 0.083978, -3.601065, 0.067183, 0.039235, 1.712347, 9.500000, 7.561106, 1.938894, 0.522885, 0.597266, 0.075230, 0.001364 46, 13095, 764116.9, 3494367, 19.204355, 2.092473, 9.177827, -0.074609, 0.020398, -3.657707, -0.337452, 0.104380, -3.232902, 0.104021, 0.050090, 2.076661, 17.000000, 15.441075, 1.558925, 0.455956, 0.576878, 0.213784, 0.003467 47, 13097, 707288.7, 3731361, 27.001012, 1.505667, 17.932924, -0.146631, 0.018322, -8.002964, -0.419165, 0.113268, -3.700644, 0.084053, 0.040104, 2.095890, 12.000000, 20.960797, -8.960797, -2.439888, 0.618653, 0.092826, 0.037360 48, 13099, 703495.1, 3467152, 18.713680, 2.536789, 7.376917, -0.065175, 0.023968, -2.719217, -0.351935, 0.133539, -2.635458, 0.112937, 0.064293, 1.756593, 9.400000, 9.201063, 0.198937, 0.055488, 0.559229, 0.135477, 0.000030 49, 13101, 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0.147911, 0.000692 128, 13261, 763457.1, 3551752, 20.685430, 1.690728, 12.234627, -0.088696, 0.017764, -4.992976, -0.316879, 0.089315, -3.547864, 0.078125, 0.043847, 1.781777, 15.900000, 12.435222, 3.464778, 0.924448, 0.612953, 0.055237, 0.003064 129, 13263, 734217.9, 3623162, 23.625745, 1.401385, 16.858854, -0.114740, 0.016535, -6.939219, -0.333560, 0.090162, -3.699563, 0.062009, 0.040795, 1.520009, 7.100000, 7.952706, -0.852706, -0.242691, 0.629253, 0.169709, 0.000738 130, 13265, 884376.9, 3717493, 27.089792, 1.566017, 17.298530, -0.146418, 0.017474, -8.379044, -0.193156, 0.096119, -2.009559, -0.038055, 0.039240, -0.969798, 5.600000, 3.951246, 1.648754, 0.457485, 0.553403, 0.126435, 0.001858 131, 13267, 963427.8, 3560039, 19.217058, 2.200403, 8.733426, -0.091326, 0.020954, -4.358374, -0.201567, 0.110553, -1.823263, 0.042392, 0.045119, 0.939556, 6.500000, 8.798031, -2.298031, -0.606947, 0.527615, 0.035842, 0.000840 132, 13269, 759410.8, 3608179, 23.039230, 1.416355, 16.266563, 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14.000000, 10.323671, 3.676329, 0.979917, 0.548679, 0.053355, 0.003319 137, 13279, 941734.4, 3567586, 19.466237, 1.985710, 9.803160, -0.089505, 0.019441, -4.604034, -0.217248, 0.099299, -2.187827, 0.041084, 0.041683, 0.985641, 11.400000, 12.017499, -0.617499, -0.171186, 0.518705, 0.124870, 0.000256 138, 13281, 797981.7, 3872640, 27.974459, 1.810354, 15.452483, -0.165515, 0.022514, -7.351677, -0.187459, 0.150689, -1.244015, -0.001395, 0.047718, -0.029238, 11.400000, 8.798515, 2.601485, 0.703537, 0.537970, 0.080385, 0.002654 139, 13283, 919077.6, 3595170, 20.817967, 1.766099, 11.787543, -0.093618, 0.018007, -5.198952, -0.242249, 0.090264, -2.683772, 0.029578, 0.039579, 0.747331, 6.300000, 10.242200, -3.942200, -1.061068, 0.533919, 0.071614, 0.005327 140, 13285, 682616.8, 3660254, 24.628583, 1.429226, 17.232112, -0.122024, 0.018024, -6.769916, -0.434456, 0.108277, -4.012437, 0.105230, 0.045825, 2.296363, 13.600000, 15.337945, -1.737945, -0.462263, 0.636860, 0.049325, 0.000680 141, 13287, 819399.6, 3514927, 19.212872, 1.905344, 10.083674, -0.079944, 0.018122, -4.411469, -0.306889, 0.092643, -3.312610, 0.090595, 0.041947, 2.159766, 7.200000, 9.733350, -2.533350, -0.696716, 0.563038, 0.110766, 0.003708 142, 13289, 832935, 3623868, 23.494623, 1.415751, 16.595167, -0.112114, 0.015528, -7.219911, -0.282337, 0.080497, -3.507406, 0.026196, 0.036111, 0.725423, 4.800000, 6.145666, -1.345666, -0.359394, 0.587368, 0.057089, 0.000480 143, 13291, 777040.1, 3858779, 27.581783, 1.780050, 15.494948, -0.159725, 0.021761, -7.339813, -0.225600, 0.145778, -1.547555, 0.020375, 0.045908, 0.443822, 10.100000, 7.482873, 2.617127, 0.732462, 0.561207, 0.141350, 0.005417 144, 13293, 752165.2, 3639192, 24.448551, 1.349780, 18.112984, -0.123045, 0.015880, -7.748661, -0.319512, 0.087064, -3.669837, 0.048127, 0.037840, 1.271852, 9.000000, 13.053828, -4.053828, -1.066415, 0.618836, 0.028113, 0.002018 145, 13295, 658870.4, 3842167, 24.393692, 2.128643, 11.459738, -0.096237, 0.028760, -3.346260, 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24.474271, 2.100238, 11.653092, -0.104160, 0.027046, -3.851167, -0.506701, 0.169414, -2.990907, 0.159743, 0.057374, 2.784246, 12.000000, 12.207216, -0.207216, -0.055252, 0.608355, 0.053997, 0.000011 155, 13315, 838551.5, 3538547, 19.592759, 1.793470, 10.924498, -0.084198, 0.017164, -4.905592, -0.288637, 0.087414, -3.301955, 0.075524, 0.038778, 1.947599, 7.600000, 5.316609, 2.283391, 0.620364, 0.560011, 0.088819, 0.002301 156, 13317, 891228.5, 3749769, 27.962051, 1.636165, 17.089996, -0.159172, 0.018915, -8.415137, -0.150957, 0.107657, -1.402208, -0.054321, 0.040961, -1.326172, 10.400000, 12.568266, -2.168266, -0.580414, 0.538262, 0.061387, 0.001351 157, 13319, 858796.9, 3637891, 23.916868, 1.456211, 16.424038, -0.114371, 0.015790, -7.243026, -0.271957, 0.081857, -3.322330, 0.013616, 0.037177, 0.366235, 8.800000, 8.890582, -0.090582, -0.025045, 0.576505, 0.120216, 0.000005 158, 13321, 801018.1, 3487328, 18.929377, 2.092550, 9.046081, -0.075227, 0.019763, -3.806492, -0.330297, 0.102164, -3.233022, 0.105827, 0.046739, 2.264206, 6.300000, 8.176916, -1.876916, -0.499318, 0.559498, 0.049672, 0.000799 libpysal-4.9.2/libpysal/examples/georgia/georgia_GS_F_summary.txt000066400000000000000000000210041452177046000252130ustar00rootroot00000000000000***************************************************************************** * Semiparametric Geographically Weighted Regression * * Release 1.0.90 (GWR 4.0.90) * * 12 May 2015 * * (Originally coded by T. Nakaya: 1 Nov 2009) * * * * Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * * Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * * (c) GWR4 development team * * (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * * (3) University of Glasgow, (4) Arizona State University * ***************************************************************************** Program began at 7/25/2016 2:05:10 AM ***************************************************************************** Session: Session control file: C:\Users\IEUser\Desktop\goergia_GS_F.ctl ***************************************************************************** Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv Number of areas/points: 159 Model settings--------------------------------- Model type: Gaussian Geographic kernel: fixed Gaussian Method for optimal bandwidth search: Golden section search Criterion for optimal bandwidth: AICc Number of varying coefficients: 4 Number of fixed coefficients: 0 Modelling options--------------------------------- Standardisation of independent variables: OFF Testing geographical variability of local coefficients: OFF Local to Global Variable selection: OFF Global to Local Variable selection: OFF Prediction at non-regression points: OFF Variable settings--------------------------------- Area key: field1: AreaKey Easting (x-coord): field12 : X Northing (y-coord): field13: Y Cartesian coordinates: Euclidean distance Dependent variable: field6: PctBach Offset variable is not specified Intercept: varying (Local) intercept Independent variable with varying (Local) coefficient: field5: PctRural Independent variable with varying (Local) coefficient: field9: PctPov Independent variable with varying (Local) coefficient: field10: PctBlack ***************************************************************************** ***************************************************************************** Global regression result ***************************************************************************** < Diagnostic information > Residual sum of squares: 2639.559476 Number of parameters: 4 (Note: this num does not include an error variance term for a Gaussian model) ML based global sigma estimate: 4.074433 Unbiased global sigma estimate: 4.126671 -2 log-likelihood: 897.927089 Classic AIC: 907.927089 AICc: 908.319245 BIC/MDL: 923.271610 CV: 18.100197 R square: 0.485273 Adjusted R square: 0.471903 Variable Estimate Standard Error t(Est/SE) -------------------- --------------- --------------- --------------- Intercept 23.854615 1.173043 20.335661 PctRural -0.111395 0.012878 -8.649661 PctPov -0.345778 0.070863 -4.879540 PctBlack 0.058331 0.029187 1.998499 ***************************************************************************** GWR (Geographically weighted regression) bandwidth selection ***************************************************************************** Bandwidth search Limits: 54486.3131542225, 279451.547243655 Golden section search begins... Initial values pL Bandwidth: 54486.313 Criterion: 914.115 p1 Bandwidth: 140415.386 Criterion: 900.141 p2 Bandwidth: 193522.474 Criterion: 903.842 pU Bandwidth: 279451.547 Criterion: 906.294 iter 1 (p1) Bandwidth: 140415.386 Criterion: 900.141 Diff: 53107.088 iter 2 (p1) Bandwidth: 107593.401 Criterion: 896.616 Diff: 32821.985 iter 3 (p1) Bandwidth: 87308.298 Criterion: 895.290 Diff: 20285.103 iter 4 (p2) Bandwidth: 87308.298 Criterion: 895.290 Diff: 12536.883 iter 5 (p1) Bandwidth: 87308.298 Criterion: 895.290 Diff: 7748.220 iter 6 (p2) Bandwidth: 87308.298 Criterion: 895.290 Diff: 4788.663 Best bandwidth size 87308.298 Minimum AICc 895.290 ***************************************************************************** GWR (Geographically weighted regression) result ***************************************************************************** Bandwidth and geographic ranges Bandwidth size: 87308.298470 Coordinate Min Max Range --------------- --------------- --------------- --------------- X-coord 635964.300000 1059706.000000 423741.700000 Y-coord 3401148.000000 3872640.000000 471492.000000 Diagnostic information Residual sum of squares: 2030.010213 Effective number of parameters (model: trace(S)): 16.304601 Effective number of parameters (variance: trace(S'S)): 10.141574 Degree of freedom (model: n - trace(S)): 142.695399 Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 136.532371 ML based sigma estimate: 3.573144 Unbiased sigma estimate: 3.855949 -2 log-likelihood: 856.178266 Classic AIC: 890.787468 AICc: 895.290158 BIC/MDL: 943.893632 CV: 18.212841 R square: 0.604138 Adjusted R square: 0.538515 *********************************************************** << Geographically varying (Local) coefficients >> *********************************************************** Estimates of varying coefficients have been saved in the following file. Listwise output file: C:\Users\IEUser\Desktop\goergia_GS_F_listwise.csv Summary statistics for varying (Local) coefficients Variable Mean STD -------------------- --------------- --------------- Intercept 23.315956 3.742747 PctRural -0.116469 0.034541 PctPov -0.290012 0.105098 PctBlack 0.053228 0.060773 Variable Min Max Range -------------------- --------------- --------------- --------------- Intercept 18.016084 29.440723 11.424639 PctRural -0.185429 -0.058428 0.127001 PctPov -0.661246 -0.100954 0.560292 PctBlack -0.064110 0.222182 0.286293 Variable Lwr Quartile Median Upr Quartile -------------------- --------------- --------------- --------------- Intercept 19.466237 23.494623 26.843339 PctRural -0.145428 -0.108359 -0.087985 PctPov -0.337452 -0.285118 -0.224337 PctBlack 0.003985 0.055632 0.102447 Variable Interquartile R Robust STD -------------------- --------------- --------------- Intercept 7.377102 5.468571 PctRural 0.057443 0.042582 PctPov 0.113114 0.083851 PctBlack 0.098462 0.072989 (Note: Robust STD is given by (interquartile range / 1.349) ) ***************************************************************************** GWR ANOVA Table ***************************************************************************** Source SS DF MS F ----------------- ------------------- ---------- --------------- ---------- Global Residuals 2639.559 155.000 GWR Improvement 609.549 18.468 33.006 GWR Residuals 2030.010 136.532 14.868 2.219909 ***************************************************************************** Program terminated at 7/25/2016 2:05:11 AM libpysal-4.9.2/libpysal/examples/georgia/georgia_GS_NN.ctl000066400000000000000000000013731452177046000235360ustar00rootroot00000000000000 C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv FORMAT/DELIMITER: 1 Number_of_fields: 13 Number_of_areas: 159 Fields AreaKey 001 AreaKey X 012 X Y 013 Y Gmetric 0 Dependent 006 PctBach Offset Independent_geo 4 000 Intercept 005 PctRural 009 PctPov 010 PctBlack Independent_fix 0 Unused_fields 6 002 Latitude 003 Longitud 004 TotPop90 007 PctEld 008 PctFB 011 ID MODELTYPE: 0 STANDARDISATION: 0 GTEST: 0 VSL2G: 0 VSG2L: 0 KERNELTYPE: 3 BANDSELECTIONMETHOD: 1 Goldrangeflag: 0 Goldenmax: Goldenmin: Fixedbandsize: IntervalMax: IntervalMin: IntervalStep: Criteria: summary_output: C:\Users\IEUser\Desktop\georgia_GS_NN_summary.txt listwise_output: C:\Users\IEUser\Desktop\georgia_GS_NN_listwise.csv predictflag: 0 prediction_def: prediction_output: libpysal-4.9.2/libpysal/examples/georgia/georgia_GS_NN_listwise.csv000066400000000000000000001644601452177046000255010ustar00rootroot00000000000000Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_PctRural, se_PctRural, t_PctRural, est_PctPov, se_PctPov, t_PctPov, est_PctBlack, se_PctBlack, t_PctBlack, y, yhat, residual, std_residual, localR2, influence, CooksD 0, 13001, 941396.6, 3521764, 21.626865, 1.457152, 14.841875, -0.099036, 0.015041, -6.584203, -0.301756, 0.078805, -3.829137, 0.058822, 0.035097, 1.675968, 8.200000, 9.355951, -1.155951, -0.296583, 0.548471, 0.025265, 0.000284 1, 13003, 895553, 3471916, 20.960601, 1.471999, 14.239551, -0.093944, 0.015137, -6.206105, -0.317883, 0.078902, -4.028843, 0.077650, 0.035724, 2.173583, 6.400000, 5.386899, 1.013101, 0.263500, 0.547804, 0.051486, 0.000469 2, 13005, 930946.4, 3502787, 21.340670, 1.474600, 14.472180, -0.097237, 0.015138, -6.423569, -0.305775, 0.079193, -3.861155, 0.065534, 0.035436, 1.849372, 6.600000, 8.982485, -2.382485, -0.626296, 0.548923, 0.071458, 0.003758 3, 13007, 745398.6, 3474765, 21.652602, 1.362160, 15.895783, -0.093473, 0.014904, -6.271598, -0.354833, 0.076919, -4.613079, 0.086734, 0.034545, 2.510765, 9.400000, 7.986999, 1.413001, 0.370642, 0.559055, 0.067443, 0.001237 4, 13009, 849431.3, 3665553, 25.292516, 1.277116, 19.804392, -0.126365, 0.014113, -8.953548, -0.286840, 0.076082, -3.770127, 0.014001, 0.032322, 0.433170, 13.300000, 15.470543, -2.170543, -0.563221, 0.550567, 0.047028, 0.001949 5, 13011, 819317.3, 3807616, 26.978495, 1.333220, 20.235594, -0.148715, 0.015739, -9.449124, -0.260333, 0.091248, -2.853020, 0.007208, 0.034424, 0.209374, 6.400000, 8.201092, -1.801092, -0.467232, 0.524135, 0.046529, 0.001326 6, 13013, 803747.1, 3769623, 27.413438, 1.366252, 20.064702, -0.155327, 0.016220, -9.576329, -0.244799, 0.095199, -2.571454, 0.001305, 0.035223, 0.037042, 9.200000, 13.795686, -4.595686, -1.181006, 0.529843, 0.028379, 0.005071 7, 13015, 699011.5, 3793408, 25.928870, 1.348245, 19.231566, -0.135314, 0.015806, -8.560655, -0.351185, 0.091376, -3.843275, 0.058437, 0.034661, 1.685933, 9.000000, 12.533795, -3.533795, -0.907169, 0.539704, 0.026342, 0.002772 8, 13017, 863020.8, 3520432, 21.263945, 1.402561, 15.160798, -0.094378, 0.014629, -6.451384, -0.317260, 0.076506, -4.146880, 0.070311, 0.034527, 2.036377, 7.600000, 12.051309, -4.451309, -1.143223, 0.541964, 0.027221, 0.004553 9, 13019, 859915.8, 3466377, 20.946762, 1.437381, 14.572870, -0.092411, 0.014981, -6.168355, -0.328129, 0.077704, -4.222829, 0.082514, 0.035502, 2.324250, 7.500000, 9.455091, -1.955091, -0.509968, 0.546717, 0.056922, 0.001954 10, 13021, 809736.9, 3636468, 24.532429, 1.242482, 19.744688, -0.119568, 0.013828, -8.646600, -0.311192, 0.074254, -4.190933, 0.034437, 0.031432, 1.095605, 17.000000, 18.067824, -1.067824, -0.280841, 0.564208, 0.072364, 0.000766 11, 13023, 844270.1, 3595691, 23.262635, 1.262917, 18.419766, -0.107683, 0.013697, -7.861986, -0.315491, 0.073405, -4.297938, 0.044395, 0.031850, 1.393871, 10.300000, 12.246973, -1.946973, -0.498717, 0.556205, 0.022063, 0.000698 12, 13025, 979288.9, 3463849, 22.023185, 1.396647, 15.768609, -0.101057, 0.014628, -6.908428, -0.317358, 0.076963, -4.123500, 0.063226, 0.034002, 1.859490, 5.800000, 6.431159, -0.631159, -0.167422, 0.563934, 0.088082, 0.000337 13, 13027, 827822, 3421638, 21.329854, 1.366845, 15.605175, -0.093531, 0.014621, -6.396952, -0.343350, 0.076056, -4.514434, 0.085757, 0.034564, 2.481142, 9.100000, 9.857815, -0.757815, -0.194052, 0.555169, 0.021434, 0.000103 14, 13029, 1023145, 3554982, 22.981765, 1.347595, 17.053916, -0.107186, 0.014312, -7.489271, -0.305664, 0.075797, -4.032678, 0.042913, 0.032692, 1.312639, 11.800000, 10.945100, 0.854900, 0.221137, 0.559079, 0.041022, 0.000260 15, 13031, 994903.4, 3600493, 23.375461, 1.335470, 17.503547, -0.109687, 0.014217, -7.714992, -0.298428, 0.075780, -3.938083, 0.033330, 0.032506, 1.025339, 19.900000, 9.101424, 10.798576, 2.820742, 0.552292, 0.059614, 0.062787 16, 13033, 971593.8, 3671394, 24.524585, 1.271622, 19.286061, -0.118149, 0.013720, -8.611196, -0.293751, 0.074107, -3.963890, 0.019467, 0.031047, 0.627025, 9.600000, 8.097758, 1.502242, 0.389514, 0.546350, 0.045594, 0.000902 17, 13035, 782448.2, 3684504, 25.845630, 1.264212, 20.444069, -0.134835, 0.014378, -9.377889, -0.301626, 0.078752, -3.830055, 0.027407, 0.032060, 0.854867, 7.200000, 12.215750, -5.015750, -1.291497, 0.560975, 0.032201, 0.006908 18, 13037, 724741.2, 3492653, 22.163047, 1.303120, 17.007678, -0.097085, 0.014424, -6.730702, -0.357953, 0.075290, -4.754320, 0.082866, 0.032980, 2.512604, 10.100000, 5.951669, 4.148331, 1.087798, 0.564186, 0.066852, 0.010553 19, 13039, 1008480, 3437933, 22.298215, 1.354182, 16.466182, -0.102417, 0.014323, -7.150385, -0.322044, 0.075664, -4.256225, 0.062073, 0.033138, 1.873175, 13.500000, 15.024108, -1.524108, -0.396779, 0.566825, 0.053252, 0.001102 20, 13043, 964264.9, 3598842, 23.220616, 1.345121, 17.262846, -0.108483, 0.014277, -7.598441, -0.296664, 0.076121, -3.897282, 0.033632, 0.032948, 1.020737, 9.900000, 11.459604, -1.559604, -0.401240, 0.548581, 0.030562, 0.000632 21, 13045, 678778.6, 3713250, 25.157218, 1.269114, 19.822667, -0.126873, 0.014523, -8.736229, -0.363915, 0.080214, -4.536773, 0.067373, 0.032358, 2.082123, 12.000000, 12.267665, -0.267665, -0.068432, 0.559438, 0.018319, 0.000011 22, 13047, 670055.9, 3862318, 25.331887, 1.282320, 19.754728, -0.127703, 0.014738, -8.664685, -0.349253, 0.082617, -4.227359, 0.057703, 0.032827, 1.757798, 8.100000, 15.625499, -7.525499, -1.969222, 0.530409, 0.062910, 0.032406 23, 13049, 962612.3, 3432769, 21.933757, 1.374678, 15.955558, -0.100015, 0.014470, -6.911946, -0.324232, 0.076267, -4.251280, 0.068344, 0.033790, 2.022611, 6.400000, 7.847483, -1.447483, -0.375138, 0.564887, 0.044685, 0.000819 24, 13051, 1059706, 3556747, 23.289156, 1.293318, 18.007290, -0.108878, 0.013869, -7.850502, -0.311044, 0.073959, -4.205622, 0.042239, 0.031514, 1.340328, 18.600000, 18.989849, -0.389849, -0.104014, 0.560384, 0.098616, 0.000147 25, 13053, 704959.2, 3577608, 23.135000, 1.280280, 18.070272, -0.105939, 0.014463, -7.324642, -0.357265, 0.075946, -4.704196, 0.075003, 0.032382, 2.316225, 20.200000, 20.288682, -0.088682, -0.023620, 0.575797, 0.095466, 0.000007 26, 13055, 653026.6, 3813760, 25.238584, 1.281379, 19.696426, -0.126760, 0.014709, -8.617714, -0.360001, 0.082164, -4.381511, 0.064186, 0.032783, 1.957891, 5.900000, 10.724024, -4.824024, -1.237205, 0.537294, 0.024480, 0.004781 27, 13057, 734240.9, 3794110, 26.738871, 1.424971, 18.764502, -0.146237, 0.016977, -8.613888, -0.320312, 0.102328, -3.130254, 0.043669, 0.036422, 1.198981, 18.400000, 16.409754, 1.990246, 0.518110, 0.537072, 0.053174, 0.001877 28, 13059, 832508.6, 3762905, 27.241892, 1.337499, 20.367791, -0.151566, 0.015698, -9.655305, -0.241590, 0.089623, -2.695614, -0.005162, 0.034409, -0.150026, 37.500000, 17.916007, 19.583993, 5.637950, 0.528664, 0.225787, 1.153944 29, 13061, 695793.9, 3495219, 22.465026, 1.270648, 17.679979, -0.099292, 0.014146, -7.018840, -0.361903, 0.074478, -4.859210, 0.082242, 0.032021, 2.568401, 11.200000, 4.612950, 6.587050, 1.736724, 0.565559, 0.076960, 0.031304 30, 13063, 745538.8, 3711726, 26.037250, 1.288900, 20.201146, -0.137682, 0.014805, -9.299565, -0.322383, 0.082647, -3.900711, 0.041183, 0.032699, 1.259476, 14.700000, 23.639943, -8.939943, -2.380434, 0.559406, 0.094981, 0.074028 31, 13065, 908046.1, 3428340, 21.390248, 1.409248, 15.178488, -0.096206, 0.014752, -6.521417, -0.328048, 0.077266, -4.245695, 0.077908, 0.034791, 2.239323, 6.700000, 9.218220, -2.518220, -0.653012, 0.559225, 0.045789, 0.002547 32, 13067, 724646.8, 3757187, 26.497784, 1.374988, 19.271281, -0.143156, 0.016165, -8.855953, -0.335996, 0.094592, -3.552069, 0.049705, 0.035141, 1.414433, 33.000000, 24.274999, 8.725001, 2.347843, 0.549968, 0.113879, 0.088184 33, 13069, 894463.9, 3492465, 20.885014, 1.484667, 14.067134, -0.093591, 0.015185, -6.163280, -0.311700, 0.079148, -3.938196, 0.074614, 0.035770, 2.085908, 11.100000, 9.725475, 1.374525, 0.352331, 0.541801, 0.023428, 0.000371 34, 13071, 808691.8, 3455994, 20.974560, 1.432494, 14.641986, -0.090631, 0.015182, -5.969623, -0.341884, 0.077899, -4.388810, 0.088745, 0.035863, 2.474588, 10.000000, 9.940225, 0.059775, 0.015399, 0.547955, 0.033141, 0.000001 35, 13073, 942527.9, 3722100, 25.350877, 1.257987, 20.151939, -0.126053, 0.013781, -9.146791, -0.285976, 0.074525, -3.837321, 0.011209, 0.030816, 0.363745, 23.900000, 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-0.338248, 0.078975, -4.282987, 0.087129, 0.036500, 2.387132, 6.300000, 8.316193, -2.016193, -0.518987, 0.545081, 0.031607, 0.001094 libpysal-4.9.2/libpysal/examples/georgia/georgia_GS_NN_summary.txt000066400000000000000000000204021452177046000253420ustar00rootroot00000000000000***************************************************************************** * Semiparametric Geographically Weighted Regression * * Release 1.0.90 (GWR 4.0.90) * * 12 May 2015 * * (Originally coded by T. Nakaya: 1 Nov 2009) * * * * Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * * Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * * (c) GWR4 development team * * (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * * (3) University of Glasgow, (4) Arizona State University * ***************************************************************************** Program began at 7/25/2016 2:04:57 AM ***************************************************************************** Session: Session control file: C:\Users\IEUser\Desktop\georgia_GS_NN.ctl ***************************************************************************** Data filename: C:\Users\IEUser\Desktop\georgia\georgia\GData_utm.csv Number of areas/points: 159 Model settings--------------------------------- Model type: Gaussian Geographic kernel: adaptive Gaussian Method for optimal bandwidth search: Golden section search Criterion for optimal bandwidth: AICc Number of varying coefficients: 4 Number of fixed coefficients: 0 Modelling options--------------------------------- Standardisation of independent variables: OFF Testing geographical variability of local coefficients: OFF Local to Global Variable selection: OFF Global to Local Variable selection: OFF Prediction at non-regression points: OFF Variable settings--------------------------------- Area key: field1: AreaKey Easting (x-coord): field12 : X Northing (y-coord): field13: Y Cartesian coordinates: Euclidean distance Dependent variable: field6: PctBach Offset variable is not specified Intercept: varying (Local) intercept Independent variable with varying (Local) coefficient: field5: PctRural Independent variable with varying (Local) coefficient: field9: PctPov Independent variable with varying (Local) coefficient: field10: PctBlack ***************************************************************************** ***************************************************************************** Global regression result ***************************************************************************** < Diagnostic information > Residual sum of squares: 2639.559476 Number of parameters: 4 (Note: this num does not include an error variance term for a Gaussian model) ML based global sigma estimate: 4.074433 Unbiased global sigma estimate: 4.126671 -2 log-likelihood: 897.927089 Classic AIC: 907.927089 AICc: 908.319245 BIC/MDL: 923.271610 CV: 18.100197 R square: 0.485273 Adjusted R square: 0.471903 Variable Estimate Standard Error t(Est/SE) -------------------- --------------- --------------- --------------- Intercept 23.854615 1.173043 20.335661 PctRural -0.111395 0.012878 -8.649661 PctPov -0.345778 0.070863 -4.879540 PctBlack 0.058331 0.029187 1.998499 ***************************************************************************** GWR (Geographically weighted regression) bandwidth selection ***************************************************************************** Bandwidth search Limits: 48, 159 Golden section search begins... Initial values pL Bandwidth: 48.000 Criterion: 896.274 p1 Bandwidth: 50.363 Criterion: 896.244 p2 Bandwidth: 51.823 Criterion: 897.469 pU Bandwidth: 54.186 Criterion: 897.673 iter 1 (p1) Bandwidth: 50.363 Criterion: 896.244 Diff: 1.460 iter 2 (p1) Bandwidth: 49.460 Criterion: 896.184 Diff: 0.902 iter 3 (p2) Bandwidth: 49.460 Criterion: 896.184 Diff: 0.558 Best bandwidth size 49.000 Minimum AICc 896.184 ***************************************************************************** GWR (Geographically weighted regression) result ***************************************************************************** Bandwidth and geographic ranges Bandwidth size: 49.460274 Coordinate Min Max Range --------------- --------------- --------------- --------------- X-coord 635964.300000 1059706.000000 423741.700000 Y-coord 3401148.000000 3872640.000000 471492.000000 Diagnostic information Residual sum of squares: 2312.592458 Effective number of parameters (model: trace(S)): 8.033359 Effective number of parameters (variance: trace(S'S)): 5.454906 Degree of freedom (model: n - trace(S)): 150.966641 Degree of freedom (residual: n - 2trace(S) + trace(S'S)): 148.388187 ML based sigma estimate: 3.813739 Unbiased sigma estimate: 3.947752 -2 log-likelihood: 876.900473 Classic AIC: 894.967192 AICc: 896.184041 BIC/MDL: 922.689706 CV: 17.914091 R square: 0.549033 Adjusted R square: 0.516564 *********************************************************** << Geographically varying (Local) coefficients >> *********************************************************** Estimates of varying coefficients have been saved in the following file. Listwise output file: C:\Users\IEUser\Desktop\georgia_GS_NN_listwise.csv Summary statistics for varying (Local) coefficients Variable Mean STD -------------------- --------------- --------------- Intercept 24.095617 1.993847 PctRural -0.118018 0.019034 PctPov -0.315782 0.031402 PctBlack 0.047231 0.026783 Variable Min Max Range -------------------- --------------- --------------- --------------- Intercept 20.871637 27.413438 6.541801 PctRural -0.155327 -0.089579 0.065748 PctPov -0.366066 -0.241590 0.124476 PctBlack -0.005162 0.088745 0.093907 Variable Lwr Quartile Median Upr Quartile -------------------- --------------- --------------- --------------- Intercept 22.299833 24.112693 25.928870 PctRural -0.134835 -0.115582 -0.100015 PctPov -0.343436 -0.317883 -0.293751 PctBlack 0.024811 0.050079 0.068774 Variable Interquartile R Robust STD -------------------- --------------- --------------- Intercept 3.629037 2.690168 PctRural 0.034820 0.025812 PctPov 0.049685 0.036831 PctBlack 0.043963 0.032589 (Note: Robust STD is given by (interquartile range / 1.349) ) ***************************************************************************** GWR ANOVA Table ***************************************************************************** Source SS DF MS F ----------------- ------------------- ---------- --------------- ---------- Global Residuals 2639.559 155.000 GWR Improvement 326.967 6.612 49.452 GWR Residuals 2312.592 148.388 15.585 3.173099 ***************************************************************************** Program terminated at 7/25/2016 2:04:57 AM libpysal-4.9.2/libpysal/examples/juvenile/000077500000000000000000000000001452177046000206315ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/juvenile/README.md000066400000000000000000000006711452177046000221140ustar00rootroot00000000000000juvenile ======== Residences of juvenile offenders in Cardiff, UK ----------------------------------------------- * juvenile.dbf: attribute data. (k=3) * juvenile.gwt: spatial weights in GWT format. * juvenile.shp: Point shapefile. (n=168) * juvenile.shx: spatial index. Source: Bailey, T. and A. Gatrell (1995). Interactive Spatial Data Analysis. New York: Wiley, p. 95. Updated URL: https://geodacenter.github.io/data-and-lab/juvenile/ libpysal-4.9.2/libpysal/examples/juvenile/juvenile.dbf000066400000000000000000000113421452177046000231300ustar00rootroot00000000000000d¨WIDN XN YN 1 94 93 2 80 95 3 79 90 4 78 92 5 76 92 6 66 93 7 64 90 79 27 70 8 58 88 9 57 92 10 53 92 11 50 90 12 49 90 13 32 90 14 31 87 15 22 87 16 21 87 17 21 86 18 22 81 19 23 83 20 27 85 21 27 84 22 27 83 23 27 82 24 30 84 25 31 84 26 31 84 27 32 83 28 33 81 29 32 79 30 32 76 31 33 77 32 34 86 33 34 84 34 38 82 35 39 81 36 40 80 37 41 83 38 43 75 39 44 81 40 46 81 41 47 82 42 47 81 43 48 80 44 48 81 45 50 85 46 51 84 47 52 83 48 55 85 49 57 88 50 57 81 51 60 87 52 69 80 53 71 82 54 72 81 55 74 82 56 75 81 57 77 88 58 80 88 59 82 77 60 66 62 61 64 71 62 59 63 63 55 64 64 53 68 65 52 59 66 51 61 67 50 75 68 50 74 69 45 61 70 44 60 71 43 59 72 42 61 73 39 71 74 37 67 75 35 70 76 31 68 77 30 71 78 29 61 80 26 69 81 24 68 82 7 52 83 11 53 84 34 50 85 36 47 86 37 45 87 37 56 88 38 55 89 38 50 90 39 52 91 41 52 92 47 49 93 50 57 94 52 56 95 53 55 96 56 57 97 69 52 98 69 50 99 71 51 100 71 51 101 73 48 102 74 48 103 75 46 104 75 46 105 86 51 106 87 51 107 87 52 108 90 52 109 91 51 110 87 42 111 81 39 112 80 43 113 79 37 114 78 38 115 75 44 116 73 41 117 71 44 118 68 29 119 62 33 120 61 35 121 60 34 122 58 36 123 54 30 124 52 38 125 52 36 126 47 37 127 46 36 128 45 33 129 36 32 130 22 39 131 21 38 132 22 35 133 21 36 134 22 30 135 19 29 136 17 40 137 14 41 138 13 36 139 10 34 140 7 37 141 2 39 142 21 16 143 22 14 144 29 17 145 30 25 146 32 26 147 39 28 148 40 26 149 40 26 150 42 25 151 43 24 152 43 16 153 48 16 154 51 25 155 52 26 156 57 27 157 60 22 158 63 24 159 64 23 160 64 27 161 71 25 162 50 10 163 48 12 164 45 14 165 33 8 166 31 7 167 32 6 168 31 8libpysal-4.9.2/libpysal/examples/juvenile/juvenile.gwt000066400000000000000000002136441452177046000232070ustar00rootroot000000000000001 168 juvenile ID 1 2 14.1421356 2 1 14.1421356 2 58 7 2 57 7.61577311 2 5 5 2 4 3.60555128 2 3 5.09901951 2 6 14.1421356 3 2 5.09901951 3 57 2.82842712 3 5 3.60555128 3 4 2.23606798 3 6 13.3416641 3 56 9.8488578 3 55 9.43398113 3 54 11.4017543 3 53 11.3137085 3 52 14.1421356 3 58 2.23606798 3 59 13.3416641 4 2 3.60555128 4 3 2.23606798 4 57 4.12310563 4 5 2 4 7 14.1421356 4 6 12.0415946 4 56 11.4017543 4 55 10.7703296 4 54 12.5299641 4 53 12.2065556 4 58 4.47213595 5 2 5 5 3 3.60555128 5 4 2 5 7 12.1655251 5 6 10.0498756 5 56 11.045361 5 55 10.198039 5 54 11.7046999 5 53 11.1803399 5 52 13.892444 5 57 4.12310563 5 58 5.65685425 6 2 14.1421356 6 3 13.3416641 6 4 12.0415946 6 5 10.0498756 6 51 8.48528137 6 49 10.2956301 6 48 13.6014705 6 9 9.05538514 6 8 9.43398113 6 7 3.60555128 6 10 13.0384048 6 52 13.3416641 6 53 12.083046 6 54 13.4164079 6 55 13.6014705 6 57 12.083046 7 4 14.1421356 7 5 12.1655251 7 6 3.60555128 7 51 5 7 49 7.28010989 7 48 10.2956301 7 9 7.28010989 7 8 6.32455532 7 47 13.892444 7 11 14 7 10 11.1803399 7 50 11.4017543 7 52 11.1803399 7 53 10.6301458 7 54 12.0415946 7 55 12.8062485 7 57 13.1529464 79 19 13.6014705 79 18 12.083046 79 81 3.60555128 79 80 1.41421356 79 21 14 79 22 13 79 23 12 79 27 13.9283883 79 28 12.5299641 79 29 10.2956301 79 30 7.81024968 79 31 9.21954446 79 73 12.0415946 79 74 10.4403065 79 75 8 79 76 4.47213595 79 77 3.16227766 79 78 9.21954446 8 6 9.43398113 8 7 6.32455532 8 49 1 8 48 4.24264069 8 9 4.12310563 8 47 7.81024968 8 46 8.06225775 8 45 8.54400375 8 12 9.21954446 8 11 8.24621125 8 10 6.40312424 8 50 7.07106781 8 44 12.2065556 8 43 12.8062485 8 42 13.0384048 8 41 12.5299641 8 40 13.892444 8 51 2.23606798 8 52 13.6014705 9 6 9.05538514 9 7 7.28010989 9 8 4.12310563 9 49 4 9 48 7.28010989 9 47 10.2956301 9 46 10 9 45 9.89949494 9 12 8.24621125 9 11 7.28010989 9 10 4 9 50 11 9 41 14.1421356 9 51 5.83095189 10 6 13.0384048 10 7 11.1803399 10 8 6.40312424 10 9 4 10 47 9.05538514 10 46 8.24621125 10 45 7.61577311 10 12 4.47213595 10 11 3.60555128 10 44 12.083046 10 43 13 10 42 12.5299641 10 41 11.6619038 10 40 13.0384048 10 48 7.28010989 10 49 5.65685425 10 50 11.7046999 10 51 8.60232527 11 7 14 11 8 8.24621125 11 9 7.28010989 11 10 3.60555128 11 45 5 11 12 1 11 37 11.4017543 11 44 9.21954446 11 43 10.198039 11 42 9.48683298 11 41 8.54400375 11 40 9.8488578 11 39 10.8166538 11 36 14.1421356 11 46 6.08276253 11 47 7.28010989 11 48 7.07106781 11 49 7.28010989 11 50 11.4017543 11 51 10.4403065 12 8 9.21954446 12 9 8.24621125 12 10 4.47213595 12 11 1 12 37 10.6301458 12 44 9.05538514 12 43 10.0498756 12 42 9.21954446 12 41 8.24621125 12 40 9.48683298 12 39 10.2956301 12 36 13.453624 12 35 13.453624 12 34 13.6014705 12 45 5.09901951 12 46 6.32455532 12 47 7.61577311 12 48 7.81024968 12 49 8.24621125 12 50 12.0415946 12 51 11.4017543 13 27 7 13 26 6.08276253 13 25 6.08276253 13 24 6.32455532 13 14 3.16227766 13 22 8.60232527 13 21 7.81024968 13 20 7.07106781 13 19 11.4017543 13 17 11.7046999 13 16 11.4017543 13 15 10.4403065 13 30 14 13 29 11 13 23 9.43398113 13 18 13.453624 13 28 9.05538514 13 31 13.0384048 13 32 4.47213595 13 33 6.32455532 13 34 10 13 35 11.4017543 13 36 12.8062485 13 37 11.4017543 14 13 3.16227766 14 26 3 14 25 3 14 24 3.16227766 14 22 5.65685425 14 21 5 14 20 4.47213595 14 19 8.94427191 14 17 10.0498756 14 16 10 14 15 9 14 23 6.40312424 14 18 10.8166538 14 27 4.12310563 14 28 6.32455532 14 29 8.06225775 14 30 11.045361 14 31 10.198039 14 32 3.16227766 14 33 4.24264069 14 34 8.60232527 14 35 10 14 36 11.4017543 14 37 10.7703296 15 13 10.4403065 15 14 9 15 17 1.41421356 15 16 1 15 18 6 15 19 4.12310563 15 20 5.38516481 15 21 5.83095189 15 22 6.40312424 15 23 7.07106781 15 24 8.54400375 15 25 9.48683298 15 26 9.48683298 15 27 10.7703296 15 28 12.5299641 15 29 12.8062485 15 32 12.0415946 15 33 12.3693169 16 13 11.4017543 16 14 10 16 15 1 16 17 1 16 18 6.08276253 16 19 4.47213595 16 20 6.32455532 16 21 6.70820393 16 22 7.21110255 16 23 7.81024968 16 24 9.48683298 16 25 10.4403065 16 26 10.4403065 16 27 11.7046999 16 28 13.4164079 16 29 13.6014705 16 32 13.0384048 16 33 13.3416641 17 13 11.7046999 17 14 10.0498756 17 15 1.41421356 17 16 1 17 18 5.09901951 17 19 3.60555128 17 20 6.08276253 17 21 6.32455532 17 22 6.70820393 17 23 7.21110255 17 24 9.21954446 17 25 10.198039 17 26 10.198039 17 27 11.4017543 17 28 13 17 29 13.0384048 17 32 13 17 33 13.1529464 18 79 12.083046 18 13 13.453624 18 14 10.8166538 18 15 6 18 17 5.09901951 18 16 6.08276253 18 19 2.23606798 18 20 6.40312424 18 21 5.83095189 18 22 5.38516481 18 23 5.09901951 18 24 8.54400375 18 25 9.48683298 18 26 9.48683298 18 27 10.198039 18 28 11 18 29 10.198039 18 30 11.1803399 18 31 11.7046999 18 32 13 18 33 12.3693169 18 77 12.8062485 18 80 12.6491106 18 81 13.1529464 19 79 13.6014705 19 13 11.4017543 19 14 8.94427191 19 17 3.60555128 19 16 4.47213595 19 15 4.12310563 19 18 2.23606798 19 20 4.47213595 19 21 4.12310563 19 22 4 19 23 4.12310563 19 24 7.07106781 19 25 8.06225775 19 26 8.06225775 19 27 9 19 28 10.198039 19 29 9.8488578 19 30 11.4017543 19 31 11.6619038 19 32 11.4017543 19 33 11.045361 19 77 13.892444 20 13 7.07106781 20 14 4.47213595 20 22 2 20 21 1 20 19 4.47213595 20 17 6.08276253 20 16 6.32455532 20 15 5.38516481 20 23 3 20 18 6.40312424 20 24 3.16227766 20 25 4.12310563 20 26 4.12310563 20 27 5.38516481 20 28 7.21110255 20 29 7.81024968 20 30 10.2956301 20 31 10 20 32 7.07106781 20 33 7.07106781 20 34 11.4017543 20 35 12.6491106 20 36 13.9283883 20 37 14.1421356 21 13 7.81024968 21 14 5 21 20 1 21 22 1 21 19 4.12310563 21 17 6.32455532 21 16 6.70820393 21 15 5.83095189 21 23 2 21 18 5.83095189 21 79 14 21 24 3 21 25 4 21 26 4 21 27 5.09901951 21 28 6.70820393 21 29 7.07106781 21 30 9.43398113 21 31 9.21954446 21 32 7.28010989 21 33 7 21 34 11.1803399 21 35 12.3693169 21 36 13.6014705 21 37 14.0356688 21 77 13.3416641 22 13 8.60232527 22 14 5.65685425 22 20 2 22 21 1 22 19 4 22 17 6.70820393 22 16 7.21110255 22 15 6.40312424 22 23 1 22 18 5.38516481 22 79 13 22 24 3.16227766 22 25 4.12310563 22 26 4.12310563 22 27 5 22 28 6.32455532 22 29 6.40312424 22 30 8.60232527 22 31 8.48528137 22 32 7.61577311 22 33 7.07106781 22 34 11.045361 22 35 12.1655251 22 36 13.3416641 22 37 14 22 77 12.3693169 23 13 9.43398113 23 14 6.40312424 23 20 3 23 21 2 23 22 1 23 19 4.12310563 23 17 7.21110255 23 16 7.81024968 23 15 7.07106781 23 18 5.09901951 23 79 12 23 80 13.0384048 23 24 3.60555128 23 25 4.47213595 23 26 4.47213595 23 27 5.09901951 23 28 6.08276253 23 29 5.83095189 23 30 7.81024968 23 31 7.81024968 23 32 8.06225775 23 33 7.28010989 23 34 11 23 35 12.0415946 23 36 13.1529464 23 37 14.0356688 23 77 11.4017543 24 13 6.32455532 24 14 3.16227766 24 22 3.16227766 24 21 3 24 20 3.16227766 24 19 7.07106781 24 17 9.21954446 24 16 9.48683298 24 15 8.54400375 24 77 13 24 23 3.60555128 24 18 8.54400375 24 25 1 24 26 1 24 27 2.23606798 24 28 4.24264069 24 29 5.38516481 24 30 8.24621125 24 31 7.61577311 24 32 4.47213595 24 33 4 24 34 8.24621125 24 35 9.48683298 24 36 10.7703296 24 37 11.045361 25 13 6.08276253 25 14 3 25 24 1 25 22 4.12310563 25 21 4 25 20 4.12310563 25 19 8.06225775 25 17 10.198039 25 16 10.4403065 25 15 9.48683298 25 77 13.0384048 25 23 4.47213595 25 18 9.48683298 25 26 1.41421456e-05 25 27 1.41421356 25 28 3.60555128 25 29 5.09901951 25 30 8.06225775 25 31 7.28010989 25 32 3.60555128 25 33 3 25 34 7.28010989 25 35 8.54400375 25 36 9.8488578 25 37 10.0498756 25 39 13.3416641 26 13 6.08276253 26 14 3 26 25 1.41421456e-05 26 24 1 26 22 4.12310563 26 21 4 26 20 4.12310563 26 19 8.06225775 26 17 10.198039 26 16 10.4403065 26 15 9.48683298 26 77 13.0384048 26 23 4.47213595 26 18 9.48683298 26 27 1.41421356 26 28 3.60555128 26 29 5.09901951 26 30 8.06225775 26 31 7.28010989 26 32 3.60555128 26 33 3 26 34 7.28010989 26 35 8.54400375 26 36 9.8488578 26 37 10.0498756 26 39 13.3416641 27 13 7 27 26 1.41421356 27 25 1.41421356 27 24 2.23606798 27 14 4.12310563 27 22 5 27 21 5.09901951 27 20 5.38516481 27 19 9 27 17 11.4017543 27 16 11.7046999 27 15 10.7703296 27 77 12.1655251 27 30 7 27 29 4 27 23 5.09901951 27 18 10.198039 27 79 13.9283883 27 28 2.23606798 27 31 6.08276253 27 32 3.60555128 27 33 2.23606798 27 34 6.08276253 27 35 7.28010989 27 36 8.54400375 27 37 9 27 38 13.6014705 27 39 12.1655251 27 40 14.1421356 27 73 13.892444 27 75 13.3416641 28 22 6.32455532 28 21 6.70820393 28 20 7.21110255 28 19 10.198039 28 17 13 28 16 13.4164079 28 15 12.5299641 28 27 2.23606798 28 26 3.60555128 28 25 3.60555128 28 24 4.24264069 28 14 6.32455532 28 13 9.05538514 28 77 10.4403065 28 31 4 28 30 5.09901951 28 29 2.23606798 28 23 6.08276253 28 18 11 28 79 12.5299641 28 76 13.1529464 28 80 13.892444 28 32 5.09901951 28 33 3.16227766 28 34 5.09901951 28 35 6 28 36 7.07106781 28 37 8.24621125 28 38 11.6619038 28 39 11 28 40 13 28 41 14.0356688 28 42 14 28 73 11.6619038 28 75 11.1803399 29 13 11 29 27 4 29 28 2.23606798 29 22 6.40312424 29 21 7.07106781 29 20 7.81024968 29 19 9.8488578 29 17 13.0384048 29 16 13.6014705 29 15 12.8062485 29 26 5.09901951 29 25 5.09901951 29 24 5.38516481 29 14 8.06225775 29 77 8.24621125 29 30 3 29 23 5.83095189 29 18 10.198039 29 79 10.2956301 29 76 11.045361 29 81 13.6014705 29 80 11.6619038 29 31 2.23606798 29 32 7.28010989 29 33 5.38516481 29 34 6.70820393 29 35 7.28010989 29 36 8.06225775 29 37 9.8488578 29 38 11.7046999 29 39 12.1655251 29 40 14.1421356 29 73 10.6301458 29 74 13 29 75 9.48683298 30 13 14 30 27 7 30 28 5.09901951 30 29 3 30 22 8.60232527 30 21 9.43398113 30 20 10.2956301 30 19 11.4017543 30 26 8.06225775 30 25 8.06225775 30 24 8.24621125 30 14 11.045361 30 77 5.38516481 30 23 7.81024968 30 18 11.1803399 30 79 7.81024968 30 76 8.06225775 30 81 11.3137085 30 80 9.21954446 30 31 1.41421356 30 32 10.198039 30 33 8.24621125 30 34 8.48528137 30 35 8.60232527 30 36 8.94427191 30 37 11.4017543 30 38 11.045361 30 39 13 30 73 8.60232527 30 74 10.2956301 30 75 6.70820393 31 28 4 31 22 8.48528137 31 21 9.21954446 31 20 10 31 19 11.6619038 31 27 6.08276253 31 26 7.28010989 31 25 7.28010989 31 24 7.61577311 31 14 10.198039 31 13 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9.48683298 154 152 12.0415946 154 155 1.41421356 154 156 6.32455532 154 157 9.48683298 154 158 12.0415946 154 159 13.1529464 154 160 13.1529464 155 119 12.2065556 155 120 12.7279221 155 121 11.3137085 155 122 11.6619038 155 123 4.47213595 155 124 12 155 125 10 155 128 9.89949494 155 127 11.6619038 155 126 12.083046 155 154 1.41421356 155 151 9.21954446 155 150 10.0498756 155 149 12 155 148 12 155 147 13.1529464 155 164 13.892444 155 153 10.7703296 155 152 13.453624 155 156 5.09901951 155 157 8.94427191 155 158 11.1803399 155 159 12.3693169 155 160 12.0415946 156 118 11.1803399 156 119 7.81024968 156 120 8.94427191 156 121 7.61577311 156 122 9.05538514 156 128 13.4164079 156 126 14.1421356 156 125 10.2956301 156 124 12.083046 156 155 5.09901951 156 154 6.32455532 156 123 4.24264069 156 157 5.83095189 156 158 6.70820393 156 159 8.06225775 156 160 7 156 161 14.1421356 157 118 10.6301458 157 119 11.1803399 157 120 13.0384048 157 121 12 157 122 14.1421356 157 156 5.83095189 157 155 8.94427191 157 154 9.48683298 157 123 10 157 153 13.4164079 157 158 3.60555128 157 159 4.12310563 157 160 6.40312424 157 161 11.4017543 158 118 7.07106781 158 122 13 158 121 10.4403065 158 120 11.1803399 158 119 9.05538514 158 157 3.60555128 158 156 6.70820393 158 155 11.1803399 158 154 12.0415946 158 123 10.8166538 158 159 1.41421356 158 160 3.16227766 158 161 8.06225775 159 118 7.21110255 159 121 11.7046999 159 120 12.3693169 159 119 10.198039 159 158 1.41421356 159 157 4.12310563 159 156 8.06225775 159 155 12.3693169 159 154 13.1529464 159 123 12.2065556 159 160 4 159 161 7.28010989 160 118 4.47213595 160 122 10.8166538 160 121 8.06225775 160 120 8.54400375 160 119 6.32455532 160 159 4 160 158 3.16227766 160 157 6.40312424 160 156 7 160 155 12.0415946 160 154 13.1529464 160 123 10.4403065 160 161 7.28010989 161 120 14.1421356 161 119 12.0415946 161 118 5 161 160 7.28010989 161 159 7.28010989 161 158 8.06225775 161 157 11.4017543 161 156 14.1421356 162 164 6.40312424 162 163 2.82842712 162 153 6.32455532 162 152 9.21954446 163 153 4 163 154 13.3416641 163 162 2.82842712 163 151 13 163 164 3.60555128 163 152 6.40312424 164 153 3.60555128 164 154 12.5299641 164 155 13.892444 164 162 6.40312424 164 163 3.60555128 164 149 13 164 148 13 164 151 10.198039 164 150 11.4017543 164 152 2.82842712 164 165 13.4164079 165 152 12.8062485 165 164 13.4164079 165 168 2 165 167 2.23606798 165 166 2.23606798 165 144 9.8488578 165 143 12.5299641 166 165 2.23606798 166 144 10.198039 166 143 11.4017543 166 142 13.453624 166 167 1.41421356 166 168 1 167 165 2.23606798 167 168 2.23606798 167 166 1.41421356 167 144 11.4017543 167 143 12.8062485 168 165 2 168 167 2.23606798 168 166 1 168 144 9.21954446 168 143 10.8166538 168 142 12.8062485 libpysal-4.9.2/libpysal/examples/juvenile/juvenile.shp000066400000000000000000000113041452177046000231650ustar00rootroot00000000000000' bè@@€W@ÀW@ €W@@W@ T@ÀW@ ÀS@€V@ €S@W@ S@W@ €P@@W@ P@€V@ ;@€Q@ M@V@ €L@W@ €J@W@ I@€V@ €H@€V@ @@€V@ ?@ÀU@ 6@ÀU@ 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(n=32, k=13) * mexico.gal: spatial weights in GAL format. * mexicojoin.shp: Polygon shapefile. (n=32) Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) "Interregional inequality dynamics in Mexico." Spatial Economic Analysis, 5: 277-298. libpysal-4.9.2/libpysal/examples/mexico/mexico.csv000066400000000000000000000067341452177046000223070ustar00rootroot00000000000000State,pcgdp1940,pcgdp1950,pcgdp1960,pcgdp1970,pcgdp1980,pcgdp1990,pcgdp2000,hanson03,hanson98,esquivel99,inegi,inegi2 Aguascalientes,10384.000,6234.000,8714.000,16078.000,21022.000,20787.000,27782.000,2.000,2.000,3.000,4.000,4.000 Baja California,22361.000,20977.000,17865.000,25321.000,29283.000,26839.000,29855.000,1.000,1.000,5.000,1.000,1.000 Baja California Sur,9573.000,16013.000,16707.000,24384.000,29038.000,25842.000,26103.000,2.000,2.000,6.000,1.000,1.000 Campeche,3758.000,4929.000,5925.000,10274.000,12166.000,51123.000,36163.000,6.000,5.000,4.000,5.000,5.000 Chiapas,2934.000,4138.000,5280.000,7015.000,16200.000,8637.000,8684.000,5.000,5.000,7.000,5.000,5.000 Chihuahua,8578.000,13997.000,16265.000,19178.000,23399.000,25332.000,30735.000,1.000,1.000,5.000,1.000,2.000 Coahuila,8537.000,9673.000,12318.000,20562.000,25688.000,26084.000,28460.000,1.000,1.000,5.000,2.000,2.000 Colima,6909.000,6049.000,6036.000,12551.000,17427.000,18313.000,21358.000,3.000,3.000,6.000,4.000,4.000 Distrito Federal,17816.000,17119.000,23174.000,32386.000,42028.000,43810.000,54349.000,4.000,4.000,1.000,3.000,3.000 Durango,12132.000,8859.000,9323.000,12700.000,16726.000,17353.000,17379.000,2.000,2.000,3.000,1.000,2.000 Guanajuato,4359.000,5686.000,8209.000,11635.000,13864.000,13607.000,15585.000,3.000,3.000,3.000,4.000,4.000 Guerrero,2181.000,3629.000,4991.000,6497.000,8727.000,9084.000,11820.000,5.000,5.000,7.000,5.000,5.000 Hidalgo,4414.000,5194.000,6399.000,7767.000,12391.000,13091.000,12348.000,3.000,3.000,2.000,3.000,3.000 Jalisco,5309.000,8232.000,9953.000,16288.000,20659.000,20133.000,21610.000,3.000,3.000,6.000,4.000,4.000 Mexico,3408.000,4972.000,9053.000,17164.000,20165.000,18547.000,16322.000,4.000,4.000,1.000,3.000,3.000 Michoacan,3327.000,5272.000,5244.000,8109.000,11206.000,10980.000,11838.000,3.000,3.000,7.000,4.000,4.000 Morelos,6936.000,8962.000,10499.000,13892.000,16513.000,17701.000,18170.000,3.000,3.000,2.000,3.000,3.000 Nayarit,4836.000,7515.000,7621.000,10880.000,13354.000,12757.000,11478.000,2.000,2.000,6.000,4.000,4.000 Nuevo Leon,9073.000,11490.000,20117.000,28206.000,34856.000,34726.000,38672.000,1.000,1.000,5.000,2.000,2.000 Oaxaca,1892.000,4538.000,4140.000,5230.000,7730.000,8465.000,9010.000,5.000,5.000,7.000,5.000,5.000 Puebla,3569.000,6415.000,6542.000,9775.000,13374.000,11895.000,15685.000,3.000,3.000,2.000,3.000,5.000 Quertaro,11016.000,5560.000,7110.000,14073.000,20088.000,22441.000,26149.000,3.000,3.000,3.000,3.000,4.000 Quintana Roo,21965.000,28747.000,9677.000,17046.000,26695.000,25049.000,33442.000,6.000,5.000,4.000,5.000,5.000 San Luis Potosi,4372.000,7533.000,6440.000,9721.000,12691.000,15436.000,15866.000,2.000,2.000,3.000,4.000,4.000 Sinaloa,4840.000,6663.000,9613.000,14477.000,15312.000,15823.000,15242.000,2.000,2.000,6.000,1.000,1.000 Sonora,6399.000,10345.000,12134.000,22662.000,23181.000,24784.000,24068.000,1.000,1.000,5.000,1.000,1.000 Tabasco,2459.000,3857.000,6494.000,9367.000,42361.000,16055.000,13360.000,6.000,5.000,4.000,5.000,5.000 Tamaulipas,7508.000,8536.000,8383.000,17128.000,21937.000,19983.000,23546.000,1.000,1.000,5.000,2.000,2.000 Tlaxcala,3605.000,4178.000,4357.000,6245.000,9882.000,10339.000,11701.000,3.000,3.000,2.000,3.000,3.000 Veracruz,5203.000,10143.000,11404.000,12240.000,14252.000,13796.000,12191.000,3.000,3.000,4.000,5.000,5.000 Yucatan,7990.000,8428.000,10067.000,11665.000,15239.000,13979.000,17509.000,6.000,5.000,4.000,5.000,5.000 Zacatecas,3734.000,6435.000,5821.000,7426.000,8876.000,11656.000,11130.000,2.000,2.000,3.000,4.000,4.000 libpysal-4.9.2/libpysal/examples/mexico/mexico.gal000066400000000000000000000016331452177046000222500ustar00rootroot0000000000000032 0 2 31 13 1 2 2 25 2 1 1 3 3 30 22 26 4 3 19 26 29 5 4 6 9 24 25 6 5 18 23 31 9 5 7 2 13 15 8 2 16 14 9 6 5 6 31 13 17 24 11 5 15 14 16 20 19 10 5 23 21 31 15 13 12 6 21 23 29 20 28 14 13 8 17 31 0 23 10 15 7 9 14 8 21 12 28 20 16 11 15 8 15 6 7 13 10 21 14 11 16 4 14 8 20 11 17 4 24 9 31 13 18 4 6 27 23 31 19 4 11 20 29 4 20 7 29 19 11 16 14 28 12 21 5 23 12 14 15 10 22 2 30 3 24 4 25 5 9 17 23 9 18 27 29 12 21 10 31 6 13 25 3 1 5 24 26 3 3 4 29 27 3 18 29 23 28 3 12 20 14 29 7 26 4 19 20 12 23 27 30 2 3 22 31 8 18 23 10 0 13 17 9 6 libpysal-4.9.2/libpysal/examples/mexico/mexicojoin.dbf000066400000000000000000000201021452177046000231100ustar00rootroot00000000000000 aßPOLY_IDNAREANCODECNAMECPERIMETERN ACRESN HECTARESN PCGDP1940NPCGDP1950NPCGDP1960NPCGDP1970NPCGDP1980NPCGDP1990NPCGDP2000NHANSON03NHANSON98NESQUIVEL99NINEGININEGI2NMAXPNGR4000NGR5000NGR6000NGR7000NGR8000NGR9000NLPCGDP40NLPCGDP50NLPCGDP60NLPCGDP70NLPCGDP80NLPCGDP90NLPCGDP00NTESTN 172527513755.000MX02Baja California Norte 2040312.38517921867.2627252751.37622361.0020977.0017865.0025321.0029283.0026839.0029855.001.001.005.001.001.002.000.130.150.220.070.010.054.354.324.254.404.474.434.481.00 272259877689.000MX03Baja California Sur 2912880.77217855733.2147225987.7699573.0016013.0016707.0024384.0029038.0025842.0026103.002.002.006.001.001.002.000.440.210.190.03-0.050.003.984.204.224.394.464.414.422.00 327319568586.000MX18Nayarit 1034770.3416750785.4122731956.8594836.007515.007621.0010880.0013354.0012757.0011478.002.002.006.004.004.001.000.380.180.180.02-0.07-0.053.683.883.884.044.134.114.063.00 479610082850.000MX14Jalisco 2324727.43619672001.1997961008.2855309.008232.009953.0016288.0020659.0020133.0021610.003.003.006.004.004.004.000.610.420.340.120.020.033.733.924.004.214.324.304.334.00 55467029851.100MX01Aguascalientes 313895.5301350927.093546702.98510384.006234.008714.0016078.0021022.0020787.0027782.002.002.003.004.004.004.000.430.650.500.240.120.134.023.793.944.214.324.324.445.00 630344905851.000MX11Guanajuato 918758.2417498359.5413034490.5854359.005686.008209.0011635.0013864.0013607.0015585.003.003.003.004.004.005.000.550.440.280.130.050.063.643.753.914.074.144.134.196.00 712032399965.000MX22Queretaro de Arteaga 619581.7092973258.8901203239.99711016.005560.007110.0014073.0020088.0022441.0026149.003.003.003.003.004.005.000.380.670.570.270.110.074.043.753.854.154.304.354.427.00 821235327145.000MX13Hidalgo 953861.2445247342.6242123532.7154414.005194.006399.007767.0012391.0013091.0012348.003.003.002.003.003.003.000.450.380.290.20-0.00-0.033.643.723.813.894.094.124.098.00 959473519192.000MX16Michoacan de Ocampo 1431015.87714696167.8595947351.9193327.005272.005244.008109.0011206.0010980.0011838.003.003.007.004.004.003.000.550.350.350.160.020.033.523.723.723.914.054.044.079.00 1021476282978.000MX15Mexico 888381.8075306883.8692147628.2983408.004972.009053.0017164.0020165.0018547.0016322.004.004.001.003.003.005.000.680.520.26-0.02-0.09-0.063.533.703.964.234.304.274.2110.00 111326256296.900MX09Distrito Federal 149985.707327723.757132625.63017816.0017119.0023174.0032386.0042028.0043810.0054349.004.004.001.003.003.005.000.480.500.370.220.110.094.254.234.374.514.624.644.7411.00 125726784868.300MX08Colima 354755.5351415113.699572678.4876909.006049.006036.0012551.0017427.0018313.0021358.003.003.006.004.004.004.000.490.550.550.230.090.073.843.783.784.104.244.264.3312.00 135055028040.200MX17Morelos 335390.3251249119.635505502.8046936.008962.0010499.0013892.0016513.0017701.0018170.003.003.002.003.003.005.000.420.310.240.120.040.013.843.954.024.144.224.254.2613.00 1437890016803.000MX31Yucatan 955594.9759362789.6033789001.6807990.008428.0010067.0011665.0015239.0013979.0017509.006.005.004.005.005.001.000.340.320.240.180.060.103.903.934.004.074.184.154.2414.00 1550165837229.000MX04Campeche 1575361.14612396198.7585016583.7233758.004929.005925.0010274.0012166.0051123.0036163.006.005.004.005.005.001.000.980.870.790.550.47-0.153.573.693.774.014.094.714.5615.00 1634260003971.000MX21Puebla 1472803.2848465797.4853426000.3973569.006415.006542.009775.0013374.0011895.0015685.003.003.002.003.005.003.000.640.390.380.210.070.123.553.813.823.994.134.084.2016.00 1751238434535.000MX23Quintana Roo 1756848.57812661242.2645123843.45421965.0028747.009677.0017046.0026695.0025049.0033442.006.005.004.005.005.001.000.180.070.540.290.100.134.344.463.994.234.434.404.5217.00 183973410003.600MX29Tlaxcala 319017.395981847.067397341.0003605.004178.004357.006245.009882.0010339.0011701.003.003.002.003.003.003.000.510.450.430.270.070.053.563.623.643.803.994.014.0718.00 1964755232407.000MX12Guerrero 1387049.88816001302.3976475523.2412181.003629.004991.006497.008727.009084.0011820.005.005.007.005.005.003.000.730.510.370.260.130.113.343.563.703.813.943.964.0719.00 2092691433795.000MX20Oaxaca 1995816.28422904460.4849269143.3801892.004538.004140.005230.007730.008465.009010.005.005.007.005.005.003.000.680.300.340.240.070.033.283.663.623.723.893.933.9520.00 2124255651555.000MX27Tabasco 1244472.6005993678.0542425565.1562459.003857.006494.009367.0042361.0016055.0013360.006.005.004.005.005.001.000.740.540.310.15-0.50-0.083.393.593.813.974.634.214.1321.00 2273391573763.000MX05Chiapas 1477195.19918135380.2867339157.3762934.004138.005280.007015.0016200.008637.008684.005.005.007.005.005.003.000.470.320.220.09-0.270.003.473.623.723.854.213.943.9422.00 23180066243824.000MX26Sonora 2735537.38644495159.87918006624.3826399.0010345.0012134.0022662.0023181.0024784.0024068.001.001.005.001.001.002.000.580.370.300.030.02-0.013.814.014.084.364.374.394.3823.00 24248054569980.000MX06Chihuahua 2393736.22861295373.94524805456.9988578.0013997.0016265.0019178.0023399.0025332.0030735.001.001.005.001.002.002.000.550.340.280.200.120.083.934.154.214.284.374.404.4924.00 25150192356644.000MX07Coahuila De Zaragoza 2107437.83537113191.12115019235.6648537.009673.0012318.0020562.0025688.0026084.0028460.001.001.005.002.002.004.000.520.470.360.140.040.043.933.994.094.314.414.424.4525.00 2657798879721.000MX25Sinaloa 2090624.51214282357.0895779887.9724840.006663.009613.0014477.0015312.0015823.0015242.002.002.006.001.001.002.000.500.360.200.02-0.00-0.023.683.823.984.164.194.204.1826.00 27120343805563.000MX10Durango 1866079.59529737483.02512034380.55612132.008859.009323.0012700.0016726.0017353.0017379.002.002.003.001.002.004.000.160.290.270.140.020.004.083.953.974.104.224.244.2427.00 2874805700364.000MX32Zacatecas 2165307.92118484817.1817480570.0363734.006435.005821.007426.008876.0011656.0011130.002.002.003.004.004.001.000.470.240.280.180.10-0.023.573.813.763.873.954.074.0528.00 2964148547178.000MX24San Luis Potosi 1529201.48715851387.8126414854.7184372.007533.006440.009721.0012691.0015436.0015866.002.002.003.004.004.001.000.560.320.390.210.100.013.643.883.813.994.104.194.2029.00 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¶2@JÃWÀñœ2@€5ÁWÀ€ÜŽ2@ÀW³WÀ€Â…2@@¥WÀ€¹02@@¦žWÀÀ‹%2@€ÁŠWÀØ22@ ÒˆWÀ`Š52@€ †WÀ€ä'2@@ð…WÀ`2@JƒWÀ`Ýý1@€Ñ„WÀÀká1@Àô}WÀÖÔ1@!{WÀÀî¾1@ÀWwWÀ í¿1@ævWÀ`ã¸1@@|oWÀ ¯1@€lWÀ¶1@€ÙiWÀ€Â…1@€¸jWÀÀès1@À¸eWÀ `1@libpysal-4.9.2/libpysal/examples/mexico/mexicojoin.shx000066400000000000000000000005441452177046000231670ustar00rootroot00000000000000' ²èÕG]À@á-@@ ¯UÀ€C\@@2TŠ" °\ø Ðàüˆˆ˜$`ˆ”€ Ø ôØ!Ð0#š%¢ )Fr,¼ð-°Ø0Œ4 74@:xê?f˜CpGvèLb O†(S²(VÞ¸Zš°_Nlibpysal-4.9.2/libpysal/examples/networks/000077500000000000000000000000001452177046000206645ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/networks/README.md000066400000000000000000000014341452177046000221450ustar00rootroot00000000000000networks ======== Datasets used for network testing --------------------------------- * eberly_net.dbf: attribute data. (k=3) * eberly_net.shp: Line shapefile. (n=29) * eberly_net.shx: spatial index. * eberly_net_pts_offnetwork.dbf: attribute data for points off network. (k=2) * eberly_net_pts_offnetwork.shp: Point shapefile. (n=100) * eberly_net_pts_offnetwork.shx: spatial index。 * eberly_net_pts_onnetwork.dbf: attribute data for points on network. (k=1) * eberly_net_pts_onnetwork.shp: Point shapefile. (n=110) * eberly_net_pts_onnetwork.shx: spatial index. * nonplanarsegments.dbf: attribute data. (k=1) * nonplanarsegments.prj: ESRI projection file. * nonplanarsegments.qpj: QGIS projection file. * nonplanarsegments.shp: Line shapefile. (n=2) * nonplanarsegments.shx: spatial index. libpysal-4.9.2/libpysal/examples/networks/eberly_net.dbf000066400000000000000000000025041452177046000234720ustar00rootroot00000000000000q *FNODENTNODENONEWAYL 1 4F 1 0F 4 5F 4 8F 0 5F 5 3F 3 2F 8 10F 7 6F 7 9F 6 9F 10 11F 10 14F 11 14F 11 19F 14 16F 12 13F 13 15F 16 17F 17 19F 17 22F 19 22F 19 18F 19 25F 18 24F 24 25F 21 20F 21 23F 20 23Flibpysal-4.9.2/libpysal/examples/networks/eberly_net.shp000066400000000000000000000051341452177046000235330ustar00rootroot00000000000000' .èð?5@"@(ð?@@@ð?@@@(@ð?@ð?@@(@@@@@@@@(@@@"@@@@"@(@@@@@@(@@@@@@@@(@@@@@@@@(@@@"@@"@@@ (@@ÍÌÌÌÌÌ@@ÍÌÌÌÌÌ@@@@ (ÍÌÌÌÌÌ@@@@ÍÌÌÌÌÌ@@@@ (@@@@@@@@ (@@ @@@@ @@ (@@'@@@@'@@( @@'@@ @@'@@( @@0@@ @@0@@('@@+@@'@@+@@("@ð?&@@"@ð?&@@(&@@(@@&@@(@@(+@@,@@+@@,@@(,@@0@@,@@0@@(,@@2@!@,@@2@!@(0@@2@!@0@@2@!@(0@ð?0@@0@@0@ð?(0@@5@@0@@5@@(0@ð?5@ð?0@ð?5@ð?(5@ð?5@@5@ð?5@@(1@@2@ @2@ @1@@(2@@3@ @2@ @3@@(1@@3@@1@@3@@libpysal-4.9.2/libpysal/examples/networks/eberly_net.shx000066400000000000000000000005141452177046000235400ustar00rootroot00000000000000' ¦èð?5@"@2(^(Š(¶(â((:(f(’(¾(ê((B(n(š(Æ(ò((J(v(¢(Î(ú(&(R(~(ª(Ö((libpysal-4.9.2/libpysal/examples/networks/eberly_net_pts_offnetwork.dbf000066400000000000000000000042251452177046000266260ustar00rootroot00000000000000_daWIDN variableN 0 0 1 1 2 0 3 1 4 0 5 1 6 0 7 1 8 0 9 1 10 0 11 1 12 0 13 1 14 0 15 1 16 0 17 1 18 0 19 1 20 0 21 1 22 0 23 1 24 0 25 1 26 0 27 1 28 0 29 1 30 0 31 1 32 0 33 1 34 0 35 1 36 0 37 1 38 0 39 1 40 0 41 1 42 0 43 1 44 0 45 1 46 0 47 1 48 0 49 1 50 0 51 1 52 0 53 1 54 0 55 1 56 0 57 1 58 0 59 1 60 0 61 1 62 0 63 1 64 0 65 1 66 0 67 1 68 0 69 1 70 0 71 1 72 0 73 1 74 0 75 1 76 0 77 1 78 0 79 1 80 0 81 1 82 0 83 1 84 0 85 1 86 0 87 1 88 0 89 1 90 0 91 1 92 0 93 1 94 0 95 1 96 0 97 1 98 0 99 1libpysal-4.9.2/libpysal/examples/networks/eberly_net_pts_offnetwork.shp000066400000000000000000000055241452177046000266700ustar00rootroot00000000000000' ªèH5ï'æÄµ?ÚeäÉsð? Ù0S5@¶d°éݰ @ C·‡‡H9@½Å“²8`@ % |1@0ëßäA5ö? ¸¨7þ @aúÏ­[@ s¦c.ž1@É® Îa@ ÈÛ¼Œ*@™ð\ó? DÚIúl@NúR]  ñ? ßκÔ&@ú8ÑŒ‰¹ô? mKŸ—&@D¡TÔéè@ ð=›P-@I<å< @ Ç=Lü'%@ÉSL>9@ ):¢ýÚ,@ ýóTË@ â ?ö]@Úì'S@ ëcW¶¢Ü)@Z4C@œc @ ºÂsë@ù@æò4ý? €Ó¹Wt!@`’#Ì@ ™rQ,LÚ@º„”/oñ? –9Û®D&@@„HÖäú@ !ÊûxY‰0@Sk¦ºR( @ ¹ë£n+@â„ÇÑ"ž@ º‡³°/%@ᘿ×e@ âÙWõÒâ@Öšù˜9@ °~ÈÍŒT@AKÛ %@ n½Z›]&@|3c¦B@  Ù0S5@p¾âNÙFö? à sV@"4N•qø@ ˆ-ð@pÎÃN4@ »-3!Ï!$@qÔ-cT@ ãß‹Ž@Î ³K n@ v/ã\ÅF2@ôÞRX@ Pn¯;Ç!@ž{“E‹ò@ '’ ×õD@Ü*‰í`@ Ãe€ëF %@êr©Vœ@! c©~D40@™©aæÈM@" €´)“G60@ô©10L@# oÚg®þý?ˆíÍ1%5@$ *M,tØÂ2@ù>Ú2Ã<ó?% q1 6ØÖ@Xâƒð¢@&  C!0±e1@ðC…ø­Å@' ••ËJ|20@Ÿÿz#Ö@( I@eô{L%@hõwÆ@) CD•¿…¡+@5Hg+·@@* ¶³ö~ô=@=œ²%†@+ IU¶`„/@ýÌrõ@, <Ñâ+@¶d°éݰ @- í¥±#!@ÙiUóN@. 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Ù0S5@¶d°éݰ @2 @ N \ j x † ” ¢ ° ¾ Ì Ú è ö    . < J X f t ‚  ž ¬ º È Ö ä ò    * 8 F T b p ~ Œ š ¨ ¶ Ä Ò à î ü   & 4 B P ^ l z ˆ – ¤ ² À Î Ü ê ø   " 0 > L Z h v „ ’   ® ¼ Ê Ø æ ô    , : H V d r € Ž œ libpysal-4.9.2/libpysal/examples/networks/eberly_net_pts_onnetwork.dbf000066400000000000000000000023731452177046000264720ustar00rootroot00000000000000_nA WvariableN 3 3 2 3 3 3 2 4 3 2 3 2 2 2 3 2 4 4 2 4 2 2 2 4 4 2 2 2 2 2 2 4 4 4 4 4 4 3 3 3 2 4 4 4 2 3 4 2 2 2 2 2 3 4 2 3 2 3 4 2 4 2 3 3 3 4 2 3 3 3 2 4 3 3 2 2 2 3 2 2 3 2 2 3 3 4 4 4 4 4 4 3 2 3 2 3 4 3 2 3 4 3 2 3 4 3 2 2 2 2libpysal-4.9.2/libpysal/examples/networks/eberly_net_pts_onnetwork.shp000066400000000000000000000061541452177046000265320ustar00rootroot00000000000000' 6蘻q ¼?ð?5@"@ ð?@ @@ à?@ Vˆ}Øùß@bvþ·@ @€˜éj@@ ’1³)8@ô6g&ëã@ s<ç:=Ä@¦JJOH³@ KC°ÌÆ!@ó[8¶Ýw@ ÅS&¼ßK@@ ûlkš@|¯ßæû @ œŸÏªŒ?@ûðøT á@ ­KÇ™X@@ D”ç® °@4CIóßï@  ¹:@{£bð~~@  ÇW¼V @8êÿPª@ Ð=ôÕt>Þ?@ ˜»q ¼?Ë| M¡P@ åFä-t@êkƒÝð\@ žêü:§@@ =$öS8~@dƒ^v“þ@ @"@ w}óAK@ –˜”MQ!@ ~ƒg#¥@òl$ @ 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æ ô    , : H V d r € Ž œ ª ¸ Æ Ô â ð þ   ( libpysal-4.9.2/libpysal/examples/networks/nonplanarsegments.dbf000066400000000000000000000001271452177046000250770ustar00rootroot00000000000000_A idN 2 1libpysal-4.9.2/libpysal/examples/networks/nonplanarsegments.prj000066400000000000000000000002171452177046000251370ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]libpysal-4.9.2/libpysal/examples/networks/nonplanarsegments.qpj000066400000000000000000000004011452177046000251310ustar00rootroot00000000000000GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]] libpysal-4.9.2/libpysal/examples/networks/nonplanarsegments.shp000066400000000000000000000004641452177046000251420ustar00rootroot00000000000000' šèŸ.ËÉ-ó¿CÆ·÷?>Ÿx¢ˆŽï¿}“Ñ7ø?0¼dL|-ó¿CÆ·÷?>Ÿx¢ˆŽï¿4†q/âø?¼dL|-ó¿Æ-û༣÷?–‰ ø°ï¿4†q/âø?>Ÿx¢ˆŽï¿CÆ·÷?0Ÿ.ËÉ-ó¿ ?·œCk÷?ðC”î?ð¿}“Ñ7ø?Ÿ.ËÉ-ó¿C[[2Ê,ø??ò67o’ð¿}“Ñ7ø?ðC”î?ð¿ ?·œCk÷?libpysal-4.9.2/libpysal/examples/networks/nonplanarsegments.shx000066400000000000000000000001641452177046000251470ustar00rootroot00000000000000' :èŸ.ËÉ-ó¿CÆ·÷?>Ÿx¢ˆŽï¿}“Ñ7ø?20f0libpysal-4.9.2/libpysal/examples/remotes.py000066400000000000000000000652711452177046000210530ustar00rootroot00000000000000"""Handle remote datasets.""" import warnings import requests from bs4 import BeautifulSoup from .base import Example # remote_dict holds the metadata for remote datasets from the geoda center # to update prior to release run _remote_data() _remote_dict = { "AirBnB": { "download_url": "https://geodacenter.github.io/data-and-lab//data/airbnb.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//airbnb/", "n": "77", "k": "20", "description": "Airbnb rentals, socioeconomics, and crime in Chicago", }, "Atlanta": { "download_url": ( "https://geodacenter.github.io/data-and-lab/" "/data/atlanta_hom.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//atlanta_old/", "n": "90", "k": "23", "description": "Atlanta, GA region homicide counts and rates", }, "Baltimore": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/baltimore.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//baltim/", "n": "211", "k": "17", "description": "Baltimore house sales prices and hedonics", }, "Bostonhsg": { "download_url": "https://geodacenter.github.io/data-and-lab//data/boston.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//boston-housing/", "n": "506", "k": "23", "description": "Boston housing and neighborhood data", }, "Buenosaires": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/buenosaires.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//buenos-aires_old/", "n": " 209", "k": " 21", "description": " Electoral Data for 1999 Argentinean Elections", }, "Cars": { "download_url": ( "https://geodacenter.github.io/data-and-lab/" "/data/Abandoned_Vehicles_Map.csv" ), "explain_url": ( "https://geodacenter.github.io/data-and-lab//1-source-and-description/" ), "n": "137,867", "k": "21", "description": "2011 abandoned vehicles in Chicago (311 complaints).", }, "Charleston1": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/CharlestonMSA.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//charleston-1_old/", "n": " 117", "k": " 30", "description": " 2000 Census Tract Data for Charleston, SC MSA and counties", }, "Charleston2": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/CharlestonMSA2.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//charleston2/", "n": " 44", "k": " 97", "description": ( " 1998 and 2001 Zip Code Business Patterns (Census Bureau)" " for Charleston, SC MSA" ), }, "Chicago Health": { "download_url": "https://geodacenter.github.io/data-and-lab//data/comarea.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//comarea_vars/", "n": " 77", "k": " 86", "description": " Chicago Health + Socio-Economics", }, "Chicago commpop": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/chicago_commpop.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//commpop/", "n": " 77", "k": " 8", "description": ( " Chicago Community Area Population Percent Change for 2000 and 2010" ), }, "Chicago parcels": { "download_url": ( "https://geodacenter.github.io/data-and-lab/" "/https://uchicago.box.com/s/j2d2ch5uvckse24y8l7vh9198wnq216i" ), "explain_url": "https://geodacenter.github.io/data-and-lab//parcels/", "n": " 592,521", "k": " 5", "description": " Tax parcel polygons of Cook county", }, "Chile Labor": { "download_url": "https://geodacenter.github.io/data-and-lab//data/flma.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//FLMA/", "n": "141", "k": "62", "description": "Labor Markets in Chile (1982-2002)", }, "Chile Migration": { "download_url": ( "https://geodacenter.github.io/data-and-lab/" "/https://uchicago.box.com/s/yqc97nq23hoeeqo5lkc2grlg98skokgk" ), "explain_url": "https://geodacenter.github.io/data-and-lab//CHIM/", "n": " 304", "k": " 10", "description": " Internal Migration in Chile (1977-2002)", }, "Cincinnati": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/walnuthills_updated.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//walnut_hills/", "n": " 457", "k": " 89", "description": " 2008 Cincinnati Crime + Socio-Demographics", }, "Cleveland": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/cleveland.zip" ), "explain_url": ( "https://geodacenter.github.io/data-and-lab//clev_sls_154_core/" ), "n": " 205", "k": " 9", "description": " 2015 sales prices of homes in Cleveland, OH.", }, "Columbus": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/columbus.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//columbus/", "n": " 49", "k": " 20", "description": " Columbus neighborhood crime", }, "Elections": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/election.zip" ), "explain_url": ( "https://geodacenter.github.io/data-and-lab/" "/county_election_2012_2016-variables/" ), "n": " 3,108", "k": " 74", "description": " 2012 and 2016 Presidential Elections", }, "Grid100": { "download_url": "https://geodacenter.github.io/data-and-lab//data/grid100.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//grid100/", "n": " 100", "k": " 34", "description": " Grid with simulated variables", }, "Groceries": { "download_url": "https://geodacenter.github.io/data-and-lab//data/grocery.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//chicago_sup_vars/", "n": " 148", "k": " 7", "description": " 2015 Chicago supermarkets", }, "Guerry": { "download_url": "https://geodacenter.github.io/data-and-lab//data/guerry.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//Guerry/", "n": " 85", "k": " 23", "description": " Moral statistics of France (Guerry, 1833)", }, "Health+": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/income_diversity.zip" ), "explain_url": ( "https://geodacenter.github.io/data-and-lab/" "/co_income_diversity_variables/" ), "n": " 3,984", "k": " 64", "description": " 2000 Health, Income + Diversity", }, "Health Indicators": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/healthIndicators.zip" ), "explain_url": ( "https://geodacenter.github.io/data-and-lab//healthindicators-variables/" ), "n": " 77", "k": " 31", "description": " Chicago Health Indicators (2005-11)", }, "Hickory1": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/HickoryMSA.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//hickory1/", "n": " 68", "k": " 30", "description": " 2000 Census Tract Data for Hickory, NC MSA and counties", }, "Hickory2": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/HickoryMSA2.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//hickory2/", "n": " 29", "k": " 55", "description": ( " 1998 and 2001 Zip Code Business Patterns " "(Census Bureau) for Hickory, NC MSA" ), }, "Home Sales": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/kingcounty.zip" ), "explain_url": ( "https://geodacenter.github.io/data-and-lab//KingCounty-HouseSales2015/" ), "n": " 21,613", "k": " 21", "description": " 2014-15 Home Sales in King County, WA", }, "Houston": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/houston_hom.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//houston/", "n": " 52", "k": " 23", "description": " Houston, TX region homicide counts and rates", }, "Juvenile": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/juvenile.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//juvenile/", "n": " 168", "k": " 3", "description": " Cardiff juvenile delinquent residences", }, "Lansing1": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/LansingMSA.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//lansing1/", "n": " 117", "k": " 30", "description": " 2000 Census Tract Data for Lansing, MI MSA and counties", }, "Lansing2": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/LansingMSA2.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//lansing2/", "n": " 46", "k": " 55", "description": ( " 1998 and 2001 Zip Code Business Patterns " "(Census Bureau) for Lansing, MI MSA" ), }, "Laozone": { "download_url": "https://geodacenter.github.io/data-and-lab//data/laozone.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//ozone/", "n": " 32", "k": " 8", "description": " Ozone measures at monitoring stations in Los Angeles basin", }, "LasRosas": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/lasrosas.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//lasrosas/", "n": " 1,738", "k": " 34", "description": ( " Corn yield, fertilizer and field data for precision " "agriculture, Argentina, 1999" ), }, "Liquor Stores": { "download_url": "https://geodacenter.github.io/data-and-lab//data/liquor.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//liq_chicago/", "n": " 571", "k": " 2", "description": " 2015 Chicago Liquor Stores", }, "Malaria": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/malariacolomb.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//colomb_malaria/", "n": " 1,068", "k": " 50", "description": ( " Malaria incidence and population (1973, 95, 93 " "censuses and projections until 2005) \xa0 \xa0 \xa0" ), }, "Milwaukee1": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/MilwaukeeMSA.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//milwaukee1/", "n": " 417", "k": " 31", "description": " 2000 Census Tract Data for Milwaukee, WI MSA", }, "Milwaukee2": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/MilwaukeeMSA2.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//milwaukee2/", "n": " 83", "k": " 55", "description": ( " 1998 and 2001 Zip Code Business Patterns " "(Census Bureau) for Milwaukee, WI MSA" ), }, "NCOVR": { "download_url": "https://geodacenter.github.io/data-and-lab//data/ncovr.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//ncovr/", "n": "3,085", "k": " 69", "description": " US county homicides 1960-1990", }, "Natregimes": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/natregimes.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//natregimes/", "n": " 3,085", "k": " 73", "description": " NCOVR with regimes (book/PySAL)", }, "NDVI": { "download_url": "https://geodacenter.github.io/data-and-lab//data/ndvi.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//ndvi/", "n": " 49", "k": " 5", "description": " Normalized Difference Vegetation Index grid", }, "Nepal": { "download_url": "https://geodacenter.github.io/data-and-lab//data/nepal.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//nepal/", "n": " 75", "k": " 61", "description": " Health, poverty and education indicators for Nepal districts", }, "NYC": { "download_url": "https://geodacenter.github.io/data-and-lab///data/nyc.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//nyc/", "n": " 55", "k": " 34", "description": ( " Demographic and housing data for New York City subboroughs, 2002-09" ), }, "NYC Earnings": { "download_url": "https://geodacenter.github.io/data-and-lab//data/lehd.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//LEHD_Data/", "n": " 108,487", "k": " 70", "description": " Block-level Earnings in NYC (2002-14)", }, "NYC Education": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/nyc_2000Census.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//NYC-Census-2000/", "n": " 2,216", "k": " 56", "description": " NYC Education (2000)", }, "NYC Neighborhoods": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/nycnhood_acs.zip" ), "explain_url": ( "https://geodacenter.github.io/data-and-lab//NYC-Nhood-ACS-2008-12/" ), "n": " 195", "k": " 98", "description": " Demographics for New York City neighborhoods", }, "NYC Socio-Demographics": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/nyctract_acs.zip" ), "explain_url": ( "https://geodacenter.github.io/data-and-lab//NYC_Tract_ACS2008_12/" ), "n": " 2,166", "k": " 113", "description": " NYC Education + Socio-Demographics", }, "Ohiolung": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/ohiolung.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//ohiolung/", "n": " 88", "k": " 42", "description": " Ohio lung cancer data, 1968, 1978, 1988", }, "Orlando1": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/OrlandoMSA.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//orlando1/", "n": " 328", "k": " 30", "description": " 2000 Census Tract Data for Orlando, FL MSA and counties", }, "Orlando2": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/OrlandoMSA2.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//orlando2/", "n": " 94", "k": " 59", "description": ( " 1998 and 2001 Zip Code Business Patterns " "(Census Bureau) for Orlando, FL MSA" ), }, "Oz9799": { "download_url": "https://geodacenter.github.io/data-and-lab//data/oz9799.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//oz96/", "n": " 30", "k": " 78", "description": " Monthly ozone data, 1997-99", }, "Phoenix ACS": { "download_url": "https://geodacenter.github.io/data-and-lab//data/phx2.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//phx/", "n": " 685", "k": " 17", "description": ( " Phoenix American Community Survey Data (2010, 5-year averages)" ), }, "Pittsburgh": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/pittsburgh.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//pitt93/", "n": " 143", "k": " 8", "description": " Pittsburgh homicide locations", }, "Police": { "download_url": "https://geodacenter.github.io/data-and-lab//data/police.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//police/", "n": " 82", "k": " 21", "description": " Police expenditures Mississippi counties", }, "Sacramento1": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/sacramento.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//sacramento1/", "n": " 403", "k": " 30", "description": " 2000 Census Tract Data for Sacramento MSA", }, "Sacramento2": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/SacramentoMSA2.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//sacramento2/", "n": " 125", "k": " 53", "description": ( " 1998 and 2001 Zip Code Business Patterns " "(Census Bureau) for Sacramento MSA" ), }, "SanFran Crime": { "download_url": ( "https://geodacenter.github.io/data-and-lab/" "/data/SFCrime_July_Dec2012.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//SFcrimes_vars/", "n": " 3,384", "k": " 13", "description": ( " July-Dec 2012 crime incidents in San Francisco " "(points + area) - for CAST" ), }, "Savannah1": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/SavannahMSA.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//savannah1/", "n": " 77", "k": " 30", "description": " 2000 Census Tract Data for Savannah, GA MSA and counties", }, "Savannah2": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/SavannahMSA2.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//savannah2/", "n": " 24", "k": " 55", "description": ( " 1998 and 2001 Zip Code Business Patterns " "(Census Bureau) for Savannah, GA MSA" ), }, "Scotlip": { "download_url": "https://geodacenter.github.io/data-and-lab//data/scotlip.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//scotlip/", "n": " 56", "k": " 11", "description": " Male lip cancer in Scotland, 1975-80", }, "Seattle1": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/SeattleMSA.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//seattle1/", "n": " 664", "k": " 30", "description": " 2000 Census Tract Data for Seattle, WA MSA and counties", }, "Seattle2": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/SeattleMSA2.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//seattle2/", "n": " 145", "k": " 59", "description": ( " 1998 and 2001 Zip Code Business Patterns " "(Census Bureau) for Seattle, WA MSA" ), }, "SIDS": { "download_url": "https://geodacenter.github.io/data-and-lab//data/sids.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//sids/", "n": " 100", "k": " 13", "description": " North Carolina county SIDS death counts", }, "SIDS2": { "download_url": "https://geodacenter.github.io/data-and-lab//data/sids2.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//sids2/", "n": " 100", "k": " 17", "description": " North Carolina county SIDS death counts and rates", }, "Snow": { "download_url": "https://geodacenter.github.io/data-and-lab//data/snow.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//snow/", "n": " NA", "k": " NA", "description": " John Snow & the 19th Century Cholera Epidemic", }, "South": { "download_url": "https://geodacenter.github.io/data-and-lab//data/south.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//south/", "n": " 1,412", "k": " 69", "description": " US Southern county homicides 1960-1990", }, "Spirals": { "download_url": "https://geodacenter.github.io/data-and-lab//data/spirals.csv", "explain_url": "https://geodacenter.github.io/data-and-lab//spirals/", "n": " 301", "k": " 2", "description": " Synthetic spiral points", }, "StLouis": { "download_url": "https://geodacenter.github.io/data-and-lab//data/stlouis.zip", "explain_url": "https://geodacenter.github.io/data-and-lab//stlouis/", "n": " 78", "k": " 23", "description": " St Louis region county homicide counts and rates", }, "Tampa1": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/TampaMSA.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//tampa1/", "n": " 547", "k": " 30", "description": " 2000 Census Tract Data for Tampa, FL MSA and counties", }, "US SDOH": { "download_url": ( "https://geodacenter.github.io/data-and-lab//data/us-sdoh-2014.zip" ), "explain_url": "https://geodacenter.github.io/data-and-lab//us-sdoh/", "n": " 71,901", "k": " 25", "description": " 2014 US Social Determinants of Health Data", }, } def _remote_data(): """Helper function to get remote metadata for each release. Returns ------- datasets : dict Remote data sets keyed by the dataset name. Values are dictionaries with the following keys 'download_url', 'explain_url', 'n', 'k', 'description'. """ url = "https://geodacenter.github.io/data-and-lab//" try: page = requests.get(url) except: # noqa E722 warnings.warn("Remote data sets not available. Check connection.") # noqa B028 return {} soup = BeautifulSoup(page.text, "html.parser") samples = soup.find(class_="samples") rows = samples.find_all("tr") datasets = {} for row in rows[1:]: data = row.find_all("td") name = data[0].text.strip() description = data[1].text n = data[2].text k = data[3].text targets = row.find_all("a") download_url = url + targets[1].attrs["href"] explain_url = url + targets[0].attrs["href"] datasets[name] = { "download_url": download_url, "explain_url": explain_url, "n": n, "k": k, "description": description, } return datasets def _build_remotes(): """Build remote meta data. Returns ------- datasets : dict Example datasets keyed by the dataset name. """ datasets = {} for name in _remote_dict: description = _remote_dict[name]["description"] n = _remote_dict[name]["n"] k = _remote_dict[name]["k"] download_url = _remote_dict[name]["download_url"] explain_url = _remote_dict[name]["explain_url"] datasets[name] = Example(name, description, n, k, download_url, explain_url) # Other Remotes # rio name = "Rio Grande do Sul" description = "Cities of the Brazilian State of Rio Grande do Sul" n = 497 k = 3 download_url = "https://github.com/sjsrey/rio_grande_do_sul/archive/master.zip" explain_url = ( "https://raw.githubusercontent.com/sjsrey/rio_grande_do_sul/master/README.md" ) datasets[name] = Example(name, description, n, k, download_url, explain_url) # nyc bikes name = "nyc_bikes" description = "New York City Bike Trips" n = 14042 k = 27 download_url = "https://github.com/sjsrey/nyc_bikes/archive/master.zip" explain_url = "https://raw.githubusercontent.com/sjsrey/nyc_bikes/master/README.md" datasets[name] = Example(name, description, n, k, download_url, explain_url) # taz name = "taz" description = "Traffic Analysis Zones in So. California" n = 4109 k = 14 download_url = "https://github.com/sjsrey/taz/archive/master.zip" explain_url = "https://raw.githubusercontent.com/sjsrey/taz/master/README.md" datasets[name] = Example(name, description, n, k, download_url, explain_url) # clearwater name = "clearwater" description = "mgwr testing dataset" n = 239 k = 14 download_url = "https://github.com/sjsrey/clearwater/archive/master.zip" explain_url = "https://raw.githubusercontent.com/sjsrey/clearwater/master/README.md" datasets[name] = Example(name, description, n, k, download_url, explain_url) # newHaven name = "newHaven" description = "Network testing dataset" n = 3293 k = 5 download_url = "https://github.com/sjsrey/newHaven/archive/master.zip" explain_url = "https://raw.githubusercontent.com/sjsrey/newHaven/master/README.md" datasets[name] = Example(name, description, n, k, download_url, explain_url) # remove Cars dataset as it is broken datasets.pop("Cars") return datasets class Remotes: """Remote datasets.""" def __init__(self): """Initialize Remotes.""" self._datasets = None @property def datasets(self): """Create dictionary of remotes.""" if self._datasets is None: self._datasets = _build_remotes() return self._datasets datasets = Remotes() libpysal-4.9.2/libpysal/examples/sids2/000077500000000000000000000000001452177046000200345ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/sids2/README.md000066400000000000000000000007071452177046000213170ustar00rootroot00000000000000sids2 ===== North Carolina county SIDS death counts and rates ------------------------------------------------- * sids2.dbf: attribute data. (k=18) * sids2.html: metadata. * sids2.shp: Polygon shapefile. (n=100) * sids2.shx: spatial index. * sids2.gal: spatial weights in GAL format. Source: Cressie, Noel (1993). Statistics for Spatial Data. New York, Wiley, pp. 386-389. Rates computed. Updated URL: https://geodacenter.github.io/data-and-lab/sids2/ libpysal-4.9.2/libpysal/examples/sids2/sids2.dbf000066400000000000000000000564021452177046000215440ustar00rootroot00000000000000gdaèWAREAN PERIMETERN CNTY_N CNTY_IDN NAMEC FIPSCFIPSNONCRESS_IDNBIR74N SID74N NWBIR74N BIR79N SID79N NWBIR79N SIDR74NSIDR79NNWR74NNWR79N 0.114 1.442 1825 1825Ashe 37009 37009 5 1091.000000 1.000000 10.000000 1364.000000 0.000000 19.000000 0.916590 0.000000 9.165903 13.929619 0.061 1.231 1827 1827Alleghany 37005 37005 3 487.000000 0.000000 10.000000 542.000000 3.000000 12.000000 0.000000 5.535055 20.533881 22.140221 0.143 1.630 1828 1828Surry 37171 37171 86 3188.000000 5.000000 208.000000 3616.000000 6.000000 260.000000 1.568381 1.659292 65.244668 71.902655 0.070 2.968 1831 1831Currituck 37053 37053 27 508.000000 1.000000 123.000000 830.000000 2.000000 145.000000 1.968504 2.409639 242.125984 174.698795 0.153 2.206 1832 1832Northampton 37131 37131 66 1421.000000 9.0000001066.000000 1606.000000 3.000000 1197.000000 6.333568 1.867995 750.175932 745.330012 0.097 1.670 1833 1833Hertford 37091 37091 46 1452.000000 7.000000 954.000000 1838.000000 5.000000 1237.000000 4.820937 2.720348 657.024793 673.014146 0.062 1.547 1834 1834Camden 37029 37029 15 286.000000 0.000000 115.000000 350.000000 2.000000 139.000000 0.000000 5.714286 402.097902 397.142857 0.091 1.284 1835 1835Gates 37073 37073 37 420.000000 0.000000 254.000000 594.000000 2.000000 371.000000 0.000000 3.367003 604.761905 624.579125 0.118 1.421 1836 1836Warren 37185 37185 93 968.000000 4.000000 748.000000 1190.000000 2.000000 844.000000 4.132231 1.680672 772.727273 709.243697 0.124 1.428 1837 1837Stokes 37169 37169 85 1612.000000 1.000000 160.000000 2038.000000 5.000000 176.000000 0.620347 2.453386 99.255583 86.359176 0.114 1.352 1838 1838Caswell 37033 37033 17 1035.000000 2.000000 550.000000 1253.000000 2.000000 597.000000 1.932367 1.596169 531.400966 476.456504 0.153 1.616 1839 1839Rockingham 37157 37157 79 4449.00000016.0000001243.000000 5386.000000 5.000000 1369.000000 3.596314 0.928333 279.388627 254.177497 0.143 1.663 1840 1840Granville 37077 37077 39 1671.000000 4.000000 930.000000 2074.000000 4.000000 1058.000000 2.393776 1.928640 556.552962 510.125362 0.109 1.325 1841 1841Person 37145 37145 73 1556.000000 4.000000 613.000000 1790.000000 4.000000 650.000000 2.570694 2.234637 393.958869 363.128492 0.072 1.085 1842 1842Vance 37181 37181 91 2180.000000 4.0000001179.000000 2753.000000 6.000000 1492.000000 1.834862 2.179441 540.825688 541.954232 0.190 2.204 1846 1846Halifax 37083 37083 42 3608.00000018.0000002365.000000 4463.00000017.000000 2980.000000 4.988914 3.809097 655.487805 667.712301 0.053 1.171 1848 1848Pasquotank 37139 37139 70 1638.000000 3.000000 622.000000 2275.000000 4.000000 933.000000 1.831502 1.758242 379.731380 410.109890 0.199 1.984 1874 1874Wilkes 37193 37193 97 3146.000000 4.000000 200.000000 3725.000000 7.000000 222.000000 1.271456 1.879195 63.572791 59.597315 0.081 1.288 1880 1880Watauga 37189 37189 95 1323.000000 1.000000 17.000000 1775.000000 1.000000 33.000000 0.755858 0.563380 12.849584 18.591549 0.063 1.000 1881 1881Perquimans 37143 37143 72 484.000000 1.000000 230.000000 676.000000 0.000000 310.000000 2.066116 0.000000 475.206612 458.579882 0.044 1.158 1887 1887Chowan 37041 37041 21 751.000000 1.000000 368.000000 899.000000 1.000000 491.000000 1.331558 1.112347 490.013316 546.162403 0.064 1.213 1892 1892Avery 37011 37011 6 781.000000 0.000000 4.000000 977.000000 0.000000 5.000000 0.000000 0.000000 5.121639 5.117707 0.086 1.267 1893 1893Yadkin 37197 37197 99 1269.000000 1.000000 65.000000 1568.000000 1.000000 76.000000 0.788022 0.637755 51.221434 48.469388 0.128 1.554 1897 1897Franklin 37069 37069 35 1399.000000 2.000000 736.000000 1863.000000 0.000000 950.000000 1.429593 0.000000 526.090064 509.930220 0.108 1.483 1900 1900Forsyth 37067 37067 3411858.00000010.0000003919.00000015704.00000018.000000 5031.000000 0.843313 1.146205 330.494181 320.364238 0.170 1.680 1903 1903Guilford 37081 37081 4116184.00000023.0000005483.00000020543.00000038.000000 7089.000000 1.421157 1.849779 338.791399 345.081050 0.111 1.392 1904 1904Alamance 37001 37001 1 4672.00000013.0000001243.000000 5767.00000011.000000 1397.000000 2.782534 1.907404 266.053082 242.240333 0.180 2.151 1905 1905Bertie 37015 37015 8 1324.000000 6.000000 921.000000 1616.000000 5.000000 1161.000000 4.531722 3.094059 695.619335 718.440594 0.104 1.294 1907 1907Orange 37135 37135 68 3164.000000 4.000000 776.000000 4478.000000 6.000000 1086.000000 1.264223 1.339884 245.259166 242.518982 0.077 1.271 1908 1908Durham 37063 37063 32 7970.00000016.0000003732.00000010432.00000022.000000 4948.000000 2.007528 2.108896 468.255960 474.309816 0.142 1.640 1913 1913Nash 37127 37127 64 4021.000000 8.0000001851.000000 5189.000000 7.000000 2274.000000 1.989555 1.349008 460.333250 438.234727 0.059 1.319 1927 1927Mitchell 37121 37121 61 671.000000 0.000000 1.000000 919.000000 2.000000 4.000000 0.000000 2.176279 1.490313 4.352557 0.131 1.521 1928 1928Edgecombe 37065 37065 33 3657.00000010.0000002186.000000 4359.000000 9.000000 2696.000000 2.734482 2.064694 597.757725 618.490479 0.122 1.516 1932 1932Caldwell 37027 37027 14 3609.000000 6.000000 309.000000 4249.000000 9.000000 360.000000 1.662510 2.118145 85.619285 84.725818 0.080 1.307 1936 1936Yancey 37199 37199100 770.000000 0.000000 12.000000 869.000000 1.000000 10.000000 0.000000 1.150748 15.584416 11.507480 0.118 1.899 1937 1937Martin 37117 37117 59 1549.000000 2.000000 883.000000 1849.000000 1.000000 1033.000000 1.291156 0.540833 570.045190 558.680368 0.219 2.130 1938 1938Wake 37183 37183 9214484.00000016.0000004397.00000020857.00000031.000000 6221.000000 1.104667 1.486312 303.576360 298.269166 0.118 1.601 1946 1946Madison 37115 37115 58 765.000000 2.000000 5.000000 926.000000 2.000000 3.000000 2.614379 2.159827 6.535948 3.239741 0.155 1.781 1947 1947Iredell 37097 37097 49 4139.000000 4.0000001144.000000 5400.000000 5.000000 1305.000000 0.966417 0.925926 276.395265 241.666667 0.069 1.201 1948 1948Davie 37059 37059 30 1207.000000 1.000000 148.000000 1438.000000 3.000000 177.000000 0.828500 2.086231 122.618061 123.087622 0.066 1.070 1950 1950Alexander 37003 37003 2 1333.000000 0.000000 128.000000 1683.000000 2.000000 150.000000 0.000000 1.188354 96.024006 89.126560 0.145 1.791 1951 1951Davidson 37057 37057 29 5509.000000 8.000000 736.000000 7143.000000 8.000000 941.000000 1.452169 1.119978 133.599564 131.737365 0.134 1.755 1958 1958Burke 37023 37023 12 3573.000000 5.000000 326.000000 4314.00000015.000000 407.000000 1.399384 3.477051 91.239854 94.343996 0.100 1.331 1962 1962Washington 37187 37187 94 990.000000 5.000000 521.000000 1141.000000 0.000000 651.000000 5.050505 0.000000 526.262626 570.552147 0.099 1.411 1963 1963Tyrrell 37177 37177 89 248.000000 0.000000 116.000000 319.000000 0.000000 141.000000 0.000000 0.000000 467.741935 442.006270 0.116 1.664 1964 1964McDowell 37111 37111 56 1946.000000 5.000000 134.000000 2215.000000 5.000000 128.000000 2.569373 2.257336 68.859198 57.787810 0.201 1.805 1968 1968Randolph 37151 37151 76 4456.000000 7.000000 384.000000 5711.00000012.000000 483.000000 1.570916 2.101208 86.175943 84.573630 0.180 2.142 1973 1973Chatham 37037 37037 19 1646.000000 2.000000 591.000000 2398.000000 3.000000 687.000000 1.215067 1.251043 359.052248 286.488741 0.094 1.307 1979 1979Wilson 37195 37195 98 3702.00000011.0000001827.000000 4706.00000013.000000 2330.000000 2.971367 2.762431 493.517018 495.112622 0.134 1.590 1980 1980Rowan 37159 37159 80 4606.000000 3.0000001057.000000 6427.000000 8.000000 1504.000000 0.651324 1.244749 229.483283 234.012759 0.168 1.791 1984 1984Pitt 37147 37147 74 5094.00000014.0000002620.000000 6635.00000011.000000 3059.000000 2.748331 1.657875 514.330585 461.039940 0.106 1.444 1986 1986Catawba 37035 37035 18 5754.000000 5.000000 790.000000 6883.00000021.000000 914.000000 0.868961 3.050995 137.295794 132.790934 0.168 1.995 1988 1988Buncombe 37021 37021 11 7515.000000 9.000000 930.000000 9956.00000018.000000 1206.000000 1.197605 1.807955 123.752495 121.132985 0.207 1.851 1989 1989Johnston 37101 37101 51 3999.000000 6.0000001165.000000 4780.00000013.000000 1349.000000 1.500375 2.719665 291.322831 282.217573 0.144 1.690 1996 1996Haywood 37087 37087 44 2110.000000 2.000000 57.000000 2463.000000 8.000000 62.000000 0.947867 3.248071 27.014218 25.172554 0.094 3.640 2000 2000Dare 37055 37055 28 521.000000 0.000000 43.000000 1059.000000 1.000000 73.000000 0.000000 0.944287 82.533589 68.932956 0.203 3.197 2004 2004Beaufort 37013 37013 7 2692.000000 7.0000001131.000000 2909.000000 4.000000 1163.000000 2.600297 1.375043 420.133730 399.793744 0.141 2.316 2013 2013Swain 37173 37173 87 675.000000 3.000000 281.000000 883.000000 2.000000 406.000000 4.444444 2.265006 416.296296 459.796149 0.070 1.105 2016 2016Greene 37079 37079 40 870.000000 4.000000 534.000000 1178.000000 4.000000 664.000000 4.597701 3.395586 613.793103 563.667233 0.065 1.093 2026 2026Lee 37105 37105 53 2252.000000 5.000000 736.000000 2949.000000 6.000000 905.000000 2.220249 2.034588 326.820604 306.883689 0.146 1.778 2027 2027Rutherford 37161 37161 81 2992.00000012.000000 495.000000 3543.000000 8.000000 576.000000 4.010695 2.257973 165.441176 162.574090 0.142 1.655 2029 2029Wayne 37191 37191 96 6638.00000018.0000002593.000000 8227.00000023.000000 3073.000000 2.711660 2.795673 390.629708 373.526194 0.154 1.680 2030 2030Harnett 37085 37085 43 3776.000000 6.0000001051.000000 4789.00000010.000000 1453.000000 1.588983 2.088119 278.336864 303.403633 0.118 1.506 2032 2032Cleveland 37045 37045 23 4866.00000010.0000001491.000000 5526.00000021.000000 1729.000000 2.055076 3.800217 306.411837 312.884546 0.078 1.384 2034 2034Lincoln 37109 37109 55 2216.000000 8.000000 302.000000 2817.000000 7.000000 350.000000 3.610108 2.484913 136.281588 124.245651 0.125 1.601 2039 2039Jackson 37099 37099 50 1143.000000 2.000000 215.000000 1504.000000 5.000000 307.000000 1.749781 3.324468 188.101487 204.122340 0.181 1.980 2040 2040Moore 37125 37125 63 2648.000000 5.000000 844.000000 3534.000000 5.000000 1151.000000 1.888218 1.414827 318.731118 325.693265 0.143 1.887 2041 2041Mecklenburg 37119 37119 6021588.00000044.0000008027.00000030757.00000035.00000011631.000000 2.038169 1.137952 371.826941 378.157818 0.091 1.321 2042 2042Cabarrus 37025 37025 13 4099.000000 3.000000 856.000000 5669.00000020.000000 1203.000000 0.731886 3.527959 208.831422 212.206738 0.130 1.732 2044 2044Montgomery 37123 37123 62 1258.000000 3.000000 472.000000 1598.000000 8.000000 588.000000 2.384738 5.006258 375.198728 367.959950 0.103 1.461 2045 2045Stanly 37167 37167 84 2356.000000 5.000000 370.000000 3039.000000 7.000000 528.000000 2.122241 2.303389 157.045840 173.741362 0.095 1.471 2047 2047Henderson 37089 37089 45 2574.000000 5.000000 158.000000 3679.000000 8.000000 264.000000 1.942502 2.174504 61.383061 71.758630 0.078 1.202 2056 2056Graham 37075 37075 38 415.000000 0.000000 40.000000 488.000000 1.000000 45.000000 0.000000 2.049180 96.385542 92.213115 0.104 1.548 2065 2065Lenoir 37107 37107 54 3589.00000010.0000001826.000000 4225.00000014.000000 2047.000000 2.786291 3.313609 508.776818 484.497041 0.098 1.389 2067 2067Transylvania 37175 37175 88 1173.000000 3.000000 92.000000 1401.000000 4.000000 104.000000 2.557545 2.855103 78.431373 74.232691 0.091 1.470 2068 2068Gaston 37071 37071 36 9014.00000011.0000001523.00000011455.00000026.000000 2194.000000 1.220324 2.269751 168.959396 191.532082 0.060 1.036 2071 2071Polk 37149 37149 75 533.000000 1.000000 95.000000 673.000000 0.000000 79.000000 1.876173 0.000000 178.236398 117.384844 0.131 1.677 2082 2082Macon 37113 37113 57 797.000000 0.000000 9.000000 1157.000000 3.000000 22.000000 0.000000 2.592913 11.292346 19.014693 0.241 2.214 2083 2083Sampson 37163 37163 82 3025.000000 4.0000001396.000000 3447.000000 4.000000 1524.000000 1.322314 1.160429 461.487603 442.123586 0.082 1.388 2085 2085Pamlico 37137 37137 69 542.000000 1.000000 222.000000 631.000000 1.000000 277.000000 1.845018 1.584786 409.594096 438.985737 0.120 1.686 2088 2088Cherokee 37039 37039 20 1027.000000 2.000000 32.000000 1173.000000 1.000000 42.000000 1.947420 0.852515 31.158715 35.805627 0.172 1.835 2090 2090Cumberland 37051 37051 2620366.00000038.0000007043.00000026370.00000057.00000010614.000000 1.865855 2.161547 345.821467 402.502844 0.121 1.978 2091 2091Jones 37103 37103 52 578.000000 1.000000 297.000000 650.000000 2.000000 305.000000 1.730104 3.076923 513.840830 469.230769 0.163 1.716 2095 2095Union 37179 37179 90 3915.000000 4.0000001034.000000 5273.000000 9.000000 1348.000000 1.021711 1.706808 264.112388 255.641950 0.138 1.621 2096 2096Anson 37007 37007 4 1570.00000015.000000 952.000000 1875.000000 4.000000 1161.000000 9.554140 2.133333 606.369427 619.200000 0.098 1.262 2097 2097Hoke 37093 37093 47 1494.000000 7.000000 987.000000 1706.000000 6.000000 1172.000000 4.685408 3.516999 660.642570 686.987104 0.167 2.709 2099 2099Hyde 37095 37095 48 338.000000 0.000000 134.000000 427.000000 0.000000 169.000000 0.000000 0.000000 396.449704 395.784543 0.204 1.871 2100 2100Duplin 37061 37061 31 2483.000000 4.0000001061.000000 2777.000000 7.000000 1227.000000 1.610954 2.520706 427.305679 441.843716 0.121 1.855 2107 2107Richmond 37153 37153 77 2756.000000 4.0000001043.000000 3108.000000 7.000000 1218.000000 1.451379 2.252252 378.447025 391.891892 0.051 1.096 2109 2109Clay 37043 37043 22 284.000000 0.000000 1.000000 419.000000 0.000000 5.000000 0.000000 0.000000 3.521127 11.933174 0.177 2.916 2119 2119Craven 37049 37049 25 5868.00000013.0000001744.000000 7595.00000018.000000 2342.000000 2.215406 2.369980 297.205181 308.360764 0.080 1.188 2123 2123Scotland 37165 37165 83 2255.000000 8.0000001206.000000 2617.00000016.000000 1436.000000 3.547672 6.113871 534.811530 548.719908 0.195 1.783 2146 2146Onslow 37133 37133 6711158.00000029.0000002217.00000014655.00000023.000000 3568.000000 2.599032 1.569430 198.691522 243.466394 0.240 2.004 2150 2150Robeson 37155 37155 78 7889.00000031.0000005904.000000 9087.00000026.000000 6899.000000 3.929522 2.861230 748.383826 759.216463 0.125 2.868 2156 2156Carteret 37031 37031 16 2414.000000 5.000000 341.000000 3339.000000 4.000000 487.000000 2.071251 1.197963 141.259321 145.852052 0.225 2.107 2162 2162Bladen 37017 37017 9 1782.000000 8.000000 818.000000 2052.000000 5.000000 1023.000000 4.489338 2.436647 459.034792 498.538012 0.214 2.152 2185 2185Pender 37141 37141 71 1228.000000 4.000000 580.000000 1602.000000 3.000000 763.000000 3.257329 1.872659 472.312704 476.279650 0.240 2.365 2232 2232Columbus 37047 37047 24 3350.00000015.0000001431.000000 4144.00000017.000000 1832.000000 4.477612 4.102317 427.164179 442.084942 0.042 0.999 2238 2238New Hanover 37129 37129 65 5526.00000012.0000001633.000000 6917.000000 9.000000 2100.000000 2.171553 1.301142 295.512125 303.599827 0.212 2.024 2241 2241Brunswick 37019 37019 10 2181.000000 5.000000 659.000000 2655.000000 6.000000 841.000000 2.292526 2.259887 302.154975 316.760829libpysal-4.9.2/libpysal/examples/sids2/sids2.gal000066400000000000000000000070071452177046000215510ustar00rootroot000000000000000 100 sids2 FIPSNO 37009 3 37189 37193 37005 37005 3 37193 37171 37009 37171 5 37067 37197 37193 37169 37005 37053 2 37055 37029 37131 4 37083 37185 37091 37015 37091 3 37015 37073 37131 37029 3 37139 37073 37053 37073 5 37041 37143 37139 37029 37091 37185 4 37069 37181 37131 37083 37169 3 37067 37157 37171 37033 4 37135 37001 37145 37157 37157 4 37081 37033 37001 37169 37077 5 37183 37063 37069 37181 37145 37145 4 37063 37135 37077 37033 37181 3 37069 37185 37077 37083 6 37117 37065 37127 37015 37131 37185 37139 3 37143 37029 37073 37193 8 37003 37097 37027 37189 37197 37171 37005 37009 37189 4 37027 37011 37193 37009 37143 3 37041 37139 37073 37041 2 37143 37073 37011 5 37111 37023 37027 37121 37189 37197 5 37059 37097 37067 37171 37193 37069 5 37183 37127 37077 37185 37181 37067 6 37057 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SIDS2

Data provided "as is," no warranties

Description

Sudden Infant Death Syndrome sample data for North Carolina counties, two time periods (1974-78 and 1979-84). Same as SIDS data set, except that the computed rates are included.

Type = polygon shape file, unprojected, lat-lon

Observations = 100

Variables = 18

Source

Cressie, Noel (1993). Statistics for Spatial Data. New York, Wiley, pp. 386-389. Rates computed.

Variables

Variable Description
AREA county area (computed by ArcView)
PERIMETER county perimeter (computed by ArcView)
CNTY_ county internal ID
CNTY_ID county internal ID
NAME county name
FIPS county fips code, as character (state code + county code)
FIPSNO county fips code, numeric, used in GeoDa User's Guide and tutorials
CRESS_ID county ID used by Cressie
BIR74 live births, 1974-78
SID74 SIDS deaths, 1974-78
NWBIR74 non-white births, 1974-78
BIR79 live births, 1979-84
SID79 SIDS deaths, 1979-84
NWBIR79 non-white births, 1979-84
SIDR74 SIDS death rate per 1,000 (1974-78)
SIDR79 SIDS death rate per 1,000 (1979-84)
NWR74 non-white birth rate (non-white per 1000 births), 1974-78
NWR79 non-white birth rate (non-white per 1000 births), 1979-84


Prepared by Luc Anselin

UIUC-ACE Spatial Analysis Laboratory

Last updated June 16, 2003

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@!ð?ð?ð?ð?@ð?ð?ð?@ð?ð?@-*!ð?ð?ð?ð?ð?@'&ð?ð?ð?ð?ð?@$ ð?ð?ð?ð?ð?@)' ð?ð?ð?ð?ð?ð?@.) ð?ð?ð?ð?ð?@/.  ð?ð?ð?ð?ð?ð?@+#ð?ð?ð?ð?ð?@/ ð?ð?ð?ð?ð?@/$ ð?ð?ð?ð?ð?@50 ð?ð?ð?ð?ð?@-"ð?ð?ð?@ 20#ð?ð?ð?ð?ð?@!*(3ð?ð?ð?ð?ð?ð?@"4-%ð?ð?ð?ð?@#28 +ð?ð?ð?ð?ð?ð?@$>5/ ð?ð?ð?ð?ð?ð?@%64"ð?ð?ð?@& C@3(1D'ð?ð?ð?ð?ð?ð?ð?ð?ð?"@'1)&ð?ð?ð?ð?ð?@(3&!ð?ð?ð?ð?@)1.E'ð?ð?ð?ð?ð?ð?@*?<-3!ð?ð?ð?ð?ð?ð?@+8,V#ð?ð?ð?ð?ð?@,V+ð?ð?@-4*<"ð?ð?ð?ð?ð?ð?@.E/B)ð?ð?ð?ð?ð?ð?@/B;$>.ð?ð?ð?ð?ð?ð?ð?ð? @0=:5 2ð?ð?ð?ð?ð?ð?@1FD)'&ð?ð?ð?ð?ð?@2I:8Z #0ð?ð?ð?ð?ð?ð?ð?@3@&(*!ð?ð?ð?ð?ð?@4G6<-%"ð?ð?ð?ð?ð?ð?@5N>=0$ð?ð?ð?ð?ð?ð?@6JA94%ð?ð?ð?ð?ð?@7Vð?ð?@8ZOV+2#ð?ð?ð?ð?ð?ð?@9MH6Að?ð?ð?ð?@:I=20ð?ð?ð?ð?@;B>/ð?ð?ð?@<LG?*4-ð?ð?ð?ð?ð?ð?@=WNI:05ð?ð?ð?ð?ð?ð?@>QNB5$;/ð?ð?ð?ð?ð?ð?ð?@?K<@*ð?ð?ð?ð?@@KC&3?ð?ð?ð?ð?ð?@AMJ69ð?ð?ð?ð?@BXUE>Q;/.ð?ð?ð?ð?ð?ð?ð?ð? @CKSD@&ð?ð?ð?ð?ð?@DSF1C&ð?ð?ð?ð?ð?@EXTFB.)ð?ð?ð?ð?ð?ð?@FSETD1ð?ð?ð?ð?ð?@GLJ<4ð?ð?ð?ð?@HPM9ð?ð?ð?@IWRZ=2:ð?ð?ð?ð?ð?ð?@JGA6ð?ð?ð?@KC@?ð?ð?ð?@L<Gð?ð?@MYPAH9ð?ð?ð?ð?ð?@N_Q`W=>5ð?ð?ð?ð?ð?ð?ð?@OZ8ð?ð?@PMYHð?ð?ð?@Q_]UN>Bð?ð?ð?ð?ð?ð?@R\W^ZIð?ð?ð?ð?ð?@STFDCð?ð?ð?ð?@TXESFð?ð?ð?ð?@U][QBð?ð?ð?ð?@V7,8+ð?ð?ð?ð?@W`R\I=Nð?ð?ð?ð?ð?ð?@X[TBEð?ð?ð?ð?@YMPð?ð?@Z^O8RI2ð?ð?ð?ð?ð?ð?@[]UXð?ð?ð?@\`^RWð?ð?ð?ð?@]a_QU[ð?ð?ð?ð?ð?@^Z\Rð?ð?ð?@_a`NQ]ð?ð?ð?ð?ð?@`cba\W_Nð?ð?ð?ð?ð?ð?ð?@ac`_]ð?ð?ð?ð?@bc`ð?ð?@cb`að?ð?ð?@libpysal-4.9.2/libpysal/examples/snow_maps/000077500000000000000000000000001452177046000210165ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/snow_maps/README.md000066400000000000000000000020671452177046000223020ustar00rootroot00000000000000snow_maps ========= Public water pumps and Cholera deaths in London 1854 (John Snow's Cholera Map) ----------------------------------------------------------------- * SohoPeople.dbf: attribute data for Cholera deaths. (k=2) * SohoPeople.prj: ESRI projection file. * SohoPeople.sbn: spatial index. * SohoPeople.sbx: spatial index. * SohoPeople.shp: Point shapefile for Cholera deaths. (n=324) * SohoPeople.shx: spatial index. * SohoWater.dbf: attribute data for public water pumps. (k=1) * SohoWater.prj: ESRI projection file. * SohoWater.sbn: spatial index. * SohoWater.sbx: spatial index. * SohoWater.shp: Point shapefile for public water pumps. (n=13) * SohoWater.shx: spatial index. * Soho_Network.dbf: attribute data for street network. (k=1) * Soho_Network.prj: ESRI projection file. * Soho_Network.sbn: spatial index. * Soho_Network.sbx: spatial index. * Soho_Network.shp: Line shapefile for street network. (n=118) * Soho_Network.shx: spatial index. Original data: Snow, J. (1849). On the Mode of Communication of Cholera. 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èç"ÏÀ|S šYAÜØÌÀ½Å›YA2(^(Š(¶(â((:(f(’0Æ0ú`^(Š8Æ(ò((J8†(²0æ8"(N(z(¦(Ò0(2(^8š(Æ0ú(&(R0†HÒ(þ(*(V0Š(¶(â8(J(v(¢(Î(ú( &@ j0 ž0 Ò0 ( 2( ^( Š0 ¾( ê0 ( J( v0 ª0 Þ0 0 F0 z( ¦( Ò8 ( :@ ~0 ²( Þ( (6(b@¦(Ò@0J0~0²(Þ( (6(b@¦(Ò00:(f(’(¾0ò(0R(~(ª0Þ( (6(b0–0Ê0þ(*0^0’(¾(ê(0J(v0ª(Ö((.(Z(†(libpysal-4.9.2/libpysal/examples/stl/000077500000000000000000000000001452177046000176125ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/stl/README.md000066400000000000000000000017251452177046000210760ustar00rootroot00000000000000stl === Homicides and selected socio-economic characteristics for counties surrounding St Louis, MO. Data aggregated for three time periods: 1979-84 (steady decline in homicides), 1984-88 (stable period), and 1988-93 (steady increase in homicides). --------------------------------------------------------------------------- * stl.gal: queen contiguity weights in GAL format. * stl_hom.csv: attribute data and WKT geometry. * stl_hom.dbf: attribute data. (k=22) * stl_hom.html: metadata. * stl_hom.shp: Polygon shapefile. (n=78) * stl_hom.shx: spatial index. * stl_hom.txt: selected attribute data. * stl_hom.wkt: a Well-Known-Text representation of the geometry. * stl_hom_rook.gal: rook contiguity weights in GAL format. Source: S. Messner, L. Anselin, D. Hawkins, G. Deane, S. Tolnay, R. Baller (2000). An Atlas of the Spatial Patterning of County-Level Homicide, 1960-1990. Pittsburgh, PA, [National Consortium on Violence Research (NCOVR)](http://www.ncovr.heinz.cmu.edu). libpysal-4.9.2/libpysal/examples/stl/stl.gal000066400000000000000000000030021452177046000210740ustar00rootroot0000000000000078 1 3 7 3 6 2 3 10 8 5 3 3 7 4 1 4 4 9 5 3 7 5 4 10 4 9 2 6 5 16 12 11 7 1 7 8 19 9 11 18 1 6 3 4 8 3 15 10 2 9 7 20 19 13 10 7 4 5 10 9 17 15 9 13 20 21 5 2 8 11 4 18 16 6 7 12 3 16 14 6 13 3 20 9 10 14 3 22 16 12 15 4 23 10 17 8 16 8 28 27 18 22 14 12 6 11 17 6 30 26 23 21 10 15 18 7 33 32 19 16 27 11 7 19 6 24 20 18 33 7 9 20 6 24 21 19 9 13 10 21 6 35 26 24 20 17 10 22 4 29 28 14 16 23 5 31 25 17 30 15 24 5 35 33 21 19 20 25 3 42 31 23 26 5 34 30 21 35 17 27 7 41 39 32 28 36 16 18 28 5 29 36 27 22 16 29 4 38 36 22 28 30 6 43 34 31 26 17 23 31 6 44 42 43 30 23 25 32 4 33 27 41 18 33 8 46 40 35 32 41 18 24 19 34 5 43 35 45 26 30 35 7 45 37 33 21 24 34 26 36 6 47 39 38 29 28 27 37 6 51 45 40 46 49 35 38 4 48 47 29 36 39 6 52 50 41 47 36 27 40 3 46 37 33 41 6 50 46 39 27 33 32 42 4 53 31 44 25 43 8 61 59 54 44 45 34 30 31 44 5 54 53 43 31 42 45 7 60 59 51 37 35 43 34 46 7 49 50 57 41 33 40 37 47 7 56 55 52 48 38 36 39 48 3 55 47 38 49 5 63 51 46 57 37 50 6 58 57 52 39 41 46 51 6 64 60 49 63 37 45 52 6 62 58 56 47 39 50 53 4 65 54 44 42 54 5 65 61 43 44 53 55 3 56 48 47 56 4 62 55 47 52 57 7 67 63 66 58 50 49 46 58 5 66 52 62 50 57 59 6 69 61 60 70 45 43 60 5 70 64 51 45 59 61 6 72 65 59 69 43 54 62 5 68 66 56 52 58 63 5 64 57 67 49 51 64 6 71 70 63 67 51 60 65 4 61 72 54 53 66 6 73 67 62 68 58 57 67 7 76 75 71 66 57 64 63 68 3 73 62 66 69 6 77 72 70 74 59 61 70 7 74 71 78 64 60 69 59 71 5 78 75 67 64 70 72 4 77 69 61 65 73 3 76 66 68 74 4 77 78 70 69 75 4 78 76 67 71 76 3 73 67 75 77 3 74 69 72 78 4 75 71 74 70 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3.263947,12,8,4,108864,95227,122551, 2.490555, 4.229330, 4.833954, 0.447269, 0.696222, 0.855499 "POLYGON ((-92.030677795410156 38.011772155761719,-91.974563598632812 38.011104583740234,-91.958641052246094 38.041805267333984,-91.933311462402344 38.035964965820312,-91.923751831054688 38.047489166259766,-91.903495788574219 38.053779602050781,-91.639785766601562 38.051868438720703,-91.638160705566406 38.157077789306641,-91.542747497558594 38.157341003417969,-91.53155517578125 38.154808044433594,-91.535430908203125 37.787075042724609,-91.816192626953125 37.787055969238281,-91.810272216796875 37.746814727783203,-91.818557739257812 37.714012145996094,-91.8201904296875 37.59881591796875,-92.031288146972656 37.604129791259766,-92.022674560546875 37.777976989746094,-92.029937744140625 37.785987854003906,-92.030677795410156 38.011772155761719))",Phelps,Missouri,29,161,29161, 29161, 3.885759, 4.036001, 3.282163,8,7,7,205880,173439,213274, 3.479951, 2.293591, 2.424375, -0.156076, 0.052126, 0.198348 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36.886070251464844,-91.6669921875 37.04888916015625))",Shannon,Missouri,29,203,29203, 29203, 6.314859, 7.794030, 4.334822,3,3,2,47507,38491,46138, 2.273782, 4.356775, 2.221061, 0.767792, 0.730994, 0.713000 "POLYGON ((-90.680656433105469 36.925613403320312,-90.697952270507812 36.926803588867188,-90.699165344238281 36.966693878173828,-90.718193054199219 36.967864990234375,-90.7186279296875 36.994609832763672,-90.739395141601562 36.9962158203125,-90.74029541015625 37.050163269042969,-90.788192749023438 37.052379608154297,-90.783943176269531 37.140842437744141,-90.760856628417969 37.141082763671875,-90.758384704589844 37.165592193603516,-90.743385314941406 37.166652679443359,-90.742828369140625 37.271846771240234,-90.560089111328125 37.273578643798828,-90.555435180664062 37.312156677246094,-90.222129821777344 37.311878204345703,-90.221672058105469 37.086109161376953,-90.114952087402344 37.086696624755859,-90.114639282226562 37.048614501953125,-90.152122497558594 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5.832951 -0.991256 -0.940265 -0.845005 2Adams Illinois 1700117001 17001 4.464496 2.655839 2.255492 19 9 9 425580 338876 399026 2.304996 4.255254 5.457145 -0.509511 -0.391588 -0.271549 3Menard Illinois 1712917129 17129 4.307312 1.742433 1.467890 3 1 1 69649 57391 68125 5.183402 3.012367 2.316253 -1.340772 -1.114002 -0.859035 4Cass Illinois 1701717017 17017 2.258866 1.437029 2.484256 2 1 2 88540 69588 80507 3.955886 2.263292 5.263547 -0.754894 -0.511259 -0.276122 5Brown Illinois 1700917009 17009 5.935246 0.000000 0.000000 2 0 0 33697 28462 35051 5.755396 4.728186 4.653891 0.096642 -0.130929 -0.305658 6Macon Illinois 1711517115 17115 3.613635 6.036815 9.048673 28 37 64 774843 612906 707286 4.818652 4.947508 5.131238 -0.664045 -0.437859 -0.229741 7Sangamon Illinois 1716717167 17167 5.774120 5.441418 6.029489 61 48 65 1056438 882123 1078035 5.444019 5.802772 6.218296 -0.790212 -0.689591 -0.586439 8Marion Missouri 2912729127 29127 1.742342 0.000000 1.800385 3 0 3 172182 140910 166631 6.758459 4.272589 4.413135 -0.409684 -0.210570 -0.016762 9Morgan Illinois 1713717137 17137 3.124540 2.166249 4.581251 7 4 10 224033 184651 218281 6.651120 6.023764 7.330383 -0.477808 -0.621812 -0.733578 10Pike Illinois 1714917149 17149 2.643242 3.298008 3.790607 3 3 4 113497 90964 105524 2.861865 3.352330 3.189221 0.094225 -0.064360 -0.185323 11Christian Illinois 1702117021 17021 1.830580 1.696334 1.447436 4 3 3 218510 176852 207263 5.022701 4.429574 4.201170 -0.788136 -0.690250 -0.576590 12Moultrie Illinois 1713917139 17139 0.000000 1.401227 1.191966 0 1 1 87628 71366 83895 4.812834 4.606847 4.626925 -1.074839 -0.943344 -0.769862 13Scott Illinois 1717117171 17171 2.750729 0.000000 0.000000 1 0 0 36354 29070 33911 3.212093 4.484042 4.602858 -0.463517 -0.561029 -0.621619 14Coles Illinois 1702917029 17029 3.789541 3.059402 1.608017 12 8 5 316661 261489 310942 4.752988 5.918855 4.483694 -0.790710 -0.666694 -0.527957 15Ralls Missouri 2917329173 29173 0.000000 4.551143 1.949812 0 2 1 53222 43945 51287 3.245325 4.022056 3.738318 -0.753998 -0.685714 -0.563780 16Shelby Illinois 1717317173 17173 1.409721 1.747030 0.745090 2 2 1 141872 114480 134212 3.431133 2.806484 3.108761 -0.783326 -0.757273 -0.701496 17Pike Missouri 2916329163 29163 5.798838 3.680620 4.173318 6 3 4 103469 81508 95847 2.446999 2.834324 3.490980 0.218643 0.098237 -0.019780 18Montgomery Illinois 1713517135 17135 3.101593 1.897425 3.783252 6 3 7 193449 158109 185026 3.951064 4.785976 4.261148 -0.689677 -0.557645 -0.437371 19Macoupin Illinois 1711717117 17117 1.014034 1.637640 2.085136 3 4 6 295848 244254 287751 3.929380 4.976053 4.866562 -0.874243 -0.662668 -0.450123 20Greene Illinois 1706117061 17061 4.039874 2.545112 2.176302 4 2 2 99013 78582 91899 4.356019 4.107227 3.197658 -0.110275 -0.249014 -0.337745 21Calhoun Illinois 1701317013 17013 0.000000 3.631346 6.309347 0 1 2 34772 27538 31699 1.733588 4.053718 3.036013 -0.211948 -0.337103 -0.400206 22Cumberland Illinois 1703517035 17035 3.032738 1.850310 10.855743 2 1 7 65947 54045 64482 3.248611 3.147752 2.636248 -0.618608 -0.618063 -0.577743 23Audrain Missouri 2900729007 29007 5.796504 0.804887 4.211354 9 1 6 155266 124241 142472 1.972892 1.978266 1.819227 -0.698537 -0.441817 -0.181760 24Jersey Illinois 1708317083 17083 1.620916 2.944756 0.804810 2 3 1 123387 101876 124253 2.634261 3.733333 3.940559 -0.994095 -1.013107 -0.958868 25Boone Missouri 2901929019 29019 3.756586 3.377637 3.215331 23 18 22 612258 532917 684222 4.690075 3.193314 3.371067 -0.796220 -0.458816 -0.248171 26Lincoln Missouri 2911329113 29113 3.693717 5.406741 2.833664 5 7 5 135365 129468 176450 2.416028 3.322543 2.691207 -0.873278 -0.889609 -0.878730 27Fayette Illinois 1705117051 17051 1.500409 0.925198 1.592040 2 1 2 133297 108085 125625 4.386029 2.709202 2.242668 -0.316859 -0.291364 -0.239199 28Effingham Illinois 1704917049 17049 1.070223 0.000000 1.571158 2 0 3 186877 156669 190942 4.849288 6.511668 5.743387 -0.890475 -0.903469 -0.895745 29Jasper Illinois 1707917079 17079 0.000000 1.804761 3.127590 0 1 2 68404 55409 63947 5.745876 2.505821 4.561346 -0.445535 -0.522597 -0.588317 30Montgomery Missouri 2913929139 29139 2.907864 3.567161 4.416896 2 2 3 68779 56067 67921 3.386317 4.704345 4.361601 -0.228905 -0.356297 -0.409705 31Callaway Missouri 2902729027 29027 5.187018 3.769152 3.017486 10 6 6 192789 159187 198841 3.670967 4.059123 4.381741 -1.141395 -0.946103 -0.751213 32Bond Illinois 1700517005 17005 4.137104 2.575594 9.924245 4 2 9 96686 77652 90687 5.971977 4.646272 4.757433 -0.208688 -0.499207 -0.734983 33Madison Illinois 1711917119 17119 6.416621 6.715531 7.973957 95 83 120 1480530 1235941 1504899 3.985334 5.033145 5.373271 -0.892535 -0.756274 -0.600238 34Warren Missouri 2921929219 29219 8.614748 7.870032 5.005464 8 7 6 92864 88945 119869 3.689965 5.767779 5.064063 -0.849236 -0.912759 -0.941746 35St. Charles Missouri 2918329183 29183 3.422831 2.305501 2.463891 31 21 32 905683 910865 1298759 4.471081 5.500872 5.441824 -1.919986 -1.875634 -1.749552 36Clay Illinois 1702517025 17025 0.000000 0.000000 0.000000 0 0 0 93388 75023 86899 2.103981 3.544417 3.385375 -0.192009 -0.241513 -0.259604 37St. Louis Missouri 2918929189 29189 6.940802 5.860835 7.377974 407 289 441 5863876 4931038 5977251 7.330506 7.924867 7.658709 -1.126490 -1.179486 -1.160006 38Richland Illinois 1715917159 17159 4.583540 1.147276 1.003875 5 1 1 109086 87163 99614 2.198550 1.992825 2.247494 -0.769040 -0.609570 -0.432524 39Marion Illinois 1712117121 17121 3.770739 3.702093 3.190047 10 8 8 265200 216094 250780 3.860683 3.610808 4.116008 -0.317751 -0.096444 0.075644 40St. Louis City Missouri 2951029510 29510 46.574796 36.000126 45.905406 1238 763 1090 2658090 2119437 2374448 9.048990 9.811838 8.296243 2.102164 2.304683 2.387951 41Clinton Illinois 1702717027 17027 1.503548 0.592031 2.447597 3 1 5 199528 168910 204282 3.021907 3.467414 5.309556 -1.122761 -1.114063 -1.026503 42Cole Missouri 2905129051 29051 4.024966 6.140481 1.294958 14 19 5 347829 309422 386113 5.048534 5.808927 5.658484 -1.168948 -1.082279 -0.976713 43Gasconade Missouri 2907329073 29073 0.000000 4.395604 5.933098 0 3 5 79838 68250 84273 3.567118 2.776865 3.159485 -0.517604 -0.672277 -0.736440 44Osage Missouri 2915129151 29151 2.763156 1.648397 4.133997 2 1 3 72381 60665 72569 2.947368 3.404168 2.619479 -0.624319 -1.008650 -1.267035 45Franklin Missouri 2907129071 29071 4.165134 4.457906 4.298311 18 17 21 432159 381345 488564 4.050914 5.670080 4.200815 -0.923090 -1.019771 -1.040774 46St. Clair Illinois 1716317163 17163 22.983237 20.158470 27.483827 369 268 435 1605518 1329466 1582749 6.017517 5.639493 3.953419 0.470741 0.591781 0.612341 47Wayne Illinois 1719117191 17191 4.509258 2.208554 0.969791 5 2 1 110883 90557 103115 4.446095 3.606536 3.587421 -0.193537 -0.292316 -0.361366 48Edwards Illinois 1704717047 17047 0.000000 2.502127 0.000000 0 1 0 48352 39966 44762 4.010349 5.726588 1.960369 -0.503923 -0.544807 -0.533802 49Monroe Illinois 1713317133 17133 2.463823 0.951095 2.934466 3 1 4 121762 105142 136311 5.525868 6.201612 6.482686 -1.413166 -1.661495 -1.768530 50Washington Illinois 1718917189 17189 0.000000 0.000000 4.456427 0 0 4 92348 75846 89758 2.857732 1.923077 2.901461 -0.775652 -0.941118 -1.034995 51Jefferson Missouri 2909929099 29099 4.486810 4.159251 4.629264 40 33 48 891502 793412 1036882 3.458919 4.598213 4.435842 -1.570242 -1.415649 -1.218788 52Jefferson Illinois 1708117081 17081 3.999876 10.015550 4.941533 9 19 11 225007 189705 222603 3.855739 3.964437 5.848205 -0.198362 -0.092251 -0.018258 53Miller Missouri 2913129131 29131 6.146821 3.941081 3.990041 7 4 5 113880 101495 125312 3.457034 3.872621 2.307257 -0.336407 -0.171547 -0.041974 54Maries Missouri 2912529125 29125 2.161695 7.537120 2.064324 1 3 1 46260 39803 48442 3.232243 2.701116 1.647875 -0.140636 -0.104383 -0.067993 55White Illinois 1719317193 17193 2.764722 2.269143 3.040253 3 2 3 108510 88139 98676 4.467023 2.150338 2.712660 -0.185614 -0.073686 0.043951 56Hamilton Illinois 1706517065 17065 3.595829 2.235586 3.905411 2 1 2 55620 44731 51211 3.672357 2.415505 2.094327 -0.013288 0.096903 0.229475 57Randolph Illinois 1715717157 17157 3.743916 4.534839 4.332839 8 8 9 213680 176412 207716 2.669165 3.732117 3.551273 -0.958592 -0.812091 -0.642228 58Perry Illinois 1714517145 17145 3.793857 1.821245 3.894111 5 2 5 131792 109815 128399 5.272408 3.674044 3.965845 -0.708670 -0.537094 -0.379090 59Crawford Missouri 2905529055 29055 10.004275 9.644545 6.828794 11 9 8 109953 93317 117151 4.051217 7.177962 3.251068 -0.092806 -0.204787 -0.279292 60Washington Missouri 2922129221 29221 11.022928 8.400979 3.263947 12 8 4 108864 95227 122551 2.490555 4.229330 4.833954 0.447269 0.696222 0.855499 61Phelps Missouri 2916129161 29161 3.885759 4.036001 3.282163 8 7 7 205880 173439 213274 3.479951 2.293591 2.424375 -0.156076 0.052126 0.198348 62Franklin Illinois 1705517055 17055 3.843877 7.543185 3.295762 10 16 8 260154 212112 242736 3.982430 4.722793 3.732272 -0.063766 0.160936 0.356135 63Ste. Genevieve Missouri 2918629186 29186 2.201649 5.125642 7.249679 2 4 7 90841 78039 96556 1.984755 3.503128 4.093092 -1.185591 -1.004837 -0.794867 64St. Francois Missouri 2918729187 29187 3.126527 4.428286 3.041846 8 10 9 255875 225821 295873 3.590924 4.891725 4.813624 -0.366205 -0.115440 0.094908 65Pulaski Missouri 2916929169 29169 2.760906 3.429187 1.618018 7 7 4 253540 204130 247216 2.226750 3.893749 1.614509 -0.269436 -0.217094 -0.160734 66Jackson Illinois 1707717077 17077 6.716332 2.610668 4.910801 25 8 18 372227 306435 366539 4.672627 5.133963 5.515054 -0.041680 0.398210 0.685298 67Perry Missouri 2915729157 29157 5.976750 7.234930 1.991457 6 6 2 100389 82931 100429 5.132361 3.092059 4.429066 -0.525864 -0.696155 -0.774723 68Williamson Illinois 1719917199 17199 5.497033 6.842239 3.146192 19 20 11 345641 292302 349629 2.439993 3.482278 3.390967 -0.572489 -0.284405 -0.023560 69Dent Missouri 2906529065 29065 6.864203 1.428490 7.266650 6 1 6 87410 70004 82569 2.285714 2.568807 2.940044 0.446578 0.630576 0.802583 70Iron Missouri 2909329093 29093 2.998771 9.329751 3.110904 2 5 2 66694 53592 64290 2.602022 4.251766 2.480339 -0.286364 0.165999 0.552370 71Madison Missouri 2912329123 29123 3.092146 5.436063 2.980271 2 3 2 64680 55187 67108 2.853900 1.882216 1.970193 0.725502 0.622569 0.478929 72Texas Missouri 2921529215 29215 4.721770 6.577835 3.866767 6 7 5 127071 106418 129307 3.199566 2.255443 2.409820 0.520598 0.513432 0.492522 73Union Illinois 1718117181 17181 8.371470 4.392081 1.868408 9 4 2 107508 91073 107043 1.890804 2.133409 2.287785 0.207753 0.111398 -0.032038 74Reynolds Missouri 2917929179 29179 0.000000 5.856001 12.577034 0 2 5 43056 34153 39755 2.644231 2.106138 2.675363 0.337338 0.387728 0.447416 75Bollinger Missouri 2901729017 29017 1.627180 5.763799 7.803599 1 3 5 61456 52049 64073 2.772277 3.963666 2.221157 0.571877 0.210119 -0.041183 76Cape Girardeau Missouri 2903129031 29031 3.673967 2.651026 3.471490 13 8 13 353841 301770 374479 4.501471 7.458181 6.022567 -0.554227 -0.485215 -0.425275 77Shannon Missouri 2920329203 29203 6.314859 7.794030 4.334822 3 3 2 47507 38491 46138 2.273782 4.356775 2.221061 0.767792 0.730994 0.713000 78Wayne Missouri 2922329223 29223 5.863555 0.000000 8.451537 4 0 6 68218 57181 70993 2.481962 3.551555 3.314986 1.087340 1.257024 1.320757libpysal-4.9.2/libpysal/examples/stl/stl_hom.html000066400000000000000000000062721452177046000221540ustar00rootroot00000000000000 SAL Data Sets - St Louis Region Homicides

St Louis Region Homicides

Data provided "as is," no warranties

Description

Homicides and selected socio-economic characteristics for counties surrounding St Louis, MO. Data aggregated for three time periods: 1979-84 (steady decline in homicides), 1984-88 (stable period), and 1988-93 (steady increase in homicides).

Type = polygon shape file, unprojected, lat-lon

Observations = 78

Variables = 21

Source

S. Messner, L. Anselin, D. Hawkins, G. Deane, S. Tolnay, R. Baller (2000). An Atlas of the Spatial Patterning of County-Level Homicide, 1960-1990. Pittsburgh, PA, National Consortium on Violence Research (NCOVR).

Variables

Variable Description
NAME county name
STATE_NAME state name
STATE_FIPS state fips code (character)
CNTY_FIPS county fips code (character)
FIPS combined state and county fips code (character)
FIPSNO fips code as numeric variable
HR7984 homicide rate per 100,000 (1979-84)
HR8488 homicide rate per 100,000 (1984-88)
HR8893 homicide rate per 100,000 (1988-93)
HC7984 homicide count (1979-84)
HC8488 homicide count (1984-88)
HC8893 homicide count (1988-93)
PO7984 population total (1979-84)
PO8488 population total (1984-88)
PO8893 population total (1988-93)
PE77 police expenditures per capita, 1977
PE82 police expenditures per capita, 1982
PE87 police expenditures per capita, 1987
RDAC80 resource deprivation/affluence composite variable, 1980
RDAC85 resource deprivation/affluence composite variable, 1985
RDAC90 resource deprivation/affluence composite variable, 1990


Prepared by Luc Anselin

UIUC-ACE Spatial Analysis Laboratory

Last updated June 16, 2003

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30 15 24 5 35 33 21 19 20 25 3 42 31 23 26 5 34 30 21 35 17 27 7 41 39 32 28 36 16 18 28 5 29 36 27 22 16 29 4 38 36 22 28 30 6 43 34 31 26 17 23 31 6 44 42 43 30 23 25 32 4 33 27 41 18 33 8 46 40 35 32 41 18 24 19 34 5 43 35 45 26 30 35 7 45 37 33 21 24 34 26 36 6 47 39 38 29 28 27 37 6 51 45 40 46 49 35 38 4 48 47 29 36 39 6 52 50 41 47 36 27 40 3 46 37 33 41 6 50 46 39 27 33 32 42 4 53 31 44 25 43 8 61 59 54 44 45 34 30 31 44 5 54 53 43 31 42 45 7 60 59 51 37 35 43 34 46 7 49 50 57 41 33 40 37 47 7 56 55 52 48 38 36 39 48 3 55 47 38 49 5 63 51 46 57 37 50 6 58 57 52 39 41 46 51 6 64 60 49 63 37 45 52 6 62 58 56 47 39 50 53 4 65 54 44 42 54 5 65 61 43 44 53 55 3 56 48 47 56 4 62 55 47 52 57 7 67 63 66 58 50 49 46 58 5 66 52 62 50 57 59 6 69 61 60 70 45 43 60 5 70 64 51 45 59 61 6 72 65 59 69 43 54 62 5 68 66 56 52 58 63 5 64 57 67 49 51 64 6 71 70 63 67 51 60 65 4 61 72 54 53 66 6 73 67 62 68 58 57 67 7 76 75 71 66 57 64 63 68 3 73 62 66 69 6 77 72 70 74 59 61 70 7 74 71 78 64 60 69 59 71 5 78 75 67 64 70 72 4 77 69 61 65 73 3 76 66 68 74 4 77 78 70 69 75 4 78 76 67 71 76 3 73 67 75 77 3 74 69 72 78 4 75 71 74 70 libpysal-4.9.2/libpysal/examples/street_net_pts/000077500000000000000000000000001452177046000220525ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/street_net_pts/README.md000066400000000000000000000004441452177046000233330ustar00rootroot00000000000000street_net_pts ============== Street network points --------------------- * street_net_pts.dbf: attribute data. (k=1) * street_net_pts.prj: ESRI projection file. * street_net_pts.qpj: QGIS projection file. * street_net_pts.shp: Point shapefile. (n=303) * street_net_pts.shx: spatial index. libpysal-4.9.2/libpysal/examples/street_net_pts/street_net_pts.dbf000066400000000000000000000065061452177046000256000ustar00rootroot00000000000000_/A WIDN 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 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libpysal-4.9.2/libpysal/examples/tests/000077500000000000000000000000001452177046000201525ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/tests/__init__.py000066400000000000000000000000001452177046000222510ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/tests/test_available.py000066400000000000000000000045651452177046000235150ustar00rootroot00000000000000#!/usr/bin/env python3 import errno import os import platform import tempfile from unittest.mock import patch import pandas from platformdirs import user_data_dir from .. import available, get_url, load_example from ..base import get_data_home os_name = platform.system() original_path_exists = os.path.exists original_makedirs = os.makedirs class TestExamples: def test_available(self): examples = available() assert type(examples) == pandas.core.frame.DataFrame assert examples.shape == (98, 3) def test_data_home(self): pth = get_data_home() head, tail = os.path.split(pth) assert tail == "pysal" if os_name == "Linux": if "XDG_DATA_HOME" in os.environ: assert head == os.environ["XDG_DATA_HOME"] else: heads = head.split("/") assert heads[-1] == "share" assert heads[-2] == ".local" elif os_name == "Darwin": heads = head.split("/") assert heads[1] == "Users" assert heads[-1] == "Application Support" assert heads[-2] == "Library" elif os_name == "Windows": heads = head.split("\\") assert heads[1] == "Users" assert heads[-2] == "Local" assert heads[-3] == "AppData" @patch("os.makedirs") @patch("os.path.exists") def test_data_home_fallback(self, path_exists_mock, makedirs_mock): data_home = user_data_dir("pysal", "pysal") def makedirs_side_effect(path, exist_ok=False): # noqa ARG001 if path == data_home: raise OSError(errno.EROFS) def path_exists_side_effect(path): if path == data_home: return False return original_path_exists(path) makedirs_mock.side_effect = makedirs_side_effect path_exists_mock.side_effect = path_exists_side_effect pth = get_data_home() head, tail = os.path.split(pth) assert tail == "pysal" assert head == tempfile.gettempdir() def test_get_url(self): assert get_url("10740") is None url = "https://geodacenter.github.io/data-and-lab//data/baltimore.zip" assert get_url("Baltimore") == url def test_load_example(self): taz = load_example("taz") flist = taz.get_file_list() assert len(flist) == 4 libpysal-4.9.2/libpysal/examples/tokyo/000077500000000000000000000000001452177046000201555ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/tokyo/README.md000066400000000000000000000027541452177046000214440ustar00rootroot00000000000000tokyo ====== Tokyo Mortality data -------------------- * Tokyomortality.csv: Attribute data (n=262, k=9) * Readme_tokyomortality.txt: Metadata * tokyomet262.shp: Polygon shapefile (n=262) For testing against GWR4 software --------------------------------- * tokyo_BS_NN_listwise.csv: bisquare nearest neighbor kernel model output * tokyo_BS_NN_summary.txt: bisquare nearest neighbor kernel model summary * tokyo_BS_NN.ctl: bisquare nearest neighbor kernel control file * tokyo_BS_NN_OFF_listwise.csv: bisquare nearest neighbor kernel model w/ offset output * tokyo_BS_NN_OFF_summary.txt: bisquare nearest neighbor kernel model w/ offset summary * tokyo_BS_NN_OFF.ctl: bisquare nearest neighbor kernel w/ offset control file * tokyo_GS_NN_listwise.csv: Gaussian nearest neighbor kernel model output * tokyo_GS_NN_summary.txt: Gaussian nearest neighbor kernel model summary * tokyo_GS_NN.ctl: Gaussian nearest neighbor kernel control file * tokyo_BS_F_listwise.csv: bisquare fixed kernel model output * tokyo_BS_F_summary.txt: bisquare fixed kernel model summary * tokyo_BS_F.ctl: bisquare fixed kernel control file * tokyo_GS_F_listwise.csv: Gaussian fixed kernel model output * tokyo_GS_F_summary.txt: Gaussian fixed kernel model summary * tokyo_GS_F.ctl: Gaussian fixed kernel control file Source: Nakaya T, Fotheringham A, Brunsdon C, Charlton M. Geographically weighted poisson regression for disease association mapping. Statistics in Medicine. 2005;24(17):2695–2717. https://doi.org/10.1002/sim.2129libpysal-4.9.2/libpysal/examples/tokyo/Readme_tokyomortality.txt000066400000000000000000000032351452177046000253100ustar00rootroot00000000000000Dataset name: Tokyo Mortality Data File name tokyomortality.txt File type text file (space delimited data matrix) Fields IDnum0: sequential areal id X_CENTROID: x coordinate of areal centroid Y_CENTROID: y coordinate of areal centroid db2564: observed number of working age (25-64 yrs) deaths eb2564: expected number of working age (25-64 yrs) deaths OCC_TEC: proportion of professional workers OWNH: proportion of owned houses POP65: proportion of elderly people (equal to or older than 65) UNEMP: unemployment rate Areal unit 262 municipality Areal extent The Tokyo metropolitan area is enclosed by an approximate 70 km radius from the centroid of the Chiyoda ward of Tokyo where the Imperial Palace is located. Year 1990 Source Vital Statistics and Population Census of Japan Sample session control file SampleTokyoMortalityGWPR.ctl Note 1: This sample fit a semiparametric geographically weighted Poisson regression model used in Nakaya et al. (2005). Note 2: Since all of the explanatory variables are standardised in the paper, the standardisation option is turned on in this sample. Additional file: tokyomet262.shp, shx, dbf, prj: ESRI shape file of the Tokyo metropolitan area Note 1: IDnum0 in tokyomortality.txt can be matched with AreaID in this shapefile dbf. Note 2: Coordinates are projected using UTM54 (Tokyo datum). Note 3: distance unit is metre. Reference Nakaya, T., Fotheringham, S., Brunsdon, C. and Charlton, M. (2005): Geographically weighted Poisson regression for disease associative mapping, Statistics in Medicine 24, 2695-2717. History 15 May 2012 The dataset is prepared for GWR4 sample dataset by TN. libpysal-4.9.2/libpysal/examples/tokyo/SampleTokyoMortalityGWPR.ctl000066400000000000000000000013341452177046000255360ustar00rootroot00000000000000Semiparametric GWPR: Tokyo mortality data (see Nakaya et al. 2005, Stat in Med.) Tokyomortality.txt FORMAT/DELIMITER: 0 Number_of_fields: 9 Number_of_areas: 262 Fields AreaKey 001 IDnum0 X 002 X_CENTROID Y 003 Y_CENTROID Gmetric 0 Dependent 004 db2564 Offset 005 eb2564 Independent_geo 3 000 Intercept 006 OCC_TEC 009 UNEMP Independent_fix 2 007 OWNH 008 POP65 Unused_fields 0 MODELTYPE: 1 STANDARDISATION: 1 GTEST: 0 VSG2F: 0 VSF2G: 0 KERNELTYPE: 0 BANDSELECTIONMETHOD: 2 Goldrangeflag: 0 Goldenmax: Goldenmin: Fixedbandsize: IntervalMax: 20000 IntervalMin: 10000 IntervalStep: 1000 Criteria: 1000 summary_output: defaultGWRsummary.txt listwise_output: defaultGWRlistwise.csv predictflag: 0 prediction_def: prediction_output: libpysal-4.9.2/libpysal/examples/tokyo/Tokyomortality.csv000066400000000000000000000353321452177046000237520ustar00rootroot00000000000000IDnum0,X_CENTROID,Y_CENTROID,db2564,eb2564,OCC_TEC,OWNH,POP65,UNEMP 0,378906.83,17310.41,189,194.572,0.126,0.606,0.104,2.865 1,334095.21,25283.2,95,97.526,0.107,0.671,0.111,3.401 2,378200.19,-877.05,70,83.235,0.106,0.733,0.11,1.724 3,357191.03,29064.39,48,52.392,0.075,0.767,0.14,1.829 4,358056.34,10824.73,65,67.664,0.075,0.812,0.146,1.961 5,366747.61,-3073.12,107,120.745,0.14,0.623,0.078,2.636 6,351099.27,11800.35,65,67.196,0.066,0.824,0.121,1.603 7,377929.98,4635.1,76,87.746,0.13,0.778,0.083,2.438 8,367529.91,20192.51,192,190.255,0.227,0.449,0.101,1.783 9,389231.47,3489.35,27,23.939,0.075,0.821,0.146,2.081 10,389427.64,9290.1,28,23.832,0.088,0.61,0.119,2.589 11,381089.82,9125.81,63,62.498,0.124,0.674,0.099,2.661 12,371082.66,6843.9,34,36.137,0.113,0.914,0.078,2.08 13,388281.84,-1760.78,17,16.77,0.062,0.92,0.161,2.509 14,386771.66,-4857.11,25,20.209,0.055,0.959,0.166,1.824 15,397029.93,4912.15,17,14.952,0.07,0.961,0.178,1.725 16,399583.28,1217.51,31,24.675,0.062,0.931,0.165,2.261 17,389413.79,18915.59,27,32.009,0.069,0.938,0.168,1.45 18,374811.31,23395.2,10,16.171,0.098,0.892,0.159,1.997 19,366291.01,3851.09,42,40.773,0.076,0.929,0.102,2.704 20,362053.67,7027.25,20,19.021,0.075,0.935,0.148,2.479 21,350567.45,26456.28,40,38.2,0.048,0.945,0.146,1.784 22,356783.59,23682.89,15,14.404,0.066,0.891,0.149,1.983 23,356225.47,19763.98,47,34.798,0.063,0.828,0.135,1.677 24,338360.54,25697.56,68,63.104,0.066,0.672,0.082,1.782 25,337846.31,18213.38,17,14.095,0.064,0.874,0.134,2.416 26,344074.13,27136.92,57,48.675,0.05,0.85,0.094,2.25 27,349087.82,19336.47,40,24.57,0.057,0.932,0.138,1.458 28,343402.57,18620.67,47,40.968,0.064,0.77,0.124,2.265 29,359036.48,1198.74,49,47.597,0.129,0.817,0.08,2.01 30,370771.99,-1522.12,49,48.444,0.107,0.856,0.095,2.752 31,376842.13,-7139.16,27,29.49,0.106,0.949,0.097,2.667 32,318049.53,32744.59,120,120.934,0.088,0.687,0.121,2.725 33,325761.21,31092.21,28,25.762,0.054,0.937,0.163,1.68 34,318112.24,28405.62,16,17.138,0.062,0.914,0.143,1.617 35,310480.1,28809.03,14,17.677,0.062,0.881,0.138,2.226 36,306513.97,32751.48,43,49.712,0.096,0.446,0.082,2.421 37,311395.51,33538.42,29,38.093,0.07,0.843,0.109,2.222 38,314408.34,-4572.95,401,451.407,0.126,0.657,0.083,2.507 39,303850.22,22478,210,230.061,0.11,0.638,0.103,2.74 40,337540.25,-12310.61,711,665.501,0.098,0.519,0.073,2.718 41,330948.96,-8687.59,544,620.327,0.149,0.514,0.084,2.674 42,327143.99,-3103.01,557,622.818,0.129,0.587,0.09,2.645 43,312830.72,21412.1,132,123.957,0.099,0.734,0.114,2.43 44,312874.14,-17053.63,395,440.38,0.157,0.578,0.073,2.8 45,293680.38,-8010.1,97,109.945,0.12,0.722,0.116,2.828 46,325185.11,20460.45,91,86.221,0.095,0.766,0.118,2.115 47,305971.31,8472,97,122.976,0.117,0.661,0.087,2.394 48,335330.5,-108.19,148,162.923,0.099,0.711,0.081,3.026 49,339115.66,3202.05,269,271.36,0.107,0.624,0.064,2.994 50,309271.13,-10589.17,183,224.968,0.133,0.645,0.072,2.589 51,319972.62,24634.35,86,84.885,0.089,0.792,0.126,2.33 52,317013.43,12374.14,86,105.836,0.113,0.715,0.084,2.577 53,323345.93,2314.46,244,294.09,0.113,0.562,0.066,2.759 54,327610.55,-7504.02,120,116.831,0.152,0.5,0.091,2.936 55,343813.29,-11626.17,297,306.205,0.091,0.514,0.062,2.712 56,342508.85,-4698.16,393,414.887,0.103,0.637,0.062,2.758 57,333426.78,-13559.3,103,114.209,0.132,0.452,0.094,3.357 58,330824.95,-14794.45,136,129.291,0.098,0.351,0.065,3.456 59,304617.97,-15261.45,160,197.739,0.137,0.674,0.07,2.886 60,338062.78,-13156.47,102,96.519,0.076,0.621,0.085,3.045 61,325419.58,-15527.5,142,150.75,0.126,0.434,0.063,3.041 62,324052.62,-12510.9,83,94.684,0.135,0.599,0.062,3.13 63,327521.88,-17674.68,78,83.535,0.144,0.463,0.069,2.77 64,322114.34,-17894.35,201,208.889,0.122,0.606,0.066,2.516 65,320355.21,5840.48,87,107.467,0.11,0.713,0.079,2.539 66,330341.07,12925.79,89,97.163,0.123,0.649,0.077,2.809 67,318527.16,8318.24,80,93.243,0.115,0.665,0.072,2.399 68,347297.26,-13547.71,105,106.606,0.06,0.605,0.061,3.229 69,321375.58,-10594.12,114,140.264,0.109,0.575,0.066,2.479 70,318675.19,-8454.47,101,94.489,0.126,0.439,0.078,3.459 71,350174.04,-12060.87,181,172.497,0.091,0.579,0.052,2.388 72,329442.54,5939.52,82,92.061,0.126,0.782,0.085,2.424 73,307098.94,992.68,93,126.491,0.121,0.539,0.067,2.431 74,337247.61,14030.91,67,78.427,0.094,0.658,0.085,3.416 75,306612.95,-2173.79,55,79.955,0.132,0.607,0.054,2.304 76,301727,-6640.87,68,74.512,0.113,0.758,0.09,2.324 77,326724.18,5478.1,47,38.462,0.103,0.809,0.069,2.164 78,311503.39,15708.66,33,40.921,0.117,0.77,0.081,2.083 79,316934.08,-10632.09,43,55.32,0.108,0.643,0.057,2.695 80,317980.71,-13171.59,52,51.875,0.104,0.73,0.065,2.308 81,298790.59,-2464.08,75,55.068,0.181,0.651,0.088,2.833 82,294903.64,214.57,33,19.923,0.1,0.866,0.14,2.113 83,284950.61,-7897.72,11,4.858,0.084,0.948,0.224,2.895 84,302616.14,12642.65,23,17.416,0.087,0.819,0.117,2.735 85,298937.62,11074.43,43,27.544,0.108,0.831,0.109,2.48 86,292980.66,10621.27,46,52.123,0.107,0.836,0.124,2.887 87,291341.64,3602.46,19,13.33,0.081,0.934,0.143,2.289 88,296052.78,6812.78,11,8.054,0.08,0.89,0.128,1.858 89,314476.95,3490.04,30,32.284,0.085,0.944,0.124,1.73 90,311673.48,10101.08,27,28.431,0.078,0.931,0.124,2.36 91,300937.58,3470.02,18,24.098,0.156,0.938,0.102,2.262 92,286991.93,9571.27,7,7.428,0.071,0.946,0.189,2.323 93,307386.12,16090.18,7,11.046,0.08,0.928,0.146,1.973 94,300604.96,17843.82,15,17.145,0.086,0.854,0.119,2.527 95,303917.55,29223.91,44,42.828,0.081,0.877,0.121,1.844 96,296097.2,19299.56,25,18.829,0.073,0.883,0.131,1.749 97,291327.94,19385.45,15,17.662,0.074,0.889,0.114,2.012 98,288651.19,16782.21,53,53.478,0.091,0.821,0.13,3.125 99,321850.11,16542.18,24,28.667,0.066,0.921,0.124,1.794 100,309623.17,24691.81,7,6.125,0.062,0.881,0.128,1.775 101,317273.81,16350.36,13,12.349,0.066,0.962,0.154,1.957 102,330127.65,26472.36,18,16.755,0.07,0.896,0.112,2.779 103,330024.3,22050.13,24,22.623,0.086,0.917,0.128,2.115 104,335366.5,8522.69,45,50.9,0.105,0.711,0.076,2.512 105,330795.7,8625.57,56,59.574,0.096,0.838,0.09,2.449 106,324461.1,12021.3,33,33.073,0.071,0.874,0.116,1.967 107,333249.02,19193.73,35,33.069,0.096,0.816,0.107,2.911 108,330905.38,16199.04,36,41.484,0.12,0.663,0.065,3.294 109,338740.71,9995.65,58,59.655,0.098,0.787,0.084,2.434 110,345541.9,-607.56,37,34.33,0.074,0.871,0.082,2.536 111,348908.69,-5077.51,53,65.655,0.088,0.678,0.069,2.275 112,343120.93,5902.89,49,55.521,0.101,0.845,0.091,2.039 113,377836.69,-36378.58,1070,1243.759,0.136,0.523,0.074,2.519 114,356153.1,-24448.15,547,607.845,0.137,0.422,0.074,2.635 115,363934.49,-23252.2,660,811.312,0.13,0.518,0.073,2.921 116,362715.03,-62961.03,175,193.735,0.101,0.62,0.1,3.467 117,355515.39,-15862.17,594,664.519,0.137,0.48,0.067,2.571 118,350331.74,2259.59,189,175.679,0.115,0.743,0.094,2.796 119,390869.17,-52824.71,121,132.775,0.123,0.718,0.117,2.875 120,391663.46,-13955.69,125,116.253,0.102,0.499,0.091,2.183 121,381676.42,-24737.89,175,218.258,0.124,0.772,0.086,2.416 122,396227.77,-39792.02,85,70.583,0.099,0.772,0.132,2.541 123,365535.4,-28214.23,200,220.195,0.149,0.461,0.072,2.81 124,358761.78,-6929.68,389,454.815,0.145,0.584,0.071,2.711 125,376070.01,-56303.59,381,386.197,0.11,0.604,0.084,2.772 126,353788.69,-7340.62,173,208.943,0.136,0.672,0.079,2.628 127,371670.71,-21543.62,206,230.127,0.117,0.579,0.072,2.925 128,367522.64,-7189.91,142,185.604,0.159,0.645,0.081,2.372 129,361892.77,-18166,148,146.777,0.109,0.723,0.068,2.281 130,366450.66,-74634.65,141,142.727,0.097,0.602,0.108,3.16 131,355381.88,-79216.89,106,95.232,0.071,0.808,0.15,2.477 132,353694.67,-32885.23,116,143.243,0.146,0.424,0.043,2.633 133,378726.25,-28678.9,84,113.452,0.135,0.759,0.078,2.445 134,365246.77,-57670.24,78,76.222,0.113,0.709,0.096,2.529 135,389517.13,-31493.43,88,74.094,0.077,0.823,0.102,2.707 136,344415.42,11511.89,31,39.895,0.054,0.928,0.082,2.317 137,365053.58,-11168.12,60,59.313,0.099,0.762,0.069,2.316 138,387334.51,-21934.03,25,27.294,0.116,0.765,0.082,3.312 139,393097.28,-22717.2,57,56.813,0.075,0.766,0.079,2.46 140,380945.88,-17224.24,17,13.907,0.065,0.985,0.166,2.451 141,367373.14,-14712.42,47,51.913,0.121,0.826,0.063,2.205 142,374567.12,-13256.38,51,53.296,0.135,0.738,0.086,2.093 143,380862.18,-12688.75,11,7.634,0.072,0.989,0.177,2.358 144,383629.18,-9335.24,23,31.803,0.114,0.947,0.099,2.559 145,394378.41,-45752.97,64,55.296,0.102,0.849,0.139,3.293 146,402514.52,-43075.49,61,32.211,0.066,0.91,0.168,3.676 147,402518.42,-36236.31,37,36.163,0.08,0.854,0.16,2.236 148,396061.3,-30927.23,22,21.578,0.061,0.935,0.142,2.462 149,408226.18,-35513.98,18,7.467,0.063,0.969,0.172,3.462 150,403471.42,-31311.84,20,18.775,0.073,0.852,0.163,2.315 151,406033.77,-29345.02,20,24.596,0.064,0.82,0.154,2.725 152,399386.5,-22290.57,18,14.326,0.052,0.847,0.166,1.836 153,397587.4,-62378.67,25,19.169,0.089,0.808,0.171,2.747 154,391305.58,-63700.85,11,13.486,0.084,0.943,0.181,2.306 155,396203.55,-57412.21,23,19.247,0.085,0.92,0.166,2.395 156,397924.17,-52596.47,28,20.984,0.064,0.933,0.173,2.727 157,382876.06,-53653.14,19,13.687,0.085,0.868,0.176,2.881 158,385919.86,-61374.5,26,19.642,0.092,0.942,0.196,2.329 159,340110.86,-28521.01,70,64.958,0.135,0.499,0.166,1.36 160,342259.81,-30180.57,117,110.919,0.1,0.441,0.153,2.131 161,338829.19,-32435.83,256,255.792,0.16,0.392,0.135,2.711 162,335964.11,-27144.87,429,457.126,0.171,0.335,0.124,3.599 163,339454.02,-25208.35,285,280.924,0.193,0.439,0.138,2.557 164,342858.93,-25291.03,362,301.482,0.087,0.509,0.158,3.346 165,345601.8,-25527.73,452,371.254,0.086,0.469,0.13,3.008 166,345909.97,-30691.34,728,629.225,0.098,0.398,0.094,3.72 167,338436.09,-37186.98,562,553.703,0.133,0.379,0.116,2.33 168,334457.21,-35114.84,354,375.609,0.181,0.382,0.123,2.985 169,338261.22,-41722.33,1072,1038.344,0.127,0.403,0.116,2.91 170,329570.75,-34216.76,1037,1166.108,0.192,0.365,0.11,2.554 171,334989.23,-30867,309,312.454,0.189,0.363,0.124,3.036 172,331704.61,-26241.59,472,469.179,0.174,0.329,0.118,4.446 173,328431.53,-27940.2,699,775.103,0.196,0.345,0.117,3.251 174,336081.28,-23805.64,449,403.901,0.148,0.335,0.125,3.286 175,337470.12,-19925.9,615,587.955,0.117,0.35,0.126,3.652 176,342348.74,-22559.69,369,326.979,0.087,0.483,0.137,3.67 177,332725.59,-19390.99,812,800.918,0.131,0.363,0.099,3.208 178,327487.37,-22298.2,904,981.601,0.164,0.416,0.094,2.68 179,343411.59,-18219.15,1215,1037.79,0.085,0.417,0.093,3.634 180,349055.69,-20887.1,802,709.067,0.098,0.452,0.106,3.633 181,351097.21,-27497.53,983,874.668,0.095,0.427,0.08,3.223 182,297942.15,-33105.95,639,694.592,0.161,0.502,0.092,2.886 183,308339.92,-26657.04,251,230.764,0.142,0.395,0.093,3.353 184,322572.21,-26917.93,160,196.476,0.205,0.334,0.113,2.827 185,322565.6,-29522.07,233,246.203,0.189,0.338,0.104,2.841 186,293318.13,-17312.06,166,182.621,0.143,0.613,0.108,2.405 187,315646.55,-31303.34,259,315.043,0.189,0.397,0.088,2.625 188,304719.13,-27487.57,151,157.977,0.146,0.422,0.09,3.497 189,321954.36,-32546,270,299.219,0.182,0.356,0.086,3.286 190,311343.05,-41967,474,532.694,0.169,0.472,0.082,2.77 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258,299215.09,-41823.07,32,30.727,0.14,0.745,0.071,2.602 259,288944.87,-47144.31,28,41.043,0.103,0.851,0.098,1.87 260,290541.99,-38708.26,10,16.491,0.13,0.673,0.118,2.608 261,285964.14,-39392.46,12,16.168,0.117,0.889,0.14,2.132libpysal-4.9.2/libpysal/examples/tokyo/Tokyomortality.txt000066400000000000000000000510561452177046000237770ustar00rootroot00000000000000 IDnum0 X_CENTROID Y_CENTROID db2564 eb2564 OCC_TEC OWNH POP65 UNEMP 0 378906.83 17310.41 189 194.572 0.126 0.606 0.104 2.865 1 334095.21 25283.20 95 97.526 0.107 0.671 0.111 3.401 2 378200.19 -877.05 70 83.235 0.106 0.733 0.110 1.724 3 357191.03 29064.39 48 52.392 0.075 0.767 0.140 1.829 4 358056.34 10824.73 65 67.664 0.075 0.812 0.146 1.961 5 366747.61 -3073.12 107 120.745 0.140 0.623 0.078 2.636 6 351099.27 11800.35 65 67.196 0.066 0.824 0.121 1.603 7 377929.98 4635.10 76 87.746 0.130 0.778 0.083 2.438 8 367529.91 20192.51 192 190.255 0.227 0.449 0.101 1.783 9 389231.47 3489.35 27 23.939 0.075 0.821 0.146 2.081 10 389427.64 9290.10 28 23.832 0.088 0.610 0.119 2.589 11 381089.82 9125.81 63 62.498 0.124 0.674 0.099 2.661 12 371082.66 6843.90 34 36.137 0.113 0.914 0.078 2.080 13 388281.84 -1760.78 17 16.770 0.062 0.920 0.161 2.509 14 386771.66 -4857.11 25 20.209 0.055 0.959 0.166 1.824 15 397029.93 4912.15 17 14.952 0.070 0.961 0.178 1.725 16 399583.28 1217.51 31 24.675 0.062 0.931 0.165 2.261 17 389413.79 18915.59 27 32.009 0.069 0.938 0.168 1.450 18 374811.31 23395.20 10 16.171 0.098 0.892 0.159 1.997 19 366291.01 3851.09 42 40.773 0.076 0.929 0.102 2.704 20 362053.67 7027.25 20 19.021 0.075 0.935 0.148 2.479 21 350567.45 26456.28 40 38.200 0.048 0.945 0.146 1.784 22 356783.59 23682.89 15 14.404 0.066 0.891 0.149 1.983 23 356225.47 19763.98 47 34.798 0.063 0.828 0.135 1.677 24 338360.54 25697.56 68 63.104 0.066 0.672 0.082 1.782 25 337846.31 18213.38 17 14.095 0.064 0.874 0.134 2.416 26 344074.13 27136.92 57 48.675 0.050 0.850 0.094 2.250 27 349087.82 19336.47 40 24.570 0.057 0.932 0.138 1.458 28 343402.57 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338436.09 -37186.98 562 553.703 0.133 0.379 0.116 2.330 168 334457.21 -35114.84 354 375.609 0.181 0.382 0.123 2.985 169 338261.22 -41722.33 1072 1038.344 0.127 0.403 0.116 2.910 170 329570.75 -34216.76 1037 1166.108 0.192 0.365 0.110 2.554 171 334989.23 -30867.00 309 312.454 0.189 0.363 0.124 3.036 172 331704.61 -26241.59 472 469.179 0.174 0.329 0.118 4.446 173 328431.53 -27940.20 699 775.103 0.196 0.345 0.117 3.251 174 336081.28 -23805.64 449 403.901 0.148 0.335 0.125 3.286 175 337470.12 -19925.90 615 587.955 0.117 0.350 0.126 3.652 176 342348.74 -22559.69 369 326.979 0.087 0.483 0.137 3.670 177 332725.59 -19390.99 812 800.918 0.131 0.363 0.099 3.208 178 327487.37 -22298.20 904 981.601 0.164 0.416 0.094 2.680 179 343411.59 -18219.15 1215 1037.790 0.085 0.417 0.093 3.634 180 349055.69 -20887.10 802 709.067 0.098 0.452 0.106 3.633 181 351097.21 -27497.53 983 874.668 0.095 0.427 0.080 3.223 182 297942.15 -33105.95 639 694.592 0.161 0.502 0.092 2.886 183 308339.92 -26657.04 251 230.764 0.142 0.395 0.093 3.353 184 322572.21 -26917.93 160 196.476 0.205 0.334 0.113 2.827 185 322565.60 -29522.07 233 246.203 0.189 0.338 0.104 2.841 186 293318.13 -17312.06 166 182.621 0.143 0.613 0.108 2.405 187 315646.55 -31303.34 259 315.043 0.189 0.397 0.088 2.625 188 304719.13 -27487.57 151 157.977 0.146 0.422 0.090 3.497 189 321954.36 -32546.00 270 299.219 0.182 0.356 0.086 3.286 190 311343.05 -41967.00 474 532.694 0.169 0.472 0.082 2.770 191 318030.71 -27812.93 139 156.098 0.206 0.368 0.098 2.888 192 315373.57 -24947.11 213 249.881 0.190 0.400 0.089 2.639 193 308034.74 -32419.40 195 245.676 0.198 0.405 0.079 2.613 194 314330.79 -21499.59 195 215.951 0.165 0.484 0.102 3.143 195 313503.88 -27427.64 114 152.567 0.219 0.425 0.092 2.867 196 311593.95 -29691.66 89 96.433 0.204 0.347 0.092 3.061 197 320162.13 -24446.09 95 115.318 0.166 0.403 0.090 3.039 198 321749.08 -23511.00 116 154.730 0.174 0.436 0.100 3.331 199 302066.12 -24471.58 82 85.460 0.129 0.372 0.079 3.503 200 324399.73 -35058.09 109 117.435 0.177 0.409 0.091 3.068 201 310172.99 -22574.96 96 119.523 0.137 0.493 0.075 3.306 202 318769.50 -18902.57 140 111.022 0.169 0.395 0.100 3.607 203 318576.81 -21935.03 156 180.434 0.153 0.432 0.072 2.770 204 306334.75 -22731.84 125 98.953 0.096 0.432 0.072 3.789 205 311907.21 -35905.87 176 191.709 0.202 0.411 0.057 2.412 206 316724.95 -35492.33 89 83.526 0.165 0.415 0.066 2.842 207 298239.30 -24996.76 63 78.267 0.127 0.716 0.089 3.141 208 300046.48 -21453.27 72 73.702 0.138 0.465 0.063 2.296 209 303145.80 -20159.37 40 44.426 0.080 0.640 0.083 3.084 210 292145.09 -22376.41 22 24.446 0.143 0.873 0.128 1.943 211 289344.08 -25302.15 37 33.367 0.133 0.795 0.149 2.325 212 281144.54 -26368.40 4 6.991 0.099 0.936 0.252 3.360 213 276385.40 -15692.77 18 16.121 0.092 0.854 0.209 2.990 214 333949.36 -49547.84 449 385.205 0.127 0.446 0.100 2.931 215 328639.25 -51354.28 350 322.831 0.148 0.437 0.108 2.875 216 328766.37 -54568.85 145 128.505 0.128 0.485 0.138 3.508 217 332223.73 -57396.73 329 215.339 0.120 0.435 0.123 4.385 218 327365.26 -57784.04 448 334.183 0.129 0.510 0.116 3.907 219 325148.15 -54327.48 295 315.196 0.151 0.494 0.092 3.083 220 328631.89 -61735.25 263 266.041 0.163 0.474 0.092 2.883 221 329641.62 -66529.75 204 301.101 0.174 0.570 0.092 2.655 222 327916.01 -45847.58 376 448.358 0.179 0.444 0.082 2.325 223 321117.24 -60823.26 290 357.977 0.173 0.551 0.071 2.510 224 325433.51 -61656.41 294 348.879 0.167 0.552 0.067 2.528 225 320495.28 -52701.22 361 401.562 0.166 0.554 0.081 2.899 226 320831.13 -45866.47 432 578.273 0.194 0.455 0.061 2.207 227 316915.15 -53631.62 156 188.688 0.135 0.490 0.081 3.125 228 323595.31 -65306.25 153 191.558 0.174 0.552 0.070 2.389 229 318116.79 -58966.35 186 201.211 0.137 0.634 0.078 3.118 230 339293.11 -47659.81 446 346.615 0.090 0.407 0.107 4.943 231 334055.07 -44545.18 248 227.145 0.118 0.373 0.097 4.169 232 331453.95 -41443.10 260 260.364 0.194 0.286 0.088 3.265 233 327954.56 -39544.67 236 229.340 0.165 0.322 0.070 3.575 234 321981.74 -36838.07 175 238.622 0.206 0.343 0.067 3.078 235 324590.43 -40337.23 192 242.805 0.173 0.405 0.052 2.771 236 317357.31 -39249.39 118 184.698 0.214 0.499 0.076 2.883 237 333435.39 -77430.79 735 704.883 0.127 0.622 0.113 3.698 238 302126.02 -67286.13 346 364.360 0.132 0.510 0.091 3.115 239 321943.14 -68916.22 247 299.298 0.206 0.606 0.139 2.508 240 314598.50 -64965.34 463 523.326 0.161 0.483 0.091 2.599 241 310206.19 -67759.02 279 314.882 0.159 0.574 0.098 3.118 242 326961.41 -72189.14 93 100.995 0.181 0.687 0.153 3.157 243 306522.23 -44854.32 686 763.182 0.148 0.508 0.069 3.152 244 332009.57 -86587.48 105 89.021 0.080 0.699 0.122 3.004 245 291250.63 -62212.96 200 212.373 0.161 0.517 0.081 2.481 246 302274.84 -54583.90 209 264.407 0.152 0.506 0.067 2.443 247 313999.38 -53197.40 265 288.902 0.139 0.454 0.071 2.977 248 299312.82 -60819.66 88 124.432 0.174 0.525 0.082 2.759 249 308373.06 -57401.82 121 152.899 0.146 0.596 0.065 2.645 250 309678.63 -51875.49 140 160.488 0.152 0.515 0.066 2.678 251 311833.72 -57007.52 104 114.083 0.100 0.630 0.059 2.315 252 327926.88 -75230.85 41 50.776 0.173 0.756 0.145 2.713 253 307926.73 -64266.21 64 64.614 0.104 0.554 0.070 2.539 254 299390.82 -71196.86 49 51.988 0.165 0.737 0.138 2.696 255 295866.34 -72353.52 44 48.284 0.173 0.634 0.116 2.788 256 299578.37 -47629.99 35 57.237 0.080 0.622 0.074 2.322 257 293314.45 -52457.68 6 6.325 0.110 0.664 0.118 1.999 258 299215.09 -41823.07 32 30.727 0.140 0.745 0.071 2.602 259 288944.87 -47144.31 28 41.043 0.103 0.851 0.098 1.870 260 290541.99 -38708.26 10 16.491 0.130 0.673 0.118 2.608 261 285964.14 -39392.46 12 16.168 0.117 0.889 0.140 2.132 libpysal-4.9.2/libpysal/examples/tokyo/tokyo_BS_F.ctl000066400000000000000000000014071452177046000226610ustar00rootroot00000000000000 C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt FORMAT/DELIMITER: 0 Number_of_fields: 9 Number_of_areas: 262 Fields AreaKey 001 IDnum0 X 002 X_CENTROID Y 003 Y_CENTROID Gmetric 0 Dependent 004 db2564 Offset Independent_geo 5 000 Intercept 006 OCC_TEC 007 OWNH 008 POP65 009 UNEMP Independent_fix 0 Unused_fields 1 005 eb2564 MODELTYPE: 1 STANDARDISATION: 0 GTEST: 0 VSL2G: 0 VSG2L: 0 KERNELTYPE: 1 BANDSELECTIONMETHOD: 1 Goldrangeflag: 0 Goldenmax: Goldenmin: Fixedbandsize: IntervalMax: IntervalMin: IntervalStep: Criteria: summary_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_F_summary.txt listwise_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_F_listwise.csv predictflag: 0 prediction_def: prediction_output: libpysal-4.9.2/libpysal/examples/tokyo/tokyo_BS_F_listwise.csv000066400000000000000000002775711452177046000246360ustar00rootroot00000000000000Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_OCC_TEC, se_OCC_TEC, t_OCC_TEC, est_OWNH, se_OWNH, t_OWNH, est_POP65, se_POP65, t_POP65, est_UNEMP, se_UNEMP, t_UNEMP, y, yhat, localpdev, Ginfluence 0, 0, 378906.83, 17310.41, 3.733837, 0.971933, 3.841659, 7.604286, 1.860931, 4.086281, -2.415012, 0.448544, -5.384117, -3.546584, 2.463207, -1.439823, 0.716628, 0.137907, 5.196449, 189.000000, 136.016202, 0.862581, 0.661318 1, 1, 334095.21, 25283.2, 7.633481, 0.465515, 16.397925, 5.986063, 1.988897, 3.009740, -5.484860, 0.463578, -11.831582, 7.358664, 1.633550, 4.504706, -0.284917, 0.074182, -3.840764, 95.000000, 84.898655, 0.610052, 0.519933 2, 2, 378200.19, -877.05, 6.370555, 0.418279, 15.230399, 5.515393, 1.095030, 5.036752, -3.511897, 0.229639, -15.293144, -5.897899, 1.291468, -4.566818, 0.155022, 0.089419, 1.733661, 70.000000, 54.568387, 0.815542, 0.406916 3, 3, 357191.03, 29064.39, 7.469382, 0.655363, 11.397321, 1.570668, 1.784117, 0.880361, -2.392275, 0.839200, -2.850661, -8.992308, 2.602416, -3.455369, -0.335384, 0.127733, -2.625658, 48.000000, 48.422571, 0.863300, 0.491301 4, 4, 358056.34, 10824.73, 7.987869, 0.418374, 19.092663, -2.836901, 0.895043, -3.169568, -5.062321, 0.306180, -16.533813, -4.015883, 1.206674, -3.328060, 0.414899, 0.063573, 6.526341, 65.000000, 48.998729, 0.824660, 0.243879 5, 5, 366747.61, -3073.12, 7.497491, 0.300542, 24.946529, 1.246136, 0.779104, 1.599448, -6.041447, 0.201584, -29.969930, -1.080732, 1.111288, -0.972504, 0.575874, 0.065769, 8.755945, 107.000000, 208.895547, 0.853538, 0.179771 6, 6, 351099.27, 11800.35, 8.029923, 0.316657, 25.358430, -0.243812, 0.771837, -0.315886, -4.449882, 0.291634, -15.258461, -5.934544, 1.105122, -5.370035, 0.140027, 0.047073, 2.974672, 65.000000, 47.158434, 0.764173, 0.186211 7, 7, 377929.98, 4635.1, 6.208413, 0.554347, 11.199513, 3.995825, 1.179244, 3.388462, -3.237152, 0.287947, -11.242188, -6.422843, 1.345907, -4.772131, 0.231338, 0.097082, 2.382919, 76.000000, 69.426794, 0.808589, 0.184727 8, 8, 367529.91, 20192.51, 11.614562, 0.867568, 13.387496, -9.521557, 1.693613, -5.622038, -6.567208, 0.503411, -13.045413, -12.104769, 2.300356, -5.262127, -0.021523, 0.094549, -0.227636, 192.000000, 189.338366, 0.902443, 0.983594 9, 9, 389231.47, 3489.35, 3.769475, 0.627516, 6.006980, 21.493203, 3.217808, 6.679456, -2.836678, 0.290188, -9.775322, 5.811257, 2.584423, 2.248570, -0.239897, 0.110822, -2.164702, 27.000000, 30.017181, 0.782592, 0.161108 10, 10, 389427.64, 9290.1, 2.665869, 0.804780, 3.312544, 16.721931, 1.894008, 8.828858, -1.803195, 0.387105, -4.658156, 2.597253, 2.024469, 1.282931, 0.262312, 0.117576, 2.230995, 28.000000, 56.016583, 0.776402, 0.612415 11, 11, 381089.82, 9125.81, 4.836019, 0.736322, 6.567802, 6.511210, 1.525004, 4.269635, -2.554751, 0.343713, -7.432808, -4.265225, 1.803603, -2.364835, 0.372463, 0.113588, 3.279060, 63.000000, 89.154680, 0.816103, 0.213814 12, 12, 371082.66, 6843.9, 8.335360, 0.554873, 15.022107, -2.055332, 1.168828, -1.758456, -4.679877, 0.317288, -14.749620, -8.933284, 1.383107, -6.458851, 0.241643, 0.083178, 2.905148, 34.000000, 37.770251, 0.840681, 0.279449 13, 13, 388281.84, -1760.78, 6.320805, 0.499328, 12.658629, 9.201957, 2.111719, 4.357568, -2.724512, 0.268417, -10.150282, -3.582488, 2.067774, -1.732534, -0.385577, 0.103333, -3.731413, 17.000000, 17.125746, 0.768526, 0.198115 14, 14, 386771.66, -4857.11, 7.032976, 0.435842, 16.136532, 5.846528, 1.665537, 3.510295, -2.708396, 0.253950, -10.665095, -6.893154, 1.703541, -4.046368, -0.382963, 0.095606, -4.005636, 25.000000, 18.437675, 0.762763, 0.229960 15, 15, 397029.93, 4912.15, 4.698214, 0.908195, 5.173133, 15.879515, 5.337109, 2.975303, -1.956176, 0.445832, -4.387701, -0.751298, 4.725634, -0.158983, -0.366157, 0.142363, -2.571995, 17.000000, 23.677903, 0.653981, 0.587128 16, 16, 399583.28, 1217.51, 9.282182, 1.028441, 9.025488, -6.914470, 5.994592, -1.153451, -1.222725, 0.462169, -2.645622, -19.054402, 5.331866, -3.573683, -0.717006, 0.173508, -4.132415, 31.000000, 19.105972, 0.690451, 0.296910 17, 17, 389413.79, 18915.59, -1.444570, 1.339507, -1.078434, 18.332063, 2.653094, 6.909693, -0.006101, 0.616420, -0.009898, 5.633864, 2.661627, 2.116700, 1.203928, 0.194438, 6.191846, 27.000000, 12.265678, 0.800026, 0.396445 18, 18, 374811.31, 23395.2, 8.322475, 1.155321, 7.203607, -2.248595, 2.015408, -1.115702, -4.853512, 0.534383, -9.082460, -9.113687, 3.211376, -2.837939, 0.309455, 0.141394, 2.188599, 10.000000, 18.947776, 0.896108, 0.678688 19, 19, 366291.01, 3851.09, 8.250102, 0.336219, 24.537874, -3.499448, 0.799923, -4.374730, -5.643844, 0.259624, -21.738540, -4.551783, 1.130188, -4.027458, 0.511471, 0.066088, 7.739300, 42.000000, 38.851594, 0.845606, 0.171456 20, 20, 362053.67, 7027.25, 7.937159, 0.347437, 22.844877, -3.768914, 0.811737, -4.643020, -5.615485, 0.282803, -19.856506, -2.368716, 1.197546, -1.977974, 0.585352, 0.064609, 9.059932, 20.000000, 33.265764, 0.833016, 0.284790 21, 21, 350567.45, 26456.28, 6.699485, 0.510671, 13.118982, 1.696692, 1.273124, 1.332700, -2.783038, 0.541687, -5.137723, -3.260913, 1.802777, -1.808828, -0.146944, 0.059139, -2.484711, 40.000000, 30.347495, 0.721773, 0.198322 22, 22, 356783.59, 23682.89, 7.521661, 0.470271, 15.994310, 0.183615, 1.139243, 0.161173, -3.222286, 0.460777, -6.993153, -6.152544, 1.604247, -3.835161, -0.128441, 0.072878, -1.762417, 15.000000, 32.828450, 0.855263, 0.203513 23, 23, 356225.47, 19763.98, 7.370494, 0.438452, 16.810262, -0.087218, 0.979894, -0.089008, -3.461085, 0.359128, -9.637478, -5.300757, 1.354300, -3.914020, 0.018124, 0.061982, 0.292407, 47.000000, 45.331976, 0.837784, 0.183057 24, 24, 338360.54, 25697.56, 7.325895, 0.470302, 15.576997, 2.977798, 2.067034, 1.440614, -4.511422, 0.467491, -9.650290, 3.348098, 1.615829, 2.072062, -0.192354, 0.073590, -2.613861, 68.000000, 83.306280, 0.559448, 0.751542 25, 25, 337846.31, 18213.38, 8.260727, 0.384536, 21.482328, 5.381755, 1.641807, 3.277947, -5.198496, 0.377272, -13.779175, 0.549191, 1.433826, 0.383025, -0.282858, 0.062709, -4.510672, 17.000000, 31.559675, 0.665322, 0.133065 26, 26, 344074.13, 27136.92, 7.040797, 0.490913, 14.342264, -0.457900, 1.721871, -0.265931, -3.656991, 0.497673, -7.348183, 0.056151, 1.664941, 0.033726, -0.096749, 0.066386, -1.457354, 57.000000, 40.328288, 0.515414, 0.327510 27, 27, 349087.82, 19336.47, 7.102234, 0.411731, 17.249697, 1.233253, 0.987872, 1.248394, -3.258769, 0.385098, -8.462191, -4.275916, 1.301114, -3.286350, -0.057746, 0.050717, -1.138582, 40.000000, 31.851575, 0.709362, 0.170408 28, 28, 343402.57, 18620.67, 7.663989, 0.401878, 19.070421, 2.865434, 1.194525, 2.398806, -4.031918, 0.378089, -10.663936, -3.068325, 1.296041, -2.367459, -0.159044, 0.053891, -2.951244, 47.000000, 54.712457, 0.653796, 0.187461 29, 29, 359036.48, 1198.74, 6.469415, 0.262081, 24.684759, 2.295594, 0.599811, 3.827196, -5.322184, 0.174799, -30.447368, 1.470154, 1.035025, 1.420404, 0.686545, 0.052393, 13.103826, 49.000000, 50.143528, 0.787073, 0.166990 30, 30, 370771.99, -1522.12, 8.314961, 0.380332, 21.862399, -1.496272, 0.929511, -1.609741, -5.610503, 0.226350, -24.786857, -7.353750, 1.072503, -6.856626, 0.453143, 0.079499, 5.699995, 49.000000, 49.437611, 0.868163, 0.193506 31, 31, 376842.13, -7139.16, 7.183744, 0.358872, 20.017556, 3.666425, 1.322337, 2.772686, -4.676953, 0.189537, -24.675713, -7.072792, 1.525613, -4.636032, 0.346706, 0.071824, 4.827137, 27.000000, 29.153068, 0.847765, 0.155855 32, 32, 318049.53, 32744.59, -1.221789, 0.531201, -2.300049, 44.971748, 3.478212, 12.929560, -3.485904, 0.373881, -9.323567, 32.732967, 2.811636, 11.641965, 0.072368, 0.088393, 0.818701, 120.000000, 89.909104, 0.822513, 0.365642 33, 33, 325761.21, 31092.21, 4.729386, 0.365786, 12.929382, 17.453029, 2.533433, 6.889082, -4.715922, 0.324256, -14.543827, 15.430865, 2.062461, 7.481775, -0.149649, 0.081050, -1.846369, 28.000000, 33.678223, 0.787159, 0.317704 34, 34, 318112.24, 28405.62, -0.184483, 0.480994, -0.383545, 42.090621, 3.091484, 13.615022, -4.069493, 0.321902, -12.642023, 31.402013, 2.338520, 13.428154, -0.011893, 0.078673, -0.151172, 16.000000, 23.971727, 0.810556, 0.150475 35, 35, 310480.1, 28809.03, -3.337650, 0.612938, -5.445326, 49.638282, 3.550811, 13.979419, -3.353446, 0.381077, -8.799924, 36.523097, 2.990828, 12.211699, 0.546023, 0.110361, 4.947593, 14.000000, 20.929776, 0.816584, 0.182015 36, 36, 306513.97, 32751.48, -4.195024, 0.682282, -6.148522, 51.054057, 4.141751, 12.326683, -2.441784, 0.484623, -5.038520, 34.292241, 3.827634, 8.959122, 0.684335, 0.145314, 4.709346, 43.000000, 59.493579, 0.808041, 0.885831 37, 37, 311395.51, 33538.42, -4.274782, 0.680904, -6.278098, 49.563020, 3.876084, 12.786881, -2.054468, 0.471215, -4.359934, 32.722189, 3.533555, 9.260415, 0.747399, 0.130974, 5.706452, 29.000000, 14.733918, 0.815480, 0.324677 38, 38, 314408.34, -4572.95, 7.406713, 0.225974, 32.776870, 6.290525, 0.566725, 11.099787, -3.236082, 0.158611, -20.402595, 3.592911, 0.819191, 4.385923, -0.584608, 0.046529, -12.564381, 401.000000, 135.072714, 0.331797, 0.059570 39, 39, 303850.22, 22478, 1.222417, 0.342641, 3.567627, 28.026817, 2.048276, 13.683127, -4.722063, 0.346857, -13.613870, 22.368784, 2.531664, 8.835607, 0.501208, 0.099814, 5.021420, 210.000000, 144.024687, 0.810371, 0.420977 40, 40, 337540.25, -12310.61, 8.221082, 0.123959, 66.320939, -5.274182, 0.273901, -19.255804, -4.031887, 0.108510, -37.156728, 1.117622, 0.309132, 3.615359, 0.036478, 0.018254, 1.998394, 711.000000, 327.797760, 0.441941, 0.094209 41, 41, 330948.96, -8687.59, 9.230254, 0.139814, 66.018029, -6.859851, 0.314378, -21.820394, -4.381734, 0.107722, -40.676165, 8.300839, 0.327806, 25.322406, -0.414191, 0.020625, -20.082375, 544.000000, 256.121003, 0.413358, 0.083743 42, 42, 327143.99, -3103.01, 11.678093, 0.218926, 53.342568, -3.636787, 0.518421, -7.015121, -6.374268, 0.142889, -44.610054, 12.447424, 0.532685, 23.367331, -1.169905, 0.042738, -27.373915, 557.000000, 243.014395, 0.438707, 0.082569 43, 43, 312830.72, 21412.1, 0.989689, 0.350563, 2.823141, 31.481093, 1.930041, 16.311103, -4.630783, 0.274011, -16.899991, 25.894769, 2.037724, 12.707693, 0.332626, 0.072897, 4.562979, 132.000000, 87.148071, 0.804690, 0.132460 44, 44, 312874.14, -17053.63, 6.722654, 0.207863, 32.341805, -2.510850, 0.518528, -4.842267, -2.074131, 0.150278, -13.801960, 15.239653, 0.691859, 22.027095, -0.489511, 0.036480, -13.418677, 395.000000, 130.512132, 0.161965, 0.108927 45, 45, 293680.38, -8010.1, 5.012179, 0.317275, 15.797599, 6.756179, 1.042320, 6.481868, -1.875680, 0.252137, -7.439125, -5.864103, 1.117595, -5.247072, 0.130836, 0.062223, 2.102684, 97.000000, 63.972156, 0.542988, 0.128300 46, 46, 325185.11, 20460.45, 7.127632, 0.319733, 22.292477, 14.359577, 1.776265, 8.084141, -6.401153, 0.272214, -23.515129, 12.597069, 1.509631, 8.344468, -0.332661, 0.063876, -5.207943, 91.000000, 79.153119, 0.736839, 0.206403 47, 47, 305971.31, 8472, 6.679134, 0.357640, 18.675593, 1.911287, 1.187155, 1.609973, -5.134445, 0.365351, -14.053468, 3.425133, 1.654214, 2.070551, 0.308213, 0.087688, 3.514893, 97.000000, 94.135553, 0.664403, 0.116036 48, 48, 335330.5, -108.19, 10.581691, 0.205588, 51.470485, -1.748511, 0.528343, -3.309424, -5.956540, 0.135985, -43.802811, 4.973659, 0.524948, 9.474575, -0.680308, 0.040915, -16.627328, 148.000000, 91.628613, 0.556158, 0.121754 49, 49, 339115.66, 3202.05, 10.018815, 0.201924, 49.616806, 3.110638, 0.532672, 5.839690, -5.821353, 0.142043, -40.983120, -3.556576, 0.720957, -4.933128, -0.438662, 0.042376, -10.351702, 269.000000, 177.355043, 0.620772, 0.160145 50, 50, 309271.13, -10589.17, 5.567444, 0.216781, 25.682297, 5.106108, 0.555771, 9.187438, -1.203855, 0.155436, -7.745044, -1.767200, 0.828429, -2.133194, -0.177201, 0.041682, -4.251280, 183.000000, 132.166071, 0.167956, 0.084673 51, 51, 319972.62, 24634.35, 2.235513, 0.387041, 5.775907, 31.898950, 2.436073, 13.094414, -4.527053, 0.273461, -16.554659, 23.443734, 1.916072, 12.235310, -0.102880, 0.069410, -1.482219, 86.000000, 66.906221, 0.788743, 0.126175 52, 52, 317013.43, 12374.14, 6.346523, 0.283608, 22.377833, 14.846060, 1.014106, 14.639562, -6.042177, 0.218360, -27.670681, 13.525016, 1.178795, 11.473596, -0.178826, 0.056951, -3.139989, 86.000000, 79.777189, 0.710684, 0.080855 53, 53, 323345.93, 2314.46, 8.972585, 0.269036, 33.350840, 8.895213, 0.790091, 11.258466, -5.197367, 0.189193, -27.471196, 5.637106, 0.858059, 6.569601, -0.830617, 0.051153, -16.237938, 244.000000, 170.238479, 0.519241, 0.135920 54, 54, 327610.55, -7504.02, 10.442891, 0.163893, 63.717794, -8.112325, 0.363493, -22.317677, -5.102387, 0.114887, -44.412133, 13.283851, 0.382331, 34.744378, -0.803649, 0.027096, -29.658770, 120.000000, 246.643080, 0.388342, 0.078910 55, 55, 343813.29, -11626.17, 8.113200, 0.122719, 66.111887, -3.654085, 0.294473, -12.408894, -4.232524, 0.103913, -40.731451, -2.381647, 0.313669, -7.592878, 0.178621, 0.019773, 9.033405, 297.000000, 380.664444, 0.524461, 0.154447 56, 56, 342508.85, -4698.16, 8.623004, 0.179492, 48.041251, -1.967639, 0.419848, -4.686553, -4.787788, 0.119543, -40.050912, 1.015837, 0.369374, 2.750161, -0.060339, 0.032882, -1.835003, 393.000000, 193.849616, 0.582535, 0.089399 57, 57, 333426.78, -13559.3, 8.114147, 0.130800, 62.034759, -5.809815, 0.266103, -21.832978, -3.809481, 0.115572, -32.962104, 3.416227, 0.312365, 10.936664, -0.031685, 0.018067, -1.753739, 103.000000, 343.822118, 0.392370, 0.054799 58, 58, 330824.95, -14794.45, 7.988677, 0.136693, 58.442666, -5.929035, 0.264825, -22.388493, -3.624144, 0.122126, -29.675409, 4.835080, 0.315739, 15.313538, -0.069532, 0.018212, -3.817847, 136.000000, 497.469763, 0.362875, 0.399874 59, 59, 304617.97, -15261.45, 4.443233, 0.244461, 18.175633, 6.963434, 0.603309, 11.542065, -0.445021, 0.173098, -2.570914, -5.746508, 0.820376, -7.004727, 0.071478, 0.045426, 1.573487, 160.000000, 134.465624, 0.147026, 0.220787 60, 60, 338062.78, -13156.47, 8.201557, 0.122844, 66.763732, -5.231937, 0.269527, -19.411530, -4.047315, 0.109607, -36.925612, 0.453080, 0.308288, 1.469665, 0.066243, 0.018067, 3.666485, 102.000000, 252.338100, 0.438639, 0.119407 61, 61, 325419.58, -15527.5, 7.850610, 0.151535, 51.807266, -6.129914, 0.287194, -21.344165, -3.220183, 0.129128, -24.937937, 9.640302, 0.329567, 29.251426, -0.247467, 0.019707, -12.557461, 142.000000, 253.528003, 0.324859, 0.114730 62, 62, 324052.62, -12510.9, 8.606349, 0.157293, 54.715501, -7.462160, 0.318477, -23.430745, -3.598669, 0.119923, -30.008282, 13.748053, 0.356288, 38.586870, -0.499742, 0.022269, -22.441350, 83.000000, 113.470274, 0.319020, 0.090024 63, 63, 327521.88, -17674.68, 7.767114, 0.144852, 53.620913, -5.891738, 0.268556, -21.938544, -3.442820, 0.133099, -25.866627, 6.667126, 0.315535, 21.129573, -0.102733, 0.018542, -5.540506, 78.000000, 244.726072, 0.338433, 0.070992 64, 64, 322114.34, -17894.35, 7.440423, 0.163001, 45.646570, -5.711885, 0.308616, -18.508036, -2.866805, 0.138596, -20.684660, 13.140480, 0.339210, 38.738487, -0.313395, 0.020845, -15.034317, 201.000000, 161.589612, 0.308794, 0.090444 65, 65, 320355.21, 5840.48, 7.463686, 0.296061, 25.209991, 12.866700, 0.900983, 14.280730, -5.180899, 0.211977, -24.440861, 8.224624, 0.958771, 8.578298, -0.548750, 0.054602, -10.050052, 87.000000, 84.903050, 0.608988, 0.067967 66, 66, 330341.07, 12925.79, 9.846863, 0.326198, 30.186760, 5.376013, 1.183150, 4.543814, -7.228884, 0.272752, -26.503474, 4.111297, 1.074537, 3.826110, -0.424304, 0.053691, -7.902773, 89.000000, 139.947321, 0.726982, 0.133612 67, 67, 318527.16, 8318.24, 7.452532, 0.310412, 24.008555, 11.688301, 0.954728, 12.242549, -5.754218, 0.229486, -25.074406, 9.848185, 1.014011, 9.712113, -0.410883, 0.056374, -7.288553, 80.000000, 109.234129, 0.656944, 0.117626 68, 68, 347297.26, -13547.71, 8.062163, 0.123722, 65.163554, -3.448764, 0.298179, -11.566078, -4.405075, 0.105471, -41.765868, -4.378503, 0.314794, -13.909100, 0.287569, 0.020427, 14.077592, 105.000000, 347.804143, 0.570404, 0.221654 69, 69, 321375.58, -10594.12, 9.741930, 0.198518, 49.073224, -8.429942, 0.405252, -20.801753, -4.440917, 0.134797, -32.945331, 18.899502, 0.512158, 36.901691, -0.864113, 0.035727, -24.186535, 114.000000, 215.890155, 0.293577, 0.141446 70, 70, 318675.19, -8454.47, 9.292138, 0.209489, 44.356272, -4.458974, 0.459162, -9.711106, -4.286690, 0.140992, -30.403781, 15.032095, 0.652012, 23.054953, -0.832577, 0.039424, -21.118491, 101.000000, 170.898285, 0.274093, 0.153946 71, 71, 350174.04, -12060.87, 7.724683, 0.143460, 53.845660, -1.096714, 0.349892, -3.134439, -4.610616, 0.109901, -41.952361, -5.622646, 0.332137, -16.928691, 0.398749, 0.024844, 16.050237, 181.000000, 274.560223, 0.643878, 0.185035 72, 72, 329442.54, 5939.52, 10.485843, 0.252850, 41.470662, 5.785750, 0.762496, 7.587914, -5.960074, 0.179677, -33.171046, -2.080352, 0.955357, -2.177565, -0.796277, 0.049476, -16.094159, 82.000000, 85.376041, 0.628383, 0.163108 73, 73, 307098.94, 992.68, 6.819145, 0.329757, 20.679294, 6.645124, 0.896223, 7.414585, -4.095353, 0.276128, -14.831333, 1.068802, 1.110176, 0.962732, -0.145988, 0.072600, -2.010844, 93.000000, 169.444107, 0.535486, 0.385692 74, 74, 337247.61, 14030.91, 9.381961, 0.323266, 29.022394, 6.512867, 1.307413, 4.981493, -6.495872, 0.294517, -22.056015, 1.022044, 1.186167, 0.861635, -0.367437, 0.054484, -6.743963, 67.000000, 94.783526, 0.710967, 0.366925 75, 75, 306612.95, -2173.79, 6.232024, 0.286311, 21.766622, 7.822977, 0.791220, 9.887229, -2.992823, 0.220999, -13.542243, -2.014701, 0.972011, -2.072714, -0.158913, 0.057959, -2.741798, 55.000000, 144.471953, 0.464682, 0.300404 76, 76, 301727, -6640.87, 5.035785, 0.275054, 18.308332, 8.170797, 0.759854, 10.753112, -1.661879, 0.202970, -8.187827, -5.871201, 0.952604, -6.163319, 0.045090, 0.052837, 0.853365, 68.000000, 71.934875, 0.412437, 0.101648 77, 77, 326724.18, 5478.1, 9.711995, 0.285077, 34.067914, 7.256669, 0.860010, 8.437886, -5.705141, 0.197142, -28.939211, 1.485641, 0.935684, 1.587758, -0.761919, 0.051058, -14.922627, 47.000000, 73.529624, 0.609055, 0.159449 78, 78, 311503.39, 15708.66, 4.141344, 0.257873, 16.059640, 12.923000, 1.119355, 11.545040, -5.106140, 0.241135, -21.175404, 13.404180, 1.555867, 8.615247, 0.482982, 0.074160, 6.512676, 33.000000, 45.304118, 0.753266, 0.192785 79, 79, 316934.08, -10632.09, 8.478430, 0.203816, 41.598522, -4.574868, 0.462250, -9.896966, -3.567851, 0.140704, -25.357051, 15.796205, 0.669546, 23.592403, -0.710880, 0.037571, -18.920806, 43.000000, 107.208768, 0.239103, 0.096541 80, 80, 317980.71, -13171.59, 8.473116, 0.199504, 42.470954, -7.057898, 0.425190, -16.599405, -3.461861, 0.138992, -24.906958, 20.450176, 0.562637, 36.347014, -0.732764, 0.034993, -20.940417, 52.000000, 127.737760, 0.246255, 0.115064 81, 81, 298790.59, -2464.08, 5.519227, 0.321254, 17.180274, 5.769114, 0.993292, 5.808077, -2.687167, 0.272447, -9.863095, -4.317603, 1.253006, -3.445795, 0.163502, 0.061660, 2.651666, 75.000000, 133.933213, 0.534626, 0.657915 82, 82, 294903.64, 214.57, 5.835873, 0.368619, 15.831704, 0.610837, 1.260745, 0.484505, -3.971215, 0.384622, -10.324974, -3.172193, 1.505850, -2.106580, 0.577706, 0.093224, 6.196970, 33.000000, 25.392909, 0.616649, 0.178259 83, 83, 284950.61, -7897.72, 6.556748, 0.377637, 17.362560, 3.570807, 1.300759, 2.745171, -3.520705, 0.333533, -10.555793, -4.537579, 1.369561, -3.313163, 0.076968, 0.091553, 0.840698, 11.000000, 15.263633, 0.716600, 0.335960 84, 84, 302616.14, 12642.65, 5.625498, 0.293094, 19.193515, 1.836153, 1.296021, 1.416761, -5.181827, 0.326018, -15.894301, 4.276221, 1.847474, 2.314631, 0.684802, 0.101498, 6.746957, 23.000000, 50.127860, 0.718028, 0.219692 85, 85, 298937.62, 11074.43, 6.351558, 0.346974, 18.305573, -0.174646, 1.422952, -0.122735, -5.760060, 0.393487, -14.638488, 4.280949, 2.004205, 2.135984, 0.628737, 0.111008, 5.663912, 43.000000, 35.586889, 0.701005, 0.096670 86, 86, 292980.66, 10621.27, 6.926095, 0.442921, 15.637314, -2.017581, 1.575853, -1.280310, -6.094146, 0.518529, -11.752749, 2.525633, 2.305945, 1.095270, 0.624941, 0.127030, 4.919631, 46.000000, 41.799827, 0.715814, 0.224526 87, 87, 291341.64, 3602.46, 6.022480, 0.439151, 13.713916, -0.887602, 1.544336, -0.574747, -4.429273, 0.489349, -9.051352, -2.518016, 1.929422, -1.305062, 0.641364, 0.126099, 5.086182, 19.000000, 18.571872, 0.641909, 0.128850 88, 88, 296052.78, 6812.78, 6.395125, 0.423789, 15.090355, -1.748511, 1.511036, -1.157160, -5.138580, 0.474522, -10.828958, -0.228112, 1.988039, -0.114742, 0.669221, 0.119254, 5.611743, 11.000000, 18.103811, 0.652946, 0.167571 89, 89, 314476.95, 3490.04, 7.969387, 0.290575, 27.426267, 10.137717, 0.798212, 12.700534, -5.457497, 0.226116, -24.135822, 8.454139, 0.958679, 8.818531, -0.599320, 0.068146, -8.794681, 30.000000, 40.073075, 0.569590, 0.156007 90, 90, 311673.48, 10101.08, 6.841848, 0.297539, 22.994813, 7.341919, 0.982911, 7.469567, -5.795509, 0.268200, -21.608923, 9.434818, 1.311417, 7.194371, 0.000982, 0.071955, 0.013647, 27.000000, 24.315983, 0.684238, 0.110118 91, 91, 300937.58, 3470.02, 6.197677, 0.382772, 16.191562, 1.454017, 1.209751, 1.201914, -4.015535, 0.373716, -10.744878, -2.193053, 1.543737, -1.420613, 0.392506, 0.085841, 4.572496, 18.000000, 27.718217, 0.607942, 0.328972 92, 92, 286991.93, 9571.27, 6.923271, 0.497429, 13.918120, -2.545901, 1.644814, -1.547835, -5.227230, 0.598738, -8.730420, -3.725020, 2.716777, -1.371117, 0.654638, 0.141816, 4.616098, 7.000000, 13.656931, 0.739065, 0.419667 93, 93, 307386.12, 16090.18, 4.146471, 0.262521, 15.794798, 8.397684, 1.182300, 7.102839, -4.661845, 0.278293, -16.751567, 8.861780, 1.714889, 5.167552, 0.693122, 0.089093, 7.779731, 7.000000, 23.416545, 0.760438, 0.146639 94, 94, 300604.96, 17843.82, 4.121478, 0.286778, 14.371671, 8.640455, 1.340085, 6.447690, -4.584931, 0.324727, -14.119359, 7.316767, 2.101661, 3.481421, 0.680864, 0.104158, 6.536868, 15.000000, 34.476048, 0.785731, 0.120764 95, 95, 303917.55, 29223.91, -2.643584, 0.560950, -4.712692, 53.164441, 3.912689, 13.587699, -4.161008, 0.431351, -9.646456, 39.324447, 3.792356, 10.369398, 0.203584, 0.119370, 1.705491, 44.000000, 23.270797, 0.809555, 0.177838 96, 96, 296097.2, 19299.56, 4.111861, 0.319495, 12.869887, 10.769804, 1.523625, 7.068539, -4.530288, 0.368524, -12.293058, 5.594407, 2.652352, 2.109225, 0.643341, 0.114253, 5.630870, 25.000000, 15.734950, 0.806908, 0.181462 97, 97, 291327.94, 19385.45, 4.890539, 0.372449, 13.130765, 10.335983, 1.853579, 5.576229, -4.814233, 0.432290, -11.136577, 2.213396, 3.441946, 0.643065, 0.585604, 0.127274, 4.601125, 15.000000, 16.545446, 0.820930, 0.250257 98, 98, 288651.19, 16782.21, 7.452557, 0.442157, 16.855009, 1.457044, 1.831080, 0.795729, -6.418561, 0.517156, -12.411271, -0.410727, 3.755461, -0.109368, 0.538865, 0.137446, 3.920567, 53.000000, 51.731578, 0.815008, 0.595608 99, 99, 321850.11, 16542.18, 6.557259, 0.310472, 21.120306, 18.747853, 1.516106, 12.365791, -6.754668, 0.235151, -28.724754, 15.825711, 1.340683, 11.804214, -0.292793, 0.057504, -5.091721, 24.000000, 20.301308, 0.751169, 0.095014 100, 100, 309623.17, 24691.81, -1.013365, 0.426214, -2.377594, 38.877092, 2.318708, 16.766706, -4.327815, 0.332736, -13.006756, 31.932756, 2.590469, 12.327016, 0.491862, 0.094091, 5.227539, 7.000000, 12.738887, 0.814011, 0.108014 101, 101, 317273.81, 16350.36, 4.096083, 0.286272, 14.308352, 25.201495, 1.524278, 16.533398, -5.890396, 0.219245, -26.866763, 20.496975, 1.572366, 13.035755, -0.033813, 0.058598, -0.577030, 13.000000, 24.123396, 0.755465, 0.123587 102, 102, 330127.65, 26472.36, 7.610302, 0.443836, 17.146653, 9.059044, 2.035632, 4.450236, -6.290736, 0.432548, -14.543446, 11.603852, 1.705749, 6.802790, -0.328214, 0.075223, -4.363183, 18.000000, 19.994567, 0.677587, 0.220331 103, 103, 330024.3, 22050.13, 8.230511, 0.415519, 19.807785, 8.315199, 1.800115, 4.619259, -6.504106, 0.412893, -15.752536, 9.114558, 1.560099, 5.842296, -0.362166, 0.068879, -5.257980, 24.000000, 29.430605, 0.675632, 0.138766 104, 104, 335366.5, 8522.69, 10.570103, 0.229342, 46.088788, 3.739182, 0.802140, 4.661508, -6.543031, 0.183171, -35.720938, -4.342394, 1.007303, -4.310909, -0.496843, 0.048816, -10.177853, 45.000000, 113.582699, 0.692883, 0.086085 105, 105, 330795.7, 8625.57, 10.478403, 0.261735, 40.034455, 4.649335, 0.853279, 5.448790, -6.508568, 0.197311, -32.986409, -1.810223, 1.007217, -1.797252, -0.610735, 0.049949, -12.227172, 56.000000, 45.238543, 0.687199, 0.070863 106, 106, 324461.1, 12021.3, 8.745709, 0.355032, 24.633561, 8.910784, 1.227186, 7.261152, -6.671530, 0.267990, -24.894710, 7.358995, 1.033892, 7.117758, -0.432367, 0.053390, -8.098249, 33.000000, 34.835430, 0.715250, 0.096452 107, 107, 333249.02, 19193.73, 8.482396, 0.384472, 22.062475, 7.153017, 1.659712, 4.309796, -6.171382, 0.376327, -16.398969, 5.558480, 1.469982, 3.781325, -0.353856, 0.063552, -5.567985, 35.000000, 40.363324, 0.676334, 0.118022 108, 108, 330905.38, 16199.04, 9.207971, 0.348096, 26.452426, 8.214210, 1.404078, 5.850254, -7.260345, 0.311775, -23.287161, 7.468243, 1.239679, 6.024336, -0.417506, 0.057740, -7.230778, 36.000000, 89.142617, 0.719931, 0.263849 109, 109, 338740.71, 9995.65, 10.260736, 0.241733, 42.446601, 3.840631, 0.935867, 4.103823, -6.799621, 0.215058, -31.617590, -2.577258, 1.021857, -2.522133, -0.363454, 0.049723, -7.309638, 58.000000, 65.672340, 0.709763, 0.082427 110, 110, 345541.9, -607.56, 8.318732, 0.209962, 39.620253, 3.300694, 0.501319, 6.584023, -5.350369, 0.139096, -38.465341, -1.316131, 0.573047, -2.296725, 0.043310, 0.041459, 1.044661, 37.000000, 49.636334, 0.656997, 0.101157 111, 111, 348908.69, -5077.51, 7.443811, 0.209538, 35.524801, 2.911204, 0.506816, 5.744103, -4.841513, 0.131466, -36.827047, -3.785916, 0.456118, -8.300294, 0.342057, 0.040701, 8.404192, 53.000000, 138.990200, 0.678004, 0.149281 112, 112, 343120.93, 5902.89, 9.574685, 0.205354, 46.625355, 5.305214, 0.588839, 9.009610, -6.101011, 0.161321, -37.819070, -3.884690, 0.866403, -4.483697, -0.269143, 0.044565, -6.039306, 49.000000, 57.562331, 0.677118, 0.123613 113, 113, 377836.69, -36378.58, 21.787188, 0.530679, 41.055339, -18.681104, 1.506680, -12.398854, -9.910203, 0.331168, -29.924972, -0.526501, 1.154661, -0.455979, -2.869207, 0.108796, -26.372317, 1070.000000, 895.183872, 0.889955, 0.840623 114, 114, 356153.1, -24448.15, 7.562334, 0.169401, 44.641629, -4.827139, 0.387895, -12.444460, -4.359713, 0.147503, -29.556705, -5.304317, 0.334641, -15.850784, 0.540745, 0.025797, 20.961390, 547.000000, 442.996145, 0.603269, 0.215934 115, 115, 363934.49, -23252.2, 7.600983, 0.215684, 35.241357, -2.609109, 0.643092, -4.057132, -4.798309, 0.135094, -35.518217, -0.333307, 0.427044, -0.780499, 0.411978, 0.037762, 10.909736, 660.000000, 385.804803, 0.737474, 0.152696 116, 116, 362715.03, -62961.03, 29.167648, 2.062674, 14.140698, -126.283291, 14.204511, -8.890365, -0.103189, 1.617565, -0.063793, -95.024737, 11.487472, -8.272032, -0.489596, 0.159873, -3.062410, 175.000000, 172.286217, 0.949761, 0.999788 117, 117, 355515.39, -15862.17, 7.164504, 0.173536, 41.285413, -0.104200, 0.419194, -0.248572, -4.770197, 0.127514, -37.409106, -6.183012, 0.346690, -17.834422, 0.603661, 0.030302, 19.921518, 594.000000, 402.737672, 0.693102, 0.177270 118, 118, 350331.74, 2259.59, 7.573433, 0.225945, 33.518950, 5.246458, 0.559625, 9.374946, -5.979276, 0.155695, -38.403709, 2.390502, 0.800740, 2.985366, 0.293773, 0.042322, 6.941396, 189.000000, 119.136654, 0.723723, 0.171726 119, 119, 390869.17, -52824.71, 10.350300, 0.695374, 14.884509, -0.547863, 2.914695, -0.187966, -6.049121, 0.892877, -6.774863, -12.561474, 2.717905, -4.621749, 0.182459, 0.128194, 1.423308, 121.000000, 147.602328, 0.967298, 0.665243 120, 120, 391663.46, -13955.69, 7.817277, 0.502912, 15.544018, 3.778428, 2.113735, 1.787560, -2.178059, 0.318020, -6.848815, -12.014226, 2.223815, -5.402528, -0.516578, 0.109880, -4.701310, 125.000000, 133.598847, 0.694023, 0.865009 121, 121, 381676.42, -24737.89, 9.156131, 0.326638, 28.031451, 10.226239, 1.177131, 8.687430, -5.410854, 0.163228, -33.149012, -8.078592, 1.585401, -5.095616, -0.366890, 0.067043, -5.472429, 175.000000, 106.254826, 0.881641, 0.189049 122, 122, 396227.77, -39792.02, 10.878174, 0.423331, 25.696599, -1.236017, 2.036436, -0.606951, -9.613243, 0.414036, -23.218377, -2.299812, 1.301080, -1.767617, 0.545285, 0.096238, 5.665980, 85.000000, 82.795918, 0.925503, 0.219159 123, 123, 365535.4, -28214.23, 9.034374, 0.264820, 34.115157, -7.469792, 0.889050, -8.401990, -4.802087, 0.138672, -34.629027, 1.307600, 0.558629, 2.340732, 0.092102, 0.040961, 2.248543, 200.000000, 428.617976, 0.716519, 0.408661 124, 124, 358761.78, -6929.68, 6.428426, 0.235326, 27.317150, 6.301939, 0.637850, 9.879970, -5.507818, 0.145128, -37.951531, -1.850994, 0.719312, -2.573283, 0.658149, 0.048200, 13.654523, 389.000000, 323.261838, 0.796758, 0.279355 125, 125, 376070.01, -56303.59, 13.345040, 0.906320, 14.724429, 6.096086, 3.304773, 1.844631, -11.344943, 0.734353, -15.448901, 5.590458, 2.707498, 2.064806, -0.657920, 0.122402, -5.375057, 381.000000, 333.321734, 0.973985, 0.751583 126, 126, 353788.69, -7340.62, 6.827818, 0.215859, 31.630929, 5.126036, 0.556612, 9.209352, -5.154026, 0.134366, -38.358188, -6.254956, 0.508575, -12.298992, 0.615458, 0.043050, 14.296405, 173.000000, 178.526413, 0.744145, 0.155321 127, 127, 371670.71, -21543.62, 9.976665, 0.246956, 40.398602, -5.877120, 0.874685, -6.719125, -6.183195, 0.131882, -46.884184, -1.378728, 1.162886, -1.185609, 0.027488, 0.049813, 0.551819, 206.000000, 295.924834, 0.832370, 0.287788 128, 128, 367522.64, -7189.91, 7.351073, 0.288868, 25.447836, 2.971584, 0.849537, 3.497884, -5.838876, 0.179685, -32.495065, -1.977311, 1.095416, -1.805077, 0.518168, 0.061590, 8.413234, 142.000000, 168.411881, 0.855070, 0.332469 129, 129, 361892.77, -18166, 6.780151, 0.209683, 32.335173, 1.047887, 0.564073, 1.857715, -4.966956, 0.137719, -36.065784, -1.771813, 0.411774, -4.302876, 0.608679, 0.038886, 15.652816, 148.000000, 96.657503, 0.757812, 0.133210 130, 130, 366450.66, -74634.65, 28.632797, 2.097502, 13.650903, -127.606495, 14.562538, -8.762655, 1.076168, 1.664541, 0.646525, -98.487455, 11.907996, -8.270700, -0.407878, 0.161082, -2.532120, 141.000000, 145.079268, 0.949297, 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22.148017, 22.370311, 1.578713, 14.169968, -6.109164, 0.215090, -28.402819, 3.018668, 1.467977, 2.056346, -0.079141, 0.072288, -1.094806, 88.000000, 35.185447, 0.918441, 0.215647 136, 136, 344415.42, 11511.89, 9.176860, 0.274366, 33.447516, 4.069599, 0.858612, 4.739741, -5.831845, 0.276457, -21.094975, -3.272365, 1.116366, -2.931266, -0.191107, 0.045913, -4.162371, 31.000000, 26.404549, 0.714976, 0.205549 137, 137, 365053.58, -11168.12, 6.845324, 0.256301, 26.708120, 3.544984, 0.692109, 5.122005, -5.577386, 0.156270, -35.690803, -1.407917, 0.934920, -1.505923, 0.604766, 0.054220, 11.153862, 60.000000, 70.091438, 0.833493, 0.132298 138, 138, 387334.51, -21934.03, 6.345661, 0.404881, 15.672889, 19.819131, 1.610896, 12.303169, -4.842733, 0.188940, -25.631070, -1.268579, 1.842721, -0.688427, -0.107764, 0.078569, -1.371571, 25.000000, 88.151167, 0.876417, 0.554347 139, 139, 393097.28, -22717.2, 5.916184, 0.438753, 13.484086, 20.324507, 1.975305, 10.289302, -3.719710, 0.251872, -14.768273, -2.308724, 1.971704, -1.170929, -0.231701, 0.095261, -2.432280, 57.000000, 46.472007, 0.835232, 0.659586 140, 140, 380945.88, -17224.24, 7.516031, 0.335868, 22.377941, 7.250470, 1.121036, 6.467648, -4.947145, 0.171667, -28.818309, -6.073731, 1.636804, -3.710727, 0.136627, 0.067238, 2.031974, 17.000000, 11.484590, 0.850189, 0.222238 141, 141, 367373.14, -14712.42, 7.570291, 0.245858, 30.791303, 0.310876, 0.704165, 0.441481, -5.654789, 0.147151, -38.428482, -0.977744, 0.952432, -1.026576, 0.483341, 0.052071, 9.282305, 47.000000, 51.481410, 0.828595, 0.134876 142, 142, 374567.12, -13256.38, 8.060525, 0.308234, 26.150671, 0.955797, 1.156403, 0.826526, -5.278233, 0.152945, -34.510748, -7.776018, 1.590800, -4.888117, 0.356347, 0.063303, 5.629191, 51.000000, 79.147899, 0.845702, 0.207651 143, 143, 380862.18, -12688.75, 6.908284, 0.346058, 19.962774, 5.448395, 1.213940, 4.488191, -4.369492, 0.191746, -22.787874, -5.650760, 1.555966, -3.631673, 0.234999, 0.070056, 3.354443, 11.000000, 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0.534075, -13.461276, -5.153385, 1.718801, -2.998243, 1.771710, 0.079720, 22.224219, 28.000000, 4.164857, 0.575914, 0.109455 260, 260, 290541.99, -38708.26, 0.838891, 0.390746, 2.146898, 20.227467, 0.930306, 21.742806, -1.094166, 0.262461, -4.168871, -11.061643, 1.181667, -9.361051, 1.032313, 0.063748, 16.193717, 10.000000, 61.502987, 0.539925, 0.206375 261, 261, 285964.14, -39392.46, 1.068904, 0.519274, 2.058458, 30.022341, 1.334604, 22.495311, -2.975280, 0.326218, -9.120541, -8.408828, 1.557555, -5.398736, 0.793670, 0.078583, 10.099795, 12.000000, 11.604152, 0.548893, 0.170905 libpysal-4.9.2/libpysal/examples/tokyo/tokyo_BS_F_summary.txt000066400000000000000000000237321452177046000245000ustar00rootroot00000000000000***************************************************************************** * Semiparametric Geographically Weighted Regression * * Release 1.0.90 (GWR 4.0.90) * * 12 May 2015 * * (Originally coded by T. Nakaya: 1 Nov 2009) * * * * Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * * Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * * (c) GWR4 development team * * (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * * (3) University of Glasgow, (4) Arizona State University * ***************************************************************************** Program began at 7/25/2016 8:23:34 AM ***************************************************************************** Session: Session control file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_F.ctl ***************************************************************************** Data filename: C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt Number of areas/points: 262 Model settings--------------------------------- Model type: Poisson Geographic kernel: fixed bi-square Method for optimal bandwidth search: Golden section search Criterion for optimal bandwidth: AICc Number of varying coefficients: 5 Number of fixed coefficients: 0 Modelling options--------------------------------- Standardisation of independent variables: OFF Testing geographical variability of local coefficients: OFF Local to Global Variable selection: OFF Global to Local Variable selection: OFF Prediction at non-regression points: OFF Variable settings--------------------------------- Area key: field1: IDnum0 Easting (x-coord): field2 : X_CENTROID Northing (y-coord): field3: Y_CENTROID Cartesian coordinates: Euclidean distance Dependent variable: field4: db2564 Offset variable is not specified Intercept: varying (Local) intercept Independent variable with varying (Local) coefficient: field6: OCC_TEC Independent variable with varying (Local) coefficient: field7: OWNH Independent variable with varying (Local) coefficient: field8: POP65 Independent variable with varying (Local) coefficient: field9: UNEMP ***************************************************************************** ***************************************************************************** Global regression result ***************************************************************************** < Diagnostic information > Number of parameters: 5 Deviance: 24597.455544 Classic AIC: 24607.455544 AICc: 24607.689919 BIC/MDL: 24625.297266 Percent deviance explained 0.526746 Variable Estimate Standard Error z(Est/SE) Exp(Est) -------------------- --------------- --------------- --------------- --------------- Intercept 8.432403 0.061613 136.859875 4593.526955 OCC_TEC -4.270431 0.156467 -27.292831 0.013976 OWNH -4.789311 0.046070 -103.957933 0.008318 POP65 -1.252659 0.178384 -7.022265 0.285744 UNEMP 0.061305 0.010099 6.070542 1.063223 ***************************************************************************** GWR (Geographically weighted regression) bandwidth selection ***************************************************************************** Bandwidth search Limits: 17528.9489150947, 134660.843497798 Golden section search begins... Initial values pL Bandwidth: 26029.625 Criterion: 13294.025 p1 Bandwidth: 28341.975 Criterion: 14337.915 p2 Bandwidth: 29771.086 Criterion: 14934.143 pU Bandwidth: 32083.436 Criterion: 15739.259 iter 1 (p1) Bandwidth: 28341.975 Criterion: 14337.915 Diff: 1429.111 iter 2 (p1) Bandwidth: 27458.736 Criterion: 13940.360 Diff: 883.239 iter 3 (p1) Bandwidth: 26912.864 Criterion: 13690.874 Diff: 545.872 iter 4 (p1) Bandwidth: 26575.497 Criterion: 13538.151 Diff: 337.367 iter 5 (p1) Bandwidth: 26366.993 Criterion: 13445.104 Diff: 208.504 iter 6 (p1) Bandwidth: 26238.130 Criterion: 13387.798 Diff: 128.863 iter 7 (p1) Bandwidth: 26158.488 Criterion: 13352.102 Diff: 79.642 iter 8 (p1) Bandwidth: 26109.267 Criterion: 13329.966 Diff: 49.221 iter 9 (p1) Bandwidth: 26078.847 Criterion: 13316.257 Diff: 30.420 iter 10 (p1) Bandwidth: 26060.046 Criterion: 13307.773 Diff: 18.801 iter 11 (p1) Bandwidth: 26048.426 Criterion: 13302.525 Diff: 11.620 iter 12 (p1) Bandwidth: 26041.245 Criterion: 13299.279 Diff: 7.181 iter 13 (p1) Bandwidth: 26036.807 Criterion: 13297.273 Diff: 4.438 iter 14 (p1) Bandwidth: 26034.064 Criterion: 13296.032 Diff: 2.743 iter 15 (p1) Bandwidth: 26032.368 Criterion: 13295.266 Diff: 1.695 iter 16 (p1) Bandwidth: 26031.321 Criterion: 13294.792 Diff: 1.048 iter 17 (p1) Bandwidth: 26030.673 Criterion: 13294.499 Diff: 0.648 iter 18 (p1) Bandwidth: 26030.273 Criterion: 13294.318 Diff: 0.400 iter 19 (p1) Bandwidth: 26030.026 Criterion: 13294.206 Diff: 0.247 iter 20 (p1) Bandwidth: 26029.873 Criterion: 13294.137 Diff: 0.153 iter 21 (p1) Bandwidth: 26029.778 Criterion: 13294.094 Diff: 0.094 iter 22 (p1) Bandwidth: 26029.720 Criterion: 13294.067 Diff: 0.058 iter 23 (p1) Bandwidth: 26029.684 Criterion: 13294.051 Diff: 0.036 iter 24 (p1) Bandwidth: 26029.661 Criterion: 13294.041 Diff: 0.022 The lower limit in your search has been selected as the optimal bandwidth size. A new sesssion is recommended to try with a smaller lowest limit of the bandwidth search. Best bandwidth size 26029.625 Minimum AICc 13294.025 ***************************************************************************** GWR (Geographically weighted regression) result ***************************************************************************** Bandwidth and geographic ranges Bandwidth size: 26029.625402 Coordinate Min Max Range --------------- --------------- --------------- --------------- X-coord 276385.400000 408226.180000 131840.780000 Y-coord -86587.480000 33538.420000 120125.900000 Diagnostic information Effective number of parameters (model: trace(S)): 66.434760 Effective number of parameters (variance: trace(S'WSW^-1)): 51.742762 Degree of freedom (model: n - trace(S)): 195.565240 Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 180.873243 Deviance: 13115.103705 Classic AIC: 13247.973224 AICc: 13294.024739 BIC/MDL: 13485.035334 Percent deviance explained 0.747666 *********************************************************** << Geographically varying (Local) coefficients >> *********************************************************** Estimates of varying coefficients have been saved in the following file. Listwise output file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_F_listwise.csv Summary statistics for varying (Local) coefficients Variable Mean STD -------------------- --------------- --------------- Intercept 6.956437 3.585435 OCC_TEC 2.808094 16.199511 OWNH -4.025297 2.176207 POP65 0.709104 13.441155 UNEMP 0.083338 0.534893 Variable Min Max Range -------------------- --------------- --------------- --------------- Intercept -4.698846 29.167648 33.866494 OCC_TEC -127.606495 60.091449 187.697944 OWNH -12.474311 3.217910 15.692220 POP65 -98.487455 39.324447 137.811902 UNEMP -2.869207 1.771710 4.640917 Variable Lwr Quartile Median Upr Quartile -------------------- --------------- --------------- --------------- Intercept 5.487963 7.375409 8.489884 OCC_TEC -4.826044 1.873720 7.407511 OWNH -5.189891 -4.171458 -2.506044 POP65 -5.189369 -0.656530 6.532191 UNEMP -0.277246 0.052468 0.483072 Variable Interquartile R Robust STD -------------------- --------------- --------------- Intercept 3.001921 2.225294 OCC_TEC 12.233554 9.068610 OWNH 2.683847 1.989508 POP65 11.721560 8.689074 UNEMP 0.760318 0.563616 (Note: Robust STD is given by (interquartile range / 1.349) ) ***************************************************************************** GWR Analysis of Deviance Table ***************************************************************************** Source Deviance DOF Deviance/DOF ------------ ------------------- ---------- ---------------- Global model 24597.456 257.000 95.710 GWR model 13115.104 180.873 72.510 Difference 11482.352 76.127 150.832 ***************************************************************************** Program terminated at 7/25/2016 8:23:42 AM libpysal-4.9.2/libpysal/examples/tokyo/tokyo_BS_NN.ctl000066400000000000000000000014111452177046000230020ustar00rootroot00000000000000 C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt FORMAT/DELIMITER: 0 Number_of_fields: 9 Number_of_areas: 262 Fields AreaKey 001 IDnum0 X 002 X_CENTROID Y 003 Y_CENTROID Gmetric 0 Dependent 004 db2564 Offset Independent_geo 5 000 Intercept 006 OCC_TEC 007 OWNH 008 POP65 009 UNEMP Independent_fix 0 Unused_fields 1 005 eb2564 MODELTYPE: 1 STANDARDISATION: 0 GTEST: 0 VSL2G: 0 VSG2L: 0 KERNELTYPE: 2 BANDSELECTIONMETHOD: 1 Goldrangeflag: 0 Goldenmax: Goldenmin: Fixedbandsize: IntervalMax: IntervalMin: IntervalStep: Criteria: summary_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_NN_summary.txt listwise_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_NN_listwise.csv predictflag: 0 prediction_def: prediction_output: libpysal-4.9.2/libpysal/examples/tokyo/tokyo_BS_NN_OFF.ctl000066400000000000000000000014151452177046000235000ustar00rootroot00000000000000 C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt FORMAT/DELIMITER: 0 Number_of_fields: 9 Number_of_areas: 262 Fields AreaKey 001 IDnum0 X 002 X_CENTROID Y 003 Y_CENTROID Gmetric 0 Dependent 004 db2564 Offset 005 eb2564 Independent_geo 5 000 Intercept 006 OCC_TEC 007 OWNH 008 POP65 009 UNEMP Independent_fix 0 Unused_fields 0 MODELTYPE: 1 STANDARDISATION: 0 GTEST: 0 VSL2G: 0 VSG2L: 0 KERNELTYPE: 2 BANDSELECTIONMETHOD: 0 Goldrangeflag: 0 Goldenmax: Goldenmin: Fixedbandsize: 100 IntervalMax: IntervalMin: IntervalStep: Criteria: summary_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results\tokyo_BS_NN_OFF_100_summary.txt listwise_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results\tokyo_BS_NN_OFF_100_listwise.csv predictflag: 0 prediction_def: prediction_output: libpysal-4.9.2/libpysal/examples/tokyo/tokyo_BS_NN_OFF_listwise.csv000066400000000000000000002775711452177046000254560ustar00rootroot00000000000000Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_OCC_TEC, se_OCC_TEC, t_OCC_TEC, est_OWNH, se_OWNH, t_OWNH, est_POP65, se_POP65, t_POP65, est_UNEMP, se_UNEMP, t_UNEMP, y, yhat, localpdev, Ginfluence 0, 0, 378906.83, 17310.41, 0.190926, 0.189581, 1.007098, -1.544184, 0.493528, -3.128868, -0.340089, 0.120284, -2.827371, 2.106230, 0.601909, 3.499251, -0.011423, 0.033762, -0.338340, 189.000000, 190.069178, 0.430166, 0.169991 1, 1, 334095.21, 25283.2, 0.109053, 0.243659, 0.447562, -1.397581, 0.633099, -2.207523, -0.142401, 0.177875, -0.800565, 1.595709, 0.804852, 1.982612, -0.024374, 0.044034, -0.553529, 95.000000, 93.532367, 0.330260, 0.290894 2, 2, 378200.19, -877.05, 0.196020, 0.178653, 1.097206, -2.274916, 0.465536, -4.886664, -0.339069, 0.109996, -3.082558, 2.327111, 0.466099, 4.992736, 0.007552, 0.031193, 0.242104, 70.000000, 81.207744, 0.625957, 0.119069 3, 3, 357191.03, 29064.39, 0.281538, 0.215131, 1.308678, -1.259806, 0.530923, -2.372861, -0.268462, 0.149378, -1.797199, 1.351389, 0.714549, 1.891246, -0.046636, 0.038902, -1.198810, 48.000000, 57.042355, 0.257838, 0.130946 4, 4, 358056.34, 10824.73, 0.236405, 0.176395, 1.340200, -1.821119, 0.446716, -4.076678, -0.328990, 0.113764, -2.891849, 2.004754, 0.504128, 3.976677, -0.011757, 0.031054, -0.378593, 65.000000, 74.954011, 0.466956, 0.160332 5, 5, 366747.61, -3073.12, 0.251438, 0.163019, 1.542383, -2.596579, 0.420675, -6.172406, -0.323817, 0.102634, -3.155062, 2.101766, 0.378404, 5.554286, 0.006795, 0.028565, 0.237874, 107.000000, 105.816789, 0.684930, 0.041579 6, 6, 351099.27, 11800.35, 0.268743, 0.183927, 1.461145, -1.861662, 0.445990, -4.174221, -0.333762, 0.118738, -2.810904, 1.967001, 0.549317, 3.580811, -0.019181, 0.032396, -0.592067, 65.000000, 72.655433, 0.427429, 0.164914 7, 7, 377929.98, 4635.1, 0.176225, 0.182966, 0.963153, -1.948520, 0.475524, -4.097626, -0.342963, 0.113375, -3.025030, 2.335310, 0.512055, 4.560665, 0.002540, 0.031878, 0.079682, 76.000000, 75.986967, 0.563366, 0.066055 8, 8, 367529.91, 20192.51, 0.228473, 0.191417, 1.193589, -1.430526, 0.493219, -2.900385, -0.315294, 0.127603, -2.470892, 1.781975, 0.624174, 2.854934, -0.022654, 0.034459, -0.657423, 192.000000, 172.453844, 0.343022, 0.716351 9, 9, 389231.47, 3489.35, 0.063528, 0.188614, 0.336816, -1.872697, 0.503667, -3.718127, -0.308254, 0.114714, -2.687160, 2.611813, 0.536720, 4.866250, 0.022630, 0.032879, 0.688293, 27.000000, 26.414842, 0.572204, 0.045739 10, 10, 389427.64, 9290.1, 0.089160, 0.191613, 0.465312, -1.707219, 0.506532, -3.370407, -0.321403, 0.117112, -2.744408, 2.519319, 0.564448, 4.463333, 0.012610, 0.033458, 0.376904, 28.000000, 25.696257, 0.527451, 0.027351 11, 11, 381089.82, 9125.81, 0.166929, 0.186784, 0.893701, -1.776889, 0.486654, -3.651235, -0.344356, 0.115507, -2.981264, 2.320602, 0.546250, 4.248244, -0.000422, 0.032607, -0.012953, 63.000000, 59.041988, 0.519995, 0.028861 12, 12, 371082.66, 6843.9, 0.188988, 0.179412, 1.053373, -1.840255, 0.465143, -3.956321, -0.338069, 0.113890, -2.968381, 2.217243, 0.506557, 4.377090, -0.001872, 0.031396, -0.059637, 34.000000, 30.827624, 0.526231, 0.052544 13, 13, 388281.84, -1760.78, 0.068741, 0.179817, 0.382283, -2.138195, 0.485831, -4.401112, -0.295700, 0.109578, -2.698542, 2.587456, 0.485656, 5.327757, 0.029850, 0.031558, 0.945878, 17.000000, 19.593423, 0.620771, 0.044383 14, 14, 386771.66, -4857.11, 0.090847, 0.178128, 0.510011, -2.329981, 0.482650, -4.827475, -0.292229, 0.108391, -2.696066, 2.531569, 0.470629, 5.379125, 0.030665, 0.031445, 0.975217, 25.000000, 23.682895, 0.642818, 0.073527 15, 15, 397029.93, 4912.15, -0.030719, 0.188072, -0.163334, -1.733124, 0.514432, -3.369007, -0.280571, 0.114848, -2.442986, 2.810490, 0.548749, 5.121635, 0.038824, 0.032917, 1.179450, 17.000000, 17.295009, 0.571247, 0.063174 16, 16, 399583.28, 1217.51, -0.071357, 0.187171, -0.381240, -1.813545, 0.518312, -3.498946, -0.262665, 0.113747, -2.309207, 2.896545, 0.541433, 5.349777, 0.050229, 0.032926, 1.525512, 31.000000, 29.048022, 0.594572, 0.070873 17, 17, 389413.79, 18915.59, 0.144879, 0.203719, 0.711174, -1.459867, 0.525407, -2.778547, -0.326070, 0.127002, -2.567443, 2.228504, 0.656530, 3.394369, -0.007508, 0.036806, -0.203981, 27.000000, 35.439057, 0.430129, 0.140087 18, 18, 374811.31, 23395.2, 0.220137, 0.193547, 1.137384, -1.400246, 0.503342, -2.781896, -0.321527, 0.128572, -2.500755, 1.827231, 0.648266, 2.818642, -0.022141, 0.035380, -0.625786, 10.000000, 16.871828, 0.349867, 0.050613 19, 19, 366291.01, 3851.09, 0.206948, 0.174561, 1.185535, -2.047118, 0.448117, -4.568263, -0.329007, 0.110971, -2.964809, 2.165268, 0.456260, 4.745688, 0.000730, 0.030612, 0.023844, 42.000000, 39.510632, 0.575066, 0.074604 20, 20, 362053.67, 7027.25, 0.215609, 0.173929, 1.239639, -1.939989, 0.443894, -4.370388, -0.324545, 0.111511, -2.910438, 2.082471, 0.467026, 4.459001, -0.003750, 0.030596, -0.122553, 20.000000, 20.310019, 0.531829, 0.052977 21, 21, 350567.45, 26456.28, 0.265518, 0.220301, 1.205255, -1.248533, 0.533594, -2.339857, -0.242465, 0.151709, -1.598228, 1.399710, 0.735324, 1.903529, -0.049906, 0.039964, -1.248767, 40.000000, 41.872140, 0.273696, 0.105269 22, 22, 356783.59, 23682.89, 0.282733, 0.203357, 1.390329, -1.403866, 0.500383, -2.805584, -0.303040, 0.135722, -2.232796, 1.557034, 0.666589, 2.335822, -0.038558, 0.036593, -1.053714, 15.000000, 15.535262, 0.294855, 0.035412 23, 23, 356225.47, 19763.98, 0.275582, 0.193155, 1.426741, -1.537934, 0.477453, -3.221118, -0.325532, 0.125475, -2.594404, 1.739699, 0.609083, 2.856258, -0.030382, 0.034279, -0.886307, 47.000000, 38.191824, 0.338485, 0.084599 24, 24, 338360.54, 25697.56, 0.172067, 0.237414, 0.724755, -1.277960, 0.581157, -2.198992, -0.177992, 0.168263, -1.057818, 1.517759, 0.779903, 1.946088, -0.038665, 0.042792, -0.903556, 68.000000, 64.615270, 0.313980, 0.251748 25, 25, 337846.31, 18213.38, 0.245898, 0.218577, 1.124994, -1.889542, 0.518532, -3.644019, -0.276589, 0.143284, -1.930363, 1.730226, 0.670713, 2.579681, -0.022572, 0.039974, -0.564675, 17.000000, 14.974733, 0.359468, 0.027090 26, 26, 344074.13, 27136.92, 0.220075, 0.234138, 0.939938, -1.156317, 0.565796, -2.043700, -0.191899, 0.165125, -1.162145, 1.385927, 0.774796, 1.788764, -0.051157, 0.042200, -1.212255, 57.000000, 49.376989, 0.284038, 0.082826 27, 27, 349087.82, 19336.47, 0.288771, 0.199491, 1.447537, -1.623913, 0.473674, -3.428336, -0.323834, 0.126882, -2.552244, 1.779369, 0.609293, 2.920385, -0.034279, 0.035454, -0.966875, 40.000000, 26.883008, 0.350070, 0.069504 28, 28, 343402.57, 18620.67, 0.282340, 0.210489, 1.341350, -1.697631, 0.485213, -3.498733, -0.304868, 0.134708, -2.263182, 1.751649, 0.642453, 2.726503, -0.034510, 0.037786, -0.913314, 47.000000, 44.290631, 0.349761, 0.058795 29, 29, 359036.48, 1198.74, 0.233622, 0.170665, 1.368895, -2.374106, 0.426124, -5.571396, -0.304242, 0.107585, -2.827912, 2.022770, 0.406158, 4.980255, 0.004877, 0.030054, 0.162286, 49.000000, 40.984421, 0.637914, 0.036201 30, 30, 370771.99, -1522.12, 0.229405, 0.167280, 1.371385, -2.418988, 0.433956, -5.574266, -0.333938, 0.104979, -3.180986, 2.199236, 0.409366, 5.372301, 0.005879, 0.029282, 0.200787, 49.000000, 44.266967, 0.656847, 0.061871 31, 31, 376842.13, -7139.16, 0.240204, 0.165541, 1.451020, -2.755715, 0.433437, -6.357814, -0.327805, 0.102802, 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-13171.59, 0.136894, 0.152816, 0.895810, -2.178258, 0.328646, -6.627986, -0.350340, 0.117865, -2.972379, 1.418610, 0.531078, 2.671188, 0.049712, 0.026234, 1.894923, 52.000000, 45.168017, 0.515505, 0.036434 81, 81, 298790.59, -2464.08, -0.591780, 0.251130, -2.356470, -0.773126, 0.632521, -1.222293, -0.017916, 0.176950, -0.101252, 3.330097, 0.692599, 4.808118, 0.117327, 0.044728, 2.623115, 75.000000, 48.941521, 0.312978, 0.145667 82, 82, 294903.64, 214.57, -0.594998, 0.258264, -2.303836, -0.500778, 0.653792, -0.765959, -0.008633, 0.185894, -0.046439, 3.330000, 0.701508, 4.746919, 0.102761, 0.046487, 2.210533, 33.000000, 20.545809, 0.301045, 0.053062 83, 83, 284950.61, -7897.72, -0.326596, 0.260324, -1.254574, -1.125432, 0.638241, -1.763334, -0.124662, 0.185158, -0.673275, 2.652036, 0.725377, 3.656079, 0.082440, 0.045997, 1.792289, 11.000000, 6.514525, 0.302726, 0.075682 84, 84, 302616.14, 12642.65, -0.817836, 0.259333, -3.153613, 0.942010, 0.770957, 1.221871, 0.088231, 0.193246, 0.456572, 4.010534, 0.780803, 5.136422, 0.074006, 0.049018, 1.509772, 23.000000, 17.556226, 0.268578, 0.030190 85, 85, 298937.62, 11074.43, -0.788363, 0.262430, -3.004094, 0.795415, 0.754358, 1.054426, 0.063684, 0.193128, 0.329751, 4.000965, 0.748400, 5.346024, 0.075280, 0.050005, 1.505449, 43.000000, 26.817322, 0.272299, 0.031238 86, 86, 292980.66, 10621.27, -0.715269, 0.266919, -2.679723, 0.620827, 0.746081, 0.832118, 0.038272, 0.198081, 0.193214, 3.832201, 0.735250, 5.212108, 0.066714, 0.051914, 1.285086, 46.000000, 54.847600, 0.272762, 0.136351 87, 87, 291341.64, 3602.46, -0.614563, 0.265587, -2.313978, -0.126420, 0.685765, -0.184348, 0.003932, 0.194590, 0.020207, 3.423800, 0.722722, 4.737369, 0.085749, 0.049322, 1.738559, 19.000000, 14.221712, 0.288338, 0.038180 88, 88, 296052.78, 6812.78, -0.736923, 0.267515, -2.754693, 0.441066, 0.735157, 0.599962, 0.036287, 0.196392, 0.184770, 3.865226, 0.736524, 5.247930, 0.083052, 0.051062, 1.626469, 11.000000, 7.892287, 0.280623, 0.022390 89, 89, 314476.95, 3490.04, -0.823639, 0.246570, -3.340389, -0.392074, 0.652622, -0.600767, 0.086877, 0.160855, 0.540092, 3.486485, 0.759639, 4.589662, 0.159427, 0.047593, 3.349797, 30.000000, 30.197232, 0.289029, 0.105039 90, 90, 311673.48, 10101.08, -0.790055, 0.241308, -3.274050, 0.348291, 0.717828, 0.485201, 0.104521, 0.177444, 0.589039, 3.541189, 0.779129, 4.545063, 0.104732, 0.045596, 2.296945, 27.000000, 29.025554, 0.275840, 0.062170 91, 91, 300937.58, 3470.02, -0.767781, 0.260345, -2.949094, 0.047528, 0.692164, 0.068665, 0.051105, 0.185588, 0.275370, 3.786392, 0.720700, 5.253772, 0.113038, 0.047546, 2.377447, 18.000000, 22.456980, 0.290012, 0.153154 92, 92, 286991.93, 9571.27, -0.628666, 0.267035, -2.354246, 0.274766, 0.712914, 0.385412, 0.020044, 0.198235, 0.101114, 3.503239, 0.730177, 4.797792, 0.065181, 0.051743, 1.259692, 7.000000, 9.286750, 0.273459, 0.063675 93, 93, 307386.12, 16090.18, -0.752219, 0.250302, -3.005247, 0.763541, 0.775476, 0.984610, 0.104932, 0.194534, 0.539403, 3.665681, 0.822641, 4.455994, 0.065795, 0.047566, 1.383242, 7.000000, 11.861687, 0.269098, 0.030865 94, 94, 300604.96, 17843.82, -0.774516, 0.262686, -2.948450, 1.110406, 0.806585, 1.376676, 0.083575, 0.203773, 0.410139, 4.005433, 0.818817, 4.891735, 0.050924, 0.050483, 1.008748, 15.000000, 17.105343, 0.263956, 0.024320 95, 95, 303917.55, 29223.91, -0.618759, 0.267169, -2.315983, 0.692340, 0.873667, 0.792453, 0.109146, 0.224066, 0.487115, 3.397121, 0.923602, 3.678122, 0.025222, 0.051109, 0.493491, 44.000000, 42.427389, 0.265663, 0.088930 96, 96, 296097.2, 19299.56, -0.747238, 0.265800, -2.811280, 1.105402, 0.805922, 1.371598, 0.065215, 0.206205, 0.316261, 4.015952, 0.797558, 5.035309, 0.044417, 0.051585, 0.861042, 25.000000, 18.731634, 0.262804, 0.057830 97, 97, 291327.94, 19385.45, -0.737870, 0.274256, -2.690443, 1.218879, 0.834869, 1.459964, 0.051362, 0.214349, 0.239620, 4.095840, 0.806886, 5.076105, 0.035486, 0.054844, 0.647026, 15.000000, 16.572195, 0.259796, 0.040936 98, 98, 288651.19, 16782.21, -0.686140, 0.267236, -2.567543, 0.777175, 0.762846, 1.018784, 0.041288, 0.202084, 0.204312, 3.780449, 0.740993, 5.101869, 0.049246, 0.052406, 0.939694, 53.000000, 57.002907, 0.264142, 0.221496 99, 99, 321850.11, 16542.18, -0.201947, 0.224168, -0.900874, -1.429641, 0.689043, -2.074822, -0.081177, 0.168054, -0.483040, 2.346793, 0.793340, 2.958116, 0.048914, 0.044908, 1.089196, 24.000000, 28.888207, 0.333796, 0.058929 100, 100, 309623.17, 24691.81, -0.531319, 0.245509, -2.164157, 0.092558, 0.779058, 0.118807, 0.079618, 0.199768, 0.398555, 3.018216, 0.845225, 3.570903, 0.040943, 0.046919, 0.872636, 7.000000, 6.146901, 0.280741, 0.015184 101, 101, 317273.81, 16350.36, -0.424445, 0.227047, -1.869416, -0.657275, 0.700990, -0.937638, 0.010065, 0.175193, 0.057449, 2.728570, 0.804899, 3.389953, 0.062743, 0.044859, 1.398664, 13.000000, 13.442486, 0.305492, 0.040620 102, 102, 330127.65, 26472.36, 0.028501, 0.248505, 0.114690, -1.464316, 0.698148, -2.097430, -0.099751, 0.185126, -0.538829, 1.693641, 0.822792, 2.058407, -0.007972, 0.044920, -0.177467, 18.000000, 16.824780, 0.335108, 0.041087 103, 103, 330024.3, 22050.13, 0.071193, 0.241011, 0.295393, -1.870347, 0.715713, -2.613265, -0.138743, 0.177848, -0.780120, 1.684323, 0.808669, 2.082833, 0.002955, 0.045676, 0.064688, 24.000000, 22.735451, 0.347660, 0.040360 104, 104, 335366.5, 8522.69, 0.258222, 0.193707, 1.333055, -2.710474, 0.490523, -5.525684, -0.315495, 0.126992, -2.484360, 1.832370, 0.513156, 3.570783, 0.012594, 0.036319, 0.346766, 45.000000, 46.996125, 0.466283, 0.019541 105, 105, 330795.7, 8625.57, 0.184693, 0.203109, 0.909330, -2.624003, 0.532944, -4.923596, -0.298548, 0.132182, -2.258619, 1.970210, 0.573373, 3.436173, 0.025530, 0.039256, 0.650332, 56.000000, 55.128432, 0.433825, 0.052876 106, 106, 324461.1, 12021.3, -0.089415, 0.220531, -0.405452, -1.902027, 0.648353, -2.933631, -0.171731, 0.155299, -1.105804, 2.304764, 0.742600, 3.103643, 0.052150, 0.044545, 1.170729, 33.000000, 32.920595, 0.354117, 0.055355 107, 107, 333249.02, 19193.73, 0.174969, 0.224128, 0.780666, -2.042332, 0.588404, -3.470967, -0.230715, 0.152618, -1.511721, 1.736393, 0.712115, 2.438361, -0.004742, 0.042292, -0.112114, 35.000000, 31.856099, 0.363492, 0.054293 108, 108, 330905.38, 16199.04, 0.155968, 0.216737, 0.719619, -2.396001, 0.607245, -3.945687, -0.243496, 0.145639, -1.671918, 1.827288, 0.675572, 2.704803, 0.016242, 0.042400, 0.383063, 36.000000, 36.766694, 0.379975, 0.067478 109, 109, 338740.71, 9995.65, 0.284557, 0.191537, 1.485654, -2.524204, 0.463439, -5.446681, -0.325052, 0.124754, -2.605546, 1.831089, 0.506237, 3.617057, -0.000792, 0.034848, -0.022733, 58.000000, 55.803181, 0.460245, 0.033254 110, 110, 345541.9, -607.56, 0.269720, 0.164881, 1.635852, -2.723763, 0.393618, -6.919813, -0.303416, 0.110611, -2.743085, 1.719481, 0.397917, 4.321206, 0.014763, 0.029625, 0.498347, 37.000000, 33.728514, 0.660120, 0.045155 111, 111, 348908.69, -5077.51, 0.258027, 0.140803, 1.832532, -2.769305, 0.331520, -8.353349, -0.298027, 0.099327, -3.000460, 1.762237, 0.343302, 5.133198, 0.018642, 0.024562, 0.758970, 53.000000, 64.115378, 0.726713, 0.037524 112, 112, 343120.93, 5902.89, 0.290739, 0.174940, 1.661935, -2.533804, 0.414004, -6.120239, -0.328211, 0.115040, -2.853024, 1.845475, 0.437847, 4.214889, 0.000082, 0.031405, 0.002600, 49.000000, 51.539740, 0.549270, 0.045467 113, 113, 377836.69, -36378.58, 0.199289, 0.125440, 1.588724, -3.438737, 0.383608, -8.964184, -0.281309, 0.082970, -3.390473, 2.480429, 0.310381, 7.991559, 0.036240, 0.020524, 1.765770, 1070.000000, 1080.555407, 0.769561, 0.311547 114, 114, 356153.1, -24448.15, 0.250411, 0.110934, 2.257289, -2.807798, 0.259614, -10.815267, -0.363322, 0.092702, -3.919261, 1.910780, 0.329806, 5.793651, 0.027663, 0.017382, 1.591488, 547.000000, 564.889701, 0.818291, 0.157759 115, 115, 363934.49, -23252.2, 0.266370, 0.114283, 2.330795, -3.105353, 0.291744, -10.644111, -0.341976, 0.087030, -3.929413, 2.085271, 0.316828, 6.581724, 0.023853, 0.019020, 1.254095, 660.000000, 739.580385, 0.816586, 0.137805 116, 116, 362715.03, -62961.03, -0.057748, 0.092537, -0.624055, -2.244992, 0.261021, -8.600810, -0.339918, 0.077676, -4.376125, 2.620837, 0.324408, 8.078830, 0.084177, 0.015390, 5.469750, 175.000000, 205.444327, 0.761128, 0.103138 117, 117, 355515.39, -15862.17, 0.262704, 0.119485, 2.198643, -2.892620, 0.283049, -10.219517, -0.321840, 0.090247, -3.566196, 1.910475, 0.323071, 5.913477, 0.020588, 0.020492, 1.004669, 594.000000, 597.007200, 0.794783, 0.168844 118, 118, 350331.74, 2259.59, 0.265435, 0.168928, 1.571288, -2.516063, 0.408423, -6.160437, -0.305513, 0.108762, -2.809014, 1.867972, 0.415388, 4.496932, 0.004675, 0.030032, 0.155668, 189.000000, 165.076700, 0.624127, 0.133186 119, 119, 390869.17, -52824.71, 0.057499, 0.156641, 0.367075, -3.209142, 0.515709, -6.222782, -0.246432, 0.091419, -2.695616, 2.721033, 0.407321, 6.680321, 0.061688, 0.023579, 2.616219, 121.000000, 130.347768, 0.715195, 0.116417 120, 120, 391663.46, -13955.69, 0.044237, 0.181772, 0.243364, -2.765942, 0.519875, -5.320395, -0.239175, 0.107808, -2.218531, 2.524938, 0.481719, 5.241511, 0.054416, 0.032709, 1.663651, 125.000000, 115.248475, 0.677706, 0.199290 121, 121, 381676.42, -24737.89, 0.216748, 0.151003, 1.435388, -3.542572, 0.445857, -7.945529, -0.258888, 0.091288, -2.835947, 2.235277, 0.339911, 6.576068, 0.037649, 0.026981, 1.395397, 175.000000, 189.896403, 0.749788, 0.114865 122, 122, 396227.77, -39792.02, 0.108587, 0.173703, 0.625127, -3.517311, 0.561939, -6.259236, -0.218585, 0.094778, -2.306285, 2.428043, 0.425499, 5.706340, 0.060390, 0.028169, 2.143876, 85.000000, 75.366806, 0.710002, 0.054190 123, 123, 365535.4, -28214.23, 0.257527, 0.111230, 2.315263, -3.127257, 0.292761, -10.681937, -0.350708, 0.086268, -4.065344, 2.163016, 0.316303, 6.838430, 0.026439, 0.018066, 1.463484, 200.000000, 191.412904, 0.822642, 0.057385 124, 124, 358761.78, -6929.68, 0.266673, 0.150293, 1.774356, -2.923690, 0.378046, -7.733687, -0.293312, 0.099164, -2.957844, 1.904085, 0.337338, 5.644443, 0.015501, 0.026303, 0.589316, 389.000000, 390.937253, 0.743341, 0.167182 125, 125, 376070.01, -56303.59, -0.026305, 0.112653, -0.233507, -2.638342, 0.338905, -7.784892, -0.275844, 0.082512, -3.343096, 2.874693, 0.357256, 8.046585, 0.068777, 0.017377, 3.957959, 381.000000, 366.991095, 0.744606, 0.136114 126, 126, 353788.69, -7340.62, 0.253906, 0.136739, 1.856872, -2.841082, 0.328632, -8.645174, -0.291826, 0.095856, -3.044410, 1.845801, 0.328923, 5.611648, 0.018873, 0.023712, 0.795901, 173.000000, 182.890674, 0.747998, 0.087844 127, 127, 371670.71, -21543.62, 0.325033, 0.137066, 2.371367, -3.552242, 0.380204, -9.342982, -0.324569, 0.091052, -3.564651, 2.101314, 0.315302, 6.664438, 0.016833, 0.024297, 0.692804, 206.000000, 212.864845, 0.793814, 0.056292 128, 128, 367522.64, -7189.91, 0.279292, 0.151806, 1.839799, -2.895420, 0.391950, -7.387227, -0.324245, 0.097412, -3.328601, 2.056363, 0.341984, 6.013038, 0.009694, 0.026579, 0.364745, 142.000000, 151.859867, 0.730500, 0.102079 129, 129, 361892.77, -18166, 0.270342, 0.118182, 2.287512, -3.064263, 0.295925, -10.354862, -0.330472, 0.087087, -3.794728, 2.008287, 0.314810, 6.379366, 0.021791, 0.020245, 1.076389, 148.000000, 130.658748, 0.802894, 0.066971 130, 130, 366450.66, -74634.65, -0.057777, 0.100776, -0.573316, -2.318552, 0.307518, -7.539555, -0.360816, 0.077843, -4.635183, 2.794195, 0.360578, 7.749210, 0.086062, 0.016502, 5.215228, 141.000000, 153.664872, 0.750589, 0.068625 131, 131, 355381.88, -79216.89, -0.003394, 0.105504, -0.032173, -2.374702, 0.314214, -7.557599, -0.417660, 0.084441, -4.946168, 2.530600, 0.359386, 7.041451, 0.090071, 0.016649, 5.409970, 106.000000, 104.537278, 0.766051, 0.268851 132, 132, 353694.67, -32885.23, 0.229348, 0.113298, 2.024294, -2.654331, 0.249636, -10.632794, -0.408861, 0.103598, -3.946613, 1.828383, 0.327321, 5.585909, 0.038707, 0.015783, 2.452416, 116.000000, 123.166612, 0.820051, 0.077428 133, 133, 378726.25, -28678.9, 0.243042, 0.138521, 1.754547, -3.607038, 0.418367, -8.621707, -0.275838, 0.086834, -3.176613, 2.298923, 0.317692, 7.236327, 0.032324, 0.024107, 1.340890, 84.000000, 93.359688, 0.767206, 0.071802 134, 134, 365246.77, -57670.24, -0.056288, 0.095033, -0.592302, -2.281711, 0.261532, -8.724397, -0.315640, 0.079357, -3.977477, 2.669432, 0.328490, 8.126369, 0.078743, 0.015534, 5.069165, 78.000000, 70.185919, 0.763320, 0.053171 135, 135, 389517.13, -31493.43, 0.149802, 0.166157, 0.901572, -3.572932, 0.519389, -6.879108, -0.227053, 0.093919, -2.417555, 2.328214, 0.393544, 5.916015, 0.053236, 0.028392, 1.875037, 88.000000, 79.419566, 0.724488, 0.090325 136, 136, 344415.42, 11511.89, 0.291302, 0.189458, 1.537557, -2.124814, 0.442689, -4.799790, -0.334190, 0.122148, -2.735951, 1.909616, 0.529964, 3.603292, -0.016954, 0.033555, -0.505252, 31.000000, 39.251548, 0.437825, 0.077038 137, 137, 365053.58, -11168.12, 0.294637, 0.142096, 2.073512, -3.135580, 0.370237, -8.469106, 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1.943773, 0.548038, 3.546787, 0.091960, 0.033016, 2.785331, 28.000000, 32.675032, 0.445734, 0.127728 260, 260, 290541.99, -38708.26, 0.079550, 0.214540, 0.370793, -2.073583, 0.547619, -3.786544, -0.439602, 0.152690, -2.879049, 1.666628, 0.600994, 2.773117, 0.072363, 0.035194, 2.056103, 10.000000, 14.914484, 0.368221, 0.022894 261, 261, 285964.14, -39392.46, 0.038342, 0.218075, 0.175818, -1.954303, 0.550624, -3.549252, -0.415982, 0.154553, -2.691519, 1.742411, 0.621148, 2.805144, 0.074232, 0.036521, 2.032592, 12.000000, 13.806140, 0.350257, 0.068405 libpysal-4.9.2/libpysal/examples/tokyo/tokyo_BS_NN_OFF_summary.txt000066400000000000000000000172051452177046000253160ustar00rootroot00000000000000***************************************************************************** * Semiparametric Geographically Weighted Regression * * Release 1.0.90 (GWR 4.0.90) * * 12 May 2015 * * (Originally coded by T. Nakaya: 1 Nov 2009) * * * * Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * * Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * * (c) GWR4 development team * * (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * * (3) University of Glasgow, (4) Arizona State University * ***************************************************************************** Program began at 9/7/2016 12:36:11 PM ***************************************************************************** Session: Session control file: C:\Users\IEUser\Desktop\tokyo\tokyo_results\tokyo_BS_NN_OFF_100.ctl ***************************************************************************** Data filename: C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt Number of areas/points: 262 Model settings--------------------------------- Model type: Poisson Geographic kernel: adaptive bi-square Method for optimal bandwidth search: fixed value Criterion for optimal bandwidth: AICc Number of varying coefficients: 5 Number of fixed coefficients: 0 Modelling options--------------------------------- Standardisation of independent variables: OFF Testing geographical variability of local coefficients: OFF Local to Global Variable selection: OFF Global to Local Variable selection: OFF Prediction at non-regression points: OFF Variable settings--------------------------------- Area key: field1: IDnum0 Easting (x-coord): field2 : X_CENTROID Northing (y-coord): field3: Y_CENTROID Cartesian coordinates: Euclidean distance Dependent variable: field4: db2564 Offset variable: field5: eb2564 Intercept: varying (Local) intercept Independent variable with varying (Local) coefficient: field6: OCC_TEC Independent variable with varying (Local) coefficient: field7: OWNH Independent variable with varying (Local) coefficient: field8: POP65 Independent variable with varying (Local) coefficient: field9: UNEMP ***************************************************************************** ***************************************************************************** Global regression result ***************************************************************************** < Diagnostic information > Number of parameters: 5 Deviance: 389.281580 Classic AIC: 399.281580 AICc: 399.515955 BIC/MDL: 417.123303 Percent deviance explained 0.594601 Variable Estimate Standard Error z(Est/SE) Exp(Est) -------------------- --------------- --------------- --------------- --------------- Intercept 0.007470 0.065139 0.114679 1.007498 OCC_TEC -2.287906 0.162000 -14.122903 0.101479 OWNH -0.259692 0.047050 -5.519470 0.771289 POP65 2.199387 0.198270 11.092878 9.019480 UNEMP 0.064025 0.010997 5.822059 1.066119 ***************************************************************************** GWR (Geographically weighted regression) bandwidth selection ***************************************************************************** Bandwidth search : 100 ***************************************************************************** GWR (Geographically weighted regression) result ***************************************************************************** Bandwidth and geographic ranges Bandwidth size: 100.000000 Coordinate Min Max Range --------------- --------------- --------------- --------------- X-coord 276385.400000 408226.180000 131840.780000 Y-coord -86587.480000 33538.420000 120125.900000 Diagnostic information Effective number of parameters (model: trace(S)): 25.145091 Effective number of parameters (variance: trace(S'WSW^-1)): 17.142370 Degree of freedom (model: n - trace(S)): 236.854909 Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 228.852188 Deviance: 311.245301 Classic AIC: 361.535483 AICc: 367.110273 BIC/MDL: 451.261832 Percent deviance explained 0.675868 *********************************************************** << Geographically varying (Local) coefficients >> *********************************************************** Estimates of varying coefficients have been saved in the following file. Listwise output file: C:\Users\IEUser\Desktop\tokyo\tokyo_results\tokyo_BS_NN_OFF_100_listwise.csv Summary statistics for varying (Local) coefficients Variable Mean STD -------------------- --------------- --------------- Intercept 0.038565 0.295395 OCC_TEC -2.132644 1.004125 OWNH -0.275802 0.157200 POP65 2.169549 0.626589 UNEMP 0.047531 0.038121 Variable Min Max Range -------------------- --------------- --------------- --------------- Intercept -0.879764 0.408928 1.288692 OCC_TEC -3.607038 1.218879 4.825918 OWNH -0.547011 0.111386 0.658397 POP65 1.319626 4.095840 2.776214 UNEMP -0.051157 0.159427 0.210584 Variable Lwr Quartile Median Upr Quartile -------------------- --------------- --------------- --------------- Intercept 0.001123 0.090003 0.254783 OCC_TEC -2.661121 -2.503268 -1.837056 OWNH -0.375947 -0.321084 -0.208341 POP65 1.676893 2.083871 2.420870 UNEMP 0.022264 0.044555 0.075398 Variable Interquartile R Robust STD -------------------- --------------- --------------- Intercept 0.253660 0.188036 OCC_TEC 0.824066 0.610872 OWNH 0.167607 0.124245 POP65 0.743976 0.551502 UNEMP 0.053134 0.039388 (Note: Robust STD is given by (interquartile range / 1.349) ) ***************************************************************************** GWR Analysis of Deviance Table ***************************************************************************** Source Deviance DOF Deviance/DOF ------------ ------------------- ---------- ---------------- Global model 389.282 257.000 1.515 GWR model 311.245 228.852 1.360 Difference 78.036 28.148 2.772 ***************************************************************************** Program terminated at 9/7/2016 12:36:11 PM libpysal-4.9.2/libpysal/examples/tokyo/tokyo_BS_NN_listwise.csv000066400000000000000000002775711452177046000247640ustar00rootroot00000000000000Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_OCC_TEC, se_OCC_TEC, t_OCC_TEC, est_OWNH, se_OWNH, t_OWNH, est_POP65, se_POP65, t_POP65, est_UNEMP, se_UNEMP, t_UNEMP, y, yhat, localpdev, Ginfluence 0, 0, 378906.83, 17310.41, 6.981148, 0.382489, 18.251914, 1.278293, 0.864533, 1.478595, -3.489627, 0.236971, -14.725942, -7.767023, 1.124270, -6.908502, 0.219992, 0.069022, 3.187270, 189.000000, 127.740980, 0.799454, 0.410547 1, 1, 334095.21, 25283.2, 7.895318, 0.421526, 18.730305, 5.671716, 1.783639, 3.179858, -5.555414, 0.402457, -13.803755, 5.471330, 1.448255, 3.777876, -0.265160, 0.066358, -3.995882, 95.000000, 88.236162, 0.647998, 0.460995 2, 2, 378200.19, -877.05, 7.173261, 0.301423, 23.797978, 2.271269, 0.714557, 3.178570, -4.202776, 0.175817, -23.904278, -9.342388, 0.991280, -9.424572, 0.365941, 0.061546, 5.945852, 70.000000, 51.244096, 0.810739, 0.227173 3, 3, 357191.03, 29064.39, 7.588873, 0.378109, 20.070619, -0.688604, 0.901905, -0.763499, -3.986198, 0.322474, -12.361305, -3.779300, 1.189256, -3.177870, 0.022004, 0.046961, 0.468565, 48.000000, 54.106687, 0.804137, 0.226219 4, 4, 358056.34, 10824.73, 7.752999, 0.294689, 26.309115, -1.632266, 0.678929, -2.404178, -4.767460, 0.241295, -19.757788, -4.700026, 1.024001, -4.589864, 0.380323, 0.050590, 7.517689, 65.000000, 45.559119, 0.822351, 0.202609 5, 5, 366747.61, -3073.12, 7.257934, 0.265644, 27.322062, 1.772214, 0.661584, 2.678743, -5.620995, 0.168210, -33.416569, -2.740331, 0.859815, -3.187116, 0.582308, 0.053016, 10.983630, 107.000000, 205.487136, 0.854208, 0.147547 6, 6, 351099.27, 11800.35, 8.028044, 0.268402, 29.910527, -0.206335, 0.672227, -0.306942, -4.406742, 0.255136, -17.272101, -6.322605, 1.046918, -6.039255, 0.135527, 0.043609, 3.107746, 65.000000, 46.315869, 0.779120, 0.165651 7, 7, 377929.98, 4635.1, 7.019021, 0.322701, 21.750870, 1.803822, 0.746353, 2.416848, -3.872084, 0.193664, -19.993870, -8.756609, 0.996122, -8.790697, 0.328969, 0.065698, 5.007306, 76.000000, 74.908312, 0.806463, 0.120370 8, 8, 367529.91, 20192.51, 8.130722, 0.411586, 19.754605, -1.877605, 0.876180, -2.142945, -4.277861, 0.281348, -15.204859, -7.909090, 1.190161, -6.645396, 0.171512, 0.062319, 2.752144, 192.000000, 198.488953, 0.823656, 0.921107 9, 9, 389231.47, 3489.35, 6.505467, 0.335098, 19.413650, 3.789934, 0.767639, 4.937130, -3.248436, 0.198668, -16.351049, -7.561067, 1.043412, -7.246484, 0.185300, 0.066558, 2.784025, 27.000000, 30.097397, 0.729777, 0.109602 10, 10, 389427.64, 9290.1, 6.176318, 0.356077, 17.345464, 3.964406, 0.815313, 4.862436, -3.048296, 0.213408, -14.283871, -6.514182, 1.111681, -5.859758, 0.200187, 0.071928, 2.783166, 28.000000, 82.172553, 0.728697, 0.330466 11, 11, 381089.82, 9125.81, 6.521322, 0.355131, 18.363150, 2.749082, 0.818253, 3.359699, -3.375880, 0.212463, -15.889262, -7.397540, 1.077143, -6.867741, 0.271132, 0.070181, 3.863332, 63.000000, 97.124222, 0.780390, 0.136083 12, 12, 371082.66, 6843.9, 7.982195, 0.347556, 22.966664, -1.181106, 0.783327, -1.507808, -4.596383, 0.228951, -20.075829, -8.789749, 0.970862, -9.053556, 0.323862, 0.065266, 4.962155, 34.000000, 37.927218, 0.833291, 0.200952 13, 13, 388281.84, -1760.78, 6.832406, 0.320079, 21.346031, 3.958139, 0.749840, 5.278645, -3.564375, 0.185018, -19.264988, -8.327457, 1.014583, -8.207766, 0.182055, 0.062678, 2.904601, 17.000000, 18.442186, 0.741230, 0.119735 14, 14, 386771.66, -4857.11, 7.044233, 0.310670, 22.674312, 4.504898, 0.782767, 5.755095, -3.904444, 0.174701, -22.349246, -8.512018, 1.027822, -8.281609, 0.191754, 0.060956, 3.145788, 25.000000, 11.993985, 0.758289, 0.095810 15, 15, 397029.93, 4912.15, 6.338613, 0.349916, 18.114649, 4.446757, 0.801790, 5.546036, -2.901260, 0.211516, -13.716536, -6.986268, 1.075199, -6.497653, 0.079398, 0.070292, 1.129545, 17.000000, 15.723887, 0.671925, 0.146465 16, 16, 399583.28, 1217.51, 6.636614, 0.328570, 20.198457, 4.490998, 0.780817, 5.751665, -3.081212, 0.201544, -15.287999, -7.793680, 1.003226, -7.768616, 0.059231, 0.065100, 0.909844, 31.000000, 18.072884, 0.689585, 0.104803 17, 17, 389413.79, 18915.59, 6.252293, 0.363994, 17.176918, 3.205111, 0.837159, 3.828558, -3.063420, 0.222735, -13.753639, -6.489067, 1.159840, -5.594796, 0.220619, 0.071982, 3.064914, 27.000000, 16.940241, 0.761202, 0.164379 18, 18, 374811.31, 23395.2, 7.731427, 0.444089, 17.409637, -0.503167, 0.945861, -0.531967, -3.810451, 0.282959, -13.466444, -8.580394, 1.240530, -6.916714, 0.139223, 0.069063, 2.015895, 10.000000, 24.457584, 0.808565, 0.233181 19, 19, 366291.01, 3851.09, 7.925930, 0.288560, 27.467192, -2.136783, 0.667745, -3.199997, -5.358072, 0.215178, -24.900701, -5.153865, 0.980240, -5.257761, 0.511223, 0.061032, 8.376363, 42.000000, 38.188622, 0.843031, 0.143790 20, 20, 362053.67, 7027.25, 7.739533, 0.300070, 25.792431, -2.818312, 0.687997, -4.096401, -5.388388, 0.238509, -22.591930, -2.711168, 1.093140, -2.480166, 0.560874, 0.058970, 9.511158, 20.000000, 32.432792, 0.834878, 0.242982 21, 21, 350567.45, 26456.28, 7.420661, 0.384778, 19.285568, 0.042411, 0.925782, 0.045811, -3.675216, 0.342484, -10.731072, -3.863457, 1.185006, -3.260284, -0.041548, 0.048456, -0.857434, 40.000000, 27.424355, 0.749127, 0.116209 22, 22, 356783.59, 23682.89, 7.742560, 0.345492, 22.410271, -0.829614, 0.826997, -1.003164, -4.038094, 0.290652, -13.893215, -5.136214, 1.076842, -4.769701, 0.062888, 0.044715, 1.406435, 15.000000, 31.477570, 0.803891, 0.126940 23, 23, 356225.47, 19763.98, 7.860539, 0.328656, 23.917231, -0.992961, 0.790434, -1.256222, -4.120106, 0.278756, -14.780322, -5.909061, 1.036975, -5.698364, 0.097378, 0.044050, 2.210600, 47.000000, 42.611027, 0.806162, 0.140486 24, 24, 338360.54, 25697.56, 7.649432, 0.420180, 18.205139, 3.483176, 1.456738, 2.391080, -4.704810, 0.386191, -12.182585, 1.612875, 1.289425, 1.250848, -0.182063, 0.058279, -3.124005, 68.000000, 92.342870, 0.641948, 0.690685 25, 25, 337846.31, 18213.38, 8.541858, 0.362381, 23.571474, 6.425882, 1.555598, 4.130811, -5.649886, 0.348467, -16.213545, 1.238173, 1.317262, 0.939960, -0.311651, 0.059447, -5.242504, 17.000000, 30.817413, 0.707070, 0.117822 26, 26, 344074.13, 27136.92, 7.376008, 0.420136, 17.556222, 0.257388, 1.007524, 0.255466, -3.883106, 0.381990, -10.165477, -1.790673, 1.243331, -1.440222, -0.067724, 0.050445, -1.342530, 57.000000, 43.273255, 0.680618, 0.140213 27, 27, 349087.82, 19336.47, 7.851370, 0.341596, 22.984400, -0.286690, 0.830822, -0.345068, -3.934686, 0.306635, -12.831811, -5.846926, 1.082117, -5.403226, -0.010685, 0.045091, -0.236963, 40.000000, 28.373255, 0.750146, 0.118991 28, 28, 343402.57, 18620.67, 8.203201, 0.352525, 23.269861, 0.275197, 0.890558, 0.309017, -4.446599, 0.325329, -13.667992, -4.453096, 1.113072, -4.000727, -0.082040, 0.046055, -1.781333, 47.000000, 57.912215, 0.715426, 0.160538 29, 29, 359036.48, 1198.74, 6.550302, 0.253111, 25.879201, 2.223852, 0.584351, 3.805682, -5.278761, 0.162860, -32.412952, 0.849941, 0.922041, 0.921804, 0.664584, 0.047984, 13.850057, 49.000000, 50.817476, 0.807209, 0.137593 30, 30, 370771.99, -1522.12, 7.663730, 0.290321, 26.397437, 0.293713, 0.687943, 0.426944, -5.266652, 0.181074, -29.085617, -7.524673, 0.943853, -7.972294, 0.536460, 0.063509, 8.447044, 49.000000, 51.852175, 0.858039, 0.154572 31, 31, 376842.13, -7139.16, 7.410573, 0.279867, 26.478907, 3.479690, 0.935630, 3.719089, -4.765319, 0.153054, -31.134953, -8.785457, 1.151890, -7.626994, 0.368879, 0.060430, 6.104244, 27.000000, 29.628678, 0.823232, 0.111860 32, 32, 318049.53, 32744.59, 1.419000, 0.410870, 3.453642, 31.648338, 2.413305, 13.114104, -3.631013, 0.289583, -12.538751, 22.119867, 1.927420, 11.476415, 0.020112, 0.069111, 0.291011, 120.000000, 84.847583, 0.699840, 0.247941 33, 33, 325761.21, 31092.21, 4.403506, 0.337139, 13.061388, 16.567510, 1.957585, 8.463239, -3.904257, 0.263980, -14.789953, 12.156812, 1.667245, 7.291558, -0.082662, 0.067238, -1.229395, 28.000000, 32.542622, 0.685944, 0.221007 34, 34, 318112.24, 28405.62, 1.719096, 0.388014, 4.430502, 31.510210, 2.277169, 13.837447, -3.927400, 0.274371, -14.314181, 22.063361, 1.846514, 11.948654, -0.003611, 0.066485, -0.054313, 16.000000, 25.338043, 0.708498, 0.123695 35, 35, 310480.1, 28809.03, 0.375899, 0.359543, 1.045490, 34.798858, 1.985183, 17.529294, -4.307890, 0.297508, -14.479914, 28.306385, 2.108676, 13.423774, 0.228558, 0.067923, 3.364982, 14.000000, 23.411518, 0.709335, 0.117369 36, 36, 306513.97, 32751.48, 0.515531, 0.351352, 1.467278, 33.054239, 1.960778, 16.857719, -4.174878, 0.306908, -13.603009, 26.249535, 2.035365, 12.896723, 0.275825, 0.070729, 3.899731, 43.000000, 104.269333, 0.701067, 0.731489 37, 37, 311395.51, 33538.42, 0.092823, 0.392644, 0.236405, 37.386385, 2.217316, 16.861099, -4.034377, 0.306083, -13.180642, 29.009071, 2.167542, 13.383392, 0.134106, 0.068619, 1.954357, 29.000000, 15.940490, 0.700865, 0.149871 38, 38, 314408.34, -4572.95, 7.120490, 0.272849, 26.096833, 11.258282, 0.818876, 13.748450, -3.491995, 0.206454, -16.914170, 9.508298, 1.139033, 8.347695, -0.803691, 0.068964, -11.653835, 401.000000, 151.279293, 0.385370, 0.093487 39, 39, 303850.22, 22478, 2.919080, 0.284714, 10.252680, 13.737755, 1.229091, 11.177162, -3.980916, 0.290116, -13.721798, 11.271257, 1.747567, 6.449686, 0.639167, 0.086109, 7.422780, 210.000000, 121.834838, 0.709080, 0.270104 40, 40, 337540.25, -12310.61, 8.593829, 0.150626, 57.054240, -5.390188, 0.351209, -15.347521, -4.024782, 0.135761, -29.646128, 3.311213, 0.337147, 9.821272, -0.135749, 0.021700, -6.255754, 711.000000, 347.054508, 0.466109, 0.146011 41, 41, 330948.96, -8687.59, 13.643524, 0.263915, 51.696671, -14.058219, 0.640710, -21.941634, -6.092492, 0.166675, -36.553069, 27.143635, 0.700190, 38.766104, -1.831951, 0.053378, -34.320555, 544.000000, 329.882990, 0.428429, 0.198848 42, 42, 327143.99, -3103.01, 12.465946, 0.303351, 41.094120, -1.069600, 0.734606, -1.456020, -6.682931, 0.192553, -34.706961, 14.946407, 0.827549, 18.061063, -1.561792, 0.060652, -25.749864, 557.000000, 275.703268, 0.476180, 0.145954 43, 43, 312830.72, 21412.1, 1.709372, 0.317943, 5.376342, 28.336174, 1.768370, 16.023896, -4.647766, 0.261658, -17.762756, 23.220482, 1.891376, 12.277031, 0.281906, 0.065582, 4.298529, 132.000000, 84.382378, 0.726901, 0.119559 44, 44, 312874.14, -17053.63, 4.837183, 0.277818, 17.411337, 2.849541, 0.734736, 3.878317, 0.128994, 0.202783, 0.636115, 11.272008, 1.334764, 8.444943, -0.397610, 0.049665, -8.005842, 395.000000, 158.962243, 0.138049, 0.183871 45, 45, 293680.38, -8010.1, 4.960215, 0.298778, 16.601667, 8.483250, 0.936171, 9.061648, -1.982949, 0.228458, -8.679693, -5.660378, 0.987850, -5.729997, 0.096157, 0.058332, 1.648455, 97.000000, 64.189759, 0.591959, 0.105585 46, 46, 325185.11, 20460.45, 6.985312, 0.311628, 22.415580, 14.600853, 1.729309, 8.443171, -6.248756, 0.258249, -24.196672, 12.215887, 1.473462, 8.290600, -0.313140, 0.062492, -5.010901, 91.000000, 78.651701, 0.725035, 0.196045 47, 47, 305971.31, 8472, 6.681146, 0.385577, 17.327660, 1.043266, 1.245172, 0.837849, -5.328314, 0.406632, -13.103515, 3.974178, 1.825556, 2.176969, 0.377423, 0.094753, 3.983245, 97.000000, 92.804692, 0.672579, 0.127057 48, 48, 335330.5, -108.19, 11.467144, 0.240628, 47.655067, 0.280718, 0.636745, 0.440864, -5.965516, 0.163017, -36.594389, -0.279222, 0.954055, -0.292669, -0.905404, 0.052620, -17.206517, 148.000000, 89.218139, 0.592046, 0.171455 49, 49, 339115.66, 3202.05, 10.428717, 0.229585, 45.424151, 5.020257, 0.633354, 7.926468, -5.828639, 0.162548, -35.857848, -8.813981, 1.044750, -8.436447, -0.494985, 0.051152, -9.676692, 269.000000, 196.907735, 0.669023, 0.231933 50, 50, 309271.13, -10589.17, 4.060642, 0.240822, 16.861560, 8.898793, 0.640793, 13.887146, -0.150140, 0.175442, -0.855779, -1.692074, 1.063328, -1.591300, -0.034171, 0.048888, -0.698968, 183.000000, 139.357580, 0.224199, 0.107498 51, 51, 319972.62, 24634.35, 2.968771, 0.349143, 8.503023, 27.098207, 2.092797, 12.948318, -4.330371, 0.253174, -17.104317, 18.892071, 1.714605, 11.018322, -0.072580, 0.064049, -1.133201, 86.000000, 64.209301, 0.714618, 0.110506 52, 52, 317013.43, 12374.14, 5.848682, 0.432616, 13.519344, 18.748491, 1.884646, 9.948018, -7.137589, 0.297867, -23.962367, 20.363479, 1.643043, 12.393758, -0.073397, 0.073122, -1.003762, 86.000000, 80.260083, 0.745695, 0.113571 53, 53, 323345.93, 2314.46, 8.376387, 0.371786, 22.530151, 13.177070, 1.150338, 11.454960, -5.170741, 0.256816, -20.134054, 8.221768, 1.112028, 7.393487, -0.868753, 0.062292, -13.946501, 244.000000, 164.874861, 0.620462, 0.233159 54, 54, 327610.55, -7504.02, 13.506643, 0.276107, 48.918059, -9.046932, 0.629671, -14.367720, -6.744622, 0.173535, -38.866039, 26.396492, 0.609241, 43.326839, -1.889846, 0.053721, -35.178798, 120.000000, 273.880422, 0.412251, 0.192904 55, 55, 343813.29, -11626.17, 8.150003, 0.144592, 56.365641, -3.289736, 0.345283, -9.527646, -4.368083, 0.119936, -36.420180, -2.124040, 0.336996, -6.302869, 0.173337, 0.023889, 7.255995, 297.000000, 381.397339, 0.579584, 0.195449 56, 56, 342508.85, -4698.16, 8.538621, 0.226036, 37.775534, -0.637114, 0.524038, -1.215777, -4.805556, 0.143170, -33.565342, 1.497324, 0.519075, 2.884599, -0.077389, 0.045410, -1.704238, 393.000000, 198.592330, 0.619080, 0.118590 57, 57, 333426.78, -13559.3, 8.746646, 0.157070, 55.686204, -5.988864, 0.332000, -18.038773, -3.940017, 0.148196, -26.586498, 6.329910, 0.347368, 18.222469, -0.286614, 0.020507, -13.976333, 103.000000, 332.978391, 0.443755, 0.088245 58, 58, 330824.95, -14794.45, 9.163816, 0.175422, 52.238768, -6.784058, 0.351981, -19.273918, -4.229137, 0.166594, -25.385906, 10.233305, 0.384986, 26.580969, -0.480524, 0.022137, -21.706646, 136.000000, 411.177913, 0.439919, 0.476065 59, 59, 304617.97, -15261.45, 3.925540, 0.262646, 14.946101, 8.181316, 0.654049, 12.508713, -0.049792, 0.187106, -0.266114, -6.392096, 0.966349, -6.614689, 0.135047, 0.049177, 2.746165, 160.000000, 141.899492, 0.260179, 0.265025 60, 60, 338062.78, -13156.47, 8.296187, 0.143211, 57.929919, -5.005506, 0.326660, -15.323302, -3.962505, 0.133241, -29.739479, 1.736098, 0.331714, 5.233717, -0.017344, 0.020468, -0.847380, 102.000000, 257.188252, 0.476896, 0.178998 61, 61, 325419.58, -15527.5, 9.395607, 0.220616, 42.588086, -10.381396, 0.412389, -25.173800, -3.627012, 0.183411, -19.775345, 26.418190, 0.609730, 43.327657, -0.993959, 0.034606, -28.722462, 142.000000, 173.320938, 0.411575, 0.157016 62, 62, 324052.62, -12510.9, 10.554844, 0.253487, 41.638551, -10.876587, 0.491101, -22.147367, -4.246890, 0.175531, -24.194566, 31.907717, 0.664735, 48.000635, -1.418441, 0.046874, -30.260604, 83.000000, 59.215010, 0.372031, 0.142643 63, 63, 327521.88, -17674.68, 8.644453, 0.188828, 45.779432, -6.819010, 0.355068, -19.204813, -4.138162, 0.178179, -23.224763, 13.238149, 0.417839, 31.682422, -0.447377, 0.023569, -18.981390, 78.000000, 226.047179, 0.435818, 0.112592 64, 64, 322114.34, -17894.35, 8.282135, 0.235807, 35.122526, -11.677637, 0.443638, -26.322460, -2.665978, 0.192716, -13.833708, 33.119219, 0.815397, 40.617303, -0.919356, 0.040074, -22.941320, 201.000000, 166.442607, 0.383248, 0.143286 65, 65, 320355.21, 5840.48, 7.195339, 0.383708, 18.752122, 14.531231, 1.278331, 11.367349, -5.293193, 0.269376, -19.649839, 9.830608, 1.084251, 9.066730, -0.530560, 0.061242, -8.663342, 87.000000, 85.563126, 0.680346, 0.086768 66, 66, 330341.07, 12925.79, 9.865192, 0.337466, 29.233117, 5.792572, 1.256163, 4.611321, -7.457604, 0.289498, -25.760460, 5.418564, 1.134592, 4.775780, -0.434900, 0.055824, -7.790547, 89.000000, 138.835376, 0.751428, 0.150776 67, 67, 318527.16, 8318.24, 6.576748, 0.400164, 16.435134, 16.042820, 1.391768, 11.526935, -5.682527, 0.280015, -20.293652, 12.464841, 1.205525, 10.339762, -0.356880, 0.068489, -5.210763, 80.000000, 108.213234, 0.709509, 0.154292 68, 68, 347297.26, -13547.71, 8.025915, 0.142138, 56.465635, -2.346761, 0.344158, -6.818845, -4.643276, 0.119168, -38.964216, -5.160312, 0.340361, -15.161290, 0.329705, 0.023705, 13.908940, 105.000000, 338.920219, 0.629912, 0.258284 69, 69, 321375.58, -10594.12, 10.236198, 0.265159, 38.603964, -6.762133, 0.552669, -12.235421, -4.514650, 0.178762, -25.255015, 23.300770, 0.686495, 33.941641, -1.223342, 0.048815, -25.061003, 114.000000, 223.271323, 0.339302, 0.218807 70, 70, 318675.19, -8454.47, 7.945587, 0.290521, 27.349456, 4.670450, 0.775427, 6.023070, -3.057030, 0.208381, -14.670419, 13.681004, 0.929824, 14.713541, -0.978712, 0.053221, -18.389505, 101.000000, 130.814512, 0.302845, 0.265556 71, 71, 350174.04, -12060.87, 7.246933, 0.184379, 39.304485, 1.967781, 0.448884, 4.383722, -4.786194, 0.130704, -36.618658, -7.009964, 0.387853, -18.073756, 0.530410, 0.033478, 15.843525, 181.000000, 259.017387, 0.684682, 0.224365 72, 72, 329442.54, 5939.52, 12.155740, 0.404473, 30.053294, 0.693312, 1.150658, 0.602536, -7.480808, 0.267941, -27.919571, -0.878639, 1.181563, -0.743624, -0.850687, 0.066038, -12.881766, 82.000000, 70.552433, 0.719540, 0.231092 73, 73, 307098.94, 992.68, 6.617891, 0.376771, 17.564778, 5.671726, 1.027764, 5.518510, -4.123638, 0.353151, -11.676691, 1.837416, 1.529063, 1.201661, -0.043376, 0.088137, -0.492142, 93.000000, 163.888923, 0.574594, 0.465509 74, 74, 337247.61, 14030.91, 9.375504, 0.325054, 28.842941, 6.545392, 1.326787, 4.933264, -6.493945, 0.298253, -21.773251, 1.063739, 1.208995, 0.879854, -0.369192, 0.054924, -6.721914, 67.000000, 94.350222, 0.734252, 0.369405 75, 75, 306612.95, -2173.79, 6.255161, 0.325425, 19.221524, 7.117091, 0.869229, 8.187823, -3.271295, 0.269414, -12.142270, -0.285985, 1.186627, -0.241007, -0.116114, 0.069281, -1.675980, 55.000000, 137.823045, 0.512724, 0.347711 76, 76, 301727, -6640.87, 5.031777, 0.286575, 17.558298, 8.112693, 0.832473, 9.745289, -1.794028, 0.217577, -8.245496, -4.859556, 1.035444, -4.693211, 0.051538, 0.055917, 0.921688, 68.000000, 71.596947, 0.479992, 0.117808 77, 77, 326724.18, 5478.1, 10.997099, 0.462037, 23.801359, 4.649693, 1.395059, 3.332972, -6.977827, 0.312209, -22.349891, 2.902521, 1.251141, 2.319900, -0.844135, 0.067740, -12.461419, 47.000000, 66.988418, 0.708266, 0.234319 78, 78, 311503.39, 15708.66, 3.710889, 0.287250, 12.918666, 14.681967, 1.357200, 10.817833, -5.267424, 0.261451, -20.146902, 15.436543, 1.760013, 8.770696, 0.552880, 0.084407, 6.550198, 33.000000, 43.587599, 0.730602, 0.217425 79, 79, 316934.08, -10632.09, 6.605816, 0.296956, 22.245094, 4.437266, 0.743651, 5.966867, -1.651771, 0.215205, -7.675320, 11.516523, 0.925642, 12.441657, -0.724692, 0.052235, -13.873582, 43.000000, 112.882726, 0.242103, 0.148202 80, 80, 317980.71, -13171.59, 7.843733, 0.287166, 27.314324, -3.618750, 0.650931, -5.559343, -2.347441, 0.201244, -11.664638, 20.553342, 0.842722, 24.389233, -0.884951, 0.050104, -17.662355, 52.000000, 155.594451, 0.256188, 0.213982 81, 81, 298790.59, -2464.08, 5.519833, 0.321376, 17.175645, 5.760029, 0.993778, 5.796093, -2.688709, 0.272623, -9.862374, -4.313655, 1.253731, -3.440655, 0.163979, 0.061689, 2.658137, 75.000000, 133.887367, 0.590509, 0.658226 82, 82, 294903.64, 214.57, 5.658746, 0.327617, 17.272431, 4.557639, 1.038286, 4.389578, -3.167305, 0.287125, -11.031089, -3.948110, 1.260887, -3.131217, 0.268242, 0.064946, 4.130258, 33.000000, 29.538205, 0.648080, 0.135631 83, 83, 284950.61, -7897.72, 5.041330, 0.298242, 16.903497, 10.109903, 0.932462, 10.842161, -2.536346, 0.228687, -11.090904, -4.622694, 0.994371, -4.648860, 0.075543, 0.058799, 1.284779, 11.000000, 14.430156, 0.650760, 0.208742 84, 84, 302616.14, 12642.65, 5.411803, 0.275892, 19.615638, 2.101648, 1.206038, 1.742605, -4.449075, 0.293459, -15.160825, 1.299968, 1.665762, 0.780405, 0.673420, 0.090387, 7.450426, 23.000000, 51.663610, 0.694053, 0.191544 85, 85, 298937.62, 11074.43, 5.644299, 0.285714, 19.755087, 1.229497, 1.209169, 1.016812, -4.122324, 0.291418, -14.145749, -1.726411, 1.524251, -1.132629, 0.634107, 0.088574, 7.159096, 43.000000, 41.923248, 0.691361, 0.074510 86, 86, 292980.66, 10621.27, 5.730451, 0.297360, 19.271076, 0.558503, 1.253828, 0.445438, -4.255307, 0.301019, -14.136332, -2.674501, 1.543869, -1.732337, 0.691662, 0.100347, 6.892692, 46.000000, 49.302042, 0.693411, 0.179511 87, 87, 291341.64, 3602.46, 6.054722, 0.343991, 17.601395, 2.023564, 1.125693, 1.797616, -4.074105, 0.318790, -12.779882, -2.863003, 1.310220, -2.185132, 0.443032, 0.077841, 5.691501, 19.000000, 20.452532, 0.681972, 0.090456 88, 88, 296052.78, 6812.78, 6.344656, 0.343571, 18.466798, 0.564764, 1.205821, 0.468365, -4.658661, 0.337419, -13.806743, -1.261887, 1.447082, -0.872022, 0.516832, 0.086311, 5.988013, 11.000000, 20.955098, 0.683733, 0.121111 89, 89, 314476.95, 3490.04, 7.400764, 0.339160, 21.820856, 10.906500, 0.881876, 12.367383, -5.489298, 0.274865, -19.970860, 11.173931, 1.207961, 9.250245, -0.487365, 0.082046, -5.940171, 30.000000, 39.978642, 0.621623, 0.212365 90, 90, 311673.48, 10101.08, 6.583548, 0.358664, 18.355748, 5.958333, 1.152991, 5.167720, -6.022030, 0.351490, -17.132856, 10.347294, 1.646318, 6.285111, 0.200630, 0.087658, 2.288777, 27.000000, 24.491866, 0.706096, 0.143897 91, 91, 300937.58, 3470.02, 6.225688, 0.360770, 17.256671, 2.572146, 1.120023, 2.296511, -4.013283, 0.340439, -11.788565, -1.850839, 1.388869, -1.332623, 0.321139, 0.078470, 4.092520, 18.000000, 29.967504, 0.645452, 0.313551 92, 92, 286991.93, 9571.27, 5.953172, 0.308892, 19.272656, 0.325157, 1.235952, 0.263082, -4.384083, 0.305153, -14.366840, -3.330355, 1.444633, -2.305329, 0.668121, 0.103535, 6.453108, 7.000000, 15.666482, 0.701263, 0.199122 93, 93, 307386.12, 16090.18, 4.246929, 0.257935, 16.465107, 7.923344, 1.132789, 6.994548, -4.569425, 0.268214, -17.036503, 8.077508, 1.617258, 4.994570, 0.677532, 0.084759, 7.993660, 7.000000, 23.487227, 0.713028, 0.135545 94, 94, 300604.96, 17843.82, 4.345835, 0.270293, 16.078215, 5.144274, 1.191489, 4.317518, -3.850278, 0.289767, -13.287513, 2.479873, 1.737301, 1.427429, 0.734088, 0.093815, 7.824811, 15.000000, 38.484273, 0.699008, 0.098362 95, 95, 303917.55, 29223.91, 1.956935, 0.304592, 6.424778, 21.178363, 1.353785, 15.643810, -3.944424, 0.289224, -13.637968, 17.148592, 1.708029, 10.039988, 0.470806, 0.073675, 6.390285, 44.000000, 23.482808, 0.703882, 0.099295 96, 96, 296097.2, 19299.56, 4.394918, 0.274039, 16.037544, 4.108075, 1.197200, 3.431402, -3.445443, 0.288473, -11.943711, -0.534350, 1.647275, -0.324384, 0.753391, 0.096701, 7.790921, 25.000000, 18.177100, 0.695214, 0.118285 97, 97, 291327.94, 19385.45, 4.530947, 0.278667, 16.259387, 3.149167, 1.218165, 2.585172, -3.264387, 0.290282, -11.245574, -2.747252, 1.632024, -1.683341, 0.768876, 0.100860, 7.623205, 15.000000, 22.103505, 0.693813, 0.113402 98, 98, 288651.19, 16782.21, 4.847563, 0.279977, 17.314146, 2.280919, 1.216459, 1.875047, -3.366528, 0.283289, -11.883728, -4.054651, 1.548443, -2.618534, 0.759215, 0.102970, 7.373187, 53.000000, 62.592732, 0.696284, 0.451641 99, 99, 321850.11, 16542.18, 7.642001, 0.379418, 20.141403, 16.648165, 1.878604, 8.861988, -7.753601, 0.295648, -26.225800, 17.186499, 1.541995, 11.145623, -0.404326, 0.063580, -6.359359, 24.000000, 20.195864, 0.750210, 0.116798 100, 100, 309623.17, 24691.81, 0.719759, 0.341703, 2.106391, 31.420378, 1.891820, 16.608545, -4.523533, 0.293772, -15.398090, 26.601920, 2.110031, 12.607359, 0.360192, 0.071887, 5.010528, 7.000000, 15.286612, 0.716875, 0.100320 101, 101, 317273.81, 16350.36, 4.299772, 0.313397, 13.719903, 24.083983, 1.721067, 13.993636, -6.023866, 0.231821, -25.985042, 20.351862, 1.683322, 12.090293, -0.024512, 0.062325, -0.393293, 13.000000, 24.060528, 0.746180, 0.132812 102, 102, 330127.65, 26472.36, 6.803407, 0.343701, 19.794565, 9.062456, 1.786805, 5.071878, -5.024642, 0.287003, -17.507260, 7.635163, 1.493375, 5.112690, -0.214981, 0.064892, -3.312891, 18.000000, 24.371289, 0.673560, 0.176748 103, 103, 330024.3, 22050.13, 8.188909, 0.376332, 21.759783, 8.444276, 1.708482, 4.942560, -6.414478, 0.354838, -18.077179, 8.393266, 1.469587, 5.711308, -0.341276, 0.064803, -5.266326, 24.000000, 29.535111, 0.691371, 0.122111 104, 104, 335366.5, 8522.69, 11.209881, 0.273097, 41.047243, 1.322137, 1.037881, 1.273882, -7.418223, 0.237848, -31.188981, -3.016247, 1.129282, -2.670942, -0.451906, 0.053243, -8.487684, 45.000000, 111.043960, 0.744465, 0.101302 105, 105, 330795.7, 8625.57, 11.430771, 0.364793, 31.334956, 0.784825, 1.250703, 0.627507, -7.776524, 0.287319, -27.065858, 0.792655, 1.170351, 0.677280, -0.563514, 0.058557, -9.623339, 56.000000, 39.673323, 0.748212, 0.088272 106, 106, 324461.1, 12021.3, 9.428353, 0.413710, 22.789772, 8.050329, 1.447967, 5.559745, -7.551337, 0.309062, -24.433070, 9.467540, 1.150792, 8.226978, -0.490632, 0.060016, -8.175012, 33.000000, 34.233974, 0.750341, 0.119711 107, 107, 333249.02, 19193.73, 8.499400, 0.383114, 22.185036, 7.206944, 1.652814, 4.360408, -6.200209, 0.374206, -16.568957, 5.607625, 1.462513, 3.834239, -0.355115, 0.063297, -5.610309, 35.000000, 40.373437, 0.683589, 0.117449 108, 108, 330905.38, 16199.04, 9.214378, 0.347534, 26.513600, 8.151991, 1.396401, 5.837860, -7.256586, 0.310372, -23.380281, 7.404199, 1.232144, 6.009198, -0.415968, 0.057575, -7.224804, 36.000000, 89.351103, 0.737347, 0.263255 109, 109, 338740.71, 9995.65, 10.286503, 0.264758, 38.852435, 4.120299, 1.052807, 3.913630, -6.911990, 0.240326, -28.760909, -2.204311, 1.104511, -1.995734, -0.366267, 0.051787, -7.072624, 58.000000, 64.968331, 0.744222, 0.090216 110, 110, 345541.9, -607.56, 8.070652, 0.222540, 36.266011, 4.488900, 0.532734, 8.426160, -5.323316, 0.149786, -35.539470, -1.745679, 0.678815, -2.571659, 0.101469, 0.044738, 2.268068, 37.000000, 48.443681, 0.691494, 0.114959 111, 111, 348908.69, -5077.51, 7.138691, 0.228191, 31.283852, 4.664457, 0.558924, 8.345430, -4.909794, 0.145925, -33.645939, -3.946414, 0.547857, -7.203367, 0.408138, 0.045503, 8.969455, 53.000000, 131.175113, 0.706333, 0.170296 112, 112, 343120.93, 5902.89, 9.566121, 0.225002, 42.515754, 5.728284, 0.689014, 8.313741, -6.313064, 0.184865, -34.149672, -3.488280, 1.001623, -3.482627, -0.231409, 0.050307, -4.599909, 49.000000, 55.749189, 0.719934, 0.157695 113, 113, 377836.69, -36378.58, 9.917888, 0.206119, 48.117404, -0.154285, 0.817864, -0.188643, -5.949407, 0.129010, -46.116027, -1.197839, 0.747064, -1.603397, -0.232288, 0.040188, -5.780015, 1070.000000, 451.055751, 0.732482, 0.253113 114, 114, 356153.1, -24448.15, 7.811110, 0.157356, 49.639733, -5.362386, 0.357832, -14.985770, -4.470878, 0.139868, -31.965037, -5.438364, 0.331261, -16.417177, 0.498816, 0.023830, 20.932210, 547.000000, 446.613941, 0.618443, 0.203974 115, 115, 363934.49, -23252.2, 7.437131, 0.174959, 42.507924, -1.787577, 0.513745, -3.479506, -4.742958, 0.115461, -41.078452, -2.521298, 0.341325, -7.386790, 0.478732, 0.029968, 15.974539, 660.000000, 388.469060, 0.710179, 0.122831 116, 116, 362715.03, -62961.03, 10.244930, 0.163939, 62.492250, -5.322190, 0.590103, -9.019084, -5.194680, 0.129492, -40.115799, -4.158972, 0.560429, -7.421051, -0.222848, 0.021887, -10.181697, 175.000000, 199.971400, 0.694908, 0.231447 117, 117, 355515.39, -15862.17, 7.099751, 0.177677, 39.958749, 0.329437, 0.429471, 0.767077, -4.804670, 0.130515, -36.813077, -6.143734, 0.351613, -17.472987, 0.616288, 0.031064, 19.839170, 594.000000, 408.090348, 0.712483, 0.183627 118, 118, 350331.74, 2259.59, 7.222726, 0.238747, 30.252622, 6.589881, 0.613647, 10.738886, -5.977683, 0.171495, -34.856301, 3.682592, 0.918403, 4.009777, 0.340069, 0.046416, 7.326532, 189.000000, 125.982089, 0.744216, 0.200503 119, 119, 390869.17, -52824.71, 8.804347, 0.241824, 36.408148, 5.477001, 1.005936, 5.444682, -5.154248, 0.150324, -34.287621, -6.083489, 0.870313, -6.990003, -0.080160, 0.044380, -1.806222, 121.000000, 125.843716, 0.800689, 0.185895 120, 120, 391663.46, -13955.69, 6.844645, 0.296150, 23.112066, 10.230422, 1.110800, 9.209956, -4.591702, 0.160791, -28.556919, -4.864434, 1.128599, -4.310154, 0.131743, 0.057003, 2.311136, 125.000000, 230.861782, 0.772661, 0.626493 121, 121, 381676.42, -24737.89, 8.275874, 0.247495, 33.438553, 6.867294, 0.986146, 6.963767, -5.306764, 0.140760, -37.700747, -4.873364, 1.006367, -4.842533, -0.017730, 0.053537, -0.331169, 175.000000, 96.414035, 0.763847, 0.125289 122, 122, 396227.77, -39792.02, 7.545447, 0.240044, 31.433637, 11.255522, 1.099000, 10.241608, -5.058090, 0.158282, -31.956138, -4.627508, 0.910666, -5.081455, 0.066310, 0.051629, 1.284349, 85.000000, 74.631259, 0.800850, 0.083500 123, 123, 365535.4, -28214.23, 8.116652, 0.178673, 45.427313, -4.105438, 0.559028, -7.343890, -4.614917, 0.110482, -41.770659, -2.680859, 0.345694, -7.755002, 0.332785, 0.028752, 11.574219, 200.000000, 454.667989, 0.657788, 0.279904 124, 124, 358761.78, -6929.68, 6.506019, 0.229599, 28.336417, 5.982513, 0.621986, 9.618410, -5.479589, 0.141231, -38.798882, -2.339805, 0.664109, -3.523222, 0.650856, 0.046569, 13.976037, 389.000000, 321.098661, 0.814084, 0.266772 125, 125, 376070.01, -56303.59, 12.129673, 0.282570, 42.926262, -7.198840, 1.083727, -6.642672, -6.228370, 0.134754, -46.220173, -6.238783, 0.731692, -8.526514, -0.496792, 0.038538, -12.890814, 381.000000, 291.400599, 0.773299, 0.234122 126, 126, 353788.69, -7340.62, 6.504182, 0.230185, 28.256375, 6.817323, 0.605980, 11.250089, -5.264253, 0.144503, -36.429944, -6.132374, 0.606925, -10.104001, 0.686602, 0.047507, 14.452696, 173.000000, 183.771545, 0.764065, 0.174421 127, 127, 371670.71, -21543.62, 8.380907, 0.193772, 43.251360, -0.913249, 0.653100, -1.398329, -5.809669, 0.115248, -50.410156, 1.305529, 0.757302, 1.723921, 0.222079, 0.036578, 6.071394, 206.000000, 285.385058, 0.762730, 0.165342 128, 128, 367522.64, -7189.91, 7.038816, 0.230785, 30.499391, 3.118911, 0.656918, 4.747796, -5.592672, 0.142964, -39.119501, -1.885001, 0.840960, -2.241487, 0.565385, 0.046105, 12.263015, 142.000000, 166.643704, 0.852359, 0.238431 129, 129, 361892.77, -18166, 6.800555, 0.195521, 34.781774, 0.842471, 0.531529, 1.584995, -4.899801, 0.128618, -38.095788, -2.550824, 0.376687, -6.771735, 0.616894, 0.035481, 17.386801, 148.000000, 97.857018, 0.758121, 0.119948 130, 130, 366450.66, -74634.65, 8.276015, 0.194015, 42.656517, 0.683798, 0.602478, 1.134975, -3.811483, 0.157020, -24.273793, -8.234494, 0.718125, -11.466661, 0.077813, 0.028811, 2.700823, 141.000000, 222.381632, 0.718999, 0.207125 131, 131, 355381.88, -79216.89, 6.092229, 0.193341, 31.510293, 2.011100, 0.541947, 3.710882, -2.171742, 0.153452, -14.152595, -6.112513, 0.644890, -9.478377, 0.340299, 0.028542, 11.922837, 106.000000, 81.964921, 0.555538, 0.341609 132, 132, 353694.67, -32885.23, 9.242217, 0.149873, 61.667000, -8.813355, 0.348556, -25.285308, -4.757348, 0.156300, -30.437261, -4.464364, 0.327895, -13.615220, 0.166590, 0.016019, 10.399840, 116.000000, 485.421196, 0.478607, 0.380652 133, 133, 378726.25, -28678.9, 8.747655, 0.217931, 40.139597, 3.158372, 0.867880, 3.639181, -5.644266, 0.130584, -43.223356, -1.824861, 0.853930, -2.137014, -0.021790, 0.045884, -0.474882, 84.000000, 109.335647, 0.743708, 0.189356 134, 134, 365246.77, -57670.24, 12.038529, 0.180260, 66.784352, -11.308782, 0.689809, -16.394067, -5.895819, 0.122052, -48.305938, -6.091212, 0.491898, -12.383073, -0.396027, 0.021910, -18.074838, 78.000000, 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0.266462, 24.754258, 13.510567, 1.128057, 11.976850, -4.962520, 0.158256, -31.357614, -1.979918, 1.091399, -1.814110, 0.141713, 0.051319, 2.761433, 57.000000, 54.612736, 0.780272, 0.327602 140, 140, 380945.88, -17224.24, 8.206885, 0.283239, 28.975077, 5.289483, 0.996483, 5.308151, -5.196528, 0.141281, -36.781505, -8.256891, 1.238938, -6.664493, 0.119110, 0.058861, 2.023587, 17.000000, 10.521455, 0.787402, 0.125687 141, 141, 367373.14, -14712.42, 7.399078, 0.216670, 34.149064, 0.886261, 0.634714, 1.396316, -5.630789, 0.130395, -43.182460, 0.099208, 0.765368, 0.129621, 0.482522, 0.044401, 10.867377, 47.000000, 50.678149, 0.819624, 0.110627 142, 142, 374567.12, -13256.38, 7.799759, 0.229923, 33.923405, 2.069059, 0.787910, 2.626010, -5.437631, 0.127948, -42.498904, -5.886135, 1.087956, -5.410270, 0.400088, 0.046862, 8.537512, 51.000000, 81.230216, 0.818404, 0.140355 143, 143, 380862.18, -12688.75, 7.817831, 0.293217, 26.662228, 5.169949, 1.023358, 5.051946, -4.951500, 0.145975, -33.920238, 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-47144.31, 3.128914, 0.282635, 11.070494, 9.357168, 0.623523, 15.006943, -1.959170, 0.218843, -8.952387, -13.981412, 1.046928, -13.354704, 1.028433, 0.052382, 19.633356, 28.000000, 19.657121, 0.595229, 0.125451 260, 260, 290541.99, -38708.26, 2.894726, 0.277214, 10.442221, 11.267686, 0.596214, 18.898743, -1.021710, 0.207539, -4.922984, -12.640997, 0.954033, -13.250058, 0.753552, 0.049071, 15.356327, 10.000000, 63.151762, 0.567780, 0.142821 261, 261, 285964.14, -39392.46, 2.745031, 0.289122, 9.494353, 12.451695, 0.625127, 19.918660, -1.151215, 0.214098, -5.377059, -12.372224, 0.961801, -12.863601, 0.766503, 0.050801, 15.088282, 12.000000, 21.769771, 0.588884, 0.138953 libpysal-4.9.2/libpysal/examples/tokyo/tokyo_BS_NN_summary.txt000066400000000000000000000206051452177046000246220ustar00rootroot00000000000000***************************************************************************** * Semiparametric Geographically Weighted Regression * * Release 1.0.90 (GWR 4.0.90) * * 12 May 2015 * * (Originally coded by T. Nakaya: 1 Nov 2009) * * * * Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * * Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * * (c) GWR4 development team * * (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * * (3) University of Glasgow, (4) Arizona State University * ***************************************************************************** Program began at 7/25/2016 8:23:10 AM ***************************************************************************** Session: Session control file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_NN.ctl ***************************************************************************** Data filename: C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt Number of areas/points: 262 Model settings--------------------------------- Model type: Poisson Geographic kernel: adaptive bi-square Method for optimal bandwidth search: Golden section search Criterion for optimal bandwidth: AICc Number of varying coefficients: 5 Number of fixed coefficients: 0 Modelling options--------------------------------- Standardisation of independent variables: OFF Testing geographical variability of local coefficients: OFF Local to Global Variable selection: OFF Global to Local Variable selection: OFF Prediction at non-regression points: OFF Variable settings--------------------------------- Area key: field1: IDnum0 Easting (x-coord): field2 : X_CENTROID Northing (y-coord): field3: Y_CENTROID Cartesian coordinates: Euclidean distance Dependent variable: field4: db2564 Offset variable is not specified Intercept: varying (Local) intercept Independent variable with varying (Local) coefficient: field6: OCC_TEC Independent variable with varying (Local) coefficient: field7: OWNH Independent variable with varying (Local) coefficient: field8: POP65 Independent variable with varying (Local) coefficient: field9: UNEMP ***************************************************************************** ***************************************************************************** Global regression result ***************************************************************************** < Diagnostic information > Number of parameters: 5 Deviance: 24597.455544 Classic AIC: 24607.455544 AICc: 24607.689919 BIC/MDL: 24625.297266 Percent deviance explained 0.526746 Variable Estimate Standard Error z(Est/SE) Exp(Est) -------------------- --------------- --------------- --------------- --------------- Intercept 8.432403 0.061613 136.859875 4593.526955 OCC_TEC -4.270431 0.156467 -27.292831 0.013976 OWNH -4.789311 0.046070 -103.957933 0.008318 POP65 -1.252659 0.178384 -7.022265 0.285744 UNEMP 0.061305 0.010099 6.070542 1.063223 ***************************************************************************** GWR (Geographically weighted regression) bandwidth selection ***************************************************************************** Bandwidth search Limits: 50, 262 Golden section search begins... Initial values pL Bandwidth: 50.000 Criterion: 13285.297 p1 Bandwidth: 54.513 Criterion: 13883.832 p2 Bandwidth: 57.302 Criterion: 14277.220 pU Bandwidth: 61.814 Criterion: 14823.882 iter 1 (p1) Bandwidth: 54.513 Criterion: 13883.832 Diff: 2.789 iter 2 (p1) Bandwidth: 52.789 Criterion: 13595.764 Diff: 1.724 iter 3 (p1) Bandwidth: 51.724 Criterion: 13457.435 Diff: 1.065 The lower limit in your search has been selected as the optimal bandwidth size. A new sesssion is recommended to try with a smaller lowest limit of the bandwidth search. Best bandwidth size 50.000 Minimum AICc 13285.297 ***************************************************************************** GWR (Geographically weighted regression) result ***************************************************************************** Bandwidth and geographic ranges Bandwidth size: 50.000000 Coordinate Min Max Range --------------- --------------- --------------- --------------- X-coord 276385.400000 408226.180000 131840.780000 Y-coord -86587.480000 33538.420000 120125.900000 Diagnostic information Effective number of parameters (model: trace(S)): 51.200710 Effective number of parameters (variance: trace(S'WSW^-1)): 37.243822 Degree of freedom (model: n - trace(S)): 210.799290 Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 196.842402 Deviance: 13157.416861 Classic AIC: 13259.818280 AICc: 13285.297045 BIC/MDL: 13442.520052 Percent deviance explained 0.746852 *********************************************************** << Geographically varying (Local) coefficients >> *********************************************************** Estimates of varying coefficients have been saved in the following file. Listwise output file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_BS_NN_listwise.csv Summary statistics for varying (Local) coefficients Variable Mean STD -------------------- --------------- --------------- Intercept 6.999979 2.285454 OCC_TEC 2.878608 8.847676 OWNH -3.894854 1.746973 POP65 1.787710 10.486526 UNEMP 0.025665 0.509711 Variable Min Max Range -------------------- --------------- --------------- --------------- Intercept 0.092823 13.643524 13.550701 OCC_TEC -15.109710 37.386385 52.496096 OWNH -7.776524 0.128994 7.905518 POP65 -13.981412 34.398356 48.379768 UNEMP -1.889846 1.238068 3.127914 Variable Lwr Quartile Median Upr Quartile -------------------- --------------- --------------- --------------- Intercept 5.389016 7.348540 8.493162 OCC_TEC -3.950324 2.752242 7.473013 OWNH -5.048143 -4.141796 -2.717312 POP65 -5.428713 -1.972543 6.458122 UNEMP -0.283051 0.096767 0.340126 Variable Interquartile R Robust STD -------------------- --------------- --------------- Intercept 3.104146 2.301072 OCC_TEC 11.423338 8.468004 OWNH 2.330831 1.727821 POP65 11.886835 8.811590 UNEMP 0.623177 0.461955 (Note: Robust STD is given by (interquartile range / 1.349) ) ***************************************************************************** GWR Analysis of Deviance Table ***************************************************************************** Source Deviance DOF Deviance/DOF ------------ ------------------- ---------- ---------------- Global model 24597.456 257.000 95.710 GWR model 13157.417 196.842 66.842 Difference 11440.039 60.158 190.168 ***************************************************************************** Program terminated at 7/25/2016 8:23:12 AM libpysal-4.9.2/libpysal/examples/tokyo/tokyo_GS_F.ctl000066400000000000000000000014071452177046000226660ustar00rootroot00000000000000 C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt FORMAT/DELIMITER: 0 Number_of_fields: 9 Number_of_areas: 262 Fields AreaKey 001 IDnum0 X 002 X_CENTROID Y 003 Y_CENTROID Gmetric 0 Dependent 004 db2564 Offset Independent_geo 5 000 Intercept 006 OCC_TEC 007 OWNH 008 POP65 009 UNEMP Independent_fix 0 Unused_fields 1 005 eb2564 MODELTYPE: 1 STANDARDISATION: 0 GTEST: 0 VSL2G: 0 VSG2L: 0 KERNELTYPE: 0 BANDSELECTIONMETHOD: 1 Goldrangeflag: 0 Goldenmax: Goldenmin: Fixedbandsize: IntervalMax: IntervalMin: IntervalStep: Criteria: summary_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_F_summary.txt listwise_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_F_listwise.csv predictflag: 0 prediction_def: prediction_output: libpysal-4.9.2/libpysal/examples/tokyo/tokyo_GS_F_listwise.csv000066400000000000000000002775711452177046000246430ustar00rootroot00000000000000Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_OCC_TEC, se_OCC_TEC, t_OCC_TEC, est_OWNH, se_OWNH, t_OWNH, est_POP65, se_POP65, t_POP65, est_UNEMP, se_UNEMP, t_UNEMP, y, yhat, localpdev, Ginfluence 0, 0, 378906.83, 17310.41, 3.583271, 1.027423, 3.487630, 7.503378, 1.913482, 3.921323, -2.707419, 0.461286, -5.869289, -2.758479, 2.654843, -1.039036, 0.839897, 0.147079, 5.710524, 189.000000, 149.519270, 0.912745, 0.762091 1, 1, 334095.21, 25283.2, 7.605694, 0.473666, 16.057094, 6.168525, 1.937685, 3.183451, -5.661664, 0.494936, -11.439174, 8.024303, 1.771118, 4.530642, -0.262738, 0.072732, -3.612410, 95.000000, 86.823391, 0.751551, 0.638654 2, 2, 378200.19, -877.05, 7.332111, 0.446111, 16.435606, 2.886554, 1.028578, 2.806353, -3.823065, 0.240810, -15.875840, -7.680216, 1.303281, -5.892986, 0.059754, 0.099284, 0.601850, 70.000000, 59.977644, 0.855759, 0.544328 3, 3, 357191.03, 29064.39, 7.769326, 0.714282, 10.877117, 0.665213, 1.832467, 0.363015, -2.765460, 0.826580, -3.345664, -9.576680, 2.462993, -3.888229, -0.258572, 0.095482, -2.708079, 48.000000, 48.640000, 0.870092, 0.562028 4, 4, 358056.34, 10824.73, 8.243570, 0.399626, 20.628200, -3.395993, 0.821400, -4.134397, -5.186489, 0.275598, -18.819043, -3.607446, 1.292753, -2.790514, 0.358330, 0.065784, 5.447071, 65.000000, 52.114302, 0.855175, 0.329222 5, 5, 366747.61, -3073.12, 7.564261, 0.317851, 23.798150, 0.577583, 0.828971, 0.696747, -5.719636, 0.190156, -30.078716, -3.545991, 0.985423, -3.598447, 0.561150, 0.065277, 8.596466, 107.000000, 197.234731, 0.873885, 0.229012 6, 6, 351099.27, 11800.35, 8.106939, 0.318757, 25.432986, -0.155342, 0.736326, -0.210969, -4.730732, 0.266637, -17.742225, -4.033351, 1.124534, -3.586687, 0.130943, 0.051685, 2.533501, 65.000000, 50.422125, 0.815303, 0.266846 7, 7, 377929.98, 4635.1, 6.714674, 0.565174, 11.880721, 3.374096, 1.169632, 2.884751, -3.352372, 0.293283, -11.430483, -7.515642, 1.429521, -5.257453, 0.135820, 0.103493, 1.312356, 76.000000, 70.280322, 0.849061, 0.258663 8, 8, 367529.91, 20192.51, 11.146358, 0.796408, 13.995790, -8.451825, 1.542881, -5.477951, -6.349870, 0.474024, -13.395660, -11.903423, 2.091466, -5.691425, 0.044412, 0.093230, 0.476377, 192.000000, 191.261883, 0.944671, 0.988064 9, 9, 389231.47, 3489.35, 5.165397, 0.581240, 8.886859, 11.651789, 1.594139, 7.309142, -2.003645, 0.319804, -6.265234, -3.417730, 1.783715, -1.916074, -0.243895, 0.114620, -2.127859, 27.000000, 29.599423, 0.822465, 0.206989 10, 10, 389427.64, 9290.1, 2.085473, 0.894051, 2.332611, 16.162142, 2.041381, 7.917261, -0.987040, 0.451632, -2.185499, 0.871715, 2.039607, 0.427394, 0.335267, 0.125729, 2.666580, 28.000000, 48.297877, 0.824849, 0.685177 11, 11, 381089.82, 9125.81, 4.345788, 0.715714, 6.071957, 8.001751, 1.504088, 5.320002, -2.350789, 0.348904, -6.737629, -3.783707, 1.780801, -2.124721, 0.421303, 0.112712, 3.737873, 63.000000, 90.023658, 0.867041, 0.277608 12, 12, 371082.66, 6843.9, 8.305331, 0.509114, 16.313305, -2.237278, 1.059384, -2.111867, -4.685393, 0.313517, -14.944626, -7.925982, 1.406658, -5.634620, 0.220273, 0.085624, 2.572573, 34.000000, 36.966460, 0.862134, 0.379282 13, 13, 388281.84, -1760.78, 6.778143, 0.475673, 14.249590, 7.005023, 1.711122, 4.093818, -2.591444, 0.271794, -9.534578, -6.004250, 1.807191, -3.322422, -0.409358, 0.113716, -3.599836, 17.000000, 17.022965, 0.851631, 0.266099 14, 14, 386771.66, -4857.11, 7.081289, 0.445029, 15.911986, 5.469208, 1.638971, 3.336977, -2.923965, 0.267361, -10.936390, -6.487191, 1.768168, -3.668877, -0.329698, 0.104184, -3.164577, 25.000000, 18.169965, 0.861207, 0.305453 15, 15, 397029.93, 4912.15, 4.753586, 0.842220, 5.644113, 11.000473, 4.019815, 2.736562, -1.156187, 0.427339, -2.705546, -5.191879, 3.642218, -1.425472, -0.229300, 0.158028, -1.451006, 17.000000, 22.039289, 0.703781, 0.613688 16, 16, 399583.28, 1217.51, 7.586034, 0.780414, 9.720521, -1.517378, 4.795840, -0.316395, -1.390538, 0.380046, -3.658871, -13.141668, 3.903645, -3.366512, -0.439975, 0.180023, -2.443998, 31.000000, 20.784505, 0.740272, 0.397756 17, 17, 389413.79, 18915.59, -1.730128, 1.299855, -1.331016, 19.145329, 2.586813, 7.401127, 0.215032, 0.584278, 0.368031, 7.572270, 2.610111, 2.901129, 1.147673, 0.194376, 5.904386, 27.000000, 15.315532, 0.862948, 0.562543 18, 18, 374811.31, 23395.2, 9.785750, 1.247184, 7.846277, -5.133577, 2.130728, -2.409307, -5.690811, 0.594372, -9.574486, -12.194400, 3.509347, -3.474835, 0.239084, 0.139760, 1.710677, 10.000000, 15.564568, 0.945480, 0.766519 19, 19, 366291.01, 3851.09, 8.260723, 0.353326, 23.379893, -2.978271, 0.715336, -4.163459, -5.436460, 0.249291, -21.807706, -4.907749, 1.239158, -3.960551, 0.424655, 0.071007, 5.980481, 42.000000, 37.772440, 0.863505, 0.234206 20, 20, 362053.67, 7027.25, 8.182824, 0.361668, 22.625251, -3.537416, 0.728688, -4.854497, -5.471257, 0.259767, -21.062134, -3.195925, 1.302194, -2.454262, 0.455804, 0.066410, 6.863486, 20.000000, 31.779904, 0.862923, 0.354732 21, 21, 350567.45, 26456.28, 6.567726, 0.516736, 12.710027, 2.343458, 1.265270, 1.852141, -2.316452, 0.560271, -4.134523, -5.673143, 1.870016, -3.033741, -0.139021, 0.062073, -2.239624, 40.000000, 30.413083, 0.745055, 0.253060 22, 22, 356783.59, 23682.89, 7.972697, 0.572028, 13.937600, -0.458254, 1.322276, -0.346565, -3.355186, 0.539526, -6.218771, -7.940967, 1.745047, -4.550575, -0.164140, 0.079425, -2.066605, 15.000000, 31.320945, 0.873779, 0.284045 23, 23, 356225.47, 19763.98, 8.056675, 0.488213, 16.502360, -1.273385, 1.068521, -1.191727, -3.794711, 0.399926, -9.488528, -6.777468, 1.445978, -4.687116, -0.046108, 0.069638, -0.662113, 47.000000, 46.625584, 0.864269, 0.241495 24, 24, 338360.54, 25697.56, 7.238068, 0.488495, 14.817068, 2.218634, 1.786882, 1.241623, -4.505027, 0.504016, -8.938259, 3.221451, 1.721941, 1.870826, -0.127814, 0.068806, -1.857612, 68.000000, 80.924279, 0.722950, 0.801990 25, 25, 337846.31, 18213.38, 8.157668, 0.403901, 20.197192, 5.181904, 1.411085, 3.672283, -5.135945, 0.377811, -13.593940, 1.050249, 1.439262, 0.729714, -0.294510, 0.059855, -4.920430, 17.000000, 30.867093, 0.719785, 0.177535 26, 26, 344074.13, 27136.92, 6.677757, 0.497390, 13.425604, 0.557351, 1.424316, 0.391311, -3.167179, 0.509139, -6.220661, -1.423756, 1.746432, -0.815237, -0.052758, 0.062907, -0.838666, 57.000000, 42.990723, 0.694171, 0.385570 27, 27, 349087.82, 19336.47, 7.267366, 0.417034, 17.426312, 1.453143, 1.004612, 1.446473, -3.239515, 0.405461, -7.989704, -5.000441, 1.365174, -3.662858, -0.104307, 0.054018, -1.930976, 40.000000, 32.747531, 0.772776, 0.234407 28, 28, 343402.57, 18620.67, 7.649818, 0.412220, 18.557621, 2.703995, 1.140916, 2.370020, -3.953814, 0.397841, -9.938172, -3.180580, 1.407996, -2.258941, -0.177137, 0.054491, -3.250781, 47.000000, 53.668724, 0.737241, 0.254496 29, 29, 359036.48, 1198.74, 6.600899, 0.266005, 24.814945, 2.526918, 0.613045, 4.121911, -5.139025, 0.168999, -30.408696, -0.478942, 0.878720, -0.545045, 0.634242, 0.051182, 12.391885, 49.000000, 52.709712, 0.843163, 0.192972 30, 30, 370771.99, -1522.12, 8.137645, 0.397337, 20.480465, -1.166856, 0.964096, -1.210311, -5.412199, 0.229516, -23.580963, -6.392830, 1.101124, -5.805733, 0.406421, 0.083276, 4.880393, 49.000000, 48.964753, 0.867845, 0.252182 31, 31, 376842.13, -7139.16, 7.442065, 0.374870, 19.852398, 1.941134, 1.240975, 1.564201, -4.911828, 0.205570, -23.893700, -5.619974, 1.477559, -3.803554, 0.336744, 0.079476, 4.237046, 27.000000, 28.203888, 0.878352, 0.215892 32, 32, 318049.53, 32744.59, -1.521117, 0.539068, -2.821754, 42.894933, 3.445524, 12.449467, -3.188179, 0.393720, -8.097585, 31.574086, 3.022992, 10.444647, 0.252377, 0.095792, 2.634620, 120.000000, 96.693831, 0.854139, 0.511346 33, 33, 325761.21, 31092.21, 5.156148, 0.370234, 13.926718, 18.229031, 2.742931, 6.645822, -5.249772, 0.353953, -14.831843, 15.808644, 2.174628, 7.269585, -0.193020, 0.086131, -2.241007, 28.000000, 32.266009, 0.859828, 0.415415 34, 34, 318112.24, 28405.62, -0.404019, 0.461059, -0.876285, 40.874466, 2.867247, 14.255648, -3.969552, 0.324411, -12.236181, 30.994300, 2.370526, 13.074863, 0.128699, 0.081356, 1.581922, 16.000000, 23.157511, 0.849047, 0.202074 35, 35, 310480.1, 28809.03, -2.767849, 0.522226, -5.300095, 43.383580, 2.688902, 16.134311, -2.972523, 0.383728, -7.746436, 32.133177, 2.767619, 11.610404, 0.642311, 0.104016, 6.175109, 14.000000, 23.743367, 0.838679, 0.218844 36, 36, 306513.97, 32751.48, -3.613954, 0.610979, -5.915021, 46.082495, 3.355164, 13.734795, -1.776735, 0.487460, -3.644887, 28.608728, 3.713935, 7.703078, 0.700645, 0.137135, 5.109171, 43.000000, 57.957940, 0.823292, 0.870919 37, 37, 311395.51, 33538.42, -3.823673, 0.641158, -5.963702, 46.740425, 3.674373, 12.720652, -1.774326, 0.481964, -3.681448, 30.347904, 3.698050, 8.206462, 0.695315, 0.127172, 5.467513, 29.000000, 16.532202, 0.832093, 0.405390 38, 38, 314408.34, -4572.95, 6.941494, 0.230080, 30.169902, 6.152912, 0.576925, 10.665021, -2.944005, 0.157853, -18.650351, 5.216627, 0.797879, 6.538118, -0.502366, 0.048568, -10.343480, 401.000000, 142.038363, 0.386441, 0.089966 39, 39, 303850.22, 22478, 1.196177, 0.329627, 3.628881, 26.700529, 1.547638, 17.252443, -4.760161, 0.343980, -13.838500, 22.498100, 2.422600, 9.286757, 0.580904, 0.104698, 5.548348, 210.000000, 149.191825, 0.838450, 0.487867 40, 40, 337540.25, -12310.61, 8.640373, 0.133895, 64.531100, -5.875519, 0.293408, -20.025108, -4.160014, 0.116399, -35.739165, 2.084691, 0.316873, 6.578943, -0.081690, 0.019467, -4.196339, 711.000000, 342.295397, 0.475725, 0.138292 41, 41, 330948.96, -8687.59, 9.406310, 0.152157, 61.819773, -6.015903, 0.357931, -16.807437, -4.194242, 0.114151, -36.743052, 9.216549, 0.343475, 26.833265, -0.565182, 0.021977, -25.716813, 544.000000, 275.061307, 0.416837, 0.131180 42, 42, 327143.99, -3103.01, 10.763039, 0.198847, 54.127198, -2.739570, 0.486418, -5.632132, -5.511744, 0.127558, -43.209703, 9.901238, 0.441590, 22.421792, -0.974440, 0.035165, -27.710469, 557.000000, 241.762914, 0.469297, 0.105496 43, 43, 312830.72, 21412.1, 1.146120, 0.327173, 3.503107, 29.003930, 1.536848, 18.872343, -4.687563, 0.271064, -17.293194, 24.493427, 1.914466, 12.793871, 0.456108, 0.075173, 6.067483, 132.000000, 88.008443, 0.833012, 0.165067 44, 44, 312874.14, -17053.63, 6.026608, 0.219445, 27.462903, 0.571960, 0.527432, 1.084425, -1.338212, 0.156901, -8.529021, 9.826961, 0.631248, 15.567513, -0.378202, 0.038312, -9.871533, 395.000000, 148.609649, 0.245262, 0.151501 45, 45, 293680.38, -8010.1, 5.564062, 0.335400, 16.589334, 5.156718, 1.007509, 5.118286, -2.381827, 0.247294, -9.631558, -4.181724, 1.085420, -3.852633, 0.079076, 0.065504, 1.207191, 97.000000, 66.802288, 0.644372, 0.190243 46, 46, 325185.11, 20460.45, 7.198158, 0.330520, 21.778254, 14.939964, 1.734099, 8.615404, -6.556824, 0.289450, -22.652689, 14.082451, 1.558134, 9.038023, -0.401987, 0.067500, -5.955342, 91.000000, 81.978255, 0.784165, 0.269604 47, 47, 305971.31, 8472, 6.317833, 0.367643, 17.184674, 2.603623, 1.202230, 2.165661, -4.921982, 0.352602, -13.959014, 3.626600, 1.546280, 2.345370, 0.329918, 0.090737, 3.635980, 97.000000, 87.738854, 0.715712, 0.153915 48, 48, 335330.5, -108.19, 10.415249, 0.195444, 53.290305, -1.219953, 0.479659, -2.543375, -5.600556, 0.133864, -41.837796, 0.345078, 0.496586, 0.694901, -0.562226, 0.038997, -14.417121, 148.000000, 103.454691, 0.600146, 0.175609 49, 49, 339115.66, 3202.05, 9.648633, 0.202784, 47.580883, 2.335679, 0.509337, 4.585728, -5.619107, 0.139363, -40.319914, -5.272906, 0.698492, -7.548988, -0.256601, 0.043552, -5.891812, 269.000000, 197.649945, 0.691854, 0.237725 50, 50, 309271.13, -10589.17, 4.960458, 0.221082, 22.437177, 7.151584, 0.557675, 12.823936, -0.908742, 0.158535, -5.732133, -0.994945, 0.798922, -1.245358, -0.123233, 0.042609, -2.892153, 183.000000, 139.044507, 0.292890, 0.115902 51, 51, 319972.62, 24634.35, 2.361738, 0.371231, 6.361906, 31.822797, 2.327004, 13.675436, -4.936541, 0.277295, -17.802460, 24.852927, 1.951095, 12.737936, -0.077743, 0.071372, -1.089264, 86.000000, 69.030868, 0.836368, 0.166581 52, 52, 317013.43, 12374.14, 6.668260, 0.297600, 22.406823, 10.789453, 1.048452, 10.290837, -5.848149, 0.230062, -25.419907, 10.470762, 1.224324, 8.552280, -0.079478, 0.061816, -1.285717, 86.000000, 79.899494, 0.766892, 0.121473 53, 53, 323345.93, 2314.46, 9.686595, 0.267960, 36.149435, 5.801151, 0.718487, 8.074119, -5.667896, 0.185506, -30.553747, 5.833067, 0.798395, 7.305993, -0.840758, 0.047814, -17.583895, 244.000000, 185.322921, 0.597309, 0.213298 54, 54, 327610.55, -7504.02, 9.714134, 0.167354, 58.045333, -5.428247, 0.391100, -13.879427, -4.446383, 0.115527, -38.487735, 12.119492, 0.368801, 32.861868, -0.757595, 0.025763, -29.406188, 120.000000, 255.807471, 0.403702, 0.123333 55, 55, 343813.29, -11626.17, 8.446375, 0.139143, 60.702827, -4.196504, 0.310036, -13.535553, -4.701372, 0.114969, -40.892345, -2.017476, 0.323634, -6.233825, 0.155200, 0.022689, 6.840463, 297.000000, 381.383903, 0.589158, 0.214246 56, 56, 342508.85, -4698.16, 8.452075, 0.179695, 47.035696, -0.938219, 0.408167, -2.298616, -4.703138, 0.125348, -37.520769, -1.254245, 0.403593, -3.107700, 0.014205, 0.032393, 0.438517, 393.000000, 204.578820, 0.612652, 0.133604 57, 57, 333426.78, -13559.3, 8.434160, 0.140759, 59.919009, -5.818788, 0.289117, -20.126090, -3.826566, 0.125641, -30.456245, 5.076432, 0.328404, 15.457890, -0.180651, 0.018673, -9.674534, 103.000000, 332.690696, 0.427785, 0.083845 58, 58, 330824.95, -14794.45, 8.237176, 0.148088, 55.623561, -5.663974, 0.289185, -19.585959, -3.713510, 0.133358, -27.846115, 7.004592, 0.338587, 20.687689, -0.215743, 0.018793, -11.479767, 136.000000, 440.696221, 0.413337, 0.455718 59, 59, 304617.97, -15261.45, 3.840805, 0.263773, 14.561033, 9.121915, 0.645796, 14.125079, -0.104860, 0.188197, -0.557181, -6.284887, 0.880282, -7.139631, 0.134195, 0.049031, 2.736965, 160.000000, 143.620801, 0.324843, 0.311094 60, 60, 338062.78, -13156.47, 8.572324, 0.132138, 64.874243, -5.912995, 0.286176, -20.662128, -4.196812, 0.118024, -35.558820, 1.331234, 0.315468, 4.219867, -0.028369, 0.019190, -1.478327, 102.000000, 255.567879, 0.482131, 0.174581 61, 61, 325419.58, -15527.5, 7.978480, 0.165517, 48.203528, -5.762072, 0.312781, -18.422090, -3.412864, 0.141813, -24.065974, 12.216134, 0.364511, 33.513786, -0.364314, 0.021044, -17.312330, 142.000000, 228.837943, 0.392213, 0.142500 62, 62, 324052.62, -12510.9, 8.304182, 0.172190, 48.226978, -5.641980, 0.342909, -16.453273, -3.442209, 0.131314, -26.213666, 14.661421, 0.382649, 38.315592, -0.549234, 0.024155, -22.737545, 83.000000, 106.755259, 0.373587, 0.120831 63, 63, 327521.88, -17674.68, 7.986641, 0.156880, 50.909221, -5.630770, 0.291783, -19.297773, -3.718915, 0.143766, -25.867913, 8.916665, 0.343360, 25.968817, -0.218330, 0.019483, -11.206413, 78.000000, 236.140690, 0.405352, 0.096593 64, 64, 322114.34, -17894.35, 7.626033, 0.176599, 43.182725, -5.862999, 0.325705, -18.000974, -3.075031, 0.148784, -20.667764, 14.326457, 0.375410, 38.162193, -0.381298, 0.023199, -16.435951, 201.000000, 153.466138, 0.373636, 0.122395 65, 65, 320355.21, 5840.48, 8.204914, 0.302291, 27.142394, 9.741222, 0.878002, 11.094767, -5.449843, 0.217210, -25.090212, 6.055519, 0.954339, 6.345252, -0.547466, 0.053639, -10.206522, 87.000000, 88.153645, 0.673059, 0.098714 66, 66, 330341.07, 12925.79, 9.671619, 0.317689, 30.443645, 6.213818, 1.200853, 5.174503, -6.895192, 0.252845, -27.270395, 4.499135, 1.121706, 4.010977, -0.518703, 0.058976, -8.795117, 89.000000, 127.783450, 0.741707, 0.183475 67, 67, 318527.16, 8318.24, 7.463312, 0.311354, 23.970497, 10.226915, 0.930737, 10.987974, -5.510866, 0.228453, -24.122563, 7.351382, 1.037470, 7.085871, -0.330051, 0.058173, -5.673598, 80.000000, 111.295525, 0.714947, 0.168044 68, 68, 347297.26, -13547.71, 8.302775, 0.139473, 59.529771, -3.178209, 0.314947, -10.091251, -5.001857, 0.118651, -42.156199, -4.286554, 0.331785, -12.919669, 0.295632, 0.023094, 12.801066, 105.000000, 323.460047, 0.644413, 0.285291 69, 69, 321375.58, -10594.12, 8.511757, 0.190128, 44.768511, -4.488192, 0.391738, -11.457113, -3.580699, 0.129688, -27.610164, 15.775353, 0.431489, 36.560229, -0.696984, 0.030738, -22.675290, 114.000000, 195.779995, 0.346419, 0.180894 70, 70, 318675.19, -8454.47, 8.282021, 0.214851, 38.547808, -0.969505, 0.476632, -2.034075, -3.516127, 0.143391, -24.521182, 13.031313, 0.570519, 22.841140, -0.730248, 0.039407, -18.530950, 101.000000, 165.140728, 0.330218, 0.228780 71, 71, 350174.04, -12060.87, 7.691862, 0.159771, 48.143174, 0.177653, 0.371571, 0.478112, -4.958274, 0.125034, -39.655434, -5.212899, 0.353783, -14.734729, 0.411824, 0.027361, 15.051245, 181.000000, 257.122418, 0.696736, 0.232092 72, 72, 329442.54, 5939.52, 10.354961, 0.280736, 36.885096, 3.637696, 0.801463, 4.538819, -6.348881, 0.193141, -32.871664, 1.095688, 0.884476, 1.238798, -0.656300, 0.051187, -12.821690, 82.000000, 77.536351, 0.689327, 0.205983 73, 73, 307098.94, 992.68, 6.267891, 0.354550, 17.678435, 4.121365, 0.890409, 4.628621, -3.480062, 0.272704, -12.761300, 0.337112, 1.085886, 0.310449, 0.036437, 0.070203, 0.519023, 93.000000, 148.708357, 0.578436, 0.416415 74, 74, 337247.61, 14030.91, 9.444446, 0.317411, 29.754663, 4.536747, 1.202909, 3.771480, -6.152800, 0.278072, -22.126682, -0.671004, 1.253928, -0.535122, -0.383173, 0.057004, -6.721816, 67.000000, 86.176957, 0.743359, 0.411816 75, 75, 306612.95, -2173.79, 6.010946, 0.322893, 18.615910, 4.767723, 0.792336, 6.017298, -2.801243, 0.230355, -12.160540, -1.066857, 0.975403, -1.093760, -0.010387, 0.061033, -0.170185, 55.000000, 128.816520, 0.516044, 0.372098 76, 76, 301727, -6640.87, 5.259818, 0.301381, 17.452372, 5.323939, 0.806431, 6.601853, -1.821194, 0.208860, -8.719695, -4.937780, 0.970733, -5.086651, 0.106522, 0.055368, 1.923881, 68.000000, 72.538345, 0.510646, 0.151214 77, 77, 326724.18, 5478.1, 9.994544, 0.300588, 33.250013, 5.159399, 0.854498, 6.037927, -6.189602, 0.206698, -29.945091, 3.181647, 0.903610, 3.521039, -0.705501, 0.051178, -13.785262, 47.000000, 67.451100, 0.675895, 0.189558 78, 78, 311503.39, 15708.66, 4.513018, 0.260756, 17.307469, 13.057198, 1.148995, 11.364012, -5.465895, 0.259413, -21.070256, 13.731346, 1.670263, 8.221070, 0.419251, 0.078353, 5.350775, 33.000000, 45.490839, 0.802316, 0.274229 79, 79, 316934.08, -10632.09, 7.478278, 0.213853, 34.969248, -0.707349, 0.482041, -1.467405, -2.761794, 0.146029, -18.912587, 12.809140, 0.585250, 21.886609, -0.611526, 0.038442, -15.907881, 43.000000, 110.842367, 0.296537, 0.128693 80, 80, 317980.71, -13171.59, 7.562756, 0.201378, 37.555080, -3.424116, 0.419561, -8.161184, -2.730137, 0.138819, -19.666928, 15.777193, 0.478383, 32.980236, -0.596872, 0.033471, -17.832404, 52.000000, 129.228859, 0.304514, 0.168480 81, 81, 298790.59, -2464.08, 5.755969, 0.358489, 16.056208, 2.306106, 1.081050, 2.133210, -2.832708, 0.279765, -10.125317, -3.899745, 1.226201, -3.180348, 0.244501, 0.066559, 3.673458, 75.000000, 107.638128, 0.627644, 0.668158 82, 82, 294903.64, 214.57, 5.881583, 0.398597, 14.755710, 0.743616, 1.278801, 0.581495, -3.469304, 0.364457, -9.519110, -3.526588, 1.535306, -2.296993, 0.401957, 0.088817, 4.525672, 33.000000, 27.306957, 0.711099, 0.233801 83, 83, 284950.61, -7897.72, 6.736148, 0.385127, 17.490735, 4.613211, 1.173537, 3.931033, -3.435947, 0.322294, -10.660914, -4.831297, 1.494367, -3.233005, -0.035278, 0.086216, -0.409183, 11.000000, 14.614875, 0.787951, 0.460851 84, 84, 302616.14, 12642.65, 6.026411, 0.319627, 18.854485, 3.942054, 1.350449, 2.919070, -5.877459, 0.351267, -16.732183, 7.355485, 1.876469, 3.919854, 0.503175, 0.105445, 4.771909, 23.000000, 44.367411, 0.782367, 0.279268 85, 85, 298937.62, 11074.43, 6.368607, 0.369946, 17.214943, 1.499849, 1.421390, 1.055199, -5.694560, 0.405573, -14.040765, 4.101891, 2.012505, 2.038202, 0.526231, 0.113321, 4.643701, 43.000000, 34.833171, 0.775973, 0.128542 86, 86, 292980.66, 10621.27, 6.455342, 0.424860, 15.194061, -0.345165, 1.525309, -0.226292, -5.216536, 0.499186, -10.450092, -0.958515, 2.579570, -0.371579, 0.625118, 0.130087, 4.805391, 46.000000, 42.234789, 0.799225, 0.278817 87, 87, 291341.64, 3602.46, 5.939725, 0.446779, 13.294545, -0.664484, 1.434578, -0.463192, -3.890562, 0.471596, -8.249778, -4.422730, 1.996161, -2.215618, 0.586876, 0.121122, 4.845317, 19.000000, 19.356452, 0.773075, 0.167632 88, 88, 296052.78, 6812.78, 6.125102, 0.431338, 14.200241, -0.251831, 1.443402, -0.174471, -4.535758, 0.469641, -9.657933, -1.271855, 2.078733, -0.611841, 0.558521, 0.117727, 4.744201, 11.000000, 18.973951, 0.747599, 0.217084 89, 89, 314476.95, 3490.04, 7.009442, 0.295016, 23.759543, 8.802390, 0.719060, 12.241522, -4.429471, 0.215825, -20.523434, 5.405182, 0.925512, 5.840207, -0.325065, 0.064884, -5.009971, 30.000000, 39.809468, 0.611182, 0.232261 90, 90, 311673.48, 10101.08, 6.257951, 0.310927, 20.126772, 6.514004, 0.990045, 6.579504, -5.181916, 0.279011, -18.572477, 7.001462, 1.329514, 5.266181, 0.176755, 0.076547, 2.309111, 27.000000, 25.203965, 0.736107, 0.158431 91, 91, 300937.58, 3470.02, 6.050491, 0.405512, 14.920605, 0.873182, 1.242295, 0.702878, -3.820509, 0.387671, -9.855039, -1.738876, 1.647713, -1.055327, 0.374831, 0.087971, 4.260830, 18.000000, 26.405466, 0.670832, 0.392093 92, 92, 286991.93, 9571.27, 6.385783, 0.493898, 12.929366, -1.608171, 1.680658, -0.956870, -4.486312, 0.612342, -7.326477, -6.272054, 3.171698, -1.977507, 0.712525, 0.146255, 4.871804, 7.000000, 12.149989, 0.830976, 0.543251 93, 93, 307386.12, 16090.18, 4.370464, 0.269030, 16.245290, 11.565871, 1.255109, 9.215032, -5.538475, 0.298947, -18.526621, 12.874738, 1.922981, 6.695197, 0.565734, 0.095752, 5.908333, 7.000000, 23.381223, 0.808676, 0.216934 94, 94, 300604.96, 17843.82, 4.444737, 0.285227, 15.583175, 12.262004, 1.428339, 8.584798, -5.714073, 0.348203, -16.410166, 12.169209, 2.255484, 5.395387, 0.547033, 0.110433, 4.953553, 15.000000, 31.498488, 0.828504, 0.160817 95, 95, 303917.55, 29223.91, -2.149387, 0.489579, -4.390281, 42.600722, 2.348609, 18.138702, -3.096083, 0.402472, -7.692662, 29.990912, 2.913140, 10.295045, 0.526686, 0.114608, 4.595556, 44.000000, 24.194284, 0.832854, 0.253575 96, 96, 296097.2, 19299.56, 4.631822, 0.328522, 14.098989, 12.742291, 1.617126, 7.879591, -5.454123, 0.381991, -14.278152, 9.193423, 2.633241, 3.491295, 0.491438, 0.120356, 4.083216, 25.000000, 16.607968, 0.841248, 0.266236 97, 97, 291327.94, 19385.45, 5.413879, 0.396148, 13.666309, 9.278881, 2.014526, 4.605988, -5.212708, 0.448894, -11.612337, 2.895386, 3.380919, 0.856390, 0.513931, 0.138829, 3.701908, 15.000000, 16.954313, 0.850357, 0.332882 98, 98, 288651.19, 16782.21, 6.443561, 0.436136, 14.774201, 3.062543, 2.120818, 1.444039, -5.384273, 0.512539, -10.505109, -1.906411, 3.743423, -0.509269, 0.606951, 0.148555, 4.085689, 53.000000, 51.969281, 0.849642, 0.686109 99, 99, 321850.11, 16542.18, 7.208563, 0.316820, 22.752893, 13.867840, 1.394415, 9.945273, -6.538716, 0.237238, -27.561884, 13.169184, 1.343427, 9.802682, -0.323779, 0.061993, -5.222851, 24.000000, 23.424968, 0.785811, 0.137815 100, 100, 309623.17, 24691.81, -0.956391, 0.406671, -2.351753, 35.486106, 1.860811, 19.070234, -3.957886, 0.325856, -12.146122, 28.715313, 2.308319, 12.439924, 0.639715, 0.091787, 6.969537, 7.000000, 13.038477, 0.839173, 0.131625 101, 101, 317273.81, 16350.36, 5.413363, 0.281824, 19.208316, 16.343646, 1.272856, 12.840138, -5.870407, 0.223514, -26.264209, 15.310578, 1.445868, 10.589196, 0.001864, 0.062732, 0.029718, 13.000000, 24.684832, 0.800626, 0.175318 102, 102, 330127.65, 26472.36, 7.474826, 0.429288, 17.412157, 10.070675, 2.174234, 4.631827, -6.281409, 0.433964, -14.474503, 11.894098, 1.834084, 6.485034, -0.329622, 0.078651, -4.190935, 18.000000, 19.448116, 0.798297, 0.282432 103, 103, 330024.3, 22050.13, 7.874223, 0.410070, 19.202130, 10.667131, 1.857044, 5.744146, -6.486140, 0.399028, -16.254853, 11.186851, 1.650689, 6.777079, -0.409675, 0.070791, -5.787110, 24.000000, 30.242840, 0.754131, 0.193070 104, 104, 335366.5, 8522.69, 10.500521, 0.246304, 42.632379, 2.722036, 0.824782, 3.300311, -6.560579, 0.178297, -36.795701, -3.327436, 0.957691, -3.474435, -0.470628, 0.052088, -9.035326, 45.000000, 108.490260, 0.733764, 0.114613 105, 105, 330795.7, 8625.57, 10.303610, 0.289869, 35.545732, 3.905608, 0.924820, 4.223102, -6.658912, 0.207526, -32.087152, 0.737870, 0.973212, 0.758180, -0.569320, 0.054367, -10.471734, 56.000000, 43.402741, 0.723928, 0.092177 106, 106, 324461.1, 12021.3, 8.950555, 0.357852, 25.011863, 8.458492, 1.217920, 6.945032, -6.739488, 0.256340, -26.291163, 7.616565, 1.093324, 6.966432, -0.496272, 0.059739, -8.307271, 33.000000, 35.450942, 0.752170, 0.144400 107, 107, 333249.02, 19193.73, 8.250311, 0.405329, 20.354608, 8.174430, 1.614541, 5.063005, -6.077624, 0.380995, -15.951987, 6.754605, 1.500257, 4.502298, -0.395800, 0.064795, -6.108531, 35.000000, 38.331902, 0.721929, 0.144616 108, 108, 330905.38, 16199.04, 8.946628, 0.348543, 25.668640, 8.236716, 1.425685, 5.777375, -6.715608, 0.301628, -22.264531, 6.942807, 1.302939, 5.328573, -0.477532, 0.062197, -7.677785, 36.000000, 78.325935, 0.736401, 0.320943 109, 109, 338740.71, 9995.65, 10.252841, 0.245069, 41.836589, 2.863774, 0.876203, 3.268393, -6.435154, 0.192850, -33.368661, -4.350535, 1.045429, -4.161483, -0.380564, 0.052072, -7.308345, 58.000000, 65.188775, 0.752843, 0.104491 110, 110, 345541.9, -607.56, 7.518741, 0.213149, 35.274533, 4.009937, 0.493419, 8.126834, -4.702380, 0.141959, -33.125016, -4.138285, 0.581207, -7.120158, 0.243537, 0.041139, 5.919807, 37.000000, 54.487659, 0.695693, 0.154223 111, 111, 348908.69, -5077.51, 6.933395, 0.203977, 33.991096, 4.069842, 0.491802, 8.275374, -4.570377, 0.137572, -33.221599, -4.600359, 0.475202, -9.680845, 0.440266, 0.038086, 11.559882, 53.000000, 131.237047, 0.704331, 0.185398 112, 112, 343120.93, 5902.89, 9.087658, 0.217743, 41.735764, 3.817245, 0.579457, 6.587619, -5.671535, 0.152710, -37.139183, -4.867926, 0.795570, -6.118794, -0.103542, 0.044489, -2.327379, 49.000000, 56.072676, 0.747564, 0.160080 113, 113, 377836.69, -36378.58, 18.006124, 0.315302, 57.107607, -8.848749, 1.125586, -7.861460, -8.387429, 0.219465, -38.217573, -3.446741, 0.803834, -4.287874, -2.151689, 0.071128, -30.251053, 1070.000000, 846.326749, 0.913022, 0.730348 114, 114, 356153.1, -24448.15, 7.927891, 0.170042, 46.623064, -5.779444, 0.394586, -14.646849, -4.376893, 0.150999, -28.986244, -4.737646, 0.329322, -14.386043, 0.456575, 0.025107, 18.185514, 547.000000, 464.772101, 0.665366, 0.282921 115, 115, 363934.49, -23252.2, 7.999369, 0.225040, 35.546471, -5.141162, 0.643307, -7.991773, -4.698500, 0.146887, -31.987144, -2.513073, 0.385648, -6.516496, 0.424970, 0.040718, 10.436963, 660.000000, 385.707070, 0.748200, 0.211888 116, 116, 362715.03, -62961.03, 18.264326, 1.392250, 13.118571, -39.047375, 5.809098, -6.721762, -7.490201, 0.891833, -8.398658, -26.956515, 3.954656, -6.816400, -0.541645, 0.145799, -3.715000, 175.000000, 164.528322, 0.938008, 0.901228 117, 117, 355515.39, -15862.17, 7.389652, 0.167763, 44.048248, 0.726729, 0.391116, 1.858091, -4.988973, 0.132645, -37.611326, -5.629094, 0.346007, -16.268711, 0.527541, 0.028861, 18.278958, 594.000000, 434.269264, 0.752276, 0.254329 118, 118, 350331.74, 2259.59, 6.773598, 0.249043, 27.198521, 5.122841, 0.579560, 8.839195, -4.770030, 0.158199, -30.152104, -1.549121, 0.675446, -2.293479, 0.418625, 0.045056, 9.291308, 189.000000, 126.906427, 0.766210, 0.250984 119, 119, 390869.17, -52824.71, 10.634642, 0.682669, 15.578045, -2.860874, 2.985962, -0.958108, -5.953111, 0.762436, -7.808014, -13.223909, 2.426532, -5.449716, 0.160860, 0.127594, 1.260720, 121.000000, 137.509827, 0.973285, 0.715319 120, 120, 391663.46, -13955.69, 7.337520, 0.502840, 14.592165, 7.909354, 2.105347, 3.756794, -2.476627, 0.314379, -7.877830, -9.268059, 2.239436, -4.138568, -0.516405, 0.112516, -4.589614, 125.000000, 139.453586, 0.823976, 0.901280 121, 121, 381676.42, -24737.89, 9.244666, 0.327952, 28.189079, 11.109141, 1.196463, 9.284983, -5.601485, 0.174705, -32.062451, -5.067852, 1.580829, -3.205819, -0.481814, 0.070948, -6.791075, 175.000000, 109.722683, 0.887252, 0.252308 122, 122, 396227.77, -39792.02, 9.626280, 0.392573, 24.520986, 1.649411, 2.002396, 0.823719, -8.189124, 0.392335, -20.872762, -4.097362, 1.428625, -2.868046, 0.584867, 0.101764, 5.747305, 85.000000, 82.505387, 0.933145, 0.303425 123, 123, 365535.4, -28214.23, 9.097398, 0.254711, 35.716517, -9.775554, 0.841798, -11.612708, -4.539383, 0.140082, -32.405212, -0.922826, 0.443849, -2.079144, 0.174398, 0.040319, 4.325400, 200.000000, 392.210599, 0.703624, 0.446976 124, 124, 358761.78, -6929.68, 6.473877, 0.246003, 26.316297, 6.052926, 0.655943, 9.227822, -5.240269, 0.150459, -34.828615, -4.924665, 0.644901, -7.636313, 0.671259, 0.049350, 13.601949, 389.000000, 317.781906, 0.827641, 0.350033 125, 125, 376070.01, -56303.59, 15.169104, 0.944983, 16.052251, -2.844005, 3.355325, -0.847610, -11.685117, 0.721031, -16.206128, 4.263528, 2.530801, 1.684655, -0.826415, 0.121690, -6.791170, 381.000000, 352.751960, 0.978530, 0.842214 126, 126, 353788.69, -7340.62, 6.547340, 0.215857, 30.331829, 5.692115, 0.549815, 10.352782, -4.874274, 0.142362, -34.238476, -5.699104, 0.504123, -11.304986, 0.608528, 0.040804, 14.913432, 173.000000, 180.356650, 0.765183, 0.209985 127, 127, 371670.71, -21543.62, 9.815762, 0.247556, 39.650623, -5.900171, 0.878850, -6.713516, -6.011917, 0.135161, -44.479532, -2.698278, 1.045433, -2.581014, 0.075912, 0.052607, 1.442993, 206.000000, 290.705120, 0.834138, 0.362214 128, 128, 367522.64, -7189.91, 7.585091, 0.300970, 25.202185, 1.497669, 0.895136, 1.673118, -5.835783, 0.175562, -33.240488, -4.140377, 1.022724, -4.048383, 0.554830, 0.061048, 9.088433, 142.000000, 154.443229, 0.878979, 0.377062 129, 129, 361892.77, -18166, 7.287138, 0.204950, 35.555740, -0.147923, 0.516647, -0.286314, -4.947794, 0.144322, -34.282963, -3.905252, 0.386846, -10.095103, 0.551619, 0.038911, 14.176578, 148.000000, 108.466977, 0.796829, 0.192098 130, 130, 366450.66, -74634.65, 18.550903, 1.557921, 11.907476, -71.639003, 11.012882, -6.505018, 0.621159, 1.632501, 0.380495, -59.137061, 9.627336, -6.142619, -0.187886, 0.166993, -1.125113, 141.000000, 147.722618, 0.950649, 0.987659 131, 131, 355381.88, -79216.89, -4.544321, 1.038079, -4.377623, 8.980470, 0.849641, 10.569728, 4.070490, 1.118872, 3.638029, 6.735041, 2.323302, 2.898909, 1.716280, 0.134055, 12.802759, 106.000000, 103.931284, 0.973366, 0.985179 132, 132, 353694.67, -32885.23, 8.618975, 0.173057, 49.804144, -10.219125, 0.430206, -23.754002, -4.245170, 0.175044, -24.252025, -3.280142, 0.372156, -8.813899, 0.300632, 0.019180, 15.673912, 116.000000, 394.472392, 0.564401, 0.492473 133, 133, 378726.25, -28678.9, 12.943786, 0.282262, 45.857428, 1.596959, 1.110802, 1.437663, -6.762583, 0.183691, -36.814916, -5.623973, 1.162935, -4.836017, -1.102543, 0.063572, -17.343344, 84.000000, 133.244856, 0.892585, 0.411665 134, 134, 365246.77, -57670.24, 18.812885, 1.204551, 15.618168, -32.560824, 4.440631, -7.332477, -9.908252, 0.635925, -15.580851, -16.049196, 2.144686, -7.483239, -0.769640, 0.126241, -6.096592, 78.000000, 101.638041, 0.946952, 0.770451 135, 135, 389517.13, -31493.43, 8.322346, 0.314135, 26.492925, 15.772679, 1.580173, 9.981614, -6.171662, 0.205836, -29.983347, -0.465696, 1.434243, -0.324698, -0.235055, 0.075102, -3.129814, 88.000000, 43.543897, 0.918027, 0.288699 136, 136, 344415.42, 11511.89, 9.338354, 0.263994, 35.373380, 2.070399, 0.761325, 2.719467, -5.623901, 0.252031, -22.314319, -4.866713, 1.142556, -4.259498, -0.188483, 0.047532, -3.965348, 31.000000, 29.826302, 0.780698, 0.239345 137, 137, 365053.58, -11168.12, 7.201483, 0.257931, 27.920173, 2.940623, 0.742350, 3.961235, -5.601527, 0.154243, -36.316362, -4.823406, 0.759185, -6.353404, 0.605368, 0.052286, 11.577915, 60.000000, 73.219256, 0.864475, 0.197328 138, 138, 387334.51, -21934.03, 6.895450, 0.429648, 16.049069, 17.358895, 1.684391, 10.305739, -4.098833, 0.205672, -19.928991, -4.415487, 1.976437, -2.234064, -0.328289, 0.091582, -3.584651, 25.000000, 75.498341, 0.860337, 0.654613 139, 139, 393097.28, -22717.2, 6.537526, 0.464859, 14.063452, 17.297978, 2.008997, 8.610257, -2.749030, 0.271916, -10.109843, -6.278513, 2.099629, -2.990297, -0.500836, 0.107806, -4.645694, 57.000000, 54.655958, 0.833769, 0.765682 140, 140, 380945.88, -17224.24, 7.182918, 0.352788, 20.360438, 8.600471, 1.182421, 7.273610, -4.784278, 0.176917, -27.042511, -4.785658, 1.787038, -2.677983, 0.104266, 0.072285, 1.442440, 17.000000, 12.068184, 0.867663, 0.289302 141, 141, 367373.14, -14712.42, 7.717936, 0.252884, 30.519704, 0.162934, 0.753453, 0.216249, -5.611752, 0.147800, -37.968549, -4.068904, 0.836625, -4.863472, 0.517056, 0.052615, 9.827074, 47.000000, 53.845023, 0.857886, 0.182741 142, 142, 374567.12, -13256.38, 7.844395, 0.307143, 25.539891, 0.827178, 1.111476, 0.744216, -5.559650, 0.162019, -34.314748, -4.911882, 1.517039, -3.237810, 0.419279, 0.062389, 6.720397, 51.000000, 74.304433, 0.878647, 0.258810 143, 143, 380862.18, -12688.75, 6.823971, 0.360730, 18.917136, 6.176647, 1.263399, 4.888911, -4.513292, 0.202279, -22.312180, -4.577565, 1.733601, -2.640495, 0.231424, 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624.493873, 0.890725, 0.885425 238, 238, 302126.02, -67286.13, 6.794947, 0.368907, 18.419115, 2.411572, 1.145581, 2.105107, -8.469653, 0.410011, -20.657146, 5.254789, 1.501176, 3.500447, 0.800868, 0.075859, 10.557258, 346.000000, 319.462552, 0.757701, 0.630845 239, 239, 321943.14, -68916.22, 4.939631, 0.240585, 20.531763, 7.167947, 0.743800, 9.636932, -3.096898, 0.221359, -13.990359, -5.242166, 0.846837, -6.190288, 0.573395, 0.042905, 13.364169, 247.000000, 190.360665, 0.658967, 0.513854 240, 240, 314598.5, -64965.34, 5.307815, 0.275287, 19.281004, 9.845913, 0.774763, 12.708283, -4.468496, 0.240464, -18.582832, -0.981771, 0.829427, -1.183674, 0.408586, 0.051018, 8.008613, 463.000000, 301.055876, 0.630885, 0.347647 241, 241, 310206.19, -67759.02, 5.938055, 0.331030, 17.938130, 9.499366, 0.947625, 10.024390, -6.315061, 0.297453, -21.230477, 1.095447, 0.972356, 1.126590, 0.490735, 0.063499, 7.728289, 279.000000, 235.350126, 0.709427, 0.327936 242, 242, 326961.41, -72189.14, 3.673470, 0.261511, 14.047098, 4.209398, 0.767571, 5.484054, -0.526382, 0.233335, -2.255905, -12.708785, 0.876334, -14.502223, 0.945476, 0.044034, 21.471285, 93.000000, 166.362700, 0.779023, 0.457223 243, 243, 306522.23, -44854.32, 3.518479, 0.243461, 14.451944, 3.590584, 0.525170, 6.836996, -1.042204, 0.187793, -5.549745, -9.795264, 0.843927, -11.606774, 0.944456, 0.044629, 21.162173, 686.000000, 337.477717, 0.522116, 0.357494 244, 244, 332009.57, -86587.48, 1.566352, 0.507622, 3.085667, 6.885538, 1.329601, 5.178648, 0.212372, 0.427108, 0.497232, -27.090353, 1.603785, -16.891508, 1.903855, 0.080404, 23.678466, 105.000000, 107.748487, 0.930756, 0.982294 245, 245, 291250.63, -62212.96, 7.454667, 0.567110, 13.145002, -2.144407, 1.237228, -1.733235, -8.185119, 0.628161, -13.030287, -4.084026, 2.195355, -1.860304, 1.005760, 0.118226, 8.507092, 200.000000, 154.809427, 0.732061, 0.570346 246, 246, 302274.84, -54583.9, 3.732571, 0.300178, 12.434508, 5.838769, 0.736308, 7.929789, -3.846621, 0.257536, 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8.490293, 0.492702, 17.232105, -3.925160, 1.403947, -2.795804, -10.876421, 0.626536, -17.359606, 11.822271, 2.378936, 4.969563, 0.753921, 0.105553, 7.142611, 44.000000, 80.552073, 0.802217, 0.464202 256, 256, 299578.37, -47629.99, 2.751404, 0.351591, 7.825577, 6.381897, 0.760867, 8.387660, -2.505954, 0.267870, -9.355126, -15.862070, 1.224738, -12.951402, 1.505534, 0.061466, 24.493769, 35.000000, 55.997637, 0.645352, 0.422272 257, 257, 293314.45, -52457.68, 4.350808, 0.427375, 10.180300, 3.973282, 1.084012, 3.665348, -4.681116, 0.367174, -12.749029, -16.052798, 1.794470, -8.945705, 1.487575, 0.078556, 18.936417, 6.000000, 15.784668, 0.716516, 0.356346 258, 258, 299215.09, -41823.07, 2.662286, 0.315556, 8.436818, 8.188125, 0.664188, 12.328028, -1.401700, 0.239266, -5.858326, -12.647961, 1.088299, -11.621768, 1.125050, 0.053519, 21.021324, 32.000000, 120.751255, 0.563070, 0.540024 259, 259, 288944.87, -47144.31, 3.440681, 0.499681, 6.885751, 10.079538, 1.172426, 8.597161, -3.700774, 0.388682, -9.521337, -14.828896, 1.853637, -7.999892, 1.284640, 0.078486, 16.367786, 28.000000, 9.762612, 0.676371, 0.208265 260, 260, 290541.99, -38708.26, 2.270856, 0.361718, 6.277982, 16.688539, 0.807689, 20.662092, -2.287035, 0.271928, -8.410437, -9.551355, 1.319205, -7.240235, 0.896833, 0.061683, 14.539328, 10.000000, 61.134170, 0.598134, 0.296692 261, 261, 285964.14, -39392.46, 2.354988, 0.451343, 5.217736, 20.741199, 1.011741, 20.500508, -2.927544, 0.314018, -9.322858, -10.527290, 1.560097, -6.747844, 0.831214, 0.075129, 11.063839, 12.000000, 11.910844, 0.627052, 0.245258 libpysal-4.9.2/libpysal/examples/tokyo/tokyo_GS_F_summary.txt000066400000000000000000000237301452177046000245030ustar00rootroot00000000000000***************************************************************************** * Semiparametric Geographically Weighted Regression * * Release 1.0.90 (GWR 4.0.90) * * 12 May 2015 * * (Originally coded by T. Nakaya: 1 Nov 2009) * * * * Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * * Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * * (c) GWR4 development team * * (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * * (3) University of Glasgow, (4) Arizona State University * ***************************************************************************** Program began at 7/25/2016 8:24:34 AM ***************************************************************************** Session: Session control file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_F.ctl ***************************************************************************** Data filename: C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt Number of areas/points: 262 Model settings--------------------------------- Model type: Poisson Geographic kernel: fixed Gaussian Method for optimal bandwidth search: Golden section search Criterion for optimal bandwidth: AICc Number of varying coefficients: 5 Number of fixed coefficients: 0 Modelling options--------------------------------- Standardisation of independent variables: OFF Testing geographical variability of local coefficients: OFF Local to Global Variable selection: OFF Global to Local Variable selection: OFF Prediction at non-regression points: OFF Variable settings--------------------------------- Area key: field1: IDnum0 Easting (x-coord): field2 : X_CENTROID Northing (y-coord): field3: Y_CENTROID Cartesian coordinates: Euclidean distance Dependent variable: field4: db2564 Offset variable is not specified Intercept: varying (Local) intercept Independent variable with varying (Local) coefficient: field6: OCC_TEC Independent variable with varying (Local) coefficient: field7: OWNH Independent variable with varying (Local) coefficient: field8: POP65 Independent variable with varying (Local) coefficient: field9: UNEMP ***************************************************************************** ***************************************************************************** Global regression result ***************************************************************************** < Diagnostic information > Number of parameters: 5 Deviance: 24597.455544 Classic AIC: 24607.455544 AICc: 24607.689919 BIC/MDL: 24625.297266 Percent deviance explained 0.526746 Variable Estimate Standard Error z(Est/SE) Exp(Est) -------------------- --------------- --------------- --------------- --------------- Intercept 8.432403 0.061613 136.859875 4593.526955 OCC_TEC -4.270431 0.156467 -27.292831 0.013976 OWNH -4.789311 0.046070 -103.957933 0.008318 POP65 -1.252659 0.178384 -7.022265 0.285744 UNEMP 0.061305 0.010099 6.070542 1.063223 ***************************************************************************** GWR (Geographically weighted regression) bandwidth selection ***************************************************************************** Bandwidth search Limits: 8764.47445754735, 67330.4217488991 Golden section search begins... Initial values pL Bandwidth: 8764.474 Criterion: 11283.153 p1 Bandwidth: 10011.123 Criterion: 12806.152 p2 Bandwidth: 10781.594 Criterion: 13629.486 pU Bandwidth: 12028.243 Criterion: 14799.293 iter 1 (p1) Bandwidth: 10011.123 Criterion: 12806.152 Diff: 770.471 iter 2 (p1) Bandwidth: 9534.946 Criterion: 12254.404 Diff: 476.177 iter 3 (p1) Bandwidth: 9240.652 Criterion: 11895.443 Diff: 294.294 iter 4 (p1) Bandwidth: 9058.768 Criterion: 11666.303 Diff: 181.884 iter 5 (p1) Bandwidth: 8946.358 Criterion: 11521.794 Diff: 112.410 iter 6 (p1) Bandwidth: 8876.885 Criterion: 11431.353 Diff: 69.473 iter 7 (p1) Bandwidth: 8833.948 Criterion: 11375.019 Diff: 42.937 iter 8 (p1) Bandwidth: 8807.411 Criterion: 11340.034 Diff: 26.536 iter 9 (p1) Bandwidth: 8791.011 Criterion: 11318.348 Diff: 16.400 iter 10 (p1) Bandwidth: 8780.875 Criterion: 11304.920 Diff: 10.136 iter 11 (p1) Bandwidth: 8774.610 Criterion: 11296.612 Diff: 6.264 iter 12 (p1) Bandwidth: 8770.739 Criterion: 11291.473 Diff: 3.872 iter 13 (p1) Bandwidth: 8768.346 Criterion: 11288.296 Diff: 2.393 iter 14 (p1) Bandwidth: 8766.867 Criterion: 11286.332 Diff: 1.479 iter 15 (p1) Bandwidth: 8765.953 Criterion: 11285.118 Diff: 0.914 iter 16 (p1) Bandwidth: 8765.388 Criterion: 11284.367 Diff: 0.565 iter 17 (p1) Bandwidth: 8765.039 Criterion: 11283.903 Diff: 0.349 iter 18 (p1) Bandwidth: 8764.824 Criterion: 11283.617 Diff: 0.216 iter 19 (p1) Bandwidth: 8764.690 Criterion: 11283.440 Diff: 0.133 iter 20 (p1) Bandwidth: 8764.608 Criterion: 11283.330 Diff: 0.082 iter 21 (p1) Bandwidth: 8764.557 Criterion: 11283.262 Diff: 0.051 iter 22 (p1) Bandwidth: 8764.525 Criterion: 11283.221 Diff: 0.031 iter 23 (p1) Bandwidth: 8764.506 Criterion: 11283.195 Diff: 0.019 iter 24 (p1) Bandwidth: 8764.494 Criterion: 11283.179 Diff: 0.012 The lower limit in your search has been selected as the optimal bandwidth size. A new sesssion is recommended to try with a smaller lowest limit of the bandwidth search. Best bandwidth size 8764.474 Minimum AICc 11283.153 ***************************************************************************** GWR (Geographically weighted regression) result ***************************************************************************** Bandwidth and geographic ranges Bandwidth size: 8764.474458 Coordinate Min Max Range --------------- --------------- --------------- --------------- X-coord 276385.400000 408226.180000 131840.780000 Y-coord -86587.480000 33538.420000 120125.900000 Diagnostic information Effective number of parameters (model: trace(S)): 80.249343 Effective number of parameters (variance: trace(S'WSW^-1)): 56.479620 Degree of freedom (model: n - trace(S)): 181.750657 Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 157.980934 Deviance: 11050.508287 Classic AIC: 11211.006974 AICc: 11283.152841 BIC/MDL: 11497.364277 Percent deviance explained 0.787389 *********************************************************** << Geographically varying (Local) coefficients >> *********************************************************** Estimates of varying coefficients have been saved in the following file. Listwise output file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_F_listwise.csv Summary statistics for varying (Local) coefficients Variable Mean STD -------------------- --------------- --------------- Intercept 6.906204 3.107262 OCC_TEC 2.860801 11.608416 OWNH -3.926832 2.110848 POP65 0.391512 10.603676 UNEMP 0.089393 0.519691 Variable Min Max Range -------------------- --------------- --------------- --------------- Intercept -4.544321 18.812885 23.357206 OCC_TEC -71.639003 46.740425 118.379428 OWNH -11.685117 4.070490 15.755607 POP65 -59.137061 32.133177 91.270237 UNEMP -2.151689 1.903855 4.055543 Variable Lwr Quartile Median Upr Quartile -------------------- --------------- --------------- --------------- Intercept 5.562201 7.334816 8.453658 OCC_TEC -5.092611 1.811224 7.880221 OWNH -5.242645 -4.012946 -2.522777 POP65 -4.995400 -1.404697 6.833327 UNEMP -0.297128 0.075154 0.423059 Variable Interquartile R Robust STD -------------------- --------------- --------------- Intercept 2.891457 2.143408 OCC_TEC 12.972832 9.616629 OWNH 2.719868 2.016210 POP65 11.828727 8.768515 UNEMP 0.720188 0.533868 (Note: Robust STD is given by (interquartile range / 1.349) ) ***************************************************************************** GWR Analysis of Deviance Table ***************************************************************************** Source Deviance DOF Deviance/DOF ------------ ------------------- ---------- ---------------- Global model 24597.456 257.000 95.710 GWR model 11050.508 157.981 69.948 Difference 13546.947 99.019 136.812 ***************************************************************************** Program terminated at 7/25/2016 8:24:42 AM libpysal-4.9.2/libpysal/examples/tokyo/tokyo_GS_NN.ctl000066400000000000000000000014111452177046000230070ustar00rootroot00000000000000 C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt FORMAT/DELIMITER: 0 Number_of_fields: 9 Number_of_areas: 262 Fields AreaKey 001 IDnum0 X 002 X_CENTROID Y 003 Y_CENTROID Gmetric 0 Dependent 004 db2564 Offset Independent_geo 5 000 Intercept 006 OCC_TEC 007 OWNH 008 POP65 009 UNEMP Independent_fix 0 Unused_fields 1 005 eb2564 MODELTYPE: 1 STANDARDISATION: 0 GTEST: 0 VSL2G: 0 VSG2L: 0 KERNELTYPE: 3 BANDSELECTIONMETHOD: 1 Goldrangeflag: 0 Goldenmax: Goldenmin: Fixedbandsize: IntervalMax: IntervalMin: IntervalStep: Criteria: summary_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_NN_summary.txt listwise_output: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_NN_listwise.csv predictflag: 0 prediction_def: prediction_output: libpysal-4.9.2/libpysal/examples/tokyo/tokyo_GS_NN_listwise.csv000066400000000000000000002775711452177046000247710ustar00rootroot00000000000000Area_num, Area_key, x_coord, y_coord, est_Intercept, se_Intercept, t_Intercept, est_OCC_TEC, se_OCC_TEC, t_OCC_TEC, est_OWNH, se_OWNH, t_OWNH, est_POP65, se_POP65, t_POP65, est_UNEMP, se_UNEMP, t_UNEMP, y, yhat, localpdev, Ginfluence 0, 0, 378906.83, 17310.41, 9.074036, 0.073984, 122.648331, -5.142149, 0.187186, -27.470845, -5.248993, 0.055641, -94.336143, -3.748701, 0.211766, -17.702104, 0.063281, 0.012091, 5.233592, 189.000000, 153.956623, 0.669769, 0.017050 1, 1, 334095.21, 25283.2, 8.410777, 0.093472, 89.981413, -3.826044, 0.210624, -18.165278, -4.991247, 0.066799, -74.720395, -1.291807, 0.253416, -5.097581, 0.071839, 0.015879, 4.524037, 95.000000, 115.954351, 0.637569, 0.060948 2, 2, 378200.19, -877.05, 9.266997, 0.078175, 118.541648, -5.418820, 0.194849, -27.810354, -5.317602, 0.059268, -89.720968, -4.646020, 0.217230, -21.387574, 0.068904, 0.012438, 5.539975, 70.000000, 81.656996, 0.675216, 0.033105 3, 3, 357191.03, 29064.39, 8.819506, 0.078174, 112.818937, -4.725862, 0.193624, -24.407415, -5.178919, 0.057417, -90.199013, -2.674651, 0.221580, -12.070818, 0.069701, 0.013202, 5.279692, 48.000000, 69.816366, 0.662878, 0.042822 4, 4, 358056.34, 10824.73, 9.134424, 0.079429, 115.001420, -5.429366, 0.195983, -27.703250, -5.331736, 0.058886, -90.544115, -3.155001, 0.222122, -14.203894, 0.053647, 0.013087, 4.099294, 65.000000, 56.964223, 0.666054, 0.035981 5, 5, 366747.61, -3073.12, 9.343691, 0.076804, 121.655760, -5.931095, 0.190135, -31.194118, -5.359711, 0.058112, -92.231188, -3.932469, 0.213200, -18.444977, 0.050443, 0.012093, 4.171209, 107.000000, 148.483203, 0.660986, 0.025549 6, 6, 351099.27, 11800.35, 9.083190, 0.080970, 112.180119, -5.437597, 0.195029, -27.880971, -5.316756, 0.059557, -89.271287, -2.504094, 0.224224, -11.167820, 0.041363, 0.013385, 3.090139, 65.000000, 60.740515, 0.656304, 0.038487 7, 7, 377929.98, 4635.1, 9.210313, 0.077351, 119.071609, -5.322749, 0.194119, -27.420016, -5.305609, 0.058473, -90.736491, -4.409552, 0.217023, -20.318348, 0.068710, 0.012430, 5.527824, 76.000000, 66.162583, 0.675688, 0.025443 8, 8, 367529.91, 20192.51, 9.013437, 0.077806, 115.845185, -4.988202, 0.197298, -25.282531, -5.263056, 0.057886, -90.921786, -3.555127, 0.222052, -16.010346, 0.071542, 0.013021, 5.494533, 192.000000, 197.649432, 0.673927, 0.204641 9, 9, 389231.47, 3489.35, 9.194835, 0.075021, 122.563621, -5.293082, 0.186581, -28.368882, -5.258926, 0.056708, -92.737218, -4.371140, 0.211092, -20.707284, 0.060387, 0.011979, 5.041098, 27.000000, 52.865691, 0.671303, 0.027392 10, 10, 389427.64, 9290.1, 9.141264, 0.074693, 122.383814, -5.183981, 0.186998, -27.722182, -5.246591, 0.056377, -93.062607, -4.237223, 0.211628, -20.021991, 0.063926, 0.012019, 5.318651, 28.000000, 171.730664, 0.671966, 0.036983 11, 11, 381089.82, 9125.81, 9.157106, 0.076461, 119.762361, -5.203088, 0.192631, -27.010683, -5.279801, 0.057695, -91.512183, -4.295181, 0.216166, -19.869835, 0.069048, 0.012367, 5.583183, 63.000000, 111.258088, 0.675491, 0.017910 12, 12, 371082.66, 6843.9, 9.186409, 0.078873, 116.471335, -5.298157, 0.198732, -26.659802, -5.324574, 0.059392, -89.651995, -4.230761, 0.221438, -19.105863, 0.073229, 0.012807, 5.717804, 34.000000, 34.583360, 0.677431, 0.022704 13, 13, 388281.84, -1760.78, 9.247242, 0.075910, 121.818623, -5.377844, 0.187730, -28.646717, -5.272459, 0.057462, -91.754793, -4.564542, 0.211969, -21.533983, 0.059052, 0.012044, 4.902815, 17.000000, 32.343811, 0.671604, 0.025496 14, 14, 386771.66, -4857.11, 9.283836, 0.076489, 121.374318, -5.443946, 0.188653, -28.856895, -5.283609, 0.057975, -91.135542, -4.678866, 0.212575, -22.010445, 0.058079, 0.012092, 4.803201, 25.000000, 25.704914, 0.671464, 0.025031 15, 15, 397029.93, 4912.15, 9.154871, 0.073950, 123.797997, -5.212980, 0.183267, -28.444755, -5.225559, 0.055830, -93.597224, -4.279668, 0.208880, -20.488641, 0.059318, 0.011812, 5.021672, 17.000000, 22.392866, 0.669267, 0.024045 16, 16, 399583.28, 1217.51, 9.168450, 0.073158, 125.323834, -5.271901, 0.180344, -29.232492, -5.216248, 0.055256, -94.401444, -4.223207, 0.206449, -20.456457, 0.054280, 0.011640, 4.663153, 31.000000, 30.300505, 0.666263, 0.023312 17, 17, 389413.79, 18915.59, 9.047782, 0.071651, 126.275723, -5.110305, 0.180358, -28.334227, -5.207427, 0.053977, -96.474067, -3.665047, 0.205657, -17.821124, 0.059159, 0.011628, 5.087487, 27.000000, 26.593260, 0.664824, 0.025958 18, 18, 374811.31, 23395.2, 8.999106, 0.074599, 120.633246, -4.990847, 0.189421, -26.347875, -5.230304, 0.055832, -93.678941, -3.533458, 0.214398, -16.480873, 0.067797, 0.012353, 5.488264, 10.000000, 30.512712, 0.669952, 0.023886 19, 19, 366291.01, 3851.09, 9.223767, 0.079786, 115.606018, -5.464277, 0.199318, -27.414922, -5.348168, 0.059998, -89.139151, -4.082177, 0.222215, -18.370382, 0.070852, 0.012875, 5.502888, 42.000000, 37.160080, 0.674355, 0.026699 20, 20, 362053.67, 7027.25, 9.183685, 0.080617, 113.916836, -5.417327, 0.200624, -27.002372, -5.348398, 0.060130, -88.947354, -3.717818, 0.224682, -16.546986, 0.066457, 0.013178, 5.042947, 20.000000, 29.699854, 0.672858, 0.027742 21, 21, 350567.45, 26456.28, 8.784883, 0.080222, 109.507285, -4.716307, 0.194644, -24.230420, -5.171281, 0.058585, -88.269742, -2.311228, 0.224823, -10.280218, 0.064987, 0.013539, 4.800032, 40.000000, 31.504735, 0.657831, 0.025902 22, 22, 356783.59, 23682.89, 8.902883, 0.078129, 113.950515, -4.926818, 0.193251, -25.494427, -5.222062, 0.057521, -90.784721, -2.728844, 0.221115, -12.341289, 0.062867, 0.013135, 4.786385, 15.000000, 38.203355, 0.663177, 0.026242 23, 23, 356225.47, 19763.98, 8.964701, 0.079165, 113.241165, -5.049515, 0.195340, -25.849823, -5.257602, 0.058277, -90.217964, -2.813649, 0.223123, -12.610303, 0.059704, 0.013291, 4.491980, 47.000000, 55.344059, 0.664599, 0.037684 24, 24, 338360.54, 25697.56, 8.549179, 0.088387, 96.724930, -4.204848, 0.203537, -20.658924, -5.063401, 0.063573, -79.646686, -1.549956, 0.241424, -6.420043, 0.067885, 0.014966, 4.535871, 68.000000, 129.402746, 0.644564, 0.099890 25, 25, 337846.31, 18213.38, 8.723631, 0.087477, 99.725252, -4.652824, 0.200282, -23.231309, -5.151293, 0.063417, -81.229477, -1.377811, 0.239016, -5.764515, 0.042412, 0.014721, 2.881016, 17.000000, 46.593449, 0.638758, 0.027529 26, 26, 344074.13, 27136.92, 8.661581, 0.083777, 103.388539, -4.468582, 0.198221, -22.543386, -5.116281, 0.060674, -84.323712, -1.912029, 0.231625, -8.254833, 0.067503, 0.014164, 4.765830, 57.000000, 58.059217, 0.651764, 0.025659 27, 27, 349087.82, 19336.47, 8.904696, 0.080340, 110.838172, -5.041168, 0.193427, -26.062404, -5.233113, 0.058843, -88.932740, -2.237014, 0.224131, -9.980849, 0.050943, 0.013450, 3.787539, 40.000000, 33.303240, 0.655176, 0.027516 28, 28, 343402.57, 18620.67, 8.837015, 0.082639, 106.935308, -4.949619, 0.194506, -25.447079, -5.203588, 0.060280, -86.323034, -1.787178, 0.228264, -7.829427, 0.045217, 0.013826, 3.270327, 47.000000, 80.986586, 0.647142, 0.031662 29, 29, 359036.48, 1198.74, 9.290659, 0.077763, 119.473605, -5.981911, 0.190454, -31.408672, -5.362232, 0.058148, -92.216336, -3.224461, 0.215445, -14.966516, 0.042072, 0.012360, 3.403937, 49.000000, 52.702201, 0.654747, 0.022895 30, 30, 370771.99, -1522.12, 9.289626, 0.078859, 117.800380, -5.584178, 0.196384, -28.435039, -5.346731, 0.059775, -89.448140, -4.420775, 0.218521, -20.230461, 0.069147, 0.012518, 5.523913, 49.000000, 48.697933, 0.672346, 0.028635 31, 31, 376842.13, -7139.16, 9.353759, 0.077575, 120.576468, -5.673078, 0.192070, -29.536528, -5.332972, 0.059049, -90.314517, -4.677717, 0.214235, -21.834530, 0.059114, 0.012198, 4.846099, 27.000000, 29.824825, 0.669125, 0.025759 32, 32, 318049.53, 32744.59, 7.942592, 0.097688, 81.305675, -2.859484, 0.218220, -13.103690, -4.704135, 0.069642, -67.547769, -0.678335, 0.256249, -2.647174, 0.095991, 0.016066, 5.974700, 120.000000, 103.411338, 0.615086, 0.030253 33, 33, 325761.21, 31092.21, 8.084150, 0.097091, 83.263911, -3.136318, 0.216501, -14.486366, -4.798250, 0.069067, -69.471916, -0.931550, 0.258448, -3.604396, 0.093430, 0.016223, 5.758997, 28.000000, 30.690347, 0.625407, 0.040066 34, 34, 318112.24, 28405.62, 7.933674, 0.099510, 79.727619, -2.789166, 0.221509, -12.591674, -4.694129, 0.070956, -66.155589, -0.581574, 0.263312, -2.208689, 0.089609, 0.016479, 5.437714, 16.000000, 34.193838, 0.610364, 0.033816 35, 35, 310480.1, 28809.03, 7.758545, 0.102915, 75.388186, -2.325516, 0.230033, -10.109504, -4.574500, 0.073329, -62.383610, -0.466270, 0.268661, -1.735530, 0.094159, 0.016893, 5.573730, 14.000000, 41.657447, 0.599226, 0.029520 36, 36, 306513.97, 32751.48, 7.819008, 0.098348, 79.503645, -2.551863, 0.221465, -11.522626, -4.612073, 0.070363, -65.546642, -0.459679, 0.254678, -1.804940, 0.091014, 0.015951, 5.705707, 43.000000, 298.776253, 0.601945, 0.225674 37, 37, 311395.51, 33538.42, 7.856876, 0.098226, 79.988133, -2.659599, 0.220291, -12.073105, -4.643633, 0.070141, -66.204220, -0.545735, 0.255067, -2.139573, 0.095300, 0.015999, 5.956542, 29.000000, 49.818790, 0.607635, 0.020736 38, 38, 314408.34, -4572.95, 8.686217, 0.085858, 101.169350, -5.120449, 0.204415, -25.049284, -4.891797, 0.062262, -78.567915, 2.311693, 0.227586, 10.157463, -0.122148, 0.014045, -8.697109, 401.000000, 111.360871, 0.496724, 0.019093 39, 39, 303850.22, 22478, 7.692588, 0.104895, 73.336033, -2.018841, 0.237504, -8.500230, -4.496125, 0.075038, -59.917871, -0.312057, 0.276607, -1.128161, 0.073561, 0.017294, 4.253544, 210.000000, 118.084796, 0.582158, 0.018113 40, 40, 337540.25, -12310.61, 9.154546, 0.080822, 113.268209, -7.426310, 0.189014, -39.289682, -5.014334, 0.059123, -84.811781, 0.656472, 0.215260, 3.049665, -0.045759, 0.012377, -3.697262, 711.000000, 313.524427, 0.498825, 0.048447 41, 41, 330948.96, -8687.59, 9.052416, 0.082188, 110.143109, -7.034787, 0.191726, -36.691890, -5.008471, 0.059833, -83.706880, 1.606830, 0.215339, 7.461858, -0.075423, 0.012660, -5.957587, 544.000000, 213.400699, 0.498491, 0.020420 42, 42, 327143.99, -3103.01, 9.001686, 0.081347, 110.657272, -6.378519, 0.191231, -33.354995, -5.099398, 0.059529, -85.662011, 1.397353, 0.216359, 6.458502, -0.078274, 0.012806, -6.112505, 557.000000, 164.718437, 0.533774, 0.014958 43, 43, 312830.72, 21412.1, 7.798962, 0.104633, 74.536438, -2.280390, 0.234413, -9.728103, -4.577930, 0.074725, -61.263552, -0.317631, 0.281271, -1.129270, 0.072723, 0.017499, 4.155851, 132.000000, 77.750761, 0.589471, 0.020448 44, 44, 312874.14, -17053.63, 8.560752, 0.083886, 102.052173, -5.390806, 0.204476, -26.363982, -4.624174, 0.061143, -75.628395, 3.698231, 0.214073, 17.275582, -0.153164, 0.013544, -11.308570, 395.000000, 131.991244, 0.405937, 0.027164 45, 45, 293680.38, -8010.1, 8.232986, 0.089730, 91.752534, -3.216455, 0.214507, -14.994646, -4.663579, 0.066109, -70.544076, 0.985854, 0.247824, 3.978045, -0.074799, 0.014892, -5.022877, 97.000000, 80.056623, 0.507185, 0.025693 46, 46, 325185.11, 20460.45, 8.232413, 0.097408, 84.514562, -3.394082, 0.215912, -15.719721, -4.867942, 0.069882, -69.659253, -0.602126, 0.265750, -2.265758, 0.056726, 0.016502, 3.437486, 91.000000, 68.721077, 0.613337, 0.024838 47, 47, 305971.31, 8472, 8.000630, 0.100482, 79.622771, -2.564990, 0.233538, -10.983174, -4.593543, 0.072550, -63.315872, 0.352386, 0.277122, 1.271591, -0.015931, 0.016963, -0.939191, 97.000000, 105.289303, 0.550473, 0.021199 48, 48, 335330.5, -108.19, 9.135363, 0.081822, 111.649110, -6.448630, 0.190371, -33.873940, -5.228087, 0.060267, -86.748894, 0.023699, 0.218219, 0.108603, -0.039466, 0.012874, -3.065416, 148.000000, 105.876734, 0.576136, 0.029859 49, 49, 339115.66, 3202.05, 9.142456, 0.081871, 111.669146, -6.164959, 0.190339, -32.389384, -5.283206, 0.060376, -87.505604, -0.785409, 0.220256, -3.565902, -0.015011, 0.013041, -1.151060, 269.000000, 162.531835, 0.605252, 0.033527 50, 50, 309271.13, -10589.17, 8.564056, 0.088114, 97.192585, -4.713345, 0.212330, -22.198241, -4.741562, 0.063918, -74.181586, 2.784304, 0.227496, 12.238901, -0.149561, 0.014551, -10.278076, 183.000000, 109.085262, 0.454006, 0.024999 51, 51, 319972.62, 24634.35, 8.006377, 0.099299, 80.628907, -2.922593, 0.220714, -13.241555, -4.733640, 0.070951, -66.716574, -0.527917, 0.266357, -1.981987, 0.077899, 0.016591, 4.695232, 86.000000, 61.081627, 0.608800, 0.024045 52, 52, 317013.43, 12374.14, 8.142152, 0.100322, 81.159891, -2.942106, 0.227251, -12.946498, -4.738385, 0.072330, -65.510401, 0.119162, 0.282748, 0.421441, 0.005009, 0.017177, 0.291615, 86.000000, 85.171501, 0.573861, 0.021889 53, 53, 323345.93, 2314.46, 8.828517, 0.084727, 104.200079, -5.434900, 0.197517, -27.516048, -5.072206, 0.062167, -81.590586, 1.130946, 0.230456, 4.907416, -0.069916, 0.013758, -5.081717, 244.000000, 189.717667, 0.553270, 0.033347 54, 54, 327610.55, -7504.02, 9.004084, 0.081456, 110.539904, -6.758980, 0.191457, -35.302813, -5.014819, 0.059218, -84.683502, 1.891151, 0.213912, 8.840804, -0.087990, 0.012661, -6.949938, 120.000000, 217.688285, 0.501673, 0.018214 55, 55, 343813.29, -11626.17, 9.304268, 0.077571, 119.945374, -7.373975, 0.184549, -39.956823, -5.161793, 0.056770, -90.925297, -0.563319, 0.210608, -2.674726, -0.027742, 0.011957, -2.320206, 297.000000, 354.218119, 0.543667, 0.076596 56, 56, 342508.85, -4698.16, 9.269433, 0.078108, 118.674146, -6.915417, 0.184835, -37.413983, -5.253566, 0.057400, -91.526314, -0.731690, 0.211007, -3.467606, -0.022338, 0.012132, -1.841302, 393.000000, 164.621860, 0.579845, 0.028182 57, 57, 333426.78, -13559.3, 9.056815, 0.080684, 112.250622, -7.350790, 0.188869, -38.920073, -4.925358, 0.058835, -83.714796, 1.483955, 0.213664, 6.945271, -0.064816, 0.012333, -5.255533, 103.000000, 324.457620, 0.473867, 0.023013 58, 58, 330824.95, -14794.45, 8.936049, 0.082948, 107.730583, -7.282431, 0.192261, -37.877778, -4.793595, 0.060643, -79.046091, 2.227006, 0.217195, 10.253506, -0.076085, 0.012610, -6.033703, 136.000000, 615.001235, 0.441654, 0.210126 59, 59, 304617.97, -15261.45, 8.459330, 0.086288, 98.036397, -4.346632, 0.210165, -20.682016, -4.681810, 0.063465, -73.770265, 2.479917, 0.225329, 11.005775, -0.133218, 0.014198, -9.382753, 160.000000, 89.785991, 0.448484, 0.033986 60, 60, 338062.78, -13156.47, 9.172157, 0.079726, 115.046009, -7.459610, 0.187386, -39.808713, -5.020073, 0.058260, -86.166381, 0.572646, 0.213456, 2.682734, -0.045992, 0.012215, -3.765230, 102.000000, 220.608795, 0.499282, 0.038072 61, 61, 325419.58, -15527.5, 8.813493, 0.081916, 107.591765, -6.887234, 0.192606, -35.758217, -4.723881, 0.059445, -79.466802, 3.072931, 0.213167, 14.415589, -0.103190, 0.012596, -8.192385, 142.000000, 322.251095, 0.426183, 0.052383 62, 62, 324052.62, -12510.9, 8.857636, 0.081798, 108.286960, -6.708618, 0.193279, -34.709481, -4.825792, 0.059134, -81.607413, 2.909369, 0.212557, 13.687492, -0.109516, 0.012706, -8.619340, 83.000000, 134.144743, 0.450648, 0.034024 63, 63, 327521.88, -17674.68, 8.787145, 0.082854, 106.056265, -7.078216, 0.192656, -36.740121, -4.638671, 0.060709, -76.408024, 3.034659, 0.216239, 14.033818, -0.091759, 0.012580, -7.294136, 78.000000, 263.853763, 0.406600, 0.031408 64, 64, 322114.34, -17894.35, 8.681605, 0.081765, 106.177254, -6.560620, 0.194131, -33.794811, -4.604517, 0.059450, -77.451992, 3.674796, 0.211889, 17.343039, -0.118679, 0.012662, -9.372692, 201.000000, 153.670700, 0.399827, 0.028938 65, 65, 320355.21, 5840.48, 8.626226, 0.086946, 99.213610, -4.694769, 0.201678, -23.278507, -4.993306, 0.063710, -78.375580, 0.822509, 0.240527, 3.419608, -0.046654, 0.014384, -3.243550, 87.000000, 89.666649, 0.564945, 0.019928 66, 66, 330341.07, 12925.79, 8.689988, 0.088733, 97.934468, -4.674772, 0.200457, -23.320542, -5.106916, 0.064799, -78.811175, -0.509172, 0.242827, -2.096851, 0.010353, 0.014823, 0.698469, 89.000000, 120.358994, 0.612726, 0.024230 67, 67, 318527.16, 8318.24, 8.429517, 0.093026, 90.614211, -3.894917, 0.213350, -18.255975, -4.886640, 0.067660, -72.223576, 0.578169, 0.260691, 2.217835, -0.033021, 0.015705, -2.102571, 80.000000, 109.332821, 0.566357, 0.027601 68, 68, 347297.26, -13547.71, 9.368688, 0.076160, 123.012522, -7.415535, 0.182964, -40.530015, -5.186470, 0.055834, -92.891189, -1.060009, 0.208951, -5.073002, -0.022648, 0.011755, -1.926649, 105.000000, 283.766320, 0.551387, 0.081002 69, 69, 321375.58, -10594.12, 8.851191, 0.080875, 109.442401, -6.376750, 0.193020, -33.036807, -4.888213, 0.058549, -83.489491, 2.754456, 0.211251, 13.038786, -0.114429, 0.012733, -8.986639, 114.000000, 189.336603, 0.469121, 0.038299 70, 70, 318675.19, -8454.47, 8.806059, 0.082697, 106.485805, -5.971967, 0.197305, -30.267619, -4.897320, 0.059844, -81.834201, 2.768539, 0.215865, 12.825325, -0.124687, 0.013198, -9.447201, 101.000000, 295.397007, 0.475941, 0.044772 71, 71, 350174.04, -12060.87, 9.400735, 0.076226, 123.327947, -7.262887, 0.183626, -39.552709, -5.241138, 0.056161, -93.324267, -1.602847, 0.209540, -7.649345, -0.010460, 0.011780, -0.887917, 181.000000, 269.580719, 0.573751, 0.077437 72, 72, 329442.54, 5939.52, 8.900900, 0.085892, 103.629164, -5.430347, 0.196987, -27.567077, -5.166534, 0.063268, -81.661503, 0.163531, 0.233025, 0.701773, -0.035940, 0.014019, -2.563782, 82.000000, 60.535351, 0.586686, 0.024369 73, 73, 307098.94, 992.68, 8.333437, 0.092656, 89.939209, -3.603964, 0.218892, -16.464557, -4.745236, 0.067337, -70.470010, 1.214098, 0.252753, 4.803493, -0.078759, 0.015538, -5.068719, 93.000000, 186.709850, 0.525545, 0.056355 74, 74, 337247.61, 14030.91, 8.832866, 0.086860, 101.690449, -4.963474, 0.198395, -25.018151, -5.198991, 0.063329, -82.094811, -1.198296, 0.236611, -5.064408, 0.024027, 0.014497, 1.657357, 67.000000, 137.769662, 0.631933, 0.055280 75, 75, 306612.95, -2173.79, 8.416704, 0.090254, 93.255543, -3.916069, 0.214707, -18.239094, -4.773155, 0.065750, -72.595549, 1.529464, 0.243675, 6.276655, -0.096779, 0.015064, -6.424642, 55.000000, 129.294200, 0.512968, 0.050312 76, 76, 301727, -6640.87, 8.351187, 0.091858, 90.913880, -3.598496, 0.219673, -16.381169, -4.695856, 0.067078, -70.006407, 1.539042, 0.246311, 6.248373, -0.109069, 0.015376, -7.093532, 68.000000, 71.531315, 0.493648, 0.022572 77, 77, 326724.18, 5478.1, 8.841172, 0.087238, 101.345605, -5.235552, 0.200148, -26.158390, -5.120137, 0.064148, -79.817363, 0.488567, 0.237804, 2.054495, -0.048322, 0.014318, -3.374792, 47.000000, 59.671961, 0.575504, 0.025685 78, 78, 311503.39, 15708.66, 7.784809, 0.107671, 72.302027, -1.983474, 0.244244, -8.120860, -4.522011, 0.077016, -58.714914, -0.176239, 0.297813, -0.591779, 0.043103, 0.018312, 2.353782, 33.000000, 63.199892, 0.571319, 0.031200 79, 79, 316934.08, -10632.09, 8.741881, 0.083770, 104.355838, -5.844014, 0.200501, -29.147034, -4.815866, 0.060469, -79.641561, 3.179767, 0.216236, 14.705098, -0.139105, 0.013424, -10.362707, 43.000000, 124.025290, 0.452781, 0.033652 80, 80, 317980.71, -13171.59, 8.725353, 0.082817, 105.357381, -6.059352, 0.198526, -30.521774, -4.756070, 0.059815, -79.512844, 3.434530, 0.213124, 16.115164, -0.137367, 0.013156, -10.441179, 52.000000, 92.707543, 0.434766, 0.031255 81, 81, 298790.59, -2464.08, 8.216008, 0.093873, 87.522978, -3.107793, 0.222907, -13.942086, -4.656027, 0.068567, -67.904772, 0.954272, 0.256703, 3.717410, -0.077216, 0.015752, -4.902016, 75.000000, 88.911901, 0.516261, 0.063185 82, 82, 294903.64, 214.57, 8.158973, 0.091166, 89.495635, -3.039032, 0.215970, -14.071538, -4.668312, 0.066894, -69.786802, 0.641450, 0.252407, 2.541331, -0.045831, 0.015148, -3.025469, 33.000000, 44.937569, 0.534231, 0.028759 83, 83, 284950.61, -7897.72, 8.195580, 0.083010, 98.729543, -3.308942, 0.198932, -16.633517, -4.695978, 0.061751, -76.047344, 0.669262, 0.235426, 2.842775, -0.032853, 0.013533, -2.427596, 11.000000, 33.806238, 0.527415, 0.072899 84, 84, 302616.14, 12642.65, 7.940405, 0.097975, 81.044857, -2.583191, 0.226587, -11.400433, -4.601313, 0.070912, -64.887364, 0.140945, 0.266022, 0.529826, 0.016099, 0.016285, 0.988555, 23.000000, 55.020916, 0.565616, 0.025488 85, 85, 298937.62, 11074.43, 8.047553, 0.092328, 87.162314, -2.940540, 0.215350, -13.654704, -4.669117, 0.067337, -69.339550, 0.268027, 0.251481, 1.065794, 0.005942, 0.015203, 0.390849, 43.000000, 49.101470, 0.565007, 0.020635 86, 86, 292980.66, 10621.27, 8.035974, 0.090741, 88.559032, -2.885990, 0.212345, -13.591045, -4.661227, 0.066388, -70.211497, 0.207250, 0.248574, 0.833753, 0.007141, 0.014868, 0.480265, 46.000000, 48.264731, 0.563498, 0.030914 87, 87, 291341.64, 3602.46, 8.103798, 0.089763, 90.279992, -2.961275, 0.211900, -13.974841, -4.667471, 0.065954, -70.768834, 0.409152, 0.249463, 1.640132, -0.020822, 0.014794, -1.407436, 19.000000, 33.628196, 0.547327, 0.025391 88, 88, 296052.78, 6812.78, 8.088176, 0.091382, 88.509421, -2.966272, 0.214700, -13.815883, -4.669673, 0.066879, -69.823120, 0.391872, 0.251299, 1.559385, -0.013402, 0.015090, -0.888148, 11.000000, 41.269499, 0.554136, 0.026134 89, 89, 314476.95, 3490.04, 8.494652, 0.089551, 94.858361, -4.228239, 0.209726, -20.160776, -4.875384, 0.065184, -74.794266, 1.210518, 0.247021, 4.900475, -0.068733, 0.014923, -4.605993, 30.000000, 35.307120, 0.542079, 0.026754 90, 90, 311673.48, 10101.08, 8.069599, 0.100136, 80.586052, -2.775403, 0.230478, -12.041950, -4.658880, 0.072185, -64.540761, 0.333470, 0.279729, 1.192118, -0.009134, 0.017004, -0.537199, 27.000000, 34.311950, 0.559284, 0.025113 91, 91, 300937.58, 3470.02, 8.177087, 0.092321, 88.571882, -3.158530, 0.217763, -14.504407, -4.690914, 0.067447, -69.549422, 0.702570, 0.253529, 2.771160, -0.042090, 0.015388, -2.735287, 18.000000, 26.068630, 0.541512, 0.037415 92, 92, 286991.93, 9571.27, 8.059381, 0.087674, 91.923909, -2.963251, 0.206142, -14.374816, -4.676418, 0.064450, -72.558512, 0.191183, 0.242684, 0.787786, 0.007678, 0.014280, 0.537632, 7.000000, 32.429448, 0.562336, 0.045475 93, 93, 307386.12, 16090.18, 7.796841, 0.104380, 74.696878, -2.157056, 0.238092, -9.059759, -4.530081, 0.074894, -60.486316, -0.108316, 0.283290, -0.382350, 0.043050, 0.017496, 2.460593, 7.000000, 32.769029, 0.571946, 0.029901 94, 94, 300604.96, 17843.82, 7.860009, 0.098396, 79.881158, -2.460024, 0.225868, -10.891441, -4.580587, 0.071041, -64.478424, -0.067973, 0.263060, -0.258395, 0.042950, 0.016188, 2.653140, 15.000000, 46.389440, 0.576123, 0.022013 95, 95, 303917.55, 29223.91, 7.834931, 0.097545, 80.321085, -2.570482, 0.220809, -11.641222, -4.610867, 0.070019, -65.851275, -0.347262, 0.254165, -1.366286, 0.079949, 0.015829, 5.050678, 44.000000, 39.980746, 0.596318, 0.022928 96, 96, 296097.2, 19299.56, 7.981525, 0.091370, 87.354307, -2.883521, 0.211447, -13.637120, -4.665190, 0.066488, -70.165406, -0.009572, 0.244986, -0.039071, 0.038685, 0.014838, 2.607068, 25.000000, 41.184856, 0.580503, 0.028108 97, 97, 291327.94, 19385.45, 8.048255, 0.087022, 92.484773, -3.092561, 0.203039, -15.231395, -4.704628, 0.063712, -73.841971, 0.018049, 0.235479, 0.076650, 0.034354, 0.014045, 2.445992, 15.000000, 40.779069, 0.580331, 0.019415 98, 98, 288651.19, 16782.21, 8.077411, 0.085438, 94.540867, -3.155685, 0.200381, -15.748395, -4.714341, 0.062770, -75.105286, 0.079271, 0.233337, 0.339728, 0.027013, 0.013788, 1.959170, 53.000000, 55.394967, 0.576204, 0.035407 99, 99, 321850.11, 16542.18, 8.179030, 0.100377, 81.482768, -3.101649, 0.223010, -13.908130, -4.810186, 0.072177, -66.644611, -0.299924, 0.278815, -1.075710, 0.035810, 0.017180, 2.084370, 24.000000, 35.558977, 0.596475, 0.026396 100, 100, 309623.17, 24691.81, 7.653010, 0.107935, 70.903884, -1.917576, 0.241539, -7.939005, -4.492220, 0.076791, -58.498982, -0.433682, 0.284947, -1.521972, 0.090763, 0.017905, 5.069144, 7.000000, 39.728453, 0.588948, 0.033285 101, 101, 317273.81, 16350.36, 8.036568, 0.101079, 79.507868, -2.796783, 0.226847, -12.328941, -4.708505, 0.072639, -64.820375, -0.138624, 0.279906, -0.495251, 0.038662, 0.017171, 2.251545, 13.000000, 29.274339, 0.586840, 0.032901 102, 102, 330127.65, 26472.36, 8.275005, 0.095020, 87.087225, -3.547155, 0.212465, -16.695212, -4.910435, 0.067835, -72.388395, -1.054172, 0.256431, -4.110935, 0.077473, 0.016047, 4.827982, 18.000000, 41.437468, 0.630418, 0.028876 103, 103, 330024.3, 22050.13, 8.361050, 0.095195, 87.830339, -3.712050, 0.211924, -17.515987, -4.955063, 0.068217, -72.636854, -0.940596, 0.258616, -3.637042, 0.061207, 0.016145, 3.790976, 24.000000, 33.350632, 0.626436, 0.021498 104, 104, 335366.5, 8522.69, 8.960338, 0.085244, 105.113783, -5.457006, 0.195170, -27.960318, -5.233783, 0.062672, -83.511208, -0.707414, 0.230778, -3.065346, -0.005868, 0.013957, -0.420454, 45.000000, 99.248615, 0.613842, 0.020456 105, 105, 330795.7, 8625.57, 8.851176, 0.087425, 101.242958, -5.153479, 0.198799, -25.923032, -5.168219, 0.064238, -80.454684, -0.237543, 0.237734, -0.999198, -0.017159, 0.014430, -1.189109, 56.000000, 52.566138, 0.601011, 0.021261 106, 106, 324461.1, 12021.3, 8.519877, 0.091442, 93.172245, -4.202002, 0.206456, -20.352979, -4.991851, 0.066671, -74.872675, -0.028636, 0.253157, -0.113116, 0.000264, 0.015385, 0.017139, 33.000000, 47.268972, 0.594882, 0.024329 107, 107, 333249.02, 19193.73, 8.540404, 0.093861, 90.989837, -4.089696, 0.209732, -19.499602, -5.057007, 0.067492, -74.927846, -1.099436, 0.255292, -4.306581, 0.047101, 0.015967, 2.949800, 35.000000, 56.868829, 0.630778, 0.032439 108, 108, 330905.38, 16199.04, 8.597662, 0.089791, 95.751679, -4.406931, 0.202196, -21.795299, -5.070481, 0.065215, -77.749948, -0.733314, 0.245499, -2.987038, 0.029698, 0.015077, 1.969701, 36.000000, 116.426415, 0.620766, 0.063206 109, 109, 338740.71, 9995.65, 8.981511, 0.084637, 106.118519, -5.428698, 0.194837, -27.862727, -5.258500, 0.062093, -84.686998, -1.165543, 0.229747, -5.073158, 0.008998, 0.013909, 0.646918, 58.000000, 69.065434, 0.626969, 0.019405 110, 110, 345541.9, -607.56, 9.263791, 0.077955, 118.834418, -6.544783, 0.185035, -35.370529, -5.312185, 0.057536, -92.328081, -1.399113, 0.212256, -6.591637, -0.004515, 0.012239, -0.368905, 37.000000, 56.057998, 0.610086, 0.024163 111, 111, 348908.69, -5077.51, 9.339219, 0.076888, 121.465196, -6.825379, 0.184043, -37.085727, -5.304487, 0.056764, -93.447449, -1.675161, 0.210138, -7.971721, -0.002628, 0.011960, -0.219715, 53.000000, 151.499066, 0.603017, 0.033940 112, 112, 343120.93, 5902.89, 9.139057, 0.082424, 110.878613, -5.896474, 0.192334, -30.657468, -5.314611, 0.060681, -87.582084, -1.490800, 0.223187, -6.679608, 0.006795, 0.013302, 0.510779, 49.000000, 50.950670, 0.627447, 0.019584 113, 113, 377836.69, -36378.58, 9.521815, 0.069308, 137.384160, -6.486440, 0.169733, -38.215591, -5.192437, 0.053025, -97.924794, -3.730247, 0.195299, -19.100171, -0.014282, 0.010870, -1.313941, 1070.000000, 273.690754, 0.619642, 0.030726 114, 114, 356153.1, -24448.15, 9.516726, 0.070266, 135.439368, -7.410792, 0.174443, -42.482606, -5.181560, 0.051756, -100.114721, -1.958560, 0.197548, -9.914365, -0.036455, 0.010998, -3.314814, 547.000000, 434.343360, 0.557297, 0.053736 115, 115, 363934.49, -23252.2, 9.572760, 0.070075, 136.607884, -7.059817, 0.174232, -40.519535, -5.273598, 0.052646, -100.171583, -2.957955, 0.196938, -15.019697, -0.020327, 0.010932, -1.859456, 660.000000, 283.710427, 0.598971, 0.022716 116, 116, 362715.03, -62961.03, 8.936764, 0.064947, 137.599923, -5.796463, 0.162042, -35.771401, -4.714944, 0.048881, -96.457107, -1.575245, 0.183106, -8.602924, -0.005616, 0.010597, -0.529902, 175.000000, 190.768950, 0.539735, 0.029939 117, 117, 355515.39, -15862.17, 9.492065, 0.073219, 129.639025, -7.247102, 0.179529, -40.367282, -5.271515, 0.054282, -97.113432, -2.199058, 0.204005, -10.779442, -0.013879, 0.011338, -1.224097, 594.000000, 325.663221, 0.584515, 0.042361 118, 118, 350331.74, 2259.59, 9.253315, 0.079397, 116.544656, -6.159241, 0.189633, -32.479714, -5.346368, 0.058735, -91.025760, -2.239751, 0.217192, -10.312319, 0.020649, 0.012632, 1.634676, 189.000000, 83.083718, 0.636297, 0.025201 119, 119, 390869.17, -52824.71, 9.106728, 0.065569, 138.888012, -5.656118, 0.160625, -35.213203, -4.988430, 0.049703, -100.365240, -2.839897, 0.186870, -15.197171, 0.012519, 0.010469, 1.195778, 121.000000, 93.042532, 0.608909, 0.018094 120, 120, 391663.46, -13955.69, 9.338796, 0.075644, 123.456545, -5.569283, 0.184122, -30.247761, -5.260867, 0.057412, -91.634238, -4.735919, 0.209749, -22.578930, 0.045633, 0.011864, 3.846363, 125.000000, 335.024411, 0.666301, 0.126770 121, 121, 381676.42, -24737.89, 9.529395, 0.073854, 129.029687, -6.162088, 0.180508, -34.137443, -5.290384, 0.056605, -93.461527, -4.599924, 0.205363, -22.399019, 0.016117, 0.011483, 1.403496, 175.000000, 75.525023, 0.649607, 0.024055 122, 122, 396227.77, -39792.02, 9.290514, 0.069178, 134.299062, -5.795311, 0.166980, -34.706671, -5.118324, 0.052705, -97.112263, -3.771933, 0.195704, -19.273674, 0.014021, 0.010874, 1.289308, 85.000000, 73.933308, 0.635181, 0.019427 123, 123, 365535.4, -28214.23, 9.570699, 0.068908, 138.890683, -7.029125, 0.171516, -40.982332, -5.230660, 0.051830, -100.919418, -2.950813, 0.194028, -15.208166, -0.027698, 0.010814, -2.561353, 200.000000, 337.557597, 0.593597, 0.030755 124, 124, 358761.78, -6929.68, 9.414445, 0.074790, 125.878135, -6.590384, 0.182989, -36.015275, -5.349821, 0.056009, -95.516860, -2.898908, 0.207403, -13.977142, 0.017134, 0.011634, 1.472700, 389.000000, 176.826739, 0.630253, 0.027476 125, 125, 376070.01, -56303.59, 9.127928, 0.065235, 139.924058, -5.902506, 0.160980, -36.666125, -4.920588, 0.049419, -99.569186, -2.459700, 0.185264, -13.276705, -0.003057, 0.010493, -0.291334, 381.000000, 198.646695, 0.584117, 0.017768 126, 126, 353788.69, -7340.62, 9.394591, 0.076218, 123.259356, -6.813655, 0.184343, -36.961759, -5.321063, 0.056601, -94.009406, -2.316354, 0.209773, -11.042193, 0.008728, 0.011831, 0.737687, 173.000000, 113.536509, 0.612501, 0.024045 127, 127, 371670.71, -21543.62, 9.563760, 0.071591, 133.588014, -6.614897, 0.177187, -37.332759, -5.314827, 0.054563, -97.407826, -3.841568, 0.200182, -19.190367, 0.001151, 0.011129, 0.103457, 206.000000, 230.282596, 0.630478, 0.026069 128, 128, 367522.64, -7189.91, 9.411252, 0.074618, 126.125233, -6.195559, 0.184509, -33.578677, -5.357195, 0.056553, -94.729502, -3.786280, 0.207550, -18.242696, 0.033924, 0.011653, 2.911274, 142.000000, 114.955639, 0.651137, 0.030275 129, 129, 361892.77, -18166, 9.553402, 0.072100, 132.502138, -7.044873, 0.178473, -39.473072, -5.308160, 0.054160, -98.008801, -2.980321, 0.201848, -14.765148, -0.007689, 0.011179, -0.687830, 148.000000, 113.013868, 0.605302, 0.026311 130, 130, 366450.66, -74634.65, 8.772185, 0.064143, 136.760224, -5.338262, 0.160376, -33.285883, -4.689544, 0.048177, -97.340335, -1.500028, 0.181590, -8.260532, 0.016238, 0.010463, 1.551955, 141.000000, 204.501530, 0.550554, 0.020527 131, 131, 355381.88, -79216.89, 8.590386, 0.064557, 133.066335, -5.111012, 0.162274, -31.496222, -4.550432, 0.048257, -94.296452, -0.900889, 0.181394, -4.966483, 0.022795, 0.010564, 2.157918, 106.000000, 87.539147, 0.524127, 0.034971 132, 132, 353694.67, -32885.23, 9.391715, 0.067938, 138.238656, -7.291044, 0.169866, -42.922348, -5.004519, 0.049531, -101.038785, -1.341203, 0.191361, -7.008761, -0.049791, 0.010831, -4.597017, 116.000000, 410.155136, 0.522855, 0.092355 133, 133, 378726.25, -28678.9, 9.562000, 0.071650, 133.453753, -6.413917, 0.175625, -36.520429, -5.270220, 0.054916, -95.967991, -4.206852, 0.200539, -20.977775, -0.000723, 0.011156, -0.064776, 84.000000, 78.737795, 0.636085, 0.027673 134, 134, 365246.77, -57670.24, 9.070266, 0.065097, 139.334384, -6.026757, 0.161969, -37.209355, -4.807549, 0.049058, -97.998161, -1.882134, 0.183799, -10.240162, -0.013455, 0.010578, -1.271987, 78.000000, 117.446041, 0.551705, 0.018351 135, 135, 389517.13, -31493.43, 9.457534, 0.072706, 130.079097, -5.988615, 0.175463, -34.130313, -5.220572, 0.055619, -93.863831, -4.471147, 0.203461, -21.975451, 0.013534, 0.011328, 1.194722, 88.000000, 72.273750, 0.648169, 0.024193 136, 136, 344415.42, 11511.89, 9.024614, 0.084854, 106.354179, -5.343316, 0.198129, -26.968861, -5.295771, 0.062031, -85.373200, -1.894513, 0.231204, -8.194136, 0.028343, 0.014075, 2.013689, 31.000000, 41.759168, 0.644971, 0.024456 137, 137, 365053.58, -11168.12, 9.473068, 0.074261, 127.564441, -6.512516, 0.183315, -35.526304, -5.354014, 0.056225, -95.224520, -3.576398, 0.206500, -17.319085, 0.022641, 0.011522, 1.965063, 60.000000, 95.034086, 0.637821, 0.026003 138, 138, 387334.51, -21934.03, 9.441026, 0.074462, 126.790460, -5.877595, 0.180913, -32.488519, -5.269324, 0.056810, -92.754200, -4.667615, 0.206698, -22.581825, 0.028202, 0.011610, 2.429195, 25.000000, 84.677821, 0.657552, 0.047934 139, 139, 393097.28, -22717.2, 9.392469, 0.074829, 125.519087, -5.713492, 0.180659, -31.625853, -5.241423, 0.056951, -92.034529, -4.726910, 0.207896, -22.736871, 0.033033, 0.011681, 2.827923, 57.000000, 105.305025, 0.660513, 0.033079 140, 140, 380945.88, -17224.24, 9.461466, 0.075320, 125.617310, -5.953431, 0.184779, -32.219249, -5.312512, 0.057542, -92.323409, -4.684196, 0.208309, -22.486717, 0.034248, 0.011741, 2.917014, 17.000000, 23.290234, 0.658697, 0.023188 141, 141, 367373.14, -14712.42, 9.517994, 0.073896, 128.802325, -6.561065, 0.182886, -35.875119, -5.348781, 0.056192, -95.188148, -3.795550, 0.205705, -18.451408, 0.018854, 0.011450, 1.646652, 47.000000, 60.853265, 0.636136, 0.028519 142, 142, 374567.12, -13256.38, 9.460162, 0.074545, 126.904861, 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0.189788, -17.897312, -4.438375, 0.058250, -76.195308, 1.307813, 0.217025, 6.026087, -0.002277, 0.012369, -0.184108, 12.000000, 45.863155, 0.460624, 0.033337 libpysal-4.9.2/libpysal/examples/tokyo/tokyo_GS_NN_summary.txt000066400000000000000000000206041452177046000246260ustar00rootroot00000000000000***************************************************************************** * Semiparametric Geographically Weighted Regression * * Release 1.0.90 (GWR 4.0.90) * * 12 May 2015 * * (Originally coded by T. Nakaya: 1 Nov 2009) * * * * Tomoki Nakaya(1), Martin Charlton(2), Chris Brunsdon (2) * * Paul Lewis (2), Jing Yao (3), A Stewart Fotheringham (4) * * (c) GWR4 development team * * (1) Ritsumeikan University, (2) National University of Ireland, Maynooth, * * (3) University of Glasgow, (4) Arizona State University * ***************************************************************************** Program began at 7/25/2016 8:24:18 AM ***************************************************************************** Session: Session control file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_NN.ctl ***************************************************************************** Data filename: C:\Users\IEUser\Desktop\tokyo\tokyo\Tokyomortality.txt Number of areas/points: 262 Model settings--------------------------------- Model type: Poisson Geographic kernel: adaptive Gaussian Method for optimal bandwidth search: Golden section search Criterion for optimal bandwidth: AICc Number of varying coefficients: 5 Number of fixed coefficients: 0 Modelling options--------------------------------- Standardisation of independent variables: OFF Testing geographical variability of local coefficients: OFF Local to Global Variable selection: OFF Global to Local Variable selection: OFF Prediction at non-regression points: OFF Variable settings--------------------------------- Area key: field1: IDnum0 Easting (x-coord): field2 : X_CENTROID Northing (y-coord): field3: Y_CENTROID Cartesian coordinates: Euclidean distance Dependent variable: field4: db2564 Offset variable is not specified Intercept: varying (Local) intercept Independent variable with varying (Local) coefficient: field6: OCC_TEC Independent variable with varying (Local) coefficient: field7: OWNH Independent variable with varying (Local) coefficient: field8: POP65 Independent variable with varying (Local) coefficient: field9: UNEMP ***************************************************************************** ***************************************************************************** Global regression result ***************************************************************************** < Diagnostic information > Number of parameters: 5 Deviance: 24597.455544 Classic AIC: 24607.455544 AICc: 24607.689919 BIC/MDL: 24625.297266 Percent deviance explained 0.526746 Variable Estimate Standard Error z(Est/SE) Exp(Est) -------------------- --------------- --------------- --------------- --------------- Intercept 8.432403 0.061613 136.859875 4593.526955 OCC_TEC -4.270431 0.156467 -27.292831 0.013976 OWNH -4.789311 0.046070 -103.957933 0.008318 POP65 -1.252659 0.178384 -7.022265 0.285744 UNEMP 0.061305 0.010099 6.070542 1.063223 ***************************************************************************** GWR (Geographically weighted regression) bandwidth selection ***************************************************************************** Bandwidth search Limits: 50, 262 Golden section search begins... Initial values pL Bandwidth: 50.000 Criterion: 21070.385 p1 Bandwidth: 54.513 Criterion: 21300.064 p2 Bandwidth: 57.302 Criterion: 21467.046 pU Bandwidth: 61.814 Criterion: 21628.107 iter 1 (p1) Bandwidth: 54.513 Criterion: 21300.064 Diff: 2.789 iter 2 (p1) Bandwidth: 52.789 Criterion: 21186.366 Diff: 1.724 iter 3 (p1) Bandwidth: 51.724 Criterion: 21119.354 Diff: 1.065 The lower limit in your search has been selected as the optimal bandwidth size. A new sesssion is recommended to try with a smaller lowest limit of the bandwidth search. Best bandwidth size 50.000 Minimum AICc 21070.385 ***************************************************************************** GWR (Geographically weighted regression) result ***************************************************************************** Bandwidth and geographic ranges Bandwidth size: 50.000000 Coordinate Min Max Range --------------- --------------- --------------- --------------- X-coord 276385.400000 408226.180000 131840.780000 Y-coord -86587.480000 33538.420000 120125.900000 Diagnostic information Effective number of parameters (model: trace(S)): 11.723460 Effective number of parameters (variance: trace(S'WSW^-1)): 7.749046 Degree of freedom (model: n - trace(S)): 250.276540 Degree of freedom (residual: n - 2trace(S) + trace(S'WSW^-1)): 246.302127 Deviance: 21045.741163 Classic AIC: 21069.188082 AICc: 21070.384849 BIC/MDL: 21111.021425 Percent deviance explained 0.595081 *********************************************************** << Geographically varying (Local) coefficients >> *********************************************************** Estimates of varying coefficients have been saved in the following file. Listwise output file: C:\Users\IEUser\Desktop\tokyo\tokyo_results_no_off\tokyo_GS_NN_listwise.csv Summary statistics for varying (Local) coefficients Variable Mean STD -------------------- --------------- --------------- Intercept 8.599793 0.585380 OCC_TEC -5.080341 1.478737 OWNH -4.701071 0.496447 POP65 0.046837 2.647462 UNEMP -0.013079 0.063278 Variable Min Max Range -------------------- --------------- --------------- --------------- Intercept 7.477564 9.572760 2.095196 OCC_TEC -7.742931 -1.917576 5.825356 OWNH -5.362232 -3.589268 1.772965 POP65 -4.885369 4.836340 9.721710 UNEMP -0.153164 0.095991 0.249155 Variable Lwr Quartile Median Upr Quartile -------------------- --------------- --------------- --------------- Intercept 8.093103 8.594024 9.143286 OCC_TEC -6.116852 -5.256411 -3.711580 OWNH -5.168985 -4.721204 -4.342757 POP65 -2.208547 0.299896 2.173021 UNEMP -0.061169 -0.002452 0.038315 Variable Interquartile R Robust STD -------------------- --------------- --------------- Intercept 1.050183 0.778490 OCC_TEC 2.405272 1.783003 OWNH 0.826228 0.612474 POP65 4.381568 3.248012 UNEMP 0.099484 0.073747 (Note: Robust STD is given by (interquartile range / 1.349) ) ***************************************************************************** GWR Analysis of Deviance Table ***************************************************************************** Source Deviance DOF Deviance/DOF ------------ ------------------- ---------- ---------------- Global model 24597.456 257.000 95.710 GWR model 21045.741 246.302 85.447 Difference 3551.714 10.698 332.002 ***************************************************************************** Program terminated at 7/25/2016 8:24:21 AM libpysal-4.9.2/libpysal/examples/tokyo/tokyomet262.dbf000066400000000000000000000205021452177046000227360ustar00rootroot00000000000000p GEOCODEC AREANAMECAreaIDN 08203 Tsuchiura-shi 0 08204 Koga-shi 1 08208 Ryugasaki-shi 2 08210 Shimotsuma-shi 3 08211 Mitsukaido-shi 4 08217 Toride-shi 5 08218 Iwai-shi 6 08219 Ushiku-shi 7 08220 Tsukuba-shi 8 08441 Edosaki-machi 9 08442 Miho-mura 10 08443 Ami-machi 11 08445 Kukizaki-machi 12 08446 Shintone-mura 13 08447 Kawachi-mura 14 08448 Sakuragawa-mura 15 08449 Azuma-mura 16 08461 Dejima-mura 17 08465 Niihari-mura 18 08482 Ina-machi 19 08483 Yawara-mura 20 08521 Yachiyo-machi 21 08522 Chiyokawa-mura 22 08523 Ishige-machi 23 08541 Sowa-machi 24 08542 Goka-mura 25 08543 Sanwa-machi 26 08544 Sashima-machi 27 08546 Sakai-machi 28 08561 Moriya-machi 29 08563 Fujishiro-machi 30 08564 Tone-machi 31 10207 Tatebayashi-shi 32 10521 Itakura-machi 33 10522 Meiwa-mura 34 10523 Chiyoda-machi 35 10524 Oizumi-machi 36 10525 Ora-machi 37 11201 Kawagoe-shi 38 11202 Kumagaya-shi 39 11203 Kawaguchi-shi 40 11204 Urawa-shi 41 11205 Omiya-shi 42 11206 Gyoda-shi 43 11208 Tokorozawa-shi 44 11209 Hanno-shi 45 11210 Kazo-shi 46 11212 Higashimatsuyama 47 11213 Iwatsuki-shi 48 11214 Kasukabe-shi 49 11215 Sayama-shi 50 11216 Hanyu-shi 51 11217 Konosu-shi 52 11219 Ageo-shi 53 11220 Yono-shi 54 11221 Soka-shi 55 11222 Koshigaya-shi 56 11223 Warabi-shi 57 11224 Toda-shi 58 11225 Iruma-shi 59 11226 Hatogaya-shi 60 11227 Asaka-shi 61 11228 Shiki-shi 62 11229 Wako-shi 63 11230 Niiza-shi 64 11231 Okegawa-shi 65 11232 Kuki-shi 66 11233 Kitamoto-shi 67 11234 Yashio-shi 68 11235 Fujimi-shi 69 11236 Kamifukuoka-shi 70 11237 Misato-shi 71 11238 Hasuda-shi 72 11239 Sakado-shi 73 11240 Satte-shi 74 11241 Tsurugashima-mac 75 11242 Hidaka-shi 76 11301 Ina-machi 77 11304 Fukiage-machi 78 11322 Oi-machi 79 11324 Miyoshi-machi 80 11326 Moroyama-machi 81 11327 Ogase-machi 82 11330 Naguri-mura 83 11341 Namegawa-machi 84 11342 Ranzan-machi 85 11343 Ogawa-machi 86 11344 Tokigawa-mura 87 11345 Tamagawa-mura 88 11346 Kawajima-machi 89 11347 Yoshimi-machi 90 11348 Hatoyama-machi 91 11369 Higashichichibu- 92 11401 Osato-mura 93 11402 Konan-machi 94 11403 Menuma-machi 95 11406 Kawamoto-machi 96 11407 Hanazono-machi 97 11408 Yorii-machi 98 11421 Kisai-machi 99 11422 Minamikawara-mur 100 11423 Kawasato-mura 101 11424 Kitakawabe-machi 102 11425 Otone-machi 103 11442 Miyashiro-machi 104 11445 Shiraoka-machi 105 11446 Shobu-machi 106 11461 Kurihashi-machi 107 11462 Washimiya-machi 108 11464 Sugito-machi 109 11465 Matsubushi-machi 110 11466 Yoshikawa-machi 111 11468 Showa-machi 112 12100 Chiba-shi 113 12203 Ichikawa-shi 114 12204 Funabashi-shi 115 12206 Kisarazu-shi 116 12207 Matsudo-shi 117 12208 Noda-shi 118 12210 Mobara-shi 119 12211 Narita-shi 120 12212 Sakura-shi 121 12213 Togane-shi 122 12216 Narashino-shi 123 12217 Kashiwa-shi 124 12219 Ichihara-shi 125 12220 Nagareyama-shi 126 12221 Yashiyo-shi 127 12222 Abiko-shi 128 12224 Kamagaya-shi 129 12225 Kimitsu-shi 130 12226 Futtsu-shi 131 12227 Urayasu-shi 132 12228 Yotsukaido-shi 133 12229 Sodegaura-machi 134 12230 Yachimata-shi 135 12303 Sekiyado-machi 136 12305 Shonan-machi 137 12322 Shisui-machi 138 12324 Tomisato-machi 139 12325 Imba-mura 140 12326 Shiroi-machi 141 12327 Inzai-machi 142 12328 Motono-mura 143 12329 Sakae-machi 144 12402 Oamishirasato-ma 145 12403 Kujukuri-machi 146 12404 Naruto-machi 147 12405 Sambu-machi 148 12406 Hasunuma-mura 149 12407 Matsuo-machi 150 12408 Yokoshiba-machi 151 12409 Shibayama-machi 152 12421 Ichinomiya-machi 153 12422 Mutsuzawa-machi 154 12423 Chosei-mura 155 12424 Shirako-machi 156 12426 Nagara-machi 157 12427 Chonan-machi 158 13101 Chiyoda-ku 159 13102 Chuo-ku 160 13103 Minato-ku 161 13104 Shinjuku-ku 162 13105 Bunkyo-ku 163 13106 Taito-ku 164 13107 Sumida-ku 165 13108 Koto-ku 166 13109 Shinagawa-ku 167 13110 Meguro-ku 168 13111 Ota-ku 169 13112 Setagaya-ku 170 13113 Shibuya-ku 171 13114 Nakano-ku 172 13115 Suginami-ku 173 13116 Toshima-ku 174 13117 Kita-ku 175 13118 Arakawa-ku 176 13119 Itabashi-ku 177 13120 Nerima-ku 178 13121 Adachi-ku 179 13122 Katsushika-ku 180 13123 Edogawa-ku 181 13201 Hachioji-shi 182 13202 Tachikawa-shi 183 13203 Musashino-shi 184 13204 Mitaka-shi 185 13205 Ome-shi 186 13206 Fuchu-shi 187 13207 Akishima-shi 188 13208 Chofu-shi 189 13209 Machida-shi 190 13210 Koganei-shi 191 13211 Kodaira-shi 192 13212 Hino-shi 193 13213 Higashimurayama- 194 13214 Kokubunji-shi 195 13215 Kunitachi-shi 196 13216 Tanashi-shi 197 13217 Hoya-shi 198 13218 Fussa-shi 199 13219 Komae-shi 200 13220 Higashiyamato-sh 201 13221 Kiyose-shi 202 13222 Higashikurume-sh 203 13223 Musashimurayama- 204 13224 Tama-shi 205 13225 Inagi-shi 206 13226 Akigawa-shi 207 13227 Hamura-shi 208 13303 Mizuho-machi 209 13305 Hinode-machi 210 13306 Itsukaichi-machi 211 13307 Hinohara-mura 212 13308 Okutama-machi 213 14101 Tsurumi-ku 214 14102 Kanagawa-ku 215 14103 Nishi-ku 216 14104 Naka-ku 217 14105 Minami-ku 218 14106 Hodogaya-ku 219 14107 Isogo-ku 220 14108 Kanazawa-ku 221 14109 Kohoku-ku 222 14110 Totsuka-ku 223 14111 Konan-ku 224 14112 Asahi-ku 225 14113 Midori-ku 226 14114 Seya-ku 227 14115 Sakae-ku 228 14116 Izumi-ku 229 14131 Kawasaki-ku 230 14132 Saiwai-ku 231 14133 Nakahara-ku 232 14134 Takatsu-ku 233 14135 Tama-ku 234 14136 Miyamae-ku 235 14137 Asao-ku 236 14201 Yokosuka-shi 237 14203 Hiratsuka-shi 238 14204 Kamakura-shi 239 14205 Fujisawa-shi 240 14207 Chigasaki-shi 241 14208 Zushi-shi 242 14209 Sagamihara-shi 243 14210 Miura-shi 244 14211 Hadano-shi 245 14212 Atsugi-shi 246 14213 Yamato-shi 247 14214 Isehara-shi 248 14215 Ebina-shi 249 14216 Zama-shi 250 14218 Ayase-shi 251 14301 Hayama-machi 252 14321 Samukawa-machi 253 14341 Oiso-machi 254 14342 Ninomiya-machi 255 14401 Aikawa-machi 256 14402 Kiyokawa-mura 257 14421 Shiroyama-machi 258 14422 Tsukui-machi 259 14423 Sagamiko-machi 260 14424 Fujino-machi 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¨¸!dà"H°"ü˜#˜Ø$t%€%Œ˜&(°&Ü`'@p'´€(8À(ü ) À*dÀ+(Ð+üˆ,ˆú-† .*ð/Ø/úX1V3\"4‚ˆ5¢7´È8€ø9|2:²ê; ø<œ`>è>ìè?ØhAD°AøÐBÌCâFÈFΰG‚ÈHNÐI" IÆ JÔNàˆPlòQb˜RþPTRÈUÐUòøXî(Z@[^Ø\:è]&À]ê€^n°_"z` €a$¨aЈb\Ðc0dL¸e¨f´°ghlibpysal-4.9.2/libpysal/examples/us_income/000077500000000000000000000000001452177046000207715ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/us_income/README.md000066400000000000000000000010171452177046000222470ustar00rootroot00000000000000us_income ========= Per-capita income for the lower 48 US states 1929-2009 ------------------------------------------------------ * spi_download.csv: regional per capita income time series 1969-2008. (source: Regional Economic Information System, Bureau of Economic Analysis, U.S. Department of Commerce) * states48.gal: contiguity weights in GAL format. * us48.dbf: attribute data. (k=8) * us48.shp: Polygon shapefile. (n=48) * us48.shx: spatial index. * usjoin.csv: 48 US states per capita income time series 1929-2009. libpysal-4.9.2/libpysal/examples/us_income/spi_download.csv000066400000000000000000000434771452177046000242070ustar00rootroot00000000000000"Per capita personal income 2/","FIPS","AreaName","1969 ","1970 ","1971 ","1972 ","1973 ","1974 ","1975 ","1976 ","1977 ","1978 ","1979 ","1980 ","1981 ","1982 ","1983 ","1984 ","1985 ","1986 ","1987 ","1988 ","1989 ","1990 ","1991 ","1992 ","1993 ","1994 ","1995 ","1996 ","1997 ","1998 ","1999 ","2000 ","2001 ","2002 ","2003 ","2004 ","2005 ","2006 ","2007 ","2008 " "400","00","United States 3/",3836 ,4084 ,4340 ,4717 ,5230 ,5708 ,6172 ,6754 ,7402 ,8243 ,9138 ,10091 ,11209 ,11901 ,12583 ,13807 ,14637 ,15338 ,16137 ,17244 ,18402 ,19354 ,19818 ,20799 ,21385 ,22297 ,23262 ,24442 ,25654 ,27258 ,28333 ,30318 ,31149 ,31470 ,32284 ,33899 ,35447 ,37728 ,39430 ,40208 "400","01","Alabama",2734 ,2962 ,3206 ,3526 ,3943 ,4336 ,4766 ,5313 ,5794 ,6464 ,7139 ,7825 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"400","18","Indiana",3692 ,3791 ,4086 ,4439 ,5083 ,5423 ,5837 ,6504 ,7167 ,7950 ,8726 ,9353 ,10287 ,10673 ,11184 ,12402 ,13065 ,13739 ,14522 ,15424 ,16668 ,17454 ,17865 ,19099 ,19885 ,20973 ,21644 ,22655 ,23607 ,25169 ,25899 ,27461 ,28054 ,28534 ,29588 ,30645 ,31302 ,32881 ,33756 ,34605 "400","19","Iowa",3665 ,3878 ,4025 ,4498 ,5409 ,5603 ,6231 ,6596 ,7267 ,8379 ,8994 ,9573 ,10840 ,11236 ,11529 ,12815 ,13370 ,13993 ,14786 ,15206 ,16484 ,17350 ,17700 ,18789 ,18700 ,20367 ,21006 ,22787 ,23747 ,24898 ,25539 ,27295 ,27900 ,28832 ,29444 ,31674 ,32306 ,33853 ,35699 ,37402 "400","20","Kansas",3555 ,3824 ,4152 ,4619 ,5275 ,5710 ,6206 ,6716 ,7279 ,8020 ,9119 ,9939 ,11197 ,12056 ,12475 ,13629 ,14312 ,14957 ,15567 ,16207 ,17008 ,18034 ,18605 ,19710 ,20371 ,21235 ,21870 ,23255 ,24504 ,26032 ,26826 ,28479 ,29670 ,29759 ,30822 ,31918 ,33130 ,35756 ,37389 ,38820 "400","21","Kentucky",2964 ,3176 ,3383 ,3705 ,4132 ,4595 ,4940 ,5457 ,6075 ,6758 ,7603 ,8113 ,8992 ,9552 ,9861 ,11035 ,11503 ,11819 ,12485 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,19004 ,21279 ,20854 ,23177 ,23502 ,25625 ,26699 ,27369 ,29761 ,30339 ,32353 ,33602 ,36695 ,39870 "400","39","Ohio",3911 ,4088 ,4322 ,4688 ,5209 ,5724 ,6101 ,6751 ,7492 ,8283 ,9172 ,10022 ,10922 ,11470 ,12173 ,13437 ,14249 ,14873 ,15529 ,16549 ,17672 ,18638 ,19013 ,20025 ,20676 ,21818 ,22653 ,23545 ,24912 ,26418 ,27293 ,28694 ,29280 ,29855 ,30698 ,31617 ,32498 ,34093 ,35307 ,36021 "400","40","Oklahoma",3204 ,3475 ,3710 ,4016 ,4515 ,4976 ,5493 ,5974 ,6570 ,7343 ,8395 ,9487 ,10943 ,11816 ,11737 ,12618 ,13171 ,13312 ,13455 ,14166 ,15192 ,16077 ,16434 ,17269 ,17772 ,18427 ,18973 ,19936 ,20899 ,21949 ,22757 ,24606 ,26228 ,26232 ,26929 ,28810 ,30492 ,33280 ,34336 ,35985 "400","41","Oregon",3675 ,3927 ,4197 ,4605 ,5109 ,5701 ,6185 ,6898 ,7526 ,8409 ,9295 ,10086 ,10802 ,11116 ,11849 ,12811 ,13429 ,14059 ,14724 ,15776 ,16956 ,17895 ,18469 ,19201 ,20077 ,21219 ,22531 ,23751 ,24854 ,26016 ,27016 ,28719 ,29238 ,29761 ,30549 ,31598 ,32488 ,34623 ,35712 ,36297 "400","42","Pennsylvania",3804 ,4069 ,4289 ,4678 ,5155 ,5692 ,6182 ,6796 ,7473 ,8262 ,9142 ,10040 ,11100 ,11880 ,12448 ,13468 ,14318 ,15017 ,15857 ,17067 ,18412 ,19433 ,20171 ,21050 ,21655 ,22355 ,23226 ,24384 ,25566 ,27367 ,28348 ,30111 ,30704 ,31506 ,32427 ,33852 ,34978 ,37326 ,39058 ,40140 "400","44","Rhode Island",3847 ,4098 ,4285 ,4613 ,4955 ,5385 ,5851 ,6402 ,6975 ,7636 ,8497 ,9645 ,10733 ,11540 ,12389 ,13629 ,14538 ,15445 ,16387 ,17909 ,19389 ,19821 ,19981 ,20820 ,21600 ,22199 ,23364 ,24299 ,25621 ,26945 ,27741 ,29485 ,31166 ,32158 ,33469 ,35090 ,36233 ,38392 ,40219 ,41368 "400","45","South Carolina",2821 ,3055 ,3265 ,3594 ,4016 ,4451 ,4730 ,5255 ,5668 ,6301 ,6988 ,7736 ,8606 ,9078 ,9790 ,10900 ,11590 ,12194 ,12939 ,13906 ,14931 ,15844 ,16256 ,17010 ,17651 ,18579 ,19384 ,20359 ,21287 ,22573 ,23550 ,25082 ,25653 ,26080 ,26704 ,27933 ,29270 ,31031 ,32065 ,32666 "400","46","South Dakota",3030 ,3286 ,3564 ,4089 ,5175 ,5187 ,5706 ,5600 ,6352 ,7302 ,8059 ,8054 ,9410 ,9946 ,10293 ,11621 ,11898 ,12452 ,13133 ,13631 ,14653 ,16075 ,16666 ,17740 ,18248 ,19503 ,19610 ,21672 ,22085 ,23736 ,24816 ,26428 ,27870 ,28073 ,30452 ,32175 ,33150 ,33767 ,36489 ,38661 "400","47","Tennessee",2957 ,3176 ,3439 ,3800 ,4284 ,4685 ,5026 ,5570 ,6089 ,6862 ,7555 ,8227 ,9120 ,9696 ,10293 ,11413 ,12152 ,12895 ,13754 ,14783 ,15718 ,16574 ,17242 ,18527 ,19331 ,20283 ,21339 ,22136 ,23031 ,24462 ,25370 ,26692 ,27535 ,28143 ,29026 ,30297 ,31360 ,32986 ,34287 ,34976 "400","48","Texas",3364 ,3628 ,3840 ,4175 ,4659 ,5170 ,5738 ,6347 ,6943 ,7856 ,8832 ,9870 ,11320 ,11965 ,12348 ,13377 ,14110 ,14182 ,14453 ,15245 ,16165 ,17260 ,17763 ,18765 ,19413 ,20161 ,21070 ,22260 ,23812 ,25376 ,26399 ,28504 ,29166 ,28935 ,29581 ,31073 ,33172 ,35275 ,36829 ,37774 "400","49","Utah",3105 ,3389 ,3649 ,3971 ,4316 ,4738 ,5173 ,5755 ,6344 ,7055 ,7792 ,8492 ,9347 ,9953 ,10506 ,11371 ,11926 ,12322 ,12652 ,13162 ,13941 ,14847 ,15479 ,16135 ,16845 ,17775 ,18765 ,19899 ,21001 ,22188 ,22943 ,24519 ,25536 ,25648 ,25830 ,26827 ,28599 ,30320 ,31739 ,31944 "400","50","Vermont",3380 ,3625 ,3848 ,4163 ,4528 ,4855 ,5203 ,5747 ,6153 ,6979 ,7756 ,8599 ,9650 ,10324 ,10930 ,11977 ,12867 ,13731 ,14755 ,15822 ,17195 ,17643 ,17869 ,18941 ,19446 ,20255 ,21057 ,22106 ,23168 ,24921 ,26268 ,28184 ,29482 ,30013 ,31013 ,32713 ,33416 ,36021 ,37717 ,38686 "400","51","Virginia",3560 ,3792 ,4091 ,4486 ,4971 ,5485 ,5960 ,6549 ,7193 ,8025 ,8950 ,10107 ,11227 ,12095 ,12993 ,14298 ,15284 ,16188 ,17191 ,18442 ,19614 ,20312 ,20953 ,21842 ,22596 ,23534 ,24360 ,25354 ,26695 ,28199 ,29617 ,31641 ,33263 ,33776 ,35029 ,36912 ,38980 ,41367 ,43275 ,44224 "400","53","Washington",4085 ,4189 ,4361 ,4713 ,5284 ,5892 ,6535 ,7165 ,7797 ,8820 ,9847 ,10810 ,11834 ,12435 ,13144 ,13972 ,14619 ,15422 ,16090 ,17055 ,18405 ,19637 ,20583 ,21581 ,22139 ,22981 ,23778 ,25280 ,26749 ,28821 ,30521 ,32407 ,32950 ,33107 ,33869 ,35986 ,36773 ,39623 ,42020 ,42857 "400","54","West Virginia",2792 ,3109 ,3369 ,3673 ,4009 ,4438 ,4973 ,5468 ,6026 ,6667 ,7373 ,8066 ,8767 ,9340 ,9575 ,10355 ,10851 ,11212 ,11619 ,12532 ,13398 ,14436 ,15086 ,16081 ,16549 ,17269 ,17817 ,18567 ,19373 ,20472 ,21049 ,22174 ,23610 ,24388 ,24916 ,25784 ,26684 ,28722 ,30144 ,31641 "400","55","Wisconsin",3747 ,3981 ,4241 ,4601 ,5127 ,5619 ,6090 ,6679 ,7403 ,8247 ,9197 ,10085 ,10973 ,11573 ,12026 ,13112 ,13719 ,14424 ,15182 ,15953 ,17192 ,17986 ,18494 ,19674 ,20398 ,21550 ,22387 ,23509 ,24777 ,26619 ,27652 ,29140 ,30102 ,30809 ,31656 ,32736 ,33689 ,35665 ,37008 ,37767 "400","56","Wyoming",3587 ,3910 ,4257 ,4692 ,5389 ,6150 ,6721 ,7224 ,8152 ,9366 ,10515 ,11668 ,12808 ,13349 ,12703 ,13416 ,14137 ,14244 ,14287 ,14821 ,16382 ,17910 ,18589 ,19344 ,20065 ,20741 ,21358 ,22233 ,23774 ,25496 ,27192 ,29280 ,31319 ,32082 ,33929 ,36274 ,39464 ,44700 ,46741 ,48608 "400","91","New England",4175 ,4438 ,4674 ,5025 ,5477 ,5954 ,6376 ,6954 ,7586 ,8407 ,9381 ,10598 ,11800 ,12833 ,13770 ,15342 ,16440 ,17592 ,18958 ,20612 ,21848 ,22462 ,22867 ,24077 ,24773 ,25804 ,27048 ,28521 ,30087 ,32128 ,33581 ,36603 ,37979 ,38113 ,38788 ,40842 ,42391 ,45652 ,48027 ,49146 "400","92","Mideast",4308 ,4606 ,4889 ,5272 ,5742 ,6273 ,6771 ,7346 ,8026 ,8845 ,9758 ,10874 ,12081 ,13048 ,13901 ,15241 ,16257 ,17209 ,18303 ,19874 ,21301 ,22542 ,22899 ,23939 ,24460 ,25217 ,26357 ,27691 ,29125 ,30776 ,31932 ,34183 ,35133 ,35509 ,36317 ,38317 ,40137 ,43156 ,45859 ,47001 "400","93","Great Lakes",4034 ,4195 ,4473 ,4867 ,5447 ,5918 ,6360 ,7026 ,7780 ,8609 ,9483 ,10263 ,11215 ,11773 ,12394 ,13661 ,14479 ,15208 ,15921 ,16955 ,18117 ,19021 ,19407 ,20585 ,21273 ,22491 ,23404 ,24476 ,25699 ,27294 ,28187 ,29819 ,30441 ,30922 ,31843 ,32824 ,33717 ,35430 ,36793 ,37566 "400","94","Plains",3592 ,3849 ,4104 ,4514 ,5265 ,5566 ,6073 ,6492 ,7154 ,8074 ,8886 ,9540 ,10757 ,11451 ,11975 ,13279 ,13984 ,14628 ,15375 ,15986 ,17121 ,18048 ,18589 ,19674 ,20071 ,21325 ,22119 ,23661 ,24692 ,26299 ,27231 ,29018 ,29896 ,30496 ,31667 ,33154 ,34096 ,35926 ,37647 ,39115 "400","95","Southeast",3078 ,3325 ,3580 ,3941 ,4406 ,4825 ,5185 ,5713 ,6255 ,7009 ,7778 ,8629 ,9638 ,10230 ,10918 ,12024 ,12794 ,13439 ,14183 ,15229 ,16353 ,17186 ,17757 ,18709 ,19403 ,20305 ,21233 ,22230 ,23215 ,24561 ,25481 ,27050 ,27996 ,28477 ,29255 ,30858 ,32514 ,34516 ,35800 ,36336 "400","96","Southwest",3325 ,3599 ,3826 ,4159 ,4633 ,5119 ,5635 ,6206 ,6794 ,7676 ,8649 ,9672 ,11050 ,11684 ,12057 ,13070 ,13792 ,13971 ,14277 ,15035 ,15930 ,16902 ,17394 ,18284 ,18917 ,19693 ,20536 ,21639 ,22997 ,24467 ,25407 ,27372 ,28227 ,28133 ,28793 ,30366 ,32378 ,34499 ,35892 ,36745 "400","97","Rocky Mountain",3441 ,3765 ,4053 ,4451 ,4964 ,5494 ,5930 ,6461 ,7049 ,7950 ,8789 ,9787 ,10909 ,11542 ,12055 ,12962 ,13497 ,13907 ,14359 ,15087 ,16278 ,17248 ,17932 ,18792 ,19729 ,20614 ,21672 ,22829 ,23993 ,25528 ,26836 ,29111 ,30419 ,30447 ,30818 ,32289 ,34061 ,36312 ,37799 ,38275 "400","98","Far West 3/",4412 ,4671 ,4898 ,5308 ,5815 ,6435 ,7037 ,7734 ,8434 ,9412 ,10506 ,11654 ,12796 ,13395 ,14162 ,15425 ,16241 ,16974 ,17823 ,18888 ,19930 ,20913 ,21381 ,22154 ,22556 ,23307 ,24315 ,25582 ,26834 ,28805 ,30236 ,32678 ,33170 ,33368 ,34270 ,36176 ,37869 ,40481 ,42331 ,42845 "Source: Regional Economic Information System, Bureau of Economic Analysis, U.S. Department of Commerce" "=HYPERLINK(""http://www.bea.gov/regional/docs/footnotes.cfm?tablename=SA1-3"",""SA1-3 Footnotes"")","http://www.bea.gov/regional/docs/footnotes.cfm?tablename=SA1-3" "Regional Economic Information System" "Bureau of Economic Analysis" "'October 2009'" libpysal-4.9.2/libpysal/examples/us_income/states48.gal000066400000000000000000000014771452177046000231460ustar00rootroot0000000000000048 0 4 7 8 21 39 1 5 3 4 25 28 41 2 6 15 21 22 33 39 40 3 3 1 25 34 4 7 1 13 24 28 33 41 47 5 3 18 29 36 6 3 17 27 35 7 2 0 8 8 5 0 7 30 37 39 9 6 23 25 34 41 44 47 10 5 11 12 14 22 46 11 4 10 14 19 32 12 6 10 20 22 24 38 46 13 4 4 22 24 33 14 7 10 11 22 32 39 43 45 15 3 2 21 40 16 1 26 17 4 6 35 43 45 18 5 5 26 29 36 42 19 3 11 32 46 20 4 12 31 38 46 21 4 0 2 15 39 22 8 2 10 12 13 14 24 33 39 23 4 9 31 38 47 24 6 4 12 13 22 38 47 25 5 1 3 9 34 41 26 3 16 18 42 27 3 6 29 35 28 5 1 4 33 40 41 29 5 5 18 27 35 42 30 4 8 37 39 43 31 3 20 23 38 32 5 11 14 19 35 45 33 6 2 4 13 22 28 40 34 4 3 9 25 44 35 6 6 17 27 29 32 45 36 2 5 18 37 2 8 30 38 6 12 20 23 24 31 47 39 8 0 2 8 14 21 22 30 43 40 4 2 15 28 33 41 6 1 4 9 25 28 47 42 3 18 26 29 43 5 14 17 30 39 45 44 2 9 34 45 5 14 17 32 35 43 46 4 10 12 19 20 47 6 4 9 23 24 38 41 libpysal-4.9.2/libpysal/examples/us_income/us48.dbf000066400000000000000000000102621452177046000222520ustar00rootroot00000000000000c 0!SAREAN PERIMETERN STATE_N STATE_IDN STATE_NAMECSTATE_FIPSCSUB_REGIONCSTATE_ABBRC 20.750 34.956 1 1Washington 53PacificWA 45.132 34.527 2 2Montana 30Mtn MT 9.571 18.899 3 3Maine 23N Eng ME 21.874 21.353 4 4North Dakota 38W N CenND 22.598 22.746 5 5South Dakota 46W N CenSD 27.966 21.987 6 6Wyoming 56Mtn WY 16.477 21.891 7 7Wisconsin 55E N CenWI 24.391 28.529 8 8Idaho 16Mtn ID 2.794 8.450 9 9Vermont 50N Eng VT 25.577 29.510 10 10Minnesota 27W N CenMN 28.187 24.787 11 11Oregon 41PacificOR 2.677 8.375 12 12New Hampshire 33N Eng NH 15.853 18.790 13 13Iowa 19W N CenIA 2.309 13.105 14 14Massachusetts 25N Eng MA 21.606 23.383 15 15Nebraska 31W N CenNE 13.875 28.487 16 16New York 36Mid AtlNY 12.550 16.841 17 17Pennsylvania 42Mid AtlPA 1.392 5.722 18 18Connecticut 09N Eng CT 0.293 3.643 19 19Rhode Island 44N Eng RI 2.057 8.419 20 20New Jersey 34Mid AtlNJ 9.932 16.110 21 21Indiana 18E N CenIN 29.969 23.608 22 22Nevada 32Mtn NV 22.967 19.992 23 23Utah 49Mtn UT 41.533 42.260 24 24California 06PacificCA 11.300 16.130 25 25Ohio 39E N CenOH 15.396 19.999 26 26Illinois 17E N CenIL 0.553 4.075 28 28Delaware 10S Atl DE 6.493 18.196 29 29West Virginia 54S Atl WV 2.625 21.881 30 30Maryland 24S Atl MD 28.041 22.025 31 31Colorado 08Mtn CO 10.645 21.152 32 32Kentucky 21E S CenKY 21.982 21.105 33 33Kansas 20W N CenKS 10.512 31.199 34 34Virginia 51S Atl VA 18.648 23.596 35 35Missouri 29W N CenMO 28.859 23.257 36 36Arizona 04Mtn AZ 18.031 25.557 37 37Oklahoma 40W S CenOK 12.628 34.583 38 38North Carolina 37S Atl NC 10.876 21.460 39 39Tennessee 47E S CenTN 65.060 64.807 40 40Texas 48W S CenTX 30.936 23.538 41 41New Mexico 35Mtn NM 12.897 17.238 42 42Alabama 01E S CenAL 11.871 22.060 43 43Mississippi 28E S CenMS 14.599 20.103 44 44Georgia 13S Atl GA 7.794 16.127 45 45South Carolina 45S Atl SC 13.517 20.877 46 46Arkansas 05W S CenAR 11.225 32.570 47 47Louisiana 22W S CenLA 13.348 41.085 48 48Florida 12S Atl FL 16.928 40.823 51 51Michigan 26E N CenMIlibpysal-4.9.2/libpysal/examples/us_income/us48.shp000066400000000000000000005541541452177046000223260ustar00rootroot00000000000000' l6è Ï._À@ºô8@¾PÀ•¯H@O¼ Ï._À@‰ÅF@Ã:]À €H@ôख़^ÀÀÙH@ Š^Àà@H@€^À`xH@€ —^À@·H@Ù ^À,H@€œ¢^À@öH@À€ ^À ‚ H@Í™^À@–H@ !˜^À`%H@  ¤^ÀÀ 5H@ ›ª^À€ß4H@ ®¬^ÀàK?H@`צ^À€nBH@ g¡^À ²:H@`>ž^Àà.;H@àA ^À€¡GH@Àh›^À ¾LH@ #Ÿ^À@ÂQH@ ž¡^À`[H@ ÿ ^À aH@ ¬^ÀÀÎfH@`0°^À€tH@`´^Àà¶yH@ ‡¯^À`]zH@ â°^À ýH@àÅ6^À ÿH@Àêµ]À€H@@ÀŒ]À €H@\]ÀàÿH@€ûA]À€H@€ËA]À€HkH@àkB]À ìH@UB]ÀPüG@ œB]ÀG®G@¦B]Àà¡G@ÀŽB]À@IG@ žB]À 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hlibpysal-4.9.2/libpysal/examples/us_income/usjoin.csv000066400000000000000000000513151452177046000230220ustar00rootroot00000000000000"Name","STATE_FIPS",1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009 "Alabama",1,323,267,224,162,166,211,217,251,267,244,252,281,375,518,658,738,784,754,805,881,833,909,1045,1106,1161,1139,1273,1356,1421,1468,1526,1558,1587,1667,1758,1890,2030,2169,2294,2516,2748,2979,3225,3544,3960,4351,4765,5323,5817,6500,7199,7892,8712,9185,9783,10800,11583,12202,12912,13842,14899,15832,16536,17462,17991,18860,19683,20329,21129,22123,22987,23471,24467,25161,26065,27665,29097,30634,31988,32819,32274 "Arizona",4,600,520,429,321,308,362,416,462,504,478,490,505,638,917,1004,1050,1124,1117,1186,1324,1305,1367,1623,1716,1716,1696,1752,1850,1893,1885,1974,2059,2103,2167,2204,2310,2412,2587,2754,3092,3489,3843,4145,4487,4929,5329,5528,6074,6642,7586,8604,9590,10658,10945,11654,12885,13808,14463,15130,15795,16568,17211,17563,18131,18756,19774,20634,21611,22781,24133,25189,25578,26232,26469,27106,28753,30671,32552,33470,33445,32077 "Arkansas",5,310,228,215,157,157,187,207,247,256,231,249,260,342,481,559,687,740,756,741,886,813,847,957,1027,1066,1074,1176,1230,1244,1314,1412,1405,1511,1574,1656,1777,1886,2094,2219,2409,2619,2849,3096,3415,3985,4376,4655,5155,5670,6509,7088,7586,8564,8952,9476,10560,11264,11734,12184,13016,13813,14509,15255,16425,16995,17750,18546,19442,20229,21260,22244,22257,23532,23929,25074,26465,27512,29041,31070,31800,31493 "California",6,991,887,749,580,546,603,660,771,795,771,781,844,1013,1286,1549,1583,1583,1671,1693,1763,1744,1877,2080,2207,2249,2227,2379,2495,2579,2596,2740,2823,2880,3004,3102,3274,3417,3663,3878,4207,4540,4815,5034,5451,5947,6553,7091,7815,8570,9618,10846,12029,13205,13774,14491,15927,16909,17628,18625,19713,20765,21889,22024,22722,22927,23473,24496,25563,26759,28280,29910,32275,32750,32900,33801,35663,37463,40169,41943,42377,40902 "Colorado",8,634,578,471,354,353,368,444,542,532,506,516,545,649,895,1039,1065,1189,1211,1359,1454,1430,1521,1796,1880,1813,1773,1869,1960,2104,2157,2261,2340,2417,2471,2539,2638,2800,2982,3142,3381,3686,4055,4413,4791,5310,5864,6321,6895,7567,8539,9596,10809,12141,12945,13570,14751,15416,15772,16417,17285,18548,19703,20487,21447,22526,23498,24865,26231,27950,29860,31546,32949,34228,33963,34092,35543,37388,39662,41165,41719,40093 "Connecticut",9,1024,921,801,620,583,653,706,806,860,769,836,918,1145,1418,1593,1599,1565,1580,1702,1720,1663,1891,2158,2298,2393,2350,2477,2684,2810,2731,2832,2926,3042,3175,3250,3401,3583,3874,4195,4443,4847,5090,5300,5697,6241,6813,7239,7885,8712,9720,10971,12439,13865,14903,15799,17580,18763,20038,21895,24007,25797,26736,26863,28635,29602,30532,31947,33472,35596,37452,39300,40640,42279,42021,42398,45009,47022,51133,53930,54528,52736 "Delaware",10,1032,857,775,590,564,645,701,868,949,795,899,1027,1164,1291,1464,1504,1526,1565,1664,1685,1805,2075,2155,2244,2341,2306,2507,2749,2645,2651,2724,2805,2815,2933,3049,3210,3468,3610,3785,4079,4421,4608,4892,5303,5871,6347,6729,7349,7913,8658,9549,10803,11873,12727,13529,14816,16056,16781,17933,19312,20930,21636,22342,23094,23823,24530,25391,26640,27405,29571,30778,31255,32664,33463,34123,35998,37297,39358,40251,40698,40135 "Florida",12,518,470,398,319,288,348,376,450,487,460,495,522,609,787,1009,1114,1174,1169,1167,1201,1208,1304,1388,1474,1567,1562,1673,1791,1836,1887,2010,2023,2039,2113,2200,2348,2498,2685,2909,3249,3659,4006,4286,4703,5235,5616,5895,6376,7010,7921,8879,10049,11195,11789,12637,13764,14705,15423,16415,17593,19045,19855,20189,20661,21652,22340,23512,24616,25722,26930,27780,28145,28852,29499,30277,32462,34460,36934,37781,37808,36565 "Georgia",13,347,307,256,200,204,244,268,302,313,290,309,337,419,567,725,831,879,845,887,988,969,1065,1204,1280,1326,1302,1423,1499,1523,1583,1660,1698,1744,1844,1961,2082,2258,2462,2655,2891,3162,3394,3666,4038,4497,4867,5152,5694,6221,6989,7722,8474,9435,10054,10849,12185,13143,13990,14820,15876,16803,17738,18289,19333,20129,21170,22230,23586,24547,26134,27340,27940,28596,28660,29060,29995,31498,32739,33895,34127,33086 "Idaho",16,507,503,374,274,227,403,399,475,423,426,437,463,596,914,1025,1095,1135,1203,1278,1345,1277,1329,1497,1644,1550,1559,1596,1728,1783,1817,1891,1898,1975,2092,2168,2250,2554,2591,2755,2910,3269,3558,3761,4150,4709,5382,5571,6116,6510,7319,7894,8735,9405,9621,10315,11069,11647,11968,12611,13548,14803,15866,16195,17236,18258,18846,19630,20353,20830,21923,22835,24180,25124,25485,25912,27846,29003,30954,32168,32322,30987 "Illinois",17,948,807,671,486,437,505,573,650,731,648,704,751,892,1036,1258,1386,1465,1534,1639,1816,1685,1831,2026,2091,2209,2177,2270,2452,2522,2511,2642,2700,2789,2902,2981,3131,3360,3599,3785,4045,4354,4580,4874,5266,5903,6464,6986,7623,8385,9277,10243,11077,12250,12771,13289,14682,15508,16284,17289,18461,19634,20756,21320,22764,23386,24440,25643,27005,28347,29974,31145,32259,32808,33325,34205,35599,36825,39220,41238,42049,40933 "Indiana",18,607,514,438,310,294,359,421,481,547,472,518,551,724,913,1138,1198,1251,1204,1313,1456,1361,1524,1711,1778,1943,1805,1907,2008,2042,2015,2131,2209,2246,2399,2486,2617,2856,3041,3153,3401,3714,3810,4105,4455,5100,5440,5830,6516,7199,8006,8814,9449,10355,10698,11203,12445,13143,13821,14664,15616,16770,17625,18055,19269,20112,21153,21845,22775,23748,25182,26143,27011,27590,28059,29089,30126,30768,32305,33151,33978,33174 "Iowa",19,581,510,400,297,253,269,425,393,523,458,475,501,609,835,1024,1003,1092,1245,1217,1642,1356,1532,1638,1725,1657,1788,1670,1758,1939,1990,2026,2061,2180,2284,2432,2549,2833,3067,3093,3312,3652,3862,4005,4473,5398,5596,6192,6580,7283,8438,9114,9671,10968,11227,11485,12798,13395,14020,14899,15315,16562,17380,17859,18939,18929,20498,21181,22713,23798,24844,25615,26723,27315,28232,28835,31027,31656,33177,35008,36726,35983 "Kansas",20,532,467,401,266,250,287,362,387,428,383,382,425,552,851,1045,1169,1165,1136,1310,1352,1299,1463,1602,1827,1739,1791,1753,1821,1911,2098,2106,2165,2227,2305,2384,2516,2689,2894,3032,3268,3548,3816,4145,4613,5274,5717,6186,6721,7307,8082,9240,10038,11248,11989,12373,13602,14330,14904,15583,16331,17093,18182,18832,19955,20510,21352,21889,23121,24355,25687,26824,27816,28979,29067,30109,31181,32367,34934,36546,37983,37036 "Kentucky",21,393,325,291,211,205,233,265,294,341,297,305,320,395,538,701,767,803,826,865,995,938,990,1153,1237,1305,1289,1346,1437,1491,1543,1604,1633,1731,1822,1906,1972,2134,2329,2501,2718,2971,3184,3391,3715,4145,4607,4933,5462,6095,6784,7640,8231,9110,9589,9859,11062,11558,11995,12782,13570,14602,15484,16241,17320,17815,18514,19215,20155,21215,22353,23237,24294,24816,25297,25777,26891,27881,29392,30443,31302,31250 "Louisiana",22,414,355,318,241,227,265,290,330,353,348,360,364,449,592,786,876,889,833,885,1019,1074,1117,1210,1278,1343,1339,1397,1502,1616,1635,1685,1690,1741,1806,1909,2003,2139,2331,2527,2749,2901,3106,3328,3593,3994,4510,4956,5557,6135,6951,7813,8833,10037,10558,10865,11628,12121,12028,12266,13113,13997,15223,16076,16968,17717,18779,19541,20254,21209,22352,22847,23334,25116,25683,26434,27776,29785,33438,34986,35730,35151 "Maine",23,601,576,491,377,371,416,430,506,510,471,495,526,631,857,1102,1102,1079,1134,1169,1240,1183,1195,1319,1438,1449,1448,1581,1669,1720,1797,1850,1919,1903,1983,2049,2212,2383,2539,2658,2872,3140,3423,3594,3864,4319,4764,5019,5708,6142,6751,7497,8408,9231,9873,10551,11665,12533,13463,14595,15813,16886,17479,17662,18350,18810,19531,20240,21293,22305,23529,24603,25623,27068,27731,28727,30201,30721,32340,33620,34906,35268 "Maryland",24,768,712,638,512,466,523,548,618,665,633,663,710,870,1117,1287,1324,1312,1315,1355,1500,1496,1642,1815,1944,2017,1938,2047,2184,2267,2258,2324,2407,2507,2638,2722,2881,3055,3284,3516,3831,4209,4573,4894,5291,5822,6382,6878,7536,8191,9062,10035,11230,12403,13246,14228,15693,16961,17966,19216,20626,22001,23023,23571,24358,25104,26046,26896,27844,29222,30850,32465,33872,35430,36293,37309,39651,41555,43990,45827,47040,47159 "Massachusetts",25,906,836,759,613,559,609,643,714,732,672,724,779,901,1073,1262,1299,1348,1396,1435,1512,1480,1656,1817,1895,1947,1930,2071,2194,2296,2321,2430,2511,2605,2734,2799,2932,3101,3331,3583,3903,4207,4486,4748,5106,5551,6024,6439,6994,7636,8480,9472,10673,11830,12803,13859,15549,16720,17954,19504,21334,22458,23223,23749,24876,25664,26841,28051,29618,31332,33394,35551,37992,39247,39238,39869,41792,43520,46893,49361,50607,49590 "Michigan",26,790,657,540,394,347,453,530,619,685,572,625,680,829,1050,1354,1391,1325,1329,1466,1563,1527,1718,1896,1985,2202,2076,2238,2275,2309,2246,2358,2438,2427,2592,2728,2949,3215,3450,3554,3906,4145,4194,4501,4966,5552,5926,6279,7084,7957,8834,9701,10369,11125,11462,12243,13576,14734,15573,16130,17198,18276,19022,19318,20278,21390,22862,23975,24447,25570,26807,28113,29612,30196,30410,31446,31890,32516,33452,34441,35215,34280 "Minnesota",27,599,552,457,363,308,358,451,472,540,494,517,524,615,797,947,1006,1110,1190,1270,1451,1328,1437,1582,1633,1711,1724,1790,1838,1930,2016,2065,2155,2236,2331,2460,2540,2781,2997,3182,3463,3779,4053,4275,4628,5431,5838,6216,6729,7559,8471,9409,10320,11320,11992,12594,14255,15093,15881,16899,17592,18966,20011,20489,21698,22068,23467,24583,26267,27548,29503,30793,32101,32835,33553,34744,36505,37400,39367,41059,42299,40920 "Mississippi",28,286,202,175,127,131,174,177,229,224,201,205,215,307,439,533,629,629,611,670,802,705,770,851,906,940,928,1045,1051,1063,1156,1245,1237,1322,1362,1499,1557,1688,1839,2001,2197,2408,2641,2867,3208,3613,3936,4205,4757,5259,5806,6549,7076,7901,8301,8615,9463,9922,10293,10913,11695,12540,13164,13806,14711,15468,16549,17185,18044,18885,20013,20688,20993,22222,22540,23365,24501,26120,27276,28772,29591,29318 "Missouri",29,621,561,491,365,334,367,420,466,508,475,504,519,640,806,963,1068,1130,1191,1224,1376,1327,1427,1554,1659,1737,1726,1818,1905,1949,2044,2126,2156,2215,2330,2427,2533,2738,2895,3067,3370,3561,3843,4107,4443,4937,5282,5733,6306,6991,7787,8713,9390,10457,11035,11716,12960,13868,14505,15250,16086,17083,17751,18560,19542,20295,21267,22094,23099,24252,25403,26376,27445,28156,28771,29702,30847,31644,33354,34558,35775,35106 "Montana",30,592,501,382,339,298,364,476,475,512,517,533,569,713,901,1150,1183,1206,1308,1484,1642,1412,1654,1806,1823,1810,1771,1894,1929,1983,2068,2023,2075,2025,2363,2330,2366,2548,2730,2805,2955,3284,3625,3789,4355,5012,5380,5794,6200,6636,7721,8299,9143,10244,10672,11045,11705,11900,12465,12996,13362,14623,15524,16509,17114,18072,18129,18764,19383,20167,21324,22019,22569,24342,24699,25963,27517,28987,30942,32625,33293,32699 "Nebraska",31,596,521,413,307,275,259,409,396,415,405,400,442,551,822,1019,1091,1186,1186,1274,1558,1346,1560,1641,1761,1676,1753,1650,1675,1936,2025,2022,2125,2134,2292,2341,2392,2656,2890,2990,3167,3572,3796,4121,4527,5269,5465,6168,6453,6993,8120,8784,9272,10685,11228,11601,12968,13743,14215,15035,15984,16878,18088,18766,19688,20167,21168,22196,24045,24590,25861,27049,27829,29098,29499,31262,32371,33395,34753,36880,38128,37057 "Nevada",32,868,833,652,550,495,546,658,843,762,780,861,895,982,1558,1511,1483,1611,1757,1770,1758,1795,1991,2211,2397,2447,2415,2527,2488,2562,2594,2749,2890,2957,3184,3174,3209,3299,3471,3660,4114,4520,4946,5227,5557,6114,6490,7009,7719,8550,9780,10765,11780,12780,12986,13465,14435,15332,16027,16886,18180,19568,20674,21283,22694,23465,24635,25808,27142,28201,29806,31022,30529,30718,30849,32182,34757,37555,38652,40326,40332,38009 "New Hampshire",33,686,647,558,427,416,476,498,537,565,533,561,579,708,851,976,1054,1111,1150,1217,1291,1274,1348,1508,1577,1653,1703,1829,1900,2007,2004,2124,2197,2281,2392,2427,2552,2708,2963,3162,3441,3744,3896,4102,4423,4880,5279,5592,6258,6892,7786,8781,9915,11079,11906,13041,14534,15819,16974,18371,19759,20635,20713,21326,22154,22521,23820,25008,26042,27607,29679,31114,33332,33940,34335,34892,36758,37536,39997,41720,42461,41882 "New Jersey",34,918,847,736,587,523,573,625,709,747,697,749,820,957,1167,1429,1554,1582,1526,1571,1648,1624,1802,2000,2114,2232,2219,2300,2448,2551,2525,2660,2764,2830,2990,3064,3224,3414,3652,3892,4237,4525,4835,5127,5521,6043,6576,7037,7703,8462,9408,10469,11778,13057,13999,15036,16549,17652,18711,20230,22142,23595,24766,25153,26597,27101,27885,29277,30795,32372,34310,35551,36983,37959,38240,38768,40603,42142,45668,48172,49233,48123 "New Mexico",35,410,334,289,208,211,247,292,343,362,338,357,378,474,636,773,881,942,929,1013,1124,1145,1204,1346,1423,1444,1462,1543,1626,1733,1825,1895,1884,1952,2006,2054,2134,2250,2386,2490,2709,2921,3197,3431,3761,4137,4568,5045,5527,6087,6847,7619,8402,9334,9894,10367,11215,11999,12226,12686,13322,14085,14960,15744,16425,17226,17946,18852,19478,20233,21178,21853,22203,24193,24446,25128,26606,28180,29778,31320,32585,32197 "New York",36,1152,1035,881,676,626,680,722,808,838,789,825,869,996,1169,1384,1538,1644,1697,1725,1771,1729,1858,2002,2057,2144,2175,2295,2416,2522,2553,2695,2788,2866,2980,3070,3254,3422,3657,3923,4295,4603,4887,5179,5538,5980,6492,6955,7477,8153,8979,9927,11095,12364,13344,14188,15739,16734,17827,19031,20604,21966,23315,23942,25199,25589,26359,27721,29266,30480,32236,33890,34547,35371,35332,36077,38312,40592,43892,47514,48692,46844 "North Carolina",37,332,292,248,187,208,253,271,297,324,295,315,324,422,572,693,766,823,866,900,1003,969,1077,1192,1230,1274,1293,1368,1436,1424,1505,1581,1629,1689,1797,1877,2009,2143,2363,2525,2754,3051,3285,3510,3899,4365,4743,5039,5584,6058,6780,7461,8247,9184,9690,10480,11788,12649,13444,14325,15461,16539,17367,17879,19120,20042,20931,21938,22940,24188,25454,26003,27194,27650,27726,28208,29769,31209,32692,33966,34340,33564 "North Dakota",38,382,311,187,176,146,180,272,234,326,282,319,355,529,665,966,1031,1046,1088,1494,1483,1211,1360,1444,1318,1336,1364,1490,1556,1598,1847,1679,1821,1653,2320,2142,2112,2463,2507,2592,2719,3052,3214,3669,4377,6172,6120,6334,6184,6427,8136,8398,8095,10342,10990,11386,12307,12811,13126,13565,12745,14357,15880,16270,17692,17830,19033,19084,21166,20798,22767,23313,25068,26118,26770,29109,29676,31644,32856,35882,39009,38672 "Ohio",39,771,661,563,400,385,455,516,593,648,561,615,658,821,1021,1253,1312,1340,1310,1401,1539,1456,1608,1835,1916,2024,1965,2087,2182,2243,2177,2314,2391,2405,2515,2604,2754,2948,3181,3309,3616,3934,4101,4328,4691,5218,5733,6087,6753,7511,8326,9251,10103,10982,11485,12167,13449,14295,14933,15675,16739,17825,18792,19217,20242,20999,22063,22887,23613,24913,26164,27152,28400,28966,29522,30345,31240,32097,33643,34814,35521,35018 "Oklahoma",40,455,368,301,216,222,252,298,321,376,346,349,374,432,626,782,947,970,952,1028,1143,1166,1144,1290,1401,1475,1458,1516,1593,1657,1794,1857,1916,1947,1994,2055,2196,2361,2517,2702,2948,3198,3477,3711,4020,4524,4986,5475,5974,6586,7387,8485,9580,11003,11817,11725,12687,13265,13288,13464,14257,15265,16214,16721,17526,18085,18730,19394,20151,21106,22199,22953,23517,25059,25059,25719,27516,29122,31753,32781,34378,33708 "Oregon",41,668,607,505,379,358,439,458,548,556,531,571,609,818,1118,1381,1389,1357,1379,1504,1646,1604,1657,1830,1912,1916,1867,1978,2070,2055,2106,2244,2283,2349,2457,2541,2685,2852,3033,3201,3448,3677,3940,4212,4625,5135,5726,6181,6913,7556,8476,9415,10196,10862,11128,11832,12866,13547,14162,14911,16062,17222,18253,18806,19558,20404,21421,22668,23649,24845,25958,27023,28350,28866,29387,30172,31217,32108,34212,35279,35899,35210 "Pennsylvania",42,772,712,600,449,417,482,517,601,636,563,601,650,776,951,1146,1250,1280,1289,1364,1436,1405,1552,1713,1789,1894,1827,1915,2063,2174,2164,2240,2301,2334,2439,2511,2662,2830,3040,3246,3507,3815,4077,4294,4683,5168,5704,6170,6800,7493,8305,9225,10151,11184,11887,12455,13512,14445,15186,16142,17323,18725,19823,20505,21550,22211,22864,23738,24838,26092,27358,28605,29539,30085,30840,31709,33069,34131,36375,38003,39008,38827 "Rhode Island",44,874,788,711,575,559,600,645,711,731,672,720,750,934,1149,1201,1274,1278,1369,1459,1433,1378,1553,1717,1765,1855,1847,1958,1998,2024,2067,2183,2234,2329,2457,2552,2691,2870,3113,3336,3595,3865,4114,4295,4625,4972,5405,5844,6411,7004,7693,8595,9742,10815,11605,12439,13717,14685,15587,16651,18271,19657,20194,20363,21257,22137,22762,24046,25123,26631,28012,29377,29685,31378,32374,33690,35318,36461,38610,40421,41542,41283 "South Carolina",45,271,243,205,159,175,211,229,258,273,250,276,310,394,545,648,732,753,780,793,911,873,925,1115,1196,1233,1166,1229,1260,1286,1316,1392,1437,1498,1602,1669,1787,1958,2176,2340,2570,2827,3064,3274,3603,4029,4459,4720,5257,5684,6334,7044,7794,8651,9071,9775,10910,11666,12258,13056,14045,14834,16050,16409,17165,17805,18686,19473,20403,21385,22544,23545,24321,24871,25279,25875,27057,28337,29990,30958,31510,30835 "South Dakota",46,426,366,241,189,129,184,309,244,323,320,345,361,475,757,846,979,1086,1132,1270,1526,1116,1283,1497,1327,1436,1457,1342,1419,1669,1740,1564,1870,1863,2092,2020,2000,2278,2500,2577,2773,2995,3256,3538,4065,5163,5178,5667,5591,6351,7347,8158,8142,9451,9915,10195,11619,11942,12486,13217,13807,14767,16238,16961,17966,18565,19607,19848,21736,22275,23797,25045,26115,27531,27727,30072,31765,32726,33320,35998,38188,36499 "Tennessee",47,378,325,277,198,204,245,264,304,334,300,311,340,436,561,728,864,912,872,892,965,951,1028,1122,1178,1276,1274,1327,1422,1476,1512,1598,1618,1690,1771,1855,1965,2128,2332,2473,2741,2967,3189,3451,3808,4298,4696,5017,5574,6108,6895,7618,8319,9196,9695,10276,11453,12247,12995,13909,14910,15883,16821,17503,18840,19741,20696,21800,22450,23324,24576,25574,26239,27059,27647,28501,29734,30764,32314,33578,34243,33512 "Texas",48,479,412,348,266,257,294,326,372,418,404,417,438,528,719,944,1045,1058,1047,1147,1214,1300,1363,1488,1564,1598,1630,1697,1784,1856,1871,1948,1955,2021,2079,2155,2284,2433,2630,2840,3105,3373,3646,3861,4192,4683,5194,5738,6362,6979,7912,8929,9957,11391,11961,12303,13396,14196,14165,14486,15324,16323,17458,18150,19146,19825,20590,21526,22557,24242,25803,26858,27871,28519,28295,28929,30392,32448,34489,36020,36969,35674 "Utah",49,551,498,369,305,298,310,389,463,444,444,458,480,594,880,1126,1049,1121,1094,1180,1257,1262,1348,1544,1596,1604,1573,1666,1758,1862,1888,1970,2035,2091,2230,2281,2386,2494,2605,2721,2900,3103,3391,3658,3979,4326,4743,5150,5739,6328,7041,7786,8464,9290,9807,10333,11233,11846,12248,12638,13156,13977,14996,15661,16354,17031,17912,18858,19955,21156,22294,23288,23907,24899,25010,25192,26169,27905,29582,31009,31253,30107 "Vermont",50,634,576,474,365,338,383,414,471,485,457,491,516,643,775,932,953,1035,1090,1127,1192,1125,1169,1334,1380,1432,1456,1524,1655,1720,1732,1832,1923,1994,2072,2128,2266,2456,2729,2885,3128,3388,3634,3856,4176,4548,4869,5192,5753,6179,7036,7853,8702,9717,10287,10968,12048,12994,13842,14992,16197,17517,18055,18218,19293,19785,20553,21359,22295,23362,24803,25889,26901,28140,28651,29609,31240,31920,34394,36018,36940,36752 "Virginia",51,434,384,370,284,285,320,350,390,423,390,426,467,582,785,843,900,950,1002,1015,1148,1132,1257,1420,1510,1533,1554,1637,1712,1738,1784,1885,1936,2005,2121,2218,2403,2563,2746,2967,3252,3558,3795,4092,4486,4972,5484,5934,6534,7192,8040,8995,10176,11291,12075,12936,14298,15286,16237,17332,18556,19780,20538,21092,21965,22773,23709,24456,25495,26768,28343,29789,31162,32747,33235,34451,36285,38304,40644,42506,43409,43211 "Washington",53,741,658,534,402,376,443,490,569,599,582,614,658,864,1196,1469,1527,1419,1401,1504,1624,1595,1721,1874,1973,2066,2077,2116,2172,2262,2281,2380,2436,2535,2680,2735,2858,3078,3385,3566,3850,4097,4205,4381,4731,5312,5919,6533,7181,7832,8887,9965,10913,11903,12431,13124,14021,14738,15522,16300,17270,18670,20026,20901,21917,22414,23119,23878,25287,26817,28632,30392,31528,32053,32206,32934,34984,35738,38477,40782,41588,40619 "West Virginia",54,460,408,356,257,259,313,337,390,418,370,388,407,498,613,739,820,888,925,1032,1110,1023,1056,1182,1247,1276,1225,1318,1477,1594,1551,1597,1625,1671,1762,1853,1974,2126,2271,2417,2573,2798,3117,3378,3682,4026,4457,4974,5479,6053,6703,7432,8172,8866,9439,9626,10417,10936,11464,11950,12708,13529,14579,15219,16118,16724,17413,17913,18566,19388,20246,20966,21915,23333,24103,24626,25484,26374,28379,29769,31265,31843 "Wisconsin",55,673,588,469,362,333,380,461,518,551,507,513,547,672,866,1053,1109,1182,1211,1296,1434,1387,1506,1736,1799,1839,1774,1875,1990,2060,2075,2225,2258,2304,2412,2458,2616,2789,3025,3180,3441,3751,3983,4238,4595,5130,5622,6061,6670,7417,8292,9281,10161,11006,11592,12046,13182,13845,14530,15358,16201,17299,18160,18711,19872,20639,21699,22573,23554,24790,26245,27390,28232,29161,29838,30657,31703,32625,34535,35839,36594,35676 "Wyoming",56,675,585,476,374,371,411,496,551,607,561,587,602,779,944,1150,1224,1258,1362,1506,1610,1647,1719,1955,1912,1932,1858,1913,2011,2132,2172,2278,2312,2387,2502,2535,2588,2743,2906,3121,3315,3584,3919,4269,4709,5410,6172,6701,7212,8152,9384,10572,11753,12879,13251,12723,13493,14242,14004,14194,14968,16383,17996,18867,19550,20287,20957,21514,22098,23820,24927,26396,27230,29122,29828,31544,33721,36683,41548,43453,45177,42504 libpysal-4.9.2/libpysal/examples/virginia/000077500000000000000000000000001452177046000206205ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/virginia/README.md000066400000000000000000000016401452177046000221000ustar00rootroot00000000000000virginia ======== Virginia counties shapefile --------------------------- * virginia.dbf: attribute data. (k=7) * virginia.gal: rook contiguity weights in GAL format. * virginia.json: attribute and shape data in JSON format. * virginia.prj: ESRI projection file. * virginia.shp: Polygon shapefile. (n=136) * virginia.shx: spatial index. * virginia_queen.dat: queen contiguity weights in DAT format. * virginia_queen.dbf: queen contiguity weights in DBF format. * virginia_queen.gal: queen contiguity weights in GAL format. * virginia_queen.mat: queen contiguity weights in MATLAB MAT format. * virginia_queen.mtx: queen contiguity weights in Matrix Market MTX format. * virginia_queen.swm: queen contiguity weight in ArcGIS SWM format. * virginia_queen.txt: queen contiguity weights in TXT format. * virginia_queen.wk1: queen contiguity weights in Lotus Wk1 format. * virginia_rook.gal: rook contiguity weights in GAL format. libpysal-4.9.2/libpysal/examples/virginia/vautm17n.dbf000066400000000000000000000262211452177046000227620ustar00rootroot00000000000000_ˆRPOLY_IDN NAMEC STATE_NAMECSTATE_FIPSCCNTY_FIPSCFIPSCKeyN 1Frederick Virginia 51069510691147 2Loudoun Virginia 51107511071177 3Clarke Virginia 51043510431188 4Winchester Virginia 51840518401207 5Shenandoah Virginia 51171511711237 6Fairfax Virginia 51059510591245 7Warren Virginia 51187511871252 8Fauquier Virginia 51061510611259 9Prince William Virginia 51153511531271 10Arlington Virginia 51013510131273 11Falls Chruch Virginia 51610516101284 12Fairfax City Virginia 51600516001287 13Rappahannock Virginia 51157511571292 14Alexandria Virginia 51510515101297 15Rockingham Virginia 51165511651298 16Page Virginia 51139511391303 17Manassas Park City Virginia 51685516851307 18Manassas City Virginia 51683516831309 19Culpeper Virginia 51047510471331 20Madison Virginia 51113511131349 21Stafford Virginia 51179511791357 22Highland Virginia 51091510911359 23Augusta Virginia 51015510151388 24Greene Virginia 51079510791390 25Harrisonburg Virginia 51660516601392 26Orange Virginia 51137511371404 27Spotsylvania Virginia 51177511771409 28King George Virginia 51099510991410 29Fredericksburg Virginia 51630516301418 30Westmoreland Virginia 51193511931423 31Albemarle Virginia 51003510031424 32Bath Virginia 51017510171427 33Caroline Virginia 51033510331443 34Staunton Virginia 51790517901457 35Essex Virginia 51057510571462 36Louisa Virginia 51109511091467 37Richmond Virginia 51159511591477 38Waynesboro Virginia 51820518201479 39Rockbridge Virginia 51163511631481 40Charlottesville Virginia 51540515401486 41Nelson Virginia 51125511251490 42Accomack Virginia 51001510011494 43Northumberland Virginia 51133511331495 44Hanover Virginia 51085510851499 45Fluvanna Virginia 51065510651500 46King and Queen Virginia 51097510971509 47Alleghany Virginia 51005510051515 48King William Virginia 51101511011522 49Goochland Virginia 51075510751526 50Clifton Forge Virginia 51560515601539 51Lancaster Virginia 51103511031541 52Amherst Virginia 51009510091546 53Covington Virginia 51580515801548 54Lexington Virginia 51678516781550 55Botetourt Virginia 51023510231552 56Buckingham Virginia 51029510291553 57Middlesex Virginia 51119511191555 58Cumberland Virginia 51049510491559 59Buena Vista Virginia 51530515301560 60Henrico Virginia 51087510871576 61Powhatan Virginia 51145511451579 62Craig Virginia 51045510451582 63New Kent Virginia 51127511271591 64Bedford Virginia 51019510191594 65Richmond City Virginia 51760517601597 66Gloucester Virginia 51073510731606 67Chesterfield Virginia 51041510411612 68Appomattox Virginia 51011510111613 69Northampton Virginia 51131511311614 70Buchanan Virginia 51027510271622 71Mathews Virginia 51115511151623 72Amelia Virginia 51007510071624 73Charles City Virginia 51036510361625 74Giles Virginia 51071510711630 75Lynchburg Virginia 51680516801633 76James City Virginia 51095510951638 77Campbell Virginia 51031510311642 78Roanoke Virginia 51161511611648 79Prince Edward Virginia 51147511471649 80York Virginia 51199511991660 81Montgomery Virginia 51121511211661 82Bedford City Virginia 51515515151665 83Tazewell Virginia 51185511851668 84Prince George Virginia 51149511491673 85Roanoke City Virginia 51770517701674 86Hopewell Virginia 51670516701675 87Salem Virginia 51775517751677 88Bland Virginia 51021510211680 89Dickenson Virginia 51051510511681 90Nottoway Virginia 51135511351683 91Colonial Heights Virginia 51570515701684 92Williamsburg Virginia 51830518301685 93Dinwiddie Virginia 51053510531688 94Charlotte Virginia 51037510371691 95Surry Virginia 51181511811692 96Petersburg Virginia 51730517301693 97Pulaski Virginia 51155511551694 98Newport News Virginia 51700517001695 99Franklin Virginia 51067510671696 100Wise Virginia 51195511951699 101Poquoson City Virginia 51735517351704 102Radford Virginia 51750517501707 103Isle of Wight Virginia 51093510931708 104Russell Virginia 51167511671709 105Pittsylvania Virginia 51143511431710 106Floyd Virginia 51063510631712 107Lunenburg Virginia 51111511111713 108Sussex Virginia 51183511831714 109Hampton Virginia 51650516501715 110Wythe Virginia 51197511971721 111Halifax Virginia 51083510831727 112Brunswick Virginia 51025510251728 113Smyth Virginia 51173511731730 114Southampton Virginia 51175511751751 115Norfolk Virginia 51710517101760 116Norton Virginia 51720517201763 117Virginia Beach Virginia 51810518101767 118Carroll Virginia 51035510351768 119Washington Virginia 51191511911770 120Suffolk Virginia 51800518001772 121Greensville Virginia 51081510811774 122Mecklenburg Virginia 51117511171775 123Lee Virginia 51105511051776 124Scott Virginia 51169511691778 125Patrick Virginia 51141511411781 126Chesapeake Virginia 51550515501783 127Henry Virginia 51089510891785 128Portsmouth Virginia 51740517401787 129Grayson Virginia 51077510771791 130Emporia Virginia 51595515951797 131Martinsville Virginia 51690516901798 132South Boston Virginia 51780517801799 133Galax Virginia 51640516401800 134Franklin City Virginia 51620516201801 135Danville Virginia 51590515901808 136Bristol Virginia 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libpysal-4.9.2/libpysal/examples/virginia/virginia_queen.mat000066400000000000000000004413001452177046000243320ustar00rootroot00000000000000MATLAB 5.0 MAT-file Platform: posix, Created on: Thu Jan 15 05:46:56 2015IM8BˆˆWEIGHT Bð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?libpysal-4.9.2/libpysal/examples/virginia/virginia_queen.mtx000066400000000000000000000115341452177046000243630ustar00rootroot00000000000000%%MatrixMarket matrix coordinate real general %Generated by PySAL 136 136 586 1 3 1 1 4 1 1 5 1 1 7 1 2 3 1 2 6 1 2 8 1 2 9 1 3 1 1 3 2 1 3 7 1 3 8 1 4 1 1 5 1 1 5 7 1 5 15 1 5 16 1 6 2 1 6 9 1 6 10 1 6 11 1 6 12 1 6 14 1 7 1 1 7 3 1 7 5 1 7 8 1 7 13 1 7 16 1 8 2 1 8 3 1 8 7 1 8 9 1 8 13 1 8 19 1 8 21 1 9 2 1 9 6 1 9 8 1 9 17 1 9 18 1 9 21 1 10 6 1 10 11 1 10 14 1 11 6 1 11 10 1 12 6 1 13 7 1 13 8 1 13 16 1 13 19 1 13 20 1 14 6 1 14 10 1 15 5 1 15 16 1 15 23 1 15 24 1 15 25 1 15 31 1 16 5 1 16 7 1 16 13 1 16 15 1 16 20 1 16 24 1 17 9 1 17 18 1 18 9 1 18 17 1 19 8 1 19 13 1 19 20 1 19 21 1 19 26 1 19 27 1 20 13 1 20 16 1 20 19 1 20 24 1 20 26 1 21 8 1 21 9 1 21 19 1 21 27 1 21 28 1 21 29 1 21 33 1 22 23 1 22 32 1 23 15 1 23 22 1 23 31 1 23 32 1 23 34 1 23 38 1 23 39 1 23 41 1 24 15 1 24 16 1 24 20 1 24 26 1 24 31 1 25 15 1 26 19 1 26 20 1 26 24 1 26 27 1 26 31 1 26 36 1 27 19 1 27 21 1 27 26 1 27 29 1 27 33 1 27 36 1 27 44 1 28 21 1 28 30 1 28 33 1 28 35 1 29 21 1 29 27 1 30 28 1 30 35 1 30 37 1 30 43 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q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q  q  q  q  q  q ð? q  q  q  q  q ð? q  q  q ð? q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q ! q " q # q $ q % q & q ' q ( q ) q * q + q , q - q . q / q 0 q 1 q 2 q 3 q 4 q 5 q 6 q 7 q 8 q 9 q : q ; q < q = q > q ? q @ q A q B q C q D q E q F q G q H q I q J q K q L q M q N q O q P q Q q R q S q T q U q V q W q X q Y q Z q [ q \ q ] q ^ q _ q ` q a q b q c q d q e q f q g q h q i q j q k q l q m q n q o q p q q q r q s q t q u q v q w q x q y q z q { q | q } q ~ q  q € q  q ‚ q ƒ q „ q … q † q ‡ q  q  q  q  q  q ð? q  q  q  q ð? q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q ! q " q # q $ q % q & q ' q ( q ) q * q + q , q - q . q / q 0 q 1 q 2 q 3 q 4 q 5 q 6 q 7 q 8 q 9 q : q ; q < q = q > q ? q @ q A q B q C q D q E q F q G q H q I q J q K q L q M q N q O q P q Q q R q S q T q U q V q W q X q Y q Z q [ q \ q ] q ^ q _ q ` q a q b q c q d q e q f q g q h q i q j q k q l q m q n q o q p q q q r q s q t q u q v q w q x q y q z q { q | q } q ~ q  q € q  q ‚ q ƒ q „ q … q † q ‡ q  q  q  q  q  q ð? q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q ! q " q # q $ q % q & q ' q ( q ) q * q + q , q - q . q / q 0 q 1 q 2 q 3 q 4 q 5 q 6 q 7 q 8 q 9 q : q ; q < q = q > q ? q @ q A q B q C q D q E q F q G q H q I q J q K q L q M q N q O q P q Q q R q S q T q U q V q W q X q Y q Z q [ q \ q ] q ^ q _ q ` q a q b q c q d q e q f q g q h q i q j q k q l q m q n q o q p q q q r q s q t q u q v q w q x q y q z q { q | q } q ~ q  q € q  q ‚ q ƒ q „ q … q † q ‡ q  q  q  q  q  q  q ð? q ð? q  q  q  q  q  q  q  q ð? q  q  q ð? q ð? q  q  q  q  q  q  q  q  q  q  q  q  q  q ! q " q # q $ q % q & q ' q ( q ) q * q + q , q - q . q / q 0 q 1 q 2 q 3 q 4 q 5 q 6 q 7 q 8 q 9 q : q ; q < q = q > q ? q @ q A q B q C q D q E q F q G q H q I q J q K q L q M q N q O q P q Q q R q S q T q U q V q W q X q Y q Z q [ q \ q ] q ^ q _ q ` q a q b q c q d q e q f q g q h q i q j q k q l q m q n q o q p q q q r q s q t q u q v q w q x q y q z q { q | q } q ~ q  q € q  q ‚ q ƒ q „ q … q † q ‡ q  q  q  q  q  q ð? q  q  q  q ð? q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q  q ! q " q # q $ q % q & q ' q ( q ) q * q + q , q - q . q / q 0 q 1 q 2 q 3 q 4 q 5 q 6 q 7 q 8 q 9 q : q ; q < q = q > q ? q @ q A q B q C q D q E q F q G q H q I q J q K q L q M q N q O q P q Q q R q S q T q U q V q W q X q Y q Z q [ q \ q ] q ^ q _ q ` q a q b q c q d q e q f q g q h q i q j q k q l q m q n q o q p q q q r q s q t q u q v q w q x q y q z q { q | q } q ~ q  q € q  q ‚ q ƒ q „ q … q † q ‡ q q q q qð? q q q q q  q  q  q  q  q qð? q q q q q q qð? qð? qð? q q q q q qð? q q  q! q" q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q qð? q qð? q q q  q  q  q ð? q  qð? q q q q qð? q q q qð? q q q q q q q q q  q! q" q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q q qð? q  q  q  q  q  q q q qð? q q q q q q q q q q q q q q q  q! q" q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q q qð? q  q  q  q  q  q q qð? q q q q q q q q q q q q q q q q  q! q" q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q qð? q q  q  q  q ð? q  q q q q q qð? qð? q q q q qð? qð? q q q q q q  q! q" q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q q q q  q  q  q ð? q  q qð? q q qð? q q q q qð? q qð? q q q q q q q  q! q" q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q qð? qð? q  q  q  q  q  q q q q qð? q q q q q q q qð? qð? qð? q q q q ð? q! q" q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q q q q  q  q  q  q  q q q q q q q q qð? q q q q q q q q qð? q  q! q" q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q q q q  q  q  q  q  qð? q q q q q q qð? q q q q q q q q qð? qð? q  q!ð? q" q# q$ q%ð? q&ð? q' q(ð? q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q q q q  q  q  q  q  qð? qð? q q q qð? q q q q q qð? q q q q qð? q q  q! q" q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q q q q  q  q  q  q  qð? q q q q q q q q q q q q q q q q q q  q! q" q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q q q q  q  q  q  q  q q q q qð? qð? q q q qð? q q qð? q q q qð? q q  q! q" q#ð? q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q q q q  q  q  q  q  q q q q qð? q qð? q q q q qð? q q qð? q q q q ð? q! q" q#ð? q$ q% q& q' q( q) q* q+ð? q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: q; q< q= q> q? q@ qA qB qC qD qE qF qG qH qI qJ qK qL qM qN qO qP qQ qR qS qT qU qV qW qX qY qZ q[ q\ q] q^ q_ q` qa qb qc qd qe qf qg qh qi qj qk ql qm qn qo qp qq qr qs qt qu qv qw qx qy qz q{ q| q} q~ q q€ q q‚ qƒ q„ q… q† q‡ q q q q q q q q q q  q  q  q  q  q q q q q q qð? q q q q q q q q qð? q q q ð? q! q"ð? q# q$ q% q& q' q( q) q* q+ q, q- q. q/ q0 q1 q2 q3 q4 q5 q6 q7 q8 q9 q: 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qBe qBf qBg qBh qBi qBj qBk qBl qBm qBn qBo qBp qBq qBr qBs qBt qBu qBv qBw qBx qBy qBz qB{ qB| qB} qB~ qB qB€ qB qB‚ qBƒ qB„ qB… qB† qB‡ qC qC qC qC qC qC qC qC qC qC  qC  qC  qC  qC  qC qC qC qC qC qC qC qC qC qC qC qC qC qC qC qC qC qC qC  qC! qC" qC# qC$ qC% qC& qC' qC(ð? qC) qC* qC+ qC, qC- qC. qC/ qC0 qC1 qC2 qC3ð? qC4 qC5 qC6 qC7ð? qC8 qC9 qC: qC; qC< qC= qC> qC? qC@ qCA qCB qCC qCD qCE qCF qCG qCH qCI qCJ qCK qCLð? qCM qCNð? qCO qCP qCQ qCR qCS qCT qCU qCV qCW qCX qCY qCZ qC[ qC\ qC]ð? qC^ qC_ qC` qCa qCb qCc qCd qCe qCf qCg qCh qCi qCj qCk qCl qCm qCn qCo qCp qCq qCr qCs qCt qCu qCv qCw qCx qCy qCz qC{ qC| qC} qC~ qC qC€ qC qC‚ qCƒ qC„ qC… qC† qC‡ qD qD qD qD qD qD qD qD qD qD  qD  qD  qD  qD  qD qD qD qD qD qD qD qD qD qD qD qD qD qD qD qD qD qD qD  qD! qD" qD# qD$ qD% qD& qD' qD( qD)ð? qD* qD+ qD, qD- qD. qD/ qD0 qD1 qD2 qD3 qD4 qD5 qD6 qD7 qD8 qD9 qD: qD; qD< qD= qD> qD? qD@ qDA qDB qDC qDD qDE qDF qDG qDH qDI qDJ qDK qDL qDM qDN qDO qDP qDQ qDR qDS qDT qDU qDV qDW qDX qDY qDZ qD[ qD\ qD] qD^ qD_ qD` qDa qDb qDc qDd qDe qDf qDg qDh qDi qDj qDk qDl qDm qDn qDo qDp qDq qDr qDs qDt qDu qDv qDw qDx qDy qDz qD{ qD| qD} qD~ qD qD€ qD qD‚ qDƒ qD„ qD… qD† qD‡ qE qE qE qE qE qE qE qE qE qE  qE  qE  qE  qE  qE qE qE qE qE qE qE qE qE qE qE qE qE qE qE qE qE qE qE  qE! qE" qE# qE$ qE% qE& qE' qE( qE) qE* qE+ qE, qE- qE. qE/ qE0 qE1 qE2 qE3 qE4 qE5 qE6 qE7 qE8 qE9 qE: qE; qE< qE= qE> qE? qE@ qEA qEB qEC qED qEE qEF qEG qEH qEI qEJ qEK qEL qEM qEN qEO qEP qEQ qERð? qES qET qEU qEV qEW qEXð? qEY qEZ qE[ qE\ qE] qE^ qE_ qE` qEa qEb qEc qEd qEe qEf qEgð? qEh qEi qEj qEk qEl qEm qEn qEo qEp qEq qEr qEs qEt qEu qEv qEw qEx qEy qEz qE{ qE| qE} qE~ qE qE€ qE qE‚ qEƒ qE„ qE… qE† qE‡ qF qF qF qF qF qF qF qF qF qF  qF  qF  qF  qF  qF qF qF qF qF qF qF qF qF qF qF qF qF qF qF qF qF qF qF  qF! qF" qF# qF$ qF% qF& qF' qF( qF) qF* qF+ qF, qF- qF. qF/ qF0 qF1 qF2 qF3 qF4 qF5 qF6 qF7 qF8 qF9 qF: qF; qF< qF= qF> qF? qF@ qFAð? qFB qFC qFD qFE qFF qFG qFH qFI qFJ qFK qFL qFM qFN qFO qFP qFQ qFR qFS qFT qFU qFV qFW qFX qFY qFZ qF[ qF\ qF] qF^ qF_ qF` qFa qFb qFc qFd qFe qFf qFg qFh qFi qFj qFk qFl qFm qFn qFo qFp qFq qFr qFs qFt qFu qFv qFw qFx qFy qFz qF{ qF| qF} qF~ qF qF€ qF qF‚ qFƒ qF„ qF… qF† qF‡ qG qG qG qG qG qG qG qG qG qG  qG  qG  qG  qG  qG qG qG qG qG qG qG qG qG qG qG qG qG qG qG qG qG qG qG  qG! qG" qG# qG$ qG% qG& qG' qG( qG) qG* qG+ qG, qG- qG. qG/ qG0 qG1 qG2 qG3 qG4 qG5 qG6 qG7 qG8 qG9ð? qG: qG; qG<ð? qG= qG> qG? qG@ qGA qGBð? qGC qGD qGE qGF qGG qGH qGI qGJ qGK qGL qGM qGNð? qGO qGP qGQ qGR qGS qGT qGU qGV qGW qGX qGYð? qGZ qG[ qG\ð? qG] qG^ qG_ qG` qGa qGb qGc qGd qGe qGf qGg qGh qGi qGj qGk qGl qGm qGn qGo qGp qGq qGr qGs qGt qGu qGv qGw qGx qGy qGz qG{ qG| qG} qG~ qG qG€ qG qG‚ qGƒ qG„ qG… qG† qG‡ qH qH qH qH qH qH qH qH qH qH  qH  qH  qH  qH  qH qH qH qH qH qH qH qH qH qH qH qH qH qH qH qH qH qH qH  qH! qH" qH# qH$ qH% qH& qH' qH( qH) qH* qH+ qH, qH- qH. qH/ qH0 qH1 qH2 qH3 qH4 qH5 qH6 qH7 qH8 qH9 qH: qH;ð? qH< qH= qH>ð? qH? qH@ qHA qHBð? qHC qHD qHE qHF qHG qHH qHI qHJ qHKð? qHL qHM qHN qHO qHP qHQ qHR qHSð? qHT qHUð? qHV qHW qHX qHY qHZ qH[ qH\ qH] qH^ð? qH_ qH` qHa qHb qHc qHd qHe qHf qHg qHh qHi qHj qHk qHl qHm qHn qHo qHp qHq qHr qHs qHt qHu qHv qHw qHx qHy qHz qH{ qH| qH} qH~ qH qH€ qH qH‚ qHƒ qH„ qH… qH† qH‡ qI qI qI qI qI qI qI qI qI qI  qI  qI  qI  qI  qI qI qI qI qI qI qI qI qI qI qI qI qI qI qI qI qI qI qI  qI! qI" qI# qI$ qI% qI& qI' qI( qI) qI* qI+ qI, qI- qI. qI/ qI0 qI1 qI2 qI3 qI4 qI5 qI6 qI7 qI8 qI9 qI: qI; qI< qI=ð? qI> qI? qI@ qIA qIB qIC qID qIE qIF qIG qIH qII qIJ qIK qIL qIM qIN qIO qIPð? qIQ qIR qIS qIT qIU qIV qIWð? qIX qIY qIZ qI[ qI\ qI] qI^ qI_ qI`ð? qIa qIb qIc qId qIe qIf qIg qIh qIi qIj qIk qIl qIm qIn qIo qIp qIq qIr qIs qIt qIu qIv qIw qIx qIy qIz qI{ qI| qI} qI~ qI qI€ qI qI‚ qIƒ qI„ qI… qI† qI‡ qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ  qJ  qJ  qJ  qJ  qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ qJ  qJ! qJ" qJ# qJ$ qJ% qJ& qJ' qJ( qJ) qJ* qJ+ qJ, qJ- qJ. qJ/ qJ0 qJ1 qJ2 qJ3ð? qJ4 qJ5 qJ6 qJ7 qJ8 qJ9 qJ: qJ; qJ< qJ= qJ> qJ?ð? qJ@ qJA qJB qJC qJD qJE qJF qJG qJH qJI qJJ qJK qJLð? qJM qJN qJO qJP qJQ qJR qJS qJT qJU qJV qJW qJX qJY qJZ qJ[ qJ\ qJ] qJ^ qJ_ qJ` qJa qJb qJc qJd qJe qJf qJg qJh qJi qJj qJk qJl qJm qJn qJo qJp qJq qJr qJs qJt qJu qJv qJw qJx qJy qJz qJ{ qJ| qJ} qJ~ qJ qJ€ qJ qJ‚ qJƒ qJ„ qJ… qJ† qJ‡ qK qK qK qK qK qK qK qK qK qK  qK  qK  qK  qK  qK qK qK qK qK qK qK qK qK qK qK qK qK qK qK qK qK qK qK  qK! qK" qK# qK$ qK% qK& qK' qK( qK) qK* qK+ qK, qK-ð? qK. qK/ qK0 qK1 qK2 qK3 qK4 qK5 qK6 qK7 qK8 qK9 qK: qK; qK< qK= qK>ð? qK? qK@ qKAð? qKB qKC qKD qKE qKF qKG qKHð? qKI qKJ qKK qKL qKM qKN qKOð? qKP qKQ qKR qKS qKT qKU qKV qKW qKX qKY qKZ qK[ð? qK\ qK] qK^ qK_ qK` qKað? qKb qKc qKd qKe qKf qKg qKh qKi qKj qKk qKl qKm qKn qKo qKp qKq qKr qKs qKt qKu qKv qKw qKx qKy qKz qK{ qK| qK} qK~ qK qK€ qK qK‚ qKƒ qK„ qK… qK† qK‡ qL qL qL qL qL qL qL qL qL qL  qL  qL  qL  qL  qL qL qL qL qL qL qL qL qL qL qL qL qL qL qL qL qL qL qL  qL! qL" qL# qL$ qL% qL& qL' qL( qL) qL* qL+ qL, qL- qL. qL/ qL0 qL1 qL2 qL3ð? qL4 qL5 qL6 qL7 qL8 qL9 qL: qL; qL< qL= qL> qL?ð? qL@ qLA qLB qLCð? qLD qLE qLF qLG qLH qLI qLJð? qLK qLL qLM qLN qLO qLP qLQ qLR qLS qLT qLU qLV qLW qLX qLY qLZ qL[ qL\ qL]ð? qL^ qL_ qL` qLa qLb qLc qLd qLe qLf qLg qLhð? qLi qLj qLk qLl qLm qLnð? qLo qLp qLq qLr qLs qLt qLu qLv qLw qLx qLy qLz qL{ qL| qL} qL~ qL qL€ qL qL‚ qLƒ qL„ qL… qL† qL‡ qM qM qM qM qM qM qM qM qM qM  qM  qM  qM  qM  qM qM qM qM qM qM qM qM qM qM qM qM qM qM qM qM qM qM qM  qM! qM" qM# qM$ qM% qM& qM' qM( qM) qM* qM+ qM, qM- qM. qM/ qM0 qM1 qM2 qM3 qM4 qM5 qM6ð? qM7 qM8 qM9 qM: qM; qM< qM=ð? qM> qM?ð? qM@ qMA qMB qMC qMD qME qMF qMG qMH qMI qMJ qMK qML qMM qMN qMO qMPð? qMQ qMR qMS qMTð? qMU qMVð? qMW qMX qMY qMZ qM[ qM\ qM] qM^ qM_ qM` qMa qMbð? qMc qMd qMe qMf qMg qMh qMið? qMj qMk qMl qMm qMn qMo qMp qMq qMr qMs qMt qMu qMv qMw qMx qMy qMz qM{ qM| qM} qM~ qM qM€ qM qM‚ qMƒ qM„ qM… qM† qM‡ qN qN qN qN qN qN qN qN qN qN  qN  qN  qN  qN  qN qN qN qN qN qN qN qN qN qN qN qN qN qN qN qN qN qN qN  qN! qN" qN# qN$ qN% qN& qN' qN( qN) qN* qN+ qN, qN- qN. qN/ qN0 qN1 qN2 qN3 qN4 qN5 qN6 qN7ð? qN8 qN9ð? qN: qN; qN< qN= qN> qN? qN@ qNA qNB qNCð? qND qNE qNF qNGð? qNH qNI qNJ qNK qNL qNM qNN qNO qNP qNQ qNR qNS qNT qNU qNV qNW qNX qNYð? qNZ qN[ qN\ qN]ð? qN^ qN_ qN` qNa qNb qNc qNd qNe qNf qNg qNh qNi qNjð? qNk qNl qNm qNn qNo qNp qNq qNr qNs qNt qNu qNv qNw qNx qNy qNz qN{ qN| qN} qN~ qN qN€ qN qN‚ qNƒ qN„ qN… qN† qN‡ qO qO qO qO qO qO qO qO qO qO  qO  qO  qO  qO  qO qO qO qO qO qO qO qO qO qO qO qO qO qO qO qO qO qO qO  qO! qO" qO# qO$ qO% qO& qO' qO( qO) qO* qO+ qO, qO- qO. qO/ qO0 qO1 qO2 qO3 qO4 qO5 qO6 qO7 qO8 qO9 qO: qO; qO< qO= qO> qO? qO@ qOA qOB qOC qOD qOE qOF qOG qOH qOI qOJ qOKð? qOL qOM qON qOO qOP qOQ qOR qOS qOT qOU qOV qOW qOX qOY qOZ qO[ð? qO\ qO] qO^ qO_ qO` qOað? qOb qOc qOdð? qOe qOf qOg qOh qOi qOj qOk qOlð? qOm qOn qOo qOp qOq qOr qOs qOt qOu qOv qOw qOx qOy qOz qO{ qO| qO} qO~ qO qO€ qO qO‚ qOƒ qO„ qO… qO† qO‡ qP qP qP qP qP qP qP qP qP qP  qP  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qe  qe qe qe qe qe qe qe qe qe qe qe qe qe qe qe qe qe qe qe  qe! qe" qe# qe$ qe% qe& qe' qe( qe) qe* qe+ qe, qe- qe. qe/ qe0 qe1 qe2 qe3 qe4 qe5 qe6 qe7 qe8 qe9 qe: qe; qe< qe= qe> qe? qe@ qeA qeB qeC qeD qeE qeF qeG qeH qeI qeJ qeK qeL qeM qeN qeO qePð? qeQ qeR qeS qeT qeU qeV qeW qeX qeY qeZ qe[ qe\ qe] qe^ qe_ qe`ð? qea qeb qec qed qee qef qeg qeh qei qej qek qel qem qen qeo qep qeq qer qes qet qeu qev qew qex qey qez qe{ qe| qe} qe~ qe qe€ qe qe‚ qeƒ qe„ qe… qe† qe‡ qf qf qf qf qf qf qf qf qf qf  qf  qf  qf  qf  qf qf qf qf qf qf qf qf qf qf qf qf qf qf qf qf qf qf qf  qf! qf" qf# qf$ qf% qf& qf' qf( qf) qf* qf+ qf, qf- qf. qf/ qf0 qf1 qf2 qf3 qf4 qf5 qf6 qf7 qf8 qf9 qf: qf; qf< qf= qf> qf? qf@ qfA qfB qfC qfD qfE qfF qfG qfH qfI qfJ qfK qfL qfM qfN qfO qfP qfQ qfR qfS qfT qfU qfV qfW qfX qfY qfZ qf[ qf\ qf] qf^ð? qf_ qf` qfa qfb qfc qfd qfe qff qfg qfh qfi qfj qfk qfl qfm qfn qfo qfp qfqð? qfr qfs qft qfu qfv qfwð? qfx qfy qfz qf{ qf| qf} qf~ qf qf€ qf qf‚ qfƒ qf„ qf…ð? qf† qf‡ qg qg qg qg qg qg qg qg qg qg  qg  qg  qg  qg  qg qg qg qg qg qg qg qg qg qg qg qg qg qg qg qg qg qg qg  qg! qg" qg# qg$ qg% qg& qg' qg( qg) qg* qg+ qg, qg- qg. qg/ qg0 qg1 qg2 qg3 qg4 qg5 qg6 qg7 qg8 qg9 qg: qg; qg< qg= qg> qg? qg@ qgA qgB qgC qgD qgEð? qgF qgG qgH qgI qgJ qgK qgL qgM qgN qgO qgP qgQ qgRð? qgS qgT qgU qgV qgW qgXð? qgY qgZ qg[ qg\ qg] qg^ qg_ qg` qga qgb qgcð? qgd qge qgf qgg qgh qgi qgj qgk qgl qgm qgn qgo qgpð? qgq qgr qgs qgt qgu qgvð? qgw qgx qgy qgz qg{ð? qg| qg} qg~ qg qg€ qg qg‚ qgƒ qg„ qg… qg† qg‡ qh qh qh qh qh qh qh qh qh qh  qh  qh  qh  qh  qh qh qh qh qh qh qh qh qh qh qh qh qh qh qh qh qh qh qh  qh! qh" qh# qh$ qh% qh& qh' qh( qh) qh* qh+ qh, qh- qh. qh/ qh0 qh1 qh2 qh3 qh4 qh5 qh6 qh7 qh8 qh9 qh: qh; qh< qh= qh> qh?ð? qh@ qhA qhB qhC qhD qhE qhF qhG qhH qhI qhJ qhK qhLð? qhM qhN qhO qhP qhQ qhR qhS qhT qhU qhV qhW qhX qhY qhZ qh[ qh\ qh] qh^ qh_ qh` qha qhbð? qhc qhd qhe qhf qhg qhh qhi qhj qhk qhl qhm qhnð? qho qhp qhq qhr qhs qht qhu qhv qhw qhx qhy qhz qh{ qh| qh} qh~ð? qh qh€ qh qh‚ qhƒ qh„ qh… qh†ð? qh‡ qi qi qi qi qi qi qi qi qi qi  qi  qi  qi  qi  qi qi qi qi qi qi qi qi qi qi qi qi qi qi qi qi qi qi qi  qi! qi" qi# qi$ qi% qi& qi' qi( qi) qi* qi+ qi, qi- qi. qi/ qi0 qi1 qi2 qi3 qi4 qi5 qi6 qi7 qi8 qi9 qi: qi; qi< qi= qi> qi? qi@ qiA qiB qiC qiD qiE qiF qiG qiH qiI qiJ qiK qiL qiMð? qiN qiO qiPð? qiQ qiR qiS qiT qiU qiV qiW qiX qiY qiZ qi[ qi\ qi] qi^ qi_ qi`ð? qia qibð? qic qid qie qif qig qih qii qij qik qil qim qin qio qip qiq qir qis qit qiuð? qiv qiw qix qiy qiz qi{ qi|ð? qi} qi~ qi qi€ qi qi‚ qiƒ qi„ qi… qi† qi‡ qj qj qj qj qj qj qj qj qj qj  qj  qj  qj  qj  qj qj qj qj qj qj qj qj qj qj qj qj qj qj qj qj qj qj qj  qj! qj" qj# qj$ qj% qj& qj' qj( qj) qj* qj+ qj, qj- qj. qj/ qj0 qj1 qj2 qj3 qj4 qj5 qj6 qj7 qj8 qj9 qj: qj; qj< qj= qj> qj? qj@ qjA qjB qjC qjD qjE qjF qjG qjH qjI qjJ qjK qjL qjM qjNð? qjO qjP qjQ qjR qjS qjT qjU qjV qjW qjX qjYð? qjZ qj[ qj\ qj]ð? qj^ qj_ qj` qja qjb qjc qjd qje qjf qjg qjh qji qjj qjk qjl qjm qjn qjoð? qjp qjq qjr qjs qjt qju qjv qjw qjx qjyð? qjz qj{ qj| qj} qj~ qj qj€ qj qj‚ qjƒ qj„ qj… qj† qj‡ qk qk qk qk qk qk qk qk qk qk  qk  qk  qk  qk  qk qk qk qk qk qk qk qk qk qk qk qk qk qk qk qk qk qk qk  qk! qk" qk# qk$ qk% qk& qk' qk( qk) qk* qk+ qk, qk- qk. qk/ qk0 qk1 qk2 qk3 qk4 qk5 qk6 qk7 qk8 qk9 qk: qk; qk< qk= qk> qk? qk@ qkA qkB qkC qkD qkE qkF qkG qkH qkI qkJ qkK qkL qkM qkN qkO qkP qkQ qkR qkSð? qkT qkU qkV qkW qkX qkY qkZ qk[ qk\ð? qk] qk^ð? qk_ qk` qka qkb qkc qkd qke qkf qkg qkh qki qkj qkk qkl qkm qkn qko qkp qkqð? qkr qks qkt qku qkv qkw qkxð? qky qkz qk{ qk| qk} qk~ qk qk€ qk qk‚ qkƒ qk„ qk… qk† qk‡ ql ql ql ql ql ql ql ql ql ql  ql  ql  ql  ql  ql ql ql ql ql ql ql ql ql ql ql ql ql ql ql ql ql ql ql  ql! ql" ql# ql$ ql% ql& ql' ql( ql) ql* ql+ ql, ql- ql. ql/ ql0 ql1 ql2 ql3 ql4 ql5 ql6 ql7 ql8 ql9 ql: ql; ql< ql= ql> ql? ql@ qlA qlB qlC qlD qlE qlF qlG qlH qlI qlJ qlK qlL qlM qlN qlOð? qlP qlQ qlR qlS qlT qlU qlV qlW qlX qlY qlZ ql[ ql\ ql] ql^ ql_ ql` qlað? qlb qlc qldð? qle qlf qlg qlh qli qlj qlk qll qlm qln qlo qlp qlq qlr qls qlt qlu qlv qlw qlx qly qlz ql{ ql| ql} ql~ ql ql€ ql ql‚ qlƒ ql„ ql… ql† ql‡ qm qm qm qm qm qm qm qm qm qm  qm  qm  qm  qm  qm qm qm qm qm qm qm qm qm qm qm qm qm qm qm qm qm qm qm  qm! qm" qm# qm$ qm% qm& qm' qm( qm) qm* qm+ qm, qm- qm. qm/ qm0 qm1 qm2 qm3 qm4 qm5 qm6 qm7 qm8 qm9 qm: qm; qm< qm= qm> qm? qm@ qmA qmB qmC qmD qmE qmF qmG qmH qmI qmJ qmK qmL qmM qmN qmO qmP qmQ qmR qmS qmT qmU qmV qmWð? qmX qmY qmZ qm[ qm\ qm] qm^ qm_ qm`ð? qma qmb qmc qmd qme qmf qmg qmh qmi qmj qmk qml qmm qmn qmo qmpð? qmq qmr qms qmt qmuð? qmv qmw qmx qmy qmz qm{ qm| qm} qm~ qm qm€ð? qm qm‚ qmƒ qm„ qm… qm† qm‡ qn qn qn qn qn qn qn qn qn qn  qn  qn  qn  qn  qn qn qn qn qn qn qn qn qn qn qn qn qn qn qn qn qn qn qn  qn! qn" qn# qn$ qn% qn& qn' qn( qn) qn* qn+ qn, qn- qn. qn/ qn0 qn1 qn2 qn3 qn4 qn5 qn6 qn7 qn8 qn9 qn: qn; qn< qn= qn> qn? qn@ qnA qnB qnC qnD qnE qnF qnG qnH qnI qnJ qnK qnLð? qnM qnN qnO qnP qnQ qnR qnS qnT qnU qnV qnW qnX qnY qnZ qn[ qn\ qn]ð? qn^ qn_ qn` qna qnb qnc qnd qne qnf qng qnhð? qni qnj qnk qnl qnm qnn qno qnp qnq qnr qns qnt qnu qnv qnw qnx qnyð? qnz qn{ qn| qn} qn~ qn qn€ qn qn‚ qnƒð? qn„ qn… qn† qn‡ qo qo qo qo qo qo qo qo qo qo  qo  qo  qo  qo  qo qo qo qo qo qo qo qo qo qo qo qo qo qo qo qo qo qo qo  qo! qo" qo# qo$ qo% qo& qo' qo( qo) qo* qo+ qo, qo- qo. qo/ qo0 qo1 qo2 qo3 qo4 qo5 qo6 qo7 qo8 qo9 qo: qo; qo< qo= qo> qo? qo@ qoA qoB qoC qoD qoE qoF qoG qoH qoI qoJ qoK qoL qoM qoN qoO qoP qoQ qoR qoS qoT qoU qoV qoW qoX qoYð? qoZ qo[ qo\ð? qo] qo^ qo_ qo` qoa qob qoc qod qoe qof qog qoh qoi qojð? qok qol qom qon qoo qop qoq qor qos qot qou qov qow qoxð? qoyð? qoz qo{ qo| qo} qo~ qo qo€ qo qo‚ qoƒ qo„ qo… qo† qo‡ qp qp qp qp qp qp qp qp qp qp  qp  qp  qp  qp  qp qp qp qp qp qp qp qp qp qp qp qp qp qp qp qp qp qp qp  qp! qp" qp# qp$ qp% qp& qp' qp( qp) qp* qp+ qp, qp- qp. qp/ qp0 qp1 qp2 qp3 qp4 qp5 qp6 qp7 qp8 qp9 qp: qp; qp< qp= qp> qp? qp@ qpA qpB qpC qpD qpE qpF qpG qpH qpI qpJ qpK qpL qpM qpN qpO qpP qpQ qpRð? qpS qpT qpU qpV qpWð? qpX qpY qpZ qp[ qp\ qp] qp^ qp_ qp` qpa qpb qpc qpd qpe qpf qpgð? qph qpi qpj qpk qpl qpmð? qpn qpo qpp qpq qpr qps qpt qpu qpvð? qpw qpx qpy qpz qp{ qp| qp} qp~ qp qp€ð? qp qp‚ qpƒ qp„ qp… qp† qp‡ qq qq qq qq qq qq qq qq qq qq  qq  qq  qq  qq  qq qq qq qq qq qq qq qq qq qq qq qq qq qq qq qq qq qq qq  qq! qq" qq# qq$ qq% qq& qq' qq( qq) qq* qq+ qq, qq- qq. qq/ qq0 qq1 qq2 qq3 qq4 qq5 qq6 qq7 qq8 qq9 qq: qq; qq< qq= qq> qq? qq@ qqA qqB qqC qqD qqE qqF qqG qqH qqI qqJ qqK qqL qqM qqN qqO qqP qqQ qqR qqS qqT qqU qqV qqW qqX qqY qqZ qq[ qq\ qq] qq^ð? qq_ qq` qqa qqb qqc qqd qqe qqfð? qqg qqh qqi qqj qqkð? qql qqm qqn qqo qqp qqq qqr qqs qqt qqu qqv qqwð? qqxð? qqy qqz qq{ qq| qq} qq~ qq qq€ qq qq‚ qqƒ qq„ qq…ð? qq† qq‡ qr qr qr qr qr qr qr qr qr qr  qr  qr  qr  qr  qr qr qr qr qr qr qr qr qr qr qr qr qr qr qr qr qr qr qr  qr! qr" qr# qr$ qr% qr& qr' qr( qr) qr* qr+ qr, qr- qr. qr/ qr0 qr1 qr2 qr3 qr4 qr5 qr6 qr7 qr8 qr9 qr: 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q‡‡libpysal-4.9.2/libpysal/examples/virginia/virginia_rook.gal000066400000000000000000000047611452177046000241570ustar00rootroot000000000000000 136 virginia POLY_ID 1 4 7 5 4 3 2 4 9 8 6 3 3 4 8 7 2 1 4 1 1 5 4 16 15 7 1 6 6 14 12 11 10 9 2 7 6 16 13 8 5 3 1 8 7 19 13 9 21 2 3 7 9 6 21 18 17 6 2 8 10 3 14 11 6 11 2 10 6 12 1 6 13 5 20 16 8 19 7 14 2 10 6 15 6 25 23 24 31 16 5 16 6 24 20 13 7 5 15 17 2 18 9 18 2 9 17 19 6 26 20 21 27 8 13 20 5 24 19 26 13 16 21 7 33 29 27 28 9 19 8 22 2 32 23 23 8 41 39 38 34 32 31 15 22 24 5 31 20 26 16 15 25 1 15 26 6 36 31 19 27 24 20 27 7 44 36 33 29 21 26 19 28 4 35 33 30 21 29 2 21 27 30 4 43 37 35 28 31 9 56 45 41 40 36 24 26 23 15 32 4 47 23 39 22 33 7 44 35 46 48 27 28 21 34 1 23 35 6 46 37 57 30 28 33 36 6 49 45 44 27 26 31 37 4 51 43 30 35 38 1 23 39 9 64 59 55 54 52 47 41 23 32 40 1 31 41 6 68 52 56 31 23 39 42 1 69 43 3 51 37 30 44 7 60 49 48 63 33 27 36 45 5 58 56 36 49 31 46 7 76 63 48 66 57 35 33 47 6 62 55 53 50 39 32 48 4 63 46 44 33 49 6 61 58 44 60 36 45 50 1 47 51 2 37 43 52 6 77 75 64 68 41 39 53 1 47 54 1 39 55 5 78 62 64 39 47 56 6 79 68 58 45 31 41 57 3 66 46 35 58 6 79 61 72 56 49 45 59 1 39 60 6 67 65 63 73 44 49 61 4 72 67 49 58 62 5 81 78 74 55 47 63 6 76 73 46 48 60 44 64 9 105 99 82 78 77 75 52 39 55 65 2 67 60 66 4 76 71 57 46 67 10 96 93 91 86 72 73 84 60 65 61 68 6 94 77 79 56 41 52 69 1 42 70 3 104 89 83 71 1 66 72 6 90 79 93 67 61 58 73 6 95 84 76 63 67 60 74 4 97 88 81 62 75 3 77 52 64 76 7 92 80 98 66 46 63 73 77 7 105 68 94 111 64 75 52 78 8 106 87 85 81 64 99 55 62 79 7 107 94 72 90 58 56 68 80 5 109 101 98 92 76 81 6 102 97 106 78 74 62 82 1 64 83 4 113 104 88 70 84 7 108 96 95 93 86 73 67 85 1 78 86 2 84 67 87 1 78 88 5 113 110 97 74 83 89 3 100 104 70 90 5 112 107 93 72 79 91 2 96 67 92 2 76 80 93 8 121 112 96 108 84 67 90 72 94 6 111 122 107 79 68 77 95 5 114 108 103 84 73 96 4 84 93 91 67 97 7 118 110 102 81 106 74 88 98 3 109 80 76 99 6 127 125 106 105 64 78 100 5 124 123 116 89 104 101 1 80 102 2 81 97 103 4 134 120 114 95 104 7 124 119 113 83 70 100 89 105 6 135 127 111 77 64 99 106 6 125 118 99 78 97 81 107 5 122 90 112 79 94 108 5 121 114 95 84 93 109 2 80 98 110 5 129 113 118 97 88 111 5 132 122 94 105 77 112 5 122 121 90 93 107 113 6 119 110 129 88 83 104 114 6 134 121 103 120 108 95 115 2 117 126 116 1 100 117 2 126 115 118 6 133 129 106 125 97 110 119 5 136 124 113 129 104 120 4 128 126 103 114 121 5 130 108 114 93 112 122 4 112 107 111 94 123 2 124 100 124 4 119 104 100 123 125 4 127 106 99 118 126 4 128 117 120 115 127 4 131 105 99 125 128 2 126 120 129 5 133 110 118 119 113 130 1 121 131 1 127 132 1 111 133 2 118 129 134 2 103 114 135 1 105 136 1 119 libpysal-4.9.2/libpysal/examples/wmat/000077500000000000000000000000001452177046000177605ustar00rootroot00000000000000libpysal-4.9.2/libpysal/examples/wmat/README.md000066400000000000000000000015201452177046000212350ustar00rootroot00000000000000wmat ==== Datasets used for spatial weights testing ----------------------------------------- * geobugs_scot: spatial weights in GeoBUGS text format. * lattice10x10.shp: Polygon shapefile for 10 * 10 regular lattices. (n=100) * lattice10x10.shx: spatial index. * ohio.swm: spatial weights in ArcGIS SWM format. * rook31.dbf: attribute data. (k=2) * rook31.gal: rook contiguity weights in GAL format. * rook31.shp: Polygon shapefile. (n=3) * rook31.shx: spatial index. * spat-sym-us.mat: spatial weights in MATLAB MAT format. * spat-sym-us.wk1: spatial weights in Lotus Wk1 format. * spdep_listw2WB_columbus: spatial weights in GeoBUGS text format. * stata_full.txt: full spatial weights matrix. * stata_sparse.txt: sparse spatial weights matrix. * wmat.dat: spatial weights in DAT format. * wmat.mtx: spatial weights in Matrix Market MTX format. libpysal-4.9.2/libpysal/examples/wmat/geobugs_scot000066400000000000000000000022441452177046000223700ustar00rootroot00000000000000list( num = c(3, 2, 1, 3, 3, 0, 5, 0, 5, 4, 0, 2, 3, 3, 2, 6, 6, 6, 5, 3, 3, 2, 4, 8, 3, 3, 4, 4, 11, 6, 7, 3, 4, 9, 4, 2, 4, 6, 3, 4, 5, 5, 4, 5, 4, 6, 6, 4, 9, 2, 4, 4, 4, 5, 6, 5 ), adj = c( 19, 9, 5, 10, 7, 12, 28, 20, 18, 19, 12, 1, 17, 16, 13, 10, 2, 29, 23, 19, 17, 1, 22, 16, 7, 2, 5, 3, 19, 17, 7, 35, 32, 31, 29, 25, 29, 22, 21, 17, 10, 7, 29, 19, 16, 13, 9, 7, 56, 55, 33, 28, 20, 4, 17, 13, 9, 5, 1, 56, 18, 4, 50, 29, 16, 16, 10, 39, 34, 29, 9, 56, 55, 48, 47, 44, 31, 30, 27, 29, 26, 15, 43, 29, 25, 56, 32, 31, 24, 45, 33, 18, 4, 50, 43, 34, 26, 25, 23, 21, 17, 16, 15, 9, 55, 45, 44, 42, 38, 24, 47, 46, 35, 32, 27, 24, 14, 31, 27, 14, 55, 45, 28, 18, 54, 52, 51, 43, 42, 40, 39, 29, 23, 46, 37, 31, 14, 41, 37, 46, 41, 36, 35, 54, 51, 49, 44, 42, 30, 40, 34, 23, 52, 49, 39, 34, 53, 49, 46, 37, 36, 51, 43, 38, 34, 30, 42, 34, 29, 26, 49, 48, 38, 30, 24, 55, 33, 30, 28, 53, 47, 41, 37, 35, 31, 53, 49, 48, 46, 31, 24, 49, 47, 44, 24, 54, 53, 52, 48, 47, 44, 41, 40, 38, 29, 21, 54, 42, 38, 34, 54, 49, 40, 34, 49, 47, 46, 41, 52, 51, 49, 38, 34, 56, 45, 33, 30, 24, 18, 55, 27, 24, 20, 18 ), sumNumNeigh = 234)libpysal-4.9.2/libpysal/examples/wmat/lattice10x10.shp000066400000000000000000000326041452177046000226200ustar00rootroot00000000000000' Âè$@$@@ð?ð?ð?ð?ð?ð?@ð?ð?@ð?@ð?@ð?ð?ð?@@ð?@@@ð?@ð?@@@@ð?@@@ð?@ð?@@@@ð?@@@ð?@ð?@@@@ð?@@@ð?@ð?@@@@ð?@@@ð?@ð?@@@@ð? @@ @ð? @ð?@@ @ @ð?"@ @"@ð?"@ð? @ @ @"@ð?$@"@$@ð?$@ð?"@"@ @ð?@ð?ð?ð?ð?@ð?@ð? 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è@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@X@@@@@@@@@@@@@@@libpysal-4.9.2/libpysal/examples/wmat/rook31.shx000066400000000000000000000001741452177046000216240ustar00rootroot00000000000000' >è@@2@v@ºXlibpysal-4.9.2/libpysal/examples/wmat/spat-sym-us.mat000066400000000000000000000006401452177046000226650ustar00rootroot00000000000000MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Wed Sep 17 16:55:32 2008 IMxœ•TQ Â0 Í”} ^DÄ3ì"@¦ ·ðÈv2·&ÍKf¡”¤/iò’t/"ïNdWÎr|Ïqm'ù8m”}({¸]î§ç¹^×ñx ~SvßÉoR-%ÙÞÍZ¬Àè— eߊë9 ©™ "y#Œ Èu‘º²ØŒ¬ÆqÙ’6”ÑjêàšpºR„×DK…@+Û¨òjðÛ%°P„® .À®ýÚ:ä%¯,í<Š0’]¯&CíJ FQþ2”ô'ªZ>Eªk·?µ¤òáNa¹Š@,R{éaøSN†ù/’Vc| ÌÐm‚YË\qjÛÂÓOÉ–ÈÃhsÚ§!¢Mù [ßô³EÄŸÛ,™ +ñÖbulibpysal-4.9.2/libpysal/examples/wmat/spat-sym-us.wk1000066400000000000000000000042551452177046000226140ustar00rootroot00000000000000--/2ÿñHd 1$&%*ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ ñ ñ ñ ñ% ñ ñ ñ ñ' ñ ñ ñ ñ  ñ% ñ& ñ ñ ñ ñ ñ" ñ ñ ñ! ñ ñ) ñ ñ ñ ñ ñ# ñ% ñ  ñ  ñ'  ñ*  ñ-  ñ  ñ  ñ  ñ  ñ,  ñ  ñ  ñ  ñ  ñ  ñ  ñ  ñ  ñ$  ñ,  ñ  ñ  ñ  ñ  ñ  ñ ñ ñ% ñ) ñ+ ñ ñ ñ& ñ ñ ñ ñ! ñ) ñ+ ñ ñ ñ ñ" ñ( ñ  ñ ñ, ñ  ñ ñ$ ñ, ñ ñ ñ ñ% ñ ñ  ñ  ñ  ñ ñ ñ  ñ% ñ  ñ ñ$ ñ- ñ  ñ  ñ ñ$ ñ- ñ ñ ñ  ñ' ñ ñ ñ( ñ ñ ñ! ñ ñ  ñ& ñ' ñ ñ ñ ñ! ñ( ñ ñ ñ$ ñ  ñ ñ ñ! ñ+ ñ  ñ  ñ  ñ  ñ&  ñ! ñ! ñ! ñ! ñ! ñ+! ñ" ñ" ñ# ñ $ ñ$ ñ$ ñ$ ñ$ ñ-$ ñ% ñ% ñ% ñ% ñ% ñ% ñ)% ñ& ñ& ñ& ñ & ñ' ñ ' ñ' ñ' ñ-' ñ( ñ( ñ( ñ) ñ) ñ) ñ%) ñ+) ñ * ñ+ ñ+ ñ+ ñ!+ ñ)+ ñ , ñ , ñ, ñ, ñ - ñ- ñ- ñ$- ñ'-libpysal-4.9.2/libpysal/examples/wmat/spdep_listw2WB_columbus000066400000000000000000000100071452177046000244620ustar00rootroot00000000000000list(adj = c(2, 3, 1, 3, 4, 1, 2, 4, 5, 2, 3, 5, 8, 3, 4, 6, 8, 9, 11, 15, 5, 9, 8, 12, 13, 14, 4, 5, 7, 11, 12, 13, 5, 6, 10, 15, 20, 22, 25, 26, 9, 17, 20, 22, 5, 8, 12, 15, 16, 7, 8, 11, 13, 14, 16, 7, 8, 12, 14, 7, 12, 13, 16, 18, 19, 5, 9, 11, 16, 25, 26, 11, 12, 14, 15, 18, 24, 25, 10, 20, 23, 14, 16, 19, 24, 14, 18, 24, 9, 10, 17, 22, 23, 27, 32, 33, 35, 40, 24, 30, 34, 9, 10, 20, 26, 27, 28, 17, 20, 32, 16, 18, 19, 21, 25, 29, 30, 9, 15, 16, 24, 26, 28, 29, 9, 15, 22, 25, 28, 29, 20, 22, 28, 33, 22, 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0.2000libpysal-4.9.2/libpysal/graph/000077500000000000000000000000001452177046000162735ustar00rootroot00000000000000libpysal-4.9.2/libpysal/graph/__init__.py000066400000000000000000000000561452177046000204050ustar00rootroot00000000000000from .base import Graph, read_parquet # noqa libpysal-4.9.2/libpysal/graph/_contiguity.py000066400000000000000000000265171452177046000212150ustar00rootroot00000000000000from collections import defaultdict import geopandas import numpy import pandas import shapely from packaging.version import Version from ._utils import _neighbor_dict_to_edges, _resolve_islands, _validate_geometry_input GPD_013 = Version(geopandas.__version__) >= Version("0.13") _VALID_GEOMETRY_TYPES = ["Polygon", "MultiPolygon", "LineString", "MultiLineString"] def _vertex_set_intersection(geoms, rook=True, ids=None, by_perimeter=False): """ Use a hash map inversion to construct a graph Parameters --------- geoms : geopandas.GeoDataFrame, geopandas.GeoSeries, numpy.array The container for the geometries to compute contiguity. Regardless of the containing type, the geometries within the container must be Polygons or MultiPolygons. rook : bool (default: True) whether to compute vertex set intersection contiguity by edge or by point. By default, vertex set contiguity is computed by edge. This means that at least two adjacent vertices on the polygon boundary must be shared. ids : numpy.ndarray (default: None) names to use for indexing the graph constructed from geoms. If None (default), an index is extracted from `geoms`. If `geoms` has no index, a pandas.RangeIndex is constructed. by_perimeter : bool (default: False) whether to compute perimeter-weighted contiguity. By default, this returns the raw length of perimeter overlap betwen contiguous polygons or lines. In the case of LineString/MultiLineString input geoms, this is likely to result in empty weights, where all observations are isolates. """ _, ids, geoms = _validate_geometry_input( geoms, ids=ids, valid_geometry_types=_VALID_GEOMETRY_TYPES ) # initialise the target map graph = {} for idx in ids: graph[idx] = {idx} # get all of the vertices for the input vertices, offsets = shapely.get_coordinates(geoms.geometry, return_index=True) # use offsets from exploded geoms to create edges to avoid a phantom edge between # parts of multipolygon _, single_part_offsets = shapely.get_coordinates( geoms.geometry.explode(ignore_index=True), return_index=True ) # initialise the hashmap we want to invert vert_to_geom = defaultdict(set) # populate the hashmap we intend to invert if rook: for i, vertex in enumerate(vertices[:-1]): if single_part_offsets[i] != single_part_offsets[i + 1]: continue edge = tuple(sorted([tuple(vertex), tuple(vertices[i + 1])])) # edge to {polygons, with, this, edge} vert_to_geom[edge].add(offsets[i]) else: for i, vertex in enumerate(vertices): # vertex to {polygons, with, this, vertex} vert_to_geom[tuple(vertex)].add(offsets[i]) # invert vert_to_geom for nexus in vert_to_geom.values(): if len(nexus) < 2: continue nexus_names = {ids[ix] for ix in nexus} for geom_ix in nexus: gid = ids[geom_ix] graph[gid] |= nexus_names graph[gid].remove(gid) heads, tails, weights = _neighbor_dict_to_edges(graph) if by_perimeter: weights = numpy.zeros(len(heads), dtype=float) non_isolates = heads != tails # can't pass isolates to _perimeter_weigths weights[non_isolates] = _perimeter_weights( geoms, heads[non_isolates], tails[non_isolates] ) return heads, tails, weights def _queen(geoms, ids=None, by_perimeter=False): """ Construct queen contiguity using point-set relations. Queen contiguity occurs when two polygons touch at exactly a point. Overlapping polygons will not be considered as neighboring under this rule, since contiguity is strictly planar. Parameters ---------- geoms : geopandas.GeoDataFrame, geopandas.GeoSeries, numpy.array The container for the geometries to compute contiguity. Regardless of the containing type, the geometries within the container must be Polygons or MultiPolygons. ids : numpy.ndarray (default: None) names to use for indexing the graph constructed from geoms. If None (default), an index is extracted from `geoms`. If `geoms` has no index, a pandas.RangeIndex is constructed. by_perimeter : bool (default: False) whether to compute perimeter-weighted contiguity. By default, this returns the raw length of perimeter overlap betwen contiguous polygons or lines. In the case of LineString/MultiLineString input geoms, this is likely to result in empty weights, where all observations are isolates. Returns ------- (heads, tails, weights) : three vectors describing the links in the queen contiguity graph, with islands represented as a self-loop with zero weight. """ _, ids, geoms = _validate_geometry_input( geoms, ids=ids, valid_geometry_types=_VALID_GEOMETRY_TYPES ) heads_ix, tails_ix = shapely.STRtree(geoms).query(geoms, predicate="touches") heads, tails = ids[heads_ix], ids[tails_ix] if by_perimeter: weights = _perimeter_weights(geoms, heads, tails) else: weights = numpy.ones_like(heads_ix, dtype=int) return _resolve_islands(heads, tails, ids, weights=weights) def _rook(geoms, ids=None, by_perimeter=False): """ Construct rook contiguity using point-set relations. Rook contiguity occurs when two polygons touch over at least one edge. Overlapping polygons will not be considered as neighboring under this rule, since contiguity is strictly planar. Parameters ---------- geoms : geopandas.GeoDataFrame, geopandas.GeoSeries, numpy.array The container for the geometries to compute contiguity. Regardless of the containing type, the geometries within the container must be Polygons or MultiPolygons. ids : numpy.ndarray (default: None) names to use for indexing the graph constructed from geoms. If None (default), an index is extracted from `geoms`. If `geoms` has no index, a pandas.RangeIndex is constructed. by_perimeter : bool (default: False) whether to compute perimeter-weighted contiguity. By default, this returns the raw length of perimeter overlap betwen contiguous polygons or lines. In the case of LineString/MultiLineString input geoms, this is likely to result in empty weights, where all observations are isolates. Returns ------- (heads, tails, weights) : three vectors describing the links in the rook contiguity graph, with islands represented as a self-loop with zero weight. """ _, ids, geoms = _validate_geometry_input( geoms, ids=ids, valid_geometry_types=_VALID_GEOMETRY_TYPES ) heads_ix, tails_ix = shapely.STRtree(geoms).query(geoms) mask = shapely.relate_pattern( geoms.values[heads_ix], geoms.values[tails_ix], "F***1****" ) heads, tails = ids[heads_ix][mask], ids[tails_ix][mask] if by_perimeter: weights = _perimeter_weights(geoms, heads, tails) if not by_perimeter: weights = numpy.ones_like(heads, dtype=int) return _resolve_islands(heads, tails, ids, weights) def _perimeter_weights(geoms, heads, tails): """ Compute the perimeter of neighbor pairs for edges describing a contiguity graph. Note that this result will be incorrect if the head and tail polygon overlap. If they do overlap, it is an "invalid" contiguity, so the length of the perimeter of the intersection may not express the correct value for relatedness in the contiguity graph. This is a private method, so strict conditions on input data are expected. """ intersection = shapely.intersection(geoms[heads].values, geoms[tails].values) geom_types = shapely.get_type_id(shapely.get_parts(intersection)) # check if the intersection resulted in (Multi)Polygon if numpy.isin(geom_types, [3, 6]).any(): raise ValueError( "Some geometries overlap. Perimeter weights require planar coverage." ) return shapely.length(intersection) def _block_contiguity(regimes, ids=None): """Construct spatial weights for regime neighbors. Block contiguity structures are relevant when defining neighbor relations based on membership in a regime. For example, all counties belonging to the same state could be defined as neighbors, in an analysis of all counties in the US. Parameters ---------- regimes : list-like list-like of regimes. If pandas.Series, its index is used to encode Graph ids : list-like, optional ordered sequence of IDs for the observations to be used as an index, by default None. If ``regimes`` is not a pandas.Series and ids=None, range index is used. Returns ------- dict dictionary of neighbors """ regimes = pandas.Series(regimes, index=ids) rids = regimes.unique() neighbors = {} for rid in rids: members = regimes.index[regimes == rid].values for member in members: neighbors[member] = members[members != member] return neighbors def _fuzzy_contiguity( geoms, ids, tolerance=None, buffer=None, predicate="intersects", ): """Fuzzy contiguity builder Parameters ---------- geoms : array-like of shapely.Geometry objects Could be geopandas.GeoSeries or geopandas.GeoDataFrame, in which case ids need to match geoms.index. ids : array ids to be used index of the adjacency tolerance : float, optional The percentage of the length of the minimum side of the bounding rectangle for the ``geoms`` to use in determining the buffering distance. Either ``tolerance`` or ``buffer`` may be specified but not both. By default None. buffer : float, optional Exact buffering distance in the units of ``geoms.crs``. Either ``tolerance`` or ``buffer`` may be specified but not both. By default None. predicate : str, optional The predicate to use for determination of neighbors. Default is 'intersects'. If None is passed, neighbours are determined based on the intersection of bounding boxes. See the documentation of ``geopandas.GeoSeries.sindex.query`` for allowed predicates. Returns ------- tuple tuple of ``heads``, ``tails``, ``weights`` arrays """ if buffer is not None and tolerance is not None: raise ValueError( "Only one of `tolerance` and `buffer` can be speciifed, not both." ) if not isinstance(geoms, geopandas.base.GeoPandasBase): geoms = geopandas.GeoSeries(geoms, index=ids) if tolerance is not None: minx, miny, maxx, maxy = geoms.total_bounds buffer = tolerance * 0.5 * abs(min(maxx - minx, maxy - miny)) if buffer is not None: geoms = geoms.buffer(buffer) # query tree based on set predicate if GPD_013: head, tail = geoms.sindex.query(geoms.geometry, predicate=predicate) else: head, tail = geoms.sindex.query_bulk(geoms.geometry, predicate=predicate) # remove self hits itself = head == tail heads = ids[head[~itself]] tails = ids[tail[~itself]] weights = numpy.ones_like(heads, dtype=int) return _resolve_islands(heads, tails, ids.values, weights=weights) libpysal-4.9.2/libpysal/graph/_kernel.py000066400000000000000000000310611452177046000202650ustar00rootroot00000000000000import numpy from scipy import optimize, sparse, spatial, stats from ._utils import ( _build_coincidence_lookup, _jitter_geoms, _resolve_islands, _sparse_to_arrays, _validate_geometry_input, ) try: from sklearn import metrics, neighbors HAS_SKLEARN = True except ImportError: HAS_SKLEARN = False _VALID_GEOMETRY_TYPES = ["Point"] def _triangular(distances, bandwidth): u = numpy.clip(distances / bandwidth, 0, 1) return 1 - u def _parabolic(distances, bandwidth): u = numpy.clip(distances / bandwidth, 0, 1) return 0.75 * (1 - u**2) def _gaussian(distances, bandwidth): u = distances / bandwidth return numpy.exp(-((u / 2) ** 2)) / (numpy.sqrt(2) * numpy.pi) def _bisquare(distances, bandwidth): u = numpy.clip(distances / bandwidth, 0, 1) return (15 / 16) * (1 - u**2) ** 2 def _cosine(distances, bandwidth): u = numpy.clip(distances / bandwidth, 0, 1) return (numpy.pi / 4) * numpy.cos(numpy.pi / 2 * u) def _boxcar(distances, bandwidth): r = (distances < bandwidth).astype(int) return r def _identity(distances, _): return distances _kernel_functions = { "triangular": _triangular, "parabolic": _parabolic, "gaussian": _gaussian, "bisquare": _bisquare, "cosine": _cosine, "boxcar": _boxcar, "discrete": _boxcar, "identity": _identity, None: _identity, } def _kernel( coordinates, bandwidth=None, metric="euclidean", kernel="gaussian", k=None, ids=None, p=2, taper=True, coincident="raise", ): """ Compute a kernel function over a distance matrix. Paramters --------- coordinates : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries over which to compute a kernel. If a geopandas.Geo* object is provided, the .geometry attribute is used. If a numpy.ndarray with a geometry dtype is used, then the coordinates are extracted and used. bandwidth : float (default: None) distance to use in the kernel computation. Should be on the same scale as the input coordinates. metric : string or callable (default: 'euclidean') distance function to apply over the input coordinates. Supported options depend on whether or not scikit-learn is installed. If so, then any distance function supported by scikit-learn is supported here. Otherwise, only euclidean, minkowski, and manhattan/cityblock distances are admitted. kernel : string or callable (default: 'gaussian') kernel function to apply over the distance matrix computed by `metric`. The following kernels are supported: - triangular: - parabolic: - gaussian: - bisquare: - cosine: - boxcar/discrete: all distances less than `bandwidth` are 1, and all other distances are 0 - identity/None : do nothing, weight similarity based on raw distance - callable : a user-defined function that takes the distance vector and the bandwidth and returns the kernel: kernel(distances, bandwidth) k : int (default: None) number of nearest neighbors used to truncate the kernel. This is assumed to be constant across samples. If None, no truncation is conduted. ids : numpy.narray (default: None) ids to use for each sample in coordinates. Generally, construction functions that are accessed via Graph.build_kernel() will set this automatically from the index of the input. Do not use this argument directly unless you intend to set the indices separately from your input data. Otherwise, use data.set_index(ids) to ensure ordering is respected. If None, then the index from the input coordinates will be used. p : int (default: 2) parameter for minkowski metric, ignored if metric != "minkowski". taper : bool (default: True) remove links with a weight equal to zero """ if metric != "precomputed": coordinates, ids, _ = _validate_geometry_input( coordinates, ids=ids, valid_geometry_types=_VALID_GEOMETRY_TYPES ) else: assert ( coordinates.shape[0] == coordinates.shape[1] ), "coordinates should represent a distance matrix if metric='precomputed'" if ids is None: ids = numpy.arange(coordinates.shape[0]) if ( metric == "haversine" and not ( (coordinates[:, 0] > -180) & (coordinates[:, 0] < 180) & (coordinates[:, 1] > -90) & (coordinates[:, 1] < 90) ).all() ): raise ValueError( "'haversine' metric is limited to the range of latitude coordinates " "(-90, 90) and the range of longitude coordinates (-180, 180)." ) if k is not None: if metric != "precomputed": d = _knn(coordinates, k=k, metric=metric, p=p, coincident=coincident) else: d = coordinates * (coordinates.argsort(axis=1, kind="stable") < (k + 1)) else: if metric != "precomputed": dist_kwds = {} if metric == "minkowski": dist_kwds["p"] = p if HAS_SKLEARN: sq = metrics.pairwise_distances( coordinates, coordinates, metric=metric, **dist_kwds ) else: if metric not in ("euclidean", "manhattan", "cityblock", "minkowski"): raise ValueError( f"metric {metric} is not supported by scipy, and scikit-learn " "could not be imported." ) d = spatial.distance.pdist(coordinates, metric=metric, **dist_kwds) sq = spatial.distance.squareform(d) # ensure that self-distance is dropped but 0 between co-located pts not # get data and ids for sparse constructor data = sq.flatten() i = numpy.tile(numpy.arange(sq.shape[0]), sq.shape[0]) j = numpy.repeat(numpy.arange(sq.shape[0]), sq.shape[0]) # remove diagonal data = numpy.delete(data, numpy.arange(0, data.size, sq.shape[0] + 1)) i = numpy.delete(i, numpy.arange(0, i.size, sq.shape[0] + 1)) j = numpy.delete(j, numpy.arange(0, j.size, sq.shape[0] + 1)) # construct sparse d = sparse.csc_array((data, (i, j))) else: d = sparse.csc_array(coordinates) if bandwidth is None: bandwidth = numpy.percentile(d.data, 25) elif bandwidth == "auto": if (kernel == "identity") or (kernel is None): bandwidth = numpy.nan # ignored by identity else: bandwidth = _optimize_bandwidth(d, kernel) if callable(kernel): d.data = kernel(d.data, bandwidth) else: d.data = _kernel_functions[kernel](d.data, bandwidth) if taper: d.eliminate_zeros() heads, tails, weights = _sparse_to_arrays(d, ids=ids) return _resolve_islands(heads, tails, ids, weights) def _knn(coordinates, metric="euclidean", k=1, p=2, coincident="raise"): """internal function called only within _kernel, never directly to build KNN""" coordinates, ids, geoms = _validate_geometry_input( coordinates, ids=None, valid_geometry_types=_VALID_GEOMETRY_TYPES ) n_coincident, coincident_lut = _build_coincidence_lookup(geoms) max_at_one_site = coincident_lut.input_index.str.len().max() n_samples, _ = coordinates.shape if max_at_one_site <= k: if metric == "haversine": # sklearn haversine works with (lat,lng) in radians... coordinates = numpy.fliplr(numpy.deg2rad(coordinates)) query = _prepare_tree_query(coordinates, metric, p=p) d_linear, ixs = query(coordinates, k=k + 1) self_ix, neighbor_ix = ixs[:, 0], ixs[:, 1:] d_linear = d_linear[:, 1:] self_ix_flat = numpy.repeat(self_ix, k) neighbor_ix_flat = neighbor_ix.flatten() d_linear_flat = d_linear.flatten() if metric == "haversine": d_linear_flat * 6371 # express haversine distances in kilometers d = sparse.csr_array( (d_linear_flat, (self_ix_flat, neighbor_ix_flat)), shape=(n_samples, n_samples), ) return d else: if coincident == "raise": raise ValueError( f"There are {len(coincident_lut)} unique locations in the dataset, " f"but {len(coordinates)} observations. At least one of these sites " f"has {max_at_one_site} points, more than the {k} nearest neighbors " f"requested. This means there are more than {k} points in the same " "location, which makes this graph type undefined. To address " "this issue, consider setting `coincident='clique'` or consult the " "documentation about coincident points." ) if coincident == "jitter": # force re-jittering over and over again until the coincidence is broken return _knn( _jitter_geoms(coordinates, geoms)[-1], metric=metric, k=k, p=p, coincident="jitter", ) if coincident == "clique": raise NotImplementedError( "clique-based resolver of coincident points is not yet implemented." ) # # implicit coincident == "clique" # heads, tails, weights = _sparse_to_arrays( # _knn( # coincident_lut.geometry, # metric=metric, # k=k, # p=p, # coincident="raise" # ) # ) # adjtable = pandas.DataFrame.from_dict( # dict(focal=heads, neighbor=tails, weight=weights) # ) # adjtable = _induce_cliques(adjtable, coincident_lut, fill_value=0) # return sparse.csr_array( # adjtable.weight.values, # (adjtable.focal.values, adjtable.neighbor.values), # shape=(n_samples, n_samples), # ) raise ValueError( f"'{coincident}' is not a valid option. Use one of " "['raise', 'jitter', 'clique']." ) def _distance_band(coordinates, threshold, ids=None): coordinates, ids, _ = _validate_geometry_input( coordinates, ids=ids, valid_geometry_types=_VALID_GEOMETRY_TYPES ) tree = spatial.KDTree(coordinates) sp = sparse.csr_array(tree.sparse_distance_matrix(tree, threshold)) return sp def _prepare_tree_query(coordinates, metric, p=2): """ Construct a tree query function relevant to the input metric. Prefer scikit-learn trees if they are available. """ if HAS_SKLEARN: dist_kwds = {} if metric == "minkowski": dist_kwds["p"] = p if metric in neighbors.VALID_METRICS["kd_tree"]: tree = neighbors.KDTree else: tree = neighbors.BallTree return tree(coordinates, metric=metric, **dist_kwds).query else: if metric in ("euclidean", "manhattan", "cityblock", "minkowski"): tree_ = spatial.KDTree(coordinates) p = {"euclidean": 2, "manhattan": 1, "cityblock": 1, "minkowski": p}[metric] def query(target, k): return tree_.query(target, k=k, p=p) return query else: raise ValueError( f"metric {metric} is not supported by scipy, and scikit-learn could " "not be imported" ) def _optimize_bandwidth(d, kernel): """ Optimize the bandwidth as a function of entropy for a given kernel function. This ensures that the entropy of the kernel is maximized for a given distance matrix. This will result in the smoothing that provide the most uniform distribution of kernel values, which is a good proxy for a "moderate" level of smoothing. """ kernel_function = _kernel_functions.get(kernel, kernel) assert callable(kernel_function), ( f"kernel {kernel} was not in supported kernel types " f"{_kernel_functions.keys()} or callable" ) def _loss(bandwidth, d=d, kernel_function=kernel_function): k_u = kernel_function(d.data, bandwidth) bins, _ = numpy.histogram(k_u, bins=int(d.shape[0] ** 0.5), range=(0, 1)) return -stats.entropy(bins / bins.sum()) xopt = optimize.minimize_scalar( _loss, bounds=(0, d.data.max() * 2), method="bounded" ) return xopt.x libpysal-4.9.2/libpysal/graph/_parquet.py000066400000000000000000000036271452177046000204750ustar00rootroot00000000000000import json import libpysal def _to_parquet(graph_obj, destination, **kwargs): """Save adjacency as a Parquet table and add custom metadata Metadata contain transformation and the libpysal version used to save the file. This allows lossless Parquet IO. Parameters ---------- graph_obj : Graph Graph to be saved destination : str | pyarrow.NativeFile path or any stream supported by pyarrow **kwargs additional keyword arguments passed to pyarrow.parquet.write_table """ try: import pyarrow as pa import pyarrow.parquet as pq except (ImportError, ModuleNotFoundError): raise ImportError("pyarrow is required for `to_parquet`.") from None table = pa.Table.from_pandas(graph_obj._adjacency.to_frame()) meta = table.schema.metadata d = {"transformation": graph_obj.transformation, "version": libpysal.__version__} meta[b"libpysal"] = json.dumps(d).encode("utf-8") schema = table.schema.with_metadata(meta) pq.write_table(table.cast(schema), destination, **kwargs) def _read_parquet(source, **kwargs): """Read libpysal-saved Graph object from Parquet Parameters ---------- source : str | pyarrow.NativeFile path or any stream supported by pyarrow **kwargs additional keyword arguments passed to pyarrow.parquet.read_table Returns ------- tuple tuple of adjacency table and transformation """ try: import pyarrow.parquet as pq except (ImportError, ModuleNotFoundError): raise ImportError("pyarrow is required for `read_parquet`.") from None table = pq.read_table(source, **kwargs) if b"libpysal" in table.schema.metadata: meta = json.loads(table.schema.metadata[b"libpysal"]) transformation = meta["transformation"] else: transformation = "O" return table.to_pandas()["weight"], transformation libpysal-4.9.2/libpysal/graph/_plotting.py000066400000000000000000000177341452177046000206600ustar00rootroot00000000000000import geopandas as gpd import numpy as np import pandas as pd import shapely def _plot( graph_obj, gdf, focal=None, nodes=True, color="k", edge_kws=None, node_kws=None, focal_kws=None, ax=None, figsize=None, limit_extent=False, ): """Plot edges and nodes of the Graph Creates a ``maptlotlib`` plot based on the topology stored in the Graph and spatial location defined in ``gdf``. Parameters ---------- graph_obj : Graph Graph to be plotted gdf : geopandas.GeoDataFrame Geometries indexed using the same index as Graph. Geometry types other than points are converted to centroids encoding start and end point of Graph edges. focal : hashable | array-like[hashable] | None, optional ID or an array-like of IDs of focal geometries whose weights shall be plotted. If None, all weights from all focal geometries are plotted. By default None nodes : bool, optional Plot nodes as points. Nodes are plotted using zorder=2 to show them on top of the edges. By default True color : str, optional The color of all objects, by default "k" edge_kws : dict, optional Keyword arguments dictionary to send to ``LineCollection``, which provides fine-grained control over the aesthetics of the edges in the plot. By default None node_kws : dict, optional Keyword arguments dictionary to send to ``ax.scatter``, which provides fine-grained control over the aesthetics of the nodes in the plot. By default None focal_kws : dict, optional Keyword arguments dictionary to send to ``ax.scatter``, which provides fine-grained control over the aesthetics of the focal nodes in the plot on top of generic ``node_kws``. Values of ``node_kws`` are updated from ``focal_kws``. Ignored if ``focal=None``. By default None ax : matplotlib.axes.Axes, optional Axis on which to plot the weights. If None, a new figure and axis are created. By default None figsize : tuple, optional figsize used to create a new axis. By default None limit_extent : bool, optional limit the extent of the axis to the extent of the plotted graph, by default False Returns ------- matplotlib.axes.Axes Axis with the resulting plot """ try: import matplotlib.pyplot as plt from matplotlib import collections except (ImportError, ModuleNotFoundError) as err: raise ImportError("matplotlib is required for `plot`.") from err if ax is None: f, ax = plt.subplots(figsize=figsize) if node_kws is not None: if "color" not in node_kws: node_kws["color"] = color else: node_kws = {"color": color} if edge_kws is not None: if "color" not in edge_kws: edge_kws["color"] = color else: edge_kws = {"color": color} # get array of coordinates in the order reflecting graph_obj._adjacency.index.codes # we need to work on int position to allow fast filtering of duplicated edges and # cannot rely on gdf remaining in the same order between Graph creation and # plotting coords = shapely.get_coordinates(gdf.reindex(graph_obj.unique_ids).centroid) if focal is not None: if not pd.api.types.is_list_like(focal): focal = [focal] subset = graph_obj._adjacency[focal] codes = subset.index.codes else: codes = graph_obj._adjacency.index.codes # avoid plotting both ij and ji edges = np.unique(np.sort(np.column_stack([codes]).T, axis=1), axis=0) lines = coords[edges] ax.add_collection(collections.LineCollection(lines, **edge_kws)) if limit_extent: xm, ym = lines.min(axis=0).min(axis=0) xx, yx = lines.max(axis=0).max(axis=0) x_margin = (xx - xm) * 0.05 y_margin = (yx - ym) * 0.05 ax.set_xlim(xm - x_margin, xx + x_margin) ax.set_ylim(ym - y_margin, yx + y_margin) else: ax.autoscale_view() if nodes: if focal is not None: used_focal = coords[np.unique(subset.index.codes[0])] used_neighbor = coords[np.unique(subset.index.codes[1])] ax.scatter(used_neighbor[:, 0], used_neighbor[:, 1], **node_kws, zorder=2) if focal_kws is None: focal_kws = {} ax.scatter( used_focal[:, 0], used_focal[:, 1], **dict(node_kws, **focal_kws), zorder=3, ) else: ax.scatter(coords[:, 0], coords[:, 1], **node_kws, zorder=2) return ax def _explore_graph( g, gdf, focal=None, nodes=True, color="black", edge_kws=None, node_kws=None, focal_kws=None, m=None, **kwargs, ): """Plot graph as an interactive Folium Map Parameters ---------- g : libpysal.Graph graph to be plotted gdf : geopandas.GeoDataFrame geodataframe used to instantiate to Graph focal : list, optional subset of focal observations to plot in the map, by default None. If none, all relationships are plotted nodes : bool, optional whether to display observations as nodes in the map, by default True color : str, optional color applied to nodes and edges, by default "black" edge_kws : dict, optional additional keyword arguments passed to geopandas explore function when plotting edges, by default None node_kws : dict, optional additional keyword arguments passed to geopandas explore function when plotting nodes, by default None focal_kws : dict, optional additional keyword arguments passed to geopandas explore function when plotting focal observations, by default None. Only applicable when passing a subset of nodes with the `focal` argument m : Folilum.Map, optional folium map objecto to plot on top of, by default None **kwargs : dict, optional additional keyword arguments are passed directly to geopandas.explore, when ``m=None`` by default None Returns ------- folium.Map folium map """ geoms = gdf.centroid.reindex(g.unique_ids) if node_kws is not None: if "color" not in node_kws: node_kws["color"] = color else: node_kws = {"color": color} if focal_kws is not None: if "color" not in node_kws: focal_kws["color"] = color else: focal_kws = {"color": color} if edge_kws is not None: if "color" not in edge_kws: edge_kws["color"] = color else: edge_kws = {"color": color} coords = shapely.get_coordinates(geoms) if focal is not None: if not pd.api.types.is_list_like(focal): focal = [focal] subset = g._adjacency[focal] codes = subset.index.codes adj = subset else: codes = g._adjacency.index.codes adj = g._adjacency # avoid plotting both ij and ji edges, indices = np.unique( np.sort(np.column_stack([codes]).T, axis=1), return_index=True, axis=0 ) lines = coords[edges] lines = gpd.GeoSeries( shapely.linestrings(lines), crs=gdf.crs, ) adj = adj.iloc[indices].reset_index() edges = gpd.GeoDataFrame(adj, geometry=lines)[ ["focal", "neighbor", "weight", "geometry"] ] m = ( edges.explore(m=m, **edge_kws) if m is not None else edges.explore(**edge_kws, **kwargs) ) if nodes is True: if focal is not None: # destinations geoms.iloc[np.unique(subset.index.codes[1])].explore(m=m, **node_kws) if focal_kws is None: focal_kws = {} # focals geoms.iloc[np.unique(subset.index.codes[0])].explore( m=m, **dict(node_kws, **focal_kws) ) else: geoms.explore(m=m, **node_kws) return m libpysal-4.9.2/libpysal/graph/_set_ops.py000066400000000000000000000151401452177046000204610ustar00rootroot00000000000000import numpy as np import pandas from packaging.version import Version from ._utils import _resolve_islands class SetOpsMixin: """ This implements common useful set operations on weights and dunder methods. """ # dunders def __le__(self, other): # <= return self.issubgraph(other) def __ge__(self, other): # >= return other.issubgraph(self) def __lt__(self, other): # < return self.issubgraph(other) & (len(self) < len(other)) def __gt__(self, other): # > return other.issubgraph(self) & (len(self) > len(other)) def __eq__(self, other): # == return self.equals(other) def __ne__(self, other): # != return not self.equals(other) def __and__(self, other): # & return self.intersection(other) def __or__(self, other): # | return self.union(other) def __xor__(self, other): # ^ return self.symmetric_difference(other) def __iand__(self, other): raise TypeError("Graphs are immutable.") def __ior__(self, other): raise TypeError("Graphs are immutable.") def __len__(self): return self.n_edges # methods def intersects(self, right): """ Returns True if left and right share at least one link, irrespective of weights value. """ intersection = self._adjacency.index.drop(self.isolates).intersection( right._adjacency.index.drop(right.isolates) ) if len(intersection) > 0: return True return False def intersection(self, right): """ Returns a binary Graph, that includes only those neighbor pairs that exist in both left and right. """ from .base import Graph intersection = self._adjacency.index.drop(self.isolates).intersection( right._adjacency.index.drop(right.isolates) ) return Graph.from_arrays( *_resolve_islands( intersection.get_level_values("focal"), intersection.get_level_values("neighbor"), self.unique_ids, np.ones(intersection.shape[0], dtype=np.int8), ) ) def symmetric_difference(self, right): """ Filter out links that are in both left and right Graph objects. """ from .base import Graph if not (self.unique_ids == right.unique_ids).all(): raise ValueError( "Cannot do symmetric difference of Graphs that are based on " "different sets of unique IDs." ) sym_diff = self._adjacency.index.drop(self.isolates).symmetric_difference( right._adjacency.index.drop(right.isolates) ) return Graph.from_arrays( *_resolve_islands( sym_diff.get_level_values("focal"), sym_diff.get_level_values("neighbor"), self.unique_ids, np.ones(sym_diff.shape[0], dtype=np.int8), ) ) def union(self, right): """ Provide the union of two Graph objects, collecing all links that are in either graph. """ from .base import Graph if not (self.unique_ids == right.unique_ids).all(): raise ValueError( "Cannot do union of Graphs that are " "based on different sets of unique IDs." ) union = self._adjacency.index.drop(self.isolates).union( right._adjacency.index.drop(right.isolates) ) return Graph.from_arrays( *_resolve_islands( union.get_level_values("focal"), union.get_level_values("neighbor"), self.unique_ids, np.ones(union.shape[0], dtype=np.int8), ) ) def difference(self, right): """ Provide the set difference between the graph on the left and the graph on the right. This returns all links in the left graph that are not in the right graph. """ from .base import Graph diff = self._adjacency.index.drop(self.isolates).difference( right._adjacency.index.drop(right.isolates) ) return Graph.from_arrays( *_resolve_islands( diff.get_level_values("focal"), diff.get_level_values("neighbor"), self.unique_ids, np.ones(diff.shape[0], dtype=np.int8), ) ) def issubgraph(self, right): """ Return True if every link in the left Graph also occurs in the right Graph. This requires both Graphs are labeled equally. Isolates are ignored. """ join = ( self._adjacency.drop(self.isolates) .reset_index(level=1) .merge( right._adjacency.drop(right.isolates).reset_index(level=1), on=("focal", "neighbor"), how="outer", indicator=True, ) ) return not (join._merge == "left_only").any() def equals(self, right): """ Check that two graphs are identical. This reqiures them to have 1. the same edge labels and node labels 2. in the same order 3. with the same weights This is implemented by comparing the underlying adjacency series. This is equivalent to checking whether the sorted list of edge tuples (focal, neighbor, weight) for the two graphs are the same. """ try: pandas.testing.assert_series_equal( self._adjacency, right._adjacency, check_dtype=False ) except AssertionError: return False return True def isomorphic(self, right): """ Check that two graphs are isomorphic. This requires that a re-labelling can be found to convert one graph into the other graph. Requires networkx. """ try: import networkx as nx except ImportError: raise ImportError( "NetworkX is required to check for graph isomorphism" ) from None nxleft = self.to_networkx() nxright = right.to_networkx() if not nx.faster_could_be_isomorphic(nxleft, nxright): return False elif not nx.could_be_isomorphic(nxleft, nxright): # https://github.com/networkx/networkx/issues/7038 if Version(nx.__version__) == Version("3.2"): return nx.is_isomorphic(nxleft, nxright) return False else: return nx.is_isomorphic(nxleft, nxright) libpysal-4.9.2/libpysal/graph/_spatial_lag.py000066400000000000000000000013111452177046000212600ustar00rootroot00000000000000def _lag_spatial(graph, y): """Spatial lag operator If w is row standardized, returns the average of each observation's neighbors; if not, returns the weighted sum of each observation's neighbors. Parameters ---------- graph : Graph libpysal.graph.Graph y : array numpy array with dimensionality conforming to w Returns ------- numpy.array array of numeric values for the spatial lag """ sp = graph.sparse if len(y) != sp.shape[0]: raise ValueError( "The length of `y` needs to match the number of observations " f"in Graph. Expected {sp.shape[0]}, got {len(y)}." ) return graph.sparse @ y libpysal-4.9.2/libpysal/graph/_triangulation.py000066400000000000000000000472301452177046000216720ustar00rootroot00000000000000import warnings from functools import wraps import numpy import pandas from packaging.version import Version from scipy import sparse, spatial from libpysal.cg import voronoi_frames from ._contiguity import _vertex_set_intersection from ._kernel import _kernel, _kernel_functions, _optimize_bandwidth from ._utils import ( _build_coincidence_lookup, _induce_cliques, _jitter_geoms, _validate_geometry_input, _vec_euclidean_distances, ) try: from numba import njit # noqa E401 HAS_NUMBA = True except ModuleNotFoundError: from libpysal.common import jit as njit HAS_NUMBA = False PANDAS_GE_21 = Version(pandas.__version__) >= Version("2.1.0") _VALID_GEOMETRY_TYPES = ["Point"] __author__ = """" Levi John Wolf (levi.john.wolf@gmail.com) Martin Fleischmann (martin@martinfleischmann.net) Serge Rey (sjsrey@gmail.com) """ # This is in the module, rather than in `utils`, to ensure that it # can access `_VALID_GEOMETRY_TYPES` without defining a nested decorator. def _validate_coincident(triangulator): """This is a decorator that validates input for coincident points""" @wraps(triangulator) def tri_with_validation( coordinates, ids=None, coincident="raise", kernel=None, bandwidth=None, seed=None, **kwargs, ): # validate geometry input coordinates, ids, geoms = _validate_geometry_input( coordinates, ids=ids, valid_geometry_types=_VALID_GEOMETRY_TYPES ) # check for coincident points n_coincident, coincident_lut = _build_coincidence_lookup(geoms) # resolve coincident points prior triangulation if n_coincident > 0: if coincident == "raise": raise ValueError( f"There are {len(coincident_lut)} unique locations in " f"the dataset, but {len(geoms)} observations. This means there " "are multiple points in the same location, which is undefined " "for this graph type. To address this issue, consider setting " "`coincident='clique'` or consult the documentation about " "coincident points." ) elif coincident == "jitter": coordinates, geoms = _jitter_geoms(coordinates, geoms, seed=seed) elif coincident == "clique": raise NotImplementedError( "clique-based resolver of coincident points is not yet implemented." ) else: raise ValueError( f"Recieved option coincident='{coincident}', but only options " "'raise','clique','jitter' are suppported." ) # generate triangulation (triangulator is the wrapped function) heads_ix, tails_ix = triangulator(coordinates, **kwargs) # map ids heads, tails = ids[heads_ix], ids[tails_ix] # process weights if kernel is None: weights = numpy.ones(heads_ix.shape, dtype=numpy.int8) else: distances = _vec_euclidean_distances( coordinates[heads_ix], coordinates[tails_ix] ).squeeze() sparse_d = sparse.csc_array((distances, (heads_ix, tails_ix))) if bandwidth == "auto": bandwidth = _optimize_bandwidth(sparse_d, kernel) _, _, weights = _kernel( sparse_d, metric="precomputed", kernel=kernel, bandwidth=bandwidth, taper=False, ) # create adjacency adjtable = pandas.DataFrame.from_dict( {"focal": heads, "neighbor": tails, "weight": weights} ) # TODO: fix this # reinsert points resolved via clique if (n_coincident > 0) & (coincident == "clique"): # Note that the kernel is only used to compute a fill value for the clique. # In the case of the voronoi weights. Using boxcar with an infinite # bandwidth also gives us the correct fill value for the voronoi weight: 1. fill_value = _kernel_functions[kernel](numpy.array([0]), bandwidth).item() adjtable = _induce_cliques(adjtable, coincident_lut, fill_value=fill_value) if PANDAS_GE_21: # ensure proper sorting sorted_index = ( adjtable[["focal", "neighbor"]] .map(list(ids).index) .sort_values(["focal", "neighbor"]) .index ) else: # ensure proper sorting sorted_index = ( adjtable[["focal", "neighbor"]] .applymap(list(ids).index) .sort_values(["focal", "neighbor"]) .index ) # return data for Graph.from_arrays return heads[sorted_index], tails[sorted_index], weights[sorted_index] return tri_with_validation @_validate_coincident def _delaunay(coordinates): """ Constructor of the Delaunay graph of a set of input points. Relies on scipy.spatial.Delaunay and numba to quickly construct a graph from the input set of points. Will be slower without numba, and will warn if this is missing. Parameters ---------- coordinates : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries containing locations to compute the delaunay triangulation. If a geopandas object with Point geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray with shapely geoemtry is used, then the coordinates are extracted and used. If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y coordinates. ids : numpy.narray (default: None) ids to use for each sample in coordinates. Generally, construction functions that are accessed via Graph.build_kernel() will set this automatically from the index of the input. Do not use this argument directly unless you intend to set the indices separately from your input data. Otherwise, use data.set_index(ids) to ensure ordering is respected. If None, then the index from the input coordinates will be used. bandwidth : float (default: None) distance to use in the kernel computation. Should be on the same scale as the input coordinates. kernel : string or callable kernel function to use in order to weight the output graph. See the kernel() function for more details. Notes ----- The Delaunay triangulation can result in quite a few non-local links among spatial coordinates. For a more useful graph, consider the weights.Voronoi constructor or the Gabriel graph. The weights.Voronoi class builds a voronoi diagram among the points, clips the Voronoi cells, and then constructs an adjacency graph among the clipped cells. This graph among the clipped Voronoi cells generally represents the structure of local adjacencies better than the "raw" Delaunay graph. The weights.gabriel.Gabriel graph constructs a Delaunay graph, but only includes the "short" links in the Delaunay graph. However, if the unresricted Delaunay triangulation is needed, this class will compute it much more quickly than Voronoi(coordinates, clip=None). """ if not HAS_NUMBA: warnings.warn( "The numba package is used extensively in this module" " to accelerate the computation of graphs. Without numba," " these computations may become unduly slow on large data.", stacklevel=3, ) edges, _ = _voronoi_edges(coordinates) heads_ix, tails_ix = edges.T return heads_ix, tails_ix @_validate_coincident def _gabriel(coordinates): """ Constructs the Gabriel graph of a set of points. This graph is a subset of the Delaunay triangulation where only "short" links are retained. This function is also accelerated using numba, and implemented on top of the scipy.spatial.Delaunay class. For a link (i,j) connecting node i to j in the Delaunay triangulation to be retained in the Gabriel graph, it must pass a point set exclusion test: 1. Construct the circle C_ij containing link (i,j) as its diameter 2. If any other node k is contained within C_ij, then remove link (i,j) from the graph. 3. Once all links are evaluated, the remaining graph is the Gabriel graph. Parameters ---------- coordinates : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries containing locations to compute the delaunay triangulation. If a geopandas object with Point geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray with shapely geoemtry is used, then the coordinates are extracted and used. If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y coordinates. ids : numpy.narray (default: None) ids to use for each sample in coordinates. Generally, construction functions that are accessed via Graph.build_kernel() will set this automatically from the index of the input. Do not use this argument directly unless you intend to set the indices separately from your input data. Otherwise, use data.set_index(ids) to ensure ordering is respected. If None, then the index from the input coordinates will be used. bandwidth : float (default: None) distance to use in the kernel computation. Should be on the same scale as the input coordinates. kernel : string or callable kernel function to use in order to weight the output graph. See the kernel() function for more details. """ if not HAS_NUMBA: warnings.warn( "The numba package is used extensively in this module" " to accelerate the computation of graphs. Without numba," " these computations may become unduly slow on large data.", stacklevel=3, ) edges, dt = _voronoi_edges(coordinates) droplist = _filter_gabriel( edges, dt.points, ) heads_ix, tails_ix = numpy.row_stack( list(set(map(tuple, edges)).difference(set(droplist))) ).T return heads_ix, tails_ix @_validate_coincident def _relative_neighborhood(coordinates): """ Constructs the Relative Neighborhood graph from a set of points. This graph is a subset of the Delaunay triangulation, where only "relative neighbors" are retained. Further, it is a superset of the Minimum Spanning Tree, with additional "relative neighbors" introduced. A relative neighbor pair of points i,j must be closer than the maximum distance between i (or j) and each other point k. This means that the points are at least as close to one another as they are to any other point. Parameters ---------- coordinates : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries containing locations to compute the delaunay triangulation. If a geopandas object with Point geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray with shapely geoemtry is used, then the coordinates are extracted and used. If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y coordinates. ids : numpy.narray (default: None) ids to use for each sample in coordinates. Generally, construction functions that are accessed via Graph.build_kernel() will set this automatically from the index of the input. Do not use this argument directly unless you intend to set the indices separately from your input data. Otherwise, use data.set_index(ids) to ensure ordering is respected. If None, then the index from the input coordinates will be used. bandwidth : float (default: None) distance to use in the kernel computation. Should be on the same scale as the input coordinates. kernel : string or callable kernel function to use in order to weight the output graph. See the kernel() function for more details. """ if not HAS_NUMBA: warnings.warn( "The numba package is used extensively in this module" " to accelerate the computation of graphs. Without numba," " these computations may become unduly slow on large data.", stacklevel=3, ) edges, dt = _voronoi_edges(coordinates) output, _ = _filter_relativehood(edges, dt.points, return_dkmax=False) heads_ix, tails_ix, distance = zip(*output, strict=True) heads_ix, tails_ix = numpy.asarray(heads_ix), numpy.asarray(tails_ix) return heads_ix, tails_ix @_validate_coincident def _voronoi(coordinates, clip="extent", rook=True): """ Compute contiguity weights according to a clipped Voronoi diagram. Parameters --------- coordinates : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries containing locations to compute the delaunay triangulation. If a geopandas object with Point geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray with shapely geoemtry is used, then the coordinates are extracted and used. If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y coordinates. ids : numpy.narray (default: None) ids to use for each sample in coordinates. Generally, construction functions that are accessed via Graph.build_kernel() will set this automatically from the index of the input. Do not use this argument directly unless you intend to set the indices separately from your input data. Otherwise, use data.set_index(ids) to ensure ordering is respected. If None, then the index clip : str (default: 'bbox') An overloaded option about how to clip the voronoi cells passed to ``libpysal.cg.voronoi_frames()``. Default is ``'extent'``. Options are as follows. * ``'none'``/``None`` -- No clip is applied. Voronoi cells may be arbitrarily larger that the source map. Note that this may lead to cells that are many orders of magnitude larger in extent than the original map. Not recommended. * ``'bbox'``/``'extent'``/``'bounding box'`` -- Clip the voronoi cells to the bounding box of the input points. * ``'chull``/``'convex hull'`` -- Clip the voronoi cells to the convex hull of the input points. * ``'ashape'``/``'ahull'`` -- Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alphashape, using ``libpysal.cg.alpha_shape_auto``). * Polygon -- Clip to an arbitrary Polygon. rook : bool, optional Contiguity method. If True, two geometries are considered neighbours if they share at least one edge. If False, two geometries are considered neighbours if they share at least one vertex. By default True. Notes ----- In theory, the rook contiguity graph for a Voronoi diagram is the delaunay triangulation of the generators of the voronoi diagram. Yet, this is *not* the case when voronoi cells are clipped to an arbitrary shape, including the original bounding box of the input points or anything tighter. This can arbitrarily delete links present in the delaunay. However, clipped voronoi weights make sense over pure delaunay triangulations in many applied contexts and generally will remove "long" links in the delaunay graph. """ cells, _ = voronoi_frames(coordinates, clip=clip) heads_ix, tails_ix, weights = _vertex_set_intersection(cells, rook=rook) return heads_ix, tails_ix #### utilities @njit def _edges_from_simplices(simplices): """ Construct the sets of links that correspond to the edges of each simplex. Each simplex has three "sides," and thus six undirected edges. Thus, the input should be a list of three-length tuples, that are then converted into the six non-directed edges for each simplex. """ edges = [] for simplex in simplices: edges.append((simplex[0], simplex[1])) edges.append((simplex[1], simplex[0])) edges.append((simplex[1], simplex[2])) edges.append((simplex[2], simplex[1])) edges.append((simplex[2], simplex[0])) edges.append((simplex[0], simplex[2])) return numpy.asarray(edges) @njit def _filter_gabriel(edges, coordinates): """ For an input set of edges and coordinates, filter the input edges depending on the Gabriel rule: For each simplex, let i,j be the diameter of the circle defined by edge (i,j), and let k be the third point defining the simplex. The limiting case for the Gabriel rule is when k is also on the circle with diameter (i,j). In this limiting case, then simplex ijk must be a right triangle, and dij**2 = djk**2 + dki**2 (by thales theorem). This means that when dij**2 > djk**2 + dki**2, then k is inside the circle. In contrast, when dij**2 < djk**2 + dji*2, k is outside of the circle. Therefore, it's sufficient to take each observation i, iterate over its Delaunay neighbors j,k, and remove links whre dij**2 > djk**2 + dki**2 in order to construct the Gabriel graph. """ edge_pointer = 0 n_edges = len(edges) to_drop = [] while edge_pointer < n_edges: edge = edges[edge_pointer] cardinality = 0 # look ahead to find all neighbors of edge[0] for joff in range(edge_pointer, n_edges): next_edge = edges[joff] if next_edge[0] != edge[0]: break cardinality += 1 for ix in range(edge_pointer, edge_pointer + cardinality): i, j = edges[ix] # lookahead ensures that i is always edge[0] dij2 = ((coordinates[i] - coordinates[j]) ** 2).sum() for ix2 in range(edge_pointer, edge_pointer + cardinality): _, k = edges[ix2] if j == k: continue dik2 = ((coordinates[i] - coordinates[k]) ** 2).sum() djk2 = ((coordinates[j] - coordinates[k]) ** 2).sum() if dij2 > (dik2 + djk2): to_drop.append((i, j)) to_drop.append((j, i)) edge_pointer += cardinality return set(to_drop) @njit def _filter_relativehood(edges, coordinates, return_dkmax=False): """ This is a direct implementation of the algorithm from Toussaint (1980), RNG-2 1. Compute the delaunay 2. for each edge of the delaunay (i,j), compute dkmax = max(d(k,i), d(k,j)) for k in 1..n, k != i, j 3. for each edge of the delaunay (i,j), prune if any dkmax is greater than d(i,j) """ n = edges.max() out = [] r = [] for edge in edges: i, j = edge pi = coordinates[i] pj = coordinates[j] dkmax = 0 dij = ((pi - pj) ** 2).sum() ** 0.5 prune = False for k in range(n): pk = coordinates[k] dik = ((pi - pk) ** 2).sum() ** 0.5 djk = ((pj - pk) ** 2).sum() ** 0.5 distances = numpy.array([dik, djk, dkmax]) dkmax = distances.max() prune = dkmax < dij if (not return_dkmax) & prune: break if prune: continue out.append((i, j, dij)) if return_dkmax: r.append(dkmax) return out, r def _voronoi_edges(coordinates): dt = spatial.Delaunay(coordinates) edges = _edges_from_simplices(dt.simplices) edges = ( pandas.DataFrame(numpy.asarray(list(edges))) .sort_values([0, 1]) .drop_duplicates() .values ) return edges, dt libpysal-4.9.2/libpysal/graph/_utils.py000066400000000000000000000230021452177046000201410ustar00rootroot00000000000000import warnings from itertools import permutations import geopandas import numpy as np import pandas as pd import shapely from packaging.version import Version GPD_013 = Version(geopandas.__version__) >= Version("0.13") def _sparse_to_arrays(sparray, ids=None): """Convert sparse array to arrays of adjacency""" sparray = sparray.tocoo(copy=False) if ids is not None: ids = np.asarray(ids) if sparray.shape[0] != ids.shape[0]: raise ValueError( f"The length of ids ({ids.shape[0]}) does not match " f"the shape of sparse {sparray.shape}." ) sorter = sparray.row.argsort() head = ids[sparray.row][sorter] tail = ids[sparray.col][sorter] data = sparray.data[sorter] else: sorter = sparray.row.argsort() head = sparray.row[sorter] tail = sparray.col[sorter] data = sparray.data[sorter] ids = np.arange(sparray.shape[0], dtype=int) return _resolve_islands(head, tail, ids, data) def _jitter_geoms(coordinates, geoms, seed=None): """ Jitter geometries based on the smallest required movements to induce uniqueness. For each point, this samples a radius and angle uniformly at random from the unit circle, rescales it to a circle of values that are extremely small relative to the precision of the input, and then displaces the point. For a non-euclidean geometry, like latitude longitude coordinates, this will distort according to a plateé carree projection, jittering slightly more in the x direction than the y direction. """ rng = np.random.default_rng(seed=seed) dtype = coordinates.dtype if dtype not in (np.float32, np.float64): # jittering requires us to cast ints to float # and the rng.random generator only works with float32 and float64 dtype = np.float32 # the resolution is the approximate difference between two floats # that can be resolved at the given dtype. resolution = np.finfo(dtype).resolution r = rng.random(size=coordinates.shape[0], dtype=dtype) ** 0.5 * resolution theta = rng.random(size=coordinates.shape[0], dtype=dtype) * np.pi * 2 # converting from polar to cartesian dx = r + np.sin(theta) dy = r + np.cos(theta) # then adding the displacements coordinates = coordinates + np.column_stack((dx, dy)) geoms = geopandas.GeoSeries(geopandas.points_from_xy(*coordinates.T, crs=geoms.crs)) return coordinates, geoms def _induce_cliques(adjtable, clique_to_members, fill_value=1): """ induce cliques into the input graph. This connects everything within a clique together, as well as connecting all things outside of the clique to all members of the clique. This does not guarantee/understand ordering of the *output* adjacency table. """ adj_across_clique = ( adjtable.merge( clique_to_members["input_index"], left_index=True, right_index=True ) .explode("input_index") .rename(columns={"input_index": "subclique_focal"}) .merge(clique_to_members["input_index"], left_on="neighbor", right_index=True) .explode("input_index") .rename(columns={"input_index": "subclique_neighbor"}) .reset_index() .drop(["focal", "neighbor", "index"], axis=1) .rename(columns={"subclique_focal": "focal", "subclique_neighbor": "neighbor"}) ) is_multimember_clique = clique_to_members["input_index"].str.len() > 1 adj_within_clique = ( clique_to_members[is_multimember_clique]["input_index"] .apply(lambda x: list(permutations(x, 2))) .explode() .apply(pd.Series) .rename(columns={0: "focal", 1: "neighbor"}) .assign(weight=fill_value) ) new_adj = pd.concat( (adj_across_clique, adj_within_clique), ignore_index=True, axis=0 ).reset_index(drop=True) return new_adj def _neighbor_dict_to_edges(neighbors, weights=None): """ Convert a neighbor dict to a set of (head, tail, weight) edges, assuming that the any self-loops have a weight of zero. """ idxs = pd.Series(neighbors).explode() with warnings.catch_warnings(): warnings.filterwarnings( "ignore", "Downcasting object dtype arrays on .fillna, .ffill, .bfill ", FutureWarning, ) idxs = idxs.fillna(pd.Series(idxs.index, index=idxs.index)) # self-loops heads, tails = idxs.index.values, idxs.values tails = tails.astype(heads.dtype) if weights is not None: with warnings.catch_warnings(): warnings.filterwarnings( "ignore", "Downcasting object dtype arrays on .fillna, .ffill, .bfill ", FutureWarning, ) data_array = pd.Series(weights).explode().fillna(0).values if not pd.api.types.is_numeric_dtype(data_array): data_array = pd.to_numeric(data_array) else: data_array = np.ones(idxs.shape[0], dtype=int) data_array[heads == tails] = 0 return heads, tails, data_array def _build_coincidence_lookup(geoms): """ Identify coincident points and create a look-up table for the coincident geometries. """ valid_coincident_geom_types = set(("Point",)) # noqa C405 if not set(geoms.geom_type) <= valid_coincident_geom_types: raise ValueError( "coindicence checks are only well-defined for " f"geom_types: {valid_coincident_geom_types}" ) max_coincident = geoms.geometry.duplicated().sum() if GPD_013: lut = ( geoms.to_frame("geometry") .reset_index() .groupby("geometry")["index"] .agg(list) .reset_index() ) else: lut = ( geoms.to_wkb() .to_frame("geometry") .reset_index() .groupby("geometry")["index"] .agg(list) .reset_index() ) lut["geometry"] = geopandas.GeoSeries.from_wkb(lut["geometry"]) lut = geopandas.GeoDataFrame(lut) return max_coincident, lut.rename(columns={"index": "input_index"}) def _validate_geometry_input(geoms, ids=None, valid_geometry_types=None): """ Ensure that input geometries are always aligned to (and refer back to) inputted geometries. Geoms can be a GeoSeries, GeoDataFrame, numpy.array with a geometry dtype, or a point array. is will always align to geoms. the returned coordinates will always pertain to geoms, but may be longer than geoms (such as when geoms represents polygons). """ if isinstance(geoms, geopandas.GeoSeries | geopandas.GeoDataFrame): geoms = geoms.geometry if ids is None: ids = geoms.index ids = np.asarray(ids) geom_types = set(geoms.geom_type) if valid_geometry_types is not None: if isinstance(valid_geometry_types, str): valid_geometry_types = (valid_geometry_types,) valid_geometry_types = set(valid_geometry_types) if not geom_types <= valid_geometry_types: raise ValueError( "This Graph type is only well-defined for " f"geom_types: {valid_geometry_types}." ) coordinates = shapely.get_coordinates(geoms) geoms = geoms.copy() geoms.index = ids return coordinates, ids, geoms elif isinstance(geoms.dtype, geopandas.array.GeometryDtype): return _validate_geometry_input( geopandas.GeoSeries(geoms), ids=ids, valid_geometry_types=valid_geometry_types, ) else: if (geoms.ndim == 2) and (geoms.shape[1] == 2): return _validate_geometry_input( geopandas.points_from_xy(*geoms.T), ids=ids, valid_geometry_types=valid_geometry_types, ) raise ValueError( "input geometry type is not supported. Input must either be a " "geopandas.GeoSeries, geopandas.GeoDataFrame, a numpy array with a geometry " "dtype, or an array of coordinates." ) def _validate_sparse_input(sparse, ids=None): assert ( sparse.shape[0] == sparse.shape[1] ), "coordinates should represent a distance matrix if metric='precomputed'" return _sparse_to_arrays(sparse, ids) def _vec_euclidean_distances(x_vec, y_vec): """ compute the euclidean distances along corresponding rows of two arrays """ return ((x_vec - y_vec) ** 2).sum(axis=1) ** 0.5 def _evaluate_index(data): """Helper to get ids from any input.""" if isinstance(data, pd.Series | pd.DataFrame): return data.index elif hasattr(data, "shape"): return pd.RangeIndex(0, data.shape[0]) else: return pd.RangeIndex(0, len(data)) def _resolve_islands(heads, tails, ids, weights): """ Induce self-loops for a collection of ids and links describing a contiguity graph. Induced self-loops will have zero weight. """ islands = np.setdiff1d(ids, heads) if islands.shape != (0,): heads = np.hstack((heads, islands)) tails = np.hstack((tails, islands)) weights = np.hstack((weights, np.zeros_like(islands, dtype=int))) # ensure proper order after adding isolates to the end adjacency = pd.Series(weights, index=pd.MultiIndex.from_arrays([heads, tails])) adjacency = adjacency.reindex(ids, level=0).reindex(ids, level=1) return ( adjacency.index.get_level_values(0), adjacency.index.get_level_values(1), adjacency.values, ) libpysal-4.9.2/libpysal/graph/base.py000066400000000000000000001410271452177046000175640ustar00rootroot00000000000000import math from functools import cached_property import numpy as np import pandas as pd from scipy import sparse from libpysal.weights import W from ._contiguity import ( _block_contiguity, _fuzzy_contiguity, _queen, _rook, _vertex_set_intersection, ) from ._kernel import _distance_band, _kernel from ._parquet import _read_parquet, _to_parquet from ._plotting import _explore_graph, _plot from ._set_ops import SetOpsMixin from ._spatial_lag import _lag_spatial from ._triangulation import _delaunay, _gabriel, _relative_neighborhood, _voronoi from ._utils import _evaluate_index, _neighbor_dict_to_edges, _sparse_to_arrays ALLOWED_TRANSFORMATIONS = ("O", "B", "R", "D", "V") # listed alphabetically __author__ = """" Martin Fleischmann (martin@martinfleischmann.net) Eli Knaap (ek@knaaptime.com) Serge Rey (sjsrey@gmail.com) Levi John Wolf (levi.john.wolf@gmail.com) """ class Graph(SetOpsMixin): """Graph class encoding spatial weights matrices The :class:`Graph` is currently experimental and its API is incomplete and unstable. """ def __init__(self, adjacency, transformation="O"): """Weights base class based on adjacency list It is recommenced to use one of the ``from_*`` or ``build_*`` constructors rather than invoking ``__init__`` directly. Each observation needs to be present in the focal, at least as a self-loop with a weight 0. Parameters ---------- adjacency : pandas.Series A MultiIndexed pandas.Series with ``"focal"`` and ``"neigbor"`` levels encoding adjacency, and values encoding weights. By convention, isolates are encoded as self-loops with a weight 0. transformation : str, default "O" weights transformation used to produce the table. - **O** -- Original - **B** -- Binary - **R** -- Row-standardization (global sum :math:`=n`) - **D** -- Double-standardization (global sum :math:`=1`) - **V** -- Variance stabilizing """ if not isinstance(adjacency, pd.Series): raise TypeError( f"The adjacency table needs to be a pandas.Series. {type(adjacency)}" ) if not adjacency.index.names == ["focal", "neighbor"]: raise ValueError( "The index of the adjacency table needs to be a MultiIndex named " "['focal', 'neighbor']." ) if not adjacency.name == "weight": raise ValueError( "The adjacency needs to be named 'weight'. " f"'{adjacency.name}' was given instead." ) if not pd.api.types.is_numeric_dtype(adjacency): raise ValueError( "The 'weight' needs to be of a numeric dtype. " f"'{adjacency.dtype}' dtype was given instead." ) if adjacency.isna().any(): raise ValueError("The adjacency table cannot contain missing values.") if transformation.upper() not in ALLOWED_TRANSFORMATIONS: raise ValueError( f"'transformation' needs to be one of {ALLOWED_TRANSFORMATIONS}. " f"'{transformation}' was given instead." ) # adjacency always ordered i-->j on both levels ids = adjacency.index.get_level_values(0).unique().values adjacency = adjacency.reindex(ids, level=0).reindex(ids, level=1) self._adjacency = adjacency self.transformation = transformation def __getitem__(self, item): """Easy lookup based on focal index Parameters ---------- item : hashable hashable represting an index value Returns ------- pandas.Series subset of the adjacency table for `item` """ if item in self.isolates: return pd.Series( [], index=pd.Index([], name="neighbor"), name="weight", ) return self._adjacency.loc[item] def copy(self, deep=True): """Make a copy of this Graph's adjacency table and transformation Parameters ---------- deep : bool, optional Make a deep copy of the adjacency table, by default True Returns ------- Graph libpysal.graph.Graph as a copy of the original """ return Graph( self._adjacency.copy(deep=deep), transformation=self.transformation ) @cached_property def adjacency(self): """Return a copy of the adjacency list Returns ------- pandas.Series Underlying adjacency list """ return self._adjacency.copy() @classmethod def from_W(cls, w): # noqa N802 """Create an experimental Graph from libpysal.weights.W object Parameters ---------- w : libpysal.weights.W Returns ------- Graph libpysal.graph.Graph from W """ return cls.from_weights_dict(dict(w)) def to_W(self): # noqa N802 """Convert Graph to a libpysal.weights.W object Returns ------- libpysal.weights.W representation of graph as a weights.W object """ ids, labels = pd.factorize( self._adjacency.index.get_level_values("focal"), sort=False ) neighbors = ( self._adjacency.reset_index(level=1) .groupby(ids) .apply( lambda group: list( group[ ~((group.index == group.neighbor) & (group.weight == 0)) ].neighbor ) ) ) neighbors.index = labels[neighbors.index] weights = ( self._adjacency.reset_index(level=1) .groupby(ids) .apply( lambda group: list( group[ ~((group.index == group.neighbor) & (group.weight == 0)) ].weight ) ) ) weights.index = labels[weights.index] return W(neighbors.to_dict(), weights.to_dict(), id_order=labels.tolist()) @classmethod def from_adjacency( cls, adjacency, focal_col="focal", neighbor_col="neighbor", weight_col="weight" ): """Create a Graph from a pandas DataFrame formatted as an adjacency list Parameters ---------- adjacency : pandas.DataFrame a dataframe formatted as an ajacency list. Should have columns "focal", "neighbor", and "weight", or columns that can be mapped to these (e.g. origin, destination, cost) focal : str, optional name of column holding focal/origin index, by default 'focal' neighbor : str, optional name of column holding neighbor/destination index, by default 'neighbor' weight : str, optional name of column holding weight values, by default 'weight' Returns ------- Graph libpysal.graph.Graph """ cols = dict( zip( [focal_col, neighbor_col, weight_col], ["focal_col", "neighbor_col", "weight_col"], strict=True, ) ) for col in cols: assert col in adjacency.columns.tolist(), ( f'"{col}" was given for `{cols[col]}`, but the ' f"columns available in `adjacency` are: {adjacency.columns.tolist()}." ) return cls.from_arrays( adjacency[focal_col].values, adjacency[neighbor_col].values, adjacency[weight_col].values, ) @classmethod def from_sparse(cls, sparse, ids=None): """Convert a ``scipy.sparse`` array to a PySAL ``Graph`` object. Parameters ---------- sparse : scipy.sparse array sparse representation of a graph ids : list-like, default None list-like of ids for geometries that is mappable to positions from sparse. If None, the positions are used as labels. Returns ------- Graph libpysal.graph.Graph based on sparse """ return cls.from_arrays(*_sparse_to_arrays(sparse, ids)) @classmethod def from_arrays(cls, focal_ids, neighbor_ids, weight): """Generate Graph from arrays of indices and weights of the same length The arrays needs to be sorted in a way ensuring that focal_ids.unique() is equal to the index of original observations from which the Graph is being built Parameters ---------- focal_index : array-like focal indices neighbor_index : array-like neighbor indices weight : array-like weights Returns ------- Graph libpysal.graph.Graph based on arrays """ w = cls( pd.Series( weight, name="weight", index=pd.MultiIndex.from_arrays( [focal_ids, neighbor_ids], names=["focal", "neighbor"] ), ) ) return w @classmethod def from_weights_dict(cls, weights_dict): """Generate Graph from a dict of dicts Parameters ---------- weights_dict : dictionary of dictionaries weights dictionary with the ``{focal: {neighbor: weight}}`` structure. Returns ------- Graph libpysal.graph.Graph based on weights dictionary of dictionaries """ idx = {f: list(neighbors) for f, neighbors in weights_dict.items()} data = {f: list(neighbors.values()) for f, neighbors in weights_dict.items()} return cls.from_dicts(idx, data) @classmethod def from_dicts(cls, neighbors, weights=None): """Generate Graph from dictionaries of neighbors and weights Parameters ---------- neighbors : dict dictionary of neighbors with the ``{focal: [neighbor1, neighbor2]}`` structure weights : dict, optional dictionary of neighbors with the ``{focal: [weight1, weight2]}`` structure. If None, assumes binary weights. Returns ------- Graph libpysal.graph.Graph based on dictionaries """ head, tail, weight = _neighbor_dict_to_edges(neighbors, weights=weights) return cls.from_arrays(head, tail, weight) @classmethod def build_contiguity(cls, geometry, rook=True, by_perimeter=False, strict=False): """Generate Graph from geometry based on contiguity Contiguity builder assumes that all geometries are forming a coverage, i.e. a non-overlapping mesh and neighbouring geometries share only points or segments of their exterior boundaries. In practice, ``build_contiguity`` is capable of creating a Graph of partially overlapping geometries when ``strict=False, by_perimeter=False``, but that would not strictly follow the definition of queen or rook contiguity. Parameters ---------- geometry : array-like of shapely.Geometry objects Could be geopandas.GeoSeries or geopandas.GeoDataFrame, in which case the resulting Graph is indexed by the original index. If an array of shapely.Geometry objects is passed, Graph will assume a RangeIndex. rook : bool, optional Contiguity method. If True, two geometries are considered neighbours if they share at least one edge. If False, two geometries are considered neighbours if they share at least one vertex. By default True by_perimeter : bool, optional If True, ``weight`` represents the length of the shared boundary between adjacent units, by default False. For row-standardized version of perimeter weights, use ``Graph.build_contiguity(gdf, by_perimeter=True).transform("r")``. strict : bool, optional Use the strict topological method. If False, the contiguity is determined based on shared coordinates or coordinate sequences representing edges. This assumes geometry coverage that is topologically correct. This method is faster but can miss some relations. If True, the contiguity is determined based on geometric relations that do not require precise topology. This method is slower but will result in correct contiguity even if the topology of geometries is not optimal. By default False. Returns ------- Graph libpysal.graph.Graph encoding contiguity weights """ ids = _evaluate_index(geometry) if hasattr(geometry, "geometry"): # potentially cast GeoDataFrame to GeoSeries geometry = geometry.geometry if strict: # use shapely-based constructors if rook: return cls.from_arrays( *_rook(geometry, ids=ids, by_perimeter=by_perimeter) ) return cls.from_arrays( *_queen(geometry, ids=ids, by_perimeter=by_perimeter) ) # use vertex-based constructor return cls.from_arrays( *_vertex_set_intersection( geometry, rook=rook, ids=ids, by_perimeter=by_perimeter ) ) @classmethod def build_kernel( cls, data, kernel="gaussian", k=None, bandwidth=None, metric="euclidean", p=2, coincident="raise", ): """Generate Graph from geometry data based on a kernel function Parameters ---------- data : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries over which to compute a kernel. If a geopandas object with Point geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray with shapely geoemtry is used, then the coordinates are extracted and used. If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y coordinates. If metric="precomputed", data is assumed to contain a precomputed distance metric. kernel : string or callable (default: 'gaussian') kernel function to apply over the distance matrix computed by `metric`. The following kernels are supported: - ``"triangular"``: - ``"parabolic"``: - ``"gaussian"``: - ``"bisquare"``: - ``"cosine"``: - ``'boxcar'``/discrete: all distances less than `bandwidth` are 1, and all other distances are 0 - ``"identity"``/None : do nothing, weight similarity based on raw distance - ``callable`` : a user-defined function that takes the distance vector and the bandwidth and returns the kernel: kernel(distances, bandwidth) k : int (default: None) number of nearest neighbors used to truncate the kernel. This is assumed to be constant across samples. If None, no truncation is conduted. bandwidth : float (default: None) distance to use in the kernel computation. Should be on the same scale as the input coordinates. metric : string or callable (default: 'euclidean') distance function to apply over the input coordinates. Supported options depend on whether or not scikit-learn is installed. If so, then any distance function supported by scikit-learn is supported here. Otherwise, only euclidean, minkowski, and manhattan/cityblock distances are admitted. p : int (default: 2) parameter for minkowski metric, ignored if metric != "minkowski". coincident: str, optional (default "raise") Method for handling coincident points when ``k`` is not None. Options are ``'raise'`` (raising an exception when coincident points are present), ``'jitter'`` (randomly displace coincident points to produce uniqueness), & ``'clique'`` (induce fully-connected sub cliques for coincident points). Returns ------- Graph libpysal.graph.Graph encoding kernel weights """ ids = _evaluate_index(data) head, tail, weight = _kernel( data, bandwidth=bandwidth, metric=metric, kernel=kernel, k=k, p=p, ids=ids, coincident=coincident, ) return cls.from_arrays(head, tail, weight) @classmethod def build_knn(cls, data, k, metric="euclidean", p=2, coincident="raise"): """Generate Graph from geometry data based on k-nearest neighbors search Parameters ---------- data : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries over which to compute a kernel. If a geopandas object with Point geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray with shapely geoemtry is used, then the coordinates are extracted and used. If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y coordinates. k : int number of nearest neighbors. metric : string or callable (default: 'euclidean') distance function to apply over the input coordinates. Supported options depend on whether or not scikit-learn is installed. If so, then any distance function supported by scikit-learn is supported here. Otherwise, only euclidean, minkowski, and manhattan/cityblock distances are admitted. p : int (default: 2) parameter for minkowski metric, ignored if metric != "minkowski". coincident: str, optional (default "raise") Method for handling coincident points. Options include ``'raise'`` (raising an exception when coincident points are present), ``'jitter'`` (randomly displace coincident points to produce uniqueness), & ``'clique'`` (induce fully-connected sub cliques for coincident points). Returns ------- Graph libpysal.graph.Graph encoding KNN weights """ ids = _evaluate_index(data) head, tail, weight = _kernel( data, bandwidth=np.inf, metric=metric, kernel="boxcar", k=k, p=p, ids=ids, coincident=coincident, ) return cls.from_arrays(head, tail, weight) @classmethod def build_triangulation( cls, data, method="delaunay", bandwidth=np.inf, kernel="boxcar", clip="extent", rook=True, coincident="raise", ): """Generate Graph from geometry based on triangulation Parameters ---------- data : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries containing locations to compute the delaunay triangulation. If a geopandas object with Point geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray with shapely geoemtry is used, then the coordinates are extracted and used. If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y coordinates. method : str, (default "delaunay") method of extracting the weights from triangulation. Supports: - ``"delaunay"`` - ``"gabriel"`` - ``"relative_neighborhood"`` - ``"voronoi"`` bandwidth : float, optional distance to use in the kernel computation. Should be on the same scale as the input coordinates, by default numpy.inf kernel : str, optional kernel function to use in order to weight the output graph. See :meth:`Graph.build_kernel` for details. By default "boxcar" clip : str (default: 'bbox') Clipping method when ``method="voronoi"``. Ignored otherwise. Default is ``'extent'``. Options are as follows. - ``'none'``/``None``: No clip is applied. Voronoi cells may be arbitrarily larger that the source map. Note that this may lead to cells that are many orders of magnitude larger in extent than the original map. Not recommended. - ``'bbox'``/``'extent'``/``'bounding box'``: Clip the voronoi cells to the bounding box of the input points. - ``'chull``/``'convex hull'``: Clip the voronoi cells to the convex hull of the input points. - ``'ashape'``/``'ahull'``: Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alphashape, using :func:`libpysal.cg.alpha_shape_auto`). - ``shapely.Polygon``: Clip to an arbitrary Polygon. rook : bool, optional Contiguity method when ``method="voronoi"``. Ignored otherwise. If True, two geometries are considered neighbours if they share at least one edge. If False, two geometries are considered neighbours if they share at least one vertex. By default True coincident: str, optional (default "raise") Method for handling coincident points. Options include ``'raise'`` (raising an exception when coincident points are present), ``'jitter'`` (randomly displace coincident points to produce uniqueness), & ``'clique'`` (induce fully-connected sub cliques for coincident points). Returns ------- Graph libpysal.graph.Graph encoding triangulation weights """ ids = _evaluate_index(data) if method == "delaunay": head, tail, weights = _delaunay( data, ids=ids, bandwidth=bandwidth, kernel=kernel, coincident=coincident ) elif method == "gabriel": head, tail, weights = _gabriel( data, ids=ids, bandwidth=bandwidth, kernel=kernel, coincident=coincident ) elif method == "relative_neighborhood": head, tail, weights = _relative_neighborhood( data, ids=ids, bandwidth=bandwidth, kernel=kernel, coincident=coincident ) elif method == "voronoi": head, tail, weights = _voronoi( data, ids=ids, clip=clip, rook=rook, coincident=coincident ) else: raise ValueError( f"Method '{method}' is not supported. Use one of ['delaunay', " "'gabriel', 'relative_neighborhood', 'voronoi']." ) return cls.from_arrays(head, tail, weights) @classmethod def build_distance_band( cls, data, threshold, binary=True, alpha=-1.0, kernel=None, bandwidth=None ): """Generate Graph from geometry based on a distance band Parameters ---------- data : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries containing locations to compute the delaunay triangulation. If a geopandas object with Point geometry is provided, the .geometry attribute is used. If a numpy.ndarray with shapely geometry is used, then the coordinates are extracted and used. If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y coordinates. threshold : float distance band binary : bool, optional If True :math:`w_{ij}=1` if :math:`d_{i,j}<=threshold`, otherwise :math:`w_{i,j}=0`. If False :math:`wij=dij^{alpha}`, by default True. alpha : float, optional distance decay parameter for weight (default -1.0) if alpha is positive the weights will not decline with distance. Ignored if ``binary=True`` or ``kernel`` is not None. kernel : str, optional kernel function to use in order to weight the output graph. See :meth:`Graph.build_kernel` for details. Ignored if ``binary=True``. bandwidth : float (default: None) distance to use in the kernel computation. Should be on the same scale as the input coordinates. Ignored if ``binary=True`` or ``kernel=None``. Returns ------- Graph libpysal.graph.Graph encoding distance band weights """ ids = _evaluate_index(data) dist = _distance_band(data, threshold) if binary: head, tail, weight = _kernel( dist, kernel="boxcar", metric="precomputed", ids=ids, bandwidth=np.inf, ) elif kernel is not None: head, tail, weight = _kernel( dist, kernel=kernel, metric="precomputed", ids=ids, bandwidth=bandwidth, ) else: head, tail, weight = _kernel( dist, kernel=lambda distances, alpha: np.power(distances, alpha), metric="precomputed", ids=ids, bandwidth=alpha, ) adjacency = pd.DataFrame.from_dict( {"focal": head, "neighbor": tail, "weight": weight} ).set_index("focal") # drop diagonal counts = adjacency.index.value_counts() no_isolates = counts[counts > 1] adjacency = adjacency[ ~( adjacency.index.isin(no_isolates.index) & (adjacency.index == adjacency.neighbor) ) ] # set isolates to 0 - distance band should never contain self-weight adjacency.loc[~adjacency.index.isin(no_isolates.index), "weight"] = 0 return cls.from_arrays( adjacency.index.values, adjacency.neighbor.values, adjacency.weight.values ) @classmethod def build_block_contiguity(cls, regimes): """Generate Graph from block contiguity (regime neighbors) Block contiguity structures are relevant when defining neighbor relations based on membership in a regime. For example, all counties belonging to the same state could be defined as neighbors, in an analysis of all counties in the US. Parameters ---------- regimes : list-like list-like of regimes. If pandas.Series, its index is used to encode Graph. Otherwise a default RangeIndex is used. Returns ------- Graph libpysal.graph.Graph encoding block contiguity """ ids = _evaluate_index(regimes) return cls.from_dicts(_block_contiguity(regimes, ids=ids)) @classmethod def build_fuzzy_contiguity( cls, geometry, tolerance=None, buffer=None, predicate="intersects", ): """Generate Graph from fuzzy contiguity Fuzzy contiguity relaxes the notion of contiguity neighbors for the case of geometry collections that violate the condition of planar enforcement. It handles three types of conditions present in such collections that would result in missing links when using the regular contiguity methods. The first are edges for nearby polygons that should be shared, but are digitized separately for the individual polygons and the resulting edges do not coincide, but instead the edges intersect. This case can also be covered by ``build_contiguty`` with the ``strict=False`` parameter. The second case is similar to the first, only the resultant edges do not intersect but are "close". The optional buffering of geometry then closes the gaps between the polygons and a resulting intersection is encoded as a link. The final case arises when one polygon is "inside" a second polygon but is not encoded to represent a hole in the containing polygon. It is also possible to create a contiguity based on a custom spatial predicate. Parameters ---------- geoms : array-like of shapely.Geometry objects Could be geopandas.GeoSeries or geopandas.GeoDataFrame, in which case the resulting Graph is indexed by the original index. If an array of shapely.Geometry objects is passed, Graph will assume a RangeIndex. tolerance : float, optional The percentage of the length of the minimum side of the bounding rectangle for the ``geoms`` to use in determining the buffering distance. Either ``tolerance`` or ``buffer`` may be specified but not both. By default None. buffer : float, optional Exact buffering distance in the units of ``geoms.crs``. Either ``tolerance`` or ``buffer`` may be specified but not both. By default None. predicate : str, optional The predicate to use for determination of neighbors. Default is 'intersects'. If None is passed, neighbours are determined based on the intersection of bounding boxes. See the documentation of ``geopandas.GeoSeries.sindex.query`` for allowed predicates. Returns ------- Graph libpysal.graph.Graph encoding fuzzy contiguity """ ids = _evaluate_index(geometry) heads, tails, weights = _fuzzy_contiguity( geometry, ids, tolerance=tolerance, buffer=buffer, predicate=predicate ) return cls.from_arrays(heads, tails, weights) @cached_property def neighbors(self): """Get neighbors dictionary Notes ----- It is recommended to work directly with :meth:`Graph.adjacency` rather than using the :meth:`Graph.neighbors`. Returns ------- dict dict of tuples representing neighbors """ return ( self._adjacency.reset_index(level=1) .groupby(level=0) .apply( lambda group: tuple( group[ ~((group.index == group.neighbor) & (group.weight == 0)) ].neighbor ) ) .to_dict() ) @cached_property def weights(self): """Get weights dictionary Notes ----- It is recommended to work directly with :meth:`Graph.adjacency` rather than using the :meth:`Graph.weights`. Returns ------- dict dict of tuples representing weights """ return ( self._adjacency.reset_index(level=1) .groupby(level=0) .apply( lambda group: tuple( group[ ~((group.index == group.neighbor) & (group.weight == 0)) ].weight ) ) .to_dict() ) @cached_property def sparse(self): """Return a scipy.sparse array (COO) Returns ------- scipy.sparse.COO sparse representation of the adjacency """ # pivot to COO sparse matrix and cast to array return sparse.coo_array( self._adjacency.astype("Sparse[float]").sparse.to_coo(sort_labels=True)[0] ) def transform(self, transformation): """Transformation of weights Parameters ---------- transformation : str Transformation method. The following are valid transformations. - **B** -- Binary - **R** -- Row-standardization (global sum :math:`=n`) - **D** -- Double-standardization (global sum :math:`=1`) - **V** -- Variance stabilizing Returns ------- Graph transformed weights Raises ------ ValueError Value error for unsupported transformation """ transformation = transformation.upper() if self.transformation == transformation: return self.copy() if transformation == "R": standardized = ( (self._adjacency / self._adjacency.groupby(level=0).transform("sum")) .fillna(0) .values ) # isolate comes as NaN -> 0 elif transformation == "D": standardized = (self._adjacency / self._adjacency.sum()).values elif transformation == "B": standardized = self._adjacency.astype(bool).astype(int) elif transformation == "V": s = self._adjacency.groupby(level=0).transform( lambda group: group / math.sqrt((group**2).sum()) ) n_q = self.n / s.sum() standardized = (s * n_q).fillna(0).values # isolate comes as NaN -> 0 else: raise ValueError( f"Transformation '{transformation}' is not supported. " f"Use one of {ALLOWED_TRANSFORMATIONS[1:]}" ) standardized_adjacency = pd.Series( standardized, name="weight", index=self._adjacency.index ) return Graph(standardized_adjacency, transformation) @cached_property def _components(self): """helper for n_components and component_labels""" # TODO: remove casting to matrix once scipy supports arrays here return sparse.csgraph.connected_components(sparse.coo_matrix(self.sparse)) @cached_property def n_components(self): """Get a number of connected components Returns ------- int number of components """ return self._components[0] @cached_property def component_labels(self): """Get component labels per observation Returns ------- numpy.array Array of component labels """ return pd.Series( self._components[1], index=self.unique_ids, name="component labels" ) @cached_property def cardinalities(self): """Number of neighbors for each observation Returns ------- pandas.Series Series with a number of neighbors per each observation """ cardinalities = self._adjacency.astype(bool).groupby(level=0).sum() cardinalities.name = "cardinalities" return cardinalities @cached_property def isolates(self): """Index of observations with no neighbors Isolates are encoded as a self-loop with the weight == 0 in the adjacency table. Returns ------- pandas.Index Index with a subset of observations that do not have any neighbor """ nulls = self._adjacency[self._adjacency == 0].reset_index(level=1) # since not all zeros are necessarily isolates, do the focal == neighbor check return nulls[nulls.index == nulls.neighbor].index.unique() @cached_property def unique_ids(self): """Unique IDs used in the Graph""" return self._adjacency.index.get_level_values("focal").unique() @cached_property def n(self): """Number of observations.""" return self.unique_ids.shape[0] @cached_property def n_nodes(self): """Number of observations.""" return self.unique_ids.shape[0] @cached_property def n_edges(self): """Number of observations.""" return self._adjacency.shape[0] - self.isolates.shape[0] @cached_property def pct_nonzero(self): """Percentage of nonzero weights.""" p = 100.0 * self.sparse.nnz / (1.0 * self.n**2) return p @cached_property def nonzero(self): """Number of nonzero weights.""" return (self._adjacency.drop(self.isolates) > 0).sum() def asymmetry(self, intrinsic=True): r"""Asymmetry check. Parameters ---------- intrinsic : bool, optional Default is ``True``. Intrinsic symmetry is defined as: .. math:: w_{i,j} == w_{j,i} If ``intrinsic`` is ``False`` symmetry is defined as: .. math:: i \in N_j \ \& \ j \in N_i where :math:`N_j` is the set of neighbors for :math:`j`, e.g., ``True`` requires equality of the weight to consider two links equal, ``False`` requires only a presence of a link with a non-zero weight. Returns ------- pandas.Series A ``Series`` of ``(i,j)`` pairs of asymmetries sorted ascending by the focal observation (index value), where ``i`` is the focal and ``j`` is the neighbor. An empty ``Series`` is returned if no asymmetries are found. """ if intrinsic: wd = self.sparse.transpose() - self.sparse else: transformed = self.transform("b") wd = transformed.sparse.transpose() - transformed.sparse ids = np.nonzero(wd) if len(ids[0]) == 0: return pd.Series( index=pd.Index([], name="focal"), name="neighbor", dtype=self._adjacency.index.dtypes["focal"], ) else: i2id = dict( zip(np.arange(self.unique_ids.shape[0]), self.unique_ids, strict=True) ) focal, neighbor = np.nonzero(wd) focal = focal.astype(self._adjacency.index.dtypes["focal"]) neighbor = neighbor.astype(self._adjacency.index.dtypes["focal"]) for i in i2id: focal[focal == i] = i2id[i] neighbor[neighbor == i] = i2id[i] ijs = pd.Series( neighbor, index=pd.Index(focal, name="focal"), name="neighbor" ).sort_index() return ijs def higher_order(self, k=2, shortest_path=True, diagonal=False, lower_order=False): """Contiguity weights object of order :math:`k`. Proper higher order neighbors are returned such that :math:`i` and :math:`j` are :math:`k`-order neighbors if the shortest path from :math:`i-j` is of length :math:`k`. Parameters ---------- k : int, optional Order of contiguity. By default 2. shortest_path : bool, optional If True, :math:`i,j` and :math:`k`-order neighbors if the shortest path for :math:`i,j` is :math:`k`. If False, :math:`i,j` are `k`-order neighbors if there is a path from :math:`i,j` of length :math:`k`. By default True. diagonal : bool, optional If True, keep :math:`k`-order (:math:`i,j`) joins when :math:`i==j`. If False, remove :math:`k`-order (:math:`i,j`) joins when :math:`i==j`. By default False. lower_order : bool, optional If True, include lower order contiguities. If False return only weights of order :math:`k`. By default False. Returns ------- Graph higher order weights """ # TODO: remove casting to matrix once scipy implements matrix_power on array. # [https://github.com/scipy/scipy/pull/18544] binary = self.transform("B") sp = sparse.csr_matrix(binary.sparse) if lower_order: wk = sum(sp**x for x in range(2, k + 1)) shortest_path = False else: wk = sp**k rk, ck = wk.nonzero() sk = set(zip(rk, ck, strict=True)) if shortest_path: for j in range(1, k): wj = sp**j rj, cj = wj.nonzero() sj = set(zip(rj, cj, strict=True)) sk.difference_update(sj) if not diagonal: sk = {(i, j) for i, j in sk if i != j} return Graph.from_sparse( sparse.coo_array( ( np.ones(len(sk), dtype=np.int8), ([s[0] for s in sk], [s[1] for s in sk]), ), shape=sp.shape, ), ids=self.unique_ids, ) def lag(self, y): """Spatial lag operator If weights are row standardized, returns the mean of each observation's neighbors; if not, returns the weighted sum of each observation's neighbors. Parameters ---------- y : array-like array-like (N,) shape where N is equal to number of observations in self. Returns ------- numpy.ndarray array of numeric values for the spatial lag """ return _lag_spatial(self, y) def to_parquet(self, path, **kwargs): """Save Graph to a Apache Parquet Graph is serialized to the Apache Parquet using the underlying adjacency object stored as a Parquet table and custom metadata containing transformation. Requires pyarrow package. Parameters ---------- path : str | pyarrow.NativeFile path or any stream supported by pyarrow **kwargs additional keyword arguments passed to pyarrow.parquet.write_table See also -------- read_parquet """ _to_parquet(self, path, **kwargs) def to_networkx(self): """Convert Graph to a ``networkx`` graph. If Graph is symmetric, returns ``nx.Graph``, otherwise returns a ``nx.DiGraph``. Returns ------- networkx.Graph | networkx.DiGraph Representation of libpysal Graph as networkx graph """ try: import networkx as nx except ImportError: raise ImportError("NetworkX is required.") from None graph_type = nx.Graph if self.asymmetry().empty else nx.DiGraph return nx.from_pandas_edgelist( self._adjacency.reset_index(), source="focal", target="neighbor", edge_attr="weight", create_using=graph_type, ) def plot( self, gdf, focal=None, nodes=True, color="k", edge_kws=None, node_kws=None, focal_kws=None, ax=None, figsize=None, limit_extent=False, ): """Plot edges and nodes of the Graph Creates a ``maptlotlib`` plot based on the topology stored in the Graph and spatial location defined in ``gdf``. Parameters ---------- gdf : geopandas.GeoDataFrame Geometries indexed using the same index as Graph. Geometry types other than points are converted to centroids encoding start and end point of Graph edges. focal : hashable | array-like[hashable] | None, optional ID or an array-like of IDs of focal geometries whose weights shall be plotted. If None, all weights from all focal geometries are plotted. By default None nodes : bool, optional Plot nodes as points, by default True color : str, optional The color of all objects, by default "k" edge_kws : dict, optional Keyword arguments dictionary to send to ``LineCollection``, which provides fine-grained control over the aesthetics of the edges in the plot. By default None node_kws : dict, optional Keyword arguments dictionary to send to ``ax.scatter``, which provides fine-grained control over the aesthetics of the nodes in the plot. By default None focal_kws : dict, optional Keyword arguments dictionary to send to ``ax.scatter``, which provides fine-grained control over the aesthetics of the focal nodes in the plot on top of generic ``node_kws``. Values of ``node_kws`` are updated from ``focal_kws``. Ignored if ``focal=None``. By default None ax : matplotlib.axes.Axes, optional Axis on which to plot the weights. If None, a new figure and axis are created. By default None figsize : tuple, optional figsize used to create a new axis. By default None limit_extent : bool, optional limit the extent of the axis to the extent of the plotted graph, by default False Returns ------- matplotlib.axes.Axes Axis with the resulting plot Notes ----- If you'd like to overlay the actual geometries from the ``geopandas.GeoDataFrame``, create an axis by plotting the ``GeoDataFrame`` and plot the Graph on top. ax = gdf.plot() gdf_graph.plot(gdf, ax=ax) """ return _plot( self, gdf, focal=focal, nodes=nodes, color=color, node_kws=node_kws, edge_kws=edge_kws, focal_kws=focal_kws, ax=ax, figsize=figsize, limit_extent=limit_extent, ) def explore( self, gdf, focal=None, nodes=True, color="black", edge_kws=None, node_kws=None, focal_kws=None, m=None, **kwargs, ): """Plot graph as an interactive Folium Map Parameters ---------- gdf : geopandas.GeoDataFrame geodataframe used to instantiate to Graph focal : list, optional subset of focal observations to plot in the map, by default None. If none, all relationships are plotted nodes : bool, optional whether to display observations as nodes in the map, by default True color : str, optional color applied to nodes and edges, by default "black" edge_kws : dict, optional additional keyword arguments passed to geopandas explore function when plotting edges, by default None node_kws : dict, optional additional keyword arguments passed to geopandas explore function when plotting nodes, by default None focal_kws : dict, optional additional keyword arguments passed to geopandas explore function when plotting focal observations, by default None. Only applicable when passing a subset of nodes with the `focal` argument m : Folilum.Map, optional folium map objecto to plot on top of, by default None **kwargs : dict, optional additional keyword arguments are passed directly to geopandas.explore, when ``m=None`` by default None Returns ------- folium.Map folium map """ return _explore_graph( self, gdf, focal=focal, nodes=nodes, color=color, edge_kws=edge_kws, node_kws=node_kws, focal_kws=focal_kws, m=m, **kwargs, ) def _arrange_arrays(heads, tails, weights, ids=None): """ Rearrange input arrays so that observation indices are well-ordered with respect to the input ids. That is, an "early" identifier should always preceed a "later" identifier in the heads, but the tails should be sorted with respect to heads *first*, then sorted within the tails. """ if ids is None: ids = np.unique(np.hstack((heads, tails))) lookup = list(ids).index input_df = pd.DataFrame.from_dict( {"focal": heads, "neighbor": tails, "weight": weights} ) return ( input_df.set_index(["focal", "neighbor"]) .assign( focal_loc=input_df.focal.apply(lookup).values, neighbor_loc=input_df.neighbor.apply(lookup).values, ) .sort_values(["focal_loc", "neighbor_loc"]) .reset_index() .drop(["focal_loc", "neighbor_loc"], axis=1) .values.T ) def read_parquet(path, **kwargs): """Read Graph from a Apache Parquet Read Graph serialized using `Graph.to_parquet()` back into the `Graph` object. The Parquet file needs to contain adjacency table with a structure required by the `Graph` constructor and optional metadata with the type of transformation. Parameters ---------- path : str | pyarrow.NativeFile | file-like object path or any stream supported by pyarrow **kwargs additional keyword arguments passed to pyarrow.parquet.read_table Returns ------- Graph deserialized Graph """ adjacency, transformation = _read_parquet(path, **kwargs) return Graph(adjacency, transformation) libpysal-4.9.2/libpysal/graph/tests/000077500000000000000000000000001452177046000174355ustar00rootroot00000000000000libpysal-4.9.2/libpysal/graph/tests/test_base.py000066400000000000000000000723251452177046000217710ustar00rootroot00000000000000import os import string import tempfile import geodatasets import geopandas as gpd import numpy as np import pandas as pd import pytest from scipy import sparse from libpysal import graph, weights class TestBase: def setup_method(self): self.neighbor_dict_int = {0: 1, 1: 2, 2: 5, 3: 4, 4: 5, 5: 8, 6: 7, 7: 8, 8: 7} self.weight_dict_int_binary = { 0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, } self.index_int = pd.Index( [0, 1, 2, 3, 4, 5, 6, 7, 8], dtype="int64", name="focal", ) self.neighbor_dict_str = { string.ascii_letters[k]: string.ascii_letters[v] for k, v in self.neighbor_dict_int.items() } self.weight_dict_str_binary = { string.ascii_letters[k]: v for k, v in self.weight_dict_int_binary.items() } self.index_str = pd.Index( [string.ascii_letters[k] for k in self.index_int], dtype="object", name="focal", ) self.adjacency_int_binary = pd.Series( self.weight_dict_int_binary.values(), name="weight", index=pd.MultiIndex.from_arrays( [self.index_int, self.neighbor_dict_int.values()], names=["focal", "neighbor"], ), ) self.adjacency_str_binary = pd.Series( self.weight_dict_str_binary.values(), name="weight", index=pd.MultiIndex.from_arrays( [self.index_str, self.neighbor_dict_str.values()], names=["focal", "neighbor"], ), ) # one isolate, one self-link self.W_dict_int = { 0: {0: 1, 3: 0.5, 1: 0.5}, 1: {0: 0.3, 4: 0.3, 2: 0.3}, 2: {1: 0.5, 5: 0.5}, 3: {0: 0.3, 6: 0.3, 4: 0.3}, 4: {1: 0.25, 3: 0.25, 7: 0.25, 5: 0.25}, 5: {2: 0.3, 4: 0.3, 8: 0.3}, 6: {3: 0.5, 7: 0.5}, 7: {4: 0.3, 6: 0.3, 8: 0.3}, 8: {5: 0.5, 7: 0.5}, 9: {}, } self.W_dict_str = { string.ascii_letters[k]: { string.ascii_letters[k_]: v_ for k_, v_ in v.items() } for k, v in self.W_dict_int.items() } self.g_int = graph.Graph.from_weights_dict(self.W_dict_int) self.g_str = graph.Graph.from_weights_dict(self.W_dict_str) rng = np.random.default_rng(seed=0) self.letters = np.asarray(list(string.ascii_letters[:26])) rng.shuffle(self.letters) self.W_dict_str_unordered = { self.letters[k]: {self.letters[k_]: v_ for k_, v_ in v.items()} for k, v in self.W_dict_int.items() } self.g_str_unodered = graph.Graph.from_weights_dict(self.W_dict_str_unordered) self.nybb = gpd.read_file(geodatasets.get_path("nybb")).set_index("BoroName") def test_init(self): g = graph.Graph(self.adjacency_int_binary) assert isinstance(g, graph.Graph) assert hasattr(g, "_adjacency") assert g._adjacency.shape == (9,) pd.testing.assert_series_equal(g._adjacency, self.adjacency_int_binary) assert hasattr(g, "transformation") assert g.transformation == "O" g = graph.Graph(self.adjacency_str_binary) assert isinstance(g, graph.Graph) assert hasattr(g, "_adjacency") assert g._adjacency.shape == (9,) pd.testing.assert_series_equal(g._adjacency, self.adjacency_str_binary) assert hasattr(g, "transformation") assert g.transformation == "O" with pytest.raises(TypeError, match="The adjacency table needs to be"): graph.Graph(self.adjacency_int_binary.values) with pytest.raises(ValueError, match="The index of the adjacency table"): adj = self.adjacency_int_binary.copy() adj.index.names = ["foo", "bar"] graph.Graph(adj) with pytest.raises( ValueError, match="The adjacency needs to be named 'weight'" ): graph.Graph(self.adjacency_int_binary.rename("foo")) with pytest.raises(ValueError, match="The 'weight' needs"): graph.Graph(self.adjacency_int_binary.astype(str)) with pytest.raises( ValueError, match="The adjacency table cannot contain missing" ): adj = self.adjacency_int_binary.copy() adj.iloc[0] = np.nan graph.Graph(adj) with pytest.raises(ValueError, match="'transformation' needs to be"): graph.Graph(self.adjacency_int_binary, transformation="foo") def test_copy(self): g_copy = self.g_str.copy() assert g_copy == self.g_str g_copy._adjacency.iloc[0] = 2 assert g_copy != self.g_str def test_adjacency(self): g = graph.Graph(self.adjacency_int_binary) adjacency = g.adjacency pd.testing.assert_series_equal(adjacency, self.adjacency_int_binary) # ensure copy adjacency.iloc[0] = 100 pd.testing.assert_series_equal(g._adjacency, self.adjacency_int_binary) def test_w_roundtrip(self): w = self.g_int.to_W() pd.testing.assert_series_equal( self.g_int._adjacency.sort_index(), w.to_adjlist(drop_islands=False) .set_index(["focal", "neighbor"])["weight"] .sort_index(), check_index_type=False, check_dtype=False, ) g_roundtripped = graph.Graph.from_W(w) assert self.g_int == g_roundtripped w = self.g_str.to_W() pd.testing.assert_series_equal( self.g_str._adjacency.sort_index(), w.to_adjlist(drop_islands=False) .set_index(["focal", "neighbor"])["weight"] .sort_index(), check_index_type=False, check_dtype=False, ) g_roundtripped = graph.Graph.from_W(w) assert self.g_str == g_roundtripped w = weights.lat2W(3, 3) g = graph.Graph.from_W(w) pd.testing.assert_series_equal( g._adjacency.sort_index(), w.to_adjlist().set_index(["focal", "neighbor"])["weight"].sort_index(), check_index_type=False, check_dtype=False, ) w_exp = g.to_W() # assert w.neighbors == w_exp.neighbors assert w.weights == w_exp.weights w.transform = "r" g_rowwise = graph.Graph.from_W(w) pd.testing.assert_series_equal( g_rowwise._adjacency.sort_index(), w.to_adjlist().set_index(["focal", "neighbor"])["weight"].sort_index(), check_index_type=False, check_dtype=False, ) w_trans = g_rowwise.to_W() # assert w.neighbors == w_trans.neighbors assert w.weights == w_trans.weights diag = weights.fill_diagonal(w) g_diag = graph.Graph.from_W(diag) pd.testing.assert_series_equal( g_diag._adjacency.sort_index(), diag.to_adjlist().set_index(["focal", "neighbor"])["weight"].sort_index(), check_index_type=False, check_dtype=False, ) w_diag = g_diag.to_W() # assert diag.neighbors == W_diag.neighbors assert diag.weights == w_diag.weights w = weights.W( neighbors={"a": ["b"], "b": ["a", "c"], "c": ["b"], "d": []}, silence_warnings=True, ) g_isolate = graph.Graph.from_W(w) pd.testing.assert_series_equal( g_isolate._adjacency.sort_index(), w.to_adjlist().set_index(["focal", "neighbor"])["weight"].sort_index(), check_index_type=False, check_dtype=False, ) w_isolate = g_isolate.to_W() # assert w.neighbors == w_isolate.neighbors assert w.weights == w_isolate.weights w = self.g_str_unodered.to_W() assert w.id_order_set np.testing.assert_array_equal(w.id_order, self.letters[:10]) def test_from_sparse(self): row = np.array([0, 0, 1, 2, 3, 3]) col = np.array([1, 3, 3, 2, 1, 3]) data = np.array([0.1, 0.5, 0.9, 0, 0.3, 0.1]) sp = sparse.coo_array((data, (row, col)), shape=(4, 4)) g = graph.Graph.from_sparse(sp) expected = graph.Graph.from_arrays(row, col, data) assert g == expected, "sparse constructor does not match arrays constructor" g = graph.Graph.from_sparse(sp.tocsr()) assert g == expected, "csc input does not match coo input" g = graph.Graph.from_sparse(sp.tocsc()) assert g == expected, "csr input does not match coo input" ids = ["zero", "one", "two", "three"] g_named = graph.Graph.from_sparse( sp, ids=ids, ) expected = graph.Graph.from_arrays( ["zero", "zero", "one", "two", "three", "three"], ["one", "three", "three", "two", "one", "three"], data, ) assert g_named == expected g = graph.Graph.from_sparse( sp.tocsr(), ids=ids, ) assert g == expected, "sparse csr with ids does not match arrays constructor" g = graph.Graph.from_sparse( sp.tocsc(), ids=ids, ) assert g == (expected), "sparse csr with ids does not match arrays constructor" dense = np.array( [ [0, 0, 1, 1, 0], [0, 0, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 0, 0, 1], [0, 1, 0, 1, 0], ] ) sp = sparse.csr_array(dense) g = graph.Graph.from_sparse( sp, ids=["staten_island", "queens", "brooklyn", "manhattan", "bronx"] ) expected = graph.Graph.from_arrays( [ "staten_island", "staten_island", "queens", "queens", "brooklyn", "brooklyn", "manhattan", "manhattan", "bronx", "bronx", ], [ "brooklyn", "manhattan", "brooklyn", "manhattan", "queens", "manhattan", "queens", "bronx", "queens", "manhattan", ], np.ones(10), ) assert ( g == expected ), "sparse csr nybb with ids does not match arrays constructor" np.testing.assert_array_equal(g.sparse.todense(), sp.todense()) with pytest.raises(ValueError, match="The length of ids "): graph.Graph.from_sparse(sp, ids=["staten_island", "queens"]) def test_from_arrays(self): focal_ids = np.arange(9) neighbor_ids = np.array([1, 2, 5, 4, 5, 8, 7, 8, 7]) weight = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1]) g = graph.Graph.from_arrays(focal_ids, neighbor_ids, weight) pd.testing.assert_series_equal( g._adjacency, self.adjacency_int_binary, check_index_type=False, check_dtype=False, ) focal_ids = np.asarray(list(self.neighbor_dict_str.keys())) neighbor_ids = np.asarray(list(self.neighbor_dict_str.values())) g = graph.Graph.from_arrays(focal_ids, neighbor_ids, weight) pd.testing.assert_series_equal( g._adjacency, self.adjacency_str_binary, check_index_type=False, check_dtype=False, ) def test_from_weights_dict(self): weights_dict = { 0: {2: 0.5, 1: 0.5}, 1: {0: 0.5, 3: 0.5}, 2: { 0: 0.3, 4: 0.3, 3: 0.3, }, 3: { 1: 0.3, 2: 0.3, 5: 0.3, }, 4: {2: 0.5, 5: 0.5}, 5: {3: 0.5, 4: 0.5}, } exp_focal = pd.Index( [0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5], dtype="int64", name="focal" ) exp_neighbor = [2, 1, 0, 3, 0, 4, 3, 1, 2, 5, 2, 5, 3, 4] exp_weight = [ 0.5, 0.5, 0.5, 0.5, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.5, 0.5, 0.5, 0.5, ] expected = graph.Graph.from_arrays(exp_focal, exp_neighbor, exp_weight) g = graph.Graph.from_weights_dict(weights_dict) assert g == expected def test_from_dicts(self): g = graph.Graph.from_dicts(self.neighbor_dict_int) pd.testing.assert_series_equal( g._adjacency, self.adjacency_int_binary, check_dtype=False, check_index_type=False, ) g = graph.Graph.from_dicts(self.neighbor_dict_str) pd.testing.assert_series_equal( g._adjacency, self.adjacency_str_binary, check_dtype=False, ) @pytest.mark.parametrize("y", [3, 5]) @pytest.mark.parametrize("id_type", ["int", "str"]) @pytest.mark.parametrize("rook", [True, False]) def test_from_dicts_via_w(self, y, id_type, rook): w = weights.lat2W(3, y, id_type=id_type, rook=rook) g = graph.Graph.from_dicts(w.neighbors, w.weights) pd.testing.assert_series_equal( g._adjacency.sort_index(), w.to_adjlist().set_index(["focal", "neighbor"])["weight"].sort_index(), check_index_type=False, check_dtype=False, ) w.transform = "r" g_rowwise = graph.Graph.from_dicts(w.neighbors, w.weights) pd.testing.assert_series_equal( g_rowwise._adjacency.sort_index(), w.to_adjlist().set_index(["focal", "neighbor"])["weight"].sort_index(), check_index_type=False, check_dtype=False, ) diag = weights.fill_diagonal(w) g_diag = graph.Graph.from_dicts(diag.neighbors, diag.weights) pd.testing.assert_series_equal( g_diag._adjacency.sort_index(), diag.to_adjlist().set_index(["focal", "neighbor"])["weight"].sort_index(), check_index_type=False, check_dtype=False, ) w = weights.W( neighbors={"a": ["b"], "b": ["a", "c"], "c": ["b"], "d": []}, silence_warnings=True, ) g_isolate = graph.Graph.from_dicts(w.neighbors, w.weights) pd.testing.assert_series_equal( g_isolate._adjacency.sort_index(), w.to_adjlist().set_index(["focal", "neighbor"])["weight"].sort_index(), check_index_type=False, check_dtype=False, ) def test_neighbors(self): expected = { 0: (0, 1, 3), 1: (0, 2, 4), 2: (1, 5), 3: (0, 4, 6), 4: (1, 3, 5, 7), 5: (2, 4, 8), 6: (3, 7), 7: (4, 6, 8), 8: (5, 7), 9: (), } assert self.g_int.neighbors == expected expected = { "a": ("a", "b", "d"), "b": ("a", "c", "e"), "c": ("b", "f"), "d": ("a", "e", "g"), "e": ("b", "d", "f", "h"), "f": ("c", "e", "i"), "g": ("d", "h"), "h": ("e", "g", "i"), "i": ("f", "h"), "j": (), } assert self.g_str.neighbors == expected def test_weights(self): expected = { 0: (1.0, 0.5, 0.5), 1: (0.3, 0.3, 0.3), 2: (0.5, 0.5), 3: (0.3, 0.3, 0.3), 4: (0.25, 0.25, 0.25, 0.25), 5: (0.3, 0.3, 0.3), 6: (0.5, 0.5), 7: (0.3, 0.3, 0.3), 8: (0.5, 0.5), 9: (), } assert self.g_int.weights == expected expected = { "a": (1.0, 0.5, 0.5), "b": (0.3, 0.3, 0.3), "c": (0.5, 0.5), "d": (0.3, 0.3, 0.3), "e": (0.25, 0.25, 0.25, 0.25), "f": (0.3, 0.3, 0.3), "g": (0.5, 0.5), "h": (0.3, 0.3, 0.3), "i": (0.5, 0.5), "j": (), } assert self.g_str.weights == expected def test_sparse(self): sp = self.g_int.sparse expected = np.array( [ [1.0, 0.5, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.3, 0.0, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.5, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0], [0.3, 0.0, 0.0, 0.0, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0], [0.0, 0.25, 0.0, 0.25, 0.0, 0.25, 0.0, 0.25, 0.0, 0.0], [0.0, 0.0, 0.3, 0.0, 0.3, 0.0, 0.0, 0.0, 0.3, 0.0], [0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.3, 0.0, 0.3, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.5, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ] ) np.testing.assert_array_equal(sp.todense(), expected) sp = self.g_str.sparse np.testing.assert_array_equal(sp.todense(), expected) sp_old = self.g_int.to_W().sparse.todense() np.testing.assert_array_equal(sp.todense(), sp_old) sp_old = self.g_str.to_W().sparse.todense() np.testing.assert_array_equal(sp.todense(), sp_old) # check proper sorting nybb = graph.Graph.build_contiguity(self.nybb) nybb_expected = np.array( [ [0, 0, 0, 0, 0], [0, 0, 1, 1, 1], [0, 1, 0, 1, 0], [0, 1, 1, 0, 1], [0, 1, 0, 1, 0], ] ) np.testing.assert_array_equal(nybb.sparse.todense(), nybb_expected) def test_sparse_roundtrip(self): g = graph.Graph(self.adjacency_int_binary) sp = g.sparse g_sp = graph.Graph.from_sparse(sp, self.index_int) assert g == g_sp g = graph.Graph(self.adjacency_str_binary) sp = g.sparse g_sp = graph.Graph.from_sparse(sp, self.index_str) assert g == g_sp def test_cardinalities(self): expected = pd.Series( [3, 3, 2, 3, 4, 3, 2, 3, 2, 0], index=pd.Index( ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"], dtype="object", name="focal", ), name="cardinalities", ) pd.testing.assert_series_equal(self.g_str.cardinalities, expected) def test_isolates(self): expected = pd.Index(["j"], name="focal") pd.testing.assert_index_equal(self.g_str.isolates, expected) self.g_str._adjacency.iloc[1] = 0 # zero weight, no isolate pd.testing.assert_index_equal(self.g_str.isolates, expected) def test_n(self): assert self.g_int.n == 10 assert self.g_str.n == 10 assert graph.Graph(self.adjacency_int_binary).n == 9 def test_pct_nonzero(self): assert self.g_int.pct_nonzero == 26.0 assert graph.Graph(self.adjacency_int_binary).pct_nonzero == pytest.approx( 11.1111111111 ) def test_nonzero(self): assert self.g_int.nonzero == 25 assert graph.Graph(self.adjacency_int_binary).nonzero == 9 def test_transform_r(self): expected_w = [ 0.5, 0.25, 0.25, 0.33333333, 0.33333333, 0.33333333, 0.5, 0.5, 0.33333333, 0.33333333, 0.33333333, 0.25, 0.25, 0.25, 0.25, 0.33333333, 0.33333333, 0.33333333, 0.5, 0.5, 0.33333333, 0.33333333, 0.33333333, 0.5, 0.5, 0.0, ] exp = self.g_int.adjacency exp.iloc[:] = expected_w expected = graph.Graph(exp) assert self.g_int.transform("r") == expected assert self.g_int.transform("r").transformation == "R" assert self.g_int.transform("R") == expected w = self.g_int.to_W() w.transform = "r" g_from_w = graph.Graph.from_W(w) assert g_from_w == self.g_int.transform("r") def test_transform_b(self): expected_w = [ 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, 0, ] exp = self.g_int.adjacency exp.iloc[:] = expected_w expected = graph.Graph(exp) assert self.g_int.transform("b") == expected assert self.g_int.transform("b").transformation == "B" assert self.g_int.transform("B") == expected w = self.g_int.to_W() w.transform = "b" g_from_w = graph.Graph.from_W(w) assert g_from_w == self.g_int.transform("b") def test_transform_d(self): expected_w = [ 0.10416667, 0.05208333, 0.05208333, 0.03125, 0.03125, 0.03125, 0.05208333, 0.05208333, 0.03125, 0.03125, 0.03125, 0.02604167, 0.02604167, 0.02604167, 0.02604167, 0.03125, 0.03125, 0.03125, 0.05208333, 0.05208333, 0.03125, 0.03125, 0.03125, 0.05208333, 0.05208333, 0.0, ] exp = self.g_int.adjacency exp.iloc[:] = expected_w expected = graph.Graph(exp) assert self.g_int.transform("d") == expected assert self.g_int.transform("d").transformation == "D" assert self.g_int.transform("D") == expected assert self.g_int.transform("D")._adjacency.sum() == pytest.approx(1) w = self.g_int.to_W() w.transform = "d" g_from_w = graph.Graph.from_W(w) assert g_from_w == self.g_int.transform("d") def test_transform_v(self): expected_w = [ 0.55154388, 0.27577194, 0.27577194, 0.39000042, 0.39000042, 0.39000042, 0.47765102, 0.47765102, 0.39000042, 0.39000042, 0.39000042, 0.33775027, 0.33775027, 0.33775027, 0.33775027, 0.39000042, 0.39000042, 0.39000042, 0.47765102, 0.47765102, 0.39000042, 0.39000042, 0.39000042, 0.47765102, 0.47765102, 0.0, ] exp = self.g_int.adjacency exp.iloc[:] = expected_w expected = graph.Graph(exp) assert self.g_int.transform("v") == expected assert self.g_int.transform("v").transformation == "V" assert self.g_int.transform("V") == expected w = self.g_int.to_W() w.transform = "v" g_from_w = graph.Graph.from_W(w) assert g_from_w == self.g_int.transform("v") def test_transform(self): # do not transform if transformation == current transformation binary = self.g_int.transform("b") fast_tracked = binary.transform("b") assert binary == fast_tracked with pytest.raises(ValueError, match="Transformation 'X' is not"): self.g_int.transform("x") def test_asymmetry(self): neighbors = { "a": ["b", "c", "d"], "b": ["b", "c", "d"], "c": ["a", "b"], "d": ["a", "b"], } weights_d = {"a": [1, 0.5, 1], "b": [1, 1, 1], "c": [1, 1], "d": [1, 1]} g = graph.Graph.from_dicts(neighbors, weights_d) intrinsic = pd.Series( ["b", "c", "a", "a"], index=pd.Index(["a", "a", "b", "c"], name="focal"), name="neighbor", ) pd.testing.assert_series_equal(intrinsic, g.asymmetry()) boolean = pd.Series( ["b", "a"], index=pd.Index(["a", "b"], name="focal"), name="neighbor", ) pd.testing.assert_series_equal(boolean, g.asymmetry(intrinsic=False)) empty = pd.Series( index=pd.Index([], name="focal"), name="neighbor", dtype="int64", ) pd.testing.assert_series_equal(self.g_int.asymmetry(False), empty) def test_parquet(self): pytest.importorskip("pyarrow") with tempfile.TemporaryDirectory() as tmpdir: path = os.path.join(tmpdir, "g_int.parquet") self.g_int.to_parquet(path) g_int = graph.read_parquet(path) assert self.g_int == g_int path = os.path.join(tmpdir, "g_str.parquet") self.g_str.to_parquet(path) g_str = graph.read_parquet(path) assert self.g_str == g_str row_wise = self.g_str.transform("r") path = os.path.join(tmpdir, "row.parquet") row_wise.to_parquet(path) row_read = graph.read_parquet(path) assert row_wise == row_read assert row_read.transformation == "R" path = os.path.join(tmpdir, "pandas.parquet") self.g_str._adjacency.to_frame().to_parquet(path) g_pandas = graph.read_parquet(path) assert self.g_str == g_pandas def test_getitem(self): expected = pd.Series( [1, 0.5, 0.5], index=pd.Index(["a", "b", "d"], name="neighbor"), name="weight", ) pd.testing.assert_series_equal(expected, self.g_str["a"]) expected = pd.Series( [1, 0.5, 0.5], index=pd.Index([0, 1, 3], name="neighbor"), name="weight", ) pd.testing.assert_series_equal(expected, self.g_int[0]) # isolate expected = pd.Series( [], index=pd.Index([], name="neighbor"), name="weight", ) pd.testing.assert_series_equal(expected, self.g_str["j"]) def test_lag(self): expected = np.array([4.0, 2.7, 4.0, 3.9, 5.0, 5.1, 6.0, 6.3, 7.0, 0.0]) lag = self.g_str.lag(list(range(1, 11))) np.testing.assert_allclose(expected, lag) with pytest.raises(ValueError, match="The length of `y`"): self.g_str.lag(list(range(1, 15))) def test_higher_order(self): cont = graph.Graph.build_contiguity(self.nybb) k2 = cont.higher_order(2) expected = graph.Graph.from_arrays( self.nybb.index, ["Staten Island", "Queens", "Bronx", "Manhattan", "Brooklyn"], [0, 0, 1, 0, 1], ) assert k2 == expected diagonal = cont.higher_order(2, diagonal=True) expected = graph.Graph.from_arrays( [ "Staten Island", "Queens", "Brooklyn", "Brooklyn", "Manhattan", "Bronx", "Bronx", ], [ "Staten Island", "Queens", "Brooklyn", "Bronx", "Manhattan", "Brooklyn", "Bronx", ], [0, 1, 1, 1, 1, 1, 1], ) assert diagonal == expected shortest_false = cont.higher_order(2, shortest_path=False) expected = graph.Graph.from_arrays( [ "Staten Island", "Queens", "Queens", "Queens", "Brooklyn", "Brooklyn", "Brooklyn", "Manhattan", "Manhattan", "Manhattan", "Bronx", "Bronx", "Bronx", ], [ "Staten Island", "Brooklyn", "Manhattan", "Bronx", "Queens", "Manhattan", "Bronx", "Queens", "Brooklyn", "Bronx", "Queens", "Brooklyn", "Manhattan", ], [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], ) assert shortest_false == expected lower = cont.higher_order(2, lower_order=True) assert lower == expected def test_n_components(self): nybb = graph.Graph.build_contiguity(self.nybb) assert nybb.n_components == 2 nybb = graph.Graph.build_knn(self.nybb.set_geometry(self.nybb.centroid), k=2) assert nybb.n_components == 1 def test_component_labels(self): nybb = graph.Graph.build_contiguity(self.nybb) expected = pd.Series( [0, 1, 1, 1, 1], index=pd.Index(self.nybb.index.values, name="focal"), dtype=int, name="component labels", ) pd.testing.assert_series_equal( expected, nybb.component_labels, check_dtype=False ) libpysal-4.9.2/libpysal/graph/tests/test_builders.py000066400000000000000000000424701452177046000226660ustar00rootroot00000000000000import geodatasets import geopandas as gpd import numpy as np import pandas as pd import pytest from numpy.testing import assert_array_almost_equal from scipy.sparse import csr_matrix from shapely import get_coordinates from libpysal import graph TRIANGULATIONS = ["delaunay", "gabriel", "relative_neighborhood", "voronoi"] """ This file tests Graph initialisation from various build_* constructors. The correctness of the underlying data shall be tested in respective constructor test suites. """ class TestContiguity: def setup_method(self): self.gdf = gpd.read_file(geodatasets.get_path("nybb")) self.gdf_str = self.gdf.set_index("BoroName") def test_vertex_intids(self): g = graph.Graph.build_contiguity(self.gdf) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_vertex_strids(self): g = graph.Graph.build_contiguity(self.gdf_str) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_rook_intids(self): g = graph.Graph.build_contiguity(self.gdf, strict=True) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_rook_strids(self): g = graph.Graph.build_contiguity(self.gdf_str, strict=True) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_queen_intids(self): g = graph.Graph.build_contiguity(self.gdf, rook=False, strict=True) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_queen_strids(self): g = graph.Graph.build_contiguity(self.gdf_str, rook=False, strict=True) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_vertex_intids_perimeter(self): g = graph.Graph.build_contiguity(self.gdf, by_perimeter=True) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_vertex_strid_perimeters(self): g = graph.Graph.build_contiguity(self.gdf_str, by_perimeter=True) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_rook_intids_perimeter(self): g = graph.Graph.build_contiguity(self.gdf, strict=True, by_perimeter=True) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_rook_strid_perimeters(self): g = graph.Graph.build_contiguity(self.gdf_str, strict=True, by_perimeter=True) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_queen_intid_perimeters(self): g = graph.Graph.build_contiguity( self.gdf, rook=False, strict=True, by_perimeter=True ) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_queen_strids_perimeter(self): g = graph.Graph.build_contiguity( self.gdf_str, rook=False, strict=True, by_perimeter=True ) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_block_contiguity(self): regimes = ["n", "n", "s", "s", "e", "e", "w", "w", "e", "l"] g = graph.Graph.build_block_contiguity(regimes) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) regimes = pd.Series( regimes, index=["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"] ) g = graph.Graph.build_block_contiguity(regimes) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_fuzzy_contiguity_intids(self): g = graph.Graph.build_fuzzy_contiguity(self.gdf) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_fuzzy_contiguity_strids(self): g = graph.Graph.build_fuzzy_contiguity(self.gdf_str) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) class TestTriangulation: def setup_method(self): gdf = gpd.read_file(geodatasets.get_path("geoda liquor_stores")).explode( ignore_index=True ) self.gdf = gdf[~gdf.geometry.duplicated()] self.gdf_str = self.gdf.set_index("placeid") @pytest.mark.parametrize("method", TRIANGULATIONS) def test_triangulation_intids(self, method): g = graph.Graph.build_triangulation(self.gdf, method) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) @pytest.mark.parametrize("method", TRIANGULATIONS) def test_triangulation_strids(self, method): g = graph.Graph.build_triangulation(self.gdf_str, method) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) @pytest.mark.parametrize("method", TRIANGULATIONS) def test_triangulation_intids_kernel(self, method): g = graph.Graph.build_triangulation( self.gdf, method, kernel="parabolic", bandwidth=7500 ) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) @pytest.mark.parametrize("method", TRIANGULATIONS) def test_triangulation_strids_kernel(self, method): g = graph.Graph.build_triangulation( self.gdf_str, method, kernel="parabolic", bandwidth=7500 ) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_invalid_method(self): with pytest.raises(ValueError, match="Method 'invalid' is not supported"): graph.Graph.build_triangulation( self.gdf, method="invalid", kernel="parabolic", bandwidth=7500 ) class TestKernel: def setup_method(self): self.gdf = gpd.read_file(geodatasets.get_path("geoda liquor_stores")).explode( ignore_index=True ) self.gdf_str = self.gdf.set_index("placeid") def test_kernel_precompute(self): sklearn = pytest.importorskip("sklearn") df = gpd.read_file(geodatasets.get_path("nybb")) df = df.to_crs(df.estimate_utm_crs()) distmat = csr_matrix( sklearn.metrics.pairwise.euclidean_distances(get_coordinates(df.centroid)) ) g = graph.Graph.build_kernel(distmat, metric="precomputed") expected = np.array( [ 0.07131664, 0.14998932, 0.09804811, 0.0402638, 0.07131664, 0.18556845, 0.17529176, 0.16394507, 0.14998932, 0.18556845, 0.17495794, 0.11561449, 0.09804811, 0.17529176, 0.17495794, 0.19116432, 0.0402638, 0.16394507, 0.11561449, 0.19116432, ] ) assert_array_almost_equal(g.adjacency.values, expected, 3) def test_kernel_intids(self): g = graph.Graph.build_kernel(self.gdf) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_kernel_strids(self): g = graph.Graph.build_kernel(self.gdf_str) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_knn_intids(self): g = graph.Graph.build_knn(self.gdf, k=3) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_knn_strids(self): g = graph.Graph.build_kernel(self.gdf_str, k=3) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) class TestDistanceBand: def setup_method(self): df = gpd.read_file(geodatasets.get_path("nybb")) self.gdf = df.set_geometry(df.centroid) self.gdf_str = self.gdf.set_index("BoroName") def test_distance_band_intids(self): g = graph.Graph.build_distance_band(self.gdf, 50000) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_distance_band_strids(self): g = graph.Graph.build_distance_band(self.gdf_str, 50000) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_distance_band_intids_weighted(self): g = graph.Graph.build_distance_band(self.gdf, 50000, binary=False) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_distance_band_strids_weighted(self): g = graph.Graph.build_distance_band(self.gdf_str, 50000, binary=False) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_distance_band_intids_kernel(self): g = graph.Graph.build_distance_band( self.gdf, 50000, binary=False, kernel="gaussian" ) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_distance_band_strids_kernel(self): g = graph.Graph.build_distance_band( self.gdf_str, 50000, binary=False, kernel="gaussian" ) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) class TestAdjacency: def setup_method(self): self.gdf = gpd.read_file(geodatasets.get_path("nybb")) self.gdf_str = self.gdf.set_index("BoroName") self.expected_adjacency_intid = pd.DataFrame( { "focal": { 0: 0, 1: 1, 2: 1, 3: 1, 4: 2, 5: 2, 6: 3, 7: 3, 8: 3, 9: 4, 10: 4, }, "neighbor": { 0: 0, 1: 2, 2: 3, 3: 4, 4: 1, 5: 3, 6: 1, 7: 2, 8: 4, 9: 1, 10: 3, }, "weight": { 0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1, 10: 1, }, } ) self.expected_adjacency_strid = pd.DataFrame( { "focal": { 0: "Staten Island", 1: "Queens", 2: "Queens", 3: "Queens", 4: "Brooklyn", 5: "Brooklyn", 6: "Manhattan", 7: "Manhattan", 8: "Manhattan", 9: "Bronx", 10: "Bronx", }, "neighbor": { 0: "Staten Island", 1: "Brooklyn", 2: "Manhattan", 3: "Bronx", 4: "Queens", 5: "Manhattan", 6: "Queens", 7: "Brooklyn", 8: "Bronx", 9: "Queens", 10: "Manhattan", }, "weight": { 0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1, 10: 1, }, } ) def test_adjacency_intids(self): g = graph.Graph.from_adjacency( self.expected_adjacency_intid, ) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_numeric_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_adjacency_strids(self): g = graph.Graph.from_adjacency( self.expected_adjacency_strid, ) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["focal"]) assert pd.api.types.is_string_dtype(g._adjacency.index.dtypes["neighbor"]) assert pd.api.types.is_numeric_dtype(g._adjacency.dtype) def test_adjacency_rename(self): adj = self.expected_adjacency_intid adj.columns = ["focal", "neighbor", "cost"] # no longer named weight _ = graph.Graph.from_adjacency(adj, weight_col="cost") def test_adjacency_wrong(self): adj = self.expected_adjacency_intid adj.columns = ["focal", "neighbor", "cost"] # no longer named weight with pytest.raises(AssertionError, match='"weight" was given for `weight_col`'): graph.Graph.from_adjacency(adj) def test_adjacency_match_contiguity(self): contiguity = graph.Graph.build_contiguity(self.gdf) built = graph.Graph.from_adjacency(self.expected_adjacency_intid) assert contiguity == built contiguity_str = graph.Graph.build_contiguity(self.gdf_str) built_str = graph.Graph.from_adjacency(self.expected_adjacency_strid) assert contiguity_str == built_str libpysal-4.9.2/libpysal/graph/tests/test_contiguity.py000066400000000000000000000256311452177046000232530ustar00rootroot00000000000000""" For completeness, we need to test a shuffled dataframe (i.e. always send unsorted data) with: - numeric ids - string ids - mixed polygon/multipolygon dataset - mixed line/multiline dataset - dataset with islands """ import geodatasets import geopandas import numpy import pandas import pytest import shapely from libpysal.graph._contiguity import ( _block_contiguity, _fuzzy_contiguity, _queen, _rook, _vertex_set_intersection, ) numpy.random.seed(111211) rivers = geopandas.read_file(geodatasets.get_path("eea large_rivers")).sample( frac=1, replace=False ) rivers["strID"] = rivers.NAME rivers["intID"] = rivers.index.values + 2 nybb = geopandas.read_file(geodatasets.get_path("ny bb")) nybb["strID"] = nybb.BoroName nybb["intID"] = nybb.BoroCode parametrize_ids = pytest.mark.parametrize("ids", [None, "strID", "intID"]) parametrize_geoms = pytest.mark.parametrize("geoms", [rivers, nybb], ["rivers", "nybb"]) parametrize_perim = pytest.mark.parametrize( "by_perimeter", [False, True], ids=["binary", "perimeter"] ) parametrize_rook = pytest.mark.parametrize("rook", [True, False], ids=["rook", "queen"]) parametrize_pointset = pytest.mark.parametrize( "pointset", [True, False], ids=["pointset", "vertex intersection"] ) @parametrize_pointset @parametrize_rook @parametrize_ids def test_user_rivers(ids, rook, pointset, data=rivers): """ Check whether contiguity is constructed correctly for rivers in Europe. """ data = data.reset_index(drop=False).rename(columns={"index": "original_index"}) ids = "original_index" if ids is None else ids data.index = data[ids].values ids = data.index.values # implement known_heads, known_tails if rook: known_heads = known_tails = ids[numpy.arange(len(data))] known_weights = numpy.zeros_like(known_heads) else: known_heads = numpy.array(["Sava", "Danube", "Tisa", "Danube"]) known_tails = numpy.array(["Danube", "Sava", "Danube", "Tisa"]) isolates = data[~data.strID.isin(known_heads)].index.values tmp_ = ( data.reset_index(drop=False) .rename(columns={"index": "tmp_index"}) .set_index("strID") ) known_heads = tmp_.loc[known_heads, "tmp_index"].values known_tails = tmp_.loc[known_tails, "tmp_index"].values known_heads = numpy.hstack((known_heads, isolates)) known_tails = numpy.hstack((known_tails, isolates)) known_weights = numpy.ones_like(known_heads) known_weights[known_heads == known_tails] = 0 if pointset: f = _rook if rook else _queen derived = f(data, ids=ids) derived_by_index = f(data, ids=None) else: derived = _vertex_set_intersection(data, ids=ids, rook=rook) derived_by_index = _vertex_set_intersection(data, rook=rook, ids=None) assert set(zip(*derived, strict=True)) == set( zip(known_heads, known_tails, known_weights, strict=True) ) assert set(zip(*derived_by_index, strict=True)) == set( zip(known_heads, known_tails, known_weights, strict=True) ) @parametrize_rook @parametrize_perim @parametrize_ids def test_user_vertex_set_intersection_nybb(ids, rook, by_perimeter): """ check whether vertexset contiguity is constructed correctly for nybb """ data = nybb.copy() if ids is not None: data.index = data[ids].values ids = data.index.values # implement known_heads, known_tails known_heads = numpy.array([1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 0]) known_tails = numpy.array([2, 3, 4, 1, 3, 2, 1, 4, 1, 3, 0]) known_heads = data.index.values[known_heads] known_tails = data.index.values[known_tails] if by_perimeter: head_geom = data.geometry.loc[known_heads].values tail_geom = data.geometry.loc[known_tails].values known_weights = head_geom.intersection(tail_geom).length else: known_weights = numpy.ones_like(known_heads) known_weights[known_heads == known_tails] = 0 f = _rook if rook else _queen derived = f(data, by_perimeter=by_perimeter, ids=ids) derived_by_index = f(data, by_perimeter=by_perimeter, ids=None) assert set(zip(*derived, strict=True)) == set( zip(known_heads, known_tails, known_weights, strict=True) ) assert set(zip(*derived_by_index, strict=True)) == set( zip(known_heads, known_tails, known_weights, strict=True) ) @parametrize_rook @parametrize_perim @parametrize_ids def test_user_pointset_nybb(ids, by_perimeter, rook): """ check whether pointset weights are constructed correctly for nybb """ data = nybb.copy() if ids is not None: data.index = data[ids].values ids = data.index.values # implement known_heads, known_tails known_heads = numpy.array([1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 0]) known_tails = numpy.array([2, 3, 4, 1, 3, 2, 1, 4, 1, 3, 0]) known_heads = data.index.values[known_heads] known_tails = data.index.values[known_tails] if by_perimeter: head_geom = data.geometry.loc[known_heads].values tail_geom = data.geometry.loc[known_tails].values known_weights = head_geom.intersection(tail_geom).length else: known_weights = numpy.ones_like(known_heads) known_weights[known_heads == known_tails] = 0 f = _rook if rook else _queen derived = f(data, by_perimeter=by_perimeter, ids=ids) derived_by_index = f(data, by_perimeter=by_perimeter, ids=None) assert set(zip(*derived, strict=True)) == set( zip(known_heads, known_tails, known_weights, strict=True) ) assert set(zip(*derived_by_index, strict=True)) == set( zip(known_heads, known_tails, known_weights, strict=True) ) @parametrize_pointset def test_correctness_rook_queen_distinct(pointset): """ Check that queen and rook generate different contiguities in the case of a shared point but no edge. """ data = geopandas.GeoSeries((shapely.box(0, 0, 1, 1), shapely.box(1, 1, 2, 2))) if pointset: rook_ = _rook(data.geometry) queen_ = _queen(data.geometry) else: rook_ = _vertex_set_intersection(data.geometry, rook=True) queen_ = _vertex_set_intersection(data.geometry, rook=False) assert set(zip(*rook_, strict=True)) != set(zip(*queen_, strict=True)) def test_geom_type_raise(): """ Check the error for point geoms """ data = geopandas.GeoSeries((shapely.Point(0, 0), shapely.Point(2, 2))) with pytest.raises(ValueError, match="This Graph type is only well-defined"): _vertex_set_intersection(data) def test_overlap_raise(): data = nybb.set_index("BoroName").geometry.copy() data.iloc[1] = shapely.union( data.iloc[1], shapely.Point(1021176.479, 181374.797).buffer(10000) ) with pytest.raises(ValueError, match="Some geometries overlap."): _vertex_set_intersection(data, by_perimeter=True) def test_correctness_vertex_set_contiguity_distinct(): """ Check to ensure that vertex set ignores rook/queen neighbors that share an edge whose nodes are *not* in the vertex set. The test case is two offset squares """ data = geopandas.GeoSeries((shapely.box(0, 0, 1, 1), shapely.box(0.5, 1, 1.5, 2))) vs_rook = _vertex_set_intersection(data, rook=True) rook = _rook(data) assert set(zip(*vs_rook, strict=True)) != set(zip(*rook, strict=True)) vs_queen = _vertex_set_intersection(data, rook=False) queen = _queen(data) assert set(zip(*vs_queen, strict=True)) != set(zip(*queen, strict=True)) @pytest.mark.parametrize( "regimes", [ ["n", "n", "s", "s", "e", "e", "w", "w", "e", "j"], [0, 0, 2, 2, 3, 3, 4, 4, 3, 1], ], ) def test_block_contiguity(regimes): neighbors = _block_contiguity(regimes) wn = { 0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8], 6: [7], 7: [6], 8: [4, 5], 9: [], } assert {f: n.tolist() for f, n, in neighbors.items()} == wn ids = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"] neighbors = _block_contiguity(regimes, ids=ids) wn_str = {ids[f]: [ids[o] for o in n] for f, n in wn.items()} assert {f: n.tolist() for f, n, in neighbors.items()} == wn_str regimes = pandas.Series(regimes, index=ids) neighbors = _block_contiguity(regimes) assert {f: n.tolist() for f, n, in neighbors.items()} == wn_str def test_fuzzy_contiguity(): # integer head, tail, weight = _fuzzy_contiguity(nybb.set_index("intID"), nybb["intID"]) numpy.testing.assert_array_equal( head, [5, 4, 4, 4, 3, 3, 1, 1, 1, 2, 2], ) numpy.testing.assert_array_equal( tail, [5, 3, 1, 2, 4, 1, 4, 3, 2, 4, 1], ) numpy.testing.assert_array_equal(weight, [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) # string head, tail, weight = _fuzzy_contiguity(nybb.set_index("strID"), nybb["strID"]) numpy.testing.assert_array_equal( head, [ "Staten Island", "Queens", "Queens", "Queens", "Brooklyn", "Brooklyn", "Manhattan", "Manhattan", "Manhattan", "Bronx", "Bronx", ], ) numpy.testing.assert_array_equal( tail, [ "Staten Island", "Brooklyn", "Manhattan", "Bronx", "Queens", "Manhattan", "Queens", "Brooklyn", "Bronx", "Queens", "Manhattan", ], ) numpy.testing.assert_array_equal(weight, [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) # tolerance head, tail, weight = _fuzzy_contiguity( nybb.set_index("intID"), nybb["intID"], tolerance=0.05 ) numpy.testing.assert_array_equal( head, [5, 4, 4, 4, 3, 3, 3, 1, 1, 1, 2, 2], ) numpy.testing.assert_array_equal( tail, [3, 3, 1, 2, 5, 4, 1, 4, 3, 2, 4, 1], ) numpy.testing.assert_array_equal(weight, [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) # buffer head, tail, weight = _fuzzy_contiguity( nybb.set_index("intID"), nybb["intID"], buffer=5000 ) numpy.testing.assert_array_equal( head, [5, 4, 4, 4, 3, 3, 3, 1, 1, 1, 2, 2], ) numpy.testing.assert_array_equal( tail, [3, 3, 1, 2, 5, 4, 1, 4, 3, 2, 4, 1], ) numpy.testing.assert_array_equal(weight, [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) # predicate head, tail, weight = _fuzzy_contiguity( nybb.set_index("intID"), nybb["intID"], predicate="within" ) numpy.testing.assert_array_equal( head, [5, 4, 3, 1, 2], ) numpy.testing.assert_array_equal( tail, [5, 4, 3, 1, 2], ) numpy.testing.assert_array_equal(weight, [0, 0, 0, 0, 0]) with pytest.raises(ValueError, match="Only one"): _fuzzy_contiguity( nybb.set_index("intID"), nybb["intID"], tolerance=0.05, buffer=5000 ) libpysal-4.9.2/libpysal/graph/tests/test_kernel.py000066400000000000000000000306251452177046000223340ustar00rootroot00000000000000""" For completeness, we need to test a shuffled dataframe (i.e. always send unsorted data) with: - numeric ids - string ids - point dataframe - coordinates - check two kernel functions - check two tree types - scikit/no scikit """ import geodatasets import geopandas import numpy as np import pandas as pd import pytest from libpysal.graph._kernel import ( HAS_SKLEARN, _distance_band, _kernel, _kernel_functions, ) grocs = geopandas.read_file(geodatasets.get_path("geoda groceries"))[ ["OBJECTID", "geometry"] ].explode(ignore_index=True) grocs["strID"] = grocs.OBJECTID.astype(str) grocs["intID"] = grocs.OBJECTID.values kernel_functions = list(_kernel_functions.keys()) def my_kernel(distances, bandwidth): output = np.cos(distances / distances.max()) output[distances < bandwidth] = 0 return output kernel_functions.append(my_kernel) metrics = ("euclidean", "haversine") np.random.seed(6301) # create a 2-d laplace distribution as a "degenerate" # over-concentrated distribution # and rescale to match the lenght-scale in groceries lap_coords = np.random.laplace(size=(200, 2)) / 50 # create a 2-d cauchy as a "degenerate" # spatial outlier-y distribution cau_coords = np.random.standard_cauchy(size=(200, 2)) data = (grocs, lap_coords) parametrize_ids = pytest.mark.parametrize("ids", [None, "strID", "intID"]) parametrize_data = pytest.mark.parametrize("data", [grocs, lap_coords, cau_coords]) parametrize_kernelfunctions = pytest.mark.parametrize("kernel", kernel_functions) parametrize_metrics = pytest.mark.parametrize("metric", metrics, metrics) # how do we parameterize conditional on sklearn in env? @parametrize_ids def test_neighbors(ids): data = grocs.set_index(ids) if ids else grocs head, tail, weight = _kernel(data, bandwidth=5000, kernel="boxcar") assert head.shape[0] == 437 assert tail.shape == head.shape assert weight.shape == head.shape np.testing.assert_array_equal(pd.unique(head), data.index) known = np.linspace(9, 436, 10, dtype=int) head_exp = [2, 16, 28, 41, 55, 72, 92, 111, 135, 147] if ids: head_exp = data.index.values[head_exp] np.testing.assert_array_equal(head.values[known], head_exp) tail_exp = [13, 92, 66, 31, 33, 73, 16, 103, 9, 147] if ids: tail_exp = data.index.values[tail_exp] np.testing.assert_array_equal(tail.values[known], tail_exp) np.testing.assert_array_equal( weight[known], np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 0], dtype="int64"), ) @parametrize_data def test_no_taper(data): head, tail, weight = _kernel(data, taper=False) assert head.shape[0] == len(data) * (len(data) - 1) assert tail.shape == head.shape assert weight.shape == head.shape if hasattr(data, "index"): np.testing.assert_array_equal(np.unique(head), data.index) else: np.testing.assert_array_equal(np.unique(head), np.arange(len(data))) @parametrize_ids def test_ids(ids): data = grocs.set_index(ids) if ids else grocs head, tail, _ = _kernel(data) np.testing.assert_array_equal(pd.unique(head), data.index) assert np.in1d(tail, data.index).all() def test_distance(): _, _, weight = _kernel(grocs, kernel="identity") known = np.linspace(9, weight.shape[0], 10, dtype=int, endpoint=False) np.testing.assert_array_almost_equal( weight[known], np.array( [ 39028.10991144, 51086.85388002, 21270.55278224, 8999.11607504, 91203.25966722, 36548.75743352, 58917.81440314, 63359.35143896, 24952.48387721, 65860.55093353, ] ), ) _, _, weight = _kernel(lap_coords, kernel="identity") known = np.linspace(9, weight.shape[0], 10, dtype=int, endpoint=False) np.testing.assert_array_almost_equal( weight[known], np.array( [ 0.0112305, 0.03158595, 0.06027445, 0.01274032, 0.07559474, 0.02240698, 0.07024776, 0.05554498, 0.06012029, 0.03836196, ] ), ) _, _, weight = _kernel(cau_coords, kernel="identity") known = np.linspace(9, weight.shape[0], 10, dtype=int, endpoint=False) np.testing.assert_array_almost_equal( weight[known], np.array( [ 5.16946327, 2.29669606, 1.60345402, 1.23831204, 19.2459334, 2.99545291, 2.33316209, 2.09711302, 2.12594958, 6.19347592, ] ), ) @parametrize_data def test_k(data): head, tail, weight = _kernel(data, k=3, kernel="identity") assert head.shape[0] == data.shape[0] * 3 assert tail.shape == head.shape assert weight.shape == head.shape # TODO: what shall be the behaviour when k is set but bandwidth # TODO: is too small so weight is zero, eliminating the link with taper? # TODO: now the affected focal has less neighbors than K without warning # TODO: test it with the code above and default kernel @parametrize_kernelfunctions def test_kernels(kernel): _, _, weight = _kernel(grocs, kernel=kernel, taper=False) if kernel == "triangular": assert weight.mean() == pytest.approx(0.09416822598434019) assert weight.max() == pytest.approx(0.9874476868358023) elif kernel == "parabolic": assert weight.mean() == pytest.approx(0.10312196315841769) assert weight.max() == pytest.approx(0.749881829575671) elif kernel == "gaussian": assert weight.mean() == pytest.approx(0.1124559308071747) assert weight.max() == pytest.approx(0.22507021331712948) elif kernel == "bisquare": assert weight.mean() == pytest.approx(0.09084085210598618) assert weight.max() == pytest.approx(0.9372045972129259) elif kernel == "cosine": assert weight.mean() == pytest.approx(0.1008306468068958) assert weight.max() == pytest.approx(0.7852455006403666) elif kernel in ["boxcar", "discrete"]: assert weight.mean() == pytest.approx(0.2499540356683214) assert weight.max() == 1 elif kernel in ["identity", None]: assert weight.mean() == pytest.approx(39758.007361814016) assert weight.max() == pytest.approx(127937.75271993055) else: # function assert weight.mean() == pytest.approx(0.6880384553732511) assert weight.max() == pytest.approx(0.9855481738848647) @parametrize_data @pytest.mark.parametrize("bandwidth", [None, 0.05, 0.4]) def test_bandwidth(data, bandwidth): head, tail, weight = _kernel(data, bandwidth=bandwidth) assert tail.shape == head.shape assert weight.shape == head.shape if hasattr(data, "index"): np.testing.assert_array_equal(np.unique(head), data.index) else: np.testing.assert_array_equal(np.unique(head), np.arange(len(data))) @pytest.mark.parametrize( "metric", [ "euclidean", "minkowski", "cityblock", "chebyshev", "haversine", ], ) def test_metric(metric): data = grocs.to_crs(4326) if metric == "haversine" else grocs if not HAS_SKLEARN and metric in ["chebyshev", "haversine"]: pytest.skip("metric not supported by scipy") head, tail, weight = _kernel(data, metric=metric, kernel="identity", p=1.5) assert head.shape[0] == len(data) * (len(data) - 1) assert tail.shape == head.shape assert weight.shape == head.shape np.testing.assert_array_equal(pd.unique(head), data.index) if metric == "euclidean": assert weight.mean() == pytest.approx(39758.007362) assert weight.max() == pytest.approx(127937.75272) elif metric == "minkowski": assert weight.mean() == pytest.approx(42288.642129) assert weight.max() == pytest.approx(140674.095752) elif metric == "cityblock": assert weight.mean() == pytest.approx(49424.576155) assert weight.max() == pytest.approx(173379.431622) elif metric == "chebyshev": assert weight.mean() == pytest.approx(36590.352895) assert weight.max() == pytest.approx(123955.14249) else: assert weight.mean() == pytest.approx(0.115835) assert weight.max() == pytest.approx(0.371465) @pytest.mark.parametrize( "metric", [ "euclidean", "minkowski", "cityblock", "chebyshev", "haversine", ], ) def test_metric_k(metric): data = grocs.to_crs(4326) if metric == "haversine" else grocs if not HAS_SKLEARN and metric in ["chebyshev", "haversine"]: pytest.skip("metric not supported by scipy") head, tail, weight = _kernel(data, k=3, metric=metric, kernel="identity", p=1.5) assert head.shape[0] == len(data) * 3 assert tail.shape == head.shape assert weight.shape == head.shape np.testing.assert_array_equal(pd.unique(head), data.index) if metric == "euclidean": assert weight.mean() == pytest.approx(4577.237441) assert weight.max() == pytest.approx(18791.085051) elif metric == "minkowski": assert weight.mean() == pytest.approx(4884.254721) assert weight.max() == pytest.approx(20681.125211) elif metric == "cityblock": assert weight.mean() == pytest.approx(5665.288523) assert weight.max() == pytest.approx(23980.893147) elif metric == "chebyshev": assert weight.mean() == pytest.approx(4032.283559) assert weight.max() == pytest.approx(16374.141739) else: assert weight.mean() == pytest.approx(0.00021882448) assert weight.max() == pytest.approx(0.000897441) # def test_precomputed(data, ids): # raise NotImplementedError() def test_coincident(): grocs_duplicated = pd.concat( [grocs, grocs.iloc[:10], grocs.iloc[:3]], ignore_index=True ) # plain kernel head, tail, weight = _kernel(grocs_duplicated) assert head.shape[0] == len(grocs_duplicated) * (len(grocs_duplicated) - 1) assert tail.shape == head.shape assert weight.shape == head.shape np.testing.assert_array_equal(pd.unique(head), grocs_duplicated.index) # k, raise with pytest.raises(ValueError, match="There are"): _kernel(grocs_duplicated, k=2) # k, jitter head, tail, weight = _kernel( grocs_duplicated, taper=False, k=2, coincident="jitter" ) assert head.shape[0] == len(grocs_duplicated) * 2 assert tail.shape == head.shape assert weight.shape == head.shape np.testing.assert_array_equal(pd.unique(head), grocs_duplicated.index) # k, clique with pytest.raises(NotImplementedError): _kernel(grocs_duplicated, k=2, coincident="clique") def test_shape_preservation(): coordinates = np.vstack( [np.repeat(np.arange(10), 10), np.tile(np.arange(10), 10)] ).T head, tail, weight = _kernel( coordinates, k=3, metric="euclidean", p=2, kernel="boxcar", bandwidth=0.5, taper=False, ) np.testing.assert_array_equal(head, np.repeat(np.arange(100), 3)) assert tail.shape == head.shape, "shapes of head and tail do not match" np.testing.assert_array_equal(weight, np.zeros((300,), dtype=int)) head, tail, weight = _kernel( coordinates, k=3, metric="euclidean", p=2, kernel="boxcar", bandwidth=0.5, taper=True, ) np.testing.assert_array_equal(head, np.arange(100)) assert tail.shape == head.shape, "shapes of head and tail do not match" np.testing.assert_array_equal(weight, np.zeros((100,), dtype=int)) def test_haversine_check(): with pytest.raises(ValueError, match="'haversine'"): _kernel(grocs, k=2, metric="haversine") def test_distance_band_colocated(): coordinates = np.array([[0, 0], [1, 0], [1, 0], [2, 0], [3, 0]]) dist = _distance_band(coordinates, 1) assert dist.shape == (5, 5) np.testing.assert_array_equal( dist.data, np.array( [ 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, ] ), ) libpysal-4.9.2/libpysal/graph/tests/test_plotting.py000066400000000000000000000303661452177046000227160ustar00rootroot00000000000000import geodatasets import geopandas import numpy as np import pytest import shapely from libpysal import graph from libpysal.graph.tests.test_utils import fetch_map_string class TestPlotting: def setup_method(self): _ = pytest.importorskip("matplotlib") self.nybb = geopandas.read_file(geodatasets.get_path("nybb")) self.G = graph.Graph.build_contiguity(self.nybb) self.nybb_str = self.nybb.set_index("BoroName") self.G_str = graph.Graph.build_contiguity(self.nybb_str) self.expected_paths = [ np.array( [ [941639.45038754, 150931.99114113], [941639.45038754, 150931.99114113], ] ), np.array( [ [1034578.07840646, 197116.60422991], [998769.11468895, 174169.76072687], ] ), np.array( [ [1034578.07840646, 197116.60422991], [993336.96493848, 222451.43672456], ] ), np.array( [ [1034578.07840646, 197116.60422991], [1021174.78976724, 249937.98006968], ] ), np.array( [ [998769.11468895, 174169.76072687], [993336.96493848, 222451.43672456], ] ), np.array( [ [993336.96493848, 222451.43672456], [1021174.78976724, 249937.98006968], ] ), ] def test_default(self): ax = self.G.plot(self.nybb) # nodes and edges assert len(ax.collections) == 2 # edge geom linecollection = ax.collections[0] paths = linecollection.get_paths() for i, path in enumerate(paths): np.testing.assert_almost_equal(self.expected_paths[i], path.vertices) # node geom pathcollection = ax.collections[1] np.testing.assert_array_equal( pathcollection.get_offsets().data, shapely.get_coordinates(self.nybb.centroid), ) # edge color np.testing.assert_array_equal( linecollection.get_color(), np.array([[0.0, 0.0, 0.0, 1.0]]) ) # node color np.testing.assert_array_equal( pathcollection.get_facecolor(), np.array([[0.0, 0.0, 0.0, 1.0]]) ) def test_string_id(self): ax = self.G_str.plot(self.nybb_str) assert len(ax.collections) == 2 linecollection = ax.collections[0] paths = linecollection.get_paths() for i, path in enumerate(paths): np.testing.assert_almost_equal(self.expected_paths[i], path.vertices) pathcollection = ax.collections[1] np.testing.assert_array_equal( pathcollection.get_offsets().data, shapely.get_coordinates(self.nybb.centroid), ) def test_misaligned(self): ax = self.G_str.plot(self.nybb_str.sort_index()) assert len(ax.collections) == 2 linecollection = ax.collections[0] paths = linecollection.get_paths() for i, path in enumerate(paths): np.testing.assert_almost_equal(self.expected_paths[i], path.vertices) pathcollection = ax.collections[1] np.testing.assert_array_equal( pathcollection.get_offsets().data, shapely.get_coordinates(self.nybb.centroid), ) def test_no_nodes(self): ax = self.G.plot(self.nybb, nodes=False) assert len(ax.collections) == 1 linecollection = ax.collections[0] paths = linecollection.get_paths() for i, path in enumerate(paths): np.testing.assert_almost_equal(self.expected_paths[i], path.vertices) def test_focal(self): ax = self.G_str.plot(self.nybb_str, focal="Queens") assert len(ax.collections) == 3 linecollection = ax.collections[0] paths = linecollection.get_paths() np.testing.assert_almost_equal(self.expected_paths[1], paths[0].vertices) np.testing.assert_almost_equal(self.expected_paths[2], paths[1].vertices) np.testing.assert_almost_equal(self.expected_paths[3], paths[2].vertices) assert len(paths) == 3 pathcollection = ax.collections[1] np.testing.assert_array_equal( pathcollection.get_offsets().data, shapely.get_coordinates(self.nybb.centroid)[2:], ) pathcollection_focal = ax.collections[2] np.testing.assert_array_equal( pathcollection_focal.get_offsets().data, shapely.get_coordinates(self.nybb.centroid)[[1]], ) def test_focal_array(self): ax = self.G_str.plot(self.nybb_str, focal=["Queens", "Bronx"]) assert len(ax.collections) == 3 linecollection = ax.collections[0] paths = linecollection.get_paths() np.testing.assert_almost_equal(self.expected_paths[1], paths[0].vertices) np.testing.assert_almost_equal(self.expected_paths[2], paths[1].vertices) np.testing.assert_almost_equal(self.expected_paths[3], paths[2].vertices) np.testing.assert_almost_equal(self.expected_paths[5], paths[3].vertices) assert len(paths) == 4 pathcollection = ax.collections[1] np.testing.assert_array_equal( pathcollection.get_offsets().data, shapely.get_coordinates(self.nybb.centroid)[1:], ) pathcollection_focal = ax.collections[2] np.testing.assert_array_equal( pathcollection_focal.get_offsets().data, shapely.get_coordinates(self.nybb.centroid)[[1, -1]], ) def test_color(self): ax = self.G.plot(self.nybb, color="red") linecollection = ax.collections[0] np.testing.assert_array_equal( linecollection.get_color(), np.array([[1.0, 0.0, 0.0, 1.0]]) ) pathcollection = ax.collections[1] np.testing.assert_array_equal( pathcollection.get_facecolor(), np.array([[1.0, 0.0, 0.0, 1.0]]) ) def test_kws(self): ax = self.G.plot( self.nybb, edge_kws={"linestyle": "dotted"}, node_kws={"marker": "+"} ) linecollection = ax.collections[0] assert linecollection.get_linestyle() == [(0.0, [1.5, 2.4749999999999996])] pathcollection = ax.collections[1] np.testing.assert_array_equal( pathcollection.get_paths()[0].vertices, np.array([[-0.5, 0.0], [0.5, 0.0], [0.0, -0.5], [0.0, 0.5]]), ) def test_ax(self): ax = self.nybb.plot() self.G.plot(self.nybb, ax=ax) assert len(ax.collections) == 3 # edge geom linecollection = ax.collections[1] paths = linecollection.get_paths() for i, path in enumerate(paths): np.testing.assert_almost_equal(self.expected_paths[i], path.vertices) # node geom pathcollection = ax.collections[2] np.testing.assert_array_equal( pathcollection.get_offsets().data, shapely.get_coordinates(self.nybb.centroid), ) def test_figsize(self): ax = self.G.plot(self.nybb, figsize=(12, 12)) np.testing.assert_array_equal( ax.figure.get_size_inches(), np.array([12.0, 12.0]) ) def test_limit_extent(self): ax = self.G_str.plot(self.nybb_str) self.G_str.plot( self.nybb_str, focal="Bronx", limit_extent=True, ax=ax, color="red" ) assert ax.get_ylim() == (194475.53543792566, 252579.0488616723) assert ax.get_xlim() == (991274.9092650851, 1036640.134079854) def test_focal_kws(self): ax = self.G_str.plot( self.nybb_str, focal="Queens", focal_kws={"color": "blue"}, node_kws={"edgecolor": "pink"}, ) pathcollection = ax.collections[1] np.testing.assert_array_almost_equal( pathcollection.get_edgecolor(), np.array([[1.0, 0.75294118, 0.79607843, 1.0]]), ) pathcollection_focal = ax.collections[2] # inherit node_kws np.testing.assert_array_almost_equal( pathcollection_focal.get_edgecolor(), np.array([[1.0, 0.75294118, 0.79607843, 1.0]]), ) # apply own kws np.testing.assert_array_equal( pathcollection_focal.get_facecolor(), np.array([[0.0, 0.0, 1.0, 1.0]]) ) class TestExplore: def setup_method(self): # skip tests when no folium installed pytest.importorskip("folium") self.nybb_str = geopandas.read_file(geodatasets.get_path("nybb")).set_index( "BoroName" ) self.G_str = graph.Graph.build_contiguity(self.nybb_str) def test_default(self): m = self.G_str.explore(self.nybb_str) s = fetch_map_string(m) # nodes assert s.count("Point") == 5 # edges assert s.count("LineString") == 6 # tooltip assert '"focal":"Queens","neighbor":"Bronx","weight":1}' in s # color assert s.count('"__folium_color":"black"') == 11 # labels assert s.count("Brooklyn") == 3 def test_no_nodes(self): m = self.G_str.explore(self.nybb_str, nodes=False) s = fetch_map_string(m) # nodes assert s.count("Point") == 0 # edges assert s.count("LineString") == 6 # tooltip assert '"focal":"Queens","neighbor":"Bronx","weight":1}' in s # color assert s.count('"__folium_color":"black"') == 6 # labels assert s.count("Brooklyn") == 2 def test_focal(self): m = self.G_str.explore(self.nybb_str, focal="Queens") s = fetch_map_string(m) # nodes assert s.count("Point") == 4 # edges assert s.count("LineString") == 3 # tooltip assert '"focal":"Queens","neighbor":"Bronx","weight":1}' in s assert '"focal":"Queens","neighbor":"Manhattan","weight":1}' in s assert '"focal":"Queens","neighbor":"Brooklyn","weight":1}' in s # color assert s.count('"__folium_color":"black"') == 7 # labels assert s.count("Brooklyn") == 2 def test_focal_array(self): m = self.G_str.explore(self.nybb_str, focal=["Queens", "Bronx"]) s = fetch_map_string(m) # if node is both focal and neighbor, both are plottted as you can style # them differently to see both assert s.count("Point") == 6 # edges assert s.count("LineString") == 4 # tooltip assert '"focal":"Queens","neighbor":"Bronx","weight":1}' in s assert '"focal":"Queens","neighbor":"Manhattan","weight":1}' in s assert '"focal":"Queens","neighbor":"Brooklyn","weight":1}' in s assert '"focal":"Bronx","neighbor":"Manhattan","weight":1}' in s # color assert s.count('"__folium_color":"black"') == 10 # labels assert s.count("Brooklyn") == 2 def test_color(self): m = self.G_str.explore(self.nybb_str, color="red") s = fetch_map_string(m) assert s.count('"__folium_color":"red"') == 11 def test_kws(self): m = self.G_str.explore( self.nybb_str, focal=["Queens", "Bronx"], edge_kws={"color": "red"}, node_kws={"color": "blue", "marker_kwds": {"radius": 8}}, focal_kws={"color": "pink", "marker_kwds": {"radius": 12}}, ) s = fetch_map_string(m) # color assert s.count('"__folium_color":"red"') == 4 assert s.count('"__folium_color":"blue"') == 4 assert s.count('"__folium_color":"pink"') == 2 assert '"radius":8' in s assert '"radius":12' in s def test_m(self): m = self.nybb_str.explore() self.G_str.explore(self.nybb_str, m=m) s = fetch_map_string(m) # nodes assert s.count("Point") == 5 # edges assert s.count("LineString") == 6 # geoms assert s.count("Polygon") == 5 def test_explore_kwargs(self): m = self.G_str.explore(self.nybb_str, tiles="OpenStreetMap HOT") s = fetch_map_string(m) assert "tile.openstreetmap.fr/hot" in s libpysal-4.9.2/libpysal/graph/tests/test_set_ops.py000066400000000000000000000126041452177046000225250ustar00rootroot00000000000000import geodatasets import geopandas import pandas as pd import pytest from libpysal.graph.base import Graph class TestSetOps: def setup_method(self): self.grocs = geopandas.read_file(geodatasets.get_path("geoda groceries"))[ ["OBJECTID", "geometry"] ].explode(ignore_index=True) self.distance500 = Graph.build_distance_band(self.grocs, 500) self.distance2500 = Graph.build_distance_band(self.grocs, 2500) self.knn3 = Graph.build_knn(self.grocs, 3) self.distance2500_id = Graph.build_distance_band( self.grocs.set_index("OBJECTID"), 2500 ) def test_intersects(self): assert self.distance2500.intersects(self.knn3) assert self.knn3.intersects(self.distance2500) assert not self.knn3.intersects(self.distance2500_id) assert not self.distance2500.intersects(self.distance2500_id) def test_intersection(self): result = self.distance2500.intersection(self.knn3) assert len(result) == 115 assert result._adjacency.shape[0] == 185 pd.testing.assert_index_equal(result.unique_ids, self.distance2500.unique_ids) def test_symmetric_difference(self): result = self.distance2500.symmetric_difference(self.knn3) assert len(result) == 334 assert result._adjacency.shape[0] == 340 pd.testing.assert_index_equal(result.unique_ids, self.distance2500.unique_ids) with pytest.raises(ValueError, match="Cannot do symmetric difference"): self.distance2500_id.symmetric_difference(self.knn3) def test_union(self): result = self.distance2500.union(self.knn3) assert len(result) == 449 assert result._adjacency.shape[0] == 449 pd.testing.assert_index_equal(result.unique_ids, self.distance2500.unique_ids) with pytest.raises(ValueError, match="Cannot do union"): self.distance2500_id.union(self.knn3) def test_difference(self): result = self.distance2500.difference(self.knn3) assert len(result) == 5 assert result._adjacency.shape[0] == 148 pd.testing.assert_index_equal(result.unique_ids, self.distance2500.unique_ids) result = self.knn3.difference(self.distance2500) assert len(result) == 329 assert result._adjacency.shape[0] == 340 pd.testing.assert_index_equal(result.unique_ids, self.knn3.unique_ids) def test_issubgraph(self): assert self.distance500.issubgraph(self.distance2500) assert not self.distance2500.issubgraph(self.distance500) assert not self.knn3.issubgraph(self.distance2500) assert self.knn3.issubgraph(self.knn3) def test_equals(self): assert self.distance2500.equals(self.distance2500.copy()) assert not self.distance2500.equals(self.distance2500.transform("r")) def test_isomorphic(self): pytest.importorskip("networkx") assert self.distance2500.isomorphic(self.distance2500_id) def test___le__(self): assert self.distance500 <= self.distance2500 assert not self.knn3 <= self.distance2500 assert self.distance2500 <= self.distance2500 def test___ge__(self): assert self.distance2500 >= self.distance500 assert not self.knn3 >= self.distance2500 assert self.distance2500 >= self.distance2500 def test___lt__(self): assert self.distance500 < self.distance2500 assert not self.knn3 < self.distance2500 assert not self.distance2500 < self.distance2500 def test___gt__(self): assert self.distance2500 > self.distance500 assert not self.knn3 > self.distance2500 assert not self.distance2500 > self.distance2500 def test___eq__(self): assert self.distance2500 == self.distance2500.copy() assert not self.distance2500 == self.distance2500_id # noqa SIM201 def test___ne__(self): assert not self.distance2500 != self.distance2500.copy() # noqa SIM202 assert self.distance2500 != self.distance2500_id def test___and__(self): result = self.distance2500 & self.knn3 assert len(result) == 115 assert result._adjacency.shape[0] == 185 pd.testing.assert_index_equal(result.unique_ids, self.distance2500.unique_ids) def test___or__(self): result = self.distance2500 | self.knn3 assert len(result) == 449 assert result._adjacency.shape[0] == 449 pd.testing.assert_index_equal(result.unique_ids, self.distance2500.unique_ids) with pytest.raises(ValueError, match="Cannot do union"): self.distance2500_id | self.knn3 def test___xor__(self): result = self.distance2500 ^ self.knn3 assert len(result) == 334 assert result._adjacency.shape[0] == 340 pd.testing.assert_index_equal(result.unique_ids, self.distance2500.unique_ids) with pytest.raises(ValueError, match="Cannot do symmetric difference"): self.distance2500_id ^ self.knn3 def test___iand__(self): with pytest.raises(TypeError, match="Graphs are immutable."): self.distance2500 &= self.knn3 def test___ior__(self): with pytest.raises(TypeError, match="Graphs are immutable."): self.distance2500 |= self.knn3 def test___len__(self): assert len(self.distance2500) == 120 assert len(self.distance500) == 8 assert len(self.knn3) == len(self.grocs) * 3 libpysal-4.9.2/libpysal/graph/tests/test_spatial_lag.py000066400000000000000000000020711452177046000233260ustar00rootroot00000000000000import numpy as np from libpysal import graph from libpysal.graph._spatial_lag import _lag_spatial from libpysal.weights import lat2W class TestLag: def setup_method(self): self.neighbors = { "a": ["b"], "b": ["c", "a"], "c": ["b"], "d": [], } self.weights = {"a": [1.0], "b": [1.0, 1.0], "c": [1.0], "d": []} self.g = graph.Graph.from_dicts(self.neighbors, self.weights) self.y = np.array([0, 1, 2, 3]) def test_lag_spatial(self): yl = _lag_spatial(self.g, self.y) np.testing.assert_array_almost_equal(yl, [1.0, 2.0, 1.0, 0]) g = graph.Graph.from_W(lat2W(3, 3)) y = np.arange(9) yl = _lag_spatial(g, y) ylc = np.array([4.0, 6.0, 6.0, 10.0, 16.0, 14.0, 10.0, 18.0, 12.0]) np.testing.assert_array_almost_equal(yl, ylc) g_row = g.transform("r") yl = _lag_spatial(g_row, y) ylc = np.array([2.0, 2.0, 3.0, 3.33333333, 4.0, 4.66666667, 5.0, 6.0, 6.0]) np.testing.assert_array_almost_equal(yl, ylc) libpysal-4.9.2/libpysal/graph/tests/test_triangulation.py000066400000000000000000000436251452177046000237400ustar00rootroot00000000000000""" For completeness, we need to test a shuffled dataframe (i.e. always send unsorted data) with: - numeric ids - string ids - point dataframe - coordinates - check two kernel functions - numba/nonumba """ import geodatasets import geopandas import numpy as np import pandas as pd import pytest import shapely from libpysal.graph._kernel import _kernel_functions from libpysal.graph._triangulation import ( _delaunay, _gabriel, _relative_neighborhood, _voronoi, ) from libpysal.graph.base import Graph stores = geopandas.read_file(geodatasets.get_path("geoda liquor_stores")).explode( index_parts=False ) stores_unique = stores.drop_duplicates(subset="geometry") kernel_functions = [None] + list(_kernel_functions.keys()) def my_kernel(distances, bandwidth): output = np.cos(distances / distances.max()) output[distances < bandwidth] = 0 return output kernel_functions.append(my_kernel) # optimal, small, and larger than largest distance. bandwidths = [None, "auto", 0.5] np.random.seed(6301) # create a 2-d laplace distribution as a "degenerate" # over-concentrated distribution lap_coords = np.random.laplace(size=(5, 2)) # create a 2-d cauchy as a "degenerate" # spatial outlier-y distribution cau_coords = np.random.standard_cauchy(size=(5, 2)) parametrize_ids = pytest.mark.parametrize( "ids", [None, "id", "placeid"], ids=["no id", "id", "placeid"] ) parametrize_kernelfunctions = pytest.mark.parametrize( "kernel", kernel_functions, ids=kernel_functions[:-2] + ["None", "custom callable"] ) parametrize_bw = pytest.mark.parametrize( "bandwidth", bandwidths, ids=["no bandwidth", "auto", "fixed"] ) parametrize_constructors = pytest.mark.parametrize( "constructor", [_delaunay, _gabriel, _relative_neighborhood, _voronoi], ids=["delaunay", "gabriel", "relhood", "voronoi"], ) # @parametrize_constructors # @parametrize_ids # @parametrize_kernelfunctions`` # @parametrize_bw # def test_option_combinations(constructor, ids, kernel, bandwidth): # """ # NOTE: This does not check for the *validity* of the output, just # the structure of the output. # """ # heads, tails, weights = constructor( # stores_unique, # ids=stores_unique[ids] if ids is not None else None, # kernel=kernel, # bandwidth=bandwidth # ) # assert heads.dtype == tails.dtype # assert ( # heads.dtype == stores_unique.get(ids, stores_unique.index).dtype, # 'ids failed to propagate' # ) # if kernel is None and bandwidth is None: # np.testing.assert_array_equal(weights, np.ones_like(heads)) # assert ( # set(zip(heads, tails)) == set(zip(tails, heads)), # "all triangulations should be symmetric, this is not" # ) def test_correctness_voronoi_clipping(): noclip = _voronoi(lap_coords, clip=None, rook=True) extent = _voronoi(lap_coords, clip="extent", rook=True) alpha = _voronoi(lap_coords, clip="ashape", rook=True) g_noclip = Graph.from_arrays(*noclip) g_extent = Graph.from_arrays(*extent) g_alpha = Graph.from_arrays(*alpha) assert g_alpha < g_extent assert g_extent <= g_noclip extent_known = [ np.array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4]), np.array([1, 2, 3, 4, 0, 3, 4, 0, 3, 0, 1, 2, 0, 1]), ] alpha_known = [ np.array([0, 0, 0, 1, 2, 2, 3, 3, 4, 4]), np.array([2, 3, 4, 4, 0, 3, 0, 2, 0, 1]), ] np.testing.assert_array_equal( g_extent.adjacency.index.get_level_values(0), extent_known[0] ) np.testing.assert_array_equal( g_extent.adjacency.index.get_level_values(1), extent_known[1] ) np.testing.assert_array_equal( g_alpha.adjacency.index.get_level_values(0), alpha_known[0] ) np.testing.assert_array_equal( g_alpha.adjacency.index.get_level_values(1), alpha_known[1] ) def test_correctness_delaunay(): head, tail, weight = _delaunay(cau_coords) known_head = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4]) known_tail = np.array([1, 2, 4, 0, 2, 3, 0, 1, 3, 4, 1, 2, 4, 0, 2, 3]) np.testing.assert_array_equal(known_head, head) np.testing.assert_array_equal(known_tail, tail) np.testing.assert_array_equal(np.ones(head.shape), weight) head, tail, weight = _delaunay(lap_coords) known_head = np.array([0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4]) known_tail = np.array([1, 2, 3, 4, 0, 3, 4, 0, 3, 4, 0, 1, 2, 0, 1, 2]) np.testing.assert_array_equal(known_head, head) np.testing.assert_array_equal(known_tail, tail) np.testing.assert_array_equal(np.ones(head.shape), weight) def test_correctness_voronoi(): head, tail, weight = _voronoi(cau_coords) known_head = np.array([0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 4, 4]) known_tail = np.array([1, 2, 4, 0, 2, 0, 1, 3, 4, 2, 0, 2]) np.testing.assert_array_equal(known_head, head) np.testing.assert_array_equal(known_tail, tail) np.testing.assert_array_equal(np.ones(head.shape), weight) head, tail, weight = _voronoi(lap_coords) known_head = np.array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4]) known_tail = np.array([1, 2, 3, 4, 0, 3, 4, 0, 3, 0, 1, 2, 0, 1]) np.testing.assert_array_equal(known_head, head) np.testing.assert_array_equal(known_tail, tail) np.testing.assert_array_equal(np.ones(head.shape), weight) def test_correctness_gabriel(): head, tail, weight = _gabriel(cau_coords) known_head = np.array([0, 0, 1, 1, 2, 2, 2, 3, 4, 4]) known_tail = np.array([1, 4, 0, 2, 1, 3, 4, 2, 0, 2]) np.testing.assert_array_equal(known_head, head) np.testing.assert_array_equal(known_tail, tail) np.testing.assert_array_equal(np.ones(head.shape), weight) head, tail, weight = _gabriel(lap_coords) known_head = np.array([0, 0, 0, 1, 2, 2, 3, 3, 4, 4]) known_tail = np.array([2, 3, 4, 4, 0, 3, 0, 2, 0, 1]) np.testing.assert_array_equal(known_head, head) np.testing.assert_array_equal(known_tail, tail) np.testing.assert_array_equal(np.ones(head.shape), weight) def test_correctness_relative_n(): head, tail, weight = _relative_neighborhood(cau_coords) known_head = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4]) known_tail = np.array([1, 2, 4, 0, 2, 3, 0, 1, 3, 4, 1, 2, 4, 0, 2, 3]) np.testing.assert_array_equal(known_head, head) np.testing.assert_array_equal(known_tail, tail) np.testing.assert_array_equal(np.ones(head.shape), weight) head, tail, weight = _relative_neighborhood(lap_coords) known_head = np.array([0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4]) known_tail = np.array([1, 2, 3, 4, 0, 4, 0, 3, 0, 2, 0, 1]) np.testing.assert_array_equal(known_head, head) np.testing.assert_array_equal(known_tail, tail) np.testing.assert_array_equal(np.ones(head.shape), weight) @parametrize_ids def test_ids(ids): data = stores_unique.sample(frac=1) if ids is not None: data = data.set_index(ids) head, tail, weight = _delaunay(data) assert head.shape[0] == 3368 assert tail.shape == head.shape assert weight.shape == head.shape np.testing.assert_array_equal(pd.unique(head), data.index) head, tail, weight = _voronoi(data) assert head.shape[0] == 3308 assert tail.shape == head.shape assert weight.shape == head.shape np.testing.assert_array_equal(pd.unique(head), data.index) head, tail, weight = _gabriel(data) assert head.shape[0] == 2024 assert tail.shape == head.shape assert weight.shape == head.shape np.testing.assert_array_equal(pd.unique(head), data.index) head, tail, weight = _relative_neighborhood(data) # assert head.shape[0] == 3346 # relativehood returns unstable results atm see #573 assert tail.shape == head.shape assert weight.shape == head.shape np.testing.assert_array_equal(pd.unique(head), data.index) def test_kernel(): _, _, weight = _delaunay(cau_coords, kernel="gaussian") expected = np.array( [ 0.1305618, 0.17359059, 0.22312817, 0.1305618, 0.1339654, 0.03259559, 0.17359059, 0.1339654, 0.06948067, 0.180294, 0.03259559, 0.06948067, 0.02004257, 0.22312817, 0.180294, 0.02004257, ] ) np.testing.assert_array_almost_equal(expected, weight) def test_coincident_raise_voronoi(): with pytest.raises(ValueError, match="There are"): _voronoi(stores, clip=False) def test_coincident_jitter_voronoi(): cp_heads, cp_tails, cp_w = _voronoi(stores, clip=False, coincident="jitter") unique_heads, unique_tails, unique_w = _voronoi(stores_unique, clip=False) assert not np.array_equal(cp_heads, unique_heads) assert not np.array_equal(cp_tails, unique_tails) assert not np.array_equal(cp_w, unique_w) assert cp_heads.shape[0] == 3392 assert unique_heads.shape[0] == 3368 def test_coincident_clique_voronoi(): with pytest.raises(NotImplementedError, match="clique-based"): _voronoi(stores, clip=False, coincident="clique") # G_voronoi_cp_heads, G_voronoi_cp_tails, cp_w = _voronoi( # stores, clip=False, coincident="clique" # ) # G_voronoi_unique_heads, G_voronoi_unique_tails, unique_w = _voronoi( # stores_unique, clip=False # ) # assert not np.array_equal(G_voronoi_cp_heads, G_voronoi_unique_heads) # assert not np.array_equal(G_voronoi_cp_tails, G_voronoi_unique_tails) # assert not np.array_equal(cp_w, unique_w) class TestCoincident: def setup_method(self): self.geom = [ shapely.Point(0, 0), shapely.Point(1, 1), shapely.Point(2, 0), shapely.Point(3, 1), shapely.Point(0, 0), # coincident point shapely.Point(0, 5), ] self.df_int = geopandas.GeoDataFrame( geometry=self.geom, ) self.df_string = geopandas.GeoDataFrame( geometry=self.geom, index=["zero", "one", "two", "three", "four", "five"] ) self.mapping = {0: "zero", 1: "one", 2: "two", 3: "three", 4: "four", 5: "five"} def test_delaunay_error(self): with pytest.raises( ValueError, match="There are 5 unique locations in the dataset, but 6 observations", ): _delaunay(self.df_int) def test_delaunay_jitter(self): heads, tails, weights = _delaunay(self.df_int, coincident="jitter", seed=0) exp_heads = np.array( [0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5] ) exp_tails = np.array( [1, 2, 4, 0, 2, 3, 4, 5, 0, 1, 3, 1, 2, 5, 0, 1, 5, 1, 3, 4] ) exp_w = np.ones(exp_heads.shape, dtype="int8") np.testing.assert_array_equal(heads, exp_heads) np.testing.assert_array_equal(tails, exp_tails) heads, tails, weights = _delaunay(self.df_string, coincident="jitter", seed=0) np.testing.assert_array_equal(heads, np.vectorize(self.mapping.get)(exp_heads)) np.testing.assert_array_equal(tails, np.vectorize(self.mapping.get)(exp_tails)) np.testing.assert_array_equal(weights, exp_w) @pytest.mark.xfail def test_delaunay_clique(self): # TODO: fix the implemntation to make this pass heads, tails, weights = _delaunay(self.df_int, coincident="clique", seed=0) exp_heads = np.array( [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5] ) exp_tails = np.array( [1, 2, 4, 5, 0, 2, 3, 5, 0, 1, 3, 1, 2, 5, 0, 1, 2, 5, 0, 1, 3] ) exp_w = np.ones(exp_heads.shape, dtype="int8") np.testing.assert_array_equal(heads, exp_heads) np.testing.assert_array_equal(tails, exp_tails) heads, tails, weights = _delaunay(self.df_string, coincident="clique", seed=0) np.testing.assert_array_equal(heads, np.vectorize(self.mapping.get)(exp_heads)) np.testing.assert_array_equal(tails, np.vectorize(self.mapping.get)(exp_tails)) np.testing.assert_array_equal(weights, exp_w) def test_gabriel_error(self): with pytest.raises( ValueError, match="There are 5 unique locations in the dataset, but 6 observations", ): _gabriel(self.df_int) def test_gabriel_jitter(self): heads, tails, weights = _gabriel(self.df_int, coincident="jitter", seed=0) exp_heads = np.array([0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5]) exp_tails = np.array([2, 4, 2, 3, 4, 5, 0, 1, 3, 1, 2, 5, 0, 1, 1, 3]) exp_w = np.ones(exp_heads.shape, dtype="int8") np.testing.assert_array_equal(heads, exp_heads) np.testing.assert_array_equal(tails, exp_tails) heads, tails, weights = _gabriel(self.df_string, coincident="jitter", seed=0) np.testing.assert_array_equal(heads, np.vectorize(self.mapping.get)(exp_heads)) np.testing.assert_array_equal(tails, np.vectorize(self.mapping.get)(exp_tails)) np.testing.assert_array_equal(weights, exp_w) @pytest.mark.xfail def test_gabriel_clique(self): # TODO: fix the implemntation to make this pass heads, tails, weights = _gabriel(self.df_int, coincident="clique", seed=0) exp_heads = np.array( [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5] ) exp_tails = np.array( [1, 2, 4, 5, 0, 2, 3, 5, 0, 1, 3, 1, 2, 5, 0, 1, 2, 5, 0, 1, 3] ) exp_w = np.ones(exp_heads.shape, dtype="int8") np.testing.assert_array_equal(heads, exp_heads) np.testing.assert_array_equal(tails, exp_tails) heads, tails, weights = _gabriel(self.df_string, coincident="clique", seed=0) np.testing.assert_array_equal(heads, np.vectorize(self.mapping.get)(exp_heads)) np.testing.assert_array_equal(tails, np.vectorize(self.mapping.get)(exp_tails)) np.testing.assert_array_equal(weights, exp_w) def test_relative_neighborhood_error(self): with pytest.raises( ValueError, match="There are 5 unique locations in the dataset, but 6 observations", ): _relative_neighborhood(self.df_int) def test_relative_neighborhood_jitter(self): heads, tails, weights = _relative_neighborhood( self.df_int, coincident="jitter", seed=0 ) exp_heads = np.array([0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5]) exp_tails = np.array([1, 2, 4, 0, 3, 4, 5, 0, 3, 1, 2, 5, 0, 1, 5, 1, 3, 4]) exp_w = np.ones(exp_heads.shape, dtype="int8") np.testing.assert_array_equal(heads, exp_heads) np.testing.assert_array_equal(tails, exp_tails) heads, tails, weights = _relative_neighborhood( self.df_string, coincident="jitter", seed=0 ) np.testing.assert_array_equal(heads, np.vectorize(self.mapping.get)(exp_heads)) np.testing.assert_array_equal(tails, np.vectorize(self.mapping.get)(exp_tails)) np.testing.assert_array_equal(weights, exp_w) @pytest.mark.xfail def test_relative_neighborhood_clique(self): # TODO: fix the implemntation to make this pass heads, tails, weights = _relative_neighborhood( self.df_int, coincident="clique", seed=0 ) exp_heads = np.array( [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5] ) exp_tails = np.array( [1, 2, 4, 5, 0, 2, 3, 5, 0, 1, 3, 1, 2, 5, 0, 1, 2, 5, 0, 1, 3] ) exp_w = np.ones(exp_heads.shape, dtype="int8") np.testing.assert_array_equal(heads, exp_heads) np.testing.assert_array_equal(tails, exp_tails) heads, tails, weights = _relative_neighborhood( self.df_string, coincident="clique", seed=0 ) np.testing.assert_array_equal(heads, np.vectorize(self.mapping.get)(exp_heads)) np.testing.assert_array_equal(tails, np.vectorize(self.mapping.get)(exp_tails)) np.testing.assert_array_equal(weights, exp_w) def test_voronoi_error(self): with pytest.raises( ValueError, match="There are 5 unique locations in the dataset, but 6 observations", ): _voronoi(self.df_int) def test_voronoi_jitter(self): heads, tails, weights = _voronoi(self.df_int, coincident="jitter", seed=0) exp_heads = np.array([0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5]) exp_tails = np.array([1, 2, 4, 0, 2, 3, 4, 5, 0, 1, 3, 1, 2, 5, 0, 1, 1, 3]) exp_w = np.ones(exp_heads.shape, dtype="int8") np.testing.assert_array_equal(heads, exp_heads) np.testing.assert_array_equal(tails, exp_tails) heads, tails, weights = _voronoi(self.df_string, coincident="jitter", seed=0) np.testing.assert_array_equal(heads, np.vectorize(self.mapping.get)(exp_heads)) np.testing.assert_array_equal(tails, np.vectorize(self.mapping.get)(exp_tails)) np.testing.assert_array_equal(weights, exp_w) @pytest.mark.xfail def test_voronoi_clique(self): # TODO: fix the implemntation to make this pass heads, tails, weights = _voronoi(self.df_int, coincident="clique", seed=0) exp_heads = np.array([0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5]) exp_tails = np.array([1, 4, 0, 2, 3, 5, 1, 3, 1, 2, 5, 0, 1, 1, 3]) exp_w = np.ones(exp_heads.shape, dtype="int8") np.testing.assert_array_equal(heads, exp_heads) np.testing.assert_array_equal(tails, exp_tails) heads, tails, weights = _voronoi(self.df_string, coincident="clique", seed=0) np.testing.assert_array_equal(heads, np.vectorize(self.mapping.get)(exp_heads)) np.testing.assert_array_equal(tails, np.vectorize(self.mapping.get)(exp_tails)) np.testing.assert_array_equal(weights, exp_w) def test_wrong_resolver(self): with pytest.raises( ValueError, match="Recieved option coincident='nonsense'", ): _delaunay(self.df_int, coincident="nonsense") libpysal-4.9.2/libpysal/graph/tests/test_utils.py000066400000000000000000000122501452177046000222060ustar00rootroot00000000000000# ruff: noqa: N811 import geodatasets import geopandas import numpy import pytest import shapely from libpysal.graph._contiguity import _VALID_GEOMETRY_TYPES as contiguity_types from libpysal.graph._kernel import _VALID_GEOMETRY_TYPES as kernel_types from libpysal.graph._triangulation import _VALID_GEOMETRY_TYPES as triang_types from libpysal.graph._utils import _validate_geometry_input columbus = geopandas.read_file(geodatasets.get_path("geoda columbus")) columbus["intID"] = columbus.POLYID.values columbus["strID"] = columbus.POLYID.astype(str) rivers = geopandas.read_file(geodatasets.get_path("eea large_rivers")) rivers["strID"] = rivers.NAME rivers["intID"] = rivers.index.values + 1 id_types = [None, "intID", "strID"] id_type_names = ["no index", "int index", "string index"] geom_names = ["columbus", "columbus centroids", "rivers"] geoms = [columbus, columbus.set_geometry(columbus.geometry.centroid), rivers] shuffle = [False, True] external_ids = [False, True] input_type = ["gdf", "gseries", "array"] parametrize_inputs = pytest.mark.parametrize("geoms", geoms, ids=geom_names) parametrize_idtypes = pytest.mark.parametrize("ids", id_types, ids=id_type_names) parametrize_shuffle = pytest.mark.parametrize( "shuffle", shuffle, ids=["input order", "shuffled"] ) parametrize_input_type = pytest.mark.parametrize( "input_type", input_type, ids=input_type ) parametrize_external_ids = pytest.mark.parametrize( "external_ids", external_ids, ids=["use set_index", "use id vector"] ) @parametrize_shuffle @parametrize_external_ids @parametrize_inputs @parametrize_idtypes @parametrize_input_type def test_validate_input_geoms(geoms, ids, shuffle, external_ids, input_type): """ Test that all combinations of geometries and ids get aligned correctly """ if ids is not None: geoms = geoms.set_index(ids) input_ids = geoms.index if external_ids else None if shuffle: geoms = geoms.sample(frac=1, replace=False) if input_type == "gdf": geoms = geoms geom_type = geoms.geometry.iloc[0].geom_type elif input_type == "gseries": geoms = geoms.geometry geom_type = geoms.iloc[0].geom_type elif input_type == "array": geoms = geoms.geometry.values geom_type = geoms[0].geom_type else: raise ValueError( "input_type not in supported testing types: 'gdf', 'gseries', 'array'" ) coordinates, ids, out_geoms = _validate_geometry_input(geoms, ids=input_ids) assert (out_geoms.index == ids).all(), "validated ids are not equal to input ids" if geom_type == "Point": assert coordinates.shape[0] == len(geoms), ( "Point inputs should be cast to coordinates, " "but the output coordinates and input geometries are not equal length" ) assert coordinates.shape[0] == len(ids), ( "Point inputs should be cast to coordinates, " "but the output coordinates and output ids are not equal length" ) if hasattr(geoms, "geometry"): coords = shapely.get_coordinates(geoms.geometry) else: coords = shapely.get_coordinates(geoms) numpy.testing.assert_array_equal(coordinates, coords) @parametrize_shuffle @parametrize_idtypes def test_validate_input_coords(shuffle, ids): """ Test that input coordinate arrays get validated correctly """ data = columbus.sample(frac=1, replace=False) if shuffle else columbus input_coords = shapely.get_coordinates(data.centroid) if ids is not None: ids = data[ids].values coordinates, ids, out_geoms = _validate_geometry_input(input_coords, ids=ids) assert coordinates.shape[0] == len(out_geoms) assert coordinates.shape[0] == len(ids) def test_validate_raises( kernel_types=kernel_types, contiguity_types=contiguity_types, triang_types=triang_types, ): # kernels with pytest.raises(ValueError): # no lines for kernels _validate_geometry_input(rivers, valid_geometry_types=kernel_types) with pytest.raises(ValueError): # no polygons for kernels _validate_geometry_input(columbus, valid_geometry_types=kernel_types) # triangulation with pytest.raises(ValueError): # no lines for triangulation _validate_geometry_input(rivers, valid_geometry_types=triang_types) with pytest.raises(ValueError): # no polygons for triangulation _validate_geometry_input(columbus, valid_geometry_types=triang_types) # contiguity with pytest.raises(ValueError): # no point gdf for contiguity _validate_geometry_input( columbus.set_geometry(columbus.centroid), valid_geometry_types=contiguity_types, ) with pytest.raises(ValueError): # no point gseries for contiguity _validate_geometry_input( columbus.set_geometry(columbus.centroid).geometry, valid_geometry_types=contiguity_types, ) with pytest.raises(ValueError): # no point arrays for contiguity _validate_geometry_input( numpy.arange(20).reshape(-1, 2), valid_geometry_types=contiguity_types ) def fetch_map_string(m): out = m._parent.render() out_str = "".join(out.split()) return out_strlibpysal-4.9.2/libpysal/io/000077500000000000000000000000001452177046000156015ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/__init__.py000066400000000000000000000001741452177046000177140ustar00rootroot00000000000000from . import fileio from .iohandlers import * from .tables import * from .util import * open = fileio.FileIO # noqa A001 libpysal-4.9.2/libpysal/io/fileio.py000066400000000000000000000323441452177046000174300ustar00rootroot00000000000000""" FileIO: Module for reading and writing various file types in a Pythonic way. This module should not be used directly, instead... ``` import pysal.core.FileIO as FileIO ``` Readers and Writers will mimic python file objects. .seek(n) seeks to the n'th object .read(n) reads n objects, default == all .next() reads the next object """ # ruff: noqa: ARG002, N801, N802, N803, N806, SIM115 __author__ = "Charles R Schmidt " __all__ = ["FileIO"] import os.path from warnings import warn from ..common import MISSINGVALUE class FileIO_MetaCls(type): """This meta class is instantiated when the class is first defined. All subclasses of `FileIO` also inherit this meta class, which registers their abilities with the FileIO registry. Subclasses must contain ``FORMATS`` and ``MODES`` (both are ``type(list)``). Raises ------ TypeError FileIO subclasses must have ``FORMATS`` and ``MODES`` defined. """ def __new__(cls, name, bases, dict_): cls = type.__new__(cls, name, bases, dict_) if name != "FileIO" and name != "DataTable": if "FORMATS" in dict_ and "MODES" in dict_: # msg = "Registering %s with FileIO.\n\tFormats: %r\n\tModes: %r" # msg = msg % (name, dict["FORMATS"], dict["MODES"]) FileIO._register(cls, dict_["FORMATS"], dict_["MODES"]) else: raise TypeError( "FileIO subclasses must have 'FORMATS' and 'MODES' defined." ) return cls class FileIO(metaclass=FileIO_MetaCls): # should be a type? """Metaclass for supporting spatial data file read and write. How this works: ``FileIO.open(\\*args) == FileIO(\\*args)`` When creating a new instance of `FileIO` the ``.__new__`` method intercepts. ``.__new__`` parses the filename to determine the ``fileType``. Next, ``.__registry`` and checked for that type. Each type supports one or more modes (``['r', 'w', 'a', etc.]``). If we support the type and mode, an instance of the appropriate handler is created and returned. All handlers must inherit from this class, and by doing so are automatically added to the ``.__registry`` and are forced to conform to the prescribed API. The metaclass takes care of the registration by parsing the class definition. It doesn't make much sense to treat weights in the same way as shapefiles and dbfs, so... * ... for now we'll just return an instance of `W` on ``mode='r'``. * ... on ``mode='w'``, ``.write`` will expect an instance of `W`. """ __registry = {} # {'shp':{'r':[OGRshpReader,pysalShpReader]}} def __new__(cls, dataPath="", mode="r", dataFormat=None): """Intercepts the instantiation of ``FileIO`` and dispatches to the correct handler. If no suitable handler is found a python file object is returned. """ if cls is FileIO: try: newCls = object.__new__( cls.__registry[cls.getType(dataPath, mode, dataFormat)][mode][0] ) except KeyError: return open(dataPath, mode) return newCls else: return object.__new__(cls) @staticmethod def getType(dataPath: str, mode: str, dataFormat=None) -> str: """Parse the ``dataPath`` and return the data type.""" if dataFormat: ext = dataFormat else: ext = os.path.splitext(dataPath)[1] ext = ext.replace(".", "") ext = ext.lower() if ext == "txt": with open(dataPath, mode) as f: l1 = f.readline() l2 = f.readline() try: n, k = l1.split(",") n, k = int(n), int(k) fields = l2.split(",") assert len(fields) == k return "geoda_txt" except AssertionError: return ext return ext @classmethod def _register(cls, parser, formats, modes): """This method is called automatically via the Metaclass of `FileIO` subclasses This should be private, but that hides it from the Metaclass. """ assert cls is FileIO for format_ in formats: if format_ not in cls.__registry: cls.__registry[format_] = {} for mode in modes: if mode not in cls.__registry[format_]: cls.__registry[format_][mode] = [] cls.__registry[format_][mode].append(parser) # cls.check() @classmethod def check(cls): """Prints the contents of the registry.""" print("PySAL File I/O understands the following file extensions:") for key, val in list(cls.__registry.items()): print(f"Ext: '.{key}', Modes: {list(val.keys())!r}") @classmethod def open(cls, *args, **kwargs): # noqa A001 """Alias for ``FileIO()``.""" return cls(*args, **kwargs) class _By_Row: def __init__(self, parent): self.p = parent def __repr__(self) -> str: if not self.p.ids: return "keys: range(0,n)" else: return "keys: " + list(self.p.ids.keys()).__repr__() def __getitem__(self, key) -> list | str: if isinstance(key, list): r = [] if self.p.ids: for k in key: r.append(self.p.get(self.p.ids[k])) else: for k in key: r.append(self.p.get(k)) return r if self.p.ids: return self.p.get(self.p.ids[key]) else: return self.p.get(key) __call__ = __getitem__ def __init__(self, dataPath="", mode="r", dataFormat=None): self.dataPath = dataPath self.dataObj = "" self.mode = mode # pos Should ALWAYS be in the range 0,...,n # for custom IDs set the ids property. self.pos = 0 self.__ids = None # {'id':n} self.__rIds = None self.closed = False self._spec = [] self.header = [] def __getitem__(self, key): return self.by_row.__getitem__(key) @property def by_row(self): return self._By_Row(self) def __getIds(self): return self.__ids def __setIds(self, ids: list | (dict | None)): """Property method for ``.ids``. Takes a list of ids and maps then to a 0-based index. Need to provide a method to set ID's based on a ``fieldName`` preferably without reading the whole file. Raises ------ AssertionError Raised when IDs are not unique. """ if isinstance(ids, list): try: assert len(ids) == len(set(ids)) except AssertionError: raise KeyError("IDs must be unique.") from None # keys: ID values: i self.__ids = {} # keys: i values: ID self.__rIds = {} for i, id_ in enumerate(ids): self.__ids[id] = i self.__rIds[i] = id_ elif isinstance(ids, dict): self.__ids = ids self.__rIds = {} for id_, n in list(ids.items()): self.__rIds[n] = id_ elif not ids: self.__ids = None self.__rIds = None ids = property(fget=__getIds, fset=__setIds) @property def rIds(self) -> dict | None: return self.__rIds def __iter__(self): self.seek(0) return self @staticmethod def _complain_ifclosed(closed): """From `StringIO`. Raises ------ ValueError Raised when a file is already closed. """ if closed: raise ValueError("I/O operation on closed file.") def cast(self, key, typ): """Cast ``key`` as ``typ``. Raises ------ TypeError Raised when a cast object in not callable. KeyError Raised when a key is not present. """ if key in self.header: if not self._spec: self._spec = [lambda x: x for k in self.header] if typ is None: self._spec[self.header.index(key)] = lambda x: x else: try: assert callable(typ) self._spec[self.header.index(key)] = typ except AssertionError: raise TypeError("Cast objects must be callable.") from None else: raise KeyError("%s" % key) def _cast(self, row) -> list: """ Raises ------ ValueError Raised when a value could not be cast a particular type. """ if self._spec and row: try: return [f(v) for f, v in zip(self._spec, row, strict=True)] except ValueError: r = [] for f, v in zip(self._spec, row, strict=True): try: if not v and f != str: raise ValueError r.append(f(v)) except ValueError: msg = "Value '%r' could not be cast to %s, " msg += "value set to MISSINGVALUE." msg = msg % (v, str(f)) warn(msg, RuntimeWarning, stacklevel=2) r.append(MISSINGVALUE) return r else: return row def __next__(self) -> list: """A `FileIO` object is its own iterator, see `StringIO`. Raises ------ StopIteration Raised at the EOF. """ self._complain_ifclosed(self.closed) r = self.__read() if r is None: raise StopIteration return r def close(self): """Subclasses should clean themselves up and then call this method.""" if not self.closed: self.closed = True del self.dataObj, self.pos def get(self, n: int) -> list: """Seeks the file to ``n`` and returns ``n``. If ``.ids`` is set ``n`` should be an id, else, ``n`` should be an offset. """ prev_pos = self.tell() self.seek(n) obj = self.__read() self.seek(prev_pos) return obj def seek(self, n: int): """Seek the `FileObj` to the beginning of the ``n``'th record. If IDs are set, seeks to the beginning of the record at ID, ``n``. """ self._complain_ifclosed(self.closed) self.pos = n def tell(self) -> int: """Return ID (or offset) of next object.""" self._complain_ifclosed(self.closed) return self.pos def read(self, n=-1) -> list | None: """Read at most ``n`` objects, less if read hits EOF. If size is negative or omitted read all objects until EOF. Returns ``None`` if EOF is reached before any objects. Raises ------ StopIteration Raised at the EOF. """ self._complain_ifclosed(self.closed) if n < 0: # return list(self) result = [] while 1: try: result.append(self.__read()) except StopIteration: break return result elif n == 0: return None else: result = [] for _i in range(0, n): try: result.append(self.__read()) except StopIteration: break return result def __read(self) -> list: """Gets one row from the file handler, and if necessary casts it's objects. Raises ------ StopIteration Raised at the EOF. """ row = self._read() if row is None: raise StopIteration row = self._cast(row) return row def _read(self): """Must be implemented by subclasses that support 'r' subclasses. Should increment ``.pos`` and redefine this doc string. Raises ------ NotImplementedError """ self._complain_ifclosed(self.closed) raise NotImplementedError def truncate(self, size=None): """Should be implemented by subclasses and redefine this doc string. Raises ------ NotImplementedError """ self._complain_ifclosed(self.closed) raise NotImplementedError def write(self, obj): """Must be implemented by subclasses that support 'w' subclasses Should increment ``.pos``. Subclasses should also check if ``obj`` is an instance of type(list) and redefine this doc string. Raises ------ NotImplementedError """ self._complain_ifclosed(self.closed) "Write obj to dataObj" raise NotImplementedError def flush(self): """ Raises ------ NotImplementedError """ self._complain_ifclosed(self.closed) raise NotImplementedError libpysal-4.9.2/libpysal/io/geotable/000077500000000000000000000000001452177046000173635ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/geotable/__init__.py000066400000000000000000000000521452177046000214710ustar00rootroot00000000000000from .file import read_files, write_files libpysal-4.9.2/libpysal/io/geotable/dbf.py000066400000000000000000000136761452177046000205050ustar00rootroot00000000000000"""Miscellaneous file manipulation utilities. """ import pandas as pd from ..fileio import FileIO def check_dups(li): """Checks duplicates in a list of ID values. ID values must be read in as a list. Author(s) -- Luc Anselin Parameters ---------- li : list A collection of ID values. Returns ------- dups : list The duplicate IDs. """ dups = list({x for x in li if li.count(x) > 1}) return dups def dbfdups(dbfpath, idvar): """Checks duplicates in a ``.dBase`` file ID variable must be specified correctly. Author(s) -- Luc Anselin Parameters ---------- dbfpath : str The file path to ``.dBase`` file. idvar : str The ID variable in ``.dBase`` file. Returns ------- dups : list The duplicate IDs. """ db = FileIO(dbfpath, "r") li = db.by_col(idvar) dups = list({x for x in li if li.count(x) > 1}) return dups def df2dbf(df, dbf_path, my_specs=None): """Convert a ``pandas.DataFrame`` into a ``.dbf``. Author(s) -- Dani Arribas-Bel , Luc Anselin Parameters ---------- df : pandas.DataFrame Pandas dataframe object to be entirely written out to a ``.dbf``. dbf_path : str Path to the output ``.dbf``. It is also returned by the function. my_specs : list A list with the ``field_specs`` to use for each column. Defaults to ``None`` and applies the following scheme. * ``int`` -- ``('N', 14, 0)`` * for all ``int`` types * ``float`` -- ``('N', 14, 14)`` * for all ``float`` types * ``str`` -- ``('C', 14, 0)`` * for ``str``, ``object``, and category * for all variants for different type sizes Returns ------- dbf_path : str Path to the output ``.dbf`` Notes ----- Use of ``dtypes.name`` may not be fully robust, but preferred approach of using ``isinstance`` seems too clumsy. """ if my_specs: specs = my_specs else: # new approach using dtypes.name to avoid numpy name issue in type type2spec = { "int": ("N", 20, 0), "int8": ("N", 20, 0), "int16": ("N", 20, 0), "int32": ("N", 20, 0), "int64": ("N", 20, 0), "float": ("N", 36, 15), "float32": ("N", 36, 15), "float64": ("N", 36, 15), "str": ("C", 14, 0), "object": ("C", 14, 0), "category": ("C", 14, 0), } types = [df[i].dtypes.name for i in df.columns] specs = [type2spec[t] for t in types] db = FileIO(dbf_path, "w") db.header = list(df.columns) db.field_spec = specs for _i, row in list(df.T.items()): db.write(row) db.close() return dbf_path def dbf2df(dbf_path, index=None, cols=False, incl_index=False): """Read a ``.dbf`` file as a ``pandas.DataFrame``, optionally selecting the index variable and which columns are to be loaded. Author(s) -- Dani Arribas-Bel Parameters ---------- dbf_path : str Path to the ``.dbf`` file to be read. index : str Name of the column to be used as the index of the ``pandas.DataFrame``. cols : list List with the names of the columns to be read into the ``pandas.DataFrame``. Defaults to ``False``, which reads the whole ``.dbf`` incl_index : bool If ``True`` index is included in the ``pandas.DataFrame`` as a column too. Defaults to ``False``. Returns ------- df : pandas.DataFrame The resultant ``pandas.DataFrame`` object. """ db = FileIO(dbf_path) if cols: if incl_index: cols.append(index) vars_to_read = cols else: vars_to_read = db.header data = {var: db.by_col(var) for var in vars_to_read} if index: index = db.by_col(index) db.close() return pd.DataFrame(data, index=index, columns=vars_to_read) else: db.close() return pd.DataFrame(data, columns=vars_to_read) def dbfjoin(dbf1_path, dbf2_path, out_path, joinkey1, joinkey2): """Wrapper function to merge two ``.dbf`` files into a new ``.dbf`` file. Uses ``dbf2df`` and ``df2dbf`` to read and write the ``.dbf`` files into a ``pandas.DataFrame``. Uses all default settings for ``dbf2df`` and ``df2dbf`` (see docs for specifics). Author(s) -- Luc Anselin Parameters ---------- dbf1_path : str Path to the first (left) ``.dbf`` file. dbf2_path : str Path to the second (right) ``.dbf`` file. out_path : str Path to the output ``.dbf`` file (returned by the function). joinkey1 : str Variable name for the key in the first ``.dbf``. Must be specified. Key must take unique values. joinkey2 : str Variable name for the key in the second ``.dbf``. Must be specified. Key must take unique values. Returns ------- dp : str Path to output file. """ df1 = dbf2df(dbf1_path, index=joinkey1) df2 = dbf2df(dbf2_path, index=joinkey2) dfbig = pd.merge(df1, df2, left_on=joinkey1, right_on=joinkey2, sort=False) dp = df2dbf(dfbig, out_path) return dp def dta2dbf(dta_path, dbf_path): """Wrapper function to convert a stata ``.dta`` file into a ``.dbf`` file. Uses ``df2dbf`` to write the ``.dbf`` files from a ``pandas.DataFrame``. Uses all default settings for ``df2dbf`` (see docs for specifics). Author(s) -- Luc Anselin Parameters ---------- dta_path : str Path to the Stata ``.dta`` file. dbf_path : str Path to the output ``.dbf`` file. Returns ------- dp : str path to output file """ db = pd.read_stata(dta_path) dp = df2dbf(db, dbf_path) return dp libpysal-4.9.2/libpysal/io/geotable/file.py000066400000000000000000000055761452177046000206710ustar00rootroot00000000000000import os from ...weights.contiguity import Queen, Rook from ..fileio import FileIO from .dbf import dbf2df, df2dbf from .shp import series2shp, shp2series from .utils import insert_metadata def read_files(filepath, **kwargs): """Reads a ``.dbf``/``.shp`` pair, squashing geometries into a 'geometry' column. Parameters ---------- filepath : str The file path. **kwargs : dict Optional keyword arguments for ``dbf2df()``. Returns ------- df : pandas.DataFrame The results dataframe returned from ``dbf2df()``. """ # keyword arguments wrapper will strip all around dbf2df's required arguments geomcol = kwargs.pop("geomcol", "geometry") weights = kwargs.pop("weights", "") dbf_path, shp_path = _pairpath(filepath) df = dbf2df(dbf_path, **kwargs) df[geomcol] = shp2series(shp_path) if weights != "" and isinstance(weights, str): if weights.lower() in ["rook", "queen"]: if weights.lower() == "rook": w = Rook.from_dataframe(df) else: w = Queen.from_dataframe(df) insert_metadata(df, w, name="W", inplace=True) else: try: w_path = os.path.splitext(dbf_path)[0] + "." + weights w = FileIO(w_path).read() insert_metadata(df, w, name="W", inplace=True) except OSError: print("Weights construction failed! Passing on weights.") return df def write_files(df, filepath, **kwargs): """Writes dataframes with potential geometric components out to files. Parameters ---------- df : pandas.DataFrame The dataframe to write out. filepath : str The file path. **kwargs : dict Optional keyword arguments for ``df2dbf()``. Returns ------- dbf_path : str Path to the output ``.dbf`` paths : tuple The file paths for ``dbf_out``, ``shp_out``, ``W_path``. """ geomcol = kwargs.pop("geomcol", "geometry") weights = kwargs.pop("weights", "gal") dbf_path, shp_path = _pairpath(filepath) if geomcol not in df.columns: dbf_path = df2dbf(df, dbf_path, **kwargs) return dbf_path else: shp_out = series2shp(df[geomcol], shp_path) not_geom = [x for x in df.columns if x != geomcol] dbf_out = df2dbf(df[not_geom], dbf_path, **kwargs) if hasattr(df, "W"): w_path = os.path.splitext(filepath)[0] + "." + weights FileIO(w_path, "w").write(df.W) else: w_path = "no weights written" paths = dbf_out, shp_out, w_path return paths def _pairpath(filepath: str) -> tuple: """Return ``.dbf``/``.shp`` paths for any ``.shp``, ``.dbf``, or basepath passed to function. """ base = os.path.splitext(filepath)[0] paths = base + ".dbf", base + ".shp" return paths libpysal-4.9.2/libpysal/io/geotable/shp.py000066400000000000000000000014561452177046000205350ustar00rootroot00000000000000import pandas as pd from ..fileio import FileIO def shp2series(filepath): """Reads a shapefile, stuffing each shape into an element of a ``pandas.Series``. Parameters ---------- filepath : str Path to the file. Returns ------- s : pandas.Series The data cast a ``pandas.Series`` object. """ f = FileIO(filepath) s = pd.Series(poly for poly in f) f.close() return s def series2shp(series, filepath): """Writes a ``pandas.Series`` of PySAL polygons to a file Parameters ---------- series : pandas.Series The data to write out. Returns ------- filepath : str Path to the file. """ f = FileIO(filepath, "w") for poly in series: f.write(poly) f.close() return filepath libpysal-4.9.2/libpysal/io/geotable/tests/000077500000000000000000000000001452177046000205255ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/geotable/tests/__init__.py000066400000000000000000000000001452177046000226240ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/geotable/tests/test_utils.py000066400000000000000000000005461452177046000233030ustar00rootroot00000000000000import pytest @pytest.mark.skip("skpping converters and metadata inserters") class TestUtils: def test_converters(self): """Make a round trip to geodataframe and back.""" raise Exception def test_insert_metadata(self): """Add an attribute to a dataframe and see if it is pervasive over copies.""" raise Exception libpysal-4.9.2/libpysal/io/geotable/utils.py000066400000000000000000000051561452177046000211040ustar00rootroot00000000000000from warnings import warn from ...cg.shapes import asShape as pShape from ...common import requires as _requires @_requires("geopandas") def to_df(df, geom_col="geometry", **kw): """Convert a ``geopandas.GeoDataFrame`` into a normal ``pandas.DataFrame`` with a column containing PySAL shapes. Parameters ---------- df : geopandas.GeoDataFrame A ``geopandas.GeoDataFrame`` (or ``pandas.DataFrame``) with a column containing geo-interfaced shapes. geom_col : str The column name in ``df`` contains the geometry. Default is ``'geometry'``. **kw : dict Optional keyword arguments for ``pandas.DataFrame()``. Returns ------- df : pandas.DataFrame The data converted into a ``pandas.DataFrame`` object. See Also -------- pandas.DataFrame """ import pandas as pd from geopandas import GeoDataFrame, GeoSeries df[geom_col] = df[geom_col].apply(pShape) if isinstance(df, GeoDataFrame | GeoSeries): df = pd.DataFrame(df, **kw) return df @_requires("geopandas") def to_gdf(df, geom_col="geometry", **kw): """Convert a ``pandas.DataFrame`` with geometry column to a ``geopandas.GeoDataFrame``. Parameters ---------- df : pandas.DataFrame A ``pandas.DataFrame`` with a column containing geo-interfaced shapes. geom_col : str The column name in ``df`` contains the geometry. Default is ``'geometry'``. **kw : dict Optional keyword arguments for ``geopandas.GeoDataFrame()``. Returns ------- gdf : geopandas.GeoDataFrame The data converted into a ``geopandas.GeoDataFrame`` object. See Also -------- geopandas.GeoDataFrame """ from geopandas import GeoDataFrame from shapely.geometry import shape df[geom_col] = df[geom_col].apply(shape) gdf = GeoDataFrame(df, geometry=geom_col, **kw) return gdf def insert_metadata(df, obj, name=None, inplace=True, overwrite=False): """Insert/update metadata for a dataframe.""" if not inplace: new = df.copy(deep=True) insert_metadata(new, obj, name=name, inplace=True) return new if name is None: name = type(obj).__name__ if hasattr(df, name): if overwrite: warn( f"Overwriting attribute {name}! This may break the dataframe!", stacklevel=2, ) else: raise Exception( f"Dataframe already has attribute {name}. Cowardly refusing " "to break dataframe." ) df._metadata.append(name) df.__setattr__(name, obj) libpysal-4.9.2/libpysal/io/geotable/wrappers.py000066400000000000000000000036511452177046000216050ustar00rootroot00000000000000import contextlib import pandas as pd import shapely.geometry as sgeom from ...cg import asShape from ...common import requires from .file import read_files, write_files @requires("geopandas") def geopandas(filename, **kw): """Wrapper for ``geopandas.read_file()``. Parameters ---------- filename : str Path to the file. **kw : dict Optional keyword arguments for ``geopandas.read_file()``. Returns ------- gdf : geopandas.GeoDataFrame The shapefile read in as a ``geopandas.GeoDataFrame``. """ import geopandas gdf = geopandas.read_file(filename, **kw) return gdf @requires("fiona") def fiona(filename, geom_type="shapely", **kw): """Open a file with ``fiona`` and convert to a ``pandas.DataFrame``. Parameters ---------- filename : str Path to the file. geom_type : str Package/method to use from creating geometries. Default is ``'shapely'``. **kw : dict Optional keyword arguments for ``fiona.open()``. Returns ------- df : pandas.DataFrame The file read in as a ``pandas.DataFrame``. """ if geom_type == "shapely": converter = sgeom.shape elif geom_type is None: def converter(x): return x else: converter = asShape import fiona props = {} with fiona.open(filename, **kw) as f: for i, feat in enumerate(f): idx = feat.get("id", i) with contextlib.suppress(ValueError): idx = int(idx) props.update({idx: feat.get("properties", {})}) props[idx].update({"geometry": converter(feat["geometry"])}) df = pd.DataFrame().from_dict(props).T return df _readers = {"read_shapefile": read_files, "read_fiona": fiona} _writers = {"to_shapefile": write_files} _pandas_readers = { k: v for k, v in list(pd.io.api.__dict__.items()) if k.startswith("read_") } libpysal-4.9.2/libpysal/io/iohandlers/000077500000000000000000000000001452177046000177315ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/iohandlers/__init__.py000066400000000000000000000005551452177046000220470ustar00rootroot00000000000000import warnings from . import ( arcgis_dbf, arcgis_swm, arcgis_txt, csvWrapper, dat, gal, geobugs_txt, geoda_txt, gwt, mat, mtx, pyDbfIO, pyShpIO, stata_txt, wk1, wkt, ) try: from . import db except: # noqa E722 warnings.warn("SQLAlchemy not installed, database I/O disabled") # noqa B028 libpysal-4.9.2/libpysal/io/iohandlers/arcgis_dbf.py000066400000000000000000000167601452177046000224000ustar00rootroot00000000000000from .. import fileio from ...weights.weights import W from ...weights.util import remap_ids __author__ = "Myunghwa Hwang " __all__ = ["ArcGISDbfIO"] class ArcGISDbfIO(fileio.FileIO): """Opens, reads, and writes weights file objects in ArcGIS dbf format. Spatial weights objects in the ArcGIS ``.dbf`` format are used in ArcGIS Spatial Statistics tools. This format is the same as the general ``.dbf`` format, but the structure of the weights ``.dbf`` file is fixed unlike other ``.dbf`` files. This ``.dbf`` format can be used with the "Generate Spatial Weights Matrix" tool, but not with the tools under the "Mapping Clusters" category". The ArcGIS ``.dbf`` file is assumed to have three or four data columns. When the file has four columns, the first column is meaningless and will be ignored in PySAL during both file reading and file writing. The next three columns hold origin IDs, destinations IDs, and weight values. When the file has three columns, it is assumed that only these data columns exist in the stated order. The name for the orgin IDs column should be the name of ID variable in the original source data table. The names for the destination IDs and weight values columns are NID and WEIGHT, respectively. ArcGIS Spatial Statistics tools support only unique integer IDs. Therefore, the values for origin and destination ID columns should be integer. For the case where the IDs of a weights object are not integers, `ArcGISDbfIO` allows users to use internal id values corresponding to record numbers, instead of original ids. An exemplary structure of an ArcGIS dbf file is as follows: ``` [Line 1] Field1 RECORD_ID NID WEIGHT [Line 2] 0 72 76 1 [Line 3] 0 72 79 1 [Line 4] 0 72 78 1 ``` Unlike the ArcGIS text format, this format does not seem to include self-neighbors. References ---------- http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=Convert_Spatial_Weights_Matrix_to_Table_(Spatial_Statistics) """ FORMATS = ["arcgis_dbf"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): self._varName = "Unknown" args = args[:2] fileio.FileIO.__init__(self, *args, **kwargs) self.file = fileio.FileIO(self.dataPath, self.mode) def _set_varName(self, val): if issubclass(type(val), str): self._varName = val def _get_varName(self): return self._varName varName = property(fget=_get_varName, fset=_set_varName) def read(self, n=-1): self._complain_ifclosed(self.closed) return self._read() def seek(self, pos): self.file.seek(pos) self.pos = self.file.pos def _read(self): """Reads ArcGIS dbf file Returns ------- w : libpysal.weights.W A ``libpysal.weights.W`` object. Raises ------ StopIteration Raised at the EOF. ValueError Raised when the weights data structure is incorrect. TypeError Raised when the IDs are not integers. Examples -------- Type ``dir(w)`` at the interpreter to see what methods are supported. Open an ArcGIS ``.dbf`` file and read it into a PySAL weights object. >>> import libpysal >>> w = libpysal.io.open( ... libpysal.examples.get_path('arcgis_ohio.dbf'), 'r', 'arcgis_dbf' ... ).read() Get the number of observations from the header. >>> w.n 88 Get the mean number of neighbors. >>> w.mean_neighbors 5.25 Get neighbor distances for a single observation. >>> w[1] == dict({2: 1.0, 11: 1.0, 6: 1.0, 7: 1.0}) True """ if self.pos > 0: raise StopIteration id_var = self.file.header[1] startPos = len(self.file.header) if startPos == 3: startPos = 0 elif startPos == 4: startPos = 1 else: msg = "Wrong structure, a weights '.dbf' " msg += "file requires at least three data columns." raise ValueError(msg) self.varName = id_var id_type = int id_spec = self.file.field_spec[startPos] if id_spec[0] != "N": raise TypeError("The data type for IDs should be integer.") self.id_var = id_var weights = {} neighbors = {} for row in self.file: i, j, w = tuple(row)[startPos:] i = id_type(i) j = id_type(j) w = float(w) if i not in weights: weights[i] = [] neighbors[i] = [] weights[i].append(w) neighbors[i].append(j) self.pos = self.file.pos w = W(neighbors, weights) return w def write(self, obj, useIdIndex=False): """Write a weights object to the opened ``.dbf`` file. Parameters ---------- obj : libpysal.weights.W A ``libpysal.weights.W`` object. useIdIndex : bool Use the `W` IDs and remap (``True``). Default is ``False``. Raises ------ TypeError Raised when the IDs in input ``obj`` are not integers. TypeError Raised when the input ``obj`` is not a PySAL `W`. Examples -------- >>> import tempfile, libpysal, os >>> testfile = libpysal.io.open( ... libpysal.examples.get_path('arcgis_ohio.dbf'), 'r', 'arcgis_dbf' ... ) >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.dbf') Reassign to a new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w', 'arcgis_dbf') Write the Weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created text file. >>> wnew = libpysal.io.open(fname, 'r', 'arcgis_dbf').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up the temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if issubclass(type(obj), W): self.file.header = [self.varName, "NID", "WEIGHT"] id_type = type(obj.id_order[0]) if id_type is not int and not useIdIndex: raise TypeError("ArcGIS DBF weight files support only integer IDs.") if useIdIndex: id2i = obj.id2i obj = remap_ids(obj, id2i) id_spec = ("N", len(str(max(obj.id_order))), 0) self.file.field_spec = [id_spec, id_spec, ("N", 13, 6)] for id in obj.id_order: neighbors = list(zip(obj.neighbors[id], obj.weights[id])) for neighbor, weight in neighbors: self.file.write([id, neighbor, weight]) self.pos = self.file.pos else: raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) def flush(self): self._complain_ifclosed(self.closed) self.file.flush() def close(self): self.file.close() libpysal-4.9.2/libpysal/io/iohandlers/arcgis_swm.py000066400000000000000000000245511452177046000224500ustar00rootroot00000000000000import numpy as np from struct import pack, unpack from .. import fileio from ...weights import W from ...weights.util import remap_ids __author__ = "Myunghwa Hwang " __all__ = ["ArcGISSwmIO"] class ArcGISSwmIO(fileio.FileIO): """Opens, reads, and writes weights file objects in ArcGIS ``.swm`` format. Spatial weights objects in the ArcGIS ``.swm`` format are used in ArcGIS Spatial Statistics tools. Particularly, this format can be directly used with the tools under the category of Mapping Clusters. The values for``ORG_i`` and ``DST_i`` should be integers, as ArcGIS Spatial Statistics tools support only unique integer IDs. For the case where a weights object uses non-integer IDs, `ArcGISSwmIO` allows users to use internal IDs corresponding to record numbers, instead of original IDs. The specifics of each part of the above structure is as follows. .. table:: ArcGIS SWM Components ============ ============ ==================================== ================================ Part Data type Description Length ============ ============ ==================================== ================================ ID_VAR_NAME ASCII TEXT ID variable name Flexible (Up to the 1st ;) ESRI_SRS ASCII TEXT ESRI spatial reference system Flexible (Btw the 1st ; and \\n) NO_OBS l.e. int Number of observations 4 ROW_STD l.e. int Whether or not row-standardized 4 WGT_i ORG_i l.e. int ID of observaiton i 4 NO_NGH_i l.e. int Number of neighbors for obs. i (m) 4 NGHS_i DSTS_i l.e. int IDs of all neighbors of obs. i 4*m WS_i l.e. float Weights for obs. i and its neighbors 8*m W_SUM_i l.e. float Sum of weights for " 8 ============ ============ ==================================== ================================ """ FORMATS = ["swm"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): self._varName = "Unknown" self._srs = "Unknown" fileio.FileIO.__init__(self, *args, **kwargs) self.file = open(self.dataPath, self.mode + "b") def _set_varName(self, val): if issubclass(type(val), str): self._varName = val def _get_varName(self): return self._varName varName = property(fget=_get_varName, fset=_set_varName) def _set_srs(self, val): if issubclass(type(val), str): self._srs = val def _get_srs(self): return self._srs srs = property(fget=_get_srs, fset=_set_srs) def read(self, n=-1): self._complain_ifclosed(self.closed) return self._read() def seek(self, pos): if pos == 0: self.file.seek(0) self.pos = 0 def _read(self): """Read an ArcGIS ``.swm`` file. Returns ------- w : libpysal.weights.W A PySAL `W` object. Raises ------ StopIteration Raised at the EOF. Examples -------- Type ``dir(w)`` at the interpreter to see what methods are supported. Open an ArcGIS ``.swm`` file and read it into a PySAL weights object. >>> import libpysal >>> w = libpysal.io.open(libpysal.examples.get_path('ohio.swm'), 'r').read() Get the number of observations from the header, >>> w.n 88 Get the mean number of neighbors. >>> w.mean_neighbors 5.25 Get neighbor distances for a single observation. >>> w[1] == dict({2: 1.0, 11: 1.0, 6: 1.0, 7: 1.0}) True """ if self.pos > 0: raise StopIteration header = self.file.readline() header = header.decode() if header.upper().strip().startswith("VERSION@"): # deal with the new SWM version w = self.read_new_version(header) else: # deal with the old SWM version w = self.read_old_version(header) return w def read_old_version(self, header): """Read the old version of ArcGIS(<10.1) ``.swm`` file. Parameters ---------- header : str The first line of the ``.swm`` file. Returns ------- w : libpysal.weights.W A PySAL `W` object. """ id_var, srs = header[:-1].split(";") self.varName = id_var self.srs = srs self.header_len = len(header) + 8 no_obs, row_std = tuple(unpack("<2l", self.file.read(8))) neighbors = {} weights = {} for i in range(no_obs): origin, no_nghs = tuple(unpack("<2l", self.file.read(8))) neighbors[origin] = [] weights[origin] = [] if no_nghs > 0: neighbors[origin] = list( unpack("<%il" % no_nghs, self.file.read(4 * no_nghs)) ) weights[origin] = list( unpack("<%id" % no_nghs, self.file.read(8 * no_nghs)) ) w_sum = list(unpack(" 0: neighbors[origin] = list( unpack("<%il" % no_nghs, self.file.read(4 * no_nghs)) ) if fixedWeights: weights[origin] = list(unpack(">> import tempfile, libpysal, os >>> testfile = libpysal.io.open(libpysal.examples.get_path('ohio.swm'), 'r') >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.swm') Reassign to a new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w') Add properities to the file to write. >>> o.varName = testfile.varName >>> o.srs = testfile.srs Write the weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created text file. >>> wnew = libpysal.io.open(fname, 'r').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up the temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if not issubclass(type(obj), W): raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) if not (type(obj.id_order[0]) in (np.int32, np.int64, int)) and not useIdIndex: raise TypeError("ArcGIS SWM files support only integer IDs.") if useIdIndex: id2i = obj.id2i obj = remap_ids(obj, id2i) unk = str("%s;%s\n" % (self.varName, self.srs)).encode() self.file.write(unk) self.file.write(pack(">> import libpysal >>> w = libpysal.io.open( ... libpysal.examples.get_path('arcgis_txt.txt'), 'r', 'arcgis_text' ... ).read() Get the number of observations from the header. >>> w.n 3 Get the mean number of neighbors. >>> w.mean_neighbors 2.0 Get neighbor distances for a single observation. >>> w[1] {2: 0.1, 3: 0.14286} """ if self.pos > 0: raise StopIteration id_var = self.file.readline().strip() self.varName = id_var id_order = None id_type = int try: dbf = os.path.join(self.dataPath + ".dbf") if os.path.exists(dbf): db = fileio.FileIO(dbf, "r") if id_var in db.header: id_order = db.by_col(id_var) id_type = type(id_order[0]) else: msg = "ID_VAR:'%s' was in in the DBF header, " msg += "proceeding with unordered string IDs." msg = msg % id_var warn(msg, RuntimeWarning) else: msg = "DBF relating to ArcGIS TEXT was not found, " msg += "proceeding with unordered string IDs." warn(msg, RuntimeWarning) except: msg = "Exception occurred will reading DBF, " msg += "proceeding with unordered string IDs." warn(msg, RuntimeWarning) if (id_type is not int) or (id_order and type(id_order)[0] is not int): raise TypeError("The data type for IDs should be integer.") if id_order: self.n = len(id_order) self.shp = os.path.split(self.dataPath)[1].split(".")[0] self.id_var = id_var weights, neighbors = self._readlines(id_type) for k in neighbors: if k in neighbors[k]: k_index = neighbors[k].index(k) if weights[k][k_index] == 0.0: del neighbors[k][k_index] del weights[k][k_index] self.pos += 1 w = W(neighbors, weights) return w def write(self, obj, useIdIndex=False): """ Parameters ---------- obj : libpysal.weights.W A PySAL `W` object. useIdIndex : bool Use the `W` IDs and remap (``True``). Default is ``False``. Raises ------ TypeError Raised when the IDs in input ``obj`` are not integers. TypeError Raised when the input ``obj`` is not a PySAL `W`. Examples -------- >>> import tempfile, libpysal, os >>> testfile = libpysal.io.open( ... libpysal.examples.get_path('arcgis_txt.txt'), 'r', 'arcgis_text' ... ) >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.txt') Reassign to a new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w', 'arcgis_text') Write the Weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created text file. >>> wnew = libpysal.io.open(fname, 'r', 'arcgis_text').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up the temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if issubclass(type(obj), W): id_type = type(obj.id_order[0]) if id_type is not int and not useIdIndex: raise TypeError("ArcGIS TEXT weight files support only integer IDs.") if useIdIndex: id2i = obj.id2i obj = remap_ids(obj, id2i) header = "%s\n" % self.varName self.file.write(header) self._writelines(obj) else: raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) libpysal-4.9.2/libpysal/io/iohandlers/csvWrapper.py000066400000000000000000000061011452177046000224350ustar00rootroot00000000000000from .. import tables import csv from typing import Union __author__ = "Charles R Schmidt " __all__ = ["csvWrapper"] class csvWrapper(tables.DataTable): """Read a ``.csv`` file in a `DataTable` object. Examples -------- >>> import libpysal >>> stl = libpysal.examples.load_example('stl') >>> file_name = stl.get_path('stl_hom.csv') >>> f = libpysal.io.open(file_name,'r') >>> y = f.read() >>> f.header ['WKT', 'NAME', 'STATE_NAME', 'STATE_FIPS', 'CNTY_FIPS', 'FIPS', 'FIPSNO', 'HR7984', 'HR8488', 'HR8893', 'HC7984', 'HC8488', 'HC8893', 'PO7984', 'PO8488', 'PO8893', 'PE77', 'PE82', 'PE87', 'RDAC80', 'RDAC85', 'RDAC90'] >>> f._spec [str, str, str, int, int, int, int, float, float, float, int, int, int, int, int, int, float, float, float, float, float, float] """ __doc__ = tables.DataTable.__doc__ FORMATS = ["csv"] READ_MODES = ["r", "Ur", "rU", "U"] MODES = READ_MODES[:] def __init__(self, *args, **kwargs): tables.DataTable.__init__(self, *args, **kwargs) self.__idx = {} self.__len = None self._open() def __len__(self): return self.__len def _open(self): self.fileObj = open(self.dataPath, self.mode) if self.mode in self.READ_MODES: self.dataObj = csv.reader(self.fileObj) data = list(self.dataObj) if self._determineHeader(data): self.header = data.pop(0) else: self.header = ["field_%d" % i for i in range(len(data[0]))] self._spec = self._determineSpec(data) self.data = data self.fileObj.close() self.__len = len(data) def _determineHeader(self, data: list) -> bool: HEADER = True headSpec = self._determineSpec([data[0]]) restSpec = self._determineSpec(data[1:]) if headSpec == restSpec: HEADER = False return HEADER @staticmethod def _determineSpec(data: list) -> list: cols = len(data[0]) spec = [] for j in range(cols): isInt = True isFloat = True for row in data: val = row[j] if not val.strip().replace("-", "").replace(".", "").isdigit(): isInt = False isFloat = False break else: if isInt and "." in val: isInt = False if isInt: spec.append(int) elif isFloat: spec.append(float) else: spec.append(str) return spec def _read(self) -> Union[list, None]: if self.pos < len(self): row = self.data[self.pos] self.pos += 1 return row else: return None libpysal-4.9.2/libpysal/io/iohandlers/dat.py000066400000000000000000000063601452177046000210600ustar00rootroot00000000000000from . import gwt from ...weights import W __author__ = "Myunghwa Hwang " __all__ = ["DatIO"] class DatIO(gwt.GwtIO): """Opens, reads, and writes file objects in ``.dat`` format. Spatial weights objects in ``.dat`` format are used in Dr. LeSage's MatLab Econ library. This ``.dat`` format is a simple text file with a ``.DAT`` or ``.dat`` extension. Without a header line, it includes three data columns for origin ID, destination ID, and weight values as follows: ``` [Line 1] 2 1 0.25 [Line 2] 5 1 0.50 ``` Origin/destination IDs in this file format are simply record numbers starting with 1. IDs are not necessarily integers. Data values for all columns should be numeric. """ FORMATS = ["dat"] MODES = ["r", "w"] def _read(self): """Reads in a ``.dat`` file as a PySAL `W` object. Returns ------- w : libpysal.weights.W A PySAL `W` object. Raises ------ StopIteration Raised at the EOF. Examples -------- Type ``dir(w)`` at the interpreter to see what methods are supported. Open ``.dat`` file and read it into a PySAL weights object, >>> import libpysal >>> w = libpysal.io.open(libpysal.examples.get_path('wmat.dat'), 'r').read() Get the number of observations from the header. >>> w.n 49 Get the mean number of neighbors. >>> w.mean_neighbors 4.73469387755102 Get neighbor distances for a single observation. >>> w[1] == dict({2.0: 0.3333, 5.0: 0.3333, 6.0: 0.3333}) True """ if self.pos > 0: raise StopIteration id_type = float weights, neighbors = self._readlines(id_type) self.pos += 1 w = W(neighbors, weights) return w def write(self, obj): """Write a weights object to the opened ``.dat`` file. Parameters ---------- obj : libpysal.weights.W A PySAL `W` object. Examples -------- >>> import tempfile, libpysal, os >>> testfile = libpysal.io.open(libpysal.examples.get_path('wmat.dat'), 'r') >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.dat') Reassign to a new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w') Write the weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created ``.dat`` file. >>> wnew = libpysal.io.open(fname, 'r').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up the temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if issubclass(type(obj), W): self._writelines(obj) else: raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) libpysal-4.9.2/libpysal/io/iohandlers/db.py000066400000000000000000000043771452177046000207030ustar00rootroot00000000000000from .. import fileio from shapely import wkb errmsg = "" try: from sqlalchemy.ext.automap import automap_base from sqlalchemy import create_engine from sqlalchemy.orm import Session nosql_mode = False except ImportError: nosql_mode = True errmsg += ( "No module named sqlalchemy. Please install" " sqlalchemy to enable this functionality." ) class SQLConnection(fileio.FileIO): """Reads an SQL mappable.""" FORMATS = ["sqlite", "db"] MODES = ["r"] def __init__(self, *args, **kwargs): if errmsg != "": raise ImportError(errmsg) self._typ = str fileio.FileIO.__init__(self, *args, **kwargs) self.dbname = args[0] self.Base = automap_base() self._engine = create_engine(self.dbname) self.Base.prepare(autoload_with=self._engine) self.metadata = self.Base.metadata def read(self, *args, **kwargs): return self._get_gjson(*args, **kwargs) def seek(self): pass def __next__(self): pass def close(self): self.file.close() fileio.FileIO.close(self) def _get_gjson(self, tablename: str, geom_column="GEOMETRY"): gjson = {"type": "FeatureCollection", "features": []} for row in self.session.query(self.metadata.tables[tablename]): feat = {"type": "Feature", "geometry": {}, "properties": {}} feat["GEOMETRY"] = wkb.loads(getattr(row, geom_column)) attributes = row._asdict() attributes.pop(geom_column, None) feat["properties"] = attributes gjson["features"].append(feat) return gjson @property def tables(self) -> list: if not hasattr(self, "_tables"): self._tables = list(self.metadata.tables.keys()) return self._tables @property def session(self): """Create an ``sqlalchemy.orm.Session`` instance. Returns ------- self._session : sqlalchemy.orm.Session An ``sqlalchemy.orm.Session`` instance. """ # What happens if the session is externally closed? Check for None? if not hasattr(self, "_session"): self._session = Session(self._engine) return self._session libpysal-4.9.2/libpysal/io/iohandlers/gal.py000066400000000000000000000150251452177046000210510ustar00rootroot00000000000000from .. import fileio from ...weights.weights import W, WSP from scipy import sparse import numpy as np __author__ = "Charles R Schmidt " __all__ = ["GalIO"] class GalIO(fileio.FileIO): """Opens, reads, and writes file objects in `GAL` format.""" FORMATS = ["gal"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): self._typ = str fileio.FileIO.__init__(self, *args, **kwargs) self.file = open(self.dataPath, self.mode) def read(self, n=-1, sparse=False): """Read in a ``.gal`` file. Parameters ---------- n : int Read at most ``n`` objects. Default is ``-1``. sparse: bool If ``True`` return a ``scipy`` sparse object. If ``False`` return PySAL `W` object. Default is ``False``. Returns ------- w : {libpysal.weights.W, libpysal.weights.WSP} A PySAL `W` object or a thin PySAL `WSP`. """ self._sparse = sparse self._complain_ifclosed(self.closed) w = self._read() return w def seek(self, pos): if pos == 0: self.file.seek(0) self.pos = 0 def _get_data_type(self): return self._typ def _set_data_type(self, typ): """ Raises ------ TypeError Raised when ``typ`` is not a callable. """ if callable(typ): self._typ = typ else: raise TypeError("Expecting a callable.") data_type = property(fset=_set_data_type, fget=_get_data_type) def _read(self): """Reads in a `GalIO` object. Returns ------- w : {libpysal.weights.W, libpysal.weights.WSP} A PySAL `W` object or a thin PySAL `WSP`. Raises ------ StopIteration Raised at the EOF. Examples -------- >>> import tempfile, libpysal, os Read in a file `GAL` file. >>> testfile = libpysal.io.open(libpysal.examples.get_path('sids2.gal'), 'r') Return a `W` object. >>> w = testfile.read() >>> w.n == 100 True >>> print(round(w.sd,6)) 1.515124 >>> testfile = libpysal.io.open(libpysal.examples.get_path('sids2.gal'), 'r') Return a sparse matrix for the `W` information. >>> wsp = testfile.read(sparse=True) >>> wsp.sparse.nnz 462 """ if self._sparse: if self.pos > 0: raise StopIteration header = self.file.readline().strip().split() header_n = len(header) n = int(header[0]) if header_n > 1: n = int(header[1]) ids = [] idsappend = ids.append row = [] extend = row.extend # avoid dot in loops col = [] append = col.append counter = 0 typ = self.data_type for i in range(n): id, n_neighbors = self.file.readline().strip().split() id = typ(id) n_neighbors = int(n_neighbors) neighbors_i = list(map(typ, self.file.readline().strip().split())) nn = len(neighbors_i) extend([id] * nn) counter += nn for id_neigh in neighbors_i: append(id_neigh) idsappend(id) self.pos += 1 row = np.array(row) col = np.array(col) data = np.ones(counter) ids = np.unique(row) row = np.array([np.where(ids == j)[0] for j in row]).flatten() col = np.array([np.where(ids == j)[0] for j in col]).flatten() spmat = sparse.csr_matrix((data, (row, col)), shape=(n, n)) w = WSP(spmat) else: if self.pos > 0: raise StopIteration neighbors = {} ids = [] # handle case where more than n is specified in first line header = self.file.readline().strip().split() header_n = len(header) n = int(header[0]) if header_n > 1: n = int(header[1]) w = {} typ = self.data_type for i in range(n): id, n_neighbors = self.file.readline().strip().split() id = typ(id) n_neighbors = int(n_neighbors) neighbors_i = list(map(typ, self.file.readline().strip().split())) neighbors[id] = neighbors_i ids.append(id) self.pos += 1 w = W(neighbors, id_order=ids) return w def write(self, obj): """Write a weights object to the opened `GAL` file. Parameters ---------- obj : libpysal.weights.W A PySAL `W` object. Raises ------ TypeError Raised when the input ``obj`` is not a PySAL `W`. Examples -------- >>> import tempfile, libpysal, os >>> testfile = libpysal.io.open(libpysal.examples.get_path('sids2.gal'), 'r') >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.gal') Reassign to the new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w') Write the weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created gal file. >>> wnew = libpysal.io.open(fname, 'r').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up the temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if issubclass(type(obj), W): IDS = obj.id_order self.file.write("%d\n" % (obj.n)) for id in IDS: neighbors = obj.neighbors[id] self.file.write("%s %d\n" % (str(id), len(neighbors))) self.file.write(" ".join(map(str, neighbors)) + "\n") self.pos += 1 else: raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.9.2/libpysal/io/iohandlers/geobugs_txt.py000066400000000000000000000173611452177046000226450ustar00rootroot00000000000000from .. import fileio from ...weights import W __author__ = "Myunghwa Hwang " __all__ = ["GeoBUGSTextIO"] class GeoBUGSTextIO(fileio.FileIO): """Opens, reads, and writes weights file objects in the text format used in `GeoBUGS `_. `GeoBUGS` generates a spatial weights matrix as an R object and writes it out as an ASCII text representation of the R object. An exemplary `GeoBUGS` text file is as follows. ``` list([CARD], [ADJ], [WGT], [SUMNUMNEIGH]) ``` where ``[CARD]`` and ``[ADJ]`` are required but the others are optional. PySAL assumes ``[CARD]`` and ``[ADJ]`` always exist in an input text file. It can read a `GeoBUGS` text file, even when its content is not written in the order of ``[CARD]``, ``[ADJ]``, ``[WGT]``, and ``[SUMNUMNEIGH]``. It always writes all of ``[CARD]``, ``[ADJ]``, ``[WGT]``, and ``[SUMNUMNEIGH]``. PySAL does not apply text wrapping during file writing. In the above example, ``` [CARD]: num = c([a list of comma-splitted neighbor cardinalities]) [ADJ]: adj = c ([a list of comma-splitted neighbor IDs]) If caridnality is zero, neighbor IDs are skipped. The ordering of observations is the same in both ``[CARD]`` and ``[ADJ]``. Neighbor IDs are record numbers starting from one. [WGT]: weights = c([a list of comma-splitted weights]) The restrictions for [ADJ] also apply to ``[WGT]``. [SUMNUMNEIGH]: sumNumNeigh = [The total number of neighbor pairs] the total number of neighbor pairs is an integer value and the same as the sum of neighbor cardinalities. ``` Notes ----- For the files generated from R the ``spdep``, ``nb2WB``, and ``dput`` functions. It is assumed that the value for the control parameter of the ``dput`` function is ``NULL``. Please refer to R ``spdep`` and ``nb2WB`` functions help files. References ---------- * **Thomas, A., Best, N., Lunn, D., Arnold, R., and Spiegelhalter, D.** (2004) GeoBUGS User Manual. R spdep nb2WB function help file. """ FORMATS = ["geobugs_text"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): args = args[:2] fileio.FileIO.__init__(self, *args, **kwargs) self.file = open(self.dataPath, self.mode) def read(self, n=-1): """Read a GeoBUGS text file. Returns ------- w : libpysal.weights.W A PySAL `W` object. Examples -------- Type ``dir(w)`` at the interpreter to see what methods are supported. Open a `GeoBUGS` text file and read it into a PySAL weights object. >>> import libpysal >>> w = libpysal.io.open( ... libpysal.examples.get_path('geobugs_scot'), 'r', 'geobugs_text' ... ).read() Get the number of observations from the header. >>> w.n 56 Get the mean number of neighbors. >>> w.mean_neighbors 4.178571428571429 Get neighbor distances for a single observation. >>> w[1] == dict({9: 1.0, 19: 1.0, 5: 1.0}) True """ self._complain_ifclosed(self.closed) w = self._read() return w def seek(self, pos) -> int: if pos == 0: self.file.seek(0) self.pos = 0 def _read(self): """Reads in a `GeoBUGSTextIO` object. Raises ------ StopIteration Raised at the EOF. Returns ------- w : libpysal.weights.W A PySAL `W` object. """ if self.pos > 0: raise StopIteration fbody = self.file.read() body_structure = {} for i in ["num", "adj", "weights", "sumNumNeigh"]: i_loc = fbody.find(i) if i_loc != -1: body_structure[i] = (i_loc, i) body_sequence = sorted(body_structure.values()) body_sequence.append((-1, "eof")) for i in range(len(body_sequence) - 1): part, next_part = body_sequence[i], body_sequence[i + 1] start, end = part[0], next_part[0] part_text = fbody[start:end] part_length, start, end = len(part_text), 0, -1 for c in range(part_length): if part_text[c].isdigit(): start = c break for c in range(part_length - 1, 0, -1): if part_text[c].isdigit(): end = c + 1 break part_text = part_text[start:end] part_text = part_text.replace("\n", "") value_type = int if part[1] == "weights": value_type = float body_structure[part[1]] = [value_type(v) for v in part_text.split(",")] cardinalities = body_structure["num"] adjacency = body_structure["adj"] raw_weights = [1.0] * int(sum(cardinalities)) if "weights" in body_structure and isinstance(body_structure["weights"], list): raw_weights = body_structure["weights"] no_obs = len(cardinalities) neighbors = {} weights = {} pos = 0 for i in range(no_obs): neighbors[i + 1] = [] weights[i + 1] = [] no_nghs = cardinalities[i] if no_nghs > 0: neighbors[i + 1] = adjacency[pos : pos + no_nghs] weights[i + 1] = raw_weights[pos : pos + no_nghs] pos += no_nghs self.pos += 1 w = W(neighbors, weights) return w def write(self, obj): """Writes a weights object to the opened text file. Parameters ---------- obj : libpysal.weights.W A PySAL `W` object. Raises ------ TypeError Raised when the input ``obj`` is not a PySAL `W`. Examples -------- >>> import tempfile, libpysal, os >>> testfile = libpysal.io.open( ... libpysal.examples.get_path('geobugs_scot'), 'r', 'geobugs_text' ... ) >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='') Reassign to a new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w', 'geobugs_text') Write the Weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created text file. >>> wnew = libpysal.io.open(fname, 'r', 'geobugs_text').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up the temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if issubclass(type(obj), W): cardinalities, neighbors, weights = [], [], [] for i in obj.id_order: cardinalities.append(obj.cardinalities[i]) neighbors.extend(obj.neighbors[i]) weights.extend(obj.weights[i]) self.file.write("list(") self.file.write("num=c(%s)," % ",".join(map(str, cardinalities))) self.file.write("adj=c(%s)," % ",".join(map(str, neighbors))) self.file.write("sumNumNeigh=%i)" % sum(cardinalities)) self.pos += 1 else: raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.9.2/libpysal/io/iohandlers/geoda_txt.py000066400000000000000000000053661452177046000222730ustar00rootroot00000000000000from .. import tables __author__ = "Charles R Schmidt " __all__ = ["GeoDaTxtReader"] from typing import Union class GeoDaTxtReader(tables.DataTable): """GeoDa Text File Export Format. Examples -------- >>> import libpysal >>> f = libpysal.io.open(libpysal.examples.get_path('stl_hom.txt'),'r') >>> f.header ['FIPSNO', 'HR8488', 'HR8893', 'HC8488'] >>> len(f) 78 >>> f.dat[0] ['17107', '1.290722', '1.624458', '2'] >>> f.dat[-1] ['29223', '0', '8.451537', '0'] >>> f._spec [int, float, float, int] """ __doc__ = tables.DataTable.__doc__ FORMATS = ["geoda_txt"] MODES = ["r"] def __init__(self, *args, **kwargs): tables.DataTable.__init__(self, *args, **kwargs) self.__idx = {} self.__len = None self.pos = 0 self._open() def _open(self): """ Raises ------ TypeError Raised when the input 'geoda_txt' is not valid. """ if self.mode == "r": self.fileObj = open(self.dataPath, "r") n, k = self.fileObj.readline().strip().split(",") n, k = int(n), int(k) header = self.fileObj.readline().strip().split(",") self.header = [f.replace('"', "") for f in header] try: assert len(self.header) == k except AssertionError: raise TypeError("This is not a valid 'geoda_txt' file.") dat = self.fileObj.readlines() self.dat = [line.strip().split(",") for line in dat] self._spec = self._determineSpec(self.dat) self.__len = len(dat) def __len__(self) -> int: return self.__len def _read(self) -> Union[list, None]: if self.pos < len(self): row = self.dat[self.pos] self.pos += 1 return row else: return None def close(self): self.fileObj.close() tables.DataTable.close(self) @staticmethod def _determineSpec(data) -> list: cols = len(data[0]) spec = [] for j in range(cols): isInt = True isFloat = True for row in data: val = row[j] if not val.strip().replace("-", "").replace(".", "").isdigit(): isInt = False isFloat = False break else: if isInt and "." in val: isInt = False if isInt: spec.append(int) elif isFloat: spec.append(float) else: spec.append(str) return spec libpysal-4.9.2/libpysal/io/iohandlers/gwt.py000066400000000000000000000234051452177046000211100ustar00rootroot00000000000000import os.path from .. import fileio as FileIO from ...weights.weights import W from warnings import warn __author__ = "Charles R Schmidt " __all__ = ["GwtIO"] class unique_filter(object): """(Util function) When a new instance is passed as an arugment to the builtin filter it will remove duplicate entries without changing the order of the list. Be sure to ceate a new instance everytime, unless you want a global filter. Examples -------- >>> l = ['a', 'a', 'b', 'a', 'c', 'v', 'd', 'a', 'v', 'd'] >>> list(filter(unique_filter(),l)) ['a', 'b', 'c', 'v', 'd'] """ def __init__(self): self.exclude = set() def __call__(self, x) -> bool: if x in self.exclude: return False else: self.exclude.add(x) return True class GwtIO(FileIO.FileIO): FORMATS = ["kwt", "gwt"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): self._varName = "Unknown" self._shpName = "Unknown" FileIO.FileIO.__init__(self, *args, **kwargs) self.file = open(self.dataPath, self.mode) def _set_varName(self, val): if issubclass(type(val), str): self._varName = val def _get_varName(self) -> str: return self._varName varName = property(fget=_get_varName, fset=_set_varName) def _set_shpName(self, val): if issubclass(type(val), str): self._shpName = val def _get_shpName(self) -> str: return self._shpName shpName = property(fget=_get_shpName, fset=_set_shpName) def read(self, n=-1): """ Parameters ---------- n : int Read at most ``n`` objects. Default is ``-1``. Returns ------- w : libpysal.weights.W A PySAL `W` object. """ self._complain_ifclosed(self.closed) w = self._read() return w def seek(self, pos): if pos == 0: self.file.seek(0) self.pos = 0 def _readlines(self, id_type, ret_ids=False): """Reads the main body of gwt-like weights files into two dictionaries containing weights and neighbors. This code part is repeatedly used for many weight file formats. Header lines, however, are different from format to format. So, for code reusability, this part is separated out from the ``_read()`` function by Myunghwa Hwang. Parameters ---------- id_type : type Cast IDs as this type. ret_ids : bool Return IDs (``True``). Default is ``False``. Returns ------- weights : dict Dictionary of weight values. neighbors : dict Dictionary of neighbor ID values. ids : list List of ID values. """ data = [row.strip().split() for row in self.file.readlines()] ids = list(filter(unique_filter(), [x[0] for x in data])) ids = list(map(id_type, ids)) WN = {} # note: fromkeys is no good here, all keys end up sharing the say dict value for id in ids: WN[id] = {} for i, j, v in data: i = id_type(i) j = id_type(j) WN[i][j] = float(v) weights = {} neighbors = {} for i in WN: weights[i] = list(WN[i].values()) neighbors[i] = list(WN[i].keys()) if ret_ids: return weights, neighbors, ids else: return weights, neighbors def _read(self): """Reads ``.gwt`` file. Returns ------- w : libpysal.weights.W A PySAL `W` object. Raises ------ StopIteration Raised at the EOF. Examples -------- Type ``dir(f)`` at the interpreter to see what methods are supported. Open ``.gwt`` file and read it into a PySAL weights object. >>> import libpysal >>> f = libpysal.io.open(libpysal.examples.get_path('juvenile.gwt'), 'r').read() Get the number of observations from the header. >>> f.n 168 Get the mean number of neighbors. >>> f.mean_neighbors 16.678571428571427 Get neighbor distances for a single observation. >>> f[1] {2: 14.1421356} """ if self.pos > 0: raise StopIteration flag, n, shp, id_var = self.file.readline().strip().split() self.shpName = shp self.varName = id_var id_order = None id_type = str try: base = os.path.split(self.dataPath)[0] dbf = os.path.join(base, self.shpName.replace(".shp", "") + ".dbf") if os.path.exists(dbf): db = FileIO.FileIO(dbf, "r") if id_var in db.header: id_order = db.by_col(id_var) id_type = type(id_order[0]) else: msg = "ID_VAR:'%s' was in in the DBF header, " msg += "proceeding with unordered string IDs." msg = msg % id_var warn(msg, RuntimeWarning) else: msg = "DBF relating to GWT was not found, " msg += "proceeding with unordered string IDs." warn(msg, RuntimeWarning) except: msg = "Exception occurred will reading DBF, " msg += "proceeding with unordered string IDs." warn(msg, RuntimeWarning) self.flag = flag self.n = n self.shp = shp self.id_var = id_var if id_order is None: weights, neighbors, id_order = self._readlines(id_type, True) else: weights, neighbors = self._readlines(id_type) self.pos += 1 w = W(neighbors, weights, id_order) # w.transform = 'b' # set meta data w._shpName = self.shpName w._varName = self.varName # msg = "Weights have been converted to binary. " # msg += "To retrieve original values use w.transform='o'" # warn(msg, RuntimeWarning) return w def _writelines(self, obj): """Writes the main body of gwt-like weights files. This code part is repeatedly used for many weight file formats. Header lines, however, are different from format to format. So, for code reusability, this part is separated out from write function by Myunghwa Hwang. Parameters ---------- obj : libpysal.weights.W A PySAL `W` object. """ for id in obj.id_order: neighbors = list(zip(obj.neighbors[id], obj.weights[id])) str_id = "_".join(str(id).split()) for neighbor, weight in neighbors: neighbor = "_".join(str(neighbor).split()) self.file.write("%s %s %6G\n" % (str_id, neighbor, weight)) self.pos += 1 def write(self, obj): """Write a weights object to the opened `GWT` file. Parameters ---------- obj : libpysal.weights.W A PySAL `W` object. Raises ------ TypeError Raised when the input ``obj`` is not a PySAL `W`. Examples -------- >>> import tempfile, libpysal, os >>> testfile = libpysal.io.open(libpysal.examples.get_path('juvenile.gwt'), 'r') >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.gwt') Reassign to a new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w') Write the weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created ``.gwt`` file. >>> wnew = libpysal.io.open(fname, 'r').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up the temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if issubclass(type(obj), W): # transform = obj.transform # obj.transform = 'o' if hasattr(obj, "_shpName"): self.shpName = obj._shpName if hasattr(obj, "_varName"): self.varName = obj._varName header = "%s %i %s %s\n" % ("0", obj.n, self.shpName, self.varName) self.file.write(header) # obj.transform = transform self._writelines(obj) else: raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) def close(self): self.file.close() FileIO.FileIO.close(self) @staticmethod def __zero_offset(neighbors: dict, weights: dict, original_ids=None) -> dict: if not original_ids: original_ids = list(neighbors.keys()) old_weights = weights new_weights = {} new_ids = {} old_ids = {} new_neighbors = {} for i in original_ids: new_i = original_ids.index(i) new_ids[new_i] = i old_ids[i] = new_i neighbors_i = neighbors[i] new_neighbors_i = [original_ids.index(j) for j in neighbors_i] new_neighbors[new_i] = new_neighbors_i new_weights[new_i] = weights[i] info = {} info["new_ids"] = new_ids info["old_ids"] = old_ids info["new_neighbors"] = new_neighbors info["new_weights"] = new_weights return info libpysal-4.9.2/libpysal/io/iohandlers/mat.py000066400000000000000000000115451452177046000210720ustar00rootroot00000000000000import scipy.io as sio from .. import fileio from ...weights import W from ...weights.util import full, full2W __author__ = "Myunghwa Hwang " __all__ = ["MatIO"] class MatIO(fileio.FileIO): """Opens, reads, and writes weights file objects in MATLAB Level 4-5 MAT format. ``.mat`` files are used in Dr. LeSage's MATLAB Econometrics library. The ``.mat`` file format can handle both full and sparse matrices, and it allows for a matrix dimension greater than 256. In PySAL, row and column headers of a MATLAB array are ignored. PySAL uses `scipy.io `_. Thus, it is subject to all limits of ``scipy.io.loadmat`` and ``scipy.io.savemat``. Notes ----- If a given weights object contains too many observations to write it out as a full matrix, PySAL writes out the object as a sparse matrix. References ---------- `MathWorks `_ (2011) "MATLAB 7 MAT-File Format." """ FORMATS = ["mat"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): self._varName = "Unknown" fileio.FileIO.__init__(self, *args, **kwargs) self.file = open(self.dataPath, self.mode + "b") def _set_varName(self, val): if issubclass(type(val), str): self._varName = val def _get_varName(self) -> str: return self._varName varName = property(fget=_get_varName, fset=_set_varName) def read(self, n=-1): """ Parameters ---------- n : int Read at most ``n`` objects. Default is ``-1``. Returns ------- w : libpysal.weights.W A PySAL `W` object. """ self._complain_ifclosed(self.closed) w = self._read() return w def seek(self, pos): if pos == 0: self.file.seek(0) self.pos = 0 def _read(self): """Reads MATLAB ``.mat`` file. Returns ------- w : libpysal.weights.W A PySAL `W` object. Raises ------ StopIteration Raised at the EOF. Examples -------- Type ``dir(w)`` at the interpreter to see what methods are supported. Open a MATLAB ``.mat`` file and read it into a PySAL weights object. >>> import libpysal >>> w = libpysal.io.open(libpysal.examples.get_path( ... 'spat-sym-us.mat'), 'r' ... ).read() Get the number of observations from the header. >>> w.n 46 Get the mean number of neighbors. >>> w.mean_neighbors 4.086956521739131 Get neighbor distances for a single observation. >>> w[1] == dict({25: 1, 3: 1, 28: 1, 39: 1}) True """ if self.pos > 0: raise StopIteration mat = sio.loadmat(self.file) mat_keys = [k for k in mat if not k.startswith("_")] full_w = mat[mat_keys[0]] self.pos += 1 w = full2W(full_w) return w def write(self, obj): """Write a weights object to the opened MATLAB ``.mat`` file. Parameters ---------- obj : libpysal.weights.W A PySAL `W` object. Raises ------ TypeError Raised when the input ``obj`` is not a PySAL `W`. Examples -------- >>> import tempfile, libpysal, os >>> testfile = libpysal.io.open( ... libpysal.examples.get_path('spat-sym-us.mat'), 'r' ... ) >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.mat') Reassign to a new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w') Write the weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created ``.mat`` file. >>> wnew = libpysal.io.open(fname, 'r').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if issubclass(type(obj), W): try: w = full(obj)[0] except ValueError: w = obj.sparse sio.savemat(self.file, {"WEIGHT": w}) self.pos += 1 else: raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.9.2/libpysal/io/iohandlers/mtx.py000066400000000000000000000164441452177046000211240ustar00rootroot00000000000000import scipy.io as sio from .. import fileio from ...weights.weights import W, WSP __author__ = "Myunghwa Hwang " __all__ = ["MtxIO"] class MtxIO(fileio.FileIO): """ Opens, reads, and writes weights file objects in Matrix Market ``.mtx`` format. The Matrix Market MTX format is used to facilitate the exchange of matrix data. In PySAL, it is being tested as a new file format for delivering the weights information of a spatial weights matrix. Although the MTX format supports both full and sparse matrices with different data types, it is assumed that spatial weights files in the ``.mtx``. format always use the sparse (or coordinate) format with real data values. For now, no additional assumption (e.g., symmetry) is made of the structure of a weights matrix. With the above assumptions, the structure of a MTX file containing a spatial weights matrix can be defined as follows: ``` %%MatrixMarket matrix coordinate real general <--- header 1 (constant) % Comments starts <--- % .... | 0 or more comment lines % Comments ends <--- M N L <--- header 2, rows, columns, entries I1 J1 A(I1,J1) <--- ... | L entry lines IL JL A(IL,JL) <--- ``` In the MTX format, the index for rows or columns starts with 1. PySAL uses ``mtx`` tools in `scipy.io `_. Thus, it is subject to all limits that ``scipy`` currently has. Reengineering may be required, since ``scipy`` reads in the entire entry into memory. References ---------- `MTX format specification `_ `Matrix Market files `_ in ``scipy``. """ FORMATS = ["mtx"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): fileio.FileIO.__init__(self, *args, **kwargs) self.file = open(self.dataPath, self.mode + "b") def read(self, n=-1, sparse=False): """ Parameters ---------- n : int Read at most ``n`` objects. Default is ``-1``. sparse : bool Flag for returning a sparse weights matrix (``True``). Default is ``False``. Returns ------- w : libpysal.weights.W A PySAL `W` object. """ self._sparse = sparse self._complain_ifclosed(self.closed) return self._read() def seek(self, pos): if pos == 0: self.file.seek(0) self.pos = 0 def _read(self): """Reads MatrixMarket ``.mtx`` file. Returns ------- w : {libpysal.weights.W, libpysal.weights.WSP} A PySAL `W` object. Raises ------ StopIteration Raised at the EOF. Examples -------- Type ``dir(w)`` at the interpreter to see what methods are supported. Open a MatrixMarket ``.mtx`` file and read it into a PySAL weights object. >>> import libpysal >>> f = libpysal.io.open(libpysal.examples.get_path('wmat.mtx'), 'r') >>> w = f.read() Get the number of observations from the header. >>> w.n 49 Get the mean number of neighbors. >>> w.mean_neighbors 4.73469387755102 Get neighbor weights for a single observation. >>> w[1] {2: 0.3333, 5: 0.3333, 6: 0.3333} >>> f.close() >>> f = libpysal.io.open(libpysal.examples.get_path('wmat.mtx'), 'r') >>> wsp = f.read(sparse=True) Get the number of observations from the header. >>> wsp.n 49 Get a row from the weights matrix. Note that the first row in the sparse matrix (the 0th row) corresponds to ID 1 from the original ``.mtx`` file read in. >>> print(wsp.sparse[0].todense()) [[0. 0.3333 0. 0. 0.3333 0.3333 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]] """ if self.pos > 0: raise StopIteration mtx = sio.mmread(self.file) # matrix market indexes start at one ids = list(range(1, mtx.shape[0] + 1)) wsp = WSP(mtx, ids) if self._sparse: w = wsp else: w = wsp.to_W() self.pos += 1 return w def write(self, obj): """Write a weights object to the opened MatrixMarket ``.mtx`` file. Parameters ---------- obj : libpysal.weights.W A PySAL `W` object. Raises ------ TypeError Raised when the input ``obj`` is not a PySAL `W`. Examples -------- >>> import tempfile, libpysal, os >>> testfile = libpysal.io.open(libpysal.examples.get_path('wmat.mtx'), 'r') >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.mtx') Reassign to a new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w') Write the weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created mtx file. >>> wnew = libpysal.io.open(fname, 'r').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up temporary file created for this example. >>> os.remove(fname) Go to the beginning of the test file. >>> testfile.seek(0) Create a sparse weights instance from the test file. >>> wsp = testfile.read(sparse=True) Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w') Write the sparse weights object into the open file. >>> o.write(wsp) >>> o.close() Read in the newly created ``.mtx`` file. >>> wsp_new = libpysal.io.open(fname, 'r').read(sparse=True) Compare values from old to new. >>> wsp_new.s0 == wsp.s0 True Clean up the temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if issubclass(type(obj), W) or issubclass(type(obj), WSP): w = obj.sparse sio.mmwrite( self.file, w, comment="Generated by PySAL", field="real", precision=7 ) self.pos += 1 else: raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.9.2/libpysal/io/iohandlers/pyDbfIO.py000066400000000000000000000302261452177046000216020ustar00rootroot00000000000000from .. import tables from ...common import MISSINGVALUE import datetime import struct import os import time from typing import Union __author__ = "Charles R Schmidt " __all__ = ["DBF"] class DBF(tables.DataTable): """PySAL DBF Reader/Writer. This DBF handler implements the PySAL DataTable interface and initializes an instance of the PySAL's DBF handler. Parameters ----------- dataPath : str Path to file, including file name and extension. mode : str Mode for file interaction; either ``'r'`` or ``'w'``. Attributes ---------- header : list A list of field names. The header is a python list of strings. Each string is a field name and field name must not be longer than 10 characters. field_spec : list A list describing the data types of each field. It is comprised of a list of tuples, each tuple describing a field. The format for the tuples is ``('Type', len, precision)``. Valid values for ``'Type'`` are ``'C'`` for characters, ``'L'`` for bool, ``'D'`` for data, and ``'N'`` or ``'F'`` for number. Examples -------- >>> import libpysal >>> dbf = libpysal.io.open(libpysal.examples.get_path('juvenile.dbf'), 'r') >>> dbf.header ['ID', 'X', 'Y'] >>> dbf.field_spec [('N', 9, 0), ('N', 9, 0), ('N', 9, 0)] """ FORMATS = ["dbf"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): tables.DataTable.__init__(self, *args, **kwargs) if self.mode == "r": self.f = f = open(self.dataPath, "rb") # from dbf file standards numrec, lenheader = struct.unpack(" int: """ Raises ------ IOError Raised when a file is open ``'w'`` mode. """ if self.mode != "r": msg = "Invalid operation, cannot read from a file opened in 'w' mode." raise IOError(msg) return self.n_records def seek(self, i): self.f.seek(self.header_size + (self.record_size * i)) self.pos = i def _get_col(self, key: str) -> list: """Return the column vector. Raises ------ AttributeError Raised when a field does not exist in the header. """ if key not in self._col_index: raise AttributeError("Field: %s does not exist in header." % key) prevPos = self.tell() idx, offset = self._col_index[key] typ, size, deci = self.field_spec[idx] gap = self.record_size - size f = self.f f.seek(self.header_size + offset) col = [0] * self.n_records for i in range(self.n_records): value = f.read(size) value = value.decode() f.seek(gap, 1) if typ == "N": value = value.replace("\0", "").lstrip() if value == "": value = MISSINGVALUE elif deci: try: value = float(value) except ValueError: value = MISSINGVALUE else: try: value = int(value) except ValueError: value = MISSINGVALUE elif typ == "D": try: y, m, d = int(value[:4]), int(value[4:6]), int(value[6:8]) value = datetime.date(y, m, d) except ValueError: value = MISSINGVALUE elif typ == "L": value = (value in "YyTt" and "T") or (value in "NnFf" and "F") or "?" elif typ == "F": value = value.replace("\0", "").lstrip() if value == "": value = MISSINGVALUE else: value = float(value) if isinstance(value, str) or isinstance(value, str): value = value.rstrip() col[i] = value self.seek(prevPos) return col def read_record(self, i: int) -> list: self.seek(i) rec = list(struct.unpack(self.record_fmt, self.f.read(self.record_size))) rec = [entry.decode() for entry in rec] if rec[0] != " ": return self.read_record(i + 1) result = [] for (name, typ, size, deci), value in zip(self.field_info, rec): if name == "DeletionFlag": continue if typ == "N": value = value.replace("\0", "").lstrip() if value == "": value = MISSINGVALUE elif deci: try: value = float(value) except ValueError: value = MISSINGVALUE else: try: value = int(value) except ValueError: value = MISSINGVALUE elif typ == "D": try: y, m, d = int(value[:4]), int(value[4:6]), int(value[6:8]) value = datetime.date(y, m, d) except ValueError: # value = datetime.date.min#NULL Date: See issue 114 value = MISSINGVALUE elif typ == "L": value = (value in "YyTt" and "T") or (value in "NnFf" and "F") or "?" elif typ == "F": value = value.replace("\0", "").lstrip() if value == "": value = MISSINGVALUE else: value = float(value) if isinstance(value, str) or isinstance(value, str): value = value.rstrip() result.append(value) return result def _read(self) -> Union[list, None]: """ Raises ------ IOError Raised when a file is open ``'w'`` mode. """ if self.mode != "r": msg = "Invalid operation, cannot read from a file opened in 'w' mode." raise IOError(msg) if self.pos < len(self): rec = self.read_record(self.pos) self.pos += 1 return rec else: return None def write(self, obj: list): """ Raises ------ IOError Raised when a file is open ``'r'`` mode. TypeError Raised when a row length and header length are not equivalent. """ self._complain_ifclosed(self.closed) if self.mode != "w": msg = "Invalid operation, cannot read from a file opened in 'r' mode." raise IOError(msg) if self.FIRST_WRITE: self._firstWrite() if len(obj) != len(self.header): raise TypeError("Rows must contains %d fields." % len(self.header)) self.numrec += 1 # deletion flag self.f.write(" ".encode()) for (typ, size, deci), value in zip(self.field_spec, obj): if value is None: if typ == "C": value = " " * size else: value = "\0" * size elif typ == "N" or typ == "F": v = str(value).rjust(size, " ") # if len(v) == size: # value = v # else: value = (("%" + "%d.%d" % (size, deci) + "f") % (value))[:size] elif typ == "D": value = value.strftime("%Y%m%d") elif typ == "L": value = str(value)[0].upper() else: value = str(value)[:size].ljust(size, " ") try: assert len(value) == size except: print(value, len(value), size) raise self.f.write(value.encode()) self.pos += 1 def flush(self): self._complain_ifclosed(self.closed) self._writeHeader() self.f.flush() def close(self): if self.mode == "w": self.flush() # End of file self.f.write("\x1A".encode()) self.f.close() tables.DataTable.close(self) def _firstWrite(self): """ Raises ------ IOError Raised when there is no specified header. IOError Raised when there is no field specification. """ if not self.header: raise IOError("No header, DBF files require a header.") if not self.field_spec: raise IOError("No field_spec, DBF files require a specification.") self._writeHeader() self.FIRST_WRITE = False def _writeHeader(self): """Modified from: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/362715 """ POS = self.f.tell() self.f.seek(0) ver = 3 now = datetime.datetime.utcfromtimestamp( int(os.environ.get("SOURCE_DATE_EPOCH", time.time())), ) yr, mon, day = now.year - 1900, now.month, now.day numrec = self.numrec numfields = len(self.header) lenheader = numfields * 32 + 33 lenrecord = sum(field[1] for field in self.field_spec) + 1 hdr = struct.pack( ">> import tempfile >>> f = tempfile.NamedTemporaryFile(suffix='.shp') >>> fname = f.name >>> f.close() >>> import libpysal >>> i = libpysal.io.open(libpysal.examples.get_path('10740.shp'),'r') >>> o = libpysal.io.open(fname,'w') >>> for shp in i: ... o.write(shp) >>> o.close() >>> one = libpysal.io.open(libpysal.examples.get_path('10740.shp'),'rb').read() >>> two = libpysal.io.open(fname,'rb').read() >>> one[0].centroid == two[0].centroid True >>> one = libpysal.io.open(libpysal.examples.get_path('10740.shx'),'rb').read() >>> two = libpysal.io.open(fname[:-1]+'x','rb').read() >>> one[0].centroid == two[0].centroid True >>> import os >>> os.remove(fname); os.remove(fname.replace('.shp','.shx')) """ FORMATS = ["shp", "shx"] MODES = ["w", "r", "wb", "rb"] def __init__(self, *args, **kwargs): fileio.FileIO.__init__(self, *args, **kwargs) self.dataObj = None if self.mode == "r" or self.mode == "rb": self.__open() elif self.mode == "w" or self.mode == "wb": self.__create() def __len__(self) -> int: if self.dataObj != None: return len(self.dataObj) else: return 0 def __open(self): """ Raises ------ TypeError Raised when an invalid shape is passed in. """ self.dataObj = shp_file(self.dataPath) self.header = self.dataObj.header self.bbox = self.dataObj.bbox try: self.type = STRING_TO_TYPE[self.dataObj.type()] except KeyError: msg = "%s does not support shapes of type: %s." msg = msg % (self.__class__.__name__, self.dataObj.type()) raise TypeError(msg) def __create(self): self.write = self.__firstWrite def __firstWrite(self, shape): """ Parameters ---------- shape : libpysal.cg.{Point, Chain, Polygon} Geometric shape. """ self.type = TYPE_TO_STRING[type(shape)] if self.type == "POINT": if len(shape) == 3: self.type = "POINTM" if len(shape) == 4: self.type = "POINTZ" self.dataObj = shp_file(self.dataPath, "w", self.type) self.write = self.__writer self.write(shape) def __writer(self, shape): """ Parameters ---------- shape : libpysal.cg.{Point, Chain, Polygon} Geometric shape. Raises ------ TypeError Raised when an invalid shape is passed in. """ if TYPE_TO_STRING[type(shape)] != self.type: raise TypeError("This file only supports %s type shapes." % self.type) rec = {} rec["Shape Type"] = shp_file.SHAPE_TYPES[self.type] if self.type == "POINT": rec["X"] = shape[0] rec["Y"] = shape[1] if len(shape) > 2: rec["M"] = shape[2] if len(shape) > 3: rec["Z"] = shape[3] shape = rec else: rec["BBOX Xmin"] = shape.bounding_box.left rec["BBOX Ymin"] = shape.bounding_box.lower rec["BBOX Xmax"] = shape.bounding_box.right rec["BBOX Ymax"] = shape.bounding_box.upper if self.type == "POLYGON": holes = [hole[::-1] for hole in shape.holes if hole] # holes should be in CCW order rec["NumParts"] = len(shape.parts) + len(holes) all_parts = shape.parts + holes else: rec["NumParts"] = len(shape.parts) all_parts = shape.parts partsIndex = [0] for l in [len(part) for part in all_parts][:-1]: partsIndex.append(partsIndex[-1] + l) rec["Parts Index"] = partsIndex verts = sum(all_parts, []) verts = [(x, y) for x, y in verts] rec["NumPoints"] = len(verts) rec["Vertices"] = verts self.dataObj.add_shape(rec) self.pos += 1 def _read(self): """ Returns ------- shape : libpysal.cg.{Point, Chain, Polygon} Geometric shape. """ try: rec = self.dataObj.get_shape(self.pos) except IndexError: return None self.pos += 1 if self.dataObj.type() == "POINT": shp = self.type((rec["X"], rec["Y"])) elif self.dataObj.type() == "POINTZ": shp = self.type((rec["X"], rec["Y"])) shp.Z = rec["Z"] shp.M = rec["M"] else: if rec["NumParts"] > 1: partsIndex = list(rec["Parts Index"]) partsIndex.append(None) parts = [ rec["Vertices"][partsIndex[i] : partsIndex[i + 1]] for i in range(rec["NumParts"]) ] if self.dataObj.type() == "POLYGON": is_cw = [cg.is_clockwise(part) for part in parts] vertices = [part for part, cw in zip(parts, is_cw) if cw] holes = [part for part, cw in zip(parts, is_cw) if not cw] if not holes: holes = None shp = self.type(vertices, holes) else: vertices = parts shp = self.type(vertices) elif rec["NumParts"] == 1: vertices = rec["Vertices"] if self.dataObj.type() == "POLYGON" and not cg.is_clockwise(vertices): ### SHAPEFILE WARNING: Polygon %d topology has been fixed. (ccw -> cw) msg = "SHAPEFILE WARNING: Polygon %d " msg += "topology has been fixed. (ccw -> cw)." msg = msg % self.pos warn(msg, RuntimeWarning) print(msg) shp = self.type(vertices) else: warn("Polygon %d has zero parts." % self.pos, RuntimeWarning) shp = self.type([[]]) # raise ValueError, "Polygon %d has zero parts"%self.pos if self.ids: # shp IDs start at 1. shp.id = self.rIds[self.pos - 1] else: # shp IDs start at 1. shp.id = self.pos return shp def close(self): self.dataObj.close() fileio.FileIO.close(self) libpysal-4.9.2/libpysal/io/iohandlers/stata_txt.py000066400000000000000000000175221452177046000223250ustar00rootroot00000000000000from .. import fileio from ...weights import W __author__ = "Myunghwa Hwang " __all__ = ["StataTextIO"] class StataTextIO(fileio.FileIO): """Opens, reads, and writes weights file objects in STATA text format. Spatial weights objects in the STATA text format are used in STATA ``sppack`` library through the ``spmat`` command. This format is a simple text file delimited by a whitespace. The ``spmat`` command does not specify which file extension to use. But, ``.txt`` seems the default file extension, which is assumed in PySAL. The first line of the STATA text file is a header including the number of observations. After this header line, it includes at least one data column that contains unique IDs or record numbers of observations. When an ID variable is not specified for the original spatial weights matrix in STATA, record numbers are used to identify individual observations, and the record numbers start with 1. The ``spmat`` command seems to allow only integer IDs, which is also assumed in PySAL. A STATA text file can have one of the following structures according to its export options in STATA. Structure 1: Encoding using the list of neighbor IDs. ``` [Line 1] [Number_of_Observations] [Line 2] [ID_of_Obs_1] [ID_of_Neighbor_1_of_Obs_1] [ID_of_Neighbor_2_of_Obs_1] ... [ID_of_Neighbor_m_of_Obs_1] [Line 3] [ID_of_Obs_2] [Line 4] [ID_of_Obs_3] [ID_of_Neighbor_1_of_Obs_3] [ID_of_Neighbor_2_of_Obs_3] ... ``` Note that for island observations their IDs are still recorded. Structure 2: Encoding using a full matrix format. ``` [Line 1] [Number_of_Observations] [Line 2] [ID_of_Obs_1] [w_11] [w_12] ... [w_1n] [Line 3] [ID_of_Obs_2] [w_21] [w_22] ... [w_2n] [Line 4] [ID_of_Obs_3] [w_31] [w_32] ... [w_3n] ... [Line n+1] [ID_of_Obs_n] [w_n1] [w_n2] ... [w_nn] ``` where :math:`w_{ij}` can be a form of general weight. That is, :math:`w_ij` can be both a binary value or a general numeric value. If an observation is an island, all of its ``w`` columns contain 0. References ---------- Drukker D.M., Peng H., Prucha I.R., and Raciborski R. (2011) "Creating and managing spatial-weighting matrices using the spmat command" Notes ----- The ``spmat`` command allows users to add any note to a spatial weights matrix object in STATA. However, all those notes are lost when the matrix is exported. PySAL also does not take care of those notes. """ FORMATS = ["stata_text"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): args = args[:2] fileio.FileIO.__init__(self, *args, **kwargs) self.file = open(self.dataPath, self.mode) def read(self, n=-1): """ Parameters ---------- n : int Read at most ``n`` objects. Default is ``-1``. Returns ------- w : libpysal.weights.W A PySAL `W` object. """ self._complain_ifclosed(self.closed) return self._read() def seek(self, pos): if pos == 0: self.file.seek(0) self.pos = 0 def _read(self): """Reads STATA Text file Returns a pysal.weights.weights.W object Returns ------- w : libpysal.weights.W A PySAL `W` object. Raises ------ StopIteration Raised at the EOF. Examples -------- Type ``dir(w)`` at the interpreter to see what methods are supported. Open a text file and read it into a PySAL weights object. >>> import libpysal >>> w = libpysal.io.open( ... libpysal.examples.get_path('stata_sparse.txt'), 'r', 'stata_text' ... ).read() Get the number of observations from the header. >>> w.n 56 Get the mean number of neighbors. >>> w.mean_neighbors 4.0 Get neighbor distances for a single observation. >>> w[1] == dict({53: 1.0, 51: 1.0, 45: 1.0, 54: 1.0, 7: 1.0}) True """ if self.pos > 0: raise StopIteration n = int(self.file.readline().strip()) line1 = self.file.readline().strip() obs_01 = line1.split(" ") matrix_form = False if len(obs_01) == 1 or float(obs_01[1]) != 0.0: def line2wgt(line): row = [int(i) for i in line.strip().split(" ")] return row[0], row[1:], [1.0] * len(row[1:]) else: matrix_form = True def line2wgt(line): row = line.strip().split(" ") obs = int(float(row[0])) ngh, wgt = [], [] for i in range(n): w = float(row[i + 1]) if w > 0: ngh.append(i) wgt.append(w) return obs, ngh, wgt id_order = [] weights, neighbors = {}, {} l = line1 for i in range(n): obs, ngh, wgt = line2wgt(l) id_order.append(obs) neighbors[obs] = ngh weights[obs] = wgt l = self.file.readline() if matrix_form: for obs in neighbors: neighbors[obs] = [id_order[ngh] for ngh in neighbors[obs]] self.pos += 1 w = W(neighbors, weights) return w def write(self, obj, matrix_form=False): """Write a weights object to an opened text file. Parameters ---------- obj : libpysal.weights.W A PySAL `W` object. matrix_form : bool Flag for matrix form (``True``). Default is ``False``. Raises ------ TypeError Raised when the input ``obj`` is not a PySAL `W`. Examples -------- >>> import tempfile, libpysal, os >>> testfile = libpysal.io.open( ... libpysal.examples.get_path('stata_sparse.txt'), 'r', 'stata_text' ... ) >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.txt') Reassign to a new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w', 'stata_text') Write the weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created text file. >>> wnew = libpysal.io.open(fname, 'r', 'stata_text').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up the temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if issubclass(type(obj), W): header = "%s\n" % obj.n self.file.write(header) if matrix_form: def wgt2line(obs_id, neighbor, weight): w = ["0.0"] * obj.n for ngh, wgt in zip(neighbor, weight): w[obj.id2i[ngh]] = str(wgt) return [str(obs_id)] + w else: def wgt2line(obs_id, neighbor, weight): return [str(obs_id)] + [str(ngh) for ngh in neighbor] for id in obj.id_order: line = wgt2line(id, obj.neighbors[id], obj.weights[id]) self.file.write("%s\n" % " ".join(line)) else: raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.9.2/libpysal/io/iohandlers/template.py000066400000000000000000000126001452177046000221150ustar00rootroot00000000000000""" Example Reader and Writer These are working readers/writers that parse '.foo' and '.bar' files. """ from .. import fileio as FileIO __author__ = "Charles R Schmidt " __all__ = ["TemplateWriter", "TemplateReaderWriter"] # Always subclass FileIO class TemplateWriter(FileIO.FileIO): # REQUIRED, List the formats this class supports. FORMATS = ["foo"] # REQUIRED, List the modes supported by this class. # One class can support both reading and writing. # For simplicity this class will only support one. # You could support custom modes, but these could be hard to document. MODES = ["w"] # Use ``.__init__`` to open any need file handlers def __init__(self, *args, **kwargs): # initialize the parent class... FileIO.__init__(self, *args, **kwargs) # this gives you: # self.dataPath == the connection string or path to file # self.mode == the mode the file should be opened in self.fileObj = open(self.dataPath, self.mode) # Writers must subclass ``.write()`` def write(self, obj): """ ``.write`` method of the 'foobar' template Parameters ---------- obj : str Some string. Raises ------ TypeError Raised when a ``str`` is expected, but got another type. """ # GOOD TO HAVE, this will prevent invalid operations on closed files. self._complain_ifclosed(self.closed) # It's up to the writer to understand the object, you should check # that object is of the type you expect and raise a TypeError is its now. # we will support writing string objects in this example, # all string are derived from basestring... if issubclass(type(obj), str): # Non-essential... def foobar(c): if c in "foobar": return True else: return False # e.g. 'foobara' == filter(foobar,'my little foobar example') result = list(filter(foobar, obj)) # do the actual writing... self.fileObj.write(result + "\n") # REQUIRED, increment the internal pos pointer. self.pos += 1 else: raise TypeError("Expected a string, got: %s." % (type(obj))) # default is to raise "NotImplementedError" def flush(self): self._complain_ifclosed(self.closed) self.fileObj.flush() # REQUIRED def close(self): self.fileObj.close() # clean up the parent class too.... FileIO.close(self) class TemplateReaderWriter(FileIO.FileIO): FORMATS = ["bar"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): FileIO.__init__(self, *args, **kwargs) self.fileObj = open(self.dataPath, self.mode) # Notice reading is a bit different def _filter(self, st): def foobar(c): if c in "foobar": return True else: return False # e.g. 'foobara' == filter(foobar,'my little foobar example') return list(filter(foobar, st)) def _read(self): """The ``_read`` method should return only ONE object. Returns ------- obj_plus_break : str only ONE object. Raises ------ StopIteration Raised at the EOF. """ line = self.fileObj.readline() obj = self._filter(line) # REQUIRED self.pos += 1 if line: obj_plus_break = obj + "\n" return obj_plus_break else: # REQUIRED raise StopIteration def write(self, obj): """The ``.write`` method of the 'foobar' template, receives an ``obj``. Paramters --------- obj : str Some string. Raises ------ TypeError Raised when a ``str`` is expected, but got another type. """ self._complain_ifclosed(self.closed) if issubclass(type(obj), str): result = self._filter(obj) self.fileObj.write(result + "\n") self.pos += 1 else: raise TypeError("Expected a string, got: %s" % (type(obj))) def flush(self): self._complain_ifclosed(self.closed) self.fileObj.flush() def close(self): self.fileObj.close() FileIO.close(self) if __name__ == "__main__": "NOTE, by running OR importing this module " "it's automatically added to the pysal fileIO registry." pysal.open.check() lines = [ "This is an example of template FileIO classes", "Each call to write expects a string object", "that string is filtered and only letters 'f','o','b','a','r' are kept", "these kept letters are written to the file", "and a new line char is appends to each line", "likewise the reader filters each line from a file.", ] f = pysal.open("test.foo", "w") for line in lines: f.write(line) f.close() f = pysal.open("test.bar", "w") for line in lines: f.write(line) f.close() f = pysal.open("test.bar", "r") s = "".join(f.read()) f.close() print(s) f = open("test.foo", "r") s2 = f.read() f.close() print(s == s2) libpysal-4.9.2/libpysal/io/iohandlers/tests/000077500000000000000000000000001452177046000210735ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/iohandlers/tests/__init__.py000066400000000000000000000000001452177046000231720ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/iohandlers/tests/test_arcgis_dbf.py000066400000000000000000000036321452177046000245730ustar00rootroot00000000000000import os import tempfile import warnings import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..arcgis_dbf import ArcGISDbfIO class Testtest_ArcGISDbfIO: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("arcgis_ohio.dbf") self.obj = ArcGISDbfIO(test_file, "r") def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) def test_read(self): with warnings.catch_warnings(record=True) as warn: warnings.simplefilter("always") w = self.obj.read() if len(warn) > 0: assert issubclass(warn[0].category, RuntimeWarning) assert ( "Missing Value Found, setting value to libpysal.MISSINGVALUE." in str(warn[0].message) ) assert w.n == 88 assert w.mean_neighbors == 5.25 assert [1.0, 1.0, 1.0, 1.0] == list(w[1].values()) def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj.read) self.obj.seek(0) self.test_read() def test_write(self): with warnings.catch_warnings(record=True) as warn: warnings.simplefilter("always") w = self.obj.read() if len(warn) > 0: assert issubclass(warn[0].category, RuntimeWarning) assert ( "Missing Value Found, setting value to libpysal.MISSINGVALUE." in str(warn[0].message) ) f = tempfile.NamedTemporaryFile(suffix=".dbf") fname = f.name f.close() o = psopen(fname, "w", "arcgis_dbf") o.write(w) o.close() f = psopen(fname, "r", "arcgis_dbf") wnew = f.read() f.close() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_arcgis_swm.py000066400000000000000000000021311452177046000246370ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..arcgis_swm import ArcGISSwmIO class Testtest_ArcGISSwmIO: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("ohio.swm") self.obj = ArcGISSwmIO(test_file, "r") def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) def test_read(self): w = self.obj.read() assert w.n == 88 assert w.mean_neighbors == 5.25 assert [1.0, 1.0, 1.0, 1.0] == list(w[1].values()) def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj.read) self.obj.seek(0) self.test_read() def test_write(self): w = self.obj.read() f = tempfile.NamedTemporaryFile(suffix=".swm") fname = f.name f.close() o = psopen(fname, "w") o.write(w) o.close() wnew = psopen(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_arcgis_txt.py000066400000000000000000000044461452177046000246630ustar00rootroot00000000000000import os import tempfile import warnings import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..arcgis_txt import ArcGISTextIO class Testtest_ArcGISTextIO: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("arcgis_txt.txt") self.obj = ArcGISTextIO(test_file, "r") def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) def test_read(self): with warnings.catch_warnings(record=True) as warn: warnings.simplefilter("always") w = self.obj.read() if len(warn) > 0: assert issubclass(warn[0].category, RuntimeWarning) assert ( "DBF relating to ArcGIS TEXT was not found, proceeding with unordered string IDs." in str(warn[0].message) ) assert w.n == 3 assert w.mean_neighbors == 2.0 assert [0.1, 0.05] == list(w[2].values()) def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj.read) self.obj.seek(0) self.test_read() def test_write(self): with warnings.catch_warnings(record=True) as warn: warnings.simplefilter("always") w = self.obj.read() if len(warn) > 0: assert issubclass(warn[0].category, RuntimeWarning) assert ( "DBF relating to ArcGIS TEXT was not found, proceeding with unordered string IDs." in str(warn[0].message) ) f = tempfile.NamedTemporaryFile(suffix=".txt") fname = f.name f.close() o = psopen(fname, "w", "arcgis_text") o.write(w) o.close() with warnings.catch_warnings(record=True) as warn: warnings.simplefilter("always") wnew = psopen(fname, "r", "arcgis_text").read() if len(warn) > 0: assert issubclass(warn[0].category, RuntimeWarning) assert ( "DBF relating to ArcGIS TEXT was not found, proceeding with unordered string IDs." in str(warn[0].message) ) assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_csvWrapper.py000066400000000000000000000054121452177046000246420ustar00rootroot00000000000000from sys import version as V from .... import examples as pysal_examples from ...util import WKTParser from .. import csvWrapper PY3 = int(V[0]) > 2 class TesttestCsvWrapper: def setup_method(self): stl = pysal_examples.load_example("stl") self.test_file = test_file = stl.get_path("stl_hom.csv") self.obj = csvWrapper.csvWrapper(test_file, "r") def test_len(self): assert len(self.obj) == 78 def test_tell(self): assert self.obj.tell() == 0 self.obj.read(1) assert self.obj.tell() == 1 self.obj.read(50) assert self.obj.tell() == 51 self.obj.read() assert self.obj.tell() == 78 def test_seek(self): self.obj.seek(0) assert self.obj.tell() == 0 self.obj.seek(55) assert self.obj.tell() == 55 self.obj.read(1) assert self.obj.tell() == 56 def test_read(self): self.obj.seek(0) objs = self.obj.read() assert len(objs) == 78 self.obj.seek(0) objsB = list(self.obj) assert len(objsB) == 78 for rowA, rowB in zip(objs, objsB): assert rowA == rowB def test_casting(self): self.obj.cast("WKT", WKTParser()) verts = [ (-89.585220336914062, 39.978794097900391), (-89.581146240234375, 40.094867706298828), (-89.603988647460938, 40.095306396484375), (-89.60589599609375, 40.136119842529297), (-89.6103515625, 40.3251953125), (-89.269027709960938, 40.329566955566406), (-89.268562316894531, 40.285579681396484), (-89.154655456542969, 40.285774230957031), (-89.152763366699219, 40.054969787597656), (-89.151618957519531, 39.919403076171875), (-89.224777221679688, 39.918678283691406), (-89.411857604980469, 39.918041229248047), (-89.412437438964844, 39.931644439697266), (-89.495201110839844, 39.933486938476562), (-89.4927978515625, 39.980186462402344), (-89.585220336914062, 39.978794097900391), ] if PY3: for i, pt in enumerate(self.obj.__next__()[0].vertices): assert pt[:] == verts[i] else: for i, pt in enumerate(self.obj.next()[0].vertices): assert pt[:] == verts[i] def test_by_col(self): for field in self.obj.header: assert len(self.obj.by_col[field]) == 78 def test_slicing(self): chunk = self.obj[50:55, 1:3] assert chunk[0] == ["Jefferson", "Missouri"] assert chunk[1] == ["Jefferson", "Illinois"] assert chunk[2] == ["Miller", "Missouri"] assert chunk[3] == ["Maries", "Missouri"] assert chunk[4] == ["White", "Illinois"] libpysal-4.9.2/libpysal/io/iohandlers/tests/test_dat.py000066400000000000000000000021061452177046000232530ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..dat import DatIO class Testtest_DatIO: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("wmat.dat") self.obj = DatIO(test_file, "r") def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) def test_read(self): w = self.obj.read() assert w.n == 49 assert w.mean_neighbors == 4.7346938775510203 assert [0.5, 0.5] == list(w[5.0].values()) def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj.read) self.obj.seek(0) self.test_read() def test_write(self): w = self.obj.read() f = tempfile.NamedTemporaryFile(suffix=".dat") fname = f.name f.close() o = psopen(fname, "w") o.write(w) o.close() wnew = psopen(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_db.py000066400000000000000000000037121452177046000230740ustar00rootroot00000000000000import os import platform import pytest import shapely from .... import examples as pysal_examples from ... import geotable as pdio from ...fileio import FileIO as psopen try: import sqlalchemy missing_sql = False except ImportError: missing_sql = True windows = platform.system() == "Windows" @pytest.mark.skipif(windows, reason="Skipping Windows due to `PermissionError`.") @pytest.mark.skipif(missing_sql, reason="Missing dependency: SQLAlchemy.") class TestSqliteReader: def setup_method(self): path = pysal_examples.get_path("new_haven_merged.dbf") if path is None: pysal_examples.load_example("newHaven") path = pysal_examples.get_path("new_haven_merged.dbf") df = pdio.read_files(path) df["GEOMETRY"] = shapely.to_wkb(shapely.points(df["geometry"].values.tolist())) # This is a hack to not have to worry about a custom point type in the DB del df["geometry"] self.dbf = "iohandlers_test_db.db" engine = sqlalchemy.create_engine(f"sqlite:///{self.dbf}") self.conn = engine.connect() df.to_sql( "newhaven", self.conn, index=True, dtype={ "date": sqlalchemy.types.UnicodeText, # Should convert the df date into a true date object, just a hack again "dataset": sqlalchemy.types.UnicodeText, "street": sqlalchemy.types.UnicodeText, "intersection": sqlalchemy.types.UnicodeText, "time": sqlalchemy.types.UnicodeText, # As above re: date "GEOMETRY": sqlalchemy.types.BLOB, }, ) # This is converted to TEXT as lowest type common sqlite def test_deserialize(self): db = psopen(f"sqlite:///{self.dbf}") assert db.tables == ["newhaven"] gj = db._get_gjson("newhaven") assert gj["type"] == "FeatureCollection" self.conn.close() os.remove(self.dbf) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_gal.py000066400000000000000000000022611452177046000232500ustar00rootroot00000000000000"""Unit tests for gal.py""" import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..gal import GalIO class Testtest_GalIO: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("sids2.gal") self.obj = GalIO(test_file, "r") def test___init__(self): assert self.obj._typ == str def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) def test_read(self): # reading a GAL returns a W w = self.obj.read() assert w.n == 100 assert w.sd == pytest.approx(1.5151237573214935) assert w.s0 == 462.0 assert w.s1 == 924.0 def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj.read) self.obj.seek(0) self.test_read() def test_write(self): w = self.obj.read() f = tempfile.NamedTemporaryFile(suffix=".gal") fname = f.name f.close() o = psopen(fname, "w") o.write(w) o.close() wnew = psopen(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero libpysal-4.9.2/libpysal/io/iohandlers/tests/test_geobugs_txt.py000066400000000000000000000034031452177046000250360ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..geobugs_txt import GeoBUGSTextIO class Testtest_GeoBUGSTextIO: def setup_method(self): self.test_file_scot = test_file_scot = pysal_examples.get_path("geobugs_scot") self.test_file_col = test_file_col = pysal_examples.get_path( "spdep_listw2WB_columbus" ) self.obj_scot = GeoBUGSTextIO(test_file_scot, "r") self.obj_col = GeoBUGSTextIO(test_file_col, "r") def test_close(self): for obj in [self.obj_scot, self.obj_col]: f = obj f.close() pytest.raises(ValueError, f.read) def test_read(self): w_scot = self.obj_scot.read() assert w_scot.n == 56 assert w_scot.mean_neighbors == 4.1785714285714288 assert [1.0, 1.0, 1.0] == list(w_scot[1].values()) w_col = self.obj_col.read() assert w_col.n == 49 assert w_col.mean_neighbors == 4.6938775510204085 assert [0.5, 0.5] == list(w_col[1].values()) def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj_scot.read) pytest.raises(StopIteration, self.obj_col.read) self.obj_scot.seek(0) self.obj_col.seek(0) self.test_read() def test_write(self): for obj in [self.obj_scot, self.obj_col]: w = obj.read() f = tempfile.NamedTemporaryFile(suffix="") fname = f.name f.close() o = psopen(fname, "w", "geobugs_text") o.write(w) o.close() wnew = psopen(fname, "r", "geobugs_text").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_geoda_txt.py000066400000000000000000000011421452177046000244600ustar00rootroot00000000000000"""GeoDa Text File Reader Unit Tests""" import pytest from .... import examples as pysal_examples from ..geoda_txt import GeoDaTxtReader as GTR class Testtest_GeoDaTxtReader: def setup_method(self): test_file = pysal_examples.get_path("stl_hom.txt") self.obj = GTR(test_file, "r") def test___init__(self): assert self.obj.header == ["FIPSNO", "HR8488", "HR8893", "HC8488"] def test___len__(self): expected = 78 assert expected == len(self.obj) def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_gwt.py000066400000000000000000000031521452177046000233060ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..gwt import GwtIO class Testtest_GwtIO: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("juvenile.gwt") self.obj = GwtIO(test_file, "r") def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) def test_read(self): w = self.obj.read() assert w.n == 168 assert w.mean_neighbors == 16.678571428571427 w.transform = "B" assert [1.0] == list(w[1].values()) def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj.read) self.obj.seek(0) self.test_read() # Commented out by CRS, GWT 'w' mode removed until we # can find a good solution for retaining distances. # see issue #153. # Added back by CRS, def test_write(self): with pytest.warns(RuntimeWarning, match="DBF relating to GWT was not found"): w = self.obj.read() f = tempfile.NamedTemporaryFile(suffix=".gwt") fname = f.name f.close() o = psopen(fname, "w") # copy the shapefile and ID variable names from the old gwt. # this is only available after the read() method has been called. # o.shpName = self.obj.shpName # o.varName = self.obj.varName o.write(w) o.close() wnew = psopen(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_mat.py000066400000000000000000000024611452177046000232700ustar00rootroot00000000000000import os import tempfile import warnings import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..mat import MatIO class Testtest_MatIO: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("spat-sym-us.mat") self.obj = MatIO(test_file, "r") def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) def test_read(self): w = self.obj.read() assert w.n == 46 assert w.mean_neighbors == 4.0869565217391308 assert [1.0, 1.0, 1.0, 1.0] == list(w[1].values()) def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj.read) self.obj.seek(0) self.test_read() def test_write(self): w = self.obj.read() f = tempfile.NamedTemporaryFile(suffix=".mat") fname = f.name f.close() o = psopen(fname, "w") with warnings.catch_warnings(record=True) as warn: warnings.simplefilter("always") o.write(w) if len(warn) > 0: assert issubclass(warn[0].category, FutureWarning) o.close() wnew = psopen(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_mtx.py000066400000000000000000000027251452177046000233220ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..mtx import MtxIO class TesttestMtxIO: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("wmat.mtx") self.obj = MtxIO(test_file, "r") def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) def test_read(self): w = self.obj.read() assert w.n == 49 assert w.mean_neighbors == 4.7346938775510203 assert [0.33329999999999999, 0.33329999999999999, 0.33329999999999999] == \ list(w[1].values()) s0 = w.s0 self.obj.seek(0) wsp = self.obj.read(sparse=True) assert wsp.n == 49 assert s0 == wsp.s0 def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj.read) self.obj.seek(0) self.test_read() def test_write(self): for i in [False, True]: self.obj.seek(0) w = self.obj.read(sparse=i) f = tempfile.NamedTemporaryFile(suffix=".mtx") fname = f.name f.close() o = psopen(fname, "w") o.write(w) o.close() wnew = psopen(fname, "r").read(sparse=i) if i: assert wnew.s0 == w.s0 else: assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_pyDbfIO.py000066400000000000000000000071531452177046000240060ustar00rootroot00000000000000import os import tempfile from .... import examples as pysal_examples from ..pyDbfIO import DBF class Testtest_DBF: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("10740.dbf") self.dbObj = DBF(test_file, "r") def test_len(self): assert len(self.dbObj) == 195 def test_tell(self): assert self.dbObj.tell() == 0 self.dbObj.read(1) assert self.dbObj.tell() == 1 self.dbObj.read(50) assert self.dbObj.tell() == 51 self.dbObj.read() assert self.dbObj.tell() == 195 def test_cast(self): assert self.dbObj._spec == [] self.dbObj.cast("FIPSSTCO", float) assert self.dbObj._spec[1] == float def test_seek(self): self.dbObj.seek(0) assert self.dbObj.tell() == 0 self.dbObj.seek(55) assert self.dbObj.tell() == 55 self.dbObj.read(1) assert self.dbObj.tell() == 56 def test_read(self): self.dbObj.seek(0) objs = self.dbObj.read() assert len(objs) == 195 self.dbObj.seek(0) objsB = list(self.dbObj) assert len(objsB) == 195 for rowA, rowB in zip(objs, objsB): assert rowA == rowB def test_random_access(self): self.dbObj.seek(0) db0 = self.dbObj.read(1)[0] assert db0 == [1, "35001", "000107", "35001000107", "1.07"] self.dbObj.seek(57) db57 = self.dbObj.read(1)[0] assert db57 == [58, "35001", "001900", "35001001900", "19"] self.dbObj.seek(32) db32 = self.dbObj.read(1)[0] assert db32 == [33, "35001", "000500", "35001000500", "5"] self.dbObj.seek(0) assert next(self.dbObj) == db0 self.dbObj.seek(57) assert next(self.dbObj) == db57 self.dbObj.seek(32) assert next(self.dbObj) == db32 def test_write(self): f = tempfile.NamedTemporaryFile(suffix=".dbf") fname = f.name f.close() self.dbfcopy = fname self.out = DBF(fname, "w") self.dbObj.seek(0) self.out.header = self.dbObj.header self.out.field_spec = self.dbObj.field_spec for row in self.dbObj: self.out.write(row) self.out.close() orig = open(self.test_file, "rb") copy = open(self.dbfcopy, "rb") orig.seek(32) # self.dbObj.header_size) #skip the header, file date has changed copy.seek(32) # self.dbObj.header_size) #skip the header, file date has changed # PySAL writes proper DBF files with a terminator at the end, not everyone does. n = self.dbObj.record_size * self.dbObj.n_records # bytes to read. assert orig.read(n) == copy.read(n) # self.assertEquals(orig.read(1), copy.read(1)) # last byte may fail orig.close() copy.close() os.remove(self.dbfcopy) def test_write_nones(self): import datetime import time f = tempfile.NamedTemporaryFile(suffix=".dbf") fname = f.name f.close() db = DBF(fname, "w") db.header = ["recID", "date", "strID", "aFloat"] db.field_spec = [("N", 10, 0), ("D", 8, 0), ("C", 10, 0), ("N", 5, 5)] records = [] for i in range(10): d = datetime.date(*time.localtime()[:3]) rec = [i + 1, d, str(i + 1), (i + 1) / 2.0] records.append(rec) records.append([None, None, "", None]) records.append(rec) for rec in records: db.write(rec) db.close() db2 = DBF(fname, "r") assert records == db2.read() db2.close() os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_pyShpIO.py000066400000000000000000000044511452177046000240430ustar00rootroot00000000000000import os import tempfile from .... import examples as pysal_examples from ..pyShpIO import PurePyShpWrapper class Testtest_PurePyShpWrapper: def setup_method(self): test_file = pysal_examples.get_path("10740.shp") self.test_file = test_file self.shpObj = PurePyShpWrapper(test_file, "r") f = tempfile.NamedTemporaryFile(suffix=".shp") shpcopy = f.name f.close() self.shpcopy = shpcopy self.shxcopy = shpcopy.replace(".shp", ".shx") def test_len(self): assert len(self.shpObj) == 195 def test_tell(self): assert self.shpObj.tell() == 0 self.shpObj.read(1) assert self.shpObj.tell() == 1 self.shpObj.read(50) assert self.shpObj.tell() == 51 self.shpObj.read() assert self.shpObj.tell() == 195 def test_seek(self): self.shpObj.seek(0) assert self.shpObj.tell() == 0 self.shpObj.seek(55) assert self.shpObj.tell() == 55 self.shpObj.read(1) assert self.shpObj.tell() == 56 def test_read(self): self.shpObj.seek(0) objs = self.shpObj.read() assert len(objs) == 195 self.shpObj.seek(0) objsB = list(self.shpObj) assert len(objsB) == 195 for shpA, shpB in zip(objs, objsB): assert shpA.vertices == shpB.vertices def test_random_access(self): self.shpObj.seek(57) shp57 = self.shpObj.read(1)[0] self.shpObj.seek(32) shp32 = self.shpObj.read(1)[0] self.shpObj.seek(57) assert self.shpObj.read(1)[0].vertices == shp57.vertices self.shpObj.seek(32) assert self.shpObj.read(1)[0].vertices == shp32.vertices def test_write(self): out = PurePyShpWrapper(self.shpcopy, "w") self.shpObj.seek(0) for shp in self.shpObj: out.write(shp) out.close() orig = open(self.test_file, "rb") copy = open(self.shpcopy, "rb") assert orig.read() == copy.read() orig.close() copy.close() oshx = open(self.test_file.replace(".shp", ".shx"), "rb") cshx = open(self.shxcopy, "rb") assert oshx.read() == cshx.read() oshx.close() cshx.close() os.remove(self.shpcopy) os.remove(self.shxcopy) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_stata_txt.py000066400000000000000000000036121452177046000245210ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..stata_txt import StataTextIO class Testtest_StataTextIO: def setup_method(self): self.test_file_sparse = test_file_sparse = pysal_examples.get_path( "stata_sparse.txt" ) self.test_file_full = test_file_full = pysal_examples.get_path("stata_full.txt") self.obj_sparse = StataTextIO(test_file_sparse, "r") self.obj_full = StataTextIO(test_file_full, "r") def test_close(self): for obj in [self.obj_sparse, self.obj_full]: f = obj f.close() pytest.raises(ValueError, f.read) def test_read(self): w_sparse = self.obj_sparse.read() assert w_sparse.n == 56 assert w_sparse.mean_neighbors == 4.0 assert [1.0, 1.0, 1.0, 1.0, 1.0] == list(w_sparse[1].values()) w_full = self.obj_full.read() assert w_full.n == 56 assert w_full.mean_neighbors == 4.0 assert [0.125, 0.125, 0.125, 0.125, 0.125] == list(w_full[1].values()) def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj_sparse.read) pytest.raises(StopIteration, self.obj_full.read) self.obj_sparse.seek(0) self.obj_full.seek(0) self.test_read() def test_write(self): for obj in [self.obj_sparse, self.obj_full]: w = obj.read() f = tempfile.NamedTemporaryFile(suffix=".txt") fname = f.name f.close() o = psopen(fname, "w", "stata_text") if obj == self.obj_sparse: o.write(w) else: o.write(w, matrix_form=True) o.close() wnew = psopen(fname, "r", "stata_text").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_wk1.py000066400000000000000000000021261452177046000232070ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO as psopen from ..wk1 import Wk1IO class Testtest_Wk1IO: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("spat-sym-us.wk1") self.obj = Wk1IO(test_file, "r") def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) def test_read(self): w = self.obj.read() assert w.n == 46 assert w.mean_neighbors == 4.0869565217391308 assert [1.0, 1.0, 1.0, 1.0] == list(w[1].values()) def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj.read) self.obj.seek(0) self.test_read() def test_write(self): w = self.obj.read() f = tempfile.NamedTemporaryFile(suffix=".wk1") fname = f.name f.close() o = psopen(fname, "w") o.write(w) o.close() wnew = psopen(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.9.2/libpysal/io/iohandlers/tests/test_wkt.py000066400000000000000000000013711452177046000233130ustar00rootroot00000000000000import pytest from .... import examples as pysal_examples from ..wkt import WKTReader class Testtest_WKTReader: def setup_method(self): self.test_file = test_file = pysal_examples.get_path("stl_hom.wkt") self.obj = WKTReader(test_file, "r") def test_close(self): f = self.obj f.close() pytest.raises(ValueError, f.read) # w_kt_reader = WKTReader(*args, **kwargs) # self.assertEqual(expected, w_kt_reader.close()) def test_open(self): f = self.obj expected = ["wkt"] assert expected == f.FORMATS def test__read(self): polys = self.obj.read() assert len(polys) == 78 assert polys[1].centroid == (-91.195784694307383, 39.990883050220845) libpysal-4.9.2/libpysal/io/iohandlers/wk1.py000066400000000000000000000434651452177046000210210ustar00rootroot00000000000000import struct from .. import fileio from ...weights import W __author__ = "Myunghwa Hwang " __all__ = ["Wk1IO"] class Wk1IO(fileio.FileIO): """MATLAB ``wk1read.m`` and ``wk1write.m`` that were written by Brian M. Bourgault in 10/22/93. Opens, reads, and writes weights ile objects in Lotus Wk1 format. Lotus Wk1 files are used in Dr. LeSage's MATLAB Econometrics library. A `Wk1` file holds a spatial weights object in a full matrix form without any row and column headers. The maximum number of columns supported in a `Wk1` file is 256. `Wk1` starts the row (column) number from 0 and uses little endian binary endcoding. In PySAL, when the number of observations is ``n``, it is assumed that each cell of an ``n\\*n(=m)`` matrix either is a blank or has a number. The internal structure of a `Wk1` file written by PySAL is as follows: ``` [BOF][DIM][CPI][CAL][CMODE][CORD][SPLIT][SYNC][CURS][WIN] [HCOL][MRG][LBL][CELL_1]...[CELL_m][EOF] ``` where ``[CELL_k]`` equals to ``[DTYPE][DLEN][DFORMAT][CINDEX][CVALUE]``. The parts between ``[BOF]`` and ``[CELL_1]`` are variable according to the software program used to write a ``.wk1`` file. While reading a ``.wk1`` file, PySAL ignores them. Each part of this structure is detailed below. .. table:: Lotus WK1 fields +-------------+---------------------+-------------------------+-------+-----------------------------+ |Part |Description |Data Type |Length |Value | +=============+=====================+=========================+=======+=============================+ |[BOF] |Begining of field |unsigned character |6 |0,0,2,0,6,4 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[DIM] |Matrix dimension | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [DIMDTYPE] |Type of dim. rec |unsigned short |2 |6 | | [DIMLEN] |Length of dim. rec |unsigned short |2 |8 | | [DIMVAL] |Value of dim. rec |unsigned short |8 |0,0,n,n | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[CPI] |CPI | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [CPITYPE] |Type of cpi rec |unsigned short |2 |150 | | [CPILEN] |Length of cpi rec |unsigned short |2 |6 | | [CPIVAL] |Value of cpi rec |unsigned char |6 |0,0,0,0,0,0 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[CAL] |calcount | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [CALTYPE] |Type of calcount rec |unsigned short |2 |47 | | [CALLEN] |Length calcount rec |unsigned short |2 |1 | | [CALVAL] |Value of calcount rec|unsigned char |1 |0 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[CMODE] |calmode | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [CMODETYP] |Type of calmode rec |unsigned short |2 |2 | | [CMODELEN] |Length of calmode rec|unsigned short |2 |1 | | [CMODEVAL] |Value of calmode rec |signed char |1 |0 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[CORD] |calorder | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [CORDTYPE] |Type of calorder rec |unsigned short |2 |3 | | [CORDLEN] |Length calorder rec |unsigned short |2 |1 | | [CORDVAL] |Value of calorder rec|signed char |1 |0 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[SPLIT] |split | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [SPLTYPE] |Type of split rec |unsigned short |2 |4 | | [SPLLEN] |Length of split rec |unsigned short |2 |1 | | [SPLVAL] |Value of split rec |signed char |1 |0 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[SYNC] |sync | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [SYNCTYP] |Type of sync rec |unsigned short |2 |5 | | [SYNCLEN] |Length of sync rec |unsigned short |2 |1 | | [SYNCVAL] |Value of sync rec |singed char |1 |0 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[CURS] |cursor | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [CURSTYP] |Type of cursor rec |unsigned short |2 |49 | | [CURSLEN] |Length of cursor rec |unsigned short |2 |1 | | [CURSVAL] |Value of cursor rec |signed char |1 |1 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[WIN] |window | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [WINTYPE] |Type of window rec |unsigned short |2 |7 | | [WINLEN] |Length of window rec |unsigned short |2 |32 | | [WINVAL1] |Value 1 of window rec|unsigned short |4 |0,0 | | [WINVAL2] |Value 2 of window rec|signed char |2 |113,0 | | [WINVAL3] |Value 3 of window rec|unsigned short |26 |10,n,n,0,0,0,0,0,0,0,0,72,0 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[HCOL] |hidcol | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [HCOLTYP] |Type of hidcol rec |unsigned short |2 |100 | | [HCOLLEN] |Length of hidcol rec |unsigned short |2 |32 | | [HCOLVAL] |Value of hidcol rec |signed char |32 |0*32 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[MRG] |margins | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [MRGTYPE] |Type of margins rec |unsigned short |2 |40 | | [MRGLEN] |Length of margins rec|unsigned short |2 |10 | | [MRGVAL] |Value of margins rec |unsigned short |10 |4,76,66,2,2 | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[LBL] |labels | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [LBLTYPE] |Type of labels rec |unsigned short |2 |41 | | [LBLLEN] |Length of labels rec |unsigned short |2 |1 | | [LBLVAL] |Value of labels rec |char |1 |' | +-------------+---------------------+-------------------------+-------+-----------------------------+ |[CELL_k] | +-------------+---------------------+-------------------------+-------+-----------------------------+ | [DTYPE] |Type of cell data |unsigned short |2 |[DTYPE][0]==0: end of file | | | | | | ==14: number | | | | | | ==16: formula | | | | | | ==13: integer | | | | | | ==11: nrange | | | | | | ==else: unknown | | [DLEN] |Length of cell data |unsigned short |2 | | | [DFORMAT] |Format of cell data |not sure |1 | | | [CINDEX] |Row, column of cell |unsigned short |4 | | | [CVALUE] |Value of cell |double, [DTYPE][0]==14 |8 | | | | |formula,[DTYPE][0]==16 |8 + |[DTYPE][1] - 13 | | | |integer,[DTYPE][0]==13 |2 | | | | |nrange, [DTYPE][0]==11 |24 | | | | |else, [DTYPE][0]==else | |[DTYPE][1] | | [EOF] |End of file |unsigned short |4 |1,0,0,0 | +-------------+---------------------+-------------------------+-------+-----------------------------+ """ FORMATS = ["wk1"] MODES = ["r", "w"] def __init__(self, *args, **kwargs): self._varName = "Unknown" fileio.FileIO.__init__(self, *args, **kwargs) self.file = open(self.dataPath, self.mode + "b") def _set_varName(self, val): if issubclass(type(val), str): self._varName = val def _get_varName(self) -> str: return self._varName varName = property(fget=_get_varName, fset=_set_varName) def read(self, n=-1): """ Parameters ---------- n : int Read at most ``n`` objects. Default is ``-1``. Returns ------- w : libpysal.weights.W A PySAL `W` object. """ self._complain_ifclosed(self.closed) return self._read() def seek(self, pos): if pos == 0: self.file.seek(0) self.pos = 0 def _read(self): """Reads Lotus Wk1 file. Returns ------- w : libpysal.weights.W A PySAL `W` object. Raises ------ StopIteration Raised at the EOF. ValueError Raised when the header of the file is invalid. Examples -------- Type ``dir(w)`` at the interpreter to see what methods are supported. Open a Lotus Wk1 file and read it into a PySAL weights object. >>> import libpysal >>> w = libpysal.io.open( ... libpysal.examples.get_path('spat-sym-us.wk1'), 'r' ... ).read() Get the number of observations from the header. >>> w.n 46 Get neighbor distances for a single observation. >>> w[1] == dict({25: 1.0, 3: 1.0, 28: 1.0, 39: 1.0}) True """ if self.pos > 0: raise StopIteration bof = struct.unpack("<6B", self.file.read(6)) if bof != (0, 0, 2, 0, 6, 4): raise ValueError("The header of your file is wrong!") neighbors = {} weights = {} dtype, dlen = struct.unpack("<2H", self.file.read(4)) while dtype != 1: if dtype in [13, 14, 16]: self.file.read(1) row, column = struct.unpack("<2H", self.file.read(4)) format, length = " 0: ngh = neighbors.setdefault(row, []) ngh.append(column) wgt = weights.setdefault(row, []) wgt.append(value) if dtype == 16: self.file.read(dlen - 13) elif dtype == 11: self.file.read(24) else: self.file.read(dlen) dtype, dlen = struct.unpack("<2H", self.file.read(4)) self.pos += 1 w = W(neighbors, weights) return w def write(self, obj): """Write a weights object to the opened ``.wk1`` file. Parameters ---------- obj : libpysal.weights.W A PySAL `W` object. Raises ------ ValueError Raised when the `WK1` file has more than 256 observations. TypeError Raised when the input ``obj`` is not a PySAL `W`. Examples -------- >>> import tempfile, libpysal, os >>> testfile = libpysal.io.open( ... libpysal.examples.get_path('spat-sym-us.wk1'), 'r' ... ) >>> w = testfile.read() Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.wk1') Reassign to a new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Open the new file in write mode. >>> o = libpysal.io.open(fname, 'w') Write the weights object into the open file. >>> o.write(w) >>> o.close() Read in the newly created text file. >>> wnew = libpysal.io.open(fname, 'r').read() Compare values from old to new. >>> wnew.pct_nonzero == w.pct_nonzero True Clean up the temporary file created for this example. >>> os.remove(fname) """ self._complain_ifclosed(self.closed) if issubclass(type(obj), W): f = self.file n = obj.n if n > 256: raise ValueError( "WK1 file format supports only up to 256 observations." ) pack = struct.pack f.write(pack("<6B", 0, 0, 2, 0, 6, 4)) f.write(pack("<6H", 6, 8, 0, 0, n, n)) f.write(pack("<2H6B", 150, 6, 0, 0, 0, 0, 0, 0)) f.write(pack("<2H1B", 47, 1, 0)) f.write(pack("<2H1b", 2, 1, 0)) f.write(pack("<2H1b", 3, 1, 0)) f.write(pack("<2H1b", 4, 1, 0)) f.write(pack("<2H1b", 5, 1, 0)) f.write(pack("<2H1b", 49, 1, 1)) f.write( pack( "<4H2b13H", 7, 32, 0, 0, 113, 0, 10, n, n, 0, 0, 0, 0, 0, 0, 0, 0, 72, 0, ) ) hidcol = tuple(["<2H32b", 100, 32] + [0] * 32) f.write(pack(*hidcol)) f.write(pack("<7H", 40, 10, 4, 76, 66, 2, 2)) f.write(pack("<2H1c", 41, 1, "'".encode())) id2i = obj.id2i for i, w_i in enumerate(obj): row = [0.0] * n for k in w_i[1]: row[id2i[k]] = w_i[1][k] for c, v in enumerate(row): cell = tuple(["<2H1b2H1d", 14, 13, 113, i, c, v]) f.write(pack(*cell)) f.write(pack("<4B", 1, 0, 0, 0)) self.pos += 1 else: raise TypeError("Expected a PySAL weights object, got: %s." % (type(obj))) def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.9.2/libpysal/io/iohandlers/wkt.py000066400000000000000000000052001452177046000211050ustar00rootroot00000000000000from .. import fileio from ..util import WKTParser __author__ = "Charles R Schmidt " __all__ = ["WKTReader"] ##################################################################### ## ToDo: Add Well-Known-Binary support... ## * WKB spec: ## http://webhelp.esri.com/arcgisserver/9.3/dotNet/index.htm#geodatabases/the_ogc_103951442.htm ##################################################################### class WKTReader(fileio.FileIO): """Reads Well-Known Text into PySAL polygon objects. Examples -------- Read in WKT-formatted file. >>> import libpysal >>> f = libpysal.io.open(libpysal.examples.get_path('stl_hom.wkt'), 'r') Convert ``wkt`` to PySAL polygons. >>> polys = f.read() Check length. >>> len(polys) 78 Return centroid of polygon at index 1. >>> polys[1].centroid (-91.19578469430738, 39.990883050220845) Type ``dir(polys[1])`` at the python interpreter to get a list of supported methods. """ MODES = ["r"] FORMATS = ["wkt"] def __init__(self, *args, **kwargs): fileio.FileIO.__init__(self, *args, **kwargs) self.__idx = {} self.__pos = 0 self.__open() def open(self): self.__open() def __open(self): self.dataObj = open(self.dataPath, self.mode) self.wkt = WKTParser() def _read(self): """ Returns ------- shape : libpysal.cg.Polygon Geometric shape. """ fileio.FileIO._complain_ifclosed(self.closed) if self.__pos not in self.__idx: self.__idx[self.__pos] = self.dataObj.tell() line = self.dataObj.readline() if line: shape = self.wkt.fromWKT(line) shape.id = self.pos self.__pos += 1 self.pos += 1 return shape else: self.seek(0) return None def seek(self, n): """ Raises ------ IndexError Raised when an incorrect index is used. """ fileio.FileIO.seek(self, n) pos = self.pos if pos in self.__idx: self.dataObj.seek(self.__idx[pos]) self.__pos = pos else: while pos not in self.__idx: s = self._read() if not s: msg = "%d not in range(0,%d)." % (pos, max(self.__idx.keys())) raise IndexError(msg) self.pos = pos self.__pos = pos self.dataObj.seek(self.__idx[pos]) def close(self): self.dataObj.close() fileio.FileIO.close(self) libpysal-4.9.2/libpysal/io/tables.py000066400000000000000000000207301452177046000174270ustar00rootroot00000000000000__all__ = ["DataTable"] from warnings import warn import numpy as np from ..common import requires from . import fileio __author__ = "Charles R Schmidt " class DataTable(fileio.FileIO): """`DataTable` provides additional functionality to `FileIO` for data table file tables `FileIO` handlers that provide data tables should subclass this instead of `FileIO`. """ class _By_Col: # noqa N801 def __init__(self, parent): self.p = parent def __repr__(self) -> str: return "keys: " + self.p.header.__repr__() def __getitem__(self, key): return self.p._get_col(key) def __setitem__(self, key, val): self.p.cast(key, val) def __call__(self, key): return self.p._get_col(key) def __init__(self, *args, **kwargs): fileio.FileIO.__init__(self, *args, **kwargs) def __repr__(self) -> str: return "DataTable: %s" % self.dataPath def __len__(self): """__len__ should be implemented by `DataTable` subclasses.""" raise NotImplementedError @property def by_col(self): return self._By_Col(self) def _get_col(self, key): """Returns the column vector. Raises ------ AttributeError Raised when the header is not set. AttributeError Raised when a field does not exist. """ if not self.header: raise AttributeError("Please set the header.") if key in self.header: return self[:, self.header.index(key)] else: raise AttributeError("Field: %s does not exist in header." % key) def by_col_array(self, *args): """Return columns of table as a ``numpy.ndarray``. Parameters ---------- *args : iterable Any number of strings of length :math:`k` names of variables to extract. Returns ------- results : numpy.ndarray An array of shape :math:`(n,k)`. Notes ----- If the variables are not all of the same data type, then ``numpy`` rules for casting will result in a uniform type applied to all variables. If only strings are passed to the function, then an array with those columns will be constructed. If only one list of strings is passed, the output is identical to those strings being passed. If at least one list is passed and other strings or lists are passed, this returns a tuple containing arrays constructed from each positional argument. Examples -------- >>> import libpysal >>> dbf = libpysal.io.open(libpysal.examples.get_path('NAT.dbf')) >>> hr = dbf.by_col_array('HR70', 'HR80') >>> hr[0:5] array([[ 0. , 8.85582713], [ 0. , 17.20874204], [ 1.91515848, 3.4507747 ], [ 1.28864319, 3.26381409], [ 0. , 7.77000777]]) >>> hr = dbf.by_col_array(['HR80', 'HR70']) >>> hr[0:5] array([[ 8.85582713, 0. ], [17.20874204, 0. ], [ 3.4507747 , 1.91515848], [ 3.26381409, 1.28864319], [ 7.77000777, 0. ]]) >>> hr = dbf.by_col_array(['HR80']) >>> hr[0:5] array([[ 8.85582713], [17.20874204], [ 3.4507747 ], [ 3.26381409], [ 7.77000777]]) Numpy only supports homogeneous arrays. See Notes above. >>> hr = dbf.by_col_array('STATE_NAME', 'HR80') >>> hr[0:5] array([['Minnesota', '8.8558271343'], ['Washington', '17.208742041'], ['Washington', '3.4507746989'], ['Washington', '3.2638140931'], ['Washington', '7.77000777']], dtype='>> y, X = dbf.by_col_array('STATE_NAME', ['HR80', 'HR70']) >>> y[0:5] array([['Minnesota'], ['Washington'], ['Washington'], ['Washington'], ['Washington']], dtype='>> X[0:5] array([[ 8.85582713, 0. ], [17.20874204, 0. ], [ 3.4507747 , 1.91515848], [ 3.26381409, 1.28864319], [ 7.77000777, 0. ]]) """ if any(isinstance(arg, list) for arg in args): results = [] for namelist in args: if isinstance(namelist, str): results.append([self._get_col(namelist)]) else: results.append([self._get_col(vbl) for vbl in namelist]) if len(results) == 1: results = np.array(results[0]).T else: results = tuple(np.array(lst).T for lst in results) else: results = np.array([self._get_col(name) for name in args]).T return results def __getitem__(self, key) -> list: """DataTables fully support slicing in 2D. To provide slicing, handlers must provide ``__len__``. Slicing accepts up to two arguments. For example, * ``table[row]`` * ``table[row, col]`` * ``table[row_start:row_stop]`` * ``table[row_start:row_stop:row_step]`` * ``table[:, col]`` * ``table[:, col_start:col_stop]`` * etc. ALL indices are Zero-Offsets. For example, * ``>>> assert index in range(0, len(table))`` Raises ------ TypeError Raised when two dimensions are not provided for slicing. TypeError Raised when an unknown key is present. """ prev_pos = self.tell() if issubclass(type(key), str): raise TypeError("index should be int or slice") if issubclass(type(key), int) or isinstance(key, slice): rows = key cols = None elif len(key) > 2: raise TypeError( "DataTables support two dimmensional slicing, % d slices provided." % len(key) ) elif len(key) == 2: rows, cols = key else: raise TypeError("Key: % r, is confusing me. I don't know what to do." % key) if isinstance(rows, slice): row_start, row_stop, row_step = rows.indices(len(self)) self.seek(row_start) data = [next(self) for i in range(row_start, row_stop, row_step)] else: self.seek(slice(rows).indices(len(self))[1]) data = [next(self)] if cols is not None: if isinstance(cols, slice): col_start, col_stop, col_step = cols.indices(len(data[0])) data = [r[col_start:col_stop:col_step] for r in data] else: # col_start, col_stop, col_step = cols, cols+1, 1 data = [r[cols] for r in data] self.seek(prev_pos) return data @requires("pandas") def to_df(self, n=-1, read_shp=False, **df_kws): """Convert a ``libpysal.DataTable`` to a ``pandas.DataFrame``. Parameters ---------- n : int Lines to read from file. Default is ``-1``. read_shp : bool Read in from a shapefile (``True``). Default is ``False``. **df_kws : dict Optional keyword arguments to pass into ``pandas.DataFrame()``. Returns ------- df : pandas.DataFrame Pandas dataframe representation of the data. """ import pandas as pd self.seek(0) header = self.header records = self.read(n) df = pd.DataFrame(records, columns=header, **df_kws) if read_shp is not False: if read_shp is True or self.dataPath.endswith(".dbf"): read_shp = self.dataPath[:-3] + "shp" try: from .geotable.shp import shp2series df["geometry"] = shp2series(self.dataPath[:-3] + "shp") except OSError as e: warn( "Encountered the following error in attempting to read" " the shapefile {}. Proceeding with read, but the error" " will be reproduced below:\n" " {}".format(self.dataPath[:-3] + "shp", e), stacklevel=2, ) return df def _test(): import doctest doctest.testmod(verbose=True) if __name__ == "__main__": _test() libpysal-4.9.2/libpysal/io/tests/000077500000000000000000000000001452177046000167435ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/tests/__init__.py000066400000000000000000000000001452177046000210420ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/tests/test_FileIO.py000066400000000000000000000005241452177046000214640ustar00rootroot00000000000000from ...examples import get_path from ..fileio import FileIO def test_by_col_exists(): """Test if the Metaclass is initializing and providing readers to its children.""" fh1 = FileIO.open(get_path("columbus.dbf")) fh2 = FileIO.open(get_path("usjoin.csv")) assert hasattr(fh1, "by_col") assert hasattr(fh2, "by_col") libpysal-4.9.2/libpysal/io/tests/test_Tables.py000066400000000000000000000020471452177046000215710ustar00rootroot00000000000000import numpy as np # import pysal_examples from ... import examples as pysal_examples from ...common import pandas from ..fileio import FileIO as psopen PANDAS_EXTINCT = pandas is None class TestTable: def setup_method(self): self.filehandler = psopen(pysal_examples.get_path("columbus.dbf")) self.df = self.filehandler.to_df() self.filehandler.seek(0) self.shapefile = psopen(pysal_examples.get_path("columbus.shp")) self.csvhandler = psopen(pysal_examples.get_path("usjoin.csv")) self.csv_df = self.csvhandler.to_df() self.csvhandler.seek(0) def test_to_df(self): for column in self.csv_df.columns: if column.lower() == "name": continue np.testing.assert_allclose( self.csvhandler.by_col(column), self.csv_df[column].values ) for column in self.df.columns: if column == "geometry": continue np.testing.assert_allclose(self.filehandler.by_col(column), self.df[column]) libpysal-4.9.2/libpysal/io/util/000077500000000000000000000000001452177046000165565ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/util/__init__.py000066400000000000000000000000771452177046000206730ustar00rootroot00000000000000from .shapefile import * from .wkb import * from .wkt import * libpysal-4.9.2/libpysal/io/util/shapefile.py000066400000000000000000001015061452177046000210730ustar00rootroot00000000000000"""A Pure Python Shapefile Reader and Writer This module is self-contained and does not require pysal. It returns and expects dictionary-based data structures. It should be wrapped into your native data structures. Contact: Charles Schmidt GeoDa Center Arizona State University Tempe, AZ http://geodacenter.asu.edu """ __author__ = "Charles R Schmidt " from struct import calcsize, unpack, pack from itertools import islice import array import sys import io from typing import Union if sys.byteorder == "little": SYS_BYTE_ORDER = "<" else: SYS_BYTE_ORDER = ">" STRUCT_ITEMSIZE = {} STRUCT_ITEMSIZE["i"] = calcsize("i") STRUCT_ITEMSIZE["d"] = calcsize("d") __all__ = ["shp_file", "shx_file"] # SHAPEFILE Globals def struct2arrayinfo(struct: tuple) -> list: struct = list(struct) names = [x[0] for x in struct] types = [x[1] for x in struct] orders = [x[2] for x in struct] lname, ltype, lorder = struct.pop(0) groups = {} g = 0 groups[g] = { "names": [lname], "size": STRUCT_ITEMSIZE[ltype], "fmt": ltype, "order": lorder, } while struct: name, type, order = struct.pop(0) if order == lorder: groups[g]["names"].append(name) groups[g]["size"] += STRUCT_ITEMSIZE[type] groups[g]["fmt"] += type else: g += 1 groups[g] = { "names": [name], "size": STRUCT_ITEMSIZE[type], "fmt": type, "order": order, } lname, ltype, lorder = name, type, order return [groups[x] for x in range(g + 1)] HEADERSTRUCT = ( ("File Code", "i", ">"), ("Unused0", "i", ">"), ("Unused1", "i", ">"), ("Unused2", "i", ">"), ("Unused3", "i", ">"), ("Unused4", "i", ">"), ("File Length", "i", ">"), ("Version", "i", "<"), ("Shape Type", "i", "<"), ("BBOX Xmin", "d", "<"), ("BBOX Ymin", "d", "<"), ("BBOX Xmax", "d", "<"), ("BBOX Ymax", "d", "<"), ("BBOX Zmin", "d", "<"), ("BBOX Zmax", "d", "<"), ("BBOX Mmin", "d", "<"), ("BBOX Mmax", "d", "<"), ) UHEADERSTRUCT = struct2arrayinfo(HEADERSTRUCT) RHEADERSTRUCT = (("Record Number", "i", ">"), ("Content Length", "i", ">")) URHEADERSTRUCT = struct2arrayinfo(RHEADERSTRUCT) def noneMax(a: Union[float, None], b: Union[float, None]) -> float: if a is None: return b if b is None: return a return max(a, b) def noneMin(a: Union[float, None], b: Union[float, None]) -> float: if a is None: return b if b is None: return a return min(a, b) def _unpackDict(structure, fileObj): """Utility function that requires a tuple of tuples that describe the element structure. Parameters ---------- structure : tuple A tuple of tuples in the form: ``(('FieldName 1','type','byteOrder'),('FieldName 2','type','byteOrder'))``. fileObj : file An open file at the correct position. Returns ------- d : dict Dictionary in the form: ``{'FieldName 1': value, 'FieldName 2': value}``. Notes ----- The file is at new position. Examples -------- >>> import libpysal >>> _unpackDict( ... UHEADERSTRUCT, ... open( ... libpysal.examples.get_path('10740.shx'), 'rb') ... ) == \ ... { ... 'BBOX Xmax': -105.29012, ... 'BBOX Ymax': 36.219799000000002, ... 'BBOX Mmax': 0.0, ... 'BBOX Zmin': 0.0, ... 'BBOX Mmin': 0.0, ... 'File Code': 9994, ... 'BBOX Ymin': 34.259672000000002, ... 'BBOX Xmin': -107.62651, ... 'Unused0': 0, ... 'Unused1': 0, ... 'Unused2': 0, ... 'Unused3': 0, ... 'Unused4': 0, ... 'Version': 1000, ... 'BBOX Zmax': 0.0, ... 'Shape Type': 5, ... 'File Length': 830 ... } True """ d = {} for struct in structure: items = unpack(struct["order"] + struct["fmt"], fileObj.read(struct["size"])) for i, name in enumerate(struct["names"]): d[name] = items[i] return d def _unpackDict2(d, structure, fileObj): """Utility Function, used arrays instead from struct. Parameters ---------- d : dict Dictionary in to be updated. structure : tuple A tuple of tuples in the form: ``(('FieldName 1','type','byteOrder'),('FieldName 2','type','byteOrder'))``. fileObj : file An open file at the correct position. Returns ------- d : dict The updated dictionary. """ for name, dtype, order in structure: dtype, n = dtype result = array.array(dtype) result.frombytes(fileObj.read(result.itemsize * n)) if order != SYS_BYTE_ORDER: result.byteswap() d[name] = result.tolist() return d def _packDict(structure, d) -> str: """Utility Function for packing a dictionary with byte strings. Parameters ---------- structure : tuple A tuple of tuples in the form: ``(('FieldName 1','type','byteOrder'),('FieldName 2','type','byteOrder'))``. d : dict Dictionary in the form: ``{'FieldName 1': value, 'FieldName 2': value}``. Examples -------- >>> s = _packDict( ... (('FieldName 1', 'i', '<'), ('FieldName 2', 'i', '<')), ... {'FieldName 1': 1, 'FieldName 2': 2} ... ) >>> s == pack('>> unpack(' 1: string += pack(order + dtype, *d[name]) else: string += pack(order + dtype, d[name]) return string class shp_file: """Reads and writes the SHP compenent of a shapefile. Parameters ---------- filename : str The name of the file to create. mode : str The mode for file interaction, either ``'r'`` (read) or ``'w'`` (write). Default is ``'r'``. shape_type : str Must be one of the following: ``'POINT'``, ``'POINTZ'``, ``'POINTM'``, ``'ARC'``, ``'ARCZ'``, ``'ARCM'``, ``'POLYGON'``, ``'POLYGONZ'``, ``'POLYGONM'``, ``'MULTIPOINT'``, ``'MULTIPOINTZ'``, ``'MULTIPOINTM'``, ``'MULTIPATCH'``. Default is ``None``. Attributes ---------- header : dict Contents of the SHP header. For contents see ``HEADERSTRUCT``. shape : int See ``SHAPE_TYPES`` and ``TYPE_DISPATCH``. Examples -------- >>> import libpysal >>> shp = shp_file(libpysal.examples.get_path('10740.shp')) >>> shp.header == { ... 'BBOX Xmax': -105.29012, ... 'BBOX Ymax': 36.219799000000002, ... 'BBOX Mmax': 0.0, ... 'BBOX Zmin': 0.0, ... 'BBOX Mmin': 0.0, ... 'File Code': 9994, ... 'BBOX Ymin': 34.259672000000002, ... 'BBOX Xmin': -107.62651, ... 'Unused0': 0, ... 'Unused1': 0, ... 'Unused2': 0, ... 'Unused3': 0, ... 'Unused4': 0, ... 'Version': 1000, ... 'BBOX Zmax': 0.0, ... 'Shape Type': 5, ... 'File Length': 260534 ... } True >>> len(shp) 195 Notes ----- The header of both the SHP and SHX files are indentical. """ SHAPE_TYPES = { "POINT": 1, "ARC": 3, "POLYGON": 5, "MULTIPOINT": 8, "POINTZ": 11, "ARCZ": 13, "POLYGONZ": 15, "MULTIPOINTZ": 18, "POINTM": 21, "ARCM": 23, "POLYGONM": 25, "MULTIPOINTM": 28, "MULTIPATCH": 31, } def __iswritable(self) -> bool: """ Raises ------ IOError Raised when a bad file name is passed in. """ try: assert self.__mode == "w" except AssertionError: raise IOError("[Errno 9] Bad file descriptor.") return True def __isreadable(self) -> bool: """ Raises ------ IOError Raised when a bad file name is passed in. """ try: assert self.__mode == "r" except AssertionError: raise IOError("[Errno 9] Bad file descriptor.") return True def __init__(self, fileName, mode="r", shape_type=None): """ Raises ------ Exception Raised when an invalid shape type is passed in. Exception Raised when an invalid mode is passed in. """ self.__mode = mode if ( fileName.lower().endswith(".shp") or fileName.lower().endswith(".shx") or fileName.lower().endswith(".dbf") ): fileName = fileName[:-4] self.fileName = fileName if mode == "r": self._open_shp_file() elif mode == "w": if shape_type not in self.SHAPE_TYPES: raise Exception("Attempt to create shp/shx file of invalid type.") self._create_shp_file(shape_type) else: raise Exception("Only 'w' and 'r' modes are supported.") def _open_shp_file(self): """Opens a shp/shx file.""" self.__isreadable() fileName = self.fileName self.fileObj = open(fileName + ".shp", "rb") self._shx = shx_file(fileName) self.header = _unpackDict(UHEADERSTRUCT, self.fileObj) self.shape = TYPE_DISPATCH[self.header["Shape Type"]] self.__lastShape = 0 # localizing for convenience self.__numRecords = self._shx.numRecords # constructing bounding box from header h = self.header self.bbox = [h["BBOX Xmin"], h["BBOX Ymin"], h["BBOX Xmax"], h["BBOX Ymax"]] self.shapeType = self.header["Shape Type"] def _create_shp_file(self, shape_type: str): """Creates a shp/shx file. Examples -------- >>> import libpysal, os >>> shp = shp_file('test', 'w', 'POINT') >>> p = shp_file(libpysal.examples.get_path('Point.shp')) >>> for pt in p: ... shp.add_shape(pt) >>> shp.close() >>> open('test.shp','rb').read() == open( ... libpysal.examples.get_path('Point.shp'), 'rb' ... ).read() True >>> open('test.shx', 'rb').read() == open( ... libpysal.examples.get_path('Point.shx'), 'rb' ... ).read() True >>> os.remove('test.shx') >>> os.remove('test.shp') """ self.__iswritable() fileName = self.fileName self.fileObj = open(fileName + ".shp", "wb") self._shx = shx_file(fileName, "w") self.header = {} self.header["Shape Type"] = self.SHAPE_TYPES[shape_type] self.header["Version"] = 1000 self.header["Unused0"] = 0 self.header["Unused1"] = 0 self.header["Unused2"] = 0 self.header["Unused3"] = 0 self.header["Unused4"] = 0 self.header["File Code"] = 9994 self.__file_Length = 100 self.header["File Length"] = 0 self.header["BBOX Xmax"] = None self.header["BBOX Ymax"] = None self.header["BBOX Mmax"] = None self.header["BBOX Zmax"] = None self.header["BBOX Xmin"] = None self.header["BBOX Ymin"] = None self.header["BBOX Mmin"] = None self.header["BBOX Zmin"] = None self.shape = TYPE_DISPATCH[self.header["Shape Type"]] # self.__numRecords = self._shx.numRecords def __len__(self) -> int: return self.__numRecords def __iter__(self): return self def type(self) -> str: return self.shape.String_Type def __next__(self) -> int: """Returns the next shape in the shapefile. Raises ------ StopIteration Raised at the EOF. Examples -------- >>> import libpysal >>> list(shp_file(libpysal.examples.get_path('Point.shp'))) == [ ... { ... 'Y': -0.25904661905760773, ... 'X': -0.00068176617532103578, ... 'Shape Type': 1 ... }, ... { ... 'Y': -0.25630328607387354, ... 'X': 0.11697145363360706, ... 'Shape Type': 1 ... }, ... { ... 'Y': -0.33930131004366804, ... 'X': 0.05043668122270728, ... 'Shape Type': 1 ... }, ... { ... 'Y': -0.41266375545851519, ... 'X': -0.041266375545851552, ... 'Shape Type': 1 ... }, ... { ... 'Y': -0.44017467248908293, ... 'X': -0.011462882096069604, ... 'Shape Type': 1 ... }, ... { ... 'Y': -0.46080786026200882, ... 'X': 0.027510917030567628, ... 'Shape Type': 1 ... }, ... { ... 'Y': -0.45851528384279472, ... 'X': 0.075655021834060809, ... 'Shape Type': 1 ... }, ... { ... 'Y': -0.43558951965065495, ... 'X': 0.11233624454148461, ... 'Shape Type': 1 ... }, ... { ... 'Y': -0.40578602620087334, ... 'X': 0.13984716157205224, ... 'Shape Type': 1 ... } ... ] True """ self.__isreadable() nextShape = self.__lastShape if nextShape == self._shx.numRecords: self.__lastShape = 0 raise StopIteration else: self.__lastShape = nextShape + 1 return self.get_shape(nextShape) def __seek(self, pos: int): if pos != self.fileObj.tell(): self.fileObj.seek(pos) def __read(self, pos: int, size: int): self.__isreadable() if pos != self.fileObj.tell(): self.fileObj.seek(pos) return self.fileObj.read(size) def get_shape(self, shpId: int) -> dict: self.__isreadable() if shpId + 1 > self.__numRecords: raise IndexError fPosition, byts = self._shx.index[shpId] self.__seek(fPosition) # the index does not include the 2 byte record header # (which contains, Record ID and Content Length) rec_id, con_len = _unpackDict(URHEADERSTRUCT, self.fileObj) return self.shape.unpack(io.BytesIO(self.fileObj.read(byts))) # return self.shape.unpack(self.fileObj.read(bytes)) def __update_bbox(self, s: dict): h = self.header if s.get("Shape Type") == 1: h["BBOX Xmax"] = noneMax(h["BBOX Xmax"], s.get("X")) h["BBOX Ymax"] = noneMax(h["BBOX Ymax"], s.get("Y")) h["BBOX Mmax"] = noneMax(h["BBOX Mmax"], s.get("M")) h["BBOX Zmax"] = noneMax(h["BBOX Zmax"], s.get("Z")) h["BBOX Xmin"] = noneMin(h["BBOX Xmin"], s.get("X")) h["BBOX Ymin"] = noneMin(h["BBOX Ymin"], s.get("Y")) h["BBOX Mmin"] = noneMin(h["BBOX Mmin"], s.get("M")) h["BBOX Zmin"] = noneMin(h["BBOX Zmin"], s.get("Z")) else: h["BBOX Xmax"] = noneMax(h["BBOX Xmax"], s.get("BBOX Xmax")) h["BBOX Ymax"] = noneMax(h["BBOX Ymax"], s.get("BBOX Ymax")) h["BBOX Mmax"] = noneMax(h["BBOX Mmax"], s.get("BBOX Mmax")) h["BBOX Zmax"] = noneMax(h["BBOX Zmax"], s.get("BBOX Zmax")) h["BBOX Xmin"] = noneMin(h["BBOX Xmin"], s.get("BBOX Xmin")) h["BBOX Ymin"] = noneMin(h["BBOX Ymin"], s.get("BBOX Ymin")) h["BBOX Mmin"] = noneMin(h["BBOX Mmin"], s.get("BBOX Mmin")) h["BBOX Zmin"] = noneMin(h["BBOX Zmin"], s.get("BBOX Zmin")) if not self.shape.HASM: self.header["BBOX Mmax"] = 0.0 self.header["BBOX Mmin"] = 0.0 if not self.shape.HASZ: self.header["BBOX Zmax"] = 0.0 self.header["BBOX Zmin"] = 0.0 def add_shape(self, s: dict): self.__iswritable() self.__update_bbox(s) rec = self.shape.pack(s) con_len = len(rec) self.__file_Length += con_len + 8 rec_id, pos = self._shx.add_record(con_len) self.__seek(pos) self.fileObj.write(pack(">ii", rec_id, con_len // 2)) self.fileObj.write(rec) def close(self): self._shx.close(self.header) if self.__mode == "w": self.header["File Length"] = self.__file_Length // 2 self.__seek(0) self.fileObj.write(_packDict(HEADERSTRUCT, self.header)) self.fileObj.close() class shx_file: """Reads and writes the SHX compenent of a shapefile. Parameters ---------- filename : str The name of the file to create. Default is ``None``. The extension is optional, will remove ``'.dbf'``, ``'.shx'``, ``'.shp'`` and append ``'.shx'``. mode : str The mode for file interaction. Must be ``'r'`` (read). Attributes ---------- index : list Contains the file offset and length of each recond in the SHP component. numRecords : int The number of records. Examples -------- >>> import libpysal >>> shx = shx_file(libpysal.examples.get_path('10740.shx')) >>> shx._header == { ... 'BBOX Xmax': -105.29012, ... 'BBOX Ymax': 36.219799000000002, ... 'BBOX Mmax': 0.0, ... 'BBOX Zmin': 0.0, ... 'BBOX Mmin': 0.0, ... 'File Code': 9994, ... 'BBOX Ymin': 34.259672000000002, ... 'BBOX Xmin': -107.62651, ... 'Unused0': 0, ... 'Unused1': 0, ... 'Unused2': 0, ... 'Unused3': 0, ... 'Unused4': 0, ... 'Version': 1000, ... 'BBOX Zmax': 0.0, ... 'Shape Type': 5, ... 'File Length': 830 ... } True >>> len(shx.index) 195 >>> shx = shx_file(libpysal.examples.get_path('Point.shx')) >>> isinstance(shx, shx_file) True """ def __iswritable(self) -> bool: """ Raises ------ IOError Raised when a bad file name is passed in. """ try: assert self.__mode == "w" except AssertionError: raise IOError("[Errno 9] Bad file descriptor.") return True def __isreadable(self) -> bool: """ Raises ------ IOError Raised when a bad file name is passed in. """ try: assert self.__mode == "r" except AssertionError: raise IOError("[Errno 9] Bad file descriptor.") return True def __init__(self, fileName=None, mode="r"): self.__mode = mode if ( fileName.endswith(".shp") or fileName.endswith(".shx") or fileName.endswith(".dbf") ): fileName = fileName[:-4] self.fileName = fileName if mode == "r": self._open_shx_file() elif mode == "w": self._create_shx_file() def _open_shx_file(self): """Opens the SHX file.""" self.__isreadable() self.fileObj = open(self.fileName + ".shx", "rb") self._header = _unpackDict(UHEADERSTRUCT, self.fileObj) self.numRecords = numRecords = (self._header["File Length"] - 50) // 4 index = {} fmt = ">%di" % (2 * numRecords) size = calcsize(fmt) dat = unpack(fmt, self.fileObj.read(size)) self.index = [(dat[i] * 2, dat[i + 1] * 2) for i in range(0, len(dat), 2)] def _create_shx_file(self): """Creates the SHX file.""" self.__iswritable() self.fileObj = open(self.fileName + ".shx", "wb") self.numRecords = 0 self.index = [] # length of header self.__offset = 100 # record IDs start at 1 self.__next_rid = 1 def add_record(self, size: int): """Add a record to the shx index. Parameters ---------- size : int The length of the record in bytes NOT including the 8-byte record header. Returns ------- rec_id : int The sequential record ID, 1-based. pos : int See ``self.__offset`` in ``_create_shx_file``. Notes ----- The SHX records contain (Offset, Length) in 16-bit words. Examples -------- >>> import libpysal, os >>> shx = shx_file(libpysal.examples.get_path('Point.shx')) >>> shx.index [(100, 20), (128, 20), (156, 20), (184, 20), (212, 20), (240, 20), (268, 20), (296, 20), (324, 20)] >>> shx2 = shx_file('test', 'w') >>> [shx2.add_record(rec[1]) for rec in shx.index] [(1, 100), (2, 128), (3, 156), (4, 184), (5, 212), (6, 240), (7, 268), (8, 296), (9, 324)] >>> shx2.index == shx.index True >>> shx2.close(shx._header) >>> open('test.shx', 'rb').read() == open( ... libpysal.examples.get_path('Point.shx'), 'rb' ... ).read() True >>> os.remove('test.shx') """ self.__iswritable() pos = self.__offset rec_id = self.__next_rid self.index.append((self.__offset, size)) # the 8-byte record header. self.__offset += size + 8 self.numRecords += 1 self.__next_rid += 1 return rec_id, pos def close(self, header: dict): if self.__mode == "w": self.__iswritable() header["File Length"] = (self.numRecords * calcsize(">ii") + 100) // 2 self.fileObj.seek(0) self.fileObj.write(_packDict(HEADERSTRUCT, header)) fmt = ">%di" % (2 * self.numRecords) values = [] for off, size in self.index: values.extend([off // 2, size // 2]) self.fileObj.write(pack(fmt, *values)) self.fileObj.close() class NullShape: Shape_Type = 0 STRUCT = ("Shape Type", "i", "<") def unpack(self) -> None: return None def pack(self, x=None) -> str: return pack(">> import libpysal >>> shp = shp_file(libpysal.examples.get_path('Point.shp')) >>> rec = shp.get_shape(0) >>> rec == {'Y': -0.25904661905760773, 'X': -0.00068176617532103578, 'Shape Type': 1} True >>> # +8 byte record header >>> pos = shp.fileObj.seek(shp._shx.index[0][0] + 8) >>> dat = shp.fileObj.read(shp._shx.index[0][1]) >>> dat == Point.pack(rec) True """ Shape_Type = 1 String_Type = "POINT" HASZ = False HASM = False STRUCT = (("Shape Type", "i", "<"), ("X", "d", "<"), ("Y", "d", "<")) USTRUCT = [ {"fmt": "idd", "order": "<", "names": ["Shape Type", "X", "Y"], "size": 20} ] @classmethod def unpack(cls, dat) -> dict: """ Parameters ---------- dat : file An open file at the correct position. """ return _unpackDict(cls.USTRUCT, dat) @classmethod def pack(cls, record: dict) -> str: rheader = _packDict(cls.STRUCT, record) return rheader class PointZ(Point): Shape_Type = 11 String_Type = "POINTZ" HASZ = True HASM = True STRUCT = ( ("Shape Type", "i", "<"), ("X", "d", "<"), ("Y", "d", "<"), ("Z", "d", "<"), ("M", "d", "<"), ) USTRUCT = [ { "fmt": "idddd", "order": "<", "names": ["Shape Type", "X", "Y", "Z", "M"], "size": 36, } ] class PolyLine: """Packs and unpacks a shapefile PolyLine type. Examples -------- >>> import libpysal >>> shp = shp_file(libpysal.examples.get_path('Line.shp')) >>> rec = shp.get_shape(0) >>> rec == { ... 'BBOX Ymax': -0.25832280562918325, ... 'NumPoints': 3, ... 'BBOX Ymin': -0.25895877033237352, ... 'NumParts': 1, ... 'Vertices': [ ... (-0.0090539248870159517, -0.25832280562918325), ... (0.0074811573959305822, -0.25895877033237352), ... (0.0074811573959305822, -0.25895877033237352) ... ], ... 'BBOX Xmax': 0.0074811573959305822, ... 'BBOX Xmin': -0.0090539248870159517, ... 'Shape Type': 3, ... 'Parts Index': [0] ... } True >>> # +8 byte record header >>> pos = shp.fileObj.seek(shp._shx.index[0][0] + 8) >>> dat = shp.fileObj.read(shp._shx.index[0][1]) >>> dat == PolyLine.pack(rec) True """ HASZ = False HASM = False String_Type = "ARC" STRUCT = ( ("Shape Type", "i", "<"), ("BBOX Xmin", "d", "<"), ("BBOX Ymin", "d", "<"), ("BBOX Xmax", "d", "<"), ("BBOX Ymax", "d", "<"), ("NumParts", "i", "<"), ("NumPoints", "i", "<"), ) USTRUCT = [ { "fmt": "iddddii", "order": "<", "names": [ "Shape Type", "BBOX Xmin", "BBOX Ymin", "BBOX Xmax", "BBOX Ymax", "NumParts", "NumPoints", ], "size": 44, } ] @classmethod def unpack(cls, dat) -> dict: """ Parameters ---------- dat : file An open file at the correct position. """ record = _unpackDict(cls.USTRUCT, dat) contentStruct = ( ("Parts Index", ("i", record["NumParts"]), "<"), ("Vertices", ("d", 2 * record["NumPoints"]), "<"), ) _unpackDict2(record, contentStruct, dat) # record['Vertices'] = [ # (record['Vertices'][i], record['Vertices'][i+1]) # for i in range(0, record['NumPoints']*2, 2) # ] verts = record["Vertices"] # Next line is equivalent to: zip(verts[::2],verts[1::2]) record["Vertices"] = list( zip(islice(verts, 0, None, 2), islice(verts, 1, None, 2)) ) if not record["Parts Index"]: record["Parts Index"] = [0] return record # partsIndex = list(partsIndex) # partsIndex.append(None) # parts = [ # vertices[partsIndex[i]:partsIndex[i+1]] for i in range(header['NumParts']) # ] @classmethod def pack(cls, record: dict) -> str: rheader = _packDict(cls.STRUCT, record) contentStruct = ( ("Parts Index", "%di" % record["NumParts"], "<"), ("Vertices", "%dd" % (2 * record["NumPoints"]), "<"), ) content = {} content["Parts Index"] = record["Parts Index"] verts = [] [verts.extend(vert) for vert in record["Vertices"]] content["Vertices"] = verts content = _packDict(contentStruct, content) return rheader + content class PolyLineZ(object): HASZ = True HASM = True String_Type = "ARC" STRUCT = ( ("Shape Type", "i", "<"), ("BBOX Xmin", "d", "<"), ("BBOX Ymin", "d", "<"), ("BBOX Xmax", "d", "<"), ("BBOX Ymax", "d", "<"), ("NumParts", "i", "<"), ("NumPoints", "i", "<"), ) USTRUCT = [ { "fmt": "iddddii", "order": "<", "names": [ "Shape Type", "BBOX Xmin", "BBOX Ymin", "BBOX Xmax", "BBOX Ymax", "NumParts", "NumPoints", ], "size": 44, } ] @classmethod def unpack(cls, dat) -> dict: """ Parameters ---------- dat : file An open file at the correct position. """ record = _unpackDict(cls.USTRUCT, dat) contentStruct = ( ("Parts Index", ("i", record["NumParts"]), "<"), ("Vertices", ("d", 2 * record["NumPoints"]), "<"), ("Zmin", ("d", 1), "<"), ("Zmax", ("d", 1), "<"), ("Zarray", ("d", record["NumPoints"]), "<"), ("Mmin", ("d", 1), "<"), ("Mmax", ("d", 1), "<"), ("Marray", ("d", record["NumPoints"]), "<"), ) _unpackDict2(record, contentStruct, dat) verts = record["Vertices"] record["Vertices"] = list( zip(islice(verts, 0, None, 2), islice(verts, 1, None, 2)) ) if not record["Parts Index"]: record["Parts Index"] = [0] record["Zmin"] = record["Zmin"][0] record["Zmax"] = record["Zmax"][0] record["Mmin"] = record["Mmin"][0] record["Mmax"] = record["Mmax"][0] return record @classmethod def pack(cls, record: dict) -> str: rheader = _packDict(cls.STRUCT, record) contentStruct = ( ("Parts Index", "%di" % record["NumParts"], "<"), ("Vertices", "%dd" % (2 * record["NumPoints"]), "<"), ("Zmin", "d", "<"), ("Zmax", "d", "<"), ("Zarray", "%dd" % (record["NumPoints"]), "<"), ("Mmin", "d", "<"), ("Mmax", "d", "<"), ("Marray", "%dd" % (record["NumPoints"]), "<"), ) content = {} content.update(record) content["Parts Index"] = record["Parts Index"] verts = [] [verts.extend(vert) for vert in record["Vertices"]] content["Vertices"] = verts content = _packDict(contentStruct, content) return rheader + content class Polygon(PolyLine): """Packs and unpacks a shapefile Polygon type identical to PolyLine. Examples -------- >>> import libpysal >>> shp = shp_file(libpysal.examples.get_path('Polygon.shp')) >>> rec = shp.get_shape(1) >>> rec == { ... 'BBOX Ymax': -0.3126531125455273, ... 'NumPoints': 7, ... 'BBOX Ymin': -0.35957259110238166, ... 'NumParts': 1, ... 'Vertices': [ ... (0.05396439570183631, -0.3126531125455273), ... (0.051473095955454629, -0.35251390848763364), ... (0.059777428443393454, -0.34254870950210703), ... (0.063099161438568974, -0.34462479262409174), ... (0.048981796209073003, -0.35957259110238166), ... (0.046905713087088297, -0.3126531125455273), ... (0.05396439570183631, -0.3126531125455273) ... ], ... 'BBOX Xmax': 0.063099161438568974, ... 'BBOX Xmin': 0.046905713087088297, ... 'Shape Type': 5, ... 'Parts Index': [0] ... } True >>> # +8 byte record header >>> pos = shp.fileObj.seek(shp._shx.index[1][0] + 8) >>> dat = shp.fileObj.read(shp._shx.index[1][1]) >>> dat == Polygon.pack(rec) True """ String_Type = "POLYGON" class MultiPoint: String_Type = "MULTIPOINT" def __init__(self): raise NotImplementedError("No MultiPoint support at this time.") class PolygonZ(PolyLineZ): String_Type = "POLYGONZ" class MultiPointZ: String_Type = "MULTIPOINTZ" def __init__(self): raise NotImplementedError("No MultiPointZ support at this time.") class PointM: String_Type = "POINTM" def __init__(self): raise NotImplementedError("No PointM support at this time.") class PolyLineM: String_Type = "ARCM" def __init__(self): raise NotImplementedError("No PolyLineM support at this time.") class PolygonM: String_Type = "POLYGONM" def __init__(self): raise NotImplementedError("No PolygonM support at this time.") class MultiPointM: String_Type = "MULTIPOINTM" def __init__(self): raise NotImplementedError("No MultiPointM support at this time.") class MultiPatch: String_Type = "MULTIPATCH" def __init__(self): raise NotImplementedError("No MultiPatch support at this time.") TYPE_DISPATCH = { 0: NullShape, 1: Point, 3: PolyLine, 5: Polygon, 8: MultiPoint, 11: PointZ, 13: PolyLineZ, 15: PolygonZ, 18: MultiPointZ, 21: PointM, 23: PolyLineM, 25: PolygonM, 28: MultiPointM, 31: MultiPatch, "POINT": Point, "POINTZ": PointZ, "POINTM": PointM, "ARC": PolyLine, "ARCZ": PolyLineZ, "ARCM": PolyLineM, "POLYGON": Polygon, "POLYGONZ": PolygonZ, "POLYGONM": PolygonM, "MULTIPOINT": MultiPoint, "MULTIPOINTZ": MultiPointZ, "MULTIPOINTM": MultiPointM, "MULTIPATCH": MultiPatch, } libpysal-4.9.2/libpysal/io/util/tests/000077500000000000000000000000001452177046000177205ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/util/tests/__init__.py000066400000000000000000000000001452177046000220170ustar00rootroot00000000000000libpysal-4.9.2/libpysal/io/util/tests/test_shapefile.py000066400000000000000000000407711452177046000233020ustar00rootroot00000000000000import pytest import io import os # import pysal_examples from .... import examples as pysal_examples from ..shapefile import ( MultiPatch, MultiPoint, MultiPointM, MultiPointZ, NullShape, Point, PointM, PointZ, PolygonM, PolygonZ, PolyLine, PolyLineM, PolyLineZ, noneMax, noneMin, shp_file, shx_file, ) def bufferIO(buf): """Temp stringIO function to force compat.""" return io.BytesIO(buf) class TestNoneMax: def test_none_max(self): assert 5 == noneMax(5, None) assert 1 == noneMax(None, 1) assert None is noneMax(None, None) class TestNoneMin: def test_none_min(self): assert 5 == noneMin(5, None) assert 1 == noneMin(None, 1) assert None is noneMin(None, None) class test_shp_file: def test___init__(self): shp = shp_file(pysal_examples.get_path("10740.shp")) assert shp.header == { "BBOX Xmax": -105.29012, "BBOX Ymax": 36.219799000000002, "BBOX Mmax": 0.0, "BBOX Zmin": 0.0, "BBOX Mmin": 0.0, "File Code": 9994, "BBOX Ymin": 34.259672000000002, "BBOX Xmin": -107.62651, "Unused0": 0, "Unused1": 0, "Unused2": 0, "Unused3": 0, "Unused4": 0, "Version": 1000, "BBOX Zmax": 0.0, "Shape Type": 5, "File Length": 260534, } def test___iter__(self): shp = shp_file(pysal_examples.get_path("Point.shp")) points = [pt for pt in shp] expected = [ {"Y": -0.25904661905760773, "X": -0.00068176617532103578, "Shape Type": 1}, {"Y": -0.25630328607387354, "X": 0.11697145363360706, "Shape Type": 1}, {"Y": -0.33930131004366804, "X": 0.05043668122270728, "Shape Type": 1}, {"Y": -0.41266375545851519, "X": -0.041266375545851552, "Shape Type": 1}, {"Y": -0.44017467248908293, "X": -0.011462882096069604, "Shape Type": 1}, {"Y": -0.46080786026200882, "X": 0.027510917030567628, "Shape Type": 1}, {"Y": -0.45851528384279472, "X": 0.075655021834060809, "Shape Type": 1}, {"Y": -0.43558951965065495, "X": 0.11233624454148461, "Shape Type": 1}, {"Y": -0.40578602620087334, "X": 0.13984716157205224, "Shape Type": 1}, ] assert points == expected def test___len__(self): shp = shp_file(pysal_examples.get_path("10740.shp")) assert len(shp) == 195 def test_add_shape(self): shp = shp_file("test_point", "w", "POINT") points = [ {"Shape Type": 1, "X": 0, "Y": 0}, {"Shape Type": 1, "X": 1, "Y": 1}, {"Shape Type": 1, "X": 2, "Y": 2}, {"Shape Type": 1, "X": 3, "Y": 3}, {"Shape Type": 1, "X": 4, "Y": 4}, ] for pt in points: shp.add_shape(pt) shp.close() for a, b in zip(points, shp_file("test_point")): assert a == b os.remove("test_point.shp") os.remove("test_point.shx") def test_close(self): shp = shp_file(pysal_examples.get_path("10740.shp")) shp.close() assert shp.fileObj.closed == True def test_get_shape(self): shp = shp_file(pysal_examples.get_path("Line.shp")) rec = shp.get_shape(0) expected = { "BBOX Ymax": -0.25832280562918325, "NumPoints": 3, "BBOX Ymin": -0.25895877033237352, "NumParts": 1, "Vertices": [ (-0.0090539248870159517, -0.25832280562918325), (0.0074811573959305822, -0.25895877033237352), (0.0074811573959305822, -0.25895877033237352), ], "BBOX Xmax": 0.0074811573959305822, "BBOX Xmin": -0.0090539248870159517, "Shape Type": 3, "Parts Index": [0], } assert expected == shp.get_shape(0) def test_next(self): shp = shp_file(pysal_examples.get_path("Point.shp")) points = [pt for pt in shp] expected = { "Y": -0.25904661905760773, "X": -0.00068176617532103578, "Shape Type": 1, } assert expected == next(shp) expected = { "Y": -0.25630328607387354, "X": 0.11697145363360706, "Shape Type": 1, } assert expected == next(shp) def test_type(self): shp = shp_file(pysal_examples.get_path("Point.shp")) assert "POINT" == shp.type() shp = shp_file(pysal_examples.get_path("Polygon.shp")) assert "POLYGON" == shp.type() shp = shp_file(pysal_examples.get_path("Line.shp")) assert "ARC" == shp.type() class test_shx_file: def test___init__(self): shx = shx_file(pysal_examples.get_path("Point.shx")) assert isinstance(shx, shx_file) def test_add_record(self): shx = shx_file(pysal_examples.get_path("Point.shx")) expectedIndex = [ (100, 20), (128, 20), (156, 20), (184, 20), (212, 20), (240, 20), (268, 20), (296, 20), (324, 20), ] assert shx.index == expectedIndex shx2 = shx_file("test", "w") for i, rec in enumerate(shx.index): id, location = shx2.add_record(rec[1]) assert id == (i + 1) assert location == rec[0] assert shx2.index == shx.index shx2.close(shx._header) new_shx = open("test.shx", "rb").read() expected_shx = open(pysal_examples.get_path("Point.shx"), "rb").read() assert new_shx == expected_shx os.remove("test.shx") def test_close(self): shx = shx_file(pysal_examples.get_path("Point.shx")) shx.close(None) assert shx.fileObj.closed == True class TestNullShape: def test_pack(self): null_shape = NullShape() assert b"\x00" * 4 == null_shape.pack() def test_unpack(self): null_shape = NullShape() assert None is null_shape.unpack() class TestPoint: def test_pack(self): record = {"X": 5, "Y": 5, "Shape Type": 1} expected = b"\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14\x40\x00\x00\x00\x00\x00\x00\x14\x40" assert expected == Point.pack(record) def test_unpack(self): dat = bufferIO( b"\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14\x40\x00\x00\x00\x00\x00\x00\x14\x40" ) expected = {"X": 5, "Y": 5, "Shape Type": 1} assert expected == Point.unpack(dat) class TestPolyLine: def test_pack(self): record = { "BBOX Ymax": -0.25832280562918325, "NumPoints": 3, "BBOX Ymin": -0.25895877033237352, "NumParts": 1, "Vertices": [ (-0.0090539248870159517, -0.25832280562918325), (0.0074811573959305822, -0.25895877033237352), (0.0074811573959305822, -0.25895877033237352), ], "BBOX Xmax": 0.0074811573959305822, "BBOX Xmin": -0.0090539248870159517, "Shape Type": 3, "Parts Index": [0], } expected = b"""\x03\x00\x00\x00\xc0\x46\x52\x3a\xdd\x8a\x82\ \xbf\x3d\xc1\x65\xce\xc7\x92\xd0\xbf\x00\xc5\ \xa0\xe5\x8f\xa4\x7e\x3f\x6b\x40\x7f\x60\x5c\ \x88\xd0\xbf\x01\x00\x00\x00\x03\x00\x00\x00\ \x00\x00\x00\x00\xc0\x46\x52\x3a\xdd\x8a\x82\ \xbf\x6b\x40\x7f\x60\x5c\x88\xd0\xbf\x00\xc5\ \xa0\xe5\x8f\xa4\x7e\x3f\x3d\xc1\x65\xce\xc7\ \x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4\x7e\x3f\ \x3d\xc1\x65\xce\xc7\x92\xd0\xbf""" assert expected == PolyLine.pack(record) def test_unpack(self): dat = bufferIO( b"""\x03\x00\x00\x00\xc0\x46\x52\x3a\xdd\x8a\x82\ \xbf\x3d\xc1\x65\xce\xc7\x92\xd0\xbf\x00\xc5\ \xa0\xe5\x8f\xa4\x7e\x3f\x6b\x40\x7f\x60\x5c\ \x88\xd0\xbf\x01\x00\x00\x00\x03\x00\x00\x00\ \x00\x00\x00\x00\xc0\x46\x52\x3a\xdd\x8a\x82\ \xbf\x6b\x40\x7f\x60\x5c\x88\xd0\xbf\x00\xc5\ \xa0\xe5\x8f\xa4\x7e\x3f\x3d\xc1\x65\xce\xc7\ \x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4\x7e\x3f\ \x3d\xc1\x65\xce\xc7\x92\xd0\xbf""" ) expected = { "BBOX Ymax": -0.25832280562918325, "NumPoints": 3, "BBOX Ymin": -0.25895877033237352, "NumParts": 1, "Vertices": [ (-0.0090539248870159517, -0.25832280562918325), (0.0074811573959305822, -0.25895877033237352), (0.0074811573959305822, -0.25895877033237352), ], "BBOX Xmax": 0.0074811573959305822, "BBOX Xmin": -0.0090539248870159517, "Shape Type": 3, "Parts Index": [0], } assert expected == PolyLine.unpack(dat) class TestMultiPoint: def test___init__(self): pytest.raises(NotImplementedError, MultiPoint) class TestPointZ: def test_pack(self): record = {"X": 5, "Y": 5, "Z": 5, "M": 5, "Shape Type": 11} expected = b"\x0b\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@" assert expected == PointZ.pack(record) def test_unpack(self): dat = bufferIO( b"\x0b\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@" ) expected = {"X": 5, "Y": 5, "Z": 5, "M": 5, "Shape Type": 11} assert expected == PointZ.unpack(dat) class TestPolyLineZ: def test___init__(self): pytest.raises(NotImplementedError, PolyLineZ) class TestPolyLineZ: def test_pack(self): record = { "BBOX Ymax": -0.25832280562918325, "NumPoints": 3, "BBOX Ymin": -0.25895877033237352, "NumParts": 1, "Vertices": [ (-0.0090539248870159517, -0.25832280562918325), (0.0074811573959305822, -0.25895877033237352), (0.0074811573959305822, -0.25895877033237352), ], "BBOX Xmax": 0.0074811573959305822, "BBOX Xmin": -0.0090539248870159517, "Shape Type": 13, "Parts Index": [0], "Zmin": 0, "Zmax": 10, "Zarray": [0, 5, 10], "Mmin": 2, "Mmax": 4, "Marray": [2, 3, 4], } expected = b"""\r\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbf=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?k@\x7f`\\\x88\xd0\xbf\x01\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbfk@\x7f`\\\x88\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x10@\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x08@\x00\x00\x00\x00\x00\x00\x10@""" assert expected == PolyLineZ.pack(record) def test_unpack(self): dat = bufferIO( b"""\r\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbf=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?k@\x7f`\\\x88\xd0\xbf\x01\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbfk@\x7f`\\\x88\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x10@\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x08@\x00\x00\x00\x00\x00\x00\x10@""" ) expected = { "BBOX Ymax": -0.25832280562918325, "NumPoints": 3, "BBOX Ymin": -0.25895877033237352, "NumParts": 1, "Vertices": [ (-0.0090539248870159517, -0.25832280562918325), (0.0074811573959305822, -0.25895877033237352), (0.0074811573959305822, -0.25895877033237352), ], "BBOX Xmax": 0.0074811573959305822, "BBOX Xmin": -0.0090539248870159517, "Shape Type": 13, "Parts Index": [0], "Zmin": 0, "Zmax": 10, "Zarray": [0, 5, 10], "Mmin": 2, "Mmax": 4, "Marray": [2, 3, 4], } assert expected == PolyLineZ.unpack(dat) class TestPolygonZ: def test_pack(self): record = { "BBOX Xmin": 0.0, "BBOX Xmax": 10.0, "BBOX Ymin": 0.0, "BBOX Ymax": 10.0, "NumPoints": 4, "NumParts": 1, "Vertices": [(0.0, 0.0), (10.0, 10.0), (10.0, 0.0), (0.0, 0.0)], "Shape Type": 15, "Parts Index": [0], "Zmin": 0, "Zmax": 10, "Zarray": [0, 10, 0, 0], "Mmin": 2, "Mmax": 4, "Marray": [2, 4, 2, 2], } dat = bufferIO(PolygonZ.pack(record)) assert record == PolygonZ.unpack(dat) class TestMultiPointZ: def test___init__(self): pytest.raises(NotImplementedError, MultiPointZ) # multi_point_z = MultiPointZ() class TestPointM: def test___init__(self): pytest.raises(NotImplementedError, PointM) # point_m = PointM() class TestPolyLineM: def test___init__(self): pytest.raises(NotImplementedError, PolyLineM) # poly_line_m = PolyLineM() class TestPolygonM: def test___init__(self): pytest.raises(NotImplementedError, PolygonM) # polygon_m = PolygonM() class TestMultiPointM: def test___init__(self): pytest.raises(NotImplementedError, MultiPointM) # multi_point_m = MultiPointM() class TestMultiPatch: def test___init__(self): pytest.raises(NotImplementedError, MultiPatch) # multi_patch = MultiPatch() class _TestPoints: def test1(self): """Test creating and reading Point Shape Files.""" shp = shp_file("test_point", "w", "POINT") points = [ {"Shape Type": 1, "X": 0, "Y": 0}, {"Shape Type": 1, "X": 1, "Y": 1}, {"Shape Type": 1, "X": 2, "Y": 2}, {"Shape Type": 1, "X": 3, "Y": 3}, {"Shape Type": 1, "X": 4, "Y": 4}, ] for pt in points: shp.add_shape(pt) shp.close() shp = list(shp_file("test_point")) for a, b in zip(points, shp): assert a == b os.remove("test_point.shp") os.remove("test_point.shx") class _TestPolyLines: def test1(self): """Test creating and reading PolyLine Shape Files.""" lines = [[(0, 0), (4, 4)], [(1, 0), (5, 4)], [(2, 0), (6, 4)]] shapes = [] for line in lines: x = [v[0] for v in line] y = [v[1] for v in line] rec = {} rec["BBOX Xmin"] = min(x) rec["BBOX Ymin"] = min(y) rec["BBOX Xmax"] = max(x) rec["BBOX Ymax"] = max(y) rec["NumPoints"] = len(line) rec["NumParts"] = 1 rec["Vertices"] = line rec["Shape Type"] = 3 rec["Parts Index"] = [0] shapes.append(rec) shp = shp_file("test_line", "w", "ARC") for line in shapes: shp.add_shape(line) shp.close() shp = list(shp_file("test_line")) for a, b in zip(shapes, shp): assert a == b os.remove("test_line.shp") os.remove("test_line.shx") class _TestPolygons: def test1(self): """Test creating and reading PolyLine Shape Files.""" lines = [ [(0, 0), (4, 4), (5, 4), (1, 0), (0, 0)], [(1, 0), (5, 4), (6, 4), (2, 0), (1, 0)], ] shapes = [] for line in lines: x = [v[0] for v in line] y = [v[1] for v in line] rec = {} rec["BBOX Xmin"] = min(x) rec["BBOX Ymin"] = min(y) rec["BBOX Xmax"] = max(x) rec["BBOX Ymax"] = max(y) rec["NumPoints"] = len(line) rec["NumParts"] = 1 rec["Vertices"] = line rec["Shape Type"] = 5 rec["Parts Index"] = [0] shapes.append(rec) shp = shp_file("test_poly", "w", "POLYGON") for line in shapes: shp.add_shape(line) shp.close() shp = list(shp_file("test_poly")) for a, b in zip(shapes, shp): assert a == b os.remove("test_poly.shp") os.remove("test_poly.shx") libpysal-4.9.2/libpysal/io/util/tests/test_weight_converter.py000066400000000000000000000155361452177046000247210ustar00rootroot00000000000000import pytest from ..weight_converter import WeightConverter from ..weight_converter import weight_convert from ...fileio import FileIO as psopen import tempfile import os import warnings # import pysal_examples from .... import examples as pysal_examples @pytest.mark.skip("This function is deprecated.") class Testtest_WeightConverter: def setup_method(self): test_files = [ "arcgis_ohio.dbf", "arcgis_txt.txt", "ohio.swm", "wmat.dat", "wmat.mtx", "sids2.gal", "juvenile.gwt", "geobugs_scot", "stata_full.txt", "stata_sparse.txt", "spat-sym-us.mat", "spat-sym-us.wk1", ] self.test_files = [pysal_examples.get_path(f) for f in test_files] dataformats = [ "arcgis_dbf", "arcgis_text", None, None, None, None, None, "geobugs_text", "stata_text", "stata_text", None, None, ] ns = [88, 3, 88, 49, 49, 100, 168, 56, 56, 56, 46, 46] self.dataformats = dict(list(zip(self.test_files, dataformats))) self.ns = dict(list(zip(self.test_files, ns))) self.fileformats = [ ("dbf", "arcgis_dbf"), ("txt", "arcgis_text"), ("swm", None), ("dat", None), ("mtx", None), ("gal", None), ("", "geobugs_text"), ("gwt", None), ("txt", "stata_text"), ("mat", None), ("wk1", None), ] def test__set_w(self): for f in self.test_files: with warnings.catch_warnings(record=True) as warn: # note: we are just suppressing the warnings here; individual warnings # are tested in their specific readers warnings.simplefilter("always") wc = WeightConverter(f, dataFormat=self.dataformats[f]) assert wc.w_set() == True assert wc.w.n == self.ns[f] def test_write(self): for f in self.test_files: with warnings.catch_warnings(record=True) as warn: # note: we are just suppressing the warnings here; individual warnings # are tested in their specific readers warnings.simplefilter("always") wc = WeightConverter(f, dataFormat=self.dataformats[f]) for ext, dataformat in self.fileformats: if f.lower().endswith(ext): continue temp_f = tempfile.NamedTemporaryFile(suffix=".%s" % ext) temp_fname = temp_f.name temp_f.close() with warnings.catch_warnings(record=True) as warn: # note: we are just suppressing the warnings here; individual warnings # are tested in their specific readers warnings.simplefilter("always") if ext == "swm": wc.write(temp_fname, useIdIndex=True) elif dataformat is None: wc.write(temp_fname) elif dataformat in ["arcgis_dbf", "arcgis_text"]: wc.write(temp_fname, dataFormat=dataformat, useIdIndex=True) elif dataformat == "stata_text": wc.write(temp_fname, dataFormat=dataformat, matrix_form=True) else: wc.write(temp_fname, dataFormat=dataformat) with warnings.catch_warnings(record=True) as warn: # note: we are just suppressing the warnings here; individual warnings # are tested in their specific readers warnings.simplefilter("always") if dataformat is None: wnew = psopen(temp_fname, "r").read() else: wnew = psopen(temp_fname, "r", dataformat).read() if ( ext in ["dbf", "swm", "dat", "wk1", "gwt"] or dataformat == "arcgis_text" ): assert wnew.n == wc.w.n - len(wc.w.islands) else: assert wnew.n == wc.w.n os.remove(temp_fname) def test_weight_convert(self): for f in self.test_files: inFile = f inDataFormat = self.dataformats[f] with warnings.catch_warnings(record=True) as warn: # note: we are just suppressing the warnings here; individual warnings # are tested in their specific readers warnings.simplefilter("always") if inDataFormat is None: in_file = psopen(inFile, "r") else: in_file = psopen(inFile, "r", inDataFormat) wold = in_file.read() in_file.close() for ext, dataformat in self.fileformats: if f.lower().endswith(ext): continue temp_f = tempfile.NamedTemporaryFile(suffix=".%s" % ext) outFile = temp_f.name temp_f.close() outDataFormat, useIdIndex, matrix_form = dataformat, False, False if ext == "swm" or dataformat in ["arcgis_dbf", "arcgis_text"]: useIdIndex = True elif dataformat == "stata_text": matrix_form = True with warnings.catch_warnings(record=True) as warn: # note: we are just suppressing the warnings here; individual warnings # are tested in their specific readers warnings.simplefilter("always") weight_convert( inFile, outFile, inDataFormat, outDataFormat, useIdIndex, matrix_form, ) with warnings.catch_warnings(record=True) as warn: # note: we are just suppressing the warnings here; individual warnings # are tested in their specific readers warnings.simplefilter("always") if dataformat is None: wnew = psopen(outFile, "r").read() else: wnew = psopen(outFile, "r", dataformat).read() if ( ext in ["dbf", "swm", "dat", "wk1", "gwt"] or dataformat == "arcgis_text" ): assert wnew.n == wold.n - len(wold.islands) else: assert wnew.n == wold.n os.remove(outFile) libpysal-4.9.2/libpysal/io/util/tests/test_wkt.py000066400000000000000000000040111452177046000221320ustar00rootroot00000000000000import pytest from ..wkt import WKTParser from ....cg.shapes import Point, Chain, Polygon class Testtest_WKTParser: def setup_method(self): # Create some Well-Known Text objects self.wktPOINT = "POINT(6 10)" self.wktLINESTRING = "LINESTRING(3 4,10 50,20 25)" self.wktPOLYGON = "POLYGON((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2))" self.unsupported = [ "MULTIPOINT(3.5 5.6,4.8 10.5)", "MULTILINESTRING((3 4,10 50,20 25),(-5 -8,-10 -8,-15 -4))", "MULTIPOLYGON(((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2)),((3 3,6 2,6 4,3 3)))", "GEOMETRYCOLLECTION(POINT(4 6),LINESTRING(4 6,7 10))", "POINT ZM (1 1 5 60)", "POINT M (1 1 80)", ] self.empty = ["POINT EMPTY", "MULTIPOLYGON EMPTY"] self.parser = WKTParser() def test_point(self): pt = self.parser(self.wktPOINT) assert issubclass(type(pt), Point) assert pt[:] == (6.0, 10.0) def test_line_string(self): line = self.parser(self.wktLINESTRING) assert issubclass(type(line), Chain) parts = [[pt[:] for pt in part] for part in line.parts] assert parts == [[(3.0, 4.0), (10.0, 50.0), (20.0, 25.0)]] assert line.len == 73.455384532199886 def test_polygon(self): poly = self.parser(self.wktPOLYGON) assert issubclass(type(poly), Polygon) parts = [[pt[:] for pt in part] for part in poly.parts] assert parts == [ [(1.0, 1.0), (1.0, 5.0), (5.0, 5.0), (5.0, 1.0), (1.0, 1.0)], [(2.0, 2.0), (2.0, 3.0), (3.0, 3.0), (3.0, 2.0), (2.0, 2.0)], ] assert poly.centroid == (2.9705882352941178, 2.9705882352941178) assert poly.area == 17.0 def test_from_wkt(self): for wkt in self.unsupported: pytest.raises(NotImplementedError, self.parser.fromWKT, wkt) for wkt in self.empty: assert self.parser.fromWKT(wkt) == None assert self.parser.__call__ == self.parser.fromWKT libpysal-4.9.2/libpysal/io/util/weight_converter.py000066400000000000000000000201641452177046000225110ustar00rootroot00000000000000import os from ..fileio import FileIO as psopen from warnings import warn __author__ = "Myunghwa Hwang " __all__ = ["weight_convert"] class WeightConverter(object): """Opens and reads a weights file in a format, then writes the file in other formats. `WeightConverter` can read a weights file in the following formats: `GAL`, `GWT`, `ArcGIS DBF/SWM/Text`, `DAT`, `MAT`, `MTX`, `WK1`, `GeoBUGS Text`, and `STATA Text`. It can convert the input file into all of the formats listed above, except `GWT`. Currently, PySAL does not support writing a weights object in the `GWT` format. When an input weight file includes multiple islands and the format of an output weight file is `ArcGIS DBF/SWM/TEXT`, `DAT`, or `WK1`, the number of observations in the new weights file will be the original number of observations substracted by the number of islands. This is because `ArcGIS DBF/SWM/TEXT`, `DAT`, `WK1` formats ignore islands. """ def __init__(self, inputPath, dataFormat=None): warn("WeightConverter will be deprecated in PySAL 3.1.", DeprecationWarning) self.inputPath = inputPath self.inputDataFormat = dataFormat self._setW() def _setW(self): """Reads a weights file and sets a ``pysal.weights.W`` object as an attribute. Raises ------ IOError Raised when there is a problem reading in the file. RuntimeError Raised when there is a problem creating the weights object. Examples -------- Create a WeightConvert object. >>> import libpysal >>> wc = WeightConverter( ... libpysal.examples.get_path('arcgis_ohio.dbf'), dataFormat='arcgis_dbf' ... ) Check whether or not the `W` object is set as an attribute. >>> wc.w_set() True Get the number of observations included in the `W` object. >>> wc.w.n 88 """ try: if self.inputDataFormat: f = psopen(self.inputPath, "r", self.inputDataFormat) else: f = psopen(self.inputPath, "r") except: raise IOError("A problem occurred while reading the input file.") else: try: self.w = f.read() except: raise RuntimeError( "A problem occurred while creating a weights object." ) finally: f.close() def w_set(self) -> bool: """Checks if a source `W` object is set.""" return hasattr(self, "w") def write(self, outputPath, dataFormat=None, useIdIndex=True, matrix_form=True): """ Parameters ---------- outputPath : str The path to the output weights file. dataFormat : str The type of data format. Options include: ``'arcgis_dbf'`` for the `ArcGIS DBF` format, ``'arcgis_text'`` for the `ArcGIS Text` format, ``'geobugs_text'`` for the `GeoBUGS Text` format, and ``'stata_text'`` for the `STATA Text` format. Default is ``None``. useIdIndex : bool Applies only to `ArcGIS DBF/SWM/Text` formats. Default is ``True``. matrix_form : bool Applies only to the `STATA Text` format. Default is ``True``. Raises ------ RuntimeError Raised when there is no weights file passed in. IOError Raised when there is a problem creating the file. RuntimeError Raised when there is a problem writing the weights object. Examples -------- >>> import tempfile, os, libpysal Create a `WeightConverter` object. >>> wc = WeightConverter(libpysal.examples.get_path('sids2.gal')) Check whether or not the `W` object is set as an attribute. >>> wc.w_set() True Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.dbf') Reassign to the new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Write the input ``.gal`` file in the `ArcGIS` ``.dbf`` format. >>> wc.write(fname, dataFormat='arcgis_dbf', useIdIndex=True) Create a new weights object from the converted ``.dbf`` file. >>> wnew = psopen(fname, 'r', 'arcgis_dbf').read() Compare the number of observations in the two `W` objects. >>> wc.w.n == wnew.n True Clean up the temporary file. >>> os.remove(fname) """ ext = os.path.splitext(outputPath)[1] ext = ext.replace(".", "") # if ext.lower() == "gwt": # msg = "Currently, PySAL does not support writing " # msg += "a weights object into a gwt file." # raise TypeError(msg) if not self.w_set(): raise RuntimeError("There is no weights object to write out.") try: if dataFormat: o = psopen(outputPath, "w", dataFormat) else: o = psopen(outputPath, "w") except: raise IOError("A problem occurred while creating the output file.") else: try: if dataFormat in ["arcgis_text", "arcgis_dbf"] or ext == "swm": o.write(self.w, useIdIndex=useIdIndex) elif dataFormat == "stata_text": o.write(self.w, matrix_form=matrix_form) else: o.write(self.w) except: raise RuntimeError( "A problem occurred while writing out the weights object." ) finally: o.close() def weight_convert( inPath, outPath, inDataFormat=None, outDataFormat=None, useIdIndex=True, matrix_form=True, ): """ A utility function for directly converting a given weight file into the format specified in ``outPath``. Parameters ---------- inPath : str The path to the input weights file. outPath : str The path to the output weights file. indataFormat : str The type of data format. Options include: ``'arcgis_dbf'`` for the `ArcGIS DBF` format, ``'arcgis_text'`` for the `ArcGIS Text` format, ``'geobugs_text'`` for the `GeoBUGS Text` format, and ``'stata_text'`` for the `STATA Text` format. Default is ``None``. outdataFormat : str The type of data format. Options include: ``'arcgis_dbf'`` for the `ArcGIS DBF` format, ``'arcgis_text'`` for the `ArcGIS Text` format, ``'geobugs_text'`` for the `GeoBUGS Text` format, and ``'stata_text'`` for the `STATA Text` format. Default is ``None``. useIdIndex : bool Applies only to `ArcGIS DBF/SWM/Text` formats. Default is ``True``. matrix_form : bool Applies only to the `STATA Text` format. Default is ``True``. Examples -------- >>> import tempfile, os, libpysal Create a temporary file for this example. >>> f = tempfile.NamedTemporaryFile(suffix='.dbf') Reassign to the new variable. >>> fname = f.name Close the temporary named file. >>> f.close() Create a `WeightConverter` object. >>> weight_convert( ... libpysal.examples.get_path('sids2.gal'), ... fname, ... outDataFormat='arcgis_dbf', ... useIdIndex=True ... ) Create a new weights object from the ``.gal`` file. >>> wold = libpysal.io.open(libpysal.examples.get_path('sids2.gal'), 'r').read() Create a new weights object from the converted ``.dbf`` file. >>> wnew = libpysal.io.open(fname, 'r', 'arcgis_dbf').read() Compare the number of observations in the two `W` objects. >>> wold.n == wnew.n True Clean up the temporary file. >>> os.remove(fname) """ converter = WeightConverter(inPath, dataFormat=inDataFormat) converter.write( outPath, dataFormat=outDataFormat, useIdIndex=useIdIndex, matrix_form=matrix_form, ) libpysal-4.9.2/libpysal/io/util/wkb.py000066400000000000000000000201231452177046000177110ustar00rootroot00000000000000""" Load WKB into PySAL shapes. Where PySAL shapes support multiple parts, "MULTI"type shapes will be converted to a single multi-part shape: MULTIPOLYGON -> Polygon MULTILINESTRING -> Chain Otherwise a list of shapes will be returned: MULTIPOINT -> [pt0, ..., ptN] Some concepts aren't well supported by PySAL shapes. For example: wkt = 'MULTIPOLYGON EMPTY' -> '\x01 \x06\x00\x00\x00 \x00\x00\x00\x00' | < | WKBMultiPolygon | 0 parts | ``pysal.cg.Polygon`` does not support 0 part polygons. ``None`` is returned in this case. REFERENCE MATERIAL: SOURCE: http://webhelp.esri.com/arcgisserver/9.3/dotNet/index.htm#geodatabases/the_ogc_103951442.htm Basic Type definitions byte : 1 byte uint32 : 32 bit unsigned integer (4 bytes) double : double precision number (8 bytes) Building Blocks : Point, LinearRing """ from io import StringIO from ... import cg import sys import struct __author__ = "Charles R Schmidt " __all__ = ["loads"] """ enum wkbByteOrder { wkbXDR = 0, Big Endian wkbNDR = 1 Little Endian }; """ DEFAULT_ENDIAN = "<" if sys.byteorder == "little" else ">" ENDIAN = {"\x00": ">", "\x01": "<"} def load_ring_little(dat) -> list: """ LinearRing { uint32 numPoints; Point points[numPoints]; } """ npts = struct.unpack(" list: npts = struct.unpack(">I", dat.read(4))[0] xy = struct.unpack(">%dd" % (npts * 2), dat.read(npts * 2 * 8)) return [cg.Point(xy[i : i + 2]) for i in range(0, npts * 2, 2)] def loads(s: str): """ WKBGeometry { union { WKBPoint point; WKBLineString linestring; WKBPolygon polygon; WKBGeometryCollection collection; WKBMultiPoint mpoint; WKBMultiLineString mlinestring; WKBMultiPolygon mpolygon; } }; Returns ------- geom : {None, libpysal.cg.{Point, Chain, Polygon}} The geometric object or ``None``. Raises ------ TypeError Raised when an unsupported shape type is passed in. """ # To allow recursive calls, read only the bytes we need. if hasattr(s, "read"): dat = s else: dat = StringIO(s) endian = ENDIAN[dat.read(1)] typ = struct.unpack("I", dat.read(4))[0] if typ == 1: """ WKBPoint { byte byteOrder; uint32 wkbType; 1 Point point; } Point { double x; double y; }; """ x, y = struct.unpack(endian + "dd", dat.read(16)) geom = cg.Point((x, y)) elif typ == 2: """ WKBLineString { byte byteOrder; uint32 wkbType; 2 uint32 numPoints; Point points[numPoints]; } """ n = struct.unpack(endian + "I", dat.read(4))[0] xy = struct.unpack(endian + "%dd" % (n * 2), dat.read(n * 2 * 8)) geom = cg.Chain([cg.Point(xy[i : i + 2]) for i in range(0, n * 2, 2)]) elif typ == 3: """ WKBPolygon { byte byteOrder; uint32 wkbType; 3 uint32 numRings; LinearRing rings[numRings]; } WKBPolygon has exactly 1 outer ring and `n` holes. Multipart Polygons are NOT support by WKBPolygon. """ nrings = struct.unpack(endian + "I", dat.read(4))[0] load_ring = load_ring_little if endian == "<" else load_ring_big rings = [load_ring(dat) for _ in range(nrings)] geom = cg.Polygon(rings[0], rings[1:]) elif typ == 4: """ WKBMultiPoint { byte byteOrder; uint32 wkbType; 4 uint32 num_wkbPoints; WKBPoint WKBPoints[num_wkbPoints]; } """ npts = struct.unpack(endian + "I", dat.read(4))[0] geom = [loads(dat) for _ in range(npts)] elif typ == 5: """ WKBMultiLineString { byte byteOrder; uint32 wkbType; 5 uint32 num_wkbLineStrings; WKBLineString WKBLineStrings[num_wkbLineStrings]; } """ nparts = struct.unpack(endian + "I", dat.read(4))[0] chains = [loads(dat) for _ in range(nparts)] geom = cg.Chain(sum([c.parts for c in chains], [])) elif typ == 6: """ wkbMultiPolygon { byte byteOrder; uint32 wkbType; 6 uint32 num_wkbPolygons; WKBPolygon wkbPolygons[num_wkbPolygons]; } """ npolys = struct.unpack(endian + "I", dat.read(4))[0] polys = [loads(dat) for _ in range(npolys)] parts = sum([p.parts for p in polys], []) holes = sum([p.holes for p in polys if p.holes[0]], []) # MULTIPOLYGON EMPTY, isn't well supported by PySAL shape types. if not parts: geom = None else: geom = cg.Polygon(parts, holes) elif typ == 7: """ WKBGeometryCollection { byte byte_order; uint32 wkbType; 7 uint32 num_wkbGeometries; WKBGeometry wkbGeometries[num_wkbGeometries] } """ ngeoms = struct.unpack(endian + "I", dat.read(4))[0] geom = [loads(dat) for _ in range(ngeoms)] try: return geom except NameError: raise TypeError("Type (%d) is unknown or unsupported." % typ) if __name__ == "__main__": # TODO: Refactor below into Unit Tests wktExamples = [ "POINT(6 10)", "LINESTRING(3 4,10 50,20 25)", "POLYGON((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2))", "MULTIPOINT(3.5 5.6,4.8 10.5)", "MULTILINESTRING((3 4,10 50,20 25),(-5 -8,-10 -8,-15 -4))", # This MULTIPOLYGON is not valid, the 2nd shell instects the 1st. #'MULTIPOLYGON(((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2)),((3 3,6 2,6 4,3 3)))', "MULTIPOLYGON(((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2)),((5 3,6 2,6 4,5 3)))", "GEOMETRYCOLLECTION(POINT(4 6),LINESTRING(4 6,7 10))", #'POINT ZM (1 1 5 60)', <-- ZM is not supported by WKB ? #'POINT M (1 1 80)', <-- M is not supported by WKB ? #'POINT EMPTY', <-- NOT SUPPORT "MULTIPOLYGON EMPTY", ] # shapely only used for testing. try: import shapely.wkt, shapely.geometry from pysal.contrib.shapely_ext import to_wkb except ImportError: print("shapely is used to test this module.") raise for example in wktExamples: print(example) shape0 = shapely.wkt.loads(example) shape1 = loads(shape0.to_wkb()) if example.startswith("MULTIPOINT"): shape2 = shapely.geometry.asMultiPoint(shape1) elif example.startswith("GEOMETRYCOLLECTION"): shape2 = shapely.geometry.collection.GeometryCollection( list(map(shapely.geometry.asShape, shape1)) ) elif example == "MULTIPOLYGON EMPTY": # Skip Test shape2 = None else: shape2 = shapely.geometry.asShape(shape1) print(shape1) if shape2: assert shape0.equals(shape2) print(shape0.equals(shape2)) else: print("Skip") print("") libpysal-4.9.2/libpysal/io/util/wkt.py000066400000000000000000000077571452177046000177550ustar00rootroot00000000000000from ... import cg import re __author__ = "Charles R Schmidt " __all__ = ["WKTParser"] class WKTParser: """Class to represent `OGC WKT`, supports reading and writing. Modified from... # URL: http://dev.openlayers.org/releases/OpenLayers-2.7/lib/OpenLayers/Format/WKT.js # Reg Ex Strings copied from OpenLayers.Format.WKT Examples -------- >>> import libpysal Create some Well-Known Text objects. >>> p = 'POLYGON((1 1, 5 1, 5 5, 1 5, 1 1), (2 2, 3 2, 3 3, 2 3, 2 2))' >>> pt = 'POINT(6 10)' >>> l = 'LINESTRING(3 4, 10 50, 20 25)' Instantiate the parser. >>> parser = libpysal.io.wkt.WKTParser() Inspect our WKT polygon. >>> parser(p).parts [[(1.0, 1.0), (1.0, 5.0), (5.0, 5.0), (5.0, 1.0), (1.0, 1.0)], [(2.0, 2.0), (2.0, 3.0), (3.0, 3.0), (3.0, 2.0), (2.0, 2.0)]] >>> parser(p).centroid (2.9705882352941178, 2.9705882352941178) >>> parser(p).area 17.0 Inspect ``pt``, our WKT point object. >>> parser(pt) (6.0, 10.0) Inspect our WKT linestring. >>> parser(l).len 73.45538453219989 >>> parser(l).parts [[(3.0, 4.0), (10.0, 50.0), (20.0, 25.0)]] Read in WKT from a file. >>> f = libpysal.io.open(libpysal.examples.get_path('stl_hom.wkt')) >>> f.mode 'r' >>> f.header [] See local doctest output for the items not tested. """ regExes = { "typeStr": re.compile(r"^\s*([\w\s]+)\s*\(\s*(.*)\s*\)\s*$"), "spaces": re.compile(r"\s+"), "parenComma": re.compile(r"\)\s*,\s*\("), "doubleParenComma": re.compile(r"\)\s*\)\s*,\s*\(\s*\("), # can't use {2} here "trimParens": re.compile(r"^\s*\(?(.*?)\)?\s*$"), } def __init__(self): self.parsers = p = {} p["point"] = self.Point p["linestring"] = self.LineString p["polygon"] = self.Polygon def Point(self, geoStr): """Returns a ``libpysal.cg.Point`` object.""" coords = self.regExes["spaces"].split(geoStr.strip()) return cg.Point((coords[0], coords[1])) def LineString(self, geoStr): """Returns a ``libpysal.cg.Chain`` object.""" points = geoStr.strip().split(",") points = list(map(self.Point, points)) return cg.Chain(points) def Polygon(self, geoStr): """Returns a ``libpysal.cg.Polygon`` object.""" rings = self.regExes["parenComma"].split(geoStr.strip()) for i, ring in enumerate(rings): ring = self.regExes["trimParens"].match(ring).groups()[0] ring = self.LineString(ring).vertices rings[i] = ring return cg.Polygon(rings) def fromWKT(self, wkt): """Returns geometric representation from WKT or ``None``. Raises ------ NotImplementedError Raised when a unknown/unsupported format is passed in. """ matches = self.regExes["typeStr"].match(wkt) if matches: geoType, geoStr = matches.groups() geoType = geoType.lower().strip() try: return self.parsers[geoType](geoStr) except KeyError: raise NotImplementedError("Unsupported WKT Type: %s." % geoType) else: return None __call__ = fromWKT if __name__ == "__main__": p = "POLYGON((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2))" pt = "POINT(6 10)" l = "LINESTRING(3 4,10 50,20 25)" wktExamples = [ "POINT(6 10)", "LINESTRING(3 4,10 50,20 25)", "POLYGON((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2))", "MULTIPOINT(3.5 5.6,4.8 10.5)", "MULTILINESTRING((3 4,10 50,20 25),(-5 -8,-10 -8,-15 -4))", "MULTIPOLYGON(((1 1,5 1,5 5,1 5,1 1),(2 2, 3 2, 3 3, 2 3,2 2)),((3 3,6 2,6 4,3 3)))", "GEOMETRYCOLLECTION(POINT(4 6),LINESTRING(4 6,7 10))", "POINT ZM (1 1 5 60)", "POINT M (1 1 80)", "POINT EMPTY", "MULTIPOLYGON EMPTY", ] wkt = WKTParser() libpysal-4.9.2/libpysal/weights/000077500000000000000000000000001452177046000166445ustar00rootroot00000000000000libpysal-4.9.2/libpysal/weights/__init__.py000066400000000000000000000004411452177046000207540ustar00rootroot00000000000000from .weights import * # noqa I001 from .distance import * from .contiguity import * from .spintW import * from .util import * from .user import * from .set_operations import * from .spatial_lag import * from .raster import * from .gabriel import Gabriel, Delaunay, Relative_Neighborhood libpysal-4.9.2/libpysal/weights/_contW_lists.py000066400000000000000000000070661452177046000216760ustar00rootroot00000000000000from ..cg.shapes import Polygon, Chain import itertools as it import collections QUEEN = 1 ROOK = 2 __author__ = "Jay Laura jlaura@asu.edu" def _get_verts(shape): if isinstance(shape, (Polygon, Chain)): return shape.vertices else: return _get_boundary_points(shape) def _get_boundary_points(shape): """ Recursively handle polygons vs. multipolygons to extract the boundary point set from each. """ if shape.geom_type.lower() == "polygon": shape = shape.boundary return _get_boundary_points(shape) elif shape.geom_type.lower() == "linestring": return list(map(tuple, list(zip(*shape.coords.xy)))) elif shape.geom_type.lower() == "multilinestring": return list(it.chain(*(list(zip(*shape.coords.xy)) for shape in shape.geoms))) elif shape.geom_type.lower() == "multipolygon": return list( it.chain(*(_get_boundary_points(part.boundary) for part in shape.geoms)) ) else: raise TypeError( "Input shape must be a Polygon, Multipolygon, LineString, " f" or MultiLinestring and was instead: {shape.type}" ) class ContiguityWeightsLists: """ Contiguity for a collection of polygons using high performance list, set, and dict containers """ def __init__(self, collection, wttype=1): """ Parameters ---------- collection: PySAL PolygonCollection wttype: int 1: Queen 2: Rook """ self.collection = list(collection) self.wttype = wttype self.jcontiguity() def jcontiguity(self): numPoly = len(self.collection) w = {} for i in range(numPoly): w[i] = set() geoms = [] offsets = [] c = 0 # PolyID Counter if self.wttype == QUEEN: for n in range(numPoly): verts = _get_verts(self.collection[n]) offsets += [c] * len(verts) geoms += verts c += 1 items = collections.defaultdict(set) for i, vertex in enumerate(geoms): items[vertex].add(offsets[i]) shared_vertices = [] for item, location in list(items.items()): if len(location) > 1: shared_vertices.append(location) for vert_set in shared_vertices: for v in vert_set: w[v] = w[v] | vert_set try: w[v].remove(v) except: pass elif self.wttype == ROOK: for n in range(numPoly): verts = _get_verts(self.collection[n]) for v in range(len(verts) - 1): geoms.append(tuple(sorted([verts[v], verts[v + 1]]))) offsets += [c] * (len(verts) - 1) c += 1 items = collections.defaultdict(set) for i, item in enumerate(geoms): items[item].add(offsets[i]) shared_vertices = [] for item, location in list(items.items()): if len(location) > 1: shared_vertices.append(location) for vert_set in shared_vertices: for v in vert_set: w[v] = w[v] | vert_set try: w[v].remove(v) except: pass else: raise Exception(f"Weight type {self.wttype} Not Understood!") self.w = w libpysal-4.9.2/libpysal/weights/adjtools.py000066400000000000000000000227411452177046000210430ustar00rootroot00000000000000import numpy as np def adjlist_apply( X, W=None, alist=None, func=np.subtract, skip_verify=False, to_adjlist_kws=dict(drop_islands=None), ): """ apply a function to an adajcency list, getting an adjacency list and result. Parameters ---------- X : iterable an (N,P)-length iterable to apply ``func'' to. If (N,1), then `func` must take 2 arguments and return a single reduction. If P>1, then func must take two P-length arrays and return a single reduction of them. W : pysal.weights.W object a weights object that provides adjacency information alist : pandas DataFrame a table containing an adajacency list representation of a W matrix func : callable a function taking two arguments and returning a single argument. This will be evaluated for every (focal, neighbor) pair, or each row of the adjacency list. If `X` has more than one column, this function should take two arrays and provide a single scalar in return. Example scalars include: lambda x,y: x < y, np.subtract Example multivariates: lambda (x,y): np.all(x < y)'' lambda (x,y): np.sum((x-y)**2) sklearn.metrics.euclidean_distance skip_verify: bool Whether or not to skip verifying that the W is the same as an adjacency list. Do this if you are certain the adjacency list and W agree and would like to avoid re-instantiating a W from the adjacency list. to_adjlist_kws : dict Keyword arguments for ``W.to_adjlist()``. Default is ``dict(drop_islands=None)``. Returns ------- an adjacency list (or modifies alist inplace) with the function applied to each row. """ try: import pandas as pd except ImportError: raise ImportError("pandas must be installed to use this function") W, alist = _get_W_and_alist(W, alist, to_adjlist_kws, skip_verify=skip_verify) if len(X.shape) > 1: if X.shape[-1] > 1: return _adjlist_mvapply( X, W=W, alist=alist, func=func, skip_verify=skip_verify, to_adjlist_kws=to_adjlist_kws, ) else: vec = np.asarray(X).flatten() ids = np.asarray(W.id_order)[:, None] table = pd.DataFrame(ids, columns=["id"]) table = pd.concat((table, pd.DataFrame(vec[:, None], columns=("att",))), axis=1) alist_atts = pd.merge(alist, table, how="left", left_on="focal", right_on="id") alist_atts = pd.merge( alist_atts, table, how="left", left_on="neighbor", right_on="id", suffixes=("_focal", "_neighbor"), ) alist_atts.drop(["id_focal", "id_neighbor"], axis=1, inplace=True) alist_atts[func.__name__] = alist_atts[["att_focal", "att_neighbor"]].apply( lambda x: func(x.att_focal, x.att_neighbor), axis=1 ) return alist_atts def _adjlist_mvapply( X, W=None, alist=None, func=None, skip_verify=False, to_adjlist_kws=dict() ): try: import pandas as pd except ImportError: raise ImportError("pandas must be installed to use this function") assert len(X.shape) == 2, "data is not two-dimensional" W, alist = _get_W_and_alist(W, alist, to_adjlist_kws, skip_verify=skip_verify) assert X.shape[0] == W.n, "number of samples in X does not match W" try: names = X.columns.tolist() except AttributeError: names = list(map(str, list(range(X.shape[1])))) ids = np.asarray(W.id_order)[:, None] table = pd.DataFrame(ids, columns=["id"]) table = pd.concat((table, pd.DataFrame(X, columns=names)), axis=1) alist_atts = pd.merge(alist, table, how="left", left_on="focal", right_on="id") alist_atts = pd.merge( alist_atts, table, how="left", left_on="neighbor", right_on="id", suffixes=("_focal", "_neighbor"), ) alist_atts.drop(["id_focal", "id_neighbor"], axis=1, inplace=True) alist_atts[func.__name__] = list( map( func, list( zip( alist_atts.filter(like="_focal").values, alist_atts.filter(like="_neighbor").values, ) ), ) ) return alist_atts def _get_W_and_alist(W, alist, to_adjlist_kws, skip_verify=False): """ Either: 1. compute a W from an alist 2. adjacencylist from a W 3. raise ValueError if neither are provided, 4. raise AssertionError if both W and adjlist are provided and don't match. If this completes successfully, the W/adjlist will both be returned and are checked for equality. """ if (alist is None) and (W is not None): alist = W.to_adjlist(**to_adjlist_kws) elif (W is None) and (alist is not None): from .weights import W W = W.from_adjlist(alist, **to_adjlist_kws) elif (W is None) and (alist is None): raise ValueError("Either W or Adjacency List must be provided") elif (W is not None) and (alist is not None) and (not skip_verify): from .weights import W as W_ np.testing.assert_allclose( W.sparse.toarray(), W_.from_adjlist(alist).sparse.toarray() ) return W, alist def adjlist_map( data, funcs=(np.subtract,), W=None, alist=None, focal_col="focal", neighbor_col="neighbor", to_adjlist_kws=dict(drop_islands=None), ): """ Map a set of functions over a W or adjacency list Parameters ---------- data : np.ndarray or pandas dataframe N x P array of N observations and P covariates. funcs : iterable or callable a function to apply to each of the P columns in ``data'', or a list of functions to apply to each column of P. This function must take two arguments, compare them, and return a value. Examples may be ``lambda x,y: x < y'' or ``np.subtract''. W : pysal.weights.W object a pysal weights object. If not provided, one is constructed from the given adjacency list. alist : pandas dataframe an adjacency list representation of a weights matrix. If not provided, one is constructed from the weights object. If both are provided, they are validated against one another to ensure they provide identical weights matrices. focal_col : string name of column in alist containing the focal observation ids neighbor_col: string name of column in alist containing the neighboring observation ids to_adjlist_kws : dict Keyword arguments for ``W.to_adjlist()``. Default is ``dict(drop_islands=None)``. Returns ------- returns an adjacency list (or modifies one if provided) with each function applied to the column of the data. """ try: import pandas as pd except ImportError: raise ImportError("pandas must be installed to use this function") if isinstance(data, pd.DataFrame): names = data.columns data = data.values else: names = [str(i) for i in range(data.shape[1])] assert data.shape[0] == W.n, "shape of data does not match shape of adjacency" if callable(funcs): funcs = (funcs,) if len(funcs) == 1: funcs = [funcs[0] for _ in range(data.shape[1])] assert data.shape[1] == len( funcs ), "shape of data does not match the number of functions provided" W, alist = _get_W_and_alist(W, alist, to_adjlist_kws) fnames = set([f.__name__ for f in funcs]) for i, (column, function) in enumerate(zip(data.T, funcs)): alist = adjlist_apply( column, W=W, alist=alist, skip_verify=True, to_adjlist_kws=to_adjlist_kws ) alist.drop(["att_focal", "att_neighbor"], axis=1, inplace=True) alist = alist.rename( columns={function.__name__: "_".join((function.__name__, names[i]))} ) fnames.update((function.__name__,)) return alist def filter_adjlist(adjlist, focal_col="focal", neighbor_col="neighbor"): """ This dedupes an adjacency list by examining both (a,b) and (b,a) when (a,b) is enountered. The removal is done in order of the iteration order of the input adjacency list. So, if a special order of removal is desired, you need to sort the list before this function. Parameters ---------- adjlist : pandas DataFrame a dataframe that contains focal and neighbor columns focal_col : string the name of the column with the focal observation id neighbor_col: string the name of the column with the neighbor observation id Returns ------- an adjacency table with reversible entries removed. """ edges = adjlist.loc[:, [focal_col, neighbor_col]] undirected = set() to_remove = [] for index, *edge in edges.itertuples(name=None): edge = tuple(edge) if edge in undirected or edge[::-1] in undirected: to_remove.append(index) else: undirected.add(edge) undirected.add(edge[::-1]) adjlist = adjlist.drop(to_remove) return adjlist libpysal-4.9.2/libpysal/weights/contiguity.py000066400000000000000000000624131452177046000214220ustar00rootroot00000000000000import itertools import warnings import numpy from ..cg import voronoi_frames from ..io.fileio import FileIO from ._contW_lists import ContiguityWeightsLists from .util import get_ids, get_points_array from .weights import WSP, W from .raster import da2W, da2WSP try: from shapely.geometry import Point as shapely_point from ..cg.shapes import Point as pysal_point point_type = (shapely_point, pysal_point) except ImportError: from ..cg.shapes import Point as point_type WT_TYPE = {"rook": 2, "queen": 1} # for _contW_Binning __author__ = "Sergio J. Rey , Levi John Wolf " __all__ = ["Rook", "Queen", "Voronoi"] class Rook(W): """ Construct a weights object from a collection of pysal polygons that share at least one edge. Parameters ---------- polygons : list a collection of PySAL shapes to build weights from ids : list a list of names to use to build the weights **kw : keyword arguments optional arguments for :class:`pysal.weights.W` See Also -------- :class:`libpysal.weights.weights.W` """ def __init__(self, polygons, **kw): criterion = "rook" ids = kw.pop("ids", None) polygons, backup = itertools.tee(polygons) first_shape = next(iter(backup)) if isinstance(first_shape, point_type): polygons, vertices = voronoi_frames(get_points_array(polygons)) polygons = list(polygons.geometry) neighbors, ids = _build(polygons, criterion=criterion, ids=ids) W.__init__(self, neighbors, ids=ids, **kw) @classmethod def from_shapefile(cls, filepath, idVariable=None, full=False, **kwargs): """ Rook contiguity weights from a polygon shapefile. Parameters ---------- shapefile : string name of polygon shapefile including suffix. sparse : boolean If True return WSP instance If False return W instance Returns ------- w : W instance of spatial weights Examples -------- >>> from libpysal.weights import Rook >>> import libpysal >>> wr=Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"), "POLYID") >>> "%.3f"%wr.pct_nonzero '8.330' >>> wr=Rook.from_shapefile(libpysal.examples.get_path("columbus.shp"), sparse=True) >>> pct_sp = wr.sparse.nnz *1. / wr.n**2 >>> "%.3f"%pct_sp '0.083' Notes ----- Rook contiguity defines as neighbors any pair of polygons that share a common edge in their polygon definitions. See Also -------- :class:`libpysal.weights.weights.W` :class:`libpysal.weights.contiguity.Rook` """ sparse = kwargs.pop("sparse", False) if idVariable is not None: ids = get_ids(filepath, idVariable) else: ids = None w = cls(FileIO(filepath), ids=ids, **kwargs) w.set_shapefile(filepath, idVariable=idVariable, full=full) if sparse: w = w.to_WSP() return w @classmethod def from_iterable(cls, iterable, sparse=False, **kwargs): """ Construct a weights object from a collection of arbitrary polygons. This will cast the polygons to PySAL polygons, then build the W. Parameters ---------- iterable : iterable a collection of of shapes to be cast to PySAL shapes. Must support iteration. Can be either Shapely or PySAL shapes. **kw : keyword arguments optional arguments for :class:`pysal.weights.W` See Also -------- :class:`libpysal.weights.weights.W` :class:`libpysal.weights.contiguity.Rook` """ new_iterable = iter(iterable) w = cls(new_iterable, **kwargs) if sparse: w = WSP.from_W(w) return w @classmethod def from_dataframe( cls, df, geom_col=None, idVariable=None, ids=None, id_order=None, use_index=None, **kwargs, ): """ Construct a weights object from a (geo)pandas dataframe with a geometry column. This will cast the polygons to PySAL polygons, then build the W using ids from the dataframe. Parameters ---------- df : DataFrame a :class: `pandas.DataFrame` containing geometries to use for spatial weights geom_col : string the name of the column in `df` that contains the geometries. Defaults to active geometry column. idVariable : string DEPRECATED - use `ids` instead. the name of the column to use as IDs. If nothing is provided, the dataframe index is used ids : list-like, string a list-like of ids to use to index the spatial weights object or the name of the column to use as IDs. If nothing is provided, the dataframe index is used if `use_index=True` or a positional index is used if `use_index=False`. Order of the resulting W is not respected from this list. id_order : list DEPRECATED - argument is deprecated and will be removed. An ordered list of ids to use to index the spatial weights object. If used, the resulting weights object will iterate over results in the order of the names provided in this argument. use_index : bool use index of `df` as `ids` to index the spatial weights object. Defaults to False but in future will default to True. See Also -------- :class:`libpysal.weights.weights.W` :class:`libpysal.weights.contiguity.Rook` """ if geom_col is None: geom_col = df.geometry.name if id_order is not None: warnings.warn( "`id_order` is deprecated and will be removed in future.", FutureWarning, stacklevel=2, ) if id_order is True and ((idVariable is not None) or (ids is not None)): # if idVariable is None, we want ids. Otherwise, we want the # idVariable column id_order = list(df.get(idVariable, ids)) else: id_order = df.get(id_order, ids) if idVariable is not None: if ids is None: warnings.warn( "`idVariable` is deprecated and will be removed in future. " "Use `ids` instead.", FutureWarning, stacklevel=2, ) ids = idVariable else: warnings.warn( "Both `idVariable` and `ids` passed, using `ids`.", UserWarning, stacklevel=2, ) if ids is None: if use_index is None: warnings.warn( "`use_index` defaults to False but will default to True in future. " "Set True/False directly to control this behavior and silence this " "warning", FutureWarning, stacklevel=2, ) use_index = False if use_index: ids = df.index.tolist() else: if isinstance(ids, str): ids = df[ids] if not isinstance(ids, list): ids = ids.tolist() if len(ids) != len(df): raise ValueError("The length of `ids` does not match the length of df.") if id_order is None: id_order = ids return cls.from_iterable( df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs ) @classmethod def from_xarray( cls, da, z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, sparse=True, **kwargs, ): """ Construct a weights object from a xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. sparse : boolean type of weight object. Default is True. For libpysal.weights.W, sparse = False k : int Order of contiguity, this will select all neighbors upto kth order. Default is 1. include_nodata : boolean If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. **kwargs : keyword arguments optional arguments passed when sparse = False Returns ------- w : libpysal.weights.W/libpysal.weights.WSP instance of spatial weights class W or WSP with an index attribute Notes ----- 1. Lower order contiguities are also selected. 2. Returned object contains `index` attribute that includes a `Pandas.MultiIndex` object from the DataArray. See Also -------- :class:`libpysal.weights.weights.W` :class:`libpysal.weights.weights.WSP` """ if sparse: w = da2WSP(da, "rook", z_value, coords_labels, k, include_nodata) else: w = da2W(da, "rook", z_value, coords_labels, k, include_nodata, **kwargs) return w class Queen(W): """ Construct a weights object from a collection of pysal polygons that share at least one vertex. Parameters ---------- polygons : list a collection of PySAL shapes to build weights from ids : list a list of names to use to build the weights **kw : keyword arguments optional arguments for :class:`pysal.weights.W` See Also -------- :class:`libpysal.weights.weights.W` """ def __init__(self, polygons, **kw): criterion = "queen" ids = kw.pop("ids", None) polygons, backup = itertools.tee(polygons) first_shape = next(iter(backup)) if isinstance(first_shape, point_type): polygons, vertices = voronoi_frames(get_points_array(polygons)) polygons = list(polygons.geometry) neighbors, ids = _build(polygons, criterion=criterion, ids=ids) W.__init__(self, neighbors, ids=ids, **kw) @classmethod def from_shapefile(cls, filepath, idVariable=None, full=False, **kwargs): """ Queen contiguity weights from a polygon shapefile. Parameters ---------- shapefile : string name of polygon shapefile including suffix. idVariable : string name of a column in the shapefile's DBF to use for ids. sparse : boolean If True return WSP instance If False return W instance Returns ------- w : W instance of spatial weights Examples -------- >>> from libpysal.weights import Queen >>> import libpysal >>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp")) >>> "%.3f"%wq.pct_nonzero '9.829' >>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"),"POLYID") >>> "%.3f"%wq.pct_nonzero '9.829' >>> wq=Queen.from_shapefile(libpysal.examples.get_path("columbus.shp"), sparse=True) >>> pct_sp = wq.sparse.nnz *1. / wq.n**2 >>> "%.3f"%pct_sp '0.098' Notes Queen contiguity defines as neighbors any pair of polygons that share at least one vertex in their polygon definitions. See Also -------- :class:`libpysal.weights.weights.W` :class:`libpysal.weights.contiguity.Queen` """ sparse = kwargs.pop("sparse", False) if idVariable is not None: ids = get_ids(filepath, idVariable) else: ids = None w = cls(FileIO(filepath), ids=ids, **kwargs) w.set_shapefile(filepath, idVariable=idVariable, full=full) if sparse: w = w.to_WSP() return w @classmethod def from_iterable(cls, iterable, sparse=False, **kwargs): """ Construct a weights object from a collection of arbitrary polygons. This will cast the polygons to PySAL polygons, then build the W. Parameters ---------- iterable : iterable a collection of of shapes to be cast to PySAL shapes. Must support iteration. Contents may either be a shapely or PySAL shape. **kw : keyword arguments optional arguments for :class:`pysal.weights.W` See Also --------- :class:`libpysal.weights.weights.W` :class:`libpysal.weights.contiguiyt.Queen` """ new_iterable = iter(iterable) w = cls(new_iterable, **kwargs) if sparse: w = WSP.from_W(w) return w @classmethod def from_dataframe( cls, df, geom_col=None, idVariable=None, ids=None, id_order=None, use_index=None, **kwargs, ): """ Construct a weights object from a (geo)pandas dataframe with a geometry column. This will cast the polygons to PySAL polygons, then build the W using ids from the dataframe. Parameters ---------- df : DataFrame a :class: `pandas.DataFrame` containing geometries to use for spatial weights geom_col : string the name of the column in `df` that contains the geometries. Defaults to active geometry column. idVariable : string DEPRECATED - use `ids` instead. the name of the column to use as IDs. If nothing is provided, the dataframe index is used ids : list-like, string a list-like of ids to use to index the spatial weights object or the name of the column to use as IDs. If nothing is provided, the dataframe index is used if `use_index=True` or a positional index is used if `use_index=False`. Order of the resulting W is not respected from this list. id_order : list DEPRECATED - argument is deprecated and will be removed. An ordered list of ids to use to index the spatial weights object. If used, the resulting weights object will iterate over results in the order of the names provided in this argument. use_index : bool use index of `df` as `ids` to index the spatial weights object. Defaults to False but in future will default to True. See Also -------- :class:`libpysal.weights.weights.W` :class:`libpysal.weights.contiguity.Queen` """ if geom_col is None: geom_col = df.geometry.name if id_order is not None: warnings.warn( "`id_order` is deprecated and will be removed in future.", FutureWarning, stacklevel=2, ) if id_order is True and ((idVariable is not None) or (ids is not None)): # if idVariable is None, we want ids. Otherwise, we want the # idVariable column id_order = list(df.get(idVariable, ids)) else: id_order = df.get(id_order, ids) if idVariable is not None: if ids is None: warnings.warn( "`idVariable` is deprecated and will be removed in future. " "Use `ids` instead.", FutureWarning, stacklevel=2, ) ids = idVariable else: warnings.warn( "Both `idVariable` and `ids` passed, using `ids`.", UserWarning, stacklevel=2, ) if ids is None: if use_index is None: warnings.warn( "`use_index` defaults to False but will default to True in future. " "Set True/False directly to control this behavior and silence this " "warning", FutureWarning, stacklevel=2, ) use_index = False if use_index: ids = df.index.tolist() else: if isinstance(ids, str): ids = df[ids] if not isinstance(ids, list): ids = ids.tolist() if len(ids) != len(df): raise ValueError("The length of `ids` does not match the length of df.") if id_order is None: id_order = ids return cls.from_iterable( df[geom_col].tolist(), ids=ids, id_order=id_order, **kwargs ) @classmethod def from_xarray( cls, da, z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, sparse=True, **kwargs, ): """ Construct a weights object from a xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. sparse : boolean type of weight object. Default is True. For libpysal.weights.W, sparse = False k : int Order of contiguity, this will select all neighbors upto kth order. Default is 1. include_nodata : boolean If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. **kwargs : keyword arguments optional arguments passed when sparse = False Returns ------- w : libpysal.weights.W/libpysal.weights.WSP instance of spatial weights class W or WSP with an index attribute Notes ----- 1. Lower order contiguities are also selected. 2. Returned object contains `index` attribute that includes a `Pandas.MultiIndex` object from the DataArray. See Also -------- :class:`libpysal.weights.weights.W` :class:`libpysal.weights.weights.WSP` """ if sparse: w = da2WSP(da, "queen", z_value, coords_labels, k, include_nodata) else: w = da2W(da, "queen", z_value, coords_labels, k, include_nodata, **kwargs) return w def Voronoi(points, criterion="rook", clip="ahull", **kwargs): """ Voronoi weights for a 2-d point set Points are Voronoi neighbors if their polygons share an edge or vertex. Parameters ---------- points : array (n,2) coordinates for point locations kwargs : arguments to pass to Rook, the underlying contiguity class. Returns ------- w : W instance of spatial weights Examples -------- >>> import numpy as np >>> from libpysal.weights import Voronoi >>> np.random.seed(12345) >>> points= np.random.random((5,2))*10 + 10 >>> w = Voronoi(points) >>> w.neighbors {0: [2, 3, 4], 1: [2], 2: [0, 1, 4], 3: [0, 4], 4: [0, 2, 3]} """ from ..cg.voronoi import voronoi_frames region_df, _ = voronoi_frames(points, clip=clip) if criterion.lower() == "queen": cls = Queen elif criterion.lower() == "rook": cls = Rook else: raise ValueError( "Contiguity criterion {} not supported. " 'Only "rook" and "queen" are supported.'.format(criterion) ) return cls.from_dataframe(region_df, **kwargs) def _from_dataframe(df, **kwargs): """ Construct a voronoi contiguity weight directly from a dataframe. Note that if criterion='rook', this is identical to the delaunay graph for the points if no clipping of the voronoi cells is applied. If the input dataframe is of any other geometry type than "Point", a value error is raised. Parameters ---------- df : pandas.DataFrame dataframe containing point geometries for a voronoi diagram. Returns ------- w : W instance of spatial weights. """ try: x, y = df.geometry.x.values, df.geometry.y.values except ValueError: raise NotImplementedError( "Voronoi weights are only" " implemented for point geometries. " "You may consider using df.centroid." ) coords = numpy.column_stack((x, y)) return Voronoi(coords, **kwargs) Voronoi.from_dataframe = _from_dataframe def _build(polygons, criterion="rook", ids=None): """ This is a developer-facing function to construct a spatial weights object. Parameters ---------- polygons : list list of pysal polygons to use to build contiguity criterion : string option of which kind of contiguity to build. Is either "rook" or "queen" ids : list list of ids to use to index the neighbor dictionary Returns ------- tuple containing (neighbors, ids), where neighbors is a dictionary describing contiguity relations and ids is the list of ids used to index that dictionary. NOTE: this is different from the prior behavior of buildContiguity, which returned an actual weights object. Since this just dispatches for the classes above, this returns the raw ingredients for a spatial weights object, not the object itself. """ if ids and len(ids) != len(set(ids)): raise ValueError( "The argument to the ids parameter contains duplicate entries." ) wttype = WT_TYPE[criterion.lower()] geo = polygons if issubclass(type(geo), FileIO): geo.seek(0) # Make sure we read from the beginning of the file. neighbor_data = ContiguityWeightsLists(polygons, wttype=wttype).w neighbors = {} # weights={} if ids: for key in neighbor_data: ida = ids[key] if ida not in neighbors: neighbors[ida] = set() neighbors[ida].update([ids[x] for x in neighbor_data[key]]) for key in neighbors: neighbors[key] = set(neighbors[key]) else: for key in neighbor_data: neighbors[key] = set(neighbor_data[key]) return ( dict( list(zip(list(neighbors.keys()), list(map(list, list(neighbors.values()))))) ), ids, ) def buildContiguity(polygons, criterion="rook", ids=None): """ This is a deprecated function. It builds a contiguity W from the polygons provided. As such, it is now identical to calling the class constructors for Rook or Queen. """ # Warn('This function is deprecated. Please use the Rook or Queen classes', # UserWarning) if criterion.lower() == "rook": return Rook(polygons, ids=ids) elif criterion.lower() == "queen": return Queen(polygons, ids=ids) else: raise Exception('Weights criterion "{}" was not found.'.format(criterion)) libpysal-4.9.2/libpysal/weights/distance.py000066400000000000000000001007731452177046000210200ustar00rootroot00000000000000__all__ = ["KNN", "Kernel", "DistanceBand"] __author__ = "Sergio J. Rey , Levi John Wolf " from ..cg.kdtree import KDTree from .weights import W, WSP from .util import ( isKDTree, get_ids, get_points_array_from_shapefile, get_points_array, WSP2W, ) import copy from warnings import warn as Warn from scipy.spatial import distance_matrix import scipy.sparse as sp import numpy as np def knnW(data, k=2, p=2, ids=None, radius=None, distance_metric="euclidean"): """ This is deprecated. Use the pysal.weights.KNN class instead. """ # Warn('This function is deprecated. Please use pysal.weights.KNN', UserWarning) return KNN(data, k=k, p=p, ids=ids, radius=radius, distance_metric=distance_metric) class KNN(W): """ Creates nearest neighbor weights matrix based on k nearest neighbors. Parameters ---------- kdtree : object PySAL KDTree or ArcKDTree where KDtree.data is array (n,k) n observations on k characteristics used to measure distances between the n objects k : int number of nearest neighbors p : float Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance Ignored if the KDTree is an ArcKDTree ids : list identifiers to attach to each observation Returns ------- w : W instance Weights object with binary weights Examples -------- >>> import libpysal >>> import numpy as np >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)] >>> kd = libpysal.cg.KDTree(np.array(points)) >>> wnn2 = libpysal.weights.KNN(kd, 2) >>> [1,3] == wnn2.neighbors[0] True >>> wnn2 = KNN(kd,2) >>> wnn2[0] {1: 1.0, 3: 1.0} >>> wnn2[1] {0: 1.0, 3: 1.0} now with 1 rather than 0 offset >>> wnn2 = libpysal.weights.KNN(kd, 2, ids=range(1,7)) >>> wnn2[1] {2: 1.0, 4: 1.0} >>> wnn2[2] {1: 1.0, 4: 1.0} >>> 0 in wnn2.neighbors False Notes ----- Ties between neighbors of equal distance are arbitrarily broken. Further, if many points occupy the same spatial location (i.e. observations are coincident), then you may need to increase k for those observations to acquire neighbors at different spatial locations. For example, if five points are coincident, then their four nearest neighbors will all occupy the same spatial location; only the fifth nearest neighbor will result in those coincident points becoming connected to the graph as a whole. Solutions to this problem include jittering the points (by adding a small random value to each observation's location) or by adding higher-k neighbors only to the coincident points, using the weights.w_sets.w_union() function. See Also -------- :class:`libpysal.weights.weights.W` """ def __init__( self, data, k=2, p=2, ids=None, radius=None, distance_metric="euclidean", **kwargs ): if radius is not None: distance_metric = "arc" if isKDTree(data): self.kdtree = data self.data = self.kdtree.data else: self.kdtree = KDTree(data, radius=radius, distance_metric=distance_metric) self.data = self.kdtree.data self.k = k self.p = p # these are both n x k+1 distances, indices = self.kdtree.query(self.data, k=k + 1, p=p) full_indices = np.arange(self.kdtree.n) # if an element in the indices matrix is equal to the corresponding # index for that row, we want to mask that site from its neighbors not_self_mask = indices != full_indices.reshape(-1, 1) # if there are *too many duplicates per site*, then we may get some # rows where the site index is not in the set of k+1 neighbors # So, we need to know where these sites are has_one_too_many = not_self_mask.sum(axis=1) == (k + 1) # if a site has k+1 neighbors, drop its k+1th neighbor not_self_mask[has_one_too_many, -1] &= False not_self_indices = indices[not_self_mask].reshape(self.kdtree.n, -1) to_weight = not_self_indices if ids is None: ids = list(full_indices) named_indices = not_self_indices else: named_indices = np.asarray(ids)[not_self_indices] neighbors = {idx: list(indices) for idx, indices in zip(ids, named_indices)} W.__init__(self, neighbors, id_order=ids, **kwargs) @classmethod def from_shapefile(cls, filepath, *args, **kwargs): """ Nearest neighbor weights from a shapefile. Parameters ---------- data : string shapefile containing attribute data. k : int number of nearest neighbors p : float Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance ids : list identifiers to attach to each observation radius : float If supplied arc_distances will be calculated based on the given radius. p will be ignored. Returns ------- w : KNN instance; Weights object with binary weights. Examples -------- Polygon shapefile >>> import libpysal >>> from libpysal.weights import KNN >>> wc=KNN.from_shapefile(libpysal.examples.get_path("columbus.shp")) >>> "%.4f"%wc.pct_nonzero '4.0816' >>> set([2,1]) == set(wc.neighbors[0]) True >>> wc3=KNN.from_shapefile(libpysal.examples.get_path("columbus.shp"),k=3) >>> set(wc3.neighbors[0]) == set([2,1,3]) True >>> set(wc3.neighbors[2]) == set([4,3,0]) True Point shapefile >>> w=KNN.from_shapefile(libpysal.examples.get_path("juvenile.shp")) >>> w.pct_nonzero 1.1904761904761905 >>> w1=KNN.from_shapefile(libpysal.examples.get_path("juvenile.shp"),k=1) >>> "%.3f"%w1.pct_nonzero '0.595' Notes ----- Ties between neighbors of equal distance are arbitrarily broken. See Also -------- :class:`libpysal.weights.weights.W` """ return cls(get_points_array_from_shapefile(filepath), *args, **kwargs) @classmethod def from_array(cls, array, *args, **kwargs): """ Creates nearest neighbor weights matrix based on k nearest neighbors. Parameters ---------- array : np.ndarray (n, k) array representing n observations on k characteristics used to measure distances between the n objects **kwargs : keyword arguments, see Rook Returns ------- w : W instance Weights object with binary weights Examples -------- >>> from libpysal.weights import KNN >>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)] >>> wnn2 = KNN.from_array(points, 2) >>> [1,3] == wnn2.neighbors[0] True >>> wnn2 = KNN.from_array(points,2) >>> wnn2[0] {1: 1.0, 3: 1.0} >>> wnn2[1] {0: 1.0, 3: 1.0} now with 1 rather than 0 offset >>> wnn2 = KNN.from_array(points, 2, ids=range(1,7)) >>> wnn2[1] {2: 1.0, 4: 1.0} >>> wnn2[2] {1: 1.0, 4: 1.0} >>> 0 in wnn2.neighbors False Notes ----- Ties between neighbors of equal distance are arbitrarily broken. See Also -------- :class:`libpysal.weights.weights.W` """ return cls(array, *args, **kwargs) @classmethod def from_dataframe( cls, df, geom_col=None, ids=None, use_index=True, *args, **kwargs ): """ Make KNN weights from a dataframe. Parameters ---------- df : pandas.dataframe a dataframe with a geometry column that can be used to construct a W object geom_col : string the name of the column in `df` that contains the geometries. Defaults to active geometry column. ids : list-like, string a list-like of ids to use to index the spatial weights object or the name of the column to use as IDs. If nothing is provided, the dataframe index is used if `use_index=True` or a positional index is used if `use_index=False`. Order of the resulting W is not respected from this list. use_index : bool use index of `df` as `ids` to index the spatial weights object. See Also -------- :class:`libpysal.weights.weights.W` """ if geom_col is None: geom_col = df.geometry.name pts = get_points_array(df[geom_col]) if ids is None and use_index: ids = df.index.tolist() elif isinstance(ids, str): ids = df[ids].tolist() return cls(pts, *args, ids=ids, **kwargs) def reweight(self, k=None, p=None, new_data=None, new_ids=None, inplace=True): """ Redo K-Nearest Neighbor weights construction using given parameters Parameters ---------- new_data : np.ndarray an array containing additional data to use in the KNN weight new_ids : list a list aligned with new_data that provides the ids for each new observation inplace : bool a flag denoting whether to modify the KNN object in place or to return a new KNN object k : int number of nearest neighbors p : float Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance Ignored if the KDTree is an ArcKDTree Returns ------- A copy of the object using the new parameterization, or None if the object is reweighted in place. """ if new_data is not None: new_data = np.asarray(new_data).reshape(-1, 2) data = np.vstack((self.data, new_data)).reshape(-1, 2) if new_ids is not None: ids = copy.deepcopy(self.id_order) ids.extend(list(new_ids)) else: ids = list(range(data.shape[0])) elif (new_data is None) and (new_ids is None): # If not, we can use the same kdtree we have data = self.kdtree ids = self.id_order elif (new_data is None) and (new_ids is not None): Warn("Remapping ids must be done using w.remap_ids") if k is None: k = self.k if p is None: p = self.p if inplace: self._reset() self.__init__(data, ids=ids, k=k, p=p) else: return KNN(data, ids=ids, k=k, p=p) class Kernel(W): """ Spatial weights based on kernel functions. Parameters ---------- data : array (n,k) or KDTree where KDtree.data is array (n,k) n observations on k characteristics used to measure distances between the n objects bandwidth : float or array-like (optional) the bandwidth :math:`h_i` for the kernel. fixed : binary If true then :math:`h_i=h \\forall i`. If false then bandwidth is adaptive across observations. k : int the number of nearest neighbors to use for determining bandwidth. For fixed bandwidth, :math:`h_i=max(dknn) \\forall i` where :math:`dknn` is a vector of k-nearest neighbor distances (the distance to the kth nearest neighbor for each observation). For adaptive bandwidths, :math:`h_i=dknn_i` diagonal : boolean If true, set diagonal weights = 1.0, if false (default), diagonals weights are set to value according to kernel function. function : {'triangular','uniform','quadratic','quartic','gaussian'} kernel function defined as follows with .. math:: z_{i,j} = d_{i,j}/h_i triangular .. math:: K(z) = (1 - |z|) \\ if |z| \\le 1 uniform .. math:: K(z) = 1/2 \\ if |z| \\le 1 quadratic .. math:: K(z) = (3/4)(1-z^2) \\ if |z| \\le 1 quartic .. math:: K(z) = (15/16)(1-z^2)^2 \\ if |z| \\le 1 gaussian .. math:: K(z) = (2\\pi)^{(-1/2)} exp(-z^2 / 2) eps : float adjustment to ensure knn distance range is closed on the knnth observations Attributes ---------- weights : dict Dictionary keyed by id with a list of weights for each neighbor neighbors : dict of lists of neighbors keyed by observation id bandwidth : array array of bandwidths Examples -------- >>> from libpysal.weights import Kernel >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)] >>> kw=Kernel(points) >>> kw.weights[0] [1.0, 0.500000049999995, 0.4409830615267465] >>> kw.neighbors[0] [0, 1, 3] >>> kw.bandwidth array([[20.000002], [20.000002], [20.000002], [20.000002], [20.000002], [20.000002]]) >>> kw15=Kernel(points,bandwidth=15.0) >>> kw15[0] {0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701} >>> kw15.neighbors[0] [0, 1, 3] >>> kw15.bandwidth array([[15.], [15.], [15.], [15.], [15.], [15.]]) Adaptive bandwidths user specified >>> bw=[25.0,15.0,25.0,16.0,14.5,25.0] >>> kwa=Kernel(points,bandwidth=bw) >>> kwa.weights[0] [1.0, 0.6, 0.552786404500042, 0.10557280900008403] >>> kwa.neighbors[0] [0, 1, 3, 4] >>> kwa.bandwidth array([[25. ], [15. ], [25. ], [16. ], [14.5], [25. ]]) Endogenous adaptive bandwidths >>> kwea=Kernel(points,fixed=False) >>> kwea.weights[0] [1.0, 0.10557289844279438, 9.99999900663795e-08] >>> kwea.neighbors[0] [0, 1, 3] >>> kwea.bandwidth array([[11.18034101], [11.18034101], [20.000002 ], [11.18034101], [14.14213704], [18.02775818]]) Endogenous adaptive bandwidths with Gaussian kernel >>> kweag=Kernel(points,fixed=False,function='gaussian') >>> kweag.weights[0] [0.3989422804014327, 0.2674190291577696, 0.2419707487162134] >>> kweag.bandwidth array([[11.18034101], [11.18034101], [20.000002 ], [11.18034101], [14.14213704], [18.02775818]]) Diagonals to 1.0 >>> kq = Kernel(points,function='gaussian') >>> kq.weights {0: [0.3989422804014327, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 0.3989422804014327, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 0.3989422804014327, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 0.3989422804014327, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 0.3989422804014327, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 0.3989422804014327]} >>> kqd = Kernel(points, function='gaussian', diagonal=True) >>> kqd.weights {0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]} """ def __init__( self, data, bandwidth=None, fixed=True, k=2, function="triangular", eps=1.0000001, ids=None, diagonal=False, distance_metric="euclidean", radius=None, **kwargs ): if radius is not None: distance_metric = "arc" if isKDTree(data): self.kdtree = data self.data = self.kdtree.data data = self.data else: self.kdtree = KDTree(data, distance_metric=distance_metric, radius=radius) self.data = self.kdtree.data self.k = k + 1 self.function = function.lower() self.fixed = fixed self.eps = eps if bandwidth: try: bandwidth = np.array(bandwidth) bandwidth.shape = (len(bandwidth), 1) except: bandwidth = np.ones((len(data), 1), "float") * bandwidth self.bandwidth = bandwidth else: self._set_bw() self._eval_kernel() neighbors, weights = self._k_to_W(ids) if diagonal: for i in neighbors: weights[i][neighbors[i].index(i)] = 1.0 W.__init__(self, neighbors, weights, ids, **kwargs) @classmethod def from_shapefile(cls, filepath, idVariable=None, **kwargs): """ Kernel based weights from shapefile Parameters ---------- shapefile : string shapefile name with shp suffix idVariable : string name of column in shapefile's DBF to use for ids Returns ------- Kernel Weights Object See Also -------- :class:`libpysal.weights.weights.W` """ points = get_points_array_from_shapefile(filepath) if idVariable is not None: ids = get_ids(filepath, idVariable) else: ids = None return cls.from_array(points, ids=ids, **kwargs) @classmethod def from_array(cls, array, **kwargs): """ Construct a Kernel weights from an array. Supports all the same options as :class:`libpysal.weights.Kernel` See Also -------- :class:`libpysal.weights.weights.W` """ return cls(array, **kwargs) @classmethod def from_dataframe(cls, df, geom_col=None, ids=None, use_index=True, **kwargs): """ Make Kernel weights from a dataframe. Parameters ---------- df : pandas.dataframe a dataframe with a geometry column that can be used to construct a W object geom_col : string the name of the column in `df` that contains the geometries. Defaults to active geometry column. ids : list-like, string a list-like of ids to use to index the spatial weights object or the name of the column to use as IDs. If nothing is provided, the dataframe index is used if `use_index=True` or a positional index is used if `use_index=False`. Order of the resulting W is not respected from this list. use_index : bool use index of `df` as `ids` to index the spatial weights object. See Also -------- :class:`libpysal.weights.weights.W` """ if geom_col is None: geom_col = df.geometry.name pts = get_points_array(df[geom_col]) if ids is None and use_index: ids = df.index.tolist() elif isinstance(ids, str): ids = df[ids].tolist() return cls(pts, ids=ids, **kwargs) def _k_to_W(self, ids=None): allneighbors = {} weights = {} if ids: ids = np.array(ids) else: ids = np.arange(len(self.data)) for i, neighbors in enumerate(self.kernel): if len(self.neigh[i]) == 0: allneighbors[ids[i]] = [] weights[ids[i]] = [] else: allneighbors[ids[i]] = list(ids[self.neigh[i]]) weights[ids[i]] = self.kernel[i].tolist() return allneighbors, weights def _set_bw(self): dmat, neigh = self.kdtree.query(self.data, k=self.k) if self.fixed: # use max knn distance as bandwidth bandwidth = dmat.max() * self.eps n = len(dmat) self.bandwidth = np.ones((n, 1), "float") * bandwidth else: # use local max knn distance self.bandwidth = dmat.max(axis=1) * self.eps self.bandwidth.shape = (self.bandwidth.size, 1) # identify knn neighbors for each point nnq = self.kdtree.query(self.data, k=self.k) self.neigh = nnq[1] def _eval_kernel(self): # get points within bandwidth distance of each point if not hasattr(self, "neigh"): kdtq = self.kdtree.query_ball_point neighbors = [ kdtq(self.data[i], r=bwi[0]) for i, bwi in enumerate(self.bandwidth) ] self.neigh = neighbors # get distances for neighbors bw = self.bandwidth kdtq = self.kdtree.query z = [] for i, nids in enumerate(self.neigh): di, ni = kdtq(self.data[i], k=len(nids)) if not isinstance(di, np.ndarray): di = np.asarray([di] * len(nids)) ni = np.asarray([ni] * len(nids)) zi = np.array([dict(list(zip(ni, di)))[nid] for nid in nids]) / bw[i] z.append(zi) zs = z # functions follow Anselin and Rey (2010) table 5.4 if self.function == "triangular": self.kernel = [1 - zi for zi in zs] elif self.function == "uniform": self.kernel = [np.ones(zi.shape) * 0.5 for zi in zs] elif self.function == "quadratic": self.kernel = [(3.0 / 4) * (1 - zi**2) for zi in zs] elif self.function == "quartic": self.kernel = [(15.0 / 16) * (1 - zi**2) ** 2 for zi in zs] elif self.function == "gaussian": c = np.pi * 2 c = c ** (-0.5) self.kernel = [c * np.exp(-(zi**2) / 2.0) for zi in zs] else: print(("Unsupported kernel function", self.function)) class DistanceBand(W): """ Spatial weights based on distance band. Parameters ---------- data : array (n,k) or KDTree where KDtree.data is array (n,k) n observations on k characteristics used to measure distances between the n objects threshold : float distance band p : float DEPRECATED: use `distance_metric` Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance binary : boolean If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0 If false wij=dij^{alpha} alpha : float distance decay parameter for weight (default -1.0) if alpha is positive the weights will not decline with distance. If binary is True, alpha is ignored ids : list values to use for keys of the neighbors and weights dicts build_sp : boolean DEPRECATED True to build sparse distance matrix and false to build dense distance matrix; significant speed gains may be obtained dending on the sparsity of the of distance_matrix and threshold that is applied silent : boolean By default libpysal will print a warning if the dataset contains any disconnected observations or islands. To silence this warning set this parameter to True. Attributes ---------- weights : dict of neighbor weights keyed by observation id neighbors : dict of neighbors keyed by observation id Examples -------- >>> import libpysal >>> points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)] >>> wcheck = libpysal.weights.W({0: [1, 3], 1: [0, 3], 2: [], 3: [0, 1], 4: [5], 5: [4]}) WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> w=libpysal.weights.DistanceBand(points,threshold=11.2) WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> libpysal.weights.util.neighbor_equality(w, wcheck) True >>> w=libpysal.weights.DistanceBand(points,threshold=14.2) >>> wcheck = libpysal.weights.W({0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 2, 1], 5: [4]}) >>> libpysal.weights.util.neighbor_equality(w, wcheck) True inverse distance weights >>> w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False) WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> w.weights[0] [0.1, 0.08944271909999159] >>> w.neighbors[0].tolist() [1, 3] gravity weights >>> w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False,alpha=-2.) WARNING: there is one disconnected observation (no neighbors) Island id: [2] >>> w.weights[0] [0.01, 0.007999999999999998] """ def __init__( self, data, threshold, p=2, alpha=-1.0, binary=True, ids=None, build_sp=True, silence_warnings=False, distance_metric="euclidean", radius=None, ): """Casting to floats is a work around for a bug in scipy.spatial. See detail in pysal issue #126. """ if ids is not None: ids = list(ids) if radius is not None: distance_metric = "arc" self.p = p self.threshold = threshold self.binary = binary self.alpha = alpha self.build_sp = build_sp self.silence_warnings = silence_warnings if isKDTree(data): self.kdtree = data self.data = self.kdtree.data else: if self.build_sp: try: data = np.asarray(data) if data.dtype.kind != "f": data = data.astype(float) self.kdtree = KDTree( data, distance_metric=distance_metric, radius=radius ) self.data = self.kdtree.data except: raise ValueError("Could not make array from data") else: self.data = data self.kdtree = None self._band() neighbors, weights = self._distance_to_W(ids) W.__init__( self, neighbors, weights, ids, silence_warnings=self.silence_warnings ) @classmethod def from_shapefile(cls, filepath, threshold, idVariable=None, **kwargs): """ Distance-band based weights from shapefile Parameters ---------- shapefile : string shapefile name with shp suffix idVariable : string name of column in shapefile's DBF to use for ids Returns ------- Kernel Weights Object """ points = get_points_array_from_shapefile(filepath) if idVariable is not None: ids = get_ids(filepath, idVariable) else: ids = None return cls.from_array(points, threshold, ids=ids, **kwargs) @classmethod def from_array(cls, array, threshold, **kwargs): """ Construct a DistanceBand weights from an array. Supports all the same options as :class:`libpysal.weights.DistanceBand` """ return cls(array, threshold, **kwargs) @classmethod def from_dataframe( cls, df, threshold, geom_col=None, ids=None, use_index=True, **kwargs ): """ Make DistanceBand weights from a dataframe. Parameters ---------- df : pandas.dataframe a dataframe with a geometry column that can be used to construct a W object geom_col : string the name of the column in `df` that contains the geometries. Defaults to active geometry column. ids : list-like, string a list-like of ids to use to index the spatial weights object or the name of the column to use as IDs. If nothing is provided, the dataframe index is used if `use_index=True` or a positional index is used if `use_index=False`. Order of the resulting W is not respected from this list. use_index : bool use index of `df` as `ids` to index the spatial weights object. """ if geom_col is None: geom_col = df.geometry.name pts = get_points_array(df[geom_col]) if ids is None and use_index: ids = df.index.tolist() elif isinstance(ids, str): ids = df[ids].tolist() return cls(pts, threshold, ids=ids, **kwargs) def _band(self): """Find all pairs within threshold.""" if self.build_sp: self.dmat = self.kdtree.sparse_distance_matrix( self.kdtree, max_distance=self.threshold, p=self.p ).tocsr() else: if str(self.kdtree).split(".")[-1][0:10] == "Arc_KDTree": raise TypeError( "Unable to calculate dense arc distance matrix;" ' parameter "build_sp" must be set to True for arc' " distance type weight" ) self.dmat = self._spdistance_matrix(self.data, self.data, self.threshold) def _distance_to_W(self, ids=None): if self.binary: self.dmat[self.dmat > 0] = 1 self.dmat.eliminate_zeros() tempW = WSP2W( WSP(self.dmat, id_order=ids), silence_warnings=self.silence_warnings ) neighbors = tempW.neighbors weight_keys = list(tempW.weights.keys()) weight_vals = list(tempW.weights.values()) weights = dict(list(zip(weight_keys, list(map(list, weight_vals))))) return neighbors, weights else: weighted = self.dmat.power(self.alpha) weighted[weighted == np.inf] = 0 weighted.eliminate_zeros() tempW = WSP2W( WSP(weighted, id_order=ids), silence_warnings=self.silence_warnings ) neighbors = tempW.neighbors weight_keys = list(tempW.weights.keys()) weight_vals = list(tempW.weights.values()) weights = dict(list(zip(weight_keys, list(map(list, weight_vals))))) return neighbors, weights def _spdistance_matrix(self, x, y, threshold=None): dist = distance_matrix(x, y) if threshold is not None: zeros = dist > threshold dist[zeros] = 0 return sp.csr_matrix(dist) def _test(): import doctest # the following line could be used to define an alternative to the '' flag # doctest.BLANKLINE_MARKER = 'something better than ' start_suppress = np.get_printoptions()["suppress"] np.set_printoptions(suppress=True) doctest.testmod() np.set_printoptions(suppress=start_suppress) if __name__ == "__main__": _test() libpysal-4.9.2/libpysal/weights/gabriel.py000066400000000000000000000322661452177046000206340ustar00rootroot00000000000000from scipy.spatial import Delaunay as _Delaunay from scipy import sparse from libpysal.weights import W, WSP import pandas, numpy, warnings try: from numba import njit except ModuleNotFoundError: from libpysal.common import jit as njit __author__ = """" Levi John Wolf (levi.john.wolf@gmail.com) Martin Fleischmann (martin@martinfleischmann.net) """ #### Classes class Delaunay(W): """ Constructor of the Delaunay graph of a set of input points. Relies on scipy.spatial.Delaunay and numba to quickly construct a graph from the input set of points. Will be slower without numba, and will warn if this is missing. Parameters ---------- coordinates : array of points, (N,2) numpy array of coordinates containing locations to compute the delaunay triangulation **kwargs : keyword argument list keyword arguments passed directly to weights.W Notes ----- The Delaunay triangulation can result in quite a few non-local links among spatial coordinates. For a more useful graph, consider the weights.Voronoi constructor or the Gabriel graph. The weights.Voronoi class builds a voronoi diagram among the points, clips the Voronoi cells, and then constructs an adjacency graph among the clipped cells. This graph among the clipped Voronoi cells generally represents the structure of local adjacencies better than the "raw" Delaunay graph. The weights.gabriel.Gabriel graph constructs a Delaunay graph, but only includes the "short" links in the Delaunay graph. However, if the unresricted Delaunay triangulation is needed, this class will compute it much more quickly than Voronoi(coordinates, clip=None). """ def __init__(self, coordinates, **kwargs): try: from numba import njit except ModuleNotFoundError: warnings.warn( "The numba package is used extensively in this module" " to accelerate the computation of graphs. Without numba," " these computations may become unduly slow on large data." ) edges, _ = self._voronoi_edges(coordinates) ids = kwargs.get("ids") if ids is not None: ids = numpy.asarray(ids) edges = numpy.column_stack((ids[edges[:, 0]], ids[edges[:, 1]])) del kwargs["ids"] else: ids = numpy.arange(coordinates.shape[0]) voronoi_neighbors = pandas.DataFrame(edges).groupby(0)[1].apply(list).to_dict() W.__init__(self, voronoi_neighbors, id_order=list(ids), **kwargs) def _voronoi_edges(self, coordinates): dt = _Delaunay(coordinates) edges = _edges_from_simplices(dt.simplices) edges = ( pandas.DataFrame(numpy.asarray(list(edges))) .sort_values([0, 1]) .drop_duplicates() .values ) return edges, dt @classmethod def from_dataframe(cls, df, geom_col=None, ids=None, use_index=None, **kwargs): """ Construct a Delaunay triangulation from a geopandas GeoDataFrame. Not that the input geometries in the dataframe must be Points. Polygons or lines must be converted to points (e.g. using df.geometry.centroid). Parameters ---------- df : geopandas.GeoDataFrame GeoDataFrame containing points to construct the Delaunay Triangulation. geom_col : string the name of the column in `df` that contains the geometries. Defaults to active geometry column. ids : list-like, string a list-like of ids to use to index the spatial weights object or the name of the column to use as IDs. If nothing is provided, the dataframe index is used if `use_index=True` or a positional index is used if `use_index=False`. Order of the resulting W is not respected from this list. use_index : bool use index of `df` as `ids` to index the spatial weights object. **kwargs : keyword arguments Keyword arguments that are passed downwards to the weights.W constructor. """ if isinstance(df, pandas.Series): df = df.to_frame("geometry") if geom_col is None: geom_col = df.geometry.name geomtypes = df[geom_col].geom_type.unique() if ids is None: if use_index is None: warnings.warn( "`use_index` defaults to False but will default to True in future. " "Set True/False directly to control this behavior and silence this " "warning", FutureWarning, stacklevel=2, ) use_index = False if use_index: ids = df.index.tolist() elif isinstance(ids, str): ids = df[ids].tolist() try: assert len(geomtypes) == 1 assert geomtypes[0] == "Point" point_array = numpy.column_stack( (df[geom_col].x.values, df[geom_col].y.values) ) return cls(point_array, ids=ids, **kwargs) except AssertionError: raise TypeError( f"The input dataframe has geometry types {geomtypes}" f" but this delaunay triangulation is only well-defined for points." f" Choose a method to convert your dataframe into points (like using" f" the df.centroid) and use that to estimate this graph." ) class Gabriel(Delaunay): """ Constructs the Gabriel graph of a set of points. This graph is a subset of the Delaunay triangulation where only "short" links are retained. This function is also accelerated using numba, and implemented on top of the scipy.spatial.Delaunay class. For a link (i,j) connecting node i to j in the Delaunay triangulation to be retained in the Gabriel graph, it must pass a point set exclusion test: 1. Construct the circle C_ij containing link (i,j) as its diameter 2. If any other node k is contained within C_ij, then remove link (i,j) from the graph. 3. Once all links are evaluated, the remaining graph is the Gabriel graph. Parameters ---------- coordinates : array of points, (N,2) numpy array of coordinates containing locations to compute the delaunay triangulation **kwargs : keyword argument list keyword arguments passed directly to weights.W """ def __init__(self, coordinates, **kwargs): try: from numba import njit except ModuleNotFoundError: warnings.warn( "The numba package is used extensively in this module" " to accelerate the computation of graphs. Without numba," " these computations may become unduly slow on large data." ) edges, dt = self._voronoi_edges(coordinates) droplist = _filter_gabriel( edges, dt.points, ) output = numpy.row_stack(list(set(map(tuple, edges)).difference(set(droplist)))) ids = kwargs.get("ids") if ids is not None: ids = numpy.asarray(ids) output = numpy.column_stack((ids[output[:, 0]], ids[output[:, 1]])) del kwargs["ids"] else: ids = numpy.arange(coordinates.shape[0]) gabriel_neighbors = pandas.DataFrame(output).groupby(0)[1].apply(list).to_dict() W.__init__(self, gabriel_neighbors, id_order=list(ids), **kwargs) class Relative_Neighborhood(Delaunay): """ Constructs the Relative Neighborhood graph from a set of points. This graph is a subset of the Delaunay triangulation, where only "relative neighbors" are retained. Further, it is a superset of the Minimum Spanning Tree, with additional "relative neighbors" introduced. A relative neighbor pair of points i,j must be closer than the maximum distance between i (or j) and each other point k. This means that the points are at least as close to one another as they are to any other point. Parameters ---------- coordinates : array of points, (N,2) numpy array of coordinates containing locations to compute the delaunay triangulation **kwargs : keyword argument list keyword arguments passed directly to weights.W """ def __init__(self, coordinates, binary=True, **kwargs): try: from numba import njit except ModuleNotFoundError: warnings.warn( "The numba package is used extensively in this module" " to accelerate the computation of graphs. Without numba," " these computations may become unduly slow on large data." ) edges, dt = self._voronoi_edges(coordinates) output, dkmax = _filter_relativehood(edges, dt.points, return_dkmax=False) row, col, data = zip(*output) if binary: data = numpy.ones_like(col, dtype=float) sp = sparse.csc_matrix((data, (row, col))) # TODO: faster way than this? ids = kwargs.get("ids") if ids is None: ids = numpy.arange(sp.shape[0]) else: del kwargs["ids"] ids = list(ids) tmp = WSP(sp, id_order=ids).to_W() W.__init__(self, tmp.neighbors, tmp.weights, id_order=ids, **kwargs) #### utilities @njit def _edges_from_simplices(simplices): """ Construct the sets of links that correspond to the edges of each simplex. Each simplex has three "sides," and thus six undirected edges. Thus, the input should be a list of three-length tuples, that are then converted into the six non-directed edges for each simplex. """ edges = [] for simplex in simplices: edges.append((simplex[0], simplex[1])) edges.append((simplex[1], simplex[0])) edges.append((simplex[1], simplex[2])) edges.append((simplex[2], simplex[1])) edges.append((simplex[2], simplex[0])) edges.append((simplex[0], simplex[2])) return numpy.asarray(edges) @njit def _filter_gabriel(edges, coordinates): """ For an input set of edges and coordinates, filter the input edges depending on the Gabriel rule: For each simplex, let i,j be the diameter of the circle defined by edge (i,j), and let k be the third point defining the simplex. The limiting case for the Gabriel rule is when k is also on the circle with diameter (i,j). In this limiting case, then simplex ijk must be a right triangle, and dij**2 = djk**2 + dki**2 (by thales theorem). This means that when dij**2 > djk**2 + dki**2, then k is inside the circle. In contrast, when dij**2 < djk**2 + dji*2, k is outside of the circle. Therefore, it's sufficient to take each observation i, iterate over its Delaunay neighbors j,k, and remove links whre dij**2 > djk**2 + dki**2 in order to construct the Gabriel graph. """ edge_pointer = 0 n = edges.max() n_edges = len(edges) to_drop = [] while edge_pointer < n_edges: edge = edges[edge_pointer] cardinality = 0 # look ahead to find all neighbors of edge[0] for joff in range(edge_pointer, n_edges): next_edge = edges[joff] if next_edge[0] != edge[0]: break cardinality += 1 for ix in range(edge_pointer, edge_pointer + cardinality): i, j = edges[ix] # lookahead ensures that i is always edge[0] dij2 = ((coordinates[i] - coordinates[j]) ** 2).sum() for ix2 in range(edge_pointer, edge_pointer + cardinality): _, k = edges[ix2] if j == k: continue dik2 = ((coordinates[i] - coordinates[k]) ** 2).sum() djk2 = ((coordinates[j] - coordinates[k]) ** 2).sum() if dij2 > (dik2 + djk2): to_drop.append((i, j)) to_drop.append((j, i)) edge_pointer += cardinality return set(to_drop) @njit def _filter_relativehood(edges, coordinates, return_dkmax=False): """ This is a direct implementation of the algorithm from Toussaint (1980), RNG-2 1. Compute the delaunay 2. for each edge of the delaunay (i,j), compute dkmax = max(d(k,i), d(k,j)) for k in 1..n, k != i, j 3. for each edge of the delaunay (i,j), prune if any dkmax is greater than d(i,j) """ edge_pointer = 0 n = edges.max() n_edges = len(edges) out = [] r = [] for edge in edges: i, j = edge pi = coordinates[i] pj = coordinates[j] dkmax = 0 dij = ((pi - pj) ** 2).sum() ** 0.5 prune = False for k in range(n): pk = coordinates[k] dik = ((pi - pk) ** 2).sum() ** 0.5 djk = ((pj - pk) ** 2).sum() ** 0.5 distances = numpy.array([dik, djk, dkmax]) dkmax = distances.max() prune = dkmax < dij if (not return_dkmax) & prune: break if prune: continue out.append((i, j, dij)) if return_dkmax: r.append(dkmax) return out, r libpysal-4.9.2/libpysal/weights/raster.py000066400000000000000000000650651452177046000205320ustar00rootroot00000000000000from .util import lat2SW from .weights import WSP, W import numpy as np from warnings import warn import os import sys from scipy import sparse if os.path.basename(sys.argv[0]) in ("pytest", "py.test"): def jit(*dec_args, **dec_kwargs): """ decorator mimicking numba.jit """ def intercepted_function(f, *f_args, **f_kwargs): return f return intercepted_function else: from ..common import jit __all__ = ["da2W", "da2WSP", "w2da", "wsp2da", "testDataArray"] def da2W( da, criterion="queen", z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, **kwargs, ): """ Create a W object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) criterion : {"rook", "queen"} Type of contiguity. Default is queen. z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. k : int Order of contiguity, this will select all neighbors upto kth order. Default is 1. include_nodata : boolean If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. **kwargs : keyword arguments Optional arguments for :class:`libpysal.weights.W` Returns ------- w : libpysal.weights.W instance of spatial weights class W with an index attribute Notes ----- 1. Lower order contiguities are also selected. 2. Returned object contains `index` attribute that includes a `Pandas.MultiIndex` object from the DataArray. Examples -------- >>> from libpysal.weights.raster import da2W, testDataArray >>> da = testDataArray().rename( {'band': 'layer', 'x': 'longitude', 'y': 'latitude'}) >>> da.dims ('layer', 'latitude', 'longitude') >>> da.shape (3, 4, 4) >>> da.coords Coordinates: * layer (layer) int64 1 2 3 * latitude (latitude) float64 90.0 30.0 -30.0 -90.0 * longitude (longitude) float64 -180.0 -60.0 60.0 180.0 >>> da.attrs {'nodatavals': (-32768.0,)} >>> coords_labels = { "z_label": "layer", "y_label": "latitude", "x_label": "longitude" } >>> w = da2W(da, z_value=2, coords_labels=coords_labels) >>> "%.3f"%w.pct_nonzero '30.000' >>> w[(2, 90.0, 180.0)] == {(2, 90.0, 60.0): 1, (2, 30.0, 180.0): 1} True >>> len(w.index) 10 >>> w.index[:2] MultiIndex([(2, 90.0, 60.0), (2, 90.0, 180.0)], names=['layer', 'latitude', 'longitude']) See Also -------- :class:`libpysal.weights.weights.W` """ warn( "You are trying to build a full W object from " "xarray.DataArray (raster) object. This computation " "can be very slow and not scale well. It is recommended, " "if possible, to instead build WSP object, which is more " "efficient and faster. You can do this by using da2WSP method." ) wsp = da2WSP(da, criterion, z_value, coords_labels, k, include_nodata, n_jobs) w = wsp.to_W(**kwargs) # temp addition of index attribute w.index = wsp.index return w def da2WSP( da, criterion="queen", z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, ): """ Create a WSP object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) criterion : {"rook", "queen"} Type of contiguity. Default is queen. z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. k : int Order of contiguity, this will select all neighbors upto kth order. Default is 1. include_nodata : boolean If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. Returns ------- wsp : libpysal.weights.WSP instance of spatial weights class WSP with an index attribute Notes ----- 1. Lower order contiguities are also selected. 2. Returned object contains `index` attribute that includes a `Pandas.MultiIndex` object from the DataArray. Examples -------- >>> from libpysal.weights.raster import da2WSP, testDataArray >>> da = testDataArray().rename( {'band': 'layer', 'x': 'longitude', 'y': 'latitude'}) >>> da.dims ('layer', 'latitude', 'longitude') >>> da.shape (3, 4, 4) >>> da.coords Coordinates: * layer (layer) int64 1 2 3 * latitude (latitude) float64 90.0 30.0 -30.0 -90.0 * longitude (longitude) float64 -180.0 -60.0 60.0 180.0 >>> da.attrs {'nodatavals': (-32768.0,)} >>> coords_labels = { "z_label": "layer", "y_label": "latitude", "x_label": "longitude" } >>> wsp = da2WSP(da, z_value=2, coords_labels=coords_labels) >>> wsp.n 10 >>> pct_sp = wsp.sparse.nnz *1. / wsp.n**2 >>> "%.3f"%pct_sp '0.300' >>> print(wsp.sparse[4].todense()) [[0 0 1 0 0 1 1 1 0 0]] >>> wsp.index[:2] MultiIndex([(2, 90.0, 60.0), (2, 90.0, 180.0)], names=['layer', 'latitude', 'longitude']) See Also -------- :class:`libpysal.weights.weights.WSP` """ z_id, coords_labels = _da_checker(da, z_value, coords_labels) shape = da.shape if z_id: slice_dict = {} slice_dict[coords_labels["z_label"]] = 0 shape = da[slice_dict].shape slice_dict[coords_labels["z_label"]] = slice(z_id - 1, z_id) da = da[slice_dict] ser = da.to_series() dtype = np.int32 if (shape[0] * shape[1]) < 46340 ** 2 else np.int64 if "nodatavals" in da.attrs and da.attrs["nodatavals"]: mask = (ser != da.attrs["nodatavals"][0]).to_numpy() ids = np.where(mask)[0] id_map = _idmap(ids, mask, dtype) ser = ser[ser != da.attrs["nodatavals"][0]] else: ids = np.arange(len(ser), dtype=dtype) id_map = ids.copy() n = len(ids) try: import numba except (ModuleNotFoundError, ImportError): warn( "numba cannot be imported, parallel processing " "and include_nodata functionality will be disabled. " "falling back to slower method" ) include_nodata = False # Fallback method to build sparse matrix sw = lat2SW(*shape, criterion) if "nodatavals" in da.attrs and da.attrs["nodatavals"]: sw = sw[mask] sw = sw[:, mask] else: k_nas = k if include_nodata else 1 if n_jobs != 1: try: import joblib except (ModuleNotFoundError, ImportError): warn( f"Parallel processing is requested (n_jobs={n_jobs})," f" but joblib cannot be imported. n_jobs will be set" f" to 1.", stacklevel=2, ) n_jobs = 1 if n_jobs == 1: sw_tup = _SWbuilder( *shape, ids, id_map, criterion, k_nas, dtype ) # -> (data, (row, col)) else: if n_jobs == -1: n_jobs = os.cpu_count() # Parallel implementation sw_tup = _parSWbuilder( *shape, ids, id_map, criterion, k_nas, dtype, n_jobs ) # -> (data, (row, col)) sw = sparse.csr_matrix( sw_tup, shape=(n, n), dtype=np.int8, ) # Higher_order functionality, this uses idea from # libpysal#313 for adding higher order neighbors. # Since diagonal elements are also added in the result, # this method set the diagonal elements to zero and # then eliminate zeros from the data. This changes the # sparcity of the csr_matrix !! if k > 1 and not include_nodata: sw = sum(map(lambda x: sw ** x, range(1, k + 1))) sw.setdiag(0) sw.eliminate_zeros() sw.data[:] = np.ones_like(sw.data, dtype=np.int8) index = ser.index wsp = WSP(sw, index=index) return wsp def w2da(data, w, attrs={}, coords=None): """ Creates xarray.DataArray object from passed data aligned with W object. Parameters ---------- data : array/list/pd.Series 1d array-like data with dimensionality conforming to w w : libpysal.weights.W Spatial weights object aligned with passed data attrs : Dictionary Attributes stored in dict related to DataArray, e.g. da.attrs Default is {} empty dictionary. coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates Coordinates corresponding to DataArray, e.g. da.coords Returns ------- da : xarray.DataArray instance of xarray.DataArray Examples -------- >>> from libpysal.raster import da2W, testDataArray, w2da >>> da = testDataArray() >>> da.shape (3, 4, 4) >>> w = da2W(da, z_value=2) >>> data = np.random.randint(0, 255, len(w.index)) >>> da1 = w2da(data, w) """ if not isinstance(w, W): raise TypeError("w must be an instance of weights.W") if hasattr(w, "index"): da = _index2da(data, w.index, attrs, coords) else: raise AttributeError( "This method requires `w` object to include `index` attribute that is built as a `pandas.MultiIndex` object." ) return da def wsp2da(data, wsp, attrs={}, coords=None): """ Creates xarray.DataArray object from passed data aligned with WSP object. Parameters ---------- data : array/list/pd.Series 1d array-like data with dimensionality conforming to wsp wsp : libpysal.weights.WSP Sparse weights object aligned with passed data attrs : Dictionary Attributes stored in dict related to DataArray, e.g. da.attrs Default is {} empty dictionary. coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates coordinates corresponding to DataArray, e.g. da.coords Returns ------- da : xarray.DataArray instance of xarray.DataArray Examples -------- >>> from libpysal.raster import da2WSP, testDataArray, wsp2da >>> da = testDataArray() >>> da.shape (3, 4, 4) >>> wsp = da2WSP(da, z_value=2) >>> data = np.random.randint(0, 255, len(wsp.index)) >>> da1 = w2da(data, wsp) """ if not isinstance(wsp, WSP): raise TypeError("wsp must be an instance of weights.WSP") if hasattr(wsp, "index"): da = _index2da(data, wsp.index, attrs, coords) else: raise AttributeError( "This method requires `wsp` object to include `index` attribute that is built as a `pandas.MultiIndex` object." ) return da def testDataArray(shape=(3, 4, 4), time=False, rand=False, missing_vals=True): """ Creates 2 or 3 dimensional test xarray.DataArray object Parameters ---------- shape : tuple Tuple containing shape of the DataArray aligned with following dimension = (lat, lon) or (layer, lat, lon) Default shape = (3, 4, 4) time : boolean Type of layer, if True then layer=time else layer=band Default is False. rand : boolean If True, creates a DataArray filled with unique and random data. Default is false (generates seeded random data) missing_vals : boolean Create a DataArray filled with missing values. Default is True. Returns ------- da : xarray.DataArray instance of xarray.DataArray """ try: from xarray import DataArray except ImportError: raise ModuleNotFoundError("xarray must be installed to use this functionality") if not rand: np.random.seed(12345) coords = {} n = len(shape) if n != 2: layer = "time" if time else "band" dims = (layer, "y", "x") if time: layers = np.arange( np.datetime64("2020-07-30"), shape[0], dtype="datetime64[D]" ) else: layers = np.arange(1, shape[0] + 1) coords[dims[-3]] = layers else: dims = ("y", "x") coords[dims[-2]] = np.linspace(90, -90, shape[-2]) coords[dims[-1]] = np.linspace(-180, 180, shape[-1]) data = np.random.randint(0, 255, shape) attrs = {} if missing_vals: attrs["nodatavals"] = (-32768.0,) miss_ids = np.where(np.random.randint(2, size=shape) == 1) data[miss_ids] = attrs["nodatavals"][0] da = DataArray(data, coords, dims, attrs=attrs) return da def _da_checker(da, z_value, coords_labels): """ xarray.dataarray checker for raster interface Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) z_value : int/string/float Select the z_value of 3D DataArray with multiple layers. coords_labels : dictionary Pass dimension labels for coordinates and layers if they do not belong to default dimensions, which are (band/time, y/lat, x/lon) e.g. coords_labels = {"y_label": "latitude", "x_label": "longitude", "z_label": "year"} Default is {} empty dictionary. Returns ------- z_id : int Returns the index of layer dims : dictionary Mapped dimensions of the DataArray """ try: from xarray import DataArray except ImportError: raise ModuleNotFoundError("xarray must be installed to use this functionality") if not isinstance(da, DataArray): raise TypeError("da must be an instance of xarray.DataArray") if da.ndim not in [2, 3]: raise ValueError("da must be 2D or 3D") if not ( np.issubdtype(da.values.dtype, np.integer) or np.issubdtype(da.values.dtype, np.floating) ): raise ValueError("da must be an array of integers or float") # default dimensions def_labels = { "x_label": coords_labels["x_label"] if "x_label" in coords_labels else ("x" if hasattr(da, "x") else "lon"), "y_label": coords_labels["y_label"] if "y_label" in coords_labels else ("y" if hasattr(da, "y") else "lat"), } if da.ndim == 3: def_labels["z_label"] = ( coords_labels["z_label"] if "z_label" in coords_labels else ("band" if hasattr(da, "band") else "time") ) z_id = 1 if z_value is None: if da.sizes[def_labels["z_label"]] != 1: warn("Multiple layers detected. Using first layer as default.") else: z_id += tuple(da[def_labels["z_label"]]).index(z_value) else: z_id = None return z_id, def_labels def _index2da(data, index, attrs, coords): """ Creates xarray.DataArray object from passed data Parameters ---------- data : array/list/pd.Series 1d array-like data with dimensionality conforming to index index : pd.MultiIndex indices of the DataArray when converted to pd.Series attrs : Dictionary Attributes stored in dict related to DataArray, e.g. da.attrs coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates coordinates corresponding to DataArray, e.g. da[n-1:n].coords Returns ------- da : xarray.DataArray instance of xarray.DataArray """ try: from xarray import DataArray except ImportError: raise ModuleNotFoundError("xarray must be installed to use this functionality") data = np.array(data).flatten() idx = index dims = idx.names indexer = tuple(idx.codes) shape = tuple(lev.size for lev in idx.levels) if coords is None: missing = np.prod(shape) > idx.shape[0] if missing: if "nodatavals" in attrs: fill_value = attrs["nodatavals"][0] else: min_data = np.min(data) fill_value = min_data - 1 if min_data < 0 else -1 attrs["nodatavals"] = tuple([fill_value]) data_complete = np.full(shape, fill_value, data.dtype) else: data_complete = np.empty(shape, data.dtype) data_complete[indexer] = data coords = {} for dim, lev in zip(dims, idx.levels): coords[dim] = lev.to_numpy() else: fill = attrs["nodatavals"][0] if "nodatavals" in attrs else 0 data_complete = np.full(shape, fill, data.dtype) data_complete[indexer] = data da = DataArray(data_complete, coords=coords, dims=dims, attrs=attrs) return da @jit(nopython=True, fastmath=True) def _idmap(ids, mask, dtype): """ Utility function computes id_map of non-missing raster data Parameters ---------- ids : ndarray 1D array containing ids of non-missing raster data mask : ndarray 1D array mask array dtype : type Data type of the id_map array Returns ------- id_map : ndarray 1D array containing id_maps of non-missing raster data """ id_map = mask * 1 id_map[ids] = np.arange(len(ids), dtype=dtype) return id_map @jit(nopython=True, fastmath=True) def _SWbuilder( nrows, ncols, ids, id_map, criterion, k, dtype, ): """ Computes data and orders rows, cols, data for a single chunk Parameters ---------- nrows : int Number of rows in the raster data ncols : int Number of columns in the raster data ids : ndarray 1D array containing ids of non-missing raster data id_map : ndarray 1D array containing id_maps of non-missing raster data criterion : str Type of contiguity. k : int Order of contiguity, Default is 1 dtype : type Data type of the id_map array Returns ------- data : ndarray 1D ones array containing weight of each neighbor rows : ndarray 1D ones array containing row value of each id in the sparse weight object cols : ndarray 1D ones array containing columns value of each id in the sparse weight object """ rows, cols = _compute_chunk(nrows, ncols, ids, id_map, criterion, k, dtype) data = np.ones_like(rows, dtype=np.int8) return (data, (rows, cols)) @jit(nopython=True, fastmath=True, nogil=True) def _compute_chunk( nrows, ncols, ids, id_map, criterion, k, dtype, ): """ Computes rows cols for a single chunk Parameters ---------- nrows : int Number of rows in the raster data ncols : int Number of columns in the raster data ids : ndarray 1D array containing ids of non-missing raster data id_map : ndarray 1D array containing id_maps of non-missing raster data criterion : str Type of contiguity. k : int Order of contiguity, Default is 1 dtype : type Data type of the rows and cols array Returns ------- rows : ndarray 1D ones array containing row value of each id in the sparse weight object cols : ndarray 1D ones array containing columns value of each id in the sparse weight object ni : int Number of rows and cols """ n = len(ids) # Setting d which is used for row, col preallocation d = 4 if criterion == "rook" else 8 if k > 1: d = int((k / 2) * (2 * 8 + (k - 1) * 8)) rows = np.empty(d * n, dtype=dtype) cols = np.empty_like(rows) ni = 0 # -> Pointer to store rows and cols in array for order in range(1, k + 1): condition = ( (order - 1) if criterion == "queen" else ((k - order) if ((k - order) < order) else (order - 1)) ) for i in range(n): id_i = ids[i] og_id = id_map[id_i] if ((id_i + order) % ncols) >= order: # east neighbor id_neighbor = id_map[id_i + order] if id_neighbor: rows[ni], cols[ni] = og_id, id_neighbor ni += 1 rows[ni], cols[ni] = id_neighbor, og_id ni += 1 # north-east to south-east neighbors for j in range(condition): if (id_i // ncols) < (nrows - j - 1): id_neighbor = id_map[(id_i + order) + (ncols * (j + 1))] if id_neighbor: rows[ni], cols[ni] = og_id, id_neighbor ni += 1 rows[ni], cols[ni] = id_neighbor, og_id ni += 1 if (id_i // ncols) >= j + 1: id_neighbor = id_map[(id_i + order) - (ncols * (j + 1))] if id_neighbor: rows[ni], cols[ni] = og_id, id_neighbor ni += 1 rows[ni], cols[ni] = id_neighbor, og_id ni += 1 if (id_i // ncols) < (nrows - order): # south neighbor id_neighbor = id_map[id_i + (ncols * order)] if id_neighbor: rows[ni], cols[ni] = og_id, id_neighbor ni += 1 rows[ni], cols[ni] = id_neighbor, og_id ni += 1 # south-west to south-east neighbors for j in range(condition): if (id_i % ncols) >= j + 1: id_neighbor = id_map[id_i + (ncols * order) - j - 1] if id_neighbor: rows[ni], cols[ni] = og_id, id_neighbor ni += 1 rows[ni], cols[ni] = id_neighbor, og_id ni += 1 if ((id_i + j + 1) % ncols) >= j + 1: id_neighbor = id_map[id_i + (ncols * order) + j + 1] if id_neighbor: rows[ni], cols[ni] = og_id, id_neighbor ni += 1 rows[ni], cols[ni] = id_neighbor, og_id ni += 1 if criterion == "queen" or ((k / order) >= 2.0): if (id_i % ncols) >= order: # south-west neighbor id_neighbor = id_map[id_i + (ncols * order) - order] if id_neighbor: rows[ni], cols[ni] = og_id, id_neighbor ni += 1 rows[ni], cols[ni] = id_neighbor, og_id ni += 1 if ((id_i + order) % ncols) >= order: # south-east neighbor id_neighbor = id_map[id_i + (ncols * order) + order] if id_neighbor: rows[ni], cols[ni] = og_id, id_neighbor ni += 1 rows[ni], cols[ni] = id_neighbor, og_id ni += 1 return rows[:ni], cols[:ni] @jit(nopython=True, fastmath=True) def _chunk_generator( n_jobs, starts, ids, ): """ Construct chunks to iterate over within numba in parallel Parameters ---------- n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. starts : ndarray (n_chunks+1,) array of positional starts for ids chunk ids : ndarray 1D array containing ids of non-missing raster data Yields ------ ids_chunk : numpy.ndarray (n_chunk,) array containing the chunk of non-missing raster data """ chunk_size = starts[1] - starts[0] for i in range(n_jobs): start = starts[i] ids_chunk = ids[start : (start + chunk_size)] yield (ids_chunk,) def _parSWbuilder( nrows, ncols, ids, id_map, criterion, k, dtype, n_jobs, ): """ Computes data and orders rows, cols, data in parallel using numba Parameters ---------- nrows : int Number of rows in the raster data ncols : int Number of columns in the raster data ids : ndarray 1D array containing ids of non-missing raster data id_map : ndarray 1D array containing id_maps of non-missing raster data criterion : str Type of contiguity. k : int Order of contiguity, Default is 1 dtype : type Data type of the rows and cols array n_jobs : int Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Returns ------- data : ndarray 1D ones array containing weight of each neighbor rows : ndarray 1D ones array containing row value of each id in the sparse weight object cols : ndarray 1D ones array containing columns value of each id in the sparse weight object """ from joblib import Parallel, delayed, parallel_backend n = len(ids) chunk_size = n // n_jobs + 1 starts = np.arange(n_jobs + 1) * chunk_size chunk = _chunk_generator(n_jobs, starts, ids) with parallel_backend("threading"): worker_out = Parallel(n_jobs=n_jobs)( delayed(_compute_chunk)(nrows, ncols, *ids, id_map, criterion, k, dtype) for ids in chunk ) rows, cols = zip(*worker_out) rows = np.concatenate(rows) cols = np.concatenate(cols) data = np.ones_like(rows, dtype=np.int8) return (data, (rows, cols)) libpysal-4.9.2/libpysal/weights/set_operations.py000066400000000000000000000423471452177046000222660ustar00rootroot00000000000000""" Set-like manipulation of weights matrices. """ __author__ = "Sergio J. Rey , Charles Schmidt , David Folch , Dani Arribas-Bel " import copy from .weights import W, WSP from scipy.sparse import isspmatrix_csr from numpy import ones __all__ = [ "w_union", "w_intersection", "w_difference", "w_symmetric_difference", "w_subset", "w_clip", ] def w_union(w1, w2, **kwargs): """ Returns a binary weights object, w, that includes all neighbor pairs that exist in either w1 or w2. Parameters ---------- w1 : W object w2 : W object **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- w : W object Notes ----- ID comparisons are performed using ==, therefore the integer ID 2 is equivalent to the float ID 2.0. Returns a matrix with all the unique IDs from w1 and w2. Examples -------- Construct rook weights matrices for two regions, one is 4x4 (16 areas) and the other is 6x4 (24 areas). A union of these two weights matrices results in the new weights matrix matching the larger one. >>> from libpysal.weights import lat2W, w_union >>> w1 = lat2W(4,4) >>> w2 = lat2W(6,4) >>> w = w_union(w1, w2) >>> w1[0] == w[0] True >>> w1.neighbors[15] [11, 14] >>> w2.neighbors[15] [11, 14, 19] >>> w.neighbors[15] [19, 11, 14] """ neighbors = dict(list(w1.neighbors.items())) for i in w2.neighbors: if i in neighbors: add_neigh = set(neighbors[i]).union(set(w2.neighbors[i])) neighbors[i] = list(add_neigh) else: neighbors[i] = copy.copy(w2.neighbors[i]) return W(neighbors, **kwargs) def w_intersection(w1, w2, w_shape="w1", **kwargs): """ Returns a binary weights object, w, that includes only those neighbor pairs that exist in both w1 and w2. Parameters ---------- w1 : W object w2 : W object w_shape : string Defines the shape of the returned weights matrix. 'w1' returns a matrix with the same IDs as w1; 'all' returns a matrix with all the unique IDs from w1 and w2; and 'min' returns a matrix with only the IDs occurring in both w1 and w2. **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- w : W object Notes ----- ID comparisons are performed using ==, therefore the integer ID 2 is equivalent to the float ID 2.0. Examples -------- Construct rook weights matrices for two regions, one is 4x4 (16 areas) and the other is 6x4 (24 areas). An intersection of these two weights matrices results in the new weights matrix matching the smaller one. >>> from libpysal.weights import lat2W, w_intersection >>> w1 = lat2W(4,4) >>> w2 = lat2W(6,4) >>> w = w_intersection(w1, w2) >>> w1[0] == w[0] True >>> w1.neighbors[15] [11, 14] >>> w2.neighbors[15] [11, 14, 19] >>> w.neighbors[15] [11, 14] """ if w_shape == "w1": neigh_keys = list(w1.neighbors.keys()) elif w_shape == "all": neigh_keys = set(w1.neighbors.keys()).union(set(w2.neighbors.keys())) elif w_shape == "min": neigh_keys = set(w1.neighbors.keys()).intersection(set(w2.neighbors.keys())) else: raise Exception("invalid string passed to w_shape") neighbors = {} for i in neigh_keys: if i in w1.neighbors and i in w2.neighbors: add_neigh = set(w1.neighbors[i]).intersection(set(w2.neighbors[i])) neighbors[i] = list(add_neigh) else: neighbors[i] = [] return W(neighbors, **kwargs) def w_difference(w1, w2, w_shape="w1", constrained=True, **kwargs): """ Returns a binary weights object, w, that includes only neighbor pairs in w1 that are not in w2. The w_shape and constrained parameters determine which pairs in w1 that are not in w2 are returned. Parameters ---------- w1 : W object w2 : W object w_shape : string Defines the shape of the returned weights matrix. 'w1' returns a matrix with the same IDs as w1; 'all' returns a matrix with all the unique IDs from w1 and w2; and 'min' returns a matrix with the IDs occurring in w1 and not in w2. constrained : boolean If False then the full set of neighbor pairs in w1 that are not in w2 are returned. If True then those pairs that would not be possible if w_shape='min' are dropped. Ignored if w_shape is set to 'min'. **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- w : W object Notes ----- ID comparisons are performed using ==, therefore the integer ID 2 is equivalent to the float ID 2.0. Examples -------- Construct rook (w2) and queen (w1) weights matrices for two 4x4 regions (16 areas). A queen matrix has all the joins a rook matrix does plus joins between areas that share a corner. The new matrix formed by the difference of rook from queen contains only join at corners (typically called a bishop matrix). Note that the difference of queen from rook would result in a weights matrix with no joins. >>> from libpysal.weights import lat2W, w_difference >>> w1 = lat2W(4,4,rook=False) >>> w2 = lat2W(4,4,rook=True) >>> w = w_difference(w1, w2, constrained=False) >>> w1[0] == w[0] False >>> w1.neighbors[15] [10, 11, 14] >>> w2.neighbors[15] [11, 14] >>> w.neighbors[15] [10] """ if w_shape == "w1": neigh_keys = list(w1.neighbors.keys()) elif w_shape == "all": neigh_keys = set(w1.neighbors.keys()).union(set(w2.neighbors.keys())) elif w_shape == "min": neigh_keys = set(w1.neighbors.keys()).difference(set(w2.neighbors.keys())) if not neigh_keys: raise Exception("returned an empty weights matrix") else: raise Exception("invalid string passed to w_shape") neighbors = {} for i in neigh_keys: if i in w1.neighbors: if i in w2.neighbors: add_neigh = set(w1.neighbors[i]).difference(set(w2.neighbors[i])) neighbors[i] = list(add_neigh) else: neighbors[i] = copy.copy(w1.neighbors[i]) else: neighbors[i] = [] if constrained or w_shape == "min": constrained_keys = set(w1.neighbors.keys()).difference(set(w2.neighbors.keys())) island_keys = set(neighbors.keys()).difference(constrained_keys) for i in island_keys: neighbors[i] = [] for i in constrained_keys: neighbors[i] = list(set(neighbors[i]).intersection(constrained_keys)) return W(neighbors, **kwargs) def w_symmetric_difference(w1, w2, w_shape="all", constrained=True, **kwargs): """ Returns a binary weights object, w, that includes only neighbor pairs that are not shared by w1 and w2. The w_shape and constrained parameters determine which pairs that are not shared by w1 and w2 are returned. Parameters ---------- w1 : W object w2 : W object w_shape : string Defines the shape of the returned weights matrix. 'all' returns a matrix with all the unique IDs from w1 and w2; and 'min' returns a matrix with the IDs not shared by w1 and w2. constrained : boolean If False then the full set of neighbor pairs that are not shared by w1 and w2 are returned. If True then those pairs that would not be possible if w_shape='min' are dropped. Ignored if w_shape is set to 'min'. **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- w : W object Notes ----- ID comparisons are performed using ==, therefore the integer ID 2 is equivalent to the float ID 2.0. Examples -------- Construct queen weights matrix for a 4x4 (16 areas) region (w1) and a rook matrix for a 6x4 (24 areas) region (w2). The symmetric difference of these two matrices (with w_shape set to 'all' and constrained set to False) contains the corner joins in the overlap area, all the joins in the non-overlap area. >>> from libpysal.weights import lat2W, w_symmetric_difference >>> w1 = lat2W(4,4,rook=False) >>> w2 = lat2W(6,4,rook=True) >>> w = w_symmetric_difference(w1, w2, constrained=False) >>> w1[0] == w[0] False >>> w1.neighbors[15] [10, 11, 14] >>> w2.neighbors[15] [11, 14, 19] >>> set(w.neighbors[15]) == set([10, 19]) True """ if w_shape == "all": neigh_keys = set(w1.neighbors.keys()).union(set(w2.neighbors.keys())) elif w_shape == "min": neigh_keys = set(w1.neighbors.keys()).symmetric_difference( set(w2.neighbors.keys()) ) else: raise Exception("invalid string passed to w_shape") neighbors = {} for i in neigh_keys: if i in w1.neighbors: if i in w2.neighbors: add_neigh = set(w1.neighbors[i]).symmetric_difference( set(w2.neighbors[i]) ) neighbors[i] = list(add_neigh) else: neighbors[i] = copy.copy(w1.neighbors[i]) elif i in w2.neighbors: neighbors[i] = copy.copy(w2.neighbors[i]) else: neighbors[i] = [] if constrained or w_shape == "min": constrained_keys = set(w1.neighbors.keys()).difference(set(w2.neighbors.keys())) island_keys = set(neighbors.keys()).difference(constrained_keys) for i in island_keys: neighbors[i] = [] for i in constrained_keys: neighbors[i] = list(set(neighbors[i]).intersection(constrained_keys)) return W(neighbors, **kwargs) def w_subset(w1, ids, **kwargs): """ Returns a binary weights object, w, that includes only those observations in ids. Parameters ---------- w1 : W object ids : list A list containing the IDs to be include in the returned weights object. **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- w : W object Examples -------- Construct a rook weights matrix for a 6x4 region (24 areas). By default PySAL assigns integer IDs to the areas in a region. By passing in a list of integers from 0 to 15, the first 16 areas are extracted from the previous weights matrix, and only those joins relevant to the new region are retained. >>> from libpysal.weights import lat2W, w_subset >>> w1 = lat2W(6,4) >>> ids = range(16) >>> w = w_subset(w1, ids) >>> w1[0] == w[0] True >>> w1.neighbors[15] [11, 14, 19] >>> w.neighbors[15] [11, 14] """ neighbors = {} ids_set = set(list(ids)) for i in ids: if i in w1.neighbors: neigh_add = ids_set.intersection(set(w1.neighbors[i])) neighbors[i] = list(neigh_add) else: neighbors[i] = [] return W(neighbors, id_order=list(ids), **kwargs) def w_clip(w1, w2, outSP=True, **kwargs): """ Clip a continuous W object (w1) with a different W object (w2) so only cells where w2 has a non-zero value remain with non-zero values in w1. Checks on w1 and w2 are performed to make sure they conform to the appropriate format and, if not, they are converted. Parameters ---------- w1 : W W, scipy.sparse.csr.csr_matrix Potentially continuous weights matrix to be clipped. The clipped matrix wc will have at most the same elements as w1. w2 : W W, scipy.sparse.csr.csr_matrix Weights matrix to use as shell to clip w1. Automatically converted to binary format. Only non-zero elements in w2 will be kept non-zero in wc. NOTE: assumed to be of the same shape as w1 outSP : boolean If True (default) return sparse version of the clipped W, if False, return W object of the clipped matrix **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- wc : W W, scipy.sparse.csr.csr_matrix Clipped W object (sparse if outSP=Ture). It inherits ``id_order`` from w1. Examples -------- >>> from libpysal.weights import lat2W First create a W object from a lattice using queen contiguity and row-standardize it (note that these weights will stay when we clip the object, but they will not neccesarily represent a row-standardization anymore): >>> w1 = lat2W(3, 2, rook=False) >>> w1.transform = 'R' We will clip that geography assuming observations 0, 2, 3 and 4 belong to one group and 1, 5 belong to another group and we don't want both groups to interact with each other in our weights (i.e. w_ij = 0 if i and j in different groups). For that, we use the following method: >>> import libpysal >>> w2 = libpysal.weights.block_weights(['r1', 'r2', 'r1', 'r1', 'r1', 'r2']) To illustrate that w2 will only be considered as binary even when the object passed is not, we can row-standardize it >>> w2.transform = 'R' The clipped object ``wc`` will contain only the spatial queen relationships that occur within one group ('r1' or 'r2') but will have gotten rid of those that happen across groups >>> wcs = libpysal.weights.w_clip(w1, w2, outSP=True) This will create a sparse object (recommended when n is large). >>> wcs.sparse.toarray() array([[0. , 0. , 0.33333333, 0.33333333, 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. ], [0.2 , 0. , 0. , 0.2 , 0.2 , 0. ], [0.2 , 0. , 0.2 , 0. , 0.2 , 0. ], [0. , 0. , 0.33333333, 0.33333333, 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. ]]) If we wanted an original W object, we can control that with the argument ``outSP``: >>> wc = libpysal.weights.w_clip(w1, w2, outSP=False) >>> wc.full()[0] array([[0. , 0. , 0.33333333, 0.33333333, 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. ], [0.2 , 0. , 0. , 0.2 , 0.2 , 0. ], [0.2 , 0. , 0.2 , 0. , 0.2 , 0. ], [0. , 0. , 0.33333333, 0.33333333, 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. ]]) You can check they are actually the same: >>> wcs.sparse.toarray() == wc.full()[0] array([[ True, True, True, True, True, True], [ True, True, True, True, True, True], [ True, True, True, True, True, True], [ True, True, True, True, True, True], [ True, True, True, True, True, True], [ True, True, True, True, True, True]]) """ from .util import WSP2W if not w1.id_order: w1.id_order = None id_order = w1.id_order if not isspmatrix_csr(w1): w1 = w1.sparse if not isspmatrix_csr(w2): w2 = w2.sparse w2.data = ones(w2.data.shape) wc = w1.multiply(w2) wc = WSP(wc, id_order=id_order) if not outSP: wc = WSP2W(wc, **kwargs) return wc libpysal-4.9.2/libpysal/weights/spatial_lag.py000066400000000000000000000213461452177046000215040ustar00rootroot00000000000000""" Spatial lag operations. """ __author__ = "Sergio J. Rey , David C. Folch , Levi John Wolf >> import libpysal >>> import numpy as np >>> w = libpysal.weights.lat2W(3, 3) >>> y = np.arange(9) >>> yl = libpysal.weights.lag_spatial(w, y) >>> yl array([ 4., 6., 6., 10., 16., 14., 10., 18., 12.]) Row standardize the weights matrix and recompute the spatial lag >>> w.transform = 'r' >>> yl = libpysal.weights.lag_spatial(w, y) >>> yl array([2. , 2. , 3. , 3.33333333, 4. , 4.66666667, 5. , 6. , 6. ]) Explicitly define data vector as 9x1 and recompute the spatial lag >>> y.shape = (9, 1) >>> yl = libpysal.weights.lag_spatial(w, y) >>> yl array([[2. ], [2. ], [3. ], [3.33333333], [4. ], [4.66666667], [5. ], [6. ], [6. ]]) Take the spatial lag of a 9x2 data matrix >>> yr = np.arange(8, -1, -1) >>> yr.shape = (9, 1) >>> x = np.hstack((y, yr)) >>> yl = libpysal.weights.lag_spatial(w, x) >>> yl array([[2. , 6. ], [2. , 6. ], [3. , 5. ], [3.33333333, 4.66666667], [4. , 4. ], [4.66666667, 3.33333333], [5. , 3. ], [6. , 2. ], [6. , 2. ]]) """ return w.sparse * y def lag_categorical(w, y, ties="tryself"): """ Spatial lag operator for categorical variables. Constructs the most common categories of neighboring observations, weighted by their weight strength. Parameters ---------- w : W PySAL spatial weightsobject y : iterable iterable collection of categories (either int or string) with dimensionality conforming to w (see examples) ties : str string describing the method to use when resolving ties. By default, the option is "tryself", and the category of the focal observation is included with its neighbors to try and break a tie. If this does not resolve the tie, a winner is chosen randomly. To just use random choice to break ties, pass "random" instead. Returns ------- an (n x k) column vector containing the most common neighboring observation Notes ----- This works on any array where the number of unique elements along the column axis is less than the number of elements in the array, for any dtype. That means the routine should work on any dtype that np.unique() can compare. Examples -------- Set up a 9x9 weights matrix describing a 3x3 regular lattice. Lag one list of categorical variables with no ties. >>> import libpysal >>> import numpy as np >>> np.random.seed(12345) >>> w = libpysal.weights.lat2W(3, 3) >>> y = ['a','b','a','b','c','b','c','b','c'] >>> y_l = libpysal.weights.lag_categorical(w, y) >>> np.array_equal(y_l, np.array(['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'])) True Explicitly reshape y into a (9x1) array and calculate lag again >>> yvect = np.array(y).reshape(9,1) >>> yvect_l = libpysal.weights.lag_categorical(w,yvect) >>> check = np.array( [ [i] for i in ['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b']] ) >>> np.array_equal(yvect_l, check) True compute the lag of a 9x2 matrix of categories >>> y2 = ['a', 'c', 'c', 'd', 'b', 'a', 'd', 'd', 'c'] >>> ym = np.vstack((y,y2)).T >>> ym_lag = libpysal.weights.lag_categorical(w,ym) >>> check = np.array([['b', 'd'], ['a', 'c'], ['b', 'c'], ['c', 'd'], ['b', 'd'], ['c', 'c'], ['b', 'd'], ['c', 'd'], ['b', 'c']]) >>> np.array_equal(check, ym_lag) True """ if isinstance(y, list): y = np.array(y) orig_shape = y.shape if len(orig_shape) > 1: if orig_shape[1] > 1: return np.vstack([lag_categorical(w, col) for col in y.T]).T y = y.flatten() output = np.zeros_like(y) labels = np.unique(y) normalized_labels = np.zeros(y.shape, dtype=int) for i, label in enumerate(labels): normalized_labels[y == label] = i for focal_name, neighbors in w: focal_idx = w.id2i[focal_name] neighborhood_tally = np.zeros(labels.shape) for neighb_name, weight in list(neighbors.items()): neighb_idx = w.id2i[neighb_name] neighb_label = normalized_labels[neighb_idx] neighborhood_tally[neighb_label] += weight out_label_idx = _resolve_ties( focal_idx, normalized_labels, neighborhood_tally, neighbors, ties, w ) output[focal_idx] = labels[out_label_idx] return output.reshape(orig_shape) def _resolve_ties(idx, normalized_labels, tally, neighbors, method, w): """ Helper function to resolve ties if lag is multimodal first, if this function gets called when there's actually no tie, then the correct value will be picked. if 'random' is selected as the method, a random tiebeaker is picked if 'tryself' is selected, then the observation's own value will be used in an attempt to break the tie, but if it fails, a random tiebreaker will be selected. Parameters ---------- idx : int index (aligned with `normalized_labels`) of the current observation being resolved. normalized_labels : (n,) array of ints normalized array of labels for each observation tally : (p,) array of floats current tally of neighbors' labels around `idx` to resolve. neighbors : dict of (neighbor_name : weight) the elements of the weights object, identical to w[idx] method : string configuration option to use a specific tiebreaking method. supported options are: 1. tryself: Use the focal observation's label to tiebreak. If this doesn't successfully break the tie, (which only occurs if it induces a new tie), decide randomly. 2. random: Resolve the tie randomly amongst winners. 3. lowest: Pick the lowest-value label amongst winners. 4. highest: Pick the highest-value label amongst winners. w : pysal.W object a PySAL weights object aligned with normalized_labels. Returns ------- integer denoting which label to use to label the observation. """ (ties,) = np.where(tally == tally.max()) # returns a tuple for flat arrays if len(tally[tally == tally.max()]) <= 1: # no tie, pick the highest return np.argmax(tally).astype(int) elif method.lower() == "random": # choose randomly from tally return np.random.choice(np.squeeze(ties)).astype(int) elif method.lower() == "lowest": # pick lowest tied value return ties[0].astype(int) elif method.lower() == "highest": # pick highest tied value return ties[-1].astype(int) elif ( method.lower() == "tryself" ): # add self-label as observation, try again, random if fail mean_neighbor_value = np.mean(list(neighbors.values())) tally[normalized_labels[idx]] += mean_neighbor_value return _resolve_ties(idx, normalized_labels, tally, neighbors, "random", w) else: raise KeyError("Tie-breaking method for categorical lag not recognized") libpysal-4.9.2/libpysal/weights/spintW.py000066400000000000000000000223121452177046000205020ustar00rootroot00000000000000""" Spatial weights for spatial interaction including contiguity OD weights (ODW), network based weights (netW), and distance-decay based vector weights (vecW). """ __author__ = "Taylor Oshan " from scipy.sparse import kron from .weights import W, WSP from .distance import DistanceBand from collections import OrderedDict def ODW(Wo, Wd, transform="r", silence_warnings=True): """ Constructs an o*d by o*d origin-destination style spatial weight for o*d flows using standard spatial weights on o origins and d destinations. Input spatial weights must be binary or able to be sutiably transformed to binary. Parameters ---------- Wo : W object for origin locations o x o spatial weight object amongst o origins Wd : W object for destination locations d x d spatial weight object amongst d destinations transform : Transformation for standardization of final OD spatial weight; default is 'r' for row standardized Returns ------- W : spatial contiguity W object for assocations between flows o*d x o*d spatial weight object amongst o*d flows between o origins and d destinations Examples -------- >>> import libpysal >>> O = libpysal.weights.lat2W(2,2) >>> D = libpysal.weights.lat2W(2,2) >>> OD = libpysal.weights.ODW(O,D) >>> OD.weights[0] [0.25, 0.25, 0.25, 0.25] >>> OD.neighbors[0] [5, 6, 9, 10] >>> OD.full()[0][0] array([0. , 0. , 0. , 0. , 0. , 0.25, 0.25, 0. , 0. , 0.25, 0.25, 0. , 0. , 0. , 0. , 0. ]) """ if Wo.transform != "b": try: Wo.tranform = "b" except: raise AttributeError( "Wo is not binary and cannot be transformed to " "binary. Wo must be binary or suitably transformed to binary." ) if Wd.transform != "b": try: Wd.tranform = "b" except: raise AttributeError( "Wd is not binary and cannot be transformed to " "binary. Wd must be binary or suitably transformed to binary." ) Wo = Wo.sparse Wo.eliminate_zeros() Wd = Wd.sparse Wd.eliminate_zeros() Ww = kron(Wo, Wd, format="csr") Ww.eliminate_zeros() Ww = WSP(Ww).to_W(silence_warnings=silence_warnings) Ww.transform = transform return Ww def netW(link_list, share="A", transform="r", **kwargs): """ Create a network-contiguity based weight object based on different nodal relationships encoded in a network. Parameters ---------- link_list : list of tuples where each tuple is of the form (o,d) where o is an origin id and d is a destination id share : string denoting how to define the nodal relationship used to determine neighboring edges; defualt is 'A' for any shared nodes between two network edges; options include: O a shared origin node; D a shared destination node; OD; a shared origin or a shared destination node; C a shared node that is the destination of the first edge and the origin of the second edge - i.e., a directed chain is formed moving from edge one to edge two. transform : Transformation for standardization of final OD spatial weight; default is 'r' for row standardized **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- W : nodal contiguity W object for networkd edges or flows W Object representing the binary adjacency of the network edges given a definition of nodal relationshilibpysal.weights.spintW. Examples -------- >>> import libpysal >>> links = [('a','b'), ('a','c'), ('a','d'), ('c','d'), ('c', 'b'), ('c','a')] >>> O = libpysal.weights.netW(links, share='O') >>> O.neighbors[('a', 'b')] [('a', 'c'), ('a', 'd')] >>> OD = libpysal.weights.netW(links, share='OD') >>> OD.neighbors[('a', 'b')] [('a', 'c'), ('a', 'd'), ('c', 'b')] >>> any_common = libpysal.weights.netW(links, share='A') >>> any_common.neighbors[('a', 'b')] [('a', 'c'), ('a', 'd'), ('c', 'b'), ('c', 'a')] """ neighbors = {} neighbors = OrderedDict() edges = link_list for key in edges: neighbors[key] = [] for neigh in edges: if key == neigh: continue if share.upper() == "OD": if key[0] == neigh[0] or key[1] == neigh[1]: neighbors[key].append(neigh) elif share.upper() == "O": if key[0] == neigh[0]: neighbors[key].append(neigh) elif share.upper() == "D": if key[1] == neigh[1]: neighbors[key].append(neigh) elif share.upper() == "C": if key[1] == neigh[0]: neighbors[key].append(neigh) elif share.upper() == "A": if ( key[0] == neigh[0] or key[0] == neigh[1] or key[1] == neigh[0] or key[1] == neigh[1] ): neighbors[key].append(neigh) else: raise AttributeError( "Parameter 'share' must be 'O', 'D'," " 'OD', or 'C'" ) netW = W(neighbors, **kwargs) netW.tranform = transform return netW def vecW( origin_x, origin_y, dest_x, dest_y, threshold, p=2, alpha=-1.0, binary=True, ids=None, build_sp=False, **kwargs ): """ Distance-based spatial weight for vectors that is computed using a 4-dimensional distance between the origin x,y-coordinates and the destination x,y-coordinates Parameters ---------- origin_x : list or array of vector origin x-coordinates origin_y : list or array of vector origin y-coordinates dest_x : list or array of vector destination x-coordinates dest_y : list or array of vector destination y-coordinates threshold : float distance band p : float Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance binary : boolean If true w_{ij}=1 if d_{i,j}<=threshold, otherwise w_{i,j}=0 If false wij=dij^{alpha} alpha : float distance decay parameter for weight (default -1.0) if alpha is positive the weights will not decline with distance. If binary is True, alpha is ignored ids : list values to use for keys of the neighbors and weights dicts build_sp : boolean True to build sparse distance matrix and false to build dense distance matrix; significant speed gains may be obtained dending on the sparsity of the of distance_matrix and threshold that is applied **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- W : DistanceBand W object that uses 4-dimenional distances between vectors origin and destination coordinates. Examples -------- >>> import libpysal >>> x1 = [5,6,3] >>> y1 = [1,8,5] >>> x2 = [2,4,9] >>> y2 = [3,6,1] >>> W1 = libpysal.weights.vecW(x1, y1, x2, y2, threshold=999) >>> list(W1.neighbors[0]) [1, 2] >>> W2 = libpysal.weights.vecW(x1, y2, x1, y2, threshold=8.5) >>> list(W2.neighbors[0]) [1, 2] """ data = list(zip(origin_x, origin_y, dest_x, dest_y)) W = DistanceBand( data, threshold=threshold, p=p, binary=binary, alpha=alpha, ids=ids, build_sp=False, **kwargs ) return W def mat2L(edge_matrix): """ Convert a matrix denoting network connectivity (edges or flows) to a list denoting edges Parameters ---------- edge_matrix : array where rows denote network edge origins, columns denote network edge destinations, and non-zero entries denote the existence of an edge between a given origin and destination Returns ------- edge_list : list of tuples where each tuple is of the form (o,d) where o is an origin id and d is a destination id """ if len(edge_matrix.shape) != 2: raise AttributeError( "Matrix of network edges should be two dimensions" "with edge origins on one axis and edge destinations on the" "second axis with non-zero matrix entires denoting an edge" "between and origin and destination" ) edge_list = [] rows, cols = edge_matrix.shape for row in range(rows): for col in range(cols): if edge_matrix[row, col] != 0: edge_list.append((row, col)) return edge_list libpysal-4.9.2/libpysal/weights/test.py000066400000000000000000000000001452177046000201630ustar00rootroot00000000000000libpysal-4.9.2/libpysal/weights/tests/000077500000000000000000000000001452177046000200065ustar00rootroot00000000000000libpysal-4.9.2/libpysal/weights/tests/__init__.py000066400000000000000000000000001452177046000221050ustar00rootroot00000000000000libpysal-4.9.2/libpysal/weights/tests/test_Wsets.py000066400000000000000000000055571452177046000225400ustar00rootroot00000000000000"""Unit test for set_operations module.""" import numpy as np from .. import set_operations from ..util import block_weights, lat2W class TestSetOperations: """Unit test for set_operations module.""" def test_w_union(self): """Unit test""" w1 = lat2W(4, 4) w2 = lat2W(6, 4) w3 = set_operations.w_union(w1, w2) assert w1[0] == w3[0] assert set(w1.neighbors[15]) == set([11, 14]) assert set(w2.neighbors[15]) == set([11, 14, 19]) assert set(w3.neighbors[15]) == set([19, 11, 14]) def test_w_intersection(self): """Unit test""" w1 = lat2W(4, 4) w2 = lat2W(6, 4) w3 = set_operations.w_union(w1, w2) assert w1[0] == w3[0] assert set(w1.neighbors[15]) == set([11, 14]) assert set(w2.neighbors[15]) == set([11, 14, 19]) assert set(w3.neighbors[15]) == set([19, 11, 14]) def test_w_difference(self): """Unit test""" w1 = lat2W(4, 4, rook=False) w2 = lat2W(4, 4, rook=True) w3 = set_operations.w_difference(w1, w2, constrained=False) assert w1[0] != w3[0] assert set(w1.neighbors[15]) == set([10, 11, 14]) assert set(w2.neighbors[15]) == set([11, 14]) assert set(w3.neighbors[15]) == set([10]) def test_w_symmetric_difference(self): """Unit test""" w1 = lat2W(4, 4, rook=False) w2 = lat2W(6, 4, rook=True) w3 = set_operations.w_symmetric_difference(w1, w2, constrained=False) assert w1[0] != w3[0] assert set(w1.neighbors[15]) == set([10, 11, 14]) assert set(w2.neighbors[15]) == set([11, 14, 19]) assert set(w3.neighbors[15]) == set([10, 19]) def test_w_subset(self): """Unit test""" w1 = lat2W(6, 4) ids = list(range(16)) w2 = set_operations.w_subset(w1, ids) assert w1[0] == w2[0] assert set(w1.neighbors[15]) == set([11, 14, 19]) assert set(w2.neighbors[15]) == set([11, 14]) def test_w_clip(self): """Unit test for w_clip""" w1 = lat2W(3, 2, rook=False) w1.transform = "R" w2 = block_weights(["r1", "r2", "r1", "r1", "r1", "r2"]) w2.transform = "R" wcs = set_operations.w_clip(w1, w2, outSP=True) expected_wcs = np.array( [ [0.0, 0.0, 0.33333333, 0.33333333, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.2, 0.0, 0.0, 0.2, 0.2, 0.0], [0.2, 0.0, 0.2, 0.0, 0.2, 0.0], [0.0, 0.0, 0.33333333, 0.33333333, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ] ) np.testing.assert_array_equal( np.around(wcs.sparse.toarray(), decimals=8), expected_wcs ) wc = set_operations.w_clip(w1, w2, outSP=False) np.testing.assert_array_equal(wcs.sparse.toarray(), wc.full()[0]) libpysal-4.9.2/libpysal/weights/tests/test__contW_lists.py000066400000000000000000000117161452177046000240740ustar00rootroot00000000000000import os import geopandas as gpd from ... import examples as pysal_examples from ...io.fileio import FileIO as ps_open from .._contW_lists import QUEEN, ROOK, ContiguityWeightsLists from ..weights import W class TestContiguityWeights: def setup_method(self): """Setup the binning contiguity weights""" shpObj = ps_open(pysal_examples.get_path("virginia.shp"), "r") self.binningW = ContiguityWeightsLists(shpObj, QUEEN) shpObj.close() def test_w_type(self): assert isinstance(self.binningW, ContiguityWeightsLists) def test_queen(self): assert QUEEN == 1 def test_rook(self): assert ROOK == 2 def test_contiguity_weights_lists(self): assert hasattr(self.binningW, "w") assert issubclass(dict, type(self.binningW.w)) assert len(self.binningW.w) == 136 def test_nested_polygons(self): # load queen gal file created using Open Geoda. geodaW = ps_open(pysal_examples.get_path("virginia.gal"), "r").read() # build matching W with pysal pysalWb = self.build_W( pysal_examples.get_path("virginia.shp"), QUEEN, "POLY_ID" ) # compare output. for key in geodaW.neighbors: geoda_neighbors = list(map(int, geodaW.neighbors[key])) pysalb_neighbors = pysalWb.neighbors[int(key)] geoda_neighbors.sort() pysalb_neighbors.sort() assert geoda_neighbors == pysalb_neighbors def test_true_rook(self): # load queen gal file created using Open Geoda. geodaW = ps_open(pysal_examples.get_path("rook31.gal"), "r").read() # build matching W with pysal # pysalW = pysal.rook_from_shapefile(pysal_examples.get_path('rook31.shp'),','POLY_ID') pysalWb = self.build_W(pysal_examples.get_path("rook31.shp"), ROOK, "POLY_ID") # compare output. for key in geodaW.neighbors: geoda_neighbors = list(map(int, geodaW.neighbors[key])) pysalb_neighbors = pysalWb.neighbors[int(key)] geoda_neighbors.sort() pysalb_neighbors.sort() assert geoda_neighbors == pysalb_neighbors def test_true_rook2(self): # load queen gal file created using Open Geoda. stl = pysal_examples.load_example("stl") gal_file = test_file = stl.get_path("stl_hom_rook.gal") geodaW = ps_open(gal_file, "r").read() # build matching W with pysal pysalWb = self.build_W(stl.get_path("stl_hom.shp"), ROOK, "POLY_ID_OG") # compare output. for key in geodaW.neighbors: geoda_neighbors = list(map(int, geodaW.neighbors[key])) pysalb_neighbors = pysalWb.neighbors[int(key)] geoda_neighbors.sort() pysalb_neighbors.sort() assert geoda_neighbors == pysalb_neighbors def test_true_rook3(self): # load queen gal file created using Open Geoda. geodaW = ps_open(pysal_examples.get_path("virginia_rook.gal"), "r").read() # build matching W with pysal pysalWb = self.build_W(pysal_examples.get_path("virginia.shp"), ROOK, "POLY_ID") # compare output. for key in geodaW.neighbors: geoda_neighbors = list(map(int, geodaW.neighbors[key])) pysalb_neighbors = pysalWb.neighbors[int(key)] geoda_neighbors.sort() pysalb_neighbors.sort() assert geoda_neighbors == pysalb_neighbors def test_shapely(self): pysalneighbs = ContiguityWeightsLists( ps_open(pysal_examples.get_path("virginia.shp")), ROOK ) gdf = gpd.read_file(pysal_examples.get_path("virginia.shp")) shplyneighbs = ContiguityWeightsLists(gdf.geometry.tolist(), ROOK) assert pysalneighbs.w == shplyneighbs.w pysalneighbs = ContiguityWeightsLists( ps_open(pysal_examples.get_path("virginia.shp")), QUEEN ) shplyneighbs = ContiguityWeightsLists(gdf.geometry.tolist(), QUEEN) assert pysalneighbs.w == shplyneighbs.w def build_W(self, shapefile, type, idVariable=None): """Building 2 W's the hard way. We need to do this so we can test both rtree and binning""" dbname = os.path.splitext(shapefile)[0] + ".dbf" db = ps_open(dbname) shpObj = ps_open(shapefile) neighbor_data = ContiguityWeightsLists(shpObj, type).w neighbors = {} weights = {} if idVariable: ids = db.by_col[idVariable] assert len(ids) == len(set(ids)) for key in neighbor_data: id = ids[key] if id not in neighbors: neighbors[id] = set() neighbors[id].update([ids[x] for x in neighbor_data[key]]) for key in neighbors: neighbors[key] = list(neighbors[key]) binningW = W(neighbors, id_order=ids) else: neighbors[key] = list(neighbors[key]) binningW = W(neighbors) return binningW libpysal-4.9.2/libpysal/weights/tests/test_adjlist.py000066400000000000000000000166601452177046000230620ustar00rootroot00000000000000import geopandas import numpy as np import pytest from ... import examples, io, weights from ...common import ATOL, RTOL from .. import adjtools as adj from ..util import lat2W class Test_Adjlist: def setup_method(self): self.knownW = io.open(examples.get_path("columbus.gal")).read() def test_round_trip_drop_islands_true(self): adjlist = self.knownW.to_adjlist( remove_symmetric=False, drop_islands=True ).astype(int) w_from_adj = weights.W.from_adjlist(adjlist) np.testing.assert_allclose( w_from_adj.sparse.toarray(), self.knownW.sparse.toarray() ) def test_round_trip_drop_islands_false(self): adjlist = self.knownW.to_adjlist( remove_symmetric=False, drop_islands=True ).astype(int) w_from_adj = weights.W.from_adjlist(adjlist) np.testing.assert_allclose( w_from_adj.sparse.toarray(), self.knownW.sparse.toarray() ) def test_filter(self): grid = lat2W(2, 2) alist = grid.to_adjlist(remove_symmetric=True, drop_islands=True) assert len(alist) == 4 with pytest.raises(AssertionError): # build this manually because of bug libpysal#322 alist_neighbors = alist.groupby("focal").neighbor.apply(list).to_dict() all_ids = set(alist_neighbors.keys()).union( *map(set, alist_neighbors.values()) ) for idx in set(all_ids).difference(set(alist_neighbors.keys())): alist_neighbors[idx] = [] badgrid = weights.W(alist_neighbors) np.testing.assert_allclose(badgrid.sparse.toarray(), grid.sparse.toarray()) assert set(alist.focal.unique()) == {0, 1, 2} assert set(alist.neighbor.unique()) == {1, 2, 3} assert alist.weight.unique().item() == 1 grid = lat2W(2, 2, id_type="string") alist = grid.to_adjlist(remove_symmetric=True, drop_islands=True) assert len(alist) == 4 with pytest.raises(AssertionError): # build this manually because of bug libpysal#322 alist_neighbors = alist.groupby("focal").neighbor.apply(list).to_dict() all_ids = set(alist_neighbors.keys()).union( *map(set, alist_neighbors.values()) ) for idx in set(all_ids).difference(set(alist_neighbors.keys())): alist_neighbors[idx] = [] badgrid = weights.W(alist_neighbors) np.testing.assert_allclose(badgrid.sparse.toarray(), grid.sparse.toarray()) tuples = set([tuple(t) for t in alist[["focal", "neighbor"]].values]) full_alist = grid.to_adjlist(drop_islands=True) all_possible = set([tuple(t) for t in full_alist[["focal", "neighbor"]].values]) assert tuples.issubset(all_possible), ( "the de-duped adjlist has links " "not in the duplicated adjlist." ) complements = all_possible.difference(tuples) reversed_complements = set([t[::-1] for t in complements]) assert reversed_complements == tuples, ( "the remaining links in the duplicated" " adjlist are not the reverse of the links" " in the deduplicated adjlist." ) assert alist.weight.unique().item() == 1 def apply_and_compare_columbus(self, col): import geopandas df = geopandas.read_file(examples.get_path("columbus.dbf")).head() W = weights.Queen.from_dataframe(df) alist = adj.adjlist_apply(df[col], W=W, to_adjlist_kws=dict(drop_islands=True)) right_hovals = alist.groupby("focal").att_focal.unique() assert (right_hovals == df[col]).all() allpairs = np.subtract.outer(df[col].values, df[col].values) flat_diffs = allpairs[W.sparse.toarray().astype(bool)] np.testing.assert_allclose(flat_diffs, alist["subtract"].values) return flat_diffs def test_apply(self): self.apply_and_compare_columbus("HOVAL") def test_mvapply(self): import geopandas df = geopandas.read_file(examples.get_path("columbus.dbf")).head() W = weights.Queen.from_dataframe(df) ssq = lambda x_y: np.sum((x_y[0] - x_y[1]) ** 2).item() ssq.__name__ = "sum_of_squares" alist = adj.adjlist_apply( df[["HOVAL", "CRIME", "INC"]], W=W, func=ssq, to_adjlist_kws=dict(drop_islands=True), ) known_ssq = [ 1301.1639302990804, 3163.46450914361, 1301.1639302990804, 499.52656498472993, 594.518273032036, 3163.46450914361, 499.52656498472993, 181.79100173844196, 436.09336916344097, 594.518273032036, 181.79100173844196, 481.89443401250094, 436.09336916344097, 481.89443401250094, ] # ugh I hate doing this, but how else? np.testing.assert_allclose( alist.sum_of_squares.values, np.asarray(known_ssq), rtol=RTOL, atol=ATOL ) def test_map(self): atts = ["HOVAL", "CRIME", "INC"] df = geopandas.read_file(examples.get_path("columbus.dbf")).head() W = weights.Queen.from_dataframe(df) hoval, crime, inc = list(map(self.apply_and_compare_columbus, atts)) mapped = adj.adjlist_map(df[atts], W=W, to_adjlist_kws=dict(drop_islands=True)) for name, data in zip(atts, (hoval, crime, inc)): np.testing.assert_allclose( data, mapped["_".join(("subtract", name))].values ) def test_sort(self): from libpysal import examples from libpysal.weights import Rook us = geopandas.read_file(examples.get_path("us48.shp")) w = Rook.from_dataframe(us.set_index("STATE_FIPS"), use_index=True) unsorted_al = w.to_adjlist(sort_joins=False) sorted_al = w.to_adjlist(sort_joins=True) sv = ["01"] * 4 sv.append("04") sv = np.array(sv) usv = np.array(["53", "53", "30", "30", "30"]) np.testing.assert_array_equal(unsorted_al.focal.values[:5], usv) np.testing.assert_array_equal(sorted_al.focal.values[:5], sv) def test_ids(self): df = geopandas.read_file(examples.get_path("columbus.dbf")).head() df["my_id"] = range(3, len(df) + 3) W = weights.Queen.from_dataframe(df, ids="my_id") W_adj = W.to_adjlist(drop_islands=True) for i in range(3, 8): assert i in W_adj.focal assert i in W_adj.neighbor for i in W_adj.focal: assert i in list(range(3, len(df) + 3)) for i in W_adj.neighbor: assert i in list(range(3, len(df) + 3)) def test_str_ids(self): df = geopandas.read_file(examples.get_path("columbus.dbf")).head() snakes = ["mamba", "boa", "python", "rattlesnake", "cobra"] df["my_str_id"] = snakes W = weights.Queen.from_dataframe(df, ids="my_str_id") W_adj = W.to_adjlist(drop_islands=True) for i in snakes: (W_adj.focal == i).any() (W_adj.neighbor == i).any() for i in W_adj.focal: assert i in snakes for i in W_adj.neighbor: assert i in snakes def test_lat2w(self): w = lat2W(5, 5) manual_neighbors = w.to_adjlist().groupby("focal").neighbor.agg(list).to_dict() for focal, neighbors in w.neighbors.items(): assert set(manual_neighbors[focal]) == set(neighbors) libpysal-4.9.2/libpysal/weights/tests/test_contiguity.py000066400000000000000000000152301452177046000236160ustar00rootroot00000000000000# ruff: noqa: N815 import numpy as np import pytest from ... import examples as pysal_examples from ...io import geotable as pdio from ...io.fileio import FileIO from .. import contiguity as c from .. import util from ..weights import W class ContiguityMixin: polygon_path = pysal_examples.get_path("columbus.shp") point_path = pysal_examples.get_path("baltim.shp") f = FileIO(polygon_path) # our file handler polygons = f.read() # our iterable f.seek(0) # go back to head of file cls = object # class constructor known_wi = None # index of known w entry to compare known_w = dict() # actual w entry known_name = known_wi known_namedw = known_w idVariable = None # id variable from file or column known_wspi_da = None known_wsp_da = dict() known_wi_da = None known_w_da = dict() try: from .. import raster da = raster.testDataArray((1, 4, 4), missing_vals=False) except ImportError: da = None def setup_method(self): self.__dict__.update( { k: v for k, v in list(ContiguityMixin.__dict__.items()) if not k.startswith("_") } ) def test_init(self): # basic w = self.cls(self.polygons) assert w[self.known_wi] == self.known_w # sparse # w = self.cls(self.polygons, sparse=True) # srowvec = ws.sparse[self.known_wi].todense().tolist()[0] # this_w = {i:k for i,k in enumerate(srowvec) if k>0} # self.assertEqual(this_w, self.known_w) # ids = ps.weights2.utils.get_ids(self.polygon_path, self.idVariable) # named ids = util.get_ids(self.polygon_path, self.idVariable) w = self.cls(self.polygons, ids=ids) assert w[self.known_name] == self.known_namedw def test_from_iterable(self): w = self.cls.from_iterable(self.f) self.f.seek(0) assert w[self.known_wi] == self.known_w def test_from_shapefile(self): # basic w = self.cls.from_shapefile(self.polygon_path) assert w[self.known_wi] == self.known_w # sparse ws = self.cls.from_shapefile(self.polygon_path, sparse=True) srowvec = ws.sparse[self.known_wi].todense().tolist()[0] this_w = {i: k for i, k in enumerate(srowvec) if k > 0} assert this_w == self.known_w # named w = self.cls.from_shapefile(self.polygon_path, idVariable=self.idVariable) assert w[self.known_name] == self.known_namedw def test_from_array(self): # test named, sparse from point array pass def test_from_dataframe(self): # basic df = pdio.read_files(self.polygon_path) w = self.cls.from_dataframe(df) assert w[self.known_wi] == self.known_w # named geometry df.rename(columns={"geometry": "the_geom"}, inplace=True) w = self.cls.from_dataframe(df, geom_col="the_geom") assert w[self.known_wi] == self.known_w def test_from_geodataframe(self): df = pdio.read_files(self.polygon_path) # named active geometry df.rename(columns={"geometry": "the_geom"}, inplace=True) df = df.set_geometry("the_geom") w = self.cls.from_dataframe(df) assert w[self.known_wi] == self.known_w # named geometry + named obs w = self.cls.from_dataframe(df, geom_col="the_geom", ids=self.idVariable) assert w[self.known_name] == self.known_namedw def test_from_geodataframe_order(self): import geopandas south = geopandas.read_file(pysal_examples.get_path("south.shp")) expected = south.FIPS.iloc[:5].tolist() for ids_ in ("FIPS", south.FIPS): w = self.cls.from_dataframe(south, ids=ids_) assert w.id_order[:5] == expected def test_from_xarray(self): pytest.importorskip("xarray") w = self.cls.from_xarray(self.da, sparse=False, n_jobs=-1) assert w[self.known_wi_da] == self.known_w_da ws = self.cls.from_xarray(self.da) srowvec = ws.sparse[self.known_wspi_da].todense().tolist()[0] this_w = {i: k for i, k in enumerate(srowvec) if k > 0} assert this_w == self.known_wsp_da class TestQueen(ContiguityMixin): def setup_method(self): ContiguityMixin.setup_method(self) self.known_wi = 4 self.known_w = { 2: 1.0, 3: 1.0, 5: 1.0, 7: 1.0, 8: 1.0, 10: 1.0, 14: 1.0, 15: 1.0, } self.cls = c.Queen self.idVariable = "POLYID" self.known_name = 5 self.known_namedw = {k + 1: v for k, v in list(self.known_w.items())} self.known_wspi_da = 1 self.known_wsp_da = {0: 1, 2: 1, 4: 1, 5: 1, 6: 1} self.known_wi_da = (1, -30.0, -60.0) self.known_w_da = { (1, -90.0, -180.0): 1, (1, -90.0, -60.0): 1, (1, -90.0, 60.0): 1, (1, -30.0, -180.0): 1, (1, -30.0, 60.0): 1, (1, 30.0, -180.0): 1, (1, 30.0, -60.0): 1, (1, 30.0, 60.0): 1, } def test_linestrings(self): import geopandas eberly = geopandas.read_file(pysal_examples.get_path("eberly_net.shp")).iloc[ 0:8 ] eberly_w = { 0: [1, 2, 3], 1: [0, 4], 2: [0, 3, 4, 5], 3: [0, 2, 7], 4: [1, 2, 5], 5: [2, 4, 6], 6: [5], 7: [3], } eberly_w = W(neighbors=eberly_w).sparse.toarray() computed = self.cls.from_dataframe(eberly).sparse.toarray() np.testing.assert_array_equal(eberly_w, computed) class TestRook(ContiguityMixin): def setup_method(self): ContiguityMixin.setup_method(self) self.known_w = {2: 1.0, 3: 1.0, 5: 1.0, 7: 1.0, 8: 1.0, 10: 1.0, 14: 1.0} self.known_wi = 4 self.cls = c.Rook self.idVariable = "POLYID" self.known_name = 5 self.known_namedw = {k + 1: v for k, v in list(self.known_w.items())} self.known_wspi_da = 1 self.known_wsp_da = {0: 1, 2: 1, 5: 1} self.known_wi_da = (1, -30.0, -180.0) self.known_w_da = { (1, 30.0, -180.0): 1, (1, -30.0, -60.0): 1, (1, -90.0, -180.0): 1, } class TestVoronoi: def test_voronoi_w(self): np.random.seed(12345) points = np.random.random((5, 2)) * 10 + 10 w = c.Voronoi(points) assert w.n == 5 assert w.neighbors == { 0: [2, 3, 4], 1: [2], 2: [0, 1, 4], 3: [0, 4], 4: [0, 2, 3], } libpysal-4.9.2/libpysal/weights/tests/test_distance.py000066400000000000000000000324501452177046000232150ustar00rootroot00000000000000import numpy as np from ... import cg, weights from ... import examples as pysal_examples from ...cg.kdtree import RADIUS_EARTH_KM, KDTree from ...common import ATOL, RTOL, pandas from ...io import geotable as pdio from ...io.fileio import FileIO as psopen from .. import contiguity as c from .. import distance as d from .. import raster from ..util import get_points_array # All instances should test these four methods, and define their own functional # tests based on common codepaths/estimated weights use cases. class DistanceMixin: polygon_path = pysal_examples.get_path("columbus.shp") arc_path = pysal_examples.get_path("stl_hom.shp") points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)] euclidean_kdt = KDTree(points, distance_metric="euclidean") polygon_f = psopen(polygon_path) # our file handler poly_centroids = get_points_array(polygon_f) # our iterable polygon_f.seek(0) # go back to head of file arc_f = psopen(arc_path) arc_points = get_points_array(arc_f) arc_f.seek(0) arc_kdt = KDTree( arc_points, distance_metric="Arc", radius=cg.sphere.RADIUS_EARTH_KM ) cls = object # class constructor known_wi = None # index of known w entry to compare known_w = dict() # actual w entry known_name = known_wi def setup_method(self): self.__dict__.update( { k: v for k, v in list(DistanceMixin.__dict__.items()) if not k.startswith("_") } ) def test_init(self): # test vanilla, named raise NotImplementedError( "You need to implement this test " "before this module will pass" ) def test_from_shapefile(self): # test vanilla, named, sparse raise NotImplementedError( "You need to implement this test " "before this module will pass" ) def test_from_array(self): # test named, sparse raise NotImplementedError( "You need to implement this test " "before this module will pass" ) def test_from_dataframe(self): # test named, columnar, defau raise NotImplementedError( "You need to implement this test " "before this module will pass" ) class TestKNN(DistanceMixin): def setup_method(self): DistanceMixin.setup_method(self) self.known_wi0 = 7 self.known_w0 = [3, 6, 12, 11] self.known_wi1 = 0 self.known_w1 = [2, 1, 3, 7] self.known_wi2 = 4 self.known_w2 = [1, 3, 9, 12] self.known_wi3 = 40 self.known_w3 = [31, 38, 45, 49] ########################## # Classmethod tests # ########################## def test_init(self): w = d.KNN(self.euclidean_kdt, k=2) assert w.neighbors[0] == [1, 3] def test_from_dataframe(self): df = pdio.read_files(self.polygon_path) w = d.KNN.from_dataframe(df, k=4) assert w.neighbors[self.known_wi0] == self.known_w0 assert w.neighbors[self.known_wi1] == self.known_w1 # named geometry df.rename(columns={"geometry": "the_geom"}, inplace=True) w = d.KNN.from_dataframe(df, k=4, geom_col="the_geom") assert w.neighbors[self.known_wi0] == self.known_w0 assert w.neighbors[self.known_wi1] == self.known_w1 def test_from_geodataframe(self): df = pdio.read_files(self.polygon_path) # named active geometry df.rename(columns={"geometry": "the_geom"}, inplace=True) df = df.set_geometry("the_geom") w = d.KNN.from_dataframe(df, k=4) assert w.neighbors[self.known_wi0] == self.known_w0 assert w.neighbors[self.known_wi1] == self.known_w1 def test_from_array(self): w = d.KNN.from_array(self.poly_centroids, k=4) assert w.neighbors[self.known_wi0] == self.known_w0 assert w.neighbors[self.known_wi1] == self.known_w1 def test_from_shapefile(self): w = d.KNN.from_shapefile(self.polygon_path, k=4) assert w.neighbors[self.known_wi0] == self.known_w0 assert w.neighbors[self.known_wi1] == self.known_w1 ########################## # Function/User tests # ########################## def test_reweight(self): w = d.KNN(self.points, k=2) new_point = [(21, 21)] wnew = w.reweight(k=4, p=1, new_data=new_point, inplace=False) assert wnew[0] == {1: 1.0, 3: 1.0, 4: 1.0, 6: 1.0} def test_arcdata(self): w = d.KNN.from_shapefile( self.polygon_path, k=4, distance_metric="Arc", radius=cg.sphere.RADIUS_EARTH_KM, ) assert w.data.shape[1] == 3 class TestDistanceBand(DistanceMixin): def setup_method(self): DistanceMixin.setup_method(self) self.grid_path = pysal_examples.get_path("lattice10x10.shp") self.grid_rook_w = c.Rook.from_shapefile(self.grid_path) self.grid_f = psopen(self.grid_path) self.grid_points = get_points_array(self.grid_f) self.grid_f.seek(0) self.grid_kdt = KDTree(self.grid_points) ########################## # Classmethod tests # ########################## def test_init(self): w = d.DistanceBand(self.grid_kdt, 1) for k, v in w: assert v == self.grid_rook_w[k] def test_from_shapefile(self): w = d.DistanceBand.from_shapefile(self.grid_path, 1) for k, v in w: assert v == self.grid_rook_w[k] def test_from_array(self): w = d.DistanceBand.from_array(self.grid_points, 1) for k, v in w: assert v == self.grid_rook_w[k] def test_from_dataframe(self): import pandas as pd geom_series = pdio.shp.shp2series(self.grid_path) random_data = np.random.random(size=len(geom_series)) df = pd.DataFrame({"obs": random_data, "geometry": geom_series}) w = d.DistanceBand.from_dataframe(df, 1) for k, v in w: assert v == self.grid_rook_w[k] def test_from_geodataframe(self): import geopandas as gpd import pandas as pd geom_series = pdio.shp.shp2series(self.grid_path) random_data = np.random.random(size=len(geom_series)) df = pd.DataFrame({"obs": random_data, "geometry": geom_series}) w = d.DistanceBand.from_dataframe(df, 1) for k, v in w: assert v == self.grid_rook_w[k] # named geometry df = gpd.GeoDataFrame(df) df.rename(columns={"geometry": "the_geom"}, inplace=True) w = d.DistanceBand.from_dataframe(df, 1, geom_col="the_geom") for k, v in w: assert v == self.grid_rook_w[k] # named active geometry df = df.set_geometry("the_geom") w = d.DistanceBand.from_dataframe(df, 1) for k, v in w: assert v == self.grid_rook_w[k] ########################## # Function/User tests # ########################## def test_integers(self): """ see issue #126 """ grid_integers = [tuple(map(int, poly.vertices[0])) for poly in self.grid_f] self.grid_f.seek(0) grid_dbw = d.DistanceBand(grid_integers, 1) for k, v in grid_dbw: assert v == self.grid_rook_w[k] def test_arcdist(self): arc = cg.sphere.arcdist kdt = KDTree( self.arc_points, distance_metric="Arc", radius=cg.sphere.RADIUS_EARTH_KM ) npoints = self.arc_points.shape[0] full = np.array( [ [arc(self.arc_points[i], self.arc_points[j]) for j in range(npoints)] for i in range(npoints) ] ) maxdist = full.max() w = d.DistanceBand(kdt, maxdist, binary=False, alpha=1.0) np.testing.assert_allclose(w.sparse.todense(), full) assert w.data.shape[1] == 3 def test_dense(self): w_rook = c.Rook.from_shapefile(pysal_examples.get_path("lattice10x10.shp")) polys = psopen(pysal_examples.get_path("lattice10x10.shp")) centroids = [p.centroid for p in polys] w_db = d.DistanceBand(centroids, 1, build_sp=False) for k in w_db.id_order: np.testing.assert_equal(w_db[k], w_rook[k]) def test_named(self): import pandas as pd geom_series = pdio.shp.shp2series(self.grid_path) random_data = np.random.random(size=len(geom_series)) names = [chr(x) for x in range(60, 160)] df = pd.DataFrame({"obs": random_data, "geometry": geom_series, "names": names}) w = d.DistanceBand.from_dataframe(df, 1, ids=df.names) class TestKernel(DistanceMixin): def setup_method(self): DistanceMixin.setup_method(self) self.known_wi0 = 0 self.known_w0 = {0: 1, 1: 0.500000049999995, 3: 0.4409830615267465} self.known_wi1 = 0 self.known_w1 = {0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701} self.known_w1_bw = 15.0 self.known_wi2 = 0 self.known_w2 = { 0: 1.0, 1: 0.59999999999999998, 3: 0.55278640450004202, 4: 0.10557280900008403, } self.known_w2_bws = [25.0, 15.0, 25.0, 16.0, 14.5, 25.0] self.known_wi3 = 0 self.known_w3 = [1.0, 0.10557289844279438, 9.9999990066379496e-08] self.known_w3_abws = [ [11.180341005532938], [11.180341005532938], [20.000002000000002], [11.180341005532938], [14.142137037944515], [18.027758180095585], ] self.known_wi4 = 0 self.known_w4 = { 0: 0.3989422804014327, 1: 0.26741902915776961, 3: 0.24197074871621341, } self.known_w4_abws = self.known_w3_abws self.known_wi5 = 1 self.known_w5 = { 4: 0.0070787731484506233, 2: 0.2052478782400463, 3: 0.23051223027663237, 1: 1.0, } self.known_wi6 = 0 self.known_w6 = {0: 1.0, 2: 0.03178906767736345, 1: 9.9999990066379496e-08} # stick answers & params here ########################## # Classmethod tests # ########################## def test_init(self): w = d.Kernel(self.euclidean_kdt) for k, v in list(w[self.known_wi0].items()): np.testing.assert_allclose(v, self.known_w0[k], rtol=RTOL) def test_from_shapefile(self): w = d.Kernel.from_shapefile(self.polygon_path, idVariable="POLYID") for k, v in list(w[self.known_wi5].items()): np.testing.assert_allclose((k, v), (k, self.known_w5[k]), rtol=RTOL) w = d.Kernel.from_shapefile(self.polygon_path, fixed=False) for k, v in list(w[self.known_wi6].items()): np.testing.assert_allclose((k, v), (k, self.known_w6[k]), rtol=RTOL) def test_from_array(self): w = d.Kernel.from_array(self.points) for k, v in list(w[self.known_wi0].items()): np.testing.assert_allclose(v, self.known_w0[k], rtol=RTOL) def test_from_dataframe(self): df = pdio.read_files(self.polygon_path) w = d.Kernel.from_dataframe(df) for k, v in list(w[self.known_wi5 - 1].items()): np.testing.assert_allclose(v, self.known_w5[k + 1], rtol=RTOL) def test_from_geodataframe(self): df = pdio.read_files(self.polygon_path) # named geometry df.rename(columns={"geometry": "the_geom"}, inplace=True) w = d.Kernel.from_dataframe(df, geom_col="the_geom") for k, v in list(w[self.known_wi5 - 1].items()): np.testing.assert_allclose(v, self.known_w5[k + 1], rtol=RTOL) # named active geometry df = df.set_geometry("the_geom") w = d.Kernel.from_dataframe(df) for k, v in list(w[self.known_wi5 - 1].items()): np.testing.assert_allclose(v, self.known_w5[k + 1], rtol=RTOL) ########################## # Function/User tests # ########################## def test_fixed_bandwidth(self): w = d.Kernel(self.points, bandwidth=15.0) for k, v in list(w[self.known_wi1].items()): np.testing.assert_allclose((k, v), (k, self.known_w1[k])) np.testing.assert_allclose(np.ones((w.n, 1)) * 15, w.bandwidth) w = d.Kernel(self.points, bandwidth=self.known_w2_bws) for k, v in list(w[self.known_wi2].items()): np.testing.assert_allclose((k, v), (k, self.known_w2[k]), rtol=RTOL) for i in range(w.n): np.testing.assert_allclose(w.bandwidth[i], self.known_w2_bws[i], rtol=RTOL) def test_adaptive_bandwidth(self): w = d.Kernel(self.points, fixed=False) np.testing.assert_allclose( sorted(w[self.known_wi3].values()), sorted(self.known_w3), rtol=RTOL ) bws = w.bandwidth.tolist() np.testing.assert_allclose(bws, self.known_w3_abws, rtol=RTOL) w = d.Kernel(self.points, fixed=False, function="gaussian") for k, v in list(w[self.known_wi4].items()): np.testing.assert_allclose((k, v), (k, self.known_w4[k]), rtol=RTOL) bws = w.bandwidth.tolist() np.testing.assert_allclose(bws, self.known_w4_abws, rtol=RTOL) def test_arcdistance(self): w = d.Kernel( self.points, fixed=True, distance_metric="Arc", radius=cg.sphere.RADIUS_EARTH_KM, ) assert w.data.shape[1] == 3 libpysal-4.9.2/libpysal/weights/tests/test_gabriel.py000066400000000000000000000022441452177046000230260ustar00rootroot00000000000000import geopandas import numpy from ... import examples from .. import gabriel path = examples.get_path("columbus.shp") df = geopandas.read_file(path) geoms = df.geometry.centroid coords = numpy.column_stack((geoms.x, geoms.y)) def test_delaunay(): a = gabriel.Delaunay(coords) b = gabriel.Delaunay.from_dataframe(df.centroid) assert a.neighbors == b.neighbors assert a[13] == {6: 1, 11: 1, 12: 1, 18: 1, 20: 1} def test_gabriel(): c = gabriel.Gabriel(coords) d = gabriel.Gabriel.from_dataframe(df.centroid) c2 = gabriel.Delaunay(coords) assert c.neighbors == d.neighbors assert c[13] == {12: 1, 18: 1} for focal, neighbors in c.neighbors.items(): dneighbors = c2[focal] assert set(neighbors) <= set(dneighbors) def test_rng(): e = gabriel.Relative_Neighborhood(coords) f = gabriel.Relative_Neighborhood.from_dataframe(df.centroid) dty = gabriel.Delaunay(coords) assert e.neighbors == f.neighbors assert e[1] != dty[1] assert list(e[1].keys()) == [0, 3, 6, 30, 38] for focal, neighbors in e.neighbors.items(): dneighbors = dty[focal] assert set(neighbors) <= set(dneighbors) libpysal-4.9.2/libpysal/weights/tests/test_nx.py000066400000000000000000000016651452177046000220540ustar00rootroot00000000000000import numpy as np import pytest from ..util import lat2W from ..weights import W networkx = pytest.importorskip("networkx") class TestNetworkXConverter: def setup_method(self): self.known_nx = networkx.random_regular_graph(4, 10, seed=8879) self.known_amat = networkx.to_numpy_array(self.known_nx) self.known_W = lat2W(5, 5) def test_round_trip(self): W_ = W.from_networkx(self.known_nx) np.testing.assert_allclose(W_.sparse.toarray(), self.known_amat) nx2 = W_.to_networkx() np.testing.assert_allclose(networkx.to_numpy_array(nx2), self.known_amat) nxsquare = self.known_W.to_networkx() np.testing.assert_allclose( self.known_W.sparse.toarray(), networkx.to_numpy_array(nxsquare) ) W_square = W.from_networkx(nxsquare) np.testing.assert_allclose( self.known_W.sparse.toarray(), W_square.sparse.toarray() ) libpysal-4.9.2/libpysal/weights/tests/test_raster.py000066400000000000000000000075401452177046000227250ustar00rootroot00000000000000"""Unit test for raster.py""" import numpy as np import pandas as pd import pytest from .. import raster class Testraster: def setup_method(self): pytest.importorskip("xarray") self.da1 = raster.testDataArray() self.da2 = raster.testDataArray((1, 4, 4), missing_vals=False) self.da3 = self.da2.rename({"band": "layer", "x": "longitude", "y": "latitude"}) self.data1 = pd.Series(np.ones(5)) self.da4 = raster.testDataArray((1, 1), missing_vals=False) self.da4.data = np.array([["test"]]) def test_da2_w(self): w1 = raster.da2W(self.da1, "queen", k=2, n_jobs=-1) assert w1[(1, -30.0, -180.0)] == {(1, -90.0, 60.0): 1, (1, -90.0, -60.0): 1} assert w1[(1, -30.0, 180.0)] == {(1, -90.0, -60.0): 1, (1, -90.0, 60.0): 1} assert w1.n == 5 assert w1.index.names == self.da1.to_series().index.names assert w1.index.tolist()[0] == (1, 90.0, 180.0) assert w1.index.tolist()[1] == (1, -30.0, -180.0) assert w1.index.tolist()[2] == (1, -30.0, 180.0) assert w1.index.tolist()[3] == (1, -90.0, -60.0) w2 = raster.da2W(self.da2, "rook") assert sorted(w2.neighbors[(1, -90.0, 180.0)]) == [ (1, -90.0, 60.0), (1, -30.0, 180.0), ] assert sorted(w2.neighbors[(1, -90.0, 60.0)]) == [ (1, -90.0, -60.0), (1, -90.0, 180.0), (1, -30.0, 60.0), ] assert w2.n == 16 assert w2.index.names == self.da2.to_series().index.names assert w2.index.tolist() == self.da2.to_series().index.tolist() coords_labels = { "z_label": "layer", "y_label": "latitude", "x_label": "longitude", } w3 = raster.da2W(self.da3, z_value=1, coords_labels=coords_labels) assert sorted(w3.neighbors[(1, -90.0, 180.0)]) == [ (1, -90.0, 60.0), (1, -30.0, 60.0), (1, -30.0, 180.0), ] assert w3.n == 16 assert w3.index.names == self.da3.to_series().index.names assert w3.index.tolist() == self.da3.to_series().index.tolist() def test_da2_wsp(self): w1 = raster.da2WSP(self.da1, "rook", n_jobs=-1) rows, cols = w1.sparse.shape n = rows * cols pct_nonzero = w1.sparse.nnz / float(n) assert pct_nonzero == 0.08 data = w1.sparse.todense().tolist() assert data[3] == [0, 0, 0, 0, 1] assert data[4] == [0, 0, 0, 1, 0] assert w1.index.names == self.da1.to_series().index.names assert w1.index.tolist()[0] == (1, 90.0, 180.0) assert w1.index.tolist()[1] == (1, -30.0, -180.0) assert w1.index.tolist()[2] == (1, -30.0, 180.0) assert w1.index.tolist()[3] == (1, -90.0, -60.0) w2 = raster.da2WSP(self.da2, "queen", k=2, include_nodata=True) w3 = raster.da2WSP(self.da2, "queen", k=2, n_jobs=-1) assert w2.sparse.nnz == w3.sparse.nnz assert w2.sparse.todense().tolist() == w3.sparse.todense().tolist() assert w2.n == 16 assert w2.index.names == self.da2.to_series().index.names assert w2.index.tolist() == self.da2.to_series().index.tolist() def test_w2da(self): xarray = pytest.importorskip("xarray") w2 = raster.da2W(self.da2, "rook", n_jobs=-1) da2 = raster.w2da(self.da2.data.flatten(), w2, self.da2.attrs, self.da2.coords) da_compare = xarray.DataArray.equals(da2, self.da2) assert da_compare == True def test_wsp2da(self): wsp1 = raster.da2WSP(self.da1, "queen") da1 = raster.wsp2da(self.data1, wsp1) assert da1["y"].values.tolist() == self.da1["y"].values.tolist() assert da1["x"].values.tolist() == self.da1["x"].values.tolist() assert da1.shape == (1, 4, 4) def test_da_checker(self): pytest.raises(ValueError, raster.da2W, self.da4) libpysal-4.9.2/libpysal/weights/tests/test_spatial_lag.py000066400000000000000000000041741452177046000237050ustar00rootroot00000000000000import numpy as np from ..spatial_lag import lag_categorical, lag_spatial from ..util import lat2W from ..weights import W class Test_spatial_lag: def setup_method(self): self.neighbors = {"c": ["b"], "b": ["c", "a"], "a": ["b"]} self.weights = {"c": [1.0], "b": [1.0, 1.0], "a": [1.0]} self.id_order = ["a", "b", "c"] self.weights = {"c": [1.0], "b": [1.0, 1.0], "a": [1.0]} self.w = W(self.neighbors, self.weights, self.id_order) self.y = np.array([0, 1, 2]) self.wlat = lat2W(3, 3) self.ycat = ["a", "b", "a", "b", "c", "b", "c", "b", "c"] self.ycat2 = ["a", "c", "c", "d", "b", "a", "d", "d", "c"] self.ym = np.vstack((self.ycat, self.ycat2)).T self.random_seed = 503 def test_lag_spatial(self): yl = lag_spatial(self.w, self.y) np.testing.assert_array_almost_equal(yl, [1.0, 2.0, 1.0]) self.w.id_order = ["b", "c", "a"] y = np.array([1, 2, 0]) yl = lag_spatial(self.w, y) np.testing.assert_array_almost_equal(yl, [2.0, 1.0, 1.0]) w = lat2W(3, 3) y = np.arange(9) yl = lag_spatial(w, y) ylc = np.array([4.0, 6.0, 6.0, 10.0, 16.0, 14.0, 10.0, 18.0, 12.0]) np.testing.assert_array_almost_equal(yl, ylc) w.transform = "r" yl = lag_spatial(w, y) ylc = np.array([2.0, 2.0, 3.0, 3.33333333, 4.0, 4.66666667, 5.0, 6.0, 6.0]) np.testing.assert_array_almost_equal(yl, ylc) def test_lag_categorical(self): yl = lag_categorical(self.wlat, self.ycat) np.random.seed(self.random_seed) known = np.array(["b", "a", "b", "c", "b", "c", "b", "c", "b"]) np.testing.assert_array_equal(yl, known) ym_lag = lag_categorical(self.wlat, self.ym) known = np.array( [ ["b", "c"], ["a", "c"], ["b", "c"], ["c", "d"], ["b", "d"], ["c", "c"], ["b", "d"], ["c", "d"], ["b", "d"], ] ) np.testing.assert_array_equal(ym_lag, np.asarray(known)) libpysal-4.9.2/libpysal/weights/tests/test_spintW.py000066400000000000000000000343641452177046000227150ustar00rootroot00000000000000import numpy as np from ..spintW import ODW, mat2L, netW, vecW from ..util import lat2W class TestODWeights: def setup_method(self): self.O = lat2W(2, 2) self.D = lat2W(2, 2) self.ODW = np.array( [ [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, ], [ 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, ], [ 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, ], [ 0.0, 0.25, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.25, 0.0, ], [ 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.25, ], [ 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.25, ], [ 0.0, 0.25, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.25, 0.0, ], [ 0.0, 0.25, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.25, 0.0, ], [ 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.25, ], [ 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.25, ], [ 0.0, 0.25, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.25, 0.0, ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, ], [ 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, ], [ 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.25, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, ], ] ) def test_odw_full(self): W = ODW(self.O, self.D) np.testing.assert_allclose(self.ODW, W.full()[0]) class TestNetW: def setup_method(self): self.link_list = [ ("a", "b"), ("a", "c"), ("a", "d"), ("b", "a"), ("b", "c"), ("b", "d"), ("c", "a"), ("c", "b"), ("c", "d"), ("d", "a"), ("d", "b"), ("d", "c"), ] self._all = np.array( [ [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0], [1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0], [1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0], [1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0], [1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0], [1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0], [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0], ] ) self.O = np.array( [ [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0], ] ) self.D = np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], ] ) self.OD = np.array( [ [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0], ] ) self.C = np.array( [ [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0], ] ) self.edge_list = [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)] def test_net_od(self): netW_OD = netW(self.link_list, share="OD") np.testing.assert_allclose(netW_OD.full()[0], self.OD) def test_net_o(self): netW_O = netW(self.link_list, share="O") np.testing.assert_allclose(netW_O.full()[0], self.O) def test_net_d(self): netW_D = netW(self.link_list, share="D") np.testing.assert_allclose(netW_D.full()[0], self.D) def test_net_c(self): netW_C = netW(self.link_list, share="C") np.testing.assert_allclose(netW_C.full()[0], self.C) def test_net_all(self): netW_all = netW(self.link_list, share="A") np.testing.assert_allclose(netW_all.full()[0], self._all) def test_mat2_l(self): mat = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]) edge_list = mat2L(mat) assert edge_list == self.edge_list class TestVecW: def setup_method(self): self.origin_x = np.array([2, 6, 9, 2]) self.origin_y = np.array([4, 8, 2, 5]) self.dest_x = np.array([9, 1, 6, 3]) self.dest_y = np.array([3, 6, 2, 7]) self.continuous = np.array( [ [0.0, 0.09759001, 0.12598816, 0.13736056], [0.09759001, 0.0, 0.10783277, 0.18257419], [0.12598816, 0.10783277, 0.0, 0.10425721], [0.13736056, 0.18257419, 0.10425721, 0.0], ] ) def test_vec_w(self): W = vecW( self.origin_x, self.origin_y, self.dest_x, self.dest_y, threshold=np.inf, binary=False, ) np.testing.assert_allclose(self.continuous, W.full()[0]) libpysal-4.9.2/libpysal/weights/tests/test_user.py000066400000000000000000000011631452177046000223760ustar00rootroot00000000000000import os import pytest from ... import examples from .. import user from ..contiguity import Rook, Voronoi class Testuser: def test_min_threshold_dist_from_shapefile(self): f = examples.get_path("columbus.shp") min_d = user.min_threshold_dist_from_shapefile(f) assert min_d == pytest.approx(0.61886415807685413) def test_build_lattice_shapefile(self): of = "lattice.shp" user.build_lattice_shapefile(20, 20, of) w = Rook.from_shapefile(of) assert w.n == 400 os.remove("lattice.dbf") os.remove("lattice.shp") os.remove("lattice.shx") libpysal-4.9.2/libpysal/weights/tests/test_util.py000066400000000000000000000257731452177046000224120ustar00rootroot00000000000000"""Unit test for util.py""" import geopandas as gpd import numpy as np import pytest from ... import examples from ...io.fileio import FileIO as psopen from .. import util from ..contiguity import Queen, Rook from ..distance import KNN, DistanceBand from ..util import fuzzy_contiguity, lat2W, nonplanar_neighbors from ..weights import WSP, W class Testutil: def setup_method(self): self.w = Rook.from_shapefile(examples.get_path("10740.shp")) self.rio = examples.load_example("Rio Grande do Sul") def test_lat2_w(self): w9 = lat2W(3, 3) assert w9.pct_nonzero == 29.62962962962963 assert w9[0] == {1: 1.0, 3: 1.0} assert w9[3] == {0: 1.0, 4: 1.0, 6: 1.0} def test_lat2_sw(self): w9 = util.lat2SW(3, 3) rows, cols = w9.shape n = rows * cols assert w9.nnz == 24 pct_nonzero = w9.nnz / float(n) assert pct_nonzero == 0.29629629629629628 data = w9.todense().tolist() assert data[0] == [0, 1, 0, 1, 0, 0, 0, 0, 0] assert data[1] == [1, 0, 1, 0, 1, 0, 0, 0, 0] assert data[2] == [0, 1, 0, 0, 0, 1, 0, 0, 0] assert data[3] == [1, 0, 0, 0, 1, 0, 1, 0, 0] assert data[4] == [0, 1, 0, 1, 0, 1, 0, 1, 0] assert data[5] == [0, 0, 1, 0, 1, 0, 0, 0, 1] assert data[6] == [0, 0, 0, 1, 0, 0, 0, 1, 0] assert data[7] == [0, 0, 0, 0, 1, 0, 1, 0, 1] assert data[8] == [0, 0, 0, 0, 0, 1, 0, 1, 0] def test_block_weights(self): regimes = np.ones(25) regimes[list(range(10, 20))] = 2 regimes[list(range(21, 25))] = 3 regimes = np.array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 3.0, 3.0, 3.0, 3.0, ] ) w = util.block_weights(regimes) ww0 = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] assert w.weights[0] == ww0 wn0 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 20] assert w.neighbors[0] == wn0 regimes = ["n", "n", "s", "s", "e", "e", "w", "w", "e"] w = util.block_weights(regimes) wn = { 0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8], 6: [7], 7: [6], 8: [4, 5], } assert w.neighbors == wn ids = ["id-%i" % i for i in range(len(regimes))] w = util.block_weights(regimes, ids=np.array(ids)) w0 = {"id-1": 1.0} assert w["id-0"] == w0 w = util.block_weights(regimes, ids=ids) w0 = {"id-1": 1.0} assert w["id-0"] == w0 def test_comb(self): x = list(range(4)) l = [] for i in util.comb(x, 2): l.append(i) lo = [[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]] assert l == lo def test_order(self): w3 = util.order(self.w, kmax=3) w3105 = [1, -1, 1, 2, 1] assert w3105 == w3[1][0:5] def test_higher_order(self): w10 = lat2W(10, 10) w10_2 = util.higher_order(w10, 2) w10_20 = {2: 1.0, 11: 1.0, 20: 1.0} assert w10_20 == w10_2[0] w5 = lat2W() w50 = {1: 1.0, 5: 1.0} assert w50 == w5[0] w51 = {0: 1.0, 2: 1.0, 6: 1.0} assert w51 == w5[1] w5_2 = util.higher_order(w5, 2) w5_20 = {2: 1.0, 10: 1.0, 6: 1.0} assert w5_20 == w5_2[0] def test_higher_order_sp(self): w10 = lat2W(10, 10) w10_3 = util.higher_order_sp(w10, 3) w10_30 = {30: 1.0, 21: 1.0, 12: 1.0, 3: 1.0} assert w10_30 == w10_3[0] w10_3 = util.higher_order_sp(w10, 3, lower_order=True) w10_30 = { 20: 1.0, 30: 1.0, 21: 1.0, 10: 1.0, 1: 1.0, 11: 1.0, 2: 1.0, 12: 1.0, 3: 1.0, } assert w10_30 == w10_3[0] def test_higher_order_classes(self): wdb = DistanceBand.from_shapefile(examples.get_path("baltim.shp"), 34) wknn = KNN.from_shapefile(examples.get_path("baltim.shp"), 10) wrook = Rook.from_shapefile(examples.get_path("columbus.shp")) wqueen = Queen.from_shapefile(examples.get_path("columbus.shp")) wsparse = wqueen.sparse ww = W(wknn.neighbors, wknn.weights) util.higher_order(wdb, 2) util.higher_order(wknn, 3) util.higher_order(wrook, 4) util.higher_order(wqueen, 5) util.higher_order(wsparse, 2) util.higher_order(ww, 2) ww.transform = "r" wsparse_notbinary = wrook.sparse with pytest.raises(ValueError): util.higher_order(wsparse, 2) util.higher_order(ww, 3) def test_shimbel(self): w5 = lat2W() w5_shimbel = util.shimbel(w5) w5_shimbel024 = 8 assert w5_shimbel024 == w5_shimbel[0][24] w5_shimbel004 = [-1, 1, 2, 3] assert w5_shimbel004 == w5_shimbel[0][0:4] def test_full(self): neighbors = { "first": ["second"], "second": ["first", "third"], "third": ["second"], } weights = {"first": [1], "second": [1, 1], "third": [1]} w = W(neighbors, weights) wf, ids = util.full(w) wfo = np.array([[0.0, 1.0, 0.0], [1.0, 0.0, 1.0], [0.0, 1.0, 0.0]]) np.testing.assert_array_almost_equal(wfo, wf, decimal=8) idso = ["first", "second", "third"] assert idso == ids def test_full2_w(self): a = np.zeros((4, 4)) for i in range(len(a)): for j in range(len(a[i])): if i != j: a[i, j] = np.random.random(1)[0] w = util.full2W(a) np.testing.assert_array_equal(w.full()[0], a) ids = ["myID0", "myID1", "myID2", "myID3"] w = util.full2W(a, ids=ids) np.testing.assert_array_equal(w.full()[0], a) w.full()[0] == a def test_wsp2_w(self): sp = util.lat2SW(2, 5) wsp = WSP(sp) w = util.WSP2W(wsp) assert w.n == 10 assert w[0] == {1: 1, 5: 1} for weights in w.weights.values(): assert isinstance(weights, list) w = psopen(examples.get_path("sids2.gal"), "r").read() wsp = WSP(w.sparse, w.id_order) w = util.WSP2W(wsp) assert w.n == 100 assert w["37135"] == { "37001": 1.0, "37033": 1.0, "37037": 1.0, "37063": 1.0, "37145": 1.0, } def test_fill_diagonal(self): w1 = util.fill_diagonal(self.w) r1 = {0: 1.0, 1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0} assert w1[0] == r1 w1 = util.fill_diagonal(self.w, 20) r1 = {0: 20, 1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0} assert w1[0] == r1 diag = np.arange(100, 100 + self.w.n) w1 = util.fill_diagonal(self.w, diag) r1 = {0: 100, 1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0} assert w1[0] == r1 def test_remap_ids(self): w = lat2W(3, 2) wid_order = [0, 1, 2, 3, 4, 5] assert wid_order == w.id_order wneighbors0 = [2, 1] assert wneighbors0 == w.neighbors[0] old_to_new = {0: "a", 1: "b", 2: "c", 3: "d", 4: "e", 5: "f"} w_new = util.remap_ids(w, old_to_new) w_newid_order = ["a", "b", "c", "d", "e", "f"] assert w_newid_order == w_new.id_order w_newdneighborsa = ["c", "b"] assert w_newdneighborsa == w_new.neighbors["a"] def test_get_ids_shp(self): polyids = util.get_ids(examples.get_path("columbus.shp"), "POLYID") polyids5 = [1, 2, 3, 4, 5] assert polyids5 == polyids[:5] def test_get_ids_gdf(self): gdf = gpd.read_file(examples.get_path("columbus.shp")) polyids = util.get_ids(gdf, "POLYID") polyids5 = [1, 2, 3, 4, 5] assert polyids5 == polyids[:5] def test_get_points_array_from_shapefile(self): xy = util.get_points_array_from_shapefile(examples.get_path("juvenile.shp")) xy3 = np.array([[94.0, 93.0], [80.0, 95.0], [79.0, 90.0]]) np.testing.assert_array_almost_equal(xy3, xy[:3], decimal=8) xy = util.get_points_array_from_shapefile(examples.get_path("columbus.shp")) xy3 = np.array( [ [8.82721847, 14.36907602], [8.33265837, 14.03162401], [9.01226541, 13.81971908], ] ) np.testing.assert_array_almost_equal(xy3, xy[:3], decimal=8) def test_min_threshold_distance(self): x, y = np.indices((5, 5)) x.shape = (25, 1) y.shape = (25, 1) data = np.hstack([x, y]) mint = 1.0 assert mint == util.min_threshold_distance(data) def test_attach_islands(self): w = Rook.from_shapefile(examples.get_path("10740.shp")) w_knn1 = KNN.from_shapefile(examples.get_path("10740.shp"), k=1) w_attach = util.attach_islands(w, w_knn1) assert w_attach.islands == [] assert w_attach[w.islands[0]] == {166: 1.0} def test_nonplanar_neighbors(self): df = gpd.read_file(examples.get_path("map_RS_BR.shp")) w = Queen.from_dataframe(df) assert w.islands == [ 0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374, ] wnp = nonplanar_neighbors(w, df) assert wnp.islands == [] assert w.neighbors[0] == [] assert wnp.neighbors[0] == [23, 59, 152, 239] assert wnp.neighbors[23] == [0, 45, 59, 107, 152, 185, 246] def test_fuzzy_contiguity(self): rs = examples.get_path("map_RS_BR.shp") rs_df = gpd.read_file(rs) wf = fuzzy_contiguity(rs_df) assert wf.islands == [] assert set(wf.neighbors[0]) == set([239, 59, 152, 23]) buff = fuzzy_contiguity(rs_df, buffering=True, buffer=0.2) assert set(buff.neighbors[0]) == set([175, 119, 239, 59, 152, 246, 23, 107]) rs_index = rs_df.set_index("NM_MUNICIP") index_w = fuzzy_contiguity(rs_index) assert set(index_w.neighbors["TAVARES"]) == set( ["SÃO JOSÉ DO NORTE", "MOSTARDAS"] ) wf_pred = fuzzy_contiguity(rs_df, predicate="touches") assert set(wf_pred.neighbors[0]) == set([]) assert set(wf_pred.neighbors[1]) == set([142, 82, 197, 285, 386, 350]) libpysal-4.9.2/libpysal/weights/tests/test_weights.py000066400000000000000000000547101452177046000231000ustar00rootroot00000000000000import os import tempfile import warnings import geopandas import numpy as np import pytest import scipy.sparse from ... import examples from ...io.fileio import FileIO as psopen from .. import util from ..contiguity import Rook from ..distance import KNN from ..util import WSP2W, lat2W from ..weights import WSP, W, _LabelEncoder class TestW: def setup_method(self): self.w = Rook.from_shapefile( examples.get_path("10740.shp"), silence_warnings=True ) self.neighbors = { 0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7], } self.weights = { 0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1, 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1], } self.w3x3 = util.lat2W(3, 3) self.w_islands = W({0: [1], 1: [0, 2], 2: [1], 3: []}) def test_w(self): w = W(self.neighbors, self.weights, silence_warnings=True) assert w.pct_nonzero == 29.62962962962963 def test___getitem__(self): assert self.w[0] == {1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0} def test___init__(self): w = W(self.neighbors, self.weights, silence_warnings=True) assert w.pct_nonzero == 29.62962962962963 def test___iter__(self): w = lat2W(3, 3) res = {} for i, wi in enumerate(w): res[i] = wi assert res[0] == (0, {1: 1.0, 3: 1.0}) assert res[8] == (8, {5: 1.0, 7: 1.0}) def test_asymmetries(self): w = lat2W(3, 3) w.transform = "r" result = w.asymmetry() assert result == [ (0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7), ] def test_asymmetry(self): w = lat2W(3, 3) assert w.asymmetry() == [] w.transform = "r" assert not w.asymmetry() == [] def test_asymmetry_string_index(self): neighbors = { "a": ["b", "c", "d"], "b": ["b", "c", "d"], "c": ["a", "b"], "d": ["a", "b"], } weights_d = {"a": [1, 1, 1], "b": [1, 1, 1], "c": [1, 1], "d": [1, 1]} w = W(neighbors, weights_d) assert w.asymmetry() == [("a", "b"), ("b", "a")] w.transform = "r" assert w.asymmetry() == [ ("a", "b"), ("a", "c"), ("a", "d"), ("b", "a"), ("b", "c"), ("b", "d"), ("c", "a"), ("c", "b"), ("d", "a"), ("d", "b"), ] def test_asymmetry_mixed_index(self): neighbors = { 3000: [45, 99.99, "-"], 45: [45, 99.99, "-"], 99.99: [3000, 45], "-": [3000, 45], } weights_d = {3000: [1, 1, 1], 45: [1, 1, 1], 99.99: [1, 1], "-": [1, 1]} w = W(neighbors, weights_d, id_order=list(neighbors.keys())) assert w.asymmetry() == [(3000, 45), (45, 3000)] w.transform = "r" assert w.asymmetry() == [ (3000, 45), (3000, 99.99), (3000, "-"), (45, 3000), (45, 99.99), (45, "-"), (99.99, 3000), (99.99, 45), ("-", 3000), ("-", 45), ] def test_cardinalities(self): w = lat2W(3, 3) assert w.cardinalities == {0: 2, 1: 3, 2: 2, 3: 3, 4: 4, 5: 3, 6: 2, 7: 3, 8: 2} def test_diag_w2(self): np.testing.assert_array_almost_equal( self.w3x3.diagW2, np.array([2.0, 3.0, 2.0, 3.0, 4.0, 3.0, 2.0, 3.0, 2.0]) ) def test_diag_wt_w(self): np.testing.assert_array_almost_equal( self.w3x3.diagW2, np.array([2.0, 3.0, 2.0, 3.0, 4.0, 3.0, 2.0, 3.0, 2.0]) ) def test_diag_wt_w_ww(self): np.testing.assert_array_almost_equal( self.w3x3.diagWtW_WW, np.array([4.0, 6.0, 4.0, 6.0, 8.0, 6.0, 4.0, 6.0, 4.0]), ) def test_full(self): wf = np.array( [ [0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0], ] ) ids = list(range(9)) wf1, ids1 = self.w3x3.full() np.testing.assert_array_almost_equal(wf1, wf) assert ids1 == ids def test_get_transform(self): assert self.w3x3.transform == "O" self.w3x3.transform = "r" assert self.w3x3.transform == "R" self.w3x3.transform = "b" def test_higher_order(self): weights = { 0: [1.0, 1.0, 1.0], 1: [1.0, 1.0, 1.0], 2: [1.0, 1.0, 1.0], 3: [1.0, 1.0, 1.0], 4: [1.0, 1.0, 1.0, 1.0], 5: [1.0, 1.0, 1.0], 6: [1.0, 1.0, 1.0], 7: [1.0, 1.0, 1.0], 8: [1.0, 1.0, 1.0], } neighbors = { 0: [4, 6, 2], 1: [3, 5, 7], 2: [8, 0, 4], 3: [7, 1, 5], 4: [8, 0, 2, 6], 5: [1, 3, 7], 6: [4, 0, 8], 7: [3, 1, 5], 8: [6, 2, 4], } wneighbs = { k: {neighb: weights[k][i] for i, neighb in enumerate(v)} for k, v in list(neighbors.items()) } w2 = util.higher_order(self.w3x3, 2) test_wneighbs = { k: {ne: weights[k][i] for i, ne in enumerate(v)} for k, v in list(w2.neighbors.items()) } assert test_wneighbs == wneighbs def test_histogram(self): hist = [ (0, 1), (1, 1), (2, 4), (3, 20), (4, 57), (5, 44), (6, 36), (7, 15), (8, 7), (9, 1), (10, 6), (11, 0), (12, 2), (13, 0), (14, 0), (15, 1), ] assert self.w.histogram == hist def test_id2i(self): id2i = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8} assert self.w3x3.id2i == id2i def test_id_order_set(self): w = W(neighbors={"a": ["b"], "b": ["a", "c"], "c": ["b"]}) assert not w.id_order_set def test_islands(self): w = W( neighbors={"a": ["b"], "b": ["a", "c"], "c": ["b"], "d": []}, silence_warnings=True, ) assert w.islands == ["d"] assert self.w3x3.islands == [] def test_max_neighbors(self): w = W( neighbors={"a": ["b"], "b": ["a", "c"], "c": ["b"], "d": []}, silence_warnings=True, ) assert w.max_neighbors == 2 assert self.w3x3.max_neighbors == 4 def test_mean_neighbors(self): w = util.lat2W() assert w.mean_neighbors == 3.2 def test_min_neighbors(self): w = util.lat2W() assert w.min_neighbors == 2 def test_n(self): w = util.lat2W() assert w.n == 25 def test_neighbor_offsets(self): d = { 0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7], } assert self.w3x3.neighbor_offsets == d def test_nonzero(self): assert self.w3x3.nonzero == 24 def test_order(self): w = util.lat2W(3, 3) o = { 0: [-1, 1, 2, 1, 2, 3, 2, 3, 0], 1: [1, -1, 1, 2, 1, 2, 3, 2, 3], 2: [2, 1, -1, 3, 2, 1, 0, 3, 2], 3: [1, 2, 3, -1, 1, 2, 1, 2, 3], 4: [2, 1, 2, 1, -1, 1, 2, 1, 2], 5: [3, 2, 1, 2, 1, -1, 3, 2, 1], 6: [2, 3, 0, 1, 2, 3, -1, 1, 2], 7: [3, 2, 3, 2, 1, 2, 1, -1, 1], 8: [0, 3, 2, 3, 2, 1, 2, 1, -1], } assert util.order(w) == o def test_pct_nonzero(self): assert self.w3x3.pct_nonzero == 29.62962962962963 def test_s0(self): assert self.w3x3.s0 == 24.0 def test_s1(self): assert self.w3x3.s1 == 48.0 def test_s2(self): assert self.w3x3.s2 == 272.0 def test_s2array(self): s2a = np.array( [[16.0], [36.0], [16.0], [36.0], [64.0], [36.0], [16.0], [36.0], [16.0]] ) np.testing.assert_array_almost_equal(self.w3x3.s2array, s2a) def test_sd(self): assert self.w3x3.sd == 0.66666666666666663 def test_set_transform(self): w = util.lat2W(2, 2) assert w.transform == "O" assert w.weights[0] == [1.0, 1.0] w.transform = "r" assert w.weights[0] == [0.5, 0.5] def test_shimbel(self): d = { 0: [-1, 1, 2, 1, 2, 3, 2, 3, 4], 1: [1, -1, 1, 2, 1, 2, 3, 2, 3], 2: [2, 1, -1, 3, 2, 1, 4, 3, 2], 3: [1, 2, 3, -1, 1, 2, 1, 2, 3], 4: [2, 1, 2, 1, -1, 1, 2, 1, 2], 5: [3, 2, 1, 2, 1, -1, 3, 2, 1], 6: [2, 3, 4, 1, 2, 3, -1, 1, 2], 7: [3, 2, 3, 2, 1, 2, 1, -1, 1], 8: [4, 3, 2, 3, 2, 1, 2, 1, -1], } assert util.shimbel(self.w3x3) == d def test_sparse(self): assert self.w3x3.sparse.nnz == 24 def test_trc_w2(self): assert self.w3x3.trcW2 == 24.0 def test_trc_wt_w(self): assert self.w3x3.trcWtW == 24.0 def test_trc_wt_w_ww(self): assert self.w3x3.trcWtW_WW == 48.0 def test_symmetrize(self): symm = self.w.symmetrize() np.testing.assert_allclose(symm.sparse.toarray(), self.w.sparse.toarray()) knn = KNN.from_shapefile( examples.get_path("baltim.shp"), k=10, silence_warnings=True ) sknn = knn.symmetrize() assert not np.allclose(knn.sparse.toarray(), sknn.sparse.toarray()) np.testing.assert_allclose(sknn.sparse.toarray(), sknn.sparse.toarray().T) knn.symmetrize(inplace=True) np.testing.assert_allclose(sknn.sparse.toarray(), knn.sparse.toarray()) np.testing.assert_allclose(knn.sparse.toarray().T, knn.sparse.toarray()) def test_connected_components(self): disco = {0: [1], 1: [0], 2: [3], 3: [2]} disco = W(disco) assert disco.n_components == 2 def test_roundtrip_write(self): with tempfile.TemporaryDirectory() as tmpdir: path = os.path.join(str(tmpdir), "tmp.gal") self.w.to_file(path) new = W.from_file(path) np.testing.assert_array_equal(self.w.sparse.toarray(), new.sparse.toarray()) def test_plot(self): pytest.importorskip("matplotlib") df = geopandas.read_file(examples.get_path("10740.shp")) with warnings.catch_warnings(record=True) as record: self.w.plot(df) assert len(record) == 0 def test_to_sparse(self): sparse = self.w_islands.to_sparse() np.testing.assert_array_equal(sparse.data, [1, 1, 1, 1, 0]) np.testing.assert_array_equal(sparse.row, [0, 1, 1, 2, 3]) np.testing.assert_array_equal(sparse.col, [1, 0, 2, 1, 3]) sparse = self.w_islands.to_sparse("bsr") assert isinstance(sparse, scipy.sparse.bsr_array) sparse = self.w_islands.to_sparse("csr") assert isinstance(sparse, scipy.sparse.csr_array) sparse = self.w_islands.to_sparse("coo") assert isinstance(sparse, scipy.sparse.coo_array) sparse = self.w_islands.to_sparse("csc") assert isinstance(sparse, scipy.sparse.csc_array) sparse = self.w_islands.to_sparse() assert isinstance(sparse, scipy.sparse.coo_array) def test_sparse_fmt(self): with pytest.raises(ValueError) as exc_info: sparse = self.w_islands.to_sparse("dog") def test_from_sparse(self): sparse = self.w_islands.to_sparse() w = W.from_sparse(sparse) assert w.n == 4 assert len(w.islands) == 0 assert w.neighbors[3] == [3] class Test_WSP_Back_To_W: # Test to make sure we get back to the same W functionality def setup_method(self): self.w = Rook.from_shapefile( examples.get_path("10740.shp"), silence_warnings=True ) wsp = self.w.to_WSP() self.w = wsp.to_W(silence_warnings=True) self.neighbors = { 0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7], } self.weights = { 0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1, 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1], } self.w3x3 = util.lat2W(3, 3) w3x3 = WSP(self.w3x3.sparse, self.w3x3.id_order) self.w3x3 = WSP2W(w3x3) def test_w(self): w = W(self.neighbors, self.weights, silence_warnings=True) assert w.pct_nonzero == 29.62962962962963 def test___getitem__(self): assert self.w[0] == {1: 1.0, 4: 1.0, 101: 1.0, 85: 1.0, 5: 1.0} def test___init__(self): w = W(self.neighbors, self.weights, silence_warnings=True) assert w.pct_nonzero == 29.62962962962963 def test___iter__(self): w = util.lat2W(3, 3) res = {} for i, wi in enumerate(w): res[i] = wi assert res[0] == (0, {1: 1.0, 3: 1.0}) assert res[8] == (8, {5: 1.0, 7: 1.0}) def test_asymmetries(self): w = util.lat2W(3, 3) w.transform = "r" result = w.asymmetry() assert result == [ (0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7), ] def test_asymmetry(self): w = util.lat2W(3, 3) assert w.asymmetry() == [] w.transform = "r" assert not w.asymmetry() == [] def test_cardinalities(self): w = util.lat2W(3, 3) assert w.cardinalities == {0: 2, 1: 3, 2: 2, 3: 3, 4: 4, 5: 3, 6: 2, 7: 3, 8: 2} def test_diag_w2(self): np.testing.assert_array_almost_equal( self.w3x3.diagW2, np.array([2.0, 3.0, 2.0, 3.0, 4.0, 3.0, 2.0, 3.0, 2.0]) ) def test_diag_wt_w(self): np.testing.assert_array_almost_equal( self.w3x3.diagW2, np.array([2.0, 3.0, 2.0, 3.0, 4.0, 3.0, 2.0, 3.0, 2.0]) ) def test_diag_wt_w_ww(self): np.testing.assert_array_almost_equal( self.w3x3.diagWtW_WW, np.array([4.0, 6.0, 4.0, 6.0, 8.0, 6.0, 4.0, 6.0, 4.0]), ) def test_full(self): wf = np.array( [ [0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0], ] ) ids = list(range(9)) wf1, ids1 = self.w3x3.full() np.testing.assert_array_almost_equal(wf1, wf) assert ids1 == ids def test_get_transform(self): assert self.w3x3.transform == "O" self.w3x3.transform = "r" assert self.w3x3.transform == "R" self.w3x3.transform = "b" def test_higher_order(self): weights = { 0: [1.0, 1.0, 1.0], 1: [1.0, 1.0, 1.0], 2: [1.0, 1.0, 1.0], 3: [1.0, 1.0, 1.0], 4: [1.0, 1.0, 1.0, 1.0], 5: [1.0, 1.0, 1.0], 6: [1.0, 1.0, 1.0], 7: [1.0, 1.0, 1.0], 8: [1.0, 1.0, 1.0], } neighbors = { 0: [4, 6, 2], 1: [3, 5, 7], 2: [8, 0, 4], 3: [7, 1, 5], 4: [8, 0, 2, 6], 5: [1, 3, 7], 6: [4, 0, 8], 7: [3, 1, 5], 8: [6, 2, 4], } wneighbs = { k: {neighb: weights[k][i] for i, neighb in enumerate(v)} for k, v in list(neighbors.items()) } w2 = util.higher_order(self.w3x3, 2) test_wneighbs = { k: {ne: w2.weights[k][i] for i, ne in enumerate(v)} for k, v in list(w2.neighbors.items()) } assert test_wneighbs == wneighbs def test_histogram(self): hist = [ (0, 1), (1, 1), (2, 4), (3, 20), (4, 57), (5, 44), (6, 36), (7, 15), (8, 7), (9, 1), (10, 6), (11, 0), (12, 2), (13, 0), (14, 0), (15, 1), ] assert self.w.histogram == hist def test_id2i(self): id2i = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8} assert self.w3x3.id2i == id2i def test_id_order_set(self): w = W(neighbors={"a": ["b"], "b": ["a", "c"], "c": ["b"]}) assert not w.id_order_set def test_islands(self): w = W( neighbors={"a": ["b"], "b": ["a", "c"], "c": ["b"], "d": []}, silence_warnings=True, ) assert w.islands == ["d"] assert self.w3x3.islands == [] def test_max_neighbors(self): w = W( neighbors={"a": ["b"], "b": ["a", "c"], "c": ["b"], "d": []}, silence_warnings=True, ) assert w.max_neighbors == 2 assert self.w3x3.max_neighbors == 4 def test_mean_neighbors(self): w = util.lat2W() assert w.mean_neighbors == 3.2 def test_min_neighbors(self): w = util.lat2W() assert w.min_neighbors == 2 def test_n(self): w = util.lat2W() assert w.n == 25 def test_nonzero(self): assert self.w3x3.nonzero == 24 def test_order(self): w = util.lat2W(3, 3) o = { 0: [-1, 1, 2, 1, 2, 3, 2, 3, 0], 1: [1, -1, 1, 2, 1, 2, 3, 2, 3], 2: [2, 1, -1, 3, 2, 1, 0, 3, 2], 3: [1, 2, 3, -1, 1, 2, 1, 2, 3], 4: [2, 1, 2, 1, -1, 1, 2, 1, 2], 5: [3, 2, 1, 2, 1, -1, 3, 2, 1], 6: [2, 3, 0, 1, 2, 3, -1, 1, 2], 7: [3, 2, 3, 2, 1, 2, 1, -1, 1], 8: [0, 3, 2, 3, 2, 1, 2, 1, -1], } assert util.order(w) == o def test_pct_nonzero(self): assert self.w3x3.pct_nonzero == 29.62962962962963 def test_s0(self): assert self.w3x3.s0 == 24.0 def test_s1(self): assert self.w3x3.s1 == 48.0 def test_s2(self): assert self.w3x3.s2 == 272.0 def test_s2array(self): s2a = np.array( [[16.0], [36.0], [16.0], [36.0], [64.0], [36.0], [16.0], [36.0], [16.0]] ) np.testing.assert_array_almost_equal(self.w3x3.s2array, s2a) def test_sd(self): assert self.w3x3.sd == 0.66666666666666663 def test_set_transform(self): w = util.lat2W(2, 2) assert w.transform == "O" assert w.weights[0] == [1.0, 1.0] w.transform = "r" assert w.weights[0] == [0.5, 0.5] def test_shimbel(self): d = { 0: [-1, 1, 2, 1, 2, 3, 2, 3, 4], 1: [1, -1, 1, 2, 1, 2, 3, 2, 3], 2: [2, 1, -1, 3, 2, 1, 4, 3, 2], 3: [1, 2, 3, -1, 1, 2, 1, 2, 3], 4: [2, 1, 2, 1, -1, 1, 2, 1, 2], 5: [3, 2, 1, 2, 1, -1, 3, 2, 1], 6: [2, 3, 4, 1, 2, 3, -1, 1, 2], 7: [3, 2, 3, 2, 1, 2, 1, -1, 1], 8: [4, 3, 2, 3, 2, 1, 2, 1, -1], } assert util.shimbel(self.w3x3) == d def test_sparse(self): assert self.w3x3.sparse.nnz == 24 def test_trc_w2(self): assert self.w3x3.trcW2 == 24.0 def test_trc_wt_w(self): assert self.w3x3.trcWtW == 24.0 def test_trc_wt_w_ww(self): assert self.w3x3.trcWtW_WW == 48.0 class TestWSP: def setup_method(self): self.w = psopen(examples.get_path("sids2.gal")).read() self.wsp = WSP(self.w.sparse, self.w.id_order) w3x3 = util.lat2W(3, 3) self.w3x3 = WSP(w3x3.sparse) def test_wsp(self): assert self.w.id_order == self.wsp.id_order assert self.w.n == self.wsp.n np.testing.assert_array_equal( self.w.sparse.todense(), self.wsp.sparse.todense() ) def test_diag_wt_w_ww(self): np.testing.assert_array_almost_equal( self.w3x3.diagWtW_WW, np.array([4.0, 6.0, 4.0, 6.0, 8.0, 6.0, 4.0, 6.0, 4.0]), ) def test_trc_wt_w_ww(self): assert self.w3x3.trcWtW_WW == 48.0 def test_s0(self): assert self.w3x3.s0 == 24.0 def test_from_wsp(self): w = W.from_WSP(self.wsp) assert w.n == 100 assert w.pct_nonzero == 4.62 def test_label_encoder(self): le = _LabelEncoder() le.fit(["NY", "CA", "NY", "CA", "TX", "TX"]) np.testing.assert_equal(le.classes_, np.array(["CA", "NY", "TX"])) np.testing.assert_equal( le.transform(["NY", "CA", "NY", "CA", "TX", "TX"]), np.array([1, 0, 1, 0, 2, 2]), ) libpysal-4.9.2/libpysal/weights/tests/test_weights_IO.py000066400000000000000000000016171452177046000234650ustar00rootroot00000000000000import os import tempfile import libpysal class TestWIO: def setup_method(self): self.swmFile1 = libpysal.examples.get_path("ohio.swm") self.swmFile2 = libpysal.examples.get_path("us48_CONTIGUITY_EDGES_ONLY.swm") self.swmFile3 = libpysal.examples.get_path("us48_INVERSE_DISTANCE.swm") self.files = [self.swmFile1, self.swmFile2, self.swmFile3] def test_swmio(self): for file in self.files: f1 = libpysal.io.open(file) w1 = f1.read() f = tempfile.NamedTemporaryFile(suffix=".swm") fname = f.name f.close() f2 = libpysal.io.open(fname, "w") f2.varName = f1.varName f2.srs = f1.srs f2.write(w1) f2.close() w2 = libpysal.io.open(fname, "r").read() assert w1.pct_nonzero == w2.pct_nonzero os.remove(fname)libpysal-4.9.2/libpysal/weights/user.py000066400000000000000000000076661452177046000202130ustar00rootroot00000000000000""" Convenience functions for the construction of spatial weights based on contiguity and distance criteria. """ __author__ = "Sergio J. Rey " from .util import get_points_array_from_shapefile, min_threshold_distance from ..io.fileio import FileIO as ps_open from .. import cg import numpy as np __all__ = [ "min_threshold_dist_from_shapefile", "build_lattice_shapefile", "spw_from_gal", ] def spw_from_gal(galfile): """ Sparse scipy matrix for w from a gal file. Parameters ---------- galfile : string name of gal file including suffix Returns ------- spw : sparse_matrix scipy sparse matrix in CSR format ids : array identifiers for rows/cols of spw Examples -------- >>> import libpysal >>> spw = libpysal.weights.spw_from_gal(libpysal.examples.get_path("sids2.gal")) >>> spw.sparse.nnz 462 """ return ps_open(galfile, "r").read(sparse=True) def min_threshold_dist_from_shapefile(shapefile, radius=None, p=2): """ Get the maximum nearest neighbor distance between observations in the shapefile. Parameters ---------- shapefile : string shapefile name with shp suffix. radius : float If supplied arc_distances will be calculated based on the given radius. p will be ignored. p : float Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance Returns ------- d : float Maximum nearest neighbor distance between the n observations. Examples -------- >>> import libpysal >>> md = libpysal.weights.min_threshold_dist_from_shapefile(libpysal.examples.get_path("columbus.shp")) >>> md 0.6188641580768541 >>> libpysal.weights.min_threshold_dist_from_shapefile(libpysal.examples.get_path("stl_hom.shp"), libpysal.cg.sphere.RADIUS_EARTH_MILES) 31.846942936393717 Notes ----- Supports polygon or point shapefiles. For polygon shapefiles, distance is based on polygon centroids. Distances are defined using coordinates in shapefile which are assumed to be projected and not geographical coordinates. """ points = get_points_array_from_shapefile(shapefile) if radius is not None: kdt = cg.kdtree.Arc_KDTree(points, radius=radius) nn = kdt.query(kdt.data, k=2) nnd = nn[0].max(axis=0)[1] return nnd return min_threshold_distance(points, p) def build_lattice_shapefile(nrows, ncols, outFileName): """ Build a lattice shapefile with nrows rows and ncols cols. Parameters ---------- nrows : int Number of rows ncols : int Number of cols outFileName : str shapefile name with shp suffix Returns ------- None """ if not outFileName.endswith(".shp"): raise ValueError("outFileName must end with .shp") o = ps_open(outFileName, "w") dbf_name = outFileName.split(".")[0] + ".dbf" d = ps_open(dbf_name, "w") d.header = ["ID"] d.field_spec = [("N", 8, 0)] c = 0 for i in range(ncols): for j in range(nrows): ll = i, j ul = i, j + 1 ur = i + 1, j + 1 lr = i + 1, j o.write(cg.Polygon([ll, ul, ur, lr, ll])) d.write([c]) c += 1 d.close() o.close() def _test(): import doctest # the following line could be used to define an alternative to the '' flag # doctest.BLANKLINE_MARKER = 'something better than ' start_suppress = np.get_printoptions()["suppress"] np.set_printoptions(suppress=True) doctest.testmod() np.set_printoptions(suppress=start_suppress) if __name__ == "__main__": _test() libpysal-4.9.2/libpysal/weights/util.py000066400000000000000000001335011452177046000201760ustar00rootroot00000000000000from ..io.fileio import FileIO as psopen from .weights import W, WSP from .set_operations import w_subset import numpy as np from scipy import sparse from scipy.spatial import KDTree import copy import scipy.spatial import os import scipy from warnings import warn import numbers from collections import defaultdict from itertools import tee from ..common import requires from packaging.version import Version try: import geopandas as gpd GPD_013 = Version(gpd.__version__) >= Version("0.13.0") except ImportError: warn("geopandas not available. Some functionality will be disabled.") try: from shapely.geometry.base import BaseGeometry HAS_SHAPELY = True except ImportError: HAS_SHAPELY = False __all__ = [ "lat2W", "block_weights", "comb", "order", "higher_order", "shimbel", "remap_ids", "full2W", "full", "WSP2W", "insert_diagonal", "fill_diagonal", "get_ids", "get_points_array_from_shapefile", "min_threshold_distance", "lat2SW", "w_local_cluster", "higher_order_sp", "hexLat2W", "neighbor_equality", "attach_islands", "nonplanar_neighbors", "fuzzy_contiguity", ] KDTREE_TYPES = [scipy.spatial.KDTree, scipy.spatial.cKDTree] def hexLat2W(nrows=5, ncols=5, **kwargs): """ Create a W object for a hexagonal lattice. Parameters ---------- nrows : int number of rows ncols : int number of columns **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- w : W instance of spatial weights class W Notes ----- Observations are row ordered: first k observations are in row 0, next k in row 1, and so on. Construction is based on shifting every other column of a regular lattice down 1/2 of a cell. Examples -------- >>> from libpysal.weights import lat2W, hexLat2W >>> w = lat2W() >>> w.neighbors[1] [0, 6, 2] >>> w.neighbors[21] [16, 20, 22] >>> wh = hexLat2W() >>> wh.neighbors[1] [0, 6, 2, 5, 7] >>> wh.neighbors[21] [16, 20, 22] """ if nrows == 1 or ncols == 1: print("Hexagon lattice requires at least 2 rows and columns") print("Returning a linear contiguity structure") return lat2W(nrows, ncols) n = nrows * ncols rid = [i // ncols for i in range(n)] cid = [i % ncols for i in range(n)] r1 = nrows - 1 c1 = ncols - 1 w = lat2W(nrows, ncols).neighbors for i in range(n): odd = cid[i] % 2 if odd: if rid[i] < r1: # odd col index above last row # new sw neighbor if cid[i] > 0: j = i + ncols - 1 w[i] = w.get(i, []) + [j] # new se neighbor if cid[i] < c1: j = i + ncols + 1 w[i] = w.get(i, []) + [j] else: # even col # nw jnw = [i - ncols - 1] # ne jne = [i - ncols + 1] if rid[i] > 0: w[i] if cid[i] == 0: w[i] = w.get(i, []) + jne elif cid[i] == c1: w[i] = w.get(i, []) + jnw else: w[i] = w.get(i, []) + jne w[i] = w.get(i, []) + jnw return W(w, **kwargs) def lat2W(nrows=5, ncols=5, rook=True, id_type="int", **kwargs): """ Create a W object for a regular lattice. Parameters ---------- nrows : int number of rows ncols : int number of columns rook : boolean type of contiguity. Default is rook. For queen, rook =False id_type : string string defining the type of IDs to use in the final W object; options are 'int' (0, 1, 2 ...; default), 'float' (0.0, 1.0, 2.0, ...) and 'string' ('id0', 'id1', 'id2', ...) **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- w : W instance of spatial weights class W Notes ----- Observations are row ordered: first k observations are in row 0, next k in row 1, and so on. Examples -------- >>> from libpysal.weights import lat2W >>> w9 = lat2W(3,3) >>> "%.3f"%w9.pct_nonzero '29.630' >>> w9[0] == {1: 1.0, 3: 1.0} True >>> w9[3] == {0: 1.0, 4: 1.0, 6: 1.0} True """ n = nrows * ncols r1 = nrows - 1 c1 = ncols - 1 rid = [i // ncols for i in range(n)] # must be floor! cid = [i % ncols for i in range(n)] w = {} r = below = 0 for i in range(n - 1): if rid[i] < r1: below = rid[i] + 1 r = below * ncols + cid[i] w[i] = w.get(i, []) + [r] w[r] = w.get(r, []) + [i] if cid[i] < c1: right = cid[i] + 1 c = rid[i] * ncols + right w[i] = w.get(i, []) + [c] w[c] = w.get(c, []) + [i] if not rook: # southeast bishop if cid[i] < c1 and rid[i] < r1: r = (rid[i] + 1) * ncols + 1 + cid[i] w[i] = w.get(i, []) + [r] w[r] = w.get(r, []) + [i] # southwest bishop if cid[i] > 0 and rid[i] < r1: r = (rid[i] + 1) * ncols - 1 + cid[i] w[i] = w.get(i, []) + [r] w[r] = w.get(r, []) + [i] neighbors = {} weights = {} for key in w: weights[key] = [1.0] * len(w[key]) ids = list(range(n)) if id_type == "string": ids = ["id" + str(i) for i in ids] elif id_type == "float": ids = [i * 1.0 for i in ids] if id_type == "string" or id_type == "float": id_dict = dict(list(zip(list(range(n)), ids))) alt_w = {} alt_weights = {} for i in w: values = [id_dict[j] for j in w[i]] key = id_dict[i] alt_w[key] = values alt_weights[key] = weights[i] w = alt_w weights = alt_weights return W(w, weights, ids=ids, id_order=ids[:], **kwargs) def block_weights(regimes, ids=None, sparse=False, **kwargs): """ Construct spatial weights for regime neighbors. Block contiguity structures are relevant when defining neighbor relations based on membership in a regime. For example, all counties belonging to the same state could be defined as neighbors, in an analysis of all counties in the US. Parameters ---------- regimes : list, array ids of which regime an observation belongs to ids : list, array Ordered sequence of IDs for the observations sparse : boolean If True return WSP instance If False return W instance **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- W : spatial weights instance Examples -------- >>> from libpysal.weights import block_weights >>> import numpy as np >>> regimes = np.ones(25) >>> regimes[range(10,20)] = 2 >>> regimes[range(21,25)] = 3 >>> regimes array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1., 3., 3., 3., 3.]) >>> w = block_weights(regimes) >>> w.weights[0] [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] >>> w.neighbors[0] [1, 2, 3, 4, 5, 6, 7, 8, 9, 20] >>> regimes = ['n','n','s','s','e','e','w','w','e'] >>> n = len(regimes) >>> w = block_weights(regimes) >>> w.neighbors == {0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8], 6: [7], 7: [6], 8: [4, 5]} True """ rids = np.unique(regimes) neighbors = {} NPNZ = np.nonzero regimes = np.array(regimes) for rid in rids: members = NPNZ(regimes == rid)[0] for member in members: neighbors[member] = members[NPNZ(members != member)[0]].tolist() w = W(neighbors, **kwargs) if ids is not None: w.remap_ids(ids) if sparse: w = WSP(w.sparse, id_order=ids) return w def comb(items, n=None): """ Combinations of size n taken from items Parameters ---------- items : list items to be drawn from n : integer size of combinations to take from items Returns ------- implicit : generator combinations of size n taken from items Examples -------- >>> x = range(4) >>> for c in comb(x, 2): ... print(c) ... [0, 1] [0, 2] [0, 3] [1, 2] [1, 3] [2, 3] """ items = list(items) if n is None: n = len(items) for i in list(range(len(items))): v = items[i : i + 1] if n == 1: yield v else: rest = items[i + 1 :] for c in comb(rest, n - 1): yield v + c def order(w, kmax=3): """ Determine the non-redundant order of contiguity up to a specific order. Parameters ---------- w : W spatial weights object kmax : int maximum order of contiguity Returns ------- info : dictionary observation id is the key, value is a list of contiguity orders with a negative 1 in the ith position Notes ----- Implements the algorithm in :cite:`Anselin1996b`. Examples -------- >>> from libpysal.weights import order, Rook >>> import libpysal >>> w = Rook.from_shapefile(libpysal.examples.get_path('10740.shp')) WARNING: there is one disconnected observation (no neighbors) Island id: [163] >>> w3 = order(w, kmax = 3) >>> w3[1][0:5] [1, -1, 1, 2, 1] """ ids = w.id_order info = {} for id_ in ids: s = [0] * w.n s[ids.index(id_)] = -1 for j in w.neighbors[id_]: s[ids.index(j)] = 1 k = 1 while k < kmax: knext = k + 1 if s.count(k): # get neighbors of order k js = [ids[j] for j, val in enumerate(s) if val == k] # get first order neighbors for order k neighbors for j in js: next_neighbors = w.neighbors[j] for neighbor in next_neighbors: nid = ids.index(neighbor) if s[nid] == 0: s[nid] = knext k = knext info[id_] = s return info def higher_order(w, k=2, **kwargs): """ Contiguity weights object of order k. Parameters ---------- w : W spatial weights object k : int order of contiguity **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- implicit : W spatial weights object Notes ----- Proper higher order neighbors are returned such that i and j are k-order neighbors iff the shortest path from i-j is of length k. Examples -------- >>> from libpysal.weights import lat2W, higher_order >>> w10 = lat2W(10, 10) >>> w10_2 = higher_order(w10, 2) >>> w10_2[0] == {2: 1.0, 11: 1.0, 20: 1.0} True >>> w5 = lat2W() >>> w5[0] == {1: 1.0, 5: 1.0} True >>> w5[1] == {0: 1.0, 2: 1.0, 6: 1.0} True >>> w5_2 = higher_order(w5,2) >>> w5_2[0] == {10: 1.0, 2: 1.0, 6: 1.0} True """ return higher_order_sp(w, k, **kwargs) def higher_order_sp( w, k=2, shortest_path=True, diagonal=False, lower_order=False, **kwargs ): """ Contiguity weights for either a sparse W or W for order k. Parameters ---------- w : W sparse_matrix, spatial weights object or scipy.sparse.csr.csr_instance k : int Order of contiguity shortest_path : boolean True: i,j and k-order neighbors if the shortest path for i,j is k. False: i,j are k-order neighbors if there is a path from i,j of length k. diagonal : boolean True: keep k-order (i,j) joins when i==j False: remove k-order (i,j) joins when i==j lower_order : boolean True: include lower order contiguities False: return only weights of order k **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- wk : W WSP, type matches type of w argument Examples -------- >>> from libpysal.weights import lat2W, higher_order_sp >>> w25 = lat2W(5,5) >>> w25.n 25 >>> w25[0] == {1: 1.0, 5: 1.0} True >>> w25_2 = higher_order_sp(w25, 2) >>> w25_2[0] == {10: 1.0, 2: 1.0, 6: 1.0} True >>> w25_2 = higher_order_sp(w25, 2, diagonal=True) >>> w25_2[0] == {0: 1.0, 10: 1.0, 2: 1.0, 6: 1.0} True >>> w25_3 = higher_order_sp(w25, 3) >>> w25_3[0] == {15: 1.0, 3: 1.0, 11: 1.0, 7: 1.0} True >>> w25_3 = higher_order_sp(w25, 3, shortest_path=False) >>> w25_3[0] == {1: 1.0, 3: 1.0, 5: 1.0, 7: 1.0, 11: 1.0, 15: 1.0} True >>> w25_3 = higher_order_sp(w25, 3, lower_order=True) >>> w25_3[0] == {5: 1.0, 7: 1.0, 11: 1.0, 2: 1.0, 15: 1.0, 6: 1.0, 10: 1.0, 1: 1.0, 3: 1.0} True """ id_order = None if issubclass(type(w), W) or isinstance(w, W): if np.unique(np.hstack(list(w.weights.values()))) == np.array([1.0]): id_order = w.id_order w = w.sparse else: raise ValueError("Weights are not binary (0,1)") elif scipy.sparse.isspmatrix_csr(w): if not np.unique(w.data) == np.array([1.0]): raise ValueError( "Sparse weights matrix is not binary (0,1) weights matrix." ) else: raise TypeError( "Weights provided are neither a binary W object nor " "a scipy.sparse.csr_matrix" ) if lower_order: wk = sum(map(lambda x: w**x, range(2, k + 1))) shortest_path = False else: wk = w**k rk, ck = wk.nonzero() sk = set(zip(rk, ck)) if shortest_path: for j in range(1, k): wj = w**j rj, cj = wj.nonzero() sj = set(zip(rj, cj)) sk.difference_update(sj) if not diagonal: sk = set([(i, j) for i, j in sk if i != j]) if id_order: d = dict([(i, []) for i in id_order]) for pair in sk: k, v = pair k = id_order[k] v = id_order[v] d[k].append(v) return W(neighbors=d, **kwargs) else: d = {} for pair in sk: k, v = pair if k in d: d[k].append(v) else: d[k] = [v] return WSP(W(neighbors=d, **kwargs).sparse) def w_local_cluster(w): r""" Local clustering coefficients for each unit as a node in a graph. Parameters ---------- w : W spatial weights object Returns ------- c : array (w.n,1) local clustering coefficients Notes ----- The local clustering coefficient :math:`c_i` quantifies how close the neighbors of observation :math:`i` are to being a clique: .. math:: c_i = | \{w_{j,k}\} |/ (k_i(k_i - 1)): j,k \in N_i where :math:`N_i` is the set of neighbors to :math:`i`, :math:`k_i = |N_i|` and :math:`\{w_{j,k}\}` is the set of non-zero elements of the weights between pairs in :math:`N_i` :cite:`Watts1998`. Examples -------- >>> from libpysal.weights import lat2W, w_local_cluster >>> w = lat2W(3,3, rook=False) >>> w_local_cluster(w) array([[1. ], [0.6 ], [1. ], [0.6 ], [0.42857143], [0.6 ], [1. ], [0.6 ], [1. ]]) """ c = np.zeros((w.n, 1), float) w.transformation = "b" for i, id in enumerate(w.id_order): ki = max(w.cardinalities[id], 1) # deal with islands Ni = w.neighbors[id] wi = w_subset(w, Ni).full()[0] c[i] = wi.sum() / (ki * (ki - 1)) return c def shimbel(w): """ Find the Shimbel matrix for first order contiguity matrix. Parameters ---------- w : W spatial weights object Returns ------- info : list list of lists; one list for each observation which stores the shortest order between it and each of the the other observations. Examples -------- >>> from libpysal.weights import lat2W, shimbel >>> w5 = lat2W() >>> w5_shimbel = shimbel(w5) >>> w5_shimbel[0][24] 8 >>> w5_shimbel[0][0:4] [-1, 1, 2, 3] """ info = {} ids = w.id_order for i in ids: s = [0] * w.n s[ids.index(i)] = -1 for j in w.neighbors[i]: s[ids.index(j)] = 1 k = 1 flag = s.count(0) while flag: p = -1 knext = k + 1 for j in range(s.count(k)): neighbor = s.index(k, p + 1) p = neighbor next_neighbors = w.neighbors[ids[p]] for neighbor in next_neighbors: nid = ids.index(neighbor) if s[nid] == 0: s[nid] = knext k = knext flag = s.count(0) info[i] = s return info def full(w): """ Generate a full numpy array. Parameters ---------- w : W spatial weights object Returns ------- (fullw, keys) : tuple first element being the full numpy array and second element keys being the ids associated with each row in the array. Examples -------- >>> from libpysal.weights import W, full >>> neighbors = {'first':['second'],'second':['first','third'],'third':['second']} >>> weights = {'first':[1],'second':[1,1],'third':[1]} >>> w = W(neighbors, weights) >>> wf, ids = full(w) >>> wf array([[0., 1., 0.], [1., 0., 1.], [0., 1., 0.]]) >>> ids ['first', 'second', 'third'] """ return w.full() def full2W(m, ids=None, **kwargs): """ Create a PySAL W object from a full array. Parameters ---------- m : array nxn array with the full weights matrix ids : list User ids assumed to be aligned with m **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- w : W PySAL weights object Examples -------- >>> from libpysal.weights import full2W >>> import numpy as np Create an array of zeros >>> a = np.zeros((4, 4)) For loop to fill it with random numbers >>> for i in range(len(a)): ... for j in range(len(a[i])): ... if i!=j: ... a[i, j] = np.random.random(1) Create W object >>> w = full2W(a) >>> w.full()[0] == a array([[ True, True, True, True], [ True, True, True, True], [ True, True, True, True], [ True, True, True, True]]) Create list of user ids >>> ids = ['myID0', 'myID1', 'myID2', 'myID3'] >>> w = full2W(a, ids=ids) >>> w.full()[0] == a array([[ True, True, True, True], [ True, True, True, True], [ True, True, True, True], [ True, True, True, True]]) """ if m.shape[0] != m.shape[1]: raise ValueError("Your array is not square") neighbors, weights = {}, {} for i in range(m.shape[0]): # for i, row in enumerate(m): row = m[i] if ids: i = ids[i] ngh = list(row.nonzero()[0]) weights[i] = list(row[ngh]) ngh = list(ngh) if ids: ngh = [ids[j] for j in ngh] neighbors[i] = ngh return W(neighbors, weights, id_order=ids, **kwargs) def WSP2W(wsp, **kwargs): """ Convert a pysal WSP object (thin weights matrix) to a pysal W object. Parameters ---------- wsp : WSP PySAL sparse weights object **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- w : W PySAL weights object Examples -------- >>> from libpysal.weights import lat2W, WSP, WSP2W Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells (rook contiguity), then construct a PySAL sparse weights object (wsp). >>> sp = lat2SW(2, 5) >>> wsp = WSP(sp) >>> wsp.n 10 >>> wsp.sparse[0].todense() matrix([[0, 1, 0, 0, 0, 1, 0, 0, 0, 0]], dtype=int8) Convert this sparse weights object to a standard PySAL weights object. >>> w = WSP2W(wsp) >>> w.n 10 >>> print(w.full()[0][0]) [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.] """ data = wsp.sparse.data indptr = wsp.sparse.indptr id_order = wsp.id_order if id_order: # replace indices with user IDs indices = [id_order[i] for i in wsp.sparse.indices] else: id_order = list(range(wsp.n)) neighbors, weights = {}, {} start = indptr[0] for i in range(wsp.n): oid = id_order[i] end = indptr[i + 1] neighbors[oid] = indices[start:end] weights[oid] = data[start:end].tolist() start = end ids = copy.copy(wsp.id_order) w = W(neighbors, weights, ids, **kwargs) w._sparse = copy.deepcopy(wsp.sparse) w._cache["sparse"] = w._sparse return w def insert_diagonal(w, val=1.0, wsp=False): warn("This function is deprecated. Use fill_diagonal instead.") return fill_diagonal(w, val=val, wsp=wsp) def fill_diagonal(w, val=1.0, wsp=False): """ Returns a new weights object with values inserted along the main diagonal. Parameters ---------- w : W Spatial weights object diagonal : float, int or array Defines the value(s) to which the weights matrix diagonal should be set. If a constant is passed then each element along the diagonal will get this value (default is 1.0). An array of length w.n can be passed to set explicit values to each element along the diagonal (assumed to be in the same order as w.id_order). wsp : boolean If True return a thin weights object of the type WSP, if False return the standard W object. Returns ------- w : W Spatial weights object Examples -------- >>> from libpysal.weights import lat2W >>> import numpy as np Build a basic rook weights matrix, which has zeros on the diagonal, then insert ones along the diagonal. >>> w = lat2W(5, 5, id_type='string') >>> w_const = insert_diagonal(w) >>> w['id0'] == {'id5': 1.0, 'id1': 1.0} True >>> w_const['id0'] == {'id5': 1.0, 'id0': 1.0, 'id1': 1.0} True Insert different values along the main diagonal. >>> diag = np.arange(100, 125) >>> w_var = insert_diagonal(w, diag) >>> w_var['id0'] == {'id5': 1.0, 'id0': 100.0, 'id1': 1.0} True """ w_new = copy.deepcopy(w.sparse) w_new = w_new.tolil() if issubclass(type(val), np.ndarray): if w.n != val.shape[0]: raise Exception("shape of w and diagonal do not match") w_new.setdiag(val) elif isinstance(val, numbers.Number): w_new.setdiag([val] * w.n) else: raise Exception("Invalid value passed to diagonal") w_out = WSP(w_new, copy.copy(w.id_order)) if wsp: return w_out else: return WSP2W(w_out) def remap_ids(w, old2new, id_order=[], **kwargs): """ Remaps the IDs in a spatial weights object. Parameters ---------- w : W Spatial weights object old2new : dictionary Dictionary where the keys are the IDs in w (i.e. "old IDs") and the values are the IDs to replace them (i.e. "new IDs") id_order : list An ordered list of new IDs, which defines the order of observations when iterating over W. If not set then the id_order in w will be used. **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- implicit : W Spatial weights object with new IDs Examples -------- >>> from libpysal.weights import lat2W >>> w = lat2W(3,2) >>> w.id_order [0, 1, 2, 3, 4, 5] >>> w.neighbors[0] [2, 1] >>> old_to_new = {0:'a', 1:'b', 2:'c', 3:'d', 4:'e', 5:'f'} >>> w_new = remap_ids(w, old_to_new) >>> w_new.id_order ['a', 'b', 'c', 'd', 'e', 'f'] >>> w_new.neighbors['a'] ['c', 'b'] """ if not isinstance(w, W): raise Exception("w must be a spatial weights object") new_neigh = {} new_weights = {} for key, value in list(w.neighbors.items()): new_values = [old2new[i] for i in value] new_key = old2new[key] new_neigh[new_key] = new_values new_weights[new_key] = copy.copy(w.weights[key]) if id_order: return W(new_neigh, new_weights, id_order, **kwargs) else: if w.id_order: id_order = [old2new[i] for i in w.id_order] return W(new_neigh, new_weights, id_order, **kwargs) else: return W(new_neigh, new_weights, **kwargs) def get_ids(in_shps, idVariable): """ Gets the IDs from the DBF file that moves with a given shape file or a geopandas.GeoDataFrame. Parameters ---------- in_shps : str or geopandas.GeoDataFrame The input geographic data. Either (1) a path to a shapefile including suffix (str); or (2) a geopandas.GeoDataFrame. idVariable : str name of a column in the shapefile's DBF or the geopandas.GeoDataFrame to use for ids. Returns ------- ids : list a list of IDs Examples -------- >>> from libpysal.weights.util import get_ids >>> import libpysal >>> polyids = get_ids(libpysal.examples.get_path("columbus.shp"), "POLYID") >>> polyids[:5] [1, 2, 3, 4, 5] >>> from libpysal.weights.util import get_ids >>> import libpysal >>> import geopandas as gpd >>> gdf = gpd.read_file(libpysal.examples.get_path("columbus.shp")) >>> polyids = gdf["POLYID"] >>> polyids[:5] 0 1 1 2 2 3 3 4 4 5 Name: POLYID, dtype: int64 """ try: if type(in_shps) == str: dbname = os.path.splitext(in_shps)[0] + ".dbf" db = psopen(dbname) cols = db.header var = db.by_col[idVariable] db.close() else: cols = list(in_shps.columns) var = list(in_shps[idVariable]) return var except IOError: msg = ( 'The shapefile "%s" appears to be missing its DBF file. ' + ' The DBF file "%s" could not be found.' % (in_shps, dbname) ) raise IOError(msg) except (AttributeError, KeyError): msg = ( 'The variable "%s" not found in the DBF/GDF. The the following ' + "variables are present: %s." % (idVariable, ",".join(cols)) ) raise KeyError(msg) def get_points_array(iterable): """ Gets a data array of x and y coordinates from a given iterable Parameters ---------- iterable : iterable arbitrary collection of shapes that supports iteration Returns ------- points : array (n, 2) a data array of x and y coordinates Notes ----- If the given shape file includes polygons, this function returns x and y coordinates of the polygons' centroids """ first_choice, backup = tee(iterable) try: if HAS_SHAPELY: data = np.vstack( [ np.array(shape.centroid.coords)[0] if isinstance(shape, BaseGeometry) else np.array(shape.centroid) for shape in first_choice ] ) else: data = np.vstack([np.array(shape.centroid) for shape in first_choice]) except AttributeError: data = np.vstack([shape for shape in backup]) return data def get_points_array_from_shapefile(shapefile): """ Gets a data array of x and y coordinates from a given shapefile. Parameters ---------- shapefile : string name of a shape file including suffix Returns ------- points : array (n, 2) a data array of x and y coordinates Notes ----- If the given shape file includes polygons, this function returns x and y coordinates of the polygons' centroids Examples -------- Point shapefile >>> import libpysal >>> from libpysal.weights.util import get_points_array_from_shapefile >>> xy = get_points_array_from_shapefile(libpysal.examples.get_path('juvenile.shp')) >>> xy[:3] array([[94., 93.], [80., 95.], [79., 90.]]) Polygon shapefile >>> xy = get_points_array_from_shapefile(libpysal.examples.get_path('columbus.shp')) >>> xy[:3] array([[ 8.82721847, 14.36907602], [ 8.33265837, 14.03162401], [ 9.01226541, 13.81971908]]) """ f = psopen(shapefile) data = get_points_array(f) return data def min_threshold_distance(data, p=2): """ Get the maximum nearest neighbor distance. Parameters ---------- data : array (n,k) or KDTree where KDtree.data is array (n,k) n observations on k attributes p : float Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance Returns ------- nnd : float maximum nearest neighbor distance between the n observations Examples -------- >>> from libpysal.weights.util import min_threshold_distance >>> import numpy as np >>> x, y = np.indices((5, 5)) >>> x.shape = (25, 1) >>> y.shape = (25, 1) >>> data = np.hstack([x, y]) >>> min_threshold_distance(data) 1.0 """ if issubclass(type(data), scipy.spatial.KDTree): kd = data data = kd.data else: kd = KDTree(data) nn = kd.query(data, k=2, p=p) nnd = nn[0].max(axis=0)[1] return nnd def lat2SW(nrows=3, ncols=5, criterion="rook", row_st=False): """ Create a sparse W matrix for a regular lattice. Parameters ---------- nrows : int number of rows ncols : int number of columns rook : {"rook", "queen", "bishop"} type of contiguity. Default is rook. row_st : boolean If True, the created sparse W object is row-standardized so every row sums up to one. Defaults to False. Returns ------- w : scipy.sparse.dia_matrix instance of a scipy sparse matrix Notes ----- Observations are row ordered: first k observations are in row 0, next k in row 1, and so on. This method directly creates the W matrix using the strucuture of the contiguity type. Examples -------- >>> from libpysal.weights import lat2SW >>> w9 = lat2SW(3,3) >>> w9[0,1] == 1 True >>> w9[3,6] == 1 True >>> w9r = lat2SW(3,3, row_st=True) >>> w9r[3,6] == 1./3 True """ n = nrows * ncols diagonals = [] offsets = [] if criterion == "rook" or criterion == "queen": d = np.ones((1, n)) for i in range(ncols - 1, n, ncols): d[0, i] = 0 diagonals.append(d) offsets.append(-1) d = np.ones((1, n)) diagonals.append(d) offsets.append(-ncols) if criterion == "queen" or criterion == "bishop": d = np.ones((1, n)) for i in range(0, n, ncols): d[0, i] = 0 diagonals.append(d) offsets.append(-(ncols - 1)) d = np.ones((1, n)) for i in range(ncols - 1, n, ncols): d[0, i] = 0 diagonals.append(d) offsets.append(-(ncols + 1)) data = np.concatenate(diagonals) offsets = np.array(offsets) m = sparse.dia_matrix((data, offsets), shape=(n, n), dtype=np.int8) m = m + m.T if row_st: m = sparse.spdiags(1.0 / m.sum(1).T, 0, *m.shape) * m m = m.tocsc() m.eliminate_zeros() return m def write_gal(file, k=10): f = open(file, "w") n = k * k f.write("0 %d" % n) for i in range(n): row = i / k col = i % k neighs = [i - i, i + 1, i - k, i + k] neighs = [j for j in neighs if j >= 0 and j < n] f.write("\n%d %d\n" % (i, len(neighs))) f.write(" ".join(map(str, neighs))) f.close() def neighbor_equality(w1, w2): """ Test if the neighbor sets are equal between two weights objects Parameters ---------- w1 : W instance of spatial weights class W w2 : W instance of spatial weights class W Returns ------- Boolean Notes ----- Only set membership is evaluated, no check of the weight values is carried out. Examples -------- >>> from libpysal.weights.util import neighbor_equality >>> from libpysal.weights import lat2W, W >>> w1 = lat2W(3,3) >>> w2 = lat2W(3,3) >>> neighbor_equality(w1, w2) True >>> w3 = lat2W(5,5) >>> neighbor_equality(w1, w3) False >>> n4 = w1.neighbors.copy() >>> n4[0] = [1] >>> n4[1] = [4, 2] >>> w4 = W(n4) >>> neighbor_equality(w1, w4) False >>> n5 = w1.neighbors.copy() >>> n5[0] [3, 1] >>> n5[0] = [1, 3] >>> w5 = W(n5) >>> neighbor_equality(w1, w5) True """ n1 = w1.neighbors n2 = w2.neighbors ids_1 = set(n1.keys()) ids_2 = set(n2.keys()) if ids_1 != ids_2: return False for i in ids_1: if set(w1.neighbors[i]) != set(w2.neighbors[i]): return False return True def isKDTree(obj): """ This is a utility function to determine whether or not an object is a KDTree, since KDTree and cKDTree have no common parent type """ return any([issubclass(type(obj), KDTYPE) for KDTYPE in KDTREE_TYPES]) def attach_islands(w, w_knn1, **kwargs): """ Attach nearest neighbor to islands in spatial weight w. Parameters ---------- w : libpysal.weights.W pysal spatial weight object (unstandardized). w_knn1 : libpysal.weights.W Nearest neighbor pysal spatial weight object (k=1). **kwargs : keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- : libpysal.weights.W pysal spatial weight object w without islands. Examples -------- >>> from libpysal.weights import lat2W, Rook, KNN, attach_islands >>> import libpysal >>> w = Rook.from_shapefile(libpysal.examples.get_path('10740.shp')) >>> w.islands [163] >>> w_knn1 = KNN.from_shapefile(libpysal.examples.get_path('10740.shp'),k=1) >>> w_attach = attach_islands(w, w_knn1) >>> w_attach.islands [] >>> w_attach[w.islands[0]] {166: 1.0} """ neighbors, weights = copy.deepcopy(w.neighbors), copy.deepcopy(w.weights) if not len(w.islands): print("There are no disconnected observations (no islands)!") return w else: for island in w.islands: nb = w_knn1.neighbors[island][0] if type(island) is float: nb = float(nb) neighbors[island] = [nb] weights[island] = [1.0] neighbors[nb] = neighbors[nb] + [island] weights[nb] = weights[nb] + [1.0] return W(neighbors, weights, id_order=w.id_order, **kwargs) def nonplanar_neighbors(w, geodataframe, tolerance=0.001, **kwargs): """ Detect neighbors for non-planar polygon collections Parameters ---------- w: pysal W A spatial weights object with reported islands geodataframe: GeoDataframe The polygon dataframe from which w was constructed. tolerance: float The percentage of the minimum horizontal or vertical extent (minextent) of the dataframe to use in defining a buffering distance to allow for fuzzy contiguity detection. The buffering distance is equal to tolerance*minextent. **kwargs: keyword arguments optional arguments for :class:`pysal.weights.W` Attributes ---------- non_planar_joins : dictionary Stores the new joins detected. Key is the id of the focal unit, value is a list of neighbor ids. Returns ------- w: pysal W Spatial weights object that encodes fuzzy neighbors. This will have an attribute `non_planar_joins` to indicate what new joins were detected. Notes ----- This relaxes the notion of contiguity neighbors for the case of shapefiles that violate the condition of planar enforcement. It handles three types of conditions present in such files that would result in islands when using the regular PySAL contiguity methods. The first are edges for nearby polygons that should be shared, but are digitized separately for the individual polygons and the resulting edges do not coincide, but instead the edges intersect. The second case is similar to the first, only the resultant edges do not intersect but are "close". The final case arises when one polygon is "inside" a second polygon but is not encoded to represent a hole in the containing polygon. The buffering check assumes the geometry coordinates are projected. Examples -------- >>> import geopandas as gpd >>> import libpysal >>> df = gpd.read_file(libpysal.examples.get_path('map_RS_BR.shp')) >>> w = libpysal.weights.Queen.from_dataframe(df) >>> w.islands [0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374] >>> wnp = libpysal.weights.nonplanar_neighbors(w, df) >>> wnp.islands [] >>> w.neighbors[0] [] >>> wnp.neighbors[0] [23, 59, 152, 239] >>> wnp.neighbors[23] [0, 45, 59, 107, 152, 185, 246] Also see `nonplanarweights.ipynb` References ---------- Planar Enforcement: http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6 """ gdf = geodataframe assert ( gdf.sindex ), "GeoDataFrame must have a spatial index. Please make sure you have `libspatialindex` installed" islands = w.islands joins = copy.deepcopy(w.neighbors) candidates = gdf.geometry fixes = defaultdict(list) # first check for intersecting polygons for island in islands: focal = gdf.iloc[island].geometry neighbors = [ j for j, candidate in enumerate(candidates) if focal.intersects(candidate) and j != island ] if len(neighbors) > 0: for neighbor in neighbors: if neighbor not in joins[island]: fixes[island].append(neighbor) joins[island].append(neighbor) if island not in joins[neighbor]: fixes[neighbor].append(island) joins[neighbor].append(island) # if any islands remain, dilate them and check for intersection if islands: x0, y0, x1, y1 = gdf.total_bounds distance = tolerance * min(x1 - x0, y1 - y0) for island in islands: dilated = gdf.iloc[island].geometry.buffer(distance) neighbors = [ j for j, candidate in enumerate(candidates) if dilated.intersects(candidate) and j != island ] if len(neighbors) > 0: for neighbor in neighbors: if neighbor not in joins[island]: fixes[island].append(neighbor) joins[island].append(neighbor) if island not in joins[neighbor]: fixes[neighbor].append(island) joins[neighbor].append(island) w = W(joins, **kwargs) w.non_planar_joins = fixes return w @requires("geopandas") def fuzzy_contiguity( gdf, tolerance=0.005, buffering=False, drop=True, buffer=None, predicate="intersects", **kwargs, ): """ Fuzzy contiguity spatial weights Parameters ---------- gdf: GeoDataFrame tolerance: float The percentage of the length of the minimum side of the bounding rectangle for the GeoDataFrame to use in determining the buffering distance. buffering: boolean If False (default) joins will only be detected for features that intersect (touch, contain, within). If True then features will be buffered and intersections will be based on buffered features. drop: boolean If True (default), the buffered features are removed from the GeoDataFrame. If False, buffered features are added to the GeoDataFrame. buffer : float Specify exact buffering distance. Ignores `tolerance`. predicate : {'intersects', 'within', 'contains', 'overlaps', 'crosses', 'touches'} The predicate to use for determination of neighbors. Default is 'intersects'. If None is passed, neighbours are determined based on the intersection of bounding boxes. **kwargs: keyword arguments optional arguments for :class:`pysal.weights.W` Returns ------- w: PySAL W Spatial weights based on fuzzy contiguity. Weights are binary. Examples -------- >>> import libpysal >>> from libpysal.weights import fuzzy_contiguity >>> import geopandas as gpd >>> rs = libpysal.examples.get_path('map_RS_BR.shp') >>> rs_df = gpd.read_file(rs) >>> wq = libpysal.weights.Queen.from_dataframe(rs_df) >>> len(wq.islands) 29 >>> wq[0] {} >>> wf = fuzzy_contiguity(rs_df) >>> wf.islands [] >>> wf[0] == dict({239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0}) True Example needing to use buffering >>> from shapely.geometry import Polygon >>> p0 = Polygon([(0,0), (10,0), (10,10)]) >>> p1 = Polygon([(10,1), (10,2), (15,2)]) >>> p2 = Polygon([(12,2.001), (14, 2.001), (13,10)]) >>> gs = gpd.GeoSeries([p0,p1,p2]) >>> gdf = gpd.GeoDataFrame(geometry=gs) >>> wf = fuzzy_contiguity(gdf) >>> wf.islands [2] >>> wfb = fuzzy_contiguity(gdf, buffering=True) >>> wfb.islands [] >>> wfb[2] {1: 1.0} Example with a custom index >>> rs_df_ix = rs_df.set_index("NM_MUNICIP") >>> wf_ix = fuzzy_contiguity(rs_df) >>> wf_ix.neighbors["TAVARES"] ['SÃO JOSÉ DO NORTE', 'MOSTARDAS'] Notes ----- This relaxes the notion of contiguity neighbors for the case of feature collections that violate the condition of planar enforcement. It handles three types of conditions present in such collections that would result in islands when using the regular PySAL contiguity methods. The first are edges for nearby polygons that should be shared, but are digitized separately for the individual polygons and the resulting edges do not coincide, but instead the edges intersect. The second case is similar to the first, only the resultant edges do not intersect but are "close". The final case arises when one polygon is "inside" a second polygon but is not encoded to represent a hole in the containing polygon. Detection of the second case will require setting buffering=True and exploring different values for tolerance. The buffering check assumes the geometry coordinates are projected. References ---------- Planar Enforcement: http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6 """ if buffering: if not buffer: # buffer each shape minx, miny, maxx, maxy = gdf.total_bounds buffer = tolerance * 0.5 * abs(min(maxx - minx, maxy - miny)) # create new geometry column new_geometry = gdf.geometry.buffer(buffer) gdf["_buffer"] = new_geometry old_geometry_name = gdf.geometry.name gdf.set_geometry("_buffer", inplace=True) neighbors = {} if GPD_013: # query tree based on set predicate inp, res = gdf.sindex.query(gdf.geometry, predicate=predicate) else: inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate) # remove self hits itself = inp == res inp = inp[~itself] res = res[~itself] # extract index values of neighbors for i, ix in enumerate(gdf.index): ids = gdf.index[res[inp == i]].tolist() neighbors[ix] = ids if buffering: gdf.set_geometry(old_geometry_name, inplace=True) if drop: gdf.drop(columns=["_buffer"], inplace=True) return W(neighbors, **kwargs) if __name__ == "__main__": from libpysal.weights import lat2W assert (lat2W(5, 5).sparse.todense() == lat2SW(5, 5).todense()).all() assert (lat2W(5, 3).sparse.todense() == lat2SW(5, 3).todense()).all() assert ( lat2W(5, 3, rook=False).sparse.todense() == lat2SW(5, 3, "queen").todense() ).all() assert ( lat2W(50, 50, rook=False).sparse.todense() == lat2SW(50, 50, "queen").todense() ).all() libpysal-4.9.2/libpysal/weights/weights.py000066400000000000000000001500071452177046000206730ustar00rootroot00000000000000""" Weights. """ __author__ = "Sergio J. Rey " import copy from os.path import basename as BASENAME import math import warnings import numpy as np import scipy.sparse from scipy.sparse.csgraph import connected_components from collections import defaultdict # from .util import full, WSP2W resolve import cycle by # forcing these into methods from . import adjtools from ..io.fileio import FileIO as popen __all__ = ["W", "WSP"] class _LabelEncoder(object): """Encode labels with values between 0 and n_classes-1. Attributes ---------- classes_: array of shape [n_classes] Class labels for each index. Examples -------- >>> le = _LabelEncoder() >>> le.fit(["NY", "CA", "NY", "CA", "TX", "TX"]) >>> le.classes_ array(['CA', 'NY', 'TX']) >>> le.transform(["NY", "CA", "NY", "CA", "TX", "TX"]) array([1, 0, 1, 0, 2, 2]) """ def fit(self, y): """Fit label encoder. Parameters ---------- y : list list of labels Returns ------- self : instance of self. Fitted label encoder. """ self.classes_ = np.unique(y) return self def transform(self, y): """Transform labels to normalized encoding. Parameters ---------- y : list list of labels Returns ------- y : array array of normalized labels. """ return np.searchsorted(self.classes_, y) class W(object): """ Spatial weights class. Class attributes are described by their docstrings. to view, use the ``help`` function. Parameters ---------- neighbors : dict Key is region ID, value is a list of neighbor IDS. For example, ``{'a':['b'],'b':['a','c'],'c':['b']}``. weights : dict Key is region ID, value is a list of edge weights. If not supplied all edge weights are assumed to have a weight of 1. For example, ``{'a':[0.5],'b':[0.5,1.5],'c':[1.5]}``. id_order : list An ordered list of ids, defines the order of observations when iterating over ``W`` if not set, lexicographical ordering is used to iterate and the ``id_order_set`` property will return ``False``. This can be set after creation by setting the ``id_order`` property. silence_warnings : bool By default ``libpysal`` will print a warning if the dataset contains any disconnected components or islands. To silence this warning set this parameter to ``True``. ids : list Values to use for keys of the neighbors and weights ``dict`` objects. Attributes ---------- asymmetries cardinalities component_labels diagW2 diagWtW diagWtW_WW histogram id2i id_order id_order_set islands max_neighbors mean_neighbors min_neighbors n n_components neighbor_offsets nonzero pct_nonzero s0 s1 s2 s2array sd sparse trcW2 trcWtW trcWtW_WW transform Examples -------- >>> from libpysal.weights import W >>> neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]} >>> weights = {0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1, 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1]} >>> w = W(neighbors, weights) >>> "%.3f"%w.pct_nonzero '29.630' Read from external `.gal file `_. >>> import libpysal >>> w = libpysal.io.open(libpysal.examples.get_path("stl.gal")).read() >>> w.n 78 >>> "%.3f"%w.pct_nonzero '6.542' Set weights implicitly. >>> neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]} >>> w = W(neighbors) >>> round(w.pct_nonzero,3) 29.63 >>> from libpysal.weights import lat2W >>> w = lat2W(100, 100) >>> w.trcW2 39600.0 >>> w.trcWtW 39600.0 >>> w.transform='r' >>> round(w.trcW2, 3) 2530.722 >>> round(w.trcWtW, 3) 2533.667 Cardinality Histogram: >>> w.histogram [(2, 4), (3, 392), (4, 9604)] Disconnected observations (islands): >>> from libpysal.weights import W >>> w = W({1:[0],0:[1],2:[], 3:[]}) UserWarning: The weights matrix is not fully connected: There are 3 disconnected components. There are 2 islands with ids: 2, 3. """ def __init__( self, neighbors, weights=None, id_order=None, silence_warnings=False, ids=None ): self.silence_warnings = silence_warnings self.transformations = {} self.neighbors = neighbors if not weights: weights = {} for key in neighbors: weights[key] = [1.0] * len(neighbors[key]) self.weights = weights self.transformations["O"] = self.weights.copy() # original weights self.transform = "O" if id_order is None: self._id_order = list(self.neighbors.keys()) self._id_order.sort() self._id_order_set = False else: self._id_order = id_order self._id_order_set = True self._reset() self._n = len(self.weights) if (not self.silence_warnings) and (self.n_components > 1): message = ( "The weights matrix is not fully connected: " "\n There are %d disconnected components." % self.n_components ) ni = len(self.islands) if ni == 1: message = message + "\n There is 1 island with id: %s." % ( str(self.islands[0]) ) elif ni > 1: message = message + "\n There are %d islands with ids: %s." % ( ni, ", ".join(str(island) for island in self.islands), ) warnings.warn(message) def _reset(self): """Reset properties.""" self._cache = {} def to_file(self, path="", format=None): """ Write a weights to a file. The format is guessed automatically from the path, but can be overridden with the format argument. See libpysal.io.FileIO for more information. Parameters ---------- path : string location to save the file format : string string denoting the format to write the weights to. Returns ------- None """ f = popen(dataPath=path, mode="w", dataFormat=format) f.write(self) f.close() @classmethod def from_file(cls, path="", format=None): """ Read a weights file into a W object. Parameters ---------- path : string location to save the file format : string string denoting the format to write the weights to. Returns ------- W object """ f = popen(dataPath=path, mode="r", dataFormat=format) w = f.read() f.close() return w @classmethod def from_shapefile(cls, *args, **kwargs): # we could also just "do the right thing," but I think it'd make sense to # try and get people to use `Rook.from_shapefile(shapefile)` rather than # W.from_shapefile(shapefile, type=`rook`), otherwise we'd need to build # a type dispatch table. Generic W should be for stuff we don't know # anything about. raise NotImplementedError( "Use type-specific constructors, like Rook, Queen, DistanceBand, or Kernel" ) @classmethod def from_WSP(cls, WSP, silence_warnings=True): """Create a pysal W from a pysal WSP object (thin weights matrix). Parameters ---------- wsp : WSP PySAL sparse weights object silence_warnings : bool By default ``libpysal`` will print a warning if the dataset contains any disconnected components or islands. To silence this warning set this parameter to ``True``. Returns ------- w : W PySAL weights object Examples -------- >>> from libpysal.weights import lat2W, WSP, W Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells (rook contiguity), then construct a PySAL sparse weights object (wsp). >>> sp = lat2SW(2, 5) >>> wsp = WSP(sp) >>> wsp.n 10 >>> wsp.sparse[0].todense() matrix([[0, 1, 0, 0, 0, 1, 0, 0, 0, 0]], dtype=int8) Create a standard PySAL W from this sparse weights object. >>> w = W.from_WSP(wsp) >>> w.n 10 >>> print(w.full()[0][0]) [0 1 0 0 0 1 0 0 0 0] """ data = WSP.sparse.data indptr = WSP.sparse.indptr id_order = WSP.id_order if id_order: # replace indices with user IDs indices = [id_order[i] for i in WSP.sparse.indices] else: id_order = list(range(WSP.n)) neighbors, weights = {}, {} start = indptr[0] for i in range(WSP.n): oid = id_order[i] end = indptr[i + 1] neighbors[oid] = indices[start:end] weights[oid] = data[start:end] start = end ids = copy.copy(WSP.id_order) w = W(neighbors, weights, ids, silence_warnings=silence_warnings) w._sparse = copy.deepcopy(WSP.sparse) w._cache["sparse"] = w._sparse return w @classmethod def from_adjlist( cls, adjlist, focal_col="focal", neighbor_col="neighbor", weight_col=None ): """ Return an adjacency list representation of a weights object. Parameters ---------- adjlist : pandas.DataFrame Adjacency list with a minimum of two columns. focal_col : str Name of the column with the "source" node ids. neighbor_col : str Name of the column with the "destination" node ids. weight_col : str Name of the column with the weight information. If not provided and the dataframe has no column named "weight" then all weights are assumed to be 1. """ if weight_col is None: weight_col = "weight" try_weightcol = getattr(adjlist, weight_col) if try_weightcol is None: adjlist = adjlist.copy(deep=True) adjlist["weight"] = 1 grouper = adjlist.groupby(focal_col) neighbors = dict() weights = dict() for ix, chunk in grouper: neighbors_to_ix = chunk[neighbor_col].values weights_to_ix = chunk[weight_col].values mask = neighbors_to_ix != ix neighbors[ix] = neighbors_to_ix[mask].tolist() weights[ix] = weights_to_ix[mask].tolist() return cls(neighbors=neighbors, weights=weights) def to_adjlist( self, remove_symmetric=False, drop_islands=None, focal_col="focal", neighbor_col="neighbor", weight_col="weight", sort_joins=False, ): """ Compute an adjacency list representation of a weights object. Parameters ---------- remove_symmetric : bool Whether or not to remove symmetric entries. If the ``W`` is symmetric, a standard directed adjacency list will contain both the forward and backward links by default because adjacency lists are a directed graph representation. If this is ``True``, a ``W`` created from this adjacency list **MAY NOT BE THE SAME** as the original ``W``. If you would like to consider (1,2) and (2,1) as distinct links, leave this as ``False``. drop_islands : bool Whether or not to preserve islands as entries in the adjacency list. By default, observations with no neighbors do not appear in the adjacency list. If islands are kept, they are coded as self-neighbors with zero weight. focal_col : str Name of the column in which to store "source" node ids. neighbor_col : str Name of the column in which to store "destination" node ids. weight_col : str Name of the column in which to store weight information. sort_joins : bool Whether or not to lexicographically sort the adjacency list by (focal_col, neighbor_col). Default is False. """ try: import pandas except (ImportError, ModuleNotFoundError): raise ImportError( "pandas must be installed & importable to use this method" ) if (drop_islands is None) and not (self.silence_warnings): warnings.warn( "In the next version of libpysal, observations with no neighbors will be included in adjacency lists as loops (row with the same focal and neighbor) with zero weight. In the current version, observations with no neighbors are dropped. If you would like to keep the current behavior, use drop_islands=True in this function", DeprecationWarning, ) drop_islands = True links = [] focal_ix, neighbor_ix = self.sparse.nonzero() idxs = np.array(self.id_order) focal_ix = idxs[focal_ix] neighbor_ix = idxs[neighbor_ix] weights = self.sparse.data adjlist = pandas.DataFrame( {focal_col: focal_ix, neighbor_col: neighbor_ix, weight_col: weights} ) if remove_symmetric: adjlist = adjtools.filter_adjlist(adjlist) if not drop_islands: island_adjlist = pandas.DataFrame( {focal_col: self.islands, neighbor_col: self.islands, weight_col: 0} ) adjlist = pandas.concat((adjlist, island_adjlist)).reset_index(drop=True) if sort_joins: return adjlist.sort_values([focal_col, neighbor_col]) return adjlist def to_networkx(self): """Convert a weights object to a ``networkx`` graph. Returns ------- A ``networkx`` graph representation of the ``W`` object. """ try: import networkx as nx except ImportError: raise ImportError("NetworkX 2.7+ is required to use this function.") G = nx.DiGraph() if len(self.asymmetries) > 0 else nx.Graph() return nx.from_scipy_sparse_array(self.sparse, create_using=G) @classmethod def from_networkx(cls, graph, weight_col="weight"): """Convert a ``networkx`` graph to a PySAL ``W`` object. Parameters ---------- graph : networkx.Graph The graph to convert to a ``W``. weight_col : string If the graph is labeled, this should be the name of the field to use as the weight for the ``W``. Returns ------- w : libpysal.weights.W A ``W`` object containing the same graph as the ``networkx`` graph. """ try: import networkx as nx except ImportError: raise ImportError("NetworkX 2.7+ is required to use this function.") sparse_array = nx.to_scipy_sparse_array(graph) w = WSP(sparse_array).to_W() return w @property def sparse(self): """Sparse matrix object. For any matrix manipulations required for w, ``w.sparse`` should be used. This is based on ``scipy.sparse``. """ if "sparse" not in self._cache: self._sparse = self._build_sparse() self._cache["sparse"] = self._sparse return self._sparse @classmethod def from_sparse(cls, sparse): """Convert a ``scipy.sparse`` array to a PySAL ``W`` object. Parameters ---------- sparse : scipy.sparse array Returns ------- w : libpysal.weights.W A ``W`` object containing the same graph as the ``scipy.sparse`` graph. Notes ----- When the sparse array has a zero in its data attribute, and the corresponding row and column values are equal, the value for the pysal weight will be 0 for the "loop". """ coo = sparse.tocoo() neighbors = defaultdict(list) weights = defaultdict(list) for k, v, w in zip(coo.row, coo.col, coo.data): neighbors[k].append(v) weights[k].append(w) return W(neighbors=neighbors, weights=weights) def to_sparse(self, fmt="coo"): """Generate a ``scipy.sparse`` array object from a pysal W. Parameters ---------- fmt : {'bsr', 'coo', 'csc', 'csr'} scipy.sparse format Returns ------- scipy.sparse array A scipy.sparse array with a format of fmt. Notes ----- The keys of the w.neighbors are encoded to determine row,col in the sparse array. """ disp = {} disp["bsr"] = scipy.sparse.bsr_array disp["coo"] = scipy.sparse.coo_array disp["csc"] = scipy.sparse.csc_array disp["csr"] = scipy.sparse.csr_array fmt_l = fmt.lower() if fmt_l in disp: adj_list = self.to_adjlist(drop_islands=False) data = adj_list.weight row = adj_list.focal col = adj_list.neighbor le = _LabelEncoder() le.fit(row) row = le.transform(row) col = le.transform(col) n = self.n return disp[fmt_l]((data, (row, col)), shape=(n, n)) else: raise ValueError(f"unsupported sparse format: {fmt}") @property def n_components(self): """Store whether the adjacency matrix is fully connected.""" if "n_components" not in self._cache: self._n_components, self._component_labels = connected_components( self.sparse ) self._cache["n_components"] = self._n_components self._cache["component_labels"] = self._component_labels return self._n_components @property def component_labels(self): """Store the graph component in which each observation falls.""" if "component_labels" not in self._cache: self._n_components, self._component_labels = connected_components( self.sparse ) self._cache["n_components"] = self._n_components self._cache["component_labels"] = self._component_labels return self._component_labels def _build_sparse(self): """Construct the sparse attribute.""" row = [] col = [] data = [] id2i = self.id2i for i, neigh_list in list(self.neighbor_offsets.items()): card = self.cardinalities[i] row.extend([id2i[i]] * card) col.extend(neigh_list) data.extend(self.weights[i]) row = np.array(row) col = np.array(col) data = np.array(data) s = scipy.sparse.csr_matrix((data, (row, col)), shape=(self.n, self.n)) return s @property def id2i(self): """Dictionary where the key is an ID and the value is that ID's index in ``W.id_order``. """ if "id2i" not in self._cache: self._id2i = {} for i, id_i in enumerate(self._id_order): self._id2i[id_i] = i self._id2i = self._id2i self._cache["id2i"] = self._id2i return self._id2i @property def n(self): """Number of units.""" if "n" not in self._cache: self._n = len(self.neighbors) self._cache["n"] = self._n return self._n @property def s0(self): r"""``s0`` is defined as .. math:: s0=\sum_i \sum_j w_{i,j} """ if "s0" not in self._cache: self._s0 = self.sparse.sum() self._cache["s0"] = self._s0 return self._s0 @property def s1(self): r"""``s1`` is defined as .. math:: s1=1/2 \sum_i \sum_j \Big(w_{i,j} + w_{j,i}\Big)^2 """ if "s1" not in self._cache: t = self.sparse.transpose() t = t + self.sparse t2 = t.multiply(t) # element-wise square self._s1 = t2.sum() / 2.0 self._cache["s1"] = self._s1 return self._s1 @property def s2array(self): """Individual elements comprising ``s2``. See Also -------- s2 """ if "s2array" not in self._cache: s = self.sparse self._s2array = np.array(s.sum(1) + s.sum(0).transpose()) ** 2 self._cache["s2array"] = self._s2array return self._s2array @property def s2(self): r"""``s2`` is defined as .. math:: s2=\sum_j \Big(\sum_i w_{i,j} + \sum_i w_{j,i}\Big)^2 """ if "s2" not in self._cache: self._s2 = self.s2array.sum() self._cache["s2"] = self._s2 return self._s2 @property def trcW2(self): """Trace of :math:`WW`. See Also -------- diagW2 """ if "trcW2" not in self._cache: self._trcW2 = self.diagW2.sum() self._cache["trcw2"] = self._trcW2 return self._trcW2 @property def diagW2(self): """Diagonal of :math:`WW`. See Also -------- trcW2 """ if "diagw2" not in self._cache: self._diagW2 = (self.sparse * self.sparse).diagonal() self._cache["diagW2"] = self._diagW2 return self._diagW2 @property def diagWtW(self): """Diagonal of :math:`W^{'}W`. See Also -------- trcWtW """ if "diagWtW" not in self._cache: self._diagWtW = (self.sparse.transpose() * self.sparse).diagonal() self._cache["diagWtW"] = self._diagWtW return self._diagWtW @property def trcWtW(self): """Trace of :math:`W^{'}W`. See Also -------- diagWtW """ if "trcWtW" not in self._cache: self._trcWtW = self.diagWtW.sum() self._cache["trcWtW"] = self._trcWtW return self._trcWtW @property def diagWtW_WW(self): """Diagonal of :math:`W^{'}W + WW`.""" if "diagWtW_WW" not in self._cache: wt = self.sparse.transpose() w = self.sparse self._diagWtW_WW = (wt * w + w * w).diagonal() self._cache["diagWtW_WW"] = self._diagWtW_WW return self._diagWtW_WW @property def trcWtW_WW(self): """Trace of :math:`W^{'}W + WW`.""" if "trcWtW_WW" not in self._cache: self._trcWtW_WW = self.diagWtW_WW.sum() self._cache["trcWtW_WW"] = self._trcWtW_WW return self._trcWtW_WW @property def pct_nonzero(self): """Percentage of nonzero weights.""" if "pct_nonzero" not in self._cache: self._pct_nonzero = 100.0 * self.sparse.nnz / (1.0 * self._n**2) self._cache["pct_nonzero"] = self._pct_nonzero return self._pct_nonzero @property def cardinalities(self): """Number of neighbors for each observation.""" if "cardinalities" not in self._cache: c = {} for i in self._id_order: c[i] = len(self.neighbors[i]) self._cardinalities = c self._cache["cardinalities"] = self._cardinalities return self._cardinalities @property def max_neighbors(self): """Largest number of neighbors.""" if "max_neighbors" not in self._cache: self._max_neighbors = max(self.cardinalities.values()) self._cache["max_neighbors"] = self._max_neighbors return self._max_neighbors @property def mean_neighbors(self): """Average number of neighbors.""" if "mean_neighbors" not in self._cache: self._mean_neighbors = np.mean(list(self.cardinalities.values())) self._cache["mean_neighbors"] = self._mean_neighbors return self._mean_neighbors @property def min_neighbors(self): """Minimum number of neighbors.""" if "min_neighbors" not in self._cache: self._min_neighbors = min(self.cardinalities.values()) self._cache["min_neighbors"] = self._min_neighbors return self._min_neighbors @property def nonzero(self): """Number of nonzero weights.""" if "nonzero" not in self._cache: self._nonzero = self.sparse.nnz self._cache["nonzero"] = self._nonzero return self._nonzero @property def sd(self): """Standard deviation of number of neighbors.""" if "sd" not in self._cache: self._sd = np.std(list(self.cardinalities.values())) self._cache["sd"] = self._sd return self._sd @property def asymmetries(self): """List of id pairs with asymmetric weights sorted in ascending *index location* order. """ if "asymmetries" not in self._cache: self._asymmetries = self.asymmetry() self._cache["asymmetries"] = self._asymmetries return self._asymmetries @property def islands(self): """List of ids without any neighbors.""" if "islands" not in self._cache: self._islands = [i for i, c in list(self.cardinalities.items()) if c == 0] self._cache["islands"] = self._islands return self._islands @property def histogram(self): """Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit. """ if "histogram" not in self._cache: ct, bin = np.histogram( list(self.cardinalities.values()), list(range(self.min_neighbors, self.max_neighbors + 2)), ) self._histogram = list(zip(bin, ct)) self._cache["histogram"] = self._histogram return self._histogram def __getitem__(self, key): """Allow a dictionary like interaction with the weights class. Examples -------- >>> from libpysal.weights import lat2W >>> w = lat2W() >>> w[0] == dict({1: 1.0, 5: 1.0}) True """ return dict(list(zip(self.neighbors[key], self.weights[key]))) def __iter__(self): """ Support iteration over weights. Examples -------- >>> from libpysal.weights import lat2W >>> w=lat2W(3,3) >>> for i,wi in enumerate(w): ... print(i,wi[0]) ... 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 >>> """ for i in self._id_order: yield i, dict(list(zip(self.neighbors[i], self.weights[i]))) def remap_ids(self, new_ids): """ In place modification throughout ``W`` of id values from ``w.id_order`` to ``new_ids`` in all. Parameters ---------- new_ids : list, numpy.ndarray Aligned list of new ids to be inserted. Note that first element of ``new_ids`` will replace first element of ``w.id_order``, second element of ``new_ids`` replaces second element of ``w.id_order`` and so on. Examples -------- >>> from libpysal.weights import lat2W >>> w = lat2W(3, 3) >>> w.id_order [0, 1, 2, 3, 4, 5, 6, 7, 8] >>> w.neighbors[0] [3, 1] >>> new_ids = ['id%i'%id for id in w.id_order] >>> _ = w.remap_ids(new_ids) >>> w.id_order ['id0', 'id1', 'id2', 'id3', 'id4', 'id5', 'id6', 'id7', 'id8'] >>> w.neighbors['id0'] ['id3', 'id1'] """ old_ids = self._id_order if len(old_ids) != len(new_ids): raise Exception( "W.remap_ids: length of `old_ids` does not match that of" " new_ids" ) if len(set(new_ids)) != len(new_ids): raise Exception("W.remap_ids: list `new_ids` contains duplicates") else: new_neighbors = {} new_weights = {} old_transformations = self.transformations["O"].copy() new_transformations = {} for o, n in zip(old_ids, new_ids): o_neighbors = self.neighbors[o] o_weights = self.weights[o] n_neighbors = [new_ids[old_ids.index(j)] for j in o_neighbors] new_neighbors[n] = n_neighbors new_weights[n] = o_weights[:] new_transformations[n] = old_transformations[o] self.neighbors = new_neighbors self.weights = new_weights self.transformations["O"] = new_transformations id_order = [self._id_order.index(o) for o in old_ids] for i, id_ in enumerate(id_order): self.id_order[id_] = new_ids[i] self._reset() def __set_id_order(self, ordered_ids): """Set the iteration order in w. ``W`` can be iterated over. On construction the iteration order is set to the lexicographic order of the keys in the ``w.weights`` dictionary. If a specific order is required it can be set with this method. Parameters ---------- ordered_ids : sequence Identifiers for observations in specified order. Notes ----- The ``ordered_ids`` parameter is checked against the ids implied by the keys in ``w.weights``. If they are not equivalent sets an exception is raised and the iteration order is not changed. Examples -------- >>> from libpysal.weights import lat2W >>> w=lat2W(3,3) >>> for i,wi in enumerate(w): ... print(i, wi[0]) ... 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 >>> w.id_order [0, 1, 2, 3, 4, 5, 6, 7, 8] >>> w.id_order=range(8,-1,-1) >>> list(w.id_order) [8, 7, 6, 5, 4, 3, 2, 1, 0] >>> for i,w_i in enumerate(w): ... print(i,w_i[0]) ... 0 8 1 7 2 6 3 5 4 4 5 3 6 2 7 1 8 0 """ if set(self._id_order) == set(ordered_ids): self._id_order = ordered_ids self._id_order_set = True self._reset() else: raise Exception("ordered_ids do not align with W ids") def __get_id_order(self): """Returns the ids for the observations in the order in which they would be encountered if iterating over the weights. """ return self._id_order id_order = property(__get_id_order, __set_id_order) @property def id_order_set(self): """Returns ``True`` if user has set ``id_order``, ``False`` if not. Examples -------- >>> from libpysal.weights import lat2W >>> w=lat2W() >>> w.id_order_set True """ return self._id_order_set @property def neighbor_offsets(self): """ Given the current ``id_order``, ``neighbor_offsets[id]`` is the offsets of the id's neighbors in ``id_order``. Returns ------- neighbor_list : list Offsets of the id's neighbors in ``id_order``. Examples -------- >>> from libpysal.weights import W >>> neighbors={'c': ['b'], 'b': ['c', 'a'], 'a': ['b']} >>> weights ={'c': [1.0], 'b': [1.0, 1.0], 'a': [1.0]} >>> w=W(neighbors,weights) >>> w.id_order = ['a','b','c'] >>> w.neighbor_offsets['b'] [2, 0] >>> w.id_order = ['b','a','c'] >>> w.neighbor_offsets['b'] [2, 1] """ if "neighbors_0" not in self._cache: self.__neighbors_0 = {} id2i = self.id2i for j, neigh_list in list(self.neighbors.items()): self.__neighbors_0[j] = [id2i[neigh] for neigh in neigh_list] self._cache["neighbors_0"] = self.__neighbors_0 neighbor_list = self.__neighbors_0 return neighbor_list def get_transform(self): """Getter for transform property. Returns ------- transformation : str, None Valid transformation value. See the ``transform`` parameters in ``set_transform()`` for a detailed description. Examples -------- >>> from libpysal.weights import lat2W >>> w=lat2W() >>> w.weights[0] [1.0, 1.0] >>> w.transform 'O' >>> w.transform='r' >>> w.weights[0] [0.5, 0.5] >>> w.transform='b' >>> w.weights[0] [1.0, 1.0] See also -------- set_transform """ return self._transform def set_transform(self, value="B"): """Transformations of weights. Parameters ---------- transform : str This parameter is not case sensitive. The following are valid transformations. * **B** -- Binary * **R** -- Row-standardization (global sum :math:`=n`) * **D** -- Double-standardization (global sum :math:`=1`) * **V** -- Variance stabilizing * **O** -- Restore original transformation (from instantiation) Notes ----- Transformations are applied only to the value of the weights at instantiation. Chaining of transformations cannot be done on a ``W`` instance. Examples -------- >>> from libpysal.weights import lat2W >>> w=lat2W() >>> w.weights[0] [1.0, 1.0] >>> w.transform 'O' >>> w.transform='r' >>> w.weights[0] [0.5, 0.5] >>> w.transform='b' >>> w.weights[0] [1.0, 1.0] """ value = value.upper() self._transform = value if value in self.transformations: self.weights = self.transformations[value] self._reset() else: if value == "R": # row standardized weights weights = {} self.weights = self.transformations["O"] for i in self.weights: wijs = self.weights[i] row_sum = sum(wijs) * 1.0 if row_sum == 0.0: if not self.silence_warnings: print(("WARNING: ", i, " is an island (no neighbors)")) weights[i] = [wij / row_sum for wij in wijs] weights = weights self.transformations[value] = weights self.weights = weights self._reset() elif value == "D": # doubly-standardized weights # update current chars before doing global sum self._reset() s0 = self.s0 ws = 1.0 / s0 weights = {} self.weights = self.transformations["O"] for i in self.weights: wijs = self.weights[i] weights[i] = [wij * ws for wij in wijs] weights = weights self.transformations[value] = weights self.weights = weights self._reset() elif value == "B": # binary transformation weights = {} self.weights = self.transformations["O"] for i in self.weights: wijs = self.weights[i] weights[i] = [1.0 for wij in wijs] weights = weights self.transformations[value] = weights self.weights = weights self._reset() elif value == "V": # variance stabilizing weights = {} q = {} k = self.cardinalities s = {} Q = 0.0 self.weights = self.transformations["O"] for i in self.weights: wijs = self.weights[i] q[i] = math.sqrt(sum([wij * wij for wij in wijs])) s[i] = [wij / q[i] for wij in wijs] Q += sum([si for si in s[i]]) nQ = self.n / Q for i in self.weights: weights[i] = [w * nQ for w in s[i]] weights = weights self.transformations[value] = weights self.weights = weights self._reset() elif value == "O": # put weights back to original transformation weights = {} original = self.transformations[value] self.weights = original self._reset() else: raise Exception("unsupported weights transformation") transform = property(get_transform, set_transform) def asymmetry(self, intrinsic=True): r""" Asymmetry check. Parameters ---------- intrinsic : bool Default is ``True``. Intrinsic symmetry is defined as: .. math:: w_{i,j} == w_{j,i} If ``intrinsic`` is ``False`` symmetry is defined as: .. math:: i \in N_j \ \& \ j \in N_i where :math:`N_j` is the set of neighbors for :math:`j`. Returns ------- asymmetries : list Empty if no asymmetries are found. If there are asymmetries, then a ``list`` of ``(i,j)`` tuples is returned sorted in ascending *index location* order. Examples -------- >>> from libpysal.weights import lat2W >>> w=lat2W(3,3) >>> w.asymmetry() [] >>> w.transform='r' >>> w.asymmetry() [(0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7)] >>> result = w.asymmetry(intrinsic=False) >>> result [] >>> neighbors={0:[1,2,3], 1:[1,2,3], 2:[0,1], 3:[0,1]} >>> weights={0:[1,1,1], 1:[1,1,1], 2:[1,1], 3:[1,1]} >>> w=W(neighbors,weights) >>> w.asymmetry() [(0, 1), (1, 0)] """ if intrinsic: wd = self.sparse.transpose() - self.sparse else: transform = self.transform self.transform = "b" wd = self.sparse.transpose() - self.sparse self.transform = transform ids = np.nonzero(wd) if len(ids[0]) == 0: return [] else: ijs = list(zip(ids[0], ids[1])) ijs.sort() i2id = {v: k for k, v in self.id2i.items()} ijs = [(i2id[i], i2id[j]) for i, j in ijs] return ijs def symmetrize(self, inplace=False): """Construct a symmetric KNN weight. This ensures that the neighbors of each focal observation consider the focal observation itself as a neighbor. This returns a generic ``W`` object, since the object is no longer guaranteed to have ``k`` neighbors for each observation. """ if not inplace: neighbors = copy.deepcopy(self.neighbors) weights = copy.deepcopy(self.weights) out_W = W(neighbors, weights, id_order=self.id_order) out_W.symmetrize(inplace=True) return out_W else: for focal, fneighbs in list(self.neighbors.items()): for j, neighbor in enumerate(fneighbs): neighb_neighbors = self.neighbors[neighbor] if focal not in neighb_neighbors: self.neighbors[neighbor].append(focal) self.weights[neighbor].append(self.weights[focal][j]) self._cache = dict() return def full(self): """Generate a full ``numpy.ndarray``. Parameters ---------- self : libpysal.weights.W spatial weights object Returns ------- (fullw, keys) : tuple The first element being the full ``numpy.ndarray`` and second element keys being the ids associated with each row in the array. Examples -------- >>> from libpysal.weights import W, full >>> neighbors = {'first':['second'],'second':['first','third'],'third':['second']} >>> weights = {'first':[1],'second':[1,1],'third':[1]} >>> w = W(neighbors, weights) >>> wf, ids = full(w) >>> wf array([[0., 1., 0.], [1., 0., 1.], [0., 1., 0.]]) >>> ids ['first', 'second', 'third'] """ wfull = self.sparse.toarray() keys = list(self.neighbors.keys()) if self.id_order: keys = self.id_order return (wfull, keys) def to_WSP(self): """Generate a ``WSP`` object. Returns ------- implicit : libpysal.weights.WSP Thin ``W`` class Examples -------- >>> from libpysal.weights import W, WSP >>> neighbors={'first':['second'],'second':['first','third'],'third':['second']} >>> weights={'first':[1],'second':[1,1],'third':[1]} >>> w=W(neighbors,weights) >>> wsp=w.to_WSP() >>> isinstance(wsp, WSP) True >>> wsp.n 3 >>> wsp.s0 4 See also -------- WSP """ return WSP(self.sparse, self._id_order) def set_shapefile(self, shapefile, idVariable=None, full=False): """ Adding metadata for writing headers of ``.gal`` and ``.gwt`` files. Parameters ---------- shapefile : str The shapefile name used to construct weights. idVariable : str The name of the attribute in the shapefile to associate with ids in the weights. full : bool Write out the entire path for a shapefile (``True``) or only the base of the shapefile without extension (``False``). Default is ``True``. """ if full: self._shpName = shapefile else: self._shpName = BASENAME(shapefile).split(".")[0] self._varName = idVariable def plot( self, gdf, indexed_on=None, ax=None, color="k", node_kws=None, edge_kws=None ): """Plot spatial weights objects. **Requires** ``matplotlib``, and implicitly requires a ``geopandas.GeoDataFrame`` as input. Parameters ---------- gdf : geopandas.GeoDataFrame The original shapes whose topological relations are modelled in ``W``. indexed_on : str Column of ``geopandas.GeoDataFrame`` that the weights object uses as an index. Default is ``None``, so the index of the ``geopandas.GeoDataFrame`` is used. ax : matplotlib.axes.Axes Axis on which to plot the weights. Default is ``None``, so plots on the current figure. color : str ``matplotlib`` color string, will color both nodes and edges the same by default. node_kws : dict Keyword arguments dictionary to send to ``pyplot.scatter``, which provides fine-grained control over the aesthetics of the nodes in the plot. edge_kws : dict Keyword arguments dictionary to send to ``pyplot.plot``, which provides fine-grained control over the aesthetics of the edges in the plot. Returns ------- f : matplotlib.figure.Figure Figure on which the plot is made. ax : matplotlib.axes.Axes Axis on which the plot is made. Notes ----- If you'd like to overlay the actual shapes from the ``geopandas.GeoDataFrame``, call ``gdf.plot(ax=ax)`` after this. To plot underneath, adjust the z-order of the plot as follows: ``gdf.plot(ax=ax,zorder=0)``. Examples -------- >>> from libpysal.weights import Queen >>> import libpysal as lp >>> import geopandas >>> gdf = geopandas.read_file(lp.examples.get_path("columbus.shp")) >>> weights = Queen.from_dataframe(gdf) >>> tmp = weights.plot(gdf, color='firebrickred', node_kws=dict(marker='*', color='k')) """ try: import matplotlib.pyplot as plt except ImportError: raise ImportError( "W.plot depends on matplotlib.pyplot, and this was" "not able to be imported. \nInstall matplotlib to" "plot spatial weights." ) if ax is None: f = plt.figure() ax = plt.gca() else: f = plt.gcf() if node_kws is not None: if "color" not in node_kws: node_kws["color"] = color else: node_kws = dict(color=color) if edge_kws is not None: if "color" not in edge_kws: edge_kws["color"] = color else: edge_kws = dict(color=color) for idx, neighbors in self.neighbors.items(): if idx in self.islands: continue if indexed_on is not None: neighbors = gdf[gdf[indexed_on].isin(neighbors)].index.tolist() idx = gdf[gdf[indexed_on] == idx].index.tolist()[0] else: neighbors = list(neighbors) centroids = gdf.loc[neighbors].centroid centroids = np.stack([centroids.x, centroids.y], axis=1) focal = np.hstack(gdf.loc[idx].geometry.centroid.xy) seen = set() for nidx, neighbor in zip(neighbors, centroids): if (idx, nidx) in seen: continue ax.plot(*list(zip(focal, neighbor)), marker=None, **edge_kws) seen.update((idx, nidx)) seen.update((nidx, idx)) centroids = gdf.centroid ax.scatter(centroids.x, centroids.y, **node_kws) return f, ax class WSP(object): """Thin ``W`` class for ``spreg``. Parameters ---------- sparse : scipy.sparse.{matrix-type} NxN object from ``scipy.sparse`` Attributes ---------- n : int description s0 : float description trcWtW_WW : float description Examples -------- From GAL information >>> import scipy.sparse >>> from libpysal.weights import WSP >>> rows = [0, 1, 1, 2, 2, 3] >>> cols = [1, 0, 2, 1, 3, 3] >>> weights = [1, 0.75, 0.25, 0.9, 0.1, 1] >>> sparse = scipy.sparse.csr_matrix((weights, (rows, cols)), shape=(4,4)) >>> w = WSP(sparse) >>> w.s0 4.0 >>> w.trcWtW_WW 6.395 >>> w.n 4 """ def __init__(self, sparse, id_order=None, index=None): if not scipy.sparse.issparse(sparse): raise ValueError("must pass a scipy sparse object") rows, cols = sparse.shape if rows != cols: raise ValueError("Weights object must be square") self.sparse = sparse.tocsr() self.n = sparse.shape[0] self._cache = {} if id_order: if len(id_order) != self.n: raise ValueError( "Number of values in id_order must match shape of sparse" ) else: self._id_order = id_order self._cache["id_order"] = self._id_order # temp addition of index attribute import pandas as pd # will be removed after refactoring is done if index is not None: if not isinstance(index, (pd.Index, pd.MultiIndex, pd.RangeIndex)): raise TypeError("index must be an instance of pandas.Index dtype") if len(index) != self.n: raise ValueError("Number of values in index must match shape of sparse") else: index = pd.RangeIndex(self.n) self.index = index @property def id_order(self): """An ordered list of ids, assumed to match the ordering in ``sparse``.""" # Temporary solution until the refactoring is finished if "id_order" not in self._cache: if hasattr(self, "index"): self._id_order = self.index.tolist() else: self._id_order = list(range(self.n)) self._cache["id_order"] = self._id_order return self._id_order @property def s0(self): r"""``s0`` is defined as: .. math:: s0=\sum_i \sum_j w_{i,j} """ if "s0" not in self._cache: self._s0 = self.sparse.sum() self._cache["s0"] = self._s0 return self._s0 @property def trcWtW_WW(self): """Trace of :math:`W^{'}W + WW`.""" if "trcWtW_WW" not in self._cache: self._trcWtW_WW = self.diagWtW_WW.sum() self._cache["trcWtW_WW"] = self._trcWtW_WW return self._trcWtW_WW @property def diagWtW_WW(self): """Diagonal of :math:`W^{'}W + WW`.""" if "diagWtW_WW" not in self._cache: wt = self.sparse.transpose() w = self.sparse self._diagWtW_WW = (wt * w + w * w).diagonal() self._cache["diagWtW_WW"] = self._diagWtW_WW return self._diagWtW_WW @classmethod def from_W(cls, W): """Constructs a ``WSP`` object from the ``W``'s sparse matrix. Parameters ---------- W : libpysal.weights.W A PySAL weights object with a sparse form and ids. Returns ------- A ``WSP`` instance. """ return cls(W.sparse, id_order=W.id_order) def to_W(self, silence_warnings=False): """ Convert a pysal WSP object (thin weights matrix) to a pysal W object. Parameters ---------- self : WSP PySAL sparse weights object. silence_warnings : bool Switch to ``True`` to turn off print statements for every observation with islands. Default is ``False``, which does not silence warnings. Returns ------- w : W PySAL weights object. Examples -------- >>> from libpysal.weights import lat2SW, WSP, WSP2W Build a 10x10 ``scipy.sparse`` matrix for a rectangular 2x5 region of cells (rook contiguity), then construct a ``libpysal`` sparse weights object (``self``). >>> sp = lat2SW(2, 5) >>> self = WSP(sp) >>> self.n 10 >>> print(self.sparse[0].todense()) [[0 1 0 0 0 1 0 0 0 0]] Convert this sparse weights object to a standard PySAL weights object. >>> w = WSP2W(self) >>> w.n 10 >>> print(w.full()[0][0]) [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.] """ indices = list(self.sparse.indices) data = list(self.sparse.data) indptr = list(self.sparse.indptr) id_order = self.id_order if id_order: # replace indices with user IDs indices = [id_order[i] for i in indices] else: id_order = list(range(self.n)) neighbors, weights = {}, {} start = indptr[0] for i in range(self.n): oid = id_order[i] end = indptr[i + 1] neighbors[oid] = indices[start:end] weights[oid] = data[start:end] start = end ids = copy.copy(self.id_order) w = W(neighbors, weights, ids, silence_warnings=silence_warnings) w._sparse = copy.deepcopy(self.sparse) w._cache["sparse"] = w._sparse return w libpysal-4.9.2/notebooks/000077500000000000000000000000001452177046000153565ustar00rootroot00000000000000libpysal-4.9.2/notebooks/Raster_awareness_API.ipynb000066400000000000000000010062511452177046000224270ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Raster awareness API\n", "\n", "This notebook will give an overview of newly developed raster interface. We'll cover \n", "basic usage of the functionality offered by the interface which mainly involves:\n", "1. converting `xarray.DataArray` object to the PySAL's weights object (`libpysal.weights.W`/`WSP`).\n", "2. going back to the `xarray.DataArray` from weights object.\n", "\n", "using different datasets:\n", "- with missing values.\n", "- with multiple layers.\n", "- with non conventional dimension names." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from libpysal.weights import Rook, Queen, raster\n", "import matplotlib.pyplot as plt\n", "from splot import libpysal as splot\n", "import numpy as np\n", "import xarray as xr\n", "import pandas as pd\n", "from esda import Moran_Local" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading Data\n", "\n", "*The interface only accepts `xarray.DataArray`*, this can be easily obtained from raster data\n", "format using `xarray`'s I/O functionality which can read from a variety of data formats some of them are listed below: \n", "- [GDAL Raster Formats](https://svn.osgeo.org/gdal/tags/gdal_1_2_5/frmts/formats_list.html) via `open_rasterio` method.\n", "- [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) via `open_dataset` method.\n", "\n", "In this notebook we'll work with `NetCDF` and `GeoTIFF` data. \n", "\n", "### Using xarray example dataset\n", "First lets load up a `netCDF` dataset offered by xarray." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "[3869000 values with dtype=float32]\n", "Coordinates:\n", " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", "Attributes:\n", " long_name: 4xDaily Air temperature at sigma level 995\n", " units: degK\n", " precision: 2\n", " GRIB_id: 11\n", " GRIB_name: TMP\n", " var_desc: Air temperature\n", " dataset: NMC Reanalysis\n", " level_desc: Surface\n", " statistic: Individual Obs\n", " parent_stat: Other\n", " actual_range: [185.16 322.1 ]\n" ] } ], "source": [ "ds = xr.tutorial.open_dataset(\"air_temperature.nc\") # -> returns a xarray.Dataset object\n", "da = ds[\"air\"] # we'll use the \"air\" data variable for further analysis\n", "print(da)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`xarray`'s data structures like `Dataset` and `DataArray` provides `pandas` like functionality for multidimensional-array or ndarray. \n", "\n", "In our case we'll mainly deal with `DataArray`, we can see above that the `da` holds the data for air temperature, it has 2 dims coordinate dimensions `x` and `y`, and it's layered on `time` dimension so in total 3 dims (`time`, `lat`, `lon`).\n", "\n", "We'll now group `da` by month and take average over the `time` dimension\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coordinates:\n", " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", " * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n" ] } ], "source": [ "da = da.groupby('time.month').mean()\n", "print(da.coords) # as a result time dim is replaced by month " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "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": [ "# let's plot over month, each facet will represent the mean air temperature in a given month.\n", "da.plot(col=\"month\", col_wrap=4,) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use `from_xarray` method from the contiguity classes like `Rook` and `Queen`, and also from `KNN`.\n", "\n", "This uses a util function in `raster.py` file called `da2W`, which can also be called directly to build `W` object, similarly `da2WSP` for building `WSP` object.\n", "\n", "**Weight builders (`from_xarray`, `da2W`, `da2WSP`) can recognise dimensions belonging to this list `[band, time, lat, y, lon, x]`, if any of the dimension in the `DataArray` does not belong to the mentioned list then we need to pass a dictionary (specifying that dimension's name) to the weight builder.** \n", "\n", "e.g. `dims` dictionary:\n", "```python\n", ">>> da.dims # none of the dimension belong to the default dimension list\n", "('year', 'height', 'width')\n", ">>> coords_labels = { # dimension values should be properly aligned with the following keys\n", " \"z_label\": \"year\",\n", " \"y_label\": \"height\",\n", " \"x_label\": \"width\"\n", " }\n", "```\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/data/GSoC/libpysal/libpysal/weights/raster.py:119: UserWarning: You are trying to build a full W object from xarray.DataArray (raster) object. This computation can be very slow and not scale well. It is recommended, if possible, to instead build WSP object, which is more efficient and faster. You can do this by using da2WSP method.\n", " warn(\n" ] } ], "source": [ "coords_labels = {}\n", "coords_labels[\"z_label\"] = \"month\" # since month does not belong to the default list we need to pass it using a dictionary\n", "w_queen = Queen.from_xarray(\n", " da, z_value=12, coords_labels=coords_labels, sparse=False) # We'll use data from 12th layer (in our case layer=month)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`index` is a newly added attribute to the weights object, this holds the multi-indices of the non-missing values belonging to `pandas.Series` created from the passed `DataArray`, this series can be easily obtained using `DataArray.to_series()` method." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MultiIndex([(12, 75.0, 200.0),\n", " (12, 75.0, 202.5),\n", " (12, 75.0, 205.0),\n", " (12, 75.0, 207.5),\n", " (12, 75.0, 210.0)],\n", " names=['month', 'lat', 'lon'])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen.index[:5] # indices are aligned to the ids of the weight object" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then obtain raster data by converting the `DataArray` to `Series` and then using indices from `index` attribute to get non-missing values by subsetting the `Series`. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data = da.to_series()[w_queen.index]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have the required data for further analysis (we can now use methods such as ESDA/spatial regression), for this example let's compute a local Moran statistic for the extracted data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Quickly computing and loading a LISA\n", "np.random.seed(12345)\n", "lisa = Moran_Local(np.array(data, dtype=np.float64), w_queen)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After getting our calculated results it's time to store them back to the `DataArray`, we can use `w2da` function directly to convert the `W` object back to `DataArray`. \n", "\n", "*Your use case might differ but the steps for using the interface will be similar to this example.* " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "array([[[0.018, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n", " [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n", " [0.003, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n", " ...,\n", " [0.002, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n", " [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n", " [0.002, 0.001, 0.001, ..., 0.001, 0.002, 0.006]]])\n", "Coordinates:\n", " * month (month) int64 12\n", " * lat (lat) float64 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", " * lon (lon) float64 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n" ] } ], "source": [ "# Converting obtained data back to DataArray\n", "moran_da = raster.w2da(lisa.p_sim, w_queen) # w2da accepts list/1d array/pd.Series and a weight object aligned to passed data\n", "print(moran_da)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "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": [ "moran_da.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using local `NetCDF` dataset\n", "\n", "In the earlier example we used an example dataset from xarray for building weights object. Additonally, we had to pass the custom layer name to the builder. \n", "\n", "In this small example we'll build `KNN` distance weight object using a local `NetCDF` dataset with different dimensions names which doesn't belong to the default list of dimensions.\n", "\n", "We'll also see how to speed up the reverse journey (from weights object to `DataArray`) by passing prebuilt `coords` and `attrs` to `w2da` method. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dimensions: (latitude: 73, longitude: 144, time: 62)\n", "Coordinates:\n", " * longitude (longitude) float32 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5\n", " * latitude (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n", " * time (time) datetime64[ns] 2002-07-01T12:00:00 ... 2002-07-31T18:00:00\n", "Data variables:\n", " tcw (time, latitude, longitude) float32 ...\n", " tcwv (time, latitude, longitude) float32 ...\n", " lsp (time, latitude, longitude) float32 ...\n", " cp (time, latitude, longitude) float32 ...\n", " msl (time, latitude, longitude) float32 ...\n", " blh (time, latitude, longitude) float32 ...\n", " tcc (time, latitude, longitude) float32 ...\n", " p10u (time, latitude, longitude) float32 ...\n", " p10v (time, latitude, longitude) float32 ...\n", " p2t (time, latitude, longitude) float32 ...\n", " p2d (time, latitude, longitude) float32 ...\n", " e (time, latitude, longitude) float32 ...\n", " lcc (time, latitude, longitude) float32 ...\n", " mcc (time, latitude, longitude) float32 ...\n", " hcc (time, latitude, longitude) float32 ...\n", " tco3 (time, latitude, longitude) float32 ...\n", " tp (time, latitude, longitude) float32 ...\n", "Attributes:\n", " Conventions: CF-1.0\n", " history: 2004-09-15 17:04:29 GMT by mars2netcdf-0.92\n" ] } ], "source": [ "# Lets load a netCDF Surface dataset\n", "ds = xr.open_dataset('ECMWF_ERA-40_subset.nc') # After loading netCDF dataset we obtained a xarray.Dataset object\n", "print(ds) # This Dataset object containes several data variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Out of 17 data variables we'll use `p2t` for our analysis. This will give us our desired `DataArray` object `da`, we will further group `da` by day, taking average over the `time` dimension." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('day', 'latitude', 'longitude')\n" ] } ], "source": [ "da = ds[\"p2t\"] # this will give us the required DataArray with p2t (2 metre temperature) data variable\n", "da = da.groupby('time.day').mean()\n", "print(da.dims)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**We can see that the none of dimensions of `da` matches with the default dimensions (`[band, time, lat, y, lon, x]`)**\n", "\n", "This means we have to create a dictionary mentioning the dimensions and ship it to weight builder, similar to our last example. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "coords_labels = {}\n", "coords_labels[\"y_label\"] = \"latitude\"\n", "coords_labels[\"x_label\"] = \"longitude\"\n", "coords_labels[\"z_label\"] = \"day\"\n", "w_rook = Rook.from_xarray(da, z_value=13, coords_labels=coords_labels, sparse=True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "data = da.to_series()[w_rook.index] # we derived the data from DataArray similar to our last example " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the last example we only passed the `data` values and weight object to `w2da` method, which then created the necessary `coords` to build our required `DataArray`. This process can be speed up by passing `coords` from the existing `DataArray` `da` which we used earlier.\n", "\n", "Along with `coords` we can also pass `attrs` of the same `DataArray` this will help `w2da` to retain all the properties of original `DataArray`.\n", "\n", "Let's compare the `DataArray` returned by `w2da` and original `DataArray`. For this we'll ship the derived data straight to `w2da` without any statistical analysis." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da1 = raster.wsp2da(data, w_rook, attrs=da.attrs, coords=da[12:13].coords)\n", "xr.DataArray.equals(da[12:13], da1) # method to compare 2 DataArray, if true then w2da was successfull" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using local `GeoTIFF` dataset\n", "\n", "Up until now we've only played with `netCDF` datasets but in this example we'll use a `raster.tif` file to see how interface interacts with it. We'll also see how these methods handle missing data. \n", "\n", "Unlike earlier we'll use weight builder methods from `raster.py`, which we can call directly. Just a reminder that `from_xarray` uses methods from `raster.py` and therefore only difference exists in the API. \n", "\n", "To access GDAL Raster Formats `xarray` offers `open_rasterio` method which uses `rasterio` as backend. It loads metadata, coordinate values from the raster file and assign them to the `DataArray`. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "[827200 values with dtype=float32]\n", "Coordinates:\n", " * band (band) int64 1\n", " * y (y) float64 50.18 50.18 50.18 50.18 ... 49.45 49.45 49.45 49.45\n", " * x (x) float64 5.745 5.746 5.747 5.747 ... 6.525 6.526 6.527 6.527\n", "Attributes:\n", " transform: (0.0008333333297872345, 0.0, 5.744583325, 0.0, -0.0008333...\n", " crs: +init=epsg:4326\n", " res: (0.0008333333297872345, 0.0008333333295454553)\n", " is_tiled: 0\n", " nodatavals: (-99999.0,)\n", " scales: (1.0,)\n", " offsets: (0.0,)\n", " AREA_OR_POINT: Area\n" ] } ], "source": [ "# Loading raster data with missing values\n", "da = xr.open_rasterio('/data/Downloads/lux_ppp_2019.tif')\n", "print(da)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ItFnyp5U9nWvfhjy9xgnXmk4cpl6DOAbfrFp9Vem0KD3zEouGC7kcLYN+tEZzG2HAmAdytqmK4FQ87hCL4KIXHHPPXu4E0bCp4/TjjhMUGdlmE4xAkmBb7cnqkzmUsfQSB4GjVim6UA9dRL4AbOFcq56qPigip4F3Aw8AXwD+mqreKtj3EeBtQAS8Q1Xfssix7gcZTW8vTSrY4z57Pe4Iz33u50mRGvEZ4v5SrSKNOlKvQ6/nTzFihTDmuGUsvcQicafSFr9RVb9KVR/0z98IfEhVnwd8yD8fgIhEwP8NvAz4CuDbReQrDmCse0KWqCy51MOYQrEx/J+tILo97I1bJDduzqfgqUSJuWO+pf8i8gUR+biIfExEHvevnRaRx0Tks/7/qXHHOIyQyyuAd/nH7wK+pWCbrwX+UFU/p6od4Of9fiWmgEQRplbHNBqupVwYApoUGvHhI1OrHwyVMVSQ9H+22STZ3nF89hnYLaNQeuclFgXr+4pO+psBMzvAIRb9i1XggyLyhIi8zr92QVUvA/j/5wv2uwd4Mnj+lH9tCCLyOhF5XEQev3bt2hyHPh5H2Uik/G6pVpFqtf/GNMlXH8fOOh8dlsZMQY4h1U6fZUxH+XMqcXvDsVyiqf72gWkc4AyLZrm8SFUvich54DER+fSU+xVNaYXZPFV9O/B2gAcffPBAeW0TG1ccIjRJXEs5mN3L9XF2xzhJXI/SsF3dIZTqm2oVqcS+h2kydUejMo5eYlGYsQXd2TSM4vF2b7sGD+kcYAX+jX9/wAH2tnQkFmrQVfWS/39VRN6HC6VcEZGLfnAXgasFuz4F3Bc8vxe4tMix7hUTjfoiKImTsJ9WdDmYivuK2G4wVy6Shz7i2NrrZrRHiUBtbzFjKFFiBswQTrkehFFGYa8OcIaFradFZFlEVtPHwMPAJ4BfBl7jN3sN8EsFu/828DwRebaIVIFX+f2OJB6z7x3t/U3J7jhySGPaYrLwy4C87ryQarB72mdGgcwVZKlVbLeH9rrjw0Fj7uVD5pVHdkVV4vZDynKZV+l/6AADAw4wwBgHOMMiLckF4NdF5HeB/wH8F1X9APAW4CER+SzwkH+OiNwtIr8KoKo94G8BjwKfAt6jqp9c4Fjngj0v6WfheB+g8dckwbZbWel91px6zmNIK1cH2EIhayigPobdjdJ9w79pkBr20riX2C/mxXLZpwOcYWEhF1X9HPDCgtdvAC8peP0S8PLg+a8Cv7qo8d0WSHnZqeeaGrRx8eNAjxz68e59U/8WGDYaqZk+Tis+XT2UcgAlDgmqQm9+laIXgPeJCDi7/HOq+gER+W3gPSLyPcCXgLFeSFn6Pyc8ZF6ZGdN5UO3S/dWagXhx2FFoKPyRGvNKjHZ7EBlotw+Mxz2k95KPiaeCYeF4ppDYHTmB5Y49zfFKlJgn5lVYNKsDPAqlQd8DhpbqaRw4MLDOcM3hZKlh9MdMmSeAY58YARHEGNRapF6DxEK3h7bbcxjADENNkkEjHoRMilYNUx1z2m1LQ17igHEUK0VLgz4DCmOuQTx50BOOhj3UvSJjd/i+m7WaM5CnT0KlgtZiEMF0E+gl2EvPZGOZW0hiWg94FEslSZxq5Azx90L530NASX0sMQqlQT9uGGPAjC/qyZJ4k5KfI9+SLF5slpeQagXOnCJZqbu0tiX7H126HuxTgf1ozAxc0JwkfGc4zlCoaNwEuaCQS34Sf8i8sjTqJYCZeegHgtKgB8j/eMMfbhojn8kgWcU06pilBvbWOrbbS98o2tj9D/ttpuyNRt29dfokRJHTOVmqYlodpNUFa9GlmitdA5DUsw2YI6MQhIqOXG/P/P2eqqXd3nMYj6x9ty9cgkdbP8tD0bcdTh1BidsGM5b1Lxx3vEEfR10bem/GH7YmiYtjq04fOkjVEFOGS73mPHJV9NoNtNN1yc7LV1C1qBikEiNLDRd+2dmFXm/6zkbMMSwzL+yFFhncN3cIl0wN8xpFyeH0M37MvtfJ9Pp9H668aigHkPLhS5QA5z/1ygYXRwMHxUFWq6jX885CJ9N2IBID1mI3tjJj4op7Yq+DYKDXw7bavqCgiXZnM+b9Ux2NePVEhK31xqx0UmbMLInpD3Z+DoCHq98x9l6UYZcSKcqQy1FD3hsMWCVziTur5EIAU1oYtdhOB3wIwA3VMWe02Qw2c8dOdnYHzzvLGJlRJiBk9PhK0qkrYqcI/2SFRgX9UKeuVJ3xs8tCavSLlcLPLu/dFzKdgMeSd8903hK3L8oY+lFEyiDJ4sjzjZnuiwOeG8e+aZC5tnemEvcLj2aJO3t+fCoFMPW4xrWgS6mNVS8B0G47/n2OBpmFVabpYDTr55if3Iq6Sk3YNwzhlDj+0NKgH01khveASusHZGC9QTWV2CVOF9nByBtyAOIYY3xjiSRx7d6mPb83rjNPWCOOnXrmEkVgJrS0m+X+5Hu35vfPJ4UJWEW93HY+jo5Usu2yRPes4ypxLHDUkqJHK6J/kAhFoPZIq9vXuYF8IdJCjXkKH8qx3R622cI2m+6v1T6Y848cljrhrXYb/CQxrTZL7kD9v/R5/r288FcyyAZK9eTzxx2in/rJUaLIyfvGlew7VerEHH+ozlecax64cz30Q/amUlqhq/b0pfozdLWfBzKPsz+oBZ9wTKjEh3EQdQZ9XLhjkZz6HLOlMPmaeuuRARUkrdw10o+7H0UFzRJzhpCULJfDR+Y9HZb+R8DESHnPBzqGNOySesBJ0o9XL3Is4+LeqZpizxYmWbMwSF5eYB5Ij5cLv2R5heB8EkWY5SX3eKmBttuOxdTrDYRsbgvGUIl9o4yhHzJCES0Xr/UfyCzx43lh3kaJfhhnUmxbkwSsIpW4MI4803lnvY5piplyx3WGfEFGMp90LUKQRNZu1xl1ESfDUHeFX/R6rl6g2epP1CWOLUotl0NGPq6pSQI9i9RqEEWIVcQsODG5KMxCP0zDG2klae4Yeznv1PATz9iq1DCJuciQ0CyTkVpst+e873abpDMo+ZtOUFKtQrUC3bKj0rGHkhVnHxXcUQY9g1/aA24Z32z135pnGPuolo3PMx497fmKkpSTMI7mOO5c055n1MpgzP79FczgF0UTb9S7PbC2v/IrcaxRslwOCSNZByErIs+QmBXTdqYvarNWtNmMHe4njS39M/Uaplp1zIxxTJIpxjgVUsM867GCQp+Z9t3PJDXu8x/1XcmzYxKLdns8XP2OvY+jxJGH+qToNH8HhTvTQ18gBmLQk0IK0x5rHsnKIIyhQUhpbMy8iGmy1zHM6GWHicWZRMPmUbG6F+RCXhJFaK/Lw5VXoVbLCtJjijLkcsA4UD5wvoBlXsebM/YcNw853PMY2xjDOrJydR7GeI9JXKlWs8lw1CTjqIsWqdSyuHuJ44uS5XInYJLB2K9kbVF1Y9r+bZKuyl6M4V5j4GOOl9IQM972PD3w3HnmKQvs7nMEvW5hviUVUEuVG0scX6iWBn3hmKtHvsCkpsSufDyjt02bqEy3y4x6UL2YFsPMo6FF6InP8R5kOYY4+Oq12wxo6Exxvql6t2ZsnnSnPSaD/XHEV5ROYug4vZlgbGWR0bHFUaMtLvybJiKRiPyOiPyKf/5CEflNEfm4iPxnEVkbsd/3i8gnROSTIvK3pznXZ5743BQDMkhccX+TSssX9WNMy++LjPkU+zqWThfb7WXGxbV466K97vA+YRJ22uTkAsM91vc7ddIDrT0V4aRho8zTH3VN4YQ0bdK76Fj+M5taWXLOE2GJownV6f4OCgfhOnw/8Kng+TuAN6rqnwDeB/xAfgcR+UrgbwBfi+uE/U0i8rx9jUKGGzlnr4/DvOPi+eOmw0gnl2mTekF15aRjFxq1g8AYI5sZxlFGdprJR3MNJ45w7uKh6NvmfswShwtFsNZM9XdQWOiZRORe4C/jjHiKFwAf8Y8fA/6Xgl3/GPBbqrqrqj3g14C/sq/B5IzaVH0+YWAiyFrCzZNOGI5vlm1n5WbPEj6adfuiffcTrvL321SrmHqtL3pVhBGTwth9ph3DtPTTSfBjKwW7jh90yr+DwqKnjrcCfx/XxjjFJ4Bv9o9fCdxXsN8ngG8QkTMisgS8fMR2iMjrRORxEXm8y+RE1ICy3ixL76DCcS8dgYoH0zdEczvmqPPAdMYn7/nPyh/fL5ffH0OTxIWkkoRUd33qcaSaL1NslxntvRj/MqxyZ8MnRaf5mwYF4enTIvKYiHzW/z816RgLM+gi8k3AVVV9IvfWa4HXi8gTwCowJHqhqp8C/hnOg/8A8LtAIf9LVd+uqg+q6oN//E//sYU0Fkj1w4+s4NIkYzSKaiimX1wUGrV88vCwjJan/YW5gmn3A0ZPAmmBVbWKVKuukXe9NmTY9/15585dNr04hpivi54PT78R+JCqPg/4kH8+FotkubwI+GYReTlQB9ZE5GdU9dXAwwAi8nxcSGYIqvqTwE/67f4J8NS0Jw5/OPtZ5ooRL28rSMR0Xt9BIxWN8mMrZH6MiteDa3JRrbrNkgTtdA5m4pqWcbLHxHR6jRJBETXSdVpKIME14R53/rEnmhBaCt4Pv4ulcT8emBdtMQhP/xjwd/3LrwBe7B+/C/gw8IPjjrMwg66qPwT8EICIvBh4g6q+WkTOq+pVETHAjwA/UbR/sN39wF8Fvn4v48j/cKY28HkjIsLCZVH3ULSTGibTaKDWIr2ezw8UGJpQGCvdt9MF36QBq54OuOBimFwl6MjNpqEmjtgPcEqSgHZ7fkLub+P44r66My+0FZ53EvbI+S8bTd/+UMDaqQ36WRF5PHj+dlV9e/D8rbjw9Grw2gVVvQygqpdF5PykkxwGD/3bReT1/vEvAu8EEJG7gXeo6sv9e/9RRM4AXeD1qnprHidPf0QTDXvKYfZe3IGEHfZwjrR1nSaJM0SxM87aag97j7njp4qLqB1YFTpju0BtdB0jU9sfhEtAxyZjs8ySZ8gKrwKZg3wxVl5oa6z08DhPPNWcCRhU00xEpVG/zaHA9B76dVV9sOiNMDztnd8940AMuqp+GLdcQFXfBrytYJtLuORn+vzPL3JMUxl2tYN9JfeCKdgeWZXnXioa0/Lybm826mOwXWZcA3ExCY99GDH0lJKo3Zm99DQnoKncbTIYDx+grqbGuBJDUKAV8vkzjz3/WQbsJwBpNFz4xip2a8vduwmff9lU+vbGnDjmheFp4IqIXPTe+UXg6qQDHbtK0VlwIDSyKYxQWi6+3/NMPfmMCe30GyGbw0sCB+PbS97Cdjr9sE6we5oklTTEBKi1mBNrrntTkjgp5bZjS6ln2BTeh3Dyq1aQeg09exo2t8FaZGkJ2drqTwT+ekYhbbwClEJetxPm8BMZE57+58BrgLf4/7806Vh3rEFfqDGflYOdD0HsYf+pEcSvnUzAoFCXbbcGt52XlEDBOEbF+PvDKfCKZzi22r5mjEQu+SnGe+TLS9h7zmEu34BqBV1qINdvuq5DPl8ixoWyTLUKkXGxdg1UIH0ISJMEmi1Mkrjfd2Kx29sDE3V6r4dWYaGXH0VItdqX3Q0Kp0ojfxQxPSVxj3gL8B4R+R7gSzia91gca4NeZLQfs+9dvGc+L+M3T2XDFKGhm2bb/Yacxhw7wx5olyFTZ6QXrxaI3LXGMebsaeyZNXon6qgIveWIykqVaLeHJAmcP42sbyOqaKuVJYpts+kMff7YuXHZze2hcQ6svsTnLAYuzbWxM42Ga4ohgrTaYGSgjV0pw3tEMedFbC48fQN4ySz7H1uDPspoL8yYz1PZLyzTXwQ0J1p1mCgw5vnJZijkkca9Pd2SPNUyKIpSq5iG286eWqNzZglbM6iAGugtxYhV1MTEGyCry7DbhHsuQLMDzRZs72Sfa/7zHXyeZOfNxpGGriRsIJ1bdfR6btIAzPlzsLWNhtz7gkKpTEog9x0pY/EHCAWdnuVyIDi2Bn2kJ16Q2Nqv4UyTcKZRd13gA8Gs0TuZwZDHqPDDNGPbiye/n2ue9p5Ns13g6WbyCo2Ge8tXiiLp/Rzc1SwteU+2SrK51eeWDwzB/+CiCHP1JtXYIK0OGIOtVRBrkS9c8glRxXa7iDFIu4Nd3xz4HIcm66JVRm4V0f8uDE9UEKwuvCefPHVpxErE/R/QhCmrVI8ASoN+uMj/CPYQf04fhzFO1IKqM0bdLtqeLEOQxmkzNkQ4nimLVaa6hhytbt+riDH0vbQRhDNm1idZ3T2yzWZm5AopiClPvtPBrK4g589CpwOtNrrbzCbLzMD2ehAZ7PZ2/7oCj9806pjlJfdekmBv3ESvXsN645mxYSIDnr8PfhXtPebsuOOue8T9GZiAxlA11U9a+fcHVnzz1g4qMR8cseLxY2fQZw2pSFzJPLtpDZ37gfc96/SHmGxvz/TDcz9kkzFLpuI/71VzBEYalLkjSaBWwyw1oBI7g97tIdb6xGKgE6M2S1y6oSpCgt3YQnab2D/+HJJ6TLzdQT7/NDRbWXs3bbUhMtlniIibSMVgVpa99+7oh3Znd2A1lKcyjppcsgl8mpXGJIxbSRV8xgPfx9xKZhTzpuS2HzBKg36EkBnLyFVadnt9jfIRP9BpmipMhSyOPYYalx4v/SGnhv8oasrkVi/abqOdjgtfnDyB3diExE8sAcMm9ZJDBgpR5Kh/9RrmD74Ez72P5sUlarVnET91Hd3YwtRW3fGMYDe3nQG3CstLIOIaNasdMuQHcR+GzjVlZWyGvEdexP4Zs0p6uPIqPtj9+alO9ciJ17oJN4og8udJLNrtop1udu/KZGwBZissOhAcO4P+mH2vizNOEYZIl93OK2TyD3/eBmEazzkwlAdqmPaC1LNMEkQFaTScNsyNmy7Jlzal8BNTqpWjSeJCHt6wkyTIyRP07jsLIsRXN5ELDXbvqmHO3YNYpXa9jVglurKBdLrY7R23MkhjztOESdIK0igaTlTudVVT5OkX0VKnGZuY7P7kq1zH1hLEFV7a+C4ebf77kYd/KPo2l69IKaxGMi5+eh6pxJkD8VD0baVRL0DZJHrBCAs0xiL9kXmDaVvt/UnY7iUxOeUP+8BCJdMiP+4gbJRCE7CbW4W7h6El58WnUgOJ0940gt64Sdxu03vevbTvO0VnxYBA42mXm4h+/wtgnWSBrK6QpOcqaso84TNZqOhakcHNjHNx0jirPK3V3KolijIKo/GPs+bT4WehNgtHjevGFSZW0+NowtC9y9colOGcApQslwPADEZVe110jLwsOPncwkRe+n695nbvdEb+SPc7zpAxcSgIJskBg+QeOL76NBNUwWQ7IJSVgKiPqVerRLd24dQSSIXGlS4qUP3s5f4EDOhW8cSxL+THOcLz3gvS3MnIsF6qy9Ppem66QZYbSL0OXZcTMPRDL2EyOPW2H2397NBxH668auB6pqkxGJA9gNJTz0GOmId+rFLnU/UUTZFqX09hhNKYb5G+dpEuyMjns8Ivuffk/c8D/v6kJe6mXnP/l5bcX8oDz3mJY483AmFuIu2Paq/fRG7cwux0iFpK7RNfJPqNT5Bcveb6e47qoToPpNcy6pomXeek78KkVYN3ILK/Zgt712nsfReQkyfAi7AVeeJF8fPUmI9dhRaMO7+9GBmcGO5kTKuFfoBG//h56NN6TtMwR9I4as8WGw6f2NRW2x8mldidzzxZqN63vwMOvzbmXqUUREQwJ0/A6jJqDFy5Bj5hNjbZN+OElte0SQ2ZfPESq1frWVz8UFcqkzDhu7TnY1hFugmd88tE9XOYMycwT13B7u66wqggPBKGVAaaaO+HqZMmav1xSk8dQMqk6KGjyNudByc79GbTL/+4opSpDh3QI/eLwNPGiCtp9yJWheMKxaeWGujZk0i7C1euods7k69rgjGfhvWReqdSiT0LIwmYPvsvCEvHWaRtUzi+Cfz78F7ueQIuqpyNIhdyuXSNWscpa7Kz66pLve5Maljy5x0IZ+3XKQjyORJFpVGHA/W+p8GxMujP/9PPgd9hmEOcw0Jof+GXvRJ7TvSM0q+zFBcF+4zV8c4284JTqXG0Ful1+/HR3LmyexfHcGLNGfNegqyuoDtNQh7+yOvII5zoQoMcbD9wv8QV/Ig0HJ1AFbOyjG02nSrinOAUE30islrp67akFapTTMap4ZSay6fQbs8+6YzIL9Drohtd91lv76RvDGwz8TstBjGDRICx++Q/m9x3Mv2u3/GJ0iO2UDxWBh0GudoDBu4g4tCpUfdsg6z7zxQUSlOtgtqpNLSLzpmdbxJjI+Qbpz/yUT/sKMJu72BUHaMkkJWdyhiMey+/QgqNWd5YqUW6LsRjN7fn/hmmevIhBgzeNKEK8ZotkyqEx322+ftAv5+tY6MMx7+ndU6y70Xq7MSVrGhdfIOUVD9+4Ds4QO3Uws/vjjXqJQ998RiZJDuomKs3sJOYDIW77oNnPsmQp+Eb9WXy6QRgO0P15v3EZKsNakk2Noa3ITV6c5ooR+U01KI7TffbSVcU8/wsRzJN9nPIMWOcMo7d11r3OYw4diJe4cQzTR6IYaMvRnyf3KjP2+90xmoLFX2/shqOIFE6bUHTLHi4+h1Z8ncct/4wcNRYLsfKoH/mic/xZ+TZgy8eFkMkf85xCUnN6ZDnMSNDYtRYBnpqjpWcnXweU4nduHyce97Iio9qNaTimjjrToLr6jyn+Pk8EGrHBM26Zx5jQTWoWi/rG7y254kiOI9adYlUErfyCipCZznW0EQRRTxy4rVor+cqd4H3X/nX040rh0zCIw0P+Wbej5x4LQAf2PipPR137rjdDLqI/C3gZ+fV03PhCPnSaSVckhyp4pw+r52pwjFhwu2olP2P9BTnDG23XRx9aYno1ImsInTfrfH2OymEUgfp82rVhW6yxhczHG/E5D+XblajzmcNmvT2vjLMEsDpeBMXckq7Qd11loe/9h8SbezCzi66tQ3GOP0dMWivh3Y6PLrz09khi7SYMnJA2/ZrRkoUYhoP/S7gt0XkfwI/BTyqetQKXh2e/6efw2OPv5uHzCv7gk1h0+QUI348c9MzDyF9BUJg2KMdwzIZGBMHnA8YhwM4b0qJlGrVeXvVqiuqCSe0RVXnTjVA3ygkijJvVKoVaHfQnd3px1E0lqFwx/6GWnTssE1f4Rj2iijCnDoJSw3sUhUSJTmxBKeXkc4psGDaXbi5ju46eeSHq9/Rd1byv4eBfIqvJoYhLrzEFVBbWFAVYkB+eA44aiGXidOdqv4I8DzgJ4H/FfisiPwTEfmyaU4gIpGI/I6I/Ip//kIR+U0R+biI/GcRWRux398RkU+KyCdE5D+ISH3ai3rMvjeLA/uDZdWcA+fwHryp1zCN+thy6b0i866TBET63ks6cYyiDGac9mC7UeGQoEgqFbva56D7Mdy4spD7MhbBtUmj7pg2vR7a67kEntWsyGkmhOynsIhmH/crZQ5JrQpLS30aYVi4NircNurzPyikv5E9jCH7nklfE0niCubMaVheQisx0fUtoi89g9lpE12+idluI50e3LjlPstGHXPxgvvtpQ4YjGdJ+b+BoiurWX7lkROv5ZFT31u4+8OVV833u6y40v9p/g4IU8XQVVVF5BngGaAHnAJ+QUQeU9W/P2H37wc+BaSG+x24Jqi/JiKvBX4A+NFwBxG5B/g/gK9Q1aaIvAd4FfDvprusfoxYmy1MJe6HCPL0vIyNspgflhtH4paKI8YwvNOY5Xp+3/QH5T2Uflx7ivMUIP3CR+fPudPtNp3eSvDjWbgRSg1vFCFx7EMYXms+jrMEoXZ7mHrNSS5Ms+oJWCST9FSmRVr8RBSh65v9PqPjdMwPe4U1DSaMMd9aT6oVtwptttD1DXdfUtXLm+tus0qMWVlGW20XZ7/7LrRRdfr53R72ytXh7lOzDLkSuzGsrvCyB/4O9BLe/9SPAy6xmq1055pUn9+h5oFpYuj/B67j9HWcMf4BVe2KiAE+C4w06CJyL/CXgR8D/q5/+QXAR/zjx4BHyRn0YGwNEekCS8ClaS5oCGr7krhDbymadFwThUVjwT9eTRLXzMFadNczfcYt60cdx/+Ykms3soIWc+ok2mpBq42kTIt5X08RF35lue+dt9qIMaCaKQKK77uZLtXDhszptQx5oOFkmfPQpw25DXQisiZrH5exRCYlllNDPymOP6KeYiGTap6bPsP3RYzFNluI16LvH1L6n4E/ZnKrl90n+9Rl93q14j5X8cwbktkmWv850O25HEvK1Y8ML13+69g03LqA3+BRC7lM46GfBf6qqn4xfFFVrYh804R934oz+KvBa58Avhn4JVwX6/vyO6nq0yLy/8Z1um4CH1TVDxadQEReB7wO4P7775/icsITzRZ7TZeWAwbtCDAu0h+VNpue5ub45QPbTBuH9cYmayKxmSA7zcVL9xZ4z3ZzG+NbwwEuD2Ktj68P9tlMwxwDBVGdHIV1wmeVeZ1SGR2KSPnmAUMpTQqqLVB6TM+bXmPR8zHIqnsjN5lljaPn/b3LrwrHVRCnhjqQYujL8Nr+89x3rl+16idcTzHOs6T25KGrHVyVikHCsGuAx+x7EZlTGOSIGfRpYuhvyhvz4L1PjdrPG/urqvpE7q3XAq8XkSdwhn7IPRaRU8ArgGcDdwPLIvLqEWN4u6o+qKoPnjt3Lnt9roUOqTGv1ZyhGBjAcAjkoCFGMCsrmLNnkBNrLiZZrWbGfey4wtcDQ2Pbrcy7CoWw5uEdpvHWoVh9yo2m74Xq7i5Sq7pYurX9pJv4MBODnqDtuhWEbbaGf8xFIZAC3rf4Yp6xse9xrxXFz/Ne+yTPPFcBLMYgcewldfdojEbF84swYeLLb5OFmoL/RRWs6iUnwvez71YqtrbX3EJwXsDlq8JJXsz8C6DuIHGuFwHfLCIvB+rAmoj8jKq+GngYQESejwvJ5PGXgM+r6jW/3S8Cfxb4mVkGMM2HN1XLupTRAO6HFXZuz5fe54xGZqD2o7U+DXzS1d5az9q8jVw+5yl3BGMMMSpcMCtyRiQrmAFkeQl6CXZrK7unUqu5ylkfJ0fVTaSV2C2rr10f1JIZOt2E0EmBUU+bOGu4+poiHJKeD3CTUiUe1CufdJzcMbNJLY4R0w/n2GZz8FrDxHHaG3WSkuKsKPLSJz3PvxfQbkfWK4ST3hzyC07bxrpGKUZ4tDX/Yidw4ZbbMeSyJ6jqDwE/BCAiL8YlQl8tIudV9aqPwf8I8BMFu38J+DoRWcKFXF4CPL6IcaZGf6JhVx8nHGMsstiqh6nXyPpcwmzL5BmKidQqttVG7PpAs+Nx+2WVrItGmOBMDV+1iqyuQK3mqIhxjGnUXUItvVe9nvNGV1ey51jFNlsDk1XR5zHUbDn0+rONhg2T0znJfbYTEqwSRc6jTxLMvRfdW09dHo6nTzJUYb0BznFIVyEpm2NohSFpqGcKEbdwHEVGdtRKc7+hnSwklQuPpY9rNRdK6vb2nMgfPqX2V9SLZlgfsQYXh8HS/3YR+QzwaVyi850AInK3iPwqgKp+FPgF4H8CH/fjfPsiBzXVUqyI5pWjUIXLRe10nGFKqVKzeGpRhKnEjlKZhk5GhQBwBto2W4NsnlHnK1r+zzLRTDsRpNdRrzmOtvc8iWPs+oZjNbTaTr1xe8eVoy81snZ1ycYmybUbbtuNTefFh0ybos+j8HK1/yNPP4s8bXEUjbFodVNADZVaDVpt7IklzLkzZGJo4bYTvNmQhmebTRc26nUHrzH8rMLPborPUOIKpl4jWl7CNBqklN2MJRVQX+fGTy+4l2rV1Rc0Gm7iC9oTDn23wrDcNJTcYJKzzRba7fHSxnctTMM99dIn/U08jkhdRP6HiPyup2v/A//6aRF5TEQ+6/+fGnecAyn9V9UPAx/2j98GvK1gm0vAy4PnbwbefBDjS/GYfe+gpx5m/mcxeGr7ib1ZGTSp95SGASDrYJN5keMwIpFV+N4Eb7FQ3IzUw8p5vSO82EzXxsNUYmy7ne1nW+3MoGiTfvwat8LRfGn6KE9yiqX/kCc7KrSSn7SGJvDgaTqsbo9obRX5wiVnSML48SxGUcNOQlNmsiexmdKJdWXZMYci4+mEtwYE1/ISwPtG/ljBOLXbyxK803yHpg7FhCu1Xj8pupBqW2CO8fE28BdVdVtEKsCvi8j7gb8KfEhV3yIibwTeCPzgqIMchod+pJF56uFydtKXqMgLmUbjZIS3kSWLfJIv7eAzdLyQ+pb+5eL3aWHQUDHNuHGMYAeIEUyj7roVNRoFsfjCixn4SxNiAz+8tDuPZz1ItdL33Nrt0YZ3Fszizc7g9YbjsrfWHRMnZFAEoZmFhLim+AwyB6Hddlr43Z5bFXkWUMhWGZz8+ucYWsVMg1EOhNrs+5xf1Y491l4+f7/fIkTDmNI7n8ZDV4dt/7Ti/xRHDnmXf/1dwLeMO05p0AuQGfUZf9AzIV1Khi3msrdksgFIE2j5tnhBrDRrNuyLPoZ++MHyWuLKwLI73GbIaFRiZHWF6PTJyZPFOBQYTu10nEduC5gSgVGRKOq3wEvfO2jkz5lYUHXx/jBpmUoY1GuD93i/Y/aT+SSjmDkIqUywMdlEmc8rpKyTodDOfscZnGNhGDPxTEV+2AumZ7mcFZHHg7/XDQ3fVdV/DLgKPOZDzxdU9TKA/39+3HCOldriPPFY8u79h18mwR/HJdD6y0LTaGRLYGfURi+DR45Hnba6qcT9RGNwLQMGp1bDrK6Atdid3azBQxiPzwSYOt0sQSm1qlvCN1tZcdZedUfyrJV0UssfL1N59F2XTKOejXfmStZpE8+TwhBico3CNRtr9nhtFZadNIDeuOUKYKYJoY2D2umah2erRkiuXutPBEVJ4txx5tKcfJFG/DDOE0CmP+V1VX1w3AaqmgBfJSIngfeJyFfOOp7SoI/BUEx9HAqoV4U9Qf37plp1Rgmc/kXYgafqmg/YGzdHn29KY5AVQYVGKWfItNN1lZjViout5qr7BrZNElBBt7ZgKzhWevxZvbEiyhr+fkm/YGXg/CS+M5BvG5dew5QFKenKSJMEqVb6Mfr8Pcrft1FQ20/sBYZSbT8hrq02khr93d1hg7oX5EMjk6AjmDzjdikK8wXHmyvmGb+/jaGq6yLyYeAR4IqIXFTVyyJyEee9j0QZcpmAMPwyVso08LZNteqYBEtLWbx5IDSRxhC7PSSO0Y1NV/683AAj6PYO9sbNfrhh1i95PrYejG/osf+B2u1tetdvuiTuhKX2gChSYPgzNkLI8JiEEZNMur9pNDDVqmP8BAlTej03AUHGu58K6XmMuCRhpYJUK/1jj0uIjrwEze7bUCFNeq86HfTmOvaZa3vTxBnFcNpLbDn/+c8S/tnrOWcd1yjMGqYKQnTzVloEZgm5jB+myDnvmSMiDVwtzqeBX8ZJr+D//9K445Qe+h5Q2OotSKKKWtRaFwoQ48u2B3tTqlUkIuNVpx56ymke+MFPm+FPkd9uzP5Fyc8hYzOG8YGEBSMBt31GDzd/HrXqCkNSymNikXoNu7nl2BFiMm1tQ9152VMyitIKU8d/7vYnhLx3PglFq4oR1+VyAwGrY9Lx85/ZIgzo7egRzzBxh6J1U3H1Zx7LdAnPKXEReJeIRDhH+z2q+isi8pvAe0Tke3D1OWNDBqVBnwJh6CXkM+c1PVKkPSqdN59Ar+BHPBQyGQ5xqFXnOUZRJkg1SjVyJIKEZr8RQX8M0+w/FgPjNfR/NHvQfskZdjGSNUPg5Aqouli0tS6EsbriJstuDza3+k2vJ5wjpZJKMAGMLTqaZswTtgmbM089WUyLKWL82TEHvg9HrMxxHgjIAEBA+e3yWPLu+Z9vTrdQVX8P+FMFr9/AFVZOhdKgT4nH7Hvdkm2a2HWQhJpmu4GXcgJJttvDiMk0ZEwU+cTbFF576qX4vpSEkgBTGowBxo3a8eGCwGDM3JYuMDTZDzL9v7qCNmrOb1lbRnbbyPIy1Kuw20Q3t4ZL48ch/HyCyWNu/VHHnDev9JgvOttTO78pvgMOUT+Ml98v3E7GVJAeBKRfyTrT9zX9vqd5Cz/Bf7Dzc4sb6xGbE0uDPgOGmC+LhjeQmUe5B3lTF0MUxBioVoYZL1MiTejR7SJeXqDwhxYyYiaNy28farsASKPuNMbBdShqO9leAFaW0JUGKoLZ2Mk6GPVlV2f8haVhEzFIFIx/0YYsW9nJ7GOegKHcxTSGMfjcUqXOAc2ifQ8qp3k0Dn4spuLO7xQcB4kGY3Vh/HgXwj0PT8VMLJcDQWnQZ8RMzJd5IAxphBVzo5Jk+ZeSBG0WFAnlfvTjfmSaJNBselZIddDLmzDmkeGAXGhJ/LGJYxdiadRdAUyz5ZQWk8Rpv/QSpNVBdnZg1YVhbGtvk1Q4loGVUZDgzucC5t0Qe6gadz9x7SI6IjAU/hqVU8nyO2Z4bPuBmCzpPMQmGgXN9TGYFPpLVzz+PGGf0oXhCIpzlSyXPWDuEpwzwvgiFVOJMwbIUIER9BN9BayErNhkgnHOtEW6PbetZ3NMzWKZAbbVxm7vOM0W36TAnDnlVBZrNbAJdDrYS5ezZtEsNYjOnx1hyPaHgcrJZA/e/0Ejl6QdQsBmGVtduiAWy746XvkxjavAljTXNOdJd/y4pvw7IJQGfY+YaNRlQrn3XqoE02VrGmuu1QYohNN4Pf1DSf9HPc1+4Q/K6uBKIaCGDV3zlAnATOIglDvodNHdJlhF1zewz1zFPuNpuEniKjOTxHWT3y+KJj7NcfJnwZiqxYljGHG8aauHTb2WTfiDb/c/84VNTiPGZwOe/qIQ6uEvhKJYeNIp/w4IZchlEQgTfEVxyHFhkzwKPGu6vo1Xuz178iqfENzHD8wZiP5XaF8VhUPX6ah+0usiu7uukEiMK0tPufPXbrhwTNbIYAovfdZr3sv1pCEG6WuUzyXhKiGLKP96enj17fqG8y0D38O9OBR+v2ziTicHX7w2Nlm+SBR9pnu9vllPfcQWbaVBnwMkMyiSxQgnqgNCFibJysa7vX5XoKGTBGyM/LKz6AczC/87uIbsrTHe1ICnl280Ma8f70A8N3Gdi1IdlCChaLd3nFaNbyxR1PosGPjg/3mNteBea5IgtdiX/1sIFCb3jBGslCJDWjiBjHo8A8SIi1OLuF6z2zvOoB808oVoIVXR3+dUMnehydHSoB8vZIqGjbrzWJqtvmEZ8wPOBLFSnjk4PjUU7zuOCpnjGYfbz3QtlTj7caYSuYXGIh9PXqQHFni36X3NEEUILjQj1Qqm0XByvNGICWaBdMSiMWvYEGUWSLEnHWqGh3RSgeH2gNPy0meEWoVOx60Qd3bBWiefsBf21F7Hkb/+9LeXqnPKgqmKKbRkuRwrPGbf67yAMK4dRdgJ+s6I1+BIeyimEJNVtiE63FUo5HnvdXlb9CNKvf84diyWbteFc45gEjCTX0jpmEtL0Gy5H3clRpLESSpME36ZBvn7Ne7+h9uGn3N/8NOdK4+C/dLCNqmaTH9nauxnYlML+ErptG/rmLh/4flmzbGMOnYUYc6dARNBuw0ry7CxBc0mL61/p9sujnl0+13jj7UfHLGfSGnQ94mUcqettmv/lteR9l/YgVZ3Rd62j49mlW0MqvU5A1EJduhvN0speWaMisIEo7ysKSmSc8eIH37GTe50+7HcSoycO4NYdYZmewfd3R1Oas467jxzRHM8+7AIBnd/M0G0cYZu2sl4xGtpxXARJXXRyMKKKa1w1HWOMOQDfXb3mMfJVmpWYWfbfQeWGsjZ03zgs/8vAB6ufge02+4/LMRrL2PoxwyzlhOH7JgBPnueb+7/Dyg2Bl1l0g5GIxNleYwL2aQhAtsb3iesNk2SrGI1a0QdriL2mEAc98PvL+eD8I8fr0Rk4RZttRER7GoNExmk20VOn0Sv3UB7vUwfp4hvPhFTGFqpxM64eEpnYUGOv65sos7yBIHoW8B7H+v9FsTTR03UC8EcYvC0Z1hZ5JwKTZK+IqmIE2sLuiDBYgz4EI6YQS9pi4eIkdTHPE0w6OySdfdJDVQYssljFtpcSNvLeZcpv1dqNczKslOPrLjwjMSVwbj2qPPPEE5IYeq10VS99P74zjdiDPaLT2HWd+DaTeytdexTvh2c75I00Ahiv0YvxyyxrTba62Z8/SGD75khphJjTqxlnZ8kdkqPEkWY1VXM2kq/MUlR45DcPc0ULuMKplEf/VksAntgkqS1DGmbvpkm1fB3MRDKUsyZ08hd5zB338XLHvg7M49rT5iWsljSFu8czFx5OkvcfI/JpoEwj/qKPTFIksDurms/5yeTST/KVE53wDuehn0jhmRnd+z1ZAwbn3xEDMmXns6qBfNe8L7iteH5Z2APFR4rjpFTdfe0lzhVzijCrm+4lUSe9z4qsRtOKr0uJAGL56h46QVGfxZef2GDDfH9BCLjjPnpU+jairuX1roitAOAUIZcShRgIL4Oe/Jm5wXn7Rnv+Q6eO+Wu22bTvzRdMZPaIHxQ5K3vJbmbDdgMTBqoazYhldhps2TVngpEfe9ulkTduG3ph8Ukdo1JMJKtCvqH0EwrBmtdmKBeg+1d7MYWUq9l3Y7SyWkqzZO0YQWwJ4XLUQjj3ZMmxRETSNZiMQsniUvgFjkCBcn//Dmy+1ytunHFMbKyjJ5YRrabWQvA9z851IN+YbjjDLrX930ceFpVv0lEXgj8BLACfAH4TlXdzO3zAiAMTj8HeJOqvnXR4z0sPBR92+Ql7Lz50wWwkzjF+aTgBAzoUI9jPez1mlKjpv1QgxhBGnWk4pPI3oDa9Y0htcN0+0F64IgxFpx7IPThVwa2VcwQEiMuBxHHoAo7TdKG0uonSXd+15Vp6pDGgr4PGTUwExAr7gEwrngui5cDGIPt7BbnfQpyAv3jJK5QK46zXgSyvOQmxChC1redtw4HaszduA/2dJNwEB769wOfAtb883cAb1DVXxOR1wI/APxouIOq/gHwVZBNCE8D7zuAsR44pi1R7vfYTAYN6iw/5uAHGGKv1aZjDU4B3W9uoaHCQ3jVxdRT32miURuzuoK99zzSTTAn1rBPXXJ8/3zCt1qBbg+bccf74834ztUKrilGrz+RhNcQRW7yIDBy+etLrOu/Wq1CpzNclDODB7xopNTagfOHnnIUjXYA/MRvm013f1O20agVUsG+WY/dSow0Gq5/ba0Ky3XYbcPWNtr2SVARPnDz387r0qfHnWTQReRe4C8DPwb8Xf/yC4CP+MePAY+SM+g5vAT4I1X94qLGeViYRW8i9ZCccQljw7N57VkvU697sW+M8rrzRn8Uk2UvksAjh9KvvNVuz8WmV5aRVg/pdJ0xHeKFe4+PimPDFF0buGpUYwqrdNMVgm6P15RxjCCFSsUZ82Yrqw7uh6L2QbGcJ4Lvl5hU4jhy9y8yLgntVS7DgqdwX/fQSREMvJ6uakZVuuYretPJVFzhniSJW+HUa9Du8IFb75j/9U+DI6i2uGgP/a3A3wdWg9c+AXwzrjfeK4H7JhzjVcB/GPWmiLwOeB3A/fffv4+hHiz2Kh6kVrOKzr2IRqXGY6w0Qd6rnsSbDuPik+LPfvuMyeHj37bV3rcnmiXQ4hiz1MBubaPXbyJnTkG7g712vR9nt0Hc2SrQGtQkyaPXG+2M5b31gnFJrZYVPw3smiZ2p/0sZ7lHgdE0q6uOl58/36QwXhZiczRBoigLZQ3RKqddgaW0zOA+h9+H8Bql0XCdqXwlKOAmZiO8/wv/YuItWDjuFIMuIt8EXFXVJ0TkxcFbrwV+XETehGuAOrIRpIhUccb/h0Zto6pvB94O8OCDDx6x2zsaA3HbnEczatssTisCVrOElZ2yl2bf8MwwEWRGoaCPanDcMIk2LnmWxWWjCERc4wyrWTu4iV2eRp0fF/8XI85wtT3V8sQq9uQy5qYz9K45R7irIsY6jn0WhvENwYNuQgMTwV4nHWOQRsN5lo06stQg2m2S3Npg5GcyTQJ5DNQq8d13DcTo0+MO1jiMPQhpnYIkiZNXmECPLOy7O3B6GQhjZTUOiXXfcd8AXMS4frv1mushay3v/9JbJ132geFOKv1/EfDNIvJyoA6sicjPqOqrgYcBROT5uJDMKLwM+J+qemWB4zxwPFx5Vd9L9dSr1DssQubhLTXAeI2QRuS8xlRXY14a0HmPK+dJjcNYOd6golKtIrFnPGzv9D38fdIK0wlOrSIk/dZ7iY+vV4bDKtmk6BUshzz07PnepV/Fe7Xa6UDTwIbjAMhSo9+ZaRRyseupOgj5e5neb3vtuj9U4BGHBhWg0x1f05Af1oSOQePez/rkWoXEYtZWvL69xZxcc4ZdxBnvp3586jEdBu6YkIuq/hDes/Ye+htU9dUicl5Vr4qIAX4Ex3gZhW9nTLjldsUHuz/Pyy6+HpaXXCz76nVvlCH01EJvVpMEvfc8KkJ0c8tVQHqa20IrBPfgEY46TlrdKUaySlOHfTQ+8AhZEVKteH0Ti+w2Ma22mzT9/RraJx1iSK2EoVj5XuGahm/56w5WUyH9c0o4Q1wZnniGN8xWHwOdrvx7qTMhxvgm5P1iq0JjnL1HPwaenwDVjrxPmRCdVVfZ2+k6JtJd56DTRc6fdkb8xga02lCrHnljftBFQ9PgMHjo3y4ir/ePfxF4J4CI3A28Q1Vf7p8vAQ8B/49DGOPCoedOkazW0dhQAewz1xgl/ZoyIcwfPYVZW3E6JZ0Cb2qRtMZJS/8JceSB7byXOWSQZg0vBJ65VGLM0lLGebbbO86It9qZ0Fi+ucMQFS89337v34hCpKwvZiDbMHXTh4H7m2tblztmKiU8cHwJjG6aVO+BpvtXK0hUc4ZdzERvXYxglpfcITsdsDoY+gsZQun4fUhMqlV41t1oFJEsVegtV1AD9Wd24MQqbG4ffWOe4k406Kr6YeDD/vHbgCGyqKpeAl4ePN8FzhzE+A4D0k2In76B3tpAg0RZyPpw3nc/RGC3tmB7x5fde42TnDEYJ3u7t4HmGhpk0gP+/VHnCEIs2RI8H8PfQ+l4flwAZmXZJT4rMbZWwbS7mOVl2NmBkyfg2g3Y3R0cb1i8Mu8JcFxycCAhPX2IY+y5guR1amQligZ7reZCaSEtUIz0heXSiXaaexMZxIdHBoTdwlVOUCgV5o20GtO8exmNBNNTaleaSKeHbVR49PdvD2NeVoqWyPD+T/1TXvbA30F7rhHyxCU0/QSeev5yahjyCda+Nzos+p9uMxXCxJlapOaMBbu7SFR1LwcNOUb1NS0MCe3XmKciT1aRM6ewaw1svUK03oRbG44aeOYUGhvkxCpSrWA3tpjLZLKPcc9y78dum6spkLjiG4I3M8PcLwoqLopKq0yHVlbTjFGMWylCdr6+6FhOAz51CqoVFz5Ui3QTKls91AimZ9GKQSPh0Sf+weRzHyHIEZOYLg36ISKlXaXynpMQNma2QRI19XozjewwnpxKlfrlcJpYm4X/7SovG+5xJUZWV92k4sM+Yen9QPelcMIBspXDiHOkx8r48ZOMWurp7jad6FWzi1Yilzhe33CND6ox9vQq0mogvR60jEsAHmQj4RSzrASm+Ez8A3fv/cSqSf/9lPVTGOveC4LPI1ylmXqtWIMmuBZNgLZfRZw7Azstqt0Eu1xDI+GDv/WmvY3pMDHHGLqI3Af8NHAXYIG3q+rbROQ0rmr+AVxl/V9T1VujjlMa9COAUOYz46eP8GZTudoixoNazRgEZmUZGk4Aim4PvPCT1GoutmwtkulqjOaZZy3gvFDWkM5IOrY4duqIPo4tvmjGxbL7q4Z8B580/i1+rLrTHNxmlFEXl8yTWg3WVtxriXWJtZVl7P3nnffX6mJubLpuNt0e6GAoamRF5zwxz4R1ELowayuOPdNquy5CXiulb7wT8nmCcOU21sCLGfqsTCUe6FaVTioDoZ0CpDo2UvWrut1dpGng3Gke/e037+0+HBHMMeTSA/6eqv5PEVkFnhCRx4D/FfiQqr5FRN4IvBH4wVEHKQ36EUNeX/0h88rMy5JaLWtX5xoo94aqPTVJHHNmRzAirsQc5zGnFLXMex7gXY8x6oEAlJjYGexAO8W9bhAx6OoydrlGdH0Trt/A1Gv+B58MaKdn19RoILVqv3oyMgh9kauRFa3qqj6104FLV9x9uec80knQmvPWWxdXqHYturaMXL6e6aKn5++PZW9Vt2MRsEDc8xHFOLMeM2DmSK0Gy0tO12R9w92PdhvtjGANheyUyCAZ82c4JNOPe/fj/kPftRnorOnKjSjiA9ffPtt1H2XMyaCr6mXgsn+8JSKfAu4BXgG82G/2LlwusjTotytCzfSX3ff9EEeuKKXZct7Y1WsDXlNmNNttbNhBSa37sZM+TQ20dZQyn7gc9yPNqg5PnYTVZfdis4W9ct0Vm3R7SLuNiWNsGpKZZLySBKpV7PmTSLPrjlGruiKStMR7cxu036UnDD2JMb7JBX6ycwa7e7KOxoJYi9zcHEzapR5zmBhdAByjo+GkBapVF9/2tL9xDZ5HHCwLbeGT1Npuu36izZZbCbkDjj1mKl2QxbRxnnf2nl9JhZ59OuHtC16g69DK9BeEGTz0syLyePD87b4ocviYIg8Afwr4KHDBG3tU9bKInB93ktKg3yZ42T3fB0sNqMQu0RfHQM/FtptNsuV1yioZ1YEoxIBWSdV1gGkNM2fC5bdaxV6/gWxuuUMGMrHas8V0t3AVAANeJlGEbu9gGnXsUo3kKx/AtBJMpwdfvIR2+5PCgJRrkriORd2e8+RVkWu3XJjJuIIiNdA9USeqncfsnkI+9+RQCGIotjyqyCk0/GOYPdnDNJ8Rxy4Ukt7jNGEZ+4KeNOQ17rj+nokRZGU5C7PoTtOFW4Iw0tgJIihS0iRBejKYsxjIyfSvaej+7AGHkrM4CExv0K+r6oOTNhKRFeA/An9bVTfT7860KA36bQLd2s4EimS3ib12o6/1DYHRnD3xlYUB4niQBgdZ0+oBbe+EIcGlUZ69ySiZzntOJ4XMC2w23fkuX8GcOYVtnMZ0esi1W47XHK4a8nkFcR3fpVbF3n2WpF6ht1IhavaItzvYiqFys4l56gqqtr9imHRvRhnzKe7pgDY7uHxF2+nUDEx2OXnYdN9RBtlN1upoqz4pjVoXAksleLs9mEHWYSCEMmkSyMY6O9QqtNtIFPHSlde4WLq//ts6/KLMtfRfRCo4Y/6zqvqL/uUrInLRe+cXgavjjlEa9NsE2unA1namFmhOn8LeWkeiCjZPeyz44Q3odgTGN0ueJUBa/g4DIkmDhTgppqiilMEmDUXnRaW/bl3fJK5UsMt1xA7z68NYb0pblEqMveccWolIlmJ6DYPpOgpc9dou5tLVrCHH0MSUl9wdR62cssgpXR2Z5SVnaH0iOg35mCVH/bS7u74FXezCS+JWGdpqYzud/pgC3nyq6pik0gFeXz0r3hGBDsV67rkx7gmB8wBBQjuOXTivKGQXFlWJIqrY7R2yAqPbGPPkoYtzxX8S+JSq/n+Ct34ZeA3wFv//l8YdpzTotwnUKrTamOUlJ7vabGJOnoAkIapWPZ+92TeigYct9ZoTwYoiZzS2d9B2e8BDy+Lj9ZpLtp064QzExmbWGCLPepjG0y2sHg1ZO6k4lrqyeNncwvQSJ2QVxyg4hkStBu32gMeuSYJubiOfadH9My+gst4m3jaoEaRn2X7uGo21GvHvfQ6sRVMRKD+GNOHnKnSnFDibgOx6IDPiEsf9LjsAIphGwyVprbokc7XqtNTVDhrzcJIJWEepBEAaO0/L6UcmXefJtAnH5QvNiOOxekTpd6Hf6LxyPMIwOjeay4uA7wI+LiIf86/9MM6Qv0dEvgf4Ek6hdiRKg36bQIw4A16vIWur6PWb2Os3svf73lzQ6UaMYzOsLEO9hi7VkK2mUxz0S+D0xynVCub0KddAoBJjl5z3JHHkmkQ8+bR7HoXnimYz8OmYcrHYLJxge9j1DaTdcTHwlJUSrBpSgxAKcGm3R/Wjf4CcPcPOV17AVoT69Q5Rx1L50nU0jUOK9ENI3iMXI6PVKsNxTmkQTSV21E118XO7u4t2OpiVFYhcMQ4wNJlqbgwDK5LwngUeb7iPNnNyyAGbaF/MmlFIJ5deF9Fo8vbp2HyoTJOEDzZ/bvI+Rxzz8tBV9ddxTn8RXjLtcUqDfrsgirC31l1RRmKR1RW02x1kkgwp3SXQtk5tz7c9y+uBh2wRqhXsSgOztYvZ2nEiSXEEqyuYixecjKlnV9DpOo8sLCiZNsZasF2/HF7Qnd1BLzVJCjv7aEK2KgHQK1eRLz+P1gy9RkRSNS4k0ai70Ae4iaLbRRJLsrkFRJmRj06fIrl5ayhs0Kc4RpmBDO9fPpwlvV42IZlGw3W5980viibA1HBHa65tgHa7br8kcffa3+OQ9pkfX2G4bdQKaV4oSr6PC1MFIZ8Pdn9+gQM7IJTiXCVmwSMnXuuWdJ6Drru76Oa2+x/EVweaNYxCQGFMGRPm7Bl0a9tzlztIs4VJEqdUuLGZ6V7LbhMqriEvcezCH80m0kuyUM9w44hBDIVrCl4PZQamKkfPGw9rqP9/P445e5qNP3s/OxcM3aW7qG5bqus9TM8S39qFRJH1TeJqBXv3edoXlqj9+u87rn5K4UzHlGsmEo5LjGRjlWrVSRwnLlxEZDAry2i7janVsNs7wwyg0Iv2nHqp+fBYow7rG24F4pPDo2Lg8zDaY3Xsp9k/5a17YbBxwmvHwph73El66CWmxEvr3+mMto+jpuqAGdKwSOQ00ENmRBYLzjYdrasSSqbK6oorSqnErjfj9g72xs3B7VNj2247DXavXW5WlmG5AatVpNVGW22kavzYBo1CKEVQaLwDDMgWTEJO7Am1mXiYvbVOZfdeGjeF5hlB1GDjCmu/fxN55gbJ+jrmxJrzrp+8TD05T++rn4/pWidPvN3CbLewV6/149KjhlGtuvth1YW1dnZdJWSjge56eVwK6JwD9yhI9p5wXrp95prTyk+NY57FNOM9GrqGoUlh77OCGHGTvG/xJ7Wa0ycqCK/lC+dud5QGvcQQbLeHWNfkImxWMWD8Uk8wUGYc13ghTZ65J6mRTCAxYAS9cdMZ6FAnPP8jz5KPZMeRyCe20iYE9RrmrnOutH5nN2N36OYWttnCVFwhTCYBXAkMdsqHNnbQMI9iY4RGPCwuOnsGvbXuPFx/f+KdHtsXY9RAbSNh+ZNXYXsHVleQZtMZn1oNzp52HruF3koF6SmmHaEnltELJ4ieuk7v0jO5YQyW3lOvOXmFtHlGFGXx7TBROES/FN/sIc11pIlrYzCnT7quPbtNF25ptd3KycviTuVJ55KoUvPVmmGFcXCcTPdnVk9dDCkXfogKeYyNuQu5HK2YS2nQjwJSY23EFefkvPO+jjYusdeow+7uxLZhWWx5IPaa9M/l6Y5u8+m+mI473oIbt5Azp9DlumvxttOG02uocT9uqVSIul3srXX3POd5mxNrIILd2s4MjbbbWRNioL8aCYS7ZKmBnjuFtLrYlQZac63sOLuGabaRdo/uXWtc/ro6GsOZTyY0nt6h9Zyz7F64yOknrmMuXkCrFXonG5h2D+lZoq0W0S0LsWHzy0+y8qVdoisboEp87gxJunpJKzaXfRf6bg977YYz7OnqKjchj4wlQ9YpKa34lKUl5+nvNtGb6/39fQJ7VOhqHLLK2l7P5VIoDoEN5EJmmTRC9ko63nRFmV9JHTOU8rklijEugZXR1gSb6npPeUyg8Mc5iy55qCUjSw1Hr/MeunQTos0d6CXOEBvfVq/dzjxWs7oCS0uws+Mol0nitFuSBLO26ihvG5sudLG6gn3mapY7MCc9fTJJoJdg7z3vFBUbNf7o70UsNTrsfO4EGivRziqVbVADZz+eoJGw/mURG88+wdmPd1i60qV9zxrSU6pXtog2m6T879a9J6hdc/d29Q83MRu70O3CUgO9ftNNQN2eC4vVa+69ZstVbHa6QzIHefnY9D5mieuh5GigOd/zHrn2Rc0GmoLP4j3nEpfiJ/d8EnUgCT1rHL2owjR8L67NdrzbCaVBL5HiITOWUjqEmeKcOc9oZEOKoEgkfX+kB2iMM2aAvXodbtx0Rj7lrCfWhR6iyCkePn3ZhRFWV0jOrqJyEulZTKeHjQ3S7iK3tmB1GanX0GoFbVSQpfuQ3bZbCSwvoY0q0nUGXTo9tp6zStyyLC9tsbNb41u/8Te5WN0A4P/62IupfrrBxnMi7n33F1h+6izSTeicbmA66QQmaGxoX1zFRoJWhOqtDlqJaJ+tY2NB7AqNp3cwX3yGzYe+HBSqWwn13/i0k0hoNp2GzIBevIsjZ2Gk3D12D1P54sH3ZbmB1L065s4O5sJZdHPbr8T20aIvN5nnJ3IxnsoZmb7Y25RFVJn+OQxPNuHKMrH9VdcxQtngosTBIBB/Mo062usFdLuctx7wmx19TxiQW42DAhARdGPLhRV8rFSbLaTddomwTtcZJl/ABECnC70e0UYTFUGrfsnfsyRrDfTUErYSoZHTXhF18e/IGPTkMrZeIWlE9JYibCTUbnWprfewNcOFf1qltxrz4Ytfzz968zt4unuab/2K3+E/fe7PknShd985xFo0NpiepXrVUQd3nnuKXmON3nJE3LJ0ViOe+ZolVr8EzfMgvoD1XHeJeO1Z1NZ71H7rD5wn7sWxTKNB0nEa5K5i0sW/Zdk3mthpBvz2fjgim1jDiVStmwh2my4PUK0iDQbCN3v9HmT/Rxhpta7YylRi7CTJ4v5O/XBKBNrrYuq1QuXGVKv9g83jw2zJoFo2uCjhMKt3PoBxJdzemJvVVadauLyEtNpYL686RG0MfvSaJJjlJUwcOzpiwHPXhKwoJu/Fa+KW8hJFvgy84/TTE4ucOuk8d3Dl/L5a0zZqxNe3YWcX1lbonl0mvrGLbO3AypKbCBpVok4PW12mstXj0ovq2GpE+64eX/bcy3z29++BquXsR+FNb/5e6rcSSJT7mi1MJ0GsJdpoYpdrVG420WqMrceIwuYDFXbugd6KsPbcW/Q+c4rmOUEFNIbuKohVol//OLERWFrCrNUyMTBV61rfpSsbI26VUq0i3S4sg4lMMUsmjS+rdVW8K8voxmZf+rjTdQnc9P6OMrShamPBOSDUMC/4zvjJXJOEZGd3eP8xkg7p/rKyDM1+jgNRl5gWyUJuj7Z+tvg4xwFHy56XBv0wsC9jDhO9J4icXka77Tyn3WZxU+ngWGmi1PqiHvdWnls+2ltMGTeJT+RlWh1e7tcuVdHYgAXT8bFca10Yp9NFLGijAmYZub7uJoQogrsvULnZpHeiTnUTbAXkjzcxopz/shucauzywq99mvf8+tdx/6OwfV+FqBsTtZXapqXz7GUa17tEOz20Yog221z9UzHVTXjW+3dJahG1z25xcbnnQjw9b1S3tr0sry+p397JYtwhG8k19WiAqpNIsOoMfSVGGg1swIIwK8tIo+7yCJ0OUq07o9dqDzCNCkNe4zxsd8Pdv3SCgaxxiFh1dQhpgVZYfDZldW8+iZo2rdCdpmtc3um6vq7VCnrZ60dFER/Y+KnJx7+NUYZcSjiEP5L9aG8UeOuZtGynQ/L0M9n7k6RVwx6TU7eoK9JlSY3LUsMZZd9JSBLF3PICY+sbroGzD2HET113zTjiCCoV17TBWri5DmuryEqN2oaSfNsN/tLFP6JtY1iDX3v3n2btFW3+xJ/8Ajd+7QGWr/SobCV012I6q4blp1pIojz1l5Z51i/fQiPhWf/qUy5EtL1DlOYFdsDeuIlUq9id3eyabbPlL9MxkAYaZvesE/5Kxb887NbW0L1xN0ihWnGqmeubzsimoatAu2VWvvlAsw6vcy5rq644KTLopSue6STFIZzwu1ZgvIfO5bdTq0hKQ40MbGzBidVjp3k+EkomlndUsHCDLiIR8DjwtKp+k4i8EPgJYAXXI+87VXWzYL+TwDuAr8Tduteq6m8uerwHAZOqzMWx01VJBY5yvN1C9kn4w0u1TaJoJENmZmbEOMbCqO1zY848/GYL2h1kZRkDzqC1O66ytNly70fGacisLJGs1NFqRNzpoidX4Mln4L67SFbrYBXTVewvnOHX7RnUQP2WZa1m+f1fej5xC+o15ZE3/f/46M0H+Nyn7ufLfr7D1R9os3FlldXPQOfMErVPfgmWllwlbLOVUSFJEleUlBrzoBgn1GE3J0/461bsZvC5ZbdjWJEya9W2s+tCTAFSBku+peBEemLATsm6WamrZSCK0M0t2NmdrvFFwWtFBV4SRZjlJRdGiyPodLDPXMVubrukaqPB+//wn48e83HE0bLnB+Khfz/wKWDNP38H8AZV/TUReS3wA8CPFuz3NuADqvqtIlIFlg5grAtF2gzaNOqukXFQMg8M09tSFHHFU+562OYsQCZ/65sHHxhSdoNaR10U43RURNzzOHbL9HNnnBAYOOOw28REAm2DLtUgUXjW3Whs0NjQOVnB9KC2YTEJNE8btu+JSCpQvwVJFUxPefe//0b++d/8Sb7mubd45uVCVw2v+dh307p2kvXn17hw6QT2S087lcpn3QvtjjNM2zuuWMnEmXbKgFFLhby2dxznPElcOKaZ9JPKvklIpr0yQkp2IIa+tORlGWxfmSkZ0Sik8Ha7UJf6lQRq0a3u+DzLKKSsl3SCMAa7szt8L+pVt+pK3DWnnv/7r/3E9Oc6JrijQi4ici/wl4EfA/6uf/kFwEf848eAR8kZdBFZA74B1yAVVe0A89E3PQQ8ZF6Z/YilEiOnTzoe99XrLoYacI1D2leKkQY5x13PjES1OiDHmumdz4LcMjw858A24evpMjyUIkgcP106HR+f9zTK9Q1nxOo1p0q4vQNXrmPWVrAbm07DxFrEGCqNOhWRrIpST67Q/NozxE0lagtJDTSCrXsN/8/X/QzPq9ykQsRdkeUNTz3Ea573W/zaqefz8Y8/i927znPys+c4+Xs34epN7MZW/5rS1m5FZfJiMl68vXAKrcZET10n8vFzKhXPv++AtSS3Nkg99OGOP/3VmPR6mTRw2C1qYrgrLBwrSnaPqCAeiTDUIooYk3WLCqUbSBL0qcsQKFhqr8ejOz89/bmOEe40lstbgb8PrAavfQL4ZpxQ+yuB+wr2ew5wDXinD9E8AXy/qu7kNxSR1wGvA7j//vvnOfZ9I5/87HOW1XGrz5/1Hp1Fr9/wnl2BAQ/DGvnY+hDHGAg7/UyKf48ory9s+DAKYfl4ECpQ60MJnW6WLE2vLDOavR7WOmaE9rpOxMq6Rg/OuFpsy3HMswrNTodzH9p1RUzAzp+44JQVVfkXP/wd/NN/9m/4jn/wN/kLf/ujdGzEf7/5XC5tr3Hm2be4eWKFnc06p3bbTmvFCFgnHyxLDZcMLbp2ta64Z3ML0+thL55zRs0Y103qhG8pZwza7mST61jDrBbbamOqlX6ycka9eX/TpzvfhGOFTJhs9ZgePxUQC9QtAT7Yuf0lcPeMO0ltUUS+Cbiqqk+IyIuDt14L/LiIvAnXjaPI846Brwa+T1U/KiJvA95IQWjGN1p9O8CDDz54xG6vR1iCb5Xk0hXMzZrTEomMM2KehVLY2MC9URxTDxF4yaHHlhmocEgZGyJgRYTiWREBd3362OtQOXkwlkzHfIBF4/tapi3tPA/bxYLNIDtHrUtCrS1Dt4u9fjOLaUcdS1IRUPjhN7yOmloev34flSjh6vYKGzdXiK9UOPVHwvlf+FT2O5Q4dgnEVjvz0KniWSHD12tbbbftzXXXBMSrJNor1wZXSH6CGrzpwyE1MYLd3B4MaeyhgGjPfPVwTOlzzTXk9oqTcLyUEvcLV1h0tEzOIj30FwHfLCIvB+rAmoj8jKq+GngYQESejwvJ5PEU8JSqftQ//wWcQb89kS2j+4JZttlEOh0XU853miky3PmY6CjveeBcvuFCNRoM56Q/Yt8+jEqMpJNOWgyjmvGiJ2LKkExYdDLQdSivsmjViXwlyYADZFZXnWBVJYZuNxMCEwvVWx2ny9LuOiqkKp//7/fQfrabKJ7zLqWyvoF55ubgJFGJnYE+dYLuxRNEOx3MjU2nzyL97k/ZGAMvuHf95sC4NfGFXEmCGB9ii9NeokFrv8jpz0i95nMdXtMGt6qZemU04p7nE6xDyFWuhuMyqytor+cSnTiqJapOP6jEMPZQvLtILKweV1V/SFXvVdUHgFcB/01VXy0i5wHEWZAfwTFe8vs+AzwpIi/wL70E+P1FjXVReMy+l8fse/sv6KBORtrAYGzbsLxxD1+bQvAoNT7GGw+pVjGNuvsBB80ptNNxHYKiCO10sSE3elrkr6EgH1A4bs+LVusacGTeZrXi+ewVolR90BhY33AhgSTBnj1JvNUlvraFuXQduXbL/W01uf8DO1z81Qr3vi+mcyL28gFeDTCNyXe6roF0JSapRSTLTqvFLC/5JGkQP07vZy6/kf6h1vd3VWRt1RUM5YS61KtqaqeL7jQz2lumR75fpBzzSbUK+Umg0XDGO4qyyT1DZNCdJtpq89KV1+x/jMcIojrV30HhMHjo3y4ir/ePfxF4J4CI3A28Q1Vf7t/7PuBnPcPlc8B3H/hI54QBox5gIMY+wjhnZeUwKNA0xX6Ak3g9fxatREjPIjduoe1O39PsdMnobkXHmUbdb2Kh0+Dx0vFlKpBFoRvf7IEocnowqtg0Du/vQXTmFGogavsiJWvR0ycRVSclUDGc+OQtUKV7doVkpU58swLNVtY4Qq3FRBFYS/13Po+qhZVlJ2mQNncOCn5cGIohg5giOuVpjcYglYq7zl6Y6AzCTz1Lsr7eP0xKP91DyGVm5L432nQGO9WUT8dgt7b6jKnE7j20cxxxJ8XQQ6jqh4EP+8dvw1ES89tcAl4ePP8Y8OBBjO+oIi1icRQyQeJecfMLGIrPSuqFN+qOXpYKJDXq6MbWwL6pxnZGgfQl/ES+4tRM2cqswPMeei/gzw8h5H+r9dRAR5HLGCBx7Pt2NrB3nUUSxRqnmKgXztA91cBW3Ll6S4Zot4K5tU31yZtoo4qeOQFnT8Lla6CKObHmujZ96enMW9Zmyz0u6DU6aXJzErgN1xxjTFK6UH8+neDyCoj5e7kX5MNi+TCgimO3RBWyDzvj4idZgjvTVC8BlFouJTweir6t0LsOk1MSRa7RwYlV7FIVc30DNrYwtarzsiOTVWGS6pundLK01Ht7J0vUpTFxzXmcrrhmCalVHVUtVUxMl4riVbMmxGQz5b6MgpmTZM3FpMP903EM9OpcbvS95Gavv6KoxI4e+JnPoyIYVVdNurNLpXsSu1zD1iNEI2w1wmxsOerklosLs9SARt11bfKt9WRjM6Mwamdwsilsp1aQ71CrWeXoSLZKWHhVFOfWoBo1jhHPSR8bE899DgPnHJWPyTGTst3S2oF0LDku/kgJiTsVd1BStEQBHq68ajAJKK4PY7isB2fQzMk1l5Ts9pCmwJIzcMkpnwxs9TCtjjPqqmBvOW9btc8aST2xoDx90Ovz3nKzhbbbTmuk14N2G6lWiU6sOQ32sGAoj5RTHTvq3qjwTbod+IbINl0hOCMhyw2MGGcUez1ILMm1G4E36ZQNs+NbBXUTlF3fdEleazFPtvrJoWoFvecCWonQSkRvqYKoEjV7ROu7JCeXMM0ushP7Rhq+2US+SUX+OgqQNZIAHPe/eBUyabWTJYqTxMkTRzVn2NOcxzhu+uCARp8kNxGB6yaVOefVKo/u/DQPV15Fyp0fKjC606GULejuVLy08V0ubFKruSSJT/6liUqp1xzTIbGZcp/d2sacPU1yaoVkuUL1mU3YbRJ5bWmNnK63rtRdwm/pLqdo+KXLfU+cAqOUS8w6z8uFF8QnQsWIi6l63rEzVAkhzXEgbKA2kIsdPo87eGBgrHWeNo7+lXo6mRGMY9c8IunHnFOWSV/VsT1wLu10XFLX00MBR4P8/FNZV6DaxfMkJxqY3Q567QayXKN9fpmaKrK5FYQ9NPNQJ4VZspZ0Xis+YwqNC0FNgp9AtdkapBTOipDuOoqBBP3vIWSrvEfOvq6kKU5C6aHfWUgNOZGj0rmYcOIZFh1XRn7fXdhqjHnSOrqYSEYj09UltGIwncR5k7UKctk1L3ZxbgPnT7sk3IZr9hxKto40RuGPvKA4KexFme3i9ULAJy3z9LhpCmE8T942Wxiz5Mrnod9gOIpcC7ZWe3T5exCP73usvh9mWviSn7R8VapWIjf5gTP6iVJ/ch195trgymJEsdT48bhCo2xCCCpC94SBugIYW/0Zfp7p9efPnb6eN+ZZuMu6RG6t6kXVEh459b13jtjWXjAney4iPwWktTtf6V87DbwbeACne/XXVPXWuOOUBn2BeOnyXwcjmLXVrA+m1mLssvPkpJ04A6nq+NNrq64lmwhaiSESpNUl/qNLLumXxi/PnUHSkEqzhTQ7zphYHWAiTBUuGGVsAmMikTPmsuYLfns97Ppm1plnVlmBlIoITrBK0rJ5qyQbm4MhlnBCSZsYJwmm6il2vZ6jWAZ89rSRdbafGDC487S6yKVrbvtOB9PuYutVzMXz8NTlAapmFv7KF/0Ued5i0HbbtaoDt7LRCXzwaVBklNPXizCJ0jrqdSNIqi+Uhuf8SuiRs68D4APX3z7j4I8/xM4t5vLvgH8JhBoKbwQ+pKpvEZE3+uc/OO4gpUFfAB459b3I6gr6J5+HGnHl69UIW1vBRkLnRIxGIKk3bKC6mbD0+XWX9DMGabYdU8KHZ8AZKanE8MxVZ1zrNScsdekZshZwMJx0TJGvPIUgnEJ2jvw2muDi6M0WZnkp855n4k3nmRvtdhYyCUMned2T0KiqlwgQUezubiYMNXiJSqqYlB2r10XiCnZr28nWpjH/SoxevuoLeiw64sdZ1B8UyJXbO6aI03Ch//qc2CmmWh2gFKrVgRXMQE4mfZx+rj4WP9BsI/iOpFILg6f1fVyTxH0nofTW81DmVlikqh8RkQdyL78CeLF//C4cU7A06AeFP/8t/5zazQ7meffTvtAg3nG9M21FMD2ledZ1y7GREHUUjSBqq2trBq7BwvZu31iRcpOD5gedDirGJf8aDcebpp9YHGnMYXDJHXh+abJrrAiYD8GkBT1Ta6UH5xnUeUkTca4v5ZDMb54nnYp++XCVK87pDLyfniN8nj7OcgSZ4ctJ3w5VtRYb44yTjSvWUp+07u+zGBaI+4zi4ok0nZSjCJMWMyXWhU4qFeh0smYdtEd/P4YSnp5CaU6fzKpvS/QhzFQ0dFZEHg+ev93LlozDBVW9DKCql9OizHEoDfoe8dCf+zHaZ6okFcky3bZmsJUIW4mo3uoQbbXonlmieq2FWd+i/qUadqUOPYu5ehOWl2Bnl96zLhB/8YrjQ6fMD1/koV4eQILiHzGuvZfd3cWcPtXnjG9s+krIMSGXEUvyIQM9YmmeJg1NNeWSR8PGOHe+TNgp530DmWZI/vWi1zJPOMcI6p9K+hrxIySIw2sbZKEM6s2MDE2lFMulJeTkCaTZxG5ueYmEGVQtCzRUhhgs4b2bwHBJE8HaanuKatetSMCNLY5d2G5AcMv2pQhIJyvP6U/bBYJLYPsQWYkcpjfo11V14XU1pUGfES/9mn+AdBOkGhPvJtjVmO6yAXEUpqidYGOhtxzTXVmhdqPlGhWvLsGTz2QsAlUXdiCxxJ+75DyqM6eQ7V1XDBTH6LpbvqtXT0zpjVJ3ol5Sr6NrK0i749q4RZHrR+mRhVKCmHQekxJ+mWEMQzTVikueJYmjGBbvmO075AnntERSOmLGW88dJ0ReeGwg1ODbxWXPo2hA4Cvtbj+gN54zoBPvBfSN4/UbWSeglNqXT8iOg6n0f36jKjCnDdukKxhtt/vX26i7783SkhOAC5uoaNoU3DqBspVl9PRakNgVR5tc33bGfHtI6LQELHrVckVELnrv/CJwddIOpUGfEo+88EfpnF9GT9ep3Gpj6xHxVodop0fDCLZqqFzfQbZb6LIrWU+WfNik03MsirTaMV+YEhn0vovYpQqSnCSpx3ROVqhuniHe6mCevOIaL7faLoa8upL9yKTZzhg02ulmzJOsn+TqCvQS14S40ylsvBA2ych3bR82oNadJxOZMv33AyOeGdN2u8+G6R+0/zAhM/qzcJwzzncUYU6ddLK16xve2/S6876Nne1tQlxzSVFAeyMmoVGrk9x1mXNnXMXt5av9z5TpjW8YIpFqFTl7Gr1xy/Uy3d3tHyccz6QkKOFn5btY7e5iTp7IGn2H26RKmuKbSHNyDa1Vsr6vu/cuUdlJqNSrRDe3Jl/TnYg5xtBH4JeB1wBv8f9/adIOpUGfAg9/3T+EpSrVS1vY5RrRxi7RFzcGquaiyJA8/34iEccDt5bIU7+sj2n2OdXBt8AbMXP1Jr3nXYSKIdrtUQWaF2rUIyGWu5yMmgW6CfqZz7uwgxHXxzEbhIFeGv9xjBe6PVeQ5MW2ChEkCY2nrw0bAbLHYmzWvs2sLLuq1FR7RdV5sJBpmw+cJ4+CApdpILHrzSmnT7o4sbXI8pJbAXkPk0oMnS7m7Bk3rijCXrtBX054RHxkVOioEmNWV9yk0eog6X3t7S0Bqt2eC5e12q4NXq+H8aG1MFYuRjCNFdTarMdpdq5cmKofUvFFWM2WKxQbusTBnIRcvQGdDqZew959nsYzLaRnIZI7r63cDJgXy0VE/gMuAXpWRJ4C3owz5O8Rke8BvoTrHzEWpUGfArYaIYkizTbR1RvuxdjHG5PEeYetdhY6yQps8lrehE/7lDyJIpL7zmE6FluLsI2YVBa2fbJCdznCVgyV7YTGp5+BtVVXhFSvOcU+6PfmjGP0xk2013Peo7XorfXiBgoBpBI741ytui9pp5st08OmxZmnmhb5VGJYXnIt5nw4w968NTAhFF3/fpB6l3RwBvXGLXd9Rtw1+ASevXbDhbIadWcwjWBOrGLXNwZiyYXjy3vF6njyyc11p4We57lPHvRw+Eptxh4Kw2MDlEsvp6DW9pUi8yi4t6nI10BfVILkt090S4SLvbfb7hytNubWJuapJvaBizz6m2+afG13LHRuIRdV/fYRb71kluOUBn0K/NeP/J889KJ/7LjFcQS9hPdf/r8Htnnpymv6SajUWPhQCEVhjtQwxjGcPIGtRmw+u0FSFeK2svJkm7VPXIftXddy7f4TdNYi6mvLSGKd8TaCrCxjr99057EW4sh1QlrfxO7uDnt0IyBR5BpugPufJO74UcX3vOwNJBrNqqdN+iQanY4rCNradoYnP3nkGTa51/IqjOG+A5NIsL9UK/2EpL+X2mpjmxuD17vrEnxy8bxbrTRbLn8RxuwLim2gry0TMmUmTk4FyVjHva9mCex0VeOSwgUyAUEIbGhlNc3kmEoMpBTOAjpq+rrEBjmxhrTaLuRz7QZmaYlHf/vNk89zJ0M5csyf0qBPicf++4+Mff/R7Xfx0vp3Dnh8eZGn1HgNFMyows11Ks0WZz6vdJ53F9F2F9PqoLHB3nuW9tk6SVVY+4MNF1Jp1NFajLS60EsyI2jXNzDtjtP3brUxYTIsM4gaGIv+l9G22kTNFnr+DNLtuRVCHGcTwlCBT+yMkt30IZ9UGRFc4+N2GxnVLNkdcPB/eosKvN3Ue5UlX1marj58SCvtWpQ15WgOHxdwlNAbN7MJdigclKdzTkPNLLiGAepkOIF5TzjrjBSwWEaeY78rmyLaaNHqo9t1xrzTQeLKHdnweU9YbAx9ZpQGfd6YJUGmrk8l1QrS6dD7srvZfFYN06tl3cQlUarblsp2Qvd0g8q1bdfgYWfXMWU8xMetOXUC2dh0xUy7uy4kYqTvGaZFPElC2LJcjLhxbO24Sabnmm+YRt2FT0zcVwBcXnLD39l1HnrPKSGmfUDTsUi16mL4KeUuR/3Lx+bDsQwlSKMIOXPKMYQuPUNaYWuWl6BaBVWSK9f6+/vVRtpARHd2YWe3f9+LYvuh51q0upiAbCXhWUB2py9qplYhZdYkCeqPOZU0w7QYtU8u4Z2uPsLuSUC2sny0+e+nP+cdjjupBd0dg0dOvDZbek1M7KUeUxRhzp91iUsj9O4+Q/NCndqmpb1mSGruR7b8TEJls0u02cbc2HDnWWo4Slmj4TxlgN0mLLt2YayuIM1WsKTHJ1F7jqGRVmq22lCJXTeaJMHeuIk0Gk7Pe9cZP+vFoaSx7DxvI45W2Ww5Wpxv8pwZjQTY3XWStCvL6JXrQEBPDD39AspkSEM09VomuIUIbG55DroPYzWbTu53cztjyUij0b/VYcel0HhOKoYiMM456uXQRB0yYDyt1KwsQ6WCAV9G71cqSXCOcQVIBRNe0dj6FzoivJXb11RiNyFXXELZ7uz2q3SXl9znuWt5uPod2TEfS949epwlypDLsUQUDSr/wVhGR0a5a7awF8/RPd1wLBZvyyo7SmdNMF3YPR/TPhHRuFnBPmsF01WWPnkZWVtzxrgWI7tt8OwLrbmE6gA3OoVV91evOVbIUsNVp3oD7RKNHR/O8FWC9Ri11nm8ccz2n7yArRiijqV+pUn0R13XkzMwImrVhW12mpgTTpvG3lrPmBeZjrtal5RL9wkNlVo36Z1Yg6UGemIFVUWaHYwRV8bedfRJ9fdUuz2X0FXNKJrpscd+LsFnEz43nvapO7suf0LkQ1YyZEDFl+Ubv4qw5086xkqn46UFgolgQvHWwJjz4ZzIUTLN0pJrML617a7bTDbqAyqU1notfTd2u73j9HpqNaerz2CYrUQBVPtyG0cEpUGfB3KNI0iLZ4qWvrkfqNlpUbu+TnLvWXbuqrJ0pUt31Vm56rYStZS1T91Eepbu+VUnE9BqO156p4O5/x60UXUUs+1dp5veqMOFc+jTlxEiFwJYXeknMUXoPes8veUK1Ru7mKeuoru2z11vNNx27TbJc+6GROHmFlQrrPzW592XuObi5drpZJ6p7XgOtJG+9kuj7lhAafEQYE6fQleXkW7P6dCoIiRZTDy7Xd2uWy2cPUFvtYpGgvSW4N6TmHaCdC1mqwmXrvhenrbP4x6F/Ocyxqii6qiP993lOrzvtrHPXHXj9eO0aVtA3/HHXr/h+nO2wtBWmvSNsolL4srIRGXROLP9Uj2XUL0z3WVcRWmQJNVmwKIJ5IbDFY3xx31p47vKEMw4lB768cMHNn7KJUQLMBBmCPnCSeLib1vbznB1E9Y+32TnnjqtU4Z4F5audJ0BF4Gb68SXrjhKXtVzsM+ccoe9etN14en1XIFIJSJZqSNnVoiubGBPr9A6t4QaiHcTomYXFCrrbYwvGjF3nYdaDa1XUFXsUpXmxSVnJIwQn6r57VYx7YRop+NWBimzx4gzAn6pLlHkpAt6vcGKVTHYGzddl6FuN/PU1SrYPi1PvPofSYLGxhnwxCI9S7JU6cd9PctmUqf7QW8zyj6bUduKEWR5CV1bJlmuYbqJu89hX80hg+s1b5pNFyNPVxyRgR7esNsshBOydtKxZ9+XnBdvqlGWC9FO13nRxiBnT7sJdXvX00U7U7NghhLT4mmMs1Iy72SUBv144tHWzwK+tVyuYGXgR5r+eJKE5NaGez2OMU9ewT73HhpX2jSuCdF2l+jzl9z+atHE9hkSVW/Qdnbh1Ak4ecJ9seo1136tFhM/fcN5tnedRHqWymaH+NoWnXtOsnPvEkuXW3RP1th+4B5EXfLVxkJ3WZyYWFdZutJj6XefxN59FvPUVVfAE8fY0yvYRgWqMaYWY1q+0fSNW06SoNP1cgW4661GmLUV6HRdEljE67Z3svuTYqCU/+wZUEu00SRZq2N22kgnwXzxGVcpu7LsNj19Em6uDykGZseMon58PS3VB8eWCSmBaUjMT0B2cws2t4iqVZcPaLackU7CXQZbtDlj6FUYu37F1kuvMeWE02+xd+Y0qGKfvpwlqtOxyqmTaKMGkcCTz7iagrSsv1qFWhW9ej1TUUwnolFVuUXPU4Odxtfp9SAKvH6i0ksfBaUfwjoiWLhBF5EIeBx4WlW/SUReCPwEsIITbf9OVd0s2O8LwBbuV9A7CGGbeSBNIj0UfdvIGORAoi7pL3ujT30Ref59SDtBnrzsKht3djOvUKoVxBgXMz13huT0smtDt77lCooqEeaZm3DhFL17z7L5nAaNq13i3R67F+vUlivEO13WPnaT3vkTxDtd6k+u0zuzwvrzG9hYOPnZFre+vI5FaJ+KqD5wAdPqohfOOAmDSOicbtBdieg1hPqNGlHbYjoJ0VIN/ewX/YTjjVtknKd79qSjGDY7sLHlNGAClUUJvFkxLmlMzZfqx6afF1CF0ycde2Zzy5X2+3vUD2v0BieGtDPSuTNOZ/7yVTc5JokLfXj2SRZzT41jWpq/1IBuF7nrPFy74cSvut2seGqgAYX/P16+2OURJIrcpLy8hDlzGvXiamZt1U1WImijgq1XkOfd7ya0Jy+h3R7J+vpwWCbBxcEhGFtBiKnIg4+i/vcsigYNVRlLH4HZq4MXjYPw0L8f+BSw5p+/A3iDqv6aiLwW+AHgR0fs+42qev0Axjh3pIb94cqr+nHOIM4+8IP34luIQT7xR87bBdRrnPeFpHxxi7WItURfeCZjtWAMdrmGMSdp3bXMzl0xaoTNZ1cxHWcY2yeqrH4JkJNoJPSWYjqna6jgBMUacOMr66BQbapLyl6sI1on6iimbbEVQWNBrLL6+RbRTtvx4bd3nAcOWaNhZ/h66M4u0uu5MnS8sfFt9KRa6TeFaNShEpOcaKDGEK/vul6gsUE6PZKVOqYau/ZxcQ2Wa0jPOr77TjMLZZhKPGi4/L21T1/OlBohNeDDxjf97F5a/85+orVRR1NGkZdyyCOdRPbStu1ld/3vTnStY9CzJ0lOLCGJJanH2KrBVA3Rxi7Uaq6hh1XHlAmZL/jVhfE8+k6XIXmDIJRjUh0XyJQ8M/qpj/OXnvkYKHdWUlRE7gX+MvBjwN/1L78A+Ih//BjwKKMN+m2PD3Z/3nV86XRdp3mffEqb/vaNuzMupGHkMFmXGifrk2DViit3b7edwfGiVNHNJajXMBeWidvQqyvSATVOjx1Rtu6rAlWWr/TYvhijMag/VdRxNElJlF7dUNvoUf/cDVjf7OuB3HcXWnE0QvP0Nde5KJigxEgmx2q9YUgNoDTqaNvHeHu27xW2287Qnl7DNmpOZqHXw9Yqjqq5tQ3WEq8sw4lVWvedwMbumkzXUm21vSa5Ojpes8Vwu7bhMFiIInpeGkY7CLz/mX819NpL//SbqX7+MnrmFETiJB1SNlKKXKLdNluut+kUhkb9CkWqFRf2yTUSH6n9U6KPOyyG/lbg7wOrwWufAL4Zpxz2SuC+Efsq8EERUeDfjBKDF5HXAa8DuP/+++cz6jkjbN31yOm/MfhmmijLo6ByMfWWXnbub6I9ZwQHwjrVCjTqxNtdqnVDdRPinYTOyZhew6A9SGouVr59T4z0yCrdkhpoBNsXI6pbytrnm671Xc8pAKZiYHLpKnj9GJvnZafeok/cZRWT3Z6jVDa7fUMjpt+JyXPapd3DWJBegsYRsrnteNypAFKni0ZC7fIWrXvWnOfaTkjOnSTq9tDNrYztMq3Q11HmWT/6xD/gkVPfi/7RFwBvZEdoz6d5Gk0SkrSAahzScEyv2w855XIZs4il3bG4Uwy6iKQNT58QkRcHb70W+HEReRNOHnKUG/AiVb3ku3Q8JiKfVtWP5Dfyhv7tAA8++ODRursF+MDNfzv2/Ycrr8oef7Dzc4XbzFKW/Zf+wj+hdqNDXaF1toqNDb01yJrbCsRN96cCpz7bJt7oOAbOyrILpYgAXgWwuz14giK6XSV2qn3bO35VURlMWEpf3jcV1aLTRW/eQk6dxJ5Y8j+UFSf6tbIEWzsukfr5pzCNBnWge3YZ07Gu+cfmFllzkGNkiD5w6x1ZPmZcRylgsHBpSrjm0J6qWJGS1TIT9M4x6MCLgG8WkZcDdWBNRH5GVV8NPAwgIs/HhWSGoKqX/P+rIvI+4Gvph2qOLfYSfx2H//prPwzAw1//j4h3LVHTsnQFqptdOmsV2qciWqcMtQ1l6UqX2tOb0O2hN26RPPc+R/Db2u4b7pz3O9TLMjKO8x7HmOVlp1HuQyEDvUO7PaAFz7kPuglcuebCUiKoMZhWh97ZZTizRLTVwegSEpksoWqu3CD28XQSiw067Exj/GYxekcBM01S+XqHIkmA7POKMLXYqy1qlocoMQWU/urxiGBhBl1Vfwj4IQDvob9BVV8tIue9kTbAj+AYLwMQkWXAqOqWf/ww8A8XNdY7AR/8zcE0xUN/7seobLsippN/2KHxyUvuy9npuj6lqkSXrrsqyWSEEQfX9QYcBU8MnD6JXa2jxhBtNeGe80i75xKSYaLSa8dgLb0zS1S2luBkhd65VbprVSSpIarOA+/00Gs3kErFlfqrYnd2nTEHUl2XiRSywKAd5VBLHkXMqZGce2+oU/GvgR6kgZFPk56pxo/Uauju7sD7JabAHeShj8K3i8jr/eNfBN4JICJ3A+9Q1ZcDF4D3ieMLx8DPqeoHDmGsxxaP/bqTBF77gw2SlRr27EnM5Wv9xJiqM55hYw7fDWiglRnemFcqWUNiaUcuVNJL0JSJ4ql+JD4ZJ45NYXqW+MYu3WefR43QXYuxcb9Pa9RMsHevEq/WiDaamDhGU4VHVbQSIUnIWgHE8FhSHK66nfFY8u6BkNxIpJXKkUGimpukw88wDdH4+HlK3cwaYI/rEVsiwNEr/Rc9YjPMfvDggw/q448/PnnDEhkeOfFa1yjDa6MM6beH1Yt55DXKY9/RZ2U5042n2XLSrNs7fdVFyDjemiSOO336JPR6JOdP0brYcMtZgcbTO5CoY7vEsWvW0em4uL7/MZmTay55u7Xlh6wj8w/HCQ+ZEQ1sRoWT8qGYnLRuum907gyI8P6nfnyOoz1aEJEn9lvbciI+p19/8q9Mte2jN/7tvs83DW6vQGKJ+UPVaaanZfgT4qd5oauU5SJZc4wqdq3hhZ/EqyK2ho/jG0xrt4f6pgrsNF0D7p4iidJ4ahtzfQPT7qKn11zVpK/yFNOPC9ut7b48r5g7wpgDPGbfW/yG2kHvOv88fS2HjMIo0lfxLDEeqeDdpL8DQln6f4cjE2ZKcu3NCppgQL8sfvBF42iKjXpW0dm9a414vYnsmCHRrfS4AwnVes0rRgoYoXatibm24aonb65nx0hb6WWywOCSqUds6XtQGGXUHzKvLA6ZjBCMy/TaE+MapZw7s4DRHkMcsQhH6aHf4dBULRD6nlzABBEjmEqMaTQwjYZv3hC7pFq10t8PvA5IhHQT4hu7yM1NdGt7rGaLGGfAtZPK8ArStSSNCiw3nFJjp4Pd2XUefS4clDdQZWWjw2P2vaM9+ACpzo2pVt1nXK+5yl1VXvbA3zmAkd7GSNs+TvN3QCg99BIORUtwIyAV17ChUceeO4HZbEK74+iI4JUfq85zP7lG87lnqV/edgJdm1tDTSb6p+sXwjjtER8Xt5bGF9fRazewrfawBxRMNvOmeB5HpEZ9ZLwdvEyDb+3X7SGdjstrBNK8JUbgiHnopUEvARR4z2myrBK7UEi7jXQTl+A8c8KVl+82EVXsXachUVSE3pKhd6JO5coNVx2aM+YjE6w+1m4ueemeTtd5/CljI9BcuZ0oh0cFj9n3Dhj1gedqnbRDpjNkEGYrYLszoUeOs18a9DscaQLRyf4OQ7s92Nx0HPOnrjhdmJ1mpu0ip06ilYj2+TrdlYilyy2kk6C7zcIydTFS6FmnevLa9uGZQPmwpM/NB/kQzDQhmRJjcCfK55a4PZCpQ1a/AwhobGrR7R1sGiJJRbgqcb8DUatH1PJ0xF5CtL7jGlingl4BVW4UAyUVwnrk9N/IpG0LW8iVKHGUcMScjdKglwD6HnpYiZgPjQwIcSW+ZdzODhIZzEqNeLPl2tl5YzygwMh06oWp1k3msZfGvMQRhXL0vp+lQS8xgKIY9zhD/LIL/xuAq9a09HW1fTI0FX+alRueP+eokFCJEocGvTMbXJS4DbDXROP7r/zr7PFL/9Sb3QPVvixwQWPsgxxfiRKLxFFLih6r0n8RuQZ8cU6HOwschW5J5TgGUY5jEEdlHHB0xjLNOJ6lquf2cxIR+YA/1zS4rqqP7Od80+BYGfR5QkQePwp9TMtxlOO4HcYBR2csR2Uch4GyUrREiRIljglKg16iRIkSxwSlQR+Nwh6mh4ByHIMoxzGIozIOODpjOSrjOHCUMfQSJUqUOCYoPfQSJUqUOCYoDXqJEiVKHBPc0QZdRL4gIh8XkY+JyFDvOhE5ISL/WUR+V0Q+KSLffYhjOSUi7xOR3xOR/yEiX7mgcZwUkV8QkU+LyKdE5Otz74uI/LiI/KEfy1cf0ji+XER+U0TaIvKGRYxhynF8p78PvycivyEiLzykcbzCj+FjIvK4iPy5wxhHsN3XiEgiIt+6iHFMMxYRebGIbPh78jERedOixnJkoKp37B/wBeDsmPd/GPhn/vE54CZQPaSx/HPgzf7xlwMfWtA43gV8r39cBU7m3n858H5AgK8DPnpI4zgPfA3wY8AbFvgdmTSOPwuc8o9fdoj3Y4V+TuxPAp8+jHH41yPgvwG/CnzrIX42LwZ+ZVHnP4p/d7SHPgUUWBURwf1gbgK98bssDF8BfAhAVT8NPCAiF+Z5AhFZA74B+El/no6qruc2ewXw0+rwW8BJEbl40ONQ1auq+ttAd57n3sM4fkNVb/mnvwXce0jj2FZvxYBl3Hf3wMfh8X3AfwSuznsMexjLHYU73aAr8EEReUJEXlfw/r8E/hhwCfg48P2qC1PjmTSW3wX+KoCIfC3wLOZvPJ4DXAPeKSK/IyLvEJHl3Db3AE8Gz5/yrx30OA4Cs47je3Crl0MZh4j8FRH5NPBfgNcexjhE5B7grwCL7o4x7Wfz9T5k+n4R+eMLHtOh40436C9S1a/GLZVfLyLfkHv/pcDHgLuBrwL+pfcMDmMsbwFOicjHcB7Q7zD/1UIMfDXwr1X1TwE7wBtz2xS0G5q7NzjNOA4CU49DRL4RZ9B/8LDGoarvU9UvB74F+EeHNI63Aj+oqotWrZpmLP8Tp9nyQuD/Av7Tgsd06LijDbqqXvL/rwLvA742t8l3A7/owwt/CHweF78+8LGo6qaqfreqfhXw13Ex/c/PeRhPAU+p6kf981/A/Wjy29wXPL8Xt4I56HEcBKYah4j8SeAdwCtU9cZhjSOFqn4E+DIRmVY4ap7jeBD4eRH5AvCtwL8SkW+Z8zimGov/zWz7x78KVBZwT44U7liDLiLLIrKaPgYeBj6R2+xLwEv8NheAFwCfO4yx+Ix+2rX3e4GPqOrmPMehqs8AT4rIC/xLLwF+P7fZLwN/3bNdvg7YUNXLhzCOhWOacYjI/cAvAt+lqp85xHE81+d68MyjKjDXyWWacajqs1X1AVV9AGdk/3dV/U/zHMe0YxGRu4J78rU4e7eICffI4E7WQ78AvM9/3jHwc6r6ARH5mwCq+hO4Zeu/E5GP40INP6iqi5AHnWYsfwz4aRFJcF/c71nAOMCFc37WTx6fA747N45fxTFd/hDYxa1iDnwcInIX8DiwBlgR+dvAV8x7kps0DuBNwBmcJwrQ08Uo/U0ax/+Cm2i7QBP4tiBJepDjOEhMGsu3Av+biPRw9+RVC7onRwZl6X+JEiVKHBPcsSGXEiVKlDhuKA16iRIlShwTlAa9RIkSJY4JSoNeokSJEscEpUEvUaJEiWOC0qCXKFGixDFBadBLlChR4pigNOgljiW8HvfviUjdV+J+UhakIV+ixFFBWVhU4thCRP4xUAcaON2Pf3rIQypRYqEoDXqJYwtfEv7bQAv4swegAFiixKGiDLmUOM44jWtMsorz1EuUONYoPfQSxxYi8svAzwPPBi6q6t865CGVKLFQ3MlqiyWOMUTkr+OUD39ORCLgN0TkL6rqfzvssZUosSiUHnqJEiVKHBOUMfQSJUqUOCYoDXqJEiVKHBOUBr1EiRIljglKg16iRIkSxwSlQS9RokSJY4LSoJcoUaLEMUFp0EuUKFHimOD/D8x5G2eVRAGfAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "da.where(da.values>da.attrs[\"nodatavals\"][0]).plot() # we can see that the DataArray contains missing values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll look at how weight builders handle missing values. Firstly we'll slice the `DataArray` to reduce overall size for easier visualization.\n", "\n", "This time we'll create `WSP` object using `da2WSP` method inside `raster.py`. Since our DataArray is single banded and all of its dimensions belong to the default list, we only have to ship the DataArray and the type of contiguity we need." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Slicing the dataarray\n", "da_s = da[:, 330:340, 129:139]\n", "w_queen = raster.da2WSP(da_s) # default contiguity is queen\n", "w_rook = raster.da2WSP(da_s, \"rook\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After plotting both contiguities and sliced `DataArray`, we can see that the missing values are ignored by the `da2WSP` method and only indices of non missing values are stored in `index` attribute of `WSP` object. " ] }, { "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": [ "f,ax = plt.subplots(1,3,figsize=(4*4,4), subplot_kw=dict(aspect='equal'))\n", "da_s.where(da_s.values>da_s.attrs[\"nodatavals\"][0]).plot(ax=ax[0])\n", "ax[0].set_title(\"Sliced raster\")\n", "splot.plot_spatial_weights(w_rook, data=da_s, ax=ax[1])\n", "ax[1].set_title(\"Rook contiguity\")\n", "splot.plot_spatial_weights(w_queen, data=da_s, ax=ax[2])\n", "ax[2].set_title(\"Queen contiguity\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### `higher_order` neighbors\n", "\n", "In some cases `Rook` and `Queen` contiguities don't provide sufficient neighbors when performing spatial analysis on a raster data, this is because `Rook` contiguity provides max 4 neighbors and `Queen` provides max 8.\n", "\n", "Therefore we've added `higher_order` functionality inside the builder method. We can now pass `k` value to the weight builder to obtain upto kth order neighbors. Since this can be computionally expensive we can take advantage of parallel processing using `n_jobs` argument. Now lets take a look at this functionality." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Building a test DataArray \n", "da_s = raster.testDataArray((1,5,10), rand=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we can see that builder selected all the neighbors of order less than equal to 2, with `rook` contiguity" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda/lib/python3.8/site-packages/scipy/sparse/_index.py:124: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", " self._set_arrayXarray(i, j, x)\n" ] }, { "data": { "text/plain": [ "(
, )" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1)\n", "splot.plot_spatial_weights(w_rook2, data=da_s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Few times we require the kth order neighbors for our analysis even if lower order neighbors are absent, hence we can use `include_nas` argument to do the same.\n", "\n", "We can also look in both the examples we used `n_jobs` parameter, and assigned -1 which equats to all the cores present in the computer for multithreading" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
, )" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1, include_nodata=True)\n", "splot.plot_spatial_weights(w_rook2, data=da_s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Additional resources\n", "\n", "1. [Reading and writing files using Xarray](http://xarray.pydata.org/en/stable/io.html)\n", "2. [Xarray Data Structures](http://xarray.pydata.org/en/stable/data-structures.html)\n", "3. Dataset links:\n", " - [ECMWF_ERA-40_subset.nc](https://www.unidata.ucar.edu/software/netcdf/examples/files.html)\n", " - [lux_ppp_2019.tif](https://data.humdata.org/dataset/worldpop-population-counts-for-luxembourg)" ] }, { "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 } libpysal-4.9.2/notebooks/examples.ipynb000066400000000000000000002014001452177046000202340ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Datasets for use with libpysal\n", "As of version 4.2, libpysal has refactored the `examples` package to:\n", "\n", "- reduce the size of the source installation\n", "- allow the use of remote datasets from the [Center for Spatial Data Science at the Unversity of Chicago](https://spatial.uchicago.edu/), and other remotes\n", "\n", "This notebook highlights the new functionality" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Backwards compatibility is maintained" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work. \n", "\n", "For example:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from libpysal.examples import get_path \n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/serge/Documents/p/pysal/src/subpackages/libpysal/libpysal/examples/mexico/mexicojoin.dbf'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_path(\"mexicojoin.dbf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a `builtin` dataset." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import libpysal\n", "dbf = libpysal.io.open(get_path(\"mexicojoin.dbf\"))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['POLY_ID',\n", " 'AREA',\n", " 'CODE',\n", " 'NAME',\n", " 'PERIMETER',\n", " 'ACRES',\n", " 'HECTARES',\n", " 'PCGDP1940',\n", " 'PCGDP1950',\n", " 'PCGDP1960',\n", " 'PCGDP1970',\n", " 'PCGDP1980',\n", " 'PCGDP1990',\n", " 'PCGDP2000',\n", " 'HANSON03',\n", " 'HANSON98',\n", " 'ESQUIVEL99',\n", " 'INEGI',\n", " 'INEGI2',\n", " 'MAXP',\n", " 'GR4000',\n", " 'GR5000',\n", " 'GR6000',\n", " 'GR7000',\n", " 'GR8000',\n", " 'GR9000',\n", " 'LPCGDP40',\n", " 'LPCGDP50',\n", " 'LPCGDP60',\n", " 'LPCGDP70',\n", " 'LPCGDP80',\n", " 'LPCGDP90',\n", " 'LPCGDP00',\n", " 'TEST']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dbf.header" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `available` is also available but has been updated to return a Pandas DataFrame. In addition to the builtin datasets, `available` will report on what datasets are available, either as builtin or remotes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from libpysal.examples import available" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "df = available()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(98, 3)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "98 datasets available, 27 installed, 71 remote.\n" ] } ], "source": [ "libpysal.examples.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that there are 98 total datasets available for use with PySAL. On an initial install (i.e., `examples` has not been used yet), 27 of these are builtin datasets and 71 are remote. The latter can be downloaded and installed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Downloading Remote Datasets" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameDescriptionInstalled
010740Albuquerque, New Mexico, Census 2000 Tract Dat...True
1AirBnBAirbnb rentals, socioeconomics, and crime in C...False
2AtlantaAtlanta, GA region homicide counts and ratesFalse
3BaltimoreBaltimore house sales prices and hedonicsFalse
4BostonhsgBoston housing and neighborhood dataFalse
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" ], "text/plain": [ " Name Description Installed\n", "0 10740 Albuquerque, New Mexico, Census 2000 Tract Dat... True\n", "1 AirBnB Airbnb rentals, socioeconomics, and crime in C... False\n", "2 Atlanta Atlanta, GA region homicide counts and rates False\n", "3 Baltimore Baltimore house sales prices and hedonics False\n", "4 Bostonhsg Boston housing and neighborhood data False" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The remote `AirBnB` can be installed by calling `load_example`:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading AirBnB to /home/serge/.local/share/pysal/AirBnB\n" ] } ], "source": [ "airbnb = libpysal.examples.load_example(\"AirBnB\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "98 datasets available, 28 installed, 70 remote.\n" ] } ], "source": [ "libpysal.examples.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we see that the number of remotes as declined by one and the number of installed has increased by 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trying to load an example that doesn't exist will return None and alert the user:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Example not available: dataset42\n" ] } ], "source": [ "libpysal.examples.load_example('dataset42')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting remote urls\n", "\n", "If the url, rather than the dataset, is needed this can be obtained on a remote with `get_url`. \n", "As the `Baltimore` dataset has not yet been downloaded in this example, we can grab it's url:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://geodacenter.github.io/data-and-lab//data/baltimore.zip'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "balt_url = libpysal.examples.get_url('Baltimore')\n", "balt_url" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explaining a dataset" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "taz\n", "===\n", "\n", "Dataset used for regionalization\n", "--------------------------------\n", "\n", "* taz.dbf: attribute data. (k=14)\n", "* taz.shp: Polygon shapefile. (n=4109)\n", "* taz.shx: spatial index.\n", "\n" ] } ], "source": [ "libpysal.examples.explain('taz')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading taz to /home/serge/.local/share/pysal/taz\n" ] } ], "source": [ "taz = libpysal.examples.load_example('taz')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/home/serge/.local/share/pysal/taz/taz-master/taz.dbf',\n", " '/home/serge/.local/share/pysal/taz/taz-master/taz.shp',\n", " '/home/serge/.local/share/pysal/taz/taz-master/README.md',\n", " '/home/serge/.local/share/pysal/taz/taz-master/taz.shx']" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "taz.get_file_list()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.explain('Baltimore')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading Baltimore to /home/serge/.local/share/pysal/Baltimore\n" ] } ], "source": [ "balt = libpysal.examples.load_example('Baltimore')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameDescriptionInstalled
010740Albuquerque, New Mexico, Census 2000 Tract Dat...True
1AirBnBAirbnb rentals, socioeconomics, and crime in C...True
2AtlantaAtlanta, GA region homicide counts and ratesFalse
3BaltimoreBaltimore house sales prices and hedonicsTrue
4BostonhsgBoston housing and neighborhood dataFalse
............
93tazTraffic Analysis Zones in So. CaliforniaTrue
94tokyoTokyo Mortality dataTrue
95us_incomePer-capita income for the lower 48 US states 1...True
96virginiaVirginia counties shapefileTrue
97wmatDatasets used for spatial weights testingTrue
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98 rows × 3 columns

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" ], "text/plain": [ " Name Description Installed\n", "0 10740 Albuquerque, New Mexico, Census 2000 Tract Dat... True\n", "1 AirBnB Airbnb rentals, socioeconomics, and crime in C... True\n", "2 Atlanta Atlanta, GA region homicide counts and rates False\n", "3 Baltimore Baltimore house sales prices and hedonics True\n", "4 Bostonhsg Boston housing and neighborhood data False\n", ".. ... ... ...\n", "93 taz Traffic Analysis Zones in So. California True\n", "94 tokyo Tokyo Mortality data True\n", "95 us_income Per-capita income for the lower 48 US states 1... True\n", "96 virginia Virginia counties shapefile True\n", "97 wmat Datasets used for spatial weights testing True\n", "\n", "[98 rows x 3 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.available()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Working with an example dataset\n", "\n", "`explain` will render maps for an example if available" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from libpysal.examples import explain\n", "explain('Tampa1')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading Tampa1 to /home/serge/.local/share/pysal/Tampa1\n" ] } ], "source": [ "from libpysal.examples import load_example\n", "tampa1 = load_example('Tampa1')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tampa1.installed" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shp',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.prj',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.kml',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.dbf',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.kml',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbn',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mif',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.prj',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sqlite',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbn',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mid',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.geojson',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mid',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mif',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.dbf',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shp',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.gpkg',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.xlsx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.geojson',\n", " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',\n", " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',\n", " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',\n", " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\n", " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',\n", " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/._TampaMSA']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tampa1.get_file_list()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "tampa_counties_shp = tampa1.load('tampa_counties.shp')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tampa_counties_shp" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import geopandas" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "tampa_df = geopandas.read_file(tampa1.get_path('tampa_counties.shp'))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "tampa_df.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other Remotes\n", "\n", "In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories. " ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rio_Grande_do_Sul\n", "======================\n", "\n", "Cities of the Brazilian State of Rio Grande do Sul\n", "-------------------------------------------------------\n", "\n", "* 43MUE250GC_SIR.dbf: attribute data (k=2)\n", "* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)\n", "* 43MUE250GC_SIR.shx: spatial index\n", "* 43MUE250GC_SIR.cpg: encoding file \n", "* 43MUE250GC_SIR.prj: projection information \n", "* map_RS_BR.dbf: attribute data (k=3)\n", "* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)\n", "* map_RS_BR.prj: projection information\n", "* map_RS_BR.shx: spatial index\n", "\n", "\n", "\n", "Source: Renan Xavier Cortes \n", "Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495\n", "\n", "\n" ] } ], "source": [ "libpysal.examples.explain('Rio Grande do Sul')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the `explain` function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading Rio Grande do Sul to /home/serge/.local/share/pysal/Rio_Grande_do_Sul\n" ] } ], "source": [ "rio = libpysal.examples.load_example('Rio Grande do Sul')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'AirBnB': ,\n", " 'Atlanta': ,\n", " 'Baltimore': ,\n", " 'Bostonhsg': ,\n", " 'Buenosaires': ,\n", " 'Charleston1': ,\n", " 'Charleston2': ,\n", " 'Chicago Health': ,\n", " 'Chicago commpop': ,\n", " 'Chicago parcels': ,\n", " 'Chile Labor': ,\n", " 'Chile Migration': ,\n", " 'Cincinnati': ,\n", " 'Cleveland': ,\n", " 'Columbus': ,\n", " 'Elections': ,\n", " 'Grid100': ,\n", " 'Groceries': ,\n", " 'Guerry': ,\n", " 'Health+': ,\n", " 'Health Indicators': ,\n", " 'Hickory1': ,\n", " 'Hickory2': ,\n", " 'Home Sales': ,\n", " 'Houston': ,\n", " 'Juvenile': ,\n", " 'Lansing1': ,\n", " 'Lansing2': ,\n", " 'Laozone': ,\n", " 'LasRosas': ,\n", " 'Liquor Stores': ,\n", " 'Malaria': ,\n", " 'Milwaukee1': ,\n", " 'Milwaukee2': ,\n", " 'NCOVR': ,\n", " 'Natregimes': ,\n", " 'NDVI': ,\n", " 'Nepal': ,\n", " 'NYC': ,\n", " 'NYC Earnings': ,\n", " 'NYC Education': ,\n", " 'NYC Neighborhoods': ,\n", " 'NYC Socio-Demographics': ,\n", " 'Ohiolung': ,\n", " 'Orlando1': ,\n", " 'Orlando2': ,\n", " 'Oz9799': ,\n", " 'Phoenix ACS': ,\n", " 'Pittsburgh': ,\n", " 'Police': ,\n", " 'Sacramento1': ,\n", " 'Sacramento2': ,\n", " 'SanFran Crime': ,\n", " 'Savannah1': ,\n", " 'Savannah2': ,\n", " 'Scotlip': ,\n", " 'Seattle1': ,\n", " 'Seattle2': ,\n", " 'SIDS': ,\n", " 'SIDS2': ,\n", " 'Snow': ,\n", " 'South': ,\n", " 'Spirals': ,\n", " 'StLouis': ,\n", " 'Tampa1': ,\n", " 'US SDOH': ,\n", " 'Rio Grande do Sul': ,\n", " 'nyc_bikes': ,\n", " 'taz': ,\n", " 'clearwater': ,\n", " 'newHaven': }" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.remote_datasets.datasets # a listing of all remotes" ] }, { "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": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 4 } libpysal-4.9.2/notebooks/fetch.ipynb000066400000000000000000000714061452177046000175220ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import libpysal" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "downloading dataset from https://s3.amazonaws.com/geoda/data/guerry.zip to /home/jovyan/pysal_data\n", "Extracting files....\n", "downloading dataset from https://github.com/sjsrey/rio_grande_do_sul/archive/master.zip to /home/jovyan/pysal_data\n", "Extracting files....\n", "downloading dataset from https://s3.amazonaws.com/geoda/data/ncovr.zip to /home/jovyan/pysal_data\n", "Extracting files....\n", "downloading dataset from https://github.com/sjsrey/nyc_bikes/archive/master.zip to /home/jovyan/pysal_data\n", "Extracting files....\n", "downloading dataset from https://s3.amazonaws.com/geoda/data/SacramentoMSA2.zip to /home/jovyan/pysal_data\n", "Extracting files....\n", "downloading dataset from https://s3.amazonaws.com/geoda/data/south.zip to /home/jovyan/pysal_data\n", "Extracting files....\n", "downloading dataset from https://github.com/sjsrey/taz/archive/master.zip to /home/jovyan/pysal_data\n", "Extracting files....\n" ] } ], "source": [ "libpysal.examples.fetch_all()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from libpysal.examples import nat" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "already exists, not downloading\n" ] } ], "source": [ "nat.fetch_nat()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from os import environ" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/jovyan/pysal_data'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "environ.get(\"PYSALDATA\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from libpysal.examples import south" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "already exists, not downloading\n" ] } ], "source": [ "sd = south.fetch_south()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from libpysal.examples import guerry" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "already exists, not downloading\n" ] } ], "source": [ "guerry.fetch_guerry()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "guerry\n", "======\n", "\n", "Andre-Michel Guerry data on \"moral statistics\" 1930 crime, suicide, literacy and other “moral statistics†in 1830s France.\n", "\n", "- Observations = 85\n", "- Variables = 23\n", "- Years = 1915-1934\n", "- Support = polygon\n", "\n", "Files\n", "-----\n", "Guerry.dbf Guerry_documentation.html Guerry.geojson Guerry.prj Guerry.shp Guerry.shx README.md\n", "\n", "\n", "Variables\n", "---------\n", "\n", "dept, code_de \tDepartment ID: Standard numbers for the departments \t \n", "region \tRegion of France (‘N’=’North’, ‘S’=’South’, ‘E’=’East’, ‘W’=’West’, ‘C’=’Central’). Corsica is coded as NA. \t \n", "dprtmnt \tDepartment name: Departments are named according to usage in 1830, but without accents. A factor with levels Ain Aisne Allier … Vosges Yonne \t \n", "crm_prs \tPopulation per Crime against persons. \tA2. Compte général, 1825-1830\n", "crm_prp \tPopulation per Crime against property. \tCompte général, 1825-1830\n", "litercy \tPercent of military conscripts who can read and write. \tA2 \n", "donatns \tDonations to the poor. \tA2. Bulletin des lois\n", "infants \tPopulation per illegitimate birth. \tA2. Bureau des Longitudes, 1817-1821\n", "suicids \tPopulation per suicide. \tA2. Compte général, 1827-1830\n", "maincty \tSize of principal city (‘1:Sm’, ‘2:Med’, ‘3:Lg’), used as a surrogate for population density. Large refers to the top 10, small to the bottom 10; all the rest are classed Medium. \tA1. An ordered factor with levels: 1:Sm < 2:Med < 3:Lg\n", "wealth \tPer capita tax on personal property. A ranked index based on taxes on personal and movable property per inhabitant. \tA1\n", "commerc \tCommerce and Industry, measured by the rank of the number of patents / population. \tA1\n", "clergy \tDistribution of clergy, measured by the rank of the number of Catholic priests in active service population. \tA1. Almanach officiel du clergy, 1829\n", "crim_prn \tCrimes against parents, measured by the rank of the ratio of crimes against parents to all crimes – Average for the years 1825-1830. \tA1. Compte général\n", "infntcd \tInfanticides per capita. A ranked ratio of number of infanticides to population – Average for the years 1825-1830. \tA1. Compte général\n", "dntn_cl \tDonations to the clergy. A ranked ratio of the number of bequests and donations inter vivios to population – Average for the years 1815-1824. \tA1. Bull. des lois, ordunn. d’autorisation\n", "lottery \tPer capita wager on Royal Lottery. Ranked ratio of the proceeds bet on the royal lottery to population — Average for the years 1822-1826. \tA1. Compte rendu par le ministre des finances\n", "desertn \tMilitary desertion, ratio of number of young soldiers accused of desertion to the force of the military contingent, minus the deficit produced by the insufficiency of available billets – Average of the years 1825-1827. \tA1. Compte du ministère du guerre, 1829 état V\n", "instrct \tInstruction. Ranks recorded from Guerry’s map of Instruction. Note: this is inversely related to Literacy \t \n", "Prsttts \tNumber of prostitutes registered in Paris from 1816 to 1834, classified by the department of their birth \tParent-Duchatelet (1836), De la prostitution en Paris\n", "distanc \tDistance to Paris (km). Distance of each department centroid to the centroid of the Seine (Paris) \tCalculated from department centroids\n", "area \tArea (1000 km^2). \tAngeville (1836)\n", "pop1831 \tPopulation in 1831, in 1000s \tTaken from Angeville (1836), Essai sur la Statistique de la Population français\n", "Details\n", "\n", "Note that most of the variables (e.g., Crime_pers) are scaled so that more is “betterâ€. \n", "\n", "Values for the quantitative variables displayed on Guerry’s maps were taken from Table A2 in the English translation of Guerry (1833) by Whitt and Reinking. Values for the ranked variables were taken from Table A1, with some corrections applied. The maximum is indicated by rank 1, and the minimum by rank 86. \n", "Sources\n", "\n", "Angeville, A. (1836). Essai sur la Statistique de la Population française Paris: F. Doufour. \n", "\n", "Guerry, A.-M. (1833). Essai sur la statistique morale de la France Paris: Crochard. English translation: Hugh P. Whitt and Victor W. Reinking, Lewiston, N.Y. : Edwin Mellen Press, 2002. \n", "\n", "Parent-Duchatelet, A. (1836). De la prostitution dans la ville de Paris, 3rd ed, 1857, p. 32, 36 \n", "References\n", "\n", "Dray, S. and Jombart, T. (2011). A Revisit Of Guerry’s Data: Introducing Spatial Constraints In Multivariate Analysis. The Annals of Applied Statistics, Vol. 5, No. 4, 2278-2299., DOI: 10.1214/10-AOAS356. \n", "\n", "Brunsdon, C. and Dykes, J. (2007). Geographically weighted visualization: interactive graphics for scale-varying exploratory analysis. Geographical Information Science Research Conference (GISRUK 07), NUI Maynooth, Ireland, April, 2007. \n", "\n", "Friendly, M. (2007). A.-M. Guerry’s Moral Statistics of France: Challenges for Multivariable Spatial Analysis. Statistical Science, 22, 368-399. \n", "\n", "Friendly, M. (2007). Data from A.-M. Guerry, Essay on the Moral Statistics of France (1833). \n", "See Also\n", "\n", "The Guerry package for maps of France: gfrance and related data. \n", "\n", "Prepared by Center for Spatial Data Science. Last updated July 3, 2017. Data provided “as is,†no warranties.\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "libpysal.examples.explain('guerry')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/jovyan/pysal_data/guerry/Guerry.geojson'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.get_path('Guerry.geojson')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "south\n", "=====\n", "\n", "Homicides and selected socio-economic characteristics for Southern U.S. counties.\n", "---------------------------------------------------------------------------------\n", "\n", "- Observations = 1,412\n", "- Variables = 69\n", "- Years = 1960-90s\n", "- Support = polygon\n", "\n", "Files\n", "-----\n", "south.gdb README.md south.dbf south.gpkg south.kml south.mif south.shp south.sqlite\n", "codebook.pdf south.csv south.geojson south.html south.mid south.prj south.shx south.xlsx\n", "\n", "Variables\n", "---------\n", "NAME \tcounty name\n", "STATE_NAME \tstate name\n", "STATE_FIPS \tstate fips code (character)\n", "CNTY_FIPS \tcounty fips code (character)\n", "FIPS \tcombined state and county fips code (character)\n", "STFIPS \tstate fips code (numeric)\n", "COFIPS \tcounty fips code (numeric)\n", "FIPSNO \tfips code as numeric variable\n", "SOUTH \tdummy variable for Southern counties (South = 1)\n", "HR** \thomicide rate per 100,000 (1960, 1970, 1980, 1990)\n", "HC** \thomicide count, three year average centered on 1960, 1970, 1980, 1990\n", "PO** \tcounty population, 1960, 1970, 1980, 1990\n", "RD** \tresource deprivation 1960, 1970, 1980, 1990 (principal component, see Codebook for details)\n", "PS** \tpopulation structure 1960, 1970, 1980, 1990 (principal component, see Codebook for details)\n", "UE** \tunemployment rate 1960, 1970, 1980, 1990\n", "DV** \tdivorce rate 1960, 1970, 1980, 1990 (% males over 14 divorced)\n", "MA** \tmedian age 1960, 1970, 1980, 1990\n", "POL** \tlog of population 1960, 1970, 1980, 1990\n", "DNL** \tlog of population density 1960, 1970, 1980, 1990\n", "MFIL** \tlog of median family income 1960, 1970, 1980, 1990\n", "FP** \t% families below poverty 1960, 1970, 1980, 1990 (see Codebook for details)\n", "BLK** \t% black 1960, 1970, 1980, 1990\n", "GI** \tGini index of family income inequality 1960, 1970, 1980, 1990\n", "FH** \t% female headed households 1960, 1970, 1980, 1990\n", "\n" ] } ], "source": [ "libpysal.examples.explain('south')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/jovyan/pysal_data/south/south.shp'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.get_path('south.shp')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "missing.shp not found.\n" ] } ], "source": [ "libpysal.examples.get_path('missing.shp')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "pth = libpysal.examples.get_path('south.shp')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/jovyan/pysal_data/south/south.shp'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pth" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "df = gpd.read_file(pth)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NAMESTATE_NAMESTATE_FIPSCNTY_FIPSFIPSSTFIPSCOFIPSFIPSNOSOUTHHR60...BLK90GI59GI69GI79GI89FH60FH70FH80FH90geometry
0HancockWest Virginia540295402954295402911.682864...2.5572620.2236450.2953770.3322510.3639349.9812977.89.78579712.604552POLYGON ((-80.6280517578125 40.39815902709961,...
1BrookeWest Virginia54009540095495400914.607233...0.7483700.2204070.3184530.3141650.35056910.9293378.010.21499011.242293POLYGON ((-80.52625274658203 40.16244888305664...
2OhioWest Virginia540695406954695406910.974132...3.3103340.2723980.3584540.3769630.39053415.62164312.914.71668117.574021POLYGON ((-80.52516937255859 40.02275085449219...
3MarshallWest Virginia540515405154515405110.876248...0.5460970.2276470.3195800.3209530.37734611.9628348.88.80325313.564159POLYGON ((-80.52446746826172 39.72112655639648...
4New CastleDelaware10003100031031000314.228385...16.4802940.2561060.3296780.3658300.33270312.03571410.715.16948016.380903POLYGON ((-75.77269744873047 39.38300704956055...
\n", "

5 rows × 70 columns

\n", "
" ], "text/plain": [ " NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS STFIPS COFIPS \\\n", "0 Hancock West Virginia 54 029 54029 54 29 \n", "1 Brooke West Virginia 54 009 54009 54 9 \n", "2 Ohio West Virginia 54 069 54069 54 69 \n", "3 Marshall West Virginia 54 051 54051 54 51 \n", "4 New Castle Delaware 10 003 10003 10 3 \n", "\n", " FIPSNO SOUTH HR60 ... BLK90 GI59 GI69 GI79 \\\n", "0 54029 1 1.682864 ... 2.557262 0.223645 0.295377 0.332251 \n", "1 54009 1 4.607233 ... 0.748370 0.220407 0.318453 0.314165 \n", "2 54069 1 0.974132 ... 3.310334 0.272398 0.358454 0.376963 \n", "3 54051 1 0.876248 ... 0.546097 0.227647 0.319580 0.320953 \n", "4 10003 1 4.228385 ... 16.480294 0.256106 0.329678 0.365830 \n", "\n", " GI89 FH60 FH70 FH80 FH90 \\\n", "0 0.363934 9.981297 7.8 9.785797 12.604552 \n", "1 0.350569 10.929337 8.0 10.214990 11.242293 \n", "2 0.390534 15.621643 12.9 14.716681 17.574021 \n", "3 0.377346 11.962834 8.8 8.803253 13.564159 \n", "4 0.332703 12.035714 10.7 15.169480 16.380903 \n", "\n", " geometry \n", "0 POLYGON ((-80.6280517578125 40.39815902709961,... \n", "1 POLYGON ((-80.52625274658203 40.16244888305664... \n", "2 POLYGON ((-80.52516937255859 40.02275085449219... \n", "3 POLYGON ((-80.52446746826172 39.72112655639648... \n", "4 POLYGON ((-75.77269744873047 39.38300704956055... \n", "\n", "[5 rows x 70 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from libpysal.examples.sacramento2 import fetch_sacramento2" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "already exists, not downloading\n" ] } ], "source": [ "fetch_sacramento2()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "sacramento2\n", "===========\n", "\n", "2000 Census Tract Data for Sacramento MSA\n", "-----------------------------------------\n", "\n", "- Observations = 83\n", "- Variables = 66\n", "- Years = 1998, 2001\n", "- Support = polygon\n", "\n", "Files\n", "-----\n", " SacramentoMSA2.gdb SacramentoMSA2.kml SacramentoMSA2.shp\n", " README.md SacramentoMSA2.mid SacramentoMSA2.shx\n", " SacramentoMSA2.csv SacramentoMSA2.mif SacramentoMSA2.sqlite\n", " SacramentoMSA2.dbf SacramentoMSA2.prj SacramentoMSA2.xlsx\n", " SacramentoMSA2.geojson SacramentoMSA2.sbn 'Variable Info for Zip Code File.pdf'\n", " SacramentoMSA2.gpkg SacramentoMSA2.sbx\n", "\n", "Variables\n", "---------\n", "ZIP ZIP code\n", "PO_NAME \tName of ZIP code area\n", "STATE \tSTATE\n", "MSA \tMSA\n", "CBSA_CODE \tCBSA code\n", "MAN98 \t1998 total manufacturing establishments (MSA)\n", "MAN98_12 \t1998 total manufacturing establishments, 1-9 employees (MSA)\n", "MAN98_39 \t1998 total manufacturing establishments 10+ employees (MSA)\n", "MAN01 \t2001 total manufacturing establishments (MSA)\n", "MAN01_12 \t2001 total manufacturing establishments, 1-9 employees (MSA)\n", "MAN01_39 \t2001 total manufacturing establishments, 10+ employees (MSA)\n", "MAN98US \t1998 total manufacturing establishments (US)\n", "MAN98US12 \t1998 total manufacturing establishments, 1-9 employees (US)\n", "MAN98US39 \t1998 total manufacturing establishments 10+ employees (US)\n", "MAN01US \t2001 total manufacturing establishments (US)\n", "MAN01US_12 \t2001 total manufacturing establishments, 1-9 employees (US)\n", "MAN01US_39 \t2001 total manufacturing establishments, 10+ employees (US)\n", "OFF98 \t1998 total office establishments (MSA)\n", "OFF98_12 \t1998 total office establishments, 1-9 employees (MSA)\n", "OFF98_39 \t1998 total office establishments, 10+ employees (MSA)\n", "OFF01 \t2001 total office establishments (MSA)\n", "OFF01_12 \t2001 total office establishments, 1-9 employees (MSA)\n", "OFF01_39 \t2001 total office establishments, 10+ employees (MSA)\n", "OFF98US \t1998 total office establishments (US)\n", "OFF98US12 \t1998 total office establishments, 1-9 employees (US)\n", "OFF98US39 \t1998 total office establishments, 10+ employees (US)\n", "OFF01US \t2001 total office establishments (US)\n", "OFFUS01_12 \t2001 total office establishments, 1-9 employees (US)\n", "OFFUS01_39 \t2001 total office establishments, 10+ employees (US)\n", "INFO98 \t1998 total information establishments (MSA)\n", "INFO98_12 \t1998 total information establishments, 1-9 employees (MSA)\n", "INFO98_39 \t1998 total information establishments, 10+ employees (MSA)\n", "INFO01 \t2001 total information establishments (MSA)\n", "INFO01_12 \t2001 total information establishments, 1-9 employees (MSA)\n", "INFO01_39 \t2001 total information establishments, 10+ employees (MSA)\n", "INFO98US \t1998 total information establishments (US)\n", "INFO98US12 \t1998 total information establishments, 1-9 employees (US)\n", "INFO98US39 \t1998 total information establishments, 10+ employees (US)\n", "INFO01US \t2001 total information establishments (US)\n", "INFO01US_1 \t2001 total information establishments, 1-9 employees (US)\n", "INFO01US_3 \t2001 total information establishments, 10+ employees (US)\n", "INDEX \tIndex\n", "NUMSEC \tNumber of sectors represented in ZIP code\n", "EST98 \tTotal establishments in ZIP code, 1998\n", "EST01 \tTotal establishments in ZIP code, 2001\n", "PCTNGE \tNational growth effect, percent (N)\n", "PCTIME \tIndustry mix effect, percent (M)\n", "PCTCSE \tCompetitive shift effect, percent (S)\n", "PCTGRO \tPercent growth establishments, 1998-2001 (R)\n", "ID \tUnique ZIP code ID for ID variables in weights matrix creation window\n", "\n", "Source: US Census Bureau, 2000 Census (Summary File 3). Extracted from http://factfinder.census.gov in April 2004.\n", "\n" ] } ], "source": [ "libpysal.examples.explain('sacramento2')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/jovyan/libpysal/examples/10740/10740.shx'" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.get_path(\"10740.shx\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "from libpysal.examples.nyc_bikes import fetch_bikes" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "already exists, not downloading\n" ] } ], "source": [ "fetch_bikes()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/jovyan/pysal_data/nyc_bikes/nyct2010.shp'" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.get_path('nyct2010.shp')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "already exists, not downloading\n" ] } ], "source": [ "from libpysal.examples.rio_grande_do_sul import fetch_rio\n", "fetch_rio()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/jovyan/pysal_data/rio_grande_do_sul/map_RS_BR.shp'" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.get_path('map_RS_BR.shp')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from libpysal.examples.taz import fetch_taz\n", "fetch_taz()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "libpysal.examples.get_path('taz.dbf')" ] }, { "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } libpysal-4.9.2/notebooks/io.ipynb000066400000000000000000000134271452177046000170370ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "import os\n", "\n", "sys.path.append(os.path.abspath('..'))\n", "import libpysal" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "w = libpysal.weights.lat2W(5,5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "25" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "12.8" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.pct_nonzero" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[5, 1]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.neighbors[0]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 10, 6]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.neighbors[5]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['georgia',\n", " '__pycache__',\n", " 'tests',\n", " 'newHaven',\n", " 'Polygon_Holes',\n", " 'nat',\n", " 'Polygon',\n", " '10740',\n", " 'berlin',\n", " 'rio_grande_do_sul',\n", " 'sids2',\n", " 'sacramento2',\n", " 'burkitt',\n", " 'arcgis',\n", " 'calemp',\n", " 'stl',\n", " 'virginia',\n", " 'geodanet',\n", " 'desmith',\n", " 'book',\n", " 'nyc_bikes',\n", " 'Line',\n", " 'south',\n", " 'snow_maps',\n", " 'Point',\n", " 'street_net_pts',\n", " 'guerry',\n", " '__pycache__',\n", " 'baltim',\n", " 'networks',\n", " 'us_income',\n", " 'taz',\n", " 'columbus',\n", " 'tokyo',\n", " 'mexico',\n", " '__pycache__',\n", " 'chicago',\n", " 'wmat',\n", " 'juvenile',\n", " 'clearwater']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.available()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': 'baltim',\n", " 'description': 'Baltimore house sales prices and hedonics 1978',\n", " 'explanation': ['* baltim.dbf: attribute data. (k=17)',\n", " '* baltim.shp: Point shapefile. (n=211)',\n", " '* baltim.shx: spatial index.',\n", " '* baltim.tri.k12.kwt: kernel weights using a triangular kernel with 12 nearest neighbors in KWT format.',\n", " '* baltim_k4.gwt: nearest neighbor weights (4nn) in GWT format.',\n", " '* baltim_q.gal: queen contiguity weights in GAL format.',\n", " '* baltimore.geojson: spatial weights in geojson format.']}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.explain('baltim')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "pth = libpysal.examples.get_path('baltim.shp')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/examples/baltim/baltim.shp'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pth" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "shp_file = libpysal.io.open(pth)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "shapes = [shp for shp in shp_file]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(907.0, 534.0)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shapes[0]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "w = libpysal.io.open(libpysal.examples.get_path('baltim_q.gal')).read()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "211" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 } libpysal-4.9.2/notebooks/voronoi.ipynb000066400000000000000000004251721452177046000201270ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Voronoi Polygons for 2-D Point Sets\n", "\n", "Author: Serge Rey (http://github.com/sjsrey)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic Usage" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath('..'))\n", "import libpysal" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "from libpysal.cg.voronoi import voronoi, voronoi_frames" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "regions, vertices = voronoi(points)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regions" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 4.21783296, 4.08408578],\n", " [ 7.51956025, 3.51807539],\n", " [ 9.4642193 , 19.3994576 ],\n", " [ 14.98210684, -10.63503022],\n", " [ -9.22691341, -4.58994414],\n", " [ 14.98210684, -10.63503022],\n", " [ 1.78491801, 19.89803294],\n", " [ 9.4642193 , 19.3994576 ],\n", " [ 1.78491801, 19.89803294],\n", " [ -9.22691341, -4.58994414]])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vertices" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "region_df, point_df = voronoi_frames(points)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "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": [ "fig, ax = plt.subplots()\n", "region_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n", "point_df.plot(ax=ax, color='red')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Larger Problem" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "n_points = 200\n", "np.random.seed(12345)\n", "points = np.random.random((n_points,2))*10 + 10\n", "results = voronoi(points)\n", "mins = points.min(axis=0)\n", "maxs = points.max(axis=0)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "regions, vertices = voronoi(points)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "regions_df, points_df = voronoi_frames(points)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "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": [ "fig, ax = plt.subplots()\n", "points_df.plot(ax=ax, color='red')" ] }, { "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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "regions_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n", "points_df.plot(ax=ax, color='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Trimming" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "points = np.array(points)\n", "maxs = points.max(axis=0)\n", "mins = points.min(axis=0)\n", "xr = maxs[0] - mins[0]\n", "yr = maxs[1] - mins[1]\n", "buff = 0.05\n", "r = max(yr, xr) * buff\n", "minx = mins[0] - r\n", "miny = mins[1] - r\n", "maxx = maxs[0] + r\n", "maxy = maxs[1] + r" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "regions_df.plot(ax=ax, edgecolor='black', facecolor='blue', alpha=0.2 )\n", "points_df.plot(ax=ax, color='red')\n", "plt.xlim(minx, maxx)\n", "plt.ylim(miny, maxy)\n", "plt.title(\"buffer: %f, n: %d\"%(r,n_points))\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Voronoi Weights" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "from libpysal.weights.contiguity import Voronoi as Vornoi_weights" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "w = Vornoi_weights(points)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "200" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.915" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.pct_nonzero" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(3, 3),\n", " (4, 28),\n", " (5, 52),\n", " (6, 65),\n", " (7, 34),\n", " (8, 10),\n", " (9, 5),\n", " (10, 2),\n", " (11, 0),\n", " (12, 1)]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.histogram" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "idx = [i for i in range(w.n) if w.cardinalities[i]==12]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[16.50851787, 13.12932895]])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "points[idx]" ] }, { "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 } libpysal-4.9.2/notebooks/weights.ipynb000066400000000000000000064060031452177046000201040ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "sys.path.append(os.path.abspath('..'))\n", "import libpysal" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['georgia',\n", " '__pycache__',\n", " 'tests',\n", " 'newHaven',\n", " 'Polygon_Holes',\n", " 'nat',\n", " 'Polygon',\n", " '10740',\n", " 'berlin',\n", " 'rio_grande_do_sul',\n", " 'sids2',\n", " 'sacramento2',\n", " 'burkitt',\n", " 'arcgis',\n", " 'calemp',\n", " 'stl',\n", " 'virginia',\n", " 'geodanet',\n", " 'desmith',\n", " 'book',\n", " 'nyc_bikes',\n", " 'Line',\n", " 'south',\n", " 'snow_maps',\n", " 'Point',\n", " 'street_net_pts',\n", " 'guerry',\n", " '__pycache__',\n", " 'baltim',\n", " 'networks',\n", " 'us_income',\n", " 'taz',\n", " 'columbus',\n", " 'tokyo',\n", " 'mexico',\n", " '__pycache__',\n", " 'chicago',\n", " 'wmat',\n", " 'juvenile',\n", " 'clearwater']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.available()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': 'mexico',\n", " 'description': 'Decennial per capita incomes of Mexican states 1940-2000',\n", " 'explanation': ['* mexico.csv: attribute data. (n=32, k=13)',\n", " '* mexico.gal: spatial weights in GAL format.',\n", " '* mexicojoin.shp: Polygon shapefile. (n=32)',\n", " 'Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) \"Interregional inequality dynamics in Mexico.\" Spatial Economic Analysis, 5: 277-298.']}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libpysal.examples.explain('mexico')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weights from GeoDataFrames" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import geopandas\n", "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n", "gdf = geopandas.read_file(pth)\n", "\n", "from libpysal.weights import Queen, Rook, KNN" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = gdf.plot()\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Contiguity Weights\n", "\n", "The first set of spatial weights we illustrate use notions of contiguity to define neighboring observations. **Rook** neighbors are those states that share an edge on their respective borders:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "w_rook = Rook.from_dataframe(gdf)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "32" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_rook.n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "12.6953125" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_rook.pct_nonzero" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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7hvPnz4sTJkyos8Yrdc3ff/9tPnToUKbJZOrP8/y5mo6zcOHCv7p27drn2WefZRnGMi+oyWTC0qVLRYPBwAJowvN8FT5DBYXHj7LjL0Kr1WaGh4f/lJube/3ChQvBycnJaNGihYrjyulH7+0N3LwJXL0K0qED2rZty6WlpblnZWX9X1hYWIRWq72r0+mCABw9fvx4v0OHDh3WarUZFcwrhoWFbc7Kyhqi1+udWrRoUc6EVWBlBfTpA7i7A889B4wcSX9u1oy6hk6eBC5fphXIhw8DLi5AWhq9MVhV7t26cOECAEj+/v5PbDzIy8uLcXZ2to2Li3sjKiqq16FDh2K1Wm0FxRnlIwhCDxsbmxmjRo2yYlnL73Esy8LPz4/5+++/oVKpmoeFhRXqdLpYrVZb3ctQUHhkKIa/GFqttjAkJORsWFjY8qysrHYXLlzwzsrKImazmW3YsOGDLlcMQ10q+/YB7dqBcXSEr68v5+joaB8fHz9ep9MVyrIsN2rUaFDHjh19kpOTe/Xq1WtVJfOawsLC8lQq1cAOHTrUqC8wALrTX7CApp4OHkx3+kFBQL9+QI8eQP/+1NCnpgJHjwJ79wKCQIPGDANkZACOjkDRzS4uLg6HDx9GkyZN5NatWz+xhh8A3Nzc0K1bN87GxqZlQkLCiLCwsN+1Wm22pecfPnz4LT8/v2BfX99q33itra0REBCABg0a+CYnJw81Go3XtVrtxeqOo6DwqFBcPRUgCAIHQMswTHeVSvVaixYtvEJDQ61LPAGsW0eN6Mcf3/enZ2Zm4rfffivIzs7+EcDotm3bul68eFFUqVQ/m0ym7XPmzNlbwXzvAPjBxcUlb8SIEXbu7u41W3hiIjB1Kl2bWl35sZJEnwQuXKDG/tgxICoK6NQJ8PfH0bw8JGRlyf0mTyZuNV3PY+DIkSPmI0eOGFmWDdfr9X8CuMjzfERFxwuC0IzjuOgJEyZYu7i41GruxMRE/Prrr3pZllebTKYZNe3YpqDwMFEMvwUIgmCjVqu3urq6al977TWNSqWibxiNwCef0DTMPn3uH5+fn48lS5YAQH+VSrWIYZiOfn5+5PLly39PmzatXCkHQRDe4jhurizLzn379rUKCgqq+YKTk6lv/8ABGgyuDqJI4wNXryI+IQHypk1yq6QkgtmzAZWKxgp8fGhF8xPcUCYnJwfXr19HfHy8/uLFi9YAPgawDcDN4tk/giDYEEJ+l2V56OzZs2Gpb78y8vPzsW/fvsK4uLgbRqOxfV1kGyko1CWKq8cCtFqtOSws7Hej0aiNj4939/f3V7EsS7NtGjUCNm8GunQBipq4q9VqmEwmY3JycrDJZOrBsmy/nJwcd5PJJIWFhS3XarUlkuIFQWipVqt3h4aGOl2+fJmkp6frn3nmGVWNF2xnR337eXm0MKw6MAzQuDHg5wdTq1bYYDSS3hs2ADY2QH4+kJUFvPMOTSUlBPj7bxovcHOrMl7wKLGysoK7uzvatWvHNW7cGAUFBT3y8/M/ZFm2WVhY2B6dTgedTkfUavUvTZs2HTxixAjOwaFuZH/UajV8fX1V58+ftzWbzU3DwsL2KD5/hSeJJ9pv+yTB87xkMBiGpKamnj927NgDw92lC91Vb91a4vh+/fqpCSEeANyMRuPzBoPhhNFo9AbwTDnDd/P29jb5+vrCy8urMDMzU2MyVSsztCxffw0UFFCXTw1xdXWF2WyGQaWi1zlqFDBuHHDlCrBhA40paDQ0XjBwIE0v/eUXYPly4NAhoLbXUEe0atUKY8eObTBlyhRrZ2fnUQDMHMdFEEL+q9Foho0aNcq60iyuGkAIwZtvvqlhGGYcgJZ1OriCQi1RDH814HlebzAYXg0PD2dWrVqVn5FRlKgzaxYtrrp8ucTx1tbWEoBYtVp9wmAwhABw4Xn+aOlx1Wr1ABsbGw4AnJ2dVSqV6u7KlSvza9IY/j4MA3TtCnz1FVUWrdEQDNRqtZyZmVn2TXd3+jQxdiwwbx41/nPm0NdYlt543n2X1h98/z0tOrtw4bFKUVhbW2PChAm2jRo1ksxmc3CHDh3eHjt2rEZdVSykFvO5ubmZWZYd/1AmUFCoIYqrp5potdosnU63Kj8/3yopKamjv7+/imFZmjkzYgSt7C1KB2zSpIna1tZWvHv3rg3LsqNYls3u3bv3meLjCYLAsCw7LTs7uxHDMFxkZCQnSVIHs9lccPTo0ZDk5GRz+/btmRrl9zduTOMPX38N9O5NbwbV5NSpU2Ljxo0ZV1fXyg8khGY6ubvTG86rr9LMojZtqDvs2DFgyxbgxg1g0yZg504gPp7eKFQ192pVl7i4OJw+fZpMmTIFAQEBnEajeajzeXt7W50+fTqwqMDP4iwjBYWHibLjrwE8z9+RZXlmcnJy+IoVK4wpKSk0dXLqVODTT+8f17hxYzz77LPswIEDNT179mxHCJlRzljS9OnTg/Lz8/vqdLoDKpXqEoCkGTNm8ABw5coVrlYBeA8PIC4O+H//r9qnZmdno7CwkLWqqe+eYYAWLahxX7iQxgWmTQMmTaKuosWL6VPAkiXAzz/XbI5qIEkSdu3aJWq1WtnO7qHI+5TB2dkZAQEBhOO4dx7JhAoKFqAY/hrC87zeaDS+mJeXt3fVqlWmwsJCuuMnBPjzzxLH+vv7o0ePHjCbzZ6CIDhXMN7xzz77bMCMGTP8eJ6/55tppFar4/bv329Bc+AKYBhg2TKax3/2bLVOzc3NhSzLpFwdoZqiVtPYQ8eOwLBhVGrCYKBusvPngaFD6ZPAQyAsLAwsyzLPPPPMI01HCgwMtCKEfPzll1+GPsp5FRQqQjH8tYDnecP06dNDWZZdtX79+jxoNNTV88svQHbJp3qWZeHp6akH0K8a46cYjcbAc+fOZV69erXmC/XwoPn9n35KM30sxMvLCxzHSVUql1aXW7doFXHXrkBEBP1+8WKqgOrnR2sKhgwB3niDppeKtZfAKSgowMmTJ+WhQ4eS6lTm1gVubm7o06ePjSiK2wRB8HykkysolINi+CtAEITGlsrxGo3GvwghNNPH35/m0K8qW6gbFBRkp1ar1y5YsCB9wYIFlWr43KOoQfubGzduLFy9enXuiRMnINbEEL7yCnWxREdX6zRXV1dy5cqVutVkPnuWxgMkicpN38PFBZg/n0pNzJ1L4xPHj1NRuqgo2gLTbK7RlFu2bJGaNm0q+fj41M01VJPu3e+Xb1Tdx0FB4SGjGP5yEAShCYBEtVq9xIJjObVa/Z/AwED7+y8OG0bz3Y8cKXGsr68vM3XqVOvx48c7y7Lsa+l65syZs0cUReekpKSx+/btw61bNVQAHjiQGv9Sqaf3kCQJW7ZswapVq+SNGzciMTERHTt2JJcuXao714jZDCQkUFfP6NHARx/Rz6o0nTrRXX/PnsD//kefBP7v/6hrKC+PCuVZGPu4efMmEhMTmRdeeOGxCc0RQhAcHGxWq9XLFH1/hceNYvjLRw0AkiRNKNJnr4wmZrPZp4Qf3N6eVvIeOVLGtaJSqeDi4gKWZdXz5s0bbemCeJ7X8zy/i+O42fv27cs312Tna2VFu3txXLnGdvv27fKVK1fg4+MDa2trcd26dfjzzz9lZ2fnuqs8PXSIFoLZ2tJCszVrgNWrKz8nIIB+pvv3AwcPAtevAx98QLWStm+n/1bCtm3bxKCgIMnR0bHOLqMmBAUFcSqVqgPLsusFQai+VKqCQh2hGP5yIIREOjo6SpIkWQOwruxYnuevcxy3NzY2tuQbzz1Hjf7u3WXOsbKywrBhw6ytrKw+ru7azGbzV1lZWeG7du3S1yjbp3t3qi80YQJ1tRQjMTFR0mq18nPPPUdCQ0PZTz/9FK+88goZO3Zs3f2dHDsGvPzyg5/feQcorEYPG2dn6k6LjgYGDABOnKB1AklJ9Jpu3ixxeGRkJMxmM9O7d+/H/rdeVEdg1apVq+Ecx8VWFOhXUHjYKFo95TB37tz/yrI8iWGY/NmzZ9tXcex4WZZ/sbW1hbNzyf/HqsJCaFeuxPExY5Dr5lbivfz8fBQVgLXgef5addYnCIKtWq0+26FDB+++ffuqASA1NRWenp6Wac0YDNRIzp9PA6pFXLlyBdu3b8fkyZNhWyQ/UecsXkwrgJs0efDawoXUpdO7d83HTUoCPv+cFoz9/jug18O4dCmWLVsmDxs2jPj6WuxZeyTs27fPcPbs2WMmk+klnufTHvd6FOoXiuGvAEEQrACYAdhUpLAoCMLHNjY2XxYWFtpotVqUV2TlcOYM3PfsQcynn0IulQ8fEREBAK/yPL+xButzUqvVB8xmcydJku77rgcOHAh7e3v4+Pig0opUUaT+8h9+oNk1RaxevVp0dnbGiBEj6t4ffvfuA+XQ4jeo776jQdx7jedry969QEwM9plMcvf//pc4bt4M0rXrE6UlZDKZsGnTJvPNmze/mzFjxpTHvR6F+oVi+KtAEASZ47gslmULAEQYDIZtAG5YWVnNMBgMLwLARx99BEII7Ozsyt9xf/oprWB9660SLy9evFhfUFDwfzUx/EVrYwFYMwyzRJKkiQB4AAJAbwCBgYGVD7BmDc2fX7r0/ks5OTn47rvvMGbMGHh7e9dkWRVz+DB1fS1eXPL13FwqKte2Le0HUEcsW7xYfNnKivV68UX6hJOaSl1Nt27RzKHHQF5eHqKioqDVapGVlYXVq1dnmc3mF3iej3osC1KolyiSDVVw7NixAZIktezatWuDNm3a+JtMpgEMw7zWtm3bto0aNTLfvXuXO378OI4fP467d+/q/f39yzby6N2bavYHB5eQSXZycuJiY2OfCw8P36PValOquzatVitrtVpTSEjIHp1O9x+e5w/pdDpbAD2HDx9e+Y4foJkzDEODq889B4DGH8xmM/bs2QNXV1fZ1dW17jJQ5s6lRW7NS2U0WllRnZ/kZOCZ8jTsasaV2FhibNFCbtGpE8GIEdSdlJ1Nfw/Nm9PYQmYm7Vn8CIiLi8PatWvlrKwsREZGyh4eHiQ2NtYawPiIiIjdWq327iNZiEK957EHvJ50jEbjIIZhFh87dgyyLItvvPGG/eTJk+1feOEF6yFDhliPGTMGzZo1k0NCQhAQEFC+L8HGhroz3n23REDV19cXWq3WgeO4yIULF66tTXEPz/OFAEAIGQrAMh89IVR1c98+oFiBWJ8+fRAUFIRDhw6hRtlD5WE20w5fHTqU//4771TdOKaa+Pr6kpiYGPqDtTXNDmrbluoFDRxIXUsjRtAsoy+/pJ3IHiJ//fWX+Mwzz+DDDz8kQUFB2L59Oxo3biwzDKMCcHbRokWnihq+Kyg8VBTDXwU8z2fOmDFjGoDWYWFh+bt37zakFxkIQghatmyJ1157jWi1WrRv377i3fG9vPVibg5CCHr27Im33367QefOnUezLBsrCEKt/CuyLH/s6uqaa/EJzs5UWfPnn4GUBw8dvXr1giiK0vLly+U6Mf43bgDPPkvnK4/u3WmmTlTdeTw6d+6MrKwsprB01pBaTW/Gy5ZRV1dmJk29PXaMFt4JgsU1ApZSUFCAzMxMtkuXLoQQgt69ezNDhw5FYmIiee655+QZM2agRYsW/gCm1unECgrloBh+C+F5Ps5sNvv/888/i3/44YfCahdREUIrejMygHPnSrzl5uaG/v37q9u0aUMADKjlUnWpqakNUlNTLT+jQQPq8vn4QXapWq3GBx98wGZnZ9eNq+fEiar1+b28gLVr62Q6gF6DRqMRr1+/XvFBhNB59++naqK2tjRDKDmZuoROnKjVGnJycrB582asWbMGHh4eYvFmLzY2NhBFEeHh4SQnJwfNmzdXq9XqQbWaUEHBAhQffzXQarXZwcHB4eHh4TLHcb1btWpVvcyXBg1oNs3+/dTvX0ozRhRF1fXr15seOnToJ61WWyOBGq1Wazpy5MgbJ06ccAwMDITKUsnjnj1pO8V//gFatwYAyLKMY8eOya1btyYNGjSoyXIesHgxbfZemWRChw70iaBhwzrLwElISGCys7PFtm3bWrbJCQigFcMMA+Tk0KD8rFk0VbR5c9p60kIiIyOxdetWODo6Ss2bNye9evViirvgXFxc0Lt3bxT1ZGYyMzPNt24zfrQKAAAgAElEQVTdOhAREbFN6dil8DBRdvw1QJblsFOnTnHx8fHIz8+HJFVDyub556lR2batzFt+fn4wGAwBADYVNXuvEZIkDQaA9evXF1jcyUutptWxixYBRcVoDMPc/6oVWVk0qNq3b+XH2dnRHf/GGiU5lUtAQADi4+OrfwEaDU09bd+eVgnHxNB/LSQsLAxHjx7FSy+9hFGjRjHPPvss3ErVcgD0M/b09GRv3rwpdurUibOxsRkK4Nlqr1dBoRoohr8G8Dx/nGGY17ds2XJnyZIlWLx4ceGVK1csO5kQquB59GiJgCpAjcDYsWPRsGHD5ziOy5g/f/6aopTN6q7vIgA2IyNj73/+8x/D2rVrc8LDwxEZGSlXmr7r60szb1JSaCN5ACaTidx7ajAajSgoKKjucoDTp2nNgCXNZCZMoGJtdUTbtm1hMBhIuV3EqiI3l+7+9+yhbqrYWKokOm5clacaDAZ4eXmJjRo1qlJUz93dHenp6cTNzQ1F0h91nEeroFASxdVTQ4KDg8+HhYUtAzDPbDYfjI2NHU0IUefk5MDFxaVMMZcsy8jKyoK1tTWIoyMtZvr7b6qTX2xH7ezsjMDAQHVaWhp7586djgCWaLXaajev1Wq1cq9evbaEh4evzs7OPnPr1q2rV69eDXZwcICHh0fFJzZrRusOrlwBtFrEx8dLFy5ckKOjo7F3715y7NgxqNVquLi4gOMsfCi5eJG6kVpa0Hq2SROA5+nxddAHlxCCf/75R7Szs2M8PauRNBUbS7N9CgupxMSYMdTgu7jQfgLt2lG3XQWZSAUFBYiKimJOnjyJY8eOoU2bNhVmWp0/fx4ARH9/f+bUqVOGnJycmJCQkPDqX62CgmUohr8WaLXae7n0iREREWdu374tx8TE2BiNRnsfH58SO/WkpCSsWLECTZs2hZOTE82h//Zb2qqwnGKivLw8+caNGzclScrX6XR3tFqt5Zk6JdeYr9Vqo0NCQsJ0Ot0XMTExqNJ/HBxMNW+aNoV/UBCJj4/HjRs3yIQJE+Dm5iYeOXKE6HQ6olKp0KS49EJ5yDLdJQ8bBpTj6iiX6Gh6syiqLagtycnJTFJSkhgQEGDZE25SEm2l2a4dfQJJSwN0Oipt7e5Ob9aCQIPh779f7pOMm5sbnJyc4O3tjdTUVNHJyYlxd3cvd7pjx44hPz+fadSoEVq2bGl15syZnhEREfu1Wu3tWl24gkIFKK6eOmL27Nl/fv755+ONRmOvEydO5ERFRUm5ubkQBAG//PILfvzxRwCAvX0x6Z8ff6S5/Tk5Zcbr2rUr6+Pj4wngewA3v/zyyym18fsX4adSqQz30hvNZnP5bohGjWgh1bBh4PLyYGNjQ+zs7GRXV1d06dKF/eSTT4hWq5VPnz5ddXDj9m2aMtm+fYWHZGVlYdWqVeKKFSvEnJwc4LPPqGsoP79GF2k0GkvEXbp27YqEhAS2yliMLAMLFtD17tv3QEzOxwf444+Sx371FZW3Pn+eykaXgmEYdOzYEc888wxEUaw0DhQcHAyNRoOff/4Zzs7OGDVqFMtx3CFFxE3hYaHs+OsYrVZbEBYWtiUxMfH5qKgoJ1mWb+Tn59+QJMnDzc0NrVu3xv0MGSsralS+/PJB28YiCCFITU1lExISSMeOHQnLsr30ev1LYWFhP9Q040Or1aYePXrU58aNG21NJpPqp59+wuHDh1Fu9o+nJ0xXr2Lb4cNigtlMRo8eTYr3qXV1dSUnTpzAnTt34OvrW7Hz/uBBICSkUsO/ZMkS5OTkMM2bN5f//PNPwqjVxHX1arAmE0inTtW6xh07dsh//PEHOXz4MKKionDs2DHp+vXrkl6vZ3x8fEreeItjMtFag3nzaDFZixYP3uvTh+74hw9/8Boh9AnmwAGa9VNq53/z5k3s3bsX586dk3Jzc9kBAwZUWEnt6OgIg8GApKQk+Pv7w8vLC8nJyeb09PTckJCQyGp9AAoKFqDs+B8CPM9fNxqNAZIkefI832LGjBmdAfRKSUnBqlWrkJZWTIyxf3+aIlhOs/Fu3boRADh37hwCAgKsjEajP4BaiacZjcZ3b9++PS88PHw7ISQFAP744w+D0ViyrW98fDyWOTrKgUeOMJPs7UlpN4VGo8Frr71GYmJiSKVtIaOjS8hUlEaSJMiyjI8//hgjRoxgR44cSY4fPy5vcnbG3gsXpOoUj4WHh+PKlSvkvffew/Tp0/Hee+9hzJgxjJ+fH2NnZyf//vvvck45T1dISaG/h3PnaN5+6ZTT9eupa6c8xo+n17hpE1UdlSRERkZi/fr1yM7OlhMSEpi33nqrykrqtLQ0mM1m+fvvv0dERITYo0cPDcuyU2sS3FdQqAplx/+Q0Gq1klarLSj28y2dTvcVy7I5p0+f7iVJEvHy8mIYjqN58998AwQF0ZaERajVavj5+eHs2bNS8+bN5czMTIMkSaGHDh3apNVqDTVclxgcHHy0V69em0JCQpbodLr/5Ofnt7p8+XKLrl27qggh2LdvHw4ePIi+ffui3fPPE27VKlp1XMqXrdFocOPGDdFoNDItiu+Q72E2A+HhtIG6RlPues6cOYOkpCRZq9USAGjYsCF69OhBWvbpg4YffUT+uHZNvi1JJCYmBjt37pRiYmJktVpNTp8+DRcXF9jY2AAAzp49i/DwcIwbNw7u7u5gWRbW1tawt7eHt7c36datG0lLS5P//PNPtGrV6sHTy4UL9Lpycqg/v7zU1ZUraSpqRcFsQgArK8hnz2JTRoYUffYseWXsWPTt25d06tQJxYu2KqJVq1bo1asXsbe3x5EjR8iAAQPIqVOniMlkSgkJCTld5QAKCtVAUed8DAiC0ITjuB22trb+kyZNUnEcR6WE//qLyghUkPaYn5+PpUuXSrIsN+R5vpx+hTVeD1Gr1UkDBw50P3bsmFhQUMC8+uqr5H72T1wcMG0asGVLGeO3f/9+KSUlhRlXXorjP/8AkydTN0kpbty4gfT0dISHh0uBgYEkODi4zEXLn32GDJbFtubNJZZl5Y4dO7JxcXHi7du3GVtbWzklJYWxs7MTn332WXbPnj0YMWIE2rRpU+F1yrIMnU4nHTt2jIwaNYo0z8ykmTorV1bcC6CwkGYa3b1bseEHVd1cs2aN6P/330xIVBRhY2JqVIT2ww8/yDY2NhgzZgyJj4/H1q1bcyRJ+laSpDAAF3mer7aYn4JCaRTD/5gQBEGjVqt3qdXq7hqNhgns3NnGb88eWAUEACNHlnuOJEnYsGGD/tatW8tnzJhRZ5ougiAQQohelmW1nZ0dJk2aBKviRkuSaOPzN96g2TnFOH36NI4fPy6+//77ZV0SX3xBlS/ff7/MW0uXLpVEUWQCAgKkPn36MFblGUmTCfjpJ+D118sYUVmWkZubC51OJ547d44dNGiQ3KVLF4vkJY7odDL35Zdy0Dff0O19HTRpOXLkCHQ6HT6aPBl2Z8/SMc+doy6kahAXF4fNmzfD1tZWfu6554gkSbh9+7Z0+fLlQoPBYDKZTH0AXOF5Xl/rRSvUWxQf/2OC5/kCo9HYLy8vr09GRsbav8LDo7ebTIW4dAlITCz3HIZh0KlTJ2uWZetOu5jyhizL9/oMm8sYYYah1bS3b9Pag2I4OzsjNzeXiYqKQlbpPr5qNdUnKofmzZszjRs3FgcMGFC+0QcAlQqIiCibUQMa/La3t8eQIUPYWbNmwVKjD1FE+7Q04hkfz8DZuWqjP28ebfJeBb1790bDhg3Frdu2SVLv3sDZs/RpopqZSa1atcLMmTPRtGlTeefOnXB3d8fAgQOZTz75xHb48OGODMOcYhgme8GCBbsFQVAqfBVqhLLjf0IQBEEFwPghy8LRyQl4771yXT5GoxFff/11QWFhoRZANAA7nudrrOMuCIItgDwAmDx5MgwGAyrKN8emTVRz58SJ+75ws9mMgwcPijdu3CBpaWkMx3Gyvb299M7IkSw3fTqwYkW5RU4xMTHYunUrZs6cWfkCjx+nmkbdutX0Eh+QkAC89BLOT5qEo9nZ4nsffFB14PTAAdrAZcyYKg81Go349ttvJY1Gg1deeYVxtrWlMYSlS2mAmK1enHblypVio0aNWD8/P7Rq1eq+RHZBQQFOnDiByMj7CT/NARgBmABIADJ4nlf+YytUSG3zwhXqCJ7nTfPnz/96pShO/OjQIWvrbt1oLnsp1Go1GjRoYC4sLOwG4AQAGbV4clOpVJ+3bNmy8OWXX7ap8uBXXqFpmb/8Qt0+ADiOw4ABA1iAuqLS0tLI5s2bmdP/+x+e8fQs1+iHh4cjKioKvXr1ElFVltIzz9Bd+fbttXPJXLxIew0PH444jUZsZGtrmRVu3Zqmc1qAWq3Ghx9+yPz888/Q6XRiaGgoCw8PIC+PVvuq1dXy+wcFBbFhYWFidHQ06+TkJKekpBCGYeDo6Ci/8847pEuXLvj111/NeXl5V0wmk5VKpTLIsgxZlrFo0aJrer3+HZ7njwqCQAC4A8i617dBoX6juHqeIGbMmDHFBPBh3bsXYuHCCo9LT0+3AdAaAAgh5yo80AJYlm3ZokWLqo3+Pby9qdunHJE5hmHg5uaGHj16EPXq1bKxAiXO2NhYUavVIiQkpGrjSwiVS66NPPL//ge89BIVoZsxA5xKBYufdNu3p08dFsJxHLp3747o6Gj2xo0bQOPGtN3k6dP0JpJmeV/1Dh06YMqUKezrr78Oe3t7fPbZZ5gyZQoKCwvlu3fvwsnJCZMnT+ZmzJhhNW3aNMyYMcNq5syZVp988olVv379fAEcEQThBbVavQdAEoACQRCcLF6AwlOLYvifMCRJWn7WZBJTV66s8BgvLy8zy7LvAMCcOXM612Y+WZZJfjl+6Bs3biAvr5we8w0aUNeFpyeVNiiHjh06wCMnh6y7dUssr2LVZDLdT8O0iEWLaNzDUqXRexiNtKWjnx+wc+d9SWW1Wk0sVi1NSaGS1dXA398fHh4eKNEHIDgY+M9/qALp0aPVGs/T0xNjxowh1tbWsLOzAyEEbCm3UfHP08bGBp07d8bo0aPRoEGDTUajceDUqVOh0WjyANRdU2OFfy2K4X/C4Hk+X5blA79VIk3cp08fG4ZhZAC1dnybzWbX8PBw7Nu3z3zPh5yamopffvkFd+7cKf8kf3+aejp1aolWkvdgEhPh8n//h3x7e0ZXLJVTkiRs3rxZzs3NZavU+CkOy9LU0N27LT9HlqnE86lTgJPT/R4DAJCdnW3ZGJGRVK20BrLUDg4OSEhIkO4/WTAMdZVduUIrgG/XXIZHluUyhr88WrdujY8++sh2+vTpsLW1vSccaGGDBoWnGcXwP4GIoqgtkyFTDG9vb/Tp08fGysrqq9pWdoqiGAEAJ06c4L766its3bpV+v777wEALStT05w+nbZLvHmz7Htnz4ITRQT16EH+/vtv+fvvvzd/++235mXLlkl3797FxIkT4VrdBufTplneBOXCBSqCN2QIvVkUK6A6e/asfP36dTKogmyjEqSkANXttFbEoEGDkJKSgo0bN0oZGRkP3ujYEbh2jbqw3ngD0Fc/K1OWZWKpMirDMFCr1UhPT4fBYJAB1OyCFJ4qFMP/ZFLBVvsBXbp0IU5OTj05jksXBKFHLeb6CkAHAJ8CwKVLl+7/TSxfvrziQCDHUbmCgQNp6mJxLl6kypagbpVu3bpxwcHB3ODBg5mJEycSJ6cauJk7dqSGvLI2igDtcRAbS3seeHiUyKTR6/XYu3cvefHFF4kl1bQIDQV++636awVgbW2NcePGMXFxccy3335bUgzP3p42f9+wAZgypdpjS5KEM2fOoLTMRmVERUUZACxXgrsKgGL4n1TGEEIqlZJUq9WYMGGCrb+/vw2A6jmhi8HzvMTz/Hme55cAcOE4LhqAkeO4P9LT023++uuvig2MoyOVXL5+/YH/3WCgFb79++Pu3bto3Lix2K1bN3To0AFt27atUKisSjiOBmgvXqz4mBUrgAEDqJxzJd2yyjRfr4i2bWnz9Rri4OBwv3vZ7dKuHWdnqs/0+uvV3vkHBweTEydOIC4uzqLj8/LycP78edlsNv/X4kkUnmoUw/9kclelUlmkxRMQEKBWqVTTBUGoZVNcgOf59JkzZ7bjed7KbDa/DABRUVFYsGABEhMTUa5g2tixdIc9fTr9+dQpanzVamRkZMDFxaXuRMa+/BI4fJg2QClOVhYNOPfuDYSFlXDtFIfjODRq1Eg+d+6cZSk9K1cCL7xQ4+VqNBrMnj0bWq0W69evx+rVq8WNGzeKCQkJtKagSRPqkgJohbIFmUa3b99GZGSk1KRJE6lcfaRy2Lt3byEh5Pva1HsoPF0ohv/J5K6mAlGz0jRr1gzu7u4sgCoa2lYPnucl0L+P3hzHfb127drcr776Cn/99VfZG8Abb1CDn5tLjXCRbLQoirLFzd4twcqK6gb9+WfJ1+fOBaKiaIevSoLGGRkZSEpKIoGBgVVX+ebkAKmpkCrrVlYFN27cwE8//SSfOXNGZFlWNpvNbGxsLLt27Vpgxw76VKJWA6tX00bzQ4bQz7ACDh8+jF9++QXdu3fHa6+9xlhbW1e5hmvXriE+Pj7XZDLNrvGFKDx1KJW7TxiCIHir1eob/fv3R5cuXSw65+TJk/KhQ4dOGAyGYACeABLqunJTEIQJAFYCgEajMfbq1Yvr3r07c7/FZGEhlWfw8QEmTgS6dcP27dtRUFAgjhkzpu52/f/8Q90+7dtTrf8vvqA9ce3tLerpe/z4cTk8PBxarZYEBQWVeX/16tViTk4OPGJjmSG//UaWTZ0KGxsbqVGjRqRr167E19fXoubzWVlZWL58Obp16yZ7eXkRvV6PiIgI2cHBAV5eXuT5558veYIo0hvBCy/QG2eRjPPWrVslKysrJjk5WUpPT2deeeWVe315q0SWZXz99dcFubm5r8yZM6dstxiFeotSufvk8abJZEJ0dLTFomNdunQhsbGx/gkJCbdNJpOLtbX1fgAD6nhdqxiGuStJ0o6CggL1gQMHkJeXJ/fr14+u0cYG6NoV+O476rLw8MCgffsQcecOm96kCRqePEkNWlYWzckfNoy6bZo0ob11b96kNw2zmX65udFU0dJPDK1a0bz8zZuBM2doENeSQG0R3bt3J87OztiyZQs8PDxKGNGTJ08iJSWF9ff3xzNjx4JZtgwzrayQmJjIXL58Wdq3bx927dpFgoKCqmxfqdPpYG9vL/ft25fcS73s3Lnzg9/nyy/T4PjgwfRnlgW+/54Wxo0cCaxfj29/+03KyMhgXFxcZGdnZ3nUqFEo3gynKpKSkmAwGDIUo69QGkWP/wlDp9P9CYDNzMwknTp1giWP84QQ+Pv7q7Kysri0tDTZaDS21ul0P2m12nK6jtSMokrbGJ1OdxfAYAC4desWKWEAtVraV8DfH3j+eXCXLiFDkuT47GypVUoKAy8vaqwjIqg/ftYsmi4pirS4yd6etjT84gvg+eeBzp2ppPOdOzT7pWFDYMYM4NIlYM0aavyfqb5eXcOGDREXFyfq9XqmVVGKaGpqKrZs2QI/Pz9cuXIFz/7xB6xv3wYTFARHR0e0atWKBAUFkcaNG2Pfvn3Iy8uDm5tbhb8fT09P6HQ6kpmZKZXboezkSSrJ0bhxydfbtgXi42G2t8eJs2fJ2IkT0bdvX+Lv789UNzB+8uRJ8datW2uCg4MPVOtEhacexdXzBCEIQm8Ah4u/NmfOHBALXBgAcOfOHfzyyy/JZrN5qyiKi3meT3gY61y4cOFOg8Ew1NbWVpw6dWpJN44kUZdLVBTQsycKCgrw9ddfIzQ0FG3atLHITVJiLEmiTwDp6bQQ684d4PPPaUwhLo6+Z8HNsTSxsbHYunUr1Gq15OzsTPR6PfH09JSHDx9OfvjhB7HniRPEb+hQprQMNUAF5iIiIqSMjAzy+uuvk127dslFaavw8/MDQH3rmzZtwltvvVW2ZuHedVWQix8XF4e7CxbAU6dDs4gIsNUpdivGf//739ysrKzneZ4/VqMBFJ5aFFfPk8VNlmU3iKJ4Xwry+PHjUlBQkEXWMjMzE4SQ1FmzZk16eEsEDAbDWAA5+fn5ZX33DEON/gsvAKmp0Gg06NevH3bs2CG7u7vL48aNYywtPgLD0C+Oe7AzbtEC+PBDajiNRsDFhergVFPArXXr1pg6dSoSExOZ6Oho6dSpU6Rp06YSANbHx4e9IElmv2HDyv3c27Rpg4YNGzLff/89fvrpJ7i7u8PW1lbatWsXk5GRgeDgYKSkpMDJyUlydXUtO8bBg7RGoJRURnZ2NtauXSvq9Xq21eDBotFkYr2uXgUrioCFfv175OXlITc3lwUV8lNQKIGy43/CEATBBUAqIeQ3WZZnqlSqk6GhoS5qtRpVpe9t3LgxLzY29gOe59c+gnWOBfArAEyaNAkNS/fVNZmoYcvNBZo0gdlsxvLly0VnZ2fy6quvMpZIDlTI9u10p//SS9Tt0749sGABzS6qSFK6ChISEiBJEpo3b44DmzfjudGjwej1ZWMMlXDz5k1s2LABnp6ecnJyMmnSpIk4evToshcqSUByMi0wu39J2+Xz58+TTp06iYMGDWLv3xxXraLSE9u2VevaoqOjsXv37qPTpk2roLWYQn1GSed8wuB5Po1hmGWyLL9qZWV1SBRFh82bN2P9+vVli4BKIdO7uOXlnLVjA4AVAPDdd9/h4MGDEAQBt+5JHKhUwNtv329iwnEc3n33XTY5ORmnTp2q3W7jxAn6VAFQoy/LwNatVAfHQuLi4nDt2jXk5ORAkiR4e3vD29sber0eBisr/O+bb8TqGH0AaNq0Kd566y3Y2NhIBoMB2dnZrFi65gCgFbuXLt3/8c8//8T58+fJSy+9hKFDh7IlnojeeYfGP3Q64Px5i9dy8+ZNk8Fg+KtaF6BQb1CCu08gISEhB3Q63RmO49qazeaWAMAwzP+Li4vr4OXlpapIbiA3N9fq1q1bGb179y5XzUwQBLVOp2O0Wm2lVcGWoNVqodVq9xQFo9+6Z/CTkpIKu3btSi3m8OG0gUlSEuDgAJZloVaryZEjR9C9e3dCCEFSUhKsrKwsEh27T9euQEgIzSQCaExhwgTqDunSBcjMBHr1KvfUGzdu4OeffxYvXLjAxMTEyJGRkUSn0yEqKgpHjhzB0aNH0SA8HC0lCe79+1sWXCmGlZUVDh06BE9PTzk7O1u6e/eu7Ovry5SI03z9NdXnDw4GQOUdLl26JCckJOCZZ54hZWI6TZtSd9aUKcBrr5Xb46A0Bw8ezM/Ly1us1Wqr0LhQqI8orp4nmKJeuGNlWV6nUql+NJlM4QA2MAyDWbNmlQn6pqamYvXq1Xkmk6kbz/NXBEHorlKpdjMMs/nzzz9/XxCEdQDG8TxfbYNWxTqtGIbZJEnSMACYNWvWA0Muyw/kCYYPhyRJWLJkidSlSxcmOztbvHz5MqvRaORRo0YRT09PyyacNo0azu++K/teWBj199+6RYPBRVk7er0eGzdulO7cucP07NlT6tmz5/1Yg8lkQkFBAYxGI1xcXKiUc0YGyPLl1f4sfv/9dzk7O1t+8803Gb1ej+XLl0uBgYHo06dPpU/XkiRh/vz5ePfdd8u6ze5x+zatUH7pJaCcGoR7iKKI+fPnmyRJcuZ5vhxtbYX6jmL4/wUIgqDheb6g6PsVACY2atQo7/nnn7dr3rx5iWPPnTsn7dmzx8AwzH6j0diGENLWzs5Or9frfzGZTEdAXTR+PM9fKjtTrdfZWqVS/ebh4eE7fvx4zX3jn5ZGg7CZmYCTE/755x9ERkaKHMfJo0aN4iIjI3H27FkMGDBA6ty5c9Xux7VrqeF/772Kjxk9mt50fv8dALBv3z7ExcXJr7/+OrG3t6+Dqy1LfHw8Nm/ejIkTJ8LZ2RkAlVj4+eefMXPmTHqjNpupb//mzQdPLADOnTuHsLAwecqUKWV3/MVxcKBB7fz8CuWic3Jy8N1332XPmDFD0d5XKBfFx/8v4J7RL/r+XQBccnLyxN9///3umjVr8otLOHfs2JGZNGmSTfv27YcD8JVlmUycONHGx8fndSsrq1cBgGGYxQ9pnbEmkykoOTk58ocffsi/L0fs4kK7WDVpAphM6NChA9577z32nXfe4ezt7TFw4EAMGzYMBw4cYCxqkBIYWHVzlI0bgY0bkTF7Nq537CifOXMGHTt2FC0y+l5eVNq5GhQUFGDbtm1S37595XtGHwAaN24MhmGQk1NUUiGK9ImlVCOazMxMODk5SRUa/UOHaO/fmBjaC6GStNjCwkKwLFtnNRwKTx+K4f8XwvO8yPP8BqPR2PTOnTvzV69eXZhWrKWfg4MDhg4dinHjxmHWrFnQaDQYNmyYtdlsfp5l2VyO4x5aXjfP8yaDwfB8RkbGrJUrVxZeuGdAu3enwUmWpQqepWjfvj00Go24f//+cqKhpdi8GVi3rurFEIJd2dlSTq9e8gfvvYdgV1fL8kg/+4zq/lhITk4Oli9fLjVv3hyldYDuBY/vV9wmJwOvvlpmjCZNmiApKYmtMPD96afAjz/SzB4fH1r1WwGFhYUghGRUeIBCvUcx/P9ieJ43zZo1a75er3/vxx9/LCzdMcvHx+e+r93GxgYTJ07kWrVqZSeKooXO9BqvS541a9bXJpOp5+7du29v375dbzQaqcEaO5Zq+JdDu3bt2PT09KonGDIEGDHCorXcbtSIabloEWP/v//RgHBV3LlDm6tb2BoyOTkZK1askNu0aSOPHDmSKS/uwjAMCgqKHtrmzqWSDKVo2bIlQkNDsWfPHpJbXKjtP/+hef+nTz9QQLW3p8Vr5dxAAdyby4IPUqG+ohj+p4DZs2evNRqNr65du7YwIaHiYl0XFxf07NmTsCw7fu7cuXWt5VMGnufPmkwm3+jo6IO7d++mIvjLlgG//ifOspEAACAASURBVFpWWhmAvb098vPzqw4837hBpaCrQJIkmM1m2o923DjqV09NBfr3p37y8vjpJ6qjYwERERFYs2YNAgMDpSFDhrDluWlatGiBxo0bS+vWrZONRiPdtd9LRS2G0WhEeHi46OTkVLIR/JUrtLlM8bHt7IBjx4CMspt6k8mEixcvQpblZIsuQqFeohj+p4Q5c+bsMJlMQzZs2JAfGRkpVeQr9/LywgsvvKCxtrb+/FGsi+f5XJPJNPbKlStiamoqdVV4etKvHTtKHOvu7o7s7GzGUMFO9j7nzlGtmypgGAYcxyHpXlN4a2uqIurgQOsMypNAnjmz8mYvAMxmM1atWiWePHkSY8aMQZ8+fco1+vcYN24cYzQa5cjISBmvvAJER99/78cffzTPmzcPCxcuBCGETJw4Efb29rT+gefpjWjChLKD5uTQNM+7JSX2MzMzER0dDb1eP7rSi1Co1yiG/ylizpw5h0wmU7cjR478tWzZssKTJ0/K5RUQeXh4wGQyBS1YsOAnQRDaPex18TyfLcvy3G3bthUUFhbS3evmzVSZstjutmnTpmjQoIEYFhZWuZ9/6lRg3rwq542MjITZbMbNmzeLT0I7hOXkAK6utCFKccaPB44cqXDMgoICrFu3DmazmZk0aRK8vb2rXAfDMBBFEZ6engR6/X03UnR0NFJTU7kpU6Zg9uzZeO+99xg1x9GnoT596FdF2NvTHX+pal43N7d731YdK1GotyiG/ymD5/nozz//fIBer+998ODBMwcOHCjj03B1dcUHH3yg7t69+ziO404IglAzFbBqIIri0oyMjJ9XrVpVkJWVRf3tp09Tw1XM7fLyyy+zZ86cYYsHq8uwZAlV6ayEixcvQqfTYeTIkejRo0fZ7biDA3D5MuDtTX3n954KOA6ooAnOpUuX8PXXX4NlWXHUqFHExsI4AEC18TUaDbBzJ1CUgvvXX3+JvXv3lu3s7B7UZISGAq+8Ql1TVUg/w9YWCAigMtfFCAgI0AOYbPHiFOodiuF/SuF5/rTRaAw9e/aslJdXtobH3t4effr04bp06WLFcVz1O35Xfz3S9OnTJ+Xl5c3+4YcfClNSUoBu3YBvvy1Rierm5obGjRvLJ06cqLi6OCCAVuhWQEFBAXbs2IHhw4ejffv2FS/Kx4c+cRw8SLNtRJF2w+rW7f4hZrMZhw8fxoEDB7Bz504MHz4cr732Gls8ZdMSbG1tZXHZsvtG/8KFC8jLy2PT0tLodeblUbfNokU0DmIJDEPjFaXcTLm5uUYAd8o/SUFBMfxPNTzP3wLw/3777bf8io4hhMBsNj+yQODMmTOXGQyG6fv27csDw9BA6ocfAi++eP+YFi1akPuaP+XRpg1txlIBaWlpUKvVaNfOAi8WITRe0KkTLayysaE3gSJOnz6N8PBwxMfHSy+//LJlY5aDJEnICA29H9fYvXu37OjoCLPZTK32G29QeYu2bak7ylKWLKExj2IN5HNzc2Uohl+hEhRZ5qcck8k0KzU1dVJ2djbK0/hJTEwsBBD/KNcky/IPiYmJ86Kjo+Hr60v96tnZ998PCAjA0aNHyYEDB6R+/fqVSZHE7t1ASkqFsgXp6emwtra+1zO4akQROHqU+vwzM4HffgPOnYPUvj3OpKXJQ156CZ27davVJslkMiF382assrZG1v79YBiGDBw4EM3NZgb79wPr11vUbL1cJk4Eli8Hhg4tuhyRALCgEk6hvqLs+J9yeJ6XOI6LuHbtWonXc3JysHXr1sLk5OR0AHsf8Zr0DMNM2bZtW254eLhZ7tyZBjIDAoCDB+Hg4IC3336bnDlzhixatAhRUVEl3T7Dh9N6gHLQ6/XYt2+f3KFDB8usqCjSXsFLl9IK4zVraIHVtGlAy5YYtHMncf/kE7pT37+fSkXUgEGDBrFt/vwTQxs0wIsvvgiDwYC7d+9KWLWKdi2zsqpRQxkAVJeoyOgDgLW1tQzA8n6UCvUORZ2zHnDo0CEbQshzfn5+agDIyMjAihUr9Onp6d+ZTKZRxSUhHhW9e/c+Gx4e/tOdO3cGZWdnO7Rq3ZojhYXUZ21rC41Ggy5dupDDhw8jISGB/PPPP9KpU6dkSZLQKCaGsHFxVLqhFFFRUcjLy5NHjhxZudyn2UwN7urVNFA8bBiNNRw9Snf9gweD+Ppip729VNiundzC25vBgQO0LWRGBu0d7OxcYSC4NC4uLrCbPBl2Wi0cHR3hcPQoTKtXk8zPPpPdP/7/7J13WFTX1sbffc40OggISFFEpQiKqIAFwULssZPYE5NYU9R8iUZjxpOqubkxRm+KehNbjCUqVsSGFBVFQaUoioJIEZUidZiZc/b3xwYEAUvaNcn8nmcemTl1n3HW3nvttd41/7eL5vn6soSuwECkpaVpi4uLI0NCQi4//kAD/0QMrp5/BgevX7/+dUZGBgoKCmhMTIyGUvr2okWLvv1f3pRarS4QBKFXcnJyUosWLVr3fPttHpcusRF4fDwkSQLHcRg/fjzKysq4iooKxMfHi9p9+3hvCwva4vXXGxnMoqIi2NjYPNrNc+kSM9zx8ayAi6srG+lXVjLZ43r+ckopym1tWY3goCBWYGbfPpaElZnJ/vXzY7UHLCwaLbTWcf0661xSUsBxHDq5uSEtMpLeysmROnfu/Buq0tTwySd1rq8bN26YA/ABsOs3n9fA3xKDq+cfgFqtvi1J0tQdO3bkxMbG/qLVagP/10a/FrVaXarVakOOHDnCL1u2jFI3N6BnT4AQHDlyRK9QKKi5uTl8fX3Rq1cvzJ07l9e98Qb29e7dZNRPWVkZLCwseIDJEzdAr2eF3t98k43at26ti7IBxwH9+zPjuWEDq5IFYOTIkdzly5f5uiQwuZwtRH/6KUusevttduzMmUwtdNcuVjRFeuj2VCrmolqzBggOxvrKSml3aCgJCgr67UYfYKUuf/kFuqNHaz/5zTUXDPx9McgyG3gmEAShC8/zkZ6enmYjRoxQcStXIj48HPcXLcLgeto+er0eCSNGSC3Ky4njgQPExMSkQV2C77//XuzUqRNva2uLLVu2wMLCQgoICKCBKhWPBQuY797dnSlw1nLzJptlpKSwEbufH5NW8PMDABw+fJieP3+eTJw4ES7NRdxQyvICiorYsTExwL/+BVhaMpeUKDJXUkEBEBWFtB49cOjQIVGj0fDe3t5i9+7d+bKyMlpaWgp3d3diZmb29A9x4kTES5LumLd3yuLFi/2e/gQG/ikYDL+BZwZBEIwVCkW4t7d30PCqKlVRcjK+USqxcOFC1BZNOX36NHK+/x6OMhk91ro1sba2ll555RVOqVQCAL7++mt93759ZTKZDAcOHJDsLCw46/Bw9BkxAqZGRmxR+GF3zPLlzIXz/vvs/c2bbGRfrzBMbGwsYmNjMWnSJLi4uIBSikuXLiEnJ0dycnLiOnXq1LAwTnExEBUFnDvHEq0++YQlqun1DS5969YtHD9+XMrLy+O09RLZpkyZgodrLTwOSik+XLoUcp0uedGnn3Z6qoMN/KMwuHoMPDOo1epKrVY78cKFC1y0nR3iAwPFkVu3oujkybp95HI5SoyMUGFjQ+fNm4eqqip6tZ5gm06nIyYmJqiuroZUXs51+/JL2Ofl4ZusLCR5e1MK4NKlS6hvZEEp89HXEhMDvPFGzSY2MAoKCkKXLl2wd+9eWlBQgIiICPHQoUO0pKSEO3TokHT06NGGfiUrqwcuofnzmRw10KDWLsDkmKdOncotWLAAAwYMwNixY+Hr64vdu3dT/UOdRFPcuXMH9RVNJ23ejJd/+MHksQca+EdjiOox8EwREhJSeeLEiXY5OTm+PM8jID+fWPfvDzg6AmBCbl1SU9Huzh2iGDUKFRUVXFRUFHJzc8XDhw8TrVbLBXbtirabNsFj0yZsGzUKqT4+sLS3F21tbTlJkrBt2zZcuHCBuri4EPO8POaXrz8TcHCAdOoUNhQVSYcOHSI9evQAx3Fwc3NDUlKSGB8fT+7du4fXXnuN8/f3x82bN1FSUkJ9fX2bHkilp7MkqxdeYJIN4eFMmrpeMXdCCFxcXNCyZUt4eHjgzJkzUlJSEnQ6HYyNjYmRkREyMzNx7NgxMTw8nIuPj5cSExPpyZMnyblz51BdXQ1XV1dcdXaW9rZuvbV3SMifGqJr4K+FIarHwLPIfAAvhIWFqYxfeolJGbzyCvDf/4LjOKZnXyNDERoaWmuQeb1eD9uMDFx6/30qy84m58eMQZWJCVq0aCFWVFQQZ2dnqFQq8DwPZ2dnsm7dOkz18EDr4OA6N01lZSVSsrJQUVFBucuXoTU2xpdffomXXnoJLVu2xMyZM2t/M3V+nby8PDpmzJjmF2nPnGGLwCtWME2gFi2YQmfHjix+vwlmzpzJnz59GhcvXhSjo6N5SilkMhlt1aoVr9frodPpOG9vbzg5OVErKyuyc+dOeubMGeLg4AC/s2dfBCGloPS93+XbMPC3w+DqMfDMoVari+RyeWpdJI1Wy3Tpa+WaExKAiIi6/du2bYsxgwfDtqBAGn7gAGydnUnnX37BvM8/BwC4urryWq2Ws7e3R1xcnGhmZib2798fVpaWVD5/PjR+D9ZBt2/fLp48eVJy02rJRGNjTq1Ww8bGRv/999/j/PnzTd6vTCajzZaMpJQVTWnXjq0h7NgBDBrE1EHbtmUhpU2gUqnQt29fzJ49m1+4cCFeeuklvPPOO6S2qlqHDh2kpKQk2NraEicnJ7z11lvkueeeQ25uLleqUrWoViialekwYMAw4jfwrKKU17pCXFyAkyeBw4dZhayCgoaFWM6fh3bRItiYmHBmKSmws7Gp29SyZUvY2NhALpcjJiYGly9f5mbPnk0sLCzQQacjMlNTGPn6AgDKy8uRk5PDz507F6ajRtUtxE6bNk32xRdfSElJSaRVq1bEwcGh4Y0qlShrStsfYCP80lKmwePoyBQ3p01jiWCrVjF10MhIIDS02Tq6HMfBscbVVfu+W7duXGZm5oOSjgC6d+8OOzs7rF+/HndtbbfOJMQXlF54wudt4B+EYcRv4JmEUmqiqKfaCQA4e5YtvE6fDixbBlRUsFq05eW4M2wYLnXtipKHKmvNmjULfn5+sLCw0F+5ckUcPXo0MTIyQmJiIvirV6WLEyeKIASiKGLnzp1Sy5YtRVNTU8DamoVh1swywsLCOFNTU7J+/XrExsbS2sXh0tJSlJaW1kUVNWL3bqYBRAjT0JfLgd69gdWrgXv3WMTPG2+w3IEnJDs7G+Hh4VJwcDCtb/gJIXX1ATqmpFwD8ASFiQ38EzGM+A08q1y/cuWKs4ODw4P/o7XhlsOGsSgZd3c2KvfxgVNwMAKOHsWGDRswYcKEBgVSFApFnW/+2rVr+OKLL2BibEwHpqQQ52nTOIBp49+7d4+88cYbzFd/7Ro7f41Bd3Z2Rrt27ejVq1fJ+fPnpdOnT/P9+/dHVFSU5OXlBW9v78Ypu5SyovC76iXQHjzI8gU6d2YJZE5OQGIi23f8eOCDDwBPz2YfiiRJ+PHHHwGACwwMbHIfW1tbHO/fH8dDQ19VP8GDNvDPw2D4DTyTaDSaKadPn77s6upq0aZNmwcbKGUhkXfuMJmFem6dAQMGwMjICD/99BPCwsLQrl27BudMTk7GgQMHaL9+/RDYoQNBTg4QEACAjaJ9fHw4uVzOYu47d2bupRq2bNki3rp1ixs/fjzat2/Pnzt3DgcPHoQkSVxqairy8vKkF154gbO0tHwQz3/pEiusUs9Ng8JCttB75gxT1Lx3j8lHODoyyYdz54A2bZot9s5xHJRKJa2uriZ6vR4833hNeeTIkVi7di08U1LOUELOE0q7Pd3TN/B3xxDOaeBPRRCEPjExMZNOnDiRGhISUtXcfiEhIeVRUVFpN2/eHB4QEPDA50MIk0e4fx/Yv5/p39TDxcUFt2/fRmZmpti5c+cGrsydO3dKRkZG3IgRIwj57DNW/atG38bExIQ7evQo4o8dg8f336Ng+HBQW1vwPI/IyEgxPT2dmzdvHrGzswMAWFpa4uTJkxg3bhz8/Pxw+/ZteuTIERITE4Pz589TW1tb0iIhgcXzd6qXS2Vtzf7t2JEliG3ezOrqjhvHFDa9vdlMQyZrUoSutLQUiYmJ0Ol0xMfHByYmjUP2VSoVYmNjUWlkhDJLS6OfMjK+DgkJeXxSgIF/DIYRv4E/FZVKtVySpABCiC+AcY/al1K6v7y8XHPjxg3Ttm3b1j8J8/HHxDBRte+/byBpnJ+fLwYGBjYaCrdq1YrT6/UiAXjs2cP87zV4eHjAOz2ddtBqyalvv8XV9HRRm5zM6XQ6Ym5ujhkzZhBVvWsoFArI5XKYmJigdevWaNOmDQewcNDExESyfetWTEpPp87vvUdAKfbu3SumpaXxpqamdIxSSRwWLwZZsQJYuJDVH758mRl9ngfduRNn4uPFqsWLSd+FCznUk2/Yt28fraysJLXhpU0hl8thZWVVXgxsvejt/Wr3+PgyQRDs1Wr1I+pZGvgtCIKgAqAAUAngUwBBALYASARwXq1Wa+rtawnADEAfmUzWS6/X/6hWqxP+zPs1jPgN/KnExsZKWq12JCFEERUVxUdHR6eGhIQ0qgsMACEhITQ6OvpaamrqoMzMTL2FhYXC0tKSbeR5Fu2TmspCJIcOBQhBUlISLl68yAUHB8Pc3BwAkJCQgHPnzuHy5cu0e/fucCgqInBza1TTtuVXX5ELKhXsBw3CuHHjuKCgIBIUFIQePXpwxg/JL+/evRscx0l9+/Yl9aUa5HI5XFxc0F6SgJUryY92djQ7OxsZGRlk0qRJhOd5khATQ9ts3UouBQVRm5YtSWppqcSNHEmohwd4Nzd8vWMHvVZWRnqvX8+ZpKSAr1edrLS0lOTk5KB169bUzs6uWTlnMzMzRUZGhoVrevr24JgY/6RevdoG9emz4+m+LQOPQxCEjmfOnImUJGkFx3HvUkqXAugFwKlTp059CwoKpgN4Pzo6eml0dPSS6OhoEcBhAPMAjJYkqbtcLp94/PjxwyEhIXl/1n0btHoM/KkIgkAALALwcc1Hw9Rq9YHHHGMKYJGNjc0bc+bMMW2wUZLYyH/FCmDjRuhNTLBjxw4pMzOTCwkJoT169CDfffcd7ty5g+eee07q0aMHh6++YjOEmTPZOVJSAEGAuGkTPl+xAo8UY6th7969KC8vFydMmNB04tZ770Hy8sIpV1dkZmZi0KBBsLW1rbllCZe2bUNSWpqYp1LxlFJ45+RQnV5Pcl1cQM3MxHnz5vGrVq4Ue3buzHfdtYsplr74IgDW6ej1enHcuHHNJo1RSrF///6qxMREIwDgCfnX+x988O4jG2XgkQiCYAbArfZlZGTUX6/XBw0cOFDl4+NDFAoFatddagcDGo0Gx48fR0JC0wP6ESNGiIQQ/sCBA4Ucx32xcOHCZX9GWwyuHgN/Kmq1mgqCsAxAFIB7arX66hMcU/7ZZ5+18vLyalz1hOOAPn2YLv6XX0L2zjsYP348d/36dWzdupW0adMGlpaW1M7OTurRowcPSWJ6+0OHAgBycnKgX7sW1WVldM/XXxMjIyPRxcXlsVLJHTp0QHh4eNP71VyDCw1Fb3t79O7d+6Fb5uCbng7f4mI+8+23sWnTJgz/z39I9ty5GJiUBNNffuEBwN3Tk0+4ckVqbWnJWSQmQj58OIq1Wly+fBmUUr62XkFTEEIwbNgwo8TERFgWFuLNVavegFq9GJQaSjI+BkEQOgLoQghpr1QqfQgh7jqdzpnneZWZmVlVixYt0LJlS2MHBweZq6sr6iup1ooJ1qJSqTBkyBAMGTIEO3fuBMdx4pAhQ/jS0lIYGRnB1NSUp5RCq9VaR0VFTQRgMPwG/p6o1WoRwKmnOUav1+dID2vc18JxwHvvAQcOQDt+PBJefhlphYVUoVAQa2trZGVlQZIk/vTp02hXWAibEydA3nsPVKOBZtAgRI0YIVm/9ho3xN0d3t7ejzT6kiQhOjoaZ86coV26dJEANN4/JYVV5rK3b749kyYhZd06VOTlgeM4JCUloduyZcCcOUza2dYWPXv2xPnz57nvlUq4tG9PRw4aRCoAGI0eLZaWlvK3b9/GyZMnpeeff77JPIIa/R+azZbEgwxGv3kEQbACMEypVC4wMjJydXFxEW1tbU2sra05KysrtGjRAqampiCEyB97skdACIFSqayb/dV+5uLiAkmSLARBIGq1+g93wxgMv4G/BDzPmyuVyuYTDgkBhg7FhQ0bULl9O203fDjtMm4cUSqV8PX1xf3793H27Fk9v2OH7Ky9Pap37RK9y8t5aLV4+YMPOFlzCVgPsW7dOn1FRQU/YsQI4uHh0XQnUSvR8Agi09Nhd/IkCquqpPY9e1JPT08eJibAunW43749dg4ZQgvbtKFarZbr06cPMjIy6Jb+/SXH0lIyuEULPlWnE9euXcsD4G7fvk1fffVVYtRECOj48ePJihUrNBumTNFPJWQXgDEw+HfrEATBUqFQfMbz/MsuLi46f39/0w4dOjQ7k/otPOqxW1paQi6X2+r1ejWApb/7xR/CYPgNPPN8+OGHfWUy2UvOzs51n127dg0nTpyQfHx8HiQyEYKMUaPgdfEifA8c4NCrF2BpicGDBzOHq14vg06HzP79oV+wgE9xdpaSJ03iXisqwsMyDE2RmJiIwsJC2ezZs2Fh0Uwtc0qZHMPB5sUxNRoNLly4gBkeHuimVHJ44YUHG2UyRHfqhC5ZWUQ5Zw5xd3cHz/Po27fvA0v07rvwiIzkB0RH4/qNG9i3bx9JSUlB9+7dG11LpVKhZ8+e8tOUfgDACoANgLuPbew/AEEQestksgNeXl6K0NBQpbGx8ZP1/r8SvV4PubzpCYMoiuA4joqi+KdIbBgMv4FnGkEQXGQy2Z4XXnjBTKVSYfXq1aJOp4NGo+F9fX1JVFQUzp8/L/bv35/38PBAWVmZWNCvH0FmJsHZsyyzt3b0ffYscPUqXF95BaiqQvuPPuJG2Ns/0eiuoKAAhw4dwpgxYxobfUqBsjJWfGXDBhaDL5czVdHz5yENHozN//qXvkSn4xQ2NpRSypuamko2q1dzOHOGrQnU3ENRUREudeuGAd7eMP72W6bn8zCffw6o1bD4739hduoULHv0oD4+Ps1G+AQGBvKnTp0aJCxd6qdeurTpzLB/JrF2dnYY8VAuyB+BRqNBZmYmHTt2bJOzxEuXLqGqqioOwN4//GZgMPwGnmEEQfDmef5wUFCQsaOjI77++mvJy8uLtGnThnNzc4ORkRHx9/fHpUuX+N27d2Pw4MG4c+cOP3HiRGDgQGDTJiaBoFazpKiSEqa988UXQGwsgCcTq5LKyrB71SppgL093BMTOSQlsfMkJTGl0I4dAVNT5puPjGSdTVER8/UvWoSjcjme++YbmcrHB+mLF8O/d29c3bePYNUqtjYxZQrw+ecoVyqxdu1a6h8QIBkHBvL45BPmNmrfvtE9Zd29i5MpKbTT5cuk44svUhXHEQBIS0uDVquFm5tb3aKjUqlE7969FSdjYz8DMAiE9AKlib/bF/UXRBCEAQCr7/BnsHPnTsnR0ZG2b9++keEXRRGJiYnlOp3uP2q1+k+plWww/AaeWTiOe0EURYeoqCgaExMDV1dXDB06lKsfN29tbY2+ffvCysqKHjx4kMjl8geKlZMnM4McH88E3d56C/DxYaGRtVRXs5F6cTFLoiotZSP40lLmrtFoUO3tDe+LF7kuLi5Ahw4sE7dlS1ao/c03mdyCUsnkGGpDRVUqwMsLmuefx/kVK2j7EyeIvasrAigFbt6Eu0JBsHo14ODA6vEOHYr7LVuinZcXDe3alYe5OYtUWryYFXWvt2aQnJyMXbt2oUPfvrBeuhQdvvuOgyCgOiYGO3fuBM/ztE+fPqR+NFHXrl356OjowTmOjtZOOTn/eMlmmUw2Ua/XN1A9fWK6dQNyc5lS7BOQn5+PrKwsbtasWY221YTdVpeWliYA2P/0N/PrMBh+A88sS5YsWSIIwlIAZhzHZQcGBpqRh+vl1uDr60vatm2L7OzshhtGjQJ++YVV2MrJYWqeN28yg7ppE/sRd+rEau7m5QF9+zJjbG7O/m7bFkYmJri8bp2Yb2mJR8XOY+RIJr3w5pt1H92/fx+SJJGcnBzR1dWVByHMBbR0KfB//8c6DCMjXCQEaatWUYv8fI5Mn84M/eTJrIOZPx/Y+8ADULse0bVrV9KqVSskDhtG0zMzSaFaDU+dTrzv60uSk5Nha2tL3N3dAQDGxsawtrbWb5g1a/5iQoYBmANKz/2a7+XvgFwu76LX69FIAfZxZGcDa9YwragnICEhAceOHaOBgYG0RYsWDSaYlFLExMSIaWlpuVqtdgQAK0EQTNRq9c2nu6mnx2D4DTyT1CR69QbgwfO8DwB5/XjppjA3N4e3t3fjDWPHAt9+y0b0lpaAszMQHMzkkFu2bFYHvz5GRkakfm3bRlDKEqxefrnBx3Z2dpg8eTI2btzI9+7ZE+TsWXbdjRsBLy/kHjqErKVL6cnQUNLrlVfQtWtXlmeQmckMvlzOavd+9BGwZAkAYPfu3aKnpyc6dOjAnzp1Sjpx4gTHe3vD5cIFPH/0KK9fsgRH4+Oxfft2vPPOO6iVmpg4caLJ2rVrF6Z7eJxwv3Kl6LGN/huj1+tdTUxM8MsvvyA8PBxz5szB3bt30b4Jt1od4eEs3Pb6deY6DAlhs0Ljxukld+/exbZt28TKykp+2LBhpGPHjg1GLFVVVdi2bVtlfn5+plar/TchpIhSKiOEbAQw9XdubiMMht/AM4cgCLxSqYxVKpU+rVu35lq0aGHk6+tL6uQafg2+vuxHO2oUM7yff97kD7YpkpOTcf36dS6gRsmzSV54Afj44ybP6eLiAiO9HpWffAKTixeZKHvcQwAAIABJREFU6mdN+OiV8+fhf+kSCTx0CDzPM+OgUjFxt8OHmSTF/v1sneLIEeDYMVRVVsLS0pL77rvvpOLiYtK3b18cPnwYt7p0ofjuO3JCEPRuR47Irrz4YoOFa3Nzc0ybNs14nSSFdD90aE4IIV+A0ifzV/zN4DiuYsKECeaUUuzYsYOuXLmSAMBrr72GVq1aNT6gpITVUdi794EulI0NcxHW+871ej327NlDr1y5Qrp06YL+/fvj4RwLSil27dpVmZeXt12n082Ry+U3/Pz8ZGfPntVSSuf8ca1+gMHwG3gWkWm1Wv8pU6bwTf4Inxa9HujXj2XrlpYCcXHs32YMf15eHnbu3ClqNBpCKYVOp+NsbW1x5swZ2NraomvXrsjOzsb+/fvF6upqjH/uOd7+6lWmrd8UGRnwu3RJytRoiPf+/aT+DOO2szPOfP21NICQpqcd7u5s1J+QAERFAatXY/ayZfynM2agbfv28Pb2JtHR0bRLly70+eef5wDgpqUlCWjZEi+NHQtFRQWoXF4nIWBlZYWXX37ZmH7xxdyUjh3NvYHXfsuj/avCcdzd6upqB1dXV8ydO5dUVlbi7Nmz0pYtWzB37lyuQQZuVRVTcf3kEzb7quWXX4Djx9n/r9atcfHiRURGRkoWFhZ49dVXiZ2dXZNuwaSkJJqdnZ2n0+lmAnDU6XR2ycnJlQDGqtXq8j+25QyDSJuBZ46QkBAxOjr6xrVr1wb5+/vLf3MyzenTzJ8+cyYbac+cCSgUTOStXz/AwQEFBQU4ceIEjh07Jp4+fZrz9fVF3759OV9fXxIcHIwePXrg5s2bNDU1ldy8eRPx8fFSmzZtOIVCQap376Ztdu4k9RVC60hKAj77DGZ+fmSDvT0puHNH8vLyIoQQrF+/Xn8zO5sbs2gRKfL1hVlTbgZBAH78Edizh1XyOnEC3H/+g+Bx49Bp0CBySaORbpmYcGFhYXUJXCeTkqh8wgTiXlxMKvv0wbd37iC/slJ0cHDgVCoVjI2NUT5lCtkmip5RUVGXgoODHyub8XcjJibmw549e5rUiu/ViOuRtLQ0euXKFdq5c2fWU1LKaj5bWwNhYSxRsD7jx6OyuBjrrl4VU1JSuIEDB5KhQ4eS+pXR6lNSUoKff/5Zo9Pp+qvV6vzo6Oj7AD4QRfHNDz74YOsf2eb6GAy/gWeS4ODgSzExMa+0adPGqtlkqSdFqWSFVepLO/M8YGGBiw4OiFyxQjyRlsYplUrR09OTHzx4MDp27EjMzc1hZmYGhUIBQggcHR1JSUmJaGZmRs3NzTFs2DDOJjGROH31FTH6v/9Dxo0buH//PszMzMARAvz0E3PTzJwJ47AwVFZW4tKlSyQ3N1fy8vIihw8fJlOmTCHV+fl0X04OOZ6cLFVWVhI3Nzd2j1otcOMGi0aytGSuhXfeYdFEvXsDgYHQuLuTdhs2UNW338Js1iwCAFZWVtzhw4cRk51N7rZsSUfMnUsqfvwR29PTiaOjY538gP+778rNr18fuy4nJzIkJCT3tz3kvw6CIHCSJH06YMAArn4hG0II2rdvT44dO0ZMTEzYIvqiRcDXXwP/+lcjoy9JEva3bIm9xcXo4OREx0+Zwjk7O6O5AAQAiIuL0+fm5n6/ZMmSjQBToA0JCRGCg4PP/0HNbRKDq8fAM4skSccyMzNdnZ2dm/8lPQnffNNIghkA7o4YgfDVq7H42295umUL5MOHP1Knx9bWtmFUD6XgevfG1qlTUbVypajT6XiO40DLytC3tFTqdvcud2PGDLT180NleTmSk5Npv379SExMDPftt99Ktra2cHR0JNycOWR8UREy7ey4iIgIGhoaSqSqKpT7+eHAsGFiiShi/PjxvKWzM6s6lpTEFhiDgtAJwC+JibQyPx+t8vMJQkPhHh6OhQsXkj179sB5yBDSkuNgFRnJpY8e3UBQTLlsGcx5Xi6PiTkgCILnP0ivn+c4TqfRaPiHM2nNzc0xatQo7N69m7q5uhKLrl1ZcMBDXL58Gfv375eMjY3xekEBZ/7zzzzCwh574bt371bpdLo/VXu/KQzF1g08s2i12pVxcXFVj4ymeRIOHqwrml4fuVwOTibD8jffxK7KSune1KmQUlKe/LybNqFVWBjGvPMOBg8ezL/99tt4d8wYTL9zBxUpKdznnp7YGR+P5cuX0//85z+0ffv2Uu/evdG6dWuprKyMmzBhAsdxHHD4MCx27ECtq+bu3buI3bgRN83M4DZ2LK/RaLjExJp8q9GjWXtGjQLApCuuiiLX+cMPOVhbs8iili2B2bMxIjUVfn5+KLe3x1czZ9IeoaHUbtw44G6NYkNwMNxv3sSQ8nJLpVK5WxCEx6qS/k3geJ6/V1BQ0ORGDw8P+NnaSmjfHlJwMNC1a9220tJSrF27VgwPD0dISAiZNWsWZ/7hh8DOnU904atXr5oB+Oj3aMRvweDqMfDMEhIScjc6Orrsxo0bffz8/BS/ytev1bLyjL6+jabqKpUKJiYm0AFUkiRis3kzKXJ1hXWXLo19uU1RVAT06AHTbt1ga2sLPioKOH0aKmdnkHnzENirF0JDQ+Hm5kasra1J3759OY7jkJqaSouKioi/vz+L+HB1BXx90cLDA/fv36fXV6+mDhERxOTnn+HTqROioqJIdnY2Tp8+Tc+dOye5LVzImZw6BbRpgy1xcWKXLl04T09P5r7q04e5tu7dY+4hhQKVU6cizc0Nw196iclYdO7MfNcmJsD69WipUnGX7eysy8vL80JCQv4UrZj/FYIgOCmVyqi2bds6BQUFyZtzy7RWKLiLqan0gr093N3diSRJiIyMxJ49e+Dk5EQnTZrEtWnThhXhUamYZMdzzwHTpjUbHpydnY0LFy4AgGVISIjwx7Xy8RgMv4FnmqioqARRFPtrNBpHNze3px+RRkQAr776oOjKQ7Rq1QqdO3cmqampSOjQgfSeOBFGgYEsoetR4Zvbt7PMzYkTmRH9+Wcm1xAaCjJ6NCytrGBsbAxCCMzMzODo6FgXWhkREUEnTpxI6konajTAK6+AvPwyWrduTYyXLYOiXz+0CwsjhBBYWlpSNzc39OnTh+j1ehyMikLQ2LEEgwYhs1Mn7nJhIVq1agVJklBXKczXlxn4igrg6FHE2NuToP37mdvC2ZklrXXpAsyaBdK3L4x0OsWN3NwWvXv3/vGpn/FfiNOnT1/q2bOn25AhQ5TN+uInTgRvYQHF/PkkMjKSSJKEXbt2ScXFxQgLCyM9e/bkGiV+mZkBZ84AgwaxKKx6SJKE7OxsbNq0SUsp5QGMDAkJSf9jWvhkGFw9Bp5p1Go1ra6ufvHcuXOFe/bs0VQ34bJ5JM7OwNtvP3Y3W1tbolQqqaWlJavnO2ECkJwMVFY2fcCOHSwktLKSLQDeucN0d4KCHnmdyspKaDQaroFUgKUlcOUKoNNBtXIl2v30E2m/fHldSUcfHx/i5+dHbG1tUVFRQSRJIv++eFG66u4O5/h4mJuaYtOmTVi9ejUuXrzY4Hr3TE3xXb9+tE27diKKiljWcE4Oyzvo0oWJwE2YAI/XX4dOp/OvqTL1t0QQBDNRFFsFBQXJmjX6ej2b7fn7w87ODgEBATQ2NhaBgYHk9ddfJ61bt276OJmMRV9t3MhmgvVITU3F+vXrIUnSXQDLCSEv/r4te3oMht/AM49arb6r0+k80tLSDuzYseOxOjMlJSXYsmULMjIymCJnQABKS0vRbCEXAP369UN5eTnS0tJQHBjIImhefpkZ84dJSWGZwGPGsEQwU1Omp1NPNro5VCoVeJ5HZf0ORalkujznzzPDUVOk4/jx49i4caNYu9uVK1eQkpJCOnbsSIcNG8alqtWif0YGZp09iwULFiAkJATh4eFISkqqO3VCQgLu379PevbqxePHH5nBz8kBLl5kxurzz4FOncCdPAlHR8dqAIMe24i/Ll6WlpZVzboMDx8GXnkF2Ly5ThvJ3NyctGjRQurVqxepHwHUgNqQz88/B959lz3betTmolBKHQEsoJQaDL8BA0+CWq0u1Wq1r2ZlZclKS0sfue+mTZvEqqoqumPHDpQsWoRf1qwRV65ciVWrVol6vb7JYziOg6Wlpbh//366adMmVjHjzBlWy/fLL5muD8B+5OPH10kjo2tXNuJvoghKU+j1eshkMpqb+1D05GuvAVu2AMeOQZLLcX7zZpyKikLlqVN87LJlQFUVvGJj4VBcTO8eOULEBQukYcHBvDwwENi+Haq8PAQHB8PU1BQHDx7E8ePHIUkSBg8eDFtbW/HGjRsPCoGMGAGcOMHWBGxsAE9PwM0NY1asMFcqlROfqCF/Tbzt7e2bdxdevgy0adPgI0pp0+GZGg2bESYmMrfaDz8wAb8tW5gcSD2sra0xul7iF8dxS35TK34HDIbfwF8GtVpdQgj5ZMOGDZXXr19HSUlJ3bbS0lIkJiZizZo1UmlpKR8cHEzmzZ2L1K+/lqyee4689dZbMDMzI//973+btvwAZs2aJevevTupM5A8zxbqCgvZi1L2GjQIsLNjawfDhzdcCNZqWRo/AOzaxdYBbtwA5s5FaUkJzg4aREPj4mBnYQG4uQHR0cxoHDvG3C5+frgVFES9pk/HNBMTjMvMhNMPP1BtaSmweTPsCgtpf19f4MYNknDypITu3Zl7afBgQKfD1KlT0bt3b8TGxmLdunUSAHTv3p0/c+YM/emnn6TTp0/j/v37TBH0xg2WjZqWBpSUwCw5GWFr1gz7bNGiFiDE/A/4Cv+nyGSy3g4ODiaNNkgS+y7HjGEJc/VoYPirqths4No1YMAANiNs3559fzNmMJG+YcOaXNy9efOm5sHlpD8tUas5DHH8Bv5S6PX6j0tKSm5v3rx5DQBMmDABhw8fFouLi3mFQgFfX1+8+OKLMDc3BzZsQK+tWzlERAAAgoODufDwcGbVJYm9SkqQdeMGYk6flqpv3CCVRkZkVI8eBIcPs8XRmBigVy/mfvH3B86dYyPkiAim+Pnaa2ykN3QoGzG+/DKT7L1wAZg3j80YXFyACxeQeOYMtFZW6D10KIG9PVMI9fZmCqE//8xcPl26QHvpEinRammrL78k1MYGd8zNybXYWHRcsAB3zp+XNL16ceaDB5M9mzaB79ePBrz1FqH29ij4979x3sdHunjxImdiYiIFBARwANC9e3e4u7qSjV99RQq3bsW9khJqMW8ewTffsAXsiROB558HWbMGeT4++m67dmVJgIojRAmgDYCsv3K5RkEQZAqF4isjI6Owjh07Nt4hI4PlRtjYNNpEKUXHEyeYKme/fsyP37YtcwvVLqTXC/dsjvT09PrP7zfV7f09IH/h79PAPxhBEOwAjOF5/lNRFC18fX3h4eEBZ2fnB5EtO3YwhcvWrYE33sD9f/8bGZaWtCvPE5qVBbJyJej06cgVRRSFhdH2qamkNCAALfPyQAoLH2RtOjmxTNmICJaNa23NRn3ffMPCJ11dmZianx+bJTThGkhLS0N4eDjGjx8PV1fXphtFKRAQgIIOHfBDx45oXVoqdtbp+Pzycol3d+f6JifjSloatXBzIw6mpsgbPBgnd+yA/aBBSC8slF744ANuZ1gY/OfPh5enJ5tNxMUB3t7IevddWuLgQMpcXanvoEHEbMgQJj1dS24uW7f4+GPcDwlBbmGhZsfYsSbqpUsvAwgHsARAZ1D6P08+ehoEQXBQKpXb7ezs/F588UXjRnWJf/mFhbUOGvTge9NomJ/+gw+QNmQINBER1G/QIIK5c3/1feTm5mLv3r3SvXv3PlqyZMnSX9+i3weD4Tfwl0YQBAWAF1Uq1Wi9Xu/bqVMnl8GDBxOZTMYMaUICM3BOTkg4dgznrl0TvTp35mNPn0a7Dh1w48YNuLi4iBMnTuQflWpfhyiyKf+uXUB5OTB79mMPuXXrFjZt2oTnn3++adnokhI2irx3DygsRNTGjbh//77k3rYtZ7x8OX6eOhWUUgQEBCApKUkc6e/Pu5WUAO3aoXzePFSlpiJr2DB03boVpKwMZPJkwMODaRR17QpMm4Zv1q8XB4wbx3fo0KHpmzx5ks1Wrl4FpRQ//vBDRbvNm2Xd4uOPGGs0owH0B/ATWM3egQDOgdJnNtNXEAQVIWQqz/NfBAYGKvv27dtY84lSNtt66y0W4dS6NRAayjrvDRuAU6dw2tERyTduiNOnT//NyW2rV68uLSwsHKJWq0/+1nP9VgyuHgN/adRqtRbARgAbBUHwTEtL25CcnNzJy8uLBgQEqBz8/QEAWq0WpaamKNZq+fjERPQPDcXp06dpQEAA7d+//5P/qHmeRfGUlLBRvygyn+4jOo19+/ZJnTt3Jt7e3k3vxPPM7cNxKJHLkaLTSVPXrePM/+//gLIyLFiwABkZGYiIiKA6nY5XOjqyNQdLS5j26wfTjAzYzp3LJIMrK4H795n09IIFdZfQm5lRrVbbfLt69GDuKTDNmpenTTM5U1amSwT43idOAEAWAJsap/enADaBkE0AxoHSb5/4+f3BCIJgw3HcNgD9KKWYNm1aXeGaBhQXs/oGixez8MsJE5iv/vvvmevO2Bho3x5iXNwjtXceRf31AY1Gg5KSEhWAZ2LGZDD8Bv42qNXqywD8BUFwSk5OnpiWlvZ+aGiocbdu3biTJ08iLi4OEyZMqCu2ERAQQMiv/VXXVtlauJDJJZ8502iXb775Rrp//z6hlHLV1dVSUFAQMTdvYs20qAjSiBHgAGzdulW0a9uWGMXEMOOTmQly5gzad+0Kk9hYcjczE1bDhjGjvnYt8PzzLGR15EgWppmXx1wX9WSF8/PzUV5eLnuk2N2JE6xNNZIVhBBctrCQZ4eEDC5Xqe4MOHJEJ9PrWcYZpX41Ow0BMBPAtyDk/wAcAqVPoXnx+yIIQjsA12rDdt999100cu3o9SxkduVKVonNwYEptzaTcdtsVM8jkCQJx44d02s0Gv3w4cNVAHDz5k0oFIoL77777iN63z8Pg+E38LdDrVbnAFguCMLOo0ePHjlx4kTr6upqMmTIkAYVln6z3DPADP+AAWwEnprKfP41FBUVcRYWFhg5ciQiIyMRHR2N4cOHNzpFxRtvID89nR6YPVsqLy/nx4wZA7mtLfMzf/8980PHxeGsQoEKd3f4eHkB6elsjUEmAz79lC1MEsLq/yYns3u6fh0wNUVkZCRt164ddXZ2btRgjUYDpVIJ4uDA3Bz1GD9+PJYvX44zgYGWCd26wWT+fDp58+bM72bM+EmSybRYuvQGgC5qtvsgAOkghAKYAUrffPhavxVBEFQA2gNwJ4R4yuXyNlqtdimAzgD2AQAhJAuAjUKhkJeXlyvrDP/Nm8C+few7MjZmrpzOnR8rzfG0hv/OnTv49ttvAUBmaWlZCUBFKcXZs2erqqurdz1tm/8oDIbfwN8WtVqdIQhCJwCxLi4u7t27d29CMP/pyMjIwJUrV6DRaNCxY0d4enoyI/vjjyyu/+bNOmNiamqq79+/v8zZ2Rk+Pj7cuXPnRAC8VqvFjh07aEFBgaTRaHjq749+8+bBQ6sl586dQ0VFBWxtbFhM+eefo3ZRMTsgQCouLubEwkJwSiVbvB0yhLkm6uPjw3IPaoxeUFAQ2bp1K/nuu++kkJAQzsPDo27XdevWUZlMRod07865jBzZ4DQqlQrjx49HSUkJIiIiwIki9D4+rkH9+r1Pysrolfz8yuLi4kWCIAxTUzoAAEBIKACHmr/XA1gHSuN+zbMWBIHwPP+NQqHwFUWxEwBjc3PzMltbW2pqampy8eJFHsC0eodMpZTuBaCtrq6u+Oabb/BBz54g27YBgYFMqO+991iU1RPytIY/Jyen9s/T1dXVnQH2fyY3NzdfkqSvnvhEfzAGw2/gb41arS4TBGFcZmbm1YSEBKl79+6/epgvSRK2bdsGV1dXUS6Xk/DwcFJeXk4yMjLo3bIy0eyDD/hWy5aRruHh0GzbBmNjYz4zM5N6eXmRNm3a4Pjx45wkSTh69CgKCwsxdOhQ3sbGBiaTJ0M1fjyBmxuxsrKiP//8MybHxJBcSZJM/v1vzhtAcXExHB0dOWNRlOS+vhw+/hjYtq35m504EZgyBejQAW7vv4/nn38et27d4sLDwzF48GCkpqaKAGBpaUlu377Nnf3qK2p0/jxs09IaWLnaxWD/mrUSAHC4cwdwcSF9MjNNYq5e9YiLi0v+/PPPL+v1+n26pUs/UqvVR2p25QGUgpDBAKaD0lFP+qwFQWinUCg2abXawM6dO6OyslLKyMigLVq0MJMkCRqNpv7u/6p5lfA8H26k13dzSU+nA0SRkKIilrA2YMATJ9nVR5KkJzL8VVVVKC8vR1ZWlk6pVMYAKPf19VUBQGFhISilx9Vq9VPqjfxxGAy/gb89arX6miAInkeOHDlhbm5u5+7u/qvPJYoiwsLCeJlMhpMnT0qnTp2Ck5OT1KdPH1lJSQnVSpJ4S6vl9/74I+RaLcnPz4eNjY3k7+/PWVlZ0RUrVpDq6mpMmDCBtKnNEtXpmF8egL+/P6moqJDSjx4l3MCB3IkDB2h2dja5HB+PwTt34siAAZw+MhKypqKDHmbw4Lravj4+PvDx8cGdO3fEY8eOwdXVlb9y5QoopQgLC0OrMWPIro8+wqiKCpiYNM5xakDLlkB6OoiDA4KXL+d8Z882uctx3Q4cOOB1//79DABbAACUTgYAEGIEILnm73gAi4WlS7UANimVygscxykIIaaVlZVBAGBkZHRFLpe3DgoKUhw7dgyOjo5wdXXlLC0tIUkSLl++TO3t7cmkSZOwefNmAHgHwDtWRUXwTE2Fd58+sDc3B5kwoVEW7dPyJFGP1dXVWLlyZTWAexzHZVVXV78ok8meP3fuXAgAhSiKcq1We+033cjvjCGc08A/BkEQeiuVysOjR482cnV1xcNFOJ6Ejz/+GPPmzXukcayoqIDs4kXIBw7E8tdfh5OXlzR58mSuoKAAx44dE/v168fb29uznWtFwWp1YFJTgTfegBQRAaJQYNWqVdQ8L4+GhIZyjqtXI3PxYnTo1u2R95ibm4tdu3aJvr6+fFC7dsDrr7OchnprGpIk4d///rfUuXNn7rnnngMSE3H2gw+klNGjMXnyZO6Jno1eD/TsyRZL27dH7u3bWL9+fZler++gVqtvP7y7IAgmo3/5JfLQoEG9OqamomNqKm5u2oQWLVpALpdj61aW0Dpt2jRYW1vD2NgYt2/fZpLX9XRytm/frjcyMpINHz4c1zMykPDdd9ILGg2noxSVrVvDYvx4kCfQTXoSjh07hlu3bokvvfRSs5FfcXFxUlxc3L6FCxc28JUJgtAKQK02h69arb7Y+Oj/DYYRv4F/DGq1Ou7jjz+euXv37nk6nc7L2dm52svLy8zT0xPN1UgFmKJmamoqEhMTRblc3qhq08OYmJgwg3jqFBa4uwNffcVBkmBnZ4cJEyY0NCCrV7NXRgZ7X1gIuLszHz6AvjduoM2PP3KFkyZBvns3monCb8C2bdvE6upq/uLFi2JQt2487t2DNj8fpUolbGxskJ+fj40bN1JTU1MSUluZ7PJldLt3j4spKpLi4uIQHBz8+MVvmYxFFAGAqyscFyxAQECA6ty5c5sEQRgCQALgBuA5mUz2Is/z3XaNHasEANfZs2F75w5aBwWxyJrNm7Fo4UJU63QNvou6DrIeoihCrtUC27bBTaOBW0ICh+nToXjhBShkv69Jq/HxNzs6zsvLQ1xcXHV1dfX7D29Tq9V5Qo0ExLNk9AGD4TfwD+P999+vjfm3ysrKGpifnx92+PDhwZ6enrRnz55GTRmaNWvWiIQQzs3NDZMnT0YjLfbm8PEBd/s2i74ZP75p9c5Jkx5EAr31FjB1KsugXb8eiIxEu6++ItE9eiBx717a4+5d9O3b95EO55KSEpSXl/O9evVCUlIS0fI8Iv/v/2jFwoVEJ5cj38ND0ul0XGBgIO3Xrx9X579+8UVwL7yAPomJXEREBGJiYmBtbS05OTnRfv368U2GodZn82agSxeEHDkiLxPFHmkyWbEkSQojI6NqU1NTWWFhoWrevHmQyWSQyWQNRvBYswbo3RvyRYsgP3qURTNpNGiyeP3t2+iycydfXlpKxe7dCT9iBBAdDb1ej5iYGFRXV6NVq1bw8fH5XaK2HrW4SynF5s2bNVqtdpZarW4ujPVdQkhJM9v+ZxhcPQb+8QiCYM3z/EyO4+ZzHGdkY2OjdXR0NPby8pK7uLhg+fLldNy4ccTCwgLW1ta/LqGnrIxlie7Zw7Jqa9m6lUk9uLgwsbXvvmOZxkePsu2zZkGn0yExMRHHjh1DUFAQHB0dcefOHXh7e8PU1BTbt2+XCgoKJEmS0LNnT9nx48dpWFgYiY2NlW7fvg1Jkrg3r12DqroaecuWQaVSNW7H2rVMjuLECdy9exd6vR65ubk4d+4cVSgU0rRp054syW3kSKBtW9xXq2FkZASFQgG9Xo9ly5bhlVdeaTqZCkBWVhasjIxgcfs2KxJjbMyE67y82DrFxYtML2fJEminTcN2U1Mpv7ISo0aN4tq1a4eLFy/iwIEDaN26tXjnzh1Oo9EgKCiIeHp6wsTEBKqmOpEn4PDhwygoKNBPnjxZBgAFBQXQ6XRwcnJCSkoK9u7dW6DT6RzUavVfypAaDL8BAzUIgkAAOAHw4nm+p0wmmwLArrq62ojneS3HcWUtW7ZUjRw50sSmCUGvR0Ip8PHHLDTzzh2mzAkw4bcBA5jhnzGDdQpDh7JwzIc4e/asdP78eZSXl3NKpZKWl5fD1NQUer0e/fr1I+Xl5YiJiQHAYvCdnZ2xc+dOsVOnTrynhwcTpTt+vFG8fs3JWU2Ah/RokpKSEBUVRefPn/90vd28eSyhbMcOAMC6deskV1dXrn///o12TU9Px7Zt28BxHExMTERbW1syKTj2H6JNAAAQNElEQVSYg5MTy0DesgUYOJB1KmPHAjyP6upq/Oc//0FZWRkAQCaTwdXVVax1paWnp+PgwYO0qqqK6PV6zJkzB9bW1k/VBACIjIzE3bt3xUmTJvEpKSnYs2dPKSFEb25ublFcXKyXJCngWXPjPAkGw2/AwCMQBMETwOsAvgSQxfP86zzPL5s+fbrq1xgS6HRM0vnHH1mYIQBMn87UHr/4gilAtmjx2NNQSrFixQpaXV1Npk+fXmfU8vLyYGRkBCsrq8YH3bvHZITPnXvQ8dRy4wYz1PUS0LRaLb766iupd+/epGfPnk9n+JOTgaws1p60NGxIS5Nu375NJk6cSJycnBrsum3bNiiVSjE0NJS/evUqDh48iMWLFz/YwdaWuZIGDgTAZgdbt26l1dXVBGCJeFOnToWdnR2rYfwQy5cvR9u2bXWmpqa1xq5RwvZD7wkAWnMtvqqqCl5eXlJubi6Xl5e3Sq/Xq2Uy2VYAhxcvXrziqZ7LM4LBx2/AwCOokYGYU++jlR9//LG0devWz2bMmGEie9rFRLmcLeRaWrIR/s8/swxbpZIZ3Scw+gAzVJIk0dGjR5P6HVBttacmsbEBbt1if5eUsHuo5cABJttQz/Cnp6dDFEUuMDCwwWkopbh8+TJKS0ulwMDAph3pPj7s9fXXwMqVmJKRwUVGRko//PADcXd3x/Dhw+tUVG/fvq0PCQmRmZiYwN7eHnK5XEL9WiF37gCEICIigiYnJ1NJkjgTExNMnz4dVlZW+PDDDxEZGYnXXnutuZZXp6WlLQdwv/bxPbT9Ue9NAdicPn36Zs37SLVafR/A4OYu9lfAYPgNGHhKRFFcXVZWNuiHH34I9vPzM+rWrdvTrSK2aMFcP1evMt//8uXARx8B77/PInyekMrKSq5ZiefmMDUFJk9mHQATX2OMGsWkp+vRsWNHRERESOnp6ZynpycopUhISKAJCQn03r17nEKhaNQpNOLNN4E5c0CuXMHADz/kHL/5BvEJCdL69evp7NmzeY1Gg4qKClltLVulUglRFLmHz5GiUiHJyoqMHTuWtGvXDhzH1RnnMWPGICIiAsnJyfDx8WnqLkQAa2ukPAzAUIHLgIGnpqYA/PP5+fmzDx8+fPvUqVNUFMXHH1gfQpi4W24uU4jMyWEuGIBpw0+Zwv4WhAcVvZrgV7lqv/uOlZKsr9aZmMjcTfXgOA7dunXjIiMj6alTp7Bx40bp+PHj8PHx4VxcXKi9vb30RNevUTQl3brBx9cX44ODueLiYj4+Ph4rV66krq6uYq2AXG2S1g8//KCXJAmSJCHp1i16PT8fL730Ejp06NAoWsfb2xstWrQQU1NTG9YybojBp10Pg4/fgIHfgCAI7kqlcpNcLvcaMGCAye8SRpiVxcIZO3RgRV4uXGDGet8+tgD7009Ar15YuWeP6OHhQQYOHPj0FywrY/rzp06xBeUvv2TF3n/6qcFukiRhx44dtLi4mDo4OHD9+/eHqakpNBoNVq5cKYWEhJCAgIAn9//n5gJubti8bJmUWVbG+fv70+eee66Bz/3evXvYvHkzdXV1JZmZmZKxXI4JY8dypnZ2zZ72/PnzYnx8PCktLSXTpk0jdvX2Xb58eaVGo2mvVqvznuIJ/a0xGH4DBn4HBEHoq1Qqv7S1tW0/bdo0k1+r9twsN28y11BoKCuuMn8+rioUsJsxAxZFRcyAW1mxspBPyq5dbHZhbMyKy0hSnXTEk3Djxg38/PPP6NKli37IkCFP7jYuKsKFmzfBvfwy2m/fDqMmisPcunULW7duRbt27cTnIyN5/sqVhq6pJpAkCeHh4WJ6ejr33nvv1X0BBsPfGIPhN2Dgd0IQBE6hUJw3Nzd369atm5m3t/fjdW9+AwWpqTi3aBF8Vq2Cy9y5bO1g7VqgXTsgNpZJKlRUPLozWLaMReC0a8cWnR8a8T+O7OxsbNy4ET169ICpqSmMjY0bvIyMjCCXy+uiZkRRxMmTJ6W4qChu+s6dsImIYFE7j0qKy81lM5T6+Q/NUCuLbGlpWUophV6v5yoqKowBtFSr1YVP1bi/MQbDb8DA74ggCDIA/ZVK5Qy9Xj/YxcVF7+fnZ+ru7v6rtIEex5EjR5CQkAAvLy9x+PDhLB920SLgs8+ApUtZwtjFi0y++Y03WHhlXh5zIQGsNGVsLODry8I9w8Ke+h7i4uKQkZEBjUYjarVaqtfriV6vJ6Iocnq9HpIkged51EZAyeVyady4cZxLrTxyq1bAhx8Cr77a9AVyctj6w7RpTW+vR3V1NXbs2KHJysqSiaK4AMAeAKVqtfruUzfsb4zB8Bsw8AchCIIpgFEqlWqWXq/38/T0lLp06WLUunXr36cITA3379/HunXrJG9vbzpw4MCmM2yXLAFefJEZ/VGjWL3ghQtZ4Zbp01mH8N57wLBhv9t91aLX61FZWYmioiJs2LAB06dPb5jBm5wMuLszmWlvb1b/tj6HDrHooKtXn/iaNXWOy/R6/fgPPvjgwO/UlL8NBsNvwMCfgCAIrTiOmyCXy2dSSh1tbW213bp1M/fx8WmoW/MrWbdundS2bVvSr1+/xy8uSBJT6vzyS8DeHhg3jrlaTE1ZIteMGaykY0AAcOkSyyxWqZie/W/osNLS0rBjxw4sWLCgaQmFsWNZVawlS371Nepz7do1bN++vUqv178OYGdN/L0BGAy/AQN/OoIgOAPoKpfLP9LpdN4zZsxoUoXyafjXv/4lhYWFcbXx8E+NRsOiiVxcWNFxV1fWQaxZw1xHs2cD+flAXBxzGU2cyPZNSWEzidJSptNfk5T1MNnZ2di2bZvk6OhIGymUPszLL7Pz7dzJ3ut0LMLp+vWn7nji4+N1kZGRcp7nL4ii+AlYB/CPN3oGw2/AwP8IQRBMOI47CsC/f//+xMnJidja2jYuEP4IsrKyEB8fj+vXr+Odd955cuXQp4VS5h6iFDh4kEk/ZGUB27ez8pChoWxG8PXXrATlnDms40hPB+bPx4FPPgFt3x5DXnrp8W6ujAzmkurYka1P9OvHVEvXrKkrLPM0aDQaXLhwAWfOnKmoqqrasHDhwjmPP+rvjcHwGzDwP0YQBH+lUvkWIaSrVqt1VSgUeltbW32rVq2M7Ozs5G3btkVtgtPDfP/997h9+zZee+21R8s1/BnUdg5nzzItoHPngIgI4PPPUdipE3SenrCfPJnJVC9dynz72dksc/nQIRam6uLyYFS/YgXw3/+yWUVuLpOc+BWGvxaNRoNVq1ZVVVZWjlar1Yd+n0b/NTEYfgMGniEEQeAAuADwJoR4K5XKQL1eH9qqVStJr9dj9OjRprXaPEeOHEFSUhImTZr0vzf6jyAnJwcbN27ErFmzYGVkxBZpnZyY7HJSEqsQNnXqA8mIbduYu2nNGra2MHw4e/38/+3db2gTdxzH8c8ll5x2taE4xdXNOQsLo5SNsmmVIhZWmCiICpMp2yMfzOEjn2wo5OcVcdDB/oiiMPbIzVVFV9YHE3QtaWJBN5U+8E9x1lWZTYmITROTu8vltwftxM0/a9PYJP19XtBHyYVvn7wJl7vv/Tj2e8QUHDp0yB0eHrYALBBCjE79vytPDD9RiRu/Omitx+N51TAMsXXr1tkPHjxAR0cHgsGgbGlpKfDdYpPjui7OnTsng8Gg9qSNpX19fThz5ozcsWPHY1sxHyPl2FK2QGBscdzo6NgVPx9/PHaT2hTvi7h48SI6OzsBAJqmfSalbFPxnD/DT1RG9uzZ84nX6/06m836ampq3FWrVnlr/7tieRqNjIygvb09dffu3b+y2ezr1dXV6ZqaGm39+vWz4vE45s+fDwBoa2uTmzdv1h5eu19kvb29OH36NADME0LcLfY80827e/fuYs9ARBO0cuXK37q7u/1+v39hMpmsjMViqfPnz+v37t3z1NbW5vd0sDwlEgkcPHgQIyMj/lwuNxcAMpmMLx6P6z6fD8ePH8etW7csy7L0wcFBVFdXa68U6CHoU9XX15eLxWJ/CiE+L/YsxcBv/ERlyjTNBQDeANAFAI2NjW5TU5P3ea6JAMZ24kSj0Ww4HNY8Hs8fHo8nbdv2W/+87vP57jiOswCPbP/VdR11dXVy+fLl/1qgVijpdHpS7z98+HAuFotdCoVCbxd8mDLA8BPNAKZpBv1+/07btj/atm3bw1MshZbL5dDe3p4eHBy8bNv2hwD6n3aO3DTNeQBOAmgCxtY853I5bN++Pa/HID5NT09PNhwOS03TnIke47puhaZpn4ZCobaCDVJGGH6iGWTv3r07XNfdq+t6rqKiwpkzZ46sqqrSA4HArLlz53rr6+snvTPItm0MDAzgypUr6f7+fk3TtKuWZTVP9E7Y8WcZtwD4HsC8hoYGd/Xq1d5JP73sCe7fv48DBw6ks9nsG0KIwf8/ggCGn2jGGV8U9yKABY/+GYbR4vF4Gpubm2c3NDRoz1oVIaXE5cuXceHChdHbt28bfr+/L5PJ/CCl/FkIcXMKsy0yDONbwzCa1q1bV7FkyZJ8PwoAcOTIkQc3b978YteuXbun9EGKYfiJFGKa5juGYXyl6/pbS5curVi0aJG2cOFCSCkRj8chpUQikUBXV1cymUzesixrD4BfhBD3CzlHa2vrWl3Xv6urq6tas2bNrHy+/d+4cQNHjx6NOY7zmhAiU8j5ZjqGn0hBpmk2+Xy+971eb4tt27W6ruc0TRvyeDwZKWXSsqzPpZQ/Pc9r3E3TnGMYxtFAILByy5YtL1RVVU34WNd1sW/fvtTo6OgHoVCo83nNOFMx/ESKM01zNoAlAK5M981Mpmlquq7v1HV916ZNm2YvXrx4QsedPXvWjUQi0fHfGhixSWL4iajoWltbW3RdP7lx48bKYDD4zPcmEgns378/7TjOm0KI69M04oxSuKdBEBHlKRQKnXYcp/nEiRPJ69ef3fJTp06lAexn9PPH8BNRSRBC/O44zrvHjh1LdnZ25oaGhh57TyqVwtWrV2c5jvNlEUacMXiqh4hKimmatQD+AIANGzagvr7+X693dHRY165d67Isaw3P7+eH3/iJqKQIIW4AqAaA7u7ux8K+YsUKw7bt9wC8Pt2zzRQMPxGVnPH7Bl5KpVK3w+Fw9tHXLl26lAXwjRCivzjTlT+Gn4hKkhAiZtt2Y29v71BPT8/D+A8MDGSklOeLOVu5Y/iJqGQJIYZs214WjUZj/8TfcZwcxn8DoPww/ERU0oQQQ47jLItGo8ORSCRbWVmpAXi52HOVM4afiEqeEOKO4zjLIpHIcCwWSwP4tdgzlTNezklEZcM0zQCArBAiVexZyhnDT0SkGJ7qISJSDMNPRKQYhp+ISDEMPxGRYhh+IiLFMPxERIph+ImIFMPwExEphuEnIlIMw09EpBiGn4hIMQw/EZFiGH4iIsUw/EREimH4iYgUw/ATESmG4SciUgzDT0SkGIafiEgxDD8RkWIYfiIixTD8RESKYfiJiBTD8BMRKYbhJyJSDMNPRKQYhp+ISDEMPxGRYhh+IiLFMPxERIph+ImIFPM3SEgQfL3nRRkAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(edgecolor='grey', facecolor='w')\n", "f,ax = w_rook.plot(gdf, ax=ax, \n", " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", " node_kws=dict(marker=''))\n", "ax.set_axis_off()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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127.225988e+10MX03Baja California Sur2912880.7721.785573e+077225987.7699573.016013.016707.0...0.003.984.204.224.394.464.414.422.0(POLYGON ((-111.2061233520508 25.8027763366699...
232.731957e+10MX18Nayarit1034770.3416.750785e+062731956.8594836.07515.07621.0...-0.053.683.883.884.044.134.114.063.0(POLYGON ((-106.6210784912109 21.5653114318847...
347.961008e+10MX14Jalisco2324727.4361.967200e+077961008.2855309.08232.09953.0...0.033.733.924.004.214.324.304.334.0POLYGON ((-101.52490234375 21.85663986206055, ...
455.467030e+09MX01Aguascalientes313895.5301.350927e+06546702.98510384.06234.08714.0...0.134.023.793.944.214.324.324.445.0POLYGON ((-101.8461990356445 22.01176071166992...
\n", "

5 rows × 35 columns

\n", "
" ], "text/plain": [ " POLY_ID AREA CODE NAME PERIMETER \\\n", "0 1 7.252751e+10 MX02 Baja California Norte 2040312.385 \n", "1 2 7.225988e+10 MX03 Baja California Sur 2912880.772 \n", "2 3 2.731957e+10 MX18 Nayarit 1034770.341 \n", "3 4 7.961008e+10 MX14 Jalisco 2324727.436 \n", "4 5 5.467030e+09 MX01 Aguascalientes 313895.530 \n", "\n", " ACRES HECTARES PCGDP1940 PCGDP1950 PCGDP1960 \\\n", "0 1.792187e+07 7252751.376 22361.0 20977.0 17865.0 \n", "1 1.785573e+07 7225987.769 9573.0 16013.0 16707.0 \n", "2 6.750785e+06 2731956.859 4836.0 7515.0 7621.0 \n", "3 1.967200e+07 7961008.285 5309.0 8232.0 9953.0 \n", "4 1.350927e+06 546702.985 10384.0 6234.0 8714.0 \n", "\n", " ... GR9000 LPCGDP40 \\\n", "0 ... 0.05 4.35 \n", "1 ... 0.00 3.98 \n", "2 ... -0.05 3.68 \n", "3 ... 0.03 3.73 \n", "4 ... 0.13 4.02 \n", "\n", " LPCGDP50 LPCGDP60 LPCGDP70 LPCGDP80 LPCGDP90 LPCGDP00 TEST \\\n", "0 4.32 4.25 4.40 4.47 4.43 4.48 1.0 \n", "1 4.20 4.22 4.39 4.46 4.41 4.42 2.0 \n", "2 3.88 3.88 4.04 4.13 4.11 4.06 3.0 \n", "3 3.92 4.00 4.21 4.32 4.30 4.33 4.0 \n", "4 3.79 3.94 4.21 4.32 4.32 4.44 5.0 \n", "\n", " geometry \n", "0 (POLYGON ((-113.1397171020508 29.0177764892578... \n", "1 (POLYGON ((-111.2061233520508 25.8027763366699... \n", "2 (POLYGON ((-106.6210784912109 21.5653114318847... \n", "3 POLYGON ((-101.52490234375 21.85663986206055, ... \n", "4 POLYGON ((-101.8461990356445 22.01176071166992... \n", "\n", "[5 rows x 35 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 22]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_rook.neighbors[0] # the first location has two neighbors at locations 1 and 22" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Baja California Norte\n", "1 Baja California Sur\n", "22 Sonora\n", "Name: NAME, dtype: object" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf['NAME'][[0, 1,22]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, Baja California Norte has 2 rook neighbors: Baja California Sur and Sonora." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Queen** neighbors are based on a more inclusive condition that requires only a shared vertex between two states:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "w_queen = Queen.from_dataframe(gdf)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen.n == w_rook.n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(w_queen.pct_nonzero > w_rook.pct_nonzero) == (w_queen.n == w_rook.n)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(edgecolor='grey', facecolor='w')\n", "f,ax = w_queen.plot(gdf, ax=ax, \n", " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", " node_kws=dict(marker=''))\n", "ax.set_axis_off()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(1, 1), (2, 6), (3, 6), (4, 6), (5, 5), (6, 2), (7, 3), (8, 2), (9, 1)]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen.histogram" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(1, 1), (2, 6), (3, 7), (4, 7), (5, 3), (6, 4), (7, 3), (8, 1)]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_rook.histogram" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "c9 = [idx for idx,c in w_queen.cardinalities.items() if c==9]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "28 San Luis Potosi\n", "Name: NAME, dtype: object" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf['NAME'][c9]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[5, 6, 7, 27, 29, 30, 31]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_rook.neighbors[28]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3, 5, 6, 7, 24, 27, 29, 30, 31]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen.neighbors[28]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-105., -95., 21., 26.])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "f,ax = plt.subplots(1,2,figsize=(10, 6), subplot_kw=dict(aspect='equal'))\n", "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[0])\n", "w_rook.plot(gdf, ax=ax[0], \n", " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", " node_kws=dict(marker=''))\n", "ax[0].set_title('Rook')\n", "ax[0].axis(np.asarray([-105.0, -95.0, 21, 26]))\n", "\n", "ax[0].axis('off')\n", "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[1])\n", "w_queen.plot(gdf, ax=ax[1], \n", " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", " node_kws=dict(marker=''))\n", "ax[1].set_title('Queen')\n", "ax[1].axis('off')\n", "ax[1].axis(np.asarray([-105.0, -95.0, 21, 26]))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "w_knn = KNN.from_dataframe(gdf, k=4)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(4, 32)]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_knn.histogram" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(edgecolor='grey', facecolor='w')\n", "f,ax = w_knn.plot(gdf, ax=ax, \n", " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", " node_kws=dict(marker=''))\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weights from shapefiles (without geopandas)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n", "from libpysal.weights import Queen, Rook, KNN" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "w_queen = Queen.from_shapefile(pth)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "w_rook = Rook.from_shapefile(pth)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/weights/weights.py:170: UserWarning: The weights matrix is not fully connected. There are 2 components\n", " warnings.warn(\"The weights matrix is not fully connected. There are %d components\" % self.n_components)\n" ] } ], "source": [ "w_knn1 = KNN.from_shapefile(pth)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The warning alerts us to the fact that using a first nearest neighbor criterion to define the neighbors results in a connectivity graph that has more than a single component. In this particular case there are 2 components which can be seen in the following plot:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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79uw5cNCgQSzD2OcFtVgs+PTTT0WTycQCaM3zfA0+QwWFh4+y469Aq9XejI+PX1VaWnrlzJkzA3JyctCuXTsVx9noR9+smbzrbtkShBB07NiRy8/P9y0qKvp7XFzcPq1We0On00UBOHjo0KEhe/fu3a/VagurmFeMi4tbX1RU9KTRaGzWrl07GxPWgNUqG/+pUx0+tSbOnDkDAFJYWFiDjQf5+/sznp6erqmpqS8nJib237t3b4pWq62iOMM2giD01Wg0M8eOHevEsvbf41iWRZcuXZjDhw9DpVK1jYuLM+h0uhStVuvoZSgoPDAUw38HWq3WEBMTczIuLm5pUVFRpzNnzgQWFRURq9XKNm/e/M8uVwwDtGwJfPYZ0Lo1mKZNERoayjVt2tQ9LS3tJZ1OZ6CU0hYtWozo1q1bUE5OTv/+/fuvqGZeS1xcXJlKpRretWtXx/sCb94MxMXJrp56JDU1Ffv370fr1q1phw4dGqzhBwAfHx/06tWL02g07a9evfpMXFzcL1qtttje8/fv3/9Kly5dBoSGhjp843V2dkZ4eDiaNGkSmpOTM8psNl/RarVnHR1HQeFBoRh+G2i1WnNcXNwmo9F4KCsr61JqamrAjRs3XEJCQri7XADp6UBgoKznA7lhS5cuXVRpaWnRFoslz2AwhLi6urpmZ2f7JCYmto6PjxdjYmJSbc2p0+mibt68+cy5c+fKAgIC1G5uDiSqSJKcDtq6dV0u+x6SkpJQVlZGhw4dyjjarvJhwHEcKlx0qszMzH8kJib23bt3r6dOp3PRarXpVZ0nCEIbjuPWjR492tnFxaVWc2s0GrRu3RoBAQHqc+fOPXHgwIEWcXFxB7RarR1KgAoKDxbFx28HgiBo1Gr1Rm9vb+2kSZNcVCrVnx/evCn7/Pv2vf1WeXk5lixZAgBDVSrVIoZhunXp0oWcP3/+8HvvvWdTykEQhFc4jvuQUuo5ePBgp6ioKPsWV1YmZ/xcuADUs3E+duwYDh8+LE2dOrVB7/ZtUVJSgitXriAtLc149uxZZwDvAPgNwLU7s38EQdAQQn6hlI6aM2cO7PXtV0d5eTliY2MNqamp6WazuXN9ZBspKNQnyo7fDrRarTUuLu4Xs9msTUtL8w0LC1Pd9gOfPw9s2yZX9lagVqthsVjMOTk5AywWS1+WZYeUlJT4WiwWKS4ubqlWq70rKV4QhPZqtXrbmDFjmp0/f54UFBQYe/furYI9HD8OXLwo5/zXMyzL4tChQyQ6Orrex77fODk5wdfXF506deJatWoFvV7ft7y8/C2WZdvExcVt1+l00Ol0RK1Wrw4ICBj5zDPPcB71lBGlVqsRGhqqOn36tKvVag2Ii4vbrvj8FRoSf7md3MOC53nJZDI9mZeXdzopKelPwx0RAXz0EVBJyGzIkCFqQkhLAD5ms/lxk8l0xGw2BwLobWP4XoGBgZbQ0FD4+/sbbt686WKx2JkZmp4OfP99bS+rWry9vWG1WmFqIFXEtSU4OBgTJkxoMn36dGdPT8+xAKwcx+0jhHzh4uLy1NixY52rzeKqBYQQTJ482YVhmIkA2tfr4AoKdUQx/A7A87zRZDK9GB8fz6xYsaK8sLAiUYdSucL28uW7jnd2dpYApKjV6iMmkykGgBfP8wcrj6tWq4dpNBoOADw9PVUqlerGt99+W15jY3iDAbiPWj8Mw0CtVtObN2/etzkeJM7OzpgyZYprixYtJKvVOqBr166vTpgwwUWtVt+3+Xx8fKwsy750XyZQUKgliqvHQbRabZFOp1tRXl7ulJWV1S0sLEzFsCwwaZJcXCWKctYPgNatW6tdXV3FGzduaFiWHcuybHF0dPSJO8cTBIFhWfa94uLiFgzDcAkJCZwkSV2tVqv+4MGDMTk5OdbOnTszNvP79XqgSROgX7/7dr3Hjh0TW7VqxXh7e9fvwFlZ8g3TyfEkprqQmpqK48ePk+nTpyM8PJyrbTDXXgIDA52OHz8eWVHgZ3eWkYLC/UTZ8dcCnuezKaWzcnJy4pctW2bOzc2Vq2s3bADeeef2ca1atcKgQYPY4cOHu/Tr168TIWSmjbGkGTNmRJWXlw/W6XS7VCrVOQBZM2fO5AHgwoULXJUB+GnT5GYx94ni4mIYDAbWqb6NsyQBYWHAqlX1O26N00rYunWrqNVqqUNZU3XA09MT4eHhhOO4+q2sU1CoA0pWTx0QBMFJrVb/IoriE//6179UGkoBi8WmbIIoiliwYIFZFMWWPM/bLOayMb6PWq0+2K1bt8Dhw4ff7Y+QJKBjR2DPHiAgoH4uqBKZmZlYs2YN3n///XrJdrnN8ePA7NlAbGz9jWkHe/bswdmzZ+kbb7xBHCnSqiu5ubn47rvvDJIkjZ89e/amBzaxgkIVKDv+OsDzvGnGjBljWJZd8dNPP5XBxQVwcQEmTgSK736qZ1kWfn5+RgBDHBg/12w2R546dermpUuX7v7w9Glg9er7ZvQBwN/fHxzHSTUqlzpKRgbwxhtyUHrfvvoduwr0ej2OHj1KR40a9UCNPiAXlw0cOFAjiuJvgiD4PdDJFRRsoBj+KhAEoZW9crxms3k3IUTO9FGp5MYtNnLqo6Ki3NRq9Y8LFiwoWLBgQbUaPreoaNA+ed26dYaVK1eWHjlyBKIoAt99J++c7zPe3t7kwoUL9avJfPKk/FTUsSPQqlW9Dl0VGzZskAICAqSgoKAHMl9l+vS5Xb5Rcx8HBYX7jGL4bSAIQmsAmWq1eokdx3JqtfqTyMhI99tvPv44cOwYcODAXceGhoYy7777rvNLL73kSSkNtXc9c+fO3S6KomdWVtaE2NhYZFy7Jks1P/tszSc7gCRJ2LBhA1asWEHXrVuHzMxMdOvWjZw7d67+lDmtVuDqVbnSuF8/WfeoqKjehrfFtWvXkJmZyTzxxBMPTWiOEIIBAwZY1Wr1Z4q+v8LDRjH8tlEDgCRJUyr02aujtdVqDbpHT7+sTH5VQqVSwcvLCyzLqufNmzfO3gXxPG/keX4rx3FzLsyfb5R+/x3w9bX3dLvYtGkTvXDhAoKCguDs7CyuWbMGO3fupJ6envUXCNq7Fygv//OJaN68e26Q9c1vv/0mRkVFSU2bNr2v89REVFQUp1KpurIs+5MgCM0e6mIUGjWK4bcBISShadOmkiRJzgCcqzuW5/krHMftSElJufuDxx4Dhg0Djhy55xwnJyc89dRTzk5OTu/c82ENWK3Wj1sdO3b9HKWW+g7MZ2ZmSlqtlj722GNkzJgx7L///W+88MILZMKECfX3d5KUBDz//J8/f/EF8OST9TZ8ZRISEmC1Wpno6OiH/rdeUUfgFBwcPJrjuBRBEDwf9poUGidKVo8NPvzwwy8opdMYhimfM2eOew3HvkQpXe3q6gpPz7v/H6sMBmiXL0fc1KkQVXcrMJSXl6OiAKwdz/N3V35VByFqvbPz+K9mzpwRFhEROHjwYDUA5OXlwc/Pr07ZNxcuXMCmTZvw5ptv4r6Jsi1eDIwde7eg3MKFstunnqUhzGYzPvvsM/rUU0+R0FC7PWsPhNjYWNPJkyeTLBbLczzP5z/s9Sg0LhTDXwWCIDgBsALQ3GquYuOYdzQazUcGg0Gj1Wphq8iKUgpiNgOUglbKh98nZ7S8yPP8OrsXRshwAK8KH3wwWa1W77Jard0lSbrtux4+fDjc3d0RFBSE2lSkrly5UvT09MQzzzxT//7wGzeAd98F1qy5XeQGADh6VM5OatGiXqf75ZdfqF6vpy+//LLtAriHiMViwa+//mq9du3a1zNnzpz+sNej0LhQDH8NCIJAOY4rYllWD2CfyWT6DUC6k5PTTJPJ9DQAvP322yCEwM3NzfaO+7335AyWv//9rrcXL15s1Ov1f3fQ8EcA8AGlsYIgsACcGYZZIknS6wB4AAIg3wAiIyMdvt6SkhJ8/fXXGD9+PAIDAx0+v1r275cF7RYvvvezlBTAxweoRz/8p59+Kj7xxBNsx44d623MulJWVobExERotVoUFRVh5cqVRVar9Qme5xMf9toUGg+K4a+BBQsWJImi2CcyMhLu7u5ITk4uKS0tRbt27dSiKOLkyZO3YwAdOnQwjhs37t6YgNH4pzTBHTvP8+fPY9OmTcVWqzWa5/kzNS6GEDWAIwCiQWnpnR8JgqDhed4gCMInAP797rvv1tpdEx8fj8TERDz99NM0NDS0/rbKkyfLqa4DB9772fTpcuvIenT3/PDDD9Tf358OGTLkofv3AVkuYuPGjVSj0cBsNtPhw4cz27ZtgyiKFkmSIuvSNlJBwREcb/PXyDCbzSNUKtWMpKSkfw8dOlR8+eWXb/v8KaXo1KkTEhISaGBgIPH29ratbeDsDGRmytLJu3bddnOEhobi5s2bHvv27UtYuHDhbyaTaSbP81k2x5CJAFBa2egDAM/zBgAghIyilNbJRz9w4EBQSrF3714EBwfDZvtJR7FagcJCoGtX25//3//VfY5KhIaGkmPHjtEhQ+yumbuv7N69W+zduzczcOBAcvDgQbpp0yYEBgbSzMxMlSRJJxctWnTcaDQ+y/N8+sNeq8KjTYPYCTVkeJ6/OXPmzPcAdIiLiyvftm2bqaCgAICcm92+fXtMmjSJaLVadO7cuerdcatWspLmHa4gQgj69euHV199tUmPHj3GsSybIghCdf4VVwDPV/M5KKXveHt733NjcJT+/ftDFEVp6dKl1Gq11nU4WT560CDAs5pElrfeAg7ZVddmFz169EBRURFjMBjqbczaotfrcfPmTTYiIoIQQhAdHc2MGjUKmZmZ5LHHHqMzZ85Eu3btwgC8+7DXqvDooxh+O+F5PtVqtYb98ccfi5cvX27IyMhwbABCZD//F18Ap+5+ovfx8cHQoUPVISEhBMCwKs7nAMxBDemlAHR5eXlN8vLyHFtfJdRqNd544w22uLi4flw9R47IOkbV8corQJcu9TIdIF+Di4uLeOXKlXob01FKSkqwfv16fP/992jZsqV4Z7MXjUYDURQRHx9PSkpK0LZtW7VarR7x0Bar0GhQDL8D8Dx/bfbs2XOsVutHZ86cqV0v1U6dqtz1hoSEuDg5Of1TEARb6TiuALaD0mqtGM/z5QzDXP3mm2+g1+trtcQ7UalUtK43EQDAzp1AeHj1x4SFyRpEJSV1n68CPz8/NiUlRazzQMePOywql5CQgKVLl0KSJKlz584YOXLkXZlSISEhmD17Njw8PMSUlBTk5+dbrVbrIaWyV+F+oxj+WkApjTt27BiXlpaG8vJySJIDUjZDhsga+v/73z0fdenSBSaTKRzAr4IgVHaszwGQbM8UkiSNBICffvpJb3cnLxswDHP7VSeKimTRusGDaz520yagHnfo4eHhSEtLq9sFlJbKwnsOxCHi4uJw8OBBPPfccxg7diwzaNAg+Pj43HMcwzDw8/Njr127Jnbv3p3TaDSjAAy6d0QFhfpDMfy1gOf5QwzD/G3Dhg3ZS5YsweLFiw0XLlywf4DSUiAh4Z63GYbBhAkT0Lx588c4jiucP3/+94IgsBUFAmMAXLRzfWcBsIWFhTs++eQT048//lgSHx+PhIQE6mgWl8ViIbeay5vN5to9RRw/DkRG3pXRVCWLF1cdAK4FHTt2hMlkIrXqIlZaCixYIMtLnDsH/P47MGuWfBOoAZPJBH9/f7FFixayqF41+Pr6oqCggPj4+KBC+qOe82gVFO5G6cBVSwYMGHA6Li7uMwDzrFbrnpSUlHGEEHVJSQm8vLzuKeailKKoqAjOzs4gTZvKkg6XLwPu7ncFfD09PREZGanOz89ns7OzuwFYot23LwDANlB61t71abVa2r9//w3x8fEri4uLT2RkZFy6dOnSAA8PD7Rs2dLu60xLS5POnDlDk5OTsWPHDpKUlAS1Wg0vLy/7s33OnpULtNrb2Xr2+eflYx1YZ1UQQvDHH3+Ibm5ujJ+fA4rIlMo3qpMn5ZsWy8o/e3nJnc86dZK7rVVRJKfX65GYmMgcPXoUSUlJCAkJqTLT6vTp0wAghoWFMceOHTOVlJRcjImJiXf8ahUU7EMx/HVAq9VCq9VSrVabuW/fvhPXr1+nFy9e1JjNZvegoKC7/LlZWVlYtmwZAgIC0KwCAgRMAAAgAElEQVRZhT7XlClAcLBNA1dWVkbT09OvSZJU3uvIkTFqi6UZPvjg3seEmtdYrtVqk2NiYuJ0Ot0HFy9ehFartfv8sLAwkpaWhvT0dDJlyhT4+PiIBw4cIDqdjqhUKrS+U3rBFpTKu+SnnpILtOyhe3cgJOTu6t46kJOTw2RlZYnh4eH2DZiVJesHvfwy0Lfv3evw9ZXfEwS529rUqTafZHx8fNCsWTMEBgYiLy9PbNasGeNbhaheUlISysvLmRYtWqB9+/ZOJ06c6Ldv377ftVrt9VpdsIJCDSiunnpizpw5O99///2XzGZz/yNHjpQkJiZKpaWlEAQBq1evxnfffQcAcHe/Q/rn11+BHj0A871x4p49e7JBQUF+AL7RxcRM+c/LLzex4fd3lC4qlcp0K73RarXW6IbgOA4ajYa4ublRb29vREREsP/617+IVqulx48frzm4cf06cP480LlzlYcUFRVhxYoV4rJly8SSkhJ5t/+//8kqnrXAbDbfFXfp2bMnrl69ytYYi6FUfgrz85ObxFTXsOXjj4GNG+VgtI14DcMw6NatG3r37g1RFKuNAw0YMAAuLi744Ycf4OnpibFjx7Icx+1VRNwU7hfKjr+e0Wq1+ri4uA2ZmZmPJyYmNqOUppeXl6dLktTSx8cHHTp0QJMmTeSDCZFbJ86Zc4+2PiEEeXl5rKTTkZ56Pbn0+OMRRqPxubi4uOWO7NgrrS3v4MGDQenp6R0tFotq1apV2L9/PyIjI6GqJCIHyO6KlStXivn5+WTcuHHkzj613t7e5MiRI8jOzka11b179gAxMdUa/iVLlqCkpIRp27Yt3blzJ2EYhnjGxoILDQWpLu/fBps3b6b//e9/yf79+5GYmIikpCTpypUrktFoZIKCgu6+8VbmyhXgtddkH37z5tVPRIj8BLNrl9xGstLO/9q1a9ixYwdOnTollZaWssOGDatSO6lp06YwmUzIyspCWFgY/P39kZOTYy0oKCiNiYlx+ClPQaEmFMmG+0TF7rwZz/N5FT/3A3AQAKZOnQovLy/5QFGUdftt9OktKyvD2cceQ7mLC9wXLbLu2LGDBaDmeb7WFVWCIDixLPuOSqXqZTKZ+lFKfYKCgkwvvPCC052GKS0tDf/9739pQECA9PTTT9tsuF5QUIAVK1bg+eefR7t27WxP+PHHQJ8+VWb0SJKEjz/+GG+//TaaNGmC1NRUbN26lRoMBuLj7S39ffJkxt5YQnx8PA4fPoxXXnkFHh4e0Ov1KCsrw+XLl+nJkydhNpvx2muvkXuMf26uvGv/+9//9O07giTJT29btgA//4yEpCTodDp4enrS/Px8Mm3aNNTUC2DHjh04ffo0FUWR9OvXTwwODmZXr16dZ7FYWvI8X/d0VAWFO1B2/PcJrVYrabVa/R0/Z+h0uo9Zli05fvx4f0mSiL+/P8NwnKzjM3Ys0Lv3XTcANcehSdOm2ObkJAUGB9ObN2+aJEkas3fv3l+1Wq2plusSBwwYcLB///6/xsTELNHpdJ+Ul5cHnz9/vl3Pnj1VhBDExsZiz549GDx4MB5//PEqDa+LiwvS09NFs9nM2DT8VisQHw+MGiX3IrbBiRMnkJWVRbVaLQGA5s2bo2/fvqRHjx5o89JL5JeMDHrDYiEXL17Eli1bpIsXL1K1Wk2OHz8OLy8vaDQaAMDJkycRHx+PiRMnwtfXFyzLwtnZGe7u7ggMDCS9evUi+fn5dOfOnQgODr7r6QXl5XLFcN++jht9QD7HyQn05En8WlgoJZ88SV6YMAGDBw8m3bt3h4eNm3plgoOD0b9/f+Lu7o4DBw6QYcOGkWPHjhGLxZIbExNz/3tsKjQqlB3/Q0AQhNYcx212dXUNmzZtmorjOODwYSAiArjTyOp0wIcfyl2rIGv4f/rppxKltDnP8/XWr1AQBKJWq7OGDx/um5SUJOr1eubFF18k9mT//P7771Jubi4z0VaK4x9/AG++KV9HJdLT01FQUID4+HgpMjKSDBgw4B6LS7OzcUWvx964OIllWdqtWzc2NTVVvH79OuPq6kpzc3MZNzc3cdCgQez27dvxzDPPICQkpMq1Ukqh0+mkpKQkMnbsWNL25k25knr16hqvsybKysrw/fffi2GHDzMxiYmEvXjxT2E+B1i+fDnVaDQYP348SUtLw8aNG0skSfpKkqQ4AGd5ns+t82IVGj2K4X9ICILgolart6rV6j4uLi5MZGSkJrSsDC75+cAzz8gHJSTIGjfjxwOQ3SJr1641ZmRkLJ05c2a9aboIgkAIIUZKqdrNzQ3Tpk2DLdeOLY4fP45Dhw6JU6dOvTcS+sEHgLe37P+uxKeffiqJosiEh4dLAwcOZGzORymwYoUsblfpc0opSktLodPpxFOnTrEjRoygERERdm3XD+h09Fx8vPT6rFks0tKAemjScuDAAeh0Orz95ptwO3lSHvPUKWDoUIfGSU1Nxfr16+Hq6kofe+wxIkkSrl+/Lp0/f95gMpksFotlIIALPM8b67xohUaLktXzkOB5Xm82m4eUlZUNLCws/HH37t3JW3fuNNzOCxdF4KOPgJEjb5/DMAy6d+/uzLJs73pezsuU0lt9hq32Gn1ArjsoLS1lEhMTUVS5abpaDYywLT3Ttm1bplWrVuKwYcNsG31AdqHk5QE2iq8IIXB3d8eTTz7Jzp49G/YafQDonJ9P+m/ezEKlqhejDwDR0dFo3ry5uPG33yQpOlrO/5840eHMpODgYMyaNQsBAQF0y5Yt8PX1xfDhw5l//etfrqNHj27KMMwxhmGKFyxYsE0QBKXCV6FWKDv+BoIgCCoA5ldfeQV+588Dbm7Av/8NHDx413Fmsxmff/653mAwaCFLOLjxPH+jDvO6AigDgDfffBMmkwlV5Zvbwmq1Ys+ePWJ6ejrJz89nOI6j7u7u0mvPPstyM2YAy5bZLHK6ePEiNm7ciFmzZtU8icUC2Mg6cpirV4HkZBxq2hQnjh4V//nGG/XaZcxsNuOrr76SXFxc8MILLzCerq7AmTPAp58CP/1UfXqoDb799luxRYsWbJcuXRAcHIxbKql6vR5HjhxBwp/V320BmAFYAEgACnmeV/5jK1SJosffQOB53jJ//vzP16xa9frbp045O7/xBvCf/9xznFqtRpMmTawGg6EX5KYsFHV4clOpVO+3b9/e8Pzzz2tqcz7HcRg2bBgLyK6o/Px8sn79eub4//6H3n5+No3+rUYv/fv3FwFUbw0plSUc9u6teyVvYSFw+TKut2ghtvDzq/fWkmq1Gm+99Rbzww8/QKfTiWPGjGHRsqWctaXXy78LB56moqKi2Li4ODE5OZlt1qwZzc3NJQzDoGnTpvS1114jERER+M9//mMtKyu7YLFYnFQqlYlSCkopFi1adNloNL7G8/zBCtE3XwBFt/o2KDRuFMPfgJg5c+b0efPmZW969ll+HM+7YPBgYO5cOb/c3//2rregoEADoAMAEELq1LWJZdn27dq1q5XRrwzDMPDx8UHfvn0JmTqVml97jdjKXE9JSRG1Wi3br1+/mo0vIXLg+1btQ23Yvl3WC5o7F+jeHdyWLaiLeF11cByHPn36YNu2bWz37t1l7Z1t24B9+4BJk+R13ErlrYGuXbuia9eubFZWFuLj4/Hyyy/DarXim2++oTdu3CCBgYF48803OQCcwWCARqNxAgCDwYDk5OTQbdu2HRAEYaRarZ5qNpuHA4AgCJ48z9dCuEjhUULx8TcwJElaevnyZanwk0/kNEhAlnbIyAAuXgTmzYO/v7/V3WB4jbFaMXfu3B51mY9SSspt+KHT09NRVmazx3yNdOvaFS1LSsiajAzRVsWqxWK5nYZpF66ucj2Ao8baapVVQfv0kdNlK1Cr1eR+GX4ACAsLQ8uWLXFXH4ABA4BPPpFdeJXcdzXh5+eH8ePHE2dnZ7i5uYEQAraS2+jO36dGo0GPHj0wbtw4NGnS5Fez2Tz83XffhYuLSxmA+mtqrPCXRTH8DQye58sppbv+c+gQ0K2b/OauXUBQkJzjHxmJgQMHaqJ1OtULv/76/0AIB0KWghAGhDQBIQ7lEFqtVu/4+HjExsZab/mQ8/LysHr1amRnZ9fqGpjMTHj9/e8od3dndHekckqShPXr19PS0lK2Ro2fuwZk5DoAR5VBV60CliyRq3A7dLj9dnFxsWPj1AIPDw9cvXpVuh1DYxjghReACxeA0aNlKYtaQim9x/DbokOHDnj77bddZ8yYAVdX11vCgfUQLFH4q6O4ehogoihq78mQAWSBMF9fBALI+uwz9jedbvC4b79dGnj16mFQKoGQFyEH+t4HIe8D+BFAAQAGlNos+BJFcR+AQUeOHOGOHDmCzp07S+fOnWMAoL29apqVOXkSnCgiKjqaxMXF0eTkZFEURZhMJkatVpPXX3/9T6E6e5k+XZZJtofkZFn/aPJkG0s7Sa9cuUL++c9/Oja/g4wYMQJffvkl1q1bJw0bNozxvCU90a2brAdUViZXCn/zjdyT2QEopcTeamaGYaBWq1FQUACTyUQBONg6TuFRRNnxN0xq3GpHRESQZs2a9fvp1VevCx98kAYAoHQ5gBkVh+QDKAcQCWArAICQsSCka8X3t9zvHwPoCuDfAHDL6APA0qVLaxcIPHtWli2G7Fbp1asXN2DAAG7kyJHM66+/Thw2+oDstuneXXbd1MT587JbjGXvyqQxGo3YsWMHefrpp4k91bR1wdnZGRMnTmRSU1OZr7766m4xPHd32dinpMh6/w4iSRJOnDgBsw1xv6pITEw0AViqBHcVAMXwN1TGE0KqlZJUq9WYMmWKa1hYmAZAv9sf3PItUPodKC0FpQkAhld8agJgASHuAK6BEMJ/8EFv/oMPWvA8v8SttNSb47hkAGaO4/5bUFCg2b17t0MGBiYTsGEDMHQobty4gVatWom9evVC165d0bFjxyqFymqE4+SdfHUG+7vvgHXr5AK456vuSf+gmq97eHjc7l52vbJrx9NTFrAbM0aWfzbaX481YMAAcuTIEaSmptp1fFlZGU6fPk2tVusXdk+i8EijGP6GyQ2VSmWXFk94eLhapVLNEASh6rQXSqWKr5tA6XlQWgKgdcVNgq144V+ffho/a/bskfwHH7SbMWfOfwEgMSEBCxYsQGZm5u088mo5dgwYNgxQq1FYWAgvL6/6S5uUJLm2obKUtNEoB3779QOio6s8neM4tGjRgp46deqB5Li7uLhgzpw50Gq1+Omnn7By5Upx3bp14tWrV+UDnJ1vPxlh1So5dbUGrl+/joSEBKl169ZSlcJ4ldixY4eBEPJNXeo9FB4tFMPfMLnhUoWoWWXatGkDX19fFoAdDW3vgFJLxdeDoHRnxbuRANIBuDCUNgXAvPX557/3PXx46+pVq0p3jhmD3bt2VX8DKCoCKmSjRVGktuSea42TkxykrfwEMmcOsHatXIXr71/l6YWFhcjKyiKRkZF2V/k61E+5Eunp6Vi1ahU9ceKEyLIstVqtbEpKCvvjjz/+eZBaDaxcKdcqPPlktXGM/fv3Y/Xq1ejTpw8mTZrEONsRG7h8+TLS0tJKLRbLnFpfiMIjhxLcbWAIghCoVqvRv39/u/Mdw8LCmuTm5r4vCMIOAH4ArtaqcpPSWz6QFAApPAB88MHoIbGx5FyHDq81Kyz8fE9SEtivvxbb+/kxrZctI2TzZmDgQOCW7PCuXcCECQAAT09PkpGRUXORliO8+ipw4wag0chPFwEBspCdHUbQx8cHQ4cOpVu2bEFxcTGJioq655iVK1eKJSUlsFqtjNVqJVarFRqNRmrRogXp2bMnCQ0Ntav5fFFREdauXYtevXqhT58+rNFoxL59+6i/vz/8/f3vvvFwnJxyGhAA7N8v3zgr2jRu3LhRcnJyYnJycqSCggLmxRdfRJs2bezasFFKsWXLFr3Vap3M83wtmiUrPKoohr/hMdlisSA5Odlu0bGIiAiSkpISdvXq1esWi8XL2dn5dwDD6mU1lBoBoFgQvowbNiwdkrQ5MTycPWqxoMeePXTIgQMEAwYABw7IGSrx8bI+TcuWGBEbi33Z2WxB69ZofvSobNCKioDMTLkV4/79QOvW8i792jU5ZdVqlV8+PrJrp/ITg14v32hOngTi4uR+uA40punTpw/x9PTEhg0b0LJly1vNzQEAR48eRW5uLhsWFobevXvDzc0NTk5OyMzMZM6fPy/FxsZi69atJCoqqsb2lTqdDu7u7nTw4MHkVupljx49qv73ZFn59/fbb3JTnp9+wlc//ywVFhYyXl5e1NPTk44dOxZ3yUnXQFZWFkwmU+HcuXPvbRGm0KhR9PgbGDqdbicA9ubNm6R79+6w53GeEIKwsDBVUVERl5+fT81mcwedTrdKq9WW1Ne6tFotYmJiLup0uhuUZUdaVSpkZGQQ7YIFco69r6/sYx84UM5Z794d3LlzKJQkmlZcLAXn5jLw9wdOnJCrWKOj5c5VGRmyz/6TT+Rsl48/llU9H39cbkup0wHZ2XI6Z/PmwMyZcuOUjAyA5+WbhYM0b94cqampotFoZIKDgwHItQsbNmxAly5dcOHCBURHR8PV1RUVEgkIDg4mUVFRpFWrVoiNjUVZWRl8fHyq/Pfx8/ODTqcjN2/elKrtUFaZjh2BtDRY3d1x5ORJMuH11zF48GASFhbGOBoYP3r0qJiRkfH9gAEDdjl0osIjjyLS1oAQBCEawP4735s7d+6twpsayc7OxurVq3OsVutGURQX8zx/9X6sc+HChVtMJtMoV1dX8d13363WjaPX6/H5559jzJgxCAkJsctNchtJkl9WK1BQADRrJt8Erl6Vd/zvvCN/Vos4QkpKCjZu3Ai1Wi15enoSo9FI/Pz86OjRo8ny5ctFPz8/PPnkkzav7eLFi9i3b59UWFhI/va3v5GtW7fSirRVdOnSBYDsW//111/xyiuvwNvb26G1paam4saCBfDT6dBm3z6wjhS73cEXX3xRWlRU9DjP80m1GkDhkUVx9TQsrrEsu1YUxfG33jh06JAUFRVll7W8efMmCCF5s2fPnnb/lgiYTKYJAErKy8tr9N27uLhgyJAh2Lx5M/X19aUTJ060u5UiGEZ+cRzQqpX8Xrt28mvQIDl1tGdP2WXkYG1Ahw4d8O677yIzM5NJTk6Wjh07RgICAiQAbFBQEJudnV1lBDskJATNmzdnvvnmG6xatQq+vr5wdXWVtm7dyhQWFmLAgAHIzc1Fs2bNJG9vb7vvdMXFxfjxxx9Fo9HIBo8cKZotFtb/0iWwogjc4ZKyh7KyMpSWlrKQhfwUFO5C2fE3MARB8AKQRwj5mVI6S6VSHR0zZoyXWq2uuq9tBevWrStLSUl5g+f5Hx/AOicA+A8ATJs2Dc1raE5utVqxdOlS0dPTk7z44ouMPZIDdlFQILuAtm+Xm7o74AO/k6tXr0KSJLRt2xaxsbEoLCy0jh8/3qGN0bVr17B27Vr4+fnRnJwc0rp1a3HcuHF2XeimTZvo6dOnSffu3cURI0awt2+OK1YAP/4o+/4dkMtOTk7Gtm3bDr733ntV57cqNFqUdM4GBs/z+QzDfEYpfdHJyWmvKIoe69evx08//XRvEVAlqHwXd6Daqk6sBbAMAL7++mvs2bMHgiAgI8O2IgDHcfjHP/7B5uTk4NixY/W322jeXM5/37tXDhzbSWpqKi5fvoySkhJIkoTAwEAEBgbCaDTCarWC4ziHm+8GBATglVdegUajkUwmE4qLi1mxcs2BDXbu3InTp0+T5557DqNGjWLveiJ67TU5/qHTAadP272Wa9euWUwm025Hr0GhcaAEdxsgMTExu3Q63QmO4zpardb2AMAwzP+lpqZ29ff3V1UlN1BaWuqUkZFRGB0dvc3W54IgqHU6HaPVamufnF6BVquFVqvdXhGMfuWWwc/KyjL07NnTptOdZVmo1Wpy4MAB9OnThxBCkJWVBScnJ7tEx6qEEDkY7O4O/POfcrC5bVubh6anp+OHH34Qz5w5w1y8eJEmJCQQnU6HxMREHDhwAAcPHkR2djbatm1Lg4ODHd4YOTk5Ye/evfDz86PFxcXSjRs3aGhoKFNdnMbZ2Rnnzp2jV69eRe/evck9xwYEyHLO06fL0s52BHn37NlTXlZWtlir1V6p8WCFRofi6mnAVPTCnUApXaNSqb6zWCzxANYyDIPZs2ffE/TNy8vDypUryywWSy+e5y8IgtBHpVJtYxhm/fvvvz9VEIQ1ACbyPO/wbraGdToxDPOrJElPAcDs2bOrNOSSJGHJkiVSREQEU1xcLJ4/f551cXGhY8eOJX5+fnVfzOXLcsOWGzfkG0GFC8poNGLdunVSdnY2069fP6lfv363Yw0WiwV6vR5msxled2jl2xtUv5NffvmFFhcX08mTJzNGoxFLly6VImVF1WpvIpIkYf78+fjHP/5Rtdvs+nW5OfwzzwC9q+6+KYoi5s+fb5EkyZPn+dppays80ig7/gZMRQrlaZ1Ot3jOnDm/abXaMzqdzpdS2vPChQtlzZs3V98peObq6oomTZpwly5deiUhIaGnKIqTKaUBGo0mLD4+3l+SpDgAz+h0uo1arTavHtcpxsTE/KLT6dapVKq+ly9fbhoeHq6ylcFDCIGbmxs5efKkaDQa6eTJkxmDwUB27twJV1dXqWXLlnW7KTVrJmf5/PST3MCme3cAwO7du5GdnY3XXnuNhISEkDvXxrIsnJ2db0sX33o5SlpaGg4ePEgmTZpEXF1doVar0bZtW7J161YSHR1d7Zh//PEHMjMz6aBBg+7d8d/C3f12MxmEhFQ5VllZGY4dO1Y2Z86cDx2+CIVGgWL4/wJotVrLHd9v1+l0H5WXl6cnJyf3TUlJIUFBQepb+eS+vr6ka9euKr1e3/HGjRveAMhbb72lys3N7VJWVuYmimIHhmHaxsTErL0P6yyIi4v7wWg0Rp89e9a3Xbt2alsNV3x9fdGrVy8mIiKCcXJyQnBwMDw9PREbG0t69+5dN7fPLXr3Brp3R+7q1Ti1YIGUJEmkd+/eYocOHe5LXEuv12P16tXSwIEDERwcfNtyu7u74+DBg+jatWu1NRnJyckwmUxS9+7dba/v8mW5mnfcOCA8vNq1FBUV4fTp03n9+/f/v1pejsIjjhLc/QvC87zI8/xas9kckJ2dPX/lypWG/Pz82597eHhg1KhRmDhxImbPng0XFxc89dRTzlar9XGWZUs5jrtved08z1tMJtPjhYWFs7/99lvDmTNn7Dqvc+fOcHFxEX///feao6EOsDElRRKjovDG1KkYEBJyX9KXS0pKsHTpUqlt27aorAN0K3hcU8Vt69atkZWVxVYZ+F60CEhIkHf9NWAwGEAIKXTgEhQaGYrh/wvD87xl9uzZ841G4z+/++47Q+WOWUFBQbd3zxqNBq+//joXHBzsJopiPTjTq10XnT179ucWi6Xftm3brm/atMloj7Rzp06d2IKCgnpdy00XFybi1VcZ9+RkOfBbz+Tk5GDZsmU0JCSEPvvss/cEcfPy8sAwDPQ1dA9r3749xowZg+3bt5PSO4Xa4uJkiYtvv5Wrou2gYq76/UUqPFIohv8RYM6cOT+azeYXf/zxR8NtyV8beHl5oV+/foRl2Zc+/PDD+tHyqQae509aLJbQ5OTkPdu2batRBN/d3R3l5eX1FniWJAkVImtAVBSwfj1QUiKLutVBdfMW+/btw/fff4/IyEjpySefZG355tu1a4dWrVpJa9asodXd/MxmM+Lj48VmzZrhroSL8+dlw29nzMFiseDs2bOglOY4fEEKjQbF8D8izJ07d7PFYnly7dq15QkJCVJVzcT9/f3xxBNPuDg7O7//INbF83ypxWKZcOHCBTEvr/p4sq+vL4qLixmTya5WBDXCMAw4jkNWVpb8BsvKBr9lS7ki2IHmJ3ditVqxYsUK8ejRoxg/fjwGDhxo0+jfYuLEiYzZbKYJCQn3uHG+++4767x587Bw4UIQQsjrr78Od3d34PvvZcG7adNk5U47uXnzJpKTk2E0GsfV6uIUGgWK4X+EmDt37l6LxdLrwIEDuz/77DPD0aNHqa0CopYtW8JisUQtWLBglSAIne73unieL6aUfvjbb7/pq+t+FRAQgCZNmohxcXH14udPSEiA1WrFtWvX/jS4TZvK0s56PRARIfe+dQC9Xo81a9bAarUy06ZNQ2BgYI3nMAwDURTh5+d3190hOTkZeXl53PTp0zFnzhz885//ZNQqlVyQFhIC2DF2ZXx8fG59W6+xEoVHC8XwP2LwPJ/8/vvvDzMajdF79uw5sWvXrnv8C97e3njjjTfUffr0mchx3BFBEGqnAuYAoih+WlhY+MOKFSv0NhvJV/D888+zJ06cYO8MVteGs2fPQqfT4dlnn0Xfvn3v3Y67uACJibLEw/r1djVyP3fuHD7//HOwLCuOHTuW2MpYqgpKKSo319m9e7cYHR1N3dzc/kz1/OQT4IcfgP79a6U8CgDh4eFGAG/W6mSFRoFi+B9ReJ4/bjabx5w8eVIqs7GrdXd3x8CBA7mIiAgnjuOmP4D1SDNmzJhWVlY2Z/ny5Ybc3Fybx/n4+KBVq1b0yJEjtXbC6/V6bN68GaNHj0bnzp2rPtDDQ95dHzsmC77ZKGa0Wq3Yv38/du3ahS1btmD06NGYNGkS6+np6dCaXF1d6W2XE4AzZ86grKyMzc/Pl6/TbJZdTxMnygVadaC0tNQMILvGAxUaLYrhf4TheT4DwP/9/PPP5VUdQwiB1Wp9YIHAWbNmfWYymWbExsZW6WNp164dqUrzxx7y8/OhVqvRqZMdXixC5F22lxfw0ktyP4A7YhHHjx9HfHw80tLSpOeff96+MW0gSdJd9Qnbtm2jTZs2hdVqlbf6X3wBfPkl4OdXfUN5OygtLaVQDL9CNSiyzAOL4MIAACAASURBVI84Fotldl5e3rTi4mLY0vjJzMw0AEh7kGuilC7PzMycl5ycjNDQ0Hs+Dw8Px8GDB8muXbukIUOGVKtzY4uCggI4OztLcHRj87e/Ac89JxdJlZZC2rgR8ZJEn3zySfTo0aNOm6SKrmrYu3cvCCFgGIYMHz4cbd3dGVy9Crz1lhxwrgdEUSQAbEf3FRSg7PgfeXielziO23f58uW73i8pKcHGjRsNOTk5BQB2POA1GRmGmf7bb7+VxsfHWyvrRXl4eODVV18lJ06cIIsWLUJiYqLdbh+j0YjY2FjatWtXx0WoBg2SVT6bNJEbu7dvDycnJ+o1a5ZcPCVJclC4FowYMYI1mUx09OjRePrpp2EymXDjxg0JsbHAf/8rC6/Z26egBpydnSmAuj02KDzSKIa/EWA0GjenpKTcdvcUFhZi6dKlxpSUlK8tFksYz/M15tjXNzNmzPjearUGHzp06ML27dtNlY2/l5cX3nrrLWIymRAXF8d89dVX0tKlS6VDhw5Vmw9/5MgReHp60piYGMd1HwiR3S2//AK0agXm6afh6emJC6NHSwgLAy5e/LOI6uxZwIEAdEhICF555RUSEhKCoKAgDPH3x5WVK5k/wsIo3nnH4aVWh0ajIQCa1uugCo8UiuFvHOy4dOkS0tLSkJCQQJcvX26glL4zc+bM9x6meiPP8zlms7nfmTNnriclJd2TfihJEhiGwbhx4xAdHc1069aNOXTokLRkyRKcO3fO5lNAYWEhvLy8al+dNXHiXdk0lFKUeXvLUgmhocDBg/IHW7bIUskmE7B4sc3AcFUwDIPIDh3Q3GKhmZmZda8kq8Tly5fdAYTV97gKjw6K4W8E8Dx/Q5KkSRs2bMg8cODARrPZ3GfmzJnLHva6AIDn+RKz2azdvXs3u3DhQnrnzn/37t1WtVpN3d3d0a1bN/Tr1w9vv/0226tXL9gqhgKA0tJSeHh4sIAsT+wwUVHA6tW3K3tHjx7NJCcns7czcm719501S+4BUFYm++YJAb7+GthW0QqhqsrggweBxYvx48WL0qH27Ul0dHQ9tSKTuaNwr95vKAqPDooev0KDQBCE7izL/h4aGtrkqaeecmYYBvPnz0dERASGDx9++zir1Yply5aJbm5u5LnnnmNuSSnfYvny5WJ4eDjr7e2Nn3/+GR4eHlLv3r1pnz597DewPXoA330nfwWwa9cuevz4cTJ+/HgEBARUfV5yMqDRyD0AwsOB1FTZHeTiIj8xSJKcMXT5Ms57eGDnzp2i0Whku3TpIvbq1YstLS2lJSUlCAkJIU2aNHH8lwhg1apVpuzs7POzZs3qUasBFBoFiiyzQoNAq9XeiI+PX1pUVNS3pKSkVceOHbk2bdpg586d6Nu3L27p5x8+fBipqalM06ZN6c6dO8m5c+ek8PBwcqupSmJiohQSEsIAQHp6utS8eXPm+PHjTHh4OOwuuBo2TE6prDC+7dq1IwDwv//9D4GBgfDw8AClFKdPn8bx48clg8FAWrRoAeLtLVcGOznJnbJcXeWnh6NHgdxcWSNo0iSgdWt4e3sjKiqKadOmDZKTk3HgwAHyxx9/kNTUVJKUlITAwEA0c7CBPKUU27Zt4yRJytdqtQ3iiU6hYaK4ehQaDDzP681m8/hTp04xOp0OZ8+eFSVJwp0aPyqV6rY7Y/r06TAYDDQl5f+3d99hUVzrH8C/Z2YbRYqAIFWwIAiK2JCArBpb7BpN7IlGExPvTczvptwk173jNb2ZbhKT2GKNPUZFLIBiQcAKFhREaSJSpO3uzJzfHwMEFBDRqJHzeR6ehK1nVn1n9pz3vO+56vvNZjOxsrKC0WiEyWTi0tPTAQDffPMNkpKSaFXAbrBaaEwM8I9/AEB1wbTw8HB07doVW7Zsobm5udi+fbu0Y8cOWlhYyO3YsUOOioqqPa9UFbRfegl49VXgzTeVbwM38fDwwLRp07g33ngDjz/+OJ588kkEBQVh48aNVBTF235mV69eRR0VTXfe9olMs8amepiHjiAIS1Qq1TQ3Nze5d+/enG+NblOyLEOWZVRd4e/atQvx8fHw8fGRsrKyOKPRSGbMmAEnJycsX75cTktL4wDA0dFRCg4O5l1dXbF06VJYW1vTp556iri5ud06gKtXIb/1FpaFhcnZ2dnca6+9BpVKBVmW8cMPP4iFhYU8z/P0ueee4+zt7fHrr79Ss9ksP/PMM/VPJ8mysg7QyD0JCxculNRqNQkKCiK+vr7EwcEBaWlpSExMlM6dO8er1WpZp9OhsLCQI4SgV69e6N+/P2JjY+UDBw5899Zbb825k8+caV7YBi7mYfQqgKfGjx+vu7m+DcdxqNk2ccCAAWjbti2SkpJ4URRhMpmwYcMG+dq1a5wsyxwAtGzZUiotLSUeHh7Q6XTgeR4eHh5k8eLFePrpp9GhQ4fqdYKysjKcSk9HaWkp5VJSYLK0xGeffYZnnnkGrVq1wgsvvFD1b6Y6gmdlZdGxY8fWH/R//hno2FFJB/Xza1S1zRdeeIE/ePAgjh8/LkVHR/OUUqhUKurq6sqLogiz2cwFBATA3d2d2tvbk/Xr19PDhw+T1q1bgxAyAgAL/Ey92Bw/89DR6/Xlhw4dGu3u7u7amJo49vb28Pf3R1xcnCyKIuncuTMZNWoUBg4ciJiYGPj7+3OZmZlk8ODBiIqKkioqKuioUaO48+fP04SEBNKzZ0+oK7N1fv31V+ncuXN4LC2Ne8zdnfQ1GHD27Flx7969nLW1NepqCH/kyBG5bdu2XM1G7bUUFCiloAkBnJ2rG8A3RKVSwdvbGz169OAee+wxtGvXDoMGDSJBQUEIDw9HTk6OnJKSQsLCwoirqytCQkKITqdDUlISkWXZdt++fZ/q9frbd79hmiV2xc88rLRVwbgxCgsLYTQauX/961+1qmC2atUKjo6OUKvViImJQUpKCvfiiy8SW1tbODs7EwcHB9nS0pIDlCblV65c4V955RVYjx4NVM6xT58+XfXJJ5/ISUlJxNXVlbRu3br2QLVa3KivumdRkVJpU6MB2rcHCgsBSVJ6AzQSx3GoOSXFcRy6d+/OpaWl1Wrp2KNHDzg7O2PJkiUA4ADgge3RYB5ubHGXeShRSq00Gk2jH5+bmwtCCG4u+Tx79mwEBwfD1tZWPHPmjDRmzBhiYWGBxMREFBYWyhpJoigshCRJ2PfJJ7K7lZVkLcvA9u3KlExlO8vx48dz1tbWZMmSJYiNja3ePVxcXIzi4mJOq9XWPbAVK5TCb1VGjwZOn76zD+MmGRkZ2LRpkxwREUFrBn5CSM3+AOl39SbMI40FfuZhdeHMmTO3T2sxm4HCQvj6+qJv69b47auvcCk9Hfj2W2U3bUICNEuX4oUXXlC9mJnJWyYk4Iv33kP7/v0BAMPKy3m8+y527doltd6zh0wKD+dBKZCVBbzwAhAeDpjN8PDwQLt27ajZbEZCQoK8cOFCJCQk4IcffpD9/f1pQEBA3eN76SWg5nTq7t1Kjn8TybKMX375BWVlZVxISEidK8VOTk4AAEEQejb5jZhHGgv8zEOpoqJi6sGDB0ur0jFrKSsDWrUCMjOVnbAzZgAAQnNz0d/BAb+uXInCw4eVk4KtLeCh9Jk5160bNqSk0PBBg2iL1FQ8//zznMXzzwMff4yMjAzkv/MOUXXrpjznf/8DFiwAfv8dOHQIB156SY6KisKECRPwyiuv8P369cMff/yB0tJS7vTp09z3338vFxQU1O6Xe/w48P33tTN5ZBl4/XVluqcJOI6DVqulgLKZrS6jRo2q+t/DgiCw6VzmFmxxl7mvBEHoExMTM3nfvn2n9Xp9vcXh9Hp9yd69e5MvXbo0vFevXrXnfCQJuHwZ2LULeOUV4KmnlNvDw9EqNBQ5OTk47eEhdQkO5tCyJdCuHQBgzd69strWlhs5ahQhN2ULWVlZcVFRUYiNjUVKSorcokULwnEceGdnJCxfLnX45Re+z9KlxNnVFSAEdnZ2OHDgAMaNG4fg4GDk5OTQXbt2kZiYGCQkJFAnJyfSUqtV1gnat//zjTgOSEsDOnX6s/zDHSguLkZiYiLMZjMJDAyElZXVLY/R6XSIjY2tfDtu+r59+xbp9frbf3timg12NcDcVzqd7kNZlnsRQoIAjGvosZTS30tKSiouXrxo7VOzDaFOByxcqGy0mjZNuarW6arvzs7Oluoq0eDq6sqJoigRQm65r2PHjtDpdLRt27aE53nu999/l0wmE2c2m4mNrS3apqRAt2kTsGULsGYNNBoN1Go1rKys4OXlhTZt2nCAkg6amJhI1q5ejb7duskhgwdzoBRbtmyRkpOTeWtrazp48GDSzmxGfRn9lFJERUVJ169fx1NPPVVrrFu3bqVlZWWkKr20Lmq1Gvb29iUFBQWrZVl+DkCRIAiuBoPh7vpZMvUSBEEHQAOgDMB7AMIBrASQCCDBYDBU1HisHYAWAPqoVKrHRFH8xWAwxN/P8bIrfua+io2NlU0m0yhCiGbv3r18dHT06frSDvV6PY2Ojj5/+vTpwWlpaaKtra3Gzq6y2jDPA56eykLpunXA0KEAIUhKSsLx48e5iIgI2NjYAADi4+Nx9OhRpKSk0B49eqB169Z1xlyO43D48GHSuXNnjBs3jgsPDyfh4eHo3bs3Z2llBbi5AceOAeHh2LZmDaiFhdy3b19Ss1aQWq2Gp6cn2ssybJ57jvzCcTQjIwOpqalk8uTJhOd5cvi336jPzJnkZFgYdXR0JCdOnJBXrlxJOnXqBLVajS+//JJmZGSQ3Nxcrlu3bqi5cFxcXEyuXLkCLy8v6uzsXO9usBYtWmhSU1NtJUn6EkB/tVrtEx4evu7O/8SYhgiC0Onw4cM7ZVn+nOO41yml/wXwGAD3zp07983NzZ0F4J3o6Oj/RkdH/yc6OloCEAlgLoAxsiz3UKvVk/bs2ROp1+uzGnire4rt3GXuK0EQCIC3ACyovGmYwWDYdpvnWAN4y9HR8R8vvfSSda07ZVm58v/8c2DZMohWVli3bp2clpbG6fV62rt3b7Jo0SJcvXoVAwcOlHv37l3vupYkSfjoo49w22JsK1ag9F//wvb586UnZ82qNy9TFkXEHTqEtLQ0DB48uHrRVZZlHImLw+GEBKmkpISnlMLGxoYajUZCKYVarZbmzp3Lf/XVV1JISAjfo0ePWq+7ceNGiKIojRs3rt73ppTi999/L09MTLQAAJ7nP37nnXder/+gmNsRBKEFgLZVPxYWFv1FUQwfNGiQLjAwkGg0GoiiCJ7nqzcEVlRUYM+ePYiPr/uCfuTIkRIhhN+2bVs+x3GfvPnmmx/cj2NhUz3MfWUwGKggCB8A2AvgmsFgONeI55S8//77rv7+/pa33MlxQJ8+QFwc8NlnUL32GiZMmMBduHABq1evJm3atIGdnR11dnaWe/fufUugvHLlCq5cuYK0tDTp8uXLvIWFheTp6dlwkv3kychTqXA1KYnHmjV/rjFUkWXgrbfAzZ+PsLAwhIWF3TRkDiEODgixteXTRozA8uXL8dJLL5HDhw/D2dkZ3t7ePAD4+vryCQkJsqOjI+fi4gILCwsUFBQgJSUFlFK+ql9BXQghGDZsmEViYiIAQJIkVrStkQRB6ASgKyGkvVarDSSE+JrNZg+e53UtWrQob9myJVq1amXZunVrlbe3N2pWUlXd1EVNp9PhiSeewBNPPIH169eD4zjpiSee4IuLi2FhYQFra2ueUgqTyeSwd+/eSQBY4GceTQaDQQIQdyfPEUXxilxfjXuOA/79b2DbNpgmTED8s88iOT+fajQa4uDggPT0dMiyzFdVvawsawBKKdatW0dlWabe3t78E088gYCAgAaDvizLiI6OxuH0dBrRurWM117jodcrO3KrGI2Al5eyaau+41GrcerMGZS2bw+O45CUlITQ0NBajwkNDUVCQgK3cuVKeHp60m7dupHz58/DwsJCKi4u5nNycnDgwAF5xIgRde4jIITA09OTZmRkEIPBkNbQcTV3giDYAxim1WrfsLCw8Pb09JScnJysHBwcOHt7e7Rs2RLW1tYghNz5inwNhBBotdrqb39Vt3l6ekKWZVtBEIjBYPjLp2FY4Gf+Fniet9FqtfWnHxMCDB2KY0uXomztWtpu+HDaddw4otVqERQUhKKiIhw5ckSMjo7mAZAOHTpIPj4+/I0bN8hbb71Fbr5Sq8/ixYvF0tJSfuTIkaRjx448XnwROHIEeP55YM0apSSz2QzMnt3g6+w8exYpDg6wTUiQ27dvT/38/KpPONu2bcOJEyeoSqWiJpOJ69OnD1JTU+nOnTtla2trEhERwZ88eVL68ccfeQBcTk4Ofe6550hdZacnTJhAPv/88wpBEDo05ttVcyMIgp1Go3mf5/lnPT09zT179rTu0KFDvd+k7kZD0+p2dnZQq9VOoigaAPz3nr/5TVjgZx568+fP76tSqZ7xqMzHB4Dz589j3759cmBgIBdSVfSMEKSOHg3/48cRtG0bh8ceA+zsMGTIkKpFUBUAXLx4EfHx8fzu3bslAHxeXh5uLsNQl8TEROTn56tefPFF2NpW9jLXaIAuXZSSywUFyu8REcAff1TvH7hZRUUFjh07hhmlpXDheQ7//Get+xMSEuDq6kpCQ0OJr68veJ5H3759a0Wi4OBgvqioCBcuXMDWrVvJqVOncPNaAKBMNYSGhqoPHjz4AYAxtz3IZkQQhDCVSrXN399fM2DAAK2lpWU926/vDVEUUV8ZEkmSwHEclSTp2F85hios8DMPNUEQPFUq1eannnqqhU6nw9dffy2ZzWZUVFTwQUFBZO/evUhISJD69+/Pd+zYETdu3JBy+/UjSEsjOHJEyaOvzOOv4uPjg8r00AbnyWvKzc3Fjh07MHbs2D+DfpUWLYBVq4DYWKVn74gRSo5+Tg6QkAB5yBCs+PhjsdBs5jSOjpRSyltbW8sur73G3ZzLf/36dXAch6eeegq368Jla2uLnJwc2NnZ0cDAwHozfEJCQvi4uLjBgiB0NBgMZ257sM1HrLOzM0aOHPmXv1FFRQXS0tLok08+WedU4okTJ1BeXr4fwJa/fDBgO3eZh5ggCAE8zx8KDw+3dHNzw9KlS+U2bdqQAQMG8K+88gqGDBlCZs2aBX9/f37jxo04duwYrl69yj/22GMcXnhBmWufN08ph1yPxgR9WZaxfPlyuWfPnnLN3gC3CA8HXn5Z2e17/bqSbfTyy4iKisLAb79VTT10iOvapQv/wpw5GNy+PcGPPwLe3soY8/NRUlKCH3/8kfbs2VO6XdBPT0/HBx98QBMTE+Hn50d1lfsYkpOTcezYsVpF47RaLcLCwjRarfb92x5sMyEIwuMA4OLicl/eb/369bKbm5vcvuZmvkqSJCExMbHEbDZ/YzAY7kuvZHbFzzy0OI57SpKk1nv37qUxMTHw9vbG0KFDuZp58w4ODujbty/s7e3pH3/8QdRq9Z8VK6dMAaytgUOHgIoKZUqmCQoLC1FaWvrnlFJ98vOVwD93rvK7vz8qRoxAwuef0/b79hEXb2/0ohS4dAm+rVsTZGcri8IWFsDQoShq1Qrt/P3pgG7dGlxgPnnyJDZs2IAOHTogIiICtra2HAAYjUasX78ePM/TPn36kJrZRN26deOjo6OHCILAVy6uN2sqlWqSKIqosxHP7QwapFxM1FVOpA7Z2dlIT0/nZtex7lOZdmssLi6OB/D7nQ+madgVP/PQ+s9//vMfKBcnLTmOuxESElIr6NcUFBRE5syZg2HDhtW+Y/RopfftW28pzc+boGXLlnB1dZW2b9/ecMB8/31g+fJaNxUVFUGWZXLlyhXluYQA7u7KBjR3d+DJJ4GhQ3H8u+8Q4+hILbKzOTJrFvDGG8CpU3XW9Klaj+jWrRtxdXXF2bNn6YIFC/Dxxx/D1tZWcnZ2pidPnqRna3zTsbS0hIODg6hSqd5q0ofwiFGr1V0B4E4qwFZ7/31g0aJGPTQ+Ph5Lly6lISEh8s29JSiliImJkZKTkzONRuNIAPaCIHjV/Ur3Fgv8zENJEAQiCEI4gOk8z88HoL7d9IeNjQ3qrJL55JNKoN3W4D6xBllYWJD8/PyG+yZ+/DEweXKtm5ydnTFlyhRUddG6WXp6Oj755BN5565d8JwxA/2+/x5YvFjZG/Dqq8DIkcqUUY2+whs3bpT8/PykDh06IC4uTt6xYwdRq9WQJAkhISH8hAkTODc3N7J27VpUVFRXCsCkSZOsdDrdmwsWLHi5yR/EI0IURW8rKyv89ttvePfdd1FYWIjzt7sw2LULeO89IDhYmdbT65WCgXXIy8vD119/Le3duxfDhg0j/fr1qxVry8vLsXTp0rK4uLgzJpNpASHkOoA8Qsj8e3SIDWJTPcxDRxAEXqvVxmq12kAvLy+uZcuWFkFBQaS6XENTtG+vtD1sgpMnT+LChQtcr1696n/Q888D//d/QIcOt9zl6ekJnudRUFCAm6/64uPj4ebmhvHjx4PneeXEotMpzdojI5WSFGfPKs3a9+8HOA7l5eWws7PjFi1aJBcUFJC+ffsiMjISFhYWNCgoiCxbtsycnZ2tVqvVtdYwbGxsMH36dMvFixe/t2DBgpJ33nnnpyZ9II8AjuNKJ06caFO1l+OLL74gADBz5sw6u6wBUAJ+VZc1Kyvl/wsKgBoF/0RRxObNm+mZM2dI165d0b9/f9y8x4JSig0bNpRlZWWtNZvNL6nV6ovBwcGqI0eOmCilL/01R1wbC/zMw0hlMpl6Tp06la/3H+GdEEWgXz9lbrYRsrKysH79eqmiooJQSmE2mzknJyccPnwYTk5O6NatGzIyMvD7779LRqMRTz/9NN968mSgTZt6X9PS0lLaunUrmTZtWq0rP6PRCEdHR/D1deTq1Anw8QFKSpSNagCGDh3Kr1ixAm3btkVAQACJjo6mXbt2pSNGjOAAoLi4mBs4cCDatGkDjUYDSml1CQF7e3s8++yzlj/99NNX8+fPL503b97qRn0ojxiO4/KMRmNrb29vvPLKK6SsrAxHjhyRV65ciVdeeYWrta+jvFwppf3hh0DXrn/e/ttvwJ49yt8vLy8cP34cO3fulG1tbfHcc88RZ2fnOv9Qk5KSaEZGRpbZbH4BgJvZbHY+efJkGYAnDQbDfemaxoq0MQ8dvV4vRUdHXzx//vzgnj17qu96M83Bg8C//qU0VqlHbm4u9u3bh927d0sHDx7kgoKC0LdvXy4oKIhERESgd+/euHTpEj19+jS5dOkSDh06JLdp04bTaDTEtGoVbTNjBmlop66LiwsXHR1NcnNzZX9/f0IIwZIlS8RLly5x165dI56enremiVaRZaBbt+pfW7ZsCb1ej86dO5MtW7ZIN27c4MaPH1+9gevw4cPU2dmZ+Pr6kl9//VXavHkzd/36dal169acTqeDpaUl2rdvrz5x4sTQvXv3noiIiGh2G7tiYmLmh4aGWlW16awsrkeSk5PpmTNnaJcuXf6c1iMEuHFDCfo3rzFNmICyggIsPndOOnXqFDdo0CAydOhQUrMzWk2FhYVYtWpVhdls7m8wGLKjo6OLAMyTJOmf9/MkzAI/81CKiIg4ERMTM6NNmzb29QbExtJqlYyemqWdKx0/fhyrVq2SDh8+zGm1WsnPz48fMmQIOnXqRGxsbNCiRQtoNBoQQuDm5kYKCwulFi1aUBsbGwwbNoyzJoSoFy2C84wZJPXiRRQVFaFFixa3pIna2dmhrKwMJ06cIJmZmbK/vz+JjIwkU6dOJXZ2dvLmzZvJoUOH5LKyMtK2bdvag/y//wOuXKkV/KuoVCouJyeHZmVlyZ07d+YAwN7enouMjERMTAwpKSkhM2fOJCdOnMCuXbuIm5tbdfkBHx8f9cmTJ0fu2bMnSq/XZ97dh/z3IQgCJ8vye48//jhX85sWIQTt27cnu3fvJlZWVsoi+q+/KsH+8cdvCfqyLOP3Vq2wpaAAHdzd6YSpUzkPDw/Ul4AAAPv37xczMzO//89//rMMUCrQ6vV6ISIiIuEvOtw6sake5qEly/LutLQ0bw8Pj4YXVW/n22+Vhbib5OXlYdOmTRg8eDAXHBwMtVrdYBqlk5MTalXEpBSW1tZYO3IkifziC8lsNvMcx8FkMsHPz08aOXIkn5qaig4dOqCsrAwnT56k/fr1IzExMdx3330nOzk5wc3NjXh4eHAdO3ZERkYGt337djpgwAAiyzIOHDiApKQkSe3riwmjRvF1rXAEBwfjxIkTtOaJxtfXF2+++SbZvHkzPDw8iLOzM8LDw7m1a9fW2hTm5uaG0aNHW27cuHGbIAh+zaheP89xnLmiooK/eSetjY0NRo8ejY0bN9K2bdsSW52u1hx+lZSUFPz++++ypaUl5uTmcjarVvEYP/62b5yXl1duNpvva+39urDAzzy0TCbTF/v375/UqVMnSwcHh6a/0B9/AL1733Jz1eLnjh07SHp6uhwQEMD5+fk1vk7LiRNwffllzFizBvn5+Xz79u2hVqtx8eJF/Prrr/zZs2fB8zxkWaYcx6F9+/ZyWFgYf+nSJTkjI4N77rnnqt+rZcuWyMvLA6CckOLj45GcnEz1PXrw+YsW0cSOHdGvX79bhnD+/HlkZWVxc+bMueW+qh2pJSUlWL9+PR08eDBuruHv5+eHjIwMm6SkpI2CIOibSY4/x/P8tdzcXPe6MsU6duyIrq1bywf/+U8y8JdfuJp/H4qLi7FmzRrp2rVr/OOPP066detGuOJipbdyI5w7d64FgP8BWHGPjqVJ2FQP89DS6/V50dHRNy5evNgnODhY06S5fpNJSYkMCrrlq7pOp4OVlRUkSaKyLJO4uDhiZ2fX+N2cLi7AuHGwtreHk5NT9QKtnZ0dvLy80Lt3bwwYMABt27YlDg4OpG/fvhzHcTh9+jS9fv066dmzZRzobgAAIABJREFUZ62Mj5YtW6KoqIju3r2bZmVlYezYsSTA1RVZy5eT/SoVDh48SI8ePSq3bduWq2q5uHLlSqlr166cXwMZSykpKcjIyMDYsWPr/Obk4+PDp6SkOJSUlGTp9fr7UivmQREEwV2r1e718fFxDw8PV9c3LeOlUnHZkZE4aWkJX19fIssydu7cic2bN8Pd3Z1OnjyZa9OmjdKER6dT1gAGDgSmT69ehL9ZRkYGjh07BgB2er1e+OuO8vZYHj/zUJMk6evi4uLDu3fvrrNL121FRlZ356pL9+7dMWXKFMLzPCwtLeHl1cj9M1FRwE8/KWl9NyGEoE2bNnB0dAQhBK6urujZs2f1ieHy5cuYOnXqLbV4OI7DgAEDOFtbW4SHh6Nt27aAiwvsFy6kTzzxBH3mmWdIx44dyeLFi6s3BKhUKv7o0aO4ePEi8vPz6xyqi4sLysvL650u4zgOoaGhVjqd7tnGHfzfl0aj2d+7d+9O48ePt6x3Ln7LFqh9feH37bfk1KlTJDo6Gp9//rl89uxZOmnSJDz55JP8Lb2OPTyAgADlQuMmsizj0qVLWLZsWdWdo+7tUd05FviZh5rBYKBGo/Hpo0eP5m/evLnCaDTe2Qt4eCiLo7fh5OREtFotbfReAW9vJdXyDpWVlaGiooKrr1SATqfDzJkzubCwMOVq8qWXEHjtGgkODiZOTk4oLS0lsiyTTz/9VF64cCFu3LgBGxsbLF++HF9//TWOHz9e6/WuXbuGVatWUR8fnwancNq1awez2dyzssvUI0kQhBaSJLmGh4er6g36oqhs9LtxA87OzujVqxeNjY1FSEgImTNnDqn3wkClAn75BVi2TKnTVMPp06exZMkSyLKcB+BDQsjT9/bI7hwL/MxDz2Aw5JnN5o7Jycnb1q1bV3q7xxcWFmLlypVITU1VauX36oXi4mLU28gFQL9+/VBSUoLk5GQUFBQ0/AanTgF2dsDtavfUQafTged5lNWz47OmPXv24Ld27SQEBwMAzpw5g1OnTpFOnTrRYcOGcV5eXtK4ceMwe/ZsvPHGG9Dr9di0aROSkpKqXyM+Ph5FRUUkNDS0wYVrnU4HNzc3I4DBd3xQfx/+dnZ25fVOGZ4/r9Rb+v57oHKjnY2NDWnZsqX82GOPkXr3WlCqXOl/9JGS73/TybdqLwql1A3AG5RSFvgZpjEMBkOxyWR6Lj09XVVcXNzgY5cvXy6Vl5fTdevWofCtt/DbDz9IX3zxBb766itJFMU6n8NxHOzs7KTff/+dLl++vOEOSH/8obR6bAJRFKFSqWhmZv3Zk1UZPYejopB37hy/JToaAODv7w+NRkNPnTpFTsfESMMGD+a91Wrghx+g27ABEQCsra2R/d//InrLFshZWRgiinBycpKu7twJmpqqvEFsrNIspqgIuHhRuS0vD0Ft2thotdpJTTqwv4cAFxeX+k+Au3YpU3g11Nz8VktFBXDyJJCYqKwf/fyzsmt75UqlH0MNDg4OGDPmz1YIHMf95+4O4+6xwM/8bRgMhkJCyLtLly4tu3DhAgoLC6vvKy4uRmJiIn744Qe5uLiYj4iIIHNfeQWnv/xSth84kLz88sto0aIF+emnn+qO/ABmz56t6tGjB2moUxJkWbmqGz78jsdfXFyMTz/9lDo6OlLnmq0aAWDJEqWu/88/4/Czz9IrK1ZgqosLnszKgnHzZtlkMgEbNqBrYiJGjhyJngsW8Mc2bJBBCBAdDWzZAqhUmDZtGvycnHAwLg5rf/pJxunT6NGjB5+/fTvd+/338sGDB2F+910lcMXGoroJjF6PgI8/hiiKAwVBaHnL4B8BKpUqrHXr1rcuylCqlMV48UVg0qSb7qoR+MvLgRUrlG8Gjz8OPPusUgrk55+Vkh2jRgHDhtW5uHvp0qXqokmyLD/w3dIsnZP5WxFFcUFhYWHOihUrfgCAiRMnIjIyUiooKOA1Gg2CgoLw9NNPw8bGBli6FI+tXs1h+3YAQEREBLdp0yYlqsuy8lNYiPSLFxFz8KBsvHiRlFlYkNG9exNERiqbvmJilAVcJyfg6FHgxx+Vq7qOHe947AkJCVCr1ZgxY8atkeHkSWDmTMDODlp7e+JKqez2zTccnTsXnb//njt16hSCO3fG1SNHJE+eV+HAAUQtX07kK1dorxUriDx6NDIzM3GisFA+7uPDaTQa2W/AAA5duqAHgOIffyTLFi4k+ZGRaGk2U9/Llwk6dwY6d64aHNQ6HXrt2sXFx8efff/991/597///WsT/5geKoIgqDQazUILC4vxnepalzl/XtnZvWXLLUkAlFJ02rcPyMhQyn788ouyETAy8s/8/jo21t3s7NmzNa8m7qpv773A0jmZvxW9Xo+IiIjE6OjoRQDSkpOTw0pLSy26dOmCsLAwBAYG/rld/vx5pUzyhg2AlRX4//s/2GRmEteVKwl97z2Qjh1Bhw9H6erVsHV3R9+UFNLeywuuCQkgsbFK4F++HCgsVApypaQoVTPDwurNEqpPcnIyoqKiMH78eGJvb3/rAwYNUtIBn3kGnIUFNvv4kMs+PpIoy9yRfv0kURS5jqGhOHDunOzo6Mj5+fnBw8ODbNmyhVQYjdhnYyMH/PvfZI+DAxn21FMYOXIkcXF2VqpJhoYi8sUX5cATJ0hpeLjcc8QITtexI9CqFdC/v/L+ubnARx+h7cyZKhsbG8tz58712bNnz6d6vf4vb/z9VxIEobVWq93m4uLyxLPPPmt5S95+Tg7g5QVMmPDnn2lFBZCQAMyYgWJLS3CnTqF1ly4EEycC06YpCQP1tFCsj5eXl/rKlStyeXn5/Hnz5v12jw6vyRr+WsswDzlBEDQAntbpdGNEUQzq3Lmz55AhQ5Tm6ZQC8fGAjQ3g7o743btx9Px5yb9LFz724EG069ABFy9ehKenpzRp0iS+oa32d+Py5ctYvnw5RowYUXfZ6IoKpf7+F18AeXnYu2wZioqKZF8fH87yww+xato0UErRq1cvJCUlScOHD+c7VFYBTU1NRWRkpNSqVSt+1JUr4DduBBkzRuk+9vrrwKefAjNn4vOffpKGDh1a/bxbFBQA+/YBo0eDUopffvmlNCsrS5YkqYfBYKi/hdlDShAEHSFkGs/zn4SEhGj79u17a80nSpUT37ffKmseXl7AgAFKCe+lS4G4OBx0c8PJixelWbNmNbg43hhff/11cX5+/hMGg+HA3b7W3WJTPczfmsFgMAFYBmCZIAh+ycnJS0+ePNnZ39+f9urVS9e6Z08AgMlkQrG1NQpMJv5QYiL6DxiAgwcP0l69etH+/fvf9T/qhmzdulXu0qULCQgIqPvMIknV0wWFajVOmc3ytMWLOZt//Qu4cQNvvPEGUlNTsX37dmo2m3lLS0vg2DHA2RntHBzQ7pdfeBw5oiw6e3srm4lefVV57cpUVp7nqamOHPNq9vZK0xoo+xCeffZZq8jISHNCQsIsALfPh31ICILgyHHcGgD9KKWYPn16deOaWsrLgd27lTWO/fuVk+7PPysZPR06KNM47dtD2r+/wdo7Dam5PlBRUYHCwkIdgAdergFggZ95hBgMhhQAPQVBcD958uSk5OTkdwYMGGDZvXt37sCBA9i/fz8mTpyIqr6nvXr1IuSvuswH8O2338pFRUWEUsoZjUY5PDyc2NjY3PpACwvIkyeDA7B69WrJ2ceHWMTEKMEnPR3k9Gm09/VFqa0tMf72G+zmzFGuSMPDlZ+VK5XXGTpUOYHcVF8mOzsbJSUlqgaL3R09qgTBymwlQgiysrLUZrP51fnz5z9NKR1tMBiO3MOP554TBKEdgPNVabuvv/46qiqWVhNF5bP79lsldXPmTGV+v54dt/Vm9TRAlmXs3r1brKioEIcPH64DgEuXLkGj0Rx7/fXXm7YR8R5jgZ955BgMhisAPhQEYX1UVNSuffv2eRmNRvLEE0+gZrPruy73fBvXr1/nbG1tMWrUKOzcuRPR0dEYXkc2UOEbb+DEsWM0adgwuaSkhB87dizUTk5AWppSHfLUKeDHHxGdng5LX1/0sLQE3n77zxeo2cDbxUWZOpo+HfjqK8DKCjt37qTt2rWjHh4etxxwRUUFtFotSHCwMtVTw4QJE/Dhhx+CUuoK4LAgCOA47kNZlk0AzAAuAlh1vxqEC4KgA9AegC8hxE+tVrcxmUz/BdAFwFYAIISkA3DUaDTqkpISbXXgv3QJ2LpVaWyTmgqEhgJffnnbtZo7DfxXr17Fd999BwAqOzu7MgA6SimOHDlSbjQaN9zpMf9VWOBnHlkGgyFVEITOAGI9PT19e/Toobvb10xNTcWZM2dQUVGBTp06oaEaOdbW1mL//v1VHh4eCAwM5I4ePSoB4E0mE9atW0dzc3PliooKntraot9bb6GjyUSOHj2K0tJSODk6KtM2Z84oGUUAiL29nAVwkiQ1fNLS6ZTUQp1yuOHh4WT16tVk0aJFsl6v5zrWyEhavHgxValU9IkePTjPkpJaOeg6nQ4TJkxAYWEhtldmRoWGhr5RVXju/PnzZQUFBW8JgjDMYDCk3cXHWidBEAjP899qNJogSZI6A7C0sbG54eTkRK2tra2OHz/OA5he4ynTKKVbAJiMRmPpt99+i3mhoSBr1iib7YxGpZOZgwNQWtqoBfo7DfxXrlyp+t+DRqOxC6D8ncnMzMyWZXlho1/oL8YCP/NIMxgMNwRBGJeWlnYuPj5e7tGjR5Mv82VZxpo1a+Dt7S2p1WqyadMmUlJSQlJTU2leXp5kbW3NW1lZEScnJ3To0AGWlpZ8Wloa9ff3J23atMGePXs4WZYRFRWF/Px8DB06lHd0dAS/ZQvsevYksLIi9vb2dNWqVRiZk0PyL12S7Rcs4AKcnFBQUAA3NzdOrVbLarX69scwYgRw+DBQWIi2gwZhxIgRuHz5Mrdp0yYMGTIEp0+flgDAzs6O5OTkcDuXL6dhly/DLyKiVpSrWgzuWblWUgPp27evVUxMTMf9+/ef/Oijj1JEUdxqNpv/ZzAY7jpjRBCEdhqNZrnJZArp0qULysrK5NTUVNqyZcsWsizX6iUM4OPKn0Ke5zdZiGJ3z7Nn6eOSRMj160qRvscfBy5fBmbPVkoy1NMo5WayLDcq8JeXl6OkpATp6elmrVYbA6AkKChIBwD5+fmglO4xGAx3WG/kr8MCP/PIMxgM5wVB8Nu1a9c+GxsbZ19f3ya/liRJGD9+PK9SqXDgwAE5Li4O7u7ucp8+fVSFhYW0qKhIio2N5WNjYwGAZGdnw9HRUe7Zsydnb29PP//8c2I0GjFx4kTSpqpV4+HDSkN4AD179iSlpaXy5shIEjR8OBe3bRvNyMggx44dgyRJkGWZq9z925jBKj8AAgMDERgYiKtXr0q7d++Gt7c3f+bMGVBKMX78eLi6upLPPvsMc0tLcUsBsnoQQhAREcEFBQVZ5eXldd+2bZt/UVFRKoCVt3uuIAjhAJZrtdpjHMdpCCHWZWVl4QBgYWFxRq1We4WHh2t2794NNzc3eHt7c3Z2dpBlGSkpKdTFxYVMnjwZK1asAIDXALxmf/06/E6fRkCfPnCxsQGZOPHPbzCUKlNijZjeqakxWY9GoxFffPGFEcA1juPSjUbj0yqVasTRo0f1ADSSJKlNJtNtOrnfXyydk2k2BEEI02q1kWPGjLHw9vbGzU04GmPBggWYO3dug8GxtLQUPM8jPz8fixcvho+PjzxlyhQuNzcXu3fvlvr168fXW/r52jVg9mzIK1aAaDT46quvqCzLdOjQoZyrqysuX76MjrfZPJaZmYkNGzZIQUFBfHhYGLB9OzBkSK2AJ8syPv30U7lLly7cwIEDgaQkHJk3Tz41ejSmTJnCNeWzyczMxJIlS26IotjBYDDk3Hy/IAhWAAxQAjUAoG/fvmjZsiXUajVWr1Y2tE6fPh0ODg6wtLRETk5OrZLXALB27VrRwsJCNXz4cFxITUX8okXyUxUVnJlSlHl5wXbCBBAPjz/fWBSVNM1Vq5Q1kDuwe/duXL58WXrmmWfqzfzav3+/vH///q1vvvlmraqbgiC4AqiqzRFkMBiO3/rsB4MFfqZZWbBgwVS1Wj3XbDb7e3h4GP39/Vv4+fmhvh6pgFJR8/Tp00hMTJQKCwv5uXPnQtNAf92aZFkGpbT+Zuqxscou0P/9r+oJSnphnz4AgB07dsinTp0iY8eOJd7e3o16z88++0wyGo18ixYtpDkvvMBj5kyY3n0XxRoNHB0dkZ2djWXLllFra2vMnDmTaDQaICcH4pkzWHj0qNytWzcuIiKiSYvfUVFR5qNHj0YbjcYnAMgA2gIYqFKpnqaUdpckSQsAo0aNgre3N2pmOZnNZhiNxgb/LABg1apVor1GoxosispC9s8/A7NmKZvrbv4mRKlywjtzpkm7raOiopCZmSlOmzatzq9YWVlZWLZsWbnRaOxpMBhO3Xy/IAgUAAwGw1+WPdYUbKqHaVbeeeedqpx/+/T09EHZ2dnjIyMjh/j5+dHQ0FCLuq7Ef/jhB4kQwrVt2xZTpkxpdNAHGpE55OcHVKVZfvKJsqGoMugDSpkJURSxatUq2rt3b/Tt27fBAFJYWIiSkhL+scceQ1JSEjHJMnaOGUNP//ADsSgthdHJSTabzVxISAjt168fVz1/7eQElaMj+lhactu3b0dMTAwcHBxkd3d32q9fP77ONNQ66PV69ZUrV3pnZmYWyLKssbCwMFpbW6vy8/N1c+fOhUqlgkqlqvNEqFarb/8tLCcHXdev50uKi6nUowfhR44EoqMhiiJiYmJgNBrh6uqKwMBA5bN//XVlumfYsEaN/2YNLe5SSrFixYoKk8k0u66gX+l1QkhhPfc9MOyKn2n2BEFw4Hn+BY7jXuU4zsLR0dHk5uZm6e/vr/b09MSHH35Ix40bR2xtbeHg4NDkDT11ys5Wskw0GmDHDiUPvzKLp4rZbEZiYiJ2796N8PBwuLm54erVqwgICIC1tTXWrl0r5+bmyrIsIzQ0VLVnzx46fvx4EhsbK+fk5ECWZe4Fe3vYXLyIrH/8Azqd7tbj2LRJSR1dtw55eXkQRRGZmZk4evQo1Wg08vTp0+9ok1tRUREsLCyg0WggiiI++OADzJgxo+7NVADS09Nhb2+POvcaUKqsg2RkAP/5D0zTp2OttbWcXVaG0aNHc+3atcPx48exbds2eHl5SVevXuUqKioQHh5OOllbw8LdHTpHxzsZfrXIyEjk5uaKU6ZMUQFAbm4uzGYz3N3dcerUKWzZsiXXbDa3vhcL2vcTC/wMU0kQBALAHYA/z/OhKpVqKgBno9FowfO8ieO4G61atdKNGjXKyrGJgeQWU6YoG69KS4EZMxp86JEjR+SEhASUlJRwWq2WlpSUwNraGqIool+/fqSkpAQxMTEAlBx8Dw8PrF+/XurcuTPvVzXNUV5eZ/Pw+iQlJWHv3r301Vdfvauz3eLFi2Vvb2+uf1VtoBrOnj2LNWvWgOM4WFlZSU5OTmTy5Ml/flWaM0fZbDVqlLIIzvMwGo345ptvcOPGDQCASqWCt7e3NHHiRB4ALi9ejKsrVmDnwIEQRREvvfQSmtK3eefOncjLy5MmT57Mnzp1Cps3by4mhIg2Nja2BQUFoizLvR6mufvGYoGfYRogCIIfgDkAPgOQzvP8HJ7nP5g1a5burhrA13ThgnJFO3Fio59CKcXnn39OjUYjmTVrVnVQy8rKgoWFBeosBCfLQI8ewMaNgKdn7fsuXgSuXKk1zWQymbBw4UI5LCyMhIaG3lXgX7p0qZyTk0MmTZpE3N3da923Zs0aaLVaacCAAfy5c+fwxx9/4O2aG9ScnZXOVoMGAVC+HaxevZoajUYCKNNp06ZNg7Oz8589jMvLgcxMoF07fPjhh/Dx8TFbW1tXBbtbNmzf9DsBQCvfiy8vL4e/v7+cmZnJZWVlfSWKokGlUq0GEPn2229/fjefy4PC5vgZpgGVZSBeqnHTFwsWLJBXr179/vPPP2/VqLTK+hQWAi+9BCxefEdBH1AClSzLdMyYMaTmCaiq21OdOE6p3W9t/eeiZ5XcXGVXa43Af/bsWUiSxIXc1GmMUoqUlBQUFxfLISEhjVoBnjp1Krdz5075559/Jr6+vhg+fDgsK7955OTkiHq9XmVlZQUXFxeo1WoZNXuF5OQAhGD79u305MmTVJZlzsrKCrNmzYK9vT3mz5+PnTt3YubMmUpW1OTJyi7ddu2qXsGYnJz8IYCiqo/v5o+zgd+tATgePHjwUuXvOw0GQxGAIY057ocVC/wMc4ckSfr6xo0bg3/++eeI4OBgi+7duzdtU1h5uRLUzGbg5poyjVBWVsY1NtOnmrW1kkW0aZNSr6ZK797KTw2dOnXC9u3b5bNnz3J+fn6glCI+Pp7Gx8fTa9eucRqN5paTQn0IIRg0aBDn5uaGQ4cOyUuWLKEvvvgiX1FRgdLSUlVVL1utVgtJkmp/nv/8J07pdEiytydPPvkkadeuHTiOqw7OY8eOxfbt23Hy5EkEBgQAH3xwc9lkCcCPlaU8GLAOXAxzxyobwI/Izs5+MTIyMicuLo5KUoO9zOvWurVSIbKRGTN1adJUbXg48O9/175t+3bg89qzFhzHoXv37tzOnTtpXFwcli1bJu/ZsweBgYGcp6cndXFxke/k/QkhCAwMxIQJE7iCggL+0KFD+OKLL6i3t7dUtahbtUnr559/FmVZhizLSLp8mV7IzsYzzzyDDh063JIpFRAQgJYtW0otZs9GeVKS0grxVmxOuwbWiIVhmkCv11O9Xn98z5492zIzM0Pi4+PtrKysNK1atbq3WT8NOHbsmFRSUoJ27drd2Ruq1crJ5h//ADp1UhrHW1gAbm5Kc5YavLy8kJGRgUuXLlEnJyfu6aefJu3bt4efnx/Zs2cPVavVcHd3v6P312g0OH/+vHz8+HHSrVs3jBw5sjqtlBACf39/xMfHk6tXr5KdO3fKee3bY+TcucShnowgAKCU0pMlJdiRkYH2vr6k5l6AAwcOmEVR/FKv19+4k3E+yljgZ5i7oNfr88PCwn7ctWvXwbS0tC7nzp1r0bVrV839CP5WVlbckSNHEBIScudvRohyAujYUfmvWq2cAG7ao0AIQUBAAOnevTvp2LFj9R4GlUoFV1dXsmXLFlJSUiK2b9/+jmYPOI4j58+fx4QJE8jNufuWlpZwc3Mj0dHR8PHxkSfExfG6RYuAZ56p+8W+/hquhYWc/6uvkrz8fDkqKoqEh4dXfyaVgf8rFvj/xLJ6GOYeEQSB02g0CTY2Nm27d+/eIiAgoNF1b5oiNzcXP/74I6ZOnQrPm7N0GuvCBaUGf0WFUq74ww/v6OkZGRlYtmwZevfuDWtra1haWtb6sbCwgFqtrv4WJEkSDhw4IMfGxnJBQUF06NChtz9pZWYqzWXq23mbWVkVoXJ/w3fffQc7O7tiSilEUeRKS0stAbQyGAz5d3RwjzAW+BnmHhIEQQWgv1arfV4UxSGenp5icHCwta+vb5NqA93Orl27EB8fD39/f2n48OF8vaUh6nP5srJxbObMJo9h//79SE1NRUVFhWQymagoikQURSJJEieKImRZBs/z1YXl1Gq1PG7cOK7RJ6srV5QF6enTa99+/DjwzTfADz9U32Q0GrFu3bqK9PR0lSRJbwDYDKDYYDDkNfkAH0Es8DPMX0QQBGsAo3U63WxRFIP9/Pzkrl27Wnh5ed3TJjBFRUVYvHixHBAQQAcNGtS0NpKLFinpj48/fs/GVUUURZSVleH69etYunQpZs2aVe8O3jrt2KF0Bzt3rvbtRqPSpKaybWVNlX2Ob4iiOGHevHnb7vIQHjks8DPMfSAIgivHcRPVavULlFI3JycnU/fu3W0CAwPrL+B2BxYvXiz7+PiQfv36NW1xYfx4ICxMCbB/keTkZKxbtw5vvPEGdLq76IlTUaHse/jllz/rHNXh/PnzWLt2bbkoinMArK/Mv2fAAj/D3HeCIHgA6KZWq/9nNpsDnn/+edRbprmRPv74Y3n8+PFcVT78wyYjIwNr1qyR3dzcaFVZhUYzm5UG6Bcu/NkXNzZWSUu9jUOHDpl37typ5nn+mCRJ70I5ATT7oMeyehjmPtPr9cV6vf7Mnj17lnEc1y8xMdFVrVaDUko0Gs0drQWkp6djx44duHbtGhk0aNA9+fbwV9iwYQNat25NnnzySe6OM554HkhKUmoavf22sgntscca9VR3d3c+JCQElpaWLvn5+UMppW5hYWF/NOEQHinsip9hHjBBEHpqtdqXCSHdTCaTt0ajEZ2cnERXV1cLZ2dntY+PT91VKwF8//33yMnJwcyZMxsu1/CAff/997RDhw6kb9++TXuBzEzA0VGZ0+/QAWjR4o5foqKiAl999VV5WVnZGIPBsKNpA3k0sMDPMA8RQRA4AJ4AAgghAVqtNkQUxQGurq6yKIoYM2aMdVVtnl27diEpKQmTJ09+qIP+lStXsGzZMsyePbvu4nGN4eAADB8OLFlyV2NZtGiRlJubawTgYjAYmm1ePwv8DPOQq8wOGsZxnJdWqzU899xzFmVlZdi0aRN8fX3pgAEDHmh3J0mScPjwYerr60vqqlh6/PhxREVF0VdfffWWqpiNFhenTPn06nVXY01MTMTWrVsBAISQNymlHzXHOX8W+Bnmb2TBggUv8jy/UBRFtaurq6TX6/m2bds+sPEUFRVh9erVpdeuXcsURbGDvb19uaurKxk9erQuLy8PrSpLQHz00Ud04sSJpMkbze6xuLg47Nq1CwCcDAbDtQc9nvuNLe4yzN9Inz594vfu3avRaDRuJSUl1jk5OaVHjhxRXb9+nWvbtu19qxMEAMXFxfjuu+9QVFSkkWXZAQAqKirUeXl5KrVajXXr1iEjI8NoNBpVly5dgr29PfGo2QT9ATp+/Lick5Oc6x9MAAAE+UlEQVSTbjAY3n/QY3kQ2BU/w/xNCYLgAsAPwB4ACAkJkcLCwvi/skwEoDSQ379/vxgdHU04jkvlOK7cZDJVl8RUq9VZZrPZBTWq/6pUKnTq1In27t2bODs73/MxlZeX39Hjly9fLufk5CTNmzev+z0fzN8AC/wM8wgQBMFXo9G8ZTKZps6ePbt6iuVek2UZq1evLr906dJpk8k0BcDZ+ubIBUFwArABQBiglHmWZRlz5sxpUhvE+sTExIjR0dGUEGJu7HMkSbIkhLwxb968j+7ZQP5GWOBnmEfIe++996okSe+pVCrZ0tLS3KJFC2pjY6OytbXVOTg48IGBgXdcM8hkMuHixYtITk4uP3v2LCGEpBiNxr6N3Qlb2ct4AIAVAJyCg4OlIUOG8HfVvaxSYWEhvvnmm3JRFP0MBsOl2z+DAVjgZ5hHTmWhOEcALjV/tFrtAI7jQvr27WsRHBxMGtrsRSnF6dOnkZCQcOPy5ctajUZzvKKi4ldK6RaDwZB2F2Pz1Gq1P2q12rCRI0da+vj4NPWlAAArV64sS0tL+/jtt9/+7129UDPDAj/DNCOCIPTQarWfq1SqoJ49e1p6enoSNzc3UEqRl5cHSimKi4uxZ8+ekpKSkgyj0bgAwHaDwVB4L8cxf/78YSqV6qdOnTrZDB06VNeUq/8LFy5gzZo1OWaz2dtgMFTcy/E96ljgZ5hmSBCEMLVaPZ7n+QEmk6mtSqWSCSHZHMdVUEpLjEbj+5TSjX9ljrsgCC20Wu0aW1vbPpMmTbKyuYMWlJIk4csvvyy9cePGhHnz5m39q8b4qGKBn2GaOUEQLAD4AEi+35uZBEEgKpXqLZVK9fZTTz1l0aZNm0Y978CBA1JsbOz+yrUGFsTuEAv8DMM8cPPnzx+gUqk2jB071trX17fBxxYXF+Prr78uN5vNXQwGw/n7NMRHyr3rBsEwDNNE8+bN22U2m/uuX7++5Pz5hmP5jh07ygF8zYJ+07HAzzDMQ8FgMBw1m82Pr127tmTr1q1ydnb2LY8pLS1FSkqKzmw2f/YAhvjIYFM9DMM8VARBaAsgFQDGjBmDwMDAWvdv2rTJeObMmT1Go3Eom99vGnbFzzDMQ8VgMFwAYA8Ae/fuvSWwh4aGak0m02AAHe732B4VLPAzDPPQqdw30Lq0tPRydHS0WPO+pKQkEcAXBoPh7IMZ3d8fC/wMwzyUDAZDjslkComLi8uOiYmpDv4XL16soJQeeZBj+7tjgZ9hmIeWwWDINplMvfbv359TFfzNZrOMyjUApmlY4GcY5qFmMBiyzWZzr/379+fGxsaK1tbWBID7gx7X3xkL/AzDPPQMBkOW2WzuFRsbm5uTk1MOYPeDHtPfGUvnZBjmb0MQBFsAosFgKH3QY/k7Y4GfYRimmWFTPQzDMM0MC/wMwzDNDAv8DMMwzQwL/AzDMM0MC/wMwzDNDAv8DMMwzQwL/AzDMM0MC/wMwzDNDAv8DMMwzQwL/AzDMM0MC/wMwzDNDAv8DMMwzQwL/AzDMM0MC/wMwzDNDAv8DMMwzQwL/AzDMM0MC/wMwzDNDAv8DMMwzQwL/AzDMM0MC/wMwzDNDAv8DMMwzQwL/AzDMM0MC/wMwzDNDAv8DMMwzQwL/AzDMM0MC/wMwzDNDAv8DMMwzQwL/AzDMM0MC/wMwzDNDAv8DMMwzQwL/AzDMM3M/wMwwXBq1UcrRQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(edgecolor='grey', facecolor='w')\n", "f,ax = w_knn1.plot(gdf, ax=ax, \n", " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", " node_kws=dict(marker=''))\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two components are separated in the southern part of the country, with the smaller component to the east and the larger component running through the rest of the country to the west. For certain types of spatial analytical methods, it is necessary to have a adjacency structure that consists of a single component. To ensure this for the case of Mexican states, we can increase the number of nearest neighbors to three:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "w_knn3 = KNN.from_shapefile(pth,k=3)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(edgecolor='grey', facecolor='w')\n", "f,ax = w_knn3.plot(gdf, ax=ax, \n", " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", " node_kws=dict(marker=''))\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lattice Weights" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "from libpysal.weights import lat2W" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "w = lat2W(4,3)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "23.61111111111111" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.pct_nonzero" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: [3, 1],\n", " 3: [0, 6, 4],\n", " 1: [0, 4, 2],\n", " 4: [1, 3, 7, 5],\n", " 2: [1, 5],\n", " 5: [2, 4, 8],\n", " 6: [3, 9, 7],\n", " 7: [4, 6, 10, 8],\n", " 8: [5, 7, 11],\n", " 9: [6, 10],\n", " 10: [7, 9, 11],\n", " 11: [8, 10]}" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.neighbors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Handling nonplanar geometries" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "rs = libpysal.examples.get_path('map_RS_BR.shp')" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/weights/weights.py:168: UserWarning: There are 29 disconnected observations \n", " Island ids: 0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374\n", " \" Island ids: %s\" % ', '.join(str(island) for island in self.islands))\n" ] } ], "source": [ "rs_df = gpd.read_file(rs)\n", "wq = libpysal.weights.Queen.from_dataframe(rs_df)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "29" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(wq.islands)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{}" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wq[0]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "wf = libpysal.weights.fuzzy_contiguity(rs_df)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf.islands" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0}" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wf[0]" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.rcParams[\"figure.figsize\"] = (20,15)\n", "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n", "f,ax = wq.plot(rs_df, ax=ax, \n", " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", " node_kws=dict(marker=''))\n", "\n", "ax.set_axis_off()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n", "f,ax = wf.plot(rs_df, ax=ax, \n", " edge_kws=dict(color='r', linestyle=':', linewidth=1),\n", " node_kws=dict(marker=''))\n", "ax.set_title('Rio Grande do Sul: Nonplanar Weights')\n", "ax.set_axis_off()\n", "plt.savefig('rioGrandeDoSul.png')\n" ] }, { "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 } libpysal-4.9.2/pyproject.toml000066400000000000000000000066411452177046000162760ustar00rootroot00000000000000[build-system] requires = ["setuptools>=61.0", "setuptools_scm[toml]>=6.2"] build-backend = "setuptools.build_meta" [tool.setuptools_scm] [project] name = "libpysal" dynamic = ["version"] authors = [ # in alphabetical order { name = "Serge Rey", email = "sjsrey@gmail.com" }, { name = "Levi Wolf", email = "levi.john.wolf@gmail.com" }, ] maintainers = [{ name = "PySAL Developers" }] license = { text = "BSD 3-Clause" } description = "Core components of PySAL - A library of spatial analysis functions" keywords = ["spatial statistics", "spatial graphs"] 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 = ">=3.10" dependencies = [ "beautifulsoup4>=4.10", "geopandas>=0.10.0", "numpy>=1.22", "packaging>=22", "pandas>=1.4", "platformdirs>=2.0.2", "requests>=2.27", "scipy>=1.8", "shapely>=2.0.1", ] [project.urls] Home = "https://github.com/pysal/libpysal/" Repository = "https://github.com/pysal/libpysal" [project.optional-dependencies] plus = [ "joblib>=1.2", "networkx>=2.7", "numba>=0.55", "pyarrow>=7.0", "scikit-learn>=1.1", "sqlalchemy>=2.0", "xarray>=2022.3", "zstd", ] dev = [ "black", "pre-commit", "watermark", ] docs = [ "mkdocs-jupyter", "myst-parser", "nbsphinx", "numpydoc", "pandoc", "sphinx", "sphinxcontrib-bibtex", "sphinx_bootstrap_theme", ] tests = [ "codecov", "geodatasets>=2023.3.0", "matplotlib>=3.6", "pytest", "pytest-mpl", "pytest-cov", "pytest-xdist", ] [tool.setuptools.packages.find] include = ["libpysal", "libpysal.*"] [tool.black] line-length = 88 [tool.ruff] line-length = 88 select = ["E", "F", "W", "I", "UP", "N", "B", "A", "C4", "SIM", "ARG"] target-version = "py310" ignore = [ "B006", "B008", "B009", "B010", "C408", "E731", "N803", "N806", "N999", "UP007" ] exclude = ["libpysal/tests/*", "docs/*"] [tool.ruff.per-file-ignores] "*__init__.py" = [ "F401", # imported but unused "F403", # star import; unable to detect undefined names ] [tool.coverage.run] source = ["./libpysal"] [tool.coverage.report] exclude_lines = [ "raise NotImplementedError", "except ModuleNotFoundError:", "except ImportError", ] ignore_errors = true omit = ["libpysal/tests/*", "docs/conf.py"] [tool.pytest.ini_options] filterwarnings = [ "ignore:The numba package is used", "ignore:numba cannot be imported", "ignore:Numba not imported", "ignore:The weights matrix is not fully connected", "ignore:You are trying to build a full W object from", "ignore:Multiple layers detected. Using first layer as default", "ignore:Geometry is in a geographic CRS", "ignore:`use_index` defaults to False", "ignore:Objects based on the `Geometry` class will deprecated", "ignore:PolygonLocator is deprecated", "ignore:SegmentGrid is deprecated", "ignore:In the next version of libpysal, observations with no neighbors", "ignore:divide by zero encountered", "ignore:invalid value encountered", "ignore:Passing a SingleBlockManager", # https://github.com/geopandas/geopandas/issues/3060 "ignore:Passing a BlockManager", # https://github.com/geopandas/geopandas/issues/3060 ]