pax_global_header00006660000000000000000000000064146641356030014522gustar00rootroot0000000000000052 comment=a6401ac0b78022bdb4bb75985a19b7cbfdfc035d libpysal-4.12.1/000077500000000000000000000000001466413560300134265ustar00rootroot00000000000000libpysal-4.12.1/.gitattributes000066400000000000000000000001031466413560300163130ustar00rootroot00000000000000*.ipynb linguist-language=Python libpysal/_version.py export-subst libpysal-4.12.1/.github/000077500000000000000000000000001466413560300147665ustar00rootroot00000000000000libpysal-4.12.1/.github/CONTRIBUTING.md000066400000000000000000000016011466413560300172150ustar00rootroot00000000000000Thank 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. At the time of this writing, Python 3.10, 3.11, and 3.12 are the officially supported versions. 2. This project implements the linting and formatting conventions of [`ruff`](https://docs.astral.sh/ruff/) on all incoming Pull Requests. To ensure a PR is properly linted and formatted prior to creating a Pull Request, [install `pre-commit`](https://pre-commit.com/#installation) in your development environment and then [set up the `libpysal` configuration of pre-commit hooks](https://pre-commit.com/#3-install-the-git-hook-scripts). libpysal-4.12.1/.github/ISSUE_TEMPLATE.md000066400000000000000000000025751466413560300175040ustar00rootroot00000000000000Thank 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-4.12.1/.github/PULL_REQUEST_TEMPLATE.md000066400000000000000000000020121466413560300205620ustar00rootroot00000000000000Hello! 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-4.12.1/.github/dependabot.yml000066400000000000000000000011671466413560300176230ustar00rootroot00000000000000# 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-4.12.1/.github/release.yml000066400000000000000000000005171466413560300171340ustar00rootroot00000000000000changelog: 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-4.12.1/.github/workflows/000077500000000000000000000000001466413560300170235ustar00rootroot00000000000000libpysal-4.12.1/.github/workflows/build_docs.yml000066400000000000000000000035261466413560300216630ustar00rootroot00000000000000 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-latest.yaml] experimental: [false] defaults: run: shell: bash -l {0} steps: - name: Checkout repo uses: actions/checkout@v4 with: fetch-depth: 0 # Fetch all history for all branches and tags. - name: Setup micromamba uses: mamba-org/setup-micromamba@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-4.12.1/.github/workflows/release_and_publish.yml000066400000000000000000000034641466413560300235450ustar00rootroot00000000000000 # 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@v5 with: python-version: "3.x" - name: Install Dependencies run: | python -m pip install --upgrade pip build twine python -m build twine check --strict dist/* - name: Create Release Notes uses: actions/github-script@v7 with: github-token: ${{secrets.GITHUB_TOKEN}} script: | await github.request(`POST /repos/${{ github.repository }}/releases`, { tag_name: "${{ github.ref }}", generate_release_notes: true }); - name: Publish distribution 📦 to PyPI uses: pypa/gh-action-pypi-publish@release/v1 with: user: __token__ password: ${{ secrets.PYPI_PASSWORD }} libpysal-4.12.1/.github/workflows/reverse.yml000066400000000000000000000026721466413560300212300ustar00rootroot00000000000000name: Test reverse dependencies on: push: branches: [main] schedule: - cron: "0 0 * * 1,4" workflow_dispatch: inputs: version: description: Manual reverse dependency testing default: test required: false jobs: reverse_dependencies: name: Reverse dependency testing runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - uses: scientific-python/reverse-dependency-testing-action@main with: package_name: libpysal ignore: >- fine tigernet cenpy autoesda region greedy pysal mesa-geo spvcm include: >- mapclassify install: >- setuptools-scm h3-py hdbscan pandana astropy geodatasets bokeh pulp dask-geopandas kdepy matplotlib statsmodels osmnx installation_command: >- pip install -e .; python -c 'import libpysal; libpysal.examples.fetch_all()'; fail_on_failure: true xfail: >- mgwr tobler geosnap pointpats verbose: true parallel: false libpysal-4.12.1/.github/workflows/unittests.yml000066400000000000000000000041211466413560300216060ustar00rootroot00000000000000name: 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-latest.yaml - ci/311-latest.yaml - ci/312-min.yaml - ci/312-latest.yaml - ci/312-dev.yaml include: - environment-file: ci/312-latest.yaml os: macos-13 # Intel - environment-file: ci/312-latest.yaml os: macos-14 # Apple Silicon - environment-file: ci/312-latest.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 . - name: Spatial versions run: | python -c 'import geopandas; geopandas.show_versions();' - name: Download test files run: | python -c ' import geodatasets import libpysal geodatasets.fetch("nybb") geodatasets.fetch("geoda liquor_stores") geodatasets.fetch("eea large_rivers") geodatasets.fetch("geoda groceries") geodatasets.fetch("geoda guerry") libpysal.examples.fetch_all() ' - name: Test libpysal run: | pytest -v --color yes -n auto --cov libpysal --cov-append --cov-report term-missing --cov-report xml . - name: Codecov uses: codecov/codecov-action@v4 libpysal-4.12.1/.gitignore000066400000000000000000000037001466413560300154160ustar00rootroot00000000000000*.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-4.12.1/.pre-commit-config.yaml000066400000000000000000000003231466413560300177050ustar00rootroot00000000000000files: "libpysal\/" repos: - repo: https://github.com/astral-sh/ruff-pre-commit rev: "v0.5.0" hooks: - id: ruff - id: ruff-format ci: autofix_prs: false autoupdate_schedule: quarterly libpysal-4.12.1/CHANGELOG.md000066400000000000000000006520051466413560300152470ustar00rootroot00000000000000# 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-4.12.1/LICENSE.txt000066400000000000000000000027771466413560300152660ustar00rootroot00000000000000Copyright (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-4.12.1/README.md000066400000000000000000000047721466413560300147170ustar00rootroot00000000000000# 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) [![Discord](https://img.shields.io/badge/Discord-join%20chat-7289da?style=flat&logo=discord&logoColor=cccccc&link=https://discord.gg/BxFTEPFFZn)](https://discord.gg/BxFTEPFFZn) ## libpysal modules - `libpysal.cg` – Computational geometry - `libpysal.examples` – Built-in example datasets - `libpysal.graph` – Graph class encoding spatial weights matrices - `libpysal.io` – Input and output - `libpysal.weights` – Spatial weights ## 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 [Discord](https://discord.gg/BxFTEPFFZn). ## 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-4.12.1/THANKS.txt000066400000000000000000000032121466413560300151550ustar00rootroot00000000000000PySAL 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-4.12.1/ci/000077500000000000000000000000001466413560300140215ustar00rootroot00000000000000libpysal-4.12.1/ci/310-latest.yaml000066400000000000000000000007331466413560300165050ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.10 - beautifulsoup4 - geopandas - jinja2 - packaging - pandas - platformdirs - requests - scipy - shapely # testing - codecov - matplotlib - tobler - h3-py - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional - geodatasets - joblib - networkx - numba - pyarrow - scikit-learn - sqlalchemy - xarray - zstd - pandana - pip - pip: - pulp libpysal-4.12.1/ci/310-oldest.yaml000066400000000000000000000010661466413560300165030ustar00rootroot00000000000000name: 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 - pandana - pip - pip: - pulp - platformdirs==2.0.2 libpysal-4.12.1/ci/311-latest.yaml000066400000000000000000000007351466413560300165100ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 - beautifulsoup4 - geopandas - jinja2 - packaging - pandas - platformdirs - requests - scipy - shapely # testing - codecov - matplotlib - tobler - h3-py - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional - geodatasets - joblib - networkx - numba - pyarrow - scikit-learn - sqlalchemy - xarray - zstd - pandana - pip - pip: - pulp libpysal-4.12.1/ci/312-dev.yaml000066400000000000000000000014771466413560300157770ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 - beautifulsoup4 - jinja2 - platformdirs - requests # testing - codecov - matplotlib - h3-py - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional - Cython - fiona - geodatasets - geos - joblib - networkx - numba - packaging - pyarrow - pyproj - sqlalchemy - zstd - pandana - 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 - git+https://github.com/pysal/tobler.git@main - pulp libpysal-4.12.1/ci/312-latest.yaml000066400000000000000000000011711466413560300165040ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 - beautifulsoup4 - geopandas - jinja2 - packaging - pandas - platformdirs - requests - scipy - shapely # testing - codecov - matplotlib - tobler - h3-py - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional - geodatasets - joblib - networkx - numba - pyarrow - scikit-learn - sqlalchemy - zstd - pandana - xarray # for docs build action (this env only) - myst-parser - nbsphinx - numpydoc - pandoc - sphinx - sphinxcontrib-bibtex - sphinx_bootstrap_theme - pip - pip: - pulp libpysal-4.12.1/ci/312-min.yaml000066400000000000000000000005641466413560300160000ustar00rootroot00000000000000name: 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 - scikit-learn - pytest - pytest-cov - pytest-mpl - pytest-xdist # optional used in ci - geodatasets libpysal-4.12.1/codecov.yml000066400000000000000000000005431466413560300155750ustar00rootroot00000000000000codecov: 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-4.12.1/docs/000077500000000000000000000000001466413560300143565ustar00rootroot00000000000000libpysal-4.12.1/docs/.buildinfo000066400000000000000000000003461466413560300163350ustar00rootroot00000000000000# Sphinx build info version 1 # This file hashes the configuration used when building these files. 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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-4.12.1/docs/api.rst000066400000000000000000000117531466413560300156700ustar00rootroot00000000000000.. _api_ref: .. currentmodule:: libpysal libpysal API reference ====================== Spatial Graph ------------- Modern implementation of spatial graphs encoding spatial weights matrices. .. autosummary:: :toctree: generated/ libpysal.graph.Graph libpysal.graph.GraphSummary libpysal.graph.read_parquet libpysal.graph.read_gal libpysal.graph.read_gwt Spatial Weights --------------- Legacy implementation of spatial weights matrices. .. 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 libpysal-4.12.1/docs/conf.py000066400000000000000000000251741466413560300156660ustar00rootroot00000000000000# -*- 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"), ("User Guide", "user-guide/intro"), ("API", "api"), ("References", "references"), ], } # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # html_sidebars = {'sidebar': ['localtoc.html', 'sourcelink.html', 'searchbox.html']} # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = "%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-4.12.1/docs/index.rst000066400000000000000000000076601466413560300162300ustar00rootroot00000000000000.. 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://img.shields.io/badge/Discord-join%20chat-7289da?style=flat&logo=discord&logoColor=cccccc :target: https://discord.gg/BxFTEPFFZn .. image:: https://badge.fury.io/py/libpysal.svg :target: https://badge.fury.io/py/libpysal .. raw:: html ************ Introduction ************ **libpysal** offers five modules that form the building blocks in many upstream packages in the `PySAL family `_: - Spatial Weights: libpysal.weights - Spatial Graphs: libpysal.graph - Input-and output: libpysal.io - Computational geometry: libpysal.cg - Built-in example datasets libpysal.examples .. Note:: A new Graph class is being added to libpysal. For users interested in migration from using Weights to the new Graph class, see the `migration guide `_. For developers interested in the technical details details see `W and Graph Components `_. Examples demonstrating some of **libpysal** functionality are available in the `User Guide `_. 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 discord_. .. _discord : https://discord.gg/BxFTEPFFZn **************** 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`_) Documentation contents ---------------------- .. toctree:: :maxdepth: 1 Home installation API reference references user-guide/intro migration .. _PySAL: https://github.com/pysal/pysal libpysal-4.12.1/docs/installation.rst000066400000000000000000000027401466413560300176140ustar00rootroot00000000000000.. Installation Installation ============ libpysal supports python >= `3.10`_ 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. .. _3.10: https://docs.python.org/3.10/ .. _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-4.12.1/docs/migration.rst000066400000000000000000000715121466413560300171070ustar00rootroot00000000000000 W to Graph Member Comparisions ============================== Overview -------- This guide compares the members (attributes and methods) from the `W` class and the `Graph` class. It is intended for developers. Users interested in migrating to the new Graph class from W should see the `migration guide `_. Members common to W and Graph ----------------------------- +-----------------------------------------------------------------------------------------+------------------+ | Member | Typee | +=========================================================================================+==================+ | `asymmetry `_ | builtins.method | +-----------------------------------------------------------------------------------------+------------------+ | `from_sparse `_ | builtins.method | +-----------------------------------------------------------------------------------------+------------------+ | `n `_ | builtins.int | +-----------------------------------------------------------------------------------------+------------------+ | `n_components `_ | builtins.int | +-----------------------------------------------------------------------------------------+------------------+ | `neighbors `_ | builtins.dict | +-----------------------------------------------------------------------------------------+------------------+ | `pct_nonzero `_ | builtins.float | +-----------------------------------------------------------------------------------------+------------------+ | `plot `_ | builtins.method | +-----------------------------------------------------------------------------------------+------------------+ | `to_networkx `_ | builtins.method | +-----------------------------------------------------------------------------------------+------------------+ | `weights `_ | builtins.dict | +-----------------------------------------------------------------------------------------+------------------+ Members common to W and Graph with different types -------------------------------------------------- +------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------+ | Member | Queen Type | Graph Type | +==================+===============================================================================================+==========================================================================================================+ | cardinalities | `builtins.dict `_ | `pandas.core.series.Series `_ | +------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------+ | component_labels | `numpy.ndarray `_ | `pandas.core.series.Series `_ | +------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------+ | nonzero | `builtins.int `_ | `numpy.int64 `_ | +------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------+ | sparse | `scipy.sparse._csr.csr_matrix `_ | `scipy.sparse._csr.csr_array `_ | +------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------+ | transform | `builtins.str `_ | `builtins.method `_ | +------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------+ Members unique to W ------------------- +---------------------------------------------------------------------------------------------+-----------------+ | Member | Type | +=============================================================================================+=================+ | `asymmetries `_ | builtins.list | +---------------------------------------------------------------------------------------------+-----------------+ | `diagW2 `_ | numpy.ndarray | +---------------------------------------------------------------------------------------------+-----------------+ | `diagWtW `_ | numpy.ndarray | +---------------------------------------------------------------------------------------------+-----------------+ | `diagWtW_WW `_ | numpy.ndarray | +---------------------------------------------------------------------------------------------+-----------------+ | `from_WSP `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `from_adjlist `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `from_dataframe `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `from_file `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `from_iterable `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `from_networkx `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `from_shapefile `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `from_xarray `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `full `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `get_transform `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `histogram `_ | builtins.list | +---------------------------------------------------------------------------------------------+-----------------+ | `id2i `_ | builtins.dict | +---------------------------------------------------------------------------------------------+-----------------+ | `id_order `_ | builtins.list | +---------------------------------------------------------------------------------------------+-----------------+ | `id_order_set `_ | builtins.bool | +---------------------------------------------------------------------------------------------+-----------------+ | `islands `_ | builtins.list | +---------------------------------------------------------------------------------------------+-----------------+ | `max_neighbors `_ | builtins.int | +---------------------------------------------------------------------------------------------+-----------------+ | `mean_neighbors `_ | numpy.float64 | +---------------------------------------------------------------------------------------------+-----------------+ | `min_neighbors `_ | builtins.int | +---------------------------------------------------------------------------------------------+-----------------+ | `neighbor_offsets `_ | builtins.dict | +---------------------------------------------------------------------------------------------+-----------------+ | `remap_ids `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `s0 `_ | numpy.float64 | +---------------------------------------------------------------------------------------------+-----------------+ | `s1 `_ | numpy.float64 | +---------------------------------------------------------------------------------------------+-----------------+ | `s2 `_ | numpy.float64 | +---------------------------------------------------------------------------------------------+-----------------+ | `s2array `_ | numpy.ndarray | +---------------------------------------------------------------------------------------------+-----------------+ | `sd `_ | numpy.float64 | +---------------------------------------------------------------------------------------------+-----------------+ | `set_shapefile `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `set_transform `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `silence_warnings `_ | builtins.bool | +---------------------------------------------------------------------------------------------+-----------------+ | `symmetrize `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `to_WSP `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `to_adjlist `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `to_file `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `to_sparse `_ | builtins.method | +---------------------------------------------------------------------------------------------+-----------------+ | `transformations `_ | builtins.dict | +---------------------------------------------------------------------------------------------+-----------------+ | `trcW2 `_ | numpy.float64 | +---------------------------------------------------------------------------------------------+-----------------+ | `trcWtW `_ | numpy.float64 | +---------------------------------------------------------------------------------------------+-----------------+ | `trcWtW_WW `_ | numpy.float64 | +---------------------------------------------------------------------------------------------+-----------------+ Members unique to Graph ----------------------- +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | Member | Type | +===============================================================================================================+================================+ | `adjacency `_ | pandas.core.series.Series | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `aggregate `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `apply `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `assign_self_weight `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `build_block_contiguity `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `build_contiguity `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `build_distance_band `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `build_fuzzy_contiguity `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `build_h3 `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `build_kernel `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `build_knn `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `build_raster_contiguity `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `build_spatial_matches `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `build_triangulation `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `copy `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `describe `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `difference `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `eliminate_zeros `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `equals `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `explore `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `from_W `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `from_adjacency `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `from_arrays `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `from_dicts `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `from_weights_dict `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `generate_da `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `higher_order `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `index_pairs `_ | builtins.tuple | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `intersection `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `intersects `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `isolates `_ | pandas.core.indexes.base.Index | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `isomorphic `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `issubgraph `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `lag `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `n_edges `_ | builtins.int | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `n_nodes `_ | builtins.int | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `subgraph `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `summary `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `symmetric_difference `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `to_W `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `to_gal `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `to_gwt `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `to_parquet `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `transformation `_ | builtins.str | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `union `_ | builtins.method | +---------------------------------------------------------------------------------------------------------------+--------------------------------+ | `unique_ids `_ | pandas.core.indexes.base.Index | +---------------------------------------------------------------------------------------------------------------+--------------------------------+libpysal-4.12.1/docs/references.rst000066400000000000000000000001461466413560300172320ustar00rootroot00000000000000.. reference for the docs References ========== .. bibliography:: _static/references.bib :cited: libpysal-4.12.1/docs/user-guide/000077500000000000000000000000001466413560300164275ustar00rootroot00000000000000libpysal-4.12.1/docs/user-guide/data/000077500000000000000000000000001466413560300173405ustar00rootroot00000000000000libpysal-4.12.1/docs/user-guide/data/examples.ipynb000066400000000000000000002016221466413560300222240ustar00rootroot00000000000000{ "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/para/1_projects/code-pysal-libpysal/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": [ "(99, 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": [ "99 datasets available, 29 installed, 70 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": [ "99 datasets available, 30 installed, 69 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": [], "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/README.md',\n", " '/home/serge/.local/share/pysal/taz/taz-master/taz.shx',\n", " '/home/serge/.local/share/pysal/taz/taz-master/taz.shp']" ] }, "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
............
94tazTraffic Analysis Zones in So. CaliforniaTrue
95tokyoTokyo Mortality dataTrue
96us_incomePer-capita income for the lower 48 US states 1...True
97virginiaVirginia counties shapefileTrue
98wmatDatasets used for spatial weights testingTrue
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99 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", "94 taz Traffic Analysis Zones in So. California True\n", "95 tokyo Tokyo Mortality data True\n", "96 us_income Per-capita income for the lower 48 US states 1... True\n", "97 virginia Virginia counties shapefile True\n", "98 wmat Datasets used for spatial weights testing True\n", "\n", "[99 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.mif',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbn',\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.sqlite',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.kml',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shp',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.xlsx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.geojson',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.gpkg',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shp',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.geojson',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.kml',\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_final_census2.mid',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbn',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mid',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.dbf',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\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/a00000007.CatRelTypesByBackwardLabel.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.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.CatRelTypesByDestItemTypeID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n", " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.prj',\n", " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\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_final_census2.sbx',\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']" ] }, "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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", "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': ,\n", " 'chicagoSDOH': }" ] }, "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": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 4 } libpysal-4.12.1/docs/user-guide/graph/000077500000000000000000000000001466413560300175305ustar00rootroot00000000000000libpysal-4.12.1/docs/user-guide/graph/intro.rst000066400000000000000000000002211466413560300214100ustar00rootroot00000000000000 ============= Spatial Graph ============= .. toctree:: :maxdepth: 1 Graph Matching Migration Guide libpysal-4.12.1/docs/user-guide/graph/matching-graph.ipynb000066400000000000000000037625111466413560300235030ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spatial matching graph\n", "\n", "Author: [Levi John Wolf](http://github.com/ljwolf)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic Usage" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath('..'))\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import geopandas\n", "import pandas" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from libpysal.graph import Graph" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "points = np.row_stack([(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3), (7,4)])\n", "gdf = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(*points.T))" ] }, { "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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uuGOrtj3mmGPiwQcfjD322KOKU0HFlJSUxPnnnx8lJSUR8a+HRH7nO99JnAq231/+8pes8bp162L16tXx/vvvxwsvvBA333xz9OzZM2655RZL/lHtlL34sXbt2jjooIOyHgK8OcXFxXHXXXfFlClT4qmnnoo999yzqmJCpRs3blwsWrQoM87Pz/e5hGqpRYsWMXHixBgwYEBMmzYtioqK4oQTTojTTjstTjvttOjUqVPk5+fHZ599FlOmTIlhw4ZlXcQ++OCDY/To0Za4JKlOnTpljRctWhQLFy6MNm3abNX+U6ZMKTdnGTRqkuXLl2eNK3rNrlGjRtGgQYOs55GtWLEimjdvXhnxyCF31sBO4uSTT46nn346nnvuuSgoKEgdB8q59dZbMw/Ua9iwYfzf//1f4kSQO1OnTo3jjz8+fvrTn2YtuQOplf3DcfDgwZmipmHDhnHVVVfF+PHjY86cOfHaa6/FiBEjolevXln7TJs2LU499dQoLi7OVWzYbiNGjMgan3rqqRV+hgLkSmFhYbzyyitx9913R8+ePaO0tDQefvjhOOOMM6Jbt27RoUOHOOyww+LKK6/MFDW77rpr/OIXv4iXXnrJxTySa9WqVey7775Zcw8++OBW7btmzZp49NFHy82vWrWqUrJBLqxevTprnJ+fX+FjlN3Hv4GayZ01sJMYO3ZsbNiwIRo0aBBHHnlk6jiQZfbs2fE///M/mfFNN93kmUrUeAUFBdGvX784+OCDo3PnztGiRYuoVatWfP755/H666/H3/72t3j66acz25eWlsbNN98cGzdujFtuuSVhcviXdevWlVsDfuHChRER8fWvfz3GjRsXe+21V9brBxxwQAwePDhuu+22uPrqqzPzU6ZMiV/+8pdx/fXXV31w2E5LliyJJ598MmvOEmhUdxs2bIgNGzZE/fr1Iy8vb4tf/thrr73i5z//eQwcODDq1q2bw5Sweeecc0787Gc/y4x/9atfxbnnnvuVXzb92c9+tsm7aFyopiYpW9Y0aNCgwsfIz8/PWvqs7DGpGdxZAzuA//7v/4733nsv8zN79ux48cUX4/e//3307t07Iv61FMlTTz0VRx11VFxyySWxYcOGxKnhXzZu3BhDhgzJXBA88MAD47LLLkucCrbdwQcfHE8//XR8+OGHcffdd8f3vve9OOKII6Jz586x7777xuGHHx6XXHJJjBs3Lv75z39Ghw4dsva/9dZb4/HHH0+UHv5jc58VmjZtusmi5suuuuqquOKKK7Lmfve73/mjkRrhgQceyLoTrF27dnHUUUclTARbNnny5OjcuXNcdNFFMXny5Ni4ceMWt//www9j8ODB8bWvfS3uueeeHKWELbvkkkuiadOmmfHy5cujb9++WUtSlvXb3/42br/99k2+VquWS57UXNuyNKXlLHcMfnPBDqBFixZRWFiY+encuXP06tUrLrnkkpgwYUK8+OKLWQ8bu/POO+P73/9+wsTwH3fccUe8/PLLERFRp06duOeee6J27dqJU8G269evXxx//PFb9WG5Z8+e8fLLL0fHjh2z5n/84x8r1Ulul1122eSFjiuvvHKLRc2/3XTTTVkXXZYuXRpjx46t1IxQFe67776s8fnnn+8CCNXWhAkTok+fPrFgwYLMXEFBQdx6660xbdq0WL58eaxfvz4WL14c48aNi0GDBkWdOv9aZGXJkiVxwQUXxPe//33LsJJcs2bNyi1BOXPmzOjcuXNcc801MXHixHj77bdj+vTpMXLkyPjGN74RV111Vea9W/b5NpaupCZp1KhR1rioqKjCxyi7T9ljUjMoa2An0KtXr5g4cWLsuuuumbkRI0b45jbJzZ8/P2tJnCuvvDK6d++eLhAk0KJFi3jooYeyLgTOmTMnJk6cmDAV/EvDhg3LzZ177rlbve+AAQOy5iZNmlQZsaDKvPzyyzFr1qzMuHbt2nHeeeelCwRbsGTJkjjrrLOyHij9zW9+M2bPnh3XXnttdO/ePZo2bRp169aNli1bxgknnBAjR46MF198Metvw+HDh8evfvWrFP8JkGXAgAFxxx13ZH1ZZNWqVfHrX/86evfuHZ06dYoePXrE4MGD46WXXspsc9lll8Wxxx6bdSxlDTWJsoZ/U9bATmKfffaJ//7v/86a84GclEpLS+OCCy6ItWvXRkRE27ZtY+jQoWlDQSIHHHBAHH/88Vlz48aNS5QG/qPshY6WLVtW6Jlihx56aNb4rbfeqoRUUHXuvfferHHfvn2jdevWidLAlv32t7+NJUuWZMadOnWKhx9+OJo0abLF/Q499NAYNWpU1tyNN94Yn376aZXkhIq47LLLYuzYsbHvvvt+5baNGjWKO++8M26//fZyy6W1atWqqiJCpfvy3egRkfW7fWusXr26XFmjsKyZlDWwExk4cGDW+OWXX47ly5enCcNOb/jw4fHcc89lxsOGDYv8/PyEiSCtE088MWv8xhtvJEoC/1F2ib4999yzQvuXvcj9+eefb3cmqCpr1qwpdwF7yJAhidLAV3vkkUeyxtdee+1WP5T62GOPjW984xuZcVFRUfzlL3+p1HywrY4//viYNWtW/PWvf40hQ4ZE586do0WLFlG3bt0oKCiIXr16xW9+85t455134gc/+EHk5eXFnDlzso7Rs2fPROmh4so+x/T999+v0P5lt2/RokU0b958u3ORe3VSBwByZ4899ojmzZvHsmXLIuJfD3Z/7733okePHomTsTO64YYbMv+7X79+0b59+6y1tjdl8eLFWeOSkpJy+7Ru3Trq1atXWTEhZ8rerVDRb1NBVejSpUtMmDAhM65fv36F9i+7/ZeX6oHq5pFHHolVq1Zlxi1btoz+/fsnTASbt2bNmnj33Xez5souA/VV+vTpEy+++GJm/Morr1RKNqgMtWvXjgEDBpRbUnVTPvzww1i4cGFmXFBQEAUFBVUZDypV586ds8bz5s2r0P7z58/PGn/961/f7kykoayBnUzdunWzxuvWrUuUhJ3dl2/R/fvf/x777LNPhY+xaNGicvtNmzbNc2+okcreWbYt6xRDZevWrVvWuKJ35Jbd/svPSIDqpuwSaOeee27mQexQ3Wzq93FFl30qu/1nn322PZEgmS9/sSQi4uijj04TBLZR165ds8ZvvPFGrF27NnbZZZet2n/y5MlbPB41h2XQYCfyxRdflPsA3rJly0RpAPiysr+fd9ttt0RJ4D/69u0beXl5mfH8+fMrdHfMm2++mTVu06ZNpWWDyjR37tysh1VHWAKN6m1TzyJYs2ZNhY6xevXqrLGHUVNTlS3bv/e97yVKAttmzz33zPqSVElJSbnPJVsyadKkrHHfvn0rKxo5pqyBnciECRNi48aNmfEuu+zi1mCAaqLs0iMeaE110Lp16zjssMMy4+Li4nLfXt2ScePGZY2//HwEqE5GjBiRNe7Vq9dWPdwaUmnYsGE0adIka27atGkVOsZrr72WNfZAdmqil156Keui9r777uvOGmqkb3/721nj++67b6v2mzNnTtbfkg0bNozjjz++UrORO8oa2Els3Lgxbrrppqy5E0880bM9SGb58uVRWlpaoZ+JEydmHWPvvfcut40l0KiJvvjii3j00Uez5vyRSXUxePDgrPFvf/vbrdrvxRdfjFdffTUzrlWrVvTr169Ss0Fl2LBhQzzwwANZc+6qoSYo+1nh7rvv3up9Fy9eHE888UTWnEKdmmbt2rVx4YUXZs394he/SJQGts/ZZ58dtWvXzowfffTReOedd75yv1/+8pdZ4zPOOCMaNGhQ6fnIDWUN1DC///3v4+OPP67QPsXFxTFkyJBy39q++OKLKzMaANvol7/8ZSxatCgzrl27dpx00kkJE8F/DB48OOuhp88999xXFjaffvppuZLnjDPOiHbt2lVJRtgef//737M+Xzdu3DhOP/30hIlg65x55plZ41GjRsUf//jHr9xv3bp18d3vfjdrGbRGjRrFCSecUOkZoSJKSkq2etvVq1fHSSedFLNmzcrMnXrqqXHqqadWRTSoch06dIhBgwZlxuvXr4/zzjtvi0sQP/744zFy5MjMuF69enHDDTdUZUyqmLIGaph777032rVrF+ecc048+eSTsWrVqs1uW1RUFA899FD06NEj65d3RMR3v/vd6N27dxWnBdi5PPjgg/HJJ59UaJ/hw4fHjTfemDV33nnnxd57712Z0WCb1a5dO+64446oVes/fzpcddVVcfnll8eyZcvKbT9+/Pg44ogj4t13383MNW/ePG6++eac5IWKKvusg4EDB0bDhg0TpYGtN3DgwNh///0z49LS0jj33HPj8ssv3+wX/CZOnBiHHnpojB8/Pmv+2muvjebNm1dpXvgqw4YNi969e8fIkSNjyZIlm9xm9erVcf/990enTp2yntNRWFgYd911V46SsjNauHBhLFiwoNzP4sWLs7YrKSnZ5HYLFiwo95zSsm688cas38X/+Mc/ok+fPjFnzpys7datWxe///3vy3255KqrrvJ3ZA2XV1paWpo6BLD1unfvHjNmzMiM8/Lyon379lFYWBjNmjWLevXqxapVq+L999+P2bNnR3Fxcblj9O/fP0aPHh3169fPZXTYbpMmTYpjjjkmM957771jwYIF6QJBGUcffXS8+uqrcfrpp8cZZ5wRRx999GYv+E2dOjVuvvnmeOyxx7LmCwoKYurUqdaNp9r5wx/+EJdeemnWXN26dePQQw+NgoKCKCoqiunTp8f777+ftU29evXiiSee8I1tqqVPPvkk2rRpk/Vt7ldeeSUOPvjghKlg682bNy+OOOKI+PTTT7Pma9WqFd26dYu2bdtGfn5+LF26NKZNm1buomJERL9+/WLMmDFRt27dXMWGTfryZ428vLzYZ599Yt99943mzZvH2rVrY/HixfH666/H+vXrs/bbZ5994plnnon27duniM1OorCwsNzn3IoaNGhQuS9TlzVp0qQ44YQTst7neXl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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(*points.T)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "g1 = Graph.build_spatial_matches(gdf.geometry, k=1)\n", "g2 = Graph.build_spatial_matches(gdf.geometry, k=2)\n", "g3 = Graph.build_spatial_matches(gdf.geometry, k=3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax =plt.subplots(1,3)\n", "for i,g in enumerate((g1, g2, g3)):\n", " g.plot(gdf, ax=ax[i])\n", " ax[i].set_title(f\"k = {i+1}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Larger Problem" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import geodatasets\n", "stores = geopandas.read_file(geodatasets.get_path(\"geoda liquor_stores\")).explode(\n", " index_parts=False\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idplaceidgeometry
00ChIJnyLZdBTSD4gRbsa_hRGgPtcPOINT (1161395.910 1928443.285)
13ChIJ5Vdx0AssDogRVjbNIyF3Mr4POINT (1178227.792 1881864.522)
24ChIJb5I6QwYsDogRe8R4E9K8mkkPOINT (1178151.911 1879212.002)
36ChIJESl0mMfMD4gRy23-8soxKuwPOINT (1141552.993 1910193.701)
47ChIJg28YOdvMD4gRiV2lZcjSVyQPOINT (1144074.399 1910643.753)
\n", "
" ], "text/plain": [ " id placeid geometry\n", "0 0 ChIJnyLZdBTSD4gRbsa_hRGgPtc POINT (1161395.910 1928443.285)\n", "1 3 ChIJ5Vdx0AssDogRVjbNIyF3Mr4 POINT (1178227.792 1881864.522)\n", "2 4 ChIJb5I6QwYsDogRe8R4E9K8mkk POINT (1178151.911 1879212.002)\n", "3 6 ChIJESl0mMfMD4gRy23-8soxKuw POINT (1141552.993 1910193.701)\n", "4 7 ChIJg28YOdvMD4gRiV2lZcjSVyQ POINT (1144074.399 1910643.753)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stores.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "stores = stores.set_index(stores.placeid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solving for this graph in larger data will take time. The solution technique is somewhere between $O(n^2)$ and $O(n^3)$ if the solver recognizes it's a matching problem." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "g1 = Graph.build_spatial_matches(stores.geometry, k=1)\n", "g5 = Graph.build_spatial_matches(stores.geometry, k=5)\n", "g10 = Graph.build_spatial_matches(stores.geometry, k=10)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax =plt.subplots(1,3)\n", "for i,g in enumerate((g1, g5, g10)):\n", " g.plot(stores, ax=ax[i], nodes=False)\n", " ax[i].set_title(f\"k = {(1, 5, 10)[i]}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-matching" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sources = stores.sample(100)\n", "sinks = stores[~stores.index.isin(sources.index)].sample(100)\n", "ax = sources.plot(color='red')\n", "sinks.plot(color='blue', ax=ax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from libpysal.graph._matching import _spatial_matching\n", "import shapely" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "sources = stores.sample(100)\n", "sinks = stores[~stores.index.isin(sources.index)].sample(100)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "source_coordinates = sources.geometry.get_coordinates().values\n", "sink_coordinates = sinks.geometry.get_coordinates().values" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "crosspattern_heads, crosspattern_tails, weights, mip = _spatial_matching(x=sink_coordinates, y = source_coordinates, n_matches=1, return_mip=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mip.sol_status" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "lines = shapely.linestrings(\n", " list( \n", " zip(\n", " map(list, source_coordinates[crosspattern_heads]),\n", " map(list, sink_coordinates[crosspattern_tails])\n", " )\n", ")\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sources.plot(color='red')\n", "sinks.plot(color='blue', ax=ax)\n", "geopandas.GeoSeries(lines).plot(linewidth=1, color='k', ax=ax)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 4 } libpysal-4.12.1/docs/user-guide/graph/w_g_migration.ipynb000066400000000000000000047472601466413560300234440ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "27d7d8f2-a943-481c-be10-365391d39458", "metadata": {}, "source": [ "# W to Graph Migration Guide\n", "\n", "Author: [Serge Rey](http://github.com/sjsrey)" ] }, { "cell_type": "markdown", "id": "57537223-f1c3-4771-b8f0-f7b32962e995", "metadata": {}, "source": [ "## Introduction\n", "\n", "Beginning in the fall of 2023, the PySAL project released a new `graph` module that offers a modern implementation of spatial weights. This module's [Graph](../../generated/libpysal.graph.Graph.html) class is set to eventually replace the [W](../../generated/libpysal.weights.W.html) class, which has been the cornerstone for spatial weights in PySAL for the past 15 years. The `W` class has significantly contributed to the library's success, but as the scientific landscape evolves, new opportunities necessitate updated interfaces and designs for spatial weights.\n", "\n", "While the application programming interfaces (API) of the `W` and `Graph` classes are similar, there are important [differences](../../migration.rst)\n", "to consider when transitioning from weights-based resources to graph-based implementations.\n", "\n", "This guide is designed to provide users with an overview of migrating from the `W` class to the `Graph` class.\n", "\n", "Beyond the specifics that we outline below, it is important to note two utility methods are available to convert between the two classes:\n", "\n", "- `Graph.to_W()` will generate a `W` instance from a `Graph` object\n", "- `Graph.from_W()`will generate a `Graph` instance from a `W` object\n", "\n" ] }, { "cell_type": "markdown", "id": "2d4e2096-0b7c-4acc-9875-32321256693b", "metadata": {}, "source": [ "## Imports\n", "To access the `W` and `Graph` class, use the following imports:" ] }, { "cell_type": "code", "execution_count": 1, "id": "f46293f2-1362-4e44-840f-eef77a67285d", "metadata": {}, "outputs": [], "source": [ "from libpysal import weights\n", "from libpysal import graph" ] }, { "cell_type": "markdown", "id": "c5d73325-f533-466d-8108-ef034705fa58", "metadata": {}, "source": [ "## Example Data Set\n", "\n", "To illustrate the migration from `W` to `Graph` we will utilize a built-in data set from `libpysal`. In addition to the relevant `libpysal` modules we will also import the other packages needed:" ] }, { "cell_type": "code", "execution_count": 2, "id": "a2254e95-de33-4856-bc71-2817e52af346", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Last updated: 2024-07-18\n", "\n", "Python implementation: CPython\n", "Python version : 3.12.2\n", "IPython version : 8.21.0\n", "\n", "libpysal: 4.2.3.dev1352+gcfa4e0ce\n", "\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import seaborn as sns\n", "import pandas as pd\n", "import geopandas as gpd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from libpysal import examples\n", "\n", "%load_ext watermark\n", "%watermark -v -d -u -p libpysal" ] }, { "cell_type": "code", "execution_count": 3, "id": "8c3bbda5-833a-4e73-82cc-eb6cfc906856", "metadata": {}, "outputs": [], "source": [ "dbs = examples.available()" ] }, { "cell_type": "code", "execution_count": 4, "id": "acd84693-21e0-49fc-b878-840ba66840e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sids2\n", "=====\n", "\n", "North Carolina county SIDS death counts and rates\n", "-------------------------------------------------\n", "\n", "* sids2.dbf: attribute data. (k=18)\n", "* sids2.html: metadata.\n", "* sids2.shp: Polygon shapefile. (n=100)\n", "* sids2.shx: spatial index.\n", "* sids2.gal: spatial weights in GAL format.\n", "\n", "Source: Cressie, Noel (1993). Statistics for Spatial Data. New York, Wiley, pp. 386-389. Rates computed.\n", "Updated URL: https://geodacenter.github.io/data-and-lab/sids2/\n", "\n" ] } ], "source": [ "examples.explain(\"sids2\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "8422e23d-a7f4-48b9-a31c-725a06dde2f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 100 entries, 0 to 99\n", "Data columns (total 19 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 AREA 100 non-null float64 \n", " 1 PERIMETER 100 non-null float64 \n", " 2 CNTY_ 100 non-null int64 \n", " 3 CNTY_ID 100 non-null int64 \n", " 4 NAME 100 non-null object \n", " 5 FIPS 100 non-null object \n", " 6 FIPSNO 100 non-null int64 \n", " 7 CRESS_ID 100 non-null int64 \n", " 8 BIR74 100 non-null float64 \n", " 9 SID74 100 non-null float64 \n", " 10 NWBIR74 100 non-null float64 \n", " 11 BIR79 100 non-null float64 \n", " 12 SID79 100 non-null float64 \n", " 13 NWBIR79 100 non-null float64 \n", " 14 SIDR74 100 non-null float64 \n", " 15 SIDR79 100 non-null float64 \n", " 16 NWR74 100 non-null float64 \n", " 17 NWR79 100 non-null float64 \n", " 18 geometry 100 non-null geometry\n", "dtypes: float64(12), geometry(1), int64(4), object(2)\n", "memory usage: 15.0+ KB\n" ] } ], "source": [ "# Read the file in\n", "gdf = gpd.read_file(examples.get_path(\"sids2.shp\"))\n", "\n", "gdf.info()" ] }, { "cell_type": "code", "execution_count": 6, "id": "c116377a-e1c6-4a0c-90ed-928db6f08220", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 POLYGON ((-81.47276 36.23436, -81.54084 36.272...\n", "1 POLYGON ((-81.23989 36.36536, -81.24069 36.379...\n", "2 POLYGON ((-80.45634 36.24256, -80.47639 36.254...\n", "3 MULTIPOLYGON (((-76.00897 36.31960, -76.01735 ...\n", "4 POLYGON ((-77.21767 36.24098, -77.23461 36.214...\n", " ... \n", "95 POLYGON ((-78.26150 34.39479, -78.32898 34.364...\n", "96 POLYGON ((-78.02592 34.32877, -78.13024 34.364...\n", "97 POLYGON ((-78.65572 33.94867, -79.07450 34.304...\n", "98 POLYGON ((-77.96073 34.18924, -77.96587 34.242...\n", "99 POLYGON ((-78.65572 33.94867, -78.63472 33.977...\n", "Name: geometry, Length: 100, dtype: geometry" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf.geometry" ] }, { "cell_type": "code", "execution_count": 7, "id": "248f154c-d874-496b-88ad-b8ad2f367bbc", "metadata": {}, "outputs": [], "source": [ "gdf = gdf.set_crs(\"epsg:4326\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "592bad5b-95fc-4f62-9dfe-8139188c6eef", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf.explore()" ] }, { "cell_type": "markdown", "id": "ed1f23f4-968f-4ab8-a876-2d1b07fdd794", "metadata": {}, "source": [ "## Building Spatial Weights\n", "With a GeoDataFrame in hand, we can build spatial weights using the `W` class as:" ] }, { "cell_type": "code", "execution_count": 9, "id": "4e4ea913-d462-4943-ae66-41a3878c60b0", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_616347/4235704840.py:1: FutureWarning: `use_index` defaults to False but will default to True in future. Set True/False directly to control this behavior and silence this warning\n", " w_queen = weights.Queen.from_dataframe(gdf)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen = weights.Queen.from_dataframe(gdf)\n", "w_queen" ] }, { "cell_type": "markdown", "id": "d6f48b63-6a66-4bc5-97b6-00a276dcdb1f", "metadata": {}, "source": [ "For the `Graph`, weights are constructed from the dataframe as:" ] }, { "cell_type": "code", "execution_count": 10, "id": "3e77b0cb-313c-4960-9cc6-f5deb602748a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen = graph.Graph.build_contiguity(gdf, rook=False)\n", "g_queen" ] }, { "cell_type": "markdown", "id": "c601e671-09b3-41cf-860d-92db22326e1b", "metadata": {}, "source": [ "Two things to be aware of here are:\n", "\n", "- the methods have different names for the two classes\n", "- the `W` relies on different methods to generate `Rook` or `Queen` contiguity weights, while the `Graph` relies on the `rook` keyword argument to do so." ] }, { "cell_type": "markdown", "id": "4192da75-3d27-4a61-99aa-8b9f11e11bd1", "metadata": {}, "source": [ "## Neighbors\n", "From the output in the previous cells, we see different information reported in the two cases.\n", "\n", "The neighbors of a spatial unit are those units that satisfy the specific contiguity relationship specified by the user. In our case of `Queen` contiguity, and pair of polygons that share at least one vertex are considered neighbors.\n", "\n", "This information is encoded differently in the two classes. For the `W` class, information on the neighbors is stored in the `neighbors` attribute which is a `dict` using the unit's id as the key, while the `list` of neighbor ids is the value:" ] }, { "cell_type": "code", "execution_count": 11, "id": "302e07a0-6bad-4ba2-9866-1194caa08c8f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(w_queen.neighbors)" ] }, { "cell_type": "code", "execution_count": 12, "id": "4e035e8a-6ac7-46aa-96d8-866cd3f01b4e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[96, 97, 98]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen.neighbors[99]" ] }, { "cell_type": "markdown", "id": "5f75edf4-9cab-4e29-9246-fa71365c057a", "metadata": {}, "source": [ "For the `Graph` class the neighbor information is stored in the `adjacency` attribute:" ] }, { "cell_type": "code", "execution_count": 13, "id": "f4e3cae4-854f-4a56-8afc-f0fb2524b67a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "focal neighbor\n", "0 1 1\n", " 17 1\n", " 18 1\n", "1 0 1\n", " 2 1\n", " ..\n", "98 96 1\n", " 99 1\n", "99 96 1\n", " 97 1\n", " 98 1\n", "Name: weight, Length: 490, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen.adjacency" ] }, { "cell_type": "code", "execution_count": 14, "id": "3be036b5-97ef-4eb2-8429-93d867d017da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(g_queen.adjacency)" ] }, { "cell_type": "markdown", "id": "b686532a-fb2e-4b45-a362-c7f7bdb13609", "metadata": {}, "source": [ "This is encoded as a pandas Series, with a multi-index. The first index is for the focal unit, and the second is for the neighboring unit.\n", "So we see here that the observation with the identifier of 99 has three neighbors: 96, 97, 98.\n", "This agrees with what we had for `W` so the question is why the need for the change?\n", "\n", "Part of the answer is in facilitating easier access to this information:" ] }, { "cell_type": "code", "execution_count": 15, "id": "075208ba-584e-403e-bc01-87834241fcc6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "neighbor\n", "96 1\n", "97 1\n", "98 1\n", "Name: weight, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen[99]" ] }, { "cell_type": "markdown", "id": "23bfd089-9b67-493f-81f0-5900ed4368f6", "metadata": {}, "source": [ "here we can query the graph with an id to get the neighbor information, along with the weights attached to each neighbor in the form of a pandas series:" ] }, { "cell_type": "code", "execution_count": 16, "id": "589290ec-aec6-4f93-b70e-1f3a80d63643", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(g_queen[99])" ] }, { "cell_type": "markdown", "id": "81380f43-5245-4502-a2bf-567d47ec7a6d", "metadata": {}, "source": [ "While we could also query the `W` object with an id, we get back a `dict`." ] }, { "cell_type": "code", "execution_count": 17, "id": "5e1d8999-5d5d-4136-904a-05df8889919c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{96: 1.0, 97: 1.0, 98: 1.0}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen[99]" ] }, { "cell_type": "markdown", "id": "ec3c3a30-982a-4568-b942-e3f5204d29b1", "metadata": {}, "source": [ "As we will see below, the pandas series will offer substantial gains in efficiency and scope over encoding the weights as `dicts`." ] }, { "cell_type": "markdown", "id": "3591c67e-d29b-4630-a401-0b2892e6ab6d", "metadata": {}, "source": [ "At the same time, if the neighbors are needed in the form of a `dict`, the graph has such an attribute:" ] }, { "cell_type": "code", "execution_count": 18, "id": "afc97b96-a76a-4001-a59e-a1d027c7c653", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: (1, 17, 18),\n", " 1: (0, 2, 17),\n", " 2: (1, 9, 17, 22, 24),\n", " 3: (6, 55),\n", " 4: (5, 8, 15, 27),\n", " 5: (4, 7, 27),\n", " 6: (3, 7, 16),\n", " 7: (5, 6, 16, 19, 20),\n", " 8: (4, 14, 15, 23, 30),\n", " 9: (2, 11, 24, 25),\n", " 10: (11, 13, 26, 28),\n", " 11: (9, 10, 24, 25, 26),\n", " 12: (13, 14, 23, 29, 36),\n", " 13: (10, 12, 28, 29),\n", " 14: (8, 12, 23),\n", " 15: (4, 8, 23, 27, 30, 32, 35),\n", " 16: (6, 7, 19),\n", " 17: (0, 1, 2, 18, 22, 33, 38, 40),\n", " 18: (0, 17, 21, 33),\n", " 19: (7, 16, 20),\n", " 20: (7, 19),\n", " 21: (18, 31, 33, 42, 45),\n", " 22: (2, 17, 24, 38, 39),\n", " 23: (8, 12, 14, 15, 30, 36, 53),\n", " 24: (2, 9, 11, 22, 25, 39, 41),\n", " 25: (9, 11, 24, 26, 41, 46),\n", " 26: (10, 11, 25, 28, 46, 47),\n", " 27: (4, 5, 15, 35, 43),\n", " 28: (10, 13, 26, 29, 47),\n", " 29: (12, 13, 28, 36, 47),\n", " 30: (8, 15, 23, 32, 36, 48, 53),\n", " 31: (21, 34, 45),\n", " 32: (15, 30, 35, 48, 50),\n", " 33: (17, 18, 21, 40, 42, 51),\n", " 34: (31, 37, 45, 52),\n", " 35: (15, 27, 32, 43, 50, 56),\n", " 36: (12, 23, 29, 30, 47, 53, 62),\n", " 37: (34, 52, 54),\n", " 38: (17, 22, 39, 40, 49, 51, 64, 67, 68),\n", " 39: (22, 24, 38, 41, 49),\n", " 40: (17, 33, 38, 51),\n", " 41: (24, 25, 39, 46, 49, 69, 70),\n", " 42: (21, 33, 45, 51, 60, 63, 64),\n", " 43: (27, 35, 44, 56, 86),\n", " 44: (43, 86),\n", " 45: (21, 31, 34, 42, 52, 60),\n", " 46: (25, 26, 41, 47, 66, 69),\n", " 47: (26, 28, 29, 36, 46, 59, 62, 66),\n", " 48: (30, 32, 50, 53, 58, 61),\n", " 49: (38, 39, 41, 68, 69, 70),\n", " 50: (32, 35, 48, 56, 58, 73, 90),\n", " 51: (33, 38, 40, 42, 63, 64),\n", " 52: (34, 37, 45, 54, 60, 71, 74),\n", " 53: (23, 30, 36, 48, 61, 62, 78),\n", " 54: (37, 52, 57, 65, 71, 74),\n", " 55: (3, 86),\n", " 56: (35, 43, 50, 79, 86, 90),\n", " 57: (54, 65, 72, 77),\n", " 58: (48, 50, 61, 73),\n", " 59: (47, 62, 66),\n", " 60: (42, 45, 52, 63, 71, 76),\n", " 61: (48, 53, 58, 73, 78, 87),\n", " 62: (36, 47, 53, 59, 66, 78, 81),\n", " 63: (42, 51, 60, 64, 75),\n", " 64: (38, 42, 51, 63, 67, 75),\n", " 65: (54, 57, 74, 77),\n", " 66: (46, 47, 59, 62, 69, 81, 85, 88, 91),\n", " 67: (38, 64, 68, 75, 83),\n", " 68: (38, 49, 67, 70, 83),\n", " 69: (41, 46, 49, 66, 70, 84, 88),\n", " 70: (41, 49, 68, 69, 83, 84),\n", " 71: (52, 54, 60, 74, 76),\n", " 72: (57, 77, 80),\n", " 73: (50, 58, 61, 82, 87, 90),\n", " 74: (52, 54, 65, 71),\n", " 75: (63, 64, 67),\n", " 76: (60, 71),\n", " 77: (57, 65, 72, 80, 89),\n", " 78: (53, 61, 62, 81, 87, 95, 96),\n", " 79: (56, 90),\n", " 80: (72, 77, 89),\n", " 81: (62, 66, 78, 85, 93, 95),\n", " 82: (73, 87, 90, 92, 94),\n", " 83: (67, 68, 70, 84),\n", " 84: (69, 70, 83, 88),\n", " 85: (66, 81, 88, 91, 93),\n", " 86: (43, 44, 55, 56),\n", " 87: (61, 73, 78, 82, 92, 96),\n", " 88: (66, 69, 84, 85, 91),\n", " 89: (77, 80),\n", " 90: (50, 56, 73, 79, 82, 94),\n", " 91: (66, 85, 88, 93),\n", " 92: (82, 87, 94, 96),\n", " 93: (81, 85, 91, 95, 97),\n", " 94: (82, 90, 92),\n", " 95: (78, 81, 93, 96, 97),\n", " 96: (78, 87, 92, 95, 97, 98, 99),\n", " 97: (93, 95, 96, 99),\n", " 98: (96, 99),\n", " 99: (96, 97, 98)}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen.neighbors" ] }, { "cell_type": "markdown", "id": "5d9c48c6-9b9e-45da-bcab-ebf1302ff4ce", "metadata": {}, "source": [ "## Weights\n", "\n", "The value of a weight specifies the \"strength\" of the neighbor relationship between to geographical units.\n", "For our contiguity weights, these will be binary valued.\n", "\n", "In the `W` case, these values are stored in the `weights` attribute:" ] }, { "cell_type": "code", "execution_count": 19, "id": "81111346-a12e-4ada-a9b4-de7318450d8b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1.0, 1.0, 1.0]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen.weights[99]" ] }, { "cell_type": "markdown", "id": "e0bfecb4-2b20-47ff-87d2-c92226132977", "metadata": {}, "source": [ "For the `Graph`, the values of the weights are stored in the `adjacency` attribute:" ] }, { "cell_type": "code", "execution_count": 20, "id": "75126118-bd99-4467-967a-6e91a745acdc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "neighbor\n", "96 1\n", "97 1\n", "98 1\n", "Name: weight, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen[99]" ] }, { "cell_type": "markdown", "id": "ebee5f1c-88b3-4d23-a9cd-aee2eb42ce1b", "metadata": {}, "source": [ "Again, the underlying types of these attributes need to be kept in mind. `weights` is a `dict` for `W`, and as part of the `ajacency` attribute of the `Graph`, which is of type:\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "1814d24f-3283-45a0-8ff5-f14a16ee22b5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(g_queen[0])" ] }, { "cell_type": "markdown", "id": "d2a6f2fb-7dce-4668-9529-b080207ad373", "metadata": {}, "source": [ "And, the helper `weights` attribute on the `Graph` mimics that on the `W`." ] }, { "cell_type": "code", "execution_count": 22, "id": "ce459560-5b7b-433a-bede-5bfaac273bb5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 1, 1)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen.weights[99]" ] }, { "cell_type": "markdown", "id": "ef3bccc1-a36c-4b47-afd3-c02cd1015b4f", "metadata": {}, "source": [ "Individual weight values will be identical between the two implementations:" ] }, { "cell_type": "code", "execution_count": 23, "id": "b6f6d449-1d55-4d22-b889-76088cb106e6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen[99][97] == w_queen[99][97]" ] }, { "cell_type": "markdown", "id": "30c912f4-f367-405d-bd9e-f9d3589e4cc0", "metadata": {}, "source": [ "As well as the neighbor sets for a given unit:" ] }, { "cell_type": "code", "execution_count": 24, "id": "6fa4e245-68e2-4dc2-bbdf-b05ed7527d76", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "neighbor\n", "96 True\n", "97 True\n", "98 True\n", "Name: weight, dtype: bool" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen[99] == w_queen.weights[99]" ] }, { "cell_type": "code", "execution_count": 25, "id": "b3240892-ab52-4f99-a106-c9ebee1b3131", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1.0, 1.0, 1.0]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen.weights[99]" ] }, { "cell_type": "markdown", "id": "bb9e8401-d6ab-47c2-92cf-5617dbee8b69", "metadata": {}, "source": [ "We are implicitly assuming that the order of the neighbor ids in the `W` matches that of the `Graph`.\n", "Here we see one advantage of the `Graph` in that the information about the neighbor ids comes along for the ride in the adjacency attribute, or any pandas like queries on that attribute.\n", "\n", "To be safe, we would have to double check the ordering of the weights in `W`:" ] }, { "cell_type": "code", "execution_count": 26, "id": "57ef256a-8130-4124-9916-5fbe288a76db", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{96: 1.0, 97: 1.0, 98: 1.0}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen[99]" ] }, { "cell_type": "markdown", "id": "ea43bd1a-9525-4b5a-8d24-22fc37db423d", "metadata": {}, "source": [ "So in this case our equality check above happened to be comparing the values for the same $i,j$ observations, but this is not guaranteed to always be the case. Handling the proper alignment of the ids and the weights was a key motivation for developing the `Graph`." ] }, { "cell_type": "markdown", "id": "a11ed77e-1f95-406e-8ad1-72ffaf7f37a3", "metadata": {}, "source": [ "The key take-away here is that the `Graph` combines the information about who are the neighbors *and* the values of the associated weights in the *same* data structure, the `adjacency` attribute, while in `W` there are *two different* `dicts` that handle the neighbor information and the weights information (`neighbor` and `weights`, respectively)." ] }, { "cell_type": "markdown", "id": "b60d44ce-d9a8-4a3c-a4a2-6d2f9b1336c5", "metadata": {}, "source": [ "## Cardinalities\n", "The `cardinalities` attribute contains information on the number of neighbors for each unit." ] }, { "cell_type": "code", "execution_count": 27, "id": "f420b789-0ede-4ade-8b17-ff7f88e3c9e0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 3,\n", " 1: 3,\n", " 2: 5,\n", " 3: 2,\n", " 4: 4,\n", " 5: 3,\n", " 6: 3,\n", " 7: 5,\n", " 8: 5,\n", " 9: 4,\n", " 10: 4,\n", " 11: 5,\n", " 12: 5,\n", " 13: 4,\n", " 14: 3,\n", " 15: 7,\n", " 16: 3,\n", " 17: 8,\n", " 18: 4,\n", " 19: 3,\n", " 20: 2,\n", " 21: 5,\n", " 22: 5,\n", " 23: 7,\n", " 24: 7,\n", " 25: 6,\n", " 26: 6,\n", " 27: 5,\n", " 28: 5,\n", " 29: 5,\n", " 30: 7,\n", " 31: 3,\n", " 32: 5,\n", " 33: 6,\n", " 34: 4,\n", " 35: 6,\n", " 36: 7,\n", " 37: 3,\n", " 38: 9,\n", " 39: 5,\n", " 40: 4,\n", " 41: 7,\n", " 42: 7,\n", " 43: 5,\n", " 44: 2,\n", " 45: 6,\n", " 46: 6,\n", " 47: 8,\n", " 48: 6,\n", " 49: 6,\n", " 50: 7,\n", " 51: 6,\n", " 52: 7,\n", " 53: 7,\n", " 54: 6,\n", " 55: 2,\n", " 56: 6,\n", " 57: 4,\n", " 58: 4,\n", " 59: 3,\n", " 60: 6,\n", " 61: 6,\n", " 62: 7,\n", " 63: 5,\n", " 64: 6,\n", " 65: 4,\n", " 66: 9,\n", " 67: 5,\n", " 68: 5,\n", " 69: 7,\n", " 70: 6,\n", " 71: 5,\n", " 72: 3,\n", " 73: 6,\n", " 74: 4,\n", " 75: 3,\n", " 76: 2,\n", " 77: 5,\n", " 78: 7,\n", " 79: 2,\n", " 80: 3,\n", " 81: 6,\n", " 82: 5,\n", " 83: 4,\n", " 84: 4,\n", " 85: 5,\n", " 86: 4,\n", " 87: 6,\n", " 88: 5,\n", " 89: 2,\n", " 90: 6,\n", " 91: 4,\n", " 92: 4,\n", " 93: 5,\n", " 94: 3,\n", " 95: 5,\n", " 96: 7,\n", " 97: 4,\n", " 98: 2,\n", " 99: 3}" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen.cardinalities" ] }, { "cell_type": "code", "execution_count": 28, "id": "358a43aa-7859-4574-9150-780ef3b4c2c6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "focal\n", "0 3\n", "1 3\n", "2 5\n", "3 2\n", "4 4\n", " ..\n", "95 5\n", "96 7\n", "97 4\n", "98 2\n", "99 3\n", "Name: cardinalities, Length: 100, dtype: int64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen.cardinalities" ] }, { "cell_type": "markdown", "id": "5bbcf980-4658-4e43-80f3-3955151b39a9", "metadata": {}, "source": [ "Here we see that, although the attribute name is common to both classes, the data types are different. (Note: other cases of common names but different types that we won't cover here are listed [here](../../migration.rst)).\n" ] }, { "cell_type": "code", "execution_count": 29, "id": "0ff66bd7-c78f-4ac3-8eae-27fbd09646d6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(dict, pandas.core.series.Series)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(w_queen.cardinalities), type(g_queen.cardinalities)" ] }, { "cell_type": "markdown", "id": "88aaf817-8b72-4da4-93e3-41976bda415f", "metadata": {}, "source": [ "Summaries of the cardinality distribution can be obtained for the `W` as:" ] }, { "cell_type": "code", "execution_count": 30, "id": "18a20a7e-9377-4f66-bd77-48e89866a874", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(2, 8), (3, 15), (4, 17), (5, 23), (6, 19), (7, 14), (8, 2), (9, 2)]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen.histogram" ] }, { "cell_type": "markdown", "id": "8f2f0877-4c46-4502-9391-820719b77fcc", "metadata": {}, "source": [ "which indicates that 8 units have 2 neighbors, 15 have 3 neighbors and so on.\n", "\n", "For the `Graph` we can more easily visualize this distribution with:" ] }, { "cell_type": "code", "execution_count": 31, "id": "2364e921-4e95-4441-a8a2-37a8b2ea351c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g_queen.cardinalities.hist(bins=range(2, 10))" ] }, { "cell_type": "markdown", "id": "072e73ce-f9ba-4f5e-9d7a-904ce50f12af", "metadata": {}, "source": [ "While to get a similar visualization for `W` requires other packages:" ] }, { "cell_type": "code", "execution_count": 32, "id": "519487e9-8a23-4a15-8731-4cc1796ddda3", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.displot(pd.Series(w_queen.cardinalities), bins=range(2, 10));" ] }, { "cell_type": "markdown", "id": "b8fe49b2-8bb8-4e93-bfb3-dc9e93410df2", "metadata": {}, "source": [ "## Geovisualization of the Weights\n", "\n", "Both `W` and `Graph` afford the ability to visualize the connectivity structure as a graph embedded in the geographic space.\n", "For the `W`, the `plot` method can be used" ] }, { "cell_type": "code", "execution_count": 33, "id": "0cb29997-4555-4883-b118-3802d2adda9e", "metadata": {}, "outputs": [], "source": [ "gdf = gdf.to_crs(gdf.estimate_utm_crs())" ] }, { "cell_type": "code", "execution_count": 34, "id": "72a287f3-c85e-411b-939f-a1ba5fdf5ff1", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "w_queen.plot(gdf);" ] }, { "cell_type": "markdown", "id": "068e0437-0e1e-47cd-a0e5-8f477bc9d2d9", "metadata": {}, "source": [ "and the same can be done with the `Graph`:" ] }, { "cell_type": "code", "execution_count": 35, "id": "b560cc2e-9438-4e65-a3a2-d70bec954f8b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g_queen.plot(gdf)" ] }, { "cell_type": "markdown", "id": "ff87e52b-5035-44c3-9e25-adff9d1ffcfe", "metadata": {}, "source": [ "However, the `Graph` adds an `explore` function allowing for a richer visualization:" ] }, { "cell_type": "code", "execution_count": 36, "id": "e372a3c5-cc3c-45b1-b1be-b73756ff1279", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = gdf.explore(tiles=\"CartoDB Positron\")\n", "g_queen.explore(gdf, m=m)" ] }, { "cell_type": "markdown", "id": "b44a171d-d458-4773-859f-00eef92aea6e", "metadata": {}, "source": [ "This can be leveraged to look at the spatial distribution of the cardinalities, for example:" ] }, { "cell_type": "code", "execution_count": 37, "id": "8cfb4407-c8a3-4e87-a634-e5b993436a74", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "focal\n", "0 3\n", "1 3\n", "2 5\n", "3 2\n", "4 4\n", " ..\n", "95 5\n", "96 7\n", "97 4\n", "98 2\n", "99 3\n", "Name: cardinalities, Length: 100, dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen.cardinalities" ] }, { "cell_type": "code", "execution_count": 38, "id": "5ebf7c0d-f74d-4345-b6ab-d71a2cab197b", "metadata": {}, "outputs": [], "source": [ "gdf[\"cardinalities\"] = g_queen.cardinalities" ] }, { "cell_type": "code", "execution_count": 39, "id": "2359c40d-bb0d-4823-aca5-5a46fba40192", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = gdf.explore(tiles=\"CartoDB Positron\", column=\"cardinalities\", legend=True)\n", "g_queen.explore(gdf, m=m)" ] }, { "cell_type": "markdown", "id": "ca807531-72df-4027-a946-0f65f847c0b2", "metadata": {}, "source": [ "## Transformations\n", "\n", "Transformation of the weight values is often required in various spatial statistics and operations. How these are handled is a major change between the `W` and `Graph` classes.\n", "\n", "PySAL currently supports the following transformations:\n", "\n", "- O: original, returning the object to the initial state.\n", "- B: binary, with every neighbor having assigned a weight of one.\n", "- R: row, with all the neighbors of a given observation adding up to one.\n", "- V: variance stabilizing, with the sum of all the weights being constrained to the number of observations.\n", "- D: double, with all the weights across all observations adding up to one.\n", "\n", "\n", "For the `W`, the `transform` property stores the type of transformation that is associated with the weight values:" ] }, { "cell_type": "code", "execution_count": 40, "id": "c4a0cb0a-76f2-41e5-b439-d99a24e7c280", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'O'" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen.transform" ] }, { "cell_type": "markdown", "id": "394d8a97-1496-449b-bc46-3b3c815c1ff8", "metadata": {}, "source": [ "An 'O' here means the weight values are set to the original transformation upon construction. In this case they are binary:" ] }, { "cell_type": "code", "execution_count": 41, "id": "7fdba8fd-8520-40e2-bc3c-b839f0e14bad", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{96: 1.0, 99: 1.0}" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen[98]" ] }, { "cell_type": "markdown", "id": "66ae843c-9563-45c3-8239-40d802e1ddd9", "metadata": {}, "source": [ "In some cases, rather than using the binary weights, we may need to row-standardize the weights, so that in the full $n \\times n$ weights matrix, the row sums would all be equal to unity. The relevant transform in this case is 'r':" ] }, { "cell_type": "code", "execution_count": 42, "id": "8827dd89-b78f-46bc-b3bf-cc7762af0f4b", "metadata": {}, "outputs": [], "source": [ "w_queen.transform = \"r\"" ] }, { "cell_type": "code", "execution_count": 43, "id": "bb868961-6809-4812-99f1-e5c225e9cc7c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{96: 0.5, 99: 0.5}" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w_queen[98]" ] }, { "cell_type": "markdown", "id": "126d3420-97f9-4682-babf-ff0e61268821", "metadata": {}, "source": [ "Since `transform` is a property of `W`, setting the `transform` will update the values of the weights.\n", "\n", "For the `Graph` class, things have changed in terms of these standardizations." ] }, { "cell_type": "code", "execution_count": 44, "id": "c6bb10a2-e4d2-49f2-a67e-6d57e923d058", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "neighbor\n", "96 1\n", "99 1\n", "Name: weight, dtype: int64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen[98]" ] }, { "cell_type": "code", "execution_count": 45, "id": "9c161b95-9cdc-479c-a449-e33d9a584ffc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ ">" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen.transform" ] }, { "cell_type": "markdown", "id": "35ad4391-6829-48d2-a7c0-403b1b1688cc", "metadata": {}, "source": [ "This tells us that the `transform` member is now a method of the `Graph`.\n", "\n", "The related change is the addition of a `transformation` property:" ] }, { "cell_type": "code", "execution_count": 46, "id": "ed78f5f3-d306-4b32-b159-3b2fb3aad0b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'O'" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen.transformation" ] }, { "cell_type": "markdown", "id": "c94993a4-8c24-497d-a803-25b9a7343e2e", "metadata": {}, "source": [ "which plays a similar role to the `W.transform` property, in the sense it informs us as to what standardization is associated with the value of the spatial weights.\n", "\n", "However, `Graph.transformation` is not a setter in the sense that if the user changes its value, the weight values will not be affected. It is an information-only property.\n", "\n", "This is because, by design, the weights for the `Graph` are **immutable**.\n", "In other words, once the `Graph` instance is created, its state cannot be changed.\n", "\n", "If transformed spatial weights are required, the `Graph` method `transform` can be called with the type of transformation required:" ] }, { "cell_type": "code", "execution_count": 47, "id": "a18cb028-c9a3-4bd3-86c7-dd28f8bf52eb", "metadata": {}, "outputs": [], "source": [ "g1 = g_queen.transform(\"r\")" ] }, { "cell_type": "code", "execution_count": 48, "id": "86f2ed73-49c0-493e-a41a-55915ac402cf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "neighbor\n", "96 0.5\n", "99 0.5\n", "Name: weight, dtype: float64" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g1[98]" ] }, { "cell_type": "markdown", "id": "3f2f5116-4ba1-4619-aaea-0d262d3d11cd", "metadata": {}, "source": [ "This will return a new `Graph` instance with the weight values suitably transformed:\n" ] }, { "cell_type": "code", "execution_count": 49, "id": "30a3a8eb-c917-4093-9e8d-d88477fe55b5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'R'" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g1.transformation" ] }, { "cell_type": "markdown", "id": "a8e3b89a-5e0b-4ffc-a0bd-68b0fd9b9358", "metadata": {}, "source": [ "## Spatial Lag" ] }, { "cell_type": "markdown", "id": "00295e4f-283d-4b34-be43-087749b7ba5e", "metadata": {}, "source": [ "The spatial lag of a variable is a weight sum, or weighted average, of the neighboring values for that variable:\n", "\n", "$$[Wy]_i = \\sum_j w_{i,j} y_j$$" ] }, { "cell_type": "markdown", "id": "5766e374-e416-4595-a71d-456dcb468076", "metadata": {}, "source": [ "The implementation of the spatial lag has changed between the `W` and `Graph` classes.\n", "\n", "To illustrate, we will pull out the Sudden Infant Death Rate in 1979 for the counties into the variable `y`:" ] }, { "cell_type": "code", "execution_count": 50, "id": "34f5d317-00bb-4d79-9f49-1ec6b02b45a6", "metadata": {}, "outputs": [], "source": [ "y = gdf.SID79" ] }, { "cell_type": "markdown", "id": "ec106df5-4899-4113-b8aa-7f6013c6e8b1", "metadata": {}, "source": [ "To calculate the spatial lag as a weighted average, we need to use the weights that have been row-standardized. For the `W` class, the calculation of the spatial lag is done with a function that takes the `W` as an argument together with the variable of interest:" ] }, { "cell_type": "code", "execution_count": 51, "id": "7b499f38-3e54-4aa6-b70a-ac237b4d3cf9", "metadata": {}, "outputs": [], "source": [ "from libpysal.weights import lag_spatial" ] }, { "cell_type": "code", "execution_count": 52, "id": "47159a15-996d-455d-bf65-74cb2a6234fe", "metadata": {}, "outputs": [], "source": [ "w_queen.transform = 'r'\n", "wlag = lag_spatial(w_queen, y)" ] }, { "cell_type": "code", "execution_count": 53, "id": "3f20b6ee-702c-46e0-9d6e-dacba84b0831", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 3.66666667, 4.33333333, 6.8 , 1.5 , 7.25 ,\n", " 3.33333333, 2.66666667, 2.4 , 6.6 , 16.75 ,\n", " 6.5 , 14.8 , 12.6 , 8.5 , 2. ,\n", " 3.85714286, 1.33333333, 3.375 , 4. , 2.33333333,\n", " 1. , 6.4 , 7.8 , 11.42857143, 9.42857143,\n", " 9.83333333, 11. , 5.2 , 8.4 , 9.6 ,\n", " 12.14285714, 2. , 9.8 , 7.66666667, 6.75 ,\n", " 7.66666667, 8.42857143, 9. , 11.55555556, 8. ,\n", " 10.5 , 13.42857143, 10.14285714, 2. , 0. ,\n", " 7.33333333, 12.16666667, 12.875 , 11.16666667, 8.5 ,\n", " 9. , 9.83333333, 5.14285714, 12.57142857, 6.5 ,\n", " 1. , 5.16666667, 4.25 , 15.25 , 6. ,\n", " 11.16666667, 9.16666667, 17. , 15.4 , 20.5 ,\n", " 4.25 , 13.88888889, 13.4 , 12.8 , 7.28571429,\n", " 9.5 , 7.6 , 2. , 10.83333333, 9.75 ,\n", " 21. , 8. , 1.8 , 16.85714286, 11. ,\n", " 1.33333333, 9.33333333, 13.2 , 16.5 , 7.75 ,\n", " 22.2 , 1.25 , 11.5 , 7.8 , 2. ,\n", " 6. , 11. , 4. , 20.2 , 14.33333333,\n", " 21.4 , 10.14285714, 10. , 4.5 , 9.66666667])" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wlag" ] }, { "cell_type": "markdown", "id": "4874f241-ae96-4296-a941-e21c311a807b", "metadata": {}, "source": [ "For the `Graph`, the `lag` is now a method:" ] }, { "cell_type": "code", "execution_count": 54, "id": "25c264fd-55fd-440c-b4f4-bb3c6b1aa1d2", "metadata": {}, "outputs": [], "source": [ "glag = g1.lag(y)" ] }, { "cell_type": "code", "execution_count": 55, "id": "d4aec77e-b453-444f-ae8e-ba5c18aa2c63", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 3.66666667, 4.33333333, 6.8 , 1.5 , 7.25 ,\n", " 3.33333333, 2.66666667, 2.4 , 6.6 , 16.75 ,\n", " 6.5 , 14.8 , 12.6 , 8.5 , 2. ,\n", " 3.85714286, 1.33333333, 3.375 , 4. , 2.33333333,\n", " 1. , 6.4 , 7.8 , 11.42857143, 9.42857143,\n", " 9.83333333, 11. , 5.2 , 8.4 , 9.6 ,\n", " 12.14285714, 2. , 9.8 , 7.66666667, 6.75 ,\n", " 7.66666667, 8.42857143, 9. , 11.55555556, 8. ,\n", " 10.5 , 13.42857143, 10.14285714, 2. , 0. ,\n", " 7.33333333, 12.16666667, 12.875 , 11.16666667, 8.5 ,\n", " 9. , 9.83333333, 5.14285714, 12.57142857, 6.5 ,\n", " 1. , 5.16666667, 4.25 , 15.25 , 6. ,\n", " 11.16666667, 9.16666667, 17. , 15.4 , 20.5 ,\n", " 4.25 , 13.88888889, 13.4 , 12.8 , 7.28571429,\n", " 9.5 , 7.6 , 2. , 10.83333333, 9.75 ,\n", " 21. , 8. , 1.8 , 16.85714286, 11. ,\n", " 1.33333333, 9.33333333, 13.2 , 16.5 , 7.75 ,\n", " 22.2 , 1.25 , 11.5 , 7.8 , 2. ,\n", " 6. , 11. , 4. , 20.2 , 14.33333333,\n", " 21.4 , 10.14285714, 10. , 4.5 , 9.66666667])" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glag" ] }, { "cell_type": "markdown", "id": "891ae30f-34ff-4240-8205-aaf8288aaf81", "metadata": {}, "source": [ "## Pandas operations\n", "\n", "As mentioned above, gains in efficiency and scope have been the main motivating forces behind the development of the new `Graph` class. Here we highlight a few additional gains." ] }, { "cell_type": "markdown", "id": "4e649b10-e9e7-490f-b42f-e30418a7bca6", "metadata": {}, "source": [ "The `adjacency` attribute for the `Graph` lets users leverage the power of pandas series. For example, the operation:" ] }, { "cell_type": "code", "execution_count": 56, "id": "9921c8c7-e2f8-4bd8-8c94-1b1cdd11b49d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "neighbor\n", "96 1\n", "97 1\n", "98 1\n", "Name: weight, dtype: int64" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen[99]" ] }, { "cell_type": "markdown", "id": "4798679f-ad54-4488-9b45-afd2fd978b4a", "metadata": {}, "source": [ "is actually a [slice](https://pandas.pydata.org/docs/user_guide/indexing.html) of the `Graph` that returns another pandas series where the index is the id of the neighboring unit and the value is the weight of that neighbor relationship.\n" ] }, { "cell_type": "code", "execution_count": 57, "id": "ea8dca41-4b87-40aa-a6eb-caa425c18a96", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MultiIndex([( 0, 1),\n", " ( 0, 17),\n", " ( 0, 18),\n", " ( 1, 0),\n", " ( 1, 2),\n", " ( 1, 17),\n", " ( 2, 1),\n", " ( 2, 9),\n", " ( 2, 17),\n", " ( 2, 22),\n", " ...\n", " (96, 99),\n", " (97, 93),\n", " (97, 95),\n", " (97, 96),\n", " (97, 99),\n", " (98, 96),\n", " (98, 99),\n", " (99, 96),\n", " (99, 97),\n", " (99, 98)],\n", " names=['focal', 'neighbor'], length=490)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen.adjacency.index" ] }, { "cell_type": "code", "execution_count": 58, "id": "c2128226-0976-49ea-8adf-133b8dabc9ae", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index([96, 97, 98], dtype='int64', name='neighbor')" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen[99].index" ] }, { "cell_type": "code", "execution_count": 59, "id": "406c3323-2d58-42ca-9ebc-a362de2046cb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "focal neighbor\n", "96 78 1\n", " 87 1\n", " 92 1\n", " 95 1\n", " 97 1\n", " 98 1\n", " 99 1\n", "97 93 1\n", " 95 1\n", " 96 1\n", " 99 1\n", "98 96 1\n", " 99 1\n", "Name: weight, dtype: int64" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_queen.adjacency.loc[[96, 97, 98]]" ] }, { "cell_type": "markdown", "id": "4cfe70cb-18f2-432d-95e0-cbe2c7fe7b4f", "metadata": {}, "source": [ "Another way the `Graph` makes use of the powerful indexing afforded by pandas is seen it the method `subgraph`:" ] }, { "cell_type": "code", "execution_count": 60, "id": "760dc3be-6c4d-4e58-bbff-a2907f99f502", "metadata": {}, "outputs": [], "source": [ "g1 = g_queen.subgraph([96, 97, 98])" ] }, { "cell_type": "code", "execution_count": 61, "id": "fc216581-a49a-4205-b5c0-688bd200eb3d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "focal neighbor\n", "96 97 1\n", " 98 1\n", "97 96 1\n", "98 96 1\n", "Name: weight, dtype: int64" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g1.adjacency" ] }, { "cell_type": "code", "execution_count": 62, "id": "1ecf948a-3b0c-4e03-8044-fde8621dae6e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g1.n" ] }, { "cell_type": "markdown", "id": "abf50253-917a-48ce-ba84-979f9fd04126", "metadata": {}, "source": [ "This allows more efficient extraction of subgraphs from the weights graph, relative to the way this is done with the `W` class:" ] }, { "cell_type": "code", "execution_count": 63, "id": "d145dc1b-aa21-4b67-b3d3-fe324e2bec2f", "metadata": {}, "outputs": [], "source": [ "from libpysal.weights import w_subset" ] }, { "cell_type": "code", "execution_count": 64, "id": "15d60171-3229-497e-9ed3-55812b0abdb3", "metadata": {}, "outputs": [], "source": [ "w1 = w_subset(w_queen, [96, 97, 98])" ] }, { "cell_type": "code", "execution_count": 65, "id": "9b7dabec-f7ce-49d5-9f41-91ea51245302", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{96: [1.0, 1.0], 97: [1.0], 98: [1.0]}" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w1.weights" ] }, { "cell_type": "code", "execution_count": 66, "id": "50fda366-2a8e-494b-a1c7-9926fef979c3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w1.n" ] }, { "cell_type": "markdown", "id": "9a723115-c894-4bc2-8e12-d2668cfbec18", "metadata": {}, "source": [ "The index of the dataframe can also be set to a more informative attribute than simple integers:" ] }, { "cell_type": "code", "execution_count": 67, "id": "0a88d13f-0573-4680-ba61-e33735fa4d97", "metadata": {}, "outputs": [], "source": [ "ngdf = gdf.set_index('NAME')" ] }, { "cell_type": "markdown", "id": "7fb4873d-ed96-46f7-8d89-26c6a6e9bd98", "metadata": {}, "source": [ "Once this is done, the new `NAME` based index will propagate to any `Graph` built on this dataframe:" ] }, { "cell_type": "code", "execution_count": 68, "id": "80c5aec6-9640-4793-80e4-6171d32e9c73", "metadata": {}, "outputs": [], "source": [ "g = graph.Graph.build_contiguity(ngdf, rook=False)" ] }, { "cell_type": "markdown", "id": "823c2b51-1dd0-4dee-90cf-7c3d7a1c8736", "metadata": {}, "source": [ "This facilities more user-friendly queries. For example, we can ask who are the neighbors for Ashe county:" ] }, { "cell_type": "code", "execution_count": 69, "id": "8466eb37-cb73-4bd4-aab0-86d83230dc18", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "neighbor\n", "Alleghany 1\n", "Wilkes 1\n", "Watauga 1\n", "Name: weight, dtype: int64" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g['Ashe']" ] }, { "cell_type": "markdown", "id": "c28a39d5-50d2-415b-98be-ec265663daa7", "metadata": {}, "source": [ "We encountered the `explore` method of the `Graph` above. This can also make handy use of the name-based indexing:" ] }, { "cell_type": "code", "execution_count": 70, "id": "726d4da2-52e0-4817-b91d-8782d7b4646e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = ngdf.loc[g['Ashe'].index].explore(color=\"#25b497\")\n", "ngdf.loc[['Ashe']].explore(m=m, color=\"#fa94a5\")\n", "g.explore(ngdf, m=m, focal='Ashe')" ] }, { "cell_type": "markdown", "id": "4bd40414-eee6-4efa-8e8c-31648fea1eaf", "metadata": {}, "source": [ "## Conclusion\n", "\n", "This notebook highlights the main areas that users need to be aware of when considering porting their code from the `W` class to the new `Graph`.\n", "More details on the specifications and differences of the graphs can be found in the [W to Graph Member Comparisions](../../migration.rst).\n", "\n", "Downstream packages in the pysal family are in the process of supporting the new `Graph` while preserving backwards compatibility with the `W` class." ] }, { "cell_type": "markdown", "id": "10c4e285-977c-408a-9c9e-ffa80e8ffb9e", "metadata": {}, "source": [ "## Further Reading\n", "\n", "- Anselin, L. and S.J. Rey (2014) *[Modern Spatial Econometrics in Practice, Chs 3,4](https://www.amazon.com/Modern-Spatial-Econometrics-Practice-GeoDaSpace/dp/0986342106)*. GeoDa Press.\n", "- Arribas-Bel, D. (2019). *[Geographic Data Science, Lab 4](https://darribas.org/gds_course/content/bE/lab_E.html)*\n", "- Fleischmann, M. (2024). *[Spatial Data Science for Social Geography, Ch 4](https://martinfleischmann.net/sds/chapter_04/hands_on.html)*\n", "- Knaap, E. (2024). *[Urban Analysis & Spatial Science, Ch 9](https://knaaptime.com/urban_analysis/03_esda/spatial_graphs.html)*\n", "- Rey, S.J., D. Arribas-Bel, & L.J. Wolf. (2023) *[Geographic Data Science with Python](https://www.routledge.com/Geographic-Data-Science-with-Python/Rey-Arribas-Bel-Wolf/p/book/9781032445953?gad_source=1&gclid=CjwKCAjw1920BhA3EiwAJT3lScLq-TnzytfU0JRyROH9eOG97U4r7YX3G0ZCs3p5mVGUymqptvNe6hoCkqcQAvD_BwE)*. CRC/Taylor Francis. [Chapter 4](https://geographicdata.science/book/notebooks/04_spatial_weights.html).\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 } libpysal-4.12.1/docs/user-guide/intro.rst000066400000000000000000000010571466413560300203170ustar00rootroot00000000000000========== User Guide ========== This user guide covers essential features of libpysal, mostly in the form of interactive Jupyter notebooks. Reading this guide, you will learn: - work with built-in example spatial data sets - how to use spatial weights - how to use spatial graphs Notebooks cover just a small selection of functions as an illustration of principles. For a full overview of momepy capabilities, head to the `API <../api.rst>`_. .. toctree:: :maxdepth: 1 Datasets Weights Graphs libpysal-4.12.1/docs/user-guide/weights/000077500000000000000000000000001466413560300201015ustar00rootroot00000000000000libpysal-4.12.1/docs/user-guide/weights/Raster_awareness_API.ipynb000066400000000000000000010062661466413560300251600ustar00rootroot00000000000000{ "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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\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 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 4 } libpysal-4.12.1/docs/user-guide/weights/categorical_lag.ipynb000066400000000000000000000463631466413560300242600ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "aae4bcad-de91-4b2d-88c7-4aa5a87322cd", "metadata": {}, "source": [ "# Categorical Spatial Lags" ] }, { "cell_type": "code", "execution_count": null, "id": "0e80fbeb-d8f9-4d33-903d-ae9c8c0ca54b", "metadata": {}, "outputs": [], "source": [ ">>> from libpysal.graph._spatial_lag import _lag_spatial\n", ">>> import numpy as np\n", ">>> from libpysal.weights.util import lat2W\n", ">>> from libpysal.graph import Graph\n", ">>> graph = Graph.from_W(lat2W(3,3))\n" ] }, { "cell_type": "code", "execution_count": 36, "id": "61a1c34b-3ede-4ff9-b4b4-f2f8ea63892e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 4., 6., 6., 10., 16., 14., 10., 18., 12.])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ ">>> y = np.arange(9)\n", ">>> _lag_spatial(graph, y)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "229bacbc-d286-4cf8-9cd2-0ac370dc60e4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'], dtype=object)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ ">>> y = np.array([*'ababcbcbc'])\n", ">>> _lag_spatial(graph, y, categorical=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "84523f3b-3909-4e33-a7b9-11a70b263308", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "689efe78-b402-4bbc-b8f1-28c24faf9777", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 28, "id": "e4b99d6e-1cea-450d-8a56-7f113f50e725", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['a', 'b', 'a', 'a', 'c', 'b', 'c', 'b', 'c'], dtype='>> y[3] = 'a'\n", ">>> y" ] }, { "cell_type": "code", "execution_count": 34, "id": "efb331be-e162-41ce-a758-13a05000229a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['a', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'], dtype=object)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ ">>> np.random.seed(12345)\n", ">>> _lag_spatial(graph, y, categorical=True, ties='random')" ] }, { "cell_type": "code", "execution_count": 35, "id": "cbd5848f-0b97-4b34-b04b-7e7e946e04ae", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'], dtype=object)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ ">>> _lag_spatial(graph, y, categorical=True, ties='random')" ] }, { "cell_type": "code", "execution_count": 30, "id": "42f588b7-ccde-4ed5-8a1b-a876ca59ca4b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['a', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'], dtype=object)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ ">>> _lag_spatial(graph, y, categorical=True, ties='tryself')" ] }, { "cell_type": "code", "execution_count": 31, "id": "603cb33c-3fdb-4d0e-aa7e-8b99374ec3fc", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "There are 2 ties that must be broken to define the categorical spatial lag for these observations. To address this issue, consider setting `ties='tryself'` or `ties='random'` or consult the documentation about ties and the categorical spatial lag.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43m_lag_spatial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcategorical\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/para/1_projects/code-pysal-libpysal/libpysal/libpysal/graph/_spatial_lag.py:58\u001b[0m, in \u001b[0;36m_lag_spatial\u001b[0;34m(graph, y, categorical, ties)\u001b[0m\n\u001b[1;32m 56\u001b[0m n_ties \u001b[38;5;241m=\u001b[39m gb\u001b[38;5;241m.\u001b[39mapply(_check_ties)\u001b[38;5;241m.\u001b[39msum()\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_ties \u001b[38;5;129;01mand\u001b[39;00m ties \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m---> 58\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 59\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThere are \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_ties\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m ties that must be broken \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto define the categorical \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspatial lag for these observations. To address this \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124missue, consider setting `ties=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtryself\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor `ties=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrandom\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m` or consult the documentation \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mabout ties and the categorical spatial lag.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 65\u001b[0m )\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m ties \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrandom\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtryself\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m gb\u001b[38;5;241m.\u001b[39mapply(_get_categorical_lag)\u001b[38;5;241m.\u001b[39mvalues\n", "\u001b[0;31mValueError\u001b[0m: There are 2 ties that must be broken to define the categorical spatial lag for these observations. To address this issue, consider setting `ties='tryself'` or `ties='random'` or consult the documentation about ties and the categorical spatial lag." ] } ], "source": [ "_lag_spatial(graph, y, categorical=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "b77d9c3b-e923-428b-8b61-f383a1af42fe", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "b120ef88-dd80-4b9d-8c7c-8b413d476e08", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "id": "2e60d703-41f3-4dd8-80ad-c968e1e72e86", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ ">>> import libpysal\n", ">>> import numpy as np\n", ">>> np.random.seed(12345)\n", ">>> w = libpysal.weights.lat2W(3, 3)\n", ">>> y = np.array([*'ababcbcbc'])\n", ">>> y_l = libpysal.weights.lag_categorical(w, y)\n", ">>> np.array_equal(y_l, np.array(['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b']))\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "cd0dcf01-ddfe-49a0-b6c5-da9a6d7d73cc", "metadata": {}, "outputs": [], "source": [ "from libpysal import graph" ] }, { "cell_type": "code", "execution_count": 3, "id": "86ffb3f3-4743-4339-acf7-36f42789093f", "metadata": {}, "outputs": [], "source": [ "g = graph.Graph.from_W(w)" ] }, { "cell_type": "code", "execution_count": 4, "id": "947f99bb-8b2f-44b0-810b-e1699559eaae", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "focal neighbor\n", "0 1 1.0\n", " 3 1.0\n", "1 0 1.0\n", " 2 1.0\n", " 4 1.0\n", "2 1 1.0\n", " 5 1.0\n", "3 0 1.0\n", " 4 1.0\n", " 6 1.0\n", "4 1 1.0\n", " 3 1.0\n", " 5 1.0\n", " 7 1.0\n", "5 2 1.0\n", " 4 1.0\n", " 8 1.0\n", "6 3 1.0\n", " 7 1.0\n", "7 4 1.0\n", " 6 1.0\n", " 8 1.0\n", "8 5 1.0\n", " 7 1.0\n", "Name: weight, dtype: float64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g.adjacency" ] }, { "cell_type": "code", "execution_count": 5, "id": "f58b41ad-a1c1-49a5-ac06-1e50b2b7d66c", "metadata": {}, "outputs": [], "source": [ "from libpysal.graph._spatial_lag import _lag_spatial" ] }, { "cell_type": "code", "execution_count": 6, "id": "38db58fa-01ba-4243-9137-69cd4893732c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'], dtype=object)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy\n", "_lag_spatial(g, numpy.array(y), categorical=True)" ] }, { "cell_type": "markdown", "id": "afe32b9a-000b-4cb5-8e2d-7e708586e7e5", "metadata": {}, "source": [ "## No Ties" ] }, { "cell_type": "code", "execution_count": 7, "id": "5d075def-1b9c-4c19-b7fd-4d219fe5e754", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'], dtype=object)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_lag_spatial(g, numpy.array(y), categorical=True, ties='tryself')" ] }, { "cell_type": "code", "execution_count": 8, "id": "b65e6f7e-081b-4d36-9e5a-edbe5fe12896", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array_equal(y_l, _lag_spatial(g, numpy.array(y), categorical=True, ties='random'))" ] }, { "cell_type": "code", "execution_count": 9, "id": "d3bb409f-7cd0-4258-b880-270461413340", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array_equal(y_l, _lag_spatial(g, numpy.array(y), categorical=True, ties='raise'))" ] }, { "cell_type": "markdown", "id": "e1c9b0f4-018f-44a8-9ed8-a6b7523d8108", "metadata": {}, "source": [ "## Ties" ] }, { "cell_type": "code", "execution_count": 10, "id": "de2f2e7b-a6f6-4e43-bf81-69facd3e0cff", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['a', 'b', 'a', 'a', 'c', 'b', 'c', 'b', 'c'], dtype=' 2\u001b[0m np\u001b[38;5;241m.\u001b[39marray_equal(y_l, \u001b[43m_lag_spatial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumpy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcategorical\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mties\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mraise\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m)\n", "File \u001b[0;32m~/para/1_projects/code-pysal-libpysal/libpysal/libpysal/graph/_spatial_lag.py:58\u001b[0m, in \u001b[0;36m_lag_spatial\u001b[0;34m(graph, y, categorical, ties)\u001b[0m\n\u001b[1;32m 56\u001b[0m n_ties \u001b[38;5;241m=\u001b[39m gb\u001b[38;5;241m.\u001b[39mapply(_check_ties)\u001b[38;5;241m.\u001b[39msum()\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_ties \u001b[38;5;129;01mand\u001b[39;00m ties \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m---> 58\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 59\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThere are \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_ties\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m ties that must be broken \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto define the categorical \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspatial lag for these observations. To address this \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124missue, consider setting `ties=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtryself\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor `ties=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrandom\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m` or consult the documentation \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mabout ties and the categorical spatial lag.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 65\u001b[0m )\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m ties \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrandom\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtryself\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m gb\u001b[38;5;241m.\u001b[39mapply(_get_categorical_lag)\u001b[38;5;241m.\u001b[39mvalues\n", "\u001b[0;31mValueError\u001b[0m: There are 2 ties that must be broken to define the categorical spatial lag for these observations. To address this issue, consider setting `ties='tryself'` or `ties='random'` or consult the documentation about ties and the categorical spatial lag." ] } ], "source": [ "y_l = libpysal.weights.lag_categorical(w, y)\n", "np.array_equal(y_l, _lag_spatial(g, numpy.array(y), categorical=True, ties='raise'))" ] }, { "cell_type": "code", "execution_count": null, "id": "e23094f6-94b7-49c1-a537-206ef4b89d37", "metadata": {}, "outputs": [], "source": [ "y_l" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9f175a-4108-408e-b17b-7147861d1375", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 } libpysal-4.12.1/docs/user-guide/weights/intro.rst000066400000000000000000000003551466413560300217710ustar00rootroot00000000000000 =============== Spatial Weights =============== .. toctree:: :maxdepth: 1 Spatial Weights Overview Raster Awareness Voronoi Weights Categorical Spatial Lags libpysal-4.12.1/docs/user-guide/weights/matching-graph.ipynb000066400000000000000000037625071466413560300240610ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spatial matching graph\n", "\n", "Author: [Levi John Wolf](http://github.com/ljwolf)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic Usage" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath('..'))\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import geopandas\n", "import pandas" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from libpysal.graph import Graph" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "points = np.row_stack([(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3), (7,4)])\n", "gdf = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(*points.T))" ] }, { "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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uuGOrtj3mmGPiwQcfjD322KOKU0HFlJSUxPnnnx8lJSUR8a+HRH7nO99JnAq231/+8pes8bp162L16tXx/vvvxwsvvBA333xz9OzZM2655RZL/lHtlL34sXbt2jjooIOyHgK8OcXFxXHXXXfFlClT4qmnnoo999yzqmJCpRs3blwsWrQoM87Pz/e5hGqpRYsWMXHixBgwYEBMmzYtioqK4oQTTojTTjstTjvttOjUqVPk5+fHZ599FlOmTIlhw4ZlXcQ++OCDY/To0Za4JKlOnTpljRctWhQLFy6MNm3abNX+U6ZMKTdnGTRqkuXLl2eNK3rNrlGjRtGgQYOs55GtWLEimjdvXhnxyCF31sBO4uSTT46nn346nnvuuSgoKEgdB8q59dZbMw/Ua9iwYfzf//1f4kSQO1OnTo3jjz8+fvrTn2YtuQOplf3DcfDgwZmipmHDhnHVVVfF+PHjY86cOfHaa6/FiBEjolevXln7TJs2LU499dQoLi7OVWzYbiNGjMgan3rqqRV+hgLkSmFhYbzyyitx9913R8+ePaO0tDQefvjhOOOMM6Jbt27RoUOHOOyww+LKK6/MFDW77rpr/OIXv4iXXnrJxTySa9WqVey7775Zcw8++OBW7btmzZp49NFHy82vWrWqUrJBLqxevTprnJ+fX+FjlN3Hv4GayZ01sJMYO3ZsbNiwIRo0aBBHHnlk6jiQZfbs2fE///M/mfFNN93kmUrUeAUFBdGvX784+OCDo3PnztGiRYuoVatWfP755/H666/H3/72t3j66acz25eWlsbNN98cGzdujFtuuSVhcviXdevWlVsDfuHChRER8fWvfz3GjRsXe+21V9brBxxwQAwePDhuu+22uPrqqzPzU6ZMiV/+8pdx/fXXV31w2E5LliyJJ598MmvOEmhUdxs2bIgNGzZE/fr1Iy8vb4tf/thrr73i5z//eQwcODDq1q2bw5Sweeecc0787Gc/y4x/9atfxbnnnvuVXzb92c9+tsm7aFyopiYpW9Y0aNCgwsfIz8/PWvqs7DGpGdxZAzuA//7v/4733nsv8zN79ux48cUX4/e//3307t07Iv61FMlTTz0VRx11VFxyySWxYcOGxKnhXzZu3BhDhgzJXBA88MAD47LLLkucCrbdwQcfHE8//XR8+OGHcffdd8f3vve9OOKII6Jz586x7777xuGHHx6XXHJJjBs3Lv75z39Ghw4dsva/9dZb4/HHH0+UHv5jc58VmjZtusmi5suuuuqquOKKK7Lmfve73/mjkRrhgQceyLoTrF27dnHUUUclTARbNnny5OjcuXNcdNFFMXny5Ni4ceMWt//www9j8ODB8bWvfS3uueeeHKWELbvkkkuiadOmmfHy5cujb9++WUtSlvXb3/42br/99k2+VquWS57UXNuyNKXlLHcMfnPBDqBFixZRWFiY+encuXP06tUrLrnkkpgwYUK8+OKLWQ8bu/POO+P73/9+wsTwH3fccUe8/PLLERFRp06duOeee6J27dqJU8G269evXxx//PFb9WG5Z8+e8fLLL0fHjh2z5n/84x8r1Ulul1122eSFjiuvvHKLRc2/3XTTTVkXXZYuXRpjx46t1IxQFe67776s8fnnn+8CCNXWhAkTok+fPrFgwYLMXEFBQdx6660xbdq0WL58eaxfvz4WL14c48aNi0GDBkWdOv9aZGXJkiVxwQUXxPe//33LsJJcs2bNyi1BOXPmzOjcuXNcc801MXHixHj77bdj+vTpMXLkyPjGN74RV111Vea9W/b5NpaupCZp1KhR1rioqKjCxyi7T9ljUjMoa2An0KtXr5g4cWLsuuuumbkRI0b45jbJzZ8/P2tJnCuvvDK6d++eLhAk0KJFi3jooYeyLgTOmTMnJk6cmDAV/EvDhg3LzZ177rlbve+AAQOy5iZNmlQZsaDKvPzyyzFr1qzMuHbt2nHeeeelCwRbsGTJkjjrrLOyHij9zW9+M2bPnh3XXnttdO/ePZo2bRp169aNli1bxgknnBAjR46MF198Metvw+HDh8evfvWrFP8JkGXAgAFxxx13ZH1ZZNWqVfHrX/86evfuHZ06dYoePXrE4MGD46WXXspsc9lll8Wxxx6bdSxlDTWJsoZ/U9bATmKfffaJ//7v/86a84GclEpLS+OCCy6ItWvXRkRE27ZtY+jQoWlDQSIHHHBAHH/88Vlz48aNS5QG/qPshY6WLVtW6Jlihx56aNb4rbfeqoRUUHXuvfferHHfvn2jdevWidLAlv32t7+NJUuWZMadOnWKhx9+OJo0abLF/Q499NAYNWpU1tyNN94Yn376aZXkhIq47LLLYuzYsbHvvvt+5baNGjWKO++8M26//fZyy6W1atWqqiJCpfvy3egRkfW7fWusXr26XFmjsKyZlDWwExk4cGDW+OWXX47ly5enCcNOb/jw4fHcc89lxsOGDYv8/PyEiSCtE088MWv8xhtvJEoC/1F2ib4999yzQvuXvcj9+eefb3cmqCpr1qwpdwF7yJAhidLAV3vkkUeyxtdee+1WP5T62GOPjW984xuZcVFRUfzlL3+p1HywrY4//viYNWtW/PWvf40hQ4ZE586do0WLFlG3bt0oKCiIXr16xW9+85t455134gc/+EHk5eXFnDlzso7Rs2fPROmh4so+x/T999+v0P5lt2/RokU0b958u3ORe3VSBwByZ4899ojmzZvHsmXLIuJfD3Z/7733okePHomTsTO64YYbMv+7X79+0b59+6y1tjdl8eLFWeOSkpJy+7Ru3Trq1atXWTEhZ8rerVDRb1NBVejSpUtMmDAhM65fv36F9i+7/ZeX6oHq5pFHHolVq1Zlxi1btoz+/fsnTASbt2bNmnj33Xez5souA/VV+vTpEy+++GJm/Morr1RKNqgMtWvXjgEDBpRbUnVTPvzww1i4cGFmXFBQEAUFBVUZDypV586ds8bz5s2r0P7z58/PGn/961/f7kykoayBnUzdunWzxuvWrUuUhJ3dl2/R/fvf/x777LNPhY+xaNGicvtNmzbNc2+okcreWbYt6xRDZevWrVvWuKJ35Jbd/svPSIDqpuwSaOeee27mQexQ3Wzq93FFl30qu/1nn322PZEgmS9/sSQi4uijj04TBLZR165ds8ZvvPFGrF27NnbZZZet2n/y5MlbPB41h2XQYCfyxRdflPsA3rJly0RpAPiysr+fd9ttt0RJ4D/69u0beXl5mfH8+fMrdHfMm2++mTVu06ZNpWWDyjR37tysh1VHWAKN6m1TzyJYs2ZNhY6xevXqrLGHUVNTlS3bv/e97yVKAttmzz33zPqSVElJSbnPJVsyadKkrHHfvn0rKxo5pqyBnciECRNi48aNmfEuu+zi1mCAaqLs0iMeaE110Lp16zjssMMy4+Li4nLfXt2ScePGZY2//HwEqE5GjBiRNe7Vq9dWPdwaUmnYsGE0adIka27atGkVOsZrr72WNfZAdmqil156Keui9r777uvOGmqkb3/721nj++67b6v2mzNnTtbfkg0bNozjjz++UrORO8oa2Els3Lgxbrrppqy5E0880bM9SGb58uVRWlpaoZ+JEydmHWPvvfcut40l0KiJvvjii3j00Uez5vyRSXUxePDgrPFvf/vbrdrvxRdfjFdffTUzrlWrVvTr169Ss0Fl2LBhQzzwwANZc+6qoSYo+1nh7rvv3up9Fy9eHE888UTWnEKdmmbt2rVx4YUXZs394he/SJQGts/ZZ58dtWvXzowfffTReOedd75yv1/+8pdZ4zPOOCMaNGhQ6fnIDWUN1DC///3v4+OPP67QPsXFxTFkyJBy39q++OKLKzMaANvol7/8ZSxatCgzrl27dpx00kkJE8F/DB48OOuhp88999xXFjaffvppuZLnjDPOiHbt2lVJRtgef//737M+Xzdu3DhOP/30hIlg65x55plZ41GjRsUf//jHr9xv3bp18d3vfjdrGbRGjRrFCSecUOkZoSJKSkq2etvVq1fHSSedFLNmzcrMnXrqqXHqqadWRTSoch06dIhBgwZlxuvXr4/zzjtvi0sQP/744zFy5MjMuF69enHDDTdUZUyqmLIGaph777032rVrF+ecc048+eSTsWrVqs1uW1RUFA899FD06NEj65d3RMR3v/vd6N27dxWnBdi5PPjgg/HJJ59UaJ/hw4fHjTfemDV33nnnxd57712Z0WCb1a5dO+64446oVes/fzpcddVVcfnll8eyZcvKbT9+/Pg44ogj4t13383MNW/ePG6++eac5IWKKvusg4EDB0bDhg0TpYGtN3DgwNh///0z49LS0jj33HPj8ssv3+wX/CZOnBiHHnpojB8/Pmv+2muvjebNm1dpXvgqw4YNi969e8fIkSNjyZIlm9xm9erVcf/990enTp2yntNRWFgYd911V46SsjNauHBhLFiwoNzP4sWLs7YrKSnZ5HYLFiwo95zSsm688cas38X/+Mc/ok+fPjFnzpys7datWxe///3vy3255KqrrvJ3ZA2XV1paWpo6BLD1unfvHjNmzMiM8/Lyon379lFYWBjNmjWLevXqxapVq+L999+P2bNnR3Fxcblj9O/fP0aPHh3169fPZXTYbpMmTYpjjjkmM957771jwYIF6QJBGUcffXS8+uqrcfrpp8cZZ5wRRx999GYv+E2dOjVuvvnmeOyxx7LmCwoKYurUqdaNp9r5wx/+EJdeemnWXN26dePQQw+NgoKCKCoqiunTp8f777+ftU29evXiiSee8I1tqqVPPvkk2rRpk/Vt7ldeeSUOPvjghKlg682bNy+OOOKI+PTTT7Pma9WqFd26dYu2bdtGfn5+LF26NKZNm1buomJERL9+/WLMmDFRt27dXMWGTfryZ428vLzYZ599Yt99943mzZvH2rVrY/HixfH666/H+vXrs/bbZ5994plnnon27duniM1OorCwsNzn3IoaNGhQuS9TlzVp0qQ44YQTst7neXl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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(*points.T)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "g1 = Graph.build_spatial_matches(gdf.geometry, k=1)\n", "g2 = Graph.build_spatial_matches(gdf.geometry, k=2)\n", "g3 = Graph.build_spatial_matches(gdf.geometry, k=3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax =plt.subplots(1,3)\n", "for i,g in enumerate((g1, g2, g3)):\n", " g.plot(gdf, ax=ax[i])\n", " ax[i].set_title(f\"k = {i+1}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Larger Problem" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import geodatasets\n", "stores = geopandas.read_file(geodatasets.get_path(\"geoda liquor_stores\")).explode(\n", " index_parts=False\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idplaceidgeometry
00ChIJnyLZdBTSD4gRbsa_hRGgPtcPOINT (1161395.910 1928443.285)
13ChIJ5Vdx0AssDogRVjbNIyF3Mr4POINT (1178227.792 1881864.522)
24ChIJb5I6QwYsDogRe8R4E9K8mkkPOINT (1178151.911 1879212.002)
36ChIJESl0mMfMD4gRy23-8soxKuwPOINT (1141552.993 1910193.701)
47ChIJg28YOdvMD4gRiV2lZcjSVyQPOINT (1144074.399 1910643.753)
\n", "
" ], "text/plain": [ " id placeid geometry\n", "0 0 ChIJnyLZdBTSD4gRbsa_hRGgPtc POINT (1161395.910 1928443.285)\n", "1 3 ChIJ5Vdx0AssDogRVjbNIyF3Mr4 POINT (1178227.792 1881864.522)\n", "2 4 ChIJb5I6QwYsDogRe8R4E9K8mkk POINT (1178151.911 1879212.002)\n", "3 6 ChIJESl0mMfMD4gRy23-8soxKuw POINT (1141552.993 1910193.701)\n", "4 7 ChIJg28YOdvMD4gRiV2lZcjSVyQ POINT (1144074.399 1910643.753)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stores.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "stores = stores.set_index(stores.placeid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solving for this graph in larger data will take time. The solution technique is somewhere between $O(n^2)$ and $O(n^3)$ if the solver recognizes it's a matching problem." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "g1 = Graph.build_spatial_matches(stores.geometry, k=1)\n", "g5 = Graph.build_spatial_matches(stores.geometry, k=5)\n", "g10 = Graph.build_spatial_matches(stores.geometry, k=10)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax =plt.subplots(1,3)\n", "for i,g in enumerate((g1, g5, g10)):\n", " g.plot(stores, ax=ax[i], nodes=False)\n", " ax[i].set_title(f\"k = {(1, 5, 10)[i]}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cross-matching" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sources = stores.sample(100)\n", "sinks = stores[~stores.index.isin(sources.index)].sample(100)\n", "ax = sources.plot(color='red')\n", "sinks.plot(color='blue', ax=ax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from libpysal.graph._matching import _spatial_matching\n", "import shapely" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "sources = stores.sample(100)\n", "sinks = stores[~stores.index.isin(sources.index)].sample(100)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "source_coordinates = sources.geometry.get_coordinates().values\n", "sink_coordinates = sinks.geometry.get_coordinates().values" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "crosspattern_heads, crosspattern_tails, weights, mip = _spatial_matching(x=sink_coordinates, y = source_coordinates, n_matches=1, return_mip=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mip.sol_status" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "lines = shapely.linestrings(\n", " list( \n", " zip(\n", " map(list, source_coordinates[crosspattern_heads]),\n", " map(list, sink_coordinates[crosspattern_tails])\n", " )\n", ")\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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cEhERkcvFxppfbsRRW7cChw7lT1lERKSPwSURERG5nLu7w3q6PCIiUmNwSURERC63a9fdXR4REakxuCQiIiKXEgLYty9/y9y7VyqXiIg8h8ElERERuVRqqtl1LS8DmAhgDwCrQ2UmJQHXrzt0KhERuQiDSyIiInKpjAyzZ0QDmASgJYCKAF5zqNz0dIdOIyIiF2FwSURERC7l52f2jKg8/74M4KJD5fr7O3QaERG5CINLIiIicqnAQCA42GjqdAAbFPt6my4zOBgICDB9GhERuRCDSyIiInIpiwVo1sxo6i0AbuQ9G0BP02U2by6VS0REnsPgkoiIiFyuVSujKaMUr+8HUMaN5RERkbswuCQiIiKXGzDASCoBdXBpvkus8fKIiMidGFwSERGRy4WFAe3a2Ut1FMAZxT7zwWV4ONCwoenTiIjIxRhcEhERkVuMHm0vhbLVshKARm4oh4iI8gODSyIiInKLiAh73VWVwWUEpAl9jIuMBHr1MlkxIiJyCwaXRERE5DZz5wKhoVpHkgD8odhnrktsaCgwZ46DFSMiIpdjcElERHSXEAJISQESEqSfQni6RkBICLB+vda6l78AyM7zugiATobzDQ6W8g0Jcb6ORETkGgwuiYiICrHYWGDsWKBLFynQCgoCypSRfoaESPvHjgUOHfJcHcPCgC1blC2Yyi6xnQAUM5RfaKiUX1iYiypIREQuweCSiIioEIqOlmZJbdQImDYN2LQJSEqSp0lKkvZPmyYFYuHhwNq1nqlvWBgQEyONkZRaLNcpUhjrEhsZKeXDwJKIqOBhcElERFSIJCZKAVbv3sC2bebO3bZNmmRn4EApn/wWEgIsWQLMmLEDwFXF0Qib54aHSwH1kiXsCktEVFD5eLoCREREZExMDNCzJxAX51w+S5cCmzdLYxY90QJ49aq8S2xAQBh8favIWl6Dg4HmzYFWraQZZ7mOJRFRwcfgkoiIqBCIiQE6dFB3fXVUXBzQvr1nxi5GR0fLXg8b1htTpgDXrwPp6YC/PxAQAFjMrUpCREQexm6xREREBVxiotRi6arAMkdSEtCjR/52kT137hxiY2Nl+yIiImCxAIGBQOnS0k8GlkREhQ+DSyIiogJu2DDnu8LqiYsDhg93T95alK2WpUqVQuvWrfOvAkRE5DYMLomIiAqw6Ghg2TL3lrF0qVROflAGl7169YK3t3f+FE5ERG7F4JKIiKgAmz49f8qZMcP9Zdy4cQObNm2S7YuIsD1LLBERFR4MLomIiAqo2Fjzy404autW4NAh95bx22+/IT09Pfe1t7c3unfv7t5CiYgo3zC4JCIiKqDc3R02v8tTdolt27YtgoOD3VsoERHlGwaXREREBdSuXXdPeUIIREXJ17dkl1giorsLg0siIqICSAhg3778LXPvXqlcdzh44AAuXrwo29e7d2/3FEZERB7h4+kKEBERkVpqqtl1LS8D2Ang5O2tLIBJpspMSgKuX5fWmXSJ2Fipr+2uXYj+4w/ZoRpeXrjv1VeB++8HIiOBhg1dVCgREXkKg0siIqICKCPD7BnrATyb53V9mA0uASA93QXBZXS0NM1tntmIohRJIqxWWH77DfjtN2DaNKBdO+Dtt4FevZwsnIiIPIXdYonyiRBASgqQkCD9dFfXMyK6O/j5mT2jluL1KQBW0+X6+5s+5Y7ERKkVsndvWWAZD6lNNS9Vh9ht24CICGDgQCkfIiIqdBhcErlRbCwwdizQpQsQEgIEBQFlykg/Q0Kk/WPHun/6fyIqfAIDAXMTqSqDy3QAF7US6goOBgICTJ1yR0wM0KiR5pSz6wDk/T6tOID2evksXSrlExvrYEWIiMhTGFwSuUF0NBAeLj0fTZsGbNqkHjuVlCTtnzYNCAuT0q9d65n6ElHBY7EAzZqZOaMcpLAtr5OmymzeXCrXtJgYoEMHIC5O87CyS2xXADYbSOPigPbtGWASERUyDC6JXEinR5gh7BFGREqtWplJbYG69dJccGmuvNsSE4GePXVnH8oE8Itin6E5YpOSgB49+AeRiKgQYXBJ5CI2eoSZwh5hRJRjwACzZyiDyxNuLg/AsGG6LZYAsB1AimKf4Sl74uKA4cMdqBQREXkCg0siF7DTI8w09ggjIkDqMt+unZkzHG+5DA93YDWQ6Gi736gpu8Q2B1DBTBlLl0rlEBFRgcfgkshJdnqEOYw9wogIAEaPNpO6tuK18eDSXDm3TZ9uN4kyuDTUJVZpxgxHziIionzG4JLISXZ6hDmFPcKIKCLCTHdVrZZL++seRUY6sLxkbKzdweUnABxX7HMouNy6ldNqExEVAgwuiZxgoEeY09gjjIjmzgVCQ42kVAaXtwBcsnlGaCgwZ44DlTLwx0/5p6scAFMT4Josj4iIPIvBJZETDPQIcwn2CCO6t4WEAOvXG1n3sgKAoop9+l1jg4OlfENCHKjUrl12kyi7xEbAiQcPA+UREZFnMbgkcpCBHmEuwx5hRBQWBmzZYq8F0wtATcU+7eAyNFTKLyzMgcoIAezbZzNJCoCtin1dHSgq1969UrlERFRgMbgkclB+99BijzAiCguTZqeOjLSVyv5yJJGRUj4OBZYAkJpqdxaz9ZDWuMxrv4PFAZDKu37dmRyIiMjNGFwSOSi/e2ixRxgRAVIX1iVLgKgoafkQNf0ZY8PDpTHcS5Y42BU2R0aGzcNbAQzR2D8bwCknikV6uvG0QgApKUBCgvSTrZ5ERG7H4JLIAQZ6hNlwC0AfAOaiRfYII6K8IiKkbq2xscDYsUCXLjljMuUtlwEBJzF2rJRuyxYHZoXV4uenuTsOwEAA7QFotWv6AXCqh7+/v+3jeW9GSAgQFASUKSP9DAmR9o8dy3EGRERuwuCSyAEGeoTZ8BaAnwC0ATAFQLahs9gjjIi0NGwITJkCbNggrYv788/q5Ujef1+gYUMXFhoYKJtdKAPAhwDqAliqc0ofAMdu/3RIcDAQEKB9LDpaapZt1AiYNg3YtEn9RzopSdo/bZrUHzg8HFi71tHaEBGRBgaXRA6w0yPMhmgAn97+dzaAdwB0AnDe0NlmeoQR0b3HYgHCwuTB5fXr1/Hvv/+6vqBm0qIiGwA0hvS1md73X1UArIY0l63DmjeXys0rMVEaQNq7t/kZ1rZtk5p/Bw6U8iEiIqcxuCRygE6PMAO+1ti3FdKj2Qq7Z9vrEUZEBUw+jfvL2xu0SZNKAOR/LCIiTrq8N+i5unXxGIBuAI7aSdvfFQW2aiV/HRMjtVQ6O9vZ0qVSPrGxzuVDREQMLokcoegRZsL3ACZC/at3DcATAJ4BkKp5pq0eYUSFyV0/z0o+jvvT6g167ZoXgBqydHv3nnBZb9C0tDS8//77qLdgAX4weE6E48XdMWDAnX/HxAAdOgBxca7IWcqnfXsGmERETmJwSeSAPD3CTPIBMAHANgDVNI5/A6AJgJ2qI1o9wogKi3tinpV8HPdnvzeoetxlDmd6g0ZHR6Nhw4YYP348bqWlqY63ADBGsS8IwIPmilELD0fuoNHERKBnT2cGvmtLSgJ69GAXWSIiJzC4JHKQsoeWOW0AHIA0r6LSaUiPYu8j72Q/zpVH5Bn3xDwr+Tzuz1hvUP3lSHKY6Q166tQpPPTQQ+jduzdOnVIvJhICYD6kr8UOK471AOBrvwjbRo++8+9hw1zXYqkUFwcMH+6evImI7gEMLokclLeHlmOCACy+vZVQHMsGMB5ABwDnXFRewXHXd4uke2eelXwe92e8N6h+y2Ve9nqD3rx5E+PHj0eDBg0QFRWlOu7l5YVXatfGcQAvQpo1doMijdNdYiMj76yfEh3t/L22Z+lSqRwiIjKNwSWRg8LCgHbtXJHTQEitmG00jm0H0Bj33feda5cR8IB7olskAbiH5lnJ53F/5nqDagWX2t/iaPUGFUJg1apVqFevHt5//32ka0xV3aZNG+zZswef/fUXSoWGAgA2A7iZJ40FQE8j1dUTGgrMmXPn9fTpzuRm3IwZ+VMOEdFdhsElkRPy9tRyTnUAW6A92U8yjh4dgMGDByMlJcVVBeabe6JbJOW6Z+ZZ8cC4P3O9QZXBZTIA/WbgvL1Bjx49iu7du6Nfv344f169TFK5cuWwaNEibN++HU2bNpW+HVq/HggOhrK97wEApY1WWSk4WMo3JER6HRtrvhncUVu38psuIiIHMLgkckJEhCu7q+ad7Ke66uiiRYvQtGlT7Nixw1UFutU90y2Sct1T86zk87g/871BK0M90vGEzTOWLk1Fv35vISwsDBs2KDu3At7e3njttddw7NgxPPXUU7DknWEsLAxi82ZEeXvLznG4S2xoKLBli/RtUw53d4dVyu/yiIjuAgwuiZw0d670HOQ6OZP9PKU6cvr0abRt2xaTJ09GVlaWKwt1qXumWyTJ3DPzrHhg3J/53qA+UH9JpT3uUuouuwzAfVi16kPNvy0dO3bEwYMH8dFHHyEoKEgzl8Pe3jibnS3b19tstQHpW6mYGHlgCQC7djmSm+PyuzwiorsAg0siJ+XpEeZCJRAcvAgffLAUJUrIJ/vJzs7GhAkT0KFDB5w9e9ZQbvk5gc490y2SZO6peVbyedyf471B7c8YC8RCmjgsEoD6l7ZixYr47rvvsGnTJjRo0MBmadGKN6eyvz/CdNJqCg+X3uAlS+50hc0hBLBvn5ncnLd3L2cbIyIyicElkQuEhUk9uFzVgpnTI2z06AE4ePAg2rZtq0rzxx9/oHHjxli6dKlmHp6YQOee6hZJMvfMPCseGPfneNBua8bYawBGAGgKYKvqTF9fX7z99ts4evQonnjiCXkXWB3K2WQjnn0Wlrx/iJTfwAUH3/lDFBsr/dHLmRVWKTXV9B+WVEgj2WcC+I+pM29LSgKuX3fkTCKie5cgcoNDhw4JSH2tBABx6NAhT1cpXyQkCBEZKYT0dbdjW2SklE9emZmZYvLkycLb21t2X3O2QYMGieTkZCGEEFFRQrRrZ67Mdu2EiI52/voHDHDu2o3cGyp4YmLc+74rt9hYD17smDH5e7Fjx4rOnR09fa7ib0UrAWQLYKEAymr+LQEgQkK6i2PHjpm6LYmJicLLy0uWT1RUlDyR1SpESooQ8fHST6vVeAHx8aYu/hQgLIrruuDITYyPN3UfiIg8zdPP4Gy5JHKhkBCpR1dUlNTDywxbPcJ8fHwwfvx4bNu2DdWrqyf7Wbx4MRo1aoKuXf/02AQ691S3SJK5p+ZZyedxeGLnLid6gypbLo8BeBDAswD+1UhfDcBqZGevQ+3adUyV9Msvv8Bqtea+Llq0KDp16iRPZLEAgYFA6dLSTwOtobn8/EzVpxqAQMW+3aZyuM3f35GziIjuWQwuidwgIkLq4eWqHmE5HnjgARw4cABPP/206ti5c2ewcWM4gEkAHJvsx5kJdO6ZbpGkcs/MsyJEvo/7S9173IFu5qkAdgJQ1jUZgNZs0/6QZqo+DKAPrl2zmO4NquwS26lTJxQtWtRcJrYEBpoa2O4FoIVin+mPTXAwEBBg9iwionsag0siN2rYEJgyBdiwQWoRTEkB4uOln4mJ0v4pU6R0RpUoUQLffPMNli1bpjFrYzaktTLbAzjjUJ0dmUCHy8/duzwQb3lunhUHxv3l2Adp5tSVJs/LuHbD5BnfACgBoDWAcQbSPwwpqJwI4E4wmJ5uvMSsrCysW7dOtq93b4fmidVnsQDNmpk6paXitemWy+bNzbWuEhERg0ui/OJMjzAtTz75JDZvPgg/v3YaR/8E0ATAEofyNjuBzj3VLZJkzMdbsQCiAdx0uEyPzbOSkWH6lCMA+gFoDumqJ0D6CsgoP5gtU91tXlut2zVaA6CG6qiZ3qA7duxAkuJDEBHh8AqX+lq1MpVcGVzugTQAyV3lERERg0uiQm3GjKrIyPgdwHsAvBVHUwAMur0lm87bzLqC90y3SFIxH299CakNLwRABIDVDpVrpmXNZUyO+wOAGACr8rw+DGC5ifMDkYrgkmZCojJ2jvsCmArgEADtfvhme4Mqu8Q2atQIlStXNp6BUQMGmEquDC6vQX+lT1eUR0REDC6JCq07E+h4A3gHwB/QaoGQWi+b3D5ujpEJdBzvFikA9IW0UEC8qTO5/FzBYS7eEpBaywAgDcBaAEcdKtcj86yYHPcHAI8DqrUeJ8L4qGhLcDCaNTeSMgvAHAAP2En3EIAxkMZZajPbG1QZXLq8S2yOsDCgnVZPDW2VAZRV7DPcNTY83Nx4BSIiAsDg0ikXL17EypUr8dlnn2Hq1KmYP38+oqKiEB9v7kGZyBHqCXTuB3AAwGCN1GcBhMPcY63E3gQ6jg9D2wGp1epNABUBPAFgIwCrjXMkXH6u4DAXbx2F9FnMy84sVho8Ns+KA+P+vABMVuw7AWCx0QyaN0erVvYivS0AmkFat9JeL4VLdos00xv07Nmz+Pvvv2X73BZcAsDo0YaTWuDEuEsT5RAR0R0FNrg8ffo0li9fjjfffBMdOnRAiRIlYLFYcrdq1ap5pF5WqxVLlixB48aNUalSJTz++ON49dVXMW7cOLz00kt46KGHUKFCBXTr1g0bN250Wz3mzZsnux8529mzZ91WJhUc+hPoBAL4H4DvACgn+7FCmkk2HMBpw2XZm0DHgWFot32V59+ZAFYA6AqgNoBpsPcQ7JFukaRiLt5SNoNXgrpdzz6PzrPSqhUEgBQEIgEhSEGg3XF8fSCNucxrMmBsNGWrVjZ6Z8YBiATQAdJYViPsdww10xs0WtG1oXTp0mjlzrGKERGmKqisiaHgMjLS/tTdRESkqUAFl5s3b0b37t0REhKCmjVr4sknn8TMmTOxZcsWpKamerp6uHz5Mtq1a4dBgwYhJiZGN112djY2bNiArl27YsiQIUh38VPwP//8g9H8VvWeZn9CmycgjfbSWmzzL0jdZA23ndgsz4FhaJDGg36nc+w0gLGQOrU9CmAdtKZA4fJzBYfxWGKt4nUvSO1L7irPdXKXFfptLEKQiCCkoAwSEIQUhCARXbABYzEFh9BAda4F6tbLMwAWGil4wACN3qAZAD4EUBeA2dmt4mGrddNsb1Bll9iePXvC21s5/tvF5s4FQkMNJVW2XO6Dnb4boaHAnDkOVoyIiApUcHngwAH8+uuvuHr1qqerohIXF4dWrVrhzz//lO0vWbIkunfvjgEDBqB79+6qpSG+/PJLPP300xAuHCA2ZMgQpKSkuCw/KnyMTWhTBcBvAKZAPdlPKoCnAAyEkcl+bJXnwDA0SC2sv96uQxGdNNkAfoQUgNSA9Hh+AQCXnytojDUkpQBQNrc71jqUn/OsREdLAVejRsC0acCmnQFIQilZmiSUwiZ0wTSMRRgOIRxbsBY9ZWl6Qj0a8n1II0915Yn07nyfuAFAIwBvAdDqG94IwOd2rkq/9dLM95Y3btzA77//Ltvn1i6xOUJCgPXrDf3hUQaXtwD8rZUQkPJbv17Kn4iIHFKggks9/v7+qFmzpsfKz8zMRL9+/fDPP//k7gsICMDnn3+Of//9F+vXr8fSpUuxfv16xMfH47PPPkPx4sVz065YsQITJkxwSV2+/fZbrF0rffsfGBjokjypcDE3gY43pFbAPwFo/Q4tBdAYwHabudiaQMeBYWiQ2nLaAlgEqWvfHNjuHnke0iIOVQE8hCpVfkJ2trmxo+Q+xuZZ2QB5m5EfgM6my8qveVYSE6Xekb17m1/DdRvCEYG1GIjFSLwdiFogzemc1wXIO4er5In0GjY8h0qV+gHoBuCYRuKSAD4FsBeAvehbO7g02xt006ZNsp45Pj4+6Natm/EMnBEWBmzZYrcFszSAaop9ml1jQ0Ol/MLMd9MmIqI7Clxw6evriyZNmuCFF17Al19+ib179yI1NRVff/21x+q0YMEC7NixI/e1v78/fv31V7z88svw9fWVpfX19cUrr7yC9evXwy9Pf8EZM2Y4PR7y33//xciRI3NfT5s2zan8qHBybAKdVgD2Q3uyn3MA2gN4F3odxuxNoONcN8VgAMMAHIQ0yc9zAIrppLUCiMLBg31QtWpVjB8/nuOMCwj7LV7KLrHtAZhvfs6PEQExMVJLpbPrqS7FQDRCDGIhRcOdIF11XlOhs+Ln7UgvLS0N77//PurVq4cLF1ZppQTwPIDjAIYC8IEUaFayUTN1cOlIb1Bll9i2bduiZMmS5jJxRliY9GZFRtpMZndSn8hIKR8GlkREzhMFyNWrV8WtW7c0j/3+++8C0jz2AoCoWrVqvtTJarWK6tWry8p+7733DJ07ceJE2XlPPfWUU3V57LHHcvNq3bq1yM7OluUPQJw5c8apMlzl0KFDsnodOnTI01W6a8THCyG1I5rZsgSwSAA1br8nPqrPjrS1FsBJzTzi4/XrFBPjSJ1sbckC+EIAzXTqeWezWCyie/fuYuXKlSIjIyP/3ghSGTBA7/3MFkB5xXv3ienPRWSk+6/h4EEhgoNd+3kORqKIQUMhALFV4zM8U3lCaKgQCQni559/FjVq1FClv7O1EMBOnXK72zjvGXn9gqXfYTOsVqsIDQ2VX8fMme55U4yIihIiPFzzDZihuP6mOcfCw4WIjvZcnYmI3MDTz+AFKri0xVPBZUxMjKzcIkWKiKSkJEPnJiYmiiJFisjOTU1NdageP/zwQ24+vr6+IjY2VgghVA8NDC7vfsnJjjzgfqXxgNlY58EzQADfCMAqyyMlxXa92rVzdYCZs+0RwEsCCLTxsCxtZcuWFaNHjxYnTpzInzeDZBISpLhI/R7u1Xi/jpv6HNyOtzxUf+e3UFwQCSglBCC6Ku5FaUCk5CQMDhYno6NF7969dT/nJUuGiKCgr4QUtOuV+bqN35W2svtqNrAUQoh9+/ap8j169Kjr3xSzYmOFGDtWiC5dcr8l+F1RTx8vL3Frzx5P15SIyC08/Qxe4LrFFjRbt26Vvb7//vsNd/spVaoUWra80yEnLS1NNW27EUlJSRg6dGju69GjR6MhF3e+Zzk2gc5AAMqxScUgTfbjo9h/HVL32UgA1wAYm0DHfd0VmwOYB2ls5n9Rt+79uin//fdfTJ8+HbVr10anTp3w3XffuXy2ZtKnP8+KsktsLUhLzhiTX/OsDBsGxMW5J+84VMRwSP1OlWMvEwDMBXCzfHmMf/xx1O/bV9XlFAC8vLzwyiuv4NSp4zh16gVERtr6L9zW/xFSt1hneoMq61ezZk3UqVPHfEau1rAhMGUKsGGDNHA2JQXNz5yBJc/aNVlWKw5kZnqwkkREdy8Gl3ZcuHBB9tpsUBem+F/bkeDytddew+XLlwEAderUwTvvvGM6D7p7ODaBTlEA4xX7cpYk+RPSw77Sd5Am+9lmaF1Bk8vPOSAAkZHP4ejRHTh48CBeffVVm1/0/P777xgwYAAqVqyIUaNG4ejRo+6sHN2mPc+K8u9ehOH88mueleho58dY2rMUAxGNXrgfgHJO1Wk+Pqjr5YX3589HhsbisW3atMGePXvw2WefoVSpUggJAZYsAaKipEmO1NRLotxxGd9/n4olSxwP2JX/l/Xu3VsWwBUIFgsQGIjAatVQr1492aHduw2teElERCYxuLRDuSyK2ckKlOljY40udC355Zdf8M033wAALBYL5s+fD38u8HfPc2wCnecAVFfsewdSy+D+28eVzgPogFu3xiPTwDf9JpafMy3vhCONGjXC3LlzERcXh0WLFqFt27a65yUmJuKjjz5CvXr10K5dO3z77be4deuWeypJAJTzrCQA2KlIYWxK0vycZ2X6dPeXAQAz8BYA9bqX17OycEGj2bRcuXJYtGgRtm/fjqZNm6qOR0RIwXfuOpxdclqO69usR61apxy8AuDKlSvYpVifKF+WIHFC3l5EAFT1JyIi12BwaYefYoV4s13slOmPHj0Kq9Vq6Nzr16/jP//5T+7rF154Ae3btzdVPt2dHGsh9AMwUbFvP4BVkGbt/C+A7yHN3pqXFX/88T7atWuHU6dsP5CaWH7OFL1ukUWLFsVTTz2Fbdu24fDhw3jttdcQYqMpZvv27Xj66acRGhqK4cOHm/6yh4zLaVkbNWo9pGEfOYpBPWeqXHi41JLoTMuaGbGx5pcbcdRWtMeh1i+g1qhRqF21qm46b29vvP766zh27Bieeuopu62C6t6gAahSpZpu+pMn9de6tGfdunUQ4s57GhAQgHDt5tMCQxlcsuWSiMg9GFzaoXxQvXTpkqnzlenT0tJw/vx5Q+eOHj06N2358uUxY8YMU2XT3cvYuoJaBgKop9g3HkD27X8/BmlJkA6qM3fu3IkmTZrgm2++kT1YatXNwPJzhhntFlmvXj189NFHuHjxIpYtW4aOHTvqpr127Rrmzp2LRo0aoXXr1liwYAFu3LjhmgqTTFycfLxl6dJdEBws730RHCy1uI0dKwV6W7aYW2/RWe7uDisn8E65jqi7dClOnDunmaJjx444ePAgZs2ahaCgINMl3O4NirAw/a6xzgSXyi6x3bp1U30RW9Aog8tjx44hOTnZQ7UhIrp7Mbi0QzlOI+96l0ZopTfyH9q2bdvwxRdf5L6eO3du/q4flse///6Lv//+29TmzIMLGePYBDreUHfIOwpgcZ7XlQFsBPABlJP9XL9+Hc888wwGDBiAJBuLbRpcfs4uR7pF+vv748knn8Rvv/2G48ePY/To0Shbtqxu+p07d+L5559HhQoVMGTIEOzbt8+5SlOu7OxsrF+/Xrbv/fd75cyzgvh46WdiotTiNmWK1AKX3/Kvh2QsgA5Ys2ag7heV/v7+WLZsGRo0sDVm0hhbeTj6NzojIwO//PKLbF9B7xILAI0bN1atS713714P1YaI6C6Wr3PTOsFTS5FcunRJNd36n3/+aejcbdu2aU4Dv23bNpvn3bp1S9SpUyc3/UMPPaSbVpm3O5YimTBhgt0lIOxtXIrEPfTXFbS1ZQugqeI9qiaAdFXa7t13i9q1a2u+p5UrVxZbtmyxW0cby8/pbq5efi49PV2sXLlSdOvWTVgsFruf12bNmol58+aJ5ORk11XiHrR9+3bVvT1//rynqyVjtbp+XUv1liSA4QLwNvT38vXXX3fJtS1atEi3jPbt2zuU58aNG1V5Xbp0ySX1dbfmzZvL6v3BBx94ukpERC7HpUgKuPLly6Nz586yfaNGjUJ2drbOGZKsrCyMGjVK81hqaqrNc999910cP34cABAYGIjPP//cRI3pXuLYBDpeAN5X7DsLaczlHaGhwJIlLbBv3z688MILqlz++ecfdOjQAePGjbM52Y/+hCN3uLtbpJ+fH/r164dffvkFp0+fxjvvvIMKFSropt+3bx+GDBmCChUq4Pnnn8fOnTttdgUmbWvXyrvEhoWFoXLlyh6qjbbUVMBGI7yGowD2AjAydt4KYCGAOgDm4E738zu6d++Ovn37yvZ9/vnniHPBmii2Wi5PnDjhUJ7KLrEtW7ZE+fLlHcorv7VSzITGcZdERG6Qr6GsEzzVcimEEL/99pvqm9oBAwaItLQ0zfS3bt0S/fv31/3GeP369bpl7d69W3h73/l2e+7cuTbrpsybLZf3npgYR1perAJoo3ifQgVwU0Bax121sPrKlStFcHCw5vvbsmVLceLECcN1tlqFSEkRIj5e+mm1uvimGJCZmSnWrFkjIiIihJeXl93PcFhYmJgzZ464evVq/le2kGrcuLHsHr799tuerpJKfLzZ353/3L6ecgIYLIDfddLtEUBr3c9TlSrVxOrVq4XVahWnTp0SPj4+suOvvvqq09d248YNmy31169fN52nsifDpEmTnK5nflmwYIHiPaji6SoREbmcp1suGVwaNHz4cNV/zNWrVxczZ84UO3bsEMePHxc7duwQH374oahWrVpumiJFioiQkBDZeTt27NAsIyMjQ4SFheWma926tcjOzrZZr/wILq9cuSIOHTpkalu9ejWDy3wUEyNEaKjZh+TfVZ8fYKYIDVUHljn++ecf0alTJ80H1eLFi4sFCxYIqyciRSedP39eTJo0SVSuXNlukFmkSBHx1FNPiW3bthXKa80vFy5cUN27rVu3erpaKsnJZr+UUX5GPlSkSRDASwLQC+r8BTBBXLlyU1aPF198UZbOz89PnDt3zunrq1Wrlu5nOUbvF13HsWPHVHns2bPH6Trml9jYWFX9L1++7OlqERG5FINLgzwdXGZkZIgBAwbYffDMu3l5eYnvv/9eVK1aVbb/6NGjmmVMnDgxN42vr6+IjY21W6/8CC4d4ekP9r0oIUGIyEizAWYX2fvk719anDmTYrOc7OxsMX36dFVLS872+OOPF9rWvaysLLF27VrxyCOPyHoQ6G316tUTs2bNEvHx8Z6ueoEzf/582b0qWbKkyMzM9HS1VMyNuVQHJ8Dft49lCeALAZSy8ZnpI4BTIjhY3Vp/7tw54efnJ0v/4osvOn19ffr00a3PqlWrTOU1a9Ys2fkVKlSw+wVoQZKVlSWKFy8uu4aff/7Z09UiInIpTz+Dc8ylQb6+vli6dCk+/vhjlCpVym766tWrY9OmTejXr59qVsBy5cqp0h86dAhTp07NfT169Gg09MS0iVRo5awrGBUlrRNozBTZq/T0BHz77Sc2z/Dy8sJbb72FHTt2oE6dOqrj33//PRo1aoQtW7YYrUSB4e3tjZ49e+LHH3/EP//8g6lTp6J69eq66Y8cOYJRo0ahYsWKGDBgAH7//XeOzbxNOd6ye/fu8PHx0UntORYL0KyZ0dRrFa+rQlra5y8ArQC8DOCqxnm1bp+7GkANNG8ulZtXlSpVZOsaA8DChQtx+vRpo5XT5MoZY5XjLSMiIuDlVXgeI7y9vdFM8WZz3CURkWsVnv8VCoiRI0fizJkzmDdvHh599FHUqFEDgYGBKFKkCGrUqIHevXvj22+/xd9//40OHTrg1KlTyMjIyD2/Vq1amkuKTJkyJTddhQoVMGjQIJw9e9bupnThwgXZ8ZSUFHfdCiqgzE2g0wodOjwsOzZz5kxcvar1gCzXvHlz7Nu3Dy+++KLq2IULF9CxY0eMHTvW5mQ/BVmFChUwZswYnDx5Ehs2bED//v1VSxnkyMjIwHfffYdOnTqhTp06mD59Oq5cuZLPNS440tPTsXHjRtm+Xvm5cKVJinlebFAGlx0APAegDQCtJWyKAZgK4BCAnnbLGzt2LIoUKZL7OisrC5MnK5cOMsfWl5Rmgsvk5GRs3bpVti8iIsLhenmKcr1LBpdERC6Wr+2kTvB0t1hHKaeCHzhwoGY6W12XnNk+/vjj/L3g2zzdJE9ytibQOXjwoGrSD7MTr6xatUqUKqXdHbBFixbi+PHjLr4iz7hy5Yr48MMPZUsF6W0+Pj6iX79+Yv369YWq66ArbNiwQXYvLBaLuHLliqerpSsmxkiX2GtCvZRIcRufgf4COK+Zl60RD6+//rosHy8vL92hFEYcPHhQt44dO3Y0nM+KFStk5/r5+YnU1FSH6+Upy5Ytk11H6dKlOXaaiO4qnn4GZ8ulm23atEn2ukOHDp6pCN3TLBYgMBAoXVr6mbdLXqNGjfDkk0/K0s+ZMweXL182nH/fvn0RExODTp06qY7t2bMHTZs2xYIFCwp9l9GyZcvijTfewNGjR7F582YMHDgQ/v7+mmmzsrLwww8/oEePHqhZsybef/99XLx4MZ9r7BnKLrEtW7ZE2bJlPVQb+8LCgHbt7KXaAPVSIjc00tUHsAnAcgDqZVfCwwFbIx5Gjx6N4sWL5762Wq2YOHGivcrpqlu3Lry9vTWPmWm5VHaJ7dixIwICAhyul6colyNJSEjAuXPnPFQbIqK7D4NLN0pNTcXKlStzXwcEBOCJJ57wYI2ItE2aNEn2AHrz5k1MmzbNVB4VK1bEhg0bMGPGDFX30Rs3buD5559H//79DXW5LegsFgvat2+PxYsXIy4uDp988gnq16+vm/7s2bMYP348qlSpgj59+iAqKsruWrmFmdbYvIJu9Gh7Kb63czwQwCwABwCov2QxWk7ZsmUxfPhw2b7ly5fj0KFD9iqoyd/fH7Vq1dI89s8//+DWrVt288jOzlZ9YVAY3lMt1apVR6lSIbJ97BpLROQ6DC7daM6cObhx484325GRkQgMDNRMu3r1aghp9l5Tm9KZM2dkx0eOHOmuy6O7SO3atfHMM8/I9s2bNw/nz583lY+XlxfefPNN7NixA3Xr1lUdX7lyJRo3bozNmzc7UduCpVSpUhgxYgQOHTqEP/74A8888wyKFi2qmdZqteKnn37CQw89hGrVqmHChAmm73FBd/LkSRw/fly2ryCPt8wREQEMGKB1JAPAdNgOLp8CcAzA6wC0x+UCQGQkYORWvPHGGyhRokTuayEEJkyYYP9EHbbGXZ45c8bu+bt370Z8fLxsX2EKLvOOPy9d2oKrV1vIjr/xxm6MHQs4GL8TEVEeDC7d5OjRo5gy5c5MnAEBARg7dqwHa0Rk27vvvgs/P7/c1xkZGXjvvfccyqtZs2bYu3cvXnrpJdWxCxcuoFOnTnj77bdlk10VdhaLBW3atMHChQsRFxeHzz77DI0bN9ZNf+HCBUyePBnVqlVDr1698OOPPxbayY/yWrdunex1uXLlVDN0FlRz5wKhoXn3bADQCMDbkIauKDUGsA3AIgAVbOYdGgrMmWOsHqVKlcJrr70m27dq1Srs37/fWAYKzs4Yq2yJrl+/PmrUqOFQXfJTdLTUDblRI2DaNGDTJiApCQDkk/qcP78L06ZJ3aPDw4G1ynmbiIjIsHsyuLRYLLLNSCtKVlaW4fyPHTuGLl26yLobTZs2DVWrVnWkukT5okqVKqpgcOHChThx4oRD+RUvXhzz5s3Djz/+iJAQeTc0IQSmT5+ONm3aqFq57gYlS5bEK6+8gv3792PXrl148cUXdcenCSGwbt06PProo6hSpQrGjh3r9PITnqQMRHr27FlolqsICQHWrwdKlDgHoB+AbpBaJJW8AHwKYA+AtnbzDQ6W8lX8Gtj02muvIVgxzfO7775rPIM8nA0uo6KiZK8LeqtlYqLUSty7N7Btm1aKlorXe5EznnbbNqkVe+BAKR8iIjKnwP2Pr1xKI2dTTi6SlZWluzxHQkKCy+v1zDPPYODAgYiKitIdoxIXF4eJEyeiadOmsok7IiIiMHToUJfXicjVxo4dK+vSmZ2d7VR3PAB45JFHEBMTg86dO6uO7d27F02bNsXXX39d6Cf70WKxWNCyZUvMnz8fcXFxmD9/vmophLwuX76MadOmoWbNmujatStWrFhRqFp3b9y4ofqyrjB0ic2RlpaG1avfQ0ZGPQCrbKR8DsBQAPbX7QwNlZYGCgszV5egoCC8+eabsn1RUVHYsWOHuYzg3HIkFy5cwIEDB2T7evfubboO+SUmRmqpXLbMVirl7+B1KL9EWLpUyic21sUVJCK62+Xr3LQGVK1a1enlNwYPHmyzDGX633//3W69+vXrl5ve19dXNGrUSDz88MNi4MCBIiIiQoSFhamWcwAgunXrJm7cuOGam2PgWs6cOeO2sszw9DTI5LjRo0erlpGIiYlxOt/s7Gwxc+ZM4evrq/l7269fP5GYmOiCKyj49u/fL1555RVRokQJu3/PSpcuLd544w1x7NgxT1fbrp9++klWd29vb3Ht2jVPV8uQn3/+WdSoUcPg/zPrDSxdIkRkpBAJCY7XKTU1VZQpU0ZWdteuXU3nk5GRoft716VLF5vnfvnll7L0JUuWFJmZmY5eklsdPChEcLCRZWWEACoq7sX/NNMFB0tL1RARFRaefgYvcC2XhUFmZiZiYmLw008/YcmSJYiOjkZsbKys5cXHxwdjxoxBdHQ0ihUr5sHaEpnz1ltvqSYTGT9+vNP5enl5YdSoUdi5cyfuu+8+1fEffvgBjRo1wm+//eZ0WQVdkyZN8NlnnyEuLg4LFy5EmzZtdNMmJCRg5syZqFu3Ljp06IAlS5YgLS0tH2trnHJG0bZt2yIoKMhDtTHm1KlT6N27Nx566CHN7shFiyq7MxcF0N5mnuHh0ni/JUvMdYVVCggIwGjF9LIbNmzANu2+nrp8fX1Rp04dzWP2Wi6VXWJ79OgBHx/7Lbb5LTER6NkzZ0ylEa0Ur7VnjE1KAnr0YBdZIiKjGFwa9OSTT6JTp04oUqSIzXSBgYF4/vnncejQIUydOrVA/idMZEupUqUwatQo2b41a9Zg165dLsm/adOm2Lt3L4YMGaI6dvHiRXTp0gWjR48uVN1BHVW8eHE888wz+OOPP3Do0CGMGDFCNc4ury1btmDQoEEIDQ3FyJEj8ffff+djbW0TQqiCy4LcJfbmzZsYP3486tevrxonCkhfhgwdOhS9e/eU7S9dujOCg+X/DwQHSzORjh0rdaPcssXYrLBGvPzyyyhfvrxs3/jx4013I9frGnv+/Hmkp6drHrt16xY2btwo21dQu8QOGwbExZk5Q9k1Vn85krg4QLE6DBER6cnXdtK7QHp6uti9e7dYunSpmDVrlnjvvffEtGnTxH//+1/x119/iYyMDE9XsUDwdJM8OSc5OVmEhIQ43R3PntWrV6vKydmaNWsmjh496vIyC7pbt26JxYsXi/bt2xvqotmmTRuxcOFCt3a/NyI2NlZVt4L4e2+1WsXKlStFlSpVdO/pgw8+KPbv3y+ysrJE6dKlZcc+++wzYbUKkZIiRHy89NNqdW+d58yZo6rjxo0bTeUxefJk3evV+z1bu3atLJ2Xl5dIcKafr5tERRntCpt326C4D34CSLd5TlSUp6+UiMg+Tz+DM7gkt/D0B/teYrUKkZwsPegmJ7vuQffDDz9UPYRu3rzZNZnnERcXJ7p27ar50FusWDExf/58YXX303sBdfToUfHGG2+oAhytrUSJEuKVV14R+/fv90hdp0+fLqtPlSpVCtz7dvjwYdGlSxfde1iuXDmxaNGi3Hrv2LFDleb06dP5Xu+0tDRRuXJlWT1at25t6v7+8MMPutcdpRM1vfLKK6qguyBq186R4DJJ417stnlOeLinr5SIyD5PP4OzWyxRIZR3UfCQECAoCChTRvoZEnKni54zi4IPHToUFSrI1+4bN26cy2d1rVChAtavX49Zs2bJ1tkEpK6L//nPf9CvXz8k3oODnurWrYsPP/wQFy5cwPLly9GlSxfdtCkpKfj888/RtGlTtGrVCl999RVSU1Pzra7KrqURERGwWCz5Vr4tqampeOutt9CoUSNVN08A8Pb2xuuvv47jx4/jqaeeyq23cs3OevXqoXr16vlS57z8/f3xzjvvyPbt2LFDVT9bzM4YK4RQjbcsiF1iY2P1lhuxpySA2op9+l1jAWDrVuf+phIR3RPyNZSle4anvzW5W0VFmf+Wvl07IaKjHSvvs88+U327v3btWtdeVB779+8X9erV02xdCQ0NNd0V8G508uRJMWbMGFG+fHm7rZkBAQHixRdfFLt27XJrK2JSUpLw9vaWlf3zzz+7rTyjrFarWLJkiahQoYLuPerYsaPu36eWLVvK0o4aNSqfr+COjIwMUb16dVl9mjdvbvh9zcrKEv7+/pr3YOjQoar0Wt2cY2NjXX1ZThszxpFWy5wtUnGNz9o9Z+xYT18xEZFtnn4GZ8slUSFgf1Fwfc4sCv7CCy+gWrVqsn3vvPOO29akbNKkCfbs2YOXX35ZdSwuLg5du3bFW2+9dU9M9qOnZs2amDp1Ks6fP49Vq1ahZ8+eui2E169fx1dffYVWrVqhadOm+Pzzz5GcnOzyOm3YsAHZ2dm5r/39/dGxY0eXl2NGTEwMOnTogIEDB+LSpUuq45UqVcLy5cuxadMmNGjQQHX8ypUr2L1b3pLlyQmKfH198e6778r27d27F2vWrDF0vre3t+YszYB2y6Wy1bJKlSqa98nTnJtnzPikPq4pj4jo7sfgkqiAM7YouH2OLAru5+eHiRMnyvbt27cPq1bZWmDeOcWKFcPnn3+On376CaVLl5YdE0Lgww8/ROvWrXH06FG31aEw8PX1Rd++fbF27VqcOXMG7777LipVqqSb/uDBg7ldnZ999ln8+eefLvuSQNkltmPHjihevLhL8jbr2rVrGDFiBJo1a4atW7eqjvv6+mLMmDE4cuQI+vfvrxuY//LLL7LXAQEBaNu2rVvqbNSgQYNUS4q8++67sFqths7X6xprJLjs3bt3genmnEMIYN8+Z3JQLkdyGMANm2fs3SuVS0RE2hhcEhVgMTFAhw5mp9jXFxcHtG9vLsAcNGiQqsVj/PjxspYqd3jooYcQExODbt26qY7t378fzZo1w5dffum2VtTCpGrVqpg0aRLOnj2LqKgoPPzww/D29tZMe+vWLfzvf//Dgw8+iLCwMMyePRtXr151uGyr1aoa++eJFj6r1YqFCxeiTp06mDNnjubns0ePHrnLRAUEKNevlFMuq9K1a1fVmOD85uPjo/qyJzY2Ft9//72h8/VaHs+ePYvMzMzc14mJifjrr79kaQrieMvUVDPrWgLASQBzAcwAMAnAnwDy/p5YAdiOVpOSgOvXzdWTiOiekq+dcOme4en+3neDhAQhQkOdGU+kv4WGSvkbtWLFCtX4q0WLFrnv4vPIzs4WH3/8sfDz89McL/bII4+I+Pj4fKlLYXLhwgXx3nvviapVq9odm+nv7y8GDhwoNm/ebHps5q5du1T5nTp1yk1XpW337t3i/vvv172+atWqidWrVxu+tszMTFGyZElZHl999ZWbr8KY7Oxs0aBBA1nd7rvvPpGVlWX33DVr1ujeoxMnTuSmW7x4sexY0aJFxc2bN915WQ6Jjzf7t+97xXXXFUBjxb5ZdvPhnxsiKsg8/QzOlkuiAsr8ouDGmV0UvF+/fmjSpIls38SJE2WtHe7i5eWFkSNHYteuXahfv77q+OrVq3VnAb2XVaxYEe+88w5Onz6N9evXo1+/fvDx8dFMm56ejiVLlqBDhw647777MHPmTMTHxxsqR9nCV7duXdSoUcPp+huRmJiIl156Ca1atcLOnTtVx4sUKYKJEyfi8OHD6NOnj+FunTt27MC1a9dk+3r06OGKKjvNy8sLkyZNku07evQoli5davdcozPGKrvEdunSBUWLFjVZU/cz35CsvIZbcGTcpb+/2XKJiO4dDC6JCqDoaOfHWNqzdKlUjhFeXl54//33ZftOnz6NBQsWuKFm2ho3bow9e/Zg6NChqmOXLl1C165d8cYbbyA9PT3f6lQYeHl5oXv37li5ciUuXLiADz74ALVq1dJNf/z4cbz55puoWLEi+vfvj40bN9oc06e1BIm7ZWdnY968eahTpw7mz5+v2TW6T58+OHz4MCZMmGA6MFIGzI0aNbI5njW/9e3bV/Vlz6RJk+x+2VOtWjUUK1ZM81hOcJmVlYX169fLjhXELrEAEBgIBAebOUP5ObgJdXBpe8ae4GDATo9qIqJ7GoNLogJo+vT8KWfGDONpe/XqhQceeEC2b/Lkybh165aLa6WvaNGi+PTTT/Hzzz+jTJkyquOzZs1C69atceTIkXyrU2FSrlw5jB49GseOHcOmTZvw5JNP6o4jzMzMxPfff4+uXbuidu3amDp1qmrWVU/MqPrXX3+hVatWePnllzXHitauXRtr167F6tWrHV6TsiCMIbXFy8sLkydPlu07deoUFi1aZPe8evXqaR47ceIEAODPP/9UtdoWtOvPYbEAzZqZOcNIy+VpAPrTajdvLpVLRETaGFwSFTCOLwpunplFwS0WC6ZMmSLbFxcXhy+++MINNbOtd+/eiImJ0eyqeODAATRv3hzz5s3jZD86vLy80KlTJyxbtgwXL17ErFmzdJepAKRW6nHjxqFy5cro27cv1q1bh+zsbM0ZVdu1a+eWOl+5cgXPPvss2rRpg30aU4QWK1YM06ZNQ2xsLHr27OlwORcvXsSBAwdk+wpicNW7d2+0aiWf7XTy5Ml2W+7tzRir7BLbpEmTAtVqq9RKOeGrTcpW21sAGgAooti/x0XlERHdexhcEhUw7u4O60x5HTt2ROfOnWX7pk2bhtTUVBfXyr7y5csjOjoan3zyiar17datW3j55ZfRt29fJCQk5HvdCpPSpUvj9ddfx+HDh7Ft2zY89dRTKFJE+bAtyc7OxurVq9GrVy/UqFEDMxRN3+6YUTUrKwuzZ89GnTp18L///U8zTf/+/XH06FG8/fbb8HdyQJyyS2hQUJCqxb4gsFgseO+992T7zp8/j//+9782z9ObMTYnuFR2c3aqS6wQQEoKkJAg/XTDlz0DBphJrWy5zOnu3VSxX3/cpbnyiIjuPQwuiQoY5xbpNr88iNnylK2XCQkJmD17tulyXcHLywsjRozA7t27NR+a16xZg7CwMGzYsMEDtStcLBYL2rZti0WLFiEuLg5z585FWFiYbvrz58/j77//lu3r3r27S+u0ZcsWNG3aFCNHjkRKSorqeP369bFp0yYsX74clStXdkmZyvGW3bt3150IyZOEAO6/vyvuv1++9uaUKVNsdlXXCy7PnDmD48eP4/Dhw7L9psfQxsYCY8cCXboAISFAUBBQpoz0MyRE2j92rPEuE3aEhQHGG8u1xt4an9QnPBywMScSEREBXIqE3MPT0yAXVlarEMHBji4xkiKAZgL4ytR5wcFSuWY89NBDsvc3KChIXL161T03xaCbN2+KV199VXephddff12kpaV5tI6FjdVqFTt27BDPPfecKFasmN0lTcqXLy/GjRsnzpw541S5Fy5cEAMGDNAtJzAwUHz00UciIyPDNRd6W3p6uggMDJSV9b///c+lZTgjJkaIMWOE6Nw579+J31X35623PtbN49y5c7r3dcKECbLXZcqUMbTEiRBCiKgoIdq1M/dHq107IaKjnb4vUVFGi4zXuO5LAvhWsa+C5vkuqCoRkdt5+hmcwSW5hac/2IVVcrKjgaVVAI/mueevCCDd8PkpKebqeeDAAdVD2pgxY9xzU0yKjo4WZcuW1Xx4bty4sfj77789XcVCKTk5WXzxxReiWbNmdoNMi8UiunXrJr7//nuRnp5uuIz09HQxffp0Ubx4cd28n3rqKREXF+eWa/ztt99U5V2+fNktZZlhP27rpKh3WdGmzXXNYMhqtaoC6JytRYsWsteDBw+2X7mEBCEGDHBu4d3ISHML72owVoXrGtd9WgBHNfZfUFWRiKgw8PQzOINLcgtPf7ALK/OLguds72s8HLUVwGVD5zuyKPgTTzwhK69YsWIF4kFcCCEuX74sevbsqfkAXaRIEfH5558Lq9nmWsq1Z88eUapUKbtBJgBRtmxZ8dZbb4njx4/bzPOXX34RdevW1c2ncePGYtu2bW69rjfeeENWZvPmzd1anj3G47Y/NO7ZB7px2/333695j318fGSvV6xYYbuCBw8KERrqXGCZs4WGSk2zTtwr+1XJ1rjuv2/vL6HYv0pWNSdjXyKifOPpZ3COuSQqQByfC+Waxr7tAJrDXYuCT5o0CV5ed/6E3Lx5E9OmTTOfkRuUK1cO0dHRmD17tmqCl7S0NLzyyivo06cP4uPjPVTDwq1MmTKay4Bo+ffffzFjxgzUqVMnd4bavDOanjt3Dv369UP37t1x7Ngx1fklS5bEZ599hj179qBt27aq466kHG/pyVliY2KARo2MTrjVBoByhtwZAFKwdKmUT2zsnSN6M8ZmZWXl/tvHxwfdunWzXcEOHYC4OCMVtC8uDmjfXl5RE0JCgPXr7a176QVA+Uf21u39LRT7pb+bwcFSviEhDlWLiOiew+CSqAAxvyh4jg8BLIZ6Sv2LANoB+Eb3TEcXBa9bty4GDx4s2/fFF1/gn3/+MZ+ZG1gsFgwfPhy7d+/WfJj++eef0ahRI/z6668eqF3hplwHslSpUti/fz+GDRuGkiVL6p73+++/IzIyEhUrVsTw4cMxfPhw1KtXD6tWrVKltVgseOGFF3D8+HG88sorbp9U59y5c6rJbDwVXDoWt01WvL4KQJpoSxm36U3qk1d4eDiCgoK0DyYmAj17AklJZipoX1IS0KOHlL8DwsKALVuA0FBbqbTWugS0JvUJDZXyszGvFRERKTC4JCpAzC8KntdAAH8AUM6amQ7gGQAjAGSqznJmUfAJEybA19c393VGRoZqeQRPCwsLw65duzB8+HDVscuXL6N79+547bXXkJaW5oHaFU7K5Sp69OiBJk2aYM6cOYiLi8OiRYtstjImJiZi7ty5mDt3rubMpi1btsSOHTvw1VdfoUyZMi6vvxZlwBwSEoKWLZUBh/s5Hre1ANBHsW8WACmjvHGbkeDS5iyxw4a5rsVSKS4O0PhdNSosTArOIyP1UiiDy5u3f8oXsPT13YODBwUDSyIikxhcEhUwzi3S3QzSAuDtNY7NAdANgLwrqDPlVa1aFS+99JJs34IFC3LXzCsoihYtitmzZ2Pt2rUoW7as6vgnn3yC+++/X7W0BqmlpaVh06ZNsn15W/iKFi2Kp556Ctu2bcPhw4fx2muvIcRgn8IiRYpgwoQJ2LFjB1rl82r1yi6xPXr0gLe3d77WAXA2blO2XiYD+Cj3VU7cptctNi/d9S2jo92/GO/SpVI5DgoJAZYsAaKipOVD5Iy1XGZmXkNSUsH6O0ZEVBgwuCQqYJxfpLssgA0AtL793wyphWOfy8obN24ciha988CWnZ2NiRMnOpepm/Ts2RMxMTGa3R1jYmLQokULfPbZZxBCeKB2hcPWrVtx8+bN3NcWi0V3fct69erho48+wvHjx/HII4/AYqeJPC0tDZMmTUKbNm3w3//+F9evX3dp3W2Vaytgzi/Ox22NAPRX7PsEQELuq6VLgX37Ktjsvly7dm3UqVNH++D06c5U0LgZM5zOIiJC6taad+lNLy95cFm8+C106QKMGVMJISHlZMd277Y/Xp2IiOQYXBIVMOYWBdfjC2m81UIAytl6zgN4EMASlywKXr58eQwbNky2b+nSpTjkokXSXa1cuXKIiorC3LlzNSf7efXVV/Hwww/j33//9VANCzZll9jWrVujdOnSmmmFEPjhhx/QpEkTrF692nDQvnPnTrzwwgsIDQ3FkCFDsHfvXqfrbYuZgNmdXBO3TYT8v/brkCb3uePDDy02u8bqdomNjQW2bXO2gsZs3Qq46G9Iw4bAlCnAhg1As2bFZMfmzLmFDRuAqVMteOABeeslg0siIvMYXBIVQKNHuyqnZwBsA1BRsT8NwCCEhIySzRDpqLfeegslSpTIfS2EwLvvvut0vnYJAaSkAAkJ0k+DwYvFYsGrr76KPXv2IExjUFVUVBQaNWqE9evXu7rGhZ7RGVWPHDmCbt264bHHHtOc5Kls2bIYNmwYunfvrtuimZqaii+//BItWrRA8+bNMW/ePKSkpDh/EQrKa2rdurXhrryu4rq4rR4A5YDDTwFczn21dSsQGqr/rZJul1h3d4fNh/Ly9rIAgLS0O2N+lWNsGVwSEZnH4JKoAIqIcEX32BwtAewFoJ5g5ccfP0KPHj2Q6ODsjDlCQkLw+uuvK/L+0T0PZ3n7uIWEAEFBQJky0s+QEGn/2LGGWj0aNmyIXbt2YcSIEapjV65cQc+ePTFy5EhO9nPb8ePHVeNpla1cqampePPNN9GoUSNs3LhRlYe3tzdef/11nDhxAnPmzMH69etx+vRpvPPOO6hQoYJu2fv27cPLL7+MChUq4Pnnn8eOHTtc1n1ZGVz27Klc1sP9XBtHTQCQd7zoLQAfyFJcu6bdcunn54d2el0ndu1ySe0Mc0N5yuAy74RSyuBy3759LvnyjYjonpKvq2rSPcPTC7jeDYwtCm5mSxfAy5qLp1erVk0cOHDAqfomJyeLkJAQWb7dunVz0d0QQkRFCdGunbmLbtdOiOhoQ9mvW7dOlCtXTvP+hIWFidjYWNddSyH18ccfy+5LhQoVhNVqFUIIYbVaxZIlS0SFChU07yEA0alTJ5t/CzIzM8WaNWtE7969hZeXl24+ed+XOXPmiKtXrzp8TcePH1flu2fPHofzc1Tnzq78XRcCeF5xXf4C+Cf3eLNmmzTvaWhoqHYFrVYhgoOFAIQVEMkIFPEIEckIFFbXVvzOFhwsletCffr0kV3ve++9l3ssPj5edT+c/btIRJTfPP0MzpZLogLK2KLgZvgB+BzFin0FX1/5QuJnz57FAw88gOXLlzuce4kSJTBa0Z/3119/xdatWx3OE4C0dkJkJNC7t/l+g9u2Sc3AAwfaXTuvR48eiImJ0RxvFhsbixYtWuDTTz+9pyf7UY637NWrFywWC2JiYtC+fXsMHDgQly5dUp1XqVIlrFixAhs3bpSN9VP2avb29sHDDz+Mn3/+GefOncOkSZNQubJyaZ07YmNjMXz4cISGhuLpp5/Gtm3bTL8/yiVIypUrh6ZNm5rKw1lCAPv22U9nznhIY69zpAOYkvvq1CntbrF6XZRjd9zA2KQ30AUbEIJEBCEFZZCAIKQgBInogg0Yiyk4BHmLaDaAjQAGI2fVTROSkgAXT+pkq+WydOnSqF69uuw4u8YSEZmUr6Es3TM8/a3J3SQmxnUtmKGhUn5//vmnbgvT6NGjRVZWlkN1vXHjhihfvrwsv7Zt2+a2bpl28KDrL94Oq9UqPv30U1GkSBHN+9OrVy9x5coVx66nEEtNTRW+vr6ye7Fo0SIxfPhw4e3trXmvfH19xZgxY0RqampuPjExQowZI7XU3W4IkzVUde4sHc9pKM7KyhJr164Vffv21S0n73bfffeJWbNmifj4eEPX1aNHD9n5zzzzjDtun03Jyc58tC8J4E0BJGocU/ZU8BXAmdzjgYElVPevSJEiIjs7O7duDnUYwBYRjZ7iB0BUzJN3PcB8K6fB99GoZ599Vna9r732mux4//79Zcf/85//uLR8IiJ38/QzOINLcgtPf7DvNgkJQkRGOhdbRUZK+eS4ePGieOCBBzQf0Lt37y4SExMdquunn36qym/dunXmMzp4UB19OLsFBxsKMIWQPsONGjXSvD9ly5YVa9euNX9Nhdjq1atl98Db21uULl1a8/4AED169BDHjh3LPd8VvZrj4uLE1KlTRY0aNXTLzdn8/PzEk08+KTZt2iQLlvK6ceOG8Pf3l523YsUKd99Klfh4Rz7OcQIYIYCcL0HGa6S5IKTusHnvzXO5xytUqKh57y5cuCASEoQYMMC5X7cueEeV9x6zmaSkuPRev/LKK7L6DBkyRHb8ww8/lB1v2rSpS8snInI3Tz+DM7gkt/D0B/tuFRUlRHi4uWez8HD9YYdpaWnixRdf1HzArFmzpkPjDNPT00XVqlVleTVv3txc66XrB5ze2UJD5VG2Dbdu3RKvvfaabgAzbNgwcevWLdP3qDDS+5wot2rVqonVq1fnvt+uCFKUX4xkZ2eLDRs2iP79+6taU7W2WrVqiQ8++EBcvnxZdk1RUVGydN7e3iIpKSkf76rEsZbLpxTXWUIAVzXSjVCk8xbACQFYRYkS6pZLAOLrr3930a9ftvBGFVnew81k4IYxl6NGjZLV5+mnn5Yd37x5s+y4j4/PPfM7TkR3B08/gzO4JLfw9Af7bhcbK8TYsUJ06aLdtbBLF+m40dhw3rx5wsfHR/WQWbx4cbFy5UrT9VuwYIEqrx9++MF4Bs5GI0aiFRPWr1+vO9lPw4YNRYzB1tDCymq1qro7K7ciRYqIiRMnips3b+aelx+9mv/991/x4Ycfijp16tisX06g0K9fP7F+/XqRnZ2tasUKDw/Px7t6R565ckxshwVgUVzjuxrpLgmgmCLdIFGixFHd+1Ss2Fcu/HUbL8u7DCAyjJ7cpYvL7/U778hbUx9//HHZ8ZSUFGGxyO/rX3/95fJ6EBG5i6efwRlcklt4+oN9L7FapZ5j8fHST0e/6N+2bZtuADVu3DhT4zAzMzNF3bp1ZXnUr1/fWB5RUe4NLHO2qChT9+fff/8VvXv31rw//v7+Yvbs2Y6PLS3AsrKyxLhx42wGbY888og4ffq07Lz87tVstVrF5s2bxcCBA1VdXfVaWEuWLCnb98EHH+TDHdXm2GyxTyquK0gASRrp3lKks4gqZYbauD+jXfi+qWfj/dnoyWPHuvw+T5kyRVaX3r17q9LUr19flmb27NkurwcRkbt4+hmcs8USFXIWCxAYCJQuLf3UmezRrrZt22LPnj1o1aqV6tiUKVPQp08fXLt2zVBePj4+mDRpkmzf4cOHsczIYn7Tpxsqw2kzZphKXqZMGfz000/4/PPPUaRIEdmx9PR0jBgxAhEREbhy5Yora+lRf/31F1q2bIkpU6ZoHq9duzbWrVuHH3/8UTbLZmIi0LOnNNmnKyUlAT16aE/8a7FY0L59eyxevBhxcXH45JNPZDPTKp09e1b1ee7atatrK2yCxq+dAeMB5P2FTwbwiUa6NwEE5HktkBD/Pxv5nrRxzKzaAFrL9nxr9FTXLfabq1ixYrLXeWeLzaFc75IzxhIRGcfgkohyVapUCVu2bMEzzzyjOhYdHY1WrVrhyJEjhvJ6/PHH0bhxY9m+CRMmIDMzU/+k2Fjzy404autW4NAhU6dYLBa8/PLL2Lt3r+raAGlZi7CwMNWSHYXNlStX8Mwzz6BNmzbYv3+/6riPjw+mTZuG2NhY9OjRQ3V82DAgLs49dYuLA4YPt52mVKlSGDFiBGJjY/HHH3/gmWeeUS1BoeXhhx/GhAkTcP78eRfV1jjH4qj6AB5X7PsEwDXFvtIARsr23MQNG/m6MrgEgKdkr9ZAXUOV8HCgofZyKc6wtRRJDuUXbAwuiYiMY3BJRDJFihTBggULMHfuXHh7e8uOnThxAvfffz/WrFljNx8vLy+8//77sn2nT5/GwoUL9U8y0rLpSg6WV79+fezcuROvv/666lh8fDx69+6NYcOGaT64FmRZWVmYPXs26tSpg2+++UY33X//+1+8/fbb8Pf3Vx2Ljnb/27h0qVSOPRaLBW3atMHChQsRFxeHzz77DE2aNNFNf/HiRUyePBnVqlVDr1698OOPP9r+MsSFwsKAdu0cOXO84nUygDka6V4HEGQwz5OQelO5yhPIu+ZmOoCV9k5RrJnrKkaCS2XL5bFjx5CcnOyW+hAR3XXytRMu3TM83d+bXGPz5s2iTJkymuOyJkyYoLvEQw6r1Spat24tO69SpUr6sy86NvBMWAFx2ZEBYS6YMOSXX37RneymQYMG4uDBg06XkR9+//130bBhQxvj8KStaNGiskl7lMwuN+Lo5ujcOzljM5WTtuht5cuXF2PGjBGnTp1y8M4a5/hw48cV9S4pgGsa6d4zdM3SFufi9+wRWf7hthKbnHDLjBUrVsjqcd9996nSpKWlqWYh3rRpk9vqRETkSp5+BmfLJRHpat++Pfbs2YNmzZqpjk2aNAl9+/ZFSkqK7vkWi0U1Xu/ChQuYN2+eOrEQwL59DtUzCkA1SG04qWZO3LtXKtcJ3bp1Q0xMDB5++GHVsb///hutWrXC7NmzIZwsx10uXryIAQMGoGPHjjik0U3Y19dX9rpTp066XUwLeK9mANJnMikpSfZ+WGwMVL58+TKmTZuGmjVrokuXLlixYgXS09MdqbJdERGOdo9Vtl5eg3br5Qj4objBPF3dNfZp2autAM5qJfP3B8qUcezNNcBIy6W/v7+q2zu7xhIRGcPgkohsqlKlCrZv345Bgwapjv3000+4//77cezYMd3zO3XqhE6dOsn2TZ06FdevX5cnTE11aAaYTABvAEgD8D6k6UO+BpBt5OSkJEBZDweUKVMGq1evxhdffKF6eE1PT8fIkSPRq1cvXL582emyXCUjIwMzZsxA3bp18d1332mmGTRoEAIDA2X7evXqpZtnIenVjLVr18ped+rUCfv378crr7yCEiVK6J63adMmPPHEE6hUqRLefPNNm597R82dC4SGmj0rDEA/xb6PIXWRvSMUKRgIo918XR1c9gIQLNuzWCtZejowe7bUTzg8HFC8V84yElwCnNSHiMhRDC6JyK6iRYti0aJF+Pjjj1XjMI8ePYpWrVrZnMRG2XoZHx+P2bNnyxNlZDhUt3kAjud5fQXAiwCaAthgJAMXtUJZLBYMGTIEe/fu1RzXt379ejRq1AhRUVEuKc8Zv/76K8LCwjB69GjcuKGe2KVx48bYtm0bXn31VVy9elV2zFZwuWuXy6tqk6HyhABSUoCEBCAlBcJqVQWXvXr1QpMmTfDZZ58hLi4OCxcuRJs2bXSzTEhIwMyZM3Hfffehffv2WLJkCdLS0py8GklICLB+PRAcbD+t3LuK10kA5ua+CsZVrEcPhMDo75mrg0t/SGMv71gE2B7ZuW2b1Jw7cKD2FMEOYHBJRORm+doJl+4Znu7vTe6zceNGERISohqjZbFYxHvvvac7DlO5RmRQUJC4evXqnQTJyQ4N5hoHCG8bY8d6AeJvW3mkpLj8HqWlpYlRo0bp1mno0KE2xy3msGZbRfI/ySL+aIJI/idZWLOdW0fz7Nmz4tFHH9WtV8mSJcVnM2eKrMuXhUhOFuMVC87Xr19fv65W169raW8LDtZZ1zUmRogxY6QxvIpKHQwMVF33kSNHNK/p0KFDYsSIESI4OFj3nuVswcHBYsSIES75W5eQIETPno7cE+V7GyyAZBGKCyIGDYUARGM713Fn62+gvJsCOGmifn+qytlh9OTQUP1FTk3Yt2+frHwfHx/NdLGxsaq6Xr582enyiYjczdPP4AwuyS08/cEm9zpz5oxo3Lix5kPpo48+KlI0Arb9+/er0o7Nu0i6E9HJYUBE2HhQ9gbEy4C4Yjg6cY1ff/1VVKhQQbNO9evXFwcOHFCdE7PymBjzwO+ic/BeEWy5Kq+u5aroHLxXjHngdxG76rjhety6dUtMnjxZFClSRLMuFkC8GBoq/g0Kkt2f5t7esnRvvPGGbhnmvxuwCuCcAL4XwJsCaC+AZNNvv+yjFhVld0ahaYprr+bvL6xRUXbv3+LFi0X79u11P2N5twceeEAsXLhQ3Lhxw/B7lOPgQSmOcuDXQAAHVHWpg6dFAkoJAYjzBup+Z2umU8YVAXwhgAgBFBXAAybf81qycoaaucDgYKcDzCNHjqiuNSMjQ5UuKytLFC9eXJbu559/dqpsIqL84OlncAaX5Bae/mCT+924cUM8+eSTmg+mDRo0ECdOnFCd079/f1m64sWLiytXrtxJ4OBssTnbBkA0svHAXAIQHwDiVs45Lpgt1p74+HjRp08fzfr4+fmJjz76SGRnZ4uoCbtEuxIHTF1yuxIHRPTEXTbL/+mnn0SNGjV070lLQOzUyDxOI+3vYWFCREfrXKfZtytNAL6KMn4z/bbHxwupqW/AAEMntFNc0ys5xyIjpXzsOHr0qHjjjTdE6dKlde9p7uetRAnx8ssvi/379xv6rBw86IrW376yOlgQLP5CPSEA8YWd+sq3EkIKBpX5r1ekswjgXxP1myQ7vxQg0s1cYGioofdJz9mzZ1XXmpycrJk2PDxclu7dd991uFwiovzi6WdwBpfkFp7+YFP+sFqtYsaMGcLLy0v1wFayZEmxbt06WfojR46o0o4cOfJOgjFjnH2yFlmA+BoQ5W08OFcFxHeAsI4Zk2/36csvvxRFixbVrE/5Iq2EM0s/RFbdLhKOJ8rKPHHihIiIiNC9B6Vv36dsnUwXKNIHAiIjt0B1IOZYr+aWinp9YDqPlD9jDTf1JUHdhToqbxoTXS/T0tLE8uXLRZcuXXTvcd6tRYsWYv78+Zqt+kJIt9PxFsu8235V2YF4WySglOit2F/Mbr2vaOR/SwDFFekWmajfKVU5q81epBNLlfz777+q8vW6uyq7tvfs2dPhcomI8ounn8EZXJJbePqDTfnrl19+0RyXZrFYxAcffCCsebqePvPMM7I0/v7+4p9//pEOxsS44ulaCECkAmI8IIraeHhu3aiR+PPPP/PtPh05ckQ0bdpUpz6lBfCTw5cc6hUnYlYeE9evXxfjxo0Tfn5+muV4AeJVQFy1k2E/xXn9VAXKAzHHejUPVdTvUVPnB5fIFNaSxgtdobgmf0DcUGVqvuvlyZMnxZgxY3TXO827FS9eXLzwwgti165dst8Lgw2vBrc+inJDRF98Jooo6vKinboCfxjM38j4zDtbScjXU33UkYu005VZT2pqquo6z5w5o5n2u+++k6UrXbq07D0jIiqIPP0MzuCS3MLTH2zKfydPnhQNGzZUPbgBEP379xfXr18XQkjjNZULlP/nP/+5k5GdMXNmt/OAeMrOQ/QTTzwhTp8+nS/3KS0tTbz55ps26vOyAG44cKlWURwLRfnS2mM8AYgHvb3FfgOZZUBqqcx77gKttIpAzHyv5m8Udaxk6vwufltMFfiM4pp66KV1sOtlRkaGWLVqlejZs6ewWCw2P3MAROPGjcWnn34qvvsuyYUf+QwhjWFVlif/UscLEJcBUctmHb/RKWO+Il3Q7XKN1bGnIjj1g/0vO1RbeLhDv39ZWVmq6zx8+LBm2lOn1K2seoEoEVFB4elncC5FQkQuUbNmTfz111947LHHVMdWrFiBNm3a4PTp06hWrRpefPFF2fEFCxbg1KlT0ovRo11ar8qQljzYDSBcJ83y5ctx3333YfTo0UhOTtZJ5Rr+/v4Y/eLbCLEsB6C1oOEXAFoAOGAi1yMAuuEGnsXlhEuqo+XLlsW3JUtiW3Y2mhjI7Q8AqYp9PbUSJiUBPXrkLhPRqpWJKgMAWipeXwAQZ/jsVhnbDKe1Alin2Ke7qEpcHDB8uOG8c/j6+qJv375Yu3Ytzpw5g3fffReVKlXSTX/w4EG8+uqriIwMBfAMgD8hPQs4IwXSSq9K8rVMK6A6juNBvIUiNvLSW45EeeeSIdXdmHE4Br88rzMArDB89m1btwKHDpk9C97e3vD19ZXt01uOpHr16ggJCZHt45IkRES2MbgkIpcJCAjAihUrMHXqVFgsFtmxmJgYtGzZEhs3bsS4ceNQpMidh9qsrCxMnDhRehERAQwY4PK6tQCwGcCPAGoFBKiOZ2RkYMaMGahVqxY+//xzZGVlubwOOYZ1PYJE0R9ADIBHNFIcAXA/gI8ghUV6UgG8CaARgI2qoz4+Phg1ahSOhYdj0LVrsKhSaFOuWNocQHm9xHkCMfNvW10AgYp9xh/eB2CZ4bT7Ia2BmpdmwJxj6VLAxtqt9lStWhWTJk3C2bNnERUVhYcffli1RmwOq/UWgG8APAigIYBPADi6rmMIgMka++XrcF7ESwjHdvwH12FBXZ289ILLipBWks3L2Pqt4diCB3EUDyv2LzJ0tsIy4+9/XkbXurRYLGjRooVs3678XsyViKiQYXBJRC5lsVgwZswYREdHIygoSHbs6tWr6N69O5YtW4ahQ4fKji1ZsgR///239GLuXCBUq1XPyboBeCQ0FH8fP46PP/4YwRor1SckJGDo0KFo1KgR1q5dCyGcbUmSi564G8vOPXj7VQiAVQDmAyimSJkBYBSAHgCUrZECwBJIwdlMAOpAuFH1Fjh48CBmduyIEitXmqrjWsVr3Ra+HLcDsbAwoF07MyV5Qd16aezhPRxb0BB/Gy5JeU11ANSyd9KMGbKXQgApKUBCgvTTyEfD29sbERERWLNmDc6dO4f33nsPVatWtXHGYQCvQQrgBgLYAvOtmUMANLCTpndODSE0g1FAP7jMe34OY4H4aEwHADyl2P8ngFOGcsjDwUDPaHAJAK0UzfFsuSQisiNfO+HSPcPT/b2pYDh+/LioV6+e5niuRx99VAQqFrR/9NFH75wcE+OKdRlsjhFMTEwUI0eOFD4+Prrjzrp27SoOHjzosnuiv9zIUSGtLahVjxABrLmd7qAA2tkYJ1dJACtEuxL7bhdobgzrGY08DS10f3sMXFSU2bflbUV5XQydF42epgpqrbimEQbPi/nhuBgzRhpPqvw4BgdL+8eMESI21vhnIDs7W/zyyy+iX79+wmLR/+zd2eoIYIbQnr1Vb9toI79qQr7MSLYAKmukCxLay5EIAezQSH/KZp0isTj3RTqkGYvznj/B3AfH4XVqq1WrJiv3p59+0k37008/ydIGBASIrKws02USEeUXTz+DM7gkt/D0B5sKjpSUFPHII49oPuRqza65Z8+eOyfHxLhqfQaby0wcO3ZMt44AhJeXl3jhhRfEpUuXnLoXMSuP2almugDeEtLagVp1aSgA9bIv0uYrgDECuJ6bX+zHG0zfp88U+ZaGtLyLofNvR1jmZj5dpbiOICEFO8aCFCNbPCAsiuv6xc45Uegl2mGLqdvXrp3uMqCarFYhgoIuC2kJllq6nz/5e/y4AH61e4+kTe8z/ZJG2lk6aRN08s4WQBlF2jm6dQnFBZGAUrKdryrKqgEIq5kbDgihs7SLLcovvJYvX66b9tKlS6p78vfff5suk4gov3j6GZzdYonIrQIDA/HDDz9g8mR117vLly+rxma+8847d16EhQExMUBkpHOViIyU8gkL0zxcp04d/Pjjj9i8eTOaNWumOm61WvH111+jVq1aeP/993Hz5k2HqrFslr3JavwATIc0flKrW/AhaI/B7HH72FQAxe+UN/Oi6Toqu4/2AKA9UlDD7TFw5no1K2cBSoat7pih/gmYA3OT7fwC6X/YHMWgP7lTIkohEkvQG9HYpptK27Zt0pDhgQNz5ziyKTUVSE4uB2A0gGMAfgPwJCCb7iavTADfA+gGoBqAJrA9kc5MAD4a+7M19g3RyeOIzn4vqDtMa4+7DMZVrEcPhOBq7r5TALYq0p2GmWmBbktPN3uGqlusrd/n8uXLqyZlYtdYIiJ9DC6JyO28vLwwfvx4rFmzBoGB8glchBCy1+vXr8e2bXlmAQ0JAZYsAaKigHBzD/sID5cmZVmyRMrHjvbt22P37t345ptvULFiRdXxGzduYPz48ahbty6+/fZbWK22JttR23W0hMGUnSBN9tPNTrpqANZACgnrqMu7UtVE7YBbkMKbvCLMZHB7DFxICLB+PaAxpFVDRagDae2xdMHBAuv9+8iCFCOUs8R2BjTnSI1BGBohBsvg3JcZS5cCjRoBsbG202Vk5H3lBaAjgGUALkKazOk+G2f/A+AggLYA+kL6DCiDxpqQvh5QWgJAGVAVA1BBI+0CG3VQjrvcDOC6bE8oLmIL2iMM8pldQyB9wpW+tVGaJn9/s2eYGnMJAC1byscFM7gkItLH4JKI8s3DDz+MXbt2oW5dvdkpJePGjVMFnYiIALZskZ7Yx44FunRRRy/BwdL+sWOldFu2AL3sTkcj4+XlhaeffhrHjx/H5MmTUbx4cVWaCxcu4Omnn0arVq2wdauy/UWbsArsu1bdRE1CIC0foZxNNa/qkObB1Z4Hdm9WIwjNI9o2Qwowc3jBfngrL3Cv1FkRUiPxli1GWzCVrZfq4DI0FNgSfQNhKebatrIBrFfs0/pExCAMHbAZcVB/qeCIuDigfXvbAaafXgMlSkOa1OcwgG0AnoZ2OAxIbbKrIX0NUB3AJEiBZ24pGufcgjTLsJJyciUA+AHKmWbv6Ap5y2gG8s5aHIkliEEjVWAJACUBVNHIcTkAw22RwcGAxszP9jC4JCJyHwaXRJSv7rvvPuzcuRO9eytbPe7Ytm0bfv31V+2DDRsCU6YAGzZIfQ9TUoD4eOlnYqK0f8oUKZ0TihUrhvHjx+PEiRN4/vnnVd13AWDv3r1o3749Hn30UZw8aWtmTSA1LhVJwlBTXh7BgM1WtN8hLUOyWvNoEkrhOow/fKcCyNvW2QZAKcNnQ1r38vqdlivjvZptB5e5vZpr6wU5+nZDvahHkuJ1IkqhJ9YhydzV2qVYBlQlMNBe664FUsvkN5DW/5wLoL6N9P8AmAipRbs3pMBwk07a+VC3Xmp1G08B8JVOHkFQdzCORji2IBq9sASDbLYyN9bYdw1GFzUB0Lw5oPF7aU+xYvKZmc0GlwcOHECGvNmZiIhuY3BJRPkuKCgIa9aswfjx43XTjBgxQt16qWSxSE/opUtLPx140LSnQoUK+Prrr7F//3507txZM82PP/6I+vXr4/XXX0dSkjJ0kWTcyDRZsgAwGMCXefZptWImQuoW+RKAG6qj6TDebbA/gDMA/gbwIYBXDJ+Zt0B5u5OxXs3K4HI/gAx1r2b9pj5dWgtkjAOQd3GWYZjrshZLpTzLgKpYLIDGEF8dwQBehTS2dqidtFZIV/4YpDGsWrKgbr3UW75kKnIC0ZIllQGxvON0GfwPm9EBvVSdkdW0gkvARNfYVsrPjTFmWy6Va11mZGQgJkarUy8RETG4JCKP8PLywuTJk7Fq1SoEaHRtO3bsGF566SX7AWY+ady4MTZs2ICoqCjcd596LFxmZiY+/vhj1KxZE7Nnz1a1bPgV9zVZogVAJcW+VACPAJqB0HwAzQHsk+31N97JMLfU+gDeADDA1Jk5BWoHs7Z7NbdQpM7Ad9/FqHs122/qU9EKcQSkFSQ3AYhGL6fHWNpzexlQTebjIwuk0F/52XDEl5B3hNYLLi8D+AIAcO2a9D7mdBjYu1ceXMYjC/sNlq4XXEYDSDCSwQCHPqHawaUQuouYlixZEnXqyMc0s2ssEZE2BpdE5FF9+/bFjh07ULNmTdWxr776Cs8//zzSHZgR0h0sFgsiIiIQExODTz/9FKVLl1alSUpKwsiRI9GgQQOsXr06NzgODA1EsEW7VVPfBEgBY16rIU320k8j/TEArSEFH1YE4yoCFBOsuJWBMXDavZqDULu2fBxuYqLGpD7mmvpwGcBenWMZkML0d9DHcH7OmDFDe79j8VFRAOrZl6WpimyN0VXKBmQz79aF/mPBB5C+3AC+++5Oh4GmTeugVq1aspQ6cbRKI539WQC+gb/t8cLh4Q53fVcFl8uXS03jQUFAmTLSz5CQO+O3Dx3iuEsiIoMYXBKRxzVo0AC7d+9G48bqtoyFCxeiffv2uHjR/LIa7uLr64uhQ4fixIkTePPNN+Gn0V3z5MmT6Nu3Lzp27Ih9+/bB4mVBs5JnTJbkB2AxpGAir+EAPgfwX+RdekSSCeAtAN3QwHuTzlQ/bmJyDFzeXs2tW8ub8Hbt0p4x1kxTn3IiH2Xb8XUABzAOwFHDeTpq61bgkHpeG4SFAe3aOZLj01C3NJ6C1LH5v9BvF1T6GsD/IE2j4w+gtk66BEhjPnMnBQZw5wuXvIyOmawJoIjOJ/QNNEEIEtEFGzAWU3BIea2jRxssRa3opUuy17cuXpQGyOaVlARs2gRMmwaEhaHlX3/JDjO4JCLSxuCSiAqE4OBg7N69G2XLllUd27lzJ1q0aIE//zS9Cp5blSxZEjNmzMDRo0fRv39/zTRbtmxBixYtMHjwYNxX/ZQDpdwHYJZi3xUA/wHwLKTxicpupQCwCXutz+JHB0p0mINj4KRTDQaXJpr6lGt29oY0rlQuAdKcuP+ojrja7WVAVRyLk7wBTFPsOwtpxOJzAF4wkdezkLpavw7tOVxzzASQnHdSYABQTc61G9In1JZo9EJHbEGaarxtjp1IQjw2oQumYSzCcAjh2IK16CnN8GRyFmgAUlN5ZCSKrl4t2217xKWk5enTsteHDx/GpUs3tHrREhHd0xhcElGB4evri6++0p6Z8vLly+jQoQPmz5+fz7Wyr3r16li+fDn++OMP3H///arjQggsWrQI/z30NKSurma7qg6BesXJNZBaqGpDWnp+DJRLktwSN/AopDBUPdWPGzg4Bg5QB5dHjx5FcrLGZDQGm/qyACjnG44AsAjSAhpy/wDoDvW8sq6lFy9HRDh663oDUN6L9yFN4jMEwBZIrdhVIQWjtiQC+BjABhtpkgB8rJwUGOHh4bJx0wLAupIldUophUgsQW9EYxvCYbuFdbHs1TaEIwJrMTB9ge4MvLpiYqTFR5ctU/UDMBJcNoH8DlqtVoSG7tPrRUtEdM9icElEBcpDDz2kCjRyZGZm4qWXXsKQIUMK5FIAbdq0wV9//YVly5ahShV1C1BaRhqksXJ1IC1Or1z0Xo8FUiBZRrF/BIATkDp8TgXwG7QmevkKQDPojz90CSfGwAHShEm+vnc6rgohsHevTo0NNPX9BfU8qT0hdfxcBfX8tMARSCtgGgn8r0Adutq3d0eGbgvX3LlG1wTNywJgumJfIqQxtz6Qlgnpe3uf0c+aPR8DSJRNCuzn54du3eQroka3bKmafCkGYWiEGMUESnojLwGpFdaq2rv0B380amR7DVF5wTFAhw7S1L1QdzJXLsiiFI1e6IEtyFYFwne6xip60SI8HFirbDonIroHMLgkogLFYrFgypQpNtN8+eWX6NixIy4pxk4VBBaLBU8++SSOHj2KadOmITBQa4KVSwCehzRZj946hErlIAWYed0EMAjSOEsA6ADgIKQlKOSOA3gAwAxoPa67gBNj4ADA398fTZo0ke3THddmoKlP+VzfBEBO7BYA4DsUh9TlOK9dAB4FdGfYFQCWQRrr2A/AOZt1UEq67idr8csrJARYv970ZLiQ3tW+in0fQVoXE5DWvHTlMispAGaqJgVWjrv8ZccOZGzcmBsxxyAMHbBZY8kXWy2X5wBs1zwSFwe0b28gwExMBHr2lI2pNNpyqW5ltb0ea17btkkf04ED9dc5JSK6GzG4JKICp3PnzujYsaPNNH/++SdatGiBnTt35lOtzClatCjefvttnDx5EkOGDIGXl9af24MAugB4CMYmlXkI0nqWee2C1BUyRykMqDICCxYsQPHi8sl+MgGMvl3iBYPXYYijY+AUDI+7BOw29SmDS2XtAlEEUutjZcWRDZAmy1G29GVBCjwjIbUEXgfwImB7TlOV9ANHdI+FhUnLfJhvwZwGeafNWwAm3f53eUgt2noT9eRlr+tsjpn46y9599leivc/NTUV269dA2JikNjvP+iJdUhCKY28bLVcAlJHZm1JSUCPHnaCt2HDclsscxgJLrVbWVsqUtmf1GfpUphrZSUiKuQYXBJRgaPXeunj4yN7HRcXh/DwcCxYsCC/qmZa2bJl8cUXXyAmJgY9e/bUSRUFoCGAV2F/hb9ZkLrV5vU+pI6gQKjXJczdWB/PPvssDhw4oFpCAQB+h/RIv8r4ZegLDQXmzHFFTuaCSxtNfRcAKJe4VwaXfsiAFFj+CiBEcXQFgGGQB44+AJRLz2yAujXZNv81K2weDwuTenFGmlp6sy6klvC8/os7X1iEQnrX1cv9yGUDeBdAMTvpstC9ezc0a9YM8+bNQ0pKCsqXL48WLeQTS0VFRQEhIRjm96VGi2WOEgCq2yjre9gaFRkXBwwfrnMwOlpzFiXl1Slz129lVf4unYaRcbqGW1mJiO4GgsgNDh06JCA9mQkA4tChQ56uEhVCERERss9RYGCgqFy5smxfzjZ06FCRkZHh6SrbtX79etGwYUPNa5C2IAF8KIA0Ic1BqbXtFoCP4rwaoiTOiZiVx2TlZWRkiLFjxwqLxaJZ3vOASNUvyPYWHCxETIzL7s2RI0dU9bt48aLtk2JihAgNldVrviKPYEBkKupuBUQwEm+/3CWA4hr3513FJV8TQCVFmhIC+MfY7UKisHbuYvh+REUJER5u9O24KICiirr1VaQ5L4CqNj57EEBFAfSzk0a+FStWTDz33HPi+eefl+2vU6eOiIoyUvc+dspYbjePqCiNG9iunWbinxT5V8tzLAGlRCgu6JSTIYAiirqtN/j+SB/ThARzvxNERGZ5+hmcwSW5hac/2HR32L9/v+pB8/XXXxcdO3bUfAgNDw8XV65c8XS17crMzBTz588XpYJCbDxQVxfACgFYdR5W31ed80jHR3XL3Lx5s6hcvrxmWbUBsdvoE3LeJ2UXBpZCCJGdnS1KlCghq9vq1avtn5iQIERkZG7dHlFc3xM619AZG/K83CAAP437M1tx2lqNND1tvE93ti74VQrIrVZT9yU2VoixL1wRXfBrnoBYaxunUbc/FWnOCXUQqtx6a+yz9YWI/taixXEDH6d3FecFK15H2M0jPFxx02JidBNvUNSxXJ5jA7DETlkPKOr2noHru7NFRpp664mITPP0Mzi7xRJRgdWkSRM8/vjjsn1ffvklFi9ejJEjR6rSb926Fc2bN9efZbSA8PHxwYsvvoiz/5zBa0Neg7fFTyPVGUirMrYFoDWu9G0AD8r2rP59FX744QfNMtu3b4+Dhw+jf58+qmMnIE0L8wEMzikaGSn13QwLM5LaMC8vL1U3XptdY3OEhABLlgBRUcho2xYbFYf1RoO2kk3I0gXAEiiXc5Fm5F2a53U3qCfQWQdbYwNl5SnX8TCgYUNgyldlsaHde0hECFZoTNgkeRPqLr5vQXq+yFEFwCd2StRajiTy9mbOnj1zFOVraax4rZwJej2Af23msHWrYgkQvUVFoT/mMhq9FGMstZgfd5nX0qVSb10iorsVg0siKtAmT54smwznxo0bmDlzJj7++GMsWrQI/oppKy9cuIAHH3wQixbZf9j3tMDAQHz0xUc4ffYEOoT10En1J4DWkB7s885O6o2WgWNQ1E8+ac9//vMfxCkmMMkRHByM7378Ef/73/8QUFT+iJ0FaaVMm5P9hIdLT8ZLlkgBnRs4FFzmiIjA9kmTVIuJyO5scHDugoQDFnZXpHwMwDyNjAdDCiABYACAHzXSjMSdGVq1DcDtgCddbzZaO0aPhgXA4/gBA2QBb44gAO8o9m2HNKY3r2cBFIc+rfrFAvgAPj7yL0IsFmUwrvQpgPqQZrDVG0+sDC5vQB4CZgPQDxZzyOJJG58bveByOozMeOxccAkAM2aYPoWIqPDI13ZSumd4ukme7i6DBw+WfZ78/f3FP//8I4QQYvfu3aJSJeU4OGkbMWJEoRiHmWPnzp2iWb3mNroZ+otqRZ4Ro1pFidhVx4UQQnzzzTeqdF27dhXZ2dk2yzpx4oRo1aqVZjnBgFgJSF04u3QRYuxYqW9mPli1apWsLkFBQXavJa9Ro0bJzm/ZsqUQKSlCxMdLP/N2SU1OFu2wRaP7orrLsdSV9A+h3fU0Z3tI6HWPDcfmOy9SUhy/QQMG2BkbmCaAaop61RdAliLdizauQ2trJCIjhRg2bJhsv7e3t3jjjTdEjRo1DOThJ4AnBLBJANl56pItgABFWmXX9+aa9zXv1iVnOKvVKn12dRIe1qjbfjSwm7+0HdO4Lr0xmvpbPv06EdE9yNPP4AwuyS08/cGmu8vp06eFr6+v7DP10ksv5R6/cuWKCA8P13yg7dixo4iPj/dg7c2xWq1i5cqVNh/Wy5QpI+bNmycyMzOF1WoVjz/+uCrN7Nmz7ZaVkZEhxo0bpzvZz3PPPitSU1Pz4arvuHDhgqoeR48eNXx+vXr1ZOdOmDBBP7HVKqKKPqbx8G8VwAiNe1JSAB/YCaAWawYT0egp/aNECdNjLmUSEnInMIpBQ50xmIs16vVfRZpYO9ch3yyWEiIhQYi4uDhRpIh8UpvBgweL7OxssWHDBvHoo48azLOmAKYJ4NLt+ijHMj6rcc7fNgO23OGsyck2I7szGvUJxd8COCSAjwTwmo3Ts4U06Vbe81fZrJfWNnas4x8BIiJbPP0MzuCS3MLTH2y6+7z88suyz5SPj484depU7vGMjAzx6quvaj7IVq1aVezbt8+DtTcvLS1NzJo1SwQFKR9k72z169cX69atE4mJiSI0NFR2zN/fX8QabB7ZsmWLqFKlimYZtWrVErt27XLz1copr2XRokWGzjt9+rSq/jt37rR9UtWqOpO4ZAtgkMY9KWMnaCqVJ2C6PYkLFt95Ua2a8zcoJia3ZS4GDTVaMLMF0ERRr4oCuKlIV9HOtci369evCyHUrcNeXl6yLwD0vujR3nwE8KiQJu3Ju7+/ACoo9r1tN2hLSRFSK7WNRJc161Euz7+9BHDVRhadFeeOsVsv5dbF+KTBRESmePoZnGMuiahQeOedd1CkSJHc11lZWZg4cWLua19fX8ydOxcLFiyAn598XNi5c+fw4IMPYpmNST4KGn9/f7z++us4efIkhg0bplrjEwAOHz6Mnj17IjIyUnYvACA9PR0DBw5EuoHxfeHh4Th48CCeeOIJ1bGTJ0+iTZs2mDZtGrKzDU334zRT613msW7dOtnrMmXKqNZeVLFYMBfDEIqLigNeABYAiFDsj7dTi6sAXoH0fzoQiouYA72FGB0UFgZs2QKEhiIMhxCDRojEkjwJvABMV5x0EcBcxb5nTRV76tQpAMDo0aNRvPidMZtWq1X2+Xv00UcVZxYB4A9tWZBWXFXOcvMLgM6KfYsBWG3WMT0dgJ/WBFl3KMdcSq7l+bcVwG82cnB+3OXevVKYSUR0t2FwSUSFQmhoKIYOHSrbt3jxYhw+fFi279lnn8XWrVsRGhoq23/r1i1ERkbizTffRFZWltvr6yqlS5fGnDlzcOjQITz88MOaaX755RcMGTIEYYrZW2NiYvDOO8oJXrSVLFkSy5YtwzfffIOAgADZsaysLIwdOxadO3fGP//849iFmOBocLl27VrZ6x49esgmg1IRArh6FSG4ivXogWBcVSTwBbACyll51eooXv8IYAWCb+cbkjffq1ddE1WEhUkz9kZGIgRXsQSDEIUIhGPL7QRdoQ7OpgGya3wN6tlx9Z04cQKAFLQPHy4PmJcvX45Dt6drjYhQBuRpkCYV+gRAA4OlJUMdSF4AsNnmWf7+AAIDpYmbdJxAQ429yi8htGbMzaEMLvcg58sEoxyYNJiIqHDI13ZSumd4ukme7k7x8fEiIEA+8Ue/fv000166dEm0adNGsyte165dRWJiYj7X3jV+++030aSJssujvIti3tcWi0Vs2rTJVBknT54UrVu31sy/ZMmSYsWKFW66OsnGjRtlZfr5+Yn09HSb59y8eVMULSpfv3Hp0qW2C1KMzdPuYipud5EMs9O9U37fvVBKbMZ9NvpuulBUlLTQ4+38Y9FAjMX7ohU+1ajnG4rq1LVzXXe2t956K7fIxMRE1Zqkjz56Z53VunWV+U64XZ5VSBMjPSPsr7cJAfgqXj+j29VUtoRo586aie5MhOStyHek4nU1ob926T8C6CakyZ1WC+Cibp1sbYVoKDgRFSKefgZncElu4ekPNt29xo8fr3oA3bt3r2ba9PR0MWTIEM2H1urVq4uDBw/mc+1dIysrSyxcuFA1NlFvq1Spkrh69aqpMjIyMsT48eNVwWrO9uyzz4oUVwdJt127dk1V3u7du22es27dOll6Ly8vkZCQYLsgjbF5CSglIjUn5bkogOqGAzEA4vH8jipiY6WZYrp0yR2T+aSiTl7wE0EBp/JUZ5bh6wkJCRE3b97MLW7ixImqNDljm19//XXFMa3ZXq8J4DOhHh9qayty+zw74xjHjNG893fG1wYq8lXPugyc0CzHVZubfn2I6B7n6WdwBpfkFp7+YNPd69q1ayI4OFj2+erZs6fNc+bPn6+abRaAKFasmNtb4dzp+vXrYuLEiaJYsWJ2H8r79+8vrA7MUrp161bdyX5q1qxpf8IcB913332ysj777DOb6ZVLZLRp08Z+ITZmFY1CLxGOzYrdJ4R84hf72yuAuOGJqMJqFSIlRZzctUv4+PjI6jR48ODc1VkSEtJ1v0DQ2nr06CFu3bolhND+Xezdu7cQQohNmzZpnB+nc7utAtgt1LOw6m1BQprc56QsH9kMrDExmu/pnZdlFXlu0HhvPzcVLJrZZK2sREQu5OlncAaX5Bae/mDT3e2DD9TLQWzfvt3mOX/88YcoX7685sPq22+/LbKysvKp9q534cIF8cwzz+guKZKzzZw506H8k5KSxIABAzTz9Pb2Fu+//77L79/TTz+tCohsqVWrliz9+++/b78QO+shCtzpYtoFv95e9mO/AIobDsYAiJKAeB0QJzwUVShnUbZYLCImJib3eKNGjUxdT69evURaWpoQQoipU6eqjv/1118iPT1dBASUUBxTLoei3HqZqoe0dRbAcgGkqdeObNdOVoB8TdOqinx+FurZgR9xKoC0tXG2WCJyF08/gzO4JLfw9Aeb7m7Xr18X5crJWxnat29vt2Xu4sWL4v7779d8SO3Ro4fprqMFzb59+0THjsrF5+XbCy+8IJKSkkznbbVaxbfffisCA5XdCaUtPDxcnDt3zmXX8umn8vGC9erV0017/PhxVX0MLz2jMzZPa7MCIgUBYhkCNO+Bt4FgqHtIiPjpp5/y9cuMK1euqMYqR0RE5B6fN2+e6aDuoYceEunp6SI1NVWUKSNfnqVrtWpCdO4sHlP1Fuhr5xaPVqS3COBlYWRspq9vaTFq1Cj5mqhRUbmZx6Choqz7FHmsEMAixb4SAsg0HTga2bjOJRG5i6efwRlcklt4+oNNd785c+aoHjB//fVXu+elpaWJ559/XvMBtWbNmoX+s2q1WsWaNWtEnTp1dB/EQ0JCxJw5c0RGRobp/E+dOiUeeEC54P3tFrqSJcXy5ctdch07d+6U5W2xWMS1a9c0037yySeytBUqVDDeBVhnbJ69rZTOve0HiKIGgrOqVauKadOmiX///dcl98ueSZMmqeqwefNmIYQQN27cMNU1Nmfr06ePSE9PF7NmqcdtbgXE/1TnBAggzcZtXaVRzjkBXBGAn+F6hYeHi2+//VYaH1q1qhCAGIMpirKaKs77RkjddpX5/eHIx8PuZnAJWiIi0zz9DM7gktzC0x9suvulpaWpxgK2bNnSUFBhtVrF559/rhqLBkAEBASIH374IR+uwL0yMjLEnDlzhL+/v+5DeJ06dcSaNWtMj8XMzMwU7777rm5AIo3pc25cYVpammqcrN6st926dZOle+6554wXpDE2z8jWVeeeegPia0AUNxgI+fn5iUGDBom//vrLoTGxRqWmpqpa+1u1apVbZtOmymDL2PZo794iuV8/UUGxvz0gLgPCojrnVxu39ZxGGUtvHxus2K+c7VW9BQcEiOGAiAVEZ2xQlKWcSXre7f3KWYEn3N6vN3Os+S083G1vMxGRx5/BGVySW3j6g033hq+//lr1QLl69WrD52/dulWULauc2EPa3nnnHZGdne3G2uePs2fPqpaMUG4dO3Y03o00j+3bt4uqVatq5lmjRg2xY8cOp+resmVLWZ7Tpk27c9BqFSI5WVw/e1b4+clbtVauXGmuIMXYPCPbCBv30x8Qo00GaQBE06ZNxddffy1u3Ljh1H3T8/nnn6vKzLlXX3zxhen65myPA+ITjf0bAXG/av8IG7c1QyP/YbePbdQ4NlX4QbsVXbl5o6UAFgjg+u38OivSfHx7/yjF/poCaC2AlWY/IrpbdLRb3l4iIiGE55/BGVySW3j6g033hszMTFG7dm3ZZ61hw4amxrOdP39etGjRQvOBtHfv3rpdMQuT7du3253sx2KxiGeffVZcvHjRVN7Xrl0TkZGR2g/03t7ivffec3h84dChQ2X59e3cWerG2rlz7kQ8PynK9PHyEtf+/NNcQXnG5hnd5tkJZgIB8aDG/po1a9k8D5C6F7/22mvi+PHjDt03PRkZGaqJj2rXri0yMjJEfHy8qh5FihQxFLjlBJiVFPtaA2KSKm1NYbsVUDm+st3t/VkCqCg75o9XRQwaiqOAeAMQpQ3VtYSQxnG2U+zP6Tb7i855OfVwbouMdOlbSkSk4ulncAaX5Bae/mDTvWPp0qWqB8ElS5aYyuPmzZti8GBltztpq1u3rjhy5Iibap9/3nnnHUNBQrFixcTEiRPF9evXTeW/ePFi3cl+2rZtK86ePWu6zt98I197sKLG0/oQRVntc461a2e4ichqFSL5sedEPEJEMgKF1UCUsFVRrpfGdYcAIkC1P1RIrXBPCSPjCLt16ybWrFnjsgmAVqxYoSrjiy++EEKYnzVWubXS2DdHM+1RG7e2siJt+TzH5BP+lIWPyMpzcjogVgCii0P1f+d2NjcFoNeV/LS9j4XNLTRUCHtLrxIROcvTz+AMLsktPP3BpntHdna2CAuTj5OqVauW6clqrFarmD17tvD2Vo/lCgwMFGvWrHHTFeSPjIwMVTdTW1toaKhYuHChqa7Bp0+fFm3aKMeySVtQUJBYtmyZqTof+esvVT4X8zytWwFRRXF8uvKJPjJS84k+JkbVCJq7BSNRdMYGMQZTRCwaaEYK8RrXqBXU+Giu3fjC7Wz+FcAHQr0shnqrUqWKmDp1qrhy5Yqpe6hktVpFq1atZHmXK1dOpKamipkzZ6rKtdV6qR5PqQ6mmwOq8ZhNEWkjCHtQkadFADduHzukKu9XnYxOAmIMIMob/LxLYy13CalVtYtOmnE26m17Cw6WPnNERO7m6WdwBpfkFp7+YNO9ZfXq1aoHwa+++sqhvH7//XdRunRpzYfLSZMmFepxmMeOHRPFihWTXVPx4sVF8eL66zY2bdpU/Pbbb4bLyMzMFBMnTtSd7Ofpp58WycnJ9jM6eFBkV6ggSijOX53nif1vjfxjtZ7sQ0Nzn+yjoswPsWyHLSIaPVUHyijKXgGIDpr3UWvpkl/yZJUlpHUWe9gNgvz8/MTAgQPFn3/+6fAEQL///rsq38mTJ4uTJ0+q9mtNepV38zUQuCmD7o6AiEIvEY7NGvf7GY08dglAiHBsFjUhHz88yM6blwGIVYDwQRchBar26rtBADN0jlUQjixNkufjR0Tkdp5+BmdwSW7h6Q823Vu0WmMqV66cu9C7WWfPntWdPfORRx4xFhwVUF9++aXqmrp27SpefPFFm8tRPPzww/I1BO34448/RLVq1TTzqlGjhvjrr7/0Tz54MLc5sbPi3LF5nto/VByrDOh2aU0IqiEG9EwyHRjk3SKxWCSgVO6O9orypwLiD9QXvmiocd3Ke1tFACka5ZwU0qQywXYDoSZNmoivvvrKdBdmIYTo1auXLK+AgADx77//2lzCRmsLgjSBka00VRWvfQBx7fYFx6KBGIv3RRf8KoKRKKTZWeXpe+GR3BbkjxXHigEi1cCbJ80We1YA7wqgkk5dQ4UUPO63cT2rzX1mtBvOiYjcxtPP4AwuyS08/cGme8+vv/6qehCcPXu2w/nduHFDDBw4UPMBs169euLYsWMurH3+sVqt4qGHHlJd07x580RsbKxqWQ9ZUODjI4YPHy4SDD4tX7t2TQwaNEgzL29vbzFp0iSRmZkpPykhQWrquf10PkZxXpc8T+4dFcde0nnCP4gwEYoLpoICvS0UF0QMGgoB9XjPCJS8HSBdEYB8ointbYiNsm4KYIHw9m5uN5+goCAxcuRIU5/JgwcPqiZ5Gj58uBg3bpyBesu3RwHhZyeNt+L1Co2LtgJitsZ4x1fzpLmskdc3Bt44+TqXWQLor1HPnG6v2QLQ7r0ARBj6nFQun85ZYYnIIzz9DM7gktzC0x/su8Xt1RZEfLz0043L4BV6VqtVtG/fXva5K1u2rEOtOnnznDVrlmaLXlBQkIgupE+PV65cUS3BUrRo0dyWyXXr1on69evrBgolS5YUs2bNMtwyvGTJEt3lUB588EFx5syZO4kHDJA9pf+ovO+AyAZEMqQWsLzH1mg85R9E2O2Az/nAMmcLRqKIQUPVZDW+aJQn3RkhtYTZC8422S2vdOmd4sknB9tcszRn69q1q1i9erU6aNfw9NNPy+vv6yt+/PFHA3VWfOkAiC9gu4usct3PwToX+7PGueGKNL0Ux7vo5JV3i0FDxa73FOUUE/IJe7S/WJJaoM/bfc9iYx35zSQicp6nn8EZXJJbePqDLUThDcxsTjQSLO0fM4YPL1q2b9+uehiUrY3ooA0bNohSpUqp8rZYLGLKlCkOj33zpOjoaNX1tGjRIncipMzMTPHFF1+IMmXK6AYMNWvWFCtXrjR0/WfOnBEPPqicrEXaSpQoIZYuXaq5JMgFjfRHAfGDYp8f1N0jE1DKZS2Wyi0UF8QqKGfHLSakVq+cdLHCfvfW6gJItVteZKQQ8fHxYvr06brdjfNulStXFlOmTBGXL1/WfU/OnTunCliffPJJUaFCBbv5K7cISMG9MuDX28pA+pJAeaF7tT4fkHd3XqY4brn9ObF3E9thS56XysmLuimS/89G/SfaLCq8IfvBEpHnePoZnMEluYWnPtiFOTBzaKIR46st3DN69uwp++wFBweLpKQkp/M9ffq07lIN/fr1E6mpqc5XPp+98sorqmt55513ZGmSk5PF22+/bbPVrG3btmLXrl12y8vMzBSTJk3SnJEXgBhUtqxI1vighyrSLQLE84p9Wq1XA7DE1O+T2e2R3mc1ruOsIt2fQgo6bQVbwwyVFxUl3cesrCwRFRUlevXqZXf9Ul9fXxEZGSm2b9+u+SXAqFGjVOc89thjduqrva1FzuQ5xtLv0LjIyzppz+ZJcxNQTfSkmiVYY4tCrzwvP1OUoVzH8qKNulcWUtda7aL4N5mIPInBJd2V8vuDXZgDs4QEVU9A0xsnjbhj7969qofB8ePHuyTv69evi/79tcZqQTRs2FCcPHnSJeXklxs3boi6devKrsPLy0ts27ZNlfbMmTNiwIABNoOFgQMHinPnztkt988//xTVq1fXzKM6IP5QfMAfUaQZCvXyFh/ZDCTctVlF8eJBimtYq5FunQBsz7oKWaua9hYerr6XJ0+eFG+++aZmy7pya9y4sfjyyy9lXcUTEhJEUJD8GvQms7K31YU0O+tKqMdFam3vaFxkts65yi7PzymON4D+ZE55tztfOCxQlNFCI3lZVT3ubNGaRUT2c2wSMSIiV2FwSXel/PpgF/bA7OBB2dwlTm2c7v4OZctLzkyYrmC1WsUHH3yg2WIUHBws1q9f75Jy8suePXtUy01Uq1ZNd0bcHTt26K5lCUjrIo4dO1akpKTYLDc5OVk89dRTmnl4A2IiIDJvf7inKo431DjnqOIXop2BYM0VW/HirRV1mamTdomNQAUCqCnurOeov+n1urh586ZYuHChobVMg4KCxIgRI3LH2H7wwQea76MjAWZOkL8cEEUAUdtG2qY6F1lJI+17ijSbNdLsM/CG3ekqvUxxfgON5D1VZdzZ+qjSh5bN4Jd8RORxDC7prpQfH+zCHpjlWW3BZRsX6pYcPnxYNQnPqFGjXFrG+vXrRcmSJVUPnV5eXmLGjBmFahzm1KlTVdcxePBg3fRWq1WsWLFCt/URgChXrpyYP3++yMrKsln20qVLVS1nOVsbQJwBxEbFfmXLVg3IW63Uk7e4c3tOUe/nbaSdYyNYgQBes1ve2LH2389du3aJZ555xtAEQF26dBHLli0ToaHyyYe0PttGtiBAXLld2UuA+NVOeq2xkq000j2mSJMNiCqKNK8ZfNNi0FAUx7eKMqprJP3ERt29hdR19vbf3hKZ/NtLRAUCg0u6K7n7g13YA7P/s3fd8TWeX/ybHQSJnai9995bjKpVShHUaKlWjVaLaEtbFKWlSlWLomZRfhVapWbtHbP2XokRIzLv9/fHm8R9533vzb0u+nw/n/PhffY4781z3nOecxTRFpxKISHCRJZUe8L09/fn1atXndrH6dOnWaZMGc3DZ+fOndPlqfZpIikpifXq1VPN4ddffzWsFxcXx4kTJ+p6ggXAcuXKcd26dYbtXDh/nnW9tc1GswD80YaAkhFgTYBdIIUueRltCawj8C+Bxy55z56Q0jFMLRvlRxrMxYPAdsP6TZqY39fo6GhOnDiRhQsXNlw/QNK6OyJMalFfqwFbANYzKPujxiRf1ShXDJIm+wOAl1LKjVCUyY0n2m5b9AMKKPrIo1Hsso25jiFAhgQ9EoKlgIDAMwMhXAq8kHAlY78Igll6TXltUViY6+fwrOPs2bMqc8933nnH6f3cv3+f7du31zx8VqhQgefOnXN6n67A+fPnVUJiUFAQL1++bLPurVu32L9/f11HPQDYokULHjt2TLuBmBgmAvwC+nf1lA5c7KMQArUJhBEYQeBHAn8ROE0gLp3vm9LrbiABi0F5CwG1I6UnVIJSjEvt+kFB9nu+Tk5O5tq1a9myZUubDoCcQR4AD1oNerNB2TYak3xXp83RKf/PDEkoPa5Rbq3JjdumqptVp6jRXdmC7JJ/q/iYJyAg8ExBCJcCLyRcydjPu2CmEW3BJZTqWfK/jH79+sn40Nvbm2fPnnV6PxaLhWPGjNE8uGfLlo0bNmxwep+uwC+/KE0FwdDQUCYnJ5uqf/z4cbZq1Ur3MO7l5cV+/frx5s2b8opRUWmMuxOSmaurBaAn5EEgL4E6lITPlpQ8iW4gcIZAvI137bxGm9ds1EkkUMFgTEMN69u4zmqIs2fPcujQocyePbtL17U+5KbKoTrlMgJ8rJjgWJ2yyjiabwOspkjrYrzwabRPUc8DPjpFXzKc57plyxzfDAEBAQEXQAiXAi8kXMXYL4JgZq9XW0dJy7Pkfw1XrlxROSZ54403XNZfRESEpomop6cnv/nmm2f+HqbFYmGnTp1U4//mm2/samfDhg2sUEFfeMqcOTPHjRvHx48fSxViYmTMGwOwh8GB/umSR4qAUZdAdwKfEphN4G8CZylpPpWhRjYYvJtJlEKPGPXpSWCXbhtRUenf68ePH3PevHmsXr26y9ZuqdWgdxiU+1MxwZ9NtO0Byavwd4p0/xT+MdgAEuAxjTYPoTRHYAyb4C8G4XZK0eaG4+jQoUP6N0NAQEDAiRDCpcALCVcx9vMumEVGPp3xp9KzGM/zaeODDz6Q8aKnpyePHz/usv5OnjzJkiVLah5Eu3btytjYWJf17QzcuXOHL70k19b4+voy0s5LZUlJSZw9ezbz5MmjezAvUKAAFy9eTEtysuYl6iWQHMQYHe7DAa5OETKGAGwJXwKVCdgOzeEc8iTgo0jrSmATJa1momJaiQT0tbtPqCT1THbTo7nUwt69e9mrVy+7PcR62sjPD/CR1cBf0Sn3nmKC60z0neq8JwrquJpztBZNQec02nxolW8BeB8B/ADGa+Lt7c0bN244d0MEBAQE0gF3C5eeEBB4TnDkCLBt29Ppa+tW4OhR57e7eLHz23yW+nsWMXz4cAQEBKQ9WywWjBw50mX9lShRArt370bbtm1VeQsXLkTdunVx6dIll/WfXgQFBWH+/Pnw8PBIS0tISEBYWBji4uJMt+Pl5YXevXvj9OnTGDlyJDJkyKAqc/HiRXTp0gW169TBziJFVPmdABwGUM+gnxwAWgF4D8AkAIvgB2A/gNsAYgBEAvgdwLcA3gfQDkAlAEGm52IMC4BERdpCAI0AFALgD6AggIYAegIYC6A1gBI22j0JQM2nQUGAFTs7BVWrVsWcOXNw5coVTJo0CZkyZTJVL8RG/iVIe5KKL3TKrYF0AjLbbhEAY1L+nwNAC0X+fKPKZcoAANTcCDy2+r8HgMx4iApQ87yXl1fa/5OSkjBv3jwbIxYQEBD4D+GpirIC/xm44qtJePjT1fqZcflvL0JDHRmLhVIIg40E7thV1x7Pki8yPvnkE5XG4cCBAy7tMzk5mZ999pmmtiNnzpzcvHmzS/tPLz788EO1tuj99x1u7/LlyyoPvkp6PUWjpGTkJIBjDOqFAbxnpXF6YtJoi+4ROERgFYG3DMfmHvIgsPOpv9NHjx51muOfDHji3ZXQ9gQLSGaqqWVu22hzs2Ijl2mUuai36UeOkBERvKcRq9V6nBaAMcjMtVCbuSsdhRUtWvSZN3kXEBD478DdmkshXAq4BK5gbMcEM8fJ2Yc4i8XR8CkXFYebggTaEfiCwGoCV6jnndIRz5IvIu7evauK2/fKK688lb5XrVrFzJkzqw6oXl5enDp16jN7KI2Li9O8N7l+/fp0tbtv3z42aNBAV3DwBfgRngiL1pTNQOAoAMkDKAGGYr2D771WmJDSBJYSmExgEIE2BMoTUO+p66gggcYEerN+/S84f/58btu2jZcvX7YZR9RR9OnTx2njt3ayc1inzFdWZSwA/XTKddPYuMdQm09/qbXBVvcd4uPjVW3/jmIMx1iGYj2DPFI/5N0yNceNGze6ZB8EBAQE7IUQLgVeSDibsR0XzEggikC5lINjJI3DBLhOMFP4LLGDVpk43OQk0IzAMAJLCJwkkEzA+feznld8+eWXqnXbvn37U+n7+PHjLFasmObe9ezZ84ljm2cMR48epZ+fn2y8ISEhvH37drratVgsXLlyJYsWLarL0zkATgOYkPIi3DJxwPcE+CnAYfjCwXctmdr3IXtq/G5YKFkSHCDwnUadTCbe2/STj48PixQpwtDQUL755pscM2YMFyxYwH/++YdXrlwx7elXiStXrjBDhgxOG2eq4E9IGmplfi3FZuTTaWeRzub1VZQrCbm3WgLkmjUyHvT09FS0f0ijaQuhob308ckpe+7cuXO63gkBAQEBZ0EIlwIvJJzN2I4LZiTwk+JgUILAxykHCWNB05mCmVW0BTtplIMHukwEarN37/6cNWsW9+/fz7i4OOdN6DnDw4cPmStXLtkaNWzY8KlpDu/evcuWLVtq7lW1atVMxZN0B7799lvVeDt06OCUdYuPj+eUKVMYFBSky8clAUYAnG8H75dHBkqeXB153+4S0PoQMF23TsmSiQR8FeW3EogmsI/AcgKTCLxHSXgtw6clfPr6+rJo0aJs2rQp+/Tpw7Fjx3LhwoXcvn07r127Zih8hoeHO20clSCZNxNSfEqlMyAPgHdS8i0AA3Xa+UxnE9RxK8G91mWUMaYiIpjJUxmXdYfOHlfWGEth1TpHOcONr4CAgEA6IYRLgRcSzmZsxwUzUtLo6R16ihIIJ7CfWoKmM88KjgvI3xGoRLVHSvvJ29ubFSpUYM+ePTllyhRu2bKFMTExzpvkM44pU6ao1iS9Zp72IDk5WfP+JwDmypWL27Zte2pjMYvk5GQ2a6Z+h+bOneu0Pm7fvs3333+fPj76PJ5bJ720TroXMhKYr/le26ajVAt/3gT+0SwfEEACZRXlZ9row0LJqmIvgWU050HW+eTn58fixYuzWbNm7Nu3L7/88ksuXryYO3fu5L///sts2Yy97nbv2JHBJr3MtgV4LWUBumjkz0jJm2PQRnudBbUALKQoOyA1PySEjI6WmC06muzShYdRjp4qj8J/6+zV6xpj8SIg1+rbG7JHQEBAwBUQwqXAC4lnR3N5O+UQYOagVZhS8PI9TD2QOlNzmT7TXlIK5n6QwBxKcfLq0Vn3vooUKcIOHTpw7NixXLt2La9fv+68iT9DePz4sSrMRvXq1Z/6vcfly5czUya15srb25szZsx45u5hXr16ldmzZ5eNNSAggGfPnnVqP6dOnWI7He2uHg0DOBbqcBRPqDMlbaS979syjbbyELiqWd7LSymADLKzPwuBgYZz9fDwYNWqVVmqVCmnmqwakdJ5jZKaNm3KpKQkFsyd21x7kMxiF2rklQR4BWBmo98qg0UcqSibA2BCYKAUA4okDx8mQ0J4GOVSnD7lU7QfodP0CJ3xNJA9FypU8pl7dwUEBP57EMKlwAuJZ+fOZTKB7QTe1zhIGFEB+vkN4c6du5x6WHC+U6JkAqcJ/EpJA/syAXOHPFuUJ08etmjRguHh4fz11195+vRph+9vPUv46SelmTT4v//976mP40hkJIsUKqS59m+99dYzZ8K8YsUK1Tjr1KnDxMREp/e1Zc4cVvEy91EoNOVl2A2wqG65/JTMVO19v4ZrtFWL0oceZdlRinLNHOgviZKzLv35+vn5cfv27bRYLLxx4wZ37drFJUuWcPz48ezXrx9ffvlllihRQnVX1pVUokQJTedPtiijRlqgiXr3dRbwlEbZ36dOlZjq8GEyKIjRyMYQXEmpUkJRfpnOvszRGYt6ryIinj3rAwEBgf8WhHAp8ELi2fQWm0xgF4EhBAqYPgDly5ePgwcP5vbt29MtXD29cCrXCKxhgwZj2aFDBxYpUsTug58WZc6cmfXq1ePAgQP5888/89ChQ0xISEj33j5NJCQkqBzJlC9f/ukIzpGREhOEhpJBQbwDsLnOWtesWZNXr151/ZjsQO/evVXjHDNmjEv6Sj50iPMDA5nXBk/6A0xMYfwHAHvrlvUk8AmBBDveoyRqm9X30yi7VFHmJQff3VhKAqz+nLNmzcojR44Yr19yMq9fv86dO3dy8eLFHDduHPv27ctmzZqxePHiT1X4dDZtN1jAWoqyHTt2lExhQ0JIgF2w0Kp4RUXb83Wa3aozlmIEisvSChbs7pL3QUBAQMAshHAp8ELi2Y9zaaF012kolY4ZjCgkJIQDBgzg1q1bHQoBEBn5tIRLiazPn/fu3eOWLVs4ZcoU9ujRg+XLl7dp8maGfH19WblyZb755pv87rvv+M8///DBgwfp3m+HYLFINtRRUdK/OlrnhQsXquaxePFi140rIoKsV09zk5IgmXZqrW2eoCDu2LHDdeOyE/fv32fhwvL3xdvbm3v27HFNh9HRfPT66wy2wYOlIY99+CuMNGA1CJyx4z26TUBLwzxLUS5So0yMg+9uFLWdCj2h4OBgnjt3zuGlTU5O5rVr17h9+3YuXLiQY8eOZZ8+fdi0aVMWK1aMvr5KB0XPDqXezdSi7xUf0vz8/Hj3tddIgBF4RVFcKcTr3ZO9ZjAepcmsP5csuePEl0BAQEDAPgjhUuCFhCsY23WCmYWSQ59wSg5+zB1w8uTJw/79+3PTpk12CZo6MobTySqkmy7i4uK4b98+zpo1i/3792ft2rU17wLaSx4eHixevDg7derE8ePHc926dbx161Y6dt8ACm2gbBGCgqT08HCZpJ2cnMyyZeUOWIoVK+Z8E88U5yFmNmwJtM0EfTw9+dPkyc4dVzqwY8cOeilMVosVK8aHDx+6pL9r14wO9nJ6FZJpJAFeql6dDcqV0ykbQGAuzTv7OURAecfRl8BuqzJxlLSj1mV2m2xfTa1bn2GOHDlpNN+iRYvyxo0bLln35ORkXrlyhf/88w8XLFjAN954I92/C86iZgAPQPIuKws3UrMmb9++rXIM9WNKfj1sUaxzY0XbU3T2w0Igo854vqHS2VqRIlNdsicCAgICZiCES4EXEq5ibNcLZhYCh5g//ycsUUJ5H0efcuXKxX79+nHDhg02BZSIiKcjXFqFdLMLSUlJPHnyJBcvXsyhQ4eyadOmzJEjh1MOhXnz5mXLli35ySefcMWKFTx37pzjd1oNtIG6VK9e2sKsXLlSNb5Zs2Y5NhYtpDgPsWd8hwAW1Fm7fh07Mj4+3nnjSwdGjRqlGt/bb7/tkr7mzJHfd8vk48OcBl5lvT09ObhbN96+fZtJSUkcN24cPT31NPSdKMWqNLM9am03kJfADasychPJwYN/Zv369rFo/fpP3t3du3fbdNxTqVIl3rt3zyVrbw2LxcKSJUvqjiM0NJS9e/d2mgm+WcoCsDzANgAHVq3KyZMns0aNGrIy9QBGoqzGeiudR40z2JvyOmPoSLU32XKMjBSOfQQEBNwDIVwKvJBwFWM/TcHMYrHwyJEjHDVqFEuXLm36sJMjRw726dOHf/31l+59RJPKLIdJGdItvbBYLLx8+TJXr17NL774gu3atWPBggWdcjgMDAxkw4YN+f7773P+/Pk8cuSIsYBuhzbQaIEsUVGsVq2abCz58+c3dKRj0uo2zXmII2OLhuSkRmut6lSq9Ex48k1ISFAd4AHw999/d3pfHTp0kPWR6tXYFl8FBQVx8uTJjI+PZ7Vqe6hvZpqPUGm09Oh9jfr1+eQeZ1tZ3tChQ0lKSvMRI8gmTbSV602aSPla1yj/97//0dNTqRGVU4MGDfj48WOnr70Sf/zxh+4YihYtSovFwpiYGGbNmtUpvw3OpEIoQmAwJe3kKkra6LaKciMN9r69TtvZCKxTpZcrt1NzPwUEBARcDSFcCryQcCVju0swO3bsGD///HOW0zW1U1O2bNn45ptv8o8//pBpnaz8SzidQjyuMrp+O5UpqCtw584dbty4kV9//TW7devGMmXKqEwmHSE/Pz9WrVqVffr04ffff8+dO3fy0aNHDmkD9RcqhOt++EHV99SpcpM2u61unbC5iQCH6KxN3uBg7t6926X7aganTp1SmVDnzJnTqWaaCQkJzJIli6yPOXPmcMOGDaZ5qWjRosyUaSWB+wTe1CnnQenunNLZTyKByQQuWD031Kg/MCU/XJbeqlUr1ZwsFinEUVSU9K8Zxf306dNtzvPVV191iedeJYzM5rdu3UqSHDFCO3RHJsVzZoD10/lb4VyqQuBbAv+jdIf2vhUvDDWot4vqu/u9CciMJQQEBASeCoRwKfBCwpWM7VLBzCrWthFOnjzJMWPGsGLFiqYPLkFBQezZsycjIiIYFxfHyMj0xr1UUxBuMxJl5YlP+XQTGxvLPXv2cObMmezXrx9r1KjhlJh8np6eLOXpyTCAEwFugKTlS8+CWQIDWb9KFVk/uXPn5sOHDx23um0wwWkbuhCSN1TlWvj5+XHOnDlPbU/1oBXW5ZVXXnFa+J7Nmzer2r927Rrv3bunSu/evbsNJzT1CewjsJxAkE6Z6pRC+6RugbUQW4uS4BFJ7bBGv1DyNvokrXDhwk5ZB5IcOtRIuJGod+/eLo+z2KVLF93+e/XqRZK8efMm/f390/3OPxv0fgov/GhQZgyBLxVpGWnt0CkszNzfFgEBAYH0QgiXAi8kXM3YLhHMgp7E2rYHp06d4rhx41i5cmXTB5asWbOye/fu/Pbb/zE4+LFTxh+CK2rB0prceLpJTEzksWPHuGDBAg4ZMoSNGzdmUJDeAd8+ygfpvtUogKsAXoTCyYcN2pY9u6rNChXGp2svwrCA0cjmFMbcDzC/ztwHDBjg1lAwFouFr776qmpc33//vVPaVwpUlSpVSstT3v+bNm0az507x06dOtngme4E9hBopJMfQOBnSvev+2jkexCoTEB5j9OfwAJZmoeHB2NjY52yFsnJyezcubPN9yHVFNdV2LpVLywHmClTpjRP0e+9955mmexOeOe1KCAggOXLl2f16tX50ksvObHtCSmv4kaDMg0IXNfgiRmy1zkkxLG/MQICAgL2QAiXAi8kngZjR0Y61ULSKX/0z549ywkTJqju8hkfijKzQIEwAispxbhzoTDzDJ1uLBYLL168yFWrVnHUqFFs06YN8+XT0gjZT9kg3Vv8EJL27zikkB966/JycLCijSAC99LHU7aEfTvoFsCGOnNt0KABb9686bZ9jIqKYp48eWRjypAhA0+cOJHutpUefT/++OO0PKX30jfeeCMtb8eOHaxZs6YBj2Qg8DGBz6kWCFLpNQKBdvJeflXawYMH070OqYiLi2PdunVtjmPixIlO61MJi8VCDw8P3b5TNeoXLlzQDXXk54R3XI+8vLzYoUMHrlu5kgVU+cUI1KHkiEl/DnJamvIaXjIo40PJhLadIr2S6nV29COmgICAgFkI4VLghcTTYuzoaEkhZ8c5XUWuUuidP3+ekyZNsnHIlZOnZyZKHiyXE3hkc+z1sZlr0MK+CT/jp5uoqCiuX7+eX331Fbt06cJS+fLR0wmHzgwAawDsB3AmwD0AH6esyT7NOkbOPUwutZaZsoOUAHCQztzy5cvHffv2uW3PtBy9VK5cOV3ebS9dUh/mt2/fnpY/bdo0WV7JkiVl9S0WC5csWcICBQoY8EUeAp9Q39lPJkqaTHt4Te7JduHChQ6vgRbu3LljY05yIc8VMPIeXa9evbRyeuFLcmqk2YplqvqtNFFG3U82AvEpr1QcgUE6dbNa/T81nEwyJe20Xn+rCWg5PNqnep3NXr8QEBAQcARCuBR4IfG0GTsiguly+e9qXLp0iZMnT2adOnXMH548M9IHbSl9OX9IpAgrTfAXR2AMj6BMOtRqz9Hppl49PgS4E+D3APsArArnaD+8AJYF+Dp86Q2lR+AASsHs0ycXhuCK00xkCXCuztz9/f35yy+/uG2btMwgw8PDHW5v5syZsrayZcsmiye7Z88eVX9aITkeP37MIkXGE8hswAtlCbTQyfOgFG4izEYb2jRs2DCH10APFy5cYMaMenEXU38/PLlq1Sqn902SDRs2NOz79OnTJCUnaGbXKZcD7++szz6z6zdVopVWr9NwnTI+BI4Q+JeA9bWFMgbtDiSQRLX2uq/mq+xsj94CAgICqRDCpcALCXcxdnpc/j8tXLlyhVOnTmX9+vUNzcusKQPANvDiQoD3tU4qjtDzcLqJjDTU5EUCnAdwMMAGALM6cEA1pjqUNBJXKN3Bc3CpscBpwiUhaV1f8vPTHPP777//VLyGKhEbG8tSpUrJxuLh4ZHmQdRetG3bVtZWly5dZPlxcXEqBz4bNmzQbCs8nARuEniHgFFYjyqUa62sqSolZz4rKMU1NOekysvLi127duXvv/9uGObGXvzzzz82fz/8/Py4efNmp/WZClvOhazNl7Xu5OqRve/vtm3bSJIHDx5k3759bQrcEmUnsITAWp099CLwHbXf97YG7ZZMKfOFIj2AwAPNVzkiwulbIyAgICCES4EXE+5mbNIxl/9PG9euXeP06dPZsGFDm7Hs0g6MANsCXADwng0hxCY966cbSSowTRaA5wCuAPgJwJYAQ+w8sOpTTgLNCAxLOZyepGQqZ3Kp8YpTBcwbWbOyXr16mmNt3Lgxo6Kinvp2HThwgD4+crPQAgUKaGoUjRAXF6cKeaGlla1evbqszJdffqnZnvwbxVHqaylThYu8OnmZCMymJHg8ILCIksChd29TToGBgezVqxfXrVvnlA8AX36p9FCqpixZsvDAgQPp7ssaWl6CrSlv3rxpWubdu3ebfse87Xwnp0+fLhvX3bt3OWXKFBYvXtyuduR7v9HgtRtio/4lApep/oDxk2Z79es7dVsEBAQESLr/DC6ESwGXwN2M/Tzixo0bnDFjBkNDQ03fMfQF2AqS9u6u/olIn571001oqMPCl7XH2JsA/wQ4DmAngMUBejh0+NQSNmoT6E9gFoH9lO5yaSw1NmsLiQA3OzjH+Oho9u/fX3NsBQsW5KFDh576lk2YMEE1lm7dutnVxvr162X1PTw8eOvWLVU5pSluu3btdNtUh5VZR8AoZq2/hpCQSh0I3LZq6y6BHnbxTo4cOdivXz9u3rxZZu5rL+rXr2+zr1y5cvHUqVMO96HEpk2bbPb5559/ppVv3LixZhkPDw/T1hta1LdvX83xWSwWrl+/nu2KF7fzvnZxG6/cDEV5ZUzf2SnlWivSq+u26U4rGgEBgRcT7j6DC+FSwCVwN2M/14iM5C2APwJsBuleoJmDkQ/AFgDnALxtfEJ6Pk43FovD8WYSAVYD+IXBWtwHOA+FKJnA9abk2VGudXOMvAlUINCTUlzELUyNd5d6TzYKUqzOOpCE3BwpY7Z7rinayVmzZmnGeMyQIQMXL178VLctKSmJDRo0UI3F1jgsFjImRppS//7vy+rWqFFDs878+fK4knnz5tVtPyJCawmTKGmVctvYT630lwhssmrLQuBNh3gmODiYgwYN4o4dO+yOU3nv3j1myZLFZh8FCxbk1atX7WpbD5cvX7bZ32uvvZZWXvmxwJpat1YKYuZJjy/SEBnJSwBLmW7Th8BU6nuKVs5DyRudUsr9rtH2Ic02R4xwypYICAgIpMHdZ3AhXAq4BO5m7OcaClPQaICzIQmOZs3GvAE2B/gTJEFG56T0bJ9uYmIcEiwJKfxI6lpkAvgBwMsa5cIxVpEUT+BPjUOjM6gIS6IMx0L6AKDM3+LIXO/fT1uunTt3MlgVUkWioUOHpks7Zi8uXrzIrFmzysYQGBjIS5cuycpFRkrsHhqq/I5QQlb33Xc/0+znxIkTqrleuXJFd1xduugt5X1KoUmMvIFqkQclM2nJA2knzGF1jXLZAwNNt1mgQAF+9NFH3L9/v2lBc+3atabaLlu2LO/cuWN+I3WQnJxMf3/jtfL29ubt27dJktu2bdMtV7NmTRWvmKWMGTPyzp0kRkVJPxeay1WvHifZ3XYmAv0o3bG15pPzNuplp2Qqn0i1aXV/Td5r0iTd2yEgICAgg7vP4EK4FHAJ3M3YzzUMTEHvQPIW2hKSptLMQckLYBNI4TduPk+nm6gohwRLC8ByGuvgA7A3wBNWZUOxXqeZ9xX1PQlMJhBO4GUaa7oco5YATwNMNjvXoCDVafratWusVauWZvvNmjVLO+w/DSxatEg1hkaNGjE5OZkREVpmqql0RmP8e1ivntq7c3Jyskprt3LlSt0xRUfbio17iUA3B/avCnNhK6ORjZd13s0fv/mGkyZNYtWqVU23W6xYMX7yySemfj979Ohhqs3atWvz0aNH6dxdskwZI8+pEo0bN46xsbE270DqmXabo1OyVyI0VPpgkWqQMf+DD3Q8S3sSUGv71VSPwGJKHxCSaNu6ITX0yKeK9KzUCi+l8RoLCAgIpAvuPoML4VLAJXA3Yz+3sMMU9B7AXwC2gfmwHJ4AG0EK6XH9WT/dOKi53GVjDTwAtkspFyS7N2dNNylpL6zrvq4oc43AGgJjKd3BK5KOA/ITygywHsCBAH8GeAiSZ1zVIHU+CsTFxbFv376abRcuXJiRTzHGaVhYmGoMFStOtLGF3ynq5KS14yRlXNrQ0FBZeVvhTyIjzbxieygJFeb3zR8enAXp40Y7rX319uaJEydIkmfOnOHYsWNZvnx50+2XKVOGo0eP1r07effuXV3NtZJatGjBhISEdO2tGS+wuXLlsulZFpBiYyo9DZunZTp7mMi8eY0c8LxE4G87+slNKSZqYUV6HsXzlyn9X6Ck2bbOm6s5VisDBAEBAYF0w91ncCFcCrgE7mbs5xYOClTrrNbarKMaD0jhO74DePXff909czXScefyEMAwmAm03oiSYxetsAMfa5Q/kJJ3lICWkHSP0h3LKZQcvJSnM0xsfQFWBvhmyn79A/DBhx8aLt8PP/yg8twKgJkyZeKyZcueyhbevXuX+fLlU4zBh3r3zyR6RVH+DVWZkBBJSCTJ8PBwWfnQ0NC0/q3vcVqbTUZG2tJgMoUnVtDejwavQbpPq5VX4qWXGBMTI1ujY8eOceTIkSxRooTpPipXrswJEybwwoULsrZWr15tuo2uXbsyOTnZ4b398MMP083X1vTVV185WPcTjb27Q8mzs6268zXSbHntVgqMSuGyhtU4Xlbk1dbkNQej9QgICAhowt1ncCFcCrgE7mbs5xYOmIImA6yqcQjKDdDf5AHNw8ODdevW5ZQpU3j58mV3r8ITpMNbLAGeBfiuqXWoRGApJbO31Op3CQQqyjWhdBcr9QC608Qw4iiZys0i0J/V4M1MDh2i1XtWvHhxdurUiePHj+e6detUHlX/+ecf5s6tbcI7YsSIp3IPc/PmzRoeQcsQiNVYq1iq7z0u0VzXoCBJSFy5cqWsfEBAFg4fnqxxj1NuNrltm6QFtb1/8QS+0eAFfcphkNe2bVuVUBcZSQ4fbmH16gfp7z+cQEHTfdWsWZNTpkxJc9bTrZt5s95BgwbZ7UAoFT/88IPdPOvj46PLjw0aNGCbNm0ceBdaK/brOIGiOmWDFM+dNcqcomTSatb0XcnbXgQepozlN43yRzX5TMvsW0BAQMARuPsMLoRLAZfA3Yz93MIBzeVGg4OPJySPs20BZrTjwFarVi1+/fXXvHjxonvXw844l3p0A+AImAnSXpTATAKPU6qOtVG+FrW1nvp0HwFMAngSkpMm+w/TxpQ3b162atWKn376KVesWMEdO3ao4kGmUosWLXj37l2XbmF0NBkQMEyj/0Ea66N0TONJSQulvZYhIeSRI1c12j5hai/q1SNHvXOD9bHZRPnolDGnXxM9evRokjS4e2ohsJvS3V+9mJty8vDwYIMGDThp0iTmzJnT9FjGjBnj0L5u2LDB7nmPGTOGU6ZM0c1fsGCBprbdmApYrdtqApk1yvhQChOifJ+1vOxGp7QVT+nDhn3m0RKtSWkjgWohdbAhnynNvgUEBATshbvP4EK4FHAJ3M3Yzy0cNAXdDLCSwWEnK8AvAS4F2AVggB0HperVq3PixIk8d+7c01+PyEgS0j22GGRmFLIzBpllMSztoRiAXwHMY3PeeQhMIHCVQC4bZX81PYQg3JaNfbNGez9BCqHSDmBBuw+1OvufNSvz5FGa70lUrFgxHjt2zGVbKHlojSdQUaP/dYo1ek+RX9fmmr72Gpkhg1IAm2cXa4RhAbehNkdgDJvgL9Vd3CDcZhP8xREYw9Uoyrbp3A9JEFxjcnzJBLZS8jZqixcl8vS0ZdoppxkzZti9rxcuXLCrj4oVKzIhIYEPHz5k9uzZNcs0atTI1B1NNd2hJDhqxczMTWB7ylpeNNHWZY09iKRksaC8h61H1h9OwhV52fjk45X+R5OneDVaQEDgBYO7z+BCuBRwCdzN2M81HDQFTYbkAMZIcCoC8DeAsQBXAewGMIuXMhC4PlWpUoXjx4/nmdOntS+zORFpYSoC92oe9kOxnuEYmxY70h56DCmOaAabWqGslExhjcoUsnlYTKUm+EuWkAi1CeVkRaU7kLTTX6fsV5msWellx56ZIT8/P44fP94pXkStIY8teYxqk9c8BKJS8i1UO0v50qq+EbVT1NMO+2BEIbjCSJQlIX3MuI8ARiE77yNA9THjGsBBkN4nx9c9K4HTdo4zkcAGAm9RbeLpOHl4ePDXX3+1a2+TkpI0Y6vqkbUA+8UXX+iWW7t2Lf397fXG3FgnvQrVwmJDG22dUpS3phgCI02Mp7RVHS3vxwtt7nWq2beAgICAvXD3GVwIlwIugbsZ+7lGOk1BHwD8BMb3DBsCPJBSPm7oUK5evZo9evSwK95cJYBjAZ5KPQkpYwA4COMwFdpUD1u4Bi3sXqthGE3J22RlOw+zSvrKVJcjMEaV2EvRVgOjBkJCyOhoxsbGcs+ePZw5cyb79evHGjVqMEOGDOmcg0QlS5ZkWFgYJ06cyA0bNqQrfIl6H5WeYEGgPSXB8qRG3kGTWzlOUa+avaxAQPpokSpgGlGEE9ZZojIEHjg0VkkbvIa+vt2ZKZOWKah95OPjw/Xr19u1v/Z4eC1XrlzaXdM7d+4wICBAs1z58o0JzHHC2nal9r3e2TbqHbax7gm07fQHlAu1yg9UDUztccrrLiAgIGAX3H0GF8KlgEvgbsZ+rpFiCppeugjJY6re4ccDUtzHaxs3pnUdHx/PtWvXsnfv3gwKMq8ZKQ/JlDMthqQD3imio42C3JujMCxgNLKZrhCJsin/tRBYT0Ae1sI8ZSFwy2aXWlrW/yna8gQYpVXZhiojMTGRx44d44IFCzhkyBA2btzYrj00ovz587NNmzYcNWoUV61axYsXL9p0BKPNxhaqPWiCkjDxjSItmObvsypDSvhScqRkPw+F4IpNHprqhDV9Qq/ZMU9tCg5+zHnzfmOnTp3S9ZEhU6ZM3L17t+l3tnXr1na1v2jRorS6xt5mN1NysuXIPDwpeXLWW9MYqjXo1rTLxJqb8SA8x6r8Uo38f03tbViY6e0QEBAQIOn+Mzieam8C/xm4m7Gfe9irujOgnQBr2jhQjh07lrGxsbIhJFy/znUNG/ItgNntONyVAfgZwKOpJyMTn94PHzYTGsK8cGBG+5RK9XyVHl/3UDrwa93fMiJjU8z62KyZEQu1s6U5qkk5cAnLYqHl3j1eOHCAqxYt4qiRI9mmTRuN0CCOUfbs2RkaGsoPP/yQCxcu5PHjx2XeZ/UV8NcJ5FC0F0CgjiLtTTv2/Z7Gfu1xmIfCsMCwwGAnrJ+cMqfMvzuBUZTujG6jdOc3WWsI6jGnCCEPHjzg4sWL+eqrr9pltmq9r8ePHzfFYu+//75dbRcsWJDx8fEkyatXrxqML5TADgfWMSuBP0ysl5aX2FTaZKJ+cxNj6WJVPp5SvFbr/A9N7SsgWXMICAgImIW7z+B4qr0J/GfgbsZ+7iG/rJZusgBcBDCfwWEof/78XLx4saSRUkh7iQDXA3wbYE47DnulAH4aEMDDy5fraroOH3Y4nKUumTVvZFAQI6ad18k+SUnAMeu90pNSGATtrozMdtsr2mpjnW+P+8i0i6rGcTiitm7l+vXrOWHCBFaunF6T4CeUMWNG1qxZk/369WPJkjMpCXha91FXatRXCocr7Nz3kor609LFQxF4RTdzIsDqMA454jzyJ1CKQEtKDo++IbCKkpMZuUmtUgi5d+8ep06darcH1kyZMnHp0qU2Q9VMnz7d7vlMnTo1rX7fvn0Nyv5D+7SXxWhWG6j2SmxNa03UVzqe0qIclH8YUGpqc9Csdr1+fXOvv4CAgADp/jM4nmpvLxiuXLnCZcuWcdq0aRw7dixnzpzJ1atXq2LNpQeXL1/mn3/+yR9++IHjx4/n+PHjOXPmTEZERDi1H2fD3Yz9QiC9NqIaFAtwDGAYZ7FWhQrclTmzbhuJkBzMvAsplqbZw1/xAgU4YsQIHjhwIE3QjI52nsZSSTbNG620gcZLfYXAEEraNVvzrEBJSyFvI6zBFemEqNPJfEU7/gAf1qlj3rTYoYuqT0yX//77b2bLls30XtpHXgTKUtLKfUNJM3SXklMao3qL7dzzNxT130gX/+hpmpUUA/AQJEdZkyC9Fy8DLOaStdSiXARqEghj/vyfcM6cOdy8eTMvXbqUJhwuWrTIobZz5crFgQMHcvv27arYnCS5bt06w/o5cig11FJaTEwMSfLMmTMGnm2bEthox3iH2LG/idSPY7ncRP0pJse036rOvxr5S02POZ1X2QUEBP5DcPcZHE+1Nztw9uxZLlmyhB9++CEbNGjAzJnlDgsKFCjglnElJydzwYIFLF++vO4fFC8vLzZt2tRu5wik9KX5l19+YdeuXRkSEmLzj1eFChX4ww8/8PHjxy6YreNwN2O/EHCh5HUtd272CgvTCG7/hLoCvGSjnSSAWwC+BzDY9CEQLFKkCIcNG8bmzfcxvffNjEjXvFGhDTS31LcpmbrZml9OApOZqlWSOeU4coQcMYJs0kSmWbwN0EvRzooVK8zxSLovqkprcf78eVasWFFzTlWqVOG7777LBg0a2OX0yZjy03Zoh3ACSSanMk1Rt2S6+ccRT8TWVM0p6+Q4+fj4sFixYmzevDkLFiyYrrby58/PDz/8kPv27Uv7OHT27FnDOkWLFtVM//TTT9NYuHNnIxPVfwgEmhxjFgKP7NiewTrt/GKiboTJMY1X1GugyG9ierwjRjjzj4uAgMCLDHefwfFUe7OBTZs2sVmzZqa+oLtDuLx+/Tpr165t1x/kt99+m3Fxcaba/+677xy6IwOApUqV4t69e128AubhbsZ+YRAZ6QKb0SeOYfbv388GDZQHnieUAeCnkDzQ2mo3GeA/kEI05LWLfwsR+IhS0HjnC5oy88b69XW1geaW2kLzpnrZ6O8/ilu36pi0Wizk/ftSOJf79xkaKncm1L17d2PecOpFVUmL++jRI3bpoi1Aly5dmqdOnaLFYuG5c+e4YsUKfvLJJ2zZsqWpD2GOU2OacZYkmeAq695L17Joefe1h3qqxlOegLeN+WalJBgbOZ1xLxUtWpQff/wxDx48aBhT09vbW/MDVsaMGXn9+nWS5KFDhwz6akZJg2lmXHkIRNuxPQd02vmRkvXBcUrm2w816mp5Ntbj3dQ6SdT2lnzG1HibNHH4r4iAgMB/DO4+g+Op9mYDkydPNv3H7WkLl1evXtV0hBEYGMjmzZuzS5cubN68ueZX/ddff92mZ0WSHDJkiOZcs2TJwjp16rBdu3bs3Lkz69evr+kRMGPGjNy6detTWA3bcDdjv1CIjHS6EGENi8XC3377jUWCg3Xft2BIMTSTTfaTDMmR0AcA85t8pyXKT+ADSs48zDkysUX1Aw9Ln/1N2JWZW+o/7ZiP9F4OGjSIFy9eNOz7u+/kB8+goCAmJCRoF3bJRVXpo4PFYuGkSZM0hYasWbNy7dq1mkO6efMm//zzT44bN47t2r1OoDjtd4qkRz6UQpZMp2QqeZ7ABUqmhpEE9lEyt1UKbuGUQk845jlWGZfUXpqgmkcdSsKEMi6nlqB0mpJDn38IzCfwGSVT37oEXCnMO5f0rHz69euXxjuvvPKKA20rP0IH0L6PU7cphYJRtpubkil36rOW99g4mgtH4kNJo96Bkga2CtXxScNNjTcoyCXhhAUEBF5AuPsM/lwIl35+fixSRO76+2kKlwkJCaxZs6as/4CAAH7//feqw19CQgKnT5/OTJnk5l7WZkB6sBYuX3rpJY4YMYL79u3TdKrw8OFDTpo0SSVkBgUFPRN3Md3N2C8coqMl80XzJyc12XAME1enDicCzGJwWKoCcKud/VoA7gb4EcCCdh0eXyIwiJLXzPQJmvbcV7K91BYC9RRjtX3Q9Pb2Zo8ePXjs2DHNfi9duqSq8/fff2sP0EXm0snBwXx85QpjYmL466+/an4s8/Dw4JtvvsmIiAj+9ttvXLx4MefNm8cff/yR3333Hb/++muOHfsl/f1HUboH14lAfUrCZjY6T+C0h246tCRBuE1LOtbzd9U4gvhEAPonZU30xuxBY+cysZS0axEEplIy82xDL69yqr8/7iQ9ax8vLy/++++/JMlt27Y50LaWBnivjS15ROkub8uU+lofdLMrnufqtFXQgTF7EFA6McpNKXambZa6f9/875iAgMB/F+4+gz9zwqWPjw8rVqzIt956izNnzuT+/fuZkJDATZs2yRbqaQqXP/zwg6xvPz8/7tixw7DOtm3bZCaufn5+PH/+vGGdIUOGsFy5cly+fLmm8wQt7N69WxWM+u233zY7NZfB3Yz9wiIiwtAxjCYZmIKmwSoo4S2A70CKt6h3SHoN4Fl7xoAnguY+gLUqDKS5WHGpFEzJQ+MWmr+D94Tsua9k7hrjVgcOlk+oTZs23LRpE+/cucPr16/z4sWLPHXqFEuXLq0q9/vvv3PZsmVcuHAh58yZwxlVq/JbgF8BHA3JbHkoJHPkdyDFLu0KsCMkr7PNATYCWBvSx4FyAIsDLABJI50NkoMnn3TM59mnS3bzTCrdR4DDwuUhzTulN6yKJFPSxup5c/UjMIP2movHxFh48+ZN7ty5kwsXLuTo0aPZq1cvNmjQwMUmzGry9vamv7+2iW+HDh3S3ru6des6ob+3DNbFQqCEovwAjTbyKJ71NItNHBzjJI2030zta1SUg383BAQE/lNw9xncgyTxjODu3bvIkCED/P39VXmbN29Go0aN0p4LFCiACxcuuHxMJFGkSBGcP38+LW306NH45JNPbNb9/PPP8dlnn6U9d+/eHfPnz9ctf+nSJeTLlw8eHh52jXHatGkYMGBA2nNgYCBu3boFHx8fu9pxJo4dO4ayZcumPR89ehRlypRx23heOBw9CixeDOzZA+zfD9y9+yQvKAioUgWoXh3o0gWw2gddjBgBjBsnSzoG4AMAf+lU8QUwCMDHALLaOfwmBU/j7wtFABwGsCyFTpusnRtAewAdAdQD4K1TjgASAcSjfv14LF6cgPj4eMTHxyMhQfv/p08n4Muxj3EvJhFAAoD4FNL6fwSAa1b9eaT0KfDsoT6AugAqAagIoDAAT1M1o5ADOXDboV5vIhvyIBZAnFXqJgANFSUfpYzrjE5LbQHMApDDVL9RUUAOg6IzZ85Ev379ZGl+fn6Ij4831b4z0aVLF9SpUwfR0dGyv5eOIQuAWwD8dPI/BPC11XNBSHtzwyotK4AYq+f2AFZYPV+F9Kv4JfT3ywgjAGwGsMMq7WUAf9isef8+kDmzA10KCAj8p+DuM/gzJVwawV3C5ZEjR1C+fPm0Z39/f1y/fh2BgYE26965cwd58+ZFXFxcWt2oqCgEBAQ4dYyxsbHInj17Wj8AsHv3blSvXt2p/dgDdzP2fwok8PAhEB8P+PkBAQGAnR8o0KQJ8Pff6qYhHXmGADipUzUHgJEAugCwwFgcS6UenrPxyOJplRMH4DKAQ5DE2jsmB+4FIABABkjCXYJVmwkm2xD47yELgAqQhM1UKg1A/UHuPjIjMx461Mt9ZEZWFIHE16mYDuBdjdIWALUB7NZpLQ+A+QCa2u7XhhBCEi1atMC6detk6SEhIbh27ZpOrecFKyAJhFo4BGmvrVER8v1RoiSAyZAEyr8g/T7ZizYAXoW0dy8BmAegp1W+B4DzAArothAUkIDb933t/mkXEBD478HdZ3C9T/4CKdi6davsuUaNGqYESwDIli0bqlWrhm3btgEA4uLisGbNGnTq1MmpY8yYMSNKlCiBw4cPp6U9/wcEAdPw8JBOko5+0iaBAwe0mwbwCqQj0UwAo6AW+6IBDEwh07C8afcwtZEMScsQY6ugQDrh6ekJb29vJCRoC+358+dHgQIF4O/vDz8/P/j6+mLbNj9ERflB0nP7pZD1/38DsMeqldIArgOw0sTDF8BgANMAxGr0nAPAPQBJdszmPoBtKWTdT1k8ETYrIhD5EOCgYAkAmfEAviiKBJnwclyntCeAPwFUBXBWI/8GgGaQ7Am+hJ52LihI+r5kBA8PD/z4448oW7YsHjx4kJZOEtmzZ8ft245pap8NzIW+cFkBQBnIBURbH6FOAmhhR/8ekPYy2SqtPYAeVs8dIdl9pP5uEcBsAF/otlrFOxIeHlXtGIeAgICAeyCESxu4cuWK7LmsGRNDK5QrVy5NuATgEuESALy95VupdwAUEFDhwQO5Wa0GfAC8B6ArgNEAvoN9R3kBx+Hj44PChQvD984d+EVFqcQzZ/xfM3/gQPh+9hn8/Pzg5+cHLy8vAMDp06fx6quv4vhxuZB06dIl1KlTB7NmzULGjBkBAGvWAK1a6c2MAKYo0voCKAWguVVaAiSN0W5I+vGjijrRkMxMRwC4BOAzAFdgPxIAHEghCffggZKQ6zcrAchpskWPkBC85FkK52TDOWFQIxDAKgA1IZnKauEbAH8DWARJGJejShVzhgv58+fHxIkTZeax169fR7t27fDnn3/i8ePHthsBkCdPHkRFRSE5Odl24aeC1QCqQdI4FlZQMKRfsRFW5bUE+fQgFJJm/DertL8gFy4zAugGSYudijmQbEC0j2XV760Djvqbu+YgICAg4EYI4dIG7tyR62nMai31yh85ciSdI1KDJM6dOydLCw4Odno/Ai8o7PgQEQTpaNsPkhiwxUVDcjU8PDzShCYPDz/cu5ceEW01AOv32gtyrQUAfIYJE0LRsKFvSp8e+OOPP/Djjz+q3l0lEhMTMXv2bNQZNUrTdNllOH5cUoMpUKxYMezatQs9evTAypUrZXmLFy/GiRMnsHLlShQsWBAtW0rXfhcv1urgEOR33QBJT14MklbnW0XZBQB2QeK8RYp6myHdf1sOoDsA6/vDZVPaPZhC0ToT1gJxCsApAEutUvPCWr8p/VsQks4qDWFhwNSpqNh3i0K41NNcWo/3ZwCvG5Q5DKAKnryNT3q25zZE37598euvv2Ljxo1paStXrsTo0aMxatQoWCyWtHQPDw9o3aIJDw/H1KlTcfasXEjz9PSU1ffy8nqKAui+FFLCD5JZqjUcuWeaFUAjSB8ClEiGZOthLVxugGT2bH3Ptw/kwuVVSJcQWmv2GIKr0os0dqwD4xUQEBB4iniq7oPSAXd5i33vvfdk/X744Yd21VfGrvT39zftCdYs1q9fL+vD19eXMTExTu3DXrjbU5WAHYiJccgT5nGT3hF9IXkkDYbkobQ4QC+UphTzrTaBRgSaE2hDoCOBrgR6E3iHUiiSoQQ+JTCcwMtUe3M0T1myZGFYWBhXrVrFx48fkyTrlbEn8LoWnaY8Lh4IZFY8F2Hd0tdUS5+UlMQVK1awWrVqhuP28vJiX19fnnZggPMArgLsD6lhI7BecnIyR48erTne7Nmzp4VR0Y+cMkZRr6hVXizVMQg9KMWytFAKRq8VisKH6lAPWfgklI2FwGUCvxP4glK8yYIO85M1BQJsCPD9vHk5f8gQHjlyhImJiVy16rhG+TsmtmCoyb5bE7iVVs+esDskee7cOVXokoIFC/Kbb74x1X9wcDDr1VOG5lFT2bJlWbVqVaes9dMnDwK1CHxOYCeBxJT1fkmjbC0CZzXSD2rscXWNvdTmh/rYTDZpYt/mCggI/Cfh7jO4EC5tYNSoUbJ+u3btalf9sLAw1R8ZWyFJ7IUyAHWLFi2c2r4jcDdjC9gBi0USJGyfdmX0GOBBgEcAfgkwu8HhrDjA1Xgi4IR6b7K3OyuyEDhE4CMCeR0+MAYEBLBFiy4EVlASZtIjYPZRtK8UNkHgG92Dv8Vi4d9//82mTZsajtkT4OsA95sc2F2AQSl1qwP8C3YKmSYC6/3+++/MkiWLaqxeXl6cPHkyLRYLIyO1WEwZ/3CgIv8QAV9FmXwE7qbk77Bj/0/YmOodAhsJfE2gGyXBVmsP7SN/f39WqVKF6tie200sfxIBY354QnkIrGP9+o79BEybNk3VZv/+/Tls2DBT/WvFQ9WiGTNmpHtN3Uc+BIpR+hDWj8BXVH8AAYHKKfunDLU0QWOPf1KU8aT08UObJ45kqW34wUdAQECAdP8ZXAiXNrBkyRJZv0WKFLGrfuHChVV/fA4dOuS08S1fvlzVvmbg9XTg5s2bPHr0qF20atUqtzK2gJ0IDU2PZEUCjAE4HKCfwQGtCcBIgOEFF6W3uxRKpqTNepOAuQOuNmUi8DqBXwk8dGAcl6gWhHIqnoP4fveTNrdi3759pjRBzQD+DWNh8VONeg0A/mN2YiYD6508eZIlSihjCErUvXt3xsbGMjLSWoMZTekgbV32D40hTNRoM8wq/yYlzbet/Z3nwJ7GEthDYCaBfsycuQb9/DKkg8esKU/KPCYS2EDgts4YommPZrVt28FpGnl7kJyczAYNGqja27hxI7t06eLwPJUa0WLFijlp/VJJyUPPAmWhZGVRX5HexGpfTxH4jMA5AgGKcl/o8AI5AmNMffAREBD4b0MIlybhLuHy+vXrqj8eO3bsMFV327Ztmn98tm3b5pSxnTt3jkFBQbK2O3bs6JS2raHU3jpCQrh8xhEe7gxJjwR4DpJ2TY8XPAF2KFmbkmDgtG4JPKakhWxPtaBnD2Ug8BqBJQQe2NH/IEU7Sm0VmC9zD1PbERcXx4wZM5oabzWAKwAmKwYUBTDAoF4LmNCA2nGQvXfvHlu3bq3ZV5UqVXjp0iVGR5NhYSSwSGPNtbTHydQSHqtVW2hVJpGALQ1bfyfxWCJbtjzGGTMWcMiQIWzcuLHqN9hxyk/JNHwUgVUELlLS0h8g4K8o601Au9/y5cs79Ht7+vRpZsggF56LFCnCefPmafajFBy1qGjRonavQ9OmTU19XHk+qTjlH52mU23GnZ+S1lrNf03wl+kPPgICAv9dCOHSJNwlXJJkaGiorO9atWoxKSnJsE5iYiKrV1fep5Bo7dq16R5TTEwMy5SRm+QEBwczygV/eIRw+R9AZKQzpTwS4DaAVQ14wssrMyVTsThnd03JdHIWJcFELeSZJ39K9/IWEohJadtCYAiBdYpD4A1KGlDr+vkUz948efJfU1vy+uuvy9fLxlhLAJwNMD5lQMkAFwEsZqPeawCPaS2ijTuXWkhOTubIkSM1+8mZMye3bNlCkmzUqJsiv6XBXl4iECgrnyVLVubKdUFRbiUlrZHWPIP55J5c+ikkRHplSMmk+cKFC1y1ahVHjRrFNm3aMF8+5b47StkJhBJ4RSMvhEBjzXr+/v6cNm0aLXbu3+TJk1VtGX3ksCVgOiJc+vv789KlSzx06BB9fHyctI7W7+OblCwUtIXmp0u1CezTSNfS4pNBuE1LjNBcCggIGEMIlybhTuFy48aNqh//Ll26MC4uTrP848ePVQdDa/rzzz/TNZ74+HiVwOvr6+s0jagSQrj8j6BePacLmMkA5wPMa8gfhQgsoyS0OX0InDfvMr/66itWqFAhnXzsR0mz9JlVWj5KzobOpvQ3QqOeXIvasmVbU9uxaJFcu+cDcCTAXDbGmRfg1wDvpyxAIsBZAPMZ1PEA2B3gGeuFS4fzkJUrVzIgQGnuB3p7e3Pq1KnMnj27LL1EiemqO5lBQdIQRowgJ05comqratUG9PJSanj+JVBWZ54N6UxteVDQEwFTC1FRUVy/fj2/+uor1q6tvF/qLCpGX9/B9PHR1tS3atWKN2/eNL1vSUlJdo/VSPjUEw5tCY29e/cmqXaoZz95EGhA4FtK926tf2PiqPxoYUyzCCxiQbzJpniJ9eBF6aNFevfwHIFKirT2unx3P0bcuRQQEDCGEC5Nwp3CJUkOHDhQ9UehUKFCnDRpEnft2sVTp05x165dnDhxIgsWLJhWxt/fX3WQ2rVrl8PjSEpKYvv27WXteXt787fffnPibOUQdy7/I4iIcL5kl0IPAX4GMKPhIasepa/4zus6LEw+xaNHjzI8PJwFChRwwqFQSY0I/EC19kzp2APctGmTze24d++e6hC+FGAswO8BFrIxniBIdy5vpSxGHMCpAHMb1PEG+DbAy4Ak1aUDx44dM625OnfuHC0WyQo3Kkr6V6l069ZNqe0EtZ2kPKTkdVirr7yUvH06h79CQiRvuLZw6NAhjbH0JFCV0keL9PGel5cX/fy028mdOzf/+OMP0/t28uRJ+vsrzXAlql+/vuZHA73ymnwZFMRx48bJ0jw95XcnPTw8GBoaqtmXY1SRwDhKgpz1HipNUkHJcY+6jdZ4jUdQJq1yFLKn8JqyrDcl4bCSyb0dQ0Dp6MibwHVNnhNWsQICArYghEuTcLdwmZCQYLdjA09PTy5btkx1kD150rZTDy0kJyeze/fuqj4WLlzo5NmmH+5mbAEH0aWLywRMArz86qsqHlZTDwJX092d0cE/OTmZfy6NoOT1MZtd77Vt0jpQBsqeK1WqZCokUfPmzWX1ulhNMBGS2Wt5G+PJAHAAwAt4IuiPxxMvslrkB/D97t3t0npp4e7du2zRooXh+EqVKmWqrXv37ml8FPChdCdRuf8W6oes8aEUysQ5mvKwV+7aHHtsbKxKgGrefG9KGwkEIimZaQ6mpGnL6mSeBFu3bs0zZ86YMpX94osvVPV9fX155coV/vnnn/TyknvS9fT0pIeHOfPzDBky8Nq1ayqBVLk+9pE9pu/VCXxDySvrVh3+UNcLV2x8DDKn/DeHoqyXVbF/NNoqqXguReAeAaUGeJwmvwl/PgICArbg7jO4EC7txOTJk5ktm+3DaKFChbhp0yZaLBb6+spNlu7evWt3vxaLhX36yMMdeHh4cM6cOc6fpBPgbsb+z8JikeJWRkVJ/9rrtl4/KKFT1Tx79uxhnTp1DN6hjJRiyj1yqCtbJoskabkXwyDcJhBP4H+UvMWa18DYR2qv0XPnzrW5HcrQDVm8vNLuVKaSBeBaSF5gjcbgBbAbpNAxBHgPkpmtkdOfTJky8eOPP3boNysVSUlJHDFCy1xYos6dO5tua+vWrRpCTCkdPulhY0+60jHPwGqKKPMRuWaN4diLFJFrsOfNm8eICLJ+fa02LZS0bCsIfELpTmqIU3gxW7ZsbNasGYcNG8YlS5bw33//VX3o6NWrl6qeh4dHmtXN7NmzVfn2CIeTJk1iuXLlnDIfiZQCnlmqS+17uuq7pO2hfu+k34/KGvVTzbUTNdr/WKP8AUqxfZW/GcnWXTIoS6KIRCIgIGAT7j6DC+HSAcTExPCHH35g+/btWbhwYWbOnJn+/v4sXLgwW7VqxV9++YWxsbEkJQ981uMuWrSoQ332799f9QdpxowZzpyWU+Fuxv5PITJS8vYaGqoOJhgUJKWHh5uPrq4dlDB9pCHtWSwWLl26VGZGrqaXCPyiOmQZkbWzFUNYLBrxNmMIzKUUX9DZYQ7knjhDQkL48OFDwyFeu3ZN1c6fBpPfAbCtibG0wpNwJFEAPwTob1A+MDCQY8eO5YMHD8zxkAaWLVumeT/P29ubP/zwg+l2wsPDNcb4nsZyTDexJ2UphYVIH3vXx2bpP2FhuupypSfd4cOHp+UdOSJZITdpov0Kp9493bz5Jv/880++9tprTuXNTJkysXbt2uzfvz/ff/993XKlSpVKC3Wi57hJi0JC0icYa8VRlVN6nHZpUVdVWhmNjQ/FegIdNOpbf+x4VaPtAoq0DymZayvb2SDrMh3XoAUEBP5DcPcZXAiXLsb8+fNl4+7atavdbWj9sZ8yZYoLRus8uJux/xOIiLDfCU+9ejY1LCSpCEqYPrIh7T1+/Jjjxo1jQEBmg8NedZoJPG9wttdEeIGFBu1dIzCZ0r04Zx5cn9Dnn39uc4w1a9aU1elXtKjNNT8GsAekO5RG/dcFGAFJC3MV4LsAfQw0UDlz5uQ333zjUCxFkhwzZoxu23379tV1kmaN+Ph4VqigpS1aq1iGPSb3ITMlT7PpY/O0+3g6/D5smDxcSps2bTTnZ+vuaSreeecd1VxCQkJYsWJFF3hZfULh4eEp47SwZ8+eLusnlWbPns09Gk717KNytM/8XR2P0xtggmLTwzGWwFCN+nesin2vyMtNKQ6mdVpeSlpOpTb3dRmPpfMatICAwH8E7j6DC+HSxejRo4ds3D/99JNd9YcOVf/hmjhxootG6zy4m7FfaERHp/9upBkp7ElQQtf2k4IbN26wT58+Nu5udSJwQdVN/frmZGYlIt+cYnIqJyl5hVWbt6aHPD09+c033xhqBMePHy+rE5w7N5ODg02t/0WAg2DLkRJYDuACgInBwTx/4AB79uxpaOaYN29e/vDDD0xISLBrvTt16mQ4jlq1avHatWs229mx4wSVmmDpjuUtq+nHUR3v1MgUexjTE65kBMY8edDQ1M+dO1fWX7FixexaOyXi4+M1PbsOGzaM8fHxPHjwIOfMmcOwsDDTMVPNUuPGjTl27FjOnz/fhFbRPGXIkIF58+aVpZUoUYKJ168zNF1tZ6YUR/UPSubSjo75FIMQzVr4h7+hLSNRlpIXWWW5a1a8cVojf4VG2kYCUxVpPrTmabPGJwICAv9tuPsMLoRLF+L+/fuyOGABAQG8b8dt/E8++UT1B2js2LEuHLHz4G7GfmFx+PBT0yimQf9imD45Ku1R8qzZuLF2/D6J/OjvH86GDe9zxIh0HrgiI1kPW+yYmoWS+dp7lAdDTx/5+fmxc+fO3L59u8rhysmTJ1Xld/7yi12my1EARwHMZmMcBUNCOG3aND569IgnTpwwDKkEgIULF+b8+fNtxv0lpdi/gYGBNtciODiYO3fuNGwrJkZLIwQCbSl31KOMNWzrnl8jOhqupAn+Ur9fVh9W9uyRa1I9PT0d1gCn4tq1awwOVofD+PXXX2XlHj9+zEGDBjmNX51B2bNn19R8ao3zx2+/5YZ09/mz1fY8JrCKQBeqP1IY0e+yLc6Ce8yFXzXKnbAqZ6EUbsk6fyKB8oq0NylpPJX3vicSIH197bPKEBAQ+O/C3WdwIVy6EEoTsL59+5qu+/nnn6v+YI0aNcp1g3Uy3M3YLyQOH34qdyF1YfZimBM+r1ssFv7vf/9jsWJq87RUyp07N2fNmmVKsDFCRJmPHFy+BAJrqB9X0THKmTMnhw8fzuvXr6eNsVSpUrIyw4YNc8h0+QHAyQBfMjGGMWPG8M6dOzx48CBbtWplWL506dJcvny5oSfSf/5Re86cNm0aM2RQH+59fX05a9YsA/4gAwMtBF7RGM9PVlNWxklsS+kupnZcSIlCCOyw/1XCbVqUiVaxcO7fv6/q6/Dhw+niXZLcvn27ygw2U6ZMPKLxHv7xxx/MnTu35ryVHmBdTcWLFydJNmjQQJb+8ssvs0qVKrK0HDlycICvL33S1acPgW0ae/fAjjb6atSP1ii3RlHmbUV+U0phdKzTslISepXetIsz9YOJMrSSgICAgBbcfQYXwqWLcOLECdmhKSAggBcuXDBV96uvvlL9sUq95/K8wN2M/cLhKXlxNQ2zF8PSifj4eE6ePNlQ41WxYkVu3LjR8U4iItgFRncvbZGFwJB0HHr1qVSpUpwxY4bKPL5EiRLS2B00XY4H+DPAUjb6DwgI4JAhQ3jlyhXu3LmToaGhhuUrV67MtWvXagqZSo+x5cuXJ0kePHhQ16nTu+++y/j4eM1tCw0lgRtUa5Az8omTnvmKvJCU9L0EtPuUyINAGO0NV3IfAerEiIi0MefLl0/Wz5IlSxznWyt8/71ai1u0aFFNL783b960+bEAAN977z0OHTqUVapUkVngOJMWLVrEmTNnytI8PT25ZMkSJ7Sv95sxRGPvtLSXWub5RXX2XlnuW0X+ckW+PyUeVfaxgtrhUTZrsZOAgICAJtx9Bv9PCpfKH24zAc0TExNNt3/y5EnVvZHvvvvOVN2pU5V3LsAhQ4aY7vtZgbsZ+4WDi+NPPuufxKOjo/nee+8ZaldeffVVnj592rH22/dhCK6kYwktlO7sOf8ADmhrlY4fP/5kAg6aLievXs1Vq1axRo0ahv37+PjwzTff5MmTJ/n333+rnAwpqW7duty8ebNsjStWrCgrY+0tNSoqStcUul69erxx44Zqz8LDU6fyu0a96pQ0y2qTYqTt8x1Kmkyjte9Ae8KVRCG75jqnQhm3dOTIkQ7xqxIWi4W9eytDWYCvvPKKZjxVi8XC6dOnq2JNWu93rly5XMbPSlLe79XTrtpHeuFJ5mrsnZazH28CpRVpnpQ0ncr6yvvJgxX5dzTK/EUppql1WntKvyXKWJhdtdhJQEBAQBPuPoM/c8Ll5cuXef78eRUtXrxYtlB58+bVLHf+/HlGRUUZ9qH8I2JGuOzatSvDwsK4evXqtDAjSly9epWjRo1SmXm1bNnSVODq2bNnq5yZtG/fXneeRpSeuHTOgLsZ+4VCRIRrBctUeg4+iR8/fpyvvKJlCvnkUPzBBx/Yz//R0YzM2Tglbl16BMwPtcfl7a07Zkfp3XffVc/DQdNli8XCzZs38+WXXzbs08PDg6+99hp3797N1atXs0KFCoblmzZtyj179vDKlSuqvK1bt8rGkJiYqBsG46WXXuKePXtk5SMjraenNDsEgZGUQthkVaT/ptizbygJEnrzKEPgX1M8oKm5BNLWe/DgwbK2O3ToYB+fGuDx48esVq2aavxGAuzOnTvT7Vk2c+bM7NmzJ/v27Wtoxu4e0tI+ztbYorw69btppM3TqK80s35Vo4zyg8yHBH5UpPkRuEvga430J79NwrGPgICAEdx9Bn/mhMsCBQqk+w9Kjx49DPtQljcjXFrHFfPx8WH58uXZpk0bdu3alS1btmS5cuU0vVw2a9aMjx49MjV35d2T9JC772e6m7FfKNgbbsRReo4+ia9bt45lypTR5f/s2bNz+vTpdlkcMDKSkVlqO0GD+YHT3mNbFBgYyF69emm/Xw6aLh88eJCdO3c29BYLgKGhoVy3bh2XLFnCEiVKGJZVai2zZs2quze//PKLpkbNz8+Pc+fOlZV98mo8pDp8hCelu5NNFOnDNfZtB4F8uuMHAigXStWkeecylVJiSPz4o1yYKFOmjHn+NIFLly4xZ061o6lVq1aRlD4iHDt2jJMnT2aLFi3o7YSPHj4+PoxMube9YoWWF1Tnki3Px7YpO6V4ttZbVFSnbEWNtAYaW6wMo1RFo8ynijIVKGk0lYLpbAJRGulTlOwkICAgoAl3n8GFcAn7hUsz5O3tzfDwcLsOt0K4FFBBrp5xPT1Hn8QTExM5Y8YM5sihZ/4mOZr5448/zDcaGcnoPGUYhgXpWEYLS8BYAwiA/v7+NgU4eyhnzpwcPHgwjxw5YspSwhbOnDnDfv360c/Pz7DfKlWqcMmSJZw9e7bu3UkldezY0bDvffv2qe4nptLAgQPTQqDIlfp7qNZAFqZam9xYZ9+iCdS3Mfah1AtXovIWK8tsQlLt1MjHx8e+DyAmsHnzZpUZdYYMGdi+fXu+9NJLTuM3JQ8kJiYyMjLSsFzmzEaxbJ8mKZ14GXkRVn409iBwWVFfKdDn0WCDbRptX6ek5dTiz86K9NJMvQOcwk4CAgICmnD3GVwIlzAnXC5btoyNGzfWvaNi/ccz9W6SvRDCpYAKTy6WPR16Dj+J3717lx9++KGhed/LL7/MY8eOmWswxUlOBF5hfStHGmaoPjZzDVrQ0qULB/bta/M97dOnj9PeeWsKCQnh8OHDeejQoXQLmtevX+fw4cNtxjMsVqwYZ8yYwW+//VYzPIY11atXjxcvXjTs9+bNm7q/iQ0bNuStW7dIKq8jj9Eor3RClJmSuazWHiYTUJuWPqEAymMYWr061nEulRQURFosvH37tqpNR/5WGCE+Pp4DBgxwmHdseY2tUKEC3333XVX6l19+yYcPH7qEn51P3nzi9IlUh6yxJq37mOMVW1xQke9J6c6vdZkEqjWcvxCqUCYelO4Fb9Tod7s1OwkICAhowt1n8GdOuHzWER8fz71793LRokX8+uuvOXr0aI4bN46zZ8/mzp077Q4q/qLC3Yz9wkByiWk3rQH4L6BvpqdHz/En8TNnzrB9+/a6h0QvLy/279/f5p3sNKQ4yTmCMhyBMWyCv1R3MoNwm03wF0dgDI+gjCy+p8Vi4XvvKUNhyCl79uxs06aNLE3LvD49lC9fPoaHh3P//v3pEjTv3bvHCRMmME+ePIb9BQcHc+zYsRwzZgyzZ8+uW87X15cDBgyQhVxRIiEhQVdQyp8/P/fv369wpJxEoI6JdTlu8BpcoXGokpaUtJzyekdQxvjdSolxrHRW89tvvzm8J6TEZ6dOneK0adPYunVrBgQEOMQn9evX55YtW3ju3DnDj7xNmjTh+fPnVeFxfH19eezYMZsfFuyhl19+mXXr1nXq+/CEqhG4SEkbaPRhV8tT7hMtInQ1n6c12KCNoky3FF5U7tmklPaV5ro9lOwkICAgoIK7z+BCuBRwCdzN2C8ELBaH4lomAsycsu4vAewBcD7Aq2bqvwCfxDdv3sxKlSrpHhazZs3Kr7/+WjfEhQpWTnIsgUG8jwBGITvvI4CWQOP4nhaLRVPLY02DBg1SaV1btGjBL7/80vBeqSNUoEABDh06lHv27HFY0Hz8+DFnzpzJIkWKGPaVNWtWDhkyRNPJjDVlzJiRw4YN4+3bt3X7nDNnDn191QKfv78/FyxYwMhI61flHNUaIqXAruWUxZqG21jL/AR2ppWvj822362UjxoNGzaUtTVmzBi79+Du3btcvnw5+/bta9oU2YgqVKjAhIQE7ty505RwmDNnTk6aNEnTy2t6HQRZU4O6dcmoKI75VHlf0ZmUPYXsrbffanu1vC3/ocEG03Ta6qh4rpRSXhkLMwMlhz9p7CQgICCggrvP4EK4FHAJ3M3YLwRiYhzSWu4wOBCVBPgewJUA7+q18QJ8Ek9KSuKcOXMMtWxFixblypUr7ROyHHCSk5yczH79+umOI0uWLHznnXdU6du2baPFYuGhQ4f40UcfqcIbpZfy58/PIUOGcNeuXQ4JmklJSVy6dKmhIA+Y18RmyZKFn3/+OWNiYjT72717t+4aDBkyhAcOJFppMOfa6K+/jdfoLoEgG214E5hMwMI1aGH73Ux5r5QfG7p27WpzrRMTE7l9+3aOGjWKtWrVcupdXS8vLx44cIA///yzpgBvJCyWLKkMmeF8OgvwIcBgJ2v000+DrbZXS/P5nQYbnNJpSylEgpJG8wbV94inWbOTgICAgAruPoML4VLAJXA3Y78QiIpySLgcbfJw5AmwGsDhANcDjE1t4wX6JP7gwQN+8sknhnelGzVqxIMHD7p0HMnJyexrcAdz4MCBzJZNfrerWrVqshiFSUlJ3Lhxo66jm/RQvnz5OHjwYG7fvl0zLqIRLBYL161bx0aNGpnqq2nTpppCTCplz56dEydO1Az5dP36ddapo2322qRJE546Fc2wMFIyKVRqg6ypmolXaZKp+byEqvofalLJyiJg2jS59qpSuXLShySFgH/u3Dn+8MMPbN++PbNmzWr3nlaqVInDhg3jhAkTDIXR8PBwDho0SDOvSJEiPHr0KNetW2fTHNoWeXh4GO67Hn2asoYzHe67EI3NnLXIjBfdXHzi3KmZRv4gDVawECigUXYaAaXG+JOUOh0U6eUZ6HWPlog1DvwSCQgI/Bfg7jO4EC4FXAJ3M/YLAQc1l+MA5nHgEOYHsBHAMZ9+yp07dzrdi6U7cfHiRXbp0sXw4Pvmm28a3v9LL5KTk/nWW29pr72fH0ePHq1KX7BggaqdWbNmOXjINkd58+blwIEDuXXrViYlJdk1x127drFdu3Y2tZWNGjViq1atDIWe4OBgTp8+XWW+HB8fr6sJLlSoEA8dOsSICLJWrdsEQnTa9yEQZ+NVekx1eBKt+3dgIYD7jBpLvcscGcm/FXyYAWAywJjAQP6vfHn2r1yZxRxwbJcnTx6+8cYbXLBgAW/evClbswkTtDRjkhOmxo0ba+Y1adIkzVT5+vXrHDt2LIOCbGlzjWnSpEl23ynOBzAJkrl/CYf67UXJmU5DO+pkNFluTcoWK+9SgsArOuyg5cRrMAFljNfClITRdary1TBVaiwsTHJAJiAgIGAFd5/BhXAp4BK4m7FfCDh455KQHPkcAzgVYFuAWR04lGXJkoWtW7fmlClTnBbewt3YuXMna9TQuh8lUUBAAMeOHaupNXMGkpOT2bt3b82+W7ZsyeLFi8sP1vnyqcZy8+ZN1QF94MCBbNSokdOdAeXJk4f9+/fnpk2b7BI0T5w4wV69etkcT+XKlVm/vnH4jwIFCnDOnDmqjx0//vijpslmxowZuXTp0pQy6w3a3m3iVfpZo15rzfZ8AX4PHSdar7+eFpTzukbdagC97dwbPz8/Nm3alBMnTmRkZKTh+5mYmKjSjANSiBKttgcPHsxbt25x1qxZDA0NdZoZ7qJFi/jNN99o77NBvb9S1vE3h/oNTtkGoxic6tig5qhzStuva+QV1+GpZRplXyawTyN9ByUPxgVl6RVR5UmDISFSyCoBAQGBFLj7DC6ESwGXwN2M/cLAQW+xSkoEuBvglwBDIWkp7T1I5c6dm126dOHs2bN54cIFd6+Mw7BYLFy0aJGheWmBAgW4ZMkSlwjUycnJ7Nmzp2a/WuaJY8eOVbWh9KD55ptvkiQvX77Mr776ihUqVHDwsKxPuXLlYr9+/fj333+b0mo/fvzYZuimVCpSpAgrV65sWKZEiRJcunSpzGx3x44dug5ohg0bxqSkJL7/vlIjlEpad+KUlERA6VSpAIMwm1mhHbKjE8D7Og1eAvgTJEHUkT0oW7YshwwZwnXr1tn1AeTrr7821b6Pjw/79evHNm3aONUxTyp9/PHHJMlBXbuq8rwA1tep1x5PPprV0sj3sNn3KQLxdMxxjxH5E7hHoLtGng+1Y6Kqw9EAeSlpKYsr0lPvBstD7GSAp5zHgoKEgCkgIJAGd5/BhXAp4BK4m7FfGLgozuVjgH8DHAGwBqT7l/YerIoUKcK+ffty6dKlaTEHnyfExsZy9OjRzJRJ29QRAGvXrs3du3c7ve+kpCTNsCkeHh4qwTAgIEBlrqsUFnLmzKnSLB49epTh4eFOiR2spBw5crBPnz7866+/dMMvrVunNufT0p5ZU548eViiRAnDMhUqVODq1avTBP+rV6+yZs2ammWbN2/Oa9eusVw5rVARTU2+LqtVdccgIy9BW9ABwOIAD0NyRLMG4CCApRxY55wAwwDODQzk1Q0bHOK1U6dOmRLy/f39TX8MyJYtG998802+/rqWxk6fXn31VTI6mknBwQzVyC8KsIpGugckwZwAtzrEs6+n7KVxaCDHaDa1TV1ByXOxFk+VVpTzoCT8fq5Iz0HJpPcqofiYMVPZaEiIMJEVEBAg6f4zuBAuBVwCdzP2C4PISJcIl0q6C3AVwAFhYSxdWnnwMUcVKlTgBx98wDVr1vDBgwfuXjnTuHr1qk0Tzq5du/LSpUtO7TcpKUlTkPL29laNpU+fPrK6Z86cUdXbtm2bZj/Jycnctm0b+/XrZ1O4c4RSBY0//vhDdj9SqYWtWbMmHz58yKlTpzJ//vyGbWbNmtVmmZo1a/Lvv/8mScbFxeneZy1SpAhXrFhBLy+lptGbwB0Tr4eFQD1Z3ewA7wFMADhEZ3yekLRx9qylD6R7z+MA7od0FzNtIA5op5KTk1mvXj27xqBHmTJlYteuXRkRESHb5/Xr15uObVm0aFGySxcS4CNoW1A0hCRkKtNLAoxLWYt8do/fk1LcybUmy3sRGEjJaY+tsg2pL7T+pcNTH2qUPZkyRmV66r3OtrL0qloNh4XZxR8CAgIvJtx9BhfCpYBL4G7GfqGQclfL5VS/flqX165d44IFC9irVy+bh3wt8vb2Zp06dThy5Ehu3brVfExJN2L//v2G9/8yZMjAkSNH8uHDh07r8+rVqxpCD1Rpnp6ejFQIFkpt3AcffGCzv/j4eP7vf//j66+/blpLZQ8FBQWxZ8+ejIiIYNGi8gDwX3zxRdo4EhISOH/+fJtxPP39/Zkrl/EBv3HjxtyxYwctFgtnzJhBb2+1p89MmTKxVatWqvTQ3MUYjtFsgr8YhNuy1yEIt9kEf3EExvAXFFLV/SSl4DVImklHTV0BMDckDecDW++ondoppWdae8nX15ft2rXjr7/+ykePHun2ExUVxbZt29psz8PD44lXaoBv65TrAMnRkTK9IyTnPnr1jKggPCgJmWbK10sZopa5qxbpeYL+Xmcrt2iUnZ+Sp7wT3jUlfY2qzgGtxiMiTPOHgIDAiwl3n8GFcCngEribsV8oREQ8HeFyjbZre4vFwtOnT/OHH35gx44dmT27/feWMmbMyJdffpkTJ07kgQMH7A538bRgsVi4YsUKFi5cWHcuISEhnDt3rtPmMHToUFNr2LRpU9kd0JEjR8ryCxcubNcd0ZiYGM6dO5dNmzZ1atxEPdq3b59qDMnJyfz9999Zu3Ztw7re3t4MDAw0LNOyZUsePHiQW7dutSmQWtMvgYEkpPt89xHAKGTnfQSoHPO8qhwTHDN1DQJYT5GWBTqOgLTIpHbq3LlzhibfeuTh4cFmzZrx559/5r1790zzk8Vi4cyZM22GG7EWiDYZlHtFJ/1tSI6TtPIKOI1fa6YM0cghlDU11En/QGcb46kWdMNS8qYq0jMReMhgXGReRZ13tBq3+kgoICDw34S7z+BCuBRwCdzN2C8cUkzJXEZ2mFMlJyfz4MGDnDRpElu0aOHQATZ79uzs0KEDZ8yYwVOnTj1znmjj4uI4ceJEZsmSRXcOVapU4datW9Pd1+3btw37saa1a9em1Ttw4IAqX6ndNItr165x8uTJrFKlipMO52qBpUuXLly5cqWuI5pt27axZcuWNtvJmNE4TETHjh25adMmVqtWzdTYsgQE8HyWLLrvhgVgJMCP0rkGPgA/g6R5u6iRf8We93XUKMP9PHPmjOEHEiPSciBlD06cOMGAgADd9gdbzSMRYDaDsQTopLfQSa/jNJ4tlTLEh5TuQ9oqn0MnvY3BNiodiqV6l71J5f3KTJjJSJTlZ4r2M0O616tq/MiRdO2hgIDA8w13n8GFcCngEribsV84REdLJnH2HEDNUjodQcTHx3Pbtm0cNWoU69atq2mWaIvy5cvHnj178pdffuG1a9ecuHDpw82bN9mvXz9DzV6HDh147ty5dPWjFeNSi0qXLp3mqdVisahMlq1NTx3FyZMn+emnnzosnNiigIAAdu7cmcuXL9c0tTx8+DC7du2qaS5sTUYaMk9PT3br1o2vvfaaqTHVq1yZScHBae/ETYALAfYAGOzAHPNAEia18t6CdN8wkyJ9vb3vrSLG4dWrVzl58mRWr17d9DhLlixJPz8/WZqXlxe3bNmSLh7asGGDYb/9UtaAAN8wKGfbC6z9dbzgRbVgp6TMVkudnvegtMEWKkPa+FK630tKoUms+BMBJCSnRkrna3O0Gh8xIl37JyAg8HzD3WdwIVwKuATuZuwXEpGRDse91CUXuLB/8OAB165dyyFDhrBixYoOHcpKlSrF9957jytXruTdu3edOj5HcOTIETZr1kx3vL6+vhw6dChjYmIcav/+/fvMkUNP+yGnGTNmpNUbOHCgLK9SpUrOmjItFgt37tzJ9957jzlzOhoH0JgyZszIjh07cunSpaq7rOfOnWP//v1t3g01+pjh7e3NunXr2hRUAfDNbt04vHRpVnJgHlkAtgM4A+DZlHfrFMAKOuXLAyyrSPvWgff3dp48/HHkSLtinBYpUoQjR47kiRMnSJIrV65UlcmVKxcvX77sMO/cvXvX5jhKQjKRXeECvjKihgihN34xUXZ7yjJrx6UFAm224enpRylOpdb2fadR52hK3nxZuhfAWykVWyrq1NRqvGLFdL37AgICzzfcfQYXwqWAS+Buxn5hERnpPA3mUwq+HRUVxV9//ZVvv/22ysmLGfL09GT16tUZHh7ODRs22BXfz5mwWCxcs2aNYaiMnDlzcubMmaqwIGZgNhZhzpw50+7Cbdq0SZXvihikCQkJXLt2Lbt27WrTLNVRypAhA9u3b8/Fixfz/v37aX3fvHmTH3/8sc07l0baZR8fH2bIkMEl4wbA7RUqkK+/rnrHYgH21RuT4vltk+/tA0ha1VYabRhRjRo1uGfPHk0T9I8//lhVvnr16oyLi3OYZ8x8LPEBOBbpc4ZkP3kTOEHbwmFVSoLhTJ38kqb68i93CgABAABJREFU+3nORdb3/kdjK49olB9HgKyNtfRT3K+cllJxlUYfkcrGvb3JZ+yqgYCAwNODu8/gQrgUcAnczdgvNKKjJZM4k4dRTVKY1D1NXLhwgXPmzGFYWBjz5Mlj9+HQz8+PjRs35tixY7lr1640M9GnhYSEBE6dOtUwrEe5cuW4fv16u9qNjY1lSEiIqTUYNmwYSTIxMVE1jm+//dYV007DgwcPuGDBArZo0cKURtAR8vPzY9u2bblgwYI0QTomJoYTJ060GfbCSINnVrunRQUgCYpzAWZV5DVs2JCWxo1137cFUJvBKqm+wfsaB0mo6ARtL6pG5OnpycWLFxvuaVJSElu0aKGq+9ZbbznMJ3qxR7Uoh+LZOtRIDRfwF/AWgbdNlJtDQH23WaKXTPW1cfVqEuARlOEIjLHyTPxYVfYl5OcRlCFT9to6r1YKLyRCbao9QItvdu1yeO8EBASeb7j7DC6ESwGXwN2M/Z9ARITkGdDgUKqi+vV1vcK6AxaLhUePHuW3337LNm3amHZsY01ZsmRhmzZt+O233/Lo0aNPzTnQnTt3OHjwYEOzzFatWvHkyZOm25wxY4apOfv6+vL8+fMkyR49esjyGjZs6KIZq3Hz5k1+9913dgkS9pKvry9btWrFefPm8e7du4yLi+NPP/3EYsWKuaxPQLob2rp1a3733Xf89+RJWmJiyKgo8v59TvzqK1X5PwICDN+94wDLGPSXQ1E+EeBfAHtBLcyaJU9PT9P3J+/cucMiRYqo2pg5c6ZDvNGtWzeH1z4TJGH7Y0jxRO0VqG2TN4HfTJTLTSCagJZptieBWjbb+PGbb1S8kOqZOLvCWZAPwL2QPOVqzTnV5PpjRXogIAvxQoAcPNihfRMQEHj+4e4zuBAuBVwCdzP2fwpHjkgOHJo0Ud/JDAqS0keMeC48CCYmJnLXrl0cO3YsGzdurHI2Yoby5MnDrl27cs6cObx48aLLx/zvv/+ydWulc44n5O3tzUGDBvH27ds224qPj2ehQvKYinoCd9OmTUmSq1atUgkU0W7QSp85c4YDBgxwshAgJx8fH77yyiucM2cOb926xeXLl7vEw+348eMNY7M+fvyY+fLJncJUAJisPOAr6CEkJ0F6/d4EuB3gewBzmRyr0sGLNc2dO9euPYyMjFSZPfv4+HDHjh1288Pnn3+erj34HeBJSObCZhz72O/8pw8lD622yg2lvhA50mb9oYMG6fJDI609M2hrTEq9cxp585XtN2pk954JCAi8GHD3GVwIlwIugbsZ+z8Li4W8fz9Nw/K837uJjY3lhg0bGB4ezurVqzsUj7Fo0aJ8++23+euvvzIqKsplY92wYQPLly+vO46goCB+++23TEhIMGxn3rx5qrqVKlXSbHP27Nl89OiR6j6hvUKFs6C8N+rK+Jne3t5s3Lgx+/Xrx2bNmtl0/GMPZcqUiTdu3DCc688//6yqt0BHiFDSHEhhNpR3JvOYHJ8XpFiZRp5sX3/9dYf2cOnSpaq2goOD7fbivHDhQsM5BNiYr9m1cJx8CHxkstwbmnkeHh/Qy8s4rmf79u3JlFiqSnpPo/ww6H9YKIUn8VCbKfLqKdsPCnruf/8FBAQcg7vP4EK4FHAJ3M3YAi8m7t69y1WrVnHAgAEsVaqUQ4fKihUrcsiQIVy7di0fPHjg1PElJSXxxx9/ZK5cuXT7L1GiBFevXq1rvpuUlKSaW4kSJdioUSO1kOHlxX379vHVV1+Vpb/66qtOnZdZhIaGysbx5ptv8q+//mKPHj2YOXNmFwsLzqXg4GCZYyElkpKSWKZMGVmdgpDuR2oJEkraCzC3nWOqB/B7gIthbCqbM2fOdH1I+egjtdBVp04dQ22uErt377Y5n5chedl1ZH8KO2Wfu9JcHMvKOunlWLt2e8O65cuXJxs00OSBqRrlKwOsatDegZS6yzTyjiv7MOBfAQGBFxfuPoML4VLAJXA3Ywv8N3D16lX+8ssv7Nmzp8pM0QylhqoYNWoUt27datfh2QgxMTEcNmyYYSzGpk2b8oiOqfKyZctU5X/66SdNT7WZMmVSmSBmyJBBM4akK3H//n36+PjIxrFy5cq0/NjYWC5dupStW7d2KBaqOygkJISnT5/WnfPvv/+uqjNFQ4hIpSsAv7YhPGgJGxMhxTi0pPzfyBQWAJcuXZquvUxMTFR9KADA/v37m27j9u3bpuY3E+BrDuyNc+5h+hCom476nixRwjisScaMGWkZOFCTH/50oM8hKXXjodZwfqDsw4WWGgICAs8u3H0GF8KlgEvgbsYW+O/BYrHw1KlTnDFjBjt06GDozVWPMmXKxJdffpmTJk3iwYMHmZycnK4xnTt3jh07dtTtz9PTk2+//TZv3rwpq5ecnKwyhS1YsCBv3bqlGVIjMDBQZYJqLdg9DSjjJfr4+Ohq/qKjozljxgzWrZueg705eumllxy6u2stHPzxxx+a87BYLKo5ZIfkhCb1gB8F8AeADWD+XmAJgJ8B/NeqnViA3UzUbZ8zp1OcWkVFRbFAgQKq9n/++WfTbQQFBbl8f9NPaiFaIuXvh56J9/cMCDB2RHb1f//TFC617k7aohCASSn1hyryskOhOReaSwGB/yTcfQYXwqWAS+BuxhYQSE5O5oEDBzhx4kS+/PLLDsVnzJEjBzt27MgffviBp0+fdvjQvm3bNlatWlW3nyxZsnDChAmyuIJr1qxRlfv++++5aNEizTaU2sAePXo4aSXNoU+fPrL+GzdubKre2bNn+e677xqaEjuLlJpVe6hfv36a+799+3ZV2Y8gOVhpAdDbjj4yQTJ7TL1XF5EiLFyBOW1nNoDXs2Z12l27AwcOqO6y+vn5ce/evabqV6tWzeV7mn7yJhCgkf6Jyfr16enZ2bDMls2bpdiTkAuXSXAsxuffKfVPaeQtTm1f3LkUEPjPwt1ncCFcCrgE7mZsAQEl4uPjuWXLFo4cOZJ16tRxyDQzf/787NWrFxcsWMDr16/b1X9ycjLnzZtnGMuycOHCXL58OS0WCy0WC2vXri3LDwkJ4aNHj1inTh3bgka2bE8tBqjFYmHevHll/U+aNEm3/OXLlzlnzhx26tSJ2bNnd4qQ4OPj43Jz2zx58nDbtm2q+bRt29ah9rJppN1LEQ6+TXmuB/N3M3+B8zVW8+fPV/WTL18+3rp1y2bdLl26uHQ/1OTovV6t+9sbCZj1RPyDYX6xYrO5plB/lXAZr8MDgOS0Sa+9N63aUHqcbZSa16SJ03hAQEDg+YK7z+BCuBRwCdzN2AICtvDgwQOuWbOGH3zwAStUqODQobR06dIcMGAAV61axXv37pnq9+HDhxw1apSmeWsq1a9fn/v27eOmTZtUeZMmTeLevXtNjW/jxo0uXkUJhw8fVvV9/PjxtPxHjx5x7dq1HDx4MEuXLu1yISNr1qyG913TS82bN+fRo0eZmJjIP//80y7hMgvAngDXAXykIUTsBDjBRhta9w1b4onG09l37QYOHKjqr2HDhmkfLywWMjUMaEzME4XZsGHDTK+LLzwYhF8oxZZ0dG9+oHZMSluk9VHiWwL/mKxva57hBMgwLGA0spEALwKsYVDnDYO8rAAfp+z1Yo38U4AUfkpAQOA/CXefwYVwKeASuJuxBQTsxa1bt7h06VL27dtXM5i8LfL09GSNGjU4YsQI/v3333z8+LFhf5cvX2b37t112/Pw8GCPHj1Yr149WXqOHDl4//59U0HqBw4c+FTWbty4cbJ+CxQowAMHDnDChAkMDQ21W9Dz8fFhw4YN+eWXX3L79u1ctmwZ27dvb3c7Xl5eLFCggEMm0WbI7F1Of4AdAf5mJRSkUglF2aJ4m0Am3bbywkelycwC8LJ1u06+a5eQkMD69eurxlK9+gcMDVWH182S5QLz5/+AXl72CnqdKcWfdHRPVhL4nYCXA3WVXmNfTpmPGe2r2tGWnDqmrU0IrnA6CuhqLFNpoI38FSkNxkG6a2mdNxR4LuIaCwgIuAbuPoML4VLAJXA3YwsIpBcXLlzg7NmzGRYWxty57dem+Pv7MzQ0lF9++SV3797NpKQkzX52796tMn+1JcB88cUXvHTpks3YjsHBwU5x7mILSsHDSCurRyVKlOCAAQMYERGhGyLm7t27nDVrFhs1akQPDzMhJJ5QtmzZWL58+acaEiUPJFPV+4DKJDKVaqk0df2pH/oihID6o8Is6zZddNfuxo0bKtNniRZaTWc3gU40I9wFAGylmfe2RlrVFLK15tNTxqGOFWs/ZSIQR+ASAfv5WU6VUsaVSEmLabtOc4B+Bvntrfb8A0VeLh8fp3m+FhAQeP7g7jO4EC4FXAJ3M7aAgDNhsVh49OhRTpkyha1bt2aWLMbeIbUoa9asbNu2LadOncpjx47JhD6LxcKlS5dqeufUoixZsvD27dv85BPbTkeWL1/ukjV5/Pgx169fzwEDBjh04A4KCmLHjh35008/8cKFC1ZroW1iqcTly5f51VdfOWTSXLZsWYe0047QHmgLldHIxi5YSOBjRR37BJkG8H5iDgvX3bWLjiabNdtNQKk9zkDgazoS0uN4zpz0VWlptbTTYwkk0LZg9pHVEn/thP1bltLWF+lsJzOBqwQamK6TEWBdg3xfgHdTJntcI3/ZsmUu4QMBAYFnH+4+gwvhUsAlcDdjCwi4EomJidy5cyfHjBnDRo0aOXS/Lzg4mF27duWcOXN48eJFkpLANm7cOAYEaHmvlNPw4cN5//59m1rVgIAAnjx5Mt1zThWwv/76azZv3tym1lRJXl5erFu3Lr/44gvu2rVLpsmNjCTDw6lpYhkUJKWHh+tb+h09epTh4eGmhfOnSY0AufAH8DDKMQRXUh4XpKP9TMyFnYxE2Sftu+Cu3eHDZEhIaheznLY240f+QcA4TqREbayWbzOBrDrlfAkssir7UTrH2CqlnVgC+dPZVk7N9JYGdZShZ5ShbKw11vUUec2aNXM6HwgICDwfcPcZXAiXAi6BuxlbQOBpIjY2luvXr+fw4cNZrVo1VcxJM1SsWDH269ePy5Yt47Fjx/jWW28Zmn56eXlxz549/Omnn2y2HRwczH///dfueUVFRXHRokXs2bOnoZdbPSpSpAjfeecdrly5UtPhUUQEWa+ersWoJtWrR65Zoz3e5ORkbtu2jW+//bZDMRZdFZfxT8gFyyDctprTPoO6wZQ0cHohVKYRIINw+4mA6eS7docPKwX+KwQqmph3JgIDCJQx4I/vCVgoCXFGbWUg8MhqDCeoviOZSl4ExhFISmm7Zjr2zovAjZQ+f3UyX/gQ+Ja/42Vm0SnTSfGsFC4bW/HVfGVZDw+eO3fOqbwgICDwfMDdZ3AhXAq4BO5mbAEBd+Lu3btcuXIl33vvPZYqpRXmwJg8PDxYqVIlvvHGG4Zmn15eXhw+fDjLlNE/wKdSSEgIT506ZTju+Ph4btq0ieHh4axSpYrd9xoBsFy5cvz+++955swZ3X6io8kuXewTKpUUFia1YzSX//3vf3z99ddNO95xFYUAfAjJFPaJxpIEHhDQ8zRbg5IpJSmZhWqVGUpJgJIcxUTXauUoy+ru0xON5UEC3antWdWaQgiMJ3AnpV4ng7L9U8pcIWBrj1YpeKC1jfINCVymdG8yPfv3dUp/FgL6d6Pto3wEdhEg62Mzq+iUqwTQ06AdD0gxUAkwNjCQgYr7xB9//LFT+UFAQOD5gLvP4EK4FHAJ3M3YAgLPEq5evcpffvmFPXr04EsvvWT3YdTb29vQDNWsxi1v3rw8ffp02rgsFgtPnjzJqVOnsmXLlsyUSd9LqRZpaWit29eC3MQyfRQSIpnU2kJMTAznzp3Lhg0b2i0wZ8iQgY0aNbLbDFi1RwCroheB+JTxnyNQXqd8PQKPU8o9IlDUoO2RaesR1vBKellVhs6dkwlEEGhkx1yXKPZJeafUmhqmlDlOwJa2v6ei3d9MjCWIwHJqx7G0JiOeKMdUAV4SsB3nAYleIRAtm0sLHTNfD4AVFGlKJz+TrF4E5f3n4ODgpxbrVkBA4NmBu8/gQrgUcAnczdgCAs8qLBYL//33X37//fd87bXXmC1bNiccWM1TSEgIp02bxrfeeov589t/jyx//vzs06cPly1bxuHDh8vyihUrZjh3tYll+ikoyFjATEhI4Nq1a9m9e/d0eYp15KOANhUkMJxAdoMy4VZzHGKizTFp5SMi0s+jsbGx7N//B9oOsQGqBbNMBI5ajf9ng7o5KAluoSb6yUHJ1DW13XgCZkPM2G89IKcDVv0aCfq21ulLAslWbUlU10B4b6F4DlY8VwoKSlPhR0ZGquqvWrUq/QwhICDwXMHdZ3AhXAq4BO5mbAGB5wXJycncv38/v/rqKzZv3txlMRkdpUyZMrFVq1acOnUqT548KfNyW7Om/D7boEGDdOcpN7F0LoWEyE1kk5OTuWXLFvbr1485cuRw6fpUguTZ07ntvpoyt51Ua/T0vNxOJEDWr+84L964cYOffvqpiTXzJ9CPwElK4T+U+cUI3E2ZwzYbbf1ox7psVex9WY0yeVywz4Os+tQzUbZFJRRjf0JlC8zQrReqePbXKHP8+HHdd7Jly5aOM4SAgMBzCXefwYVwKeASuJuxBQSeV8TFxXHLli0cOXIk69SpQ29vW3fcnEseHh6sUqUKw8PDuXnzZt14eVFRUSoT03Xr1unOK713LG1Rly4W7tu3j0OGDDGtZfTx8VGtryPmr4sAjgfo7fC6K0PblKBkFqvUuPlRcmYzU6edbwnY79PnyJEj7N27twmvx7kJjCYQZbX2Fkomq8qyrShp6a7baFOpwTUy8f5Ase/VNMp8QeB1B/dBj3LwiTnzTgfbyKwY+xPKnHmvbr0QjbQcgYGyZ+u7lXPmzJHleXp68tKlS/YxhICAwHMNd5/BhXAp4BK4m7EFBF4U3L9/nxEREXz//fdZvrzeHb30UUhICHv16sXFixfz1q1bpsa1cOFCWRsZM2bk48ePNctGRLhSsDxO6d5hMVNz9fDwYKNGjfjjjz/y9u3bbNKkiSz/o48+4tq1a9myZUu7BPsiAEek/Jv+PfEiMEwjfZzVvL/Tqfu9qWgkFouF69atY/PmzU2MpyyBOZSc42jtwWMCVTXqjaIkfNpzl3cZgSY6eakeaFen9BusUaZmSp9zCdgO6WOeVlnN1X5HVxJZewm2pnuG9YoUKiR7rl+/vuy5UKFCaRYFDx8+VMXh/eyzz0z/3ggICDz/cPcZXAiXAi6BuxlbQOBFxc2bN/njjz86TaMZHBzMCxcu2D2Orl27ytpp3bq1bll7w43YpguUPJJWMD3P6tWrc/Lkybx69apsbCNGjJCVa9y4cVregwcPOHfuXFauXNm0IyBvSM57nCfUpFIVAomKdfhGs2ypUrN09yIuLo5z5sxh2bJaJqVKak7gLz5xaGNEFylp+JRt/M+OfWqW0td5GgukQ6kvkHnxiRB3hpLXXWesfzuruZq5w6mlVVWa9T6hnDn149XWri33UtugQQNVmR07dqTt8TvvvCPLy5cvnyyurICAwIsNd5/BPSEgICAg8Mzj4sWL+Omnn9C/f38MGzYMSUlJTmn3+vXrKFWqFIYPH469e/ciOTnZZp3k5GT8+eefsrRXXnlFs+yRI8C2bc4Y6U0A0wDUAVAQwHAAhw1rlClTBmPGjMGZM2ewe/duDB48GCEhIbIy1atXlz3v3bsXFosFABAQEIAePXpg//79uHHjBsaMGYN8fn6GfSYBuGvPtEzBE8DbGunvAxinSj1xog/mz/9FlhYdHY0xY8agQIEC6N27N44eParZk6+vL3x9ewM4AuBPAE0BeJgYY34AP2qkdweQx0R9HwDfpfRVEMBXBmWDAZzQyUsGsCjl/0UAbAPwiYn+bSECQHTK/+uaKF9FI225bulixYrr5sXGxsqeIyMjUaJECVnawoUL0/7fp08fWd7ly5exbt06g7EKCAgIOBFPVZQV+M/A3V9NBASedzx48ICrV6/mgAEDWKKEGa+dzqHAwEC++uqr/O6773j8+HGZA59U7Nypvnemp/0MD0+PhvIuJXPMprQdqiJ1/IU4YsQIHjF58fDatWuqNqwdpMhgsZBBQTwFsPNT2g85ZSfwFoE/CSRYrdPnqrKenp5csmQJT548ybffftvmXdIcOXJw5MiRPH36hoN7lUgptIjeuG3NLVzRXrJBe0tT+EKvrcqq8eXBK05Y/6kp7c02UbYRAaUn6OKaaxcURPbu/aZuWxkyZFClvffee6r9S0hISGPVKlWqyPLbtm1r6n0QEBB4/uHuM7gQLgVcAncztoDA84bk5GTu27ePX375JRs2bEgfHx+HDsBvv/02Dx48yFq1ajnhMC3dx+zWrRt//vnnNMcgn376qaxM6dKldecVGuqIoHKYksdUWw5mUikPJY+euxgaqhaGbUHpAGju3LnaBWNiZANdnu71NSN06VEQgV4E1lK6CzlCVcaMKW+JEiU4c+ZMxsbGkiSjohz9EPB+OuYSQuChRptnKHmmVZZfSOAjG20ekrXVEd3SuVfgE6H1iMn9aaxI8yBwSzXPJk3ICRMmGLaXO7fcbPbzz9UfFNasWZPGqjNnyp0+eXl5qUzCBQQEXky4+wwuzGIFBAQE3ISrV69i7ty5CAsLQ+7cuVG1alWMGDECmzdvRmJios36JUuWRKZMmWRpR48eRYUKFfDNN984ZYzXrl3DggUL0KtXL+TPnx8lSpTADz/8ICujZxJLAgcOONKrB4BVABIMygQBeAvA3wCuAJgCoAYOHPAAaV9vStPYPXv2aBdMkI/nNQC97OtKgdsAXgJQ04G6dwH8DOAVSGanVwC0lZWgwUI0btwYEREROH78OPr27YsMGTIAAHx9HRgKCCDekYopeAtAJo30IgBGaqRPhb5ZbCp+lj31wW6HRibHAQBHAZQC4G2j7F0AJRVpBLBEVbJ6daB4cblZrLe3vP2cOXPKnk+ePIkaNWrI0qxNY7t06SL7bUhOTsbPP8vXREBAQMAVEMKlgICAwFNCbGws1q1bhw8++ABly5bFSy+9hF69emHx4sWIjo62WT979uzo3Lkz5syZg8uXL+PEiROYOHGirMz27dvx559/ombNmujcubOqDU/P9P3snzp1ClFRUbK01Dtdjx49kqU/eADcvetIL+UAlNFIzwigC4DfAdwA8BOAxgC80krcvQs8fGhfb6aFSw3J61sAhe3rToErAHZD+46eWdwDMB/A/wxLeXt7o3v37jh48CD+/vtvtGzZUsUPmTMDQUH29u8BYDqAOQCM76RqI6tB3qsaabshCXpGWIjUjxP1sQVNcBqBDoxMjXmQ+K2QibKXNNJ+UaV06aIWLpV3qh88eCB73rZtG8LCwmRpq1atwsMU5s+cOTO6dOkiy581a1bafWIBAQEBl+Gp6kkF/jNwt0peQOBZgMVi4eHDh/nVV1+xSZMm9PPzs8sMz9vbmw0aNODYsWO5b98+Jicnq/qIj49nwYIFZfUqV65Mi8XC8+fPq7zK+vn5sVOnTqb6tmesgBQ3sn79+vz888/5zz//8No163uB9tKYlHZ9CLQhsJjappNqioqyb582btyomodmWJWUO5fWnVkA9tdZjxwA5wPMh9cIlLZ7PZ1JXl5eXLx4san1cMyUOZX2EDAXZ/QJvWnQ3qZ0zHsFAXINWpAA6ztlLXNTul9q+x1Sxy9NpRNp86tfX1rzuLg40x6JU2nv3r308vKSpS1YsCBtH3fv3q2qYxSLVkBA4MWAu8/gQnMpICAg4ETcvHkTCxYswBtvvIGQkBBUqFABQ4cOxYYNGxAfb9t0sHjx4hgwYABWr16NO3fuYPPmzRgxYgSqVKmiqXX09fXFZ599Jks7cOAAfvvtNxQsWBA9e/aU5cXHx6N+/frYsWOHyqzOGklJSciWLRtGjx6N9u3bI8iEOisxMRFbt27FqFGjULduXRQvng1ASwDfQPLsao/WpBuAWZC8xP4PQGdom06qYcOhqwpVqlSBh8cTj6iJiYk4fFjDE62HB1C5ctrjY0i+UKfrtNssJb8bKkMyp9wMSftqFnkBOGSnqkJycjK6deuGzz//XKVhVkKhyLUT1QDsB9DAjjrHDPKupWMscxCGhXgFfwAAKqSjpSe4CeAvAKEmyt6HtoHYE+3lsGHSv35+fihYsKCslI+Pj+w5c+bMsufjx4+jadOmsrRFixal/b9atWqoUEE+659++snEuAUEBATSgacqygr8Z+DuryYCAk8Ljx8/5oYNGzh06FBWqFDBbk1IYGAgO3TowB9//JHnz593aAxJSUksWbKkrN1SpUoxKSmJ9+7dU2k3cuXKxaSkJCYnJ3PhwoXMly+f7vh8fHw4c+ZMJiYmcteuXTa9jhpTzhSNz48EzupoqtJHQUGSgtFelC4t1yxOnTpVu2CK+9vLAKuYmPPvACNR1mqMZjyNKikrAfu03kbk6+vL1157jUuWLOGDBw9UU4yMdMZeJFBysmR7PB4IoH4szUkm2tDT+HnyKALTGpvlpPUDOlLiXzNl1Z5egfwEkhkWJl/35s2by8oVKVJE9lygQAHZ81tvvcVffvlFlubl5cVbt26ltTlt2jRZvre3N2/cuGH/CyIgIPDcwN1ncCFcCrgE7mZsAQFXwWKx8NixY5w8eTJffvllzTABRuTl5cU6derw888/586dO5mYmOiUcf3666+qvubNm0dSfWgFwJ9//jmt7qNHj/jFF18YCo7Vq1dXeaAEwMGDB7N27doqAdYcFaRkErmIgKMhMNSeNx1Bjx49ZGPr1q2bdsHISG4HmFtjPh4AfRRpOQHeAFgPW1LGmEhXm8jasxf+/v5s164dFy5cyJiYmLRp1qvnLIH/F5oJI5MVh3Xqf2BiHhl18yZYNbbHaWvsS+A2AftNx1Mpe/ZNjI6Ws9aAAQNkZSpXrix7DgoKkj2XKFGCDx48UP0GTZs2La3Nu3fvqvInTJjg2EsiICDwXMDdZ3AhXAq4BO5mbAEBZyIqKoqLFy9mr169mDdvXrsPkoUKFWK/fv3422+/8e7duy4ZY3JyMitWrKjqNz4+nv/8849qTLly5eLDhw9lbVy9epUdO3Y0Pa8KFSqk1b1//z4jIiL4/vvvs3z58g4eustS0nb9TiBGR9gwphEjHFu/6dOny8ZSvHhxzXKzZs2ij8bduMwAVwP8SWNerwBcjRZW41ylMXdHhHNjypAhA4sVK2a6vJ+fH9u0acP58+dz6dJ7Dq2/mo6Ymtt0FGAIrmjU76woW1+jfmbddktCuhdLgLEAPZ22vtMJ2P9bkErt2vVW8dZ3330nK1O2bFmb7dy4cYOdO8vXqFatWrJ2lR9OihYtqhm/VkBA4MWAu8/gQrgUcAnczdgCAulBfHw8N2/ezBEjRrBq1ap2O9rInDkz27Zty+nTp/PMmTNPbdwRERGqscyYMYPJyckqrQcAfvbZZ7rtmDF//fDDD3XHcvPmTS5ZsoTt279FoJADB3AvAjUJfExgI6VYjraFmSNHHFu7vXv3qsZg/SEgISFBpVlKO6wDPJ4yAAvAthplvgfYBQtTxmkhUFtRJivTF/dSn7JmzcqQkBC76vj4+DBbtpYE5hK4Y2rt1WQh0MBUf58AjEY2hmGBog1l/UnUNjXVpx1WDZZ02rr6U1vQNf8b8ejRIxkPrlu3TrVvefLkkaUpnYItX76cq1evVrV/9uzZtHa3b9+uyt+4caNjL4qAgMAzD3efwYVwKeASuJuxBQTsgcVi4b///svvvvuOrVu3ZkBAgF0HRQ8PD9aoUYOffvopt23bxoSEBLfNo1atWrKxhYSEMDY2lm+//bZq3BkzZuSVK1c02zp//jxz5cplOO/s2bNz3rx5ml5srSGZWJ4j8BMlTZRxu/qH+aYExhPYSyBJIYQ88bzpCOLj4+nr6yvrc/369STJ6OhoNm7cWHNczQDeUQzkFsA8inIZAO5EVivt3DaN9nI7SfDRJkc8AEvkTeBlSvdFo1Xrrk8LTffhB3BbSsUIvML62JzShlLzuoi2zFEDFM99rAbVyalr6pOu+krvvefPn1eVUVoS5MyZU/Y8cOBAJiQkMHt2+YeJ0aNHy34XlHeKO3fu7PjLIiAg8EzD3WdwIVwKuATuZmwBAVu4c+cOly9fzr59+6ocZZihfPny8a233uKvv/7K27dvu3s6aVCG1QDAr7/+WqUVSaWePXvqtnX+/HlDZz+pVLVqVW7btk23nYgILY1WJIHJBFrRyKxRnwIJtCMwjVJoBwvXrEnf2tWoUUPWx9ixYxkZGclChbQ1rx9mysREaEtWf2iUrwxwP0ozCLdTir1i55yzEqhBfQc2jpGvr68dgqcXgWaUnDLd0pp6CsUQyGPXOLwATscTM9YjKENf+CrK2RZYCyieMwN8lNLmWCeuW3qpRYsWMv5LSkpSaSY//fRT1V5ZP1eqVIkk+c4778jSS5YsKTN9nTJliqqdKHtj9ggICDwXcPcZXAiXAi6BuxlbQECJxMREbt++nSNHjmTNmjXp6WnbyYg1ZcyYkS1btuS3337LEydOPNN3lkJDQ2Vjz5EjB6Ojo5klizrunoeHBw8cOKDb1tmzZ00JmADYoUMHnjt3TrOdLl2MNFwJBHYQGE3JDFIpUNimDBlC2L17d86dO5eXL192aN2UZq/VqlVjpkyZVH35+flx/vz5kltVRdxLa3pPY5zhANejBAPQn/pxEI0oA4FvCXxGwP6PInoUHBzM99//ij4+rexYfy8CoQRmELipmL6WIx5zfNQL4GOA9zXyJkG9H0rKD/Xdyl+QqhV1znrZIi8v29YPXl5eKs+tZcqUkZX55ptvDNvw9PTkvXv3NO9VW7/Xt2/fVgmuX3/9tUPviYCAwLMNd5/BhXAp4BK4m7EFXAiLhYyJkSLVx8Q4FvfhKeHs2bOcMWMG27VrpylY2aLKlStz+PDh3LRpE+Pi4tw9HdPYtWuXai6jR49mly5dNOfZqFEjQ2H5+PHjpu+d+vr6ctiwYTLPoyQZHU2GhJg1p3xEYB2BoQSq0BFNXfHixfnOO+9wxYoVpjXLyrAOWhQSEsLdu3c/qRQZqTuxRwBLaQkVds1FzxPqh+zUKZ6//baBYWFhKsHBEfL0zEfgPIF7BBYQaEvzIVA8CTSk5OhmI9VOfFowJKS16bFUBbhJI/0LxbOHFg9CMle2TmucsieX07lGZik4ONhUucmTJ8t4sF27drL8YcOGqUIcKb0Br127lhaLhQULFpSlDxkyRNZ2165dZflK7aaAgMCLAXefwYVwKeASuJuxBZyMyEgpvl9oqFpTExQkpYeHO+5NxUmIiYnhqlWr+O6777Jo0aIOHQh79OjBRYsWyWLFPY9o3Vp+kM+aNStnz9aPsfi///1Pt62///7b7rXMlSsXZ86cyaSkpLR2bCj6DOg2gRUE3iVQwu6xeHh4sEqVKhw6dCjXrVuncqSSin///dewnZo1a/LatWvqitHRZFiY5uD3wV5hUklVqXb+I1HTpk3TBOc7d+7w+++/Z9WqVdPRFyiZ3h6zmsJ9AosJtKd079WRNn1ZvfppfvCBXJtpK2RKVo3nNxRplXXqasW0PAfJ5DabyXFnT8c65smTR+WMR4tSzVpTMWzYMFl+u3bt+NFHH8nSMmeWm5GHh4eTJEeMGCFLDwkJkb1/mzdvVvVvZM4uICDwfMLdZ3AhXAq4BO5mbAEnISLC/oB39eox3ZffTCIpKYm7d+/m6NGjWa9ePbsdlvj7+7N58+b8+uuveeTIkRfqK/6hQ4dU8x0yZIjqzlYqFS9eXNcR0ZAhQwzX0UhIKFeuHDds2JDWloGizw66TGAeM2R4g7ly2R8OwtfXlw0aNOAXX3zB7du3p8379OnTuubSvXr1sq29joiQvApBCnvxA8ASJsZTxFDLFcisWeOYN6/aIRMAFi5cmJGRkbJhHD58mIMHD1Y5ebGPelEde/QBgaUEOtIotqSaXiIwmd27j5Wl2+s4qxTAaoq0iQDLaZRdDDBIkTYqZSINTfbX2+G1k8xVw8LCTJU9YvVRTvkBqEyZMvzrr78M69etW5ek+u8uAP79999pbVssFhYvXlyW3717d7t+VwQEBJ59uPsMLoRLAZfA3YwtkE5ER9u6JGebwsKoihLuBFy6dImzZs1ix44dNcNr2KLy5cvzww8/5F9//cXY2Finj+9ZQqdOnWRzz5gxo+o+pjVNnTpVs51SpUrZFAqCg4MNhczWrVvz33//JWmo6LObvSwWC0+ePMnp06ezffv2DAwMtJsnMmfOzBo1ajBjRrXA5OHhwSlTppj+8HDjxg1++vbbzJHBdriMJjVr8uTJk7x9+7bhuK9elbSlM2fOpI+P2kNpxowZ+euvv6rGEhcXx+XLl7NFixZ23zGWyItATwJnNfbgIYHllLz/2r4HqUW+GnMxogaQnPNYp/0JcL9G2aIA+ynSCgBMBjjIZH8/Q+151h4yu+ZDhw5N27Nt2+RehP38/PjgwQPD0EC+vr58/PgxSapMaHv3lsfTnDhxoizf39+fd+7cMcXbAgICzwfcfQYXwqWAS+BuxhZIBw4fdoZqSaKQEElVlQ48fPiQERERHDhwIEuWLGn3AS9Xrlzs2rUr582bp23S+ALj33//VQl8RsJltmzZVAfNc+fOqcotWbJE0+SvcuXKbNGihW773t7eHDRoUJopp5WizzTVr2+sGE9KSuLevXs5fvx4Nm3alBlMCHlGVKpUKVNrfeTIEfbu3VtXM6xFgYGBvHTpEkly/PjxuuWstU/bt2/XNbcMDw+XmUFa4/Llyxw7diyLFCniwDp4EHiNwCGdfYklsJKOmCzbQ1k10i6mDCKXRt47WmsJcI4izVenvzkAR7hwPqmUN2/etH27efOmKv/8+fNs1qyZLE15B3rLli0kya+++kqWniVLljTBkyRv3bql+kCh91FJQEDg+YS7z+BCuBRwCdzN2AIO4vBhRy/F6VNQkF0CZnJyMvfv389x48axUaNGmpoaI/L19WXjxo05YcIEHjx40GYMxhcdvXv3Vq2P0fp98MEHsvrTp0+X5efIkYNJSUk8fvw4c+dWx2WsW7cuf//9d5YrV063j2zZsnHq1Klp5qhHjpAjRpBNmmhf6W3SRMp35EpvXFwcN23axE8++YS1atWyec9PiwoVKsS33nqLixcv5s2bN9PatlgsXLduHZs3b26zjbJly7Jv376q9EaNGjE5OZmxsbHMm1fbxPe7776TzenKlSuqsCmp1KJFC0NNlMVi4ZYtW9ijRw9NTa1takZgC6VwMtZ7FUm1Ex9H42qao0x4ErakudZvAcBi+fPL0rpCW9OpRdMBRkN9Z9bfBXNJNR23WCzMmjWrLG/dunUqjaPyPR4zZgxJ6SOCUvBcsWKFjAdef/11WX65cuVeqCsBAgL/dbj7DC6ESwGXwN2MLeAA7HPnab8G08BE9urVq5w7dy7DwsJUQcLNUOnSpTl48GCuXbuWDx8+fIqL9uzjwoULKgFdSyhMJR8fH54+fTqtfsuWLWX53bp1S8s7duwYc+XKpWqjXr16vHfvHmfOnKmZn0olS5ZkRESE7GBrsZD370vOiO/fd74z4piYGK5evZp9+vRxULiS7sE1bdrUVHzU5s2b86+//kqbo9Y9vEmTJpEkZ82apdnGu+++q5pHXFyc6sNBKhUtWtTUb25MTAxLlvyJQC0H1qE6gVUEkikJmvUU+X6UHANFUDKtDXRorY2ostVvzFs6ZfIrhEt/gDdgzsnSJNh3RzM99MYbb6TtS7Vq1WR53333HQ8fPmxYv1mzZmn1GzZsKMtr3769bN83bNigqr9z506H3icBAYFnD+4+gwvhUsAlcDdjCziA9N6xtEVhYWldxcbGct26dRwyZAjLli1r90EsW7Zs7NSpE2fPnp1mViigj/fee0+2frbugr322mskycePH6vMShctWiRr+8iRI5ofBBo0aMCHDx8yJiaGw4YNM9SYNm3aVObUxNXYu3cvX3rpJZcJCr6+vuzdu7fmnO7evauKG+rr68tDhw4xMTFRdb81dS21YLFYOG3aNE1HVgEBAfztt98M18FisdYUHyPwIYEcds63JAG1RhYYqfgJiCfwBz1gvwMmPSoA8GFKByMNyin5fSbA0ooymTTqjU5pe5tGXnruYmpRpkyZ0j6MKUOGDBgwgBaLxfCjUEBAABMTE0mSP/30k4q/7t69m7bvycnJLFy4sKyM8m6mgIDA8wt3n8GFcCngEribsQXsRESESwVLC8BIgBN792bTpk3tjsnn7e3N+vXrc8yYMdy7d6/uvTIBbVy/ft3uu4dbt27ln3/+qTqkR2tooCMjI5kjh1ooadSoUVrYj7Nnz7JDhw6GAkC/fv1cHgJmwYIFms5RMmbMyGXLlvHs2bOsVKmSw0KCt7c3GzRowAkTJnDfvn2avLpp0yaV6WKZMmUYGxvLVatWqdrMnDmz4Zy2bNmiqyH+9NNPdU3DY2K0XtcEAj/Q8bAjIFCQ0j1MrfYHycpqxam0h8oBPAPJM68Rb1k/1wTYRVGmmEa9EVYDz6rc53SOW4t++eUXkuTnn38uS2/evDlJslu3bob19+3bR1IKS6P8mDNr1izZ3o8bN06WnzFjRlVsWgEBgecT7j6DC+FSwCVwN2ML2Al7w42YoJsAF0CKS5fHgYNWsWLF2L9/f/7++++8f/++u1foucfQoUMN11spfFatWpUDBgyQpdWuXVu3/cOHD2uGvmjcuLEsruTWrVtZpUoV3XFkyZKFX331le2wH3YiKSlJFS8wlQoWLMhDhw6llTVyrmMvBQUFsX379pw+fTpPnjyZZh6rtR+DBg2ixWLRFG7Pnz9vOL9Lly7prmurVq147949VZ2oKKNX+BQB+03UJXqNQLROu9/Jymo54rGXAv39Oeqdd+yq84Hy90ajzHtWA++lkW/vRzJblGraunjxYll6oUKFSJLz58+XpSuF5m+++SZtb1999VVZXqNGjWR7f/36dZXGe8aMGQ69WwICAs8W3H0GF8KlgEvgbsYWsAORkU4RJuMgeWIcBrCSAwerrFmz8rXXXuPMmTN57tw5d6/KC4fo6GhmyZJFd/21NI9KM7xUpyF6OHToELNly6Zqp0mTJrKwL8nJyZw3bx5DQkJ0x1O4cGEuX77cKY5G7t69q+vFtmHDhoyKikorGx8fz/DwcFM8q9Q+mqG8efPyjTfe4KxZs1i6dGlV/rp167hx40ZVeq9evWzOMzY2lm+88YZmvyVKlOCJEydk5bU1l9Z0nI4LmF6UhMwLijb/kJXLqKhXCGB/gIEO9Wl+b16rXVs1DqUW9WWrgW/UaMMoPIgj5OnpyatXr3L//v2qucTFxfHatWuG9du1a5e2t8uWLVO1ceXKFdn+t2vXTlamUqVK6XzTBAQEngW4+wwuhEsBl8DdjC1gB8LDHTZ1PQ5wCsBXNA6JZg5StWvX5meffcYdO3ak3RcScB0+++wzwz0pWLCgYf7+/ftt9nHw4EHN+KPNmjWThUQgpTAzI0eONDTZrV+/vql+9XDy5ElV4PhU6t+/f5rH2jt37nD8+PG6HltTydvbm927d+eBAwf46NEjrlu3jh999BErV67skLCprBMcHMyoqChmzpxZlu7r68sbN27YnK/FYuGUKVM0veJmzpyZv//+u1VZM86hIwmoNdKAPTFmKxLYQIDMgr2GZX0AJgDcYOc6alGhQoV087TMiPMo9qK4p2eaq+KEAweYKZN98TwdiS06evRo3r9/X5V+7NgxkjT0wpwje3Zabt0iY2L4ODZW9TFp4sSJMl75448/VG2kmtYKCAg8v3D3GVwIlwIugbsZW8AOhIbaLVi+DzCfA4e9ggDfzpuXK1askDmYEHg6iImJ0TRdTSWlIxHZwTtPHtNhXfbv38/AwEBVGy+//LJKwCQlk06j+2QeHh7s2bMnr169atd816xZo6mt9fHx4Y8//kiSPHPmDAcMGGBTcMiQIQOHDx+u0v5Y4/bt21y+fDnfeecdXYHWDBUvXlzTxLV///6m575x40bdvf7888/T9tLc63+AzvH2mpOF0N2mp9ZjABemuy9oatGtSckbSsEtQ4YMMs251p1hI9PYQYMGmRqnkje3bdvG4OBgWfrKlStJkkOGDDGsfzx104KC2EvRhlIzmZSUpPKm27dvX9M8JiAg8GzC3WdwIVwKuATuZmwBkzCnulDRqyYPSgEA20CKF3caKTHpgoKcH19CwDSUQdatqXLlymzcuLFmnhmzTGvs27dPU8Bs0aKF7n3K3bt3s7bCXNGaMmXKxNGjR8tMbLVgsVg4fvx4TU1irly5uHXrVv7zzz9s166daW1jp06d7Jo/KcUcnDt3Lrt3725oAmyWvLy8eObMGdP9nz9/nhUrVtRs69VXX+X9+/ftMFzYQ0DfrBrIQ6AxAfu1dYGK518hhQExrBMYqBKM7CWll+Pq1auryljfxZ07d65d7Y8ePZpff/213ePy8PBQhbmZMGECSXLdunWGdccjK5NTNk1L+3v8+HEZj3zxxRey/ICAAD548MBuXhcQEHh24O4zuBAuBVwCdzO2gEnYvnSlSd/rHYoAVgP4CcCtkMzbNNsQDnrchkePHjFPnjy6h9O1a9dqClzLli2zu689e/aoAsIDYMuWLXUFTIvFwiVLlhjGkMyXLx8XLlyoeR/z0aNH7NKli2a9SpUqcfr06ZpChJKKFCkiey5evLjd81fO68SJE5w2bRrbtWunKXiboZCQEE6ePJmRkZGm7qM+evRIM7YmIMWHXb36lB2v/g4CATpjW5VS5haBTwkYaw2tKZvieRTUDnc8NeotWrSIe/bs4euvv+6QCaqWx15lmbFjx6at5c2bNzXb0es79YOE2Tu8RvTmm2+S0dGM7diRfoZlu9IDyQxCNGtiC4Mg16x+XKaMLObw5cuXVeP/6aef0sXrAgIC7oW7z+BCuBRwCdzN2AImYewuUpfOWO3tSwDfBLgUYLTZNqwcqAg8fUybNk33cDp16lSGhoaq0v/55x+H+tq9e7emaWqrVq0MPcLGxsbyyy+/ZECAnjAD1qhRQxb8/dKlS6xcubJm2YoVK9qMbenl5cVOnTpx9+7d3Ldvnyr/zp07Dq2BFpKSkrh3716OHz+eTZo0MYwDqke5cuVi586dOWvWLENvshaLhZMmTdIUgrJmzcoyZdbY8frP1hlPTwLJVuUsBNYR0N4PI2oPsLMiTetOd4YMGXj48GGSUqgbe+9EKknLjLhWrVr/Z++8w6Ooujj82/QAIQmhht5rAoIgIASQXgSkKIT2AUqxISACAaQ3KRZAQBSVDoK0BCkCRqQKCAmhJaHXFNJJ3/v9Mdlk770zs7Ob3QT0vs9zn2TOrTszm8yZc+451LnU8mLCUBo0aJB7/t977z3ZNlrX3LpxY0K8vclMzCT2aKvSthJzvcZT9VUBoi9alJBdu3I/05tvvkm1adasmdXuc4FAUPAU9jO4UC4FNqGwb2yBRiy0XBKAfAdpf4/ekv7CclmopKenK1oG33jjDdmIox06dLA4cuvp06e5ADUASM+ePUl6erpq38ePH5N3331X1X114MCBZOfOnYq5Hk0pbm5ubmTixInkzp071Dli+x0+fNiiz6+F1NRU0q1bt3wpR9WqVSPvvfce2bZtm2y+0MOHD8vuQ5TO7QIiKYRqX91sAryusoZRCmM8IYA/cYA2F2QHgFTQ2LZatWokNjaWEEIUFbj8FJ1OR0UTZnNQqhVHR8fcva1y+xvzzr3psZyhIxVxOed8Kru2S+WO0bnnX5KcNFQ2bkxIUBDZt28f18bYHVggELxcFPYzuFAuBTahsG9sgUYs3HOZryL2XL4QrF+/XvbB1M7OjlSsWFG2LigoyOL5Tp06JWuF7N27d27EVjX++ecf0q5dO6sqDpUrVybLly9XTB7/2muvUe1NpWLJL0lJSZw7bn6Kr68vGT9+PAkMDMzNFRsZGakScbQfAZJUvr4/aZj3QyKnYHZDIJmE2aQ63iXKrrWWlS5dupCsrCwyd+5cq45rKOvXr8+9RnIWbbVy8eLF3L4zZsyQbaM9X2YFAvxJgH9MtNtodO71BKhN1XdGVeriZA4YQMozwX/MCR4lEAheLAr7GVwolwKbUNg3tsAMLIgWm6/SoUNhf2IBISQzM5PUrk0/dJoqdevWzVfKmL/++ktWwezTp48mBVOv15M9e/aQGjVq5EtZaNasGdm+fbvJz/LRRx9R/Xr27GnxZ9fKmTNnZNOI5Lc4ODiQli1bkhkzZpDffvtNNvKpVBoQIELmqxtH+JyXZQjgKDPGeGKsYDohjRkrzOqfr2vXrmT8eNoF1Frn0cnJiYwcOZL89ttvJC0tjYvkqlYGDBiQe223bNlihfXYEWCazLUwLqOY8z2HqS9JtqEXdYFnMN9Ld3d3kpKSYvP7XSAQWJ/CfgYXyqXAJhT2jS0wAwvzXFpcAgIK+xMLcti+fbvZD7fffvttvuY8ceKE7D6zvn37alIwCZFcVufMmWOW8qDT6UifPn3IX3/9pdm9d+PGjdQYZcuWtdg12By0ul6+++675O233yYlS5Y0+zq6uLiQmjVrKrhlehJpv6TxV/dDmXaBBPiVAHLXYQpRdrNNN3u9lhR7e3vZgFL5KZ6enqRmzZqa27u6upLk5GRCiBTgypy5XJ1cVOqVUwoBdZnzHcG10WEfuYnquY3uQArIZtzmp59+svm9LhAIrE9hP4ML5VJgEwr7xhaYQUhIwSqXoaGF/YkFOWRnZ5OGDRuqPuCyikvJkiVJfHx8vuYNDg4mRYoU4ebq37+/Jsvo5cuXFV135RSMoUOHmpXCw8CNGze48e7fv2/JRzaLzMxM0rx5c9nPYnxcrVo1kp6eTrKzs8mlS5fIsmXLSLdu3VSDIGkvdkTa26cnkhsmGwyol9HXertMPQgwU+HPgJ5ICqxxW217D1/GsmTJEkIIIc+ePTOzb34sr1HMOWfvJ39SGbeoC9OFGaNly5Y2v9cFAoH1KexncDsIBIL/Nj4+QOvWBTOXnx/QoEHBzCUwiZ2dHebNm6faZty4cdRxTEwMFixYkK95/fz8cODAARQpUoSS//LLLxgyZAiysrIU+y5duhSNGzfG/fv3Nc2VnZ2NrVu3Ys2aNUhISDBrnTVq1ICHhwclO3funFljWIKDgwM2bdoEFxcXSp6dnU0d37p1C9999x3s7OzQsGFDTJgwAUFBQXj27Bn++usvzJ49G35+fnB0dLRgFXoAnwFoAWBkzrEBFwBfGR2/DWADAB0zxmwAcveKDsCrjKylBWt8OViyZAlSUlLg6emJEiVKmNEzmzlmz68afzHHg5jjPbiLUpiFmbmSUUyLU6dOISwszIw5BQKBABCWS4FNKOy3JgIzCQwsGKtlPgLCCGyDXq+XtZIBUrTLpKQk0rt3b0ru5OREbt26le+5jx07RlxdXbl5Bw4cSFkws7Ozyb59+0iVKlVMWmxcXJRdCUuWLEm+/fZbs/aNduzYkRpj8uTJ+f7cWvn222+5z8BG3S1dunRusB4lkpOTycGDB8mkSZNI48aNNUcoVS5TFb7i8kGigCUybccybdSi0L78ZenSpYQQ81KZ8KUOAbTuNx7PnO+nhLeEbiLuiMttlAGQMsw448aNs/2NLhAIrEphP4ML5VJgEwr7xhZYwMCBtlUs/f0L+xMKFDh69KjsA2r79u0JIZJ7qIODA1VnSBBvjbnlFMJBgwaRpKQksmbNGk173N58801y/PhxkpGRQVatWiWbs9BQ6tevTw4ePKhpfdOmTaP6tmvXziqfWwt6vV7WfZgts2bNMmvcmJgY8ssvv5AxY8aYtX8wr+gI0JQAU0ixIgdJO+wnAZhHuiKQAGsU+nzN/ElYxtSru2dbWqpWrUq6d+9O2rdvT2rVqmVGZFbrFnt7e/L222+TBg0a5GMcNwIkEmCEhrZNmPNNCNCFadOVAIT8ahTcZyozTokSJUhqaqptbnCBQGATCvsZXCiXAptQ2De2wAJiYgjx9raNYuntLY0veGFp0aIF94D63nvv5daPGzeOqz916pRV5j5y5IisgmlKEbC3tyfvvvsuuX79OjdmXFwcmThxInF0lItmKpVu3bqRq1evqq5t7969VB83NzeSlZVllc+thTfeeINbt50dvb+xWLFi5MmTJxbPce/ePfLjjz+SwYMHmxUJNfc66XTEF14EmEeA0wT4UqHtaqM/C3uZOvkcpXyxbB9irVq1yKJFi8jDhw/JgwcPOGv8y1Me55y/X4h6Shc7Iimixn+KNzJt7AnwlLTEX7mNImTG2rx5sxXvaIFAYGsK+xlcKJcCm1DYN7bAQkJCrJ/30tNTGlfwQrNhwwbuobJRo0a59bGxscTTkw7C0rx5c6tFTz106JCqIsiW7t27k6ioKJPjhoeHqyoS9vb25MMPPyQxCi8/Hj16xPUJCwuzymfWwoQJEzSdjw8//NAq8+n1enL16lUycuTIfChAxQlQT6Huh5w/DVdl6iprGNuPAFsI4GvR2uzt7cmbb75Jdu/eTSZOnGhW35IlS5L69etzrskFWzoQKRLvt0TZSmwoB3POtaEkEYC1hK8kJRBD/c3uwIzTpk0bq9xbAoGgYCjsZ3AR0EcgEOTh4wMEBwPe3tYZz9tbGs/HxzrjCWzGgwcPONnly5fx7NkzAECJEiXw+eefU/VnzpzBjh078jUvIQSHDx/G8uXLkZmZabJ90aJFceDAAQQGBqJUqVIm29eoUQO7d+/GsWPH0KhRI64+OzsbK1euRI0aNfDll18iIyODqi9XrhwqVqxIyQoiqI+BevXqaWq3du1aREZG5ns+nU6H2rVr5zOQSyKAqwp17wLYCKAq+AA1lTWMXR/AQACXABwA4GfWyrKzs7F//3689dZb2LBhA4oVK6a5b2JiIs6cOYPExETEx8djyJAhZs1tHX4HsAjA+wDGmGh7gjkuBqAXI9uMOHhS4ZreY1oEBwfj5s2bZq9UIBD8NxHKpUAgoPHxAUJCAH///I3j7y+NIxTLl4IDBw5wMkIIPvvss9zj999/HzVq1KDaTJ48GWlpaWbPl56ejh9//BG+vr7o3LkzDh06ZLJP/fr1ERISgq5du5o9X7t27XD+/Hl8//33KFOmDFcfHx+PCRMmoEGDBti3bx8IIbl1TZs2pdoWpHJZt25dTe0yMzMxY8YMq8z5888/48yZMybbOTg4QKczJ4IpIL1IHwqgBwAvpk5LJFXD+dAB6AogGMBJAD3NXAcQHR2N5ORkze0zMjJw9OhRAIC7uzv+97//mT2n7XAE0JSRscolAR819jQI7uAJyuZKegNgX9usW7fOCmsUCAT/BYRyKRAIeLy8gM2bgcBAKX2IOfj5AUFBUn8v9uFR8CISFxeHU6dOydZt2LABUVFRAAAnJyd88cUXVP3du3fx9ddfa54rJiYG8+bNQ+XKlTFixAhcuXJFc98mTZqgSpUqmtuz2NvbY+TIkQgPD0dAQACcnZ25NuHh4ejVqxc6dOiAkJAQAECzZs2oNi+icgkAW7duxcWLF/M1X1xcHPVCAZCsv9u3b+esfFlZWbC3t4e39ygAH0GyKmrlKIAYRsYeyyF3PloC2AvgCooXb2HGGswnMDAw9/dWrVrBzc3NpvNp53XAKK2IxFkAcQC2QVLmRwPoBF6p34Ik5H0OJwDDmBY//fQT0tPSgMREICZG+mn0AkYgEAhyKVAnXMF/hsL29xZYmdBQQgICCOnQgd+T6ekpyQMCpHaCl45t27ap7t365JNPctvq9Xri5+dH1bu5uZGnT5+qznH9+nUyevRo1VQhWsro0aNJdna2VT73nTt3yIABAxTnsrOzI++99x7ZuXMnJXdwcCjQCJrmBNnp2LFjvuZ6//33uTF/++03QgghYWFhKtFl/0eAVCIFnNlMpIimWvZQmlseMPsI6VKsmHzkY2uVcuXKUfuM+/bta/YYbORl80sbAjQjQFkj2VACxBEpkq9xW+M9lm4ESCF8Gpg65AHKUCfyhsy824sW5f/2t29PyNSp4m+/QPACUdjP4EK5FNiEwr6xBTZEryckMZGQ6Gjpp5UCuggKj6FDh6o+zDo5OZH79+/ntj9//jzXZuzYsdy4er2eHDt2jPTo0cPkA7Nc7kUXFxdib89HBx07dqzVAgkRQsjJkydV8w8WK1aMW9+ZM2esNr8p2IixVatWVT2XR44csWie8+fPc5/zrbfeotrExcWRbt26KczdlAD3jfQPPQEiCLCWAP0JkL8XC0BRAmRR+g1fruVzDtPF+Nr/+OOP+R7PsnQwfkQK6nMn5xzfyvn8bEoX9nu1lQB/ceP9LXMy2zBtOqifeEJatxa5jAWCF4DCfgYXbrECgcA8dDrAzQ0oWVL6afaeK8GLhF6vx2+//UbJXF1dqeOMjAzMnTs397hJkyZcMJO1a9fmBoHJyMjApk2b0KRJE7zxxhuUKyGLIVgOYVzsqlSpgrNnz2Lnzp1wcHCg6lavXo2PPvqI62MpLVu2xOnTp7Fp0yZUqFCBq09OTubmKsygPr6+vvDw8FBsP2XKFOj1esV6OfR6PT744APqc7q6uuLLL7+k2nl4eGDfvn2YNm2azCh/A3gVwF85xzoA1QGMArADQDKAvjl1jjnFHFIAlAHQH8AaAOGQnp2MKW/mmObToUOH3L2Xluz/ZalcubKM1MlErz8hBfWpCmAkgCAAjwC0Ztqx+1g3QXIjrkJJt8rMMIo5/h2AasioEyeA7t2BQYOA2Fi1lgKB4N9Mgaqygv8Mhf3WRCAQaOPvv//mrBj9+vXjZA4ODiQiIiK3371794irqytt2ejQgSxatIiUL19e1eri4OBA3n77bdncmgBIu3btSHR0dO5cv/76q6wr4UcffWRVCyYhhKSkpJA5c+aQIkXYlA106dq1q1XnVWPVqlXU3A0bNjTpyrxt2zaz5vj++++5MebPn6/a55dffiFFixaVmd+BSDkt9TIGrmwCTCDAJQJctMBix5aKRHLJ3UiARwQgpFgxtfyPIMWLF7fCvCC1a9cmK1euJK+88kq+xqlXrx55/fXXGXmfnPNkzlg6AtRhZKy1WMptCQRQcm+AZDEXKxUgJZg5pvIXVDm3sUhBJRAUCoX9DC6US4FNKOwbWyAQaGP27NncA/P+/ftlH14HDx5M9Z0xY4ZZD78eHh5kypQp5I8//lB0Bfzwww9JRkYGt86dO3fKusiOGzfO6gomIYQ8ePCADBs2TPXzDBkyhHIXthXHjx+n5nVxcSFZWVlk8ODBimurXr06SU9P1zR+bGwsKVmyJNW/Zs2aJC0tzWTfkJBQYmdXTWEd7xEgTUUHSZHpI6esai92dnWJh4cHJZN72TF8+HBVV2hzitx9aU5xdnYmK1euZOQuBIgigGe+xpaKM3P8DQHCuHZHZS7SOKZNGYBkKF9QkeNYIHgBKOxncKFcCmxCYd/YAoFAG6+99hr1XR0/fjx5/vy5rEVKp9Plfpf1ej05fPgwcXZmH1zlFZ0VK1aQpKQkEhgYKGs5cnR0JOvWrVNd644dO2Qf5MePH28TBZMQaR9i69atFT+bq6srmTVrFklOTrbJ/IQQ8vTpU27eiIgIEh8fTypVqqS4tpUrV2oaf+xYNsALyMGDBzWvz88vlgCdFNbRggAPVXQQVvGzZP+hemnevDlniba3tydHjx4lx48fJ507d7b6nOaWy5cvy1jnfyTAaBvM1zTn3Dei5CNkLtAVmf6/Kl9MeQtmTIylt75AILCAwn4GF8qlwCYU9o0tEAhMExUVxQVwMQSDUYqC+dZbb5Ht27dzSqlcadWqFfn1119JVlYW0ev1ZOHChbKBe0qXLk3++usvTWvetm0bsbOz48aYOHGizRRMvV5Ptm7dKrt2QylfvjzZsGGD1SLZsvOXKFGCmm///v2EEEKCg4MV11W6dGmSlJSkOrZcEJ8+ffqYtb6pUwmRAu1MVjg/5QhwSkH/aMO0raVyTwUTYD0BBhE6UqplpWTJkuTu3buEEEIuXrxI3Nzc8j2mpeXYsWOkZ8+ejLw9AU5qHMP0Sx66TOSuV3FIrrDsRWrJ9O0ifyGVi7+/ZTe+QCCwiMJ+BhfKpcAmFPaNLRAITLNx40bqe1q0aNFcV0i2zpzi7OxMjh07ljtPSkqKYsqPJk2akHv37pm17i1btsgqmJMmTbKZgkkI0eRG+eqrr2pWlM2hVatW1DyLFy/OrZs6dariembPnq04ZnZ2NveZXF1dcxUurYSEGOsSWwngKrMWRwKsk9E9RjLtlJRGR0JHitUTybXzGwL0IoC7Rfeqh4cHWbp0Kbly5Qr58MMPLb7n81u+++47smPHDkauI1L03TIaxxlPgGEK519b2clfIPIT00YHkDsy7VRLYKDF975AIDCPwn4GF9FiBQKB4D/KgQMHqOP27dvD2dkZANC9e3cuSqtW0tPTc6Np3rt3D61atcK2bdu4dv7+/jhx4kRuxFitDBw4EBs2bICdHf0vbMmSJQgICLBaFFmW5s2bm2xz/vx5tGrVCu+88w7u3LljtbnZiLFXr17N/X3WrFlo3LixbL8lS5YgKipKtm79+vVc1NsZM2agUqVKZq3NxwdonRukdACA02CjkQKZAN6DFOE0w0heg2mXoDCLCwB7o2MdgHoAPgKwB0AMGmE1Fri4oCYT7ViN+Ph4fPrpp2jQoAE2bdqkuZ+1uX79Onr06IHixYsbSQl0um0Aemsc5RtIkXQvWryOGQCuM7L+ANypVQE/mDvwF19YvCaBQPByIZRLgUAg+A+SnZ2NgwcPUrJu3brl/h4REQEvLy+T41SuXBnLly9H586dKfmyZcuwa9cuNG3aFP/88w9Vp9PpsHjxYmzatIlLe6KVQYMG4aeffoKOSYWzaNEiTJ8+3SYKZtOmTaljLy8vtG/fXrbtjh07UKdOHUydOhWJiYn5nrtu3brU8bVr13J/d3JywubNm2XPZXJyMubNm8fJY2NjMWXKFEpWq1YtTJgwwaL1TZ5sfNQQwHkAcudmdY78Sc5xTaY+TWGGJABXFeoAwAHzsQ9T09IwLzWVqnFxdOReRMgRHx8vP7KFL1nM4dixY3B1dUW/fv0oeeXKmyAp5FrIhqQKxgDwZuoaaRrhGoC6ABoAmA0gDEARAIOZdusBZGlcFQDgzz+BK1fM6SEQCF5WCtROKvjPUNgmeYFAoM7Jk/xerlu3bpFff/2Vc8GUK25ubmTbtm0kMzOTEELI1atXuWA7cnsBixcvToKsmGj9p59+kp1n+vTpVneRvXHjBjfP3bt3yf79+0nt2rUVz1Xp0qXJd999R7Kysiye+9ChQ9SYxYoV4z4fm7LEUBwdHUlkZCTVdvRoPlDMoUOHLF4fIYQMHMh6Q2YS5XQa5QlwjkgpSbS6bY5Q9Lr0x6bcgxNMv6IAeda3L9mzYQP56KOPSPXq1S12G7VVsbOzI2FhYeTYsWNc3RtvXCaAEyPn3cLzigcB2CBFgwmgfI+qlboAGSUj36d0MZRKQEC+7i+BQKCNwn4GF8qlwCYU9o0tEAjUmT59OvUdLVeunNkP3WxE0Q8++EC1fa1atcj169et/ll+/PFHWQVz5syZVp0nOzubS3Oxc+dOQgghGRkZ5OuvvyaensqpI3x9fcnRo0ctmvvevXvceOxeVb1eT7p16yY7t79RUJW///6bO1/9+vWz/MTkEBMjBQfl9YpNhM+3CCIFoVltxj3nRAy5LI2LNx6QGJTIFUTK9E0AnXtx3bp1Filati5+fn4y99Bkwgc6Ug4uJRU2InMlAsxnZEVJWbxJHE2OJV/e5C+0eunQId/3mEAgME1hP4ML5VJgEwr7xhYI/vXo9YQkJBASHS39NNNK17hx43w/CL/66quU9UzOemkoXbt2JXFxcVY+CXn88MMPsvOqBbSxhI4dO1Ljf/bZZ1R9bGws+fjjj2XSSuSVnj17khs3bpg1r16vJ8WKFaPGkbM0PnnyhJQqVUp23osXL5Ls7GzStGlTSl6kSBGzgyopERIipTfkdYsLBKiocE6KKJ4rvkyhxvVELAlBA2qy5zL9ruV2yMu9OGXKFK5dfnNW2qaUJ8BQGbnWQD+GckpGtosAbCAhbcUOIPf5C61cPD3N/jslEAjMp7CfwcWeS4FAIHhZCA0FAgKADh0ALy/A3R0oVUr66eUlyQMC5Pc2EQIkJgIxMTiydy8uXjQd9MPb25vbS2nM+fPnsWfPHgDA5cuX0bVrV2RnZ3PtevXqhf3798PDw0PrJzWbESNG4LvvvuPkM2fOlN1zaCnNmjWjjv/++2/quESJEvj6669x5coV9OjRQ3aMffv2oX79+hg/fjzi4uI0zavT6VSD+hgoU6YMvv/+e9kxJk+ejB9++IFb84wZM8wOqqSEjw8QHAx4s1v+0BjSPsw2Mr2emzHDakj7LwFvPEQw2sAH9P3uCsCT6fXQ8EtcHNClCxAbi3nz5qFTp05UO7n7t/B5CCBcRl7CzHFuA/BjZBsBvAmgOCVtBaC8idH0AH40Z/q4OCA52ZweAoHgZaRAVVnBf4bCfmsiELwUaLU+BgYS0rq1eS5orVsTsmqVlISwfXuS7eFBAgHSToNFolGjRmTjxo0kPT2dhIWFqbatX78+2bZtG5ek3rhUq1YtN8WJrVm7dq3sGubPn2+V8ffu3UuN6+bmprqX8siRI6RBgwaK56ZEiRJkxYoVJCMjw+Tcw4YNo/qOGjVKse2oUaNk52NzOdauXZukp6dbdC7UiImR0hvyt2YGAT62yFKWV5YTf2yiXGHZUp/ps4Ftk+MmHBsbS6pWrWpyTnt7ezJhwgTV+7xwSlvmWM3FdQyR0sEYyxwJEEuA4ZS8EUCyAXIKIBMAUilHXokZsxJAshSugWyJjrb6vSYQCGgK+xlcKJcCm1DYN7ZA8MISEpKr8HH+g56eknzqVEJCQ6UndD5KilnlOUDWAKS2hgfVHs2akePHj1Ournq9ntSqpZbYXltZunRpgZ3i1avl9/EtXLgw32M/evSIGzcsLEy1T2ZmJlmzZo2iuyoAUqdOHRIUFKQahGjx4sVUn1atWim2TU5O1rSH9vDhwxafCy0EBhLi5yd3a/5IpD2X5t9LpeBCMkzc952YPovk2uXkXrx06RJxdTWdG3LJkiXk8uXL+f4umC7m7IH0JwDrgq2kANcjQJzMeV9NgN/5+9roXOkBchYgP8uM+5uJa0GVxESb3m8CgaDwn8GFcimwCYV9YwsELxyWWB+dnCxWKp8AZAZASmp4QG0DkOuGvv7+klJrxGeffUa1d3ZWVwpatGjB7etzd3cn0QVotVCKnLp48eJ8j12xIr138Mcff9TULz4+nnz22WfEyYmN/JlXOnXqpPj3cv/+/VTbEiVKqCqj586dkw10ZCj9+/e35ONbRGioFCy0QwfjdyrniLSf0HwFbJOJ+/9/TPuP5dr5+eWub8uWLSbnrFevnuLLli5dushG4LWslCfARDPal5ORKSmY0QTox8heJ0AWN86ncCJ6mfP2CjPmWyauRW4Rey4FggKhsJ/BhXIpsAmFfWMLBC8MVrA+mlNCATICIE5mPMw+ZMcxiqpJCCGnTskFApEvI0eOJGlpaeTMmTNc3UcffVSgp37FihWya8yvFbVv377UeGPHjjWrf2RkJOnXj33Azyt2dnZk7NixJCoqiuoXERHBtX369KnqXLNmzZKdo2jRouT+/ftmf3ZroNdLBqzoaEIiIp6QRo0ambyvHJjjhoCs4mMoAUz7fkptQ0Nz1zVhglLalLxy9uxZsmzZMtm64OBgzu3Y8lKHAKbddZWLnMIJAvQmwF4ZeSTh08ZUJR6IIe1xhEzFfBKK+oQAZLXMtXmsci1yi4gWKxAUCIX9DC6US4FNKOwbWyB4Ibh8WSk3g1WLHiCHANJZw0Mna8l8RWlco6ia2dnZpGzZsibHXr58OWVJGzhwIFVvb29Prl27VqCX4KuvvlJcq6Ww7qlNmjSxaJzg4GDVqL3u7u5kyZIluftVs7KyiIsLndLj+PHjqnNkZmaSMmX4qKJNmza1aM22ICoqyiIF6rDKd2Il07alUluj3IuZmZmkYcOGqnOOHj2ahIeHy9ZVr16dlCxZ0qLPUrBlPgG8GNlsIkX0ZduepE5XawSTX9CBFGHaLVS5FnLnWiAQ2I7CfgYX0WIFAoHAFoSEAG3bAo8e2WyKdEjRGn0BdAZwSKWtob4sI++m1MEoqqadnR169uxpcj12dnbQ6XS5xwsXLoSzs3PucXZ2Nj777DOT41iTcePGYfny5Zx8woQJ+Prrry0ak40Ye/nyZaSlpZk9jp+fH/7++2/89NNP8ObDqyIhIQGTJk1C/fr1sXv3btjZ2aF27dpUG7mIscbEx8cjPT2dk//9998ID5eLQFrwlCpVCqVKlTLZrgJzvESlLXs2Fb+F587l/urg4GAysvC2bdtQvnx51KpVi6uLjIy06D4oeKYB8GFkmwA0AlCbkW+mjk7AD/1xBGWZqL/rIEWPVWXgQDPXKRAIXkaEcikQCATWJjYW6NpVUtBsQAyAeQAqAxgBQCbxCADAKac+FMBBAHVl2ioql4CkGH/8MdLT03H79m2T61qwYAFSUlJyjytXrowJEyZQbfbv349jx46ZHMuajB8/HsuWLePkn3zyCVasWGH2eE2aNKGU6KysLFy6dMmitdnZ2WHYsGG4efMmPv/8c7i6unJtIiMj0adPH7Rr1w7lypWj6q5du6Y6fkBAAOLj42XrunbtCr3epEpQIFStWtVkm2LM8REAlxTayimXRK7hhQuSXc3QT0bJNyYhIQG7d+9WTDOTXGipNnSmm1D8yRyHA/gbwCBGvgNAJtf7FhYxx8Bxten8/IAGDcxco0AgeBkRyqVAIBBYm48+sonF8gaAMQAqApgB4KlCO6+c+nsAfgBgeKT7jWlXAsBrJuZ8vGUL2jZsiCNHjnB1NWvWpI6joqLwzTffULIpU6agdOnSlGzixIkFnk9wwoQJ+OKLLzj5xx9/jFWrVpk1lpubG+rWrUvJzhlZwCyhaNGimD17Nm7cuIFBg9gHfIng4GAcPHiQkqlZLs+ePauY8xKQlNZFixYp1hckZcuyNnWe65BemBizVKEtqyJmAIiVa8jkXpRT7ll+/PFHdO/e3WS7gkVWdVZBD14hXQ0gipHFADgs0/81sNZPPsusEZMnm7c8gUDw0iKUS4FAILAmQUHA1q1WG45Asgi8CaAOgLUAlBzvaufU3wcwB0AZpv4Ac9wZgL3K3OcAvArgzI0bsvXVqlXjHrK/+OILylJWvHhxzJkzh2pz6dIlbNiwQWVm2zBp0iRZZerDDz/E6tWrzRqLdY3Nr3JpoGLFiti0aRPOnDmDFi1amGyvpFxmZ2fjgw8+ADGyyskpTp9//jnCwsIsX7CV8PLy0tQugzneAqAtpJcuCwDczZGXBa86Kb7uMXIb1qJc/v7776hYsSKKFy9usu2LDauQ7gawUqbdZhmZDsB7XO9ouWn8/YFuqj4SAoHgX4RQLgUCgcCaLF5slWEyIO2CagLgDQCBKm3b5dRfBTAKgNzjcTqA3xlZV5UxNwLwg8oDOYBjx45hypQplCw+Ph5Ll9L2pJEjR6J+/fqUbNq0aZQLbUExefJkLFiwgJO///77WLt2reZxbKVcGnjttddw8uRJbNu2DZUqVVJs9+TJE6xbt45SIgFg3bp1uHDhAiWbPXs2OnbsSMmys7PRr18/2X2ZBUmRIkUs6kcABEN6qTINwJMcuSOA0kxbxXvZaF+wFuUSALZu3YpOnTqZsVI5HPLZ39okAvCQke8FIOfuOxiAS+5RJoCfc34nABLhhpgy9ZE4fwWIuYZVgUDw0iKUS4FAILAWoaHAiRP5GiIOwGIA1QAMAfCPQjuHnPq/ARwD0B3qf9BPADBW5XSQLJcsWQA+BTAUkkJqTOXKleHgkPdAnJmZiQcPHqB///5Uu6+++gpRUXnudQ4ODpzC+fjxYyxZohaSxXZMnTpVNnDLmDFjsG7dOk1jsMpleHg44qy8x1an0+Gdd97B9evXMX/+fBQrxu46lBg1ahRatmyJM2fOAABiYmIQEBBAtalbty7GjRuHdevWwd6etldfv34d06dPt+razcX4vsoPxmq4pqA+np6A0XnVqlyuXLkS3fJtjVPf35mHs+kmALQ/0qm1kwus9BySgsniCYD+7i9ESbTHYXghFu5IRKmnV+BetQS8vIAOHYCAAOCK0iZxgUDwr0AolwKBQGAt8uEOGwngY0j7KacAeKjQziOn/g6ADQC2AqgJ4H+Q9jyFQT5qI+sS2xS8ZScOkpLKh74B3vD0xIULFzjL1549ezBnzhzY2eX9O0lJSeHcT7t06YLOnWl19osvvsDDh0qf1LZMmzaNc9cFJEXthx9+MNnfx8eHioQLAOfPn7fa+oxxdXVFQEAAwsPDMXLkSNk2BjfaQYMG4aOPPuIU3ZUrV8LJyQmVK1fGBx98wPVfunRpgQdaMiYzkw8aAwDm2DOdQLuCa1IumzQBjIIzaVUuo6OjkZCQQAV2Mh/5z8yTDskWawo9JH+HzwHw0Wzpdk0U6pQCd8m5xgKsa+wzxOAYnBCHEpQ8Lg44ehRYuBDw8ZHi+xxg/ygJBIJ/BwWa+ETwn6Gwc+wIBIVC+/Zm56i8ApA+ANGZyE1XHSArAJLE9H9Npq0HQLoCZC5AjgEkGSC1mTazmHGuAqSGwtwfAyTDw4MQvZ6sXbuWqnNzcyNpaWlk2LBhlNzZ2Zncv3+fOj2hoaHEzs6Oajds2LDCuVY5zJw5k/u8Op2OrF+/3mTf5s2bU/3mTZ9OSHQ0IQkJhBjl+7Q27du3Nyuv4TvvvEP1j4mJIcWKFePaVahQgTx79sxm61ZjwIAB3HqKAaQpI2OPqfUz9/R7TP0YjbkX7e3tNZ1XNzc3Lk+ps7OzWdcGqCkja2tGfyfmeEbOR9MToIVKv9kEaKJQJ5fT1p4AT7nTJ81Th2k7SKadfPH3JyQmphBuOIHgX0xhP4MLy6VAIBBYA0KAixfN7pYE4FdAMdZjq5z6GwA+BJ2OIRWA3IzxkCLDzoC0X9MdwE2mjbFDXyCk2I8RTBtHSNFmvwbgGB8PJCejZ8+elLUmKSkJx48fx8yZM+HomGddSU9P51xPGzRogPfeoy0dGzZswEULzpu1mDlzJmbMmEHJCCEYOXIkfv75Z4VeEs2qVaOOz82bB5QqBbi7w5Z+gFoC/Rhwdnbm3I+9vLwwdepUru2DBw8wduxYbv9mQfD48WNOVgp5kY4N+ABYAXm3q4cA9hgda7JcyuReVNr/Wb16deo4KSkJCQkJlMzYgq+NXjKyFADyFmqeoszxRuRFgpULzmPgLKS/EnIWzgQZWTaktCQsfGAfYCeAZypz57FlC+DrK+0oEAgE/xIKVJUV/Gco7LcmAkGBk5BgttXSUFowVgJ7gLwDkLMm+p0wy0JCl4o5c/RQqC8DkJPsnNHRhBBCWrSgLSKjRo0ihBAyduxYSu7g4EAiIyOp0/TkyRPOata2bVuit6GlzxR6vZ5MmzaNOwc6nY5s2LCB7xAYSEjr1mSTzDnTK12v1q0JCQqyynq3bNlCzevp6Unc3d0Vr3Xjxo1JcHAwNUZycjIpV66cbPuNGzdaZZ3mULMmb8GrB5CljOy1nPP5B0DsFD7vTIBkA+Q7Rv4qe038/GTXUrp0adlxR48ebfH3TblMJYBORh5G5K2abHGQkf1p9DFZq6KhuBMgiwB3CVBC41qby97aQDThLahfKbSVL56ehISEFPBNJxD8SynsZ3ChXApsQmHf2AJBgRMdbbFyuTPne+IGkAkAuaOxXyZALkJylx0IkEpWeuCtAcldl5szMZEQQsgXX3xBtS9TpgzJzs4mDx8+JC4uLlTdkCFDuFO1YMECbs49e/YU9BWj0Ov1ZOrUqdy6dDpdnrIVE0PIwIG55+OmzLm7Z+q6WcEP8NKlS9y858+fN+mS2adPHxIREZE7DuvibCjFixcnt2/fztcazUXOTbcOQH5jZG7IU+AbqXzWngDZwci82WuhoOxXrlxZdsxOnTqRTp06WeU7llc6E6CijHwoAfZqHKM6c/yu0cecpdLvUk6bi4RXcJXupUiFW5t1a65HJJdZ7X8Kvb2Fi6xAYA0K+xlcKJcCm1DYN7ZAUODkw3KZBZBvAZJgYX/jch8g2wEyDpKlxj4fD771ATIKID8BJLx4caLPziaEEHLz5k2u7alTpwghhEycOJGS63Q6EhYWRp2q58+fk0qVKlHtatasSdLT0wv8shmj1+vJZ599xn02Ozs7snnhQunp1+hc6yHtbzVuu1PrU3Q+zDTPnz8nOh2tDPTs2VPTNXVyciKffvopiY+PJ5mZmaRWrVqy7Vq3bk2ysrKseHaVSUxMlF2Dd879zMrv5pzHhiY+axX2OuZ81whylHwF6tSRt/aVLVuW7Nmzx+Lvk3wpQYC3ZeQfE0k5e0XDGL2YY3cCpObcbvtV+n1jdFuy94/cvksQYK7CbX1Mpu1Jk18FtqhcFoFAoJHCfgYXey4FAoHAGri5SWkNLMAewFgA1kjJXgHA2wC+gpSmJAHAcQDzIe2zlE9mIU8YpAi0/wNQMzERZcuVw1tvvYU9e/agSpUqVNvdu3cDAKZMmUKlzCCE4PPPP6faurq6ctFkw8PDsWbNGjNWZ310Oh0WLVqETz/9lJLr9XoMmToV2x/Ru/Z0kKLuGqMp2+WjR0CbNhZvNHN1dUU1Zr/nvn37qOO2bduibt26XN+MjAwsXboUNWvWxPfff4+5c+fKznHixIkCSxXz6JF8BsooSNFf2e+FYQfrPRPj3mGO9Tljwtsb+OYbxX5KEWOfPHmCV199FaVLs3GW88MzSPGeWeIg3WGfaBgjKKetgQQA+3N+r63Szzg68ESm7gmk+LssmyE9r7K0BVCDkX2nMrc8W7YAQUFmdxMIBC8SBarKCv4zFPZbE4GgULAgWmxBljXInyVTrZQtW5Y8fvyYEELI559/ztVfuHCBOlV6vZ40a9aMauPp6UliY2ML48pxa5swYQL3GewhWYWNz+k0pk1bc65JPvwAe/TooXgtihUrRh4+fEgyMzPJypUriZeXl2LbevXqkdq1a8vWOTg4kPPnz1v57PIcP35ccX2RxYtze5K/AEiihffpeTc3k1bjli1bKvY/fPgwZ51XL8U1tJkkIytCgEQChFj4neyRc5tlEPl9mSBAUZLnuppNgEpMvdKezwsKt/Ripp0rAeJMfg3YorAVViAQaKSwn8GF5VIgEAisRbNmhb0CWTIAvA9gDKSYj8bUBnAKUor0zyBFp9Wast2YJ0+eoFy5cqhRowZu3rzJRdycPn06dazT6bB8+XJKFhcXx0WYLQx0Oh2WLl2KTz75hJJnA/CHFAvTAHvFz4M/x4o8egR8/LFFa6xXr55i3axZs+Dt7Q0HBwd88MEHCA8Px4QJE6hovgauXr2KGzduyI6TlZWFQYMG4fnz5xatUStKlksAiPjiCzRg7qUwAPctnCt8+nQp0aIKarkuL1++jOHDh5sxY0PQVkU5omRkzwHsUqjTwsGcvo4Aqiu0SUGe9dIOwGCmPlqhn1LOy2EAHIyOU1XaKvPnn1YPsCwQCAoQoVwKBAKBtZBJa1DYRAPoCGC1TF03SAkJWgDoCWAxgBOQnOpOA1gKoA+AMjJKiRKRkZHYtm0bp5D89ttv+Oabb5CcnJwre/3119G/f3+q3cqVKxERwSZFKXh0Oh2Wt28PVvXLBjAAUnoYgHeLTQZw3ZyJLPQDVFIu69Wrh48ZhdXT0xPLli1DWFgYevXqZdY8N27cwKRJk8xenznIpSExEJGdjfrTplGyKwDuMu1Y9Y110DQw4auvTN5fasplSEgI6tevj6ZN2SuvhAuAESba3IT849hPAJ5qnIclC8C2nN/l0o0YML62rHIZD3ln/a2Qf4VSBkBvRvYdIOtGq87WrWZ3EQgELwhCuRQIBAJr4eMDtG5d2KvI5RKAVwH8KVM3BcA+SDkwWZwBNIe0C2sXgMe7dyMyMhIbNmzAmDFj4OPjQ+W61Mq4cePg4eGBJk2a4KOPPsK2bdvw8ccfw8kpb29XZmYmJk+ebPbYtkD3xRf4CsAHjDwbwDuQciqWA1CRqde079KYL74we21y+ykBYNWqVbIWSgCoWbMm9uzZg2PHjqFhw4aa5/r2229x4MABs9eoFTXLZXh4OOozHgHXwO+nZHdBPgQwS2a8x48fo2nTpjh48KDinKYslwDMsF6mA1hmos0lAJVk5MEArmqcR46vcn6q7bv8B8DvOb/XBdCEqS8j0+cxgD8UxmNzXoZA2v1tHufM/hIJBIIXBaFcCgQCgTV5QRSjXwC8Dj7oiQuALQAWQgokZBJ/f+i6d0e1atUwZMgQrF69GiEhIYiLi8PChQvNXld2djYuXryIlStXYuDAgWjdujWcnWlH3F9//RV//imnEhcgoaHAiRPQAVgBKeCSMVkA+kNyJ2ZdY81+lLbADzAxMZGTdevWDW3btjXZt127drhw4QK+//57lCkjpzzwDB8+HFFRlrpoqqPqFhsRgfr161Oy5wBCGzemZK+Ad8jE++/jzTff5MaMj49Ht27dsGjRIhDCW9XUlMtr164hIyMDAwYM4O5beVIgvcIpq9ImFfJKHKCsxLHIrfk2JFdVJbdYA59AuqMBYAhTpxQ2aYuCvAOAKoxsnYn5eS5ckHZgCgSClw+hXAoEAoE16d493+6xBEAi3BADLyTCzSynMj2AGZAixrI75SoA+AuA5tWpRNV0d3fHxIkT4eHhQcn79++PgQMHonLlyprXnJSUxMm6d++OadOm4cCBA4iLi9M8ltUw8svTAVgJac+qMQYFk43Aa5HRxQw/wKysLFlX1R49emgew97eHiNHjkR4eDimTp1qUlGKiorCu+++K6uM5Zf795V3UEZERKBs2bIoUaIEJb/q5kYd1xk9GgMHDKBkK3fsQGNGCTVACMHUqVMxYMAApKSkUHXsfmFjMjMzce3aNXh6eqJPnz5Undw5tLePgPSNll9HHi4K8hAT/QwUgeTgzrIBwFoTfcOM2gwA/dopHbxdGJB2HqfJyO0AvJvzuyeAcdAW8ZYmLg4w8qAXCAQvEUK5FAgEAmuzYoWkmJlBKBogAPPRAUfghVi4IxGlEAN3JMILseiAIwjAfFxBfcUxEiHteJILifM6pGAzrNObIp6ewMGDgJeXYhNHR0dOoXn27Bm2bNmCO3fu4MGDBxgzhlXJtJGcnIwFCxage/fuKFGiBOrXr49Ro0bhp59+Qnh4uE2UHArGL88OwCoAo5hmmQA2MbLLkH/sNmc+NdasWYNLly5x8qdPzd+f5+bmhgULFuD69et45513VNvu378fy5aZcvE0n3v3lJOK3Lp1C3q9nrNe3r1L77qsXKcOPp06lZLFxMTg1q1bqnPv2LEDLVq0oNqxlsuiRYtSxyEhksLHusamp6dz42dnJwBYAqCO6jqU7xj2xYuShTMWkj+CnLvzJRNzA9Irqdic8TsxdXIJjBIhpUCRYwSkb8UjSK65yn+z1JA5nQKB4GWgQGPTCv4zFHYYZIGg0AkJIcTT02Tc/UB0I60RbFao/tYIJkHoSgnDAVJPIS3BewBJN2cCb2+T6RoM7Ny5k5rL3t6ePHv2LLc+MzOT1KxJpzQoU6YM6datG/H09DQjtQJdSpUqRXr16kUWL15M/vrrL5Kammq9a6fXK167bIC8q2F9p83Nv+DpKc1rgqdPnxJ3d3fZOfv375/vjx4UFETs7e1VP9vEiROter6dnZ1V57tz5w4ZM2YMJStatCh1vGvXLkIIIZ07d6bk5cqV4/rpdDpuDk9PT3L48GFCCCEBAQFUXfny5bnPTwghWVlZpGLFitz9z46t09kRYJyJe6aKxnt/jErdWAJcsvg7BQTk3I5bGDl/vqTyllm3uLklMdFqt5hA8J+isJ/BheVSIBAIbIGPDxAcrGjBjEUJ+GMzeiAIJ+Bn1tAn4IfuOIBB2IRYlMBhSFFL2dAf9pDcOddCPh26LP7+QEiIyXQNBrp06QIXlzyXvuzsbAQZRT91cHDA7NmzqT5Pnz7FoEGDEBMTg6tXr+L777/H8OHDUb58ea2rRHR0NPbu3YvJkyejVatWcHd3R8uWLfHpp59i9+7dFlnxcklKkvzyZLCDdD7lwrnoIO04WwWgsrlzavQDnDx5MhISEmTrrl7NT/AXiW7dumHWrFmqbZYtW4Y6dergl19+ybcFOSkpibP42dvTu4EjIiLQoEEDSsa6slaqJAXEYd2F2Ui0KSkp2LNnD9zd3Sl5XFwcunTpgqVLl1L3M8BbLg1Bfezt7TFs2DCqTi7QFSF66HQ/cnIaNv6tEl2hbAncDikoj7bvbh46ALMBGKLy9gJtrSSQv6ODAMh/T/KLpydQTM5gKhAIXnwKVJUV/Gco7LcmAsELQ0wMIf7+1Cv5y/Ah3nhghbf7elIcM4mdjFXBy8GBHDdnMD8/QoKCLPqIb775JjV33759qfrs7Gzi4+NDtalRowbJyMig2un1etK6det8WF7oUr16dTJkyBCyZs0aEhISQrKysrR9oOhok+crGyD/k5nTGSCHLL2g0dGqy/rrr79UP6+joyPJzMw069rJkZycTMqWLavpHLdq1Yr8/fffFs91/fp1bszSpUtTx6tXrybHjx9XXUdUVBQhRLqHXnnlFdW2d+7cITdv3iR169aVrW/UqBF13LBhQ+q4VKlSRJ9jZY6IiLDa/aqtnCPAdpX6vQT4woJxY5nbcRhTX16h3zqLbnVTpUOHfN/GAsF/lsJ+BheWS4FAILAlXl7A5s1AYCDg54cQ+KAt/sAjaLfSyZMGYDgSMRt6psbX1xfnw8PRNjQUCAgAOnSQTAHGeHpK8oAAKTJqcDDQrZtFK+nduzd1/NtvvyE1NTX32M7ODnPnzqXaRERE4Oeff6ZkOp0OK1as4Kw/PXv2xLJly9C3b1+ULasWdZMmMjISGzduxJgxY+Dr64sSJUqgS5cumDNnDo4ePSobSAgA4GTazmsH4HsAQxl5OiS7z+9cDw2oBNXJysrCBx/QSVGKMaadzMxMREZGWjIzRdGiRU1aLw389ddfaNq0KYYNG4aHDx+aPZdcjssqVapQx3IRY41xcXFByZIlAUj3kKm8nI8ePULNmjVx9uxZ7t4FwO1nZVO7REdH51rGq1evjjZt2qjOpx0t8ZtLA+gH5QiwmwAMgvkhNU4yx2zOy4eg4/Ea2GzmPNpoxoZgFggELw8FqsoK/jMU9lsTgeBFJCaGEO/SGVZ4s/+QAK/JWhL69u1LkpKS+Mn1emkTU3S09FPD/j6tREVFETs7O2od+/btY6bXk2bNmlFtKlasSNLS0rjxRowYQbXT6XTk0qVLuePcunWLbNy4kYwdO5b4+vrK7qHTUuzs7Mgrr7xCPvjgA7JlyxZy584dySKlsueSLVkAGSwztgtAfjfnoprYc/nNN99wc3z55ZeclW/37t1WuaYZGRmkVq1aZp3PIkWKkFmzZpGUlBTN8/z444/cOMOHD6eOe/XqRQgh3Gc1lFq1alFjZmZmksqVKyuuc+fOnblts7OzyZw5c1Q/16uvvsrt8Tx48GDuGD/99JNF959lJTXnltmoUO9MgHgCdDSSlSdAOxPjTmJuySwCeDNtasj00xHgvubbXGsJDc3f/SsQ/Jcp7GdwoVwKbEJh39gCwYvIwIHWePA6QwA6SImh+PjMIdnZ2YXy2fz8/Ki1jBgxgmtz+PBhbs1ff/011+7hw4fcw3z79u1zXRFZEhISyKFDh8jMmTNJhw4dSLFixSx+eC9fvjzp378/+apmTXIOIBkaLkoWQPxlxnIFyDGtF1bFD/DJkyekePHizLX2IZmZmaRt27aUfP78+ZZfRIZffvnF4nO4ceNGTffi+PHjuf7ffvstdVy/fn1CCCHt2skrSB07duTG/eqrrxTX980333Dt9+7dS9zc3GTbly1blrz2Gv0yZ/Hixbl9k5OTVe85S19+AA7MsbPRLZNFACeFfj8QYAcBhhLg95y2/5iY6zWZ23IS06aEQt8lmm5xrcXPLx83rUAgKPRncOEWKxAIBAVAUJBZqQwV+BmAHwDWlbAYgN0IDZ2B334rnD/rrHvhvn37kJWVRck6dOjAuRDOnz+fC87i7e2NyZMnU7KjR4/iwIEDsnMXL14cnTp1wqxZs3DkyBHEx8fjn3/+wapVq+Dv78+5Warx8OFD/PLLL/gkPBzNALgDaAsp1EkQgGcyfewhXZkBjDwVQHcAf2iZWMUPcPLkyUhMTKRkK1euhIODA+rVq0fJrRHUx0Dfvn3RjFkX6yIqx8OHDzFkyBA0b94cJ0+y7pY0N27coI6LFCnCucBGRkbKpiMxYAjmY8zIkSPhybqC5/Do0SNO1rNnT5w7dw61a9fm6p48ecLlWjUE9QEkN2K1NC6EEMU6dYoyx3pIz4qAdNcpuQpvhpSB9WcA7XPa1jQx1wUAKYyMdY19JrMmw3zWg/nqCwSCl40CVWUF/xkK+62JQPCi0bp1ft7mZxKAt/BIpRoBQgv9rf+tW7e4tQUHB3Pt5ILSLFy4kGuXkpLCpYCoU6cOFwRIKw8fPiS//PIL+eSTT0izZs2IgwNrFdJe6kJKR7IeIDcAos85+ZkAeVumfRGABJu6yAp+gCdOnODGGzx4cG79ihUrqLrGjRtbdH6UkAuk4+REW8ycnZ1VrXNvv/02uX37tuz49evXp9pWqVKFPHz4kBvj/v37ZM2aNbLjz549W3ZsNqWIoQwZMkTx88bHx3NWSrnSoEEDqp+pYEsdOnQw+z4rKis/ZXTbfKLQV8lVVT3li2TlZPv4Mm1qKvQNU729tRZ/f4tvVYFAkENhP4ML5VJgEwr7xhYIXiRCQvLzwPWM0PunjEt7AsRwfQprvxIbVXP8+PGy7bp160a18/T0JHFxcVy7n3/+mfvMq1atsspaU1JSSHBwMFmwYAHp0aMHKVFCyeXPdCkJkJ4AWQSQ4wB5S0FR+FPpIiu8EcjMzCS+vvTDffHixcnjx49z2xw9epSqd3V1tbprdNeuXak5XFxcuM/XvHlz0r59e8Vz5OzsTKZOnUoSmeSFJUuWpNr5+fkRvV5PihQpQsmPHz8uq2gDID/99JPsuh8/fswpwoDkYq3GH3/8YfKaOzg4UPuF9Xq96h5VPz8/znXcsjLa6NaRV7al8oXMrcbuWWVdeWfK9GEjz7oqzBcg09e84u0t7UsXCAT5o7CfwYVbrEAgENgYy91hrwJoBuCITN04AAcBeFlxvvzBusbu2bNH1iVw3rx51HFcXByWL1/OtRs8eDAaN25MyWbOnIn4+Ph8r7VIkSLw8/PD1KlTsX//fsTExODatWv4/vvvMWLECFn3SCViAOwDMAVAOwCBAEowbVIAdAMfkxOAoh/gt99+i5CQEEo2Z84cKmJu3bp1qfrU1FTcvas1Z6I2Fi5cSEXwTUtLQ61atag2Z86cQbdu3bB//36uDgDS09OxcOFC1KxZE99//z2ys7MBgHP3rV27NnQ6HWrUqEHJw8PDzXKLBYCyZcti6FA2ni/w4MED2fYG2Ci8cmRlZSEsLCz3WKfT4X//+59i+5MnT+K7774zOa5ptkGKFA0A/HnOY5OMrDRzzEZ/PSHTxx9SHkwDqZD7mwNsgfQcaxmensDBg1JwbYFA8JJToKqs4D9DYb81EQheJNq3t+RN/l4ZywKIFMRjvWrfwsoR988//3DrNUR5ZenXrx/VrlixYrm5Co2RsyJNmjTJ1h+FEEJIdHQ02e/nR6YAxA9SBFj+ephXnAHyM6QgQATKfoCPHz9WDOJjjF6vJx4eHlS7IAvzlaoxePBgag5HR0cuF6aTkxMJCQkh6enp5KuvviKenp6K56Fhw4ayAZ5WrlxJCCGkT58+lPyzzz4jhBBSrhwfzCoiIkJx3deuXePau7i4qH7WsLAwTdeyefPW5OnT1Nwgvw8ePFB1D962bRupWLFivu8hYFvOd/2BiXaXmb8NXZj6osyxKwHSZf6msC691RXmOynTV5vFMiTEOvepQCAo/GdwYbkUCAQCG0IIcPGiub1CAPQGkMzIy0IKDzNctfeFC9K8BU3Dhg254Dl79uyRbTtnzhzY2eX9C0pOTsbixYu5dm3atMFbb71Fyb7++mvcvn073+s1RcmSJdHj11+x0NsbwQASAJwFsBxSpsFyFoyZDmAYpEBBnZ2dMadiRfz+++9czs3PPvuMs+qtWrUKDg60tUmn09k0qI+BuXPnwsko/2dmZiYaNGhAtcnIyMCgQYOg1+sxbtw4RERE4OOPP+bWDEgBcTp16sTJX3vtNQDgLJcREREAgMqVK3N9KlSooLjuOnXq4I033qBkaWlpSEhIUOzj6urKyVq06MjJzpw5gTJl2sLT8yE6dABWrSqP11/vojhuYGAgfHx8FOu1Y8gP6w2giEo7NtAOmxszjTlOBSD3x4oN7KP03TM/sI+/PxASAljltAgEgheDAlVlBf8ZCvutiUDwopCQYOkepI8Yq0DTHEuFtv7M1rYC45NPPqHW3ahRI8W2Q4cOpdo6OzuTBw8ecO1u3rzJBeB5++23bfkxaEJCZPNe6gFyGyCbAPI+QBoCxM5Ca5SdnR1p1KgR+eCDD8iMGTO4+qFDhyoub+TIkVRbuTQw1oC9tjqdjgwZMoRb64QJE6h+165dI927d9d0Hgw5Mr/77jtK7uvrSwghpGfPnpRc0Qqp10tfvuhocujXX7l5li9frvg5Hz9+LLO2JwRopbDusgT4K+e22KH42by8vMi4ceMsuj/oYkekXLeEAI1U2pUnQLbRLbtSpk1t5lhur2YiYfdaVqhQQWaskgTQlsfXz48QGxjYBQIBKfxncGG5FAgEAhuSkWFpz2WQdvABkuUgGEB5zb3T0y2dN3+w+y4vXbqkaGWcOXMmZdVKT0/n9mMCQM2aNfHhhx9Ssh07duDUqVP5X7AWfHyA4GDA25sS6wBUATAIwCoAlwDEATgMYBaAjgDcNE6h1+tx6dIlrFq1CnPnzqXqnJ2dMWDAAGRmZsr2LQjLJQBMmzYNbm55n4gQgqioKPj6+lLtli9fjqNHj+Ye16lTB4GBgTh8+DBn7WRZv349MjMzZS2XhBD1/ZChoUBAANChg7R5z90dKFUKHfv0Afuws3r1atn9wIC85RLIADBBYeInkL6rawH0BL1HMY/Y2Fg4Ozsrr18zeuRZCdX2XT6E9HfDgNye1TrM8Z8ybdwgeVLkYbwHN48YvP32YXToIO2hNMbTU7osAQHSZQoOBrp1U1m6QCB4aRHKZT54+PAhdu7ciVWrVmHBggX47rvvEBgYiOjoaKvPFR8fjwMHDmDdunVYuHAh1q1bhwMHDlglsIVAILAdRp6EZuIIYAeA1QA2AJB74FXGKs+wFvD666/Di4nKsXfvXtm21apVw7vvvkvJvv/+e9y6dYtrO2PGDC5v4YQJExQVBKvj4yP57/n7qzYrDkmpnAlJyYyDpHR+A8mJ0RLS09PRrVs3uLu7o02bNggICEBgYCCePZOybsopl7Y4LyVLluTyjx46dAjjxo3jlKZhw4blrs9Ax44d8c8//2DNmjUoVaqU7BwfffQRGjZsiPv371Py58+f48mTJ5QrNSCdm/Q9ewA/P8DXF1i4EDh6FDDKS6mD5FBuTHh4OI4tXCi7hogIue9aKoCGsu0lMgGMAfAx5HNBSsjl2LQEB/wAySjBKpfFmWPjwD5yjtxsBJ2TkJRXliHU0f3797lrAQD29ptx5AgQGwskJgLR0dLP2FjgyBFg/nzAxPsFgUDwslOgdlIziIyMJNu2bSOffvopadOmDXFzc6NMvJUrVy6UdWVnZ5NNmzZx4eGNi729PenYsSM5cuRIvue7ePEi6d27t2w4dUByI+vduzf5559/8v/hrEhhm+QFghcFvV7Wo9KmxdOT5AYZKQyGDx9Off/btGmj2PbBgwdcagslF9CvvvqK+xu4detWG30KFQIDJb8+cy6Knx9J3b2bdOnCBlXJX6lTpw555513OPnDhw9t8tGTk5O5QD5NmzYlX375JbeGfv36Eb3CjRgfH0+KFZMLWJVX7OzsqOPg4GDSt29frt1lDee/rcz4nZETUMko/8Xly4R4eOiJ5Hpq3P4SkVxM1dcsFfn/1wBIvXr1rHbt3fA7Adh0PaWY4+IEeJ5zGmJlxhkqIwuROYWZhE1lUqFCNa5vkSJFSFJSkk3uPYFAoI3CfgZ/oZTL48ePk06dOmnKN1YYyuXjx49Jy5YtzfrjP3r0aCoXljksXLiQODo6aprHycmJLF682Mqf2HIK+8YWCF4kLIsWa3kprGixBvbu3Ut9/+3s7Eh0dLRi+wkTJnDtr169yrVLT08nNWrUoNpWqlSJPH/+3JYfR5nQUEICAqQTzr5B8PSU5AEBhOzaRcjUqYS0b09SPTxIJ5m/4cWcnEjtKlXyrXAYysiRI8mff/5pk3OzevVqbr7t27eTjh35fKw///yz7Bh6vV7z/zdD+eGHH0iLFi04+WYNX4rBCmNeBnLDlcbESL9KXdhIqqdz5OwzgPm5K9mX5ZaWgShBSmIvI3eQabsjZ+3ZBGCj2bYlAHvfrVQ4jfR+UZ2upMxcIBs3brT6PScQCLRT2M/gL5RyKffmU6kUtHL58OFD2RDiHh4epHPnzmTgwIGkc+fOxN3dnWvz9ttvK769VWL+/PncOK6ursTPz4+88847pHXr1rKJrL/44gsbnQHzKOwbWyB4kZg6tWCVy4CAwv28z58/J0WKFKH+Bqxfv16xfVRUFGfF6tevn2zb3bt3c3/3Fi5caKuPoh29XoqiFB0t/dTrJQtn69bcBXoOkI4a/s+1bNmSjBw5ktSpU8dsxcNQHB0dyWuvvUbGjx9Pdu7cSR49epTvj5qRkUFq1qxJzVOrVi1y584d7uWwm5sbuXXrFjfGvXv3uLWWLCmvrBjKG2+8Qby9vTl5gIYvxWcKYw4xtPH0JAO7xhl1YddyNEc+lpEPIMAWwga8UStVrPQSoQRAroB/5gBYBbyn0edi21civPXyHYXTeF5mLv5zd+3aNd/3mEAgsJzCfgZ/KZRLZ2dnUr06nVepIJXLjIwM0rx5c2r+YsWKkW+//ZZkZGRwbVetWkWKFqXfes6YMUPzfPv37+dyZY0aNYp78x8VFUXeffddqp1OpyO//fabVT53fijsG1sgeJEICSlY5TI0tLA/MZ+nsGfPnqrtp0+fzv3tv3jxItdOr9eTNm3aUO3c3NzI06dPbfVRzCcmhpCBA1UvUgpA2qsoDu6OjuSJkfU2JiaG7N+/n0ydOpW0adOGuLpqV2bYUrVqVTJo0CDy7bffkkuXLpGsrCyzP+Ivv/zCjbtmzRqya9cuTv76669z+TkPHTrEtVu2bBn58ccfZXNZqpVeGr4UXyn0dQDIPYAEohvThX2ZHJgjX8PI6+XI/yFAZU3rLV++PHWcn2u5CyBenHwcc+xAgOicddaQqVvLyLwJoJc5jXoCsC862PGkrUEv1PdRIPiPUdjP4C+ccuno6EgaNWpE3n33XbJ27Vpy4cIFkpGRQY4fP06dqIJULtesof+ZODs7k1OnTqn2OXHiBLVP0tnZmdy+fdvkXFlZWaR2bTo0+Pjx41X7sOHh69WrZ9HDgjUp7BtbIHjRkDFg2aT4+RX2J5XYsGED9TfAxcWFJCcnK7aPi4sjHh4eVJ9u3brJtr1w4QL3Am7MmDG2+ijmcfmysW+lSQWznYLSMBVQzS6fnp5Ozp07R7788kvSr1+/fLlaurm5kY4dO5JZs2aRw4cPk0QNeWz0ej1p2rQpNU7ZsmVJcnIyt+cWAJk3bx7VX27/7Pbt2wkhhCQlJZEZM2Zw6WeUSnUN53qHSv+JAGmNYKYLm6Ljlxz5KUZuT4DUnLpoArQzuV7WHZjdw2pO6QmQFoysDToSO27f57c5a5Rz4z0iI4tQOJWsVxXrPiyVFStW5P+7JBAILKKwn8FfKOXy2bNnJDU1VbausJRLvV5PqlatSs09d+5cTX1nzZpF9RsyZIjJPuvXr6f61K5d2+SezbS0NE4hVdrnUlAU9o0tELxoBAYWjHL5ouSOi42NJfb29tTfgV9//VW1z4IFC7iH1JMnT8q2ZXNk2tnZFf7fmcuXzY7eFAEQe5mHcy+AhALSeAoKpjGsu7CDgwMXEEdrsbOzIw0bNiTvv/8+2bRpE7l9+7bs1o5jx45xfefNm0cSExNJtWrVuPWcO3cut+/o0aO5vn/++Sc1/ubNmzWtVwdJUVc7z3+p9C8KOwLEM10aMe025MiTCL9v8bxRv0zC72FUL56enibbKO1PdQDI24xsjIwMeD1nfQNlxgkibLAe4EeFU3lbpr8HJ2vevHn+v08CgcAiCvsZ/IVSLtUoLOUyJCSEmtfFxYXExcVp6hsbG0vti3RxcTEZRa1t27bUfGvWrNE017fffkv1a9++vaZ+tqKwb2yB4EXEhKdkvou/f2F/Qpo33niD+jtg6gVbcnIyKV2afsht27atrGJz//59zp2wS5cutvoopqGjwWgug1QUilIACUOOBdMooqkcN2/e5PrfunWLHDlyhMyePZt06tQpX9bNcuXKkX79+pHly5eTs2fPkvT0dEII4aLfurm5kejoaHLy5ElOua1Vq1au9VouME9ERAT1mW7fllNk5Mv7kPayKp3nWybHWMx0Yde31qiOdQX9genbx8RcfDH1IoDdw2xcujHHb3h6kn39+8u0jSTAJzLyLwnARuEdrnLbstbP6jJj8tdTIBAUDIX9DC6USxOsXLmSmlctpL4crVu3pvpv27ZNsW1MTAz1pt/JyUmTexIhhCQkJFBvNh0cHEhsbKxZa7UmhX1jCwQvIhbqH5qKBv2jwFmxYgX1d8DT05Pbp87y9ddfcw+pSmmdPv/8c67twYMHbfFRTGPBm4M/NCgdZQByFabfHGRmZnIpq4KDg6k2WVlZ5PLly2T16tVk8ODBnHXRnOLi4kL8/PzIiBEjuDrDVg656zN69GhCCOFcoAGQlJQUbr1KabjkSiWAbAWIXuZcp5ns702AdKMubzD1XxnVsYrYOGY6Oeugenn11VdNtilVik0zkvM8VKkSdVyhQgWSnp5OXF29mLZzCcB7B0hBitjvXQ2VW3cd01befXnOnDlW/IIJBAKtFPYzOJ8BV0Dx4MED6riBmdl/fXx8qOOgoCDFtkeOHEF2dnbucZMmTeDm5qZpnuLFi6Nx48a5x1lZWThy5IhZaxUIBLbFyws4eBDw9LTuuJ6e0rhebD70QqZXr17UcVxcHE6cOKHaZ/To0ahYsSIlmzZtGgghXNtJkyahbNmylGzixInIysqycMUWEhQEbN1qVpdMAB8wMncATRnZUwDtAFzfskWaRwEHBwfUrl2bkl27do06tre3h6+vL8aMGYONGzciMjISjx49wq5duzBhwgQ0b94cjo6OmtaflpaGP//8E+vXr+fqvvnmGxw9ehTTpk1Ds2bNqLq1a9di8+bNiI+Pp+Rubm4oUqQIt95q1appWg8A3AMwEMDrAM4ydc4A1L8ejwBsMTp2ZepTjX73ZeouM8cuqjPJUatWLZNtoqOj4ezszMnv3rtHHT948ACZmZkoUeIdpuUmACVlRr4BoDUjiwDwWGEl/SCdUQNZAMpxrTZv3iz7vRUIBP9uhHJpgmfPnlHHHh4eZvVn24eGhiq2vXLlCnXcokULs+Zq2bIldRwWFmZWf4FAYHt8fIDgYMDb2zrjeXtL4zHvsV4IKlasiCZNmlCy3bt3q/ZxdnbG559/TsnOnTuH/fv3c22LFSuG+fPnU7KwsDBZhcemLF5sdpcVANi/0PMBHAXQkpEbFMwbs2apjlmvXj3q+OrVqybXUa5cOfTp0wfLli3D6dOnkZCQgBMnTmDRokXo2bMnSpaUU0bUyc7ORocOHVCuXDkULVqUU1g//PBDrk+FChVkx6pRo4bZ858G0BzAIAD3jeTsV64O13MppBf9gLpy2ZCpu2zUD6AVLwAorbzYHDyCg022AYD09HRN7cLDI5CYOJiR3gCQKNP6JiSFuTgjV3oR5AHgTUbmxLW6ceMG/vnnH5NrFQgE/y6EcmkCJyf6D6bWP+xK7a9fvw69Xi/bln0QMPefavXq1VXHEwgELwY+PkBICODvn79x/P2lcV5ExdLAW2+9RR3v2bPHpDVj2LBh3N+/6dOny/7tHDZsGBo2pB/2Z8yYgcREuYdoGxAaCpiwxrI8AjCTkb0CYAwANwC/QVKOjHkCoN358whXsV7WrVuXOrbkf4CrqytatWqFyZMnY+/evYiKisKNGzewfv16vPvuu9wcajx79gzHjx9HZmYmJWetloCk5MphiXJpYAuAWgA+B5AMXrl8jesRBunsA8rK5UEA85i6OADGXk6s5dK0VfLCw4cob7KVhL29PSfT6XTU8eXLN5GU1BwAa/k9JzPiAwBpAPwg3YnjAOwE0EFlFaziele21ebNm1XGEAgE/0aEcmkCL8bP7PFjJTcRedj2aWlpuMe4sBiIiIigjitVqmTWXGz78PBws/oLBIKCw8sL2LwZCAwE/PzM6+vnJ3lIbt784rnCsvTu3Zs6fvDgAS5evKjax9HREbNnz6ZkoaGh2L59O9fW3t4ey5Yto2RRUVFYbIE10SLMdIcFgE8hKTvGrAJgUBmKQ1JhmjFtHgNoN3Ag97/CAGu5ZN1iLUGn06FWrVoYPnw41q1bh6tXryI2NhaBgYEICAhAmzZt4OrKKmLmk5SUhEuXLlFbQwDzlMuqMrI0AHMB1ACQwtS5AajDPQYtyfmppFymAjgvM1OI0e+s5dJbZjyaswBKQKfaxkB2djanTLIvbG7cuAlAB14J/F1h1HAAewBcBPAVgL4ASqisoit4R+MqXKutW7dy11QgEPy7EcqlCdi3tGfOnDGrv1z7hIQE2bbs29zSpU270qi1V5rHXKKiohAWFmZWUXr4EQgENN27S26toaFAQADQoQO/J9PTU5IHBEjtgoOBbt0KZ73mUq9ePU5B2LNnj8l+AwYM4Pa4z5w5U3Y/Zfv27dGjRw9KtmzZMty9K29NsSrn5CxByvwBgFVHhwNgN0G4AzgEfg/mw6QktGvXDpGRkdzYrHL58OFDq/0fMKZEiRLo3r075s+fjz/++AMJCQk4d+4cuuXjpvz777/xyiuvwMPDAx07dsSsWbNw+PBhRYsmwD/AbIDk2Oou0/YpgL8Y2SMAk8Baw/+ApDyyyuDznJ+VFVZjvO+StVzqIe1TVCcUyhZ91m3YlPX/7l3Dy+VBTM0ztmkON5D3ekMLTgDYPZ3PuVaPHz/GH3/8Yca4AoHgpadAwwflg8KKFvv48WMuAtqpU6c09T1x4oRsBLUTJ07ItmdzXYWGhpq11suXL1P9S5QoYVZ/JWbOnGl25Du2iGixAoF29HpCEhMJiY6Wfspk4nip+PTTT6m/B/Xr19fUj83dCIB8//33sm2vXbvG5dX0t3VuFr3erLyWGQCpx3weD4A8VekTB5BXZf6mVqxYkURGRlLLSU9P587BmTNnbHsOjEhOTiZlypSh5i9dujR55ZVXLM65qdOxOSWlYgeQmozsh5xzFgWQohrGfgVSFFknlGDq3ibAFEY2MOeSxCiM97bRZVvI1PUgAJ8T1JxSo1Il0rRpU83tW7RoQTw99DnracbU28v0maP1NjYqp7hxnGTWMnz48AK7BwUCgYgW+8JTtmxZtG/fnpJNnDjRpJtHVlYWJk6cKFuXlJQkK09Oph2lXFzMizjHuiax4wkEgpcDnQ5wcwNKlpR+6rR5y72wsPsuw8LCNLnt9+rVC02b0ra7OXPmyO59r1OnDsaOHUvJtmzZgnNmWhbNIikJiIvT3NwewETQ8TrnQz3ciweAwwAaM/L79++jXbt2uHPnTq7MycmJsxIX5N77okWLYubMmZQsKioK06ZNQ0JCAn7//Xe8+uqrZo1JFCx0zuCdTw0BkkpB3aHTwD8AZgBohkZMzU5IDrXGGNxiSwAoKjOaseWSXVkagDaQcxvVyq17j7Bm0VrNHk03b95EY1/DXlfWNVYu7sMNAHsB9ILkkF0RgKlN4c0B0LEe5Dbz7Nq1C2lp7PkUCAT/VoRyqYFp06ZRx6dPn8aQIUMUg/ukpaVh0KBBig81dnbaTju7p8La7QUCgaAgeO2111CmTBlKtnfvXpP9dDod5s2jg6fcu3cP3333nWz7mTNnwt3dnZJNmDDBdukQMjLMam4HYASkx/j3AbwKYLSGfp4AjgCcCnTv3j20bduWcv+1RlCf/PDuu+9yCm5AQABcXFzQvn17fPXVV7L9zI1KmwrgCiPbDWA/gBiZ9hVlZIC0wzIUf4BWCPXgA98YlEsd5F1jw5HnFsq+GE6HdPX/p7AK0+iRha7dgEWLdsgG9GGJjY1Fg/qGM/EOaJdXue/DTUixdfcB+BtSkB/5+BB58Hs6n8q0SkxMVE3DJhAI/l0I5VID7dq1w8cff0zJtm7dirp162LZsmU4e/YswsPDcfbsWSxduhR169bFjh07AEjWRzYokFI6k2LFilHHqampsu2UYNuz41nK+++/jytXrphVtOypEggE/w3s7e3Rs2dPSmYqJYmBjh07wo+JeDR//nykpLDhWSQFZfr06ZTs5MmT2LVrl5kr1ogTn35BCyUgBfA5Ce273EpACsXSkJHfvXsXbdu2zQ0UZ4ugPubg6OiIBQsWULKbN2/mpoe5deuWbD8nJyfcuHEDv/76KyZOnIgWLVpozrlp4DaAnpAsl2zovW8gBfaRIwF6SAqgMX8zx8b/X+WUSz3ybKdylksAGKawAgPqVsmo9BhM/LgFPv10uYlxJF5taXjpUBpAJxOtbwAoy8i0BDCklcskQDY/t4gaKxD8hyhQJ9x8UFh7Lg1kZGSQgQMHmrVHws7Ojvzyyy+kcuXKlPz69euyc1SsWJFqd+7cObPWePbsWap/pUqVrPHRLaKw/b0FAsGLxYEDB6i/CTqdjjx+/FhTX7n964sWLZJtm5aWRqpWrUq1rVq1KklLS7Pmx5Ewc8+lNUq0uzvx9fXlzkfVqlXJvXv3yKZNmzh5QaPX67n9geXKlSPJyclk6tSpiv8zhwwZQo2TmppK/vrrL9KyZUuz/vfKFTeAdMvXGE2NLsMYhTbrcuo3M3Jfo77tVeZoS4CRKvXTCUBIuXJ60r//YJNrXrt2LWntcVlhTXJlP3PsSgDDvk210oLqV6NGDW5sR0dH8uzZswK/FwWC/yKF/QwuLJcacXR0xJYtW/Dll1+iRAnTuzmqVq2Ko0ePom/fvlw6EtY9zADrzhUdHW3WGqOioqhjJQupQCAQFDRvvPEG5U1BCMH+/fs19W3VqhW6dOlCyRYvXiwbCdXZ2RlffPEFJbt9+zZWrFhhwapNoNMBjdndkLalZNOmOHr0KHyY5Ka3b99G27Ztuf9Pd+7cwfPnfBRPW6LT6bBo0SJK9vjxY3z99deqltSNGzdS6WZcXFzw+uuv45132KikwOsAtGfclCxqB8xoz2PKcgnk7btUslwCUmxgJcIBrAPvVmtgMYBf8fixDjrdWjRq1EhlLOC7777D5I7/5Bz1gvxeUWNYb4BUSGfOFLT1Ui5Kc2Zmpu08CAQCwQuFUC7N5JNPPsHt27exZs0a9OnTB9WqVYObmxtcXFxQrVo19OjRAxs3bkRYWBjatm2LyMhIZBjty6lRo4ai0lezZk3q2Nww+mx7djyBQCAoLJydnblUFea4z7N7L+Pi4rB8ubx7YN++ffH6669z/WNi5Hbj5ZNmbDZKG9OsGUqWLImjR49yqVpu3bqFDz/8kNp/TwjBjRs3CnaNkF4mdO7cmZItXrwYV66wOyVpxowZg/v371MyuVyXUwFcBVCbkVvmqKwFLcqlIdel3J5LA29BymQqx0NIGVCV6jMh5Z+cgR07XPDRR7tVX3ZfuHABxbrqMRBbICmWfRTbSsh9P0y7xvaFHRyMV5mZKRt4SLjGCgT/DYRyaQHFixfH6NGjsWvXLkRGRiIxMRGpqamIjIzE/v37MXjw4NzIradPn6b6vvbaa4rjsoEYzM0Vye5lYccTCASCwqR3797U8e+//47ExERNfZs0aYI+feiH4+XLl8sqjDqdjlM8ExISMHv2bPMWrIWBA60/pob5SpUqhaNHj3J7LG/dusUFfCnooD4GWOtlYmKibH5OY+Lj4zFs2DDo9XkRTStV4mOQeuT89GXk70HaLcmkirUCtwFsh+RlJhcTFZAslwTqlssiAAaozHMTkhKpxjwAvfDDD57Yvn27apDAkfPnY1HTDfDGQ/BRY1nugM8Sqq5ceuMh1mIa2AynRYoU4dr+8ccfePDggYk1CASClx2hXNqYo0ePUsdt27ZVbMu+hWYVU1OcPHlSdTyBQCAoTLp160YFacnIyMDBgwc19587dy5llUtOTsbixYtl2zZr1gz+/nQqhdWrV+P69etmrtoEPj5A69bWHVMJPz/A6O966dKlcezYMe5FYlZWFnVc0EF9DDRq1AiDBg2iZEQmcm+VKlWo4+PHj+PLL7/MPZZTngz+QPUZ+XVIUXhZFWkSgA9Bp4ExDz0kpbA4gEUKbRIgRVhVs1wCQD+VecIAxGtYTyBOnWqG58/Lc0q8MZGRkVge+TcOogs80Ah80B5jbgAox8iUlUtPPMNBdIEXnmEIU2ecIseYrVu3qswvEAj+DQjl0oYkJSVh586ducfFihWT3TtioGPHjtQb5wsXLijmxJSb6+LFi7nHDg4O6NixowWrFggEAtvg7u6ON954g5KZ4xpbr149DB5MW19WrlyJR48eybZfuHAhlS84Ozsbn332mfYFa2XyZOuPqXGeMmXK4NixY6hTp45it8KyXALSCwFTUV979uyJihXpZCEBAQG4fFnaw/jkyROuz5Oi0v5B9hVqGNdSojWAFQA+YuSlwCui6iQDCFSpPw9euTS2XN7OWYWSA+8/gGyqEDluon//11CrVi306tVLsdU3z54hEVfwJ9qjKN5UHU+rcumNhwhGG/jkJITp8c47XNwIOYvzli1bVOYXCAT/BoRyaUO++eYbKly+v7+/bIhuAyVLlkSrVq1yjzMyMjT/Id68eTMyM/Ncafz8/DQFHhIIBIKChHWNDQoKovalm2LWrFlwcMjb4ZWWlob58+fLtq1UqRImTJhAyfbv3895lOSb7t1t7x7r7w90Y50PJcqWLYtjx46hVq1asvWhoaG2XJkqVatWxdixY0222bBhA2WVzsjIwKBBg5CamiobfyDif/8DvL05y+UTALGQMjDK4c0cVwLwrFw5hO7ahTVr1qBBg6EwlRJEnQGQspcWzRmnBqTQQwRS7szmkCyESve8knpsgFbgMjKS0Lt3b9SrVw/Fi8vv1SSQ8qvWwBXsw3bZNhKRANiAg7xi74/NCIFvrmIJb2+4rFqF/v37M2vjP+OlS5cK9WWHQCAoAAo0Nm0+KOxUJOZy7do14urqmrveYsWKkTt37pjs98MPP1Cfs3bt2iZD6KelpZHatWtT/X7++WdrfRSLKOwwyAKB4MXk0aNHXJqCgwcPmjXG6NGjuTQHt27dkm2bmJhISpcuTbVv2LAhycrKssbHySMmhhBvb9ukIPH2lsY3wcOHD0nNmjVl00zcv3/fup/XDKKiooibm5tiCozt27cTQgj57LPPuLpx48aRWbNmcfKhQ4cSEhNDMgcMIE5MXTBAqjKyvTnnMoiRl3N1pc5t+/aEABkEKKOSssPcUosAbxDAUUPbKibqFxDgFdk6uRQ1xuVTgOgBUlt1/OHM8WDqVqyPEBKErnkCT09CQkIIIYQEBwdz49nb23OygICAQrkPBYL/CoX9DP6ftFzqdDqq/PHHHyb7sHtY1Lhx4wY6dOiA1NS86HILFy5E5cqVTfYdNmwYatfOi39348YNBAQEqPaZOnUqFQ2wXr163D4XgUAgeBEoV64cmjdvTsnMcY0FgOnTp8PZOS9oSmZmpmKwHjc3N8ydO5eSXb58GT///LNZc5rEyws4eBDwtHIoGU9PaVwvL5NNvb29cfz4cVStWpWra9u2rdnpraxFqVKlMGnSJMX6cuUkV8w5c+Zw6TW+/vpr2fgDERERgJcXHLZuRR1mz2YYtFsun6anIyvHnZMQQNpd4ghAztraI6fOXG4COAY+UI/cWKYC3jwD8BcA/n98SEgIJzNmOSTbaT+4mpjDGNotNgw+6I4DGIRNiC1bHwgOlvYdQ0oZxLrCyj33bNmyRXbvrUAg+HfwwimXDx48wJ07d7jC7rnIysqSbXfnzh2bhJv/3//+h0GDBiEwMJBSGo159OgRZs2ahVdeeQUPHz7MlXfv3h0ffPCBpnns7e2xdOlSyj1o+fLlGD16NGJjY6m2MTExGDVqFBX4QKfTYdmyZVy0QIFA8B+FECAxEYiJkX6+AA91rGvs3r17qeigpqhQoQLef/99SrZx40bFwDUjRozgApxNnz4dycnJmufUhI+P9LDtzaowFuLtTT28a6F8+fL4888/uf8BkZGRaN++faEpmOPGjVOs8845X87Ozti8eTO1TxaQAvywhIeH5/5ev0ULqu5KhQoAEwSIFC0KdOgAb2Yder0eT58+BQAkJQFxcYaaETIrdYO0Z3Ko4mcxD7mosKZeZF+EFHF2I4BlMOcxTg/AH874Dr+ptGKDD8nvudyCQfBFCEKRd2/a2dlxe6KfPXvG9b1z5w5OnTqlcdUCgeClo0DtpBqoXLlyvl1Qhg0bpjoH2/748eMm19W3b9/c9o6OjsTX15f07NmTDBo0iHTv3p34+PgQnU7Hjd2pUyeSkpJi9nmYP38+N5arqytp27YtGTBgAGnTpg3ldmsoixcvNnsuW1DYJnmB4D9NSAghU6dKPn6enrSLpaenJJ86lZDQ0EJZ3vXr17m/XadPnzZrjKdPn5KiRYtSY/Tv31+x/aFDh7g5P//88/x+FHliYgjx98+fK6y/vyZXWCVatWql6DoZHR1txQ+rjVu3bin+z2b/R65YsULT//r4+HhCCCHz5s2j5G3atCHVq1enZHt27yaEEJKdnU0cHWn31HPnzhFCCImONr4EaTJzOhLgSU79dJW1DSeAHwF4l9D8lzLMrXKEeHqWMHOMKQRQclPuzhx7qt6mRl6xhBBCrl69yo3Jfk8BkLFjxxbIfScQ/Bcp7GdwoVzCfOVSS3FwcCBTp04lmZmZFp+LBQsWcP8ElYqjoyNZtGiRxXNZm8K+sQWC/ySBgYS0bm2eEtO6NSFBQQW+1Lp161J/IyZPnmz2GNOmTeP+Fl68eFGxfZcuXai2rq6utt2LGBhIiJ+fedfDz88q1+Pjjz9W/H/RqFEjEpMPxdUSgoKCZNdiZ2fH/Z/U6/XctZIrFy5cIIQQsmfPHkru5eXFKZe7c5RLQgipVKkSVbdnzx5CCCEJCcaXQk8A/oUxMC2n/neVtS0hQNN8P8solwMEiCfIUe4iI28RXxkFTrnYE0B+b678utNUb1l2S3CTJk2o/uy1AEBKlChBMjIybH7fCQT/RQr7GfyFc4t9URkwYADeeOMNzl2Hxc3NDSNHjsSVK1ewYMECKqqhuUydOhVnz55Fr1694OQkH7bcyckJvXr1wrlz5zC5oMLhCwSCF4vYWCmaaI8ewIkT5vU9cUKKdjpokDROAcG6xpq77xIAPv30U3h4eFCyGTNmKLZfunQplTMxNTUV06ZNM3tezXTvLrm1hoYCAQFAhw78nkxPT0keECC1Cw5WjAprDmzuS2MuXbqEjh07yros2goll2W9Xo/169dTMp1Oh/Xr18PLxD7TiIgIAED9+nTM2NjYWFU3a2/GbdmQysbNzfjy6ADZvYnfQkpHwqfZyGMOgL8ZmSMkV9Z7ALZBSkfSBIAlW1i6AfAE4IMiRcbgr61bsCklBf1NdcslG4CSe/Q9GRkfMdaYR4+Ajz/OOx4yhM56+eABv4/02bNnOHz4sPoyBQLBy0mBqrL/AtLT08nff/9NtmzZQpYtW0bmzp1LFi5cSH744Qdy+vRpm72Je/bsGQkMDCRr164lCxYsIGvXriWBgYHk2bNnNpkvvxT2WxOB4D/D5cvWi1Lq7U37uNmQs2fPctaMq1evmj2O3BaCU6dOKbYfM2YM1/78+fP5+SjmodcTkpgo+WAmJkrHNkAucidbGjduXGD/Q0aOHKm4jnLlysluH/n1119V1z9//nxCCCFZWVnExcWFqitfvjx1/Ouvv+aO26dPH6pu+vTpuXVStFhD8VKY+2sCpJphKXQnwDGFr10SASqZMZZyKQOQ+lYYB3Bmjk9r+vMRGCidwydPnnBRYr28+HM5cOBA2950AsF/lMJ+BhfKpcAmFPaNLRD8J7h8md9Tmd/CbqKyEdnZ2ZwCsGDBArPHSUpK4lKNtGvXTrH906dPubQYbdq0IXobKXmFRVRUFPcwX7ZsWU7WpEkTEhcXZ/P1tGzZUlWhUbr27du3V+wzfPjw3HaNGzem6kqVKkUdGyuXH374oeI4U6cafx0qKMxdhQCZBODPJ18qEyDMxNdutEp/Bw1zWLt4M8e/avrT4eeXd926du1KjVG1alVuHldXV5KUlGT9m00g+I9T2M/gwi1WIBAIXkZiY4GuXY3DW1qHuDigSxebu8ja2dmhV69elMwS19hixYph6tSplOz48eM4evSobPvSpUtz6Z2Cg4Oxd+9es+d+kSlVqhRKlixJyWbNmoUKFSpQsgsXLqBTp05ISEiw2VoIIYpusQYWLVrERUQHgPbt2yv2oSLGMq6xGRkZ3BoMKLnFAsDAgcY1Sik77gDYCaCy4tokXgVwBkA9E+3UHFq7muhrC4oyx/IRY1n+/BO4ckX6nY0ae+fOHa59amqqRd95gUDwYiOUS4FAIHgZ+egjabOTLWA3UdkIdt/luXPnqDROWhkzZgynNE2bNo1SKIz55JNPuPx7kyZN4hSSl5169WilJi4uDsePH0f58uUp+d9//43OnTvbTMGMiopCnImXIImJiViwYAEnN6QJkUNNuUxPZ1Nq5MF+/kePHuVm66lcGWjVylCjlg9yCYAKKvVNAfwBoKxKGwOvq9Q1V5CvxIgRS9C7Rw+U1jCDebCxItT3XBqzdav0s3fv3ihWrFiunBDCfUcBYPPmzZYsUCAQvMAI5VIgEAheNoKC8p7ibMWWLdI8NqRNmzZwz0lgb8ASC6KLiwsXyOfs2bMIDAxUbL9o0SJKFhERgdWrV5s994sMG9Tn2rVrqFGjBo4fP45y5cpRdWfPnkWXLl2QmJho9XWYsloaWLlyJe7evUvJ7t2TCzAj8fTpUyTfuQMkJqJBPiyXV648grs7UKoU4O4OXLpkqFFTLi8CUAue1RW8BVAJFwAeCnXtZKV1697FDz98it0//ognAMIB/AxgFICKGmdVJoI51ma5BIBz56SfRYoUQd++fak6uSBLhw8fRlRUlLkLFAgELzBCuRQIBIKXjcWLC2aeL76w6fBOTk7o0aMHJbPUTW748OGoXr06JZs+fbpi1NB33nkHr732GiWbPXt2gUZQtTWs5fLq1asAgJo1a8oqmGfOnEGXLl2QlJRk1XUY5pXD3j4vWmpGRgZmzpxJ1bPKJsv3VasC7u6oP2gQJVe67kFBwOTJtHJJSCyAPEtncrLhtyJMbzZ6rZpSdEWlTg656L7FIUWU5aPOZ2fnvDhxcoIOQA0AQwGsBXDAzJl5Mplj7crlhQvSDkyAd4199OgRFa0ZkK7Tjh07LFmkQCB4QRHKpUAgELxMhIaan27EUow3UdkI1jX2+PHjiI+PN3scR0dHzJ49m5KFhITgl19+kW2v0+mwfPlyShYXF4e5c+eaPfeLCqtcXrt2LVfpql27No4dO4ayZWm3zdOnT6Nr165WVTDVLJeDGKVww4YNCA0NzT1Ws1wCwOeQnDYrJSaq2gkTE0lutp6LF71lWsgpUKzlspVMGyUum9EWAJrJyFwAOAGoz9XcvHkNt2/flvKnMOl4qkNKpCKPejo1ebQrl3Fxecp5u3btOCsx644OCNdYgeDfhlAuBQKB4GXC1u6wBTxfly5d4OzsnHuclZWFIAvdcQcMGMDtvfv888+RlZUl275ly5Z4++23KdmqVauovXwvM6xbbEpKCpVzsE6dOjh27BjKlClDtTt58iS6d++O5DwTXr5QUy5nz57N7c0zBGh6fv8+YmJiqPZVmf5JAIZDUqZ4FSyPiRONb2V38Iqj3P5lto2zTBslInNWpxU5y6XBgthStkdQUBCg0wG+vpTcFWpZOB1gfpAg7colABi2u9rb28Pf35+qk9vXe+bMGURGRpq5JoFA8KIilEuBQCB4mTBsavqXzFesWDF07NiRklnqGmtvb89ZHm/evImNGzcq9lm0aBGcnJxyjzMzMzF58mSL5n/R8Pb2RvHixSkZ66Jat25dHDt2DKVKlaLkJ06cQPfu3ZGSkpLvdSgpl+7u7qhSpQomTZpEyYOCgvDnjz/iXpMmXB8/mXEOAvgW6splXJxxcCcdANZ6KRdIilUu5a3gyoSabpJLbRlZAoBkKAX1yX0J8+qrXF0txXmSAbwD4DNofwR8CiBbY1vA6F0RhgwZQtU9e/YMLi689XTLli2axxcIBC82QrkUCASClwVCgIsXC3ZO401UNoJ1jf3tt9+QlpZm8VivMg/bs2fPVoweWrVqVYwbN46S7d69G8HBwRbN/yKh0+k466Xc/sd69erJKph//vknevTokS8FMyEhgUr1YYzBZXLChAkoXZqOeTr5vfdwNzqaknlB3r4HAJ8CKKlQJw+rXGqxXJr7PQjJ/c3fn2DLFsBPTjsGANSRkelRr95RrF7dVLbH8ePHpWszbBhXp6xcAsBGAIsB+Kq2Ml4HEGOyFQB4ujyHkSEavr6+8GUsq2y0XkByjVWK7iwQCF4uhHIpEAgELwtJSRbntfwJwBCY/3hMbaKyEW+++SZ0urxdYikpKYp5Kk2h0+kwb948Snb37l2sW7dOsU9AQACXE3LChAmKQWFeJuT2XcrRoEEDHD16lDsPf/zxB9588008f/7covmvX7+uWGcIKFSsWDEukM+Z7GywcYMrAaipMFYagF9VV8Le+ayCI6dcOqqOaJrL8POTgght3qzDwIFAcLC0bTogAOjQAfD0NLQtA7nAPS1bBmLUqNpwdOTXkp6eLn1PfH25fZfqyuUxAPcg7c7UijbX2CZFrkHHbPiUC+zDcuPGDVws6BdnAoHAJgjlUiAQCF4WLMjDmA1gEqR9aZsAzLFkXpWcgdagdOnSeP11Otff7t27LR6vU6dOaN26NSWbP3++ooLk4eHBBQO6ePHivyLQiFLEWDl8fHxw9OhReHnRUVGPHz+Onj17IjU11ez51fZbGgd7ee+997hov2wM0UqQoqIqYd6uPVOWy1gA+xT6joFayBwDDRteRnAw0K0bLW/QAJg/HzhyBIiNBRITgehoHYoUceLGOHDgAHQ6HapUqSI7R65rLONarq5cEkjWy1KqrWi0KZfNnv/BeTr4+/tTL49SU1PhwSjDgAjsIxD8WxDKpUAgELwsOPEPn6b4H4ClRsezYP7OMWoTlY146623qON9+/YhO1v7Pi9jdDod5s+fT8mePHmClStXKvYZNWoU50I6depUiy12LwpyuS7V3A99fX1x9OhRlChRgpIfPXoUvXr1MlvB1KpcOjo6Yj6TFzGWaV8Z5tnaaNjPrKZcRgJoAeABeNYBWA2gj8kZIyJCTFq/dTop4GtJBZ/eR48e4dKlS2jUqJFs/YEDB6TryeR5VVcuASkrpvWVy4FpP3KeDuXLl8cbb7xByTzzTLa5bN261eLvvEAgeHEQyqVAIBC8LLi5GfvRaWII+D/0wwBc0DqApyeoTVQ2olevXtRxdHQ0Tp8+bfF4rVu3RufOnSnZ4sWLZaNVAoCDgwOWLFlCyR4+fIhly5ZZvAYWQiQrVUyM9LMgtpixlsu4uDg8ffpUtU/Dhg3x+++/cwrAkSNH0Lt3b7P2w6opl2yezf6nToEP4ZNHJQBFAZRj5PYybU2jpFyegRRARy5i8KsA3s35fZJMPU1KSoqULkQDhBBkKHgmBAYGok2bNrJ1Dx48QEhICODjAzTPC/xTGaacesMBmLOX1rRy6YdgNECYrKcDG9hHLn/pkydP8Mcff5ixJoFA8CIilEuBQCB4WdDpgMaNzerSCcBXjCwVQE/I7zLjaNIE3CYqG1C9enX4+PhQsvy4xgLg9l4+e/YMX375pWL7bt26oUOHDpRs8eLFePzYvFQMxhjvr/PyAtzdgVKlpJ9eXpI8IMB26UQrV64MV1c6MI2aa6yBV155Bb///jvnvnj48GG89dZbmhVMrZZLhIbC7q+/sFhlLEN6DdY1toumlWixXO4C0A7KwWuMM2m+BqA110LHfFcuX9aW7zIxMVExZU5QUBC6sb61RgQGBkq/TJ+eK7OHnAtxWeY4hGuhzBOTLSYbrp6Mp0OfPn2o+1Cv13MvFwDhGisQ/BsQyqVAIBC8TDSTS7auzocARjOyRwB6ATDp9GnBfJbCusbu2bNHWwRJBZPgq6++yo25fPlyxMayDpcSOp0Oy5Yt44ILzWBcDrUQFCRFBvX1BRYuBI4e5WMxxcVJ8oULJcOTnx9w4IDZU6liZ2eHOnXoSKRqCp8xjRs3llUwDx48iL59+ypG4DWQlpaGW7duKdZTykVOEsr2kF6IyFE55yerNHlDUgnNg1UuEwH0gxQaSAnWJZi3XrL367lzlzVZqtWsyefOnUORIkVgby9vo83dd9m9OzBwYK6cd41l052wngFqL5HUX7D4YzO64TdFTwc3NzcuKrTc59m5c6fFkaIFAsGLgVAuBQKB4GXC6OFRKzoAK8A/gJ8HMAImIshaMJ+lsA+ft27dwhUlk55Gk+DcoUMpZTEpKQmLFyvbx3x9fTFy5EhKtn79es0WqNhYwN8f6NEDOHFCU5dcTpyQ9INBg6RxrIU5QX1YmjRpgsOHD8Pd3Z2SHzhwwKSCefPmTdU9h5Tl0iif6iKF9gbLJRsxNhLSDkJ3mAOrXMrhwxyzymV3mdXQLF58WZOlWk25JITg4MGD3D5YA2fOnEFMTI61dcUKIOe8ssqlM+xAK5CsW6zaXwJl5dIbD/ENPpYOVDwdWNfYBw8ecJbepKSkPEusQCB4KRHKpUAgELxM+PgArXl3PFM4QgrkwwZE2Q5grlInPz8ptGUB0ahRI1SqVImS7dmzh25kpkmw/ltvwZ+JlrJy5UpVV9e5c+eiaNE8F0hCCCZMmGDSihoSIi0rxwhnMVu2SOOEhuZvHANacl2q0bRpUxw6dAjFixen5EFBQejfv7/iXkFTFtJcyyWTv/UVAB1l2hvsWazlMgJARQBrVGdjr11RqKuj7wFgLdascmkH4DPVWY1dT9Us1ab2wQYGBqJatWqydQblE4CkwR48CHh6csplJZyCAxQTbZpA/vviiWc4iC7wwjNJoOLp0LFjRy6fKft9B4RrrEDwsiOUS4FAIHjZmDzZom5eAPYDKM7IZ0IhgqyF81iKTqfjrJe5+y7zYRKcFR1NBX1JTU3loskaU7ZsWUyZMoWSHTt2LM/9UIaQEKBtW0AmhZ9FPHoEtGljHQVTa65LNV577TUcOnQIbm5ulHz//v14++23ZRVMtXnc3d1RpEgR6UAmf2sPmT6GZDF1AfhBsrovAGAIuTQAQD+zclOy1kuD8rYQwFoAbky9XKTcIVAPKXQbksstj7Gl+tYtdeXy8OHD8PX1VaynrH0+PkBwMGox1+o+0jEbEarzKPMYrILujYcIRhv4wMgMq+Lp4ODggIFMfbJMDt2goCDEWZjPVyAQFD5CuRQIBIKXDWZvlTnUhWStNBlB1t+fT9BXALB7JP/55x/cPXgwXybBGgBGMrLvvvsOd+7cUewzYcIEVKhQgZJNmjQJmZmZXNvYWKBrV96Aml/i4oAuXfLvIssql0+fPsWzZ8/MHqd58+Y4dOgQijF76vbu3YsBAwZw50ZzMB8ZxVTO2fZnAKEAGgAIBvADgKmQdkoaWA5nAHKpc+SitrLK5RBIQX2mQHIfdWXq5ZRLZwBVZOTGqAfO2bIFmDs3SrVNYmIiZ/Uz5tChQ3RAIB8f1DpzhmqTBqAPHsKy2M9pMFaS/bEZIfClFUsNng6sa2xsbCycmBRLmZmZ2LVrl0WrFAgEhY9QLgUCgeBlxGhvlbl0AcDGTE2FFODnMSCN+803+VqepbRq1YrbW7a3T598mwRngFY5MjMzMWfOHMX2RYoUwYIFCyjZ9evX8d1333FtP/rIehZLlkePgI8/zt8Y1atXh6MjbdGzxHoJAC1atMDBgwcpt2FAsjAPHDiQUjA1pyGRyd/KJ6qQ7GYBJtZXAclwwBsyNavAK4dyQX2M81dqUS4BKSemGqb36yYnq1suASk1jhLx8fE4deoUJStTty7cmBcBDwC8bXImJR7DD8EIQjdsxuA8V1gDGjwdGjduzAWYYl/iAMI1ViB4mRHKpUAgELyMGO2tsoSPAIxiZA8B9LK3R+qePdL4hYCDgwPefPNNSrY7VemhXjsVAIxlZD///DNu3Lih2GfQoEFo0oTOvDhz5kzEx8fnHgcF5X+PpSm2bJHmsRQHBwfUqkXvwDN336Uxr7/+On777TdOwdy1axf8/f2RmZmJ7Oxs3Lx5U3EMynIpk7/1nkK/QABqTtE6ANW4ncWAFB+ZVX5Y5ZJV3uSUS7l9t3xcVhotKT9MK5cnT57kAuAYw7pt63Q61KpNR4i9CclLQRnlx8If0BXBaCtFhWXR6Omg0+k466XcftM//vgDDx48MDmeQCB48RDKpUAgELys5OytssSCqQOwEkBbRv53djZGLF+uLQWIjWBdY/8EYI3gqVNBZyrU6/WYOXOmYns7OzssX76cksXGxlL7NVUCz1qVL77IX//8BvVhad26NQ4cOJC3bzKHnTt3YvDgwQgPD1eNJEsplzL5W5WUS0BSEdXuzqaK6URWADhovAqmnjU/s8olIO+wW1lGZoyWSMOmlcvw8HAuaq8xcnuCa9ako9neBNAKebtLWTyhHN3XBXfkK8z0dPD396eOU1JSuL28ALDV1m9tBAKBTRDKpUAgELzM+PhI0WSYBzYtOALYCT6C7LZt2zBv3jxrrM4iOnbsCFejROx6SBar/FIawDhGtn37dtU0I35+fujTpw8l++abb3Dr1i2EhpqfbsRS/vyTT19hDtYI6sPi5+cnq2Du2LEDo0bRdnEHBwfqmHKLBbgoo3JusQZOA9irUj8Kf6nUDgcQnfN7eaZOi3IpZ0U3pVyGAsg20ca0cglAVgkzEBYWxu0jZi3WN3U62AEYqjBGUQU5oBAv1tNT8qAww9OhSpUq8POjo9aWKlWKa7dp0ybNYwoEghcHoVwKBALBy46XF7B5MxAYKAXVMKernx/2r17NpZn4/PPPCy2oRpEiRdCZ2Su2x0pjfwo+AcX06dNV+yxevJjas5iRkYEpU6bY3B2WJT/z5SfXpRpt2rRBYGAgXF1pRewEo3WzyqU3a203ClCVCCCemYdNWDEVQBbk8cN1KEdwfQLJIZxA3nJpbBOVUy6fy8hMKZfPIWXjVEObcpmdra6kstZLTrmsUAEoUUJRuVSLtfuEFXh7S54TPmw+UNOwrrF37/KvE0JCQhAWFmb22AKBoHARyqVAIBD8W+jeXXrYCw2VMrV36MDvyfT0zMvkHhoKBAej7pgx2L59O+zs6H8JQ4YMwUWj/IMFRmgoejMhUg9B/rHeXDwBTGJkgYGBOMNE1jSmRo0a+PDDDynZL7/8gsOHT1phRdo5d87yvqxb7P3795GUlJTPFUm0a9cO+/fvh4uLi2IbViniLJdG+Vvvy/Rnc7FeB/CTypqUVwJIryrWg1cu00CrtVotl+Vh+nFKbd9lSk4xzZMnnIpHYUq5vP3wITKuXEFVf3+0kemv5m5MWS79/SWPCQsUSwDo168fnI28E7Kzs2Wj4W7ZssWi8QUCQeEhlEuBQCD4t9GgATB/PnDkiJTHIjERiI6WfsbGSvL586m0AV26dMGyZcuoYVJTU9GzZ088fiyfQN1mbN2KHqBtT6kADltp+HEAWCe8adOmqfaZMWMGF8X20qUJgMoeNWtz4QJg6VbYWrVqcS8Prl+/boVVSbRv315VwWTTlHCWSyA32ihrwyoNYDCAxox8JpRfOLhxbqgOzPE4AHyORdo1Vu6zyCmXjuBdbFnU9l2qpyExDuKj16vfb8eOHUNKSp6iyu651Ov1uJWQAGzejGHjWCdxdRX3MSB5RgQFSZ4S+Qj65eHhwQXuMlY2DWzevLlQ938LBALzEcqlQCAQ/JvR6aRonCVLSj9Vok2OGzcO7733HiV7+PAhevfujVQrRGzVzLlz8ALAOvjusdLwxSC5VRpz7NgxHDt2TLGPp6cnF/wnO/scpKyhWrkG4GJO+QdgUzmYIC4OkMk5rwkXFxdUr07vrrWWa6yBDh06YO/evbJKAgtnuQRy87eywXwqQ3pYYWMnPYIUokcOXi0szhynABgBoKTMqAa05ro0rFINNeWSdYmlnVPZPJBqpKenU/exh4cHZxE0RPHtN3cu9zJALVXr42rVJM8IK+W/HTx4MHV8/z5vs7579y6XYkUgELzYCOVSIBAIBAAkC8nKlSvRpg3tMHfu3DmMGDGiYCwIhAA5rri9mar9UN5nZy5jwduapk2bpvoZx4wZw1mCgClQVjhY/AE0ySmNAbmUDiZQCcBqEtY11hpBfVg6deqEH374QbWNu7s7FwQolxUrcI/Zb2vYb9kBQEem+ULIq+i8Q2s8gJGM7Bz4vZla0pHIwe4KZVHzaWaVS9qurhZ1Vw6T+y5zlEs3Nzd07tyZqssCUFTh2jx+Zt7LEFN07doVXoz1s2LFilw7kfNSIHi5EMqlQCAQCHJxcnLCrl27UK0anaxg27ZtVAoOm5GUJJnoAPRiqp5BPsfhNACzAfwI4HcACRqmcQEwg5GdOXNGNp2DAScnJyxZsoSR3gPwlYYZAfUdbdrQYBRUxFZBfVjKlCmjWi/rEmvAywt3maBUxmrbIqZ5AiQFk4VXLvUARgNgXw6wip2piLFaLZfshbKH8qsRdg3lIFlNLSMoKIh6SaKkXALAiBEjuP5lypaVHTc+Ph5paUppXszHyckJ77zzDiWTG3/btm2cW7VAIHhxEcqlQCAQCCi8vLywf/9+LoLsjBkzbB9BNiMj99fK4PfZ7WGOCYAvAcyC5OTYEVLiBy2MAJ/vb/r06ar72nr27Im2bdsy0oXQGu2TxjwFwtMTYIx6ZmHtXJdKmLKIJiQkqJ7je4mJ1LGx2tYYwACm/QrweTHZHZYA8PrrtwFsgnIkWcB6yiXrbvsYwC2FvnLKZRVaIudGrMCDBw8QGpr3LVBTLrt37871z8pS9g8wFVDIXFjX2OjoaC6ycFxcHA4dOmTVeQUCge0QyqVAIBAIOOrVq4dt27ZxQWCGDh2Kf/75x3YTM/vLejPVe0Db/2LBP+7zjnXyOEJSSo25fPkydu7cqdhHp9Nh2bJlVJAVIAlSeBlT5M9y2aSJ6pZZk7CWy9u3b9tkL60p5fLRo0f44IMPFF2Q792jVUXW4XQeaOUxHfx1lKN9+zAEBjZD5cpqra2lXKaCTnpDACxX6MsG9CkDoA49emX1PZ1sKpjAwLzMsGrKpb29PZc78+lT5Rcl1g7u1bx5c24vsHCNFQheboRyKRAIBAJZunbtiqVLl1Ky58+f2zaCrJsblT6lN1N9D1IoHANsCBAd+AQTavgDqMco0J9//rmq9aZx48YYOpTNFLgOwBUzZgbMtVw2a2bm8Ax16tAKi16vpxQNa8Eql3KK0Zo1a/Dhhx9yCmZWVhYePqT3PVb66isqf2t1AGOY8X4GffZ1MibeK1euoHt3ICJiCho1aqmw+kdUth4fH0uVy2fg93j+BPnIsKwyVwZAbUqiluZFDmP3bla5fPz4MZWGpkKFClS92h5Pa3/vdTodZ72MiYnh2u3evRvJlkazEggEBYpQLgUCgUCgyCeffIJ3332Xkj148ABvvfWWbSLI6nRA4zxn2AaQlAlj9hj9ziqX3lBPBM9iD2BO/fqU7MaNG9i0aZNqv/nz58PFxVjx0AP41MRs+bNcDhyYr+4oVqwYKlWi7YC2COrDjllMwZf322+/xccff0wpmI8ePeJyYlYeNIjL3zrD3R3Go+oBBJQsmZe/lVGkASAsLAwA4ODggF27Nsquq1y5+1S2nhIl8hPQpyfouzEdwEqZdqaVy9jYWM5NnVoV8108c+YMYnNyxVavXp2xtAPh4eG5v8tZCpWwxUslVrlMSkriAj6lp6djz549Vp9bIBBYH6FcCgQCgUARnU6HVatWcRFkz549i5EjR9omgqyRiU4HeddYA+xeO+2PyXn06dEDTZo0oWSzZ89GhtH+T5by5ctj8uTPGOkhAAfNmFm75dLPj0pLajG2Durz7Nkzzq1SzeK0cuVKfPLJJ7n3EesS6+rqmhtRlNRvgMTJ8xGz9Qhc7sZhwpQpVNv9MTE40aUL0KABp0wBQERERG7AmGrVqmHFCj6RyZMnj5GdnWe1Zt1NlTNrFgW/zzIVUoZOY1aBzyYpp1zSynFERAQ6dmRj5dIYpyzR6/U4eFC6F11cXDjrsbHFmk1VooYtlMsaNWqgefPmlKysTFAhUy98BALBi4FQLgUCgUCgipOTE3bu3MlFkN26dSsWLFhg/QkZE11vpjoUQGTO76zl0hLlUufvj3nz5lGyO3fu4Pvvv1ftN2nSJJQowQZa+RTKUUEtV8QnT7a4K4WtlUvWaung4JBrQTPA7uP95ptvMH78eBBCcPfuXaquTJlKmDZNhw4dAC8vwN0dKFUKcPfQ4Zs1AXB0pNN2TJ48WfGFR3Z2Nm7cuJF7PGzYMC6gDSEEixblxaTllUs1az3rGnsXvDX7GaS4xsawymVpsJbL1NRUTgFjYS2QavsujS2X5iiX1g7oY2DIkCHU8YMHD7g2R44cQVSUnFuxQCB4kRDKpUAgEAhMUrJkSezbt48L/jF9+nT8+uuv1p3Mxwdo3Tr3sAXYzH951ktWuTSVbZAjxyTYuXNntGrViqqaN28enj9XslQBRYsWxdKlbHqWMADqeR7Nxd/fannrbZ7rkh2vWrVqnOXyq6++gr09HbH166+/xqeffsopl3fuVMbChcDRo7kZanKJj3dDZubnlOz06dPYt2+frOUSyHONBSSr/Pr167k2c+bMyW3HKpcVKpirXNYDwEZkXY68FxAZkHJwGlMGrVuX5b5rFStWVPxcALgoqwcPHszdO6wW1KewLZcA8M4771Drz8jIQIkSJag2er0e27dvt8n8AoHAegjlUiAQCASaqF+/vmwE2SFDhlg/gqyRqc4efM7LPTk/8+0WmzOPTqfj8ng+fvwY3377rWr3oUOHwsenESOdASBRpjWLabdYb2/gm280DKUR1nJ58+ZNq+YQ1BLMZ+TIkdi8eTN3Hy1fvhxbtuxgWpt6XTAKbEKZoUOnIjNT3npprFwCkmJlcLs1kJmZiUGDBiE9PZ3b+/fKK2rKJbtWg6I8iZHfBmB4ISNniSuDKVN0XACmqKgoNG3aVHF2NhBSfHw8Tp8+DUBduSxVin11o4ytlEsvLy90Y96gyO2JFa6xAsGLj1AuBQKBQKCZbt26YcmSJZTMEEHWqi5z3btT7rG9meqTkB7L8+UWy5gE/fz80KlTJ6rJwoULkZiorCja29vjq6+WMdJoAItkWpvnFuvpCRw8KLmDWgvWcpmVlYWIiAirjc8ql2XKlKGO3d3dUaRIEbzzzjvYtGkTp2CGhV1mRjSlXDoBoF8KJCZeQ0hIrGzrK1f4iL5Vq1blZJcvX8aMGTM4y2XZsqkqgZXkLJcA4AeAVQqXQLofWJdYBwwc6Ilu3YDatWnX2OvXr6NHjx5KkyM5OZnrY4gaK6dcGtyHXwTLJcC7xrL7bwHg3LlziIyM5OQCgeDFQSiXAoFAIDCL8ePHY8SIEZTswYMH6N27d27AFKuwYoVkugPQHqCigxJI1suHTBfNbrEKJkF27+WzZ8/w5Zdfqg71xhtv4M0332Sky5GnXCihbLn09pYCpPr4mBjCTDw9PblgKdZ0jWXHYiOcenvnJYoZOHAgNmzYwCmYNOr5HSXeBvAKJcnOZl87SLCWS3ZNxixduhTR0dGULDU11fi2NLFWg3KkA2+9PA8gGKxyaWdXGitWSOeDVRRvhIWhuwn/6Jo1a1LHhn2XrHIZHx+fm/LDHOUyKiqKi+ZrLXr06AF3d3dKJndttmzZYpP5BQKBdRDKpUAgEAjMQqfTYfXq1fAzyj0I2CCCrJeXZLrz9IQLgK5M9XYA7GOuJsulikmwadOm6N27NyVbtmwZF5SGZcmSJcyet3QAU5lW2s6Lvz8QEmJ9xdKArYL6PH/+nNszaRzBFADKlaMDIA0aNAgrV/4EZUVbyxW1A7CYkcm7+kZG3kK7ds8REABcuQIgNBTe9+UVUUIIgn75hZKlpqYa35YMrHL50GgdfQCwFtIlYJXLWrXKwOuRlHKlDrO/8EZwMF7p0AHezs6y6wX48x0WFoa7d++iUqVKXJ3BNdYct1i9Xs8p3NbCxcUF/fv3p2RyiuzGjRttE6VaIBBYBaFcCgQCgcBsnJycsGvXLs6lcMuWLVi4cKH1JvLxkUx43t6ca+wJdk3gA/9waDAJzp07lwqckpSUhC+++EJ12Nq1a2Ps2LGMdCuAsyq9aIXKzw8ICgI2b7auKyyLrYL63Lhxg3ro1+l0nHIgZ4k6cWIIpAiqcgrmbo2zdwTQQUM7gj/+uIaFC6VbwM83Dsn/KKdKSWLWn5qjPBvdlkawyqUeebZ1ewATmPoDAEIoSeWndwBfX2DhQtRmXHgfAEiJj0e39HTF9d67d4/bQxoUFAR7e3vUqFGDkhuUS3Msl0DBusY+ffqUs2yHh4fj4sWLNluDQCDIH0K5FAgEAoFFlCxZEvv37+eiWk6bNg27d2tVCjTg4wOEhKBbv34wtg2ytqmKMPFPTaNJsEGDBhjIbKxbsWKFyYfqmTNnwsPDg5I1ajQBU6cSdOgA2NnR1paiRYEOHYCAACA0VFJWrBUVVg1bWS7lgvmwVi5WuQwKArZuBYBhAGbKjLoCwGyNK5Db5ypHnmvsCfhhE+g8L25scyNSz58HBg0CYmMNtyX8/Q21JSDluzTG2JI7PKeNMYepozJGIXFrgle3bwJQ3nUp7RVl9w2r7bsEpKjHbOAiNWypXLZq1QqVKtHO7ewxAGzevNlmaxAIBPlDKJcCgUAgsJj69etj69atXIqEwYMH49KlS9abyMsLHr/8gnavvKLYRNGB0gKT4OzZs6l0GampqSZzenp5eWH69OmU7NKlU3jllZ04cgRgttDhxx91OHIEmD8faNBA07KsAqtcXr9+3Sr76Fjlsm7dupwiwrrFLqa8WRsrjDwLwBwNK2gC4B0N7dh9l+WpIxcAnMdrDqkAsGWLZF0MDYWXl3RbBQYCfn46KAf1ASTF8wOqVqdjAiAx66jCjHYD0v5jJ8iTmZmJ+vXrU7Jjx47h+fPnZqUjYdOaGGNL5dLOzg6DBw+mZHFsDhpIyqWt9n4KBIL8IZRLgUAgEOSL7t27F0wEWQBvvfeeYl2ucunpmW+TYI0aNTB8+HBKtnbtWm5PIcuHH36IatXo1BiTJ09Geno6t09MJWWhTWHdYtPS0kx+Li2wFtC6devi0aNHlMzYchkaCpygfJvV1jATbFRYeeYBUFaMJNiIsbQ1NQbAKoWeuVlPHz0C2rSRPgSk4MbBwUCrVsrKpacn4Of3IRwcXHJl7D1Bx9YFmPcRuA4psFU7hfUBgFNsLPViJC0tDceOHTMrHYmvr6/i+LZULgFwymVCQgKcmX2mUVFROH78uE3XIRAILEMolwKBQCDINxMmTOCUsfv37+Ott96yagTZnj17KtZVGj8eSEwEYmNhDZPgjBkzqCAomZmZmDNH3YLm7OzM7c+8ffs2VqxYwbVlrb0FRenSpbkE9dZwjZWzXKopl5I7rDF86gma6QBM7eetAWC0iTas5ZJWLgmABvDAUJmeVHTiuDigSxfpfsuhQQNauRw69C6io/Nuy+Dg0hg5cpjiyljlsg5zfCPnZ3fFEYCwNWvQksmHGRQUxCmX4eHh0Ov1AHjLJWvdNsbaL4xY6tatiyZNmlCy8uXLc+1E1FiB4MVEKJcCgUAgyDeGCLKtW7em5GfOnMG7775rteiO5cuXR7NmzWTrKtauDbi5Wc0kWKlSJYwZM4aS/fzzz5TFR44+ffqgVatWlGzu3LnIzJSPYFrQ6HQ6znqZX+UyMzMT4eHhlKxy5cpISUmhZMZusefOsaOwymVHmZkCwEeGZZkB9cebuwCSjI69ADhSLT7FeKwAwIa6iQVAhZJ59Aj4+OPcQ3Z/4KNHd1GyJH1bTpw4UTE2rhbLJaCuXJ5LTUUP5oVOUFAQl6YkPT0d93Mi5bLKpbu7Ozz5cLgAbG+5BPjAPg8fskmHgB07dlg39ZFAILAKQrkUCAQCgVVwdnbGrl27UKVKFUq+efNmLFqkNdiKad566y1ZecWKmhKRmEVAQAAV7CQ7OxszZ8oFnslDp9Nh+fLllCwxMZFLZ1JYlkuAt0zlN2JsZGQksrKyKBmb4xLIUy4JAfiAn6xbbE8AK2VmmwIpEY0SZcDuo+QxVqZ1YK2Xh+GDE+gGegethD+M3GMBaQ9mTtCcypVpy6Wcu3HNmze5yMcGWGWWVS5vQopBW41bcR7XAbRh9jvfv38fT58+5a6JUsTY2NhYvP3227LjF4RyOWDAAMq1Nz09nVt7SkpKbh5PgUDw4iCUS4FAIBBYjVKlSmH//v0oVqwYJQ8ICMCePXusMgebh9KAXFTJ/FKmTBmMGzeOkm3btg0hISEKPSSaNm2KQYMGUbL4+HhrL89irB0xllVOS5cujdTUVErm7u6eq6gnJUlepTSs5bIypAA43zDyjpAUTzUqmKhXd40FHuELfAY5G/kNAJNYYY4rNKtc3rt3L9f1NJfFi/n+OZhyi02FlJIEUN53SQCkAKjM7FM8cOCA4r5Lds9ldHS0TGodiYJQLsuUKcNFvZWzpG7atMnmaxEIBOYhlEuBQCAQWJUGDRooRpC9fPlyvsevU6cOl18TsI3lEgAmTZoEd3d3SjZjxgyT/RYsWAAXFxfF+sK0XMrlusyP67K5+y0zMtgRMgCwSovhZcFHAL7K+b0zgL0AXE2syN5EvXrEWOAR/kQbPEB12d7fQspSmcuffwJXrnDKZXp6Op2OJSeKUQsAjWTGZWMZlwWfGsXgGjtAdmUS5wF0Z/Jhyu27VLJcRkVFoWHDhrIpSh4/fmw1N3c12MA+9+7xe3KDgoJko8kKBILCQyiXAoFAILA6PXr04ALbpKSk4M0338TTp0/zPf5rr71GHdvZ2XEKoLXw9PTEp59+Ssn27duHs2fPqvarVKkSJk6caJM15RfWcpmUlCS7r00r5iqXRnGScngIyeZmjLElehyAXQD2wLRiCfAZIlnUI8YC0tp/x5uKIwwHEGUs2LoV5cqV49J4UK6xRlGMesuMeYw51oF3jTUE9ekM5U95Dnw+zNOnT6NCBdqiq6ZcAvxLCEBSmBMSEhRmth69e/emPCAIIShThrbtZmVlYefOnTZfi0Ag0I5QLgUCgUBgEyZOnGizCLKsBUav1+P69esKrfPPuHHjULJkSUrG5rSUY/LkydwDsYHCtFxWqFCBc13Oj2ssq1zWq1dPNcelm5uUmiMPdm+iGwAPRtYHUvZHa2DaLRYArqAplIgC8C6MVOJz52Bvb89Z0Cnl0iiKEW97B5bIyJQixjpCOR/n3wDaAnC1y3vM0+v1SE5OptopKZcxMTHQ6/Vo37697PgF4RpbpEgR9O3bl5LZ2fGPrRs2bLD5WgQCgXaEcikQCAQCm2CIIMtGTj19+jRGjRqVL9c6uSTve/futXg8U7i5uWHq1KmU7PfffzeZa8/NzQ1z58612bosRS5irKVBfeQUe1OWS50OaNzYuJZ1eawE09ZHNUz1fQgg3nh1TL209lDZXZd57AewznBw4QJAiHJQHyaKUTR4joKJRgvliLFydQbuAUgE0N6edg+OjIykju/cuYP09HRuz2V2djbi4uLQmL5IuRSEcgnwrrGPHz/mXsr89ddfuVFvBQJB4SOUS4FAIBDYDGdnZ/z6669cBNmNGzdi8WJTKSWUefDgASfbvXu3xeNpYezYsZSCBADTpk0zqSSPGDECPj4+nLyw0yhYK6jPgwcPuJQjdevWVbVcAgCdUUZOucwPWl5cGFsvvSFZSutBChjUFgCQIBOTld0XOR5SFFfExQHJycrKJRPFSMk5fClzrGS5BIDXFcYAJOtldyb9DevKrdfrcevWLU65BCTXWNZDwMA///yjMrP1aNeuHfedk9tbvW3btgJZj0AgMI1QLgUCgUBgU9QiyFpqbZSzVJw9e5azllkTV1dXLpDP6dOnceDAAYUeEvb29li2bBknt7UybApr5bpk+7m5ucHb21vVcgkAAwcaH7FusZVhOemgU40A8o87xok23wAQB0nhPAxgRY6cd8OdzRw/BzAYQCYApKcrK5dMFCNqv6YROyCdjU0AaoBPxvIAgMG51VdhDED6dGw+zPj4eJQoUYKS3bx5E87Oztye5aioKC43poGDBw+qzGw97O3t4e/vT8mSkpK4dj///HOBrEcgEJhGKJcCgUAgsDlyEWQJIRg0aJBFEWTlIkcCUqAdWzJixAguUu306dP5dBMMHTt25CJv7tmzp1Dd+eQsl5a4KssF89HpdCaVSx8foHVrw5G1LJdpkPZmxjJyucedzUa/K7nR2sGZkTQDMIaR/Q1gDgA4Oysrl0wUIyXLZTaALwGEAogEECzT5mbOT3nVL29NFQH4NmhAyYsWLUqPpZKOpFixYihfns8Zeu7cOZP3vLUYMmQIdRwXF8e5xYeFhSEsjN1HKxAICgOhXAoEAoGgQOjRowfnCpuSkoKePXuaHUFWSSmzVi5NJZycnDBr1ixKdunSJezatctkX9ZilJGRgWnTpllzeWbBKpfPnj2j02ZoRE65TEpK4lxlWbdYAJg82fCbNZTLVEgxWOUsyR8AaMDILgJQV+498YyLTZsKYBn4/Y4LAJy8fJnLt5qrXDJRjNTu+O8BXFKpN7jG1lBpcw4A8fBA9x503FitQX0MEWNr1+Z3diYmJuLPP/9Umd16+Pr6wteXttGyUW8BYPPmzZxMIBAUPEK5FAgEAkGB8emnn2LYsGGU7N69e+jTpw/Smbx8SiQkJCAxMVG27tixYzZPkzBo0CDOpfTzzz9Hdna2aj8nPv8GNm7ciPPnz1t1fVqpUqUKnJ1pu5wlQX20pCEB5JXL7t2BAQMI8u8W+xxATwCHFOprABjNyAiAWaqjNsEFsJkeUwEUgeS2amw/0wMYMnQovLzoXZkJCQnSPclEMWKVS+MHshRIlkclDMqlFwClBDzPANyuV49TLtm8kKaUS6V9lz/99JPKCq0LG9hH7mXUzz//XGDWVIFAoIxQLgUCgUBQYOh0Oqxduxavv06HIjl16pTmCLJqrqSZmZkm90DmF3t7e8yZM4eSXb9+HZs2bVLtp/TZJk6cWCBJ6Vns7e1Rpw4dLsaSfZdyyiUbzMfd3Z1zCzYwe3YsJJXNGHMslymQsjr+rtKGAOggI/8R/P7M/7N33uFRVF0YfzedhJAGBAJSgxQJUgQRgRB6EektND+7AqKAgoCgFAEBEUURRaRXEQJEOiTSBKmhSCB0EmpI72W+P5YNe++dmZ3d7G6inN/z8JA5U+7dzSaZd86573lCExyTzVwCwAsQ119eu3YNX3/9tXCdgjLuxy5G+RDXXPKzS4AyBsdYHUxkL8uUQdOmTYWsuTGWZC4B4LfffhOyoLYiNDSUKanPyMgQynvj4uJw5MgRu8yHIAhlSFwSBEEQdsXgIMuvTVu+fDm++uork+fz4pLPCNq6NBYAevbsiQYNGjCxzz//HNmcaYsW/vzzT7vMWY7Cmvo8ePAA8fHs+kZTbUh4UlP5klhHiK1BFM8G0BkA3xJGbFWjl2F89lgCMF7x6gOwRlFcAsBYAM25/StXroS3tzcTKyiNfexilAAglztvpOIsRIwdY9XE5d9eXnB0dESnTp0Uj7l79y6Sk5Nl11wCypnLtLQ0TeXg1qBChQpo3bo1E+P7zgIw+YCHIAjbQ+KSIAiCsDtly5aVdZD99NNPTZry8OKSL7f8448/bN7mw8HBAdOmTWNi169fxy+//KJ4jlp28pNPPrFImBYWft2luWWx/PEuLi6oWrWqWeKyQHgVUAHy4pAnGUBHAPzaPx8A9bmY9PiafGMPAAgDcEiItkQk6uK8qrh0BLACgCfXT5J3NC14jY9djOScYttA71krB19QHA199hMwkbm8ehUA0KUL7xvLcvnyZbMzl4B9XVp5Yx+5CobVq1cjh2u/QhCEfSFxSRAEQRQJQUFBWL16teAgGxoaiqioKMXzeKfYOnXqMNdITU3Fvn37rD9hjk6dOqFZs2ZMbNq0acjI4Es8TRMTE4MffvjBWlPTTGF7XfLismbNmnBycjLZ49IY/vtZpoyWktgkAB0gikI/APsAlFI47zmF+FjwvTHHQm8+pSYuAaAKgO8//JCJ8etvGQE9dqyw3tIbgCuAjxVmx886A/qWJIC6uDx58iRyc3PRoUMHODgo3/JdunRJUVxWqVJFcGc1sH//fpmHA7ahZ8+eKFHiyXcjPz9fWN+anJyMnTuV1t0SBGEPSFwSBEEQRUbXrl0xc+ZMJpaWloauXbsW3Nzy8BmLwMBAQeTZo8xUp9Phyy+/ZGJxcXGKIpHPXPIZoSlTpuDRo0fWnaQJ+LLYO3fuIDExUfP5custAZiVueTFZbt2lbBtG9CypdIZiQDaAfiLi5eGXljWlznH8N7zjrEGDgHYWrAVilXojO0ATItLhIZi0OzZ6Nu3r9KEWQHWpQvucZ9X/8f/dwAQJHN+QwCeXEyLY2x6ejouXLgAX19f4WfEGDlxaSiLdXR0RGCg8ijLly9XmYH18PT0RPfu3ZmYm5vYh9Re8yEIQh4SlwRBEESR8vHHH2PIkCFMTM1BlheXlSpVEm46w8LCTLq3WoPg4GC0a9eOic2cOVO20TvPW2+9xWwnJCQIRkG2JjAwUMhKmVMay2c6DeLSnMwln/mqVKkSunQBIiOBs2eB8eOBtm0NXTweQV9AynuplgUQAcDQskKpb6VS5hIAPgWQhwDE4lt8UBBVFZcBAcC330Kn02HhwoWyPSEB8TXe4xxcDeJSB2CMzPmuEFufGEx91MQloO9JCehbASlx6dIlYc1lfHw8cnP1K0N54ydjli1bZjdDKr40NjY2VjgmLCxM088fQRC2gcQlQRAEUaTodDr89NNPQmbl0KFDsg6yvLh85plnBHF5//59/PUXn9myDfzay4cPH+Kbb74RjuNfR+3atdGvXz8m9v333xe4d9oDFxcX1KhRg4mZUxpri8ylsdFT3brA9OnA7t1AdPRD1KvXBvr+lMaUg15YqglHw3uvdswFuOMH7EBH+OFJBllRXPr4ADt2AI9LM319fRWzZoK45FxW/Y2+7iZz/kGI4tKQufQH4AFl/v5bL8TV1l3KZS4B/WcZUDb1AYArV67g0CFxzaotaNeunTBP/rOVnZ1dZAZZBEGQuCQIgiCKAa6urti0aZPQgH758uWYPXt2wbYkSbLiMjAwEHXrsiWP9rrBbNKkCbp1YyXBnDlzTJa46nQ6zJw5k+k1mZubi7Fjx9pknkrwpbFaM5cpKSm4ffs2E7OGuOQ/A4C+RLNNm9aIijrNxMuWDUDZshEAanNnKGUuq0KUi0/wxCgE4hwT449OB/QZy8hIvUGPEa1bt8bo0aOF6969e5cxmeL7NBrLpWsy84oAUIaLGcSlyXYkjzOXzz33nOx7C+jFpa+vL7N2GdBm6gPYz9jHyckJAx477hqQM++yp9EQQRAsJC4JgiCIYoHBQZbvXzdu3LgCB9kHDx4IpbKGG2Y+e7lp0ya7letNnTqVuTFPTk5mRDEg7xZbpUoVjBzJNqHYvHkzIiIibDJPOSw19bl48SKz7eDggGeffRYpKSlIS0tj9imVxWZmZgpCixdA9+7dQ0hICM6ePcvEK1asiEOHInHhQk2EhpqareG9d4QoRI3GQi4WcDEhcxkYCERFCcLSwPTp04X3FADWrVtX8DW/ntjf6Fpy774E4DwXM373A595RnYuABAVFYWMjAzodDrF7GVycjIePXok9MM01Y7EwPr165Genq56jLUYNGgQs/3o0SPBrGjfvn2Ka7YJgrAtJC4JgiCIYkO9evVkHWQHDhyIs2fPCllLR0fHAuHCi8srV67g/Hn+ltw2BAUFoX///kxs/vz5uHv3ruI5htc4fvx4oWff6NGjkZ+fL3ea1bG01yWf4axatSrc3NyErCWgLC7l2kkYi8s7d+6gVatWwvexUqVKiIyMRGBgIPz8gFWrwJkAKWUuAb401heuzPaX0PehNCCIy5deKiiFlcPV1ZURkgZGjx5dkM3mBbX/8OEFL0Dp3ecbrtwGkPbyy0B4OAJV1HV+fj5OnToFwPS6S0vakQB6cWqvSoFGjRoJa0ArVqzIbEuSJPs9IAjC9pC4JAiCIIoVr776KmbMmMHEUlNT0bVrVyF7FRAQAMfHPQYbNmyIZ7gMjj3XXn3xxRcFcwGAjIwMxk1WKYvq5eUlGPmcPHnSbg3h+SzbjRs3hMyjHErrLXkzHy8vL7i7u8tegy+J9fb2RqlS+jYisbGxaNWqlZAhrVKlCiIjI1GtWjUmbmwCVLUqP5L+vffxAQID2fLpimW9mO1EQN+ExMcHaNsW7s2bM/u1tJqpW7euUAocHx+Pd999F5IkieLS37/gBZxvLd/tUrS2Ai599x3QubOqmyvwZN1lSEgI086DuZaKuCxdurRwHr9tr1JUnU4nGPvEx8cLxy1ZssQu8yEIgoXEJUEQBFHs+OSTTwQH2Rs3bggizFhM6nQ6IXtpT3FZo0YNvPbaa0xs0aJFgoCS46233hIyiOPHj9ck8gpLzZo1hbV2vKCTwxpmPnJOsYA+oxkcHCyYG1WvXh2RkZGoUqWK4jXr1gVq1GBfz5QpQHIyEB8PfPMNm7m8kZWFPn36MLH5bm64feYMsHs3SnClpFr7mDZs2FCIbdiwAcuXL5cXl4+5wK1jNYYv/zR8n0yJy8OHDwPQC8LWCuJVTVzqdDqhXJl/WLJ7925hDa6tCOUytWlpaUJbktOnTyMmJsYu8yEI4gkkLgmCIIhih06nw6JFi/DSSy8x8WvXWLsT/oaXF5cnTpyQLb20FZMmTYKLi0vBdnZ2doEg5m/GjQWdk5MT5syZw+yPjY3F3LlzbThbPSVKlEBVLtWnxdSHP8aQAS2sU+yNGzcQHByMK1euMPtq1KiBiIgIRVMaNdzdJXh6Ajqd3tjGmKSkJIwYMYJpyZKZmYnPv/gCgJih0youlQTw8OHDBRMag7jMyspSFUR8qXR0tN7Wx5S4PHjwYMHXSusu5dqRGNZcyo2RmZkJT88n3TclSbJbtr1KlSpoyTVCNRboBlatWmWX+RAE8QQSlwRBEESxxM3NTdZB1hi+DLZFixbw0TdELMCe2ctKlSrhnXfeYWJLly7F5cuXTZ7bqVMnoWfmrFmzZNcwWhtzTX2ys7MF8WdJj0u5stjg4GDhIULNmjUREREhrK1Tgs/EGlOpUiWULFmSiaWnpwt9R3/99VdcuHDBYnFp3FLFmFSuDQmAgoxhdHS0ICD5z4QxhsxlQECAkLkzJi4uDgkJ+pWkauJSKXMJ6NcV8/BrOO3Z85I39pHLmi5ZssRu8yEIQg+JS4IgCKLY4u/vjy1btggOsgZ4cens7IyuXbsyMXv3vBs/fjwjSPLy8jB58mTVzKVhe+7cuUzpY3p6Oj777DPbThjmi8vLly8jLy+PiRlMVgpTFrtlyxYhVrt2bURERKhexxTG772Dg4Pwes+fP49JkyYxa0Pz8/MxYcIEq4tLHg8Pj4LPN/++V6pUCZ9++qniuYbMpYODg+Z1l5UqVZIVijExMYKxlLG4rF+/vnBOmzZtmO2LFy8WtD6xNX369GHa+OTl5cHLi10/e/PmTZw8yfdFJQjClpC4JAiCIIo1zz//PFatWiWbjZLLSvClsZGRkSZ7TlqTcuXK4YMPPmBia9euRU5Ojslzg4KC8MYbbzCxX3/9FadPn7bmFAXM7XXJi6CAgICCG/vCZC6TkpKY7bp16yIiIgLlypVTnQ+PWuYSEEtjz58/j3Llygk9Kjdv3izM0drikllvyb2vderUQatWrdCoUSPZcy9dulSQ6TQlLrdt21bwtVz2Mjs7W3jfjMti+c8IoDdrql69OhOzl7GPt7e38CDJuEzXwIoVK+wyH4Ig9JC4JAiCIIo93bp1w9SpU4X4zJkzmRtgAGjfvj1TIpiXl8fcWNuDTz75pMD1FNCL4OTkZOYYJQE0ZcoUpmxTkiSMHj3apuV9fCYvJiZG6CdqjJKZD6A9c5mfn6+6Hvb555/H/v37hVJNS+Dfu7p1WcfYc+fOAQDGjBkjZO9Wr17NbBdGXMp9z43XOcqJS51Oh48//lh2jPT09IJyUF5cGq8hBYC9e/cWfK1UGsv3qjTOXMqJ16ioKAwdOpSJrVmzRlhTaiu0lMYuX75cyLITBGE7SFwSBEEQxR5JArp1GyTE79y5g549ezJCyMPDAx06dGCOs3dprK+vr5AFUxNrxpQrVw7jxo1jYvv27bOpQOb7Bubn5wtOrcZYQ1zev39f8T1p0KAB9u7dKwg9rZibubxw4QIkSUKpUqUwceJEYZ8xWsVlmTJlmLJNAIIQA9iel3w/T8M8e/XqpWgQpGTqw5fzGq/7bdq0KXx9fYVrJSYmMtvJyckFQtHd3V1Y1xkdHS20BUlMTMTWrVtl52ptOnXqJLwO/mFEQkIC9u/fb5f5EARB4pIgCIIoppw9C4wfD7Rtq+9ZHxQk3+bg4MGDeO+995jsFF8au2PHDiErY2s++ugjVXGkJoBGjRolrCf9+OOPNZXWWkKpUqUEsxy10lglcZmSkiK0T1Eqi1W64X/hhRewd+9e+Pn5mZy3pfDiMjU1taD89d1331VtdaL1c+Tg4CCYUYWEhAji5/r169i6dSuys7MF4ydDRtnJyQmjRo2SHUdJXPKflZycnIIMrZOTEzp27Chci2+RArClsfz35Pr166hSpQpatWrFxO1VGuvi4oJ+/foxMbkM/9KlS+0yH4IgSFwSBEEQxYzwcKBlS6BePWDGDGDvXkBvdKncL/LXX3/Fm28+advxyiuvMMY4GRkZ2LNnjw1nLeLp6SlkILVSokQJfPnll0wsOjoaixYtssbUZNFq6pOXl1cgaAwo9bgE5MXlmTNnBFddAHjxxRexe/duwfHXXHjhzguOChUqCOYvBuHl6uqKadOmKV47IyNDc4kyXxobFxeH559/XjjujTfewJEjR4TyTeOM8Ouvvy6bbVTqdSlXmvrLL78UfC1XGnvt2jWhnNZYXPLfS8P6Wr6/644dO3D37l3h+raAz5w+ePBA+P5v3LhRc8aZIIjCQeKSIAiCKBbExwOhocArrwAHDsgdwa/PY/+ELVnyCVq12ob4eKB06dJo0aIFs3/Tpk1Wna8W3n//fcWyUFOlm6GhoXjhhReY2Oeffy6ULloLraY+N27cEISLUhsSLy8vxoEVAE6ePInWrVsjJSWFiZcqVQq7du2Ct7e3JdM3C51OJ2vqY2DAgAGyIhDQlwxrzSDz4vLGjRuymc8HDx4ImcmKFSsyAtjDwwPvv/++cG5UVFTB8cY9VgEIAnrXrl0FX3fs2FH4DJ47d07VMZbvh2owyurVqxfj6JyXl2e3HpNNmzYVTIUqVKjAbGdmZiI8PNwu8yGIpx0SlwRBEESRExWlz1SuWaN2FC8uW3HbEiIjB6B27XM4exbo0aMHs3fr1q3Izc0t/GTNoESJEsIaPq04ODjg66+/ZmLx8fGYPn26NaYmoDVzyYtOb2/vAsdTU+stjx8/jjZt2si693bp0oUxQSoMpjKXgLxjrAEHBwfMmjVL8fqWmvrcuHFDtvQUgNAyg/9+AMDw4cPh7OzMxAxOwo6OjqhWrRqzjy+tvnTpUsHPgK+vryCg7927pyou+QcQ6enpkCQJJUuWRO/evZl9S5cutUuPSZ1OJxj78K7DALB48WKbz4UgCBKXBEEQRBETFQW0agXIVFRy8GWxLwP4koul4sGDrmjR4gFq1uzG7ImPj8ehQ4cKNVdLeOONN2TX8Gm58W7RogV69erFxL799ltcuXLFWtMrgBcz0dHRsmKcF5cGR1NAXVwePXoUbdu2Vcy88tknW6PkGGugffv2CAkJkT3XFuKSR05c+vv749VXX2ViKSkpBSWofGksP35ubi4OHz5csM2vTQbAOBUD6r0uJUkqeD28WdG5c+dw6tQp4fq2gBeXKSkpggjfs2cPEvT19QRB2BASlwRBEESRER8PdOpkWFNpCj5z+QyAcQB4F9nrSErqhddfD0BQUH1mT1GUxrq4uODzzz8X4gfka38FZs2axdwoZ2dnW7yWUw0+K5WTk4OrV68Kx6k5xSr1uDx8+DDatWsnm1EywJvfFAZLMpf//PNPQc9IwzWUspeFEZd8ObAScuISACZNmiTEZs+eDcB0OxIAWLlyZcHXPXv2FPbzDr7Gay7lSoVPnDgBAAgODhZer72MfQIDA9G0aVMmxvdGzcvLw2+//WaX+RDE0wyJS4IgCKLIGDFCS8bSAC8uKwHQAfgZQFNu3wHcufMuJKk7E928ebNdSvV4Bg0aBEdHRya2bNkyTf33qlevjhEjRjCx3377DQcPHrTqHP38/AQnU7nSWHPbkBw4cAAdOnQQRBWfWZLrC2lLeHGZkZGBa9euMbHGjRsL5Z6A3iVVC/xr4p10AeDNN9/UND8D9erVE9axLlu2DDk5OYK4jI2NZdZCAsD27dsLvq5bt67QLoUvWTbOXPJrGYEn4tLBwQFDhgxh9q1atQrZ2dmyr8Pa8MY+coZCP/30k13mQhBPMyQuCYIgiCIhPNzUGktj0gE85GKG9WRuADYZbRv4FefOJTORGzdu4MyZM2bOtPA4OjoKN/k3btzQbHoyceJEwSl09OjRTKbNGvDZS15cSpIkxNQyl2lpaejYsSNSU1OZePv27QVTHHtnLv39/YXWGnxpLADBtRfQlyZroUKFCoxrMY+Liwu++eYbYa0kIPacNIYv6Y2Pj8f69esFcXn58mU0aNCAid2+fRs3btwAoH+f+LEfPmR/zozFpaurq2AaZPye8eIyPj4ef/zxh+LrsCb9+vVjMrU5OTnCz9zx48dx6xb/kIogCGtC4pIgCIIoElT8UmSQ63FpLCbLAdgCwJ07Zh6cnf2ZyObNm80Z2GrwN+WA3v1VS2bHx8dHKK09duwY1q5da63pARBLMfks5d27d4XSVrXM5U8//SS4o77yyiv46quvhLF58xlbY8ox1kCNGjWE711YWJhqH1ADzs7Oim7BgF7genh44N133xX2vf/++4plxC+99JIQmz17trBuNSkpCc2bNxeONXZO5a/FZ1eNxSUAwXQpJiam4OvAwEBhPHv1mPTz80Pnzp2ZmFxLG3u52BLE0wqJS4IgCMLunD2r1G5ECT7b4A3Ak4vVB7CSi0nIyWEXdBbFukslrl27hiVLlmg69t1338Wzzz7LxMaNG2fV/n2mHGN5QVWiRAmm9JMXl3x2slu3bti4caNQsli6dGkhy1QYtGQuAXXHWGP49iiSJGH8+PGa5qJW7mtw2ZX7Ht64cUMohzZQs2ZNIXbmzBnExMQI6yzl+oxu3bq14OuOHTsqzg9g11wCEEqnb99mH/zwxj7h4eHCNWwFXxrLzw0g11iCsDUkLgmCIAi7o70c1gDvFKuU5eoBgG/VwWYGo6KiZI1qioqpU6dqEojOzs4Fxi0Gbt26hXnz5lltLnxZ7MWLF5nSW15c1qxZs6DsMyUlRXZNoYFevXphw4YNcHFxwc2b7PfTmiWx5mDKMdaAnPDdvHkzjhw5YnIMLeJSqe3LihUrsG7dOiEuJy4B4Ouvvxacid3c3ITj9u/fX5BRDgoKUpwfoM9cGotzPsP88OFDxlW4T58+zJi5ublYY/4PvEW88sorQm9PPnt55coVxYcIBEEUHhKXBEEQhN05dszcM/jMpQ+ALLkDAXwKYKDq1cLCwsydQKFRyp7FxcVh4cKFmq7RtWtXoT3GjBkzNLe3MAWfuUxPT2eEoDlmPsb069cPa9asKTDxMaz5M2BtcWlp5vLixYuy7VdKlCghe/7YsWNNGkQVRlwC+ow1n4GrVauW7LG7d+8WMot37twR1lVmZWVh3759AIBq1aoJ75cxGRkZzEODGjVqMPslSWKMkLy8vAQXWnuVxrq5uaFPnz5MjDeOsud8COJphMQlQRAEYVckCeD6xWuAF5d/AigBvWNsKwBvQN/zch2A4wC+AvCi4tWKU2ksoBeIWlpU6HQ6zJ07lxEDqampsu0pLKFcuXJC5sdY+KiJyw0bNsheMzQ0FCtXrmRu8vnMpb2dYg3w4jI7O1u2h6iSuDxw4ACzflEOU+IyNzcX0dHRisckJiZi6NChTAa5fPnyQj9KA7whT0xMjNCmAwC2bdsGQL8W2JS4Ny5rlTMfunTpErPNl8aeOnUKUVFRqmNYC740ll8zCujFpbXNsAiC0EPikiAIgrArKSla+1oaw5fFAoAEveiMBLAEwAQA/QE0AVABwD8AxKwFABw8eFD2ptOWqGW4Hj58iPnz52u6ToMGDYSb98WLF+Ps2bOFmh+gF69qpj68uDQcu3HjRkyePFm43uDBg7F8+XJhHaCty2K1Zi5Lly5dkD00IFcyybf+MGbcuHGqLWXUxGXZsmVx5coVwdSJz/zt27ePKX/W6XSK2Utjgx3DdpMmTYTjwsPDC94XpZ6aBox/VuTWcPLiuE2bNkLbEnv1vGzevLnweeJ7Xj58+BCHDx+2y3wI4mmDxCVBEARhVyxre2dJ+4BkADmyeyRJQr169dCpUycMHz4c8+bNQ1hYGM6dOye4m9qKZs2aMdtz5sxBgkbVPW3aNEbw5OfnY8yYMVaZl5KpT1JSktBqpHbt2li3bh369esnZILKly+PX3/9VejvCdi+LNYc+Oyl3LpLpcwloBejK1fyRlJPMJW55Eti/f39sXjxYmFt4/jx45k2OkrrLvnvQ0xMDBo3biwcd/v27YJsIm8UxWNKXPKZS0dHRyGDuGrVKtmSY2vj4OCAQYMGMTG5n2ky9iEI20DikiAIgrArMh05TGDIUFqXe/fuYceOHfj+++8xatQodO/eHUFBQfDw8EBAQACaN2+OoUOHYsqUKVi1ahWOHDkimJuYA3/eW2+9xWwnJSUJhj1KVKhQAZ988gkT27VrF3bs2GHR3IxR6nXJZy0dHR1x/PhxhIaGymbuWrduLSss8/LyhDWEti6LVfueaXGM5cUlnwn77LPPkJmZKXt9NeHs7+8vjFenTh34+Phg2bJlTAY2OzsbAwcOLBhHSVzyPHz4EFWrVpXtt2ko6TVHXPKvHZBfM8pn1+/du4edO3dqmnNh4cVlcnKy8FncsGGDpjZABEGYB4lLgiAIwq54egIy7edUSASQysVOAzgF4DcAswC8A6AtgKoAREFjLnfu3MGhQ4ewfPlyTJ48GYMGDUKzZs3g7++PUqVK4fnnn0fPnj3x8ccf48cff8SuXbtw5coVofWGGoGBgejfvz8Tmz9/vmZznjFjxgg9FEePHl3o7JBcWawkSYK4LFOmDP73v/8prl3jyyIN3L17V5ijrcti1eAdY7WIy+DgYGb71q1b+P7772Wv7+HhgdKlS8vuk8tcGsRuSEiIkI0+f/48xo0bB0DZ1EeOuLg4PP/880LcsO7SlLg0XnOppSzWML8XX2TXPdvLSKd27dpo1KgRE+NFcXp6Onbt2mWX+RDE0wSJS4IgCMKu6HRAw4bmnCGXtawFfV/LXgA+AfAjgN0ArgLIABADYCeAHwCMQenS3QWjGktJTU1FVFQUNm3ahDlz5uC9995Dhw4dEBgYiBIlSqB69epo164d3n33XcyePRsbN27E6dOnZbNnX3zxBZNRSU9Px4wZMzTNw8PDA9Ons21XLly4UOhyP15cGspheRF09+5dVVMUORECiCWxrq6uKFOmjIWz1YY5mcvo6Ggho8WLy/LlywuuvdOnT0diYqLsGEriWU5cGr//U6dORf369Zn98+fPx65duzRnLgHl0ti//voLDx8+NCtz6enpKbwf9+/flzWk4rOXW7ZswaNHjzTPuzDwZbm80REAzS7NBEFoh8QlQRAEYXdk/EVU4MVlOQCuKsc7A6gOoD2A9wDMxttvb8KdO3dQqlQp4ejAwEA0a9ZMttzPXPLy8nD16lXs2bMHixYtwieffILevXujQYMGgvD45ZdfcOTIEXTo0IGJL1y4UDC8UWLIkCFo0KABE5s0aRKSk5Mtfg3PPPOMYGDzzz//CJlLHm9vb2abz6oakDPzkSvZLAzmZC55cZmbm4vLly8zMV5MZWZmYubMmUwsISEBs2bNkh1DruzX0dERXl5euHjxIhM3Fpeurq5YtWqV0Kvytddeg6+vr+bXqWTqI0kSduzYgYoVK8r2wzRgLC51Op2mdZcA0L9/f7gY1cFnZ2dj7dq1muZcWPr37888uMnKyhJe465duzS5NBMEoR0SlwRBEITdGTDAnKN5ofWM7FGmxitRogTGjh0r7IuJiUGfPn1w584dpKam4uzZs9i8eTPmzp2LYcOGoWPHjqhRo4Zsv7zCsHTpUrz22mv4448/mHh2dja6dOmCBQsW4I8//kB0dDSysuR7ejo4OGDu3LlM7MGDB5qzn0rXlFt3qSYuP/74Y6Ek2BxxaWvUMpfe3t5CCS9fGsuLy4yMDDRp0gS9e/dm4vPnz0dsbKwwhpy4LFOmDK5fvy58b3mxW6dOHXz11VdM7M6dOxg5cqRwXSXXV6XMJaAvjXVwcBD6VxrDOyvLfW/lxKWPjw+6devGxOzlGuvv74/27dszMT8/P2Y7Nze32LUlIoh/OyQuCYIgCLsTFAS0aKH1aD5zaZ64bNkSMCyrGzJkiOwxo0ePxvbt2+Hh4YG6deuiW7duGDVqFBYsWIDt27fj0qVLyMjIwPXr17F371789NNPGDduHPr06YOGDRtareTWwLlz5zBixAh06dIFtWrVQokSJVC5cmWEhITgzTffxIwZM7B+/XocP34c9evXF27g582bh+vXr1s8Pi8uo6KicPXqVdljP/30U0ycOBFpaWlMXGtZrC3EpTmZS8C0YywvLg3uo9OnT2eyYxkZGfjiiy+E68uJS7mS2DJlysiuzxw+fLiQ4f7999+FXpd8Ca2BmJgY1KlTR7alys6dO5GTk6NaGmu85hKQN/VR6tXJl8YeO3bMZBbcWvDGPnFxccIxSmtlCYKwDCfThxAEQRCE9Rk7FjhwQMuRvLg0T4wYJysrVqyIxo0b4++//2aOyc/PR//+/XHkyBHF7I+joyMqV66MypUro3Xr1sw+SZKQkJCAK1euFPy7evVqwdexsbEWu8warn/z5k3cvHkTERERwn6+3DcrKwuvv/46li5digoVKsi6tqrBvwdKLrSTJk3C559/Lpu1UhKXfObS1k6xgHrmEtCLS2NzFy2ZSyQn41lfX7w1dCh+XLKkYN8vv/yCUaNGMYY7WsWl0mdPp9Ph119/RVBQEOLj4wvivKDLy8vDc889J8w/JiYGTk5OaNiwIQ4ePMjsS0xMxOHDh1XFJZ+51FoWCwAdOnSAv78/Y1S1bNkyoazYFnTv3h0lS5ZEaqreEEySJHh6ejKlsMeOHcO9e/eEfqcEQVgGZS4JgiCIIqFLF63lsZaXxYaGAp07s7Hu3bvLHpucnIyuXbvKGn+YQqfTwdfXF40bN0b//v0xYcIE/PLLL4iIiMCtW7eQnp4uZJlefvll1K5dG66uautHtSG3xnL//v2oXLky3N3dUatWLXTu3BkjRozAN998gy1btuD8+fN6kSQDL3L41iGA3ozoiy++gE6nE/pfenl5yWbJAPuUxZqbuTTlGCuIyx07AC8voEwZTFqyBMavND8/H+PHj2eOVyqL5cfhM6jGlC9fHj///DMT40uRL126JNvv9O7du0hJSZFddwnoW5KYEpfGAl2rYywAODk5CRnEFStWyLavsTbu7u7o2bOnEONZvXq1zedCEE8LJC4JgiCIIuO77wCFpXlGWFYWGxAAfPutGFcSlwBw9epV9O7d2+r979zc3ATBM3v2bFy4cAHp6em4ffs2tmzZIogYa6zzzM7ORnR0NLZv344FCxbgo48+Qrdu3VC3bl24u7ujQoUKaNGiBV577TVMnToVq1evVlzjaaBGjRqYNGlSwTZfbqi03hKwT1ksj5bMpTGXL19+0rcyPBzuP/zA7M8waqVSHsBH3PU2bdqEv77+umBbTly6u7trzlwa6NGjB9544w3F/dHR0ejfv7+s+Lty5Yrquks1cZmbm4ukpKSCbaXMpdL7zJfGxsXFYc+ePYrjWRPeNVau1Q+5xhKE9SBxSRAEQRQZfn7Ajh1qfS/zAfBZM9NixMdHf13OvwOAfj2h2o10ZGQkhg8fXqgyVi0YxKaDgwMqVKiArl27CoZDOTk52LdvH06ePIkNGzZg5syZePvtt9GmTRtUqVLFKi6rcXFxOHjwIJYtW4ZJkyZh4MCB6Nevn+o5zZs3Z3pVahWXSUlJjEgBbFMWa27mkhd1+fn5iD56VJ/6fuUVlIiJYfbz+d6PAfAftU9Gj4YUGgrEx8PX11coTXZwcBDWHpoSlwDwzTffoHr16rL70tPT8eDBA3z44YfCvoMHDypmLv/55x/G1VUO49JYOXGZmpoqZLANBAUFoSHXf8hexj4hISHC55E39rl8+TJiuO8xQRCWQeKSIAiCKFKCgoDISKUM5j0AOVxMPXMZEKC/XlCQ/H6dTidkL52cWAuCn3/+Gd/KpT0LgRax+tFHHwk3vjNnzkSDBg3Qu3dvjB07FosWLcKePXtw7do1ZGZm4vLly9ixYwe+//57jBw5Eh4eHladtxy//vorSpQogcDAQLRv3x4rVqxg9iv1rbx1S+xZWrFiRZvM0RhT772np6cgcs937w6sWQMAKMEdz4tLLwATudgBAH+sWQPUqwfduXPCZywxMfFJdvQxamWxBqgT8xsAAQAASURBVEqWLImVK1cqrqONjo7GO++8I+xftWoVqlatKny+DBw+fBg+yk95cOXKlYKvldr2KK27BMTs5aZNm4QHDbbA0dERoaGhTEyuJPeXX36x+VwI4mmAxCVBEARR5AQFAVFR+kQRCy9GnAAoG2+EhuqvoyQsDfDiMjc3V+iBN2rUKGzfvl39QlamVKlSQvZy165d+PPPP2WPd3Z2RmBgIDp06ID3338f33zzDZYvXy4c169fP0yePBmDBw9Gs2bNrGJekpubiytXrmD37t2Iiopi9q1duxb+/v546aWXMGjQIEyePBnLly8X2q74+/ur9le0FHMzl4CMY6xRX1JT4hLQd1Tlc7DjAOTFxQHBwZDy85l9vEmOn5+foijnadq0KT777DPZfRcvXoSXl5eQBf37778RHx+vWBr7xx9/qGb0d+/eXfC1klmT0rpLAAgNDWXKvDMzM7F+/XrF460JXxqbmJgoZP2XLFli82oFgngaIHFJEARBFAv8/IBVq4Bt2/TtQ/Tw4rIiADFj07IlEB6uP18hMcPw4osvCtmXXr16MdsGB1lrtU3gb1yVBNCwYcOEuU2YMEHzjW+PHj3Qguvzsn37dgwbNgzLly/HoUOHcPfuXaSmpiIqKgqbNm3CnDlz8N5776FKlSraX5AJ7t+/j7/++gurVq3ClClTMHToUEE4Z2VlYdSoUfj+++8LWr5Ye70roC1rzItLY6sdLeLSFcBULnYOwEoAeQkJyObMd/i1f3Xq1DFLFE+YMAEvvviiEDc8EHnllVeYeF5eHr7//ntFcbl//35Uq1ZNcbxDhw4VfF26dGkhEwuoZy5Lly6NLl26MDF7lcbWq1cPQdwTJ17I379/HydOnLDLfAjivwyJS4IgCKJY0aWLvqz17FmgbVt5p1gfH6BtW2D8eP1xkZGiK6waDg4OQm/Iq1evCj0KDQ6yxu0fbI27uzsmTmSLLA8ePIidO3dqOl+n0+FrIzMZQP86Pv/8cybm4eGBoKAgdO/eHaNGjYKbm5um3piWZAWVSExMxLx58zB8+HB07twZNWvWRIkSJVClShW0bt0ab731FmbOnIn169fjxIkTSDTKJlp7joJjrNHXWsQlAIQCqMfFJgGIlTmWdyXWst7SGCcnJ6xcuVIwfdq9ezfu3r2LF154QThnwYIFqFePn6Ge7Oxs1fft7NmzBeWkDg4OstlvtcwlALz22mvM9qFDh3D58mXVc6wFn72UK8n9gTNuIgjCfEhcEgRBEMWSunWBoCA2c9mr1zNITgbi44Hdu4Hp0/XHWQJfGvvXX3/hzTffRP/+/Zn4lStXrOIgqzVzCQBvvfWWkEWcOHGi5uzlCy+8ILR/WLRokWwWVpIkjBw5EvPmzdN07ZdffhkPHz7E0aNHsXr1akydOlU2i2Up+fn5uHHjBvbv34/Fixfj008/Rb9+/fDCCy/Ax8cHfn5+TMuXJUuWFLR8yedKT41foymeS0hgtq8CSH/8tVZx6QiA7954E8B3Msfy4kbLekuewMBAwT02JycH//vf/xAYGCgc//DhQ2btJM/du3cV92VkZODo0aMF2+b0ujTQqVMnlC5dmonJlXHbgtDQUOZnLjMzUxDmGzZssEuLFIL4L0PikiAIgii28AYwNWpUgqcnYI3kWUhICDw9PQu2JUnC1q1bsWTJEqF0MCIiAiNGjLDbmiwXFxdMnjyZiZ04cQKbNm3SfI0vv/ySWc+Yl5eHjz/+mDkmPz8fw4YNw3ffsfJHp9Nh8eLFsqYxderUgZ+fH5o0aYIBAwZg5MiRjHMsAOzYsQNbt27FN998gw8++ABdunRBrVq1rJL1fPToEY4fP45169bhyy+/xBtvvIGQkBBUqlQJ7u7uqF27trBG9cGDB4o9PQ3UXr8exrOTABikOC8ucyHaTBnoCKAVF1skcxzf7sXczKWBd999V4jt2LFDsdXHzz//jGeekTfFOnv2rOpY4eHhBV/LicurV6+qPoRxcXERzHWWL1+u+FDAmlSoUAGtW7dmYrzQTU1Nxb59+2w+F4L4TyMRhA04d+6cBP3fZgmAdO7cuaKeEkEQ/0KaNGnC/C75/vvvrXr9/v37M9fv2LGjJEmSFBsbK1WoUIHZB0CaP3++xWOVKFGCudaxY8dUj8/JyZFq1qzJnFOnTh0pNzdX85gTJ04UXsOuXbskSZKkvLw86a233hL2Ozg4SKtWrZIkSZKeffZZYf+8efOYMS5evCgck5aWJjufihUrMsf16tVL6t+/v9S4cWPJ19dXuI61/1WoUEFq0aKF9Nprr0lTp06VVq9eLf3111/Sg8hIKR+QqnPHLwMkCZCuy1wr6fE+uX9HLZhbXFyc5u+rMenp6ZJOpxOu5+bmJpUtW1Z2rKZNm1r0/j3//PMF48p9dgBIFy9eVJ3vyZMnhXP27t1r0Ws3l6VLlwqfdX4ur7zyil3mQhC2oqjvwUlcEjahqD/YBEH8Nyhfvjzzu2TLli1Wvf7atWuZ6zs7O0tJSUmSJEnS8ePHBUHo4OAgbd++3aKxzBWXkiRJ69atE25+V6xYoXnM5ORkyd/fnzk/KChIysrKkv73v/8J13Z0dJTWrl1bcH737t2FY3bs2MGMsX//fma/l5eX7FxycnKEm/kTJ04wxyQkJEgnTpyQ1q9fL82YMUN66623pNatW0uVK1eWFQLW/Ffq8T/jWF9AugpIsTLH34WyuJQAqZcZY/v4+Ej5+fmav688lStXlr1uyZIlZePPPPOM8vtQqpTqXG/duiVJkiRNmjRJdn9YWJjqXPPz86WgoCDmnCFDhlj82s0hOTlZ+Dl0d3cXfgekp6fbZT4EYQuK+h6cymIJgiCIYkl2drawBqxSpUpWHaNTp07MuqucnJyCdhmNGjUS3Czz8/PRr18/ixxkJTPWXBro3bs3nn/+eSY2efJk5OQoFWWyeHp6Ytq0aUzs7NmzaNWqFX799Vcm7uTkhHXr1qFfv34FMbkelHz5ZlxcHLPNN6w3EBsbK5Q/8r0lvb290bBhQ/Tp0wfjxo3DTz/9hL179+L69evIyMjApUuXsH37dixYsACjRo1Ct27dULduXbi7uyu8A9pJfvzPmPUAqgGQ+9RtBpAlEzcwHXK+xvKY6xTLU6tWLdl4amqqbFyu36hWDKWxlrQjAfSfe97Y57fffkNKSorFc9KKp6ensNbauDQe0P8OCAsLs/lcCOK/ColLgiAIolgSFxcnCDKltWKWUqpUKbRp04aJbd68ueDrPn36CC6r9nSQdXBwEMTh1atXsWTJEs3X+N///ie0YThy5Aiz7ezsjA0bNgjtWHjRptPpBMGpVVzevMk6/7q7u8PX19f0C3iMi4sLatSogY4dO2LYsGGYO3cuNm/ejLNnzyI1NRV37tzBwYMHsWzZMsH5tbDIWby8C0BtpWBNAG9qvL6l6y0LxqpZs1DnG5OczEtsFlPi0pSpDwAMHDiQWc+bnp6OjRs3mjFLy+FdY/mWMAAwf/58u8yFIP6LkLgkCIIgiiVyYsTHx8fq4/To0YPZ/uOPPxizlUmTJjHZPOCJg6zWDCJgWeYSALp06YKmTZsysalTpyIzM1PT+Y6OjkJrEmNcXFzw+++/CxkdAEKmUZIkwRjnzp07zLaS6OC/n5UqVbJaWxOdTody5crh5ZdfxpAhQ4R2G6NHj8aZM2fw+++/Y/bs2XjvvffQvn17VK9e3WKn2zIQjX54JgPQklO1xCnWGKXMpS3Yu3cvMjMzLc5cAoC/vz86duzIxOzV87Jdu3YoW7YsE/P29ma2jx49ikePHtllPgTxX4PEJUEQBFEs4Uv3rClGjHn11VeZ66akpGD//v0F2zqdDr/++qvQN9BeDrI6nQ7Tp09nYrGxsVi4cKHmawQHB6NcuXJC3MXFBZs2bcIrr7wie57cDTYvHrRmLm/cuMFs8yWx1oT/nDg7O6NevXro0aMHxowZgx9++AE7d+5ETEwMMm7dwlUAu6FvGaL1Eyb/KlnKA/hQw3HWzlzyLTasSXp6OiIiImQ/T4C2zCUg9ryMiIjAtWvXCjs9kzg5OWHAgAFMjP+8SJKEVatW2XwuBPFfhMQlQRAEUSzhxaW1S2INlCtXTsgM8i0/SpQogbCwMEE4LVq0CAsWLNA0jqWZSwBo3bq10EZhxowZimvqjMnOzkbfvn1lexg2b94cnTt3Vjw3JiZGiPHrTQuTuSwOOLm7oyqAtgCGA3iW2/8NgL8AeHFx/jglPoFpwWptcZmTkyN8pq3Jtm3bFMXlvXv3hB6ecnTt2lWoRFixYoVV5mcKvgdsAtfjFAC+//57u8yFIP5rkLgkCIIgiiW8GLGVuATE0tiwsDChJDQgIABhYWEoUYIthvzwww+xc+dOm83NAJ+9fPDggcm1YVlZWejduzezjtSYffv24fjx44rnyxkXXbhwgdm2dM2lLcWlXCZKEU9PwEjk8AWqNwC8CIAvyB4MbZSC+s1WqVKlFN8zrQQEBKBkyZJMbMSIEfDy4iUxLC4DNiY8PBzOzs7w8/OT3a8le+nq6ipkEJctW2aXXrKNGjUSSon5npfR0dGFMj4iiKcVEpcEQRBEsUSuLNZW8OsN7927h6NHjwrHvfDCC1i6dCkTMzjIXrx4UXWMwmQuAaBp06ZC+ers2bNlsy4AkJmZiR49emDr1q2q1x01apTsDX18fDwePHggxHnBWRzLYs1CpwMaNizY5MXl+cf/8+srM6CNRMgbAhmoWbNmocu9dTqdkL189OgRfvzxR+FY/uGIJVy/fh3//PNPodZdAsDQoUOZ7atXr+LgwYOFnp8pdDqdYOyTnp4uHLdo0SKbz4Ug/muQuCQIgiCKJfYqiwWAGjVqCKWJStm+vn37YvLkyUwsKSkJXbt2tbkJyNSpU4Vx58yZIxyXkZGBbt26Yfv27Uzcw8MD7777LhM7cOCAUAYMyGctATZzmZKSgrS0NGa/nOCQJKn4Zi4BoEmTgi95n9nCikvRi5RFrt2LJfDiMjo6Gv3790fVqlWZuLVafoSHhxfKMRYAGjduLGQQ7WXsExoaymynp6czDrYAzHJlJghCD4lLgiAIolhiz7JYQMxebtq0SVGUTJo0CX369GFiMTEx6NOnj6KDbGEzlwBQv3599O3bl4nNnz8f9+/fL9hOT09H165dsWvXLuY4T09P7Nq1C/PmzUOVKlWYfZ988gmys7OZmJK4vHz5csGxfNYSkBeXiYmJwvrQ4rLmEgBgVJ7JZy5joc8+8uJSzHPJY0pclipVSuOV1OFFmiF7yJeeApZ99njU1l1qzVzK9bxcv369bBbR2lSpUgUtW7ZkYnyZ7507d3Du3Dmbz4Ug/kuQuCQIgiCKHWlpaUK5p63FJb/u8vLly4oCy8HBAUuXLkWjRo2Y+L59+/DBBx/YdN3YF198AQeHJ3++09LSMGPGDABAamoqunTpgr179zLnlCpVCrt370azZs3g5uaGWbNmMfuvXLkiGJgovfa8vLwCox/ezMfLy0vojQmIJbFy/TKtidmZy6AgoEULAEANAPyqxPOwXeZSzjTJEvjMpaFMW67np9L7Yfy5MsWhAwfgs26d7L5LkZGARlE2aNAgZtyUlBTZTLot4I19Hj58KBzzzTff2GUuBPFfgcQlQRAEUeyQM9Kwtbhs1KgRKlSowMSUSmMBfd/NsLAwIVP3448/yjpNWiNzCegzVPxatYULF+LixYvo3LkzIiIimH3e3t7Ys2cPXnzxxYJYnz598NJLLzHHTZkyBfHx8QXbSuISeFIaa6mZT0BAgE3bZVjE2LEAABcANbldthSXx48f1+SuagpeXN66dQtpaWkIDAzUfA3exIrH+BObJ0lI5LLdBi7du4f8oCCgZUvgjz9Ur1mhQgW0a9eOifHrmm1Fnz594OrqWrCdn5/PbAPAhg0bTL4vBEE8gcQlQRAEUezgxYivry88PDxsOqZOp5MtjVWjQoUKCAsLg5ubGxMfOXKkUJZqTSZNmsSIs6ysLLRq1QoHDhxgjvP19cXevXvRuHFjJq7T6fD1118zscTEREyZMqVg25bi0tYlsWZnLgGgS5eC8li+NPYcAD4fq1Vc3jexPysrC1999ZXGqylTo0YN4XVfvnzZLHFpCl5gK3WlTAcQBwAHDujf14EDAaMHFzz8w5K9e/faxanV29sbXbt2ZWJ8mXJycrLwc0UQhDIkLgmCIIhihz3NfIzhS2OPHz9u8ia3cePGsg6yffv2ZRxkrZW5BPTrxd5++20mdu8emyPz8/PDvn370NDICdWYpk2bon///kzshx9+wKVLl5CWliaUshpjEJ5ae1zy1ypW6y2N+e47ICBA1jHWVplLAJg3b57s+lVzcHd3F97XixcvwsfHB76+vpqvo1Yay0v0MyrXYVZdrl4N1KsHnD0re2z37t0ZUSdJElauXGlyrtaAL42Vc0ieO3euXeZCEP8FSFwSBEEQxQ57tiExpmXLlvD29mZiW7ZsMXlev379MGnSJCZmawfZCRMmKLaVKFOmDPbv34/nn39e9RozZsxgygBzc3PxySefmGyrUtjMpa3bkFiUuQQAPz9gxw7U5bLkthaXGRkZTNbYUpRMfWrUqKH5Gny/TGP415ysch3BLzYuDggOlhWYJUqUQL9+/ZjY0qVL7dLzslOnToL45t+DnTt3CoZXBEHIQ+KSIAiCKHbY2ynWgLOzs9BLUm3dpTGTJ09G7969mZiag2xhHTtdXFzg6ekpxP38/BAREYGgoCCT16hSpQo+/PBDJhYWFoaNGzcyMd4VNDo6Gnl5eZozl/Yuiy0UQUF4bvVqJiQnEDWLS41rSxcvXqzZZVUJuXYkAITSWDVxz7eW4dEqU2VfSUIC0LGjbIksXxp76dIl2V6z1sbFxUUQtvx64OzsbE0PmQiCIHFJEARBFEOKqiwWEFuSRERECM61cjg4OGDZsmWyDrIjR460ahbmwYMHaNOmDdOCxECDBg2Enp1qfPrppyhTpgwT4/v7PfccWyialZWFa9euac5c2rss1uLM5WOqd+4MFxcXJsZ3h9QkLkNDcU9BcPPk5eVhwoQJmo5VQskxlheXgYGBKF26tOI81HiJ21a6kVSUyXFxwAcfCOFmzZoJ87SXsc/gwYOZbbmfd36NMkEQ8pC4JAiCIIodRVUWCwAdO3ZkDHpyc3MRHh6u6VwlB9mFCxcKx1qaubx//z5at26NM2fkV7zt2bMHx48f13w9Ly8voSSTX79Zv359oQfghQsXNInLrKwsIcNp67LYwuLk5ITatWszMd7PVVVctmwJhIdDWrkS92TW8CmxceNG/PXXX5qP55Eri83PzxdE261bt/DLL78oXkfNOqset63koyqUxRqzejXA/UzpdDohe7lu3TpkZmaqXckqNG3aFNWrV2difHn80aNHkZLCP2IgCIKHxCVBEARRrJAkqcjKYgHAw8NDaI2gtTQWUHaQtUbm8u7duwgJCREau/MmLBMnTjTrum+++aZqtrN27drC/tOnTwsllHJlsbGxsULM3mWxlrz3fLY2vn59ZpsRlz4+QNu2wPjx+jWFkZFA585IS0tDRobWAlo948aNs/izwmcu09PTERsbK4jLa9euoXPnzoIplAG1GZcB4K1hLtcBZKkdIOOQy2cQExMT7VKOqtPpBGMfvpQ9Pz8fy5cvt/lcCOLfDolLgiAIoljx6NEj4YbcnuISEEtjt2/fbpZIaNy4MX799VfVY8zNXMbFxaFVq1YFZjoGKleujI8//piJ7dy506z2CU5OTpgzZ47ifjlxefLkSeE4OXHJl8R6enrCy8tL89wsobDrWQFRXN5PTWW2M156CXjwAEhO1q8h3L0bmD4dqFu34Bg+A6yFyMhI7Nixw6I5BwQECGY00dHRgrjMycnBrVu38PXXX8ua/ah1dYwH0EHDXPIBXFE74M8/Ae4hSeXKldG6dWsmZq/SWF5cpqWlCQ9tFixYYJe5EMS/GRKXBEEQRLGCL4nV6XSoUKGCXefQtWtX5sYyPT0de/bsMesa/fv3x2effWaV+dy+fRutWrUSDF+qVq2KyMhITJo0STDdmTBhglkZsI4dO6J9+/ay+2rXri2UifJ9ML28vODuzneDlHeKtYb4MwdLMoF1jUQiILZdSc/KAkqXBjw9AYXXo1Vc8j1cx44da3Ltoxw6nU7W1MfPz08Q9DExMfDw8MDKlStl248ofYfuA+iicT4m7YnWrBFCfGnszp07hffeFgQGBqJp06ZMzMfHh9m+ePGiRQ8MCOJpgsQlQRAEUazgxUj58uUF90ZbU6ZMGTRv3pyJmVMaa+Dzzz9Hr169ZPfl5uZqusbNmzcRHByMy5cvM/Hq1asjMjISlStXhru7u2AGc+DAAezatUvzXHU6HebMmSMIjRIlSsDHx0fIXPIZSa1tSOxREmuLzCVfAqwlk61ViPCZ+bNnz2I151irFTlTH51OJ2QvY2JiAABNmjTBJ598IlxHSY4/ANAJyuLTGNV1lwBw7JgQ6tWrF5N9zc/Pt1vPS74sNymJX2kLfPvtt3aZC0H8WyFxSRAEQRQritIp1hi+NHbLli1mZ5MMDrL1ufV6ADBz5kyT51+/fh3BwcG4evUqE69RowYiIyOZ9+att94ShNvEiRPNytoFBQWhYcOGTCwjIwOnTp0SxGVWFruirrg4xcphSeayatWqin1EAeuKy6ysLKF1zGeffSa8x1rQ2o7EIC4BYOrUqUy/UzXuAygNoKmpA6Ehc3niBMB9bzw8PISWPsuWLbNLz8u+ffvCycmpYDs3N1d4sGWvMl2C+LdC4pIgCIIoVhSlU6wxvLh8+PAhDh06ZPZ1PDw8ZE1J1q5di++//17xvKtXryI4OBjXr19n4rVq1UJkZKRQKuzq6orJkyczsePHj5udcZV7v0ePHo3y5cvL9tU0oLXHpT2cYq2RuXRwcFA1ObKmuLx16xamTZvGxG7cuCHrMmwKOcdYQF1cOjk5oV1IiKbrG5rfaCmNNZm5TEgAuLWsgFgae/78eZw4cULL9ApF6dKl0blzZybGlxPHxcXh0iWTr4wgnlpIXBaCR48eYdeuXVi0aBFmzZqFmTNnYuHChdi6dSvu3r1rtXHy8vJw7tw5rFq1CvPnz8f06dMxb948LFu2DCdOnJBtzk0QBPFvpSidYo2pWrUqnn/+eSZmSWksAMU1oyNHjsTu3buFeExMDIKDg4X3ok6dOoiIiFAUckOGDMGzzz7LxD777DOzMq7Xrl0TYvv370d4eLiq2CpOZbE8lma9+NJYY7SIS7k+pAAEJ+Hc3Fw0aNAALVu2ZOLTpk2TLc1Ug89c3rx5E2lpaYK45MusQ5o103R9wyvqA+BbAGpNekxmLgFAJjvbsmVLVKlShYktW7ZM0/wKC18a+/DhQ+GYr2ScbgmCeIxUTLly5Yq0du1aacyYMVJwcLDk6ekpQb8EQAIgVa5cuUjmlZ+fL61Zs0Zq3rw5Mx+5fw0aNJAWLVok5eTkWDTWjRs3pJEjR0q+vr6q43h4eEhvvvmmdOHCBSu/Wss5d+4cM8dz584V9ZQIgviXwP9+nTdvXpHNZfLkycxcqlatKuXn55t9nby8PMXf4d7e3lJ0dHTBsdHR0VJAQIBwXFBQkHTv3j2TY61du1Y4d+XKlZrnWaJECdl5Pvvss9KQIUMUX4fc9yk/P1+43oEDBzS/b5by1ltvMWN+8MEHFl1n1qxZiq/X2dnZ5Pm9evVSvD9wdXVlYocOHZKOHDkiHDthwgSz5pyWlibpdDrmGqdOnZIOHDjAxFxcXKTc3NyC8w7s2GHyvgaA5KEvZC34d9/E8fHc8cK/5GTZ1zFp0iTmOr6+vlJmZqZZ74UlZGRkSF5eXszYbm5uzLaXl5dFvwcIwh4U9T14scpcRkREoEOHDvDz80P16tXRv39/zJkzB5GRkcWice3du3fRpk0bDBgwAAcPHjR5/KlTp/DOO++gadOmTPmJFn755Rc899xzmD9/Ph49eqR6bFpaGhYvXoz69etj1qxZZo1DEARR3CguZbGAWBp77do1REVFmX0dSSVzlpiYiK5duyIhIQH//PMPgoODERcXxxxTv3597Nu3D2XLljU5Vp8+fYSM6+TJkzVVudy8eVMxI3fp0iXVLJpc5vLhw4fC9exRFsuj9v6rwTvGGpOTk2MyI6xUFlu3bl3hc33jxg00bdoUPXv2ZOJff/21WW6p7u7uwrXl2pFkZ2czPUgbvPyypnK2NADpRtt+AJwUjgVMlMb6+ABc6xQDQ4YMYbYfPXqE8HC1PKl1cHNzQ58+fYSYMUlJSThy5IjN50IQ/0aKlbg8ffo0du3aZVJMFQUPHjxASEgI9u/fz8SdnZ3RpEkT9OnTB/369cPLL78s/BI6ceIEQkJCBFMDJb777ju8+eabSOXWIZQvXx5dunTBwIED0bVrV+EPdHZ2NsaNG4dJkyZZ8AoJgiCKnry8PNy+fZuJFVVZLAA8//zzQnmepaWxaly6dAmdO3dGq1athGUVDRs2xN69e1G6dGlN13JwcMDUqVOZ2JUrVzQZkfDtRXjn2H379imeKycu+ZJYR0dHxZJea2KtVidqZbGA6dJYJXFZp04d4W+44R5h+vTpzPuekZGBL774Qst0C5BzjPX39xdanhg/+PYoWRJ1FYQezwOjrx0AlFM6ECbEZaNGim1cqlevjhYtWjAxe5XG8j0vExMThWNmzJhhl7kQxL+NYiUulXB1dUX16tWLdA4ffvghLl68yMTeffdd3L59G0ePHsX69euxdu1aHDx4EHfu3MG4ceOYPw63b9/GO++8Y3KcCxcuYPTo0UysUqVK2LZtG2JjY7Ft2zasXLkSW7ZswfXr1xERESH0Hps2bRoOHz5ciFdLEARRNNy9e1fIBhWluNTpdEL20hJxKZc54393//XXX8IavcaNG2PPnj3w9fU1a7xXXnkFL774IhObMmUKMjMzVc/jxSVvDqNWRSQnGvmHqhUqVGDcOIs7lSpVYtpi8NhCXNaqVQtvvPEGs2/x4sVmmcjImfqotSMx0FjjvdYDblvtcYHqussmTVTH4Y19/vjjD8V1rNakRYsWQvaXN7PavXu3Rb1ICeK/TrETl87Ozqhfvz7efPNNLFq0CCdOnEBKSgoWL15cZHO6fv260G/q008/xcKFC2VLlLy9vTFjxgzMnz+fie/cuRNHjx5VHeurr75iSpfKli2LQ4cOoUuXLrJPYoODg3Ho0CHUqFGjICZJkvDUmiAI4t8AXxLr4uKiqRTUlvDi8vTp04KDqyX88MMPKFdOOefTtGlT7N69W2jkrgWdTofp06czsdu3b2PRokWq5/Hi8sUXXxTaQighJy6LwikWEDOXlpbF6nQ6i019MjMzkZycLLtPTVwC+jJm4zYoeXl5Qh9TNeQyl4C6YywANOEeeChxjdu2OHM5YIDqOH369GHeh9zcXIv7f5qDg4ODkL3kP0NZWVkICwuz+VwI4t9GsRKXQ4cORXJyMk6dOoWff/4Zb7/9Nho2bGj35tk8W7duZbb9/f0Fu3c5hg0bhnr16qley9RY48ePR8WKFVXP8fHxEcoz9u3bJzR8JgiCKO7wYqRixYpCaaa9efnll+Hn58fEzM1eyombgIAAxV6Xzz33HHbu3Cm0QTCHNm3aIIRrLzF9+nRhyYUxvLisXbs2Zs6cafLvsJeXF9zd3YV4cXCKLSxq4jI9PV1xn5pTbNWqVVXFZYUKFfDhhx8y+3/77TeTD6gN8OLy0qVLkCTJdOYyWpO/K3Zy2xZlLlu2BFTWtAJAqVKlhDWoRVUaK/dzM2fOHLvMhSD+TRQrcenj4yOsVywO8M2r27dvr6nZsE6nQ9euXZkYb/1tTFJSkrDelD9fic6dOzOlRtnZ2cIfdYIgiOIOn7ksypJYA05OTnj11VeZmDXWXUZFRWHkyJGy+27fvm2VllZ89vLBgwf49ttvZY+VJElWXFavXh0ffPCB6jhKbUj4slh7iUtrZS4By9uRKJXE1qpVC46OjrLi0nien3zyiVAOPW7cOE2vhS+LTUtLQ2xsLFPlBHDi8uxZ1D11Clruwn6H3obSgJq4vAwgX25Hv34aRgJee+01Zvv06dM4c+aMpnMLQ+3atdGoUSMm5u3tzWwfO3ZMU0sagniaKFbisrjCZwBNZRKN4W+MEhISNI9jzlglSpQQzB7UxiIIgiiOFCenWGP40tgDBw7I9r9TQk4QvPbaa4ruq0lJSQUOsoXhpZdeQpcubLv72bNnyxqUPHjwQHjAaVgXOnHiRCF7a4zWHpdF4RRbWNQcYy0Rl4ZeofxnOy0tjXn/vb29MX78eOaYiIgI7NzJ5w1FAgIChLWiFy9elM1cFnw216yBM4AGJq8OJABYabStJi4zAdyS22HkVKtGSEiIcC9UVNlL/j4tLy8PS5YssctcCOLfAolLDfBrYkwZIqgdq2bK4OfnJxgd2GosgiCI4ggvRopD5hIA2rVrx5R95ufnm1zmYAr+RrVMmTLM9qVLl9C3b1/k5uYWapxp06Yx24mJibLlfBcuXGC2XV1dC5xyvb298fnnnyuOoeQAW1RlscU5c2m4nlzJN5/pHTZsmPAzMHbsWOTny+YCC9DpdHj22WeZWPSJEwj09xfmX9Dm5NgxAABvsaPkUfwuAMNs+e8+7xAhWxr7eDxTODo6YvDgwUxs5cqVmlrrFJYBAwbA0dGxYDsnJ4fZBoAFCxbYfB4E8W+CxKUGeCvskydPaj73xIkTzHbjxo0Vj3V1dUUTzjlN61hXr15lnkSXKlVKKH8hCIIo7hTHslhAXx3SsWNHJmZOaawpcdOhQwdcuHAB9evXZ+J79uzBRx99pHkcOerXry/07fvmm2+ENYF8SWzNmjWZG+l33nlHWMtnQM50KCMjQxijuGSizSEgIEBx7WthMpfOzs5CxpcXl25uboJBX1RUlGlTm7NnUYt74Bw9bhzK166NEtyhMTExgCQBj+83eHGp9GgjHcBgAHlQN/QBFEx9TpzQj6sB3jX2wYMH2LFjh6ZzC4O/vz/at2/PxEqVKsVsX7x4EfHx8TafC0H8WyBxqYE2bdowf1APHDigqYl2bGwsNm7cWLDt7OyMASac0YYNG8Zsa30ixjvTDh48WHi6RhAEUdwprmWxgFgau2vXLqsYp3Xu3BmbN29G6dKlsWXLFvhz2aUFCxbgxx9/LNQYU6ZMYbJkaWlpgpmQ3HpLY5ydnTF79mzZ68u5mfPfS+DfmbnU6XSKpbGFEZeAWCYs55UwaNAgYfyJEyciKytLvHh4uN4op1491OQy0Rehv+njm43EvPkmsHEj8LgEm38EnghAyWXiAIBhEMUl/27LZi4TEgAVcyljatasiaZNmzKxoiqNlStVnzt3rl3mQhD/BkhcasDBwQFLliwpMPHJz89H7969Va3o7927h+7duzNOchMnTlRcl2IgNDSUMY7YuHEjvvzyS9VzFi9ejO+++65gu2zZsprcbAmCIIoTWVlZwg15cclcAkCXLl2Yh3aZmZnYtWuXpnP37t0rG3/11Vfx+++/F5jZPfPMM9i8ebNgGjd8+HDs27fPwpnrDV740sIffvgBt2/fLtg2JS4Bff/M1q1bC3F+rSYgCiUfHx+hV+C/BaXSWDVxKecW6+rqimrVqhVsqznGGnB0dBQc4W/cuIGFCxc+CcTHA6GhwCuvAAcOAABYS58nAi+Qi8dcvgwYZbYDAXhzx1QQZvWERQBeUtkPqLQjkRPICvDGPlu2bLFLxrB79+7C+lUXFxdm215ClyD+DZC41EizZs2wbdu2gjUxly9fRr169fDRRx9h586duHjxIqKjo7F3716MHz8ezz33HI4fP15w/jvvvIPPPvtM01jr1q1D//79C7YnTJiAl156CT///DNOnDiBmJgYnD59GsuWLUPbtm3x1ltvFTyVLVu2LHbs2CGs3SkM9+/fx/nz5836x9ubEwRBmMJY6BgoTuLS19cXwcHBTGzTpk0mz9uxYwd69OghxNu1a4cNGzYIQrJp06aCSUheXh569+6t6jhuismTJzMtRbKyspj1mFrEpU6nk83SHD58WIgVlVMsYN3MJWCZuJTLXNasWZPxVtAiLgH9gw1+ic60adP0hlBRUUC9esCaNexY3DVuQl/KKohLblsHMXupbOWk5w4ANZcHxXYkGpz3DfTr14/5WcnJycHatWs1n28p7u7uQjsU496bABAXFyd0FiCIpxbpX8L+/fsl6CstJABS5cqVi2QeDx8+lCZOnChVrVqVmY/Sv1q1aknr16+3aKydO3dKnTt3llxcXEyO4+LiIr3xxhvS3bt3rfyKJWny5MmaXqvav3Pnzll9XgRB/Lfgf897enpK+fn5RT0thu+++46Zo4+Pj5Sdna14fHh4uOLv8IsXL6qONX78eOGcmjVrSgkJCRbP/7333mOu5+TkJF25ckVKSkoSxoqKipK9RnJysuzrOXPmDHPcpEmTmP2vvvqqxfM2l2HDhjFjv/vuu4W63p49e2Rf87x58xTPqVOnjnB8//79mWN+/PFHZn+jRo0Ur3f48GHhehPfekuSfHwkSb96kfmXJjPfU4D0IxdrIHPuBO6Y2hr+zvuq7NMBUjo/jo+PJJn58923b1/mui+88IJZ51vK7t27Tb7+//3vf3aZC0GY4ty5c0V6D06ZSzMxuPZp6XPZrFkzfPvtt+jdu7fFYzk7OwsOsjzu7u6YMGECpk2bJqzVIQiC+Lcg5xQrt5avKOnWrRuznZCQgAOPyxB5tmzZgu7duyM7O1t2v3EWUY6pU6cKGc/o6Gj069fPYgfZiRMnMv2kc3Nz8fnnn+PixYvMcQ4ODoLbqIG4uDjZ+JgxY5gMYVE5xQLWz1xaa82l8XpLQHvmEtC3leE/D18vXow7Cu1q3AFU5mLRkM9c8u8On7lUntUTxMLoJ0gQM6Ro1Agw8+ebL409fvy44HJsC0JCQoRlTXz2UksVA0E8DZC4NIOff/4Z1atXx7Rp04Q/xHIcPnwY7du3R7169XDo0CHN48TGxqJ169bo0qULwsLCmHWbcqSnp2Py5MmoUqUKRo8eTQ19CYL4V1JcnWKNeeaZZ4TG6nI3lZs2bUKvXr1U2yWYEs4ODg5Yvnw5nn/+eSa+a9cujBo1yoxZPyEgIADDhw9nYitXrsSePXuYWPXq1RUfoha0ruDYvXs34+BZlGWx1qZs2bKyfT6V/j7n5OTIrgfky2t5cfnw4UNVk6gvv/ySMWZKlyRMUZk3Xxp7EaK4TAHwgIvxjrHqdyHyeHDbwrrLJvwopmnXrp3QHs4e6x0dHR0RGhrKxPg2MomJiTh69KjN50IQxR0SlxqZPn063n77beaX/gsvvIAlS5bg8uXLSEtLQ0ZGBq5du4Y1a9YgJCSk4Lhz584hODhY0y/A27dvo3nz5ti/f39BzN3dHR988AH279+Phw8fIicnB48ePcLhw4cxfvz4Agv4rKwsfP311wgODi50421j3n//fZw7d86sf+ZY9BMEQQDF2ynWGD57tHnzZiYztmHDBvTp06fQ/SkBoGTJkrIOst999x0WLVpk0TXHjh3LGOtIkiT8fZJbb2lAKXMJAKNHjy543XzmkhdStsTamUudTie77lLpYe6DB7xc08NnLuU+43KOsQZq1aqF119/nYn9DGXDHF5cRgOoCMCFi/MrectDNPEx13+eF5fCuksT7vlyODk5CcZUK1assMrPmin4ceUeAvBtYwjiqcSuRbiFoCjXXO7du1fS6XTM+J9//rnJtUCLFi1iznN0dJQOHjyoek5wcDAzTmBgoHTp0iXVc2JjY6XGjRsz53Xq1KlI1yoVdb03QRD/Pjp16sT83pgyZUpRT0kW/vcbAOn48eOSJEnSmjVrJEdHR2H/kCFDhNi1a9c0j3n48GHJ1dWVOd/JyUnau3evRa+BXw/J/xs7dqziubNnz1Y994cffpDy8vKEtaZHjhyxaK6WMGLECGbst99+u9DXfP/994XX+v7778see+rUKeFYJycn2fW5fn5+zHHbt29Xncft27clNwcH5pw+MusmJUD6nptDw8fxWlx8qcy5PbhjXE2sOeT/lee2hxpfv2VLi78Pcj9/pt4zaxEUFMSM6+npyb5Hrq5SXl6eXeZCEEoU9T04ZS41MGHCBOap59ChQzF58mSTJU1vv/02JkyYULCdl5eHkSNHKh6/c+dOREZGFmy7uLggPDwcNWrUUB0nICAA4eHh8PV94tW2fft2bNu2TfU8giCI4sS/oSwW0GefAgPZ4sLNmzdj5cqVGDhwIPLy8ph977zzjuaexUq89NJL+OWXX5hYbm6uxQ6yo0aNKqh6kUMtc6lUFmtg8uTJuHz5srDW9N+85hKQd4xVylzKrbesVauW7Dpbc9ZdAkCFR4/wYX4+E9sA4JjMsXKZSwmmHWMBcd2lGu4ysUyZsQsYO9aMq7M899xzQmn60qVLLb6eOfDZS77XaFZWFlVuEU89JC5NEBsbi7/++ouJmdNDcty4ccyi7xMnTiAqKkr22A0bNjDbAwYMUDRU4ClTpgyGDRvGxH799VfN8yQIgihq/i1lsTqdDt27d2div/76K4YMGYJ87qZ/2LBhWLhwoezDSHPNigYOHIhPP/2UiSUkJKBr165ITEw061peXl4Yq3KDb2lZLKAvCZ0+fToTc3Z2FtbK/duQE5dKay61mPkY4MWlWlksAGDNGowFwD8aGAsIxjy8uEwDEAtt4pJfEanWkVLuZjKR2y4o3Q0NBTp3VrmaaXhjn82bN5v9M2AJoaGhzM9tdna28HM8e/Zsm8+DIIozJC5NcPr0aWa7WrVqqFq1qubzPTw80LRpUyamtOD7zJkzzHabNm00jwMAbdu21TQOQRBEcSM5OVnfs8+I4pq5BMR1l7GxsUJ2bOTIkfjuu++s6ng7bdo0Qdha6iA7fPhwRYfxWrVqKZ5nKnMJAGu4novPPPOMYIBiS+yVuXz48KHssYURl6Yylzh2DN4AJnDhCAA7uVgFyK991CIuG8nElEgF8BIX49/xRwAe+vsD335rxpXlGTBggNCzdf369YW+rikqVKiA1q1bMzHj9csA8Pfffys6RBPE0wCJSxPwT8IsefLKn6P0x6iwY2kdhyAIorjBZy0BoGLFikUwE228+OKLqq2fRo0ahXnz5hWIHDlxY4nodHBwwIoVK2QdZEePHm3WtTw8PJilGwb8/PxQqlQpxfNMZS4BCEK3uGahzaF06dIoWbIkEzNHXMqJU8BMcSlJwMmTAIBhAPjHL+MAGOfOddDmGHsZohj0ljlXjWQNx0R/9RUg47prLn5+fujatSsTK6rS2NTUVGY7Ly8PP/74o13mQhDFERKXJvD29ma21SzCleB/8fB/nKw1ltZxCIIgihu8uCxTpozQR6444ejoiOrVq8vuGzt2LObMmWOzHp0GB9myZcsy8W+//RY//fSTWdd6++234eXlxcRycnJUM31axCWPPZ1iAdtkLgEIvQ6VSjHlHpZYJXOZkgI8doN3A4Q2JGcArOFi7QD0gF54LgXQCaK4TIJ8n0pz1l2eB2CqA/glbj1yYRg6dCizfeTIEVy6pOSbaz169uzJ/G7Kz8+HoyPrpbtw4UKbz4MgiiskLk3A/yGJjo422XeS5+Tjp4wGlDKS/FinTp0ya5wTJ05oGocgCKK48W8x8zEwf/58HD58WIiPHDkSM2bM0CRuCiM+K1WqhM2bN8PFhW0qMWzYMKaVlSlcXV2Ftf3JyckICwuTPT4lJUX1waeHB1+E+WS+/wX415GcLJ+vu379OrPt4OAgmEAZ4MVlbGyscn9UrtxyMAA+HzoR7PrImQB+BzADwFAA1QBUBuDEnadl3aUpTLUriY4WGpJYTKdOnVCmTBkmZo+el56enkJpOv+5v3jxol3WgBJEcYTEpQnq1avHOOplZmZixYoVms/ftm0bYmNjmVjz5s1lj23VqhWzvWzZMs11+5Ik4eeff2ZiLVq00DxPgiCIooQ3MSnO4nLu3Ln48MMPZffVqVPHZhlLnpdeegmLFy9mYrm5uejVqxdiYuSkgjyZmbyvJ/DZZ58JrreA6axls2bNZOP2Fpe2ylxWqVKF2VZ62My/T/7+/sKDAAO8uMzPzxfuGwrgruEIvXg05joAU0WZTgB494jCOsYCgKlH79bMLDo7O2PgwIFMbMWKFYKpli3gS2PlHjLMmDHD5vMgiOLIUykudTod8y8iIkLxWEdHR/Tu3ZuJjRs3DufOnTM5zs2bN/Huu+8ysZdffhnly5eXPb5Hjx7MAvUbN25g+PDhmv4oTpo0CX///TcT4+dNEARRXPm3OMXOmjULY8aMUdyvlPGzdubSwODBgzFu3DgmZo6DbF5enuwN/7lz57Bu3Tohzpv5uLqyhZCenp6yGTqlv3v/NvhS6KysLNmHwI8esUWmSiXUAODr6ytkvhRLYz09Aa6FTBcA/CPraTC9BlKLqU99mM5GmsP58+eteDWxNPbWrVtmZe4tpV27dkJZOv/wYOXKlTafB0EUR4qduLx9+zauX78u/Lt79y5zXG5uruxx169ft7qRzaRJk5j6+sTERDRr1gwLFiyQfWqZnZ2NZcuWoVGjRsLTR7UnWVWqVBHE6M8//4xOnToJrrUGLl26hL59+2LatGlMvHXr1oJ7LEEQRHHl31AWO336dEHI8ezZs0exVNJWTJ8+Hd26dWNiFy9eRP/+/U06yF6/fl3o1Wdg8uTJQnkmn5Ez7q9sGPeLL74QrqX2ENcW2CpzKdcejBfn+fn5Qv9LtdYuOp1OeJiiKC51OqBhQzYEYBZ32EMAcxRH1KNFXLoB8JXpzWnAydE86Xn58mXlkl8LqF+/vmBuZQ9jHycnJwwYMICJ8T1M4+LicO3aNZvPhSCKG8VOXDZv3hxVq1YV/vE/xLGxsbLHVa1aVfWpsiVUrFgRq1atYhZsp6SkYMSIEShdujRatGiBfv36YcCAAQgJCYGfnx9ee+01QeROnz7dZKnqrFmz8PLLLzOxnTt3okGDBqhWrRpeffVVDBo0CN27d0etWrVQs2ZNoT9mlSpVsHz58kK+aoIgCPtR3Mtiv/jiC0ycOFGIT506lbmpzM7Oxo4dO4TjbJW5BPTr+VauXIl69eox8Z07d5r8e/jPP/8o7ouJiRHWsPHikvcKuHz5sqxxzU8//YT79++rzuXfAL/GDxCzcfHx8cL3u0GDBqrXNcvUp4m4ErIZgO5cbC6Au8KRT9AiLnMApKgI81wTBj38+yVJkuzDh8LAZy83btxolwc8gwYNYrbl1iJb+7USxL+BYicuiys9evRAWFiYYD2fkZGBgwcPYv369Vi7di0iIiIE11YPDw989913GD9+vMlxSpQogfDwcGEdAQBcu3YNW7duxapVqxAWFia7ML5ly5aIiIhAhQoVzHyFBEEQRYMkSbh9+zYTKy5lsZIk4bPPPsPnn38u7Js3bx4mTpwo9L3bvHmzfSZnhJKD7Pz584X1+MZcuHBBuI4xU6ZMYTKbfFlstWrVmO2cnBxhiQagX5Mm9x7aC2tlLt3d3YUYLy6vXLkiHMP3u+YxS1wq/H3/EuxNXTpEN1ljtIjLnQAyzeyfakwZHx/hIcpXX32Fs2fPWnxNntDQUObhf0ZGBn777TerXV+JRo0aCf1g+c9HUfwuIIiihsSlGXTp0gUXLlzAl19+qbp+woC/vz/GjBmD8+fPY/jw4ZrH8fLywsqVK7Fv3z707NlT0QTAgIODA1q3bo1169YhIiLC7pbvBEEQheHhw4eCqUxxyFxKkoTx48cLyw4AYMGCBQWmPrxzZHh4uLAOz5aZSwOVK1fGpk2bhL8Z77//vmJZKp+55Ktrbt26hUWLFhVs85nLqlWrCs7kvHO5gUWLFgli1lbYylRJrj0OL5TkXr9SGxID/N9tPpPPILMWFgBqA3idi/0EfQ9LOXhx+RBAIhdbXrq08jweo+QQDAD3L11CTe6zn5OTg4EDB8oaSVmCv78/OnfuzMTs4Rqr0+mE7CVvgpWUlIS//vrL5nMhiOIE70Rd5PD23bagME8wfX198emnn+LTTz/F7du3ceLECdy5cweJiYmQJAleXl4oU6YMGjRooGg7rpWQkBCEhIQgKysLZ86cwT///IOEhASkpqbC3d0d3t7eCAwMRMOGDamnJUEQ/1r4G2kHB4ciN4CRJAmffPIJ5swRV64tXLiQWR//6quv4r333ivYTk5Oxv79+9GhQwe7zNWYZs2a4eeff2ZKBQ0OskePHhX+LvHisk2bNsjIyGDE6PTp0/HGG2/Aw8NDyFyWL18etWvXZnwRLl68KDu3/Px8fPzxxwgPD7f05VmMtTKXcuKSN/iLiopitl1cXATjIx7NmcuzZ4EDBxSv8zmAlQAMsi0P+tYkcnK0CvQZBmNv1SsAGj3+OgHAFg2mUNXLlEGUQnuahwDaA+A/EWfPnsWE0FDM/f13k9fXwtChQ7F169aC7T///BNXr14VMuvWZuDAgUy5vNz65S+++ALbt2+36TwIolghEYQNOHfunASg4N+5c+eKekoEQRRTNm3axPy+qFixYpHOJz8/Xxo5ciQzJwCSTqeTfv75Z9lzmjZtyhz77rvvMvvj4+OF68XFxdnsNYwdO1YYr3bt2lJiYiLzOkuVKsUc88cff0iHDh0Szp0xY4YkSZJUo0YNJr5u3Tpp2LBhTKxKlSrC+cb/du7cabPXbWDMmDHMmEOHDrXKde/cuSP7ucjIyCg4plWrVsz+0qVLm7zuwYMHmXPc3Nyk/Px88cBPP5UkQPXfWJn3/JjCsVW549Ya7Vuk8j00/tfCxP4xKvv2tG4tSQ8fFvr7kpmZKfn6+jLXnjx5cqGvq4WWLVsy45YsWZLZdnV1lf9eEoSNKOp7cCqLJQiCIIqU4tSGRJIkjBgxAvPnz2fiOp0OS5YswZtvvil7Hl8aGxYWZpd+e0p8+eWXePXVV5nYP//8wzjI3rlzRzA+qV27Npo1ayaUGX711VdITEyUNfThnVAfPHjAbHt6ejLbo0ePlu2haUskG2YuJUlisrV8Jp5fBysHn7nMzMyUN0A6dszktcYC8JGJyb0DausutdoCZkB9jRU/F2OG7tuHR3Xr6jOyhcDV1VUwfly+fLldfgb50li+i0BWVhY2btxo83kQRHGBxCVBEARRpBQXp9j8/Hy8//77+P7775m4g4MDli9fjtdee03xXF5c3rlzB8dMCAFbrQsEnjjIBgUFMfEdO3bg448/BiCWxLq7uxcIe36daUJCAmbMmCE4YpYvX15YT8jfXPM33+fOncOSJUvMfEXmYc81lwBr6nPv3j1mn5bPc/ny5eHkxK5UEkpjJQk4edLktXwA8PaB+wHslTmWF5eG9ZlXABwyOZKeq9C71SqhsnoUsQDevXsXUsuWhRaYvGvstWvXcEClhNha9OnTh1nnnJ+fL3z+Zs+ebfN5EERxgcQlQRAEUaQUhx6X+fn5eOedd/Djjz8ycYNI4wUST82aNYUMnrFTpLUyZ+bg6emJrVu3Cu0gvvnmGyxevFgQl7Vq1YKDg/62oEGDBujduzezf8GCBcIYcuKSf61Dhw4V2qR89tlnSElJMe8FFQOcnZ0L3iNjDOIyJSVFEOA1atQweV1HR0fhcy+Iy5QUICFB0zyHA6j4+OvSAOYDkGuEppS5XMnF+X6mxjwC0EplLvtU9gHABgArEhOBjh2B+HgTRyvzwgsvCJ9Fexj7eHt7o2vXrkyMNzk6ceKEVft7EkRxhsQlQRAEUaQUdVlsXl4e3njjDSxevJiJOzo6Yu3atUK5nRJ89tJUGwJbZi4NKDnIvvfee9izZw8T48XxlClTGCHFZyS9vLzg7u6OsmXLwsdHufixatWqmDt3LhO7d+8eZs2aZdZrMQf+vbWWuNfpdKqmPnJmRqacYg2YNPXhHIjVcAMwC8Ak6LOQHwCQsxSSE5cSxJLYAQMGoLSKc2xNlblcBlCOi/H+ssMBXIuLAz74QOVK6uh0OiF7uWHDBtn+k9Zm8ODBzDbfki4vL0/24QxB/BchcUkQBEEUKUVZFpuXl4fXXnsNS5cuZeJOTk5Yv349+vTpo/lavLiMjo4uyA4WRebSwMsvv4yffvqJieXm5gqurby4rF27tmrGNiAgAID+pl5JQLm5uaFMmTJo27YtunTpwuybO3eu8GDh34CcuDRkLuVarVSsWFGIycE/VBHEpYm2ZDyhAL4AUErlGF5c3gOwB/pSV2OGDh2KJlWqKF4nF4DaIyH+0/8Kt50CYDCAvNWrgUK4CQ8aNIh5IJKamorfreRIq0anTp2E7K5x700AQlUEQfxXIXFJEARBFBm5ubmCSYy9xGVubi4GDx6MlSvZIkBnZ2f89ttv6Nmzp1nXe+GFFwoEl4Hi0kR96NCh+OSTT5iYwdjHAC8uAWDy5MnCWkADxq9V7lxAL5gMWcTZs2czN9yZmZkYP55fHWgdbJW5BOTF5bVr15CWlia0JQH0fRi1YDJz6ekJqGSILaEaAD5/zkugWrVq4YUXXkBjOYOhx1wG0EVxL8Cf6QzgNS52CPpsK776SuVK6gQEBKB9+/ZMzB6lsS4uLujXrx8T49vPXLp0CYkaWrsQxL8dEpcEQRBEkXHnzh3B0dEeZbE5OTkIDQ3FmjVrmLiLiwt+//13dOvWzexrOjg4KJbGyokbe5TFGvPll18Ka8OMkROI1apVU3TINe5FqpS5NP5e1q5dm+kPCgArV67E33//rTrv4oaSY+w///wj9LgELBeXfEYfOh3QsKH2iWrADU/WZhrYwW0PGTIEunPn0ISfjxGXIGYjjeE//dHQrwOtysUnAzj+55+AjEjXCl8au2/fPvG9tAF8aSxfRg6IRlkE8V+ExCVBEARRZPA3fW5ubqpru6xBdnY2+vXrhw0bNjBxV1dXhIWF4ZVX1G6T1eHF5bFjxxAbG2vx9ayJo6MjVq1aJTjIAnqhGxjIF0nqmThxItzc3IS4ceZSi7gE9JlQLy8vJjZq1Cirlw3bMnPp7u4uGz9//jzjGmtASysSQEPmEgCaNNF0LXPg7YaMJZEOwMCBA4E1a9BY5RqXAIQAkPfSlT/eE3rjIOMb0VwAAwGkFSLb2K1bN+YzJkkSVqxYYfH1tNK0aVNUr16difFrnVevXm3zeRBEUUPikiAIgigy+DV3FStWtGlGLysrC3369MGmTZuYuJubG7Zu3YqOHTsW6vrBwcGCeAoLCysWmUtA7yC7ZcsWofekJElYvly+s2GFChXw/vvvC3HjtW1KZbG8YCpTpgwmTJjAxA4ePGiXdXHWQqkdyenTp4UHCR4eHorH8/DvVWJiotCHFBrNpcxB/pGCnpAXX9Q/IDh2DGUAVFE47jL0hkFtNI6ZBH2pbDMAE7h9lwCM4SoKzKFEiRJCieqyZctsvu5Zp9MJa5T5n/E7d+7gypUrNp0HQRQ1JC4JgiCIIsOeTrGZmZno1asXtmzZwsRLlCiB8PBwtGvXrtBjuLi4CJnP4rLu0kCVKlXQuXNnIf7ee+/hzz//lD1n3Lhxwo3y/v37C75+5plnULJkSeE8ue/niBEjULUqWxA5duxYZGVlaZq/Fuy95hKAbF/TcuV4n1Rl5NYaC9nLoCCghVxTEctRE5dD3nuP6a+plL3MgL5npdq6S55Lj///TOa6P8bGYtvWrWZcjYXvSXv58mUcOXLE4utphReXcp/pyZMn23weBFGUkLgkCIIgigx7OcVmZGSgR48egkOqh4cHtm/fjtatW1ttLL40dv/+/bJGHkWRuTQg12MyJycHPXv2xNWrvFeoPuPIG/scPXoUp06dAqB/LXLZSzlx6ebmJrQhuXLlCr7//nuzXkNRoSQuo6OjhZg54tLNzU04XrY0duxYzdfUgpK4dHd11ZtaGfXXVCvKvQTzxKXh3XKGvjyWLzZ+/fXXce/ePTOu+ISmTZsK/UXtYewTGBiIpk2bMjH+87K1EKKZIP4NkLgkCIIgigw+c2kLcZmeno5u3bphxw7WqqRkyZLYsWMHgoODrTpehw4dGKfI3NxcoadkUWNokcITHx+Prl27CuWYKSkpsk3gJ06cWPA1v94MEEs9DfTu3RvNmjVjYlOmTMHDhw9Nzl0LRZG5jI+PF2JazXwMaFp32aWLVctjlcRlzz599OXTRv01Ta27fAaAVj/bS0ZfPwvgG27/g4cP8cYbb1j0vZPrebl27VpkZGSYfS1z4Y19srn+pMnJyTh48KDN50EQRQWJS4IgCKLIsHVZbFpaGrp27Yrdu3czcU9PT+zatQvNmze36niGa7dt25aJbd++XTiuqDKXGRkZuH79uuL+CxcuYMCAAcjLyyuI8e1iDPzxxx84fPgwAPksnVKPR51Oh7lz5zKxpKQkTJkyxdT0ixytaygBG4lLAPjuO4Bre2Mp1RTiBSLJyJSmEcTWJQYMYlHrp5rP874J4FUuFh4ejkWLFmm8IsvgwYOZn7Hk5GSEhYVZdC1z6Nu3L5PlN/45MvBv+JwThKWQuCQIgiCKDFuWxaampqJz587Yt28fE/fy8sLu3bvx0ksvWW0snh49ejDbe/futdlY5hIdHS1kg3i31z/++IPpi3nnzh3F602YMAGSJAkmQTqdTuj1Z0zTpk0xgMvALVy4ULa81FyKInMph1anWAP8wxVFcennB+zYYZW+lx4A+NWyPj4+aNPmsT2PUX/NkgCUCn0vAXj0+B+P3LtwidvWAVgMgJfjo0aNsugzUalSJaHc3R6lsaVLlxbWNPMOw5GRkTY3GCKIooLEJUEQBFEkZGRkCGWQ1hKXKSkp6Nixo2BQ4+Pjgz179uDFF1+0yjhKdO3alRE4cuV4RZW55EtiK1eujPDwcKEFzNdff41ffvkFgHLmEgAiIiKwd+9exj0W0Au6Bw8eqM5lxowZQgmxsagtjhSLzCWgN/eJjCx0BjMPQDYXq1evHhwdHfUbXH/NWgrXuQRAtDTS4ysTuwx96xFjygBYwsUyMjIwcOBAobxUC7yxz65du1Q/y9bCVM/L7OxsrFu3zubzIIiigMQlQRAEUSTcvn1biFlDXCYlJaFDhw44dOgQE/f19cXevXvxwgsvFHoMU5QtWxYvv/yyzcexhAsXLjDbtWvXRpUqVbBp0yY4Ozsz+wwOsvwNOZ+RnDBhAtLS0kyOxVO5cmV89NFHTGzLli1CttlcikvmsrDiks/sCwQFAVFRQGioWeMYsx+iuBQyrkb9NesrXOcagEMK+xIBVOBieQCuyxzbGcCwRo2Y2IkTJ/DFF18oXF2ZHj16MC7G+fn5WLlypdnXMZdXXnlFaEnEP3yZM2eOzedBEEUBiUuCIAiiSOBvnL28vFCqVKlCXTMxMRHt27cX2g6ULl0a+/btQ4MGDQp1fXPgS2N5ikvm0uDy2rx5c2F9m8FB9uLFi0y8fv36zPaxY8dw9OhRk2PJ8emnn6JMmTJMbPTo0bJr1YoD9hSXd+7cMd2ixc8PWLUK2LYNaNnSrPHQsiWWyzglP3rEFbcalS83gzx5ACIU9t0DMFwmrvQI4auFC1GrFpsjnTFjBg4cOKBwhjweHh7o06cPE1u6dKnNS1Ld3NyEcfkHMqdOnbJq+x2CKC6QuCQIgiCKBGs7xT569Aht27YV+g2WLVsW+/fvx/PPP1+o65tLt27d7DqeVpTEJQD873//w5gxY5j98fHx2LBhAxNr3rw5AgNZn9ETJ04IY5nKXAJAqVKlMHXqVCZ2+vRprFixwuS5Stgyc8mvn1OjsOISEH9OFOnSRV8me/YsMH480LatuCbTx0cfHz8eOHsWqeHh2PjXX8KlYmJi2IBRf021fPwphbgEvXGQIxf/Ve7gl16Ce82aWLVyJWOMI0kSBg8ejKSkJJUZiPClsf/88w+OHz9u1jUsge95yZfG5+fnY/78+TafB0HYGxKXBEEQRJFgTafY+Ph4tGnTRhA45cqVQ0REBOrWrWvxtS2levXqCAoKUtxfFJnL3NxcXL58mYnx/SlnzpyJV155hYnxrUkqVqwolCny68oAbeISAN544w0899xzTGz8+PGypbZFDZ+5VPs+misuvby8hHJK1XWXctStC0yfDuzeDcTHA8nJwIMH+v/j4/Xx6dOBunXx+++/y37fbt68KWbVHvfXVHtFat+tfQD4Tqh/Q5/VZDhyBPDyQsN27TBVZg3qiBEjVEYRad68OapWrcrE7GHs06JFC+F3Gt8r9qeffrL5PAjC3pC4JAiCIIoEaznFPnjwACEhITh9+jQTL1++PCIiIgTxZE+6d+9eZGPLceXKFaFfJf/+ODo6YtWqVYLYMyYgIAD9+/c3Kdq1lMUC+ptufg3anTt3MHv2bPZASdKLpIcP9f8rZCTtuebSzc1N9jg3NzdmvZ9WzDL1MYVOp3d7LV1a/z/3vihlhyVJwtWrV9lg06ZA5cpwAKA9d/uErQC6cLE8AAuUTkhIwMdXroAv9F2xYoVZZjgODg5Cz8vVq1fbvCTVwcFByF7y6y6vXLlitd6uBFFcIHFJEARBFAnWKIu9d+8eQkJCcPbsWSZesWJFREZGombNmoWaY2FRW3dZFJlLXuyVLVsWfn5+wnGlSpXC1q1bBQdZAwEBAXBwcBDKWXni4uKQmJioaW4dO3ZEhw4dmNhXX32F2D17npR5+vkBXl5AmTL6//38npR5njunaZzCwotL3gTJgL+/v0XfY6uKSxVu376t2iKHKY2NigLq1QMez0X8xJgmDvItSX6AcsbTEcByAPxK7HffeUfWEEyJIUOGMNsJCQnYtm2b5vMthReXco6306ZNs/k8CMKekLgkCIIgioTClsXeuXMHrVq1wvnz54XrREZGokaNGoWeY2GpX79+ocp9rY3aekueqlWr4vfffxdK+YAnTr/dunVD48aNzRpTjTlz5jDZnYyMDExs1w6YMQPYuxdISGBPSEjQx2fM0K8LbNkS+OOPYuEWa25JrAF7ictVq1Yx70tB65HHFIjLqCigVSvAyDGYd36VQ67DqdwreQSx/YgxlaEXoMYkJiVhaK9eyM/P1zAT/We5JWd2tHTpUk3nFobatWujEed86+LiwmyvXbvW5vMgCHtC4pIgCIKwO5IkFaosNjY2Fq1atRJcTKtUqYLIyEhUq1bNKvMsLDqdTrE0tjhkLk2VDLdo0UK2BcTw4cNx7do16HQ6k5kXc8Rl3bp18RbXI3AZgJNaL3DggN7YZuNGzWOaCy8ulVxtLRWX/MMIW4hLSZKwfPlyJsb/zMTExOjXaHbqJIj6KhrGkMtSHgMg96n/GmLPS2NCAfTnYvuOHcM8M3qi8sY+27dvx717wopPq8NnL/nPy71794TfYwTxb4bEJUEQBGF3kpKSkJqaysS0istbt26hVatWuHTpEhOvVq0aIiIiUKVKFWtN0yqYakliT8wVlwDQrJnYfCI+Ph5du3ZFcnIy2rVrp/qeazX1AQBEReGLHTtgvFJRAjD68f9a0XElsrbMXObmysui4py5PHnypPB94TN7MTExwIgRTMbSgKlX5ghALl9/FIDcT/l1AL+rXE8HffaSP3f83Lk4ExlpYjZ6evfuzTj95uXlYfXq1ZrOLQwDBgxgssJyDyM+//xzm8+DIOwFiUuCIAjC7si1V6hYsaLJ827cuIHg4GChVUJgYCAiIyNlWzkUNc2bNxccQAH7Zy4lSbJIXMbJiAsAOH/+PEJDQ5Gfn4969eopnq85c/m4/NL/3j2M53ZFQG8IUxzgxSVvkGTAWuLy1q1bVu/5yWctq1atitZcv8uYqChgzRrZ88vIRp8QBEDup1kCIP4k6JkN9QcIPtBnsY1/arIBDOzWDZmZmSZmBHh6eqJXr15MzB6lsf7+/mjfvj0T43tehoeH23weBGEvSFwSBEEQdocvifX39xduuHiuXbuG4OBgXLt2jYk/++yziIyM1CROiwInJye0bdu2qKeBW7duCa09CiMuAf1N8bhx42SNSgxoylxy5ZcfQsx8jYFeTGiBl+2SFZ1BeXGptO6vbFm5wlDT8OIyNzcXd+/etehacuTk5AgZu8GDB+PZZ59lYtfv3lV8v029shcBlFfYpyQDjwMwlYMMgf5zYMz5pCR82p8vmpWHd42NiooSXKZtAV8ayzvVpqamYt++fTafB0HYAxKXBEEQhN0x1yn2ypUrCA4OFkoEa9eujYiICAQEBFh9jtaEz1wAwKNHj+w6Bz6D6OnpiQoVTFuz3Llzh9nmDUnmzJmDM2fOKJ5//fp10/0qufLLEgBmcodcBvCjydkqoDI/c7G1oU/ZsmWFBy3WLI3dsWOH0P5i8ODBqF69OhPLh7wBD2BaXDaFsrhU83idrbLPwFQAz3Oxb8LCsHv3bpPnhoSECL9r7JG97N69u8m2NOQaS/xXIHFJEARB2B1znGIvX76M4OBg4Zy6deti//79KF9e6Ta2+MCvZwP0N/n2hBeXtWrV0lSay2cue/XqJbTf4AUoT3R0tPLO8HDZ8sv+AJpwsS8AJAhHigiZy1u39ONYAVuLSwcHB5ua+vAlsc2aNUNgYCB8fHyEtjRs8fkTTJXFqonLDJXz/gBwXmU/oHehXQWA7y762qBBiI+PVz3XwcFBaEuyevVqxdJma+Hu7o6ePXsyMf4BwsGDB61e/kwQRQGJS4IgCMLuaHWKjY6ORnBwMGJjY5l4vXr1sG/fPotv4O2Nmxt/Kwy79NkzxpL1loAoHJs0aYKFCxeaNbZqaeysWbJhHfQuosY8AmBxfuerryw9k8HYFEYNS8tiAduZ+iQkJGDrVnb1qrHYCgwMZPYpiUtTr6wGlMWlKeZoOOY5APx3M+7+fbz99tsmzZt4cfngwQNs377drDlawmDOBZkvjc3JycGKFStsPg+CsDUkLgmCIAi7o6Us9sKFCwgODhbETf369bFv3z6UKWMqf1K82bNnD9LT0+02nqXiks9cBgQE4I033sBHH32keeyjR4/K7zh7Vt8+RIGXAfThYt9BWfQYEDKXAPDnnwDnImsJWjOXhTFsspW43LBhAyNqXFxc0Ldv34Jta4nLBADlVParvTOrAMSq7DcwDEAHLvb7779j2bJlquc9++yzggOyPUpjQ0JChPJ9/jPyzTff2HweBGFrSFwSBEEQdsdUWey5c+fQqlUroQ9do0aNsHfvXqF8r7gjl03JzMzErl277DYHXlzWqVNH03ly4hIAZs+ejc6dOwvHu7i4wMGBvb1QdMNUcCM1ZiYA41WeOQDGmjxLAQ3jmcLFxUWTcOSz7eZgK3HJl8R27doVPj4+BdtaxaUn9OWpSlyCeuaS/2kwLrLOAfCtyrkGHAAsAcD/JhgxYgSuXr2qei5v7LNt2zZhHaq1cXR0RGhoKBPjy8ujoqJMr08miGIOiUuCIAjCruTn56tmLs+cOYOQkBA8ePCAOaZJkybYs2cPfH197TJPe7B582a7jPPw4UPh5llL5jIlJUW42TWscXV0dMSaNWtQrhybo8rOzhaMgq5duya4/AIAjh0zOYdqAD7gYr8D+NPkmTJoGM8UOp1OU/by/HlTqweVsYW4vHLlCg4dOsTE+BJRreJSB+V1lxOgXyvrC/ahgBqGT6IOQC8AvTWeFwDgZy6WmpqKQYMGKfYfBYC+ffsyax5zcnKwxgoPHkzBl8byLsuSJGHevHk2nwdB2BISlwRBEIRduX//vmCgYRCXp06dQuvWrQUh9NJLL2HXrl3w9va21zStitI6sK1bt6reBFsLPmvp4uKCqlWrmjxPrg2JsYFSqVKl0KVLF+EYuT6mkyZNYgOSBJw8aXIOgF6w8Bmq0dA7msohWxYLACdO6MctJLYWl3KGPqbWEpqCX89XunRpdOzYkYnx4vIaAKVPp1JprA8AJ+i/B2qlscb4AngXQDSA3wA01ngeAPQA8AYXO3LkCGbMmKF4jre3N3r06MHETJXTWoN69eohKCiIiTk6OjLbixcvtvk8CMKWkLgkCIIg7AovPJycnFCuXDkcP34crVu3Flp0NG/eHDt37oSXl1L79X8vjx49wgGVNYfWgheXzz77LJycnEyex6939fLyEgxtkpOTNc1h9erV7DxSUgr6WprCG3qnWGPKA9A2shEJCUBqqrlnCdg7c5mWllao1jWSJAklsQMGDBDayvDiMgeA+JhAj5K4NLZu0mrqEwtgIfRGQJbwDYDqXOyLL77AMZVMNV8ae+LECZyzwppcU/DZS54bN26o9pYliOIOiUuCIAjCrvDiskKFCjh+/Djatm2LxMREZl9wcDC2b98OT09PO87Q+qhlnexRGmtNMx8e3vlXifz8fEyePPlJgCsJNMXbAGoCqA9gL4At0ItOORQzlwDAuXRaghZxee7cOYuzjRUrVhTWrWp9n+U4dOiQUJbMl8QCgJ+fn/AQx1xTn7+NvuYzl0orVa8CMO/TwFISwEpPTyYLmJeXh4EDByJV4WFCu3bthM+zPbKXAwYMYNbsyrUfoZ6XxL8ZEpcEQRCEXeFvkr28vNCuXTskJSUx8datWyM8PNxk8/F/O5s3by50yaMp+FYg1hSX5qwH3LBhA06dOqXfcNG6Ik+PM4DdAI4DaG3WmRyualY02tAiLpOTky029XF2dhbe68Ksu+SzlrVr10ajRo2E43Q6neZ1l0prLs8DMKzS5TOXSmWyedCX4BaGpi++iIkTJzKxmJgYjBo1SvZ4R0dHDBo0iImtXLnS5mXqFStWROvW7CeYN/ZZv369TedAELaExCVBEARhV/jM5YULF5CSksLE2rVrh61bt8LDw8OeU7MZauLx5s2bTwSXjbBWj0vj9ZaAvlff3bt3mdjLL7+ses3PPvtM/4WnJ2DkVKqFZwA4mjxKJXPp4wNY4WGF1nYkxcHUJzMzUxArQ4YMUXS85cXlZYXrKr2L+QAMK2l5can23Y5W2aeJJk0wceJEvPjii0z4559/RlhYmOwpfGns3bt37eLgzJfG8oI2Pj4ep0+ftvk8CMIWkLgkCIIg7AovLvkbq44dO2LLli2am9X/F7BlaWxqaqrwnlsrc3n79m3hnNWrV6tePzw8HEeOHAF0OqBhQ03zsBqNGunHLSRaxWVh1vBZS1xu3bqVqQrQ6XQYOHCg4vFaM5cpCnEAMKx05MWl2k3nJZV9mhgwAE5OTli5cqXwUOrNN98UHoIA+nY8jRuz9kH2KI3t2bMn8xmSe/g0ZcoUm8+DIGwBiUuCIAjCrqitHevSpQs2b94MNzc3O87I9pgqe7WluLx48SKzrdPp8Oyzz2o611Tmkhc8pUqVQqVKlbB161bVdbITJkzQf9GkiaZ5mIti5tJK4/2bMpd8SWxISAjT+odHq7gUpdoTDOsueXGZANF4x0ChMpctWwJ16wLQz3/+/PnM7ocPH+L111+X/Tnks5dhYWFI0Gg0ZSmenp7o3r07E+PNlXbu3In8fCU/ZIIovpC4JAiCIOxKTIz87Wq3bt2wceNGpv/c08LZs2dx5coVm1ybL4mtVq2aZnFkKnPJPygwCKLq1asLrS+M2b9/P/bu3QsMGKBpHlbDSuPJmbDIUdTi8v79+9i+fTsTkzPyMYYXl1egXxNpzFnURSSUBWoYymE8piMN1Zj4PQBi4xo9hcpcjh3LbL7++uuCeNu+fTt++OEH4dT+/fszwi4rKwvr1q0rzGw0wa/35HtepqenC987gvg3QOKSIAiCsBtbt25FfHy8EO/ZsyfWr1//nxWWchmT0qVLM9u2yl5aut4SMF9cGvdn7Natm8nspVS3LtCiheb5aEU2c2mU3SosmZmZmo47f/68xdkna4jLNWvWMELY3d0dPXv2VD2HF5fZ0LcKAYBwdEZLRKIeohCr0ggmE3cxA2+jP/5k4rkAlFbkWpy5DA0FOndmQjqdDj///DPKlWMthMaMGSP8PPj5+aFr165MzB6lse3bt0fZskqeu3pmzZpl83kQhLUhcUkQBEHYhW3btqFXr15C/NVXX8XatWuFsrD/Oq+++iqzXdzEZUpKCtLS0piYqbJYY3EJAA1V1lQePXoU27ZtE7JONsOK4/Dvi9pxlrYQ4d/Lhw8fah7XAF8S27NnT5Ntffz9/QWH5hPu5RCKVXgF4TiAltDb/CTJnv+EvwH4g5f6VSFvBnRPwxUFAgKAb7+V3VW6dGksXbqUiWVmZmLgwIFClpAvjf3rr78QHV1oiyFVnJycMIDLpPOusUeOHNH8IIMgigskLgmCIAibExYWhp49eyInJ4eJOzo64rfffhNuqv5ryGUueXF56NAh3L9/3+pjW6vHJSCKS6WyWK1jTZw4EfmdOlm9PFbIXAYECNmtwsC7GzNjc4ZBlpbG8u8lYF6vy3PnzuHkyZNMzFRJLCDfjuR/GaOwBqFGkaMaZvA3ACfwTUtOoDLaKZxhVmmsjw+wYwfg56d4SIcOHTBixAgmdurUKbbfKvQmYnwW0R7ZS740lv/9mJuba5d5EIQ1IXFJEARB2JSNGzeid+/ewo0ToC/B+68LSyXatGnDZIgkScKWLVusOkZ2drawxtXSNiReXl6Cg69aWSygd+NUIyoqChs2bAC++06fhbIVJuZhLmqGL/x6VksdYz08PODHCSdzSmP5Na8BAQFCf0UleHGZJD3gjvhLw1XkPWNHYwzqo4LsGZrFZUAAEBkJBAWZPHTWrFnC53DWrFmIjIws2HZ2dhYcdFesWKF5ba2lNGrUCLVq1WJi/MOJbxUyswRRXCFxSRAEQdiM9evXo1+/foqNydVcK//ruLm5oVOnTkzM2qWxMTExwg2ytdqQSJJUaHEJAB9++CFyvbz0WSgz+14qIWQurfgAIzc3F48ePVLc7+3tzWxb09RHa+YyLy8PK1euZGKDBg2Co6OWLqGiuBQ9Y7WIy7+hX+3KrntMRwp+wFbZMzQVooaGAlFRmoQloBf7q1atYh5iSZKEIUOGIDExsSD22muvMefdvn0b+/bt0zSGpeh0OiF76eDA3pr/888/qp83gihukLgkCIIgbMLq1asxYMAA1af/vBj5ryJXFqvT6QRHyz179qiWXJrLhQsXmO3y5cvDy8tL07mmxOWDBw+E9WBaymL5m+e7d+9i0qRJerEQGWnbDKYVuHr1qupnmi8dLgrH2H379gnfv8GDB2seV11cpgOI0nCV+wBuQmxIcgf30AC+umrCGaqZy5YtgfBwYNUq1VJYOerXr4/p06czsZs3b2L48OEF2/Xq1UP9+vWZYwwlqZIEJCcDDx/q/zfRWcgs+Iwp/9mSJAnz5s2z3oAEYWNIXBIEQRBWZ8WKFRg8eLDglMnfLD/NmUsA6Ny5M5ycnAq2s7KysHPnTqtdvzBOsaZ6XPJZNEdHR+EYOTH7/vvvC2PNmjULly5d0gvMqCh9dqoQ6Bo0YLZN9Rk1B16w81SpUkU43tLySkvFJV8S27BhQ9Q1wyn3wQG+0iAGT7qFnoTe91UO3u35b8iJSwB4JIlil8lc+vgAbdsC48cDZ8/qHzwUYt3sqFGj0KpVKya2atUqrFmzpmCbN/ZZt+53tGqVDD8/wMsLKFNG/7+f35OpWVj1XECVKlXQsmVLJsY/gPn1118LNwhB2BESlwRBEIRV+fXXXzF06FBBWA4fPlwoGXxaxKVS5tLb2xshISFMfNOmTVYb15ZtSHihU7FiRaHsUqfTCaWxVapUQbdu3ZhYfn4+QkJCkJqaqr9zX7UK2LZNn60yB0N2q5DiVA1TmUh+DV1mZiauXbtm0ViWiMvU1FRs3LiRiZmTtQSATRurcpEMGEShupkPX9Z8DKK4vPv4f7HjZbSbG6T79/Xpwfh4YPduYPp0q7SQcXR0xLJly4SHHe+9917Bg5LQ0FA4Oj552JObm4HIyA3gl9gmJAB79wIzZuifh7RsCfzxh+Vz40tj+d8XsbGxiv2BCaK4QeKSIAiCsBo///wzXn/9deHm6MMPP8S3336LW7duMfGnRVyq0aNHD2Y7PDxcaJVgKfbMXMq5m8qN+c8//2DDhg3w4dZXxsXFoX///k8eSnTpos9WnT2rTxG1bSuuyVTIbvGmKPbMXFavXl3oYWotx1gt4vL3339Henp6wbajo6PQ8kKNsxsv4VhqWwAluD0GcaO23pLPXMqJS8PnqhEA9n3KyMxEbFYW4OkJ6PiVs4WnUqVKWLhwIRNLSkrCkCFDcP9+Hj78sCzy8vjsqGm31gMH9B/XgQP1mthc+vTpw7Rikvu8zpw50/wLE0QRQOKSIAiCsAo//vgj3n77bSE+ZswYfP3110hPTxeMKZ72NZeA2JIkKSmJcbK0lPz8fKFXnxaDHQOmMpemzHyUxrxw4QKcnZ0FwxlAL6wnTJjABuvW1Wevdu/W37knJwMPHtgku6UFU+LS398fzz33HBOz1DGWF5exsbGyrsvG8L0tO3bsCH9/f81jrpkbB/3tYXVujxZxyXMCfCsSvbiUHo/xinCGrftLDhgwQFjnGBkZiRo1voa+QvY17owDAK5ouvbq1UC9evrnHObg7e2Nrl27MjHjcnkA+O2336z6kIQgbAWJS4IgCKLQLFiwAO+9954QHzduHL766ivodDohawlQ5hIAKlSogCZNmjAxa5TG3rhxAxkZGUzMlmWxSuKSH/PChQuQJAmdO3dGixYthONnzpwprBksQKfTZ7VKl1bNbtkqc5mXl4eLFy+qHiMnLq2VuczPz0dsbKzi8bdu3RIcTrX0tjTm2MVSj7+SM/WJBXBb5ew0bjsVegMgY9IBGEyruoInKkqLWVDhWLBggfB5TU6eAOA09OW6vGHQcmglLg4IDjZfYPKly7zDdlJSEg4fPmzeRQmiCCBxSRAEQRSKefPmCY3KAeCzzz7Dl19+WXCjz4tLHx8feHh42GWORY1a5hIQS2PDwsKENavmwpfEent7a85gpaSkIC2NFQqWlsXymcukpCTcvatfd/f111/LnvPmm2/iyJEjmuZqT65duyY45PL4+/sL5jmWiktfX1+ht6haaeyqVauYz5qXl5eQEVNDypdwMtGw3lJOXPLrLUty248AVORicu1TDKWx7cA3jtm7d6+muRYGb29vLF++nHsIkQMgFEAeAL6MeBkA7T+PCQlAx47mlch26tQJvr6+qsfMmDFD+wUJooggcUkQBEFYzJw5czBq1Cgh/sUXX2DKlCnMzRsvLp+Wklgt8C1J4uLicPz48UJdU269JZ/RU4LPWgKmxaXS97NSpUqCQDKUlr7wwguCsAaA7OxsdO/eXXNfRx5bZS5NlcQCQNmyZYXM5cWLFxV7vaqh0+k0r7uUJEkoie3bty9KlODXTiqTEpeCBMmwrlVOXPIlsfW57XyZ2BkApbiYwdTHE0AV9ugzZzTNtbAEBwejVq1PuOg/AMZCLI29AeBPs64fFwd88IH2411cXNCvXz8mxhtk7dmzx2RZNEEUNSQuCYIgCIuYMWMGPv74YyE+bdo0fd9CDl4oPE0lsabETa1atVCzZk0mtnnz5kKNaU0zHy8vL0Ygpqen48GDB8wxSuLSwcFBcFA1ntvUqVNlRe/9+/fx6quv6h1kFbBl/0E5TIlLHx8fuLi4COIyOzvbYrdPXlwqCe4TJ04I33NzS2Kz04yFCy8uL0PMXJaHPvMYCH2272cATbljlNuR6HmZ3XPnTqGz9loIDwf++WcKgAbcnu+g79H5HBdfavYYq1frx9EKXxrLt7DJysoq9O8FgrA1JC4JgiAIs5k6dSrGjx8vxGfOnCkasjyGnGLV4bOXhV13yQsha663lFs/q5aJljP1MfDcc88JBisGzpw5I/RLNTaPVeo/GB5um8ylqfJWQ9mxn58fypUrZ9a5SmjNXPJZy6pVq+Lll1+WPVYJFw9noy1eXKZCLxSNSYPenCcGwBoAqwA04445A6AsFzMWl2zmOi8vD3/9ZY5pkGXMmgUALtDP2Y3b+zqAPlzsN+jfA/P46ivtxzZt2hTVq/NGSixz5841ew4EYU9IXBIEQRCakSQJkydPls1Mzp07F2PHjlU892kui+XFjVymji8PvXjxoknzGLXxbNnjks+e+fr6omRJfv3dE9TEJQB8/vnngjumgc2bN2PixIkID9f3E6xXT99fcO9eKPYftNWyPVOZy7Jln4goWznGyonLnJwcrNFbnRYwZMgQzWXQBjwDPOGjM7ypFaEXX8ZkcNv8a+oIfYsRfi0jL96eiEtvNBLmsWrVKm0TtpCzZ/XtQ/TUBjCHO+Iu9CXAxrfJaQB+N3usP/8EtH7rdTqd0POSL439+++/kZycbPY8CMJekLgkCIIgNCFJEiZOnIgpU6YI++bPny+79tKYp7ksVguNGzcW1jWGhYVZdK179+4hMTGRiVmzx6VWp1ilsXnhW716dbz++uuK58+YMQOvvLLSSBCYghVVZ89KFvUfNCY/P1+YN4+xYZKtHGPlxOWOHTvw8OFDJsaLFC3oHHRo6H3t8ZYjgGpqM4No1tMR+vWVtbg4v970yefrBd+HwprcnTt3apyxZXA6HMD7ADpxsR0A+NY9S600njL8940vjc3Pz8eSJUssmgdB2AMSlwRBEIRJJEnCuHHj8OWXXwr7FixYgA9MOFdIkvRUl8VqyVw6ODigW7duTMzS0lheBLm5uSm6ucphbubS1LX5zOX9+/cFMfTZZ5/B1dVV5Spvwrwei0+4d8+y/oPGXL9+XWjtwmMsLq3lGCu35pL/PPElsc2aNUNgIF/Wqo0mtYyzYmrX4PtXlgNQ7/HXjbl9Sdz23YKvmtRKEeZ65cqVAkdhW3DsGB/RAVgCoDQXv8xt74fe3Kew4ykTGBiIpk3Zdav874sffvjB7DkQhL0gcUkQBEGoIkkSxowZg69kFg8tWrQIw4YNM3mNhIQEpKez/e6eprJYrfClsUePHpV1bjUFLy5r1qwplNepYSpzqdUp1kC1atXg4sKWWPJzrFixomyv1CdkAegO+dYWPLx4lyzuP2hAi1OsWuby0qVLyM7ONntcXlxmZmbi/v37BdsJCQnYsmULc4y5Rj7GDBht/CChhsqRfP/Kjnjyvjfh9t1R3B4wpgJeeOEF4ep//PGH6jwtRZKAkyfl9pQDsJiLZUGfwTVmvdljnjhhntkUb+zDc/nyZdy+rdZvlCCKDhKXBEEQhCKSJOHDDz8U+hHqdDr88ssvePvttzVdhxcjOp0OFSpUsNo8iztaMpcA0KpVK5QqxbZt4IWDFnjhxmcOTWFu5tKUuHRycsKzzz6rOkcA+PTTT030Pr0H4FVYYqwCWNZ/0AAvLh0cxFsoY3HJv+e5ubm4dOmS2eOWL19eWI9qXBq7fv16RrS6uLigb9++Zo9jIKjXs2hRytAORC1zeYXb7mj0NZ+5lBeXLb1Oo26PGrKfz3BzbFbNICVFXKv7hG4A3uJieQCcoHfD3QFAvfxfjoQEQMX0WKBv377M91zOkOrbb781ex4EYQ9IXBIEQRCy5OfnY/jw4cJNjE6nw6+//qq6Ro6HL4ktV64cnJ2dFY5+enFxcUGXLl2YmCWtBwpj5gOYFpf8uj8tJbemTH0AvSHOyJEjTVzpDIDBUG9qL2YuDZjbf9AAX9Yq9/k1Fpfe3t6oWLGi6jW04OjoKFzH+P3nS2JfffVV+Pj4oDCM/cggVpXEpTP0WT0DOgBtjbaff3yMEo8AZGHsR/rWJ/yDBwDYtWuXRZleU5i+5NcQX3c+gA8AdICYydRGVpbpYwyULl0anTt3ZmL8A6lly5ZZzQWZIKwJiUuCIAhCID8/H++9956wtsfBwQErVqzA0KFDzbre0+wUC2jPXAJiS5J9+/YhKYlfs6ZOYcRlSkoK0tLSmJhxWWx+fr5F309Tpj4G6tUbA8CLi/LZzM0APjM5phLm9h8ERDHs5sY7oLJusYDtHWNjYmJw+PBhZl9hSmINdPm8MQZUPgRlccmvt2wCwM9o2xV6galMtwrb0HmyPsPJ93gFgNTUVPz5558aZ6wdF94AV6Ak9O1JjEVkPoBBAFIsHld1ObEMvLEP/zvk/v37OHPmDAiiuEHikiAIgmDIz8/HW2+9hZ9++omJOzo6YvXq1Yo9CdUgp1jtdOrUiVmfmJOTY9b6s6SkJCHzWJg2JAArLu/du4ecnBxmvxZxqSVzCQDff+8D4GMumgb9Tb8xX0IvAuQw3YLDnP6Dck6xvMMpwGYuAds7xq5YsYKJly5dGh07doQ1+G53bZTXOUNfEsqTyW3Ljcmvu2RvOd//xrPg66pVq8q2orFFaaynJ2A6sdsEwGQudgXARxaN6eMDqHTqkaVr167w8uIfsrDMmcO3UCGIoofEJUEQBFFAXl4e/ve//wlW905OTli7di369etn0XWfZqdYwLzMpaenJ9q2bcvEzCmN5XtjOjo6okYNNWMWFt7Mx8vLixFSfEmsi4uLIKrk4MXl7du3hX59T/oPjoSYHasBsSTxDQBHTY5tXBZrwJz+gzdv3hSyuSVKlBCO498HWzvG8uIyNDTUauXmvoG+2Lg8CzrIPTh4xG3LiUt+3SX7mU93emII5OzsjGrVxLYn27Zt0zRXc9DpgIYNtRz5KYCXuNgvAMx3cG7USD+uObi5uaFPnz5MjF/nu3nzZqFVCUEUNSQuCYIgCAB6w5EhQ4YIa7icnJywfv169O7d2+JrP+1lsebCl8b+8ccfyMzks0Xy8BnB6tWrC06taphr5vPMM8/Imtvw1KhRQziOF8JP+gGWhP7m3phTAIZzsSzoTVhuwRK09h/k31MvLy+hbYqrq6uQzeQzlzExMZq/j8bIZS4PHTqEa9euMfHClsSePQuMHw+0bQv4+QHNBgdCQkUTZ/lAFJKAmLlkRRD/EENu3WVMTIxFJkimaMJPTRYnACshZszfAmCeg7O28UT40tj8fHadcVpaGnbv3m3ZxQnCRpC4JAiCIJCbm4tBgwZh9erVTNzZ2RkbN24UWmSYy9NeFmtO5hLQm7IYH5Oamop9+/ZpGsvWZj7mOsUacHV1FfoZ8qKN7Qf4HgDeUfgo9A3vjTE4yBpnFpUNfZTHU4af53PPPSd8Dz09PcHDZ2vz8/MFQa0FOXHJPwSqXbs2GmpLyQmEhwMtW+p7gc6YAezda+yoyi8W5G8d20Pe5KYmRGH2BF5cyq27BGyTvRwwQOuR1QB8x8XiAfwP6oZSlo7H0qJFC5M/X3PnzrXs4gRhI0hcEgRBPOXk5OSgf//+WLduHRN3dXXF5s2b8eqrrxbq+nl5eYiNjWViT5u4NBd/f380a9aMiWktjS2suDTV49ISp1iluRiLNrH/oBtE056/oBczbbn4aehdPs1Da/9BXlzKtc6Qa6FSsmRJVKlShYlZUhrLv8eJiYnCz+uQIUNMPrTgiY8HQkOBV14xlCPLwWdaeVGltMbTEYDYv9KAlswlYJt1l0FBQIsWWo8eCqAXF9sF4HtNZ7dsCXDV0ZpxcHAQ1rjz2f+IiAihZJsgihISlwRBEE8x2dnZ6Nu3LzZu3MjEXV1dERYWJtjhW8K9e/eQm5vLxJ62slhzM5eAWBobFhamaX1Vcc1cAqIoM56rfP/B16HPHhkzGcBaAMZi5F0A44y2tWUutfYf5AVhnTp1hBJFuTWYgHUcY+UexhivV9XpdGYbbUVF6TOVpkuD+R6VPB2YLR9dAtr6nsD4ZhH4X3flPplaM5d//vmn2W7JWhg7VuuROgCLAJTn4p8AMP2gQPs48gwePJjZ5j93ubm52LBhQ+EGIQgrQuKSIAjiKSUrKwu9e/cWMmIlSpTAtm3b0KFDB/kTzYQXI87OzkLLBkKEF5f379/HX3/9pXpOZmamsA5PLsumhqnMpTXFpXFGUL7/oDOAL7jYGQB7AWwFUBr6ssUfoN5XURlT/QclSZIti+UfmMi1JjEca4wlmUs3NzeUK1dOcX/r1q3NqgaIigJatdL3/FQnA8B1lf3PwyC6vErl48ieNMTnemN3fCNMP9QKnQcq/w65e/cus62UuczNzbXJusIuXcwpV/UDsIyLZQIYCLbfJ0toKFDY53O1a9dGo0aNVI+ZP39+4QYhCCtC4pIgCOIpJDMzEz179sTWrVuZuLu7O8LDwwW30sLAm/lUrFhRkwHMfwlLMpeBgYGC26ip0thLly4JmY1atWppm+RjTGUurVkWe+3aNWRkZABQ6z84AAAvkCdBn9G8Ar3JD/9+astcAqb7D96+fRupXHqzTp06QjsWJdMkaznGqol4c4x84uOBTp3kssRynASQq7L/SUlsUrIDeg3xwKOEJ+9948ZyRj96+IcY5cqVk123Cthm3SUAfPcdwH28VWgH4EMudgZK/VYDAoBvv7V4agy8sQ//++P06dO4d++edQYjiELydP11JwiCIJCRkYFu3boJvRM9PDywfft2hISEWHU8coq1HD57uWnTJkGoGsOXxD7zzDMoaWaDPTVxmZKSggROlZjz/eSFriRJiI6OBqDWf9ARwFQuFg29k2cpzWPLoaX/IC8GPT09UaFCBWRxKU+lFiB85vLq1asWrZFTEvHu7u7o2bOn5uuMGKElY2lAPVPOr7eMiwM++ODJdqVKlRSrFO7du8eUeet0OsXs5fbt24WHJtbAzw/YsUNL30sDMwA8x8XmANjPRHx89Nf18yv8HAFgwIABcHR8Ypok9zuAbx9FEEUFiUuCIIiniPT0dHTt2hW7du1i4iVLlsTOnTvRsmVLq4/5tDvFApZlLgFRXF65ckU181XY9ZYpKSmC8DEui+UfFADmfT89PDwEgxtDyal6/8EeAPjSwC8AyNbSQmvmUkv/QTkzH51OJ7QUMb75N6ZWrVrC95v/PmlBSVz26tVL8wOE8HDt7Vf0qPUQLQmgmRBdvVo/DqD/nCtlL/Py8vDw4UMmprTu8v79+zh+/LiWCZtNUBAQGak1g+kGYBUA4yy1BGAIAP1Dl4AA/fWCgqw3R39/f7Rr1071mB9//NF6AxJEISBxSRAE8ZSQlpaGLl26YO/evUy8VKlS2L17N15++WWbjMsLkqdRXFpKw4YNhfdLrTTW2mY+ACsu+ZLYMmXKKBrZKMHPyXjOyv0AdQCmcbHrABabNTaPlv6DcustAQgiXOmBgbu7O6pXr87ErOEYa8CckthZs8wdVS1z2QasyHrCV189+bqJypus1TEWsF1pLKAXglFR+jWSpnkewJdc7DaA9zFggISoKOsKSwO8sQ/PzZs3LXpoQRDWhsQlQRDEU0BKSgo6deqEiIgIJu7t7Y09e/agadOmNhubymLly9i0oNPphOylLcUlf7Pv5eUFd3f3gm0+C23OeksDaqY+6gYrHQA052JTAaTLHKstc6nF0EXOKTY9PV1Yc6mGNRxj5d7rChUqaC5jP3tWrd2IHHEAxEz1E5RakAB//gkYXqLaukve1EcpcwnYpiWJMX5+wKpVwLZt+vYh6nwEoDUXW4suXVZbrRSWp3v37iYz1N9/r609CkHYEhKXBEEQ/3GSk5PRsWNHHODuLH18fLBnzx7Vmz9rQGWxIub0I+TF5YkTJ2TLU3NzcwvWLxooTm1IlOZkLC6DgoAWpc4onKkDMJ2L3YXWfoM8Lb1Om+w/KOcUW6dOHVnzFLU1gdZwjK1YsaIQ69evn2I5Lo955bCAekkswLcgURrPHFMfNXF58uRJ2cy6tenSRV/WevYsMH480LatuCbTx8cBzZsvg5ubNxN///33hey+tZBbW8v/Hlm1apVN1qYShDmQuCQIgvgPk5SUhPbt2+Pw4cNM3M/PD/v27TNpcV9YsrKyhBvxp1FcWpq5BIAWLVrAh7u7lcteXrt2DdlcPw9ri0v+xtkScclnLmNiYp7M++xZjE0er3J2SwDtudhMAMlczHTmcmzS+CfpNQXi4uKYfpKAXijev39fOFYtk2kNx1g50dKmTRvN5x87Zu6Ipsx8Smsar3Tp0qhatarsMby4rFGjhuo1eRMyW1K3LjB9OrB7t95hNzkZePBA/398PHDgQEUsW7aIOSc5ORlDhgzR1I/WEvjSWP73SmJiIg4ePGiTsQlCKyQuCYIg/qMkJCSgXbt2OHqUzUCUKVMG+/fvR/369W0+h9jYWCH2NJbF8piTuXR2dkbXrl2ZmJy45Eti/fz8UKZMGbPmZW6PS0vKYnnBm5ubi5iYGP3GmjXogj8wAKtVrsCvvXwEYJ5ZcwjFKnTGdpPpPF4ElixZEs8884xs5tLQUkUOPnN58+ZNQbSaYuPGjULM1VQflcdIEnDypFnDwXTm8orq3hMn9OMCyusu+c9byZIlUaFCBcVr2nLdpRo6nd7NuHRp/f+GH9++ffsKgu/PP//E7NmzbTKPkJAQ4YEPz7fW6n9CEBZC4pIgCOI/SHx8PNq2bYu///6biZctWxb79+9HkC0cJ2TgxUjJkiXh5eVll7GLE4XJXAJiaWxkZCQePXrExAq73hKwT1mst7e3IFoL5v443fUdRiAA4oMJPY0BdOdicwHEG20rZy4DEItv8QEznhJ8SWzt2rWh0+lkxWV6utzaTz01a9YUylf5a6uRkpKC33//XYhrLcFMSdHa19KYyQCmQN/fUY4Y1bMTEgBDe1Ct4hJQN/XZs2eP4NJb1Hz33XfCQ5bPPvsMJ81X8yZxdHREKOc6xD+o2rZtW7F7j4inCxKXBEEQ/zEePnyINm3aCDc35cqVQ0REhJBFsSVyTrHmZO3+q5j7HrRv3x5ubm4F23l5eUIWhxeXfPmpFtQyl7m5ubh9+zaz39IstKypj1F6zQ+PsAMd4YNHcqdDb+Rj/B6mAPhK4dgn+Dy+rp/husbpNRmUnGLNzVy6uroiMDCQiZlTGvv777/Lilet4jJbqWOLKiEAPgPwtsJ+dXEJAIZWoErrLnlDH0B93WVaWhoiIyNNjmtPvLy8sGLFCjg4PLmlzs3NxcCBA1UfOFiKqdLYrKwsbN261erjEoRWSFwSBEH8h7h//z5at26NM2dYU5SAgABERkZalM0qDOQUq6ewmUsPDw+0b8+uNeRLY22dubxz546wlsySsli5uV24cEFIrwXhHCIRrJDBrAuAt3o9hicZSjFzGYBYRCIYQTBaZ2mcXpNBzswHMF9cAuK6S3McY5cvXy4b1youXeQ7hmhkh0LctLg0VO02bNiQEV8GtGQueYdUW7vGWkKLFi0wbtw4Jnbx4kV88sknVh+rXr16JitPqDSWKEpIXBIEQfxHuHv3LkJCQnD27FkmXrFiRURGRqqWm9kK6nEpjyXZ2x49ejDbO3bsKMiMSJJkc3HJl8S6ubmhdGl1Uxcl+MzlP//8I5teC8I5RKEeQrFK5ipfAHAEEAQgDMA+iKJSjx8eIgr1WGFpwJBe45AkSbYNCWCZuLTUMfbmzZvYv3+/7D6t4tLTU3Q81YYES8Wljw9g0IUeHh6yFRN37twRHrzwmUv+gca2bdsK/bDGFkyePFkwSPv+++9tYkLEZy/53yeHDx9GfHw8CKIoIHFJEATxHyAuLg6tWrUSMi2VKlVCZGSkUJJnL6gNiR5r3Ay/8sorTPYnIyMDe/bsAaD//qekpDDHmysuU1JSkJaWxsSMy2LlnGItLXHmxeXFixeRp9BSww+PsAqDsA1d0BLGJZGBAA4DOA3gVbDCkp1XIGKelMLyKJji3L17F4mJibLzlnOLzcjIUP0+WyouV61apXhd/udLCZ0O/2fvvMOjqNY//tmE0EMISSihN6kBpQkiodcAovRQ7OK1oD+vCqLXjojtKlwbKigQUEQB6U06SolAQu+9JSGQ0AmZ3x/Dhp0zs7uzLdkN5/M8ecKcOTPnJNmE+e77fd+XRo1MTRXYAXZzXx2Ly8aNbxe+AeO8yytXrugKG4lvgomi/dChQ+zevdvh2nlBwYIFmTp1KkWKFNGMP/roo4avF08YOHCg5ndPfH1kZ2czbZqjolgSie+Q4lIikUgCnBMnTtCmTRtdj8OqVauyatUqqlWrlkc7k7ZYe7gjyiIjI2nVqpVmbNasWYDeElusWDGXhbxRD0FbcemNSrFWROF77do1DqWmOgyvxbGAVbQhmfqMYjQdWEo4NbB9lAnnHB1YSjdMVhW1Da8JiG/UFC1aNOdrNopcKoqiawVji2iLPXnyJOlOquwoimLXEgvq75fZvoZ2auo4wV7UElTRaT+nUFzPXt6laI2tUqUKISEhmjGx6rE/WmMBateuzWeffaYZO3v2LE8++aRXo60VKlSgXbt2Dud89dVXXltPInEFKS4lEokkgDl69CitW7dm3759mvHq1auzcuVKqlSpkjcbu4W0xap468FStMbOnTuXrKwsnRCqXbu2ywJWfMgPCwujaNGiOcfeqBRrJSoqioiICM3Yrt27TYXX6rOD0bzBUjqRRgQZhJJCJBmEkkYES+lEb2ZrrrH73RfDazaIkcU6derkRI6NxCU4tsbWrFlTJ5qcRS8TExMdRulu3LhhmLdoxEAxRdUUjsQlwEHT69mrGCsW9SlQoADVq1fXjImR7rxqSWKGYcOGERcXpxn7448/+P777726jmiNFdm9ezcHDjhuFyOR+AIpLiUSiSRAOXz4MK1bt9Y9QNSsWZOVK1fmeZQwMzNTZyu8U8WliLt20gceeEBznJaWxrp163KlDYmRLdZdLBaLccVYF8NrFiCUi0SSRigX7WRcOsDBevaK+Vy/ft1uxNGRuAwJCdFZPp2JSzFqWaVKFV1vS7N5lzExIAS+nXAJWCOMlRKOja2xsbEgBGqpX7++puKxFSNxLOZdirm9a9eu1f1t8RcsFgs//PCDLtr64osvsnfvXq+t89BDD+ksuCITJ0702noSiVmkuJRIJJIA5ODBg7Ru3ZrDhw9rxmvXrs3KlSupUKFC3mzMBjFqCXeuuPRW5LJKlSrcfffdmrHZs2fnSY9LT2yxoN/jrl273A2v6bDf5VLAwXr22pA4yp/zZsXY69evM336dM3Yww8/rBP1ZsUlwIgRpqcCKwFbm28BoLYwx1hcGq0TEhLCPffcoxs3UzH2+vXrGlF98+ZNFi9ebLxtP6BMmTI6YXf58mUGDx7MjRs3vLJGaGiorv+tyA8//OCXxY8k+RspLiUSiSTA2L9/P23atNE97NetW5cVK1boREFeIYrLyMhIp++03yl40utTfKCcNWuWTgi5Iy7t9bhUFMjIgCNHvGeLBTu9Ll0Pr7mPUXjtFo4qxXoiLl0p6rNo0SJSU1M1Y0OGDNGJelfEZVycK/pdtMTehxlxGR8P3boZ39Eo79JM5PLgwYO6HEN/zbu00r17d55++mnN2KZNm3jvvfe8tsbgwYMdnj9z5gwbN2702noSiRmkuJRIJJIAYu/evbRu3Von3OrXr8+KFSsoW7ZsHu1Mj6wU6xvEvMsjR46QkpKiGROFmxnEyOX+/dF06AARERAWdp7MTG1VzxkzKuFCq0YdRpFLRVFcDa8ZYipy6WCds2fPcu6ctrqsozYkVqytYezhirgULbEtW7akevXqHolLgPHjwdz7T6K47IJaodcWrbiMjgZHLRaN8i7NRC73799P165dNWMLFizQtSnxNz755BPd1zJ69GjWr1/vlft36tSJ0qVLO5wzYcIEr6wlkZhFikuJRCIJEHbv3k3r1q11IqBhw4asWLHC6UNGbiMrxd5GtKZ5ErmMiYmhatWqds+HhIToCqKYQXzI//vvcixfDmp6odjywsI331QgJkYNALrTyk8UwBcvXuT48eOuhtfcw1F4Db0ltkiRIjnFsRyJS1dtsWfPntW9MQBw7tw55s6dqxkbOnQooP89clVcRkTAokXO+l7uRx+VNBKXtwuJhYer9xXqNGkwilwa7V+MXF67dk1nB09LS/P7qFyxYsVISEigQIECOWPZ2dkMHjxY14LFHQoUKMBAJ78rv/zyi9esuBKJGaS4lEgkkgBgx44dtGnTRldZ8Z577mH58uVuN7P3JbJSrG+wWCwOc61q1qypeZg1Q1oa/POP2IrENrwlisuygJoDt2aNqgcHDVLvY5by5csTGhqqGcsRdebDa4Y4jFw6C6+hF5e1a9cm+FYfTk/EZfXq1XUFeYyilzNmzNC0NSlUqBB9+/YF9LmuropLUN3Hq1Y5+haL+YxlgIboxeUx4CrR0er9YmIcr1ujRg1NBWJAlzcOajXhkiVLasYuXbqki/z6c9VYK02aNOHtt9/WjB06dIgXXnjBK/d3Zo29dOmSX+enSvIfUlxKJBKJn5OUlETbtm11D7VNmjRh+fLlupYO/oK0xd7Gm5FL0FtjbXE13zIpCRo0gMxMR+JSFDD6KPS0aep9kpPNrWuxWHR7zRF15sJrrmMmvIZe8NlGWT0Rl8HBwdSurc1bNBKXoiW2Z8+ehN/6XhiJS3eKtsTEqD/7+Hijs6IltjPqI6MoLhXi4g6RlORcWAIEBQXpvn4xrxTU14ZoJ927dy/du3fXjPl73qWVkSNH0rJlS83Yjz/+yMyZMz2+d+PGjXXfUxHZ81KSm0hxKZFIJH7M1q1badeunc46d++997J06dKcB05/RNpifcd9991nN1rtirhMSoI2beDkyUzU1hO2lLP5txi5NK4Ue/IktG5tXmCK1lhN1Vvn4TW7GEp3s+E17FeKBc/EJTivGLtv3z7++usvzZjVEgt6cXnp0iW7rVGcEREBCQkwb55qb1a5BvwpzOxy63MoahTzNsOG7Xem1TU0btxYc3z16lWuXr2qmydaY/fs2aPrH7lt2zbVSu3nBAcHM2XKFF2kftiwYZw4ccKje1ssFqfRy6VLl3LhwgWP1pFIzCLFpUQikfgp//zzD+3atSNN8Bred999LFmyRGcb8ycURZG2WBu8HbkMDg6mZ8+ehufMisu0NOja1ZpTKUYtwbG4tP9GQXo6dOliziJrWDHWFsfhNdMo4eGYDq8Z7MN2n55UiwXnRX2mTp2qOY6KiqJz5845xxUqVCAoSPv45o411pa4OFV3JyfDwIFrAdvCRBagI+Hh0KEDVKigjV7u32/cjsQesbdVbA5iHjnoi/rs3buXFi1a6N5QC5ToZdWqVfnf//6nGTt37hyPPvoo2dnZHt170KBBDs9nZWV5JUoqkZhBikuJRCLxQzZt2kT79u11EYlWrVqxaNEiSpQokUc7M0dqaqouGnEni0tfYC/v0qy4fP55NdKoIlbsDANsc+Oc22JtOXkShg93vgcjW6zO4mkcXnOITrpXrerUCmslJSXFYfVdTyOXRuLS+jVnZ2frLLEDBw4kJCQk5zgkJETXbshTcWmlfn0oX15riW3UqCkZGZGkpcHSpdC+vWfism3btrqxDRs26MaMIpcFChSgS5cumvFAEZegtpKx5s5aWbp0KeOc5AA7o0qVKrRy0sLn66+/9mgNicQsUlxKJBKJn/H333/ToUMHzp8/rxlv06YNCxcu1Fmr/BExahkUFOQ3/TfzAm9HLgE6dOhg2DdUfCg3Yv58mD7ddsRRviWYtcXaMm2auo4jxMhlenq6/cigbXht1Cg1jCbawq3htR49NMOu5CSKUctChQpRrVq1nGNv22LPnTuXU6hr3bp1ugI3tpZYK94o6mOPRYu04rJ79y6EhoL1JVujhmfisnz58jnFkaz8/fffunli5PLYsWNcunRJl3e5bNkyU993f8BisfDNN99Qvnx5zfjIkSNJNuslt8OQIUMcnk9MTNT9XZZIfIEUlxKJROJHrFu3jk6dOunK1Ldv35758+dTrFixPNqZa4gPMdHR0S5XMJU4pkiRIjqhUqRIEV01TiPGjhVHHInLGwbnzeXPfvSR4/OVK1emcOHCmjGdNVakfn0YPVoNo6WlQUYGpKSon2+F1yz9+pnanxGOKsXevHnTsACNFTMip0qVKrqfkdUaK0Yt69atS6NGjXT38JW4PH78uC4HVIwUeiouAZ3zYtu2bbo5NWvW1I3t37+fLl26aGzBV65cYeXKlS7vIa8oVaoUP/30k2bs2rVrDBo0yDD31Cx9+/alYMGCDueIlmuJxBdIcSmRSCR+wurVq+ncuTOZmZma8U6dOjF37lxTosFfkJVitfgicglQrlw5zfG1a9e4ePGiw2uSk9X2IVpEW6ztfY8jNPPArLhcvRoEraLBqHqqpqiPMywWCA2FyEg04TUBVyKXjirFpqamOsyPMyMug4KCdHbgHTt2cOXKFWbMmKEZHzp0qOFrxVfiUmxZER4erutNKYrLw4cPa9qmmEF83e7bt083p2jRorq/G3v27KFUqVK0aNFCMx4ILUlsad++PS+99JJmLDk5mTfeeMPte5YsWZIeQsRe5Ntvv3WrsrBE4gpSXEokEokfsGLFCrp27cqlS9qKnV27dmXOnDmG9kd/RhbzcYyiqIE2T5/zRKGTnZ3ttKed1g5rxZUel8UB81WKjde7jdOiPm7giXh3VMzHkSUWzIlLMK4Y+8cff2gcCxaLxW6hFl+JS9ES27FjR53joHr16prj7Oxsl9cX73H69GlDgSpavPfu3Qtg2JIk0ETT6NGjiREKTH366acsX77c7Xs6s8YeOXLEMEoskXgTKS4lEokkj1m2bBlxcXFcvnxZM969e3dmzZqlsw0GArINyW2Sk+G777QPvqmpFsLC1BozHTqoKYSOInz2OHDggG5s9uzZDq/ZuNFo1FHk0qhSrHnxZrzebcQonkuRS5N4knNpW4BHzAcVfzfF32F7GBX1ES2x7du3p0KFCobXi79P3hCXWVlZLF26VDMmWmJBjWaKvXVdtcY2aNBAc5ydnU1SUpJunph3uWfPHgBdS5IjR44Y9gv1ZwoXLkxCQgKFChXSjD/88MOcO3fOrXt27dqVUqVKOZwzceJEt+4tkZhFikuJRCLJQxYvXkyPHj10EY9evXrx22+/6R48AgVpi1WL2cTGQoMG9qN36emwfDmMGaN2yYiNhQULzN3/xo0bhnbCefPmcePGDcNrFAX++cfojKPIpWuVYkUSEx1HaH0RuXSXtLQ0XXTSUeRSzIE2G7kUxWVycrIu4mxUyMeKGLlMTU3VuR5cZcOGDbpeiLYtUGzxNO+yatWqurFNmzbpxuxFLuvXr68T2IFUNdZKTEwMH374oWbsxIkTPP30025FYgsWLEj//v0dzpkyZQo3b950+d4SiVmkuJRIJJI8YsGCBfTs2VNXxKF3797MmDHDaXEGf+ZOtsWmpaltGbt3t81tFB8UjSN/a9aoRVEHDXLeJ/LAgQNkZWXpxs+fP8+qVasMr8nMtPa1FHEkLiOBpkCZW8fOK8Xakp4OjtJARXF5+vRpXQseVxFtsWYf1EVhW7BgQY2FUxSXYuVmd22xFy9e1DzwFy1alAcffNDu9aK4BP3vnKuIltgGDRrYrfDsqbgsW7asbmyjQYjbKHKpKAoWi0UXvQy0vEsrw4cPp0OHDpqxX3/91e3iO86ssefPn/fIeiuROEOKS4lEIskD5s6dS69evXR5Rv3792f69OmavnaBRlZWlq4p+p1ii01KchypNMu0aep9HHUncGQftWeNNa67kgmIUS9bW+zTwEbgNHAZ+BBXuXbN/rnq1avr8vp8YY01gygua9WqpdmbKC7DwsI0x2bFZcWKFR22FOrduzfFixe3e75YsWI6a6qn1lhRXNqLWoLn4lIs6APG4lKMXF64cCGnB6mYd7l+/Xq37aR5SVBQED/++CPhQludZ599lkOHDrl8v+bNm+tyWkV++OEHl+8rkZhFikuJRCLJZWbNmkXv3r111sX4+HimTp0a0MIS4NSpUzrb1Z0QuUxKgjZt4KQYBATMRi5tOXkSWre2LzCdiUujaJ1xMNxow/qHf5UiQEm769rDkbs7JCREF6Hy1BrrrcilGFUVxWXJkiU1x2bFpcVi0d3bFkeWWCveLOpz9uxZNm/erBkzyre04gtxuXPnTl2l7EqVKulSA6x5l23bttUUOjNTzMpfKV++PBMmTNCMZWZmMmTIEJctrBaLhcGDBzucM2fOHI9t1BKJPaS4lEgkklxk5syZ9OvXTycshw4dyuTJk/NFL0jRnleoUCGioqLyaDe5Q1oadO1qz3LqPunp0KWLsUXWkQA7ceKETiyA2q0jXFfoVSzmEwZ4r+1NeDg4CMIB+qI+eZV36agNCejFpRhtMisuQW+NtVK+fHnatm3r9HpvikuxkE+xYsVo2bKl3fmiuDx06JChRdsepUuX1vSqtPKPkBAcHBysW8uad1mkSBHat2+vOReo1liAPn368Mgjj2jG1q1bx1h9U1qn2KsybOXatWtOC39JJO4ixaVEIpHkEr/88gsDBgzQPYQ99thjTJw4MadRe6BjlG/prb6O/srzz9uLWFpxPXJp5eRJGD5cPy5GLkuXLq05Nnp4tFigUSPdCsKxcZ6duzRubLf9ZA6iiPPUFuuryKVYLVa0proiLsWiPlYGDx5s6m+BN8WlaIlt166dw2JiouC7ceOGSzmfwcHButcrwOrVG3XFn0RrrDVyCfqqsYsWLSLrxg21z09qqnf6/eQiX3zxha7Y0VtvvWX4RpEjatasSfPmzR3O+e6771zen0RiBikuJRKJJBdISEggPj5eZ3F68skn+e677/KNsIQ7r1Ls/Pme51g6Y9o0dR0r2dnZ7N69WzOnTZs2mmN7kYlmzcQR34pL/Xp6/KFibHp6OqdOaaO4tgJQUeD0aW3kUozIe0NcOivIYsVb4jI7O5slS5ZoxhxZYkEV1WK+qVHlYnskJ4Oi6Iv6vPnmJl17HtEybY1cgl5cnjt3jr8jIiAsDKKi8Eq/n1ykRIkSTJkyRRPVzcrKYtCgQS7bWJ29jlavXs3p06fd2qdE4ggpLiUSicTH/PTTTwwZMkTX8P5f//oX33zzjaE9LJC50yrFmnOteR49+eij2/8+duyYrqeiaIXbuXOn5kHcysCB4oijHpeeo19Pj2iLPXr0KBcdlZh1gjuRS1HQFihQgCtXajBqlKpNSpVSOHlSG7n87ddIzbGnttj69evbFZ0i3hKXW7du1UVknYlLi8XiVt6lbXueM2eMXmcbde15fv/dfuSyYlISDYR2MPOFvE2P+v3kAS1btmTUqFGasb179/Lyyy+7dJ9+/fo5TLNQFIWff/7ZrT1KJI7IX080EolE4mdMnDiRRx99VPdwO3z4cL788st8JyxBLy7zc6XY5GTbdiOu4LpNePXq24EX0TZaokQJ4uLidC0ejKKXMTHQqpXtiO8il7GxYCe1UMNdd92l+10QI7O+Rt+G5C4aNw5hzBhVm5w/nw5oc6X3HSivOb5wMtV0hMyoGmyLFi1M71f8vTpx4oTd/qaOEC2xNWvWpFq1ak6vc0VcGrfnMRKXRwCt0N27Vxu5PHDgAFlnzuTcME6I6DnNunSl308e8eabb9K0aVPN2DfffONSTmlkZCTdunVzOEcsIiSReIP891QjkUgkfsKECRN4/PHHdcLy//7v//j888/zbR7inWSLNW+H9U7el3U9UVzWqVOH4OBgHnjgAc24PWvsiBG2R76LXGrXsU+RIkV0giYxcafbaXPeiFxevixGEM+gR/u9upBlYdCYeqTFtHYaIfv99991Y2XKlDGYaYwYuczOzubEiROmr7ciiktnUUsrNWvW1BzbE5f22/PYe51tEo61kcsbN25w2OaG3YXZ21ElqlPM9PvJI0JCQpg6dSpFi2oLaz322GO6olKOcFY1dteuXXlWPEuSf5HiUiKRSHzAV199xbBhw3Tjr776Kp9++mm+FZZwZ9liDVrzmcS9n791PSNxCdCrVy/N+N9//63LIwQ1cHPbruqbyGV8PDgJnGgoX15rjX366V25mjb39987hBGxVYj4UB9268OWK0wjngYkkbwm3WGEbPLkybqxw4cPm95vRESETny4ao29cOEC69ev14yZFZdmIpeO2/OYFZcRQCnNyOKzt1uQ3Htrhi3zMYmzfj95yF133cV///tfzVhKSorhG5b26NGjhy43VmTKlClu71EiMUKKS4lEIvEy48aN49lnn9WNjxo1ig8//DDfCktFgTNnruQ0ObeSX22xigJC5wRHs4XjbCDD5TUTE9V17YnLtm3bEhoaarNHhT/++MPwXuPHQ3Q0+EJcRkfDuHHm5lrz8FatEsXc7YiKq2lzrkYuk5JgwwYxgiPu56xwXAa176ct2cANTlKe1qwimfqGEbKjR4+ycuVK3T7EViiOsFgsuuil6BpwxvLlyzVFxgoVKkTr1q1NXSuKywMHDmju5bw9jz1xafSOjTZ6OYpHSLslOIOBrsJs0+ISHPf7yWOefPJJevbsqRmbP38+3377ranrCxcuTN++fR3OmTRpkq4egETiCVJcSiQSiRf57LPPeOGFF3Tjb775Ju+//36+E5bJyeQUPImIgLJlj+vmTJxY0d+LNLpFZqYnfS0zUMXLHJeuSk+Hixfti8tChQrpKmjas8ZGRMBvv2UCYhVKz2yx4eGwaJF6f0fo8/DqCDPs2/W8mTaXlgadO19AUURLqTNbbBmM+4GqRX3SKUUXFqkiSIiQJSQkGAreXbt26SpKO8LToj6iJTY2NpZiQoEce4ji8vr16xpbrvP2PPpqsSqb0L8Zo827zOA0w7n97kWcMPtP4DIuYK/fTx5jsVj4/vvvdXbpl156SVPYyBHOrLFnzpxhjXuJ4xKJIVJcSiQSiZf46KOP+Pe//60bf+edd3jnnXfylbC0rfpoLXiiCi2x110JPvusRCAUaXSZ69ddmS0+LF8CTgC9gD7oo4f2OXEihTRBUdlWWxWtscuXLycjwzhKGh5utK774jI6GlatUqOLjjDOwxMjhQeBqw7vYy9tzpXI5fPPw+nTopANBmoKY0biUoxcglVcApyk/G0RdCtCpqSmGlpiAa5evcrBgwft7lXEE3GpKIrb+Zag9lUVixJZrbHm2vPYe52lAoeFsVrC8V6mMYj5qL7rzqg/MStXUQWmS4j9fvyEqKgoJk6cqBm7cuUKgwYNMlXAqVWrVk7dI/ZejxKJO0hxKZFIJF7ggw8+YIRB9ZIPPviAN998Mw925BuMqz7aIorL2w81AVCk0SUKFvTWnX5Djdp9g2qrdMyhQ1ohVKhQIU3j9a5duxISEpJzfOPGDRYuXGh4L30+ZhjG0TjnxMerotGMsDTOw6stHGcD+lYqIp6kzd0WQaK4rAmIP2DXxSWgEUGcPMnmwYMdVsJ1xRrribjctWuXLj/aFXHpqB2JufY8jt7EEPMu7xKO1ajdR7wKQDjQUpjhlky07ffjR3Tr1o1nnnlGM5aYmMjbb7/t9NqgoCBdmyKRX375hatXHb+RI5GYRYpLiUQSUCiKWj3S3SqSvljg3Xff5fXXX9eNf/TRR7z22mte3mDeYb/qoy1izpe+mI8fF2l0idBQ1QJqDmevowzgX0Asjuyg4eFw+LDWEnvXXXcRHHw7blOiRAnat2+vmTNr1izD+50UFF7FitHExjrZqkBsrCrSEhLMWWHt5+GFon+97DKaqENMmzMbubwtgpzlW4K74hJuiyCAyYsXa84VLlxYc5xb4lKMWlasWFHXb9QZRuLSfHuewkBJO+fEvEsxcnkSuMhqWrP9ln1ZrBo7DzdqNNv2+/EzPv74Y2rX1r4BM2bMGFOW1iFDhjg8f+nSJZfanEgkjpDiUiKR+D1iXl9YGN6tIunmAoqi8Oabb/LWW2/pbvnZZ5/xyiuvuLkh/8Nx1UdbxMilcaVYPy7SaBqLBRo1cvfqCCDSYHwdcDfwFnBNd7ZxY9i92zjf0pYHH3xQc7xgwQKuXdPfTxSXd90VzapV2l8JUUCHh9/+lUhOVm2wZqvCOs/Ds1/Uxxmups1pRZC4jphvCXpxWRr1MUqMcOrFpVUEXQfE92ZihFDvdhf+kBkV9DFbSdTIEuuqdd9IXJpvzwPmK8ZWR19hWY1qT0cteyzmXR4H3Prz4toXkGsULVqUhIQEChQokDOmKApDhgzhwoULDq+tU6cOjRs3djhn0qRJXtmnRCLFpUQi8Vvs5/XdxtUqkt5aQFEUXn/9dd577z3dbceNG8f//d//ufdF+yHOqz7aYt8WK+LHRRpN06yZ2ZniA38x1KjcUIO5N4B3gYbAat16YjGfunX1UbaePXtqhEJmZiYrVqzQzRNtseXKqQ/79evD6NGwdKn688nIgJQU9XNamjo+erQ6zyzm8vDMF/Uxwpo2ZyZyqd2LszYkYFwtFvTRS724BFUELQTEl3uPHj00x65ELsVcuqtXr3L2rLhPPZcuXWLVqlWaMVcssVaMxKVr7XnsFfVJBGwLGxUBKgtzVHG5EfWXsA5QRZjhVizO/f5CPqdRo0a6/3OOHDnCcBPvqjgr7LN48WJSU1M92p9EAlJcSiQSP8R5Xp99TOX1ebiAEhfHq/XqMWbMGN3pr776iueff961e/o5zqNNtji3xdrip0UaTXO7V6SrWFAjlz8BS4FqBnP2AK2Bp4D0nPXsVYq1pWzZsjRv3lwzZmSNFSOX0dH6NiQWi2oBjoxUP7tbl8pcHp4o6szZYm0xmzZ3W0NkoH9TRNyHgrEtFsyKy400Qyybcv/99+taf+zZs4esrCzjTQtER0drIllgzhq7atUqrttUpAoODtZZqc1gJC4TE10xo9qLXF5C/7M3zrtMpDEK6m+UaI11K+/S2u/HT3nllVdo1aqVZmzy5MnMmDHD4XUDBw7U2OdFbt686fQeEokZpLiUSCR+hbm8PufYzevzcAEFeAn4ZJf+oXfChAn861//cuu+/oq5aJMt5myxtvhpkUZTxMSA8JxnB0cPqx1QDXwj0da8tPIdUIc6dWZQqVIGx49r273Yy5MTrbFz5szR9bOzF7n0Nubz8ERRtxc1kmue1avh2DHHkUttj1KxuE4Q+hy/i+hFo2vichPVmCuMDRkyhHr1tBbc69ev5xTGcUZwcDAVKlTQjJkRl6Il9r777iMsLMzUmraI4vLKlSucPy8WiXKEo9ebs7xLNXKZTikuolattVpjywCPAm4lJlj7/fgpwcHBTJkyhRIlSmjGhw0bpvvbYEuZMmXo2LGjw3tLa6zEG0hxKZFI/AbzeX3m0OX1ebiAAgwHPhfGLRYLEydO5Mknn3R7r/6KuWiTlQtApjDmuAS+FT8t0mgKgyLBJhDDf0WBMah2wKYG88+wa1d/XQ/LoKAg7rpLjOioiC1Jzpw5w4YNGzRjZiKX3sD8GxSiUL4BHHB5PWdCVtujVLSh1gAKCWNi1BLsi0vjDosXWKqRyYWAvn36EBERQdmyWnuop3mXzvCkBYkt5cqVo0gR8es3J4xV4oGfgVWoDUVsEfMuRXF5u8/jtVs/rza3rjoJTERt9OMWBvnJ/kTlypX56quvNGPnz5/n4Ycf1r2BZIuzwj6bN2/mwAHXf98kElukuJRIJH6Ba3l95snJ69t3zqMFsoFngP8J40HAT2FhPNqzp4c79T/MR5usGD3UVjAY0+PHRRqdEhdnxh5r1mbXEPgL9S0MfTP7tWvXao6rVatGoUKiEFKpWbOmLh9z9uzZmuPcEpfm09hKcVu0WXHdGrtvn+PIpbZHqZlKsWWAOcAE4D3gRSAF9edqLnKJYIrtCYTfahkjRi99WTH2wIED7Nu3TzPmrrgMCgqievXqwqgr4rIR0B+1SrJoyxVfNOKbKHux/l4VulX8qjDQBC883Nr5nfIn4uPjGTBggGbszz//5PPPP7d7Ta9evXS9SUWmTp3qje1J7mCkuJRIJH6Ba3l9rnHyJAzvuMvtBbKBYahdCG0JAqYAQ86fD+zEQTu47hwWLbGl0UeAvLme/zB+PLimyxwlLgYDL6CKHrEGphZnYlCMXs6aNStHaGVmZnLp0iXNeV/YYrUWVDO4XzHWirPgi7ZHqZlKsaGocvBJ4A3gHdS+nLXQF/oxEpf7UN80uM1QyImQ1RcqI/lSXC4WWqFERUVx9913m15PRLTGFi7siri0RYzYJwG2vRfFyGUmcJpwzlEcL9pYw8PBiQDzBywWC1999ZXOFv3aa6+xbds2w2uKFi3KQw895PC+kyZNMl1xWCIxQopLiUSS57ie1+c60460vN3I3AVuAo8D3wvjwcA0VFOXukAAJw7awfWiieYrxXpnPf8hIgIWLXLU99Kdh7VKlCw5l08+mUGZMmI0T2XNmjWMHDmSK1eMo2Vi3uW+fftyCgKJUUvwjbjUWlDNEItqkXwRNVLY2+U1L11yXHVI26PUTKVYkXmorWL2oX/dG/0spmiOorhlAr0VIRMjl57YYp2JS9ES27lzZ4KC3H8cFMVliRL77Mx0RmO0b7pkAVttjiugjxLvpTGJDt+qcX0bjd2vWpXLhIeHM3nyZE115OvXrzNo0CCuXr1qeI0za+yRI0d09nmJxBWkuJRIJHmOa3l97mPbyNwMN1GLQvwojBcAfkE1c2kXCODEQQHXo03gaqVYET8v0uiUmBi156O5CKbzh9foaFi92sK//92XXbt2Geb0KorC2LFjiYmJYdmyZbrzjRs3pnz58poxqzVWLOYTFhZG0aJFzWzeJbQWVDO8DSwC/osaKaztcLYZxEjM7R6lFwFRjJkRl46qaoriMhtRXMYDITYRMlFc7tu3T1PN1RGuiMvr16/z559/asbctcRaEcWlxeJu5DIUfc6t7TtOQUBN4fwemunssx5ivr+QX9C2bVtefvllzdiOHTt47bXX7M535niYMmWKw/MSiSOkuJRIJHmK63l97mNtZG6GLGAI4iMhhAAzsRNLCeTEQQHXo02g5k8NBdqiFkWp4Xi6gJ8XaTRFTIxaNyo+XjzjmmqOj1fvExOjHoeHhzN+/Hi7Te4PHDhAx44defjhhzW96iwWi6E1FnIv31JrQc0tnIt3VUOYqRQrkoEqfu0hisu1wGHNyFDQRMhEcZmVlcXevXud7ENFFJfnz58nIyPDcO66des0VmiLxUKnTp1MrWOPmjW1gi8zcz/uReoBRGEnFvXRtyPpwkIyCHV7RR3u9xfKM9577z0aNmyoGfv8889ZunSpbm5wcDDx+j9QGhISEky/uSGRiEhxKZFI8pTczrObjvMHhxuokQVxawWB34EHHC4QwImDNrj3XNEbtW/jn6h2QdcjuX5epNEUERGQkADz5kFsrL1ZxuInNlZ1VyckqPexZd++fU5zoSZPnkydOnWYOnVqzlzRGrt582aOHTuWa+JSa0HNHYoJtZCMvm+qhhDzLauht16KzAVsX6jiz1IUl9pCPvWAe0ATIQsLC9Plzpm1xlasqHcI2IteipbYxo0bExUVZWode4iRy8uXL3LvvWIeqllEcem8HUksawkjgwjS6MBSRjHa9JuIOmJjQch/DQQKFSpEQkKCrrjXI488QppBw+fBgwc7vN+FCxd0ubkSiVmkuJRIJHmK+3l27lV93ah7eNFyHRgA/CqMFwJmo2/SrV8ggBMHbcibaFNAFGk0TVycapNNTobevY1FYXg4dOgAo0ap81atgm52UoN3Cb1VS5cuTcuWLXXzUlNTGTJkCF26dOHgwYPExsZSsmRJzZw//vgj13pc3rag5h41ajiPXMbEQIUK7uRbipZYMbf4ivBv7fyh3JKjQoTM3YqxhQsX1uXkmhWXnlpiASpUqKATNb17e6uoz17gvM2xPnJpJZ1SLKcDYxhFDNuJZRUL6Ora8u71FfIL6tWrx0dCasbJkycZNmyY7s2Vhg0bEmO1Rdhh8uTJDs9LJPaQ4lIikeQZ7uX1AaShlsRoAvwHWI9qZHVOIo0d2qceQ41O2lIY+APMPaYEeuLgLfIi2hQgRRpdpn59EAMFVatayMhQW/AsXQqjRzsPmIjiskGDBqxevZqvv/5a11AdYMmSJdSvX5/PP/+cboJinT17dq5FLiH309jE1p/2Ir7lyplpQ2KLkSVWzBO0FZdzsO39auFWETCDCJmvK8aePHmSpKQkzZg3xGVQUBDVqlXTjJUuvd9Nd2kDVI+ILZtt/i1GLg+CpnvobdYQSxwLGMRU0ijlfOn4ePvv7AQIzz33HJ07a/uF/vbbb/z000+6uc4K+8yZM4cLFy54dX+SOwMpLiUSSZ7hXl4fwBLUcjuJwPtAS9S2F/1Ry++ctntlOqW4iH0F8yRaU1wR1LqQprOS8kPiIHkTbQqgIo0uI4qb4GBVwLvy9Yrisk6dOgQFBfH000+za9cuwxYDV65c4dVXX2X9+vWa8ZUrV3LsmLbKqa8il5D7aWytW5v7xqalmWlDYssfqP4GKyGA+K6ArbjUZm2351bnV4MIma8rxoo2x7CwMO69917TazhCtMbu37/fjfY8oArLu4Ux27xLMXJ5E1Vg2mcag2hAEsm6n5MN0dEwbpzpXforQUFBTJw4kQjBU//8889z8KD2+zRw4EC7OdwAN27cYObMmT7ZpyR/I8WlRCLJM9yvF7DQYCwd1X72KFAOtbjM66jFNLRRzWsOei+2RhWThYGiwAL0rb2dkh8SB8n9aFOAFWn0CEcPdfYwEpdWoqOj+e2335g1a5auOizA4cOHNcdZWVkcEJpB+jJyGRMDrVr57PYaYmNB0FqGkcvLly9z6NAhYdRZ5FI0zHcGxBD/5VufTwNaQTcU7EbIRHF54MABu+0kRERxefSoWLlZb4nt2LEjBQoUMHV/ZxiJS+fteezhKO8yHNW1YovzwkcnKU9rVhkLzPBwdaNiknOAEh0dzYQJEzRjFy9eZMiQIWRl3f6/sEKFCrRr187hvaQ1VuIOUlxKJJI8w728PgWxGbkxW4APgFaoDyP9gEnAKQrhWPy1Q41PLALauLPFfJI4mNvRpgAs0phr3Lx5kz179mjGbMWllV69erFz506ee+45pwI2JSVFc+xLcQm5l842YoQ58b57925BdFpw3PbkAnpLbD/0BYDUyGURfkCNrKkUAx4sW9ZuhKxuXa2wzc7OZvdusZqtMc4il1lZWbrKod6wxFoxEpfganseK2LepfOKsWZIpxRdWKS1yEZHqxt0kn8YaDz00EM89thjmrH169czZswYzZizwj6rV682fKNCInGEFJcecO7cOZYsWcK3337L2LFj+fDDD/n666+ZO3cup0/bt+V5wqlTp5g7dy7jxo1j9OjRjB07lgkTJrBs2TLOnnW3OptEkje4l9dnQW16vgJ4FTDzUHAeNeLwGBBNLBcZBazBfqZmR1RZ6jL5KHEwt6NNAVik0TT6PouuRS6PHDmii2IZiUuAEiVKMH78eNavX6/L47Pl5s2bmmNf2mJBLXDk6zcQ7KXNGUUud+7UWmIrVKhKdLSjPp+iJbYg0BMjcRnNCSrzvma0d8GCFF+yxG6ErHjx4lSpUkUzZtYa60xcbtq0iXQhB0HMzfMEUVzaVja2357HHmLk8gRgmx+srxhrlpOUZzi3xL3Y7yef8fnnn1O9enXN2DvvvMNGm6JzvXv3pkgRx9WRp02b5pP9SfIxip9y4MAB5eeff1ZefvllpXXr1kpoaKiCGrJQAKVy5cp5sq/s7Gxl+vTpyv3336/Zj9HHPffco3z77bfKjRs3PFozKytLmThxotKkSROna1arVk159tlnlfT0dO98wW6yfft2zb62b9+ep/uR+C/t2yuKWgHHk49jCnynwEMKlHD6e2L7EQZKH1B+AOWE5xtRlA4d8vpb6lXmzfPGz8f5x/z5ef2V+pbffvtN87q76667XLp+7ty5muvDw8OV7Oxsp9ddu3ZNGT16tFKoUCGnvwuXLl1y98szTWqqokRH++Y1FB2t3l9RFGX+/Pmar83omWHkyJGaOd27d1dSUxUlPt7eGj2E71mPW+M/aX82VFZWGfwdWjZhgtPvT1xcnOaakSNHmvq+btu2Tbfe1atXc86/+eabmnP169c3dV+zHDhwQLd+qvWHYcO8eYoSG+vsZ3lT0f8dn21zfqxwLtbl18q8tzZ69ev3V/766y8lODhY8/2qUaOGkpmZmTNn4MCBDv8u1KpVy9TfGon/kNfP4H4VuVy5ciWdO3cmIiKC6tWrM2DAAD755BNWrVpFZmam8xv4mNOnT9O+fXsGDhzI2rVrnc7fsmULw4YNo3nz5jkWEVdJSkri7rvv5rHHHmPz5s1O5x88eJAvv/zSZ5FTicTbeCfPrgLwBPAbkAqsBEagVh50zAVgJvA4UB61lMRrwGrs1SB0Qj5LHMzLaFN+QvEwcmmUb2nmHgULFmTUqFEkJSXRpk0bu/OCg4M5fvy4S3tyB/fz8BzjLG1O/P6DPnJZt25dBz1KLyDmT0LfW5+1kZ8yHGEuGZqxCtHRtBFsika4WzFWjFwCmoJNvmhBYkulSpV0+ZtGzz227XlGjVLb8OhfC0GolcBtsc27FG2x5iOXVj5aIVpv8yfNmzfnjTfe0Izt37+ff//73znHzqyxe/bsYevWrb7YniSf4lficuvWrSxZsoRz587l9VZ0pKSk0LZtW1asWKEZDwkJoVmzZvTt25f+/fvTsmVLChcurJmTmJhI27Zt7fadsseCBQto0aKFzhYTHh5ObGwsffv2pW/fvrRt29bjJsgSSV7hfeESglqW50NgG3Ac+AHoA4Q5vXrbrStbA5G3rvoB1ZhlinyYOOhe1Udz5JMijT7HUTEfM9x11138+eefDBgwwPD8zZs3adCgAe+//z7X3a+0ZQr38vDs427anCgubQvqiCKoXqWpiJbYkrSiA0vpyx+a+1wBpgprDR46lODgYKd7crdibFhYGGFh2r9v1meO1NRUNm3S5i16W1wWKFCAqlWrasYcvalev77afmfpUrUdT0aG+v2+jaO8S9EWexoEMe+M1avBhWK8Ac3rr7+uqwo8YcIE5syZA0CnTp2cPkNOmTLF4XmJREOuxkmd8N///tcwJF+oUCGlevXqTi0uviQ+Pl63r6efflo5c+aMbm56eroycuRIJSgoSDO/c+fOptdbu3atUrhwYc31TZs2VZYsWWLXZrtr1y7lww8/VGrWrKns2rXL7a/VG+R1SF4SWLRqlTvWy/tZpqwG5TVQ7nZgA7L30QCUEaCsBOW60QKxsXn9rfQZSUmKEh7u3Z9HeLh63zuBX3/9VfNaql27tkvXN2/eXHP9p59+6tY+Dh486PR1Xq9ePWX9+vVu3d8VHFtQzX3Ex9+2wtqyYMECzddUsWJFzfnLly8rFotFM2fTpk1299q9e3fN3G4RZZTskuovxDLh+1fSwIK8Y8cOU9+TxMRE3bUXL140dW1MTIzmuh9++EFRFEWZNm2aZrxo0aIay6y36Nq1q2adt99+26XrX3vN9mf7m/B9KKlA9q1zVxUIEs5vcvm1M2qU178Ffsu+ffuUYsWKab5nkZGRyqlTpxRFUZThw4c7/JsQFRXlcYqXJPfI62dwv4pcghoJvPvuu3niiSf49ttvSUxMJDMzk++//z7P9nT48GFdQvNrr73G119/TenSpXXzS5YsyZgxY/jiiy8044sXL2bDhg1O17t06RKDBw/WFG946aWX2LBhg8PS4bVr12bEiBHs2bOHmjVrmvnSJBK/ILeqSL7Gp7RCrSG7BbVExERUc1uYieuTgLGoFWQjgd7A96ixUSD3vpA8wF+iTXciiqJ4HLm0UrVqVRo2bOhwzo4dO2jZsiXPPvusT5uo27egOic2FubPV693p4PEnj17dFbZ2rWNK8WeP39e1yNy4OefYDmnhtyKzJ+vOXdRKJTUuHFjXSVYexjZncWfvT3sFfURLbHt2rWjkA8qWturGGuWjbbOV13k8jxgvV8hoKpw3lzFWPvr5W9q1KiheyZNTU3lscceQ1EUhgwZ4vD6lJQU/vzzT19uUZKP8Ctx+fDDD5ORkcGWLVv47rvveOqpp2jUqBEhISF5uq+5c+dqjsuUKcNbb73l9Lpnn32WBg20OV/ivYx47bXXND3Jhg4dyqeffmo6R8disZiy30gk/kKu5PVVXkc3oT9mOdSumDNQMzXXAKOAe0zcLwP4HXgSqAjEhIXx6sqVrFixwue2wrzC9aqPxuTzIo2GiELGlZzL06dP60Seu+IS1HYlzlAUha+++oq6desye/Zst9cyg5k8vPBwdXzUKHXeqlWO83TF76/4/RctsZUrV6a4nSrPf/zxBzdu3M7ALlSoED179gSLBUJDKSJU2bXtJQjq/+FmKVKkiK7CpycVY7Ozs3XC2NuWWCueiEtFgX/+sR2pAJQVZnk37zIxUV33TuGxxx7T/e4vXLiQr7/+msaNG9t9c8WKtMZKzOJX4jI8PFyXr+gPHDx4UHPcqVMnU+/6WSwWevTooRnbt2+fw2uOHz/OV199lXMcFRXFf//7Xxd2K5EEJj7P61tax+ECBYD7gdHAP6hRzUmoXexKmlhj+4ULfPzxx7Rr147IyEgefPBBJkyYoCmqkR/Iy2jTnYoYuSpatCiVKlVy+35G4nLIkCHcc4/+bZWTJ0/y4IMP8tBDD3HihOnMY7cwysNLSVE/p6Wp46NHe6dljaN8S5EZM2Zojjt37kyJEiVyjh21cihQoIDdPFd7iHtxt6jP0aNH2bZtG2fOnNGM+6O4zMwEbacUC+bzLgsCrhd9TE+HixddvixgsVgsfPfdd5QtqxXt//73v9m9e7fTwj4zZ87k4p30DZO4jV+JS3/l0qVLmuMKFSqYvrZixYqaY7HPlMj333+v6T329NNPU6pUKQdXSCT5A59XkaxZyqUFygGPAL8AKcBa4HWgkYlrMzMzmT17NsOGDaNSpUrUr1+fV155hT///DPfRDV9EW3Kz4iRM1cQxWWtWrUICnL/v++GDRvq0iuuXLnCxo0b+fjjjw3F0qxZs6hbty5ff/012dnZbq9tlltBQSIj1c9GgV5FUYVnaqr6Wcm+PWC5fFmYq/3+i4LNnm31/PnzLFmyRDPWr18/zbEjcdm1a1fD9BlHeKti7JEjR3SW2Bo1augio95CFJepqamcP3/e1LXGfxbFytu2kctHgYXAAeAy8JnJXWq5ds2tywKWyMhIJk2apBm7evUqgwYNom/fvnauuj3P1y4GSf5AiksTiO/yiI2sHSHOdSYUf/jhB83xo48+anotiSTQ8Xlen5sLFABaAu8DicAp4Eegf+HChNtEMOyxY8cOPvnkE9q3b09ERAS9evXi22+/5ejRoy7twx/JzWhTfsIVW6y38i1t1xbX37p1KwUKFODll19mx44ddO7cWXddRkYGzzzzDK1atTIteLyN7ZsZEREQFgZRUerniALn6RC2kVFREzjc+yXthTe0jYWM2pAYMWfOHJ0lVnQkORKXrlhirbhbMVYUl8eOHWPhQm0qgK+ilgBVqlTRvelhNnpZsKDRqBi53MLtBlENgC5ANcD9NCAfpJ76PV26dOH555/XjG3ZsoVJkybRqlUrh9dKa6zEDFJcmkD8ZftHmxjgkMTERM1x06b2eyvt27dP02esevXqutLeEkl+x+d5fV5YoCzwcHw8Px8/ztm0NNatW8cbb7xB48aNnV578eJF5syZw9NPP03lypWpV68eL7/8MsuXL+dagL+NbibadKfizcilp+IyMzNTI5hAFQHWXP+qVauycOFCpk6dSmRkpO769evXc8899/Cf//zHpTdbPWH+fNVW3aABjBkDy5eLNkpIV8JZTgfGMIqnmaA5p5w9q95gwQKuXbumEz32bLGiJbZLly4aSyzYF5clS5ake/fuZr48h3s5duwYGRnOW22I4vLGjRusX79eM2b0poG3KFiwIFWqVNGMmRWXoaFGphLxeekq4L3+IeHhYCfNNt8zduxY3d+RsWPH6lqWiCxbtoxTp075cmuSfIAUlyZo3749tWrd9vevWbOGpKQkp9edOHGC3377Lec4JCSEgQ6qlmwUSpe1aNEi5987duxg5MiRNGrUiKioKAoVKkR0dDTNmjVjxIgRpqrQSiSBgs/z+ry4QIECBbjvvvt477332Lx5M6dPn2by5MkMHDjQlKV9586dfPrpp3To0IGIiAgeeOABvvnmG5f74koCC1cil2KUzVNxefLkScNxW8ubxWJh0KBB7N69m0ceeUQ398aNG7z//vs0bNiQVdoGhV4lLU19H6h7d1izxv37ZGNRbxAXx54HH9RZe42+p+np6SxdulQzJlpiwb647N+/v1t1JGrVqqUryie+BowoXbq0rh6EbZpNwYIFadOmjcv7cQV38y4tFmikyzkoBYgW3k3iJLdp3PjOfQOsSJEiJCQkaApmKorCzz//7LCIZnZ2Nj///HNubFESyORq4xMPWLFihaZnS273uVy3bp1SyKZ3Vc2aNZVDhw7ZnX/69GmlSZMmmj2/8847Dtd48cUXNfNHjx6tXLx4UXnuued0/biMPjp37qwcOHDAy1+5e+R1jx1J/iI5We1J1qGDvtdieLg6PmqUOs+fFsjKylL++usv5c0331SaNm1q6vfY9qNOnTrKSy+9pCxdutQnfekkucf06dM1P9t69eqZui49PV33ujDbM9Ee4v+n1o/WrVvbvWbZsmW6ftO2H0888YRy7tw5j/Ylsm2bokRHu9sDc4lmf0GUUZKoryigTBf2LvbAtDJp0iTNvEKFCikZGRmGcwsUKKD7nqxbt87tr7127dqae3333XemrqtZs6bdn1H79u3d3o9ZnnnmGc2aDz/8sOlrtX0urR8Dha/jcTdfD3d2n0t7jB07Vvc6qVixosP/l+6+++683rbECXn9DG7cMFGi47777mPevHnEx8eTkpLCvn37aNCgAY8//jhdunShcuXKWCwWjh8/zvLly5kwYQJpaWk51w8bNoz//Oc/DtcQrQbh4eF07NiRv/76y9QeFy9eTLNmzZgzZw4tW7Z0/Yu0w9mzZ0lJSXHpGlf7W0kkjrDm9YH6WHDxolqIoVAh1dbk8bvPPlogODiY5s2b07x5c9555x3Onj3LkiVLWLhwIYsXL9b8jTBi165d7Nq1i88++4yiRYvSrl07unbtSteuXaVlPsAxG7kULbHBwcG66JCr2ItcrlmzhtTUVEMrbPv27UlOTua9997j448/1rXb+P7775k7dy5ffPEF/fr1cykya0RSErRpo7e+uks2QbRmFatozU7BWmnWEtu1a1dCQ0MN54q5htHR0Rr3kavUq1eP3bt35xybzXGtVKmS3ar0vsy3tOJJxdiBA1XLs5amwHSbY+9FLn3d/ioQ+Pe//83ChQtZuXJlzpizCudbt25lx44dDissS+5wclXKekBeRy6tpKamKm+88YZStWpVU5GH2rVrKzNmzDB1786dO2uurVChQs6/LRaLMmDAAOX3339XkpOTle3btyuzZs1S4uPjddGQiIgI5fDhw177mt966y1TX6ujDxm5lEi0ZGVlKX///bfy1ltvKc2aNXM5qlmrVi3lxRdfVBYvXqxcuXIlr78ciROmTZum+fnVr1/f1HU//PCD7v8UT/n444/tvq4mTpzo9Ppt27YpzZo1s3uPuLg4j/4PSk31JGJp/Vgq7KusAooSzXGlOyGacy/961+6PZw7d04XjZw2bZrhfq9du6b7/R0yZIjbX7+i6P/f7dixo6nrHnvsMbs/l2S3rR3m+eOPPzRrlilTxqXrW7USf45rha8jSIGLHr42FCU21kffgADkyJEjSlhYmOb77Oz/o5EjR+b1tiUOyOvIpcy5dBHru7Vm+lzed999jBs3jj59+pi6t1iy21rcp0SJEixfvpzp06fz4IMPUr9+ferVq0evXr1ISEhg+fLlmndT09LSePzxx01+RRKJJC8IDg7m3nvv5e2332bDhg2cOXOGqVOnMmjQIMPIkciePXv4/PPP6dy5MxEREXTv3p0vv/xS15dX4p+4G7n0NN8S9C4ZW8y0GmjQoAHr169n3LhxFDeoiDJ//nzq1avH559/rsn5M8vzz4Od4KrHnKQ8q4nSjNU1KNI3e/ZsTXS2UKFCdovzLFiwQFewyVnVTWd4q2KslfLly+dKpEmMXJ45c4bMTPM9KEeMEEfuQVsNNhu1aqxn6Ne5c6lUqRJff/21Zkx8PYtMnTo1V1oSSQITKS5d4LvvvqN69eq8//77GruKPdavX0+nTp1o0KAB69atczrf3i/q5MmTadu2rd3r2rZty9SpUzVjy5cvN22nlUgkeU9UVBSDBg1i6tSpnD59mg0bNvD2229z7733OhUily9fZv78+Tz33HNUr16dWrVq8eKLL7J48eJcq+YpcYyzhzV7+EJc2rPFAixZskTX29mI4OBgnn/+eXbu3KlrzQFqf+j/+7//o3nz5mzdutX03ubPh+nTnc9zjvg7Y/3+XyeDM5ozdTdsUBe24ddff9Ucd+vWza4l1qg9Q1hYmEu7FRGF4KlTp5z2yQb74rJLly4eW5XNULVqVd06Bw4cMH19XJxoVy0KiH2MNuIJ8fF3br9dewwcOJB4FyqoHz9+nNWrV/twR5JARopLk4wePZqnnnpK859ukyZNmDhxIvv27ePSpUtcuXKFQ4cOMX36dI0Y3L59O61bt+ann35yuIbRO8Bt27blgQcecLq/nj170r59e82YKDjd5ZlnnmH79u0ufchGuxKJ+wQHB9OsWTPeeust/v77b86ePUtCQgKDBw8mKirK6fV79+7liy++oEuXLpQqVYq4uDj+97//ufSQJ/Et/hS5tN3L1atXWbJkiel7VaxYkTlz5vDrr7/qekIDbN68mSZNmjBixAguX77s9H5jx5pe2k32Atpoal2Ajz7KOT537pypKrHWuXPnztWNX7lyxaNd1qxZU1e100zepSNxmRsULlyYihUrasZcrcEwfrzYilhsSeK+uIyOhnHj3L48X/Pll19SqVIl0/O99YwpyYfkqgnXA/Iy53L58uU6//nbb7+tZGdnO7zu22+/1VwXHBysrF271u78Hj166HztkyZNMr3Pn376SXNt3bp1TV/rbfLa7y2R5Fdu3rypbNy4UXnnnXeU5s2bu5yrWbNmTWX48OHKwoULlcuXL+f1l3PHMHXqVM3PoUGDBk6vuXz5su7nu3nzZo/3IlYUrVevnlfyBdPT05Vhw4bZfe1Vq1ZNWbJkid3rk5K8UwVU/VgurF/61vgvmvHythfdykkU81wLFy6sZGZmGu75q6++Mvxav/76a7e+h7bUr1/f5XsePHhQt5egoCAlPT3d4/2YpV27dpr1x4wZ4/I9kpJsC3dPEL6mam69JsLD1ftK7LNy5UrT/6eEhobK/0P8lLx+BpeRSxO8/vrrGkvTww8/zFtvveX0neennnqK119/Pef45s2bvPDCC3bnlyxZUjfWvHlz0/sU5+7Zs8ejxt0SicT/CAoKomnTprz55pv89ddfpKSkMG3aNIYMGWIqqrlv3z7GjRtH165dKVWqFN26dWP8+PGywnMuYyZyuXfvXt3f8Nq1a3u8tmiL7dixo+Z43rx53Lhxw+X7lixZkm+++YbVq1cb7vPgwYN06tSJoUOHGlYg944d1hnafpF1DTZgZIk1chaBmrZihJkorTNEa6yZyGWFChV0r62YmBjD5wtf4UnFWCsxMbBqlTWC2Uw4exBIdel+0dHq/WJiXN7KHUXr1q159dVXTc3NzMxk3rx5Pt6RJBCR4tIJJ06c4O+//9aMvfXWW6avHzlypKbBcmJiIklJSYZz77rrLt1YuXLlTK8VrfWRcPPmTV2RIIlEkr+IiIhg4MCBTJ48mdOnT7Np0ybeffddWrRooWuPIHL16lUWLlzI8OHDqVmzJjVr1mT48OEsXLjQY1ufRIs7b/SJlthKlSpRrFgxj/aRmZmpy6l86KGHNMfp6emsWbPG7TVatWrF1q1beeuttwwbsk+ZMoU6deowefJkzfdlo2epdAL2xLtWXGrk28aNnDt3jmXLlmnm9O3b1/BOe/fu1T0fWPHG70/9+tpcQzPiMiQkhODgYM1Y3bp17cz2Dd4Ql6AKwaQkGDCgHlBEOLvZ9H3i49X7SGFpjnfffZd77rnH1FxpjZUYIcWlE8RCBNWqVXOpv1yxYsV0EcUNGzYYzjWq5GamKq2jubKYh0Ry5xAUFESTJk34z3/+w/r160lJSWH69Ok8/PDDlClTxun1+/fvZ/z48XTr1o1SpUrRtWtXxo0bZ7dvnsR9zEQud+7UCiFfFfNp3LgxjRs31ozNnj0bRYGMDEhNVT+7oo8LFSrE22+/zbZt27j//vt159PS0nj44Yfp1KkTBw4cQFHAoGirSV4FqgIVgDLAjwZzrJvXCjTNdzQxkdmzZmmqxBYuXNhulVijQj5WvCEu3akYe+jQIV0PUlfepPYG3hKXABERMH16AerWbSSccf5ORGysWqcpIUG9j8QcBQsWJCEhgcKFCzudu2DBAlJTXYsiS/I/Ulw6QYz8GRUscIZ4jb1fxAYNGjhd3xFGcyPkX1SJ5I6lVKlSDBgwgB9//JGTJ0+SmJjI+++/T8uWLU1FNRctWsQLL7zAXXfdRY0aNXjuueeYP3++Vyx/dxq6yOXNm07VWm4U8wkLC6No0aL06tVLMz5hwmxKlVIIC4OoKAgLUx/QO3SAUaPAZGcM6tSpw6pVq/j2228NK6guW7aMmJgY3nvvI9LTXbfiqqQBh4ETwFkgE+PI5Q3Ugj63qWobEUtPZ4bgzY2LizO0xGZnZ+e6uExJSTG0E9uyePFi3Vhut4wQxeWJEyc8/pvRqZO2qE9k5CbCw7VzwsNvvz6Tk1UbrKwK6x516tThk08+cTovKyuLGTNm5MKOJIGEFJdOEPMUzJRoF7l48aLm2F7uhrWFgC1mbDBWxHc1o6KiKFiwoOnrJRJJ/iUoKIhGjRrx+uuvs3btWlJTU/nll1945JFHTL1pduDAAb788ku6d+9OqVKl6Ny5M59//rnM7XZGcrL6tPvhh5phS3KyU7WWG21IrOkUpUo9qBm/du0Y589rQ4np6bB8OYwZo1oMY2NhwQLnawYFBfHUU0+xa9cuw77PV65c4a23RqBWBd3k0tejUkA4NhKpCrAf0Eb1qnI7OpMGLFu5UnPeniV2zZo1HDlyxO6OvCEuq1evrnMkOXsmWLRokW7s6NGjHu/FFapVq6Yb87T/brNm2rzLoKCNpKYqZGRASooaWU9Lg6VLYfRoqC92L5G4zDPPPGOqyrCjN1kkdyZSXDpBzGPcs2ePy+/A/SN4fRw9yPXu3VtzbPQfhT3EuZ42cZZIJPmX8PBw+vXrx6RJkzhx4gT//PMPo0eP5v7779flbIlcu3aNJUuW8H//93/Url2b6tWr8+yzzzJv3jy33oDLl8yfr6qvBg1gzBgUweIKOFRrWVlZ7N2rjbJ5I3dOFJdRUdHEx8Ozz9YFagizZzu815o1al/CQYPUB3tnlCtXjl9//ZU5c+ZQoUIFgxnbgObAi8BFg/P2EPM6szCOXIrCrBxlbITobNRaBVaKFClCXFyc4YpiIR/RJeQNcRkcHKx7Q8GRNfb69essX75cN+5IBPuCYsWK6Z6dPC0Y1rSpNnJ59uxZjh8/RmgoREZCaCjkQhvPOwqLxcKkSZPs9ne18vfff8uCcBINUlw6oUGDBoTbeC+uXr3q0rs08+bN48SJE5oxo/wTK0OHDtU82E2aNMlU4+T09HR++OEHzZi9PBGJRCKxJSgoiHvuuYdRo0axZs0aUlNTmTFjBo8++qipfK1Dhw7x1Vdf0aNHDyIiIujUqRP//e9/2b17950X1UxLUyuIdO+uqi87GD4H26i1g4mJuoqtvrDFbtpU7laRVAvQS5g9y9Q9p01TNXRysrk99OzZkx07dvD8888b5J5mA1+gltqZb+6GPAX8ASwElgH9DeYoiMV8ClCT4jYidkYBbQTUXpXYK1eu6CrKNmzYUDfHG7hSMXb9+vU6pxTkvrgEvTXW07zt6tWra57FADZ6twKUxICyZcvqni2NkIV9JLbckeLSYrFoPlYKNhhbgoODdTaekSNHmkqsP3r0KE8//bRmrGXLlg4f1mrVqsVjjz2Wc5yWlsbjjz+uS9C3JSsri8cff5w0m7eOK1WqxKBBg5zuUSKRSERKlixJ3759mThxIidOnGDLli188MEHtGrVylRUc+nSpbz00kvUqVOHatWq8cwzzzB37tz8H9VMSlJVlkFPDZck9rRp7OraVTMUFRXllRx6MXJ55YpthElrjVUjfeZEwcmT0Lq1eYFZokQJxo0bx19//UWMYRnPo0B3VKF42sndGgA9gC5Ae6AyxvJdKy7LUSpnVhqw3CZqCdCvXz/D1ebMmUNmZmbOscVioUmTJpo53hKXrlSMted0Sk1NzfU8aW8W9QH1eyxGLzdtcsdCLXGVvn376lK2RKZOnXrnvZEosYvficvjx49z+PBh3cfp09r/XLKysgznHT582OuVq958801NO5Hz589z33338b///c/wD/b169f56aefaNy4sS5qOWbMGKfrvfvuu5p+dbNmzaJr167s2bNHN3ffvn1069aNWbNuv8NssVj4/PPPZb6lRCLxGIvFwt13381rr73G6tWrSU1N5ddff+Wxxx7TWd+MOHz4MF9//TU9e/akVKlSdOzYkc8++4xdu3blr4eRpCRo00ZVWSZw5uDbJThWvBG1BH3kEmzf7LwXtdqqLXNM3zs9Hbp0MWeRzVnx3ntJTEzkgw8+IDjYqDr6DNSart+jRjXdRUG0xdazud8s4KbN69EVS2yHDh101Zh9Fbncvn273d8bR2k0uZ13WbNmTc2xN2yTYt6ljFzmHv/9738dnj9w4IDdtjySOxDFz6hcubKC+r+A2x8PP/ywwzXE+StWrHC6r99//10JDg7WXVukSBHl/vvvV/r166cMGDBAadOmjVK8eHHDfY0ePdr092HDhg1K0aJFdfdo2LCh0qdPH6Vv377K3XffbbjOW2+9ZXodX7F9+3bNnrZv357XW5JIJF4mOztb2bp1qzJmzBglNjbW8G+ko4/KlSsrTz/9tDJnzhwlMzMzr78c90lNVZToaEVR678afkwSvvbGDuYqoAwV5g8bNswrW61Zs6bwc/hFWPpJ4XxLR9s0/IiPd29vc+fuVaCdg9dMrAK7TO5jtXBtKQVCNGM/UiXngo7CWn369DHc46lTp5SgoCDN3ClTpihfffWVZuz+++/34Kd0mwMHDui+DydPntTNO3HihMPftUWLFnllP2b59ddfNetXqlTJ43vOmTNHc8/Q0FAlKyvLC7uVOOPmzZtKmTJlHL7GnnnmmbzepuQWef0MLsUl5sSloijKvHnznP5yGX0UK1ZMGT9+vMvfi1WrVrn0/QgJCVG++uorl9fxBXn9wpZIJLnP+fPnlZkzZypPPPGEUr58eZf+ToaEhCjt2rVTPv74Y2X79u1KdnZ2Xn855hk40KnamSR8vU2czG8qzP/888+9stXChYsJ3/s1wtLzhfMWBU47+/J0H/Pmube/++/PVmCSoopBo9dKQQXeVeCakz2I4jJMd6/UW5NTQAkWzs2YMcNwf59++qnu//eLFy8qkyZN0ow3atTIg5/SbW7evKl7o3np0qW6eeL6ogD+9ttvvbIfs2zZskWzvsViUa5cueLRPU+ePKn7Ge7YscNLO5Y447XXXnP4N7xUqVLKtWvX8nqbEiXvn8H9zhbrz8TFxbFz504++OADqlev7nR+mTJlePnll9mxYwfPPfecy+vFxsaSnJzMyJEjKV++vN15xYoV49FHH2X37t3861//cnkdiUQi8QZhYWH07t2b7777jmPHjpGUlMTYsWNp06YNBQqI7SK03Lhxgz///JNXXnmF+vXrU6VKFYYNG8bs2bM1+W1+x/z5hjmWIooLt1SAXcJYnYwMV3ZlSGZmJlevinmvYg2A9oBtERsFtViOa3z0kcuXADBypAV4BPU7YFQ34DrwJnAPsN7BnUTjsTafsiQFsWawzhLOFilShG52GiSKltg+ffpQrFgxTeoMeM8WGxQUpKsSbFTzQbTEilXpc7uoj/iMpCgKhw4d8uie5cqV01UZlnmXucfgwYMdnj937pxLHQ4k+RfH/9vnAYcPH/b5GooHeT6lSpXitdde47XXXuP48eMkJiZy6tQpzp8/j6IohIWFERUVxT333KNLaHeH0NBQxowZwwcffMDGjRs5cOAAp06d4ubNm0RGRlKjRg1atGhBSIhYil0ikUjyDovFQkxMDDExMbz66qtkZGSwfPlyFi5cyMKFCzl+/LjD648ePcqECROYMGECISEh3H///XTt2pWuXbtSr149gyqjecTYsW5d5mj3x9E34qgzbx785z9urWVlxQqjfFBRXBYCuqHmOlqZDTzp0lqrV6utO13tNxgXBwMHwvTppYGpwGDgX8BhYeZOoOWtc2OAMCd31hbFu4frOf/+tWxZsKnr0L17d4oVK6a7w7Zt29i2bZtmbOjQoQA+E5eg5l1u3rw551gs6nPz5k2WLFmiGatTp46meFNui8vQ0FDKlCnDmTNncsb279/vce5ws2bNNH87Nm7cyMMPP+zRPSXmqFu3Lo0aNdK117NlypQp9OzZMxd3JfFH/E5cBhIVKlSw06vL+1gsFu69917uvffeXFlPIpFIvEmJEiV48MEHefDBB1EUhR07duQIzbVr1+rabthy48YNVqxYwYoVK3j11VepWLEiXbp0oWvXrrRv354SJUrk4ldiQ3Kyw3YjtrjylqYYtSwOVNi40T21ZsMvv4jFfMKAogYze6EVl8uADMC17/P06WpDe1cZPx5WrbLWRuoCbAfeBj5DX9Tna1Tx+z/gIZtxx5FLaywwpUwZ/kxJ0ZyzVyVWbENWoUIF2rRpA0DRotrvozfFpbOKsZs2bdK1LLvvvvs0PS/zqh2JKC49pWnTpvz+++85xzJymbsMGTLEobicO3cu58+fp2TJkrm3KYnfIW2xEolEIslVLBYL9evX55VXXuHPP/8kLS2NWbNm8dRTT1GxYkWn1x87dozvvvuOhx56iIiICNq2bctHH31EcnJy7lagNWGHtYejyKXOEmud78F6ANu2iZFLe9V+uwG2bpjrgOt2N3eLeUZEwKJFcLutYTHgY2AT0MjgilNAb9RWKicMzoMoLusBhIcza9gwbtq0IClatKihJTYrK4uEhATN2ODBgwkKUh+jfB25tGXHjh2a1/nixYs15+vWrcvdd9+tGfOHXpe+qBi7detWrl275vF9JeYYOHCgw3ZU165dY+bMmbm4I4k/IsWlRCKRSPKU0NBQevXqxbfffsuRI0fYvn07H3/8Me3atXNq+c/KymLlypWMGDGCBg0aUKlSJZ588kl+//13MryQp+gQF9STJ5HLHCOhB60XFAUOHjQrLsOAdsLYbJfXTExU13WHmBg1eqntdtMI2AB8inHEdTbqd+sr9N9xbcSzbkQErFrFr+u1eZvdu3fXRSEBli9frmuJNmTIkJx/56a4zMjI0FhDxTy3Ll26ULlyZc3YiRMnHLoDfIEvxGXjxo01xzdu3CApKcnj+0rMUaZMGTp27Ohwzrfffms4riiQkQGpqern/NSJSqJFikuJRCKR+A0Wi4V69erx8ssvs3z5ctLS0pg9ezbDhg2jUqVKTq8/fvw433//Pb179yYiIoI2bdowduxYkpKSvBvVVBRwYA9zhquRS8AjtZaZCVeuOOpxKdLL5t+lncw1Jj0dLorJoy4QE6O2D42Ptx0tALyEapXtYnBVJvAsai6mfequW0dK2bL8+eefmvG+ffsazhcL+TRp0kRTaEcUlzdu3NBERD2hYsWKhIaGasas1ti0tDRdv0cjcZmdna3JwcwNfCEuw8LCqF27tmZM9rvMXWzfVDFi8+bN7N27F1AzB0aNgg4dVEdCWBhERamfIyLU8VGjVMe/JP8gxaVEIpFI/JbQ0FAeeOABvvnmGw4fPsyOHTv45JNPaN++PQULFnR4bVZWFqtWrWLkyJE0bNiQihUr8sQTT/Dbb79x4cIFzzaWmamqJ7NfB1ANqHrrw17MEByISw/U2vXrAGYjlwAPAP8G1t667lO31vXUsRgRAQkJMG8exMbanqkKLACmAVEGV9p/Wo2KiiKqVi1mzZpFdvbtiKY9S2xGRgazZs3SjFkL+VgRxSV4L3ppfcPFFmvF2GXLlmm+hiJFitCqVSsiIiJ0EdjctsaK4vLw4cNcv37dzmzzNG3aVHMs8y5zlwceeMCw4JUtDz30BLGx0KABjBkDy5fr/1ymp6vjY8aobyTFxsKCBT7cuCTXkOJSIpFIJAGBxWKhbt26/Pvf/2bZsmWkpaUxZ84cnn76aV2kxogTJ07www8/0KdPHyIjI2ndujUffvgh27Ztcz2q6eJDcj/gAHDw1sfvdualASnCmKa+pptqTdXhrkQuywGfoFZktZ9j5YxChdy+VENcnGqTtY2EhIdbgIHAbuBR0/eyRhxnzJihGe/Ro4ehJfa3337TCMUCBQowYMAAzRxfikswzrsEvSW2bdu2FC5cGIvFovudyOt2JNnZ2V7Zg5h3KSOXuUuxYsXo3bu3wzk7dqxhzZo/Hc4RWbNG/T0fNAjS0jzZoSSvkeJSIpFIJAFJ8eLF6dmzJ19//TWHDh1i165dfPbZZ3Ts2NFUVHP16tW89tpr3H333ZQvX57HHnuMX3/9lfPnzztf3Mn93UWMWhZEjXjm4KZaCw2FoCBXIpeeEx4OxYs7n+cK9eurFWiXLlUfQDMyICWlFBkZE1m2dBk1qlVzeo+6dety9uxZVqxYoRk3a4nt1q0bUVHaaKmvxaVRxVhFUQzzLa3ktbgMDw8nIiJCM7Y/MdHjhDsxcrl7927f51dLNDizxqoMBMy7O6xMm6ZGPJOTXb5U4idIcSmRSCSSgMdisVC7dm3+7//+jyVLlnDu3Dnmzp3LM888Q9WqVZ1ef+rUKSZNmkS/fv2IjIykVatWfPDBB2zZssU4qhkaalvO1GuI4rImNj3DPFBrFgtYLLkrLhs3Vtf1FRaL+mOIjFQ/t+/QnqTt2xk1apTDipZXrlzh999/19hJixUrRteuXXVzjxw5wsqVKzVjoiUW8iZyuXXrVl2RIX8SlyQnU0N4M2T/wIEeJ9w1bNhQU+hLURQSExO9smWJOdq2bUt0tLO/H2dR859d5+RJaN1aCsxARYpLiUQikeQ7ihUrRvfu3fnyyy85cOAAu3fv5r///S+dOnWikJPo382bN1m7di2vv/46jRo1Ijo6mkcffZQZM2bc7idosUAjo7YYnrFTONZYYj1Qa5mZmdy8eUkYdb1IjysI7sVcoUiRIowePZoff/zR7pwff/yRN954QzNmr0rs1KlTNcclS5ake/fuunkhISE6QXv58mUXdu4YUVxevnyZn3/+WTNWrVo1TZ5jnonL+fOxJtzVEIoI5ZT08SDhrnDhwjRo0EAzJvMuc5fg4GDitZW27DAdSHA6y4j0dOjSRVpkAxEpLiUSiUSSr7FYLNSqVYsXX3yRxYsXk5aWxrx583j22WepZsJGefr0aX788Uf69+9PVFQU999/P6NHj+afSpWEBheeY7eYD3ik1owrhfpWXA4c6NPbO6RmzZoOz6cJT6z9+vXTzVEURWeJ7d+/v903J3zZjqRcuXKEC5HyBYIY69KlCxabNx9yXVympamlfbt3VxPogBrCFLv1Yl1MuJN5l3nP4MGDTc58BnDvtXfyJAwf7talkjxEikuJRCKR3FEUK1aMuLg4/ve//7F//3727NnD559/TufOnU1FNdetW8cbb7xB40mTiAYeAX7BnewiPQ7FpQdq7dQpsZhPGMa9Ir1DbKyaHxkIBAUFUa6cXmhv3Lgxp6WCFSNLrBUx8ulNcWlUMXbnTm2c29YSC+ha9xw9etS77XhsSUpSE+WmT9cMmxaXVkwm3Il5l1Jc5j4NGzakSpUYEzMzgKGAe615pk1Tg+GSwEGKS4lEIpHcsVgsFu666y5eeOEFFi1axLlz55g/fz7PPfecrtqlEWeAn4ABQCRqbdX3gURwOap5ETgqjOV0UvRQrekjl77Ntxwxwqe3d4rFjn3YqNBTdnY2rVq14tVXX9VYWadMmaKZV716dVq0aGF3TV9GLkFvjbXNGQ0JCaFt27aa82Lk8urVq5w9e9arewJUYdmmjRpmEhDF5UEgy9n9TCTciZHLY8eO6fJPJb7HYjFT2AdgNWr1aff46CO3L5XkAVJcSiQSiURyC2uvw/Hjx7N//3727t3LF198QZcuXShcuLDDa7OB9cB/gCaoptOHUbOOzKQN7RGOLcBd1gMP1Vpuisv4eDBoF+kXPPjgg4bjN2/e5OOPP6Z+/fosWbKE69evM12Iwg0dOtSuaIXcF5e2tGrViuJCsafo6GgKFCigGfO6NTYtDbp2tdvzVTQnZ6F/A8UQJwl3tWvX1n29Mu8yd0lOhkOHBqL+pTLDf4B/3Fpr9WqX6z5J8hApLiUSiUQisUPNmjUZPnw4Cxcu5Ny5cyxYsIDnn39e1yDeiLPAZCAeKA20AN4FNmEc1RQtsVWBIuAVtSbaYosU8U2+ZXQ0jBvnk1u7hD0RaBvtM+LQoUN07tyZTp06ce7cOc05ZzlmvhaXYjsSW0RLLKhFVypUqKAZO3rUlLQzz/PPG0YsrZQCSgpjTq2xVhwk3AUHB9O4cWPNmBSXuYv63ksFoJ3JK24AgwD3Cl0J7/VI/BgpLiUSiUQiMUGRIkXo2rUr48aNY9++fezbt49x48bRtUMHHMc0VTH5N/AW0AwoCwwBpnE7qmmYb+kltSZGLgcMiPZ6J5XwcFi0SO0y4a+IOZQxMTGGVtlVq1Zpjlu1auW0+FNeRi6NxCX4uKjP/PlOn/gtuJF3aYuDhDtZ1Cdvuf3tNlvYB2A38KqH60n8HSkuJRKJRCJxgxo1avD888+zYOlSzm3cyMLixRmO3gpoRAowFfV9/CigOfC7MKdOoUJeU2ti5LJBg3KsWqVqV28QHQ2rVqldJfwBe5HLZCGP791332Xr1q20atXK4f3siTdbfC0uS5cuTWRkpG48OjrablTTp+Jy7FhT0zwSl2A34U4s6rNp0ybfFSySaFAU+CfH4dqbWx4Lk3wJmGs7Y0tiorquxP+R4lIikUgkEg8p0rQpXdav54voaPaiPkD/D4jD+WOXAmxAfU/flgvdu5NqUMXUHcTIZXR0NDExai0WU+3qHBAfr97HX4SlPYoUKaKxxRYvXpzOnTtTp04dVq5cyYQJEwgLCzO89t133+XDDz/kxo0bDu9vi7fFJRhbY8UWJLb4TFwmJ+e0G3GGx+LSTsKdGLk8d+4cBw8edPXuEjfIzLRNsw0Fejm5wvb12RKhDrYp0tPh4kWXL5PkAVJcSiQSiUTiDWzUWnXgWWAecA5YDLwI1HLhdt/99hulS5fm3nvv5e2332bDhg3cvOleOX8jcQlqUDQhAebNUwvSukJsrOpYTEjwPyuskdgKCQnRHPfs2TNHEAYFBfHkk0+ya9cuSpUqpbv22rVrvPbaazRp0sSu/TI3xGWtWvpXkKOoqs/EpQsJcB6LSzvrVapUiaioKM2YzLvMHa5fF0ecWWMVIBgYDaxCzSh3nWvX3LpMkstIcSmRSCQSibcwUGuFgU7Af1GjkwdRjWHdcd5pUlEUNm7cyDvvvEPz5s0pU6YMgwYNYurUqaSkpJjaUmZmJpcuXdKMiX0d4+JUW2tyMowaBR06oMvJDA9Xx0eNUuetWuW/VWGNyMjI0Bz369fPcI5YyMeWpKQkmjdvzgsvvEBmZqbmnCgubduaeAtRIAN06NDB7nyfiUsXEuBEcXkANzoeGqxnsVhk3mUeoU9T7oRq8HfEo8AoVJHpHk7aEEv8BCkuJRKJRCLxNg7UWlXgGWBueDhpbduyZOBAurdubeq2aWlpTJs2jSFDhlCmTBmaNWvGW2+9xd9//203qqlvQ6IXl1bq14fRo2HpUrULREYGpKSon9PS1PHRoz1quZkrOGoZAhAaGkrnzp1142JvS1Ewgir4x40bR7169Zg3b57dub6IXJ45c0ZzbLFYdC05bKlUqZLm+Pz58zqR7TLahDuniOLyOnDC1TXtJNwZ5V1KfE9oqPjmUwFgoJOrFuF699/bhIeDg5e6xI+Q4lIikUgkEl/hRK0V/vNPOk6bxr1C9CkyMpKiRR3HNRVFYdOmTbz77ru0aNGCMmXKEB8fz5QpUzh79mzOPLGYT1hYmNN7A1gs6kNkZKT62Yle8yucFXbp2bOnrm9pdnY2U6dO1YwNGzaMDRs20KBBA909jh07Ro8ePejXrx+nTp3KFXG5bds2zbGiKOzfb99oKopL8EL0Uptw55TSqC1JaqHmIL+AG7ErOwl3YuQyMTGRrKwsV+8ucRGLBRo1EkeHOLnqOKol1j0aNw6sv0F3MlJcSiQSiUSSGzhQa7t2aRuR9O/fn3PnzrF06VJeeukl6tRxXgAjLS2N6dOnM3ToUMqUKUPTpk158803dW01or1VItaPOX36tMPzffv21Y2tWbNGJ7yGDh1Ks2bN2Lx5Mx9++KFOkAL8+uuv1KlThz179mjGvS0ujxw5omulArBjxw671xQuXJgyZcro7uMR+oQ7h1hQqyPvRs1B/hwo7866Bgl3YuTyypUr7Ny50527S1xE0PVAY6C2k6umOjnvynoSf0WKS4lEIpFI8hhRXNapU4dChQrRoUMHPv30U3bu3Mnhw4f5+uuv6dmzJ8WKFXN6z82bN/Pee+/x9ttva8aN2lnkNw4dOmT3nD1L7OTJkzXH9evX5+677wbUXMcRI0aQnJxM+/btdddeuHCBhQsXasa8LS4XL15sOL7doJKqLWLe5dGjRz3biEFfUGd45WHTIOEuMjKSqlW1xWFk3mXuMFDngrXgvLDPTMC93wv9ehJ/RYpLiUQikUjykOzsbHbv1jYiMYpUVq5cmaeffpo5c+aQlpbGsmXLePnll6lXr55L661Zs4bGjRvzxhtvsG7dunxpI3TUkuKBBx7QRSAvX77Mr7/+qhkbOnSoLnezRo0aLF26lJ9++smwqqwtYhElT1m0aJHhuKPIJfigqI8+4c73OEi4k3mXeUNMDOjbww5yclUGMNfltWJj/T/PW3IbKS4lEolEIslDjh49qotyObPBFipUiPbt2/Pxxx+zfft2jhw5wrfffkuvXr0cFnix8s8//zB69Gjuv/9+SpcuTf/+/fnxxx+d2kkDBUfi0sgSO2fOHE3116CgIAYNMn5QtlgsDB06lN27dzN4sP1Izbp161i7dq0Lu7bPjRs3WLZsmeG5XBeXxgl3vsVBwp2sGJt3jBghjlQBdIpTwHVrrH4diT8jxaVEIpFIJHmIaIkNCwujbNmyLt2jUqVKPPXUU8yaNYu0tDT+/PNPXnnlFeqbeLs/PT2dGTNm8Oijj1KuXDkaNWrE66+/ztq1awM2qnngwAHD8RIlStCpUyfduGiJ7dChg9Pc1KioKKZMmcLixYt11kxQbbGtWrXiX//6F+fPnze/eQP++usvXesTK3v37uWagwaAPmlHktsJcA7WEyOXycnJPimmJNETF2dkV3VW2GchahauOeLjA6vlkUSKS4lEIpFI8hSjfEtnrTQcUbBgQdq2bctHH31EcnKyofBxxJYtW/jggw9o1aoVUVFR9OvXj0mTJumqzvoriqLYjVwaVYk9deoUS5Ys0YwNGeLsAfk2nTp1Ijk5me7duxue/+abb6hbty6//fab0yq29rBniQW4efOmYaEfKz4Rl7mdAOdgvUaNGhEUdPtx9ubNm2zZsiU3diUBxo8H7fswfQBHeblZwAxT946OhnHj3N+bJG+Q4lIikUgkkjzESFx6E9u2JADjxo3j1VdfJSYmxum158+f59dff+Wxxx4jOjqae+65h1GjRrFmzRq/jWqeOHGCy5cvG57r16+fbmzatGlkZ9/uv1esWDEefPBBl9YsVqwYTzzxhN3zp06dok+fPvTq1Ytjx465dG/Qi8vQ0FDNsSNrrCguT5065TDSaQrjhDvf4CThrnjx4tStW1czJvMuc4+ICFi0yDYNNxzo4eSqKU7Oq/dbtEi9vySwkOJSIpFIJJI8RGyd4E1xmZmZqSss061bN8aOHUtSUhLHjh3ju+++46GHHtIJFiO2bt3KmDFjiI2NJTIykr59+zJx4kROnjzptT17ij2hZdYS26dPH1PVeEXEPpdG/PHHH9StW5f//e9/3Lx509R9T58+rYvEia8RRxVjjXpduiNwdeRWIpyJdWTeZd4SEwOrVtlGMJ1F/jcA++yejY5W72fi/S+JHyLFpUQikUgkeYSiKD6NXBqJvnLlyuX8u0KFCjzxxBP89ttvpKWlsXLlSkaMGEGDBg2c3vvChQvMnDmTxx9/nPLly9OwYUNGjhzJqlWruHHjhte+Blex1+fwgQceoJDQzmLbtm0kJSVpxoYOHerWuqK4DA0NpWvXrrp5Fy9e5Pnnn+f+++8nOTnZ6X1Fy25oaCgtW7bUjDmKXJYsWZISJUpoxrxijTVOuPMuJhPuZMXYvCcmBpKS1B8ZdAUcV1O2V9gnPl69jxSWgYsUlxKJRCKR5BFnz54lPT1dMyZa/DxBzJMMCwujaNGihnNDQkJo3bo1H374Idu2beP48eN8//339O7dWydOjEhKSmLs2LG0adOGyMhIevfuzffff8+JEye88rWYxZ64NLLETpmitedVrFiRNm3auLWuKC6vXbvG/HnzmD59OqVLl9bN//vvv3OKJzkqQCNaYjt06KCzNOd6xVgr+oQ77+FCwp0Yudy3b5/u90rieyIiICEB5s0rSLly/Z3MngrczkGOjYX589XrpRU2sJHiUiKRSCSSPEKMWhYuXFgnBDxBjFw6q4BqS/ny5Xn88ceZOXMmqamprFq1ipEjR9KwYUOn12ZkZPD777/z5JNPUqFCBRo0aMCIESNYuXKlz6KaigIZGbBtm7HQ6tixo+Y4KyuLhIQEzdjgwYM1xWFMkZwMo0ZR9LnnNMPXr18nOyKCAd9/z674eB5/6CHdpVlZWXzwwQc0aNCAP//8U3f+5s2bushlly5ddFWA9+/f71Cgiq+po0ePOv2yTKFPuPMOLibcxcTE6KLSmzdv9u6eJKaJi4OZM+236VE5SJMmfzFqlPortGqVrAqbX5DiUiKRSCSSPEIUl7Vq1SI4ONhr9/dEXNoSEhJCbGwsY8aMYevWrZw4cYKJEyfSt29fwsLCnF6fnJzMRx99RNu2bYmIiOChhx7iu+++4/jx427t5/Z9YdQo6NBB1SFhYQqbNhlHLkXxsWzZMl1fT1eqxDJ/vhpuadAAxoyhyIYNuilXz5+H5csp9fnnfP/776yIiaGmwc9g//79tG/fnscee4y0tLSc8cTERM0xQOfOnXXWaUVR2L17t92t+ixyCUYJd57hRsJdSEgI99xzj2ZM5l3mLS1atKB69eoO5zRtOpXRox3Wa5IEIFJcSiQSiUSSR/i6Uqxoi7XNt/SE6OhoHn30UWbMmEFqaipr1qxh1KhRugd8IzIzM5k1axZPPfUUFStWJCYmhldffZUVK1Zw/fp1U+sLuo7ly0F1QZ4CLhheExsLCxbcPhYL+TRt2tTc9z8tTU0M694d1qzJGTYq5yPGEtskJ5N08iRv1KtHgQIFdPMnTZpEnTp1mD59Ooqi6CyxderUoXLlyhQvXpwqVapozrlSMdar4hLEhDv38SDhTuZd+hcWi4XBgx1HL3/55RfTv/OSwEGKS4lEIpFI8ghfi0tvRS4dUaBAAe6//35Gjx7NP//8w8mTJ5k0aRL9+vWjZMmSTq/fvn07H3/8Me3atSMiIoIHH3yQCRMmGFY0taPrbLAvsNasUe16gwbB4cMZzJo1S3PeVCGfpCRV0U6frjtlRlwCFAbe27GDLeHhNDconJSSkkJ8fDzdunVj9uzZmnNdunTJ+bdojXVUMdbn4hJsE+5UJe8KXki4E/MuN2zY4HZfUYl3GDRokMPz586dY+HChbm0G0luIcWlRCKRSCR5RKBGLh1Rrlw5HnnkEX755RdSUlJYu3Ytr7/+Oo0aNXJ67cWLF5k9ezbDhg2jUqVK1K9fn1deeYU///yTxMTr9nSdDcaWWFumTYN77pnJ1atXc8YKFCjAgAEDHF+YlARt2oCdtitmxaWV+ikprDt6lC9HjTJsA7No0SJdCxJbcVmvXj3NOVcil8eOHdP09vQqcXGqrdXWsyzmZIaHq+NeTLgTI5enT5/O9WJSEi01a9akefPmDueIRbUkgY8UlxKJRCKR5AEZGRm6h99AjFw6okCBArRs2ZL333+fxMRETp06xY8//kj//v0JN1EEZseOHXzyySe0b9+eJk0iOHmyF/AtYK8gjaM8u9tRrPPntZbYbt26ERkZaf/StDTo2tXqvTUkBP1DlSNxCRB0/jzP/PgjO9et44EHHnA4t1ChQsTaRAQ9EZc3btzQvfHgderXh9GjYelS9fuXkQEpKerntDR13IsJdzVr1tTl/0prbN7jzBo7d+5czp8/nzubkeQKUlxKJBKJRJIHiFHLoKAgatas6dU18lpcipQtW5aHH36Yn3/+mbNnz7Ju3TreeOMNGjdubOLqi8Ac4GmgMlAPeBlYDly7NecvE/c5DKzSjDi1xD7/vN2IpRUL+uilM3EJwMmTVPjwQ2bPns3vv/9uN7p87do13njjDS5dugTobbGHDh3i4sWLhteWLl1aV9DIJ9ZYe1gsEBoKkZHqZ4vF60sEBQXpopeyqE/e079/f8P8YivXr19n5syZubgjia+R4lIikUgkkjxAFJfVq1fXCQBPyMzMzBEiVnLDFmuWAgUKcN999/Hee++xefNmTp8+zU8//cSAAQMoVcpZA3ZQLbCfAh2ACKAnqnC0hzVyqW0/EhJSku7du9u/bP58Z17cHNwSl6B6defP58EHH2TXrl08/fTThtM+/fRT6tevz+LFi6ldu7aubYr4mrISFBRExYoVNWO5Ki5zCVnUx/+IjIykmxPLs7TG5i+kuJRIJBKJJA8QhUDdunW9en8xagn+JS5FypQpw9ChQ5k+fTpnz55l/fr1DBjwH6CJiasvAXOxtb4aowBaS+yNGwNYtsyBqB871sT6KqK4vGz6SuCjjwAICwvjX//6l91phw8fpkuXLjzxxBM6u2ueVoz1A8SiPps2bfJdbqnENM6ssatXr86Xr8c7FSkuJRKJRCLJRRRFTTvbti13i/mEhYVRtGhRr67hK4KDg2nRogUnTrwLbALOoIrCgYCZqKYRXwGzgb3C+FCrrtOTnGyvLK0hbkcuAVavhlsVXxcvXux0+rRp03R9QvO8YmweI0YuMzIy2LdvXx7tRmKlR48elChRwuGchIQEh+clgYMUlxKJRCKR+BjbwpkRERAWBosXa8Xl9u11cKANXMbf8i1dRavrSgNDgGnAWdTcyjeBpqjZjmZ4HnhIGKsONLfVdVpM2mGtiNLdJXFps57Y33LAgAGaYj5Wbty4oTl2JXJ59Ki9okiBS/ny5XWvc5l3mfcULlyYvn37OpwzZcoU2TomnyDFpUQikUgkPmL+fLWFX4MGMGYMLF9uLTh6FTiomTtvXh1iYtT5CxZ4vnagi0v7ui4YaA68g1od9gzwuZurHAV6AF/y1VcH9addFCYeRS5vrXfx4kXWCNHSAQMGsGLFCr777juHvUP/+usvneC0cidELkHmXforQ4YMcXh+9+7d/PPPP7m0G4kvkeJSIpFIJBIvk5YG8fHQvbs9V+U+QMwFqw2o8+PiYNAg9T7ukhc9Lr2JeV0Xhf57aZYbwHzgOb7+ujq1atXixRdfZPHixVy9cgVcfNj1WFwmJrLizz81ArFAgQK0a9eOoKAgnnjiCXbt2kX//v0NL79w4QJ33303GzZs0J0zEpf5MVIk5l3KyKV/0KpVKypVquRwztSpU3NpNxJfIsWlRCKRSCReJClJjVQ6dlSKVT0rAKGakWnT1PskJ7u3j0COXCqKq7puhok5IU5n7N27ly+++IIuXbpQKiKCuPR0/gccMLmL7sAw4EXgNcyVItKQns6iuXM1Q/fffz+hobdfG2XLluXnn39m3rx5ugqwADt37qRFixYMHz6czMzMnHFRXF68eJF0B307AxUxcrl161auX7+eR7uRWAkKCmLQoEEO50yfPp2srKxc2pHEV0hxKZFIJBKJl0hKgjZtnLZERC8ujYv5nDwJrVu7JzADOXKZmWm1D5vhKPC3iXmuRemuXLnCAtRMzRpATWA4sBD7Ecn/A74B/gt8ALR0aUV1hwuXLtWMdenSxXBuXFwcO3fuJDw8XH8fRWH8+PHUrVuXP/74A4AKFSroWpfkR2tskyZaSX/t2jWS3X2HRuJVnFWNPXPmDMuWLcul3Uh8hRSXEolEIpF4gbQ06NrVrCgyJy5BvV+XLq5bZAM5culaoElswF4IuM9gXpYw5yBqgaAhqNZax+wHxgPdUOvVdgXGoRqcvcV+4JAg+OyJS4DixYvTvn17u+ePHz/OAw88QJ8+fUhNTdW9BvKjuAwPD6dmzZqaMZl36R/UrVuXRo0aOZwje14GPlJcSiQSiUTiBZ5/3kzE0spO4dhxG5KTJ2H4cNf2E8jismBBV2aLlthBwI9OrukFVEVtbTIZOA1s4vXX36VFixa6CJ/IVWAR8AJwF2pk83lgAS72thRYVESbtVm2bFkaNGjg8Jp69eppjo32/ttvv1GnTh0KCt/Y/CguQeZd+jPOCvvMmjVLY+eWBB5SXEokEolE4iHz57vSteIm+l6LzntcTpumrmOGzMxMLl26pBkLJFtsaCgYuD0NOAKIxWv64bw9yVDhOIjw8Ca8995/WL9+PSkpKUyfPp2hZctS2sQuDgD/A+KACKAL8AXqT9kVM+4ioQ9ply5dsFgcfy2iuIyMjKRDhw66eRcuXODgQW1F3PwqLmXFWP9l4MCBBAcH2z1/5coVZs2alYs7kngbKS4lEolEIvGQsWNdmX0IuCaM1TV15UcfmVtBjFpCYIlLiwWcuOduIVpiSwHtnFxTGuikG23cWF0XoFSpUgwYMICfHn2UU8Bm4D3UHEpnD05XgcWoRX1qoUY1n0OtSesoqnkVWHHhgmbMkSXWSv369TXHZ8+e5eeff2by5MlEREQ4vPbQoUNO7x+IiJHLnTt3cvHixTzajcSWMmXK0LFjR4dzZNXYwEaKS4lEIpFIPCA52V67EXuI+ZYRmMn5A1i9GrZvdz5PLOYTFhZGUSEq5u8I+sAOvwrHD6JWhXUU7RsEFDC33sCBBAGNgTeAtUAq8AvwCFDWxA4PAl+iVpItBXRG7cq5B7WBSgahpBLBIopwxaZSZlBQkGEEUqRGjRqEhGgr4e7cuZMhQ4awe/duhzbERYsW6Xpq5gfuvvtuChS4/TPOzs6WPRT9CGfW2OXLlxu+QSYJDKS4lEgkEonEA8zbYa2YL+bj7nqBnG9pZeBAZzMOY2yJdYZoiXWwXkwMtGqlGQq/tcok4ATwDzAauB+wb/ZTuQYsQa0qWxsIoTJhDCGKH3mQJzRz69dv5jTyCBASEkKtWrU0Yzt27ABUi+zkyZNZsmQJ1apV01179epVYmNjGTZsGOfPn3e6VqBQpEgRYmJiNGMy79J/eOCBByhWrJjd89nZ2Ux3/Q+rxE+Q4lIikUgkEg9w/ZnVM3FpZr38IC4NdJ2AkSW27a1/24tcxgANdaOxsSC4S28zYoTdHQQB9wCjgDVACmp5oUcBMybkbI4AXwE9ULM2b5OU1JnYWFiwwPl9RGusVVxa6dixI8nJyTz++OOG10+YMIE6deowc+ZMFMW1li3+isy79F+KFStG7969Hc6RVWMDFykuJRKJRCJxE0UB1912nonLxER1XUcEco9LWxzoOvSW2IdQLbGOGIKR8HS4TlycmTAqoEY1+wITUaOaW4A3KEIUtXAe1xR/qBNYs+YZ4uL+oF+/iw5b0YhFfbYbeKeLFi3KF198Yfcep0+fpm/fvjzwwAMcO3bMyV79H1kx1r9xZo3dtm2b7E8aoEhxKZFIJBKJm2Rmmu1ractrwFuo5sqYWx/mSU8HZ7VJ8kPkEhzpusOAKBb62vzbKHJpQc231BIfD926OdnI+PHg4vfQAgQRw0T2kcJu1GzNX4HHMBfXPAV8DTzAr79GEB3dgZdf/pSdO3fqoouiuBQjl1aKFSvm1Go7d+5c6taty/jx47l586aJffonYuTy8OHDpKSk5NFuJCJt27Z1+ncpISEhl3Yj8SZSXEokEolE4ibXr7tz1QPA26hlYZIA50VbRK6JxWYF8ou4BHu6TrTERnDbEmuPdoD2RtHRMG6ciU1ERMCiRWb7owCQRAxtWMlJyt8aKQn0AX5AjWtuBcYAsThvnXKd69eX8+mnL1OvXj2qVq3K008/zZw5c7h48aLOFpuSksLZs2cN71S5cmXNsXgtwMWLFxk+fDj33XcfSUlJTvbmn9StW1dXxEpaY/2H4OBg4uPjHc5JSEggOzs7l3Yk8RZSXEokEolE4iZCT/pco1Ahx+fziy0W7Om6GcIs0RJrFKHSPsiGh6v3NVEzRyUmBlatMhXBTKMUXVlIOqXszLCg5n6OBJYBxU1uQuXIkSN8++239OrVi1KlSjFs2DBNdVSwH70UxWWPHj345ZdfKFOmjG7uxo0bady4MaNGjeLKlSsu7TGvKVCgAI2EfjZSXPoXgwcPdnj++PHjrFq1Kpd2I/EWUlxKJBKJROImoaEuBbO8Qng4FHeiRfJT5BJEXXcIEEVCX+F4jsFduuf8KzpavV+Ma45k9YKkJNVL64DnGW8TsXTG30CmMLYM+BBojVHbFFtu3LjBihUryLJpYwIwffp0MjPF+0KlSpU0x0ePHqVfv37s2rWLJ554Qjc/KyuLMWPGEBMTw/Lly51/OX6EzLv0bxo2bKir6isiC/sEHlJcSiQSiUTiJhYLCMERn9O4sbquPTIzM7l06ZJmLJAjl1asuu7uu81YYn83uIPa+iA+Xr2Py8IyZ7kISEiAefPUMrMC8+nGdByLTy2LhOO7gfbACGAlkAb8BjwBpgUrfPfdd0RERNCuXTs+/vhjtm/fjqIousjlkSNHAAgPD+e7775j5cqV3HXXXbr7HThwgA4dOvDII4+Q5qi6kB9hVDE2v1TDzS84i17OnDkz4KLmdzpSXEokEolE4gFCcCTP1zNqPp4fxCWouq5AASNLrG10bxuwW3dty5YK8+erutC0FdYRcXFq+DM5GUaNgg4dIDycsTgscWuAKC67CMclUL/G74BjQDLwEWFhbXVWWBFrVPPVV18lJiaGypUrs0DobWIVl1Zat27Ntm3b+M9//kNIiL767k8//UTt2rVJSEjwe6EmRi5TUlJ0X68kb4mPj8fi4N2yzMxM/vjjj1zckcRTpLiUSCQSicQDTHapyLX1RHFZsmRJXWGTQOXQoUNs3rxZMzZwYD+rrrvFZMNrFy0yURXWHerXh9GjYelSklemsQZ9NNM+ZwGxl01nB/MtQH3gFS5c+JO1a88xa9YsnnrqKSIjI52uduzYMZYtW6YZO378OFu2bNEIxcKFC/Puu++yZcsW7rvvPt19UlNTGTx4MF27duXQoUNO180rqlatqquOK62x/kWFChVo166dwzlTp07Npd1IvIEUlxKJRCKReEBMDLRqlTtrxcaqWsYR+amYj8ivv2p7W0ZuJHhUAAByC0lEQVRGRjJ5chuWLoW0NDh3LouoKOP2BbkRZZv+s7OqryJLhOPigF7M2eOPP0Lp1asX3377LRs2bHBxbRVFUWjUqBEVK1bkiSee4LfffuPChQuA2uJkzZo1fPXVV5QoUUJ37eLFi6lXrx6ffPKJLufTH7BYLIbWWIl/4cwau2jRItlGJoCQ4lIikUgkEg8Z4aoT0ofr5LdiPrbMmKG1xD700EM51lCLBf7+eykpKWfyYmsAuB4UEy2x7QHzJYht16tSpYouQv32228zbNgwXREfI06cOMEPP/xAnz59iIyMpHXr1nz44YckJyfz9NNPs3PnTh566CHddVeuXOGVV16hWbNmJCYmmt57biGL+vg/Dz30EEWKFLF7Pisri19++SUXdyTxBCkuJRKJRCLxkLg439tj4+PN2Trza+Ty4MGDOvHSr18/zfHkycaWWPB95FJR4B/R4eqQbGCxMCbmWzomMVFdFyAoKIi6detqzoeFhfHNN99w+PBhduzYwSeffEL79u2d3jcrK4vVq1fz2muvcffdd1OhQgXefPNNBg4cyNSpUw3fsNiyZQvNmjXj3//+t66gVF4iRi4TExO5efNmHu1GYkSJEiV44IEHHM6RVWMDBykuJRKJRCLxAuPHm2qB6BbR0TBunLm5+TVyaWSJbd26dc7xhQsXmD17di7v6jaZmZCe7soVFlRb7BhutxxxlG+pJz0dLl68fVxf8Exbe11aLBbq1q3Lv//9b5YtW0bHjh1dWufkyZNMnDiRvn378vDDD1O5cmXuvfde3bzs7Gw+++wz6tWrx8KFC11aw1eI4vLSpUvs2rUrj3YjsceQIUMcnt+4cSN79+7Npd1IPEGKS4lEIpFIvEBEhFo0xtt9L8PD1fuarXB6p4jL3r17a6qlzpw5k6tXr9q93teRy+vXXb3CAtwDjERtOXIOqOryuteu3f53vXr1NOe2b99ueE21atU0x3369OHTTz+lQ4cOFCzo2JZ78+ZN/vrrr5wcT6OKtUeOHKFbt27Ex8dz5kze2ZQBypQpo7MFy7xL/6NTp05ERUU5nCML+wQGUlxKJBKJROIlYmLU7hTe0nPR0er9XOnJmB9tsQcOHHBqic1r25wTTWaCULeuKlTo9r9Fcblz505DUS2KrQsXLvDSSy+xdOlS0tLS+OOPP/jXv/5FlSpVnK7vqJDP9OnTqVOnDhMnTszTtiUy79L/KVCgAAOd5BZMnTrV79vfSKS4lEgkEonEq8TEQFKSmiPpCfHx6n1cEZaQPyOXYtQyKiqK2NjbLT8OHz7MqlWrHN7D1w+loaHej1o7Izwcihe/fSzaYjMyMjh+/LjuusqVK2uObXs/Fi9enB49evDVV19x8OBBdu3axWeffUbHjh2dRjWNSE9P5/HHH6dVq1Z5ZmuUFWMDA2fW2EOHDrF+/fpc2o3EXaS4lEgkEonEy0REQEICzJuntg9xhdhYmD9fvd6sFdZKZmamrphKfohcOrPEina5sLCwXNmXLRYLNGqUu2s2bqyua6VChQq6liFG1lhRXB49etRQfFssFmrXrs3//d//sWTJEs6dO8fcuXN55plnqFrVNQvvunXrqF27Nh07dmTDhg1kZ2e7dL0niJHLbdu2ObRQS/KGxo0bU6tWLYdzpDXW/5HiUiKRSCQSHxEXp9pak5Nh1Cjo0EEf3QoPV8dHjVLnrVplriqsEWLUEgJfXB44cIB/hDKsffv2zfm3oii6KrFGlSdzw04naJhcX89auMcWa1EfW0RxefXqVVN9BIsVK0b37t358ssvOXDgAHv27OHzzz+nc+fOFLL159pBURSWLVtG8+bNiYqK4pFHHuHnn3/m3LlzTq/1hMaNG2OxUeFZWVls27bNp2tKXMdisTiNXv7yyy9cdz3BWZKLSHEpkUgkEomPqV8fRo+GpUshLQ0yMiAlRf2clqaOjx6tzvMEUVyWLFlS1/sw0BCjlqVLl9ZYYjds2MC+ffs0c2zFZ27i63Y0ZtazVzHWlujoaF0hHltrrBksFgt33XUXL7zwAosWLeLcuXPMnz+f5557jurVqzu9/ty5c/z0008MHDiQqKgo7rvvPt577z02b97s9ahmaGgoderU0YzJvEv/ZNCgQQ7Pp6ens2DBglzajcQdpLiUSCQSiSQXsVjU/LzISPWzra3RU/JjMZ8ZM2ZojkVLrBi1rFmzJo0M/Km5EbmMiYFWrXy+DKDap43ejDBTMTY4OJgKFSpoxlwVlyJFixalW7dujB8/nv3797N3716++OILOnbsSHBwsMNrs7Oz+euvv3jzzTdp2rQp5cqVY+jQoUyfPp20tDTzm1AU9R2b1FT1s83PXOZdBgZVqlShlZNfImmN9W+kuJRIJBKJJJ+Q34r57N+/ny1btmjGbKOS165d4+eff9acHzJkCEFBefd4M2JE3q5jVDHWKBLoqKiPN6hZsybDhw9nyZIlZGZm8vnnnxMZGWnq2rNnzzJlyhTi4+MpXbo0LVq04N1332XTpk36r8XWcx4RAWFhEBWlfo6IyPGcNxPEtIxc+i/OrLFz584l3bWmspJcRIpLiUQikUjyCfktcunMEjt//nzdQ+bgwYMN75VbLQzi4nxvj42Pt5+XK9piL1++bCgcfS0ubSlSpAgvvPACJ0+e5KOPPjKVn2klOzubv//+m7feeotmzZpRtmxZhgwZwrRXXiG1RQto0ADGjIHly0EUHOnp6viYMTQdPVpzas+ePVy4cMEbX57Ey/Tp08dhZeLr168zc+bMXNyRxBWkuJRIJBKJJJ+Q3yKXRpZYW4ulaImNjY11uYqpLxg/3nu9TkWio2HcOPvny5YtS7hQNcpMxVhfiksrISEhvPLKK+zcuZOOHTu6dY+UlBSmTp3KoE8+ofTff9MceAfYCDjK1GwAiHJl8+bNbu1B4lvCw8Pp0aOHwzl53ddWYh8pLiUSiUQiySfkJ3G5b98+tm7dqhnr169fzr9TU1N1hT2GDh0KoKkMaiU3m69HRMCiRd7vexkert7XUYsai8Wis8YaFfWpVKmS5jg3xKWVatWqsXjxYqZOnWrXKmuxWHRFh0QUYAPwNnAvUAYYDCQAqcLcQkBDYWzT3Lku712SOzizxq5Zs4bDhw/nzmYkLiHFpUQikUgk+YT8ZIsVLbFlypTRFPr45ZdfuHHjRs5x4cKF6dOnT67tzxkxMWpbGW/p++ho9X4xMc7nmqkYmxeRS1ssFguDBg1i165dOW8K2KIoCllZWVSoUIG+fftSq0oVp/dMRRWWg4HSqILzbVQBehMQO8Vs/OorNWczr3FQiOhOpWvXrpQqVcrhnISEhFzajcQVpLiUSCQSiSSfkJ8il65aYnv16kVYWBhgHLnMC2JiIClJzZH0hPh49T5mhCWYqxgrisvz58+TkZHh9h7dJTIykp9++omlS5dSrVo13fnjx4/z66+/0ursWf4BvgS6A84a7CioVtl3gOaoUc1EYc6mGzegSxe1H1BuY7IQEQY/uzuBggUL0r9/f4dzpk6dmquOBIk5pLiUSCQSiSQfkJmZyaVLlzRjgRq53Lt3r67Jva0ldvfu3bpqn0bRL1vy6iE0IgISEmDePLV9iCvExsL8+er1jqywIqK43L17Nzdv3tSMibZYyP3opS0dOnQgOTmZkSNHGrYu+f7yZboCkcAfQBqwBPg/oLaJ+6cBfwtjx4G5J09y87nnPNq7S8yfr/5gTRYiIiZGnX8H9na0V5zLyu7du0lMFN8ykOQ1UlxKJBKJRJIPEKOWELji0sgSe//99+cci8U8ypQpoykQ4y+RS1vi4lRbq23ASszJDA+/HbBKTlbn26sK6wjRFnv16lUOHjyoGStcuDBlypTRjB09etT1xbxI0aJFGTNmDImJibq+lABngP5AT+As0BH4DNgFHAS+AnrgPKppS0+g9M8/M7B1ayZPnszZs2c9/CrskJamhqC7d4c1a1y7ds0a9QU0aFDeRFnziBYtWlC9enWHcyZOnCpdxH6GFJcSiUQikeQDRHFZsmRJihZ15THbfxAtsX369MmJZmVnZ+vEZXx8vPPiL37yBFq/PoweDUuXqjohIwNSUtTPaWnq+OjR6jx3iYqKIioqSjPmLxVjzdCwYUP++usvvvjiC4oZ9CydB9QFvkDNpQSoCvwLNap5DlgKvATUMbHeOeDn1at5+OGHKVOmDE2aNOE///kP69ev10V83SIpSY1UTp/u2X2mTVPv4w95ormAxWJxGr38+uvplCqVdae7iP0KKS4lEolEIskH5JdiPnv27CEpKUkzZmuJXbVqFceOHdOcFy2x/hi5NMJigdBQiIxUP3tz22YqxvqruAQIDg5meNu27MzOprvB+UvAi0ALYJtwrhDQAfgU2AkcAr5GjVKGmFg7MTGR999/n5YtWxIVFcWAAQP46aefOHPmjOtfSFIStGkDBs4Ctzh5Elq3vmMEZnT0ICczznL+/FLpIvYjpLiUSCQSiSQfkF+K+YiW2LJly9KyZcucY7GQT0xMDA0bik0m9PhL5DK3CISKsU6ZPp1KqNHIGahFeUQ2AY2BkcAVO7epAjwNzAEm25ljj/T0dH755RceeeQRypYtS+PGjXnjjTdYt24dWVlZji9OS4OuXfU5lZ6Snp53hYhyCauLeNiwmqglmRyhdTLcoS5iv0GKS4lEIpFI8gH5JXIpiktbS+zly5eZOXOm5vzQoUN1kcpAiVz6EncqxvqduLxVtMkC9EXNrXzSYNpNYCwQAyxzcsv7DMbeBR4AihsUEhL5559/GD16NPfffz9RUVH079+fH3/8kdOnT+snP/+89yKWIidPwvDhvrl3HqN3ETu2xsJsIFM3eoe5iP0GKS4lEolEIskH5IfI5e7dux1aYmfPns3FixdzjoOCgog32efjTotciuJyz549mr6g4OfiUlHgn380Q+HABGAVUMvgkgOoRX5ecnDbiugjoNVR5UlaaCjLly3j5Zdf1n3/jDh//jwzZszg0UcfpVy5cjRq1IjXX3+dtWvXkvXHH57nWDpj2jS1+mw+wthF3B9wlFN9Bfjd8Mwd5iL2C6S4lEgkEokkH5AfxKUYtSxXrpxDS2zHjh0Nv04ZudSLyxs3brB//37NmNiO5NSpU1y7ds3nezNFZqZdO2ksap7lmxjnUDZycFsLINahtTa1KXj+PO2aNePjjz9m+/btHD16lAkTJvDggw8SGhrqdMtbtmzhgw8+oFWrVkT17k0/YBLgo9ilykcf+fLuuYp9F3Ek4Kxs8lS7Z+4AF7FfIcWlRCKRSCT5gPxgizWyxAbdqhZ68uRJli5dqjnvrLelLXda5LJUqVK614BojRUjl4CuWFKecf26w9OFgHeArUBLm/GOgLMSMM2E4022BzbiumLFijz55JP8/vvvpKamsmLFCl599VViYmKcrADns7L4FXgMKA/cDbwGrAZuOLjOZVavzjclUh27iJ1ZY5fjSMbnYxex3yHFpUQikUgk+YBAj1zu3r2bZMG71rdv35x/T5s2jezs7Jzj4sWL06tXL8N7ycilirOKsSVLlqREiRKaMb+xxhYsaGpaXVTB9jVQFvgGNTrpCFFc/oON4CtUyM52CtKmTRvGjh1LUlISx44d47vvvuOhhx4yFdXcBnwItAaigD7AD8AJp1eawNf221xg/nxnX0YPoISD8wowzeEa+dBF7JdIcSmRSCQSSYCTmZnJpUuXNGOBFrl0ZIlVFIWffvpJc75v374u9fG80yKX4F7F2KNHj/p0T6YJDYXwcFNTg1CrwR4GqpmY30Q4vgrsAHW94sVNrVmhQgWeeOIJfvvtN9LS0li5ciUjRoygQYMGTq+9APwGPAFUABqiVrtNNbWyARs3Op/j54wd62xGYdSyTo6wb421ko9cxH6LFJcSiUQikQQ4YtQSAk9czpgxQ3Pct2/fHEvstm3bdJbOIUOG2L2XjFyqBHTFWIsFGjnKntRjHHPUE4FaxMeWjQCNG7vVbDQkJITWrVvz4Ycfsm3rVo6HhfE90BvHsTYrScBnmN+/jsREtQBSgJKcrLYPcY7933mVbYDjyj35yEXst0hxKZFIJBJJgCOKy5IlS7oU1ctrdu3apRM+tpZYsZBPxYoVad26tUtr3ImRS1Fc7tu3T1ewx2/FJUAz0cDqPcSiPpu8tV5mJuUvXOBxYCZqNHIVamTSUTfWWMC5udYO6elgU0U50DDv6m0FVHIyx3n0Mh+4iP0aKS4lEolEIglwAr2Yj2iJjY6O5r771I6EWVlZJCQkaM4PGTIkJ6pphIxcqoji8ubNm+zdu1cz5tficuBAn91alJEbvbWeUIgoBFU4jkEtPnQCNdeyDxBmM6+rp+v6S5VfNzDv6g3CebmmBNTOp95YT+IOUlxKJBKJRBLgBHoxH9ESa1sldsmSJZw9e1Zz3pEl1h53YuSyRIkSVKxYUTPmrGKsX4nLmBho1contxYjlzuAS1Wren5jJ4WIolEryP6KGtVcjVpFtqen69opROTvGLQzdYKzqrEnUGPF9glwF7HfI8WlRCKRSCQBTiBHLnfu3KkrNNOvX7+cf0+ZMkVzrlmzZtSuXdvhPWXk8jbOKsaK4vLYsWOaqrx5zogRPrntPUCwzfFN1D6VHuNCIaICqEbPD4CanqzpQiEif8NBO1M71EXtZFoEaG5nzhQ74yoB7iL2e6S4lEgkEokkwAnkyKVoiS1fvjwtWrQA4MKFC8yePVtz3pXelrZkZ9+ZoQpnFWMrVdLmsN24cUP3ZkWeEhfnE3tsMaCeMLZp0yajqa7hRiEij3GzEJE/4KSdqR2mAmeAJahVZEV+Ay47vEMAu4j9HikuJRKJRCIJcAJZXDqyxM6cOZOrV6/mnAsJCaF///5275WcDKNGQc+e+gftBg2gQwf1/J1ULdJZxdgyZcpQULBy+pU1FmD8ePDBa1qXd+mtZDwfFiLyi/W8iMl2pgJ1UMsfhWJsKM4E/nB4hwB1EQcEUlxKJBKJRBLgBKotdseOHezcuVMzZmuJFavExsXFERkZqbvP/PkQG6sKyDFjYJVBytWFCwrLl6vnY2LU+QsWeOfr8GdEcXngwAGuXLmScxwUFKSLXvqduIyIgEWLTNtNzdJUqKjslcgl+LQQkV+s50VccBHbwV6BH/tVYwPYRRwQSHEpkUgkEkmAE6iRSyNLbPPmah7VoUOHWL16tea8WMgnLQ3i46F7d7FPnnOL4Jo1quNy0CD1PvmVunXrao4VRWH37t2aMTHv8ujRoz7fl8vExKjvGnjrtR0dTbMff9QMHThwgDRvvBh8WIhIR2wsCNbnQMJzF3EXoJTB+CLgrMF4QLuIAwIpLiUSiUQiCWAyMzO5dOmSZixQIpeiuOzbt2+OJXbqVG3kITw8nLi4uJzjpCQ1Umm+Z51xzuW0aep9kh33Xg9YihUrRlWhCmpAVYy1JSZG/cHHx3t2n/h4SEqiXq9eFC6szdnbvHmzZ/e24qNCRHm2jg/xzNVbEOhrMH4T+MUH60mcIcWlRCKRSCQBjBi1hMAQl44ssYqi6CyxAwYMoNCtRKmkJGjTBgy+9Fu4FpY4eRJat86/AtPVirF+Ky5BtcgmJMC8eWrUzhViY1UPdUICREQQEhJCIyFs5rW8Sx8VItIQHw/duvl2jVzA82+TPWuscdXYAHYRBwRSXEokEolEEsCI4rJkyZIUFXLJ/BGxkE+FChW49957Afj777/Zv3+/5ry1SmxaGnTt6mr7ArAXubSSng5duuRPi6yzirEBJS6txMWpNllrFacOHfTJe+Hht6s4JSer8wUx1rSptuOl1/IuwWeFiAD1vuPG+ebeuYznLuKWQCWD8U3AHs1IgLuIA4ICeb0BiUQikUgk7hOIxXwURXFoiRWjljVr1swRns8/7yhiacW9hKqTJ2H4cDWwlZ9wVjHWSFwqihIY/ULr14fRo9V/K4rawPDaNbUcaPHiTpPrmgkeyY0bN3rva7cWImrd2p13Q+wTHq7eNyLCe/fMY0aMEPOmXSEIiAc+NDg3FXhPs47Et8jIpUQikUgkAUwgFvPZsWMHu3bt0oz17avmTV27do1fftHmSg0dOhSLxcL8+a7kWIqY63M5bZrqnMxPiOLy8OHDXLTpIi+Ky4sXL5LuTTGUW1gsavnRyEj1swmBKEYuz5w5w/FduyA1FTIyVMHqCT4oRMSqVep98xGeu4jt5eFOxfq7n09cxH6PFJcSiUQikQQwgRi5FKOWFStWzIlMzp8/XydsBg8eDMDYsWZX8Czq9NFHHl3ud9SuXTsnKmzFVtxXqFBBF6kLCGusF6hRowYlQ0M1Yxvr1YOoKAgLU6ODnjZI9XIhovwmLK145iKOufUhchhYn59cxH6PFJcSiUQikQQwgRa5VBRFl2/pyBLbunVrqlSpQnKyJ7Y5MBu5BFi92n0d4Y8UKVKE6tWra8ZsrbEhISG6180dIS7nz8fSujVNMzM1w5qsy/R0vNIg1YuFiPIrnrczNS7sU7DglPzmIvZrpLiUSCQSiSSACTRxuX37dl2fRaslNjU1lfmCJ9VayMc1O6zn+XLu22/9k3xVMdZThAapYmcKh/ViPW2Q6qVCRPkVz1zExr7awoVncNdd1zzal8Q8UlxKJBKJRBLABJotVrTEVqpUKccS+/PPP5OVlZVzrnDhwvTp0wcAzztEuJY7562OFP5CvqwY6w4GDVKbClM2A9nO7uNpg1RrIaKlS1WRmpEBKSnq57Q0dXz06DuytKn7LuJKgD4qnJGRzgJ3os0St5DiUiKRSCSSACaQIpdGltg+ffrk5PuJlthevXpRokQJFAX++ceVlTyPXCYmel7LxZ9wtWLs0aNHfb6nXMdOg1QxcpmJ2MDCDt5qkOpGIaL8jvsuYmNr7NSpU72yL4lzpLiUSCQSiSRAyczM5NKlS5oxf45cbt++nT17tI/t/fr1A9QCM2KPQaslNjPTu50czJCerna1yC+I4vL48eNcuHAh5zjfRy4dNEgtB5QXxkx3u8zPDVL9AFddxGvX9iEkJER3n3nz5gVmBeQARIpLiUQikUgCFDFqCf4tLsWoZaVKlXL6DE6ZMkVzrkyZMnTs2BGA69ddXakgcMHmIwOo6vJ+r+WjNK277rqL4OBgzdjOnTtz/p3vxaWTBqku5V2KWBukSnyGWRdxy5al6Nq1q+7669ev6yz5Et8gxaVEIpFIJAGKKC5LlixJ0aJF82g3jrFXJdZisZCdna2zrQ0aNIgCBQoAULCgq6tZgBI2H6G488hTqJDLl/gthQoV4q677tKM2VpjRXGZkpLC5cuXc2VvPsdEg1Qx79LllNv82CDVT3HmIh40yNgaK76BJfENUlxKJBKJRBKgBFIxn+TkZPbu3asZs1piV61axbFjxzTnrJZYUB8g3W9P4B7h4VC8eO6u6WscVYwVxSXko7xLEw1SxcjlNsDlwHV+a5AaoPTo0YNQoXcpwNq1azl06FAe7OjOQopLiUQikUgClEAq5iNGLStXrkzTpmq8SCzk06BBAxo2bJhzbLFAo0a+36MtjRvnv7oqjsRlsWLFiBAaAeYLa6zJBqlNhOPrQJKra+W3BqkBSpEiRXjooYcMzyUkJOTybu48pLiUSCQSiSRACZTIpSNL7KVLl5g5c6bmnG3U0kozMbTkY3J7vdxAbEciVoytVKmS5jhfiEuTDUvDgFrCmOmiPm6sJ/Et9qyxU6dORclPZaD9ECkuJRKJRCIJUAIlcpmUlMS+ffs0Y1ZL7OzZs7loU5Y1KCiIeIMGdwON+6P7jNxeLzcQI5enT5/m3LlzOcf5sqiPCw1LPSrq48Z6Et/Rrl07ypQpoxvfs2cPmzdvzoMd3TlIcSmRSCQSSYASKOJSjFpWqVKFJk1UI6Joie3YsaNhBDYmBlq18t0ebYmNzZ+962vUqKFr0+Ao7/LI3r1qOc5AjfS42CBVLOrjVuQyvzVIDVCCg4MZMGCA4TnZ89K3SHEpkUgkEkmAEgi2WEVRdC0ArJbYEydOsGzZMs05I0uslREjfLLFPFsntwkJCaF27dqasRxrbHIylQWb7NGZMyEsTO1ob20kGEg5hS42SLWNXFYD7gayXV0zvzVIDWDsWWOnT5/OjRs3cnk3dw5SXEokEolEEqAEQuRy27Ztdi2x06ZNIzv79uN78eLF6dWrl917xcX53q4aHw/duvl2jbxEV9Rn/nw1VNugAZWXL9ecyzHFpqfD8uUwZowaQo6NhQULcmfDnuBig9S7gUVAKnAASMDNB+X81CA1gGnSpAk1a9bUjaekpLB06dI82NGdgRSXEolEIpEEIJmZmVy6dEkz5o+RSyNLbOPGjVEUhZ9++klzrm/fvk77dI4fD77S0NHRMG6cb+7tLxiKy1vVVMVmJCeALKObrFmjKv1Bg9QO9v6Kiw1SCwGdgQhnE53eKB81SA1gLBaLw8I+Et8gxaVEIpFIJAGIGLUE/xOXRpbYfv36YbFY2Lp1qybfDxxbYq1ERMCiRd7vexkert43wmNl4d/oKsba/FsUlzdRBaZdpk2DBg3Udh/+iGyQesdjT1zOnj2bzMzMXN7NnYEUlxKJRCKRBCCiuCxZsqTTqF9us3XrVvbv368Z69u3LwBTpkzRjFeqVInY2FhT942JgVWrvBfBjI5W7xcT4537+TP1goM1x6nA2Vv/jgDEV5DTerEnT0Lr1v4pMGWD1DueGjVq0Mygr9CVK1f4/fff82BH+R8pLiUSiUQiCUACoZiPGLWsWrUqjRs3JisrS9fMfMiQIQQFmX8siYmBpCQ1R9IT4uPV+9wJwpK0NKoNG0ZhYdgaP7agj16aakaSng5duvinRVY2SL3jsRe9FN/gkngHKS4lEolEIglA/L2Yj6IounxLqyV2yZIlnD17VnNuyJAhLq8REQEJCTBvnlpjxhViY2H+fPX6/G6FzeH55wk+dYo6wrCtNbaScM50p8uTJ2H4cLe35jNkg9Q7nn79+hm+cfXnn39y4oRD47fEDaS4lEgkEokkAPH3yOWWLVs4cOCAZsxqiRV7W957773UqlXL7bXi4lRba3Ky2i2jQwd9ql14+O1uGsnJ6vz8XBVWx/z5MH06APWEU7aZr25FLq1Mm6au40/IBql3PGXLlqVDhw66cUVRmDZtWh7sKH8jxaVEIpFIJAGIv0cuRUtstWrVaNSoEefPn2f27Nmac+5ELY2oXx9Gj4alS1WHZkYGpKSon9PS1PHRo+/Q5/+xY3P+6TNxCfDRR65e4Xtkg9Q7Hlk1NveQ4lIikUgkkgDEn8WlkSW2b9++WCwWZs6cyTWbPoAhISH079/f63uwWNRioZGR6uc7usZKcnJOuxEAUVtvB5Rb//ZYXK5eDdu3O5+Xm8gGqXc8Dz74IEWKFNGNJyUlkZSUlAc7yr9IcSmRSCQSSQDiz7bYLVu2cPDgQc1Yv379AL0lNi4ujsjIyFzb2x3JLTusFTFyeR6wvppEcXmU28LT3fX8Atkg9Y4mNDSUnj17Gp6T0UvvIsWlRCKRSCQBiD9HLsWoZfXq1bnnnns4ePAga2wiaGCut6XEQzZu1BxWRt9yZIfNOVuuAikerucXyAapdzz2rLEJCQncvHkzl3eTf5HiUiKRSCSSACMzM5NLly5pxvwlcunIEitGCEqVKkU3aSX0LYrC/7d35+FRVuf/xz8TsrAETAhrQHZZhEQETRFZZBEVpCgCSrDmp1KMIm5QI7jEWgHBfhXFUhWkVXYXqhUs2LIJqECCCmGTJQHCIlsiCUsCYX5/TJnyzJLMZNZk3q/ryiXnzDnPuYc+DXPPOc852rzZUBUm+9nLywtZ4yWF27zm9tLYzEzLuMGGA1JD2m233abatWvb1R8+fFirV6/2f0CVFMklAAAVjO2spRQ8yeXmzZuVnZ1tqBs2bJjMZrPduXL33XefoqKi/Ble6CkosJxDacPZpj5VJHWT1FvSg5JellTX3THz8qTCQnd7+QcHpIasyMhI647Vtjjz0ntILgEAqGBsk8uYmBhVr2670DEwHC2J7dixo77//nvt2bPH8BpLYv2guNhhdWk7xq6StELSbEnpkpqVZ9wrNm0KOhyQGrKcLY397LPPdPbsWT9HUzmRXAIAUMEE62Y+ZrPZ7giSYcOGyWQy2W3k07p1ayUlJfkzvNAUGemw2nbH2G0qx8Y9pakIM9IckBpybr75ZjVp0sSuvrCwUF988UUAIqp8bJfVww2nTp1SRkaGsrOzlZ+fL7PZrKuuukqNGzfWjTfeqAYNGgQ6RABAJRSsm/lkZmY6XBJbVFSkhQsXGuofeOABmUL6fBA/qVnTkiDZLI21nbkskHRQkv3H7nKIjZWio71xJf+4fECqZHlWtLDQMvMaFWV5H9ynlUZYWJiSk5P12muv2b02d+5cDff1kTUhIGiTy3379mnTpk3KyMjQpk2btHnzZhUUFFhfb9q0qXJycvwel9ls1qJFi/SXv/xF69atK7Xt9ddfr9TUVD300EMKD/fuX/W7776rRx991K4+OztbzZo18+pYAIDgEqwzl7ZLYlu1aqXrrrtOixcvVn5+vuE1Z8vT4GUmk9Spk7RihaG6saRakk5fUbdNXkouO3euuAnZ5QNSa9YMdCTwkREjRjhMLpcvX65ffvlF9evXD0BUlUdQLYtdvXq1brvtNsXFxally5a677779Oc//1lr1qwxJJaBcvToUfXp00fDhw8vM7GULOd8PfLII+rSpYvdcyaeOHjwoNLS0rx2PQBAxRKMM5fuLInt2bMnX4T6k4PlxyY53zHWF+MBwaJDhw5KTEy0qy8pKdGiRYsCEFHlElTJ5Y8//qivv/5ap06dCnQodo4fP65evXpp1apVhvqIiAglJSVp6NChuvfee3XzzTeratWqhjaZmZnq1auX9u93ezNvh1JTU3X69OmyGwIAKqVgTC4zMjLsVhQNHTpUx48f11dffWWoZyMfP3Oy1K+0TX18MR4QLJKd7BZse1wS3BdUyaUzUVFRatmyZUBjeOqpp7Rz505DXWpqqnJzc7VhwwZ9/PHHWrhwodatW6cjR47oueeeU1jY//56c3Nz9cgjj3gcx5w5c6z/SNdkyQYAhKRgXBZrO2t5zTXX6LrrrtPChQt18eJFa33VqlU1ZMgQf4cX2hISpO7d7ap9klz26GF5hhEIYs6erdy0aZN27drl52gql6BLLiMiItSxY0eNHDlS7733njIzM1VQUKBZs2YFLKacnBzNnz/fUDd+/Hj99a9/Vb169ezax8TEaPLkyXrrrbcM9cuXL9eGDRvKHcexY8f01FNPWcuTJ08u97UAABVXsM1cms1mu+ctnS2Jvfvuu1WrVi1/hgdJcvA4jW0KuF3SJR+MAwSbJk2aqIeTY2iYvfRMUCWXKSkpOn36tH744QfNnDlTo0aNUqdOnRQRERHQuL788ktDuX79+kpPTy+z3+jRo+3WdNteyx2jR4+2Lhnu0qWLww19AACVW0FBgc6cOWOoC/TM5aZNm+we/Rg6dKh27NihjIwMQz1LYgNkwAC75aq2M5dnJeV4MkZyMsd0oMJwtqnY3LlzdemSx1+zhKygSi5jY2PtnlcMBvv27TOU+/XrpygXzm8ymUwaOHCgoW737t3limHx4sX69NNPJVlmd2fOnGlYdgsACA22s5ZS4JNL2yWxrVu3VmJioubMmWOob9Cggfr27evP0HCl6dOlK2a5G0iyOdWx/Etj4+Olt98ub2/A74YMGeJwAisnJ0fffvttACKqHMhOXGD7DXHjxo1d7nv11Vcbynk250y5Ii8vT6NHj7aW09LS1IHnGQAgJNkmlzExMapevXqAonG8S+zQoUNlNpvtkssRI0Z4/WguuCEuTlq2zHIOpSw7xtp+mijXjrGxsZbrxsV5GCDgP7Vr11Z/JzPttr+74DqSSxc0aNDAUD5//rzLfW3b1q5d2+3xn376aR09elSS5dvgF154we1rAAAqh2DbzMfRkthhw4Zp9erVys3NNdSzJDYIJCRIa9ZYZzA93tQnPt5yvYQEb0QH+JWzpbEff/yxioqK/BxN5UBy6YLuNjusbd682eW+mZmZhvKNN97o1tjLly/Xhx9+KMmyzPb99993aUkuAKByCrbNfGw38mnTpo0SEhLsNvJJTEx0eLYcAiAhQdqyRUpO9iy5TE62XIfEEhXUnXfe6fD0hfz8fC1dujQAEVV8JJcu6NOnj9q0aWMtr127Vlu2bCmz36FDh/TZZ59ZyxEREU63PnaksLBQo0aNspZHjhypnj17utwfAFD5BNPMpbMlsWfPnrXuE3AZs5ZBJi5OmjdPHSZNMlTvkFRSVt8ePaSlS6V581gKiwqtWrVqGjx4sMPX2DW2fEguXRAWFqbZs2dbZwwvXbqkIUOG2B0WfaVffvlFd911l86ePWute+GFF9z6hjktLU0HDhyQZFmaO3Xq1PK9AQBApRFMM5cbN260/jt12bBhw/SPf/zDsF9BWFiY00PLEVjtR440lIsk7bU9KiY2VurbV5owQdq61bIMll1hUUk4Wxq7ZMkS6ykNcB1P1buoa9euWrJkiZKTk3X8+HHt3r1biYmJevjhh3X77beradOmMplMys3N1YoVK/T+++/r5MmT1v6PPPKIXnzxRZfHW7t2rf76179ay9OnT1dMTIw335LLjh07puPHj7vVZ8+ePT6KBgBCWzAll46WxHbo0EFjx4411Pfr1y/gz4bCsbp166pu3bqGf+e3/e1van3rrVJRkRQVJUVHSyZTAKMEfKd3795q0KCBdX+Tyy5cuKBPPvlEjzzySIAiq5hILt3Qt29f7dixQ9OmTdO8efOUnZ2tadOmadq0aU77tG3bVq+88oqGDh3q8jjnz5/XyJEjZTabJUkDBw7UkCFDPA2/3GbMmKE//vGPARsfAPA/gVoWazZLBQVScbEUGSnVqHHJbknssGHDdPjwYf3nP/8x1LMkNrh16NBBq1atspaztm3T3YMHSw6eRQMqmypVqui+++5z+Hl+zpw5JJduYlmsmy5evChJLm2q07VrV7399ttuJ4YvvfSSfv75Z0lSzZo1NWPGDPcDBQBUSv6cudy61bISsm9fy6N1V10l1a1r+W9s7EYdPHjQ0H7YsGGaN2+e9ctRyfLv2KBBg3wWIzzXvr1xW59t28p92iVQITlbGrt+/Xq78+5ROpJLN8ycOVMtW7bUq6++qp07d5bZ/ttvv1W/fv2UmJio9evXuzRGRkaG3njjDWt50qRJbp2rCQCovAoKCuzOXvbFzOXSpZY9WxITpcmTpRUrJNtjmgsKjEtiq1Vrq5yca+12iR06dGhAz+FE2UguEeo6d+6sa665xuFr8+bN83M0FRvJpYsmTpyoUaNGGf5Rv+GGGzR79mzt3r1bZ86c0blz55Sdna0FCxaoV69e1nZZWVnq2bOn9UgRZy5cuKCHHnpIJSWWfdq6dOmixx57zDdvyA2PPfaYsrKy3Pr5/PPPAx02AFQ6trOWkneTy5MnLadL3HmntHZtaS0vSTLuBnvu3DANHPiTXWLCktjg16FDB0N5165dunDhQoCiAfzPZDI5nb2cO3euYTUGSsczly5YuXKl3WY8L7/8sl566SWZbB5wb9asmZo1a6b77rtP77//vlJTU2U2m1VSUqKHH35YrVq10s033+xwnEmTJmnr1q2SLMeWzJw5U2Fhgc//69Wrp3r16gU6DAAIebbJZUxMjNdmBbdske64Q3KQvzqwQdJBm7phkmYZapo2bWp3VjSCj+3M5YULF7R7925de+21AYoI8L8RI0bo5Zdftqv/+eeftWnTJiUlJfk/qAoo8JlLBfD8888bvrFISUlRenq6XWJpa9SoUXr++eet5ZKSEj355JMO22ZlZWnSFWdNpaWl2X2TCAAIbb7azGfLFumWW1xNLCXpY5tyO0mtJc031Pbrd39QfEmK0sXGxtrdSyyNRahp1aqV0wSSMy9dx2/8Mhw6dEjff/+9oS49Pd3l/s8995yqVatmLWdmZmrLli127SZOnKji4mJJlg8L999/v3Jycsr8sZWbm2t4/fTp0y7HCgAIbr7YzOfkScuMpe0zlc7ZL4m1zFp+LemYofaLL36nK07lQhCz/UKb5BKhyNnS2IULF7JU3EUkl2X48ccfDeUWLVqoefPmLvevUaOGunTpYqjbsGGDXbtz585Z/3zkyBG1bdtWzZs3L/PHVvfu3Q2vz5492+VYAQDBzRczl2PGuDNjKUnfS8q1qRsq6SObut/o2LE2euIJT6KDv9gujc3KygpQJEDg3HvvvapSpYpd/fHjx/X1118HIKKKh+SyDPn5+YZygwYN3L6GbZ8TJ054EhIAIER5e+Zy6VJpwQJ3e31iU75WUiNJX9jUWzbymT/fMg6CGzvGAlL9+vXVt29fh6+xNNY1JJdliImJMZRtt4B3RWFhoaEcHR3tSUgAgBDl7eRyyhR3e1ySfXI57L91RVfURUi611qaOrUcwcGvbJfF7t69W0VFRU5aA5VXcnKyw/rPP/+cx81cQHJZBtt/uHft2qWzZ8+6dY3Nmzcbyo5mPz///HOZzWa3f2xlZ2cbXn/qqafcihUAELy8uSx269ayjhtx5DtJh2zqHC2JvVNSnLX0zTcSqyyDm+3OsCUlJdq1a1eAogEC5+677zbsl3LZ+fPntXjx4gBEVLGQXJYhMTFRsbGx1vL58+c1Z84cl/svWbJEhw4Z/yHu1q2b1+IDAIQOb85cur8cVrKftWwvqaqkdTb19mdblm88+EutWrV09dVXG+pYGotQVLNmTf32t791+Jo7OUCoCsnk0mQyGX5Wr17ttG2VKlU0ZMgQQ91zzz3n0oPuBw4cUGpqqqHu5ptv9uqB1wCA0FBQUGD3aIYn/55s3OhuD0dLYodKsn0Oqbak/l4YD/7GjrGAhbNdY1etWqXcXNsNzXCloEsubY/SuPxz9OhRQ7uLFy86PZ7D2xvmvPTSS4bp8fz8fHXt2lXvvPOOwyWyxcXF+vDDD9W5c2e7WcvJkyd7NTYAQGiwnbWUyp9cms2SzRMbLvhOkm0MQ2S/JHa4pEi73pmZlnERvNgxFrC47bbbVLt2bbt6s9ms+fPnO+iBy8IDHYCtbt26af/+/WW2O3TokNMjQVJSUvT3v//dazE1btxY8+bN09ChQ1VSUiLJ8g3ymDFj9Oyzz6pz586Kj49XWFiYjh49qoyMDLtNfCTLWZbdu3f3WlwAgNBhm1zGxMSoevXq5bpWQYE751petk2WjXoun/XWQdKvkvbatPudw955eVJhoVSzprvjwl/YMRawiIyM1LBhw/Tuu+/avTZ37lw9++yzAYiqYgi6mctgdffdd+uLL75Q/fr1DfXnzp3TunXr9PHHH2vhwoVavXq1XWJZo0YNTZ8+XRMmTPBnyACASsSbm/kUF5en1yhJxyR9KGmApBGyn7VsLSnJ6RVs3gKCjO2y2L179xrO4QZCibOlsVu3btVPP/3k52gqDpJLNwwYMEDbt2/XpEmT1LJlyzLb169fX+PGjdO2bdv0+OOP+yFCAEBl5c3NfCLtV626KEaWzXqWSHpK0iKb1x+QZHLa+4UXyjsu/KFdu3aGstls1o4dOwIUDRBYXbt2VZMmTRy+xpmXzgXdsticnByfj+HoCA9X1a5dW+PHj9f48eOVm5urzMxMHTlyRPn5+TKbzbrqqqtUt25dXX/99WrVqpUXo3bMk/cCAKg4vDlzWbOmFBtbnqWxV1oiKd+m7v5Se3zyibR0qTRggCfjwldq1Kih5s2bKzs721q3bds2derUKYBRAYERFham5ORkvfbaa3avzZ8/X6+99pqqVKkSgMiCW9AllxVJ48aN1bhx40CHAQAIAd6cuTSZpE6dpBUrPInIdknsLZKaltlr6lSSy2DWoUMHu+QSCFUjRoxwmFwePnxYq1atUt++fQMQVXBjWSwAABWAN5NLSUpy/mikC45L+pdNnf3Zlo58843EJqTBix1jgf/p0KGDEhMTHb7GmZeOkVwCAFABeHNZrCQNH+5J74WSLl5RribpHpd7L1jgydjwJXaMBYycbeyzePFiu7OHQXIJAECF4O2Zy4QEqfynY9kuib1bUi2Xe2/cWN5x4Wu2O8bm5OQ4PF4NCBXDnXwTV1hYqH/+859+jib4kVwCABDkCgoK7L4h93TmUpLS0srTa7ukDJs6x2dbOpOZKbEfXXBq27atwsKMHw+3b98eoGiAwLv66qvVo0cPh6+xNNYeySUAAEHOdtZS8k5yOWCANGSIu73CJaVIqvHfcgNJ7m1qkZcnMRkWnKpWrWp33BpLYxHqnC2N/frrr/XLL7/4OZrgRnIJAECQs00uY2JiVL16da9ce+JEd3u0lvR3Sb9ImiPpTyrP5vNFRW53gZ/YLo0luUSoGzJkiCIiIuzqS0pKtHDhQpnN0unT0okTlv+G8soMkksAAIKctzfzuVKDBuXtWUOWcy1Hlqt3VFR5x4WvsWMsYFS7dm3179/f4WsvvjhXcXHSVVdJdeta/hsXJ/XtK02YEHq7Y5NcAgAQ5Ly9mc+VataUYmO9djmXxMZK0dH+HROuY8dYwJ6zpbEFBRnKy9tpqMvLs5wjPHmyZfO0Hj2kr77yR5SBR3IJAECQ8+XMpckkderktcu5pHNny7gITrbLYnNzc/Xrr78GKBogONx0050KD6/p5NW5pfZdu9byjPuIEdLJk96PLZiQXAIAEOR8OXMpSUlJXr1c0I0H97Ru3Vrh4cbnaJm9RCjbskX6zW+q6eJFZ+f5zpV0qczrzJ8vJSZKW7d6NbygQnIJAECQ83Vy6eQYN5/x93hwT2RkpK655hpDHcklQtWWLdItt0iWX8PJTlrtl7TepesdPiz17Fl5E0ySSwAAgpwvl8VKlmeCunf36iWd6tFDsll1iSDEjrGAZQnrHXdYnqG06C3L8UuOuH7mZV6edPvtlXOJLMklAABBztczl5KUlub1SwZ0HHiGHWMBacyYyzOWl1WRdJ+T1h9LOu/ytQ8flp54ovyxBSuSSwAAglhBQYHOnDljqPP2zKVk2WzC18tVk5MlJ7v5I8iwYyxC3dKl0oIFjl5xvGus9KukpW6NMX++ZZzKhOQSAIAgZjtrKfkmuZSk6dMlH0yKSrJc9+23fXNteJ/tstijR4/qZGVcwwc4MWWKs1c6S2rt5LXSd411ZOpUt7sENZJLAACCmG1yGRMTo+rVq/tkrLg4adky7597GRtruW5cnHevC99p1aqVIiMjDXXMXiJUbN1qOT7EMZOcz14uleTelzDffCNVplXnJJcAAAQxX2/mYyshQVqzxnszmPHxluslJHjnevCP8PBwtWnTxlBHcolQ4Xg57JWc7Rp7QdInPhiv4iC5BAAgiPljMx9bCQmW7feTnX1+clFysuU6JJYVEzvGIlRt3FhWi1aSnB3Y6/qusa6PV3GQXAIAEMT8PXN5WVycNG+etGSJ5fgQd/ToYdmkYt48lsJWZOwYi1BkNkubN7vS0tnS2G8l7XNrzMxMy7iVQXigAwAAAM4FYubySgMGWH6ysixLtzZutHwQ+t+5b5ZnKjt3lpKSLDvOco5l5cCOsQhFBQXG32/O3SvpGUklV9TVlvR7SVXdGjMvTyoslGrWdKtbUCK5BAAgiAU6ubysQwdp4kTLn81mywehoiIpKkqKjpZMpoCEBR+yXRZ74sQJHTt2TPXq1QtQRIDvFRe72rK+pL6Sll9R10XSa+Uat6iociSXLIsFACCIBWpZbGlMJsuHoDp1LP8lsaycmjdvrqpVjTMwLI1FZWezSXIZbJfGfi3pRLnGjYoqV7egQ3IJAEAQC5aZS4SeKlWqqF27doY6lsaisqtZ053jmO6SVO2K8kVJH7s9ZmSkZQVIZUByCQBAkCooKNCZM2cMdcEwc4nQwY6xCDUmk9Spk6uta0oaZFM3z+0xi4ulr75yu1tQIrkEACBI2c5aSiSX8C92jEUoSnJ2yohDtmc2fSsp2+0xp051u0tQIrkEACBI2SaXMTExql69eoCiQShytGOsubKcmQA4MXy4O61vk2WX2CstcHvMb76x7Mpd0ZFcAgAQpIJxMx+EFttlsfn5+Xb3JVDZJCRI3bu72jpS0jCbunmS3P8SZoH7OWnQIbkEACBIsZkPAq1JkyaqUaOGoY6lsQgFaWnutLbdNXa7pJ/cHnPjRre7BB2SSwAAghQzlwi0sLAwXXvttYY6NvVBKOjf351jSbpKampT5/7GPpmZlnOEKzKSSwAAghQzlwgG7BiLUFRQYNnF1TVhMm7sEyXpvNtj5uVJhYVudwsq4YEOAAAAOEZyiWDAjrEIRa4nlpeNkLTpv/8dLKlWucYtKrKctVlRkVwCABCkWBaLYGCbXG7fvl1ms1kmkylAEQG+5/qS2MvaS/q3x+NGRXl8iYBiWSwAAEGKmUsEA9tlsQUFBTp48GCAogH8o2ZNKTbWv2PGxkrR0f4d09tILgEACEIFBQU6c+aMoY6ZSwRCo0aNVKuWcYkfS2NR2ZlMUqdO/h2zc2fLuBUZySUAAEHIdtZSIrlEYJhMJrulsWzqg1CQlFS5x/MFkksAAIKQbXIZExOj6tWrBygahDp2jEUoGj68co/nCySXAAAEITbzQTBhx1iEooQEqXt3/4zVo4dk8x1OhURyCQBAEGIzHwQT2+Ryx44dunTpUoCiAfwnLa1yjeNrJJcAAAQhZi4RTGyXxZ49e1Y5OTmBCQbwowEDfL9cNTlZ6t/ft2P4C8klAABBiJlLBJP69eurdu3ahjqWxiJUTJ8u+epXcHy89Pbbvrl2IJBcAgAQhEguEUzYMRahLC5OWrbM++dexsZarhsX593rBhLJJQAAQYhlsQg2JJcIZQkJ0po13pvBjI+3XC8hwTvXCxYklwAABBuzmZlLBB3b5y5ZFotQk5AgbdlieUbSE8nJlutUtsRSIrkEACA4bN0qTZgg9e2rgtq1debMGcPLDZ991vI6H+gRILYzlzt37lRJSUmAogECIy5OmjdPWrLEcnyIO3r0kJYutfSvTEthrxQe6AAAAAhpS5dKU6ZIa9daqw47aNbwu++k776TJk+2HLz23HOVZ3tBVAi2yWVRUZH27t2r1q1bBygiIHAGDLD8ZGVJCxZIGzdKmZlSXt7/2sTGSp07S0lJlh1nK8M5lmUhuQQAIBBOnpTGjLF8KrFhm1zGSKp+ZcXatZaf5GTLNoOV9StwBJW6deuqXr16OnbsmLUuKyuL5BIhrUMHaeJEy5/NZqmwUCoqkqKipOhoyWQKbHz+xrJYAAD8bcsWKTHRYWIpSUdsyk638pk/33KdrVu9GR3gFJv6AM6ZTFLNmlKdOpb/hlpiKZFcAgDgX1u2SLfcIh12tPjVwvaVUrfyOXxY6tmTBBN+QXIJoDQklwAA+MvJk9IddxgfynHA5ZnLy/LypNtvt1wf8CF2jAVQGpJLAAD8ZcyYUmcsL3Nr5tLa6bD0xBPliQpwme3M5c8//6wLFy4EKBoAwYbkEgAAf1i61OkzlrbKlVxKlmcwly51JyrALbbJ5YULF7R79+4ARQMg2JBcAgDgD1OmuNzU7WWxV5o61Z3WgFtiY2MVH2/8uoOlsQAuI7kEAMDXtm41nGNZlnLPXErSN99YDl4DfIRNfQA4Q3IJAICvubgcVpIKJJ2xqXNr5tLN8QB3kVwCcIbkEgAAX9u40eWmjrb7cTu5dGM8wF3sGAvAGZJLAAB8yWyWNm92ubltchkjqbq7Y2ZmWsYFfMB25nLPnj0qKioKUDQAggnJJQAAvlRQUOa5llc6ZlN2e9ZSsoxXWFienkCZrr32WkO5pKREu3btClA0AIIJySUAAL5UXOxW83tleeZyj6RvJM0o77jMJMFHatWqpSZNmhjqWBoLQCK5BADAtyIj3e5SXVJLSd0l3VLecaOiytsTKBOb+gBwhOQSAABfqllTio3175ixsVJ0tH/HREghuQTgCMklAAC+ZDJJnTr5d8zOnS3jAj7CjrEAHCG5BADA15KSKvd4CDm2M5f79u3T2bNnAxQNgGBBcgkAgK8NH165x0PIadeunaFsNpu1c+fOAEUDIFiQXAIA4GsJCVL37v4Zq0cPyWbJIuBtNWrUUIsWLQx1LI0FQHIJAIA/pKVVrnEQ8tjUB4AtkksAAPxhwADfL1dNTpb69/ftGMB/kVwCsEVyCQCAv0yfLsXH++ba8fHS22/75tqAA+wYC8AWySUAAP4SFyctW+b9cy9jYy3XjYvz7nWBUtjOXO7fv1+FhYUBigZAMCC5BADAnxISpDVrvDeDGR9vuV5CgneuB7iobdu2CgszfpTcvn17gKIBEAxILgEA8LeEBGnLFsszkp5ITrZch8QSAVC1alW1atXKUMfSWCC0kVwCABAIcXHSvHnSkiWW40Pc0aOHtHSppT9LYRFAbOoD4EoklwAABNKAAZZlrVu3ShMmSH372j+TGRtrqZ8wwdJuzRp2hUVQILkEcKXwQAcAAAAkdeggTZxo+bPZLBUWSkVFUlSUFB0tmUyBjQ9wgB1jAVyJmUsAAIKNySTVrCnVqWP5L4klgpTtzOWhQ4eUn58fmGAABBzJJQAAAMqldevWCg83LoRjx1ggdJFcAgAAoFwiIyPVunVrQx1LY4HQRXIJAACAcmNTHwCXkVwCAACg3EguAVxGcgkAAIByY8dYAJeRXAIAAKDcbGcuf/nlF508eTJA0QAIJJJLAAAAlFurVq0UGRlpqGNpLBCaSC4BAABQbuHh4Wrbtq2hbltGhnTihHT6tGQ2BygyAP5GcgkAAACPtG/UyFDOGjtWqltXuuoqKS5O6ttXmjBB4nlMoFIjuQQAAED5LF0q9eih9v/6l6HasCg2L09asUKaPFlKSJB69JC++sqvYQLwD5JLAAAAuOfkSSk5WbrzTmntWnWweTlLktPFsGvXSgMGSCNGWK4DoNIguQQAAIDrtmyREhOlBQusVe1tmpyUdKys68yfb7nO1q1eDhBAoJBcAgAAwDVbtki33CIdPmyobi6pqk1Tl/aLPXxY6tmTBBOoJEguAQAAULaTJ6U77rA8Q2mjiqRrbepcPowkL0+6/XaWyAKVAMklAAAAyjZmjN2M5ZVsl8a6tS/s4cPSE0+UJyoAQYTkEgAAAKVbutTwjKUjtsmlyzOXl82fbxkHQIVFcgkAAIDSTZlSZhPbHWO3qZQdY52ZOtXdHgCCCMklAAAAnNu61XJ8SBlsZy7zJTlfROvEN99IWW4tqAUQREguAQAA4FwZy2EvayKphk2d20tj3RgPQPAhuQQAAIBzGze61CxMXnju0o3xAAQfkksAAAA4ZjZLmze73NyjHWMvy8y0jAugwiG5BAAAgGMFBQ7PtXTmcnJZVdL1kpqWZ8y8PKmwsDw9AQRYeKADAAAAQJAqLnar+f2SfiuphaQqnoxbVCTVrOnJFQAEAMklAAAAHIuMdKt5/f/+eCwqyhtXAeBnLIsFAACAYzVrSrGx/h0zNlaKjvbvmAC8guQSAAAAjplMUqdO/h2zc2fLuAAqHJJLAAAAOJeUVLnHA+A1JJcAAABwbvjwyj0eAK8huQQAAIBzCQlS9+7+GatHD6lDB/+MBcDrSC4BAABQurS0yjUOAJ8guQQAAEDpBgzw/XLV5GSpf3/fjgHAp0guAQAAULbp06X4eN9cOz5eevtt31wbgN+QXAIAAKBscXHSsmXeP/cyNtZy3bg4714XgN+RXAIAAMA1CQnSmjXem8GMj7dcLyHBO9cDEFAklwAAAHBdQoK0ZYvlGUlPJCdbrkNiCVQaJJcAAABwT1ycNG+etGSJ5fgQd/ToIS1daunPUligUgkPdAAAAACooAYMsPxkZUkLFkgbN0qZmVJe3v/axMZKnTtLSUmWHWc5xxKotEguAQAA4JkOHaSJEy1/NpulwkKpqEiKipKioyWTKbDxAfALkksAAAB4j8kk1axp+QEQUnjmEgAAAADgMZJLAAAAAIDHSC4BAAAAAB4juQQAAAAAeIzkEgAAAADgMZJLAAAAAIDHSC4BAAAAAB4juQQAAAAAeIzkEgAAAADgMZJLAAAAAIDHSC4BAAAAAB4juQQAAAAAeIzkEgAAAADgMZJLAAAAAIDHSC4BAAAAAB4juQQAAAAAeIzkEgAAAADgsfBAB1CRnTp1ShkZGcrOzlZ+fr7MZrOuuuoqNW7cWDfeeKMaNGjg8Ri5ubnatm2bcnJylJ+fL0mKjY1Vo0aNlJSUpLp163o8BgAAAAB4KmiTy3379mnTpk3KyMjQpk2btHnzZhUUFFhfb9q0qXJycvwel9ls1qJFi/SXv/xF69atK7Xt9ddfr9TUVD300EMKD3ftr/rXX3/Vl19+qWXLlmnVqlU6fPhwqe2vu+46Pfroo0pJSVHVqlVdfh8AAAAA4E0ms9lsDnQQl61evVqTJ09WRkaGTp06VWrbQCSXR48eVXJyslatWuVWv86dO2vhwoVq1apVqe3eeecdjR07VsXFxW7H1q5dO3300Ue64YYb3O7rC9u2bVOHDh2s5aysLLVv3z6AEQEAAACVW6A/gwfVzOWPP/6or7/+OtBhOHT8+HH16tVLO3fuNNRHRETo+uuvV9OmTRUWFqbc3FxlZmbq/Pnz1jaZmZnq1auX1q1bp6ZNmzodIycnx2FiWatWLSUkJKhevXqKiorS4cOHtWnTJp07d87aZseOHerZs6eWLVum7t27e+EdAwAAAIDrgiq5dCYqKkqNGzfW3r17AxbDU089ZZdYpqam6o9//KPq1atnqM/Pz9eUKVM0depUXbp0SZLl2clHHnlEy5Ytc2m8xo0b64EHHtDgwYPVsWNHValSxfD6mTNn9O677+rFF1+0Jplnz57VoEGDtGvXLp7FBAAAAOBXQbdbbEREhDp27KiRI0fqvffeU2ZmpgoKCjRr1qyAxZSTk6P58+cb6saPH6+//vWvdomlJMXExGjy5Ml66623DPXLly/Xhg0bSh0rISFBn376qfbv36+JEyeqc+fOdomlJNWoUUNjx47V6tWrFR0dba3Py8vTiy++6M7bAwAAAACPBVVymZKSotOnT+uHH37QzJkzNWrUKHXq1EkREREBjevLL780lOvXr6/09PQy+40ePVqJiYmlXutKTzzxhH766Sfdc889Cgtz7X+apKQkTZ482VC3aNEiXbhwwaX+AAAAAOANQZVcxsbGBuWOp/v27TOU+/Xrp6ioqDL7mUwmDRw40FC3e/dup+2bNGkik8nkdnwPPfSQ4e8tPz9fP/zwg9vXAQAAAIDyCqrkMlidOXPGUG7cuLHLfa+++mpDOS8vzysxXal69epq06aNoa6sI0wAAAAAwJtILl3QoEEDQ/nKnWDLYtu2du3aXonJlu05muU5zgQAAAAAyovk0gW2R3ts3rzZ5b6ZmZmG8o033uiVmK5kNpvtlu42bNjQ6+MAAAAAgDMkly7o06ePYdnp2rVrtWXLljL7HTp0SJ999pm1HBERoeHDh3s9vhUrVhiW20ZGRuq6667z+jgAAAAA4AzJpQvCwsI0e/Zs6yY+ly5d0pAhQ5STk+O0zy+//KK77rpLZ8+etda98MILio+P93p8b775pqHcp08f1apVy+vjAAAAAIAz4WU3gSR17dpVS5YsUXJyso4fP67du3crMTFRDz/8sG6//XY1bdpUJpNJubm5WrFihd5//32dPHnS2v+RRx7xyfmTn332mb766itD3bhx47w6xrFjx3T8+HG3+uzZs8erMQAAAAAIbiSXbujbt6927NihadOmad68ecrOzta0adM0bdo0p33atm2rV155RUOHDvV6PNnZ2fr9739vqBs6dKh69+7t1XFmzJihP/7xjx5dg2QTAAAA8C3bz9xFRUV+HZ/k0k0XL16UJJfOuezatatefvll9e3b1+txnD59WgMHDjQ8a9mwYUPNmDHD62N5w1133RXoEAAAAICQcvDgQXXq1Mlv4/HMpRtmzpypli1b6tVXX9XOnTvLbP/tt9+qX79+SkxM1Pr1670WR3FxsQYPHqxt27ZZ6yIjI/Xxxx+rTp06XhsHAAAAAFzFzKWLJk6cqBdeeMFQd8MNN+ixxx5T9+7dFR8fr7CwMB09elTff/+93n//fa1atUqSlJWVpZ49e+qDDz5QSkqKR3GUlJRo+PDhWrFihbUuPDxcCxcuVLdu3Ty6tjOPPfaY28t6V65cqSeeeMIn8QAAAAAIPiaz2WwOdBCuWL16tXr16mUtN23atNTdWr1p5cqV6tu3r678q3r55Zf10ksvyWQyOe33/vvvKzU11dqvSpUqWrNmjW6++eZyxXHp0iX9v//3/zRnzhxrXVhYmObMmaPk5ORyXdNX8vPztWbNGmv56quvdmkpsaf27NljWIL7+eefq1WrVj4fF3CE+xHBhPsRwYJ7EcGkst2PRUVFOnjwoLXcs2dPxcTE+G18Zi5d8PzzzxsSy5SUFKWnp5fZb9SoUTp48KBeffVVSZZZxyeffFIZGRlux2A2m5WammpILE0mk2bNmhV0iaUkxcTEaNCgQYEOQ61atVL79u0DHQYgifsRwYX7EcGCexHBpDLcj/58xtIWz1yW4dChQ/r+++8Nda4klpc999xzqlatmrWcmZmpLVu2uB3HmDFjNHPmTEPdjBkz9OCDD7p9LQAAAADwNpLLMvz444+GcosWLdS8eXOX+9eoUUNdunQx1G3YsMGtGJ555hn95S9/MdRNmzZNqampbl0HAAAAAHyF5LIM+fn5hnKDBg3cvoZtnxMnTrjcNy0tTW+++aah7vXXX9eTTz7pdhwAAAAA4Cskl2WwfQD2zJkzbl+jsLDQUI6Ojnap34svvqipU6ca6iZOnKhx48a5HQMAAAAA+BLJZRni4+MN5V27duns2bNuXWPz5s2Gsiuzn6+88op1I6DL0tPTNWHCBLfGBgAAAAB/ILksQ2JiomJjY63l8+fPG3ZsLcuSJUt06NAhQ11Z51G+/vrrdpsGjR8/Xi+//LLL4wIAAACAP4VkcmkymQw/q1evdtq2SpUqGjJkiKHuueeeU1ZWVpnjHDhwwG7TnZtvvlkNGzZ02mf69Ol69tlnDXVjx47VpEmTyhwPAAAAAAIl6M65zM3N1cWLF+3qjx49aihfvHhROTk5Dq8RHR2tOnXqeC2ml156SXPnztW5c+ckWTb56dq1qyZNmqSHHnpI1atXN7QvLi7WggULNG7cOLvNeyZPnux0nNmzZ9tt1DN48GA9/vjjTt+rMzExMX49MBUAAABAaAu65LJbt27av39/me0OHTrk9EiQlJQU/f3vf/daTI0bN9a8efM0dOhQlZSUSJIKCgo0ZswYPfvss+rcubPi4+MVFhamo0ePKiMjw24TH8myGU/37t2djvPRRx/JbDYb6hYvXqzFixe7HXN6ejrLaAEAAAD4TdAll8Hq7rvv1hdffKGHH35Yv/zyi7X+3LlzWrduXal9a9Sooddee02PP/64r8MEAAAAgIAIyWcuy2vAgAHavn27Jk2apJYtW5bZvn79+ho3bpy2bdtGYgkAAACgUgu6mUt3ny0sD9ulp+6oXbu2xo8fr/Hjxys3N1eZmZk6cuSI8vPzZTabddVVV6lu3bq6/vrr1apVK7euXdrGQnBN3bp1DTvt1q1bN4DRINRxPyKYcD8iWHAvIphwP3qXyexJpgUAAAAAgFgWCwAAAADwApJLAAAAAIDHSC4BAAAAAB4juQQAAAAAeIzkEgAAAADgMZJLAAAAAIDHSC4BAAAAAB4juQQAAAAAeIzkEgAAAADgMZJLAAAAAIDHSC4BAAAAAB4juQQAAAAAeIzkEgAAAADgsfBABwBUBrm5udq2bZtycnKUn58vSYqNjVWjRo2UlJSkunXrBjZAAAgCR44cUUZGhrKzs1VQUKDw8HDFxsaqRYsWSkxMVL169QIdIiq5kpIS7dixQz/99JNOnDihwsJCVa9eXbVr11aHDh2UmJioiIiIQIeJADh37px27NihnTt36vjx4yosLFR0dLT13khISFB4uHdTp/z8fH377bc6dOiQTpw4oTp16qhRo0bq2rWrYmJivDqWv5BcwiP79u3Tpk2blJGRoU2bNmnz5s0qKCiwvt60aVPl5OT4Pa6LFy9q27Zthti2bt2qCxcuWNukpKTo73//e7mu/+uvv+rLL7/UsmXLtGrVKh0+fLjU9tddd50effRRpaSkqGrVquUaE6UL1XvRVe+++64effRRu/rs7Gw1a9bMp2OHIu7H/ykpKdFHH32kGTNmKCMjo9S2LVq00B133KFXX321wn6wCkbcj9KBAwf0xhtvaM6cOTp16pTTdjVq1NDw4cP1zDPPqF27dh6PC6Nguxc3b96szz//XCtXrtTGjRsN956tGjVq6N5779WTTz6pxMREj8b94Ycf9Morr+irr75ScXGx3etRUVG64447lJ6ero4dO3o0lt+ZATetWrXK3K9fP3Pt2rXNkkr9adq0qV9jmzFjhvmmm24yV6tWrczYUlJSyjXG9OnTzZGRkWVe39FPu3btzJs2bfLumw5hoX4vuurAgQPmWrVqORw7Ozvbp2OHEu5Hez/99JO5Q4cObv+u3LFjh9diCFXcj/8za9Ysc3R0tFv3YGRkpPm1117z/M0iKO/Fc+fOmVu0aFGuz3JVqlQxp6WlmYuLi8s19uTJk80REREu34dTpkzx8rv3LWYu4bYff/xRX3/9daDDcGj58uX67rvvfDpGTk6Ow2+ZatWqpYSEBNWrV09RUVE6fPiwNm3apHPnzlnb7NixQz179tSyZcvUvXt3n8YZCkL9XnRVamqqTp8+HegwKj3uR6OvvvpKQ4cO1dmzZw31sbGxSkhIUP369SVJJ06cUFZWlo4fP+7X+Co77keL6dOn64knnrCrb9iwoTp16qSYmBidPn1aW7Zs0f79+62vFxcX67nnntOZM2f0yiuv+CXWyioY78WLFy9q3759dvUmk0lt2rRRkyZNVKdOHRUWFiorK8vQtqSkRFOmTNHu3bu1aNEit5bKTpo0Sc8//7yhrlq1arrxxhvVsGFD62fH8+fPS7Lch2lpaTKZTPrDH/5QznfrXySX8JqoqCg1btxYe/fuDXQodmJiYlSjRg0dOnTIq9dt3LixHnjgAQ0ePFgdO3ZUlSpVDK+fOXNG7777rl588UVrknn27FkNGjRIu3bt4llMHwnFe9GZOXPm6KuvvpIk1axZ07D8CP4Rivfj+vXrdc8991g/IEnSjTfeqIkTJ6pXr14OP4zt3LlTX3zxhT744AOvxgKjULoft2/frrFjxxrqmjRpohkzZqh///4ymUyG19asWaNHH31UO3bssNa9+uqruv3229W1a1evxIT/CZZ7sUqVKurXr59SUlLUp08f1alTx65NZmamnnnmGX3zzTfWusWLF+vll1/Wq6++6tI4S5Ys0QsvvGCoGzVqlCZOnGgY8/jx45owYYJmzZplrUtLS1NCQoJuv/12d9+e37FbLMolIiJCHTt21MiRI/Xee+8pMzNTBQUFhv8jBEp0dLS6d++up59+WvPnz9fPP/+sU6dOaeTIkV4bIyEhQZ9++qn279+viRMnqnPnznaJpWRZnz927FitXr1a0dHR1vq8vDy9+OKLXosnlIX6vViaY8eO6amnnrKWJ0+e7JdxQxn3o+VLtfvvv9+QWD7zzDPasGGDbr31Vqff8rdt21ZpaWnatWuXrrnmGq/GFKpC/X6cOnWq4Rm6evXqaf369RowYIBdYilJPXv21Pr16w33n9ls1p/+9CevxRSqgvFejIqK0ujRo5WTk6OvvvpK9957r8PEUpI6d+6slStXavjw4Yb6119/3TDj7UxJSYnGjRsns9lsrXv66af13nvv2Y1Zt25dzZw50/Dvt9ls1tixY1VSUuLGOwyQAC/LRQV06tQp87lz5xy+tmrVqoA+x3H06FFzSUmJw9fS09O98hzH/v37zZcuXXK73/Tp0w3jx8TElHu9PixC/V4sy5AhQ6xjdOnSxVxSUsIzlz7E/WgxZswYw/UeeOABj66H8uF+NNs94zdt2jSX+n366ad2z70VFhaWO45QF4z34oULF8wHDx50u9/Zs2fNV199tSHmqVOnltlv9uzZhj5t2rQxnz9/vtQ+58+fN7dp08bQ78MPP3Q7Zn9j5hJui42NDdodT+vXr6+wMN/e1k2aNHH4jWdZHnroIcPfW35+vn744QdvhhZyQv1eLM3ixYv16aefSrJ8Yzxz5syAxhMKuB8txzLNmDHDWq5bt67efPNNn48Le6F+P/766692u8IOHDjQpb79+/c3zLAXFxfrwIEDXo0vlATjvRgeHq7GjRu73a9atWp68MEHDXWrVq0qs99HH31kKD/99NOKiooqtU9UVJSefPLJUq8TjPikAfhJ9erV1aZNG0NdWUeYAOWRl5en0aNHW8tpaWnq0KFDACNCqJg1a5Zh2VZqaqpq164dwIgQqs6cOWNX52oyUa1aNbulinl5eV6JCxXf9ddfbyiX9Vnu5MmTWrt2rbUcGRmp5ORkl8YaMWKE4dzVNWvWlHqUTjAguQT8yPZZI0e7zgKeevrpp3X06FFJUuvWre02EAB8xXYzHttv+AF/iYuLs/s398rngMti25YvSXCZu5/l/v3vfxu+dOvcubNq1qzp0li1atVSp06drOWLFy/q3//+txvR+h/JJeAnZrPZbtvrhg0bBigaVFbLly/Xhx9+KMmypfr7779f5tIbwBt2796t3Nxca7lly5Zq3rx5ACNCKIuKilJSUpKhbvPmzS713bdvn/Lz863lWrVqsckUrPbs2WMol/VZLisry1C+6aab3BrPdqfibdu2udXf30guAT9ZsWKFYVlNZGSkrrvuugBGhMqmsLBQo0aNspZHjhypnj17BjAihJKNGzcayld+gNq2bZuee+45derUSXXr1lVUVJTi4+OVlJSktLQ0bdiwwd/hIgRc+XiAJL3zzjsu9XvrrbcM5d/97ncOd4RHaLq8n8Fltl9i2Nq+fbuh3KpVK7fGa9myZanXCzYkl4Cf2G5q0adPH9WqVStA0aAySktLs2460aBBA02dOjXAESGUZGRkGMrt2rXTmTNnNGbMGCUkJGjKlCn64YcfdOLECRUXF+vIkSPatGmTpk6dqi5duuj22293eKg5UF7Jycn67W9/ay1/9tlnmjRpUql9Zs2apenTp1vL9erVU3p6us9iRMWyadMmrV+/3lB39913l9rHdqazSZMmbo1p23737t1u9fc3kkvADz777DPrQfaXjRs3LkDRoDJau3at/vrXv1rL06dPV0xMTOACQsg5cuSIoRwbG6tbb71V77zzjuFsN2eWL1+upKQkuw9ugCcWLVqk++67z1p+/vnnddNNN2nmzJnKzMzUnj179OOPP+rDDz9U37599fvf/956v9arV0/Lli1T3bp1AxU+gsiFCxf0yCOPGOq6d+9e5szllUusJct95Q7b9r/++qtb/f3N8UnGALwmOztbv//97w11Q4cOVe/evQMUESqb8+fPa+TIkdYPRAMHDtSQIUMCHBVCje0HqEmTJlmfwTSZTLr33ns1bNgwXXPNNTKZTNq9e7c++eQTLViwwHrvnjx5UoMGDVJmZqaaNm3q77eASqhq1apasGCBHnzwQb311lv6z3/+o++//17ff/+90z6RkZH63e9+p4kTJ6p+/fp+jBbB7A9/+IPhCLmIiAi9/fbbZfYrLCw0lKtVq+bWuLbtCwoK3OrvbySXgA+dPn1aAwcONDxr2bBhQ8M5cICnXnrpJf3888+SpJo1a3J/ISBsk8vLiWWtWrX0+eefq1evXobX27dvr7vuuksjR47UoEGDrB+YTp48qYcfflj/+c9//BI3QsPFixcVERGh8PDwUnf3rF69utLS0jRq1CgSS1jNnj3b7lncl19+WR07diyzr21y6e6Zn7bJpe31gg3LYgEfKS4u1uDBgw27ekVGRurjjz+2Oz8LKK+MjAy98cYb1vKkSZPKdTA04KlLly45rP/oo4/sEssr9erVS3PnzjXUrVixQt99951X40NoOnTokHr37q0BAwboiy++0NmzZ0ttf/bsWaWnp6tZs2YaO3aszp0756dIEayWLVum1NRUQ92dd96p8ePHl+t6JpPJp+0DjeQS8IGSkhINHz5cK1assNaFh4dr4cKF6tatWwAjQ2Vy4cIFPfTQQ9bzs7p06aLHHnsswFEhVEVHR9vV9erVS4MGDSqz729/+1v16dPHUGebcALuys3NVbdu3bRq1SprXfXq1fXEE09o1apVOnHihC5cuKBTp07p22+/1YQJExQbGytJKioq0htvvKGePXsaVh8htKxfv1733HOPLly4YK3r1q2bFi1a5HLSZ/u70d0vLGzbO/pdG0xILgEvu3Tpkh588EEtXrzYWhcWFqYPP/ywzB3FAHdMmjRJW7dulWR59mPmzJkKC+PXOgLD0QeeBx54wOX+tm1Xr17taUgIcffff79ycnKs5VatWunHH3/UW2+9pVtuuUVxcXEKDw9XbGysbrrpJk2cOFFZWVm68cYbrX02bdqkESNGuLQpFSqXzMxMDRgwwDDbnZSUpKVLl6p69eouX4fkEkC5mc1mpaamas6cOdY6k8mkWbNmKTk5OYCRobLJysoybKmflpamDh06BDAihDpHuxN36dLF5f62bXft2sUHepTb8uXLtWbNGms5MjJSS5cu1TXXXFNqv/j4eC1dulS1a9e21v3rX//SkiVLfBYrgs+WLVvUr18/w86s119/vZYvX+72MXJXXXWVoXz8+HG3+h87dsxQDvad4NnQB/CiMWPGaObMmYa6GTNm6MEHHwxQRKisJk6caN2UomHDhnbf0Lvq8qYrl9WuXZvzV1EurVu3tqtr2LChy/3j4+MN5ZKSEuXn51uXKQLu+OSTTwzl4cOHO7xHHalbt65Gjx6tP/3pT9a6v/3tbxo4cKBXY0Rw2r59u/r27atTp05Z6zp06KCvv/66XIndNddco6ysLGt5//79bvW3bV/WFySBRnIJeMkzzzyjv/zlL4a6adOm2T0EDnjDlctkjhw5orZt25brOt27dzeU33zzTT311FOehIYQ1b59e7u6qKgol/s7anv+/HmPYkLo+umnnwxl22d6y9K3b19DcrlhwwavxIXgtmvXLvXp08cwu9i2bVv95z//KfdmjO3atdM//vEPa3nPnj1u9d+3b5/d9YIZy2IBL0hLS9Obb75pqHv99df15JNPBigiAPCvxMREuzrb40lK46htXFycBxEhlNneTw0aNHCrv237EydOeBoSgtyePXvUu3dvHT161Fp3zTXXaOXKlR4dS2P7yIq7O2GvX7++1OsFG5JLwEMvvviipk6daqibOHGixo0bF6CIAMD/WrZsqTZt2hjqrjyKqSxXLhuTLEsTIyMjvRIbQo/t8sUzZ8641d/2LMFg30QFnsnOzlbv3r11+PBha12LFi20cuVKt5b3O3LrrbeqSpUq1nJmZqb1XN+yFBQUaPPmzdZyeHi4br31Vo/i8TWSS8ADr7zyil599VVDXXp6uiZMmBCgiBAqPv/8c5nNZrd/bGVnZxteZ0ksPHHPPfcYysuWLXO5r21b2yXbgDtsn+H94Ycf3OqfmZlpKLs784mK48CBA+rdu7cOHjxorWvatKlWrlzplXOj69SpYziGrri4WPPnz3ep77x58wzHoPTo0cOw2VQwIrkEyun1119Xenq6oW78+PF6+eWXAxMQAATYAw88YPiG/m9/+5tLZwTm5eXpgw8+MNTdeeedXo8PoeOWW24xlD/88EPrJmhlMZvNdpvz8WVH5XT48GH16dPHsCFeo0aNtHLlSjVt2tRr49getfTmm2+qqKio1D5FRUWaNm2aoS4lJcVrMfkKySWClslkMvwE05ln06dP17PPPmuoGzt2rOFoCFQewXwvIvQE8/3Ypk0bPfTQQ9byyZMn9fDDD+vixYtO+1y8eFEPP/ywTp48aa1r0qSJRowY4dNY4R3Bej/efffdioiIsJb379+vxx9/3KXjbV566SVt2rTJUDdkyBCvxwjvcvdePHbsmPr06WPYYKdhw4ZatWqVWrRo4dXYUlJSDI8N7Nq1q8xVbuPHj9euXbus5WuvvbZC/F5kt1iUS25ursMPC1c+BC1ZPjQ4Ox4hOjq63DtvOXPx4kW7oxUus324v7Cw0GlsderUcfp8xezZs+026hk8eLAef/xxt4+CiImJCfrzioJdKN+LCD7cj5bHBT7//HPrbov/+Mc/dMcdd+idd96xeyZz9+7dGj16tP79739b60wmk6ZNm8bzll4Qyvdjs2bNlJqaqunTp1vrZs6cqQMHDui1115Tx44d7fr8/PPPeuGFF+yOMendu7f69u3r/A2hTMF2L+bn5+vWW2/Vzp07rXU1atTQBx98oIiICLc/zzVr1qzU16tUqaI///nP+u1vf2v9guONN95QYWGhJk2aZNi87MSJE5owYYJh9txkMun//u//DCtDgpYZKIemTZuaJXn0k5KSUuoYtu1XrVpVZlzZ2dkexyXJ/Le//c3pGD179vTKGJLM6enpbv29w14o34vlYXv97Oxsr14/1HE/WmzYsMFcvXp1u/7XXXedeciQIeahQ4eaO3bsyO9FHwv1+/Hs2bPmm2++2WHf5s2bmwcOHGgeMWKEedCgQeY2bdo4bNesWTNzbm6u63/pcCjY7sVVq1Z55R68/OOqiRMn2vWtVq2a+ZZbbjHfd9995p49e5qrVatm12bKlCkujxFozFwCAACvSkpK0r/+9S898MADhgPAf/rpJ7vzBy+LiIjQW2+9pUcffdRfYaKSq1atmpYuXarRo0dr3rx5hteys7OVnZ1dav8ePXroo48+UqNGjXwZJkLIhAkTZDKZlJ6ebt2o59y5c06X8EZEROhPf/qT3aNYwYxnLgEAgNf16NFDW7du1XPPPVfqh/MaNWrowQcf1M6dO0ks4XVXXXWV5s6dq5UrV2rw4MFlLrcOCwtT7969tWjRIq1evdqrm7oAkuVZyg0bNmjQoEFO78fIyEgNGjRIGzduVFpamp8j9IzJbHbhyWYAAIByMpvN2rhxo/bu3asjR46opKREderUUatWrXTTTTcZNl4BfKmoqEg//fSTduzYoby8PBUWFqp69eqKiYlRq1at1KlTJ55zh9/k5eXp22+/1aFDh3Ty5EnFxcWpUaNG6tq1q2JjYwMdXrmQXAIAAAAAPMayWAAAAACAx0guAQAAAAAeI7kEAAAAAHiM5BIAAAAA4DGSSwAAAACAx0guAQAAAAAeI7kEAAAAAHiM5BIAAAAA4DGSSwAAAACAx0guAQAAAAAeI7kEAAAAAHiM5BIAAAAA4DGSSwAAAACAx0guAQAAAAAeI7kEAAAAAHiM5BIAAAAA4DGSSwAAAACAx0guAQAAAAAeI7kEAAAAAHgsPNABAAAAAACMcnJytHnzZu3fv19nzpxRVFSU4uLi1LJlS1133XWKiYkJdIh2SC4BAAAAVDj79u3Tpk2blJGRoU2bNmnz5s0qKCiwvt60aVPl5OQELsByOH/+vN599129//772rFjh9N2JpNJ7dq1U//+/fXaa6+pSpUqfozSOZPZbDYHOggAAAAAKMvq1as1efJkZWRk6NSpU6W2rWjJ5erVq5WSkqIDBw641e/cuXOqWrWqj6JyDzOXAAAAACqEH3/8UV9//XWgw/C6Dz74QI888ohKSkoM9fXr19e1116r+vXr68KFCzp27Ji2bNmiX3/9NUCRlo7kEgAAAECFFhUVpcaNG2vv3r2BDsVtn376qX7/+9/rygWl/fr10x//+Ef95je/kclksuuzefNmff7555o9e7Y/Qy0TySUAAACACiMiIkLt27fXDTfcoBtvvFE33HCDEhIStH79evXq1SvQ4bnl8OHDGjlypCGxfPPNN/XUU0+V2q9Tp07q1KmTXnrpJYWHB09KFzyRAAAAAEApUlJSlJqaGjTPGHoqNTXVsMT1pZdeKjOxvFIwJZYSySUAAACACiI2NjbQIXjNd999py+//NJabteunZ5//vkARuQ5kksAAAAAKENhYaHWr1+vw4cP6/jx46pSpYrq1aundu3aqVOnTgoLC3Preu+9956hPG7cOEVGRnozZL8juQQAAAAAJ5YvX64pU6Zo3bp1unDhgsM2derU0cMPP6y0tDSXZlcLCgr08ccfW8s1atTQsGHDvBZzoLiXXgMAAABACDhx4oRuvfVW3X777Vq1apXTxPJy2ylTpuiaa67RN998U+a1v//+e507d85a7tKli6Kjo70SdyAxcwkAAAAAV9izZ49uu+027du3z1Bfs2ZNde7cWfXr11dJSYlycnK0efNmXbp0SZJ08uRJ3XrrrfrnP/+p2267zen1N27caCjfdNNN1j9v2LBBc+bM0TfffKPc3FydO3dOderUUbNmzdSnTx8NHTpU7du39+K79R6SSwAAAAD4r7Nnz+ruu+82JJZt2rTRxIkTddddd6lKlSqG9keOHFF6erpmzpwpSSouLtb999+vH3/8UY0aNXI4RkZGhqHcrl07nThxQqNHjzYsl70sNzdXubm5WrdunV555RXde++9mj59uurUqePp2/UqlsUCAAAAwH/94Q9/UFZWlrV8xx136IcfftA999xjl1hKUsOGDfX+++/r//7v/6x1J06c0Isvvuh0jCNHjhjKkZGRuummmxwmlrbMZrMWLlyopKQk7dy505W35Dcm85UndgIAAABABbR69Wr16tXLWm7atKlycnLcusbhw4fVvHlzFRcXS5KaNWumbdu2qXr16i71HzBggL766itJloRx//79atCggV27tm3bateuXdZy48aNlZubK0mKiIjQgw8+qEGDBllj2bFjh+bOnaulS5cartOyZUtlZmbqqquucut9+gozlwAAAAAg6d1337UmlpKUnp7ucmIpSWPHjrX+ubi4WMuWLXPYLj8/31C+nFjGx8crMzNT7733nvr376927drpuuuu03333aclS5ZowYIFioiIsPbbu3evYcxAI7kEAAAAAEn//ve/rX+uUqWKhgwZ4lb/bt26KTz8f9varF271mG7yxsAXSk8PFz//Oc/lZCQ4PT69913n/785z8b6j766CMdOHDArTh9hQ19AAAAAIS88+fPKzMz01q++uqrdeLECZ04ccKt68TExFj77N2712Gb6OhoHT9+3FD3u9/9Tp07dy7z+mPGjNE777yj3bt3S5IuXLigRYsW6Q9/+INbcfoCySUAAACAkHf06FHDWZY5OTlq3ry5R9c8deqUw3pHZ1o+8MADLl3TZDLp/vvvV3p6urVu9erVQZFcsiwWAAAAQMg7efKk169ZUFDgsD4mJsZQDgsLU1JSksvX7dKli6G8Y8cOt2PzBZJLAAAAACHvyo18vMXZwRytW7c2lGvVquXWxkHx8fGGsi8S4/IguQQAAAAQ8urUqWMo9+vXT2az2aMfZ0ehtG/f3lCOiopyK1bb9ufPn3erv6+QXAIAAAAIefXr1zeUf/75Z5+NlZiYaCjbHk1SFtv2cXFxHkbkHSSXAAAAAEJerVq1DDOKOTk51h1Zva1bt26qVauWtVxUVOR0Z1lHsrKyDOXGjRt7LTZPkFwCAAAAgKTbbrvNUJ45c6ZPxomKitKdd95pqFu2bJnL/W3bdu/e3StxeYrkEgAAAAAkPfroowoP/99pjdOnT9e2bdt8MtaDDz5oKL/zzjsubSq0d+9e/eMf/zDU2SaqgUJyCQAAAACSWrVqZUj6zp8/r/79+2v79u1uXaeoqEh///vfS23Tt29f3Xrrrdbyzp07NW7cuFL7nDlzRvfff7/hPM4uXbqoV69ebsXnKyazs/1xAQAAACDI5Obm6uLFi3b133//vYYPH24tN2rUSOvWrXN4jejoaLvdYS8rLCzUzTffrC1btljrqlWrpqefflqpqam6+uqrHfY7d+6c1q9fr3/+859auHChjh8/7vQoksu2bt2q3/zmNzp37py1bvjw4Xr99dfVqFEjQ9uMjAylpqYqMzPTWhcZGam1a9e6dUamL5FcAgAAAKgwmjVrpv3793t0jZSUlFJnFg8ePKh+/fpp586ddq+1aNFCbdu2VUxMjC5evKhff/1VOTk52rNnj0pKSgxtXUm1vvjiC91zzz2GvmFhYbrxxhvVtGlTXbx4UTt27NCOHTsM/Uwmk2bNmqWHHnqozDH8heQSAAAAQIXhj+RSssxgpqamat68eeUaIyYmRnl5eS61Xbx4sUaNGqWTJ0+61D46Olpz5szRXXfdVa7YfIVnLgEAAADARnR0tObOnauffvpJ999/v2JjY8vsEx8frxEjRuiTTz7R0aNHXR5r8ODB2rZtmx599NFSz6yMjY3VU089pT179gRdYikxcwkAAAAAZbp06ZK2bNmi7du369SpU8rPz1fVqlVVq1YtNWvWTO3atXP6PKY7Ll68qG+//Vb79+/XkSNHFBYWpjp16ujaa6/VDTfcoLCw4J0fJLkEAAAAAHgseNNeAAAAAECFQXIJAAAAAPAYySUAAAAAwGMklwAAAAAAj5FcAgAAAAA8RnIJAAAAAPAYySUAAAAAwGMklwAAAAAAj5FcAgAAAAA8RnIJAAAAAPAYySUAAAAAwGMklwAAAAAAj5FcAgAAAAA8RnIJAAAAAPAYySUAAAAAwGMklwAAAAAAj5FcAgAAAAA8RnIJAAAAAPAYySUAAAAAwGMklwAAAAAAj5FcAgAAAAA8RnIJAAAAAPAYySUAAAAAwGMklwAAAAAAj5FcAgAAAAA8RnIJAAAAAPAYySUAAAAAwGMklwAAAAAAj5FcAgAAAAA8RnIJAAAAAPAYySUAAAAAwGMklwAAAAAAj5FcAgAAAAA8RnIJAAAAAPAYySUAAAAAwGMklwAAAAAAj5FcAgAAAAA89v8BiNuEHoxHe5cAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sources.plot(color='red')\n", "sinks.plot(color='blue', ax=ax)\n", "geopandas.GeoSeries(lines).plot(linewidth=1, color='k', ax=ax)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 4 } libpysal-4.12.1/docs/user-guide/weights/voronoi.ipynb000066400000000000000000004252061466413560300226500ustar00rootroot00000000000000{ "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 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 4 } libpysal-4.12.1/docs/user-guide/weights/weights.ipynb000066400000000000000000064061641466413560300226370ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spatial Weights" ] }, { "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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\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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\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 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 4 } libpysal-4.12.1/environment.yml000066400000000000000000000006551466413560300165230ustar00rootroot00000000000000name: 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 - pip - pip: - pulp libpysal-4.12.1/libpysal/000077500000000000000000000000001466413560300152455ustar00rootroot00000000000000libpysal-4.12.1/libpysal/.gitignore000066400000000000000000000003351466413560300172360ustar00rootroot00000000000000contrib/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-4.12.1/libpysal/__init__.py000066400000000000000000000013561466413560300173630ustar00rootroot00000000000000""" 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, graph, io, weights with contextlib.suppress(PackageNotFoundError): __version__ = version("libpysal") libpysal-4.12.1/libpysal/cg/000077500000000000000000000000001466413560300156365ustar00rootroot00000000000000libpysal-4.12.1/libpysal/cg/__init__.py000066400000000000000000000003521466413560300177470ustar00rootroot00000000000000""" 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-4.12.1/libpysal/cg/alpha_shapes.py000066400000000000000000000524571466413560300206550ustar00rootroot00000000000000""" 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: if xys.shape[0] == 3: multipoint = geom.MultiPoint(xys) alpha_shape = multipoint.convex_hull 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-4.12.1/libpysal/cg/kdtree.py000066400000000000000000000277511466413560300175020ustar00rootroot00000000000000""" 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) # noqa: E731 # print map(a2l,d.data) return scipy.sparse.coo_matrix((list(map(a2l, d.data)), (d.row, d.col))).todok() libpysal-4.12.1/libpysal/cg/locators.py000066400000000000000000000624241466413560300200460ustar00rootroot00000000000000""" 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-4.12.1/libpysal/cg/ops/000077500000000000000000000000001466413560300164375ustar00rootroot00000000000000libpysal-4.12.1/libpysal/cg/ops/__init__.py000066400000000000000000000000361466413560300205470ustar00rootroot00000000000000from . import atomic, tabular libpysal-4.12.1/libpysal/cg/ops/_accessors.py000066400000000000000000000025731466413560300211440ustar00rootroot00000000000000# 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-4.12.1/libpysal/cg/ops/_shapely.py000066400000000000000000000133351466413560300206220ustar00rootroot00000000000000# 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-4.12.1/libpysal/cg/ops/atomic.py000066400000000000000000000004071466413560300202660ustar00rootroot00000000000000from . 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-4.12.1/libpysal/cg/ops/tabular.py000066400000000000000000000121051466413560300204420ustar00rootroot00000000000000import 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-4.12.1/libpysal/cg/ops/tests/000077500000000000000000000000001466413560300176015ustar00rootroot00000000000000libpysal-4.12.1/libpysal/cg/ops/tests/__init__.py000066400000000000000000000000001466413560300217000ustar00rootroot00000000000000libpysal-4.12.1/libpysal/cg/ops/tests/test_accessors.py000066400000000000000000000501021466413560300231750ustar00rootroot00000000000000import 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-4.12.1/libpysal/cg/ops/tests/test_shapely.py000066400000000000000000000155051466413560300226650ustar00rootroot00000000000000from 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-4.12.1/libpysal/cg/ops/tests/test_tabular.py000066400000000000000000000042031466413560300226430ustar00rootroot00000000000000import 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-4.12.1/libpysal/cg/polygonQuadTreeStructure.py000066400000000000000000001531141466413560300232600ustar00rootroot00000000000000""" 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 """ # ruff: noqa: N999 __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-4.12.1/libpysal/cg/rtree.py000066400000000000000000000774341466413560300173500ustar00rootroot00000000000000# 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-4.12.1/libpysal/cg/segmentLocator.py000066400000000000000000000343021466413560300212000ustar00rootroot00000000000000# ruff: noqa: A002, N802, N806, N999 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, stacklevel=2) 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: # noqa: N801 def __init__(self, shpfile, nbins=500): warnings.warn( "Polyline_Shapefile_SegmentLocator " + dep_msg, FutureWarning, stacklevel=2 ) 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, stacklevel=2) 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: mask_copy = self.mask.copy().astype(float) mask_copy += self.endMask.astype(float) mask_copy[rows, cols] += 1 mask_copy[row, col] += 3 i = pylab.matshow( mask_copy, 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-4.12.1/libpysal/cg/shapely_ext.py000066400000000000000000000250321466413560300205370ustar00rootroot00000000000000from 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-4.12.1/libpysal/cg/shapes.py000066400000000000000000001454311466413560300175030ustar00rootroot00000000000000""" Computational geometry code for PySAL: Python Spatial Analysis Library. """ __author__ = "Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow, Hu Shao" # ruff: noqa: A003, N802, E402 import math import warnings 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(f"{obj!r} does not appear to be a shape object.") 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(f"{geo_type} is not supported at this time.") 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, stacklevel=2) 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) -> 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, stacklevel=2) 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, stacklevel=2) 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, stacklevel=2) if m == float("inf"): raise ArithmeticError("Slope cannot be infinite.") self.m = float(m) self.b = float(b) def x(self, y: 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: 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, stacklevel=2) 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, stacklevel=2) 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(f"{geo!r} is not a Chain.") 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) -> int | float: return math.hypot(v1[0] - v2[0], v1[1] - v2[1]) def part_perimeter(p: list) -> 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) -> int | float: """Returns the geometric length of the chain computed using 'arcdistance' (meters). """ def part_perimeter(p: list) -> 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, stacklevel=2) 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) -> int | float: return math.hypot(v1[0] - v2[0], v1[1] - v2[1]) @property def perimeter(self) -> 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) -> 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) -> 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, stacklevel=2) 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) -> 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: int | float, v2: int | float) -> float: return math.hypot(v1[0] - v2[0], v1[1] - v2[1]) def part_perimeter(part) -> int | float: return sum([dist(part[i], part[i + 1]) for i in range(-1, len(part) - 1)]) sum_perim = lambda part_type: sum( # noqa: E731 [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( # noqa: E731 [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, stacklevel=2) 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[:] # noqa: N806 x1, y2, X1, Y1 = other[:] # noqa: N806 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) -> 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) -> 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) -> 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-4.12.1/libpysal/cg/sphere.py000066400000000000000000000407501466413560300175040ustar00rootroot00000000000000""" 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 # noqa: E731 # radian to degree conversion r2d = lambda x: x * 180.0 / math.pi # noqa: E731 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-4.12.1/libpysal/cg/standalone.py000066400000000000000000001057171466413560300203530ustar00rootroot00000000000000""" 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, N803 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]) # noqa: E731 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( # noqa: E731 vertices[i], # noqa: B023 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 w_left = max(r0.left, r1.left) w_lower = max(r0.lower, r1.lower) w_right = min(r0.right, r1.right) w_upper = min(r0.upper, r1.upper) segments_a = set() common = [] 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 >= w_left and x <= w_right and y >= w_lower and y <= w_upper: x, y = b # check if point b is in the bounding box intersection if x >= w_left and x <= w_right and y >= w_lower and y <= w_upper: if a > b: segments_a.add((b, a)) else: segments_a.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 >= w_left and x <= w_right and y >= w_lower and y <= w_upper: x, y = b if x >= w_left and x <= w_right and y >= w_lower and y <= w_upper: seg = (b, a) if a > b else (a, b) if seg in segments_a: 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 = f"Should be 2D point coordinates: {X.ndim} dimensions present." 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] x_m = x * m dx = x_m - x_m.T if p % 2 != 0: dx = np.abs(dx) dx2 = dx**p d += dx2 d = d ** (1.0 / p) return d libpysal-4.12.1/libpysal/cg/tests/000077500000000000000000000000001466413560300170005ustar00rootroot00000000000000libpysal-4.12.1/libpysal/cg/tests/__init__.py000066400000000000000000000000001466413560300210770ustar00rootroot00000000000000libpysal-4.12.1/libpysal/cg/tests/data/000077500000000000000000000000001466413560300177115ustar00rootroot00000000000000libpysal-4.12.1/libpysal/cg/tests/data/alpha_05.gpkg000066400000000000000000003000001466413560300221450ustar00rootroot00000000000000SQLite 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-4.12.1/libpysal/cg/tests/data/alpha_auto.gpkg000066400000000000000000003000001466413560300226710ustar00rootroot00000000000000SQLite 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-4.12.1/libpysal/cg/tests/data/alpha_fifth.gpkg000066400000000000000000003000001466413560300230210ustar00rootroot00000000000000SQLite 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-4.12.1/libpysal/cg/tests/data/alpha_fourth.gpkg000066400000000000000000003000001466413560300232300ustar00rootroot00000000000000SQLite 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-4.12.1/libpysal/cg/tests/data/alpha_tenth.gpkg000066400000000000000000003000001466413560300230430ustar00rootroot00000000000000SQLite 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-4.12.1/libpysal/cg/tests/data/eberly_bounding_circles.gpkg000066400000000000000000003000001466413560300254270ustar00rootroot00000000000000SQLite 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-4.12.1/libpysal/cg/tests/data/pecos_points.txt000066400000000000000000000033721466413560300231640ustar00rootroot00000000000000-101.758293152, 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-4.12.1/libpysal/cg/tests/fast_point_in_polygon_algorithm.ipynb000066400000000000000000010041661466413560300265240ustar00rootroot00000000000000{ "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\nejgHWpsYuD16X0tjVPo0mmkpqVR1d15sj3G5bDSkDclAHkpNZJOqUhal7+XN+nOc7e5kQWY2izJz\nonLNWDFVttWI6Lq2eBrXRnFfuAjdli1b2LJlS1hfM6TNALqudymKsgW4HqgF/tL//C5FUTRFUTJ1\nXW8det7AwFQIISZEkaynw7Hk5WHOy8PX0ACAuaAAc87UukGOBZqmj7iMt2RI5urSGEm+9tG5i7Go\nKrU93SzLyotqkqDRfGTuYlr2bqOmp4slWbn8y4DSUdGi6zqVXR2YRwh0nz57il8fD8zsPFF5nC8s\nXSPlSIQQk97QycYHH3xwwq85lqy8WYBX1/VORVHswGbgG0A3sAl4tX9Zr2W4oFQIMTm8dK6Kk53t\nzEnLZGNhiWH9UJTY3WOquVz0vPoqusdD4urVIQeGut+PMs7ZB8ViIeuuu3Du3AmAY9UqlDDVkhVj\np2sjZ+NdX1BMs8vJm4115NkT+ejcxVHu3fAcZgv/Pt/4JcXDyXUk8oN11xh2fV3X+eb+HWztz7B7\nU9lMPjxn0aBj3hyQfVcHtjael8BUCCHGYSx3LfnAo/37TFXgz7quP68oigX4jaIohwA3cHsE+ymE\nMNAzVaf4Vf9ejxdqzuDR/IYtsVFVYjIw1XWd1l/8Am9toO6aa/dusu+9F1NKCr6WFnq3b0exWEha\nv/6SDLe630/7H/5A36FDqMnJZHzgA1hLQg/+VYeDJNkqYShdH3mPKcAtMyq4ZUZFFHskJuJER1sw\nKAV4puo0N5eVDyrllutwDMogmmeXhGFCCDEeYykXcwhYOszzXuD9keiUECK27GluuKRtVGAaqBNp\nyKUvS+vpCQalAJrTiaemBmtZGS0PPYTWE6g/6z52jKxPfAJlQDZW586d9B0MBP5aVxcdjz9Ozr33\nRvcbEGFxueRHIv4MN/s99Ll/m7OIXq+Xs12dLMrK4Zbp0V9uHK/O9XRxqrOdaSlpUdsPHA9a+1w0\nOnspS06NWHkbTdf5xdH9vF5/jhy7g88sWkFRUmxsLxBTl6zzEkKMqjgphX0tTYPaRgkEprE3Y6o6\nHKjJyWjd3f1PqJizsvDW1ASDUgDvuXNo3d2YUi/ehGlO56DXGtoW8UPX4qd+qRhdRVoGVxeW8lJd\nNQDvnjH7kgy/KVYbX1p+hRHdi2uHWpt5YPcbeDUNs6Jw39I1rJjidbohMPD79b3b8GgaWQl2vrl6\nY0Qy0r7pweNgAAAgAElEQVRcV83zNWcA6PZ6+P7B3Xxn7aawX6fH6+G7B3ZysqOdOemZ/MfCFVJL\nVozI+AJqQohx++Opo3zolRe4d+sr1PZ0Rew6/1o+j3xHYHmaikKZBKaXUEwmMj/8YazTpmEpLCT9\nttuw5OVhysoKrD/upyYmog6pDWpfvHjQc4lXyE1uvBptKa+IP59cuJyfrr+WX264nn+dNc/o7kwa\nL9ScwasFlr/4dJ3nayoN7lFs+OOpo3j6/11a+lw8W306ItdpcbkGt/tcIxw5MY+eOMye5ka6vR52\nNtXzh1NHI3IdMTnIjKkQButw96FDyIXJtzee50+njwHQ3OfkW/t28KMrN0egh3CgtZF6Zy8AGjoP\nHdnLuoLiiFxrNIoSm4EpgKWwkKy77x78XE4O6bfdRvdLL6FYraS+/e2XJCUyZ2aS/elP4z51ClN6\nOraZM6PZbRFGl0t+JGLPX6tO83jlcRJMZu6av4TFWbnDHleYmDyo7fR5+eGhPZzsaGNOeib3zF9G\ngiQbC0nikFmzJLPVoJ7EFnXI+8e+5kZ+4NnNrTNmk5+YFLbrrMkr4C9nT9DXX2ZsQ0Fkkho2uwav\nAGrpkxVBYmTyLiqEgR47dZTH+oPLd0wr587ZC8d8bmN/oHhBg6t3hCMnzunzDWq7/X78uo7JgBtw\nRYUYjUtHZF+8GPviy2dgNaWl4VixIko9EpGi6yPXMR2rfS2NbG88T549kZvKZmJSw7+4qd3dR2Vn\nO0VJyeQ5wnezGw1uv58/nT5KfW8vq3ILuGqcWcIrO9v5xbEDF16Vb+zbzu82vQ3LGDJj/+7kkWBS\npNfrz5Fhs/OhOWN//xZwW/lcTne2U9nVQWlSCrdXzDe6SzHhjor5fGXPVpw+HyoK1T1dVPd0caC1\niZ9ceW3YBkBKk1P5zppN7GqqJ8fh4Mr8yAw2bygoYW9/zWYFWB+h64jJQQJTIQzS5OoNBqUAT549\nxdWFZZSMsbbhsuw8/njqKC5/IGi8Iq8oIv0EWJVTQHFiMrW9gf2TN08rNyQohdieMRViosmPDrc2\n8+CuN7iQ3+u8s4e751+Sf3BCqrs7uX/Ha3R7PVhUlfuXrmFZdl5YrxFJPzmyl1fqagDY2liHw2xm\n1TjKswxduuj0+ej1eUkbQ2A6dGCwMYIDg5NVui2B711xNW6/H9s4y2RNRvMzsvnVxhvY3dTA/zu4\nK/h8S5+LBldvWJNElSSnjPmeY7yuKiwhzWbjVEc7s9MzWJgp9bXFyCQwFcIgF/bWDOTW/GM+vygp\nmW+t2cgb9efISLBHNEtuosXCt9dcxcG2JpItNirSMiJ2rdEoqhKYlhIiBmkTDEz3tTYy8J1hb3Pj\nxDs1xF+rK+n2eoDA+9D/Vp6Iq8D0WNvgkulH21vHFZjOy8gix+6gqX+p4eLMHNLGuKViTW4huwdk\nK1+TWxjy9aeiFpeTXx8/SLvbzbXFZWwqLJWgdBhJFiuLs3JwmM3BFUspFis5CeFPghQNS7JyWTLC\nMnkhBpLAVAiDFCYms7GghC3nAyP/q3MLmJmSFtJrlCanUhqlFPsOi4X5Gdl8Zc82jra3UJyUzH8t\nu4I8R3Rr9ikqMVkuRggIzJiqEwhMS4YkFitOSh7hyPGzqoMDAWucBQblaemDti7MSk0f1+skWax8\ne81VbKmrIcFs5prC0jGfu7m4jBSrlZOdbcxOy5RssmP09X3bOdXZDsCx9hZy7YnMy8gyuFexKc2W\nwAPL1/F45XEU4LbyeZLNVkx6EpgKYaBPL1zO9cXT0HSduRlZMZ805YnK4xxtbwGgtqeb3xw/yP1L\n10S1DwoKmizlFTFqosmPNhSU0Oh08mbDOfIciXxs3pIw9i7gnTMq2N/aSG1PN+m2BD5QsWDUc051\ntPFmQx1Zdjs3lMwwbCk/wN3zl5JssVLv7GF1biFX5I9/G0O6LYF3jLPu6KrcgnHN1E5lZ7s6go/1\n/rYEpiObnZ4ppYjElCKBqRAGUhSFuXH0odzt9Q5qd3k8Ue+DruuoptgO4OOZruvg91+SOViMTTiS\nH906cza3zpwdng4NI92WwA+vuIY2dx9pVtuoyX7OdHXw+R2vBrcfnO3q5J4FyyLWv9E4zJaIBOwi\n8hZkZgdrYpvj7PMv3A60NLG/tZHSpFQ2jjOBlxCTjdx5CBGnnq+u5I+njmJWVT42bwmrozByv7mo\njNfO1+LR/KjAW0qmR/yaQ000ucxUoLlcOHfsQNc0HKtWYRpSN9V9+jT+7m5ss2YN+lrfiRO0//73\n6H192JcvJ+3WW2N+Fj/WxMvvp0lVybaPbb/a3ubGQXvitzeeNzQwHavKzg5+eGg3HR431xdP473l\nc43u0pT3uSWreaLyOO1uN5sKS5ge4vaVyWJXUz1f2bOVC2t/WvpcvHNGhaF9mgq6PR48mp/MBLvR\nXREjkMBUANDj9fDX6kp8msYNJdPlP22Mq+3p4udH9wc/1L6zfyePbnrrJXXhwm1Oeibfv2ITx9rb\nKEtJpXyce7smYqLJZSY73e+n9Wc/w1sXKGXh3LWL7E99CtVmA6Drb3+j55//BAIlarI++UlMyYF9\njB2PPYbeX3TdtWsXCXPmYF8oJTBCES+BaSjyh+wjz4+T8jLf3Lc9uBf1sdPHmJWWEVdJniYjh9nC\nHWNYOj7Z7Wg8z8ANKdsa6yQwjbDnqyv5xbEDaLrOVQUlfGrhchl4jUHhL44m4o5f0/jCjtf446mj\nPF55nM9u20LvkCWbIra0ufsGfah5NH8wy2akFSWlsLm4zJCgFAKJj+SzZGS+lpZgUArgb27GV18f\nbPe+9trFr3V00HfwIBBYwqu5BpfPGNoWl6d7vfSdroQhdX/j3RX5Rbxn5hzyHYksyMjm3sWxX29X\n13Wa+5yDnruQfVcIow2tHRwvgz3xqs/nCwalAK+cr+FAa5PBvRLDkcBU0Ohycra7M9hu7nNS2dVu\nYI/ik9PrZX9LI+d6uiN+rVmpGRQlXszWuSAjm5wxLsuLe/rEkstMdqakJJSBM+eqippyMdOrYh+8\nGuJCW1EUEtetu/g66ekkzJsX2c5OIprHQ8tDD9Hx+ONoXZ10Pfec0V0Kq9vK5/LzDdfz1VXrL7mp\njkWKonBlfnGwnWi2sFTKVYgYcfO0cq4rnkau3cHKnHw+MneR0V2a1Py6HgxKLxiuZJ8wnizlFaRZ\nbdhNZlz+wCi/WVHIsUe3BEi8a3f38dltr9DocqIqCvfMX8bVRWMvPRAqu9nMN1dvZMv5GiyqyqbC\nUtRxBGvdHg9/PH2UDncf1xSVxcUyN03XJ5xcZiz8XV146+ow5+Zizohu3VZvYyNdzz6L3tdH4saN\n2OfPH/O5amIi6e9/P53PPAN+P8lvecug/qe/5z20/fa36C4X9iVLsC9eHPxa6o03YquoQOvpIaGi\nAjVR3gfGyn3sGN66OhQs6Dr0bNlC8rXXDh4kEFH1yYXLmZeRRafbzbr8QnKjXNpKiJGYVZW75y81\nuhtTRqLFws3Tynnq7CkA5qZnsVgGqmKSBKYCh8XCF5at4dfHDuLTNW6bOTfqtSnj3T/PVdHYv0xM\n03UeO300ooEpQLLVyo1lMyf0Gt/Yt51Dbc1AIKHJd9ZcxQyDluiOla4xoTqRY+E9f56Wn/40sN/S\nbCbzgx/ENmt8JSVCpWsabb/8Jf6OQFkFz29/i/kzn8GSO/YP0YS5c0mYO3yiF1t5OXkPPhjIvDtM\n0JQQpe9zsrnwb2ky6fj8KphMoMqiJCOZFIXriqcZ3Q0hRAz44OyFXJlXhNPvY156FmZ5f45JEpgK\nABZm5vCDddcY3Y24ZRnyBje0Hasu1CSFwFKXEx1tcRCYRj65TM9rrwWTAOHz0f3KK9ELTF2uYFAK\ngKbha2wMKTAdjaKqEjSFmW3OHOxLl+Lath+fppD2rnehjFKGRQghjOTx+3mzoQ4FuCKvcNTSUfGu\nPC26q59E6CQwjUMev5/fnTzMma5O5mdk8e6Zc8a1jFOEz3XF09naUMfxjjbsJjMfmbN49JOGeO18\nLbua6ilMTOaWGRVRCW5npqZzoqMNCGw4j/WgFPoD0wj/vg+dSQylpmfvtm14qquxlpWRuHp16Nd2\nOLAUFgYTGCkJCVhLpMZdLHCfPo3W14etvDyY5fgCRVFIv+02EjbfgP/5J3Esi1w5Fb+u80J1JfXO\nHlbmFrAoMydi1xJCTE5+TeNLu94IDlD/vfYsX1l5JSYZtBQGksA0Dj164jDPVp8G4FBbM3azmZun\nyfI7I9nNZr6+eiMtLifJVisOc2j7yrY31vGdAzuD7Ta3i7uisP/k/qVreOTEITrcbjYXlVERB6OJ\nuh75GdPka67Bc/o0vuZm1JQUUt761jGd1/Paa3Q98wwArt270b1ekq68MqRrK4pCxkc+Qs9LL6G7\n3SSuXYspbfLX+vO1tdH+6KN4GxqwlZeT/v73XxL8Ganz6afpff11AMz5+WT9+78P2z9bZho+jz/w\nexqhAZTfHDvAs9WVADxXXcmXV65nQWZ2RK4lIm97Yx27mxsoSUrhbaUzZaB5ktjVVM/TVadwmC18\noGIBBYnGJg3TdZ0DrU14/H6WZOVS09M1aNXUkfYWanq6mDZFa8uK2CCBaRyq7OoY3O7sGOFIEU0m\nRRl3co3DbS2XbUdKui2BTy+MTukHr6bxo0N72NPcQHFSCvcuWkFWCJmEe7wefnJ4H81OJ4+dPspd\nJSsjtkfElJpK9r33onV3oyYljXnG1H3q1OD2yZMhB6YApsREUm+6KeTz4lnX008HZ4ndx4/T88or\npFx/vcG9CtC93mBQCuCrr8d97NigxFEXqKqCyWrC5/ZjSYjMR+yupobgYw3Y29IggWmc2tF4nq/t\n3R5st/b1cedsqfMZ72q6u/j63m34+jPBVnV18vMN1xmaUf77h3bzSl0NEKhJ/okFy1AJvIdAYNVU\nksVqVPeEAKRcTFyan5E1qD1vSFvEn+lDRihnxNCIpabrPHn2JN/ev4Pn+mdpxuOZs6fYcr6Gbq+H\no+0t/Ozo/pDO//Wxg7zRcA6/pvNKfQ1Pnj057r6MhWIyYUpLC2kZr6Wg4LJtMTJ/9+AyS1pPj0E9\nGYaqXrq8OyFhxMMtNhPePn/EulOUlDy4nZg8wpGxq9fr5emzp3j67KkpXTd7X0vjZduxoMvj5tET\nh/nl0QPU9Ua+HNpkUNPTFQxKARpcvfT6IvN7rus6j1ce5792vs4jxw/h9V/63tPW5woGpQDH2ltp\ndjn5yNzFWFUVq2rio/OWkG130OHuY39LI81S91cYQGZM49B7y+fiMJuDe0yvL5ludJdEP73/gyjU\nUdFNhaV0ut3sbKqnMCmJD1YsjET3xuWJyuP84dRRAF6vP4eOzttKQ88GPLTYfagfenW9/YGKDigD\n2jEkefNmdK83sMe0tJTkzZuN7tK4ab29tD36KJ6qKqwlJaTfcQem5GQ0pxNvXR2mzMywltFxrFpF\nZ03/jZPJhD2CezRDpZhMpN16K+1//jP4fDhWrsRWUTHi8ZYEE54+Hw4isxT5ngXL+MnhvdQ7e1md\nW8CmwshmAA83r9/P/TteDdbPfrmumu+suWrSJ14ZTklSyqB26ZC20fy6zhd3vk5V/8/qtfpafrTu\nGtJsIw/MiED+hgSTib7+IHFacmrEZiOfqTrN708eAeBAaxN+XedDcwbfQ1hNJlRFGVTL02G28JbS\nGdzQfw+pKAo13V3ct+NVur0erKqJ/1q+Vvawi6iSwDQOmRSFf5k+8k2RMMZz1ZU8fPwQqgIfnrOI\na0MsU/CO6bN4x/TY2yt8yTLj1pZxBabr8ov4e+1Z/P0fjBsKikM6f2VOPsc7WkHTQVFYmZMfch8i\nTTGbJ80S3K6//Q3PmTMAeKqq6H7hBZI2baLlxz9G6+4Gs5mMO+4gYc6csFwvcdUqzJmZ+BobsU6f\njiU/tn6+9iVLSFiwAN3rRbXbL3usJcGMzx25GdN0WwJfWLY2Yq8fadU9XcGgFOBsdyc1Pd3MSI2d\nlSLRckPJdNrcfextbqAoKYWPzF1kdJcGaetzBYNSgE6Pm8qujrioeW2kPEciX165nudrKnGYLbx7\nxuyIXet0Z/ug9qnOdrY11NHu7mNlTj5ZdgdJFisfm7uYnx/dj1/XeXtZObP6c0oMHEh/uuoU3V4P\nAB7NzxOVxyUwFVElgakQYdDg7OEXR/dzYSzyJ0f2sSw7j8yEy9/AxoOZqWkcaG0Ktsd78zg/I5tv\nrd7I/tYmipNSWJ0b2jLXd86oICMhgWfUV/jU4uWszSscVz/E2AxdSuvv7qb3jTcCQSkEyuj84x9h\nC0wBbDNnYps5sdq8kaSYzWNa2m22qPg8kQtM4126LQGzogSXOpoVlbQYSnQVTYqi8L5Z83jfrHlG\nd2VYqVYbqVYbnR43AGZFId9hbBKfeFGRlhGVhIJzM7J4tb422PZoPr6+L7Bv+bHTx/je2k1k2R1c\nXzKdqwpL8WsajmFqWAOX5G2Il9J3YvKQwFSIMOj2eNAHtDVdp8frmRSB6W3l89D1wCjsvIysCc3W\nl6dlTKiO2KbCUl40mVmcHXpNT13T0N3uUWe7RIBjxQr6jhwBTQNFwbFyJZ6qqkHHTOY6nZrLhdbd\njSkjI6R9xgCaX0cxSWbVkWQm2Pn0ohU8fPwQECh8PxneKycjq8nEl5ZfwW+OHcSj+XnXjArDs8uK\nwW4omY6m6xxqa2Z6ShqPnz4e/FqnJ7BF6C2lMwCwmUxwmfftd02v4EBLE+edPaRZbdw+a37E+y/E\nQBKYChEG01LSmJ2WwfH+mqALMrIpirG9QuPV6OxlTnomN5XNJCMCN48d7j4eOryXcz3drMjJ487Z\nCy9bLiFQxzS0a7grK2l79FF0pxPbnDlk3HFHyMHGVJMwdy5Z99yDt6YGS1ER1tJSrGVluI8dw9fU\nhOJwkPK2txndzYhwnzpF2yOPoLvdgdIwH/84qmPsGaQ1TcdkkpmGy7kyv5gr80Nbzi+MUZ6aztdX\nbzC6G+Iy3lo6g7f2B58v1p6laUAOh1Tr2FcjZNkd/OjKzbS4nGQk2AOBrBBRJHdmQoSBWVVZnp1H\nXW8PDrOZD1TMxzQJatFtb6zjW/t24NN1kiwWvrFqIyXJ4Q24f3JkHzub6gF4uuo0uY7Ey+9h1Qm5\njmnHE0+gOwMf1O5jx3Du3Eni2vjdoxct1uJirMUXgwdTcjLZn/kM/vZ21OTkmKozGk6dzz6L7g4s\nXfTV19P7xhskX3vtmM/XfJrMmAohDPGZRSv59v4ddHrcXFNUFvK2F4uqki+z4sIgEpgKEQYHWpr4\nfX/m2m6vh2/u38GvNt5gcK8m7v/OnAzuA+vxenmuppKPz1sS1mucH5Jdt36UbLu6poccmF4IMi7Q\nhrTF2CkmE+asSV6iStMGNfUh7VFP9+uoIf6OisFcPh+Vne1kJNjHtHS0vreHHxzaQ0ufk/X5xdxe\nIUsQxdQ0Jz2T31z1FqO7IcS4yFojIcKg0dU7qN3icgazz8azhCHLeBJM4R/LWpV7MfuqCqwYJduu\npuuXXeo7nKQNF5ehqSkpOJaEN7jWh6kbJ+JX8nXXBfdhmdLTSVyzJqTzNU1HNcvH63h1ut18+s2X\nuH/na9z1+ou8Ulc96jnfPbCLo+0tNLmc/O+ZE7x6vnbUc4QQQsQWmTEVIgwWZeaQaLYEC2ivyi2Y\nFEt5Pzh7EQ/ufoM2dx/TklO5JQLlbN5XPo9ceyJ1vd0szc4bNTX9eGZMkzZuxDptGv6ODqwzZmBK\nCs8yJV3T6PjTn3Dt24ealET67bdjmxZamSAxOs3tpvv55/E2NGCbPZvkq66K6PXsCxZg+dzn8Le3\nYyksRE0IrWaj5tNQZSnvuP3zXBXnnYGVE5qu8/uTR7lqlFqtQwcHG529IxwphtPr9dLa5yLXkSj7\nCoUQhpHANMZ1edyYFJXEEVJ7i9iQ60jk22uu4rX6WlKsVq4rnm50lybkeHsrz1afxm42883VG7Ga\nTKRabSHPVI6Foigh1XzVNVDGMRllLS2F0svf3IbKtW8frr17AdC6u2n/wx/Ive++SZ2t1ghdTz+N\nc+dOADyVlagOB4mrVo3pXF3TUMZR8sCckYE5Y3wZpCX50cSYhgw8DW0PZ01uAX+rPQuAVVVZniN1\nNsfqREcbD+5+gx6vlzxHIl9buZ4s+9iTfQkhRLhIYBrDfn3sAE9XnUYF7py9kLdPKze6S+IyipKS\nua18rtHdmLAGZy9f2vU6ff3LU4+1t/LQus2DinAbaTwzpmN+bb8fX0MDamIiprTR67VqTufgdkcH\nLQ89ROZHPxryLJsYmefcuUFt77lzMEpg6m1ooO2RR/C3tmKbPZuM229HGTDAp/v9ERtA0HxSLmYi\nri2exhv15zjZ2Y7NZOIjcxaNes7H5i1hRmo6zS4na3ILmJ4yvnrLU9FvTxymxxtY7dPg7OXJsyf5\nt7mLRzy+y+Pm63u3c6KjjdnpGXx+yWpSQsj8KoQQI5HANEad6mzn6arTAGjAw8cPsrGghNRJmgVT\nxI4zXR3BoBSgtqebbq8nZm48AuViwn/Tr3k8tP7sZ3hrakBVSXvXu3CsWHHZc+wLFtDz8sto3d3B\n57y1tTh37iRp/fqw93Gqsk2fju/8+UHt0XT+5S/4W1qAQCbmntdfJ3nTJrx1dbQ9/DD+zk4S5s4l\n/f3vD3vpIM2vYTJoj6nb7+elc1V4NY2rCkti5v9tKBxmC99cvZFGl5MUq5Uki3XUc1RF4boQVl6I\ni4bmQxgtP8JvTx7mSHvg/9bhthZ+f/IId81fGrH+CSGmDllrFKPcft+gtgZ4NEmwIiamtc/F6c52\nvJdJ1lOWnIJlwNLHPEfimG4ML8fp9dLj9UzoNS7QIeQ6pmPh2rs3EJQCaBqdzzwz6jmmtDSyP/1p\nFPuQ+q6SDCmsUm68keTrriNh8WLS3v1u7GNIXqX19g7b7njiCfwdHaDr9B05Qu+2bWHvr+aP3Kz+\nZa+r6/zP7jf52dH9/Pr4QT63fQvO/n3v8cakqhQkJk34vUeM7r3lc4KJ7jJsCby97PKrszqGZDVv\nd/dFrG9CiKlFZkxj1Jy0TBZmZnOwtRmATYWlZMueDzEBr56v5QcHd+HTdaYlp/K1VRuG3btckJjM\nF5et5ZmqU9hNFm6vmDehvaVPnT3JI8cPoQHvmDaLO2cvmMB3EdmlvONhSkkh9eab6fjzn0HTMOfk\n4Fi50uhuTSqKyUTy5s0hnZO4Zg2dTz0VON9mw7FsGTBMwDpkOXY4aH7dkORHLX1ODrU1B9t1vT2c\n7GhjcVbuuF6vw91Hs8tJUVIK9jDPKovYsSgzh5+tv44GVy8lSSmjDgZcXVjK7qZ6NAKzG1cXlUWj\nmyKG/LX6NM9XnyHZauXueUvDXt9cTF3ySROjTKrKA8vXcai1GYuqMj8z2+guRYXT6+XPlcfocLvZ\nVFQ6aobWqa61z8Xjlcfx+P3cVFbOtJTUEY995PihYE3Ss92d/PNc1Yj7lpdk5bJknDezA7W7+3j4\n+CEuLAx78uxJNhQUT2j/lx6hMjz2JUtw7tiBt7YWVJXUG28c87mOZcuwlpXh7+rCUlCAOoWX3Oua\nhqeqCsVkCiScMkjiunWY8/PxNTdjmzkzWHs1cd06up59FgDFbsexNDJLEI3Ykp1ktmIzmXD3z9gr\nQIbNfvmT+vV4PTh9XrITHCiKwqHWZr6yZysuv48cu4Ovr9ogg6OTWEaCnYyEsf2urMkr5BurN3Kq\ns51ZaRlUpI0vSZiIT0faWvjF0QOBRi98Ze9WfrHhemM7JSYNCUxjmFlVWZI98eAAwKdp/PzofvY2\nN1KanMInFiwjzRZ7yVm+sW87+1ubAHi9vpbvrN0kSSxG4NM0vrjzNep6A2UVdjSd58dXXkv6CD9X\nI26U3X4/Q8PIviHL1EOlqgq6dvngtO/IEXpefRXFZiPlxhux5Iw+wKHabGTdfTe+xsYxJz8ayJyZ\niTkzM6RzxsJTVUXX88+DrpN83XXYZs4M+zXCRdc02h55BPfRowA4Vq4k7dZbDeuPbcYMbDNmDHou\nacMGLEVF+FtbsZaXY05PD/t1FTWQPZooJ2d2WCx8dvEqfnpkH15N47byuWOayXilroYfHdqNT9dZ\nmZPPfUtW87uTR3D1/19tcjl56uwp/m3u6EmIxuvlumr2tzRRmpzCzdNmTYpyW5PZ7PRMZqeH//1O\nxL7z/fccFzQ6e/FrGqZxZD8XYigJTKeIp6tO8ff+VPrNfU5+fnQ/n1uy2uBeXerwgGVoPl3naHuL\nBKYjaHO7gkEpQI/Xy9muDtKzhy+TcOfsBXzvQGAp7/SUNDZHYflVniORDfnFvFofKHa/KDOHirSJ\n3cwoqsLlJk29TU20/fa3wX2ebfX15Nx//5hKhihmM5bCwgn1L5w0p5PWX/0KvS+wh6vt4YfJ+fzn\nMSUnG9yz4Xlra4NBKRBIAnXNNeMuuxIpthkzYEjAGk6qqgRKxkTsCiNbkZPPipz8MR+v6zo/PbI3\nuJpiZ1M92xrPXzKQFcnV81vqavj+wd3Bdo/Xwx0VE1vyL4SIjAWZWdhN5uDA1bLsPAlKRdhIYDpF\nDC023hCjxcenpaRxqrMdCCxDm54c/aC02eVkd3MDWQn2kG7woi3dmkCGLYG2/sQTVtVEUdLIAcuV\n+cXMS8+i3e2mZEiCo0j6j0UruKa4DL+msSgzZ8IzIYqqoPm1Eb/ua2wclHzI39GB5nRiSkqa0HWN\n4O/oCAalALrbjb+tLWYDU4YpvzIVa7oqyuiz+qH4R20Ve1saKE1K5Z0zKjCH8f+uDviG9NWr+Xn/\nrHl8eXdgKW+u3TFqQpyJODhgQBII5lYQ8a3P5+O5mkr6fD6uKSoj15FodJdEGOQ5kvjWmo28UldD\nstXKjaWxu4pHxB8JTGNIa5+LZpeT0uTUsCeaWJ1bwIu1Z7lwO782LzZmhXRd5/maM1R3d7I0O5f7\nl097fm4AACAASURBVK7h18cO0uHpY3PRNOZmZEW1Pw3OXj6z9WW6+zPI3jK9gjsq5ke1D2NlMZl4\ncMU6Hj1xGI/m553TK8ixX/6DP5R9ROGiKMqoe4WbXL1878Bu6p09rM4t4CNzF4+YcEk1KYFlkiOw\nFhejJCQEAzpzQQFqYnzeEJmysjClp+NvDwzWqKmpmHPDs7w/EqxFRSRecQW9b74JQPL112NKHXnf\n82Sl9M+YhsOWuhp+dHgPAG9SR4/Pw4fHUNdzrFRF4b3lc/jdySMAzEhJY01uIQlmM7/ceD3NLheF\niUkkRDD50fQhe+Nllczk8D973uRwW6CszN9rz/LDddfE5BYiEbrS5FQ+MMFEhkIMRwLTGLG7qZ5v\n7NuOR9PItTv4xuqNZIYxgFianceXV65nf2sjJUmpbCgoDttrT8QfTh3l8crjAPyt9iz3LVnNZ5es\nMqw/WxvOBYNSgBdrz8ZsYAqBD4cvLb/C6G5M2I8O7QnWxXu+5gylyancUDJ8rcrRZkxNaWlkfuxj\nOLduRbHZSNq0KSJ1T6NBtVrJuvtuerZsAV0ncf161ITYvrFLfcc7SLrqKjCZYndmN8ICgyfhCUwv\n/L+44MKNfqhaXE62NtaRbLGyvqCEup5uanq6KE9N510zZrM8O59ur5uKtExs/bPcKVZbVOqgvrVk\nBj0eL/tbGylNTuVOWcYb97o87kG/qx0eN8faW1kTI4PiQojYJIFpjPjdySN4tMDNdqPLyXPVldwe\n5oBoQWY2C2Isu++e5oZB7b0tjYZ+cA0dzU2Nw+L08eL1+lqOtbdSnppBo2twyY4m18hLzdVR9phC\nYObOamDSnXAypaWRevPNRncjJKEmjppslDEk6BqrmanpwfwAgXbo/7bt7j4+s+2VYL3Jf5yr4nh7\nKz5dx24y85WVV1JuYGZVRVF4T/kc3lM+x7A+iPBKNFtIsVjp6h/oVUCW8gohRiWBaYwYumxxInUj\n40lJUgqVXR3BdvFl9khGw8aCEg63tbClrprMBDufXrTC0P5MVi/WnuWhw3v7W5UsycoJ7ns2Kwqr\ncgpGPDecN/1ChJPr4EG6//lPNHcS7upqHAtnXXKMX9d5+PhB9jQ3UJyUwr/PX3rZWcnriqfR6/Ww\nr6WRkqSUcQ1Y7m1uDAalMHjW1eX38XzNGT4pJT9EGJlUlS8uW8vPju7H5fNyy/QKWaIthBiVBKYx\n4gMVC/ja3m24/D6KEpN5W2nkMkbGko/MXYwOwT2mbzN4E72qKHxiwTLumb80bpd/xoPdQ2bKFRT+\nY+EKzjt7WJGTT3nqyCU8Akt5JTAVscXX0kL7H/4Afj+6v5zOP/ye1IovXFLT9rnq0zxTdRqAut4e\nTIoyaob0f5lewb9Mrxh33zKGLP+2qOr/Z+8+A+OorgWO/2e2V/VqdcmyLMm94d4wNhhMDZgkJJRA\nCCEJJYGXBPLSCMkLCS+FJNRQA3lATDVgmsGAe5VtWZYt2ZLV+660fWfeh5UXy1VlpV1J8/uC7rI7\ncyVLu3PmnnsOXunLdHjjIO4fVYxeBTFx/O/cpeGehkKhGEaUT6MIMSk+kccXraDV7SLVaEY7SipZ\nmjQa7orAVUklKB1cY0w9V8bTzRYWjcno1Wt708d0tJAcDmxr1+K32TBOn45h4sRwT2lQOffuxV1a\nijohAdOCBb1qATRUfK2twWrQkgy4nUidnacEpif3ADx5PBimxCdxdW4Bbx89jFmj4ev5RTx/cB8N\nTgfZlii+klsw6HOIZBvrazhsa6c4Np7J8ZFbXEyhCLVGZxftbjdZlqhRc92piGxKYBpBhqrQhGJ4\n8Pj97GiuRyuqmBKfNKKC5WvzxmP3Boph5EfF8vX8ol6/NpQVT4e71ueew1NeDoC7tBTxttvQZWeH\neVaDw7VvH21PPx0c+202olatCt+ETqJNS0O0WpFsNmQZtKkpp91rOyMxhXeqKjj+GzxziFpSfT2/\nqMff2YKUdLp8Xswa7ZCcP1KtPXqYf+zfBcDLh+G/ppynFOhRjAof1RzlzyXbkWSZbEsUD85aiFGj\nCfe0FKOcEpgqFBHI6/fzk82fcrCjFYDFqRkjar+rVqXi9uJp/XptKCueDnfeI0e+HMgy3qqqERuY\nursD8OC4rCxMMzk90Wgk/vbbcWzciPx/tSR8+5bT9nCdlpDMz6fPY0dzA+lmC8vSsvp9zja3i38f\nKsXp83FRZi7j+rBPVBCEUR+UAnxeXxP8Wga+qK9RAtMh5JdlbB43UVrdqKmtESn+eaAEqbuSYKW9\ng49qj4Z9O5VCoQSmilHL4fPyTlUFXr/EsvSskLbnGai9rc3BoBTg49oqri+YQIzSA27EFj/q3LAB\nx+bNiBYL0VdeiTr+3D18NVlZwRVTBAFNRu/SoYcjdXJyj7EmZWhWGvtCHRuLdeVKJOlxVJYzVyCd\nkpDElISBp4z+99bPOGLvAGBjQw1/nreMZKXyaZ8kGow9xkrl2KFT02Xnv7d+RqPTQbrZwq9mzB/y\nPtuj2SlFNxneNwbcfj/PHdxLdaeNaQnJrMoaG+4pKfpBCUwVo5JflvnZlg0c7GgDAu0T/jRvacSs\nIBhOKkaiEgR0orL/AwIrPSOt+JGrrAzb668HBvX1tD77LIl33XXO18Vedx22d95BstkwTJ8+YldL\nAYyzZiHZbLhKS1EnJkZsC53jaeaiOLgXeZ1eTzAoBXD5/RzqaFMC0z66sWAidq+Hwx3tFMfFj/r9\ntkPpmbK9NHa3CqvutPPvwwf4TtGUMM9q9PjW+Ik8vHsbPlkiPyqGJWMywz2lAXl8/y7WHTsCwM7m\nRkxqLUvThvf3NBopgaliVGp2OoJBKUCTy0F5RxtTTlP4osnp4MGdmzhia2dSfCL3TD7vlMAx1Api\n4rg8eyxrKstRCwLfKZqq7P3oJqoE5HM1Mh1mfI2NZx2fiWg0En3llYMxpYgjCAKWCy7AcsEF4Z7K\nWcmSjKga/JUHk1pDssFEvfPLNkuZFuugn3eksWi13DdtTrinMSq5fL6zjhWDa35KOsWxCXR43KSZ\nLKgjqJhcf5x4TRcYtyqB6TCkBKaKUcmq1WFQqXH6Ax+EoiCcktJ13BOluznU/Ya3vamBVyoOcF1+\n33sJ9tUNBRNZnVeIWhDQKNXygkZiuxjd2LGgVkP3hZl+/Pgwz0jRX5JfRhjk1VIIBOo/nzGPZ8pK\ncPh8rMrKI92sBKa91eJy8mHNUfQqFcvTc9Ap77FD7rLssexra8YrSRhUai7OUvY3DrUYnX7EbBEq\njInrkUVSGBMXxtko+ksJTBWjkkGt5ifTZvP4/t14JT+r8wpPaWFyXJvb3WPcftJ4MA32yuxwNBKL\nH2mSk4m/7TacO3ciWiyY588P95QU/SRL8qCn8R6XajLz46mzh+Rc53I8iyHSqof7JIkWl5NonT4Y\nfNo8bn74xce0uJ0AbG6o44FZC8I5zVFpakIyf5m3jKrODnKtMSSc4eawYnQ60NbC3/btpMvr5dLs\nvHPuGb1p/CTMGm1wj+nC1JFbc2EkU656FaPWpLhE/jp/2Tmfd0F6FgfaWwBQCyKLlTe7sBKEkdku\nRpuRgXYIihd56+poe+45fK2tGCZPJvrqqyOqH+hwJ/klhCFI5Y0kbx89zD8P7AEEbho/kQszcsI9\nJSBQtfi+zZ9S3WUnSqvj59PnkRsVTWlbSzAoBShpbaLd7SJ6hKwcDSepJjOpJnO4pxFRXiovZUNd\nNYlGI98tmkr8KAzYZVnmgR0b6fAEFgKeKN1DflQsBWdZBdWIYp9azykikxKYKhTncH5aFskGE0fs\nHRTFxpNtPbU34XDU6nLy4M5NHO5oZ0JcPPdMPg/TMNjHOhJXTIdS+0svBfewOrdtQ5udjWnWrDDP\nauSQ/KHZY1ra1sLaqsOY1BquyRsfsel2DY4uHt+/C6l7/Oi+nUxPSI6I1a9XDpdR3WUHoMPj5pmy\nEn45cz4JBiMCBHvJmtQaTBFS+E4xun1aW82/Du0HoLrLzsN7to3K1Xy33x8MSo9rdDrOGpgqRgYl\nMFUoeqE4LoHiuIRwTyOknjpQQll7oCXNzuZGXjpUyk3jJ4Z5Vuc2UtvFDBW/3d5jLJ00VgyMJMmI\nA1yBru3q5P4tG/BIfiAQpP5p3vmhmB4AHr+fFw+VcqzTxvTEFJan97+ac6fXEwxKASSg0+slIQK6\nfni7f37HHf955lijubVoCi8fPoBBpebWoilolKwBRQQ41tXz/bima3S+P+vVaqYnJLOtqR6AKK2O\n4thzt1BTDH9KYKpQjFJtblePcbvHdYZnRhZBZMQVPxpKxpkz6fzgAwAEvR79xMi/GTGcyCFYMS3v\naAsGUQCV9g4cXm/IKnM/Ubqbd6srAdjcWIdBpWZBanq/jpVliaIwJo79bYHtDhNiE8gwn36//lC7\nJCuPL+prsHk9aEWxRyuYCzNyIiblWKE4blpCMi8fPoC/e8/2jMTI69c8VH485Tzeq66k0+dlUWqG\n0uN2lFACU4VilFqWlsXe1iZkAn1Sh0sPM1liSKqejlTWFSvQpqfja2tDX1CAOl65Cx1KoVjRz7ZE\noRYEfN0Xp6lGc0jbRR0PIo8rbW/pd2CqEkV+OWM+X9TXIAgwJ2kMqghZfUw3W/nr/GVU2jpINZlJ\nUnq8KiJQm9tFeXsrqSYL46JjeWDmAjY21JBoMHFRZm64pxc2GpVKqdQ8CimBqWJYsHncfFpbjVal\nYnFqhtI+JQQWjckgwWCkwtbO+Jg48qJiwj2lXvF7JVSayLjwPRvZ50OI0KrK+iKlQMRgUalF/H7p\n3E88iwyLlR9Pnc1bRw9hVGv45rgJIZpdwLjoWKo6bcFxflTsgI6nValYNCYyi8JF6/RMSYjM/bnH\n1Tu6WFddiV6l5uKsXIzq0O71d/i8GFTqiKuYrIBjnTbu3fQJdq8HtSDwo8mzmJ08hkIlbVUxSkXm\nVZNCcQKH18s9G9dT6+gE4LO6Y/xixrxh8yHrkyRquzqJ1umwanXhnk4PRbHxFA2zD0DJL6FSR25g\nKjmdtD71FJ7KStSJicTedBPqOKVgw2ghqgX83oEFphBI4RusNL5bCidhVGs41mVjekIKiyM0qBwN\n2t0u7tn4Me3dhV62NdXzu/MWhuTzrdPr4ZfbPudAeyuJBiP/PX2u0us2wqytqsDu9QDgk2VerShj\ndvKYMM9KoQgfJTBVRLzS9pZgUAqwq6WRZ8r2ohZFlqdnR0T1xzPp8nr56ZZPqbC1oxVV3Dtl1qje\nM3I6Tp+P/9m1id3NjWRZovjx1Nln/Tf1eyVEdeTelOj88EM8lYH9e77GRjpef524G28M86wUQ0Wl\nFiN+D7ROpR4Whc5Gg7L21mBQCnCgvYV2jzskVZhfrSjjQHeBu0angydK9/CLGfMGfFxF6OhOyv7S\nqZTLcsXoFrnLDgpFt9iTPqAF4D+VB/m/wwe4d9N6OrvvNkaid6srqLC1A4GKkE+W7gnzjCLPKxUH\n2N7UgE+WOWRr5/HS3Wd9vt8X+SumJ5IdjjDNRBEOolrE75WQ5cgOThWRIdlo6nEhZtVoMYeodU2X\n19tj7PB5z/BMRbhcnp1PtiUKgGitjhsLlBtGitFNuTWjiHjZ1mi+NX4iLx0qRSWIPXpbNbuclHe0\nMSU+KYwzPDPppItTvzzwFL+Rpt3tPuv4ZH5vZAamvtZW/K2t6CdNwrljB7LXC4KAce7ccE+tB19z\nM7a33kJyuzHPn4++sDDcUxpRRFEIFkASQtDPVDGyZVqi+P7E6bxyuAy9Ws0t4yeFrHXN8vQcPqmt\nxun3IQoCl2QqhWQijVWr4+G5S2l3u7BodUrbIsWopwSmimFhVdZYVmWNxeXzccPHa+nqvvOrEgSS\n+pHK2+n18GzZXppdThamprMwdXD2WC1Pz+ajmqPUdHWiFgS+Ma54UM4znC0ek8HHNVX4uoP2ZWlZ\nZ32+3xd5xY+cJSW0Pf88+P2ooqOJ/da38Le0oE5JQZvev2qng0GWZVoefxx/S6Aqa2tFBQl33okm\nOTnMMxtZVGoBv09GDEGNNkmWaXE5sWq1SprfCLVkTOagVEXPjYrmz/POp6y9lXSzhWxrdMjPoRg4\nURDO2Qqlw+2myeUgzWRBH6FF9RSKUFB+uxXDil6t5qdTZ/N46W48kp/VeYWkmvreM++Pu7cGGzdv\na6onWqtnUnxiqKcbuBs6ZylH7B3E6Q0RvR/2XI7aO/iktpoorY6LMnNDdme3ODaBP8xZzL7WZrIs\nURTHJZz1+ZJPjrgVU/t774E/0HfS396Ou6wM60UXhXlWp5JdrmBQCoDfj6++PiSBqbuyEte+fajj\n4jDOmoUwiu/8iyoRv0/i8NZ6Njyzn7gMC0u/PZGoxL79/Tu8Xn62dQMHO9owqTX8dNpsimPP/veh\nUJwoyWhS2uQMY3taGtlYX8P7x47ikfwkG0w8eN5C4pSenooRSglMFRFHluWzViQsjkvgT/POH9A5\nyroLQgTHHa2DEphCIJguiBnaqqw7mxtweL1MSUjqV+sBj9/P+toq/LLMwpR02j1u7tm4HqffB8D+\ntmZ+PHV2yOabbY3u9d18v09CjLDAVDi5fVGEtjMSDQbUqan4amsBELRaNCFY0fUcOULL3/8OUmDV\n29fYSNSllw74uMOVzqThV4teRq1Vsfz2SdQeaOPXi19h7lfHccF3J2OM7l117reqDnOwow2ALp+X\nx/fvHvB7n0KhGB7erargb/t29nis3tnFG0cOcUNBaFtI9ddrlQfZ3dJIliWar+aNV1r5KQZMCUwV\nEeXJ0j2srTqMWaPlrkkzmBTX+2CxpsvO3/bupN3j4oK0bC7NHnvG5xZEx7K1e8UUYKx1ePTw7I1/\n7NvJ2qoKADLMVv5n9qI+Bad+Wea/t37GvrZmAN6pquCCtKxgUAqwubHunDcQjtvSUMuxLjuT45PI\nCUEqWSQWP7KuWkXrP/+J7HKhTknBPH9+uKd0RnE330znBx8guVyY5swJSSsbV2lpMCgFcO3dO6oD\n0/s+vgqXzUN0igm1NnChtvTbE3jzf7bx16+/w52vXoJGd+4LOG/3KvxxHsl/hmcqFKfyShLvVVVg\n83pYmJrOmH5kFynC54NjR8I9hbN6t6qCpw6UALC9qQGP38/NhZPCPCvFcBdZV3eKUW17Uz2vHynH\nK0m0uV38fufmPr3+wR2bKGltorrTzpMH9rCrueGU57S4nDQ5Hdw9aSYLUtKC6ah/37+TJufwr57q\n9vuCQSlAVaeNHU2n/hzOpt7RGQxKAY7YO04p4pRsMPUqKP1PRRm/3rGRp8v28sMvPuZAW8s5X3Mu\nfm/k7THV5eaSdN99JN57Lwl33IFojNyUbZXFQtTllxNz7bVoM0Ozr+3k4FYVP7x644aaOUZPfKY1\nGJQCxKSaue7hhcSkmnn0xveo2tN0zuNckJ5NnC6QsicKAtfkjh+0OStGnod2beax0t28dKiUH37x\nMY3OrnBPSdEH0adpGZRsMHFJZi7/PlTKTzd/ypOlu/H4w3PD6pTMs5PGCkV/KCumirCp6bLT7naT\nFxWDTqU6pRprp9eDX5JQ9XKvWm2X/aTjdzL5hGq9Lxzcx78PHwBgZWYuDp8Pb/cqT72ji5cPH+C2\n4qkD+ZbCTi2I6FQq3Cd8UJn6mMpr0WhRC2KwGJEITE9Mxo/Mu1UVRGn13N7Ln9PHNVXBr32yxIa6\nYwNKa5YkGVmSESOw2qmo1yPqT72QkNxuRF3vUjeHK8OMGfiamnCWlKCOiyP6K18J95QikiAIXP/n\nRXz2/AEevel9pq3K4fL7Zp3xJk+Cwcif551PeUcriQYTaWZlxWugjto7eGTvTuxeNxdm5LAq68yZ\nNcOZX5LY1FAbHHf5vOxubmJZurLfdLi4pXASrS4nRzttFMXEsTqvkBxrNO8fO8IL5fsBKGltQpJl\nbi6cPOTzK4iJ48Oao8Hx+CHesqQYmZTAVBEW71RV8Oi+nUhAliWK385ayPSEZOL1BppdgT6QS9Oy\neh2UAsxITGFj9wexTqVi0glFdJqcjmBQCvD20cPkR/VM3/VKw7+Vi0oU+cGE6fzvnm14JD8r0rOZ\nktC3VjpWrY47J03nsf278UkS3xhXTKrJwuXZFi7Pzu/TsWL1Bo522oLjuNMEbn0h+SREtdCr1dpw\n89bX0/rEE/jb29FmZxN7002nDVxHAkEQsK5ciXXlypAf21tXh+R0os3IQBgB1Sg1ejWLv1XMzCvz\neOS6d3nx3s9Y/eBcRNXp3+ssWi1TE5SqyaHywI6N1DsCK4dPlO4h2xLNhHMUXBuOVKJIvN5Ik+vL\nTKBkpQjSsJJoMPHHuUtPefxQ977z48o72odqSj0sT8/GK/nZ1dxItiWKa/KUjA7FwA3/T3nFsPTc\nwb0cDwOP2DtYX1vFRZm5/GHOEjY31GLRaJmTPKZPx/zhpJm8cfQQ7W43i1LTSTNbg//Pd5qgc8mY\nTI522nD7/Vg02j4HXZFqXkoas5NS8clSv9tLzE9JZ35K74vieP1+jnXZidHpe6QffWv8RB7atYUG\nZxczElK4ZICrE5G4v/RMOl57DX974ILBU1lJ5yefYF2+PMyzGl7s69ZhX7cOAG1WFnG33jrsg1Nv\nXR2eykrUKSl8/6WL+McN63jm++u57uGFPVJ/Q0GSZHa+VUFqQSwp+SNnH31/+WWZRkfPdNY6R+eI\nDEwBfjJ1No/s3Y7N6+GijJwR+32ONkWx8Xxc+2U2UnFs+LZOXJyZx8VKf1xFCA3vT3jFsKUSegYX\n6u6V0RidnhUZOf06pkal4sqccaf9fykmMxekZbGuu5jAnKQxXJiRw8zEFGocnWSZo4gaQemWKlFE\nNURbyDu9Hn68+ROO2m1oRZEfTZ7FrKRUqjtt3L9lA61uF4kGI1/LLxxwi5lI3F96JrLTedZxuDi2\nb6friy8QjUaiLr0UdYTuB5V9Puzvvx8ce44cwbV/P4aJE0N7HlkGv39IAl734cO0PPZYoLWQIBC9\nejW3PbuCZ77/MQ+u+A+rfzOPseelhORcXW0u/nHjOrxOH221XVz96zlMW5UbkmMDtLldPLRrC0fs\nHUyMS+COiTPQRXhFTpUgMCMxhc2NdQAYVGomjuBgLTcq+rQrborB4fB60alUfcr06o8L0rORZJk9\nLU1kW6O44gzXPQrFcKQEpoqw+HbhZB7esxWvJFEcG8+i1IxBP+ftE6axPD0bvywzLjoWQRCINxiJ\nD1FvUb8ksb2pHgmYnpAcDLYjSVl7K1sb60gxmlgyJjMkKbHvVlVy1B5I1/VIEv88UMKspFReKN9P\nq9sFQKPTwYuHSrlz4owBnWs4rZiaFiyg/aWXQJYR9HqMM2eGe0p4jh4NzgmgtbWVxB/9KMyzOgNB\nAFEM9oeF07TlGSBXWRltzz+P7HJhnDmTqKuuGtQ0ccfWrV9+P7KMY9Mm4qdN41uPnc+utUd4+nsf\nM25eKpffNwtL3On7FJZ9XsPHT+6jo6GLlPwY0grjKFycTnJez4rXFdsbEAS4Z+3l1JS28sQt77P9\n9cOMnZ1K9rRE0oriBrRC+0TpbkpaAwWcPq+vId1s5atjC/t9vKFyz+RZvFV1GJvHzeLUTJKN5nBP\nSTHM+WWZh3Zt5vP6GgwqNfdMmcW0QU6/X5GR0++b+Kfj6G5HVWFrZ2JcAtePmzDoAbZCcTpKYKoI\ni3kpaUyMS8Du9ZBsNKMaoj2DY6Njg1/bPG5sHjcpRnPwDdgvSVTY2jFqNH0qrS/JMg/s2Mi27hY0\nk+IS+fmMeUP2ffXGgbYWfrL5E3zdQUl1p53rQ9ALTaZnxV6pe3xy+vTp0qn7SvLJw2bF1DhtGuqk\nJHyNjWizslDHxp77RYPMW18fDEoBfA0NyH5/yAO+UBBUKqIuu4yONWtAktAXF6MbH9o9TG0vvBBc\nyXZs3oxu/HgMxcUhPceJVOaeQZDYPRYEgSkrsxm/cAxv/3EHv17yCqvumcHs1fk99p5KfomXf7aR\nudcWkDk5gbqyNo7tb+HhK95k5Q+nMWf1uGCwWVfWRnyGFVEUSC+K4961l7Nn3VEqdzTyxUtlqHUi\nP3rzMkSxf+9RTSdlAERCVfMWl5Mur5cxZssZ33s1KtWI2bahiAwbaqv5vL4GAKffx59LtvPMktDv\ntx9M/zxQEixkVGnvIFqr58pcZSVWMfSUwFQREm6/n32tTZg1WvKje3cBbtXqsGrDkz67qaGWh3Zt\nxiNJ5EfF8KuZC1CLIr/Y9hl7WgKrAN/IL+aqXr4xH+u0B4NSgN0tjVTa2smLipx9XZsb64JBKcAX\n9TUhCUyXp2ezvqaK6i47akHk+nGBY16VM46Sliacfh8mtYYrQnAx6PdJZywSE4m0aWlo09LCPY0g\nXXY2gkaD7PUCoM3Njcig9DjT7NnoJ0xAdrtRxcaGdDVTliRkl6vnY47BDa7M55+Pt7YW96FDaFJS\nTun1qjdrufJn5zHrqrH8+6ef89JPP8Mcq8eaYMSaYMBp9xCVaGThjUWIokDO9EBhs4XXF/LSTz5n\n7R93MP2yXESVyKb/K+Pu11YFj22M0nHeV/I57yv5yLLM/6x8jU+e2seim4r69XNdlJrOgfZA+ydR\nEJifEt7f8/eqK/n7vp1IssyE2AR+Pn0umu7f7Ta3i/eqK1EJAhdm5GDWaMM6V8XI4vB5zzoeDqpO\nKFJ4urFCMVSUwFQxYG6/jx9v+oRDtkChl6/kjuO6/MFbdQiFJ0p34+lewTvY0cb7xypJ0BuDQSnA\n8wf3cmlWXvDi5mwMajUC9Fg7NEZYkZakk1KWk0JUodGq1fHHuUupsncQqzcQpw+kIBbExPG3BRdw\nrNNOhsVKzGl6svXVcNpjGonUiYnE3Xorjq1bEU0mzIsXh3tK56Qym8Ec+nRLQRQxzZ1L14YNgfPE\nxqIrKgr5eU4k6vXE3XILsiyfNRhMK4zj7jWr8Hn8dLa4sDU5sDU5cTu8TFqedcoqZ/LYGO54SEAz\n1wAAIABJREFU+WIaKtrZ9tphNDoVtz69nKTc6NMeXxAErvvjQp7+3sfsX1/N6t/OIy6tb61oLsrM\nJcFgpNLewcTYhAG1gRooWZZ5fP/uYL/lktYmvmioYWFqBg6fl3s3rqe+u4fn5/U1PDR7cURutVAM\nT/NS0lhTeZCG7qyB4bgiPzU+idIT+oxP7WM1f4UiVCLrylkxLG1trA8GpQCvHC5jde74XgV04SLJ\nPdNP/dKpF4qCIAT2ufVCgsHIjQUT+WdZCcgyX88PtFiJJBekZ1PdaWNjQy0pRjPfnzAtZMfWqVQ9\n0qSPizshUA2F4bTHNFJpMzPRZmaGexoRIerSS9GNG4fU1YW+oADRNDTtNHq7QqnWqohOMRGd0rt5\nJeVEs/Ku3v1dpxbEcu/ay/ng0T387sI13LVm1Sn7VM9lRmIKMxJDU6xp4E7aUtA9rLC1B4PS4NjR\npfSEVYSMVavj4blL2dPSRLRWR2EYq+T219W5BURpdd17TBOZF+YMCMXopQSmigHTnnTnWSOKiBF+\nN/rr+UX8uWQ7kiyTZrJwfloWRrWa6QnJbGuqRwRuLJjYpyqyl2aP5aKMHGRAG4FBuSgI3Fw4OSyN\nuEPleB9ThSJU9AUF4Z5C2Kg0Istvn4zH6eOz50u56uezwz2lfhEEgevHTeDx0t3IQEF0XLDdWLze\niEoQ8HffjNSrVESPoArsishg7keLu0giCEJIiykpFP2lBKaKAZuemML8lDQ21B1DLQjcVjw1oor+\nnM6SMZkUxsTT6naSa40O9vu8b9ocarrsGFTqflXrjeRV4pFAkmRUw2iPqUIxHJhj9XS2REY7o/66\nOCuPaQnJdHo9ZFujg6m6yUYTd0+ayfMH96ESBb5VMEnZY6pQKBQRSglMFQO2pbGWLEsUS8ZkMj4m\nDqNaE+4p9Uqy0UTySfssRUEg3WwN04wU5yJLMkI/q4gqFJFOlmUcGzfiqa5Gm5WFadasITlv9d5m\ncqcPbnuLoZBiOv1e5HkpaUpqokKhUAwDSmCqGJD/VJTxdNleANSCwK9nLhiW+ysUw4PkVwJTxcjV\n9ckn2N56CwDn1q3g82GaO3fQz+tx+vC6fYN+HsXo4/H7eaZsL4dtbRTFxvPVsUURn1GlUCjCR8mJ\ni1BdXi+eExrLR6r1tdXBr32yzGf1x8I4G8VIJ0tyv/suKhSRzl1eftbxYFl26yTWPbKbqj1NyCcV\nhlMoBuK5g/t48+gh9re18PLhMtZUHAz3lBQKRQRTVkwj0CN7d/BedSVqQeR7E6ayeEzkVtCM1xs4\nYu/oMVYoBouSyqsYydQpKbjLynqMh0Lm5AQu/uE0Hrv5A1ydHtKL49GbNbQe66S9rgtLgoG0wjh0\nJjUV2xppPWZn7OwUipZmMO3iHIzR/S8m5Pb7UIsqZRVthKo4oWL/6cYKhUJxIiUwjTC7mxt5r7oS\nAJ8s8ZeSHcxLTovYojrfKZrCH3ZvobrTzrSEZC7JGhvuKSlGMEmSEZQ8D8UIZV2xAnw+PFVVaLOy\nsJx//pCde861Bcy5tgB7s5OqPc14nD5i08xEJ5uwNTk4tq8Fl93LnNUFxIwxcfDzWvasO8qbv93K\nedeMY+ktE4hK6n3BOFmW+XPJdj6sOYpBpeaHk2dGUOuZ0cHh9VLdZSfJYCQ6BH2mT2diXAIlrU09\nxgqFQnEmwmCn7QiCICupQb23qaGG3+zY1OOxl5atGjYFhULpQFsLzS4nE2ITiFLK+7OruYE1lQfR\niiq+Oa6YtFFYpOnQ5jpe/+1W7l6zKtxTCZvODRtwbNmCymIh6sorUcfFhXtKilGstaaTDx/dw5ZX\nDzFufipjxseSnBdD8thoErKsqLWnv6n66eGj/HHbFkS7hH6/D50o8ptbVpA8NrrXfV4HwuP0oTWM\n3nvz9Y4ufrzpE1rcTgwqNfdPm0PxIASNkizz5pFDHLK1URybwPL07JCfQ6FQRAZBEJBleUBv4Epg\nGmHcfh//tekTDnenu1yUkcOtRVPCPKuht6byIP88UAJArE7PQ7MX96t9y0hR7+ji9g3r8EgSEEiZ\nfmzhimBLhNGifGMdbz20jTtfvSTcUwkLV1kZrY8/Hhxrxowh4c47wzgjhSLA3uJk74fVNBxqp/5Q\nOw3lbbTVdjFuXipTL8nFHKtHVAuoVCLtDQ5e+sXnONxeZJ2Aq0iDIEDaocB/b3xkCdnTknp13r0f\nVvHho3vQGtQkj41h8kXZZE1JOGNwe2hLPe/87w4Ofl7LzCvHcs0Dc0dlgPro/l28ffRwcFwUE8+D\n5y0M44wUCsVwF4rAdPS9G0c4nUrNb89byO7mRoxqzaDcwRwOTiyQ0Op28UldNVfmjAvjjMKrutMW\nDEoBml1OOjxu4kbZnt7RvsfU19jYY+xtaAjTTIaeY8sWvI2N6AsK0OXlhXs6ipOYLComjmlCTvZj\n/MFcRJMJV5eX3e8cYc+6o3gcXiS/jN8noVKLXP/IYv7mL6XO0QnApVljubFgAltePcS/7v2M/3r3\nclTqM994k2WZF+/9jAMbjnHpT2ai0ak5uruJ5+76BEe7m+hkIwarFoNVhyFKi8GipfmojWP7W1l5\n9zS+8fAifjb7RS798YxRGZie/C6qbPFVKBSRYPS9Gw8DOpWamUmp4Z5GWJk0Gto97uB4NKYynyjH\nGo1RrcbhC7R0SDNZBm1PUCSTRnlgqsvLA7Uaun8P9OPHh3lGQ8P23nt0vv8+EGipEnvzzejz88M8\nq+HFtX8/ktOJvqAA0WQ69wv6QJYkWh97DM+RIwA4Nm4k/o470Jt0zLpqLLOuOn3tgYe8yZQcqSC6\nuYWx8SkIgsDMK/PY9toh/vaNd7nq57NJyY857Wvry9vZ91E193/yFfSmwOfDxAsyueRH02mt6aSz\nxYnD5sHZ4cFpc+O0e0jKi+amvy9Fo1dz8PNaUsfHYk0YnZk4l2fns6WxjkanA5Naw3X5xeGekkKh\nUCiBqSIy3V48jd/s2Ijd62FKfBJxOj3VnTbSR+G+SoA4vYEHZi7gjSOH0KlUXJM3flRWsZQlGVE1\n+r7v4zQpKcTfdhvOnTsRLRbM8+eHe0pDwrV375cDWca9b58SmPZB+3/+g+OLLwBQxcaS8IMfhDQ4\n9be2BoNSAF9TE97q6nOubOuaW8h69gVkp5NGlYqY667DUFzMrf9czqfP7Od/r3qLaatyuOD2yUQn\nB+YryzIbnivltQe2sOw7E4NB6Ylix5iJHWM+67lLPqyieGlG37/ZESLBYOSR+cuo7eoiwWDArNGG\ne0oKhUJx7sBUEAQd8Cmg7X7+K7Is/+KE/3838HsgXpbl1sGaqGJ0KYqN59mlF1PXZee/t37Or3ds\nRARunzCN89Oywj29sMiNiuHOSTPCPY1+2d5Uz56WRrIs0Swe0/+LQUmSh6QwSiTTZmSgzRhdF9Tq\nuDh8dXXBsSo+PoyzGV5kvx/Hxo3Bsb+1FVdpKcbp00N2DtFkQtBokL3ewAOCgCoq6pyv6/riC2Sn\ns3tifjo//hhDcTEqjcjibxUz44o83v7Ddn699BV0Bg3pE+NpPmpD8kn8ZN0VxGf2/0aly+7BHDv6\nsk5OpFOpybae+99JoVAohso5A1NZlt2CICyWZdkhCIIK+FwQhHdkWd4iCEIasAw4OugzVYw6KkHg\n8/oamlwOACTgpUOlozYwHa5OrjTd6nb2e79wOPeY+u122p59Fk91NbqcHGKuuw7RMLr2+IZL1JVX\nIvv9+Bob0Y8fj2nu3HBPadgQVCoEvf7LABAQjaFNXxUNBmK+/nU61qxB9vuxrFiBOuHc9REEbc9V\nOvGk6uvmWD3XPDCXq389h5YqO1UlzVgTDOTMSEYc4PvAgm8W8o8b1nH+tyei0oyuInIKRV/sam6g\nztHFpLgEUk2WcE9HMcL1KpVXlmVH95e67tccL7P7MPAj4I3QT02hAK3Ys9WATozMfq6KM9vcUHfS\nuHZAgWm4ChHb3n4bT2Wgx7D74EHs69YRdeml4ZlMBJH9fhDFQV3JVlksxN1006Adf6SL+drXaHvh\nBWSXC+Ps2egLC0N+Dn1REfqioj69xrxkCe7ycny1tYhWK9ZLTl9tWxAE4jOtA1ohPVl6cTwJWVa2\nvX6IWVf1TAuXZSUzQ6EAeL2ynCcP7AHAoFLz2/MWKavsikHVq8BUEAQR2A7kAo/IsrxVEIRVQLUs\nyyXKG7hisKzIyGFjQy0H2lswqtXcUjQ53FMasP1tzWysryHBYGRlRi6qEd7yJcVkPuu4LyRJRlCF\n5+cl2Ww9xv6TxqORbe1aOtevR9BoiL76agyTJoXkuH67na7PPgNZxjRnDqro6JAcd7TSFxSQ8qtf\nIfv9CKrIubmnMplIuOMOpK4uRKNxyOe28q5pPPHtD9jxViVjz0tBa1RzeHM9+9ZXkzMtiev/vBhj\ndP96aDdWdHBoSz2ZE+NJGReDGKb3LYViIN6trgh+7fT7+LSuimzrhD4fp9LWzpbGOpIMJhYNYDuP\nYuTr7YqpBEwRBMEKrBEEYQLwEwJpvMedMTr9+c9/Hvx60aJFLFq0qD9zVYxCBnWgfU6ry4lFq0Wn\nGt71usrbW7lv86f4unv7Vtlt3D5hWphnNbiuyM6n2elgV0sjOZZobh7f/+BFlmQGksHnLi8PFA6y\nWjEvWYKoPbXgh7e2Fr/djjYrq0dqoWHGDNzl5SDLIIoYp43sf7dzcVdW0vnRRwDIbjdtL76IvrAQ\nQTOwCtqyz0fL3/8ebI3j3LmThLvvRtSP/P2AsteLt7YW0WJBHRsb8uNHUlB6nCCKqCzhSQ8cOzuF\nX22+lm2vH6buYBsum4e82Sms+vEM1j+5l9+tfI27X7ukT5V7HR1unr/rEw5vayB/Tirv/2039mYn\nWVMSmXPtOKZenDOI31HfdbW5MFi1SuA8xPxeiaO7m0jOi+73zY+hEKXVUdPV2WPcV4c72rl308fB\nlneV9g5uKOh7cKuIPOvXr2f9+vUhPWafrvJlWbYJgrAeuBTIAnYLgeXSNGC7IAgzZVluPPl1Jwam\nCkVfiYJAvGFklPTf3twQDEoBtjTWneXZI4NaFLmteGpIjiVL9HuPqaeqipbHH4fuD0dfQwOx3/xm\nj+d0rl+P7a23AFAnJRF/++3BfaTGKVNQRUXhPXYMbVbWqCtAdDLZ4ej5gM+H7PUOODD1NTf36Nfq\nb2vDV1+PNitrQMeNdJLDQfPf/oavvh5EkejVqzFODc3fjeLMtAY1c1afurVgxuV5fPT4XhorOk4J\nTCVZxuP3o1f3vITyunw8euM6UsbF8KtN1wb7o9qbnZS8f5TXH9waEYFpxbYGErKswb6vx9v0XPbT\nWQPeuzsSybLMwS9qMcXoScqJQqMf+A3yN363le1vHMbR4SEqyUjm5ASypiQypiCW1IIYTDGRcSPu\ntqKpPLhzI3WOLmYmprAyI7fPx9jUUNOjD/uGumolMB0hTl5s/MUvfnHmJ/dSb6ryxgNeWZY7BEEw\nEFgl/a0sy8knPKcSmCrLctuAZ6RQjGBpJxUOGBOCQgJuv58/7N7CjqYGMi1W/mvKeSSMkED+ZJIk\n9T8wPXw4GJRCYPX0ZPZ164Jf+xoacO7ahWn27OBjupwcdDlnv7B0HzyI5HCgGzdu2BRHsn/0Ec6d\nO1FFRxN91VW9qqiqzctDnZSEr6EBAMOUKSEpqqOyWhF0OmR3dx9jtRpVzOl7WY4kjq1bA0EpgCRh\ne/vtsASmfrsd5+7diHo9hqlTEUb4VoMz6WhwoNGrSMrtmUa+t7WJB3dswu71cF5SKvdMnoWrw8Nz\nd6ynfFM9ky/M4upfz+0R4FniDcz6Sj7/d/8XuLq8p21xM1Qc7W7+ePkbaPRqMibGc9lPZ5E3M5ln\n71jPh4/uYdl3QpOOP1I47R4+e76U9U/tQ2/W0FxlZ9W901l6y0RkWeZQRxuiIJIb1bvtBpIk017X\nxcZ/l/GzT67GYNVSX97OkZ2NHNnZyBf/OkBMqolrfzcft8NLYnZUWPc7Z1is/H3B8gHtuz75emSk\nXp8oQqM3t31SgGe695mKwL9lWV570nNkzpLKq1AoAualpHGs086GumriDUZuD8FK4pqKg2xqqAWg\nvKONR/fv4r5pcwZ83EgkS/S7j6k6NbXHWJOScspzBLUa2eP5ctzH1b+O11+na8MGAFQJCSR8//sR\nH5w6S0qwrw28pfvq6mh74QXib7vtnK8TdTriv/c9XPv2IWi1fS58c8bjGo3E3nADtrfeQpYkrCtW\n9CpQHvZOuugLx8Wov6uL5j/9CX97OwCu/fuJ/cY3hnwe4dZW28lT3/2I5bdPxnRSS5m/lGzH7g28\nR2xqqOW9g4fZe/de8mal8M0/nXlPqkotkjU5kTW/2kTmpAQOba5n30fV6ExqsqcmkT0tkemX52Hu\nw0pZR6ODA5/WAFAwfwxRSee+4JdkGYNVx+/39fx3veoXs3n2zvW9Dkx3ra3kvUd2kZBpJXV8LGmF\ncYwZH0t0imnYFY6SZZmyz2pRqUWS8qJQqUX2vF/FrrcrOLS5nugUE4tvKub8WyfSUm3ndxetoaPB\nwU5NK7snBapdr0jPPmNmkOSXgqnSz3z/Y5w2D5Z4Q7Bd0ZjxsYwZH8vcrxZQX97Grxa/wgNLX8XR\nEbg5d/sLFzJ+YdoQ/CTObCD/puenZVFp7+DzumMkGU3cMSF0raoUI09v2sWUAGe9epZlOfy5KQrF\nMLF67HhWjx0fsuO1up09xy7nGZ45/MkD6GOqHzeOqCuuwLF9O6qoqNNW1I36yldoe+EF8PnQFRRg\nmDKl93Pz+wMFe7r5m5oC/SIjPB3z+IrnmcZnI2i1g7LXVpeXR8Idd4T8uJHMOHMmzh078B47Bmo1\n1jBUfPaUlweDUgDXnj1IbvcpbVxGOlEtojNpcNg8HN5Sz5iC2GDA6fD5Ak+SZXSlPjY8tI0p52Vw\nxf2zzvnedMsTy3jz99s4tLmejEkJrLx7Gl63n8odjex+p5Iju5q4/s+LT/vayh2NPPmdD1l0QxHz\nvl7A1tcO8/pvtjBubiqCKLDmgc3c+/ZlxKSevbicAMjIpzyekGmlraarRxB1Jn6fxIs//oxrfzsP\nd5ePmtJW3v3zTiq3N3LbsysoWpJ+1tcPpuMre7Is859fbaa9roslN08ge2riGV/z1kPb2fl2JaYY\nHQ3l7XjdfsYvTGP6ZXlc/9clGCxf1iKIS7dgitbx4aMlAMQs0OGLF/k06gCXZ+eftrjfb1eswefx\nkzsjmb0fVpGSH8Oca09fmT55bAy/3nItaq2K/5r8fHB+sWnmU1bvhwtREPh24WS+XTj8i1cqBt/w\nriSjUChYlJrBB8eO4pMDaaojuc/rQPuYmubMwTSn52qy5+hRPJWVaMaMwTBhAtr778f2xht4Kitp\ne/ZZoq++GtFkOvfBRTGQgupyffnQMCjYo8vPD6Qwd6c56woKzvkaz5EjtD77LFJnJ4YpU4i+5ppR\nm/IZKqJeT/z3voevsRHRbA5LQSDxpHMKBsOA9wwPR1GJRn76/pWsfXgHr/9mC7VlbSRmW5l/XSEr\ncjJ4c8NeLO+5UEsC5/9gBktWF/XqhpkxWsc1D5zagzc5L5q0wlieu+uT075u38fVrH9qHzGpJo7u\nbuKDR/egN2m44a9LgkHgur/u4snvfMjdr63qMRen3YPWoEalDvx9VmxrwHSaVV1DlJbsqYk8/8NP\nmXJRNtteO0xtWRvjF47h4h9OD+6XBTi8uZ6YVDOTL8wGwO3w8qer32bedeMZvyg8K3uSJONxeLm7\n4BlmXTUWU6yeQ5vrmHVVPk9+50OS86K48I6p5M4I7EKTZZm9H1bhc0uUrDvK1/+wgJxpSUAg8D7+\n8zqdG/++lN27aniSMqxrXYidMqbPnLzW+AVmsw6v04fH5cfr8uFx+miusrPoxiKiU0xMXplN0eKz\nB+7Hby7cu/YyksfG8M6fdvKPG9YRm2Zm6socRLWAOU7PhPMzQ/TTUygihxKYKhTDXGFsPH+Ys5g9\nLU1kWKxMiU8K95QGjSTJCCGMf1ylpbT+85/BoCz6mmvwd3bi3L4dAH9rKx2vvUbM1752zmMJgkD0\n6tW0/+tfyB4Pxpkz0Y0P3cr4YNFmZBD37W/j2rMHVXQ0pgULzvmathdfDLbPcW7fji4/f9RXKQ4F\nQaU6bYr5UNHl5mJZvpzOTz5B1OtH9Q0HS7whGERKksyBT4+x4blSKn/fyKRcM9l3juOCSwqJDVGq\nvjXBiK3Recpevr0fVvGvezaw4vtTmHxRFtYEI5tePkhHg4PCxYEg0NHuRqNXU3ewDckvo1IHXn9k\nZyO/v+R1TNE6CpekE5dm5rPnD3DLk8tOOb8gCNz8+Pn855eb+fCxEiatyGLptyfw0eN7+c2yV7nu\n4YXkzkjGafPw4eMlTL4oK/har9tPS7WdBd8sRPJJiNrBqf4s+SW62t2YY/V0trj45J/7UGkCq9uf\n/+sATpuHuHQzKrWIy+bh5seWETvGzLyvFbD5lXKe+cF64tItTLwgk/0fV9Na24kpRk/TERvpRXHB\n85wtKIVAD9z04niq9vp5Ny7Q23rhjYWMPaRHo1ej1avQ6NVo9Cq0BjUavZrUglg0ur79XDImJgCw\n4vuTSRkbjdag5vMXy5Almao9TSy7bRKLbizu83HDqcLWznvVlRjVGq7MycesObUyvmJ0E2T51JSO\nkJ5AEOTBPodCoYgsmxpqeLJ0DzLwjfxiFqSGJrVr47/LKN9UxzceXhSS47W98ALOnTuDY21eHuq4\nOBybNwcf06Sl9SmtVJYkZJ/vtK1oRoq6++7rsTJsXbUKcy8CWoVCcXp+n8RvV6zB4/Qy8YJMBFGk\nZn8LVXua+c6zy4OreSezNzt56NI3yJwUz6Kbins8r7PVxW8vXIPkk5h8YRYqrYrZ1+STOq5vrYh2\nvVPJv3/6OV1tgT2P0y/L5epfzUFv/vI9rmJbA68/uIWGwx1MWJZB6rhYkvOjyZmehM7Yu1X3jfU1\nHGhvYVx0HHOSxwQfl2WZ9/66izd/tw0AjU6FWq9i2qpczDE6HB0eCuaPAQEyJyUQnXz6DBe/V2LL\nmkMc3dVI6rhY5lw7DrVWhdvh7fUcT1bv6EQliINS0Ef2+/G1tCAajajMPVOED22p58lbP+DKn53H\npAuzh0Vw2ujs4nsbPsDpD6TDj4uO5fezT5+6rhieutPoB7TJXFkxVSgUIdXhdvPQri3B8vD/u2cr\n42PiQvLB7fdKqDWh+wAWrdYeY5XVir6oCMeWLYF+pYB+Qt/K2guiiDCCg1IIpEQf72EqWiwYJk4M\n84wUiuFNpRb5yftXUFPaSsm6owgqgcXfKiZzUgKW+DOvytaWtaI1qLnxb0tP+X+bXj6I0+amaHE6\nS789kbj0/qWHT74wmwnnZyJJ8hkDoJzpSdz56iU0VnSw/5NjNBxqZ8uacuLSLHzr0fPPeY4Pjh3h\nzyXbu0fl3F48lQvSA6nCTpsnGJQC/HLTavxe6Zz7aU+m0ojMvjqf2Vfn93i8v0EpQLKxb3PoLcnj\noeWxx/AeOQJqNTHXXoth0peFqfJmJlO8NIO3HtrOtjcquPWpCwZlHqF0oK01GJQClLW34vB5MapH\n33YBxZkpgalCoQipdo+rR88ynyzT6naFJDD1eSVUmtClFlqWLcPX2IinogJNWhrWSy5BZbEQe/PN\neA4eRJ2SoqSonob1oovQ5uQg2Wzoxo0bHVVzFRHBVVZGx8svI3k8WJYswXxCD73hThAE0grjSCuM\nO/eTu+XNTKGjvou22s5TArWmIzYu+dF0Ft1YPOC5qTQivbklmJgTRWJO4P3A1enhZ7Nf4p0/7cDd\n5aOl2k5LlZ34TAtf/Z/5PVZcN3dXlscnI3hkNjXUBgNTY5SO+z66ij9c9gb3f/yVU/rKjkTObdsC\nQSmAz0fHa6/1CEwBvvb7BZRvrOOR697hoydKmH31OAzWyL0pmm62IAoCUvdN30SDEYNKCUMUPSm/\nEQqFIqTGmCzkWqM5bAtU90w3W8iyhCZw8Xv9qLWhC0xFvZ64m2465XF9fj76/PzTvKJvfE1N+Fpa\n0Kan966A0jCi70WRJIUilGSfj7Znnw32uLW99Rba3Fy06eGrAhtuKo3IhOWZ7HirgqW39MxcOJ7m\nGi56s5bvvbSSL/51gKgkIxOWZRKfYWHTywf5w2VvcuvTFxCXFljFTTGZEdwyib+zoa73057s4rnZ\nPjInJSCoBHatraRoSXqvWuKMCCfc3D3tuFveecl897kL+eTpfXzxYhm3P38h0SmBzxpZlmms6ECl\nEYnPsJ729edSaWtnc2MdSQYji1IzBtQ2JtsazV0TZ/DGkUOYNGq+NX7SsGstpBh8SmCqUChCSi2K\nPDBzAe8dq0SWZZalZaNThSb91ueRUIUwlXcwOXftou1f/wJJQrRaib/9dtSxfdvbpVBEClmSwl4I\nSXK5gkFp8LHuIlzDhWPHDtwHD6JJScE0f35IfqZzVo/j0RvW4bR5WXbbRHRGTaCIkigGe2GGS3pR\n3CmViLOnJfLxE3v5w6Vv8N3nVjCmMI6vjS2kdkMjNUYHua8WcYU6m5o9LVTtbkIQBCatyDolBXck\nM0ybRtfmzfjq6kAUsVx00WmfJwgCY2enMHZ2Cuse2cX9572I5JfRmTSMGR9LxbZA+6+71lwSrEjc\nWxW2du7Z+HEwA6rC1s5N43vX5/ZMFqSmh6zmhGJkUoofKRSKYePtP25HluDiH0Z+em3jH/4QuKjo\nZl68GOvKlWGckSJUZK+Xro0bkV0uDNOnj+gbDp7qatqeeQa/zYZh8mSiV68Oa4Da8sQTuA8cAEAV\nG0vCnXcihqgy7mBz7twZ6JPczbxkCdYzBBx91XLMzhsPbmXH2xXoTVpkZHQGDTf9Yyk50yOzUvvW\nNYd48/fb+K+1l2OM1vHvn35OdIqJ5bcr/S4h8D7jralBNJtRx8f37jXdVZ1bqu0c3lLpAQeSAAAg\nAElEQVTP+qf2cXR3EwAPlX6zR0/Wc3mxfD8vHioNjmN1ep5e0vvPsC6vl3/s28kReweT45O4vmAC\nKmWFdERTih8pFIpRxe+VevTT6y1ZlvG3tSHq9YjGoUkFE9Q95zka+0GOVC1PPYWnvByAro0bSbjr\nrrD0HR0K7S+9hL89kJbv3LEDbW4uplmzwjaf2Ouvx7F9O7LbjWHKlGETlAK4u39nzjQeiLg0Czc8\nsoRv/GkRLrsHWQZzbGT3UZ5xeR5VJc38ctHLxI4x017fxR2vXBzuaUUMQaNBm5XVt9d0B35x6Rbi\n0i3MvHIs//uVtyjfWMd/frmJ7KmJJOZE0XCoA2uSgeKlZ07PTTypLkRf60Q8VrqLT+qqATjaaSNW\nr+fy7NGz6q3oHyUwVZxTbZedZ8v24fT7uCxrLFMSIvPu63C1t6WJv+/fidvv5+rcgmDBB8WpfB4J\ng7VvqzWy30/r00/jLi0FlYroq68ekoJGUZdeSstTTyE7HGjS0jDNnz/o5wwlb00N3tpaNJmZaBIT\n+3UMye3GuW0bsixjnDZtWAURZyI5HMGgFECy2/FUVoakMnHXxo14q6vRZmVhnDlzwMcLBamr66zj\noSao1WENjAdCfVKPWk1qasjPoVKLmGIiOyA90RX3z2LBNwrpaHSQmh+DMVoX7imNOD/4v5Uc2dlE\nxbZ6yjfV8dkLB9CbNZR9VouoEph55VjmXDuOkverGHteIC1Ya1CzZEwmFbYOPqs/RqLByJ0Tp/fp\nvMc67WcdDwa338eeliZMGg2FMb1bZVZEFiUwVZyVX5b52dbPaHQ6ANjb2sRf5p1Pqmlkrg4MNY/f\nzwM7NtLl8wLwyN4d5EfHhqxY0Ejj8/j73C7GVVISCEoB/H46Xn0Vw9Spg150QZuVRfL99yN1dSFa\nrWHfn9cXzpIS2p57LlBwQ60m7pZb0OXk9OkYst9Pyz/+gbc6cMfcsWULCd/73rBYOXaWlODatw91\nYiLmhQsRTtgjLeh0iEYjksPR/YCAKgSpvJ3r12N76y0g8LOSfT5Mc+YM+LgDZZw9m8733wdANBpP\nqQyq6D3T3LlIXV24y8vRJCdjXbUq3FMaMv6uLro+/RTZ78c0Z04w/V0QBBKyrCRk9a84j+LcBEEg\ne2oi2VO/vMH4r3s2UEYtkl8mOsXE377xLi67l/f/thuA/95wNYnZUdxcOImbC/v3Nz89IZnyjrbg\neFpC3/a49pXL5+PeTeuptHcAcHn2WG4oUFqZDTdKYKo4K7vHHQxKAbySxFG7TQlMQ6TT6wkGpQAy\n0OR0KIHpGfi9ftS6vq+YnjKWZRiCvS6CRoMqOnrQzxNqXV988WUVSJ8Px6ZNfQ5MfY2NwaAUwFdb\ni7euDm1GRiinGnKu/ftpe+aZ4Fjq6CDq8suDY0GlIvbGG2l/9VVklwvz4sVo09IGfF73wYM9x+Xl\nERGYWpcvR5uZib+9Hd24cahjYsI9pWFLEEWsK1bAihXhnsqQkv1+Wv7+d3z19UBgr23iD384IjIo\nhqtrfzePK342K9iyZ/ntk/n3Tz9n0/8F3od+ufBlcmYkccNfFlO+sY7W2k46W1wsurGo1xV+V+eN\nJ0an52injclxicxKCn2GwIm2NdUHg1KA1yrL+erYopAVX1QMDSUwHQYOd7SzubGWJIORJWMyh7S8\ntlWrI91sobo7BcOgUpMbNfwutPuirquTZw/uxeX3cVlWPpPi+5fG2BsxOj1FMfHsa2sGIF5voCC6\n9z3sTuT2+/hLyQ72tTaTFxXNDyZOx6yJ3J5m/RHoY9q3Dxl9cTGatDS8x44BYFm+fFitXobDyReM\n/dmXK5rNoFLB8RsDohh4LIJJHg+2tWt7PHa6fYDarCwS7747pOdWp6T0CE7VyYO7utAXSmsgxUD4\nW1uDQSkEbvZ4a2rQ5eWFcVZn52tspP2VV5A6OzHOmoV54cJwTymkBEHo0UdWa1Bz3R8XMv2yXA58\nWkPJ+0c5vLmeXy95hYL5Y4hJNbPplYPkz0ntdWAqCAIrMvp2Q3Mg9CcFoBpRRK0UWxp2lMA0wlXY\n2rl3U89y3TcXDl3FOlEQ+OWM+fz7UClOv49LMvNINIysfownCqQub6Che5W4pKWJv8xbRoppcC6o\nBUHg5zPm8k5VBW6/n/PTsrBo+xdMvnToAJ92FxpoaXTydFkJtxcPXfXa2q5OKm3tZFujSR2kn5fP\n7Uet6VtQKep0xH/3u3iOHkU0GgdlX9dIY734YnwNDfgaGtBkZGBZtqzPx1BZLMRcey0db7wBsox1\n5cpTqtf6u7roePllvA0N6AsKsF5ySVhvGtjXru1xAQ2gHqLfF+uKFeDz4amqQpudjeX884fkvArF\nYBMtFgS9HtnlCjygUoUk/X0wtT7zDL6GQKsV25tvok5JCUlv60g3fkEa4xekcck90/l/9u47PK7y\nTPj/95wzfTQz6nKRbVnuvWNsgytgG1MNoQRCMRCylLDskuwmb/Im2ewvm98mm93NBhYICZgECCSE\nakIxGIwLGFyEe5eLbHVpNL2d8/4x8lgjyaozmhnp+VxXrvgZnTnntrFnzn2e57lvWZGQZAlJkti4\ndh8+Z5A9608ycXExJ3fXUn/Kxezrzj9cCAUiHPuyktLZgzi8NbpMuGRmISd21VB5uAFHkZXZ146K\nO15vTNxs5qyCQSwZOpwNFSfRSTIPTZ6FIh5CZxyRmKa5bVVnY0kpwObKij5NTAHyTGYemDyzT6+Z\nKk3BQCwpBQiqKifcTUlLTAGMio7rElCprsrr6XCcTLvravjJl5sIqioGWeZHsy9hSl5Bwq8TCako\nhu5/0Uh6fVo/nU83utxcCr/zHbRQqFd7Qs3Tp2OefuHPK+err+LfswcAT00NSm4uWSksEhVqlZTK\nOTlk33BDn1xb0uvjlgwL/Y9v507CDQ2YJk5En0Yz4skmm0zk3nMPTW++iRaJYF++vMMWS8Hycvz7\n96PLz8cyZ04fRnpeuLY2bhypqYEBkJieozPEJ4yX3D6eghI7n6zdx3/e8DblO6sBmHz5CLb+6SD5\nI2y8/H+20FDhBsCabcTrDGC2Gxk6IZfc4iw+enoPIX8YV62PU3vq2LnuOIPGZLPqH2YxY1Xviz5K\nksSjU+dwz/ipGBUFoyJSnEwk/qulucJWS+hal+8WEsthMFJstXHac37pcqk9M/Z7Lhg0lE2Vp1uM\ne7/vraveLD8ce4ASVFXeOnEkKYlpOKR2u/iR0HPJLlTU+uYvXFOT1Ot1xjRuHMEjR2Jj++WXi31w\nQkI433oLzyefAOD+4APyv/1t9K2q9PZnxpEjKXjkkU6PCxw7Rt2TT8b2uIdra7GvXJns8NowTZyI\nf/duoPnB5pgxfR5DOpEVmQmLihm7YAiv/HAL5TuryR9h48eXvMyo2UVsXLsPi8PAuAVjqT7mZO6N\nY/jT9zcj6yS+/coq0DTWPvIxh7acwV5gYeSsQm752QJO76vjxe9uou60i8vuT0yhIrtBVHbOZCIx\nTXNLhgyn3OVk09nTFJgtPDKle+W6he6RJYmfXnQpLx3ehy8S5pqSMRmzdHnB4GJ+otOzt6GW0Y4c\nLk5yoYGWLLr4BMasS85HSySkonRzKa+QvkwTJ+I+cyY6kCRMEyakNJ6sJUuQzGZCp09jKC3FMnNg\nrBTJBBGnE/+ePchZWZimTk1IrQVNVfts6bhvx47z1w2F8O/ZM6AS067y7917vvAa4P/qq5Qkpjm3\n3YZn0yYibjeWGTPQ9bBlVn+j6GRu+dkCpq8sYe9Hp5h301iGTswjElbxNgaw5Z9/kDf7ulGEAhFk\nWQIk7v7N0jbnG7dgKFc+OpP9n5xu8zNhYBKJaZqTJIk146eyRpS87jN5JjMPTem7vZmJNKOgKCV9\nZm8fO4lDznoqPG6KrTZuHzMpKddRwyqKLn0SU03Tou1gzOa4liJC19iWL0fJySFcVYVx/Pi02MNl\nvfjiVIcgtBJxOqn5r/9CdUVXsljmzevVEuvg6dM0PPccEacT09Sp5Hz960n/96tkZ8fiPzcW2mq9\nxFfJ61kxwN6SdDqyFi9OybXTnSRJsb2o5yg6OS4pBTBlGTB1aReUhpQ+X+tCionEVBCEXiswW3ji\n0itwhYLY9IakVY4OByNpM2Oqer3UPf00odOnke128u67T8yAdJMkSVjnzk11GEKa8+/bF5fUebdt\nw7F6dY8/ZxpffplIY2P03GVleEePxjpvXkJivZCcW2+l4cUXiTQ0YJo2DfOszHz4mWyWefMIV1fj\n37sXpaCA7K99LdUhCUmmqiDJonquECUSU0EQEkKSpKTv7YiE02cpr3vDhlgLGrWpCeebb5J///0p\njkoQ+h/ZFt83W87K6tXDL9Xj6XCcDLrCQgr+/u+Tfp1MJ8kyjuuvF4XABhJN69M2iEJ6E4mpIAgZ\nI5JGS3nVQCBurLUaJ1K4pgYtFEI3eLD4Ak9TwfJyXOvXIykKtuXLRVuiBDJPnkzw0kvxfvYZstVK\n9m239ep81vnzcb37LgCSxdJh5Wgh8TRVpWndOgL796MrKiL7xhuRrZlRy0FIPE3VEF9rwjkiMRWE\nBKjz+1h7cA9NwQArho/k4qKhqQ6pX+qs+FHg+HGcr76KFgiQtWQJ1vnzu3zuUGUl7o8+AiBr2TL0\nRR3v1bXOm4dv5040nw9kOWn7kVzvv4/r/fcBME6cSO5dd6W016fQVsTlou6ZZ2J9GoMnTlD4ve8h\nG0V1yERxXHstjmuvTci5bJddhmH48GjrlnHjUr7fU/V6aXjpJUIVFRhHjSL7ppuSXhE7lbxbtsQq\nFIerq2mUZXK/8Y1unUMLhQieOoVstXb6WS2kN01DPHAVYkRiKiSUJxTihMtJkcVKnmngtFn41+1b\nONoU3bO0q66aX85bwmhHToqj6n86mjHVIhHqn30WzRvtQ+t87TX0w4djKO68bY7q81H31FOxfWyB\nw4cp/Kd/QjaZLvge/eDBFD72GMGTJ9EVFCSlL6Hq8+H64IPYOLBvH4EjR9KiSFBfcG3YgHvDBmSj\nkeybbkpqywb/wYO4PvgASZaxr1qFYcSILr83XFsbS0oBVLebSEMD8gDqVZlpjGPH0tXHBuHqalzr\n14OmkbVkScJnw51vvklg/34g2utUyc1NSSXavhKqro4bh1uNO6MGAtQ98QShigqQJOxXX03WwoWJ\nDFHoQ5qqiT2mQox47C4kTI3Py8ObPuCfP/+E+z95l+01lZ2/qR+IaBrHmpNSAFXTYkmqkFhqBzOm\nmt8fS0qjL2hEGhq6dN5wbW1ccRXV5WrTY7M9isOBecqUpCSlA13wxAlc69aheb1EGhqof/55tEgk\nKdeKNDbS8NxzhMrLCR47Rt0zz6C2SDQ7oy8sjFuKqGRno7SqLipkJjUQoPbJJ/Ht2IFv507qnnwS\nteXnTAJE6uvjx1383MpUpgkTaLl20zRxYqfvUb1ePFu34t2+Hd+uXdGkFEDTaHrnHTRNS1a4KeP+\n+GNq/uu/qF+7lkhTU6rDSRpN00DkpUIzMWMq9MrOmipePLIPRZLIM5mp9fsACKoqLx7ex6yC/n/D\nrkgS47LzONBYB4BOkhjrEDelyRDuIDGVrVYMo0cTPHIkOrbbMYwc2aXz6vLykCyWWGIrWyzoUtSm\noCXZbMa2fHlsP5xp0iSMo0enOKq+EXE648aaz4cWDCKZE78SI1xXhxYKxV1LbWrqcMa8JdlqJe9b\n38L98ccgy9iWLUM2GBIep9D3IvX1qC2SAtXrJVxdjaGkpNfnDlVVISkK5unTCR47Fn1RkjBN7d/t\n4UwTJpB7zz0EDh5EV1iIpZMWTarfT+1vfhObWdW1mrGWFKXfLQX17d5N09tvAxA6fRrV7++3xfU0\nTVTlFc4TianQY3V+H//fjq0E1egshr7Vvjclw74omoIBLDo9uh7s3/vBrHm8eHgfTcEgVwwrYaTd\nkYQIhUio4+JHeffcg2frVrRAAPPs2ShZXWqihmyxkPfNb+JuXjZru+IK5CQkQD1hu+wyzDNmoAWD\n6AYN6nc3YBdiHDUKJTs71tbDNGlS0v6b6AcPRrbbYwmIUlDQZsZTi0RwvvYa/v370RUWknPLLSgO\nR9w5cm69NSnxCamj5OQgZ2Whut0ASGYzSkFBr86paRqNL72Eb8cOILqnPeeuu6J7TEtLk7pkPV2Y\nxo/HNH58l44NHj8et9w3fOYMhtLSaDKvKDhWr05WmCkTPns2flzZf1egaaqGLBJToZlITIUeq/J6\nYkkpQEhVKTRbqPZ5seh03DluSgqj67pAJMy/fLmF3fU12PQGfjBrPhNyujdbZjcY+dakGUmKUDin\ns3Yxkl7f471GhuJicu++u6ehJVU6zN72NdlqJf/b38a3axeS0Yhl9uzkXctiIf+BB/Bs2hQrZCXp\n4r8ePVu24P3sMwCCTieNr75K3po1SYtJSA+yyUTe/fdHC5BpGlmXXYbSywqyoRMnYkkpgPvDDyn6\n4Q8xT57c23D7pdbtgiS9npw1a9Dc7uiDgn5Y0dc4dmx0X7OqxsaZStM0XO+8g6+sDCUnh+ybb0bX\n4sFfP1yFLfSCSEyFHhthc5BnNFMXiC7fHWLJ4j/mLaEu4CfXZCJLnxlL2f528hi762sAcIWC/O/e\nnfz6kstSHJXQnnRqFyMkn2K391lRE11+Po7rrrvgzyN1dR2Ohf5LP3gwuXfemeowBixDcTG2K6/E\nvX49kl6P42tfQzGZoItL7TORoaSEvPvuiyVzWYsWpTqkHvPt2IF7wwYgujS+8aWXyH/wwdjPRfEj\noSWRmAo9ZtXr+fnFi3jrxBEUSeLakjFYDQasGba3yhcOtxqHLnCkkGqdtYtJV55NmwgcPoxuyBBs\nl12GpCipDknoJtOUKXi2bInNYJinTUtxRH1L9Xhwb9yIFgphnT8fXX5+qkPKWPoRIzBNm4a/rAwA\n66JFccvChbZsS5diW7o01WH0KeOYMf1iWXfrh3jhVmNNE31MhfNEYir0SpHFyr0TMvsGbVnxCN49\ndZyGgB8JuKF0XKpDEi5AzcAZU8/nn+N8/fXoYO9etGAQx9VXpzYooduMo0aR/8AD+A8ciBZsmTkz\n1SH1GU1VqX3qKcJnzgDRGZCCxx7r8h5uIZ4kSeTcfjvhpUtBpxN9OPsxTVUHbN/piNOJe+PGaEVh\nRYHmqurmKa22eYniR0ILIjEV+p1T7iZ219UwPMvO5LzOi1QUmq3894Jl7Guoo8hsZZQjtc3Wheh+\n5dbFtDRNy8gZ0+Dx4/Hj8vLUBCL0mqGkJCHVWDON2tQUS0oh2qc1dPo0SheL1whtSZKEfujQVIch\nJIn/0CEaX3gB1efDOm8ejuuvT3VIfUoLhah94onYbKmclYV51ix0BQVYLroo7lhVFTOmwnkiMRX6\nlcON9Xzv842xokwPTJrBiuGlnb4v22hi/iBxk5BqEVXll2Xb2FJZgcNg5Psz5zG+uRCVGtFAkpCV\nzEpMDcOH4/vyy7ixIGQS2WpFtlpRPZ7oC4oyIAtydSbi8RA+cwZdQQFKtnjAOZA1/vGPsX63ns2b\nMY4b16V+rf1FuKYmbgmv6nZjnjoVw4gRbQ8WM6ZCCyIxFfqVj86cjKsU/O6p411KTIX0sL7iBJsr\no43TG4MBfr17O08svALI3P2l1vnz0YJBAocPox8yBNvy5akOSRC6RdLryb3nHprefBMtFCLrssvQ\n9bJlSn8Tqq6m7vHHUT2e6J/XmjX9Yn+g0H2aqqL6/XGvxR7qDBBKdjaS0YgWCADRzxAlJ6fdYzVV\nGzBt0ITOicRU6FccBmOHYyG9uUPBuLGrxTiTK/JmLV5M1uLFqQ5DEHrMMHw4+Q89lOow0pZn48ZY\n8qGFQrg++EAkpp3QVJWmdesIHDqEftAgHKtXp03/6N6QZBnL3Ll4t24FokmaccKEFEfVt2SLhdw1\na2h65x3QNOwrVqDY7e0eK4ofCS2JxLSf2ltfy6vHDqKTZb4xdhLDstr/QOhvrh85hkON9eyorWKY\n1ca3Jk5PdUhCN1w6uJjXjx/GGYw+Zb16xOjYzzI5MRUEoX9rXWm7dR9coS3Ppk14PvkEgPDZsyDL\n5Nx6a4/Opakq3i1bCFVVYZowIeXLZrNvuAHT+PGoHg/GiRMHZKEw46hRFDz8cKfHaWIpr9CC+OTs\nZ0KRCKfcTfzky034myugHW5s4KlFyzF0oUXFlsoKanxe5hQOZoi1/Q/SL6vPsrehjjGOnLTbl2lU\ndPzf2Quan8CJD7pMU2i28l8LllFWV02+yczUvMLYzzJ1Ka8gCP1f1tKl+A8eJFJbi2y1Yl+1KtUh\npb1wVVWH4+5wvfMO7o8/BsC7dSu5a9akPDk1TZqU0uungu+rr4g4nZgmTuzyPnRNjdaPEAQQiWm/\nUlZbzb/t3Iq3VV/OuoCP+oCfQRZrh+9fe3APrx47CMBLR/bxy3lLKc6yxR2z8cwpflm2LTb+u0kz\nWJmGezhFUtoz+xvqeHLvTgKRCF8bNY5lxSV9HkOeyczSoW0LJGRiqxghscI1NWiRCPpBg1IdiiDE\nURwOCh97jEhDA7LdjmwU20g6o2tVldjYiyrP/oMH48cHDqQ8MR1onG+9FZsBd73/PgWPPNKlfsea\npjFAO+oI7RCJaT/y+N4dbZJSgEFmK3mmzvdtrD9dHvu1NxxmS1UFN2XFf1FsraqIH1dWpGViKnRf\nSFX51+1bYvs6/2f3dkY5ciixpUfj93BQRTGIb6+Bquntt2MzIuZZs3q85E8QfF99hfPVV9HCYWzL\nl5O1cGFCzivpdKIoVBeFKipwvf12bGyaPr1XheF0RUXR5cDNRG/Yvufddn7SQvP58O/Z06XaCpqK\nmDEVYkRi2o8EIpG4candQak9h5tHjW/TE7I9uUZTbG8fQJ7R1OaYQZasVuOOZ2F7IhiJdGnZsZBY\n7lAwrtiQClR5PWmTmIo9pgNXpLExlpQC+LZvx7pggWi9I3Sb6vPR8OKL0PwQt+nNNzGOGYN+8OAU\nR5ZZ/Pv24d22DdlqxbZyZbf3UHq//BIteP77JlxV1auVTtmrV+OUpOge0/Hjscyf3+NzCT2j2O2E\nfb7YWL5AsaNzIk1NaMGg2HolxBGJaT/ytdJxPL2/DIACk4Ufzb6EnHaSywt5dNocfrHzc2r8XhYO\nGcaSdpZT3jJ6AvV+H3sbahntyOHOcVMSFv9pdxP/8uUWKn0epuQW8H9mzcOi0yfs/EJb7548xvOH\n9qBIMveOn8rEnDz2NUR7j+UYTYzPTp9ehWKPqRBH01IdgZCBVJ8vlpTGXnO7UxRNZgqeOkX9c8+B\nqgLRpLK7FZtla/xDbdli6VVMssVCzm239eocQu/kfP3r1P/xj6iNjZhnzcI8Y8YFj3Vv2kTTG2+A\npuGtnohl2tQ+jFRIZyIx7UeuKhnNhJx86vxeJuTkYzMYuvX+EpuDx5t7Rl6IUVF4dNqc3oR5QU/t\nK6PSFy23v7u+hteOHeK2sd0vHhCIhDEq4q92Z8563Dy5dydq8/i/d2/nfy+9gk2Vp/FFwlwxrARH\nGu2TEjOmA5eSnY114UI8GzcCYJ4xA72YLRV6QMnJwTh2LIFDh4DoElDxd6l7QqdOxZJSgODJk92e\n9bIuXEiwvJzAwYMo+fk4brghGaEKfUg/dChF//RPnR6nBoM0vflm7OFiqLKKyLD6ZIcnZAhx997P\njHJkM8qRneowesTTqoelOxzq1vtrfF5+/OUmTrldjLJn86PZC8juxozxQNMYDKC2GIc1lZAW4YZR\n41IWU0ciIZGYDmSOa67BMncuRCLohwxJdThChpIkidw1a/Dt3IkWiWCeNk0UKuom/bBhIMux5FQ/\nfHi3l2LKBgN5996LpqpIovJNv6b6fLjee4+Iy4Vl9mwMpaVxDzY0JKS4uxFhIBOfBkLaWDViNOe+\n2syKjsuGlnTr/c8d3M0ptwuAo02NvHh4X2ID7GdG2bMZ2WL/6MScPAZbbR28I7Ui4fRfyuvbtQvX\nhx8Sqqjo/GCh2/RFRegGDybidqO12lMvCF0l6XRY5szBevHFyObOCwMK8QzDhpF7550YJ07EMncu\nuXfe2eNziaS0/6t//nk8mzbhLyuj/tlnCVdVkbVs2fkD7DmYhiSn0nqd38f7p46zvaYyKecXEk/M\nmAppY1nxCIZn2TjlcTExJ69NoaXOeELxM6yebs64DjQGReHf5i7i4zMnUWSZxUOGo6RxAQI1zfeY\nNv3tb7g//BAA1wcfkP/QQxiKi1McVXryHzyI6513ALCtWoVp7NguvU/1eql76ilCFRXIdjt5994r\nZk8FIQVMkyYNyD6dQvcFjx8/P1BVgidOYF+5EvO0aaiBAIbfn0FnTHw6Uuvz8g9bPqKxuajnDaXj\nuHPc5IRfR0is9L3LEwakMdm5LB06ottJKcCqEaPQNSdWBllm5TDRxqYzFr2eK0eMYvmwkRjTvBJy\nOBRJ68TUt3Pn+UE4jP+rr1IXTBqLuFw0PPccoYoKQhUVNDz7LJEuFp9xb9gQm41Wm5pwvvVWMkMV\nBEEQeskwbNj5gSShb35gqx8yBOPIkYTDWlJawW2pqoglpQDvnDia8GsIiSdmTIV+Y07hYH61YBnH\nm5yMcWRTnNVxqXIhs4SDKjpD+iXPWiiEd/v2Nn3YlOzM3OudbJGGBrQWqxu0UAjV6exSuwk1EIgb\na63GgiAkn3fbNvwHDqArKsK2bBmSTtxKCheWc8cdNK1bh9q8x9Q4cmTcz8PBCLokPHS26+P3jjsM\nYi95JhCfJkK/UmJzpE3fTSGxwsFI2iWmmqpS98wzBI82P4nV65H0esxTp2K5+OLUBpemdEVFKHl5\nROqibYmU/Hx0hYVdeq913rxo0RqfD2S5S83bBUFIHN/OnTS+8kpsrHm9OK6/PoURCelOsdnIueWW\nC/48ElRR9In/bl84ZBhlddVsOHOSbIMxaR0lhMQSiWkG21JZQVldNSNtDpYPG6Ci3ikAACAASURB\nVCkaFPcjIVVl89nTRDSNSwYPFe1vgHAggi4Jy316I9LQcD4pBQiFyF2zBuOYMakLKs3JRiP5Dz6I\nZ9MmkCSsl1yCpO9av2L94MEUPvYYwZMn0RUWoi8qSnK0wkCn+v00vvwywRMnMIwYQfbNNyObBm61\n98CxYx2OhcRxvvEG3i++QLHbyf761/ttzYJwKDnf7bIk8cjU2Tw0ZVZa188Q4om73Qz16dlT/GLX\ntti4MRjgltETUhiRkCiqpvEvX26mrK4agHdOHuXncxehT/M9oMkWCkTQG9Prz0C2WECng3A4+oIk\nIdvFEvLOKHY79iuv7Nl7HQ7MU6YkOCJBaJ/r3Xfx794NgH/3blzZ2TiuvTbFUaWOYdgwvFu3xo2F\nxPOVleH59FMAwn4/DS+80KUeoRlJA0lOXuIoktLMkl7TD0KXba+pihvvEKWw+40zHncsKQU47Gzg\nSFNjCiNKD+FgBF26JaZmMzm33YZssyGZzTiuu07M4glCPxJuXnJ+TqS+PkWRpAfLRRdhv/pqDGPG\nYL3kEuzXXZfqkPqlSFNT3Fh1OlMUSfJJsoSmaqkOQ0gTYsY0Qw3LsrUaR2dpqrwe6vw+RtqzMYuC\nBBnJqtejSBIRLfpBLQF2gyG1QaWBdNxjCmCeMkXM4AlCP2WeNo3A/v2xsWnq1BRGkx6yFi0ia9Gi\nVIfRr5kmTsT1/vvR/fSAedasLr/X+8UXBI4cQT90aHSrRJr3ihWJqdCSyFwy1HUlY3AGA5TVVlNi\nd3DPhKl8evYUvyr7goimMdhi5f+/eDHZxp7thXGHguyrryXXZGa0IyfB0QsdyTGaeGjyTJ7eV0ZE\nU7lj3GSGWm2dv7GfS8elvF2laZrYAy4IGcgyezay1RrbY2qaILbMpAMtHAZJQuqnW1x0eXkU/P3f\n49+zB9luxzx9epfe5922LVacyrd9O6rPh3358mSG2muSBKqa6iiEdCES0wylyDJrxsc/uf3Dob2x\nWbazXg/vnyrnptHju33uxoCf72zdQJXPC8A946dy7UhRzCUZanxeNDQKzda415cVl7CsuEQkNC2E\nA+nZLqYjoepqGp57jnBtLaaJE8m5/fZet1aIuN0Ey8vR5eaiHzIkQZGmjur30/TWW4SrqjBOnIht\n6dJUhyQIcUwTJoiENI24PvwQ13vvgSThuPZarPPnpzoktEiEpnXrCB49in7oUOzXXots7F17El1e\nXrsz0xGnE3Q6FKu1zc8CR47EjYNHjkC6J6ayhCYyU6GZSEz7kdYbvHu64fuTM6diSSnAK0cPiMQ0\nCZ498BWvHT8MwLUlo7lnwrQ2x4ik9LxwMILZnllLmp1/+Qvh6uh+Yf+ePXg2bepVi5NwfT21//M/\nqC5X9Kbsxhuxzp2boGhTw/nXv+LbsQOAYHk5SlYWlosuSnFUgiCko1BVFa6//S02dr7+OqbJk1FS\nXHTO/fHHeDZuBCBUUQE6HdmrVyf8Oo1//jPezz8HScJ+9dVkLVwY93P9kCGxz9Nz43QnK5KYMRVi\n0nvhudAt906YhrF5WcsoezYrhpf26DzGVktjxF7VxDvjccWSUoA3yo9w2t3UwTuEcLB3S3m1SKTj\nn4dCeLZswf3xx0Rcrh5fp6XW54m43b06n/eLL6JJKYCm4f7oo16dLx2EKio6HAuCIJxzbs9ljKqi\nBQKpCaaFcGXlBceRpiZCZ892+h3UmcDx49GkFEDTaHrrLVSvN+4Y68KFZF12GfqSEizz52O/6qpe\nXbMzkaYmGl54gdonn8S7c2ePziH2mAotiYyjH5lVMIhnl1xJY8DPIEsWuh5ueF9WXMJnVWfYUVuF\nWdHx4KSZCY5UOLfkuqVwO68J54UCPSt+pPp81D/7LMFjx9AVFpK7Zg26/Py4YzRNo+53v4suewI8\nW7ZQ8OijyGZzj2INnjhB/fPPx1VSlAwGLDN7929JblUES+rlUrF0YBw9mnDV+SrjhtGjUxiNIAip\npoXDRJxOFLu9TY9j/bBhGEaOJHj8OADGCRNQWn2ep4Jx/Hh8LRIz47hxQPRhYuOf/wyqiqG0lLz7\n7uty3+Y2zrUlO0fT2iS7kixjX7GiZ+fvgfq1awmdOAFA8OhRlOxsjCNHduscsiQSU+E8kZj2M1l6\nA1n63i131MsyP55zCY0BPxadHkM/LS6QaF9Un6UxEGB24SByOik6NSzLzpKhw9lQcRKARUOGUWJz\n9EWYGStalbf7D1tcH35IsLkJfLi6Gucbb5B3zz1xx6hNTbGkFKItIYLl5T3eV9b48stxSal55kxs\nl1+OrqCgR+c7xzJ/Pv79+wkeO4ZksSRlqVhfs19zDbLdTri6GtOECaLCsSAMYOHaWuqeeopIQwOy\nw0H+/fejKyyM/VxSFPLuvx//3r2gKJgmTkyLLS+WWbNAlmN7TC0XXwyA8403YpV9gseO4du1C8uc\nOT26hqG0FMOYMQQPR1dbWebPR7GltjBi6PTp8wNNI1RR0e3EVJIlVJGYCs1EYipcUE8r+g5Ez+wv\n483yaGKTbzLzH/OXdpqcPjp1DleNGI2maYzNzu2LMDNaOBBBZ+z6R1bgyBEa/vAHVI8n7vXWS58A\nJLMZyWg8vyRMklAcPX9Q0PqausGDe52UAshGI/kPPEDE7UY2m/tFRUpJUbAtW5bqMARBSAOuDz4g\n0tAARHt3Nr33Hrnf+EbcMZJOh3la25oMvrIygidPYigpSckDLsuMGVhmzIh/sfVKqF6sjJIUhbx7\n7yV4/DiSXo9hxIgenytRDKWlsUQZWcZQUtLtc8g6CTUsNpkKUWKP6QAWikTQxPLRXtM0jXdOHIuN\na/0+tlWf7dJ7xzhyRFLaReFA92ZMG158sU2CiCS1W8FRNhjIueMOlLw8ZJsNx3XX9apohKXlNfR6\n5HaqJ/aGkpXVL5JSQRCElrTWy1Vbjy/A89lnNPzhD3g++YSGtWvxbtuWhOi6z75qVbQfCtFlyKZ2\nEurukBQF4+jRaZGUAuTecQfWhQsxz5hB7j33YCgu7vY5FJ1MpDkxbQoG+OTMKcrqqhMdqpAhxIzp\nAKRqGr/evZ0NFSfI0hv4zvSLmJ5flOqwMpYkSdgNBuoD/thrtl4up04UbyjEU/t2cdzlZFpeIXeN\nn9Ljas2pdqHiRxGXi4jTib6oKG7vTuuZUfOsWVgXLMAwfHi75zeNG4fpe99LSKz25ctRvV68mzdD\nKITzlVeQ9XrMrZ+mC4IgCDFZixcTOHgQze9HMhrJWrKkS+/z793bZpwO1b2t8+djHDcO1eNBP2RI\nr9uFpRvZbMZxzTW9O4dORg1rNAb8PLZ1A9XNXSFuGjWe28dOSkSYQgbpX/9ChC7ZUlnBRxXRzequ\nUJD//OpL1i5dleKoMts/TJvDL3ZtwxUKcnlxCfOK0qNE+zMHvmLDmeg+1nKXkxyjkdWl41IcVc+E\ng237mPr37aP++echHEZXUEDegw+iZGUB0RuCc+X7ZYcD+6pVfdpS4FybmHN8e/aIxFQQBKEDhmHD\nKPzudwlVVqIvKurylgpdfj4ta/OmQ0Gkc3R5eZCXl+ow0ta5GdOtVWdiSSnA68cPi8R0ABKJ6QDk\nDgXjxp5WY6H7puYV8odlVxHRtLSakTzVqgXNKXdi2qCkQigQRtdqxrRp3brYUq9wTQ3eLVuwXXEF\nAI5rrsE4Zgyq241x/Pg+LxKhy8s7v/cG2lQCFhIjcPgwTe++C4B95UqMoqqvIGQ0xW7v9kNE24oV\nqB5PdI/piBHYli/vVQzhmhrC9fUYiosTvhVDiKfoZSIhlSxdfLXirJ5WLxYymkhMB6CLi4bw56MH\nqfFHn0xdNULcyCVKOiWlEG0hdLCxPm6cqcKBtjOmnelpVd1EsK9aher1Ejp9GkNpKbbLLktZLP1V\nxOWi/tln0YLRh2v1zz5L4fe/j3KBG0lNVXG++ir+PXtQ8vPJ+frXo7MZA0jozBlCZ89iGDYsrtqp\nIGQy2Wgk5+tfT8i5fGVlNLzwAqgqst1O/kMPocsVtSCS5dyM6SWDi/mi+iyfnD2FVafnkamzUx2a\nkAIiMR2Aso0m/nPBUnbUVJFtNIr9pUlwsLGeN44fxqAo3Dp6AkWW1DxxvXnUeLINxtge0/mDhqYk\njkQIByNtZkztq1bFLeW1tFPYKFVks5ncO+5IdRj9WqShIZaUAmiBAGpj4wUTU+/WrbEG9arHQ+Mr\nr5D/d3/XJ7GmA//evdSvXRttX6HTkXfffRhHjUp1WIKQVlzr18davKhNTXi3bMF+1VUpjqr/UvQy\nQV8YWZL4x+kX8dCUmehlBTnNHvQLfUMkpgOU3WBk8dD2i8AI8MdDe/mo4gR5JjOPTJlNcVbXl4HW\n+rz8322f4otEl5jua6jliUuvQCf3fRFsSZJYMby0z6+bDO0VPzJNnEjR979PpKmpTfEjAC0UwrNp\nExGPB8vMmb2qtCt0n2vDBvxlZSi5uTiuvz7hy6l1RUUoOTmx9hJKbm6He8sijY3x4xa9ZgcCz+bN\nsRtuwmG8n30mElNBaKVNgSKxpDSpWlblBTAqIjUZyES7GEFoZfPZ07xy9AC1fh8HG+v5ZVn3ys6X\nu5yxpBSg0uuhoUXFXqFnwiEVRdf2I0ux2zEUF7dJSgEa/vhHmtatw/Pxx9Q+/jjh2tq+CFUAfLt2\n4Vq3jtDp0/i/+orGl15K+DVko5H8Bx/EumgR1kWLyH/wQWSj8YLHm6ZMgRY3nebp0xMeUzqTzOYO\nx0J6C505g/vTTwkcOZLqUPo1x7XXxv5t6IcOJevSS1McUeYJnTmDb88eIq7O61oouugeU0EAMWMq\nCG1U+eJ7X1Z5PRc4sn0jbA6MikIgEgGg0Gwhx2hKWHypsKeuhrWH9qBpGreNmcSMgr5f/i1JdKvv\nrqaq+PftOz8OBAgcOTLgihD5Dx3C+eqraMEgtmXLsF5ySZ9cN1RZGT+uqkrKdZTsbBxXX92lYw3D\nh1Pw8MP49+9Hl58/4BJT+6pVhM+eJVxdjb64GNvll6c6JKGLAkePUvf009D8veL42tewzp2btOup\nfj++7dtBkjDPno1siG+BFjx9Gv+uXcgOB9b58/tVX2VDSQlFP/whqtuNkp2NlILVThei+v2gachp\n/FDJu20bjX/+czROm438hx/ucI+urJNQIyIxFaJEYioIrcwuGMxLR/bHEssF3dyXWWC28OPZl/Da\n8UMYFYXbxkxKyTLeRHEFg/x0+5bYLPDPdm7l6UUr+jzZlmUJTe16YirJMkpeHpEWs6QDLSnVQiEa\n1q5FC0QbKTjfeANDaWlClzSrfn/0Bi4nJ+7m1Dh2LO4PP4TmhwnGsWMTds3e0A8din5o5u617g1d\nbi6F3/0uajDYJtEQ0ptvx45YUgrRm/9kJaZaKETtE08QPnMmeq3t28l/4IHYv+9QZSV1jz+OFgpF\nx6dOJazwULqQDQbkNCt45P70U5refBM0DeuiRV1+INfXXB99FPvcV10uvNu2YV+x4oLHixlToSWR\nmApCK8Ntdv794iVsraog12ji8mEju32OSbn5TMrtH0lQrd8XtzQ5EIlQ7fP2eWIqKTJqpOuJKUDu\n3XfjfPVVVLcby7x53WolooVChGtqkB2OCxbTSXeqzxdLSgHQNCKNjQlLTANHjlD/3HNofj/6oUPJ\n+9a3Yk/yjaWl5N57L/7du1Fyc8lauDAh1xR6TySlmUdutT87me2vQhUVsaQUIHTiBOGaGvSDolXd\nA4cOxZJSAP+ePUmLRYiKuN2xpBTA88knmGfMwFBc3Kvzhs6cIeJ0YigpSdgsrGw0Emkxljr5vFH0\n8XtMhYFNJKaC0I6Rdgcj7V1r7N3fDbFmMcSSxRmvG4guTR7WjWJQiSIrUrcTU31REfkPPNDta0Xc\nbuqeeIJwdTWSXk/OXXdhGjeu2+dJJTUYxH/oEMqgQUSal9UqOTkYSkoSdg3nG2+g+aP7p0MVFXg2\nb45ri2MaNy7j/twEoa+5N24kcPAgukGDsK9Y0e5++awlSwhVVBA4fBj94ME4rrsuafHINhvI8vlC\nWYoS18uz9cqTjgqOpZLq89HwwgsEy8vRDxtGzu23Z+xDRi0UiiWlsddaPnTsAc/mzThffx00DSU3\nl/yHH07IAw/H9ddT97vfofl8yA4H5hkzOjxezJgKLYnEVBD6mCcU4sm9Oyl3O5mRX8Sd46akXf/T\nloyKws/mLuSN8iNoaFw9YjQWXd9XKZRlCbUbS3l7w7N5M+HqaiB6Q9D09tsZlWBp4TB1Tz5J6ORJ\nAHSFhZhnz8YyezayxZK4C0UicUOt1VhIjVBVFarHg2HYsHaTHCF9eD7/PDoTBgQOHkQLBsm+4YY2\nx8lGI3n33NOra0UaGwlVVaEfPBjFbr/gcbq8PLJvvJGmt98GWcZx3XVxCYtp4kRsK1fi/eILFLud\n7Jtu6lVcyeJ67z0CBw4AEDx8GNe6dWkba2d0OTmYZ86MLukGDKNG9foho2v9+liyG6mvx7d9O1mL\nF/cy0uj3zbkic6rTSf3vfkfBo49ecB+ymDEVWhKJqSD0sd/uL+OTs6cAOOFqItdo4rqR6bH/7kJy\nTWbuHj8lpTHIioTWVwUSWhdZ6kbRpXQQOn06lpQChKurMU2e3OHNaE/YrriChhdfBFVFyc7GevHF\nCT2/0H3uTz6JJhSaFl1e/cADHVYqTqTAkSP4DxxAX1iI5aKL+uSama7lv9P2xokSOH6c+t/+Fi0Y\nRDKZyLv/fgzDhl3weMtFF3X439C2bBm2ZcuSEWrCtG4HlentobJvvRXLnDlo4TDGsWN7XXCq9UOr\nzpbcdlWoogKtRTXecGUlkcZGdHl57R6v6GRUMWMqNBOJqSD0sZPuprjxaXfn5dQFkHqwlLenrPPn\n49u1K1o4SafDfuWVfXLd7mp69128n32GbLORc8stsaI+stV6roxx9EBZRjYlfk+wefp09MXFRBoa\n0BcXp3WlyP5G9fnwfvEFEE0iZJMJTdNwvftu7L97qKICf1lZnySJ/kOHqP/tb2PXDjc0YF++POnX\nzXSGkhK8n39+fjyy+zUNusL90UdowSAAmt+P55NPMNx+e1KulS7Ms2ZF979qWrS68KxZqQ6pVyRJ\nwjhmTMLO57jhBhqefx4tEMAwejSWOXMScl4lLw8UJbaiRrJYkLOyLvyG9F0wJqSASEwFoY/NLhjE\nEWdDbDyzYFBCz//h6RO8c/IoWXoD35w4jaHWvt8PmgyyLPfZUl7Fbqfg0UcJV1WhZGcnfKYxEfz7\n9uFevx4A1e2m/vnnKfre9wDQFRRgv/pqmt55B0mScFx/fdJ+D7r8/AFX7TjVYlVTz54FwLd9O/kP\nP4yk00X3Brak65uv+cDevXErC/x79ojEtAvOzYAFDh1CV1QUt0c7kdos6e6jvxepZJ48GfmBBwg1\n7zHtTvG7gcA0bhxFP/pRdC+o3Y6UoC1Futxccu+4A9cHH4BOh+Oqq/ps1YaQ+fr/J5PQbxxvauSQ\ns4FR9mxGO3JSHU6P3Tp6ArlGE+UuJ9Pzi7i4KHGtOw421vPr3V9y7vbwp19u4clF/ePmsCfFj3p1\nPaMRw/DhfXa97oo0NrYZa5oWu7nIWrgw2rNUkhJ2wyGkh9DZs7GkFJqrqFZXox8yBMfq1TS+8gpE\nIhjHjsU8dWqfxNS6AM6Flu0JbVnnzcM6b15Sr2FfsYLQyZNEGhtRcnMHTA9b48iRGJM0C90fyAYD\nJKFKt2nSJEyTJiX8vEL/JxJToc/V+rwE1Ei3ZvJ21FTyr9u3ENY0ZEnin2dcnNCEri9JksSK4aVJ\nOfcpdxMtU7czXjchVUXfhT6q3lCItYf2UOn1sGDQUK7oQZucZJIUCa0PE9N0Zxw/HslsRvP5gOiy\n2tYJaDo1hhcSR7Hb21ZNbV4qZ5k1C9P48ag+H0pubp/9HbAuWECkrg7//v3oCgtxtFPAR0gdXWEh\nhf/8z0SamlDs9ujsuiAIQpoRn0xCn3rt2CGeO7gbDVgwaCjfmT4XuQuzOe+fLid8rmGzpvH+qeMZ\nm5gm08ScfEyKgr95b8eU3IIuJaUAv969nS1VFQDsrK3CYTAyN43+jKNVeUWBhHN0ubkUPPIIvrIy\n5KyshO0PEtKfkp1N9q230vT220iShP3qq1HsdjybNuF6/33Q68m+4YY+XWItNVdvTWYbE6F3JJ0O\nXW5uqsMQBEG4IJGYCn3GGw6xtjkpBdhcWcHyumqm5xd1+l67Pn6piU00iG/XEGsWP5u7iPWny7Hp\nDVxf2vVqv4ec9W3GaZWY9vFS3kygy89P+8qYQnJYZszA0qI/YOjMGZxvvBHb59nwhz9Q9OMfi71d\ngpDhvNu24fvqq+jn/YoVSSlkJwjpQiSmGeiMx8VLh/cT1lRuLB3HqAzZb6lp0Hq+K9LFNhy3jZlE\nuauJg411jHLkcOfYyYkPsJ8Y7cjp0R7ciTn5bGxuY3NunE5EYioIFxZxueKKD2mhUHSZt0hMM553\n+3aCx49jGD5ctOEZYHx79kT3jAMBIOJ2k9vPqimLGghCSyIxzTCBSIQfbPuUWn90X9mu2mr+d+EV\nZBvT/wmaVa/nplHjeeVotOH1jPxCpucVXvD4kKpS4/OSazThMBr593mLiWgaivgQS4qHp8wkz2Si\n0uth/qBiZiW4WnBvSbKM1kdVeYWBK1xbi/ONN9B8PqyXXIJ5+vRUh9QlhhEjUPLzoy2OAMOYMcgO\nR4qjEnrLs3UrzldfBcD72WeogQBZl16a4qj6B/+BA9F2WxYLtpUrUWyJqWCvhcNEnE4Uh6PXe3nb\n9Lk9caJX5xOEdCcS0wxT4/PGklIATzjEKbcrIxJTgNvHTmLh4GH4ImFGO3IumGTW+X38YNtGKjxu\n7HoDP55zSYfHC71nVHTcPb5vKnj2hJgxFfpC3TPPxJK74IkTKPn5GIqLUxxV52STiYKHH8a7YweS\nXo9l9mwxE9EPBA4ciB8fPCgS0wQInTlD/bPPxnpths6epeCRR3p93nBNDXVPPRWtfpydTd7996Mr\nKOjx+QwjRsSPS0p6GWF6Et/swjmiZGOGKTCbyW2RhFp1eoqz+q5PpaZpfFl9li2VFQSaP9C7a7jN\nzrjs3A6TzL8eO0SFxw1AUyjIcwd39+haQv8RTUxF8aP+KFhejuv99/GVlaU0Di0UiiWl0Rc0wpWV\nqQuoHRGXi8Y//5n6Z5/Fv29f3M9kq5WsSy/FevHFaVV1VYtEcK1fT8MLL+DdsSPV4WQU3aD4lSu6\nos5rMgidC50+HUtKAUKnTqH18J6mJdf778daeUUaG6PFyHrBNGkS2bfcgmnyZKwLF+K48cZexygI\n6Sx9vrmELjEqOv71okt5sXmP6ddKx5HTh7Olvyr7gk+a9yGOdeTws7mLMChKwq8T1uITkLCoxjrg\nybKYMe2PAkeOUPf007HWJ+G6OmxLl6YkFkmvR19SQqi8PDZOtxmK+ueeiy3n8+/fT/63v532M7pN\nb72FZ9MmAHw7dyLpdH3WXzXT2S6/HNXnI1hejmH4cOwrVvTZtcN1dYTr6jAMHYpstfbZdfuCvrg4\nruWSvrgYKQH3Mlo43OG4JyyzZ2OZPbvX5xGETCAS0wxUnGXnuzPm9vl16/y+WFIKcMjZwL6G2i5V\n1e2ua0pGs6WyAmcwgEFWuHnUhIRfQ8gskiKJPab9kO+rr8734wR8u3alLDEFyLvnHlwffojm82GZ\nO7dPW650RlPV+D1nqkro1Km0T0wDR4/GjYNHj4rEtIsknY7s1av7/Lr+vXupf/55iESQbTbyH3oI\nXV5en8eRLPohQ8i9+268n32GZLFgX7kyIefNWryYwOHDaH4/kslE1uLFCTmvIAwUIjEVusyk6FAk\nKa6SrlWnT8q1hlptPH7p5ZS7nBSZrWytqmDj2VNMzy9i0ZBhSbmmkN5kRRYzpv2QLienw3Ffk81m\nHFddldIYLkSSZfTDh58vgCLL6Iel/+ehfsgQwmfPnh8PHZrCaISucH3wQWypq+py4dm8Gcc116Q4\nqsQyTZiAaUJiH3obRoyg8LvfJVxZiW7QIBS7PaHnF4T+TiSmQpdZ9XoenDyTJ/bsJKKprC4dx5js\n5DXrthuMTM0rZO3BPbx67CAAH1acQJbg0sHpfzMmJJZYyts/WRcuJFRdTeDAAXRFRThSMDuUSXLv\nugvX3/6G6vFgmTs37WdLARyrVyPp9YSrqzGOHy9anmSA1nuUe7NnWVNVVLcb2WpNyHLZjoTr62l4\n4QXC1dWYJk4k+6abkn7N1hS7XSSk3SFqtAktdPpJI0mSEdgIGJqP/4umaT+RJOnfgauJtlY6Ctyt\naVpTMoMVUu+y4hIWDxlORNMwNn/Yv3PiKC8d2Y9BVvi7SdOZXTg4odcsq6uOH9dWd5qYesMh3i4/\nii8S5vLiEoZYsxIak9D3NFVDksU3WH8jKQo5N9+c6jASQvV68e/Zg2Q2Y5o8OSlVcRWbjeybbkr4\neZNJNhrJFkVbMor96qup+93v0LxedIMHY120qEfniXg81D31FOEzZ5BtNvLuuw/9kCEJjvY851/+\nEltR4Nu+Hf2QIWT1MHZBEPpep4mppmkBSZKWaJrmlSRJATZLkvQ34H3gnzVNUyVJ+jnwveb/Cf2c\nTpZjf3FOuJw8tW9XrNT3v+/6nOeXXoUpgRUhS2wOjjgbzo/tnffm+/EXmznQWAfA+tPl/PqSy/q0\nSJSQeEF/GIO5b598C0JXqT4fNb/+dayqr3n2bHJuuSXFUQm9pWkakbo6JKMxYX0uM4FhxAiKfvAD\nVJcLJTu7x7OO7g0bCJ85A0SXBDvffJP8b30rkaHGiTidHY4FQUhvXWoXo2mat/mXRqLJrKZp2npN\ni5VO/QxI//VEQsLV+/1x/af8kQjuUDCh17hvwjSuKC5hXHYuN48az5XDR3V4fFMwEEtKAZzBAAdb\njIXMFPSFMZjF7gMhPQUOHoxrNeP78kvUQCCFEQm9pakqDWvXUv3zn1P1gMIJjAAAIABJREFU05/i\n2bo11SH1KdlgQJeX16ulsFow/n5AC4V6G1aHzDNnnh8oCuZp05J6PUEQEqtLd3mSJMnAdmAU8Lim\naV+0OmQN8KcExzYgHfn8LDXl8Suitfa21bXzYvvHtffWdt7bXjDNx8UO11r+KDoIhiMMOa7SFIh+\n+QyxZLH7zFFAih2vnf9Fm/e3PndcbC1eG4vCWC0HCPHB+2VtztPyWFXTKDgWjPVZlTQ4VnaCOkNl\nO3G0+r22+cH5X7Y5tuUfWqvXNA3QVGixlK/j98f/flof2+b49o5t79yd/Lm2GNJmIEW3fkiyFF2S\nKJ37NUhS9P+RJGS5+WfNPwcJWY7+TGrxnui49Wvnz3/uvG2vE32t9oRLJKZC2mrdTkMyGpH0ySkO\nJ/QN/759+PfsiQ5UFefrr2O56KI+37OYyazz5+PbtQvN6wVFIWvJkqRez7ZsGbqiouge03HjEl5o\nK3jiBKHTp9EPH44hAwqPCUKm6dJdXvPM6AxJkuzA65IkTdQ0bR+AJEn/Bwhpmvbihd7/4x//OPbr\nxYsXs1iUz24jElb5zW1/o+pIIxMWtTP53M5WpfZ2L7W7p6m9l7p8XPwvWr7t3DkWqEVUBF3Iksyw\niI2acleb98euF/f+C51banNMR3G0fk2S4NL8YnbWVhHWVMZn52FXDGgRrd1jo5eTYjGev1bb3yut\nfj9xf2TNg0h9Hb4dO9BCIQzDhmGaOrWd97c6b8uf0f6x7f9eu/Dn2vr30N65W/xMks4l1lrz/4Oq\nabGxpp7/f7Tog4c2r507VtNavNbivWH1/DGqFrtetGuI1uI1UENhAsePMyrfg61iF5o2KCl79/qS\n6vPR+Oc/EzpzBuOoUTiuv75XxUWE1DOOGYN18WI8n36KZDCQc+utSHKXFiUJ6ar54WaMql7gCbBw\nIfpBgyh87DFCp0+jKyhAV1CQ9GuaJ09Oynl9u3fT8Pzz0b8DskzuXXdhmjgxKdfKFMFTp1CbmjCM\nHIlssfToHLY8M/s/Oc3PrniVv3tuOTlDRE2QTPHxxx/z8ccfJ/ScUnuzZx2+QZJ+CHg0TfuVJEl3\nAfcBSzVNa3fNkiRJWnevMRD53UF+OPdPFE/O45GXV6U6HKEXKn/yE1TX+eQ85447RM++Xmh85RW8\n27bFxo7rr8e6YEEKI2pfxOXCX1aGZDZjnjGjw6Sk4eWX8X1xfuGJbflybJdf3hdhCkmmqapISPsJ\nLRym7sknCZaXA2BbsQLbZZelNighZeqeeYbAgQOxsWnKFHLvvDOFEaWWe+NGmt58EwAlJ4f8b3+7\nx/uww8EI7/1mF2XvlvPtl1eRlSNqgmQiSZLQNK1XMwddqcqbT3RG1ClJkhm4HPi5JEkrgO8ACy+U\nlApdZ8oy8M1nLuetX36Z6lDShnfnTnw7dqDk5GBfuRLZbE51SJ3SNA3V6417rfVY6J5QRUX8uLmQ\nRjqJeDzU/vd/E2lsBKJLAHO/8Y0LH99iLyJAuNVYyFwiKc18wZMncf71r6h+P9aFC7GtWoVsMqEf\nnNiK830t0thI4PBhlPx8jCNHpjqcjCO3Srpajwca9/r1sV9HGhrw7dyJrqAA//796AoLsc6f3+XP\nQ51B4cpHZxLyhXni9nf5ztvXZvzKKKFnuvI3ZjCwQZKkXcDnwHuapr0D/A+QBXwgSdIOSZKeSGKc\nA0I4FEGnFzc1AIHDh2l88UUC+/fj3bKFhhcvuFI8rUiSFDebJzscmCZNSmFEmc8wenTc2DhmTIoi\nubDgoUOxpBTAX1bWYeGb1n8nBvpyMEFIF5qqUv/ss4ROnyZSW0vTa68h6fUZn5SGa2qo/tWvaHz5\nZeoefxz3xo2pDinj2K+8En1JCcgyhtJS7MuXpzqklJKMxrhxuL6e+t//Hu+WLTS9/jpN69Z173yS\nxLXfv4iAN8S6/9hBfYUbVRUrLgearrSL2Q3MbOf19Ls7zHCHt54ld6hYWw/RAgMt9/Kc60uWCRzX\nXINxzBhUtxvj+PEDqsVAMthXrULJyiJUWYlx3DjM06enOqQ25FbN1CWzucPCN1mLFyPb7YQqKjCO\nHo1pwoRkhygIQhdogUDcVgw0LbrCoTizGw94d+yIFiBq5tm0iayFC7t9nuCJE3g//xzJbMa2bFmP\n9xVmIsVmo+Chh1IdRtpw3HgjDc8/jxYIYBw/vs0e7MCBA3D11d06pyRJrHl8KR8/u5d/u+KveJ0B\nbvvFpcxZPQa9URQdGwhEtY00cvZQA6WzilIdRsJEnM5owYPCwm4XPDCMGNGiCg/oR4xIRohJ0zrR\n0MJhAFHgpgckWU56JcfeMo4ahe2KK3Bv3IhsMpF9882dLmGyzJwJM9s88xMEIYVksxnD6NEEjxyJ\njq1WDKWlKY6q91onkD1JKMM1NdQ9+WSs5UvoxAnye5GoaapK0zvvENi/H11REdk33jigEt1MZxo3\njkE/+Qmq34+SldWmnZKusLBH5x06MY/bfrGQW39+CT9a8DJv/3I79RUernx0BrIiVhX2d90uftTt\nC4jiR11WfczJL699g++uu4784fbO35DGQmfPUvvEE2g+HygKuXfc0e0lrd4vvqDp7bdRPR5kh4O8\ne+/NyOVUrvXrcb33HkgSjmuvTcvCPYIgCEKUGgzi3bwZ1e/HMmcOuvz8VIfUa1o4TP3zzxPYtw/Z\n4SD37rsxdHMW2PvFFzS+/HLca4P/7d+61BZJ9XoJVVaiy8tDcTiA6Kyt8/XXY8eYpk8n9/bb28Ye\nChE8cQLZak3aPUCoqopIfT364cNRWrV+ErpG0zRc776Lf98+dIWFZK9e3aaNVk/s+fAk/3vne9z3\n28uYvlLsjU5nfVL8SOg7haUOlt43hXX/sZ07/zu9Z4g649m8OZqUAkQiuDds6HZiGmlsRPV4AFCd\nTpyvvUb+Aw8kOtSkClVV4Xr33ehA03C+/jqmKVNQ7Jn94EEQBKG/kg2GtF+l0V2STkfemjVo4XCP\nV+7oBg+OW8mkFBR0KSkN19RQ+8QTqC4XksFA7po1GEePJlRdHX9cVVWb96qBAHVPPBErgme/6iqy\nEtxy0LtjB41/+hOoKrLdTv5DD6HLzU3oNQYCSZKwr1yJfeXKhJ538rLhXPf9i9i9/qRITAcAMSee\nZkpnFVG+s4YTZTVk8kyzZDB0OO4K9Vxie4FxJtD8/lYvaGgdFMXpqXBtLfVr11L71FP49+9P+PkF\nIVN4t22j8ic/ofKnP8W3e3eqwxHSlBYOEzx5csBVxO7NdhJDcTE5t92GYeRITJMmkXfPPV16n3vj\nxti+XS0YxPX++0DzlpcWlVfb22vv/+qruMrsTe++m/B7I/eHH9LcRBu1qQnvZ58l9PxC10Wamoi0\n3OPdbN4t4/jq3RPs+fCkKIjUz4kZ0zRTOqeIWdeM4vcPfIiik5mzejRzrhtN/ojMmmHLWrqUwOHD\nhM+eRbbbsXdzAzyA5aKL8G7bFk3uJAnrJZckIdLk0hcXYygtJXjsGADGiRNRErwsTFNV6n77WyJ1\ndQDUHztGwT/+I/oe7u8QhEwVrq2l8S9/id1kNrzwAoYf/lAszRPiaKEQtU8+GS2qJ0nYr7mGrEsv\nTXVYGcE8fXq3C9C12W+vRIvYmCZMIHfNGgIHD6IrLMQyb17bNyvxBW8knS7hbURaz/r25EF6MgVP\nnsT5xhtooRC2pUt7XAAw4nKhBYMoublp2Yql6Z13cH/0ERC9h7RfeWXsZ1m5Jm7/1ULe/sWXrPuP\n7fzj69egM4hiSP2RSEzTjM6gcNVjs1j1jzM5vqOaL/56hF9c8wa2fDOTlgxj0pJhlM4piv2DDBw9\nimfTJiSjEfuKFSjZ2Sn+HUQpWVkUPPooqtuNbLUiKd3/AFHdbpBlkCSMkyZhueiiJESaXJKikPfN\nb+Lfvx9JljFOmJDwLwTN748lpQBEIoTPnu3XiWnE5cL94YdowSDWBQvQDx2a6pCENBBxuWJJKQDh\nMKrHIxJTIY7vq6/OV3rXNJrWrcO6YIHoQZskWUuW4D9wgEhdHZLFErfU0zRhQodVyc3TpuHbvp3A\nwYOgKDhWr054fI7rrqPu979H83rRDxuWVg/BtXCY+t/9LratqeHFF9ENGdLt73fP5s3R/byahmnq\nVHJuvz2hf99VrxfJaOzRvR5El3ufS0oB3B99FN3f3aJw5rTlJUy9YgS/ue1vfP6Xwyz4+vhexy2k\nH5GYpilJkiidVUTprCK+9i/zOFFWy74Np3j9Z9uoPu5k3IIhjL8oh8Ldr2EzRJeGhk6epOA730mb\nJ2GSLPdqL2XDiy/GytsH9uzBv2cP5ilTEhVen5F0uqTGLZnN6AYPJnz2bHRsMKDP8NYGHdFUlbqn\nn479fn1ffUXhY4+lzUMZIXX0Q4eiKyqK7VXTjxiBLi8vxVEJaafVd6QkSW1eExJHyc6m8LHH+H/s\n3Xd8FHX6wPHPzPbdlE0vJIQkhBB67yAongLSbGA5RbHr2c+zneXu1NM7z5/dsyDKndiRIodioUnv\nPfQSEhLSk91sm5nfHwuBUFJ3s5tk3q/XvS7fzc7MI8lO5pn5fp/HU1SExmpFNBrrva2g0RB5221I\nxcWIJpNfqvbqO3Qg/s9/RrbbEUNDg+oGhWyzVSel3hdkpMLCBiWmittN2dy51WuDHVu34szO9kmb\nMtnppPijj3AdOIBosRB5663ergoNdKpzQV2vCYKAIitodMHzM1L5lpqYtgCiRiS1TyypfWIZ90hf\nyk/Y2bU0h63fbmPu6mRCzW76Z5TSiwKUqiqEVlBuXVGUmidjTj5BVZ1DEASi7riDisWLUZxOLEOG\ntOqLcdlmq05KwfvE2JWTg0lNTNs8Ua8n+r77sK9fjyCKmPr1a/QdfFXrZerRA/vatd6WMKJI2KRJ\nQXNDtzlJpaWUzZuHXFGBedAgzH37+u1Ygk6HLj6+cdsKgt//pgk6XXW14GAihoaiS0rCnZPjHYeE\noEtObtA+FEWp0V8UQJEkn8Rn++236qVKss1G2Zw5xDz4YIP3o42Px9izJ44tWwBvhWbtBX5fkrpG\nsfTjHfQam4rRUnvxLZvbjUPyEGU0NTgmVWCoiWkLU+FyUWmW6HdVBn0viuD4P14l57iO71YlYE2w\nkGBqHR8+QRCwDBmCbflyAMTw8AZX9W1LNKGhWP0wxSkYiWYzGqsVqbTU+4JGgy6u9fT/VTWNaDKp\n6wVVtRK0WqLuuANPQYH3fNJGq6QXz5xZnfC4Dh1CExmJIVWtehpMBFEk6s47qVy2DMXtxjJoEJrQ\n0AbtQ9TrCbnkEip/+gkAfVoaxs6+mQZ7djHHxhZ3FASBiBtvxHVyGrW+Q4cL3iya/PRA7kv+kC+e\nXMG1fxuKKfT8a4J/OXaYt7ZtwKMoDItP4tFeAxDb4A2olkbtY9qCrDx+jFe3rMUty3SJiOL5/sPh\nwAFsK1ZwOFfLF18q/HHBZKKSG3bSCmaOnTuRKysxZGU1+GSsar3c+fmUL1iA4nQSMnIkxi5dAh2S\nStVkjl27qNq6FW1kJCGjRjWpgqpKVZfcxx6rsSY7fPJktc92K+bOzUV2OtG3b++zmSSeoiIK33jD\nO8NNEAi/+mosAwf6ZN+KLF9wWvUHty9m8/8O8Y8dN2EON5zzfUlRmPLjd7jO+P1+qs9gBsYl+iQ2\n1fmpfUzbmPd3bsZ98kO2s6SIX48d5vKMDAwZGUQCJbHb+PDOn3j42/HojK3jR6smHKrz0cXF1btV\nQWujSBKKx4NoOPePcUtXuWQJjt270cXFETpuHGKQVcf0J+f+/RTPmFE95c5TWEjEddcFOCpVa2bo\n2BHnnj3egUaDXn1a2qrpEn2flGmjooh55BFchw+jjYz0SSFC5759lPznP8h2O+Z+/Qi/5ppznp46\n7W6m/n3YeZNSAFlR8JxZCA9wyb6Zvqzyr9aRvbQRZ3/IPErN8ajbunFwYwFfPbOK619Rp7KpVMFI\nttmwr10LGg3mAQMaVAikbMECbEuXVldWjLzpJj9G2rzsa9dSvmABAK59+1DcbqzXXhvgqJqPc9++\nGuvAnHv3BjAaVVsQcdNNVP78M1JFBeZ+/fySuKhaP01YmE8LPJZ89ll1TRH72rUYMjMx9exZ/X1F\nUcjfX4bOcOGnvjpR5Or0zny5fzcAHcOsDIhN8FmMKv9RE9MW5MZOXXlvxyZkIDkklO4RMUiyjObk\nVAdBELjhH8N55Yq5rPoim8FTMgMbsEoVBGSHg4pFi/AUFWHq0QNz//6Bi8XppPCtt/CcOAFA1caN\nRP/hD/WaVuUuLMS2ZEn12LF1K1U7d2JqJbMKXEeP1hyfXPvWVugSal40qUmCyt9Eo5GwceMCHYZK\nVYN8shvDhcYluTaqKlz0vKxDrfu5sVNXBsYlUul20TUiGr1aCK9FUBPTFuTy9ml0j4phb2kxn2bv\n4A+//US82cLfBgwn1uTt02cM0XPHB6N57eoFJHWNIrlbdICjVqkCq/TLL3Fs3QqAc9cuRIslYFPE\n3bm51UkpgDsnB09RUb1K/0snW6CcST5VAKoVMKSlYV+1qsa4LTH16IE0YQJVW7agjYwkbOLEQIek\nUqlUzc4yZAi2ZcuAcwtflh638cLor+kzLg1TWN1LPTLCI/wWp8o/1MS0hWlnCeU/e3ZQ6KwC4Ljd\nxuy9u3igR7/q98RnRDDlhaF8cMdPPL5wMmZr61uLplLVl/vIkRpj1+HDjUpMFUlCKi1FDA1t9NpH\nTVgYiGJ1wRFBp0NjsdRrW31qKuh04HZ7X9BqMfbq1ag4gpGpd28UtxtHdja6uDhCLr440CE1u5AR\nIwgZMaLGa4qiYF+5EldeHkplJYrbjSEz85z3qVQqVWsQPmEChowMb+HLzp1rFL40WHTIHoVRt3UL\nYIQqf1IT0xbIdVb/qfMt6O5zRRoHN+Qz8/5fuWvmZYiiWiJb1TbpUlJOt5aBRjX/lioqKHrvPTz5\n+YhmM5G33Ya+ffsG70cbFYV16lQqFi4EjYbwCRMQ65mYimYzsY88Qtn8+aAohE2ciOYCPYsVRWmR\nfRnNAwZgHjAg0GEElYr//Y/KX36p8ZozOxvBYPBZ9UuVSqUKJsasrPO+bgrVc+0LQ3n92u/565rr\n0JvUNKa1UdvFtEDbi07w/IbfcEoSJo2Wvw4YTidr5Dnvk9wyr09ZQNaIJMY82CcAkapUgSc7nVQs\nWoRUXIyxe3fM/frVvdFZyubP9xYdOkmflkb0Pff4MkyfUCSJ0i++oGrzZjRWK5E33+yTKomqwCn4\n17/w5Oae87p54ECs11wTgIhUKpUqsD59aAnH95Yy7c1RWOMtOCpdhMWc/0atqvmo7WLaqG5RMbw9\n/FKOVJTTITScaNP5P4wancj0d0fz8tg5pPSKocvI5GaOtHlINhuCICBe4OmRyreqk5+Ta+Eibrrp\nnMItwUQ0GAhv6nq9s2YpKB5P0/bnJ/Z166jauBEAqbiYki++IPbhhwMclaopdHFx501M9R06NH8w\nqlbFU1CA58QJdMnJ3mUGKlUL8ft/XcSSGTv4++VzUBQFrU7Dxbd3I6p9GP0np7fIGUMqLzUxbaFi\nTZbqgke1CY8zc8vbF/PR3T/zx/kTiUoKrXOblqT6SZYgEDpmDKFtcF1ac7OvWVOd/HhOnKD0yy+J\neeCBAEflX5Zhw6jasgW5ogK0WkJHjw50SOd1qsT+hcaqlid88mRQFFzHjyMaDIihoRg7dQpodWlV\ny1e1bRsls2aBLCOazUTfdx/aehRhU7UdlUuXYl+zBjE0FOvVV6ONiQl0SNUEQWDU9G6MuLkLLruH\nvD0lfPnnldhLHQy4smOgw1M1gZqYtgEZgxIYfVcPPrzzJ257bzRRya0jOXXn5p6eXqkoVPzvf5j7\n9kUTHh7YwFo5qaKixlg+a9waaaOjiX30Udy5uWiiotBGnjt1PhiYevakculSlCpvcTTLoEHNdmxF\nlhFOtq5S+Y5oNhNx440BjWHjwXLu+GAXvVJCufmiBF749iDPXJXGkExrQONSNV7lL79UF2GT7XZs\nq1Y1fWaJqtVwZGdTPn++d1BQQPGnnxL7yCN+OZYiyzh27ABJwti1K4JOV+9tNVqR7N+O8cHtPxGb\nGsb1r6hF4Vo6NTFtIy65ozu2YgevjPuO6JQw+oxPpc8VaUQkhgQ6tEZTTlUnrX5BOfe1VkoqK6Nq\n2zZEiwVTz55+Swik8nJcBw6giYioLhpk6tUL27JlKE4n4F3r1haIFguGjIxAh1ErbUwMMQ89hDM7\nG01EBMbOnf1+TEWWKfvmG+zr1iGGhBDx+99jSE31+3FV/rflcAX/+v4w/9tcxF+vTedokYPH/ruX\n/flV/GdFHlGhOo6Xuhje2aoW2Gthzr74b0gyoGr9zmxrdr6xryiKQsmnn+LYvh0AXfv2RN9zD4K2\n/ulJat84EjMjSOwcybbFh7FEGknuGuWXeFX+pxY/amMkt8yelblsnH+ALT8cIi7dSp8r0ug9LhVr\nQv2qgwYLRZYpnjED5+7dAJj69CHi+usDHJX/SeXlnHjtteonlab+/YmYMsXnx/EUFVH4xhvINhsA\n4ZMmYRk2zPu9wkKcu3ejiYwMWE9QVXCo2rSJkv/+t3qsiYgg7qmnAhiRylduf38nOo3AX65NJypE\nV71u63BhFde8tpVDJxxkxJsJNWoY1tlKQbmLAenh3DAs/rxrvFweGUUBg87/T9bdeXlI5eXoU1IQ\njUa/H6+lceXkUPzhh8iVlejatSPqzjvVOg2qau78fAr/7/+qb/Ybe/Qg8qabfH4cT3ExBS++WOO1\nqLvvxpCe3qD92EudLP1kBwv+sYFOQxO5779j0GjVGTzNTS1+pGowjU4k66Iksi5KYupLw9i94hgb\n5x9g4WsbSci00md8Or3HphIeF/x/oARRJPLWW3Ht3w+iiD4tLdAhNQvn7t01ps9WrVuHZfDgRrUv\nqU3Vhg3VSSl415ucSky10dFoT36tatvO/B0BdV1ra6IocFnPKKJDa/btTYk28eWDPdBpBBQF/v1T\nDiFGDSkx4Xz46zGW7iqhT2oomYkWhnTyTvctrnRz41vbkWSFRU/0xqTXoEgSgkbj87htK1ZQNncu\nKAqaqCii//AHNCEtd3aQP+iTkoh7+mlkux0xJESdhq+qQRcXR9S991K1aROakBAsw4f75TiiwVCj\ntzeAaDI1eD9mq4ExD/QhNMrE50/+5ssQVc1MTUzbMI1OpOuoZLqOSsbjkti9/Bgb5u1nwT/Xk5QV\nhSlczw3/HEFIRPDebRZEMeinV/qaGHruGuHSL74g9o9/9OlxhLOeMgiN+GOhav2M3btT8fPP1TdL\nzEOGBDii+nHn5oIooouPD3QoQUurEfBI55/x1CHm9Pngr1NOFxu5akAs7yw+ytEiJy/PPYRHVgg3\na+mXFsagjHA+/OUYWQ/9xoyI9XR25mHq1Qvrddf5NDGqWLzYm1UDUlERVRs3EjJCXXt2NkGrVavx\nqi5In5SEPinJr8cQLRasV19N6bffgiwTeuml6BITG72/zGHtsFgNfHjnT1z550HEdFB/v1sadSqv\n6hxuh4edS3J4/7bFJGZG8MDXVwR1ctoWFb7zDq4DB6rHosVC/PPP+/QYittN8ccf49yzB9FiIfLW\nW6vXmQbSj1uLsDkkJvWPUUvCBwmpvBzHrl1oQkMDPrVbURQqvv+eqi1b0ERGYp0y5ZxiVSWffVZd\nWdoybBjhkyYFItSgd89HuxjZJZJrB8c1antFUXC4ZVL/sAKzQcNPT/clLdbE5AfmMlY5wGW6XIoU\nA5lTJ2AeMMBncef/7W9IpaXV4/Arr8TSQm6YqFQNITscVP7yC7LNhqlfvxa7vl+RZVAUn8ygqCx2\n8OWfVyKKAn0nptF9dOCvW9oKX0zlVRNT1QXJksy8v69j08JD3D3zd8RnRAQ6JNVJnsJCTrz+enX1\n1ZCLLyZs7Fi/HEt2OBD0+qCY6jVzaS7/+v4wERYdERYtwztH0DXJQtfkEJIiDWqiqsK+YQOls2dX\nj/WpqUTfe2/12JWTQ+H//V+NbWKfeAJtlFos42z3z9zN4Awr1w1t2lPlA/l22kUaMehEjhU7GPrE\nb9yn383HrnROKCbeHKow5eaRvgkacOzaRcmsWSguF/qMDKJuvVUt7hPk7Bs34jpwAF1yMpY2UlDP\nF4refx/nnj3egVZLzAMPBHVf8eayZ2Uub0xdSMcB8Tz49RWBDqfNUNeYqvxK1IhMemogcRkRvHb1\nAm5+fSRdRiYHOiwV3jWeMQ89hHPXLjRWK8auXf12rGAoHKIoCv/6/gifrzzO3Ed7kRRpYOHmQrYe\nqWTm0ly2H7URYdFy80WJXDsojjBz/U5tHkmhsMKF1aLD2AwFWfzJffw4rsOH0SUk+Hy9cUviKSys\nOS4urnsj9YbGeWlEAY/c9BvLaWfULLA7ZeyKlqWeOF42beRW+1DmFoUxziHhlmRCjVq0mqb9PIxZ\nWcQ9+yyKw4EYFgaKQuWvv+I6ehR9aiqWYcPUm1hBxL52LaVffukdrF6NUlVFyMiRAY2pJVAUBefe\nvadf8HhwHTzY5hNTt8PDko+2kzkskds/uDTQ4agaSE1MVXUafG0nYjqE8dFdP3HZfb246Jau6h/1\nIKCNjEQ7dGigw/A7WVZ4+sv9rNpTyoI/9SYu3FuIZWK/WCb28zaEVxSFlXvKmLk0l5fnHeK/f+jG\ngPQL97N1uGX+szyPNxcdocIhcVFWBH+/PqN6374glZdTuXQpSBKWYcPQRkf7bN9nc+7fT9EHH4DH\nA4JAxA03YOrVy2/HC2bGrl29PRolCQBTt241vq9PSsLUrx9V69cDYLnooqDtSxtoGlFA8kFieqaM\nBDP7/m8ohpwDeErbw4dV/LS7grT7V6ARYfb93RnZpek/D9FgAIMBgIqff6Zi0SIAHFu3AhDip2Iu\n9VG1eTOeggIMmZlBsTwi0BwnK+tXj7Oz1cS0HgRBQJuQgCc399RT2HXhAAAgAElEQVQLaAOYlCqS\nROWvv+LOzcWQmRmwJ9+/zc6mqtLNPZ9ejs7g++JqKv9SE1NVvXQcEM8j303gvVt+5Pi+Uq55fgia\nFv6ESRXcCqvsfLZnF9/+WIVeMvLdo70Iv8CTUEEQGJppZWimlW/W5HPfjN3c+7tkpg6Jx6AT2XXM\nxke/HkNR4LHxKUx6dQsd48x8em83kiKNPPv1fgY9vZa/TUlnRFYEyVFNe0qseDwUvfcenoICAKq2\nbCH2j3/0WzsG+9q13qQUQFGwrVrVZhNTfVIS0ffei2PHDjQREedduxgxdSoho0YhiCLamJgARNky\naEQB6QLFj07ZcOI4H+3aiqwo/L5TV4Ym1F0sJcyshU6dMACrU+wcLnSw97idl747RFai79uWnbke\nv3rcwMTUU1iIVFqKLimpSbNIKhYvpuKHH7xf//QTUXfdhaGNVJS/EF18fPUNg1NjVf1ETptG+bx5\nSBUVWAYNCuga0/KFC7EtXQp4bwAJGg3mfv2aPY64tHBydxez9uu9dBmVRESiWpG7JVETU1W9RbcP\n45HvJvDxvb/w9u//x/R3L8GiFkVS+YEkyzyxajlLfhERRYWeQ4qRxK7U55R15YBYQowa3l2cw+Jt\nRbglhZ05Nm4ZmcjhEw66P7aaKwfE8t5tWdXbvHVLZy7KymfR5kJemHOQ/9zXDY+sEBWiIy3W1OAZ\nAlJJSXVSCiBXVHjvInfsWMtWjSdaLLWO2xp9+/Z1TmfWxXkL+ihuN1Vbt4IoYurevUGN3Vsi2eHA\ndfAgmrAwdO3a1fperSgg1VIjotzl5O+bVuM8+XT61S3r6GSNJMZU/xswaXFmvllbwD/mH2ZMryhC\nTaf//RVJQiorQxMa2qQ1orqkpNPr8E6OG8K+cSOln38OsuxtP3PffWjOUx29Pqo2bz49kGUc27a1\n+cQ05JJLkO12nAcOoE9OJnTMmECH1GJoIyOJnDYt0GEAeFv3nTk+cCAgiWnWRUlMf/cSFr2xiYWv\nbSRzWCJXPjOIkEj1erUlaN1/gVU+ZwrVc9fHv2POC2v554S53PXJZcSlWQMdlqqVOVBcyc8/aDCF\nSqT1rsShwMGKMiKNdbesEQSBy3pGMyIrgr98c4CeKaF8ek8sBp2Ioig8e3UakSHnXuReMyiOawbF\n8eKcgzwyaw9GvUh+qYtKh8SAjmE8PC6FPqn1Kz0vhoUhmM0odrv3Ba0WjR+L64SOHo376FFcBw+i\nTUggbPx4vx2rNVE8HgrffRf3kSMAVHXqRORttwVFoS9/kG02Trz5JtLJdbhhEyfWOqVVFLlguxiA\nUqezOikF8CgyhY6qBiWmADdflMjePDsLNxfS+eGVfP94b7pYFe+sg/x8b1Xw229vdOuK0MsuA8B1\n9CiG1FRCRo1q0PYVP/xQ3WdRKirCvmYNoaNHNyoWTUQEnvz8GuO2TtBo2kRlbM+JE8h2O7p27Vrl\nDTBdcjLunJzTYz+3mqlNpyGJdBqSyN5VeXzx9G/sXZVH73Ets2JxW9P6PhkqvxM1Ilc9M4j4jlZe\nu3IB094aRedhtd95VwWGXFVF+cKFSMXFmHr29GlLBn85Vuxg2hu7iYtXiM6sRBBAL2pIDmnYEwqT\nXsNL19XscSsIwnmT0jM9OTmVJyef/gOWX+bix61F3PzODi7uGslTV6YSG1b7WlTRYCBq+nTKv/8e\nRZIIHT0abRMuQCuXL8exbRva6GjCxo8/pwG5aDYTfe+9KJLkk3L7bYU7J6c6KQVw7tmDp7AQXWxs\nAKPyH/umTdVJKUDFjz/Wmphq61hjmmAJITU0nIMVZQAkmr3jhooN0/P+HV0osblZuKmQZ77Yx8zM\no9UJnGyzUT5/PtF3393gfYM38WlK1fKzP1NN+YxZr76aktmz8Zw4gTErC0sbqBOg8p7Dy+fNA0VB\nl5JC9F13tbpK0eETJiBotbiPH8eQkYF58OBAh0TG4AS6XZzMwtc2EpMaRlIXtfp6sFMTU1WjDb2+\nMzEdwphx7y+Me7gPw38f2P6FqnOVfvEFju3bAXBmZyOGhAS8z2Rt9ubZmfL6VqZf3I4JQzrzafZ2\nXLLElamZxJoCMz01LlzP74cnMKlfDK9+f5iLnlvPo+NTmD6q9psx+pQUou+5p8nHr9q8mfK5cwHv\n1CjZ4SDyppvO+141KW0Y0WLxVuQ9NV1VFIOiCrW/nH0hLOhrv8FSV/EjnSjywsAR/O/IASRF4fLk\nVIxNeBIUYdHRIyWUD385huJ21/iecmoNdQCETZxIySefoLhc6JKSmnTBrbFaG51gtwSKouA5fhxB\np/NrwbeWRJFlyr//vvo84z58mKqtWzH37RvgyHxL0OkInzgx0GGcY+KTA9iy6BBFRyvUxLQFUBNT\nVZN0GpLIw9+O571bfiRvTylXPTsIjbZ1ToNriVxnPA06NQ7WxHTjwXJuensHT1+ZytQh3uIXT/Ud\nEuCoTgs1aXnu6nRuGJrAxH9uZlTXSNJi655a3FSuM6ZGAbiPHfP7MdsKbUwMYePHU75wIYIgED55\nMpqw+k3XbonMffvi2LIF5549CHo91quuqvX9Wo2AJNe+zxCdnmvSO/swSth33M4U2vG4Po5MVz5o\ntYRecolPj9EQxsxM4v78Z+TKSjSRkeoNoAtQZJmSWbNwbNsGQMillxJ2chp1myYI3v6OZ72mah5l\n+XaKc21kjUhi55Kj7PjlKJ2GJtLzsg6BDk11HoJSS2EDnxxAEBR/H0MVWJIs85/N29j0zA7MaJn2\n9HAyBsarLWWCQPGsWTi2bKkeR95+O8bMzABGdH5LdhZzz0e7ee2mTlzWM/jvsj/+2V4SIw3cf3nj\n+oXa16/HdeQIhrS0OqvnOnbtovijj6rH5gEDsF57baOOqzo/RZarLx5bO0VRkMvLEYxGb0uVWrwy\n7xAK8KcJHZoltlMqqjy8+v1hFm06wfh0LY9N6IBOffoW9Jz79lH03ns1Xot//vk2X4wNwLZmDWXf\nfAOyjD4jg6jp01vlOtNg9fF9v7B7+TGMITqqyl3YSpy8dfS2NnHOb06CIKAoSpP+UdVPharJvti/\nm2/y98FdekKWOXn/wZ+JtpoZOb0bfSekq32kAsh67bVUWK3Va0yDMSmdu76AJ2bvY8ZdXRmU0fD1\naaVOBwoQYWi+KZjRYTrsTqnuN55H5fLl1VNz7StXorhcta79NWZlEfH73+PYvh1NdHRAnxy1Vq21\n2NH5CIKAJrx+nzOtRsDpruORqR+EmrQ8Mi6Fd37M4cNyD49PU6ffqVo2y8CBGLOykKuq0MbEtKlz\nTjC45a2LOb63hMikUJ4d+jlp/ePUpDRIqYmpqsn2lBZ7v9AIVI4yYr0mnivKk1kyYzvfvbiWYTdk\nMfymLMJj/dPDsTl4iotx5+aii49vUetmRIOB8CCu0DpjyTFeX3iErx7qQdekhvcam713J7P37QJg\ncmoGt3Tu4esQz0srCjhcjbtgd+7aVWPs2LWrzqJUpp49MfXs2ajjqVSNpREFPLWsMfUXp1tm6hvb\n6JJk4dc/96XU7l1fGmFpXcViWht9WhrG7t1rTOVVn5aepgkLa9VLBZpb+cKF2NesQQwLI2Lq1Drb\nX8VneAsQZl2UhMseuDXrqtqpiamqybpERLOx8HT5+65RMXTr355ul7Qnb08JS2Zs56+jvqL76PaM\nmt6N9j1aVkN71+HDFL3/PorTCVotUbfeiqFTp0CH1Wxkh4OSWbNw7tuHLjGRyGnT6v3E5UIUReGf\nCw7z9ZoC5j3Wi5Tohq/VLKiyVSelAHMO7uWSdh1oH+r/P/w9U0K5d8ZuhmZaGdU1skHbamNja/RU\n1J7sp6lSBZu6ih/5i1YjUGrzkBhh4N4Zu1m0pYhKh3RO/2FVcBFEkYibbsKTl4eg17eom7iqlsWx\nYweVv/wCeKt2F8+aRdzjj9e53a8fbmfNV3vpMirZ3yGqGkmdS6CqF0VRcFygKuLV6ZncnNmNwXGJ\n3Nq5O5NST7foSOgUwXV/H87zK6aQmBnJ+7ct5l9XzqM0z9ZcoZ9DrqryrimrJ9uKFd6kFMDjoXLp\nUj9FFpwqf/oJZ3Y2SBLuo0cpmzevSfuTZYUnZu9j0eYi5jcyKQVwn+dn6JQbN722oUZ1jeTDO7rw\nh4+zefqLfSzbVYKjnlMeQ8eOxTxgANr4eMyDBze6H6JK5W8aEaRa+pj677gCPz3dh14dQsiIN7Pl\n5UH0aB/CkUJHs8eiahhBENAlJqpJqcqvpJKSmuPS0nptV1XhAmDwlE54XM1zvaBqGPWJqapO+8pK\n+NuGlRQ7HfSOjuXJPkMwnFGVUBQErkqrfe2iJcLIpff05OI7ujP3pXV8/uQK7pzxu2ad4694PBTP\nnIlz924Es5nIadMwpKXVud3ZLRWEOgqGtDZSRUWNsXzWuCFcHpn7ZuzmRLmLOY/0JMzc+FNQO0so\nFyUmszT3KAADYxPoGGZt9P4aakimlR+e7M2s5Xn8fe4hdh2z0S8tjJdvyKi1Wq+o16vFi1QtglYU\nCEBeCnj7ED812Xt+3p9vZ2+enWkjEwMTTBskVVRQuWQJituNZdiwVtvbV9UyGbKyEH74AaWqCgBz\nnz712m7sQ33Q6ERm/2kF+ftKGfNg/bZTNR81MVXV6Z0dmyh2eu9UbyosYOHh/UxOa9xUVo1WZPxj\n/Xh57Bw2zj9A3wnpvgy1VvY1a3Du3g2AYrdT9tVXxP7pT3VuF/q73+E6eBBPQQGayMgmNWpvicz9\n+lG1eTNIEghCneshL6TSIXHLuzuwGDXMfqAHRl3TJ2w83KM/Y5LTkBWFLpHRzV7MoF2kkccnpvL4\nRCi3e3j/lxxGPLeO64bGkxxlpKLKQ0KEgcEZVjITzIiiWmxB1XKIooAnUJnpGYw6kQ4xJrYfrWR3\nro3Oieq6RUWWce7ejeJ2Y+zS5ZwetU3atyRR9O9/4zl+HADHli3EPPoomtBQnx1DpWoKbVQUMfff\nT9W2bWhCQzE1oCfsZff1Yt/qPHKzS5BlRf27HGTUxFRVJ5vbVXPscV/gnfWjM2i44R8jeP+2H8kc\n3o6QiOappiqfmo57gfGFaMLDiXn0UWSbDdFiaRPV9DwFBd5iT0lJGDIyiLn/fpwHD6JLTKzXU+az\nFVW4ueFNbzGTV27ohFbjmz8EgiDQJTI4poyFmbU8ekUHrh+awFs/HKWk0k2oScvmQxW8tzgHp1tm\n7h97kRRp9Nl/f2MoHg9l8+bhOngQffv2hE+a5NOLWlXroQ3QGtOztYs08vOf+zLxn5uZ+I/N7Pjn\nkIB+hoJByX//W90KTNe+PdH33OOz9iNSeXl1UgreNXzuY8fQdPZtv1qVqim0MTGEXnxxvd8vywq7\nl+XgqHRTlm+nqtyFxymhN6mpUDBRfxqqOk3skMF7OzcDEKrTc3G7xvVuPFNqn1j6Tkjniyd/4+bX\nR6LV+7+ljLlPH2wrViCXlwMQMnJkvbcVRBExJAT72rVIRUUYu3RB36GDfwINMOeePRTNmAEej7fY\n0+23Y0hPr7Pi3YXkFDmY8vo2xvaO4slJqfV+qrmzpJByl4sekTGYW1DilBhh4MWpHc95/bmv9zPg\nqbWkx5nIamchJkzPxV0jGJoZQXGlmznrCkiMMNAt2buurrEX3pLNhqDRIBrPf8OnYvFi7CtXAniL\nlBgMhE+Y0KhjqVq3QBU/Oh9BgEMnqvjnjb67seULclUVFYsWIZWWYurdu86+xL4glZbW6E/tPnIE\n18GDGDIyatmq/jQhIYghIciVlSdf0KhrRlUtXvaKY7x94yIyBiUw9uE+9Ly8g9oyJgipiamqTmNT\n0kkPj+C4vZJukTFEGRtXrOZs4x/rx4x7fuGvo75m8tMD/H6S0FitxDz8MK79+9FYrehTUhq0ffmC\nBdhOFj6qXLqU6LvvbpXJaeXy5d6kFMDjwfbbbxjSGzflek+ejamvb+OOS5K469Kkem/3nz07+HK/\nd9p1kiWUVwaPJESnr2Or4PanCR2YOiSeiioPuSVOjpe6+Ou3B8kp3oVGELhqYBzbj1byr+8Pk1fi\nJDPRwtBMK49ckYLFoMEpedCLmlo/I6Vff4199WoQRcKvvBLLoEHnvMeTn1/rWKU6RasJnsRUIwo8\nPDaFb9YWML5vzcruVdu3U7l4MWg0hI0fjyE1tdniKvnss+oWUI6dOxFDQjB0PPfGlC8Jej2IIpxR\nAE6y21Fk+bwzemSnE/vatSBJmPr1QxNSe2suQacj6vbbKZs/H8XtJvSSS9TEVNXi2Uq8s+SmvTUK\na7y6HCBYqYmpql4yrZFkWhvWFqMuBrOOu2dexq5lOXz7/GqWfLSDK58dRPvu/vsDqAkJaXQ/yFO9\n2QCQJBw7d7bKxFQ01bzxcKEnb3XZcKCcm97ZzrNXpXPt4Pq3RJEVhW8PZFePc2wVrM7PZXRSh0bF\ncTZFUQJyl9Sk15yzNu7O0UnnjanS4WFHjo1Pl+Ux+m8b6NDZRVlIAVEhep7sM5isiKhz9u/cv9+b\nlALIMmXffoupd2/Es4p1GTp3xrF9e41xXao2b6Zi8WIErZawiRMbNZ1b1fIEqo/phUiKcs7adE9x\nMSWzZnnXwAPFM2YQ99RTjT5vNZT78OHTA0XBdfiw3xNT0Wwm/KqrKPv22+r/7tJZs7BnZBA1fXqN\nKb2KLFP8wQe4Dh0CwLZ6NTEPPljnv4+uXTui77rLb/8NKlVzi2wXQvrAeP466iuueLQfo6Z3C3RI\nqvNo/YvlVEEva0QSj/9wJf0mpfPuzYv49KEllB4PXDuZC9FERdU6bi1Cx4xBG+N9IqGNiyP0sssa\nvI95m49z1eubGT4MumU2bJq2KAgYNDXvmZk0Tb+HJskyr25ey1U/zOHOpYs4WF6/8vLN4exEOcSo\nZWDHcN6+tTNjhpnZuMfO9iVW8ovdvLVtw3n3obhqrgVHlqsvWs9kGTQI6/XXYx48GOuUKYQMH15r\nbJ4TJyj57DM8+fm4jx2j+OOPkc8+VhNVLF7M8eefp+Af/8B15IhP961qPI0oBKRdzIXEhxtYtaeU\nbo+uYt3+MgCkoqIav+dKVdXpKajNQHfmzBtBQN++6Utd6sMycCDxf/sbnFEh37V3b42bTuBtq3Eq\nKQWQCgtxHz16zv5kpxPb6tXY1qxBcTetjoRKFYzS+sXx8DfjmfriML5+dpXaLiZIqU9MVUFBoxUZ\ndmMWfSem8+Nbm3lx9DeMnN6N0Xf1CJqF6RFTplDyxRdIhYUYu3dvdHXaYKeNiCD2T39Crqo65+lp\nfcxZW8D9/9lNxwFl5Bo8PLOugDeGjSbRUvv0sTM90KMvr25Zh1OSGJ6QxOD4xq1vPdPinEMszfNe\nkOXZbbyxbQOvDb2kyfv1t64ddWTKFeQfNLBtiZVj0TIbksrpmxZW432GjAx0KSnVT3DMQ4Ygms3n\n3ae5T596l9f3FBfXmDJ46sJfjPTNDArHnj1U/PAD4G1FVPzJJ8T/+c8+2beqabSiwKItRfR8bBWi\nICCK3psoogAeSSGn2EnHOBOCICAIIAogIIDAybGAgPfrs8ecfK8onPq+93viqa9P3qsRT34teHeL\n1aJj1zFb9TpTXbt2iOHhyGXeRFUbH48mIqLZ/o0irr+e8oULq9eY+mqdZ30ImnNv+ilKzRsJosWC\noNOdTjYFAU14eM1tPB6K3nkH97FjAFStX0/UXXedd/8qVUtWeKScr55ZSVisCSV47rmpzhAcV/wq\n1UmmUD0TnxjA0Bs6M/eldfzt4q944serMIUGfn2hxmol+s47Ax1Gs2lMUvrRL8d4Y9ERMoeWYg7z\n3o10yRL7ykoalJgOimvHZ5fE45AkQvW++dmXnlWFucxVd1VmRVH4987NLMk9QpTRxKM9B5DajL1S\nAUYkJjP30F5IdRKZ6MKZncjmwxXnJKaCVkv03Xfj3LsXwWDw2XRbfXJyjQt/XVISGqvv/g3ObpQu\nl5ejSJJ6URwExveNZkDHAciKt6KlrHin2ssKSJLCvnw76XHeCzwFUE5+/9SYk+/1jpWT3/d+rk69\nXzk1rt7HudsogCJ7twkza+mbGobmZIsH0Wwm+t57sa1ciaDREDJiRLP+7ogmE9arrmq2451JEEXC\nxo2jfP58UBT0HTpg6lZzeqJoNBJx002UzZmD4vEQetllaM/qSeo+dqw6KQW87dFOnEAXH9/kGB07\ndmBbvRrRYiFs7Fg0YWF1b6RS+YHD5uaju3/GYNHx/MqpapuYIKUmpqqgFN0+jOnvXsKMe35m1efZ\nXHx790CHpKqFoii8Mv8wc9YWMP+x3ry4Yym5du90Oq0gkhoWXscezqXTaND58AJzWEIScw/trW53\n9LukugukLM09ysIjBwCwV1bwry3reHP4pT6LqT5iTGZeHzaarUUFRBvNvJJ7nCOFDtweGZ225moM\nQavFmJXl0+OLZjPR992HffVqBK0Wy7BhPm2ZZMzMpMJiQbZ5p++bevZUk9IgIQgC8VbDBb+fkXD+\nJ/LNTRsZSfgVVwQ6jIAIGTECY5cuyHY7unbtzvvZMWZl1XpeEENCvI+kTz1C0mguONuiIdzHjlH8\nySfVMy48x48T8+CDTd6vStUYRYfLKTpSwTPLrlWT0iAmnD3tw+cHEATF38dQtV4HNxYw456f+cPs\nscSmNjy5UfmfJCs8MXsfGw+WM/v+7sSE6cm32/gkezt2j5vxHTrSN6bpd96byiVJLMk9wpHKcnpH\nx9Urpm8PZDMz+/SarRCdjs9GB7a1SnaujSc/34fFoOGTe7q2inL3nqIiqjZvRrRYMPfvryamKlUz\ns61eTfmCBSAIhE+ahLlv36bvc80ayr76qsZrCS+/rH6+VQHzyhXf0eN3KVx+f+9Ah9IqCYKAoihN\nuihRn5iqglpqn1iGTM3k1UnzSOoaxfDfZ9H90hSKcyrJ319Kt0uap9BEQ3hKSqhYtAjF6cQyfHit\nrVZkh4OKRYvwFBdj6tnTJxcDzcnplrl3xm5KbG7mPNKT0JPrgePMFh7rPTDA0Z3mkiSeXLOUPWXe\naaNuWa5XYjooLpGv9mdXP2W9pF3DWgz5Q2aihdn3d2fk8+tZkV3K8M7Nt57OX7RRUd7WNoKgXrSq\nVAFgGTTovO2lmkKfnOwtznSyOJWufXv1860KGIfNzeHNJ8gYlBDoUFS1UJ+YqloEt8PDpoWHWD5r\nJycOlSN7ZPRmHcNu7MzFt3Vn7+o8io5WMPjaTsh5RymfO9e7nubSSxvdHqYxFEXhxCuv4DlxAvD2\ng4t55JEL9oAr/uSTGm1oIm+7DWM92ncEg0qHh2nv7iDMpOXd6VkYdMFb5HtdQR5/3bCyxmufjR5f\nr96oubZK1hbkEm00Myyh/r1Y/e3vcw8C8PjE5uvZ6C/lixZR+dNPIAiEXnYZoaNHBzoklUrlA47s\nbOwn15iGXn55nT1UVSp/ObKtkI/v/YW+E9K54tGW9RCgpVCfmLZi5S4nvx47glYUGZ2Uck77jGDz\n49GDbCkqICU0nKtSO6Hx4Ro0AJ1Ry4ArOzLgyo7k7y8lNMqEy+Hh9Wu/54c3N5PcPRqDScuymTu4\nImsXsaZyAEr++1907do1W3NwxW6vTkoBFLcb97FjNY6vSJK3QI1Wi/OMMv4ArsOHW0RiWljh4vo3\nttMjJYSXr8+oLkQSrIxnfX60goCunr+jFq0OhySRY6ug3OUkTH/hNXfNqVdKKDOX5gY6jCZzFxR4\nk1IARaFi0SJMvXujbaXtmFSqtsSYmYkxMzPQYajaOFeVh9evXcDUF4fRf7J/+wyrmia4s502qsrj\n4U+rl3DM5i0esyzvKC8OvAhNkK4l+znnEG9t3wjA8rwc7G430zr7r1hRXLq3IqgZA0/8cCWSW8YU\npkdRFFZ/upn//qWQgZlaBnUuRkTGU1zcbImpYDajiYlBOpWcarXo2p1udaJIEkXvv49r/34ARKuV\nM+cT6FMCP1W0LmV2DxNe2cyY3tE8PTm1Xmsc95eVMGP3NlyyxLXpnekf27xTabpHxXB5ciqLjh5E\nKwjc3bVPvW72OCWJJ9YsJcdWAcCy3KO8NvQSDEEwHa1Xh1C2fFqJoigte53peXomtvQ+ioos487J\nQTAY0MXFBTocVQvgysmhfP5870yf0aN9XsRMpWrLDmzIJyzapCalLYCamAahvWXF1UkpwK6SIgrs\nNhIa0G6jOW0vLqx17E96kxZOdjURBIFBv+9J9KGlzJlrZm+uhUmjK4lPap7pl6cuRsMnTaJq40bv\nGtNhw2okxa5Dh6qTUgC5tBRT//7INhumnj1bxNNSjSiQHmdm0eYiLu4aydDM2luHOCWJ59b/Vt2e\n5aWNq3lnxKXEm5v39/mebn24KbMbOlGs9wyEo5Xl1UkpQI6tgpzKctLDA7+uM95qICpUx5x1J7hy\nQGzdGwQpbUICxq5dcezYAYCxRw+0LTiZUySJ4hkzcGZnAxBy6aWEXXZZgKNSBTPZ5aL4gw+qK1MX\nf/IJsX/8ozprQKXygUVvbuLXD7dz3UvDAh2Kqh7UxDQIRRpMiMCplvZ6UeOzXo7+kB5u5edjh6vH\nHcObt8/jmQRRJP2Pd3DHgGWs/LGMj39wMLHPEYZcl+nXp0qKLFP88cc4d+0CwDJyJOHXXXdufGf/\nHAWBsCuuQGOx+C02Xwsxavj03q78b3MR983YzdDOVp67Oo3oC/SaLXM5avQM9SgyubbKZk9MgXqt\nKT1TlNGEXhRxnWx3oBdFIo0N7+9aXy5JYsburewpKyHLGsW0zt1rnXL83m1ZTH51C7/rEUmIsWWe\nzgVRJOLmm703bAQBfXp6i34C7NyzpzopBahcvJiQiy5CNBoDGFXrong82FasQKqowNS7N/pmuvno\nL3JFRXVSCoDHg6eoSE1MVaomkmWF+S+vZ8qLQ+k1tuXXY2gLgrdaSRuWFBLKPd36EGEwEmM081iv\nAQ2+oG5O49qnc2OnrnSPjGFCh47c0rlHQOMRzWasYy5n7OYzom4AACAASURBVGtTePDr8Sz/dCf/\nvvVHyk/Y/XZM14ED1UkpgG3JEqSKinPep09OxjJixMlARcImTmxRSekpgiAwtnc0y5/vT2SIjhHP\nrec/y/OQ5XMLnUUZTKSEnG6qHq43kB4W+CeO9RFhMPJYr4Ekh4SSHBLKn3oPIsLgvwTjv3t3sPDI\nAfaVlTD/8D6+3Ler1vd3Sw4hwarn4AmH32JqDoIoYsjIwNCxY4tKSt25udjXrsWde8Za37NvJAiC\n938qnyn5/HPKFyzAtnQphW+9hTsvL9AhNYnGaq0xS0C0WNAlJgYwIpWqdTjVrzQmJayOd6qChVqV\nV9XqeVwSC1/byMrPs+k/KR1jiB6DRUd0Sii9xvjmDprzwAGK3nnn9AuCQPxzzyFeIOmU7XYQxVbz\nFGXb0Uoe+88eRFHgHzdk0CWp5tPQUqeDOQf34JQkrkjpSFJIaIAirZ/9ZaV8nL0VlyRzbXom/Zpp\nTeyz61awqTC/ejwoLpEn+wyudZu/zz3IvPUn+PDOLuf8u6v8x7F7N8Uff+xthSGKRN5yC8asLBRZ\npmTWLG+1bUEgbNw4QkaODHS4uPPyKPvmG+SqKixDh2IZMiTQITVa3hNP1FiHHDZhAiGnbvi1UFJF\nBZW//ori8RAyfDjamJhAh6RSBYRcVUXZd9/hKSzE2KULoZdc0qj9uJ0S+9cd5+N7fuGx7ycRlRzc\n1x2tgS+q8qqJqcpvNhXm8/rW9dg9bialduL6jC4Bjefw5hPsWZWL0+bBaXez6otsnlh0pU9OVoqi\nUDp7NlUbvUWgQseMafTJ9BTbb79R8fPPCHo91quvxtAxuBfty7LCp8vzeHnuIaYMiePRKzoQYgx8\nkaCGckkS05f8r3r6sU4UeXu4b9fEbj5UwZFCB7KioCh4/x9Yl5/HsrwcTp0yL0poT/fImDPe530v\nZ2wjy/DFquPsyLFxcdcIPn8gsDMW2orimTNxbN9ePTZ27UrkLbcA3vOB58QJRL0ejTVwSxvOlP/C\nC0glJdXj6PvuQ9+hQ+ACaoKCf/0LzxlPqSOnT1eLBalUrUTxrFk4tmypHlunTMHcv3+D9rF+7n5m\n/2k5CZkR9B6XyiV3qH8Xm4PaLkYVtGRF4ZVNa7B5vHe1P9+3ix5RMXSLDNxd4JReMaT0On38yqIq\ntv54mFHTuzV534IgEHH99YReeimCTtfki1H3sWOUffcdpzKU4pkziX/uOQRt8H5kRVFg2kWJjO0d\nzfNfH2DYs+t4/po0JvSNaVHTM0vPWhPrln2/JvaBmdnEhusJM2kQRQFR8P4OCYKBeFccFW4XVoMB\nR4mBDWXlCJz6PojV/3/662GdrcRbDeSWOOs8tso3RLO5xlg4YywIArrY4ClIpbjdNZJSAE9hYYtN\nTCNvuonSb75BLivD3L+/mpSqVK3ImTedgJpLJeogSzK7lx/j62dX8cCX42jfQ5150NIE71WuqkVz\nSVJ1UnpKqTO4LpqH/74L/57+IyW5NsY90geDWdfkffpq+pVUWlqdlAIoDgdyVRWa0OCfihIbpuft\nWzuzak8pj8/ex6zlebw0NYOMBHPdGweBKIOJ9iFhHKn09sIN0+lJC/PtUy+bS+KVGzJIjfVfISWV\nf4Vefjnu3FzcOTnokpIIGzMm0CFdkKDTYejUCeeePd6x0Yg+LS3AUTWeNjqa6DvvDHQYKpXKD/QZ\nGTX6wRsyMuq9ra3Eyds3LmLMg33UpLSFUhNTlV8YtVqGJySxPC8HgFiTmR5RwXWSSOsXx9M/X803\nz6/mhUu+ZsqLw+g6KjnQYQGgT01FY7V6E1S8J+qWkJSeaXAnKz8/3ZePfj3G+Fc2ccPwBB4el4LF\nENzTezWiyF8HDOfbA3twyRLjU9Kx1lLwaNmuEn7cWlQ9Fs75guonxqdeKqxwYdSpteeCkVRRgfvI\nETRRUeji4y/4Pk1YGDEPPogiSQhB0Ne2LhHTpmFbsQLZbsfcrx/ayMhAh6RSqVTnCJ84EY3ViufE\nCYxdumDsUv9lYJXF3kKArqqW3Qu7LVPXmKr8RlIUlucexe5xMzi+nV+rmTbVziVH+fzJ3+jQO5ar\nnxtEWEzgn+5J5eXY169H1OsxDxyIoGv6E91AyS918tw3B1i9p4y/XJvOFX2iW9T03tr88b97KKpw\nM7Bj+JkPufGuAOWs17yMWpFbRiZWVwxUBQdPYSGFb77pbd0hilinTsXcp0+gwzov15EjKB4P+g4d\nEGppKaRSqVRtwW+zd/P186vQGDQkpFl5ZM6EQIfU5qjFj1QqH3JVeVj4rw2sm7Ofv6yeikarXuz5\n2srsUh6fvZd4q4GXrutIelzgbwA01eOf7SUj3sz0i9sFOhRVE5UvWEDlkiXVY218PLGPPhq4gC6g\nbM4cbL/9BoAhM5PI6dPV5FSlUrVZkiTzxwmfk9cbbEMNdI+M4S/9h6FRz4vNyheJqfoTU6lO0pu0\nTHpqIMYwHTk7iureALCtXEnRhx9SNm8essvl5whbviGZ3um9F3eNZNzfN/HinIPYnFKgw6oXm9vN\nBzu38PKm1azJP12MQSMKSOrNt2YnlZdT8vnnFH3wAVVbt/pkn4K+Zr9owWDwyX59SSovr05KAZzZ\n2bgOHAhgRG2DY+dOCv/9b4pnzsRTWBjocNo0RZIoX7CAwrffpmzBAhSpZfwNUfnP9x9txn7Ajr2P\n9xy+rfgEe8tK6thKFYzUxFSlOkvGoAT2rq67YXvVpk2Uffstzt27sS1bRtm33zZDdC2fTity16VJ\nLHm2H0eKHAx/dh3fbyok2GdWvLplLfMP7+O348d4aeMqdpV4b16IInik4I69OSiSRNm8eRS8+iol\nn32G7HD49XjFn3xC1fr1OLOzKZk1C9eRI03ep2X4cHTt2wMgWiyET5zY5H36nEYDZ02DD+Zq3a2B\nOz+f4k8+wbV3L47t2yn64INaz1eKx0PxJ5+Q+9hjFLzyCp6CgmaMtvWrWLyYyiVLcB08iG3JEioW\nLw50SKoA6/G7FNwpWmL/WQ4e72fTrG25y5/aMjUxbcHybJW8uHEVT69dxtr8+pfTrotT8vDSxlVM\n+XEuj636lcIqu8/23RJkDEpgXz0S07MvhN0+uDBuS+KtBt67LYs3b+nM3787yHVvbONAvm9+1ypc\nLjaeOM4xW4VP9gewo/j0UxIZ2H0yMdWKApKsJqa2ZcuwLVuGJy+Pqo0bKZ83z2/HUhQF99GjZ75Q\nc9xIoslE9B/+QNyzzxL3zDPoTyapwURjsRA6dmx1cmoeOLDFtn1pKTx5eXDGUzmpqAilquqC77et\nWIFj2zaQZTwFBZR+/XVzhNlmuI8dq3Xc2lVt20b5okU49+0LdChBo0OHKAZd2wldroSggakds2gf\nGhbosFSNoN5mbaEUReHZ9Ss4brcBsLO4kP8bOtonH8RvDuxh1clEd3dpMe/v2sKTfQY3eb8NYfe4\n0YkadAFYH9BxUAKzn1iBLMmImgsfX9+hA7bly2uMWwL7unVU/PgjglZL+OTJGDp1Cmg8QzOt/PLn\nvrz/yzHG/n0T00Ymcv/l7TE3snpvQZWNx1YtodjpQCMIPNSjPyMSm15tOcMawdYibwl7AegYHgF4\np/Kqean3qVJtY18SBAF9hw6np7CKIrqUFJ/tO9grYIeOGoW5Xz8UjwdtRESgw2n1dMnJCHo9ysnl\nGtrExHP62J5Jqqh5Q0yurPRrfMFGkSSc+/YhaLUY0tN9vn9Dx444d+2qMW4rKpcvp3zuXO/XP/2E\noUsXwsaMQZeQEODIAq9HWAybPTBz0BgiIi2BDkfVSOoT0xbK7vFUJ6UAHkXhUEWZT/Zd7Kx5J7jY\nceE7w76mKApvbtvA1MXzuG7xXFacbDfTnMJjzYRGmTi2u/b1CaaePQm/5hqMXbsSMnIk4ZMnN1OE\njecuKKD0yy+RSkrwnDhB8SefIAdBf1mdVuTe3yXz6zP9OJBfxYjn1vO/zY2b3vvD0YMUO73TSCVF\n4Yv9u+rYon4e6zWQ0Ukd6B0dx0M9+tM9KoYqjwebx4XLo65xMmZl1Rx37uzX40VOm4Z5yBCMPXoQ\neeut6JOS/Hq8YKMJDVWT0maijYoi6s47MfXti3nIEKJuv73W95v79KmxXtk8aJC/QwwaiiRR9P77\nFH/wAUXvvkvJ55/7/BiWESMInzwZU58+hE+ejGXECJ8fI1g5tmypMXbu3Enh22/jKVHXU/a/siPh\ncWY2faOuuW/J1CemLZRFpyMtzMqBcm+fS6NGQyerby5SLkpozy85h/GcTAoubuebJxH1sbEwn8U5\nhwBwyTKvb1vP4Ph2aJq5tUjGoAT2rcojuWtUre+zDByIZeDAZoqq6aTS0hr9SxSnE9lmQwySIi8J\nEQbev6MLy3aV8MTsfcxalscLUzuSGmuq9z50Ys0nrQbRNz0mw/QG7u/et3q8r6yE59atYEeOllCd\njmlVccSYWn6V4cYy9ewJoohz7150iYmY/fy5EM1mrFde6ddjNIRUUYGg0yEag7ctlqrx9Ckp6Ov5\nVF7Xrh0xDz2Ec88etLGxGDIy/Bxd8HAdPIhr//7qcdX69YSNGYMmPNxnxxAEAcvQoViGDvXZPlsK\nTWQkHDpU4zXF4cB16FCbv1G189ejGMxanDa1h2lLpiamLdjz/Ybx+f5d2D1uxiSnEW8O8cl+u0fF\n8I/Bo9hWfIKUkHB6x8T5ZL/1YffUPKG4JAlJltE0cwP7jMEJbPr+IKNu69asx/U3fXIymshIpOJi\nAHQpKWis1gBHda4RWRH8+kxf/v1TDmNe2sgto9px/+XJmPR1/x6MT+nI2vxc9pWXEqLTcVtWT7/E\n+Gn2dsrdLgRBQ4XLzTcHsrmra2+/HKulMHXvjql790CH0awURaH088+p2rABNBqsV1+NuX//QIel\nCjBtTAzamJhAh9Hszq5sjSCoxbl8KGziRGS7HWd29umbzILQJn/XzrR05g4Wv7uVcQ/3odfY1ECH\no2oCtY+pKqjYPW7+tGoJhyvLARifks7tXXo1exyleTZevPQbHvhyHIlZkQjN/MTWn6Tycuzr1iFo\nNJgHDw6ap6UXcqzYwbNfHWDz4QpemJLOZT2j69xGUhSKHVWE6fUYNP65KHpqzTK2FZ/gWLYJySNw\n++Vx3Nutj1+OpQpejt27Kf7ww9MvaDQkvPBCvS7GFY8H+7p1KA4Hpj59fPpUSdU6uI4cQXG50Kem\nIjTzDdr6km02pNJSNDExiHo9ZXPneusvCAJhEyYQMnx4oENsddx5eZR99x2Kw4FlxAjMffvWvVEr\nlbOziLeu/x+PzptAdHu14FEg+aKPqZqYtlA2t5t3dmzkUHkZPaJjua1zj1bTSNjucbO5sACLTkfP\nqNiAxbHgnxtYN2cvHpfM+Mf6MeiawBYJauuW7Czmidn7SI8z8bcpHekQU//pvf6wtaiAv25YyYFd\nejSSlm/vGExSSHAXzVH5XtW2bZR88kmN18yDBuHJz8eQkUHI6NEIFzg3F330UXURFzE8nJiHH0Zj\nUYt2qLzK5s3DtmwZAPr0dKLuuCPoklPn/v0Uz5iB4nSiiYoi+p570ISHI9tsoNGoU9tVfvfFU79h\nMHv70F/I+oI8VuXnEmeyMDmtU0AKa7YFvkhM1Z9MC/Xhri0sz8vhqK2C7w/v57uDewMd0jlW5P0/\ne+cdHsV19eF3ZvuqS6uCUAMhEFVU0Ts24AIuuAVX4hjHLY4T7Nhf4sSOU5zEcRL3xE5s3B3HBZvq\nQjG9id6FUO9tpd3Vtpnvj8ULAtRgV7uS5n0eHnSnHkmrmXvuOed3ivjr3h18cOIwTklq93lGtYYJ\nCb0D6pQCXPXzUfxm402kZyfQUNV5AlAKF2baoGjWPTma7PQI5vx+N3/+4hQ2R+BEh4bFxPHqlNlc\nkdKHmYlpilPaQ9FnZqI5S3hJnZiIdetWHHl5NKxZg2XTpgueJ1mtzZRFpfr6ZrV5Cp2DNSeHmqVL\nMa9YgewMnto0yWr1OqUAjtxc7MeD7z1vXrEC+bSAnru6msbTNoshIW06pbLbjW3fPqw5OUinFY8V\nFDpKbUkjblfLc8zvF5G/KjrFO8cP8vKB3Z1onUJHURL/uyjn9mcs8mG/Rl+wvbyEP+3Z5h3XNNm4\nr4NpjvkN9WwuKyZGb2BWUhpiANJpBUEgNcvEd0sPkzU7jbi+PTPVTpZlGpYvx3bwIOqYGCJvvBFV\neOenzOg0Ig/NTeG67Die/G8uU36zk9/f3I/LhrUuUuUvYvQGeoeGUexoCsj9FQKPoNFguv9+7Lm5\niHo99V980Wx/Sz0WBZ0OwWBo1g9TSeXtXJoOHaLu3Xe9Y7fZTNTNNwfQorMQRc+/sxZ1u0StZjsz\n5GRZpuatt7AfOgR4WvKY7r+/a3yPCkFFydFaxt80oMX9+6orOftTube6wv9GKVw0SsS0izI6rnnP\nqtGxCQGy5MLsr6lsddwWhY1mlmxZy/snDvPigd28fDBwK1wz7xnGZT/O4q/Xf8GJbaUBsyOQWLdv\np3HdOtyVldiPHKHuww8Dak9SjJ5/3zuYZxdm8KuPcrn9pQPkByiqrRLBrVQr9GgEjQZ9ZibatLTz\neiq21MdRUKmIvuMOVCYTYmgo4Vdd1W7VVwXf4O2D28I4kIh6PRHz5sHpBVnD6NFB2a8zfM4cr+CR\nKjKS0Ha2bnHX1HidUgBnYSGO/Hy/2KjQediPH6f8mWco/dWvaPjmm065553/mM57j37H3tWncNrP\nz6JKC4todawQXChLU12UG9MzidLqOdVQx7CYOMbGJwbapGb0DY9sddwWOyvKaHKfecBsKi3mgSGB\nK+6fuDCT6ORQ/nXP1yz4zXjGXBt8EwR/4q5svrDgqqoKkCXNmTE4mvW/Hs0rXxUy+3e7+dHMJH48\nNRb54D4EUcQwYgSCRnNR15ZlGZdbxu6Scbgk7E4J++n/HS4Ju0vG7pQ4WWHD3f5MdYVuTtjs2YgG\nA86SEnT9+rWq0Kvr14/4X/yiE61TOBtNcnKr40ATMmkShhEjkJ3OoFRPB9BlZBD3+OO4a2tRx8W1\nu6ZU1OvPiwiLxp7bbsvXyJKEefly7EeOoI6LI3LBAkQ/16/LkkTN0qXeLJCGlSvR9u2Lro9/VXL7\njo7nh6/M5OOntvDmA2u588XpZM1O8+6f1CuJSpuVTWXFJBhDuGeQf5T6FXyD4ph2YS5LTgu0CS0y\nvXcqdXY72ypK6R0SyqLMYR06P+6cfpDnjgPBwClJ/OTDK3n5jtVUFZiZ85CnNYj9yBFklwt9ZuZF\nO0HBjm7QIE/t0OlJhH7w4ABbdAadRuThK1K5fmw8v/rgOJN/cYTH1HuZpKnAumsXMYsXtyg+0xL3\nvXGYT7Z70n30GhGtWkSnEdGd/l+rFpptv2l857VUUghuBFEkdNq0QJvRY5FlGcv69dhPnkSblETo\nzJktCgYZsrKQGhuxHTiA2mQi/MorO9natmnJmbBs2oR5+XIQBCLmz8eYnd3Jlp1BFRaGKqxjNfZi\nSAiRCxZQ/+mnyG43YbNno+nVq+0TFdqFdfNmLOvXA+AqL6dOFIm+7bZ2nWs/cQJXdTW6fv0QjUYE\ntbpdcxvZ4WhWmgAgmc0dN/4i6D8xkSfWXM+LC1egUp//vr+2b3+u7asIWHYFFMdUwW9cyoNgYq8k\nFpjrWFtcQIxez8PDRvvYuosjMTOaJcvm8+pdq6nKb2BOViHO/XsA0KSkYLrvvm5ZI6Pr25eYxYtp\nOnQItcmEcWzL6neBIjlGz2tXhPL5sb08ax/M/5wpLDl+kMjq6g73eCuusfPRw8OYMrBnNyxXUOhq\nWL77DvOXXwJgP3QIWZIInzOnxeNDJk4kZOLEzjLPJ7iqq6n/7DNvPWfdxx+jy8wMSN3/pWDMzsYw\nejTIctCpDXd1nBXN6yhdFe2rq2zcsAHzsmWegUoFbjeo1UTeeCPGka3rhIh6PfohQ2g6cMBzemRk\np6eflx6rIyEjOLMLFNpH95tB9xDq7E1UN9lICg3zW5/GQHP7gCHcPmBIp95TkmUqbFZCNRpCNdoL\nHhMRb+Thj6/in3eu5JtDhUw5baKzoABHXh66jAzvsY0bNmDbvRtVZCQR117bpcVNdOnpLdbLBQuq\n0FAmaSsZo17PUkc6P7BMYfHGOh64Kgadpv1RU6db7tDxCgo9FXteHubPP/dEvWbNwpAV2DS5c+sU\nHadOBcYQPyJZLM1FhiQJyWLpco4p0OFsFoX2oR84EOuWLd7PiX7gwHad10xF/PtyKpeLuo8+wpCV\n1eYCQtRtt2HbvRupqQlDVpZf04etu3Z5Sib690c/YAAVefU4bS6ikxSF/K5M9/Roujk5leX8PmcL\ndrebRGMofxg3lShd9+4VJssygp9UefMb6qm1N5EeHsmze7axr7oSrSjySFY2ExJ6X/AcnVHD1UtG\n8q/bCpk8uPp7fQoEnc57TNPBg96VR2dREZLVium++/zyPSh4UMfGEnHNNZhXrOAeYxE3zxzF7w7a\nmfrUTn5/Sz9mDI5u13VcbhmNqvNVoBUCi2S3U/fee9hzc9EkJRG1cGGHUxR7EpLd7ulheTp9r/a9\n99D07o3aZAqYTdrUVJr27m027m5oEhPRpKTgLCgAQNu3L+p4pZxA4Qz6gQOJXrQI+9GjqOPiMI4f\n367zxJAQ3NXV5+9wuZBdrjYdU0GlarWu3lc0fPstDStWAGDZsIHoO+8k77CWvtkJiKLy7u7KKI5p\nF+StYwewn17JKrE28mX+CW7r37mRxc5ClmVeOZjD10WniNTp+fnwbAZF+W7S83necf59ZB8yEKXV\nUevw9GNzSBKvHMxp0TEFSB3dG21kGEU1ISTHWAidPh1tSop3v7OsrNnx5467I67KSpzFxWgSE1HH\nBaYP7dmpeb2ApdPgq33VPPbucYamhPL0DekkxbS+kON0S4pj2gNp/Oormg4eBMBx4gTmL78k6pZb\nAmxV8CI1NjavKXO7cdfUBNQxDZk8GdnlwnHyJJrkZMJmzQqYLf5CUKuJufdejwMuCBiGD+9RkUfJ\nbse6eTOSw4ExOxt1lFJycSH0Awe2O1L6PZELFlDz5pu4a2sRdDrkJk8rtJCJExHPWngPNN+nCwMg\nyzQdPEjm1Kv475ObsTU4MIRdOONNIfhRHFOFoGZTWTGrCvMAqGqy8de9O3h92lyfXf/9E4e8/a2+\nd0q/xyW1LrUqCALjbh1GbkEaY54Zh6ht/iDUZWTQsHr1GcGgAS332eoO2E+coPr118HlArWamEWL\n0PUPDrGBy4bFMHlgFC+uLmDWM7u47/Jk7r0sCe0FRBLAEzFVq3rORE/Bg7u+vvm4ri5AlnQNVJGR\naHr39vZqFSMi0CQlBdQmQRAImzEDZswIqB3+RtRqOyUyFWzIskzN66/jyPPMC6zbthH7s5+h8rPi\nbE9Bk5hI/BNPILvdyC4X9uPHEfX6oGtVpDaZvBkDACqTibB4I8lDYvj6lb1c/WjP+9voLigzryCm\n1t5Erb3pvO239x+M7nQ6RS9jCFelBtcDw5fUOZp//3V2ewtHXhwasXlaSvTplGgBWNh/UJvnj7yq\nL3tX5Z/nlAJoU1KIuecejOPGETZnDpE33eQTm4MVy6ZNHqcUwOVqXqvSTpzFxVh37DhPuOFc5HY2\ncT8bvUbk51elseqJkezINTPtqZ2sO1RzwWMdSipvt8JVWUnToUO421CINIwY4e0bCWAcFbgWVV0B\nQaUiZvFiQi+7jNAZMzA98IDS8kPBr0iNjV6nFDyqr84gqSN219XhLC+/qPdTsCGoVIg6HYYhQ4LO\nKQWIuOYa9EOHooqJwTh+PKFTpwKgD9Oy6h97KD1WG2ALFS4WJWIapPznyD4+zTsOwE3pmSzsf6Y9\nx8jYBP45dQ7VTTaSu4j40fqSAv538hgGtZp7BmaRHtG+1JtxcYl8dOIIdaejmbOTfdsP677BI3hu\n73YcksTwmDgeHTGWPHM9kTodyaFtC0nUlVqITgptcb+uXz80vXp5lO5WrCBkwoSAprn5E+Gc/nVC\nB9N+bAcOULt0qSfCrFYT86MfnSe2JMsy9Z98gnX7dsSQEKIWLuywIFNarIG3HxjC6r1V/Pyd4wxP\nDePpG9NJjDpjr8stoVYc025B08GD1CxdCm43gsGA6f770SQkXPBY/aBBmO6/H/vJk2iSktAHScS/\nI0h2O7ZduwAwjBrl9/Q70WgkfPZsv95DQeF7RIMBwWA4k0IuCKii26cd4E8aN2zA/MUXIMvoBg0i\n+s47e1R6dWcjGo1E33HHedu1es98OCJeWSDrqgS/R9MDKWgwe51SgA9zjzAzKY0E45lUlSidvssI\nHp1qqOf5vTv4PjH2qZ2b+M/0K1C146FtMhh5fuJMdlSUEqnTMy4+0ae2jU/ozdKYq7C4nJj0BgRB\nYGhM+1uLHN5QxMCpLaeuyZJE1Wuv4SopAcC2ezexS5Z0y7Sj8DlzcBYU4CovRx0XR9jcjqVcWzdv\nPtNs3eXCunXreU5n04EDHqVBPCvlte+9R8KvfnVR9s7OMjFlYBQvrCpkxtM7uX92MotnedJ7nUrE\ntNvQuHatV11Sttlo3LAB45gxqMLCLrhIpE1LQ5uW1slW+gbZ5aL6lVdwFhUBYN2+HdMDD3TLFlYK\nPRNBrSZ60SJP/1O7nbCZMwPe/1R2Oj0tik5HSu2HDmE/cgT9oLazrhR8R/7eSg58U8CP/jkLY0Tw\n1MMqdAzlbRWEOC9Q2+iS3AGwxDeUWBo5+zuqc9ixuJyEa9v34IjRG5iT0tc/xgFGjQZjO5pHX4jD\n64u47slxLe5319d7nVLwpCE5CwtRZWZe1P2CGVVEBHFLliDZbIgGQ4fPF85JAbxQSqDU2NjquKMY\ntCoenZfGDePi+b8PTvDB5nL+cEs/jypvC/WnCl0L4Zw0+6YDB7Bt3w6iSOSCBRizswNkme9xlZd7\nnVLwqIE7y8rQBrjuU0HBl+j69CHukUcCbUZzzk3fc//4rwAAIABJREFU7cR0XrfZTMNXXyHb7YRM\nmtRMhLEncWxTCeNu7M/wK3ybWafQubQ58xIEQScIwjZBEHIEQdgvCMKvT2+PEgRhjSAIRwVBWC0I\nQtdt0Bhk9A2PYPxZkcGpickktSOtNFjJjIwm7KyeoAMio9vtlAYzDdU2KvLq6TuqZZl+VWhocwdL\npeq2qbzfczFOKUD4lVd6Wx5okpMJvfzy847RDx6MeFavvpB2SuC3RZ84A+8+OIRfXdeHR5Yeo6rB\nqURMgxTL1q3ULF1Kw5o1yO62F+zCr7rK+5kRw8ORrVbPDknCfLrdQHdBDAmBszNRRBFVaMulBgoK\nCh3DVVPjqVc/SxhN0GgIOyudXZuRga6TFp9lWab6tdewbtmCbfduql97DVfNhbUTujuGcC2NNefr\nsih0LYT2FGkLgmCUZdkqCIIK2AQ8BFwPVMuy/CdBEB4DomRZ/sUFzpW7QyF4ZyPLModqqxEFgczI\naL/18Owsii0NrC7Mw6BSMz8t46IjlMFE+ck6Xrp1FU9vvrnV4xwFBZiXLUN2OgmdNQvD0KGdZGHX\nRHY6EVr5fLjNZpoOHUIMDcUwxPdtkqx2N1/tr2beqNgu/3fXHZCammhcuxapsRExLIzGr7/27guZ\nPJmI+fPbvIbsciFZLNj278f82Wfe7WJ4OAlPPukXuwOFddcub//k8HnzFAEnBQUfYT9xgpo33vC8\no3Q6YhYvbhaddFVUIDU1oUlK6rT6UndjI+W/+U2zbVF33NHj5hlFh6p5ceFKbv7dRCViGkAEQUCW\n5UuaOLUrlVeW5dNLzOhOnyMD84Gpp7e/BawDznNMFS4OQRAYHN19Imu9Q8JYlDks0Gb4FFNKOOYK\nK00WJ/qQlh0pbUoKpgce6ETLujatOaUAqvBwQsa1nD59qRh1KuaPDkwPVoXzqV26FPuxY57BOQsF\nZ6tztoagVqOKiMCYnY0tJwdnfj6o1URcc42vzQ04xlGjFGfUhzhLS5EaGtCkpgZVH0d/ILvd1H34\nIU2HDqE2mYi69dZun+HTERrXrUN2OgGQ7XYs332HduFC7/5A9O4WjUZUUVG4a0+r0KrVLYq7dVcK\n9lfx8u2ruPG3ExSntBvQLsdUEAQR2AWkAy/JsrxDEIR4WZbLAWRZLhMEQZnJKfQoVGqRhH6RlB6t\npc9I5eOvoOBrZFnGfuLE2Rua7dckJ3foeqJWi+m++3BVVyOGhHRLETIF39G4YYM3+qyOj/e0w7nI\nUoWugGXTJmy7dwOe+uS6//4X049/HGCrgodzF03bWkTtDARRJOaeezAvX47scBAydSrq2PYLOPoL\n+4kTOAoK0Kak+KzdjMPm4viWUqoKzFTlm6kqaKDqlJma4kZu/9s0suak+eQ+CoGlvRFTCRghCEI4\n8KkgCIPxRE2bHdbS+b85K81g2rRpTJs2rcOGKig0OBwcq68h3hBCUmhYoM0BIDEzmtKjNYpjqtAt\ncDc24q6rQx0Xd8HevJ2NIAio4+NxlZZ+vwHj+PG4q6pQJyQQ3kHlZ/D059O0ENlwVVcju90t7lfo\nWTSsXu392lVejm3PHp/VtQcj5/b5ldro+9vTCL/iCpyFhbjr6lDFxhJ2AR2EQKCOjSX6zjsDbYYX\n25491L77rmchURCI+sEPPD2iL5Gc5Xkse3YHQ2YmY0oNJ31MAjGp4cSmhqEPbft9tbe6ApvLyfCY\nePSKUrlPWLduHevWrfPpNTv0m5Fl2SwIwjpgDlD+fdRUEIQEoKKl835zTv67gkJHqbRZWbJlLTX2\nJkRB4KfDRjM1MfDKc70GRFFyRGnkfCEsW7diP34cTWIiodOmIahUgTZJoRWajh6l9s03kZ1OVCYT\npvvuQxUeeNG16DvvxLxsGe7GRkLGjvWbiq555Uoav/kGAMPIkUTecotSY9zDEdRqZLu92djfOEtL\ncdfVoU1L6/TorCErC8vGjeByecajR3fq/YMddWwscY8/7ql3Dw1V3mktYMvJOZPdIstYd+/2iWNa\nlW8mc0pvbvnj5A6f++rBHFYUnASgT1gEz46bpjinPuDcYONTTz11ydds87ciCIIJcMqyXC8IggG4\nDPgjsAy4E3gWuAP4/JKtUVBogdWFedTYPWprkizz4YkjQeGYJmZGc2hdUdsH9jCsO3ZQ//HHADTt\n3YtssxF+1VUBtkqhNRpWrPDWT7mrqrBs2nRREUlfo46JIfquu/x6D7fZ7HVKwdNvOGTChC7bz1TB\nN0Rcfz21770HLhe6/v19MrluDcvmzdR/+inIMqqoKEwPPYQqrO3sIEd+Ps6iIjSpqZfUGkibnEzs\nT36C/ehR1LGx6AcPvuhrdVcElQpVhNKEojXEc34+qsjIS75m7o4yvnv7MEu+bFvs7lxsLpfXKQXI\na6gnp6qc8Qm9L9kuBd/TnuWCXsBbp+tMReBDWZZXCIKwFfhIEIRFQD5wox/tVOjhaMXmK5PaIFmp\n7J0ZTcmRGmRZVqIrZ2HPzW0+PnmyhSMVFIKACyjHK2ryCoZhw9BlZCA1NaGKjPT7M77h66+9n0V3\nbS22nTsJnT691XNs+/ZR+/bbnvNEkehFi9BfQqsSTa9eaHr18o5ltxvr1q24GxowjBiBJr7l9mit\nITudWHfsQHa7MY4adcE+1Qrdg/C5c3HX1uI4dQptaqp3gVN2uXCWlCCGhaGOimr39Rqqbfzn/m+5\n9bkpxCR1vIxLLYpoRBGnJHm3GZRoadDS5m9GluX9wMgLbK8BZvnDKAWFc7kqNZ3tFSUcq68lVKPh\nnoFZgTYJgIgEI3F9I3j7kfUs/NMUVJrOkYgPdrTJydh27vSONZewiq/QOYRdcUWzVN6QiRMDbVKn\noYqIIGTqVCzr1wOgHzZMiZYqAJ6+zJ2VUnueuE476ryt27adWViRJKzbt1+SY3oude+/j23PHgAs\nGzcS+/DDHVbqlSWJ6n/9C8fpBUrrli2YfvKTbq9y3FMRDQZifvjDZtskm42qV17BVVICokjkjTdi\nbEequOSWeOuhtYy5th9DZl5clpxGFHlo6Cj+sX8XTkliTnIfhpsuboFFwf8oSwYKLfJ10Sn+c2Q/\nAD8cOIwZvVMDZotRo+FP46dT3WQjXKtDFyQRU0EQeODdubxx7ze8umgNd782E50x8Ep9gcY4YQJS\nUxP2Y8fQJCYSfsUVgTbJ7xzZWMz/fr2FmJQwhs1OY+hlKYTFBLeCp+Rw4K6uRhUZiX7AAOKeeMIj\nfhQfHxTiR51JxNVXe+pXXS7UiYlKBkSQ4aqsxJ6bizouDl3fvoE2xy9EXHcdtUuXItvtaNPT21VP\nLYaGtjq+FGRZxrZv35nx6Wd6Rx1Td22t1ykFT79PZ2Ghz9RaFYIf665dHqcUQJIwL1/eLsd01T/2\n4LJLXLXk/GNtLhdalQpVO57VUxNTmBDfG6ckYQwCNWWFllEcU4ULUmmz8uKB3UinV2Jf2L+LrJg4\nYvSBm2iLgkCswTfpP3uqytlcVkyCMYR5aRmoL6EZts6oYfG/L+f9x77j7zcs58dLZwe9Q+JvBEEg\nbOZMwmbODLQpfsflcPPZ77aTszyPG5+ZgN3qYt/qU/zvqS0kDYoha24aWbPTiEkODiXp73HV1FD9\nyiu4a2sRjEZi7r4bbUpKu2rauisXm6bY3bHu2oUjPx9tWhrGkeclUPkdR1ER1S+95K2Bjrj++m6p\njqsfMID4X/8a2WZDDA9v1+JI+JVX4qqqwllYiDYtjfA5c3xmjyAIqKKjcVdVebepYmI6fB3RaETQ\naLy/PwQBMQiE1RQ6j/M+ye34bB/5rpiN7xzmsRXXolKfmaNJsszz+3awvqQQg0rNoyPGMiq27d6t\nGpUKTZAENRRaRnFMg4RjdTW8fewAblnmpn4DyYoJbLuCeofd65QCuGUZs8MeUMfUVxysqeI3Ozby\nfbVBiaWRB4ZeWkN6lVpk4V+m8OVfdvHXa5Zx/ztzMaUqL96ewJ4VeZzcVc4TX11HSJQegOzr+uGw\nuTi6qZi9K0+x+oU9RCaEkDUnjazZqSQOjA54RK5x7VpvU3bZasW8ciWmxYsDalN3x7p9u6e3X2oq\nxjFjAm1Ou7Bs2uQR5AGsmzd7eiWOG9epNth27Trj1ACWLVv85pi66+pwVlSgSUgIiCq1qNVCB7IV\nVOHhxD74oN90DqLvuIO6jz5CamzEOG4c+gEDOnwN0WAg6tZbqfvkE3C5CJszp9NbMllzcrDt3o0q\nMpLwuXOVGtdOxjBmDNZdu3AWFoJKRcS8ea0eX1dq4a2frOWuF2YQEd/8d7W5rJj1JYUA2Nwunt+7\ng3dmXe032xU6F8UxDQKsTidP7dxEg9MBwPFdm3l1yuyLcgItTid7qsoJ1WovyblNDYugX0QUJ+o9\nE9f+EdEkh3YPRyunqhzprPHuqnKfXFcQBK5eMpqIeCN/ve4LHnz/Cnr1b3+Bv0LX5OjGErKv6+d1\nSr9Ha1AzdFYqQ2elIrklcneUs2/1KV774RoEUWDY7DSy5qTRd1QcourManD5yTr2rDjFnpV5NFTa\nmP3QCCbcNMBbv1xfbiV/byX5eyoo2F/NZT8eRv8JiR22u7zERUlRKAOSGj0bJKn1E87C3diIIy8P\nVVTUJamA9iQaN27E/NlnAFi3bkV2OgmZMCHAVrVN0+HDzcb2I0c63TEVQ0JaHfsK+4kT1LzxBrLT\niWAwYLr3XjS9u4Zyp78WujS9ehH7k59c8nX0gweTECCVX/vx49S9+6537K6tJebuuwNiS3uRZZmS\nwzVBsYjpC0SdDtMDD+CqqEAMCWlz0eebf+4jaVAM/See/26zuJzNxja3SxGg7EYojmkQUNVk8zql\nAHa3mzKrpcOOaaPTwZItaym2eCaa89L6cfdFigRpRJHfZU9hXUkBAjAtMeWS0l2DiZRzHOzkUN+m\nLk65fRD2Rier/pHDXS/O8Om1FToHt0vypg5Jksz+r/JBhujkMEzJYRjCz0Q0jm4qZsY9Q1u9nqgS\nyRjXi4xxvbjuyXEUH65h78pTfPTLTZgrbAy9LIWIOCN7V53CUmcna04a1zyRjdag4cu/7OSbV/eR\nmBlN/t5KnDYXKVmxpA6PJWVoDMv+uIMrfzaKqgIzLodE6vBYkgfHoNG3/HgvPVbLG681EamNZUBS\nI05RT6E6i6PvHmHCDwa0+oJ319VR+Y9/IJnNIAhEXHttl3CwAo39yJHm46NHu8TPTR0X18x2dSdH\nugBCpkzBceqUp42JyUTktdf65T6Na9d6I7OyzUbjhg1E3XKLX+6l0Hk4Cgqaj/PzA2RJ+5ElmX/c\nvIL07ARu/sNEwmMvLsJrtzo5sqGYfV/lExlv5Mqfj0YUA+PACSpVM8Xn1rj8geE8f/2XrHlpD5ff\nP7zZvvHxifwv9yhlNgsA1/TJUJzSboTimAYBCcYQEowhlFk9f2RROj2pYR2PTu6oKPM6pQBfnjrB\nnQOGtupQumWZ43U16FQq+oQ37zVlUKuZm9L9RCamJCZTbrOwqayYBEMIiwcPb/ukDjL+lgH8ZuKH\nWOvsGCMV5cGugtsl8c1r+1j5jxwuf3IUI7KTee8XG3FYXUTEG6kpbKCqsAGVRsSUHEZEQghOu5uE\njPb3aRMEgaRBMSQNivE4lPlm9q3Jp77cys1/nESfUfHNJg4PvncFx7eUUl9h5dpfjsWUGuZ9CUtu\nieIjtax5eS+mlDBElcC2j49TfqKOXv2jSBsRS9rIeNJGxCKKAjVFjVQVNvDln3dy+YMjWPnX3Xxe\nNInjObX0HV1PfVkJ5SfriO8bScnRGvqN7cWIK/s0s9+6c6fHKQWQZRrXru0SDlagUSckNHfwukg9\na/jcuch2u7fGNOyyyzrdBlGrJebuu5ElCaGNBVJXZSV1n3yCZLEQMmFCh6K7wjktJM4dK3RNtKmp\nnprG0+VJXUFxW1SJXPN/2bzzsw3k7ijjxmcmMOrq9Had21jTRM6KPPavySd3exmpw2MZOiuVnJV5\nvPXQWm5/flrQdxAIizHw0PtX8Nfrv0AfqmXKHYO8+8K1Ov46cQZ7qiqI1OoYEhMbQEsVfI3g715t\ngiDISj+4tqmyWfkk7xhuWWZ+Wj8SQzoexdteXsIzu7d4xwaVmg8um9fiSpJbknh612ZyTqeyXtun\nP3dlth75UWg/b/z4GzLG9Wr2QFUILiry6ik+VEPGhF7UFDXy3pINNBjcHJ/gIuotC1q3yPWPZjPl\n9kHedFtZlrHU2qkubKC6sAFjpI7MScGV7uewuSjYX8Wp3RWcyvH8QxCITgoluncow+ekkTU3jU9+\nu42kQdEMnZWKMVJHfYWVDx7fiEavZtfnudz75uUMndVcjbtxwwbMy5Z5x+r4eOKWLOnsb7HLITud\n1C9bhrOgAE1qKhHz5imOjx+oePZZXJWV3nHM/fej69OnlTPO4Cwro/qf/0Qym1HFxBBz770d6rfY\nnWk6dgx3VRW6jAzUsb5xBGz792PduhUxJITwK69EFRHhk+te8F5792LLyUEVGUnY7Nmd1gLoUpAk\nmeev+4KEfpHk7iwncUAUN/1+Yqviik0WJ8/O/ZTeg6IZcUUfBk5NwhjhWRx32Fy8tmgN/cYlMPcn\nnS9idjFUFZh5/vovmffYaMYu6B9ocxTaQBAEZFm+pPC14ph2I2RZ5sUDu/mq6BR6lYpHssYwLr7l\nCXNOVTm/3rGx2bZ3Zl5FuLbnRPhqmmx8fPIoDsnNvNQMUi4iUt0Sh9YV8sWfdvLYCv+knSlcGk2N\nDv4w+xOiEkMp3F+FWqdi7uOjeD78ELIgoKp2I6sEnrl8WpdbkS1oMLO1ogST3sD0xJQOpzk5m1z8\nZtJH3PnidDLGNU+9kp1Oqt94A8eJEwgGA9GLFrV74q/Q+diPH/eIzjidhF52GSFjxwbaJL8hSxKl\njz12pq8nELFgQYeiprLTidtsRhURoSwcnKZx3TrMX34JgKDTYbr/fjSJHa9rPxtHQQFVL7zg/V1p\nkpKIffjhS7a1u1F4sJqXFq7ksRXXsO7fB9n+yXFu/O3E8zJZvuetn6xFVIvc9tzUC+5f/twuZEm+\nYPuVYKXseC1/v2k5Nz0zkeFXKO+aYMYXjqny1O1GCILAg0NH8aOBWWhEEVUbKU9qofl+EVAJwZ3e\n4UvcksQvt39HkaUBgC1lJbw4+TKidPo2zmwfmZN7896j31F0qJqkQR2X2PcFksOBbccOZLcb4+jR\nihLhWXz86y1kjOvFrc9NxeVw43ZJ2NUS8jcesRd3jEdWXqJrLawVNpr5+ZZvaXK7ATheX8viQR1L\nV9fo1dzw2/G8/9h3PL7mejS6MxL7gkaD6d57kSwWBL0eQZHfD1okh4OaN99EttsBqP/4Y7SpqWgS\n2m6t0BURRBFd//7Yjx71jDUadOntS3/0XkOjQX0RLVG6M5atW71fy3Y7tpycS3ZMncXFzRYQnMXF\n7UrV7mkkD45h9Px0/n3ft9zw9Hiy5qbx9k/Xk7P8JDc+M5HQ6DPzlW0fH6NgXxWPLr+mxetZapuI\nTfNfZLojSLLMioJc8hvMjDDFMyHhwoGUhIwo7ls6h5duXYXGoGbw9OROtlShM1GeAN0QvVrdplMK\nMCTaxNRenj9wAbh9wBBCelDj4Wq7zeuUAjQ4HZw01/ns+qJKZNwN/dn07pG2D/YDsiRR869/Uf/p\np5iXLaPqxReRTk9Qezp7V53i+NZSFjzlaTmh1qrQGTWEa3XckH6mHcLYuF4Mie5a0dLt5aVepxRg\nw2lZ/Y4yfG4f4vtFsualPRfcL4aEKE5pkCNbrV6n1LNBxl3nu2dcMBJ1xx2EzZlDyKRJxNx/v8/S\nTnsy5/Y2FkNDL/ma2tRUOOv5oU1LU5zSFrj2l2PJvr4fr961hrWvH+C+pXOI7BXC7y/7HxuWHqIq\n30x5bh2fPL2NRS/PRGdseR5nqbUTEu2bxfdL5b3jh/jXzhy+OpjLn9Zv5usDudRXWD3/yj3/6sos\n1JVZCIsxcOMzE/j3fd9wfGtpoE1X8CNKxDSIqLM38U1RPmpR5PLkPhj8nEYkCAI/G57Nwv6D0Ioq\nortBj9KOEKnVE6XTU2tvAkArivS+iNre1pi4MJPnr/+C9x6VuO7X49CHdJ7j766pwZGX5x27Kipw\nFhai69ev02wIRuorrLz/+Ebu+ddl6EPP7xd4W/8hTEtMweF20yc8ErGLqf2ZDM2j4rGGi4+S3/jb\nCfxh9ickDzERkxSKIUJHaLQerUF5dXQFxPBwtH36eJ8Dqqgoj0PQjRG1WsJmzQq0Gd2KiAULqH3r\nLVxVVegHDyZk4sRLvqYmMZHoRYuwbt/uqTGdPdsHlnZPVBqRybcNIvv6DP5+w3JO7iznul+NY/jc\nPqx/8yAr/7Yba52dBU9PoPfA6FavZalrIiQqsOVaNcWN5Cw/ydb395BYYEfWet6xy1Qb+Fqt8URK\n8OhVfc/35Sg6o4bN7x85r8REofug1JgGCVaXk4c3feNV5h0QGc0fx01D1cUmxV2NPHM9Lx7YRb3D\nzuzkPtyQnunze9gaHPz3yc2c3FHOHX+fRp9RnaPGKdlslD/99JnG9IJA3KOP9ugIgizLvHz7alKG\nxnD1o2MCbY5fkGWZfx/Zz7qSAkx6A49kjbmkHsQ7P89lw9JD2OrsWOrsuBxu/rT/dh9arOBPJIcD\n67ZtyA4HxjFj2uwfqKDQEv7oFemqrqbx228BCJ02rUe/n9pClmX+b/R7PPLJ1ZhSw5ttN1faiIhr\nfRHyVE4Fr9y5msdXXUdkL//0Am6J2pJGcpbnsfvLk1Tk1jNsdioFQ9xsNdWA2vOZ+mHmMOb3yehU\nuxR8i1Jj2o04Xl/rdUoBjtbVUGmzkGC89JSZrsrmsmI+PHEYjaji7oHDyIzyfd1PsaWBE/W1yMDb\nxw4SqzcyrXeKT+9hCNNy+/PTyFmex2s//IrJtw1kzkMjUGlEXFVVWLZuRdRqCZk8uU2lQFdNDQ1r\n1iA7nYROmdJq9EM0GIi69VbqP/0U2e0mbM4cv7z0XZWVWLZuRdBqCZ0yJajVDr97+zCN1Tau+Omo\nQJviNwRB4IcDh/HDgcN8cr3R89MZPd9Tp9dY28RTkz5ClmU2vnOY3O3l3PnCdO+xbrOZ+k8/xV1X\nh2HkSEInT/aJDQoXj6jVKr8HBZ/ga6dUstupevllpPp6AJoOHyZuyZKge4e4Gxowr1jhaUE0diz6\nwYMDYkdVvqf0KCaleWaXIAhtOqUNVTZeX/w1P3h2cqc5pbUljeSsyCPnyzzKT9QxdHYqcx8eyYCJ\niai1KqxOJ68d3kNBg5nhpjiuTuvZ2VwKHhTHNEgw6Q2IgoB0OrqsV6kI01x8uoVbltlYWoTN5WRi\nQhJh2vNTFoOZEksDf96zDffpn8dvd23m39OvQOfjmrYNpYXNpG02lBb63DH9nhFX9qHPqDje+dl6\nnrt2Gbf9IRvh49eQLJ4FiabDhzE99FCLL3/Z7ab6tddwV1cDYD9yhLglS1BFttxDUz94sF9fom6z\nmaoXXkCyWj02HT2K6cEHg7LZdXluHcv/souffnJ10PdwC1bsjU5EtcB/7v+WIxuKiUtvLqJR++67\nOHJzAXAWFqKOjg7YJE5BQSG4cVdVeZ1SAMlsxlVZiTbFP+/gi6XmP//BWVAAeN67poceQpuU1Ol2\nnNhWSr9xvTr8fnU7Jd649xvG3tCfrDlp/jEOcLrdnMyvpuDbMvYtP0XZsTqGzU5l9oPDyZzcG7W2\n+fzNqNHw02HdM3NJ4eJRHNMgoXdIGA8OGcV7xw+hEUV+NCjrkoSI/pSzlS3lJQB8lnec5ybM6FLC\nRqVWi9cpBY8wkdlhv6R6uQthOqeu9txxS5RZLWwsLSJcq2Vm79R2iU0BRCaEcN/bc9nw5kGev3k1\nUzLUjEj31FI4CwuRGhpaTLWTGhq8Til41BGdpaWtOqb+xpGf73VKAZwFBUgWCyofiGP4ErdT4s2H\n1nLlz0aR0C9wP6+ujt3qpLG6CX2olht/N5Gdn51ott9V2lyUwllW1qMcU3djI0iSki4bxDQdPIiz\nuBhtv37o+vbFtn8/5s8/R5Ykwq+8EuOo7ptNEWyooqMRDAZkmw0AQa9HFWSKyLIk4Sw8S0BOknAW\nFQXGMd1eRr/sjitqf/q7bWiNaq585MK9S21mB+W5dZTn1lFT1Ej2ggxiktqvt1FXZmHj58dY/uE+\n5CIn0gg9Ny0axZTL+zdTdFdQaA+KYxpEzExKZWbSpQtTmB12r1MKUGJtZH9NJePiL03evTPJiIhq\nJkzUNzzSL+JMCzMGU2Gzcri2mgGR0dw+YEib51TarPxs87c0OB0A7Kuu5OfDs9t9T1EUmLZoCOmZ\nOv6zeBUnSkKZO6aMCJO+1XYuYmgoqqgo3LW1AAhaLeoAt31Qm0wgiiBJAIhhYUGXhgWw8m+7CY02\nMPn2gYE2pUuT0C+Sn302j76j49n8/pHz1B11AwZgy8nxDESxRwltNXz9NQ2rV4MsEzJlChHz5gXa\nJIVzsGzaRP2nn3oGX31F5C23UPfRR+ByAVD34Ydo+/RBHd26gEww425owFVZiSY+HjGkc+sIO4po\nMBDzox95/27CLr8cVQdsdpw6hWS3o0tP91vPWUEU0aSk4MzPP220iDY5MO1KTm4vY8iMjkWTt//v\nOPu/KmDJl/OpKW6kPLfe44SeqPN+3dToJD49kvi+EejDtfzpis+Y94sxjLm2H3VlFk7uLGfAxESi\nEs8sONdXWNmzIo/dX5yk+EgN2tGhVF+upWlQCGgEtsTVMlNxShUuAkX8qBvidLtZ+M0XzVpG/Gnc\nNL/UaPqTMquFlQW5aEUV8/tkEKppOR3Z7LBjdTmJM4T4XUV1TWEeLx7Y7R2LwP/mXHdRQlXm7btY\n/oct7NinJWVIDKMWDCRrThrhsRd2UF1VVZhXrQKnk5CpU9H17Xux30a7kSUJ2969yDYb+qFDz2sd\nYM3JoXHtWkStlvBrrgnISnJrnNxVzj/v/oq695RoAAAgAElEQVTHV11HRLzSx9VXrHlpD5ZaO9f+\ncqx3m+x00rhunafGdPhwdBk9Q8jCXVdH+TPPNNsW+8gjl9zrUcG3VL34Io5Tp7xj3eDB2A8ebHaM\n6cEHu6xysePUKapffx25qQkxJISYe+9F06t7qpfWf/45lu++A0CTmorpxz/2m3PqbmigYdUqJIsF\n49ix6Af6f4FTdrlApWqWtrvxncOseH43yUNNzP3JCNJGxLV6jaoCM8/M+BidUYPd6iQkSk98eoTH\nCe0XSXx6BAn9IolICEEUBerKLJQcqUFyy6x9/QAntpXidslodCqWfDmfkCg9e1eeYvcXJyk6XM2Q\nmSmMurovmVOSeOloDt8W53vvPcIUx1NjlNr2noYifqRwQTQqFT/PyuYf+3fR5HazoO+ALueUAiQY\nQ7grs20Bl7XFBbywfycuWWaEKZ5fjZqA2o/90GL1zZ2baL3hotWTw7NHccuno7je5uLQukJyvszj\n8z/sIGlwDCOu7MPwuX2aOVNqk4noW2+9JPs7St3773ujYI3ffovppz9ttqptHDEC44gRnWpTR1jz\n0l7mPTpacUp9jKgS2fz+USpPmek7Jp70MQkkD4kh7LLLAm1apyOfjrg12/a9GrZC0KCKioKzHFNN\nQgKS2exN1VTHx3fpxYSGr79GbvJkGUkWC43r1hF1yy0Btsr3SE1NXqcUwJmfj/3oUb+VDajCwoi8\n4Qa/XPtC1H3yCdYtWxB0OqJuucX7fU26dSBjF2Sw5cNjvL74axIyopj78AjSx1w4cyokSs9tz08l\nNjWcuL4RF2yPBlBypIZv/rmffavz6T0wmtLjtSBDSlYsJ3eUM3BqEh/9cjNFBz3O6PQfDWHQ1CQ0\n+jMuxDV9MthRUUqD04FepeKGvr7vcNBe3LKM0+1G7+eWiwr+QYmYKnR5blrzOTb3mYnhz7LGMDXR\nv+IJHxw/zPKCXMK1Wh4eOpqMSN+lfjmbXBxaX0TO8jwOfFNAYmY0I087qZ0t8S45HJQ98USzbVEL\nF2IIYkf0XJ6c8AEPvDOXuL4RbR8chLhlmW3lJdjdLsbGJ2JUt79WfGt5McvzcwlRa7kzcygJRt9+\nfmqKGzm5o4zcHeXkbi+j4lQ9IZmhpIyMZfL0fvQZGY8hvGsJr10ste+/j23XLsAjOhZ1xx0Iflwg\nU+g47sZG6t57D2dJCbqMDCJvvBHZ7ca6YwdIEsYxY1otp/An1u3bMS9fDqJIxPz5GIYP7/A1av7z\nH5rOigAbxowh6qabfGlmUCA7nZT+3/95S0gAYhYv9kmGhquqCkdeHur4+ICIMDUdOULN6697x4Je\nT8LTT5/3LHE53GxYeohPnt7GlDsGceNvJ3ToPrIsc2xzCV+/up+ig1VMvXMwk28bSEiUnoYqG+vf\nPMTKv3kyw0Zfk87Iq/oycGpSqz2s6+xN5DeYSQoNI8YPpVftYVdlGX/esw2ry8W0xBQeHja6y/Ui\n78ooEVOFoGJreQn5DfVkxcR1WoRWlmXcstRsm0uSWjjad9ycMZCbM/yTzqPRq8manUbW7DScdjdH\nvisi58s8vnxuF3e9MIPBMzqvvkVQq5uJU4CnjrSr0NTooKHShim169h8Ln/es43NZcUApIaF8+dx\n09u1EpxnruOPOdu8St+FjWZemnK5T22L7h1KdO9+jL6mH0dqq/m/tetQ5TrIP15EwV8qsR6zEJsW\nTvqYBOY8NKJbRq1llwtEkahbbsE4bhxIEto+fRSnNAhRhYYSc889zbYJGk3A2+m4qqup+/hjr6NV\n+/77aNPTzyubaIuw2bM9gnSNjagiIwmbNcsf5gYcQaMh4rrrqP/kE5AkDKNHo/VBPbujqIjql19G\ndjhAEIi8+eZOF8OSznrXgkfkEEnyaDmcxlLbxLr/HOTbf+1n+BVpTFrYsejksc0lfPLbbThsLmYt\nHso9/5pFk8Xp7TNasK+KQdOT+dG/ZjFoWnKrzujZROr0ROr0bR/oR/62byfW0xks60oKyI7rxaRe\nwVVepNA6imOq4BM+zzvOG0f2AfD+icM8NXoSWabW6x98gSAILOw/mP8c2Q9AengkExK6z0NIo1Mx\ndFYqQ2elcuCbAv731BYyJ/futHYngigSfdtt1H7wAbLNRsiUKV1K0Kb0WB3x/SIRVV3TSai1N3md\nUoD8BjMHa6sYFdu26NWphnqvUwpQaGnA7nb7vOXS93xXWoTTAM4hWpqGaDGHhvP82Osp3F/Flg+P\nsfSn63jg3bnemilHfj62vXtRRUYSMnEigp/s8if1y5Zh+e47BLWayJtuuqgol4KCdFrN2Yvb7VE3\n76BjqklMJO7xx3HX1aGOjkboQkr8HSVk3DgMw4cjO50d/jm1hHXbNo9TCiDLWDZu7HTHVJ+Zicpk\nwl1VBYBx3LhmtbONNU08Nuxt7/iWP0xCEC8coJJkGYEz/WclSWbNi3tY/+ZBbvrdRNKzE9i3Kp9X\n71rDqT0VDJ6ezJTbBzFoevud0WBClmWsruYlFBaXUlLR1eh6nzyFoGR96Rk5dUmW2VhW1CmOKcC1\nffozypSA2Wmnf0Q02i44wW0Pg2cks/aNA3z39iGmLWpbPdhX6Pr3J+HJJzvtfr6k9GgNiQOiAm3G\nRWNQqdGIIs6zJq3hrYiAnU3/yGi0oojj9Ln9I6L85pQCxOj154wNqLUq+oyKJyUrlufmL2PjO4eZ\nfNsgnMXFVL38MpwWaHOWlBB1881+s80f2HNzsWzYAHhSC2vfew9VbCza3r0DbJlCV0OTmIg6MRFX\niUdNX5Oaijo29qKuJep0iPHxvjQvaBH1etD7LkJ3bhp3INK6RYOB2IceounIEUSD4TyhpdBoPb/f\nvZCTO8pZ+8Z+Pvy/TexadpIXC+9uJpT0v9yjvHv8EGpR4MeDRzDGGM9bD63DbnFwzRPZfPf2Yd5+\nZD2DpiUz6daBLP735V3SGT0bQRCYn5bBxyePAhBvMDK+C3WjUPDQtT+F3RhJljlWV4NaFOkXEXwT\n6xP1tawrKSBSq2deWj9i9QZO1Nd6958rEORvUsK6f99AQRC4/slx/P2m5WRfl4ExUhdok4KevF0V\n9OrCjqlerebhYaN5cf9uHJKbG9Iz213P3DskjKezJ7O6II9QjYab+vlXSfLqtAzyzPXsrCyjd0gY\n4+MTuXvdStySzO0DBnP736by1+u+YOCUJHS5R71OKUDToUN+tc0fnJtyhyRR/dJLmB58sNsqoSr4\nB0GjwXTffZ4aZUHAMHp0l8wgCAaajh711OrKMuFz56IfNKjd54ZOn44jLw/HyZOoTCYirrnGj5a2\njGg0Yhx54Z6jABFxRtwuifLcenK3lwOw49Ncsq/zZDOdaqjnrWMHAHC54dXPtvL1W26yZqchSzLL\nnt3JNU9kc8/rl6Ezdq+o+u0DhjDcFEe9w87wmHjCtD1D46A7oYgfBSGSLPP73VvYXuFpVn9lajqL\nB3V+iphbkvgi/wSlVgtj43ox8nT6YFGjmZ9u/hb76YnluPhE7h00nOf27qCg0cxwUxwPDRmFJkhf\nrLIsN1tZ7Gp89MtN5O4oZ/7jYxg4NalLfy/+wu2S+PS32zi4tpCHPriiWf+1rogsy0hw0erPnY3Z\nYeeutSu8kV6VIPDKlNkceDuX/WvyuefJVOrfXuo9XpOaSuyDDwbK3HYjSxL2I0eQnU606elUv/Ya\nrtLSZseEzZnTbWv7FIITV1UVte+8g6u6Gv2QIUQuWNAjHVvJYqH8d787k46rVhP/+OOoIjomfCc7\nnV0mDdrtksjdVsbfb1oOwDVPZGOaH8+v920CWSb0Wzvhy23MWDSEY6uKSMiI5JY/TCIkKrC1oArd\nE0X8qJtyuLba65QCLM/PZUHfAZ2ucvavw3tZUXASgFUFJ3l6zGSGxcTy1tEDXqcUYGdFKdEjx/O7\nsVM61b6OUtRo5ve7t1JqbSQ7rhc/z8oOWue5NRY8PYE9K/L4+NdbCDMZmPeLMS3KxX+P5HDgqqhA\nFRHhs3qcYMVaZ+eN+75BEGDJsvndIrIsCAJd6ZNaZ7c3Sz92yzK19iZm3D2EfatP8cWHZmZMvgyO\nempMIxYsCKC17af23Xdp2rsXAE1KCjGLF1P1wgu4q6u9x3R0EqygcKnUffQRzqIiAGw7dqBNSiJk\n4sQAW9X5uM3mM04pgMuFu66uw3+TXcUpBVCpRcJiz8wNP/v9dvg9JAPFf4kk4lMr+iGh7Hr7ONc9\nOY7s6/spi9kKQY3imAYh50ZFBAiI3PXuynLv1zKQU1VOocXMtormEYLeIcHr6NjdLppcbiJ0Ol48\nkEORpQGALeUlfFmQy7V9+gfYwo4jigIjr+pL1pw0tn9ygjcfXEvigCiuenQMyYPPV0N219dT9fLL\nuKurEbRaou+8E13/rvd9twen3c1frlnGoGlJXPvLsajUXVP0qKuTGBJKRkQUx0+n9yeHhNE3PIIC\nawMVi0M4+U4h27+wM+fey5l22xDUXaC2yV1X53VKAZwFBbhKS4n+4Q+pe/dd3LW16IcPxzB6dACt\nVOiJuOvrWx33FNQmE+q4OFwVFQCoYmJQJ7QtFNfVqS+30n9iIj/58EpkWeZUTiV7vjpF+PhYLG9Y\nqf5fOfNeHENM8oXnapU2K8/t3UFRo5kxcb24f8hIv/aCV1BojeCfDfRAMqNiuDwpjTVFpwBYmDGY\nqABIcKeEhVNms3jHyaHh7KuuaHaMUa3m8ZHjOtu0drG5rJi/7t2OQ5KYkNCbekdTs/31drtf7++U\nJF4+sJucqnKSQ8N5JGuMT3+PKrXI+Bv7M3p+OpvePczLt60kPTuBobNSSBkWS3x6BKJKxLJxozei\nIzscmFeuJLabOqaH1hYSbjKw4DfjA20Kkiz32P5palHkt9mT+broFG5ZZlbvNLSiiqd3bqJKssEP\ntGinqzi0sYR1/z7I3IdHMOHmzE5Tm74YBK3W07LhrEiwYDCgiYsj9qc/DaBlCj0d46hRNKxZ4xmo\n1eiHDQusQQFC0GiIue8+LJs8aawhEyYg6rp+xsyFqMo3U3igmgETE6kvt3hbcQmCQJ+RcfQZeVp8\nMhWYNrjVa71yMIdDtR4V4G+K80kNC+eaLrhor9A9UBzTIOWBoaO4IT0TtSgGrFHxg0NG8eqhHEos\njYyP783MpFSckpu1JQXeY+Yk9yUxSCOmL+zf5VUk3VxWzPTEFIotjQDoVSqmJvq3H+iyvON8U5wP\nQI29iVcP5vD4SN87TBqdimmLhjD+5gFs+fAYB9cWsfJvOZgrbSQNjiY+tBGTNZxeUU1EhznavmAX\nZsdnJxhzbWDb2eyqLOOve3dgczm5Kq0fizJ75iTRqNYwL+1Mw3ury0lV0xnBIEcvFSOeGUqfKj3L\nnt3B16/u56qfj2LU/HTEFtofBBLRaCTi+uu9vRNDZ8xAm9R9WlMpdF3CLr8cda9euKqq0Gdm9mjx\nLVVoKOGzZwfaDL9SfKial25bRXy/SN752Xo0BjVjr89o+8QWqLRZWx0rKHQmimMaxMQbQwJ6/wid\njsdGNI+Gzknpi0uS2FdTSZ+wCG5M71hj585CkuVmdbAAQ2Nimd47hRJLI1mmOL+nIJefFW0GqPDz\nw15n1DDtrsFMu8uzOmqtt1N4oIpT24o4sayGDfsFrA41SQOjSKvbQsqwWFKGmYjtE4EoCjTWNiG5\nJMJjO18i3xfYGhwcXl/ELX+Y5N3W6HRw0lxHgjGEOIP//54kWebPe7Z5G3x/lneckaZ4hpt6RvuG\n1jCqNQyKMnlX5kPUGgZFxRCbaOSBd6/g2KYSPv/jDr56ZS/zHhvD4BnJQVcLFTJ2LMbRo0GSulQd\nmkL3xzB0aKBNUOgECvZV8vIdq7nh6fGMujodZ5OL3B3lxPa5+M4EUxKTefvYQcBTSjaxG/WCV+h6\nKKq8Ct2W944f4oMThwFIDg3jT+OmE9KJk8k9VeX8ZucmpNOf/zsGDOH6vgM67f5nI9ntuCoraZL1\nlJyyU7C3koL9VRTsq8RSZyd5sInjW0uZuDCTHzw7OSA2Xip7VuSx6h85PLriWkRRoMJm4bEt66m2\n29CIIo8NH0u2n3ua2d1ubljzWbNtP8vK9nt0vqtgdTn5PO84VpeTy5L6nNfmSZZl9q3OZ9mfdmCM\n0DH/F2PoN7bnRn8UFBQUvufkrnJeW7SGHzw7maw5aT699uayYooaGxhhimt3SzIFhXPxhSqv4pgq\ndGuO1tVgdtgZEh2LQd35CQKHaqrYU11BSmg4k3q1bxXSLcscqKlERGBItMnvUaPGmiYK91fx4sKV\nLPliPmkj4vx6P3/RWNvEP3+4hpAoPXf8fRrvFx3h07zj3v19wyP528SZfrfj7/t2elO44w1G/jph\nptJLrYNIbokdn55g+XO7iO8XxQ+eneS3lj8Na9Zg27MHVXQ0kTfcoKjqKigoBB3Ht5by+uKvuf35\naQyeoSx0KgQnimOqoNDNOLeH7eReSSwZPtbv960taeSX2e8TmxaOWqdi0UszSMzsWqum36fyvvfo\nd6RmxRL5VAr/zT3q3d8/Ipq/TJjudztkWWZreQmNTidj43sRru2e4hudgdPu5pOnt+JscnHrc1N9\nfn1rTg51777rHWvT0zH9+Mc+v4+CgoLCxXJ4QxFvPrCWu16eQeak3oE2R0GhRZQ+pgoK3Yxcc12z\nHrbflRZxS79BJIV66mE3lhZxor6WwdEmxsT5LsUxslcIv95wI5Iks2/VKf776y089MH/s3fW4XGV\naR++z3hm4u7apE2T1F1TNwoUdyjussiy8LHAsrvswrIsLLK47eJOS1ug7i6ppB53n8n4zPn+mDA0\n1cgkM0nOfV29rryTc97zZDpyfu/7PL9nrs/V+J2Iw+akYFcV+WtKyV9XSun+OpKHRjDzzsEMmpWE\nNknL5spyigxN6BRKbszsnhosQRAYGy3dPHgCpVrOvAeH89TEz7jgD6MICPesEdyvbSXc4+pqj84v\nISEh0RacTpEtX7kyfFR+CpQaOSo/BfVlzXzz583c8tZ0qaxBok8g7ZhKSPgQRfom7l73c6vH3smd\nQ4SflkWFR3hz/299FB8eMoqJMZ5P6XHYnfx97jc47E4Sc8JJyAknITuc+Kww/AI6lpJqLSig8bvv\nEG02AqZPx2/IkDMeaysrw5SXhyIkBL8RIxBO00+tprCJf126CF2IhgET4xgwKY60kdGoTuqHaXc6\nqTI1E6zWoFVIZjU9lf89vIbQOH/m3D/Mo/NaCwupefVVdwsY7ZgxBF9yiUev0dcRHQ6Mmzbh0Ovx\nGzIEZQf6SopO52k/B3obotOJvaICQa1GEXZqT2qJ7kMUxW5dmK0pbOLZWV8zaFYSNrMDq8mO1WTH\n6RRZ8Pjo39q/SEj4MNKOqUSP5nhTA8UGPf2DQ73uQOwrJAYEcnFqf746dhABVw/bCD+XS+6myrJW\nx26qLOsSYSpXyHhk0YWUH6qnOK+G4r217PjhGKUH6giK1LqEalYYCTnhxGeHERB29l0s0Waj9t13\nEY0uV+L6jz9GGReHIiLilGNt5eXU/PvfiDYbANaSEoIvuqjVMU3VRv591RJm3TOEidcOPOu1FTKZ\nz7Yzkmg7U27K5uUrfiRrWiKJOeEAGNauxbR9O/KgIIIWLEAeHNzueVVJSYTdfjvmvXuRh4SgGz/e\n06H3eRo+/RTTzp0ANK9dS8T995/2vX86HE1N1L37LrbSUpRJSYQuXIhc1zu/K0Snk7r33sNywGXY\nFzBnDgHTur4mvidhr6tDNJtRREd32UKFvbaWuvffx15ZiTojg5Brr+2WXqjGJivhSYFc/1LXl5tI\nSPgykjCV8Arrykv4x+4tOEURP7mCv46eTFpQ+28seyPX989mQUo6AkIr05wYrT97aqtbjbsKhUpO\nQrZrp/RXnA4nlUcbKd5bS/HeGpa9souSfbXoQtTc9u5MYvufvibV2dzsFqWuB5zYa2tPe3Nq3r/f\nLUoBzLt3wwnC1KS38tq1Sxl1cb9zitLT0WixoJbL0XTQCMvWsgMb0sN2YM12O6/s3cGB+loygkO4\nJ2d4j4o/dkAol/5pLK9evYQ5Dwxj9Cho+u47AGwlJTiam4m4++4Oza1OTUWdmurJcPsUot2O/pdf\nsJeXo87IOEXcm/bs+e1YiwXLwYNtFqZNixdjKykBwFZQgH7ZslMWqnoLlkOH3KIUQL90KboJE7pF\nFPUEDGvW0PTDDyCKqDMyCL3pJgS53OPXafzmG+zlrnIaS34+hlWruqUvqqnJ0uGMJAmJ3oQkTCW8\nwncFh91tVEwOOz+VHOeOoKFejsp3OJ1hzg39czDYrBxprCc7NKJLesjWW8zsqK4kRK1mWETrlDuZ\nXEZMRggxGSGMuqgf0GL08/kh3rjxZx5ZdAG6EM0pc8oCA1HGx7tvMGWBgagSTr/TKw8JaT0O/U3s\n2iwO3rz5Z5KGRjL3gfaldDpFkRd3b2V1eTFKmYx7c4YzOTaxXXPUW8z83+Y1FDfr8Vcq+ePw8QwI\n6Rnpdv87vJ815cUAVFcYCVCpCNdoabCYmRybSP8e0B5g+PlpJOSE8+6dK9j/nYnZiTI0KlcKrr2i\nwsvR9V2aFi+mee1aAMz79iEolWhHjXL/XhEa2qp2V96OFFVnc+te0E6DoZPRSvRERIeDpkWLoOWe\nwXLoEOYDB/DLzvb4tU5+jXXHa85udVC4qxpNoCRMJSR6f9GGhE9y8m6N1gutXHoaOqWS3w8dw1u5\nc7hv0AhUHl4trjObeGD9cl7K28ZT29bzwcG95zxHEATGXt6fwbOTePfOFTjszlOPkckIu+02AmbO\nxH/KFMLvvhvZGdLxtMOGocvNRRYUhDIpiZCrrgJcu7Uf3LsSXbCay/88rt21P9uqylndIsxsTif/\nztvhXhhpK98eP0xxsx4Ag83Gewfz2nW+N6k0tb7B31RRxn8P7WNR4VEe37yaIn2TlyJrH5EpQTz4\n7fmEpoTz9rJkSmtdCyHqAZ5fpOkopt27qXjmGSqeeQZjSwprb8Z6/HjrcUFBq3HI9dejTExEHhJC\nwOzZaDIz2zy3dvRo+DVlUy53jXsp6owM1Cc8NwGzZ0u7pWeji7xLtGPG/DZQKNCOGNEl1/mViiMN\nPD7iY/b8VMik69r+3ugJGO02ypr12J2n3hdISJwJSQ1IeIWbMwfx9Lb1VJmMZASFcnFqf2+H1OfZ\nWFlGncXsHi8uPML1/du2In3hY6N47bplfPnkRi56YjRKTeuPFpmfHwEzZ7ZprqDzziPovPPcY1EU\n+fyJDTTXm7nzoznI5O1fTzM7HK3GdqcDhygia4fAdYitv1zb+2XrEEXe2r+LrVUVxPv7c1/OCEI1\nnnWZPRNjo+Ja1SjrbVb3z1ankz21VSQGBHZLLJ1FqZZzxYszSRsUyBd/V7HghgjGXD7X22EBrprI\n+o8/hpbXW8Onn6JOSelQ/WtPQZmY6M6GAFAmJbX+fXQ0Effe26G5/XJykN97L7aSElSJiShjYzsV\nqy8jyGSELlzoMj9SqVCEh5/7pD6CIJcTOHcuTYsXgyiiSk9HM7D9pRxtQTdmDIqICOyVlahSUztk\n1tUefnplF7k3ZTHnPs8au3mb/XU1PLN9A812G0kBgfxl1CSpdZpEm5CEqYRXSPAP5K3JszHZ7WiV\nPafWrTfjr1SddXw2ZHIZN742lffuWsEfhv+PQTOSGH5BGgMmxiFXuISk2WDFbLARHN0+85IfX9xB\nwY4q7vviPJTqju0Sj4yMISUgiOP6RgAWpPZH2U7zjPOS+rGuvIQ6ixmVTMaV/dq3ur248Ag/Fh0D\noNps5LV9O/m/4ePaNUdHmRKXiL9SSX5DLRlBoXx8eL/7uQDc7Yh6EiMXjsGuCyV/TQljfeQzxKnX\nu0UpAA4HDr2+VwvToPPPR6ZSYauocNWYenhXUxUfjyo+3qNz+iqCTNarxXdn8M/NRZOT4zI/ionp\nUpdmdVoa6rS0Lpv/V2pL9Oz9pYin1l/e5dfqbt7N30Oz3eUXUahv4vuCI1yTkdXm8w821LG3tpqU\nwKBTyookejeSMJXwGoIgSKLUh5gYE8/OmkpWlhYSqFLzu0Ej23W+NkjNXf+dQ2OlkR0/HOPHf+7g\nw/tXMey8VGxmO7uWFiATBG55ewbpY9rWj23tR/vZ8tURHvx2fqeMIfwUCp4bm0tebTX+SlWHakOj\ntTpemTiDgqZGorQ6t1tyW6k0tk6nrThp3NWMjIxx975NCgjk9X07abBYmBGfzJDwqG6NxVPEpAez\n+t1zp5x3F4qoKJRxcdhKS13jmJgu33HxNoJCQeAJGQ4SfRfR4cBaUIBMo0EZ5/lezr2thc6KN/MY\ne0V/tEG9byfRdlJG0cnjs7GzppKnt613l9vcmTWU2YmSQV1fQepjKuHziKLI0aYGVDJ5j0k37MnY\nnU4UHlqNrilsYvsPx5DJBUZfkk5Zfj3v3bWCm9+YTsrwSPQ1JoJjdKetGd2x6BhfPrmRB76aT0Ry\nz/9/31NbxR+3rnN/2V7RL5Or0rsmHc1XWFdewqHGOjKDwxgb7fkbVZPeymPD/scLB29AJuu+noNn\nw2k2Y9y6FUQR7ciRyPy6J11bQsKbiHY7tW++ifWYKyvEf9o0AufM8XJUp2IrLUX/008A+M+Y4bXd\neH2tiT9N+oLHl1/c7iyinsCmyjKe27kZu+gkTO3Hc2Nz27yY+9KebSwvLXSPM0PC+PuY3C6KVMKT\nSH1MJXo9TlHkbzs3uevjFqSks3DAIC9H1b2IokixQY9KLie6G/q9ekqUAoQnBTLr7iHucWCEloWv\nTeXt234hIjmQ8kP1qLQKUoZGkjwskuShkSQOjqBoVzWfPb6eu/83p1eIUoBBYZH8ddQkdtRUEqcL\nYEpc+1yB28OB+lrKjQayQ8OJ9PPOTc+SomO8vs9l/vMth7kvZwTT4pPOcVb78AtQoQ1SU19qICzB\nN9KRZRoN/hMnejsMiR6OtaQEe3k5qvgN05YAACAASURBVOTkNrfX8SaWgwfdohTAsHw5/lOn+pSB\nk9NkovaNN3C2tC+zHD9O1KOPItO2L/vFE6x+dx9Dz0tpsyhtsJh5KW8bRXo9wyOiuG3gEORdmM7c\nWcZExfL6pJlUmppJDQxuV2lQ2EneC+Hd5MUg4RtIwlTCpzlQX9vKtOWb44e5IDm920xjvI1TFHlu\n52Y2VLpSA69OH8jl7axt9DUGTIjjptenseGzgzzw1XwaKpsp2FHF8R1VfP/3rZTur0OmELjt7Zmt\n+qj2BgaGhjMwtGv/ph8Lj/Kf/bsA0CmUPDc2lwT/7hf3m0943wJsrirzuDAFiE4PpuJIg88IUwmJ\nzmLatYv6//0PRBFBqSTstttQJSd3eD7R6cTZ3IxMp+u62syTXeJlsi6tA+0I9tpatygFEI1G7DU1\nqBK7bpHwZJxOkR3fH2XNB/t56IcL2nze6/t2sr26EoClxceJ0fmzICWjq8L0CFFaHVEdWEy/NK0/\nJQY9u2uriNX5o7daeXTTas5P7se4Lsi8kfAtJGEq4dOczjS1PU6qPZ28umq3KAX4+PB+5iWltWv1\n0RfJGB9LxniXyUdYfABh8QEMP99lNmGzODAbrASE9Y3FB0+zqPCo++dmu40VpYVc3z+n2+OI1fmz\no6byt7HWv0uuE5UWROWRBrKmnL43rkTHsZWVIdrtKOPjfU5k9Gaa1693t0MRbTaMW7Z0WJg6Ghqo\nffNN7FVVyENDCbv11i5x/FVnZKAZMgTzrl0gkxF0wQUIPuYhoQgPRxYQ4DIpA2QBAd26G31gTQnf\n/XULMrmMW96aQWRKUJvPPdmToLs9Ck6mwWJmS1U5gSo1Y6I8a9illit4dJirbc+tq5dyuLEegPyG\nWl4cN42UwLY/bxI9D0mYSvg0mcFh5MYmsqqsCIAr+2USrNZ4OapuRDx12NtLtpVqOUq1JEo7iv9J\nN4PeWsS4NiMbvc3KoYY6BoaEc2UX1dNGp4dQsq+2S+buyzR+/z3Na9YAoB44kNAbbpDEaTdxcmqp\n0Ik6Zf1PP2GvqgLAUVdH05IlhF57bafiOx2CTEboNddgnzsXQaVC7t81C1GdQabREHbHHRiWLwdR\ndKUad0MNeFFeDd89u4W6Yj3zfz+SofNS2t2Le1x0nNtJXQYeF4PtocFi5ncbVlBjNgEwNzGV27OG\nevw6RrutlQB3iiJFhkZJmPZyJGEq4dMIgsDvBo/k8n4DUMpkXquXA9eHYpPVQoBS1a7aDrvTyb76\nGtQyebvdYHPCIhgVGcOWqnIALk8bQICqc0Kj2WZjX101IRo/0oNCOjWXhO9xR9Yw/rx9A9VmI0PD\nozgvqZ9X4vBTKHhw8CgAakxGjHYb6pPT/TxA2qhovn9uK1aTnUk3DCRlWGS7b/okWuPQ692iFMCy\nfz/WY8dQ9/POa6mvEXj++dirq7FXVaFKTiZg2rQOzyVara3HFktnwzsritDQLp2/sygjIwm58spu\nuVZNYRM/PLeNw5vKmXP/UMZdMQC5smOLO5f3yyTKT0dxcxNDw6LIDvNe3fHWqgq3KAVYVnycWwYO\nQe7hz12tQkl6UIh7x1Qjl9M/2LdfXxKdR3LllZBoAw0WM09sXUuhvolwjR9Pj5zQpro9u9PJk1vX\nkVdXDXRsZdEpihxvakQtl3e632ST1cJDG1e6VyEX9s9hQapv16lIdAyrw4GqC4Rge3l17w6WFR9H\nAK7LyObitP4ev4axwcLGzw+y5oMDBEVrue+zee7+uRLtx9HcTOVTT7VKzwi76y7UKSneC6oPItrt\nCIrO7R9YCwupfeMNl0BVKAi76SbU6ekeilDidOhrTCx5aSfbvj3ClJuymXJLDhqdb6U1txeLw877\nB/dSpG8iTKNhVVmx+3dBKjUfTeuallFNVgtfHD2I0W5jVkIKGZIw9Wk84corCVMJiTbw1v5d/HBC\n7d6IiGj+OGL8Oc/bWVPJk1vXtXrsw6nzvJaOvKjwCG/u3+0e6xRKPplxvldi6Sk4nE42VZZhF52M\niYpFLZcSTdpKfn0tj2xa5R4LwIdTzyOoi5w6nU6Rf126iEnXZjLiwq7Z3RNtNuxVVcgCA5EH9F7D\nJf0vv6BfuhQAv+HDCb7iCmkn2kuITifmvDxEux1Ndna7nW7tdXXYyspQRkd3SX2phAtzs42Vb+Wx\n8p29jFzQj9n3De01Xgmv7N3OT8UF7nF2aDj59bXolCoeGjKKwWGR3gtOwmeQ2sVISHQTJoej1djs\nsLfpPJWs9Y6VDM+2Y2kvmpNEVVekVvYmRFHk2Z2b3KnU/YJC+NvoyT6xE9kTsIutm6qLp3nMk8hk\nAjPvHMwPz21j+AVpHhdSjuZmal99FXtVFYJSSch116HJ7Nku2WciYPp0tCNGINrtkpjxIqIoUv/B\nB5j37QNAERtL+N13I2tHSYciNNTnU2x7Mg6bk/Wf5LP0pZ2kj43hkUUXEp7kvTZnBpsVg81KpJ/O\nY2aRxxobWo1jdf78edSkPmVGKdE9SLlOEhJtYF5iGtqWlCqFIGuzTXtWaDgzE5IB15vtpszBXnXU\nzY1NZERENAB+cgV3Zw/zWiw9gWqz0S1KAY401nOooc6LEfUsMkPCGR4R5R7PSUw9pUedpxk4JQGH\n3Un+2tJzH9wOnCYTTd9/7zaSEW02mhYt8ug1fA15cLAkSr2Ms7HRLUoB7GVl2AoKvBeQhBtRFNmx\n6BjPTPmC3UsLuP39WSx8ZapXRemGilKuX7GYW1cv449b12I7aVG9o2SFtq5pzQ6JkESpRJcgpfL2\nQIoNTWyuLCfCz49JMQlSelU3UW0ycqSxngT/wHbXetaZTShl8k4bF3mKBosZrUIp7fydA73VyvUr\nFmE/4TPs5QnTSQ6QXAHbikMUya+vRSmTdbo+qNpk5PV9O6mzmJkWl8T85NOn6278/BDbvj3CPR/P\n7dT1fsXR1ETNv/+No76+1eOKqCgiH37YI9foSzj0emxFRcgjIlBGSimAZ8NpMlHx1FNwgsCQBQai\nSkoi5KqrfK4ly8ePrGXBE6PxC/CN77quwOkUObCqhMUvbMfpFLngDyPJnBTv7bAAuHb5Ihqtvxlc\n3ZM9nBkti+OdweF08tXxQxTqGxkWHsW0+M7PKdH7kFJ5+yBF+iYe2rgCc8uX1OHGem7OHOzlqHoX\nFUYDB+prSfAPpN8JrrURfloi/LRnOfPMhHbxLlF76VMtdzpBgErFXdnDeX3fThyikyvTB0qitJ3I\nBYGsUM/suv1952YONbp2rI81NRCr82d4SwbAiYy8MI1Fz22jeG8NCdmdv7Zx8+ZTRCkKBYHz5nV6\n7r6GvbqamldewdncDDIZIVdfjd9g6TvsTMj8/Ai+/HIav/rK7ajrbGrCnJdH7RtvEH733V6OsDXr\nP85n7OUZpAyPOvfBPYzmejMbPz/E2g8P4BegZPrtgxh2fhoymec2B6wOB7VmE2Eavw4tHNudrUsl\nbE7P7JjKZTIuSxvgkbkkJM6GJEx7GJurytyiFGBVaZEkTD3I0cZ6Htu8BpPDjgy4b9BIpsQlejss\nCS8yLT6JKXGJiKLYrjZBEp6n2NDUalxkaDqtMFWo5Ey5OZtfXt/Dwlendv7CJzmjykNDCb/7buSB\n3kvZ66k0b9zoEqUATieGlSs9IkztdXU+2z+zs2iHDcNv6FAqnngC0Wx2P24tKfFiVGdGoXG9X0RR\nBFF097+1me0oNR2/7TyypQKZXCC1G0SvKIrubLSivBrWvL+PXUsLyJ6ayA0v55LcBW2pypr1/N+W\ntdSYTYRr/PjzqInE6tqXnXVNRhZv7t+FCCT6BzI51vfvX5wtWTUyQWh3Sz2J3ockTHsY4ZrWO3bh\nHdzBkzg9P5cUYGoxNnLicrGVhKmETBBASpmnyWrhv4f20WC1MCM+mZGRMd16/WERUayvcNWOKgTZ\nWZ0gx189gCfHfkptsZ6whM655+rGjsW8Zw+24mIEtZrgSy+VRGkHOTn1tLOpqKIo0vDxx5h27gRB\nIOiCC9BNmNCpOX0RQRBQxMRgO37c/ZjcxwyNfi3bUqpkGHfupPGLLxAdDgKmT8d/+nTu7/ceD/9w\nAclD25++Xbi7mhcv+oGpt2R7TJjWlxk4srmC+jIDjZVGGiqMNFY201hppK7EwJjLM6g83EBDRTMT\nrs3kydWXERDeddlPHx8+4O4PWmM28cnhAzw4ZFS75piXlMagsAjqLWYygkLRnLCoVmxowupwkhoY\n5DMlYE5R5G87N7GpsgyAqXFJ3D9ohJejkvAmkjDtYeTGJnCksY7VZcWE+2n53aCR3g6pV6E76SbJ\n38fqdyQkvMmzOzaxr74GgC1V5Tw/dgrpJ6S7dzUPDBpJSmAwdWYTk2ITSA0MPuOxfgEqxl3ZnxVv\n5XHpn8Z16royjYbwe+7BUV+PTKdDpuk9qfCi0+na1eqmenP/yZOx5OdjKy1F5u9P4AUXdGo+y+HD\nLlEKIIo0fv89fqNGtcu1tqcQdtNN1L7zDvbycuShoYTddFO753A0N2PevRtBrcZvyJBO/7/XFutp\nrDRiNlhx2F1ppA6zhYZPP3XXxeqXLUPd4l79/PzvmHZbDkc2V3DPJ3PbVItaV2rguXnfotIquPjJ\nsR2O1Wqyc3hjOQfWlHBgTQn6ahPpY2IISwwkLDGA1BFR7FtZzODZyXz7ly1UH29kxp2DyZ6e2C19\nkU9Nw+2Yg3mCf+ApfdY/PLiXL48dBGBMVCyPDh3jE+ZFRxvr3aIUYEVpIZelDSBW1/syHyTahiRM\nexiCIHDLwCHcMnCIt0Pp8fxYeJRFhUfxV6q4M3soyQFBXJzSn/11teyrryHaT8ctmdLz3B6cougT\nX3YSXUN+Q637Z6cocqihrluFqUoub1edU+5N2fxl2lfMeWAY/iGdE5OCTIYirHelmTVv2kTjt9+C\n00nAnDkETJnS5deUabWE33cfToMBmVaLoOjkbYj9pNZdTqfrXy9EptEQcdddHT7faTJR8/LLOGpd\n72NzXh6hN9zQ4fkqjjTw/PxviUkPQe2v5OiWCgAcJguKk9xgnUYjC1+dynt3rWD5G3kANFUZTxGm\nB9eX8vLlP/JqyS0AGOrMPDH6EwBeOHB9u+ITRZHSA3UcWF3CgdUlFOysJiE7jMzJ8Vz3z1wSclzv\n5/2rSnj7tl+wmV0xn/fQcP66/WqCoro3I+2i1Ax21VZitNvRKhRclNo29/9z0WixuEUpwKbKMvbV\n1ZATFnGWs7oHhezUhRGlVDLTp5GEqUSP5sQ6kPawv76G/+zf5R7/efsG3s6dg1ap5Nkxk7E4HFKP\nz3aQV1vN87s2o7dZmZWQwu1ZQ70dkoQHEEURvc2Kv1KFTBDICA7lQL3rplYG3SpKO0JwtI5Bs5NY\n++F+5twntUY6EUdTE41ff+0WcfrFi9FkZXWLS64gk3ksFVqdkYEqNRXrsWMA+E+Z0qt2tD2J5ehR\ntygFMO/di9NoRKbtmABb+c5ecm/MZv7DrtTLwxvL+delixC0OjTZ2Zj37gVcvVdVyckMTpQx654h\nbPv2CLXFBhy21gsI9WUGXr78R2RygcObytm3opifX9sNwPN7r0Mmb59g+eU/e1j93j5yZiSRe2M2\nGeNi0PircNidHN5Yzt/nfkvJvt+ejwVPjGbqzdntvo6nyAgO5bWJMykyNJHoH+gx08TT3SL5yvpx\nSmAQ5yf34/uCIwBc1W9gh00mJXoHkjCV6JE022z8bedG8upqSA0I4rFhY9tVb1vebGg1rjIZsTmd\n7pU6SZS2j3/s3kJDi0X9j0XHGBIeyZioOC9HJdEZ6i1mnty6jgJ9I5F+Wv40cgJ/GDqGjw7to8Fi\nZnp8cqfbv3QH028bxEuXLWbovFSi+5059beziA4HhpUrsVdWos7MRDvMt4WwaLGcsrMoGo1eiqZt\niE4nzRs2YK+sRDNgAJqsLASFgsD582n48ktwOFAmSp4AZ0Ie0LrWWlCrETqR8hzbP4Tlb+xh1EX9\niEoLJn1sDAk54TgdIiHXXUfzzt3gdKAdlIPFAvWlTcy+dygr395L0uAIyvLriB3g+gwx6a28dt0y\nAEQRvnp6E9ogV2xPrLwEbbC6TTFZjDYaKpqJSg1m69dHuOHfU+g3OgabxcHBdaWs/zifPcsK3ceP\nv2oA8x4aTlCkb4ihUI2fx138A1VqruyXySdHDgAwMSaerBDf6U98c+ZgFqRkIBMEQqSOAX0eSZhK\n9Eg+O3qA3bXVABxpauDd/DweGTq6zednh0agUyhpttsAGBYeJaWPnIMak5H8hjpidf6tavtEUUR/\nQt80gEartbvDk/Awnx/Np0DfCLgWbt4/uJfHho3lnpzhXo6sfcRkhDDvoeH8c8H3TL0lhxl3DEau\n9Px7vWnRIprXrgXAtHMnglzu021Q5GFhqAcMwJKfD4AqORllQoKXozo7+iVLMKxcCYBx40ZCFy5E\nPWAAde+9h7PJ5dhc/9FHKB56SOqPehpUSUkEzJmDYflyl4nXZZd1KpV68g1ZqLRKXrxkEbe+PYPU\n4VHIZAKLX9hOU7WJsvw6zv/9SKaPVPPVY6s5srmC6/6VS2CkH/MfGcF/H1pD9vREbGYHH9y3kn6j\no7nmhUkER+tAgL/N/prr/pVLdPqZMzNOTtc9trUSbbCa+784j/qyZvQ1Zt67ewXbvj3qPidtdDTz\nHxpBvzHRPmMC1NVcmT6QKXGJWB1OEgN8z7gtzMda6kl4D0mYSvRIGiwnCyHLGY48PVFaHX8fk8vK\nskL8lSrOS+rnyfB6HcWGJn6/aRUGm+2UNjqCIDArMZXFha4v/jC1H6O62a1VwvMYWxZtzjTuSUy8\nJpOBufF8+ug6dvxwjKv/MYmkwZ6tr7IcOdJ6fPSoTwtTQSYjdOFCzPv2gdPp2n1syRRxNDXRvGYN\notOJbsIEFD7i/mpuEdHu8cGDKBMT3aIUAKcTR3W1W5hai4sx79mDPCgI7dix3Wby5KsETJtGwLRp\nHptv7GUZBIb78Z/rl/HYzxcz9ZZsjE1WErLC2LH4OFaTncLd1exbWYxCJcdudRCTEcKnj63DrLfy\n97nfoq8xMXJBPy55eizHmxt5fM96mgoNxCeq+fz/1rP8jT30Gx1NvzExDJmTTHO9hfy1pS4xuqYE\nlZ+Sgbnx5N6YzeSFWSx/M4/8daUYGy28fdsvAPiHaZhz/zBGLejX5t3X3ka0tmsNhSqMzdSZTaQG\nBrdyA5aQaA/SK0eiRzI9Pol15SXYRScCMDMhud1zJAYEcn3/HI/H1hv5qfg4BptLmDiBb48fatVG\n57aBQxgcFkmT1cLIyBgpHacXMC8xjU0VZZgcdhSCwAXJ6d4OqVOExQdw50ez2fr1EV6/fhmjLu7H\n/IdHdKqv4oko4+Kwl5e3GncVotPp7g3ZGQS5HL9Bg1rPbbNR8/rrOKpdGSnmPXuIeOghn6jbVEZH\nt36Oo6KQ+fujjIvDVupqIyT4+bl3fm1lZdS8+qrbIMlWVkbwZZd1SWyOhgaa160DQUA3cWKfaieU\nNTUBTaAKq9nOiAt/W+StPNbIl09uZN1/DzD/kZEU7Kxix6JjZE1NoGBnNUqNgrkPDCNnZhIancsB\n/7mdm6kwNSOTOylVG0lO0lK6r47SA3Wsfn8//qEaHHYn6WNjyJwcz5z7hxGR7HqubRYH3zyziZqC\nJnb96GqrM/7qAYy/cgCJg8P7zO6oN1hZWsRLedtwiiJxOn/+PiaXQFXfXACQ6BySMJXokQwKi+SF\ncVPJb6glJSDIK02Z8+tr2V5dQawuoNf3OvVTtG6bo1Wc2kZnTFRsd4Uj0Q1kBIfy8oTpHG1qINE/\ngHj/nn+jLQgCoy5OJzM3nmdyvyB7aiIZ4z3zug1asABBqXTVmA4YgG5020sL2oq9tpa699/HXlGB\nOj2dkOuu87hgtNfWukUpuASXrbwcdUqKR6/TEYIuuggAW0uNqXbsWARBIOzWWzGsWoXTYkE3bpxb\nFJrz81u59pry8rpEmDotFmpefRVHfb37OpEPPtjpHq09CVOjFafdyZavDhMzIJSErDDGXJpB1pQE\n9q0s5qdXdlF51FUaMHReCte/lEv/iXHIZK3FYo3ZVeesrHCg3WKlUeOqg04cHM7AyfFkTo4nZVhU\nq3T8xkoj3/x5M1u/cWUtaAKUjLiwH7e+M9MteCW6lo8O7cXZ0se2tNnALyUFXJTa38tRSfREJGEq\n0WNJCQwiJTDIK9feX1fD41vW4Gj5IC5r1nN1RpZXYukOLkxOZ1dNFfkNtYSqNdw60HdTFCU8R5RW\nR5RW5+0wPE5diQG1Tkm/MdEem1OmVhN88cUem+90NH73nXvH0HLoEIaVKwmcM8ej15AHBSFoNIhm\nMwCCUukzqbwyPz9Crr761Md1OgLnzTvlcUV4+FnHncXR3Ix+8WJslZVuUQrgqKnBXl2NMrZvLNaJ\noojFaOP5878jfWwsx5/exIy7BjP1lhya68189MBqAObcN5TJC7MICD9zPeGk2ERWlBZii5Njm6jl\n0vlDGDUtDf/QUxdgjmwu56XLFuN0uL6HMyfHc9EfRxPb3zder30JhdA6g0MuSJ4dEh1DEqYSEh1g\nY2WpW5QCrK8o7dXCVKtU8tzYXAw2K1qFslf2KjXabPgpFFK6Vx/gyGaXuNuzrJBBs5NP2bXxVZyG\n1m7izuZmj19D5udH6I030rR4sau/6ezZyIO8swDYUUx792IrKkKVnEzAzJkYd+xAHhTk8d3S+g8/\nxHr06CmPC2p1h58z/fLlGLduRR4YSPCll6KI8H6vyXMhCAKP/XwxYQkBKNVyaoqaeP/uleSvKeWy\nZ8ZxxwezyJqa0KbP1ntyhpMdGk6jxcK4eXHE6FrXRdqtDn58cQfL/v1bu7cr/jaBMZdmoFT37fph\nb3Jz5iCe27UFq9NBelAIMzpQXiUhASCIJ9xcd8kFBEHs6mtISJyL1WVF/FxSQLBKw8IBOZ12gFtU\neIQ39+92j0dERPPHEeM7G6aEF2iyWnh623oON9YT5aflqZETiNMFnPtEiR6LKIrs/aWIxS9sRxRh\n7gPDGDQryecXJYxbt9Lw+eeufhoKBeG3344qOdnbYfkUxi1bXM9RC8FXXol2eNc4SZf/4Q+Itt9M\nwWRBQcgDAwmcOxd1evtrss379lH33nvusSI2lsjf/c4jsXY3DpuTH1/czsbPDnHti5PJnBTfqflq\nCpt46fLF1JW4Fmdi+4dwy1sziEztWYsmvRm91Uqj1UKMVodc6nLQJxEEAVEUO/VFKu2YSvR69tfX\n8M/dW/l1eaTS1MzzY6dwoL6Wn4qP469UcllaJgHt6Oc2JzGNYoOerVXlxOn8uSvbt3sWSpyZL48d\n5HCjKw2v0mTk3QN7eEJaZPBp8utrWVJ0DJ1SyeVpmQSp22eyIQgCOTOSyJ6eyIq38njz5p+54m8T\nmHhNZhdF7Bm0I0ciDw/HXlmJKiUFZVSUt0PyOUx79rQam/PyOiVMRVHEsGIFlkOHUMbGEjh3rrt2\nVJWSguXQIdeBgkDoddehSkrq8LXstbWtxo6amg7P5W30tSbkSjn6WhOvXLWE0Zeks+CJ0QSEtW9R\nuOpYI09P+m2hYf7vR7haPikk4eNrBKhU7bqPkpA4HZIwlej1HGtq4MQ9+6ONDZQY9DyxZQ3Wlgbz\nBxvqeG7slDbPKRcE7sgayh1ZQz0crUR3Y7S1boPSfIJZioTvUdZs4Imta7E4HIBLpP5zfMfaX+hr\nTCx6fjsXPj6KcZf3DKMOdUqKTxgR+SqK8HBObB4m72R9rHHDBvRLlgBgPXoU0eEguMWEKeSaa9Av\nW4ajqQntiBGdEqUA6owMBKXSvQuryc7u1Hyd5di2Sn54biuXPDWWuIFtMxisLzPw5VOb2PNTAXED\nQnHaRSZcMwC1TsVfpn/FpU+PZdj81DZnJxgbLYQnBXDr2zOJy5RqRyUkejuSMJXo9WQGhyEXBHdN\naFZoOAcb6tyiFCC/oQ6rw4Gqj/e464vMSkxlbXkJJocdmSBwfrLU09aXOdxY5xalAEeaGjDabad1\nij4X/mF+aIPVZE1JaOXyKdFzCZg9G4dej62wEFVKCgGzZnVqPmtxcauxraTE/bNMqyVowYJOzX8i\nyuhowu++G9OuXcgCA9GNG9fpOa3FxTT98AOizYb/tGn4tVHsLn5hO0v+tQP/cD/euWM5jy69CJXf\nuW8ZnQ4R/1A1KcMiqSsxcPU/JjHuCteiz9B5Kfz3odVs++4ol/9lPMHR5zZWSx4aydPrr2hTzBIS\nEj0fqcZUok+ws7qSFaWFBKvVXNFvIBVGAw9uXOm2N4/XBfDapJlejtIz5NVW89GhfQBckzGQQWGR\nXo6o4/xcXMCSoqMEqNTcNnAwsV1U+1lhbOZQQx2JAYEkB0g1S75Mgb6RB9Yvdy80xWr9+c/kjouP\nr5/ZjEIl4/zfj/RUiBK9COPWrTR89pl77J+bS+B553kxorYj2mxU/vnPv5lkyeVEPvxwm9yJt3x9\nhOK8ag5vLKd4by3jruzP1c9P6nRMFqONV69ZQll+Pf+34pI2iVMJCYmegSdqTCVhKtFnWVdewo9F\nR/FXqrhxwCCie0FbjCarhVtWLcXkcKWj+skVvDl5drtr8HyB/Ppafr9plTsN+2yLBzurK2m22xgW\nHoW2D/UO7KtsrixjUeERtAolCwfkEK31P/dJZ6Aor4Z3bv+Fp9Zd7vPmRxLdhzk/H6fRiCYzE9Oe\nPe4aU//cXIQeklljr6+n6i9/afVY6M03oxkw4IznVB1rpPxwPVGpQYQlBuKwOfjb7G+oLmji0mfG\nMXRuCoGRfu1+r9SXGVj/8UE2fnqQ0AR/JlydyaiL+0nvOQmJXoRkfiQh0QkmxMQzIaZzToFtYUd1\nBYca6xkYEtblu5fVJqNblAKYHHaqzMYeKUyLDE2taoNLm/U4nM5T3P5e37eTJUXHAEjwD+D5MVMk\ncdpLsTocfHLkACWGJibEJDAr+ejoRAAAIABJREFUofO1lgnZYciVcnYuPs6w81I9EKVET6fh668x\nbtgAgDw8nIh770U3erSXo2o/8qAgFDEx7t63Mp0OZfzZv/MqjjTw3p0rsFkcKFQygqJ1mPVWFjwx\nmt1LjvPjC9vpNzqaW985d4aR0+Fk/6oSlr6/h7IdNYy6sB93fjRbqhWVkJA4I5IwlehxNFktvJO/\nh2qTkUkxCcxO9N2byeUlBbyUtx0AAXhkyGjGd6EYjtMFEOWnpdJkBCDST0tCD219kh0ajloud9cT\nDgqLPEWUWhx2tygFKDbo2VFT2S0LDhLdz5sHdvFTcQEAm6vK8ZMrmBSb0Kk5BUHg+pdyee26pfiH\nasgYF+uBSCV6KqLdjnHjRvfYUVODOT8f7bCe57wuyGSE3X47zatXI9ps6MaNQ+5/9uyCQTOTeHTp\nAj64fxXaQDWz7x1KSKyO8KRApt82iKK8Gj5+eM1Z52isMrLxs4Os/18+Jq2T0jECxov90cU6ubR/\nsCf/RAkJiV6GJEwlehwv7N7KzppKAPbW1RCm8WNkZIyXozo9a8p/M84QgbUVJV0qTDUKBc+Onsx3\nBYcBOD85HY2iZ77NY3UB/HX0ZJaXFBCoUrMgJeOUYxSCrJV4BdB1wARHomeQX1/XanygobbTwhQg\naXAEN702jXfuWM6dH84maXBEp+dsD5ajR2levx6ZRkPArFnIg6Q6Z68hlyOo1Yhms/shmV/n+l57\nE7lOR+Dcue06Jzo9hIe+vYCl/97JO3cu508bfjMfUqrlWM2OU85xOkUObShj3UcHyF9XyrB5qVzx\nyiT+UL3Jfczu2mry6qoZEn7mNkeNFgv/2L2ZI40NZIeG87vBo/Dzse8wpyiytaocm9PJyMho1HLf\nik9CoicjvZskehzHmhpajY82NfisMI3w07Yea7RnONJzhPtpuSlzcJdfpztIDwohPSjkjL+Xy2Q8\nMGgkL+7ZisXhYE5iKkMjpN6OvZWM4FCKDE3ucf8gz6UEZoyP5arnJvKfhcu4/8vziErtnp0de3U1\ntW+9BS1tiqxFRUQ+9FC3XFviVARBIOTKK6n/5BNEiwXtmDFoMn27v21XIFfKmPe74Wz45CCGOjOh\nca6d1uAYHXarg0/+sI6L/zgGq8nOps8Pse6/B1D5KZhwbSZX/2MSfgEqDDYrsl/AecK8KtnZ63Pf\nO5jH7tpqwJUV8emR/SwcMKir/swO8fyuzayvKAVc31HPjp4sOfpLSHgISZhK9DiyQ8PdXwqylrGv\nckP/HOrMZg631JhelT7Q2yH1OsZFxzE6Kha704laujno1dw2cDA6hZLSZj3DI6LJjUv06PyDZyWz\n9+ci8n4uIuq27hGmtpIStygFsFdU4DSZevQuXU9Hk5VF9J/+BE4ngo/t1nU3CpUMm/m316dfgIo/\nLL2Iz/5vPU9P+hyL0cagmUlc/1IuycMiW5kZ+StVLBwwiPfy9+AEZsYnM/Ac39e1ZmOrcY3Z5NG/\np7PUmk3u+w+Aw431HKivZXB4z3W/9xVsDgdvHdjNgfpa0oNDuW3gYGk3ug8i/Y9L9DjuHzSCGK0/\n1WYjE6PjyQ7t3rS79uCvVPHHEeO9HUavRy4IyCVR2utRyxXclNm1uydNNSZSR3bfrrsiLg7kcmhJ\nR1dERkqitItx6PUYli9HtFrRjR+PMi7ulGMEmQxkfbu3rcVoo7HSSFhCa58Cv0AVN7w8hePbK4lM\nDUIXojnjHBekpDMlLhGb00mY5tyv69zYRPeOqQyYHNP5VH1PopbLUQgC9hO6Tfh3odme0W5DLVcg\n7wPuxZ8ePcDS4uMAFBqa0CoU3NxLsr8k2o4kTCV6HGq5guv6t61JuISEhER7yJqSwLd/2ULez0VM\nvTWHtJFRXdrSQhkZSeiNN9K8bp2rxrSd9YAS7UN0Oql94w3sFRUAmPbsIfLhh/t0XW/5oXpiMk4t\nmagrMaAL0SBXnl6gpwxv2wJOoKrtrvDT4pMJ1fhxtLGBgSFh59xh7W78lSruyh7Ga/t24nA6uTRt\nAGlnKTfpKA6nk+d3b2FDRSk6hZJHho5m6Flqc3sDxQZ9q3HJSWOJvoEkTCUkegkmu53t1RVo5HKG\nR0RL/eEkJDrApOsHMvrSdDZ9foiPHliNLkTN9S/lEpXWdam9mv790fTv32Xz+yKi3Y6jqQl5UFC3\n9gV1GgxuUQogms3YSkr6pDCtLzPwxRMbyPuliH8evAGlpvUtYXR6MGqdksMby7vVrXpoeJRXRJhT\nFClr1qNTqghRn3kXeFp8MrmxiThFEWUXvXbXlBezoSVluNlu4+U923lvqu8tWlkdDj47eoBig56R\nETHMSEju8FwjIqLZVFnWaizR95CEqYREN1Ni0PPFsXwQ4ZK0/iT4B3Z6TrPdzqObVnFc3wjAtLgk\n7hs0otPzSngXURSps5jxUyjQSm7D3YZaq2TyDVlMvDaTr/+0mXX/PcDFT471dli9BltlJbVvvomz\nsRF5WBhht9+OIsTzu06nQ6bTIQsKwtno+qxELkcR2bfqA50OJ2s+PMCSF3cQlR5MvzExp4hScO2Y\nanRK9i4v7vVtlGxOJ3/atp7dtVXIBIE7s4Yy8yx9kuUyGV25nNJst7UaG08a+wontvDaVFmGn0LR\n4XZtMxNS0MgVHGioJSMolCke9hCQ6BlIwlRCohsx2mw8vmUN9RZXK4JNVWXMS0hjSnxipwTq7toq\ntygFWF5ayE2Zg/BXqjods0TX4XA6MTnsp/1/cjidPLtzE1uqylHKZFzZbyCF+kaUcjlX9BtApJ/O\nCxH3LWRyGSMX9OPNW34mdUQ02dMSTnsDfzasRUUYVq1CUCoJmDkTRVhYF0Xbc9AvWeIWho7aWgy/\n/ELwpZd2y7UFuZywW26hadEiRKsV/ylTUET4rk+Bp6g81sDhDeVMuCaTwl3VfPHEBmbfN5T6UgOJ\nJ7VHMumt/PTqbtb/9wBTb81h2q05Xoq6+9hQUcLu2irAtXP61oHdzIhP9lrm0YToeL49fpiqlp7k\np2uX5gscqK89aVzTqT7ik2ITPNICTKLnIglTiR5NrdlEncVMkn9gj7BrLzca3KIUXOm3Xx4/yOKi\no7w4fiqxuoCznH1mdCeZLyhlsnPa8kt4l7211fx150YMNhsjIqL5w7CxKFvMVpyiyEt529lSVQ64\nVvM/PLT3t3Prqnlt4kwUfdycpTtIHBzOeQ+PYM0H+/jfI2sYNDOJERek0X9CHHLF2Z9/R2MjtW+8\ngWixAGA9fpzIRx7pdqdXZ3Mz5oMHkfn7o8nw/g2uaLOdddzVKKOjCbv55m69prfQ15j48cUdbPj0\nIOGJAUy4JpOU4VE8+N35vH3rLzRWGpn/iCu7xulwsvHTQyx6YRuZk+N57OeLCY7pGwtgzhPMjH4d\ni4C3CmKC1RpeHDeN3bVVhKo1Pldr+ysZQaGtakPTPdjCS6JvIglTiR7L+vISXti9FbvoJNE/kGdH\nT6beYubHoqNo5AouSs1ol+lCdxCl1RGgVKG3WVs9bnLY2V5d2WFhmh0awQXJ6XxfcBilTM69OcOp\nMZt4dudGSg2u1hoPDRkttVPxIV7ZuwNDyw35tuoKfikpYE5iKgBfHj3IqrKiM55bYWymzmKSdk27\nAUEQGHtZBmMvy6Chopkdi46x6B/b+fD+1Qydl8L02wed4lr6K7bycrcoBXDU1eFoakIR2n03b47m\nZmr+9S8c9fUA6CZPJmj+/G67/unwnzoV67FjiDYbgp8f/pMnezWe3oDNbOeTP6xDX20iY1wskxdm\nsfzNPax8ay+jLk5n/iMjKM6rcR8fGutPY6UR/1ANIbH+5K8t5aunN+EXpOKO92eROKj37yKfyLjo\neH4sOsbBhjoE4LqMbGRe9mkIUKk6tfvYHdw2cAhahZKSZj0ju6CFl0TfQxKmEj2W9w/mYRddrbuL\nDE18c/wQS4uPuW/2d9VW8c9xU73+5XIi/koVfxo5kU+P7Gd3bRXmlhYRAJF+2k7NfVPmIK7NyHLV\nvggCf9yylkJ9E+BqVP5dwWEuSxvQqWtIeI6Ta4ZOHOfVVZ1y/IktCsI1foSozmzO0VkqjM1sriwj\nVKNhQnS819LZypoNfFdwGJkgcFFKBhGdfI90luBoHVNvzmHqzTlUFzSx9qMDvHrNEh7+4UL8Ak9N\nx1ZGRyOoVIhW10KUPDgYeWDna8rbg3nvXrcoBWhet47A887zqjmaOi2NiEcewV5VhTImptufk96I\nTC5jz7JCJl6byfK38lj17j5SR0bx8KILiUgO5KunN+KwO2msMhIUqWXRP7YBkDk5ntdvWEbF4Xou\nfGw0Q+Z6L33Vm6jlcv46ejJHG+sJVKk6vEjc19AoFNwyUGrpIuE5JGEq0SkMNisyQfCKMYtwUpJN\nncXkFqUAx5oaaLRazuqu5w3SgoJ5fPg4CvWNvJS3nTqzienxyYyO6ry5xInpzA1WS6vfNZ6QQizh\nfS5KzeDd/DwAwtR+5Mb+ttKcGhjs7uUHMDcxjQnRcXx1/CAqmZxrM7K6zA2y0tjMgxtWuHf19yfV\nctvAIV1yrbNhsFl5bPNq6lpet9uqynll4kyf2fWPSA7koidGYzXZ+OC+ldz6zkxkstafSfLgYMJu\nvRXDypWuGtNZs7o9jffknqgyjcYnhIciJKTbDI/6AjaLnciUIIKidQyYGMfkG7JIGeYydbLX1NA/\nuJQVG408M/lzYgaEUn28CW2QmrxfCplz3zBufmM6SrVvvLe8hVImY0CIVAMOsKmylLXlJYRrtFzZ\nLxNNN39unQuHKLK8pIAGq4UJ0XHSQkIvQhBPyqv3+AUEQezqa0h4h/fy9/DN8cPIgIUDBnFBSnq3\nXn9jRSn/2L0Fm9NJckAQd2cP5febVuNoeb2FqjW8nTunz9bhLS48yhv7dwGgksl5dvQk0oOl+g9f\n4lBDHTVmE9mh4a3Szm0OBx8e2sfhxnqyQsO4Kj2r2xqs/1BwhLcO7HaPNXI5n8+8sFuufSL762t4\ndNPqVo/9e8J0kgJ8q62H3erg5SsW0398HPMeHO7tcE5BFEUaPv8c07ZtCBoNIddc0+d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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-4.12.1/libpysal/cg/tests/img/000077500000000000000000000000001466413560300175545ustar00rootroot00000000000000libpysal-4.12.1/libpysal/cg/tests/img/cell_boundary_category_rule.png000066400000000000000000000513151466413560300260350ustar00rootroot00000000000000‰PNG  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;Ad'H€ì ²$@v‚ÈNÙý?A Fñ·+”ÇIEND®B`‚libpysal-4.12.1/libpysal/cg/tests/test_ashapes.py000066400000000000000000000111111466413560300220300ustar00rootroot00000000000000import 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-4.12.1/libpysal/cg/tests/test_geoJSON.py000066400000000000000000000030351466413560300216560ustar00rootroot00000000000000# ruff: noqa: N999 from ... import examples as pysal_examples from ...io.fileio import FileIO 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 = FileIO(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-4.12.1/libpysal/cg/tests/test_locators.py000066400000000000000000000027621466413560300222460ustar00rootroot00000000000000"""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-4.12.1/libpysal/cg/tests/test_polygonQuadTreeStructure.py000066400000000000000000003021621466413560300254600ustar00rootroot00000000000000"""locators Unittest.""" # ruff: noqa: N999 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-4.12.1/libpysal/cg/tests/test_rtree.py000066400000000000000000000030411466413560300215300ustar00rootroot00000000000000"""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-4.12.1/libpysal/cg/tests/test_segmentLocator.py000066400000000000000000000026541466413560300234060ustar00rootroot00000000000000"""Segment Locator Unittest.""" # ruff: noqa: F403, F405, N999 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 self.grid.nearest(Point((5.0, 5.0))) == [0, 1, 2, 3] # Left Edge assert self.grid.nearest(Point((0.0, 5.0))) == [0] # Top Edge assert self.grid.nearest(Point((5.0, 10.0))) == [1] # Right Edge assert self.grid.nearest(Point((10.0, 5.0))) == [2] # Bottom Edge assert self.grid.nearest(Point((5.0, 0.0))) == [3] def test_nearest_2(self): # Left Edge assert self.grid.nearest(Point((-100000.0, 5.0))) == [0, 1, 3] # Right Edge assert self.grid.nearest(Point((100000.0, 5.0))) == [1, 2, 3] # Bottom Edge assert self.grid.nearest(Point((5.0, -100000.0))) == [0, 2, 3] # Top Edge assert self.grid.nearest(Point((5.0, 100000.0))) == [0, 1, 2] libpysal-4.12.1/libpysal/cg/tests/test_shapes.py000066400000000000000000000533211466413560300217000ustar00rootroot00000000000000import 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, -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-4.12.1/libpysal/cg/tests/test_sphere.py000066400000000000000000000132361466413560300217040ustar00rootroot00000000000000import 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-4.12.1/libpysal/cg/tests/test_standalone.py000066400000000000000000000556471466413560300225620ustar00rootroot00000000000000# 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 get_points_dist(pt1, pt2) == 2 ** (0.5) 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 pytest.approx(get_segment_point_dist(seg, pt)[0]) == 50 ** (0.5) 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-4.12.1/libpysal/cg/tests/test_voronoi.py000066400000000000000000000177201466413560300221130ustar00rootroot00000000000000import geopandas as gpd import numpy as np import pytest import shapely from geopandas.testing import assert_geoseries_equal from packaging.version import Version 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.points2 = [(10, 5), (4, 2), (5, 5)] 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], ] p1 = shapely.Polygon([(0, 0), (0, 1), (1, 1), (1, 0)]) p2 = shapely.Polygon([(0, 1), (0, 2), (1, 2), (1, 1)]) p3 = shapely.Polygon([(1, 1), (1, 2), (2, 2), (2, 1)]) p4 = shapely.Polygon([(1, 0), (1, 1), (2, 1), (2, 0)]) self.polygons = gpd.GeoSeries([p1, p2, p3, p4], crs="EPSG:3857") self.lines = gpd.GeoSeries( [ shapely.LineString([(0, 0), (0, 1)]), shapely.LineString([(1, 1), (1, 0)]), ], crs="EPSG:3857", ) def test_voronoi(self): with pytest.warns( FutureWarning, match="The 'voronoi' function is considered private" ): 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_from_array(self): geoms = voronoi_frames(self.points2, as_gdf=False, return_input=False) expected = gpd.GeoSeries.from_wkt( [ "POLYGON ((7.5 2.5, 7.5 5, 10 5, 10 2, 7.75 2, 7.5 2.5))", "POLYGON ((7.5 2.5, 7.75 2, 4 2, 4 3.66666666, 7.5 2.5))", "POLYGON ((7.5 2.5, 4 3.66666666, 4 5, 7.5 5, 7.5 2.5))", ], ) assert_geoseries_equal( shapely.normalize(geoms), shapely.normalize(expected), check_less_precise=True, ) def test_from_polygons(self): geoms = voronoi_frames( self.polygons, as_gdf=False, return_input=False, shrink=0.1 ) expected = gpd.GeoSeries.from_wkt( [ "POLYGON ((0.5 1, 1 1, 1 0.5, 1 0.1, 0.1 0.1, 0.1 1, 0.5 1))", "POLYGON ((1 1.5, 1 1, 0.5 1, 0.1 1, 0.1 1.9, 1 1.9, 1 1.5))", "POLYGON ((1 1.5, 1 1.9, 1.9 1.9, 1.9 1, 1.5 1, 1 1, 1 1.5))", "POLYGON ((1 0.5, 1 1, 1.5 1, 1.9 1, 1.9 0.1, 1 0.1, 1 0.5))", ], crs="EPSG:3857", ) assert_geoseries_equal( shapely.normalize(geoms), shapely.normalize(expected), check_less_precise=True, ) @pytest.mark.skipif( Version(gpd.__version__) < Version("0.13.0"), reason="requires geopandas>=0.13.0", ) def test_from_lines(self): geoms = voronoi_frames( self.lines, as_gdf=False, return_input=False, segment=0.1 ) expected = gpd.GeoSeries.from_wkt( [ "POLYGON ((0.5 0.95, 0.5 0, 0 0, 0 1, 0.5 0.95))", "POLYGON ((0.5 0.05, 0.5 1, 1 1, 1 0, 0.5 0.05))", ], crs="EPSG:3857", ) assert_geoseries_equal(geoms.simplify(0.1), expected, check_less_precise=True) @pytest.mark.skipif( Version(gpd.__version__) >= Version("0.13.0"), reason="requires geopandas<0.13.0", ) def test_from_lines_import_error(self): with pytest.raises( ImportError, match="Voronoi tessellation of lines requires geopandas 0.13.0 or later.", ): voronoi_frames(self.lines, as_gdf=False, return_input=False, segment=0.1) def test_clip_none(self): geoms = voronoi_frames( self.points2, as_gdf=False, return_input=False, clip=None ) expected = gpd.GeoSeries.from_wkt( [ "POLYGON ((16 11, 16 -4, 10.75 -4, 7.5 2.5, 7.5 11, 16 11))", "POLYGON ((-2 -4, -2 5.6666666, 7.5 2.5, 10.75 -4, -2 -4))", "POLYGON ((-2 11, 7.5 11, 7.5 2.5, -2 5.66666666, -2 11))", ], ) assert_geoseries_equal( shapely.normalize(geoms), shapely.normalize(expected), check_less_precise=True, ) def test_clip_chull(self): geoms = voronoi_frames( self.points2, as_gdf=False, return_input=False, clip="convex_hull" ) expected = gpd.GeoSeries.from_wkt( [ "POLYGON ((7.5 5, 10 5, 7.5 3.75, 7.5 5))", "POLYGON ((6 3, 4 2, 4.5 3.5, 6 3))", "POLYGON ((7.5 3.75, 6 3, 4.5 3.5, 5 5, 7.5 5, 7.5 3.75))", ], ) assert_geoseries_equal( shapely.normalize(geoms), shapely.normalize(expected), check_less_precise=True, ) def test_clip_ahull(self): geoms = voronoi_frames( self.points2, as_gdf=False, return_input=False, clip="alpha_shape" ) expected = gpd.GeoSeries.from_wkt( [ "POLYGON ((7.5 5, 10 5, 7.5 3.75, 7.5 5))", "POLYGON ((6 3, 4 2, 4.5 3.5, 6 3))", "POLYGON ((7.5 3.75, 6 3, 4.5 3.5, 5 5, 7.5 5, 7.5 3.75))", ], ) assert_geoseries_equal( shapely.normalize(geoms), shapely.normalize(expected), check_less_precise=True, ) def test_clip_polygon(self): geoms = voronoi_frames( self.points2, as_gdf=False, return_input=False, clip=shapely.box(-10, -10, 10, 10), ) expected = gpd.GeoSeries.from_wkt( [ "POLYGON ((7.5 2.5, 7.5 10, 10 10, 10 -2.5, 7.5 2.5))", "POLYGON (" "(-10 8.333333, 7.5 2.5, 10 -2.5, 10 -10, -10 -10, -10 8.333333))", "POLYGON ((7.5 2.5, -10 8.33333333, -10 10, 7.5 10, 7.5 2.5))", ], ) assert_geoseries_equal( shapely.normalize(geoms), shapely.normalize(expected), check_less_precise=True, ) def test_clip_error(self): with pytest.raises(ValueError, match="Clip type 'invalid' not understood."): voronoi_frames(self.points2, clip="invalid") def test_as_gdf(self): geoms, polys = voronoi_frames(self.polygons, as_gdf=True, return_input=True) assert isinstance(geoms, gpd.GeoDataFrame) assert isinstance(polys, gpd.GeoDataFrame) with pytest.warns( FutureWarning, match="The 'as_gdf' parameter currently defaults to True but will", ): voronoi_frames(self.points2, return_input=True) def test_return_input(self): geoms, polys = voronoi_frames(self.polygons, return_input=True, as_gdf=False) assert isinstance(geoms, gpd.GeoSeries) assert polys is self.polygons with pytest.warns( FutureWarning, match="The 'return_input' parameter currently defaults to True but will", ): voronoi_frames(self.points2, as_gdf=True) def test_radius(self): with pytest.warns(FutureWarning, match="The 'radius' parameter is deprecated"): voronoi_frames(self.points2, radius=1) @pytest.mark.parametrize("clip", ["none", "bounds", "chull", "ahull"]) def test_deprecated_clip(self, clip): with pytest.warns( FutureWarning, match=f"The '{clip}' option for the 'clip' parameter is deprecated", ): voronoi_frames(self.points2, clip=clip) libpysal-4.12.1/libpysal/cg/voronoi.py000066400000000000000000000452211466413560300177070ustar00rootroot00000000000000""" Voronoi tesslation of 2-d point sets. Adapted from https://gist.github.com/pv/8036995 """ import warnings import geopandas as gpd import numpy as np import numpy.typing as npt import shapely from packaging.version import Version from scipy.spatial import Voronoi __author__ = "Serge Rey " __all__ = ["voronoi_frames"] GPD_GE_013 = Version(gpd.__version__) >= Version("0.13.0") 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]]) """ warnings.warn( "The 'voronoi' function is considered private and will be " "removed in a future release.", FutureWarning, stacklevel=2, ) 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. """ warnings.warn( "The 'voronoi_regions' function is considered private and will be " "removed in a future release.", FutureWarning, stacklevel=2, ) new_regions = [] new_vertices = vor.vertices.tolist() center = vor.points.mean(axis=0) if radius is None: radius = np.ptp(vor.points).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. """ warnings.warn( "The 'as_dataframes' function is considered private and will be " "removed in a future release.", FutureWarning, stacklevel=2, ) 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 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. """ warnings.warn( "The 'clip_voronoi_frames_to_extent' function is considered private " "and will be removed in a future release.", FutureWarning, stacklevel=2, ) 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 def voronoi_frames( geometry: gpd.GeoSeries | gpd.GeoDataFrame | npt.ArrayLike, radius: float | None = None, clip: str | shapely.Geometry | None = "bounding_box", shrink: float = 0, segment: float = 0, grid_size: float = 1e-5, return_input: bool | None = None, as_gdf: bool | None = None, ) -> gpd.GeoSeries: """ Create Voronoi polygons from a GeoSeries of points, lines, or polygons. This is a wrapper around ``shapely.voronoi_polygons`` that handles not only points but also lines and polygons through their discretization and dissolution of the resulting polygons. Parameters ---------- geometry : GeoSeries | GeoDataFrame | array_like A GeoSeries of points, lines, or polygons or an array of coordinates. radius : float, optional Deprecated. Has no effect any longer. clip : str, shapely.geometry.Polygon, optional Polygon used to clip the Voronoi polygons, by default "bounding_box" The options are: * ``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. * ``'bounding_box'`` -- Clip the voronoi cells to the bounding box of the input points. * ``'convex_hull'`` -- Clip the voronoi cells to the convex hull of the input points. * ``'alpha_shape'`` -- Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alpha shape, using :func:`libpysal.cg.alpha_shape_auto`). * ``shapely.Polygon`` -- Clip to an arbitrary Polygon. shrink : float, optional Distance for the negative buffer of polygons required when there are polygons sharing portion of their exterior, by default 0 segment : float, optional Distance for the segmentation of lines used to add coordinates to lines or polygons prior Voronoi tessellation, by default 0 grid_size : float, optional Grid size precision under which the voronoi algorithm is generated, by default 1e-5 return_input : bool, optional Whether to return the input geometry, defaults to True as_gdf : bool, optional Whether to return the output as a GeoDataFrame (True) or GeoSeries (False), defaults to True Returns ------- GeoSeries | GeoDataFrame | tuple GeoSeries of Voronoi polygons with index allowing to link back to the input """ if radius is not None: warnings.warn( "The 'radius' parameter is deprecated and will be removed in a future " "release. It has no effect any longer.", FutureWarning, stacklevel=2, ) if isinstance(geometry, gpd.GeoDataFrame | gpd.GeoSeries): # Check if the input geometry is in a geographic CRS if geometry.crs and geometry.crs.is_geographic: raise ValueError( "Geometry is in a geographic CRS. " "Use 'GeoSeries.to_crs()' to re-project geometries to a " "projected CRS before using voronoi_polygons.", ) # Set precision of the input geometry (avoids GEOS precision issues) objects = shapely.set_precision(geometry.geometry.copy(), grid_size) geom_types = objects.geom_type mask_poly = geom_types.isin(["Polygon", "MultiPolygon"]) mask_line = objects.geom_type.isin(["LineString", "MultiLineString"]) if mask_poly.any(): # Shrink polygons if required if shrink != 0: objects[mask_poly] = objects[mask_poly].buffer( -shrink, cap_style=2, join_style=2 ) # Segmentize polygons if required if segment != 0: objects.loc[mask_poly] = shapely.segmentize(objects[mask_poly], segment) if mask_line.any(): if segment != 0: objects.loc[mask_line] = shapely.segmentize(objects[mask_line], segment) if not GPD_GE_013: raise ImportError( "Voronoi tessellation of lines requires geopandas 0.13.0 or later." ) # Remove duplicate coordinates from lines objects.loc[mask_line] = ( objects.loc[mask_line] .get_coordinates(index_parts=True) .drop_duplicates(keep=False) .groupby(level=0) .apply(shapely.multipoints) .values ) else: geometry = np.asarray(geometry) objects = geometry = gpd.GeoSeries.from_xy(geometry[:, 0], geometry[:, 1]) mask_poly = mask_line = np.array([False]) limit = _get_limit(objects, clip) # Compute Voronoi polygons voronoi = shapely.voronoi_polygons( shapely.GeometryCollection(objects.values), extend_to=limit ) # Get individual polygons out of the collection polygons = gpd.GeoSeries( shapely.make_valid(shapely.get_parts(voronoi)), crs=geometry.crs ) # temporary fix for libgeos/geos#1062 if not (polygons.geom_type == "Polygon").all(): polygons = polygons.explode(ignore_index=True) polygons = polygons[polygons.geom_type == "Polygon"] # Assign to each input geometry the corresponding Voronoi polygon # TODO: check if we still need indexing after shapely/shapely#1968 is released if GPD_GE_013: ids_objects, ids_polygons = polygons.sindex.query( objects, predicate="intersects" ) else: ids_objects, ids_polygons = polygons.sindex.query_bulk( objects, predicate="intersects" ) if mask_poly.any() or mask_line.any(): # Dissolve polygons polygons = ( polygons.iloc[ids_polygons] .groupby(objects.index.take(ids_objects)) .agg(shapely.coverage_union_all) ) if geometry.crs is not None: polygons = polygons.set_crs(geometry.crs) else: polygons = polygons.iloc[ids_polygons].reset_index(drop=True) # Clip polygons if limit is provided if limit is not None: to_be_clipped = polygons.sindex.query(limit.boundary, "intersects") polygons.iloc[to_be_clipped] = polygons.iloc[to_be_clipped].intersection(limit) if as_gdf is None: as_gdf = True warnings.warn( "The 'as_gdf' parameter currently defaults to True but will " "default to False in a future release. Set it explicitly to avoid " "this warning.", FutureWarning, stacklevel=2, ) if as_gdf: polygons = polygons.to_frame("geometry") geometry = geometry.geometry.to_frame("geometry") if return_input is None: return_input = True warnings.warn( "The 'return_input' parameter currently defaults to True but will " "default to False in a future release. Set it explicitly to avoid " "this warning.", FutureWarning, stacklevel=2, ) if return_input: return polygons, geometry return polygons def _get_limit(points, clip): if isinstance(clip, shapely.Geometry): return clip if clip is None or clip is False: return None if clip.lower() == "none": warnings.warn( "The 'none' option for the 'clip' parameter is deprecated and will " "be removed in a future release. Use None or False instead.", FutureWarning, stacklevel=3, ) return None if clip.lower() in ("bounding_box", "bounds", "bounding box", "bbox", "extent"): if clip.lower() != "bounding_box": warnings.warn( f"The '{clip}' option for the 'clip' parameter is deprecated and " "will be removed in a future release. Use 'bounding_box' instead.", FutureWarning, stacklevel=3, ) return shapely.box(*points.total_bounds) if clip.lower() in ("chull", "convex hull", "convex_hull"): if clip.lower() != "convex_hull": warnings.warn( f"The '{clip}' option for the 'clip' parameter is deprecated and " "will be removed in a future release. Use 'convex_hull' instead.", FutureWarning, stacklevel=3, ) return points.unary_union.convex_hull if clip.lower() in ( "ahull", "alpha hull", "alpha_hull", "ashape", "alpha shape", "alpha_shape", ): if clip.lower() != "alpha_shape": warnings.warn( f"The '{clip}' option for the 'clip' parameter is deprecated and " "will be removed in a future release. Use 'alpha_shape' instead.", FutureWarning, stacklevel=3, ) from .alpha_shapes import alpha_shape_auto coordinates = shapely.get_coordinates(points.values) return alpha_shape_auto(coordinates) raise ValueError( f"Clip type '{clip}' not understood. Try one of the supported options: " "[None, 'bounding_box', 'convex_hull', 'alpha_shape', shapely.Polygon]." ) libpysal-4.12.1/libpysal/common.py000066400000000000000000000062771466413560300171230ustar00rootroot00000000000000import copy import importlib 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: _simport = True, importlib.import_module(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-4.12.1/libpysal/examples/000077500000000000000000000000001466413560300170635ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/10740/000077500000000000000000000000001466413560300175365ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/10740/10740.dbf000066400000000000000000000177741466413560300207060ustar00rootroot00000000000000_ÃÁ)WGIST_IDNFIPSSTCOCTRT2000CSTFIDC TRACTIDC 135001000107350010001071.07 235001000108350010001081.08 335001000109350010001091.09 435001000110350010001101.10 535001000111350010001111.11 635001000112350010001121.12 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libpysal-4.12.1/libpysal/examples/10740/README.md000066400000000000000000000006251466413560300210200ustar00rootroot0000000000000010740 ===== 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-4.12.1/libpysal/examples/Line/000077500000000000000000000000001466413560300177525ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/Line/Line.dbf000066400000000000000000000011551466413560300213200ustar00rootroot00000000000000_{WNameCPIValueN FValueN  Eye1 1 1.100 Eye2 2 2.200 Nose 3 3.300 Mouth 4 4.400libpysal-4.12.1/libpysal/examples/Line/Line.prj000066400000000000000000000002171466413560300213560ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]libpysal-4.12.1/libpysal/examples/Line/Line.shp000066400000000000000000000010641466413560300213560ustar00rootroot00000000000000' 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-4.12.1/libpysal/examples/Line/Line.shx000066400000000000000000000002041466413560300213610ustar00rootroot00000000000000' BèÈßñuî=¥¿ÐDÓäÎyÝ¿(Yñ9àÎÁ?ö½Ëiп20f(’8ÎHlibpysal-4.12.1/libpysal/examples/Line/README.md000066400000000000000000000003011466413560300212230ustar00rootroot00000000000000Line ===== 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-4.12.1/libpysal/examples/Point/000077500000000000000000000000001466413560300201545ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/Point/Point.dbf000066400000000000000000000023241466413560300217230ustar00rootroot00000000000000_ {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-4.12.1/libpysal/examples/Point/Point.prj000066400000000000000000000002171466413560300217620ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]libpysal-4.12.1/libpysal/examples/Point/Point.shp000066400000000000000000000005401466413560300217600ustar00rootroot00000000000000' °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-4.12.1/libpysal/examples/Point/Point.shx000066400000000000000000000002541466413560300217720ustar00rootroot00000000000000' V耫ÉÊÝ ¥¿ *d@à}Ý¿`ø±ƒæÁ? ÛâåEgп2 @ N \ j x † ” ¢ libpysal-4.12.1/libpysal/examples/Point/README.md000066400000000000000000000003061466413560300214320ustar00rootroot00000000000000Point ===== 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-4.12.1/libpysal/examples/Polygon/000077500000000000000000000000001466413560300205125ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/Polygon/Polygon.dbf000066400000000000000000000007621466413560300226230ustar00rootroot00000000000000_{WNameCPIValueN FValueN  Eyes 1 1.100 Nose 2 2.200 Mouth 3 3.300libpysal-4.12.1/libpysal/examples/Polygon/Polygon.prj000066400000000000000000000002171466413560300226560ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]libpysal-4.12.1/libpysal/examples/Polygon/Polygon.shp000066400000000000000000000017401466413560300226570ustar00rootroot00000000000000' 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¿$È™ÛÁ?ðÙÆT²Ù¿,YDgŽUÂ?sVó­ŽïÙ¿ðŸ,[½?ò¶üÜ¿X•’†¬ß³?ªÔÚ[šÝ¿h’x/yœ?,:”_¼Ý¿0 ÓŒ¿™½ÇâoÜ¿yÌéð-§¿{0“wÚ¿libpysal-4.12.1/libpysal/examples/Polygon/Polygon.shx000066400000000000000000000001741466413560300226670ustar00rootroot00000000000000' >èyÌéð-§¿,:”_¼Ý¿,YDgŽUÂ?ÞKtà п2ÚPdˆlibpysal-4.12.1/libpysal/examples/Polygon/README.md000066400000000000000000000003311466413560300217660ustar00rootroot00000000000000Polygon ======= Polygon Shapefile ----------------- * Polygon.dbf: attribute data. (k=3) * Polygon.prj: ESRI projection file. * Polygon.shp: Polygon shapefile. (n=3) * Polygon.shx: spatial index. Used for testing. libpysal-4.12.1/libpysal/examples/Polygon_Holes/000077500000000000000000000000001466413560300216445ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/Polygon_Holes/Polygon_Holes.cpg000066400000000000000000000000051466413560300251130ustar00rootroot00000000000000UTF-8libpysal-4.12.1/libpysal/examples/Polygon_Holes/Polygon_Holes.dbf000066400000000000000000000007621466413560300251070ustar00rootroot00000000000000t{NameCPIValueN FValueN  Eyes 1 1.100 Nose 2 2.200 Mouth 3 3.300libpysal-4.12.1/libpysal/examples/Polygon_Holes/Polygon_Holes.prj000066400000000000000000000002171466413560300251420ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]libpysal-4.12.1/libpysal/examples/Polygon_Holes/Polygon_Holes.qpj000066400000000000000000000004011466413560300251340ustar00rootroot00000000000000GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]] libpysal-4.12.1/libpysal/examples/Polygon_Holes/Polygon_Holes.shp000066400000000000000000000027701466413560300251470ustar00rootroot00000000000000' üèyÌéð-§¿,:”_¼Ý¿,YDgŽUÂ?ÞKtà пFà/Í<#†¿jîªÔ(ÒпÌŒ$^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-4.12.1/libpysal/examples/Polygon_Holes/Polygon_Holes.shx000066400000000000000000000001741466413560300251530ustar00rootroot00000000000000' >èyÌéð-§¿,:”_¼Ý¿,YDgŽUÂ?ÞKtà п2F|ròlibpysal-4.12.1/libpysal/examples/Polygon_Holes/README.md000066400000000000000000000003171466413560300231240ustar00rootroot00000000000000Polygon_Holes ================= Example to test treatment of holes ------------------------------------- * Polygon_Holes.dbf * Polygon_Holes.prj * Polygon_Holes.qpj * Polygon_Holes.shp * Polygon_Holes.shx libpysal-4.12.1/libpysal/examples/__init__.py000066400000000000000000000041601466413560300211750ustar00rootroot00000000000000"""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-4.12.1/libpysal/examples/arcgis/000077500000000000000000000000001466413560300203335ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/arcgis/README.md000066400000000000000000000003151466413560300216110ustar00rootroot00000000000000arcgis ====== 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-4.12.1/libpysal/examples/arcgis/arcgis_ohio.dbf000066400000000000000000000525641466413560300233120ustar00rootroot00000000000000oΡ/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-4.12.1/libpysal/examples/baltim/000077500000000000000000000000001466413560300203335ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/baltim/README.md000066400000000000000000000012121466413560300216060ustar00rootroot00000000000000baltim ====== 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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0.133579 122 129 0.578002 122 130 0.207916 123 118 0.442533 123 119 0.367545 123 120 0.466506 123 121 0.00153975 123 122 0.52122 123 123 1 123 124 0.0513168 123 125 0.77812 123 126 0.168875 123 127 0.717157 123 128 0.803884 123 129 0.40257 123 130 1E-07 124 118 0.478421 124 120 0.509949 124 122 0.0490452 124 124 1 124 126 0.779137 124 127 0.0500322 124 130 0.243539 124 134 1E-07 124 136 0.387436 124 137 0.379826 124 138 0.642817 124 139 0.2423 124 205 1E-07 125 117 0.136378 125 118 0.315943 125 119 0.461682 125 120 0.352513 125 121 0.101334 125 122 0.506575 125 123 0.805052 125 125 1 125 126 0.0901699 125 127 0.655377 125 128 0.857908 125 129 0.51263 125 130 1E-07 126 118 0.51607 126 119 1E-07 126 120 0.685127 126 122 0.288675 126 123 0.123917 126 124 0.789182 126 126 1 126 127 0.263875 126 130 0.462516 126 136 0.500428 126 137 0.201497 126 138 0.607768 126 139 0.0754998 127 118 0.302514 127 119 0.535009 127 120 0.515307 127 121 0.15925 127 122 0.77202 127 123 0.671202 127 124 1E-07 127 125 0.544039 127 126 0.188185 127 127 1 127 128 0.466312 127 129 0.544039 127 130 0.132476 128 117 0.00470048 128 118 0.390386 128 119 0.345858 128 120 0.368658 128 121 1E-07 128 122 0.433238 128 123 0.828803 128 124 0.0195344 128 125 0.858828 128 126 0.111095 128 127 0.599239 128 128 1 128 129 0.390386 129 98 0.0584931 129 117 0.516845 129 118 1E-07 129 119 0.92057 129 120 0.232975 129 121 0.49764 129 122 0.679807 129 123 0.395078 129 125 0.438344 129 127 0.602849 129 128 0.292893 129 129 1 129 130 0.165038 130 119 0.147407 130 120 0.335137 130 121 0.345561 130 122 0.329253 130 124 0.225911 130 126 0.423785 130 127 0.156674 130 129 0.0681367 130 130 1 130 136 0.591351 130 138 0.311903 130 206 1E-07 130 207 0.112408 131 85 0.213121 131 89 0.0376969 131 131 1 131 132 0.421859 131 133 0.180712 131 149 0.108641 131 167 0.433831 131 168 0.424234 131 169 0.128849 131 171 0.0448411 131 174 0.33379 131 175 0.272836 131 178 1E-07 132 1 0.100974 132 85 0.162724 132 89 0.251118 132 90 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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-4.12.1/libpysal/examples/base.py000066400000000000000000000213041466413560300203470ustar00rootroot00000000000000""" 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-4.12.1/libpysal/examples/berlin/000077500000000000000000000000001466413560300203365ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/berlin/README.md000066400000000000000000000010561466413560300216170ustar00rootroot00000000000000Berlin ======== 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-4.12.1/libpysal/examples/burkitt/000077500000000000000000000000001466413560300205475ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/burkitt/README.md000066400000000000000000000007741466413560300220360ustar00rootroot00000000000000burkitt ======= 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-4.12.1/libpysal/examples/burkitt/burkitt.dbf000066400000000000000000000166061466413560300227210ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/burkitt/burkitt.shp000066400000000000000000000123641466413560300227550ustar00rootroot00000000000000' 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-4.12.1/libpysal/examples/burkitt/burkitt.shx000066400000000000000000000031041466413560300227550ustar00rootroot00000000000000' "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-4.12.1/libpysal/examples/calemp/000077500000000000000000000000001466413560300203245ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/calemp/README.md000066400000000000000000000004421466413560300216030ustar00rootroot00000000000000calemp ====== 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-4.12.1/libpysal/examples/calemp/calempdensity.csv000066400000000000000000000156271466413560300237150ustar00rootroot00000000000000"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-4.12.1/libpysal/examples/chicago/000077500000000000000000000000001466413560300204605ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/chicago/Chicago77.dbf000066400000000000000000000406231466413560300226550ustar00rootroot00000000000000j 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-4.12.1/libpysal/examples/columbus/000077500000000000000000000000001466413560300207145ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/columbus/README.md000066400000000000000000000006671466413560300222040ustar00rootroot00000000000000columbus ======== 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. Boston, Kluwer Academic, Table 12.1, p. 189.libpysal-4.12.1/libpysal/examples/columbus/columbus.dbf000066400000000000000000000235421466413560300232300ustar00rootroot00000000000000g1¡ÀWAREAN PERIMETERN COLUMBUS_N COLUMBUS_IN POLYIDNNEIGNHOVALN INCN CRIMEN OPENN PLUMBN DISCBDNXN YN NSANNSBNEWNCPNTHOUSN NEIGNON  0.309441 2.440629 2 5 1 580.46700319.53100015.725980 2.850747 0.2171555.03000038.79999944.0700001.0000001.0000001.0000000.0000001000.0000001005.000000 0.259329 2.236939 3 1 2 144.56700121.23200018.801754 5.296720 0.3205814.27000035.61999942.3800011.0000001.0000000.0000000.0000001000.0000001001.000000 0.192468 2.187547 4 6 3 626.35000015.95600030.626781 4.534649 0.3744043.89000039.82000041.1800001.0000001.0000001.0000000.0000001000.0000001006.000000 0.083841 1.427635 5 2 4 233.200001 4.47700032.387760 0.394427 1.1869443.70000036.50000040.5200001.0000001.0000000.0000000.0000001000.0000001002.000000 0.488888 2.997133 6 7 5 723.22500011.25200050.731510 0.405664 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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-4.12.1/libpysal/examples/columbus/columbus.html000066400000000000000000000056201466413560300234360ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/desmith/desmith.gal000066400000000000000000000001421466413560300226370ustar00rootroot0000000000000010 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-4.12.1/libpysal/examples/desmith/desmith.txt000066400000000000000000000001311466413560300227110ustar00rootroot0000000000000010,2 "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-4.12.1/libpysal/examples/geodanet/000077500000000000000000000000001466413560300206515ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/geodanet/README.md000066400000000000000000000015671466413560300221410ustar00rootroot00000000000000geodanet ======== 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-4.12.1/libpysal/examples/geodanet/crimes.dbf000066400000000000000000000126571466413560300226230ustar00rootroot00000000000000paPOLYID2N 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-4.12.1/libpysal/examples/geodanet/crimes.prj000066400000000000000000000007441466413560300226550ustar00rootroot00000000000000PROJCS["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-4.12.1/libpysal/examples/geodanet/crimes.sbn000066400000000000000000000062341466413560300226440ustar00rootroot00000000000000' 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-4.12.1/libpysal/examples/geodanet/crimes.shx000066400000000000000000000045341466413560300226650ustar00rootroot00000000000000' ®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-4.12.1/libpysal/examples/geodanet/schools.dbf000066400000000000000000000002221466413560300227740ustar00rootroot00000000000000pA WPOLYIDN 1 2 3 4 5 6 7 8libpysal-4.12.1/libpysal/examples/geodanet/schools.prj000066400000000000000000000007441466413560300230450ustar00rootroot00000000000000PROJCS["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-4.12.1/libpysal/examples/geodanet/schools.sbn000066400000000000000000000003241466413560300230260ustar00rootroot00000000000000' ÿÿþpjA&‹T[DÇA*ÀpJÞ‹A&6K(.ŒA*Ùûj,AT ÿÿÿÿÔþÕÿº»€ÇÿÈÿÿËÿ̮ͯÎzñ{òæçlibpysal-4.12.1/libpysal/examples/geodanet/schools.sbx000066400000000000000000000001741466413560300230430ustar00rootroot00000000000000' ÿÿþp>A&‹T[DÇA*ÀpJÞ‹A&6K(.ŒA*Ùûj,AT2 B^libpysal-4.12.1/libpysal/examples/geodanet/schools.shp000066400000000000000000000005041466413560300230360ustar00rootroot00000000000000' ¢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-4.12.1/libpysal/examples/geodanet/schools.shp.xml000066400000000000000000002563271466413560300236550ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/georgia/G_utm.shx000066400000000000000000000025341466413560300223030ustar00rootroot00000000000000' ®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-4.12.1/libpysal/examples/georgia/README.md000066400000000000000000000030701466413560300217570ustar00rootroot00000000000000georgia ======= 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-4.12.1/libpysal/examples/georgia/err.p000066400000000000000000000074401466413560300214560ustar00rootroot00000000000000cnumpy.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-4.12.1/libpysal/examples/georgia/georgia_BS_F.ctl000066400000000000000000000013711466413560300234540ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/georgia/georgia_BS_F_listwise.csv000066400000000000000000001644601466413560300254210ustar00rootroot00000000000000Area_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-4.12.1/libpysal/examples/georgia/georgia_BS_F_summary.txt000066400000000000000000000212451466413560300252700ustar00rootroot00000000000000***************************************************************************** * 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-4.12.1/libpysal/examples/georgia/georgia_BS_NN.ctl000066400000000000000000000013731466413560300236040ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/georgia/georgia_BS_NN_listwise.csv000066400000000000000000001644601466413560300255470ustar00rootroot00000000000000Area_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, 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2.095997, 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-4.12.1/libpysal/examples/georgia/georgia_BS_NN_summary.txt000066400000000000000000000211771466413560300254220ustar00rootroot00000000000000***************************************************************************** * 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-4.12.1/libpysal/examples/georgia/georgia_GS_F.ctl000066400000000000000000000013711466413560300234610ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/georgia/georgia_GS_F_listwise.csv000066400000000000000000001644601466413560300254260ustar00rootroot00000000000000Area_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-4.12.1/libpysal/examples/georgia/georgia_GS_F_summary.txt000066400000000000000000000210041466413560300252660ustar00rootroot00000000000000***************************************************************************** * 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-4.12.1/libpysal/examples/georgia/georgia_GS_NN.ctl000066400000000000000000000013731466413560300236110ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/georgia/georgia_GS_NN_listwise.csv000066400000000000000000001644601466413560300255540ustar00rootroot00000000000000Area_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, 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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-4.12.1/libpysal/examples/georgia/georgia_GS_NN_summary.txt000066400000000000000000000204021466413560300254150ustar00rootroot00000000000000***************************************************************************** * 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-4.12.1/libpysal/examples/juvenile/000077500000000000000000000000001466413560300207045ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/juvenile/README.md000066400000000000000000000006711466413560300221670ustar00rootroot00000000000000juvenile ======== 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-4.12.1/libpysal/examples/juvenile/juvenile.dbf000066400000000000000000000113421466413560300232030ustar00rootroot00000000000000d¨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-4.12.1/libpysal/examples/juvenile/juvenile.gwt000066400000000000000000002136441466413560300232620ustar00rootroot000000000000001 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-4.12.1/libpysal/examples/juvenile/juvenile.shp000066400000000000000000000113041466413560300232400ustar00rootroot00000000000000' 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-4.12.1/libpysal/examples/mexico/mexico.csv000066400000000000000000000067341466413560300223620ustar00rootroot00000000000000State,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-4.12.1/libpysal/examples/mexico/mexico.gal000066400000000000000000000016331466413560300223230ustar00rootroot0000000000000032 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-4.12.1/libpysal/examples/mexico/mexicojoin.dbf000066400000000000000000000201021466413560300231630ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/mexico/mexicojoin.shx000066400000000000000000000005441466413560300232420ustar00rootroot00000000000000' 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-4.12.1/libpysal/examples/networks/000077500000000000000000000000001466413560300207375ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/networks/README.md000066400000000000000000000014341466413560300222200ustar00rootroot00000000000000networks ======== 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-4.12.1/libpysal/examples/networks/eberly_net.dbf000066400000000000000000000025041466413560300235450ustar00rootroot00000000000000q *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-4.12.1/libpysal/examples/networks/eberly_net.shp000066400000000000000000000051341466413560300236060ustar00rootroot00000000000000' .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-4.12.1/libpysal/examples/networks/eberly_net.shx000066400000000000000000000005141466413560300236130ustar00rootroot00000000000000' ¦èð?5@"@2(^(Š(¶(â((:(f(’(¾(ê((B(n(š(Æ(ò((J(v(¢(Î(ú(&(R(~(ª(Ö((libpysal-4.12.1/libpysal/examples/networks/eberly_net_pts_offnetwork.dbf000066400000000000000000000042251466413560300267010ustar00rootroot00000000000000_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-4.12.1/libpysal/examples/networks/eberly_net_pts_offnetwork.shp000066400000000000000000000055241466413560300267430ustar00rootroot00000000000000' ªèH5ï'æÄµ?ÚeäÉsð? 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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-4.12.1/libpysal/examples/networks/eberly_net_pts_onnetwork.dbf000066400000000000000000000023731466413560300265450ustar00rootroot00000000000000_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-4.12.1/libpysal/examples/networks/eberly_net_pts_onnetwork.shp000066400000000000000000000061541466413560300266050ustar00rootroot00000000000000' 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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Ø æ ô    , : H V d r € Ž œ ª ¸ Æ Ô â ð þ   ( libpysal-4.12.1/libpysal/examples/networks/nonplanarsegments.dbf000066400000000000000000000001271466413560300251520ustar00rootroot00000000000000_A idN 2 1libpysal-4.12.1/libpysal/examples/networks/nonplanarsegments.prj000066400000000000000000000002171466413560300252120ustar00rootroot00000000000000GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]libpysal-4.12.1/libpysal/examples/networks/nonplanarsegments.qpj000066400000000000000000000004011466413560300252040ustar00rootroot00000000000000GEOGCS["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-4.12.1/libpysal/examples/networks/nonplanarsegments.shp000066400000000000000000000004641466413560300252150ustar00rootroot00000000000000' 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-4.12.1/libpysal/examples/networks/nonplanarsegments.shx000066400000000000000000000001641466413560300252220ustar00rootroot00000000000000' :èŸ.ËÉ-ó¿CÆ·÷?>Ÿx¢ˆŽï¿}“Ñ7ø?20f0libpysal-4.12.1/libpysal/examples/remotes.py000066400000000000000000000661441466413560300211260ustar00rootroot00000000000000"""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, timeout=(10, None)) 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) # Chicago SDOH name = "chicagoSDOH" description = "Chicago census tract SDOH variables" n = 791 k = 65 download_url = "https://github.com/lanselin/spreg_sample_data/archive/master.zip" explain_url = ( "https://raw.githubusercontent.com/lanselin/spreg_sample_data/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-4.12.1/libpysal/examples/sids2/000077500000000000000000000000001466413560300201075ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/sids2/README.md000066400000000000000000000007071466413560300213720ustar00rootroot00000000000000sids2 ===== 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-4.12.1/libpysal/examples/sids2/sids2.dbf000066400000000000000000000564021466413560300216170ustar00rootroot00000000000000gdaèWAREAN 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-4.12.1/libpysal/examples/sids2/sids2.gal000066400000000000000000000070071466413560300216240ustar00rootroot000000000000000 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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37149 2 37161 37089 37113 5 37043 37039 37099 37075 37173 37163 7 37017 37051 37141 37061 37191 37085 37101 37137 2 37049 37013 37039 3 37113 37043 37075 37051 6 37017 37155 37093 37163 37085 37125 37103 5 37133 37061 37031 37049 37107 37179 4 37007 37167 37025 37119 37007 4 37153 37123 37179 37167 37093 4 37155 37165 37051 37125 37095 4 37055 37177 37013 37187 37061 6 37141 37103 37133 37107 37191 37163 37153 4 37165 37007 37125 37123 37043 2 37113 37039 37049 6 37031 37137 37013 37103 37107 37147 37165 3 37155 37093 37153 37133 4 37141 37031 37103 37061 37155 5 37047 37017 37051 37093 37165 37031 3 37049 37133 37103 37017 5 37047 37141 37163 37051 37155 37141 7 37019 37129 37047 37133 37061 37017 37163 37047 4 37019 37141 37017 37155 37129 2 37019 37141 37019 3 37129 37141 37047 libpysal-4.12.1/libpysal/examples/sids2/sids2.html000066400000000000000000000052431466413560300220250ustar00rootroot00000000000000 SAL Data Sets - SIDS2

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-4.12.1/libpysal/examples/snow_maps/000077500000000000000000000000001466413560300210715ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/snow_maps/README.md000066400000000000000000000020671466413560300223550ustar00rootroot00000000000000snow_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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›YAs(x>ÍÀ|T?›YA #"ÍÀO–E›YA #"ÍÀO–E›YAx>ÍÀ|T?›YAt(¸ÍÀ+_d›YAòÑxÍÀ0ro›YAòÑxÍÀ0ro›YA¸ÍÀ+_d›YAu(º£ÍÀƒ2g›YAèM”ÍÀ¶·s›YAèM”ÍÀƒ2g›YAº£ÍÀ¶·s›YAv(x´ÍÀ¶·s›YAº£ÍÀ3å{›YAx´ÍÀ3å{›YAº£ÍÀ¶·s›YAlibpysal-4.12.1/libpysal/examples/snow_maps/Soho_Network.shx000066400000000000000000000020241466413560300242340ustar00rootroot00000000000000'  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-4.12.1/libpysal/examples/stl/000077500000000000000000000000001466413560300176655ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/stl/README.md000066400000000000000000000017251466413560300211510ustar00rootroot00000000000000stl === 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-4.12.1/libpysal/examples/stl/stl.gal000066400000000000000000000030021466413560300211470ustar00rootroot0000000000000078 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 37.048870086669922,-90.154685974121094 37.012588500976562,-90.169090270996094 37.011600494384766,-90.17120361328125 36.990734100341797,-90.1884765625 36.989276885986328,-90.190635681152344 36.973396301269531,-90.2061767578125 36.972396850585938,-90.207229614257812 36.961963653564453,-90.226219177246094 36.960037231445312,-90.22772216796875 36.936904907226562,-90.262710571289062 36.924446105957031,-90.680656433105469 36.925613403320312))",Wayne,Missouri,29,223,29223, 29223, 5.863555, 0.000000, 8.451537,4,0,6,68218,57181,70993, 2.481962, 3.551555, 3.314986, 1.087340, 1.257024, 1.320757 libpysal-4.12.1/libpysal/examples/stl/stl_hom.dbf000066400000000000000000000553601466413560300220200ustar00rootroot00000000000000gNá!WPOLY_ID_OGN NAMEC STATE_NAMECSTATE_FIPSCCNTY_FIPSCFIPSCFIPSNONHR7984NHR8488NHR8893NHC7984N HC8488N HC8893N PO7984N PO8488N PO8893N PE77NPE82NPE87N RDAC80N RDAC85NRDAC90N 1Logan Illinois 1710717107 17107 2.115428 1.290722 1.624458 4 2 3 189087 154952 184677 5.104320 6.595780 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-4.12.1/libpysal/examples/stl/stl_hom.html000066400000000000000000000062721466413560300222270ustar00rootroot00000000000000 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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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-4.12.1/libpysal/examples/street_net_pts/000077500000000000000000000000001466413560300221255ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/street_net_pts/README.md000066400000000000000000000004441466413560300234060ustar00rootroot00000000000000street_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-4.12.1/libpysal/examples/street_net_pts/street_net_pts.dbf000066400000000000000000000065061466413560300256530ustar00rootroot00000000000000_/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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.æ{±-&AÄó¥L·É*A £¸f‚ï#&ABÍSdÏÍ*A +yk¶ž/&A›k™ŠÂ*A %9†|&A ›l³¿*A ÿV¯†&AZq ßå*A Ý%`'&AÍèÊ à*A x¥·ï8&AO*¿Â*A FqäaÏ!&A¦ëÆŠÈ*A ý1$=&A YŒÆÒ*A PŽw8—%&AwÍ­“Ž*A ÿ\´2è+&A“¼g(Ñ*A Ûô®ØÚ(&AÃS ½*A fS “ï&A_w‰‘§ß*A ·ßÍn•&AËoÁ&l»*A PÙÜmˆ)&A=ì}ÇÍ*A g¿ôü;&Ad^Íð³É*A ÛAî<&Aj›Òã*A ºÊm³A4&Aa°ò·À*A E„N&A%ãtúÙÜ*A 'ær0g&AdkÃÌ*A m¶m &AIšzMÍ*A öµYL‰4&A1²°ˆ3Ù*A ýP-Ç(&A;u_P&A3F/¯+»*Aý1$=&A‡€üQå*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 ä ò    * 8 F T b p ~ Œ š ¨ ¶ libpysal-4.12.1/libpysal/examples/tests/000077500000000000000000000000001466413560300202255ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/tests/__init__.py000066400000000000000000000000001466413560300223240ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/tests/test_available.py000066400000000000000000000045661466413560300235710ustar00rootroot00000000000000#!/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 == (99, 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-4.12.1/libpysal/examples/tokyo/000077500000000000000000000000001466413560300202305ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/tokyo/README.md000066400000000000000000000027541466413560300215170ustar00rootroot00000000000000tokyo ====== 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-4.12.1/libpysal/examples/tokyo/Readme_tokyomortality.txt000066400000000000000000000032351466413560300253630ustar00rootroot00000000000000Dataset 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-4.12.1/libpysal/examples/tokyo/SampleTokyoMortalityGWPR.ctl000066400000000000000000000013341466413560300256110ustar00rootroot00000000000000Semiparametric 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-4.12.1/libpysal/examples/tokyo/Tokyomortality.csv000066400000000000000000000353321466413560300240250ustar00rootroot00000000000000IDnum0,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 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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-4.12.1/libpysal/examples/tokyo/Tokyomortality.txt000066400000000000000000000510561466413560300240520ustar00rootroot00000000000000 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 18620.67 47 40.968 0.064 0.770 0.124 2.265 29 359036.48 1198.74 49 47.597 0.129 0.817 0.080 2.010 30 370771.99 -1522.12 49 48.444 0.107 0.856 0.095 2.752 31 376842.13 -7139.16 27 29.490 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 1.680 34 318112.24 28405.62 16 17.138 0.062 0.914 0.143 1.617 35 310480.10 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 0.070 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 22478.00 210 230.061 0.110 0.638 0.103 2.740 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 0.090 2.645 43 312830.72 21412.10 132 123.957 0.099 0.734 0.114 2.430 44 312874.14 -17053.63 395 440.380 0.157 0.578 0.073 2.800 45 293680.38 -8010.10 97 109.945 0.120 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 8472.00 97 122.976 0.117 0.661 0.087 2.394 48 335330.50 -108.19 148 162.923 0.099 0.711 0.081 3.026 49 339115.66 3202.05 269 271.360 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 2.330 52 317013.43 12374.14 86 105.836 0.113 0.715 0.084 2.577 53 323345.93 2314.46 244 294.090 0.113 0.562 0.066 2.759 54 327610.55 -7504.02 120 116.831 0.152 0.500 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 -13559.30 103 114.209 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 0.070 2.886 60 338062.78 -13156.47 102 96.519 0.076 0.621 0.085 3.045 61 325419.58 -15527.50 142 150.750 0.126 0.434 0.063 3.041 62 324052.62 -12510.90 83 94.684 0.135 0.599 0.062 3.130 63 327521.88 -17674.68 78 83.535 0.144 0.463 0.069 2.770 64 322114.34 -17894.35 201 208.889 0.122 0.606 0.066 2.516 65 320355.21 5840.48 87 107.467 0.110 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 0.060 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 301727.00 -6640.87 68 74.512 0.113 0.758 0.090 2.324 77 326724.18 5478.10 47 38.462 0.103 0.809 0.069 2.164 78 311503.39 15708.66 33 40.921 0.117 0.770 0.081 2.083 79 316934.08 -10632.09 43 55.320 0.108 0.643 0.057 2.695 80 317980.71 -13171.59 52 51.875 0.104 0.730 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 0.100 0.866 0.140 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 2.480 86 292980.66 10621.27 46 52.123 0.107 0.836 0.124 2.887 87 291341.64 3602.46 19 13.330 0.081 0.934 0.143 2.289 88 296052.78 6812.78 11 8.054 0.080 0.890 0.128 1.858 89 314476.95 3490.04 30 32.284 0.085 0.944 0.124 1.730 90 311673.48 10101.08 27 28.431 0.078 0.931 0.124 2.360 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 0.080 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 296097.20 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 0.130 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 0.070 0.896 0.112 2.779 103 330024.30 22050.13 24 22.623 0.086 0.917 0.128 2.115 104 335366.50 8522.69 45 50.900 0.105 0.711 0.076 2.512 105 330795.70 8625.57 56 59.574 0.096 0.838 0.090 2.449 106 324461.10 12021.30 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 0.120 0.663 0.065 3.294 109 338740.71 9995.65 58 59.655 0.098 0.787 0.084 2.434 110 345541.90 -607.56 37 34.330 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 356153.10 -24448.15 547 607.845 0.137 0.422 0.074 2.635 115 363934.49 -23252.20 660 811.312 0.130 0.518 0.073 2.921 116 362715.03 -62961.03 175 193.735 0.101 0.620 0.100 3.467 117 355515.39 -15862.17 594 664.519 0.137 0.480 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 365535.40 -28214.23 200 220.195 0.149 0.461 0.072 2.810 124 358761.78 -6929.68 389 454.815 0.145 0.584 0.071 2.711 125 376070.01 -56303.59 381 386.197 0.110 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 -18166.00 148 146.777 0.109 0.723 0.068 2.281 130 366450.66 -74634.65 141 142.727 0.097 0.602 0.108 3.160 131 355381.88 -79216.89 106 95.232 0.071 0.808 0.150 2.477 132 353694.67 -32885.23 116 143.243 0.146 0.424 0.043 2.633 133 378726.25 -28678.90 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 -22717.20 57 56.813 0.075 0.766 0.079 2.460 140 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 0.910 0.168 3.676 147 402518.42 -36236.31 37 36.163 0.080 0.854 0.160 2.236 148 396061.30 -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 0.820 0.154 2.725 152 399386.50 -22290.57 18 14.326 0.052 0.847 0.166 1.836 153 397587.40 -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 0.920 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 -61374.50 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 1.360 160 342259.81 -30180.57 117 110.919 0.100 0.441 0.153 2.131 161 338829.19 -32435.83 256 255.792 0.160 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 345601.80 -25527.73 452 371.254 0.086 0.469 0.130 3.008 166 345909.97 -30691.34 728 629.225 0.098 0.398 0.094 3.720 167 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 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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-4.12.1/libpysal/examples/tokyo/tokyo_BS_F.ctl000066400000000000000000000014071466413560300227340ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/tokyo/tokyo_BS_F_listwise.csv000066400000000000000000002775711466413560300247110ustar00rootroot00000000000000Area_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, 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-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, 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6.660298, 0.159902, 41.652401, -0.384411, 0.408978, -0.939930, -2.134535, 0.136973, -15.583580, 7.039918, 0.491010, 14.337628, -0.184518, 0.026081, -7.074786, 361.000000, 232.588123, 0.294276, 0.105427 226, 226, 320831.13, -45866.47, 7.380323, 0.162249, 45.487520, -4.245062, 0.379771, -11.177947, -1.725040, 0.144983, -11.898209, 10.907932, 0.462735, 23.572762, -0.402715, 0.020743, -19.414625, 432.000000, 256.846765, 0.284262, 0.147986 227, 227, 316915.15, -53631.62, 5.317747, 0.177343, 29.985603, 3.354535, 0.436688, 7.681771, -1.601749, 0.153191, -10.455904, 4.075656, 0.551536, 7.389641, 0.064471, 0.030163, 2.137384, 156.000000, 248.974811, 0.338922, 0.084980 228, 228, 323595.31, -65306.25, 4.460472, 0.215632, 20.685552, 8.606188, 0.730179, 11.786406, -2.515650, 0.189918, -13.245998, -5.951276, 0.737594, -8.068496, 0.570776, 0.039270, 14.534494, 153.000000, 248.714446, 0.530003, 0.156226 229, 229, 318116.79, -58966.35, 5.394172, 0.192588, 28.008828, 5.903675, 0.509622, 11.584428, -2.697822, 0.171204, -15.757902, -0.222184, 0.618443, -0.359264, 0.228566, 0.034364, 6.651294, 186.000000, 179.091001, 0.384622, 0.242742 230, 230, 339293.11, -47659.81, 8.911340, 0.160686, 55.458147, -7.067553, 0.390057, -18.119278, -2.863803, 0.174788, -16.384473, -0.177347, 0.388239, -0.456799, -0.219621, 0.016317, -13.460039, 446.000000, 405.503283, 0.235419, 0.475031 231, 231, 334055.07, -44545.18, 8.238102, 0.154376, 53.363992, -5.376618, 0.351815, -15.282525, -2.727481, 0.159763, -17.071998, 1.939631, 0.376759, 5.148206, -0.182010, 0.015174, -11.995012, 248.000000, 409.794194, 0.251952, 0.218166 232, 232, 331453.95, -41443.1, 8.112061, 0.150813, 53.788798, -5.243250, 0.327520, -16.008952, -2.919300, 0.162467, -17.968608, 2.761862, 0.366119, 7.543615, -0.158837, 0.014618, -10.865978, 260.000000, 397.180460, 0.265736, 0.223270 233, 233, 327954.56, -39544.67, 7.918451, 0.150338, 52.670937, -5.290023, 0.320114, -16.525428, -2.856477, 0.160280, -17.821801, 4.478373, 0.361060, 12.403388, -0.169965, 0.014477, -11.740445, 236.000000, 340.928127, 0.283033, 0.188675 234, 234, 321981.74, -36838.07, 7.547574, 0.159252, 47.393953, -5.244660, 0.330087, -15.888710, -2.291328, 0.162957, -14.060909, 8.187346, 0.370149, 22.119044, -0.266693, 0.016274, -16.387716, 175.000000, 223.389849, 0.293327, 0.110196 235, 235, 324590.43, -40337.23, 7.629765, 0.156053, 48.892245, -5.320303, 0.342492, -15.534078, -2.166999, 0.157997, -13.715409, 6.532906, 0.372181, 17.553027, -0.235750, 0.014740, -15.993849, 192.000000, 249.177410, 0.282011, 0.156147 236, 236, 317357.31, -39249.39, 7.471354, 0.175385, 42.599690, -5.072565, 0.391661, -12.951417, -1.446055, 0.158014, -9.151440, 12.844766, 0.491938, 26.110538, -0.515490, 0.022756, -22.652876, 118.000000, 173.172290, 0.270699, 0.247704 237, 237, 333435.39, -77430.79, 0.285660, 0.426369, 0.669983, 8.263250, 1.101255, 7.503486, 3.217910, 0.335903, 9.579878, -27.216816, 1.291047, -21.081192, 1.650179, 0.070554, 23.388746, 735.000000, 580.270028, 0.825868, 0.843323 238, 238, 302126.02, -67286.13, 6.648560, 0.353517, 18.806911, 3.635812, 1.063642, 3.418265, -8.238447, 0.400756, -20.557278, 5.329344, 1.440606, 3.699376, 0.749728, 0.074239, 10.098808, 346.000000, 313.328073, 0.704803, 0.607075 239, 239, 321943.14, -68916.22, 3.901667, 0.232415, 16.787516, 9.142091, 0.754323, 12.119594, -2.248044, 0.210670, -10.670904, -7.547500, 0.797240, -9.467039, 0.739296, 0.042716, 17.307358, 247.000000, 186.358861, 0.603222, 0.430545 240, 240, 314598.5, -64965.34, 4.703208, 0.262442, 17.920922, 12.013407, 0.838874, 14.320869, -4.369488, 0.230230, -18.978797, -4.227815, 0.811853, -5.207614, 0.574358, 0.049356, 11.637146, 463.000000, 280.039025, 0.538188, 0.255560 241, 241, 310206.19, -67759.02, 5.873550, 0.312870, 18.773112, 9.427009, 0.944831, 9.977459, -6.113100, 0.295461, -20.690016, -0.114265, 0.993489, -0.115014, 0.501001, 0.061518, 8.143942, 279.000000, 224.628683, 0.630236, 0.231290 242, 242, 326961.41, -72189.14, 3.458200, 0.250856, 13.785582, 5.543995, 0.738460, 7.507509, -0.429170, 0.221957, -1.933569, -12.270174, 0.830051, -14.782441, 0.912324, 0.043030, 21.201837, 93.000000, 175.860768, 0.688469, 0.417000 243, 243, 306522.23, -44854.32, 4.026808, 0.237673, 16.942607, 3.420848, 0.508924, 6.721725, -0.933502, 0.180850, -5.161763, -9.382616, 0.846416, -11.085116, 0.713726, 0.041962, 17.009041, 686.000000, 287.475981, 0.408636, 0.242337 244, 244, 332009.57, -86587.48, 8.612336, 0.835964, 10.302287, -2.024864, 1.668760, -1.213395, -12.474311, 1.272645, -9.801877, 3.659702, 3.163100, 1.156999, 1.504181, 0.081849, 18.377457, 105.000000, 109.491532, 0.883143, 0.989316 245, 245, 291250.63, -62212.96, 6.241682, 0.513236, 12.161438, -0.864512, 1.179587, -0.732894, -7.080622, 0.589577, -12.009655, -6.256354, 2.123104, -2.946796, 1.227185, 0.111302, 11.025686, 200.000000, 145.429955, 0.679432, 0.473949 246, 246, 302274.84, -54583.9, 3.970989, 0.277643, 14.302493, 6.596793, 0.687260, 9.598685, -5.023711, 0.271706, -18.489495, -7.110381, 1.257576, -5.654035, 1.309785, 0.058305, 22.464286, 209.000000, 173.315221, 0.593983, 0.209360 247, 247, 313999.38, -53197.4, 4.600232, 0.186032, 24.728230, 4.982501, 0.451475, 11.036063, -1.436614, 0.159133, -9.027728, 2.441508, 0.570517, 4.279468, 0.239193, 0.031534, 7.585258, 265.000000, 251.137971, 0.399848, 0.134187 248, 248, 299312.82, -60819.66, 5.074453, 0.328488, 15.447888, 4.475117, 1.024674, 4.367357, -6.376102, 0.346973, -18.376364, -4.753446, 1.480633, -3.210415, 1.204933, 0.069443, 17.351299, 88.000000, 230.510673, 0.667093, 0.337531 249, 249, 308373.06, -57401.82, 4.393080, 0.231593, 18.968996, 9.406106, 0.571178, 16.467910, -4.181908, 0.227423, -18.388201, -3.646616, 0.747851, -4.876124, 0.761626, 0.042487, 17.925923, 121.000000, 156.245055, 0.554028, 0.167502 250, 250, 309678.63, -51875.49, 4.001262, 0.206757, 19.352525, 5.397538, 0.501537, 10.761995, -1.816161, 0.174061, -10.434064, -1.235406, 0.663302, -1.862509, 0.592958, 0.036064, 16.441700, 140.000000, 219.811172, 0.468257, 0.086268 251, 251, 311833.72, -57007.52, 4.619656, 0.204422, 22.598673, 8.040916, 0.504480, 15.939010, -3.107783, 0.193210, -16.084961, -1.331811, 0.631794, -2.107982, 0.487716, 0.035906, 13.583193, 104.000000, 91.499210, 0.484895, 0.236581 252, 252, 327926.88, -75230.85, 2.741441, 0.327120, 8.380535, 5.776966, 0.926458, 6.235543, 0.349554, 0.253503, 1.378892, -16.227335, 0.959645, -16.909730, 1.137503, 0.054110, 21.022207, 41.000000, 114.228334, 0.751198, 0.343503 253, 253, 307926.73, -64266.21, 5.137657, 0.293629, 17.497080, 10.364251, 0.854061, 12.135265, -5.748999, 0.296748, -19.373368, -1.725399, 0.948296, -1.819474, 0.687413, 0.056215, 12.228190, 64.000000, 105.125619, 0.625652, 0.348327 254, 254, 299390.82, -71196.86, 7.933906, 0.413677, 19.178999, -0.444249, 1.278663, -0.347432, -10.595750, 0.504897, -20.985959, 11.047972, 1.923563, 5.743494, 0.769400, 0.091832, 8.378348, 49.000000, 38.492351, 0.734232, 0.480124 255, 255, 295866.34, -72353.52, 8.291320, 0.474286, 17.481686, -2.103398, 1.377734, -1.526709, -11.399216, 0.630403, -18.082422, 12.266861, 2.507558, 4.891955, 0.825176, 0.105672, 7.808828, 44.000000, 83.418952, 0.737625, 0.434868 256, 256, 299578.37, -47629.99, 2.925340, 0.307559, 9.511487, 5.106422, 0.657567, 7.765633, -2.586861, 0.240808, -10.742431, -13.294132, 1.269529, -10.471702, 1.457574, 0.057678, 25.271090, 35.000000, 61.901706, 0.522154, 0.327056 257, 257, 293314.45, -52457.68, 4.287453, 0.425091, 10.085955, 3.278614, 1.119211, 2.929397, -6.019539, 0.411966, -14.611739, -11.090555, 1.600138, -6.931001, 1.686620, 0.082394, 20.470073, 6.000000, 15.089429, 0.608255, 0.245260 258, 258, 299215.09, -41823.07, 3.072393, 0.277050, 11.089678, 7.711599, 0.572342, 13.473768, -1.030781, 0.209561, -4.918764, -14.021358, 1.082301, -12.955138, 0.948636, 0.049710, 19.083357, 32.000000, 128.627298, 0.476249, 0.438285 259, 259, 288944.87, -47144.31, 4.426345, 0.564826, 7.836653, 3.013489, 1.596897, 1.887090, -7.189329, 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-4.12.1/libpysal/examples/tokyo/tokyo_BS_F_summary.txt000066400000000000000000000237321466413560300245530ustar00rootroot00000000000000***************************************************************************** * 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-4.12.1/libpysal/examples/tokyo/tokyo_BS_NN.ctl000066400000000000000000000014111466413560300230550ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/tokyo/tokyo_BS_NN_OFF.ctl000066400000000000000000000014151466413560300235530ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/tokyo/tokyo_BS_NN_OFF_listwise.csv000066400000000000000000002775711466413560300255310ustar00rootroot00000000000000Area_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, -3.188706, 2.200147, 0.386718, 5.689277, 0.012772, 0.029182, 0.437658, 27.000000, 26.272906, 0.697026, 0.053511 32, 32, 318049.53, 32744.59, -0.247365, 0.243840, -1.014455, -0.926965, 0.786639, -1.178388, 0.028041, 0.196415, 0.142766, 2.149458, 0.843408, 2.548541, 0.021761, 0.045220, 0.481220, 120.000000, 122.113836, 0.304744, 0.194182 33, 33, 325761.21, 31092.21, -0.090731, 0.254307, -0.356778, -1.220510, 0.754304, -1.618061, -0.030182, 0.195782, -0.154161, 1.838380, 0.843750, 2.178822, 0.002077, 0.044988, 0.046171, 28.000000, 28.995105, 0.322726, 0.105439 34, 34, 318112.24, 28405.62, -0.265526, 0.240853, -1.102437, -0.970020, 0.792088, -1.224636, 0.023722, 0.192996, 0.122917, 2.212323, 0.844409, 2.619967, 0.029304, 0.045713, 0.641050, 16.000000, 18.193435, 0.308164, 0.050930 35, 35, 310480.1, 28809.03, -0.480198, 0.254603, -1.886067, -0.042384, 0.834890, -0.050766, 0.092387, 0.210808, 0.438253, 2.817320, 0.888884, 3.169503, 0.030485, 0.047949, 0.635781, 14.000000, 18.680516, 0.282427, 0.038288 36, 36, 306513.97, 32751.48, -0.508643, 0.258445, -1.968090, 0.171302, 0.842744, 0.203267, 0.096777, 0.216387, 0.447239, 2.966888, 0.891248, 3.328913, 0.025342, 0.048717, 0.520197, 43.000000, 43.027917, 0.275281, 0.252608 37, 37, 311395.51, 33538.42, -0.398725, 0.252472, -1.579283, -0.343810, 0.835546, -0.411480, 0.079220, 0.209444, 0.378239, 2.558719, 0.877883, 2.914647, 0.025424, 0.047172, 0.538962, 29.000000, 37.315654, 0.287165, 0.058349 38, 38, 314408.34, -4572.95, -0.417306, 0.193383, -2.157928, -1.468377, 0.469973, -3.124388, -0.068988, 0.126921, -0.543547, 2.540501, 0.689647, 3.683769, 0.123937, 0.036778, 3.369879, 401.000000, 397.937397, 0.362033, 0.148101 39, 39, 303850.22, 22478, -0.699981, 0.259869, -2.693591, 0.878856, 0.821459, 1.069871, 0.101172, 0.209700, 0.482460, 3.660228, 0.864183, 4.235476, 0.041082, 0.049816, 0.824672, 210.000000, 219.017488, 0.265338, 0.176301 40, 40, 337540.25, -12310.61, 0.277039, 0.121119, 2.287324, -2.543204, 0.255664, -9.947431, -0.349217, 0.099545, -3.508147, 1.577459, 0.351469, 4.488192, 0.018444, 0.019692, 0.936606, 711.000000, 673.430984, 0.738627, 0.167790 41, 41, 330948.96, -8687.59, 0.260041, 0.139674, 1.861768, -2.455274, 0.281327, -8.727485, -0.361917, 0.108428, -3.337847, 1.546136, 0.398565, 3.879257, 0.021377, 0.023299, 0.917530, 544.000000, 558.617634, 0.665159, 0.125186 42, 42, 327143.99, -3103.01, 0.136774, 0.158317, 0.863923, -2.277080, 0.345304, -6.594422, -0.310312, 0.112651, -2.754625, 1.521894, 0.508631, 2.992136, 0.044787, 0.030070, 1.489424, 557.000000, 572.815283, 0.564325, 0.147968 43, 43, 312830.72, 21412.1, -0.518183, 0.242781, -2.134368, -0.076504, 0.774397, -0.098791, 0.077552, 0.195414, 0.396860, 2.911709, 0.850097, 3.425148, 0.047817, 0.046553, 1.027158, 132.000000, 121.416961, 0.286851, 0.077892 44, 44, 312874.14, -17053.63, 0.150475, 0.155909, 0.965148, -2.254038, 0.361754, -6.230858, -0.364480, 0.118615, -3.072788, 1.522845, 0.507587, 3.000167, 0.048831, 0.026271, 1.858728, 395.000000, 372.953357, 0.475765, 0.169565 45, 45, 293680.38, -8010.1, -0.372165, 0.250462, -1.485915, -1.219876, 0.621277, -1.963499, -0.113581, 0.176274, -0.644344, 2.838237, 0.691512, 4.104393, 0.095588, 0.044706, 2.138127, 97.000000, 109.835374, 0.319664, 0.108978 46, 46, 325185.11, 20460.45, -0.055688, 0.232130, -0.239899, -1.812847, 0.737354, -2.458584, -0.102706, 0.173523, -0.591886, 1.947678, 0.805517, 2.417923, 0.028838, 0.045738, 0.630504, 91.000000, 84.872599, 0.343784, 0.080987 47, 47, 305971.31, 8472, -0.879764, 0.259764, -3.386777, 0.753090, 0.756782, 0.995121, 0.111386, 0.188401, 0.591214, 3.976434, 0.772831, 5.145283, 0.102327, 0.048425, 2.113083, 97.000000, 108.297501, 0.271675, 0.058896 48, 48, 335330.5, -108.19, 0.245307, 0.163630, 1.499159, -2.554247, 0.373465, -6.839323, -0.329726, 0.114618, -2.876744, 1.672999, 0.426676, 3.921006, 0.020461, 0.029876, 0.684881, 148.000000, 155.820960, 0.598509, 0.128136 49, 49, 339115.66, 3202.05, 0.277493, 0.179938, 1.542165, -2.703278, 0.433780, -6.231908, -0.322082, 0.120612, -2.670404, 1.755128, 0.450394, 3.896872, 0.011709, 0.033079, 0.353957, 269.000000, 254.192897, 0.561882, 0.149430 50, 50, 309271.13, -10589.17, -0.216303, 0.180414, -1.198926, -1.779044, 0.450826, -3.946186, -0.168438, 0.127012, -1.326159, 2.438258, 0.638148, 3.820833, 0.090298, 0.033254, 2.715403, 183.000000, 193.201971, 0.389116, 0.089823 51, 51, 319972.62, 24634.35, -0.227204, 0.233788, -0.971838, -1.167162, 0.756679, -1.542479, -0.011402, 0.183494, -0.062136, 2.195043, 0.824337, 2.662798, 0.033466, 0.045250, 0.739585, 86.000000, 86.119682, 0.317870, 0.082565 52, 52, 317013.43, 12374.14, -0.510486, 0.224011, -2.278845, -0.557781, 0.668773, -0.834036, 0.008853, 0.166158, 0.053279, 2.946115, 0.774724, 3.802793, 0.084528, 0.044477, 1.900520, 86.000000, 95.584271, 0.302171, 0.047399 53, 53, 323345.93, 2314.46, -0.177601, 0.207863, -0.854411, -1.827458, 0.508621, -3.592969, -0.172017, 0.129963, -1.323589, 2.165530, 0.627410, 3.451540, 0.087853, 0.041358, 2.124227, 244.000000, 267.312836, 0.403813, 0.143129 54, 54, 327610.55, -7504.02, 0.215808, 0.146150, 1.476614, -2.368757, 0.297480, -7.962732, -0.352863, 0.110663, -3.188637, 1.511239, 0.447324, 3.378400, 0.030685, 0.025471, 1.204719, 120.000000, 106.449063, 0.620758, 0.023179 55, 55, 343813.29, -11626.17, 0.268234, 0.120122, 2.233006, -2.640688, 0.265969, -9.928570, -0.330724, 0.095751, -3.454003, 1.685496, 0.338443, 4.980144, 0.018875, 0.020090, 0.939519, 297.000000, 310.407049, 0.759592, 0.111308 56, 56, 342508.85, -4698.16, 0.267319, 0.140545, 1.902023, -2.636296, 0.316856, -8.320181, -0.324245, 0.103136, -3.143859, 1.656376, 0.355157, 4.663780, 0.018131, 0.024327, 0.745289, 393.000000, 391.483414, 0.703054, 0.138434 57, 57, 333426.78, -13559.3, 0.281512, 0.124000, 2.270260, -2.512158, 0.248604, -10.105056, -0.365005, 0.102540, -3.559619, 1.541298, 0.352878, 4.367793, 0.018836, 0.019607, 0.960706, 103.000000, 113.416788, 0.725471, 0.016056 58, 58, 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-0.373370, 0.115941, -3.220354, 1.507549, 0.428937, 3.514619, 0.030036, 0.023594, 1.272996, 83.000000, 83.313240, 0.617493, 0.066436 63, 63, 327521.88, -17674.68, 0.294295, 0.133811, 2.199329, -2.492740, 0.247567, -10.068945, -0.395790, 0.114112, -3.468449, 1.485965, 0.353536, 4.203150, 0.019592, 0.019907, 0.984158, 78.000000, 76.260524, 0.697977, 0.019936 64, 64, 322114.34, -17894.35, 0.285518, 0.141810, 2.013385, -2.456674, 0.268577, -9.147006, -0.420225, 0.117771, -3.568161, 1.446210, 0.378510, 3.820802, 0.024696, 0.021494, 1.149002, 201.000000, 186.892948, 0.628919, 0.077339 65, 65, 320355.21, 5840.48, -0.409904, 0.221213, -1.852981, -1.275906, 0.585732, -2.178311, -0.079535, 0.142143, -0.559542, 2.762724, 0.688959, 4.009999, 0.108345, 0.044507, 2.434367, 87.000000, 95.924860, 0.333163, 0.048101 66, 66, 330341.07, 12925.79, 0.161784, 0.210910, 0.767075, -2.541826, 0.583424, -4.356737, -0.269307, 0.139578, -1.929440, 1.922722, 0.634383, 3.030853, 0.023796, 0.041447, 0.574122, 89.000000, 86.976684, 0.397206, 0.045732 67, 67, 318527.16, 8318.24, -0.499063, 0.221688, -2.251199, -0.890571, 0.618680, -1.439469, -0.031986, 0.151636, -0.210942, 2.959515, 0.726511, 4.073601, 0.105907, 0.044393, 2.385659, 80.000000, 79.807334, 0.313640, 0.057114 68, 68, 347297.26, -13547.71, 0.261062, 0.117840, 2.215387, -2.695755, 0.264102, -10.207258, -0.322806, 0.093718, -3.444449, 1.759184, 0.332321, 5.293635, 0.019650, 0.019765, 0.994175, 105.000000, 114.883646, 0.776250, 0.059170 69, 69, 321375.58, -10594.12, 0.135554, 0.153922, 0.880664, -2.187452, 0.322476, -6.783312, -0.336271, 0.118092, -2.847545, 1.429879, 0.551485, 2.592779, 0.048032, 0.027008, 1.778466, 114.000000, 129.119207, 0.546036, 0.064735 70, 70, 318675.19, -8454.47, -0.017200, 0.160902, -0.106897, -1.961664, 0.367933, -5.331571, -0.259849, 0.117270, -2.215814, 1.556646, 0.640699, 2.429606, 0.072005, 0.030115, 2.390982, 101.000000, 93.739876, 0.473028, 0.069440 71, 71, 350174.04, -12060.87, 0.255966, 0.121558, 2.105706, -2.764608, 0.278719, -9.918971, -0.310545, 0.092904, -3.342664, 1.809368, 0.326971, 5.533720, 0.020089, 0.020689, 0.970998, 181.000000, 166.837604, 0.774450, 0.086956 72, 72, 329442.54, 5939.52, 0.137883, 0.200022, 0.689338, -2.516699, 0.512969, -4.906145, -0.289381, 0.129452, -2.235434, 1.980466, 0.565274, 3.503553, 0.035812, 0.038657, 0.926388, 82.000000, 79.208305, 0.451848, 0.086650 73, 73, 307098.94, 992.68, -0.813643, 0.250745, -3.244901, -0.325091, 0.654508, -0.496696, 0.078377, 0.170062, 0.460876, 3.664521, 0.722517, 5.071885, 0.146803, 0.045348, 3.237239, 93.000000, 102.702599, 0.293821, 0.143862 74, 74, 337247.61, 14030.91, 0.266923, 0.210547, 1.267757, -2.300512, 0.515941, -4.458863, -0.299540, 0.137497, -2.178525, 1.775117, 0.620477, 2.860892, -0.009230, 0.039153, -0.235727, 67.000000, 76.332799, 0.387021, 0.134091 75, 75, 306612.95, -2173.79, -0.714604, 0.240030, -2.977144, -0.811463, 0.604629, -1.342083, 0.032933, 0.159976, 0.205864, 3.518902, 0.716423, 4.911768, 0.148258, 0.043569, 3.402815, 55.000000, 61.028741, 0.313363, 0.098055 76, 76, 301727, -6640.87, -0.517279, 0.239909, -2.156142, -1.170391, 0.602554, -1.942383, -0.056309, 0.165426, -0.340389, 3.201948, 0.694563, 4.610021, 0.122427, 0.043352, 2.824053, 68.000000, 66.117997, 0.332036, 0.055090 77, 77, 326724.18, 5478.1, 0.017568, 0.206084, 0.085246, -2.264863, 0.530342, -4.270573, -0.248378, 0.131683, -1.886183, 2.110035, 0.602674, 3.501121, 0.054867, 0.040537, 1.353526, 47.000000, 33.027649, 0.423155, 0.041332 78, 78, 311503.39, 15708.66, -0.682608, 0.242476, -2.815160, 0.369341, 0.757408, 0.487638, 0.098987, 0.189605, 0.522068, 3.341088, 0.829204, 4.029272, 0.070453, 0.046468, 1.516160, 33.000000, 35.369483, 0.277219, 0.060474 79, 79, 316934.08, -10632.09, 0.017026, 0.157911, 0.107820, -1.998656, 0.362155, -5.518781, -0.283060, 0.117002, -2.419266, 1.544528, 0.606692, 2.545819, 0.066468, 0.028694, 2.316426, 43.000000, 49.376154, 0.470273, 0.034267 80, 80, 317980.71, -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, 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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, 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0.016249, 5.768426, 735.000000, 747.497034, 0.777854, 0.347222 238, 238, 302126.02, -67286.13, 0.028710, 0.131064, 0.219057, -2.539290, 0.373494, -6.798751, -0.471244, 0.095979, -4.909851, 2.408149, 0.412812, 5.833521, 0.102208, 0.019360, 5.279460, 346.000000, 360.990899, 0.707819, 0.079114 239, 239, 321943.14, -68916.22, 0.050282, 0.117294, 0.428679, -2.648424, 0.323409, -8.189092, -0.436045, 0.090890, -4.797496, 2.380236, 0.385191, 6.179362, 0.095746, 0.016272, 5.884033, 247.000000, 247.865373, 0.770352, 0.315718 240, 240, 314598.5, -64965.34, 0.045134, 0.118871, 0.379687, -2.647175, 0.333003, -7.949394, -0.435809, 0.091387, -4.768846, 2.386537, 0.388239, 6.147074, 0.097162, 0.016744, 5.802636, 463.000000, 463.286230, 0.750688, 0.074665 241, 241, 310206.19, -67759.02, 0.039441, 0.124904, 0.315774, -2.610529, 0.354309, -7.367943, -0.459961, 0.093910, -4.897887, 2.428972, 0.404329, 6.007409, 0.100019, 0.017933, 5.577259, 279.000000, 287.852327, 0.739085, 0.074912 242, 242, 326961.41, 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0.102369, -4.410738, 2.328810, 0.417935, 5.572181, 0.101829, 0.021194, 4.804552, 209.000000, 219.907387, 0.658914, 0.079539 247, 247, 313999.38, -53197.4, 0.039233, 0.119695, 0.327772, -2.661651, 0.330000, -8.065615, -0.396654, 0.095906, -4.135864, 2.361711, 0.383848, 6.152729, 0.094297, 0.016861, 5.592630, 265.000000, 271.421438, 0.732217, 0.076941 248, 248, 299312.82, -60819.66, 0.013175, 0.139120, 0.094699, -2.463657, 0.393307, -6.263961, -0.469104, 0.100465, -4.669314, 2.368542, 0.422243, 5.609426, 0.104094, 0.021076, 4.939063, 88.000000, 103.896206, 0.672191, 0.024531 249, 249, 308373.06, -57401.82, 0.024003, 0.127789, 0.187829, -2.567832, 0.360282, -7.127276, -0.440043, 0.096062, -4.580827, 2.393005, 0.402299, 5.948317, 0.100565, 0.018666, 5.387752, 121.000000, 126.235716, 0.710824, 0.053531 250, 250, 309678.63, -51875.49, 0.036168, 0.126167, 0.286668, -2.605632, 0.346593, -7.517847, -0.414216, 0.097932, -4.229643, 2.339680, 0.392736, 5.957391, 0.095492, 0.018136, 5.265257, 140.000000, 136.340723, 0.700782, 0.034393 251, 251, 311833.72, -57007.52, 0.033216, 0.122077, 0.272088, -2.620312, 0.342991, -7.639592, -0.424342, 0.094093, -4.509829, 2.384046, 0.391514, 6.089306, 0.097972, 0.017478, 5.605364, 104.000000, 100.307400, 0.728731, 0.130326 252, 252, 327926.88, -75230.85, 0.046352, 0.114852, 0.403576, -2.588178, 0.312894, -8.271728, -0.437476, 0.089449, -4.890809, 2.352501, 0.378739, 6.211411, 0.094521, 0.016145, 5.854537, 41.000000, 44.382101, 0.775268, 0.071660 253, 253, 307926.73, -64266.21, 0.032138, 0.126414, 0.254228, -2.582094, 0.359359, -7.185276, -0.457008, 0.094365, -4.842978, 2.413887, 0.405127, 5.958352, 0.100852, 0.018366, 5.491228, 64.000000, 60.575051, 0.725140, 0.060975 254, 254, 299390.82, -71196.86, 0.028861, 0.134120, 0.215185, -2.527445, 0.382882, -6.601111, -0.483187, 0.097702, -4.945503, 2.428595, 0.421071, 5.767658, 0.103026, 0.019961, 5.161282, 49.000000, 45.587812, 0.705722, 0.079409 255, 255, 295866.34, -72353.52, 0.023328, 0.139358, 0.167397, -2.491862, 0.398212, -6.257630, -0.493931, 0.100779, -4.901152, 2.438806, 0.432937, 5.633162, 0.104609, 0.021075, 4.963557, 44.000000, 41.709807, 0.694190, 0.032732 256, 256, 299578.37, -47629.99, 0.030792, 0.168968, 0.182234, -2.362744, 0.449864, -5.252131, -0.460086, 0.121032, -3.801364, 2.148843, 0.468714, 4.584546, 0.096186, 0.026419, 3.640773, 35.000000, 53.793766, 0.566286, 0.137486 257, 257, 293314.45, -52457.68, -0.005101, 0.174003, -0.029313, -2.259646, 0.464661, -4.862996, -0.468379, 0.123228, -3.800905, 2.202182, 0.478967, 4.597775, 0.103265, 0.028010, 3.686795, 6.000000, 5.732371, 0.559206, 0.019430 258, 258, 299215.09, -41823.07, 0.117650, 0.189264, 0.621620, -2.365511, 0.499335, -4.737326, -0.482461, 0.136750, -3.528046, 1.804427, 0.520130, 3.469188, 0.078637, 0.029186, 2.694318, 32.000000, 24.165232, 0.480773, 0.045116 259, 259, 288944.87, -47144.31, 0.005076, 0.199519, 0.025439, -2.053354, 0.515429, -3.983775, -0.451285, 0.141199, -3.196087, 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-4.12.1/libpysal/examples/tokyo/tokyo_BS_NN_OFF_summary.txt000066400000000000000000000172051466413560300253710ustar00rootroot00000000000000***************************************************************************** * 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-4.12.1/libpysal/examples/tokyo/tokyo_BS_NN_listwise.csv000066400000000000000000002775711466413560300250370ustar00rootroot00000000000000Area_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, 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-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, 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0.590548, -7.633462, 0.433179, 0.027492, 15.756257, 735.000000, 282.021852, 0.415987, 0.242787 238, 238, 302126.02, -67286.13, 4.942152, 0.228419, 21.636301, 8.877500, 0.605001, 14.673521, -4.893662, 0.215648, -22.692817, -5.503049, 0.753659, -7.301773, 0.776872, 0.040853, 19.016485, 346.000000, 254.015152, 0.597606, 0.150093 239, 239, 321943.14, -68916.22, 4.947488, 0.178914, 27.652844, 4.461157, 0.499717, 8.927362, -2.016053, 0.156667, -12.868360, -3.736515, 0.618216, -6.044027, 0.455175, 0.030280, 15.032310, 247.000000, 193.826219, 0.414164, 0.317943 240, 240, 314598.5, -64965.34, 4.816002, 0.194942, 24.704834, 8.024845, 0.542677, 14.787506, -3.271465, 0.176497, -18.535554, -3.943586, 0.648969, -6.076699, 0.532799, 0.034936, 15.250768, 463.000000, 258.207329, 0.479460, 0.159683 241, 241, 310206.19, -67759.02, 4.661372, 0.199930, 23.315076, 8.545372, 0.552014, 15.480346, -3.694831, 0.185326, -19.936926, -5.088488, 0.669206, -7.603775, 0.667859, 0.035653, 18.732049, 279.000000, 240.559119, 0.532075, 0.113122 242, 242, 326961.41, -72189.14, 5.128578, 0.174428, 29.402280, 2.952711, 0.491294, 6.010073, -1.693575, 0.154047, -10.993884, -3.951761, 0.602443, -6.559558, 0.431087, 0.028486, 15.133116, 93.000000, 191.685441, 0.401829, 0.308717 243, 243, 306522.23, -44854.32, 3.690036, 0.259531, 14.218096, 2.861401, 0.548516, 5.216619, -1.054993, 0.200170, -5.270488, -11.004938, 1.010755, -10.887838, 0.942368, 0.048579, 19.398621, 686.000000, 326.552303, 0.471467, 0.332129 244, 244, 332009.57, -86587.48, 4.438423, 0.190255, 23.328828, 3.889678, 0.534292, 7.280065, -1.158721, 0.166623, -6.954151, -6.749928, 0.637324, -10.591044, 0.612286, 0.030843, 19.851975, 105.000000, 141.948797, 0.437111, 0.371110 245, 245, 291250.63, -62212.96, 4.860810, 0.287826, 16.888046, 5.638558, 0.735865, 7.662486, -5.873508, 0.271895, -21.602128, -5.511637, 1.120786, -4.917654, 1.160513, 0.056597, 20.504868, 200.000000, 175.003193, 0.627501, 0.181055 246, 246, 302274.84, -54583.9, 4.131069, 0.238436, 17.325702, 5.186386, 0.570219, 9.095425, -3.877789, 0.210750, -18.399942, -6.430871, 0.896340, -7.174590, 1.086936, 0.046914, 23.168912, 209.000000, 177.994827, 0.588641, 0.142957 247, 247, 313999.38, -53197.4, 4.480984, 0.192387, 23.291469, 5.482900, 0.464337, 11.808031, -1.438585, 0.166805, -8.624357, 2.514699, 0.586949, 4.284359, 0.255747, 0.032485, 7.872706, 265.000000, 252.116551, 0.451342, 0.146883 248, 248, 299312.82, -60819.66, 4.759873, 0.241437, 19.714746, 6.698499, 0.593428, 11.287808, -4.807882, 0.219784, -21.875488, -5.697375, 0.837779, -6.800573, 0.940830, 0.045175, 20.826350, 88.000000, 252.103601, 0.609454, 0.128997 249, 249, 308373.06, -57401.82, 4.577484, 0.213214, 21.468940, 7.861881, 0.522303, 15.052349, -3.699727, 0.199471, -18.547707, -3.253703, 0.688245, -4.727536, 0.670664, 0.038184, 17.564153, 121.000000, 161.208311, 0.559151, 0.147869 250, 250, 309678.63, -51875.49, 3.915597, 0.216375, 18.096317, 5.886174, 0.524476, 11.222959, -2.133654, 0.188831, -11.299251, -1.089661, 0.723423, -1.506256, 0.657125, 0.039102, 16.805344, 140.000000, 221.257200, 0.520919, 0.094071 251, 251, 311833.72, -57007.52, 4.673467, 0.200449, 23.315044, 7.615701, 0.494826, 15.390671, -3.012357, 0.186724, -16.132640, -1.247852, 0.624198, -1.999128, 0.469540, 0.035267, 13.313994, 104.000000, 94.690496, 0.504261, 0.231594 252, 252, 327926.88, -75230.85, 4.913561, 0.178305, 27.557060, 3.249639, 0.506152, 6.420285, -1.528110, 0.157776, -9.685306, -4.868754, 0.610505, -7.974966, 0.489427, 0.028969, 16.895071, 41.000000, 140.098270, 0.410932, 0.238675 253, 253, 307926.73, -64266.21, 4.969128, 0.213447, 23.280345, 9.210357, 0.571208, 16.124362, -4.490972, 0.203569, -22.061130, -4.653898, 0.697608, -6.671219, 0.651919, 0.038268, 17.035658, 64.000000, 117.737421, 0.563679, 0.232929 254, 254, 299390.82, -71196.86, 4.785658, 0.227777, 21.010289, 8.597619, 0.609638, 14.102819, -4.744894, 0.213366, -22.238248, -5.889263, 0.755001, -7.800334, 0.833174, 0.040360, 20.643716, 49.000000, 62.852877, 0.598978, 0.211631 255, 255, 295866.34, -72353.52, 4.838026, 0.235559, 20.538509, 8.269920, 0.623611, 13.261336, -4.953420, 0.219851, -22.530825, -5.904348, 0.781787, -7.552373, 0.870533, 0.041832, 20.809983, 44.000000, 130.372269, 0.607714, 0.181497 256, 256, 299578.37, -47629.99, 3.183204, 0.273979, 11.618424, 5.451985, 0.582634, 9.357473, -2.087656, 0.213173, -9.793232, -13.204366, 1.140220, -11.580541, 1.238068, 0.051951, 23.831453, 35.000000, 67.932402, 0.555682, 0.294512 257, 257, 293314.45, -52457.68, 3.743547, 0.272761, 13.724658, 5.671835, 0.631539, 8.980970, -3.307270, 0.218146, -15.160835, -11.917304, 1.090726, -10.926031, 1.231454, 0.053652, 22.952700, 6.000000, 25.200319, 0.600419, 0.207435 258, 258, 299215.09, -41823.07, 3.290740, 0.265001, 12.417839, 7.425451, 0.549157, 13.521553, -1.009346, 0.202995, -4.972264, -13.948162, 1.025584, -13.600217, 0.871965, 0.047625, 18.308983, 32.000000, 128.621564, 0.521383, 0.415241 259, 259, 288944.87, -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-4.12.1/libpysal/examples/tokyo/tokyo_BS_NN_summary.txt000066400000000000000000000206051466413560300246750ustar00rootroot00000000000000***************************************************************************** * 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-4.12.1/libpysal/examples/tokyo/tokyo_GS_F.ctl000066400000000000000000000014071466413560300227410ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/tokyo/tokyo_GS_F_listwise.csv000066400000000000000000002775711466413560300247160ustar00rootroot00000000000000Area_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, 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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, 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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-4.12.1/libpysal/examples/tokyo/tokyo_GS_F_summary.txt000066400000000000000000000237301466413560300245560ustar00rootroot00000000000000***************************************************************************** * 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-4.12.1/libpysal/examples/tokyo/tokyo_GS_NN.ctl000066400000000000000000000014111466413560300230620ustar00rootroot00000000000000 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-4.12.1/libpysal/examples/tokyo/tokyo_GS_NN_listwise.csv000066400000000000000000002775711466413560300250440ustar00rootroot00000000000000Area_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, -6.122524, 0.184116, -33.253594, -5.339255, 0.056869, -93.886469, -4.310160, 0.206770, -20.845170, 0.032574, 0.011613, 2.805012, 51.000000, 80.701186, 0.653954, 0.023481 143, 143, 380862.18, -12688.75, 9.407035, 0.076293, 123.300713, -5.788029, 0.187620, -30.849714, -5.316665, 0.058159, -91.415331, -4.729152, 0.210670, -22.448136, 0.045848, 0.011935, 3.841431, 11.000000, 20.149187, 0.664421, 0.023350 144, 144, 383629.18, -9335.24, 9.344304, 0.077663, 120.317897, -5.548516, 0.191075, -29.038475, -5.303565, 0.059034, -89.839390, -4.885369, 0.214190, -22.808573, 0.057942, 0.012213, 4.744216, 23.000000, 28.614690, 0.670988, 0.024340 145, 145, 394378.41, -45752.97, 9.191923, 0.066931, 137.333311, -5.727990, 0.162867, -35.169777, -5.053751, 0.050852, -99.381236, -3.244088, 0.190275, -17.049468, 0.012800, 0.010609, 1.206521, 64.000000, 49.811978, 0.621737, 0.028431 146, 146, 402514.52, -43075.49, 9.175136, 0.067648, 135.630472, -5.603386, 0.163906, -34.186654, -5.067730, 0.051375, -98.642822, -3.434991, 0.192294, -17.863217, 0.020408, 0.010698, 1.907721, 61.000000, 40.109776, 0.630348, 0.038655 147, 147, 402518.42, -36236.31, 9.270327, 0.070226, 132.006697, -5.660316, 0.168838, -33.525090, -5.130174, 0.053421, -96.033003, -3.971882, 0.198324, -20.027201, 0.022469, 0.011022, 2.038651, 37.000000, 47.043541, 0.643346, 0.025721 148, 148, 396061.3, -30927.23, 9.372199, 0.072411, 129.429913, -5.784838, 0.174051, -33.236488, -5.191601, 0.055187, -94.072523, -4.381104, 0.202950, -21.587064, 0.021686, 0.011308, 1.917755, 22.000000, 36.468598, 0.650807, 0.021455 149, 149, 408226.18, -35513.98, 9.204996, 0.069455, 132.531443, -5.539390, 0.167317, -33.107164, -5.109229, 0.052721, -96.910494, -3.810717, 0.196674, -19.375787, 0.027282, 0.010935, 2.495017, 18.000000, 28.337304, 0.642525, 0.032195 150, 150, 403471.42, -31311.84, 9.251419, 0.070167, 131.848803, -5.614727, 0.169153, -33.193277, -5.141691, 0.053282, -96.499850, -3.944732, 0.198023, -19.920586, 0.026016, 0.011024, 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-35.691006, -3.976533, 0.055464, -71.695357, 2.007508, 0.201037, 9.985770, -0.055705, 0.011353, -4.906702, 248.000000, 483.338341, 0.340086, 0.100628 232, 232, 331453.95, -41443.1, 8.397447, 0.073010, 115.018100, -6.237646, 0.175553, -35.531389, -3.959144, 0.057081, -69.360488, 2.462353, 0.204564, 12.037089, -0.063824, 0.011405, -5.596192, 260.000000, 429.804621, 0.327409, 0.080584 233, 233, 327954.56, -39544.67, 8.282701, 0.074713, 110.860627, -6.045496, 0.178268, -33.912431, -3.887854, 0.059346, -65.511394, 3.111144, 0.208562, 14.917145, -0.072605, 0.011503, -6.311937, 236.000000, 400.023025, 0.311018, 0.077632 234, 234, 321981.74, -36838.07, 8.115081, 0.076521, 106.050196, -5.548295, 0.184335, -30.098942, -3.858003, 0.060663, -63.597776, 4.083873, 0.211858, 19.276502, -0.090183, 0.011718, -7.695945, 175.000000, 282.837500, 0.298697, 0.065843 235, 235, 324590.43, -40337.23, 8.112935, 0.074991, 108.185091, -5.584827, 0.179964, -31.032988, -3.783974, 0.060202, -62.854558, 3.626323, 0.210200, 17.251815, -0.075609, 0.011570, -6.535137, 192.000000, 268.622269, 0.300266, 0.060166 236, 236, 317357.31, -39249.39, 7.934199, 0.075927, 104.498272, -4.770744, 0.187855, -25.395947, -3.818338, 0.060312, -63.310002, 4.174783, 0.211769, 19.713892, -0.083574, 0.011859, -7.047206, 118.000000, 161.458446, 0.300878, 0.061703 237, 237, 333435.39, -77430.79, 8.185920, 0.067723, 120.874258, -4.622963, 0.169810, -27.224389, -4.220764, 0.050646, -83.339121, 0.459198, 0.187227, 2.452630, 0.034211, 0.010976, 3.116986, 735.000000, 172.757635, 0.457732, 0.045883 238, 238, 302126.02, -67286.13, 7.695633, 0.074486, 103.316032, -3.523968, 0.185387, -19.008691, -4.021627, 0.056838, -70.756248, 1.709282, 0.205954, 8.299352, 0.052541, 0.011781, 4.459990, 346.000000, 244.361854, 0.411450, 0.016921 239, 239, 321943.14, -68916.22, 7.830279, 0.072427, 108.112287, -4.159715, 0.179212, -23.211197, -3.904796, 0.055448, -70.422298, 1.669827, 0.200466, 8.329723, 0.037554, 0.011479, 3.271509, 247.000000, 138.845552, 0.393170, 0.074678 240, 240, 314598.5, -64965.34, 7.597993, 0.076070, 99.881181, -3.646931, 0.187905, -19.408396, -3.767269, 0.059223, -63.611772, 2.236142, 0.211967, 10.549502, 0.043827, 0.011883, 3.688099, 463.000000, 246.815793, 0.366101, 0.021169 241, 241, 310206.19, -67759.02, 7.670692, 0.074621, 102.795898, -3.659950, 0.185125, -19.770178, -3.908316, 0.057301, -68.206252, 1.898972, 0.206540, 9.194217, 0.049935, 0.011749, 4.250237, 279.000000, 178.963443, 0.392639, 0.028696 242, 242, 326961.41, -72189.14, 7.982312, 0.070429, 113.337673, -4.394465, 0.175167, -25.087288, -4.023536, 0.053319, -75.461661, 1.175622, 0.194378, 6.048130, 0.035624, 0.011273, 3.160010, 93.000000, 111.617692, 0.417214, 0.078768 243, 243, 306522.23, -44854.32, 7.668422, 0.079345, 96.646451, -3.304340, 0.200693, -16.464665, -3.927448, 0.062388, -62.951496, 2.987316, 0.220177, 13.567803, -0.023813, 0.012621, -1.886699, 686.000000, 203.426131, 0.344668, 0.033019 244, 244, 332009.57, -86587.48, 8.209034, 0.066568, 123.317323, -4.515122, 0.167641, -26.933237, -4.315836, 0.049529, -87.137304, 0.167619, 0.184537, 0.908319, 0.042912, 0.010812, 3.968957, 105.000000, 145.534304, 0.482077, 0.031988 245, 245, 291250.63, -62212.96, 7.735417, 0.075190, 102.877745, -3.328645, 0.187252, -17.776242, -4.155833, 0.057179, -72.681644, 1.490192, 0.209003, 7.130020, 0.048308, 0.011953, 4.041537, 200.000000, 198.640370, 0.431034, 0.022714 246, 246, 302274.84, -54583.9, 7.590615, 0.078355, 96.874541, -3.141350, 0.196214, -16.009780, -3.950704, 0.060846, -64.929700, 2.243142, 0.217890, 10.294855, 0.030018, 0.012401, 2.420535, 209.000000, 208.033136, 0.379068, 0.034222 247, 247, 313999.38, -53197.4, 7.585872, 0.076735, 98.858187, -3.645689, 0.190777, -19.109683, -3.697454, 0.061067, -60.547672, 3.089781, 0.217276, 14.220534, 0.002201, 0.012053, 0.182603, 265.000000, 277.661955, 0.331060, 0.032885 248, 248, 299312.82, -60819.66, 7.680816, 0.075713, 101.445923, -3.375155, 0.188734, -17.883164, -4.045412, 0.058036, -69.704725, 1.836593, 0.210015, 8.745075, 0.043719, 0.012001, 3.643015, 88.000000, 188.843606, 0.407226, 0.024695 249, 249, 308373.06, -57401.82, 7.488749, 0.078856, 94.967521, -3.178750, 0.196830, -16.149741, -3.765589, 0.061910, -60.823226, 2.580713, 0.220777, 11.689216, 0.034504, 0.012339, 2.796291, 121.000000, 154.372815, 0.353274, 0.034115 250, 250, 309678.63, -51875.49, 7.503222, 0.079167, 94.776563, -3.200445, 0.198803, -16.098605, -3.732602, 0.062739, -59.493985, 3.005632, 0.222908, 13.483731, 0.008023, 0.012437, 0.645066, 140.000000, 203.226629, 0.333986, 0.030122 251, 251, 311833.72, -57007.52, 7.477564, 0.078735, 94.971657, -3.297160, 0.195895, -16.831262, -3.678675, 0.062325, -59.024093, 2.820530, 0.221943, 12.708332, 0.026768, 0.012270, 2.181544, 104.000000, 157.379712, 0.338244, 0.069971 252, 252, 327926.88, -75230.85, 8.034497, 0.069555, 115.513649, -4.421714, 0.173579, -25.473800, -4.091965, 0.052362, -78.147372, 0.927252, 0.191815, 4.834096, 0.038200, 0.011175, 3.418247, 41.000000, 82.607700, 0.432509, 0.053779 253, 253, 307926.73, -64266.21, 7.578823, 0.076575, 98.972470, -3.418587, 0.190163, -17.977179, -3.855189, 0.059275, -65.039018, 2.123018, 0.212588, 9.986515, 0.049632, 0.012004, 4.134722, 64.000000, 213.181115, 0.379539, 0.057902 254, 254, 299390.82, -71196.86, 7.793609, 0.072467, 107.546718, -3.672553, 0.180690, -20.325203, -4.124214, 0.054779, -75.287825, 1.409758, 0.200403, 7.034605, 0.053292, 0.011529, 4.622333, 49.000000, 88.793492, 0.433644, 0.047500 255, 255, 295866.34, -72353.52, 7.842154, 0.071570, 109.573739, -3.710173, 0.178682, -20.764143, -4.188660, 0.053888, -77.728850, 1.254719, 0.198343, 6.325994, 0.052881, 0.011423, 4.629318, 44.000000, 126.174356, 0.446523, 0.034969 256, 256, 299578.37, -47629.99, 7.640659, 0.080691, 94.690096, -2.954323, 0.202739, -14.572015, -4.039985, 0.062604, -64.532079, 2.198968, 0.223411, 9.842714, 0.008024, 0.012866, 0.623680, 35.000000, 159.620279, 0.380325, 0.084547 257, 257, 293314.45, -52457.68, 7.724168, 0.077730, 99.371708, -3.149705, 0.193810, -16.251502, -4.158636, 0.059548, -69.836625, 1.708013, 0.216389, 7.893265, 0.028558, 0.012396, 2.303708, 6.000000, 130.973974, 0.416750, 0.058187 258, 258, 299215.09, -41823.07, 7.801636, 0.080578, 96.821153, -3.188515, 0.202027, -15.782601, -4.169991, 0.062229, -67.010845, 2.225587, 0.221869, 10.031064, -0.021187, 0.012923, -1.639555, 32.000000, 77.593803, 0.389159, 0.041615 259, 259, 288944.87, -47144.31, 7.838920, 0.077525, 101.115181, -3.216458, 0.192464, -16.712036, -4.290320, 0.059062, -72.640555, 1.490054, 0.217137, 6.862286, 0.015489, 0.012423, 1.246778, 28.000000, 56.348297, 0.436942, 0.033521 260, 260, 290541.99, -38708.26, 7.968411, 0.078516, 101.487407, -3.352787, 0.194235, -17.261530, -4.393288, 0.059683, -73.610546, 1.542132, 0.219524, 7.024887, -0.013788, 0.012635, -1.091273, 10.000000, 112.379240, 0.442772, 0.025182 261, 261, 285964.14, -39392.46, 7.990553, 0.076895, 103.914455, -3.396701, 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-4.12.1/libpysal/examples/tokyo/tokyo_GS_NN_summary.txt000066400000000000000000000206041466413560300247010ustar00rootroot00000000000000***************************************************************************** * 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-4.12.1/libpysal/examples/tokyo/tokyomet262.dbf000066400000000000000000000205021466413560300230110ustar00rootroot00000000000000p 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-4.12.1/libpysal/examples/us_income/000077500000000000000000000000001466413560300210445ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/us_income/README.md000066400000000000000000000010171466413560300223220ustar00rootroot00000000000000us_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-4.12.1/libpysal/examples/us_income/spi_download.csv000066400000000000000000000434771466413560300242620ustar00rootroot00000000000000"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 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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-4.12.1/libpysal/examples/us_income/states48.gal000066400000000000000000000014771466413560300232210ustar00rootroot0000000000000048 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-4.12.1/libpysal/examples/us_income/us48.dbf000066400000000000000000000102621466413560300223250ustar00rootroot00000000000000c 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-4.12.1/libpysal/examples/us_income/us48.shp000066400000000000000000005541541466413560300224010ustar00rootroot00000000000000' 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-4.12.1/libpysal/examples/us_income/usjoin.csv000066400000000000000000000513151466413560300230750ustar00rootroot00000000000000"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-4.12.1/libpysal/examples/virginia/000077500000000000000000000000001466413560300206735ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/virginia/README.md000066400000000000000000000016401466413560300221530ustar00rootroot00000000000000virginia ======== 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-4.12.1/libpysal/examples/virginia/vautm17n.dbf000066400000000000000000000262211466413560300230350ustar00rootroot00000000000000_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-4.12.1/libpysal/examples/virginia/virginia_queen.mat000066400000000000000000004413001466413560300244050ustar00rootroot00000000000000MATLAB 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-4.12.1/libpysal/examples/virginia/virginia_queen.mtx000066400000000000000000000115341466413560300244360ustar00rootroot00000000000000%%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> 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 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qBd 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  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qc qc qc qc qc qc qc qc  qc! qc" qc# qc$ qc% qc& qc' qc( qc) qc* qc+ qc, qc- qc. qc/ qc0 qc1 qc2 qc3 qc4 qc5 qc6 qc7 qc8 qc9 qc: qc; qc< qc= 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 qcXð? qcY qcZ qc[ qc\ qc] qc^ qc_ qc` qca qcb qcc qcd qce qcf qcgð? qch qci qcj qck qcl qcm qcn qco qcp qcq qcr qcsð? qct qcu qcv qcw qcx qcy qczð? qc{ð? 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) 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 qdOð? 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 qdlð? 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 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 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q‡‡libpysal-4.12.1/libpysal/examples/virginia/virginia_rook.gal000066400000000000000000000047611466413560300242320ustar00rootroot000000000000000 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-4.12.1/libpysal/examples/wmat/000077500000000000000000000000001466413560300200335ustar00rootroot00000000000000libpysal-4.12.1/libpysal/examples/wmat/README.md000066400000000000000000000015201466413560300213100ustar00rootroot00000000000000wmat ==== 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-4.12.1/libpysal/examples/wmat/geobugs_scot000066400000000000000000000022441466413560300224430ustar00rootroot00000000000000list( 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-4.12.1/libpysal/examples/wmat/lattice10x10.shp000066400000000000000000000326041466413560300226730ustar00rootroot00000000000000' 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?$@ð?"@"@ @ð?@ð?ð?ð?ð?@ð?@ð? 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è@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@X@@@@@@@@@@@@@@@libpysal-4.12.1/libpysal/examples/wmat/rook31.shx000066400000000000000000000001741466413560300216770ustar00rootroot00000000000000' >è@@2@v@ºXlibpysal-4.12.1/libpysal/examples/wmat/spat-sym-us.mat000066400000000000000000000006401466413560300227400ustar00rootroot00000000000000MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Wed Sep 17 16:55:32 2008 IMxœ•TQ Â0 Í”} ^DÄ3ì"@¦ ·ðÈv2·&ÍKf¡”¤/iò’t/"ïNdWÎr|Ïqm'ù8m”}({¸]î§ç¹^×ñx ~SvßÉoR-%ÙÞÍZ¬Àè— eߊë9 ©™ "y#Œ Èu‘º²ØŒ¬ÆqÙ’6”ÑjêàšpºR„×DK…@+Û¨òjðÛ%°P„® .À®ýÚ:ä%¯,í<Š0’]¯&CíJ FQþ2”ô'ªZ>Eªk·?µ¤òáNa¹Š@,R{éaøSN†ù/’Vc| ÌÐm‚YË\qjÛÂÓOÉ–ÈÃhsÚ§!¢Mù [ßô³EÄŸÛ,™ +ñÖbulibpysal-4.12.1/libpysal/examples/wmat/spat-sym-us.wk1000066400000000000000000000042551466413560300226670ustar00rootroot00000000000000--/2ÿñHd 1$&%*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-4.12.1/libpysal/examples/wmat/spdep_listw2WB_columbus000066400000000000000000000100071466413560300245350ustar00rootroot00000000000000list(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, 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0.2000libpysal-4.12.1/libpysal/graph/000077500000000000000000000000001466413560300163465ustar00rootroot00000000000000libpysal-4.12.1/libpysal/graph/__init__.py000066400000000000000000000001201466413560300204500ustar00rootroot00000000000000from .base import Graph, GraphSummary, read_parquet, read_gal, read_gwt # noqa libpysal-4.12.1/libpysal/graph/_contiguity.py000066400000000000000000000266711466413560300212710ustar00rootroot00000000000000from 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].update(nexus_names) for idx in ids: graph[idx].remove(idx) 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", **kwargs ): """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. **kwargs Keyword arguments passed to ``geopandas.GeoSeries.buffer``. 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, **kwargs) # 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-4.12.1/libpysal/graph/_indices.py000066400000000000000000000023441466413560300205000ustar00rootroot00000000000000def _build_from_h3(ids, order=1): """Generate Graph from H3 hexagons. Encode a graph from a set of H3 hexagons. The graph is generated by considering the H3 hexagons as nodes and connecting them based on their contiguity. The contiguity is defined by the order parameter, which specifies the number of steps to consider as neighbors. Requires the `h3` library. Parameters ---------- ids : array-like Array of H3 IDs encoding focal geometries order : int, optional Order of contiguity, by default 1 Returns ------- tuple(dict, dict) """ try: import h3 except ImportError as e: raise ImportError( "This function requires the `h3` library. " "You can install it with `conda install h3-py` or " "`pip install h3`." ) from e neighbors = {} weights = {} for ix in ids: rings = h3.hex_range_distances(ix, order) for i, ring in enumerate(rings): if i == 0: neighbors[ix] = [] weights[ix] = [] else: neighbors[ix].extend(list(ring)) weights[ix].extend([i] * len(ring)) return neighbors, weights libpysal-4.12.1/libpysal/graph/_kernel.py000066400000000000000000000320431466413560300203410ustar00rootroot00000000000000import numpy import pandas from scipy import optimize, sparse, spatial, stats from ._utils import ( CoplanarError, _build_coplanarity_lookup, _induce_cliques, _jitter_geoms, _reorder_adjtable_by_ids, _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, coplanar="raise", resolve_isolates=True, ): """ 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 resolve_isolates : bool Try to resolve isolates. Can be disabled if we are dealing with cliques later. """ 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, coplanar=coplanar) 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() return _sparse_to_arrays(d, ids=ids, resolve_isolates=resolve_isolates) def _knn(coordinates, metric="euclidean", k=1, p=2, coplanar="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 ) if coplanar == "jitter": coordinates, geoms = _jitter_geoms(coordinates, geoms=geoms) n_coplanar = geoms.geometry.duplicated().sum() n_samples, _ = coordinates.shape if n_coplanar == 0: 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: coplanar_lookup, nearest = _build_coplanarity_lookup(geoms) _, counts = numpy.unique(nearest, return_counts=True) max_at_one_site = counts.max() if coplanar == "raise": raise CoplanarError( f"There are {len(coordinates) - len(coplanar_lookup)} unique locations " f"in the dataset, but {len(coordinates)} observations. At least one of " f"these sites has {max_at_one_site} points, more than the {k} nearest " f"neighbors 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 `coplanar='clique'` or consult the " "documentation about coplanar points." ) if coplanar == "jitter": # force re-jittering over and over again until the coplanarity is broken return _knn( _jitter_geoms(coordinates, geoms)[-1], metric=metric, k=k, p=p, coplanar="jitter", ) if coplanar == "clique": heads, tails, weights = _sparse_to_arrays( _knn( numpy.delete(coordinates, coplanar_lookup, 0), metric=metric, k=k, p=p, coplanar="raise", ) ) adjtable = pandas.DataFrame.from_dict( {"focal": heads, "neighbor": tails, "weight": weights} ) adjtable = _induce_cliques( adjtable, coplanar_lookup, nearest, fill_value=-1 ) adjtable["focal"] = ids[adjtable.focal] adjtable["neighbor"] = ids[adjtable.neighbor] adjtable = _reorder_adjtable_by_ids(adjtable, ids) sparse_out = sparse.csr_array( ( adjtable.weight.values, (adjtable.focal.values, adjtable.neighbor.values), ), shape=(n_samples, n_samples), ) sparse_out.data[sparse_out.data < 0] = 0 return sparse_out raise ValueError( f"'{coplanar}' 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-4.12.1/libpysal/graph/_matching.py000066400000000000000000000142361466413560300206570ustar00rootroot00000000000000import warnings import numpy from sklearn.metrics import pairwise_distances from ._utils import _validate_geometry_input _VALID_GEOMETRY_TYPES = ["Point"] def _spatial_matching( x, y=None, n_matches=5, metric="euclidean", solver=None, return_mip=False, allow_partial_match=False, **metric_kwargs, ): """ Match locations in one dataset to at least `n_matches` locations in another (possibly identical) dataset by minimizing the total distance between matched locations. Letting :math:`d_{ij}` be .. math:: \\text{minimize} \\sum_i^n \\sum_j^n d_{ij}m_{ij} \\text{subject to} \\sum_j^n m_{ij} >= k \\forall i m_{ij} \\in {0,1} \\forall ij Parameters ---------- x : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries that need matches. 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. y : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame (default: None) geometries that are used as a source for matching. 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. If none, matches are made within `x`. n_matches : int (default: None) number of matches 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. solver : solver from pulp (default: None) a solver defined by the pulp optimization library. If no solver is provided, pulp's default solver will be used. This is generally pulp.COIN(), but this may vary depending on your configuration. return_mip : bool (default: False) whether or not to return the instance of the pulp.LpProblem. By default, the problem is not returned to the user. allow_partial_match : bool (default: False) whether to allow for partial matching. A partial match may have a weight between zero and one, while a "full" match (by default) must have a weight of either zero or one. A partial matching may have a shorter total distance, but will result in a weighted graph. """ try: import pulp except ImportError as error: raise ImportError("spatial matching requires the pulp library") from error if metric == "precomputed": distance_matrix = x match_between = y is not None elif y is not None: x, x_ids, _ = _validate_geometry_input( x, ids=None, valid_geometry_types=_VALID_GEOMETRY_TYPES ) y, y_ids, _ = _validate_geometry_input( y, ids=None, valid_geometry_types=_VALID_GEOMETRY_TYPES ) distance_matrix = pairwise_distances(x, y, metric=metric) match_between = True else: x, x_ids, _ = _validate_geometry_input( x, ids=None, valid_geometry_types=_VALID_GEOMETRY_TYPES ) y_ids = x_ids distance_matrix = pairwise_distances(x, metric=metric, **metric_kwargs) match_between = False n_targets, n_sources = distance_matrix.shape if match_between: row, col = numpy.meshgrid( numpy.arange(n_targets), numpy.arange(n_sources), indexing="ij" ) row = row.flatten() col = col.flatten() else: # if we are matching within, we need to row, col = numpy.triu_indices( n=n_targets, m=n_sources, k=int(not match_between) ) mp = pulp.LpProblem("optimal-neargraph", sense=pulp.LpMinimize) # a match is as binary decision variable, connecting observation i to observation j match_vars = pulp.LpVariable.dicts( "match", lowBound=0, upBound=1, indices=zip(row, col, strict=True), cat="Continuous" if allow_partial_match else "Binary", ) # we want to minimize the geographic distance of links in the graph mp.objective = pulp.lpSum( [ match_vars[i, j] * distance_matrix[i, j] for i, j in zip(row, col, strict=True) ] ) # for each observation for j in range(n_targets): # there must be exactly k other matched observations, which might live if match_between: summand = pulp.lpSum( [ # over the whole match matrix match_vars[j, i] for i in range(n_sources) ] ) sense = 1 else: summand = pulp.lpSum( [ # in the "upper" triangle, or "lower" triangle match_vars.get((i, j), match_vars.get((j, i))) for i in range(n_sources) if (i != j) ] ) sense = int(not allow_partial_match) mp += pulp.LpConstraint(summand, sense=sense, rhs=n_matches) if match_between: # but, we may choose to ignore some sources for i in range(n_sources): summand = pulp.lpSum([match_vars[j, i] for j in range(n_targets)]) mp += pulp.LpConstraint(summand, sense=-1, rhs=n_matches) status = mp.solve(solver) if (status != 1) & (not allow_partial_match): warnings.warn( f"Problem is {pulp.LpStatus[status]}, so edge weights may be non-integer!", stacklevel=2, ) edges = [ (*key, value.value()) for key, value in match_vars.items() if value.value() > 0 ] if not match_between: edges.extend([(*tuple(reversed(edge[:-1])), edge[-1]) for edge in edges]) heads, tails, weights = map(numpy.asarray, zip(*sorted(edges), strict=True)) if return_mip: return x_ids[heads], y_ids[tails], weights, mp return x_ids[heads], y_ids[tails], weights libpysal-4.12.1/libpysal/graph/_network.py000066400000000000000000000104671466413560300205600ustar00rootroot00000000000000import numpy as np from ._utils import _induce_cliques, _validate_geometry_input def _build_coplanarity_node_lookup(geoms): """ Identify coplanar points and create a look-up table for the coplanar geometries. Same function as in ``graph._utils``, but need to keep the index to use as graph ids """ # geoms = geoms.reset_index(drop=True) coplanar = [] nearest = [] r = geoms.groupby(geoms).groups for g in r.values(): if len(g) == 2: coplanar.append(g[0]) nearest.append(g[1]) elif len(g) > 2: for n in g[1:]: coplanar.append(n) nearest.append(g[0]) return np.asarray(coplanar), np.asarray(nearest) def pdna_to_adj(origins, network, node_ids, threshold): """Create an adjacency list of shortest network-based travel between origins and destinations in a pandana.Network. Parameters ---------- origins : geopandas.GeoDataFrame Geodataframe of origin geometries to begin routing. network : pandana.Network pandana.Network instance that stores the local travel network node_ids: array of node_ids in the pandana.Network aligned with the input observations in ``origins``. This is created via a call like ``pandana.Network.get_node_ids(df.geometry.x, df.geometry.y)`` threshold : int maximum travel distance (inclusive) Returns ------- pandas.DataFrame adjacency list with columns 'origin', 'destination', and 'cost' """ # map node ids in the network to index in the gdf mapper = dict(zip(node_ids, origins.index.values, strict=False)) namer = {"source": "origin", network.impedance_names[0]: "cost"} adj = network.nodes_in_range(node_ids, threshold) adj = adj.rename(columns=namer) # swap osm ids for gdf index adj = adj.set_index("destination").rename(index=mapper).reset_index() adj = adj.set_index("origin").rename(index=mapper).reset_index() adj = adj[adj.destination.isin(origins.index.values)] return adj def build_travel_graph(df, network, threshold, mapping_distance): """Compute the shortest path between gdf centroids via a pandana.Network and return an adjacency list with weight=cost. Note unlike distance_band, :math:`G_{ij}` and :math:`G_{ji}` are often different because travel networks may be directed. Parameters ---------- df : geopandas.GeoDataFrame geodataframe of observations. CRS should be the same as the locations of ``node_x`` and ``node_y`` in the pandana.Network (usually 4326 if network comes from OSM, but sometimes projected to improve snapping quality). network : pandana.Network Network that encodes travel costs. See threshold : int maximum travel cost to consider neighbors mapping_distance : int snapping tolerance passed to ``pandana.Network.get_node_ids`` that defines the maximum range at which observations are snapped to nearest nodes in the network. Default is None Returns ------- pandas.Series adjacency formatted as multiindexed (focal, neighbor) series """ df = df.copy() _validate_geometry_input(df.geometry, ids=None, valid_geometry_types="Point") df["node_ids"] = network.get_node_ids( df.geometry.x, df.geometry.y, mapping_distance ) # depending on density of the graph nodes / observations, it is common to have # multiple observations snapped to the same network node, so use the clique # expansion logic to handle these cases # get indices of multi-observations at unique nodes coplanar, nearest = _build_coplanarity_node_lookup(df["node_ids"]) # create adjacency on unique nodes adj = pdna_to_adj(df, network, df["node_ids"].values, threshold) # add clique members back to adjacency adj_cliques = _induce_cliques( adj.rename( columns={"origin": "focal", "destination": "neighbor", "cost": "weight"} ), coplanar=coplanar, nearest=nearest, ) # reorder, drop induced dupes, and return adj_cliques = ( adj_cliques.groupby(["focal", "neighbor"]) .first() .reindex(df.index, level=0) .reindex(df.index, level=1) .reset_index() ) return adj_cliques libpysal-4.12.1/libpysal/graph/_plotting.py000066400000000000000000000200221466413560300207130ustar00rootroot00000000000000import 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 representative points 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).representative_point() ) 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.representative_point().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-4.12.1/libpysal/graph/_raster.py000066400000000000000000000074551466413560300203720ustar00rootroot00000000000000from warnings import warn import numpy as np import pandas as pd from ..weights.raster import _da2wsp from ._utils import ( _sparse_to_arrays, ) def _raster_contiguity( da, criterion="queen", z_value=None, coords_labels=None, k=1, include_nodata=False, n_jobs=1, ): """ Create an input for Graph from xarray.DataArray. 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 ------- (head, tail, weight, index_names) """ try: import numba # noqa: F401 use_numba = True include_nodata = False except (ModuleNotFoundError, ImportError): warn( "numba cannot be imported, parallel processing " "and include_nodata functionality will be disabled. " "falling back to slower method", stacklevel=2, ) use_numba = False if coords_labels is None: coords_labels = {} if use_numba: (weight, (head, tail)), ser, _ = _da2wsp( da=da, criterion=criterion, z_value=z_value, coords_labels=coords_labels, k=k, include_nodata=include_nodata, n_jobs=n_jobs, use_numba=use_numba, ) order = np.lexsort((tail, head)) head = head[order] tail = tail[order] weight = weight[order] head = ser.index.to_numpy()[head] tail = ser.index.to_numpy()[tail] else: sw, ser = _da2wsp( da=da, criterion=criterion, z_value=z_value, coords_labels=coords_labels, k=k, include_nodata=include_nodata, n_jobs=n_jobs, use_numba=use_numba, ) head, tail, weight = _sparse_to_arrays(sw, ser.index.to_numpy()) return ( head, tail, weight, ser.index, ) def _generate_da(g, y): """Creates xarray.DataArray object from passed data aligned with the Graph. Parameters ---------- g : Graph Graph, ideally generated using _raster_contiguity builder to ensure it contains _xarray_index_names attribute. y : array_like flat array that shall be reshaped into a DataArray with dimensionality conforming to Graph Returns ------- xarray.DataArray instance of xarray.DataArray that can be aligned with the DataArray from which Graph was built """ if hasattr(g, "_xarray_index_names"): names = g._xarray_index_names else: warn( UserWarning, "Graph does not store xarray index names." "The output may not align with the original DataArray.", stacklevel=3, ) names = None return pd.Series( y, index=pd.MultiIndex.from_tuples(g.unique_ids, names=names), ).to_xarray() libpysal-4.12.1/libpysal/graph/_set_ops.py000066400000000000000000000150661466413560300205430ustar00rootroot00000000000000import 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) ) return len(intersection) > 0 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-4.12.1/libpysal/graph/_spatial_lag.py000066400000000000000000000147301466413560300213440ustar00rootroot00000000000000import numpy as np import pandas as pd def _lag_spatial(graph, y, categorical=False, ties="raise"): """Spatial lag operator Constructs spatial lag based on neighbor relations of the graph. Parameters ---------- graph : Graph libpysal.graph.Graph y : array numpy array with dimensionality conforming to w categorical : bool True if y is categorical, False if y is continuous. ties : {'raise', 'random', 'tryself'}, optional Policy on how to break ties when a focal unit has multiple modes for a categorical lag. - 'raise': This will raise an exception if ties are encountered to alert the user (Default). - 'random': modal label ties Will be broken randomly. - 'tryself': check if focal label breaks the tie between label modes. If the focal label does not break the modal tie, the tie will be be broken randomly. If the focal unit has a self-weight, focal label is not used to break any tie, rather any tie will be broken randomly. Returns ------- numpy.array array of numeric|categorical values for the spatial lag Examples -------- >>> from libpysal.graph._spatial_lag import _lag_spatial >>> import numpy as np >>> from libpysal.weights.util import lat2W >>> from libpysal.graph import Graph >>> graph = Graph.from_W(lat2W(3,3)) >>> y = np.arange(9) >>> _lag_spatial(graph, y) array([ 4., 6., 6., 10., 16., 14., 10., 18., 12.]) Row standardization >>> w = lat2W(3,3) >>> w.transform = 'r' >>> graph = Graph.from_W(w) >>> y = np.arange(9) >>> _lag_spatial(graph, y) array([2. , 2. , 3. , 3.33333333, 4. , 4.66666667, 5. , 6. , 6. ]) Categorical Lag (no ties) >>> y = np.array([*'ababcbcbc']) >>> _lag_spatial(graph, y, categorical=True) array(['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'], dtype=object) Handling ties >>> y[3] = 'a' >>> np.random.seed(12345) >>> _lag_spatial(graph, y, categorical=True, ties='random') array(['a', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'], dtype=object) >>> _lag_spatial(graph, y, categorical=True, ties='random') array(['b', 'a', 'b', 'c', 'b', 'c', 'b', 'c', 'b'], dtype=object) >>> _lag_spatial(graph, y, categorical=True, ties='tryself') array(['a', 'a', 'b', 'c', 'b', 'c', 'a', 'c', 'b'], dtype=object) """ 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)}." ) # coerce list to array if isinstance(y, list): y = np.array(y) if ( isinstance(y.dtype, pd.CategoricalDtype) or pd.api.types.is_object_dtype(y.dtype) or pd.api.types.is_bool_dtype(y.dtype) or pd.api.types.is_string_dtype(y.dtype) ): categorical = True if categorical: if isinstance(y, np.ndarray): y = pd.Series(y, index=graph.unique_ids) df = pd.DataFrame(data=graph.adjacency) df["neighbor_label"] = y.loc[graph.adjacency.index.get_level_values(1)].values df["own_label"] = y.loc[graph.adjacency.index.get_level_values(0)].values df["neighbor_idx"] = df.index.get_level_values(1) df["focal_idx"] = df.index.get_level_values(0) gb = df.groupby(["focal", "neighbor_label"]).count().groupby(level="focal") n_ties = gb.apply(_check_ties).sum() if n_ties and ties == "raise": raise ValueError( f"There are {n_ties} ties that must be broken " f"to define the categorical " "spatial lag for these observations. To address this " "issue, consider setting `ties='tryself'` " "or `ties='random'` or consult the documentation " "about ties and the categorical spatial lag." ) # either there are ties and random|tryself specified or # there are no ties gb = df.groupby(by=["focal"]) if ties == "random" or ties == "raise": return gb.apply(_get_categorical_lag).values elif ties == "tryself" or ties == "raise": return gb.apply(_get_categorical_lag, ties="tryself").values else: raise ValueError( f"Received option ties='{ties}', but only options " "'raise','random','tryself' are supported." ) return sp @ y def _check_ties(focal): """Reduction to determine if a focal unit has multiple modes for neighbor labels. Parameters ---------- focal: row from pandas Dataframe Data is a Graph with an additional column having the labels for the neighbors Returns ------- bool """ max_count = focal.weight.max() return (focal.weight == max_count).sum() > 1 def _get_categorical_lag(focal, ties="random"): """Reduction to determine categorical spatial lag for a focal unit. Parameters ---------- focal: row from pandas Dataframe Data is a Graph with an additional column having the labels for the neighbors ties : {'raise', 'random', 'tryself'}, optional Policy on how to break ties when a focal unit has multiple modes for a categorical lag. - 'raise': This will raise an exception if ties are encountered to alert the user (Default). - 'random': Will break ties randomly. - 'tryself': check if focal label breaks the tie between label modes. If the focal label does not break the modal tie, the tie will be be broken randomly. If the focal unit has a self-weight, focal label is not used to break any tie, rather any tie will be broken randomly. Returns ------- str|int|float: Label for the value of the categorical lag """ self_weight = focal.focal_idx.values[0] in focal.neighbor_idx.values labels, counts = np.unique(focal.neighbor_label, return_counts=True) node_label = labels[counts == counts.max()] if ties == "random" or (ties == "tryself" and self_weight): return np.random.choice(node_label, 1)[0] elif ties == "tryself" and not self_weight: self_label = focal.own_label.values[0] if self_label in node_label: # focal breaks tie return self_label else: return np.random.choice(node_label, 1)[0] libpysal-4.12.1/libpysal/graph/_summary.py000066400000000000000000000371731466413560300205670ustar00rootroot00000000000000import numpy as np class GraphSummary: r"""Graph Summary An object containing the statistical attributes summarising the Graph and its basic properties. Attributes ---------- n_nodes : int number of Graph nodes n_edges : int number of Graph edges n_components : int number of connected components n_isolates : int number of isolates (nodes with no neighbors) nonzero : int number of edges with nonzero weight pct_nonzero : float percentage of nonzero weights n_asymmetries : int number of intrinsic asymmetries cardinalities_mean : float mean number of neighbors cardinalities_std : float standard deviation of number of neighbors cardinalities_min : float minimal number of neighbors cardinalities_25 : float 25th percentile of number of neighbors cardinalities_50 : float 50th percentile (median) of number of neighbors cardinalities_75 : float 75th percentile of number of neighbors cardinalities_max : float maximal number of neighbors weights_mean : float mean edge weight weights_std : float standard deviation of edge weights weights_min : float minimal edge weight weights_25 : float 25th percentile of edge weights weights_50 : float 50th percentile (median) of edge weights weights_75 : float 75th percentile of edge weights weights_max : float maximal edge weight s0 : float S0 (global) sum of weights ``s0`` is defined as .. math:: s0=\sum_i \sum_j w_{i,j} :attr:`s0`, :attr:`s1`, and :attr:`s2` reflect interaction between observations and are used to compute standard errors for spatial autocorrelation estimators. s1 : float S1 sum of weights ``s1`` is defined as .. math:: s1=1/2 \sum_i \sum_j \Big(w_{i,j} + w_{j,i}\Big)^2 :attr:`s0`, :attr:`s1`, and :attr:`s2` reflect interaction between observations and are used to compute standard errors for spatial autocorrelation estimators. s2 : float S2 sum of weights ``s2`` is defined as .. math:: s2=\sum_j \Big(\sum_i w_{i,j} + \sum_i w_{j,i}\Big)^2 :attr:`s0`, :attr:`s1`, and :attr:`s2` reflect interaction between observations and are used to compute standard errors for spatial autocorrelation estimators. diag_g2 : np.ndarray diagonal of :math:`GG` diag_gtg : np.ndarrray diagonal of :math:`G^{'}G` diag_gtg_gg : np.ndarray diagonal of :math:`G^{'}G + GG` trace_g2 : np.ndarray trace of :math:`GG` trace_gtg : np.ndarrray trace of :math:`G^{'}G` trace_gtg_gg : np.ndarray trace of :math:`G^{'}G + GG` Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb) >>> contiguity >>> summary = contiguity.summary(asymmetries=True) >>> summary Graph Summary Statistics ======================== Graph indexed by: ['Staten Island', 'Queens', 'Brooklyn', 'Manhattan', 'Bronx'] ============================================================== number of nodes: 5 number of edges: 10 number of connected components: 2 number of isolates: 1 number of non-zero edges: 10 Percentage of non-zero edges: 44.00% number of asymmetries: 0 -------------------------------------------------------------- Cardinalities ============================================================== Mean: 2 25%: 2 Standard deviation: 1 50%: 2 Min: 0 75%: 3 Max: 3 -------------------------------------------------------------- Weights ============================================================== Mean: 1 25%: 1 Standard deviation: 0 50%: 1 Min: 1 75%: 1 Max: 1 -------------------------------------------------------------- Sum of weights ============================================================== S0: 10 S1: 20 S2: 104 -------------------------------------------------------------- Traces ============================================================== GG: 10 G'G: 10 G'G + GG: 20 >>> summary.s1 20 """ def __init__(self, graph, asymmetries=False): """Create GraphSummary Parameters ---------- graph : Graph asymmetries : bool whether to compute ``n_asymmetries``, which is considerably more expensive than the other attributes. By default False. """ self._graph = graph self.asymmetries = asymmetries self.n_nodes = self._graph.n_nodes # number of nodes / observations self.n_edges = self._graph.n_edges # number of edges excluding isolates self.n_components = self._graph.n_components self.n_isolates = len(self._graph.isolates) # nonzero self.nonzero = self._graph.nonzero self.pct_nonzero = self._graph.pct_nonzero # intrinsic assymetries if asymmetries: self.n_asymmetries = len(self._graph.asymmetry()) # statistics of cardinalities card_stats = self._graph.cardinalities.describe() self.cardinalities_mean = card_stats["mean"] self.cardinalities_std = card_stats["std"] self.cardinalities_min = card_stats["min"] self.cardinalities_25 = card_stats["25%"] self.cardinalities_50 = card_stats["50%"] self.cardinalities_75 = card_stats["75%"] self.cardinalities_max = card_stats["max"] # statistics of weights weights_stats = self._graph._adjacency.drop(self._graph.isolates).describe() self.weights_mean = weights_stats["mean"] self.weights_std = weights_stats["std"] self.weights_min = weights_stats["min"] self.weights_25 = weights_stats["25%"] self.weights_50 = weights_stats["50%"] self.weights_75 = weights_stats["75%"] self.weights_max = weights_stats["max"] # sum of weights self.s0 = self._s0() self.s1 = self._s1() self.s2 = self._s2() # diags self.diag_g2 = self._diag_g2() self.diag_gtg = self._diag_gtg() self.diag_gtg_gg = self._diag_gtg_gg() # traces self.trace_g2 = self.diag_g2.sum() self.trace_gtg = self.diag_gtg.sum() self.trace_gtg_gg = self.diag_gtg_gg.sum() def __repr__(self): n_asymmetries = f"{self.n_asymmetries:>12.0f}" if self.asymmetries else "NA" return f"""Graph Summary Statistics {'='*24} Graph indexed by: {self._graph._get_ids_repr(57)} {'='*62} {"Number of nodes:":<50}{self.n_nodes:>12.0f} {"Number of edges:":<50}{self.n_edges:>12.0f} {"Number of connected components:":<50}{self.n_components:>12.0f} {"Number of isolates:":<50}{self.n_isolates:12.0f} {"Number of non-zero edges:":<50}{self.nonzero:>12.0f} {"Percentage of non-zero edges:":<50}{self.pct_nonzero:>11.2f}% {"Number of asymmetries:":<50}{n_asymmetries} {'-'*62} Cardinalities {'='*62} {"Mean:":<20}{self.cardinalities_mean:>9.0f} {"25%:":<20}{self.cardinalities_25:>9.0f} {"Standard deviation:":<20}{self.cardinalities_std:>9.0f} {"50%:":<20}{self.cardinalities_50:>9.0f} {"Min:":<20}{self.cardinalities_min:>9.0f} {"75%:":<20}{self.cardinalities_75:>9.0f} {"Max:":<20}{self.cardinalities_max:>9.0f} {'-'*62} Weights {'='*62} {"Mean:":<20}{self.weights_mean:>9.0f} {"25%:":<20}{self.weights_25:>9.0f} {"Standard deviation:":<20}{self.weights_std:>9.0f} {"50%:":<20}{self.weights_50:>9.0f} {"Min:":<20}{self.weights_min:>9.0f} {"75%:":<20}{self.weights_75:>9.0f} {"Max:":<20}{self.weights_max:>9.0f} {'-'*62} Sum of weights {'='*62} {"S0:":<50}{self.s0:>12.0f} {"S1:":<50}{self.s1:>12.0f} {"S2:":<50}{self.s2:>12.0f} {'-'*62} Traces {'='*62} {"GG:":<50}{self.trace_g2:>12.0f} {"G'G:":<50}{self.trace_gtg:>12.0f} {"G'G + GG:":<50}{self.trace_gtg_gg:>12.0f} """ # noqa: E501 def _repr_html_(self): n_asymmetries = f"{self.n_asymmetries:12.0f}" if self.asymmetries else "NA" return f"""
Graph Summary Statistics
Number of nodes: {self.n_nodes:12.0f}
Number of edges: {self.n_edges:12.0f}
Number of connected components: {self.n_components:12.0f}
Number of isolates: {self.n_isolates:12.0f}
Number of non-zero edges: {self.nonzero:12.0f}
Percentage of non-zero edges: {self.pct_nonzero:11.2f}%
Number of asymmetries: {n_asymmetries}
Cardinalities
Mean: {self.cardinalities_mean:9.0f} 25%: {self.cardinalities_25:9.0f}
Standard deviation: {self.cardinalities_std:9.0f} 50% {self.cardinalities_50:9.0f}
Min: {self.cardinalities_min:9.0f} 75%: {self.cardinalities_75:9.0f}
Max: {self.cardinalities_max:9.0f}
Weights
Mean: {self.weights_mean:9.0f} 25%: {self.weights_25:9.0f}
Standard deviation: {self.weights_std:9.0f} 50% {self.weights_50:9.0f}
Min: {self.weights_min:9.0f} 75%: {self.weights_75:9.0f}
Max: {self.weights_max:9.0f}
Sum of weights and Traces
S0: {self.s0:12.0f} GG: {self.trace_g2:12.0f}
S1: {self.s1:12.0f} G'G: {self.trace_gtg:12.0f}
S3: {self.s2:12.0f} G'G + GG: {self.trace_gtg_gg:12.0f}
Graph indexed by: {self._graph._get_ids_repr(57)}
""" def _s0(self): r"""helper to get S0 in downstream ``s0`` is defined as .. math:: s0=\sum_i \sum_j w_{i,j} :attr:`s0`, :attr:`s1`, and :attr:`s2` reflect interaction between observations and are used to compute standard errors for spatial autocorrelation estimators. Returns ------- float global sum of weights """ return self._graph._adjacency.sum() def _s1(self): r"""S1 sum of weights ``s1`` is defined as .. math:: s1=1/2 \sum_i \sum_j \Big(w_{i,j} + w_{j,i}\Big)^2 :attr:`s0`, :attr:`s1`, and :attr:`s2` reflect interaction between observations and are used to compute standard errors for spatial autocorrelation estimators. Returns ------- float s1 sum of weights """ t = self._graph.sparse.transpose() t = t + self._graph.sparse t2 = t * t return t2.sum() / 2.0 def _s2(self): r"""S2 sum of weights ``s2`` is defined as .. math:: s2=\sum_j \Big(\sum_i w_{i,j} + \sum_i w_{j,i}\Big)^2 :attr:`s0`, :attr:`s1`, and :attr:`s2` reflect interaction between observations and are used to compute standard errors for spatial autocorrelation estimators. Returns ------- float s2 sum of weights """ s = self._graph.sparse return (np.array(s.sum(1) + s.sum(0).transpose()) ** 2).sum() def _diag_g2(self): """Diagonal of :math:`GG`. Returns ------- np.ndarray """ return (self._graph.sparse @ self._graph.sparse).diagonal() def _diag_gtg(self): """Diagonal of :math:`G^{'}G`. Returns ------- np.ndarray """ return (self._graph.sparse.transpose() @ self._graph.sparse).diagonal() def _diag_gtg_gg(self): """Diagonal of :math:`G^{'}G + GG`. Returns ------- np.ndarray """ gt = self._graph.sparse.transpose() g = self._graph.sparse return (gt @ g + g @ g).diagonal() libpysal-4.12.1/libpysal/graph/_triangulation.py000066400000000000000000000610371466413560300217460ustar00rootroot00000000000000import warnings from functools import wraps import numpy import pandas 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 ( CoplanarError, _induce_cliques, _jitter_geoms, _reorder_adjtable_by_ids, _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 _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_coplanar(triangulator): """This is a decorator that validates input for coplanar points""" @wraps(triangulator) def tri_with_validation( coordinates, ids=None, coplanar="raise", kernel=None, bandwidth=None, seed=None, **kwargs, ): if coplanar not in ["raise", "jitter", "clique"]: raise ValueError( f"Recieved option coplanar='{coplanar}', but only options " "'raise','clique','jitter' are suppported." ) # validate geometry input coordinates, ids, _ = _validate_geometry_input( coordinates, ids=ids, valid_geometry_types=_VALID_GEOMETRY_TYPES ) if coplanar == "jitter": coordinates = _jitter_geoms(coordinates, seed=seed) # generate triangulation (triangulator is the wrapped function) heads_ix, tails_ix, coplanar_loopkup = triangulator( coordinates, coplanar, **kwargs ) # 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) heads_ix, tails_ix, weights = _kernel( sparse_d, metric="precomputed", kernel=kernel, bandwidth=bandwidth, taper=False, resolve_isolates=False, # no isolates in triangulation ) # create adjacency adjtable = pandas.DataFrame.from_dict( {"focal": heads_ix, "neighbor": tails_ix, "weight": weights} ) # reinsert points resolved via clique if (coplanar_loopkup.shape[0] > 0) & (coplanar == "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. if kernel is None: # kernel not set, weights are assumed binary fill_value = 1 else: fill_value = _kernel_functions[kernel]( numpy.array([0]), bandwidth ).item() coplanar, _, nearest = coplanar_loopkup.T adjtable = _induce_cliques(adjtable, coplanar, nearest, fill_value) adjtable["focal"] = ids[adjtable.focal] adjtable["neighbor"] = ids[adjtable.neighbor] adjtable = _reorder_adjtable_by_ids(adjtable, ids) else: adjtable["focal"] = ids[adjtable.focal] adjtable["neighbor"] = ids[adjtable.neighbor] # return data for Graph.from_arrays return adjtable.focal.values, adjtable.neighbor.values, adjtable.weight.values return tri_with_validation @_validate_coplanar def _delaunay(coordinates, coplanar): """ 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. coplanar : string (default: "raise") How to deal with coplanar points. Coplanar points make all triangulations ill-posed, and thus they need to be addressed in order to create a valid graph. This parameter must be one of the following: * "raise": raise an error if coplanar points are present. This is default. * "jitter": jitter the input points by a small value. This makes the resulting depend on the seed provided to the triangulation function. * "clique": expand coplanar points into a graph clique. This creates a "unique points" triangulation using all of the unique locations in the data. Then, co-located samples are connected within a site. Finally, co-located samples are connected to other sites in the "unique points" triangulation. seed : int (default: None) An integer value used to ensure that the pseudorandom number generator provides the same value over replications. By default, no seed is used, so results will be random every time. This is only used if coplanar='jitter'. 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, _, coplanar = _voronoi_edges(coordinates, coplanar) heads_ix, tails_ix = edges.T return heads_ix, tails_ix, coplanar @_validate_coplanar def _gabriel(coordinates, coplanar): """ 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. coplanar : string (default: "raise") How to deal with coplanar points. Coplanar points make all triangulations ill-posed, and thus they need to be addressed in order to create a valid graph. This parameter must be one of the following: * "raise": raise an error if coplanar points are present. This is default. * "jitter": jitter the input points by a small value. This makes the resulting depend on the seed provided to the triangulation function. * "clique": expand coplanar points into a graph clique. This creates a "unique points" triangulation using all of the unique locations in the data. Then, co-located samples are connected within a site. Finally, co-located samples are connected to other sites in the "unique points" triangulation. seed : int (default: None) An integer value used to ensure that the pseudorandom number generator provides the same value over replications. By default, no seed is used, so results will be random every time. This is only used if coplanar='jitter'. """ 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, points, coplanar = _voronoi_edges(coordinates, coplanar) droplist = _filter_gabriel( edges, points, ) edges = numpy.row_stack(list(set(map(tuple, edges)).difference(set(droplist)))) heads_ix, tails_ix = edges.T order = numpy.lexsort((tails_ix, heads_ix)) sorted_heads_ix = heads_ix[order] sorted_tails_ix = tails_ix[order] return sorted_heads_ix, sorted_tails_ix, coplanar @_validate_coplanar def _relative_neighborhood(coordinates, coplanar): """ 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. coplanar : string (default: "raise") How to deal with coplanar points. Coplanar points make all triangulations ill-posed, and thus they need to be addressed in order to create a valid graph. This parameter must be one of the following: * "raise": raise an error if coplanar points are present. This is default. * "jitter": jitter the input points by a small value. This makes the resulting depend on the seed provided to the triangulation function. * "clique": expand coplanar points into a graph clique. This creates a "unique points" triangulation using all of the unique locations in the data. Then, co-located samples are connected within a site. Finally, co-located samples are connected to other sites in the "unique points" triangulation. seed : int (default: None) An integer value used to ensure that the pseudorandom number generator provides the same value over replications. By default, no seed is used, so results will be random every time. This is only used if coplanar='jitter'. """ 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, points, coplanar = _voronoi_edges(coordinates, coplanar) output, _ = _filter_relativehood(edges, 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, coplanar @_validate_coplanar def _voronoi(coordinates, coplanar, clip="bounding_box", 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 ``'bounding_box'``. Options are as follows. * ``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. * ``'bounding_box'`` -- Clip the voronoi cells to the bounding box of the input points. * ``'convex_hull'`` -- Clip the voronoi cells to the convex hull of the input points. * ``'alpha_shape'`` -- Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alpha shape, using :func:`libpysal.cg.alpha_shape_auto`). * ``shapely.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. coplanar : string (default: "raise") How to deal with coplanar points. Coplanar points make all triangulations ill-posed, and thus they need to be addressed in order to create a valid graph. This parameter must be one of the following: * "raise": raise an error if coplanar points are present. This is default. * "jitter": jitter the input points by a small value. This makes the resulting depend on the seed provided to the triangulation function. * "clique": expand coplanar points into a graph clique. This creates a "unique points" triangulation using all of the unique locations in the data. Then, co-located samples are connected within a site. Finally, co-located samples are connected to other sites in the "unique points" triangulation. seed : int (default: None) An integer value used to ensure that the pseudorandom number generator provides the same value over replications. By default, no seed is used, so results will be random every time. This is only used if coplanar='jitter'. 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. """ if coplanar == "raise": unique = numpy.unique(coordinates, axis=0) if unique.shape != coordinates.shape: raise CoplanarError( f"There are {len(unique)} unique locations in " f"the dataset, but {len(coordinates)} observations. This means there " "are multiple points in the same location, which is undefined " "for this graph type. To address this issue, consider setting " "`coplanar='clique'` or consult the documentation about " "coplanar points." ) cells = voronoi_frames(coordinates, clip=clip, return_input=False, as_gdf=False) heads_ix, tails_ix, weights = _vertex_set_intersection(cells, rook=rook) return heads_ix, tails_ix, numpy.array([]) #### 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 reimplementation 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_coordinates = coordinates.shape[0] out = [] r = [] for edge in edges: i, j = edge pi = coordinates[i] pj = coordinates[j] dij = ((pi - pj) ** 2).sum() ** 0.5 prune = False for k in range(n_coordinates): if (i == k) or (j == k): continue pk = coordinates[k] if (pi == pk).all() or (pj == pk).all(): # coplanar continue dik = ((pi - pk) ** 2).sum() ** 0.5 djk = ((pj - pk) ** 2).sum() ** 0.5 dkmax = numpy.array([dik, djk]).max() prune |= dkmax <= dij if prune: pass else: out.append((i, j, dij)) if return_dkmax: r.append(dkmax) return out, r def _voronoi_edges(coordinates, coplanar): dt = spatial.Delaunay(coordinates) if dt.coplanar.shape[0] > 0 and coplanar == "raise": raise CoplanarError( f"There are {len(coordinates) - len(dt.coplanar)} unique locations in " f"the dataset, but {len(coordinates)} observations. This means there " "are multiple points in the same location, which is undefined " "for this graph type. To address this issue, consider setting " "`coplanar='clique'` or consult the documentation about " "coplanar points." ) edges = _edges_from_simplices(dt.simplices) edges = ( pandas.DataFrame(numpy.asarray(list(edges))) .sort_values([0, 1]) .drop_duplicates() .values ) return edges, dt.points, dt.coplanar libpysal-4.12.1/libpysal/graph/_utils.py000066400000000000000000000320211466413560300202150ustar00rootroot00000000000000import warnings 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") PANDAS_GE_21 = Version(pd.__version__) >= Version("2.1.0") NUMPY_GE_2 = Version(np.__version__) >= Version("2.0.0") try: from numba import njit # noqa: E401 HAS_NUMBA = True except ModuleNotFoundError: from libpysal.common import jit as njit HAS_NUMBA = False class CoplanarError(ValueError): """Custom ValueError raised when coplanar points are detected.""" pass def _sparse_to_arrays(sparray, ids=None, resolve_isolates=True, return_adjacency=False): """Convert sparse array to arrays of adjacency When we know we are dealing with cliques, we don't want to resolve isolates here but will do that later once cliques are induced. """ argsort_kwds = {"stable": True} if NUMPY_GE_2 else {} 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(**argsort_kwds) head = ids[sparray.row][sorter] tail = ids[sparray.col][sorter] data = sparray.data[sorter] else: sorter = sparray.row.argsort(**argsort_kwds) head = sparray.row[sorter] tail = sparray.col[sorter] data = sparray.data[sorter] ids = np.arange(sparray.shape[0], dtype=int) if resolve_isolates: return _resolve_islands( head, tail, ids, data, return_adjacency=return_adjacency ) if return_adjacency: return pd.Series( data, index=pd.MultiIndex.from_arrays([head, tail], names=["focal", "neighbor"]), name="weight", ) return head, tail, data def _jitter_geoms(coordinates, geoms=None, 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)) if geoms is not None: geoms = geopandas.GeoSeries( geopandas.points_from_xy(*coordinates.T, crs=geoms.crs) ) return coordinates, geoms return coordinates def _induce_cliques(adjtable, coplanar, nearest, 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. """ coplanar_addition = [] for c, n in zip(coplanar, nearest, strict=True): neighbors = adjtable.neighbor[adjtable.focal == n] for n_ in neighbors: fill = adjtable.weight[ (adjtable.focal == n) & (adjtable.neighbor == n_) ].item() coplanar_addition.append([c, n_, fill]) coplanar_addition.append([n_, c, fill]) coplanar_addition.append([c, n, fill_value]) coplanar_addition.append([n, c, fill_value]) adjtable_filled = pd.concat( [ adjtable, pd.DataFrame(coplanar_addition, columns=["focal", "neighbor", "weight"]), ], ignore_index=True, ) return adjtable_filled 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() isolates = idxs.isna() if isolates.any(): 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[isolates.values] = 0 return heads, tails, data_array def _build_coplanarity_lookup(geoms): """ Identify coplanar points and create a look-up table for the coplanar geometries. """ geoms = geoms.reset_index(drop=True) coplanar = [] nearest = [] r = geoms.groupby(geoms).groups if GPD_013 else geoms.groupby(geoms.to_wkb()).groups for g in r.values(): if len(g) == 2: coplanar.append(g[0]) nearest.append(g[1]) elif len(g) > 2: for n in g[1:]: coplanar.append(n) nearest.append(g[0]) return np.asarray(coplanar), np.asarray(nearest) 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 _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, return_adjacency=False): """ Induce self-loops for a collection of ids and links describing a contiguity graph. Induced self-loops will have zero weight. """ islands = pd.Index(ids).difference(pd.Index(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], names=["focal", "neighbor"]), name="weight", ) adjacency = adjacency.reindex(ids, level=0).reindex(ids, level=1) if return_adjacency: return adjacency return ( adjacency.index.get_level_values(0), adjacency.index.get_level_values(1), adjacency.values, ) def _reorder_adjtable_by_ids(adjtable, ids): return ( adjtable.set_index(["focal", "neighbor"]) .reindex(ids, level=0) .reindex(ids, level=1) .reset_index() ) @njit def _mode(values, index): # noqa: ARG001 """Custom mode function for numba.""" array = np.sort(values.ravel()) mask = np.empty(array.shape, dtype=np.bool_) mask[:1] = True mask[1:] = array[1:] != array[:-1] unique = array[mask] idx = np.nonzero(mask)[0] idx = np.append(idx, mask.size) counts = np.diff(idx) return unique[np.argmax(counts)] @njit def _limit_range(values, index, low, high): # noqa: ARG001 nan_tracker = np.isnan(values) if (not nan_tracker.all()) & (len(values[~nan_tracker]) > 2): lower, higher = np.nanpercentile(values, (low, high)) else: return ~nan_tracker return (lower <= values) & (values <= higher) def _compute_stats(grouper, to_compute: list[str] | None = None): """Fast compute of "count", "mean", "median", "std", "min", "max", \\ "sum", "nunique" and "mode" within a grouper object. Using numba. Parameters ---------- grouper : pandas.GroupBy Groupby Object which specifies the aggregations to be performed. to_compute : list[str] A list of stats functions to pass to groupby.agg Returns ------- DataFrame """ 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, ) if to_compute is None: to_compute = [ "count", "mean", "median", "std", "min", "max", "sum", "nunique", "mode", ] agg_to_compute = [f for f in to_compute if f != "mode"] stat_ = grouper.agg(agg_to_compute) if "mode" in to_compute: if HAS_NUMBA: stat_["mode"] = grouper.agg(_mode, engine="numba") else: stat_["mode"] = grouper.agg(lambda x: _mode(x.values, x.index)) return stat_ def _percentile_filtration_grouper(y, graph_adjacency_index, q=(25, 75)): """Carry out a filtration of graph neighbours \\ based on the quantiles of ``y``, specified in ``q``""" 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, ) ## need to reset since numba transform has an indexing issue grouper = ( y.take(graph_adjacency_index.codes[-1]) .reset_index(drop=True) .groupby(graph_adjacency_index.codes[0]) ) if HAS_NUMBA: to_keep = grouper.transform( _limit_range, q[0], q[1], engine="numba" ).values.astype(bool) else: to_keep = grouper.transform( lambda x: _limit_range(x.values, x.index, q[0], q[1]) ).values.astype(bool) filtered_grouper = y.take(graph_adjacency_index.codes[-1][to_keep]).groupby( graph_adjacency_index.codes[0][to_keep] ) return filtered_grouper libpysal-4.12.1/libpysal/graph/base.py000066400000000000000000003171541466413560300176450ustar00rootroot00000000000000import math from functools import cached_property import numpy as np import pandas as pd from packaging.version import Version from scipy import __version__ as scipy_version from scipy import sparse from libpysal.weights import W from ._contiguity import ( _block_contiguity, _fuzzy_contiguity, _queen, _rook, _vertex_set_intersection, ) from ._indices import _build_from_h3 from ._kernel import _distance_band, _kernel from ._matching import _spatial_matching from ._network import build_travel_graph as _build_travel_graph from ._plotting import _explore_graph, _plot from ._raster import _generate_da, _raster_contiguity from ._set_ops import SetOpsMixin from ._spatial_lag import _lag_spatial from ._summary import GraphSummary from ._triangulation import _delaunay, _gabriel, _relative_neighborhood, _voronoi from ._utils import ( _compute_stats, _evaluate_index, _neighbor_dict_to_edges, _percentile_filtration_grouper, _resolve_islands, _sparse_to_arrays, ) from .io._gal import _read_gal, _to_gal from .io._gwt import _read_gwt, _to_gwt from .io._parquet import _read_parquet, _to_parquet ALLOWED_TRANSFORMATIONS = ("O", "B", "R", "D", "V", "C") # 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) """ __all__ = [ "Graph", "read_parquet", "read_gal", "read_gwt", ] 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", is_sorted=False): """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 - **C** -- Custom is_sorted : bool, default False ``adjacency`` capturing the graph needs to be canonically sorted to initialize the class. The MultiIndex needs to be ordered i-->j on both focal and neighbor levels according to the order of ids in the original data from which the Graph is created. Sorting is performed by default based on the order of unique values in the focal level. Sorting needs to be reflected in both the values of the MultiIndex and also the underlying MultiIndex.codes. Set ``is_sorted=True`` to skip this step if the adjacency is already canonically sorted and you are certain about it. """ if not isinstance(adjacency, pd.Series): raise TypeError( f"The adjacency table needs to be a pandas.Series. {type(adjacency)}" ) if not tuple(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." ) if not is_sorted: # 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 _get_ids_repr(self, chars=72): if len(self.unique_ids) > 5: ids = str(self.unique_ids[:5].tolist())[:-1] + ", " if len(ids) > chars: ids = str(self.unique_ids[:5].tolist())[:chars] return f"{ids}...]" else: return self.unique_ids.tolist() def __repr__(self): return ( f"" ) 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, is_sorted=True, ) @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 Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> queen_w = weights.Queen.from_dataframe(nybb, use_index=True) >>> queen_graph = graph.Graph.from_W(queen_w) >>> queen_graph """ 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 Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb) >>> contiguity.adjacency focal neighbor Staten Island Staten Island 0 Queens Brooklyn 1 Manhattan 1 Bronx 1 Brooklyn Queens 1 Manhattan 1 Manhattan Queens 1 Brooklyn 1 Bronx 1 Bronx Queens 1 Manhattan 1 Name: weight, dtype: int64 >>> w = contiguity.to_W() >>> w.neighbors {'Bronx': ['Queens', 'Manhattan'], 'Brooklyn': ['Queens', 'Manhattan'], 'Manhattan': ['Queens', 'Brooklyn', 'Bronx'], 'Queens': ['Brooklyn', 'Manhattan', 'Bronx'], 'Staten Island': []} """ grouper = self._adjacency.groupby(level=0, sort=False) neighbors = {} weights = {} for ix, chunk in grouper: if ix in self.isolates: neighbors[ix] = [] weights[ix] = [] else: neighbors[ix] = chunk.index.get_level_values("neighbor").tolist() weights[ix] = chunk.tolist() return W( neighbors=neighbors, weights=weights, id_order=self.unique_ids.tolist(), silence_warnings=True, ) @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( _sparse_to_arrays(sparse, ids, return_adjacency=True), is_sorted=True ) @classmethod def from_arrays(cls, focal_ids, neighbor_ids, weight, **kwargs): """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 **kwargs keyword arguments passed to the class constructor 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"] ), ), **kwargs, ) 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 Examples -------- >>> neighbors = { ... 'Africa': ['Asia'], ... 'Asia': ['Africa', 'Europe'], ... 'Australia': [], ... 'Europe': ['Asia'], ... 'North America': ['South America'], ... 'South America': ['North America'], ... } >>> connectivity = graph.Graph.from_dicts(neighbors) >>> connectivity.adjacency focal neighbor Africa Asia 1 Asia Africa 1 Europe 1 Australia Australia 0 Europe Asia 1 North America South America 1 South America North America 1 Name: weight, dtype: float64 You can also specify weights (for example based on the length of the shared border): >>> weights = { ... 'Africa': [1], ... 'Asia': [0.2, 0.8], ... 'Australia': [], ... 'Europe': [1], ... 'North America': [1], ... 'South America': [1], ... } >>> connectivity = graph.Graph.from_dicts(neighbors, weights) >>> connectivity.adjacency focal neighbor Africa Asia 1.0 Asia Africa 0.2 Europe 0.8 Australia Australia 0.0 Europe Asia 1.0 North America South America 1.0 South America North America 1.0 Name: weight, dtype: float64 """ head, tail, weight = _neighbor_dict_to_edges(neighbors, weights=weights) return cls.from_arrays(head, tail, weight) @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 Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> france = gpd.read_file(get_path('geoda guerry')).set_index('Dprmnt') In the GeoDa Guerry dataset, the Region column reflects the region (North, East, West, South or Central) to which each department belongs. >>> france[['Region', 'geometry']].head() Region geometry Dprtmnt Ain E POLYGON ((801150.000 2092615.000, 800669.000 2... Aisne N POLYGON ((729326.000 2521619.000, 729320.000 2... Allier C POLYGON ((710830.000 2137350.000, 711746.000 2... Basses-Alpes E POLYGON ((882701.000 1920024.000, 882408.000 1... Hautes-Alpes E POLYGON ((886504.000 1922890.000, 885733.000 1... Using the ``"Region"`` labels as ``regimes`` then identifies all departments within the region as neighbors. >>> block_contiguity = graph.Graph.build_block_contiguity(france['Region']) >>> block_contiguity.adjacency focal neighbor Ain Basses-Alpes 1 Hautes-Alpes 1 Aube 1 Cote-d'Or 1 Doubs 1 .. Vienne Mayenne 1 Morbihan 1 Basses-Pyrenees 1 Deux-Sevres 1 Vendee 1 Name: weight, Length: 1360, dtype: int32 """ ids = _evaluate_index(regimes) return cls.from_dicts(_block_contiguity(regimes, ids=ids)) @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 Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb) >>> contiguity.adjacency focal neighbor Staten Island Staten Island 0 Queens Brooklyn 1 Manhattan 1 Bronx 1 Brooklyn Queens 1 Manhattan 1 Manhattan Queens 1 Brooklyn 1 Bronx 1 Bronx Queens 1 Manhattan 1 Name: weight, dtype: int64 Weight by perimeter instead of binary weights: >>> contiguity_perimeter = graph.Graph.build_contiguity(nybb, by_perimeter=True) >>> contiguity_perimeter.adjacency focal neighbor Staten Island Staten Island 0.000000 Queens Brooklyn 50867.502055 Manhattan 103.745207 Bronx 5.777002 Brooklyn Queens 50867.502055 Manhattan 5736.546898 Manhattan Queens 103.745207 Brooklyn 5736.546898 Bronx 5258.300879 Bronx Queens 5.777002 Manhattan 5258.300879 Name: weight, dtype: float64 """ 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_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 Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] Note that the method requires point geometry (or an array of coordinates representing points) as an input. The threshold distance is in the units of the geometry projection. You can check it using the ``nybb.crs`` property. >>> distance_band = graph.Graph.build_distance_band(nybb.centroid, 45000) >>> distance_band.adjacency focal neighbor Staten Island Staten Island 0 Queens Brooklyn 1 Brooklyn Queens 1 Manhattan Bronx 1 Bronx Manhattan 1 Name: weight, dtype: int64 The larger threshold yields more neighbors. >>> distance_band = graph.Graph.build_distance_band(nybb.centroid, 110000) >>> distance_band.adjacency focal neighbor Staten Island Queens 1 Brooklyn 1 Manhattan 1 Queens Staten Island 1 Brooklyn 1 Manhattan 1 Bronx 1 Brooklyn Staten Island 1 Queens 1 Manhattan 1 Bronx 1 Manhattan Staten Island 1 Queens 1 Brooklyn 1 Bronx 1 Bronx Queens 1 Brooklyn 1 Manhattan 1 Name: weight, dtype: int64 Instead of binary weights you can use inverse distance. >>> distance_band = graph.Graph.build_distance_band( ... nybb.centroid, ... 45000, ... binary=False, ... ) >>> distance_band.adjacency focal neighbor Staten Island Staten Island 0.000000 Queens Brooklyn 0.000024 Brooklyn Queens 0.000024 Manhattan Bronx 0.000026 Bronx Manhattan 0.000026 Name: weight, dtype: float64 Or specify the kernel function to derive weight from the distance. >>> distance_band = graph.Graph.build_distance_band( ... nybb.centroid, ... 45000, ... binary=False, ... kernel='bisquare', ... bandwidth=60000, ... ) >>> distance_band.adjacency focal neighbor Staten Island Staten Island 0.000000 Queens Brooklyn 0.232079 Brooklyn Queens 0.232079 Manhattan Bronx 0.309825 Bronx Manhattan 0.309825 Name: weight, dtype: float64 """ 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_fuzzy_contiguity( cls, geometry, tolerance=None, buffer=None, predicate="intersects", **kwargs ): """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. **kwargs Keyword arguments passed to ``geopandas.GeoSeries.buffer``. Returns ------- Graph libpysal.graph.Graph encoding fuzzy contiguity Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] Example using the default parameters: >>> fuzzy_contiguity = graph.Graph.build_fuzzy_contiguity(nybb) >>> fuzzy_contiguity Example using the tolerance of 0.05: >>> fuzzy_contiguity = graph.Graph.build_fuzzy_contiguity(nybb, tolerance=0.05) >>> fuzzy_contiguity Example using a buffer of 10000 feet (CRS of nybb is in feet): >>> fuzzy_contiguity = graph.Graph.build_fuzzy_contiguity(nybb, buffer=10000) >>> fuzzy_contiguity """ ids = _evaluate_index(geometry) heads, tails, weights = _fuzzy_contiguity( geometry, ids, tolerance=tolerance, buffer=buffer, predicate=predicate, **kwargs, ) return cls.from_arrays(heads, tails, weights) @classmethod def build_raster_contiguity( cls, da, rook=False, z_value=None, coords_labels=None, k=1, include_nodata=False, n_jobs=1, ): """Generate Graph from ``xarray.DataArray`` raster object Create Graph object encoding contiguity of raster cells from ``xarray.DataArray`` object. The coordinates are flatten to tuples representing the location of each cell within the raster. Parameters ---------- da : xarray.DataArray Input 2D or 3D DataArray with shape=(z, y, x) rook : bool, optional Contiguity method. If True, two cells 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 z_value : {int, str, float}, optional Select the z_value of 3D DataArray with multiple layers. By default None coords_labels : dict, optional 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"}`` When None, defaults to empty dictionary. k : int, optional Order of contiguity, this will select all neighbors up to k-th order. Default is 1. include_nodata : bool, optional If True, missing values will be assumed as non-missing when selecting higher_order neighbors, Default is False n_jobs : int, optional Number of cores to be used in the sparse weight construction. If -1, all available cores are used. Default is 1. Requires ``joblib``. Returns ------- Graph libpysal.graph.Graph encoding raster contiguity """ if coords_labels is None: coords_labels = {} criterion = "rook" if rook else "queen" heads, tails, weights, xarray_index = _raster_contiguity( da=da, criterion=criterion, z_value=z_value, coords_labels=coords_labels, k=k, include_nodata=include_nodata, n_jobs=n_jobs, ) heads, tails, weights = _resolve_islands( heads, tails, xarray_index.to_numpy(), weights ) contig = cls.from_arrays(heads, tails, weights) contig._xarray_index_names = xarray_index.names if k > 1 and not include_nodata: contig = contig.higher_order(k, lower_order=True) return contig @classmethod def build_kernel( cls, data, kernel="gaussian", k=None, bandwidth=None, metric="euclidean", p=2, coplanar="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". coplanar: str, optional (default "raise") Method for handling coplanar points when ``k`` is not None. Options are ``'raise'`` (raising an exception when coplanar points are present), ``'jitter'`` (randomly displace coplanar points to produce uniqueness), & ``'clique'`` (induce fully-connected sub cliques for coplanar 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, coplanar=coplanar, ) return cls.from_arrays(head, tail, weight) @classmethod def build_knn(cls, data, k, metric="euclidean", p=2, coplanar="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". coplanar: str, optional (default "raise") Method for handling coplanar points. Options include ``'raise'`` (raising an exception when coplanar points are present), ``'jitter'`` (randomly displace coplanar points to produce uniqueness), & ``'clique'`` (induce fully-connected sub cliques for coplanar points). Returns ------- Graph libpysal.graph.Graph encoding KNN weights Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index('BoroName') >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... >>> knn3 = graph.Graph.build_knn(nybb.centroid, k=3) >>> knn3.adjacency focal neighbor Staten Island Queens 1 Brooklyn 1 Manhattan 1 Queens Brooklyn 1 Manhattan 1 Bronx 1 Brooklyn Staten Island 1 Queens 1 Manhattan 1 Manhattan Queens 1 Brooklyn 1 Bronx 1 Bronx Queens 1 Brooklyn 1 Manhattan 1 Name: weight, dtype: int32 Specifying k=1 identifies the nearest neighbor (note that this can be asymmetrical): >>> knn1 = graph.Graph.build_knn(nybb.centroid, k=1) >>> knn1.adjacency focal neighbor Staten Island Brooklyn 1 Queens Brooklyn 1 Brooklyn Queens 1 Manhattan Bronx 1 Bronx Manhattan 1 Name: weight, dtype: int32 """ ids = _evaluate_index(data) head, tail, weight = _kernel( data, bandwidth=np.inf, metric=metric, kernel="boxcar", k=k, p=p, ids=ids, coplanar=coplanar, ) return cls.from_arrays(head, tail, weight) @classmethod def build_spatial_matches( cls, data, k, metric="euclidean", solver=None, allow_partial_match=False, **metric_kwargs, ): """ Match locations in one dataset to at least `n_matches` locations in another (possibly identical) dataset by minimizing the total distance between matched locations. Letting :math:`d_{ij}` be .. math:: \\text{minimize} \\sum_i^n \\sum_j^n d_{ij}m_{ij} \\text{subject to} \\sum_j^n m_{ij} >= k \\forall i m_{ij} \\in {0,1} \\forall ij Parameters ---------- x : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame geometries that need matches. 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. y : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame (default: None) geometries that are used as a source for matching. If a geopandas 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. If none, matches are made within `x`. n_matches : int (default: None) number of matches 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. solver : solver from pulp (default: None) a solver defined by the pulp optimization library. If no solver is provided, pulp's default solver will be used. This is generally pulp.COIN(), but this may vary depending on your configuration. return_mip : bool (default: False) whether or not to return the instance of the pulp.LpProblem. By default, the problem is not returned to the user. allow_partial_match : bool (default: False) whether to allow for partial matching. A partial match may have a weight between zero and one, while a "full" match (by default) must have a weight of either zero or one. A partial matching may have a shorter total distance, but will result in a weighted graph. """ head, tail, weight = _spatial_matching( x=data, metric=metric, n_matches=k, solver=solver, allow_partial_match=allow_partial_match, **metric_kwargs, ) # ids need to be addressed here, rather than in the matching # because x and y can have different id sets. It's only # in W where we *know* we can just use one id vector. return cls.from_arrays(head, tail, weight) @classmethod def build_triangulation( cls, data, method="delaunay", bandwidth=np.inf, kernel="boxcar", clip="bounding_box", rook=True, coplanar="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 ``'bounding_box'``. Options are as follows. ``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. ``'bounding_box'`` Clip the voronoi cells to the bounding box of the input points. ``'convex_hull'`` Clip the voronoi cells to the convex hull of the input points. ``'alpha_shape'`` Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alpha shape, 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 coplanar: str, optional (default "raise") Method for handling coplanar points. Options include ``'raise'`` (raising an exception when coplanar points are present), ``'jitter'`` (randomly displace coplanar points to produce uniqueness), & ``'clique'`` (induce fully-connected sub cliques for coplanar points). Returns ------- Graph libpysal.graph.Graph encoding triangulation weights Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] Note that the method requires point geometry (or an array of coordinates representing points) as an input. >>> triangulation = graph.Graph.build_triangulation(nybb.centroid) >>> triangulation.adjacency focal neighbor Staten Island Brooklyn 1 Manhattan 1 Queens Brooklyn 1 Manhattan 1 Bronx 1 Brooklyn Staten Island 1 Queens 1 Manhattan 1 Manhattan Staten Island 1 Queens 1 Brooklyn 1 Bronx 1 Bronx Queens 1 Manhattan 1 Name: weight, dtype: int64 """ ids = _evaluate_index(data) if method == "delaunay": head, tail, weights = _delaunay( data, ids=ids, bandwidth=bandwidth, kernel=kernel, coplanar=coplanar ) elif method == "gabriel": head, tail, weights = _gabriel( data, ids=ids, bandwidth=bandwidth, kernel=kernel, coplanar=coplanar ) elif method == "relative_neighborhood": head, tail, weights = _relative_neighborhood( data, ids=ids, bandwidth=bandwidth, kernel=kernel, coplanar=coplanar ) elif method == "voronoi": head, tail, weights = _voronoi( data, ids=ids, clip=clip, rook=rook, coplanar=coplanar ) 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_h3(cls, ids, order=1, weight="distance"): """Generate Graph from indices of H3 hexagons. Encode a graph from a set of H3 hexagons. The graph is generated by considering the H3 hexagons as nodes and connecting them based on their contiguity. The contiguity is defined by the order parameter, which specifies the number of steps to consider as neighbors. The weight parameter defines the type of weight to assign to the edges. Requires the `h3` library. Parameters ---------- ids : array-like Array of H3 IDs encoding focal geometries order : int, optional Order of contiguity, by default 1 weight : str, optional Type of weight. Options are: * ``distance``: raw topological distance between cells * ``binary``: 1 for neighbors, 0 for non-neighbors * ``inverse``: 1 / distance between cells By default "distance". Returns ------- Graph Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> from tobler.util import h3fy >>> gdf = gpd.read_file(get_path("geoda guerry")) >>> h3 = h3fy(gdf, resolution=4) >>> h3.head() geometry hex_id 841f94dffffffff POLYGON ((609346.657 2195981.397, 604556.817 2... 841fa67ffffffff POLYGON ((722074.162 2561038.244, 717442.706 2... 84186a3ffffffff POLYGON ((353695.287 2121176.341, 329999.974 2... 8418609ffffffff POLYGON ((387747.482 2509794.492, 364375.032 2... 8418491ffffffff POLYGON ((320872.289 1846157.662, 296923.464 1... >>> h3_contiguity = graph.Graph.build_h3(h3.index) >>> h3_contiguity """ neighbors, weights = _build_from_h3(ids, order=order) g = cls.from_dicts(neighbors, weights) if weight == "distance": return g elif weight == "binary": return g.transform("b") elif weight == "inverse": return cls(1 / g._adjacency, is_sorted=True) else: raise ValueError("weight must be one of 'distance', 'binary', or 'inverse'") @classmethod def build_travel_cost( cls, df, network, threshold, kernel=None, mapping_distance=None ): """Generate a Graph based on shortest travel costs from a pandana.Network Parameters ---------- df : geopandas.GeoDataFrame geodataframe representing observations which are snapped to the nearest node in the pandana.Network. CRS should be the same as the locations of ``node_x`` and ``node_y`` in the pandana.Network (usually 4326 if network comes from OSM, but sometimes projected to improve snapping quality). network : pandana.Network pandana Network object describing travel costs between nodes in the study area. See for more threshold : int threshold representing maximum cost distances. This is measured in the same units as the pandana.Network (not influenced by the df.crs in any way). For travel modes with relatively constant speeds like walking or biking, this is usually distance (e.g. meters if the Network is constructed from OSM). For a a multimodal or auto network with variable travel speeds, this is usually some measure of travel time kernel : str or callable, optional kernel transformation applied to the weights. See libpysal.graph.Graph.build_kernel for more information on kernel transformation options. Default is None, in which case the Graph weight is pure distance between focal and neighbor mapping_distance : int snapping tolerance passed to ``pandana.Network.get_node_ids`` that defines the maximum range at which observations are snapped to nearest nodes in the network. Default is None Returns ------- Graph Examples --------- >>> import geodatasets >>> import geopandas as gpd >>> import osmnx as ox >>> import pandana as pdna Read an example geodataframe: >>> df = gpd.read_file(geodatasets.get_path("geoda Cincinnati")).to_crs(4326) Download a walk network using osmnx >>> osm_graph = ox.graph_from_polygon(df.union_all(), network_type="walk") >>> nodes, edges = ox.utils_graph.graph_to_gdfs(osm_graph) >>> edges = edges.reset_index() Generate a routable pandana network from the OSM nodes and edges >>> network = pdna.Network( >>> edge_from=edges["u"], >>> edge_to=edges["v"], >>> edge_weights=edges[["length"]], >>> node_x=nodes["x"], >>> node_y=nodes["y"],) Use the pandana network to compute shortest paths between gdf centroids and generate a Graph >>> G = Graph.build_travel_cost(df.set_geometry(df.centroid), network, 500) >>> G.adjacency.head() focal neighbor 0 62 385.609009 65 309.471985 115 346.858002 116 0.000000 117 333.639008 Name: weight, dtype: float64 """ adj = _build_travel_graph(df, network, threshold, mapping_distance) g = cls.from_adjacency(adj) if kernel is not None: arrays = _kernel( g.sparse, metric="precomputed", kernel=kernel, bandwidth=threshold, resolve_isolates=False, ids=df.index.values, ) return cls.from_arrays(*arrays) return g @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 """ grouper = self._adjacency.groupby(level=0, sort=False) neighbors = {} for ix, chunk in grouper: if ix in self.isolates: neighbors[ix] = () else: neighbors[ix] = tuple(chunk.index.get_level_values("neighbor")) return neighbors @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 """ grouper = self._adjacency.groupby(level=0, sort=False) weights = {} for ix, chunk in grouper: if ix in self.isolates: weights[ix] = () else: weights[ix] = tuple(chunk) return weights @cached_property def sparse(self): """Return a scipy.sparse array (CSR) Returns ------- scipy.sparse.CSR sparse representation of the adjacency """ # pivot to COO sparse matrix and cast to sparse CRS array return sparse.csr_array( self._adjacency.astype("Sparse[float]").sparse.to_coo(sort_labels=True)[0] ) def transform(self, transformation): """Transformation of weights Parameters ---------- transformation : str | callable 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 Alternatively, you can pass your own callable passed to ``self.adjacency.groupby(level=0).transform()``. Returns ------- Graph transformed weights Raises ------ ValueError Value error for unsupported transformation """ if isinstance(transformation, str): transformation = transformation.upper() if self.transformation == transformation: return self.copy() if transformation == "R": standardized = ( ( self._adjacency / self._adjacency.groupby(level=0, sort=False).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, sort=False).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 elif callable(transformation): standardized = self._adjacency.groupby(level=0, sort=False).transform( transformation ) transformation = "C" else: raise ValueError( f"Transformation '{transformation}' is not supported. " f"Use one of {ALLOWED_TRANSFORMATIONS[1:]} or pass a callable." ) standardized_adjacency = pd.Series( standardized, name="weight", index=self._adjacency.index ) transformed = Graph(standardized_adjacency, transformation, is_sorted=True) if hasattr(self, "_xarray_index_names"): transformed._xarray_index_names = self._xarray_index_names return transformed @cached_property def _components(self): """helper for n_components and component_labels""" return sparse.csgraph.connected_components(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, sort=False).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 """ return self.cardinalities.index[self.cardinalities == 0] @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 nodes.""" return self.unique_ids.shape[0] @cached_property def n_edges(self): """Number of edges.""" 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 > 0).sum() @cached_property def index_pairs(self): """Return focal-neighbor index pairs Returns ------- tuple(Index, Index) tuple of two aligned pandas.Index objects encoding all edges of the Graph by their nodes Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb) >>> focal, neighbor = contiguity.index_pairs >>> focal Index(['Staten Island', 'Queens', 'Queens', 'Queens', 'Brooklyn', 'Brooklyn', 'Manhattan', 'Manhattan', 'Manhattan', 'Bronx', 'Bronx'], dtype='object', name='focal') >>> neighbor Index(['Staten Island', 'Brooklyn', 'Manhattan', 'Bronx', 'Queens', 'Manhattan', 'Queens', 'Brooklyn', 'Bronx', 'Queens', 'Manhattan'], dtype='object', name='neighbor') """ focal = self._adjacency.index.get_level_values("focal") neighbor = self._adjacency.index.get_level_values("neighbor") return (focal, neighbor) 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 summary(self, asymmetries=False): """Summary of the Graph properties Returns a :class:`GraphSummary` object with the statistical attributes summarising the Graph and its basic properties. See the docstring of the :class:`GraphSummary` for details and all the available attributes. Parameters ---------- asymmetries : bool whether to compute ``n_asymmetries``, which is considerably more expensive than the other attributes. By default False. Returns ------- GraphSummary a class containing a summary statisitcs about the graph Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb) >>> contiguity >>> summary = contiguity.summary(asymmetries=True) >>> summary Graph Summary Statistics ======================== Graph indexed by: ['Staten Island', 'Queens', 'Brooklyn', 'Manhattan', 'Bronx'] ============================================================== Number of nodes: 5 Number of edges: 10 Number of connected components: 2 Number of isolates: 1 Number of non-zero edges: 10 Percentage of non-zero edges: 44.00% Number of asymmetries: 0 -------------------------------------------------------------- Cardinalities ============================================================== Mean: 2 25%: 2 Standard deviation: 1 50%: 2 Min: 0 75%: 3 Max: 3 -------------------------------------------------------------- Weights ============================================================== Mean: 1 25%: 1 Standard deviation: 0 50%: 1 Min: 0 75%: 1 Max: 1 -------------------------------------------------------------- Sum of weights ============================================================== S0: 10 S1: 20 S2: 104 -------------------------------------------------------------- Traces ============================================================== GG: 10 G'G: 10 G'G + GG: 20 >>> summary.s1 20 """ return GraphSummary(self, asymmetries=asymmetries) 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 Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> gdf = gpd.read_file(get_path("geoda guerry")) >>> contiguity = graph.Graph.build_contiguity(gdf) >>> contiguity >>> contiguity.higher_order(k=2) >>> contiguity.higher_order(lower_order=True) """ if not Version(scipy_version) >= Version("1.12.0"): raise ImportError("Graph.higher_order() requires scipy>=1.12.0.") binary = self.transform("B") sp = binary.sparse if lower_order: wk = sum(sparse.linalg.matrix_power(sp, x) for x in range(1, k + 1)) shortest_path = False else: wk = sparse.linalg.matrix_power(sp, k) rk, ck = wk.nonzero() sk = set(zip(rk, ck, strict=True)) if shortest_path: for j in range(1, k): wj = sparse.linalg.matrix_power(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} higher = 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, ) if hasattr(self, "_xarray_index_names"): higher._xarray_index_names = self._xarray_index_names return higher def lag(self, y, categorical=False, ties="raise"): """Spatial lag operator Constructs spatial lag based on neighbor relations of the graph. Parameters ---------- y : array numpy array with dimensionality conforming to w categorical : bool True if y is categorical, False if y is continuous. ties : {'raise', 'random', 'tryself'}, optional Policy on how to break ties when a focal unit has multiple modes for a categorical lag. - 'raise': This will raise an exception if ties are encountered to alert the user (Default). - 'random': modal label ties Will be broken randomly. - 'tryself': check if focal label breaks the tie between label modes. If the focal label does not break the modal tie, the tie will be be broken randomly. If the focal unit has a self-weight, focal label is not used to break any tie, rather any tie will be broken randomly. Returns ------- numpy.ndarray array of numeric|categorical values for the spatial lag Examples -------- >>> import numpy as np >>> import pandas as pd >>> import geopandas as gpd >>> from geodatasets import get_path >>> aus = gpd.read_file(get_path("abs.australia_states_territories")).set_index( ... "STE_NAME21" ... ) >>> aus = aus[aus.geometry.notna()] >>> contiguity = graph.Graph.build_contiguity(aus) Spatial lag operator for continuous variables. >>> y = np.arange(9) >>> contiguity.lag(y) array([21., 3., 9., 13., 9., 0., 9., 0., 0.]) You can also perform transformation of weights. >>> contiguity_r = contiguity.transform("r") >>> contiguity_r.lag(y) array([4.2, 1.5, 3. , 2.6, 4.5, 0. , 3. , 0. , 0. ]) """ return _lag_spatial(self, y, categorical=categorical, ties=ties) 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 Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb) >>> contiguity.to_parquet("contiguity.parquet") """ _to_parquet(self, path, **kwargs) def to_gal(self, path): """Save Graph to a GAL file Graph is serialized to the GAL file format. Parameters ---------- path : str path to the GAL file See also -------- read_gal Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb) >>> contiguity.to_gal("contiguity.gal") """ _to_gal(self, path) def to_gwt(self, path): """Save Graph to a GWT file Graph is serialized to the GWT file format. Parameters ---------- path : str path to the GWT file See also -------- read_gwt Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb).transform("r") >>> contiguity.to_gwt("contiguity.gwt") """ _to_gwt(self, path) 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 Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb) >>> nx_graph = contiguity.to_networkx() """ 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 subgraph(self, ids): """Returns a subset of Graph containing only nodes specified in ids The resulting subgraph contains only the nodes in ``ids`` and the edges between them or zero-weight self-loops in case of isolates. The order of ``ids`` reflects a new canonical order of the resulting subgraph. This means ``ids`` should be equal to the index of the DataFrame containing data linked to the graph to ensure alignment of sparse representation of subgraph. Parameters ---------- ids : array-like An array of node IDs to be retained Returns ------- Graph A new Graph that is a subset of the original Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb) >>> contiguity.subgraph(["Queens", "Brooklyn", "Manhattan", "Bronx"]) Notes ----- Unlike the implementation in ``networkx``, this creates a copy since Graphs in ``libpysal`` are immutable. """ masked_adj = self._adjacency.loc[ids, :] filtered_adj = masked_adj[ masked_adj.index.get_level_values("neighbor").isin(ids) ] sub = Graph.from_arrays( *_resolve_islands( filtered_adj.index.get_level_values("focal"), filtered_adj.index.get_level_values("neighbor"), ids, filtered_adj.values, ) ) if hasattr(self, "_xarray_index_names"): sub._xarray_index_names = self._xarray_index_names return sub def eliminate_zeros(self): """Remove graph edges with zero weight Eliminates edges with weight == 0 that do not encode an isolate. This is useful to clean-up edges that will make no effect in operations like :meth:`lag`. Returns ------- Graph subset of Graph with zero-weight edges eliminated """ # substract isolates from mask of zeros zeros = (self._adjacency == 0) != np.isin( self._adjacency.index.get_level_values(0), self.isolates ) eliminated = Graph(self._adjacency[~zeros], is_sorted=True) if hasattr(self, "_xarray_index_names"): eliminated._xarray_index_names = self._xarray_index_names return eliminated def assign_self_weight(self, weight=1): """Assign values to edges representing self-weight. The value for each ``focal == neighbor`` location in the graph is set to ``weight``. Parameters ---------- weight : float | array-like Defines the value(s) to which the weight representing the relationship with itself should be set. If a constant is passed then each self-weight will get this value (default is 1). An array of length ``Graph.n`` can be passed to set explicit values to each self-weight (assumed to be in the same order as original data). Returns ------- Graph A new ``Graph`` with added self-weights. Examples -------- >>> import geopandas as gpd >>> from geodatasets import get_path >>> nybb = gpd.read_file(get_path("nybb")).set_index("BoroName") >>> nybb BoroCode ... geometry BoroName ... Staten Island 5 ... MULTIPOLYGON (((970217.022 145643.332, 970227.... Queens 4 ... MULTIPOLYGON (((1029606.077 156073.814, 102957... Brooklyn 3 ... MULTIPOLYGON (((1021176.479 151374.797, 102100... Manhattan 1 ... MULTIPOLYGON (((981219.056 188655.316, 980940.... Bronx 2 ... MULTIPOLYGON (((1012821.806 229228.265, 101278... [5 rows x 4 columns] >>> contiguity = graph.Graph.build_contiguity(nybb) >>> contiguity_weights = contiguity.assign_self_weight(0.5) >>> contiguity_weights.adjacency focal neighbor Staten Island Staten Island 0.5 Queens Queens 0.5 Brooklyn 1.0 Manhattan 1.0 Bronx 1.0 Brooklyn Queens 1.0 Brooklyn 0.5 Manhattan 1.0 Manhattan Queens 1.0 Brooklyn 1.0 Manhattan 0.5 Bronx 1.0 Bronx Queens 1.0 Manhattan 1.0 Bronx 0.5 Name: weight, dtype: float64 """ addition = pd.Series( weight, index=pd.MultiIndex.from_arrays( [self.unique_ids, self.unique_ids], names=["focal", "neighbor"] ), name="weight", ) # drop existing self weights and replace them with a new value existing_self_weights = self._adjacency.index[ self._adjacency.index.codes[0] == self._adjacency.index.codes[1] ] adj = ( pd.concat([self._adjacency.drop(existing_self_weights), addition]) .reindex(self.unique_ids, level=0) .reindex(self.unique_ids, level=1) ) assigned = Graph(adj, is_sorted=True) if hasattr(self, "_xarray_index_names"): assigned._xarray_index_names = self._xarray_index_names return assigned def apply(self, y, func, **kwargs): """Apply a reduction across the neighbor sets Applies ``func`` over groups of ``y`` defined by neighbors for each focal. Parameters ---------- y : array_like array of values to be grouped. Can be 1-D or 2-D and will be coerced to a pandas object func : function, str, list, dict or None Function to use for aggregating the data passed to pandas ``GroupBy.apply``. Returns ------- Series | DataFrame pandas object indexed by unique_ids """ if not isinstance(y, pd.Series | pd.DataFrame): y = pd.DataFrame(y) if hasattr(y, "ndim") and y.ndim == 2 else pd.Series(y) grouper = y.take(self._adjacency.index.codes[1]).groupby( self._adjacency.index.codes[0], sort=False ) result = grouper.apply(func, **kwargs) result.index = self.unique_ids if isinstance(result, pd.Series): result.name = None return result def aggregate(self, func): """Aggregate weights within a neighbor set Apply a custom aggregation function to a group of weights of the same focal geometry. Parameters ---------- func : callable A callable accepted by pandas ``groupby.agg`` method Returns ------- pd.Series Aggregated weights """ return self._adjacency.groupby(level=0, sort=False).agg(func) def describe( self, y, q=None, statistics=None, ): """Describe the distribution of ``y`` values within the neighbors of each node. Given the graph, computes the descriptive statistics of values within the neighbourhood of each node. Optionally, the values can be limited to a certain quantile range before computing the statistics. Notes ----- The index of ``values`` must match the index of the graph. Weight values do not affect the calculations, only adjacency does. Returns numpy.nan for all isolates. The numba package is used extensively in this function to accelerate the computation of statistics. Without numba, these computations may become slow on large data. Parameters ---------- y : NDArray[np.float64] | Series An 1D array of numeric values to be described. q : tuple[float, float] | None, optional Tuple of percentages for the percentiles to compute. Values must be between 0 and 100 inclusive. When set, values below and above the percentiles will be discarded before computation of the statistics. The percentiles are computed for each neighborhood. By default None. statistics : list[str] | None A list of stats functions to compute. If None, compute all available functions - "count", "mean", "median", "std", "min", "max", "sum", "nunique", "mode". By default None. Returns ------- DataFrame A DataFrame with descriptive statistics. """ if not isinstance(y, pd.Series): y = pd.Series(y, index=self.unique_ids) if (y.index != self.unique_ids).all(): raise ValueError("The values index is not aligned with the graph index.") # reset numerical index to enable numba functionality if not isinstance(y.index.dtype, int | float): y = y.reset_index(drop=True) if q is None: grouper = y.take(self._adjacency.index.codes[1]).groupby( self._adjacency.index.codes[0], sort=False ) else: grouper = _percentile_filtration_grouper(y, self._adjacency.index, q=q) stat_ = _compute_stats(grouper, statistics) stat_.index = self.unique_ids if isinstance(stat_, pd.Series): stat_.name = None # NA isolates stat_.loc[self.isolates] = np.nan return stat_ def generate_da(self, y): """Creates xarray.DataArray object from passed data aligned with the Graph. Parameters ---------- y : array_like flat array that shall be reshaped into a DataArray with dimensionality conforming to Graph Returns ------- xarray.DataArray instance of xarray.DataArray that can be aligned with the DataArray from which Graph was built """ return _generate_da(self, y) 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 Examples -------- >>> graph.read_parquet("contiguity.parquet") """ adjacency, transformation, xarray_index_names = _read_parquet(path, **kwargs) graph_obj = Graph(adjacency, transformation, is_sorted=True) if xarray_index_names is not None: graph_obj._xarray_index_names = xarray_index_names return graph_obj def read_gal(path): """Read Graph from a GAL file The reader tries to infer the dtype of IDs. In case of unsuccessful casting to int, it will fall back to string. Parameters ---------- path : str path to a file Returns ------- Graph deserialized Graph Examples -------- >>> graph.read_parquet("contiguity.gal") """ neighbors = _read_gal(path) return Graph.from_dicts(neighbors) def read_gwt(path): """Read Graph from a GWT file Parameters ---------- path : str path to a file Returns ------- Graph deserialized Graph Examples -------- >>> graph.read_parquet("contiguity.gwt") """ head, tail, weight = _read_gwt(path) return Graph.from_arrays(head, tail, weight) libpysal-4.12.1/libpysal/graph/io/000077500000000000000000000000001466413560300167555ustar00rootroot00000000000000libpysal-4.12.1/libpysal/graph/io/_gal.py000066400000000000000000000030671466413560300202370ustar00rootroot00000000000000import contextlib def _read_gal(path): """Read GAL weights to Graph object Parameters ---------- path : str path to GAL file Returns ------- dict neighbors dict """ with open(path) as file: neighbors = {} # handle case where more than n is specified in first line header = file.readline().strip().split() header_n = len(header) n = int(header[0]) if header_n > 1: n = int(header[1]) for _ in range(n): id_, _ = file.readline().strip().split() neighbors_i = file.readline().strip().split() neighbors[id_] = neighbors_i # try casting to ints to ensure loss-less roundtrip of integer node ids with contextlib.suppress(ValueError): neighbors = {int(k): list(map(int, v)) for k, v in neighbors.items()} return neighbors def _to_gal(graph_obj, path): """Write GAL weights to Graph object Parameters ---------- graph_obj : Graph Graph object path : str path to GAL file """ grouper = graph_obj._adjacency.groupby(level=0, sort=False) with open(path, "w") as file: file.write(f"{graph_obj.n}\n") for ix, chunk in grouper: if ix in graph_obj.isolates: neighbors = [] else: neighbors = ( chunk.index.get_level_values("neighbor").astype(str).tolist() ) file.write(f"{ix} {len(neighbors)}\n") file.write(" ".join(neighbors) + "\n") libpysal-4.12.1/libpysal/graph/io/_gwt.py000066400000000000000000000016631466413560300202750ustar00rootroot00000000000000import pandas as pd def _read_gwt(path): """ Read GWT weights to Graph object Parameters ---------- path : str path to GWT file Returns ------- tuple focal, neighbor, weight arrays """ adjacency = pd.read_csv(path, sep=r"\s+", skiprows=1, header=None) return adjacency[0].values, adjacency[1].values, adjacency[2].values def _to_gwt(graph_obj, path): """ Write GWT weights to Graph object Parameters ---------- graph_obj : Graph Graph object path : str path to GAL file """ adj = graph_obj._adjacency.reset_index() adj["focal"] = adj["focal"].astype(str).str.replace(" ", "_") adj["neighbor"] = adj["neighbor"].astype(str).str.replace(" ", "_") with open(path, "w") as file: file.write(f"0 {graph_obj.n} Unknown Unknown\n") adj.to_csv(path, sep=" ", header=False, index=False, mode="a", float_format="%.7f") libpysal-4.12.1/libpysal/graph/io/_parquet.py000066400000000000000000000044401466413560300211510ustar00rootroot00000000000000import 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__} if hasattr(graph_obj, "_xarray_index_names"): d["_xarray_index_names"] = list(graph_obj._xarray_index_names) 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, transformation, and xarray_index_names """ 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" if b"_xarray_index_names" in table.schema.metadata: meta = json.loads(table.schema.metadata[b"_xarray_index_names"]) xarray_index_names = meta["_xarray_index_names"] else: xarray_index_names = None return table.to_pandas()["weight"], transformation, xarray_index_names libpysal-4.12.1/libpysal/graph/tests/000077500000000000000000000000001466413560300175105ustar00rootroot00000000000000libpysal-4.12.1/libpysal/graph/tests/test_base.py000066400000000000000000001276221466413560300220450ustar00rootroot00000000000000import os import string import tempfile import geodatasets import geopandas as gpd import numpy as np import pandas as pd import pytest from packaging.version import Version from scipy import __version__ as scipy_version from scipy import sparse from libpysal import graph, weights @pytest.mark.network 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") self.guerry = gpd.read_file(geodatasets.get_path("geoda guerry")) 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___repr__(self): expected = ( "" ) assert repr(self.g_int) == expected expected = ( "" ) assert repr(self.g_str) == expected nybb = graph.Graph.build_contiguity(self.nybb) expected = ( "" ) assert repr(nybb) == expected h3 = { "821f87fffffffff": ("821fb7fffffffff", "821f97fffffffff"), "821fb7fffffffff": ( "821f87fffffffff", "821f97fffffffff", "82186ffffffffff", "821867fffffffff", ), "821f97fffffffff": ( "821f87fffffffff", "821fb7fffffffff", "823967fffffffff", "82396ffffffffff", "82186ffffffffff", ), "823967fffffffff": ( "821f97fffffffff", "82396ffffffffff", "82186ffffffffff", ), "82396ffffffffff": ("821f97fffffffff", "823967fffffffff"), "82186ffffffffff": ( "821fb7fffffffff", "821f97fffffffff", "823967fffffffff", "821867fffffffff", ), "821867fffffffff": ("821fb7fffffffff", "82186ffffffffff"), } h3_g = graph.Graph.from_dicts(h3) expected = ( "" ) assert repr(h3_g) == expected 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 assert isinstance(w.id_order, list) 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) with_additional_zeros = self.g_str.assign_self_weight(0) pd.testing.assert_index_equal(with_additional_zeros.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_index_pairs(self): focal, neighbor = self.g_str.index_pairs exp_focal = pd.Index( [ "a", "a", "a", "b", "b", "b", "c", "c", "d", "d", "d", "e", "e", "e", "e", "f", "f", "f", "g", "g", "h", "h", "h", "i", "i", "j", ], name="focal", ) exp_neighbor = pd.Index( [ "a", "b", "d", "a", "c", "e", "b", "f", "a", "e", "g", "b", "d", "f", "h", "c", "e", "i", "d", "h", "e", "g", "i", "f", "h", "j", ], name="neighbor", ) pd.testing.assert_index_equal(exp_focal, focal) pd.testing.assert_index_equal(exp_neighbor, neighbor) 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_transform_callable(self): contig = graph.Graph.build_contiguity(self.nybb) trans = contig.transform(lambda x: x * 10) assert trans.transformation == "C" assert trans.adjacency.sum() == 100 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_gal(self): with tempfile.TemporaryDirectory() as tmpdir: path = os.path.join(tmpdir, "g_int.gal") g_int = self.g_int.transform("b") g_int.to_gal(path) g_int_ = graph.read_gal(path) assert g_int == g_int_ path = os.path.join(tmpdir, "g_str.gal") g_str = self.g_str.transform("b") g_str.to_gal(path) g_str_ = graph.read_gal(path) assert g_str == g_str_ def test_gwt(self): with tempfile.TemporaryDirectory() as tmpdir: path = os.path.join(tmpdir, "g_int.gwt") self.g_int.to_gwt(path) g_int = graph.read_gwt(path) assert self.g_int == g_int path = os.path.join(tmpdir, "g_str.gwt") self.g_str.to_gwt(path) g_str = graph.read_gwt(path) assert self.g_str == g_str 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))) @pytest.mark.skipif( Version(scipy_version) < Version("1.12.0"), reason="sparse matrix power requires scipy>=1.12.0", ) 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 @pytest.mark.skipif( Version(scipy_version) < Version("1.12.0"), reason="sparse matrix power requires scipy>=1.12.0", ) def test_higher_order_inclusive(self): # GH738 contig = graph.Graph.from_arrays( [0, 1, 2, 3, 3, 4, 4], [0, 3, 4, 1, 4, 2, 3], [0, 1, 1, 1, 1, 1, 1] ) assert len(contig) == 6 higher = contig.higher_order(2, lower_order=True) assert len(higher) == 10 assert contig < higher 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 ) def test_eliminate_zeros(self): nybb = graph.Graph.build_contiguity(self.nybb) adj = nybb._adjacency.copy() adj["Bronx", "Queens"] = 0 adj["Queens", "Manhattan"] = 0 adj["Queens", "Queens"] = 0 with_zero = graph.Graph(adj) expected = adj.drop( [("Bronx", "Queens"), ("Queens", "Manhattan"), ("Queens", "Queens")] ) pd.testing.assert_series_equal(with_zero.eliminate_zeros()._adjacency, expected) def test_subgraph(self): knn = graph.Graph.build_knn(self.nybb.set_geometry(self.nybb.centroid), k=2) sub = knn.subgraph(["Staten Island", "Bronx", "Brooklyn"]) assert sub < knn expected = pd.Series( [1, 0, 0], name="weight", index=pd.MultiIndex.from_arrays( [ ["Staten Island", "Bronx", "Brooklyn"], ["Brooklyn", "Bronx", "Brooklyn"], ], names=["focal", "neighbor"], ), ) pd.testing.assert_series_equal(expected, sub._adjacency, check_dtype=False) def test_assign_self_weight(self): contig = graph.Graph.build_contiguity(self.nybb) diag = contig.assign_self_weight() assert len(diag._adjacency) == 15 assert diag._adjacency.sum() == 15 diag_array = contig.assign_self_weight([2, 3, 4, 5, 6]) assert len(diag_array._adjacency) == 15 assert diag_array._adjacency.sum() == 30 for i, val in enumerate(range(2, 7)): assert ( diag_array._adjacency[(contig.unique_ids[i], contig.unique_ids[i])] == val ) def test_apply(self): contig = graph.Graph.build_contiguity(self.nybb) # pandas built-in expected = pd.Series( [1.62382200e09, 3.76087588e09, 3.68168493e09, 6.16961834e09, 3.68168493e09], index=pd.Index( ["Staten Island", "Queens", "Brooklyn", "Manhattan", "Bronx"], name="focal", ), ) pd.testing.assert_series_equal(contig.apply(self.nybb.area, "sum"), expected) # numpy expected = pd.Series( [1.62382200e09, 1.18692629e09, 1.84084247e09, 1.93747835e09, 1.84084247e09], index=pd.Index( ["Staten Island", "Queens", "Brooklyn", "Manhattan", "Bronx"], name="focal", ), ) pd.testing.assert_series_equal( contig.apply(self.nybb.area, np.median), expected ) # lambda over geometry expected = pd.Series( [2.06271959e09, 6.68788190e09, 7.57087991e09, 8.78957337e09, 7.57087991e09], index=pd.Index( ["Staten Island", "Queens", "Brooklyn", "Manhattan", "Bronx"], name="focal", ), ) pd.testing.assert_series_equal( contig.apply(self.nybb.geometry, lambda x: x.unary_union.convex_hull.area), expected, ) # reduction over a dataframe expected = pd.DataFrame( [ [3.30470010e05, 1.62381982e09], [1.56477261e06, 3.76087473e09], [1.25564314e06, 3.68168433e09], [2.10181756e06, 6.16961599e09], [1.25564314e06, 3.68168433e09], ], columns=["Shape_Leng", "Shape_Area"], index=pd.Index( ["Staten Island", "Queens", "Brooklyn", "Manhattan", "Bronx"], name="focal", ), ) pd.testing.assert_frame_equal( contig.apply( self.nybb, lambda x: x[["Shape_Leng", "Shape_Area"]].sum(axis=0) ), expected, ) # 1D array input expected = pd.Series( [1.62382200e09, 3.76087588e09, 3.68168493e09, 6.16961834e09, 3.68168493e09], index=pd.Index( ["Staten Island", "Queens", "Brooklyn", "Manhattan", "Bronx"], name="focal", ), ) pd.testing.assert_series_equal( contig.apply(self.nybb.area.values, "sum"), expected ) # 2D array input expected = pd.DataFrame( [ [3.30470010e05, 1.62381982e09], [1.56477261e06, 3.76087473e09], [1.25564314e06, 3.68168433e09], [2.10181756e06, 6.16961599e09], [1.25564314e06, 3.68168433e09], ], index=pd.Index( ["Staten Island", "Queens", "Brooklyn", "Manhattan", "Bronx"], name="focal", ), ) pd.testing.assert_frame_equal( contig.apply( self.nybb[["Shape_Leng", "Shape_Area"]].values, lambda x: x.sum(axis=0), ), expected, ) def test_aggregate(self): contig = graph.Graph.build_contiguity(self.nybb) expected = pd.Series( [1.0, 20.08553692, 7.3890561, 20.08553692, 7.3890561], index=pd.Index( ["Staten Island", "Queens", "Brooklyn", "Manhattan", "Bronx"], name="focal", ), name="weight", ) pd.testing.assert_series_equal( contig.aggregate(lambda x: np.exp(np.sum(x))), expected, ) def test_describe(self): contig = graph.Graph.build_knn(self.guerry.geometry.centroid, k=5) y = self.guerry.geometry.area stats = contig.describe(y) pd.testing.assert_series_equal( stats["count"], contig.cardinalities, check_names=False, check_dtype=False, ) pd.testing.assert_series_equal( stats["sum"], pd.Series(contig.lag(y), index=contig.unique_ids), check_names=False, ) r_contig = contig.transform("R") pd.testing.assert_series_equal( stats["mean"], pd.Series(r_contig.lag(y), index=contig.unique_ids), check_names=False, ) ## compute only some statistics specific_stats = contig.describe(y, statistics=["count", "sum", "mean"]) ## assert only the specified values are computed assert list(specific_stats.columns) == ["count", "sum", "mean"] pd.testing.assert_frame_equal( specific_stats[["count", "sum", "mean"]], stats[["count", "sum", "mean"]] ) percentile_stats = contig.describe(y, q=(25, 75)) for i in contig.unique_ids: neigh_vals = y[contig[i].index.values] low, high = neigh_vals.describe()[["25%", "75%"]] neigh_vals = neigh_vals[(low <= neigh_vals) & (neigh_vals <= high)] expected = neigh_vals.describe()[["count", "mean", "std", "min", "max"]] res = percentile_stats.loc[i][["count", "mean", "std", "min", "max"]] pd.testing.assert_series_equal(res, expected, check_names=False) ## test NA equivalence between filtration and pandas nan_areas = y.copy() nan_areas.iloc[range(0, len(y), 3),] = np.nan res1 = contig.describe(y, statistics=["count"])["count"] res2 = contig.describe(y, statistics=["count"], q=(0, 100))["count"] pd.testing.assert_series_equal(res1, res2) # test with isolates and string index nybb_contig = graph.Graph.build_contiguity(self.nybb, rook=False) stats = nybb_contig.describe( self.nybb.geometry.area, statistics=["count", "sum", "mode"] ) ## all isolate values should be nan assert stats.loc["Staten Island"].isna().all() # for easier comparison and na has already been checked. stats = stats.fillna(0) pd.testing.assert_series_equal( stats["sum"], pd.Series(nybb_contig.lag(self.nybb.geometry.area), index=self.nybb.index), check_names=False, ) pd.testing.assert_series_equal( stats["count"].sort_index(), nybb_contig.cardinalities.sort_index(), check_dtype=False, check_names=False, ) y = self.nybb.geometry.area for i in nybb_contig.unique_ids: neigh_vals = y.loc[nybb_contig[i].index.values] expected = neigh_vals.mode().iloc[0] if neigh_vals.shape[0] else 0 res = stats.loc[i]["mode"] assert res == expected ## test passing ndarray stats1 = nybb_contig.describe(self.nybb.geometry.area, statistics=["sum"])[ "sum" ] stats2 = nybb_contig.describe( self.nybb.geometry.area.values, statistics=["sum"] )["sum"] pd.testing.assert_series_equal( stats1, stats2, check_dtype=False, check_names=False, ) ## test index alignment with pytest.raises( ValueError, match="The values index is not aligned with the graph index." ): nybb_contig.describe(self.nybb.geometry.area.reset_index(drop=True)) def test_summary(self): assert isinstance(self.g_int.summary(), graph.GraphSummary) assert isinstance(self.g_str.summary(), graph.GraphSummary) assert isinstance(self.g_str_unodered.summary(), graph.GraphSummary) libpysal-4.12.1/libpysal/graph/tests/test_builders.py000066400000000000000000000524121466413560300227360ustar00rootroot00000000000000import sys import 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. """ @pytest.mark.network 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) def test_fuzzy_contiguity_kwargs(self): g = graph.Graph.build_fuzzy_contiguity(self.gdf_str, resolution=2) 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.network 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 ) def test_delunay_subsets(self): delaunay = graph.Graph.build_triangulation( self.gdf_str, "delaunay", kernel="identity" ) gabriel = graph.Graph.build_triangulation( self.gdf_str, "gabriel", kernel="identity" ) rn = graph.Graph.build_triangulation( self.gdf_str, "relative_neighborhood", kernel="identity" ) pd.testing.assert_series_equal( gabriel.adjacency, delaunay.adjacency.loc[gabriel.adjacency.index] ) pd.testing.assert_series_equal( rn.adjacency, delaunay.adjacency.loc[rn.adjacency.index] ) @pytest.mark.network 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, coplanar="jitter") 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, coplanar="jitter") 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.network 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) @pytest.mark.network 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 class TestMatching: def setup_method(self): pytest.importorskip("pulp") self.gdf = gpd.read_file(geodatasets.get_path("nybb")) self.gdf = self.gdf.set_geometry(self.gdf.centroid) self.gdf_str = self.gdf.set_index("BoroName") self.gdf_str = self.gdf_str.set_geometry(self.gdf_str.centroid) def test_matching_intids(self): g = graph.Graph.build_spatial_matches(self.gdf, k=1) 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_matching_strids(self): g = graph.Graph.build_spatial_matches(self.gdf_str, k=2) 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.network @pytest.mark.skipif( sys.platform.startswith("win"), reason="pandana has dtype issues on windows" ) class TestTravelNetwork: def setup_method(self): pandana = pytest.importorskip("pandana") import pooch self.net_path = pooch.retrieve( "https://spatial-ucr.s3.amazonaws.com/osm/metro_networks_8k/17140.h5", known_hash=None, ) df = gpd.read_file(geodatasets.get_path("geoda cincinnati")).to_crs(4326) self.df = df.set_geometry(df.centroid) self.network = pandana.Network.from_hdf5(self.net_path) def test_build_travel_network(self): g = graph.Graph.build_travel_cost(self.df, self.network, 500) assert_array_almost_equal( g.adjacency.head(10).to_numpy(), np.array( [ 418.28601074, 228.23899841, 196.0269928, 0.0, 341.73498535, 478.47799683, 298.91699219, 445.60501099, 174.64199829, 0.0, ] ), ) assert g.n == self.df.shape[0] def test_build_travel_network_kernel(self): g = graph.Graph.build_travel_cost( self.df, self.network, 500, kernel="triangular" ) assert_array_almost_equal( g.adjacency.head(10).to_numpy(), np.array( [ 0.16342798, 0.543522, 0.60794601, 1.0, 0.31653003, 0.04304401, 0.40216602, 0.10878998, 0.650716, 1.0, ] ), ) assert g.n == self.df.shape[0] libpysal-4.12.1/libpysal/graph/tests/test_contiguity.py000066400000000000000000000270111466413560300233200ustar00rootroot00000000000000""" 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, ) @pytest.fixture(scope="session") def rivers(): 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 return rivers @pytest.fixture(scope="session") def nybb(): nybb = geopandas.read_file(geodatasets.get_path("ny bb")) nybb["strID"] = nybb.BoroName nybb["intID"] = nybb.BoroCode return nybb parametrize_ids = pytest.mark.parametrize("ids", [None, "strID", "intID"]) 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"] ) @pytest.mark.network @parametrize_pointset @parametrize_rook @parametrize_ids def test_user_rivers(ids, rook, pointset, rivers): """ Check whether contiguity is constructed correctly for rivers in Europe. """ data = rivers.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) ) @pytest.mark.network @parametrize_rook @parametrize_perim @parametrize_ids def test_user_vertex_set_intersection_nybb(ids, rook, by_perimeter, nybb): """ 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) ) @pytest.mark.network @parametrize_rook @parametrize_perim @parametrize_ids def test_user_pointset_nybb(ids, by_perimeter, rook, nybb): """ 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) @pytest.mark.network def test_overlap_raise(nybb): 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 @pytest.mark.network def test_fuzzy_contiguity(nybb): # 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 ) # kwargs head, tail, weight = _fuzzy_contiguity( nybb.set_index("intID"), nybb["intID"], buffer=5000, resolution=2 ) 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]) libpysal-4.12.1/libpysal/graph/tests/test_indices.py000066400000000000000000000017421466413560300225430ustar00rootroot00000000000000import geopandas as gpd import pytest from geodatasets import get_path from libpysal import graph pytest.importorskip("h3") pytest.importorskip("tobler") class TestH3: def setup_method(self): from tobler.util import h3fy gdf = gpd.read_file(get_path("geoda guerry")) h3_geoms = h3fy(gdf, resolution=4) self.h3_ids = h3_geoms.index def test_h3(self): g = graph.Graph.build_h3(self.h3_ids) assert g.n == len(self.h3_ids) assert g.pct_nonzero == 1.69921875 assert len(g) == 1740 assert g.adjacency.max() == 1 @pytest.mark.parametrize("order", range(2, 6)) def test_h3_order(self, order): g = graph.Graph.build_h3(self.h3_ids, order) assert g.n == len(self.h3_ids) assert g.adjacency.max() == order def test_h3_binary(self): g = graph.Graph.build_h3(self.h3_ids, order=4, weight="binary") assert g.n == len(self.h3_ids) assert g.adjacency.max() == 1 libpysal-4.12.1/libpysal/graph/tests/test_kernel.py000066400000000000000000000317321466413560300224070ustar00rootroot00000000000000""" 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, ) from libpysal.graph._utils import CoplanarError def _grocs(): 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 return grocs @pytest.fixture(scope="session") def grocs(): return _grocs() 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) 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)) @pytest.fixture( scope="session", params=[pytest.param("grocs", marks=pytest.mark.network), "lap", "cau"], ) def data(request): if request.param == "grocs": return _grocs() elif request.param == "lap": return lap_coords elif request.param == "cau": return cau_coords parametrize_ids = pytest.mark.parametrize("ids", [None, "strID", "intID"]) parametrize_kernelfunctions = pytest.mark.parametrize("kernel", kernel_functions) # how do we parameterize conditional on sklearn in env? @pytest.mark.network @parametrize_ids def test_neighbors(ids, grocs): 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"), ) 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))) @pytest.mark.network @parametrize_ids def test_ids(ids, grocs): 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() @pytest.mark.network def test_distance(grocs): _, _, 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, ] ), ) 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 @pytest.mark.network @parametrize_kernelfunctions def test_kernels(kernel, grocs): _, _, 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) @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.network @pytest.mark.parametrize( "metric", [ "euclidean", "minkowski", "cityblock", "chebyshev", "haversine", ], ) def test_metric(metric, grocs): 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.network @pytest.mark.parametrize( "metric", [ "euclidean", "minkowski", "cityblock", "chebyshev", "haversine", ], ) def test_metric_k(metric, grocs): 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() @pytest.mark.network def test_coplanar(grocs): 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(CoplanarError, match="There are"): _kernel(grocs_duplicated, k=2) # k, jitter head, tail, weight = _kernel(grocs_duplicated, taper=False, k=2, coplanar="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 head, tail, weight = _kernel(grocs_duplicated, k=2, coplanar="clique") 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) 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)) @pytest.mark.network def test_haversine_check(grocs): 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-4.12.1/libpysal/graph/tests/test_matching.py000066400000000000000000000063231466413560300227170ustar00rootroot00000000000000""" 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 pytest from libpysal.graph._matching import _spatial_matching from libpysal.graph.base import Graph @pytest.fixture(scope="session") def stores(): stores = geopandas.read_file(geodatasets.get_path("geoda liquor_stores")).explode( index_parts=False ) return stores np.random.seed(85711) simple = np.random.random(size=(5, 2)) pulp = pytest.importorskip("pulp") pytest.importorskip("sklearn") default_solver = pulp.listSolvers(onlyAvailable=True) if len(default_solver) == 0: raise Exception("configuration of pulp has failed, no available solvers") default_solver = getattr(pulp, default_solver[0])() def test_correctness_k1(): # manual solution for simple k=1 by hungarian method known = np.row_stack([(0, 3), (1, 4), (2, 3), (3, 0), (3, 2), (4, 1)]) computed = _spatial_matching(simple, n_matches=1, solver=default_solver) np.testing.assert_array_equal(known, np.column_stack((computed[0], computed[1]))) computed_partial = _spatial_matching( simple, n_matches=1, allow_partial_match=True, solver=default_solver ) # manual solution by relaxing the above known = np.row_stack( [ (0, 2, 0.5), (0, 3, 0.5), (1, 4, 1), (2, 0, 0.5), (2, 3, 0.5), (3, 0, 0.5), (3, 2, 0.5), (4, 1, 1), ] ) np.testing.assert_array_equal(known, np.column_stack(computed_partial)) @pytest.mark.network def test_stores(stores): computed_heads, computed_tails, computed_weights = _spatial_matching( stores.head(101), n_matches=3, solver=default_solver ) computed_heads_p, computed_tails_p, computed_weights_p = _spatial_matching( stores.head(101), allow_partial_match=True, n_matches=3, solver=default_solver ) assert (computed_weights == 1).all() assert (computed_weights_p < 1).any() assert (computed_weights_p >= 0.5).all() for heads, tails in ( (computed_heads, computed_tails), (computed_heads_p, computed_tails_p), ): _, n_by_heads = np.unique(heads, return_counts=True) _, n_by_tails = np.unique(tails, return_counts=True) assert n_by_heads.max() == 4 assert n_by_heads.min() == 3 assert n_by_tails.max() == 4 assert n_by_tails.min() == 3 gf = Graph.from_arrays(computed_heads, computed_tails, computed_weights) gp = Graph.from_arrays(computed_heads_p, computed_tails_p, computed_weights_p) for g in (gf, gp): assert g.asymmetry().empty assert g.isolates.empty def test_returns_mip(): *computed, mip = _spatial_matching( simple, n_matches=4, return_mip=True, solver=default_solver ) assert mip.sol_status == 1 assert mip.objective.value() > 0 with pytest.warns(UserWarning, match="Problem is Infeasible"): *computed, mip = _spatial_matching( simple, n_matches=6, return_mip=True, solver=default_solver ) assert mip.sol_status == -1 libpysal-4.12.1/libpysal/graph/tests/test_plotting.py000066400000000000000000000305731466413560300227710ustar00rootroot00000000000000import 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 @pytest.mark.network 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( [ [943802.68511489, 147890.05410767], [943802.68511489, 147890.05410767], ] ), np.array( [ [1033983.96582281, 196127.39050293], [998506.94007314, 177674.69769287], ] ), np.array( [ [1033983.96582281, 196127.39050293], [995258.50398262, 226631.05230713], ] ), np.array( [ [1033983.96582281, 196127.39050293], [1021230.82521895, 251186.3362925], ] ), np.array( [ [998506.94007314, 177674.69769287], [995258.50398262, 226631.05230713], ] ), np.array( [ [995258.50398262, 226631.05230713], [1021230.82521895, 251186.3362925], ] ), ] 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.representative_point()), ) # 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.representative_point()), ) 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.representative_point()), ) 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.representative_point())[2:], ) pathcollection_focal = ax.collections[2] np.testing.assert_array_equal( pathcollection_focal.get_offsets().data, shapely.get_coordinates(self.nybb.representative_point())[[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.representative_point())[1:], ) pathcollection_focal = ax.collections[2] np.testing.assert_array_equal( pathcollection_focal.get_offsets().data, shapely.get_coordinates(self.nybb.representative_point())[[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.representative_point()), ) 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() == (193374.44321345096, 253939.28358198277) assert ax.get_xlim() == (993322.2308906089, 1035920.2389148154) 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-4.12.1/libpysal/graph/tests/test_raster.py000066400000000000000000000052131466413560300224220ustar00rootroot00000000000000import numpy as np import pandas as pd import pytest from packaging.version import Version from scipy import __version__ as scipy_version from libpysal import graph from libpysal.weights.raster import testDataArray as dummy_array # noqa: N813 class TestRaster: def setup_method(self): pytest.importorskip("xarray") self.da1 = dummy_array() self.da2 = dummy_array((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 = dummy_array((1, 1), missing_vals=False) self.da4.data = np.array([["test"]]) @pytest.mark.skipif( Version(scipy_version) < Version("1.12.0"), reason="sparse matrix power requires scipy>=1.12.0", ) def test_queen(self): g1 = graph.Graph.build_raster_contiguity(self.da1, rook=False, k=2, n_jobs=-1) assert g1[(1, -30.0, -180.0)].to_dict() == { (1, -90.0, 60.0): 1, (1, -90.0, -60.0): 1, } assert g1[(1, -30.0, 180.0)].to_dict() == { (1, -90.0, -60.0): 1, (1, -90.0, 60.0): 1, } assert g1.n == 5 assert g1._xarray_index_names == self.da1.to_series().index.names assert (1, 90.0, 180.0) in g1.isolates def test_rook(self): g2 = graph.Graph.build_raster_contiguity(self.da2, rook=True) assert g2.neighbors[(1, -90.0, 180.0)] == ( (1, -30.0, 180.0), (1, -90.0, 60.0), ) assert g2.neighbors[(1, -90.0, 60.0)] == ( (1, -30.0, 60.0), (1, -90.0, -60.0), (1, -90.0, 180.0), ) assert g2.n == 16 assert g2._xarray_index_names == self.da2.to_series().index.names def test_labels(self): coords_labels = { "z_label": "layer", "y_label": "latitude", "x_label": "longitude", } g3 = graph.Graph.build_raster_contiguity( self.da3, z_value=1, coords_labels=coords_labels ) assert g3.neighbors[(1, -90.0, 180.0)] == ( (1, -30.0, 60.0), (1, -30.0, 180.0), (1, -90.0, 60.0), ) assert g3.n == 16 assert g3._xarray_index_names == self.da3.to_series().index.names def test_generate_da(self): xarray = pytest.importorskip("xarray") g2 = graph.Graph.build_raster_contiguity(self.da2, rook=True, n_jobs=-1) da2 = g2.generate_da(self.da2.data.flatten()) # the order may be different but shall be align-able a, b = xarray.align(da2, self.da2) xarray.testing.assert_equal(a, b) libpysal-4.12.1/libpysal/graph/tests/test_set_ops.py000066400000000000000000000126331466413560300226020ustar00rootroot00000000000000import geodatasets import geopandas import pandas as pd import pytest from libpysal.graph.base import Graph @pytest.mark.network 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-4.12.1/libpysal/graph/tests/test_spatial_lag.py000066400000000000000000000057301466413560300234060ustar00rootroot00000000000000import numpy as np import pytest 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]) self.yc = np.array([*"ababcbcbc"]) w = lat2W(3, 3) w.transform = "r" self.gc = graph.Graph.from_W(w) 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) def test_lag_spatial_categorical(self): yl = _lag_spatial(self.gc, self.yc) ylc = np.array(["b", "a", "b", "c", "b", "c", "b", "c", "b"], dtype=object) np.testing.assert_array_equal(yl, ylc) self.yc[3] = "a" # create ties np.random.seed(12345) yl = _lag_spatial(self.gc, self.yc, categorical=True, ties="random") ylc = np.array(["a", "a", "b", "c", "b", "c", "b", "c", "b"], dtype=object) yl1 = _lag_spatial(self.gc, self.yc, categorical=True, ties="random") yls = _lag_spatial(self.gc, self.yc, categorical=True, ties="tryself") np.testing.assert_array_equal(yl, ylc) yl1c = np.array(["b", "a", "b", "c", "b", "c", "b", "c", "b"], dtype=object) np.testing.assert_array_equal(yl1, yl1c) ylsc = np.array(["a", "a", "b", "c", "b", "c", "a", "c", "b"], dtype=object) np.testing.assert_array_equal(yls, ylsc) # self-weight neighbors = self.gc.neighbors neighbors[0] = (0, 3, 1) # add self neighbor for observation 0 gc = graph.Graph.from_dicts(neighbors) self.yc[3] = "b" yls = _lag_spatial(gc, self.yc, categorical=True, ties="tryself") assert yls[0] in ["b", "a"] self.yc[3] = "a" yls = _lag_spatial(gc, self.yc, categorical=True, ties="tryself") assert yls[0] == "a" def test_ties_raise(self): with pytest.raises(ValueError, match="There are 2 ties that must be broken"): self.yc[3] = "a" # create ties _lag_spatial(self.gc, self.yc, categorical=True) def test_categorical_custom_index(self): expected = np.array(["bar", "foo", "bar", "foo"]) np.testing.assert_array_equal( expected, self.g.lag(["foo", "bar", "foo", "foo"]) ) libpysal-4.12.1/libpysal/graph/tests/test_summary.py000066400000000000000000000170761466413560300226310ustar00rootroot00000000000000import geodatasets import geopandas import numpy as np import pytest from libpysal import graph @pytest.mark.network class TestSummary: def setup_method(self): self.nybb = geopandas.read_file(geodatasets.get_path("nybb")) self.G = graph.Graph.build_contiguity(self.nybb) self.summary = self.G.summary(True) self.no_asymmetries = self.G.summary() def test_exactness(self): assert self.summary.n_nodes == 5 assert self.summary.n_edges == 10 assert self.summary.n_components == 2 assert self.summary.n_isolates == 1 assert self.summary.nonzero == 10 assert self.summary.pct_nonzero == 44.0 assert self.summary.n_asymmetries == 0 assert self.summary.cardinalities_mean == 2.0 assert self.summary.cardinalities_std == 1.224744871391589 assert self.summary.cardinalities_min == 0 assert self.summary.cardinalities_25 == 2 assert self.summary.cardinalities_50 == 2 assert self.summary.cardinalities_75 == 3 assert self.summary.cardinalities_max == 3 assert self.summary.weights_mean == 1 assert self.summary.weights_std == 0 assert self.summary.weights_min == 1 assert self.summary.weights_25 == 1 assert self.summary.weights_50 == 1 assert self.summary.weights_75 == 1 assert self.summary.weights_max == 1 assert self.summary.s0 == 10 assert self.summary.s1 == 20.0 assert self.summary.s2 == 104.0 np.testing.assert_array_equal(np.array([0, 3, 2, 3, 2]), self.summary.diag_g2) np.testing.assert_array_equal(np.array([0, 3, 2, 3, 2]), self.summary.diag_gtg) np.testing.assert_array_equal( np.array([0, 6, 4, 6, 4]), self.summary.diag_gtg_gg ) assert self.summary.trace_g2 == 10.0 assert self.summary.trace_gtg == 10.0 assert self.summary.trace_gtg_gg == 20.0 def test_repr(self): expected = """Graph Summary Statistics ======================== Graph indexed by: [0, 1, 2, 3, 4] ============================================================== Number of nodes: 5 Number of edges: 10 Number of connected components: 2 Number of isolates: 1 Number of non-zero edges: 10 Percentage of non-zero edges: 44.00% Number of asymmetries: 0 -------------------------------------------------------------- Cardinalities ============================================================== Mean: 2 25%: 2 Standard deviation: 1 50%: 2 Min: 0 75%: 3 Max: 3 -------------------------------------------------------------- Weights ============================================================== Mean: 1 25%: 1 Standard deviation: 0 50%: 1 Min: 1 75%: 1 Max: 1 -------------------------------------------------------------- Sum of weights ============================================================== S0: 10 S1: 20 S2: 104 -------------------------------------------------------------- Traces ============================================================== GG: 10 G'G: 10 G'G + GG: 20 """ assert self.summary.__repr__() == expected def test_html_repr(self): expected = """
Graph Summary Statistics
Number of nodes: 5
Number of edges: 10
Number of connected components: 2
Number of isolates: 1
Number of non-zero edges: 10
Percentage of non-zero edges: 44.00%
Number of asymmetries: 0
Cardinalities
Mean: 2 25%: 2
Standard deviation: 1 50% 2
Min: 0 75%: 3
Max: 3
Weights
Mean: 1 25%: 1
Standard deviation: 0 50% 1
Min: 1 75%: 1
Max: 1
Sum of weights and Traces
S0: 10 GG: 10
S1: 20 G'G: 10
S3: 104 G'G + GG: 20
Graph indexed by: [0, 1, 2, 3, 4]
""" assert self.summary._repr_html_() == expected def test_no_asymmetries(self): assert not hasattr(self.no_asymmetries, "n_asymmetries") _ = self.no_asymmetries.__repr__() _ = self.no_asymmetries._repr_html_() libpysal-4.12.1/libpysal/graph/tests/test_triangulation.py000066400000000000000000000423711466413560300240100ustar00rootroot00000000000000""" 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._utils import CoplanarError from libpysal.graph.base import Graph @pytest.fixture(scope="session") def stores(): stores = geopandas.read_file(geodatasets.get_path("geoda liquor_stores")).explode( index_parts=False ) return stores @pytest.fixture(scope="session") def stores_unique(stores): stores_unique = stores.drop_duplicates(subset="geometry") return stores_unique 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, coplanar="raise", clip=None, rook=True) extent = _voronoi(lap_coords, coplanar="raise", clip="bounding_box", rook=True) alpha = _voronoi(lap_coords, coplanar="raise", clip="alpha_shape", 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, 1, 2, 2, 2, 3, 4, 4]) known_tail = np.array([4, 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 = _relative_neighborhood(lap_coords) known_head = np.array([0, 0, 1, 2, 2, 3, 4, 4]) known_tail = np.array([2, 4, 4, 0, 3, 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) @pytest.mark.network @parametrize_ids def test_ids(ids, stores_unique): 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) @pytest.mark.network def test_coplanar_raise_voronoi(stores): with pytest.raises(ValueError, match="There are"): _voronoi(stores, clip=False) @pytest.mark.network def test_coplanar_jitter_voronoi(stores, stores_unique): cp_heads, cp_tails, cp_w = _voronoi(stores, clip=False, coplanar="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] == 3384 assert unique_heads.shape[0] == 3360 class TestCoplanar: 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), # coplanar 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( CoplanarError, 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, coplanar="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, coplanar="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) def test_delaunay_clique(self): # TODO: fix the implemntation to make this pass heads, tails, weights = _delaunay(self.df_int, coplanar="clique") exp_heads = np.array( [0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5] ) exp_tails = np.array( [1, 2, 4, 5, 0, 2, 3, 4, 5, 0, 1, 3, 4, 1, 2, 5, 0, 1, 2, 5, 0, 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, coplanar="clique") 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( CoplanarError, 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, coplanar="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, coplanar="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) def test_gabriel_clique(self): # TODO: fix the implemntation to make this pass heads, tails, weights = _gabriel(self.df_int, coplanar="clique") exp_heads = np.array([0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 4, 5]) exp_tails = np.array([1, 2, 4, 0, 2, 3, 4, 5, 0, 1, 3, 4, 1, 2, 0, 1, 2, 1]) 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, coplanar="clique") 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( CoplanarError, 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, coplanar="jitter", seed=0 ) exp_heads = np.array([0, 0, 1, 1, 2, 3, 3, 4, 4, 5]) exp_tails = np.array([2, 4, 3, 4, 0, 1, 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, coplanar="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) def test_relative_neighborhood_clique(self): # TODO: fix the implemntation to make this pass heads, tails, weights = _relative_neighborhood(self.df_int, coplanar="clique") exp_heads = np.array([0, 0, 1, 1, 1, 1, 2, 2, 3, 4, 4, 5]) exp_tails = np.array([1, 4, 0, 2, 4, 5, 1, 3, 2, 0, 1, 1]) 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, coplanar="clique" ) 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( CoplanarError, 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, coplanar="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, coplanar="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) def test_voronoi_clique(self): # TODO: fix the implemntation to make this pass heads, tails, weights = _voronoi(self.df_int, coplanar="clique", seed=0) exp_heads = np.array([0, 0, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5]) exp_tails = np.array([1, 4, 0, 2, 3, 4, 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, coplanar="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 coplanar='nonsense'", ): _delaunay(self.df_int, coplanar="nonsense") libpysal-4.12.1/libpysal/graph/tests/test_utils.py000066400000000000000000000130041466413560300222570ustar00rootroot00000000000000# 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 @pytest.fixture(scope="session") def guerry(): guerry = geopandas.read_file(geodatasets.get_path("geoda guerry")) guerry["intID"] = range(len(guerry)) guerry["strID"] = guerry.intID.astype(str) return guerry @pytest.fixture(scope="session") def rivers(): rivers = geopandas.read_file(geodatasets.get_path("eea large_rivers")) rivers["strID"] = rivers.NAME rivers["intID"] = rivers.index.values + 1 return rivers @pytest.fixture(params=["guerry", "guerry centroids", "rivers"]) def geoms(guerry, rivers, request): if request.param == "guerry": return guerry elif request.param == "guerry centroids": return guerry.set_geometry(guerry.geometry.centroid) elif request.param == "rivers": return rivers else: raise ValueError( "geoms not in supported testing types: " "'guerry', 'guerry centroids', 'rivers'" ) parametrize_idtypes = pytest.mark.parametrize( "ids", [None, "intID", "strID"], ids=["no index", "int index", "string index"], ) parametrize_shuffle = pytest.mark.parametrize( "shuffle", [False, True], ids=["input order", "shuffled"] ) parametrize_input_type = pytest.mark.parametrize( "input_type", ["gdf", "gseries", "array"], ) parametrize_external_ids = pytest.mark.parametrize( "external_ids", [False, True], ids=["use set_index", "use id vector"] ) @pytest.mark.network @parametrize_shuffle @parametrize_external_ids @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) @pytest.mark.network @parametrize_shuffle @parametrize_idtypes def test_validate_input_coords(shuffle, ids, guerry): """ Test that input coordinate arrays get validated correctly """ data = guerry.sample(frac=1, replace=False) if shuffle else guerry 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) @pytest.mark.network def test_validate_raises( guerry, rivers, 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(guerry, 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(guerry, valid_geometry_types=triang_types) # contiguity with pytest.raises(ValueError): # no point gdf for contiguity _validate_geometry_input( guerry.set_geometry(guerry.centroid), valid_geometry_types=contiguity_types, ) with pytest.raises(ValueError): # no point gseries for contiguity _validate_geometry_input( guerry.set_geometry(guerry.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_str libpysal-4.12.1/libpysal/io/000077500000000000000000000000001466413560300156545ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/__init__.py000066400000000000000000000001751466413560300177700ustar00rootroot00000000000000from . import fileio from .iohandlers import * from .tables import * from .util import * open = fileio.FileIO # noqa: A001 libpysal-4.12.1/libpysal/io/fileio.py000066400000000000000000000323671466413560300175100ustar00rootroot00000000000000""" 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, A003 """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(f"{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 not isinstance(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-4.12.1/libpysal/io/geotable/000077500000000000000000000000001466413560300174365ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/geotable/__init__.py000066400000000000000000000000521466413560300215440ustar00rootroot00000000000000from .file import read_files, write_files libpysal-4.12.1/libpysal/io/geotable/dbf.py000066400000000000000000000136751466413560300205570ustar00rootroot00000000000000"""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-4.12.1/libpysal/io/geotable/file.py000066400000000000000000000055761466413560300207440ustar00rootroot00000000000000import 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-4.12.1/libpysal/io/geotable/shp.py000066400000000000000000000014561466413560300206100ustar00rootroot00000000000000import 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-4.12.1/libpysal/io/geotable/tests/000077500000000000000000000000001466413560300206005ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/geotable/tests/__init__.py000066400000000000000000000000001466413560300226770ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/geotable/tests/test_utils.py000066400000000000000000000005461466413560300233560ustar00rootroot00000000000000import 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-4.12.1/libpysal/io/geotable/utils.py000066400000000000000000000051561466413560300211570ustar00rootroot00000000000000from 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-4.12.1/libpysal/io/geotable/wrappers.py000066400000000000000000000036511466413560300216600ustar00rootroot00000000000000import 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-4.12.1/libpysal/io/iohandlers/000077500000000000000000000000001466413560300200045ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/iohandlers/__init__.py000066400000000000000000000005571466413560300221240ustar00rootroot00000000000000import 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-4.12.1/libpysal/io/iohandlers/arcgis_dbf.py000066400000000000000000000170231466413560300224440ustar00rootroot00000000000000# ruff: noqa: N802, N803, N806, N815 from ...weights.util import remap_ids from ...weights.weights import W from .. import fileio __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): # noqa: ARG002 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_], strict=True)) for neighbor, weight in neighbors: self.file.write([id_, neighbor, weight]) self.pos = self.file.pos else: raise TypeError(f"Expected a PySAL weights object, got: {type(obj)}.") def flush(self): self._complain_ifclosed(self.closed) self.file.flush() def close(self): self.file.close() libpysal-4.12.1/libpysal/io/iohandlers/arcgis_swm.py000066400000000000000000000236151466413560300225230ustar00rootroot00000000000000# ruff: noqa: N802, N806, N815, SIM115 from struct import pack, unpack import numpy as np from ...weights import W from ...weights.util import remap_ids from .. import fileio __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 ============ ============ ==================================== ================================ """ # noqa: E501 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): # noqa: ARG002 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 _ 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)) ) _ = 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(f"Expected a PySAL weights object, got: {type(obj)}.") if (type(obj.id_order[0]) not 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(f"{self.varName};{self.srs}\n").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, stacklevel=2) else: msg = "DBF relating to ArcGIS TEXT was not found, " msg += "proceeding with unordered string IDs." warn(msg, RuntimeWarning, stacklevel=2) except: # noqa: E722 msg = "Exception occurred will reading DBF, " msg += "proceeding with unordered string IDs." warn(msg, RuntimeWarning, stacklevel=2) 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): # noqa: N803 """ 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 = f"{self.varName}\n" self.file.write(header) self._writelines(obj) else: raise TypeError(f"Expected a PySAL weights object, got: {type(obj)}.") libpysal-4.12.1/libpysal/io/iohandlers/csvWrapper.py000066400000000000000000000061161466413560300225160ustar00rootroot00000000000000# ruff: noqa: N801, N802, N806, N999, SIM115 import csv from .. import tables __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__ # noqa: A003 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) -> list | None: if self.pos < len(self): row = self.data[self.pos] self.pos += 1 return row else: return None libpysal-4.12.1/libpysal/io/iohandlers/dat.py000066400000000000000000000063071466413560300211340ustar00rootroot00000000000000from ...weights import W from . import gwt __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(f"Expected a PySAL weights object, got: {type(obj)}.") libpysal-4.12.1/libpysal/io/iohandlers/db.py000066400000000000000000000044001466413560300207410ustar00rootroot00000000000000from shapely import wkb from .. import fileio errmsg = "" try: from sqlalchemy import create_engine from sqlalchemy.ext.automap import automap_base 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-4.12.1/libpysal/io/iohandlers/gal.py000066400000000000000000000147621466413560300211330ustar00rootroot00000000000000# ruff: noqa: SIM115 import numpy as np from scipy import sparse from ...weights.weights import WSP, W from .. import fileio __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): # noqa: ARG002 """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 _ 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 _ 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(f"Expected a PySAL weights object, got: {type(obj)}.") def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.12.1/libpysal/io/iohandlers/geobugs_txt.py000066400000000000000000000173451466413560300227220ustar00rootroot00000000000000# ruff: noqa: SIM115 from ...weights import W from .. import fileio __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): # noqa: ARG002 """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({}),".format(",".join(map(str, cardinalities)))) self.file.write("adj=c({}),".format(",".join(map(str, neighbors)))) self.file.write("sumNumNeigh=%i)" % sum(cardinalities)) self.pos += 1 else: raise TypeError(f"Expected a PySAL weights object, got: {type(obj)}.") def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.12.1/libpysal/io/iohandlers/geoda_txt.py000066400000000000000000000053371466413560300223440ustar00rootroot00000000000000# ruff: noqa: N802, N806, SIM115 from .. import tables __author__ = "Charles R Schmidt " __all__ = ["GeoDaTxtReader"] 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] """ 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) 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.") from None 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) -> 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 GeoDaTxtReader.__doc__ = tables.DataTable.__doc__ libpysal-4.12.1/libpysal/io/iohandlers/gwt.py000066400000000000000000000233551466413560300211670ustar00rootroot00000000000000# ruff: noqa: N801, N802,N812, N815, SIM115 import os.path from warnings import warn from ...weights.weights import W from .. import fileio as FileIO __author__ = "Charles R Schmidt " __all__ = ["GwtIO"] class unique_filter: """(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): # noqa: ARG002 """ 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, stacklevel=2) else: msg = "DBF relating to GWT was not found, " msg += "proceeding with unordered string IDs." warn(msg, RuntimeWarning, stacklevel=2) except: # noqa: E722 msg = "Exception occurred will reading DBF, " msg += "proceeding with unordered string IDs." warn(msg, RuntimeWarning, stacklevel=2) 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_], strict=True)) str_id = "_".join(str(id_).split()) for neighbor, weight in neighbors: neighbor = "_".join(str(neighbor).split()) self.file.write(f"{str_id} {neighbor} {weight:.6G}\n") 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(f"Expected a PySAL weights object, got: {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()) 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-4.12.1/libpysal/io/iohandlers/mat.py000066400000000000000000000115241466413560300211420ustar00rootroot00000000000000# ruff: noqa: N802, N815, SIM115 import scipy.io as sio from ...weights import W from ...weights.util import full, full2W from .. import fileio __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): # noqa: ARG002 """ 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(f"Expected a PySAL weights object, got: {type(obj)}.") def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.12.1/libpysal/io/iohandlers/mtx.py000066400000000000000000000163251466413560300211750ustar00rootroot00000000000000# ruff: noqa: SIM115 import scipy.io as sio from ...weights.weights import WSP, W from .. import fileio __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): # noqa: ARG002 """ 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) w = wsp if self._sparse else 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(f"Expected a PySAL weights object, got: {type(obj)}.") def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.12.1/libpysal/io/iohandlers/pyDbfIO.py000066400000000000000000000276261466413560300216670ustar00rootroot00000000000000# ruff: noqa: N802, N806, N816, N999, SIM115 import datetime import os import struct import time from ...common import MISSINGVALUE from .. import tables __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 OSError(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(f"Field: {key} does not exist in header.") 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() value = MISSINGVALUE if value == "" else float(value) if isinstance(value, str | 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, _, deci), value in zip(self.field_info, rec, strict=True): 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() value = MISSINGVALUE if value == "" else float(value) if isinstance(value, str | str): value = value.rstrip() result.append(value) return result def _read(self) -> 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 OSError(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 OSError(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(b" ") for (typ, size, deci), value in zip(self.field_spec, obj, strict=True): if value is None: value = " " * size if typ == "C" else "\x00" * 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(b"\x1a") 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 OSError("No header, DBF files require a header.") if not self.field_spec: raise OSError("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 is not 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) from None 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(f"This file only supports {self.type} type shapes.") 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 = list(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, strict=True) if cw ] holes = [ part for part, cw in zip(parts, is_cw, strict=True) 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, stacklevel=2) print(msg) shp = self.type(vertices) else: warn( "Polygon %d has zero parts." % self.pos, RuntimeWarning, stacklevel=2, ) 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-4.12.1/libpysal/io/iohandlers/stata_txt.py000066400000000000000000000174611466413560300224020ustar00rootroot00000000000000# ruff: noqa: SIM115 from ...weights import W from .. import fileio __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. """ # noqa: E501 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): # noqa: ARG002 """ 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 _ 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 = f"{obj.n}\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, strict=True): w[obj.id2i[ngh]] = str(wgt) return [str(obs_id)] + w else: def wgt2line(obs_id, neighbor, _): 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("{}\n".format(" ".join(line))) else: raise TypeError(f"Expected a PySAL weights object, got: {type(obj)}.") def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.12.1/libpysal/io/iohandlers/template.py000066400000000000000000000124041466413560300221720ustar00rootroot00000000000000"""Example Reader and Writer These are working readers/writers that parse '.foo' and '.bar' files. """ # ruff: noqa: N812, SIM115 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): return c in "foobar" # 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(f"Expected a string, got: {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): return c in "foobar" # 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(f"Expected a string, got: {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() # noqa # # 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") # noqa # for line in lines: # f.write(line) # f.close() # # f = pysal.open("test.bar", "w") # noqa # for line in lines: # f.write(line) # f.close() # # f = pysal.open("test.bar", "r") # noqa # s = "".join(f.read()) # f.close() # print(s) # # f = open("test.foo") # s2 = f.read() # f.close() # print(s == s2) libpysal-4.12.1/libpysal/io/iohandlers/tests/000077500000000000000000000000001466413560300211465ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/iohandlers/tests/__init__.py000066400000000000000000000000001466413560300232450ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/iohandlers/tests/test_arcgis_dbf.py000066400000000000000000000036171466413560300246510ustar00rootroot00000000000000import os import tempfile import warnings import pytest from .... import examples as pysal_examples from ...fileio import FileIO from ..arcgis_dbf import ArcGISDbfIO class TesttestArcGISDbfIO: 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 list(w[1].values()) == [1.0, 1.0, 1.0, 1.0] def test_seek(self): self.test_read() pytest.raises(StopIteration, self.obj.read) self.obj.seek(0) self.test_read() def test_write(self): 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 = FileIO(fname, "w", "arcgis_dbf") o.write(w) o.close() f = FileIO(fname, "r", "arcgis_dbf") wnew = f.read() f.close() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.12.1/libpysal/io/iohandlers/tests/test_arcgis_swm.py000066400000000000000000000021161466413560300247150ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO from ..arcgis_swm import ArcGISSwmIO class TesttestArcGISSwmIO: 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 list(w[1].values()) == [1.0, 1.0, 1.0, 1.0] 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=".swm") fname = f.name f.close() o = FileIO(fname, "w") o.write(w) o.close() wnew = FileIO(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.12.1/libpysal/io/iohandlers/tests/test_arcgis_txt.py000066400000000000000000000044441466413560300247340ustar00rootroot00000000000000import os import tempfile import warnings import pytest from .... import examples as pysal_examples from ...fileio import FileIO from ..arcgis_txt import ArcGISTextIO class TesttestArcGISTextIO: 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 list(w[2].values()) == [0.1, 0.05] 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 = FileIO(fname, "w", "arcgis_text") o.write(w) o.close() with warnings.catch_warnings(record=True) as warn: warnings.simplefilter("always") wnew = FileIO(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-4.12.1/libpysal/io/iohandlers/tests/test_csvWrapper.py000066400000000000000000000051571466413560300247230ustar00rootroot00000000000000# ruff: noqa: N999 from .... import examples as pysal_examples from ...util import WKTParser from .. import csvWrapper 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) objs_b = list(self.obj) assert len(objs_b) == 78 for row_a, row_b in zip(objs, objs_b, strict=True): assert row_a == row_b 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), ] 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-4.12.1/libpysal/io/iohandlers/tests/test_dat.py000066400000000000000000000020731466413560300233310ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO from ..dat import DatIO class TesttestDatIO: 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 list(w[5.0].values()) == [0.5, 0.5] 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 = FileIO(fname, "w") o.write(w) o.close() wnew = FileIO(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.12.1/libpysal/io/iohandlers/tests/test_db.py000066400000000000000000000037171466413560300231540ustar00rootroot00000000000000import os import platform import pytest import shapely from .... import examples as pysal_examples from ... import geotable as pdio from ...fileio import FileIO 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={ # Should convert the df date into a true date object, just a hack again "date": sqlalchemy.types.UnicodeText, "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 = FileIO(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-4.12.1/libpysal/io/iohandlers/tests/test_gal.py000066400000000000000000000022651466413560300233270ustar00rootroot00000000000000"""Unit tests for gal.py""" import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO from ..gal import GalIO class TesttestGalIO: 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 # noqa: E721 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 = FileIO(fname, "w") o.write(w) o.close() wnew = FileIO(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero libpysal-4.12.1/libpysal/io/iohandlers/tests/test_geobugs_txt.py000066400000000000000000000033701466413560300251140ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO from ..geobugs_txt import GeoBUGSTextIO class TesttestGeoBUGSTextIO: 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 list(w_scot[1].values()) == [1.0, 1.0, 1.0] w_col = self.obj_col.read() assert w_col.n == 49 assert w_col.mean_neighbors == 4.6938775510204085 assert list(w_col[1].values()) == [0.5, 0.5] 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 = FileIO(fname, "w", "geobugs_text") o.write(w) o.close() wnew = FileIO(fname, "r", "geobugs_text").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.12.1/libpysal/io/iohandlers/tests/test_geoda_txt.py000066400000000000000000000011451466413560300245360ustar00rootroot00000000000000"""GeoDa Text File Reader Unit Tests""" import pytest from .... import examples as pysal_examples from ..geoda_txt import GeoDaTxtReader class TesttestGeoDaTxtReader: def setup_method(self): test_file = pysal_examples.get_path("stl_hom.txt") self.obj = GeoDaTxtReader(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-4.12.1/libpysal/io/iohandlers/tests/test_gwt.py000066400000000000000000000031371466413560300233640ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO from ..gwt import GwtIO class TesttestGwtIO: 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 list(w[1].values()) == [1.0] 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 = FileIO(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 = FileIO(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.12.1/libpysal/io/iohandlers/tests/test_mat.py000066400000000000000000000024461466413560300233460ustar00rootroot00000000000000import os import tempfile import warnings import pytest from .... import examples as pysal_examples from ...fileio import FileIO from ..mat import MatIO class TesttestMatIO: 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 list(w[1].values()) == [1.0, 1.0, 1.0, 1.0] 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=".mat") fname = f.name f.close() o = FileIO(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 = FileIO(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.12.1/libpysal/io/iohandlers/tests/test_mtx.py000066400000000000000000000027541466413560300233770ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO 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 list(w[1].values()) == [ 0.33329999999999999, 0.33329999999999999, 0.33329999999999999, ] 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 = FileIO(fname, "w") o.write(w) o.close() wnew = FileIO(fname, "r").read(sparse=i) if i: assert wnew.s0 == w.s0 else: assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.12.1/libpysal/io/iohandlers/tests/test_pyDbfIO.py000066400000000000000000000072501466413560300240570ustar00rootroot00000000000000# ruff: noqa: N999, SIM115 import os import tempfile from .... import examples as pysal_examples from ..pyDbfIO import DBF class TesttestDBF: 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 # noqa: E721 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) objs_b = list(self.dbObj) assert len(objs_b) == 195 for row_a, row_b in zip(objs, objs_b, strict=True): assert row_a == row_b 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-4.12.1/libpysal/io/iohandlers/tests/test_pyShpIO.py000066400000000000000000000045731466413560300241230ustar00rootroot00000000000000# ruff: noqa: N806, N999, SIM115 import os import tempfile from .... import examples as pysal_examples from ..pyShpIO import PurePyShpWrapper class TesttestPurePyShpWrapper: def setup_method(self): test_file = pysal_examples.get_path("10740.shp") self.test_file = test_file self.shp_obj = 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.shp_obj) == 195 def test_tell(self): assert self.shp_obj.tell() == 0 self.shp_obj.read(1) assert self.shp_obj.tell() == 1 self.shp_obj.read(50) assert self.shp_obj.tell() == 51 self.shp_obj.read() assert self.shp_obj.tell() == 195 def test_seek(self): self.shp_obj.seek(0) assert self.shp_obj.tell() == 0 self.shp_obj.seek(55) assert self.shp_obj.tell() == 55 self.shp_obj.read(1) assert self.shp_obj.tell() == 56 def test_read(self): self.shp_obj.seek(0) objs = self.shp_obj.read() assert len(objs) == 195 self.shp_obj.seek(0) objs_b = list(self.shp_obj) assert len(objs_b) == 195 for shp_a, shp_b in zip(objs, objs_b, strict=True): assert shp_a.vertices == shp_b.vertices def test_random_access(self): self.shp_obj.seek(57) shp57 = self.shp_obj.read(1)[0] self.shp_obj.seek(32) shp32 = self.shp_obj.read(1)[0] self.shp_obj.seek(57) assert self.shp_obj.read(1)[0].vertices == shp57.vertices self.shp_obj.seek(32) assert self.shp_obj.read(1)[0].vertices == shp32.vertices def test_write(self): out = PurePyShpWrapper(self.shpcopy, "w") self.shp_obj.seek(0) for shp in self.shp_obj: 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-4.12.1/libpysal/io/iohandlers/tests/test_stata_txt.py000066400000000000000000000035771466413560300246060ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO from ..stata_txt import StataTextIO class TesttestStataTextIO: 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 list(w_sparse[1].values()) == [1.0, 1.0, 1.0, 1.0, 1.0] w_full = self.obj_full.read() assert w_full.n == 56 assert w_full.mean_neighbors == 4.0 assert list(w_full[1].values()) == [0.125, 0.125, 0.125, 0.125, 0.125] 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 = FileIO(fname, "w", "stata_text") if obj == self.obj_sparse: o.write(w) else: o.write(w, matrix_form=True) o.close() wnew = FileIO(fname, "r", "stata_text").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.12.1/libpysal/io/iohandlers/tests/test_wk1.py000066400000000000000000000021121466413560300232550ustar00rootroot00000000000000import os import tempfile import pytest from .... import examples as pysal_examples from ...fileio import FileIO from ..wk1 import Wk1IO class TesttestWk1IO: 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 list(w[1].values()) == [1.0, 1.0, 1.0, 1.0] 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=".wk1") fname = f.name f.close() o = FileIO(fname, "w") o.write(w) o.close() wnew = FileIO(fname, "r").read() assert wnew.pct_nonzero == w.pct_nonzero os.remove(fname) libpysal-4.12.1/libpysal/io/iohandlers/tests/test_wkt.py000066400000000000000000000013701466413560300233650ustar00rootroot00000000000000import pytest from .... import examples as pysal_examples from ..wkt import WKTReader class TesttestWKTReader: 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-4.12.1/libpysal/io/iohandlers/wk1.py000066400000000000000000000434441466413560300210710ustar00rootroot00000000000000# ruff: noqa: ARG002, N802, SIM115 import struct from ...weights import W from .. import fileio __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 | +-------------+---------------------+-------------------------+-------+-----------------------------+ """ # noqa: E501 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) # noqa: N815 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, b"'")) 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 = ("<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(f"Expected a PySAL weights object, got: {type(obj)}.") def close(self): self.file.close() fileio.FileIO.close(self) libpysal-4.12.1/libpysal/io/iohandlers/wkt.py000066400000000000000000000052401466413560300211640ustar00rootroot00000000000000# ruff: noqa: SIM115 from .. 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): # noqa: A003 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-4.12.1/libpysal/io/tables.py000066400000000000000000000207251466413560300175060ustar00rootroot00000000000000__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 f"DataTable: {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(f"Field: {key} does not exist in header.") 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-4.12.1/libpysal/io/tests/000077500000000000000000000000001466413560300170165ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/tests/__init__.py000066400000000000000000000000001466413560300211150ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/tests/test_FileIO.py000066400000000000000000000005501466413560300215360ustar00rootroot00000000000000# ruff: noqa: N999 from ...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-4.12.1/libpysal/io/tests/test_Tables.py000066400000000000000000000020311466413560300216350ustar00rootroot00000000000000# ruff: noqa: N999 import numpy as np from ... import examples as pysal_examples from ...common import pandas from ..fileio import FileIO PANDAS_EXTINCT = pandas is None class TestTable: def setup_method(self): self.filehandler = FileIO(pysal_examples.get_path("columbus.dbf")) self.df = self.filehandler.to_df() self.filehandler.seek(0) self.shapefile = FileIO(pysal_examples.get_path("columbus.shp")) self.csvhandler = FileIO(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-4.12.1/libpysal/io/util/000077500000000000000000000000001466413560300166315ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/util/__init__.py000066400000000000000000000000771466413560300207460ustar00rootroot00000000000000from .shapefile import * from .wkb import * from .wkt import * libpysal-4.12.1/libpysal/io/util/shapefile.py000066400000000000000000001005071466413560300211460ustar00rootroot00000000000000"""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 """ # ruff: noqa: N801, N802, N803, N806, SIM115 __author__ = "Charles R Schmidt " import array import io import sys from itertools import islice from struct import calcsize, pack, unpack SYS_BYTE_ORDER = "<" if sys.byteorder == "little" else ">" 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) 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: float | None, b: float | None) -> float: if a is None: return b if b is None: return a return max(a, b) def noneMin(a: float | None, b: 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 OSError("[Errno 9] Bad file descriptor.") from None 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 OSError("[Errno 9] Bad file descriptor.") from None 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: # noqa: A003 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 OSError("[Errno 9] Bad file descriptor.") from None 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 OSError("[Errno 9] Bad file descriptor.") from None 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 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: # noqa: ARG002 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) content_struct = ( ("Parts Index", ("i", record["NumParts"]), "<"), ("Vertices", ("d", 2 * record["NumPoints"]), "<"), ) _unpackDict2(record, content_struct, 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), strict=True) ) 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) content_struct = ( ("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(content_struct, content) return rheader + content class PolyLineZ: 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) content_struct = ( ("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, content_struct, dat) verts = record["Vertices"] record["Vertices"] = list( zip(islice(verts, 0, None, 2), islice(verts, 1, None, 2), strict=True) ) 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) content_struct = ( ("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(content_struct, 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-4.12.1/libpysal/io/util/tests/000077500000000000000000000000001466413560300177735ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/util/tests/__init__.py000066400000000000000000000000001466413560300220720ustar00rootroot00000000000000libpysal-4.12.1/libpysal/io/util/tests/test_shapefile.py000066400000000000000000000413351466413560300233520ustar00rootroot00000000000000# ruff: noqa: SIM115 import io import os import pytest # 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 buffer_io(buf): """Temp stringIO function to force compat.""" return io.BytesIO(buf) class TestNoneMax: def test_none_max(self): assert noneMax(5, None) == 5 assert noneMax(None, 1) == 1 assert None is noneMax(None, None) class TestNoneMin: def test_none_min(self): assert noneMin(5, None) == 5 assert noneMin(None, 1) == 1 assert None is noneMin(None, None) class TestShpFile: 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 = list(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"), strict=True): 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 is 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 == rec def test_next(self): shp = shp_file(pysal_examples.get_path("Point.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 shp.type() == "POINT" shp = shp_file(pysal_examples.get_path("Polygon.shp")) assert shp.type() == "POLYGON" shp = shp_file(pysal_examples.get_path("Line.shp")) assert shp.type() == "ARC" class TestShxFile: 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")) expected_index = [ (100, 20), (128, 20), (156, 20), (184, 20), (212, 20), (240, 20), (268, 20), (296, 20), (324, 20), ] assert shx.index == expected_index 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 is True class TestNullShape: def test_pack(self): null_shape = NullShape() assert null_shape.pack() == b"\x00" * 4 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" b"\x00\x14\x40\x00\x00\x00\x00\x00\x00\x14\x40" ) assert expected == Point.pack(record) def test_unpack(self): dat = buffer_io( b"\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00" b"\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""" # noqa: E501 assert expected == PolyLine.pack(record) def test_unpack(self): dat = buffer_io( 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""" # noqa: E501 ) 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" b"\x14@\x00\x00\x00\x00\x00\x00\x14@\x00\x00" b"\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00\x14@" ) assert expected == PointZ.pack(record) def test_unpack(self): dat = buffer_io( b"\x0b\x00\x00\x00\x00\x00\x00\x00\x00\x00\x14@\x00" b"\x00\x00\x00\x00\x00\x14@\x00\x00\x00\x00\x00\x00" b"\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$@\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x10@\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x08@\x00\x00\x00\x00\x00\x00\x10@""" # noqa: E501 assert expected == PolyLineZ.pack(record) def test_unpack(self): dat = buffer_io( b"""\r\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbf=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?k@\x7f`\\\x88\xd0\xbf\x01\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\xc0FR:\xdd\x8a\x82\xbfk@\x7f`\\\x88\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\xc5\xa0\xe5\x8f\xa4~?=\xc1e\xce\xc7\x92\xd0\xbf\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00$@\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@""" # noqa: E501 ) 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 = buffer_io(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, strict=True): 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, strict=True): 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, strict=True): assert a == b os.remove("test_poly.shp") os.remove("test_poly.shx") libpysal-4.12.1/libpysal/io/util/tests/test_weight_converter.py000066400000000000000000000154131466413560300247660ustar00rootroot00000000000000import os import tempfile import warnings import pytest # import pysal_examples from .... import examples as pysal_examples from ...fileio import FileIO from ..weight_converter import WeightConverter, weight_convert @pytest.mark.skip("This function is deprecated.") class TesttestWeightConverter: 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, strict=True))) self.ns = dict(list(zip(self.test_files, ns, strict=True))) 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): # 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() is True assert wc.w.n == self.ns[f] def test_write(self): for f in self.test_files: with warnings.catch_warnings(record=True): # 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=f".{ext}") temp_fname = temp_f.name temp_f.close() with warnings.catch_warnings(record=True): # 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): # note: we are just suppressing the warnings here; # individual warnings are tested in their specific readers warnings.simplefilter("always") if dataformat is None: wnew = FileIO(temp_fname, "r").read() else: wnew = FileIO(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: in_file = f in_data_format = self.dataformats[f] with warnings.catch_warnings(record=True): # note: we are just suppressing the warnings here; individual warnings # are tested in their specific readers warnings.simplefilter("always") if in_data_format is None: in_file = FileIO(in_file, "r") else: in_file = FileIO(in_file, "r", in_data_format) wold = in_file.read() in_file.close() for ext, dataformat in self.fileformats: if f.lower().endswith(ext): continue temp_f = tempfile.NamedTemporaryFile(suffix=f".{ext}") out_file = temp_f.name temp_f.close() out_data_format, use_id_index, matrix_form = dataformat, False, False if ext == "swm" or dataformat in ["arcgis_dbf", "arcgis_text"]: use_id_index = True elif dataformat == "stata_text": matrix_form = True with warnings.catch_warnings(record=True): # note: we are just suppressing the warnings here; # individual warnings are tested in their specific readers warnings.simplefilter("always") weight_convert( in_file, out_file, in_data_format, out_data_format, use_id_index, matrix_form, ) with warnings.catch_warnings(record=True): # note: we are just suppressing the warnings here; # individual warnings are tested in their specific readers warnings.simplefilter("always") if dataformat is None: wnew = FileIO(out_file, "r").read() else: wnew = FileIO(out_file, "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(out_file) libpysal-4.12.1/libpysal/io/util/tests/test_wkt.py000066400000000000000000000040741466413560300222160ustar00rootroot00000000000000import pytest from ....cg.shapes import Chain, Point, Polygon from ..wkt import WKTParser class TesttestWKTParser: 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) is None assert self.parser.__call__ == self.parser.fromWKT libpysal-4.12.1/libpysal/io/util/weight_converter.py000066400000000000000000000204141466413560300225620ustar00rootroot00000000000000# ruff: noqa: N802, N803 import os from warnings import warn from ..fileio import FileIO __author__ = "Myunghwa Hwang " __all__ = ["weight_convert"] class WeightConverter: """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, stacklevel=2, ) 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 = FileIO(self.inputPath, "r", self.inputDataFormat) else: f = FileIO(self.inputPath, "r") except: # noqa: E722 raise OSError("A problem occurred while reading the input file.") from None else: try: self.w = f.read() except: # noqa: E722 raise RuntimeError( "A problem occurred while creating a weights object." ) from None 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 = FileIO(outputPath, "w", dataFormat) else: o = FileIO(outputPath, "w") except: # noqa: E722 raise OSError( "A problem occurred while creating the output file." ) from None 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: # noqa: E722 raise RuntimeError( "A problem occurred while writing out the weights object." ) from None 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-4.12.1/libpysal/io/util/wkb.py000066400000000000000000000202421466413560300177660ustar00rootroot00000000000000"""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 """ # noqa: E501 import struct import sys from io import StringIO from ... import cg __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. dat = s if hasattr(s, "read") else 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. geom = None if not parts else 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) from None # 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.geometry # import shapely.wkt # 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-4.12.1/libpysal/io/util/wkt.py000066400000000000000000000101611466413560300200070ustar00rootroot00000000000000# ruff: noqa: N802, N815 import re from ... import cg __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, geo_str): """Returns a ``libpysal.cg.Point`` object.""" coords = self.regExes["spaces"].split(geo_str.strip()) return cg.Point((coords[0], coords[1])) def LineString(self, geo_str): """Returns a ``libpysal.cg.Chain`` object.""" points = geo_str.strip().split(",") points = list(map(self.Point, points)) return cg.Chain(points) def Polygon(self, geo_str): """Returns a ``libpysal.cg.Polygon`` object.""" rings = self.regExes["parenComma"].split(geo_str.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: geo_type, geo_str = matches.groups() geo_type = geo_type.lower().strip() try: return self.parsers[geo_type](geo_str) except KeyError: raise NotImplementedError( f"Unsupported WKT Type: {geo_type}." ) from None 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-4.12.1/libpysal/weights/000077500000000000000000000000001466413560300167175ustar00rootroot00000000000000libpysal-4.12.1/libpysal/weights/__init__.py000066400000000000000000000004421466413560300210300ustar00rootroot00000000000000from .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-4.12.1/libpysal/weights/_contW_lists.py000066400000000000000000000071671466413560300217530ustar00rootroot00000000000000# ruff: noqa: N999 import collections import contextlib import itertools as it from ..cg.shapes import Chain, Polygon 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, strict=True)))) elif shape.geom_type.lower() == "multilinestring": return list( it.chain( *(list(zip(*shape.coords.xy, strict=True)) 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): num_poly = len(self.collection) w = {} for i in range(num_poly): w[i] = set() geoms = [] offsets = [] c = 0 # PolyID Counter if self.wttype == QUEEN: for n in range(num_poly): 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 _, 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 with contextlib.suppress(Exception): w[v].remove(v) elif self.wttype == ROOK: for n in range(num_poly): 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 _, 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 with contextlib.suppress(Exception): w[v].remove(v) else: raise Exception(f"Weight type {self.wttype} Not Understood!") self.w = w libpysal-4.12.1/libpysal/weights/adjtools.py000066400000000000000000000233041466413560300211120ustar00rootroot00000000000000# ruff: noqa: B006, C408, N802, N803, N806 import 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") from None 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") from None 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, strict=True, ) ), ) ) 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", # noqa: ARG001 neighbor_col="neighbor", # noqa: ARG001 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") from None 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 = {f.__name__ for f in funcs} for i, (column, function) in enumerate(zip(data.T, funcs, strict=True)): 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-4.12.1/libpysal/weights/contiguity.py000066400000000000000000000632251466413560300214770ustar00rootroot00000000000000# ruff: noqa: B006, N802, N803, N813 import itertools import warnings import numpy from ..cg import voronoi_frames from ..io.fileio import FileIO from ._contW_lists import ContiguityWeightsLists from .raster import da2W, da2WSP from .util import get_ids, get_points_array from .weights import WSP, W 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 = voronoi_frames( get_points_array(polygons), return_input=False, as_gdf=False ) polygons = list(polygons) 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) ids = get_ids(filepath, idVariable) if idVariable is not None else 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, # noqa: ARG003 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 = voronoi_frames( get_points_array(polygons), return_input=False, as_gdf=False ) polygons = list(polygons) 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) ids = get_ids(filepath, idVariable) if idVariable is not None else 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, # noqa: ARG003 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="alpha_shape", **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, return_input=False, as_gdf=True) if criterion.lower() == "queen": cls = Queen elif criterion.lower() == "rook": cls = Rook else: raise ValueError( f"Contiguity criterion {criterion} not supported. " "Only 'rook' and 'queen' are supported." ) 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." ) from None 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. Notes ----- 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()))), strict=True, ) ) ), 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 ValueError(f"Weights criterion '{criterion}' was not found.") libpysal-4.12.1/libpysal/weights/distance.py000066400000000000000000001012021466413560300210570ustar00rootroot00000000000000# ruff: noqa: N802, N803 __all__ = ["KNN", "Kernel", "DistanceBand"] __author__ = "Sergio J. Rey , Levi John Wolf " import copy from warnings import warn import numpy as np import scipy.sparse as sp from scipy.spatial import distance_matrix from ..cg.kdtree import KDTree from .util import ( WSP2W, get_ids, get_points_array, get_points_array_from_shapefile, isKDTree, ) from .weights import WSP, W 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) 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, strict=True) } 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", stacklevel=2) 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]} """ # noqa: E501 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: # noqa: E722 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) ids = get_ids(filepath, idVariable) if idVariable is not None else 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 = {} ids = np.array(ids) if ids else np.arange(len(self.data)) for i, _ 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, strict=True)))[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: # noqa: E722 raise ValueError("Could not make array from data") from None 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) ids = get_ids(filepath, idVariable) if idVariable is not None else 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() temp_w = WSP2W( WSP(self.dmat, id_order=ids), silence_warnings=self.silence_warnings ) neighbors = temp_w.neighbors weight_keys = list(temp_w.weights.keys()) weight_vals = list(temp_w.weights.values()) weights = dict( list(zip(weight_keys, list(map(list, weight_vals)), strict=True)) ) return neighbors, weights else: weighted = self.dmat.power(self.alpha) weighted[weighted == np.inf] = 0 weighted.eliminate_zeros() temp_w = WSP2W( WSP(weighted, id_order=ids), silence_warnings=self.silence_warnings ) neighbors = temp_w.neighbors weight_keys = list(temp_w.weights.keys()) weight_vals = list(temp_w.weights.values()) weights = dict( list(zip(weight_keys, list(map(list, weight_vals)), strict=True)) ) 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-4.12.1/libpysal/weights/gabriel.py000066400000000000000000000324461466413560300207070ustar00rootroot00000000000000import warnings import numpy import pandas from scipy import sparse from scipy.spatial import Delaunay as _Delaunay from libpysal.weights import WSP, W 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 # noqa: F401 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.", stacklevel=2, ) 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." ) from None 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 # noqa: F401 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.", stacklevel=2, ) 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): # noqa: N801 """ 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 # noqa: F401 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.", stacklevel=2, ) edges, dt = self._voronoi_edges(coordinates) output, dkmax = _filter_relativehood(edges, dt.points, return_dkmax=False) row, col, data = zip(*output, strict=True) 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 = 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 libpysal-4.12.1/libpysal/weights/raster.py000066400000000000000000000675201466413560300206030ustar00rootroot00000000000000# ruff: noqa: B006, N802 import os import sys from warnings import warn import numpy as np from scipy import sparse from .util import lat2SW from .weights import WSP, W if os.path.basename(sys.argv[0]) in ("pytest", "py.test"): def jit(*dec_args, **dec_kwargs): # noqa: ARG001 """ decorator mimicking numba.jit """ def intercepted_function(f, *f_args, **f_kwargs): # noqa: ARG001 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.", stacklevel=2, ) 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` """ try: import numba # noqa: F401 use_numba = True except (ModuleNotFoundError, ImportError): warn( "numba cannot be imported, parallel processing " "and include_nodata functionality will be disabled. " "falling back to slower method", stacklevel=2, ) use_numba = False include_nodata = False if use_numba: sw_tup, ser, n = _da2wsp( da, criterion=criterion, z_value=z_value, coords_labels=coords_labels, k=k, include_nodata=include_nodata, n_jobs=n_jobs, use_numba=use_numba, ) sw = sparse.csr_matrix( sw_tup, shape=(n, n), dtype=np.int8, ) else: sw, ser = _da2wsp( da, criterion=criterion, z_value=z_value, coords_labels=coords_labels, k=k, include_nodata=include_nodata, n_jobs=n_jobs, use_numba=use_numba, ) # 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 # sparsity of the csr_matrix !! if k > 1 and not include_nodata: sw = sum(sw**x for x in 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 _da2wsp( da, criterion="queen", z_value=None, coords_labels={}, k=1, include_nodata=False, n_jobs=1, use_numba=True, ): """Helper for da2WSP that can be reused in Graph""" 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) if use_numba: k_nas = k if include_nodata else 1 if n_jobs != 1: try: import joblib # noqa: F401 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)) else: # 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] return sw, ser return sw_tup, ser, n 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" ) from None 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" ) from None 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.", stacklevel=2, ) 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" ) from None 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]) # noqa: C409 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, strict=True): 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, strict=True) rows = np.concatenate(rows) cols = np.concatenate(cols) data = np.ones_like(rows, dtype=np.int8) return (data, (rows, cols)) libpysal-4.12.1/libpysal/weights/set_operations.py000066400000000000000000000426341466413560300223400ustar00rootroot00000000000000""" Set-like manipulation of weights matrices. """ __author__ = ( "Sergio J. Rey , " "Charles Schmidt , " "David Folch , " "Dani Arribas-Bel " ) import copy from numpy import ones from scipy.sparse import isspmatrix_csr from .weights import WSP, W __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(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): # noqa: N803 """ 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-4.12.1/libpysal/weights/spatial_lag.py000066400000000000000000000214141466413560300215530ustar00rootroot00000000000000""" 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 """ # noqa: E501 if isinstance(y, list): y = np.array(y) orig_shape = y.shape if len(orig_shape) > 1 and 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-4.12.1/libpysal/weights/spintW.py000066400000000000000000000226421466413560300205630ustar00rootroot00000000000000""" Spatial weights for spatial interaction including contiguity OD weights (ODW), network based weights (netW), and distance-decay based vector weights (vecW). """ # ruff: noqa: N802, N803, N999 __author__ = "Taylor Oshan " from collections import OrderedDict from scipy.sparse import kron from .distance import DistanceBand from .weights import WSP, W 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 ------- ww : 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: # noqa: E722 raise AttributeError( "Wo is not binary and cannot be transformed to " "binary. Wo must be binary or suitably transformed to binary." ) from None if Wd.transform != "b": try: Wd.tranform = "b" except: # noqa: E722 raise AttributeError( "Wd is not binary and cannot be transformed to " "binary. Wd must be binary or suitably transformed to binary." ) from None 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 ------- net_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'" ) net_w = W(neighbors, **kwargs) net_w.tranform = transform return net_w def vecW( origin_x, origin_y, dest_x, dest_y, threshold, p=2, alpha=-1.0, binary=True, ids=None, build_sp=False, # noqa: ARG001 **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, strict=True)) 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-4.12.1/libpysal/weights/tests/000077500000000000000000000000001466413560300200615ustar00rootroot00000000000000libpysal-4.12.1/libpysal/weights/tests/__init__.py000066400000000000000000000000001466413560300221600ustar00rootroot00000000000000libpysal-4.12.1/libpysal/weights/tests/test_Wsets.py000066400000000000000000000054761466413560300226130ustar00rootroot00000000000000"""Unit test for set_operations module.""" # ruff: noqa: N999 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]) == {11, 14} assert set(w2.neighbors[15]) == {11, 14, 19} assert set(w3.neighbors[15]) == {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]) == {11, 14} assert set(w2.neighbors[15]) == {11, 14, 19} assert set(w3.neighbors[15]) == {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]) == {10, 11, 14} assert set(w2.neighbors[15]) == {11, 14} assert set(w3.neighbors[15]) == {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]) == {10, 11, 14} assert set(w2.neighbors[15]) == {11, 14, 19} assert set(w3.neighbors[15]) == {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]) == {11, 14, 19} assert set(w2.neighbors[15]) == {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-4.12.1/libpysal/weights/tests/test__contW_lists.py000066400000000000000000000120331466413560300241400ustar00rootroot00000000000000# ruff: noqa: N999 import os import geopandas as gpd from ... import examples as pysal_examples from ...io.fileio import FileIO from .._contW_lists import QUEEN, ROOK, ContiguityWeightsLists from ..weights import W class TestContiguityWeights: def setup_method(self): """Setup the binning contiguity weights""" shp_obj = FileIO(pysal_examples.get_path("virginia.shp"), "r") self.binningW = ContiguityWeightsLists(shp_obj, QUEEN) shp_obj.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. geoda_w = FileIO(pysal_examples.get_path("virginia.gal"), "r").read() # build matching W with pysal pysal_wb = self.build_w( pysal_examples.get_path("virginia.shp"), QUEEN, "POLY_ID" ) # compare output. for key in geoda_w.neighbors: geoda_neighbors = list(map(int, geoda_w.neighbors[key])) pysalb_neighbors = pysal_wb.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. geoda_w = FileIO(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' # ) pysal_wb = self.build_w(pysal_examples.get_path("rook31.shp"), ROOK, "POLY_ID") # compare output. for key in geoda_w.neighbors: geoda_neighbors = list(map(int, geoda_w.neighbors[key])) pysalb_neighbors = pysal_wb.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 = stl.get_path("stl_hom_rook.gal") geoda_w = FileIO(gal_file, "r").read() # build matching W with pysal pysal_wb = self.build_w(stl.get_path("stl_hom.shp"), ROOK, "POLY_ID_OG") # compare output. for key in geoda_w.neighbors: geoda_neighbors = list(map(int, geoda_w.neighbors[key])) pysalb_neighbors = pysal_wb.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. geoda_w = FileIO(pysal_examples.get_path("virginia_rook.gal"), "r").read() # build matching W with pysal pysal_wb = self.build_w( pysal_examples.get_path("virginia.shp"), ROOK, "POLY_ID" ) # compare output. for key in geoda_w.neighbors: geoda_neighbors = list(map(int, geoda_w.neighbors[key])) pysalb_neighbors = pysal_wb.neighbors[int(key)] geoda_neighbors.sort() pysalb_neighbors.sort() assert geoda_neighbors == pysalb_neighbors def test_shapely(self): pysalneighbs = ContiguityWeightsLists( FileIO(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( FileIO(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): # noqa: N803 """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 = FileIO(dbname) shp_obj = FileIO(shapefile) neighbor_data = ContiguityWeightsLists(shp_obj, type_).w neighbors = {} 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]) binning_w = W(neighbors, id_order=ids) else: neighbors[key] = list(neighbors[key]) binning_w = W(neighbors) return binning_w libpysal-4.12.1/libpysal/weights/tests/test_adjlist.py000066400000000000000000000166701466413560300231360ustar00rootroot00000000000000import 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 TestAdjlist: 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 = {tuple(t) for t in alist[["focal", "neighbor"]].values} full_alist = grid.to_adjlist(drop_islands=True) all_possible = {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 = {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={"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() # noqa: E731 ssq.__name__ = "sum_of_squares" alist = adj.adjlist_apply( df[["HOVAL", "CRIME", "INC"]], W=w, func=ssq, to_adjlist_kws={"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={"drop_islands": True}) for name, data in zip(atts, (hoval, crime, inc), strict=True): 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-4.12.1/libpysal/weights/tests/test_contiguity.py000066400000000000000000000152451466413560300236770ustar00rootroot00000000000000# 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 = {} # 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 = {} known_wi_da = None known_w_da = {} 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 @pytest.mark.network 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-4.12.1/libpysal/weights/tests/test_distance.py000066400000000000000000000325041466413560300232700ustar00rootroot00000000000000import numpy as np from ... import cg from ... import examples as pysal_examples from ...cg.kdtree import KDTree from ...common import RTOL from ...io import geotable as pdio from ...io.fileio import FileIO from .. import contiguity as c from .. import distance as d 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 = FileIO(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 = FileIO(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 = {} # 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 = FileIO(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 = FileIO(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) for k, o1, o2 in zip(names, df["names"].values, w.id_order, strict=True): assert k == o1 == o2 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-4.12.1/libpysal/weights/tests/test_gabriel.py000066400000000000000000000022441466413560300231010ustar00rootroot00000000000000import 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-4.12.1/libpysal/weights/tests/test_nx.py000066400000000000000000000016651466413560300221270ustar00rootroot00000000000000import 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-4.12.1/libpysal/weights/tests/test_raster.py000066400000000000000000000075421466413560300230020ustar00rootroot00000000000000"""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 is 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-4.12.1/libpysal/weights/tests/test_spatial_lag.py000066400000000000000000000041721466413560300237560ustar00rootroot00000000000000import numpy as np from ..spatial_lag import lag_categorical, lag_spatial from ..util import lat2W from ..weights import W class TestSpatialLag: 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-4.12.1/libpysal/weights/tests/test_spintW.py000066400000000000000000000344101466413560300227600ustar00rootroot00000000000000# ruff: noqa: N999 import 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-4.12.1/libpysal/weights/tests/test_user.py000066400000000000000000000011521466413560300224470ustar00rootroot00000000000000import os import pytest from ... import examples from .. import user from ..contiguity import Rook 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-4.12.1/libpysal/weights/tests/test_util.py000066400000000000000000000256341466413560300224610ustar00rootroot00000000000000"""Unit test for util.py""" import geopandas as gpd import numpy as np import pytest from ... import examples from ...io.fileio import FileIO 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" _ = wrook.sparse util.higher_order(wsparse, 2) with pytest.raises(ValueError): 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) 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 = FileIO(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]) == {239, 59, 152, 23} buff = fuzzy_contiguity(rs_df, buffering=True, buffer=0.2) assert set(buff.neighbors[0]) == {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"]) == {"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]) == {142, 82, 197, 285, 386, 350} libpysal-4.12.1/libpysal/weights/tests/test_weights.py000066400000000000000000000546351466413560300231610ustar00rootroot00000000000000import os import tempfile import warnings import geopandas import numpy as np import pytest import scipy.sparse from ... import examples from ...io.fileio import FileIO 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 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): 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 TestWSPBackToW: # 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 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 = FileIO(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-4.12.1/libpysal/weights/tests/test_weights_IO.py000066400000000000000000000016441466413560300235400ustar00rootroot00000000000000# ruff: noqa: N999 import 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-4.12.1/libpysal/weights/user.py000066400000000000000000000077501466413560300202600ustar00rootroot00000000000000""" Convenience functions for the construction of spatial weights based on contiguity and distance criteria. """ __author__ = "Sergio J. Rey " import numpy as np from .. import cg from ..io.fileio import FileIO from .util import get_points_array_from_shapefile, min_threshold_distance __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 FileIO(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): # noqa: N803 """ 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 = FileIO(outFileName, "w") dbf_name = outFileName.split(".")[0] + ".dbf" d = FileIO(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-4.12.1/libpysal/weights/util.py000066400000000000000000001337271466413560300202630ustar00rootroot00000000000000import copy import numbers import os from collections import defaultdict from itertools import tee from warnings import warn import numpy as np # ruff: noqa: N802, N803 import scipy import scipy.spatial from packaging.version import Version from scipy import sparse from scipy.spatial import KDTree from ..common import requires from ..io.fileio import FileIO from .set_operations import w_subset from .weights import WSP, W 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.", stacklevel=2) 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] 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, strict=True))) 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 """ # noqa: E501 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(w**x for x in range(1, k + 1)) shortest_path = False else: wk = w**k rk, ck = wk.nonzero() sk = set(zip(rk, ck, strict=True)) if shortest_path: for j in range(1, k): wj = w**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} if id_order: d = {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 _ 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.", stacklevel=2) 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): # noqa: B006 """ 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 isinstance(in_shps, str): dbname = os.path.splitext(in_shps)[0] + ".dbf" db = FileIO(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 OSError: msg = ( f'The shapefile "{in_shps}" appears to be missing its DBF file. ' f' The DBF file "{dbname}" could not be found.' ) raise OSError(msg) from None except (AttributeError, KeyError): msg = ( f'The variable "{idVariable}" not found in the DBF/GDF. ' f"The the following variables are present: {','.join(cols)}." ) raise KeyError(msg) from None 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(list(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 = FileIO(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): with open(file, "w") as f: n = k * k f.write("0 %d" % n) for i in range(n): 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 ------- bool 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 return all(set(w1.neighbors[i]) == set(w2.neighbors[i]) for i in ids_1) 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]) # noqa: C419 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 isinstance(island, 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 """ # noqa: E501 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__": 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-4.12.1/libpysal/weights/weights.py000066400000000000000000001507201466413560300207500ustar00rootroot00000000000000""" Weights. """ # ruff: noqa: N802, N803 __author__ = "Sergio J. Rey " import copy import math import warnings from collections import defaultdict from os.path import basename import numpy as np import scipy.sparse from scipy.sparse.csgraph import connected_components from ..io.fileio import FileIO # from .util import full, WSP2W resolve import cycle by # forcing these into methods from . import adjtools __all__ = ["W", "WSP"] class _LabelEncoder: """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: """ 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. """ # noqa: E501 def __init__( self, neighbors, weights=None, id_order=None, silence_warnings=False, ids=None, # noqa: ARG002 ): 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 + f"\n There is 1 island with id: {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, stacklevel=2) def _reset(self): """Reset properties.""" self._cache = {} def to_file(self, path="", format=None): # noqa: A002 """ 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 = FileIO(dataPath=path, mode="w", dataFormat=format) f.write(self) f.close() @classmethod def from_file(cls, path="", format=None): # noqa: A002 """ 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 = FileIO(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 = {} weights = {} 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" ) from None 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, stacklevel=2, ) drop_islands = True 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." ) from None 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"): # noqa: ARG003 """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." ) from None 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, strict=True): 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_[:-1], ct, strict=True)) 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], strict=True))) 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], strict=True))) 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, strict=True): 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 and 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 = {} s = {} big_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] big_q += sum(s[i]) nq = self.n / big_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)] """ # noqa: E501 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], strict=True)) 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 = {} 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." ) from None 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 = {"color": color} if edge_kws is not None: if "color" not in edge_kws: edge_kws["color"] = color else: edge_kws = {"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, strict=True): if (idx, nidx) in seen: continue ax.plot( *list(zip(focal, neighbor, strict=True)), 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: """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-4.12.1/notebooks/000077500000000000000000000000001466413560300154315ustar00rootroot00000000000000libpysal-4.12.1/notebooks/Raster_awareness_API.ipynb000066400000000000000000010062511466413560300225020ustar00rootroot00000000000000{ "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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\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-4.12.1/notebooks/examples.ipynb000066400000000000000000002014001466413560300203070ustar00rootroot00000000000000{ "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-4.12.1/notebooks/fetch.ipynb000066400000000000000000000714061466413560300175750ustar00rootroot00000000000000{ "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-4.12.1/notebooks/io.ipynb000066400000000000000000000134271466413560300171120ustar00rootroot00000000000000{ "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-4.12.1/notebooks/matching-graph.ipynb000066400000000000000000037624741466413560300214140ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Spatial matching graph\n", "\n", "Author: [Levi John Wolf](http://github.com/ljwolf)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic Usage" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "import os\n", "sys.path.append(os.path.abspath('..'))\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import geopandas\n", "import pandas" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from libpysal.graph import Graph" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "points = np.row_stack([(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3), (7,4)])\n", "gdf = geopandas.GeoDataFrame(geometry=geopandas.points_from_xy(*points.T))" ] }, { "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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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(*points.T)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "g1 = Graph.build_spatial_matches(gdf.geometry, k=1)\n", "g2 = Graph.build_spatial_matches(gdf.geometry, k=2)\n", "g3 = Graph.build_spatial_matches(gdf.geometry, k=3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax =plt.subplots(1,3)\n", "for i,g in enumerate((g1, g2, g3)):\n", " g.plot(gdf, ax=ax[i])\n", " ax[i].set_title(f\"k = {i+1}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Larger Problem" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import geodatasets\n", "stores = geopandas.read_file(geodatasets.get_path(\"geoda liquor_stores\")).explode(\n", " index_parts=False\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idplaceidgeometry
00ChIJnyLZdBTSD4gRbsa_hRGgPtcPOINT (1161395.910 1928443.285)
13ChIJ5Vdx0AssDogRVjbNIyF3Mr4POINT (1178227.792 1881864.522)
24ChIJb5I6QwYsDogRe8R4E9K8mkkPOINT (1178151.911 1879212.002)
36ChIJESl0mMfMD4gRy23-8soxKuwPOINT (1141552.993 1910193.701)
47ChIJg28YOdvMD4gRiV2lZcjSVyQPOINT (1144074.399 1910643.753)
\n", "
" ], "text/plain": [ " id placeid geometry\n", "0 0 ChIJnyLZdBTSD4gRbsa_hRGgPtc POINT (1161395.910 1928443.285)\n", "1 3 ChIJ5Vdx0AssDogRVjbNIyF3Mr4 POINT (1178227.792 1881864.522)\n", "2 4 ChIJb5I6QwYsDogRe8R4E9K8mkk POINT (1178151.911 1879212.002)\n", "3 6 ChIJESl0mMfMD4gRy23-8soxKuw POINT (1141552.993 1910193.701)\n", "4 7 ChIJg28YOdvMD4gRiV2lZcjSVyQ POINT (1144074.399 1910643.753)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stores.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "stores = stores.set_index(stores.placeid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solving for this graph in larger data will take time. The solution technique is somewhere between $O(n^2)$ and $O(n^3)$ if the solver recognizes it's a matching problem." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "g1 = Graph.build_spatial_matches(stores.geometry, k=1)\n", "g5 = Graph.build_spatial_matches(stores.geometry, k=5)\n", "g10 = Graph.build_spatial_matches(stores.geometry, k=10)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax =plt.subplots(1,3)\n", "for i,g in enumerate((g1, g5, g10)):\n", " g.plot(stores, ax=ax[i], nodes=False)\n", " ax[i].set_title(f\"k = {(1, 5, 10)[i]}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cross-matching" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sources = stores.sample(100)\n", "sinks = stores[~stores.index.isin(sources.index)].sample(100)\n", "ax = sources.plot(color='red')\n", "sinks.plot(color='blue', ax=ax)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from libpysal.graph._matching import _spatial_matching\n", "import shapely" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "sources = stores.sample(100)\n", "sinks = stores[~stores.index.isin(sources.index)].sample(100)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "source_coordinates = sources.geometry.get_coordinates().values\n", "sink_coordinates = sinks.geometry.get_coordinates().values" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "crosspattern_heads, crosspattern_tails, weights, mip = _spatial_matching(x=sink_coordinates, y = source_coordinates, n_matches=1, return_mip=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mip.sol_status" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "lines = shapely.linestrings(\n", " list( \n", " zip(\n", " map(list, source_coordinates[crosspattern_heads]),\n", " map(list, sink_coordinates[crosspattern_tails])\n", " )\n", ")\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sources.plot(color='red')\n", "sinks.plot(color='blue', ax=ax)\n", "geopandas.GeoSeries(lines).plot(linewidth=1, color='k', ax=ax)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 2 } libpysal-4.12.1/notebooks/voronoi.ipynb000066400000000000000000004251721466413560300202020ustar00rootroot00000000000000{ "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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m7BMTE2737t19+74wDDl06BV27drP6dMb2LDhEur1kHfeOcbx429y6tQxTp06yunTxwiCgNHRtYyObmDNmi385j/5IP6M2cdzccB37z07/hs0RHntVsqjUml1QNbr7ZV3rdnR2Bgt0rjDbqRHonRJdCydTpNKZdoq73RztEgqlaZeh+iKtVIn0azQVqXe+Z6Ll06oUq02vqeGc9ZWWUdpkyidEpBKtd+R+83Ku1WRt871/WDeVIdzrqMSb6/Y6/XOu/T2XLpzVYIg1ewUbe8obaRe5sqnn01DGaU/zGyPc25iofN0O9DFiy++yJ49Bzl+fBXr17+fbdtaS0fk8wXGxrY3X4dhyMmTb/P2229y4sQxDh16gUyPgaDh8q/9Y579zX+3UsVvagz9i8ZnVxcY+td+V12dUVm3Uij1ejXuLJxdaafTftuYbZ9UysfzwPOibT2z2eifW6vijibtNNIqrVRM9F2VSokzZybjMoSkUpm4os42K+tUKtdRebdy4UHc2RgQBNm40z4b34kv7p991Gppvzsvcfr0qTj10lmhN/Lp0bF63EGaoVhsVOqtCt738/j+6q536cqnS78pGLR5++23efTR5/j2t3/C9HQdz/Pw/UfIZHyCIEsmkyMIsvh+niAoEAR5crkCQVBg3bqNrF69llOnNi7qOw246Ht/whOf+2rXoX+NiTadlXaUt27cYbbuPBvHooo+WnEyHT+iyrtRYUcVpR/fdUdpi1wuRzo9ilnj7tK15b4bFXdUabc6PRtlrFAuTzXvwqOy1uLfn2negTeGFDbuzGfmwlsVdqZ5R954vdwKspFeasw0bc+pRxV4FbPG3Xnjzr1KKuWad+nFYvvdevQ8kxmdc9SL8ulytlAwAE6dOsXjj+/juecmyecv4ZZbPoiZUamU47VFzlAuT1MqTVMuT1MuT3Hy5CnK5b9vTqLZv/9pyuUpoM6iu4RdyD33/FF8R51u3mlHlbZPKpVp3nmnUhkKhYB0utC8+47SGz6N1Eir0m6kbMpxOStUq6W4sm5V3o0g1EqjuI5RJ41KPCpDo3OzGFeEflvF3ejMzPaURlmKRuqlvWO0vdUSrYxZbY56aVTs7amXaAap3zE2PQh8fL/YdYy6Ui9yPjivg8H09DTPPPMCjz9+lHT6YsbGrp2xW1lUoY2OdturuFMYhhw9+jqHDx+Ah7+x6LJ89KP/I9VqqbleSqMSbzzq9TKl0pm2IYqtR5QPr7elUVp33FEOPGhLoxQoFHyCIEcqlY4r7s4RKTM7M5PQnnqZWbHXapV4ZcnZd+lmNYIgTT7vUyh0jk3P5/049TL7Lv18mUUqslTnZTCoVqs8//wBHn30Fer17Wza9GHS6eWtguh5Hps3X8DmzRcs6fM//vFf43lRXrs9hRLdnY/Gk3Bmjkrx8f3ciqVRliJKvXRW6O1DGRt36DM7ST3PdUw0isamt8ao+/5I105SpV5EknFeBYMwDHnppYPs2hUtP71x44fmXWxuKb9/ampySZ+95ZZ/vGLlWCznXMdwzZmjX2aOeml1knbOIh0dbeXTo+GNrdTLzIpdQxlFhst58X+kc47XX3+dhx56nuPHV7Fu3c/OuQ/BTGEYUipNUypNcubMJOXyNFNTpymVzjTXLC+XzzA9fZpqtYTvB9ya8N8zX1nnGpveSL10dpI28uq15mqM7ZV5awGvHJnMaNd8ulIvIueGcz4YHDlyhDvvvI8jRzzM1pHPpzhy5JF4N6RqvElFtKsQRHvIVirTHD36ejNXHy2ilYnvan3y+RGKxTWMjKxl48YtrFq1kdHRtWSzxahy/Mb/tuhy3nPPv407Kj08LxXveNT50/OiGa+pVIrNm9dQLAa0Lw2QSoUEQVSRj4xE6ZfOBbyKc+bTlXoROb+d88EA4Nix41QqIxSLjlSqhu+n8LyAVKoYj4tP4Xnp5nOzFNPTp9rutMuUy43NLsrxOP1J/v7v3+a1156lWq1glorz/VmWsjPBhz70K82dk9o3No/WX2+9rtdrnDjxHDfdtIOxsTEt4CUiK+Kcrz22bt3K7/3e/8qLL77ET35ykFptjI0bdy67w3imSqVCpTJFqTQJX1/851ev7m1+wokTR9mx4zRXXXXV4r9ERGQO53wwAEin01x22XvYseNCnn32RXbvfoBU6mI2bLhoxXLe0cgen2Jx9ZI+X6vVerqzP316Px/5yMVL+g4RkbmcF8GgIZvNcs01V/Ke9+zg8cf38cwzB8nnL2HdurEVy5lPTp5Y0ufuuecP2bRpnG3bLmNs7JKuY/1Pnz7O+vUltm7dutxiioh0OK+CQUOxWOTGG6/liiuO8+ijz/Hyyy+zatWlrFq1YVm/98UXH+OZZx7kV82wRS4AePPNn+eVV15g//6neeyx77F+/Wa2bbuU8fHLyWbzAJw4cYBPfOJidfaKyIo7L4NBw9q1a/n4xz/IG2+8wcMPP8Orr+ZZv/4y8vnRhT/cZmrqFI88cg9TUxVuvPG/4/lXn+bSH/1neqmyHfDc1ksJw5DLL7+Wyy+/llJpmldf3c/rr7/Ak0/+iDVr1rFp00WMjZUYH19w8UERkUU7r4NBw5YtW/j0pzdx6NArPPzwIxw/vpENG95DEOQW/OyBA7t56qkHufDC93HjjTeQTqf540uv5zemp3nfI3cB87cQjl7589zzK19h//3/ieuuu4ULLngX2WyOd7/7St797iupVCocPnyAl19+ghtuGNe4fhFJhIJBzPM8duy4iPHxC3jhhQP85CcPUq9fyMaNF3cdedRoDZw5U+KDH/xVNm3aBkTBoV5Pc/iLf8URz+OBB/6aXbv+gi9/+a+5995v8vTTf8uXv/znHZ3FVwLr1m3lkUfu4fjx93PVVdc33/N9nx07LiMITnLJJe9K/DqIyPlJwWCGdDrN5ZdfwrvetZ1nnnmBPXseIJ3eyYYN25t35QcOPM5TT/2oozUAUKmUeOaZB3n/+3+peW6hsIYNGy7E8zxGRlaTTo90HTU0NvYefuEXNvDQQ3dy/PgbXH/9P8T3g+b7zp1k1apVfbgCInI+UjCYQzab5dprr+KSS6KRR3v3HsTztrF//085fXqqozXQ8NRTP2D9+ovZsuXC5rF8fpQwrAOwffsOnPvZOb9zdHQtH/3o7Tz66N1873t/xg03fJo1azZQLk8xOpoiCII5PysishxKQC9gZGSEn/u567j11qvJZJ4mDDN84hOfmxUITpw4yquvvsB73/vhjuPF4ijVahQMon0C5o+/6XSa66//ZXbuvJIf/vDPOXjwec6cOcmWLWoViEhyBtYyMLObgX8DpID/4Jz7l4MqSy/WrVvHJz95A9Xqy10r9D17/padO3+WYrFzJNLatRtYv35L83Wvw0IvvfQDrF27lV277iKXW83ExAeW9weIiMxjIC0Di/ZV/GPg48BlwG1mdtkgyrIYGzduJJ8/He9o1nLo0F4mJ6e47LJrZ33GzCOXi+YJhGEIPQ04jWzadCEf+9j/RCYzTT6/cktti4jMNKg00XXAAefcyy7a7eRO4FMDKkvPPM/jH/yDrRw/frh5rFar8fTTP+C9772pa4uhfSiocyGet7gJY/n8KJdeeiVr1iy825qIyFINKhiMAa+1vT4cH+tgZreb2W4z233s2LG+FW4+O3ZcQL3eCgZ79z5IsbiV8fGdXc/3PI8wjOYaRJvLL372sHMlslm1DEQkOYMKBt1qxFmzs5xzdzjnJpxzExs2LG+piJWyevVqNm40Tp8+zuTkCV566UmuueamBT4VAlEwWOykMeccZhWNJBKRRA0qGBwG2jcL3gYcGVBZFu2KK7Zx8uRh9uy5jwsvfB+rVq2d52yLWwTEQ0wXd8mr1TL5vDafEZFkDSoYPAbsNLOLzMwHbgXuHVBZFm18fBuTk/s4fvwtrrzy+nnP9TyPxpp1UctgcZV6pVJiZEQpIhFJ1kCGljrnamb2BeD7RENL/9Q59+wgyrIUuVyOYrHGFVd8qOtS0zM1WgZRymdxwaBaLbFpk4KBiCRrYPMMnHP3AfcN6vuX66qrLuP55xce4RP1EbQHg8WniUZHFQxEJFmagbxEhYJPrVbp4UxrG020uHkGAJXKtNJEIpI4BYMlyud9wnDhYOB5Ho3MUBiGix5NZFYml1MwEJFkKRgsUTTUs9zTuY2WAYBziw0GmmMgIslTMFgi3/cx661lEKWHljaaCBQMRCR5CgZLFI0i6i0YNETLUSx20pmCgYgkT8FgiYIgwLne0kQQ9Rc4Fy5qNFHUx1Ajk5m905qIyEpSMFiiXlsG0FifKFz02kTVaoliMdDsYxFJnILBEmUyGTwvjJelXkh0mRe7aqlmH4tIvygYLEOvcw3MaLYMFpMm0oQzEekXBYNlyOV8qtWF+w2iABC1IhaT8qlUSgoGItIXCgbLsLhZyCEQEm3y1pt6XWkiEekPBYNl6D1N1OgzWOw8Aw0rFZH+UDBYhkIh6DFNtLShpWYlbWojIn2hYLAMi0kTLaXPQC0DEekXBYNliOYa9NIyiALAYkcTafaxiPSLgsEyBEGA5/XWZ9DqQO7tktfrNTIZp9nHItIXCgbL0Oss5EafQRj23oFcrZY1kkhE+kbBYBl6X5KikSYK6fWSa/axiPSTgsEy9LpYXWPSWTS0tLdLXq1qwpmI9E9iwcDM/m8ze97Mnjazu81sdXx8u5lNm9mT8ePfJ1WGpGUyGcxqzQ3v59JYqA5670CuVKJF6kRE+iHJlsH9wBXOuSuBF4Evtb33knPu6vjx+QTLkCgzI5vN9DS8NEoR9d6BHIYlRkZyyyyhiEhvEgsGzrn/6pyrxS8fAbYl9V2DVCj4VKvzB4PG0NLFdCBrwpmI9FO/+gx+A/jbttcXmdkTZvagmd3QpzIkolAIqNUW6jfwlrBqqeYYiEj/pJfzYTP7AbC5y1tfcc59Jz7nK0AN+Gb83hvAuHPubTO7BrjHzC53zp3q8vtvB24HGB8fX05RE5PP+7z5Zu9pol47kDXhTET6aVnBwDl303zvm9lngU8CP+/iXlYXDb8px8/3mNlLwLuB3V1+/x3AHQATExPz99IOSLG48JIUjQAQXQK1DERk+CQ5muhm4J8AtzjnptqOb7B4HWcz2wHsBF5OqhxJ6y1NZDgXxnsaL3zJa7UqQZAilep9uWsRkeVYVstgAf8WCID74w7UR+KRQzcC/9zMakAd+Lxz7niC5UhUEPjA5LznRENLXdxnsHAHsiaciUi/JRYMnHMXz3H8LuCupL6333zfx6z3PoNeGmPVaonVqxUMRKR/NAN5mXqbhey1pYl6bRloWKmI9I+CwTL10jJopYYcnrdwP0C1WmLVKrUMRKR/FAyWyfd9nFs4TRStWhr29DudK5HPKxiISP8oGCxTIxjMtz5RNIKoMc9g4ZaBmYaVikh/KRgsk+d55HJparXqnOeYGc65nlct1YQzEek3BYMVkM8vNPHMwzlHGDoaexvMT8FARPpLwWAF5HLzBwOzaGipcwtPOovSTRUtUicifaVgsAKKxWCBYODFrYKF5xnUahVyuXTPaxiJiKwE1TgrIFrGeu65Bq0lrBduGWj2sYgMgoLBCigUFkoTeXGaaOEO5EpF212KSP8pGKyAXC4A5p9r0AgGC11y7X0sIoOgYLACorkG8y1J0RpBtFDLQMFARAZBwWAFLLQkhZnFO53Ve+gYLpHLKRiISH8pGKyAaBjofB3I0QzkXtJEZmXNMRCRvlMwWAELrU8UzTPobQayc9MKBiLSdwoGK8D3febrQI5GE0FveyCrZSAi/adgsAJSqRSZjFGv1+Y4o7dtL6P3q3FwERHpHwWDFVIoBPNOPAvD+oJbXlarZQoFv6etMUVEVpKCwQqZb7G6aA/ksIdgoNnHIjIYiQUDM/tnZva6mT0ZPz7R9t6XzOyAmb1gZh9Lqgz9FC1JMVe/gUe9XlswGGgpChEZlHTCv/+rzrl/3X7AzC4DbgUuB7YCPzCzdzvn6gmXJVGFQkCt1j1N5HmGcwuniSoVbXcpIoMxiDTRp4A7nXNl59xB4ABw3QDKsaLmbxlAvR6y0F4G9XpZLQMRGYikg8EXzOxpM/tTM1sTHxsDXms753B87KyWy80318AMVEvfAAAO0UlEQVQjDGt43kIdw9rURkQGY1nBwMx+YGZ7uzw+BXwNeBdwNfAG8AeNj3X5VV03EDaz281st5ntPnbs2HKKmrj5ZiFH8wzcgmki7X0sIoOyrD4D59xNvZxnZl8Hvhu/PAxc0Pb2NuDIHL//DuAOgImJibl3nB8C861PZBbNIVg49ioYiMhgJDmaaEvby18E9sbP7wVuNbPAzC4CdgI/Taoc/TLfLORoobr6gmki5xQMRGQwkhxN9H+Z2dVEKaBDwD8CcM49a2bfAp4DasBvne0jiWD+Zawbm9vM14Fcr9dJpepkMpmESigiMrfEgoFz7tfmee/3gd9P6rsHIeozmK9lMP+kM004E5FB0gzkFZJOp0mlHPV690bOQmmialXDSkVkcBQMVtBcS1KYeYRhnfkud6UyrR3ORGRgFAxW0HzBoF5fKE2kloGIDI6CwQqaa+XSKAjMv3x1va4+AxEZHAWDFVQozL1y6Vx9CS0aVioig6NgsIKKxWCONJEtuJ+BWSkekSQi0n8KBison/ep12eniaL0kMNs7sutCWciMkgKBitorlnIvXQgK00kIoOkYLCCgiCYc32iaD+D7p+r1ar4vpFOJ729hIhIdwoGKyhqGXQfTRTNQO5+uavVMsWiWgUiMjgKBisoWp+oeweyc3OniapV7XAmIoOlYLCC5ksTheHc+xlUKiWKRY0kEpHBUTBYQel0GrNavHdBSzSaqD5nmija+zjXhxKKiHSnYLCCzIxcbvbEs2htorlbBs6VyOfVMhCRwVEwWGFzrU803xLW2u5SRAZNwWCFFYuz1yeKOpDnThNpjoGIDJqCwQrr1jKIJp3NnyZSMBCRQVIwWGGFgk+12hkMPM/DLKTb5XbOAWUFAxEZKAWDFRYtVjd74plzdE0T1WpVcrn0vMtbi4gkLbH1D8zsr4D3xC9XAyecc1eb2XZgH/BC/N4jzrnPJ1WOfvN9H7NTs44757ouR6G9j0VkGCQWDJxzv9p4bmZ/AJxse/sl59zVSX33IEXBYHaayDm63v1XKiXWrNGwUhEZrMRXRrOo1/RXgI8k/V3DoNvKpVF6yNEtK1etlrT3sYgMXD8S1TcAbzrn9rcdu8jMnjCzB83shj6UoW+iDWq69Rl0H00UzT5WMBCRwVpWy8DMfgBs7vLWV5xz34mf3wb8Zdt7bwDjzrm3zewa4B4zu9w5NyvRbma3A7cDjI+PL6eofdNtsTrPszgYdIu9JXK50f4UTkRkDssKBs65m+Z738zSwKeBa9o+Uya+dXbO7TGzl4B3A7u7/P47gDsAJiYm3HLK2i9Rmqja0RJoBAHP6zbPoEQ2u7F/BRQR6SLpNNFNwPPOucONA2a2wcxS8fMdwE7g5YTL0TfR+kRparVq27FGn8FcwUBpIhEZrKQ7kG+lM0UEcCPwz82sBtSBzzvnjidcjr7K56MlKTIZv3ksDN0ccwk04UxEBi/RYOCc+x+6HLsLuCvJ7x20fN7nxIlWv0ErCHQGgyiVVIk7nUVEBkfTXhNQKMxcn8jiZSc6VatlCgV/zjWLRET6RcEgATNXLm2MJprZgawdzkRkWCgYJGB2yyAyc2ipJpyJyLBQMEhAEHTOQm6MJpoZDCoVBQMRGQ4KBgkIggCzVprIzOJtL9UyEJHhpGCQgG7rE8HsPgNtdykiw0LBIAHRUNHOoaXdl6NQMBCR4aBgkIBofaL2NJGH2ewOZDNNOBOR4aBgkIBui9V16zPQ3sciMiwUDBLgeR5BkGquT9RtnkEYhnhejUwmM6hiiog0KRgkJNoLOWodNFoE7S2DajWacKbZxyIyDBQMEpLL+c1ZyGazO5ArFe19LCLDQ8EgIbNnIc8OBppjICLDQsEgIe3BIBpa2rm5jSacicgwUTBISOdidQaEtF/uWk1pIhEZHgoGCclmW8NLG5PO2je30exjERkmCgYJ6bYkRXufgVlZm9qIyNBQMEhI+2J1jZZB5x7IJXK53EDKJiIyk4JBQjpbBlEQSKVal9u5kloGIjI0lh0MzOyXzexZMwvNbGLGe18yswNm9oKZfazt+M3xsQNm9sXllmEYtS9JMXOhunq9RibjNPtYRIbGSrQM9gKfBn7cftDMLgNuBS4Hbgb+nZmlzCwF/DHwceAy4Lb43HNKdNffWqyufQ9kTTgTkWGTXu4vcM7tA7otq/Ap4E4XLd950MwOANfF7x1wzr0cf+7O+NznlluWYZJKpchkjHq91jbPIIq91WqJtWsVDERkeCTZZzAGvNb2+nB8bK7j55xcrjHxzABH43JXq2VNOBORodJTy8DMfgBs7vLWV5xz35nrY12OtWrE2ce7fe/twO0A4+PjPZR0uBQKPlNTFXw/jXNhs2WgNJGIDJuegoFz7qYl/O7DwAVtr7cBR+Lncx2f+b13AHcATExMdA0Yw6xQCDh5skw2W8S51jyDMCxRLGpYqYgMjyTTRPcCt5pZYGYXATuBnwKPATvN7CIz84k6me9NsBwDk89HaaJGi6C1lLWGlYrIcFl2B7KZ/SLw/wIbgL8xsyedcx9zzj1rZt8i6hiuAb/lnKvHn/kC8H0gBfypc+7Z5ZZjGBWL7cEgbFuoTktRiMhwWYnRRHcDd8/x3u8Dv9/l+H3Afcv97mGXzweEYTS8NBpZavFzBQMRGS6agZygmbOQW2sTKRiIyHBRMEiQ7/uYNba+jOZi1GpVgiBFKpUacOlERFoUDBIUdRK3twyMSmVacwxEZOgoGCQoWp+otSSF53lUq2WKRQUDERkuy+5AlrnN7DNwzqhWS4yMaFipiAwXtQwSlMlk8LyQMAybfQbVaonVqzXhTESGi4JBwgqFaK5BY6E650rkcmoZiMhwUTBIWC7nU62WyWQCPM/T3sciMpQUDBLWaBls3LgFz8ugOQYiMowUDBJWLAbxMtbROnuafSwiw0jBIGH5fJQmaqlokToRGToKBgkrFHzq9Wh4aa1WIZ/PNFcxFREZFqqVEta+F3K1WqJYVKtARIaPgkHC2tcn0naXIjKsFAwS1j4LuVotKRiIyFBSMEhYEAQ4VyYMQ2o1BQMRGU4KBgnrXJ+oQi6nYCAiw0fBIGGZTAazGtE8A80xEJHhpGCQMDMjl8sQhiFQVjAQkaG0rGBgZr9sZs+aWWhmE23Hf8HM9pjZM/HPj7S993dm9oKZPRk/Ni6nDGeDQiEaTqrZxyIyrJa7n8Fe4NPAn8w4/hbwD51zR8zsCuD7wFjb+59xzu1e5nefNfJ5H3BkMi7uQxARGS7LCgbOuX0QpUJmHH+i7eWzQNbMAte+7dd5pFCIAkCxGMy6ViIiw6AffQb/LfDEjEDwn+IU0T+186B2bMw6HhlRikhEhtOCLQMz+wGwuctbX3HOfWeBz14O/Cvgo22HP+Oce93MRoC7gF8D/nyOz98O3A4wPj6+UFGHVpQmUjAQkeG1YDBwzt20lF9sZtuAu4Ffd8691Pb7Xo9/njazvwCuY45g4Jy7A7gDYGJiwi2lHMMgCHw8z7FqlYKBiAynRNJEZrYa+BvgS865h9uOp81sffw8A3ySqBP6nBYEAel0qJaBiAyt5Q4t/UUzOwx8APgbM/t+/NYXgIuBfzpjCGkAfN/MngaeBF4Hvr6cMpwNfN/H90MNKxWRobXc0UR3E6WCZh7/F8C/mONj1yznO89GUTBw5HK5QRdFRKQrzUDuA9/3CQKnloGIDC0Fgz5opIm03aWIDCsFgz7wPI/f/d3fVstARIaWgkGfrFu3btBFEBGZk4KBiIgoGIiIiIKBiIigYCAiIigYiIgICgYiIoKCgYiIoGAgIiKAOXd2bBNgZseAVwZdjjmsJ9r3+Xyn69Cia9GiaxEZ1HW40Dm3YaGTzppgMMzMbLdzbmLQ5Rg0XYcWXYsWXYvIsF8HpYlERETBQEREFAxWyh2DLsCQ0HVo0bVo0bWIDPV1UJ+BiIioZSAiIgoGy2JmN5vZC2Z2wMy+OOjyJM3M/tTMjprZ3rZja83sfjPbH/9cEx83M/t/4mvztJm9b3AlX1lmdoGZ/cjM9pnZs2b22/Hx8/FaZM3sp2b2VHwt/s/4+EVm9mh8Lf7KzPz4eBC/PhC/v32Q5V9pZpYysyfM7Lvx67PmOigYLJGZpYA/Bj4OXAbcZmaXDbZUifsz4OYZx74I/NA5txP4YfwaouuyM37cDnytT2Xshxrwu865S4H3A78V/7c/H69FGfiIc+4q4GrgZjN7P/CvgK/G1+Id4HPx+Z8D3nHOXQx8NT7vXPLbwL6212fPdXDO6bGEB/AB4Pttr78EfGnQ5erD370d2Nv2+gVgS/x8C/BC/PxPgNu6nXeuPYDvAL9wvl8LIA88DvwM0eSqdHy8+f8K8H3gA/HzdHyeDbrsK/T3byO6CfgI8F3AzqbroJbB0o0Br7W9PhwfO99scs69ARD/3BgfPy+uT9y8fy/wKOfptYhTI08CR4H7gZeAE865WnxK+9/bvBbx+yeBc2VP2D8C/ncgjF+v4yy6DgoGS2ddjmloVss5f33MrAjcBfyOc+7UfKd2OXbOXAvnXN05dzXRnfF1wKXdTot/npPXwsw+CRx1zu1pP9zl1KG9DgoGS3cYuKDt9TbgyIDKMkhvmtkWgPjn0fj4OX19zCxDFAi+6Zz7dnz4vLwWDc65E8DfEfWjrDazdPxW+9/bvBbx+6uA4/0taSKuB24xs0PAnUSpoj/iLLoOCgZL9xiwMx4t4AO3AvcOuEyDcC/w2fj5Z4ny543jvx6PpHk/cLKRQjnbmZkB/xHY55z7w7a3zsdrscHMVsfPc8BNRB2oPwJ+KT5t5rVoXKNfAh5wceL8bOac+5JzbptzbjtRXfCAc+4znE3XYdCdLmfzA/gE8CJRjvQrgy5PH/7evwTeAKpEdzafI8pz/hDYH/9cG59rRKOtXgKeASYGXf4VvA4fJGrSPw08GT8+cZ5eiyuBJ+JrsRf4P+LjO4CfAgeA/w8I4uPZ+PWB+P0dg/4bErgmHwK+e7ZdB81AFhERpYlERETBQEREUDAQEREUDEREBAUDERFBwUBERFAwEBERFAxERAT4/wEjrIAp2XE+JQAAAABJRU5ErkJggg==\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-4.12.1/notebooks/weights.ipynb000066400000000000000000064060031466413560300201570ustar00rootroot00000000000000{ "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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\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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nT5+WJ02aVG2NV6qbw4cPW/fu3XvbYrEMFEXxVGXHmT9//u7OnTv37devH89x9nlBLRYLFi5cKJtMJh5AQ1EUy/EZqqg8eNQdfz56vf52WFjYiuzs7Ctnzpzpk5ycjKZNm2oEwUY/ej8/4No14NIlkPbt0apVKyE1NbVeRkbG/4WGhobr9fqbERER3QEcOHTo0IC9e/fu0+v16aXMK4eGhq7LyMh4wmg0ujdt2tTGhKVACPvS5fc9//pr4PXXgREjgK5dmTtIEIDFi4F164AVK9hX3bqsKrlWLcChbO/WmTNnAEAJDAx8aONBvr6+nIeHh3NsbOyLUVFRvfbu3Ruj1+tLKc6wjSRJPZycnGaMGTPGgeftf8bxPI+2bdtyhw8fhkajaRIaGmqIiIiI0ev1Fb0NFZX7hmr4C6HX6w0hISEnQ0NDF2VkZLQ+c+aMX0ZGBrFarXydOnXudrniOKB2bWDHDqB1a3BubggICBDc3Nxc4+LiJkZERBgopbRu3bpDO3To4J+cnNyrV69eS8uY1xIaGpqj0WiGtG/fvuJ9gTdvBkJDgdGj2YOA59nuftgwoHt3YOJE4OmngaFDgago5gpavx6YNQv47Tfg8GGWRWS1smP5D7vY2Fjs27cPDRs2pC1atHhoDT8AeHt7o0uXLoKTk1Ozq1evjg4NDf1dr9dn2nv9vn37Xm7btm2fgIAA+x+8+Tg6OqJdu3aoVatWQHJy8nCz2XxFr9efreg4Kir3C9XVUwqSJAkA9BzHddNoNM83bdrUd9SoUY5F3gBWrQJSUoB3373jT799+zZ+++23vMzMzJ8AjG3VqpXX2bNnZY1Gs9JisWyaM2fO9lLmexXAEk9Pz5zRo0e71KtXz/7FnjkD5OQwI28vigKkpQHJycB//ws0awYkJgIrV7IHhZcXTubk4LyTEx3w1lvEuyLrecDs37/fun//fjPP82FGo3EngLOiKIaXdr4kSY0FQYieNGmSo6enZ5XmTkxMxK+//mqklC6zWCwzKtuxTUXlXqIafjuQJMlJq9Vu8PLy0j///PM6jUbDDpjNwHvvAU8+CfTte+f83NxcLFiwAAAGajSaLziO69C2bVty/vz5w9OmTetWyhwvC4LwCaXUo3///g7d7TXiOTlAQABw4QJQwV7CJVAU5h5KSwPWrcPNhARkx8TQ5ufOEcyaxbSLtFr2JtGoUbUEj+8VWVlZuHLlCuLi4oxnz551BPAugI0ArhXO/pEkyYkQ8juldPjs2bNhr2+/LHJzc7Fjxw5DbGxsvNlsblMd2UYqKtWJavjtRJIkzsHB4W8vL68eEydOvGv8jx9ngd6ZM1kRVj67d+82Hzt2LNFsNnfSarV7HB0dOxmNxgSz2dxcFEVLsbGbabXaMyNHjnTcsGGDXLt2bdNbb72ls2thBw8Cn34KbNtWfTebT3JyMlasWIHpjz3GHgqhoWy+mBjAYACeew5o3Jg9fF5+mbm/HkJiY2Nx+PDh7ISEBA2ANWazeRIABQC0Wu2aBg0ajOrXr59jmQH9CkIpxQ8//JCXlZW12mw2T1aNv8rDxEPtt32YEEVRMZlMT6SkpJw+ePDg3WqmoCCgTh2WW1+IAQMGaAkh9QF4m83mQSaT6YjZbPYD0NXG8F38/PwsAQEB8PX1Ndy+fVtnsdiZGRofDyxfXtnbKhMvLy9YrVaY2rVjweLp04EtW9jbxZo1rL6AUmDJEhY/6NuXPQAWLWJryi1bCeN+0bx5c4wfP77W1KlTHT08PMYAsAqCEE4I+Uan040YM2ZMtRp9gElKvPTSSzqO4yYAaFatg6uoVBHV8FcAURSNJpPpubCwMG7p0qW56en5iToFQdLz54uc7+joqACI0Wq1R0wmUwgAT1EUDxQfV6vVDnZychIAwMPDQ6PRaG7++OOPueU2hjcYgO+/r5Z7swXHcdBqtfT27dslD/bqBej1wOTJ7A3gwAFg7lzgiSdYcPmjj5iy6XPPMdfQt98Ce/awWoMHhKOjIyZNmuRct25dxWq19mnfvv0r48eP12m12ns2n7e3t5Xn+Yn3ZAIVlUqiZvVUEL1enxEREbE0NzfXISkpqUNgYKCG43lgwACWVfPSS8zwAWjYsKHW2dlZvnnzphPP82N4ns/s3bv3icLjSZLE8Tw/LTMzsy7HcUJkZKSgKEp7q9Wad+DAgZDk5GRrmzZtOJv5/Xl5LCWzoJDrHnDs2DG5QYMGnFchN5ZNCGF+/1atgM6dgWnTgFGjWA2Blxd7M5k3jz0cd+4EPvsMeOaZctNJq5vY2FgcP36cTJ06Fe3atRN0Ovs8apXFz8/P4fjx48H5BX52ZxmpqNxL1B1/JRBF8QaldGZycnLY4sWLzbdu3WJVte+/D3zwwZ3zGjRogH79+vFDhgzR9ezZszUhZIaNsZTp06d3z83N7R8REbFLo9GcA5A0Y8YMEQAuXLgglBqHeeMNFnC9R2RmZsJgMPAOlTXOHMf0hl58EZg/H7hyBfjpJ/YmcOYMqym4jyiKgi1btsh6vZ663MOfW2E8PDzQrl07IgjCq/dlQhUVO1ANfyURRdFoNpufzMnJ2b506VKLwWC4m0e/c2eRcwMDA9GjRw9YrVYfSZI8Shnv0Icffjh4xowZbUVRNOZ/XFer1cb+/fffJZsDKwrLvw8KqvZ7KyA7OxuUUmJTR6iyaLXsZ9SnD/DOO9U3rh2EhoaC53mua9eu9zUdKTg42IEQ8u6nn3466n7Oq6JSGqrhrwKiKJqmT58+iuf5patXr86BTsdcPb/8AmQWfavneR4+Pj5GAAMqMP4ts9kcfOrUqduXLl0qevD0aTZPo0bVcSs28fX1hSAISrnKpRUlIQF4800WAA4Pr96xSyEvLw9Hjx6lw4cPJxWpzK0OvL290bdvXydZljdKkuRzXydXUbGBavhLQZKkBvbK8ZrN5t2EEJbpExjIMlyWlizU7d69u4tWq/153rx5afPmzStTw6eA/AbtL61du9awbNmy7CNHjkCWZeYyOX68IrdUKby8vMiFCxeqV5P55EmW+tmqFdCgQbUOXRrr169XGjVqpPj7+9+X+YrTrdud8o3y+zioqNxjVMNvA0mSGgJI1Gq1C+w4V9Bqtf8JDg52vfPhiBEse2X//iLnBgQEcO+//77jxIkTPSilAfauZ86cOdtkWfZISkoav2PHDiRcu8aqdJ96qgJ3VT6KomD9+vVYunQpXbt2LRITE9GhQwdy7ty56nONWK3A1atAhw4sKO3ufs8zfa5du4bExETu8ccff2BCc4QQ9OnTx6rVar9S9f1VHjSq4beNFgAURZmUr89eFg2tVqt/ET+4qyvLad+/nxU3FUKj0cDT0xM8z2vnzp07FnYiiqJRFMUtgiDMvvD550bl77+BapZR2LRpE71w4QL8/f3h6Ogor1q1Cjt37qQeHh7VV3y0dy/L7y+oMp47t8QDsrrZuHGj3L17d8XNze2ezlMe3bt3FzQaTXue51dLklRxqVQVlWpCNfw2IIREurm5KYqiOAJwLOtcURSvCIKwPSYmpuiBxx5jRn/r1hLXODg4YMSIEY4ODg7vVnRtVqv1swbHjl0/R6mluquuExMTFb1eTx977DEyatQo/oMPPsCzzz5Lxo8fX33/Tg4eZGmcBXzzDcv9v0dERkbCarVyvXv3fuD/1vPrCByaN28+UhCEmNIC/Soq9xpVssEGn3zyyTeU0jc4jsudPXu2aznnTqSU/uLs7AwPj6L/jzUGA/Q//ohD48Yh29u7yLHc3FzkF4A1FUXxst2LI0Sb5+g47rsZM6YHBgX59e/fXwsAKSkp8PHxqZLWzIULF7Bp0ya89dZbcK6q7k9pfPkl6x3QsOHdz+bPZ26f3r2rdSqz2YyvvvqKjhgxggQE2O1Zuy/s2LHDdPLkyYMWi+VpURTvf09NlRpNhSVoawJz5sx5W5KkaYqiWCVJcilNYVGSpHednJw+NRgM6NKlC2wVWaW/9BL6bNuGix98AFosHz6cZbR0BWC/4Qf664zGJ4yK0vWff/7Zdfz48Y6KotzxXQ8ZMgSurq7w9/dHRStSW7VqBU9PT3nnzp0YPXp09fvDb94E/vmHCdsVpn//e5KdtHHjRurt7U1btWr10PnUH3vsMYe0tLRe165dmwlg6oNej0rNQt3xl4MkSVQQhAye5/MAhJtMpo0A4h0cHGaYTKYnAeCdd94BIQQuLi62d9wffMA6YL38cpGPv/zyS2NeXt7/iaK41u4FERIEwBuU7pAkiQfgyHHcAkVRJgMQAUgAewAEBwdX+H6zsrLw/fffY9y4cfDz86vw9WWybx9zfX35ZcljMTGspWQ1+uEXLlwoP/7443yrVq2qbcyqkpOTg6ioKOj1emRkZGDZsmUZVqv1cVEUox702lRqDqrhL4d58+YdlGW5W3BwMFxdXREdHZ2VnZ2Npk2bamVZxsmTJ+/EAFq0aGEcO3ZsyZiAwcB8/j//DDRvfufj8+fPY9OmTZlWq7W3KIpnyl0MIVoARwD0BqXZhQ9JkuQkiqJBkqT/APjg/fffr7S7JiwsDFFRUXjyySdpQEBA9e2WX3oJGD++iIT1HaZOZfLW1ejuWblyJfX19aUDBgx44P59gMlFbNiwgTo5OcFsNtMhQ4ZwW7duhSzLFkVRgqvSNlJFpSKorp5yMJvNQzUazfSDBw9+MHDgQPnFF1+84/OnlKJ169aIjIykfn5+xMvLy7a2gZMTE1N77TVg1y4mZQAgICAAt2/frh0eHh45f/78jSaTaYYoikllLCcIQHZxow8AoigaAIAQMpxSWiUffd++fUEpxd69e9G8eXPYbD9ZUaxWID2dNZC3xX//W/U5ihEQEECOHTtGBwywu2bunrJ79265a9euXN++fcmBAwfopk2b4OfnRxMTEzWKopz84osvjhuNxqdEUYx/0GtVebR5KHZCDzOiKN6eMWPGNAAtQkNDc7du3WpKS0sDwHKzmzVrhueff57o9Xq0adOm9N1xhw7A2LFF3ByEEPTs2ROvvPJKrU6dOo3leT5GkqSy/CvOAJ4p4zgope96eXmVeDBUlF69ekGWZWXRokXUarVWdTgm0tavH+BRRiLL228Dh+yqa7OLTp06ISMjgzMYDNU2ZmXJy8vD7du3+aCgIEIIQe/evbnhw4cjMTGRPPbYY3TGjBlo2rRpIID3H/RaVR59VMNvJ6Ioxlqt1sB//vnnyyVLlhgSEhIqNgAhrKI3PR04VfSN3tvbGwMHDtS2bNmSABhcyvUCgNkoJ70UQERKSkqtlJSUiq2vGFqtFm+++SafmZlZPa6eI0eA8noMvPwy0LZttUwHsHvQ6XTylStXqm3MipKVlYV169Zh+fLlqF+/vly7ULMaJycnyLKMsLAwkpWVhSZNmmi1Wu3QB7ZYlRqDavgrgCiK12bNmjXbarV+eubMmZLCaeVRvz7zb//5p00j2LJlS52Dg8MUSZJspeM4A9gGSsu0YqIo5nIcd/WHH35AXl5ehZdYHI1GQ6v6EAHAhOvatSv7nMBApkGUlVX1+fLx8fHhY2Ji5CoPFBUF7NhRoUsiIyOxaNEiKIqitGnTBsOGDSuSKdWyZUvMmjULtWvXlmNiYpCammq1Wq2H1MpelXuNavgrAaU09NixY0JcXBxyc3OhKBWQshk0iPn4N24scaht27YwmUztAPyR3+y9MLMBRNszhaIowwBg9erVeXZ38rIBx3F3vqpERgYTrevfv/xzN21i8s3VRLt27RAXF1e1G8jOZr+3mTPtviQ0NBQHDhzA008/jTFjxnD9+vWDd7FaDoD9jH18fPhr167JHTt2FJycnIYD6Fel9aqolINq+CuBKIqHOI57Yf369TcWLFiAL7/80nDhwgX7LiaEZbccOAAUU9zkOA7jx49HnTp1HhMEIf3zzz9fLkkSn18gMArARTvXdxYAn56evv0///mP6eeff84KCwtDZGQkrWgWl8ViIQX9hc1mc+XeIo4fB4KD7WvO/uWXpQeAK0GrVq1gMpmIzS5i5ZGdzZrHODuzB9fRo8z4T5hQ7qUmkwm+vr5y3bp1maheGdSrVw9paWnE29sb+dIf1ZxHq6JSFLUDVyXp06fP6dDQ0K8AzLVarXtiYmIyB8kuAAAgAElEQVTGEkK0WVlZ8PT0LFHMRSlFRkYGHB0dQdzcWDHT4cNAjx53snwA1rgjODhYm5qayt+4caMDgAX68PBGALaC0rP2rk+v19NevXqtDwsLW5aZmXkiISHh0qVLl/rUrl0b9evXt/s+4+LilDNnztDo6Ghs376dHDx4EFqtFp6envZn+5w9ywq0mtnZevaZZ9i5FVhnaRBC8M8//8guLi6cj08FFJEpZQ+qkyeB1q1ZT+UOHQBPT9b5rHVrQJZZfwEb5OXlISoqijt69CgOHjyIli1blpppdfr0aQCQAwMDuWPHjpmysrIuhoSEhFX8blVU7EM1/FVAr9dDr9dTvV6fGB4efuL69ev04sWLTmaz2dXf37+IPzcpKQmLFy9Go0aN4O7uDnTsCHz3HRNas9HoJCcnh8bHx19TFCW3y5Ejo7QWizs+/jiyEmvM1ev10SEhIaEREREfX7x4EXq93u7rAwMDSVxcHOLj48mkSZPg7e0t79+/n0RERBCNRoOGhaUXbEEp2yWPGMEKtOyhY0dW8FZVF1M+ycnJXFJSktyuXTv7BkxKYvpBL77IHswZGcDu3azHcL167DNJAt59F3j9dZtvMt7e3nB3d4efnx9SUlJkd3d3rl4ponoHDx5Ebm4uV7duXTRr1szhxIkTPcPDw//W6/XXq3LfKiqlobp6qonZs2fv/OijjyaazeZeR44cyYqKilKys7MhSRJ++eUX/PTTTwAAV9dC0j8//cRy+20EMzt37sz7+/v7APghIiRk0q8vvljLht+/orTVaDSmgvRGq9VarhtCEAQ4OTkRFxcX6uXlhaCgIP69994jer2eHj9+vPzgxvXrrM9umzalnpKRkYGlS5fKixcvlrOysthu/3//YyqelcBsNheJu3Tu3BlXr17ly43FUApcvgz4+LAmMQUNW+rVA4pvkD77jL0FnD7N1loMjuPQoUMHdO3aFbIslxkH6tOnD3Q6HVauXAkPDw+MGTOGFwRhryripnKvUHf81Yxer88LDQ1dn5iYOCgqKsqdUhqfm5sbryhKfW9vb7Ro0QK1atViJzs4AP7+wKef3m3bmA8hBCkpKbwSEUE65+WRS4MGBRmNxqdDQ0OXVGTHXmxtKQcOHPCPj49vZbFYNCtWrMC+ffsQHByMAj9+YfLy8rBs2TI5NTWVjB07lhTuU+vl5UWOHDmCGzduoMzq3j17gJCQMg3/ggULkJWVxTVp0oTu3LmTcBxHPHbsgBAQAFJW3r8NNm/eTP/880+yb98+REVF4eDBg8qVK1cUo9HI+fv7F33wFufKFeDVV5kPv06du5/PmwfcuFH0HghhbzC7dgGzZpXY+V+7dg3bt2/HqVOnlOzsbH7w4MGlaie5ubnBZDIhKSkJgYGB8PX1RXJysjUtLS07JCSkwm95Kirloe747wGiKF4xm83tFEXxEUWx6YwZMzoB6HXr1i0sXboUqamFxBgHDmQyDitXlhinS5cupHV0NK4YDGjXrp2D2WwOBFAl8TSz2fza9evX54aFhW0ihNwCgD///NNkNhfNTo2Li8N3331H3d3d8frrr5PibgqdTofnn3+eXLx4kZRoC1mY6OiiRrQYiqKAUop3330Xo0eP5p966ily6NAh+nWdOvhp716lIsVjYWFhuHDhApkyZQqmT5+OKVOmYNy4cVzbtm05FxcX+vvvv9MsW6mit26xxu/+/sylU9x1M2EC6xFsi4kT2T3+8QdTHVUUREZGYvXq1cjMzKRXr17lXn755XIrqVNTU2G1WukPP/yA8PBwuUePHjqe59/P12NSUalW1B3/PUKv1yt6vT6v0N8TIiIiPuN5Puv48eO9FEUhvr6+HCcIQIsWwLffsq5ahQp8tIKAWm5u2OrgoPg1b05v375tUhRl1N69e//Q6/WmSq5L7tOnz4FevXr9ERISsiAiIuI/ubm5zc+fP9+0c+fOGkIIduzYgT179qB///4YNGgQV1oQV6fTIT4+XjabzVzTpk1LnmC1AmFhwPDhgE5nc4wTJ04gKSmJ6vV6AgB16tRBjx49SKdOndB44kTye0ICvWmxkIsXL+Kvv/5SLl68SLVaLTl+/Dg8PT3h5OQEADh58iTCwsIwYcIE1KtXDzzPw9HREa6urvDz8yNdunQhqampdOfOnWjevHmRtxfk5rKK4R49bGceXb0KNGlSelYSIYCDA+jJk/gjPV2JPnmSPDt+PPr37086duyIwkVbpdG8eXP06tWLuLq6Yv/+/WTw4MHk2LFjxGKx3AoJCbn3PTZVahSqSNsDQJKkhoIgbHZ2dg584403NIIgANu3s93mV1/dNTAREcAnn7CuVWAa/gsXLlQopXVEUay2foWSJBGtVps0ZMiQegcPHpTz8vK45557jtiT/fP3338rt27d4ibYSnH85x/grbfYfRQjPj4eaWlpCAsLU4KDg0mfPn1KWFV64wau5OVhb2iowvM87dChAx8bGytfv36dc3Z2prdu3eJcXFzkfv368du2bcPo0aPRsmXLUtdKKUVERIRy8OBBMmbMGNLk9m3WCOaXX0q/QbOZPRCOHCkz2JyTk4Ply5fLgYcPcyFRUYS/eJG58irIkiVLqJOTE8aNG0fi4uKwYcOGLEVRvlMUJRTAWVEUb1V4UBWVYqiG/wEhSZJOq9Vu0Wq13XQ6HRfcqZNT223b4NCu3d1eupGRTONm3DgAzC2yZs0aY0JCwqIZM2ZUm6aLJEmEEGKklGpdXFzwxhtvwMFOo3X8+HEcOnRIfv3110u6JD7+GPDyYv7vYixcuFCRZZlr166d0rdvX87mfJSypvUvvFDCiFJKkZ2djYiICPnUqVP80KFDaVBQkF0Vr/sjIui5sDBl8syZPOLigGpo0rJ//35ERETgnbfegsvJk2zMU6eYK68CxMbGYt26dXB2dqaPPfYYURQF169fV86fP28wmUwWi8XSF8AFURSNVV60So1F9fE/IERRzDObzQNycnL6pqen/7w7LCx6k8ViwLlzQGIiyxH/9FNg2LA713Ach44dOzryPN+1mpfzIqW0oM+w1V6jD7C6g+zsbC4qKgoZxZuma7VMn8gGTZo04Ro0aCAPHjzYttEH2JtPSgpgo/iKEAJXV1c88cQT/KxZs2Cv0QeANqmppNfmzTw0mvKNfng4qyYuh969e6NOnTryho0bFaV3b5b/P2FChTOTmjdvjpkzZ6JRo0b0r7/+Qr169TBkyBDuvffecx45cqQbx3HHOI7LnDdv3lZJktQKX5VKoe74HxIkSdIAML/N83Bzdwc6dWINXA4cKHKe2WzG119/nWcwGPRgEg4uoijerMK8zgByAOCtt96CyWRCafnmtrBardizZ48cHx9PUlNTOUEQqKurq/LqU0/xwvTpwOLFNoucLl68iA0bNmCmPTIIFgtgI+uowly9CkRH45CbG04cPSpPefPN8gOnJ0+yCt7SgruFMJvN+O677xSdTodnn32W83B2Bs6cARYuBFavvpseaic//vijXLduXb5t27Zo3rw5CgLdeXl5OHLkCCIj7yT8NAFgBmABoABIF0VR/Y+tUiqqHv9DgiiKls8///zrH2V58jt79zo6uroCv/5a4jytVotatWpZDQZDF7CmLBRVeHPTaDQfNWvWzPDMM884VeZ6QRAwePBgHmCuqNTUVLJu3Tru+P/+h64+PjaNfkGjl169eskoL0uJUibhsHdv1St509OBy5dxvW5dua6Pj31WuEMH+6QmwH43b7/9Nrdy5UpERETIo0aN4lG/PpCTw6p9tdoK+f27d+/Oh4aGytHR0by7uzu9desW4TgObm5u9NVXXyVBQUH49ddfrTk5ORcsFouDRqMxUUpBKcUXX3xx2Wg0viqK4oF80bd6ADIK+jao1GxUV89DxIwZM6ZaADG0WzcDpk69a/ivXCmi5pmWluYEoAUAEEKq1LWJ5/lmTZs2rZTRLw7HcfD29kaPHj2Idtkyavb3t3leTEyMrNfrERISUr7xJYRJW1TF6G/bxoLkHTsCU6ZAEATY/abbujVQAQluQRDQrVs3REdH8/Hx8UCDBqzd5PHjLHsr1f6+6u3bt8fUqVP5F154Aa6urvjwww8xdepUGAwGevPmTbi7u+Ott94SZsyY4TBt2jTMmDHDYebMmQ7vvfeew4ABAwIA7Jck6XGtVrsNQBKAPEmS3O1egMoji2r4HzIURVl00mKR0//3P5YGCQCTJjHjc/EiMHcufH19ra4Gw6uc1Yo5c+Z0qsp8lFKSa8MPHR8fj5wcmz3my6VD+/aon5VFViUkyLYqVi0Wy500TLtwdmaVshVVGrVambhat24sxz4frVZL7FYtPXaMGe8KEBgYiPr166NIH4A+fYD//AdwcSnhvisPHx8fjBs3jjg6OsLFxQWEEPDF3EaFf55OTk7o1KkTxo4di1q1av1hNpuHvP/++9DpdDkAqq+pscq/FtXwP2SIophLKd316+HDzM0AsOpQf3+W4x8cjL59+zr1jojQPPvHHx+CEAGELAIhHAipBUIqlENotVq9wsLCsGPHDmuBDzklJQW//PILbty4Ual74BIT4fl//4dcV1cuolAqp6IoWLduHc3OzubL1fgpMiDH6gAqqgy6YgWwYAErIGvR4s7HmZmZ9l2flAScOFEpzaDatWvj6tWryp03C44Dnn0WuHABGDmSSVlUEkppCcNvixYtWuCdd95xnj59OpydnQuEA6shWKLyb0f18T+EyLKsL5EhAzDNmHr14Acg6auv+I0REf3H/vjjIr+rVw+DUgWEPAcW6PsIhHwE4GcAaQA4UGqz4EuW5XAA/Y4cOSIcOXIEbdq0Uc6dO8cBQDN71TSLc/IkBFlG9969SWhoKI2OjpZlWYbJZOK0Wi2ZPHkyE6qrCFOnsiCrPURHs/z7l16ysbST9MqVK2TKlCnlj3P9OnDwYKUawA8dOhTffvst1q5dqwwePJjzKJCe6NCB6QHl5AD/93/ADz8AjuU1VSsKpZTYq4zKcRy0Wi3S0tJgMpkogAq2jlN5FFF3/A8n5W61g4KCiLu7e8/Vr7xyXfr44zgAAKVLAEzPPyUVQC6AYABbAACEjAEh7fO/L4i6fgagPYAPAKDA6APAokWLKhcIPHuW+cbB3CpdunQR+vTpIwwbNoybPHkyqbDRB5jbpmNH5ropj/PnmVuM54tk0hiNRmzfvp08+eSTxJ5qWnTpAkybVvG1AnB0dMSECRO42NhY7rvvvisqhufqyoz9X39VqLlLAYqi4MSJEygus1EWUVFRJgCL1OCuCqAa/oeVcYSQMqUktVotJk2a5BwYGOgEoOedAwW+BUp/AqXZoDQSwJD8oyYAFhDiCuAaCCHixx93FT/+uK4oigtcsrO9BEGIBmAWBOHPtLQ0p927d1fIwMBkAtavBwYOxM2bN9GgQQO5S5cuaN++PVq1alWqUFm5CALbyZdlsH/6CVi7lgnePVN6T3q7m69Pm8bSOStJ7dq173Qvu17ctePhwVpwjh3L5J+N9tdj9enThxw5cgSxsbF2nZ+Tk4PTp09Tq9X6jd2TqDzSqIb/4eSmRqOxS4unXbt2Wo1GM12SpFqlnkSpkv/nJlB6HpRmAWiY/5Dg87/w3sKFYTNnzRomfvxx0+mzZ/8JAFGRkZg3bx4SExNhl2DasWPA4MGAVov09HR4enpWn8iYorDahuJS0kYjC/z27FmmW0YQBNStW5eeOnXKvpSep58G/CrfDEun02H27NnQ6/VYvXo1li1bJq9du1a+evUqc1u1anW3D/GKFSx1tRyuX7+OyMhIpWHDhopNfSQbbN++3UAI+aEq9R4qjxaqj//h5KZOp7NrW9q4cWPUq1ePT0hI6A9gs90zUGrJ/7Nwikkw2FtBM45SNwDc219/veNojx6mXwjp2/7o0VoOb76Jvv36ld59KyODNSwBIMsy1Wg01dc43MGBBWnNZqBwVtDs2Uwy+YUXyrw8PT0dSUlJ5KkCSYyysFiARo2guLlVencUHx+P0NBQmpmZqfA8z1mtVj4mJgYxMTEQe/YE1q1jkhTLlrGU1SeeYG8stWw/w/ft24cDBw6gV69e6N27N1e8y5stLl++jLi4uGyLxTK7kreh8giiVu4+ZEiS5KfVauMHDhyIoKAgu645evQo3bt37xGTydQHgA+Aq9VWuUmIIwDy9dtvv9rl6NGv9wwciN7Hj8vNfHy4hosXE7J5M9C3L+CWnyX49tvA+PFAly7YtGkT8vLy5HHjxlWvtPDNmyzQfewYa+lYqxbzmdthCA8dOkTDwsKg1+tJ9+7dSxxftmyZnJWVBdcbN7gBGzeSX55/Hk5OTkrdunVJ586dSUBAgF3N5zMyMrBo0SJ06dKF+vr6EqPRiPDwcFq7dm34+vqSQYMGFb1AloE33wQef5w9OPNlnDds2KA4ODhwycnJSlpaGvfss88W9OUtF0opvv7667zs7Oxn58yZU7JbjEqNRd3xP3y8ZLFYEB0dbbfoWFBQEImJiQm8evXqdYvF4uno6Pg3gMHVshpKjQCQKUnfhg4eHA9F2RzVrh1/1GJBpz176ID9+wn69AH272cZKrt2sYyV+vUxdMcOhN+4wac1bIg6R48yg5aRwbSIRowA9u0DGjYEfH2Ba9dYyqrVyr68vZlrp7hUQ14ee9CcPAmEhrIm7hVoTNOtWzfi4eGB9evXo379+kWM6NGjR3Hr1i0+MDAQXcePh4skYaaDAxITE7nz588rO3bswJYtW0j37t3LbV8ZEREBV1dX2r9/f1KQetmpU6e7v8/ly1luf/Pm7O88z35+Gzcykb7Vq/Hdb78p6enpnKenJ/Xw8KBjxoxBETnpckhKSoLJZEpXjb5KcVQ9/oeMiIiInQD427dvk44dO8LRjlQ/QggCAwM1GRkZQmpqKjWbzS0iIiJW6PV6G11HKkd+pe3FiIiIm5Tnh1k1GiQkJBD9vHksx76gF+2BA0xioUcPCOfOIV1RaFxmptL81i0Ovr4sLz48nPniZ81ihWmyzIqbXF1ZodbHHwODBjG9oogI1v1q6lSWjz9jBntwnD0LzJ/PHhYVpE6dOoiNjZWNRiPXPN/wpqSkYP369Wjbti0uXLgAfVYWnBQFXIMGcHNzQ/PmzUn37t1JgwYNsGPHDuTk5MDb27vU34+Pjw8iIiLI7du3FZsdyqKj79ZmFKZVKyAuDlZXVxw5eZKMnzwZ/fv3J4GBgVxFA+NHjx6VExISlvfp02dXhS5UeeRRXT0PEZIk9Qawr/Bnc+bMgT2+XAC4ceMGfvnll2Sr1bpBluUvRVG8ei/WOX/+/L9MJtNwZ2dn+f333y/pxqGUpVO2aoW8vDx8/fXXGDVqFFq2bGmXm+QOisK+rFYgLQ1wd2cPgZUrWS78mjXsWCUE3GJiYrBhwwZotVrFw8ODGI1G4uPjQ0eOHEmWLFkid7p6lXQZPZqDDXfbxYsXER4erqSnp5MXXniBbNmyheanraJt27YAmG/9jz/+wMsvvwwvL68KrS02NhY3582DT0QEGoeHg69IsVshvvnmm+yMjIxBoigerNQAKo8salbPw8U1nufXFP7g0KFD5Tc0z+f27dsghKTMmjXrjXtl9AHAZDKNB4Dc3FzbvvvYWJYKSSl0Oh0GDBiAzZs3019++aVCrRTBcSyN09GRySbodEDTpkyu+rffWJC3Uyebss3l0aJFC7z//vt48sknubp169KUlBQIgqAAgL+/Px/dqpViy+gDQMuWLTF69GjOYrGQFStWQBAEODs7K1u2bMG+fey5fevWLbi7uys2jf7Fi0CvXiU+zszMxDfffCNv3LgRKcOGyfE9esB66RLryVBBcnJykJ2dzYMJ+amoFEHd8T9kSJLkCSCFEPIbpXSmRqM5OmrUKE+tVovy0vfWrl2bExMT86Yoij/fh3WOB/ArALzxxhuoU7yvLqUsZVGWAXd3WK1WLFq0SPbw8CDPPfccZ4/kQKls2sR2+k8/zd4E6tRhQmwhIUwLpxJcvXoViqKgSZMm+HvTJnSfMgWuSUl2K3MCrMH6mjVr4OPjQ5OTk0nDhg3lsWPHlrxRRWFKoZ6ehW5pEz19+jTp2LGjPHToUP5O1tTSpcDPPzPffwXksqOjo7F169YD06ZNq3jZscojj7rjf8gQRTGV47ivKKXPOTg47JVlufa6deuwevXqkkVAxaDsKV6BaqsqsQbAYgD4/vvvsWfPHkiShIQCJUtCgB9/ZO4YsBz61157jU9OTsaxY8eqttto1uyu9k6dOuwhs3cvCxzbSWxsLC5fvoysrCwoigI/Pz/4+fnBaDTCzHEInz1brojRB4BGjRrh5ZdfhpOTk2IymZCZmcnLxWsOABafKCSMt3PnTpw+fZo8/fTTGD58OF8kVfbVV1n8IyICOH3a7rVcu3bNYjKZdlfoBlRqDGpw9yEkJCRkV0RExAlBEFpZrdZmAMBx3H9jY2Pb+/r6akqTG8jOznZISEhI792791ZbxyVJ0kZERHB6vd5u91Fp6PV66PX6bfnB6JcLDH5SUpKhc+fOzOnevTvQtSvrouXsDJ7nodVqyf79+9GtWzdCCEFSUhIcHBzsEh27Q926RXe/hLBgsKsrMGUKcwk1aWLz0vj4eKxcuVI+c+YMd/HiRRoZGUkiIiIQFRWF/fv348CBAzCcPw8/Hx/auFOnCm+MHBwcsHfvXvj4+NDMzEzl5s2bNCAgoGjO/Z9/sgrn/IeXo6Mjzp07R69evYquXbuSEjGdRo2YrPPUqcDzz9vscVCcPXv25Obk5Hyp1+uvlHuySo1DdfU8xOT3wh1PKV2l0Wh+slgsYQDWcByHWbNmlQj6pqSkYNmyZTkWi6WLKIoXJEnqptFotnIct+6jjz56XZKkVQAmiKJYfUVVbJ0OHMf9oSjKCACYNWvWXUNOKfNn//or4O8PRVGwYMECJSgoiMvMzJTPnz/P63Q6OmbMGOLj42PfhJ99xtI6P/us5LHLl5l2/82b7EGQ74IyGo1Yu3atcuPGDa5nz55Kz549uYKdtcViQV5eHsxmMzw9PVkRVU4OyKuvVvhn8fvvv9PMzEz60ksvcUajEYsWLVKCmaJqmQ8RRVHw+eef47XXXivpNivg+nXWHH70aPZALQVZlvH5559bFEXxEEWxctraKo80quH/FyBJkk4Uxbz87xcDmFy3bt2cQYMGuTQptrM9deqUsm3bNhPHcX+bzeaWhJBWLi4uRqPR+IvFYtkP5qJpK4riuXuwzhYajea3+vXrB0ycOFF3x/gXtE7MzgZq1cI///yDyMhIWRAEOmbMGCEyMhInT57E4MGDlU727LIL/s2W5Yr57jvm73/xRQDAjh07EBsbS1944QXi6upa1Vu1SVxcHNatW4fJkyejQI3z+vXrWLlyJWbOnMke1IrC5JnXrmWB63xOnTqF0NBQOnXq1JI7/sI0asRqIL77rtRTsrKy8P3332fOmDFD1d5XsYnq4/8XUGD0879/DYCQnJw8+ffff7+5fPny3MISzh06dODeeOMNpzZt2owEEEApJZMnT3by9/d/wcHB4TkA4Djuy3u0zhiLxdI9OTk5csmSJbnp6ensgEbDMn0GDwYoRfv27TFlyhT+1VdfFVxdXTFkyBCMGDECu3bt4uxqkBIdXb6/+803gRdfxK1ffkHks88qJ06cQIcOHWS7jP6UKSxoXAHy8vKwceNGpX///vSOBDOABg0agOM4ZGXll1QoCmvCXkzy4vbt23B3d1dKNfqXL7MspshIJlFRBgaDATzPV1sNh8qjh2r4/4WIoiiLorjGbDY3unHjxufLli0zpBZq6Ve7dm0MHz4cEyZMwKxZs6DT6TBixAhHq9U6iOf5bEEQ7lletyiKFpPJNCg9PX3Wjz/+aDhz5gw70Lw5sHs3263bUPts06YNdDqd/Pfff9uIhhYjNpZJL9vBhpgYRe7eHW++/jr6tGxpX6V6v37MTWQnWVlZWLRokdKkSRMEBweT4scURblbcWsysQdgMRo2bIikpCS+1MD3F18Af/zB4iXe3mWux2AwgBCSbvcNqNQ4VMP/L0YURcusWbM+NxqNU3766SdD8Y5Z/v7+d3ztTk5OmDx5stC8eXMXWZbtdKZXel101qxZX1sslp5bt269vmnTJqPZbGZB1wULmJ/aBq1bt+bT7NlpjxjB5Izt4LZOxwW98grnGh3NdvLlYTKx8e0sCktOTsbixYtpy5Yt6VNPPVVCOC0lJQUcxyGvoHvYb7+x6uNiNGvWDKNGjcK2bdtIduGGM6GhrFL5xx+ZwbdDJjp/roq9sqjUKFTD/wgwe/bsn81m83M///yz4erV0uu2PD090bNnT8Lz/MRPPvmkerR8ykAUxZMWiyUgOjp6z9atW5na6OuvMyE3G2mOrq6uyM3NLT/w/OefLLe9HBRFgdVqZf1ou3dnaphZWazxuo1ewACA7dvLVfksIDw8HMuXL0dwcLDyxBNP8LbcNE2bNkWDBg2UVatWUbPZDLzyCvBlSU+b2WxGWFiY7O7uXrQR/PnzzPAXZC7Z6CpWGIvFgrNnz4JSmmzXTajUSFTD/4gwZ86czRaL5Yk1a9bkRkZGKqX5yn19ffH444/rHB0dP7of6xJFMdtisYy/cOGCnJKf1gmNhgmtxcUVObdevXrIzMzkTKZyWhG0b8/E2cqB4zgIgoCkpCT2Ac8zg1+/PqsKttX8ZNQoloFUBlarFUuXLpWPHj2KcePGoW/fvjaNfgETJkzgzGYzjYyMpFi5khVv5fPTTz9Z586di/nz54MQQiZPngxXV1cm4hYWBrzxBmsWv327XW8st2/fRnR0NIxGo32vRCo1EtXwP0LMmTNnr8Vi6bJ///7dX331leHo0aPUVgFR/fr1YbFYus+bN2+FJEmt7/W6RFHMpJR+snHjxjyDwcB2r3/8wQqxCu1uGzVqhFq1anOqKdkAACAASURBVMmhoaFl+/kbNWLSDeUQGRkJq9WKa9eu3Z3EzY3tuvPygKAgpiRamA0bWCpoKeTl5WHVqlWwWq3cG2+8AT87GrVwHAdZluHj40Nw/fqdbKTo6GikpKQIU6dOxezZszFlyhROq9Gwn0nLlkWbwAwYAEyfXsoMd/G+6/8vP1aiUmNRDf8jhiiK0R999NFgo9HYe8+ePSd27dpVIpLq5eWFN998U9utW7cJgiAckSSpcipgFUCW5YXp6ekrly5dmpeRkcF23bGxzH1RyPg/88wz/IkTJ/jCweoS/PADc9eUwdmzZxEREYGnnnoKPXr0KLkd1+mAqCiW8rlu3d1G7leu2Aw+A8C5c+fw9ddfg+d5ecyYMcSpcDOYcqD5ukWYNYu1XQSwe/duuXfv3tTFxeVuTcZ//sNE6Hr1uqs8mp0N7NzJJKztoF27dkYAb9m9OJUah2r4H1FEUTxuNptHnTx5UskpvqsF86f37dtXCAoKchAEYep9WI8yffr0N3JycmYvWbLEcOvWLbbjX7y4SD6+t7c3GjRoQI8cOVJ6dfE779gu3sonLy8PmzdvxsiRI9GmTZvSF1W7NnvoHDvGgrqUstaOhTT6rVYr9u3bh127duGvv/7CyJEj8fzzz/OFUzbtwdnZmWZv2wZMmgQAOHPmDHJycvjU1FR2n2Yzcz1NmMAKtApz8yZwxH6ttezsbDOAG+WeqFJjUQ3/I4woigkA/vvbb7/llnYOIQRWq/W+BQJnzpz5lclkmr5jx44cEMJcNkuXFilIatq0Kbmj+WOL06eBg6VnpKampkKr1aJ1azu8WISwXbanJ9C/P9C6NUuZzOf48eMICwtDXFyc8swzz9g3pg0URYGxbds77pqtW7dSNzc3WK1W9tT75hvg228BH5+SGv3NmwNz59o9V3Z2NoVq+FXKQO3A9YhjsVhmpaSkvJGZmQlbGj+JiYkGAHElr7x3UEqXJCYmzo2OjkZAQAAwbFiR9Ml27drhwIEDZNeuXcqAAQNK9pa9eRPIzCx1/LS0NDg6Oiqwd2Mjy6yBjLs7cPQoc61kZ0PZsAFhikKfeOIJ2FVRXAYWiwUJkZGIMBphcXICx3FkyJAhaOLqyuHqVZbpZKtXQXIy68V7+LDdSqGyLBMAdlTCqdRU1B3/I44oioogCOGXL18u8nlWVhY2bNhgSE5OTgOw/T6vychx3NSNGzdmh4WFWWn9+mzHPX48cPkyateujVdeeYWcOHGCfPHFF4iKiirq9hk4kEky28BoNGLHjh20ffv29mmRyDIwdCiwcCHw+++sHWStWqyxe7NmcHBwoJ4zZ7KKWUVhQeFKMHToUN5/zx6MbtYMTz75JEwmE27evKlgxw6WnqrVlqjmBcBy99evr5A8tKOjIwVgW8lPRQWq4a8RGI3GzTExMXfcPenp6Vi0aJExJibme4vFEiiKouF+r2n69OnLrVZr80OHDl3Ytm2biQIsx79RIwCs5uDtt98mJpMJoaGh3HfffacsWrRIOXToELWsXQt8/73NcY8cOQIPDw8aEhJSttyn1Qp8/TXw8svAvHnA5s3srePbb9kDoEEDcE8+CQ8PD1wYOVJBYCBroNK3L7v+7FmgrAB0MVq2bInA3bvRaNw4+Pv7Y4CvL64sW8b9ExhI8e67pV+4fj0LhFcAJycnAkDV6VEpFdXw1wy2X7p0CXFxcYiMjKRLliwxUErfnTFjxrQHqd4oimKy2WzueebMmesHDx6U0b07U6B87TWAUiiKAo7jMHbsWPTu3Zvr0KEDd+jQIeXHkycR6+9vM/ibnp4OT0/PsmWnT59mGT2HDgFz5rAuXgVulgkTivTxpZQix8uLSTgEBDCXEAD89ReTSjaZWEFWeWKH6ekscAyW3hncogX+n73zDoviWv/498xso3dBmmIBQVAURFQQ7L3FxMSeWGI0yb2aexNT3TuJN81fYoy5iUaT2GI0xthjV4odAbGA2ChSBaTDtpnz++MAAQFFkxgT5/M8PMruzO7M7PKeM+d93+/XwWikWVlZzR+rTsdkLu7TtObGjRvWAALuayeZxwo58D8GaLXaPEmSpm/ZsiUrNjb2J4PBEPrmm29+9WcfFwBotdoyg8EQefDgQf7DDz+ktHVrYOxYgBAcPHjQpFKpqLW1NQIDA9GnTx/Mnz+f9wsLw/ErV5qMtOXl5bCxseEBJk/cAJOJGb3/4x8sEG/a1Fi3v1cvYO3aus7esWPHcikpKXxdE1htLuKtt1gpakUFGzQIYXchu2qsEO7sDOY4IDiYDRxLlmBNaqp0qkMHEh4e3nxUV6uBVavuK/DXa9z7zZ4LMn9fZFlmmUcCQRC68Ty/39fX12rMmDEabtcubNu8GebTp2PYsGF125lMJsRMmSLZV1aSDj/8QCwsLBr4EqxcuVLs0qUL7+TkhI0bN8LGxkbq2bMnDdVoeCxcyLyAfXwAd/fmD6Z7d2D1avYvgAMHDtD4+HgyefJkeNYsRTVJSgpgZsY8ALp0YX0KhYWsZ8DCgg0OBQXAjRtItrHBvn37RJ1Ox/v7+4s9evTgy8vLaVlZGXx8fIiVpSXrTt6xg1X6tJBvv/1Wn5ubm/zWW291b/FOMo8dcuCXeWQQBMFcpVJt9/f3Dx/Vpo0mNycH35w9i9dffx21piknT55EdHQ03NzcpPT0dM7BwUGaOXMmp1arAQCff/65qV+/fgqFQoE9e/ZIzjY2nMP27eg7ZgwszcxYAvleidKMDDazrxdwY2NjERsbiylTpsDT0xOUUpw/fx5ZWVmSu7s716VLl4bGOMXFrErof/9jlpC7drE7jrNnG7zVzZs3ceTIESknJ4cz1GscmzZtGrx4njVttTCxSynFu6yx7YJWq+3Sop1kHkvkpR6ZRwatVltlMBgmnzt3josuLkaiKIp9jh5FUUpK3TZKpRIOGRlwuXQJCxYsQHV1Nb1y5Urd80ajkVhYWECv10OqqOCCP/0ULjk5+DI9HYn+/pQCOH/+PAzNdOcCYJU9L78MAHWCaeHh4ejWrRt27txJ8/PzsXfvXnHfvn20pKSE27dvn3To0KGG60p2dsDKlUBAALNMvHKF/f8OPDw8MH36dG7hwoUYOHAgnnzySQQGBuLc++9Tk1J5z6B/69YtNKFouv+uO8k89sieuzKPFJGRkVVRUVEdsrKyAnmeR6izM3Hp148toYAJuQVaWaGjgwNRde2KyspK7ujRo8jOzhYPHDhADAYDFxoUhHbr16PT+vXYPG4cLgUEwNbFRXRycuIkScLmzZtx7tw56unp2bQbV+vWkE6cwNrbt6V9+/aRXr16geM4tG/fHomJieKpU6dIYWEhZs+ezYWEhCAjIwNlBQW0q78/h5s3gQkTgGnTWOLXzQ24dQtISAB+/rnZQE4IgaenJ1q1aoVOnTqh7IsvEFVYSCsUCpibmxMzMzOkpaXh8OHD4vbt27lTp05JCQkJ9Pjx4+Ts2bPQ6/Xw8vICx3FSdnb2ufDw8Idaoivz10Je6pF55BAEwV6hUGQvWLBAY25uztywNm1i5Z5NcOPGDSQmJuL69euwT01FW0miisxMEt+tGyqsrGBvby9WV1eTSZMmcRqNBitXroS3tzeSk5PxzDPPwNvbu26ZpqqqChcvXkTl0qX0pqcnTTM358zMzPDss8/WF0BjVFYCqalYcviwNDcmhrOcO5eZrNy4weQoavnmG1aPX1jIKoNCQ+9+ASiFTq/HyZMnkZKSIpaUlPCUUigUCurq6krS0tJAKUVISAjc3d2pnZ0d2bp1Ky0vLyetW7eWbt26lf3GG2/cJRkh87gjz/hlHjkiIyOrT506Nc7d3d3V3t6eefaePs0qbmqVPX/5BejTBwBgZ2cHv7ZtcWXXLmnk1q2E9u9PfJYuReSoUYiJiYGfnx+XnZ1Nhg4dikOHDok6nY6OHTuWu3r1Ko2PjychISFQ1lTrfP/99+KVK1fQJy2N6+PuTvpptUhNTTUdPXqUs7S0hKurK/PLtbdngfyzz3DGxUVymTOHc+zThx1ffR0fSpkIXP/+rDrH2bnOAL5ZZsyAwsoKXgMHokePHlyfPn3QoUMHDBkyhAQGBiI8PBx5eXlSSkoKCQsLI66urggNDSUajQaJiYlEkiSbqKioTyIjI++yniXzOCNLNsg8qqhrgzFsbIB//5vVzRuNrImqul7PWXw8DG++CUcLC87q4kU4OzrWPdWqVSs4OjpCqVQiJiYGKSkp3Lx584iNjQ2cnZ2Jg4ODZG5uzgFARUUFsrKy+Pnz58Ny3DiWjAUwY8YMxZp//UvCu++S3BUrSOuyMlbG6eMDfPMN1F9+ifLqZnrgcnKY+UunTmy2X1LCuoXvVqL56aesk7cGjuPg5ubW4Pfg4GAuLS3tV0tHAD169ICzszPWMJMaBwB/Wo+GzKONnNyVeSShlFqo6gU/AGyt/NYtFhQ1GrbU8uqrQEUFbo0cifNBQSi5I2k7d+5cdO/eHTY2NqbLly+LTzzxBDEzM0NCQgJKSkoklShSlJRAFEVE/d//Se4WFqKlJAF79rByzqFDgcREDJw6lSvz9ydr1qxBrJ8fNdTU/5eVlaGsrKyuqqgR27YBTk6/ru2PGwdcutT8iZ88CeTmsvLPZsjMzMT27duliIgIWj/wE0Lq+wOkN/8mMo87cuCXeVS5fvnyZVODR4YNA0aPBgTh1x+9HvD0hPvLL6Nf69b4aflyZKSnM81+SoH4eKjWrsULL7ygmJedzZvHx2PZ+++j44ABAICR1dU8/vtfHDx4UGx95AiZHB7Og1LmbWtry2rzfX3h1rUrrMaNo0ajEfHx8dJnn32G+Ph4fP3115Kfnx/19/dvfAaUAuvWMeOXWg4fZjX+zZGWxrqXm0GSJHz33XeoqqriQkNDm8wUOzk5AQAEQbi3TZnMY4kc+GUeSXQ63bSTJ09WpqenN3yCUrbcs3Il8NxzbAb9738DAHrn52OAgwO+37gRJadPs+1sbOoMTK4EBeHnlBQaPmQItbp2DXPmzOHM5swBlixBZmYmit5+myiCglgH7tixQHo6sH8/EBeH4y++KB06dAgTJ07E/Pnz+f79++OXX35BZWUld+nSJW7lypVScXFxQ7/c8+fZ8dVbpoEksSayJpzRQCkwaRJz22oGjuOgVqspwJrZmmLs2LG1/z0tCIK8nCvTCDm5K/NQEQShb0xMzJSoqKhLkZGRzYrDRUZGVhw9ejQ5IyNjVM+ePX9d8yGEJUpv3GB6OwsWAE8/zZ4LD0er3r2Rl5eHSx4eYtfu3TnY29dV2Gw+elRS2thwY8aOJcTcvMH7WVhYcIcOHcKpw4fRaeVK5I8aBerkBN7ZGfHr14ve333H9127lji7ugKEwNbWFsePH8dTTz2F7t27Iy8vjx48eJDExMQgPj6eOjk5Efu4OFbPX3+Gz3FsVt+5cwMpagBMIO7oUSA8vNnrV1ZWhoSEBBiNRhIQEACLJpaENBoNYmNja96OmxEVFbUiMjKy6VFC5rFEng3IPFQ0Gs1HkiT1JIQEAmhaW7kGSunuiooK3Y0bNyzb1RNOg0bDNPNHjACmT2ezf42m7unc3FwxNDS0UfbU1dWVM5lMIiGk0XOdOnWCf2oq9TYYyImvvsKV1FTRcOECZzQaibWNDdqnpECzfTuwcyeweTNUKhWUSiUsLCzQpk0btG3blgNYOWhCQgL5cdMmTElNpR5vvEFAKXbu3CkmJyfzlpaWdOjQoaSD0YhG6zRz5wJlZaCU4tChQ+Lt27fx9NNPNzjWXbt20aqqKtJkeWkNSqUSdnZ2FcXFxZskSZoFoFQQBFetVttyOVGZ+0IQBA0AFYAqAO8DCAewEUACgHitVqurt60tACsAfRUKRR+TyfSdVquNe5jHKwd+mYeKJElfGwyGUIVCESgIwnwA32i12vKmttVqtdLixYtnb9q06Tt3d3dF3759LdvW2iLu3MmqbvLzmRVjjYVjYmIiiouLedd6cgtxcXHIyclBSkoKHTJkSLOtsOEZGeSYoyNcXFwwavRovuZ4wXEcC74jRrBGrKoq7N22DU5OTpKnp2eD5VJzc3OEhYWhg8kEcdUq8nlwMG198SLS0tK4qVOn4sqVK+TAN99Qu7VryfVt22jXwECSnJwsxf/0EzexZ0+YjRyJ5cuW0erqas5gMJDy8nJYWVnVvX6bNm1IRkYGSktLKdB47Khl4MCBljt27OhnMBj+A+A/SqXyK9xjoJW5fwRB6KzRaNZzHOdPCIEoinW3cV26dAk8f/68pmY7ABABaAEsrt3GZDJBqVROFwQhQqvVnr3z9f8o5AYumYeKIAgEwJv49cs/UqvV7rnHPpYA3nR0dHz5xRdfZGUslLKk6UcfARcuAEuXAuvWwWRhgS1btkhpaWlcZGQk7dWrF1mxYgVu3bqFwYMHS7169Wqc17p4ERAEiOvX4+OlS3FPMbYNG1D5739j77vvik8+/3zTdZlvvAHJzw8nvLyQlpaGoUOH1iVdJUnCmRMncDo+XqyoqOAppeh4+za1zcggST17QqlUigsWLOCXL18uhoaG8j169Gjw0tu2bYPJZBKfeuqpZmtCKaXYvXt3dUJCghkA8Dy/5O23337tbtdZ5u4IgmAFoH3tj5mZ2QCTyRQ+ZMgQTUBAAFGpVDCZTOB5vq4hUKfT4ciRI4iLa3pCP2bMGJEQwu/Zs6eI47j/e/311z98GOciz/hlHiparZYKgvAhgKMACrVa7ZUW7FPxwQcfuPr5+f26ME8IMyVXq4G+fdl6/6efQvHqq5g4cSJ3/fp1bNq0ibRt2xa2trbU2dlZ6tWrV6NAmZWVBdOqVdCXl9Mdn39OzMzMRE9Pz7vrIE+ZggKFArcSE3ls3vxrjqEWSQKqq8ENGoQwFxeEhYU1eJrjOIQ6OCDUxoZPGz0a69etw5Mff0xOnzmD8c7O8PLy4gHAx8eHj4+PlxwdHTkXFxeYmZmhuLgYKSkpoJTytX4FTUEIwciRI80SEhIAAKIoPhIy3H8FBEHoDKAbIaSjWq0OIIT4GI1GD57nNVZWVtX29vZo1aqVeevWrRVeXl4N7sgUd7ioaTQaDB8+HMOHD8fWrVvBcZw4fPhwvqysDGZmZrC0tOQppTAYDA5Hjx6dDEAO/DJ/T7RarQjgxP3sYzKZsqQ7Ne6HDmWuWJ06MRPzPXtgmDgRcc89h+SiIqpSqYiDgwPS09MhSRJ/8uRJtGnTBq1btwYhBFSng27oUBwdM0ZymD2bG+7jA39//7sGfUmSEB0djdPp6TSidWsJr77KIzKSdeTWcvEik2J2cWn+fJRKXLx8GZUdO8IzOxtlgwah99GjDbbp3bs34uPjuY0bN8LT05MGBQWRq1evwszMTCwrK+Pz8vJw/PhxafTo0U32EdTo/9DMzEyi1WrT7n2VH18EQbADMFKtVi80MzPz8vT0FJ2cnCwcHBw4Ozs72Nvbw9LSEoQQ5T1f7C4QQqBWq+vu/mof8/T0hCRJNoIgEK1W+4cvw8iBX+YvAc/z1mq1uuH01mRiyd1Dh5hP7ogROLd2Lap+/JF2GDWKdnvqKaJWqxEYGIjS0lKcOXPGFB0dzQMg3t7eon9FBQ+DAc8tWsQpmmvAuoPVq1ebKisr+TFjxpBOnTrxmDcPOHMGmDOHSUmo1UyHv75WTxPsT01FioMDbOLjJev+/anq9dfrBpw9e/bg/PnzVKFQUIPBwPXt2xfXrl2j+/fvlywtLUlERAR/4cIFcdWqVTwALi8vj86aNYuY1QjZ1WfixIlk6dKlOkEQvFtyd/W4IQiCrUql+oDn+ec8PT2NISEhlt7e3s3eSf0W7rasbmtrC6VS6WQymbQA/vO7v/kdyIFf5pHn3Xff7adQKJ71qKnHB4CrV68iKipKCli2jAutvdUmBNfGjYNfUhIC9+zh0KcPYGuLYcOG1SZBFQATdRNfeIG/6OEhXZgyhZt9+zZat8DXNiEhAUVFRYp58+bBxqbGy1ylArp2ZeqhxcXs9//8h2kJNYNOp8O5c+cws7ISLpRysLJq4AQWHx8PV1dX0rt3b+Lj4wOe59GvX78Gkah79+58aWkprl+/jl27dpGLFy/izlwAwJYaevfurTx58uSHAJ6450k+RgiCEKZQKPb4+fmpBg0apDY3N2/Z6P+A1CRym3xOFEVwHEdFUTz3Rx5DLXLgl3mkEQTBU6FQ7Hj66aetNBoNvvjiC9FoNEKn0/GBgYHkxC+/wO4f/6B07VrSydcX5eXlYn7//gRpaQRnzrC7gjtm3+3s7IDqanR87z1ujItLi2Z3+fn52LdvH8aPH/9r0K/FyooJt8XGAk8+CTg6shr9vDwgPh7SsGHYsGSJqcRo5FSOjpRSyltaWkour77KoaioriIJYJ7BHMfh6aefbrB23BQ2NjbIy8uDra0tDQgIaLbCJzQ0lD9x4sRQQRA6abXay/c82ceHWGdnZ4wZM+YPfyOdToe0tDT65JNPNrmUeP78eVRXVx8DsPMPPxjIgV/mEUYQBH+e5w+Eh4ebu7m54fPPP5f8/PxI27Ztufbt28PMzIyE9OiBbLWa/PLzzxg6YgRu3brFT548mfnhrl/PzNS1WiaoBgDLl7OAXNvg1ILjkCQJ69evl0JCQuDj49P8LuHhrIt4+XLm6XvxIvDmmzikVGLwl18qNAEBSH3rLYSEheHKrl0Eq1YB//0v0+8vKkKFWo1Vq1bRkJAQycrK6q65hvT0dGzatImaTCYSEhJCNRoNAYDk5GQYDAa0b9++buBQq9UICwtTHT9+/AMA41pwyn97BEEYCDB/h/tGklgj3n2wdetWyc3NjXbs2LHR5yqKIhISEiqMRuP/tFrtQ/FKlgO/zCMLx3FPi6LY+ujRozQmJgZeXl4YMWIEV9/i0MHREQ4LF0K5cyfd9ssvRKlU/qpYOXUqYGkJnDoF6HSsg/b4cfb4fVBSUoLKykou9F46+kVFwL/+xYTjAMDPD7rRoxG/dCntGBVFXLy80JNSICMDPq1bE+TmMlE2Z2dgxAiUtmqFDn5+dFBQ0F2D/oULF/Dzzz/D29sbERERsLGx4QBAr9dj69at4Hme9u3bl9SvJgoKCuKjo6OHCYLA1yTXH2sUCsVkk8nUQPW0RVDKZEDMzJhgYAvIzc1Feno6N3fu3CZejmL37t36srKyOAC77+9gHhxZq0fmkeWdd955B2xyYs9xXHloaGiDoF9HTg58V68mL730EkaOHNnwuXHjmNLlvHlA797Axo2sEes+sLe3h6urq7h37967B8wPPmB3GfUoLS2FJEkkKyuL7UsIM3rnefbvhg3AG28g6auvEOPoSM1yczny/PPAwoXsjqEJTZ/afERQUBBxdXVFamoqXbx4MZYsWQIbGxvR2dmZXrhwgaamptbtY25uDgcHB5NCoXjzvk7+b4pSqewGAI0UYO9GXByQmcmS+GvXtnCXOKxdu5aGhoZK9vV9GsCCfkxMjJicnJyt1+vHALATBKFN06/0+yI3cMk8ktQ0eoUB6MTzfADHcbNnzZqlaU6m4J4MG8Zm1zdvsjX5+2TDhg1SRUUFXnjhheYnS5SyZYA7tPYzMzOxbt06vPXWW7hz4Mretw/pn35Kjw8cSPr06UODgoKIprqa6fm8/jpLFr/2GisN9fYGAKxatUq0sbHBhAkT+BMnTkhRUVEcz/PQ6XQYNmwY/P39cejQISQlJeHVV1+FpkbOoqysDKtWraqqrq5+8+2331523xfhb8T7779fqlKprCsrK6FQKPDiiy+ioKAAHTt2bHoHk4k5p2Vns89GFNkE4pdfWOnuHRQUFGDz5s1iVVUVP3z4cHTu3LnBZ19dXY3NmzdX5ebmphkMhk8IIV9TShWEkHWLFi2a/keddy3yjF/mkUMQBF6tVh+3trb+JSAg4LOwsLCX5s2bd/egf+FCnUpnk/j5sVmalRWwfTtT7mwhFy5cwPXr17laPZ4mmTOHlXE2YbDi6ekJnudRXFzc6Ln4rCzoQkLov/71L/Tp04doNBom7Na9O3DgAOtMLixk+YOoKAAsaBBCuBUrVkjR0dGkX79+0Ol0MDMzo4GBgdi4caMxKSkJSqWyQeLa2toaM2bMMFer1e8vXrx4ZosvwN8QjuMqJ02ahFmzZsHCwoIuW7YMGzduRE5OTsMNKyqAl15in8G+fcDu3UwXysKCJfHv+ExNJhO2bt1Kv/76a7Rr1w7//Oc/4e/v3yDoU0rx888/V+Xk5PxoMBhClErlByEhIQpCiIFS2rS/6O+MrM4p88gRHR2tkiTpy2nTpmlCQkKUbdu2JZp6ImxNYmbGAmZTUgsmE5uJjxrF/v/556zbt4m6dwDIycnBmjVrxJiYGBw/fpympKQQBwcHXLlyBVZWVnB1dUVmZia+//578dixY7Rt27aclbs74O/frLNWQkKCmJmZicDAwAZT/rjkZJi3bUs7dupEmjRit7dns36Vih2/uTkcHR25w4cPExcXF3Tt2pXExsbSLl260BkzZnA8zyMqKgr9+vXj+vXrBxsbG1BK6wKPmZkZfHx8lElJSYOOHDlyPSIi4uLdL+zfkxMnTkzz8fFx9vT0RGhoaK39prR//34aEhJCOI4DqqrYbL6oCJg5k1WH1fg4AGBJ+bNnWaLX1hZJSUnYsGGDJEkSJk2aRLp168bd2ckLAImJiTQhISHTaDQOB+AqSdKbxcXFVSaT6QmtVpv8MM5fDvwyjxyRkZFidHT0jatXrw4NCQlRtqiZRq1mgT89nc3E6nPyJLsbeOEF9kc6ciSbtT37LJtZ29oiPz8fUVFROHz4sHjy5EkuMDAQ/fr14wIDA0lERAR69eqFjIwMeunSJZKRkYFTp05Jbdu25VQqFTH88ANtO3MmwV3Wi11cXLjo6GiSn58v+fn5EUII1qxZY8rIyODGvPMOKQ4Lg3W9PoU6Tp8GFi9mxjKVlcC6sfyhfwAAIABJREFUdbAfPBiRkZHo0qUL2blzp1heXs5NmDChroHr9OnT1NnZmfj4+JDvv/9e3LFjB3f79m2xdevWnEajgbm5OTp27Kg8f/78iKNHj56PiIh47Bq7YmJi3u3du7eFec0yjVKphKenJ0lOTqaXL1+mXVu1IujXj+lBeXuz79SECWg0OE+ciKriYqy+ckW8ePEiN2TIEDJixAhS3xmtPiUlJfjhhx90RqNxgFarzY2Oji4FsEgUxX8sWrRo0x982nXIgV/mkSQiIuJ8TEzMzLZt29o1qptvjqgoZnU4cGDDx9Vq1mRVX9qZEMDBAUmiiJ+//VY8kZDAqdVq0dfXlx82bBg6d+5MrK2tYWVlBZVKBUII3NzcSElJiWhlZUWtra0xcuRIzpIQolyxAs4zZ5JrN26gtLQUVlZWjXoDbG1tUVVVhfPnz5Ps7GzJz8+PHDhwgEybNo3cGDZM2h4VRU6dOiVVVVWR9u3bs50MBqBtW+DYMSAri53DiRMsSV0TgBQKBZeXl0dzcnKkLl26cABgZ2fHHThwADExMaSiooLMnj2bnD9/HgcPHiRubm518gPt2rVTXrhwYcyRI0cORUZGNm/79TdDEAROkqT3Bw4cyPH17tAIIejo6krSV60i+s6d0fq115jL2+efA0uWNAr6kiRhd6tW2FlcDG93dzpx2jTOw8OjUR6nPseOHTNlZ2evfOedd9YBQGRkJI2MjBQiIiLi/6DTbRK5nFPmkUWSpMNpaWleHh4ezf8l1WfIEPZzJ19+CURGNnq4wNcX27/8EvP37OHNv/wSyuDgu5ZROjk5oYEiJqUwt7TEj2PGkAPLlolGo5HnOA4GgwG+vr7imDFj+GvXrsHb2xtVVVW4cOEC7d+/P4mJieG++uorycnJCW5ubsQjL4/rHByMNGdnbu/evXTQoEFEkiSUhIdjf9++YkmnTpg4dixva23NEr3x8WxAcHBA9+7dcf78eVp/oPHx8cHrr79OduzYAQ8PD+Ls7Izw8HDuxx9/bNAU5ubmhnHjxplv27ZtjyAIvo+RXj/PcZxRp9Pxd3bSWhuNGAhg5b59tL2XF7EJCmJNeXeQkpKC3bt3S+bm5ngpP5+z/uEHHhMm3PONCwoKqo1G40PV3m8KOfDLPLIYDIZlx44dm9y5c2dzBweHlu20Ywerupg//9fHfvkF6NWr0aa1yc/lQ4ag4/XrUujZs5zH2LHgWtrUc/48XP/5T8zcvBlFRUV8x44doVQqcePGDXz//fd8amoqeJ6HJEmU4zh07NhRCgsL4zMyMqTMzExu1qxZ7M6gsBA2oojapZqCggLExcXh+ogRtFffvnzRypU0oVMn9O/fn73v7t3MhSw8HFevXkVOTg730ksvNTq82o7UiooKbN26lQ4dOhTOzs4NBlFfX19kZmZaJyYmbhMEIfIxqfHneJ4vzM/Pd68bCM+cYaW4y5fDfs0adF+9WkLHjryUkgKunqBaWVkZNm/eLBYWFvIDBw4kQUFBhCsrA15sWU72ypUrVgDeA7Dh9z+tliMHfplHFq1We2nx4sWvb968+YM5c+ZY8M0kThvQrRvg6/vr7wYDGwya0OKxZTo+SElJoUajkVz76SdUOTnBd/z4lh1g167Anj1wtrCAcz11Ti8vL0ydOhWWlpZwcHBAbm4uycrKQlBQEE8IqR0MfvXMHTYMANBRkuDv70/3LV5M/U+e5Lw3biQdLC0Rc+sWiTlxAnFxcVStVkuT583jnZycgOxs7Nu3T+zZsydvbW3d7GFev34dKpUKQUFBTd45DRo0SJWRkdEtNzd3GoDvWnbyf00EQXBXq9W7vby87Nu3b8+S/UVFQEAAMGNG3Xb9Q0P5sz160IKTJzF69GgiSRIOHDiAhIQEeHt7Y9KkSbCwsGDX09aWVff06MHySU0kdAFW1ltD2z/2LO+NXM4p80gjiuIXZWVlpw8fPmxo0Q6enqxbt/aP7MABVm/dzLprcHAwpk6dSnieR+LQoXDp3x946y2m+Hk3Dh0CvvmGlfXdASEEbdu2haOjIwghcHV1RUhICGoHrps3b2LatGm/LrvcugUMHQqO4zBo0CDO4OMDOm8ebd++PeDiArvPPqPDhw+nzz77LOnUqRNZvXo1xfXrwKRJUPA8f/bsWdy4cQNFRUVNHqqLiwuqq6ubXS7jOA69e/e20Gg0z939pP/6qFSqY7169eo8YcIEc0IIK+19/31W4dWtG9to8mQoz59HuzVryMWLF0l0dDSWLl0qpaam0smTJ+PJJ5/kG3kde3iwqi5D46+pJEnIyMjAunXrap8c+8ee5b2RA7/MI41Wq6V6vf6Zs2fPFu3YsUOn1+vvvdO2bXU17/DwYDIK98DJyYmo1Wpqa2sLTJnCZm+3bzdf7+/lxQzT75OqqirodDqugVSAgwOr1weg+eILzBw8mOv+7LOEEAK8+CICCgtJ9+7diZOTEyorK4kkSeST7dulz8aMQWVpKaytrLB+/Xp88cUXSEpKavB+hYWF+OGHH2i7du3uuoTToUMHGI3GkBqXqb8lgiBYiaLoGh4eriCnTgGHDzMzn6VLf93IZGKThJAQODs7o2fPnjQ2NhahoaHkpZdeIm3aNNNYq1AA330HrFvHvjf1uHTpEtasWQNJkgoAfEQIeeaPO8uWIQd+mUcerVZbYDQaOyUnJ+/ZsmVL5b22L5k8GRsVCly7do2t3fbsibKyMjQycqlH//79UVFRgeTkZBS7uDA9lk8+YX/Id3LxIru9v5d2TxNoNBrwPI+qqqpfH+R5wNoaKCsD3NzYQADgyJEj+KlDBxHduwMALl++jIsXL5LOnTvTkSNHcm28vMQ5585hrrs7Fi5ciMjISGzfvh2JiYl1Lx0XF4fS0lLSu3fvu66TaTQauLm56QEMve+T+uvgZ2tjU80RwgZ0g4EF+drE+IEDrF5/w4Y6RVdra2tib28v9enThzS71Egpe6333mPNXjWuZ7XU+j9TSt0ALKSU/umBXy7nlPlLEBkZqT9y5MiBsrKy1wIDAxVNOU7VsmrVKrHLvn0k9fhx4rpyJXaLorj73DkuKSlJDA4O5prqCyCE4OLFi+KFCxdIcnIyevbsyeq4u3VjGi25uUCtgfuaNUz0rVbx8z4wGo04c+YMdXV1JY71+w0WLGA9CC+8AEmtxokTJ3Bi/35I2dlcrlIJHx8fODk54eTJkzQ/P5+gsFAcNXgwb1ZSAuzbB8Xt22jbqhXiCwuhWbsWORYW8LSygnd2NpJNJtE2LY1ztbEBsbdnyqSurqwrNSeH9T8UFAAGgzo9L08VFhb20OrJHybR0dFDR8THj2lVWspj3DjgTnmG3btZ6W+/fnUPZWdnIzc3l/bo0aPhUplOB1y6xK5fjx6skWvgQNYX8swzDdQ7a3SSkJKSAgDgOO6diIiImD/uTO+NHPhl/jJERkbqYmNjyeXLl3s6OjoqOY5roENz6dIl7Nq1SyouLuZ7jxhBuj/zDJL8/SVlQAB5Yvx4cv36dZw9e1YMDg5u8k63R48enMFgIPn5+QgNDWWdtIQAiYksSHp7sw7gsLAHCvplZWVYtmwZdXJyoj169GjomJWczOSj3dxwatkyejMlhYxq0waBx4/j6q1bkreTE+GXLEHl1at48vx54rZlC3fVyUlqHR1NsHs3y2mMHAnvAQNgffIk9hUVIfviRcm/qIggLIwr+u47ejMtjeZZWRGX994D/8QTQEwMm6VOmgR0745WO3YgpkuXNkePHv0qMjKy+sE+pUcUQrwTevWa3nrEiG5uY8cyv4RaJIk1as2bB9yhzZ+VlYW8vDwaHBxMUF0NbNrElnXGj2fX7j//AUJCWFVP166sx6KJiUVcXJwuNzdXAQCU0jmRkZG3G230EJGremT+UphMpsUlJSV5GzZs+BoAJk2ahAMHDojFxcW8SqVCYGAgnnnmGVhbWwOLF6NPVBRXm6iNiIjgtm/fzlQJJYn9lJQg/cYNxJw8Kelv3CBVZmZkXK9eBAcOsD/kmBiWwHVyAp57ji0HHD7MfH7vk/j4eCiVSsycObNxZCgqYssEtrZQ29kRV0olty+/5Gh1NYYmJnJpISHw7tgR+qoqMXvhQoVDVhY6TZ7Mxe3aRXv89BORxo1DztWrSCopkZLateNUKpXkO2gQh65d0QNA2apVZN1nn5GiAwdgbzRSn5s3Cbp0YVLVABATAz4qCj3t7bm4uLjUDz74YP4bb7zx/QN+TI8UgiAonu7ceVOboiJf7/DwxlId166xwf3Ojm8wXZ3OUVHM23nECNbQ1b8/+w5UVrLvRVO9I3eQmppaXw3zN/n2/h7I6pwyf0kEQXAGMJ7n+fdFUbQJDAxEp06d4OHhgdo2fIwezbT4vb2Bl19G6Sef4JqtLQ3ieULT00GWLQN9/nlkiyJuT5hAO166RMp69kSrnByQoiLgzTdZ16a7O5vlHzkCtG/PpB/u0p3ZFMnJydi+fTsmTpwIr3o2iw2gFOjZE/ne3tjk6YkJP/xAc2fOJFnV1SK6deNHt2mDS/PnS+qBA7kOHIcivR4rLSwQ4eqK1Nu3pWkLF3IrX34Z/WbPhp+fH3u9998HFi7ErhkzJI+MDC5+2jTpSS8vziY4mOUVasnOZk5gixcjKSkJe/bsKTQajc4Pyxjkj6LC0vKjzTNn9ucCA/2eeeYZ80a+xD/9xAb2oUN//Ux1OiApCXjrLaRGRKAqNpZ2S0wkSEtj+RiN5r4//+zsbOzcuVMqLCx875133vnP73N2D44c+GX+0giCoALwjEajecJkMgV26dLFc9iwYUShULDAFxfHApy7O+IOH8bZq1dFv65d+diTJ9HB2xs3btyAp6enOHnyZP5urfa/hZs3b2L9+vUYPXo0/P39G2+g0zH9/WXLgIICHF23DqUlJVIXlYrjN27ED9Ong1KKnj17gi5fLvW5epXTrF4NeHriRk4OLJ56CrEff4xxv/wC7swZkNmzWbLxtddYgnr2bCz95htxxIgRvHeNtHMjjh9ndzRXroBSiu+++64yJydHEkWxh1arTW16p0eX42FhtocHD36605UrS13GjVOEjRvXWPOJUiA4GPjnP1kup00btk6v0wFbtwLvvosMMzPsDw4Wn585k29OgK+lfPHFF2VFRUXDtVrt8d/0Qr8DclWPzF8arVZr0Gq16xYuXDjWZDINS05OPvvxxx/rt2/frsvNy2Prr506waBSoczSEsUGA38qIQEDBg1CTk4O7dmzpzRlypQ/LOgDwK5du6SuXbvSJoM+wLTdg4IAACVKJa6VlEiDv/iCa2dhgTbl5Vi4cCGez8tD+3nzEBcSwhXu3Vs362zXvj2cHR3x5JNPgu/eHUSpZOWstZ28//oXYG0NnuepoYka8zp69QLOMZ9vQgiee+45ix49emiUSuXzv+e1+KMRBMHxvXffPeyZkVFsW1i4InzJErO+48c3DvrFxcDLL7OeDYOByTLs2cPuepyc2LLP+vW4OW0a0955gKBff1Kt0+lQUlKiAfCnyzUA8oxf5m+IIAjuHMdN5nn+7UGDBpkHBwdzUVFRiImJwaRJk+rMNiRJAiHkrqJav4Uvv/xSKi0tJZRSYmZmJs2cOZNrssNWkiCBNVKtWLFCbFNVRQbfusXx//gHW24aPx4wN8f1X35BYVwcOv/yCyyXLWMa/eHhbI26Qwe2/HDlCqtMqVdvnpubi++++w5Tp06FR1MKoABbxvrHP1ipag3fffcdMjMzQQjJoZSO02q1Z37nS/S7sviddzp0uHr1aqqPD1QGA+YvWoRGSzsmE/NlWLYMyMgABg9mv+/fz7yY77BHjI2NRWpqqjhr1qwWR35JknD48GGTTqczjRo1SgMAqamp2LFjx5nXXnut5+9wqr8ZObkr87dDq9VmAfhIEISthw4dOhgVFdVGr9eT4cOHN3BYapHc82/g9u3bnI2NDcaOHYv9+/cjOjoao0aNarRdycKFOH/uHE0cOVKqLi7mx8+ZA751a6Y5tGkTC0rHjmG3tTXMfXzQw9yczVRrqV+W6O3NKpDatWNNbJ6e2L9/P+3QoQP18PBodMI6nQ5qtRqkdWtg0KAGz02cOBEfffQRKKWuAE4LggCO4z6SJMkAwAjgBoAfHlYeQBAEDYCOAHwIIb5KpbKtwWD4D4CuAHZxhMD38uXym23aENHOTllRUaGuC/wZGcCuXawE89w51oCXlAS88grT3R/XtAd9fS+DlnDr1i189dVXAKCwtbWtAqChlOLMmTPVer3+599y/r8ncuCX+dui1WqvCYLQBUCsp6enT48ePe7h5nJvrl27hsuXL0On06Fz587wra8LdAeWlpamAQMGKDw8PBAQEMCdPXtWBMAbDAZs2bKF5ufnSzqdjqc2Nuj/5pvoZDAQ/ddfQ/3WW0wOwsuLWUXWiIQROzspB+BEUbz7oGVpyXRnasTmwsPDyaZNm8iKFSukyMhIrlO9iqTVq1dThUJBh/fowXmObagkoNFoMHHiRJSUlGDv3r0AgN69ey+sFZ67evVqVXFx8ZuCIIzUarVpD3hJm0UQBMLz/JcqlSpQFMUuAMytra3LnZycqKWlpUVSUhIPYIZKr8fYbduwZ+TIWdvHjdsKwAC9vvLLL7/Eot69QTZvBnr2ZIF/2TI2s798md0h1e/abYL7DfxZWVm1/z2p1+u7Auw7k52dnStJ0mcPdCH+AOTAL/O3RqvVlguC8FRaWtqVuLg4qUePHg88zZckCZs3b4aXl5eoVCrJ9u3bSUVFBbl27RotKCgQLS0teQsLC+Lk5ARvb2+Ym5vzaWlp1M/Pj7Rt2xZHjhzhJEnCoUOHUFRUhBEjRvCOjo7gd+6EbUgIgYUFOWNnR1fs24eR8+eToowMyW7xYs7fyQnFxcVwc3PjlEqlpFQq730Ob7/NJASuX0f7xYsxevRo3Lx5k9u+fTuGDRuGS5cuiQBga2tL8vLyuDOffUbN4uPhlJzcIMrVJoNDQkLufAfSr18/i5iYmE7Hjh278PHHH6eYTKZdRqPxPa1W+5vXjwVB6KBSqdYbDIbQrl27oqqqSrp27Rq1t7e3kiQJOp0OAGBRXo5KK6sl1uXlMZWWlvt5nt9uZjIFe6am0oGiSEhyMhs4p0xhAf/ll1lJbhMy3U1Ruxx4L6qrq1FRUYH09HSjWq2OAVARGBioAYCioiJQSo9otdoW6I08HOTAL/O3R6vVXhUEwffgwYNR1tbWzj4P0HxViyiKmDBhAq9QKHD8+HHpxIkTcHd3l/r27asoKSmhpaWlYmxsLB8bGwsAJDc3F46OjlJISAhnZ2dHly5dSvR6PSZNmkTatm3LXvT0aZZcXL8eIU5OpDI8XNpx4AAJHDWKO7FnD83MzCTnzp2DKIqQJIkzmUxoytKvEUplXVIyICAAAQEBuHXrlnj48GF4eXnxly9fBqUUEyZMgOv48eTn997DuMpKNBIgawZCCCIiIrjAwECLgoKC4D179viVlpZeA7DxXvsKghAOYL1arT7HcZyKEGJZVVUVDgBmZmaXlUplm/DwcNXhw4fh5uYGLy8vztbWFpIkISUlhbq4uJDpgwZB+dRT+GbWrFdXz579qt3t2/C9dAn+ffvC5dw5kP/+FwgMZB3RGg0bDK9fv69SzJbkQPV6PZYtW6YHUMhxXLper39GoVCMPnv2bCQAlSiKSoPBcLXFb/oQkJO7Mo8NgiCEqdXqA0888YSZl5cX7jThaAmLFy/GggUL7hocKysrwfM8ioqKsHr1arRr106aOnUql5+fj8OHD4v9+/fnXZrS/D97llWYLF0KacMGEJUKy5cvp5Ik0REjRnCurq64efMmOt2jeSw7Oxs///yzGBgYyIf36cM6ghctatCtKkkSPvnkE6lr167c4MGDgYQEnFm0SLr4xBOYOnUq9yDXJjs7G2vWrCk3mUzeWq02787nBUGwAKAF8GrtY/369YO9vT2USiU2bWJKETNmzICDgwPMzc2Rl5cHJycn1NfJ2fH116Y2ly4pApctw/XUVMStWiU9rdNxpuxsiMXFUK9fD3L1KpNSsLJiCd1Bg4Affqhb/mophw8fxs2bN8Vnn3222eTusWPHpGPHju16/fXXG6yVCYLgCqDW2SxQq9UmNd77z0EO/DKPFYsXL56mVCoXGI1GPw8PD72fn5+Vr68vmvNIBZii5qVLl5CQkCCWlJTwCxYsgOou/rr1kSQJlFI0K/AVG8uWHp5+mq3pm5kxq8W+fQEA+/btky5evEjGjx9Pmm38uoNPP/1U1Ov1vJWVlfjS88/z6NoVho0bUebhAUdHR+Tm5mLdunXU0tISs2fPJiqVCvj+e0jLl+PTJ5+UgoKCuIiIiAdKfh86dMh49uzZaL1ePxyABKA9gMEKheIZSmmwKIpqABg7diy8vLxQv8rJaDRCr9ff9bMApfh55UqT3/Hjik7Dh7OB8pVXWB/E9OmsTLP+wEgpm+FfvvxA3daHDh1Cdna2afr06U3eYuXk5GDdunXVer0+RKvVNjKuFwSBAoBWq/3j6oUfAHmpR+ax4u23314HYJ0gCHbp6elDcnNzJxw4cGCYr68v7d27t1lTM/Gvv/5aJIRw7du3x9SpU1sc9IEWVA75+jIl0G++YUFs1qy6oA8wmQmTyYQffviB9urVC/369btrACkpKUFFRQXfp08fJCYmEgOA/R9/TC9v3Uoc8/NR0LGjZDQaudDQUNq/f3+ubv36mWfAPf00+iYkcHv37kVMTAwcHBwkd3d32r9//7savdQnMjJSmZWV1Ss7O7tYkiSVmZmZ3tLSUlFUVKRZsGABFAoFFApFkwOhUqm8+11YTAywdCn8OI4nGRlUOnuWcGvXAmfOwOTujpjjx6HX6+Gq1yMgIIBd+9deAyIigJEjW3T8d3K35C6lFBs2bNAZDIa5TQX9Gl4jhJQ80Jv/gcgzfpnHHkEQHHief4HjuFc4jjNzdHQ0uLm5mfv5+Sk9PT3x0Ucf0aeeeorY2NjAwcHh9637z80F7O1Z7f2+fayRq57VH8BmwgkJCTh8+DDCw8Ph5uaGW7duwd/fH5aWlvjxxx+l/Px8SZIk9O7dW3HkyBE6YcIEEhsbK+Xl5UGSJO6lmzdhceYMsrdtg0ajaXweq1YB338PREWhoKAAJpMJ2dnZOHv2LFWpVNKMGTPuq4OptLQUZmZmUKlUMJlM+PDDDzFz5ky0bsIJDQDS09NhZ2cHGxubxk+WlQE//wyoVMDMmTDOno0t7dpJBeXlGDFxItehQ4damQm0adNGvHXrFqfT6RAeHk46W1rCzN0dmiZ0eFrCgQMHkJ+fb5o6daoCAPLz82E0GuHu7o6LFy9i586d+UajsfXvkdB+mMiBX0amBkEQCAB3AH48z/dWKBTTADjr9XoznucNHMeVt2rVSjN27FgLxwcMJI2YMoWZpz/7LFuuuAtnzpyR4uPjUVFRwanValpRUQFLS0uYTCb079+fVFRUICaGqf1OnDgRHh4e2Lp1q9ilSxfet1MntuxRUADUs4ms9+LAiRMNvYoBJCYm4ujRo/SVV175TaPd6tWrJS8vL27AgAGNnktNTcXmzZvBcRwsLCxEJycnMmX0aA7W1qyxbPp0oKQEWL0aGD4csLKCXq/H//73P5SXlwMAFAoFvLy8xEmTJvEAcHP1atzasAH7Bw+GyWTCiy++iBb7Ntdj//79KCgoEKdMmcJfvHgRO3bsKCOEmKytrW2Ki4tNkiT1fJTW7luKHPhlZO6CIAi+AF4C8CmAdJ7nX+J5/sPnn39e8yCBpEmiophI2uTJLd6FUoqlS5dSvV5Pnn/++bqglpOTAzMzM9jZ2TXeSRSZbtHevQ2WkwAAN24AWVkNHjcYDPjss8+ksLAw0rt3798U+NeuXSvl5eWRyZMnE3d39wbPbd68GWq1Whzi5MRnpKdja1oa3vrmGzYg6nTMD3fDhjpv4vT0dGzatInq9XoCsOW06dOnw9nZGXU+DdXV7Jp26ICPPvoI7dq1M1paWtYGO3LnXdsdvxMAtOa9+Orqavj5+UnZ2dlcTk7OcpPJpFUoFJsAHHjrrbfu3gjwiCIHfhmZ+2Tx4sUv29nZfTBnzhyLFpVVNkdxMas2OXiQmaHcJ//3f/8njRo1iruv8tRbt4BWrZgkdf38w/LlbADaurXuoQsXLmD37t1YuHBhg1wFpRQpKSkoKyuTQkNDW5QBppRi//790pkzZzgfHx+MGjQI5mo1YDLhelgYrVi/nnTNzMTtoiKsLiiQXvv3v7m646tJ0O7du5deuHCBSpLEWVhY0MmTJxM7Ozu8++67cHV1xezZs4HCQnYXtWtXXRXTRx99pNfpdB8BKK05nDsHsbv9bgnAEUBGze/7/4oz/DuRk7syMveJKIpflJeXD/32228junfvbtacscs9uX2b6fA/YM6gqqqKa2mlTx2tWgEff8yau2ocoQAwyYKwsAabdu7cGXv37pVSU1M5X19fUEoRFxdH4+LiaGFhIadSqbjQFtpPEkIwpFs3zvfSJRwoK5PSR40ifvPnE93IkTjVsycZ4enJau5v34a4ciXXYFD6xz9wUaNBop0defLJJ0mHDh3AcVzdRRs/fjz27t2LCxcuIMDfH/jww4ZGK4AIYFWNlIcMZHVOGZn7psYAfnRubu68AwcO5J04cYKK4l29zJumfXumx2Nr+8DH8kB37M8/z4TJ6pOQwMpK68FxHIKDg7n9+/fTEydOYN26ddKRI0cQEBDAeXp6UhcXF6nZ9xdFlk8AmLNVUhKIJKFNZSUmTpzIbRs2jJzy8MCyzz+n3IABok3NHU9tk9a3335rkiQJkiQh8eZNej03F88++yy8vb0bVUr5+/vD3t5etJo7F9WJiWwAaeJS3f+F+vsiWy/KyDwAkZGRNDIyMunIkSN7srOzQ+Pi4mwtLCxUrVq1+sPUPu/k3LlzYkXWzehcAAAL9klEQVRFBTp06HB/b6jRMM/drl2Bzp2ZkueePUy8bPz4Bpu2adMGmZmZyMjIoE5OTtwzzzxDOnbsCF9fX3LkyBGqVCrh7u7+6/t/+SV73T17gJUrgVGjmIF8+/bMRD4iAiqVClevXpWSkpJIUFAQxowZU1dWSgiBn58f4uLiyK1bt8j+/fulgo4dMWbBAuLQTEUQAFBK6YWKCuzLzERHHx9Svxfg+PHjRpPJ9HlkZGT5fV2nvzFy4JeR+Q1ERkYWhYWFrTp48ODJtLS0rleuXLHq1q2b6mEEfwsLC+7MmTPMH/h+IYSpeA4eDJibs1nyyJGsZLLBZgT+/v4kODiYdOrUqa6HQcHz8CCEbD9yhFjv2iW6JCdzCA1lieMuXZixyejR7EVcXFi5aj04jiNXr17FxIkTyZ21++bm5nBzcyPR0dFo166dNPHECV6zYgWrfGqKL76Aa0kJ5/fKK6SgqEg6dOgQCQ8Pr7smNYF/uRz4f0VO7srI/E4IgsCpVKp4a2vr9sHBwVb+/v4t1r15EPLz87Fq1SpMmzYNnp6eD/YiR48yKYPWrZmu//f3sNndv58F8o4dgaAgZO7Zg21ffYWgoCAovbxgbm7e4MfMzAxKpbLuLkgURRw/flyKjY3lAgMD6YgRI+49aGVnA+XlzXfeZteoItT0N3z11VewtbUto5TCZDJxlZWV5gBaabXaovu4Mn9r5MAvI/M7IgiCAsAAtVo9x2QyDfP09DR1797d0sfH54G0ge7FwYMHERcXBz8/P3HUqFF8s9IQzXHmDPDFF2w2XVgITJjw63P5+UziOT+fOXlt28aqflxdmWNXDceOHcO1a9eg0+lEg8FATSYTMZlMRBRFzmQyQZIk8DxfJyynVCqlp556imvxYJWVxfIPM2Y0fDwpCfjf/4Cvv657SK/XY8uWLbr09HSFKIoLAewAUKbVagvu78L8vZEDv4zMH4QgCJYAxmk0mrkmk6m7r6+v1K1bN7M2bdr8riYwpaWlWL16teTv70+HDBnyYMaw8+cDAwawWXVCAtMOmjyZJWZDQoDUVKA568h7YDKZUFVVhdu3b2Pt2rV4/vnnm+3gbZJ9+5g72JUrDR/X65ljWI1tZX1qfI7LTSbTxEWLFu15oAP/GyMHfhmZh4AgCK4cx01SKpUvUErdnJycDMHBwdYBAQHNC7jdB6tXr5batWtH+vfv/2DJhbZtAR8fVgq5YAGr6U9JYcE1MJC5VJmZPXDpKQAkJydjy5YtWLhwITSa3+CJo9MBkyaxktSmJB5quHr1Kn788cdqk8n0EoCtWq22tNmNHzPkwC8j85ARBMEDQJBSqXzPaDT6z5kzB03KNN8HS5YskSZMmMC1qee1e19QyoK8JLF1/HHjWBNUSQkwdSqTOP72Wxb8lyxhFTsxMUz2uFs35hTm6tqsKXlmZiY2b94subm50VpZhRZjNDJLyevXf206i41lfsP34NSpU8b9+/creZ4/J4rif8EGgMc+6MmBX0bmT0IQBAuO4w4BCBkwYABxd3cnTk5OjQ3C70J6ejpOnTqF69ev49VXX70v5dD7ojZOlJcDyclAaChb77e3B/r1Y5U8MTFs3X3LFpY32LwZ8PMDAgKwc9EimLp2xdhx4x5smWv6dLaWv2gRMHZsgxzDvdDpdDh37hxOnz5dWV1dvfb1119/8f4P4O+FHPhlZP5kBEEIUavV/ySEBBkMBi+VSmVycnIyubq6mjk7OyvbtWvXtGolgJUrVyIvLw+zZ8+Gq6vrQz7yJigvZ7IQ7dsDP/3EAn+7dsjz90fK11+j382bzPD844/ZncPYsYC7OxOIGzCgsZRELdnZgKMjW9P39mZ3GveJTqfD8uXLq6uqqp7QarX7foez/csiB34ZmUcIQRA4AJ4A/Akh/mq1OtRkMg1ydXWVTCYTnnjiCctaQbaDBw8iMTERU6ZMeTSCfjNkZWVh3bp1mDt3LuzMzFi+wN4e2L6d3TlIEvDOO8yTQBDYctHbbwNz57IZPs+zgWT8eGDNmt90LCtWrBDz8/P1AFy0Wu1jW9cvB34ZmUecmuqgkRzHtVGr1dpZs2aZVVVVYfv27fDx8aGDBg36U92dRFHE6dOnqY+PD2lKsTQpKQmHDh2ir7zySiNVzEZQyqwSlUrWDBYZyfIHn3zCyjl79vxNx5qQkIBdu3YBAAghr1NKP34c1/zlwC8j8xdi8eLF83ie/8xkMildXV3FyMhIvn379n/a8ZSWlmLTpk2VhYWF2SaTydvOzq7a1dWVjBs3TlNQUIBWrVoBAD7++GM6adIk8sCNZr8zJ06cwMGDBwHASavVFv7Zx/OwkSUbZGT+QvTt2zfu6NGjKpVK5VZRUWGZl5dXeebMGcXt27e59u3bPzSdIAAoKyvDV199hdLSUpUkSQ4AoNPplAUFBQqlUoktW7YgMzNTr9frFRkZGbCzsyMeHh4P7fjuRlJSkpSXl5eu1Wo/+LOP5c9AnvHLyPxFEQTBBYAvgCMAEBoaKoaFhfF/pEwEwAzkjx07ZoqOjiYcx13jOK7aYDDUSWIqlcoco9HognrqvwqFAp07d6a9evUizk05gP1Gqqur72v79evXS3l5eYmLFi0K/t0P5i+AHPhlZP4GCILgo1Kp3jQYDNPmzp1bt8TyeyNJEjZt2lSdkZFxyWAwTAWQ2twauSAITgB+BhAGMJlnSZLw0ksvPZANYnPExMSYoqOjKSHE2NJ9RFE0J4QsXLRo0ce/24H8hZADv4zM34j333//FVEU31coFJK5ubnRysqKWltbK2xsbDQODg58QEDAfWsGGQwG3LhxA8nJydWpqamEEJKi1+v7tbQTtsbLeBCADQCcunfvLg4bNoz/Te5lNZSUlOB///tftclk8tVqtRn33kMGkAO/jMzfjhqhOEcALvV/1Gr1II7jQvv162fWvXt3cjepCEopLl26hPj4+PKbN2+qVSpVkk6n+55SulOr1ab9hmPzVKvVq9RqddiYMWPM27Vr96AvBQDYuHFjVVpa2pK33nrrP7/phR4z5MAvI/MYIQhCD7VavVShUASGhISYe3p6Ejc3N1D6/+3dz0sUcRjH8Wd1RCVTOgYdLCEPIh0NiSBICOykkIT/ScE+fJegW5B47Q9QsAQPCdGKzuohAm9pmB4K2hUvse6iM191ulgUgthi7o/n/brODHxOb5b5wSays7MjSZJIsViUbDZbKpVKX6MoeiYib1X1x3nuyGQyD4MgeNXX19c5PDzcVsmv/83NTZmamip476+r6v557mt0hB8wyDl3p6Wl5VFzc/NQHMc9QRAcpVKpfFNT036SJKUoip4nSfLmf77j7py73NraOtXV1XV3fHz8Umdn55mvPTw8lImJifLu7u7jdDo99782NirCDxjnnGsXkRsi8umiP2ZyzqWCIHgSBMHTsbGx9u7u7jNdt7y8fBiGYe74WQMR+0eEH0DVZTKZoSAIXo+Ojnb09vaeem6xWJTJyck97/0tVd24oIkN5fz+DQIAKpROp9957+/NzMyUNjZOb/n8/PyeiEwS/coRfgA1QVU/eu/vT09Pl+bm5o7y+fyJc8rlsqytrbV5719UYWLD4FYPgJrinOsRkS8iIiMjI9Lf3//X8dnZ2Wh9fT0bRdEw9/crwy9+ADVFVTdF5IqIyMLCwomwDw4OtsZx/EBEbl70tkZB+AHUnOPvBq6Wy+Vvi4uLB38eW11dPRCRl6r6uTrr6h/hB1CTVLUQx/HtlZWV/NLS0u/4b21t7SdJ8qGa2+od4QdQs1Q1H8fxQC6XK/yKv/f+SI6fAaAyhB9ATVPVvPd+IJfLbYdheNDR0ZESkWvV3lXPCD+Amqeq3733A2EYbhcKhT0ReV/tTfWM1zkB1A3nXJeIHKhqudpb6hnhBwBjuNUDAMYQfgAwhvADgDGEHwCMIfwAYAzhBwBjCD8AGEP4AcAYwg8AxhB+ADCG8AOAMYQfAIwh/ABgDOEHAGMIPwAYQ/gBwBjCDwDGEH4AMIbwA4AxhB8AjCH8AGAM4QcAYwg/ABhD+AHAGMIPAMYQfgAwhvADgDGEHwCMIfwAYAzhBwBjCD8AGPMTcaSQ/XzNev8AAAAASUVORK5CYII=\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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D22buc3NzkZGRAbPZjODgYOTl5WHJkiU3Ll26tMFqtXa0WCx/yX/UJCUlfR0bGzsgMTHRKITARx99lJeXl5dYGOirqmoAEGSxWM78FddnjDHG/h2cxs8YY4z9j9Lr9UPq1avn/eKLL3q3bt3a3c3Nbdyj7lNJVFXt6e7uvjo+Pn5CcHDwd0ajcZ+qqt7FjlFUVQ02GAwfe3h4JNSrV++OFH0fHx9ER0cjLCwMBoMBvr6+ePbZZ93d3NyaAUj4N/pVPykp6Ztx48b9U1XVu/43kc1mezclJUURQiAvLw9WqxUANrna8DQajfv0ev2xpKSkzx+2D4wxxthfhYN9xhhj7G9MVdW6qqomqqrqXvw7g8HQOCwsTA8AcXFxcDqdzVVVjbhLO7qkpKQPJk2atHvMmDGt/up+F2U0Gts1a9bMo0OHDsbnnnvOq3r16uEmk2mqq1/lkpKSvtXpdDeMRuOJiIiIwQMHDnTX6x9spaFOp4NOpxMAHmr9vqqqMQaDYX2zZs0G+fr6jgXQ927HWiyWS4qi5F29ehVmsxlCCCMAX1VVyWg0Tq1SpUr4P//5T6OiKANVVa3zMP1gjDHG/ioc7DPGGGN/U+PHj3/FbDZvCgoKmqfX6y+MGzfuycLvVFX1cTgcsf7+/gAAk8mEBg0a6I1G4wfF21FV1cdoNP5ctmzZYR07dqyj1+t/VFU17F7XVlW1sqqqHVVV/Y+3lXM6nedzc3PtwM319yaTydR50qRJR/R6/elatWr1eeWVV0wjR450e+qpp9w9PDweuO20tDRcv349H8DWh+xWs5o1ayoJCQnUqlUrdzc3t+F3O1BVVX9N00wGgwE6nQ7R0dF2vV4/z2g0rvTx8enSqVMnN7PZjMcee8zNzc3NUuQ8P1VVdQ/ZL8YYY+xPwQX6GGOMsb8pnU73xlNPPeUeGhqK8+fPY+bMmbNUVT1psVj2GAyG96pVq2YKDAy8eXzTpk31KSkp7ceNGzfW6XSOtVgsNlVVY41G46qaNWsGdOjQwaTT6XDq1CldSkpKXwDji17Plcr+pJub22iTyRRZtmxZe0ZGhn7s2LH9Ro8e/cP9+uuqjq9YLBZn0c+dTueilJSUVxs0aGDw8fGByWTCSy+9ZD537lzVsmXLwt39jqQFQAjA6QTuM8P/66+/XrfZbCOKX9PVnyAA/gAOlbCe39NkMukBICIiAjabLUZVVaPrmbkBaGkwGDro9fpGOp0uunHjxkphPzt27OgeFBTUVlEU1K1bl4xGIwAgODiYAFQDgKSkpA91Ot1wRVGuqqrazWKxbLrHcwsFUAvATovFcuGeN8wYY4w9IC7QxxhjjP1NJSUlTa1Xr96zrVu3NgLAgQMHsGLFimydTrdLUZQmQ4YMMXt5ed12TlZWFpYuXZp/6dIlBxFZ7XZ7QHh4OPr163fzmNTUVKxYsWL7iBEjGhV+pqqqr8lkWunt7R3XokULz6ioKCiKgoyMDMycOTPfZrN1effdd9eW1E9VVd31ev3bAP7hdDo9jEbjT1artb/FYskuPGb8+PGvGY3GsSX1+Q5OJxAcDFy+DKxdC1SrBpQvf8dhOTk5+OKLL/IcDoe/xWKxFu2P0Wj8VAjRx2AwaE6n86CrP4cLj5k4ceL3LVu2fCo+Ph4A8Omnn17Pzs4eriiKt6IoYwIDA0W1atW8goODqVy5cvD09Lxnl69cuYI5c+bkXb9+fYzdbv9Or9envfrqq27p6elYvHjxxbfffvuOG1BVNd7Nze0Tp9NZOygoyHb+/HlyOBw1LBbL2Xs/IMYYY+z+eGafMcYY+5uy2Wy/Xrhw4SkARgCIiYmBj4+PX3Z2dusqVaqUOCMeEBCAwYMHm7OysvDbb795Hj58GOnp6bcdU6VKFTidzlquGeULAHrodLrpQgjTkCFDoCi3VvkFBQWhU6dO5p9++mkcgLUAoKpqRyKqLYTYAiDYaDR+EB4e7tOqVSuzt7c3fvnll3YHDhzYoKpqQ4vFkg8Ao0aN+mj8+PFVdu3aNbhFixZ3X0ZotwO5uUDHjkDlyjLg/8c/gLZtgX79ANcsOgBs3brVrijK/MJAX1VVE4CeRqPx/YiICJ8uXbq4GY1GJCcn11u3bt3u8ePHJzkcjokAFIPB0C48PPxmW926dfNcuXLlFB8fH6Vly5Ye5co9+OqF/Px8TJs2Ld9ms73jdDo/AdA3PDzc4e7ujvDwcDgcjoDi54wfP364yWRKat26tTk2NhZ6vd5txowZV8+cOVMPAAf7jDHG/mMc7DPGGGN/A67CbrUA6CC3pztkNBqHREdH3zYNHhoaitDQ0Pu2FxAQgJ49e9LHH38sypUrR1lZWQgIkDGnwWBAfHw8JScn79Y0zbN8+fIODw8P0/Hjx7Fs2TJn9+7db1tnXq5cOWiaFubqZ1Wj0biwbt26xm3btukAoGLFinjqqaduHp+YmGjMy8urcvLkyWWqqnYtDPgdDsf5TZs2KSdPntSioqKU8PBwVKhQ4daFNA1o0AA4fBg4fx6YPh2oUweoXx84fRqoVQsYNgwYNgyHDh/G3r17r9jt9pGqqtYymUzv6XS6NkFBQc4WLVp4RkTcqlPYoEEDpWrVquZly5a9deHChSEAgu12+83nAQAhISEYOnTofVIOSnbixAkA2PbOO+9MAYAxY8ZEBQcHewJAXl4edDpdXuGxqqoSEQ0wmUxJL7zwgtnX19d16xrS09Pd4aryzxhjjP2nONhnjDHGHrEJEyaMMJvN70ZERAidTkeXLl2yX7lyxRQcHOyIjY39j9q2Wq107NgxZGZmOl988UWd/vx5YO9etHI4TLGDBpk8PT2Rn5+PL774AgBw/fr1O9rIycmBoijnAECn0/WOjY3VtWnTRle5cmUcOXJE7Nmzh3744Qd06dIFer0eRIQnnnjCvHTp0qbHjh07O2HChMV2uz1dr9cnent7i4CAACU1NVVbv369AgDt27dHvRo15Cz+668DJhPg5wd8/z0QEgL06gWEhgIrVgDz5gGvv46zubk3lLJln4WbW5TBYFjTvHlzc82aNelu6fZ+fn7o37+/+969e92XL1+OB632/yCICJCDNHA9Iy+DwQAAcDgccDqd7mPHjp1oNBorG43Gpp6enh69evW6GegDgN1uBwDNYrFcVlU10mQyqYqiVHQ4HG52u30ugEMADlsslow/reOMMcZKNV6zzxhjjD1CqqoqOp3u2rBhw9zLlCnzp7d/7tw5pM+cCefKldhZvz6GzpkDU06ODKYvXACCg7G9cWOR266dqDFhgjLrmWcQnJGBkLJlnS2TknQQAtt37MD69etn2Gy2EQaD4djAgQN9yrvW0Ash8Pnnn4srV65QeHi41q9fv9tS9DMyMnDq1Cnk5+cLs9lM8fHxKAyENU3D5s2bsX79eoz48Ue4paUBY8fK2XvZOEB37qiXt24dTg8fjoDMzBe2tmz5XtSoUUHVa9R4oOfhcDjw/vvvw263Y/To0bctWbgnIeQSgyLLCArduHEDU6ZMybfb7fUtFsvBCRMmfB0TE/NCy5Yt4e7ujpMnTyI9PR0eHh6oWLEiypYtWzhAcJOmaZg8eXKBpmkpDoejVkJCgqls2bLKwoUL4evr63Rzc7t++fJlN0VRzlqt1gEWi2Xzg3X8r+Mq6BgG4JLFYsm73/GMMcb+u3Tvvffeo+4DY4wx9v/Wxo0b/XU63ett27b9c6aabTbg1CkgIwPo3Rve7u4IysvD5RMncLBqVVwfPBhRiYlAdDQQGoobgwZh/pkz1LlzZ7KmpGg+/fqh7Lx5FHb6NPkOGkQwm6HVrIkrhw9X6T916ls1hTAGDRig4NAhoEwZkF6P2rVrU3BwMHbt2oXq1auT2Wy+2R0vLy+EhIQgIiKCQkJCoNPdWiFARAhzd4e2fz+2lC8PzwYNRGbLllQmKEgGw1OnAm+9Jdfqu1y/fh0/7NiBHaGhuBAU1KbuqVOe0Tk5CpUpAwQF3ffxKIqCgIAAHDp0CFFRUSixWGB+PqAowJdfAtu2AV5eQGQkoKpAcjKQlQVs2gS4uwNeXjB4eMDHx0d/4sSJvlu2bKlps9kGZGRkgIhEREQE+fn5ISwsDMHBwfD09Lwj0C98FnFxcfqgoKCQli1bGqKioigwMBD79u2Dm5ub8sILL7glJCToPT09y6SlpTVr0qTJZw/5ZvypVFWNMxqN24xG4xsAXlm/fv13zZs3vzMthDHG2CPDafyMMcbYo5XjcDhMQogSg8AHkpEBzJkDdOsGFK6dX7MGGDQI6NgROi8vHKxQQdNOnVLM3t7Q3noLyoEDyH/qKZw9cgTuL7yAMtWqocyqVUqIEFj65pvOdampupo//qh1PXVKCfX1RYd16zy9p0+Hz4YNwGefAbNmySJ6gYEwrlmDql99hbq7d9P6d94RT3z6KUFRZMB8P888gxYXL+Ky3Y5ZAwdq11at0nU1GlGlShW41a8Pys/HxQsX4OfnB5PJhPPnz+P06dNQFAVnK1QwzQ8KwvPt2iHo1VeBsmWB776TQfg9FBbfSz9zBsFCAAEBwKuvAq1aAdeuyaUEmzcD6ekyAyIsTC4fWL4cSEkBAgOBAweAjz4CwsOBixcRazJRSGSk9zkfnz6H8vPxx6VLiIqKeqgf1MPDA9HR0bd9Fh8f7/z11191M2bMcA4YMEBnMpkA4Erh967tDs0Wi+XGw1zr36WqajOdTtfGYDC8mpiY6B4VFYWPP/443+FweEEWe2SMMfY3wcE+Y4wx9mhFurm53SCie0eoxX37LZCUBDRqBDRvDmzfDrRvD2zcCHh4yGOefvrm4YXp6lu3bkU8AF+dDjOaNXPWjolRXqpZk5CXB3h4gIjQvXt3nclkwr59+6irlxfw5JMIOHECsFqB2rUBHx852/7KK3KgoVw56AID0dhux8Hdu2n+gAF4cssW6MeMAbKzgdRUYPJkGUDXri2D6z/+AH7/HZg/H3T4MALOnsU/e/bUpaSkYMWKFXA6nTAajah89CiO5OZCI0JERIQ4ceIEATJgv3DhAoxmM3SRkcAvvwBbtwJ9+gCxscBzz8n1/oUuX5ZV/olQ5u230bBaNWEcMoRERgYoIwMoKJCBfdu2skaAmxswcaIM6kND5fICb285s2+xyP5Pniy/y8gAzp6F32+/we/SJZS5fh11Vq2Ccc0aYPRomRkQESG3E3zIAZ3GjRvrypQpgwULFuhu3LiBPXv2XC8oKPgSAFRVjTAYDJudTmfZSZMmzR4xYsSAwvPGjBnT3mQyfSSE2GO1Wp+zWCwF97uWqqpNTCbTCKvVOsZisSSX8H03d3f32XFxccbatWsbAgMD8fPPP9uEEMssFsuxh7oxxhhjfzkO9hljjLFHyGg0jqhdu7bhgU84dw5YtAgYMUKm7A8ZImfaAwPluvLHHpNBqNUK7NwJvP02cO4cevfqpew7eBDLly+Ho00bQKdDTkGBLqBfP+jee0/OUq9effMyrfz8YEpJwUd6PZ4zmeAxbhz0kycDH38sBxbOngXeeENeq3dv5OfnY0arVuLy5csEAHPr1XM+26WLDtu2AWfOyP689RbQsCHg7y+v17Qp8OmnMlBv1AgAULt2bVSrVg2HDx+Gw+FA7U8+AVaswBl/f8yePZsA4I033sCsWbMcQgh9XFycMzAwUAciOegRHw98/TWwciUwdy7gdMq/N2oExMTI64WFoXxMDJbb7YgeNQpuiiIHT4qz2YCqVYGZM+U9BwTIYH3MGPkbJCcDv/0GDBgAVKgg7w3A5f378ZuPj/A/fZraHD6MsnY78Ouv8hrduwNt2sgihKGhsv17yMnJwYIFC1C5cmW4u7sjMzNTAEgBAJPJNC0hIaFcgwYNlI8++ugpVVXHWyyW46qqhhoMhh+6detm3rFjR6WzZ8+eAzCiaLuqqpJOpxtiNBqfB5BRUFCQYjAYXq5bt67n7t27awIIv7M3qBkbG2tu06aNAsg6A3v37nXabLb3irUdDsBpsVjO3PPmGGOM/aU42GeMMcb+DaqqeplMpjl2u729wWD4zmq1vmCxWB6o6q2qqjWJqIvJZGppMBgaNm3a9P7Bfm4usHevTNUPDZXBZkaGXF8+ezZQowaQlycD6ipVgHXrZLAPAHGPsTYGAAAgAElEQVRxUJ58EjW6dUOZb7+FQ9OE9thjFLNnD841aiSqfPYZISsLmDIFeOIJICgIpp070SAri04eP45ZI0ciOzcXwdOmiX6tW5Pxp5+A8eNlmvvjj+P8+PGYu2ULCov+litXDqcvXtQdTEuDPTAQtT/4QPbj99+BhQvlTL+/P9C5s+xnsT3t3dzcULt2bfmPzZuB0FDsWb4cANChQwfh7u5Ozz//vH737t1YvXq1rnbt2igsGAgPD+C114Bp0+RMf/nyMtth9245ww4AEyciLCeHtP37kZqaijp16tz5vG02ORv/zTdAp05y1r9opkCPHnKLwNdek8sHHnvsZvthlSrB5u0tTlSuTLurVxft27eX0/nDh8tlAKmpwMmTcmvBNWtuDnSUpHB3gYoVKwIAqlSpYti/f/84VVV/MhqNjRo2bKgYDAbExMRQSkpKTwATAFQOCAiwVa1a1RwQEGD+6quvhqqqOspisTgAQFVVndFo/MTT03NA+/bt3fPy8mpduXKlfWRkJEJCQpCcnFxeVdW6Fotld7HuLEtJSXm7devWboqiIC8vD06n02GxWI4WHqCqakODwbAOAI0dO3bA6NGjF9zt3lRVDTeZTBZN067Z7faZAEJNJtPTAITVav0ewEqLxWK/68NhjDF2TxzsM8YYYw9JVVW90WhMiY6OrtiyZUvDN99809tqtc4EsOV+544fP36KwWAYXqdOHbuvr68hOjoaZrMZP/74o9i7dy9Vr17d2aNHj1tV7DQN+OknGXRGRADvvgs88wywdi3Qvz9w/Djwj38A770HNGsGPPmkPK9vX/kHkCnsmoafZ8zQEBeneF+9SgWrVyP8+HEEBQURIiKAUaOAVavkbH3TpsBvv8E9Nxd9Z83CvgEDxC+//ELnzp2jsw4HIsPDgYMHgRYtkJ2YiPPDh6Pm88+jRtu2NGPGDLRr1w7Lli0TP/zwA+llAT/A4ZDp78nJQJcuMsA9f16ur1+9GkhMLPmBbdiAEykpOFS2LKpXr474+HgCgEWLFmlHjx5Vqlev7gwICLj1vGbOlDPoGzfK55SZCVy6BDRuLFP7hw8HAPj6+sJsNmPVqlWIjo6Ge5F1/o6CAqSfO4eQKVOQGx+Pg5s2od7HH8P9tdfk+v1C1aoBS5bI36JHD2DCBKBOHaSkpKCgoEDp06cPKlWqJAN9q1UOjqxeLQdtJk0CEhJuLbm4iwULFmhEpMTHxwMA2rRp40ZEba5du/Z4QkKCW+HOBv7+/ia9Xl/YOXNhIcQyZcqgUqVKSlpa2oX3339/GwCH0WhsULZsWe/evXu7Fy2meOuV09wA7BozZoxTr9fn6fX6U3a7fROASw6HQ2e322EymeDu7g69Xm8YM2ZM4rvvvvuT6/QalStXFlFRUe5r1qwZAOCuwb7RaNwQHx9fUdM0Z2pqaj8vLy/ExMR4CyGQkpLS7sqVK2dUVY21WCxa8XNVVXUDYH3QATbGGPv/iIN9xhhj7OG18vHxKdu1a1cTAERHRxuTk5Mb4j7BvqqqzYjoZSEE0tLSdDvklnbw9fXVLl26pABAamqq7ssvv3QOGzZMh19+kenuvXsDu3bJWfdnnwWuXgWef14G9jVqyED9yhUZSLuCwuI0ACnp6Yqufn28OHkyPm3eHM369XPWjI7W4coVGYxXrSpnwGNigPXroevaFe7vvIPYggLavXu3yM7OplOnTonQ+vVJ99VX2CSE2HTjBgV37ersN2WKzmg0wtfX1zlz5kzd66+/TtnZ2Zg+fTp2Nm0qahYUkPumTTIgvnFDzuy3awf4+soMhE8/lYMWxda0p6WmInPfPlQaPFg0adLk5pdnzpyBpmm4ePEiHT16FNEREVAuXADi4uS6fUCmyrdvD7z/PrB4sVxn36MH0Lo10L8/evTogVmzZmHXrl1o2LAhDh48iEuXLiH6H/8Ql729aVaXLkBqKvz9/TXP9HRlx9q1WrTdrrRo0eJWB93c5Mx/QYHMtJg6FV5160LTNMydOxeWAQNkoN+tm/ytpkyRSy70erm8Yfx4YMFd42EIIRSdTndzMMJoNCIxMdEEwFT0uGvXrjltNtsFADAYDD0rV67sWfhd7969PbKysjzOnz/fEQD8/f1RoUKFuxaELMzQGDRokM7Ly8s7MzMzLiMjI66goEALDw9XXEUCodPp0KdPH7c5c+YsGjt27LDRo0d/B2DDyZMnlfLly8Nmsx26640BcDgc5Rs3bqyYzWaldevWBgDIzs7GpUuXULVqVY8tW7aEAlAgX18AcvmB0Wj8hIiGGQyGo6qq1rdYLLwLAGOMlYCDfcYYY+wh6XS6FtWrV78ZTKWnp1sBnL/XOaqqKiaT6ZuOHTtSTk6OZrPZKCgoSDt06JBSpUoV9O3bF5mZmZg9ezZsR47obP/8J4xOJ6DTyZTy1FQZGAMyXfzoURkwAkDLlsCGDXJrOIulxIA/PT0dANAkIQH6SZMAudWbnP5VFDn7HR4uBxJSU4F69QCDAb8sXOjcvn27TgDUtm1bbN26VduRn6/rlZyMc97eosvzz1NcXJwO3bsDv/+O4V5eujFXrmDfvn2ikaZRg6tXtfTAQGVvcDAqrl4t2i9YQFS/vtw9oJCfn6wtMHu2DNRds9KapmFeQICIf/llPNuz522RacWKFQURaQEBAcqSJUvQb906EZqfT9i+HahV69aBL74otxkMDweiopCuKHB+8AGseXk4s3+/5lalirJ161Zs3LgRPj4+mq+7u3D27ElNn3mGeuv1iIyMBBUUKNaNG5HZuLGyadMmbNmyBe+8887tD7hHDxm8z52LqocPI8BqRb2cHLme/803Zd2ASpVuPycuDhg58l6vDa5evQqHw3HPYwAgJiZGt2vXrjcmTJjQTlGUuPj4+JvZDkSEwMBABAYG3rcdAIiIiBDHjx+n1atX47nnnoO3tzciIyMBGXjfpmLFihg0aJB56tSp08eOHdsOwEAhxNXTp0+bnE7npcLjVFWtACDfYrHc3EnAaDTuOHbsWNPY2FgIIbBixYr8AwcOOA0Gw16Hw3HA6XR+Wrj0oJBer3/H19d3UP/+/XWzZs0KuXDhQksAPz7QjTHG2P8zHOwzxhhjD8loNLarWLEiAYDdbkdGRoYHgE33OkdRlCGenp4VatSoASIqDJqoc+fOgCuI8rpxAy/VrAnl5ZexLTZWBL30ElXOzsbpevWQeeQI6tatC71eL2eHs7NlobhCzZsDdrsM2vfsAYqtQ8/KygIAlA0MhHnWLPikpYmpU6dS7969ERoaKtee2+3AP/8p08uvXQM0DdeXLcPrEydi/tNPIzo6Gg0bNtQdPnwYZ7y88PSWLYoSFycvUK0aUKECxEsvofP+/XDr1ImoZ0+0e/ttxZaUhF27dkH/6qu0NjwcFU+dEhfXrydPT0/UrVsXSoUKssjg7Nny+hMmAB4emDFjBrycTmo5cKDMatDdytZ/+umn5T9mzoSWmqp9n5Cg9OjbFxHFH/yQITK132zGtJUrnRkZGTpz586ocPasSDh6lGKio6GrUAHeTz4J47/+peD99+VACgDvwjbOnIEpPx9t27bF9evXcfDgQVy8ePHmFn43eXkBY8bAo0sXDD52DIu7d4ffhx+iamH9geLCw2Wwn5srdzgoJjs7G5mZmSWfW0xQUBCGDh1qnjZtWn2n0ylycnJuW5rwoDRNw/HjxwkAOnTo8EDnBAYGQqfTKR4eHt1v3LjRVtM0X7PZDIPBEAYAEyZMeMVgMCQJIYSqqh0tFstvAGC1WietW7euTrVq1Tw2btxoO3ToUKrD4WgxatSoayVdR1XVSgaDYWSfPn3Mer0ely9f1sNVrJAxxtidONhnjDHGHt41u13WDTMYDGjUqJE1OTn5ZFJSUq5Op/sDwPWCgoLGQoh5er3ertfr23p6eob06tXLvcTU6fx8ue570SL416uHgu+/R73+/WnZ4sX4oVo10Jo1sFqtMJlMqFWrlgx6SwgO0bo1sGyZDCA//xxaZCQcDgf27NmDDRs2ICAgQFSPjib06oWX9u2j8ePHY8aMGejRvj3CvvwSHiYTRKVKwMyZoMREQKdDZNWqyoo+fXC2QgUYxo4FgoNR7bXXUK1KFTmTnZIit9MDAG9v3Hj9dQS1bAm3p56S9xQZCSOAxpqG3KNHMb1/f1gff5wOJydD0zRs3LhRGzx4sOLj4wMMHiz7vnAhtvr5IT09He3atRNUsSLB4bgt2IcQ8k9KCqp4eCjbDAZct9kEgNsecH5+PnSTJsGoKMjMzFQ0TcPQoUPh4eFBEEKuox86FFi+/K5ZEfD1vbnWv3PnziAiMWvWLAwcOJD8/f2BEyfkAMtXXwH9+kEsWgTbwYOoPm2a2D5lCp3p00e0bN2a7vjtiWTRwogIWSehiLS0NHz33XcAgK5du97lNbxl06ZNzg0bNuiEEDCZTDR9+nQkJiaWXHzwHhRFQd++fTFnzpybxQHvRtM0pKenY/Xq1ZoQgl5++WVDZmam79dff41y5crhxIkTiaqq9jcYDElDhw51y83Nxbx585aPGTPmXSHEDIvF8tPEiRN37t27t/mBAwfsNpttOYDBSUlJ0Xa7/aQQ4iuLxZJbeD29Xj+sVq1aOm9vb+zatQs6nW7r22+/fcaVNdBRr9dXczqdp4UQ31kslpyHunHGGCuFqHBdFmOMMcbuT1VVMplMqU888UR0lSpVbn6uaRquXLmCrKws2Gw2LF26FADg5eWFjh07onLlyjf3ur/NwoVy5nnAAKB6dWDqVDkbbbXiuIcHNm7cKNLT0wkARo8eLds4cUIWitPfZcx+82bk7tiBH86d09J9fRVPT09Ru3Ztql69Osr5+8u0/40bYbPZsCc5GdU6dcKWhATktGypHc/OVvQmE1q0aCFiY2Np5syZWuXKlZVatWqh7IIFcvb6iSdkv8uWlbUCnn1WrvmfNAkYOBDLBw+GpijoarcD8+fLc774AjnjxuHrAQPwWJcuaNSoEaxWK1auXKmdPn0azZo1k0XohIBj2jQc+uorXBo0SLR+8UXCqlVyj/qi6flNm8rAfPJkbNy4ERs2bECZMmVEhw4dyJVyDgDYtm0bflmzBl6enqLAaqWAwEAMGjQIuqIDB3/8IbfNq1VLXufTT+U2e4W++UbWQ5g6FQBw7do1fD9jhhCXL4vnt29XKCMD+PFH7D9+HGv27tVu3LihKIoC2GzosXAhnAYDfFavhqeXF44ePYry5cvLbApA7hpQvbosIOiSn5+P999/H2azWbz55pslL6wvwmq14vPPPxe1atWi+Ph4eHl5YfHixSI1NZWaNWuG22oMPIBJkyaJgoICatmy5W11Eorbvn071qxZg+joaK1bt26K0WiEzWZDUlISAKBu3br5WVlZjlatWnkV7iaQkZGBzZs33zh69KhwOp1dhRCBkZGR/0pISPDauXPndU9PT6O/v7/x7Nmz1uPHjx+z2Wy1LBaLU1VVvcFgyBo8eLBPYGAgZs2ade3UqVPTTCZTuKZp7apUqaIFBQW5nz9/Pv/48ePX7HZ7TYvF8mBpEYwxVkrxzD5jjDH2EIion5eXV0jRgBKQM6IBAQEIcAWJ0dHR2LlzJ9atW4cFCxbAy8vL+corr9yKMA8dkmn4CQlyrX21anIv9lOn5FZ0fn4od+0aCgN9ANi0aRM8PTxQt3170J49uGgywcvLC2lpabhy5Qo0TUNqaioMBgMCdu0S3fbsUXy3bQOVKXMrYHM45Hp8yGJvDRs1Av71LzSfMgVZS5YonVetwty5c7F+/Xr65ZdfAEDJz8931q5dW4d//EO2sXIl8MEHcmu7IUPkEoLkZDmTP2AArvfujRs3bmjo2VNBgwZy+zsi6I8fh3XyZOTmyslak8mErl27KuvXr3f+9ttvWLVqFby9vZ1Wq1WJrVyZWrq5EbZulcF2WJhcvvD77/JZWSw3g//HHnsMCQkJ+OCDD2jXrl3OyMhI3eXLl3H48GGkpaWhbLlyoqfFQtvj47Grfn1s3boVTZo0kQXqrFYgMhKYOxdo1Qr44QdZzG/fPuCVV4CoKLk0onC9fXo6vE6exKClSynF2xvfVqsmRPfulLVggVAUhdq0aaPExMRA0zQcOXIEy0wmVL92TVTq1Il+q1MHmTVqaFarVQkJCdESEhKU8LAw0OLFtwX7mzdvBgD0LFanoKgLFy5gyZIlmt1uh4eHBxGRaNGiBRUOKPXo0YOSkpKwd+9e0aJFi/sOGBRVpkwZoWkaFe58cDeVKlWCwWBAmzZtFKPRCACw2Wzw9/cXly9fppCQEHPHjh1vOycoKAg9evRwP336NObNm/cjEeVVqlTJHB4ejvDw8JupBA0aNDB9/fXXlS5dutQRck1+Wz8/P6Ww7kCDBg08HQ7HoKioKM+6desqbm5uhaeaZ86c6Th9+vRjABY/zH0zxlhpwzP7jDHG2ANSVdXdYDCce/bZZ30rVKjwQOecO3cO06ZNAyCrl7/Qti0Cz5+XAf64cTJQfucdYN48WXG/QoWbFemFENi9eze8vLzwxx9/aAcPHlScNhtiz5zRTsbEKNeuyaXNBoMB3t7eWkFBAZUrVw6+vr6iTJkySryHB4xz5gAvvAAUZiFYrUCbNnJruqNH5fZ8n3wC1K8vr6soSE1NxYEDB+BwOBAcHIz8/HwtOTlZCQ0N1QYMGCCjSadTBvft28t2T56U2/+lpeHHV18VHXr0IMOXX8rBhaFDAb0e2ZcuIb9GDexq1AgdvvoK+ueeA378Edi7F9mZmdiUny+qVKlCeXl5qFGjBtw3b5ZZD337ysr9gFznPno0MGjQbc/51KlTmDVrFsLDw7WWLVsqs2bNgs1mQ/ny5Z3x8fG6Q599hgsBAcj38oIQAl26dBG1atYkREbKHQ8KtywE5G4B33wjr/XLL7KQXu3acmZ/2zaZraDXwx4RgeTkZJw8eVJr0qSJEhYWdluF+/3792PlypV4++23kfPDD/CaMAG6efOQGxiINb/+ihMnToiwa9fw1O7dpKxZA0DO6n/xxReiadOm1KBBg7u+V/v27cOKFSvQokULXLx4EY0aNUJQUNBtx3z00Ue4fv06RowYgSLB8H0dOXIE8+fPx9NPP42qVave89ilS5c6c3Nzlf79+982MPDDDz+Iy5cvi+eff76EdBYpOzsb+fn5CAoKKnFngB07dmDt2rWHnE7na0aj8ZP27dtH1Sqa3VECp9OJyZMn37hx40ZTi8Wy527HqapqBNAVwHmLxbL5no0yxtj/KJ7ZZ4wxxh4QET0XFhZmeNBAHwAqVKiAUaNG4eSePdg1ZQpMGzfKGeTp04EvvpCBdnKy3Iru4EHAle7suh7quWbhV69eTWazGQPi4rD96FElLy8P0dHRuHTpkrNhw4a6+Pj4okHVrcjp4EGgVy+5vZ1eL/88/bT8Li9P7nPfq5dcf+9SvXp1VK9eHYAccJg0aZLinZMD/7Q0oEULoHNnoGtXIDZWBvvr1sn18+++C1itaF2pEn3y6qtoV68easTFgb76CjhzBn5mM7wnT8aVQ4fE4lmzqEd+PnREwL/+Bb8LF9Bl2TKCp6ecZbfZ5Pr9adOAESOAjh3lwMCRI3JLvWJCQkIQGxuL/Px8Zfbs2bDZbHjppZfg7++vA4Ba06dDmTQJaNsW41atwvKlS8nbbEbE558DiYm3N1b4TH79VT6XL76Qzy05Wab3Z2QArVrBcOUKGjdujMaNG5cY0J46dUoLDw9XAMD3iSeA7t2BQYPgY7ej55QpuNGxI300cSIOxsfj+vr1uGq1agcOHFD8/Py0Bg0a6Epqs1BMTAxOnTrl3LBhg+6NN95A4cx6Uf369cPKlSu1Dz/8UHFzc9OGDRumPEjRvqioKPj6+uL777/H8OHD4Vu4C0QJvLy8lAMHDpCmaTeXqQghcPLkSWrfvv09MwP8/Pzg5+d3z++dTmeN8uXLL6xRo4ZHXGExSEAOIpWwjCUtLQ1Op/OixWLZo6qqiYiG6vX6Kna7fRuARRaLxaqqaozRaFwYEBBQMTMzU6+qarzFYjl4n8fCGGP/c+462soYY4yx25lMphcaNmzo8bDn6efOReVx41DJZsPxrCzYli+XqeEdO8p91k+ckDP7e/YAJVRfdzgcyM3Npbi4OPglJ6NVfj50Oh2OHTuGatWq6dauXXszNf4OffvKon3du8vA1W6X1e7XrgU2bZJb9p08eed5Vitw7Bho0CB08fZGq19/Rcz69Yq1TBk5w/3ee3I2fNUquce8r68cCLBa4e7ujjpt22LZypVYu3atBjc3mX4/ciR0YWFoO2wYnVcU/KtbN7l/+tdfyz4CwPHjMvMgOFj2+cIFmVKvacCWLUCXLjL74fJleW9LlgDp6dAfOoRuHTsiODjYabVaC3+vm7ejKAqwYwewfz/atm2LXvPnw/7MMzgdEyO3Hrx6FThwAJg8WS6vaNMG+PhjICRE1hx47TV5jzt3Ajk58lkGBsp+jRuHm0scFi8GsrJw7do1HDx4UElISLj1TIlkxsBjjwGrV8N940bUadBA81m+HBd+/VU7e/Ys2rRpg4EDB94z0C+8n7i4OJ3BYNBKCvQBWSX/2WefVerUqaPl5eUpH3zwAT7//HPnza38CgrkNospKbcKHrr06dMHALBjx457poCGhoaSEAJHXTsYADLYLygoQKXiWw0+pD/++EMDgK5du3o3adJER1evykKKixbJ3+Kpp+44x8vLC1arNXzcuHELjUbj/vDw8AnNmzcfFhoa+hWAgokTJ+42Go072rRpE5WYmOgJQADI+486yhhjf1Mc7DPGGGMPQFVVxWq1Rt0srHY/QshU+dhY4NgxKBcuIEanw84GDTC9c2ehtW4tZ45fflnuMx8fLyvsT5ggA0iXM2fOYMqUKVAUBU2bNgXeew+GpUvRtm1bOJ1O7NmzB3a7HefOnbt7X0JDZZA8ZIhMvy8okIHs66/LQQe9Xgaws2cDWVmyWF3DhgARskwm/HL0KH7s1QtrRozAgmXLNGvt2jJALvTKK3KGf+RIWWwuNRWPP/44+vfvj507dyrOtDSZzaBpwNSpCAoKQqtWrZCbm6t88803ztv6Wr484OYmn8WqVTJlv1EjmUrftKmsyL9iBWA0AjVqyHN+/VUWONy+HfXeekv3/M8/izIFBcgcORI4fFim5QshBxR69YI3EX5PSMCeiAj8Om4cTkZEICs8HKu++grXLl4E6taV1fdDQmRWwfDhwOOPy5T+c+dkgcKQEDkgoihAzZq3diR47TVg3z4ULFiAlz78UBbi++yzW4MZVivw3HOyBsGYMUgMDFTCWrdG98aNlcGDBytxcXElF3IsQdrp0yijKARNk4MQe/bI3/PDD2UQv3QplD590KFVK+XNn39G4u+/wzs5WadUqiR/r2eflc+wTh05ENGz5806CAG1aiH24EGcXLqUcnx9kZGRAYwaBXToIJ8pZMr8pk2bBABULJKRoigK/P39tYMH/7PJck9PT8Vgs+HS999reOEF+TscOCDfkVGjZIHIJUtuO2fGjBkCAOLj459MTEys2rdvX3Pjxo0xYMAAz6Zyx4M6r7zyitloNNKMGTOuOZ3O5ywWy6n/qKOMMfY3xWn8jDHG2INxVxRFGAyG+x+5aRPw7bfAzz/LILBzZ2DYMHgFB6P1iROYP38+fTdqlNZ9xAjF19v71nlDhwI9egD79wO1akHTNMyfP18LCwtDQkKCLIJWqRKweDFCw8IAAA6HQzRu3JgK0+7vatAgWR+gTRsZ1FksgNkMvPgiULmybHf2bBnozpoFhIZCmEyYExWF3Nxc6ImQlZUFp9OpnD17FiEhISAimT7+1lsyoI6MlAF906bAtGmo2K0bggICtJONG1OV2FhCt25AUBDE889jV1wcfHx8kJmZqfv2228dAwcOlP9NcuqUXA5gMsnBiXnzgH/9SwZ5LVrIZ3vjhkylHzny1v317w8AUD76CH+sXq0ZCgp05S5elM/ys8/kEoDt24HISETZ7Sg3YADc9u6FIyEBl7t1w8U//sBJRUHzzz5DwebNcJs+HXjjDeD772XmxfHjcm0/kay3MH26DDgbNpSZBlarHDCZMgVWIXDe2xu58fFas7Q0Bdu3y/oILVvKQYydO2W//PxkkcO1a4GzZ+XAQbNmwHffyXatVvlbWCzyz40bMqOiUyfgxRdR9+hRxH/7Lc0XQuu+aJFijI6W9R+2bpWDR+7uMkNiyxaY+/VD7c8/x3WHA/ubNkUtHx9g4kSZSVI4079ggbwmACxZghYBATiwfz9+T0+Hcf9+EeTpSfj5ZyAtDTh4EDnZ2Th37hz5+vpqnp6et41QhIaGYt26dWjYsOH9//dSnMMBrFqFx7dvR/j168j96SflwsCBKD9s2K3U/eBg+bxnzJADTNHRAICQkBAymUzOtm3b3pEdUbVqVezevVszm81KZmamk4g2aJq26OE7yBhj/xs42GeMMcYezA1N08jhcEB/ty3vCo0cKYvtLVwoZ6SDg+VMNIDIyEiM6NwZjkaNlBl6vTbYYlFutqYosvhbnTq4MW8eZm7eDL1eT08++STd3CquZ08gOhreBgP8/PyQnZ1Ne/bsEa1bt75/xfXCHQS++EKm4p8+Lfd4b9VKzp4/88xth+ddv35zeYDD4YDFYsHkyZOdeXl5ug8//BAOhwNDhw5F2aAgYM4cmckwcKAMBj09YRs+HJUCA5W89HTg88+Bn34CBg5E9u7dcM/NReKnnyI7OxsLFizQ7/juOzTYuFFmILRpIwsKvvIKsH69HABISpL9nDhRBrPPPAO8/75MiS9iyalT2gmDQQeDAZ/Uri2eb9Xq/9h776iqznXt+3rmXIW26L1IR3oRBaQI9hrssWuiRk0sSUzdaWZpjKk70aiJKRp1azS2xEKs6AKpSlEQAUVpgtJ7X3N+f9ygaFVmuDsAACAASURBVMw++xvnfcc++z3zNwYDZK3Zn8Xwust1M7NZs6hiorCQKgNMTWG8YQMJaV9fGLS0ADt2wGfJEhQ0NKC2tBSDCwuhmDwZcHZGSWmpaFxczIxaWylgoKODnOBgQb5rF9M5cACm0dHM8MYNmnKwZQvKRVHMdnRkQ8vKOFy8SMGA1FRqSfDxoaCBgwMFMIyN6XtODnDsGHksvPwyeSIMGED+Bc8/Ty0IfWuAMUClQo+XFzqcnFBcW8vdUCjEoIEDyfPA0pKex+LFdM1vvAFcvgzexgb3qquRVFWFwORkMmX85BO657NnU5Bm5Uo6RkgIDHp6EL93LzBkCN6IiqIRiLdvAzY2wM8/w3THDniuWCFUNTcLeKJadNiwYVxmZiZyc3Ph6+v7Xy5NtLRQUOXoUQrwrF0LDBsG57AwqDdvhpuZmThPJmMAcPLkSWH85Mnc/vfe6zHSatmAFStY2qRJQpdSiba2Nt7Nze2ph6iqqgLHcSIARERE8Pfu3RtZXl5+Q61WvwGgXKFQrOB5PkIUxcKOjo5N69atS/uvT1xCQkLify6S2JeQkJCQkPjXiBBFkf+XSqy3bAFMTamne/p0yp4mJFCft7U1ZO7u6Ll8GVW//87FxcUhNjYWAAnq+oYGcG+9hbq5c9E4bx6ioqJEnudJyN+/D8TGokMmw5bNm6Grq6t99dVXeQMDg38u9O/eJfF06BAJ0uvXaeRfXBxlxHurBJ7EwMAAb7zxBj7//POHAQ49PT2Wnp4OpVIpcBzHzp8/j4aGBiyvqmJ8a2vfhuhob0fZuXNie3AwHE1N0dHZyeQbN6IkMhK/xMZiib8/rA4cgNW0aXihshKZ2dkQRowAN24ceQjMm0f3TFeXsvz9nebDw+keKxQQvvwS7NVXUXjrFqqrq1FUVMQBgIWFhWBnZ4djx44JPj4+fENBgTg2MpJxv/8OjB5Ngjkyksb5HTxIWfZx4+Awbhwqx48HW78euHIFJaWlyP/xR6bT0YFL+/fDMSpKbGpqQr2lJafj7Y3l27dj/8CBMJg8WTArLGQPFi0S7927xy1fvhx6enqUXe+jrAyNc+bgjoMDvN56C8qxY8FGjqTrtbGhsX+hoTTW8OWXyaRwzRoybTx4kDLetrbAhg3AkCHIO3NGaLK35/RMTWE4eDBDX4tJdjYFB1asoB73Pqf7KVNQu2ULerq7Ubx0KZy2baO16eBAAaq+VoRe6uvrH536+fMYWFZGQYuPPgJSU6Gtq0N5aSnnceUK17RwIQyNjB6+39DQEMOGDRM0Go3o6+v7dA+Ce/dobf7+O/08YgSNOszMJJ8EAC0tLVAoFNDT0xMBsK6uLmRkZHDZ77yDCbGxMsZxMDtwALEFBdyDl14CGIO9vf2fjldWVobTp0+jL+OvVCoxf/58vby8PPekpKQ9ra2tzN/fX9fNzU324MEDr0uXLo3euHHjG+++++72p567hISExH8AktiXkJCQkJB4ArVazQAMAhAGwFgmk5kxxlaJoojOzk5UV1ejpqYGpqamKCgoEIcOHco6OzthYWFBAi06GqivB+Ry4MwZ6oPPySEXfMaAMWOg89FH8Pb2FhoaGlBaWsqdOHFCrKmp6Zu5hynPPCO8WV/PcRERj6ILP/8M/PEHuHPn0NPTg6amJn7Lli14/fXXnz5a7fPPAUNDYOxYqhro7CT3f3t7MpKrraWe9EGDKPv7lPFnenp6YIzByMhIC4B3c3PjkpKSYG5uzrW2tqKsrAwdHR34u76+aGplJbR//jmnVShYW1ub2DVrFosyN9cavPEGf3nFChSMH4/q/fth5eAgWDs4cFi9GvjpJ9QPHCjcDg0Ft3o1h4ICMt47c+ah4INCQefYn9BQ3Lp0CfjuO+SnpCDT1xdgDHp6emJISAjz8PDgsrKytJWVlXx9RQXm7N7NkoOChPCDBznOzIxMAc3MSOhy3MPRfr9++y06AgIQvXYtmpqbceDAAUwOCUFNYiIAoKSkhAGAoaGhOHXqVBbn46NtrazkZ73yCpc2cqRgtmABN23aNBgaGlJLw/376N66Fby+Ph5cvy7eKypiN595Rji9aBH35q5d4NPT6dpef52y2sXFjwIxly9TRQNjD8vUAVAACYDLpEnc9xUV4Lu6kDRokNZ1wQISuamplNXPz6e2jH68JJej8ZtvcOnZZ0Wnf/yDQUeHSuOXLKF1OnEi+QvI5bCwsMCqVauwdetW1OblkQmhnx+ZGRoaQlyzBqNPnYL9V1/h9N//rn32/ff5/g75QUFBXFJSEoTr18GVldG+R4ygtaZQUNDF0JCuOyiIAi79uHbtGn777TcwxmBqasoaGxvx448/Cl4mJpielcXxfdfm7Q188QVscnKAhQv//DkAcOjQITEsLIwF97sfjDGYmZlBT0+Pq62tlSUlJbGbN282BwQEGISEhOilp6cvBiCJfQkJif9YJLEvISEhIfH/HGq12mTdunX1f/EaB2AagDt/NYdboVB8JZPJXvDw8OD09fUVMpkMFRUV3K1bt7Bnzx7hwYMHnCiKCAoKQlZWFktNTQUAzJw5E14mJmB+fiT0+zA3p6+rV8nc7OOPgenTEfrFF1zStm3YW1oK/4AAcebMmczCwgKiKIJrauIwdy5laPvmra9eDSxcCIVCgWeffRbHjh1DW1sb9uzZo122bBkJvbo6KsX+/nvqaTYwoH78I0cenU9VFbUX3LpF7vPJySQ4P/+cxNgT1Qvm5uaorq7mr169ilGjRsHIyAhBQUG4e/cuMjMzMXbsWGRmZjKPpUv5GhMTnHn2WQwZMoQ5Ojoie+9e8Y6np6hYvJgtSU0Fu3kTspkzOaxcScLM1hZF9++LolLJp+/fj5CffqJWBkvLRydw/z6NvetHQkICLmo0cFq7FvydO1i4Zw8yX3lFO33NmodZ3ZycHOgolfDU0RGv+vmxGx4enEdVFSwMDVG+axeqPvoIFmvXIvP4cTQ0NKDx3j1h7tdfc1UbNohae3u274cfRFtbW9Fz6FCuyMAA0ydNQmVlJcrLy8VFixYxjuPg5ORExwsMxDhPTw5JSUBCAjo0GnTu3YvkVaugunQJtwMCUOnvjzmbNmFBURH3U1qa9u7ixbzbwIHUj//zz4CXF62bIUOo5aGkhLL5R45Q+8YTz6W4uBgAGeW1traSa35FBa0TtZrWW38EAcLs2YhLSsKUf/yD9cydC1lUFAnuzk5aK6IItLc/XL9mZmZQcRxsf/yRglXm5mQ6mZ6Og4WFQmlpKWe+aZN2iJ8fjwEDqBXg2WeBESNgZGcHw/feE6u3b2dWxcUk9keNomBTZSV5PRQWkmdB/7F6vdzpnRIxadIkuLi4sG+//Vb08vLCM+7uHHeoX6u9UkltHdu3UzXIsGEPX+rs7ERiYiJaW1tZTEzMn47xyy+/tDY1NX0A4FcAdbW1tRGJiYlTZDJZWHd399d/2kBCQkLiPwj+ww8//Hefg4SEhISExP8R1Gq1QWpq6kmtVrsjNTX1mQsXLlyMiYmp6/8ejUYzQVdX9wDP84s1Go3y4sWLmpiYGLHfPvRFUTz66quvKn19fWUuLi7MycmJCYKAoqIi2Nrasvr6eoiiiMGDBz82ciwvLw8OurowHTUKcHZ+/OQ+/ZR60D/7jDLX8+fDqLwcdh99hFZvb3GCsTFnaGoKZm4Oxhg50g8bRuZyISGAvj5la728ADs7mJqaorS0VKytrWWMMda0Y4dQ+v33TBEWxgyPHSNTwNhYEo5PYmpKpfAeHpRBX7qURP7GjRQI0NMDrKwevr28vFx48OABMzc3h6urK+zs7Poc1+Hr6wsdHR3Ex8eL14yNmdcbb2Ds1Knw8PCAmZkZfKytOXMrKzZg4ULwL74ILi2Netjfeotm2V++jAG//cY98PSE47ffimabNzP4+z9+vjwP6OtD8PJCamoq4uLihNzcXKanp4eZc+fidlOT1qqzkxsWEMDB0JD64AG4urpyESdPwv74cXZh2DCs/Oor3ADwu6enWC6TsesNDcjOzkZ7e7v44MEDFuHnh4FBQcxq5Up2/PhxbW1tLV544QWOXb8OU40GlkuXwtXVFUFBQYz1r4K4c4eCETwPceFC1F24gKOWlrgeGiqWGBrCcNo05jNmDCZMmMDMzcyAESNgWV3NdV69Cv3oaMjt7EgE8zxl8BctAlJSqPqivJxE9pgxlA3X1X14WJVKhdzcXDEgIIDNnTuXIgFFRSR4Z88m8TtxImXuy8oAJyfwa9aA8/bGEZkMZtHRMDc3J+PGsWOpymHePKooOH8edc7OOHnyJLTZ2XBlDCZ94wXXrUP72bM4qVSyoKAgzJk7l7MZMIBE99dfk/9DdDQQFoYHKpVQYGfH/D79lG5YVBQFFD79lN7T0kKtJM8+S2u7txWhqakJcXFxEAQBJSUlyMjIELu7u1llZSWLmTEDbPHix9eIkRFVQCQnkw9Fr/FlTU0Njh07BhMTE21BQYFYWlrK3N3dHz6/u3fvdre2tjrwPO/O8/w4nuf9enp6irq7u9Xvv/9+wp8/PBISEhL/OUhiX0JCQkLiPxa1Wm2n0Wj8NBpNg0ajsVAoFBc9PT2DlixZIlcqlTalpaXz4+PjD8XExDT0baPRaHzs7OxiFy1aZHD79u0h3d3dQfHx8cdiYmKE3teHqFSqOVFRUY8NL7e2tkZUVBR8fX3h4+OD0tJSbWlpqejq6opnnnmGZWVlAQCMz58XlAkJ+IPnBW9vb44xRllTe3tg0qRHvec8Dzg4QLl2LQ4lJzPXPXtgfO0aZU2PHqVyaVNT6vPfu5dM1L77jsRbrxAfYGnJTD/8ECWmpsyzpoYZarXs954eIfy77xj09f/6xl29SuX8c+dSL39sLAmk2bNJLG7bRllkY2PA0BBmZmbs6tWrqK+vF8LDw5/qD+Dv788u37ghmGzbxq5kZCC7sVGwtLRkBiUlVJ0wejT1Yt+6RSXzEyaQOBs6FPLwcDi88w4yhgwRnV94geXn5+P06dM4ffq0kJSUhJITJ0S9PXvwY0sLysvL4enpiXHjxrExY8ZAX18fp+LiOIOJE+Hq70/3ycqKhOP9+0B+PhQ3biD0hx+QqKsrpiqVmCoIzH/fPoT94x8YOXIkwsLCmPnevYLx7t3M5OefWWZmppiWlsaWL1/O6ejo0LNqayO/A4DE6t275Bvw4AGV2jc0QJg9G7/a2wsaf39MaWhgw1NSWOSePczDwwOWlpaQvf46tSa89BLu3b2LOC8veHV2Qj86+vHgkKcnCWFLS3oGr7xC4+Z8fKi3ffRoAIBCoUBFRQWKi4vFsLAwei4GBpTl1tcn139fXzqmkRGVu/v7Q8fPD7ne3hg3fz4FlpYvp+qPvgDG4cPAtWu4bGaGzIwMTIiLg+2uXVBYWNDrEyYg388PxSUlwqJFix4FPlQqEvyZmbSWXFxwt6SEKywsRGRk5KN1k5ZG7505k8wHP/uMrr+ggCpTXF1RXFyM69evw8XFRZg5cyYLCAhgWVlZMDQ0xNCkJGoFWLLk8UXo4kKBqw8+oHugqwstY0hNTUVXVxenUChYSUkJS0hIgK2tLUxNTeHt7a0wNze3trOzC3Zycgp2cXEZpFKpomtra1cmJiaaxsfHX4qJiXl8PKSEhITEfwhSGb+EhISExH8kGzdufFkul39qaGjYWV9fr8sYQ3h4OBs2bJiMMYZe8WN88eLFP9Rqte+6det654vhQUtLi6hSqbBkyRK9ffv2TaisrNwGYFnv6wZ6enrC049KmJmZYfny5Q/LxQVBgK+vL+RyOYrKyrhyjsOdmzf5y5cvY4ijI3RCQynj+IQBGgC0tLZCJpdDdeYM9ZCnpgI//EBj5o4coRFsJiZk0LZ/P5VRJyQAGg2M3noLbh0dYoYgMKsvv2Rnz57V+tjZPd0MrT9OTtQbbm9PJdRxcSS6ZDLK7MbGAnFxEN57DzdVKuF3CwuOMYaJEyf+pTuhTCbDzJkzOfH0adRWVaGgqIizt7cXjfLymKK0FPzIkSSO33yTBOjUqYBCgZZNm1D78ceisq6O6VRWcp999hl0dHREGxsbYdy4cbyVlRXE8+eZKicH48ePh6+vLziOYwC5q6enp2t1dHT4lJQUREdHQ7FnD5Whz50LnD1L5m9NTeB0dHBdLhc7m5u5fYzB4YMPhEWGhlyfUPX29OTiDAyESzt3oqqqis2cOZP67gGIPA928CCJ07feoqkDu3YBWi0J6Lg4AMAfp05py5ua2Msvv8wUXV30zOvqaHTg22/TPa+uBt5/Hw4ffADZ9evaW7/9xrPRo2He/2Z6edF3FxcSrr1jBZGdTdf22WcQ/vgDd3/4AXl5eWz27Nl0EeXlgLMzxKYmnDx/XusoCLzhypWiRUoKy50zR2u8ciWvyM1FuY8PIpcswUOzyfXrqYy+r9/9zTcBAJYrV8KhrQ2Ozs7Q6+8ZIJfDbcYMuI0ZI3D9HSurqig4MWwYBagWLICuri4ZFfaRkEDjAS9fpiDKc89RQGv0aBoBuWYNcOMG5JMnAwBMTU0FS0tLDgCCg4O1WVlZPGbOpPv+NN58kz5DDg7AkCEwCgzEO99+i87WVhiYm7OOjg7s2rULZ86cgZmZGUxMTPCUsZXKESNG4NixYyvKy8vHqdXqqHXr1tU97XASEhIS/5ORxL6EhISExH8kMpls2qhRo5TBwcHKnp4eMMbwcDxdL6GhoVxOTo59ZWXlIgA/9f76RkNDg25PTw/kcjlmz56tu23btnlqtfrwunXrzgK4XVNT8xS3u7+G4zhMnz4dgiDgwcmTOCOTgeM4XDx/Hlx4OCI2bQKcnaHVav90jufPn4ejo6PWzMyMXggLo5JwUaR+5uZmEsevvUbGaOnplH0vKAAUCpjk5LAVvfsyNjZGUVERdu7cKRoZGbHo6Gh0dnaipaUFZmZm0NHRgYGBAVUNvPzyI3f3CxeAmho0vvQSVCoVOJUK3dOmYe+NG6LRnTts7aVLuDh8uJiakiJ6eXn9peB3dnYG4uPx69/+BgXPQ6PRsPLiYhg3NcG/oUEcsGIFg0YDQRBQvGoVCs+f16oWLuRVjInCsWMskOcRlZEBnZdeYgAe3ahRowAXF/g/Ud5///59XLt2jY+MjIS7uzsUCgUQGAi8+y71/TNGI/w+/BDgeVhZWXFOTk7ayZMn89ysWRxzd6d9T5gA7TffIEBfn8vctQuDBw+Gu7s7is+dw/VffgGrrsaE5GTkbtwIq6AgmKxeDaW/Pxnp9ZKamirm5ORwK1asYAqFgkrux4yhKgq1mjLZBw9SAGDSJBgMH441QUF8yc6d4ndnz7IF1tYwNjZGSUkJ/Pz8KOO+bRsJZ1GkazE0pK9Zs3ApI0O8umsXm3LyJI61t0PP1laMjIxkgaWlyCksRGZmJt9uaYkRR4+y6ytWoNzLi798/Lg2/MQJPi0mBq/0v5dPM3cUBHifOIFqNzfcff55+DzhF9AwcSIedHfLurq66L4DVJIPAOPHA198AYwfj0uXLsHc3JyCET09VDWwadOjMYK2tuQz4ORE2f3vvweuXUPN88+DGzYMuv3aFry8vPjc3FwBgsDhyXF+H31E9zYujrL706bR13PPQZ6WBnlsLFBcDB3G4O3tjcTERGzduhXu7u7C7Nmz/7SmDQwMMH/+fL0DBw64FBYWzgPwzZ9vkoSEhMT/bKQyfgkJCQmJ/0guXLhQVFxcPNvV1VVuZGSEp43EY4zByspKkZOTMyw+Pv7HmJiY9piYmM6UlJSp5ubmNhYWFpDJZDAyMpIXFBRMvHjxYrBMJtvg6uqqf+XKFe727dtQqVQw6jdS7J/BGIPqiy/QEx6O21otnjt2DLXZ2UgKDtZmZ2fj+PHjzNLSEgqFAu3t7VAqlTh27BjGjx/PmZqaPr6zkhLA1ZWE3h9/ULays5MyxZaWwIwZJJT69Y6rVCouOTkZTU1NrKqqCleuXEFmZiby8vKQnp6OlJQUZGdni9U1NaL1N9+wWjc3GDo7o9XbG0WNjTh08CAuXL2KtrY2IScnB9Xd3eLizz7jZFFRcDpzhrWnpbGk4mJh4NCh7MmgBUBj0pqamjBqxQqEjRqFLh8fjN66FfeNjFAzYgSzuXcPGnt74fDhw+x2WZngX17OB9XUwKazk1lv2AB9rRayF18ksWhm9mjHCQnA7t1UDdAPIyMjJCYmIiIiAo6OjpRNLikhL4KwMCrzHjSIyuJLSnCmq0sMDQnhrG1twdLSKBDQ3Q3Ex+OjujpkZWfDsqoKtseO4eadO6KvWs26mpqQFhqK+xYWSLS3R0Z7Oy7n52Pw4MEPRW5hYSFOnTrF5syZw6ytrR+dYF0dZZitrWnMn6srBW+++ope37cPxgYGrG3YMPHcuXNISUlhRUVFYmYm+UaahYUxeUsL7WPRIkClgiAIOJOWJiR3dXEeNjZwSUxEqZeXaF9RwWw//xx7q6uRW1qKgIAAcXpcHNPr7obDhg3wiY5GuLExZ/DJJyibNQuD+rv0R0WR0O4nrAVRxN8rKxGWlgbXCRPAP5H9ZoMGoejECZgOHgzjXo8EXL5M7R8TJ1JgKjMTpVZWQm1tLZXxf/ghjXl89tlHOyoqehSUoUUM2NigdNcuRLS2Iuittzg86q9Hbm4us3v7bRjn5oKbOfPRfszNqVKl7zw5jj4foaEUSJg9m/7t6wvdigqUOjkJcydPZvHJySw8PPypfz8aGxtx4cKFbq1WuykmJqb8T2+QkJCQ+B+OJPYlJCQkJP7trF+/PiYtLe3w5cuXF1+4cKEwJiam5L/aJiYmpvTSpUs3r127NqmjowPOzs78Y6ZpvRgaGqKlpYWrrq4Oi4+P3x8TEyNoNJpga2vr4AG9ZmAWFhYwMzPTvXnzpo9KpTK4d+8e19jYiOrqamRnZ8PGxoaMzP4VAgJgO2kSokeMgFF3N8qHDkWrXM4xxlBXV8eKioqQlJSEtLQ0JCQkQBAETJkwASwnhzKTqalk4Hf5MhnZGRhQhnj4cCoBFwTKxNbUAC+9RBl+CwugsRFGLi7IyckROzs72fjx46FSqfDcc89h2LBhCAsLQ1dXF+zs7Njt27cFbVkZS66vR2phITTZ2czo0iUh+uZNlunqiqbmZtbR0SHMmjWLNzAwACwtwU+bBqv2dlhu386ulpcLLoGBDAYGDy87IyMDe/bsQXp6OuxefBGWM2fC3cUFit9+Q42vL241NCDezw9lFRVMBojP1NSIPnI5uaq/8AJw4gSVul+4ANy4QZMEbG1p5zU19DV8+GO3WiaTged5IeHAAabQ1YXNJ5+Q58GUKdSvn5tLmfFZsyAMHoyK3btZ9AcfgFuzhrLyiYl0/9atg9n69QhKS0OzSgVbpRLX9fTYuaFDcdvHB29++CF8Nm1C0McfQ8/MDMXFxbh27ZpYVFTEsrKykJaWhnHjxsGrr/weoCCNhwf1sL/0EvDJJ3Q+Li6PxO7Ro8CkSXAbOZINHTqUBQYGYuTIkQwAS09PFzIyMljI8OGMeXmhOyAA/9izR7SwtGQnTpxg5ubmGODpieNGRujS1WXPnz8Pi7o6OHzyCSZOmwYvLy+GgAAaAdncTEGP1lZ8o1KJtY2NzMPDAyqVCtXV1eC/+gqyDz+kjHgv7e3tKDh+HJ7NzaLpgQMMr7322L1vvXcPg156CYUTJsDBxYV+mZVFlQd+fhQ8+OUXeEZHs8SiIhZqbw/Z+vU0/aF/JcHNmzR5IDb20e8UChysrRVMvL2Zw6uv0nM3MYGVlRUsLS1xUCbDlQEDhPb2duZSWUnPWK3GY9l+Hx/yKXjmGeT4+GDfmTPa1NRU8UpQkJhnbIwBt2+zQa+8wm6OHStePXYMHUolc3Ry6n10IvLz87F///52rVb7tiAIRzUaTZBGownUaDQ1MTExHf/aHwMJCQmJfy+S2JeQkJCQ+LeiVquZXC6/On78eFd3d3f7u3fvzoqPj78RHR1d8F9tGx0dffPixYs/VVVVjdXT07O27ROHT+Di4sIXFxdbtbW1xcTHx7dyHPfB+PHjFX0lwowxWFhYoKSkRBBFURw2bBi7ffs2jIyMoFKpEBgYCFXfzPd/RkvLI5f7554DfvgBAwIC4OvrCz8/P3blyhXB0tJSfGHsWKZNS4NBURGGXLkCuw8+ALOwoJ55Z2fqW37xRSpJ9/GhMWVffw2cP0/H+fZbKok+cIC26ewk1/6dO+Ftb8+qcnORV1WF5158ETzPg+M4yOVyeHh4wMXFBSEhIZxzezsLsrBgre7uLCoqCsHLljHDykpw/v540Nwsrl69mjPoJ+bBGGSDB6MjNhal+/czl9RUcB0dyOnqwu69e4W8vLyHkRbfqCiY944LZMbGYAYGCNy9G3mhoUK7IDD/K1eYeUkJMztwgIExyuwqFEBjI7nCHzxIpfcvvUSvPXhA97bPIK8fjl1dLHjVKsS3tMDy+++h6iutT0+nfv2FCwEDA/xy6hQKZDKYLVoEq6Agqo44dowEeFQUdFUqJA8aJFa6ubFaX1/EPvccmltaxEWLFjEdHR2wkyehM20aHH19UVpaKgqCwARBEO/du8eUSqU4ZcoU9jA7fOIEBWmee46CNl9+SeaKS5eiauhQlNbUwMLMDLh4kUwb9fXBcRx0dXXBGIODgwMGDx7MXbhwgQ0dOhQyX19kr10rhH32GfedQgGZXI7p06fDxcUFcrlcLC4uZukuLqicP18Iv3yZsTlzgK1babqCsTGQkUHrZMIEBPz8M8u6dk1MS0tjd+7cETUaDctpaUGbt7fgOGzYQ7O9w7/+Ko7eto21bdsG8y1bGO7fp9L73pF8TF8fm9vb0cxxYnBwMG1UUkJVDI6OFOQQBMiys1Flba3lX3+d0ztwAAp7+8cfoLExcOgQTRzopa2tDZcSEpiFv7/WzcODw+nTgKsrmKkpLC0tMWz/fmj19ITE0lIuYNQo6FpYIEWrmnD9NgAAIABJREFURV1dHSwtLfEw6KenB5w6hWvGxujS0cHo0aN514EDOScvL+Y0bhwzeust+IaFMb+5c1lFYSGcYmOB2lrsPHy4NTMzs7izs3OuIAjXlUrleR0dndcsLS2nd3R0rIiPj98WExPTAwkJCYn/4UhiX0JCQkLi34pGozHlOO6dmTNn8lZWVnBxcZFfv349Nj4+/kRMTEzVv7C9Ac/zc1xdXW3/SuxzHAdfX1+5Vqt1EEVxQkxMjIGTk9NjZQCMMQQGBrIhQ4Ywa2trJCcnQ6lUCgDE5ORk5unpicfE79OorQXy86lk29aWRuZ1d5OxWm4uImprWdCJE0xx9izc/P2RV1cnltnYMKPvvsNVAwPRaepUxrm4PBoj1seJE2ReFhpKbuzNzWSot3cvZbJHj6b+5EmToKyogCItTdTW1zOP3bvBbt6k4INc/lCoAQCys8Hl5MDlpZdgYmJCxxs0CAOmT0emlRVzDQl5rF+6D30TE/xRVSWW6eqy4uRkrd3mzZxneDizjIjA3bt30WvkB9kPP5Bw12phGBgInWnTELZ8OTM/cQK6N27g95EjWWlFhdbX15dM8kxMaARcYiKJ/LQ0ynx7eFDf+9WrVN7fR1ISlfW/+CKqR43C+bo6ZGRkwMXFhdoufHyA6GgIRkb48ccftSUlJVxgYCBCJk6EvLYWuH0byM+H4O2Nn5RK8Wx3N6sHWFdXlzh27Fjm5OQEPz8/9rAfvbERcHHBsYsXtQUFBdzo0aMxadIk5unpidTUVCaXy6mVQBSBSZPQwRiqOQ46nZ1oGDkS6bW1OHH8uNZ440bumFaLkoQELc6eZTtbW1l2drY2ICCAk8nISkkQBOzbt09UKBRiQEAAS0lJEc+Xl3PVFhYInDFDXDB/PjM2MYGOjg66urpYbm4u3vj0U9wEmNPHH0PxzDOUXZ8zh7L1mzfTvZs4EQovL0RERDAXFxckJyeznp4eDB85Etrjx3G2sVEYNGgQxxhDd1wc0+bnQ/v888zSyoraIsrK6JmCqip0Tp+Gx/HjMFu6lAGAsHMnYG4O1mfmN3AgCl5/HfpJSVyLvj5u+frCva9cvw9dXarkCA9/uD6rq6uRmZkJR0dH5jJtGoObGxku9j5XduQInMaN4wavXYtfmptxY8AA8caNG6yoqAgXLlxAQUEBAgMDwclkwKxZqD5wAA4FBaLP888zc3NzWFhYkPmiri5kcjkO29n1VLm4cIPu3AGmTEH8oEFa1ytXDjXY2YVwPP/N6NGjbadNm6YIDg5WXrlyRezq6joSExNTrVardTQazYTejH91TExM6z//A/HPUavVMo1GI8bExPx3diMhISHxEEnsS0hISEj8W9FoNE66urpLwsPDFQD6euRlRUVFU+Lj43+IiYnpAoANGzbMSEtL25eYmPh+cnLywsTExPUJCQmvMcbeCQoKsoyKipI9rYy/D66zE84WFlxQSIjSSqVi6OqiTGVrK/X3CgLQ0QHIZOC6ujAsIgK+fn7s+tWraGtvZ14DB8JEpaL3avtN4hJF+s4YCcivv6YMtEwGnD5NJdtdXST+bW1pJNiqVUB4OO7p6YkFra0sr7AQJSUlrLGxUfD09Hz8IpqbydDsyy/p2O7uNF7u66/J3f+992iUnZERZTIDApBmairc1dcXA+bP57paWqA4dw7YuZP2FR9P7/X1pTGAjo6PjqVUgrW3o76kROjw8GB/FTxxcnJiuZWVgsP48ZzX1KnMIi0N4vHj4h09PRaYlQWrgwehm5ZG11tdTaZso0cD8fGwKCoC9/nnuHrzJurr6zl3d/eHrvcA6HyWL6fRcTNmULZ4+nTqx3Z0BPLyKCCgUtFowpgYGAwYAD8/P6Snp6OkpERwcnJiBkolBD8/bBZFgZPLuQULFjB/f38oGxtpKoBGA5w9C21MDDoOHIBufT1rd3HRrl27lrO0tPzzRe/YAdjZoUwmY5WVlaytrU3b1NTEPDw8GM/zYkJCAtNfuxbJWVliyuTJ8PjoI5ZTXo59Fha48uABGhsbRb/AQIQdPsx016wRvSsrOUuZjAW//TaKi4uRmJgoDB06lAOAo0ePChUVFdy8efNYW1sbDh8+zESeh9bZWTv70CEOR4/SZABQFjw7OxtXBw1Cq7ExIufMAff22zTrvrmZSuC3bQN++YXWTC9GRkaIioqCXC5H0vnzmL1rF8saPx5JyclCYGAgd23rVjEjIIAVNTQgJCQE4vz56BgzBrKuLrDeAMiZAwdgUFvLlLGxZAq5bx8709yM+JIS0cbGhhkbG6NOVxcO+/bhzqpVGD9//p/vK8+TJ4WXFwV8QH8Drl69KhgbG3MeHh70+yFDaNpAVhZNKHB3h8LcHDqxsSgoLMSCBQvY6NGjYW9vj5SUFGRcvSqkJCeLOSdOiKqsLDgXFzOVSsWgVNI+vvuOAgwLF6I7Nxem1tbM4ddfgfXrEXTqlNzv999D/UtKAoZu2SJ3GjiQMcYgiiISEhIErVb7hUajMZHL5bnW1tazLS0tY5ubm1+5ePHi2ZiYmIqn/wUC1Go1p9FoTDUaTUd/Qa9Wqy1TU1MTtFrtdplM9vrly5cV8fHxCZLol5CQ+O8iufFLSEhISPxbkclkC9zc3B5zx/L392e3b982zc/P3wDgFbVaPUSpVO6eMmWKnrGxMWpqauwOHTqEqKgoREdH/8nh/iHNzSQa3d0pI25tTePrZs+mLOKuXdQvPmMG9TbHxQHHj5NhmYMD2PvvY9bHH7NrAQFwuX2b+smTkynLHhJC5fZqNZXcHz9OZdkAlU2HhgIjRtCM9Ke5nQMYM2YMN2bMGABAXl4eDh06xA0ZMgR2dnaP3nT8OGWp+18jYzTGLTqahEufU/9nnwEWFhBFkbV3d3NfnDsHURTx7saNkLW1UeXB3r1UXh4YSAJw7Fga8ff993RNr7yCAYsXcw3btgnYteupzvvW1tZYunTpo9dCQ1G+ahWeP3QIrbGxMPHwoACHoyNll9PSKBNfVgamVsPG2hqGhoZoamrCL7/8Ag8PD+3kyZMfXaCZGfXoOzgAK1eSs7tWCwQHA3/7G5V+795NYq2XpqYmADSR4KeffsKoUaMg9/QULQwMmIO3N/vuu++g29qKiXl5guuDB5zO888D48ejPTMT1osXM4/XX8cRc3N+00cfYd7ChXDp60PvY+JEwMkJY1xdmbe3N272jlZ0c3NDzLBhzHPgQOinpMBRo8FNrVYUs7LYCCcnDOvpAc/z6K2PZ7hzB2GiyLBrF60fc3MsXLiQ2759u7hx40Y4OzuLPM8za2trrZmZGZ+UlCRwHMe5u7vDx8eHx6hRD0vkwXEYMGAA/lZdjWOtrch3csKpOXOEyXZ2HASBqj9WraKMfEcHjVkcPfqxy4qIiEBaWpo2Jy2NX+zryx08eFDY8fbbGJOby1zfeQe/njiBjz/+GIwx6NbVYc2WLUj/7Tfca2vTaiMi+CxLS2R//z3qtVqmmjQJAZGRKOzpYfv27YO7m5s25JdfuFteXhiZksKwevXjFSt99PQAd+6QnwGATz/9VOzo6OBqa2tFALRBQAB9fl99la7Lzw84dQq+n38O38ZGhjFjyP9g7lysra+H7rZtXNX+/TBZtgzlPI+by5ZpbT78kMf69RSga26m44aGorWqCik1NdCfPBn+7u7Q3bABKCuDkZsbw5dfAp9/DjCG9vZ29PT0AMA9hULxQ0hIiMXIkSN5ADh58mRXRkZGNICrfZe1adOml3men6bVaot4ng/ged6HMcZ4ns9Tq9Wj1q1bVwsASqXy4KBBg/xGjRrFGhoaDPbt2/dGXV3dDQCH1Wq1vY6OzkFRFB26u7s/FgRhx7p168SnfS4lJCQknkQS+xISEhIS/1dRq9W6ADqe9h9UtVo9VKlUvjJ8+PA/1YtHRkbqFBQUzFGr1a8plcpvR40apevh4QEA0PZm1lUq1Z+FfnExlQb//jvwxhskGpcupb739etJKJaWPnp/wePWAIIg4NuPP9bW19fzAzo7tdzXX3OlpaUY+c47rN+bHm0QHEyCPi8PmDWLggEvv0yBgXv3SED/C3h7e0OpVIpFRUXsMbGvo/No/nl/TpyAWFmJji+/RMGCBSLbulVs+uILUalScTeVSk5PT0/w9vbm8vPz0dbWRtlzQ0Ng3jwKDIgiiZ3798nt/sAB+ndjIzx++w2NSiXXs2QJZKWlNK9+1iwSpy4uVB3QZzKnUAA8jwoDA3gDsLO1pXtx4QKQmUnCbOFC6rtfvZoEG4B58+bhp59+Etva2lh2djZfXV0tPjNxIrPqa2HIyqLe/atXyaOAMXqux45RhcMT3LlzBwCwcOFCLj8/HwcPHoSPmRlrzstDSlOT6GpsjHEVFayku5s77u6OZ1evhiCTYfP27dDKZNBZvBgjk5NF/upV1hgV9VB4PuTKFbpvrq6wt7eHubk5kpOTwXMcEBwM6+hoYPduqD76iFls2MDQW63QV5r/kL5Ak44OTQkAwPM8Vq1axQoLC3Hs2DHW3d0NlUrFbdmyRaivr+eMjY3BcRyVwOvo0LMbMICqPZ59FoqODkw9dQqnQ0NREhTELr37rhhZVcVknZ10vO3b6T4GBQFnzpDg73cPbWxsONNp04DvvsOcOXO4vJ4eOLm6orG3skNfX1+YPn06Z29vj7SAAOSUl2v1dHRYe3u7NvbIEb7Gykq0OniQ2f/977D384OfiwtaWlpw+J13uILOTpY5dixkp07B58gRKGbM+PNaHjKEjClHjcLBgwfFjo4OZm9vj5KSElZfX09tJgBgZUXBi19+oeCAKJILf0QEBYY++wzw8iJ/jU8+ga2ODj5dvlwcMGAAGxYZyWP/frr306bRfQGAV19FFMDd2b1be/f333mPjz6C7McfIUtMpEDhzZtkAHjwIP3toOCDHACnVCo5gAz9ioqKugCk9V2SWq1mHMd98cwzz8haW1uHmZqawsHBAUqlEn/88YfP9evXD6nV6pEAbOVyedjw4cPlHMfBxMQEHMeJAHoAQKlUbgoICAj18fHhf/311y9aWlryAVz6802UkJCQ+DN/OStXQkJCQkLiv8vGjRtXMsZa5XL5fbVaHdH/NbVazZRK5c6JEyfqPm20nYWFBUxNTfUAdFlbW3sNGjToodi2sbEBYwxxcXHo6OggIfnzzyTCg4MpUz1rFmU0tVoSOmvWUPn2mjX/9Jyzs7NRU1PD6+npoby8nC8qKmLd3d3sl19+0T72xmvXHmX2q6tJ3O7bR2XnAwYAhYUkipub/xRQ+CsGDx7MEhMTcfDgQW1XVxdw9y4ZrfUGOZ4k/exZdH38MS6dOSMWxcZyokrFD9i7l41WKqErl4tZWVnQarX45ptv8M3GjeK95ctF7YsvAiNHAhs20EiyDRvI+G/qVAqKREeDa2nB0dWrtTXNzZRJFwQq/ZfJyJNg/XoyBYyIoK/GRozZvZs1e3nRvhsaqOR6xQoSyYcPk0N+Vhb1jzc0wHL1arzl4sKek8uxYvt2tF+/znQGD37kS/DGG2RuJwhU1SCKJFaPHychLoqUqb59G8CjzD4AeHp64rnnnoNPebk4RanE2ytXsvnXrzNzf3+Y1dXB0cxMPJOQgNz0dCFi8GCMHj0aNgMGaM+EhjJmYyMEiSIZ9/WH42id9aJQKOBpbCz+uG0beiZMoPO6f5+ur39bwpO8/DLwzDN0Df08IBhjGDhwIJYtWwZTU1NRT08PdnZ2YmhoKKysrLQtLS3C5s2bxVOnTqH83j3gxx/Jwb6+HvjuOyg2b8YkKytMO3qUVdy5w847OKBjxw5gwgQSrdHRVBGwYgXwj388dkpTp05lJba2SL5xQ8t1d8P3449x28cHe/fuFQHAzMyMDRgwABzHYejChViem8sv2LmTW716Ne+o0SD41Clmb29P59JrZGnQ2oq5eXlMb8YMDI6MxKWICBRt2IDdmzcLP/30k7hr1y7s3r0be/fuxR937yLn2jXs27cP+fn5DACcnZ0FhUIhnu8zpQQetcw8eEBrpLubWhOcnSmYFxwMWFri97Nne7bv3Cl8/vnnYkdHB2OMwc7BgSpuoqPps9qfpCTMSErinV59FVuWLkXz0qW4/eWXFGDy8qL7vH8/9Do6YG1t3Q1gSldX147U1NQ2URRRUFCAtra2agDJarXaTq1Wu61bt06UyWTVzc3N8Pf3h6urK3R0dMAYw9ixY+UqlSpULpd/AWCSg4NDt7zXr6CjowO1tbW6AApp2XH2jo6OvL29PRhjIoD/li+AhITE/y6kzL6EhISExP81ZDLZy/Pnz2eNjY2WcXFxPwLoN5sM/jKZzMG3/7isfjDGsGzZMoP29nbo6urqPdmPH+DrK5hs3cpV+fhAmZ8Pq6NHaWTdgwckSvtISaFsnpsbCdPU1D8dKzs7G+np6Whvb9e2tLTw5ubm4ooVKxjP8zh37pyYnJzMjI2NqYSgvJzEbk8PidHPPiMhC5DgiIigrOOmTfS7336j7HZlJZ3HX7UcABg1ahT8/Pzw/fff84mJiRjZ0YGe6GiUlpWhq6sLhYWFQkREBGdmZoaamhqcLi5G/ubNwisDB3IwNqaM5YsvwvrcORh++SX327Bh0DExwfMeHuhev55luroK+yMj2eKQEJjp6dHs9vBwKpHesQNYt44Ev6sr7AYN4rVHjohoaGDgeeCHHx6daGEhfU9LA6qr0eHri8xBg5Dp44N5ZWWwFgTaz3vvkVh/8ICEZk4OUFFBP3t5gXNzg2NkJHYVFqLR0BAPzpyBUd96yM6mbTUaCnr8/DOJ6dGjH5ZV49NPqR3hjTcw+rXXcGvGDJSlpsLB05PM8vbtI7f/NWsoCBESguyEBFwzNWX6OTli6Pnz3A0XFzFg/nwWHBzMNzU1ieeiohDY1xLx0UePKjPmz6dSeFD1R/Hdu5j07rtMK5dDzM2lVgpT0798tg8JCqIqit4xb09iamqKlStX0mLXanmUlwO2tnxjTg6u/PqrkJyRwfht22CvVlPwJDyc1vuoUeBeew37rKzQrlAAooj8HTuEV1555VFiR6mkTLVMRs/+zTepRQRA9vDhoqO+PgV+goJQJYowNDRks2bNgr29/eMfvrVryesCIF+G8eOpPSYl5aHYR3o6FMOHI3LZMgBAd3c37t67h+gbN7iyBQug1Wr7vkSe40R5fj4MDAxEU1NTfuLEiXBxceF6enqQ2v/zeuAAtXRcuUIVKs3NFGjjODQMHy62VVTg2KpVYtfduzLfCROQkpICACjtq+ThOFqzzz9PwQKZjPaRng59pRIBkZHwj4hA7oIF+P34cbwjCOA4jgJP168D48Yh4rXXVL8pFGu7uro+bm9v1+nu7sa5c+eau7q6VjHGJvE8f4DneWzatGlHV1fXpEuXLm07e/ZsmL6+vvD6669zAFVyPPfcc3pHjhxZ/uDBA4SFhen3XaKuri5iY2O5uLi41A0bNnwhk8kGcByHu3fvorOzsx69bQJqtZoD4ACgYt26dd3/9cKTkJD434gk9iUkJCQk/im9ZfjBAHLXrVvX8P9nW1EUVfr6+rC3t8fx48dd1Wq1cd8+GGMj3d3d+X9mqscYg56e3uO/PHcO+PZblA0fzvkVF0Pz22+44+6Ot5KTofNkyTRAgvDzz+nnkSNp3viyZVQazvOIj49HYmIiACAmJoYNHToUCoXi4Ul5eHiwK1euYLiXF42327KFhObu3SToraweHcvcnPYvio96k6dMob747m4ymTt6lMqCn0AQBHz33XdCdXU1BwBJCQlwP3AAR2bPRtehQyLHcaJWq+WysrLg6emJu3fvwsPDQztnzhz+oRA9fJj63WfOhNOtWywsLg4Gzc2QTZuG9iNHEOnqyt3++Wfh7NmzmD59Oqe4fBlYvJiu4W9/o3nnn34KjBiB4JEjcXDsWPa8Ugn96mrAwuJpDwgwNsYNf38xYfBgxngeljNmkBngrl1UBfDZZxQgKS2lcXp99Pv5mS++wLZt28A/6f5/5AgJy9xcKv9vbqZn8PHH9Hpv1rcoKwsVtrboMTBA68svi+jpYcjIIAf3ggKa7T59OsTYWNQ7OoLxPPT19ZksORntly6JJ0+ehFarZdbW1iw0NJQdKS2F9+LF8Lp7l47x9tvUnlFQgFJTU1x7+22hTavlHJcuFQLNzTm5re3Te9H7EEW6B0VFlGXfsoUmD8ybR14AP/1Efg/btlEgxdmZjPVeeYXWikYDo99/x6j4eK5n/Xph4N69nJCbC87DgwSssTFVaEyahJd37MDde/dw58QJaF988c8VnH1tAD09FETx9MT27duFCWfPMndnZx6+vsDf/w4xLw9NTU1ISkrC0KFD4dQ/OOHsTIEYNzcKwgC0L29vCigkJlKFQ7+s/P3799EWECBO4HnmpKdHWfjeVQRRZPj+e3iOHk0mk724u7sjPT0d1dXVsDA3J+PL7dsBjkPP11+jIjsbV44dE6fPmMHi16wRym7d4keYmjKft98G3nwTHaWlQmZ5OWdsbPyo93/CBArGFRYC779PVSh9mX5RBLO1Rfvf/qbVarW80Cf2AZp68eqrcOd5ROjrBxZZW+/x9fWFTCaDKIq6PM/vZIxZOTk5idOmTWM7duxYJoqiQVdX1/sAzrW2tnKffvqpaGBgoJXJZMzPz49fuHDhQ5Hfn8DAQGZlZaW6du3aewYGBpy7uzt27tzZ0tXV9f66detEtVrtoVAozgKwYowVAgj468UnISHxvxnJjV9CQkJC4i9Rq9UqhUKRbWBg8KJWq33l4sWL5/6Z2/STJCcnT7S2tna2trbGnTt32hobG2/HxMRcV6vV+gqFYm90dLSZmZnZP99JnzCJjKSebRMToLoaZrNn46hKharekumrV6+KpqamzKK/KL16lQTnggWPepRlMvR88AF+Li8XLl6/Lty6dYuzsbERXVxcxHHjxnFPegDcS0mBzoULWu9ff+XabtyA/L33KLs8evRjZdgAKGvv4UFl3H0j2wDK6CuVJHAGDwbeeYfKz/v182/dulXb3d3NLVu2jOno6GBIUxOMGhrg8PrrmDx5MouIiGDh4eFgjOHWrVtie3s7mzNnDqenp0cBhWnTqGrBwQH4+WdoExJQ1NMD6wcPkCYIOFZfj6amJu2UKVP4hIQE4eLFi5xYWSmW2dqy6qYmZF+7JrpNmMCYqytlOk+fxu2gIG37V19xNpmZ4PuqF/o/l4gIQKnEDwYGTNnejgmnToFNngwVQKPanJwo2+vqSr3iFhaPu//3oqenhxs3bojp6emspKREa2ZmxhnW1dH9eeEFahl45hnK7H7yCWWzzc0fbn+rtBTnRBE+fn64aGzMovbuBRNFem9NDQUWLlwAOjpQERuLiooKtLa2YuBrryFy8WIWNW8eq6io0BYVFXG3bt1CU1MTbtbXi1FTpzL89BM9OwMDoKEB+0pLMX3LFuZdVwfHuDgmi4khod/QQGutuZl8BTQaErvffkvn8MILJDD19GhKQ2Ulic0TJ4BRoyjjHx5OQnruXGqrsLenQIO1NYn/F16AtbU1OyqTiWZffsmM580D27KF9jVyJLBkCWTe3tDLzoYiLg4XLSwQHBuLlKws8XJeHnqOHWO6QUE4eeYMTunoCKk1NYL35Mlcq1zOgr75hins7ek+LVyIzu5ulJeXC/fv32fXr19HZGQkHgvMMUbrffx4ypQ3NwMtLfQ5XbCAAmz9pjmcO3dOCAgN5ZwNDckjIjLy0WeSMXq27u6PrQ+lUon09HTx1q1bCMnMZNi9Gz0rV2L37t3a0xoNV56ejvGffcZkq1ahpLSU6wHEcYsWMbZmDZiZGQbOncvMHzwQM0xNmbazs8fZ3Z0OGBdHz8POjoJS/QIMTYaG2FtVxcUMH/5nk0Y/P3BtbXDcvFkWFBurYxcZyX3//ffampoamSiKBhzHoaamhoWFhWHw4MHytrY279bW1pmBgYFQKBSM4ziuurqa02q1XEFBAQYOHEj+Ak9BpVLBzc2NHzBgANfT04PTp09zoijO0Wg0TKFQpI8cOdJx8uTJ8suXLxtHR0d/9NSdSEhI/K9HEvsSEhISEn9JUlLS31xcXMYvWbJETxRFxb1794SoqKhT/+r258+f12WMjfDx8ZEbGRkp8/LyRl26dCmA5/lPvL29LcPDw/96XN6vv1LW+a23SPC0tdGIOX9/YMwYmJqZITw8HD4+PrC0tISNjQ1OnTrF7t27p/X396f/1Gs0QFgYrtTVQaFQQF9fH+B5VE+bhvyTJ9moIUO4IRMmYMSIEczLy+vxE6mrA7ZuhWlWFkrv3+cU+fkokstxxMpKGzh/Pvcn47U+Ro0iwdY3a7w/Li4kHPPz6buFBXD+PL6/fFmsrq7mVq9ezYyMjODs7AzL6mrox8bC3M/v4eaMMTg5OSEsLIzdvXtXuH//PvPy8iKxdOUKuf9v3w6Ym4P74APUREYi08ZGaO3uxrgTJ5jNjBmclYcHwsLCuEGDBsF061aWYWCgTbt1i6usrGQODg4o7+zEdY6DsrUV4YcPc3+Eh4vmFhbM1M3tz73obW3AiBEoPnECDUZGiEpOhu2aNTTNoK/i4cYNymi/+y49Rz+/hyZ9/QkJCWHm5ua4cuUKl5GWhmHLl4O5uVGlhL4+VR7I5VROP3UqicVewa9QKHD16lWEhITgzp07CA4OhkJHB1iyhIzYzM2BMWNwV6EQEwSBcTIZtIzBqaQEllOngre3h6+vL5eVlSV2dnaynp4eqFQqhI0cyTBtGhkOfv018NtvsMrKElNCQliFsTHqMzK0locOcdyNG+QSf+0aMHAgPV9bW2DMGBLh0dFU5j9vHq3r5cvRsHo1FDY2YNu2QYyMRNmNGyj6/XdBNzqa6ZiZPZw5/yTV5eUozMpiNvn5MH31VfD6+vS5GDeOMu2BgWCDBuGnzk509fTgjpMTTGJimHFdHZzieXosAAAgAElEQVT27GE/t7Zi1LZtCGhvZwPmzePEtja4LF8Ow9JS8h2YMQPw8oKFhQVCQkJYZGQkUlNTceXKFW1GRoZw8+ZNBAYGst6HRln8mTOBr76ic75+nYIV0dGPnXdKSooYFRXFjEJCqLVFX//xVoY+X4aBAwEAqampOHjwILRaLdPR0UFIQwNDWBjiysq0ZWVlbOnSpazHxATNeXm4pq8vZN24wSwtLeHv70/j9QBg2TLojB3LDBITMeT117m/CwIiBg+myp5586hNo7PzYdCuZ80abG5thbGNjfjss88+/Q+TrS2dI2NARgZ4X1+urKxM6O7uZoIgwMvLS/D29ma6urrw8PCQDR06VOnm5ibz8PDgzp49S8eh9SWEhIT8f+y9Z1gV59oFvJ6ZvTfsTe9dehcpKoiKgr1L7L2XWFJOYiw5GoLGk5iYWGJMYjTGxNhijxixgopSRBBEQaQoHaT3zd4z348bEKI557zfj+8773v2ui4uA5l5ZuaZZ+aadd/rXjfT/otOHX/G3bt320RR1JHJZB/b2to6jR49WlZYWIisrKysAQMG7P23BtFAAw3+66CR8WuggQYaaPBaREZG6kskkg+GDx8uZ4zBxsaGSSSSPv/DYS53uKQ7Ojpi3rx5egcOHJiur68PNzc3vEL0lUoiCocPA9nZ5Obu50f14m+//YpUmjEGMzMzmJmZISkpiUkkEjx9+pS/fv06hvj6AufOIW/NGlyMoviEkZGRevz48XxSUpIQlJrKuZaUgJ80qfs5NDdTNvboUeCDDyC5dQshKhXSDh5Ezz59kHnyJDt69CgmTpyI0tJSeHl5QalU4sSJE+KMGTOY6ttv0WJjA0NQO72EhAShvLycqdVqxvO8OGnSJOb6zjt0rLNn0bRqFcrffJNNCQujYARAWeJvvqE66NeAPXoEx0ePuMc8T4Rl8WKqZ+d5IuAlJUB2Nnzu3EFMUxPXZGEBcycn2EREULbY1RW6urrQNTTEnIkT+R+SktTFxcX8yZMnRY7jRI7jcEel4sy8vDD58GFm+PbbRLCvXCHJ+JtvkhnbkSPAhx9iVGwsvp8/H1m//ALrDuf0DowYQfXdDg4UiJg2jcoNXpPh9/b2xqU//lD79OjBs8TE7mRw6FAyQxw4kPqsr1rVKROPiooSAbCmpiZIJBKxuLiYufXoQXPz6ado3bwZhRMmiEddXdmcigo4XLhAAYiJE0kJAQCFhVgVHs62HzmC8ceOoXbnTta5Dm7e7Oxrb2tkxLQKCsQcXV12B+CTZDIsXroU0r///eW5/qm9XScOHYLKzAynZDJ1wfnz/JvjxuHwBx+INYmJzDc+XnTMzOQepKZi8MmTFBixte22e21ODhR9+kArPBynp0yB3Zkz6kWLFvFgjMolTp8GJk6ERCKBQqFQGxgYcHM++IC1rysmREZiZXMztMePBy+KFLxJSqJ7MmcO3afp0+k+WVoCgwaB1ddj8axZKKmp4a9fv478/HyoVKqXXQZcXanUwMWFnpvmZiq9eA3EjvKW99+nQF7Pni8VGoaGtG7bUVBQgNbWVigUCqywsWFoN38s//FH+Pv7c6amphg6fDhSTU3huX49V+jiAt/Jk7u/IGQy6PfogaDt23HR1hYLduygedq+nTwlcnNJgfLoEaCjA3b5MtSTJ2PIkCH/pCYDQHAwlZZs3gzvVavwe3Mz5+HhIQQGBnKOjo6vNb9ub9nXibCwMM6Q3P3/JXiex+LFi7UyMjI+MDAwgK+vLxhjePTokbKtre1cx3aRkZESAEJERIQQGRmpBSAUgAWAAgBxERERyn/rgBpooMH/GWjIvgYaaKCBBn+FCT169BA7ZPampqZoa2tzi4yMZB1t9CIjI020tbUPAfBQq9UVbW1t5wH8GBER0WFbnqdUKiUtLS3Q1taGra0tRowYISQnJ7PffvuNzZs3D46OjtTWqv3jGVVV9NE/axa528+eTWT/6lVypu9aI9+OI0eOCM+fP+emTZuG4uJi3LlzB46FhXC0t0d6cTGsra0FW1tblpiYyP/666+QyWTciD/+AK+nR7XGjo4va3cPHqSM5ZQpVJc8dSrkkycjSKGAIAgwNjbm7t+/j2+++QZSqRTnzp0Tvby8WE5ODtu6dSu809MhcBxy/P1FiUTCHB0dxZEjR3IqlQpJSUnsyJEjiIiIAACcVKmQ//774uIxY5hVcDBJ3YOD6Zx69SLDt4oKImEvXlB2e+tW4Kuv0PPZM/H2sGEMW7YQ0b9zh/YzN6dJuXcP2gUFGBgUBOsvv0SWgwNk27dTpnvUKBpnwwbAxgbzvbz4p0+fwsPDg3EcxwBg+/btQu/58zmzzZvBrV1L2c+MDJKaT5sGzJiBtm3b8I2VFWrnzwcAMsX7M7S1qR0iQPN88iRJ8i9ffiXDn5mZiQEnT/IORUXUSaErevemjgAAeSDI5cDSpRC//x4cxzFTU1N1SEgIf+/ePaG8vJx3a2kBoqJQPHQoku/dQ+mIEcKc8eN5BwcHmqfcXBpTJiOCOnUqaiwsRAQGwhlgzdbWdH41NeQd4OsLlJSAc3GBxZgxzCwuDq4PHyLewAC/btuGmRER0PoLF/6srCyk37unHvXJJ/yFKVPEcrmcrdy8GY06OhgcGsoEY2N4rl3Ldu3apR6tr88jNZUk8cePU63/++8DmZnQs7PD8zVrYB8UhF5GRrh27dpLYjl4MM3P8OHAxYuQyWR8aWkpnjx5Ivj5+XGPHz+Gs7MzBZS6ekakpdF82NrSGisrI2XCjRv0/3/5BSbGxjCqqgKfmYm0/v1FyYoVDJaW9Izk5VGmfPFiak24bdtrjQrb2tpYVFSUuHLlSoYePcjh/rvvyMQRoLl+8KBz+/bMuHrypEk8HxAAfPIJRFGEtra22NJulAgAfv7+gIEB3AIDKXjwGnC//44CpVLts2ABj/nzydciMpLm99IlKg1qaACflQXfCxeEuLg40cvL66+dNAFEPXsmFPXtC4s//hCDc3P5DD095ujo+Jfba2trw9HRUSguLmZeXl7Mp4ti59+Bubk5zDuebZDhYWpqKlQq1R4AiIyM7M/z/GWO41o+//zzNIlE0tfU1FQwMjLiX7x4oa6pqRE2b9489aOPPrr6lwfRQAMN/s9BQ/Y10EADDTR4LbS1tWf7+Ph0FqUbGBhAKpVKVCqVJ4BHAKClpXXIy8trhIeHh/TEiRPOPM/3Y4xFbNu2La2lpeU0gFae58WudfBqtRqVlZUMogirhQtxPDwcXqWl8FGrKet3/Tpt+PQpuWAvXEit7KKjgQULSC49YkTneIIgID8/n5syZQpcXV1hY2OD+Lt3ob1uHb5dtEhofPKEc3R0ZKNHj2ZWVlaiUqlk3t7eRHpyc0km/vHHRKhbW6k1mq4ukYGdO7vJ8X/99VcxNzeXAcD06dPh7OyM/fv3IzU1FQDQo0cPDM3Lg7ZEgpoFC5iFhQU4juM7rvv8+fOit7c3A4DKykpkZGRg6dKlzMramoi0hQVJ4DMyqPd4QwMRmAkTSAY/aRJlUo8dQ/mjR0x+8aIaFRU8nj8nUzxzc2oVdukSTubnCxn29hyKi+Hu7w99tRp2N24Q+dbTo+t8+hRYuxaywYPh5eX18uaXlMAS4MoyM9VcQQGPjz8mM8KwMDIabGwEkpKgMjWF5ODBTvOz2NjYV+ucraxoH7WalAf29kT4R4wAYmK6EUMjXV1EDRiAxLY2jM7Opr7yHejogNCBoCAytjt2DPn5+fD29uYBwNPTk3/48KEwYPlyjqWnw3TqVHAcB5++fflOk7nSUsrm9uhB85mRgabffsO3Bw4wtLVh9+zZUJ06BVEUseHgQUhkMsrsf/wxdWBYvRrchAkw++gjjI2PR9S5c2gdNw5qc3Mo1q4FXFzQIJPhxo0bSE9PR1tbG8JKSrir4eFq5ufHLxk/nikUCigiI2G2cSPJ3yMiYGdnx6Ju3BAtL1xghoaG9Czk5dH5+vqCO3oUDn//O6xaW7Fjxw6EhIT8qUWFL2BlhfS9e1FWXw8ASExMZDdv3hRramqYjY2NuGjRItZpOgfQvdDWJqO9wkJae7t2vVTRtCtfuOpqcDduoPrBAwEeHjxqaqizQlwcBaQSEig4VlWF12HkyJEsNjZW7PzD6tU0dlwceT84OdFx168HGIOuri7u37/P71+/Hi1jxqAhPR1iWhoYYxJ/f//ug584QS0vk5LouemKoiIIy5aBnzyZN//ySwoSAeSXMGYMPS9yOSCKEHfuRHJVFWdGhn5/idzcXDx48IAbP3Mm+LIy2GzcCHuZjHUz5vwTpFIp5s6dy6Wnp+P27dvi1q1b2dSpU7s/d/9DtLficwZQIpVKZ4SEhOi4urrqVFVVhVlbW6OrciAvLw9Hjhw5HRkZaRMREVH///qgGmigwf8qaMi+BhpooIEGryAyMpKTSCQhXYkbYwy9evWSJCcn74mMjHyb47iJCoUibMiQIdKdO3fC3NxcnD9/PmOMaWVnZ/ctKCjwb2hoUAYGBmp19JAWRRHFu3dzE7KzcT48HAXNzajIzsZpc3PY/+1v6JYXzcsj8gEQKbC2Jln2Z5+RqdmuXRAA3Lt3DxzHiVVVVQwgs7dwqVQotLXlymUyzt/NTR0WFsYDeFlrDBBxvH6dyAHHEZlLTCQSsGcP8Mcfr3y4T5w4ke3ZswdLly5FhxHghAkT2I0bN4RRo0ZxxsbGFJwQRci77HvlyhX1/fv3ebVazZqrq8WT774r5ujrc2OfPhWsPv+cw9q1dB5r1pBrfXU1kafGRiJUHcGSFSs6xywsLBSG37zJw8aGiHCHMeHo0QBjMDAwoBPgOOT06gWVSgWpry+G+/tTAMHHh4hZVhYR+YwMconPyYG6d2/4GxvD+NNPeWzbRuMHBZHqAaBSgR49IGcMq1evZj/88AOKi4vR1NQkAOguY+Z5aqP3+PHLzKubG5VqjB5NHQBMTYGyMlj4++PNW7ew89QpHDlyBDNnzoSbmxvtM3ky+RycPk2/a2kB27ej7euvYV9aijJTUxEACw0Nxc6dO1mbmRmqPvwQ16RSPPP2xhxrayoTiYujdfTee6Ry8PQEDhyA/OJFDN67F7G3bqGtrQ1vnD6NPA8PgdPS4jBxIpFJnidC2qcPlRPU1ABz5yLjxQsxvbaW2RYVIWjLFnCVlcjR14cUgL6LC0aGh8P1m28YNm7k/yzNh58fZdcBTJkyhfvll1/Es2fPqhcsWMBj5UpStdTWoi0wEC9cXFC1aJHQWFvL1AEBbMCAAQCA1tZW1NXVoa2tDRVr1kC9aRN8JBKk+fmhtLSUAcDAgQNx+/ZttmXLFri6usLOzg6ujMEyIoKIfgdJvnULuHCB1BddYWQEvlcvNOTmMsyc+fLvQ4eSCuPixZfGe6tWkWHfzz93bqalpQWO41664nMcyelnzaJ7YmhIYxUVAba2GDRoEHq6usJo2DC07toFSWgoJBIJ9u/fL5iYmLwqlT9zhpQCmZn0e14elZ6cP4+ovXuh1dCglmtr89i8mUh+SQkpVMrLKfhVVwfVjz/CwdcX006fZiWtrXhaXi7oJCSwyg0b2HA/P8DaGod++UVVXFwsCQgIEH18fBh8fIDgYBgcO0YBtK++6t76swtSUlLE33//nfE8zxQKxeuv49+EVCqFk5OTVlZW1q3IyMhFWlpaIy0sLGBpaQnL13hiODo6Ql9fX6yqqnJHe/s+DTTQ4P8+NAZ9GmiggQYavILY2NieCoVicVhYmKzr33v06MG3tLRY1tXVLbK3tw+ZMmWKjlwuR0JCglhTU8P8/f0hlUqhra2Nnj17cl5eXlIDAwP68B47FszPDw6urqjMyMBTBwekubigWUcHlpaWYmpqqsgYYzY2NnSw7GzKBne06LK1JUIilUL55Amq1q/H0fx8MaeuDgMGDEDv3r1Zh4LAKCmJXZBK0ainh2HDhnHdPn6VSiA5mT709fSohvftt4nwvvceOXQ/fEgZ9k8/pW1kMuCHH1Dl4IDSq1dF7vlz2AUHMyQnQ8/SEj6+vkze3Eyu3r//TiRj2TIgIQEt+/bhWEEBt2zfPoS5u0PR1ib2O3GCc1+5Eh5GRqykthbnGhpwTyZDmb6+WDVgAEsfNEh0GD2acXI58Le/QRg8GPezsmBtbY2mpibcuHYNBpGRrMHJCWWTJ+PJixeQyWRISEhAhbMzrkdFqdMLCjhzc3Nh9uzZTCqVCgUFBcwvIABWAwfSHFy/ToRszBjquR4SAixfDpiaIn/SJBxTqTBy4ULwS5eS/N7Pj8ixQkHBiHnzgLlzUVpVhZiYGLi5uSE8PJzJ/9w+DyAZv7Ex1XV3wMKCSP+6dWRqaGgI2NlBNmQITE1N8ejRIzx8+BBGRkaipaUlw4gR1HWgy/jPKytx8fx5DL16FW4ffcQMTE0hkUhQXl6OrNpapnfpEl7Y2EDRpw/6GRiA//BDFFVV4VcXF9xXq4WmykpRLZUyo4gIsMmTYZ2QAINjx1Dg7S0OqatjvXJzGbt8mSTys2bRQQcOpLny8QHefBPs8mX0276dufbsCaPevXGipgYZDg5o0dODXm0tht+8CePnzyExMQHn4UES8q5k0MuLMvLvvw+MHg1HJyd29epVzs3NDeoXLyBbuBDZvXrhW319pOXkQJGVxcTmZlZhaQmrJUtwoKQEsUlJSE1NxcOHD4WioiJ1m5YWCzl/nt0PCIC1nR1CQkIwcOBABAYGIj4+HtUvXsBt3z4UpKTg6d/+Jpr07s2kUim1mdPTo+DOwoWvBLs4jsPt27fZ4K7mewsXUhBoyxa6l4sWUWDO0ZHucf/+wPz5qKiuRk5ODvr16/dyUGNjuu9nz9IcV1SQpN7KitpuMgbOwADSqVPB8zwYY0hLS2P6+vrdWwECFIyaM4cCUY8e0TqNiwMmTULVli1i8P793FVPT8Fs3z5o+/kxNmwYPfdaWhRUnDULvJkZPEpLkTVzpnBeqWTqFy9Yb0FglxUK0Xf6dKa6fx8Pq6vZsIsX2QOOg5+dHePa2uiedgSwevSgwNtrCD9jjN2/fx9BgYGYN2EC021qoqBeRQXN4bFj1ELT1xfYt+8vvQ86cOvWLZExxqysrIY4ODgYBQUFSf9ZK9O4uDilUqncGxoa+uKfDqyBBhr8n4Ems6+BBhpooMErYIyNcnNze8UOXCaTYfTo0fLRo0d3+/vSpUvZnj178PXXX3f+7e9//zskDQ1EAMrLKWuelwedqVPhNngwlA8fora2Fnfv3kVVVRWUSiUXHx8PnucREBBAbeS6yrgBwNUVKoUCTw8cQIGDA+ZfucKkn30G5uf3stVdXh5YcjIsx4wRy7Ky2O3btwVnZ2cOKhXVIcfE0Pl89BHJ2FeuJPl7cTHg7U11wx2KgmvXyHlbKoV65078WFaGkbdvM/2oKAjvvANu5EjK9JqYAOPGkVz9rbco03nmDFBdDWlaGuDnh6czZsB/wQL0cXfn8Omn6KiPOL1nj1CZm8u9d/Uqnvn6ijdHjRJVpaVc4LFjMJ4+HTA3x4n9+9WSlBQ+OTQUVQ0N4hsXLqBYV5fFmZvDJCdHUKlU7O7du8zCwkIIOH0aYzMyeGV8PKysrDhRFLFv3z4OAJobGoiQbN9OmdXKSsrCtraS3L4dBgYG0NHREXbs2MHNnz8fFh0+CU+ekLHZ2rU0L1paMElOhra2NgoKCmD8mlrtiooKiB4eMH9dtnPwYKC2ljwSJk8GvvwSAODl5YUhQ4bg+vXrqK6uJvZSWUnZ43YCVFFRgYMHD6LP5MmwWbaM/B0+/hjgeUxSKBjeegvixYtQzZ6N0s2bxaioKGYgl4u3eJ5pSSTwsrLitDMy8Dg6GqYLF+LOgwd4kp6OFffvo9zYmOk8f06E65NPgPR0yloDFAyKiKASkKgooLgYks8+g3V4OKx79oSrqytkMhkaGhrw3XffodjGBuFnzuBq//4Yd/8+pAsWkCncqlU0loUFlY3ExQGZmdD38oK3p6dYOWwYSwoORsHixdB+/lwYM2YM17dvXxQUFODXX3+FVlMTGuRyOPn4YGJiInhra2DtWg4A19DQgCPGxsKbMTGc2XvvEYEGqV42vf8+kJsL8f593Fq8GHGPHrH4hw8BAKtXr4Zx//5gH3xAypYxY7rdLgMDAzLZA4hU37xJ90xXl1QP9fUU2OvT7uNZVkbBOm1tmH/9NexeR0RDQ+lZT0mh3589ozaEdXXkLRAb221zqVQKpVL5UiHQAcYoq9/eDhJZWfRTVASvzZtZ4qVLqK6u5vZMmIDRNjboq6ND7v/9+9M1VFQAkZHQ7tEDfgMGcD4vXqB5wwboOjnhb4LAbgQECPG3bnE6tbWs3sICSo5j7OOPSbVy9+7LUpvff6cSoC++oEBGSgrNTUYGLPPzscLPD9zs2VDxPCRffEH7+/uTGaWfH72DtLUpMCaKtNbWraPuBX+Cu7s7S0tLExcsWPD6/n1/glqt5gCYRkZGchEREcK/s48GGmjwvxus86WtgQYaaKCBBu347LPPTg0bNmxSnz7/vvl+Q0MDJBIJDh48KJhWVmKqQsHh11+JVA8dSrJZV1f6qG6XnIuiiK1bt0JPTw/9+/cXExISUFlZyVatWgXDbdtQ27s3ogUBhoaGyMzMVDc3N/OiKMLa0FCY09TEyQoLyVyN50kC7+JCBD0zEzWzZ2PXrl1wdnYWZkmlHBcdTQS1owZ61Cjab8wYIrrJyfSx/aeWZzU1NTh79iwKCgrg7Oysrqur48rKytibb74JCwsLVFRUdEr6UVZGWewJE4jAtBt2HT16VP3kyRMeABYuXIgePXp0jl+8Zg0e5OaKqR4ebMLs2fD29sbhw4cF28OHufyQEHW5VMrEmhpu6enTKHByEh0aGpj++vX4IT9f5GQyLFmypDvpqa8n8t6lD31ubi6ivvkGls+fw+/hQ4g2NnD5/ntwzs4v/Qq++KLbME1NTdi+fTtGjBiBfv360R/79qUMt5MTyb0XLwYGDsS9779HVEYGgoODhdTUVE5fX1+wsbERa2pq8Pz5c945PR3GNTWoWrBAmDFjxqvS5blzKfiwY0dnDf/mzZshiiKWLFkCGxsbIvqbN0MdF4eUlBRER0dDrVZj3bp10OJ5CgJYWZHp4KhRVL+dmkrBmKYmPJs+HT/FxAAA5s+fT5nhuDhcO3ZMuG1qynmlp8NqxQr0a2hAy9KlULe0IPv774U+Q4dyaGrq3hkgJ4f8Av7xD0AUIbz7Lhry86F35gxYl3r4pMRENG3bJujY2nJR7dcV0rs3QurrIS0poeCJiQmR5alTaf3I5YCrK+rCw6HatQuGXl7oqLFvaWnB7t270dbWBo7jRKVSyaZNmwbPW7dov5AQUqxcuUJmfatXE9HfsoVOqLycMvHDhpH3BcjzQqVS4dNPP+0872DG4Hn1qqg4dozpm5khLS0NLS0tqK6uFlJSUrhBgwYhODoassuXaZ47SHx2Njn8v8ZEs3riRNw2NhbHf/wxQ1QUBdk6cOkSdd7o0YPKST79lDLe27cDv/3WbZwTJ05ALperx48fz6O5ma47LIwCR/PmkRfErFmkTngN9u3bJ/Ts2ZPr378/zdPBg2QOmJlJa+jpU/IG+fZb8iDw9OwMeoiiiIMHDwpFRUXc/Pnz6TkWRXoHHTpEgYaEBFL1KBTUySEujt57dnZoamvDY55HUno6tM3NsWDBgu4n98EHFET55hsKXv3xB40XF0fn8id5/h9//IHExEQ4OzsLc+bM+ZclAampqero6GilSqVqU6vVUz/66KPL/2ofDTTQ4H83NDJ+DTTQQAMNuiEyMtKWMbZ73LhxUplM9q93aIdMJsPdrVtFq99/5+S5uUxiZ4ebU6ao7tbWwqd3b8aZmtJHa0dterssNz4+Xpw6dSrT0tJiGRkZTKVSwcrKConnzuFmQwPqeR719fVC37592YQJE9iAAQMQOGAA4wMDyaF/8GAieefOUb3vhg3Ajh3QNjKCjyDA6m9/Y7V5eTDU16cP5zFjKDs9YgRlHfX1iaxMmULBgC6krqmpCT/88IMol8sxbdo01r9/f87c3JypVCrx2rVruHv3rnj37l12//59tVtCAiebNQvcF1+QyZieHhFYAD4+PpytrS3S09Px4MEDVFVVwdPDA1CpoLdvHxxmz2YxVVVwc3ODpaUlrK2tWXlrqxB69ixv/u67LGjwYFi8+SYsb99m2hkZQGUlYkxNMWTIENYZaOiAlhbV9vfp0+leb3T/PgJu3YKdRALjZ89waMwY1MhkcHd3pzp6PT0KDrT3J3/w4AGOHDkiuru7C8OGDeM6pcHLllG3AA8PKqv48kvg0iXUtbbCPyICN6VSJjU2Fq2trTmlUsnp6OhwM2fOhI+1Nczv3cMfWlpMS0tLtLW1ZQBQfv06tOfMAXf2LGXK4+LQ6OGB7bt3Q61WQyqV4v79+ygqKhLqLCxY+YQJuHz5svDo0SPR09OTlZaWorW1FdoKBQxCQynQUV5OwYiKCqrh3rIFmDcPj0pLhby8PLZx48aXCoTkZDjdv8/6b9sGn7VrYT9hAloPH0bWoEG45ucnej95woy2bmV4++2XyhGAAhIWFsDu3Sj19sZPpaXiTQMDFrB+PbR0dEjiD6Dsu+8Eo9xcrteRI8jNzUVdXR2el5Tgbm0tekyfDsPp04kIPnpELvZnzpCSoKUFWosXQ37pEljv3iRzr6yE2tAQpZcuifWiyFxdXODi7Mx8AwIgCQoi6bdK9bKl4PTpJC0PCaEMvLY2ue1Lpd3aWDLGwPM8+vXrh/j4eAwePBh3nj8HX17O7qSm4nJaGkpKStQ1NTWorq4WDRiD8U8/sfzp0+G0dWt3ufonnxBZHjjwlfdDnq8v7jU2ol91NcOBAxQounSJ7pWzM12jkREF3UaNIt+A7dtfKSUou3IFqpYWwe35cw7BwUSQDQ3peWnFdFoAACAASURBVHZ2pk4N331HpSmvwb179wQrKyvOtr6eOkLs2kX7TptGa3rmTPJJeOedl9L8pCQgNBSMMdjZ2bHk5GSEhYVBJpPR+Uml9F6TSOja5XK6vjfeoO4IAQGAiwuinzwRYu/dY40qFZYsWYJX3q/Z2RQwWLWKyjsSEqjLhp4eBSIrKoAhQzo3d3Z2Rnp6OoqLi1nv3r2h1f78/hUsLS25gQMHSu3t7bUePXo0JSYmJmvw4MGP/ulOGmigwf9qaGT8GmiggQb/xYiMjPTmeX6ORCKxVCqVeaIoZshksq3BwcESXV3dfz0AQBmnpCRg0ybY9ujBbtnbo8jFBTGCAKSlSQC8lP4OGED18IxBKC9HdnU12tramKWlJXbt2oXm5mYYGhqK586dY7PS0zHys88gJyn/q1krqZTc8r//nmS0S5eSfNbKipz7HzyA/qFDqJPLoT1mDBGw0NBXMvedePPNzqxyS0sLjh07JpSUlDBra2tx2rRpXEcteo8ePWBnZ8diY2MRGxvLnJychNbYWP4HU1PoTJ+Okfn5cC0vB5s8GSgrg1KpRFZWFhwcHBAREYHHjx/j/NmzIkaPZnkGBjg9cKC6JSuLNzc3F319fRlAbQ5Dly/nsHcvBSmamuj61q4Ftm5F7e3bsDt9mimysykDyf1peoqKSEmhp0dZUnt7SKdPh2F5ObB6NUYrFIiOjhbGjh3LcaamlEXMzET1rFk4ffq0UF5ezo0dO5b5+Pjw3WqAhw0jj4OAAFIt3L8PFBZCx9wcrWZmkLa1wdHQkI1vd3DvRJ8+MOR5DB8+HNeuXWNSqRQGBga4evw4+nAcDPLy4PL++6hZvRr3li+H6OMDSKVoa2tDUFAQ6uvrufS0NPXcdev47B078M4773BPnz5FRkYGUlJSUFpaKixatIiDpSX5DhQUEKGdNQtoaoIIoPzzzznzgADwBQWUMV2xgiTWv/8OWWEhSfVnzcJ5NzchU1eXs7e3Z3YlJbS27t2j9dPehhIA1MbGaNu3D79yHFx694ZzWBiuZmVh9JMnaMvLw6OyMtgeOMA9X7sWiYmJKCsrw4oVK2BoaIjvv/9eOHToEPfhhx9Cam9PwROViu5bejqRxpgYUomo1XR/7O2hJZNh6rVr7LKPj2gaHc1809IgKS+nDgfjxlHN+sOHJH2Xyeg6HR2pjt7cnAIfq1e/ul5AreEAICgoCIMHD4bw6BHUy5ejZt06mDk6vmyn8dtvePH0KVJEEYJEAghCp/IAQ4eSaeFrwBij98DMmfSjVtO/ly8DdnZEzn/8kQJO27ZRAOTtt2nnzEzK8G/ahOCtW5ExahTDd9/R/MhkVALSARcXysY/fkz//gmiKIIxRkHB/v2JrG/cSIGxtWtJYdHaSufn60vr/c4dIvH79sHU2xuenp7qr7/+mlOpVKxXr17ixIkTGQSBAhdHjpDs/ttvad10UUeNHDmSKy8vR0FBAY4ePaqeP38+L5PJKNA1dy6pkrS1ScXSqxcZk27YQM9waiq9u7Zto44Qv/8OjuMQFhaG06dPo7y8HHp6/5aaH/b29li4cKH84MGDP0dGRj6PiIhIBIDIyEgDqVT6d47jZgO43traujgiIkL5bw2qgQYa/EdCk9nXQAMNNPgvxZYtWyZIJJLooKCgQR4eHgFmZmaDAIwJCgqyGjBggOSfGT0BoPr069fJVOrnn4GtW2G0bBlcR41C6JAhUKlUKCgowOjRo2Fra4vO8QwNIaxdi+pVq/CjtjaGDRsmODg4MHt7e3AcR63tBAGjLl3Cl7q6iLl5E4WFhepevXq9Xqbapw+5yjc2krRcpSJJ7bNnaNm3Dwf09ZFjZITAmTNfutq/DhIJcP48MGgQfvzxRzVjjI0bN44LCwtj0j8FCBhjMDU1xd27d2GUmMgmnzqFpKAgNOjr4+HDh3hQUSEa9ezJ2tzdsW/fPmRlZYkJCQmstbVVsJZI2OOEBNasVovXbG2Z74AB3MSJEzFw4EDWbc55/qVb/Z07pEgYMwbQ14eWtzfExkbIfvwRCaamcFGru9XcY948IhBHjhCJCQoiQrNyJRAQADMzM9y+fZsZGRlR725bW1SkpWHP3bswa++qYGdnh1fWQFwcETpjYyIeS5ZAPHMGR2/cwF0PD7TJZJjz0UeQOjl173kulQKXLsF27lyoeF6Ii4sTbbduhVb//miZMYNdu3ZNzM7OFq+o1WxYWRn06urEAmNjNnbCBAwaNAje3t7o07cvJ2tuhs+bbzJeLoeZmRlCQkJQXV0NtVot9OrVi4OFBRndKZVEdL/6Crh3D6pRo2D17rtwWbAAejU1JL9fuZIk7eXlwKlTNN9KJew++4y1KpWoSk0VSgwMBPdDhzhmYgLB2xtPa2tRYGSEzMxMnIiKQmpwsHquhwcXMGkSMzc3R4pSKVxraYHn4sVMFh0tZkydyhxmz8a5c+cwfvx4ODk5ged5uLi4sKSEBOhERQm2lpYMn35Ktd779pF64tQpyvL27En3fNYs+tfODi8mT8bZnBzWEBoqXA4IYKmPH4svQkIEi3nzOG0HB1oHfn4k+ZbLqaRkxw4ir7q69MymptLxAKoFl8sBjkNMTAxCQ0PBcRyYmRn4xEToWFsTia2u7mx9Ge3ior6XksLdvHkTeXl5gr+/Py0UuZyIaEjIK49XZWUlnj59iuDg4Jdu/OvWkW/BnDl0Pt7e1JngnXcoEDB7NilOKipoLc+bh1t9+uCJsbHoHxjIva6OHRxHwbHKyteS/aSkJMG5rIwznzcPrSNG4Phvv4n6ubmilpUVk/TtCzZiBJUS8DwFEsPDSX3h7EzKBV1d5IiiWFhczImiCEEQhL75+Rzc3Slg0UHuP/uM/ChCQ7s80jxSUlIEhUKBkpISztPTkwh6fj4FqMLDaUNvb1IYyeXUHcTOjv4mkZCKwcSEMv/r10MYNAj3HzxAWloagoKC8Od31V9BV1cXurq60mfPnvUcOHDggcjISD2pVPrY09MzdOzYsUaVlZWuTU1Nw65du3YoNDRUU/OrgQb/S6Eh+xpooIEG/4WIjIyUcRyXuGDBAh0/Pz/OxsYGzs7OnL+/v5atrS37p0RfqaRWXefOUf17WBgRSWtrgDFIpVIwxuDs7IwnT56oU1JSuNjYWBgbG3cavR3Iy1PftbXlJg0fDv++fRkkEujr68PNzY1ZWVmhp6UlLCwsUNu7t1BaWsqqq6u5jIwMtUql4hobG2HapR4dAH0ET5xIH80jR1KGLigIsrNnYRwejtScHLi7u+OfqhWqqoA1a1C3YAFib97kpk6dyrrW1v8ZeXl5kP30E+xWrsTvFhZopNZi2LhxI8BxLDU1VSg7epQZDRokLl++nNnb2+PG1atiyOrVTKGrixu9ejGlTIbBgwfD2tr6JbFuaQF++YUI25tvknqhqora4h0/Dpibg+nqwsLPDy/GjsWDU6fQ+9NPKTvs5ESZyjFjKCP5+edUqvDiBf3b7qIeFxcnZmdns+zsbDg5OUHfwwOFX3wBCy0tYdz69dxflm8MGUIZ4o5z5ThU79sH0+hoZPTsCZHjkOHhgX4rVpA82syMiAljtFacneHo58cGBAUxqyNHmPOaNcw5KAh9+vRh+fn5zM3NDf4ffojGr75iFlpaQr+lS1m3LLSzM2V+u8iVb926JcrlctbT3Jx1Oqu/8w4FbvbuBSZNwp2EBPE3Kys2duVKkmZ31ItnZxM5dnAAFAo0xccj88YNse75c3Hs9escn5rK5fn7izZOTuysSiUmv3jB9A4fRnFrqzp4wgTujbFjOZ2pU4ng2dnBx8eHNTY1sfsymRD64gXnNno0WhwckJ2Xp05PT+eamppEfZmMmSUnQ6euDlaHDjGDkBDKJuvoUCbXyIiUKevXE1EcNoyIZ0AAAEBHRwf5+flCS0uLuGLlSk5HV5fl1daK15OSuHpBECzDwpiWiQlluAcNorUE0DXOmUNZfgcHCo5dvUpZ86NHIZw/j7a0NDjb24PxPAUGRo+mgMgbbxAZ/f13YMECeHp7c6GhoZDL5SgqKhL69OlDN6mujjw6li9/ZelUVlYiOzv7JdkHXq6jadOojEalIkK9bx+t3YQEKhsZPJgCDYyhuKQEpaWlYu/evf+6Rl2tpiDZ8OHdygBEUUT8lSvM76uv2OXmZvFRUxOys7PZU0FgcY2N0NbXJ3+IyEh6z92//9JPwsEBmDQJqi1bII+KYo/t7Vn4tGkYPXIkx8aNo8BCR+cQgNQG/fq9oiTy8vJijY2N4vPnz5mdnR0sN20i1UiXtprw96e17ulJgZ7mZhpHR4eeP19fKtfYuhXyd9/F/YsXITE2Fm/dusUcHR1h0F6+869QUlKC3Nzc5IEDBx6PjY2d6+TkFD5t2jSFnp4evLy8pLGxsTaiKH4dGhra8m8NqIEGGvzHQSPj10ADDTT474SWKIqy1/Vj/qfYsoWyotOnUxu2oUP/WhYP6v/dgTNnzkAul+Pq1atCa2srt2rzZigGDqQP4r17O7dzd3cHlEqU5eSIqTzPdch/X7x4wV+5cgUAYG1tjUGDBsFdV5eIgLs7ERMfH8qAHT5MsuY33oDXt9+ixsdHOLB/P9cvOBhDutS8doO7O3D5Mq5cuAAHBwe1lZXVP5EBAG4tLXBLS0NseTlqu8wBx3EIDg5GYFISV3/1KgynTGEA0CMmBmP09bkDc+eiWle3sy5BVl9P/zF/Pn3Ez5xJGc833iDSmpJCMl61mpy5W1rog/+jj5C7caOgI5eL+OMPHpaWlIHU1aUgwZIltN3ChUTwpk3rPMeePXsyHR0dxMfH4/bt25gxYwaemJiIQQkJf02gRJFUArW13ZzBny5YIN5RKNh8R0e0kuSe/v/t20S03nuPyNLDh3R+paVU9tFulgeQhHzq1Kmdv5dGRoomH3zACfv3g1u27CVhGz6csr0ffdS5bd++fdnV6Ggitxs3UunC5MmUET10CJg/HyUlJUwikXR3cN+0iVQAGRkQvvgCscuWIU5HByZ6eizg8mUmlJTgzpQp8PvkE+xyd0edgQHjTUxgWF2NkIoKXubpSZLrI0coyIKOUxwOSCQchg4Fvv0WVtHReOfoUT4rJQW3Tp4UbdatYyZ6eujx0084WVSEhStWQH7yJN1bgJ6nRYtImdGvHwV7oqNJqeDtDfTogXHjxnHffPMNKioqEBAQgICAAD4/Px/R0dFs9+7dWL16NRG+igoi9FOn0vOxeDGRZ1dX+lm8mI5ZX4/WnBwIGzaA3bxJnTNu3SKSKZGQcV5KCp1HF+Tk5IgtLS0v14ytLWW0BeG1pQJ/aQqdnEzrqiMTb2REQYqsLFIQKZWdnglyuRxtbW2stLS0Izv96ng9e9L5Pn9OJQ4AVCoVrl69ClVFBUsZNgxW06axu3fvCgDYyhs3EG9iApOO+Th5ko6prU2y+SVLUKutjVu3bgnJbm6cC8ex8FOnoDhyRFSeO8e0nz179Rw4juYjPp4CTO24c+cObt26xQHArWvX1A7Jybzh2rXd9z1+nN5HAD1L+/bR/Bw//nIbNzfg4UOcOn5cXPXdd0zrxx/ZKYkEUVFRwvLly7l/qcwCvZ9VKpUsMjJyvEwm2+Tr69v5YGdkZEAqlaauX7+++l8OpIEGGvzHQpPZ10ADDTT4L0RoaKjyzp07q7y8vHQVCsW/v+Pp00TaLC1J1rtjB30YGxgQMdDX70b+c3JyuKqqqs7f09PToa+vj4ULFzKFQkFBgwkTiNh0qYdGZSVUL16wQmtroa6ujrm6uooODg4YOHAge/z4McTCQhh+/z2Mdu2CRKEAd/w4kYKOTGivXkBDA7B9O9iyZehx6xbzLilBlEqF+KQkdUNDA2dsbIxuPeEZg7hoEUoePxacZszgzc3NXz8Hogi8+y7YkCGoW70ap65cgVwuF3mex9ixY1lHAIULCoL2ihW0vVoNzJsH+bBheG5hgREXLqDe0BD2+fkIXLsWDW+9hYqHD5EhiigxM4Pptm1gcjk4S0sir9OnE3lYvZoy/traEFQqHCkqYuG//MIZZGeTtP7HH4nompgQoZbLiYjOmdPtErS1tZGdnY2srCxUVlbi9u3bKNbTY4Hjx0MBvNZNHQCpJ/6kdqiqqUFBUhLz++or2C9fDmtvbyLnM2dSTfSQIbRm3NwoO5yRQT9vvPGXy0ylVrMzzc0YlJ0N9uIFXTNjlN0dOrQbkaz67TcUAWLfb79lePiQsqJ+fkT+09MBxnAqIQFqtZo9e/ZMaGxsZGZKJVQrVuBmQgIaLl6E3sOHiHN0FEInTGCjxo2DQVQUMhUKFDk4wO7RIyZTKjGBMYTq6uL+xInwWrcObPx4cnNfvZquUU+PSGZGBhlQrl9PgRYfH+CTT2C6aRMCfH3Zzz174raXF0a4uKBNLkdGa6vosHQpk3Q1ujMyosz2gAE05pAhwIkTFDiZMwdXb98Wampq2IgRI8C3l6YYGhqiT58+LCUlRV1fXw8nJyfGHThA5H7dOgocSSREGEePRmRkJGJjY6Gnpwcre3vUa2nhfHk5Bm3aRMcNDSX5+J49pAKYOZOk8cbGneUw6enpYmVlJZednY309HQkJCbCasMG8WxaGrv69KmYnp4u3Lt3T0xMTBSysrLQ1NTEZWZmqtPS0piPjw/jUlPpXbJxI11vz54UoNm0iWTqCgVluIODKXg0ejRqqqqQlJLCkpOT8eDBAzQ2NqrPnDnDNTc3i46OjowxBhEAZDKwM2eAkSNRXl6On3/+WWyIiWEzjx3DzYkThWmzZrGgoCDWp08f6AYGIqqmRgwaPpzJ5XJSjkyahJqkJNRfuSIm3b6NU4WFrLGxUXB2ceGyAbywtUVQbCzLa2mBeXAwnWtXMEbvRS+vTqNMACgqKkJeXh4cc3LgkJrK3V24UBUwfHj3yMixY/S86OvT76GhgLc3apqb8fnXXyMxMVFIS0sT0tLSuNzcXOa5ezf0Q0Nh98knkF29yhLNzdWenp7/0p3f2NgYVVVVNtra2uH9+/c38/X17VR1/f7773U1NTVrQkNDH/+rcTTQQIP/XGha72mggQYa/Jdi27ZtOfPmzXOy6lrr/Vd4/33KyP76K7B1K/XCfvqU2ls1N5Mcuryc5OZZWSRHHjAA2379FS3txl99+/YVHRwcmJ2dXXcjKbWayOnx4yTBB4Cvv6a+959+CrVa3UlohIYGxKxcKTrGxjKzykp8s3o1WuRyOFtYYMa2bZA8edJdaZCQQHXbwcGAWg0hKgpJkycLD7W1xbKyMt7W1hY+Pj4wMTGBra0tcj/5BI/T0zHy119fdcruQEkJyeSjo3EiJkZ4/PgxN2fOHDg7O7+67aRJZJa2eTNJ6c+epYzjxIloW7kSn8bFETFpvz7GGCQSidjW1sYAYN2qVdCeNYtMzF6D77//HpXPnuHD1laSHHt7U6/voUPJoKyj3vlPOHv2rJiZmclGjRqF5ORkFBYWAgCCU1LQo7ZWaPjiC87Pzw/dCGhVFbUpe//9V8Y7fvy42Hj5Mlu0cSMFA7p6IyQkkOFZeTkR1l9+oSztP8k8FhUVYf/+/dj0zjvg1q8nmfySJRRsevass20cRBFNJia4MHQopu7cCRYeTlnpjrGjoyEePIjtdnZwCAwUOY4TSoqKmOGDB1wDz2NUcjJ+Gz8eplIpFqxfT/J1AOKBA4jNyVHHaml1XkhgWZl6dHMzj+XLSWkxfTplXQ0NifT7+pIU/bffgIYGPOrbF7bx8dD/8ksgMBBiUxOiFy9GZlYWdMrKsOTECdxdvBgxADw9PeHu7t5pdFdUVATfS5dgOmIEqRk6UFEBHDyIvDNnxKQZM8Rp77zzCqGrqKjAL7/8olbk5fGT6utF8+3bGdLSKGDQ2EglOL6++PzOHUilUtTV1WHBggWQyWQ4dOgQ1q9bR+oRAwMi3/7+wNixtJb79KHs+759qKyvxx+XL4v19fXM09MTarWavBO+/pprDAtDWb9+4Diu80cQBFRVVcHAwAAxFy/izXfegZGjIykW+vcHPvyQSj0AyqhLpcD+/fR7WRm9VwoKoN62DecGDBDqjYy4fInktT4choaGYmthIZty4wbS1q0THzx6xCCKcH3yBDotLRh++DC6BjnT335bvMrzbP6mTSgpKcHjx4/VdXfu8Ky6GnxIiOjn4MC8AEjaVUHVZmYoW7sWlcHB6H3tGmTPn6Nl7lwgMBBFRUUoLS1FRUUFlM+ewSwhQZ3aty+USiWnVquZWq0GAAy9fBlyqVToHRPzKil3caHA3aBBUKlU4DgOGXv2wPwf/8D1zz8XAoKCuKqqKtTV1YmWlpbM19eX9ouLQ+mTJzj0+DEWV1XB8NtvIfk3a/j/jMuXLyuTk5PvKpXKsREREY2RkZEOEolkGWPMqK2t7QKAixERERoSoYEG/+HQkH0NNNBAg/9SfP755w+mT5/ey75d5vqXaG0lKbBMRsRDpaLM6eHDRGwYo/ZXBga07aNHneS0LjMTuSkpqDEyQu8PP4Re796djvfdUFNDpCkjgwjrF19QvXdHH2pRpOO9/z5EFxfEL14sWA8bxv30008AgF4PHsCyuBjO58/jtRn5Q4eI5EybRpLYzZtR6OSE+zk5wpMnT1hjYyMDAGlrK6bW1Aiue/e++gEuCJSNXrOm04CssLAQBw4cgJWVlbho0SLKziqVZDLWpw9lCJVKuo4PPqCa5vae9YIgYO/evaIgCDAxMcGYMWOYkZERqqqqkJycjLt370KroQEfHDoEVlr6illeW1sbDq1YgeBnz+A9eTJlkS9coOucOpWywLNnE/mvqKB5BXDz5k3cvXsXS5YsgUkXNYUgCHj28CEaDxzAVUtLwcDSEjNmzOjsQoAHD+j6c3P/NC0CLl26hLS0NKy5dg2Sfv1e9nXvCgMDuv7Bg6nl2nvvEWm3taWMcxdScunSJfXTp0+xevVqHuXltB569qTWeo8fUx/ybdtQHRaG3VFR0DMwwLuiCE5LizLtXXB23jzBpLqahZw+zSCVAnv2QP3FF2g1NgYXF4fWtjYc2LwZ7x49Cu75czIhNDLCI4kEp06dgomJibqmpoZnjGHdunXgmptp3dvbU+vBwkK6rk2bSDaenIxStRqq1FScmjoV+rq6qDU1hdDWJszYu5dr7dVLNNiwgeVmZCCmulp0d3dnJSUlQnNzsyiKoqhUKrnm5mZO2tqKVfv348Knn0KQydQ6Ojpwc3Pje7q74+bYsbBZvhzOVVUUBPlz4EQUkbBoEaCvLwRMm8bxx4+D7dqFqqoq1P32G7SuX8cBd3c4uLmhqKhIbG1tZQDgWFaGeYcPA999h5qmJjQHBcHq5k2qW9fXhygIKMjMhN7x4+B278a+994TgwYPZoMGDXp57Lw8Iuft6/wV/PADGt99F6riYhjo6JAKZ+lSWgtpaRRAyc+n90HXGvgOPHsG6OpCHD4c1Q0N+G3qVPWY/v35JhcXxMfHg+M4GBgYoKysTLQ9fZo9t7NDqY0Neicmws/KCtb79r3sHtCOWnNz5K5eDbf338eOHTvg6ekJT09PeNy8Ca6qCggMJHVMcjLUlpa4MGUKHnp5gbWXs9g+eQKz0lLI2trwcPhwUWFiIhgbG8OqrY0PXLYMBcnJMDAxgba2NirKymC8dy/qli7F/qNHYWJiguHDh1P5EjoetQeIiYlRK5VK1tLSwomiCCnPY3lVFYw3baK5+QvU1tbi0rp1QvDFi9zNf/xDmBMSwuFfveNfA7VajfPnz7dkZmY2MMZiBUEY4+/vL9HX15feuXOnqampaUpERMQf/+OBNdBAg/9PoanZ10ADDTT474UoCAIAoLi4GPX19Xj27BmMjIxgZWUFxhhsduyg2up7917uJZFQffGRI5SxjowkkunuTgTX359+ABzZvVtoMjTkZstk0Lt6leqAS0ooczd1KklVLSzo4/XxY/qoLi0l6a6rK5H81FTKSs6aBXzzDdjYsQhWKDgAGDBggCojI0Mib2pCWq9e8O+QvXa9SFFEXXg4ynr1gkVKCgw8PKDavRtGNjboP2cOV2Vqqm5tbeV5nhctbWyY686dHCoqKNjQAUEgh2+5vFv9rZmZGRhjKCsrY1GTJ0Nn2jRx2IsXDDt3ovr+fejNnw+JpSU5rMfGUv/s9rZ42W1tqKysZOPGjUPvLqTG2NgYw4cPh5+fH37av1/8dsoU9mLzZvA8jxEjRogKhYKhpQU5R46IIy9fZjpffUWEs18/qi9+7z3K4FZU0JweOULlFvn5AGNITU1VDx8+nO9K9AHyGnDs1QuwsoJ7RQX3vZ6ecODAAXHu3LnMwMCAMtddiH5zczNiYmLEJ0+eoL6+nqnVaiQvXowgQehWY43SUjJLCw+nYNCaNZQ5LisjtcPp07SG6uvRMG0aKr29cV+l4s3Ky3H31i0Eh4SQBP3rr4nwf/MNjRsdjYKmJugZGOCtVavA7dlDPg1dEB8fjzRHR+49XV2Sia9ZAwQEgA8MhOKrrwCFAtoA6vX10TRuHHQ7DOYGDUJOYKAgCAIXGhrKW1tbY9euXYiNjUVYWNjLIJSTE3Wi0NGhe6xSAb6+0F65EoiLg1FVFVhlJVZs3447f/zBWWppgTt/ngn19bg6YoQYHh7OPDw8gNe0lszPz0dBVpZoHx2NjKFDeblcjlOnTqF6yBAkDB8Oi5QUOCUng4kiPRtda9dTU+GUmYnvR43iCnfuhM+DB7iydy+qq6uho1Cog5uaWM+6Oq7/iBGQy+UsZv58WD17hmfh4SiXyXDq5k2UW1nB8euvMamxEbXh4bDS1UVNTQ0OnToFbYVCsJg7F2/OmcPpDRtGczBgAB07IYFax/2Z7IeE0DaRkfghOxuLqqupHOPLL8nkMymJTBW3bKHn//JlUqu0GxN2op24suRkVN65A6uDB2H30UdAZCTc5XJ6B5GHAoOeHtSVlWieNw8sJAQ6VNZRuwAAIABJREFUa9e+4iXQ0tKCnatWYeyYMSgtLYW2trZ68uTJJBd49Iie2w8/pJIQV1fwe/eiJjwcqvx8yBjDW2+9BW1tbUjq6yF+9RWGqtUMQUE8evWiA4wdC0cdnc7j2ldWAufPQ2/LFkyfPh0nT57EsWPHAABjxoxBfX09vGfOhNvf/sbZjBnDnJ2doVQqoVAooCWVkmrnyy9fnZd2GBgYYOCmTdzNwYNRmJ3NYelSUrv4+Lx2+78Cz/N44403tIuKirRLS0snOzk5wcjICABQXl6OtLQ0u//RgBpooMH/L9CQfQ000ECD/1Ko1Wru5s2bOH/+vLquro4XBAEKhQJSqVSsr61lBrW1GDhoEAIWLnx156FDKQtXUUGS5suXyUhs/HiSN4MyvpX19ZzK0BDfAVi+fDkszc2pbjs5GThzhkifsTER6d69aUyJhFqPjRxJQYWKCpL5RkaSLL0Lhg0bJtFiTNQ6fJg9DghAfHy84OjoyPE8j4yMDLGsrAylpaVMpVJBS0tL3drayuva2cFILsfYM2dQmZCAXoGBXNiGDbD38KD0qJtbNwM6qFQUbJgzh2ppu0C4cweDr19HTFgY+sXH47pCwb4bNEiU/P3vrH7zZgzOyRECzMw4XLhApnmDB1Ntb2go7KZMga6WFlQPHrw2g2lmZob3Fy5kqj59cOwf/4CtrS2uXbvGTMrK4JeQAHNRZOaLF0Pr2TOao8OHKXt/9iz1J3/2jMoXrl4lp++kJGDjRtQHB/P/1Jhx+nRIZ87Esn/8g/v5l1/Ue/bs4d3d3dXD6+p4gxMnIP7xBxITE8UrV64wmUwGDw8Plp6eDm9vb/QaO5YIUv/+1C5QJiPZt7U13dvr12m9nDpFPwCd41tv4eqNG0BtLVrLygQriYSb9NtvyOnXj64nO5vKMRYsIPO6jz8GLlyAR14eEg4fFu7s2iUOTkrisXgxZdkbG5FRXo5b58/Dw9VVrfvWWzx+/pkIaEkJKUjagx03btwAAEgjIqiU5Pp1QBTh+vQp9+jhQ7H0zh2mq6WFQJlMLX7+OXftwAHR58ULzry2lkoarl2j0oV2eTYGDoThsmVIz8lBo7Y25qxcCa0NGxDm5UXO92vWILWwEKipYbcvXRJiY2Mhl8tFBwcHNmjQoE4m6uDgAOzdy7BrFwYuXAjIZHj69KkYExPDRFFEnlSK1hMnoF1bS8G1DRtoblQqYOtWmH35Jf4f9t47LorzXR++ntmFhYVFWHoTQUClCIiAICpYsRN7N9bElqImmiZii6ZYookak2gsscTYeyMgAiIoRSkiiqA0kd522Z35/XGzFMs5Ob/3Pd/3fN+z1+fDB4GZ2Zlnnhmf676v+7q/DAoCampQVlcH3bNneTs7O+Hdd98VYcwYCjxdugT06YNRVlZovHIFd4uKsHPJEnAcB2tLS+hlZOC6QoGUX36Bs7MzP3DgQE5XV1f4ZMUKOk9BoOCIrS0RYhMTev5ra+kiiovpXZGcTM9y9+6ARIIamQx6oaF45OcH52PHaNu7dyk4pcHmzXS/3kJqwRhUcjmyu3Rh2L2brnv6dPIY2LyZlCgDBkA0YwYMFQpSY7i4vHaYPXv2CAt+/JHphYfjfklJ+xKjceOobeX69RREs7EBn5WFRltblVQq5Xie5wwMDEh1Y2ICtmYNGfJt2ECBjSVLgJ07gfh4et9dvEjvl8xMgOPQtWtXfPnll7h9+zYuXbqEq1evoqmpCd0HDxaGTp/OYGsLgDowtMDHhwIPbxmXkpIS/NJc/uDp6alGaakIMhm9T/v2JaXPfwG2trbUoaANjI2NJTo6OhMiIyPPRkREFP2XDqiFFlr8S6El+1pooYUW/0vB8/yzvLw8zw4dOohWrFgBlUqlqWNl2LABymPH8K1cjoeNjfwkd/f26TC5nDJsFy8C8+ej0MMDKb16wauoCLbz5iF/0SL8cemSIJfLIRaLWWFhIXbv3g1ra2th/vz5DA4OVAMsCFTLnZFBRCsujhbGqakkB372jLLJ4rf/d6V48kTQkUhYrb4+7ty5w2JiYsAYg7W1NVQqFbO2thamTp3KOI4TqVQqVFVVoUOHDhB/+inke/YAcXEMmzdTZrZnTzKT++svah2nVBKJGDmSFv4aDBsGrF8PZVERrEpLIRKJsEvTzq20lLm5ufFWeXmcyNyc401NwXJyyAbezg4A8CQ6Gkd//x1dHj6E3+7dJLc/f56CAW0y7pxUCt3evTFjxgyA59E7IwPPHj0S+BcvWM2ECZAoFMCMGYCjI+2gUpGJ3ujRJO2+f5+IGMcBNjZQ2dlBBUCdl0cE/E1wdAQ2boTuiROYO3euqKKiAmfPnmXHk5Lg06ED//ivv5Cbm8uCg4PRo0cPVl1djXv37qGkpETQ19dn8PGh4MW6daQI2b+fgiUiEZG3RYuIkLdRYTwuLsatW7cwcccOdO3alYuLi8NWCwt88e67pOzIy6OxKyqia9yyBRg8GLrz52OKQsElW1mh4cEDlK1YARtdXTScPo2Y4cPxwYED0HV2FsHEhAhbeTkQFgbs2gUcPYo94eF88J9/chM7dxZ0w8IYFi6kMo/Ro+GUkABnqZR5RUejRi5H2P79onKFArmGhjiSnw+P4GD0HzqUgkD19RRM6dGDSg0ePEDdO++gMT5e0Dc1Ze0MD8eNQ9ypUwiKi0OPR4+41GPHkPPoEaKiohAcHNxeYm5pSdd78SIwejRWrlzJ6urqkJaWhitXriDv+XN07dqViGxeHpFMtZoy40FBdIxZs2DWrRvmrV3bemCJhMjo4sUUqIiJwaPhw9GYmyt8tWQJ4zgO2VlZkEVG4uDUqQCAwMBArrl+vLWrAWPURQKgbPqLFxTA05Q0rF5NpT+CQMaZAHDvHkyKi7F9zBjUGhoiQnNOY8e2b9l36dKb52cbGBkZob6+nnuQlQV3d3fyhBAEeo+cOUPt8kpLSdXxhqDl1atXUV5ezkrs7JCfmqqulUhgaWnZ3gTgiy/IZyIpCdi7F00ffICX/fuLu+vqYkD//u3Laxij4JWtLRkKjh9P7xUdHTqvhQtpHr7i71FbWyvo6elBU06R5u+PEFPT1+UeAAUyrlwh9cSwYa/9WSaTgTEGb29v9O/fX1TR1AQTgAIP9vb0PF24QMGZ/0sEBweL7t69G8rz/EoAH/5fH0gLLbT4b4eW7GuhhRZa/C8Fx3GFgwYNQkBAAEQiERnSqVS0EFy2DPd8fARVYiLT1dV9s6vzqFGoWrECxysr1SUqlahjx478gcePuYDiYkGycCHznzyZD33/fZFmMfzbb78JBQUFLDExkff396djMkaExtKSMmiMEdnV0yPiEhLyn16H5PBhrtrAADzPw9PTk92+fRsDBw5EUFCQZhXeshoXi8Wtdepdu5Ic9tw5cgPPyyOpet++FHAQBCLfQUG03b59ZFAXHQ04OUHgeRxvaOCLpkzh1Go1unfvzpeVlbHCwkIWGBDAmS1fjh8nTcLZ+np4qlT8yPp6Lu3hQ3h4eODy1auCu58fG7lmDZEzxqgkYtIkkrvHxxOJEouJPJSWAl9/DcmDB+iso8NyBgwQYnR04PHFF6ydkWB4OGXTT5ygn6uryYn9jz8AOztUbNoEk7VrYREaios//iiEzZnD3tiiS1+fMpLjx8PExAQzZszg6ocMweWLF1l5eTk/b948Tt7svfDjjz8KAFhZWRkrKyuDmakpqTAcHKgsY+TI1uPKZERcz56lAEczrKys0KFDB/7evXtC165dRRrTSI7jKADTsydteO8ecPgwEfYXL4B9+6Dv4oKeLi74a906obiqSmhoaODY/PkYNWoUJDt3tn52URGVOQwbBgwejBQzM5Q8e8YZzZkD2y5dGGJiaLvsbNz38BDuPHnCnjs4IMPHBzzPY4WPD0wDA2EKsKozZwTrb75h8POjsZo1q1Wd8cMPwMGD8M/IQExMjHDo0CHMnDmzdZCXLsXCAQNw7/vvcSM2FuXHj/Nmjx5x7hERAsdxr9+MUaOop7ogAIzBwMCgJft89epVddeuXUXw9qas+Z07FKRKTGxtfzduHM11gI5x9Sp5Vxw4QPdo4EBALkeRRAKxWNxyDl1qa4GQEHTp3RspKSk4cOAADA0N0dTU9Ob3wdy5NMbr11NGPymJPn/r1vbbLVqEMCsrHPLywheaLPPduxQUePGivcze35+UAm8whQQo6yyTyQSxWNw6boxRIOP0aXqf5eTQ718p8eF5HklJSUL//v1Zp9BQnL15U9SkUiE8PLx1I0GgeZqVRQFOGxsUvXghvL97N3vq6IiqqChBPyaGwdCQAoSGhhTguHqV3mHe3qR8GDuWVEFZWRRoeQUBAQHszp078PPzw8OHDxGwciXblZmJLmPHov+rAQWAPE4iIqid4it/k0qlsLOzU6WkpIjv3bsHAPjyyy8h0pS4HDxI8wSgefUmY9H/BEVFRVAoFOVqtXrDf3lnLbTQ4l8KLdnXQgsttPhfCo7jgq2trVuc7gHQgjQiAurMTFxKTGQAMFCTkXsFR6OieLu8PC5ALuec1q6FVCrl6uvrkZmZyRyVSsgjIkQQBJKUA7CwsGAFBQVobGx83Rk2K4sWxl99RYROJqOWfv+A7PsrFGj47jukXruG0tJSAADP87xCoeAkb1hYv4YRI4gA/vYbkfmLF6n/eGoqEZcpU4DgYMpKN0u1H37wAU6cOAGO47By5Uo0NDRAJpNxDQ0NePbsGazv3oVoyRJMnTYNCQkJgtfy5dyFly+FZBcXlpqaihcvXrDhw4fT52vGPyWFvp8+TcGFadOAjz4i+e/hw9T2bMECYNkydFQomPzoUfW3334rEovFgkwm42UymUh36FB0sbSEt+baTEyIgDUTRXNzc5j16qXerqMjasjPZ0OmTQP7/ntqf9YWAQEUACkoaFENSD/7DO/k5DDcvNku8zlixAiWl5eHu3fvorKyEmYjRlAbxK+/bi/L1kAkorr70aNb6sylUikcHBy44uJiHkALuWmpkdfg+HHax8GBlAOlpeDGjoV01ixMmzePAWDHjh1DZmYmzNt6LowbR5nMvDxUr1qFRzNn4vzgwXD38VHbZmWJMG8eXWtRETBrFmwsLdm0GTNwcuJE4bm7u9CgVLLDhw9j/PjxzFCthtXLl6zLrVsofucdmBUVQdzccQIAZYI//BBcbS169erFRUVFYc+ePfzo0aM5CwsL4PJlXLh+HckXLsDX15cPApjzjRvgevRgmvvUDsHBdEwPj5aAgsY00draupUZcxwRyf37KftbWkrGlHV1lHVftozm9JkzJCU/dIi+5s9vnn4p/KC2LeAqK4H58zHIzw9OTk44ceIEamtrwRhDU1MTdNq6vKvVVGJx5w7V7HfqRF062npf/PkneTbExCDhyBG1m0Qiaun20LEjkdBX6umxatXbZfygevuamhpWV1f35g1EIirLsLWlsSgpoXIHAOfPn4dYLGbdnZ0h69oVPocPC0nJyUypVKKxsRF6u3fTOyE1lSTwKhVl6r/+msUePIhULy9YduzI5gMUrLC3J/Lc7FeCI0eISJ892zoGWVl0TmVl7TwNDA0NMX78eBw9ehT6+vrqpA0bRHW1tUJsbCxzcHCAs7Nz++uaMIHUMvHxrQqONpg9e7YYAPLz87F3717Exsby/fr1o8GdNo2+LlygLgvOzlRm8Qa/k7dBKpWC53kzsVgcu2nTpkeCIHjwPL/h888/3/mf762FFlr8K/Gf9uDUQgsttNDi/39Yu3btNAAOHTU902traRE8ZQqQlQWeMZiZmYHjOOjp6UFj5KdBRkYGcnNzOfeDB+Fx5gyTNhMUqVQKX19fyAMDgV9/JeO9hQuB6uoWE7qamprW/3tiY4mImJiQrH/+fDL6k8uJiBQU/McXkp8PyejRMPL0RH19PUJDQ8FxHK5fv85t3LgRhYWF/2xAOI4yk+fOkeP80qW0aD91Chg8mKTJv/0GPHkCxcWLuP/FFxjG81ju5saJb92CrKQESE2F/pMncLGwgCgqCnBzg5UgoE9ZGcscNYqXvnjBPB48wIv79zHj778Fs0ePiBAEBtLx/f1Jkm9pSSTpzz+JAGn6zGdmtmQ4JRIJZsyYIfrggw8wY8YM1q1bNzZw+3bBsb5eOP3gAdLT0+m6bGwoy1hZ2XKpjx8/FtUZGWHJwoXgysqAigoiZq+ivr59RnXbNgo8vAJPT0/k5eXxxuXlSPryS6hqa5Hcowe2cpzwu6UlnzJlChISElBdXY3a2lrUW1gQ8a6paTlGYmIiMjMzMXLkSA4AHBwc0KNHDyEmJgYqlar1w2JjiVABwJ49dH/OnCHS04y6ujoeAP744w+e53kKPDx4AAwfjhs3bmC7jg4q7e2F5U1NGOPgIMKAARRAqKsj8mNpCbmFBaoyM5HZsSMbsXcvN27fPpafn88qV60CLCzQxdoa+cOHC78NG4Ynr85Rxiir6+CAvioVPvnkEzDG2M6dO5GamoqG48cx9OZNSCQSoampSXD9+GPGpaXR3PP3p7nwKhYvputtRlNTE/T19flx48a1RAbu7NkDdW0t1cj/9BMRwrNnaV5fvEgZfKWSgjBXr9Lc8/ICli5F7erVUCgUnEvbmvbDhwEDA0ilUnh6emLUqFFwdXWFIAjYsGEDvvnmG+HYrl2o9fODet06IuYffUTBhuXLaUy/+671eFevAo8eobisDPn5+aIhmjabACku/Pxev+4RI+j+vuU5VigUAIC0tLTXA4iRkUSo+/UjYiuVAnFxUCmVOHHiBNLT0zF27Fh0MDcHTp/G4CFDmLu7u/rWrVvC5jVrUD5oELBxY+vxxGLA1RWPTp3ic/r1w5Jr12Bz/jzu3r1LwRN9fQrIfPMNbX/sGI31/fsULAgIoJ8PH27tGBEWRpJ8pRKdbWywfPly+Pj4iCSHD8PF3p7JZDI+MTFRKC0txWvds1QqUv/cv//GsQGAvXv3AgDMzc1b37kLFpBKSS6n915JCQX2srMpKPgPoFQqoVarYWZm5jxkyJCwd955x47juFX/aGcttNDiXwot2ddCCy20+F+GNWvWDNXR0fl51qxZei1Z/fR0WnSqVICODnR0dCCTydSMMXz99dc4dOhQC9tvbGzEmTNnhKFDhwrGtraU/d7wBjWnvT0weTKZxg0aBNPmrJ1EImGKjAzKoickENGUSklqrsmA6enRovltve41SEkBOA7PCguhq6sr2Nrawt7evmXFWlZW9s8HJjubWrx99hl9rqMjZc3u3KEsaY8egLk5JDo6cDYz44vu3BFKjh6l2uLbt5Ezd65Q1r8/GjdtIrJ19ixw7RpMd+zA0CVLuJD0dFg+fw6eMRTX1rLfjx1DulotxDo58ecuXlThxx9JmtuzZ2s2cckSUjsMH07E6ZXFuEwmg7W1NUJDQzlrT0/mP2ECGzVqlHD27FnkakjxkiWURW/GsOY63zq1Grh8mYIrTk4UnGmLkSOp1riqin4+dYoygG9At65dMX/3bvgkJ+PbqVNxjrwJmJmrK+scFYW7R49i69at2Lp1K7Zs2YIjN2+qm4YPB69S4datW7h27RrCw8Nh1+xpwBiDp6cnA4AcjQwboPvz5ZetPz99CjQ2gh8xAieOHUPBzZsoKyvD9OnTAYC7/OGHePrTT/grMhJJu3bh5s2bUPE8Mnx8oLt3L7nA29qiPi0NF+LioFqzBrhzB4Ig4MCJE4LczEy4MWOGGu7u+Ky0FHYHDwIiEXRTU9Hp3DnGZLLWwEpb6OiQcV/fvpDq62Pu3LnMxcVFff78eZw/fx71lZUwMjISatoEPBAaSgEvkahVeq7BmDFEJtPSAADOzs5oaGjg9u3bJ+zYsQPbf/gBZX/9hXIXF2pxmJ9PWeVFi2j/9euppWDv3tTZoEOH1mMHB6Pw8mUhyMJC3WIEV1NDKoc2WXUfHx9MnjwZoaGhsCguxpjdu1lBTg6irKywUalEkaEh7ffyJe3w3nukwPjoIzKn/PlnYPVqPHjwQLCyshJkMhnKyspQX1xMQa02fe/b4eef6T3RjMbGRmRnZ6O6uhoHDhwQdHV1UVRUxE6ePNn+4Xj/fbpugAIlFhbA2bN4MGSI4LphA0YMHw4nJyfycbh6FSKRCGPGjBF9ZGXFPtq2DdX6+iSTb4tNm5DZvTt774cfYDxyJHxCQnDx4kUi/G/C8+ek1GlqAj79lAwE4+PpuQMo2GJnB5w5A8HUFH8eO6ZWbdqE7vfvo6+PD2bPns01NjYKv/76K7Zs2SI0tA3KWVkRaTc3fyNJ16icwsLC4KarS0EgjSGquTkFMxMSqNTHxYXmWFhYq9HkW1BXV4c9zYEnpVKp9vb2hkgkAsdxWqM+LbT4HwitjF8LLbTQ4n8RIiMj7cRi8dEpU6boW1hYUMZ3+HCq8X6FyBUXF7PQ0FCYm5vj2LFj3IULF9DY2Kiurq5mtra2gre3N0UKQkIoc1hSgnZGZACRnkWLgIkTIQ4NRYCLi3CnsZH5//ADVACKUlPhvHz5m0/W0JDO7c6d16XNGmRlAV5eyM3NFczMzASO49iUKVPY5cuXcffuXZw8eRL29vYtLaPeiOpqWvDHxNDxdHVJijx/PmVbKyqoFj46msbpww/RrV8/7sHx4/j54UN89dVX4DgOp4uKhLq6OmZvbw/T995Ti4ODRb379oUxEU9w5uZQT5kiNJSVMbZ9OyquXxcuFRayeldXZpSXx+67uMDBwgIytZqykXPmUMYToH9nZBBZ6927Vfqvwa+/EqHhOPg4OLDa2lrh9OnT/NKlS0WIjKSsNYALFy6gpKQEAMm2bW1tOXAcEVMbGwrA+PpS/bdIRATlq6+oDv3GDSKQbTOyALBhA3rl5XF3/PyQ4e6OLl26YOTIkRqZN8Po0VgYHY1qHx/wzbXmUdevsydnziBmzRq+RCLhwsPD4ebm1u6wUVFRAgDmoOkRrlYTKcnNJSUIQCaOAwZgQ10d3A4fhvzKFdR/8gknl8tRW1sLw5QUKK9ehb+RkXBy2jQYGxtj3vffs7SRI4U9EyeyEefOCZdWr2bP7amLWLm9PeqSk3mzxkauoaGBrZw2DZyRkQjPn5PLO0DzIzsbqK2FUqlEUVERjzclT3r0oMzwjRvAlSsICwsTbd++HbrTp/M7k5I4VWUl1xyUIBgZUaApNpYCLY8ft16nSERBoMOHge7dIRaLYWZmJjx9+pQBgE1BAexKSmA+ZAhlbk+fpqxtZCRlzOfOJQ8GjiMSXlfX0sIOtrZI8/AQ+hUUiFrKCA4epDn/qqz+4kX4Z2aiXiZDQceOeO+zz6BrZIS7X3+Nm+vXC2O+/ZaJm0sMIJWi0cEBZd9+iyd+fsLTgwfZ8+fPhcbGRsZxHNatWwee56GrUKDLjh0YqaMDHbwBzUQ6Ly8PR44cETQmdgBgbm7O5s6dC47jsH//fuzevVs9Z+ZMkdjPjww/NSVIzQGZFy9eIN7Tk02Ry2Fkb09zqqCAVA7r19MzNm4cDt25g5JDh6Cnp6cWiURoampicrmcWVlZsRpBYNycOcDAgbCVSLBk5UpsaWrC33//rZZKpczPz4/z9fWlY1++TCUN5eXkm9CtGwWoPv6YTCY1z7ebG66KROqC7GxuUseOEG3cCKmJCeDoiNkPH3J8bi7+2LcPt2/fFvr06cNagrS9e5Ocf+RIOmYbWFhYYExiolBx+zbjd+0C5+REPg7r11NQxtmZ1DupqeRXMmUKjdfq1fT7VavaBYUEQcCVK1fUNTU1TDPfy8vLRbW1tVCr1VCpVOaRkZErAVyPiIi486ZbqYUWWvzrIVq9evX/1+eghRZaaKHFvwgJCQmng4KCOnt5edEq/vPPicReuEB1rSdPAjwPlY0NbkRFMRsbG/j5+aGmpkaVmprKFRUVcXV1dWzGjBmcnqZO2cCAyMPjx2jpLf0qpFKwsDB0XryYdX74ENcWLULlihVw9/SE+C1O+wevXFF3uHKFSzQzUz8tK+N0dHRgYGDQ6lZeVwfMno36detw8vRp1q9fP2ZlZQWRSITKyko8evQIADBgwID2vgQaaLJhAQGUSV2zhszAyspIxr9wIf3+4kXK3p88SQSoZ0+I3Nwg79sXadXVePbsGVxdXZGUlCQolUpWXV2NYgMDTpGZCb/Zs6Ezfz6N0dSp6NShA0t48gRBQUHgOI4ZGhpCoVDw1dXVXGZmJpzv34eBnh5EY8ZQfbAmyLF7Ny3my8pI8RAW1qp6ePQI6okTkRgQADNraygUCkilUhYfH8/V1NQg8cEDoXLdOiQkJfFpFRVcVVWVJnOutrOzo8E0Nyc1xcaNlI0cMIA+29CQMumjRlGJQd++reOnUlEWd9IkcJ0740hQkFAtlbLa2lr06dOndTs9PWDTJkiuXYPe5MnQ09NDNzc3VujoKNhfusRGb93K3tQK8OHDh7y5uTnn7d3sQNDYSDXfGgdyQQBGjkTU4MHIr6xEl/HjkdSjBxpKSxGwZw/s5HJeuWgR3AYMYHKlkgWYmLBeS5YwHSsr2H7zDUvu0gVlHTuynhkZsBo2TOjZvz/zvHMHRfr6yHz5knE8D/8pU6A4cgS6ublEmmNiKANtYgJ89x1qiotRIhazzIICvkePHiwnJwfnzp3DpUuXBA8PD6bn4IBKxpAC4Pjx40L37t35ka6uoqC5c5E2bJhw+/ZtoVevXu1NEjt2bHWO//RTCq5wHCllCgrou6Eh/P39WY8ePdBFEOB67pxgnZ3NdMViOk+plLaNjqZ6/08+oQz7sGF0L7dsIaIIIKe0FDfz81nvR48g6dCBVB4//EBZbU2ZT3w8oFKh6rPPkJSZifiuXeE8Z47g3LUrE4lEKCws5IevXcvdfvGC7zRsGAMAPiICpevX45GjIx5bWLBSfX04OzvzgYGBXFZWFsaNG4d33nkHblOnIr+uDuU2NoKDg8Mbo3qChQUulpfzMg9oo6mSAAAgAElEQVQPbv78+ejZsyecnZ0xePBgGDSXGvj4+LDMzExEXb8u+JqZMfGkSS1BseqffwYWLMBOhULo2qeP4D53LsPs2RQgW7GC1A4JCRRkW7YM3UJD4erqCldXV65z585camoqa2xsZNXV1UJAQADvMnMmB8aAfv0gYQxdPv8cti4unL6+Prtx4wbi4+OF0l9+Edjp09jv6cmr799HtFrNu/v7c5yvL6mG3n0X8PQEbGyQk5ODi9euce+++y4znjqVyhcWL6YyBC8vsC1b4Jiezs4aGAhu06ZBPziYQSajd6C7Oz0bmueksJD2HzUKllVVLLGkBGkGBrznF18waHwWnj8nQj9qFAULhg4F+vShMoPZs6mbgZMTBTt9fFreQ4cOHeJqamqYWCwGx3GwsrJS+fv7c6amppDJZEYmJiYh5eXl02NjYz2Dg4P/etO91EILLf610JJ9LbTQQot/I0RGRprcvHlz0e3bt3+KjY0dd/369cSQkJCXr2yjHx0dPS86OnpwdHR0fkhISFXz7ydIpdJFEyZMkHAcR+7RHEdS0KlTiSDk5ACJieD++AMd4uMFhUQCx/p65hoczAUPHAgDAwPBx8eHvdp3GSYmlB0LCHhdjpudTbXIY8aAcRwK5XKh06VL7JGzM7r6+b2R7Ofn5yMmNpazGT0a4rg4Lp0xPjk5GXfv3oWrqyvT19en/vGmpjhVW6tmjAnDhg1rSUMaGhqiuLgYVVVVKCoqgqOjI9RqNV6+fAlDQ0Miqf7+lN2MiCCSr0FeHjmVf/cdyaF1dakmNzUVWLmSnMY7dIBswgQ4/fmnYPPrr+wXpVKwtLRE7969WUVFBRQKBeqkUrAePXinESMY6uvBp6WhfuZM3OzSBS6urggMDISbmxsUCgXLy8uDIWNC8KZN7GR5Obq9/z4qKyuhUqkgEonwl56e8LSujhUaGACxsaiTyWBkbg5IJDh1/bpwwtWV5eTlIT4+HgkJCUhLS4NarUZxcTEsLCz40MxMzqpLF85v/nwkJydDJBJBpVJxWVlZag8Pj9b07fjxlN2bPp0yo9OmERnIz6d/SyQU0FEqyWsgIwPYvBncvHnoExrKampq+Pz8fCaRSAQ7O7tWEjtgAO1XXg6YmQEALDt2ZBY7djBRaGi7doMalJaWcsnJybh//766pKSEs6+rg46paWuv9KtXUZSejlMdOsDAwEB4+fIlvPz9ofviheB57x6zuHyZ2S9fzkT6+iRRnjiRyjKGDUOttTWuq1SYFhkJOx0ddNTTY+a9esFgwwa4h4WxgN270cnPD0JTE4yTk1Hx7rvQ//13Mo4MCaFsvSBALJPx4pwcZpiZyVyGDcP+P/9ESUkJ1Go1e/jwoZCQkyPcLCtjgatXw2TiRH7wmDEiJpeDdesG32nT2PXr19mjR4+QmZkJY2NjdNBkUg0NSXHyzTeUtTU0pOfqwgUic927A8+fQ7JrF4y//BIiR0f2S0gIbHJy0OG772i+7tpF1+3iQs+5WEzS73feoUCNSgVMnIjcuDh0nDpVcO7Vi+HePZKVq1REAAsLqYQlPBwHc3JwsUcPdJwxQ5g2bRrr1KlTCzHv6urKvlGr8axDB1ZbWwvF8+e4XVTEZ8jlLGz/fvht2IDeS5eiW//+nFKpRHp6OoKDg2FkZAQDc3MkdewIpSAIbm5ur5H9CxcuIKmmBkJwsDB5+nSmq6sLPT09yOXydi71YrEYXklJrOHMGXYxMFDwDwxkjDFkZWXhxLlzeNnUhNquXdmUKVNoXg4ZQpn2o0cpkPXNN0SwDQygo6ODDh06QC6Xo6ioCJmZmVCr1RgwYACCgoLoeTEyorFatQqy48dhYWsLx+Bg+Pr6wk0uZ56HDjHlzp3Mxdubs79yhd2QybjYxETEx8cLj2prhe4jRzJ28SJe1tRg740b6N27N1oCW6WlNP5ubkS0hwyB5N13IajVrCw1lXWcNYvUF+PHk9nl55/TV34+BQITE4GhQ1Hh7Y1zeXno3r0769SpEx27vp7ebZ98QqUNzZ0vANAYhISQqqmqitQsVVWAjQ2K6+qQnJyM4OBgYerUqaxPnz7o0aMHxxhraXXq7OzMubu769y+fdshKipqY2xs7PLY2NjhN27cuBUSEvIf1wdooYUW/y1grxl+aKGFFlpo8T8SkZGRfjo6OlddXFx0vb299cvKyoSoqKjqpqamwIiIiBbb840bN16ysbHpa2pqqpOamqoSiUR3eJ6vEYlEIVOmTJHa2dnRIn7BAspme3m1fEZTUxM2bNiAEH9/PD9zBhbOzuqBSUkixMUBa9dSRig0lBbJbet+AcoAN5MpAETs9u6lbWfNojKB5prslIMHIXz/PazDw2H5+edgOu0FvBUVFfjhhx8w3MEBPX/6CYiLA89x+OWXX9RFRUWi7t27C0MrKhhvY4Otd+9i+vTpsG+WYrfF/v371U+ePGlJ6xtVVODDqipwGqftOXOAVz4b9fVEjvT0yP398uVWc7QrV4gIp6VRycLjxyi4eBHKgQPhFByMXePGodTKCpoFsFQqxbSyMpj/+Sfi9+4VGjduBPvgAxYyaFCrQqGpCU0TJ6J66FBc0tHB08JCoampqR3pMTU1FSZ/+y0eW1iwvydMgFNCAnzu3UOFqSk65eejKj4ely9f5i0tLbkxY8a07MfzfOvnKBTgdXRw5MgRvrKykpmbmwsZGRmcp6cn2u4DAHj4kOTGLi7UHWHbNlI1DB5MEmC1mtqJLV5MmeM2SE1NxZkzZxAeHg49PT20mL7duUPzICqq1ZshLY3G2sPjtXtXWVmJ5ORkSKVSpKen890PHGD+zs6MazYdU/zwAw5lZws+c+cK3bp147755hsEBwejv6UlnXd5OZUmCALJ/Y2NSb1y9ixOVFaqnz9/zpYsWUKDs2QJnZODA5GdgQMp6DNwIG5/+SV/5cYNThAEiMVifPrppxBXVADLl6Nm7Vrs2bYNYQoF3Orr8dLREbt4HioAHh4ePMdxQpcuXUSuq1dDvHYt+SPQxASGDkXkTz+1XK+hoSG/bNmy18sBnj2jDO/ff5Oy4+uvyXAwNJSy0iNGANHRKJg0iX8kEnFBNTWQDBhAcu23lb8AwL17qP/0U1yRShH04gUseJ4yzRrFjoMDtc27fx/Q18eJ8+eRnp6OiIiI9sdJTweCglBfUIBrMTFq2Q8/iLyTk7Fn5UrMmz+fSmiuXYNw4ACuTJ4sJN+9y/z8/DBo0CB6vszMcLamBk+ePBFGjhzJZDIZzJoDQseOHRPy8vLYhDFj4LBvH9iaNa+XFrTFjh1oKCzEN81dOEaOHIn09HS+qamJDXZxYYZ2dpBrWs0JAgWtrK2pVGf/fmoXaGREBoPXrgF9+uDWw4e4fuMGBEGAkZGR8PHHH7cf1Hv3qGPCsGFkqllXR8HTTz+l96FCQVn3jAy8KCtDeXk5jhw5gkGDBiFILseLhQtx39ZWCD1woPW4sbG0b5trVSqV2LJli9CnTx8WFBREPgA7dtCxBw2iZ3LXLirTaMb169cRGxuLd999Fy3lMEeP0vM8bRopefLz249hZSVJ+H/4gWT/lZVAYCCE4cOxzsQEwcHB7TtkvAKe57F58+YGhUIRa2Ji0tvIyIgVFBS85DiuUa1Wm6jV6kVfffXV0bffRC200OL/TWjJvhZaaKHFvwk2bdoUM3DgwD4aV3sASElJES5cuFDc1NTkGxERURQZGdlZJBJlTZkyRZyXl8f36NGDi4mJgZWVFdzd3WFgYEALuA0bKLM9eDAaGhrwyy+/oLy8HIwxCIIAqVSK+vr61kUiz1M9+61blB1cvJjIgEb23asXSdWDgojAKZX079xcIl2vtMArKirCH1u2YPCFCyjt31/oMX8+M/HwgCAIlNWPieELCgq4mTNnwjYzk+qVjY1b9j1x/DgfePAgdzMwELXm5lixYsVbywHq6+uxbc0amFRWYtrgwTD84w/K3Gtqi1/FF1/QNZw9Swvq/ftpMa8hak+eECH65BMyD5NKiTgcOoSikBAUjRoFw5oapKxfj8yMDDBBgF1BAV7a2WGGpSUsgVbjtMpK2vejj8h8zsICL1++xA5NfTiApUuXUl/1ly8pwCIWo7CwEFlffgmpuztvrlRyh9VqGBkZqRcuXCh64zg0NdG+Dx+2BFzS0tL4kydPclKpFD179nzzAn7bNjIbfP99Cgr5+JC898MPSeXQtuVcG5w/f15ISkpijDGsWtXGpPvYMZKP29gQES0spAxmTMzrwaM2SE9PFy4cOsSmT5wIGx8foLwceRMmIH7MGH7ywoUcABw9elSor68XZm3dymHQIKqHdnYmJcbWrZQpX7sWdUVFeNajB5527ozBx45RicT06ZT5j44mlcr48TSHf/kF4DioVCqUlpZiz549mD59OhkJ8jx0goMRP3iw+qpEItKvr0ev+Hi4PnyIuHnz+DGrVnHtAknx8fQ8TJtGz85PP6GyWUpfVVWFffv24bPPPoPuq6aUPE8ZV0NDask4bBgFJlavpm4JvXoBP/0EISYG2b17I2H2bLz77rtvHUsN6mtr8cP33wsDDQzgu2cPY5qWcUePUlAkMZGIb3N7xB07dqi7dOnC2rXnA2j+3rxJz3tsLFTdu+NZZiZs/Pygq6uL+vp68DyPig0b8DgxUXA9cIBZa7LJQ4cC/fpBtXw5fv31V9XLly9FTU1NTCwWQ61WgzGGJUuWwNjIiNQKFy+2KjteHaPVq+nZlUigVCqxbds2mJiYCDo6OlAqlZi3Zg3Dp59Sec6TJ3TPw8MpqHP/Pimdzp6l+x4aiuopU5AREADjx49hdecOknbuVDvv2MF1mjaNITycvAR696Yxio0laf7x43Q+5eXklwDQ+3HZMuDIEWRlZWna6wkfffQR09XVRerx4yg/dUod6u0tgsa/xMDgtfdmYWEh9uzZgyF6euhw+bIg27mT2Y0aReU9p0+Tz0h6Ot27lmHhsXHjRri5uQnh4eEMajUF8Sws6L6+qdXjkyfUqjIuDpBIwPM8bp05o3ZsbBSVfPstxF5e8Nqz53XfkDYoLS3Fo0eP4OXlBalUimfPngEAOI7DwYMHG1Qq1VcqlWpzRESEloRoocV/M7QGfVpooYUW/yZgjOk0NDRAEIQW+aq3tzerqakxj4mJebRp06YbYrHYX61Wi6OiovDs2TMuKSkJKpUKeXl5gpeXF+30yy8QKiqQaW2N5AMH8OTJE+jp6QljxoxhdXV1uHv3Lj9nzhyusrISlhrDPY4jSanGRG3YMFogKxSUDYuPp0UzzxOJdnKi38tkb8zEWVtb4+ONG1HzxRcomjOHPZs2DUnh4ep0IyNOoVDAxsaGLV68GEZGRsD585SF/+OPln0XhIdzhXv2CF7h4SwkJOTtgyYI2PLdd5h4/DicunQBN2YMSZv/I4wYQdJ1gLL+OjqU2de0EXN0JGnry5dAXh4a5XIcvXwZz58/h+jQIYhDQ2FcUYFZYWHArFl4eesW4p8+xYyffoK4spLk1IsWkVQ3KIiI3O+/t3x821ZzmtpYACR1X7UKKCmBze7dsFEqgfx8rvLyZZj364fpW7a8mehrruOvv1oN3wDIZDIOoGBITEwM+vXr1/pZGnz4IQV2jh4lKf7ChRR4cXF5K9EHgOHDh7O8vDy+rKyMS09Ph6enJ/1hwoRW1/klS4j0h4cT6X8L2W9oaMC1a9cw5+RJ6I8dix9//FFtU1LCTDiO+Q0e3HLCvXv3Zod+/JGp4uMhzsqiDH1WFl27kREwfTrSb97Eie++Q3c3N/hOnEj3cP16mltFRUR+KiuJQGmCO833wcrKCra2tsKBNhnYYaNGIbC+XpRhZYXCkhKkjBuHGrGYD/v7bw4rV5L6oVcvegYSEkgZMm0aPSuMwbjNNQKkaGl55srK6NyDgqh2fvt2IpA9e1IQYtgw8snYtQv48ks8njoVRwsKYF5fj+3bt6s76OhgxqRJIjQ1kTnfggWktImJAc6ehZ6ZGQL79mVuS5dCkMnAli2jGm57ewrs/PEHBaGa50hNTQ3XrVu39szw559JZaAJCm3eDPHDh+jUHMQAgN9++014+fIl01OpMC8vj8mb2+VBqSRyzXEQcxzee+89MUBEMTc3F2lpaWCM8cbGxnSPmz043ojSUjrfpUsBiQS6uroICQnB9evXmUaKL+Tlge3cSc9vYiIFBszNydwyJobq3pcvp8BF1644umCBurCwUMSZm2PiggXwCQoSqeLiaKyys4F160jN9OuvRPxXrKDxsrSk0qjiYvoCKFABoLq6GgYGBrxSqeSuX78uhISEMK5LFzzq1k0ILSwkKf6GDaSCahv0qauD1bJlcNPTE+oBiKuqmO7t2/R+PXiQgpdbttC9aEP2OY5Dv379cO3aNda3b1/Ijx6lOZCYSO8SFxcKerSFoyN9fnw8hKQkHJbL8ejRIxEAdA4OVo8rKBAhOZm6qPTr98bbYWFhAQsLi5af26qu3nvvPf39+/evqaur84yMjJwXERHR9PYbq4UWWvw/hbZmXwsttNDi3wTXrl27V1hY2O/Bgwf6Xbt21dVkAB0cHDgPDw8dc3NzV19fXwMjIyPcv38fMpmMd3BwYEOGDMGdO3fY48ePURsfz4vz84WH/v64kJAAsViMwMBANmnSJGZpaYnY2FiYmpry7u7unGFzRu+NqKmhvu++viT3tLMjgn/6NMlJt26l759//lZyzRiDnp4e/nz8mJzqs7I4p4cPMXnrVubTsyeTaLJa1tZENqdObclCsV27YBQSwjqNHfv2c2w2rerarRuO9+zJp3p5MXNzcxgbG7+2Kc/ziIuLQ0NDA0yfPgVzdaUWVQAFOH7+mWph9fSQn5+PXfv2wWTxYryorISJvz9ydHRQbWUFnufRpKeHelNTBPfvD65XLxgEBaHLxx+D09EhYta/P0nZO3cm4jZiRLtzMTQ0RGJiIgBg6tSpMDU1bSXhDQ1U8+7lRRm4QYMgmT0b1ampfNXx48z2nXfePh4SCbm5BwQAAExMTPDixQv+xYsXDCB3/sDAwNd13xxHJLCykshEairJ7v+DzB5AgajY2Fg8f/4cgYGBrX9wdSVy7+9P97N7dyL+48a98Zjbtm1DbU0N87p7F/v19ARTOzv037+fq3jnHd5r+HBOE/gyunwZvitWoHLBAhju3Elk/9IlwM4Oand3lN6/j4NlZbCytsakr7+GSWIiEU5jY7ofW7YQIbe0bO1J38ZLoLKyEgkJCbC0tGSdO3cWiouLWQ6ArPp6jN6zB72+/x6hYWFw9fNjOuPHE1FPSiJliL4+uZ1PnUqftXMnZWKbydLOnTuhUqmQnpYmlN29i5ozZ5jpsmUQe3lRsGXRIjqX998nl//9+4FDhyhYUVMDODhAZ8QIGMXE8AN+/50VhYdzoxcv5nSKisB8fChQEBZGARU7O8DLC+UzZuDYy5eILyxEtKsrXENCIJs3jz6H40hFEBsLTJqElLQ0ZGVlsbCwsPZml0+eALW1RJCXLiUJeRs1g1KpRFpaGkQiEVOKRPD++GMYaM5l7VpSBs2c2e5+p6en48qVK6itrcW4ceNYy/Oq8XtYtKh9oOnOHcqEf/lly+8fPnyIc+fOQaVSgdXUYMb584Ls+nXG7t4lsuviQs/f8uVUn+7gQOft4UHZ+rw8dAsM5JKSkgSZsTEKGhuFzOxs4W+1mlXa2wsd+/RhOjNnktN+bi4FIr77jmT1ajWNc10dnZOlJT33P/+MW2ZmfODevZxIJBKKHz9mTRcu8PmMscrSUvivXEmZ9/XrqXRo4kQi8fPmAfPng129Cvfly5nj+PGscu9ewaRvX2YwcSJ10vjgAwrm1NVR8KwNOnbsiPj4eEEsFsPRxYVhyBCaA9euUfBP4xPwKs6eRcPlyzjXPKZ9+vTB6IULOfHEifQe+OwzCojJZG9XSb0Benp68Pb21ikoKHCtra39ODY2dvnNmzdX3rhxIyEkJOTpPz6QFlpo8Y+gJftaaKGFFv8mCAkJKbp+/fpPSqVScufOHX+JRCI2NzdnIpEI+vr6sLKyglwuh5OTE/T09BAQEMCCgoKwd+9etUKh4BQVFeh88iTLaGpiiRIJGz9+PBs8eDBrm3WJiopSeXp6iq3bmjY1NJD0s7KSFtqCQJnuAwdooS4IJHseMIAWvgEBtMh98oTM+UaMIGInkVBNsFrdLtuvr6/P38zPZ9VGRnDKzmaZJ07gcmYm7xEYyHR0dGgxaWFBx3Z2pp0OHKAgwqut/gDK0M6cSW37OnXCVTMzvqiigtXU1LDU1FT069evnbEXQNnEY8eO4f79+/DfsgXF+vq419SEqqoqGBkbQ8fREfyKFcDEidi3b59QV1fHMjIykP30KUTDh2Poxx+j7717cBsyBLezs8HzPMninZzoA8LCiERIJEQoTpygLPfBg3S+trZkFNitG/DoEVyfPsWdpia8PH0aT54+Fdx8fRmqqmj85HLKPEdEAM7OYObmKKuqYla7d+O2Uglde3uIxGJUVlaioaEBUo1h4v37tED/4IOW63ZxcWHl5eV48eIFpFIpC2gOBLRg/XpSHWzfThnfNWvo925uVNrQXBLwJohEIjQ2NvKPHz9mpaWlvLu7Ow26vT3d/3nziADr61Om2t6+dbzawNraGua6uqjv2RMew4ezfhYWzODKFXTcvZtjmnnE84CHBw5VVal91q3jdDdtIgO2tWtRY2WFnysrhViZjM34/Xc4m5pCPngwZdjT04ksd+tGZRmffUaBBwMDIDQUvEoFhY8PLly4gPPnz0OhULCwsDD07NmTpaSkQKlUok5PD0NtbWFgYUHnr5lbjo70LBgaErHfvp3ufVgYZfudnFqIVidTU3h27oz+mzYx2xs32F9ubng+YAC6T55MRLKwkL7WraPjbdxImf+6OiKSP/0ESUMD7MLCmMTQEM5Dh+IXExMBY8cyO29vcn6Xy4nUNnt0SDt0QKdOnfDgwQPByclJCAgIYJyxMT2rpqZkBjh0KPi5c5FQUcEbenkJ3bt3Zy2Bpz//JJUGx1HwYvbsdrLzhoYGbNu2DYIg8JMnT+aGDh0KAzs7Gou0NHpfBAbSODWjsbERR44cEcLCwtjkyZPbB+b09anTgMa0ToM5cyizPmoUACAtLU04deoUGy+XY8iBA0iXy9GoVDJFeDj45GRIli+HSCRCzpYtkBYWQmfpUlRXV6Ourg6ibt2Qm5aGDt7e2F1RgTqJhOnq6kKhUKC8vJyb0b078o4fZ0kpKfCZOhXM25ueyYAAupcjRlAL0qoqCoaePUv3jeeBzZvRrXdvVldYKGQrlcxWpeK7pKVx2ebm6om//SaSHDpE74Tvv6cxUqnouE5OVD4zejQdNyUFebduMZuxY6Gbk0PziuNoHLt3f2OZg46ODnv53XeM/f03CgYMgJmZGUQDB9Jx3wY/Pzz08oJ8/37e0MpK8OjfvzXw0rkzMGkSneOwYfSObuP98p9BLBaje/fuOu7u7vo9e/aUSiQS/cLCQr5Pnz5n/vFBtNBCi38ELdnXQgsttPg3QkhICPr06fP39evXLxUUFDjHxcXZiMViZmdnx7UlsHZ2di3O3rdu3UJTUxNb0r075Eolbnh4CL169WJta/81+PvGDRbg5sYkublkKObsTOTkzBki0MXFrW7t771Hi3tfX3Innz2b5LyBga1mZ5qstVRKZDcqir5/9hlJ/2Uy2Dg5MXd3dzh6eKDI05Mvys9nIZcvsw52dtBxd6f9ExMpIzd5MmX4cnNpcd82y1hZSZ4CYjFlGsPDAW9vnDl/XlAoFBwAfPXVV8jJycFff/2F6OhoWFtbw9jYGNeuXRMACDKZTIi1sGA5Uqm6ur6eZWdn89HR0Vzckyewu3wZp3JzUSoSsZCQEEyePBmhoaHoFBAATl8fik2bcPfKFRQ7OQmDhgxp37FAKqUM8vnzpH5gjOT72dlEmC0tqZ69Tx8gKgoGp04hxccH4/fsgZWRETMyMaG2dyEhtLiOjqYMeTNZNLCzQ3znzjCJjxcU27ezP8rKcC89HQkJCcjJyVH7+vpy6NiRiH6bOl2RSARHR0fExcXB1dUV3bp1o/OtqaE6/08+Iem5iQnJfm1tKWvp60vEYs4cklD7+b1xvhoYGLDk5GSUlZW1L7ewsKCATZ8+dOzBgynooQnmtIGJiQk6RkfD9sgRmH38Mdi5c6QC0LiLA+Qj0bkzitLSYCyVMtm1a1AqFIgZOVI4cvcuMzc3ZwsWLEBqaqra47ffuJpbt6BTXIwmXV1sCAmB7uTJ0DEwgMGVK8BHH4G99x7Oy+XChawsZvbJJ6jOzcUzGxsAwIMHD3Dr1i106dJFKC0tZQBQ4OEheBUVMRw/TmZpGjBGJKxXL7r/CxaQfHzwYLrvjY3A3bswmj4dxjY20JHLof/BB5Beuyb4HDzICtzd0WHuXBTl5CDD2BhWEgk4Kysap+RkCsSsWEEKgZ49aZ5lZ0Pc1ATnzZuZ7fHj0O3WjQJD6el0T2/fpu3Ky7H34EHB1NqaTZ48mQJrGtjYAB9+CD4hAU0XLsD2+XPmP2wYE7u4tGbux42je7huHQUBX1FlNDQ0IDY2FmKxmPXt25eJxWIaD1dX8o5ISgLmzm23z6ZNm9DU1MTGjx//5naZRkZkUKgps3nyhJQPo0YBjOF2XJyg+vBDNrS6GrbvvAPBxQVXlUoU2tsjs6oKyU5OiI2JQUJsLDx+/hmHfH0Rm56O27dvC/Hx8SwuLg6Zubm44++POiMj+CYmwsbLi02IimKdx42D46FDcKioQGyXLvytbt1Yk5ub2rFPH66l5EMmo6Datm0UXNm+nc7R2RkYPRpMKkVUXR3/mOc535kzmcuqVfDq35+TLFtGJTKadnqxsVTq8f33ra3vTp8GPvgA9WPH4oBIhIFnzoAplRTUAijgsmMHBategZ2dHexu3TsbfTYAACAASURBVMLjxkb+UkUFe/r0Ke8dFMTY5MmtCqZXcObMGfWNGze44ampzD80lBlrxlwDiYTe8337UgBqxQpSVjSXLPxn0BiY6uvrQ6VSITs7W7d37947IyMjLaKjo92jo6OVISEh9f/oYFpoocVboTXo00ILLbT4N0ZkZGRXXV3dv/z9/Z0HDBig+6ZtVCoVkJAA8cqVJPlsa5bH87T4P3IE9V98AcHREU9cXJAUEIBxUikkH3wAsa0t2KumYa9i/36qMc3IoJ81GeA34elTCgT4+tJC9fPPaaG4YwfAGK5cuSI8P3kS49zcmKy8nMiRSkWE39eXpNZXr1J2E2h11V6yhD7/5s12mb+ioiIcOXIE1dXV8Pb25lNSUjiRSAS1Wg2O42BiYiLU1NRg0qRJzNHeHoKnJ1h6OgUNQBL/J0+ewL6uDrWHDiFlyBD06tWrNWPejOzsbBw5fBgf/PknTAYNolIGDZKTKUuXnU1Ey9+fAh81Na9l10pLS1FWVoYTJ05ArVZDJBJh2bJl0E9LIwm9pSVd+5kzFCBQKiF89BHS09PxNCtL7ffbb6J7gYHqTLVaVCsWw8/PD0OHDqWDL1lCpFpjJAa0GAI6OjoKM2bMYCguJnJqYkLZ3dJS6uX9Jj+AXbso4+rhQWTyFWO4R48e4dChQxg4cCB6t6njbsHKlSRXdnOjLPHPP7/RmR85ORRoCgggYvn55+0ywoiOBnbvxgVdXeF5UBALS0wUrurpsbouXdQODg6iUe7uJNm2s0P9b7+Ba2rCjf79cae5vMDIyEhdW1srsn35Eg7p6SiaNYu3t7fH33//zblkZcGlulpwWryYnYuKUhfJ5SKFQoHJkyfD1dW15RrdjI0RunMnDKOioNdWGdMWgkAE8PPPidRZWZGKAKCSg02bgIkTobazw+4ffkCZuTkkUikMDQ35+vp6bv6ePeiQn0/7ikSUPZZKKdCleeZ+/x0wNMTx48fVL548Efl4eKCXri49Q7W1UNy6hcbAQKg//hh6L18Cv/4K6V9/UfDG3p6OHRaGknv3cOHOHd7v5k3OYd06yMaOpfO9fZvmQlER8NVXVBqied6USnoPHDyIZLlcSDh3jvW+dQtl33+PgX/8QWR261bKRDNGpnp2djQurq64UlSE+IwMfPTxx61tCNuitJSChZmZpA7o0wcoL4fw9ClKFy4Urjo5YZhMxuRjxtB2AARBQFxcHH/t2jXuvZ070ejrC0VAAOqfPhUky5axEydOQCqVYurUqcjIyBCCrazYraIi3nb+fM4hLw9XPv2UH1pSwonWrWvXpu7AgQN4/Pgx9PX14efnpw4NDaXoxIcf0nPm7EyKg0mTgMWLIcyejbKGBhgZGeHWrVtISUlRL126tDWiIQjA5s0010UiGktXVwrQ1NZSy0SJBFm6ukjdsUOYuGYNQ4cOrUGWQ4eoNCIr6/VxS0mh797eaGxsxO7du4VuhYX84A0bRG8rxzl48CBfVlbGPvroIwaepzm7bt2b3wPNBqW4cKHVi0Pj/v8PkJaWhrNnz74Ui8VFKpXKpUOHDo1VVVV6IpHoT4VCMS8iIqLxHx9MCy20aAetQZ8WWmihxf9wREZGWgAYBMAGwF0AKRERES8BICIiIisyMnJyampq7NvIvrihgQjAd99Rxig3l7Ix48ZRptzaGlCrwQkCti9cCH0HB7WdnR3b9uABp9q/H/b29vzs2bM5QRAQFRWlrqioQHh4uEgkElGwYPhwWmT27Elu2a+Q4NegWQQmJ9P31FTK8jGG+pkz0UGlQryrK0txcECfkhKSeu/fTwZcN28SsdFIRnmeyPLgwdRv+tU2eiAZuJOTE5+SksKlpKRwAKBWq7F48WKo1WqkpaWxoKAgIu9VVWBmZu0WtBzHoXPnzoBSCXlREfpLpW+8Rp7nAcZwuF8/9Hd1Rde4OJLdpqaSTLxXL8qM6+lRFm/tWlqEp6aisLAQp06dUstkMtHjx48BAMOGDePNzMy4/fv2QdzYSJJ6V1cKkOjoUFaN54GqKkRfuMBX/vEHU4waJToVHs4HpqeLQjMzIf7tN4jbZsCnTSPi0AZ6zTW5ebm5rHDmTNh88w1lT+fMITXF55+//V6+/z59P3OGspCTJlFAo/n+ODs7w8DAQKivrweA1/0A9PRov4MHiSS8LQFx4gQpTJ4+pTHUEP21a6kl3Y4dQF0dApKTWeajRzgyerTQv39/5uvlJUJ2NilPzp8H1q+HdPVqwMsLg+VyBOjogNnbQy6XixobG5GQkABFjx7Iy83lcnNzAQA5XbsieNYslr1qlTr85EnR+Q0bhF7h4cypOYPp7OyMqVOn4tChQ8iYOBFLJk2CXkREa/a5LRgjdcXPPxMp69OHSJJm3ja3eBQBmLNlCxoaGkjKnprK5Wzbhr8nTBBGDxzIoHn2evem5yIsjPY9dYoUM5s3w2PSJNHR+npcTkyE04IFqK+vx/nz59UVcrlInZEBzJuH5cuWwYAxCuYwRu+GoiK8fPgQ9V99hRF1dZzxJ59AZ+5ckpNLpaSoqKqiTHJkJAUAiotJZl5YiKrr1yFbtQoP+/VjQ8eOxf9h772jqrq+7fG5z7mF3ov03lWKgIoF7CVixI49lmjsLdHERD+YqNFUSzSJJRprjL0BUekoggUREVRQQEF6k8495/vHEgFBk7zfeO83xnt3jsFI5N577jn77LPZc6255rJ+/hxcsweCqip1P/j2W3r+g4LoWPfuAV9/DTuFAjqVldAoL6fgxNChtF4YGlKQy8iIyHBtLXkXbN4Mcd48xHt4KCCK3ODly5mes/MbQ87Qq1cvzsnJCWpjxqCTuTl1vliyhMHVFS7OzsDBg2APHsC4vJxh4kQEJCRw1Vu3osLQEMlnz3JO0dEQc3Ph2IrsT506FTU1NUhJSUF4eDjfq1cv6qSgrk7Gifb2dI4LFwK3bqHq6lX8YWEB3s0NVVVVqK2t5fPy8mD6SjGC+Hhq47dvH3D6NI2TuzsZAHp707OvrY3S48fx3pEjDIsWtc3KjxxJqqeOHPYXL6bXPTygoqKCOUOGsNCQEL60ogJ6HWT2BUGAm5sbd+7cOWrfWVtLz/icOSThfwOZWVnQHT4celOmkIfLTz+RIqhTp3adWDpC586dwfO8vkwm07e1tQXP8/K6ujqcPHlybE5OThGA5X97ECWUUKJDKDP7SiihhBL/PyEkJEQDQMO6desaOnp9/fr1g2Qy2R6FQmFsZWXVoK+vr5KdnV1bUlKiwvN8fkNDwzlRFFcBGNGpU6e9c+fO1Wx3kL/+IrKsUBBxCw0lB/2ffwYyMiB27oy6hgZIpVJIJBLU1NSA4zioqKjg2rVriIyMhCiKMDY2FvPy8lhzRvzTTz8lp/jcXCJ8Z89SRm/rVpJ0+/n943Goq6tDamoqbty4IdiHhXHMxkbwnj2b0+vdm+qSm+W+M2ZQNvH+fXK7njOHsqS5uZTxfYc7/K5duwRNTU1OS0tLfPz4sVhVVcXNmDEDnTp1grz1ZrSykoIJlpYdHygsjEjIG6Z6ra+jpKQEt27dwtKiIqidOgUUFdE5W1u3bMIvXgSKitAQFITI774TkkWRc/D0FHmeF83MzLhu3bqBMYbbCQkwHDcOzNcXZn/+CcZxJAtetoyIHYCqqiqcXLkSU0+dAp+bC9TUEAnYvZuy5Gpqbetpz54l8tCqzODiiRPI++svfHDjBiR5eUQ4hg7tMHjyTuTm0v0/dAgICED648c4ceIE5s2b97p3ehsIAsmVb96ksgsvL1I8GBq2fZ+jI5HL48eJRE+YQL9/8oSCEVparw32BGdncM0EauJEegbs7cnc7fff6b80cGQw9tNPRDqbT0lVFfuDg5FrZQXGGHR1dYVZs2Zx33zzDaT19bDKzsZ7iYmizunTrLUK4dy5c7hz5w7W2NhAcukScOxY22sQRZoDP/5IpS63blHQ7dNP6b/jx799XH180JCairAjRzCytQljURFdT2ty19QEHDqEJgcHNG7ciAS5XLzh48OaFAr4+fnB1tYWVlZWEEWxffcFAGlpaTh79qzo7u4u9urVi9PW0KA2bIcPk0/D6tXUTm7iRCrtCA2l+djYiKraWnzfStGyYsUKtDH63LaNlDk//kjlIJWVbe715s2bRaGsjH06ezYFcSoqKPCWkUGqpBEjaC3bupVq5QcORH1tLU5UVUF75EiMsLWlZ1ddnYIL2tptu4GUlJCRoLk5Bc2++ILmx/DhNP/mzqVMeats9+a1azHqjz9wYswYrNm4scPbs2PHDoWqqipzd3dnXnl5jLt6lc4RAFauRNPw4ShJTUVcdDTkDQ3I8PeHppaWMG7cOE63WUGzeTOdi1xOa+q1a6/Xu3vjx4umL18y/evXcfH331GYno6pmza1bTf68mVLGUFz+RNAgZHcXJp7zQqtw4dRuHw5ymJi4NSq60QzcnNzsX//fjDGsGbNmhaPk6Ii+jvyySev39vY2IiNGzeCMYYvvviC3ltY+DrohE2b/tXfg9a4desWrly5cnrVqlV/00JFCSWUeBuUNftKKKGEEv/DCAkJ8UhISIgWRXEzY2xNQkJC4NWrV+8EBATktXqPv0wmuzBu3DiDkSNHStzd3eUODg68t7e3vHfv3nxlZaXOixcvuoui+JlMJhsZFBSk/to8SRQpc+bqShn9q1epXnjiRMqM+vtTrXenTrh1+zb279+P2NhYFBUVCY6Ojkz1lbOyhYUFCgsLhYqKClZWVsasra3h7++P9PR0JCcni4qTJwWDW7c4yW+/gUkkaGhoAP/NN5R1bW7R9xaIoojs7GxcvnxZceHCBe7FixcKd3d33v/jj+Hw3ntMVVeXZMWuruQPoKFBxDAqijbDkyZRu6zRowFn546lpa8gCAKuXLnCgoKC4OXlxe7evStWV1ez5ORkVFRUCM7OzgygDCD27qVs5cyZHR/M1pa8C7p1ayPpBch0ytTUFHZ2djDcsEHkYmKYxrFjNObh4WRi2EwiRBE5Bw7glydPxMB9+7jednboumwZc3Z2ZqampmAAxK+/Rk55Oe6KIiIcHaGurU1ZQFGk47wi8KGhoYoqHR3R++hRDhxHJN7Ojsbt1i3amPfp00JyJ0yg8Wyusw8Lg9XMmYjy88O9vn3RLTkZrG9fGv9/C21tIir29oC3NwpiYkRZYKDo6enZPqtPg06EbsYMIlvp6URIWpMVQSDHdH19IvY7dpC0uUcPylaGhdHnrl8HVq4E09Qkxcrvv5PUWyIhAjRlSltFhlxOhK9LF8qoWlggPDxcOGVszEr19QHGYGpqio8++ohdvnwZ+fn5UPA8SvX00H3AAKbapQsZUXbrBmhooK6uDunp6dAPCECn998n5/3336frOXyYvsfampQwBQWU/fX2pvr1zEwKALwZXPnhB8rKRkQgIzAQMTduwMrKqkXirq5OwQ+ptOV+cRzg4QHOwgJSNTVU3LkDRXEx88zMhO/s2dDp1Ak8z7czqRQEAQcOHEBCQgIGDx6MgIAATkVFhY5nZUVeHUFBpKQ5dAgYNozI/rVrqBg/Hk9SUnDs7FkIggA1NTW4ubm1tFwEiODb2wMffEBZ+qAgCohMm/b6LTzPs4c5OXDo3h1aAQFEEnv0oDGbNo3m8PLlRORra4GtWyGJiIBddjai6+rg8O23UK2rIwXQsmU0vn5+9Fx7edG6ERZG1zRsGD1Lfn6kYunenZ6rNwIguS9eCNGmpmyQh4do3r8/w/TpLaUXr9C1a1fu6dOnLCUlRbx17x4zTEuDMHw4VFVVUb5pE468fIkEFRVBUFVFt/v3mVhaikmrVzNVfX0KZOzZQ2oDIyMKZjBGATtra2Q7O+N0aSmr53k07twp9rh2jV3x9RWamppgY2PTchNlMuDuXbzs2hW7L15UNDU1cRYWFqR6MjSk62++150744f6erz33nvoqF1ncXEx7t69C1EUER0dDZ7nYWVlRYGa77+noMir+XPy5EmhrKyM8TwPAwMDGBoa0vi8UkzgP/+hNeGNMfsnSEhIqMnLy/sjOjo6PiAgQJmdVEKJ/wKUZF8JJZRQ4n8QISEhXhKJJPq9994zHTNmDNe7d29OU1PTLDMzc3JkZOSJgICAEgCIjY2d17Nnz37NGd7WYIxBU1MTN2/ehJ6enrho0SKpvr4+Za3OnyeCPHcuZUMDAykDHB1N5E9Pjza0VVXAmjUosLREOaAIDAzkkpKSxKSkJObs7IzIyEjFs2fPuDt37jB/f39MnToVHh4eMDIyQmpqqmBra8tZ7NzJ3QZw7NEjxMbGIjY2FikyGeKqqlBYXt7k7OzcLm1YWVmJGzduiCdPnsS9e/egqanJxo0bxwYMGMBZW1u3ZBp5vqXP+fvvUz33X38R2X/2jIzjJk/+Ry2fHjx4gMePH4tDhw5lCoUC4eHhDCB5viiKePr0qXj27Fmmrq4OEzc3Igitpe9tB5/IY2IimeW9iQsXwCorEXXvHnNJToZ8xQoKSDx/TmQiPR3o1AnHr14VbXbvZo7LlzOzDRsgHTqUCIq1NVBSgrrcXJQvWYJYY2PRfOJEJnCccPPmTfbixQtUPXjQZHHqFIeJE1FcXIzQ0FBu2rRpnKqaGhGVDz6gQMDcuaQo2LKFyG5iIhG1uXPJMyA/nzbjPXuC9eqFXosXgwmCeGTcOGRrajIXF5cOM79/C5kM4DiUeXrir5wc1htg2j//TISto/pgHR3K2JaUUCDl2jUi0M3fHRtLRNPamgICLi4UADh1irKMO3fSNTVn8aOiSLkCkIph/fp2BO41jIxI+jx0KDINDMSbhYWc2a1bcCgvF8Zv2sS6desGnudhaGiIR48eKezs7JhcRYVdfvEC7t26QfXKFQrmmJhA1dQUCTdu4OHDh1CoqUFy5QqqKypwOSsLzkuXggUFUWac50mh8MsvpE7p0YOc6d97j7womjP3CgWR/bw8YMkSGJqbQyKRiOfPn4eTkxNTbyZP1dUUvGtWMzSDMcDFBZ2Cg1lOdrZgnZLCTqWlQfjmG0RkZysKm5o4Gxub1+tLbGwskpOTMXLkSLFbt25vRgIoU61Q0HkOHEj3KCMDOHgQp0+fVvSdNInLnzhR7OLpyfJfvBDz8/NRUFDAdHV1ofnXX8CFC0Sqm4Nko0bRXH31/WVlZThz5oxgbW1NnQFa3zNBoMCCvj6tYUePUv14SQnE1auxNSgIFbq6uNOtm9jniy8Y+venshALC5ob06ZRNt/Pj7Lcs2YRua6ooHKmZg+BDhAWFiY2NjYyJ29vZubmRvewrKzN2iORSODm5gY/Pz+maWKCsqtXxVPZ2Szx7l1RNzkZVh99xIKmTGHegwaxGC0tRUNREdz27mVoaCC1w6eftgRIJ0xA/ZkzqIqIQIZMhof37+OFiQkmfvklSg4ehNbDh6zThg3sckQEs7e3h6Zmi6BLOHEC0QkJwgtzc/7Ro0dwsLODZmYmjUEr5VN+UBA0nj8XbSdPZm/+fQGA3377TWxoaHj9wpMnT6CiogLewQEaK1ag4OhRXP/tNyHs8WMxLy+PmzBhAp4/fy5KJBLY29vT55qaqHwmMJCCbn37dji+74JEIpFmZWX1aGxs/OratWvzIiIiTgYEBJT/6wMpocT/YSjJvhJKKKHE/xDWr18/QSqVng0KCtLo0qULOI4Dx3EwMTEBY4x//vx574iIiCMBAQENUVFRpqqqqoM6d+7cYR1+aWkpUlJS0L9/f2ZRVESb4bAwkkwuWEA/X3xBcuw5c0iS7ehIRNbcnLKMWVkotLVF948/5izr69F1yRIWHRODxKQkAEBWVhYkEgmbOHHi6+9ljKF7t27M6dAhaO7YAa3hw1FUVCSOHj2aWVhYoN+aNShwc8P90lKuvr4eurq6kMvlyMjIwMWLFxVXrlzhqqqqhD59+nBjxoxhXbt2bSEtrfHiBcmDQ0MpkyuT0Sa/rIwye5s2kfT2b/wBBEHArl274OnpyRwcHMBxHHr16gUPDw+kpqaK5eXlrKioiAmCgIcPHyJAoSBjulbtCNvB3R344w86nzff9/HHqLtxA5Hm5tAeMQInnj5tiktJESMZ4/JPnFDYLF3KndbUFJ4WF6ObkxMzr6igrHt5OdVfGxkBEyci0d5e+KtbN3Hu559zdnZ2cHd3Z3V1dUhJSUFtXh7X7dEj4IMPcPr0aYWOjg66d+/esmPX0KCAhKUlZWStrYkYXb5Mqg41NVIolJVRAGDkSHBdu4K9eAG9devY0/JylpWVBSsrqw7ref8JBEHAz3/+qbDp1Qve3box3L5Nc3DLFgo0vJlN1NWl7N/du+RjIJO1kJ+XL0kxkJpKY3XsGAUxOI6I+u3bROYyMohQyGQUINq6lcbgLSTuNbS0II4fj9MJCUw/Jgb6paVw0dJi+sHBrx3hVVRU4OvryyUmJoo5OTkMAHr16wf5uHFE/kaNgsrFi2CTJwtPnjxhOTk5yFVTg++2bSgpLMSdL79EfkMDOnXqBKlUilwjI0hnz0bO8+fk9QDgr8ZGmPr6QlZZSa798+ZRecPCha+vwcLCgtXW1opRUVFC9+7diQ37+pIxZWFhh60QRVHE0StX2AMnJ9TxPGwyM2GQmcnl5uRAKChAeFqaePbsWfb06VNoa2uLo0aNaj9g165RgOL4cVLueHgAGzZAXLAAz9TVcS8ignM+fBie/fszyxEj0N3KiplNmcKysrIU5Rs3cvn5+WLtnDnMsLVaRColsm9mhlxBwL59+2BhYYHg4GCujRN/fj4Rx3XrKMBz9ixlqnkeMDZGtKqqIk8uZ9N37WJSNTXRbsyYtucvkxExl0op0DBpEvDLL2hcvBjV6uqQFRSAzZlDz0QH8+XZs2dCTU0NS0tLY/7LloE9fUrvXbiww9IhQ2NjmMfGss5BQTBwdWWuL18yk6lTX895TirlorKzmc+AAZCFh9M8njePglQbNiA7OBj7RBE2dXVoGDMGFmlpovm0acx6wgRorl/P9ujrw+rJE2glJOClqyssLS3R0NCAuLg4qPz0k6hSUcENP3AAd+/eFU1372aGq1e3KdkBgNunTwuGvr6ccZ8+7c4fABISEtBM9rW1tSGVSoWCggIhLi6Oi4uLg+GBA9DOy4NmcDA3atQo6Onp4fLly8zCwkK0s7NjSEmh4NvJk1SiEB5OypN3tOrsCPr6+vDz85P5+/vT2ldbGx4QEPDkXx1ECSX+j0Np0KeEEkoo8T+A9evXD5XL5ftmzJihZtxBb/hevXrxpaWlbmlpaUkhISEDABRWVlYKbzuegYEBDPLyoK+vT1LkOXPIbXz6dHqDKJLUNiqKjJ8AagmWmUly5sWLydzs7l1EjR/fNG7IEIlGaCg+O3IE5bt2wdDFhXtcXY0Tp061//LwcODyZaj85z8wl0oxa9YsBgCWlpZA584YPX8+bj5/jqioKCQkJEAmk0EqlQpOTk782LFjoaGh0bH9c0YGcOUKEehlyyibv24dZaXWrKFAhb4+vTZqFF3PN99QlvEtYIxBXV0drwziAFAWTk9PD/PmzWPp6em4ePEi7OzsRLlcDmH7dsa99x5Jqt9+UJLGbtvWUovq4EDnungx5PPnw7BzZ+Hsy5ccXr6UGBkZYfr06SgrK+OfTJ6M+shINv7oUSb75huSngNUCztqFF2zTIb0+nrR2dmZb85uchyHYcOGQS6X4/alS4iQSKCXnIzs7Gx+6dKlHZ+npyf9pKdTMCg+nkhkcjIRjC++oKy6VEqqjwMHoArg+enTAMjY8N8iLS0NUVFRgrm5OSeXy9mQIUM48DxlsQsKyHise3fa9NvZtSX9e/bQ/KysJJl3M4qLqeSgqoqkyLt3E+Ffv57M/RITqVbZ1PS1aRtsbf/xORcXF+OnvXuhW1KCsVeu4MqaNei0cuVbhtSTe/78OURRxI8//ghBEGBkZCROOXuWaebnw//MGa5PSQlyevSA+erVKCovR9fLl3EoMRGpamqIi4sDAHjdvAmLnBycHd22FPlmXh5mXr0Kw/R0POrZE5q5ubB+Q2Wiq6vLCYKgaPPL3bspUNSjR7tzZoxh9uzZSElJEdzd3Tmtzz7Dw4cPFZr/+Q9vuXYt4qZPZ/YFBXjWpQtmzpzZnuhfvEjBtZgYmjcGBlRO4eiIlD17BLXoaG5qSQnUv/iC3n/qFJiqKmyTkmAbEsJXaGiIvzk6Mt2cHGjk5sLMzKyNYuTJ/fs4evkybG1txaysLHb37l3Rw8ODziM5mVQff/xBcnDG2hHsTE1N5mZiwkw2bICJlxeHs2dJDfLtt23eFxERoYiPj+e1tLTEAbGxrCAjAwkBAbDT1lbYDRrEnsbHiwOnT+dfmJggdPRoUeQ4CKKIhoYGvqmpCX379lUwxnjY2FDphbY2kdkxY9pPFAMD6Ny5A505c2jcWp2zpqYmLPT0hHs7dzINKyvmYm4OfuxYCvjNn4+/8vIE3syMM+zZE7ZDhwJRUQwFBYCvL9QHDIBuQQFuRkbivbt3USCTid9/9x3q6uuZmpqaqLpmDXPgefA8D297e2a+Zg22u7oKxq6u3MCBA6Gnp4fIyEjcdXHhpsya1eaUk5OTERoaCg0NDbGqqop5eXkJt2/f5ioqKjB//nzO0NAQNTU1KCgogNVnn4HjOIatWwEPDwgyGVRVVcX09HRu0KBBYHFxFASTSunvU0UFrXGC8HaVzVtw9OhRxdOnT/mGhgYNAHH/6sNKKKGEkuwroYQSSvx3IyQkhJfJZPvGjh3bIdEHiMyNHDlSrqOjYx8fH3+PMVbi6uraPuXd0ACUl0M9Oxuz9u1D1rx5ROZaZ6MUCiLMf/xBG6vWLazs7Mi9u7IS2LQJksBA5FlaEnEWRUjt7WHo6gr4+MDEyAjynj0h3r4N1lzvmZBApCIqqmPztqlTIbOwgJ+VFerq6hAbG4uGhgYsXbqUU31Tcq9QEAE8dIhM2QIC8IpauwAAIABJREFU6DpsbWkz3Zzda2iga/T0pExu83uOHiWZc2kp1cN3kMFljMHFxUXx8uXLdgEGDQ0NeHt7IzQ0FJmZmQwA0gICoNLYiBVNTR3Wsr7Ge+9Rj/T//IfkxZs3U6eDJUvAIiMx2dSUazZss7S0FPT19Tl9fX3A3h5uPj4Mjx5RhvT8eQpynDtH4xkfD8WSJShYupQPDAxs97X9+/dHgJkZMufNE4+cPcucnZ3btQBsB2dnUkikplJ2X0uLAkNffgmsWEG/69fv9duHDRsmhIWFcT/++CNWr179j6X8TU1NOHPmDORyOXfnzh1MmDChbYbW2Bh4pRqBry8pDo4ebZFQGxvTfV+9msb28GGSim/YQJnZnj2JZPbuTaUc8fE0ds0kNCGBgkH/Ak1NTbhy5QoAoNbMDH9t24b3mprAqagAWVmoNzR8Xe5RUFAAR0dHamUJYMKECVAoFDhx4gT7fvt2uLq6Kjy9vXnbjRthfeQIoKsLkyNHgPp6LPv8c+R7e+OhhgaioqJg0LkzjCwtsXjxYqiqqoIxhvr6eij8/KCVno5vVq6E+927kH70Ea6MGaOYNGkSr6amhvr6ekRERGDQoEFt53OzGWBtbYelLWZmZjAzM3t9I728vPinM2Yodnh68kbFxZj07BmYpycFW1xdWxzUs7KoTOLEibYkbc4cVJw4AfNTpziNmTMhnzGD1pSqKgp8AZSFd3CA1tGjbJSHB/6qrsa+p0/B8zykUqnIGBMHLFrEhZ4+jfcnTECXLl1YRkYGTp48iZcPHwq9zp7l2E8/0Tq2cCEFjHx9212bjY0NFxcXhxIbGyHYxYXjMjPpPBQKOvf581FaUYHY2FgeAORyOet04QIcCgvRU18fh/76C8kmJrCwsOBvrVkDndxcjCwtZaZRUShZtQq/37sHACguLmbNrTBhaUldAebMITLb2oQQoKz2H39QCUZ9fZuXTDt1wkwLCy5REHDGxgYIDRV16+uZUX09nmVmohhgU6dNg+bo0S3eGsuXA48fA6BAb6qLC54tWoTitWsx8sED9vi77wS//v05rbVr6bmZNQs9TU1RkJKCLtnZLDc3V9i+fTuno6OjqKys5D/bsAH8uHFtzBEfP36MhoYG2Nvbw9jYWOHp6clnZmZCW1ub6vABqKmpwaa5E4YoUhmNjQ24kSPRr18/dunSJTyfNAnmixdTxxGA/rbs2UP3QhBIdfMvkJ+fzysUCjDG6tauXduhma0SSijxdijJvhJKKKHEfzMkEsknhoaGWrZ/k21kjMHf319ibW2tV11drefcuoXUy5e08Rs4kDJrp05h14YN4iCplDWTXEEQEB8fD5lUitrhw0UZY6zHjRvgOnVq+0U2NhALCyHGxEBboYCoocFenUDLZvr+fTyNjobxgQNQjBkDyeHD5Jr+6ackCW82d2uNmhrKtE6dCoCIaVNTE27fvi0eOnQIfn5+oputLYeHD4l8xsaSkVpDA2UO3d3bE/YjR4gg/vUXOa+3zqI5O1O/+RUrSGb8ww8dEn6e51kzQesIs2bNQkNDA3R1daHq6ordwcE4fvw4OnfuLNrY2LDWNbGvIZORUdXNm+R4/9df1CHg88+JjPbrB++5czFs7Fj8vGgRl3/2rGgSE8OQnk4EdsIEkp//+itdY1UVACKfu7OzxZ6dOjHDt5BsTlsbNoMHMzQ0oLCwUERHLe3ehEJByoGFC4kwa2pSrfe5c2TiuHkztckaOhRePM/VdO4sxty7x/4p0RcEAYcPH1bo6OiwIUOGcElJSQpHR8eOFRwAEfPycjJt+/136t2uokIy/eBgei05mcbq++8puzt3Ll1D3750vqNGUbbw4kUqT3hXcOYN1NXWIjEpCfJjx1BbUwM1IyMs370bfHIy1Xo3NgKdO+PQ8uXipI0b2cEpU1BtaChydXWM19eHQqFAVVUVfHx84OLighcvXiD3q6/42ps3xZ3TpmH+pk2MmzevJUhhbw+T776DSUQE/P39qUylsZHKFwDg6VPIf/2ViBqAVV27gl24gOpff0W+IHD79+1TzF+4kL927Zqgrq4uenl5tR1bxui879yhoNM/QGBgIJ+WloZCIyNsHDQIHwQFwXTJEiL4sbEUSFmxgoh+qzVEFEXk5ubiyMOHGNyvn+A1dy6HEyfo/v30E/DgAV3fb78BJ06AaWtD7uYmlujpsekAdBsbUTJpErt48SK7euwYPvv+e7BX7R2dnJwwJzCQxW/ZgseZmaJGYSEz0dOje90B0QeAXr16wcrKCjExMfjxxx+FmTNncql6elA9fx4u69cjSV0dZUVFAgSBA5VOCQYWFhy+/BIQRXy4e3f7eSoIwPnzyJRKMf+HH/DA2RlXBIGrGTq0pUbewYF8JqqqaM377beWOdipE5XJNDVR2UBr7N8P4eJF3HJ3xxxra1QPHMgSXF3FvJs3me+5cxjeuzdM1dSoDMXBgdYYKytaf62tMXToUKipqeH8+fMw8/dng3x8YN+nD4eXL0m5c+AAkJUF9v776HTrFjoFBDAA7OXLl0hPT+dLS0vx0/LlWOjkBO715QrIzMyEpqYmhg0bxgDwoihCFEV0uP41z7nkZLrG77+Hz/LliP/zT8ivXEH5+vXQaX4fx5E5X0AAzasRIzr27mgFURSRn5+Pq1evitXV1czBwQFZWVm1ISEhzuvWrUt/54eVUEKJNlCSfSWUUEKJ/0aEhIRYSySSL8aMGaPakRFSR7Bq7kMP0EaqupqynqmpRGxebb7EVuSmrq4O58+fR9nly+h37RqyvvuORV+9Cv2MDDj4+IAD8OeffyoePnzIC4IAQRAg69YNBrm5CExM5DBnTlu3ZI6Di78/4h89wkYrK4yQSuH1669EbN3difAvWNBCVgDa/M2e3YZwDx48GBaqqix73z6kXLvGLBIToeXmRgZ7kya9vcVdM5KSyGsAoGMvWECGd83Q1aWa/tOnqYRh505AQwOlpaWorq6GQqFAfn5+2wzzG3jd51oUgV69MHDKFFyMiBAyMzM5QRDg6uoqDB48mNNurZAAyLFfVZXc+/fsoSy/nh6VIqiqwlRLC7h1C33q63Hs1Ck28ttvYUeD0iI5t7IiQ7a0NGSkpCCZMbFeTU3wP32ax9mzVJ/8JkpLIblwAZ9GRuLbb79lDx8+hIODQzsjRwAUgJkwgchSSgq1Kqyvp/u3fDl9v7ExzbH4eMDYGOKBA5A+fcrGlZZCiIoCt3w5BTJMTckUMT2dPB9UVfESwNGICJSUl4sqKircBx98wLS1tWFnZ/fu3TzH0VgtWEDnUF1Nzvq//AKsWkXZ0owMcpoPCqLX1NXJ7Cs0lLKD48dTrXPz/OgIz59TxtnZmTwDduyAGBkJcfFiRH76KcZER8PcwgJD168Hb2lJAbWjR1+79I8aPZo1SKWo0NHB9N27mV55OTZ8/jmW790LTR0dwNsb3I0bMHV0hOmYMWjQ12enSkrwbVwcRuzdK7paWTE4OpJXwYEDwJIlFKjYsoVq0Y8epfMMDiYfgg0bAMYoehMYCPXAQAyZOZPVXr3Kb6mqEhUA161bt3aX2dTUhGcTJyJGQ0P0TU8XOzLHfBNSqRQLFizAtm3b0KRQoFoQyETvxQsKQH35JQX3tLQAAEVFRYiMjBQfPHjAeJ6Hnp5ek9eJExIsW0aGiDk5FIwLCaHgW2IiIJXi2rVrinsjR0JRVMRbaGiALy6Gto0NPszNRd6cOeDmzm3x3li1CoYnT2JwcjLbu2+f2HfcOOSam4Nfv17RDehwTsnlctjZ2cHW1pY7fvy4Yvv27TAyMhIUCgXi16zhXubl4cNt2zgHNzfEBAUp+vTuTcf58ksKTCgU7cknxwHvvw/LigocnDYN2qWlCM7OFjVHj2bYsaOlBIUxUhZlZ9NxmtdkQ0NaG7KzKXDQjLt3gQsXwH3/Pab4+SGupATDjh+HLcCqx45FWWkpzHfsYK+DWzt2kHJIW5vKp+bOBcdx8PLyQlJSktDV359jvr40t9atozGfP58CL7t2tXTgQIuSCRkZkCYniyVlZUxdXR2HDx8WeJ7n6urqKBDVCpWVlSgoKHj3RHr2jIJ2fn74QEUFW+fPx6I322w2GwTW1VHp1erVbz3c1atXFXFxcc03hPXt2xf+/v5ISkrSioiIuPP1119fqa+vX7Fu3bqH7z4xJZRQAlCSfSWUUEKJ/08ICQlxlsvlGziO06yrq7sgiuK+devWvXz1mq1UKo0fOHCgTLc1Kf47iK86DH3yCZkb3b9PktE3zOAaGxsRHh4uREVFobi4mBNFEUOcnBQOEglvP3gw7t+/j/TqamRmZGD44MEoLS1lPXv2RI8ePaCiokLy7MZG4KOPGMLDieC0Ivwcx2H8+PE4ceKEWLxmDSvKyYF2aipkmZmUbXr2jGrpO3emDeaLFxSQACjjGhoKZGbCJS0NLra2iDY1xS4jI/gMGSL4ODtzb80YAZTFWrSIDMGaZcl9+3Zcn6+mRmRJEFDfuzcuLF/elPrkiURNTU1olmD7+vr+fYq6uhpYtgxOHh5w8vDgBEHA/fv3cerUKe7ly5fi6NGjmba2Nm2+k5MpI62pSdlYT8+WHtatXdG7dIEHgCdZWcjauhVqHh4wqa2lII6bG3DmDBr09ZG8YYPgfOQIF/bFF8KEyZN5tmABSbJTUjpugWdsDJlMBkdHx6ajR49KAGDOnDktgQuAsuVOTvTj7U2b7b17SbK/dCllw21tqXZ/xQoi3QAwbBjC1q+HtakpHIYMwe2MDBj07QtLLS0izq88AGqOHUNlSgq0evXC+Lt3maaLCzgDAyLp/foRmdXWpsx2TQ1lO98s/VBRITf0mhpqy1ZbS+M5YABllHfsoPvy888UqPDyouPs2EEGjQA9L4xRJtTfnyTogwYR0Vq4kMb6/HkKaogiHvv64tTixfD19UXndevQuflcFi5sO6c8PKC3aRMq8/LQ5dEjMfXgQfYgPh6SpibK5urq0jP67bcUAOvTB7LcXKw7dgxRu3YhOiFBdPXxYThyhMjj/v1E9nbvJvJTV0fBjW3b6F7J5R0qU/S++w4vfvsNU3x8WEJ0tFhUVAS8UnMUFhYiKSlJcefOHV4URdiVljLF3LkQo6I6Dv60Qk1NDSIiIkSO45ggCHhdYsTzFGT78kvkeHpC29wcqZ07I3LwYOgbGbHp06fD0tISHMfRHnLtWjK+Kyqi9WPPHipRkUohiiKSkpI4U1NTFhAQAL65w8bTp5CfOgWbDRuIlEZFkUHl7NnAqlVQ09DAR1OmsNtHjyLK1RV2T592GORoDcYYxo8fzysUCkgkEg4A7t27h1OnTuG3pUvx3sCBWBAXx2PkSFpPjY0peLprF0nuO4C2tjYWbt2KXbt24cLLl2zZ8OF0jba2lEX/+GMqR4mJoWd17FhaA2Uymv+3b7esC/Hx5Dfi4wMcOYL9c+aIpRzH7B89goODA9TV1aGurk4qp6QkCnAVFlJQUSajc5wxA8KxYwgvKICuTMa8VFVpfk+fTsfNzQU2bqRgzaNHHQ/Uo0ewe/QIMTExyMjIgKqqKmdnZydqa2uLr00fX42ng4OD+OjRI/bgwQPY2NhApQNDQlhbU6Bn0CAwjgN69YL0zee8sZGua9cuWhsWLmxf+vAKzUR/wIAB8PX1hezV+HXv3p338vLiY2NjhyQmJg74+uuvr9XX149ft25d6VsnhRJKKKF041dCCSWU+K8iJCRERyKRpPbt29e9c+fO9oIg+FdWVi6NjY11iYyMnCKVSr8ZNGiQrq+v77uznK0RFkbkTkODNr7TphGhfDOrDCA+Ph4+Pj6ctrY2eJ7HoMpK0XPMGB7TpoExhp49e0Ly9dfI8/JCYV0dMjIymKqqKjw8PFqIAM9TtrSsjKSoAwaQadsrqKiowEtHh9U6OuK8XA5jZ2foOjmBBQfTZjkzk2T4paX0+eRk2viqq9P/+/tTdnDQIFj36AF1AwPcunVLiIqK4qRSKczMzDomJTExRPjHjaN/371LRm1vcY8GYyi3ssL+3FwEbd/OOU6ciPfmzmV+fn7Mz8+PtVFLvGvsV66kbDFos2tsbAxHR0fcu3dPiI2N5Tw8PCA3MSHSOmcO9X+Pj2/fG74Z5eXA7du4eu2aMPzcOaY+cyak06YR+QbQNGkSYuPjxZuenvD49VfmzxinOX8+ZUg1NSmIYG5OAZVmKBREkrt2haurK9ejRw/cu3cPiYmJcHd3pw15ejrJZQcMoCxjYCDdh8WLW5y58/Mp01xZSffL3f31dRcUFIhPcnJY5O3beJibi+SaGjyQSlGmowO7OXPQ4OKCH/PyxPpp0zDm00+ZypgxYH37Ura+qYm+IzycxsbIiL772DEKEh04QAGcb78lUmNmRhnJoUNpTn33HZHp4GAi6VpaRIyrq4lg/f47nffw4ZSdnTmTSNQXX9BY+fjQHO7WjY7RLKMOCgL09XEhPByVTU344IMPOpwGdXV14Ly9sUMux325XIx8/pz5hYYyeWQkbru5QeB5yD/9FNbvv09z9OJFCkKpqxNpHT0aViNGQC8jg/EcB5XPPqN5Eh5O19WvH41Fp04UzHvxgq7nLeoTTlUVWj17QvPyZTh8/TULt7dnGq/qqLdu3QqFQoH+/fuzwMBAWKmro/bUKbavuhr6+vqva63fRG1tLVJSUnD9+nUmiiLs7e1FHx8fhpoa4ORJKGQyCIsXY9u+fUjp0gVW3bsj6NEj+N27B51Ro8BaB+pUVckY0NSUSmnu3aMMd3AwXgwaBIeFC5mTqiospkyhebt6NZVmPHxI89HPj4jp0KF0T1VVgQMHwC1dCrPr1yHR1satW7dYr169/jaAwRh77TGRmZmJP16ReJEx1CkU6PrBBxQw0tensoDgYAq0jB371q4NeXl5iI2NRX1jI+rs7AR7Ly+GIUMoa75tG11L//4U8KivJ7l6XR09pydPUuDDwoIy2lOmUDBg7VpU8Dx79uwZ7qWkoI+6OrgzZyhgsnAhlQeUl9MzunYtKRD++guCpSXKV67EM4UCfZycmO7s2bRGjhrVEiTbv5+ULB9+2PEgOTri57o6lJWVMR8fn6bg4GDO2dmZubq6tmvD5+Liwh4+fCimpKSwuLg43L59W7C0tGxf2rR3L2BggOLQUNRPndpisNgMExNStCxZ0nKOr1QENTU1uHPnDurq6lBUVITU1FQMHToUfn5+eFORxfM8bGxs+B49ekizs7NNy8rKngcEBNx856RQQon/42BicwZJCSWUUEKJf4WQkJC5jo6O3wUHB79OhxcVFeHKlSvIzs7GBx98gLcZ8nUIUSSin5ZGUvD6eiJGr6S+FRUVuHjxIgRBgCiKyMnJwaxZs9CpuZ62Tx/a9K1YQf9WKNCgqYkf1qyBura2UFJSwvE8j8DAQLi/InZtsG0bESR9fdosAi0t4b75BlsfPVJUVlbyM2bMgIWZGW3qq6qI7CQkEHGrqSHSPGDAO+syT58+rUhJSeElEgmcnZ3h5+dH7u+iSFLxzz5rS3DXrCGy/8svHR6vvLwce/bsgZGRUdO0Xr0kmD+fCKa//9+3XmtGaSkFLzrwI4iNjUXOrl0YFxsL2cmTRCpjY0kin5BApPSrr1q+q7iYAjbLlwN37+Luzp24ePEiDAwMFFKpFHZ2dryamprIJSaym4WFwuy1azmO4+ief/klEaKoKHKu19cnMtScFU1Lo+zdoUNtznHr1q2CRmkpN+3KFUiPHycCzPMtngCv5NivUVhIcvj4eCL848cTke7UCZWVlYiJiRHU1dU5JycnXL9+HamvVBuffvoptm3bJjQ1NXHLli2DvNnM7e8gijTGubkUAAgNJaLbty8pOIyMiNRkZlLQZdEiMnAEyJvh9GkKSPz0ExH4n34iUsnz7a/tHQgJCYFEIsGaNWsAANXV1bh06RLS0tIAUICrrq4OMpkMFhYWqKqqEidPmMAOfvUVRly6hBtduyJgxw4YaWtTkKe8nIJxjLXc//R0/PzDD9AuL8eEBw/A/fADEcraWiKY+/fT+2prO2zf9laUlyPryBEUHT2KxHHjxMqqKtZ8Hc0QGhpwa98+XC4thZeXl2hkZMQ8PT1RWFiI6OhosbKyUnz+/Dknl8uhqqoqzpgxg6mqqiI7KwuFK1eK9rq67Gd7+9fH09fXR58+feDu6AgcPEhzcsECIqHW1nQflywhknvrFpltamgAwcEQBg/GDW1tJBcVoffMmXDJzYVk6FAas6oqUpMsXUpBkzNn6AsbGkgW7usLBASgsLAQu3fvxmeffdaG7N++fRs2NjbIzMyEq6trG6PKe/fuISwsTKypqWH6+voCY0wMDAzkLZvLhpqaKDi0ahWR8JQU8s/ooANFRUUFDh8+rCgqKuK1tLSwbNmylhcbGojQd+9OAZ9PPgFcXCi4lphIz3FgIM3v589pzIKCgOxsnN6yRbC4fZuT9ekjdrl9mzF3dzofUaQxHDGCFCbNz9edO1AsXoyvBg7ERx99BCMjo5bzqK+nuaShQYEFS0sKYnSEiRORUFAgWh4+zNoogf4GaWlp+PPPP+Hi4oLx48e3vHDlCrBiBRquXMGmnTsxz8ICxn36tBg1NuPPP+kZ9/YGZs0ClixBTGNjU2RkpASgQI1MJhMZY+KKFSu4dxmkKhQKbN++vbqiouL9devWXV2/fv1QqVQ6raGhYfW6dety/vFFKaHE/wEoZfxKKKGEEv9FyOXyfg4ODm0c8w0NDeHl5YWHDx8iOjoaTU1NcHBwgI+PDxoaGlBWVoba2tp27bQgipT1CQmhTVFdHWUKm/sSe3qiaPp05NTWwqtbN5ExBnNzc/a6D3pODmXDWxPboiLIVq7EKiID3Pfff4+qqiqEhYXh1q1bUFVVFYODg1s+sHgxmXtNn06S6O7daRO5YQMwbBiWdO7Mn1uzRiE/epTH/v1EvGbOJEO6FSvou8vKqJ5V0bYz2JsICgriHzx4IPr6+rL4+Hjcv38f9vb2ikl9+/J49oxIRGssX95S3vAGQkNDFYmJibyLi4swfvx4+rt29iyR18hIMs3rqHPAm7h0iYhjB2Tf1sYGd7p0EWMKCljP99+H2vnzYBMm0Is9etDYf/89EVSJhCTpO3cSIWUM7gCMjIyQkZHB5+XlidevXxcVCgUb//SpyGQy7tmzZ9S6UC6noEFODh1rzx4i+/7+JEvX0aFxqK5ud44f9u7Nnfj9d1SlpUFPoaD7qa9P96UjMmxkRAT6yROSJRsZkXu4sTG0tLQwYsSI15LeMa+MEVNTU7Fp0yYA4D755JN/TvQBOg99/RbH/Natv5oVG4JAxEhPr+We8TwRylWriCgB9J7/ApoTHOrq6goAvEKhwMGDB4Xi4mLO2NgYXbp0gVQqhffixeAiIppryRkAaHl6CvHV1VzXa9cQumGDEBgdzek5OUE4ehR5gYGoLisTz7z/PqRSKXv58iVEU1OMCgkBMzZuqe3OzKT710z2a2ooK2pi0taL4m3Q0YFt//6w+OMPJFRWMi0VlXYGjVxaGnw+/hi6iYmITUhAUlISwsLCIIoiNDQ0RFtbW66oqEisr69n9fX17McffwREES4vXsA5J4f9/Eoub2lpiZycHJSUlODMmTM4yxjWrl1L92jQIArUfPcdqUgWLKDgFECSbTc34JdfwHXtip4//AD1yZPFCxcusFMNDRhmYABfX1+6xwCaNm+G5FU2vikzE5Lhw+lYr4h3cXEx1NTUxNZp5+PHjwsPHjx4PT8jIiIEFRUVaGlpscLCQtTV1TGZTMYAYODAgVwbs1OFgu7Ds2f0PO3ZQ4GvgAAqQdHRoaCihQUwcSK0c3IwPyeHT0lLE1InTgRmzOCwbh3d07AwUgKlp1OwQFub1A0uLkS8s7KAfftoPXNyomM6OgIWFvBZtIirsLJCVFOTeG7QILZ06VJoaGhQUGDlyhaVlbU1HaNfP/Djx8PmwQPxwIEDbNmyZS0dQ+TylqDA7NmAhwcAMt5rzppraGiQasXJCenq6qzzvwiQAcCTJ9TefsiQIS2/fPyYFB2XLkGirw8DAwOhccsWpnjyhPFfftn2AJWVFFTx9wcWL8bT+HhEVlVJNLS0sGLFCgiCQO38/oHhaFFREWpraysARISEhHjIZLJTtra2KpmZmXUAZv6rC1NCif/lUJJ9JZRQQon/Onp2lBmxtLRE//79UVBQoFAoFOzSpUtcZGSk0NjYyDHG0NjYiG7dumHEiBEtH7p1i3quZ2VRrfKaNeTu3LcvvT5zJlS6d4f3V19h4O7dDFlZRKxlMiJrrq60+W4t93/xog3pnj17NhobG/HTTz8hNzcXcrkcNTU1bVu39e5N8mqZjGr4y8tJ9vrZZ8CKFdAvLmYKfX061+bvevqUNveZmURyJ00iGWp4+DsHr9loSkVFBSkpKaLJb7/xcZGR8IuKatvuramJlAYPO/Zjevr0Ka+vr4/x48e3fEhFhRQRu3ZRNu2TT9qaCXaE+Hi6puDgtr/Pz4dZ585YvGwZe7ZuHfadOoWu1dXwa2xsqU0dM4bu1fr1dO3Z2e1KL0xMTJp717OcnBzs378fNnfusHxfX0RERGDGjBktb7a0JK8Gxqjm/eefifQWFhIBfbOOf+NGqP74IxyPH0fe1atIPn1a0WvePP5vyfjPP5Mi4+JFUgo8ekRBnOjoduNlZWUlpqamvt6Ib9myBWvWrHl3i8J/itJSUrGMHk3BGW1t4OpVkjzX1FAQatIkIlS9e5M0+l9CFEVER0eLjDG2ePFi/vHjxzh8+DDU1NSwevXqttehpdVOETJ16lTuMMeJJ01N2YirV7nq4mKEDhsm5m7ZAkdHR9HJxISztLDAw4cPwfM8PvroI5w7d05QV1eHn58fZ/b0KZU6XLhAGfIbN4joP3tGwb2aGsr+nzv3zs4CNZaWODptmmB5+TI3/M4dhpkzXxNOSGb2AAAgAElEQVRnAET0nj+HvZYW7F1cWF1dHbKzs2FnZ/e6lt3d3Z3FxMTAxsYGzs7OuDpvHrxv3cLBqVNhaW2NnJwc+Pv7w9raGnV1dUhNTUVoaCj2rF4tco6OmHHqFOMmT6aa9XPnyJAvLIyy9EZGJEV3dycyePUqunp7s66fforffvtNyMrKYr6+viwvLw9XduzAmK1bseWzz0Q5z4tGGRmctb29YFBezrm8IvsxMTGCjY2N2NTUxJ8+fRrPnj0TXr58yY0aNQoqKirQ09NDeHi4+Pz5c15XV1fRs2dP3tLSEo8fP1bEx8fzjs+fU/Bv1SoqnVi5koJGxcUUeElMJML+6680/86eJVVBTQ3VmQMQXF1hZm7OnX70CMKzZ+AUCiL4u3fTZ+fNoyDn3r207mzbBsyYQeonjmvxktDXp7VeTw/mAMwBuAHc119/LdbU1DCNZj+IJ09aTAu/+oqy4YwBJiYYkZnJttfUvL6n7TB6NAVox4zBmTNn8PjxY+jo6Cjq6uqYVCqFmYqK6L1gAa/xlpr5d0Emk4na2tr0YCgU9ExOmACsWAEOwNy5c7lvq6rEwYMHw2vfPhoDjkNDQwOuS6Ui9/KlmPLTT6gsL+feP3IEPXr3xqB16wDgH7f4BABNTU0IgqAPoL9cLl8eEBCg4uLiwvbu3Ttx8+bNPRsaGnYIgrB73bp1ylZ9Svyfh5LsK6GEEkr8S4SEhEgBjAVgmZ+fDwMDg9cmQgCgqqqKPpSp5AEgPz8feXl5nI6ODuzs7FBQUICDBw+K5eXl4qRJkzguKYnqLc+coayZqSmRjfh4IjYAsGgRDOrqkODujgGffEKpD3t7knOPHUsb1Dfr+kWxpT4bgNarTM64ceOE48ePc4IgsG+++QZrli6F5P59IphffknkOiaGNup6epRhXrUKsLZGYmWlaNinD0xaf5eFBW0umzeP69f/K2kyYwxSiUT0ra1loRKJuGPHDmHx4sUtNQClpcCwYYCuLnJzc/Hnn38KgYGBnIODA3Jzc1FSUvI689wG6uqkCNi5k8yuwsLadhx4E9u3tydZTU2UYQwJAXbvhvn06TDr21eMiopiCoUCAQEB4IKCiET8+SdloCWSDj0WWkNDQwOiKCLv1CnUZ2RAzM1t/6bmzf7cuXQfP/yQvAv++IOICkBz5tw5kgpLJOju74+Cq1dxcf9+LvbrrzFz5kxYvGHs2AYLFlCAqRl2dpS1bWykn1aKCG9vb+bp6YkTJ06I6enpTCqV/qsNeoe4cYOIbng4EfvevUlZMXgwncf9+xT42rKF5vq4cdT1YOVK8kz4F4GGmJgYITo6mps2bRqqq6tx/PhxuLm5oV+/fu0lw2FhHZZ/eHt7s8ePH6NTfj5ue3rCo6QEw6VSpvPtt4zduQO3GzeQPmECTp8+jV9//RU8z3MVd+5AsWIFSq9fh35ICNWl/+c/RDh37yavi+HDieAxRte0eDGVsXRQd52fn4/i4mI24ddfIT95kshkfDyV2zRDFGkdSUuDio4OnJpLQF7B0tISU6ZMoX/cv48uDx7gxJgxAMehuroan3/++et6aTU1Nfh06YK6tDT0mjOHbVq1Cj/PnYv5zX3UBw6kgF9ZGWVu4+KoPERXl4jgvHkUjPz4Y/jNmsVduHhRkZCQwIeHh0O7thb5M2di0sSJzOjDDxk3fTqijIzEuDNnEBoaqvDw8OALCgo4CwsLbN68GTKZDF27dkW/fv3arLlTpkyhkz17lkdlJWBsDEs3N/7p/PliXkICM39VooGEBFrnOI6eVYDW2gkT6BlzdSXyb29P/548GQDwfWGhUF1dzQFAxZ9/QldXl97THLBtVjWIIj2PdXUUmAOotMrVlZRbb0F9fT07efKkOOSXX5iZuTnkFy4AABoaGnDPxQXlM2YIVbq6YoGLC5zv3eMdx4wR7OzsOn74DhwALl1CUVER0tLSMGPGDJibm7espyoqLef9D9HU1ITS0lLo6+u3PBSpqaRceuU/AgASiQSWlpbC5UuXeI+dO8G5uwPduuHJkyeIevKELT92jMl+/BEvnZ2h6eCAIc+e0TrzbxRCANTV1REcHCw/duzYxfr6ermKigq0tbWxdOlS1aysLOe4uLgt+fn5KzZu3BjGcZxafX39pnXr1mX8qy9RQon/JVAa9CmhhBJK/AOEhIRMSExM/CUmJmaLIAgbNDU1h7u6ukrv3LmjiIyM5CQSiWBpacmampraESBNTU2YmpqiWXKvoaGBzp07s7i4OCQlJsJ70ybG+fpShnXqVNrwy+VUr9zK9E0ikSD2+nV4jx5NGeVFi2hjt3EjZZOWL6dMrY4OkfT9+ylLFBDQcjJZWTB88IB5du4Mg61b0en5cxiGh0MeFkabtgULiEgtWUIZyF27iGjb2gKBgUh69EiwdXTk9PX18fz5c4hffIHGGzcgLljQkhnV06PNtL09ZZlaGf61RlxcHLy8vFhTeDgsjx2DWWwscxgyhMXHx3N5eXlobGzEixcvwG7dQmOPHoh9/BhhYWFQU1NjiYmJePr0qRATE8P69OkDnw6k9wBoLH19qSRhzBhSKXRQlwuA5LWurnStAG3cO3WiQMq0aRQI0deHi4sLS4+PV/RZtYq7Y20Nq969iWybmFB2sEePv1URREZGIi8vD4NWrkRKcTEcAwNh1iow0wY+PlTbvHMnGdj16kVZNXNzyqxlZ9Nrr0opNDQ00KVLF5abmyvExMSw+vp6GBkZdSy5V1enz9bXU40tY6ToiIujAMn06W024hzHoVOnTiwpKQm2r8bJyMjob43T2kChIIM6ExP6LltbyhCfP08k+9QpGr+6OuC99yjQYmlJGetp0+j3OTlk1mdpSZLpf4AXL16wsrIyYfDgwezw4cOiqqoqpk6dytooW5ohlVKHiTeCQwYGBigqLBS1Hz6E35EjzKh7d6b6669gTk7kvH7wIAxWrUJxcbHo6+3Nhnl6wvnuXRhcvQph8WKoBAbSgZKSyHxxzBgilMHBLa0NATK3tLamgE+vXsC8eSgqLkZ0dLQQExPDzMzMBG8fHw6envS9Y8cCEye2lGuoqFCgytf33UQqMRFYsgQ35swBZ2oKDQ0NFBQUICAgoOWeTpoEtn07rLZvB7dqFSKvX0dNTQ2io6Px4MEDAQAzc3MjAmluDoSGov7sWSgMDJBhYYHjcjm6nD4N/uxZhEqlYokgcI8ePYKfnx8m6+lBb+ZM6HAcpElJkKxaBXsXF87Pzw/V1dWIj49nAHlyWFpa4sMPP4SDgwPjeZ5KOVRUKFjl6Unr1g8/0Lo5bBiSVFRwD2A1zs5w++ILuhYdnfZBnGvXSLY/bRoZ7fXoAaFnT7Bm00D6fpaXlwcnJyeFj49Pe5ItCHScnTvJhPLqVZqbBw9SAC01lbw2fH07vA21tbVQr6lh8Vpa8Pj6a9xJTcUff/whRkdHs4yMDAx69oxZWFpy6sOHc+rR0ajJyWHnnz0TYmJiYGJi0lLOBdD6M3kyDl26pDAyNhZ79+7d9nxXr6Z17R8+sxEREU2HDh3iysrK4OfnR8HDPXvoOGvXtgu4denShYuMjkbFrFmokEhQvHOneOXFC2Zrayt6cRwzt7GB7ciR0HZzo7X1wQNSL/1L6Orqonv37pK4uDh4eHjAwMAAjDHo6enB3d1damRkpGtsbOyjra3tUVpa6ty7d++D//pLlFDifwGUZF8JJZRQ4m8QEhLir6qqenzEiBF2AQEBaoMHD2Z+fn5SZ2dn9OzZkzM3N8fFixeZpaUltm/fjqSkJNy5c0cwMjJiWlpaHRIhuVyOrp07M93Nm1nckCGCq54ew4MHLTXJRkaUTR84kFzZX+H69euim5sbU1dXJ0Jia0v1zlu30uZt0aKWevfPP6fXKyqIMNXVUZY0LQ1FPXogLzER9aNHizY5OUxSVAS2fj0RnB49SPY+bRplblRU6POurjCYNw8Gp06x3xkTbsbEQExPF7PKynA6K4tlZmYqCgoKBHt7ew4yGZGNgQPfatQXFxeHbt26Mf7QIZQ1Noqmkycznudhb2+P27dvC0+ePBFzcnJEs++/ZymPH+OJnp4QFBTEhgwZgoyMDDE/P5+zsbHByJEj//4mGhmRzH7aNCLPHW12q6qIXKqr01g1NND4ZWdTEERFhcy49u6Fd0gIlxcTI8aqqjLr4cOhYWlJnzMwoOO8Q2aen5+PsLAwDBo0CE+SktDYsycGNNf/vwvTphGh6tqVau2vX2/pIPDGGKuoqMDLy4s1NDTgxo0bSExMRGVlpfD48eP/x953h1Vxdd2vM7fRi3AVEAsoHQULClLs2IKKJpYYWxJrjIkajSbxVVJNYsprizWxl9hrxIIiFoooIqKACCiKgPR+y5zfH5tLUVCT759fvu+u5/ER7h3mzsw5M/estdfeW2zXrp2gi9pqtVqU79iB2OhofvDxY+Tl5YnFxcWCzMUFJubmVLCxqqoRYTQyMkJkZCQKCwtx7949xMTEwNvbu1GktUlkZZGt+dgxEqkGD6Z5xTkJS35+VDStuJicLv/5D41RaCgJEXI5/R4QQKJMcjI5Ay5coDQPXS2AZiAIAuLi4phSqURcXBymTp3KmmwlBpDt2sWFRKuG0Gjg+tdfbFPbtkwtk4ntvb0ZmziRCN6mTSS8SSRw69mT2YwfD9lnn8E4MhJfa7UoU6u5s7MzEwSBzvONN+he9fcn98Y779TPycBAwNUVmuJipN+4Ie7IyWFtZ86E9skT7jx5Mhs4cKBQ91xp04bqHxQX01wYNozmg78/OSK6datv/9YQGRlERmfNgvOoUfD29kbHjh0RHR0NYyMjtPb2pjEZP55cFDIZIJXCz88PEokECoUCDx8+ZGlpaYiMjERkZCQyHj7ETaUSpwwNURAeDrfdu3G3ZUtYXLqEa+7uYMXFLKSsDEHLl8PDxYXOs6amfk7UOkkEQUCHDh1YQEAA+vbuDf+iInQKCQH7/XcSNadOJSFKqaTuBh07kjAaEgIMHIjKykpsu3ABylatkJ2djYyMDNHR0ZE1KXjFxNC1qxViUtLSsKWyEsUKhdY5OVkA5zBydsaNGzdQUFAgdO3atV4402opDSM0lMj+8uUkKIoiEWKVisSH2bNJwLlwgebvc88eJwMDdBg1Cnf9/LjK3Fw8f/68oFKp2JAhQzBw4EDYvPceTPv3R2u1GjYjRsBp3TpYLl7Mqqqrce3aNaSkpLDr16+L9+7dQ41KxVr5+iLV3l4Y/fHHQqM2eAkJdIyv88ys+5MEnpubKzDGkJ6ejiAXFzClkoTcZoRTrVaLtLQ0rXD1Ku+2aZMQ1bUrps2YwYRu3eg5oEtD8vQkF8vfdOnoIJFIcO/ePbGmpkZ0dXVt1DbQ2toabdq0wbNnz3hWVtatgICAf1boQw89/uXQ2/j10EMPPV4N17Zt28Ld3b3JN3V5k1evXoWJiYl22LBhkoyMDGHPnj1crVazAQMGiL169XohGmS8bx9aa7U4XlUlbM7O1r6/fXs9YzM2pmj0pUtkMa2FVCoVy8rKJC1btqRo3hdfNI7cX7tG//fpQxGTtDSKmM6YQYvQoUOBe/eQpVaLARcvCps7dmTXXV1h0qsXf7e0lD1+9gxuANmCz5+naLYo1jkMImbN4hVpaXCXy4WBX38NlprKYG0NP40GFy9elMTFxWFwbTQMCxcSAbp/n9ouPQd5VRVMpk5Fxpw5uP7gAdeZQVu1aoWZM2fWX69eveDi4AC0aFH32rRp01hKSgqOHDmC5OTkZsemEZydaWE/ZAhdhzlz6kmQjtjrWpX5+FDNg61bieB/+y0V/NNq6XpIJHA+cYJd372bb9iwgS1ZsoTIbmAg2c//+18iWQDKyspw/PhxjBkzBiUlJdixY4fo4+PDZTKZkNO+PSvRanXFqV5+/LpCe97eND7e3kRGo6KomFcTCA4ORnBwMKKiopCRkSEUFRVpb926BWtra61Go5Hk5eVB6uyMPnZ2bLCvL2JjY1lWVpb24sWLkrlz58JowwaKeuraqdVi4cKFUCgUyM7OxqFDh/DTTz/B3t4e48ePxwuR8mvXiIz170/iz9q1tMDnnNIRJk6k1A+d0LV9O0X8Fi6k3yMigDVrqI6ADm3akDsgLo6s7gEBwJkzJLI0kULBOYdEIoFKpcK+fftgY2PDzF+WapGSQqLX80JVQgIk69dj0oUL2LVnD1MqlejcuTPZ1pXK+gJkc+bQvZOVBQBw9/Dg6enpuHHjBu/RoweDmVn9cXp5EflaupTys2tRXV2NA1FR4mMfH9ba2hraiRPhHxIioKqKzj87u5406pwk2dnkerC3J4Fm/366Nv36NT6PJ0+I9I0d26idpdm5c/hgxw6sBZAaHIz+Dg6weY7QKRQK9Kl95jx9+hRHjx7Fs2fPYGBggIKCAlRUVGDKlCmU3iSV4r1Hj1AVHY32rVvDID4eQmoqOYakUorKL1lCYhpA91ZCAj2rFiyAlHMSLCZNIreJv3+9e+F6g45rDarDazQaVFVVwczMTFtYWCgZOnQoUlNTsXbtWi6RSLhMJuPdunWTBOnqobi41Ll5cnNzsX//fgQNG8YjIyMlHa5fh+WqVfhj7FgYmpmhqqoKKpWKrOcxMVQ4z8yMinPqCpYWFdW7gQoLaVt7exJk1qyhuf7ZZ/XPHlEErKxQs3EjnqWl4cnFixJPT08kJSXh1KlTAIC3334bTps20bP87l0IM2bAPTkZruPGsZiYGF5TUwOpVCo8ffpUjIiKYp3lcoRMnw5DQ8PG415aSsf3NzB06FCJtbU1oqKi4NaunSiEhAhYupTmfDPo168f+vXrJwGA4mXLMO/RI0ijo+n+3LuXXCgSCYmuO3fS9fjpp791XDowxlhCQoLQrVs32OsK2jZAcnJyeXV19dV/tHM99PhfAH3rPT300EOPVyAsLKyFVCrNnDRpkmlz+c8nTpwQMzMzeffu3SW+vr51r2dkZGD37t1wdHTUqtVqVFZWMnd3dzFILpciPx+8WzcUb90K9apVaPn0aeOdVlcTcbhwoS5/e/Xq1Ro3NzdpRVmZ1mv5cqFi5Up4DBxYHybS9a9+6y0i+/v20b8ffqB/ixeTdffyZTzKyoLC0BCVlZX4888/UVVVBQBYOnEiBAcHWqBGR9Oi+tEjImg6PH1KUb+EBPChQ6GysEDN7t3Y8MMPWPjNN/Xb7dtHx6Oz0TbArunT+VuZmezW11/jeny8ZtasWS8K0NevU7RSlxP7HC5fvizGxsZi/vz5r588Xl1NIkn79hS1tLKiXPiRI0kcuXmTFuiRkWTFffttcgV8+mmTua4rVqzgtra2fPTo0YKJiUl9S7wJE4DaaNjOnTthY2MjFhQUCN7e3uLAgQOFNWvW8PdWr2Yn+/Xj99u1Y/Pnz4fxy2oKAHR8rVpR5PvgQbKB//wzvR4VRWToJaKBKIrIzs5GYmIibt68iXfffRe2NjY03gcP1nUi2LBhg1YikQj9+/ZlDpmZFEk3NGwyJUOr1SIxMREnT56Eqamp9qOPPpKgooLy7XNzaSG/fj1ZmHVRxrIySjswNyfyoyMllZUU6TUxqd/2yRPaZv36pk8qN5fOfdEiIsErVwLduoEzhqtXr4o3b95EcXGxIJVKubGxsWhqaioZOHBg82kTAB1Xaipdax3Ky4mg1roBNmzYoBUEQRg2bBi1MKuqonurTx8i/rqc7VrcunULp0+fxpw5c2C8ZQtVTdcJI0VFJIJ06UKReQDff/89qqurMWfOHFg1dC2Ul1NBxbFjydHw7rskjGi1dHx375KgFR5ORLasjD5HJ1yUllIR0JISas8IkGDSrx8QGAjNrl1Yb2SEgoICjBs37oV8/1eBc/6im4lzuj6zZ5OI2Ls3iTOMkTgxZQqJl+3b071WUkLuFam0XgR6TRw9ehQJCQmQy+VcoVCw6dOnw9jYGBkZGVCpVKipqcFff/0FKysrbmZmxhwPHUL7Tp2g/OILXLt2DWfOnMGYMWPw4MED7fXr1yVMFDH05EmYqtXYP3w4gjIzeefkZCZ+8AEUNjYwHjKkvl1eRQWJK/7+JE6kpxNB11Wmr6qicc7Lo2eiqSkJHZWVwB9/oLy8HDdu3EBAQAA2bNgg5uXlCTKZDN7e3uLQAQPoxpbLad/bt1PaUFOIjqZ5WluV/59Co9Hg2LFj/Pbt2wwAPp08GQYnTtA4/p16HbNm0TifPEn38kcf1YtdFRUk+C1c+Lfs/KWlpdi8eTMvKytjHh4e2pCQkCYLkmZlZWHbtm0azrli2bJl4usftB56/O+A3savhx566PEK9OnTp+rixYuJd+/eDfXy8pI1taBwdnZmPXr0EJ6PLFhaWsLc3ByZmZlCbm6uUFJSwqykUrFtWJhwtrRUjKys5E69e7MjVVU4l5TEFQoFqyMhUimR6kePEKtWY+vWraiqqhKepKXBKSaGFX76KTt95QpVWOacKkTPnUsk5ZdfiDCZmdGicNMmWlyFhRE5AGBuYQFjY2NYWFigZcuWuH37NgSNBn7vvYdCf39axLq60sJ72TJa4DFGUU+ZjBarBgaIcXLC/qIipIaHY/aqVdhpYcETL13i0YmJYlxVlRgrl3PZkiU4VFiojY6LE6Ojo0XFwoV4aG0tOK5fj+KSEty+fVswMTHRVauvx40b9TbZJmBnZ8fOnTvHvL290awl+3lIpRR9P3+ecmwHDKBiXCEhdP0++YQW7G5ulBYxZw7ZTJ2dm9ydh4cHi42NFSMiIoSysjKt3eDBgvarr5CWm4ureXliREQE8/LyQmZmJtNoNOjbty+LjIwUS0tLETR3LlO5ubHUrCzY2to27p39PCoqiLz5+FDV788+oyjZ+fNE5Lp0ITeBoSGJFc9H9UD2VnNzcxQXF6O4uFjs378/Y4JA5+riUldk0dnZWbh27Rq7cfMmgqZMoc/66isqtvYcBEGAra0tbGxskH3tGu/u5iYgNJSOa/lyWui3b19PNm/fJgL/4AG939AJ8MknRIamTq1/zdSUBBpT06YLH5qYkBjRpQvl+4sisHYtnhoZ4UhcHAsKCmKDBg3CwIEDWc+ePQVvb++6YpXNYsmS+oKTOkyfTj3Zp00DAJibmwuJiYlMKpXCsaoK6NSJUld27iQnyHNkyMbGBvfu3dMmJyXBe9cuxt5/v34bQ0Ny8/z+O+Dri0fFxbh58ybmzZsHi+cFFrmc7M8ACQs9ewJnz1KEfvFier9NG7oeV66Qa6C8nEi0Wk1iSH4+uX3mzqV5f+MGRV27dYPg74/u3bsjNjZWVKvVzN7e/vVbLKrVYJWVRHJzc2msL1+m67JmDYkaEyeSYyY3l+Z0djaRYDs7uobffUf3qIfHa9djACgqv27dOl5QUMA1Gg3TarVs/vz5MDIyAmMMlpaWsLa2ho2NDTw8PFBVVQW1Wi2qo6OFO1VVuF1VhZycHJSWlsLR0RG9e/cW2rZti1u3byNXqYRPVRW6RUbyYkFgF7t0wRWZjF/OzmZRUVEwWLIEwtGj/Mn06czKxATswQMSXBMSSKTS3TcyGYleiYnkWPH0pGswbBjQujXkcjnatWsHrVaLmJgYVl1dDVEUMXbsWKYwMqL9BQQAK1bQ/aNUNu7EoEP37iSQPl/gcfBgcovpake8BOnp6di1axd/+PAhs7KyQufz59Fq61YYbtny2vn+dXjjDRJN//qLxv3mzfqCknI5PVsTE0mceM19b9++XVtQUCC8/fbbCAwMfLHIZi2ePn2K5OTkKgDixYsXKyMjI5/qnCl66PF/AXqyr4ceeujxGujdu3daZGRkW4lE0sXBweFvrXQuX76M9PR0aLVadPb05NKTJ4U4OztWERDAquPimO+JE0ju1w8FBQVMqVSKHTp0qNt/VnU1Evfs4TflcmbZooV2zJgxgmtKCrqcOsUcV66EXC7H5WPH0HPSJEiuXKHFfV4eWUpPnqToWEoKkcK+fSkS3Lv3C6QpLS2Np6enM8Y5nrRsifMVFbD/8UccefZMe6WykhcEB3OnR48EODtTJOrgwbpiYg+yslCsVovvzJ/Pbvv5wdzBgfX74gvmWFIimLz1luBgaCg4//knsxo1Smjn4yM4K5VCxz//ZC2XLoWtszNkMhkePXrE7969y/39/Rtf22fPKDe3CSLPOUdpaSni4+MxYMCAv1ckjjEiR3Z2RGJTU4n4f/01kdTqaoo8z5vXbM0BHQwNDdGzZ0/BxcUFJ06cEGJiYlCo1YodDhxAiq8vCw0NZT4+PnBzc0NpaSkuXLiAyspKPmPGDEHxww+wCwlBmUSCy5cvw8/Pr2k7P+dEYqdMIVJ+5Qod2/XrZBX38aHIdocO5ED45hsiGI8f19ueayGKIk6ePMmVSiV3d3enDzMwIAIxZAgAsmqXlJSgoqKC+/j4MDZkCBXTS00lwtKQCIsikJyMkpQU+CxdKhh4ehKJnz6drl3D87l5k1739iai+XwueZs2RE6fz78PCyPC2sA18wLataNzPnoUSE6G1NQU8jNn0Omdd2DeXFHG5tC1K90rDYWIN94g90ftuVtZWeHxo0dot3On2Mrbm6G4mOaMhwcJbCtWULS6Fvn5+YiPj4dCrUaX8+cZ+/jjxp9pZ0fX8tQpXJFIUFxSIvbp0+flk7plSxJB3N3p2KytyZLeqRO5UkaOJFHuvfdo3M6cAU6dojQWFxci1hMmUPeDBmIWYwympqYs4do1fuPUKebbpQvYtWs03zQacpZcvkwpGlu3kiA4YACJjO3bk7iYno7oc+e46ulTPJHLmZFUCrmfH20XEkI2+C5dKO+7bVuag2fP0ryOjaW5++gRzTlBoHn8knuxrKwMMTExTKPRMHt7e96pUyfR2dm5yfCzoaEhHB0dmbu7u+B48ybkffqg1NBQtLa25gEBAczT0xOMMVQ/eQK2dSvMysshy8uDundv1iU2Ft137kRAcDDr4uUFn0Q8aHEAACAASURBVORkYNAgRHt7s8vx8WBHj/LH48YxJpXCrEsXmgMNHTuCAPTqRULIpEkkKD5XuG/z5s1ifn5+3djb2NjAxsaGxlujIaJ86xYV/uvV68UTVCrpWfG8e8XLi7bXpSs1g8rKSmzcuBHm5ubso48+gm+PHkjftQs3vbxEr6FD/ybTrwVjdA/Z2ZFo8fbb9e+1akWpT1pto6K0TSElJQXbt28XCwoKJKNGjYLbKwQhCwsLmJmZyS0tLXsXFRVNFgThnfPnz8f06dPnyT86Dz30+JdBn7Ovhx566PEaCAsLk0ilUk/JK4hfU+jZsyeys7O15eXlEuMNG1iXmhqYnjiBn1atQqCJCeS1rfr+/PNP+Pv7C1euXAFANsXkO3fQ98EDZmpjA1lAALOTSKhA1aJFQGoqen34IaQzZuCmhwe6yWSQyuUUuRkyhCLSc+bQAurGDYoeurvTIlEup3zJWvj6+rKCggKtw5dfSoyWL0d6bCxyLS1RWFMjKS8ogLSgQMTu3WTt37uXSEktRFGEgYEBNzU1RY9aCzJSUmBcVYVWsbG02C0uhmtSEvDHH0QGkpLg8PAh8PQpWjKG0MJCdqiigmP7diKyv/5KUcukJIqQlZTQ8Y8aBezfj/K8PMSYmcH06VMEtGkjCqGhAkxNiaCdOEERTC8vIgvdulHlbgsL2tfZs7TwbN2axIROnahwIUA54AkJf7sVFADY2trijTfe4CdOnGBZTk7wcnNjYwQBitoFt1KpxLhx43DhwgX4+/sLct1YzZyJkJAQ3L17V9yxYwcLCQlh1tbWjXe+aFE9udJq6fwYI1I/ezZZhXXRunXr6P3iYorWZmYSQTUxAQQBhw8f5jU1Nejfv3/9ZDY1pZSL77+vcwR07twZ8fHx7KuvvsLYsWOhVCphumABeEUFLi1cKBqIIvNxdmYGK1ZAFR6OA5Mno8eWLbzPyJEvkoGqKiLCQ4dS5NvJ6cULqBMrZsx48b0vv2xUqLJZMEYtFKOiYLh/P+zz83llYCAr+O03tBs0qFErwZfC2bnxtkFB5FAYP77+Nc7R5s8/ucfRo8LR6mo4r1hBxKKiAho7Oyjc3SHVaCCVSnH//n3s37+fe3p6YlCXLgLLzW36c0eMQNmlS1Bt3owWI0YIAJCXlwepVIqioiJcu3ZNDA0NFZpM93B1pf9TU4kUHzxI9vCRI6kgWuvWJF44OtIcMTKiegx79xLZioqiHPPhw4E1a+D19Cmc+vZlD86fR7lSCePbtyEolSSqKBT0Ny4udG0cHKjvvIUFkdlaITA8LIzmQnY2HdudO3CorNTKysrYyOBgwfC778hy/vnnJCLOmUOuiJQUujfLy+n9lSvpPtUJV0ZGJNa1bg20bInknBycO3dOBCDY2toiOzu76WJ8TUCSnQ23KVPg5ulZLwzUplXYCgIGdOmC+z4+SOMct27dwrsnTqBNbb6+WdeuQGQkLA4fRgczMxRfvQrD4GD2m4ODePHKFWHc4MFoN2AAjm3Zos3KyhJmz57N5LpCk7p7ITycnscNLPelpaVgjEGXattKl04ilZIY8sknJOqFhpIl/vmotrU13W9RUY1fT00lV9NLkJSUhEOHDsHIyEicNm2aIFRUgAcG4l6fPhi3ZMn/rNfmvXtUH+WLL+iZoHMfCQKN/b175HRoJp0pOjoa4eHhACCMGDECrro5/xJIpVJ0o/op0uDgYGlSUpLriRMnzoWFhXktW7Ys6390Pnro8S+APmdfDz300OMVCAsLayWXyw+pVKpeAGBvby9OmDBBeG3beC0OLl+urU5LkwxZtAgtvLxw+NAhsSQiglkFB4tDhw2TfPPNN+Cco2XLlqIgCDAxMRE1Gg0bWFAgsbOwIPt9586U1xgQQP+8vQF3dxzt1EnrFR8v5K5bx7p164YXLI0jRlC09PffaTHl5kYL7AbE6tTRo1q/uXMlljExOH/njsZo6VKp4aef4nhiIszNzcVAURS8P/0UTyIjcae0FAUFBdzJyYkVFhbi8ePH2qlTpzathJSXE9E0MqKIuYEBLabt7WmxOnkyKpcsweEuXbQTfH0luHWLIo6//UaL/FGjKAImikDLluBPnuD333+HZ1AQOrZoAQM7OxiXl9NntW5dX7jMwgIoKKDIYUYGEaB27SgSLpPR/9HRRDI8PWn/b7xBpNPNrWl77GtArVbj8uXL/NGhQ6zXX38h6+ef0SUgAC2a2p9KRcfCGBISEnD06FEoFAosXry48XaZmUTEunal3319qaaAQkG5sG+8QTnDz0fEddfe3x+wt8eNTz9F+PHjmDJ9+ospExoNiSrP7UOXs8sYg1ajQcviYt4pOxue4eHsuo8P7gYE4BnnsLSyQnBw8IsL8Kwsyt/fvx9Ytap50j5zJgkWOsGoITIyKBKoK0D5Oigrg+rbbxGXnS3aXbokaIyNcT40lBs4ObHQ0FC8tEBfaWmdOAKAItajR1MEGiDXzJgxODBnDjLlclQ0YSGWqFTolZIC6//8B8ePH0dgYCAPCgpiSEmhVIVVq5r86Gt796Jq40bYTZ+ODHNzHhcXx3QFBgVBgEwmA2OM9+/fn3Xp0uXl16C6mkScKVPod6WSajxs2kQ/T5tGY+PqSiKSoSFFVquriTCamuLnn3/mZWVlDAC6du3KQ0JCXiuyq9Fo8M0332DhwoWorKzE1q1bIZPJYGtrC+mpU2ibm4vuul73CQmU2nH0KDltmkpn0WjIAp6fTw4FzuneSUxE+enTSPD0hNfIkTB1dET4xYtIEASIZmZ80uTJ7KX1GfbsofvH1JTcBPfuUbR9wgQioObmyMvLw2+//QYAmDdvHszkcio4+cUX9Dzr25f2JYp0nHI51Q64eROjjh3DrblzxafPnmHAgAGCt7c3sHs3PaPGjyf3VWwsOWees5cfO3YMiYmJkMlk+OijjyhVSRTpObBzJ3UAKCmpL2apw8KFlM9fUtL4dSsrsvd36tTkpbhx4waOHz8OPz8/BAcH04vXr4Nv2oQv7exgbm7Ox48fz1o1rGXxd7F7N13badNerDkwezYJ0nPmNPmnRUVFWLVqFeRyORYtWoR/Ir4DwLVr17QXL17MUqlUzsuWLdP+o53ooce/BPrIvh566KFHMwgLC3OVyWQfSKXSd318fGRt2rThERERTKVSsdWrV4sDBw4UOnfuDI1G8+q2Y3l5CL16VVL0/vto4eUFAAgVRaF0/36c8PMTtm3bJjLGBEdHR+0777yjW8EQ0+CcFmeDBlGrtTlzyAK9fz9F1WbPRm8/P8nFixeRcvEiT0tL46NHjxYaVWL+/XdahJaVAaam0Jw7h82//QaHYcNw190d5ebmsHz2TJK7ZAmm2tigv42NFGvWAG3b4mhCAkpLS4VjGg3aGhnh0n//iwJ/f25kZMRPnjzJzM3NX8wrBuorzOss3z4+RCJWr6ZIVn5+3bZPt25F7pEjtPjVRU87dybb8OLFFKmurW7/UK1Gftu2vOe4cU2TjqaKKNZW2wZA0X53d4p6rV1bv3+NhgqXDRtGrgCdA+B1o8G1kMlk6Nu3L0PfvshctQo54eGIvn0bTk5OePPNNxvb9O3tiaR7eMDb2xsxMTGihYVF/QZaLR3Ljh31RB+gY9btp21bIgxjxhAJbShCmZigvLwcEfPm4eGdO6Lxli3Cp7//DuHzz4k0NDyWX36hnNqIiEbnM3z4cDY8JAQoLIS4bRuEzZsZOnaE+q23kNu/P56lpsLC0hIWFhY4dOgQJk+eXF/87q+/yNK/Zg25OprDuXPUas/Orun3bW2JgOvEoteBqSnk330H//h4QSWToTQnB52Tkphw7hz23LvHZ65Y0TxpbdWKSHD79kScvv6aiLAoEnl++BDo2RNZxsaoqA2aTJ06Ffn5+Th37hyvrq5mVioV73H2LPvFzQ2iVAqNRsMBsGrOIWnbFk3NqrKyMlx78gStlUpI1q1D9siR2kmTJknt7OzqBLyEhATk5OSwY8eOwcLCAg7Pt3msqSGLeEQEPS+USuqGUFND0eCOHYlclpRQuoGTE0WZm8GgQYPYgQMHwBjDjRs3WMhr5HsDFFG1trYWo6OjhX79+uGTTz6pe68qKQlRjx8jLS0NTk5OJFq6uVHe9vTpJITohJX6HZKY17p1oyj4k8ePsUuphLtCoTV1cJCgpAQDHz2CV1wc0mxtmVFODj2DXF1J1LOxofGVSume/+gjSid48IDmaVAQiQ8NnumFhYV1P//3v//FUk9P+ntjYxKpfvqJ7uX+/YGcHABASEgIOnToALf27dHpjTeEg5GRSE9P13p7e0uwZw/lzwMkeFpYEAkvK2uUTz98+HAMHz4c3377LbZt28b79u3LnJ2d6fiKi0n4/fRTivY3FJy++opqI2i1dakPmlqxpLnc9pKSEpw+fRrBwcHw0xXKW7ECMDAA27ABEx88QEREBNu0aRMCAgLwt/PeN22i76ypU8mhUVn54jZLl9JzbNasJlM2LC0t4e3tLSYkJAgVFRWvrr3RDPzo+9IOgDWAZmw2eujxvwP6nH099NBDj+cQFhYmvXbt2lapVPrfbt269Rg5cqSBh4eHxNramvn4+KBr165MJpOxM2fOIC4uToyNjeW9evVqnjhotUByMpi1NYwmTap/vU0bZHt44GpaGswKC3mfQYOEzPh4dKupYXmmpkhfuRIoKIBRq1Zg27cTwTt7lizu+/ZR9GvjRsDJCQYGBnB1dUX37t3ZlStXxHPnzgkJCQn8+vXrPDY2lscmJfGi7du56fz5bCPA4zMyxCJAmFRQgB6MwffLL+E1bRpqWrcmuzNA+3d1RWFVldht0ybmk5SE6KAg+PbpIwbPmiVUVlbi8ePHaN26NXvw4IFQWVmpLSwsZEVFRTwuLk7cu3ev4O3tjZTvvuPC0aMw3ruXISCAzqNhT3Ggrmd73XVUqymP9c03ya67YAFUffviSkICv3jxIrOxseGdO3f+e7mj2dm0yOzdmyzxv/1GC+2OHUlAOXaMCrONGUPRvrt3Kdf5gw/+NuHXwUKpROetW2EzZQouxMYiLy+Pe3p61h93eTnZamuFGQsLC3bhwgWUlJSILi4uLPfhQ1SmpqJ61ChIZDKo1WrIZDL6mzFj6msvtGhB12zjRmD4cGg0Gly5cgUHDhwQIyMj2dP8fISMGcP6vfMOJGPG0Nh26EDOAl2hLCcnIkW1YlTd8Wm1RK4SEsDmzydi8f77KO7eHZJFi9By4ECMnz4dXl5eJCxERHCmUsF63Tom9fYGGz68rsp/kxBFcqkEBjYWZRpCKqW88urqJjsCvBR2dpAEB8PI2Rltdu+GSfv2yCkuZm6pqWDduzc9tsOGkZX/6VMiIDNn0jH07Uu5xQcPAtOno1fv3vD09ERcXBwSExNha2uLCRMmsPz8fNHY3p5779ghXL1wAVpBwMOHD1l1dTUSVq+GJDwcv+Xnw8LCgtvY2LCioiJs3LgRFy9ehEqlgmtoKAJUKvi4uwsWvXpBIpGAMQbGGGxtbeHs7Izq6mqcPXsWjDFuZmzMDJ49I8FkzRoaX2triqIaGZFV/8svififPk2pLlIpiYDl5SSseXlRhX9BIKJVe3/qIto2NjYoLy+HkZER7OzsXqtGxunTp1lwcDBMn3NzyKRSpLVogfM3bsDKygpKpZKOJyCA0g4GDybBIjj4pVXf9+7dqz1z9qyglssx9pNPBEWnTkCnTmBvvolLHTuK1zlnxS1acA9/f4bbt6lwYEICiTdr15KYGB1Nwt/QoRRZDg5+gWjGxcWJjx8/ZhKJBKNTUriyd2+GuXNJQAgJoeu6YAER2VqizBhDy5YtIcydCzg746FUiqKCAu4dHy/g668b59o7ONC8PnSIxqM2b72qqgoHDx7UFhQUCC1atGDR0dGwtbWl7gy66HxQEAk5DUVOzoH27XHXxYWfTUxk169f5zd++41ZLFyIrWo1v337Ni8rK2Occ1hYWECr1eLw4cNiTU2NOGbMGLrgokjur8mTgTZtYGlpia5du8LMzAzh4eF4/PgxMjIykJ+fDxMTkxdbbtZPAvreOnmSHF1Ll9LPJ0+Sg6vhPWhqSs+lFSuaLCIoiiLCw8NZTU0NAgMD6Vn4D6BWq3Hp0iXOOf+qT58+6n+0Ez30+JdAH9nXQw899HgOEolksVKpHD1p0iTDpiL2EokEPXv2rKtgn5iYKDx79gwv5FkDRE58fIg4HjxIha0+/JAWmF26oEPXrliydy+Dp6ekuGNHPI6PF8SNG7Ft7ly8uW8fVL//ztcMH87mJCdTmD89naK406fT4vI5GBgY4IMPPpCUlJTg4cOHjHNetyLnPj5ILS2FpakpuvToITEyMoJ02TJApYJ00CBkdOsmPu7evX5l/fnnwI8/IjQ0VFgTEYEPRo6E04ABwK+/Cli9GmZ9+jDOOc/IyOA2NjaIi4uTAIBCoWA1NTUAKAo24Nw5VtS+vdiSMQYXF4rQ3rrVKDrHOUejtDKplCyqutzuAQOQ9d13/LKdHRw7dMCoUaMaMQBRFPH48WOUlZXB1dW1cfRcV7MgKIgiZ0eOUEqEWk0kB6CFpUZDPzs6kl38gw8ohSA1lSzkmzbVE+PXhaMj0L8/lCUlMDExgUqlEgHUMwlX10aR6o4dO8LR0REJCQlC5d69aJedjWsjR4rl69cLOhv3wIEDeXdLS8aqqxt/1qRJAGOo+eEH/KzVclNTU969e3f25MkTZGZmckEQmEQioTxrgKLuLVoAmzfTojs9nUjOpUu0AD97lnKDd+0i0tipUyPiFXntmrZ7aamkW4N7ZNCgQTCUSJjsm294Sn4+TgsC3v/kEzRxZ9SjpoYiuq8oGoZ16+jzX7Mft1arhVarJdeNoSE0PXrg6owZYLt3o6OBAYScHLK3L19OUeWG5PXcOXIZMEb3blgY2aYPHKAIbgPyqosuiqKIqKgoWFtb46233qpz5Xz66694tnYtrltb48mTJzDlHIW1KR1Hjx5l586dQ0VFRd3+7O3tNf3795ciKIjmpZ1dk0XYBg8eDMeSEqRs3cqzkpJgZmfHhEmT6L49dIjmllJJx96xI2oPkkjutGlELtesodcrKshR06IFpSvk5wNXr4KvWwdjrRYVEgme1rYG/euvv2BsbAx3d3doNBo8evSoLkWlocMnPz+/jvC+gO+/x+CvvoK9TIZDhw6hQ4cOEEURt2/fRsuWLXF6zBjuk5XFu33/vVDZvTvWJiZCrVbDxsZGK5PJIJPJhIKCAhQUFEgAYP78+S8ICv369ROuX7+OZK2WYdAgckbpMHIkpfiEhdE1USpJXGzblqrnu7pSLv28eYBMhkGDBgkpKSlckZrK2sTGMvz4I2BpSftq25bSC3JzyQ2Qk0NOFB1WrgQ6doQP5zi5ZIlEfeoUZLU1DRrB2xv4+GMSk8rLoXr7bWzdupVzzuu6CmzcuBEHDx6kNJ+YGDqGEyeIODdsWyeTAY6OuJCYyJ4ZGKB9+/bMuWtXGJWUICgoiOXm5rLk5GQxOjqaqVQqprt2da6ynBzqwHHmzAtOGl3nk3379sHY2BgVFRWIiIiAh4cH3mxQkBI1NVQsNiSE2jru3UvHm5hI81kQSFTt3ZtEHh26dycx5saNxm4mUFvFkpISODk5iQqF4h/XD4iJiRFlMtnlxYsXV7x6az30+HdDT/b10EOP/3MICwvrxhgL5ZxfBHB+2bJlvMF7HWQy2ZJRo0YZvcqa7+DgAAcHByE9PZ0nJSWxRrbGqioqfjV3LpFKCwuKVM2cSXbNoiJaBO3aRQSrqAjFmZm4/eQJrJyctO5xcZKMJUt413nz2PgDB1Dx118wtbSkxVFKCu1r8mQiPqamjYi/IAiwtLSEpW4x2gCH33wTIV9/Devz5+sXpLVkiAsCzLKzaZGmUNQRGta3L7Te3tAEBUFOJw6sW4dOH36Impoa5Ofni7pibyUlJfjzzz+1nHOJZ3Y2nCMisHfsWIAxwXjlSiiVSnHSvn0CO3u2EdmvjVzWs/1hw2jhW0v299jbw2vrVvbe0qVoNW4cgLooD9LT07VlZWWCIAhMo9HAw8ODj9QViRs5kqLXp05RZNPVFbh/nxaSDdGpE4kCOkilNH66XtaBgXS9Vq8m625t+8LXwqJFMOncGdIBAzD8/fcbhww//ZT23aBl48SJE5GTk4Oib79Fx1at0GvBAqG8vBwVFRUoKirCkSNHwMaMEbtZWAg6evro0SNERUVxbW6u6H3woOAbGir2nTOn7rN2797Nbt++rXVxcan/fB3pf/ttIiycU+93qZSia6dPkyCgI4oNUFxcjLspKRK/8HCaK4MHA7t2QbhyBX0+/RQ4f55tP3uWqx8+ZM1F/AoKCpCSkICuo0cjc88eCB06wLmZ9oYAyGXRIPVDB1EUkZeXh2fPnsHAwAAmJiYoKSnB4cOHUVNTA0dHR25ubs5v3rwpAIDz9OkY+eQJpUbs3EmW4ZYtgW3b6qvv//wz5W8fPUoi3bFjNBc7d36hNZhMJkPXrl35jRs3mFarxfHjx+GhqyjOGISEBLTs0AE6o3yVoyOijh8XnZyc8JCuD6usrMTUqVPRhqKz0todk4vns89oDurExOxsIkwrVsDZ2RnOb70l/N6ihSifOpXVVSbXtdo8coRSEXTo0IHs6uHh9L5ORDA2JuILkJhYUADU1ID98AM+OXECJVFReLBxI6Tbt+PQoUM4cOBAk0NkZmbGzc3NeWhoqJCVlQVTU1NRKpU2JmWcU/0JFxfgwQNotVrs3r2bP378mBkbG4u15FM8oVRKVDU16DhlChxnzBB7Tpgg3Lx3T1JTU4OKigoUFhbWCYTPE30AiI6O5pzzxnUNamrqHRo//0wCQP/+RJyPHiXHT6tWFPE/fJjELldXCIMHY6ZCwR5cuIBbK1bwAFvbxpMgPp7+nzqVovJTplA+P0DC0d27sOrXDy5vvIFfbW0x4tGjpue6gwPw+eeonD8fsRcuiGK3bnzWhx9KBEHAvn37kJOTg/fee4+2bdGCxuzePfqOKS1t3Hnju+9ge+GC2CUkRPDz8yPhedYstK6fv4Ioiti9ezevrq5mo0ePFuq+M/bsoedmMykzrq6umDp1KszMzGBkZITk5GR+9OhRVlZWJk6YMEGQHzlCefmDBtE929CNk5VFtVMEgQqwVlfTc6a2EwiMjUnYO3iQviMaCIy6WgFjx44V/km+/t27d3lERERZSUmJSq1WT/vbO9BDj38h9GRfDz30+D+FsLAwmVQqjejevbtZcnLyxzU1NTFhYWETli1b9jQsLMyUMXbP399fUCgUyM/PJ3vpK9CnTx+Eh4dzX19fZmBgQFWkk5JowT1lCuVOKpVENg8coPZjFy/WE30AKC9H26+/htTWFtorVyRKhYLHxsYyrZ8fgkaMgGLiRMqxNTcnu+2yZbSIt7YmYSElhSJze/cSOWvG+soNDFDVogUVPGsYfeIcjwcM4K47d0Jz5AikV67QQrd1a8DGBpVmZqiurqYo6YgRFHE5eBDdR49maBCpViqV+OCDDyS//vKLpouLi7S8qAhgDFZWVigoKICNjQ2YUkm5mz161PcLb3SQnBattYthzjnup6ej7RdfwP3bbyGOHIlzUVG4ffu2aGBgAD8/P4mtrS1sbW3xxx9/aAVRlMDWlnLhf/yRokunThFZbKpNFUCku7iYqto3xKRJVAxv3TqKlpaWUr52fj7lPTdVTO55SCSQzZiBvseO8Vu3brGgoKD69w4datQVQQfbAwdgu3x5XbE8ExMTmJiYoFWrVnjvvfdY3sCB7EnHjmg9bhxycnKwfft2eHp6ii379pWYuLnB48oVCZ48qcuBd3NzQ2RkZNOTwsiIrMu11l88fkxjU1ND9Q2aIPt//fWX2KpVK25rayuBKJIwtGcPCSbr1qHczAwZmZls+PDhTdp7Y2JicPr0aZhyzqv69sXV69eZGBsLExMTuLq6ikOHDhVesIm7ulIUPiCgkfX+zJkzPCYmhpmammo556iqqpKIoojAwEA4Ozvj0KFDPD8/H+3atcOgQYPqCxO+9x4JSr6+FNn/7DMi/bNmUWSztJTOadiwujzsppCYmIgbN27UHayVlZUIXb0NgAj2rFlEcBYvhuGDBwju0EHQtR47d+4cAIitWrV6cXxcXSld45NPyFWi0VAuvrs7iRVt2wKMwUarFf7ctw+LjhyB4bJlNJZz5jQm+joIAjlXrl4lItrU+7rnXmYmACBuxw5Izc3Rx9MTTv7+iBwzRrxjYyOY5ObCdeJEpKSkYPDgwUhLS2MxMTHs9OnTolarbZKEIzubKv4bGcHd3R2PHj1CcXExpk+fjpYtWwoAUF5eLvnpp59w08ZGFPbvF0ZfuSLgs89g/+WXqGzXDkePHoUoiujXrx+XSqW80fUGPTMiIyMZ0MBtcO0aiVKZmTTH5XIS7yZPJvFu3Dh6flpb03a6nPpjx4DiYohjx8KiuhoazhnefpvOIzKSXDEhIfQ8AMgZ8+QJCbHBwfR9IIpAaCh69usH7ciROHjwIGbNmvVCrRNRFHE6Lk6b5OgoGRkTIwSKIoSaGogKBe7duwcAsGtY16J3b5oL5eV0b/z8c/17770HX3Nzdi80lH4fOpQEggZCjSAIeOeddxrfaH/8QZH4Dz98cewaoG2Dmgre3t4sPz9fbLlggVD8xRe85WefMfz0E8375632CkUjoRd79lBXkfv361/r0IGcRn/+SeNSC19fX0RERODhw4cv1qp4BVQqFQ4dOqTWaDSLARxdtmyZvvWeHv8noK/Gr4ceevyfQlhY2BClUrlv9uzZplqtFhcuXFDFxMRoAXyl0WjsJRLJbHNzc15dXc0qKysxbNgweHl5vTQ3kHOOffv2aR88eCAZd+YMWmZnI/3gQXjpcp+3bCFSa2lJEbrERPr3+DEtdDIzqUjUkCGI/vhjnL17F24pKFZxZgAAIABJREFUKegVFYVnHTuKrZYvF6ytrCBZsIBIqYcHLcZ1C73ffqNiR+vXU2RpzBhaUB07RqS0QeXkw4cPQxRF7WiZTAJ7eyLtqamAtTWiU1PFiPPnBSfG+FstWjCsW0cL3h9+wLfffovp06fXpyrcv09547GxL9qvY2JQFBKCp+HhcKuNqv3yyy+8tLSUmZiYIDAwEJV79ojq1q3FkloyUl5eLuTm5gq9evXSBpqaSmBtDdjbQxRFbNmyRVNQUCBt164dhigUyPvtN5wcOpT7+fmx7t271xecOngQ6WFh/PaXX7Lh+fkQJk6kyNTMmSSU7NnT/MQoK6MFeXMV2mtqaBwfPqzP6R07lkSW6upG17hJqFRImjePP7S2xlBdOzKAoorHjjVuNVVRQVHky5ebzWG/PHKkqJw0SXAZNQq7du3iRkZGYmhoaH2o648/KA9540ZAKkV4eDjS0tLEOXPmNKMCcbJ237tHdQuGDaPjmjEDSEujffXtCxgYIC8vD5s3b8acOXPqC2SFhtI1LiwELl2CaGKC7du386ysLPbxxx83qnz/5MkTbNq0CfLqaizJzqbrKpcjNjYW8fHxyMvLw4cffth09wInJyIrDeoKJCUl4dixY3zx4sVMEASIovh6RTMBEifWriVColLRvdi3LzlCPD3JmTN2bLMdBLRaLb7++msAVEyt2er4GzeSe2PoUBKgLCzoegOIi4vDqVOn6jYdNGgQb926NWtjZkYR55gYKlg3bBgVC3RxebHVWmEhft2+HV1PnEAvziHduJEI08ugUpFgMGHCK4sehtVG/RcuXAiju3dp36dOgc+fD/b0KQklffsCAwfi4cOH2LVrFxdFkc2bN+9FsefUKbKdr137isNTNR7DvXsBMzNcXreOR3TtysytrPDhhx82TtmpBeccBw4cQHJyMgbFxsLX1JScG8nJVIwPoGfjO+/QsUildI9360bH1bt3/c5+/RW4fx97evXS5uXnsxkzZggGhYUkhHl60lwsLSUx94cfKO1j715KgyksJHdQTg7V5XBwACQS7Nq1SywrK8P06dOFhsd/+PBh7f3799n7778vWJqa0nh37Ai8/TY27t8v5uTkCADg4uKiHTt2rIQxRsLC1q10X1y8WB/dj45GdEYGjy8sFD/44AMJCgpovjeVVqFDfj6lnZ0+Xd/K8VUQRaqt0q0bHj54gKK4OHhdutR8bY3p0yk9Siem0ICRAG1vX18Y8eZN+p4ZMqSuyGt2dja2bNlCHRH+ZnE+tVqNDRs2VFZWVkKtVkslEsm+mpqayQ2dfXro8b8R/7N+mXrooYce/zIYGBjM6dGjhylA1vEBAwbIZ8+ebWhvb7+0ffv2k2bPno3CwkKmUqkAACdPnkRZWdlL98k4x9itWyWzpFLcfPNN7b4PPxSPHDmCXF0v7dRUii47O1MBqMJCiiJnZdHPVVWAqSkyduxA+J076JqaKnbu2hWyn35Cqzt3hMM7d+KuKBJp7dWLCtytX0//Fiwg4eDBA2D+fBIUdu+mhWdaGkUsr14l23pKSn3v5qgocgcA5AjYtg2+vr5C3379UCmXi0hNJXfC1q1AcTEUajXU6gZ1jDp2pAVhVVXji6FWA5aWSOrVS6ttIJC0bt1aa2trq7WystJevnxZ+6xbN8Fv5UqpaUmJVCaTSc3MzASlUonIyEhJ0YgRKKmt3B4fH49nz55Je/fujdTUVKy9cQNlGg2fplQyX19fIvqLFtH5ODvDKjCQZWZm8pVFRaLmjTeIhKxf/3KiD5CN9GXbKBQ0dsOG0WddvUqRPYWiPsr6MvFcLoe1kxNzPHKk8XZpaY23KyqiRe/jx80XqwPAVSqwggL88ccfYmZmJvPx8WnsaZ08mSLgmzYhOTkZ0dHRKC8vF7J1/c6fR2wsLayDg+v7cA8fTkRCLqcI+KpVQEYGKu7cgUwmE83MzOi6TZxIkbnTp6l1mEoFQavFlClTWNu2bbWrV6+G7n7KycnBpk2bYGVlxT8dPJjOtXZx36NHD0yYMKHZauEAaB43SHkAAGdnZ8hkMnajNjVDEITXI/oAiTZz5wIDBtBY2NvTvOecxnfXLrrHvv+enCKnT1PEsXYMMzIy6nZl/9xxNcL06UQMd+0iYq2rRQHAx8cHb731Vt2xZ/73vyx7zBg8W7kSPDyc/u7RIxofieRFoi+KgKMjPnZwgG1REb/v5sZfSfTpwyiyvWvXKzcdPnw4AOD06dNEiC0sgLffJqIPEJktKQHi49E2MBBLFixgn7/7LoyaEhHatycx5RV4YQzHjQO8vOChULAesbGwUavFpog+QM/kPmfPQpmbiwdmZsBbb9G92lCMUamIpOqup0IBbN8O1YYNUD+pDfpWVgLnzwPvvov2Dg6ssrKSvhvs7ICePUmki48nYj9+PNVAUCgoBcDBgYrR2dhQ+odMVufkGj9+vFBRUYFTp07V5Q4VFhbi7t27klmzZpGdXiqlDhkFBcDatRgfGCi0a9cOAJCSkiKp1tXsePCAnvnLl9Ox6pCYCJ+wMFZUVCQpLy+n2hwNU5WeR1ERiQU3b74e0a+sJKv9gwf0/IiPxyVXVxwZPfrlRTQjIhoXAQUoNaZ/fzpfHbp0oXtu27a6l3Sie6PvoteETCbDnDlzjBYtWmS0cOFCuYGBwSgAr2HN0kOPfzf0Nn499NDjX4+wsDBbUAudlGXLlqmaeN8IgA8AQ6lU2s/d3b3R+5aWlpg8eXJdn7qPPvoIUqkUCQkJiImJ4QBYTk5OXQ68gW4Bq1JRi6PPPwfr3RuWb76J0Y6OEgDYuXOneO7cOWHChAlkv3VxoUVLRAQtqlxcyPr58cdElEALmF63bvGBFRUC1q4Fdu5E5YMHKPjlF6q+PG4cVM7OSFUoRJ6YiPYTJgimpqYUIczOJuIFUKRdF21/9IjI1IcfAjdvQrJgAZz37SM3gJERCRHbtxOxPHwY7ffsgQgwODnRAmzECODkSUxdswY1Hh71C1czM1q0u7pS3rO/P+2rtm1VQv/+vLco1l3jMWPGvPh9c/s2BhkZ0WeALKwRERHYW1XFLdu2ZW2uXMGVK1fg4OCg9fPzk0ilUpiZmcFl3DiGnTtJrAgLI5tsbi4QGAiLtWsxce9etiExkWmmTYO0Z8/Xm0Tp6bSPVyEwkM750CHKdf/lFyJhUilFCdVq4M8/kZeXhydPnqCoqAgVFRVatVoNTatWTHRxETreuAFpbRtB3LhRnycOUJ7vrVsU1W8ClZWViIqKgnlBgXD3zBmYjBnDZ8+e/WJ9BkEARo6E9osvcOfyZdj16cOrq6vZ/v37+bx58xrbdi9epGjr999TnYKGJIsx+j0lhYjCDz/AbuVKyBYvpojoL7+QK0V3T/36K6VCLF4MxMZi6tSpkm+++QaVlZWQy+UQa+fEzHffZUJKygvneeTIEd6hQwduaWnZNIvbuBG4coWufy3kcjmCg4Nx/PhxREREiCNGjBBcdPUIXhdOTiRmREfTvTJoENmzp00jst22LZ1/eDiR/zfeAKqrIRMEPvDRI3bf0BDbvv8ec774AgbNEZ2//iLxbMiQxqRLq4W7KGKZpSVw/jzEtm2xRyJBpEaDN2fNQkddGsWmTTTnYmPrCeovv5AIGB8PfPYZDBYtYr/fvQvHHTtgbGwsBgUFCU0WDgVoH9eu0XPs9OnGkdYGyMvLw7FjxwAA7ZtKCwDq2yoWF9MclsuJ0LdpQyLa8uVUONDIiJwNuh7ufxetW2NVp06wzs/HxE2bBJiZkZCigygCZ85A268fpDExMOzaFc/8/Dgo3agxvvvuhYKb+W3aYLdSidE+Psj28uKt791jv0+aBBw/jjZt2giiKCIxMRGOjo6wsLCA0bVrRHjXraMd6FoY6kS933+n9I0HD+rb6i1aBCEwEO+//76wevVqBAQEwMLCQteqlJuYmDQ+1v/8BxXff49HX34Jla0tYG6OZTqhFiBRLymJiPHBg/Q8FQQgOxuSwkIYGhry6OhoNmDzZhIrG6ZvNcSMGeRsqhWemoVGQ0T/9GkSFX18gNRUcENDpH/5JUx0rVabQnEx/U0TqUu4c4fmR0VFvdPps89IxJ45E6gtUArgH1fh10Eul2PAgAHGp06d+hzAif/RzvTQ4/9z6Mm+Hnro8a/GN99887FUKl1hZGSkqqysFMPCwkYuW7bsou79sLCwDlKpNMbS0lIGgPv7+8uabRFUC10eZYcOHRAVFYXVq1cDICeAQqFA9+7dxb6OjgJUKirqNHs2WZ8bwNHRkUVEREBTXg7pu+9SpN3OjnKwAwOpRdbHH9MieeBAwM4ObVetwn3O2a5+/TQTBg2SwscHUKnAOUdubi6MFizA7uPHucbNjbd6+pTdef99jN65E7JNm2hhuXkzEbedO+sPRLdoWr8e4BzybdugTE4WkJlJ0aB16yiC+/QpMHQoigMCcJdz7v/RR7TAsrcHGMPe+/d5sIsLw+zZRAA3bCBHwQ8/kMWyqIjO76efABsbMMbqiF2z+O47iqoOHQqYm0MQBAw4fhwuGg3b+vAhUrOywDlHz549JfHx8Th16hQMKysxdcAAKI2M6LOWLKEFtQ6cw2LBAnj07o2DHh7i+LZtX9K4qwGeG7+XQqkkguHmRgvXjz4CHzYMfOlSqO7cwfkff+QOf/zBYt59VyO3spIYGBhIZDIZ1Jxj6KhRkK5cSU4AQSASqavEXlhIlbifr7Jfi3379vHU1FRmYmICxyFDuFdQEGvfr1/zVapatMDDfv1gvn07fysoiO1LSIBCoWjcDUCrJbK+YgVFHQMDmz9viQRYsgSxHh4Y8umnAs/IANuzh/JvKyvrRYshQyjdQRCA8nIoFArx4cOHgoWFBaKjo8EYo2tw/DgQF9foI8zNzZGSkiKkpqaiScI+YkR914QG6Ny5M1q1aoX79+8Lx48fF11cXP6+c1EQSPT55BP6+fPPidjrLNvTp5MDAKDzzcpCO7Watdi6FcLVq2hRVIRHfn5o17IlKvz9YenoSIUfJRJKvZkxg/5t2kS1MPLyaLxv3aJ70NQUWLsWgr09QisrsXr1ar5r1y723nvvkWvAzY3ExZkz6V5Xq8ly7udHbpPWrdHmzTfRYc8eMT09XQAgpKSkYOzYsXBsziViaEhE7Z13UJOYCIOG+eC1aCgWnDhxAgYGBnheMK1Dg/QExMTQXC4oIBK6eDEJAQcPkiPIwIDG0tubXBUKBbmfnj2je0wU6dq1aEHz1MAAkEoxeMgQnDt3Dg++/hreMhl1zVi0iJ6tly8D776L9GPHsGfUKDqOoiIWHh7Onz17pi0sLBRKSkqEUaNGwT0qqrFQAGD37t0osbJCXPfuCMzMZKolSzBz+HDk5OTgwoULHAA7f/48zp8/D8YYPra1hVlTT5hbt+h7Yf16muOc19fGyMgAIiNhbmYGVwcHccuWLWzixInMwsICarWa1ZL+Rrt7NGIEkuPi0OXKFfjOmcMBNBYESktJoHr/fUpFcXQkgaV/f3SqrmYpKSnaAWlpzT8rLl2iZ0Dr1s1uUoeePWn8QkNJ8AsMBGQy8NrnPWOscc2KhsjIINGjqbaNRkbkKvD3J5eITEbfPxMn0n2zeXOdqKl9mUPhNaHVasE5VwNAWFiYRKFQbNFoNG+JovjWf/7zn1Ov+ns99Pi3QE/29dBDj38twsLCvORy+bezZs1SWFhYKC5fvowrV658AuCibhuFQrE5MDDQ0t/f/28v/m1tbeHq6soSExPRpUsX1NTUICMjgwvLlws8KwssI4MIbxPwS0tjrbZtQ9KwYXCSSlFYXY02I0fS4nXgQFq4rlpFYkFoKCCRwKB1awyOisLaNm2kWLUKAGAklWLUqFE4fvw49z91inUYMQIdhw2T3Fq+XOx34QKkugJ/jFHEWVcMqrT0RSslY1BbW+PI/Pl8Wo8eDPfvE1m4eZMKTdnboyw2Fur4eFqQf/ghCQf9+gFKJXYdOoRBP/4I365dKQK6fj2JFhMnUlT/0CHK/wVej+wLAi3wN20iggUArVqhjacnlg4fjpMnT+L69evYvn07jMrL4dO3Lxw/+4wXHT7MjC5fhrEoUlcCHx8izD4+wNGjkGRno29ZGX755Rdh06ZN4owZM1499gsWUPTt8OFXblp3vYOCgG3bUPTRR7j744+I6N0bXC5HO4mEB7Zrx2Z88IEUmzeTmKHL6eecSNDjxxT1XLCA9lVRQXnQZ89S66kmYG5uziwsLPjs2bOZZMkShuvXaWxeghM5OdzHzg7YuBFqZ2dkFhdLfvvtNz59+nT2LDsbLVesADt2jKKPoaEURWsGnHMUHD0K36oq/OXujoqNG9HV15fG/+5dii4XFNC5fvABOQV27IDTl18KcXFxolQqFZKSktCta1eO3r0ZJk9+4TNGjBjBzpw5g+PHj3MXFxcmiiIKCwsRExOD69evY8CAAaK/QiEgP79RrQjGGGxsbGBlZYVLly4J0dHR8PX1fY2BbABRJDK9ejWRMnNzIkxLl5IwMXIk/S+VEjGprXxvvHIlLFJTIa+sxJ7Dh2FSUYH/x957h0V1rd/ja58zM3QQkN6l92bBjh17iUZjbDEajSUx6k23oLEkliQaS6Imltg1dlEREQFFQEQBxYIoRUCQLnXmnO8fL0MRsNx787n3/n6znmeexOHMzJl99tmz1/uud71ts7MxwdGR2vtt20b32BdfkEJiyxZS9tjZUSBhxQoas7VryXNj40Zoamtj9kcfsTsDBqC4QwdYFhaSqeW335JU3NCQsvIPHlBdt5cXBc54HhMmTODo6whYtWoVbty4oWhXpzhqCaV2dtjy0UfQWrUKoqYmCjU1YW5uLkycOJGTSCTIzMxEYGAgLl++DFEUcfjw4aaZ5VdBXZ2yuHXmcjA1pQDAmTMUCNm/n9rbXb1Ka1ZQEK0t7dqRCkoQyDzvp59orEaPhm9yMl5UVsJi+3a6Z3R1aSytrIjoZmbCiecxhDGcPn0ahoaGyMvLQ9u2bSVubm4oLCzEud27UThwoKBVW8vVXL+OiooKXLlyBQAwx8gIhqmp1P7t0CFgxAiY+PjAx8eHlZaWorCwEBkZGSi4f188npsrTPrll4axFQTKsN+8ScTbw4Pu70uX6LofPkzHnTsH7NiB0d9+yz04dkw8UFCAfpMmgTHWxG+iqKgIGRkZKCkpQYqnJyp0dTEpIoLBwaFpqz1dXSLS0dFE2n/7jZ7v0wda27fD5skTHr6+dF4vIz6eAkjnztF63BJqaoh4f/AB3ROXL9O8a/QbowxQtKrKAShI8KqAgq8v/aZKpbRWKuX9y5YB9+7hSZ2iJT09vcVuM2+Dy5cvl1dVVS0PDg6WymSyP42MjIb4+flphoaGfgNARfZV+P8MVGRfBRVU+J8Fz/MTAgIC1JWZ+IcPH5ZXV1cfVf49ODi4k5aWVodOnTr90/4knTp1Qm1tLYKCgiAbPRpl1tZsU5cuCDh5EmovZycUCnL2XrwYgpMT7js4IO7UKfR8/31F7NOnvN7XXwsfbt3K8dnZdLytLWVFli+nDPzcuahZsgSlISE4cuSIOGLECCaRSODm5gYXFxeG48fBmZmx9cePK7wnT+ZjRo4UnnzzDafr7a0YM2YMX+njgwRBQKcNG6D2/fdUb/3SOdrY2CA5OZnGY/z4hsxZHXGqq+kXwRhl6utM0j788EO2d+9e4fnz5xxkMiK64eGU4VTWOJ87R9nJH3+EVvv2jJWXv36A584lGXx1NakLBg6sr6sdPHgwvD09kZSUhP6jR4MFBUEeE8O+X7MGn2lqkonWrl10Dj16EFk1N4dCEPBjXe2nTCZ7s2s/dixJTF+BgoIC3L9/HzY2NkhMTEReXp6Yn5/PmJeXOLaykn115Qqqt2+HpqMjfWZtLZEZNTX6Xrq6RCC//JI2sFFRDZJaDQ2SiLdC9AGgf//+SExMRFpaGpzatm3o5NAKRFFEYWEhi3F2FgMqK9GrshIpPj64FhPDTp48Cd1ffoEDx4lGhoZMs7aWiKinZ/3r5XI5EhMTYW1tDWMjI8QdPSrYzZzJnRsxQrzp5cVcTU1pU37gAI1dUhJlvsPDaUO/YAHQvj16W1pi465d3OGsLDDGMCQqiuHMGQoWtYC+ffsiNjaWrVy5sr42V+nMHxsby7qeO0eO6RMmNHutVCrFuHHjcPDgQTx58kQcM2YMa62uuxkuXaKyCqUMOSiISP/06ZSNXLSI7u/586k+u+6cOI6Di4sLFAoFXrx4IV66dImV6emhYswYkjSPGEEkMD+f5vr+/fQZZ8+SZFpJgJXzLzcX0NVFfm4ujAsKSGn09CmRYDU1CqpUV9NzW7bQdVu6tEWiJpfLkZ+fzxUXFzdzfgeodOj3338XTZ2dhfevXePlFy/i/OLFePLkCbZu3SrW1NQwhUIh6urqioGBgdzly5dhYmLSeub2Vbhxg+Tt+fkUVBs4kDLKAwbQ30+epA4JytZyjdU2yr70tbUofvIE13fuhIedHc2D4cNp7VG+judRVVWFiIgIdOjQAYNIWt9kIbS/fh3Vly+zCxYWcqlUytTU1HgA0CsqwuOiIsHwjz84dOlC1ysqiua1jg50dXWhq6sLW1tbPP7+e8bdvs3/pK0tzJo1i5PJZHSehYUUxG3sV2Bu3tSEU+n2n5EBRxMT9ryiQsyYN495ubtj8+bNipKSEl7pXaGjoyMUFxdzMpkMbUePFmBpyeGrr0ji3rgMwcKCAkFPnpCHgpkZ0LcvrDw8EH/zJgoHDUIzy8vCQlr7d+8mldHLuHSJSk/mz6fgwrNnFFgaOrTFS9yuXTvFo0eP+JycnIauF41x6BB5PrwKDg50z2hqkvpMU5OCzz//jHBnZ8HFxYXzaKmLy1tCXV0dPM8P43l+haWlpfvYsWM1y8rKIJfL3YKDg9/heb6TVCq1rqqqOrZkyZKD//IHqqDCfwgqsq+CCir8L0NsvJGvMwEzCQ4OZnUOu45WVlbCKw2/XgNzIyO8GxMD9OyJikGDcCg9XbD19hbV9PQamJaydVBSEpEAHx/wVlbw27kTHXgebefO5bsuXIhL+fncVkEQTc+eZdbW1si7fRvdi4uhp9z0Hj8O9XXrMO3MGRw/e1b8448/hOnTp/NAXdYkLAzgeSg2bGCpqakiy8xk0379FRumT+fLBw5EaGgoUlJSEC2TiR+eOsWMi4tJJrx0KQDKEoWFhcHMzKxhs96uHUlg09KAmTOV2RkGd3faYNXJetXU1KCpqSmKooj09HTY2NiAUwYS+vShjXtmJmXX7O2hUFMTHadPpw3n0aO0CZ0xo3mtpq0tkcLc3AbPAaVh2L59sFywAJY5OZQJNzCADIDj/fvgQ0Npk9+7NxGe7Ox674PG9dBKuXFWVhbMzc1bdO6mC23etEd1I6SkpCAyMlLMy8tjmpqaCA0NhampqWBoaChmZWXxc+bMYQYGBkBiIjTHjKGMdp8+RISvXqU3mTmTpNbJyZSV7dqVSPKyZZQNDw+njfUrwHEc3NzcWGhoqOgUFMRQUtLqsXUkDwDg7uHB0KMHLCZMgIW1NQocHUX5vn3sqaUl7ru6Cs/WreMndusG6cyZsNDVRUV5OY4dO4ZHjx4BADRrajDzwQOUlJVxNVFREO7dY0hMVPaEJ7RpQ0Gae/coQOTjQ9L1nTuh8dFHmBQWhh3Tp+Pzzz+nDfwrHME5jsPcuXNx69YtGBoawtXVFRzH4cGDB1Q7vnVrfUvCltCuXTtMmzYNmzdvZklJSTAyMsKRI0cUnp6efM+ePVueAzExND+V/dIbToa6BVy4QESlf38icvr6RPwbZRd5nkf37t1ZXFwcOI5rWrvMcZS9NzGh7KqxMQULGverP3GC/ls3j89v3SpabtggDunShU44KIiCaosXU5Bg1y7g9GkKeiUk0L3cyCSQ4zhMnz4d+/btw71799CpkX9FbW0t4uPjER4eDh0dHWHy5Mk8Jk6EpKAAwzMzIQwezIWFh0NfXx9+fn6M4zhWFzwS79+/z6qqqhq8S94ECgWN78aNFPCaPJk6gbz7Lp2zp2dD+YqZGZXm9OpF49bIQ0KorUX++++L6NOHJWZmon+HDjSWyjZvvXsDPXogJjAQZWVl6N+KN4CtkxPg6cmchw6t/3F4cfQoambPxo4JEzhjKytYARRwWLy4QebeKKDCb9yIyydPCiUlJdy5adMEr3v3uJwPP0SSXI6RZWUwajw+Li60BmZkNCXV1tbApk3oqFCwnBkzUHniBOwXL+bt9fSQ36sXdPX1oaenx1H8lQHKdXvbNgpufPIJzQvlWjxwII3fwYPAvHnItbcXnx44INaoqXGan3zSdBAEgVQ5rq6kKmmM06dprW7ThrL4aWkUjHxVPT6AcePG8bt378bevXuFhQsXNr/RXrygdeF1WLSoaZcXKyugogJuN29ymYGBCplM9upI5xtg3Lhx2tHR0R+1adOGDwgI4DmOg4GBAbp166b15MmT3y0sLLR1dHS4kJCQ0cHBwVeWLFnSev9NFVT4L4aK7Kugggr/s1AoFA9zc3MrAGgBwKhRo7T379//7YsXLwYHBwePYoy109XVbUWX+AZISqJsSVQUMGsWknx9UVhWxo0fNow23d7eVPM5aBBlWYAmMmgTpXRbTQ0SXV30Gz0a5r/9xu4UFSmS9+/nhx46JMqeP2dJly7Bc+VK2viuXQuTTZvQU1eXXdTVbWrv/sEHgJcXxrz/PpeYmIj2w4fj7IsXgme3btDT0+P8/Pxw//59aGpqsr2RkXA5cQJ9jx2DdNEigOdx/PhxUUtLC4MGDWrYhE2dSsSzEdEVBIGIdH4+1ZbXmU4xxsS7d+/ixo0b0NXVVXz6+DHPnT5NcmIDA6rBDwkB1qxB7datLPmPPxDg6EhZ0ZgYKl/44QciVJcv0wY6IKDePBBbt5JcdPBgqgv9/HM65tYtymQOGAB9OD9nAAAgAElEQVQUFcEpNRXSnTspk9+nDxlUlZZSdmrhwnoy5+/vj3PnziE3NxeJiYmQyWSik5MTKyoqErS1teHg4MClpqbCzc0NBkuXwignB7WhofUZ0KysLJw/f16RlZXFOzo6okuXLqKXlxerrq6GTCbjGGMIDg7Gxo0bYWdnB319fQy9epU24cnJdD2VhHjrVqpDvX6d2k5FRRHxOXyYMlevy3bVYdCgQVi1ahV7evIkzCsrW62xj4iIQFRUFHR0dNCnTx8iTatXAxs2YPywYUx48gTctm2Atzf//fffi/e3bhWlhYWcOGIEUlNT8ejRI7Rt2xZBJia4euWKkJSZyUmXLoWFiwsS09JgYGAgqqurNy+8VdZ3x8VREOPMGUjOnkXR6tXokZ0N9b17idh17PjK76mnp4cePXo0ec7MzAzl5eXsaXw8zNeupbFsBffu3RMkEglXVVWFP/74Ay4uLlxiYqIYGxsLNzc3NrRxZlIup2zi8eOtu4j370/XaNUqIle7d5PR3N69zQzPTE1Nxby8vGa93+thbQ1MmUJS61YIc3x8PEpLS1nPnj0bxjg2lgjt9euUuT10iMpvTEyIHP35JwWUSkvrFRrm5ubgeV5o06YND5Da49ChQ2JqairjeR6mpqbyadOm0V6Q4+g+7tIF3MKF6DdzZpNzYoxh+PDhbPny5fj+++/xzTffvLpzghKVleRJsHcvlS/Qm1HQJyOD5knHjg1zYudOmtc//khlRllZ5FFQXQ1u+nTo5OQwF2NjWG/YQO/Xvz+RXwD4+muk5efj2oULYOrqLZ+fKJLnSONAS2IitKyswPbswYurV5u6vS9dSmVGV6+SgojngaVLYZWbi4nr1nG1qakomjmTO+vmhic5xAe3bNmCNm3aYObMmQ1dBZYupTKdW7eanVJRcTHOde4s5NrZcR3S0gSXyEjOysKCAqjDhoG93N7U3JyChWPGkIJoyBC6fl99RfN4/35g2jRo79zJjI2M2Dsch5qbN6EeGtrwHs+fE9lXehsAFHAZO5aCW25uVIZw7Bj91r3BtZZKpfD09ERISAh35MgRcfTo0Q3zVxTpeikNJ18Ff3/63bCzo98AKytg9mwUr10raL/sV/BPQk9PD4MGDWrS8oExhh49ekgBSAHg1KlTlTKZLKG6uvoN3FtVUOG/Eyqyr4IKKvzPQk1NbbKTk1O9NrJt27aYNWuW1qVLlzrFxsY+5DhO6ufn94Y9uBpBaXa3eDFtRmNjAQClaWmwvHEDCiMjlGdnQ3vxYmo11rbtK9ukQVcXEASw/v3h0a8fPC5f5qGjAyxZwp5wHE6Fh+Opvz8GXLoEjBwJRZcuMPztN2by1Vd8fd0iQCTYwAC2trawtbVFaWkp7piacv8IDoagq4uHdX2pi4uLYWVlJZaam4sb2rTBgvv3ObFvXzyfNg2jJk9mRnUbx6dPn+LSF18IQ4cO5fR++gn46y+cTkoCz/N81PDhQsCQIZzEz6/+azDGIJPJoK+vLxQWFvLP9PVhWlNDBEEiIbnz2LFA374wNzfnIiIjoW9oSEZryqy9iQltdsvLKXgxYwZtJFesoEBJURFlz37/vWH809MpY1W3OY/t0gW+f/xBG9OLF+l9lZ4BffqAWVuDMYb27dsjPT1dvHv3LgMAS0tLsaCgQOR5npWVlYlnzpwBAKSlpcGod2+xqKCAyX/++eWrx48cORKenp5MKSVXa5Tdmzt3LoqKinD37l0hJSWFKyoqUkzav5/Hxo3A+vUUNAkMpIP19Ci4sX071cgfPUq+DbdvN7h4vwYSiQTq6upCpYUFh1ZUCqIoIjo6GoaGhpgzZ07DH+zt6VwWLQL344/17a+++OILhh9+YEcfPRL++OMPDgAGDhyIji9eAIsWwT44mMv56qt6We6LFy+go6PT3CSsMdTUiJCMGgUYGcGza1fK0AoCBTleQ/Zbgra2NoyNjbEnLg6fSCTQaHxvALh+/Tru3LmD/Px8oaqqihNFEWFhYRg+fDg8PDyYst48ISEBgwcPbsjwL11KASjlHG0NhoaUAb16lWqjPTyoltzMDJgzB5BIUFZWhqysLFY3Pi2D40gB8eRJA/l9CRkZGXBwcFDo6Og0ZDCrq0k+ffkydaKIiWkgYCtXkqFgYiIR08mTgb59obC1hVwuZ1FRUXjw4AGePn0q5uTksM6dO6N3796QvMyGJRIiV/r6JGF/iZhxHIegoCCcO3cOISEhGDhw4OsJ/+rV1DLUyanp866u9Dm+vqRqULbFU8r6v/qKsv55efSd7Owg9O2Lu66usO/dGy4uLuQFkJfXkC3v2xfH1q7FjK1bcbNxm73GuHOH5qGeHv37+nUKovz+O9T79IFeSopw6dIlZmlpyWQyGV2v9euJWEdH0zi7u9O6P3gwpJ06wTguDlNA9x4APHnyBLt27cLq1avRoUMHceDAgQzLltWrrF6GKIrIysriRo0ahejoaPFmUBCG6uvD9bffwE6domCOunrTrLieHs3ZsWNpDCZPpnvsr78owJKSgus//ihkl5RwGlZWGNq428L586Q2unu3QTkRE0PXPiGBWhLeukXz/eVAwyugUCgQExMj6uvrs5SUFGZgYIDeSl+RggIKRin9WV4HZaeZOjVYqbU1DO/e5Vwbta38u1FaWiqvrq7etmTJktcY0Kigwn8v+KWtLDwqqKCCCv+tCA4OVo+MjJympqY2ZcSIEdLGslyO42Bvb8+7ubmpdenSRWpg0KxK8dWYMIEyJsHB5JbPcfTw8ICdVIqH/v5I0NNDmiAorIOCOGhqIiEhAXFxcYqDBw9ydnZ2zWtjf/2VJN3z5lEmbtYs2kht34427dvDwcEBoaGhePj4scJrwQKu7B//wImRI4Wxt24xPiKCMuI8Tw+Oq88WR0REoLikRPQwNGTHYmPxhOPEdu3aiXl5eaxXr17M0dGRpaSkiJ2HDGFPS0txnedZL21tMEtLJCUl4eTJk6LP3r1cZnKyYOvgwNixY3jcsaPCXaHg2LVrELOyWEJVlVCqULDCwkJkZ2ez3NxcZslxwshVqzidGTMg3bQJ6ZmZSEtLg7mTE2XkvvgCzvPmMam2Ns6ePSvm5eUJ7dq14yQSCQUGXF2JDH76KakKUlIaWnc9ekRqCR0dMtybOpUksBIJ1TjPmoVz/v7oeOQIpI27KhgaUkad44D27fFcTQ2uY8age/furGvXrujRowd8fHxY+/btmZ+fH/P392eBgYFQPjpkZrLOz57Bc84c2NraokOHDujVqxe6desGKyur+prxl6GhoQEDAwM4OTkxb29vhIeHcxUVFbAfP542yXv3EqlzdaVN9aNHFJhwcCAyI4pE/F9qAdYaampqcOXKFdbHzg5q1683uMM3QnFxMa5fv45x48ZBT0lolFi2jAItdnZEVjmO1BRLlsBh1y6WnZMjmunqov+JEwz+/uTr0KEDdHR06t+itLQUt2/fZp07dwbf2DegpoYM0gwMqJxFQ4Oycz16UOCmoqIhO/qG3/dlpKeni7klJeyJVIrMjAyFppkZp6enhz179ohxcXFMoVDAx8eHARCnT5/OevfuXa+wYYzBwsICqampYkZGBjw8POiiFhRQD/eW2oG1BCsrCpro6VEJSVoakVKpFJUyGaKiolBRUcGioqLE2NhYpKenK7y8vDiAWidevXoVBp9/DjUTE7QWsLl//z6qqqpET09POmDuXCJ6I0cSMd28uXkLNZmMSO+ECYCzM2qHDcPTlSuR4uzMikpK8DQvD+Xl5WzOnDnw9PRsvaRFR4eUNn37kgKhca05AEtLS7Rp00a4fPkyi4yMREJCgnD79m0hNjZWvHbtmiiRSDhzpav/mjU0rrNnt5wZ1tamc7axobFQVyciHhkJWFujtH9/hNy/Lx4dMYJFe3ig8OhReCQkwC44GPz06RQcXLGiXpFRXV2NsrIyRGhqoszLS/DX1WX1JRNKFBTQOmVpSYFcbW1SCHXooAxosvj4eObp6Qmtl+vsL16k633zJs2DkSMbug+A5hhjDG3atEFgYCB0dHQQGRnJAgICwKurU4nP9evN5r+mpiYEQRAiIiIwbdo0TltbGxcuXxauW1vDdfFiphEWRnMgKIiCgzY29J04joj59u20zri50Vxu147m5uPHzHf7dtgD0O7cmYIr1dWk2urbl9YlUaQSsXHjSJHk60vrhJERlVe9YX18dXU1Hj9+jLi4OLZw4UIkJSXh3r17uHr1KjQ0NNAmKwvSGzdI8VSHsLAwSKXS5uuUEt27A3/+iYcLF4rbcnKYwt0d7fPywHr2pN/Uvxna2tpqd+/eDbx06dKBwMDA1uumVFDhvxgqsq+CCir8zyA4ONg7JiYmVBTFdcbGxr3fe+89rdZ6+mpqajbJwL4ScjkRTysrImFDh9JG/upV2vh89hnQsSPYuHGw9/PDY1FEQUEBu3v3LrKzsxEdHc3y8vI4qVSKAQMGNCVAABlvffEFZalPnCD57ooV9ZsVHR0deHl54caNGyw8NhbXXVzYx2fOMNn77xNB0tdvMCs6f57MiwBYWFggKioKMTIZM8zNRT9XVxbw3nusW7duMDc3R2lpKZKTk8XnhYVCyLNnnHpFBXrNm4frjo7i+ZgY1r59e2aflobU8nJWOniwYNW7N6vIyeG4mBjR+d49phMdjZvm5kgtLlakpKSwZ8+eMX19fYzt25crOHYMRlev4qKDA85fuIDU1FQ4OztDx8aGyOSOHbCcNQu+vr7s2rVrYmRkJGdiYoJmwZcff6RN5927JN+dN4824wcOULb7nXeI5OvoUHbQxQVX8vLQtVu35hlFxmjjPnQowh48QPu//oJEUxPMwaFVsl6Pa9fAZ2RAa/RoGBkZoU2bNlBXV2+Q4L4B1NTUYGNjg3PnzkEqlcKyY0eqp750iUz51NUp2HPkCDBpEpFjpTy2S5c3kskWFhYiNjYW/W1sKMM7enSzY2praxETE4Nu3bqhSZvJkyfJZGvxYiqnEEXayMfGAmlp4MeMgbeREXNPS2PIyWm1H7aVlRVuHDsGTzMzIqyenmRMtmIFZT3nzqVMubs7lWN8/TWREicnIhorV1Jm2tmZrutbwN3dnQUGBsJq2zbI09O5UyUliI2NRV5eHvv444/Rr18/2Nvbw9fXl8lksmbXXV1dHRYWFuzixYvM1NQUbWfMoGDa2yoN1NQoYNK+PRHO6GjgyBHIjYxQbW6OnJwcCILAALCCggIuPj4eN2/eFC9evMgeP34MxcWLouP77zN88UWLb5+eno7y8nLR29ubWnyuWkU17uPHk8Taxqb1c5NIIKip4fuCAjyxtETv8nKMiYlBoampYNe+Pbz8/F4vg7awIGKpr0/r40v3gampKevZsye8vb2hr6/P2rZty1lYWHCGhobcpTrvifzQUEi3bkX53LnQfpUDu5sbrWsffUQKnshIiO+/j206OmKomxtzHjWK+fr6Ijk5GXkGBvDfuBEGBgYQjxxB5alTuJ2XJ2rPn8/OWVkpzp8/z+Xm5oodBgxgI8aNYywggO67Dh0aPm/JElJbaWiQjL1LF7pPQYGsPXv2wM/PT+Hr60vREIWCMt5mZg2Z/dhYUuXUqWNag4mJCW7evCnm5uYyNzc3CmxKJEDHjhBFEcnJyWjbti04joOtrS27efMm5HI569atG5ydnVlsbCxLe/RI7DBzJsNHH1HGfvZs6sJgaUn3j1RKqq/Tp8n7w8eH1pwff0SboCDIU1NRlJWFu+3awcrLi8bY2Zl+R65do9+7ixdp3T59mu7ftm3psW8fzbVXeGwoFApcunRJOHDgAEtKSoK2trbQtWtX1qlTJ3h6eiIhIQGpqal4ERIiws2N1Tg74+jRo4rw8HDxwYMH3O3bt/Hs2TPRzc2t5Xn57BnuX7smqgUFsbHTp4N79oyCw4MHv3Ls/x3Q1dVFYmKiUFVVFRYREZEbEREhD1QqtVRQ4X8EKhm/Ciqo8D+B4ODgzlKpNDQoKEjLzc0NMpns3xPWj4ggAnTvHm3a+/Uj8tKpE23mbtygTWFdmyMZgNGjR+PBgwds3759yKmr0XRxcVGMGjWKl7aUbVi4kBz3d+0iA6kWHNd1dHQwbdo0dvDgQTEvL0+QhYRw+OQTChIcO0bEbts22oDXQV1dHZ9//jkDgMThw6GxebOIDz5gShJsYmICuVzO3bx5E7q6umK1TIbz+/cLLu7u/Jjly2E+bBh+9fUVKysrWeqlS5xJTo6ge+0ah127mJGnJ7KfPsV7FhaM4zhJUVERNm/ejN579woaZmZc4mefKcIfPuQKbtxgQ4YMwenTp6EsD8DEifXGZtoffoiPP/6YP3r0KA4ePAg3NzfFoEGDeDU1NZIcf/011R7v3k3kyd2dyMy5c0QMZ84kk8E//qCN6aFDsE5PJ/mujw8AUFuqlBTI5XLI5XLU1tYKz3V0uFo1NahfuEDydXNzuo6tYdq0t505LcLKygqG+vri7YQEMSAigsOFC9Rm7OxZIgre3hTE6NGDWnuFhTXIgrdsofn3CijbTZU7OUF79uwWjzl06JAIgDVpTRUSQvN5zx4iB9u3Uzaze3e6VpMnk3/Ad99RMOKDDxpee/kyZQCzs4Fp03Bm+XIMO3ECmrm5VBvcsycFLlaupAdA5FQJZQa5uLjBWO/iRRqDFvq6vwmMVq5E0YMHqE5JQVVVFYYPHw7jVxCSxrCysoK9vb2QdPUq51xQ8GaGYa3B35/cydeuRe1vv+Hyrl0wzs6G+bhxKK6tFRwdHZmDgwM7evQoNDQ0oKenB3t7e9yMj2cuq1ejteKf1NRUhZ2dHR+zbx/8Hz+G9OxZCtKsXdvEFPBlCIKAlJQU/PXXXwCAIQsXwtbWFrh5E88OHmQTpk9nYkIC2NKlr+3mAE9Pui8ePqQ58BIYY9DX12/WAq1t27aI2rYNPeLixIi5c8X7Z85wWpcvy83NzSV9+vSBpqYm0tLSkJ+fj6SkJKFXx46cx9ix1Mrt668h7NyJ1fPmofbZMzZlyhTY2NggODgYANCrVy/RysqKAUDYkCEKRX4+n8pxrMrbW6yqquJnrlkDNn8+0+jShbwLkpJImfDrrzSnZTLKauvrU/B15876dQSgtUSrqAhWlpY8Zs+m7PaQIfTaw4eJGCcn08G5uXQvr1tHvgEtQBRFqKmpoba2lspeZs2i15WWIqOoCH/99RciIyPFoKAgxnEcCgsLmTJIvX37dtTW1qJNmzb0WnV1MjSNiiLFzIIFROpjYykgsXw5BZK//ZZ+N6yswEJCoPXDD/gtNxdlT56gbVwcHB0c6L7fvZvu9eJiUlfExjYxeIStLd3bMTEUHGjhty0yMlIRHh7Oa2lpYcaMGdDX12/SAcXQ0BDz588HYwxpM2eKKZmZLHnbNhgYGHCDBg1iFhYWqK2txY4dO7Bnzx5BQ0ODy8jIULi4uHCDBg0i8t+/P+QaGpzdwoV4bGGBdmPH0m9CVlbT8/0bcOfOHVRWVt4DkA2gVCaTrQHw5d/6oSqo8G+GiuyroIIK//UIDg4OlEqlp8aMGaPl+O+q11MoyCxp8mTaPC1fTu7P2dmUAVXKSl+uM61DbGws2rZti/LycgQEBMDHx6eB6MvltHHy8yNS8+IFkahffyUZd1ERkVcbGzomNBTo2hXSwkKMrahgmysqkLxxIzw++oiyLJ060cZu/Xra4Jw7V38eHMfh/PnzuNGxI8aNGsWM09KI3IIyzRMnTsSzZ88QEhLCBg8ejBMnTvC3bt/GBA0NUc/IiE3esoU9MjXFhaAgnDA3Z1NmzoTB++8Dq1bB6vPP6bNsbaGvr49vvv4aSE7mcP483lEo+KzgYBgYGODw4cOCtbU1JBIJl5eXB0NDQ4SUlMjtDh/mHV1dWQJjuHv3LgDg3r17/KPoaMyLiQG/aBFl8V1diVA4OVFdrI4OZZ3NzMgJPSKCvmxWFmBoCJ/4eMhmzyaJ/KpVeDJgACLu3RNNTEzA8zzjeZ7z8PSE1siRdA1nzaL3uHWr9ez5t98SoYmK+ufmU3k5eQvo6GDKN9+wkHfeEYXx48HZ21Nm8do1ktkePEhz4rvvqI2VlhZliVetInLy4kUz2XRjPH/+nK57VhbV8YaHNzvGxMSE5efnixzH0Wb58WNSPKxc2SD91tYmI6+BA4nsSqWUnR82DHB0pFr00FAKVEybRp81ZAgwcCD09fXx5+TJGDJkCPwBMlF7Uyg/Pzqa7pPBg+m+eNtNu5UVHBYuhNi1K7y8vODTiLC9CbREUbQ7cQJCaCi4t1BvtAQ5Y7jatStuFBZi0rVrMJRK0SkzE5g9m1N6edS1CmMAEXJDQ0Nc3b1bNJVImGbjwAqoVKOiooK/desW9C5fRkVNDfRCQ0kS30prxrKyMiQnJ+PChQsAyN9gwYIFDQf4+qKjKLJdoii2q6kRTQMCOHcbG8g2bmxeDtAYGzeS6qagoMF88TVwd3GBe3Q0sGQJcx0yhGVnZyM/P1+Smpqq2LJlCw8AmpqaopaGhmBQVcW7dO2KYz//rBh57x4vfvghUrp3F2WjR7MPp02rL8HQ09NDSUkJDAwM6rO/zj//zD0eMgSzP/8cEomEnnd1JTXKyZO0tpeV1ZuGwsKCSK2LCym5li0jMltbS+qt4cOhk5SEj7dswR4jI9HdwYFJHBxozNPTG75geTm9fv16ul/atyeF0qVLwKlTTcYiJiZGLCwsZGVlZeLt27fh6OgIjQEDgAEDcNzCAmpqaspuEQJjDE5OTujYsSNXUlKC6upqDB48GO3bt29ab8FxpDzbvp3aL6akkCv/l1+SEsrEhNa8r74CDh0C+/VXfGZqivgJEyCdPp3Ie8eOtM4YGtL579vXTL0BgPwApk6lY997r8mfCgoKEBkZyQ8fPhze3t6ttmJUBi9cAwI418BAjHJ0BHtJcjN9+nS2bds2JooivL29+aSkJMWgQYPqo1HdundHYW0tju/YAXz9NTIWLBBdpk5lJT//DCcXl9crt/5JcBwHQRCspVLpFWdnZ6SlpQ2Fiuyr8D8GFdlXQQUV/quxfPnyd2Qy2e6xY8dqtnuVCd7b4NNPaWN26xaRDKWTdUYGZXxay5wJApExdXX0SEjAX2pqok1ODjPdsQMZR4/Cc/p0FFla4pSvrzhp/XqGqKgGQyZtbZLkP3xIWdD4eNqIWlpSuYClJZCSAunVqxi6cCETpkwhwvvxxw01qY8e0Tnn5dGGDkBmZibi4+MxZepUWCQk0Kbv/v36LIylpSUsLS0RFhYmamtrs379+sHU1BRWixcziCKMS0pQMHAgOnTogP79+zPJzp1EPjt1osyvspYyNpbGaP9+4OefwdzcYGVlhaNHjyqysrJ4Jycn/Pjjj4rS0lLezMxMUVRdzZe7uTHFpk2ibNo0JpPJMH/+fNy7eRNXDhzAi5wcaM2bh33z54v++/YxN6Wr/Jw5pH5Yu5aks43rResI4YnRo+HyxReQ5ecDjEFSVoaRJ07ARVeXISSEMmeNZcObN5Ps9fBhCl788ktz+fikSa/NqjdDeTkR1YkTiVhUVADh4WDnziEtMpJdqK5GUGOZ/YMHRKI3biQZvb9/w2eOGUNzw9eXlA22ti1+pGFdZlxqZNRiX/WysjIkJCSgnuhXVpLxYe/eNLaNYWZG9bPffEOqig0baHM/dy7VFCsl/A8f1r+k9IsvEL5xIwBAU1Pz7fqt375NmdbGkMneqHyhGfT0UBUfD01v72au/W+CLo8e8S+SkrB81SoMHToUfo2MKN8GZWVl2Lp1K0RRFDoPG8YZrlxJmd7aWsr2fvopjWcjcBwHf39/aK1dKz5at47FiiKmTp1a//dTp06JxsbG4nQLC+68mRnuxsUJAe7uXGtE/+nTp9hW50ZvZmYmTp06lbVkmufn5wcPDw925MgR8bZMBvXCQtE1M5PhvfeIKDY2cFNCQ4PUKHZ25GPyUmCiGeRyMt389VcKZILKjSwsLODj48NXVlaioqIChsHBDFev8oiPR2737rh9/jw/WCrFsU6dBHOplH324YfgG63D8+bNw8GDB3H06FHyDOB5tH34kB2qqkJxSIgwdOhQmodeXvSCUaMajP4WLSLvAV/fBmO93bvJyG7uXFpPCwoAiQRt5sxBXLduyAkJYckDBzYLIj3NyIBaVhYiqqrEwX36MDU9PSLfgYH0u6FQNDjYu7igrKyMmZubQyKRCOfPn2enTp3iOn75paBrbc21efgQxcXF8PDwYGPGjGnCVkvq2mqGhYVBX18f9nUB3GYwN6dHWBj9PvXuTf/+9ltSlDk7A1paYBMnwiY8HIpnz1DRrRs0nZ0p0Hf2LKl9XhXwWr2afAl6967/3UlOThZPnz7NPD09RW9v79czbVGkYOO4cS0Scz09PXz66adgjOHRo0dIenmdAGCQkACbI0eQMm8eEvz8mHZRETK//hp2Bw++VbnV28DNzQ26urqGUqkUGhoaSE1NtQkODtZasmTJi7/lA1VQ4W+AiuyroIIK/5UIDg425nn+E6lUOn/y5MkaZq/KPr0JBIEM3AICKIO/fTuRwvDwhrZzxcVEyDp3pmzmxx+T4/v48US0u3Wjze/587A6fRrDVqxgl8LDUWRigscpKeKDoCD2qKwML+RyVq8QiI2ljK2GBm3IwsOJQO/b13Buyv7XdY7s98+dE+/NmSN8+umnlNno04cy++PGUaZ7xgySfJuZITExEUZGRoK5uTkHCwt67xbkuRKJBLW1tejSpUvDk4yBrV0Ldz8/uN+/T62YfH2JqBgZUf28cmwSEynQsXUrZYXqyhru3bvHe3h4CFVVVWKvXr14XV1dHD16lOnr6wtVXbpwhfv3M5116wRJr16cJDoa7iNHwvjrr7Fp2jRoqalh5LJlLDEgQDRzc2P6+fmkdsjNpUz8smWtXk7GGBH6rVtRER+PuwMHCi6enjwyM6kGd3w7QiAAACAASURBVPFikiHn5lJtqqMjZbEuXCCSHh3dlNxoaLwyo94Eq1cTsZ8wgUoQBg2ijF4dwZJ17IggLS124sQJ9O7dmzaiRUVkmrVtG2WzfXzIqb+ggIiCVEp+EfPm0TiXlNQHWgSBjKA5jsOJEydEAIw3N6fN+ktQtgzT1tamDXZKCpHOGTOaHigIpDAwN6fv3a0bBUViYqg+19i4aZ1zHbZu3SrK5XLWvXt3haur62t7XQuCgP379wMA/GJjYWtvj/piComESFFODmVko6IaZP6vwZWoKFybNg1BffrUB0DeGBkZMJ48GYpPP4XP5cviuXPnmI6ODt5WNVRXq6zQ0tJiM2fO5OoN7778kgIbyclk5DZ1Ks2ZRmUGampq8DxyhNuwYQNKMjMhl8vrPSgeP34s9vbz4zBmDHq0b49wdXVOMXcuXh7s8vJy3L17F2fPngUAjBgxAq8jXjKZDOPHj+fCwsIQmpICV39/ygILAhF5R0ciiY3JE8/TdTI3pwDlq3wWliwhMt1SLbtCAQ03N2hs3UpBkDpXdtOAAKhdvozc3FxkyOXouGgR4/39ycxuzJj6l48cORKrV6/G9evXhQEaGpz6lSsYo6mJ3bt3cx07dmxodaqE8n6Oi6N1fOhQuq9evKDvt3AhXSvGyCOkDlciIxUBAQG8Z137wsY4vGsXLL28kHzvHiuytBQ+vHmTQ2wsKXN8fSnYMWIEFDY2eDR6tNA2K4sz2rQJ/v7+PEDtPI8dOyYO+e478W5QkGjj6ckcHR2bXTNra2ssWbIE0dHROHz4MBYuXPjqrgdK1UVsLN3zsbG0jhcUAIWFQEoKjHx98YLnxSOensy5qEjhd+ECL60r93gljI1pXZo9G7V//IFdBw8Kz5494wYMGAB/f/83S6mnpdF63ajF68tQKuPMzc0hl8v5P//8EwEBAXBo1BGij7o6au7cwaM+fRRXu3ThR7ZtC1l19auDFf8CGGOwUrZPBWBiYqLIzs7uBeD03/KBKqjwN+DNI/IqqKCCCn8TgoODNYODgweuWLHixx9++OHqypUrn/M8n+Xu7r7go48++teJ/mef0Qbv5EnKqA4fTlmKCxeIxOvokHx/7VrKAgG0KayspM1vXb0oIiNJESCVAvfu4URWlsLh3XeFtJkz5dnZ2WISYxBNTWFiYiLg+HHKFn/yCWXl1dToM7/8kjYmn31GBPAlZGdnIyEhgY0ePbphb29hQWQBIEI0aRJlZEURPXr0wPPnz9mFCxdQXl5OG04HB5KIN4JMJhPj4uKatw86coTqVtevJ1l8QkJDD2wfHzrHpUuJMG/bRhtwhQIA6mvkhw0bxo0bN4738fGBubk5qqurue7du/M5OTns+cSJirLycm5mQQHJRZcvh9G6dfhoyhQYGhsrFH37is+7dBGvT5qESmUwJSeHxvzRoxYvpyiKEEURGRkZOH78uCIqKkosNzambJ6tLcnlJ06kFlvLltFGf+BA2gDv2EFkf8ECIjB1RBq//UYGYS9DEIjg3LpFhPTOHbr+lpZUdnD9Oj3/0kbc3d0dhoaGwunTp0lG7+1NAQKlqZS9PWXa27Yl6W1BAT0/axZlv4OCgLIyFBYWYsWKFVizZo1YXFyM5ORk1q9fP3C1tTSfX4LSAPHFixf0nebPp6zcy9k0pYw5MJDm1ObNlBVdtoz+VlHR7L03bNggVFZWsvHjx6N3796vJPoPHjxAcHAwli9fjocPH+Lhw4e4lZeHg0lJ2LBhA5YvX44bN27QwSYmdE++ohZdCblcjpiYGISHhyPo7l3Ru3Gv9DfFBx8AW7eC19PDsGHDmKurq3Dy5ElBGVR5ExQVFeG7775DYmIi37VrV66Zs72XF91P48bRvAsIINLVCIIg4J116zC9sLCeyGVlZaG2pIRz1dUFfvsNGh4euNejh2Ljxo0IDg7GmTNnBFEUERERgXXr1uHs2bPo3r07vvnmG3i/xixOiZqaGty5cwdFRUVMLoq0lgwaROOiDLT94x/U410Jf/+GoMxL36MeO3bQ9/7mm6YBx5MnKaDJcaRS6taN5n9duzyFQoHq6mocOHBArKio4MxtbGitMTKie68OMpkMgYGBuB4Twz2dP19UZGTA2toafn5+ih07diA/P7/p+YgiqaZGjaL3mzWLAlwrV5LyQE+vxdIEhULBnJ2dmxmtVlVVQSMzEx0yM8XRo0cjKyuLW6uri0fZ2Sg8cYLWJZ7HrXHjsGHrVjFZX190nToV/i4upCgoLoalpSXmzp3L23XpwqaMGcNNmTKlRRWGEl27dgXP80JcXFyrx0ChoPUjM5OIflYWdcTo1488T8rLgdxcsDFjoJ2aykZ36wbNykr+5KhRrb9nk2EUUebvD9HEBC/27EFubi63cOFC+Pv7v9HrAVDAuxWPkZehra2NIUOGCJWVlYoDBw4gOzsbERERSEpKwo68PMXaqVPhzxg3dcECWJWWUuD+/whWVlYaHMf9czIgFVT4D0GV2VdBBRX+IwgODmYA+qipqU3jeX64sbFxjZOTk7aZmRlnZGQEfX19MMb+dRO+TZtIOu3hQdmXU6eI+PTsSWTN1ZVM+MzMKGutxOHDDf8/ZUqztw0PD4dCoeC6du3KJBIJp8xgPr5zB6PT0jhs3UpkuXG2WF2d1AXl5bQJzc4mUmVmVm9iVlVVBUEQUFpaCovGUnRbW8rQrlxJGdcffgDefRd6+/fj3XffZefOnVPEx8fzgwcPhvfo0WClpU0yiePHj+e2bduGmJgYBAQENLxvXVawHmPGEClftYoCGzU1JHmdM4cI75079UZ3qamp0NTUFBozHXV1ddjb2yuOHj3Km5iYCMPHjeOr09OhuWYNfcfgYGDWLBgyhverqnhUVmJKejrL2bYNW3ftEmt/+EFkjKHvw4ei78mTPOoMEBtDFEXs3btXfPr0KbO2tuY6derEPD09G3bmUik9pk+nR1UVmcEBRDYOHqRa9PR0qt8NDiaSq1QSKBQk9w8KIhWDmRmZK37zDQV/3NyanVNL8PX15aJOnKAgz88/N29htW4dkY/Hj0m9sXkzqRLefZdqcYuKcOTECcHIyAiWlpbi5s2beQDw9PSka9CrV4uf+8EHH+DIzz/jQWEhHHfsaEq8du4k+fKmTSTT37ePgh7HjzcYB54+TXPs22/pvOrmkb6+PldUVASNRkaHjx49wsmTJ4WAgACO4zi4ubnh2rVruHr1av0xjo6O8PDwgIVcjvOZmSiqC3KdPn0ap0+fhoGBAaw7dwa3aBH6bdqE8zt2wMXDA9bW1oiKisL169eVZpPis2fP6qMWtl9+yfD48Rtdi3oo/TPqiApjDEFBQdz69euxa9cuTJ48ufWWdKBuCDKZDJcvXxb09PTEgQMH8s7Ozi0fLJHQPXvsGJWk2NtTln/VKkAmA8dxuNKjB8Y0IkGPHz/GiJAQqP/5J82/zZsxu6aG37x5swiAxcfHc/Hx8QAANTU1cf78+extJMxJSUn15n1eXl5NCW2PHvTIyiK/hpQUWjuHDaP10s+PAqIaGpTBbkxS4+Ppe/70U0P2dtIk6rTRoweVhwCkknoJgiDA09NTlEgkLCkpCeXl5VDr04c+38+vwQkfQI8ePeBoY4Okq1fZtehoGD18KDo5OXHm5ubinj17RC0tLW769OngVq+mdWvfPgpiDBlC68+1axR4U1cnc9DvvmuyRu/Zs0esrKzkqqqqmpxfaWkpTp48Cc2KChg6OTFrd3cUFRXhzp07OOPrqxj0xRd83OnTYsWwYWJKSgrXuXNn1m3WLDIiLS1tKIn6+GOaB9u2tRrMBEDrbkkJUFGBTiUlSN+0CfZPnsC4uJiCR46O9PfaWgpc9O9PgbusLPq8zp3pHlYGES0saL0YNAia3t6oXrkSyWfPwvvhwyaZcyVKSkrw7NkzPH/+HNHR0WJVVRWTWVhg9E8/wWnsWLlMJns7/nDnDt17bwh3d3fO3d0dK1euxPbt2+vvSS8vL/Tt2xc2Q4YwFBeT4mDfPgpAvW2b3X8CZmZmUjU1tYDXH6mCCv89YKIo/qfPQQUVVPj/GYKDg5lMJtuprq7+TkBAgKanpydrrYXevwSFgmTJu3ZRJreVTbkyo3fjxg04OztD9xVSQ4Aceo8dO4Z33nlHrK6uZjdv3hRtbGxYz549secf/xDeOXqU0zYwoDZ7jSSALcLNjTbEGzbUP/X7778rbGxs+D59+jQ//vFj2vy+eEFZ9+fPiTQCuHXrFkJCQkQ7Oztx1JMnnHTw4CZO4zExMWJUVJQ4a9Ysrr4l26JFpDxQmnl16UKZsA4dyLQwOJiey8uj5yIjiUibm+P8998LQlERN/D770kSO3UqIJdD+OQTPD98GIbTpoG7fZsMvjQ0KMhy8SKR30OHKOs8YwZlPfv1g1wux9OnT3Hz5k3cT00V/jFnDveytF4QBCxfvhxSqRQzZsx4ewl3bS0Z8fXqReTb3Jx6Ze/ZQxvs776jeTN/foMDuYVFq/3QW0NRURG2rF+PGdu2wXD58ia9uAEQWXJ2pi4QEgldywkTSGr8zjuAkRFqPv4Yj8LDYRodjTaGhsjMzMTNmzeFgQMHclKplAjr1avNavd3r1mDnhs3Qn3TJpgMHUpPlpbS3Nm+nWqZlQqDpCR6XnmcEuXlFFCqrqaA2LBh+Gn3brGkpITNmDEDpqamTYhjSxg/fjzatWvXQCjnzAHGjIFYF3hZ9nKZhiDA7c4d3PHwAFdbC6GR+7eZmZmYk5PDAJKre3l5kdvd/PkUAHtVpwUlysqotOPiRVK/NEJ6ejp2794NAJgyZQqMjIygoaHRpL748ePH2LVrFwAqi5k6dSreWHUkCOR58dVXFDwcNw7w9MTypUsxWaHAX/r6AjiOq3n6FAP19ATP9HQOmzc3kSfL5XLcunULZ8+ehY+PD4a+fM1eA7lcjrVr14p+fn7M1dW1iTy5RdTWkrFdcjJ5UpSW0trw2Weknjlxgo5LTycvjxEjiNDOnUvB0hUryPuhpXWsFezYsUOUyWRs4sSJ9MSmTUTUTUyIoAPAp58iXV8f4dbWorq6uvjkyRNWU1PDDF+8QPsrV+Dm7w9df38KFLxs3Dh7NqmeDhygNTcvj4J4daUJStf/+fPnQyaTYevWrWId2WcA4PLoETy8vODe2PwQQMalS3i0bBlSvbzgNW4cEhMThYKCAu69995rWh7y668UZHNzo2DzN9/Q3OV5IqvnzlHw+d13G9Q1ZWUIe/5cdBoyhFkpSyg6dKDX6OvT+t1SZ4UtWygg0LEjBXA//5yCepGREHV0sOnIEdHd3Z31aiFouGbNGigUClFHR0do374936lTJzx48AD8zZuwW78e7OrVt1sTN2yge66uVO1N8fTpU/z+++8YO3Ys7O3tGwJxgkCfn5lJQaaMDApE/c2IjY1FaGhoilwunwogbsmSJSoSpcJ/PVSZfRVUUOE/gS4ymWz07NmzNf8uYx0AZISWlERkNTKyRbJfWlqK3377TaioqOBEUURcXJxi1qxZr5QoR0REiH5+fqKLiwu3evVq0dbWlt2Kjhbt5s6FfMYM7uD8+cKHt29zeFVvaSUSEohQrVtHxO3oUcjlchYXFydWV1ez3r17Q125yQUajNuWLSPSbGlJdbLBwfD29oajoyP75ZdfUHThAozLyoiw16FDhw4sIyND+Omnn8SFCxdSRtDIqMGED6AMWmAgZUyioym7lpNDm8WhQ+n/e/UC8vJgdvYsU8vPx2/29sIH8fFcjo8PnkoksNTWRsjBg5jYsSPUCwtpc/3DD1Q2cfUqEdy1a4kMTJ5c/9E8z+PBgwdCSkoKN3DgQA6M0fnFxgJ2dqiqqsKePXsELS0tbtiwYW9P9AEKVPTrRxLf2bPJzPDUKarrBUjNsGYNycr/BVQWFUFWUYGwoCBx0NixrFkoq7ycAiTK7Ki+PmXUv/uOxuadd/B8wQI8ys2FS1kZYGAAKysrWFlZNeywq6uJwDYi+zmZmVDcuYN7zs7oqyT0lZVEuvr3bxJUAkD3R0sZMW1tmmOZmRBOnwa3eDH8MzIQ7uODsLAwtGnTRoyPj2cAtY20t7cXBEHgbt++DU1NTYwfPx6mpqZNM8clJYCFRT2Bnj17Ntq0aYOysjJUVlbC1NQUcrkc3deuhd6PP2LTt99iwoQJkMlkMDAwYFVVVVBTU2tq8BUdTcEvpRnbq1BWRpnVFjKZdnZ2mDNnDn755Rfs3LkTAODk5CR27NiR2dragjGG0NBQwcnJCYGBgZwoim9O9AEiJpMmUWa7c2ciYqdPw8/PT2H43nu85IMPOIm+PuZs3AhJhw4cIiKalV5IJBL4+/u/nXy6DpGRkYiMjATP86xTp07Qa3zPtwaplLLfAGX6N2+m9cbIiEi0QkGPOXNoXYqNJRVARQWpaRYvfuvzfPHiBZNKpdRqDqB79IcfKPB2+jSNyYULsDt2DHYuLgwAQ0gIqtasQWhAACr19KAbHNx654DGnSM+/ZQCRRs2UIBCVxdDhgzBxYsXkZGRgdOnT0Mmk4keHh6cvb09JBIJFCtWIOn6dfH5lSvM0dERCQkJyMvLEzMzM5mZkxN6h4bikkwG18GDubi4OOTl5TUl+zNm0Jj17Uv/PnOGAkDGxhQECAyk8W3Tpsn1T1y3DvcBTHnnnSbKmlYhilSKIQi03qWnUzAxOxuorgaztYXfkCF43Eo2nDEmjhgxgrm4uNTfwI6OjqQoUFena/vdd68/DyVOniQ1w1vC3Nwcffv2xaFDhzBlypQGtRvH0e+It3dDC8Tnz9/Y8+OfhYeHB0JCQtwZY9ekUumZ4ODgbwAkq0i/Cv/N4JcqnaJVUEEFFf6PEBER4ayjozM2ICBA9ne1zAFANbNDh5Icdu5cyqg1kp5evHgRx44dg5OTkzB16lSuQ4cOuHbtGlddXQ07O7tmb1daWop169aBMYYRI0ZwampqSExMFCorK1knb2+YRkaiql07IbCmhtdcs6Z5nXRLkEho86SuTsd7esL9+nWm17s3e/DggRAWFgYHBwem09gUq00bqs2/cYOyb4mJgEwGwdERsbGxePLkCbQnTBCtR4xgyM+vJ/N1MmsuPDyctWvXjvpjd+rUkP2qqqIsu4cH1d4eOEDjlpREGafjxylg8u23gLo6TsrlQu0773D3799n0fb2SCkvF8slEkiLilivTZsQO38+7E6cAKqqII4aBURFge3aRd85O5uyrHVGfwB1Fjh37hwmTZrEnJV9nY2NkePggAOHDglhYWFMT09PmDJlCvfWPg7Xr1MWLyWloWd8ZCRtgsvLaSPcvj05z5eVEVl4gxry1qDz7rtwLSnBGXd35u3jg2bKlQcPiLwMGdLwnLK8RCIB9u7Fvbg4JHl5iR1XrWJITm5OaJ2cyPSvUQa8ZtEiSBISEN63L65duQL3fftw6Y8/8GzyZFh/9VXzE12+nDKLAU2Vqc+ePYNcLsePO3aIyWpqYhLHiZKsLG7E8eO4zxjul5WxHn36YMyYMWjfvj08PT2Zq6srAgIC0LNnT+jq6jaXw4eG0neoGwtNTU1wHAcNDQ3o6OiAMQae56HdtSvut2uHrBcvhN4uLkyjriuARCJp7uQ9bBiNQws9wJvgwgUKqNWpYFqCpqYmqqurkZWVBQDQ0NBAVFQUu3LlCq5cuYKamhpMnz6d09PTg86rTOpeBY4jlYeJCbB8OZxu3+Zu/vADkoqL8Wn37pCWl1Opxb+odBIEAevXrxcuXbrEwsPDkZ6ejj59+uC9995rGjx8U9jYUGBOFKk2Pz+fAic7dtD3KSujQOCIEXQvvWUQt6ioCKGhoUJGRgabPHlyfa95ALRO5OQQsUtOpjXdyYmCD0eOABwHSbdusJoyBYfz8mBgaQnjRiVMTXDsGGW4x4+n+61bNzJNvXUL8PCAuZ0dbty4ISQkJDBbW1tMmzaNOTg4QF9fH3p6etCprES5hQWS8vPF6OhoJpFIFPr6+mKHDh24m3l56NWpE7o5OKDcwABJDx5gwoQJDXO2tJSI99On5CGir08Kp4cPiay2bUtkVamCaoSamhp2//59FBcXCy4uLi93rmuK58+JWAcH0zXasoWuy6JFNP+kUmDyZIQ9fYqOe/eytvfvNwQfQB0moqKi2MiRI1tuaWdqSr8HPF/f5vWVqK6mcZ82rWUFwmtgaWmJuLg4hY2NDde2cRCnbVsKNOnpAd2703cdN+7Nfnf/CaSnp2PLli0AgClTpjAtLS37/Pz8DziOGxUWFlYdERFhHBgY+IraDBVU+M9ARfZVUEGF/3NERERkKhSKCWVlZQZ6enpMU1Pz398n99gx2hwOGEBEuqKCyF1di7eQkBDcunUL77//Pjp16sTxPA81NTVYWloiJCQEpaWlcHBwaHJef/75p2BsbCx+8MEHnDK7UvzsGdd7wQJW3qWLaL9xI6d9/jynER4O2ZQpb/edzMyIcN2/D8mcOTD7+mv4+fgwSKXi8ePHWXFxsejs7Nzwhnp6tOnatAl4/32IEREIOXVKSHzxgg0YMIC179iRsdWrydhv2rT6lzHGoFAohLCwMNHKyorpjR+Ppz/+iN8rKxUe6emcbM4cMrJq04ayN4GBlO3OyiL57sKFJI/V0kJubi6eP3/OampqBEdHR3H65Mlce7mcWQ8eDHlICG7k5+Pe1KlCvkyG4mXLWGlODu5NnqyomDuXU3/yBNzDh+B0dGjjDjJ1e/bsmSCKIpecnIzjx4+L8XK5oLZ6NWegpgY4ObG+fftyr83o19Q0lHC89x5tMOfMoU39o0ekTPjiCxq/jAzqke3vTwRs8eKG+uOaGspkvY1cVRSB1FQUe3ggwslJkVdQwNna2jYnH48eofbePRx4/hwuLi5NM+B2dkBAANK2bBE719Qw3c8/JzMzZZcEJT74gMiK8rnSUmju3QvzP/9ExYMHqM7LE9teuvT/2HvvqKiur338OXdmqAPSi4JUpStNQUABW2yJRuzGkmiaJUajn5jKS0yzJTGWxBYTe4k19oZKVRQLiKh0AZEuDMPAMPf+/tgMHUuSz/t9f2vNs1YWcdo9c865Z/az97P3Zgn9+uGJri569erVVPFaLpfj8uXLsJ81C6xv3yZyxvM8CgsLsWnTJmRkZKC6upopFAr2lOfZ427dkOrhgaD4eIQA8PL1hcTREZIW5LHTYmOCQA6XIUOe32qPMejZ20P06afMZutWsDlzOn/tX39RCkoLhUiH+PNPItCd1DlQ4+HDhygoKMDYsWMxbNgwpnb6VVVVCdOmTWMvFBF/HhgjZ93gwcDvv8MmPx9hv/4K0dmzJI2/eZPSfzIyaM569qRUJI6jdfr6a1IrnTpFkeF+/eie7dIFUKlQO24cfquq4j1PneJc795FdXCwMHvrVuY4ZAi4khKagw8+IKXBwYOUxmJmRtLwa9fojFy6lP594gSRa319UhJdvEi58LGxdA+Vl9Nj27ZRFP76dXKqvfsu7dlPPiEFz6hRlLdfVtZcDLVHD2DTJgjnzmFLXBwcT53CoMGDmRlAnUtsbWk81dU0BnX6T3IyqTNOnaJ1f/11wNMTEh0dmJmZ4ejRo7CysoJEIoF22/aUMhmNWZ1aoHawnTlDkWJXV1y7c0elUCi4N954A03pTo3g3nkH3d5/n/mPGMEGDBgAb29vztXVlevatSuSk5NV5fb2cLt3jxXt3Ssog4NZbx8fkprPnk3zrqtLaTp//knKM7mcxu/vTw6VTlBZWYn09HQ8efKEmZubd+7MAGhd16+n82/XLiL5H37YigQLeno4ExPDPHr3hqmJCd0bFy4Anp7YsmWLYGBgwPfp06fjg09Hh86c6Ojm+gfPQnExnceBfz/VPSYmhvn5+bW//3R0KLXk+HE6z2tryTn0X4ChoSHi4+PB8zwKCgr4CRMmiIKCgiQSicRCLBYPraysfCM6Onp9WFiY4vmfpoEG/3vQyPg10ECD/3VERkYqo6Kihty+fTvy1q1bY0Qika6ZmVlDQECAoYeHxz8n/jxP0ecff2x+bOJEMojr6gBtbWRkZKjCwsJEbfNW7ezsMH36dOzbt0/gOI4Nb2xvdvXqVZSUlHBz585tRWjuZ2er9Hr0EKVXVEC2eTPidXRQP3AgltTWtjMUXwienkRA6+sBCwv0376d6zl7Nnbu3IktW7aoZs6cKWq6vr095WBHReF+fT38T57kBpw/DwNXV3r+s88ooqPOb2xEeHg4V11drdq2bRtCp05F2r170NHRER3IzVW9uXatCHI5yUqzsiif29ycIl9PnxL5yMsDkpPhmJfH5QqCasmSJaKm6x0+DKSmQjpwIIZGROD07duoyMtj3hUVKHd3R0bPnoxbtIhP6tkTdVIpF5CYyHueP8/hq6/w4MEDvqqqSnTt2jWoVCqMGzeORUdHi7TlchTn5LCndnbC7t27hSVLlrQ3QmNiSOnw5pskNZ4wgUiCOid+/36K6BsY0Hx88QUZ/mvX0us3byai8sorFCHKyqK84//8hwjFC1Y6x5IlUJw6hTXjx4NxnIgx1tQzuxVychCTn89nGBlx69at40NCQjhvb2/cunULDx48EPz9/dmV8HDmaWVFhMvRkQjap582E1tb2+Z1TUmhPX7jBti1a+i/di3KwsLYgUmT6PnKShw+fBh+fn64e/eukJqaygAgZNYs6Bw8iKqePREbG4vMzEy+vLycA4DS0lJIpVLIZDLo6upi+PDhsLCwQNnMmTCvrCRycOoUGfEREc+OqCkURCxbpJU8C1KpFJdHjhScxo9nFpmZtGYdtcYLDGyurdAZDh0idc8LrOG1a9cAUDeFH374QaiurmYAYGpqCnkHHQr+FpRKur937yZn1PffkzPk6VM6n6ZPp/vs0CFa+5Ejgd9+o2i0oSFFVT/6iO7DBw/oM6uqIK+sxIHoaJVfaamou62t4BsRgfonTxAjVQbViQAAIABJREFUk7EUGxuhX7duDKamdA4KAu1zoLn7hr8//f+VK/T42bPkXNDToxoTAJF1xmic1tY0joICIlklJbRO9fVEzs3Nad+WlVEU++23myPB3t7knJBKkfXwIa8yNub8TE2ZLs/TWXP0KOWaL1vWXEyvurq5qKarKzlA2sDd3R3Xr1/nd+/ezQFAeHg4BqiLc6qv21bizxhdJzKSOiAAoqccB/2OWnHKZE3FAtti1qxZog0bNghp48YBp0+zgOJiOnesrOiahobgZ88GvvgCnFxO1zUyou+rVmi1rTHQCGtrawiCgJ49e+Lw4cPQ1tbusLAetm4lov/hh3QGHj9OXUaSkpp+D0tKSnD27FkeALN95x0GHR1a688+gzBiBPRra6F8nlPL35/I/oYNzakenSEtjdbtb0Imk0GhULBO0xd++omcFQoF7ePntYf8B+NoaGiAgYEBhg4dygHkPA8MDBQFBARIV65cKa+trbUA0L7NjgYa/D+EpkCfBhpo8P8UjVX5uwPw19bW/trKyqr7xIkT9V4oL7Ez3L9PJKgtOdiwgSJCQ4dixYoVwvjx41lHcn2AqlafPn2aX7hwIScWi7Fq1Sp+2LBhnGfLiupOTuC//x6Z3t549OgR+r7+Okreew+7DQ0xe/bsdn2feZ7HtWvX4Ofn1xRhfSbS0yna88UXqLO2xo4uXXiFQiHMmzevKQxcXl6OvIQEmM+di+zZs1Uhly+LsGVLc5Tozh0y+u7ebSehXLt2raB/8yYbGhYG7eHDce2ttzA0MhISLy+SXI8aBUydSsZt2+JHixahOjUVvw0cyC+oq+Mgl1Okr3t3ipIFBwPjxpGhHx1N0tXt2+k1rq7Arl1IEgRcOXlS8L5yhRlXVOByaCh4GxteJpNxAGBpaYny8nL4+fnhZkIChhobC9d1dYVuPI/H9++zUjMzfPD990y5dy+6nD5Nhq2NDRE7Hx8iFm3rNCgURIoHDSLHgNoo3LyZpPUrVjS/VhCIbF29SoTn44+fHeW/fh2wsMDRc+dUacXFovDw8NadD1rg5ltvIbe6GgO3bkVSUhIfGxvLBQYG4vr163B3d+czMjI4uVyOqVOnwlmppAj27dukrGCMSMGWLRSVdHIiya6RETk9vvsOPM9ja2wsCgsLwRgDx3EQBEGt7ABAufZjdu6EsH49Dly+jLq6ulZjFIvFGDt2LBwcHKBUKjuWricn0zWPHGmOOHdE+nNyiNgmJHQ+f22wZs2ahvDwcHGvr74iEtlZMcBDhyi63REJUyppfrZvJ5XKM6BSqfB1mzzk999/HzU1NTh37hwUCgXfo0cPLjQ09OUcedXV1PXC0ZEiun/8QXniCxaQkmT1aiLDEyfS3H38MRHotn3jn4MffviB19fXZzNmzGAtpfqCIOCrr76Cvb09HxERwUmlUhQWFsLCwuLZvdvpzc1KhIEDKcd961aK2Ofm0p5TO3ESE58f5W0DhUKBNWvWYNSoUfBoUUy0Q6Sl0T3Wty9Fytesaeqq0BK1tbV48uQJzp8/z3fr1o1TO2wBULHQ4OBW7QPr6+vx119/Cbm5uXyfM2c4Jc8zh+++g4O/f+sPrquj1Kbp0zt1bG3fvh06gqDKv3dPtODXXyG3sUH0N9+gtKxMkMvlvKKwUCQtL4eus7Nq2qJFoqZUl507SU2Rk9Oh8mX16tV8jx49MGLECO7cuXO4d+8e5s6d21q5kJ5Ohf927ybH68cfE9G/f79ZCQYqglhWVsZmzpzZSiHwuLAQ0spK6A8YgO2jRmH0jz9SqldnqKmh83L48GdH7VesIAfQ5593/ppnYM+ePXxhYSH3UZuiiK1QW0u/PWPH0m/8kiV/61rPwvHjx/kbN25wXl5ecHFxabVfVSoVVqxYUa9SqT5XqVQ/REZGqv71AWigwd+ERsavgQYa/D9FWFgYwsLCnoaFhd27cOHCRrlcbpWamurp4eEh+VvF+9RRpZCQpj7OTZDLySDt3x/XkpJ4R0fH1jmALWBiYoL79+8L58+fR25uLkpKSrjBgwdTcaS6OjLIrKzAXnsNppaWeFpejn1GRrjZmJIglUrRvXt3yGQyaGlpQR3h3bVrF8RiMeyeIdlsgpkZ5VfevQuxgQFcBw1i9w4f5nwb87wzMjKwfft24YlcLigiIvhBhw+LWL9+1NrrrbeImJqbkxw4KKidId67d2/mdeoUTB4+hO6UKTBevBh5RkawiImhYm6VlWQ8jRjRPtfylVfAJk5E2qFDzPvnn8EVFhLhWryYCrsZG5Oh+eabNFfl5cC6dWRoN8rSu3Xrhh4eHqzE1RXXlUpM2L8fKrmcFdvbg+d51NTUQFtbW+jj68tMN2yA/7Zt7OaIEYL/qlVcUHk56ycIrKaiAtnZ2XzXvn0ZZswg0hEaSo6etmubm0sk//PPiXi2NJQZI6VEy3VhjCTU7u5AZiYZ4wMHti5oqMbRozTnCxYgTyZjRUVFmDBhQrvc2v3796uOHDnCicvKBJ+JE5ll797o1q0bi4+PR2lpKe/v74+RI0dyQUFB6NmzJ6ysrCC2siJSw3GkSNi8mdqZHTnSlPsNDw+KwjIGRESAWVqipqYGOY2t6ZycnHiZTMY++OADhISEwMPDA0b6+g1ZubncJbkcJiYmqK2tRcsAAM/z0NXVFUQiEevatWvHe9Tamuo+BAQQcb11i8ZkadmaEJWV0fheQsqbkZHBVCqV4Lx0KcOECRQlFYlapzIARGzq60l23hZ37tC6ubk993ocxyEsLAyXL18GALz22mtQ52t7enoiLi6O5ebmIjExEe7u7h1HftWRaZGIyFZUFO351atpT8rl5OxSp+1IpVRLJDiY1njQICo4Nn483UNVVa3yqZ+FhIQEfvjw4VxbiTdjDL169cLZs2dZQkICHj58iMuXL+PGjRt8fX19pw5P3L1LjpLFi2mskybRPRMbS2k98+fTGru7E+GbMYO+h5HRC40XAE6dOqWqqanBiBEjnp2HzvM0J9Onk/qJ54nEKhTk4GtxPkkkEhgZGaGoqIjPzMwUAgICOIVCgcePHyO7ogK1/fqhi7s7VCoV9u3bhxMnTggSiUQYNGiQyHrCBGZXVATLW7fAfH3JyadGTg45aSZP7niMgoCzhw5hxooVXF1DA84MGwbzx4+FEicn3qpHD6579+5cwOnTQrAgsNNdunCOjo7NhRJ79aLPffSIzpdGwl9fX4/jx4+jrKyMTZo0iWlra8PBwQEJCQl8amoqtLW1mVKpRN3Zs6jq0gUGHh6Uo19SQs5tXV2K6jdK78vKynD+/Hk2bdo0tLynb926hd27d+NqejpSevRAqakpPD/7DCqZDDpBQR1/Xy0t2g9//EH3f0f3AwBcvkwKs2elHnSCP/74oyEvL0/0zjvvPLswoURC+2D6dLp/rKz+9ei+vb09s7S0RF5eHh8XF8fS0tJUPj4+HMdx4DgOHh4eory8vKC6urpJFy9ePBQWFvbivQY10OC/CA3Z10ADDf7PICwsjO/fv//JCxcu6N64caOPubm5xMTE5OVk/VVVFM1Ty5dbont3YN063DU0ROqjR9yQIUPQmUNBJBLBx8eHdevWjZWUlPCMMRYSEkIGqacnGVPz5gFiMeoSE2EaGoorQUEwMjVFTU0NnJyccPbsWf7ChQssISEBqampfHx8PGOMCbm5uay6urqhZ8+eXG1tbdP1OkW/fkQE9uxBz59+guK998Axhs1btgjh4eGIiIjgerq7c2zYMCL6ixdTS7Jx48ggGzqUCE9AQKsWZWKxGOLXXgNefx0MwFVvbySUl/OB27YRuZo2jeYyOLjjOfriC9geO4ajixcLvTIzGXbvpvxMc3PKO+/VqzlKVVVFhcd27qTnG/Mq9fX14eDggD5DhiCjRw9YP32KwdnZEKWlIc/ODvPWr2dFcXG46+YG29xc8CUlkGhrQ9vSkj328hJSR41iSZaWQtDChQzOzq0i72qHgZZEQu3W3nqLCLKfXysiqlAoULlnD+SpqdAJD2+/36RSiiYGBhLhUKmI4Kj3Tnw8PT9pEjJranDy5EmmUqlYaGhoq896/Pgxzpw5ww0ePBiDY2KY8cCBQPfuqKmpwdWrV6FUKtmwYcOYulidgYFBc+RVLCZCHxBAkXSeJ8eEVErtvG7eJPnu8OFNxKewsBBlZWXCmDFjWHh4OAsJCYGWlhYkEgkMDAzQTaHgXDZsgPHSpUhKSmoi+hMmTICjoyOqqqr4Bw8ecKmpqXB2du68JaW6a8KUKRTt+/13IkaMkQEOkGri3j1yUrwgKisrWVZWFu/n70+dGZYsIbLcWHejCZ6eJNFvO77oaFKmzJ//UgXjHj58iOrqahgaGjZVUheLxejTpw/8/f2RmpqK+Ph4ZGVl8b2dnRkTBJIyx8fT91Q7XxwcqIBgr1407l69KOo6ZAipbfT1qfPD1Kkkyd+0iQjf6NG0j+Pj6d7dupUcB++998xUiZiYGHh4eLCOorG6urpwcHCApaUl6urqGgYPHsxZW1uzS5cuQSwWt27DFxFBaon336c97+xMLSpVKkodmTCBHGZti4bq6tLZM3PmC9e5KCoqEjIzM7lu3boxk2f1Sc/KIsXQkiU0B35+dC988AHlpU+d2m5uLC0tuejoaC4uLg4xMTG4efMmSsrKeOOdO7EjM5PFXr0KmUwGQRDYnDlzmIWFBQwMDOgMyMkB9u2js1d9ZublkYOhZVFNNW7cAF55BbkuLrxq6lQEfPcdCxwxAmZhYcx12TLOYeZM2Hp5waiigknefRfRycnIzMzk09PTWWpqKtLS0vDw8WM4LF0K7upVsNGjwfM8Vq9eLeTn57Pp06c3dR/hOA6BgYGsoKAAt27d4m/duoUB8+Yx5d69qBCJoD1qFMQ7djTfD7NnUxpG//5QKpVITEyEsbFxK4fzb7/9Jjg7O2P8+PHMxMkJQaGhyLp3T5X66BFXkJbGO9XVsQ6L8dnakoNPJqMzsaP9uWcPOUlfwgkEALGxsbh58yY3ffp0WFpaPt8O8PKiNVu1in5vXqLl44tAJBLBwsICrq6uLDY2FnK5nAsLC2sal66uLnx9fbWUSqVJUVHRxIsXL+7REH4N/i9AQ/Y10ECD/3Po379/9Pnz568+ePAgLC0tTWRqaqr1TDmhGrW19AO/cmXriIwajAG1tcj/+WfBae5cZq9uY9cJGGMwNjaGu7s75+/vz1h5OZGsoUMpUtFIxMQWFii3sYFB376oqqri5XI5Kygo4HV1dYV58+Zxtra2MDAwYH5+fhg+fDiLi4tDt27deI7juF9++QUxMTHIyckRLl68yPr06dO+inkjRH36INrLq+HK9u1crwkTUDxokDB84sTmF+vrU2Rj9mwigrGxJGnnOJLfWlu3j3CuWEEGkr4+rKZNQ4ybG3Nctw6G9vb0+qlT2xvuNTVATg7Ks7MhPXIEisWLme3nn1MUc9s2kv7OmkX5vDIZRZUaGoDSUqrUPXZss1EolwNZWWDm5rB+/XWYLl6MjL/+EnxjYphlaChsAwLQRamE7cWLuOHjg4EXLrD9Y8awFG9vJNfWssKnT8FxHEJCQlhKSgpu3Lgh9OjRg/E8jx9//FEVf+4cZ7p/P8wvXSJJvoUFlEolEhIShOvXr/M2Njbc+vXrobh9W3hUWMhqevZsingJgoDMzExIpVI0NDRQhH3GDGojdfQoEVmFgvL8R40CXF2RmpqKnJwcREREtEvj2LNnjyCTyZiHhwe6rl8PzJmDcp7H3bt3MX78eAwaNKhzQq2GhQUV9PrlF5IxnzhBOdxLlrSr4F5UVIQnT56ohg0b1vGG4jige3fI7eyQnp6Ovn378tOnT2eWlpawtraGv78/69WrF65du4abN2+iV69ez2/75eZGc1FVRbn0Bw40523n578U2dfR0UFsbCwXEhJCBvUbbxDRX7SI5l4dKayqIkdOyyJ9DQ1ERgcNItL9EnB1dUV8fDwcHR3h6OjY9LhIJIKOUol+GRnwt7ODfNMmZrR0KWINDFROVVUcAgLIyfj22/SdTUwoyp+VReR54EAipx4eRJzv3CFHmo8Prd2339LzAN0fU6aQI+PmTSJUb75J6RujRrUjTmlpaUhNTWUjR47s9PwwMjKCjY0NPDw8ODMzM0ilUiQlJSEgIAAmIhFUFhZI9vSE9SuvgI0bRxHmlnOnrsTv4tKx2sDNjR4fOpTOnRcoZmhvb8/p6enh6NGjTEdHp7m1WlusWUOOhJaqDo5rbmW4eDFw/z4a+vZF8s2buHPnjnD69Gnm5uYmvPHGG6yiokIoLy9nCxcuZLaRkazPypXwHTgQAwcOREpKCp+amiq4uroyiUTS7EwoK6N88KFDKYJ98ybtKV/f5jGcOkUR9AEDgK5d8cDOjmU+fQp/f39yDJua0jjv3KEIuI0NEBYGDw8P6OnpMQMDA+jp6QkVFRXIyspidxwdES+R4F50tColP59raGjAokWL2jlwGGNwc3Njge7uXLCJCeOMjVH/9ClievTgHdavZ9ozZjST/TffbHKQicVixMTEYOjQoU1pOeXl5UhISGDh4eHMwcEBVlZWMDQ0hN2YMVy3IUPweNMmGP78MztqaSl0NzJiOm1Je//+5Ph2dm6vpmtooMh+RMTLFToFdWfJysrCrVu3cPfuXcHd3Z09V+2nrU33xtWrdJ+0LdD4L0AsFqO4uJg3NjZmvXr1avUcYwz29vacQqGQFhcX+4eEhGz/1weggQYvCU2BPg000OD/JCIjI89HRUU5FhUVTdizZ8+6kSNHGvfu3fvZrv0bN0i63VmPZQAxNjZwyM1lxn9H4jduHH32gQPNj339NaBSwTIyEpYA+vfvr7ZomiwbBweHplZ+PM+DMQZzc3Pxzp07YWtri/z8fFRXVwvV1dVs3bp1mDNnTjvFQXV1NfT09BA6cKDYxd0dpx4/hrGHB4cPP6RImrqwk1hM0esPPyRD86OPSEJ8+TKRjra9iJ2dyTiysoLuRx/hg7VrkXj7Nm+RlcVJ9u/vuHL6pEkQcnKwd9YsPnTyZC7o3XcpollVRQadiwtFNAWBnA7r1hHROXSIopp1dUT4jx0jcnPnDkUxX38dyMmBq4UFK7G0hMXatRBGj4b0f/4HJRIJPAEgLw9veHhgfWO/bCsrKzDGBADs2rVryM/PZ35+fti7d6/KoKZGFH7xInQdHUkC3lgnISkpCXFxcVAoFCIvLy/wPA+hvp6J9PQEKysrBgCpqanCuXPnUFVVxUQiEXiex6JFi6iF3nffAZcuQTV/PmRDhoDt24cKIyOYVFcjOjoaAGDeVmoOIDw8nO3evRsZDx7AZ+pU1FlY4PdffxWqq6uZvr4+fHx8Xmwfqus9CALlg7u7E2H85htKX6mrA2xsIJFImvLzO8Tt28D9+7AfOxZLly4FWuxZNVpGW/ft2yfMmDGDPTdfXSwmhUFwMBAXRwXhcnJoj1ZUvHBLQ7WzRB1lb0JGBrViU+fMWluT/Lm+vjmC/+mnJOfd/nK2Ns/zOHv2LADAWColFYWtLe3hgwep8vzRo9CfORMuP/+MPWfPCkbm5gwLFjR/SEkJpcDMnUsOuH37aL+3xJYtRBQPH6Z/29hQtPzJk/a5+p98Qv8VFlIqyoMHpALw8mpSMMXGxvJ9+vThnpuD3wL379/H0PPnBeODB/HL7NkwGzaMpV2/Dp3x4+GpVmSoMXYsRfdXruw4sq2GnR117fjySyLBL1DbQF1l/cCBA4iNjVWZmZlxxsbGrF+/fhTNLi4mx+XChe3fzBidYQsXQli5EoejooQntbWMc3aGTCZDXV0d09PTQ1BQELt37x5ycnLg/OgRdACok5reeOMN7uTJk6offvgBEolE0NfX5319fUVB06cTYZ48mc78jIxmwnrzJilo4uLIAeDoCDg54dX6eqxevRpJV64gwN2d6gzo6TXvnUZnhrm5ecszglVUVGDDhg34MDIS9QUFYF5eou2TJuHVjz56NsGdPx/YtQvc8uUwWbUKo3r25A6XlcHg1i3VKBsbkvhMm0ZFHidNAsdxYIxBJpM1fYRRI3l/+PChys3NTdTSWdSlSxcE79rFsjIzYbJvnyCaM4dVX74Mg5YOD8ZoP8+YQd+x5XhTU2mO/kbLveDgYNjb20OpVOL48ePC3r17hbfeeovrzJkFgJxjnp7kAB069G9f+wUglJSUCJs3bxYMDQ25iIiIJhUWYwwhISHihISEsKioKJvIyMj8/8YANNDgRaGJ7GuggQb/ZxEWFiaEhYWlXrx48URmZuasvn37ijs1ZuvqoDx0CFnz56NaJoNUKm0V4WpoaMDRo0eRmJQEzwEDYJmX13GOb0d48oQMvXnzSMLa0nhISyOy3NL4eQYYYyguLhbi4uKYSCTC3LlzER4ejoCAAGZra4ukpCTExsYiISEBFy9eRExMDNLT03Hu3DlcuXIF8fHxuHPnDkrMzODi4gK7c+cor1ZHhwxRbW2KEr7yCpFSdREtDw+SAZ8+TU4LNdzcKIp1/z4QEIAqV1dEV1Yy44ICweyzz1grWeaRI0BCAm5XVAiSxERWIZUKQevXM87QsLmSN6hN1JlLl6Cjrw+jxYvp2j4+lPd+4wZFPv/4g6JhdnYU8T10iEiOoSHY8OHIe/tt7NLWRj+VCiJBgLGZGYxdXCCyt4fuO+/A1MgII5YsQWpqKv/06VOuvr4emZmZUCqVuH37NqzLy9mrp0+z+x4eQvX06ezYiRMqc3NzzsTEBMnJySoDAwNOpVKpkpKSOMYY7LKzBZ/evVnXV18FAJw4cYI3NDTk7O3tUVpaCp7nYWRkBH19fRQUFEBuZATRsmXIzc/Ho7g4pNy4gYTSUiiVStja2sLFxQWnT59WxcXFCR4eHlx0dDRiYmJQX1+Pob6+MDl9Ghm9eyM5OZkBVDU8ISGBj46O5s3MzLgOW7zdu0fqiUuXKJq2bBn9+8EDIhs+PmRsv/oqMG8eRF9+CXlREXqOHs06ldemplLUrRNcuHABeXl5CAoKQmFhIcvJyYGXhwcYx1FxvvR0iv5OmED1De7epRZjCxfSGO/fp0ryX35Jr7W1pT05bRopUC5fpn3bicQ3KSlJZW1t3bq2xuTJtG/CwugetrEhAlpX1xzNvHOHiHBntQY6QEZiIhITE2GxYgXc7t2Dj58f2LJlRCb79CHVjIsLRZPd3WFgaYkzFy+yyZMnU6XwwkKguBh8WBhSCwqwy8+Pj7WzE0RaWsymJXmuqqLaIfPnNzveGKP0Bx2dplaU7WBgQE4MJyeS2J87B0yZgoa1a3FOJkNQUBDrrAZJK/A8hO7dcaKmBoUmJizLxgZd+/ZlYXPmwMDQEOfOnUNxcbGqtraWs7CwoHM0P5/Gt3x5q1SgDuHlRfUvJk0iwvWMCH9paSkeP36MiooKKJVK3t/fXyQIgvD48WMhJiaGSSQS2NTVAX36oNTcHDdu3ABjDPr6+qivr0daWhpu376NvLo67JDJ0C01lb2RlIS+S5eyQoWCf/jwIbOzs8O2bdsAAGPGjAHr35/mstFZpKOjAy8vL87Pzw/u7u7s9u3b3L179zBgwAAwNzdSaLz/Pjk8rKxozfr1o30/ciTNR3Y28OefEN27B/9ff2WiffsgsbCAJDqalFA9elDdEpWK3jt/Pjm/Wuz7mJgYBAYGQtvEBKJXXoHv5MkwEYs7d5ikpJByqqqKHEuenhCHh0Px8ce4cvUq5+rqSs7JlBRyCDo4gDEGuVyuunDhAqdWtiUlJaG6uhrZ2dmcjY0N2rY2FYvFMLewQKWBATulUiGuoEAI/vxzBnv7ZuWHiQmpLlasoDQU9Xlz4QKt/3NaXnYGQ0NDGBkZwdfXl126dEmorq7mnZ2dny8RuHCBVFempu2LtP4LsLe352QymZCens6VlpbCxcWllUMyLi6u4dGjR3cFQfghLCyM/9cHoIEGLwFNZF8DDTT4/wPSAChqamp02vVNVuPXX1Gwezd211CKHMdx+OKLL5qeXrduHf/06VNuxowZsGeMInVy+QtFnrBsGUVBY2JaP752LRGAl+y9bW9vz9LS0mBiYiJkZmbCxcWFAYCTkxM+/vhj3Lp1C1euXIGenh5MTEyQn58PLy8v+DdWh963b5/Qv39/FhgY2CyLHj+enBLqtlkGBkSof/mF/goC/b9CQdJKtdPkzTeJgOvqotLWFud1dBBYXs4XrVzJubaMoAgCGfrFxXjw7bcC/+gRGyWXc2IDA+qp3YjS0lJs3bpVkEgkrOLQIYxNSIBhTAwR0fJyut7cuTSXGzZQde2gIIpqtpChu/I8xIaGQuq4cczn3j2S6UZGAoGBYD17wis4GJBKMWLECC49PV14+PAh7+XlxXp7eXHFu3YJ7pcusZzPP0dySgpDYiJUKpUoOjoajo6OKCkpYfb29pgwYYKooaEBYrEYF7/6CmdKSxGakwN7e3sYGBggOztbyMzMZB4eHqrU1FTRmTNncPr0aVgpFCrD0lJW9uGHeGvpUk50/Tr0N27klXv3clf69UNuQwN27NgBhUIhMjExEVavXo2GhgbY2Njw1dXV3KXTpwVTiaSpMJpEIsGvv/4KAJyWlhb2798vzJo1q1m6e/06tc2aNInmccIEmrNly4Bff6Ve5GFhRJ7d3SnCJhIBggCJXE4R5Pffp79FRURO+vWjx9o6zxoaKNc9LAw4dw7mv/8O45AQYciyZcx1xAgcT0ggJUltLUm6nzwhQ14iISLj7U2t5ACS0mZkkBNn9Ghy8kRFkYLjzTdpjJ9/To6yuXMpZeTQISJHAODuji5durCioiJBfY80QSSiQnfqKPjq1bTnv/+evv/ixc/uty0I9B02bQLMzaEqLESXFStQNHo0RMbGsAoKAjd6NI37GeA4DoVZWTDZuRNYvx6PN23CgYkTBT0HByHM15e7f/8+YmNj+cDAQK7puu++S5/btltrl+UBAAAgAElEQVRIcPALyY7v37+Po7Nn85ZmZmzAjz8y++++g2jJEub48CGRr84iwVu2QPjtN5z58kvU+fqiwsQE0+bOhampaVP1/uDgYGhrayM3N1d06dIlvmr7doQmJHDc2rXkOHnRvGtvbzob9+6l/dKB0kUmk2H9+vVgjEFHR4dXKpVcaWmpsGDBAo4xhpSUFPx19Cgcjx5F8Tvv8Efj4zkTExPhypUrTKVSNdWY4DgOUqmUd3d3Z69+/jnjcnLA37uHV3fs4LZ5e+OPxhZ9TSkOEREdEkB9fX3I5XJUVVVhypQpzc7i4cNJOTJ0KN0vw4bR91OpqJaCoSGtaY8eQI8eiNHXR0JmJuaPHw+9d9+l1K9Fi4iA6uvTPSMWU1rT5s0AY5A1KkOaovje3pS6sHlzc8HHlsjIoDoV779PNTu8vIjUd+8OAxsbiMViNDl+vviiVS790KFDRdeuXcNvv/3W1KnD2toaAwYMEOzs7DpVz504cYKcHYLAZIMGQSoSkfPY1JScYcOGUbeJY8ea7xuplObtH0IsFmPUqFHckSNHMGzYsOe/4fffyfE4axYVbH1eetRLQl9fH8OHD+eSk5PRs2fPduknNjY2YgMDAwe5XJ4dFRU1IjIyMuVfHYAGGrwENGRfAw00+NcQFRVlCCBCIpH48zxfrVKp4gBcioyMrP6HH91PV1dX3Gnefl0dCr29cTwnB2+//TZkMhn27NkDNZEDAH9/fy4uLo5kyYaGRAxu3uy0+BwAitSdOUOOAb6Nc76+niStgwe/NNn39/fHzZs3G4qLi8V79+6FjY0NP2vWLA6gKFNgYGCrlm3379+Hk5NT03dZsmRJe4Ns/36K8hw7RqT42jXKn5w/nwzChQup7dnly2Qcqh0X69aR0VpeDuWMGfCTyfgeYjGHIUOaP1tdfXrlSjSMGwerDRu4qq++wlNPT7SMASXExuJSTIzw/tatzHDWLJwKCBAeZWczj/79KQJmYEDG/8cfU5qBhwf9NTcneXePHkRsq6vBicVgjCEjI4P3mTKFQ3AwyaH37ydJs0IB7N8PmwkTYGNjwwCI8PQpsH8/rK9eZTh7FrYSCaxLS/n8/HwOAAoKCvDVV19BLBZz48ePB4CmOR2oULD7jGHfvn0IDQ2FQqHglEol4zgOSqWSMcYwZcoU2Nvbgxs4UARPT0CtjAsJgYrj8GjtWiHi8mWW5OiIjN69oVQqYWJiIpSUlDAAeO211zhdXV3cWbGCZaan486uXTAwMOAXLVrE3bt3D/v374eRkZGqoaGBS01NRX8jI1pPdV0Ade5wTQ3N4YABVIk+Kam5SnpISBMxKPv0U9w9dUoYOnw4zZujI1UTz8sjY7hHDyJvubnkRAgOpr0zbhyQng55Xh74rCyEff45g64ubIOCoBUbi+Uff4xu+/YJTlOmsGD1/bNrV/v90tBAJMTMjMbp70/R6MhIIh9Pn5LMFiDZ+6RJFCFcvpwcUPHxGPfdd9zViRNVcHMToaCASJCauGzaRA6k7t1pT2tpEZG6ebN9nn5tLTkxbGxIkv3bb5Q6EhODsqFDsbGiAg1z5qCnm5sw4LXXnp+qAOBRZiYitm+HxcGDiPngA8SPGQNVXByChgwRQkNDOcYY7t+/rzIzM2tmapmZdJ+2VNeo4epKc9Py3muDhoYGHDt2jO/atSszNzcX/pLJBMXixVx4RAQk6naXP/9Mzhf1eRkQQAU6Q0Nx5soVXL16FSO/+AKv+vp2mN/v7+8Pf39/yGQybtuXX8K+vBwOx45RmsjL4N13m/u8r1/fTmWRm5sLAHjjjTfg6OjI8TyP5cuXIyUlBb169YKXlxcMqqpQ9eefOPrkCefp5YXRo0cznudx5swZmJiYQC6XIzQ0FK203Y6OyFQoUFhbC8PHj2ErFuPVX36BWE2kIyKaU2HaQCqVQiQSNUe3R46kWgrqTia6ulQAztiYzq1585rWZdeuXSg+e1aQy+UsNDycNzExoTFduEDqG/WesrQk2TtAqVUPHsCksBDTf/8dhxjDuC+/pOfmzqUzUiaj3yz1vs/OpnvdyYlqVXh5kRPpxg3g4EHEbNumCgwM5MRiMb3B3p5UXZ9+CoBqT0yePBkqlQqHDx+GUqnEq6++CgsLi2emyY0ZMwZHjhwBGMPjqVOpeOWCBaQOOn6cXrRqFaXt+PjQfXn9Op1d/wIePXoELS0tKBQKaGtrP79gn1xOZ92aNeTw+C9g8uTJ2LNnD+rr61ul3jk7O2PBggUGt2/fNjhx4sSVqKiovpGRkQ//K4PQQIPnQEP2NdBAg38FUVFRFmKx+K69vb2uvb29vlKpFDIyMqqLiop0VqxYcaOuru4sz/NpAB4BUAJoAPAUQFFkZGTtsz5bW1t7nr+/v576x53neZw6dQouLi5wdnaGbPlyVO3aBfHChejatSuqqqoAAHv27MG0adMAACEhIcjNzeU3btzIhYWFCX0GDGDYtq3zvuAAEf2NGykK2dIo5nmKkObl/e35evvtt8VyuRyrVq1Cz549hWe91uVFZIiMEZkODqaImkhEZPQ//yHj/+hRKla2cyfJq3mevpM6p/LkSaR/8gl/4+xZrldYGFLWrFEZCAJeHTxYpJeYCPbkCbZ6eqokY8eKyi0soH/rlhATE8M8HBygJRKBP3eOH7JzJ6d/6RLroq0NpqMDp7VrmXF1NUVWIiKI9PXtS9dXR/rWrqW/SiX4lSux88wZ3nPjRmZWVMRq33yTDVixgsHdnSLWCxdSBPfkSRpzejpFuQGSix84QAben38CjEEH5OTJz6eUSY7jMHXqVPA8j3Yy+aoquHh6onuXLqrExERYWVmJZs6cCUNDQ/z8889MEAQc2LgRYeXlfMCRI1xbB0/x06eoGTGClRsbwz4qCq7p6Tg5ejTu3bvXtHHULRmDXF2R//QpTjx6BGdnZwCAm5sbPvnkE4jFYtGRZcsgvXWLovZz55KzRr1Hy8tpjRMTycl09y49LpdTnYRTp4jcAdDW1gbP8/TexsdACgIiEHp6FGFszPuFtTWRmKdPAQCnLSxQOWcO/6aXF4fGQlRvubtj5cqVyM7OZtnZ2ejduzdJhdvi0iUi5g8fUnG5lStJeSCR0BquWkV95XfvJoJvZNTcG3vDhqY9UR8RgTyVisPZs0QsiovJKRASQiQyJ4ceDw+nfR8aStfLySFnw9dfU2T0229JYjx3Lsnw33oLsLND3e7d2Lh6NQy7dsXMmTMhlUqf3/ojJQXCp5/itpERlAEBcFqxAtd27oRCTw9z33kHZmZmTWvu7OzMnTp1ishAURGdJRcvdlwLw96eyJ9S2YqMFhQUgOd5WFtbY8eOHbypqSmmTp3KGGPslZZEKjWVSOGkSaTw6dmTnH4zZgDe3sgSBFxtrKju6enZaSE/AIBcDmlICEb//DNkp09DERHRlOP+Uhg1iohubCw5pxp7vQN0jtOl5ADo/gwKCmKHDx9Gt27dEB0dLXT74QeWPWgQGlSqJsccx3EYPnz4My8r1tXFpYEDMbZvX3h9/z05kD7/nO6FuXNpHJs3t34Tz0NXRwcDMzJUQv/+Ity9S2tSV0eqk0mT6F7cu5dULY3OCnWdh5ycnKb9U1hYyOrq6qCtrU1OjhUrOvydEcaPR3Z2No7u3IneTk7QNjLiMXYsB3NzulcHDyYn2bBhtJcBSr3q2pWcV+qK+kePEqmdMQOVlZVMX1+/+WIJCe0c0j0bU0WcnJwgEome3Q2mEUeOHAFjDBMnTmzqUoE1a8jRsH07OfTu3iVn39KldKakp5Oj4V+Ai4sL4uPjsXz5csyYMQPPK7CLPn3I2bBhA90PL1Ec9EXh6OgIKysr/tixY2zcuHHtFrh3795QqVSGp06dSo6KihoWGRkZ968PQgMNngMN2ddAAw3+FYjF4gWenp7S0aNHq21CFhYWZlhXV4esrKx+BQUFfZ88eVJTWVkp8DwPQRBYXV2dqLa2Vuf777/PUSqVy3me/y0yMrJVRbGoqChTsVj8uq+vb5NlqlAocP36dSQnJ4PjOPgmJED22muY3BhRNDQ0xMSJE7Fv3z788ccfwowZMxgATJ06lYuJicHJkyeZ/5dfUj5ucjLlrLdEfj4ZVr/+SsZ5W/zyC5GHgoK/PV8FBQXYsmULPD09Vf379//3KgiZmhIprqgg8jtxIhm2o0cTyRg/ngjAe+/R94uNpehxbi68p03jrA0NsatLFwwaNEjkP3Ikin74Ab9Nm4b3L13Cq4GBojyJBL69ekF69y7LsraGpY8PsgID+YYBA6Dq2xe9vv22qVJ3F5kMylmzSLqtRlJSxwUUJRIku7riSXQ0N/jwYWzduhXmhoawcHWlaNrXX5NxnpdHn1dSQg6EykqK5B47Ruv4xhutPrZXr15olMbDz8+vVXX1Vhg9GrC2xmRPz3ZrsWjRInby5EnY/vIL71RRwVpF2Rrh7OzMJSUloby8XAj86SfesbBQNOPDDxE3apSQ6+yMaTNnNudTW1vDZtw4fBEc3CoiqZWbC1y9ij6HD+Pp66+TLLctIXv4kBwmauM9NJSI7v/8D0mNtbSAxEQ8dXPD8ePHhU6NeJ6nehNiMak8Jk2iyuPh4U3frbq6Gt27d+daRtAYY5g/fz42bNgAmUyGDiPggoCKMWMgSCQoW7YMBr17w6qqqpnAqnvQh4XRfvzqq44VNhIJjL/5Bo+/+44pxo2DjnptBw+myP2DBxQ1jI4mue6BA9RisbCQ6hF8/TXVCOjdmyKNly61u0RWVhZEIpHw3nvvsecWt9u3j0jNd98hzdERNw0NYWlrq1q+ZYtILSnftGmTsHTpUqZeVk9PT3by5EkkJibC6uuvIeveXdDPymItlTpNMDBo3tuNUfCdO3c2ZGVliTmOg1gsFurq6rhp06Z1HNV0dSUVyIoV5MRsjAgrz5zBmr17UVNf3/TS+vp6qKX7HUIkAvr1Q/cff8T6ceN4WUMD6wu8RA9UAs/zUM2fD8m2bUQAly9vSr1wdXUFABw8eBB1dXXw8/OD2jG3bt06GNbVsX75+bhhbAy3nj0xcuTIF76uuq3cqZQUeJ05Q3Papw85P7dsad6LPE/3jZ0dnZ07dqC+Xz8uRVsbYQApEt5/nxxip0+TMsrTk/aBUomq997DXj8/PC4tBQAsXLgQ5eXl2LFjB//zzz+Lwtzd4fvBBxAlJ0MQBJSWliIrKwupqamqJ0+eiMRiMWpra6mjyMmT0NLSoq4Ojx7Rfp0/n/aErS0VVz15khQ6jc6qJoSH07gYg56eHlJSUoSAgADqCHDuHO2HDmpBPLe6fSPS0tIAUIeSds5nxsih1KsXOUZWrSInz4YNdD531mHhRdHQACgU6K6nh/cHDsSfe/fCMCeH1jQ7m66vq0sOz8ZxwtSU3hcfT6lBK1eSI83a+qW7AjwPvXv35hISEhrQCafy9fXl0tLSxFlZWQEANGRfg/91aMi+Bhpo8I8RFRXVT0tLa2H//v3bWY/a2tpwc3ODm5ubCEC7xDme55Gbm+t07ty5H8vLy2dGRUUNaEn4xWLxh+7u7pBIJEhKSsKZM2eaKox/+umnkK1YAX7oUBi3qdTs6uqKuXPnYv369ezgwYOIaCxCZmdnB11dXTKWv/2WDL+2ZL+4mEiVOvrdEoJAMsWJE//eZDVCbWSNGTPmv1IqGMbGzVFfBwciwWvWUCS1Xz8ihQ8fUrX+KVOAggIUnzqlOv/0qWihiQkMVSogOBh2Fy5gxpw5MPzmGxgeOwb7nj2JWC9YAMf33gPeegtegsChro6KsAUHE+HIysKj11/n7zs7s2ktSYKhIUX2r1xpN7fJyclCr169hK5du3K2trZCbm4uK/3hB6pa/eGHZLCWlhLp8vam6K2xMUW+Nm7ssEhiY4sqAEBtba2AzghLXBwZq56e7Z7SEYsx9v594M8/OejpdWgs9ujRA1+S/JZSCgBkmJjAa9EiRHh4MJSVNasZoqMBb+/myGpdHTlobt4Etm5F7Lx5MLOzEzw5rvVYy8po3hpzkAFQNE0d4dLXp88eOxZ3DhxAeXk5W7BgQcf7a/Vqkv0ePw6MGEFy4XnzSCExbBjg4ACpVIqioiKV+vuooauri+nTp2PDhg1Yv369ijEGDw8PUXh4OLB/P0qkUvwyfz4GXryIhMxMGF+/jp4eHmgXVwsIoPtvyhSSiA8Y0C7iLRaLoa2tzT958oRr6gv+7rukPujShUhQeTn9BWhfd+tGKQ9SKRHfp0+J3K1fT0TJ2prk/tOmoWHpUvSXSJh41ChyRDk70zyqVJTmUFtLFfMNDckJOGEC4OmJvGHDBP7aNcZxHHv77beRnZ0tnD9/ntnZ2bWKmOvq6kJfX194sm4de2xuDtGoUULmkSNMoVBALBZDS0uL19bW5vX09ETGxsbM/8ABlF25gvv+/sjIyADP8+L33nsPDQ0NyM3NZQ8ePEBsbCzv6OjYfhNGR9P41Gs4Zw4gCBCtWYP3oqNRYG+PbufPQ2pt3eGWaMKMGaQCGj4c2LgRgrk5V1ZW1qQ+qq6uRmZmJlJSUvjXXnutw2KSCoUCsbGxiGtM07CyshKmOjsz/YQEsOBgwNwcEokES5YswcqVK3HlyhXBz8+PZWZmwtfXF8nJyQjp3h3ddu/GvJCQZ4+3AwiCAMYYtYrU0aH98c03FOXOyKC13rePpO1//UVzdvEi0Ls3bq5Zw3S8vYUw9Vnxww901nTpQo6jsWMBQUDKhQswP3ECZZaWiIiIgI2zMziOg5mZGRYuXChKTExEzqFDqnobG1Hm0aOqx48fi3ieh6GhocrZ2VkUGhqK6upqWFhYYMuWLfj999/5d955h8h+QAD9/kRGkhNn1Cgag7Y2EdqWTsuLF0nl8pBU4uPGjeN+/fVXfPXVV/jyyy8h/PILOImk88KPLwB1WkOfZxW19fEhVUr//nQWR0bSmZeRQWlvVVXkrDU2bv73zZt0fkkkpJY7f57Oc319+h3evZscGW5uqMzLQ8OVKwhwckIXBwc6hx88IKWDjQ05qGxsyFFmYkJzFRxMDhKlkl47ZgwpPJ5Ti+NFkZGRgdOnT6Nv376d/o7zPI+srCxtQRB2AEBUVFSAjo7OJ/X19Yd4nt8RGRn5TGWfBhr8U2jIvgYaaPCPEBUVpaWlpbVv9OjRui3bdL0oOI6Dg4MDZs+erb9ly5beRUVFMwD81vjZYrFY/OGAAQN0fv75Z76mpobT0dFB165dYWxsDI4xdNm1q1kK3gZmZmZNOXVpaWno2rUrb2dnxxkbGxOB8fIiQpCRQUb+o0dE4s+fpzzLjhAURHLpTz556e+qRlJSEq5evQpDQ0NBJBK9dLTspZGcTH9/+40kzZaW9L2fPCFD8bPPAKkUTp9+KnL67DMiTF27Anl5YLdvw2r9epJIf/ghGbsmJjQPffo0V0NXRxoVCsobDgqCwxdfcBe2bMFff/0FX19fKmJkaUmS7bIyMugePgRkMiTcvg3bixdZn8mTGZYuxfiHD9khPT0oQkLIYFRXdn/1VTIqu3ShqO6aNWT8CgIZ8H5+HZJxAwMDwdnZufO5NjHpvGXjhQsk3f3gg05zfTtCOcfh6uTJWNqlC40zNJSK06WmkgFbXEwRp1u3KDK/fDlgYAD+5k2oVKr2jomVK2mPtkSfPkTa1YZ8eDgeJSTg2ubN6NFYybpDDBlCZE4NOzsiPRs2gP/oI5RHRCAzO1sYNXZsh0asmZkZJkyYAIVCIXr69CkuX76MR48eCZO3b2clYjHGA4L499/ZazyPyo0bhdInT1hsbCzu3r0rNDQ0YOLEiaR0cHAgdclbb5EjY84cwMKi6ToJCQmora3lFDU1VHTv++/JMXL2LO2HqVOJJNTX03waG7d2GLz2Gv0VBMq/rq+naGNBAeDggAoPD9Q+fozrly7x/sXF5LR68IAk2m++SXtZW5uI4dSpEAQBycnJQkpKCuM4DrW1tdi0aRMkEgkmTZoEJyendnvM38GBOa1ZA4Pff4fRoEEcDUdATU0NKioquMrKSq6iogIlJSWqTKmU4eFDTvDz4728vLjw8PCmlJNu3brBxcUFGzZs4CorK5vX9sYNWrsFC6hjhokJRYQ9PQFra3DDhyP9009V5UlJIr3QUEjLyui+6+y8trUloqlQAIcPo+y776BTUMAAoKioCBs3bgQASKVSbvfu3cL06dOZvr5+09vPnz/Px8XFcQAVMnvllVdw/PhxrFapELZxI/ru2oWijz8GMzPD3bt3eQCcj48Pq29UHty+fRsQBLgdOgS06WP+IlCpVPi6UfIeFhZGD8rl9H1GjaI2oDU1NG/ffUcOIQDw8UFZWRmqqqrg7e1N69i/P52P6qJwBw6ges8e5CxahKMjR0I1Zw7mvPsuzF1daY+0SKsIDAhA4ObNovy1a1GRlyfq378/Gp1Bre6prKwsSKVSPHn0iMPdu3Q+GBtTAc6iIjobQkPpvPbxaV+PQiSiFJjGM9jS0hKTJk3C3r178dVXXwEjRmC8jw/cX3omm6FUKiGVSpGfn8+jgxadTZBI6BwrL6fzWBCI+HfpQuOvrCRns44OEXA7Ozq79PWp9kBoKOX6a2lRtH71akBPD7wgYM2yZYCLCz755BOIXkSREB5OKQXXr9O5cfQoqX10dUlR1KMHqSZe4kxvi5MnTwoBAQFsyJAhnf62cBwHV1dXxcOHD1dHRUUd0dLS+iUgIMDi9u3bQ+RyeXhUVNQ7kZGRyr89CA00eA40ZF8DDTT4R+A4bpa1tbWJu/s/MSXoB3Hw4MH6Bw4c+ASNZB+As46ODnieR01NDQcAixcvbs4vvHSJosydSbNBuYlTp05FbW0tYmNjWUJCQlPPe+jpUYT4+nUi+8bGVKX5WfLWL7+k/PF/gKtXr6rKyspEffr0QWZmJg4fPizU1tayyZMnN+Vw/6tQF+pSV5COjKToaGPvZfTq1dzWLSeH5PJbtxK5rKkhuf/ly6SEsLMjEmFg0PoaPE/vjYkhQ8rKCubl5Xi7uBhFP/0kxHTrxlxTU2Hi7Y3u9fUUxf32WyLBFhYoMjDg+3XtyplIpUBgIPQHDIBZSQlOWFnhvS+/bH09dWqFIBD5bWigiGZGBn2HmTNpbRv3iUwmQ3V1NSsrK+t8joqLaW7aYvlyInxJSZ1XOe8ElpaWEIlEAmbMYBg8mGTmH31Ec3XwIBFKCwv6/xbVokUikZrsN6O8nAhcW9l8fDxJ2IuKmh4ys7PDqykp0EtLw+auXfnevXtzffv2bX6PXE5j+emnVh/F8zzuhYYipqBAcPz9dzZSpYKrmiy1QUvFBAB4JSUh5/x5rB48WHDPyIBDYiIz1NMjB09oKNteWorrFy7Ay8sLKSkpLDExEaPU/dpFIioYuGoVsG0bMHQoqszMsGnfPj702DFugZ0djNzcSIUwbx6tyZo19N6PPiKCNW4cOYX27CHlw+nTbQfcvIdMTam4GYCc8HBkZ2fDt3t3hrlz23/R//yHnDFGRkBVFa6mpeHMmTNsxIgRcHFxwb179zgXFxcYGRl1auyHKZXY/MorUKSlYf6gQU3zJ5VKIZVKYatWJQAihIZS5HHKlA4JlampKaysrBrWrl0rnjZ1KuyNjUmmrW6xqU6H+P13mqvGKL7/t9+KoqKiUOjvj0kyGXQCAkhRsmwZOfIYo/n9809ag8BAus+0tMAYg0gkwo4dO/isrCzOzMxMePPNN5mOjg5WrFjBVq1aBQAYMmQI4uLiIJfLOU9PT9TW1mLChAnQ0tKCl5cXy8nJwR+CgMqbN8F//TUeuLhAoafHBQcHY8CAAeA4Tq2OAV9cDO7RIyKGL4ldu3YJANjwV16B1+XL1MlixAhy2M2aRST//ffJMdiG6FVUVAAAQkND6YEBA1orhv7zH3B79yLf0xPD7ewE+7FjmampKTmqPDxoPgMCmnu8Z2fDxssLNm2dFgoF1d1IT0daaio/8tw5zrqoCLylJbjqakrVWbOGfo9yc8m5dfw4OSpaIiuLnGWffdbq4R49ejTtq9Cvv0bCw4dw37nzpecSAPLz83Hw4EHIZDKMGTPm+Rr4uDjaP++/T+dbZOTfum5LnGgsADh+/Pjnpx40NNCZMmoUzWH37hTRf+MNOmPEYvqNP36cHPunTtE50jIt4gWhVCpZZmam8CyyDwAjR47UPXnyZERNTc1oHx8fg969e6Nfv356u3btmvDkyRMVgNnq10ZFRYkAuAOoBJCvifxr8E+hIfsaaKDBP4K2tva0gIAA/ee/8vloLLjTLSoqyiUyMvI+AK3a2lrdzZs3w8zMDGFhYbxIJCJjQ6GgaMbatc8k+wBVxhUEAQ0NDezYsWPw8PBojqwMGED5w4sXk8HW2I+5HRQKihLu3//i7ac6wTvvvCM6e/Ysbt26xW7dugWJRCKYm5sLu3bt4mbPnt2ujc+/hkGD6L+rVykaLpWS4dPQQITBx4fyLMPCgMeP6TUBASR/Dgqi+cnOpqjg7dv0WV98QUT0m2+IjFpaNkuJLS1hZmQEs4gI1mPIEBz86Sd0DQxE94AAMqCHDAFefx0KhQKpK1dyoXPnIj49HTAwwNWrV1VVVVUisZERcsrKYN/WuQCQPPPRI6o/sH075bjn5ZHhFhdHaQyCAH19fbi5ueH+/fuqgQMHdiy3rKmhSFNLPH5MFd8nT35pog9QKsmJEye4xJMnEXj+PEWpXFxo/saNQ2V4OO4VFqKvvn4rnXwj2W/9Yf/5DxmwjVHVJowYQeNsAV1dXZju3o2zR47wuikpXHRhodC3b99mYzQ+ntQKLfK+8/PzsWfPHl6pVHJSS0v02bcPhvHxjLt1ixxqn3/+zPZVpjU1MHF1ZbZiMZKGDBEO2dqi17VreP3114H16zH5P/9BXWAgpFIpq6mp4cvLyxlaKhcYA8zN8fTBA9S9/TbMkpPhvH+/4LdoETixmJ5PT2990bOW//4AACAASURBVMpKUpvs3NmcIuHt/cKFM3meR3Z2NgBg0KBB7Y31hARqmdalC62Ztzd0Gyuaq+XMAerCh53h0CFg1y4U9+6NhvLyJnl5pzA2pvFXVDQ76drg7bffFl+8eBGi8HDUentD9+jR9i8yM+vw/bk6OliuowO9GTOEt8+eZUaJieT44bhmcnbxIs1po+OxS5cuDXl5eWKO47gPPvgAxsbGDKD5q6urA0DtI8+dOwddXV1hwoQJrKUjSA17e3t8vHQpGhoaoL18Obg7dyDas6fD9qdcZGSzE+JF0ej8GykSsdLdu9Gja1c6H/r3J0VNyw4Is2dTekebloD/H3vfHRbVuX293nOGmaGKSq/SBBGkCKig2BUVW2I0GjWWWFM1uTGdjElMjMnNNSZqokk0GhNbjF3sCliQKggKAqKCdOllyjnfH5uhDqjJbb/vsp7HR4WZ095y9tp77b1/+eUX2NjYgKutJZL6xRetriF1+nQcNTTE00OHwvXdd1mTHFxL5nNzKY2kpoYcCRs3Uu2EM2fIadSnD6WFKJXkcLKxgfvw4dxJUcS0t99GNzu79vcVEUFjMm9e+9/98AM5WdtAqVTCzMwMycnJuOvoiH6PKGjYGa5evaopLy/ntc9n5cqVuotyArTvh4c3pxqMHk1789y5j32+O3fuYNeuXeKQIUPYhQsXWu2F586dEz09PTufFP37kwLsnXfICTpqFL2/TUxoPU6fTs6UoCDaOy9epGe4bh3NkQ4cnLpgZ2enyc3N5YuLiyndrAMYGhrimWeeaTXRZTIZZs2aZfDll1/OUSgUioiIiHuNP9/J8/wktVrNMcZSFQrFAlBB4xxQceNgADUREREJj32hXfifRhfZ70IXuvBXUalqS5L+JDiOQ58+fbikpKRpAD4BMIQxxg8bNkwMDg5maCkfrK8nAtKiRV1nOHr0qCYlJYWbOnUq69cyymJrS17+8eObc591obyc5Lz/hH69UqkU4eHh8PDwQFpaGuzs7DhXV1ds2LCh86JZ/yz4+lIU/OLF5tzF4GCSNZaWUuTTyYn+/+uvdM+2tsD16/SMtOkPffqQISeXEyE8c4aM3pYy+uBgAIAMQK2fn6bUwIBH795EnhoNxsrKSvA8j8LCQpw5cwZmZmZC//792aBBg3Ds2DHs3bsXixcvbl9BPz29KU8VHh5UiHDlShqnQ4dIjj1sGNiFC6ipqdFYWFh0XB9h1KgmcgOASLWdHR3jMSpVN6GigpQiI0eCGzUKc9zdsaumRhxw5Ahjzz5LkaSPPwb8/JBw8KAY9fAhO3nyJIKCgtTjxo2TALQO2pH9oUN196tmjAoVLl9OzpdG9DQzw8wXXuDqXF1x1cGBHTx4EKamphSxHDQIiI9HZWUlysvLUVhYiMjISAQFBbFRo0aB09YKCA+n+9m7l4zgDz+k77YkYD/+SF0rdu8GE0VYuLhgwqZNrO/zz0NPGzVtaICetzf0Gsd76NCh3Pbvv4e6pgaS2FhyZFy7Bhw9inSex9WQEEw0MxOn3LvHY/nyjnvPb99OjqqW6Rd9+lCEbuNGeiadQCsb79atm8bAwKD9IC9dSvnZERG0Fv7xD4iMweX8+XattnTi4UOSDL//PubZ2mLr1q3QtKgqrxM8TxH12lrdZF+jAV57DSOmTMGeBQuEUlNTtkQQWLvK+nI5nbsFQkJCEBMTA0dHR7i4uIjfAGzu7Nlw+OknUu9kZxNh3r+fFDqN4+zl5cUnJSUJCxcu5FqmhWij4IMHD8bI5rnXKRFr2t9WrwaOHiUJ/YoVravFl5XR+des6exQhLo6clT27UtS+549Ybp1K44NHIh6Pz/4LFqk+3vZ2URIV69u+lF1dTUAUiggNZWuQRRbzffIqCgMu3QJriNHEpG9eLG1E2HrVspF1zpVtW1Hk5KaU58GDaJUCcaQmJiIQ4cOARYWsNRF9HfuJFK6a1d7uXlNDfDii7hVUIB7p09DFEX06NEDMTExmoqKCp7jOIwYMQJ+S5fC4E+k2GkxePBgvqamBjk5OdAWomyHrCxKI9m3jwpkCgKp4IyNaY94ArJfW1sLlUrFzp49C4AK7j711FOwtLSESqXqeH49eEDpAN99R/PhlVdIaSGR0Lvm738nx/W0ac3vKGtrSuVQqchBvGsXzamaGlr7jyjm17t3b/7mzZtITEzEGF378yMgl8sRGBgoxsfHX1QoFJ8D+EkqlVYOGDBAPmTIEC4qKso3Njb2MsdxQk1NjT5jTGNsbKyqrq5mCoXCLSIi4kFHx1YoFJYARgGI7Wr797+NLrLfhS504S+hvr7+t9jY2JB+/fp14Op/MgwYMECWmpr67qefftpXT09vQlhYGPz9/Vu/4GtrqYjPuXOPfdzS0lL4+vq2JvqCQJGs9eup33xHUaTUVJJ5Hz78Z26pQ7i6ujbJ9jdt2iS4u7ujZ8+e/5xSwaJIOZJRUc2F0K5dI4Nn7FiKVvftSyQ+L4/kvwsXUuTZxYVIh75+U29mAB23Llq4kIylV17p1DgaMmQIv3//fkRHRyPY3BzcuHFAQgKuXLkCGxsbjbGxMS+VSrFs2bKmg0ycOBE7d+4UtmzZwk2aNKmpVRQAUmO0hKkpkZbUVCJqW7aQ8sPRESOfeYYr9vIiYqwDNd9/D83ChTBxdiZCtXkz5Xk+iujX1dFzGj0aWLsWYkYGxDffRMGVK1DNmIHibt1Qk5nJMmbMgPvkyfSs//Y34PXXERgfz27NmwelhYUYGxsryc/PF4KCghjP80ytVjdb1bNnUxG7xirm7eDo2I7YaSG/fh2SuDiUbtuGJEdHxMTEiH/bvJnxe/bgm7NnRa3x3L9/f3H06NGsXdS5WzeKgk6ZQoXbHByIoBkb0xyztyeHW309FbDLzgYA9Gr5fCZOpAhqQgIQG4tEa2vhja++4iR2duRs0ubV792LmjNnUB4dDac9exjee4+cSWFhzQX4tPjoI3rmLdsSavHgASlNFi3qNB9X61DRaDS6F35SUuv/T5oE98xM2EZG4sqbbyK0TRpEO3z6KRGNNWtwpFGRUV5eDrOOakO0PO+9e+S4aonbt+k53LkD9OiBKW+/zX311Vfib7/9BktLS3h6esJaW3wvO5sISyMuXrzYVCxvzpw54Hmey87OFn7avp0b5uWFoX5+5GR49ln6QmoqPffnnsNIIyM2MjubobYWOHoUaldX7EhOxt28PIDjHq1u0AXGmknwc8/R3qodx7Q0IogdKBtQV0dpCmVlNAeTk2lNnT0L2NuDB8BGjxaSbtzgfAICdB9Dq0pqgZSUFFEqlaKXUsnQpw91LWmB6upqVFdXw6KhgfbW1atpL/XwoNSm/Hx6l8TG0l554wYVzmvTIaQlTp48KQJgz2qfe0ukp1O61fDhzW0pW2LtWuTs3y/+Nn06s7e319BX0pmXlxcLCAiAidYx3bMnXVcn19EZ9uzZIz58+JDZ29uL+vr6MDAwaL1eqqrI6WZsTOtNu2dWVdH7YPFi3cVuO4CnpycsLCw0RUVF/JIlS2DVol1jp87w8HByjGzaRCqg6OhW9T9gZkbv8YKCpm4XTdDTa37XHT9OqR+urjSWc+Z0uL/W1tZCKpUiuNGp/WcwevRomaOjY6+oqKgvCwsLRyuVynvFxcX1jDGD0NBQSWhoqAQA1Go1VCqVRF9fX3bq1CllXFzcGYVC8UxERMSNtsdUKBRyPT29WBsbm575+fnc6tWrn/rggw9OtD97u+8ZAZgIYHdERITwONevUCgYgDAA1yIiIkqe7O678O9AF9nvQhe68JcgiuLO4uLiz/Py8oz+GfJzS0tLLFq0SD87O3umvb09bNq+lAEyZEeNak8AOoGjoyOflZXVXPSsrIwKVO3ZQ1HTzEzKi2zZt1qLHTvIoGybL/lPQEVFBS5evCiWlZVxAJCbm9vUNuqRaGgg6bqlJeV/HjlC+cyDBhGRf/556nVsa0sSfQ8PilDevt0sS//jD5LfZ2WRbNnEhKLzjyOfraujaO/f/tbsINABpVKJ8vJyuLu7Y+bMmdizZw/O1dbiaT09sfjkSZaYmIiJEyfyVlZWUCqVraKmHMdh9uzZ3FdffSX8+uuvHADMnTsXTr160fglJLQuWCWV0r1Onw5ERED12WfIy89HqocHM3FwEBEVxfDzzxT5bSSC+fn5qEtNRdSBAzA9cEDsBzCLixdhpCt1QKmkPt3ffUd/nz9PRrmXF85GRWmiyst5fvFiSHfuFKRSqdC9upqbmprK9fzmG2o9xnGAmxuUEgnO+vtDrKzE5ClT2PZjx3D//n3u4cOHMDY2bs4Bb2ig8fL27ngcvviCpMI6wAwMEOLtjYH79uHBnj346dIldtXBAQ9yciCXy9msWbNgZWUFuVze+YCbmdFc2buXSIOrK0Vm09OJdH/+ORnZjbJ4AGTs5+cTMZ82jYjdwYO4O348ru3ejVBtzn4L0uXl5YXo6Gjcr6iA/fr1lFazdi2RUG1V9lu3aN0uXqw7vcLFhZwqjRLzjmBoaKgt4MldunSptcH+zDOUatKmEKfMxQUbFiyAoUSC0DlzyEmooxo9qqqIWDQWDy0oKMCAAQMeTfQBIiy7d7f+WVoaEcjz55ucjlIAc+fOZX/88Qeys7Px8OFDzbRp02gRzprVquXZ3cbUBkdHxyZn2ezZs7l7zz6LhNpaAbt3c/j+e1JDxMVRisz69URiMzPJwcpxwK5duFNRIdo7O2PGlStM9vrr4NesIUfPunU0RwIDyWmop9d5+gtjlGuurTvxzTdEqr7+mhyHWogiKY4iI0nN8fbb5MScMoXGqc1epVKpkJWVxXVaOT4oiOZkfj4EKytcunQJ0dHRjDEm4uWXaUz37Gn1FbVaDQDQ37OnmUTOmUNOpcmTiVyGh1NEW6lsLvwXGkrPtQ2hT0hIgEqlYqampqK7u3vrmygvpzop167prKKvVCpxrLxcKBg6lJs/fz4cHBw69kreuvWXFGnDhw9n+/fvh4uLC4YOHdr6On/8kcbjwQNSgWnx4oukLvnyS3Ka7tnTXCj2MbBs2TL+888/F+7fv8+1JPs6cfkyzbOzZ5vrchQU0Du75Ttp5EhynKxY0X59tcS4ceToys+nOZeSQs5xF5d2Dtfa2loolUrExcU1F4N8QjDG4O7uDkdHR/0zZ86MLy0tVQ4YMKBdbotEImlSBY0aNUravXt3j1OnTsV9+umnFYIgGEokkgJBEEwYY98xxsrs7e3NZ8+erZ+cnIwTJ0588MknnwyRSCTPiqJ4uaGh4TyAuIiIiCaPpkKh6CmVSg+r1eqBgiAYA/j+MW+hO4BjjDGlQqEYGBERkfikz0ChUEgA+ADIioiIKO/kc4YAlF0FDZ8MXWS/C13owl9CRESE+qOPPvry8uXLH0ybNk1H0+0nh7m5ecf5b1VVZEB/++0THbOhoUGQy+XNoYV+/Yikvvoq/X/kSCLMbcl+YSEZK/8CFBUV4bvvvoO5ubk4Y8YMdvz4cezevbtJsmhsbEwyapWKSNSxYxRlnTOHDOB336VUhpdeoghinz4kVzx+nPIlpVIyhjtCVhbd77BhZAQlJZGhWlNDDpBHEZMNG+i6nJ07jaD++OOPmsLCQn7IkCEYMWIEVq1ahfr6elzy9xdrjx4VXQYMgL29PdfYikwsLS1l1i3ag3Ech9dff50DgLVr14o3b95kDra24L/8UndRJcaARYtQOmwYBF9fpIwcKRSOGiWGzZvHIzGRDGmOI0fF8uU4ffq0aNyvHyvr0QM+Z8+yXBsb7Pj73yHXaND73j3hhosLN333brGHqyurXLcORocPI6p/f/HptWtZU77xV18h7ZtvOF9fX0ym1AgOAIfjx8nQ7dGjleEpvvEG+sybhyF2dujx6qt47cgR8FIpvvzyS9TU1KCkpIRXKpWQbttGpLqx9ZVOrFtHKobbt3X+mnXvDsmDB7DneQz76SfccnfH/YwM9OvXT1sn4/Ggp0ckMiCA5t7gwWTAe3mRFH/BApqPH39MzpbcXHJEeHiQFH/BAojz54N9+y2r1SFlLyoqwpnGLhjm5uY0jgsWkJogIYEcCy4uRF6uXtWZ690EjYYiw5cuUdpKB4iJiREBMP22kbupU3U6E+/evYs6AwPY9+pFzzsykj7bcv5XVND6OXIEMDbGsWPHAFCx0MfC+PFN9SYQF0f71LFjlEbTxvlpbW2NZcuW4eDBg0hPT+ePHz+uGTduHI+VK4GzZ1HN81i/fj3UajX8/PzESZMmNZE1nuchZmSITtbWHIYMobHbv59ULW++SbUDfv+dVBmNjpCin37C7i1b2PLly2GgvefkZCKlgkCy9rw8Iss//UQKi927yfHyzTfkLBo8mNatnh6R0EGDiNguWkROODs7crQcOkRz59lnifAuW0bPYvBg2q86AMdxCAwM1CQmJvI9e/bUrTxgDJr0dBSWl2O7RiPq6+tDT0+PKevr2f5FizRPT57cjjzX1dUBAPSqquj6lywhJ0ffvrT/HjpE+4pSSaTw5k26l6lT6fk1NEDN8/jhhx809fX1XHl5OfPx8RFsbGzaO9rCw2nOZ2W16i4hCAISEhJQ+umngi3Ps36ffQYHB4fOZhPtDTNndp6m1gm0jtfLly+zpsKF2jSwadMoVaFt1D44uJl4z5zZ7Kh7AgQGBnJHjx6Fh4dHxzUCAHIG2tjQ3AForaxaRW39WoLjqLju6tXN6WodgTFylu3fT6qFVato7l65QnPZyQn5+fm4dOkSpFKpGBoa+pe76sjlckyYMEEGynrrFIwxBAQEMF9fX3l5eblcKpWivLzctb6+Hnv37n0dwCVbW1s5YwylpaXqhoaGAHNzc//w8HBZdna2Q2lp6dRbt25BoVAMioiIuK5QKHie528rlUrTgQMHIjExcRIayf7q1asHS6XSdxsaGt5oqyJYvXr1OJlMtkmpVIqDBg2SXrt2LRKAhY5L7hAKhcJLKpUeZ4xZADioUCiWAKiKiIhQt/mcL2MsTiKRZH322WcPNRpNX47jqjUazZL33nvv0JOc838NXWS/C13owl+GIAg/3bp1S6FSqZrzdP9V2L4dKC5u14+7M4iiiNu3b8Pd3Z1ysK2sKBrQUomgzYu8c6fZKBJFItBbtnQo//4rqKurg0wmE5YuXMihsBAvm5jgiCgKD+bP50yKi0Xj339nCAujSKqPD/WYV6up4rylJRnAo0a1P7CHx+NdwMGDREy0kTFfXzJufviBJI0zZ+o2qgWBHAyLFlEE7hG5y9XV1Rg2bBiio6NRX1+PYcOGwcDAACP69uUwezZFfxqPIZfLhZKSEt66g17gAQEBiI6Oxp1Dh7Bg/nzIOpGGnoqPF7glS7jppqYcTE3J2AsMJKOtpoby54OCMNvTk935+GOxX0IC+2XuXCw+c0YMdHNjKeXlYuA333DDbt9GgZUV9qakoGz3bjh+8QXu3bvH9p86BRMTE/j6+sLY2Bhubm5ISUkRBUFg3NWrzUUfdRTHkgUFocHcHA+GDEGPuXPRLT0dGDAAAQEBolwuZ9HR0Uj99VeN/5df8o+U3y5b1i5q2A6GhsDhwwjZtw+OL72EYxYWwvXr17m8vDy89NJLnX+3JbR1HgoKiOh+9RVVOZ8xg6J8O3aQguGHH+icP/zQqip8WloaqqurMarNvC0tLcWWLVtgaWmpmTp1KtdKaaCtp7B+PakAGttxdQqeJ6L8iM4ZAQEB7O7duzhy5AiuXr2q8fb25kMkEponbm6tPltXV4ft27cDAJ557jnKQy4tpSj2zz83E5ojRyia25iDnZGRIZiZmXGP7VjR0yPyfPAgRZAHDaKIdyf3PHr0aDDGEBsby/fu3RtOGg3OXryImMbibU899RS8vb2bnqlw6hRyjxzB0VGj2GJtX3SJhBwNffvSeM2ZQ9Hzl14CRo1CfX09Dhw4oPHx8UH37t2byXBISHMXgF9+ab6oV1+lvcLUlEhxbS0pFGQyWoP799N+/tprNE7DhxMB03YCqKuj+bVr1+PvaSAnxvjx4/nevXtj9+7d6NWrFyxbRGTv3r2LixcvipqqKiY8fIjg+fMRGhrKNDU1YJaW2DxvHr82K0ucPn06c2qhGspNS0OvrCzIduwAXFxQ4+EB2QcfoObKFfDffguD5cvBGRjQPnP4cNP4Y8UK+tveHlEDBwoFXl48DbMewsLCuHbS9ORk+k5wcNO60eLixYu4cOECJmVnc/0WLQL/iOK0ACiPfvDgP0X2S0pKkNSYzuLj40PKOFGkcY6NJWecLtWAlVVzoTtHR0pL+fFH+vxjQBAEDB06FLdu3RLOnDkjTtbhfMHPP1N9hAMHWqs7fH0pbU2XjTBgAF3bd9+1TlHrDBIJ7TkREdTJ4Y03gJUrcRMQAHDPPfdc+5oZ/yZIJJImtZA2bWPatGkG6enpQ7Tpj7dv364B8El9ff0HDg4OMgcHBwkASVxcnHjy5Mnza9as+Q3AVY1GY6o9jlqtHqVQKKQAeKlUutnFxaVvZmbmkLVr18YLghClVCrfB2DBGDsybdo0ztnZGbdv30ZcXFzncipQtwGZTHZRFEUnlUr1EoAwU1NTs7Fjx0p37NjxDIBpMplsl0Kh+Egmk20FUN3Q0DAFQIlEIqmQyWSu1dXV2gduxBh75Dn/19FF9rvQhS78ZURERBStXbs2MSMjY2Dfvn3/dSeqraWX9cyZj/xobm4uLly4oBFFEYWFhTzP8+KIESPIkAwMbK8MkMmIPMfENBtFjFE0TVfhpD9z7XFxFC28dAk4dAgmCgWWffQRh/PnKUp09CjCV67k8o2McPHsWTbGygpVMTHILyiAhYUFHGfMoGP9iRZBOuHt3drhARDRWLqUSF1GBqU2vPNO69Z3x4+TU+RRbQoboVarOQ8PD/To0UM8f/484uPjmaGhoWbmzJm8dX5+U1RUrVajpqaG70zqPHLkSBYTE4PAa9egTkuD7MKFVr+vq6vD2bNnNXfv3mXFxcXci6++SmM7fz6R0y1b6IOGhiS7BVDz9NPolZnJACCspkZUOzqKRvb2bNCsWQzLl6M7gO4vvMCM799Hbm6uGBwczIqKivD7778LhYWFLC4uDkqlkgFgHMfhwqlTGN67NxnYLaL55eXl2LNnj+Dt7c38/f1ZmosLRuXl0XwePRro3RsTNm5kAJCTk4PzSUl89bvvinxSkhASEtKxTNfEhCTDr73WuRojPByb334bo4KCsMzbm1vz668oLS1FfHw8vL29H11wLi+PCMiRI/RMJ00iWblGQ3nXMhlF81uiuLhVZD0yMlIMDAxkbYvU7d27V/D09BSnTp2q+z61RPfDD2kcBw5sap/XITw96dnOnYuSwEDk5OSgsrISSUlJGDlyJHx8fODt7Q1LS0s8ePAA+fn57PTp0xiQlASJgUFr4go05by/+uqrzUX2zMwoei2VUiS2vJyi8i3y+Y2MjFhRURHEykpy2HXvThHCmhpSLZw7R86M6GhaV8uXU9G5qCh6fo+RD2xgYICbjd0Kdu7cCc8BA5DX2KVh0aJF7dKhUnbtEvSuXOFmiiKkU6YQqV+zhhx4UikR/cpKIuDnzyPRxASHjh8HAH7WrFmPvB6hMUebk0hI5aDF5s3N/16zhpwBTz1F9xsZSXMpJob2Jo2G1AXffdcc2c/Jod/pSrFpA1dXV0ilUpw7dw5hYWGorKzE4cOHUVJSgj59+ojD336bmQ0bBrZlCwNjkBgZAUePYmrv3tiyZQtLS0ujNq0FBcBXX2FAURHKiouxnzHUDx4Ms9OncaOkBKIowltfH+UvvYSi3r3Fyenp6PPqq6y6uhrp6emor6+Hu7s7DkyZgoIePbhxRkai29y57GhkpPDVV1+xgQMHsuGNTlUhKQkICEDGhAkotbKCl4EB9PX1kZqaCjs7O1y5cgW91WrR59dfGefu/shnAIAI6p+AKIrYtWsXHj58qC3CyLByJc3R33/v+Iu5uRTxLytr/tnVq+Rg74TsC4KAy5cv4+bNm7h//z4YY9DX12eFhYWcqamp2C6F4N49Ur21JPoHDlAaSEf1fAIDSXmkneudVNBvBxMTWg/nzgEqFYaGhHB2ogjNtGntCjn+J+Hu7g53d/cmdYCRkZFEIpFMayq82oiAgABmZ2fX/datW0uys7Nn+/n5wdvbGzzPIzMzU52Xl5cNoMjZ2dnl6aefRlFRkWFBQUHomTNnApRK5SAAI0RRRK9evZCQkKA+efJkg0qlepzCEKONjY29x40bZ3zkyJHtPM8LLi4uop2dHfr37682NzeXXLx4sZeent7WhoaGwb179667c+fOfUEQftZoNFesra2Hl5WVoby8XC0IwswPPvggsuXBFQoF17LegEKhcJRIJPGCIHz0/vvvr/+Lj/f/JLrIfhe60IV/Curr6/+4f/9+QN++ff91+8r69WS47NvX4UdEUcSZM2eEa9eucX369OENDAwQFBSEPioVj9hYMqg7IjWLFlEEXdtXffbspoJjj4QoklEoimQMHTpERvusWSTrXLeOopLLlhHRDA5G+t27Ysbf/ibMe+89HhzXVBnfZuBAPKip0Rw4fJi/c+cOjI2NhYaGBta9e3fRxcUFXl5enJWVVectvB6F27cpYte2nZkWoaFE0rZsoUjG8uWU+vDaa2Tw/PHHY7WjU6vVaGhoYEZGRrC0tGTe3t7QaDQ4dOiQ+OOPP2KwXI5B334LaXY2Nm/erLGxsWHW1tadhkneeOMNfCOXi7kuLuJToshpn4NKpcKRI0eE+/fvc0FBQczPzw8GWpK4YweN6fz5ZOxpnRzl5TAGoHRzw53u3YX8oiLOu6KCie++C/bhh8250v36wc7ODnZ2dgyg2hLLli3jRFHEnTt3miphn3zlFQR/8QVQWopDISGaW+vW8T4+PoKTkxN36NAhURRF7sKFC+LJkydh4eAgdL93j0N1NRnP9+4RzNdL9QAAIABJREFUeR42DP3y80WXn39m35iYMNy5w3McJwwaNKjj53L4MBXC64zs//ILpu3fLypjYhg4Du+cOYPff/9dPHLkCDt+/Dhef/11tJOza5GdTSQrJ4eiuF9/TTLZn34iMqpLYQJQFK1Rvq5tf2nXxnmWlpaGoqIirlMSuXo1GfaLFxNZ/vFHmocdnbcRGnt7JNy4IUTGx3NyuVxoaGjg1Go1Dh48iIMHD+Ktt96ChYUFLCwsYGJiwsXGxkJy4ACt4xZQKpWIiYmBkZERTNu23pw5kxQyvr6Up//xx+T8KCkBXnoJ477+mhVJJBDj48npt2IFRR6trKidmrEx/XvGjGap8dCh5CwaM4Zqakyc+EgysXjxYqxfT7asf0ICnvn++/ZF7pRKqFatwmWZjJMOH44Fo0eTDLu6mpx42lz50aMpyv/yy4h0dkb/8ePx3DvviPYvvsj09PRQXV2NqKgoFBYWIigoCJ6enrh//z4SExORmJgIkVpeCm+88YbuOZubS2Mok1HXAomEHDj+/uTkqK0lgpidTeqqnj3JibJ+PT2XlBT63vr10OzfjxovL0gGDIBB9+6tHGxSqVTMyMhgt27damppOWPGDHh4eNB1ubjQPC4qojabhw7BBsDUsDDEbd6Mi19/Lfa/fZvh/fdRN38+rjXmeg9NSMCg2loEPP88amtr4TB1KjQvvogHc+cy/U2b8BtjYoazM+vevbvA8zzOnz/PCY1r0+/LL5meiQmee+EFLjo6GhcvXqRUA40Gx779VjCePJlVTJ8ulKWksHPnznEajUarmBNVKhUbfewY4wwNSUHzOPD2pmfdWQ2DNlCpVNiyZUtTxwV/W1tyvIwd23F3DC3s7Mjx1XK+vv669sBNzl1BEJCSkoKKigpcu3ZNqK+v54yNjUU3Nzdx4sSJnIGBAdLT09nNmzdF7b4LgNZXSoruvPuqKnIedYaMDCL5ublPRva1MDFBVFSUcH7aNM6mvBzP7t5NdsXXX9Pa/Q9F+TvC1KlTDZOTk4M8dKhjrKysYGVlxQ0dOrSV92zOnDmGubm5hmVlZbY+Pj7gOE77WXTr1s3g559/HsHzfH1oaKhcIpEgKSmpRqVSbQTgq1AoigCIoEJ/2wFUA7ABUAigBoC/paUl5+zsjFdeeaVVfkZ4eLikoKAAKpXKQ6VS9ZdIJNcCAgLMR4wYoX/lypXlxsbGev379+dNTExw6tQpxMfHTwRwVPt9hUIRCCD2s88+++att956WaFQuEgkkotqtbongP9ZBUAX2e9CF7rwxGisvuoAYADHcb56enp2Uqk0tF2V3n8mBIGMaW2rOB1oaGjAtm3bhIcPH7LZs2fDvmXO7YIFZAjs3dvxOYyMyNjduJGK273/fusXtyhSNK60lAzjESMo6nTxIpHJceMoIubvT9cLUITP0pIi+hcvtr7ec+eYxtaW12Uc9OvXjz9//jycnJyE2bNnc0qlErGxsez27dtCXFycKJVK4ePjA2dnZ6bRaJCcnKypqqrC8OHD+ceSC8fEkOHWGYEwMSEj7dYtkuT27k0S0w8/fOy+8xKJBBzHNVfQB8lsp06dKvHz80P02bMaqb09f+Hjj0WJXM4vWbLkkcc0MDDAa9u3s2+nTkVCr15wcXHBlStXhISEBM7AwEBcsGAB165Nn1xOJCEpicb2yBGaD6+9BixeDOnYseg9fjyXX10tfD5gALd08mRYGhlRO8EvviDnQF0dETtjY5IVcxwYY2iS+sbFQTV0qOaKuzvvVlSE1NRU3snJCXl5eSwlJUVjamoqLly4UAKApaWlwcjIiOP+/neqfD5oEM2TGTOADz9EUFgYe+jsDGRlwcrKClVVVVAoFHjhhRegsxBmcnK7H4miiLy8POobznGAuTnS+vSB8dtvw75vX+DSJTw1cSLz9vbGb7/9hs8//xy+vr4CY4zz8/NrXj9RUUTwMzLImVBbS0URtdFtfX1ycOnKSd+0iaqugwhzXV0d7t+/Tyk1AOrr6xEZGSn4+/vDxMREt5W8Zg05plxdaT3OmEEE8NtvSXnTtmp9IyorK/Ft9+4wKy/HwoEDYR0e3nT848ePIzY2Fp999hkWL14MY2NjHD9+XBx34gSrvn4dlRs2tIqG5zYqFqZOnarzXBBFIujbt9PeMGkSKV+CgnBgwgTR3s+P+U2f3vz5lm26dOWUv/QS7SNjxtA+JJc3d8/oAKampvDy8kJqaiqqjYx0q24yMsAdPozx9fVg779P+eQAdVN4442mdpgAgPfeg7B5M2JdXVE+dixMU1PF02vWiMUSCQcANjY2go2NDXfgwAEkJSUhs7ENprm5OcLDw7Fnzx5OoVDAzc1NM2vWLB4aDe2tv/xCedYSCdV2MDGhlIlZsyid6quviIQJAqBWQ8jIQJ6NDQyMjNDz0CHas/LycH7DBsRv2oTB0dFoOHoUSqkUw8rKIHnnHSJfenoIGTVKTNy3j6nc3FBcV4dXXnkF3Vs6QDZupEJs5ubUnz0vD/jhB3gXFcHGwgL33NzYF4MGAbm5MK2oaH7WCgWYpyccW+6D48fDpLoa9Tdvwu/uXTbJ3h4GBgYcACgUiqaP3TpxQvDq149j772HwXPn4qxajXXr1mHRpk0Ir63lpKWl4PT0eIA6RlRWVqJbt26IiooSL54/z3JfeAFm8+Z1OA/aoU2xRi06S7vjOA7FxcVN/zacMYP2qJbKjI6wZg2p41oWTgVIMZaW1vQe/P3333Hjxg3IZDL4+voiMDAQPXr0YC1bggQGBiIwMLD5JaVWk3OqZZtULa5dIwXQo9r8zZ5NDou33qJ6KI9yXuiAra0tJ+jpQfT1heHChcCJE7QPurvTOnqM99i/CwYGBhg0aNATfYcxhl69eums6eLk5AQzM7PKkpKSiOjoaIWrq6uJi4uLvKqq6iUrKytZTk7OCgDGrq6uRhkZGe9rNBpjQ0PD6vr6egnP8/f19fWtAgMDDTs6t6WlJXr06CEtKipKt7a2FmxtbWFgYIDJkyc3bWhqtRppaWm1KpXqokKh4AAYAwgBcAgAGhoaXlQoFO8DCFCr1TZmZmbVJSUlhU/0EP4/QhfZ70IXuvBYUCgUcgDdAIRJpdKPGWM9bWxsVA4ODsaGhoase/fucH6c/ME/fwFkHDbmP5eXlyMyMlKTn5/PAYCJiYlQXFzMm5iYiG+++SbXlEOXkECG348/Pt55pkyhyH5uLkXht22jKPbGjWRsh4RQpeGYGDLGw8Opcq+bG0WhtHbKlCn0dyfFk4qLi0VBEHSy7aFDh8LT0xPm5uYcQMV7QkNDERoaymkjInFxcUJcXBwkEolob2/P9+jRA7/++qvIcRwzMzPTqNVqptFoIJVKRXt7e2ZjY8M5ODhQv/qSEqqS/jhwdydym5NDRnFlZcdtsXRALpcLmZmZnHebivK9evVCrwULeHVYGKx+/pnJn3vu8Wo+KJWQ+vhgQFgYi4yMhCiK6NmzJ2bNmoVevXp1LHdnjKKkWoeMRkPt0bRFGUtKMLiggIvq3x9bjhyBl5eXZsKHH/J6enpUbTo6mqKGzz9Pz2/LForGh4YS2V66FAbffovLxcVi9E8/sf79+yMsLAygDhCtrstTm0s+dy4Zh2fPEpE+cIDI4oED6P7LL+A//hgFBQUoKCjgAODEiRNieHg4s2zbhk+hoLl+8GDTj6KiooRz585xNjY2QkhICKc5d06M6duXveTmRobp888Dy5fDbcUKuLq6ChkZGdz169c5AEhMTIShoSFenDsX+uHhZKD7+JChfO8eFVTTYvVqIo1tI41lZfTcGq9VJpPh2WefxYEDBxAXFydyHCc2NDRwLi4uYlhYmO5xO3WKVAvLl7fOwe3Xj3JoT56kSu7fftuO3F69elXQ09NjC1JSOD47m9ZqIwwNm23Nbdu2QSaTiXp6esgOCtLcrazk0rZuZRzHwdTUVJw9ezbLysqCXC4XnZ2d26/Xp56iyP7p0zQnTp0i5cy5c4C/P0rlcubxpH3O164lx2T//lRw7Kuv6GdffknjoMNJt23bNk1ubi4PALKGhvZFM//xD6h798bOkBCA4/BUy3SoNWvapysFB6Po0CEEx8eL3lu2sNzUVG7E3LnAJ59A7/nnASpCCScnJ+zevRv29vZ49tlnm9Q05ubmUD58COf6eh5z59L4hISQymnwYFo3c+YQ4ff3J0dut26kclAogLg4FPzyC8xjYnBywQJ4fvcd7D76CD2+/x7bk5M1xfr6/NOzZ8N0xQpUVFRg37598JkzByJjMGtoAHf7Nny7deM8MzKQmpoKNWMwTU6myPDJkxTx5nmqJv/GG6TAGjUK+OgjsJkzYdbQADMvL7icOoWf//gD5YKAoLg4ZAwcKFzduJFz2r8fhUuXivb6+gx+fkRmzc0hd3OD++HDtEY8PZFy547IqVRM0NPDc889B1dXV3o5XbkCFhqKV155BUJODuR5eZDNng3WYtx4nm9yTgwdOpSzf+011I0c2WErOJ2YP7+d4icuLk48evQo8/Hx0UyePJlvqxLjeR79/f01Zp98wjsvWgRpdPRjpU4AoNQTXZ1yVq1qpZhxd3fHrVu3MHz4cNHGxoYzMjLqXK32zjtEznU4NgGQE3rgwEdfn5UVqTnUanqnPUE9CC2cnZ1hYWEhqiiNgDXVZdm5k/a9ZcvIIf7RR3+pE8J/K7p164aSkpL7giBsTU9PXzFy5EjZ8OHDZUqlEl988YXdgAEDNCNHjpTGxMRIo6Ki8MYbbxhVVVWhoKDA1cXFBZ3VOGCMYfjw4UZFRUWawYMHS3TNCUEQUFNToyeXy1+uq6vT5lsJDg4O7P79+6Kenp4oCEIiADE4OFi8dOmSHICeQqEwBqCJiIio/Vc8l/9WdJH9LnShC+2gUCi8OY6bKpPJhouiaKNWq60ZYwYSiURtZ2enGjx4sJGTkxMYY09gcfw5iKKI48ePa/rs38/Hjhghir/9JtTW1uLBgwe8vb09Gzt2LBMEAaWlpXxAQAB8fHxak4a4ODK6W7Zy6vhkFGVNTiZjQJsrO2ECGaHZ2c0kV0fRtSdFQUFBp9XQO+pIwHEcfHx84OPjo31jNr0NJ06cyHJycpCXl8dLpVLo6emhsrIS9+7dE2/evKmprq7mberqxBEHDuC2t7cwcsyYRzSSB3UkyMwkh0d6Osl7t2+nFneP6kMPGsO2OdotISkpgdO6dbp7SetCQwPw008IlslQXVsrpqSksIqKCiZ73AiNSkXX7eVFBbC0+ZbR0UjdsAHT1Wocl8nE5ORkfsiQIejZsyd9TtvZ4No1Muhu3qT58vvvJHENC8MUHx8+urQUySqVGBYW9mili58fOYRiYogIubkRaY6KAu7cwaxZs1BSUoJLly4JFRUV3P3791l0dDTs7Ozg5+fXnGcfFkYksBGCICApKYn5+vpCFEXu8q5dmunff89PPX++WYaenEwELCoKM2fO5ACgsLAQmzdvhqurK4z27sXXxcUYf+aM6MHzTBIbC/bZZ+3H/PJl3dLV0lLq7tAC7u7uWLFiBcrLy1lGRga7cOECxo8fz+ucHydO0HGPHqUib21hY0NRS7WaHHTbtrUiNampqWzEiBGMf/11Gt8WebVBQUFNUVOVSgUTExM2wsICkn79eAwd2iQxPn/+vLh+/XrG8zx4nm89nt9+SwR51SpyiDFGqQWRkURmv/wS6gMH0Pf6dWiepBe9KLbvNrBiBe05Fy5QF4x33mlVs+CTTz6BWq3mtd83rKlpr0ravBlcQwPkQUGwe/nl5j7s9LCIdAO4cOGCmJSUJBgYGHCCIGDWjRvM2NAQFiNGkCJGIqHraMzF9/DwQERERPOxNBqgpATjzp4Fd/QohJ9/pjSEiRObHTKiSGqC/Hxq2Xb2bLMDY/VqICsLac88g25XrqDCxASjFi1Cz23bsFUiwZjXX0eoRsM7r10LA1tboHt3mJqaQiKRYOOOHQAAQ0NDUS2ToSEykmHECFhZWYmjPT0Z0zoq09JoL0tNpXfEvHnULeDWLVrr587R3C0ogMlnn2G2vT02duuGofHxGLdhA5e7ezeU+vo4y5j4fG4ug5UVfVejoUhxRgYVIAwKgu2PP7IXMzNxfMoU0fbZZxl8felZhIQARkbo3q8fOcVOniQHSCfzQi2T4bJUiieqjOPsTMduLCCZmZmJCxcuMDc3N6Snp3OiKMLFxQWenp7N+3RdHexsbBjq61EDPD7RF0VywumqdO/kRNL7CxeA+fPh7e2NqqoqxMTECEqlkjM1NRWXLFnCtSN3gkCkfOVKUvXoQkEBpRTpaoOpC0FBtD5WrSJn/p9IiauoqGDt3tHade7pSaqmmzdpva5aRe+b/08glUr1JBLJ12q12vrWrVv1I0eOlDf+HG+//TbPGOMBoKysrGlOGRsbU4ehx4C7uztzd3fv0GiQSqWYP3++XlVV1cC8vDxERUXB399fOXHiRDkAiKLI9uzZY3nz5k39rKwsjSAIGp7n/TmO+06tVhsrFAqPiIgI3e1r/j9EF9nvQhe60ASFQqEnk8l2yuXyiT4+PhIHBwc9ExMTmJiYwNjYGI0b+JNr3v4CsrKyoNyxg6t96y24eHiwgoIC3sLCAmPHjoWtrW3H7uFr1ygK9ttvlOf7KOTnk4G9eTNVf963j6Lz3bqRlLtR0vgkEe1Hobq6mulsC/UXwHEcXFxc4NK+eBkDwKvVatw8epSlFxQg/to1fuiIEZ0ScaxZQ8bw0ca0OEdHIjRz51L0ZuXKdu3A2qKuro7rVPXRrx8Za4+L554jwh8ZiTFjxrBRo0Zh//794tatW9mYMWN0t9rSorqaDO0NG8jYDg8HnJ0ROXkykpKTxfG7dzOn1FTY/vSTOHfuXNajo4hsjx7kDFIqyUnxxx9ATAzY3bvw/PVXWKhUdI1mZuQsMjPTbVAyRkQjNpauJyqKSIYoAlVVcDY1hXNQEAICArjGCD8OHz6M1NRUnDhxAqtWrYJcLqd83EuXAJDE8R//+AdUKhXGjh1Lv58wgcesWejbsjq9gQE5csLDKb3ByQmWlpaQSqWiV58+zCkmBpXGxvj96FH2zO7dsLCzg2z//vYG244dFNVuJFpNyMpqXaSrETKZDJaWlrC0tERiYqLmypUrbOzYsa3XcmUlKWi++46edUeQSqkd5aBBpND49FNgzBiUlJSgrq6O+fr60jP+5hvK926Umsvlcgxv22nigw9IvXH2bCuHWlpaGsrLy3Hq1Cls27YNs8aNg9TIiOTo77/fWoYvk9G49+gBKBSQZGXBa+tWVH3wAfYVFmoGjx3LP7JvuEpFBfwM2yhdPTzIGaTR0P52/DiwbBn+OHxYVKvVzNraGg8ePABEEbGBgbBFY+j98mVawytWQBwyBLf27oWXqSkqKyuJ8BcUkGKj8T4SExMFV1dXvlu3bugRHAyj8HBqHzl/fnNdghkzqP1iyzoL16/TenjzTcDLCxZLlmCdhQXE1FQEBwdjcEuiP2kS1QXw86OftVwb+voQ33oLmePHI2nIEAyUSDBw2TLR6OZN9sK1a8icNw8OlZUwuHOH0nB8fGC0cCECAwPFy5cvs9deew2HDx8WrKys+JiYGG00vfkE9+4RGd++nRwzVVWkKjA2pv3dxYUcYVqVzJkz4KuqoPnHP3B0wwY84+wMx7ffRtr48ZB88QX3g7e3OHnrVqbn749uxcXkEOnfn5yiAISnn8a3Gzagj4sL9F97jZxTgkAdCiQSKuqo0dD8HDCAovY6nGfqnTtxb84c5JWUdD5/2qKkpMnJUlVVhT179sDPzw9hYWG4f/8+27lzJ1JSUlBcXIyRI0fS/HNxge+6ddzWl14S8vPyONddu4RZs2Y9Ohk9OZnS23SsewBEflukQwQHByM4OJi/cuUKIiMjWXJyMnzbtsr89FPq6pGd3XFNkvnzqfr/qlWP80RIZm9kRHtWdDSlBjwBsrOzoVarYW9vL6BxmbWCoyO17K2oIEXg7dsU9e/fnxyT/yXF/P4sJk+erH/v3j19c3NzGBgYtJJUtXTWZGZmaqysrHhRFP9ajSEd0KZZubu7o3///jAxMWm6DsYYpk2bpl9UVITu3bvzN27cEB88ePCaKIqyhIQEAPAG8D9D9vkPP/zwP30NXehCF/5LcPny5c22trbTFi1aZODu7s6bm5vDxMQEMpnsn75RPw6Kiopw8LffxKl79jC7V16BTf/+6N27N5ycnFpHpXShpIQM4o5ybLUQRYqqrl1LxvWGDeSV15LPr7+m/MM1a8joWLqUIlGurp32ln8cXLp0CZ6enmiXX/4vBMdxsFi6FM5vvYWorCz4+fmhXesnLUpKyOB9553WRpZEQgbLnTsk7y4tpRxqHXMkPj4ed+7cwZAhQzqV7uHsWYpOP44CY8oUMs4bpayMMfTt25dZWlriyJEjcHBwaF9EDSCDa9w4Gr/+/SlC/fTTiLp4UZQePMgGzpvHXObMgdTNDX0HDGD6ndU+SEig6/jwQ5oT3buT08LZGckeHogzMxP87ew4XLlCBHDuXIoiWlkRke3Zs/l5detGBR39/akmQJ8+JCf+8UcikwsXgvE8jI2NYW1tDWtra+jr6yM/Px9qtRqurq6AKEJ0c8MuuVw4ePYsU6lUWLp0KWtaJ1OmkNHZ1umirVNhZESGuo0NpJ9/ziobGjQehw5x4q+/ovvt2zg7YQKu2tvj8uXLaGhogLW1NSQSCURRBHv4sLlye0vcuEFkvIXioC14nufOnz/PQkNDm/eYggJyuH31FUmtHwcWFkQyfvgBePAA2cbGyMzKEocMGUIHdXGhqKkuebEWw4dTakMbmJubw97eHvb29rh++DB8ly6F1M+PjPk27fkwa1ZrstajB1Rjx+JadbUw6qOP+MSrV+E6a1bT76uqqqCnp9d076IoQkhPB7dgQZOTsrCwEDU1NdRrXNs+0tSUIscHD+KPsjKmaTzGokWLYGligpCPP4bk7bfpuBERwDffoH76dNx3dMT169eRnp6OK1euIC4uThR+/51Jk5JgNH06cnJycP36dc7d3R2DBw+GhYUFmFRKqh7teQ0Naf05OtJ+qa9PDq9ff6X9dsKEJmdghVqtKSsr427evAme5+Fob08OgexsIqDTplHbyBZ7g9rWFr8VFQm3HR3Z1GefxaAVKyAvKGCoq4P06adhPWIEDCZPpnaogYG0H02cCGcjIxa8bBkMzczQr18/LiYmRnj48CFzc3Oj1nubNxO5GzWKovDjx5PDbtkychZ5etJ9mJhA89lnqMvLQ7VaDfbmmzhdXy8U19WxwsJCODs7o1u3buippwfvefPwwNMTZteuoTori+23sBDdr15lkqwssMYofVFREW6kpcHDy4s5DRxIygEbG4ouV1QA770HTJ0KMTUVNWlp0Lz2Gkr79EFWSQn0ZDIkJCQIe375hfV7+21cNDdH/4kTmxRhSqUSSqUSjLGmP+2wciU9J0ND5ObmIiUlBS+88AIYY+jWrRuGDBmClJQUobi4mIkbNgiF5uaCzXPPcTHGxmJyY1pPZWUlC+1MdaCFTEbPUldOPUCEfPRoUnU0vj83bdqkzsrKYv7+/vDy8mJN76OqKprjy5ZRzZ2OlFuiSA6TpUsf/53MGL3b3d1p/+3T5/G+14hz586JFRUVmDt3bucOEG2tGHd3ev8cO0bjf+oURfofQxn33wiJRIIePXpALpe3qsfTFoIgcNevX4e1tTUp5P5FkMvl7eY+x3EwNjaGRCKBjY0N17t3b4mNjQ3UajUKCgomX7p0qWLIkCGx/7KL+i9CV2S/C13oAgBAoVD0kkqls5955hn9R7bg+jfhwIEDgjXADI8ff3yjPzmZDO6kJIrcdIaSEmpZZmtLhCcgoPl3Xl6UMxsURIbBunVkVKhU9FlfX/p+aWlzfv4T4NatW9BoNB1K9f9lKCkB7t1DpoEBNBpNx5/buJGiqomJuiXajFF0JC2NnCCVlUTW2/SFPnXqlDhixIj2Eui2GDyYipI9TgujF1+kSG0beHh4ICgoSLN9+3Z+8eLFsG55LTk5FIlcv74VQTsQGSncMjfnXhk0CAavvkpR0IwMKoyWlqZbydHQQHnoQ4fqNC45joNGT4+IzLRp9MN+/Ujqf+4cKUVmzKBo+ODBRIzMzMjptGtXc2/4t96iqPvZs0RUG41gd3d3uLm5oaSkBImJicjJycHTTz+N3Z9+iqqGBm7cuHFwdnZGqxaGgqCzVZ0gCCitrUXae+8h9OBBsPx89MzMhP3YsTwEAb5+fiiVyTD29ddx9+5dnDt3DlevXsWVK1eajuHj7Y37VVWo/uwzeHl5CeHaQnhHjhAh6wCJiYmIjIzE2LFjBcZY8yRbtIii2BYWHX5XJ9zcaJ1+8AGcT54E5+/f/LuePYlgzZ5NEba2WL+eVECXL+s+dkUFXFavRoOlJX6bOhULG6O27eDkRIqiFnJjAxMTZHMct/355xGUmgrxiy/AQkNxqLhYnZiYKOndu7c4YcIEZmxsjP379wv50dHc0uHDwVQqXL16FWfOnAEA9O3bV5g0aRInlUopfWDjRpSlpGDhyJFI8vFB323bYGZmBjMfH5orGg3NeX19lCYm4qcDB1DT2H3D0dFRDAkJYXfu3GGa+nrNaY2GF3fsQF5enqhSqVr1l4e1Ne1xt28TwRdFKtL47bckVc7MpCKWVlbt9orx48fz48ePR0JCAs6ePSsM/uwzjtnYkFQ/JIScb22URfnFxfCJjuZGFxaiftgwclJFRBBhmjaNzjFkCM2vwYOBkSOBlBSwrVsh/fBDGucJE8DzvOhqbS16f/MNw7Jl9D0HB5oL69dDEATcSk9HQ0MD3KZPx8PgYOQsXAi1Wo2EkBBUV1fDIipKDCosRE5+Plu6cyfumJpC+fTTQHExuLIywN0d4ePGMeHtt5F04gQK7t5lP8pk8FYqoR+AO4msAAAgAElEQVQTA4HjcPLkSQBopSQRRRE1MTHgw8MRu2ULeri4wODePTGjrIxVDhwoFEVGsjmbNrFLwcG4MWwYJoWGAk89hfI//hCjoqKYh4cH4uPjERcXB4DIl0ajgYODg2by5Ml8qyKE0dHITErCkRs3xMrKSgYADx8+bFWo0NbWVrx19Sr6HD7MZY4aJVQ9/TQKTp4UAbApU6Z07BBui+PHm/evjjBxIu2bf/wBpVKJoqIiycqVK9srhg4cICf8zJmd573Pnk0Kj5ZpL4+Dl14iZ8uaNeSQfFQrzxbIzMxkbdtZdgrGyGmxbBmpuA4eJMdjVhalh3XWReX/MEJCQlBaWorTp08Lbm5u//E2BcbGxnBzc8O1a9fkjLEnLKTyfxddkf0udKELAIALFy6Md3Z2Hu/v7/9vlel3hJKSElw9fZrN27GD8XPn6s4B1AUTE5IQPqIlF44doz+mpvQCbltIz8mJnAV9+zaTDsbIE//mm1Sobts2ImzTp1O1+v7920tvO8DJkyc1lpaWXL9+/R7vvv5ZuHgRZx0dxdPp6SwoKEjo06cPaxcNys4mwvvGG03F1TqEuTkZb5mZ5BzgeTLKASQnJyMnJ4dNmzat/TnaQk+PyMLu3a2dLm1RVUWG/6uv6nQKuLi4cHl5eZqysjKut7Y6/LVrdC+rV5PzphFJSUm4dOkSmzd/PnpMmkTEeuJEIguxsTSubSXkp09T5OqDD8i5oQP5+fnIz88XAwMDm40bU1MiSoMGURqCszMRpshIymE9epQcLOHhzc4IxmjuvfEGRYJaqFQYY/Dx8UF8fLz48OFDdu3aNficP49nHB3hNHNmc8tBgBQYI0e2cnLcuHEDu3fvFk6ePMni4uJwx9gYhhUVyPvlF+Hy+PEYeuIEY5cuAevWwaCxdkW3bt3g6+uLoUOHws7ODhUVFaisrERFYSFe/PJLXBgwAPkFBUyrFhH37kVBQACu5+fDQUehyr1796pDQkK4QYMG0UAKAkWKX36Znv2fURPJZMCoUWByOew+/ZTpjx8PScu9Y/NmIgdto1FWVrTmdRGVvDxq9RUTA8eVK3Hh3j0kJSUJHh4erB0JCgwkKXaL5y+VSnHhwgUo5XJYTp+OuGPH4LRlC2Jrajj/wYNxr7RUPHPmDEtKShKLi4uZjUol5GRlcQeyslBUVCQMHDiQjRs3DhcuXEBRURGa1ixjuJqejhP6+vDT0xNd8vIYDAzoXmxtybH36qsQV63CT4mJorGxMQsKCoKpqSmeeeYZZmZmBhcnJ/Ratozr+cUXyCsr0xQWFnLm5uYapVIp7tu3r7mgp4sLzduCAnJm9OxJfxwdyenXu3en49XNyAhJp04x68BAmC5YQPvF3/7WvmI7aJ5JDQxQcfky7ri5ib0GDKADe3nR/p6VRc950SL6QkgI7R8DBlDU+OFD4Nw5uHzxBZelVrNuSUnicY6D4+LFTDZsWOOQ5uHmzZs4fPgwiouL1Xd79RJu9+olFtfUsKqqKmZiYoKJEydi9LRpzOaFFxhnaMhO1tTAdswYeGnTZnr0oHH+4AOwefNgPWIEQkNDgR49YPfpp2J6Vha7UlUFADAyMhKDg4ObHlDsuXP49eRJ3HJ1RS7PI+vOHU2eubk4ISiI8588mQ2YPZtJw8LAevbE2KtXmeX770MWFgb3ceNYbGws4uPjkZ+fDxcXF8yaNQvu7u7o06cPcnNzuZSUFCEgIIAB9B49ZGOjuZSWxhwcHERzc3P4+fmx3i07Z+TlwWnaNC7B3R3chx+K5VIp279/PysqKmIAMGPGjNaOw86wZAk5SDtr8xcaSmoOuRwajQYxMTHw9PRsVutlZ9PceO89KszZ2T5QXU1kfcWKJyf7lpa0344ZQ8S7k04XbZGRkSHyPM98OlEtdQhbW3JgWFiQw+yPP2h91df/U9ME/1tgYGCAuLg4NrixZsR/Enfv3sVvv/1WK4ri6Hfffffn//T1/LvQFdnvQhe6oAV7NCP710OpVOLMmTNCQkIC18/KSpC88ALXTiqrCwUFZKjfvEmkriMIAuX1PXxI8sbOohAKBcl6Y2J0R7e1Ranq65sLOm3bRjmtb77Z6qOVlZXIz8+Hnp4ecnJykJOTw8+ZM+fR9/XPxo4deGhiAp+JE4UxY8a0v6ldu6ha9ZUrj19FWE+PjDcXF5Kqv/wy8MknyMjIgFwuFzmOe7x5lZdHKoElSzo28IyNqbBWJ3BxceGio6OFsLAwThIfT9Xc33qLouuNOHr0qJCcnMxNmDBBtLGxoZNZW1Nk+P33KUI1YQJFYBqdF7h6lQzVQ4c6bT3IGIMoiiIEgeT+9fVEQn/7jaTi27dT+sPJkxRl7tePIrVFRXR+R8dmWSljJI/Oy6NrmTwZtbW1OHPmDOrr61FdXc2WLFmCXbt2CYzjOD1dksqPPqLzb9yInJwc7Nu3T2hoaOCCg4NZQEAA9PT0cP/+fVRdvy64Jydz/tHRVIhPBxHTwtXVldIHtBgzBh8EBWHzli3Cvn372OhRo1heQ4N49vRpppbJEBIS0u4YMpmMlZWVNee8/vADGb4vvPDXelVLJJBMmYLoU6cwQzuGffqQw+XiRVL/tDTSHzyg9Tt/fvtjrVlDDoLbt4ERI2APwM/PT0hMTOSOHDmCWbNmtU5PUamo/kcLBwPHcQgLC8OJEycQFxeHHsOG4e+9e6Pf9esYsnMnhi5fzikXLsT58+eZj48PzPft4xsKChDw/POwtbVt6iwyZcoUtmfPHnz66afQaDQQRRGiKAImJiieOFFEURHDvHlExj/6iMhMZSVKKitRsnEjCwwMbD8ON28CMhlsvb0xw9ubz8zMxL59+/j4+Hh4e3tj/7594mvTpzP2ww+kYsrIoH0uIKB5jbaosN4RuOeeQ3hqKnpev07rKyuL1lEHMJo1C3c2bcKNmzfZUI0GTDuvtW3kZswgh83r/4+97w6L6ty6X+85M5ShShfpRYoIiKJgRVExNjT2aNRYEqMm0SQmMdF48cYkfomaWJJoYoyJvRs7IoqAEaWqCChKFQXpfYaZc35/bIYioJji/b77Yz2PjzIOM+e87ey199p7v0fKGFtbOq9//JGIZnU1ivX1YX/vHio7dWKqO3dw9uhRuPfsqS76Co7jBFtbW2HmzJlkC69YQQ6xMWNaXE9VVRUKzcxw+/FjsZevL8OUKeQI3rKlmSQdANUMWbGCle3ejTsSCTQ1NcX58+c3O9C8/vUviHV18GtUktANHjxIz6/ffgPn7Q1nb286cwoLAUNDmL76Kvr166e6yhivUqkwY8YMAGgg48bGxti6dSv3ww8/iMbGxsKdO3f4pV99xddFRsKge/eWh+rNm4CzM/jXXkPXnj0RfeUKU7fkGzRoEHqoayq0F1euPHs9WFuTos7SEidlMujq6jZXuBUV0Z4Enu3wO3uWvrO9BQSbgjFSZ9nY0LqKi2v3M8/S0lLMzs4W0Vq+fnuhp0fPW5WKlGobN5Lzt6rqqalP/9dgbm4OpVKJvLw8PJca4h/AxYsXK+rq6r4AcPWZb/4vQkdkvwMd6AAAICIioqaysnKRr6+v9KkF2/5B3L17Fz/88AOKioowLziY9Vi7lmHTphYyzxaoqSGPuINDs8htC1y/Trmmb75JhOJZnnwjIyo6VV7+9Eq6Egl9pkxGjoGiIspNHDSIor96eti4cSPS0tKEO3fuCFVVVRg0aBBz/RMtf/4SlEpUp6Tgd21tNnT4cNYirz02lgxotfH8vOjShYjxgwfA+vXoNHAgrt67175cT4CMriVLSCbf1pxPmEBR7rFj2/wYS0tLlpqaKmb//LNoe+kSk86c2VCJGgCio6MRHR3NZs6cCRcXl+bWZJcuFPkdPZocFzY2lNcbGkrf/e67zUnw/fsk91cqoVi7FtdPnhQl+/axfocPcxp9+oDNmwdWVEQR/ZISyssfP56cQTY25Ezq04dk0m++Sf/+4w+SsPbtSyRCKiXyO2cOIJPhYnExrl+/DgMDA5W/vz/n5OQEf39/5jhjBiR9+rSMWjs5AYGBuJqSgiNHjsDY2Ji9/vrrcHFxYZqampB++imMDxxA58OHmcb27eDUXQFcXdsfXf/9d0AigV3fvuzKlStiSlQU6xsTwzIHDYKTk5PKzc2tmVGckZGBnJwcLjU1lZmbm8Pk6lWwbt2oWGFr9RaegFKpbMjRrK2tbdGysbKyEueSk+G/dSsk06cTIfPyIiLi5ETzqy6Ud/o0ydAXLGj8gF27SGUyZ05jTYN6dO7cmV29ehUlJSUoKCgQPTw8Ggdp/nza/08oiywsLBAZGQlBEBAcHIzisjKMWrECWm5uQHk5+E8/hePEidC1sQEzN4e0Z08YODs3y0M1NjZG79698ccffzSk4OiXlEBkDGJmJvOKjW1cNw8f0hnk4wNZly6wtrbG2bNnYWxsjGZtGzMyKMpYT7YMDAyQlJQEmZaWOF1bm+ls2sTMDAyQ++CBsM3Tk7nl5kLT2xusXvIsdu+O6qgofJWaipycHNHR0ZE1SwOrrUXc9u3C8bo6ltqvn9Db1pZJuncncvUUFZRSpULNt98i8Px57MnJweXsbFFfX58ZGhqCd3Ag5010NKUrxMfTvPbpQ230vLyAadOwsaQE2hMnov/o0TCLjERlWpqqKiJCTKyoEJhMJs6bN49vpr45doyuqWfPFtdjb2+PstOnMXjPHqbTvTudcfr6NN8hIaQG+vhjIuYSCUqsrJBy9ChM7e3FvLo6lpycDG1tbejq6kKD45CQkSFctrNjTh4eVItBjW7daP9mZTW2hFu2jBw4Dg5ATQ1S8vNF7zNnuLEuLtAYNKjZHpXJZPDz80N0dLQol8vZK6+8woyMjaE1bFhLB+WlS5RG8c474IOC0NXFBRERETAwMMDSpUthZ2fXvlaoaly8SA7SxYuf/d5TpwCOw2WFQmCMMR8fH3Dx8VAGBgKrVoGbNevZZ09KCrXdXLCg7Xz+Z2HoULILtLUhVFYiQRBQWFgIuVwObW3tNvPRjx8/zpRKJXve/vWtguOoZeDcuXR2vPYaqRPt7Mgh8J+Pwfwl8DyP5ORklSiKaLV16QtEenq66vHjxyMkEsnrFy9evBAQEPAclYH/76KD7HegAx0AAAQEBBRHRkY6P3jwwMXDw0P6nwjyJyUloba2VliyZAmnExFB8uOm1Z5bQ10dkfKgIJJwtoaqKiIjGzYA69cT8WtPXQLGiLweO0Yy6PZEG3v3pkJhtbVESmfMAFasgFFUlKgxdiw3a9YsrlevXqzzE7ntLwTffov81FTEGxmhrKxM1aVLF66h3/j581Ste84cVNnaorS0tFkv8nZDKgV8fSH07YvaV16BQUkJrKdPb3+kdt8+knC3VajPyYmiiq1ECMrLyxETEyMmJyeLnRMSoJ2Tw103MRG6v/FGs8WcnJwsPnjwgI0cObJ1Y46xxqJhyckULVT3ec/OJtXD++9T9HbRIiAiAnm9eiFh1y6ka2uzOzY2SPLwQFheHiI8PJBoYwMLb28YjhhB0k19/cb1p1JRPQpXV6pM3rkz3eOJE0RgRJGIGFXUB2xtYV9UhGuPH4sBAQGcp6dnIyHMzSUC+8knjQZiSgpFUuuLvSUkJKCyshLm5uawsLCgyuAaGlSboVMnUnUEBZGjqlOnxmrpz8LKlYChIWQDBqBfv36sr6Eh9GNjYfz227h8+TLn5+cHQRCQnZ2N/Px87N+/HwqFQpBKpSzvyhXRec0aVj1sGLSfkdayd+9eHDlyBJGRkYiIiEBERASio6ORn5+PiIgImJiYQEdHB4cPHxY0NTWFPv37c5gxgyJod+6Q+mT16ubrx8OjkehXV5PBvW8fObyGD2/R01xTUxPdunVDfHw8Hj9+zFJTU0W1bBozZ7aZQnT58mUAFPkdNGgQNDU1ybmkTuk4e5YiuEeOkAOzVy9SIikUdJ589BEkI0aA//xzsXdkJDNYsECctmIFKzc0hJG3t+Bw6RJDp060PktLSTGSkAC2fTuMunbFlbt3oW1ggGYS7hUraE3WE8v81FQY79yJAbt3sxwnJ1zX0IDJ/Pm4AggPy8q4/NpaGG7ejD1SKSorK3EYwBVTU9h37SpmZGSw6OholJeXq4yNjTktLS3ULloE2W+/sWuDB+P9RYuYZORIOh+fkdcdFRUlXq2uZk7ffAODwYPx8OFDdvXqVWRkZIj6+vrsZnW1aD1uHOPmzaOI7K1b1EGlpob2pESCuJQU0d/fn5n4+EB3yhTY+/hw9rW1XK+bN7k+hoachpdXc+n3Sy/RnNfVNbYIBGgOPv8ct0tKVKXa2pz54cN4NHgw9BcvBnN2JudUZSXtgcGD8Uilomrt586hZ0UF8125EgUFBbh586bq7r59XNfXXsPu3r2ZlaurcPnyZfTt27e5qM7Ojj5LIiGy969/kTJJQwPw9cWljAxmqq8vOsjlDJqadD41uRee5+Hv78969+7N9NQdBszMGs/gmzfJ2fj223Su1UezHz58iNjYWHh7ezdfI+2FUknOEnXL2qchMBDw9MS5EydYlUKBnIwMISUnRyzJzmb37ezEdpFCDQ2as6cV3XwWeB74+GPcCwxEyqFDYpQoipnZ2UJ8fDyLiIhgd+/eFUJDQ1n37t2b1S1ISUkReZ5Hw77/O6ChQfM0axY5fWbOpIi/+ln0f7SYH0CFWC9fvsycnZ1RWlr67ALL/xBcXV2lvr6+qK6u1i4sLFQOHDjwzH/kQl4wOsh+BzrQgQaEh4efqaysHFldXW3q5OT0QsP7crkc58+fF42MjDgPGxsiWSEhTyeJ9+8T4QoOpoJ5reHRI3p4lpZSJebnyMsDQIbS5ctkjD9PJF4qJdLK80B1NQoBdkepFL3Hj2eYMaNZpPCFYfVq6M6dC/O+fREbG8tpamqitrYW2vHx0OjcGZX9+2PTpUuIiIhAbGws+vfv//QK+k/BD3v2KK9aWHBBXbtCe+dOisS1J+/T0ZEk7W0pKSIj23S87N27V7h79y6zjI+HRXw8l9SlCzItLVlAfZ6uGs7Ozuz69euClZUV69RWjqQoUgT/jTdIcjx7Nq0DZ2eK3g0ZQpHEhQuBOXNQq6ODQ4WFKDM0hFxLC13c3UU/f3+Wnp4OuVyOpKQkXL9+XXBycmLNInmCQFGpKVMa70lLiyLPRUVE3KXSxnzl+/fBZs9GlpubEHn7NmdsbCyam5uTwamvT9FcG5tGsh8TQ0R+yhTo6+vD1tYWSUlJSE1Nhca338L4k09QtmIFZOfO0f12795YfLFXL2pL1R6Vx6uvNo+IJiYCTk4wCghAQkKC6vr161xkZCQSExNx8+ZNDBs2DFOmTGH9bGzgzRi75OkpnsrMZHZ2djAwMEBNTQ0uX76M4uLihoj0N998g6Kiooav0NLSQqdOnWBmZoaioiKxuLiY3bhxA1FRUSgvL2fz58+nYnY8Twqb69cbWy4OH06krrKSfn7vPfrZw4P2+ldfNW+p9wR0dHTg4eGBa9euoaqqqnGNrV9PbepGjIAgCA2OmNTUVCQnJwMA3N3d0aylI8eRU2XAACoU+MsvlNO+aBE5Au7fp3ldsQI3vL2RkJnJur38MrwnT2bsww/RdfJkOERHM6SmEjH497/JATppEv3s5QXFt9/COCoKdiYm0Adoj3EcqUhmzACioyF8/TWu/fYb5DU1yHnvPcFk5EiWVFYmXo2JYYWFhZxEIsGckBCUA2Dp6UisqRFs8/PFKT16sN4zZrCBAwdCR0cH6enpuBIayoo3b8YJGxtc9/LCiNGj0VlLi87TZ3VIofFlKRcuwDkkBA5nzqDn55+jTKnEnTt32M2bN5Gdns70v/sOpgUF4ESRCrlNnEhrODwc+PhjOF65wvi8POgzRvNsakqOmBEjaF+88w4Ra6WyQdmAwEBSc6ml/Eol1SI5cgTWublcuZ6e+PukSUJsXR3HJBLR1taWKZVKPCgqgsGHH0IhkUDs2hUX5XJg8GCh15gxTMfaGh4+PvD39+ccO3VCLs+j87hxGDVqFLt27RqrqalBs9akGhp0Xj54QM+tb75p5pyWyWTs97t3me8nn0CD4yjlyMGBan506tSiSCrMzUk1pKtLTqO7d6kmyYQJzRxZt27dwr1795Cbm4snz8x24d49chK2w0lcWFgIrcGDoZuRAWZtLU5atYqL8vcX+6xYwc6dO8d0dXXFzp07t02k4+PpHP7ss7+U8lNcUoJrDx4IUY8esf6PH7PhL73E/GbN4vr168d8fX1RUlLCSkpKRJVKJTo6OjZcT+fOnVl0dDTz9vYmp93fCYmExnDGDAoeXLlCzkgnJ3Js/JUUp/8QysvLkZSUhFu3bokJCQkN5/yLBmMMGhoa0NDQYMnJyZbh4eFZERERxQEBAVUv/GJeIDpy9jvQgQ40YNWqVfKQkJDRcXFx2f37928uL/ybIYoi0tPToVAokJubi9jYWOjq6grBwcE8vviCovr1+YhtondvynNrLfovl1NudHQ05ej/leIwY8aQcaFUPjuloDWMHQuJmxuKDx0SsXQpg5kZGVoyGTkg2lOB/q8iLw+YNQvc8OGwqamBQqFAQkICtO/cwfRLl4Cff8aVujpRoVAwiUSC8ePH43nTOUpKShAaGoqsrCxRoVBI3lq+nB7oZ8+SIb59O5Gap0lDZTJyqnz5JUWzmiI/n6SOkya1+LWDBw8KOTk53IdOTtCQShlmz4aTh0eb96CtrS3GxcUhIyMD1tbWcHBwAF9RQW2YJk0iGfSCBUSKVq+mSMvGjW1ednZ2NgDggw8+gDYZ0KyiogJnzjQGDqqrq7lff/0VS5cupeuKjSWJ7oULrRtw/fvTd+bkkLPh669p/M6cwfi8PP7nAwcQFhbGujftVFFURJ+rTmdxcCDyWQ97e3usWrUKR378ERn6+nC+fBnXX30VI888EeCQyWgPjh5NEfFnFWr85hvKN1V/TnY2jSWAefPm8WfOnIGnpyccHR0hCALdvygCU6ZAOnIkRq1YwZTHjyMsLEw1YMAA/uTJkyhr0o97x44dAIC5c+fCysoKgiA86YhiCoUCJ06cQN++fWFqatp87hkjcufvT+tq0CBKBTEwIII+Zw6N8cmT7XbqqT+/mTqkSxc8KijA3g0bVOXl5byGhobo6OjIqqoabUnbtpwnmppUs2LvXro+gKKw6nP41i0IiYnIs7KCIiiIXjt/vrEt6JQpdI+FhUT6tm2j1z79FMIvv+DMp5/i1S5dSL69ciU5dqKjIRw9iqRp04TbZWXc/X79MGvePNjY2HAAsGjRIrZlyxaVtrY2RowYwevq6cF50CA4f/MNgr7+msOKFSTfrq8/4uvrC19fXy73gw/AYmJwy9sbphYWkO3aJSpiYtjjAwfwLHerQqGARCJBgbk5dO3tac3X1SE4OBj5+flKO8YkPSIioHXzJgrPnIEFz5NjOCuL7qt/f0AuR5SXl8o+O5u3KimhtXz6NNXFmDuXiNOmTdSC7/hxUhr4+JCKS01CkpLIMeHoCCxYAF1/f/R2cmK9Af6nn35ShYeH8w8fPlTl5OTwlZWVWLJkCb7ZtAk9+/eH60svYYCzM4cvviBn0qJFwLRpMJg4EQbffttwryYmJsKNGzfQt29fTiaTERHPzianw6BBVJB06lRg926Sxy9ZArfOnWH++DGKi4qg4+RELTrlclKjbNtGTubqalICMUbqIYCI8c6d9B2tkPmm5KuV/QWA2kBmZ2fD1tYWWlpaSEtLg5OTExISEtBr2jSEDxqEslGjhFmzZnFKpRJ3796Fi4tLs8/Ky8vDjz/+CL1hwyCtqoKOs7OoOnyYLRg2jNPQ0MDkyZNx4MABZmpqCisrq9YXSV0dpWz8xZTD33//XSwTRW7R4cOQbN/e8DrHcdDV1cXIkSPh7u7O9uzZw/z8/Bo6Bqhbj965cwe9nlZQ9q+A4yiI4e1NTiwzM3I2v/IKzeX/Ibi4uODDDz9ESUkJ2759OxQKxX/0emxtbWFiYmKal5d3mOf5kwBaFur4L0IH2e9ABzrQDKtWrSr8n//5n/TCwsLu/yTZz8vLw549e6CjoyPIZDIEBwdzHh4ePKqryVh52ndHRpKhev9+6wV1EhNJ1hkbSxG6523h9ST8/IjMuLiQEfYnUFJSInLa2iKWLKEXvviCojYFBRQ5zcsjY/95Kwq3FydOUJGxqVMhCAIAQC85GZwo4vrcuaKJjg77IzS0weMQHh4uuru7P9MDQXXoBFy6dAlRUVHql9n06dMbjccRI8hwXbKE5mvBgqe3OSosJEnuk2Tf3JwidE9AEATk5ORw9mlpSD12DKVz5ogDfXzY0xI1goKC+NOnT6vKS0v5hFOnYOPmJk5avJghPp6iq8ePU62G27eJZC9aRMW4WoFCocDJkydhZmamJvoAqM3PW2+9ha1bt0KhUOCVV17Bnj17sGbNGgCAv1yOQe7u0HxapKZrV4o+XrtGOcEffwylnR2uf/SRMCUigss9dar5+7dvpwJlvXuT/HvYMIpoN5Wt79uHlz/4AMjLQ5meHpLS0zFcqWzpGLGzI6eHUknOrqat7J5E//7N96K+fkP1f5lMhglNWvBxHEcEZO9e4KefGooRDh48GBs2bOB3794NY2Nj1fz583l1QafTp0/D2dm5wfhvjYhoaGg0+55W0bs3FQdbsIDmWd2ic+9eGmt1Hn87oKOjA6lUCpVKhQ0bNqg0NDRYTVkZGMdx/n36cN7e3sjKymI3btxQlZSUcACYTCZr3QF1+DCtsYsXKTKvLvD5xDno4eGB48eP4+qJEwI0NTn7yEhyGPn50Rt+/JEcPkolEdx6h4lEIkGNjg7uOzuj8+zZVETT0hKoq0O1uTlS8vOZm4MDxsycCf0mnRM0NDSwdOnS5vrhPn1I2XLjBuWSN0V5ObByJay+/BLVn3wCm99/Fx4/fswK7t1DjJMTMjU/WHQAACAASURBVH/6CavUhU1BtRW2bdsGjuNgZmYm5ObmcjU1NfSfHAfNf/+biGtGBtClC143NJRg1SooO3XC+sWL8U5yMqlffvuNznpHR7q+fv1g9vnn8Ni0ia5JKqV7zswkMv3oESleNm8mRUV+PpHHmprGzgJ37tDZNWNGC2n6vHnz+Ly8PERFRfEARADs+PHjAACjjz+Gd7dutK5nzQIeP6b5AJoXXjt+HNMcHLgzVVUqhaUlCs+dg82+ffTs0tSka9bTI7Ivl1PkvLgY8qgozP7pJ0SOGUMpUu7upGzIzKT5OHOGziwXF1Lm7N5Na+u111p1lKrhWH8me3p6tthfmZmZuHnzJuLj4+unhgPHcVCq7wtA/MyZqNXSgiozk9u+fbuQl5fHqd9ramoqlpSUsKFDhyI1NRVaWlrgioux8LvvULtlC6czalTD5zg5OcHDwwNnz54V5s2b13Kj37pFaXn15+ifhVKpRE5ODhs2ahQkMTGk7pgxg9ZSk3mys7ODlZWVavv27Vzfvn1Zjx49IJVKUVtbi7i4uH+O7DeFen+fOkVFNVetIuXHkylJ/4uhpaWFqKgoWFlZCU5OTv9ReQLHcZg9e7ZmaGgobty48V/fgq+D7HegAx1ohpCQEIlUKrX8J4k+QMWmpFIppFKpaG9vz3moZdtr1pBxtG1b27/81lsUKV6xovnrKhVFMUNDqeDZ0z7jebFkCRmTQ4b8qSh8bGys2LNnz0ajuWlO5OXLFIkbOJCk7keOUP51W1GNPwNLy4ZUB1EUYVhbK044fZopVq/GScbExLAwwd3dHdXV1QDAZWZmsuvXr8Pa2ppyu9tAfHw8Tp482SBX/uSTT1onM9raVNV8xw4y1IyNybBqjegGBhJBLS5u3vpuyRK6jyc6HXAch3e6dIE8Kwsl69fj+OXLrFd1dfP2c0/AmePwzsKFfN2sWSi8dAk6qakMY8eSYygnh6S7hw+Tsb1zJ8lo21BgXLp0CQARlydhZGSE5cuXo6qqClpaWhg/fjwyMzOh/OUX5Bga4ktbW/Q5e1YYMWJE28aPhgYRkdJSiIMGIb5LFzFt3Dj4/fvf6JGSQpFoNdE+caLx91QqMvqbGoNxcbR33noL2LULenPnQrlmDeRyeevzJpNRL/UvvqDIaVtr38uLJMJqfPUVkYy28K9/0bU0abGnr68PTU1NsX///qx///7NCObItvraPy9EkQjdsmWkWqiuprHdv795nnY7wPM83n//fXzxxRewt7fnjYyM4BYdDeMDB8B9/jkDADc3N7i5ufEAcP78ecTExOCLL74QdXV1xZqaGriWlXGWpaW45ekJz+XL0f3iRUh5vs1xlvA8FgYHQ/XSS1yaszPsIiIaazZ89hmdI4sWEeH7+WeKTH/5Je4GBwNAYy52p06kuNHVRVRcnKhKSED36mpIv/+ezt8ePWidWFi0rsQZMoRSPcaPp89Rr7vr18kxJJFApq2NV6dP5xAcDISE4EpoqBgUENDsxg4fPiyKoihaW1tz1dXV3MiRI+Hq6oqUlBQUFhZCs6yMzoxz54ioVlUB3bvj6ttvQ4iKgnT2bAjDhoFzcKAo75YtlMcdGwtl37788VWrMJExOjv+538oQtq0wOayZbTfHz6kNJRffmlUwpiaUiHXoUPJcbxnD6UIhIcDI0bA8tVXoczKEsbu2MEdnjgRXSIjxRH6+sysb1/aL0eOkMPM15eccNHRlD4wZAg5HLZuhYa/P4I/+IC/mZwsxly7Js5bt47DZ58RqYuJoWupraW1efYsamtrsfbsWWiFhOC9wEDa2zxP67pTJ/q3SkXngUxGtTqUSnIWREY+dT1LpVJYWlqKdXV1LRbflStXcPfu3YafBUGAIAgYN24czM3NIb1yBbkhITj28ssAgEePHnF9+vTB8OHDERMTg7t37zJDQ0NER0er5HI5m1pTw9lu2ACIInRaccYPGTIEmzZtYmlpaXBRd0NRY9cucpT/ReTm5kIQBPj5+dEZf+ECqXvCwlpUw585cyYfGhqKK1euqC5dusT36tVLlEgkrLKyUlQoFM2LUv6TcHOjP4MGkTLl/HlSwbm60nmbnU2qh6Zqr/8lUCgUSEtLw2uvvfa/Ig9BKpUiKytLpVQq6/7T1/JPo4Psd6ADHWhASEgIJ5VKt1laWmobt7ev/Z+ElpYWFi5ciKysLP7cuXPgeR7Dhw8nw0Qd/X4Sp06RERMf35IkpqVRS6hx48jb/VeK9rQGPz+Scu/e/ez0gieQl5eHkpISzret3sPq18PCyIjJziaZaXExRWtsbNrfBq815OYSobl5EwBgcO8e3nF2ZoiMBBwdMbeV9kHbt29Xnj59WuLt7S0GBAQwPT09MMaQmpoKW1tblJeXQ19fH3fu3FE5Ojry9+7dQ2Bg4NOl/4yRXDo9nYollpQQWWilBzs2b6Yc1KZt9rp2bV2lcfky+N9+g2zjRsgcHCCLjRVv377NWkRcamspKuLqSsZQWBj2DRyIMj8/cbG+PoO+PkWN1q0DIiIaq8Lfv09R8tu3aQ08geHDhyMtLQ3FxcUoLS1Fiy4HQEOxQ09PT3h6egKrV+MwgFxbW8TExHDtSZspFgQcmDBBcMnKYq+lp3PSIUPIIbFvXyNBuXqV8nMTEshh0dRojY0lSXFiIkUJ9fXBAZDJZKq0tDTep63I/ZtvUoQyJYWcNK1J+hUKyoMvK6N5rqujNdwazp4lKeqSJS0KZQ4dOpSFhobi6tWrIsdx0NHREQ0NDUVBEMTa2lpWXV3NampqmL29vdCnTx++TZlvU4giybMvXqQI7Y4dFM1dupTSJLS16Ww5eJCcIK20CWwL6enpAIDAwECS+Hp5kXy61csQoVKp4OHhwWRaWmJnCwtOZ+NG8HfuiF3GjUNMerp4LzKScxg6FC3rwYPW4erVeAyIF6ZNYz1eflnFGGsedVePp6kpUFmJ5MePBfv9+7lDdXXw8fGhFme1teRQO3EC8PKC3urVLMbCAp+D0lsWDx/OZNevU67w1q3kJHz5Zdqn6r3q6Eg1HZRKIsOVlZSa9MsvzUnlvXtAVRUKzM0hl8vZlStXoKur29DBIDMzk3l7e7PgemeEGg2pKVVVNH88T9H9SZOAt99G7LffinK5nJ1dvFgs43nm+/77cBo2jBw5VVVAaSl0ZDLc1tdXobiYR3g4SeI7dWp4dgiCgNraWlRoaKBSKoVNcTGkcjmdM48fk2JA3Rrv3j1a43Z29PPw4YCLCzxNTLjKgwdhYGyMbhIJM1Ona5w+TWeMUknnL0BpAYMGNTqhT5+uv8UqXHJ1ZbxCISIsjJxgly/TuDo60nhOnlw/vRrQ1NQUTU1NIYoia0bqfv6Z/n777cYCp3/8QcqANWso1eLNN9ssoMdxHB4/fszcnyiemJSUBCsrK9y9exdjxoyBhoYGOnfuDCMjo0ZHk5YWjHv3htvy5SgtLcWFCxdUf/zxB5+QkCDa29uLM2bMUD9jeBQU0NjMnUv779EjWj9Nzj9dXV34+/uzffv2ITg4WPT29qYveviQHBh/wRFeVVWFhw8fIjw8vPFFiYSuJSyMUkHy8lpEzIcPH47hw4fz8fHxSExMhI6OjlBbW8utW7dOnDdvHmvWPvCfQmUlXdubb9Jz6qefSIHy6BHtkQ0bKE0jPJzW+tatpLB6+21aU8ePUz2AYcPorH6BefNZWVnQ0tISu3Tp8sKqP1dUVIDn+TYd/25ubvzly5cHffbZZ+ErVqwY8qKu60Wjg+x3oAMdQEhICAMwSFNTc62xsbHH1KlTZS+iGr+hoSEMDQ1hbm6On3/+GY7Hj6PQzAy+rq6tN6/dv5/IxoABja8JAhHwpCSKRE2c+M/lv0+eTFXYp059rlzBixcvqlxcXHitZ0UO1dV4AXoQa2tTqzdDw0ZCN3ny8xfoOXuWCIyahM2YQRL5p0jp586dK0lKSsKxY8dYYmIiunbtquJ5nktJSWk2uNra2mzWrFmorKxUFRQUAAAviiKqqqraJq9OTmRYhYZSZG3cOGr/1RRz5rTsNTx5cssif4cP07js3AkYG+PUqVPK2tpaSZemhRgjI4mwrFxJ+cqJiVAUFGDPoUNCVn4+19ADWxRJ6rp8efP2b+oCapmZjZLgemRmZiIyMhIVFRVwdnYWtbW1n734IiOBmBgEiyKG19Rg//794vr165mxsbFq0aJFbZZc3rdvn8qoa1duwPLlTLJ9O+Wcnz9Pc/rLL1S92cEBUEfB9+1rJGe//dZQrA2PHjVI5wEgMDCQP3nyJDw8PNBmhEomo7VoZEQRzieho0OOFImEUlMWLGi948X165SOsG9fs170avTq1Quenp7IyspiArXBYsXFxeB5Hubm5jA0NISOjg5iY2O5X3/9Fd7e3qqRI0c2HzOVihwaZWUkz9fQoL3UowcZuVOm0JjV1BAByssjp9O0aUSEg4JIAv3110/t2qFUKnHw4EHo6ek15PJCFMlx8ERXgeLiYvxRTxzHjRsH9O3LwdtbTdCYLYBhPj6s7vPP8ZWrKy6tW6fq1KkT5+joyLzc3WG4YwdFnj09cai0lAUMHSo2Uz+kpdH8qI3aXr1QsnAhjly7xmkNHYrujx8LQUFBdHDU1FDqkKEhMG4c+jg7w2boUKSlpSE6OprtvH9feOPjjzmO48gpkJ1NjpDr14mg3bxJpHXNGpLM791LMnhd3cZCd+q5PnUKCA+HGajewoULF3DkyBHm5OQELS0tDBgwADExMWLv3r1b71CyfTudEaamRLZffx1gDBMnTmSHDh1SdT17lpPY22PPgQPgeR6jRo0SPV57jWHNGvS4cAEXP/yQ27R/v7Jy2jRO+c033Hvr1uHQrFlijpUVU6lU4FUqmJSVYfzevajmeRh8/z1J98+cobz2d9+lOg+DB5Ozr6SExmPzZqBbN0gYw/mAAJRXVeF3a2vh9ddfpzE+doycBd9/T+0Qv/2W/l1XR46TegiCgB07dojCw4dsyIkTXEZwMAy3bIGBhQU4iYQcvnV1tKZ5HhzHoV+/fiw8PBwPHz6ETWuOUjUOHCBHw+TJdGb16EFRcY4jAvjE/lOpVKirq0N4eDi6d+8OfX191NbW4tixYw3vMTMzaz2P3scHCAqCBs/DzMwM06ZN4+VyOe7fv88OHjzIioqKYGxoSE7DTZvoDFI/p/v1o/P+k0+afWRAQADi4+OF4uJiDqCaMNrvvAOmrw/Nn35q+76fgtLSUmzcuBGiKEJPT08VGBhIe8jUlL5fpaJ/79jR4noab9UHPj4+DAATRRHnz58Xf/jhBzZp0iT87e10S0vpvJ07l9ahiQnw66/kCLKxITVPTAxdd3Q0kXs1IiPpPfn5dHYwRjVJ9PTIhjI2Jqf2/fuUphgXR4EST09yWF66RHvgedovPgX6+vpQKBSsrXoQfycEQcC+ffvq7t69K/X09FQNHTqUj4yMFHr27Mk1bT06YMAAmJmZ4dChQ4NDQkImr1q16sBTPvb/LJgoiv/pa+hABzrwH0JISIi1pqbmZ6IovqStra09YMAAXW9v7zZ7y/6TuJeaCiM/P+ydNg2cl5eoVCpVrq6ukqFDh5LhlJJCJKwp6uooQmhiQgWa/mpufnvw669kDLz0UrveLooiNm/eLPbp04f1VhdNe16IIkXD/fwoT+/UKXqIt6UUeBK7dxPJefSISPHy5e2qjK9QKLB582ahoqKC69SpE0pKSmBnZ6fS0dFhd+7c4Xr06CG+9NJLDKBquxs3bsScOXMQHh4u3Lt3j+vXr58YEBDAnhrtv3mTjJWYGCqUpiZNQKNk9rPP6P7d3UmO2rTS/LJlJJc1MYEoivjuu+9EY2NjNnXoUIpyv/QSRaLrx6C0vBzHjx8XsrOzOTMzM2HChAmciYkJRQPnzKFiZ605Qa5fJ5I0cGCzl9euXSsaGxuLgwcP5hyfVodAjWvXqOBjenrDvaqLVe7Zswf29vbKmTNnthiwvLw87NixA4sXL26shVBVRcazpSXd67x5RIays4mk6uoSCX/wgCKSu3cT0W0FX3/9tTBq1CjOrYkToAWUSnKuXb1KOfpPGmzLlpGRqKVFTpivvmr+/3fv0jXz/N8iMy0tLcX3338vjhs3jrkZGpLhamJCvc9tbMiALS0lI/lJR9uWLZQbv3o1OZ7OnqVo4caN5DC5e5fG9ddfycE3cGCL1lc7d+5EZmYmpk6d2ig1zs6mfZmf3+y9ISEhAIDZd+8KtuvWccjLI8LV1CF25Ahw/Dhqt25FTk4OsrOzxUfXrwt2p0/zOrW1YsLo0YJPcDB/7NgxDB06FP2aKhA8PYGgIFSHhEAmk0GpVELw8cEvfn4QOA7Tjx5F6cWLUBYUwLa8HNzYsfR7mZm0B+urz+fk5ODIkSOipaUlJk2a1NxxJQh0X7//TgoYuZzWws2bFEFW5xWrMWECjcUTtTe2bNmicnJyYkFBQZwoirh48aJ47do1LFy4kDW05CooIOVBeTnNkZYW7c/ly1ut5/L7778jISEBgYGB6N+/P3D8OCp/+w1Hzc1Fr/nzWWl5OaKiomCbnIyAzz+HyeXLkJiZgT94ENDTQ0KnTqLe+fPM6cIF2jv79hEx2rWLzt9t22g9p6XRF164ALi7Q9ixA8L69Vi7ZAk+yMiA9I03iFgVF1OayOXL5IC5f5+cI6dONexPURRx5swZ1ePwcEwPDOTzT53CjQcP0D8sDJuWLxdHjBjBfHx8aE+tWdOQhvX1118Lvr6+bODAgeypTvnXX6ezrLCQ1kd9EUUsX06OgLg4UozV7+Pa2lqsXbsWABAUFAQrKytsry9aZ2ZmJk6fPp212TLN1pacGa2k22zatEksLi5mEoUCcw8dwv01a2Dq5QV7e3tSgpWUkOOplXuJiorChQsXoKmpCZVSCav0dBSYmcHAyUkYP34897zR9PDwcERGRuLtt99Gi04sFy9S7YMffyRl1LBhLdputoWjR48KN27c4IyNjVVTpkzh/3SUv66OHKYzZhAht7SkZ9Lhw/T/nTs3P3fj4ykIUl1Nz8PISKr30B7U1+5BRgapeF59lVKAevUi5Ur37uQ03bCBnsNJSeSomjWLzsPt26kgqLpV5TMIvCAI2LBhg8rExIRNmTKFe2bw409CEATExcXhNClnBHd3dy4jI0NZU1MjAQAnJ6ea9PR07R49eigfPXoEb29vibqQ7qpVq158z+kXgI7Ifgc68P8pVq9ePVwqlR7u1auXlqenp8TU1BQvIprfFhzz81F+9SoGKpXIz8/HjRs3JMnJyaqhQ4eS7O/hw8Y3iyJJIFesoDzwXr1eXA/arl0pkjV0aLs83omJiaioqGAebbWSaw8YI2Kgbjt24AAREg8Pyl/fsKFtpUFeHkUpJk4kx8jYse1rgQeSjL777rvcjh07VNnZ2bxEIsGsWbOaDnTDgtHV1YUoilRlWU8PI0eORGRkpJicnIxJkyYxy7aKCHXvTvfGGBmK27c31jNQKMjYAMhozc6m94kiGTg//URGd/08ZNy/jy4REazfihUU6d61iwzux48BxpCXl4edO3eKdnZ2bPz48fDw8Gi0Tlavplzepi2wmsLXl8jhK6/QddQbNnV1dWzq1KmsXTUu5HK63/j4Zk4Nxhic6iXvGRkZkvj4eLi6ukImkyElJQVXrlxRPXr0iO/fv7/KwMCgcfx1dIiMfvEFtQA0NCTCuGxZo4TexYX2yeeftykvB4Bu3bpxYWFhoqura9sEQiIhwj91KkXnniyGl5dHY62l1VwZAZCjZPp0MhQXLXr2WD0LNTUwrKnB2Ph41mnnTpLfZ2eTcyc+niSsT8PChRTxNDQkB4iaxBw5Qs6CpUvpXtXt4rp3p+jljz+SMc1x6N27NzIzM9FMRaKOpD0BplJh7PjxsF20iENyMuVtP4m8PGDlSmhpacHZ2RnOP/zAEBrKq8LCkK9UMsd797jQ0FAAjfutYa7i4rBjxw4h+6uvOCcnJ6G4uJhZdu3KqnR1MeX99xFqaKjKOnyYuf7xB2eSkYG6/v1RWloKm5wcSL76qoHsW1tbw9raWnj8+HFLy53jiGy88Qb9XF5OeyInh5w4t241tsz89Vdy3rUSeR4/fjy/Y8cO9OnTB4aGhrCxsWGRkZHIysoi+b5cThHF7GwqMPfOOxS55DhyAIwb1/BZQteuCFu6FNkqlaCtrc151xNi5ahROHbwIAKPHmUmoaG4tX8/6urqkNWtG1TXrkFj8WKwSZMgDhsG8aefoOXnxyLnzIGTWsnRuzflQqvrdMyZQ4S2pobW7+bNgJ4euJUrccrTE2JsLCQ8T2fOiBGURnbpEnVQcHZGyfDhKLezEzp17swJQ4eipEsX8Uy/ftC/dYubevcukwwejC6bN6OLKEK8fRveWVk4ceIEUlJShOn+/hzu3QO8vfH48WNUVVVxbm5uT39eHz9O54KREZG0ps/HL74gWffRo/Qs27gRcHWFpqYmNDU1YWdnB2tra8TFxQGA2pn1dONg61ZSP7SC2dOmMa2BAxE9f77w86xZnGFBgfB4zx7OwMBAtWTJEh6dOtH6e/fdFp/h5+cHHR0dGBgYwG7FChR4eUEydy4OHjzInTlzRjVz5sznevB3794dkZGROH/+PCbXp0Y0oGdP2oMmJvSMrapqqThrA2PHjuX69u2LsLAwbufOncL777/fvtC1IFDqgIMD1aVYvpyc8o6OtJf696dndlvIyKA9lptL5/7atZTO2B6oybmjY6ODu2kR2uJi+nvFClIWAKQIs7CgM+6HH2hvTplC4xYXB3Ttiqp165CtVMJ0927Ev/46LAsLoWlmhvS6OvTs2ZOPiIjA7t27xblz5/7tBqcoijh8+DBu374NAMMAqFJTU49yHHea5/kolUq1JT09/VcAeUlJSVJBECY9fPjQRVNT84pcLp/zd1/P/xZ0kP0OdOD/Q6xevXqUVCo9MH36dNlTZYAvCqWlwJIl0D94EB6urjAwMGAxMTFYZGnJY/RoirCo8fAhGVF791LEvz09wP9O+PmRkXT7dguZeUlJCQwMDMBxHLKysnDp0iVVbm4uHxAQ8NRicc+N/fvp77Q0ku7xPBnd7u4tCVh1NckkJ02iCvet5JM/C7NmzeLXrVsn6ujoCADaNK4MDQ1VlZWV3KRJkzhra2v07NmT27hxI6Kjo1WTJk1q2yjT0iJjYvx4IvwjRlC7xBEjyPjLy2ssdjVqFBH83bspwiiVAllZqDt+HLtKSvDeH39A5949isa+/z59PmPIyMjArl274OzsLE6ZMoVrZih/+il9TyvFF9V5vTKZjEieoSFQXY2ckhKcPXsWmpqazSrwPxXz5tF8qKM0TcAYQ0BAAC5duoTw8HBcvHhR0NLSEgoLCyXa2tr8G2+8ARMTk5ZjKJPR3J84QfdRWEiS69paMlbHjiWS+kRRwycRFBSExMREVlBQAPOntdmTSEiVoKVFRKxpS8vdu4mUrV3bPHJfV0fr9Jtv2swXfiZEkXJTU1OJvNRHomxdXLDfzAyPy8th5OgozPTze3bEqLSUjPjTp2m+V68mEvfdd3S2ABTZ6t+/0XAODSVVxp49wK5dqFqyBKfra4dIn3T6OTmRykDtBCgsxEdffonNlZXwjIpqXcJaW0u/M3UqreuzZymd4OOPwRsbwxKAZZcuzMfHB+vWrcOxY8egUCjg6+tLEcBFi2Dt4IDc/HwoFApoa2sLvoGBvGdBASwtLTFh2TIeHh747YMPVOvLy3ls2gQACB41Csbjx0P66BEsLCwgiiJyc3M5U1PTZxvi+vqkeDl3jupg+PqSQ2fcOCKUQUGtRmstLS1haWkphIaGipMnT+ZramrAGIN7166k0Pr9d0pPuXatIZqN3Fxax3I5kaH6GhiZPj64npuLgSNGsO7duzekDmVnZ+OeszPcHBxgkZUF7soV6FRUwP/aNWScP4/c+fNFp+Bgppo3TywXBBYeHAwNpVKAun6JgwM5K27epKi4VErnUVERqSEqKojwODlBXlcn2GZmcikmJnBduxbc7NnkqDA2hhgUhNtyOf4YNUrsd+MGiwgOVvU9dIiTlJez4TIZnMaOBTdrVmOqwCuvgM2fDwu5nAFAeno6t19bW5gSHs5hwgRUVFQAAL7//vtmXQ2aobSUHFZRUSTVTkoi5cUPPzS+p3Nncryp1ToLFkC5ciXkcjnS0tJw584diKKIgQMHNusv3ypiYxu7yTwJpRJ6xsbA+PEImD+fC6D54a5du4YzZ87wJSUlFGHv1q3VApkSiQQ9evRoKKxpMW4cYGKC4OBg/Pjjj/zhw4dVEyZMaBfhr66uxvbt28FxHEaMGNHyDfr6lO506RI9jxq7yzwT6jSjCRMmsLVr17LqpxWIFUWyX0JDyWk6fz6lDE2cSJFzmYzOpPbgyhVal+vWUWQ+JoZId89Wq378ORgbN6Z8NO2AUF+vBCdO0BkP4PaIEQiNjkZnhULoc/s29/DhQ5XLF19w1To6rHThQlXgokV86fLlgvOjR8CkSQwHD9KZ6uZG9lRdXfuVCa0gKSkJt2/fBsdxy1euXBlW/3JTo+e7J35lZUhIiPSjjz76ry7S10H2O9CB/88QEhISKJVKD7z66quydhW3ehFQKik/zskJJSUl2LdvH3x8fFAmCNDr0YOsL1GkKG9ICBHu33//2y+jpqYG27ZtgyiKokKhYNra2iLP82L//v3h6enJVVRUoLi4GJF1dRg4bRo6x8aC19JCaWlpgyGho6MjdunSBRkZGczBwYGfM2cOWs1F/Tvg4kJRTICM0PJyMoSnTaNoAc9ThFJXl3Jr/wTRB6hw07JlyxieQvQ5jsNbb73V7P8fPHiAiooK9OjRo33RFyMjkgtu2UJ53bNmkSFx6BBdf10dRdRCQ8nRs2kTKSzS0qD68UeK1KWk4GFFBQpv3mwo8lVZWYkDBw6Ivr6+bPjw4c2JGVyBHgAAIABJREFU/oULRCpaKRYHAKdOnUJ8fDykUimsra2Fwbt2cZ02bcJxPT2VgYEB99prr7F2p728+eZT+9UPHDgQXl5eiIiIEBMTE7mamhrOzc0NL7/88tMLHwIUGevdmyKLixdTW7TTpynq+pTWTNXV1UhOTkb37t2hoaGhKisr459K9gEyRlNSyPGSldW4rlaubCyC11TG+tZbtC6PHn365z4JlYqI5KVLRIbWriUCvmgRRSYNDaELYFp1NfLy8nD69Gns2LFDfPPNN59OTvLzyaBUr4NhwxqLqDW9ZkdHuj8DA5L4W1nROeTujlKVCnO+/Rb5FhbQeOstel1NVkaNos/PyaH1unYtbn/3HSoePEBqaiqeLIAGgKJorq4k9/7qK3LgPNELvba2FuvXrwcA9OjRgwo9AuQk8/eH3qNHoiAIcHV15fz9/Wl/XLtG7zE2BurqMD05mT85dixSUlLg5eWFE2fOIOjkSWT88QdSu3WDrq4uamtr2ejRo589P5cvk9zY15fGMiGBSO6CBURcn5JWNWbMGG7Lli0N6Q0GpaXY9dZb4siEBJhWVjLo6dF9qWFjA3z5JUrGjwerq4O+IICrrcX1gABYy2SqAQMGNGxChUKBc+fOifb29ug5cybD2bPwmjQJHoKA8mXLEOnujqyoKGb9yit4bG8vas6axeTJyVBqaTX3wsTGUjrB1183vqbOcxZFWg9Ll2Li0qXc9X37II+OxlcbNmDx4sUIy8tTPdy6lbf97DMkxMfjveBgphkQAPevvuJx9CgR8Pv3aY2cPUuf/corDYoPLS0tGBgYiPPnz2ffrV/PZUdGQis1FQ6urrC0tEReXh5UKlXLlDtRpOeAupgmADg6QjhxAjcSE3H58mWxpKSEjR49Gj179iSiWV4OQVMTsfPmCc6urlyFoyMKy8ogkUgwePDgZzt9Ll6kPTrnicCoSkVjtG1bizZ5vr6+OHPmDDZu3Ahra2txzpdfMiQlkTOnNbK3cyc5X+o7KVhaWsLJyQm3bt3iX3755XapEh8/fgyFQoGPP/647fM0P5/qK5w9S+qI8+fpfGgnHtYrEOPj4ymdBKA5zs2lM3H4cOoKIZVSoEJXl86YPwOlkmpLfPYZOXUfPqTnYVgYOcleZDpmvcPzpJWV4OXlxQUFBXFYvx52AI9ZswAAboLAo1s3jPPy4hAXR45voLEGgb4+OUrff79lClg7oW7/KwjCxfb+zqpVq/6riT4A8P/617/+09fQgQ504AUgJCSERUdHL5dKpT9Mmzbtf0dEHyAS0KcPeaY1NfHgwQMUHzqEoV9+iR+6dEFq585CN3NzJt2wAUhOJkN/4sS//LVZWVkoKSkBz/MoLCyEutr8+fPnIa+PqmhpaTFRFFlKSgqzsLDA1q1bkZiYiBItLdinpqKTtTWuFRdj3759SEhIgJ2dndizZ0+WnZ0tDhw4kA0bNqyxcNc/jREjyDN++zY5RUxMKLJ29SoRl27dKAqQn0/R39xcipQWFVEhs5KSxqrIcjlFOmtqyGBT4zmL6kRHR+PBgwfw8fFptUJ9q5BKG4sJvv8+kYmZM8mQTEuj+7G0JLn2xx9T9HTKFAjz5uHatWuIjIxEXFwcUlJSkJCQIOTn5zN1Pl6nTp3YwYMHkZubS0Tp7FnUxcTg8ZIlKGYMhYWFuH37NrS1tVFZWYmdO3eqcnNzuQkTJsDf3x+3bt0SpevWMYeff8YVZ2duwty5zKQ9KREqFRGiadPIQdMGGGPQ0tJCWlqaysTEBPn5+aywsBD6+vpoMw2iKfLyyID86iuau6go2itffkmR486dKYrXJBIdFRWFsLAwXL16FXK5nHNwcGifc8rUtDFf8+5dcmJYW1NEqa6O5lBXl4hR796UP/ysiI1SSeT0wQNyHPz0ExFtW1sivu+8Q2u6U6dmUUCpVAojIyN069aNhYWFsbi4OKh7YbcKnqdCjGqoO19IJI3VqV1c6B6nTSNl0dSp9DpjgLU1dK2ssKGiAsXGxvApKQFbsqSxpVvfvnR9Dx8CW7Yge+hQHIiOhkQuR19PT+iqo8IJCbQH8/Op5VdkJKlzNm1qrPreBDt37hQqKioYAIwZM4Yior/8QukInTvj9OnTrLKykmVnZ4sDBgxg8PQkEqBWnsyZAxYaCsdJk3ApPh6zZ8/GwIEDYXrrFji5HBldukAul0OlUqGoqEj08fFhSqWy7WJaGzY0tgKjiSCnmVLZSDZu3aI0nSdw5swZFBQUwNTUFNN9fBC4bh1crl5lPy9axG4NHixGx8ay6OhosVu3bkxTvW58fJDepw86rVqF9aII+ZYtqsDvvuNCvbw4Dw+PBoVNWVkZLl68yBRVVegHMGzbBubgAM7GBtqvvYauHAcPOztg6FA4rF3LCjQ0YP/11/CKjYVh004wVlY0dl27tlT8iCKqZs0C3n8f5Z9/jkJBwI0FC1BVVSVGRkayiooK1NXVobaiQgzW1mZGfn5gP/1ETupffqHIrrU1KW6ys+nvNWuQW1uLw4cPIy4uTgTAAgIC4Ojighvp6cLlhAR2+dYtoaKignEch8zMTMHV1bV5TZTNm8kRtmxZ4zWbmiL9yhXxeH4+DA0NWWVlJe7cuQMdHR2YmJhAoqODBHNzXAbw+rVrrFdCAnp9+y28evRon2qpb19SdDRFRgbtJXX+9xPjxxiDn58f9PX1ERcXx9zd3aHTrx85dZ6s51FeThHvuXObOaxv374tFBUVsbKyMpWLiwv3NMIvCAKOHz8uMMYEf3//th9kdnY0587OpN7YvZtUce1ERkYGMjMzxen9+jG8+y6dh3Pn0nNr/nw6C4OC6Hk8ePBfKyaclkZnbVAQ7TnG6N+HD9Mzp2l73xeEkpISMTExkfXo0aNlsVfGaE9JpTTPgYH0+owZ9PyYO5ee5xMmtNr15mkQRRFKpRLqNCcAGwMCAgr+8g39l6CjQF8HOvD/AUJCQvQ1NDR26evrB86YMUNm8ALbrTwTEREkO968mX5WqZCfm4vK77+H4bJliF+9WlBmZnKdNTVxPzgYlSqVWFFRITg7O/PDhw//U19ZXFyMTfUyVolEAo7jRFEUmbu7uyotLY0fPXo0utU/bARBwN69e1Xp6ek8z/Po168f7O3tcfiHHzD66FEcmDwZbl5eSE1Nha6urqqsrIzX0dERhgwZwnl6ejZEEIqKivD999/DzMxM+frrrz+/qqq8nAhcRgaR8cxMIuceHkSKbt6kh2ViInnIT54kAqNQkAEwdSpFkwwNKeeuqoqivzo6ZHSqVCT15HmSrdbWkiRQLieDRSKhSJ66xZpUSkSlpISMPX19uraHD4GBA1HHcYg5cwb6ZWWwe/ll6FtZ0T1kZtLnaWgQ8czKImIlk9E1FBaSwSWV0nd99x1Jt+PjieB/9BGpQCIiWjgfRFHE/fv3cfPmTSQlJTW87uXlhaSkJJiYmIiFhYVMIpHgndmzIfTpg8OjRiHb0hI8z4PneSgUCgCkVHB1dVWNHj2ab2bwKpWoyc5GgSjCtj0F+QCS1b77LkW42tHFQS6X48svv4SmpqY4ePBg5uvr2zbheviQ8kuHDSP59JIl5NQZPZqMZgsLysmVyciI3bmTiMUffwAaGrg/dSpCt29H71dfFUWplNVH+Nt3XwA5En7+mdZCbS2lW3z+Oa3D0FAi7SdPkqOhNeTnU2QQoMhdly6Ut1pVRevqOSWdJ06cQHx8PGQyGaZMmdKyWnlFBSlIHjxoHnkeN472Qf250ABBIIP6+HFa3x9+2PBfW7ZsEQsLC9nSpUuhX1RE4ztoEMlbpVLaczIZoh48QNc//kAnANIlSyhaDNDesreniP6SJeSsyc+nNVIvU2+KsLAwZGdnQ6FQiPn5+UzC81jyzTdIWbkSp8vKANAekEgk+OSTT+i8MDene+3Vi+ajvtjg5xUVeOONN2BsbEzzFhsL9O8PURRx4cIFMTo6uoGFGBsbCwMGDOC6d++OoqIiat/36BERocWLG+coL49a1PXoQY6ekyfJQRMdTfvdyKjhXrZt2wYjbW3VxPBwHhYWQN++KCgrw/f37rU6r9ra2qKtrS16ffopqzEzE6VbtzKBMZgaGeFMaKhQVlbGAgMDWW5urlhZWYm6339n3a5dg/n8+ZD16QOZvz+dIT170rUdPNiQZpCdnY0dO3aAr6vDcACmUVGInDMHcrlcGL5+Pbs2dKgqx96eqVSqhj8DQkNx3c8PBrW10CkthWZFBTLt7THHxQUVQUEofestsZjjxGpzc67HyZM4ERwMQUNDNDIzYw5BQehhYQHO0pL2y4IFEGprkXTxIk7VR/n9/Pxw/fp1LFcXpf3hByjv38f9efOwd+/ehnF54403YGFh0bhWHzxAZkICzjx4IFhYWODBgwfQ1tDAkLVrOfnhw3CtT4vYv3+/kJ6ezpmamoqenp4sNDQUI0eOFHv17Mlw/z7l4Ofnk0Onyby1CisrijCrSboo0rr76iuoo7pPw/fffy+4ublxAX36tF4Q78GDxnoRT2DHjh1idnY2W7hwIZ5WFK+qqgrr1q3D/Pnzn+3MXLuW9uiaNRTh9/JqdOS1BpWKnpX+/qj198edqip47t9P979mDV33P1EP6cIFUg3Mnk2FH+/cISXf7ds0fytXtrs+z9+Jf//73xg8eHCjsuFZyMigM8PSktbQvn3PPV67d++uTU9P12KM1Yqi6LRq1aoHf+LS/2vRIePvQAf+y1FfiO83d3d3/VGjRmk9Uw78IlFURA9TtYF95w7g4wPzR49g/u9/A19+iWFyOXd/+nQxSSoV6hQKWJiaMjc3N/7cuXPQ0tLCrVu3hICAAE4tjY2Li0NYWJg4ZsyYFv2C1UhJSQHHcRg2bBjc3d2hq6vL6qu38mZmZko3N7eGQeI4DtOnT+cTEhJgY2NDxjGAN1asgLa2NpZZWUFr4kQkJiaKBQUFMDExwYkTJ7gTJ07g3Llzore3N7O2tsaxY8dgZmaGsrKyJxkqEeCCAiLMcXFEDuvq6HVPT4rSGBkBq1aRUWFmRsTEyoqI8YcfkkGhlvUdPUpjOmYMOVHs7OjzAwOJBP5ZqFREUASBvkuhIFLGcXQf1dV07To6iIuORpq9PfQqKlDNmKqPtTXPqqvp99QGXV0dGZT6+kRGy8qIdLi4ENmpqSGHRUoKvb+mhkjq3LlUCM7MrJlRwBiDo6MjHB0dMXDgQOjo6EChUEBPTw/BwcEoLi5m27ZtwwhPT6SPGoWwqVMx9vXXMcPeHowxSCQSXLp0CREREXjvvfcgk8la6iAlEmjfuQPb0aNpvbZV0E+NxERKOVD3wG4HNDQ04OzsrMrOzuaMjIxaEv3KSpI+WlqSVL+8nNQPycmNEvWXX25MS6iXSgMgibVKRfOVlgY7pRIvnT4Ng9JSZti1K62XTz6h6/b0fHZ06KOPKJKYmEiOpJkzyQF19y6NzfbtzY30mhoi3F9/TdLf99+nvO8hQ4j0t0JynwdjxoxB7969sXPnTuzYsQOurq6qSZMm8Q1jqKdHBvKTEvMdO1r/bo6j8SwspDUH0PrnOBQWFjKAiuU17N2AAFLNWFnRvpXJUB0bK27r3p0ZWVqKCxcubN2Kramh75HJAHNzyL/5BucMDVVSqZQNHDiQ09HRQUZGBvLy8jBz5kwWFRUFKJX4+q23gNJSSCQSDBkyBM7Ozo0tL/8fe98ZFtXZbr2ePYWhd6SKHQFBBBuIit3XEhF7N9FYYkmMMWqiMaQZW2KiJprYEjV2g4pYEcWCDVFAOgiISu8DQ5m9vx83w9BF3+ScnHys6+JCh5nZ7Sn3WnfT1KRQeLmcftvYUH5sTAxsAwOVfn5+7K233uJMtbTI6/jgAZi2Nvr27cs6d+6MoqIiaGlpIT4+np09exbnzp1DZWUlTExMlAsKCuie1iT6Xl5E8FXHHzWKIo4ePaIQ9RpF3PioKPTo1EmE69dpnK1fD1NBwMCbN2FhYYFDhw7hnXfewa1bt1BcXAwLCwsUFhbytz79FP3atRO1OXeORJG2bTFt2jTO398f58+fV2o9e8Z5Xb3KCnv3FkJ79WJP8/Mx0teX796+PVdd2E8koufaowfw3ns4f/cu9LS0UGhggOLKykpjfX2RlaUl066s5DS6d4enlpa4ZMwYyGQyyCQSyLS0oBUQgH5DhgCDB8Nv0yYh9dkz1vPOHeh9/z0OLVmCfnI5pI6OSO/Th1esXs1NMTFB6aFDzODDD7FBLofjli34Yd06waukhGl5evKBbdpw3VeuxIhRoypdly0Tl5SUVLdqBAAMGQLxzJnotHEj9PX1+YKCAk5fX79WfQ3F8OF4am+PU2ZmcHJy4oqLi6Eq5Gfg4wPDGmvVpEmTuF27diErK4vduXNH0NPTQ/fu3Wlstm9PRdm++47G8s6dNE8bEgEFgXLFVeuEnx+JdLGxry6SCRLTs7KyOE9PT4qGcXWltU1V6DEjgyL/VOlqNXDr1i2kpqYyAHhVhJW2tjYcHBz4PXv2cOPGjUOTXUcGDKDQeJmMruPxY9p7a6KyktbVPn0oMuPrr4F795A0YwYuJyXB2cGB1pS/E0+e0DoD0Nw+cYL+7eBA69uhQyS2/Q8iKioKgiCgWQWJFQoSJE6cIPF+1y5K13oDYSQhIUEGYI8gCEvXrVtX8vpn/u9Gi2e/BS34F+PLL78cL5FIfp8wYYJms9qC/U/jk0+IhO7eTYa0qam6KM6KFbTh9uxZux1bFUJCQoRLly4xFxcXISoqikkkEl4mk7HCwkJVNXFh9erVDAAeP37MZ2VlCYMGDRIxxpCWlobDhw/DxcUFQ2rk4712/9fISMrpW7So2hCqqjnADxkwgJMUFODc6dPQf/4csqIiGLm5QffGDb67sTEHR0fyxltbE8np3JlChsPDKdfSxYVIRZs2REKaSge4fp1yfLOyyFjasEFNZh4+JEGlpIRI4saNTfYO/6uQl5eHy5cv4+nTp1AoFPjwww/fPKUhOppIfs+eFJY+fDh5C/v1oxBiIyMy3K2tGzUUXr58if3796Nz587C2D17WGLHjjhiYoLKykqYm5vzM2bM4LS0tFBRUQGe56HRlEe5sJDCVnfvbjIHHwCR2rAwMrxeA0+ePOFPnTrFWVpaqqsWnzlDosGGDeSdX7pUHQqpwqefkjdn2bJmH+vIkSP8yxcv2LKRIxlu36YogfnziUx5e5MXdN48EnrEYspJl0prR1b07UtG5jvvEOndt4+EAB8fMphv3yZxyN+fiL2ODj2zrl1fOz2kuQgMDMTNmzchFouxevVqmtszZtC9a6j1n5sbGe0NFe+qQsbixdD090fIrl24c+cOtLW18dG0aXSPIiJI7PjjDxLgoqKAoUNRWFKC77//HgsXLoRZY3nsWVkkfkyYACQmIiY7G4XLlgGCgHxqpyfcvn2blZaWYuXKlZCJRCjX08Put99GVo0xOHXqVHSsGTZ/5AgJhN9/r35t9mwoxWLscHYW8vLy2MqVKyHbuZPuTZ3e6yrI5XLI5XJoa2tjy4YNmLd7Ny6sWAGJmRnGentDKz0duH4dT3r1QlFREXr27Fl7LX34kNb3JUvwgOeVjoGBIsmYMRCr+r7XOM5PP/0EhUKBNWvWNJyL7eFB3zdnDo25RYvI+xsaSs9v3jzgrbcg2Npi45Il6H7/PtyfPoVWbCwJzBxH8zYmBhCLEbdsmWBx/TrbP2sW5lZUQHPWLHoeM2fS954+TWJFQQEJbE+ekGD0++/A1q3gbW0R2K0bIrt0gU5JCdr16SMMXLqUsZAQdYoDQASxqAhKjoN8wwZkzZkDTJwoVAoCrFasYDpvv03r982b4AF89dVXtfPLv/sOGDECsYwhIiICT548gb6+Prp16ya49+7N7g8ciKiBA/k+U6Zw9YTu+fNpf6mqA1FZWYmvv/4aIpEIAwcOVLq7u4savNfJycDy5SQWdetWP7z66VOK7nJ1pf936UIE8913GxxHdeHv76+MjY1ly5Yt4ziOI/I3a5a6m8jz51S3o049gPLycqxfvx4AMGHChIbrYNSB6pqrI1+awrlzFGVkbk4dOGbPpn345Elaf6OjKX3m7bdJ1Koaw7du3UJwcHC17fG3omtXOp8OHUjAdXcn+0kVNde7NxXzbSoq4S9CREQErl69qlQoFCJPT09lnz59mi4YkJBAQvXu3ST2ffst3c9r197IPlHV/hCLxVcEQRArlcph69atK3+Ta/k34h/k4mtBC1rwV8LX17erRCL5bfbs2ZrVYX7/JOTlqXO1iopImX7wgAh0WBgZp3X7NteAu7s76969OyQSCRs2bBji4uK4yMhI5YwZM0Rbt26FRCJhSUlJsLKygp+fHwcA7dq1g7+/v2BtbY3y8nJWVwBpFtEXBPI2V1TQNdy4QaHorVoBTk4w3LkTCyMiOBQWAmFh8NHQEIqyswETE3b96VP0dnPj0LcvEQvGiKjWPG4T7dHq4d49Mnjt7Ijg7d1bv9WVgwO9/sknZKhOmULE//Tp5h/nDWBoaIiJEyfi66rCTE2S56bQpw+FIK9ZQ9c4fTqF848ZQx7j9HQyvocPp/f26kXGTceOgI0NlEolAgIC+MePH3O93Nww5OxZhs8/R3s3NywrLUV4eDguXrzIyeVyaGlpNZ7nXRN6elQIb8wYiqJozIN16xYZicuXv/ZlZ2RkMKGyEoM6dWKYOZOesbExFTKytW34+VVUUG7oaxbCe+utt7itW7ciFoCdKpf93Dn6LZfTsa2s6JqvX6e5OmMGGbl9+1Lo9v795OUdMkTdFSIsjIzQr76i8PbVq2n8/Q+kEaWlpeHu3bvw9vaGn58fvvzyS7i6umJ0Rka94/M8j9TUVBR07oz4wEC+JDMTurq6XHZ2Nm9pacnJ5XIhOTlZqKys5MRGRug2a5YyKzUV7ePiRFoKBUozMqDZvj2lS2hqUpHJVq2IlP7yCx5UjatGiT5A5PLTT4nst2+PttbWOOvoKEiys1nihQvoER4O40mTkMXzgkwmYxAEKA4cgFFlJfKTkgSlUskA1C7YxvPUTSAvrzbZ//ZbvLh7F8Xh4WzkyJH0fW3a0JiqW2StCtra2tCuinz4SCLB1SFDkFJYCBQW4t6YMbATiXB+0iTh2YkTTE9PT7h27RrMzc2VY8aMESsUCpQZGaG1hgbKw8PB6euLMHEixPPm1Vr7zp8/XxkaGirW0tLiGWNcZGRkdZHNWqhJCAoLKZpp+XIiiRIJjd05c4AzZyDz80OKrS0M9fTgKpXSmvHVV7S3DB4MAOh09iwrzMtD2bff4sXz50L7hw8Z4uJoLUlOJjHh1Ckq3GZrS0VDDQyqe4vzL1/i/qZN6Nu3L/r27QsADG3bkrd7wQISBvv2JaHs5EmIXryAnosL9IyNgdBQNSlU9XV3dAQ3bx6kUqlQUFDAVNFkqKgAgoNhN28e7Ozs0L17dwQGBiIkMJAZLV6M4FGjMHrSpPpEnx4gzdMqsi8Wi7Fs2TJcuHABN2/eFHk01iWjTRvyvBYVUXFMLy+6DyphYP16EuxdXEh8jYhoVHCtqKhAVFQUOnfujIqKCpSXl+PRo0ei9957T733fvEFkft27UhQWbiQ7ncdSKVS2NnZCXFxcSw4OJh3cHB45eatmhumpqavFvZVfeWHD6dotY4dKUrJw4Oin8TiBtfZW7duQU9P7+8n+i9fksCismFEItr/kpNp3HEciY5Tp9I+9DcJqgAQEhKCy5cvw83NDUOHDoVEImma6MfGErGXSsl+0tMj0t9Y9EgjKCgogEwmg4aGBmQyGRQKBSorKweLRKI/Afzri+69DlrIfgta8C+Er6+vuVQq9R8xYoTWP5LoA6TkZmeTF6ZnTwr1XLCAiN0PPzTYhqcuVMRMJpPB2dkZzs7OIgBYtGgRIiMjhcOHDzOO46Ctrc1ra2vzBw4cEBsYGCArK0vw8fFh7eqGYCuVVCE5K4sIwf37ZCgrlbVD6i0tyTC/f5886AkJ5KVs3x74/HMyKC0tAcZgDjBzkMcq76ef8MjCgm/Xrx/3X7Xiu32b2pgFBVEBrnXrGuxnXXVz6G9RUXROW7aQoBIVRWF0Ko/M3wSVt0ihULxeLjhAwkrfvkQkZ88mz+CaNURiPDzIwPz5ZzJAFy0iIyI+nrxNX3+NkuJi3OjcWciVSDiZgQG4n38WkoqK2JOUFIx0dYWWlhbKy8thYmLCm5qavp41xPNkRAUGNlwwUqmk13fuJFGguVAqgeJidD55krkdPIhKb2/yQs+c+eoogi+/JK+aqmd8MyGTySCTyZCamgq7ugUEtbXV7fI+/ZR+ALr2khK652lpFIbu6UniS0wMeVC7daP5s307ech0dJqOUPmLUFxcjEOHDgn9+vUTunbtyllZWWH37t2IvHULDz08gH374OzsjLFV1d6/++47Xi6Xc05jxiidIiNF0To6fHlFRaWdnZ04IiKCNzIyEsaPHy8yMTGBpqYmGbM//gjezw+BHh7YV1KC9xYsoIMrlUSaP/2UvJIAKjdtEioqKtjBgwf5sWPHcto10gVyc3MRFRWFjh06oNXeveq+7gCko0ezsLAweGppodfFi+x+RgbfVSIRwPMizJgBve3bMdnQELGxsezo0aO1vcA8T3Nj/nwSYSIj1aHR5uaI3bdPmJaZCdu1a+lgcjlFnzRC9quhUEDr1i2M2r4dozp0QFRUFCpv3hQC7e2ZQqHAqFGj0K1bN/bs2TMcPXpUvH37dgiCAMOKCnhcvIguz55BOnUqLyss5DBrFpFDqRQgb7V44MCB8PDw4P7880+EhYUpnZyc6hMHqZTG+Y8/0tyYP5/W7KVLKaKlap1JXL9emPjkCdu7ZAlc/vOfqtNXQHjyBMUSCdJbtYJcLkdJSQlKSkpQpqeHmw4OaD9rFtUnefyYhIP8fPKgdu9O6+2NG1RLxMoKiIuDuGtX9OvXTwgMDGTNJ9poAAAgAElEQVShoaHKtm3bitzd3WHGcUTQc3NJENbToz3vzh0SzWbMIA+xah8yMaHIrrIyICEB+mZmfF5urqia7A8dSukSVWjTpg3mzJmDoA0bBP2CAlauoYFG6/HMmVPdHk0FPT09DBkyBDt27EB5eXnj6zNjdO737tE+OHkyCapffknpYiUlVATWwaFRop+Tk4OTJ08K6enpzM/Pr3qcmpmZVRoZGdXmIlOnEsHPyKD1p5Gq8t7e3uzixYt49OgRl5+f33gRWJ6ny8jNhY9SqTz18qXo6Ycfor2zM433jh0p+u3yZdq/09MpUufZMxLjP/2U1vmpU+n6migep6WlJVhZWf39ZD8xkTz7Ne93164kyqjQoQOJhxs3UpTVXwye5/HkyRNcvnwZM2bMQNu2bZsm+aWlNGefPiVhyMiI7KyyMkqJU62hDaC8vBzPnz9H26oohcTERBw8eLDWeziO28Tz/Jo1a9a0ePTroIXst6AF/zL4+vpyGhoa/j169DDvWqcP/D8GgkDhjf37E9mcM4e8gNu3qw3S/wImJibw8vJipqamwqNHj1hKfDw3c+BA7n5ICHrp6TETQWDIySFPY1ISGYzh4USKb96kkPpJk8jYUIXUCwJ5OWbMUOecT59Ov995h15rIg9QW1sbHh4ewpUrV7hNmzbB1tZWOXv27NfrjXP1Kv38+CMJJC9e1Kqs3ihGjiRRZfBgIvyWliQWbNtGQsXfUTyoCh988AE2bdqEK1euwMfHp/kfLC4mA2XrVhKGaoLjKKxfLqexo6lJ7frs7Kqr3ef7+ODYZ5+hp6EhG2JsDP74cSgSEljcsGF83vXr3C8xMZi3ejWio6PRoUOH178BhoZkpCQlVedw10JODhGsRsKia4HnifRoaJDn+623YDB2LPYC0AoLwzsrVrz6OwoKyCM2f/5rXYZCocCuXbtQVFSE8vJyJZpor1gLqhzZ1q3JI5mbS4S+Rw8iM6NGkTghl9OzCgqifPSePWmuKxQ0lyorKf1CJqP5VTfS5Q1w4cIF8DzP+vTpU53Pu2rVKiitrBAzeDBOtGuH8PBwjB49GmKxGIIgMDs7O95nwgQRli+H3eHDHDw9OQDo169f7ZOprKSw3pMnwZ0/D/nNmxi5axdefvIJLKysaJ1YuVJdiPHCBQz95hsm37MH4ZGRXEpKSnXIMc/z2L9/vyCRSITAwEBuVlCQoL1gATMcPx63b9/G48ePIZVK4TB9OqQrVuBDhYKDjQ3d+7g4QFcXL168wPHjx2FpaclzNV2Va9fSM9m0icbpkCHkNa2q2RHdpg3z1NRUiwvjxpG3W6lsumXXw4e03nXoAJSXw+GDD4Ddu5mzrS0AVM8jW1tbfPjhhxAEAZIvvgA2bULB2bMocXZGl1at6Dzz8ynq6MgR4I8/IJVKBUNDQ6bqiuHj41P7RJRKCmV/7z016RWLidxHRanX5Z07gZISFK5cicPBwRAxhvPnz8Pf358+4uQEDUEQxFevChoaGoKGhgY0NTUF66wsscuFCxQhsGQJ3avkZPKYtmpFZCQtjXqxy2S0NkmlwKefwnXCBJbm5QXG8yLLQ4fw5+zZwnxXV4YNG9QRSYmJNBcWLqT1TdW+ryacnWlNSUnBu+3bi/Lv36fUF4D2l9GjSUxT1UVITMSA3r3ZFwoFAFQXGK2HiAgS6OoUoAwKClICEClrdl1pDDIZ7Y2ffUZpOgEBtE9OmkRRPxER6toTQK15fPLkSaSnp7NVq1aB47iare9q8xDGKFJALqc17dixhs+lvByywkI4OjpC2LcPivBwEiDmzaNID1XBy2PHSGDcswdplZWw+fFHEZYtgyg/n+YHQEKynh5FYIwYQc8lIoI8+e+/T/Nh/Xoi/FXRII3B2dkZISEhAmrMhb8FGRkkNtVEUZG6CKUK06dTwdS4uL+0Ov+tW7dw9epV8DyPt956S2jbtm3T16uqfXDrlvrfKodOeDiNmQZSTXmex9OnT6uJ/WeffYbc3Fwkqgt5jgdwouq9sS2h+w2jhey3oAX/MjDGZhkaGnYeMGDAP3d+f/gheQPatyfjLTiYlPRXVf2tC54nj45SSZtZeTl5UNLTga5d4bhjB3OIiECgszPEd+/CtVUrKrBnakpqvqsrbd6qNlH6+rWJb5U36JX49FPy4np7N0lUPDw8WMeOHSGRSLBjxw7R0aNHhbFjx7JXerxVuagPH5IxkpT0elV2HRwo/PTGDfKUA2QMLVxIHqvDhym3728o3hgfHw9WlWPq5uYGWyIFr0Z4OKV1bNxIpPmPP2r/nTEyeOfNo/sTF0fGYY8eAICA8+f5DDMzzmrBAnAZGeCOHIHO77/DNTmZ65iRgbgLF/D06FG0s7KC20cfMRQWvrZHvLq90erVtftInz1LOat1e7fXxcOHdM/XryfjMiiIiESXLtACkHPrFnKePUNsbGx9j3tdPHhA31PXAHwFUlJSkJ+fj8WLF8PY2Pj1GzOXlZGX88QJ8oyZmBAZ+PZbmuOBgZRLqqpiX1JCIaiJiTR/09Lo37/+Ss/Ty4vSMioqSAzU1SXPZ1kZzVlz8wbHaWZmJkJDQ+Hi4oJ+/fohKiqq3ntEERFw1NBAJ6kUP/30k/Dtt98yU1NTlJWVMScnJwbGKFqnsQiKlBR61tbWwJ9/Iq2kBNmBgXymmRmnfPYMFvr65M3ftYsIEEBhwJmZcIyMRHRoaC0ylp+fj6KiIrZ27Vp2/Phx5Pn5sdCTJxGTmAjGGCZMmIDOnTurjy+TkbcxPZ3m7ezZSJw/H0ZGRvzcuXPVC8/GjSRg/uc/dE91dIjkFRcDSUm4nZ6OXENDsHnzKLz/ww/pu729KRqgsSragkB56qpru3+fyG4jUUXiFy9IbHB1Bd55B/r9+9cO0zUwoHnC84AgYICfHxTdusHv1i2lvb097O3taTxevkzrwQcf0L0dPJjG07p1RPBjYug1Ly8i6U+eAA4OcB06lBVpaODatWuYO3cuzMzMIBaLwSorAT09hvv3Gbp0IY/ounW4ZmvLGwYF0X2cN4/E5wcPyLusEkUMDChnG6AaGgCgVCJ//36kBgVhlLk5DIqK0HXOHIauXammxltv0Rq2bRtdww8/0FpT1UGhQdjYIHzMGIjs7QWTP/5gGDuWrtXNjcQ0Fen84APA2hrdR4/G/fv3cfDgQaxatap+2lTHjhRNUAfZ2dno3Lkzr6mpSdfN80QkU1PpuqOi6LXwcCKT3bvTGImOpv2krExdQNXTk/bx7Gxg7lya64MGAebmkA4Zwq/YsoWTTphAa/X331M6xgcf0P40bx55offupefn6Ulr64gRlBfv5UV/NzWl1I2lS4HgYJyaPVuYEhrKWGgoedxV4vfkySQ8ACQeaGvj+d27uLR8Of4zbBja9Oypvgk18/d1dSklRBDINnn/fZofPE+dTF5B9t3c3FhQUFDTkRJ/BU6erC/sduhA62jNeglmZnTvfvmF5uJfIOwrFArcvHlTGDx4MLO3t4eBgUHTX3r5MolDXl4kZjNWO3Jz794GCwcrFArs37+/JDs7uwyAIQB88cUXtd6zbt26k76+vjoAvAAE/HdX9u/FP5cMtKAFLXht+Pr6ijQ0NNYOGjRI+7UKzf1PgufJ6MjMJA/f8uX1q9YKAnnQEhLofYaGZFjm5tLfUlLIA7J8ORGBVavURrqtLXkf7OyAb7+FslUr3NmzB/dEIkilUoHneQiCgNH6+szB3f2vuab27Snf7MgRCvVrBIyx6rzdmTNnYt++fSw2NrY6L7W4uBhisRgy1Ub48iV5G+7cofD7LVverNgOY2QAPHqkJvsAqendu9NmXFlJRtpfnPaRmJgIc3NzvqSkhPPz8xN69OjBOnTo0HT+cmIiEYg7dyiSoSkC27s3/fzxBz2DhASUCALi4+M5AAjduxfDi4vJOKrqVa8LoNOiRbi8b5/SNTGRM7pyhSEoiLzxRkZ0z9u0IU90U5BIiJzXNAAFgQzVP/9sWDxJSKD836FDKTT/o4+ILBsY0HOq0T5q1qxZ+O2335CSktI02S8rIyNP1abrNaBKhWmWZ68u4uKo+FtSEnn59PVx584dwWbSJGZ15AiRm44dSQibMYPSGbS0aL60bw9BENQF2FQtusrKaM6np9NPTg556A4fpt9DhxLhFYlorLZpgwILCwT5+fEF2trc/ZAQCFWe6dDQUDg7O5PRvWEDpdfMnAkJgPnz57OrV68KpaWlgouLC3NwcKATEQQ6v/h4tYe7qIjmyObNVIF78GCAMezdvh0Cx3HWX36Jvhoa9OzOnKkVSisIAniRCMbTp2OUSIRLRkZ49OgRr1AokJuby1lbWys5jhNNmjQJN/X14WVkBGVystC6dWtWi+irEB+vTnvKz0dhQIAw6M8/ufj+/dHR0ZEM+tRUIls1Q7r79KHIjzlz4LJ7N65JJEhIT4fjpk3k2TQyot9NefUzMujavLxIxElMJGGrIQJRUkLvy8ige6aqBVEXYjGl4OTkQLOoCFxQEHR4HnqWliJ+5Ehwv/5Kc+bJEzq3hAR6RiNGUBh5ly70vAYNor3gyhUqCtmxI1BWVr2WJicnw8rSkgjc/PkkWAwfTmLThx+Cd3PDrehozvrgQRprPE/Xpq1Na6OhIb23oTB5kQhavXqh4sEDdFq8GOIPPqDXY2Ppd3ExjT8nJ7pvNTFmDEVVTJ1K5FSV5sJxiBwzBq319Oh6PD1JFP/mGyLeAM2DQ4cADQ10y83F/fv3AQCbN2+uX4BOFXWWlUUCRl4eYGWFKWlpooiDB1Fx4QIkxcW0H+zdS2vuwoW011pYUNi1nh7d4wMHqGL9jz/Se/39ad7WFC9Uven37gU4Dm2TkoQLAwdiZKtWkPK8OrWpVSsae4JA4xagNV+pJFIPEOk3Nyex4/ff6bWdOwEAH5SXs+94XujTvTtrZWysFmBq1vupSpu5UNXWsGdNot8QOnRQC1jx8fQchw+n88vMrN/Jowa0tbUhFouRkpJSu1DmXwmFgsSjPn1qv967N7XXrItRo0i0uXKFInz+C1RWVuKXX35RWltbs549ezJRU+tFaSnN/c6dqztu4PHj2qJ6TAytZT/9VO/jycnJyMjI0AJgB2AEgOuMsc2CIKiUAQUArFu3Tg7g3H91Yf9ytJD9FrTgXwSRSLTY2NjY7B9ZeV+FMWPIu7NgAZGvkhIyYs6eJePRxYW8nG3bUsijnR0ZDnl5ZEyrQuptbMiAVKnnjZBsMYCVq1ZBLBajvLycnTt3DhEREYiJiWlWBd9mw8mJPDbjxjWrN3jr1q1hZWXFx8bGck5OTsjLy8OOHTsAAO3y8pQDgoJEhomJSOnZE1YREdD5b5+pvT0ZQnJ57RZjbdqQt+zyZSK5UVF/aW/e7Oxs3tTUVOB5XoiMjGSXL19Genq6kJSUxKRSqTBlyhRWrz/yokVkXB8+TM+8X79XH2jqVDKKHz6EbMEC2M+ahRyJROj53XcM331XTfRV0DUygs/y5WpLpayMQnYDA0lUqWqBBomEiETXrg2nTEybRgbw4sXkAZo2jTxVa9ao35OfTyGhbdvSfVYoaPyHhzcYCcLzPK5fvy4EBwczkUhUnafYKF68IAP1DdJ2/vjjDwwaNEgwNTVtvstHEEio++gjChOtERp88eJFBgCun37Kj/rmG44dO0bGupERiQJGRoCBAcLCwoQzZ84wgHqFV1RUwNraGkxDg+a2jU3tY6rqIiiVJErl5wPR0SjLzkbAgQNon5zMdZfLUdm/P1hhIVIKCqDw9xfutm0LpxkzmEFYGCoyM1GRnQ0NAwPIZDKMGDGCoW64rbk5EZmiIhJgrl8n8lFeTs+uxtwxNDQUcnNz2bBhw0i0iIggsubpCfA8YuPjERAQgMLCQuiPHy9YOzoy3fR0QU9Pj+vUqRNsbGxgY2NTPQY9i4qAr7/GxBs3Gn8WTk5EwKoIh7tSyZ6fPo1Tx45h4bVrMJg6FeXLl0OnoSipYcOAZ8+glZuL4X374vSNGyjct0/p/vy5CEZGVHH98mWKxGgIS5bQOGeMCn4uWFCf6JeVkbATFERikKqdYBN4/vw5Ll68yL/08eGWamhgnK+v6En79sjU1RXMi4oYFi4EFi6EIAgICgoSdCorma22Noy7diUjdvNmKIYMQeyXX+KFsTEvvntX0D12DJ1WrRKZubqiZ1oazO7dA3/2LLidO+n5fPSRer7MmgVBqURlRQVaL1tGY+/XX9VinVhM19tE96ry8nIIgoDTp08L48aNq31TdHSIZH3+OXmRp0whURqg6JWOHUms7t+fCPMffwB6eigtLVVKO3QQobCQhN/evSmF4cIFSs3y8KCIml69YGFhgbVr12L37t38y+fPuYsHD/Jew4dzGh9/TF7ua9fIu/vkCc1BJyfAxga6Xbogu6CA3y2Tce/6+kJsbk73pi6Kikgwfu89ShFxdCQSnJJCItiKFUT+VVCtlVXX2dvMTPStiwuccnPRwdFRnfdeU6D8+WdKU7h4kcihat2r+Z5x42qdllQqhYWFBf/o0SNRZmam0sbGRtStW7dahVbPnTunjI6Orp5nd+/eRa9evRp+kD//TN1CVOJqQACJNYWFtFar6hQ0gpycHFRWViItLe3vI/vx8TTH6orJ+vpkT9WNUhOLaQ9T1Z1oRkvExvD777/zmpqabOLEiVyTRD8piZwfIhGlsnXrRoJJ3ei5334jAasBwVC173Ect2Xt2rWTfH19uRpEHwBeMxT0/1+0kP0WtOBfAl9f3/4SiWS9j4+PZoMtdP4JCAkhLwDHkXdq2zYiaTo6RFjef58UdWPj+pvCsGFvfFjVxq+hoYGcnBxeR0eHG/yKcLzXhrMzGY/37tX2njcBLy8v7tChQzA2NkZERITQhzG+r7+/qFBTU1QECGc+/pgvtrDgsk+ehLGxMdPX1xfGjx//Zg/X3JyISkxM7Zw+FYYMIdGFMQqRXb/+v86djo2NRWZmJjd69Gi0atUKHTp0QHFxMYKDg1FeXo6SkhK2f/9+rKiZk65QkCghCBTK7eZGHrWmIgFUkMmAXr3ATZ8Obx8fvBgwgB0eMQILvb3xyivR0KiV84/SUiok9PAhGZtt2xKpNzcn47x3b7UXVFdX3YO9a1d167YjRyhq5YcfKNXA3Z2MyVegoqICwcHBDACsra2Fjh07Nv7MVX2um/G9dVFSUgKe59GrVy/W7DUjL49C9HV0qIJyjSKXgiBAX19fsLGxYRGxsdxAOztoy+V0fo6ORNgLC5Fz5AjOnDnDWrVqhYyMDOzatQsA0L9/fzDGlEqlUuTg4IAGi4uKRECrVijU1MSR69eRk5MjGPTrx1x++w2cVAqpIAAFBeggl6Pi9m12xc9POH/gAPrq6qL0zBmUXL0KXhCQb2yM1i9eILdzZ8F5wQKWEhuLtm5ukHTujMrPP4f4wgUiq0olkf+qKuY1wfO8IJVKq+9dgakpnq1Ywdv9/juXMXUqAm7eRGFhIWQyGV+gUHA+Xbui9dtvMzx8SN6uuhgwoOnQ7shIqlFx5071S0YeHvh1yhTeOSSE07tzB36mpkhOSEC5ri6UVeS0R48eMKqKKNDT02OTDxxg7Z49Q9tx43Dt+nVRr8OHwan61u/YQSHVdcdDXBx5NXv3JgP99On6UUCPH5OXDiBi2AxSkZycjN/37cP7P/3Eyc6cgUZpKfD550jX0gJ35Agz++EHcD/8ACXH4ciRI3xCQgI3IDAQl1u3Rptbt1BWWAinTZtwNi4OuR07CtYlJczIyorLcnGB2/79CNfTQ/eICOSYmiLO3x+d4+PpwFeuUOHPqhaV5UFBGHfiBOI/+wyOEybUv/7Ro8mzW6cInCAISE9Pxy+//AIzMzNERUWxcXUIKQAiy0eOUMTA06fqVpwffqh+T24uea9jYwGZDLyWFrqPGkVCQGUlfe7XX0kU4Hmaf25uJHzl5oL74gt0dHdHv6NHYZaZyV3avZsfzRgHExNaz5YupbnYujXtr1IpXr58ifC4OM7a2poXzMy4epEdJSV0n2bOpLnXoYNaOA0OJiHH3Z3y5PfubbTAo4aGBgwNDYXw8HDWQdVWTwWeJ9FAW5vWT6mUcu2Dgih64RWYMGGCaN++fcqnT5+KIiMjYWFhAZsqsbCiogIPHjwQAYCOjo4wYMAA5uLi0vAXKZW0ttXdHz09adxv2kTrgELRYAHhgoICHDlyBABUXRn+HqSkNJzyyHFkO+Xl1befevWiYpPXrpGo9wZITU3FixcvuEWLFjXdtebIEYr+WLOGxKU1a0hEr+tEyMqi8VVHpBYEAY8ePYKBgQE0NTWF0tLSib6+vlPXrVun9PX1rc7RB2ANIP6NLub/M7SQ/Ra04F8AX19fC4lEcnry5Mmaxs0pCPa/AaWSvMt2dtT66JtvSPX/+GMyEJ89IyPGxYVCMw8cIKMiMpI8ZiEhRLoSEyms8A2r2evr6wvGxsbQURU4+ivh7k7haD16NKubwI0bNwQA7MmxY5h29Soz4DiRyNUVRr6+MOrQgdkCIkEQcOfOHSErK4sPCwvjXrx4ofTx8RFZW1sjLy8PBQUFaNOmTfPOTyWw7N/f8N87dybR5dIlMtbbtm06rLcJ8DwPPz8/wcPDAxYWFgwAVAUj3d3dWUxMDI4fP46JEyeqP1RaSvnQly6pDa7s7NfLpReJgI8/hmTGDJjn58Pi5Us8Cg2Fa1Uuf7OhqUkeegcH8l4XF5MR/uwZ5eeXlFDEiYkJGeAvXpAgtX49/d3ZmSIDJk0iQly35kATEKpImqOjI14p7ty9S+f6BoUti4qKIAgCysrKmtdyMDiYhBeplDx5Nca4UqlEcHCwIJfLWb9+/VBUVKT0NzZmk/T0OCxaRPnjx4/D78gRZcX8+aJ3YmMFm/Bwdv/BAwQEUKrl9evXIRKJREqlEvfu3cOqJipIx8fH4+XLl3BycmLe3t7qNlqqvGoDA0gmTIBjr15M7u0tiPPyELd9Oxs5ciT4oiIUZWSg9O5dRPr5scSDB2GUno7CAwdgVFICaV4erNLTwXr3pqJ2DRB9ACguLuY4jsPLly9x6NAhlJWVwZ7jUDJ3LgIDArBszx78Pno0PBct4gICAvgSgMO9ezTPGirqaGJC9zcnp+HCjhIJrZE1iKhCoUDn27c5LxcXVGzYgD46Ohg/aRLKdHXxeN06pKSkIDU1lY+Pj2f5+flcRkYG1hsYoFdUFFhCAl8uk3FPFi4UnAwNGSwsSDRSKut7DW/dIrITFETiVc1im4JAc2PAAHXxwKZQWUnX/vXXsD1+HMK4cXg2bhy6dOxInx84EF1fvsS5hAS+4O5dLuKjj9BRLudT27dn0ydPhtkvvyDF3l5w9vZmx2fMEHI++wwTQkKY3ooVDEOH0vM/dgwwNobXgAE4unt3pfaFC+KBDx+SV3zAAJq7jx/T+WRkQHPdOsi9vYXglBQh58YNrqCgAEZGRqioqEBpaSnfYft2aD5/joD33hPKyspYWVkZKy8vZ5WVldXzNTc3FzzPIzAwUDlo0CBaOAsKKDLJ35/uy++/q736daHakzZuBACUbd7MPZg2jfewsuLg60v74dmzJKKoamK0aUNk9KuvAC0tDPjPf7is4cOxbf9+DDAx4YTdu8ESEmhfHTuW1lIvLxJPjx1DaWkpJBIJZs2axdUS/FT1cPz9ab+YPl0tUKggEpEIoqFBaSP+/pS/30ihWkdHR4SFhfE8X7uWJNaupecVF0djCyBPdEhI0+OoClpaWli0aJHo1KlTiIiIqE4RKyoqqo6W09HR4ZcvX9605puYSB7punve9et03xijNJCff64WiWri/PnzQnZ2NgNQswDhX4+YmMaLARcXq/fvuvjkExor3brRuHlNnD59Gu3bt+cNDQ0bvo8VFfQs7e0pAiIggESi0aMbjha8f5/eW0e4ePToEc6cOQPGWLkgCFIA/1m3bp0SoBx9/N3FD/+FaCH7LWjB/3H4+voaS6XSy+7u7lr1Wsn9U1BZSeRn40baqADazK9dIyOhXz8q7AMQ8a8q2IQlS8iwefaMFGKAPNBvv02fc3Qkw/jAAcoNXbGC2tI5OTXa4qt9+/acv78/srOz+Xnz5v21hQ169CBDOSys8VDYGhhtasqyzp3j7S5d4jhPT7qOOkY+Ywzu7u4MAOvduzd+/vlnUXR0NM6cOaPMysoSSSQSfPLJJ807v5EjaRN+9qx+mLQKlpZ0/unpZJQGBNCze03s3LmzsqKiQtyQh4PjOJw8eRIAULMNGWQyMoZVRH/bNhoHS5e+3sG/+AJszhzIdu9G6vbtwrDx4xmWLGk4PLW50NGh83Jzo0Jm+fk0lp8+pWJTVR4d7NxJZGLq1DfuJx8REQEASEhIEFJTU1nrxtoqAhRivnv3axdeCgkJQVBQEFxcXHgtLa2m54Eg0DGuXqUUiwZSZsLCwnDr1i02YsQImJqaYvLkyaLvv/9euOzvD9fKSpQ8eQL/L74QsjhONHrsWNgkJjLwPHooFOixbh14nkdUVBQcHBzw3XffvbKGQEVVGzFXV9cm+2W3ptoPDDk5GGlhAQDgdHWhr6sL/Q4dMHrKFOTk5MDY2Bhffvkl2sfHY0B6usB0dOiGfvIJCTbLllEIc5VhmpaWBqVSqcphBQBoa2vzI/v04TQsLPCiuJi/5+AAPXd3OMhk3HVtbSE9PZ3v7OXFYds2Kv6myumuiXPnyBiv264xN5fmbRURVKHy9m10iYzEnZEjMczODroAcPUqNHNz0Ts4GL03bgTu3OFUYdUlJSVITk5Gh9WrIfXy4viZM4GFCxkGDqT82gMHKKf7/ffVB8nKIk94z55EpE+cUK+vhYUUydSlCwkBjYlOqsJvZma0pp84AUybhqiq+ghaH39cixBYWFhg5vr13PPnz/Fs61ah95UrnNeCBZDk5QHTp2PGF18wPH6MOZaWDDNmkOCmUFAIuCeOYhUAACAASURBVGouDBwICAJKJRJk2Nuj39KlJI4FBZFwUV5OAqOVFXDjBlpnZDDNkyeFuLg45cuXL0VSqVQwMzNjGhoaXMzUqdDS0YGjiQl0dHSgq6sLXV1d6OvrQyqVIjo6Gvfv38fTp0/x6NEj0cCBA8HKymjd6N+f0pGUShJsZsxodLzyVW3iAMCmbVuW0b49D4mECPqQIeoOMBIJRQtER6ufRVXesykAa2trBAUFoVOnTjDfu5fInUo0DQig53H5Mtro6YHneSEjI4PViqQZNozec+UKRZM0hLg4dW67qnjmjz9Sl4IGUtl69OjBbt68ybZs2SKsWLGCYfduGlurVlF6Q801zMeH9vLc3GYX7lW1HVStB8HBwSgrK8NqSgNoeo2LjiYinJBQPyJFX5/2zYQEPJs7F1anToF79121OFMFhUIhAGg6j/2/hSCQ2FAjuqcWBg1S13SoC5mMnCyrV5Nw8xqRe8XFxcjLy4OPj0/DH5LLaV/o3p2e3enTlBK0ZEnD+2BlJUXw1SkyWFlZiTNUd6FCEARdAJXr1q3j639BC14HLWS/BS34B8PX11cfwDDGWBeJRGLBcZwWY0wfgE1FRUU7nuclIpEIbm5u6N+/fzNcc/9LYIzyO4cOpfzdlSvJgBg7loj+9u3kcUlKIkVa1Y5IRWJnzFAbSElJ9FsuJ5LFGBmc+fn0uo8P5aDLZFRw6uVL2qgNDYHFi+GWlMRsZs7EroMHubS0NFjXbX3032LzZjKUHjxo3Ct++zZw+DBMTpyASe/eXEPhoQ3BxMQE1tbW/P3795lMJhO9++672Lt3b60cxO3btys1NDTw9ttvi+p5F0Qidc/g2bObPpi5OXmLrK3JgB4ypNnGwc2bN5GVlSWePXt2ox7jdu3aISEhATt27MC0adPQITWVvFU1CwzJ5a8fWXD8OHnsli4FNDTQ1dWVBU6ZIrzVqxfDuXN0n+sWNnoT6OuTNz88nEhwz54UPmlrS4Wt/gsYGBjAzMxMyMzMZPv27YOTk1PDbQsfPqSolzr1CF4FnucRHBwsjBo1ijk7Ozf9UJ8+JeK3YAHNt0YKOEZERAjW1tbM1dUVACCTyfD++++z8L59EbRnD18sFnMz9uxhZT//DGNV5eXHj6l41LNn4PT00KWKKHbv3h3Xr1/H8ePH0alTJxgZGWHv3r0AgHbt2gk+Pj7s4sWLAPDq7g4hISTEqDyGdcBxHExNTQFBgNv9+2ijoSFY/fQTtebU1iYS8+uvFKa8bx9FaUydCtHAgRAEAe3atYOdnR2uXr0qyOVyruzyZWj06QPv+fM5eHuTcW5oCPt33hHdLSyEl5cXiZWNCWgXLzZsHJ84QbUkagoEZ85Ax88PN774gr+XkMBZRkRQsU+plJ7ToEFEsBmj8Oqvv4aWhYW6VsnBg+CuXaPw9H79SExwcyPvYE2kpFAY88qV5InbtIleDwwk7zLHUS51QzUjVFXSP/uMcoYTE2ltrEqXOXHgAMzNzdGQUC2VStHW0hJt33qL4YsvaO2q8tRixQoSJJ88oToBsbE0/zZupOKYycl0D955BzPnzhUL776LJyNGwPn0afIofvYZqivc+/kBHAcLCwssXryYA4CoqChcvnxZ+fbbb9MiWlFB9yAkpHZHgSrY2dnheJVgLZfL8dvWrcK0VavYyc8/54udnYGDB2EdEQHX48e5PFfXWgU3VaksQp2aAFapqRiak0Nh+Bs30h5YUkJrznffETFtJPd82rRp2LBhA26eOcOPf/GCw+efq/9YFRVXvn8/ElNSBG70aCaTyWgfHT2a0jh++YVEkKZERBub2u3cJk+mSKwzZ9QF+mpAr0ps0NfSYpDLaf3U1GxYmBeLydvfv3+TRW9roqCgAIaGhggJCRGysrL4yMhIkZGRES+VSl+9cXXoQOO5sdST3r0RqVDgVFAQ7Dp0EIbcvcsMBgxAUVERdHV1wXEcUlJSOACYrhJkXgGe53H06FHo6OjA3t4e7dq1a1K4BEDjetiwWoVca8HenjqCNIYhQ8jJsn9/oykXDeHcuXMwMzPjrays6p/gmTO0Nvj7k5g3cyatEePHNx7hGBZG60q3brVefvDggeqfLi1t9P46tJD9FrTgHwhfX1+O47j5YrF4o7W1tdC6dWsdTU1NJhaLoaGhAR0dHZiYmEAqlUKpVELrDUPa/0ewfDkZnFV9UvHzz7W9P8bG1EIpK4vUZpWX8r33Gt/QADLEVRW8Fy9Wv56eTr8LCshLBZCXQeVpmD8fpqGhGJeczOs4O3PIzaUogf79yWvyww/0fW8ahmdmRp7f3bvrt8bJyiIvYWRkdesgvEYRH47jMGvWLC4uLg7W1tbQ09PDxIkTceLECRQWFqJNmzbIyckRmZmZCT/88AM/depUzqLKm1mNTp2ItDQURlwXs2eTcfnee2Qcq+53E3j+/DmCg4MxYcKEJonYlClTsHXrVhQVFeHQoUNwLCzkB7Rrx9WKa1i2rFnFDqtx7hyd77ZtgIEBYmJiEBwcDE5bm+U/fSrYHzokWMXHM8vYWAaR6M1qElRU0FjW1KTza9OGCLeqpkB0NBGZ/6LeQUpKCp+ZmcmZmpoKZWVlgru7e8NfVlxMhlVzQvBr4JdffqmUSqUiVReIRnH9Ookn48dTHYJGrkmhUCA1NZVNmzat1utaWlro7ekJyGQcVqwAvvkGOgUFRMoMDYkcZmfTPe3cmUi5iwtcXV0REhIixMTEsLot9JKSktjmzZsBAAMGDOAZY03f6KwstRDYGMrLkbdrF2xevIDBr78ydOpE86NzZwqbXrqUfpKSgJ9/RsXp0zCZNw/T+/QRrIYOZbIePdC9e3cGAFxkZG1PJGPIuH8fwYcPw+vcOQhDh4J160brTe/eVNBxyRL1+3//Hbh5k8i9CoJABGzuXPVrqak0J0+dwiBra+7e+vW4ePEiunTpou5wYGxM311cTEQ+N5c+17EjnaNq7fHxoTz8xES65vgaabBlZXTcPXtorKtqpxw8SGRh1Soy3GsiNZWEqLfeIsHhxAmKGvjmG/p7FdFNqhJuq4WsrCwi06NHkzDs5UXh58uXk1AwZAitnfb2FAVw9ixdo5ERiQpmZnQMFbFNTwc4Dk8iIvDnunVY1bMniTVff00Cc1IS7TU//0zPe+3aauJZtbeqma5EQsfMy2uwNWNISEg1WfeqrATXvTt7sn07rO3tq8enYGmJExUVUJw7h44dOyI5ORlhYWFITk7mRSIRZ2VlpZw5aJCI27mTUtUKC4HWramF3+XL5DHv3Zv2zz17qNDfd9812K1EJpNBX09PcNq8mTs8eDDeqqyEdo219MaNG7jZpQs6jh3LL7t2TSQdPJha6Q0ZQgJcc6KStm2rXUWdMSLmS5bQ+ddp4aiK1pm0YQMJFd9/3/h3a2qSQ+A1io6amJggIiICQUFBDIAIAGxsbF4d8nT4MF3HjRsN/pnneTxt1w537t7F8PR0ZZGNDSv8+GO2c+xYoUKpZLa2toKPjw8DKBKvZnRGU1AoFIiLi4Ouri4ePnwIBwcHYcKECU2fb1oa4OaGyspKXL16FYaGhuhRM0XNxITqBi1c2LBQwxg9n6lT6Tk3oxaOqqBxnz59aq+1paU0L4ODyZNva0tRMxoa9O+m9qWNG2u3q60+/eronlf0rW3B66CF7LegBf8wVLXPO21gYODl7e2t3WCRqv9L8PCo7bU2MmrYYDI1pZ67JSUUCtinDxX8ad+eDIfXhb6+uiXaxx+rX8/NBQPQfsMG7qCeHmyvXMFgW1s6flERebOXLiVDNjSUvI9Dh9K5ubuTkbh2LRnBEknDBGj2bPKMqAr5JCaSd/TyZTLQf/2VrusNIBaLa3URaN26NSoqKnD79m3cvn0b+vr6wpw5c9iZM2dw8OBBYeHChaxWfQJ7e3WRqOacg5YWGdgiEYkYH3zQaA4zAFy6dIm3t7cXHBwcmnTJcxyHadOmISQkBK1v3MC94mJuu6UlVpWVUY/ozEyKKpDLm0dmX7yge7tjR7Wn28bGBrq6urCwsFDGxcWJnnp4MLGnJz7dto2EpRs3ml/7ITOTUkyuXCHPYf/+RHgGDVK/RxBojBw6RCkTb4h+/fpxt27dQlZWFgPArly5IsyYMaO25fbsGYXU371b/RLP87h9+zbatm0LCwsLcBynyiFGz549cerUKaGwsBAlJSXixYsXo9GifKoUmuRk8gC9ojtDUVWrOb3Gait0704EztWVCNaUKSQi6OrSsxWLSWhr1w64cAF6Hh5YvXo1A4CvvvoKEokEM2fOhIWFBaKjo6GlpYXffvsNZmZmHM/zTXvDBg4k0tkY8vKALVsQdfeu8GLZMkxwc6ObwnHU3u3x42pyKrRti6cLF+LUqVPo0LFjpXdxsRgTJwLGxuDGjCHP5pIlZMTWiBgqlUggCAJapadD+fw5xC4uZHQvW1a/00T37vXFCV9fih4JDqb/37hBnrSQEEBTE1IAK1euxMaNG5GVlVW/raWODq09AK1lRkYkhIpERPhXrqR0qBs3KDUlLY3Oy8qKvJ1WVuS5Dw2ledavHxG969fV6UqCQOe5cCHVpzh7ltaLvLwGSUfa2bO4d+YMOnTqBJMxY8hTHxNDc2r4cGDiREqLcnFRzyVHR1pTVZ7gmmKVak3U1KQf1TMEed3NFAr+9rRpXC+eh+aECXT+qnGzZg1Fkd27R+t1nz6QSqXgeb72iR87RqH/NZCfn4+DBw8KeXl5bNiwYbyFWMy1HjgQbM6c+oVaPTzwfORIxBYV4dtvv0VVpw3l4H79RPoHDkD244+iX7y90Ts1lXfZsoWrviZBUBcJXbqU7idjJNxu2UKEvwEMMzBgYkFAnKkp7ty5IwwYMICp5sr169fBSkogv3mTi4mP550qKjjI5cDq1WDNjabi+fqRPsbGRNIfPCDhyMQEcrkcL1++hGLlSsHI0pLJzpxpXo2RpCS6blU7vVegX79+6NixI3bv3g2xWAxDQ0M4Ozu/muy3akWiWx0oFAqcOnVKSE5OZiKRCJ4ZGUqXoCDR9e+/F/LPnIFGfj4TDA1RVlbGtm3bBsYY1qxZg5cvX+LSpUsoKyuDp6cnDBuJFpDJZDA0NEReXl7VabRiqvUsNzcXRg2lLyQkIDUvDye3beMlEgkrLi5m5eXl6KOKVjMyoiiX3NyG634AZE+tWkVjZ8OGV94elWhfa7+Qyyl6o107EssyMii6be9edUpbY4iKojWlgTFQ45o1ARS+8uRa0Cy0kP0WtOAfBrFY/KmpqemA2bNna/2tuV9/N6KjaTMIDa3tnV29mjxEVX1y60FLizaiZcuI9G/YQKTGxeUv7QGvYWIC15kzlQEBASKdIUPQW9WXNzubfu/cSRsmQB6K1q2JYP3xB5H9kSPJ2Ll6lYzQAwfIyDx2jCIVSktJvb57lzw0rVqRB7hm2ONfAJ7nYW1tzVtbW/O2trZisVjMpFIpRo0axU6ePMnv2LEDgwcPZo6OjtRzmjFS3Y8eVadJvAoq41lVI6GxAmIA8vLyBBcXl2YN3FatWsHb2xtFX32FdDMzZFha4tq1a/ywYcM4GBhQuGFziH5aGpH848drVYfX1tbGh1TtWlRcXIzi4mLs2rULmRMnwszSkp7L1q00JhsjvsnJRIqGDSMP9JgxRBSGDKmfYsAYeRNlMjLQ37ArRlU7KaG0tJTl5+cjKSmJxcbGQkNDA9ra2hR2fv06KkaOxDdVIdXLli3D9zU8ZaampoKRkRFLS0uDXC7HvXv3oFQqmbGxsTBq1ChBV1e34ZOLjCTRZM4cEnVq1lRoBKamptDW1sbZs2eFOXPmNPy9y5YR2f/xR5rLMTH0f5GI7pOqVeHq1eRpXrsWALCmZgtDAPb29igvLwfHcTh69CgAYNCgQYKnp2f94169SiJDcXHDzyIlhcLmRSJEeHsrc1JTxWFhYYiOjuZ5nsf0LVs4bNpE+aViMeRyOQ4cOAAHBwfB+6OPyH764QcSRCIiiJxWVJDB6+ZWPW/atGkDPT095b21a0WdRo4kgjx0KBX/S06meXXvHt1rDw8Kw605fmbNUvcNz8yk2hOHDqnnJYg4SKVSQSwWNz3oLl2i69m1i9bWp08p2mjXLsrB3raN7ptqDJ8+TZ71Fy/oXPfto/W8Rw8S1a5cofd//TUR/IEDaf2uWVzRz4/WnMJCYMkS8MeOQfHZZ+iqVEJ7xQqkFRbCqFs3aHl7I3vuXBiLxZQTXQWlUomysjJoamqCmZurK/7XQE5ODvz8/ARPT0+mq6sLQ0NDFBUVQSSXI3/zZvQpLuZSS0qQ9uWX6FiValINHR0al9eukdg7dy6kHh71PbQffUTXUDXuAODq1avIyclhPiNHotOmTZzG/v0kcDRU1V4QMHLBAjilp8MSgEFFBdjq1SI4OpIwMGECempp4bS/P9fK0BDVEndoKP188kntiLOPP6bc7SdP1K3sVKishH1ICHLPnoXhpUu4efMm8/T0hIaGBk4cP64UCwI3/dw5hvJytmfyZBZlZ6d07tdPJOF56AQFoV5EWF1UVFBaQUNC4KBBFJ2yezewciV+2roVirIyzL57l3WcNQsajVXDrwuRiJ7JayAmJgYSiQSampp8Tk4OFxoaqiwqKhJFR0crX7x4wbm6ukJbW5t17twZurq6tF8bGFCaUh2EhYUhPj6eTZ48GZ06dQJjTAQAPbOzWcjkycpRR4+Kjo4dC57n+T59+nBhYWH49ttvIQgCrK2tlampqaK0tDR+4cKF1akhDx484EeNGsUZGBjgp59+UvI8z02cOJHFxcUpg4KCRHfv3uWdnJy4u3fvwsPDA+7u7ggMDESPHj1gaWkJnDiBa+3aCYUmJtxHH32Ehw8fIiIigi8vL+eeP3+OIUOGoNWgQSRSDhxY63qKi4tRXl5OhLp/fxIPr1xRO0UaQWlpKQCoU20uXCCHxZo1tJa9fEkC5dixzYvEePCABMMG0mEiIyMBABzHXQXgWO8NLXgjtJD9FrTgHwRfX18mEok+Hjt27P9tog8QSRs7tn4Y9oULzfu8hgZ5e6ZPpxzFd96hDWL58tcOW24M3bp1E1VUVCAwMFB49OiRYGxszE+YMIHWRVXeK0DkR4XoaPp96RJ59wF13+T792kDPX6cSADPq9XuusbYXwQtLS3MmTOHQ50CRDKZDO3ateNSUlLg7++PixcvCg4ODmzw4MHQqQqPLJg7F3fCwtCnT5/mdSdQhd05O6trLdSBRCJhcrm8+RcQE4Ok3btx//RpAEB1pd8zZ2p5RxuFIBAZGju2FtGvCx0dnWrj3cTSkrywql7AY8eSmFOT2CYlEelasICIvlJJ9RgGDmy0+CMAIknz5xMxqsorfxO8++67TBUSunXrVuHIkSPVVZ415HKM8vPDUVXveQB79uwBQAKHi4sLbt26xbKq2gHOnTsXxcXFOHLkCGxtbVmD4ftKJXlcZswgEviarS67dOki3L17lyUnJzfcHYIxms/nzlFthvnziSjUDeV8+FD9TBlrsHOEVCrFhx9+iAcPHiAoKAjR0dHMs07IMAAqlBgR0TDRDwsj4ursDPj6YgEgDggIEK5fv640MTERJSYmsqzcXJiGhNBY9PGBjo4OjI2NhYKCAgGqitBSKYXYA5SLP2gQEBuLrBUrIDl6FCHTpglRNjYoKysTJSUl4dmzZ2i9eTN5aSMiKALpP/9R57ZyHAkdPXrQeA4IoJSkKVOI2Kla7zUwBnmeZ82qAi4Wkwfe1ZWEkCFDyKOemUmk//x5Eja9vCh6wNycxvOsWRSltG0bnfOmTRQN9fw53ePgYCJPNjZE/H/4gQSEdevovMeNAz77DFFFRTjp7U2Xe/QodI2NlSV//CEyNjbmMzMzudatW/PTpk3jGGMICAhQhoeHi3ieh6mpqTDH3JxJqqJZ0tPTceHCBWVBQQGXn5/PADBV6zORIMAhJgaO4eFI7NABKc7OQma7dqxHU2uKlxd5o2NjYTR9OvS7das9cD75hK63Bvr06YNnz54pr5w9yxncucMS/P35vmPHcvWeQtV9083JgePXX5MIfOUKCQheXhA4DqGhoSiu+v7MzEwi3P7+JOwEBNTf93R16Xv37iURuuY4/+MPQFcXRnZ2sIiIEDQ1NQUNDQ0OJSUYuny5SMfeHicmTADT1RWQn8/Ky8uZ/u+/I/TaNaT98AN6uLmh29ixjVeVz8qiPfC99+r9qaioCDpr1kAYOxbczZtYtnkzfp00CXvffReMMcRv28ZPnz6da8zjXY0tW0jIkMubFB0LCwsRGhoKVRePKVOmoG3btlxOTg4uXbrEXbt2TWlubs55eHiwiIgIvri4WLh//z7ee+89Dnv2AMOH44/sbKVCoWDjx4/nVBFK5ubmkEqluHbtGm9nZ0f7UmQk9Hv2xPCsLBF/5gwWDx4MQ3d3jjGGvn37oqCgADo6OoiMjBQlJyejpKSEbd26Vdm6dWsuJyeHvXjxgtuxYwdEIhF0dHS4xYsXM7FYDDs7O1F4eDi0tbURGhqKvn374saNG7hz5w6kUikSEhKE4e7urG1+PiTu7tRhAUBxcTGfkZHBKRQKwdbWVti7dy83RSxGG1U6I0hkuHPnDtLS0iCVSoVVq1YxyGQ0h2/fJoFRSws8z6OkpKSWLZCZmYmdO3eic+fOyrbm5iIEB5Mg9uuvFCUYE0P75+7d1QJtkygqovWrjoirQlZWVqlYLN5cWVm59dVf1oLmooXst6AF/yyIeZ6X6b9hFe9/BASBNpG1a6kwXl389huR9y1bmvd9Fhb04+REG0vXrkT8FyyoVw33TdC9e3doaGiwjIwMdu/ePe706dMYPXr0qwvlcJzas6bauGJiyDhZsYJCf83MyBMUF0f/bqoGwV+EtLQ0nD9/ns/NzWUA2PDhw2FjY4OAgAAhKSmJfffdd7CxsUFnLy9krlyJR23aICoqCt7e/4+9946q6tq+x+c+5xZ6r6L0IogiKmLA3ruiRkWj0cSoKUZjmnm+aEwxicaSosbEEo2JNfYaBRRQQBGwgAgISpMivV0u957z/WPRuRTN+/zeG+PHHIORCPeess/e+6y51lxrTYWDg0PHThIcTATkyBGK7jUiWrq6uqyysrKBDLV9sUDfvvB6/BghhobqyspKPjQ0FD4+PmB//knPui6iqQkqFRXt+vbblr2RNUBbWxscx0GhUFCdC0dHioAUFdH/79pF97JqFd3jd9/R+RUKIqEdiHIDICn3P2y9VCeZrK3TwGJjY+Hr64u9e/eKbqGh7KmFBcDzWL58OaqrqxEeHq5mjPEDBgyAtbU1nJ2dYW1tTSkRtRgzZox469YtAbX5rPV49owcWs+eUQTxBRyNo0ePZk+ePMH+/fuxatUqyDREbRAYSOtm61aKan/0UUuVSJ1EeflykoZmZVEkqFl1eh0dHQwePBhXr15FdnY2Wsj5BYEi1rWF/Zrg4kVapyNGNDnu+PHj2fjx4yWVlZXYtGkTioqKYL52bZNUD0dHRzx58kRscczkZJK1FxYCjCF5xw5Ya2ujZ0oKG3LwIKp69ULOqlXU/7uugvmSJbSHHTpEUtjUVCLQO3c27BUREUTC61KIDhxo1dkkCILmcdcEjqNjKpWUMmVsTNLa336jufv337T+eB44fpwk+ra2dM2XLtF+tm8fEfk//iClwq+/Uu6zoyP9u0cPOk9dizsAmDED6bWtFmfPng0bGxvo6enxqampSE5O5gIDA7Fz50527tw50cbGhj148IC98cYbMDExwddff82+T09HD0dHdd6+fezx48eci4sL7+3tDbVaDX9/f0h4HqqkJIhvvQW5iwtw7RrczM1x+fJllnfjBp4+fdokzaGsrAzp6engeR4KhQKVlZWoqK4WjZycRMeEBA7nzlE6B2M0Ph99RLL52rlmCWD5d9/xiIxE1uLFCNu9m7Px8oKrqyvtT2o17R0jR9L/b9pEc9/WlhxFtXvuvbt3cf78eVhZWQk2NjZwcHDgUFlJDoZDh1p3cC9aRO+h7OyG3P3KSrre11+HIIpQKBRQp6ZyyhEjIDt0CNz69bidnIxX5s2DnZ1d3T7NAYBNv37IHT0a5UeO4JhaLc6aNYtpTPcpKWmRApWTk4OoqCghLi6O41Qq+JeVwXv2bHBffoklb74JlUqFR48e4cSJE9z27dvx9ttvw6itwrS3blH9nO+/p7QODaisrMT27dtFExMTUSaTicOHD2cODg4cAJiamiIwMLA+fx8ABgwYwKlUKqxfvx5CYiK4M2fwOCsLjw8e5J2cnITt27eLZmZmQnl5ORQKBW9lZaX29PRsGABPT2pDqasLbudOmKSk1DtZeJ6vl6F36dIFXl5eanNzc87AwICPjo5WV1ZWsokTJ4qhoaF8WVkZJk2aVO+c4zgOVlZWgq6uLldSUoLhw4fjpZdegkQiQX5+Pk6dOiXePXZMzDc05JKSk9k777wDXV1dFBQUiAAwZcoU5uDgwA4ePCiGXrvGLGxt8TA2FvHx8cjIyEDv3r1hZmaGu3fvsvT0dOpSMnAgqo4fR9yqVfDZvBkHDx5Up6am8rUpLE1k+9O8vfln8+dDJZPBJCgIT9LTEfX227Dz9xec3n+fu/LokdogPx/dunXjvby8WncSRUfT3txKUdm0tDRBpVKdXLt2bWHrE6MTzwv+s8YVOjvRiU78VzF06FAhIiJikYuLi6F+W9HD/2UolRSxmDNHMzlKTibJ17Bhz3dcfX0yjF5+maR9d+8SMXF27njetQYwxmBlZQUnJydYWFggKCgI2dnZYody/QDyVP/rX1RYa/NmimpFR5MCoXt3kstlZjbI/F8wV781xMTEoKSkBPn5+Thy5Ij65s2bnLu7uzhixAhu7Nix6NKlS120l3l7e6OgoECorq6GPs9DHhPDJCNHoqKiAklJSWo7Ozvu5MmTKC0tRWJiIuztKVpNXAAAIABJREFU7TU7PXR0KDdw924iTjNn1hs8EomEhYeHM//2Kt6LIhGdN94ALCwwYMAAztXVFRERERg4cCC4wMA2awMAIKP52TNy/nSAoHIchxs3bsDZ2bm+TRMYI8O9Vy9y2hw+TBWuu3enqNmCBRS57SiJAogIlZSQKkVDLujzQkdHB46OjpDL5ch5/BjIyMCT3r3ZqBkzRDs7O6anpwcPDw/O3d0d+vr6YIzByMiohcHFGGMRERGciYkJLOtqZly4QHnZXbtScbMXVBQxxtCzZ0+Eh4fj6dOn6KWp2jxjRFpWrqTUnOnTaaxdXFquC2tr+v2pU+Q4rCvC2Yx4XL9+HYIgwMLCommuemEhpeIsXdr0O2fP0jOeMaNpvYVGuH37NlJSUjB9+nQi3T4+wOTJEIyMcO7cOWZvb885ODigifpq2TKa07WKCGtvb+wRBDHewYHZ6Oqqu+rrcxbZ2WCzZtHa6d2b5se0aUT0//6b7nfQINo/796lOTh6NKUALVlCZL+1/toArl69iqFDh7bvqGwMnqcceV1dSjtyciLi/+gREX9RpKi+XE6R/Y0baf+eNIkK740fTwQoIID24sWLKU3LzIxIvwaiaG5ujtTUVNy8eRN5eXlCr169mLGxMZydnSGXy+Hs7MxCQ0OFhIQEbtSoUczV1RUcx8HDwwNmcjn6rVrFpU2bxiwtLfHyyy/D3t4eDg4O4B8+BNu9G/yff0Kybh2NWe07SF9fHwkJCUJCQgKzsLCAWq2GTCbD7t27xdu3b7PMzExVZmamUFBQAIVCwVW5uzOrwYPRZceOhnQQnqcxmDeP3kd5eYC5OaJu3lQfzM9HbFwcLKqr2YjqanC9etG7QKWi8b17l/a76dOhqqhAmbk5qqqqUFlZicrKShw4cADm5ubilClTOGdnZ1aTnAx+4kTEbtmCNJUKaWlpSE1NRUpKClJSUpCUlITExEQkJiXh9rVrotG//832KBTq8IgIFG3bhtIrV9hulQqxp07BNCaGVXAc+NRUcJMn4+yTJ6JELmfDhg3TOFf05s1D+aRJyPn5Z1ScOSPW9O7NioqKcODAATExMVHs3bs3Q0ICKhUKHHj0SDx9+jSLiopCVFQUJBIJevXsycZYWaHHkSO4PmSIoLh0ieX5+MCmWzeYm5tj4MCBiIyMrCehrUImI0fRhAn1DrLy8nKcO3cORUVFsLa2xg8//CA4OzuLr7zyCu/t7c117dq13fc2x3EIv3IFg5ctg6JPH8SXl+PJkyewsbER3NzcOI7jRAMDAyxYsIDr27cvZ2Nj0/SYJia0zl9+mQIao0e3sHX09PTQvXt3ztbWlllaWsLb25sbMGAAMzMz44KCguDs7IzBzep1SKVSREREMI7jMGjQIEilUvA8D319ffj4+LCeKhWzc3OD35IlqLMP9fX1uezsbNHX15dpaWnB0NCQFaWkqKW7d3PHJRJIJBKMGjUK/v7+cHNzA8/zwvnz58X+/fuzsLAwXM7MFF0uXGAZ3brhbmoq8/f3ZwAEHx8fDBkyhKlUKhgGB6M8NFRMFgScdXdn2dnZyL16FWP27EGkjg4LBqmtZDIZHxsbKwQHB7OcnBxBFEVYWlo2HbvAQNpnWkkDTExMVJWWlr5z7dq1r4cOHdp2/9VOdBiseauPTnSiE/9dbNiw4cqECRNG9Pg/kn3/n+LHH4l8rVv3f3+u9HQim7a2FNV4553W2+Y8ByIjIxEWFiZOnTqVubRVKf/xY7pPpZIiX4MHk3HcONdREEiRcOQIRfgfPCCjb82aDvcObg2CIOCvv/4SHz58yNRqNXiex5AhQ9CrVy90SBmiVKJ6xQpI1q7FvnPnhIyMDI7jOBgaGtYXDBo/fnzTSr+aIIqUj52djbyffsLZs2fFjIwM9umnn7ZNOj79lJw2jSognzp1CnFxcehSUIAFJ08iPSQEjo6OmgvJ/fILGdN+fh3uZ19aWooff/wRq1evbrj2w4fJaXD4MD2fNWuIrH/wAVUrftGq+uXlROaOHv2PpZ2Ulpbi4qJF6JubC5tLl6gGw3NAFEXs378fCoUCS+bNowjVxo00j+sKrf1DxMfH48SJE3jrrbc0F5gCaO2cOUNr9soVyu9dtapJDnqzC6daAqNHU6pBo3UeGRmJujZ8r7/+ekMrzaqqpscTBKrrkJtL0VBNqQYAqqur8cMPP6CyshJr1qyhubdqFTBsGEK1tYXw8HCupqYGAOpy5EXfsjIMXrqUg4VFE0VHZWUlzp8/j/j4eLz55puwkEqpUryjIzkIfXzomqKj6bo++ICcS1ZWDT3UdXQo8v7SSy1ycBtDqVTim2++wZrGKUdtobKSyGt+PpHZ+fOJuNjYUHrB1loV7aRJNJYZGRTJv3qVIvZ+fjTHTU07rnhpBFEUcejQITE1NZV9/PHHGiOBKpWq5e/Vahq/5OQGB1xGBr0LtLTIaRUY2KrT6urVq7h3755YUlLC6qrDcxyHT1uTICuVNOdWrqSo/tixNB+zs8m5kZaG7w8fVo/bto2/MnIkLHNyMC4hATqJifRMGxeivXEDOHQIqp9/xjcffgheT0+s29tEUWQcx4mMMZGJIiYdPMjuOTuzhJ49YWlpKchkMpHjOHAcJ3Icx3ieFzmOA8/zKCsr4yeePcsUb70FzsEBxt99B2H+fEgdHCD7+GOwoiLUHDuGU6dOiRUVFRg2bBg7cuSIUFVVxfXp00c1YcIEjWHY6q++QvKVK+L5ceNErrY1YVpaGjdjxgxkf/edUJOZyd0fNQpTp05FdXU1qqqq0PfxYxqnpKT6vTN63jyUPH0K6x9/rC8ue/bsWfH27dts6tSp8GqW5y0IAq5cuYL8vDy8fPo0ZAMG0PgD2LJli9rY2Jjl5uZyHMeJRkZG4qJFi7hWi422gnWffQZPQRAeyGScrq6umud5TqlUijU1NVxNTQ3mzZvXttJtxgxy7p8+TeuyUdHc9nDjxg0hLCyMk0gkQv/+/blBjQo5VlRUQBAEaAz2fPghKUTaS7GqqYHaxga//vvfYs++fcXGVfRFUcSePXuE0tJSrrS0FEZGRhj8+LFYFRXFwiZPxltvv91w7qoqnH/nHcE+IoJ7Mn8+7gCihYWFYH/5MpdkYMBkZmbC4IULucuXLwtvvPEGJ5FI6tUbJ0+ehFqtRu/evdUPHjzgHRwc1GM4jpcGBUH2ww+t1rN5/Pgx9u3bBwAGa9euLdP4oU48Nzpl/J3oxP8YRFEsUjar+PsPj4czZ84I1dXVGDlyJKetrf3cJKHDKCpqv4je0aOU51ebT/zCsLUlkpKbS1Ldl18m0unv36Ge9a3BzMwMCoWC/fnnn/j4449bjtXVq1TUau1aKsa2Zg2RpuZFnwAydubOpb8vXkyy2Koqqn796qstWhM9D0JDQ5GQkMDeeecd6NTm2+k+j9Etk0FuYAAEBWH48OFcfHy82svLi7e0tERsbCwuXLiAkJAQUSaTsebGWGOUlpVB9tpriN++HeGbN8PV3l4IePddvt3o4rx5LSL348aNg5ubG87v2oVgd3dEHjgAa2trcfHixU0tg/Bwavmzc2eHiT6AeiMmIy0N3b74giTiGzZQBDgoiFow7dhBRGbOHGoNFRLS4eM3gZ4eFSY7dIgIwj+Yk3XYv2+f2l8u55w+/5y12r+4DTDGMH78eJz84gsUBQbCeMwY6izwAmStNfTo0QPHjh3D+fPnW+83bW1N1eGtrclotrQkpc+5c5ojPowRwdywgcZx0ybKOdfRQURERP3H6iXsokiR5ZAQqplRVUXOnNhYSs3Q4IQQBAGnTp1SJyQk8IwxDB8+vN7JJK5bh2dLliDcyYl75dVX0Y1y8PHw4UMmefqUdZkzB7srKwWjl15iAQEBTKlU4tSpU3j48CFWrFiBZ8+e4fr16+qAgAAe339PJ/T2phz3nTtp/v3737QvnjhBUf5u3ag92aZNpA5qZ6+orKyEfmUlOQhcXGjujhtH+c6ff05ze+FCijT//DN95pdfyAny7bdEkMvKKGp/717DgZ2cKHofF0dqlYcPibQy1uAQcHUl5+vs2eQQMDamrh1BQUTMLS3p2H370r5vYgLWtSvKSkuZSqXCV199hRUrVrRwUmqUAvM8jUlZGWBgQM/z8WMan6lT266nAWDgwIGIjY0V1Go1z3EctLW1xTlz5rTOFGUyUmGsXUvn3b2b0rViY0k1MmECZpw+zVedPCkGTJ7MIquqcLamRjUTkLToOOPnB6jVSEhOFn1EEWN27mT45RdyYhEYFAqGWbOAHTtg6uyMpF9+QW5uLlfveGoNc+cSEfTzI4n0rFmkmtqzB+A4SAF4enqyv/76C2fOnFFPnDiRj4+PR0xMjGT8+PEajy1fvRqeq1czzw8/ZHj0CDh+HDdv3sThw4dhq1Zzk5YswagpU8ipm5dHjuz+/cmB2MjZ0u+nn/DE2xvnfvoJjydOFH19fZmJiYkAgL948aLo5eVVf/K8vDzs3btX0NfXh0wmYyW3bjHzmBhg5UoUFxejoqKCX7ZsGaqqqnDnzh3m6empOc2gLaSl4cPt2xG+axe3Yvhw6Onp1V0sUyqV+P7774WysrK2X2DHjlGawfjxpDyIi+twm1g/Pz/O19cXQUFBXHBwMKRSKaRSKfr27dv6O1wQKGjQEWeeVAr+o4+wdOJEBkfHJoPDGMOUKVO4W7duiTk5OWKPHj3E3u+8w1ePG4eXBg4Eq1s/Dx8C69djhK8v932XLqiqrgZjjFU/fMgNv32bdf3mG9hPnMjJZDI4OzvXj1Vt/QGYmpqKWVlZLDExEV27dkVKSgrTu3wZmd26YXppaasBiRSqRVDdSfT/s+gk+53oxP8YGGOS55JhNoJSqURZWRnKy8tx69YtUV9fn0VGRgK1+Xh1PatlMhlWrVrVtvHwPHj6lIj2vn3tv/DGjCEj8D8FS0uq4P3hh+T9//xzkru6uLwQwdLR0QHP83B0dBS0tLToQajV9KI1NyfZrVJJLZBmzqRWWW3hk0+I6M+aRcT07bfJMH73XXJ8bN36QlXbZTIZrKysBFNT0xdv6L5kCTBtGuxjY2Fvb19vnfXt2xdFRUWCXC7nzp07h6qqqoZuBbWorKzEb7/9ps7Pz+cBoKuPjzA3PZ0z+/JLHo0qaWvEggX000xGLZPJ0L17dzjPnYtsjkPkyZN4+vQp27VrFwIDA8kQiokhMrJtW6vR2dZQdP06ph48CF1ra8rFHziQooLbttFzDA1tmL+//04RvcuXqZjW6tUvVl1/3TqaP/9Qzh8ZGQnDqCjepaKCyN+LQBRhfvQoBpuZidcLCsTxS5dyxcXFqC4thbGx8X/UCfjo0aPW/yiXUxS5tJRIW69eFB27eZMIiyYlBMeRg6iqikiqvz/g44Ply5fj+++/R2lpKdLT00nKzxiRMRcXIpebN1PUd+fO+mPn5OTgypUr6p49e/I2NjaQSqW4f/8+v3TpUuzZs0dsLN1Nz8mB4sYNBLi7U64rqACml6cnoK+PktOn4W9mxl28eFFcv359fT9xAEhOTtbcknDw4Ia2eyNHEjn64gtKVYqNpftbuJBIeF4ekfCwMCLaX39NjrK5c4lMz5mDmsOHMfb8eYrSR0SQ2sjDg9aIhweN36pVDYVHS0sbVCt1TpnZsxuKA8rlRGQSE2lfjY0l1UIDMW1oiVddTbUXdHXJSVFcTA6VukrtiYkU6dXTo+Pn5wP/+hcW/P477lRXQ1pTA934eCLPhw/TdwMCaP3VpS1IJPQ8a2poLy4spFzuWbNIEWFk1FDjQUuLFGbPntG95ufT75KSINHSwnve3nzk/v2oefwYPrNnM60jR+ic587R5/v0IbVFaio5O1QqItAREeTQVqvpWnV0gMmTYUMV+hnMzdE1OhqXLl2S5ObmNqTKNEKOiwuira1Z35s3Rcyf39DCs2tXmg+//07FXn19Yc5x+PDDD/Htt9+isLAQppocYXUqA6WSntmGDUTwP/64oQCjTAZoacHNzQ0TJkxAUlISd/jwYRgZGYmBgYHtk+W6wowKBfq7uUEmk8Hw449hVlHRMIfWrqVnHBREc6AxjIzQ7do1zAoMxG+mpoiJiamvbG9TV2egFufPn4etrS2bPXs2y8zMxKWICDh6eKB/bTFcURQhkUigr68PjUU5OwJBgM6772K0hpacf/zxh6ijo8Pc20iXqcfUqZT65OFBQYDnKGrK8zzkcrkAgLt06RIMDQ0FLy8vrtVc95QUemd2NL2zLv1GQ9FaMzMzjBs3jqFRXR2tgwepUOf166RWqKgAhg+HfP58LHz2DHfi4jDo+HGcKihgqthYuLbjIJ45cyYrLy9Hly5dyK7IyOAK7t3DLWtrHDlyBDNmzNDYkrBWmSVft26d89q1a1M6drOdaA+dZL8Tnfjfg3GHiyw1Qm5uLn5u2s6OAZQf2adPH/Tq1Qvh4eGIiIiAUqlEbm4urP5TreweP24wKtqDlhaRKk05vf8EEgm1T8rIIGnwxo30Mp4+vcMedwAoLCxETU0NHj16xP342WfCODMzzjkzs6Fyt7MzOTU0VTTXBMaI5P/xR0PlYltbMvD+/ptesBs2aFYGtAFdXV1U13UDeFHY25PzIjy8SeSQ53mMGTOGAwCO48SrV68iJiZGHDp0KOfh4QFBELBlyxbY2tqyuXPnQi6XQy6Xk5Ry+XIix/v2UZS8ueNKFIngtVGsUDJzJmyXLMH06dMREhKCrKwsXL9+Ha7W1jCaMwcJkyaJ/e3tWYdfYH/8Afz1F3Jffhm59vZij5kzGcLDKTpTVkZk0Nm56Xd0dMgBEBlJBclWrCADSIMB3yYePKB7Lil5LhVCc0RERAgB9+9zel9++WIHqKwkklhQAOeVK9ml8+exefNmQalU1svS20296CCMjIxQ3LxXfGMIAhHYWbPo39u3k0z97Fl6Jr/+2mTelJeX48iRI+qysjK+rKwMukuXovTSJawICMCjoUNR2r07pFIp+tStoY8/prUPUHTfwIB+1+iYmZmZePToEV9QUKCurKzk1Go1E0URwcHBgq6uLjM0NMSWLVvEnj17it27d+eCRo/G0mZFArFmDRARAcPgYBgCcHV1ZfHx8SgrK0PPnj3x008/QRAE5ObmqocMGdJ6MYRRo4gQb9zYUOBTFCnKv3w5kfADB4ho1imiZswgUu7iAoSHowzAufnzRfcPPiADvpHiAbXtGdE4JacxwVOraU/+5BNyuiQm0n4wcSIR6epqIt5ubpr3Urm8oeBW4zxkN7eWnx05EoIgIC8vD8KIEbi8cyd0KypgM3IkzF1ciPxWVNBPejodOzi4If3i008pgnz0KBEfUWzoFZ6RQc4SHx/ag7KyaJzy88kZkJpKaQoWFvDu1w+RxcXCXzdvcgYSiThw6lRmPGcOKUusrBqKE5qa0nzV06Pz37pFe0FICP1bS4v2uexsYOBA+Fy+DKGgAEXOzrgaFSUK9vbi7HffrZeZHz58WCy2s2Njxo5lOHeOIqjBwUT2Q0Npjm7aVD9Xa3OhxWfPnjGjmhpwCQlgdnakaHj4kLoc7N1Lz8vEhK7Z3p6cPUFBpIiJiwP69AELCUFvBwe4z5rFLKOjxSKZjElsbck54+BA96jp+Xp60s/ixUBMDHpHR9N5HBzIYaylRQ72Nup9cLa2MH3jDbwfGsrSPv0U+/fvh729vWrOnDlNtvH+/fvj6NGjrKamBt26dcNkQYD6229xKioKD3r1go2NjYBmnWeeCzdvtlkN3sPDg127dg1RUVGQy+Xo27dv63tiRgY9pwkTaA8bPfq5HMKDBw/mbGxs8Mcff6CkpIT76quvMG/evIYWd43x+HGrRe00wsqK3jkdhakpBWxGjiTnxXvv1acmmJuYYKSnJ/DNNygZMkSMiIlhjVMPNMHAwKCpkzM2FqZeXnipTx/h1q1b3A8//ICFCxfWO08BUlfFxcVVAdAG0GXdunVZACSdUf5/js6c/U504n8MGzZsiJkxY4a3xg2/DWzduhUlJSV47733kJKSAmdnZ+Tl5cG5EYlRqVT4+uuvIZFIYGNjI86fP/+fh/Y//pgKpGky7DRBoSDCVFT04vnQHUFyMhlgb75JEbIPPujwi1jx4AF4iQSqYcOgLClBiY2NqKquZpFTpwreixdzHfL6N7+W5GSScze/55gYihJ98QURHk1RQA0ICQlBTEwM3n///ee7luY4fZqiBrU5kc1R11s8Pz8fPM9DEAQIggCpVIoPPvgAUk1R2OTkBuUC0NA1QRAoKvvGG20XglOpyIiXSiEIAvbs2YPCJ08wIDQU9z08kF/rpHJ1dRXNzc2Zjo4O/Pz8ml84GWC2thRx6toVxVOm4Mjq1XijpgbMyYmISXuFBOsQFUWOkStX2iySphHLllGl9rt3n+97ANLT07F3715Y5uTAExAG/vAD99w1AC5fJiK3YwdJkqVSVFdXIyEhAWZmZrC0tMS2bdugUqmEDz/88B8vypKSEmzdurXBmKupoWKajo60BoYPp581a+hZ//vftB+kpkL8/nsk2tvjiZ0ddAHUqNVIcnRElbY2yvX0IEgkYIxBFEVYZWWBt7ERJ3bpwqzMzRsI/uTJFGWLjycFx/z5La5RoVDg+++/h0QiEeRyuaBUKrkJEyZwJ0+eFEeMGMFu3bolVlRUiBzHiRUVFbyns7MYsHo1Q0gIRV4VCoqCOTrWV1RvjPj4eBw7dqz+WleuXAl9LS0irYmJRGRjYynC7uZGxyoupqJ8V67QWnnrLXpuHUBCQgL+/vtvccWKFc+3p+fnkzQ9Pp6UD5MmkWrgyRPqTrFjB6VbbNtGe1VaWrt7lEqlQm1+OWpqanD79m3x6dOnoq6uLlOpVOzhw4coLS0FQAoJhUIBX19fjB07tu1rPXCASPuZM5TG8/PPpATYvbvFNT179gzXrl2DSqWCKIqwtrYGx3EQBAGiKNbtY+KzZ8/w8OFD9t5772lWYNQhLY2KJ8bEUPpWTQ2dOz6eFAbOzuRQu3cP17ZuFZ4BXNf0dFHgOOZVWAi5vz/uODmpH0dF8aWmpliwbRuR8G++oR9BaFBNpKSQU+HOHaB/f9xfsAAypRI5XbpAv6QE9p98AuOKCpp3/v71apXc3FwUFRVBFEUYGBg0RM1Fka734UNysllZIePQISTGxMCre3dYlJXRu+fdd+ndvH49OR66dSOCyXHk+CwoIGeAjg61zFyzhpQmTk40Nu1BqSTHK8+jLCAAmzdvBgA4OjqqhwwZwp88eVIsKipiADB48GAYGxvD8+xZSEJCUL1oEYSpU6HdWl2PjuLTT2lu79/f6kdOnz4t5ubmijk5OZxUKsWqVataP96ECeRwy82l9dqRcWiGmpoarF+/HgDw4YcfUqeY5tizhwh5c4djawgKIgfMiRMd+3xyMr2X7e1JMTRqFP2+upqcsoMHAytXYs+ePYKRkRE3bdq0jh0XoLnt5UXBDWtrKJVKfP3119DV1cX7779frzDNyMjAnpbdUwrXrl2ruZpfJzqMzsh+JzrxvwdVXT/wjiI5ORklJSVwc3ODgYFBfYSrufGSlpYGbW1tcebMmWzv3r3sxo0bgp+f34sb9woFRT1fe63j39HSej6P84vCxYV+zp+nF97ixWT4z5zZuhTuwQMgOxtay5cDvXtDWlAAhYuLED53LnpNmMAenz7NFQYFqd3d3Z+vZLmzMzkdtLVbdiHo04eI5KpVlN/9/vsURWkHPj4+iIiIQFpaWsdb5mnCgAEkWS0v19jKUFdXF/PmzcPevXvFgoIC5uXlhVGjRrVdH8DFhaq8X79OtRSioxtksOvXo02Zf0QEGc+1/bI5jsOiRYuAV15BWe/eGLh5M/bt34/09HQkJSWxpKQkAICzszPMzMzA5eSQwmPBAjJEhw0jY6W8HEZr1sA/NBQVW7ZAb8SI56s87+tL92RjQ3Pou+8aWqg1wqVLl8TY2FgYGhqKXl5enJ+fH/UlfsGUmQsXLggAuEBRhKxnz+cj+tXVZCTm5VHxzEaRXblcDm9v7/p/L1iwANu2beP++usvTJgw4R9J+nV0dCBTKKDavJnG6uefaQ7cukXqgmHDqEPFzZtEJN58E3jyBMKZM9hqbo5Z27bB0dUVWQYGgkliIveKlhZ0eR6lGRnQv3cP3IgRwKBBEF96CczCguHwYVKn1BWTW7SIiE1AQIPR2gxaWlqYOXMmcnNzmVwulwQFBSEtLQ39+/dnly5dglQqFT/44AOuUVSPISmJrtvamsjRjz9qJPoAUJmRAavsbDgD8PD0hP6oURRBf/11OoalJc1Rnqd1kpVFe0FKCkm5r10joh8QQA6SJUvaHHOFQgGpVNqxlpd1uH2bosqCQEqcwECa1+bmdK0APZu6FJSKCvp9bCzVHGgGlUqF3377TczKymIAzQOVSgWpVCoaGhpy6enpKC4uhpubGxYuXIibN28iKSlJMDAw4KKiolBWVoYATb3dq6spsvndd+SAqC0AWxAXh8qHD8Hn5MDyl19wf8AAhN+5I5SXlzOFQsEAoE+fPuoHDx7wKSkpsLa2FhkBHMeBMcbq3rWxsbGipaUlc3Nza5neJgg0T//9byreaWZGa2nDBlI/XLhAUd5PPgF8fTHk4MG6ScN2794t3njwgJkUFECVksLP7t4d+tnZ9M5kjBRi/frRu+HbbylK/8EHlKoQGAiMGIHimTPVkWVlvNzWVqyoqGDucjn0zczousLC0LdvXxgaGuL4l1+i9/37wpXhwzlBEDB+/Pj6W6i/J0NDMIUCl7S14bNiBSwar48FCyj1Qa2m+SmK5OTIzCSiuW0bvSPLymhPuXGDUh+ay/Zbg0xGNQU2bMADQRDkcjmbN28e27VrF5+ZmQl9fX0mlUphZmYm3rlzB6WlpexqdTUGeHujv64uuH9K9B8/JtVMO1L4sWPHsgMHDgiCILReZLQOy5eZlxn6AAAgAElEQVSTsyM9/YX3+MbKgaKiIkRGRsLPz6/pHvzjj+Rc6ig8PTseTAkLo+LCy5aR0+z8eXL4VVeTE9bdnfZUAMXFxUwmk2Hnzp3iG2+8wTqkBPv9d0oJqi1eLJPJ8P7772PTpk04fPgwAgICIJfLYWFhgSlTpkChUIiXLl2qG8zRrR+4Ex1FJ9nvRCf+xyAIQtc2IwyNoFKpUFVVhczMTADAzFZ60dYhNTUVFhYWgq2tLT916lScPHmSs7a2fjGy+Pff9CKIi3v+7/r7UxSpA6T2H8PeniRpiYlEAnV1yaBZsIDIrVpNEYsPPiDSPXcu1SAwNgaysmBsYsLNAeUK/vXXX2hTjtsaGKPzPXqkueUgY2ToXblCEavz5ylK0Ix8Z2dn48SJE+qqqipmYmLCdHR02P79+8HzPBYtWvRiaRkWFkQ0bt9uNRdcV1cXU6dOZbt370Z5eXnHCwH6+1MExdiYDPUlS8jgastAqKlpGqUTRZIhL14M/ZdeAngeCxcuRH5+Puq6B+zYsUM49a9/cRaiiAnm5pCUlNB9nThBkdOzZ4HPPoPw5ps4a2gIy+xsLHiRFnO9epEBpKeH+4cO4XZFhRr29ryxqSnUajXy8vLEwsJCNmXKFBQWFrLQ0FAUFhaKEydOZMjIIMM+Pr7Fc42JicG9e/eEefPmcXXRx8LCQpiZmaGqqgqDevWCoa6uxgh1m+M4cSIZal9/3W4RPmNjY8yZMwdBQUHC1q1b2YgRI1i7nRgaQ60GVCpUf/01Ks6cgc7AgTCLiyM1xO7dDXn4dXmt/v5UmX7JEnIA/f03jm/ZgsqyMuhv2wab/fvhumkT17i6vaFKRVG5Bw8AtRosKYlI0c2bNHdnzaL1o6tLzzw8nIgIYxQlDQggJ4CxMWBlBQfqZ85yc3NRXV0NPz8/GBgYYBit0ZaT9IMPyLh3dCRlgp8fzYe8PJIGZ2cTEdy/H/3i4yGOGYPKsjLx8q1brP+KFWL3gABWL5N+8IAcIMeOEXG8eJHGaeZMiqa/+ip9buRIkmK3g6qqqjqy3z5EkQjqkSMkc584kfbzykqao0OGkNLIzo4+r6ND6qj168k5mJUFZGVBNDDAkydPEBwcLGZkZLBaFQObM2cOjIyM8PDhQ9ja2sLW1rZ+LJVKZX0hxdGjR2P06NHcgwcPcOTIESQkJGDChAlNyX5aGjlq33iDnEVSKaBUoubkSdzr3Vt0Ki5mf+zfj9n79iEtLAzdZ8yA26RJLO3JE9jb26Nbt268vb29ePz4cbZw4UKN5OTOnTs4deoUE0UR77zzTsvc+GnTKOpel4bFcdRNoaKCVCTe3hSdv3iRxqo2+pqamorMzExm5eKCkcuXIycnB/p1a0oU6T1z9y7VmampoffPxo3kUCwpoSKLQUEIUqt5Gzc3vPbaa+zMmTMoLi6uT5N58uQJ7t69CwMDA7VWWRnvy3Fc348/xoEDB4To6GiBTtUwLQRBYGVlZXxt6k5T5xBjDelVde+SwYNpD62spGjx/fs0Dw4eJEfu559TSkNUFOWtT53adrqSmxvUU6eiaPt2buHOnbC0scHy5cuhr6/fuJUlq71WpJ86Bf6991B5/jz0NOTYPxfq0vpa67xQi127dqnz8/P5Hj16tKhV0wKjR1PA49Ejsi8uXGjVAdgaRFGEvr4+ysrKsGfPHpiamgq3bt1ivr6+bOjQoaQasLMjJ3pHYW5OzsPS0taVOKJIe9qDB/SeNjOj392+TcVlt2yh+/v66/qv9O/fXwgODuZFUWQnT54Up02b1raHQxRpn6tbO7XQ09NDt27dVI8ePZJs3LgR/v7+6N27N3R1dXH79u26j9msXbs2u+M33YnW0Cnj70Qn/oewbt26kbq6uqdWrlyp0xGP6ebNm1FWRulMEomkoaVYK9izZ49ob2+P4cOHsz179iAjI0NjFeQOYf588tTW5Us+D959lwj4P4lIvyhSUujFlpNDROjAAYrSZWRQVPnVV8n4bUZ8CwsLsWPHDrTWJqpDmDmTokFtFZYrLqacTRMTql49eDB2794tVlRUoKKigvXq1Ut0dnZm6enpQk5OjiiTyVhiYiLn7Owszp0798VCCyEhZKi0IflTq9W4evWqGBERwaRSqTh8+PCOk8GsLDJUfHwoYtkWmisMdu8mIvLLLy2NSFEEzp+H0KMHKv38ENarF5RvvilOmTKFQaWiqFNMDBmqq1YBpqYoKCjAb7/9BiMjI/Xrr7/+Qk3lQ0JCcDMqCst27sSznj1xPTBQzRiDjY0N7+HhUU8W8vPzsXfvXshkMrVt1668T3AwcufPx9P8fHh5ecHCwgJpaWk4efKkqFQqmaGhoRoAKy8v59RqNaRSKWpqajAxLk7s26cPa89IrR+TEyeINOzaRXmXzxFxqq6uRqOcVaFZdLspHj2idcPzFPn5/HM8zsxEVFQUEj088OmaNa3nu5aW0nNu9PfkHj1ELYmEdbtzhwj0qFFE2tuLHAoCEe3r1ykaKpeTc+/ttyk9JTWVopLh4URWe/Sgv738Mqq/+QZZp0/D+soVaBcX05qbNIkI2+ef08/du2Swbt1KEU8dHYpU1cnveZ4cl05ORJaNjEjJUjvuf//9N6KiotC/f3/1aBsbntVJiPv0oYr5GzeSND0uju5l4kRSttTNd0Egef/27UT+NSA4OBhPnjxRL1y4sO05XV5OUcKyMnpmjo7k7Ny6lfZkExNySB492lBgDwDu3oW6uhoZc+fC7NkzxP38M4SffkLIiBGQSCSQSqWwsbGBv78/7J+zcKYoiti6dStKS0ubku1Tp+gdERJC74za8Xz69CkOf/cdBo0fL/bp04ept21D+Wuvwcjamop+8jztGY3m/bp16zBnzhxoaqcaHByMsLAwaGlpged5vP7661AqlTA1NUVVeTn0160jxVVdW8c6LF5MjoC69IPERCpU5+yMKwMHqq/fvMnb2Nhg5syZLVMEwsJI8XPhAr13Nm2iqP7KlTRPb94kx4aDA85Om6a2WLiQ79+/f4trT0hIwOXLlwVnZ2fO19cXZo2fmQbU1fZ5+eWX4eLiojkNSxBoXg8aRO9BQ0N6Flev0nq0sqL35qVLNFcNDUm9s3o1zVNLSzrG66/TWmmG4qIiJE6ejAEzZpDzrC1UViL77bfFmyUlzH39erh1797251uDKFI6l5NTk/aYmnDgwAH1o0ePeE9PT3H69Ontb54bNtCxu3ShzhStdSFpA4IgIDMzE3p6ejA2NsbVq1cRGhpKqVBPnzZ0f3gevPMOOVM11Rd68oSUKpMm0R7UWO0QGkr7waRJZA9p2MOjo6Nx48YN9bvvvtv2fhMbSw7NL77QeBylUokTJ04gNTVVVCqVrM7ZDaBq7dq1GvIZOvEi6Izsd6IT/yNYt26du1QqPR4QENAhon/w4EGUlZXByMgIY8aMadfAqiuKNGLECAZQlNjHxwd6jYhV46hLq1AoKBdsz552X5qtYssWevn+N2BnR5GTI0eIUDx4QNGKTz4hg7JZhfg61ElSFQpFkzF7LvTqReT1iy9a/4yREf09MpLIWng4iqVSlFdWMoDaJ9nZ2cHNza1+kqjVavA8/+L1FwYNAv78kxwhzQvV1YLneYwYMYL5+flh//79LCwsTPDx8emYTrBLF5I5lpQQ0Tp4sPUCjc7OZGjMnk0EyNycVAGNiX5VFRGw2Fjg4EFwW7dCLysLvXNysGvXLuZbWgqryEgiiVOmNCl+aGpqijfffBNbt27lY2JiGoq6NcKDBw9w6dIltaGhIZs8eTLXONIXERGBiIgIzJ49GzrLl8O2oAC2e/bwkErpmhvB3Nwcy5cvx7179/i0tDT1GU9P1ufTT5k4YIC4/84dTq1WQy6Xo3fv3vD09ER+fj5fXl4ODw8P3L9/X9DW1uZcnZxgPGwYtehqD4WFNG9++41yY5+z7gdA8v7Ro0ejV69e2LlzJ5ebmwvrWvklAJojP/1EkZq6avC7dlFU08EBtoKAfV980XqnD0EgB8SxYy2UPekbNyLt/HlMPHQIVpMn03iePUsGa1t7ImM0V06coAgpz1N+cHAwRfEWLaqXoTZpK/f0Kb778kvwrq4w7dYNrwPgvvqK5o2xMUU1TUyIdHEcRbtKSkiOPGkS1T9YuhSXQ0NRVVWF7OxsoWdxMZf78CEcHR3Ru3dvAMDoUaPQQ08PuStW8M+qqkTzn39m8PYmp0RUFJ1n0SK6j9RUMr4bz3eOIyLu7d0gKW+G6upqyGSytjfVxEQaJ6WSxqVOFv3557TOTEwo2vz2202JPgDx5Ekojx3DvjlzYJOdjdmvvQZRRwdev/8OwzoFwAuCMYZ58+Zhx44dDVHoQ4fIKbFrVwtCeODAAWFcfDzzFEWGoUMhuXYNRtraRMj//pvu4bff6B117hxgYABdXV2EhIQILi4uLQavf//+UCgU4DgO0dHRuHjxIpKSkuCSmIj+N2+iKDgYts2JPn2R0h/qyH737nTeR48wKCCAL/byQiLH4ciRI2p7e3vezs4OLk5ODbJ4Nzd65v7+5DRTKOj9kJSEh4cOweSzz8Tb06cj1sKCX/7ee+SI+uGHJpfg4eEBDw8PuqegIHp2iYmtjnVVVRV4noepqWlLoh8URGvHyYnuKTubSH6do+LePXp3rlhBa+HMGVI3XLtGzgsXF5pLZ86QQ33oUJq3S5fSPlx7PgNDQ4QPHIjeWVnQaiUlpB46OugCMNeCAtzbskV027nz+d9zKhU9m5MnO2SzvPLKK/zWrVvF8vLyjqXFfPQR/Tc3l+bgpEnPXYyV47gmxeqGDh2K0NBQBAUFYaG2dodr+TSBVEr2TXOyn5FBtQY++4yUDo33aqWSnFhOThr3GlEUkZOTg4iICNHExKT9sbl/n/L1W9m/ZTIZZlGhVpaeno68vDxcunSpQKVS/bNNpRNNwH/22Wf/7WvoRCf+f49169a5SiSSyHHjxhl4eHi02wqnqqoKp0+fBgB8/PHHMDMzazfa/Ndff4kqlUocOXIkY4yhoKBAfPDgAUJDQ1lYWBhCQkIQHh6O8vJytYuLC9fqNVy4QNXLly598QJ7rq5EtFsh1v8nqKoicvLLLyS9fPKEjC5zc4pk5eaS8WZpqbH1l0QiwY0bNwBAc7XcjsDdnQw6R8f2jY6uXanCskoFv19/Zb0WLEBGTY0QGRnJHBwcmqgx/nEFdY6jlIzHj9s2vABIpVI8e/ZMyMjI4Dw9PdsvmCQI9LyHDSOjo6SEyPfTp5or8o8ZQ0qL1FSSygYE1FcFRlERRYSHDSNJ77ffUvTI2hpgDPo1NXBcvx5Z0dHIGzsWJkuWQKIhKlybF4ozZ85gwIAB9fJRQRBw5coVBAUFYeDAgZxKpRIuXLjAxcXFqaurq7mIiAjExsYiMDCQ5oBUSkZYQQEZNX37tlAmSCQSdOnSBT169OB8fHxY1wsXmNugQcx12jR4eHhg0qRJcHFxYYaGhrC2toadnR10dHRgb2/PunbtCu19+0gu6+XV9jinptKaVKlojmtoa/Q80NPTQ2FhofrOxYvM29eXsSlTKL3EzY0cLUOGkAT05ZeJtNaeLz8/H9HR0fDx8YGrq2vLA6tUpKqZObPF/uHo4sJyABh+8gnKjh8XE954gxmVlUG+eTORBU17kiBQAcJLl4honTlDUccxY4hAu7rSer91q4GU1UIEEHfnjliuUrEybW1ERkZCaWcHxwED6Ln6+ZHR7uFBpEYmI9n7xYtEugwNkV9UhOPHjyMnJwdKpZKlpKRAX19fiImJYamPHqm8Kio4/PEHDE6eRJKbG+4FBgqeEyZQ7YV33qFr/fxzItoARbFLSkiB0Bg+PqRQsbWldIVmbUVr26oKPXr00LwZnDxJDs5+/Wie1O1xcXFEPGfPpud45gwV7Zw+vcnXc5ydsbOoCFpqNd7atAnyM2cgt7eH1oAB9GzmzOlYN5ZWoK2tjZKSEvX133/nuq9eDflbbxHpaLR+VSoVfv75ZzXHcdzoqVOZpLyc9sjp00ktdeECrRNDQyIrpaU0jsuWIb1LF6GSMU6TGkkmk8HFxQXOzs71hB8AuqWno9TQENLBg6FWqxEZGSl269aN1cvNvbxoD8vLw64LF4SrV69C28AAVr16McngwfDIzISPpSXSyssRm5rKcnJy1D4HDnD46itSAIwb1zCnN28mouXtjb+uXlWHpKVxxpMnM97JCbN+/JHpuLmBvfsu7YG//qq5EJy2NqUvtdHZJTk5GWlpaXBxcUFubi4y4uIgvvMOrhoYQOvLL5F16xZOSqVC5PDh6hsxMcL127fF4JAQjvvpJ8FWpWL45BN6zjxPrTCXLwcKC1EdGwsuORls4kQa+9mz6Tno69N+uH49rZkRI8Byc5GsUAiPHj8WHC5f5mRDhrSdZrR3L/SVSjwrL2eHMzPFu3fvMkEQ6tq0tY+KCtqjZ8/usMopOjpacHV15TusUvnlF9oLBw4k20KDCuN5UKsUw/Xr19EnMhIqHx/InkfGDzSQ/boitFVVpCDZuJFUGl5eTccjOJhUJZ9+SrU63n0Xl4uL1bmiyLp27cpUKhX27NkjhIeHM3t7e8ycObN1OxGgd+GKFeRU6EBHJG1tbezduxeiKB4GcGHo0KE1z3fDnWgNnWS/E534L2PdunWDOI4LVavVBklJSez27dvgeR42NjbIyMiAlpYWJBIJlEolAODixYs4WlvlfODAgR0inkePHhUfP37MXnnlFVaXb+3u7s78/PyYk5MT+vbtiwEDBsDLywvBwcGM53l069at5S5+9CgZ+e+993zFzZpj9myS6D5vVfEXQUQEGbe6umRQp6dTdDIpiXLRvv2WCGVdT+7ly8no0mAwmZmZ4cKFC4iLixMrKyvZc9c60NWl84lih/Jwoa1N1+XmBu3vv0dfb292Iy9P1DU0ZM8rlW0XcjlFyv382jWI9PX1WVpaGkJCQnDv3j21r69v694GlYocHDNmELkbNIjGwceHijU2Nr5v3iRSZmNDxHLpUiLQz54RwZw2jYzZr76iHF6JhK712TOK6i5cCP3PP0eQo6MYlZ3Nenh6alRhJCQk4OLFi2qlUsndu3dPfePGDYSHh4thYWEoKirCzJkzmaenJ9zd3TkfHx9IJBIWHR0NnufFgIAAZtc8kunuTob7wYNEYt98s3XiM2MG4OICvaQkGLdXs0KhoGJkgYEtIq31qKmhSHtUFBnfdbneLwq1mojz06dwuHuXc92wgQVracF50CBEu7jAcuxYcJMmEdlsNk9u376NP//8EwBV024hKRYEKr64alWra9/JyQl3undHur09k+zbJ55RKNhLGRlgXl4t2x6q1USUL1ygSKK2NkUf09OJ7Ovp0TU6OJCKoKyMilDVRrMYY+jXrx+ztbXFxIkToaOjg1u3bqlfeumlpvN51SqajytXUjS/f39Kt7Gygq6uLgwMDJCcnIzVq1fDx8cH/fr2ZW4JCdD78Ucu/s4dMbamRnw8axaLLC/H2HHjOGOVimTPr79Oc6WxIfzLL3R8TYa9jg6RWj8/2qMaOQzv3bsHuVwuNlb8AKD58eWX9N/Jk5vWDKmupoJlPXo0OJMkEoqCNttf9A0M8Cw0FC//+CO0Vq6k9WdgQARnxAiKFJaX05p+ATDGYFVQwJWeOoVCPT3Yvv02WCMJeF5eHjZv3gxBELhly5YxubMzkZMBA+i5P3pE60SlovHT1SXiVVUFHDiAIl1d0YzjmH3Pnm0SD1tbW0ClwkvffouYfv2Q2r07kpKSEBsbi8zMTFZZWal2dXXlnj59ipRHj5B18CCqvv8e17t2ZYaGhkwmk4nOzs4MVlbAqFGQXr0Ku59+YtLiYgwzN+d016yBuHgxFCoVysvLUVxcjPz8fBTL5ciRSnEwOBjp6emckZERaoyNVXkKBZJsbZFWWYnHZ88yy99+g/zpU1rnRUUN6oy6Z2do2PpeAeprHx8fL2p//jkrv3FDvK+lJfY4f56l9O+vLhg9WlQOG8Z16dKFOTo6ct27d+d69uzJmYeHQ8jJgd1337F6J9PixeQ8l0qhsrDAYV1dXHJ1hXtkJHQCA2mtSCQ0t1xckOXtjcTSUiGpsFCwf+89rhfAqnv0QEFwMOM4Dnq+vq2/d7y8IAkIgO2oUejSsyeLjIxESkoK7t27J0gkEsYYg35rRfcKC8mxsmHDc9ksYWFhorGxMWdlZQV5R1r3envTeu7Zk/ZtF5d/3G3I1NQUckDQ/+Yb9pOVFeTa2h13cAC0392+TeszOZmceNnZpJZpnl6hVkOYMgU3GROeODiIUqmU5Tg6wurTT7lTJiYsIzMTly9fRk1Njfj+++8zT0/PdoNS2LmT9uz2umzUQhRFPHz4UFFeXu4D4F9Dhw5d1/Gb7URb6JTxd6IT/wWsW7fOGMAAiUQyVS6XzwsICNAOCgpCfn4+lEqlePHiRXbx4kUAJB+vrKxs8n1zc3P0798f/fr1a/dcBw8eFLKystiiRYtg3Czax3Fci5fH5MmT2ZEjR2Bubg5ra2uUl5dDoVAgJy0NPm+9BfH0afAvvfTPBuD2bTLUJ078Z8dpCxs30stt61YyciMjiYw5OpLn2te36ee9vOjHwYGI5bBhDTmZtS97d3d3rFixAjExMSwsLAxPnz5FSUmJ2s3NjR/RUZXCq68SGamFIAjIz8+HZVv92wcOBHx9UTBvHqbdvs1um5mRRPI/CXd3yuFLTGyIpLcCS0tL9O3bV52SksKlpKTwR48exfTp05Gfnw9zc/MGpUFyMjk3fv21qSEnkxEpk0jIKJw9mwz06GiS3R45Qg4YNzeSSB4+TI6ACxea5s0qFETCwsLIuI+LAyeTIaC0lG3ZsgWPHz8WraysWlgkly5dUru7u/MGBgbQ0tLiBUGAlZUVtLS0YGpq2kQpoa2tjQEDBrDaQk1tWzeLFlEud0oKtaT84w+KmjbHhg00Fyka2zoSEmgOtpanmppKayk2lsa5rTnUFkSR5M/btlHe8EcfAW+9Ba2lS3GzTx/cvn4dSdnZKCsrw/mvvoKNjQ1sbW2RmJiorqqq4iUSiWhtbc2KiooAAJ6ennDT1IozLY1SeN59t9VL4TgOw2or16uPH2dcdDSiv/tO9Nm2jaF//4bOH3UKBj09klLXEd/Zsyny3Rh1+1VUFBngjJFzYPZs8DIZnJycANC+WlVVxSckJMDDw4Ou91//onWxdm2DgZyQQIUWt26tva00ked5xmpqoBsTA3z9Ncy6d0f2ihWw7NePyUtKxOjoaHH48OGCo6Mjj/feIynt8uVNFT5VVTTPv/uu9Wc1axbN9ylTSCFR61RSKpXQ19dvOj+fPaO9zt6e8oibqz1SU4mc1LUsBOjcGrplVFdXQ8vPT7zw4AGbrFRCS1ub5nqXLjTvbt1qKICoac63h88+g96xY7g/eTKq5XL41tRAqxE5+/333wEAK1asaEg1O3MGQkUFOFtbSkPS06O0j+xsmtPDhpHj8K+/8Oj339nYTZto7I4dI2dTK2RlqIMDaqys0GfqVAwYNAgKhQL3799HUFBQfTu7y5cvq3Nzc5mWo6PYizHeTlcXehYWuHnzJnf79u36dAROqYRsxgzMPHoUVWlp+KmiAgW1xehqfwSe50W9ykpx2i+/SGrefVc0NjZm1tbW0NHRkVhbW0OvVy/o6+vj/J9/ir27dmX6H31EBSLDwojw1zk0w8LImVjb1rAF0tKA4cNR+vrrKNfXh8TERDS2sxONk5PxskSimQkHB8MkLw9H3dzEQRYWDQNWWysoJTUVZ95/X3ztww/Z33//jbPl5epXjx3jFaWlSF29Wqh89VVOT08PR48ehb2DA0tLS+OK1q4VCx89wqyaGq4mPx/Cjh2kaFm1qqWiBaCim6mp4OPi4BwbizVr1iAtLQ3x8fHs7NmzACidYdy4cS2du9ev0x75nMEJPz8//saNG0J0dDTn5+enHjFiRNsH4DhSD8ycSeobmYwcwP8QA4yNuZpFizBq/Hjh0qVLXF5eHiZ3tFChnR3ZLvfu0V4zd67mGks7dkAdH4/1AQEQJBLO4OZNdXh4uCiKImbOm8cW5+Tgor09evToAX9/f65DNYuUSnIa79vX4XuVSCQIDAzU2lq7r3biP4dOst+JTvx/hHXr1unIZLJ1jLFZPM9bWVlZVTo6Our5+vryurq6jY1jlpCQgHPnzonu7u4sJiYGAEXx+/XrB0EQWpD21nD8+HExOzubLV68mHW0wn/37t3h6uqKgwcPQhRFSCQSmBUXC5Lqanb5rbeYcPkyzOPiRDc3N6ZQKIRu3bpxNjY27beoaYxLlxqqhf8nkZNDckgXF3q5L19OEZ8VK4g4xsS0T4g8PRt+NmwgA1wmq5crGxoaYtiwYVCpVOqnT5+iR48efGhoKPz9/TvWrmz4cMrp8/BAiZsbdu/erS4rK+MNDQ3VarUaSqWSEwSByWQyoXfv3lzXrl3h5OSE69ev40bPnujv4YE+27ejkjHoLFrUIXlchzF2LJG9jRvb/aivry/v6+uLoKAgITw8nKuVEUNfXx/z58+nqG5KChmjmoxqIyMyyKuqyCDz8iJiW1xMBGTrVjJSli0jwtVMtoxjx+h7XbpQ/nijSKiBgQGmT5+O06dPM0dHR1hYWNT/7cGDB6iuruZHjhypuTjVP0WXLkSqbGwochoR0UA267B6Nd2TWt26ESoIrRIvAERq9u8nA27v3udr+1RTQw4ta2sijaNHk8LC358is43y2v2HDYO+qSlyc3MFS0tL7vTp08jKykJWVhYGDx7Mh4aGAgBLTk6uz9NvNcrWtSulznQEPI/qw4cRsX49pr73HsO6ddTX+5VXUC+Bl0pprjZed7m5ZGxXVbWMrPn6EilUKqlGh4cHOf+MjACOg52dHby9vREcHKz2sLfn8c035ARrXuRw8mQiO6KIktJSJMXFMeuhw9wAACAASURBVJekJETPnQvDjAwx2s9PMBg+nJswcWLdlzh/f38gIYHH1Kn0vAwNW15fXBxF4dozpgcNIkemRELrxcgINTU1kMvlDQcMDycFzMqVwIgRUCiVaLI7JSUROa+VrNfjyhWK9jfDrl27xGfPnjHTQYME+bhxHHbupHVa105xxgxyyunqUirApElt30Md4uMpTz0wEPyyZXC4ckX98OFDXqlU1u+nYWFhKC8vx+JFiyCvLaSa9/XXyAfwOC4OPXx9YR8SQvNaW5vWzp079JzXrgWSk6EzaBB7On06rObPJ3XJvn107c3TkH75BdDRgfTaNQys/ZWenh4qKiqgUqlw5swZPjg4WKyqquIHDRqEIUOGAKdPY8iOHRDOnkXh0KFo1NoP+t7eqAkMROaVKyj48ktY5OTgTRsb8MuW1T1nemaiCOzbh/cDA1mLQoC1OKGtjVsTJoiTbGwYJkygdfvnnySP37aN/t2Y6FdU0PMYO5bSDfbsARYtgqWlpXhnyBBoaWlxJfHxmDhxouY0wIQE4O5d6Mybh6zr17nw8HAMHFg7KjNmIPbePeF8SAg3eMwYZrh+PfxPnEDC9u2syNUVkVu2CP4HDnC/OzmpFWo17OzsxHnz5kl+/PFHISExkQOAzQoF5EuXinP69WNYsIDmdFQUzc2PPqIUDo6j1BOFgtaMWg3G83B0dISjoyObMGEC7ty5I964cQObNm1ihoaG6tdee403qEuv6tmTnCDPAZVKBT09PchkMpSXl7cItrQKR0faQ4cNI2fefwI5OZC6u2PAgAGcoaGheOTIEebr69t2cOD/sffeUVGd7Rfofs+ZoRfpCAqKShULAqIUGyjW2Ii995ZoojHdkhi/WJJPYyXRGI0Fu8FeUFRUiiigIAIiIFUE6UM559w/HoeioFjy3bvuj70WS2Fmzpzylmfvpymho0Nj/do18rLXFy2kUADBwZDGj4eHpiaioqKkNm3a8IOV8/fZM2DBAkxwcmpcNKISly5RFOhbCn8aGhrgOK5KFMW3zFlowuvQRPab0IT/AVasWNFcLpfftLKyMvb09FQ3NTUFx3ENVnB5UXSHAcCgdyTEFy5cQEJCApsxY8arlYDfgNEvFRrDgAEcjIyADRugUChw6tQppKamguM47t69e6JCoeA6dOiAzp07w8LCouHiXEp8aOX21i0yeL74ggy+bt3Iq/rf/1J43cWLNXlrjUWHDpRPGxlJxvmzZ5R/OGECIJfDx8eHB4Dc3FxcuXIFv/76K3ieF9u3b88GDBjw+hvg6wvFtm3Y0q6d5OjoyNzd3ZGens5raGhAVVUV5eXlKC0t5W7evCnExMRwRUVFTFdXVxo2bJhkb2/PnbKwEEo2buTNy8pg0qfPG/PsG42JE4n8va5dz0vo06cP5+zsjGfPnuH+/ftISUmR9uzZIy7q1o1H+/bklWkIjFGbMYA8OqGh5D29coVI3Zgxr4YFP3pEIk737hQl8aII2sto3749rly5IiQlJfG1yf7FixeF7t27M7lc/p6FDl4DdXUiMPn5lMu8fHnd8HqOo3tsakqe0Pqqzaen0/tejuAoLCSxoGdPEgwauP5XUFBA/Y67diURITqaxvaKFVThvYG0A57n0ZnGFwcArVu3hrq6Onieh0wmQ69evVBeXg6O47Br1y5kZ2ejoqICaWlpaFn7ug4cII9+Tk6jTvfJkyfYs2ePZOngILWIjuaQk0Nh2z16UB7+4MH087LYZWQEnDyJS0FByMvPhyiKyMjIENTV1bnCwkJp8eLFHKeiQvcXADw9UaCpiX0ffSQOGTKES05OllwePuTQqRNFTLxIeyosLER4eDi6desGDRMTIDERFXv34uHp0+j74AG03dyQZmUlFM2dy1moqPBXrlxBVHQ0eJ6XevTowdycnSnX38am4XoK+flEbBoDDw9q97Z9O5CcjMrKSlEul3OQJIqkiYsDVq5Eka0tAg8cEBISEviWLVtKFhYWzMvTE7Jz58Dmzwer7QktKaGCbPVEZQiCACMjI8ycOZNjShEPoLG+cCGtlQoFCRZTpxJRrK8mR20UF5M319WVrpsxDBw4kH/w4AGOHTuGCRMmIC4qCvf37cMMhQLNv/kG90RRys7LQ1KvXsy9VSu037VLymnRgrVijITczEzydCvTsM6dowiHb76RLPPyGP76i/rD9+hBc++LL+h+WVoS4T5wgFIrXsLz588lvOhg5ezszMzMzGpa1vbvD/z9N7jnz0nklCQ67qJFwJkz4K2t0ZbjkDtvHrIOHED84cNSi2fPmM706TXpEozR+MjLe7Xq/wv06dOHnT59Gt7e3lD//HP645Ej9BkVFSpkmZ5OaT0KBe1hBQU1bRTlclQsWYK0NWs4Nzc3JCYmioMGDeLqFamzskj4cHOD3NsbE6yssGfPHiQkJAhqamroFRTEXZQkbtTkyWjbti1gZIQzz5/D2ciI27p1K4zNzcGlpWFeURGP7t1JWAGwYMECrrCwEHv37pVycnLYvPnzmba2NnD0KK2T//0vCWmVlbQujhtH1/XgAUXdRUXVSbHjOA6dO3dmnTp1QmFhIU6fPs22bt0q9erVi7n++iuN6Rf1jRqLc+fOISIiAnK5nFuwYAH09PQaFxYgk1FRyR07qI6Is/Nr6yc0CtHR1fPRzs6O6ejoSOHh4axRduGlSyR8JSbSnIiJqVus76uvaI07exYyAL0AmJqaslOnTomDBw+m/dHAgObDzp0krDY2NSEh4dXoyUZALpdDX1+/Ijc31wfA7299gCbUiyay34Qm/MtYsWKFgYqKyo3u3bub9+jR438y527cuIHw8HBMmTLl7TzuL0PZgufIkWqjWk1NDS+1o+FiYmJw+fJlKSoqirVr106ytrZmERERkru7O3Osr+3Lrl0Uqn369LufmyCQJ6m0lAy8Tp1IuU5NJbK/ZAmFDL9UaOqt4eREP2lpRJJ27yaDxMEBkMtRXl4OxhgqKiqgq6vLhYeHIzo6WrK0tGR+fn44duwYevToAUNDw5rw8ClTkJyeDgNRxKBBgzgA9UZrtG/fngcAhUIBVVVVpkySGzh5Mh9oYCA8OnCA97pyBcbjx9N1vq+nWlWVDNegoLqhvW+Arq4udHV1YWVlhZKSErZu3Tq+au5cyHx9qTBTY9CmDZF9Q0MykpQtxuzsKDQ7NJQ8/3fu0HhMSXljtXl1dXU+NTUV3Wp51gsKCvi2DXQc+ODQ0yPPJUDXM3ZsTRi6jg6JUA21lZs3j3KtawtnSUkUudKiBXnrXlfUShDo5z//qQkNP3OG5snu3TUkecCAt7qkZi9HWADVApWJiQkyMjJw+/Zt3L59G0uXLsWpU6fQrVs3mI0c2XA6Qj2IioqCkZGRNHb8eA7jxhH56NOHPPbx8UTQ6hEoRADBp09L9wEmWFigsLAQDg4OvJaWFkJDQ1/ts37pEsKOHRN1TpzgVL/9FpqDBrHmL1qLVcjlOH38OKKioqrffvfuXdHd1pZzZgzSjBnI9/RExdy5Qpfp03lroJoUdO3aVZmPzSJ+/RWOI0ZA88GDui2uXsb58xRB1Fh88QV51J89A19QIMl5nvKGDQ2BpUtRrKUF/+3bJT09PTZnzhwEBASIoaGhfJm/P3SzsnBz4EAMun8fhoaGeP78OYQTJyS7y5cZO3Pmla8yNDSUKisrmUwmI2H17NmaqBV9fXom/fvTGE1IIJI5fjx50F+OXhFFqlp++zaFb9d6XUtLC3YtWogVFy5wx0+cQLvkZPiUlCDE3V3KNjVFvqYm8/DwwOguXaCVnY1sf3+UlZWh+OlTyGbMgKKqCq+MUENDpFlbI++jj6BvZUVryalTtM49ekRziuNoTTlxggSMioo646u8vLz2vajbwk8up8JnCxdSPjRj1L5s9GiqOQIoq4wDeno45OPDBtnYoMuoUbRuK6u5BwfTfrtzZ72P28HBAadPn67dj57uubL44q5ddN7Pn1NK0bffEgF1dKwWTR8+fAi5XA53d3d4e3vXz9xKSihioH376g4jynS/oqIiXktLS8o2NMRHI0dCuZYm8DyeGxqi2ZAhcPf3R49+/ejYamoUnWRgUE02dXR0MGfOHLZjxw4hICCATZ06leNcXen7Ll2i8wboHqal0V5+/DhF2IwYUS+BZoxBV1cXH3/8MRcVFYXAEyfQ6rvvYPwOqU3l5eUSAKaqqopbt24Jvr6+/BsdGEoIAqXOzJpFQtz27W/9/XWwfTuJ3y/QtWtXXL58+c1OoJgY2nOePaOxXVpKotpnn1EtnMxMEpvXr3/lo5WVlXUv1t2dnsvRo7TevAmpqfTs5s5tzBW+gvz8fHUA21euXLlZkiT5smXL3r3TUBMANJH9JjThX8WKFSuYqqrqWScnJzMvL6//yXy7desWrly5grFjx9ZtmfUuOHKEvBPJya9VdB0dHeHo6MhiY2Nx6dIl6caNGyLHcfzx48eRnZ0NLy+vui39HB0hlZQAkvTmKICXUVRExa369yfPw2efUV79998T2e/enQyVxoaRNgKlpaXYeeKEVGBszNpPnCj0W7mSVxMEYNUqmNvY4IsvvkB8fDzOnz8PTU1NjBw5kh0/flxatWoVA6ggnKmpKezs7ODh4YHiqipIMTFwMjBoVGuf+jwvgwcP5hPt7LDP3x/Tnj2D9qRJ5CVrqKVdY+HqSh7fd0RKSgq0RFGS3brFGiU+HDlChsiff1I+obk55dwWFBApePSICOlnn5Eh++mnFHI+YAAZIFevEinOyiJy4eREY3bBAvRfuBD7d+5EVrt2MH1hIKqrq+PmzZviiBEj/j3Pfm0o87wnTiTycOcOeSDNzGisTp1KUQ0zZ9Z8JiKCUgBq105Yv54M+oMHyUtXHx49ovtQXk4G3erVNCfs7Mg7dOrUv3aZyiJmampqkkKhYACwceNGsaysjNM5e1Yw09DgsWFD9furXhQo09HRAcdxyMzMxOHDh0Vzc3MuKytLfPr0KWdkZMQBwJ27dxFy/rwwKz2dl3/+OT3jMWPIC1trjOXk5MDf3x+zDh5kTp99Bm76dNy8eVNyc3Nj6urqCA8Pf7W9qIoKnLy9uYvPnwsl16/zPUND0aJ9e6SXlWHnzz8rez6jTZs2GOHqirQ9e1j5zz9LYZLEcnx8cL9DB3w+fvwrnj+ZTAZDQ0MYlpfDpEcPXC4sROmZM+LHH39c/7grK6PUlEak0FSD46hV5fDh6Bcdzen+/Td5cfv1A1RUELBjh9isWTNMnTqVA4AFCxbwYnk5Ku3twQIC8DwjQzp58iQqKyuZIAhomZrKElVUMJBaedb5quLiYi47O7vmD5cvk/c+MJB+l8tpPe7dm7p6GBiQlzk/n8QA5f6RnExe2jZtiPArvyc3l6ICgoPx8ZkzXLGvLw6lp+Ncnz5QNGuGmTNnMmU0iba2NgmnOjqIHT1auvvPPyyuvByapaVI2rABLi4uaNmyJQRBqB5fgiAwSZLoPLp1q0mt6dmTCLoywujTT+k8li+nc+/SBXBxgfX06WzElCnY+MknUNm9mzzVQUE0r/v0IQ/04cNUiyM4mIS858+rbxdjDDzPQxAEyORyPFZREbocPcojIoI+u3gx7WnFxQ0+bg11dWgVFiJq40a4FBXRXG/VigRFCwtaF9LSKC2lrIyez5MnRNZerDM2167hhqamsOn773kTBwdh4uTJdR+0INC6AQCLFuHp06f4+++/JXV1dUkmk3E9e/ZEhw4dGJYvp++E8vHlgtPXF/UdHDjL2kVXZTKKQKqooHXu+vXqSLRJkybx69evl3744Qd4eXlJvZYvZ1i4kMitpyfN8Q4dSFzX0yPy2ro1qqNu6rEdeJ6Hk5MTzEePluLT05nxH380eD8bgrq6uqSpqSl17tyZCw0N5XR0dGrSF94EuZyeS2EhiatZWUSq3wUpKXTNZmbVfzI3N2eMsdfbDZJE7Tx3766xgzQ1aX9QKGhfycujPfYlXLx4UerUqVPdY3Mc2VinTlFkVq1IuXpx8CA963dszfztt98yURQRFxcnP3z4MH788cc1giB8vWzZsqp3OmATmsh+E5rwL+MjTU1NO29vb5W3JrXvgNDQUAQFBWHMmDF472rtsbHk3X2Lqvl1ev4CuHjxIiIiIqTo6GjMnj2baWhoICEhAQ/S0sS0p09Z0Zo1rHv37qisrERlZaWYkZEhiaLIJkyYwKm87LWLj6cQvqQk+v/IkaT2L15M+csXLpAK/gFJvhIcx6GwsJDJZDKpUC5n67t0gXNlpdDpyBHeMDYWauPHo+OgQbC0tMSGDRtgbGyMTz/9lOXn50NNTQ23b98W4+LipOvXr/OXL18Gz/NoYW0tddfWfn3e9hvQtm1b2PTqJexOSsK8UaN4HD1Khua8ee9eCdjZmcjziBHvZKSUP3mCBf/5D8OsWXWMlAYRGUmG4IQJ5LF+/pwIzL175DmMjSViMGZM3eM9fUr/2tjU1H7o2ZOMwYoK4OJFmK1dix6RkaLO339zSE1FgaUlOnTujC7DhnHo0YOMytBQMrC9vd/rWbwREyfSv/Pnk0ARFUXGavv2r1Q+x7175ImXySgHffVq8s4dPUpiSG0kJABbthBJGTqUjOPNmylq5kXhuX8TVVVVuHbtGq5evYp+/frBzc2NiaKIR48eobKyknv69ClyNm3iBQMD1L6z+/fvx6NHj6Cqqiq1bdsWiYmJMDAw4B4+fAhra2sMGDAApqamuHTpEsJCQuB7+jQfZG2NJwkJ0tAff2QGrq405wcMgCiKiIiIwM2bNyEIArQSE6tbQvbt27d64TUzMxPXr1/PtLS0xAkTJvDKKAWD3FyMio/n0377DQciIzHx5Ekcv3BB8tbSQo5Mxh7k5qLN5s2oOHkS1kOHMsyZg0ItLVR8/DGkJ0/w9OnTuikLShw9CixaBP3ISHgMHIg///yT/fHHH8LUqVP5VyIMbtygtexdonOWLoXq0KEsb+pUaPXpU+2RzszM5D55qRgit24dVI8dAzp0wBCAKYt9KRQKRE6ejCgzM+zZsweTJ0+u8zkXFxf8888/KC4upiJoP/1EodaPH9eMXysrIvMtWxLhjI4mD6OrK0WXqKtTXvnMmeStLSwkInf/fs287diRivXxPAbm5GDPnj3oaG0tGhsb17ug9UlN5fq4uNA8YAyBTk5CbGwsl5iYKBUWFnI8z4MxJslkMtHQ0JCGYG4u5TFHRhIZtrQkMdHKinL8J00izydAnn41NcSfOyclDx7MoK8PE0/PGiGufXtat4qKiEhxHN2DuDg6ZmEh4OMDoy5dsGTJEsg/+wxXBw5EbGQkg4MD1X8QRbonW7fSeqcMt66ooHM9dIiIWosWGHT5sqTdqhXDjBl0z9q0QXXqhr8/RQUUFZF39cABOsfERCL/cXEoffIE6h06YNQvv0A0NeXRsSPtmzNmkAh55AgJlIsXo7KqCnv27JEsLCxgamrKcRyHDkoxWUeHvlc5rjgO6pqakvbp00RytbTqtgdUUSEvs5oaibKffgqZiwsWLVrE7t69iwsXLrCbN29ihKGhYNOvH4+bN2kf6t6djjVxIpHNL78kcYQximKZOJFSMmpDksAPGMBuyeV4HhiIwW9pE/Tv35/r/6K4XmJionDp0iWZlZUVzBqznymhrPuTnk7j4F2QnEzHqGU76uvrQxTFho3J4mIaT+npr1bcr6gg+2DbNjpmSAjdz3376kSYvSz0AaB95eRJmg8N1ZEBSEDYt49SK98DL8RfAIAgCEsAbACQ/l4H/T+MJrLfhCb8i1BTU1vcs2dPzffug94IhIeH49KlSxg9enRNLuG7orSUckKPHn2vqu/e3t7w9vZma9euFdeuXcusrKyEx48f884VFWz22rXs8qlTePDggSiTyTgAzMTEhIuNjRXj4uLQUdkK6vp1UpKVObrr1pHhsnQpeUCrqihszt///a75NeB5Hi4uLlJYWBibMGECKy4uxqlTp7i/nzwR9HR0eOPAQNHlk0+4O716iWjZknvy5Amsra2rUyg8PT05T09PVFVVQZIkZWE4hsGDGc6epbZV7wgPDw/+9u3buNqhA7zmzqWQ1MGDSV1/XZh3Q5DLaWMPDHz9pt4AWru4YPekSRivr496MkFrMHAgkYTNm+v+XVeXxt38+TQGx49/fXcAVdUasjF9es3fHzwAADxfvJjbERcnOYWEsOS+fTFg/nzoa2lRqDNj5I17/JjIvq4ujSMrq5q84+3b6T6OH0+GUteu79xeDADl0xYX0/ccP16Tc7t1K+VGJiWRQTVyJBF9b28SbyZPJiEiO5s8pR99ROLH6NHkyef5GgFBeR//ZUiShICAACQmJkIul+NFxwJwHIe2bduioKAAEXv2INPSEtKPP9b57PPnz4WBAwfyxsbG7MKFC2K7du2k4cOHK8NlOYAiYqIvX8bCsDCorVyJUG1t8da5c9yd9u3Re9MmcHv3Ag8fYqeWFtLT0+Hh4YGOHTtC/Y8/iEBu21bnOydNmsTl5eXh2rVr7O+//xbmz5/Po7ISmD8fVb17I7yqCqUch21DhqBnz56S27p1HMvMxOCCAjyZPRu6U6dW59vrAMCCBRimqlp/KsaZMzSPjh0DDAzQDMCsWbPYnj172KZNm4TZs2fzdQTNhw8bTuloCAoFzZ+TJxHw6afCtD//5FFUBKxejadPn4LjuLrVyYOCiOAuXPjKodTU1NA9LQ0qXl44lZKCa9euCe7u7tWihFLMuH37NhWl43lKO5g1i8ik8loYo3E9dy4JaU5ONJf/8x9aq7dvJ0Lw1Vd0Lj170rh3cHil/oKxsTHU1NQQGRnJ+fj41F8Adc4cWrNGjwaqqjDY1FRZXIw9e/YMu3fvlgoLC5l71668ZkYGhczHxtI5DRpEUUL79hGZjo+nubdoER3b3R2wtkZMTAweP37MuC5dUFFRgUQNDTgpa3AsXkwRCh4elKYzaBCJdcOGVRfLE5cuBUdt1FCemoo74eFwj47miv78E5pPnoD76iu6ji1bqPZBaCgRWmXah7Y2iQKdOiFYRwc2trYwq01wFy2i7xoyhELwR4+mtdvPj+6/hgb9eHjgVGqq9CghgT+8fj3GDB5Me6mZGQmdnTuTuDB5MuDkhCKeh23nztIAa2sOurok1lRVkQg5b16d4oYcx0FStiD4808SIWqTfRpkJBBUVNCaWl4OFVVVuLq6ouNvvyE3NhY3J0yAzYoVlJKwYUPNeiaX07hq1YqiLgBa97S1SRS+dYvugygCn3wCw+XLMeTZMxw5cgR9+vSBxsvEt5GYOXOmbO3atWJJScnbGXGHD9M9v3at5p69LbKzXxHMVVVVIQgCwsLC4Orq+upnZswgok+FU+ti2jRaM44do/uqUFAxQR0dlEyahIumpmKpvj5XVlZW//ksXEhOAB+fVwVqJQ4ffn3r2UZi7969AADGWG9Jkh4sW7Ys870O+H8cTWS/CU34d2Gs/nK1338B8fHxuHDhAkaNGgWrN+QxvxHFxWQA3LvXOM9sIzBnzhzu/PnzEASB9/b2RreuXRm+/BJ9NDXRp08f5SZKVj7HsZNHjyJh507RnOPQJSSEU/npJ/KUlJTQhj92LBGyI0deyQOurKxEWloacnNzIUkSeJ6XHj58KJiZmXElJSVSenq6soVMo1y4hYWF2Lx5M+RyuTRlyhQGUF7pqFGjGABeoVAgJiaGy3Z1RZfz5zmzBw9wd9UqmG3cCK2XCNcrFY/HjaNreA+yr62tjbFjxyIgIEDy8vJiWL2aiI6rK+U+fvzx23urFy8mQ3Hq1Lf7rL8/mv3zDwp69ZJu3LjBevfu/ep7oqPJk7ZkCXmmaiMzk8KYk5LIUPzlFwoBfQ/07t0bt2/fxpUrV+C3ZAn0ra3pBWXhwNptznJzyTOXk1MTVp+aWiOaDBpE3pAbN4i8PH5MxpWLC/27bBmdr9K4a0gU0NIio+nJE3rvpk3kpZ4zh87B15cM3QsXSGAIC6OfGzfIE3rlCkU6uLlRmL6yvsH/GBEREdLjx4/Z8OHD8XJtjlu3bknnzp1jYw8dgoWDQ52xX1FRgYKCAt7Gxgba2tqYNm1aHUP6xo0buHXrliA+fcqPycuT1H19GXx84MYYdz0kBCFeXuh8/ToM/P0BNTVUTJgg9u7dm/NUkovWrYm8vASZTAZjY2P4+Phwv/32G4QtW8Dv3g0EB+PAoUNSTkoKmzJlCswzMsAHB3OwtAT69AHXvDksZs161UPn40Pz7Pr1mvZnAIVy//QThXLXqg+hoaGBqVOncgEBAeLGjRvFmTNnctXFU8vLyYvXWGRm0hiRy4EzZ1C+ZQsyjh2Dla0ttZozMZF0dHREjuNoApeVkWf1u+/qFwEFAfjlFzg7OUEjIQGHDh3ig4KCMHHiRLRu3Rra2tpgjNWNYOjfn0KFX17XnJ1p3Bobk1D35Zf0vpgYCmseN47ErFWrcC0kBM3kcjg20FVEpDwKrsHoCaUwV1JC3u1aMDAwwCJfXxa6ZYsoffstl8RxsPvkEyqippybym4gixcTMf7+e/KO79xJxSunTwdvbIyqqip06NABLVu2rBGiASJMQUG0ZrVpQyTfzo6icaZNg+jtjdWhoWCMoUOHDpLWsmWsMDgYqTNm4Mz9+5j99ClMNm6k57h3L0VLREXRtcye/UqNh5LSUrFZs2Y1i/KZMxRi3bEjpR2oqFB7xspKupY//6xTKHH06NEsIiICQUFBOBcSIgwbNozHt9+SADNwIAkNK1cif9kyPA0NRc+yMg4LF5Ioc/58TSHAFSvouletAqjgcI3H+eBB+rK4OLoXtcFYzevK9d/fH6pDh+Kivr7E9PUZRo+mtfSPP2oE527daD7FxNCa3LYtCSMAve/mTSL7a9dSUcb162FjaAgdHR3pl19+YTY2NpKfn99bh1dWVFSgrKyMe+vITFfXmnukrU1i8dtiOEtG/wAAIABJREFU3z4SxWpBmVb0vFaKSDVKSqhGxsupIAkJNJ7XraMOLMprUVMDVq4EBAFPc3Ml9dJSrrO2Nnq/3L5QCQ0NOp/PP38lhQoArSFpaa+2Pn1LJCQkQCk4fP/995ff62BNAKBs+dGEJjThX0FFRcV3Bw8eLLt9+/arlucHQmZmJo4cOYIBAwZIbT5E2O7w4eSV+UBEHyByPHz4cPj5+VGxNI6jsOOKirpvLCiAr709+zI4GP2uXuWSbW25DcOG4XHz5hB//x2Ktm1RtHgxYv/8E1e3bpXW/PMPNmzYIAUEBAjnz5+Xzp07J166dAl79uxBeHi4EBYWJly+fFnS0tKSxcbGStnZ2UxHR4e/detWo9e+w4cPi4aGhuLixYu5+sL41NTU4OLigg7Tp8Po4EF0mDULnaOipLRff5Vw+HC9pKMafn5kFDx82Oh7WR9MTExQUVHBzpw5gy1btwpRgkDG9rlz5N189OjtDmhgQGT7xo23+5ytLTB4MPr06cPCw8OlnPoqr48aRQW+evasyT3PyiJDd/Jk8i4dPUqk+d49ijIpKXm786iF69evo6qqinXt2lW0VhL9hqCmRsZsixY13s9Vq2oEh6Ii8taOHk0eDOU1K9vs/fknea6++67GyHVxoerMz5+T502SSPBITqb86rQ0Ioe7dpEhO2IEeafu3iUCqSxmdu8ekZrERCIVEybU30rpf4SEhAScPn2a9enT5xWiDwD3799nALBv3Djk1srVB4DIyEioqKjU26KvoqICFy5cQHdLS/6ThASY9+vHsGhRtYE6/UX0xqbERBx2c8OeadPg/ddfXKcXRjAAIi3KQoj1QEdTE62fP5eyrl0jb6qqKp7l5opDzM1hsXgx+MuX6fmtW0fjYNQo8gZXVdGYDQ2lA6mo0LO4dIl+FwQS2Dw96W/1FAeTy+UYO3YsZ21tLW3dulXKzMwkorV9ex1h4LVISqJzsrODuHw5Lly7htLSUl5VV5fG16JFKDlyRGzVqlUNKdy8mc6poe4NISG07svlsLe3x3fffQfGGHbv3o3S0lKUlJRAkiSUvDwXlYJTcnLdv1dVUfeFsDA6VxcX+pfngehoFJw8ifhduxB+7Biubd2KK2fP1gkLV2LevHmciYkJdu7ciXo9jgYG9B25uTW5xIWF9Ix8fYEVK9C1b1/O4JtvcNDbGytiY5GmTAECaN3R0KDnmJ9f0yps6lR6lrduwX7vXrTW0UFxcbHYuXNnCnMuKSEhIyuLyLmqKkXoqKkBnTrh2fjxiDx8GCfmzBHV1dUlLy8v3L59mwUHB6NDhw6Cvb09zBUKwSQqiqJ61qwhT35YGM3xKVOA27dRXFyMwMBAHD9+HNevX0dhYSGvLJaHiAj6nJERoKMDhYoKsjt2RFVVFQkqkZH0DGrNjY0bNwpnzpyBTCaTbG1taR+cMYOiGPT0aJybmyP1009xaMwY7PfxEZO2bCFSOG4c3R9l54LiYuqMoakJrTt3YBIfL1V/161bJPrUt//98QfN0YMHaR3Ly4NoZoZUQ0PWo0cPDioq5IUuK6spcMoYiRfLl9ekJygxfTpw8CCk589R9dNPOPn550i+cAEVT59i5syZzMnJCUlJSa+exxtQVVWFDRs2SJIkwaJWfYK3OADt73v2vP1ni4rIQ/9SRXs1NTU4OjqKdepnALR3GBvT/X65KPP69TQnzM3rFe8ljsNfrq4s2dsbffX0oJgwAT8tX46E+mq8eHqSbRgQ8Oprd+7QPveywPMWEEUR+/btA2NsGYBW73ygJtQBv1wZDtOEJjThg6NHjx73g4KCDickJCzw8PB4tRL0eyIzMxO7du2SPDw8pK5du77/wXNyyHs5btz7V3Z/E5S5dxoaVJDt8WMiQzwPbvVqqC5aBEcnJ5itWQPDb75BVEYG4hwdxesTJ+JBfr5YUFAgubu7c61atWJZWVlcamqqlJKSwqWlpcHKykqaPn0617VrV87d3Z3Z2NjAxcWF69y5M7O2tkZwcDBzdXWtv7dwLUiShPPnz7Phw4cz3caGRbdujWM8j2ba2qzFrVuUZ6inRwTyZe8Ax5GXNjycwuneES/SAsTU1FTRysqKv3LlCtx9fMCGDSPSuHYtEQlz88b3Yy8vJ89zY1u7/ec/5JkcOBDGxsZITU1FWFgYOnXqRBW8x40jT86KFTXXWlZGhtD582SQ/vQTGUfKeaKlRUZhYGB1VejGQhRFnDx5EhERERg/fjycnJw+XNEMNbUaMax7dzJ4ZTLy/MrlRDI++YTutYEBGWySRNEEEyfSPQgMpKiLtm2JVF65QveopISO7+dHHiFPTxLGunShsNl3LHr0IVFZWYnDhw+LNjY2rE+fPvW+x9LSEl6LF8PE0FC8WFgouri4cACwfv16MS4ujrVq1Urs0KHDK8+E53lEnTiBbkVF0LO1JQGo1phVU1ODqakpkpKSkKmnB4uwMHSKioJmcjKYsi5CUhIRku++e/XERBHw80PLpCT2l5eX2G3oUMZu34b422+cbWgoZN9/T15ne/u691pbmwxlNTV6vspc83nziCS2bUvP9MABOufX9JdmjMHa2poTBEEKDAxEu6gopmVgUG+ET2lpKTZu3Iiqqiq0NDPDxdmzBf0tW7ikJUtwvLAQl4KCxJSUFNaxY0c4OzuDvRBSIwsKpDaBgZzJkCFEhO/do3NtqBuAUszw9a0+x3bt2uHhw4dSSEgIE0UR6enp4Hke9rXTamQyOr6dHZHOqiqKONi7l0SAuDia50lJRCiNjVH4669ITk9HakYG7ERR6nv3LruZm4s2U6eC+fuD9/Qk4aG8HHeOHoX6uXNo4+uL1mlp4EpL6d4qo2c4ju5bnz5Eji5coDUlNpbmzbx5gJ0dDNq1g5qaGpKSkhAVFYX09HTB0caGw4QJJMZpatJ8jI2tSV0zMqL1KDoalvn5eBAVxYKSk5EQE4OQ27eFyuRk7q+8PFy5dg2Zhw+jMj4eF+Vy6fz589Kt9HRWWloKn4wM5u7lxVr36wdPT094eXjATl2d0125EoYXLnDXVVQEy06dONndu3QPjxwh4WXCBGD/fuy9fVss09GRAIh37tzhRFFE//79aa9OT6eCiF26QKFQYNfPP4ttdu1im0pKkJubK9ovXcqqDh0CRBHsRVG8oKAgThRFyGQyydvDg1OJi6PrTkmh+zhuHBARAZPp02FtbY3yigrpwu3bTCGXo83MmSQMPH5M9VwmTqS9LT4eVTIZuvz+O8f99RdFFRQXk4CpokLzt6iIUieqqmiNtLIiW6N3b2DtWrDERCS2bYvCwkLR1taWQV+fvufwYXq2MhmlXmloQOrfHzA1xf3797F9+3aUlJSgoqICCUuWSFqpqewfBwd0WLdOyt6zh91u3Vri8/PZk/x85uTkhFdqAb0G0dHRuHfvHhs7diyM31SUrj707k37Q14e/f42gsH9+5SKVauTjBI3btwQDQ0NuWrxWhDoeQwbVtdJc/MmPaPdu6meSwN7P2MMVVVVVXFxcVyUiopwx92d00xLg+eyZVDx9KRjK0UCxmhP+uILimyq3aJ3zx5Kx6mvA1MjcfDgQSE3N5cD0IoxltOjR4+Idz5YE6rx/77V0IQm/P8fRtra2iU8zzcQG/VuUBL9bt26SV5eXu9P9I8do9DljIx/n+gDZFQVFpJSv38/FRQ7cYKM9LIyCsn76y+0Hj0alRs2AI6O8OnShXtB0OvI005UaZ0rLCxU5pQ2SOxUVFQgk8mkwsJCVm8OaC08efIEACRzc/O3IorlFRViuaMjhxkzGHJzqeCTQkGbYfPmdYvnjRlDXqjS0lcL6jQSjDH07NmT6/nCSI2MjMSTJ0/IGzFzJhnwv/9OHvPZsxtXuM3ZmYyzESPqhig3hBMnqlsicRyHIUOGsF9++QUBe/eKk6ZNI2+NQkFGmyTRuezfTwW7pkxpuI3egQN0X/LyXvVY1IOEhAQUFBQgPj5efPbsGZs8eTJ7q8JKHwrKZ9ytGxlE9+/XhGnHxBB5Dwuj19q0ofsBkDfY2prI/v+gqOfb4MKFC0JKSgrS09N5ANyM19R0MDAwAFasQHMrKy4nMBD+/v6Cm5sbX1JSwk2cOBEWFhb1rllFkZHwunoVBvPmNeidt7W1ha2tLTIzM6FQKKBZWEge1r59Kc3HyopCV19GbCwJS3PnQqdbN9h/9hkL8/OT2nAcSzU1xQU7O3QtKIDv69Y/ZQ92hYIMYFEk77+FBZHcS5catX4yxtCjRw9OR0dHurN8OSyHDIE9gOzsbCQnJyMnJwdyuRxhYWEAgFsnTyL3119hnZDAbx88GOXR0TA0NISfnx/XqlUr1BaSRQBpMTH8R2fPUsjzt98SkXvdPOB5EqhqwczMDIsWLWKrV6+ubj+oWV8KwA8/0LUfP05hxAsX0ndaWBDZPnOGIiju3IFkbo4APz+0cHKCz969kAcEMGhpoWt8PALd3YWniYm8ePy4ZGdsLBpWVvK2dnZICgmRLt28ydwjImh+jB1LXt6JE0nE2LuXnsPw4bRmDRtGxPAlODk5ISoqCvn5+Xj06BFf8ttv0Fy1qiYiYOjQ6poM1VBVBZYsQbOwMLSfOxfy0lLJ4cQJFrt3Ly/398dMPT08f/4c6mvX4rqFBfT09KTu3btzLVu2xNq1a8E++ghqqanAX3+Bd3CgjiOiCNXvv4e2nh7Mx43jhYMHiZB160Yih0IB6Okhffly9Bk0iGvZpQswahTWrFmDoUOHEplesIDEgRcOu8DAQMlITw8teB7NmzeXCgsLpQORkSKvp8d9NGsWAgoKRBN7e06SJCxcuBAHDx5E5vz5aHf/PkUWpKbS/GnVCjA2BmMMzZs3R/PmzTkdHR1EREQIPj4+tO9evUrvc3enn1WrkBsWhtsFBTBUKETrNWtYs9xcxu7epWMbG9Pcq6wkMviib3w1/voLAODj5wdFbCyX5uQEc3NzcB9/DGRmoujAAaiNG4c4HR0YhYejZOdO7J0wofrj4eHhiLl7V+Lat4fTDz/ge2trYNkyFhMVhfI//hD7bd/Oh379NVKTk2HfCCL6+PFj7N69G5IkoVu3bkK7du3evWrrwoUUZZeYSPeqsUhJaXC/Mzc3Z+np6SKU0dlt2gDffFO3xk5ZGZH9IUMaZVd4e3vLPDw8EBQUxNLS0kT1rl3Zdn19fNa1K0P79iTMKNv0aWtT2su331I0G0B24+7dNB/fA9ra2hWGhoby3Nzc1jzPjwew7Y0fasIb0UT2m9CEfxErVqzQksvlAX369HmHSmkNozbR79mz5/sT/cJCyr+8cuXfJ/qCQKFelpZEfnbvJmKvq1tTeC8+njzSv/0GjBsHOYCubzwwoKOjg16N8JDzPC/l5OSwN6n1ISEhgqWlJfe2ERne3t78kSNH0Lp1a8ozPXWKekqvX08ej2nTyGPEGHnbjx6lvys9k+8JIyMjIT4+nq8OPbSwIKNw507KewwKonN43XUZGpKhGxpKnpXX4cYNMixqQS6XA6KIUZ98wsHCAkcHDxay7t7FRxzHm69ZQ5EO//nPm0OXDQxIHDAzI0/RRx81+NaKigocPXpUEgSBCYLAzZkzB4b1GP0fFJWV5K3KzqbzW7qUCEd5OUUqdOxInkOZjAxqQSDDzMqKSH1aGqVaODlReKuy0J6HB/DrrzRO/j+AmJgYREREcO3bt2fp6enw9fV9fWTMi17TRi1aYLyuLv7++2/+2LFjANBgAdGq6Gjc+u9/Jc7BAWpTp75R6ajTWvTKFRqD/v5kYB88SAaqMqS0ooLCyKdOBQoLwY0aBW83NxbSurUQoKnJjE1MJNVHj/jQ0FD07dsX9c7558+p4Fa/fkTIKiuJ4MbHUw5927ZvvX527tSJZaip4WBMDE6vW1cnTF55f42zsjBZknBPkiTu4EFpbosWnIqKSv0F6wAkJSWhQkdHlGdmcggNpcihF4SqQWzaROP2pWfDcRymT5+OU6dOSU+ePGHKIozVKCigNWXHDhrb16/XJdqhoSRcHT0KjBqFNFVV9I2Nhb6LC+S6ujRP5HLY2NjAxsaGlyQJ2dnZLCEhgQu/f188mZDA8b17M1EUwa5cIW+2hQWJB8nJtI4dOkTfrapKpH/jRhIZXkqjUFFRQe/evREYGCiYaGhwD3ftYg+GDxfLd+3iVFRUoFVRAcPISBTLZOjYsSNMan/e1RVpNjais6srx2dkwNHFpToNycTEBDA0xNgJEwAHh+qBY2lpKURkZPD9IiNrqpgvWUKRPgUFMPrmG8Tq60slKSksS1MTbXiehIqBA4HSUlw+elQy/uor0SIxkc/cvBmVlZXIzs5G69xcaPTrRyLyC2RkZIi9unblo5OTpby8PGnhwoX8unXrMHPzZlQZGWHIxo3ckXnzxBEjRnC6FRUYmpXF/e7oiBnr1sFoxAgSXgFab3fsoNQhUHTbjRs3JEdHxxrC279/tWBcUlKC/Px8VKqoIM3XF7dzcrjz7u7QLi3Fwo4dKX0kL48I/jff0HiRpHqFzBbLl+P0b79JUdu2MaP8fORbWkKjvFzq9ccfLCw4GGmtWmFsTAwsUlLQQl9f8ps0iSkL78lmz2bgeVpTX8CxY0c4/vYbjxUrsExfn4T2n34icfk12L9/PyRJwpw5c2BsbPx+7Vm2baO1JyCAama8JuKnDpR1d+qBkZERFx4eTr9IEjkRaof7BwaScHfv3lsV6VVTU8OAAQM4gFIY1qxZg9TMTFicO0c24rZtJNwdOEDCUEICVegfNIjm47Jl7y1QDxgwQP3PP/+seHEO71DooAn1oSmMvwlN+BcRHBw80MzM7OP+/fu/R/nuulAS/e7du38Yol9SQuGWbm7/LrGQJCK9U6bQxq+rS/nQ1tZkoC1ZQh6aK1coDHbPnvfvGd8AUlJS8OzZM9GhlmH2MjIzMxEUFMSNHz+eqTZQPKohGBoaoqqqCqdOnYK2tjaMTUzAzM1pg5TLKex16VJSyG1sqE3VyZOkwn8ACILARUZGCm5ubjXXx3HkTdbVpVDXs2fJe/861d/amoyjMWMafk94OBl/CxfWCXuWHTgAEx8fnCsrw20NDRRER3OdHj3imq1dC51VqyjCoLGGD2N0Dq6uZHTUQ3Jyc3Oxbds2sXnz5tLMmTM5V1dXKFurvTeUAtXOnUTSN22iXFk/P4p60NSkn3376Bk2a0bn2rEjPWd7e3rOH39c0xtdKWLMn09h4B4e5CUZPrympdbq1fQ+c/P/TbRNAygvL8f+/fslNzc31rdvX/Ts2RPVecP1QRQphPqFh1VfXx8PHz6EQqGAn59fHQGmqqoK27ZtE2L274fKL7+w0hYtYPPjj0yndnhoY+DmRuTn4kUyrG/donXN1pZC7n/9lUJPDxwgL9T69ZANGoTWHh6ca9euzMHBgXN1ckLG+fPS6atXWf62bZLWqVNMx9ubBIOqKkprWbmSwvwfPaLwVjc3Emns7GgOS9Ib1y2FQoGzZ88iMDBQvBYYyExDQxHdqRMYY+jUqZPk7OzMBg0ahBbm5hADAtD68WO0mTQJ5suWMSMjI6aqqvpaoSU4OFjQ1NTkHY2NaTzJ5RTh87pim+XlFJZbz1qnpaUFGxsbFhISgtatW9PzUyhoHvzzD43pL78k0TYrq05BOJiZ0XG9vIDu3VG8Ywey27RBay8vup9JSTSPxowBdHXBGIOWlhYsLS2Zs7Mz69ihA8q2bIEEQCcqCobjx9OcadGC5lrLluRlvHePnomxMf1ra0tpNSUldVKRysrKEB0djVn29pygpgZ4ezMtLS3I5XJR5+FDscPmzdwhU1PExMRIHh4eNcylpAStp0xh9wYPFi19fRl++IHmpaVljcgxYADdv/JyIDkZ5qtXc6YBAdBcs6amICfP01rg6wuoq0PFzg7hEydK50JCmLUkQfvTT4GJEyHp6+OcnR3zGT+e03Z1ReX69WCPHqHq4UOp2aZN2Nuxo3ghOJhFRESIUVFRUkFBAZ8RGYk2YWHos3Ejp6GhgZs3b4oFhYWSzsiRzCQgAE7l5cxwzhzgxAlo/vUXcocNE0t37IBl794ML1rNITeXxsqLYpQVFRW4fPkyGzduHNUqyM2F5O2NIw4O4sWgIAQFBbG4uDjp/v37TBRFSUNDgykUCggqKvAaPJgEl3/+oTV8714Smv/4gzzddnZ1wsCZsTEUFhZM9++/4XvhAgxWrICZlRVr37MnjB89guOIEWi7aBFkY8fCyd2dqerpgeM4EuZKSylsvr51SVkkuX9/Wmd//pm80vWQ/qqqKoSEhKBXr16we4/c82pwXE2HB1FsXLqeKFIax6pV9e51BgYGuHbtGlRWrRJb+PsztmJFzdyOj6cxP2DA67vYvPG0OVy9ehVPnjyR1Js3Z8aOjjSvnzyh1IS5cymcPyiIwvavXaMUkHfpAPQCkiQhNDQUkZGRPADbZcuWvX2hhSbUiyay34Qm/IsIDg7+yN7evl/btm0/SLJ+baLfo0eP9z+mINAm3KULbZT/BiSJiKAy7NLTk76rfXsih0ePktGWlUXK/y+/0Pn8i+A4jt28eZPT0dGRTE1N60jRkiQhKSkJ+/btg7Ozc9381LdAZGSkkJWVxcXHx+Pq1atwcXGhfMF27cjIMTEhD+5ff5ExWllJm/y7FAKqheLiYgQHB0tZWVnVYf11YGBA99/AgDZnVVUiJ/Up8np65C1VktOXUVlJxvz8+XU3+bIyoH9/GE2aBMHCQtI/fVryliRm1qYNttjZwXPyZMotfhs0a0ahoDY2NbnxLyCKIrZt2yba2tpixIgRvEwme6vcTABEXo4codDJoCAiq+PHkzEfGUkGzU8/EXHR1qbrdnIikapHDwprnTCBxJN27YiwM9awp6OykgSP3r0pJDc5mbw5yp7QrVoRAVqzhlIwfH3fOc3jfXHs2DGhoqJCGjlyZOMeWl4eEbBapD46OlrKz89nI0eOrPPWmJgYZJ0+zQ3R0WHGo0bB9quv3p7oK6GjQ0anvz8R765dKaJm7VoiZsXFZOBPm0ZeqS1bKGqla1fg0SPwADqsXctcliyBYVERS759G8aTJ0PWpQt5r1xdycjV1qaQ602bKL9740aaS8nJJND06wcwBgXPo7i4uNoDf+PGDRw4cEAMCQlhT548QUVFBfNIS5NMZDIM9fdnnp6esLa2Zs2bN4dqVRUMT56EQVwcotzdhQ6zZr3x3ldWVuLatWtiZGQk7+3tDcMtW+ic9+2j0OutW2n8voyHD6lA4Gsqhufl5SEiIgLPUlKkLikpDOPH09gdPZqu18iIRJaZM2k8K8c9Y0TsQkOBgQNxOSUFnocOgZ8yhca/igq99vw5kbKWLWnPuHgRWLAA6tOmweyHH/BUVRXXmzeXPE6eZFBXpzmWk0OC5Ndfk8dx6VK6lh9+oOM8e0ZjMSkJ2LAByTo6iEhMxNPHj5lHcDD05syBhbMzWrdujXbt2jFLNzdOtW9f2PXqhbCwMObi4gI5YzRmBg7EL2pqotfIkZyOiwt535cupagmOzsac8OH0z1YvRrgeSQZGUlBfftKTra2DNHRdO+HDydvd0AAsG4dtD/9lN0pLBTLysrQu18/xnfqhGxDQwSUlkr2Pj6irb09VyAI0B0wAK1LS9EqJYUddHJiWTzPzZgxg5mZmXHNmjXjevbsicKcHEHrwQMWWFkpVFZWcj4+PiwuLg43b96EhlwumT17xrBhA43XWbOgr6nJijdvZs1mz8ZTSUJycjJuJyYKeZMmMWNjY8bzPGQyGeLi4sSoqCjGGINRejpK7t/HP5qazMnJifXv3x++vr7Mw8MDnp6ezM3NDV5eXvAMCAC7dYsIK88TyZ41i+6BigqNx0WLqKbD06e0XmpooLS0FMHl5WKvQ4eYaUEBzJcuBf/999CNioJeUhIJqJ07076lbD/4zTc0D99kNxgb19QLaN2a/u3YkYhxs2aIj4/H3bt3kZOTg9GjR+OtK/A3BFNTEoXPnSNB8k3HffiQxIsGothkMhlcXV0R+PAhM+3ZE82cnemF3bvpHi9f/l5580pYWloiLi6ORUZGIjExEZrm5tAfORJM2b6ve3daX69cIbulb993/q6ioiIcO3ZMCA0N5VRVVX8WRbFfcHCw0KNHj/erXtwEAE1h/E1owr8KmUxmoa6u/kHm2Qcn+gB5VNTVSXH/0EhIoI2+e3ci9jNnUr4eQJ4PFxci+EoPv7Y2bfypqbSR6+tTdWKZjAy6/HwikZJExFJZwKeigl4vLCSyXFRExzQwoNDa/HyINjaoyMuDmqYmkJMDBz09VOjqIm7NGtbcwQHGnp5ASQnuP3okPrpyhSvV0JD6d+/OOkkSeQLNzYmcaWqS18jIiL6/sJAIRLNmNeeVng5oaqL45k0Ma9sW+pqaKBMEqN65Q+/LySG138yMjGWZjIyedu3IANy3r/FVuV/g8ePHuH//vpCSksLy8vI4IyMjccSIEQ278XieSMD+/dS+6IsvyHB9OeRdLieD6t69OuGR1Zgyhe7BP//Q7zk5ZPhfvkz/374dXUJDGTw8GAYNQoWeHsTVq2vqCbwtHBzoXNTU6L5raSk9+jAxMUG/fv0aTrkQRSKCVlZ0bj/9VJMWUFVFz3nFCjLEjY2JzEkS5dUbG9MzU6Yq1Pb4vKHuQ4Pn8vHH5OUZMYLGwMCBRChrgzEiEdevkwFnY0Pz6C0jTd4XCQkJ/KRaIcOvRXY2GX6ZmXVyTtu3b89SU1Px66+/CosWLaoem8KDB/C5fBnGv/xCRvf7YuVKEoNsbIhEKGsglJbSeterF5He8nLyglVVEVm0twfatQNLToY6AJmbG7ZzHHLOnoWbmxuavyxAKBS0xoWEkAF/6xalLhQVkWAZHo4jw4dLiTzPunTpgvLyciQmJkpeXl5cTEyM1LdvX2Zqagq1JUsYJk+ue+z4eDrH1q0RNnasoCorR45IAAAgAElEQVSTvTGUuKqqCjt27BDLysqkcePGoVVCAnm3lVE5CgWtrfUhIoJefw1MVFXRq7AQFhcvkjh540bd4lwAiV3KCuy15+GlS5SDPGoU2k2fDv/yckydMgXqY8ZQatOZMyT6enoSKVqzhkhYx46AKCLnn38QtXcv1NXUJMhkNUzJxoYEFk1Nihywt6c1wsWlpj4JgLigIDxNScGd48cx6tAhtDQzQ5GlJZop9yMlVFSA9eth8vXXsLa2FjZv3sw+nzuX4xITEXb2LMqrqjhe6UHV1aX1c98+IjnZ2UQ2W7aktb1HD9h06sQejh5N68iuXSR2V1WRALF1K4m96upQ7NoFGxsbTuXmTSAjA7EqKpBKS1nVpk38z6GhkMlk0Covx5yLF6FiYgKvzEwpwNCQSZJUR4we0rcvrzh0COjeXXbu3Dl06tQJEyZMYGFhYbihqSl1UVen9IbHjwErK5jcv48sKyvxv+fOcVVVVdDQ0BDNzMz4tt98I902N5fUfvyRqampoaCggAMgBgUFIeXePS7D1haOjo7wqSUc1Y404USRxoayfWlttGxJ4vD8+STCrFtHxHHjRsDQEDqjR0MqL68pvqgsKjtvHo2vIUOI2Cv3D0Gg/aeB6DOFQlEn3SU2NhZnz54VKisrOfX4eNFDX59dPHCAuV28iEJVVXbb1RV+fn4fjugDtA5Onkxz5o8/6r8vtZGeTmO/IWRlQcPNDQ6rVwtnnj/HHFHkERBAttaZM6/WnXhHWFpaom/fvjhw4ABUVFTEAwcOcM2bN8fMmTNpvywvp7Sox49J9H4H5OXlYefOnWJJSQmHF7WYysvLl754eTiADxSe938bTWS/CU34F7BixQoOwGC5XD65/dv0Tm4ASqLv7u7+YYrxAWR0fP45baYfEkeOEEH5+Wci+aNH08YvCGQQ37xJpL5FCyL7K1eS9/T58xrFW5KIEFVU0OcqK2ljKS2l8wZIpMjPp5A1U1P6fHk5GVLl5XSstDQUPXyI4JAQiAUFGDJoEJF1c3N0NjODlqYm0o4cgbqODlJiY4Xk9HS+u4oK9GxtGaerS3UDHj0ilVwQyBC9dImIebt2ZDynpZHhIZcTmb98GTA2hmlWFjNWKGAqSSRkZGYSmQ4Opvd260bXn5tLxDsvjwq3/fADGYbNm9O9ys8nz5eyMFg9uHbtmlBQUMC3b98eHh4ekDWCHACoKeyzcycZUMeP099qGzr29vQ8P/ro1TDg77+vaZ+Yn09GzQsvKT75hAyz778nLzWApLg4MMagrgyrfBdYWZEwERgIxMYiNjYWHMdhwoQJnPzxYyLfgkAC0rJlVBfg6lXyzo8ZU5NH36IFjbPffyfjSF+fIgeUUFZBNjV993OtD0+fEun88Ucq2KS817q6VKTSw+PVz3h40Hj78kt6T6tW5N36H0GSJGg0NqrAxIQ8uS8Vl7K1tYWqqiqOHTvGr1u3TpTJZKyjQsFaBAXh6uDBsOzfHx/EvK6qIsKtbPv444+0/jRrRmM5O7uGiI4bR//W40GTy+UYMGAAoqKiRH9/f87ExEQYPXo036xZMyKj5uY0rmJiar5LLieif/w48Pgx2lZWsi4HDyK2WTMhRRB4hULBnJ2d0a1bN7pUZZus2h0vjh0jEaFrVwh+fni8ZQvn+ob0quLiYuzYsUPiOE769NNPea6igozwr7+uEaP696eflStpHn/zTc0BunZtOLKrqAjYvRslDx6gIikJiePHi61mzap/fWGM1q2WLam9pLImysqV1W+xt7dHxrBhCCgqwsRNm8BNmUL7xOzZlM+9aRMRI1VVSpEAEBsbKwLgysrKuPj4eNjY2FC7tbKyGqE6KorWBl9fiip5+BAJpaWIy8hATEwMPJctk2Z06cJUfX2hNmYM7mlrS049ezKN33+n+aSMFMrNBbKy4DdgAF9kaYnfc3IkYdw4sSgxkRvo48Oap6ZSkcfSUkqHcnWlteTJk5rxoK8PtGuHsNOnkRQeLmHePIZ584iUKtuNZmcT0VcoYGVlxd+6dUsclJvLcQUFKDIzk4xyc5lVQgLsN29Gy+bNcezbb4WH7dox+40bOZtz59jis2eh8VK0lZzjIDcxgbOzMxITE4W//vqLzZo1i0s5c0acumMHh+vX6dnMnUuk8/JlOC5axEEux/Hjx+Hr68s5OjpC5DgWmp6Oa9euCSUlJXyrVq0wZswYrqKiAjne3kjo3RueDaWcXb9O3uW7d9+cetSmDYkekkTvDwyE3hdfYJKGBof0dMDbm+oHxMfTHElJoX1q5cqaVq23btGzr0fk/f3335GRkQENDQ1RTU1NAsCKioo4Hx8frkWLFiwvL48v7dkTVikpkv2zZ8zA2hodunVDy02bSPT9kIS/qoqE45wcEslfd28SEupt21kNQ0Ng1iy4Dx3Kh//3v8j6+WeYHjlCqXkfuEaNjY0Nli1bBoVCwa356ScUJiVRiuUPP9B4Hj+eyH5qKtlibxHGr1Ao8NtvvwEvCg22aNGi8smTJ7VvzAfoJd0EoInsN6EJ74QVK1YwAEYALADoA2gpk8kc5HK5nSRJbTmOa9msWbPKgQMHaug3ooL46/CvEP3z52sU2Q/RyqukhIq3qKmRETRuHBlwnTqR8aTcGGbMIGNn4kQyiKZPJ69I165UUEtpfCvxJk+fMuS5HmRmZuJcQYGYUVnJmZmZISMjA4Nnzqyj2LcbORL+/v7CycxM3qh9ewz77jsY1C76VR+mTXv96y/6gN/+6Seuy8yZr26+DVUwlyQKpVUe/7vvyIgMDqZ7N2cOGaWzZpFXZPRo4ORJJEdEID0xkR87bdq7ectlMvI0jBhB3+3kRL8rSa6DAxkfSk+hEn36kKDj7EwGrqsr5e+3bUtpG/7+r+QMqqioQJKk+it6NwbFxUQ+Fi0icefOHXjcu4eOv/yCjTyPT379FSrjxoGbPZsEH0ki0WHWLLrOjIyaYynJ8ruGjL8LcnPJ0J4791VSf/Xq63MsTUwop//0aQoTHj68pgbA/wCNKlIZH0/eN2Uf+lrQ1tZGhw4doKGhgQcPHnAPLl+G7d69uDp+PJK1tFBUVIR3Dt9X4uuvyaMbG0vrUV4ekc/27d8pPcbFxQUuLi5cXFwcLl68yO/Zs0dYsGABX8cYl8nqzmnGqFZBVhasZTI80tZGDwMD3luS8MjLS9kmk/DoEY2JZs1I3Fy3jn4fMwbo3BlnT50SSkpKuBfdRhrEyZMnBU1NTTZ16lSe4zgioV99RWTpZbRoQfOoNr74gtYTP7+av5WWUorRzp3AokWIdndHiKEhHAwM+Pj4eLRr167+MaGnR14+pSirRKdOlBozZQq8vb3xU1gYNs2Ygfn9+4Pz8yORZOlS+pGkOh+1tbXl7ty5A4DqBwCgef30KQRBQFZWFgSeR+X+/dgXHg4VFRWp7bVr6BwSwu5//DF8R4yQunTpQgv/48fQ/uwzZLRuLeUeP868AWhZWhIB++orEmqyssBra0N18mT0c3FhfEgIX3T5Mky1tUmM6dSJ5vCAATSuvvyS1kIVFVTnvgPILy4WGWPs8ePHaCWX09xo3ZoEqG3bIFE0hvTs2TMmSRKXPG4c2jg64tHGjWLPFSv4zMJC8fru3dyYxER0Skjgb8yZU2Wvrc2xAQOgGRZG68A339SIsM2aVe+ZQ4cO5Q8cOCCuWb0aKC/nYh0dYa2rC92EBNr7/fwAe3twzs7QT0sDAGhoaEAURXB+fugWH49uDg7833//LaioqPAArd8thg9Hixfr6f/D3neHRXVu3a/3nJlh6IMgiA0QQcUCghRRASt2gy0ajSUaS2Ks11SN4d58UaPGEjXRRE2sscaClVhQQFAQUVRQVLogvQ0wzDnn98dmABUUveYrv8t6Hh5wnDlzylv2XnvtvbVaLTQaDQRBgCAIEAUBsjt3IL3/PtQ5OQCo6FtOTg6kqmcql8vBGAPP89U/e/bsQWlpKSQA0qJFaJeYiHGSRNc3ezZJ/0+cIEVdx47U+SEykiLl77xD86iOlpLt27dHRkYG1Go1p1arAQATJkxA27ZtGVBT4NPd3Z2hKr2odVQUEQ+MkVPbv//bIXwtLckhPniQ1smXBYH27SPSqy6MHk0qsC++gJifj37Hj6Ni5Uoiy95SRP8ZaDTA2bN4ZG6O+evXw2jCBNrz9+whe+Cf/6T5OmAAFUANCmrwoZVKJT766COYmZmhtLQUhoaG8v3796sTExMNAIxbtmxZ7tu/oP9MNDr7jWjEa6Cquv5XCoViuiRJxsbGxhUGBgaSqampzMrKytDMzAwqlQoWFhZQKpX/ttY2KysL27dvR69evd6eoy9JtNGsW/fvO/opKVT4ato0Yq0XLSKnLzOTnJKICHqPszNt1s9vmj//TEz9/v20qX7xBW2KCxa88qtLS0tx/fp1qbi4WFSpVLxKpUJ5eTkYYygqKkJERAQcHR3ZwoULoVAosGbNGnH//v0YOHAgV7tw2/Tp0/mkpCS0adPm36u6WwtFRUXQarU1hmlDwBj1FA4IIEfi44/p9SlTUC3zDQ0lRUNRUXW9hcpp06QZlZWsSWAgkSYbN1L05tq11yv4Z25OqozVq0mi16YNkSmMkWG9ejVFgwAy5K2tibBZvpxkzEuWUA60mxsRFHVERdpUtde7dOkSBtdF5IhiTZ/5H36glIDsbJJlX7lCY6VZMzLC0tOB0aPB9esH0507McDaGqsrKwEAQ0pKpK47d75WWKasrAxbtmxBr1694PZ31Iy4eZOcnfXrn43kAvRcc3JefL0uDB5MNRZiY+n5rlxZkx7zNyA5ORmiKKJBRSp5nuTUL4mItW3bFm2fPMHg6Gjs/uwzPC4qgkwmw7p16zB+/Hi0adMG/MsKyT0PUaQxN38+kTozZpAi5swZGrMjR9J8+fHHF9MkGogOHTogPj5eVAcHc5g7l9QgOugq/M+fX+uURFzr2hUmEREwKy+XnmRmotOpU8zFyYlIptrFtKysKNo3fz5FpadPr1ZFWFhYcJIkMbVaXW8NioKCAjx8+JCfN28eOd+3blG0VNcS63no2hkOGkTnPmUKRVi9ven13FwinrZto3H211+AqSnctVrc+e03ITU1FXfu3OGnTZtWf5HGRYvI4R8+nKT5AJGXtSqGjxs3Drt27UL0yJFw//13Oq9OnahuRffudB5VxIiDgwO8vb0RHh6OoqIiWC9YAO7bbxFpZCSe+fZbTqFQwIzj0KK4GM7OzmL37t05URDAL12KOYMGwbhLFxqQ5eVAair4mTMxpnVrbjcgrD9wgF988SIUBQWkAlqzht63bBn04+Jgq6cHDBuGbeXleH/iRHLyn4MkSSj96CPojR+Pio8+gqxPHyiVSvj5+XFyuVzct2sXM6moECd6eXEmJSVgAwfSOhcTg9zcXNapUyfcvn0bTceMQd4XX0CtVvPHjh3D7N9+44ydnZGXloYrAwbAy96ebAADAyLQDx+mMb9hA72Wn09r9+TJUCqVmOLnx0lduuDMTz/hlJ4elCkp6Ny5M62dN27Q+gqgadOmUKlU0u7du5mPjw96W1kR6Vtejp49e/J79+6FRqOB4vJl2leqxvqKFSsgCAJ4nocMwLu//YYr/v7Sk9atwXbtksrKyjiFQoHKykoYGxsLGo2GEwSBSZIEhUIhSpIESZKYVqtls2bNgrGxMWQyGY1jjiNF3/XrVANi3DiyI2JiaKxYWxM5GhVVp6MPAL169UKHDh1gZGQEhUKBI0eOSIcPH2b/+Mc/6l9junUjEgGgWh+6VLaiohdbBr4utFoi8oYOJVKpLmRnEwFd35ru6lot8U/fsgWW+fmSTbdu7K3Wc9F1D7CwQOW5c5BFRiJn7lycmz4d83Xt92qDMVLSDRtGROtr1DlqWlXM09TUFIIgoMrR3wDg4tu5mEYAjc5+IxrRYAQGBnrJ5fJjjo6Oxj179tS3srICY+xvTZ5NSUmBVquFqanp29GTaTRkwG3bBjxXKOu1cOkSselZWWS4WlmR9PzRI9qUs7LIWR0+nJz/+qKCcjk5+Zcvk+FhZUXsfVERGRVVkSlJkp6JyD969AgHDx6EsbGxZG5uzmdlZYmlpaViVdSMyWQyNmbMGM7BwaH6Q7Nnz+b2798vbtmyBYsXL66OSnEcV+2Evg2IoojffvtN6Ny5M5RK5esRCJJE116fhK+2cX3mDCoqKrB/zBi2uKpNEnx9ydG/coUiPsOHU45yz57EwH/yCTlACkXdz4TnKbIWHU3OUVYWHaNHD4rwFRTQ+7ZsIYf7yhUyNLOzyQCZM4fk6PWAMQbbVq0QExODwXl5JE03MKCx8uuv9N2xsaQiOH6c0iecnMh50NcnQ083DoKC6H2tWwOiiKSzZyU9PT3WsWNHXLhwAV1fU+aekZGBwsJCREVFiW5ubm83XH79OilXvv76BYdeq9Wi8MABmGdmUpSqIWjZsqbg3L59dI/efffl1dbfAJIk4dSpU5KtrS2USuXL16DUVFIKrV798oOmpQFz54Lbvx+THB2RmpoKQRDw+++/Y+/evWjevLk4fvx47pVEWUwMkSfnz9P4GTy4pgr8rVt0L21syLn+4AMiic6ff3UbyXpgamrKxMxMVmFsjGcW/TNnnhnzSUlJOHjwINRqNVQqlWTSrRvz1GoFzJjBo3dvmo99+lCKya5ddN4LFtA8e06p4eHhwW7cuCFt3ryZff7553VG0lNTU2FkZKQ1MjKSQRSJaJ0+/eUyYICc/U6daO7q6RHB9tNPpH4xNCQZs05yDsrJnj59Og8AgYGB2LZtG5YtW1b/8UtKaA3XOfs6B+fddwEQ8de2bVvhVGIib/bjj2g7fDgRM6tXA0eO4Gl+Pm7duoX09HQhNzeXq6ioYABwYN8+fP70KfRUKtyPjJS6du2K5ORksWPnzqyXTMYwfHjNTfrlFyIKd+2iNSsoiNY+GxswAN6urjw7dw7JwcFw2LeP5o+tLdWKcXGhOdu7N0RbW+RbWkobf/0VrVu3FkeMGMHLZLLqPenSpUvC5cuX+X4lJSjevBkPHj6UPvnkE2ZsbIyBAwdybrt3Q378OJfVvDn+HD9e8nzwgHUwMgLbvh3NXV3F4uJi9O/fH/ciIlhEfDxrYmcnOjs7cwkJCaJzcDC3afp0yMzM0LJly5pr09Oj8XL7Nq3NkycT8aVLKcnIoOs8eBD5ubmwViqlzn/8wfD990QoR0RQvYlLl6D088O8efPY3r17tWVlZTI4OZGygzHY2tpCkiRs3bpVmJOXx+ta7tEjHSpGRESgsLCQTfX1ZZYxMbBbt45VkTTPrxfPL06vXmPl8poicOXlVICuWTNSjRUXU5Q8NZXsmnrIsNqdPzp16sTu3LlTTVC8Erdu0e9//YvW2bt3aX69aaRfJiMbackSOv+6SIoHD4jUfp4wvXiR9sg9e2gf7tMHZTNm4PycOeJsA4M6LyYrKwuCIKB58+YNO79z54BNm1CwciXE5csR3bOnGG5tzRkEBECdkACvlxXg8/CoUZxdvlyTwvMayMvL0/15edmyZU9f+wCNqBeN1fgb0Yg6EBgYqB8SEuIQEhJiEhISUhISEuIsk8kujRo1yszHx0duZGT0dgu41IMWLVogLi5OzMjI4KytrWFsbPzvfa8gkBH27ruvL/8VBDJ+OI5kq05OlBtpaEiGw44dFC0YM4aiOF26kFPyqvPt3p02r7w8Oq8uXSDt2QPtwoXY17SpcPLECe7CxYu4cuUKwsLCEBYWhtu3b8PHxwejRo1inTp1gqurK/Py8uLc3d05d3d3zs3NjZk/l8+oUCjg6urKQkNDpQ4dOrAG5yC/JoqKinDp0iXO3d2da/Amq0NBARmbtVtX1QNJkrBr1y5RoVCI3b296WH27091BTp2JMceIFl9t270+qef1sjaly4lieSkSZRq0KwZGSLm5lRgatAgigCtXk0OecuWFI1KSCAWf9MmIn0++4zkqT4+zxaru3Klptji+PEUPfz1V3T6/HPcHTEC7rt30zm5u5Ox1qMHvW/BAjK6p00jMkClIsNbJntxLJmbAxoNRHNzXDA1ZRMXLEDHjh1x8eJF5unp+fI+8M/BxMQEV65cQUlJCavunPA2cPQoOR0ff1xTB6AW7t+/jx2PHkH5/vsvb2f3PBgjZ83VlRQVO3bQ3HtLsv60tDRs27YN+fn5bNKkSay+nu7V2LaNoqIzZ9b/npMnae2IiKh2Rk1NTaFSqeDn5wdvb2/cvHlTvHjxIufk5PRCnQCNRoOMJUtgkpIC5uREObwDBtAY0qWGlJWRs7tmDTkAQ4fSWLS1JYfIxqbuYpMvQ3ExZFOnsrAePdBr/fqa17OyKMr53nvVL+3YsUMUBIFptVosXLiQOQ8fDiuVisO6dTSH3n2XSJH8fHJCs7OJiOvd+4XxzRiDtbU1u3XrFrKzs1/oDCKKIo4ePSpaW1tzTk5ODLt3E+laK0e+Xnh6kkFub08EW3g4rRMBAXTP6mlbqVNN6evrw8XFpf55MnQoXe+jRxSFLSoi8nbBgmpFWZcuXTiZTIZjUVGwa9VKUl24wODuDnh6IsvHR7rSpAmzsbfnOnXqxEaPHo1bly5JbRIT2d0JE8QzV64gLy+PGz58OO7fvy8+zMjg3LOywD9PmHl40LW1bUvkQ5cupPxITobZ+++j1dOnuFpaCvu0NMj++U9S3nh5Ebloagr4+4MdOACvw4cZPD0Zf/AgdyoqCjHJyZK7uzsDgPj4eMYYw4gNG5ig1aL08mXmMHQoJElCfk4Ozpw/jy6PH8PS0REFEyey4OBgWHp7w+LCBTh88AG7HB3NhLIyqXfr1txljsOCBQtYq2bNYJORwfSaNEH7pUsRGxsr3rlzh1lZWaE6NZDjiDi6eZNSLrp3p/Pu0IHG+ciRKIqLg9X33yNHT491NDWlNX/wYEo96NSJCgx27QoYGyM+Pl7kOI5r164dFStNTwe6dIGenp50+/ZtLu3+fZT0749CrRYZGRmorKxklpaWzGzHDpZ/7hxsT51664RjNTSaajUEZs0ipSBAe0xYGKmjTp2iOW9gQD+15pNGo8H27dslPz8/2NnZvZ4R5etL902tpn1y4kSKvr/JWmtqSnVjTp6su/tFdDSRAM9Hx5OT6Vp9fIC7dyGlpOCIsbFkZ2/POTg4VL+tvLwcISEhOHHihBAaGsrFxsaiZ8+eiIuLQ2RkpGhtbc0UCgXZkaWlZHeNG0cdAAwMIBobY1dSkhTj4cFaDBjA+g8YAHNzc2g0GnH06NEvv29du5IiLzX1jVRU2dnZiI2NrQQwy8/Pr+K1D9CIetEY2W9EI2ohMDBQT09P72ee58cZGBhUCoLAlZeXK3ieF4cPH65oXztn+b8Jo0aN4s6dOyfu3LmTKRQK2Nvbi7a2tryDg8Pr5T5//DFthGvXvt4J5OSQsZSZSdHWwECKkNy5Q46GmxsZu/37v1GxP0mhgMgY8OWXyD94EPGJiVJEXh6Ejz6SOlRW8p999x3EhARomjRBeXk5ysvLYWJi8noS+VpQKBRiQUEBb/GWC9noYGpqiuHDh+P06dMwNTVF29eprP/0KRlYDZDBXb16VXr69ClbtGjRyy2O2rnh96u62Pz8c00ESCYjJz0sjKKLJSXksOkk+jExdE5nz5IB2bcvyY1DQihCMWgQOeYffEDH/Phj+p7vvycjf9UqIh+srICFC3GxSxdUJCeLuHSJA8hheThiBLRZWbC3t4fidQ0oAwPE/vQTSp8+hYWJCSCTwcDAQLh8+TIGDBjQYMszLS0NBgYGolarZYmJicz5ZdWQG4rdu6no2Oef11td+Ul6urjwhx+4bZWVUmFhIXN0dESzZs0aXsRQpSJHOy6Ooqhz574ZmVcLarUa27ZtQ6tWraRhw4YxVT2OXzVEkSKoL0u/iYkhQuLYsXq7CSgUCsyYMYP/9ddfsXnzZrRp00bw9vbmS4uL0XTvXhxt2lTqHBLCKouKJJvJkxm/bt2LBzlxgsZb7e/4xz8oanXyJJGWGzeSCqWhyMwEy8uDfpMmEmpHLDMzyeEHKTQ2bdokFBUV8dOmTYNKparJz+/SheZccDCpPJYsIYcyMZGc8zr6eUuShIiICCk/Px8ajYbFxcXh8ePH0ujRo5ltVcHL1NRUFBYWshkzZjBIEh1/xYqGXZMk0ZwuLqao6fnzNFZfoQgwMTHBvHnzsH79eqxbtw5ffPFF/fUcjh0jIurePVpPCgtfeEvPnj0RFRWFnRzHZn36KQz79MGj/v1F/aIibsawYTCoJWfunp0t2QUHs+3OztywYcPQpk0bKJVKTJ8+nV+3bp1YeeMGp0hKqi4ICoDymNeuJUKzsJCiwo8eAe3bg02dCpPISORYWmJlly742MMDFgA5ze7utN59/TXg7g7us8+Q/vvvaJeXB8+0NFxMTmZX/vgDN7t1Q56xMfOpagPXRqWC5cmT+H75coiShLnr1sHNzU1UDh/OYfx49O7dG1qtFgcPH4Zrq1bigLlzOftp08QnwcGc/K+/KK1CFImEHTYMOHAATdeswYgRI7jTp0/jwYMHor1Ozg/QPJ8xg6LPJ07QHj16NDmoM2bgvKurWOTuzpV7euKRvz/s7OxoADNG5PK2bUSQ/fwz5HI5q6xKhaoujAvA09OTtW3dGiVeXjjWrh2kpCSR4zjwPA9NRQUbkpzMbvr5iWhItP51EB9Pju/48USO6dJntm6lvSk7m0iAsWOJoP7rLyJXL18m5xigeebsjHslJdBoNOzWrVvi+fPnmZ2dnVBYWMh169aNKZVKyGQySnGoDxxHhGJ2NjnsPXsSUfb7769/XX37UuAkK+vF+RYc/KzqUjcW1q2jce3lBfz6KwpWrED2hg1selVl/7Nnz0oPHjwQCwsL+aZNm4o9evTgjYyMcPr0aaxcuRIaKqTLPQgJASeK8IiKgtutWyiKiIDlkLkARKoAACAASURBVCG0ZzRpgls3byL39GksXry4mixv3rw53N3dG/Zso6Nprzt5kmoLvARpaWnIysrCo0ePyu7evavb8OQARgPY3qDva0SD0OjsN6IRtSCXy5e2aNFiXEBAgNLIyEgJECNcnUf2PwBra2tMnjyZE0URd+7cwb179/iQkBAhKCiIb9q0qejs7MzZ29vDwsLi5VH/zp3rNCrrxePHlGM2dixFRX75hdhyXcXhNm0oZ9DWtt7ovSRJyM3NRUZGBtLS0sSsrCypvLwcGo2GaTQaVllZybRaLSBJsHV0hBQYKBV37Cj26dOHd3FxYRzHAXZ24Fq2hGz2bBiMHv3GMlwdlEolEhISYGdn93q5wa+Brl274vbt27h586bYtm3bhg+cLl0aFNW/ffs2QkJCMH78ePY60etqyGRkHAEkAdVBV7hrwAAiACSJnJSwMDIiN22in2+/JWN4/XqqZK6vT06nqytFz8zNayIvwDMOyL20NGi1Wnbq1CkkJCQIZWVlvEwmQ1lZGTp06ICxY8e+1qVkZWUh+OlTcZggMDRvzpCdDW9vb/7cuXPw8/N7ZYRekiSEhoaKFy5c4BQKBfP392cnT54Ex3EvN/5ehY0byVidP7/eVoqSJCH2xg3W8p13MHTKFHby5EkhIiKCb9q0Kdq1ayf5+fmxBo1RjqOxs28fqTf09OhZ1HZ6GghJknD27FnJxMRE/OCDDxo2Qd59l4zSw4fr/v/t20mCe+JEvfm1tTFixAhERESgOCuLC1uxQiq1sRHH/vILXzl+PEv48EOE5eRIxr/8IrVr147TaDTIysqSnjx5wpydnSWnrCzW+vPPaw4mk1EnDFEkWXBaGoStW3FHkmA8dCisrKygVCqRlJSEkpISODk5PasI+fBD4JNPcGPVKiH/9m1+7dq1or6+PmvXrh3r3aMHyWtBc7KgoIAHqIjcC2RkixbkvE2ZUpOb36YNpSKEh79QPyUiIkIKDg5mVlZWcHNzk0xNTVl5eTnbu3cvxo0bhzZt2iA1NVVSqVQix3E8pk4lp6gh+bI5OUTG5eSQ05GcTA5jfj6RtkuWEJFRT96wSqXCggUL8OOPPyI8PBw9evSoe++ZM4ccl6o2mfjlF3ICnstX7tmzJ06ePInNiYkY3rs3LFNTubQ5c2Cvp0dEhFIJ5OTA5JNPuM1WVpALgtS2bVumm9syIviQ064dDFNSnh33gkDrmLU1nceYMXT///oLOHsWMmNjeMTEICgoCM+01bOzI6JizRpSPPj6YsjUqViVk4OZM2eiX0oKuEOH0HX4cBiMHw8uMxNwcoK8Z0+YPXqEJYmJlOO9aRNUs2ZxCAmh4wDo378/unfvjl82b2ZOcXHIDQlhw+fPR/mECUSi3r1b0yqySjXiYGqK+/fvIzIykktJSZFmDBrEoK9Pz/Dzz0nB8F//RQ78lClUW8DfH7KLF5H/8KEglZdj//79XMuWLaWxY8dyenp6pArIzwd+/hm35s0TYi0t+eqAxrff0pypgjnPw3zMGMz9+mtA59Tfv4+CWbOwZ+ZM7ccLFvz7/oQkUeG95GRSkF28SGTLihW0nuXnk5rMyYn2ryZNaDytXk1KttqqooICsmGCg4GgIDiKImZfuABJqeTu+/gABQV8nqkprly5IspkMqm4uJh/8OCBGBAQwL3UjtKl7Bw/TmqViAhSw2RkNLx6/4oV5BDv2/dMrQ9UVlIg5Ycfal5Tq0kJ9PAh2QYffQR4ekJZVgYAuEIKFzx69Ij5+vry7dq1g5mZGQcQAWlhYSEV3rzJxqpUMB0xAmL//iidNw9/9ughRfj6svIjR+Dg4CCOMjXleNBc0mg07I3tXTMzIvzffZeeQT32SWlpKbZt26b7p87R/wbk7NezkTTiTdHo7DeiEbXA87yfq6ursrah9tbkvP8mdM5HlQPCl5eX4+rVq1xUVJRw9uxZ/p133kGd0cioKNoIQ0MbJrG7fZs2sTlzyBD98Ud6zd2dDNSBA2tUAnUgJycHcXFxUkJCgpSdnc3JZDJJX19fsLCwkNnY2MDIyAgGBgbVBrGRkRH1wU1MBD74gOGrr/hnctP9/Oi3Wk1G3+PH5NzUyid9HQwcOJA/ePCgaGhoCD8/v7+NwbGyskJ6err06nfWwr17JIF7SaE2URQRGhoqenh4cLZv4Mw1CKNG1fydmkqO64gRZDj5+pKBwxgZJXFxZMTrDJ1XVARu3ry5dOfOHfbkyROhb9++fIsWLWBubo7IyEhcvXpVwIu5nXVCFEXs2bNHSElJ4V1dXSXHfv049O8PSBK6u7vjypUrYnx8PNelS5c6P19aWgqZTIYLFy6IUVFRHABoNBpmbW2NIUOG4MSJE8jKyhL79OnDvZbhI4oUZWvdmqrDvySVIzY2VuJSU5n9mjXgLCwwb948Pjc3F8HBwQgNDWXt2rV7PWm/rS3EP/6A9OOP4H/+maJfrq4NMkJFUURCQgKSkpKkO3fusEmTJjWcCVu9mgzxunDoEMlPd+9ukKMPAE319THMzw9YsYIhPBzYsoXPmTEDJqdOoV+/frC0tOROnTqFpKQk5OXliSqVihMEAbf/+os5HDmCh76+KIiOlmJjY0VLS0vWtm1bzsbHB5EjRgjZAwZwD4YOZZaxsej6xx/SnoEDmYGhoVReXs5kMpkYFRWFyZMnczzPQ9JooL59GzLGMGzYMN7d3R2PHz/mCgoKxLCwMOby2Wcw694d6uXLkZubKzHGWPfu3UWVSlX3gDEzo3X0zBkiTbdsIYXUmDF0n3geFRUViI6ORnBwMBs0aBCq2u5VP0BDQ0Ps27cPw4YNk2JjYyUnJycecXFELOnWyvpQWUkS9sWLyTGaPLm6zR+aNaOiXMuXkyMzcyYpdaKjyUl9zmg3MTGBk5OTdPHiRebg4ACruhQBjFFXDBcX4MEDiJ07Iyc9Hc9n816/fl0EwLm5uaHrsmXAgQNo8eGHVNdg7Vradzw94TBhAnx698bly5dZRkYGaq9/np6e3NWICCjz8lB9JoJA5z5pEsnVFy0CvvmGrikqqtpxc3Z2Rlpamrhx40ZOpVKJw4YN42xPnaLnFB8PlJej0MUFpqamsLCwEG7fvs3169ePMV1LxMuXkXDqFFLmzRM7hoZyt318RP8TJzjo6dG+FhdH+1XV9x05ckRITU1FUVkZf2rwYIwyNGRWV66gIjERDikplCt+7Bgde+1air6fP48hjx/D78gRHO/Rg1Q0Pj4Ubb5xgyK+X31Fz7dXL6qU/sknGLZmDafrWqNWq7F69WoWGhqKvjrSXE8POHsWbUJD+b6LFqHzwIH0+r17tHZUOZU4fvxFddKZM9BaWUH8dwv9XrtGY2/0aJLLjx5NSqElS2oUSt98Q+958IDIog0bgAsXatpqZmYSEaCbAyoVycqrarfoSxL0c3KAq1dhlZ5OEfrkZODSJQ4BAUhs1Qrn/vqLE/39wTdEMdmkCf00a0bPiDH67vnzG1Z7ZdMmIgg+/LAmBen+fSLddKqk334jQvCXXygXfuvW6ha8SqUS3t7eYnx8PARBYO+99x5r1apVzfFzc5H/9ddgRUWsZ0ICTMzNobd4MZCUBH19fXxYtabk5eVh69atbPXq1TAxMUF2djaMjY0FjuPePBLy6ad0f2fPfraYaS3UFaSQyWRDv/rqK/c3/t5G1ItGZ78RjahCYGBgPwMDg66vJbv+H4RSqUTv3r3h6urKr1u3rn65uG4TqnL0de1ydD+CIEDOcdArLIT+ihXgo6PB1q2jKFRaGslvu3Ylo7QeBz83NxdxcXHSzZs3UVpayszNzUUnJye+Y8eOaNKkCUND1pq2bcn4fPr02UJ0OujkcrNnkzEUFtaAu/Qi2rRpg379+nGXL1+WfH19/7baC0lJSXil/Pl5WFtTFepakCQJly5dEhISEpiJiYmYmZnJS5Ik9dIVvvo7kZ9PRtXGjXTP9fRoLNjYUKRBqyWH1tS0pkJy584vdS5HjRrFAgICwD8XsnZ2dkZ4eDh3+PBhadSoUS99KOXl5di7d69UXFzMzZkzB6ampnSsbt1IiVJcDK5v33oryD98+BB79+6FJEkwNTWVpk6dCj09PYiiCCsrK1hbW6NZs2bYtWsXioqKhICAAL5B40QQyPEtKiJy5BVtN8+cOcOmhoeD+/BDaukFwNzcHOPGjcPKlSulS5cusYl15XU+B1EUcZLatLGSkhKkFxRwDt7eotdvv7EWW7awu8OHw8DBAba2ti/cE0mSEB8fj5CQELGoqAj6+vrS1KlT+RYNTckJDKT7Xpdkc8UKcnTWrm2QYgVaLa1TbduS9P6f/6RoJajQ1qRJk6rfOrym00S1Y50cFITEmBiEHT0KSZKYu7s7n5GRId27d0/sOGkSq2jfns/Ozhade/Rgg7y8wAUEMBsfH9xjjHX39oYoityGDRvE7777DgPu3JHyBQGRgwYx54cP8U7nzrC2tta16+LMzc2F48nJfJvOnaF6+BBhYWEMAMLDwzlHR0fYPE9GpqbS2Ny+nSKxZmbkKPzjHyQJXrQIWLcOhw4dEhMTEzkXFxfJw8PjhUHn7e0NMzMzHDlyhDHGmGfHjhT9PH2aFDb14eZNciAzM0lhoSugNWkSOfsbNpBjpyN4R46keTxmDJ3j2LHkuNdysAMCAlhhYaF45MgRZmtry7KzszFixAg8ffoUBQUFSE5OhlarhZ63Nwp/+03UKJVcYWkprNesERStWqG0tFSaOnWqzN3dnTt58iS6VUW+MXYsrTdVReYAAJGR4E1MYPngAQAq0lrb2e/atSt4Kyvc2roV1m3borVWC2MbG7ARI+jeeHnRNdvYkNLDzIzm6OTJkI0ciZ49e3I3b95EYWEht3fvXvj06AG3adOQ+c9/4kRJidCjTx/+Qp8+4K2t2fXr15mhoaHk7e3NAOB2SQmO5OTAa+pUbqejIwwYg39JCRHUx47RtYSG0jOurERiYiLr1KkTJwgCPDt1guVXX5GCgTF4hoURMZaeTnvchg20rmzdCiQngzM0hHtwMLv87rtSj4ULGW9jU0OiffIJfYe3N+3dJSU0p775BujfH4Vt2kCSpBdqYeDoUZyeO1fyLSlhpqWltJ63a0fOsyTROLhw4dnWjAsXAlOm4ImfH6kRXgdqNaVUBAZS9HfAAJoLtrY0TmtDFCnC//gxrasGBuRMd+9Ofyclkc0wejTdK6Bu0osxWoNqd6gRBDpuZCTaCgKe3LsHcfBg8JaWVDC4XTsaMw4O9adFKZWU8w6QKsbZmciIs2ep4GV9CA0laX5WFjn0AF1L7eJ/wcFkcy1dSutGrZovjDH079+f69+//7PXs24dETM7dqBJfj469+uHo23bovns2air7kqTJk3w+eefs+DgYNy5c0f09/fnPD09/z3JI2NEYvj6EqFZRx2XWgUucyVJ8gOwSRCEOvKyGvE20OjsN6IRAAIDA03lcvm+gIAAgwa1mPpfBLlcDo7jIAgCSkpKkJ+fTz95eZLjggW4PGyYlNKkCav87juSzAPgeR4cx0FZWSnplZdLI3//nUu3skJy69aw1NOD5fTpuOnqilQ7O6ibNYM8J0fS27JF0tPTk5RKJfT19WFgYAC5XM4lJCRIRUVFnLm5uejl5cW7u7vjjVnhDz4go+zbb2njrAs//UQGwJUrtME/efLa+cldu3bFuXPn2NOnT+uOSP2bePLkCXJycjBmzJjXuw/e3nQ9tfDo0SNERERwvXr1YkVFRZyjoyNcXV35vz2tZPp0iuAfO1bT3ujWLYqu2NuTvB8gQzonh4qNLV9OjsH16yRtrEOhoOux/DyUSiUmT57MfvrpJxw+fBj+/v7PSKE1Gg1OnjyJlJQUoaioiG/atKk0ZcoUzvT56v+rVgGMQX7woHTo0CH07t1b8Pb2rv5CtVqN3bt3w8HBAePGjat3rFpZWWHWrFnc5s2bxRUrVqBVq1aCv78/X2+6TFkZRTScncnobkAByIqKCsR8+y0GVrXAqg1fX1929uxZxMTEvLKzwO+//y6lpKTwlpaWor6+PqZNm4a4uDhu/7VrcA0Lg7B7tyjxPDvYvj0zVanE9u3bw8vLixNFEYcOHRJzcnKYk5MTN3369NcqagiAomPP1zKRJHoOzZtTPYeGOPoREWRgp6QQQfAGNTVsNm5E+bx5WNinD3766SfJ1taWDR48mEEXGf+v/wI6deKqiYmQEFgcOoRe+/bRuqNQYO7cudzp06eh/Osv1qFnT2jd3BAdHY1u3brBysoK4eHhkkwmY87OzvzTzExcTEmBXm6uBID17t0b4eHhCAsLQ0ZGBrKyssRhw4ZxfE4OOUvvvFOTSpWQABw5QjUWvvkGCA3F08WLkdKkCfvss89e2v2gQ4cOcHd3l1JSUmCwYQODmVm9BfVQVEQGd0UFFWT7+utnybhr12r+Hj+eSLw1a6jwJsfR8xAEImzWrq1p21bVnnLixInc3r17xWvXrjEAWFdVR8HExEQyMzOTkpOTOTg7Y/aBA9zVHj3EqX/+yd1o3Zo74+vLAGDVqlUix3EMAMvMzEQznbPz7bdUfVzXt76q84udnR3s7e2RmJgo9unTp3oR5DgOLgMHIiMmRtp/+rQ4c9ky/r6NDbLd3SX3UaOYols3MFdXIjt06qMuXcjRio+HkudhYGAAtVoNf39/nDp1CtfHj0dRWhqUBgZ8ZyMjFJuaQuntzYWEhCAvL6/6JlpYWIDnechzcjDi6FGpXUwMh+bNaf1zcSFCdOBAup/t2uEdR0d2x8xMCEhI4OHnR2tlURH4bdtwzt8fE8aPB6fVIm/kSDQzNgbr1IkKmA4fDj1HR5QlJiLk6FFmzhisKipgYWxMe+L586TaAIgwP3iQ5mJVETsLuRxeDx/i0sWL6NixI0xMTOi9CgUe29mxgKVLkRUeLt1asgR6hobolZ/P2NWrNDemTKlxNh88oDSIZcvwJDISgiC8ejPKzqZ7f/gwEZtbttBcCAggFVR9mDuXlBghIZSfDxAR9dVXlGoxYQIFCVavpmtNTm541w0dsdi2LeLj43EpORk9v/yS5oSuu8ju3USOffIJdbqxs6N7W5ey8Kuv6HdcXI3c/6OPiLSsaz2LjSX1QlERjY3UVLoXxcWkRAkMrGk/W0dxV4gifSY/v0ap06oVEXa2tuD37kX4Tz9JHMcxy1dUx+/fvz/69+//VoyKsrIyVFpbo2DcOBRt2ICTjx7Bb+RIeNZqvfntt98CABhjocuWLYsD4Ps2vrsRdaPR2W9EIwAoFIrVHTp0MP6/EtWvDQMDA7Ro0UK7YcMGGWMMCoVC1NPTE015nm+rUDAbb2/m7uhYXdROqVTSprlnD7BmDcN77zH06IFm3brBWaOB2KMHyj090beyEpIkoaKiAsXFxaykpISVlpZCrVZDrVajuLgYGo0G3bp1Y+7u7pDJZP9+AjxjFPU6d65+Zx+gjbF7d3I4OY4c0yVLGpyjzHEcrK2txTNnzmDixInc287dlyRJ1zLx9T6YkUGGSq0WN8YUtWE9axfa+7sgiuQQqVQU9bp4kQzjEyfIOfnzTzK8asscdRETXYG2zEwydrZupdx9IyPKO21AZ4YmTZpg4sSJCAkJEdavX897eXmhb9++UKvV2LRpk2hqagoXFxfm5eUFPT29ug0TGxtAkjB39Wr+zx49cEGSeG9dD3EQqeDk5CQmJCRwhw8fFl5GyBgZGWHhwoXc06dPcf78efbLL7+AMQa5XC61bt1a9PHx4Zs1a0ZRqu3biRCZPLm6P/jLb7UIRXk5nOfNI6nwc/Dy8kJMTAyCg4PrdfbVajUePXqElJQUZm9vj4kTJ1bfk5YtW2LAgAHIzs6GVX4+h1Wr0Dc/H7G+vlzM3bvCtWvXIEkSWrdujUWLFr1Z7Yf798khe554WraMDNhaPePrxaxZVBF6587qnu4NgSiKz9ZRiY8HSkrQbsgQgOchl8uRnJwMnufRTtcfOyeHjGMdGCNFQlAQkWyWllDcuYMRQUEU1WQMNgDy8/OxY8cOdO3aFdHR0QwAIo4dwydnzyKpXz9BkCTm6+vLvLy8UFZWJt6+fRvJyclMo9FwDrm56LhhAxnvuhz427cp+rd0Kc2vBw+gad4c2T/9hHemTHl15wMAarVa0t68yV02MECxkxOsoqLwQgeDoCCK8nXtSqRLXc/igw8oZcvVlaT6n31Ga+mWLfRsGKPXFy8miXJpKTkg27YBLi6QGRpi0qRJ3HfffScZGxuzkSNHIicnR3J2dmYAWHXb1JwcjPDw4LB8Odrm57Mhjx+joqJCePDgAZecnMwAoFOnTs+em50drUmSRMqy06dhsHMnxOXLhQErV/I3IyNFl/XrOUyZQk6ZgQGaP3jAFnzwAY8OHWA9fjxOSJJk7OXFjq9YAVEUYVJWBs0338CoaVNJaWsrFZw/zw3fv19iubmsbPJk9OvXD+7u7nB0dERqSgraBQSgcv16KD7/HH6ZmcCnn6Lp0qU4cOiQpFKpJG9vby4qKgrKigp08fCAurSURVy6BO/kZCJXjhwhabauTWF8PCzS01n65s289vJlyESRxn5QEHjGMD4hAfHdukkZNjbSQ47jPDZvhlettV9dRegzxnDo0CE0a9ZMnDlzJofsbKox8LzjzFh1XRb58ePwj4pCrJ0dLs6cCZfAQFSAOiyUV1Tg12++gd+lS4y7cAEXjYzQMzYWrHNnWsOXLaMUgeBgmkMxMQDPQ6PRCNXKqueRmkrX37MnOeUjRgBTp1IaYNOmRCLUh6Iiup6hQ2mNKSqqcZgliZxagH4vWULFP6dNI2Lqhx9oHdEpRRqA+/fvQxTFGttC51y//z4RCbdukVoiPp6caQMDWuv19akeh5sb3SeAVJHDhtFcCQ+n/eD0aSrG5+pa86VLl1J9hthYmqPr1pHtc+ECkXKOjkSC1iZLRZHWJnt7ut60NHrPihUUSX9uLVapVOB5vsHpcW8Da9euFSorK3moVJhcUoLRBw5gt4EBPDw8wBhDdHS07nweS5I077/rvP6T0ejsN+I/HoGBgT4KhWLigAED/m+F9Gvhgw8+qD2XOfzwAweZDIiMxDNiXI2GpHgJCcSoq1RkAM+ZQ5uVoSE4AM/HJP+O6He9CAigKNQPP5ADWR9kMnqvINAGLJeT82FrW2+6QW2899573Lp166TIyMhqOebbQnBwsNCqVSvG8/zrMeUqVXXF6vz8fHAch5s3b0Iul7/9SsfP4/p1cshv3CApqIcHGWi6iKGDAzkpKhWlUpiZUQuh2mCMpPy6AmmxsaQMuHuX6gCMGEGGnqlpvY6/jY0NJk2axKelpWHfvn2Ij48XHB0deSMjI0yfPr1h+fOMgR06BFlWFsTYWBw9elQaNGgQe/ToEdq0aYOhQ4dyd+/eRVFR0Sufu0wmQ/PmzfH+++9zoigiKysLJSUl7Pr169z27dvRztRUGHTtGm/g7U3RyAYqLpKSkqBXXg59G5t674WHhweCgoJw/fp1wd3d/QVj7fTp02JcXBxnaWmJYVV5ubXBcRzNXSsrYPNmKO/eheekSfD89lv+yYgRKCoqQrt27d5sXElSdWVojBxJrwkCOYteXkRY6SKHz6OkhIzhTZvosyoVyWHdX52umZWVhQMHDiAvLw8qlUrs3Lkz5+PjA1l6Osl4q4g7e3t76dq1aywmJkb68ssv6QavXUvzKyWlxikyMKAUofXryTGeNo1y22s9k/fffx+XL1/GxYsXwXEc3NzcYGVlBe5f/8InenrPPBd/f3/Ov0qpkbBtG8LDw2H44YewbtsW1RtMYiIpk+jgwPHjKFy6VHrUvbs47MEDHtHR1ZHz+uDu6spVrlkjPbCxQYmbmxh18iR/8uRJLFu2jNb5mTOJlFu0iOTI9aG4+JlCbNDXJ6XB5MnkUNTurS2X08/Tp/T8P/mEIq5xcTBLTWUd3n0XLVq0QIsWLapvXrUKZuNGWmPCw2H+6acwv3ABUCp5T09PHD58GBkZGUJBQQFvYmJCHTk+/5zIxR9+oO/6+GMiEL/8Ev5qNZ//+DGuZmZyeuHh6BAbS8997FhyfpYsAR49ggkAy5072dGjR8HzPLy9veE9Zw4e9+6N6zY2LCUlhfE8j6NjxrDK8nL4pqWh/YQJyI2KgrmFBUw7dwbmzoVct/8pFMDDh7DW1wfHcQgNDeVCQ0Ml99hYLPjrL8b/61+4c/w4Qk+eFLvHxHDJvr4oPX4cDh99hOodSalEE3t7NPXwEH+wsuKma7VoMmsWcPIk2KBBMDx6FF0fPmRd27dna9eulTSi+MzicPnyZdy6dQtWVlZCbm4ub2dnR3vD4cM1Pdrrw/DhwPDh6HbkiNRpyxYWWVqK3I4dBcnEBHZ2dvyoUaNg0KsX2vn6Ivn99yHs3w9OLicSrk8feg6ffVbTHhWAIAhMEISa78jKolxzfX0iaGQyWvtv3GiQ2qkaX31FUfoFC2i86daS7GwaE893XPj005oWsMbGREqUlLy6jkUVWrZsiZiYGJSWlr5YXNPYmJQVOkybRkRGWhp1drh0iZQPQ4cSwSCXE+ns5VWTlrBnD+17Li409319SVWydCmRagcPksMfHU2/LS2JyNA5+idO0LrRoQN9ZvlyIhCbNqW1qnfvOq+rsrJSMjc3/29z9Ku+k76PMRju2AHV4sX40NMTjDHk5eUhKChIdz7Dly1blvzfeW7/qWh09hvxH43AwEBPuVx+6t1331X+XX3X/0fw6NGzDPL167RBGBtT9HjsWGKj9+9/o3Z5fztcXel8P/zw1UW9eJ7y3wDKPe3cmRyQV0ChUMDQ0FDUe85Y/3eh0WiQnJzM9+vX7/U/3LIloK8PQRCwoSr/0NjYWHjnnXf+vs1aEMgQGz+eoiM7dpAhFRX1bL/u338H/viDchH79qUc01fB2Zl+JIlyER88IGPxyRMyzhQKcvDqcHZbtmyJruXUXQAAIABJREFUOXPmYO3atXx4eDjGjh37eoXyevTA8MePMXjiRKyeO5dlZmYiKysLjLHqiHB6ejqXnp6OhuanVylCAAAODg6s7M4d3F63jruSmQmnQYPQqoHnV1FRgSNHjoBr0UJUrV1b74fc3Nxw/fp1hIaGcu51OMKdOnXi4uLi4Obm9moVia7ews6dwO7dsM7IgLUu1/RNwBgZ47r5KYoUXZIkkizX1RozMpLG0Jo1FLVSqQAnJ6jVahzauROiKMLOzg6+vr64evUqLly4ALlcDq1WC5lMJpaVlXFmZmbIz8+HgYEBWrVqhStXriDsyhV8dPEizHU5uwDS09MBAG3atBFQ29ZZsIDGoc7Z1mHKFIpWOznVSMdrwcHBARcvXoQoihAEAW7HjlEl+5Ur674/x4+j3fbtKJ45EwcyM6WK779nLVq0kHx8fFjbgAAiKquQ5uqK0716sfeaN+dRUUGRw6Cg+qX5AFqUlQEBAcx2wQKAMf7u3bs4tH8/wr75Bt0PHMBBDw+puEMH8b1WrfiX7mwHD774mrExpeUMGUKEQW1HB6ghtDZvBjQaRBw+jCk7dkB87z1ySJo3r9uxCw4mQqV1a5IeW1tDJpNh8ODB+OGHH/hNmzahhZWVOD0oiKtOGdJFWKOjKUK+ahWs/P1hUVCAh7m5yMrLQ4fSUlrHoqOJ8Bk6lBzNL7/Ee4cOMU1lZY3i4eBBlCcnI+3mTfj6+sLZ2RkmJia4cuWKxHJzWXhGBkzj4uBz7x4RJnPnUupYkybknIWF4d7q1eh9/DhrOngwmpSVMeOdO6kaPwAnJydcungRj9LScDwkBL5Pn0rbIyMl+/JyLiMjQ1Kr1aKtrS3v5ubGGZw+DUXHjrQnZ2aSg3fiBDmteXnQ09OTDAwMmFarra61U15eDhsbG+348eN1Y5p+azTPEjMvQZ+RIxlGjsQwUQTmzuVx5AjdrydPgF69cG/lSnTevx/8tWs0Lz7+mFJQvv2WyJ1ae7Kc5zm98nKaB/v305h48oSqsc+d26DzeQbp6fS5NWuIcN63j85Bhy1baG+q3dGha1ci6x4/JmKra1eqC7BoEa03DVD8ubq6Ijg4WAoJCcGQIUNeTf63akU/uvG5dCnZXdnZdC91HWhcXEih0q8fEREhIURw5uXRfWrWjNbRixdJATF+PAU5YmMpKDNlCqlvjh4lpdz8+bS+NhDNmzdn9+/f/2+N7FtZWSGrqiXp5itX4G5piZ6jR2Pl7Nkof3ZfuI3arUwb8beh0dlvxH8sAgMD/eRy+YnRo0cbttEVSPm/juRkkmH/9BM5wd99R8a9r2+No9W3Lxktf1NhurcCR0eSs+3eXV19tkG4erWmj/auXeRcgAq6FRYWwszMDAqFAikpKTAxMUFZWRmzeIPc4Prw9OlT7N27V1KpVGjVqhUDSNIfGRmJ0NBQdOzYEUZGRnj48KHg4uLCu7i4QKvVIjQ0VHJ1dWVKQYDsyROkpaVBLpejZ8+e8PHx+fs26aAgMqwiIylPsbKSHIw//nixXVzLljVRktGjiUAKC3vREagLjNU4/qNGkUKAMXK8KiuJVNBFdms5zLr8c8YYXpVzWCfs7CA7cQK9jYwQfPYsAGDWrFmIiIjA48ePhYKCAj4pKanBzv4zuH0b+ps3o72LC1vbsiX0k5KQSn2DRa1WKxUWFkIul0sKhQIcxzFRFCVTU1PWvXt3vqCgAKWlpViyaxfJb+tzGAG4uLjg7NmzbOXKlfjoo49gbGyMjIwMJCUlISQkBFZWVnUWcqsXXbvSc1i2jKJBf/31+vnxGk1NAS9jY/r3/Plk+C5c+GKhuP37aU5nZZGByxgZ7VXYsWOHkJOTwzdp0kS6dOkSCw8PB8dxkMvlEEURffr0kZo2bcrt3r0b+fn5sLW1xaRJk8AY4zw8PLD3xx/xoKICIYmJUOXkIDo6WiovL+fs7Ozg7u7+rJ2zcWPd11RURFGyo0epnVdICGBgAFEUIYoitm7dCgAwNzcX+/btyz1fSLMakkSOR69ewPbt6NauHboBrLCwED/++CPbs2cPZv30E/KnT5faf/sty8jIwPHjxyWLnj2ZYadOlPrw0UcU7duxo+6ie2lp5BQcP169jjvxPD4oKpKKzp5lq4YORaWJCbNWKtkPP/yAyZMn45lK3bWhr0/P5XkVhqEhRdb9/Oh3fXukQoHLjx5Jqdu2sTE9e5KjrVbT+p2URIogHT7/nNQAJSXPpGsYGxujU6dOYuWBA9zwlSu5wv79YXjjBsr09JB69y5am5jAyMCASOp33gG+/x5Py8pQKklo5uBAzjJA1fONjGgMbt4MmJtDJpdDduECSnr0QGpqKs5fuCD02bqV7/fjj1J3P7/qeeNX9ffalBR0SkykjhrvvkvrYVAQFU5dsgQA0KFtW2SVlEgJERGwrqyUOjLGcVX3lzGGycOGccVnzmC8mxuaCgK7Z20tpqenCy1btuQMDAz486dP48nJk6js3Fms7N6dQ1oazcvcXFK8ZGYC5eWY8OWX3K4JE3DS0hIcx4ExBsYYXF1dn53vWi0RtnPm1P2M6gPH0XwoL6fUK39/ICgIajc3tNiyReICAhimTiUCIiWFiBwjI3p/eDigrw/vpUtxz8QE4vz54DZvJsL9ZfL8l0EQiKhv3pzGx4MHL6b1jB5NY+x5FBbSOnrsGM2JHj2IjFu2jIIGtVK56oJarUZ5eTmzs7N7s3PnuOr8f3TvTvO4ooLuWV4eEV2//ELrwief0BrxzTekYNixg+y0zp3p2uLjaW3euJGIirZtiYh8A6hUKlZaWsqVlJS8qFh4C1Cr1YiLi4Orq2t1vZdJkyZh1apV1e+57uUF92vX0C06uizMz2+kJEnRANYBeIOISCPeBEySXq8zVCMa8f8DAgMDneVyefi4ceMM/r9x9AEyStavJ8fs/n0ywnv3pvx3P78G5RL/r0FZGTmG8+aREfI6yMxE8alTyPDyQuHkyTg3YAAEhaK6MKEgCOB5HjzPQ6lUSgDEgIAAvmXLlng+evzkyROYm5s/04LxhXxhABcuXEB4eDhatmwpBQQEMFNTU52TIBYWFjILCwuWlpZWXYW2srISEydOxNOnT3Hu3DkAAKfVwiMqChGenmAcB6VSKXl4eFQbom8NERHkrLu5UYRtyBAaK+bmlFtYV32AW7fIMdAZ7599RgZmcPCbn4ckURpJUBDldr73HuWCvvPOK42zhiAzMxMPHz5E099+g+roUQi3blVH5gHgjQ2g8HC6H+bmUA8ZgoMHD0oajUYSRVFijPHm5uZo2rQpNBoNKioqIIoieJ7HnTt3oFarwRiDi4sLhvfqRRFxc/OXfp1Go8Hy5csBAAsXLsTBgwfF1NRUrmPHjhg6dGidVZYbhJQUysXu1IlIn4bWmCgvJyLxm29o3OzYQQbq99/XtI0CiEjo25ee5fvvkxP7HP7880/p1q1brHPnzhg5ciQePHiA69evi927d+dqG966rgGlpaXo2LEj9KucYEEQUDBhApLatxeDGOMAch6Li4upaJpcLn322WfPzp89e4hsuHyZ/j1rFklz3d3peYSEkPy4VSuciY+XIiMjWdVxxYULF9LEj48nmfvza+rBgyQ3/+23Z1RTBQUF2Lx5M1q0aAGDoCCo/P1h0KEDQkJCUNX7nHL1S0vpvt6/Tw74rl0v3v8jR2rI29JSmj+nTgF9++JS69ZiTEwMN3/+fMTHx+PAgQPo378/vOuaT6JIjtywYfWnn+TmEll84gRFKOvA2rVrYWVlJb733nt0kPJyat3m60u/1Wr6rExGUdl+/ej78vLoABoNCnftgvTll4jv2hXnPDxgWlCAtg8fIr5PH2HGP//Jl06ejJwBAyDftElsbmvLGVpYYG/r1qJ+kybcKF2r0BkzSC107RpFSg0NgdJSaK2s8PPkySht1QqeXl5i93nzOL09e2jcP4d9+/Zps7KyZM2aNUPbNm3QbehQctL69iUCXV+fnLK+fSGoVPgrLk5svmwZV9USl5CeTgRXdDSlQg0e/Mx3XB40SHS/c4fTT0nRDQ6SZFfND41Ggz/27hVaHDnCXe3alXWNioLaxUXMbtGiWlkiiiKTJAmiKKJZUhLzuHQJRyZOhO41rVYLnuehUqnEwsJCTqvV6gg0ieM4SRAEThRF1PYB9EpKUKGvj5kbN6LI1layb9WKYdgwUsHMn09rXosWVHBQXx/47Tek3b+PbWFhmDp1Klq/rNDeq3DvHpE5Z8+SkqC0lAij5zFlCkX9n18zJYkCAytWPKuIiYykyHhY2EuVMseOHZNu3rzJvvjii7+33XJRESkQMjNr2tJFRNCYAYjACw8ncnTKFJpLL+uy8QpUVFTg2LFjwsOHD7nZs2ez1+4QVA9KSkqwY8cOqXahSgBYvHgxCgsLq8lRAM0BXJvx88/drTMzLwP4JyTpt7qOWVUoe5Wenp6pWq1OF0Vx17Jly2Leygn/h6Mxst+I/0jo6ekt9fHx0f//ytEfMoRy21q0IENn5UqSo9aXN/u/Hfr6VI22vJwY8gZ0SYiNjUWHDh2gaNYMvxYWCuKvv/ITUlMhMYYlkyejzMICgiDAxMQEmZmZUKvVSE9PZzExMfyuKqPaz88PN2/eFJs3b86lpaUhLy8PRkZGwqJFi3gA2L59u5CamspzHIdhw4bBxcUFGzdulHJzcxkA+Pv7s2jq8Y2ysjImk8nY4sWLWW1y4MSJE7hx4wb27NkDc3NztG/fHgUFBdqcnBxZv7AweP76KxKfPMHDhw9ZSEgIysrK0KtXL+zbt0+wtLRkI0aMeLM86/JyioCsWEHGxCef0OshIRSNSUiovy/8mjXk3Hz9Nf27qpouysspKv8m0BX3mzqVfvLzKWq5cSMZQKWlFF1zcHgjJUpUVJQQHR3NKyws0OH994XBTZo8o5J4I0c/LIwcxQkTAH9/GACYPHlyTcX3l8DBwQF79+6Fm5sbhnTuTC3OdPfzJVAoFBg1ahQOHz6MY8eOoWPHjlxqairUarX0sqrtr0Tr1hQxCgwkw9rMjByxl0GrpUJSy5YRIffFFzQu1qwhZ04UayL448aREX/1ap2HCg8Px61bt5hcLodflWrEwcEBDg4OL4xvxhg66CrZ1wKv1cL8xg2Yr13LJV+9KhgaGsLf35/XtaysKqj37D3q2bPGuBYEikDrxjDHEUG6fDm027cjfuZMBlDFdUdHR3qPJFEE886dmjahGg2RBpMmAYcOVRMnoiji5s2bOH36NJo2bSpNHj6cnbp3TwxPT+eQng6FQiFNmjSp5vwMDcmZ+uADGvdffvlsbYxTp6ii+Z499MyCguj+r1oFWFrCD+B097KiogIAkFq7KGEtiFX1QV6aHmNuTgTGxIkU4a9bYSNlZ2fXHESppEh1Vhat4127UuX7zZuppsf27TU5xqII+PjA9O5d4M8/0W3lShTIZCjIzUX3hAQMOXmSXy+KQolGw3f5+We437jBrXN3x6S8PMn52DEu6J138Gt+vlhaWip1Gj2a69u/P8Pdu+Qw9e+P2MREnPjsM/Tu0wc9Zs8GtFoON2/SudUBDw8PWWJiovj06VMWGh4udtu7l4eHB6lhystpXTp5EmjZEvzatVCq1ewF5/D2bXKQi4ufldbv3w/ExkI9dy636upV2O/ZI7i4uPAmWVnQk8tRnpICjuNQVFSE5NRU3m7xYnhXVqLzX3+hpLycy+/cGZxMBt7ICDKZDDKZDDzPQ5GcDPmAAZhMhXLB8zwEQcCjR4+gUCg4pVKJsLAwcByHPn36MKVSyfT09KCnp1etFtDVVmCMgU2fDvOYGIaMDBqHAEWrz5+n8X3xYvV8adm6NQxiYqi43ZsiM5NI3lmzSOm1f39N5f3aKCqie19X73bGiOyZO5ei5Dp4etJYGDiQ1ts6ivZlZWUhLi6OTZ069e919AGyx3QqN3t7OifdWgSQMjMzk9bhQYNIUXP0KKlXNm8mIuu77yg9oE8fqofTpQutvXVAT08PY8eO5Xfu3Cns37+fTZs2jXujYqzP4eHDh3jO0U8F0GrVqlXw8fGp/dZMAP23zprV6tPlyw/qV1SsBGO7IUnaOg47rrKy8kMvLy/k5OSI9+7dm//dd989EUWxiUwmu1tRUbEEQPCyZcvq+mwjXoJGZ78R/3EIDAw04Xl+iIuLy/9iHftrYu9eMgINDKji87RpFE37P9ZG8AW4u5Nc9fr1eov1rVixQqqKvLOkpCSEhobC0dERRUVF/MLAQBj/8AOWAkDTpjBaubLaeNFFeO3t7ZGbm4tbt27B3v7/sffeYVGdzfv4/ZyzLCBVunQQUUDFioKKLfZesJdgjN3EksSoSZA3mlhiSTGxxRohauwFxYINAbGDCNKRjlSBXWD3nN8fw7KggJry+X3fN851cdHO7p7zlHnmnrlnprl49+5dURRFrqioSOns7MyHh4fXtCi6d+8eMjMz+ZkzZ+LgwYO4c+cOTp8+DUEQGMdxEAQBu3fvhkQigZ2dHWvTpg3Mzc3Zy8b0sGHDMGTIEISGhiI1NRUDBw6EgYEB6ePjx2GoVKJTp05wc3NDbGwsbt++jdu3b8PZ2Zk9fvyYMzU1rT9S15i8eEHjOWkSGe0q8CyKBMp++KFuH+WXZf78utEUDQ0CqrduUQT375CmTdWto54+pShHSgoVlho6lGi0eno1xaFeJz179uTv3r2LVh4eypGjRvHo3p2Mv40b/9z9nThBuZSLFxOAeQsRBAFHqvOj27ZtS88XFPRGYB+gSuVxcXGIjo5GYmIiAKBLly5/XYfp6hJQDAkhJ0t+Po117Y4LteXRI4pcz5lDtGmeJ/owYwTcfHwI6N+6VdMurSF59OiR6OLiIo4bN+7PF5+MiqL7adYMo0ePrvkwxhgePnzI9axu8VZH7OzIuTR2LNFqz59/5ZL8WbOw7/lztLpyBZUuLorBy5dLpFIp3acoUsVsFdCXychhVl5O66tWJC44OBgREREwMzMT33//fYajRzH4t9+4grVrlampqdygQYNevb+mTWmPLlhA7AMbG4paiiI5Zr7+mvajvT1FQ+th4hQXF+NmdS2T+uqHpKSk4NqaNRj9++/4fdMm4cMPP+SKiooQGhoKLy8v6OnpITw8HDo6OtQNYvNmKtq3ezcB9loiCIJYUlLCiouLoa+vry7KpxqHu3cJKF+9Ss67jAxq1+jqSs6V1FRyUvftC8mZMzBu1kyMEEVmv3AhugJYuHQpX1JSgu0SiRjTvbtoVFmJvSYmXEtXV0w4eBCHfHw4UVcX3YcNw8bPPhO7M4Yut26xOFtbnDhxAk2aNKGo8969VKzyhx8opeDyZQJJGRkEptq3R/ODB9E8PZ27a2Mj6IeHc/D0JKr1s2cERA8fpig3xyFz4kTcLC9nyz/7jJxfqjo5KSnqfvAqYKVQ0OurqqCppSWKPM+USiUfHBys7HDpErNNSOCOFhcL1Z1cOIlEgh49etBr+/SBKQCHjz+me46OrjuZn3xC6/mleTGtVcX94cOH0NPTU9rb29MeqaqivXn4MOny1q3Jbli+nPbD8ePqtowaGjT3H374yjoCgL/EEL5+nWojnDlDgF+VztAQ6M7IaJih6OJCtkLtApwAOd62bqU9ExT0SoT/2LFjSicnJ87a2vr/xiYsL6f15uVFOf7OzjT+lpY0Hmlp5BT46SdiJri4UEtMVSHEhAQ6ixMTifV45Qo5bW/dojmbNIlYAR060HoZNQqjR43if/7lF6xfvx6LFi3CX61R5e7ujjZt2mDr1q2q6P5CALcYY8tu3LjhC0DV+qPGC7R+2TJ8+fXXgZwo7gEwtfb7+fv7jwHwi6enp7xPnz5aALjCwkLk5uZampqaIjk5uUNQUNBppVLJ+/v7fwvgpJ+fX8Rfeoh/kbyj8b+Tf534+/uPtrW13ePr6/tfGvJ+SW7domjL1atk/BUWEkCePJmMUFWxpCFDiH743xbpT06m4nBffPGK9zoqKgrHjh0DQMb9mDFjcPPmTTE7O5u1b98ew4cPV19cUkJAceFCihqoaWZQKBQICAgQnj17xtXuy15RUYG11YV2WrVqBT09PRQUFCinTJnCX7x4UXHv3j2JVCqFj48PrKys6u+//rYSEEApF7Ui7KIoory8HDo6OggPDxcvXLjANKoNnnbt2onVvcTrl6wsAgrLlqkjbirZsYOiA/b2DUYGauSTTyhyoopuAmSk37lD6Rb/pMTGEmB4+pRSB2bPpjXfsWODYLKsrAwBAQECAPHDDz+ki0JDKf/RzOztmQLXrxNb5rvv1L3S30L8/f0BAIsXL1b3t/4TEhAQgKSkJAwcOBCd3qK11BtJURFFJLdtIzCkAhsqEUVi2chkVGfByIgKU8lkBKBVOan6+q9fT6AxMTAwwKJFi/78PS9cSJ85YgTt6/R02uva2ji3fbvQwtiYa9GsGd23mRk5bHJzyfl17x4xS3JzgZgYCM7OuHL1qmhpaclOnTqFyspK9Hv6VPCMi+Nw+7Z6zVy8SIyFjz4iY33KFAISw4bVWVfXrl3D1atX0b17d8rzV4kgYMPGjRg0aNCr7eZqiyCQ8ysvj6jgCQk0tgcP0n4cMqTBCucRERE4f/48mjZtKmhpaXEGBgaws7PD9evXYW1tjfj4eLRo0QJyuRzPnj2DylkJkC5V2YY8z2PYsGFwd3enCP/VqzT3tRx/q1evhlQqhVwuR5MmTTBnzpyGWTNlZXTPKgeooyM5qsvK6hSV3b17tyiVSjFu3Di2b98+MTMzkwHA3OnTYdqqFc7+5z94pFDAIj4eQ4KCoBEUBMmePQjq1g3PkpPFqUZG7FhlpZBfVMQtWLAAdejLLi6kU1q2pPQSfX0CUj170t8NDHAkOVlhpqcn6Tl6NDk4bt+m9b9/PxWOa9EClfv3Y926dRhx7pwY3b493OfPh5ubG8OJE8S6mDKFAN2ECbQmz50DoNYFK1eupDxnmYwAYPWYbtq0SXRzc8OAAQPqKikVa6a8nBxVt26pa51culQ/TT0+HkhLw8mMDHTYsUO0WbyYITub9u2DBwTubW3pfDh9mqLJUik5xfftI/06YwZ95r599ab6rF+/HhMmTHh7Gn9oKAH3oiKaExsbsl+aNq3/+g8+oOfdurXh90xOJsZNQ3n9Xl5UF6C6sF5aWhr27NmDXr16oWfPf7jVuyiS08fUlAIxbm7kZDUwINDOccRcsLGh6/LyyGH2yy/q9n4Niao7gKcnpYNMmEDzOHEi6bkxY5Cbno5tEyfi02PHoL19O9Vq+eMP0vXPn9P6eQtmw5UrV3CDCp1uAzAPgD2ApJcuGwLgmp+fX5m/v3/PL/7zn1gmCPGhXl6nbg4d2kYQBEMAgra2ttmIESOaNG/evMHPO3fuHCIjI9GiRYuq+Ph4DQ0NjcyqqqrJfn5+V9/4pv+l8i6y/07+jaL4n3FyxccTANu5kwrCeHqSh//FCzokxo+nA338eDoAVNRJHR0C/l260MHyN9C6/jFxcCDK2rRpBIRBUdKioiKcPHkSAOWJqTzVbm5urLKy8lU6ngpgTZhAuaLFxeQ5rza4pk2bxgUEBOD27ducKmpeWYtK+PTpU2hqaor21ZV9+/XrJ+nXr9/f/7zh4QS+a4F9xhh0qiOtXbt2ZV26dEFhYSFu3LiB6OhosaysjPXt2xdGtftoV1TQQd6hAxlz7u6vAoP9++lwf7kYX33yxx+0jmqLnR1FKNavp+r6/5S0akVfAKUaMEZRprw8itbJ5UC3bqhUKhEREQGe53Ht2jWYmpqKM2bMUHsDunUjQGhqSrmhr2lvBoAMtN27KcKyd2/dnsevkejoaERHRyMpKUkEwAYPHqwG+m3bUsTw5TFtRORyOeLj4wGQ8+lvF0NDAqxSKYGeq1cJBKj20k8/UQrIpk1kkLduTYCpmoINT883bj2oklcMbIWC9qZCQcAmM5PWbW4uGfrnz5PTrmVLSg84dYpor2fP0no8eZLWfL9+0ExO5rQ4DoKrKzgdHYrEr11LhvM33xB46NWLDOUePSB88w1io6KYJCoKTUaPVn7++ec8AA6VlXTt4MEEsqKiiOGRmkqf7en5CtAHgPz8fFhYWNQF+tOnA6NGoaKiAkePHoWjo2PDUTaOIxbKRx+RnmeMKnqfP98wIKqWFi1a4MKFCygsLOQAqj0SGxsLAIiPj4e+vj58GAMXGorYJUvw4sULuLm5QU9PrybvmzGGTZs24fr16wT2fXzw/OlTFHzwAaJGjQLT00N8fLzYpEkTNnfuXCQkJODYsWONOz2LimhN2dpSBHfSJFpnwcG0ruzsAAB5eXmsffv2+Pbbb6Grq8v69++P4OBg5MtkMIuNhasgIPbYMaHZuHHcMRMTcebo0Uxj9Wo4OTkhNjaWPfn9d7gqFFzr48cJ6CuV5DgZOZLA3qVLarYSY0hJSUFgYCC46lopRUVFEkNnZ3J+RUYSy6JrV1orcXHAxo2QPniAz3fsQMLJkyz/+HHRatIkhtBQilBHRdHzxsYSE6MWy87JyUmhr68vqaFTL19OYHf2bACAiYkJy8/PF/By+gnHUfQ+L4+ctFpa5HiZMIEA45dfkjO3aVN1q8KdO4GUFPAff4zSpk1FODkxDBhAdkKTJuQ4UsnUqeRQKCigeRIEshVGjKD9EhhIjJ6XRBTFxlNB6hMVq2n/fnp2MzMas8bOokGDiPremOjoEFOpS5dXdbWBAUXAt2+n89XOrsbB1bKx9pR/hzx5Qh04hg6leSsuppSC5s3p906diLExeTLp2VWrqEBx69Y0xw8fkjPGzY1stpdF1R0AILq/SpKT6fvZsyh9+BDaV66I2uPGMZib0/9OnKD1N2AA6ccdO8h5euQI6dzgYNJBYWFki1lY1Ly1o6OjCuzP8fPzm+vv759V+5YkEsnmlStXnvP39+f8/f2tAci/WbVqZ+v797UATAhRAAAgAElEQVS7x8ZO1O3YkSuUSGBoaAh3d3fwr2HsDR48GNHR0ULXrl01hg8fjmfPnlkePnw4xN/f38PPzy/y7Sfl3yP/D1v47+Sd/GNyLSMjQ1tVpO2/VlJT6cDYv18dEVFF2VT0yUOH6Ht2NhlSK1ZQMa38fGpXd/EiHRDFxWRAjx1LRtjfVMTlb5P+/cnYSE8HrK2xb98+ZVpaGu/g4CBMnDiR03iJ1tdo3p2K8nr5MuW+rlhBz29oCG9vb+zdu5dVt/qCnp4eZsyYAblcDkNDQ6SkpLB27dr9s4vG0LBu3+t6hDEGIyMj8DyvlMlkfExMDGJiYtC7d2/Kl5PLKaf+6lWKJtVqSQaAnrltW3XLwjeRx4/rjyIWFVHBsH8S7NcWlVF27RoZvTdv0jqPioIyLg6JJSVItbODnYOD+P777786V1IpGTTt2xOQbwyYKJW0To4do0JptZ0pbyCnTp1CVVUV9PT0WMuWLZWdOnVS38/y5W/WyaBa4uLiEKUCEABiYmLg4eHxVvfzxjJgAM3r+vUEYg8donU5ahStqQ0bCCx06EAGO8+/+iyCQE7H588JpOTkkG569qwmutXn4kU4Xb5MYCIlhaq0X71KkdMePchRmZ9PrapkMgIEHToQILSxIabH1Km01jU0aC5rtYss1tJSHnz6lLeKiBCmnjrF4dQpAuw7dxJwW7eOjPDVq4HUVEi0tTHi11+hPHcOLcaO5dGvHwGp6dPJKXD6NIHFxYsJxE2ZQut+1ap6h1GpVOLFixecKIpqAGxkBBgbw9fXF7t27UJxcXHjlFpBoPGVyWjcFi8mPb10KTlvtbTqXcOVlZUQRRFTpkxB8+bNUVFRAblcXrdFY1AQYGEBNze3Oq9ljNWcjd7e3ggKCoK/vz/09fWVJVVVfJ/8fNiEhIh327cX33vvPda+fXtwHAdLS0toa2sLu3btYnPnzmUaGhp1gb8oUiTf0JDWVI8eBGLCwwmcaWrS/p4zBx6dOyuv37jBOzo6iuPHj2dyuRzBwcE4c+aM4PLpp5zjpEn4xNycO+HkBNHBAZyHB7BkCToeOoTCbt2gKCzEeyUlBOYjImjdxMRA3LkTLDycaPcVFYCxMZTBwfjj6lVwHIcRI0bg6dOn7OHDh2hy+zbyEhNh+t57lEqUm0vrLj+f5uKrr6CxYgWSs7Mx5PBhpmdkRPMUGkrXffEF6dktW+qMryiKdfP8tbXrnLnW1ta4ceMGFxkZCZ7nYWNjA1NdXTrHIyJoTbRsSaltW7bQ/M+cSXuoZUtiKOzcSQCy+ryTBgfjzrhxoou7e8Nr7dEjWuepqXVTs7p1I/ZWixbkHJg8uc7LqqqqcPjwYUFHR0d0dnbmezfQ871GduygCPuBAwTAdXTobG+sUGlVFe3317HIzMyIwl5aWr9jtmtXcphMmACsW4f46roW5ubmjb/vn5Vr18gR2r8/jevVq8QSad68JtiAlSspmDF9OgVejh8nwO7nR+OzcSM5qO7eJV05bBjNr6HhmzPUtLXxrKJC5DQ1RSxdSi9q3lxdo+X+fTVzy9+fdGxqKulvgJyNw4dDMXQohMGDcXTJEoXhrVuSQfn5uDxq1AIwNnOMq2vkcR+fG1AqvxYkEmOFQnHG39/fHcADAOA4TrCyspL3WbBAYuDjA9MvvlCniryh2NnZsRs3biinT5/O16rhctvf31/Lz8+v4q3e7F8k72j87+RfKWvXrn32/vvvW1vU8lL+V0llJUWTJkxQ5zirRFW06WWAp5I1a0ihL1tGB9D+/WSUh4URoFMoKK+xuJgO+W7d6Pf/vx0jpaXAwoUo/+47bPjpJ0ycOFFdMOuvSGUlRf3v3gXc3LBp0yZlz549+Y5vEvX9J2TrVoo61OrD3ZCoijCdPXsWxcXFMDAwwKQLF2BWVkaR6waAAJYuJUD1plHl4mK6p9LS+t9PqaRcyr9SjfmvikyGsk2bkLFvH2JcXGBTUSF0XLuWg7t7/fd85QoBxbS0+td2RQXtoehocpC9ReRKoVBg8+bNYnl5OXNychImTZrE1QE96enkqHiLvH8V9bdz587o378//o4iS28kWVmkJ7S0KKK5YAGBDlXBMoWConExMWQcqopp2ttTC0cTEwLFmZk0zjo6gJkZBHt7HPrlF1g4OqLHzJn0PAYG5HR8UwO2Z08C6i+nG6hk4UIU3r4t7uzVi/UVRbTv1Anc6NG0Vu3siDG0ahU5IhhD5uDB2LNvHxQKBTp27IihGRnE/iguJgM7P5+eOz2dnKdWVvT3l0TVrSMxMRGHDx8Wvb290a1bNwalksZMRwdVVVXYtWuXyHGcMHv27FcXYHExgYD8fDK0rawoZaZLF9LZvXtThP/QIQKAFy/SeFTXEThx4oT48OFD5ufn94YT/ark5+dj586dkEqlePHiBXr27Cnq6+szWxsbmHz5JQHomTPr7A1BELBhwwZRLpcznucxb948Yhz9+CMBGE1NumeV0y40lKL7cXG0xq5frymEJ169CrZ/P0UTJRLExcXh999/x5dffgnu3DnA1BS/xcdDIpGIgzw9mX5VFdjSpXjasiWut2ghzjQ3Z+A4ckzduYOdwcFKnZAQPtHFBT179oS3tzfy9+7F/vx8yCsqxClTpzIbGxsIgoDDmzYJ3r/8wp1+7z3M3rKFoqCururBefYM6N8fpefP4/sDB7CwqAj6U6aQA2XFCtLfhw4RQDU0BJo1Q2BAgOjs4oLHjx8rmzVrpmaGxceT7pRKgWfPUJmdjUsBAYJRcjL00tK4F5qacHd3h3ZREQHYPn3oe3g4fT9zhpxQU6Y0OJchISFITk5WDho0iH/06JFQXl4uDhw4kNfW1lZ3oPD2JudwPWsaH39Mjos1a4jpUAuYx8fHo6ioCE+fPkVeXp5i0aJFDSun8+fJIRcQQDqieXMCuq9bp1evEqugmp3SqOTmqp2FDVWyv3EDQmgoDubkIMnQEL6+vjAxMYEoipBKpVAoFDUdP/6UxMbSuJ48SRF6c3PSo2vX0vMnJBCwv3yZHF6jRxOzY+dOAvQTJ1JKw507tJ7atKF90LYtOZLGjSPdm5z8xqmZcrkc69atg6OjI6ZOnfr6F9QjGSkpOLdmDbItLNAiPh5G+fm416fPH5+vWOEs09RcH9qr1/hely4N2bJ8eW6fc+fMFDzPzg8cyJbm5kJ72TJwRkb0XKWl5AxbtEjd0vclEQQB+/btU7x48QKMMUgkEmZqasrHxMRg2bJl0NTUxIEDB5CUlAQAzf38/F5OIXgn1fIusv9O/q3y3+vlksvJ675rV/1gwcGBcvkqK+vPv1q5kr6XlJCn3sqKaKKq3tcq+uyDB0SRvXOH8vV0dcloGTCADmg9vX/2OV8WXV1AIkHVli2ARIKEhIS/B+xLpUBSEoFZHx8MsbbmjwQFwczMrOHe1P+kMKb2pr9GeJ5HixYtsOjjj5G+dKnwrLQUl5o25QasXQvj+gyVuXPJwPjxx7e7J0Ego74hEPbHH+Q8Sk7+U1Xz/xbR1kbpzJkIVCjgYWGBVsXFHDIzySjq148MLH19dXEnb28CbDz/aoS/tJSiuElJROF/S4rqzp07leXl5XyrVq0wcuRI7hVa8++/kwPh8eM3er+MjAzwPI9PPvnkz7fZa0wUCnVV5717CbDPmkXG+KFDBESq841RXk6ALCaGjNdjx4imrCq29uGHNMZGRg1GvAGAA2BSUYGwyEhEnT4tTp8+nRm8TS2D5GQyFF8uVJmZSU6sr74CBg5EpKkpk4kicpOSxOK5c1lhhw5wVFGFjx4lA1pDA+KCBeDj46HQ1QXHcejfv39d/ZmQQMBBR4eit19/Ddy9i+LiYhQXF+Px48eIiYlRyuVyXsUM4nlerKysZJcuXUK3bt0o1eD994GCAlRWVoLneVZRUVH3LMrLI0bF4MEEEL76itauQkG1FKKjKXd6zBhiO02bRut61y4gLAz5Xbrg2datSOrWjbXs0EGoHur6ZdAgAt8nTtT774sXL4oVFRXs888/V/1JvZB//pn2lqUl1Q6oXuMcx2HOnDns4cOHiI6OFn7++WfOqqQE0zZtQrGFBaS3bkG3dgpCt240tocOUSRaVcm7Z08we3tyANjaAosXw3n+fEAU8csvvwjz58/n8OQJeh0+jP1du7LiTz9Fwo4dYsd9+5jO3Ll4LyiInDKLFqH44UOUd+oERxcX3uvMGZw8eVJ57do1vqqqSghNS+Nck5Mx5vJlxpYvp2fIysKE0FBOOHcO2b//TilwtRgjAAhwPXmCzHPn4Pvrr9CPiaH1sWYN/f/qVQBA7qBByNHXR3j37piybx87OWIEbLOzJZ6PH9Nce3urKdIyGeDlBamXFwa3bs2hdWuUODpi79WrovaoUcy99nl//Dg5qDdvJqdlWBgBxAMH6k3Lk8lkePbsGb9nzx5YWFhwWVlZ8PT0JEB75Qq9NiOjfqAPkA49e5YisadOkTOjmo3Qorodq7a2Nq5cuVL/6wWBWCljxpBzQleXzrmIiDdjTPXooW6V+ToxMyN9FBHRMJDs1g3BN24IXS5c4LR79xbOnTvH5dTq0CCVSjF+/Hi8dcemsjJas/37k5Pp9m1iSc2YQb/v309npbs7OehU9thnn9E6nzuX3kMioTlRFessKiLmpoEB6b4zZygXf+xYmvvduxsvsAuqzG9jYyOkpaVx4eHh6Nq169s9GwAre3t8uHMnsrKykJ2djVOnTgGVlf0higbr/f0/AjAsc9gwDOjRw6KZszOM9PTgoWLG5OfTM3/yCTkzHzwgx1Xv3uQke6m98fbt25VyuZzv27cvEwQBcrkcT58+VYqiWOMc7dixo5iWliYAuAHA6tU7fifAO7D/Tv6F4u/v30pTU9PU9C1yb/+fkpkzCcirKPovS4sWlGf4008E4huKAOrrk2EPkLFQXEwg39lZTecbNIgM+KIiMqL/+IOMPHNz8s5qaVFLG2dniii9bd7e28qaNdCLjITBxYt/rm1aQ6LKj/f2RsvOneESEiLGfPutaPPzz//wA9UjHTuSQ+dN5ckTwMgI1rdvc9bffYfEhATlTydO8K0TEsQRI0awmgiwUkmOmj/jpCkrI2OvIfHxqT+P8P9Yrl27JhgYGLCeU6eyGmq0iwsVQTp8mAD8hAlkCLq7EyhcuFBtPAGUr+rnR2v7l1/e+h7y8/ORm5vLSyQSjG+IOfHJJ/T1hhIXF4cmTZr8eaCvVBIw6NiRxuDRIwKRrVvTfTg70/wWF1PEsrKSwNv48cQUGjWKoo/p6fT7ixcEkv38CCi2a0dOwVOn6G+tWhGI27mT9MKTJ2R4h4fT50ZEAA4O6NevH3r37o01a9awp0+fonPnzm/+TEeP0n2qGDC7d1NO8alTZGh7egK6ukgNCcHIK1fE6CVL8KubGwxCQgRHJyeu5jXu7sC4cYiaMAEZ69fD1cpKKPHyYhKJpK6HxtKSAMv166SDr1/Hxf37hbbffceF9eqFPG9vsV+/fnyTJk1gbm4OjuMgl8uZXC5HTZrRgAE1tNV79+4hKysLkydPpg2am0v61diY5qtVK3WNFVdX9c+tW1O00MKCHDCqQmQhIQCAR9u2CVqMceYWFooJX34pqWF5PXlClO7aOnrPHqJHNyAGBgYNe+44js6gQYNorQwZUvt18Pb2hnePHpysTRvwKSlInTIFgU5OUPzwA+zs7IQxY8ZweipdpKFBDuoTJ2gMAHJCTJtGP1+5QkD4yy8xOyAAB6ZM4SpzcyGVSGBdXIwVS5ageP9+BKemCk6xsXy5mxuKkpMZr68vhuTkiKm7dnHWAwag+6xZ0NbWRq9evfi4uDg8evRInDp1KhysrNQdSpRK0mcrVkBo3hxMqYQiPBySegrTAQDXrBnyTE1heeuWuuK7hwexpzgOR5YtEzp16sRNdXcHt3kzRgoCyjMyoFFYSG8wdixFyp2cSEc5OJDDzN0dyM6GVsuWKLp5k8kqajGUHz2itfjZZ+oK/J06kZPywQMkGRoiv/r9pVIpJBIJCgsLYWFhoVSxSL755hucW78ePW7eFE0vXGCGmZkNV7kHaH63baN1Gh5OoK26ho5KFApF/fUalEra8zExRBE3NCRQGx5O9PE3kSFDyFE2YcKbXX/gANHfe/as1wF97tw55WOe57qvXw+Nhw+5sIcPBbvhw1lVVZVQ3c6U+/333zFx4kTY29sjMzMTOjo60NfXb7g+QXQ07fGjR8lZt2ULsRFOnSI9qEqnvHiRxkDFPgAo0DJ8OOmz0aOJ+XLmDAHg7GwKsEillAaTk0O6a8wYsvM2biTHkYcH6ana3XZqCWMMM2bM4M6dO6e8cOECn5mZKY4ePbrOhbm5udDV1X1ttf5mzZqhWbNmBPYBVXGjLACYNHs2MbXatlW/QOUE6tSJHMmiSPeamkqsEkGguVq4EBg7FuHh4SgrK+MWLlzINGvVu0hNTeVsbGwEVWcUV1dXdu/ePZaYmGjp7+/vCeDeOzr/q/IO7L+Tf51IpdIlnTp1kvxX5usfOUKULkfHxiOompp0EFtavtnhqKLBiiIZcDY2dLhWVJCSTksjxa1qE6ZQ0N8eP6YDqTr3EQUFdG/Dh9Nn/0VALpfLUV5eDi0tLWzYsAEmJiaC+/nzrG9ODnP9s+3TGpPqvvM9Tpxg8qAgVn0Tf76P/J+RhAQCBK+rDKxq/zV7Ns1Zdf79lK5d+dzcXAQGBmLNmjVwd3cXRwYHMzx+TIfqn5HLlwlI1VfhGCDDX0eH/n/27J/7jL8oL168wJMnTzhfX9+6hoqDA30BZIBVVdGYpaSQcd2mjToPNCODoqaurvUWompM0tLScOTIEaG0tJQDgIXVa6le6duXANhLudL1SV5eHu7evYtG00oEgcBSx460do4cISPQ25sA3vz5FEF5+pScdKoI1urV9BobG3LoABQ9ys8nYH/qFLV3GjyY9n9yMo1RWRmxHp4/p5/19cnYVFFDY2IIiAsCfbaeHv0/LY3+//77BJjHjIGkVSuYbtwoYv16EVZWHLZsIYN45EgykOXyV2mqokjApE8fooCPHk2gx8eH9uqXX1ZfJsJcU1NsCrCqqirR2NRUHDFihNpSLyoinQXA3M0Nt1q2xMz8fE7SqlX9jsuRI8mJsHo1kJ8PuwULuKQWLTDc1xda168ztmULOYiqjVOdl9sX7tlDbCwnJxgbG0MikaCpVErzMm8epRb8+GPd9JrjxymaV7sDhLs7UX/nzSOnQK2iZtZ9+3IBOTn4culSCaZMoTmKjSUa9ldfqVt4LVtGOqORbgBGRkbQ1NQU8XKhOJXwPK01VQSyNsuishLYvBnaGRnA3Llw3LABywUBOTk5+P3337Fp0ybY29uLPM+LjDFM3rqVQ2kpAcpZs+qOf3UxymRdXVzPz8fQwYMhbd+emB1HjgAnTkCxeTNybt3iM/74A5Xt2glWAQGcnr8/m/rLL0zQ1cWL1FQYbt8OJCTAuG9fjA8MRKsDB3iEhVEUfuRI0hMlJRRl9PCABEBLW1th3eTJ3MD4eLFjx46Mlk0RAgMDFQCYVCrlK8eMEd0jIhguXCDHV7t2tA6uXIHh6NFiQUFBHUedloqRdv8+RbVlMtpbBQX0PF26kB5auxY5N26gT3AwnFW59rm5xOgYNapO9wJoaQGnTiF60SJU3riByA8+EMAYlEollEolq6ioYO618vXHjx0LWXg4lBcvoqS4GIavKfgIxmjfnj5Nuis5mSj5AwfWXKJUKsFxXN21IpORXvDxodQyjiPw+tNPtP/eVFq0eCN9WSM6OlDcuYMnGzbAYd68muBAeXk5KisrkZSUxNvY2EB36FDoGhjAYetWDrq6QI8eNYZhWVmZcPDgQY4xBsYYBEGAgYGBOH/+/LrtdI8epec5e5YYID//TDbS9u3EhHr0iM6crVspGt+pEznJqotRAiBdp9JztrbqekJKJT17cDA5wqKjgW+/Jafq4cM07z//TGN56xatpw8/JOfswYPq9qC1ZNCgQby2tjauX7/O2rRpU8PM2Lp1q/D8+XNOQ0MDdnZ2YqdOnVh9hQufP3+OoKAgAFDR60O++eabFTzPx3EcF3f8+PEWY8eOfZXRVltu3KBzZ8IESnPYupV0k64u0v384LRtGwyDgqBZ6z3y8/ORkJDAZs2axWq/t7OzM1fdjvYWY6zU399/EoDLABQAeD8/P1nDN/LvkHdg/538q8Tf399MIpFM6dKly3/f2v/1V6JqBgW9HnxyHB2s5eVk7JmYNHipKIrqarqMqQ/v06fptbm5ZOBGRRFI0NQkI8PRkb5UtL+iIjJaz5yhey0vJwBYVkaGSevWlF/4FjTvH374QZTJZDUvaNu2LVdmba3skJ7O81lZ9R5kf4eUz5mDg3p6WBETA9axo5rF8H8hRkYERBuTEyfosA8JoUPzpf7KZmZm+Pjjj1l2ZiYObt7M5IsWQaviLzi7Bw2qa1jWJ3Z26l7SjUWI/iHR09NDq1athD179nDTp0+HqmvCK/cIkPPi+XMywm7dItbKsGFEw506lSKKiYk0F02bvtGa3bNnDwBwH3zwAQwMDKDXGIPC0rJOVeOXRRRFyGQyaGlpITAwULS3t0cfiYTh+XMCzatWkYNn7Fga6wMHaI6uXaM5UBnRq1bRM1tZUdQPICqpry/9PHLkqx9+5w4Zpt26kWFZUkLOQKtqhqSxMQHFFi3oc5KS6G9yOe318nJqBXb+PBlzKprq5Mnqwl5PntD3qipkBgSgOC4OTTt3ZjXG7sGDBJhiY8kRI5OpAeXPPxOoTEggh4ShIYGAl9shXrwIua8vHs+ZA+tdu/B+hw6vTuJ339VEto2MjJBnYgKhXz/6XEtLtZNIJb16UT7tli3A48ew2bkTOsOHo8rZGdq2tpSe8ewZgdBvvqkb2QJIp0okQJcucHF0xD0AYrduNC7nztWfdrV1a/1FydzdgQcPICQnI/2jj3CvTx88efKkxtGVlpZGe0ClG1QFuH79lXT5gwf0uS1bEmugb186N3R1ScdbW6M8NxeVFRWNL34DA4oyfvABgZNu3Wi+jI1J/1+6VNP1guM4NGvWDIsXL+Z+++03JCYmMl1dXVZaWgr///wHdmZmwtSffuJ4ExNa2y+JrYMDNPr1w+Hr1+G1caPQr2tXDqtXAz/+CGMAyzp2RIW9PbQOHeIqL12CFmNgAPjSUhja2dF5YW4O7sEDNE9MpLFVsQqUSlpPCkWdLh3jd+7kCjw88Mv58ywpKUl0dXVlQUFBor29PWdpacnpfvml2Co9neHJE3K6qIpAWlgAHAcjIyNWqIriAzXV3zmOA777DoKBAdK6d4fFZ59Bq6wMOHcOoiiiZOJECOPHQ/78OZqWlICTyZDy22+w+ugjZCxZgiJ9fVSGh0MpilAqlRAEAUqlEnEWFhghlWLe8OEcrK1f1l0EZHftQvPVq4HkZKzLyoJtWBiSU1MbnWZjY2O09vJS56I3aUJFRvX1AS8vVFVVoby8vO6LSkooctu0KTnkOI4o5++9R7qjsYJ8AIKDg5GVlQV9hQLt2reHQ5s2AKj4ZG5uLqwbOf9LXrzAYUtL8M+e4dT332PkyJEICQlR5ufn8zzPg+M4uLq6Un0NVbAjLY0cP9XU/1GjRnEjRoxAXl4eDAwMIJVKsWbNGpaXl0cF/Y4fpzNDS4t0aXQ05aE3a0bA38KCHDddupA+ePSIHJ9jxhDTqrr7AgDSYSr99/XXdB9KJTnUUlPJhispIef0p5/SZwwfTvqrc2eyDX/7jfTw8uVkt125Qp/Tpw/prWphjMHe3h7Xr1/Hw4cPceHCBWVBQQHPcRw3ffp0cByHs2fPst9//x3a2tpi3759ma6uLtLS0qCvr4+bN28Kenp6HMdxoiiKrKqqygeADwAolcrusbGx52/evNmkR48e9VMggoKIUebuTuzTnBxg82bkjBmDw/fuKa2jovjOS5fCtV07BlNTYrDMno2SlBRIJBKYvGTPenh4oE2bNpBKpTh79qzm/fv3TzHGlKIo8hzHVa5Zs+aLlStXbmh0sf2Py7sCfe/kXyXffvvtr23atJk8dOhQzddf/f+QXLigbtHyNgB39mwCB9W96F8WmUyGn376CTKZDM7OzsoxY8bwL1e2B6A+dObMIWB0/Dgp4K++qrfvLgCK7GRnEyCNjCSj/PlzMq46dqQDysGhwT7RCoUC3333nejr68sMDAxQXl6ubi136xYdaKoidH+zKBQKbNiwQfT19WUWRUUUWRo0iICLqpr/PyUZGWQgT5/+6v8ePCAQynHEpngNa0NYuRIF27bh6vbtGFuP8fzG8v33NE8fftj4deXlRM3s0+fPf9afkLCwMNHW1pbl5+fj+PHjb94zWRBqCjNCFYXt1ImM0cREMkKsrQkAeHsTu8HZmcb98WNyBjRrBjCGLWfPwsrQUPSZO5cMlIaonnI5gamXHQg3b9awYTL79RN3Dx/OhoSEiGbp6TBPSGASOzuKeHfoQMbe8eME4Jo0Ubcl/Cty7x5V9/7lF3IMDBtGn+HoSMamhwdFkz/+mMakpISA/pEjBLxVTj+5nBxR/v4UvS8qIgdgcfEruuLKlSsIDw9H7969BU9Pz/oHTBBoLG/eJB3UtSulEVVW0r2OG/dqgcXqaH3Mf/4jnLG0ZNOnT2f1VttetIjmODAQpaWl2LJlC1asWAFuxw6a3zVryAB/+pTW/qVLpL8qKwm0cxy2/vijcuq2bbz+ypXEMigtpTzqPn0I+JmaEqW79nyfPw98/TVCvL3xpLQUc77/vn5qcHY2AXRT0wbTsaoeP0Zxz57Y6+uLMl1dGBkZKadNm8YbNKSXa49r586kl8ePp88oLFT3g7e1RZogoDIiAk5yOa15Q0Maj+Rkeq2NDc2NtjalduzcSU4a1Xzv30/7pQFJTk7G/v374eLiIvA8z0VHR8OQ5zGwe3e0lDNC7rYAACAASURBVMuBgQNRUlKCsrIySKVSxMfHizdu3GC6urpQxMRgso0NjP74g9auUkngqlMnlD59CllhIWT9+sHs/n1IIyLAff45sV5u3kQFqDf8l9UMEEyaBPGzz5AqlaLsyhXoDx8OG1Wx0YgIlJuZIb2qCoGBgdDU1BRFUWQTXV1hzxixgHR0SA9cu0Zr//59OncVCmT6+mJf27aito6OUFlZyaqqqjiO48QhxsbsvkKhSMvNlQiMQaOyEmNHj8bRc+dQJYqoBqRitY3OOMbEnidPMuu8PPHkokXihHXruDxbW/H61KmC59Gj7I6Pj8ikUnAcBy8vL77F5s2kZzZtUq+98nIaKzc3AqY9eiA4OBjp6emv0O9rYwOZTIaKigpxyZIlDMuWkTOjZ096Ti0twMgIPx4+LBYUFDCO4+Dt7Y2erq60ZsaPJ6YGQADUzIz23Guq38vlcqxfvx6enp4wPXZMMAwO5kK++ELw8fHhdu7cKZSUlHCmpqZKLy8v3tnZGVpaWigoKMChQ4eUBgYG4ujRoyXfrVuHFYGBiFu1Cheys5UKhYL179+fs7W1RXR0tBAWFsbZ2Nhg3LhxRDsPCSFH3qpV9dZDio+PR0BAAL6cPBmctTXt8YkTCXSPHk37Z/t2Son66ivS5126kCPNy4vO9rNnyeEWEaFOHQPIgdq6tZodNWgQFUNVpcecOkU2QUEBzWdaGjnEZs+ms2nGDBrnwYPVTpS9e0nHTZ5MDr3CwpraOzKZDIGBgUJeXh7n7OwsOjg4MFNTU1hZqdPelUolLl26JNy7d4+rrKyEvr6+qKGhIZaUlHCjRo2Ci4sLTp8+XXXv3r0ao5ExVsrz/NcKhWKdl5eXsk+fPnwdFm1KCjmMPv8clZs24b6ODgry8+E1ZQrCvb1hOnmy2P6zzxg7dIjOnOxsel1wMMQFC/Dd0qXiTCcn1nTw4Abbj6qc5QUFBWCMYf/+/ZWMsaOCIBRKJBLPqqoqTYVCMdjPz69xD9f/kLwD++/kXyP+/v6eWlpalz/66CPtv1Rl9f9aYmKIAqeKdtUjoigiIyMDMpkM5ubm6l7epaUEZrp2rdPnVyUPHz7ElStXlLNnz+Z37NghODg4iCNGjHh9fkNBAR0eFy+SsV9aSofV6+T5c1L2R49SFNDIiA5AXV2qJGxlBZibQ6FUYk11oaOVK1fWX3n844/poKtdIflvlHXr1ok+Pj6spkDPrFkEYJKTyTCupr797ZKQQCCDIsUklZUE7nv3JkPi228bf4/KSqpy3b077l25gksPHwqfffZZHTShUCggk8kaj0CrZOFCmht1oa76JT2dDJbU1AadQFlZWcjKyoKlpSUa64ZRUFCApKQkWFhYNBrBqaqqwjfffFPzO8dxaiO+IVG1hbt2jaLcSUn0jM+fEzC7eJEMeF1dMr4YI1ATF0cGk5UVOV6USuD5c1RkZuJkRga8HzyAhVxORlp4OEVjKisJJPXrRwbeb7+R0ZuQQD9v20Y/t2tHxvGsWUjp10+MmDePuRgawtXJCZLG2mX9VcnKIqYDQEAvKoqiUyqjr29fAgiFhWRc3rpFwHP+fIqA+fjQHr5x49Vieenp9OXmRmMRGUl6iOdRbGqKLVu2YOzYsa+0fmtUrl0jML1/P+kAKyuaM5Xhd/Ik0Y3z8qAAaozVmTNnwszMrO573b1LuqxfPxQWFuKHH36Ar68vbG1sKA/WyoqcCZGRtB+3b6fXVVWRIRoRgZDQUDw9exazs7Jo39ZmlBw4QNG88ePpns+fJ0fn6tVAeTn2JCeL2dnZWLZsGasX7M+YQfto8+ZGh6S4sBCX5s2DfXw8zgwdCl09PWXz5s3ZyJEjG685cuMGReVVucIvyYEDB5ScUslN7t+f4cULAvAKBa1tgPR3WhqtCQ0NWiOhobRf4uKIim5pSXvozh1ypNXSN+Xl5diwYQNUTJzCwkIcO3YM4q1bmHzsGHKuXsXvQUEQRRGVlZVo0qSJ0OPFC65zSgri4uOFPImE8751C0zVwSQ4mO5l1iwk3b4Nq99+wy0vL7Q7fhxN9fSo3gLHQXHyJNYZGGDl8uWApiYqJk5EkK2t8MTQkDXPzhZRVMT57N0LduMGwg4cEIOtrZmGhgakUqnwySefcEeOHIHnxo2idZs2DDt3qgfsu+/obBg4kBwgx49DuW4dor7/HkkvXiAxMRFz5szB6d27MXzlSuybNg0a7dqhe/fuOHLkCFyjo9Hz+nUgKgomJiZ1HEC5hw8j6sAB9A0MJL0kCDQXubm0R2NjyQkoirR2ExNp3fj5qVvQffUVgUZVnvgbSmxsLIKCgsTFixcznD5N868qRPjVV0BkJE7PmqW49+iRRCKRoMnz5+jm5gaPigrKzec4uldDQ9oDDTjMq9MjlHZ2dpylpSU7d+4cVqxYAWRnoyw1FYFRUUJWVhbn5OSkHDBgAP/48WPxypUrDAAMDAzE0tJS5uzsLKSkpHAymQwcx2Gljg4qjI2xPiUFPM/Dx8cHKmp6VlYWjhw5IhYWFrKatrX371MQoVu3V7p8nDtzBsWRkeLEbdsYjh8nxuPGjbTmbG1J77RrR3OwcCHppEWLiOIfF0fMne3b6dyQSus68EJCyBGi0oXnztHrPT3V1yQn0+cIAu233FzSXxs20NfmzcQsWrCA9hpA16psyPx80vUpKeSoeDnNqAGRy+WQyWRo2gC4PnPmDO7evYuJEyciMDAQjLF0URQ3aGhozDYyMnLo3r27dsuWLaEhkwHTp0NwcsJ9DQ0E6+qKmlpa0NbWFts2bw6vMWM4duYMOUjkchr/X38lXQsApaXY/ttvyuGrVvHc0KF4MXIk7J8+hWThwkbZhMXFxYiLi0NVVRU4jkNwcDAAbPbz81vyRgPwPyD/fVTmd/JO/oT4+/tLpVLpwSFDhvz3AX1VH/N6cqdUEhYWJly7do1pamoKMpmM19XVFUeMGMHs7e3JIGvbloz4l2iimZmZgpmZGd+kSRP4+vpyP//8s2htbV2Tm9igGBlRlA8gw66ykqIGXl5EVW2oWJuJCX2pDiK5nIDhzZvAH38gLzkZLxQKVGVkiB3t7FibOXMgUSjqj2pt3kxGw969jUaP3lSKi4tx8eJF5OfnC3p6ekz1txrZsYO+L11Kh3lgoJrx8HeKsXHdgysnhwqOzZhBYPFNPm//fjLGkpJg1a4d5BER3Jo1a+Di4iK0a9eO09TUxLFjx8SCggKmra2NcePGwc7OruZQr6qqwuPHj6GlpQUPDw9IVqx4s4rJ1tZk1FQ7m9LT01FQUABnZ2dIJBKEh4cLISEhnKGhobKgoIBfsmTJK84GQRCwe/duITs7mzM0NFQWFxfzenp6oo6OjtilSxfWunVrBlAu+/3793Hv3j0YGRkpJ02axMtfV9hQFSXu3p2A3KFDlH86fjzRw69dozXcty+ty6AgAulz5pABX7t6cS2Kdsz9+3hy6hSeuLnBz8+P1seLFwQKMzIIBOnqkoMrJYX2oSiSg6t/f3qTagM8PT0dB0ePZqM8PeH6DzmyasaipIQiUm5u6vX93nt0zyqw/+mn6ujKZ5/RdYzR+n/vPaKv2tkRuFiwgHrXq4o5WlurmUhZWWTsL1kC3LoFvfBwDLl7V7jJ86xFixZ1+443JBUVFM3/6Sd1Bfrt22lvrF5NgGfUKALxEgkkALp06cJFRERAqVS++n5Nm6IqMRHyFy+wvRrIa6pYF0uWkCPo4EGip6uAPkD709YWJRkZuH79Opra2tJ+8/QkI1+VJqGK0iUkEC23qorAT+fO+HX3bjE9PZ3Z2tqKYWFhKC8vR05ODoYOHQpDQ0NkZWXhkUIhKFxc2JCGcuar5eLly3hmYYGBOTlwnT0b0Wlp/Lnz5xEbGyu2b9+eubi4wLa+tpguLgQONm9+BeyXlpYiKSmJnzJlirqAqUpeduoAdL54ehKAMDGhKKSeHq3vlBSqxK6Krh89Cjx9iiazZsFdLociPR148ABN+/TB1ClTcNrQEL80awb9H39Eu27dxIE+Pky4cQOcry+HceMAxuBaVcXdMDISb8+fL3ZZtYqDhgZFOLOyABsb2HbsCMXBgyLz9haaTpzIY8ECqimSlgYWHQ1DV1eIhoYI+vRTRLZqBXt7e/bp5MmMLVnCYh49EqOiouBy5QrTj4hgw+bORQdKY+IwZw7aZ2bi4ocfCr4ffFBXGWtrE0AxN6+ptM6npKBdRgakyclIT09X6OnqSsaOH48MV1d0NTJCenq64siRIxIA0Bg5EsbduoF/Od0tOBg6P/+Mp927i311dVVtD0iPqHQuQAC1qoqAdatWlLfdvz852Lp3py4PjXTIaEgEQVBH/ocNo7Xu64tyS0vc9PQUmgGMz8hgAKBZWIhpJ04gIjUVhydMEMdxHEN+Pu39+PhXUs5qy/bt20UHBwcWFxeHO3fuwNvbWwmAx8KF0PnmG0yfPp3Ly8uDpaUlDwA9evRgVlZWCAgIQHFxMWvatKk4btw4TiaT4cKFC2JGRga4efOY9gcfwHfOHBi7udWpo9GsWTN89NFHbPXq1UhOTha9vb0Z2rcn3b10Ka1TlS2zfz8GrFyJtb6+7Mr33wuebm6c9vr1pI9ULfW0tKiORkUFnSe2tsQQGjeO5mDZMnKOXL1KjrbaEhJCc6kC+wMG0Hlz8qR6/zk40N/19SnIYmZG4yqRkH764gs6R377jUC9ry9d07o1OR3lcjrbHj8mvT5rFumy1xSr1tLSarQ47NChQzG0up7P3LlzcfnyZaOEhIQtVVVVLDc3d+/x48eHWZiZ6b7furWmhpcXEo8fF5Pd3TFx1ixWnW7Hql9MTInMTBrLGTPo2UJDyQGgq4vOHh78jrlzAVGE7R9/QPf8eRxjTBgbFsYZTJ0KzXq6SRgYGMCj2mFQXFysAvuL/f399/n5+T1s9OH/R+Qd2H8n/wqRSCSfW1pamr9VBOn/b6moIG/sRx/Vze2qRx49egRvb2/WrVs3XhAEhISEsICAAIwZMwYt3dzIkCsoqMkTLikpwd27d4X79+9zA6tz9A0MDODj48MOHz6MqqoqsWvXrm+WXD9vHn2XySjSZ2xM+ZulpQSmVACrHolLTcXt27fh2bMnnD74AD+vWgUtmQyu1tbiQFFkGlevUhEaxujQ6t+fgL2xMb3nyJFEHd3w19KxwsPDceHCBTg7OyudnJz43NxcZUVFBd+sPsNEVYF43z5KJcjM/Euf/YoYGFB0ISeHIperV5OR1q/f67sdlJZSga/lyynKwBjMzc2xfPly5ObmYteuXVxUVBQAwM3NDbNnz0ZQUBD27dtX79tpaWmJOTk5wqi1a3l8+WXDBfpqi4YGAcWnT/Hrr7/W/JnneWhpacHHxwc6Ojr8gQMH6hTSy83NRV5eHiIiIoTi4mLxs88+g1Qq5UtLSxEZGcmePXvGjh49ivv37wvPnj3jxOpcVVEUMXPmTN74NfmfyM+nnNrQUCp0VJ1TC46jyOann1KOo7U1jbWdHYH8mTMpYrdgAe2j5s0JVNQyxs+fPy8CYP3796diZoyp8y+NjSn6uXgxGVU//khGzcGDFPWubvelkqLqfPt/FOg/f05GlasrGYVNmhBIWbFCXTUZIIP044/VYKJjRwJ106bROvX1paj32bOUk5qcTMZ8Tg5eSWVQOYu2bAFEEVxFBTomJnIPXVyEpDVr0EomY1i/vvH7VlXPVzkQeJ70j4cHUVXT02mN1mqXJZFIoKGhgQcPHkC1nxMTE3Ht2jWlc2Qkmp86xe948gRaWlqin5+felI5jkCwqysB1JcBbkAArlWnR82bN4+M0sOH6evWLfX1CgWBwC5dyOg2MAAYg4eHB9PV1RVevHiBS5cuMR0dHZSVleHHH3+Erq6u4HLlCqdnbMwuZWWxx+vXiwMGDKhTYE09lc/x+PFjwNAQWhcvgl+7Fp0jItB0/Xpk5uaKERERLDw8HAYGBujVqxecnZ3V+87AgOonHD5M814r/SYiIgI8zyMpKQnNmzdvfF527iQnz9dfUyTz2TOij1tZ0VyFhZEDhueJljxpEiCVIkdXFxVVVXh09KjoFBbGYGoK6Zo1GBMTA+HxY6BlS3AHDzKsXQuub18CXgsWkOPsq69QznEsvVMn1iUnhxx18+cTO0Yuh/zWLUR0787uNGnCt3dygkJfH1r79kEnIABKT08oiopQoaeH0thYWPTti6lTpxK74ocfEH/sGIs6fhwP7e3FZxMmYEWHDgzFxRAqKhDHcbjdrBl69elTF+jn5lKah6qwZ79+NA6PHgF9+sBy1ChUubgw+PtDeu4cHG7fhgOAy5cv19jhDxMS4NyvH1ynTKHzRSIhR1FeHvI++ADy17Vk7dNH7YTv14+clSYmdF+RkaR3jh6l/aGqA/EG9Uiqwb4IgCkUCmSNG4eCTz8VTrRrxxkZGbFsW1s2ws+P954+HbcEAZeHDxf7rVrF9u/fj23btiln79zJs44dgR07IAgCdu7cqaysrET//v355s2bQyKRoKioCJWVlWz48OEMIDZBmzZteAgCAVgjI2hoaMDyJceTo6MjfHx8kJGRgdDQUHb9+nV4e3tj5MiR6gczMYHtmTPqCPFLoqOjI7Zq1Up9vbc3jdPKleSwatkS6NUL/IEDmGxvj6hlyxg3YwbKjY2R8scfaNmpE3Jzc7F761b0AISuXl6cNCqKxn3iRNIjCxfSWPv41F8rpaKibsFCnidbJyREXesEIOeCoaHarlKxBfLzSb98/z0x/3bsoH3ZsqW6touqPkVsLOl9Ly8K0uTkkH1obAxRFCEIAnieh1wuR2RkJNq2bQt9ff36Oy3UkiNHjihjYmL4tm3bavfv35+dP38eoii+L4oiWgUGZueXlZklzJnDlRkasiEbN0L75RTOb74h9tD06bT+Fy+m5+zYkWzLzZtravFMf/996OrqonzlSrRJSuKeHzwoPNm4kXNMS4PN5s2kb+phF8bHx9caYv7Tb7/91rCysnKxn59f/CsX/w8Jv+pPePneyTv5bxJ/f397nucDp06d2uS/JqpfUkIR8iVL1JG/RiQ5OVl4/vw52rZtyxhjcHR0hCAICA8PFzy6dGF47z0yii0skFBRgd27d6OsrEwYOHAg17ZWhNLIyAh2dnY4deoUysrKRCMjI6alpfVaJQ+ADpKBA+l78+Z0UDVtSkb/mDF00EmlNQAgMTERAQEBKCwsRFRUFEpKSpCdnY0PFixAx7FjGT9oECn43r3JwI+PJ4PlwQM6CM6coUgGQPmRfyG6HxERAalUKkybNo13dHREmzZtuJ49ezbe3q9tWzIKLCzoud9778+1tXtZlEqiDDs4UHR5zBgCHG8yB2FhZHTPnl2HDcHzPPT19eHm5oYOHTrgyZMnMDQ0hJ2dHXN3d4e3tzc6d+4MFxcXWFtbw9zcHNra2igoKADHcejQogVD//5v9nza2rR+O3ZEYUWF0tjYmJs8eTI8PDzQp08fZmJigoKCAty/fx9lZWVKZ2dnDgAOHz4shoWFMTMzM+br68upIr1SqRQODg5wd3dHUVERYmNjmSAImDRpEnr27AmFQgF3d/eG1+hPP1ExoPnzyWjo2pWegzECDoMHE0AcP54YJ25udZ9TxQQYPZoiPhcvEujU0CCjmedx8+ZN9OjRg3l6etalY6siPDduUI5zaCiBXY4jI/76dXIg1Lr30NBQ5fPnz7keL1FI/xYpLianhokJ7ZfFi+k5GCOgPGBAXQNp1y4Cgar0DY4jA7iqiubZxYWo04sWEdBXteKbNo0irIMG1X8fjAESCdj8+dCxtWVP/viDGcpkot6IEQytW6tbSNWe05wcAjKjRtVlmaSlkXEbEEBj+vw5AczqOTxw4IBQXFzM3NzcYFMdpbt69aoyMTGRT5VKuRg3N2ibmGDQoEGsDs2/pIQYVWvWkDFtaVm3evaUKXCIjsbNZs3QunVrihgaGak7OchkpCNGjKipTA9BIPDXtSvMzc3RunVr1qFDB9arVy94eXmhV69e0NLSUiqVSrgdOcIVOjig+7x57N69eyw2NhYymaymcjZAQH/btm0QRRErVqygdCcXFyArC0be3rBr3px18/aGlZUVUlJSxLi4OBYeHi62aNGC6erqEqAYO5aYHKtWEQivZhUFBgZCqVTi2bNn8PDwQL21XESRQNHs2cT0mDSJIvszZqjz9V1dCYB4elJEs107cgIwhodNm+L2ixfgHRxYh59/BrO1pfvx8QHT0wOrqqL11akTjd8HHxDQP38e0NPDc29vaG7fjgIbG8F0wQLGSySkiyMiIP34YzRzdMQ1S0vc1tPDvfR0JMTGws3GBpqBgbB/8ACXBg4Usp2cxKlHjzItc/Ma9lzL4cNRJpUKwzZv5toFBDBtLS3AzQ0Pw8KEy507Y/Ds2aqIpFpu36ZxrNWCsGZfxcfjxQ8/4G5kJOs+dCjDiBE1jvfU1FSxqqqKaWlpoaKiAm7t2sF09271ub9iBa4kJyuDJBJOR0cHHh4etCkiImi9KxQEIE1NifK8cSPpusREcuAtW0YgctAgcr7/9pu6/k/HjnS+PnpEc9NAEdZqFhWLjIwUr127xp6VlyudCwv5PitWoOd77zH3Dh2g1bYtpP7+sBwzBqcEgfXu3Rvt3d3Z7fPnuZsODuj67bdgGhrYs2ePsrKyktPR0WGhoaEsMjJSlMvlLDg4WNmmTRvRxcWFk0gksLCwoDSGwkKa90aKDJuYmMDBwQEWFhY4c+YM0tPTUduugbs76SsLi3rp3nl5ecL9+/fRqVMnVpNffuMG7d9Vq4jNMHAgwBgMd++GdWYme+jiIl4dNUqISk8XQ0NDOdMvvoBXeDiuDhsm2G/ZwrSePmW8uTmdHbNnkyPm2DGyX0aPfvUhLC1p79Z+zgEDaI7d3NS6sGlTdTTe21vNPGjShPbbixeku8aNI10eEkLsgG7diF3AcRQ46dKFmAeffkpr4KuvgO7dcerOHfHw0aNMJpOJFy5cQGxsLAsPD0d2djYcHR1r9IBSqUR8fDzkcjmioqKEiIgIISYmhgeAnJwclpCQAKlUCisrK6UiPZ2zefZMN7hHj8rOJ05IYq2t0WbUKLzSEUvV1Wf1ahonLy8KdEycCGhrQxgzBg8uXED/q1fR7OxZ6Lq5wWDSJNiZm8PUxoaZxMQgMjsbThERYLq6ND4viUQiQWlpqdzGxkYhCEK74uLilgA29OrVq6TBBfY/IO/A/jv5n5ewsLDNHh4e7VxdXf97eu1NmkQR4+ro7OvEwMCACw0NRffu3Wsutra2xuXLl5mbmxtFcq5fBwoLcVdLSwTAPvzwQ860HvqWgYEBnJycWFhYmHDz5k3uwYMHYuvWrevQbEVRRHl5OTiOq7+wlJkZGRTa2gSmXF2B99+H4rvvsKW0VJm2dy+upaQwEcCSJUsQFhaGoqIi9OnTR3Bxcan7wBoadMB160YGUM+eBAZMTcnguX2b8p4jIykC2bQpGbGv6RNbW86fPy9YWlqKLi4urwmd1xLGyGgtL1cXz7l1i+7rz1ajVyopAvDoERkZEyfWW2vhFcnNpWuXLKHXN8AA0NHRga7u/8fed4dFdW5fr/ecgaFJFxDpShFFEVQUFUuwoWKvwd5iNMYkappXo4lJLNGrxsReoiB2RcWuAQsQFamCgCgYmlKHNgxzzvn+2Ayggpp7f8/v++79sp/HR8XxzDnnbXutvfbeBnB3d8etW7fw+++/s5SUFLFbt25MW1sbRkZGMDMzQ3BwMIqKijB8+HDWs2dPJl67hpKuXRGfmIiSkhI8ffoURkZGkDd3b76+wC+/IMPCQqpVqzlvb++XZIAmJibIzs7Gw4cPOQsLC5iYmODcuXOM53ksXLjwdSegzuLi4oTi4mJOJpPhvffeg5GREVxdXV8H+pJEJImJCc09R0eKejSOUJaVETidP58AxE8/UV5xamrTbQ9lMrpO//40/7ZtA7ZsQbmZGWIfPWIunTu/XFsgPp6ijXfvksNdXk6RPo1c2t2dnD5N+7ARI1BSUoKwsDBu4sSJeKtS4a+YKKL6xAlc3LcPypgY6aKJCcuytJTc3N0ZXF3pfS1b9nokRBSJrGg8n8aNI8DWpg2tsWfPaJ3PmUME5e+/E7Hx2WcEwNPSyNluxszNzVFmY4OTCgXLefZMsDU25nSGDaP5v2wZAecXLyj6XF3dUAhQY9nZ5MzOnEljHhlJaUHa2oCdHWJiYqBUKllAQAA0ZK+1tTWXmpoqaRUUsPHHjiHdzw9+fn4vz+ddu4jsWLqUQEJcHK1tDdHg4wNVnz64nZyMe/fuIScnB0qlEmeuXIH2wIGwYozG3tCQwJdcTmTBhg0EspoZXxsbG66Dri4nGz4cxwoKWL9+/TB48GBYWlri8uXLUKlU9ZH2W7duIbuunWHfvn1pHejr0/z94gtSPE2bBjNzc3Tr1o317NkT+fn5SExMZF4aYLd0KYGDcePo2epIkvv370s1NTXMzMxM+v3339kff/wh3r9/nxUUFEiurq4MogjMnAlh/35cmT0bwX/+idu3b4P39YXdsGG079va0rz/5puG9VZQQCDD0BB2dnZQKpViamoqe/DgAXr06EHrTCNNtrSk+bR9O6XbLFtG4K+4GFi2DNpz58IyPh76T5+yq6IouX73HbvXty9a+fuD7dkDrfffh9PQofDt3x/m2dnwW78e4rFj0FqyBPcuXULLadPY6OpqJr97l4BWnQqD6evDZepUJuvRA7pXr9LYLVyIQ5WV0tChQzknJyeUlZUhIyMDSUlJSEtLE7U2bkR8x47i3bw8JCYminFxcVJsbKx0Py9PuuviIpkvWYKRhw5xzNyczvdNm4D27XEjLIy1k8lg160bHO3t4entDe6DD6iAXloaIAhIy8mBb7du7L0WLRg/fjwB988+I4Jt5Ehav/37E1Gi2f/79iXSi+NoifHmGgAAIABJREFUHJydifAfOJDGedEikn337Us56teu0bU0aoy6Vn7o0AG1goDk5GRp7NixrGfPnugzdChnlpgIXQMD2hMPH6azd8wYaO3ZgxRPT+FxVhbKli1jg8+cQezAgVI3X1928eJFKSMjg5s9ezbr3r0769OnD/T09Nj169ehra3NJk+ezL3mT6xYQftjUx0pmthLSkpKpEePHrGXCrTq69OzVlQ0SWi0adOGu3nzJisrK4ObpSXN0TFjiLRcs4b2aHd3OuOvXYP2rFmwWb+ede7Zk/P18eFaVlTAoG9f2M+bB6+qKm6foyM6XLvGdHR1SUU2bRrtA8HBtNY0aU6Nbd8+Onsa1zOQyWg83d1fLszMcbSHvKIwQ9u2NB5Tp9J4TJlCfpOuLt2DpSURAozRL3d3IhesretJn9bffMNsMjNx3cyM6erpserqamhra+P58+e4c+cOfH19kZOTg0OHDklxcXEsMTFR+vPPP1FSUsLr6OiIAJim20FtbS2G2dlxXb//HuEBAdBWKmWOiYm41a8fcnNzoaur+/o5Fx5O+3liIpHRmzbR/ffrh2epqShUKtFh/XpwU6fSObxoEb2H996DtkIB44sXIQgC5MePN+mH6evro0OHDjIrKyvZzZs3lYIgjF+5cuUfb51c/+H2t4z/b/uvtlWrVrWWyWQTe/To8b/fC+xfMVGkqOGPP9Km/Y5t6nR1dfFqsU2ZTAZLS0vx7t27GDx4MIeNG4HERLS9c4clK5WUC9eMtWrVCh9++CEPALt27ZKOHDkizZgxg6usrERERIQQHx/PS3UVg+fNm9ds4RYADVVtjx1DdHg4DJOT2diQEK7g1CmYiiJ0oqKwYsUKDWB7O9hmjA4na+uG6Mf165QXWlBAB0VdJWRUV5MD1L49OTFN5P4/e/YM5eXl/3ok1cCAvhMg4Dh5Mkmz/0KLQYgiSaKrqiiKmJNDkdG3VCyuN7WarvGO9QNMTEzw8ccfs0ePHiE0NJRLSUlBu7rWZdra2pg4cSLOnTsnhIeH86yqCgu2bMFuXV1IoMJ++vr64t27d9mCBQtYkxF1bW3g119hvHgxql7N962zzp0748mTJzh27Bg8PT0lAKz/W6r419bWamT7TasusrIaIrGOjgQaOnV6HWxmZ5PEsi6lAQDNlYoKUke8qcAfY4CdHUo3b0b8iRMQ9u2ThpeXw87cnEFfn3JlDxyg6OPs2QRU4uMJGDcuuqixoCAgLg5CWRnS09JgYGAgOjs7vzvp1IRpinWmpaWhr68vtJcvR/GlS0gZNQoJI0YwWwsLKSMykhV07QrLXbsoyvOqRUfT+iosfDmSfuYMOc537pAjvnAhve/u3QlMqFRE9pw6RcB2ypQGGSrHQaVSoaSkBOfOnUN+fn49UaNWq8FpafHbDA1hdfq0qLawQKepU7nc48eFEe+/zyu9vcFt2QLdqCiKEkdH01pLTSW5ssaWLKFoVlgY8OgR5s2axbbv3i1t27aNWVpaij179uQKCgpQVVXFXNu1E3QMDfmioiJs3boVY8aMgYuLC7ikJCIP5syh8e7blyK0GzcC336LKh0d3PzjDzgFB4Pz8sLAgQOl1NRU6cKFCxwAhOXlwfnMGehWVYEtW9agFOF5Inh++eXNRfe+/BJG7u5o16GDePjwYWn+/Pm8Zn1GRUXB3d0dNjY2KC8vBwAMGDDgddJ1wwYimxITiWQwNgYAODk5sVu3bgnZ2dl8YWEhPIcPB/fsGRE0ffrQuJqbo6auVefChQtZfn4+MjIyOIVCIcXGxrKS/HxpSlISY9eu4dfAQFS2aIHRAQEoKSlB7rZt+DMzE5U8j8Rdu8RBJ05wLSIjCYhcvdqQ7mVqioqjR/HkyRNOM/4HDx4E0FAJ3iwzE7YODqiOiAA3aRJsbG1hGRsL6cIFKI4cwalZs6R8Pz82c9IktMjOZmtat4Z04QLsPv5YsvLwYKiogN2aNcD8+XjYti0i+/QBi4qSZDIZYvr0YStHjaK1aWxM67BlSyKq4+MpuvjRRzROPXoAdnZwd3fnTpw4AY7jIIoi9PT0BCMjI16H4zhWWIhUSeIL09LQqVMnaGlpQSaTQU+hACwsGtqFPX9O4HzDBmD0aHRMSRF73L7NyXNziUDr14/kzJqo5Jo16HLjBqe2s4P2nDk0B21sSPGgsbrUgaqqqvq1HxMTIyqVSgiCwAyLizFixw528IsvJFDPeMm6d28onjzBcFdX7mm7dlL08OEStm+H1eTJqHj8mDOPjBR7nTrFHS0vF4dv3sz52dgwg7FjYR4RQeoFHx+SSnfrRmqyrVuBnj1x97ffJLGggHuWn8/0FizAlexsiQmCdOPGDfbgwQM2btw4aDpFMMbg5eWFdu3aQUtLizVZiDcw8C8R976+viwhIQFqtfrlwr4ffUTqQEl67WyWyWSwtbUVzE+f5jB7NkNWFhEpHEd1ViSJQPi4cRRcaHzdpUvhcuUKrbMff4Ts+HG0cXJC0f79MOI4irBbW9O4L1rU/JluZ0dKsMbGcUTgN1Xs1t+fyNWFC8n30TyTnh6pz54+pRSsjz6iPP0jR8ivjI4mgk/jszHWQEDfuAGt6GjEGxlhyMmT8K6qgurqVcjq6iI8fvwYeXl52L9/Pzw9PZmOjg4SExOlyspKzt7eXnB1deUdHR1haWmJO3fuiJHnz3MJhw/jvW++waCePdFy+nQcCQyEKEkwMTERjx07xpmamqrnzp0r49LS6Mx0daVn8/en99a+PaWG2dqiVXw8DkydCt8zZ6B99SqlIGmeOzQU4vLlSO/aFW67d791zpw6dUpVW1t7cOXKlaff+MH/Evsb7P9t/7W2atWqNtra2ld79OjB6b9j1dH/67ZtG8kiJ01qttVSU1ZdXQ2e5ylfuJH5+/tzwcHBKCsrE9zd3fkOz57BevVqiIsXv/O1Z8yYwW3atEncuXOnWFRUxJmZmWHKlCnQ0dHB7t27pefPn7M3gX2VSoVjx46JCoVCYozxOi1bSrxCAWvGKPqUng7m70/FYr7//q3FYpo0FxeKHu3ZQxEMUSSQcuMGRfkePCBZo1pNh7a3N9CmDR7m5uL06dPw8/MTzczMOLVajdjYWIkxJnbt2vWvK0ESEujw+egjisz8/vubPy9JlFvXsSPdV//+DcCqqOjt3/f8OQGrW7debuPzjubq6gp9fX08ePCgHuxrfu7q6sqnpqaiVcuWMJw0CV+1bw+1Wg2VSgUdHR3uhx9+wLZt26TZs2ez14r3MAY8foyqU6cgNVUYDYCHhwc8PDyQmJiIc3X3HhMTI/r6+jYLdLt3787n5+dLlpaWL3trkZHkKNnbk0NXU0OR+uZs40YCbzNnNvxs+XKaNwUF71R4saCgAL//+Sfg6cmWzZwJ3YwMmsOPHxNQWLu2Qfa9ZAlJkev6N79kXbsCXbsi3c0NhgA6bt36LwF9pVIJhUIBU1NTHDhwALU1NRhy4QKKX7zA43XrEGFmBonjMGzwYMnb25sVmpsjJyUFluHhTV/Q2poczVcLM/bvT8BeS4sczZgYkpseOEBk16efUu52VBRFsoyMyGHPz8fmKVOk0tLS+rGbMGECTE1NYWxsDI1yqKCgALGxsZxoZYWEnBxRKizk4z09kW1hAbO1a9ErIQEsOprme1BQ073p+/WjqNX27eBWr4aRoaGEuhSls2fPQqlUIjAwEJ07d+bRty8CKytxJypKOnLkCOPUany6aRPyvvsO0ebmkpaWljhhwgQeQUGoSE7G7c8/FxMcHZmujg56Xb3K0L49Ll68yObPn88sLCyQl5KCqKNHxTB3d842IAA9rayI8NHkck+eTO8qNbXZlomxnp7izZoazrC8nON5XtT8fP78+bh79664f/9+ztDQENXV1ejWrZvk6+v7OuMmlxNZ88EHtP9FRwOMoW3btjh79iwfHBwMlUqFW3I5AvPyYGViAp2JE4GnT6EyNERNTU199N/KykrTOYN5ubiA8/Njz/T0pMuzZknlcjk3d/bs+ghdTWAgDpqYIN/GBk7e3tzx2lp0u3QJ7SdMoOimvj4RCgkJeDh1quSXns6effKJxNnb1xM/mt/NsrOZRVmZVHL8OJ5fvQpFRga74ukJX3NzxI4dKw64cQM2WlpMe/Ro2A4ahF6HD6PKwQFVOTkszsJCuNuqFbpFRLAOgYFc76IirI6MhENREeN5Hp6azjYaMlCtJuWAmxuBi/Jykr/HxNS/0oCAAJSWliI9PR3t27cXRowYwWtpaeHWvn2It7REjZ6e2NvTU/Lz8+PrQaWJCY1/eDgQGwtxyRIU5ObixJdfCjh7Vqrs0kXGFi5EH4D2rtpa2tP19IiglssR17On0Dkvj8cnn9D7S0wksC2T0f2amCAnJwe7d++GgYGBIEkSjI2NWa9evTi5XA6IIvSPHMGQLl2YmtYzk957DwCgcHeHVXY2G11UxHQTE5H/0Uc4HhmJTrNnc0/nzEFXSeJKOQ5J8fFwevqU6no4OREw++UXUo/k50NcvhyxO3eK1/v355Z8+y24ykqUbN0KfTc3djU8HMVJSRg3apTkqq3NkJ9P0doWLQCOQ7PplaJIaUGayv/vYJaWljAzMxPv3LnD+TWWcffoQaklRkYv9ZwHACxciL5PnvDhPj5Cz/BwHteukYLIyIj29AEDKIAwZUqDX1ZVRYqIb76hLj0HDgCfforU0lL4HDzIzNzdiQw5e5Y+r1FcHD7c9I23bNn0md+/P/kr9+69nkY3aBC9/1cJd319Uqro69O97d1L5Pe2beQjDRxIqqWOHV/+v/36QadfP7xYtAgd4+MBPz9oR0fj+RdfQK9unezfvx+Ojo6StrY2oqOj2ZAhQzg3NzcYGhq+dGD29PHhPDdswDVRxF6lUrRdu5Zp6+uj09ChYm8/Pz43N5d79uyZWJCfL1MVFkJnxgwiJT7+mN7FzJmAhwfNMQAoLgYLCoKukRHKT56EmbMzkRq//kpntqMjwhcvlrwjImDatu0bIy337t0Ts7KytAH89qbP/TfZ3633/rb/eFu1apW5XC7fCOA9QRCMGGO1oijqSpIkGzhwIPPx8fm3omT/a3b0KMljNXnuf8FycnIQHBz8Wns1AMjOzkZsbCzS0tJEURQ5eVkZArt3R5tXD7w3WFVVFSIiItChQ4f6vNddu3aJZmZmbNSoUU1Hd+vs6dOn9QXgLC0txYCAAO61ytAKBUkTT5wgBjovj1jov2KHD1NkpGvXpqPq5eXE1p8/DygUUGtp4dGpU7B0dobx4sXIqKzEsZgYMJkMgiDgq6++ajpP9V2ssJBkdO3bU77h0aOvg8dnzwhUjhxJgL9x8aBTp0h6qela0JQJApEF335L8ti3Fe9rxu7fvy+dO3eOTZ48+aV84HqLiCBwrFEv1Flubi4OHjwoOTs7S6NHj379y0URNSYmuLl4MfxXrXrjPVRUVGDXrl1S27Zt2fAmqulqLCUlBadPn8aUKVNIMh8RQTLFoCCKmGze/PYH1kh2tbReTpEYOJAcohs3yClsXHm/GduwYYPk7Owsjhg+nMeFC5STaWlJrfwcHMh5cXSkn+nqvpHAO7N7t5R77x7TMjAQZ69YwdUX+HtHO3jwoJSZmcnk2tqSV2IiKxUE6FVWIqNtW5Q1IuP+0acPOAcHnL91S4x78oQbNGQIurw6z0SR0h6Cg5vsN43vvydnd/VqkhMPHkzqmqAgctAkieSrOjoAx0FauBBHevfGE2trBBkZwXTRIujp6b1bLZDEROCjj5CxYweCQ0Mx28UFrefNozFct45A2t69dK+vmigCoaFQRkYiXBSlFuPGMV9fX9y+fVvq3bs309XVpb02ORmwsMCD2FhcPXwYvCii3NAQzs7OYnp6+ktze+KlS5D7+KD12rXgeB6FhYVIS0tD586dYaCvD3TvDqlrV6xu2RKWlpb4YOhQiuhNmEDF4wBSIty8Se+xkUmShKzJk6WUkhKWP2ECsrOz0a1bNwx5pfZB4z212XXbcFFSvNy5Q+eLuzsUCgX09fXx5MkThBw8iHnbt+PA1KkQTEyk0UePImPaNHZPocD06dNh37hOQW4uMGkSVByHjT4+qNHVxZQpU+DUqCBibW0t9uzZIymVSvbBBx/gwYMH0ProI5gJAk4GBYnjxo2r3/+T7t1Dztdfo83ChWgbHEzApHHXmeXLCbDwPKmdLl7Es969ccDBARNGj4bz8uVE2n31FQHkw4eB2looHj1Cpp4eLNPTET9/PszWrIGdgwPO+vuLyqoq6f2+fXkTPT3a0woKCDCfO0fPp+lhnptLJJ1SSWTq/fv0LnkeFRUV2LNnj6Cnp8emTZvGpS9bhmepqYjr2xdjxoyBc14eRUtfvKBzx8gIqKpC8bBh+ENLC5UGBkjq2BGBgYEIDw8Hx3GSXC4XHdLS8N7Ro7ziu+9gO3w43cPTp6iZPh2nd+2CuaUlDDMzwWVlwcPbG9oREZDOngUbPx7Pq6txs7AQY8aOpb1LU4BUY5GR9GzNKdjy84G1a1GzYAHSR41Ch/37CWQCyMzMxMngYGnJmDEMp08TAeLmRuuP5yFWVSFi3DhRMjaG1+bNnLGjI6mk9u5FdW0typOTURQeDvvAQOjp69NYnjxJ6gUXFyKt798nxYCmsGdaGp2jYWE0NnI5nXm2tgR69fVpT23CYmJipMjISOmzzz57OS3g0CEa82PH6Frr1gEjR0J99SoK1q5F8d698EhKonm0fz+N4Y4dRB6+aj/9RK01HzwgYn/QIODYMcQ8f46YyEj0lyTJcvJkVtiiBdq2bQutnBzaD5trN7trF63T7757/d8CAoiIaoosBmjfNTRsAMaNTaUi32L+/IZCz3/+SakJPj60JzXqnlRdXY1169ZBR0dH+nzpUobx41EZHY1zPXvClOMkb6WSPfnyS5y7cAHjx49/KUjwkj18CKxfj8p//hMP4uOlVmfOwGnOHMbqCM6QkBChurCQn3DpEgzMzen5NcTtkCFERPTu/VJRYLVajdDQUNQWFWHGyJFErL/3Hs2Zs2fxtLQUD3fswIAjR5r13dLT03Hs2LGy2tpar5UrV2Y2ffP/ffZ3ZP9v+4+2VatWGWppaSV7enoad+7cWdvAwABqtRpyuRxyufzdnMn/Fyw+nhydU6feGehXVFTgypUrQnFxMSorKzmZTNYkc2dnZwc7OzuIosg9fvwYjubmkHXuTLlamgJ3bzE9Pb3XHE5LS0suNzdXlCTpjWDfwcGhXvb4wQcfNI1IDQ2peBlAz19TQ3JPT0866N+lp/2kSeQ8fPbZ68w9QA5Cx44NLdNqanBZoYDz8+eS/po1TE+plIYKAnPgedwxNYUsPZ2im3Xy179kmhaDKSn0HBxHvco1+YJnztBhe/EiOQuvvr/MTHIymwP7VVUU9YmIoHnzb5i3tzfLz8+XTp48iTlz5jDTVyO5KlWTMkJra2tMnjyZ7d27lw0cOPB1WT3HIW7JElHRsuVbWQgDAwN88sknb1ysarUaSUlJAIDQf/4T7by9xYB58zgWGQmEhr7tK8hKSylic+bM69L1tWvJefTweKcxV6lUqKysZAP69eMxbhwB+T17KK1Dkmg+p6QQmTN+/Mut216xyspKxOXkMJmtLaaHhjI8fkx7wVvs0aNHaN26NXJyciCXy5nZixcICAhgjmfOQFi3Dnzv3lAoFFAoFMjJyZHKy8tFbvVqHl26IGDtWs40Olo6f/48y8rKwpjGObHPn9fnCzdpFy7Qs61aRcDi/HlSply/ThF1nifJ68qVEK9exfqgIDg+eoQlV69Cq7ycFD3vsjer1bSeDx2Ck5UVer94Ie6WJG766dOw16R8REQ0qA+8vQn4jR5N1+c4YPJkVJiawnz9euZw7Bj0/f0xcODAhi/fuJHGDEDns2fhGR6O82vXSh07dmR2dnZcXl4e9uzZA7lcDldXV9FuwQJO9/PPac/euBGWrVvDcsMGks1euADs3w/m5gasXo0XL16QI71tGykdNm4k5YOvL0Xas7JeKvp38+ZN0TQlhevyySdoOW0aRFFssiaKZo0OHDjw7SkfjBHxtGULpZFcuQLDOiKpbdu2WLFqFWp9fWH77Jmklsshc3GB+ZkzMB83TrK2tm54Tzk5RF5OmgTtzZvhceUK7t27h5s3b9bL742NjaWhv/7KtP39WUnr1tLatWvZsGHDgK1bURITg3alpdyRI0ekjz76iBUXF+NSRIRY4evLWZiYiHBz43D9OkUwvb0JTD5/TrL6n34i4H3lClrb2MAzPFw6euoU+/r4cbq3oCD6TF29CMOyMnju3g2cOIFWenr488MPUbh+vTTpxg0uwckJT377Der33oOZXA6uZ086X1q2pLlTU0P73eXLJK0eNYqAZmkpPf+iRTBYsAAfDRzIb7t6VTh27Jjwfno6b7d0Kdr9+ityzp+HtGsXnA4ehIzj6vdOUamEkJaGzMBATNfSQu/p02Fhbw9XV1cUFxYy3L3LG4aHQ9DWRkZNDZHqM2YA3bohJSQEQlERzLdtwzlvb6jlclzNyJC0HBxQPWsW6+3gIOXfucN4U1MBJ0/y2LiR1DR5efRcnTrROUNKJFpX5eVEeGpSwIqKgLlzUZObixpdXYpUnzkDFBXB1t8f4w4cYI/Pn5fa/PADw7hxNHenTAEuXEDN06d44OrKffDNN9ATBFoLdQUgzx49KqQYGvLGH3wgzJs3j4dGBbZjB32vUknn2fPnDe1Ajx2jGg8nTtDP1q4lwHvrFgFma2uS11+8SGRBz54AY5BycvBCJoOjszPLePCAVezfD0N7e0p70NUlYGhsTHMlKIiKpZ48ifs//wwjc3PJw96ewdSUwH5JCa1xDaHQowftaUFBRBL8+COReGfO0B6YkgJ06wafFSvgGR+Py66u0vkbNyCKIsbGxkrOXbpw+Pzz5tepn9/L1fgbW0gIkR7N2ZQpzRcw1NYmZcmtW0R0e3nR+9C0nQ0OJqXG2LEAx6G4uBgAoFQq2a07d6Rex48zfZUKY3fuBP/jj0zo2RO3T5+WFiQnM/NZs5r+zuBgIkIuXoQ+gF6VlQzFxfVEXk1NDWRhYbxPz54wGDqU0rwaK7RMTSmy/8svBPx5Hjdv3sTNmzdRW1sLCwsL2ldv3KC1uWoV0KULHPr2xc1u3YRbt27x9TVMGllhYSGOHTtWXVtbG/D/E9AH/gb7f9t/uPE8v9DFxaXF4MGD36FJc/MmSRIyMjJgYGCAVq1aQZIkpKWlQalUwt3dvZ4llCQJSUlJiIqKEtRqNTMzM5N69OjBN9nD+F0tJoYOu4sXm+9P/4ppoie6urrMzc2Nk8vl6NSp0xt1xxzHNUSANBV5/40+8QEBAdi0aRNOnz4tBgQEcM31Ya2oqECbNm3w5MmTZp3Xl2zyZPpdpaKD1dqanJ78fHKma2qaL1r3j38Q8HgH1YJMLkev99+HoaEhMzAwQOvWrRkqK3F29WrB4ckTxs6d4/DsGR3AdnaUQ+bsTPfzrlH0du1IvimKFB1YvJic/t9+I5DS3Lxxcmo2aoG8PIoOhIa+1Ov93zF/f3/29OlTbN26lfrENzZ399cikBrTtDLbvHkzxo4dC0NDQ9TJ+wSe51lV+/as49at9OyNI3b/gsXev4+UxET0jYpCpz/+wD9btOBc791D23ftwiBJNKdu3aL84VdNpSIHtVcviko0I7EGgJKSEuzauVPsXVgI3QkTOHzxBf0/zVpijP7evTt9r7Y2AZh584hsaBwtBRAdHa2WyWQySZKQtHYta92jBzmcnp40h5qwffv2CdnZ2bxcLpdqlErmnZkpzbl/n8mnTQOioiCrc3SMjIxgZGQE2wsXGExMeE1rPQagR48eLDU1VUpKSmIZGRnSwIEDWefOnQkUFBY2T3ps3Upy4+pqiryUlxMxtXo1AX5NAapvvkG6qyu89u3DQ1dXaOnrk+z1+nV6F8nJby5AuXMn5ZHb2IC7eRP9T5zgYubNw+XHjzFHA/b79GkoqDh7Nr3n7dspQpaTAzx7hnwbGzzs21fwMzfnMX48SVg1BFZVFTn2WlrA3LlgEyZgWKM2XK1atYKjoyPMzc3FQYMG0cLfsIH2mCFDGlrsTZ9O4zVuHADqTa1UKunfrK0h+fhA2r4dlW3boszLC1oyGaoPHcKfffqgvLxcLC8vF0uuX5dZjBiBDtOmAUCze+Xt27dhZ2cn9OjR442btyRJKCoqwosXL6Dz0UdwbNWKAMqIEQR86kwrKQmTKisZVqwAxo1Dm6go+FhZsfoCV9evU2Rw4UJSEjGG9u3b4969e/UFAl1dXWFjY8MMXFwwfd48cB4e7NixY9K5c+fYihUrwNq1g+jnB8HLi/3zn/+UWrduzSoqKrgFCxbA3Nycqy9MtnQpdVe4eJEid4JAgHTQIEBLC9y9e7B6+pQVZ2QQcRISQoW8WrcmEunBA1pvGzZQ9PfGDdj06gXj2bNZ3o4dksLUlOWNGyeczcri27Vti/GOjlRrokMHmjdfftkg154xg+oMVFWRMsPVlQqd3boFbsIEvJ+WxieMHQsxMxMtunVDi+fPUZCUJIWdOydVV1dz4t27kMvlkkwmk2pqaph6zhzm6ekp6iUnc3rx8YC9PfTkcuh99RWpCRYvxsWsLHWNkxP55nWyc099fXhmZADr1qHFypU4GBaG4cOHM0EQoKuri2vXromOI0fy/v7+fP3ZpFAQaZyXR1Ln27epsGFqKqmNNFJ1S0v6bGQk4OcHZmCAKiMjCdnZDMnJwM2bkFlYwPzGDWwNDmajrK3hWltLa+3YMSA+HroZGZh08aKUERTEOu7eTQq6zp1RkZyMPD8/3sLCQpw/f/7rc5XjKF1BT4/W4JYtBDoPHKCx0NKiaH+bNqSU27v3ZSm7UknPVFODfT//LJoATMjJYbUFBejo4gLDP/8kBU3btrRf3b/fkO7m6kqDQHVKAAAgAElEQVTvoLYWFcXFwo3AQH5hcDAMRo8mgrFxoTyAiCBnZyKj7t2jPV4Uae7s30+EWkgI0LIl5GFhGL51KzdcJsPRo0elqpgYBkfHNy1VAuI3bzZdP0VXl/bCnj1fLjKrsUGDaI24u5OKsEOHl/+9e3e67tChRJacPEln1Sef0FmnSSuaOhXW9vbo06cPIiIiEBMTw7KystCtWzc4L1wIjB6N0H37pKnr1zNDGxuas/fv0zUHDaLvUqmIpNG0Q9YQSVOm0DurrER+ZCT63LiB87q66NBUHRtXV/KNEhKgDA/HgdxcoaysjB89ejROnDgBWxsbUsAGBtJ7a9+eFGByOXovXMgfOXpUysvLk8aNG8dpfHelUolDhw5VC4Lw1cqVK++8eTD+++xvsP+3/Uebtrb2BE9Pz9dQkVqtxuPHj/H48WOIoohOnTrVy8+bsjt37oiRkZFMFEW2cOFCPHv2DGFhYdDR0RHCwsJ4HR0dSRAEJooiZDKZ2KVLF75FixbIzMxEcHAwvvjii3dWEZSXl+PatWuCubk5Z9uyJbN9/31w337btFQMQFlZGVJSUlBdXQ1DQ0NYWFggKipKBCDNnj2bfyt4bsp69SKnxdqaHKt/wWQyGebMmcOFhIQImzdvxqhRo+DSCHiFhYVJycnJUKlUDADc3d1FSZLe/Wa1tRsKpa1eTdGC8vKGKvwtW9LB3/jwHzCAIgKrV1PxsLdY165dX/p7TmkpYvX0eOfVq1FiaQkZgBbV1ZQ3GBFB0bj4eDrAJk8mUO7o+PaWdJmZdI3kZJJmrl375gJdFRV0iA4e/PLPBYFA6NGjDYfr/4DJ5XIMHz4c+/btQ0REBF6qZLxuHQHWLVte+39lZWXQ0tJCbW0tQkNDoa2tLUmSxIyNjSFJklRWVsb51tSQQ/Fvgn3vSZMguLmJEd27c/f798c/Pv/87cRRY1u5ksiiu3eb/vdlywiwdu0KYds2sJEjm73+uV270DohgfPLywP79demny0jg6KC9+/TXB4zhpzV/fsJJPfvj+IuXVBUVIRbt27Vn8WJDx+KgwICOJw+DdX161D88ANKS0tRUFCAyMhISZIkJggCRFHkXVxcJK0TJ1i/P/6ASUQE4zTtpZqytLQmK8BPnDiRVVdX4+DBgywsLAweHTpA1qcPRVa2bm36WlVVFI2ZOJH2j8pKku2vWEG5qV27AsbGKCwqQmh6OvpIEuZGRZGTfOECgbTRownoN1a8NLbCQgIhgwZRdem9e4GcHHSPiEBkZCSUSiVeIxjnz6ff58xpuGbXrtAdOVLSdXZmsLYmkmHIEFp/Pj70jEZG1FLwwIHXnXwAVVVVor6+fsNkMDEhMBkaSuTR+fO0vhuRI3PnzsWWLVvw/fffo7a2Ftra2uC7dUOXX34Bk8mk9C5dxH6nTnHphoaStqkp16JFC27sgwfQbty6rQkTRRHx8fGSnZ0dd/v2baSnp0v6+vrs4cOHcHR0FJ2dnTm1Wi2VlpaKsbGx9QCL53l0795d8Le05HHmDOUCN27npek/raNDnQeiomif0RRk27yZ8pfrzMHBAUFBQXj+/DmuXbuGfv36wVLTaqwuPXTo0KHs4cOHSEhIQKdOncDt34+BAPQTEtitJ08AALt375b69+/PumnSmNavpzm1ahWRDG5uFOlbtgxYtAjSjRuoTUiArYUFAfyvv6ZzwcGB7hsg2fKRIzRn5s0D5HIYDB4Mu8BAlrxypegZH8/pVVWhy4ULEqZOZZg1i8Dc8uV0zSFD6D0oFIAkoay2FmcCAkSzxER0OnSIs/nzT0SOHy/mf/IJN/bOHSqUk5AATJqEbgDrBrDHjx/j0KFDqKmpYZMmTWLXfv5ZCtq5E9plZRxcXaklqKkprYfRo4lEKSpCuY0N5JpxsbZGiZsbbk+bhuq2baG1bh30k5OxeNMmiKNHw8TDAwBgY2PD16l30Lp1a9q3FAqKNq9dS+qiyEiojY2R2asXWrq6wuSTT5oki0uysxFjYCD1XrKE4dkzKD08kHrqFIpraiQfPz9cu3ZNdJ0yheaQhwcFKFavRlxoKJOdPClIo0bxz2pqUJSUJGVwHCstLYW7uzuTJOl1H0kQCCBLEpCbiydRUZLo7c3azJxJ9/zwIUnXjY1pfK5eJbm3xuqi/Dk5Ocg2NOSq27RBlaOjOHXqVO6lNpoAgf0lS2iemJrSNVu0AOzt8V5VFW+/bZuUk5EB58WLGaep59DY9PWJ/LlyhVQG9+5R4CErq97fUO3YAdm8eeDOn68PCLR68YLFurtLncaPf/2ajU2SaMyaMrmc5n9T9Uk0JpORgqy5AAJjpEJISCDyftAgIkbd3akmwpUrwPLlYH5+aBsQgIiICNTW1iIjIwMZGRn4+uuvIbO2RqYoMnVEBDgdHSILnjyha/ToQWtu61a6lqYQ4fXrFOW/dIn2+g8/hP2aNahJT8ezTZuQl5cHS0tLnDlzRlQqlax/v37M4MQJRHl5ARMmIOX2bamlpyemTp0KxhhYeTnMQkJEMSCA4zStAy9eJDXB3r1wsLND79692ZUrV5hCoYCZmRlEUURoaGhVdXV1qCiK75Dr999nf+fs/23/sbZq1SodjuMUy5Yt05LL5cjLy0N8fDySk5OFqqoqXldXV7KyshI5jsPTp095R0dHYdKkSU1GQiIjI8UHDx5IWlpaUnl5uUwQBAwePBheXl6oqKjA8+fPoaurC47j0LJly5dAwPfff4958+a9U6sshUKBo0ePilVVVcxEpRItoqO5FEdHxtnaik5OTpK3tzeviZaqVCrcuXNHvHPnDmdkZCTo6enxFRUVglKpZHp6etK0adP4N/aBf5vFxJAjt2zZv36NOrt79y6uXLmC2bNnw8LCAklJSThx4gTat2+Pfv36wdjYuNl2an/Znj4lB2PePDpw4+IIaIwYQZGAZ88oCh8V9XpxsXewPXv2CEVFRUwQBE6lUmHSpEkvkRgoLaUD848/6M+SRASAlxcxzZaWJJNjjNh2tZqY5ylTKKp59y4dzHI5RW6aqk0QFkaRlg0bGn4WGUkgqqSESJr/SZMkoLQUR+fNQ3n79phlaEgHaEgIRaH19EiK7O9PDlJd7nlYWJhQUlLCTZkyhanVamg354ykp9OYNaP+aNYSEsixSk0lx7VzZ5RVVuKXX36RbGxspMDAQM6oqUrFr5ogoOLuXdSo1TBrAsy9ZJWV2Pz112BOTmKvXr24Nm3aoKSkBDk5ObCwsIBzaSlSpk9Hibs7fEJDm5/Xd+8SGHy1UKBKBURGouLOHZTu2oWI3r3x1MkJNs7OcHR0REFBAXJzc9UqlYpTKZXcoDNnkNquHbLbtcOYMWPQqlUrZGdno+j6ddFbT4/Tq64Gc3ICaw4kfv45kUfbtr3xsUtKSrBlyxZYmJmJAy5c4CxXrUKL5tJIMjOpxsH8+RQFKymhCNeBA0RmPH4M/Pgj8vLzsXPnTgDARF9fyfXgQYZVq8hBdHKi4lWff07zo3Xrl79j82ZaO8OH0/dcvAhoaUGtVmPNmjUwNzfHggUL3vhMAFBbXY1joaFSq8RE1u/4cXLOly0j4m7ixIYif9u3U3GoxgSPJAGVlTiwaZO6u4ODzNXOjn6WnEz3nJZGYK1LF1orcnlDuzMAcXFxuHHjRn0B1c8//5whN5ec8u3bKe3D05OIV0GgqLSmsFYzJooifvzxR9TW1kImk0EURRgbGws1NTW8jY0N8vPzBblcDmNjY15LSwsBAQHgOA4KhQK7d+/G4MGDJS9PT4ZevWjsxoyh726syKipIRl7ZCQ58L/88lLurMbOnz8vxMbG8lZWVuK0adM4bW1tIpvWrKFaJQDCw8Nx9+5dLF++nNbK8eOUznDhArJKS7F//34AwJAhQ9Ctcd2SWbOIKNu3r6G4YV2xvCdPnuDQoUPwzssTi4yNJZcRI3gfHx+qSzB9Oo1LWRmB2StXUPznn9B2coKBgwOKt2+HfOlSRHfpgj/t7CB06CDK5XLONDpadGjblrMyN4fRwoV0xksSirOysPvoUdHe3h6G9+9zrKhI6B4dzeulpUEYOhS6I0cS8RwZSRHVKVMgiiIiIiJw584dDBs2TOrUqRPbsX69elBVlcxBo5wKDKTI8sKFtDf26we0b4+jR4+qdXR0ZIGBgYiOjobs449RO2mSWNimjVRTUyOplEpYhITIuMWLYV1WhixDQyk2NpYxxqSa6mq2wMYG5k+eoLCoCArGUDN2LHQvXECmi4sY8+IF16KgACNOnkS1h4dkHhLyWtpWWloazp07J4wYMYLPz8tD0m+/ocDKCsPOnUNrIyPcMjHBiIcPIUtJQZlCgeoxY3C3bVsxrnVr7uNff5V4S0u2cfRotGzZUuispcW3aNkS5x89Eg0NDeHu7s61atUKbQwMwH75hcZqxgxgwQLkhoeDv3ABO+fOhbGFhegEcH3v3YN+XW0KCALtM19/Ta0sGxEVV65cQVJSkrRo0SK2Y8cO0dzcnI0fP76BWcjIIMXAoEFEIlhbU/R5+3aSv5eWQoiJwY7ffpMU5eVYsmTJy50BJImIwbIyOosYg+Dvj2wDA5zu0EEyKClhNbq68IqKQq1cDrfDh4n4AlDp4YEkGxvJ58KFN0eDcnJIUdVUZB8gv2bQIDoT31S8+eZN8jOuXm0+VWrzZlKO3br18rUyMyGdOwfh2jUot27Fk7IynDx5Eowx6OvrS7Nnz2Y7d+4UW7VqJQUFBfHIzqY53LIlkVe3b9NePXEijY9MRkTW3Lm0t9Wd5dWBgfWdeCZOnIhLly4JADgrKysx49EjvvP9+ygYOVJoUVSEwC1beK3Hj+lakgTRzAzPzM2ljBUrWD9TU3DFxfRd334LBAfjj7g4XL16FePGjatXsl69erX23r17D2pqanquXLlS/cZx+C+1v8H+3/Yfa6tWreoJ4JadnZ1UUFAASZKYhYWF0L17d75NmzYvRX0UCgV+/vlnzJgxA1ZWVnj8+DFycnLA8zw6duwIXV1d/PTTTxg2bBhUKhXc3NyoN/072J49e4SamhpuwIABzMnJ6TXnv6KiAnFxcVJCQoJUXFzMWVlZCVOnTuW1R40CnJ2h/PFHPHz4EImJiWJOTg5nbGws2traIikpidPX1xdGjhz576UJvMmSkwmM7N37b18qLCxMysjIwMyZM1leXh5OnjwJPT09QRRF7pNPPmGNCZLa2lpUVlbC0NDwr0VnXzWVin61aUPMcVER5f716kUSw08++Wst8F6xtWvXgud5LFmypPkP1dYSgAgPJ4ckN5cKMxUVEUCdO5eipK/m1J0+TeDo1CkCT42KXOHJEyIQRo6kv0sSMfbffUfA4F8xUSQn2NiYCgodPkw5i15elPf42WfImjsXRQsXwsvVlZyPGTPo/cbGkhO/YweBuWfPgEuXcBkQ4lJTOWNjYwwcOJA5ODg0/d3e3uTcvpoi0JytWEHR4m+/pZQTTQu0OquoqMDevXuF8vJyHiCFhq2tLVxcXF4H32o14OmJYwMGiA+NjTknJyd4e3vD3d29/iM//fST2P/UKZS4uUltV6zg5V264M7HH0tP9fSgUCiYrq4uWnKc6HLyJMc5OSHdzAythg2TBgwY0PTkunOHxlDTcu61oRDxw/ffo5eZmdDm6FEuUxBYsakpMtu0QbmhIQICAmBpaQkDAwPorV4NmYsLuHnzwPE8vZeKCprj8+ZRtKo5E0WKOqtU79SnOisrCzqzZ+OpkZEY1auXtHjx4uYZuoQEWmOff07pD9u307xYtYoUDV9+Cfj6Ij09HSEhIXBwcJCm5OYyLjaWIsZ1fbmxezc5uMOHE7nVrh05vStXUoQoNfW1NkrHjh3Dw4cPMW7cuJfGUWOCICAzMxMPHjwQ0tLSeLlcLs6ZNYsz1hR769uXanzcuEGSVgsLWluaopdnztCfO3cGiorw9NYtydjTkxl3705kXlERfa6ggJ5l9uwGMKGrC+zbh5rjx7FHoQDn5iZV1dbCyMhImDVrlqzuBmlfcHSkd3j3LoE+R0d6b28wTcTY2toac+bMeeuYNraoqChcvnwZS5cuhd7x47R3TZhA5IKHBxEqNjb0bO+/T4AoLKxZFdHFixeFuLg47osvvmhYB6mpdI06IloURWzatEk0NTXF9OnTOcYYnTd9+gCOjsj+808cOHAAoijCwMAA8+fPh15MDOWZ//orAECqqQF7/Bg4eBBVn3+O6H79UKpUwvuTT5Du7Iz4IUPEz6ZM4dCxI6lGGhHYt2/fhvnMmeAFAWlubnDIykKmnR1kajVqZTIMiYjA7cOHUV1aCuOff0Z0jx7wnTwZHTt2RM7UqTC+ehVPvb0ll0OH2Ivu3aE0NIRJURHkc+bANDSU5u6mTfXdPVK/+w7nExNFMMbGjh3LNAUOt//0k7q/ubnMRbN/r1mDvAMHJL6yklWYmkolrVpJsQEBUpFSyavVaujo6IhKpZIbPXw43IqKcDgvT3zx4oWoVCp5lUrFDDhOWvjdd+y3KVNQY2mJD9q0QeVXXyGxRw9Jy88Pl4qKmLxFC8n26VNx3PbtfOKYMTD4/ns4FxRAnDgRh1atEp1++41zLyiAQUJCPVGr6Y6iUqlgYmIiTtuyhSvduROhMTFwiItDu0ePoHRxkWo//phdv34d/vHxoqWREXfQzg6j792TKp8/ZyaBgXD98kugb18IHh64Mnw47t+7B6fUVBgoFOjXpw8MkpMpIr5rF/DoEU4NGYLEx48xbdYs5OTkIO+339BeoYBbSEjDhJMkiuTKZBSdriOxtm7diqlTp8LW1hZFRUXYsWMHxowZA1dXV/JxkpJoLk2YQGA0JYVy5L/7jvbIx4+Bzp3xfMAA/Fo350aOHIlOnTpRcKF3b0Rt2IDLDx/CiDHRJSUF8e3bc7KKCnwUEgKcOgUxNRUyCwtcU6mExJQUzs7OjpmbmSEzNlZsYWEhTZox483RjrAwIj8bKxdetT59aJ9trigeQP7Hjz8SoH+T76NZ+x070l5b99n01FREf/cdBl2+jLi5cxHN85A4DjzP49NPP4VKpcKWLVswffp02NraklqjrIyIwhEjyBe6fJn2c19fkvqXl9N+v2MHKuVybNiwAYwxeHh4IC0tTXJ0dBRHjRrFa2lpofbpU3ATJoDXdMGYNYv8gJoaIgrT0pA6dap4LiaGtY2OZtLo0ehrYACTFi0QVl4uJSUlsQkTJqBNXbpDXUG+4tra2nYrV658/sYx+C+2v2X8f9t/stkCQKtWrSR/f3/OxsYGjLEmN1RDQ0N07NhR2L9/P8dxHKutrYUgCGCM4caNG3B0dBQEQeANDQ3xV4H1jBkz+LNnz+LUqVOiSqXiWrZsKTo6OsLExITLyMgQMjMzeWNjY7FTp058t27doA3w2LqVIm12dtDhOHh5ecHLy4tTq9WIjIzk8vLyMHbsWDg7O/8PhcObMXt7ilDn5zcv/31HCwwMZEeOHBF//vlnBgA+Pj4YMGAA/8MPP0iZmZmaaIdYVFTEqVQqcBwHNzc3YezYsf/6M2pr06+CAvr7998TSB49moBDYSEdFtra5CAYGBBweEeVQbt27fDgwQMUFRU1r9zQ0qKcwEWL6O9FRUQyjB1LQIsxclBKSigy7ulJnx8xgsD88+d0eOfkNBACJSV0sI0cSdG/Hj3I6XjbfQsC5THm5hKZEBREYCw3l6JpY8ZQXqu5ObHxkkRkg4MDYGCAe2vXCgUFBfAaMqThi3R1Gwo5rltHzrRSCfzjH3hv5EieqdVQRUQgy9ERzYL9EyealJC/ZDU1RIqsX0+5kTxPY9VIOqwxAwMDLFq0iE9NTcWNGzeQnp4u3Lt3j5fJZGjXrp0wfPjwhvuvqoI4bBjS9fS4Af7+KCsrE0+dOsXV1NSgXbt20NHRQUVFBWfm4IASXV0xJCQEg62t4e/rywz69kVVVRX0IyOBx4+5fB8f7NHXh1pLC+1NTZv3pH7+maJHr4B9URShVqsRHR0NtSDAKyiIbzF/PuRRUUj+6SfJ88EDZlBRAbehQ9FCsw/98580Tl260PiFhdGcfvgQeFO3iA8/JCc3MvLN772R2dvbA/HxMHB15SJUKvGNH/7uO7q+pyfd17hxVD3588/JsZ44EbhyBc7OzvDz80NkZCT7FsAHX38Ny969SdXyxx9EQA0bRqoPExOSsm7eTE5ohw4NQF+pJKLDzAxjtLWRXVGBW4cPo21VFbTXrYO4YQNKCwoQ6esr+S5cyP4YNUqSFRfzn549C22FgpN17kwgNCSEvtPLiyJbkkT7RIsW9P0mJvRv9vakDNLSQuiPP2L27NkN6/PpU4qeubgQWO/UiX6eng6UlUHKyUH5ixcwVasx8eRJhhcvgEuXZNiyhRQRbm60V02fTlH+kBAaS01k/Q2mUChEAK93NHkH8/HxweXLl7F+/Xr06dNH7Ovry8HZmZzpRYtoj1Sr6R5zc2lc6iqyN2VWVlacJEkv12JJSCBlQ50aiuM4fPzxx9yaNWsQHx9PLe9mziQw0L497H78EcuXL8fp06fFhIQEbscPP2Dx7t1gFy9StHv5cuz09IRNRQUKN24Uh+3bx90HYKRS4eCMGRA5DqMGDuSe790L07IypISESCerqxlHLd1EFBRwRr17Y3ZMDBxsbMBdv44Wp07B5uxZsJAQ4MwZ9HnvPeD8eSj8/HDJwAA3btyQXnz7LZPV1iJn9Gj4X7rEzi9ejG48j2etWuHGnDmSlra21MvUlFUUFkpt2rfnntvYiKdHjcKQr7/mJrVtyyz372d8o4CDVWYmnFatIrL2xAnA2xtxt28z+6AgcGo1a7NzJxM7doTRF18gwdsb7RYu5MrKyqR2Li5MbW2Nyg8/ZAPff19mYWEBU1NTcJLE4OoK75AQqfWZM+xp166wv3wZ+RERkuX165hrZsa4wkKmf/48z+npofPUqbTPOzuDe/IEQTdvcs83bcLl7dtFx9u3mU98PMPixSgvL4cgCJg4cSJcXV05ZGfDyN4eS/v2RdnWrSivrobu77+zg46O6DN0qLrHlCky8d49OBUUIGPAAFQbGAiTV6/m03V0cGrIEEmqqmJ+e/ZIvd57T+qRmclFm5tLP1VUsOVLloAvK6M15euLF1lZYvtOnWBvb8/Z29sjbcECcJozVWOM0bmuUgGenqhavhx7c3NhYmIi2tracgBgZmaGgQMHSqdOnUL7R48kn2vXmN6HHzKDwEAiR0WR9mYTEyLlly+nM+ePP2ARGIj5c+fi1507ER8fL3VydGQwNweWLkVVaCi6KZXw7NiRM01Lg/fGjTCsrYWOmxvN9enTgb174e/lxfNyObKyssTOK1Zwhl27so579rw9otG69cuEf1MWGkqE3JvAvrU1pdytXEnv9qOPmv6clhat+717iRBp0wbQ1UUbFxeEODtjp709JoeHo72rK/Dpp2jl7Q2O46CnpwcPDw8xODiY2dnZie+//z6P3FzyE+bPJ7927FgiwebOJfVjURGgqwu1lhYO7dlTX7A5ISEBAwYMkHr06MFrUjy0SkpoD9LYp5+Sj/LTT/QdUVFwMzTk3FxdoV63DsFduwqV27bxuydORJW+Pps2bVq9H9KoIF/g/89AH/g7sv+3/QfbqlWrWvE8n7V8+fJ36o+mVCqRlJQExhiKi4slbW1tlpubi7S0NHh5ecHDw6N5sPKOplAoEB8fj8zMTKmyslKwtLSU+fv74yWp8YYNJLEKC3tzDtb/lokibfgTJ9ZHY/4dq6qqqo/SAEBwcLA6OztbxvO86OHhwXXq1AlWVlaIi4vD9evXMXToUOjo6MDBweF/rntCVRVF4CwtST4vCBRtLS6mfE6ZjA642lqK8unrU2SmooIOKV1doKICokKBs5mZKKutRdCkSeCUSnKY5HIaO0miA1VDOqSmkrN76VJDLjDQkNt35QrdA9CQZjBzJn2uQweK1i5eTIz49OmUZtG/PzHwmrY5AAGi338nUHL2LM2lw4fp3oKCKNc/OJgO8+xsep4ePd762srLy7FlyxZMmTLlnUgvURBwdOpUDIqLg8ndu3TIL1hAOb6v2vTpVChII8nVWEYGkSILF9L/27KlHij8FauqqkJ4eDiSk5PRs2dPdOjQAVZRUSjevx8H+vYV5HI59+GHHzIAuH//vnj9+nVWU1PDhLqK8yOGDYOnlxckAMqMDOhWVhLg/Mc/6B1++SXg7o64uDjExMRIRUVF+Oqrr16fsJrIpq7ua+TMkSNHpNTUVMbzPNq2bStMnDix/gMqlQpMoYDWrVtEqgwbRmPfrRspP8aPJyCtr9+kpLre1Goi7xgjEKsBou9iWVkAgBJ9ffz866/4h6ZmRlMWEUHFv/z9G0DwjRt0jenTaf4lJdXXBPn555+l8vJytnTpUsi2bCEHUBNRmj+ffpmaUv6nUknOXa9eBDxTUmheZWTQ+nJ0RHJQkBRVWMgCLlxA3MaNAn/gAM/UakR17Yoet28jx89PUvK8NK1LF05vwADaAzSSVYWCAFfXrkSydelCBFtgIEnFG9XgqKiowKZNm/D1118ToM3KIumqXE4yc6WyvuVVbW0trl27JsTHx/MAJA8PDwQMGECFzszMiCBwcqLvr2tNhVGjaB/ZsOHN4woquLd161apQ4cOrHv37rhz5w6Kiorw9OlTycLCQpzxtugh6Bz89ddfoVAoMHPmTNg+e0aOf0wM/R4eTmOzfz8BomnTGtooNnE/W7ZskczMzKSgoCACND4+pMSZPfulz65atQomJiZYpAFwubnkxL//fn2tg7LSUgSvWwdVVRVmf/cdDORyqHfuREhMDMYlJiJm0yZUVVXBysoK7mPHQquwEGd27xYeZWTwNjk5KJMkmJaVIbNdO8hEEa6pqXCNj0emuzuK+/VTT/3wQxlKS2netm9PqhiFgubWxInA8eMQli5F0Q8/wHz/fjxnDKVRUfkv6HAAACAASURBVKJRZiZ3q39/WA0eDNR1l+GUSkBPDxzHQbuyEkaPHyP7zBk4tGgBB3NzUo506ABNC82ft25V9+7ZU9apc2ciewYORPrFi3CMiYFMI6WvrUXKqFFStasr8/LxofX0009IDg/HiYQE9B84EHxpKcwjItA6NBQxbdqgnbk59O/fRxEALUHA1ffeQ5u0NOTb2aFdaqpklpfHjo8ZAwtTU3FwaChXsno1LikU0vBvvmEP3d1xy88Pbf/8E4PPnMG+JUvEWrWas7WzQ1BQEN1TaGh9S1tp6FBc1NGR5JMns97BwdDy9yeib/ZsAqBxccDly4iJiMDD/8Ped4dFdW5fr/ecYegdpCtSBCkqICioiA272Bt2jbHEnkSjJkhijD0aY41RY2/YsASxoNhAUBErIE2aCNLrzJzz/bEZsGDLzf3d3O+6n4dHKTNzznvestfaa++9bRv8UlLQ8Nkz8EZGVGDO2RlRN27g3JkzmL52LfD119CePx/x8fE4duwYevToAQ8PD8jKyvCkZUtYR0ZC7W1V5vftw9nTp8U8Nzd0HjGCvZyjLyoUyPzmGzy3tUXZtWui0Z07zCQkBPra2qRWa9uW9tOWLWlvV7Y5nTkTuHQJz8PCsGflSkxavx4JO3bAMSAAiZ99Bu7GDTSNi6N9ZMYM2lM2baLzy9a2tqhrVVUV7ty5I5Z8+y1zDwqCwYd0Pbp/n1RG79pzX7wgKfzp0+9PUVy1iv5m7NhXfiyXyxEZGSmYmZlxjsoitJMn0+eHhwOqqsjNzSWFgyhiYGmp6FxUxGpTIGr20MrKSvz888+id0WF2PLuXU4zJASM5ymKL4oUTKiupkDBiRMQGzXCiaFDxRQnJ6Glpyd//vz5N9p2AqAzKzm5rhXxyZNUs6V/fyK+lT6qXA48eYKShASk/PKLGOrnx/T09BQvXrzg/fz8hJYtW3JbtmwpKykpmb1w4cIt738A/3/bJ7D/yf4rLTg42FUqlR5wcHBo1L9//w/T29djSUlJOHjwIKRSqWhubi62atWKO3/+PEpKSgR3d3fO29sbUqn0X5Oav2y//UbRL3NzfGwf7X+r9ehBEZwffvg/+0i5XI7t27crysrKxMrKSt7d3V309/f/ewa6rIycOC2tN4vniSIdFBUVJD9TVmkvKSGQpKlJvy8qAhISEJ2ZKT7NyGBaFRXwEgSoe3qiCoBuZSWB7DZtCOwcPEjAYehQcugrKqimQOvWlJOpUJC0296eSIjycsobVijqPv/mTXL+9fQoOjp9Or1+z5468GNlRTLV774jsFRZSZHGSZPoPd9XLPA9duLECfm9e/cks2fPfrMA2msmCAJ++eUX0cHBgXXv1IkAS7NmFOE8eJAOaqmUrumXX8gBGDaMXhwdTfLpyEiSckdEvFN2mJqaiqioKLi7u7+zr/iePXuE1NRUTi6XY6GODi6fPg2VoCDRy8uLvV5ToKKiAo8ePYKlpSWM2rUDGzGCIj1bt1KucHU1KSJatKgd14sXL8rT09P5tLQ09t3rRSBlsrr+0y/J5rOzsxEVFYWUlBQUFxfD3t5eGDBgAKf6rkr0hw7R+DFGY2hmRqoMOzuSyr8Gpmpt9mySUda0Kfwoa9UKyMyEkJ6OJUuWYPLkyW9XtGRnk4Ll/n2qhAzQ/A4Kws1Ro4SkxERx8PPnPN+5M+Djg61btyIzM7OuL3xiIpFYISE0348dIzWCVEprZ9w4kkc/eUJAoh5Vy9OnT7Fnzx7R3Nxc9PHx4fbs2YN27dohMTERHMdhzJgx9fdbnjGDZKUDBxKgHTCA1s6SJbQHhocTicFxiIuLw6VLl4Tp06dzyM0lx7hLF1pv9++T8+vggNLSUuzevVuoqqoSu3Xrxju8qyBlYWFdDQ43tzpl0jffUJQ/O5tIDUdHAqXDhwMLF6Ji7VpUfvcd1Csq8Ou0aZi9ciUeOTqCd3WFzZEjODV2rNAzPJxTEQS6Nh0dmrd79lCus7ExgeuFC3F1zBhR6+RJ5qqjAy4oiM4AuZyi6SdO1DnVPXtShO4tHU7i4uJw6tQpcd68eexd52RwcDDs7OwQ+LLaJT+fxuDoUSKlVq+GsG0bdsyaJb4oKBCnT5/OiVOn4lBlJdz8/eE8cmTda589I/LE2pqUNBkZEL77DmUyGdRtbKBYuxZ58+ZBr3lzPK6uRmhoKAxVVRU+ERFcamAg63r/PjT79aPCbxcuEPA7cYK+X7GC5nLNeVhuaopDbdoIpaWl4tSpU2kiNmlC8+elTiW7Fi6U94qKkuhv3Eh7dHk5kbD6+tj/5ZfyLomJEsPgYNrnXVywKiQEs5Yvf8W/2Ldvn1xXV1fSw8yMFC6//gp5mzaoTkpCiYmJUKWhwYyTk1m1RILDgwbBs7hYdP78c8aaN4fg4YGKL76A6syZKEhLg8ngwagaMgTizJm4cuUKhAMHhILKSqZRWMh0hw6FibMzVEpKoOvkBJlMBll2Noy9vXF771549+9PFzRnDpGXrVpBsLXF4k2bMH/BAkjy82kNzJpFe/vXX9PZGRuLVE1N4KuvYPb0KWTTp0Nr9Gg6F3JzgQEDIMydizW3boklosiUUV4TExNx0KBBzNDQEMU3b+JKUBDi27cnwqxHj9qDYfPmzWJOTg7T0dFRlL14wX+1fTukX30FNmNG3dz4+Wd6psHBkIWH4/SLF4Lk3j2O9/MTu6mrM0yaROs2IoLOb+UeUV1NueWZmXgYFoZrcjlKXF3F0tJSZpKfLw7+/Xemk5ICZmBAdSkGDSKCY+NGirirqSEtLQ179uyB88OHgsvAgZztB3QGAkDn4IYNNF/eZadO0VxVFsB7l8nltN7Xr68lZDdt2iQ+e/asdjwbNmyIEYGBUImKojlvaQl07Ijk5OTalprmVVXi6OfPmdTVlbp4ODsDAF7k50PWsiUudOwoyr28xBHl5RxbvJgI7/Dw2i4WYkoKFB4eOLhokWLYpUs8AMhOnqy/xs/x43Qm7NxJ54C/P5G88fGvpEKKEycitUEDEfv3s5hvvhH6jR7NSSQSxMfH4/Tp00JlZSWnoqJyfP78+X3fP1D//9snGf8n+6+z77//vrOKisrxLl26qHt4ePxLoeDGjRujWbNmitjYWD4xMZEl1lQkNjMz4y5fvozLly9DIpGAMYb27dujzQf2pa/XYmPpUOze/Z8F9AHKixYEim5bWv6ffCTHcZDL5aJCoeC0tbXZ9evXWZcuXf716P6WLeQEZmbWDx4Zo8NdReWDnoMXwLQePMDhw4dx4yVyNGjxYnL0pkyhQzUykoCDXE6R/OrqOideFOv6NytBC8/T9xxHB3dpKYGA8+cJ4JSVkbPHcZTbP3EikUTKCsX375Oj0bIlOaUDBxJQzcoih8DRkZxWAwMCN5GRBBIFgRyVvn3rchZXrSJAsG4dumlqSuLLy8EpK6uXldHBPW0ava+ZGUVfw8PBdegAU8YEjYQEHt27k4pCV5cILTU1AgwWFhRdmDmTZNPnzhGQGj2a5Ohffkmve4/t379frKqqYsnJyTAxMRF69erFyWQyGBsbv+I0BAYGcgBwsXNnJMyahcvFxZjfqhWrD/Spq6vDzc2Nvjl8uI6E4XmaJ2vW1KUw1FhycjKXkZHBvOtTSjBGa+m13506dUosKChgGhoagqGhIRs4cCD31mKGSuvRg5xIiYTA5ZkzNJYHDxL4vX6dwLkSKLx4QQWSVq4k4uiv2GefAaWlqKysrM17fyvY19MjB9fBgcZMQwNo3Bh3NDTEOwcOcNlWVjhfUiJ0OnqU41u2hIuLCzIzMyEINdkB9vZEbO3aRaRGu3YE+gSB5klcHM3ToKC3pq9YWVmhJl+chYSEKADwkZGRtb9fvnw5vvjiizpl1cGDROIcPEhzc84c2gMGDKDrX7yYyLNhw0gx4+ODtLQ0GBoaivL0dEh+/x0YPx6ZzZvjyeXLou/WrQxLluDZs2fYvn07TE1NMXHixHd3SCkvJ7CnTIlZsoRA0KVL9DynTiXCw8CA1rqODl1rq1Y49eSJIm/wYL6ttTWmTp0KzsYGTm5ugIEB8qysoOXkxM4UFoomlZWiXnIy56CvT4SatTW9t6srvZeVFVr17s0ehIYiLSMDxnl50FLubfHxNAYnTtCecugQOd+lpfUqv86dOyd6e3vXAf2RI0mR8lpEkeM4JCUlvfpipdqhoIDev1s3cJ06YbiDA9u8eTN++ukndNfSgoW+Poy/+oqIEWVbMZ6nsdPUpP3o0SNwixdDW0UFmD8fEn19WOSSctfN3x9WISFQWbGC1zl3DpednSELDaU5mJ1N7/fgAalxgoNpHW3ZAowfj3J/f2zfulWUvXjBFRUXQy6XQyKRkBLitfPlmZ4eV7x1K/SLiog4btuWyBsfH/AKBVhhIYS+fcFpaKBg0yaUvk7MVldDNz0dehxHZwdjQEAAJGpqkJSXQ6OggMOBA0CHDlDfuhWDjY1RMnYstnTsKI7OzGQaM2ZAe8wY4NQpmHz5JTBlClTnzAEAdO7cGejcmavMz0dKp04it3o1s5w2DZpBQZTWoqoKmJoieu5c+cVHj3in4GCmO20aPUdBINXLzp3geB6VlZXQMjGh9Tp7Np0t168TqNy6FQ+9vMR7kyezipISzAgIoD2xaVNa7x07gvP3B0tMFAZ368bb2NhAJpNBS0urdjB1kpLQ1d8fpebm7ObNm8jKyhJLSkqEqqoqThRFNnbsWBQVFfH37t0Tfxs0iA1ydITJiRMUDR4/nnLWx4wBfvoJKubmCPD15aqvXMH2rCyWOmMGrJUKn9at6SwKDaV1JpXSdXbqBDsDAxwZORJjAgKY4eXLUNy/z9SaNQMrKKDc+agoQC6HzNISV3v1EnHjBsvJyVGkpKTwurq6YkB0NIc+fd6+D7xuTk71pqy9YQ0a0Nl9/fr7/5bjKGjx4kUtUDY1NX0F7Kenp2PV6tWYPXs2Sh8/hsGkSUBMzCsR9yxVVZYyZw4cHj2igJWTEzBkCAxGjQIiIjDU15ddS05G1urVsBg1ivbtykqgWzcoKipwKS9PjFm0CDO/+opnp04BWlqQ3rlDZElk5KsdBPT0aJ+KiaE90s2NiMavvqK9u8ayc3LEzMREuLi7Y9D48bUbrqurKzQ0NLjdu3dDJpN99f5B+t+wT2D/k/1XWXBwMJNKpTsHDx6sYVtfv9GPNJ7n0atXL14mk4l3795lmpqatOmVliIqKgoaGhpC48aNue3bt+PcuXOIj4/HkCFDoK+vX9v66YN6x4eH04F58eKbhdr+CWZgQCDh2DHafP8uOf077Pnz58jNzZX07t0bz58/F/z8/LjMzExEREQoHBwcOE9Pz4+/iKqqOpne33gPTk5OmD9/PkJDQ3H37l364Z495ECbmhJwrU/2/vrP2rV7+4cIAj2Ds2cJMOflkTMaEkLOiJcX/VtVRX8fHU2ROp4nh9zQkKTwpaX0+y+/pGgZY+S0q6iQo6wEWxYWtekKUCjo7zIyAB0d8AoFSk+fhsHw4RSB3b2bwP6vv9J7TpkCfPEFSnbuROMLFziX69eJUPD1pUigEsS3bk25ubt30/UPHUqf3akTgbyJE+kwv3+fqn5fuULX6u8PuY8Pkr74Qrzk6QmT/HzRKz2d012+XHAJD+fC0tOxc/ly2ObkINHaGpaqqoKTtTVX2qgR0q9dk+cWFPBjY2LYjitX0LJtW4WKisr7izQkJBBR8+WXFLVr1KjedIJhw4ZxK1aswPXr1+Hr64vMzEwcOHAAbkZG6B4cDDx+DEEQEBERgYKCAsjlcmRlZTE/Pz/R19f3/cqVoiJyVPX1SdapbOc2dSqB0d27KaI2eza11VIW/Nq1i5yh3r3r0kc+xg4coKjKiRMUUQHwTElW1WMyiQT3Dx9GZVwcDI4dg93QoSgsLMRViYT1u3wZRjduYPOOHaLuuXOi3urV7IaamgIAr6GhQff4xx9EgHp5ERk2ZQqtKY4jUiknh4iEUaMIML1nPVdVVUFXVxeampqCjY0N5+Pjg40bNwpr1qzhhg0digbV1dBLSYFi8GDIpVKoAqTaeN3U1Ah88zzg6wuJmZmYYm/PZyxciCw/P7GodWsxeutWDoLAWoaE4JSPjyjT0WFyuRxjxoyp//mWltI9DhxYV0/j++8piq4scGVjQ2syIIBSgLy9X83N7dQJWffvi76TJ8NF2RpM2WoQgNGqVegMsKT27XHz5k1WfOoU7H/4AZyXFznPLxf+GjwYEgAZy5bhzo0baL1nD9o5OIDbvBn8tGkkbd66tQ4k7NhB0bXg4DduzdraWrh8+TKvr69P+fguLvXuhVKpFJWVlW+OzahRFMUbOJDue9gwqAGYMWMGe/DgAcI3b0afQ4eQZ2SEU+vXI9vKSnR1dWU9t28Hp6w5MnUqzf2qKiK6Ro+mNeDsDNjbg509C+MlS4i8s7UF++UXkenrU2eIw4dpT8vMpGjwxIm0tk6dAgCcPHhQMW7hQv7OZ58Jj5o1EzmO4zF2LKlSXmuNWl1dzenp6dHeERFBRGnNHpI3YwY7amMD56oqPBs0SHgSEcE5PH0qcqGhDBkZNN83boS9mxtf0qwZKbhGjKB5oKFR13rO2ZnSWgwNoZ2dDc2hQ1lhYSFS9+0TnXx8GPT0SPIMEEEYGEik7/jxgKkp1KRSOMTGss0//aQY+fPPPDp1qjsTAHgFB0vyTp0SFJ99xuDrS3Pg6FHg+HFEZmfDMTdX1NTUZLVpNytW1LWRDQuDvGlTWJw7xzrn5kJFWVBu61aa5+3akZpjzBjIWrRgdn37QmX9eqhqahLRGB9P5/eRI+B790a/fv3gbWmJR1IpM27UiDdv1gwampq1KYKurq5sSUoK1Jo3pxQYY2NKbXN2pvGPj6e1dvYspBERUPz2m5CXl8fVpmuqqdE9ZGcT2D90CGjfHjf27cOF+Hj0NDISzNu355hMRgDW2ZlUdAEBRDJ9/jkUmpq4pKvLcOkSXFxc+BYtWsCjSROGzz77uDTNlBRSSSjbR77N3Nxoj3z+nO73XcZxRMimpgJz56Jg3jzExcVxALBgwQJs3rxZzMvLY1VVVfipJt0qKD6e1nlJCaYuWIDnBQU4ePAg9h84gKCgIMDdHfLwcMDcHHxFBZiWFtigQcgpKBD5igpmsW8f7Vv+/hB9fHB97FgxSlMTwwIDSVl37RrthdHRNLc0NYlQ79KFiL/KSiLdly6lwsDJyfT18GHtbaXdv49zVlasb9Om0HutzklBQQEOHz5cDqBfUFBQ4oc/gP+/7RPY/2T/bebE87zuG3k+/6L169eP9erVqzaKr6Ojgy60idRujM+ePcPmzZuxceNGqKioCOXl5ZxUKhWVfeSnT58OfX39N9+8vJzyrX/99Z8J9JU2cyY5nHL5uwt//U1mbGyMRo0aKcLCwvgJEyZwxsbGCAsLE7Kzs/m0tDTY2tri9bZA77SCAnIy79wh5vlvNolEAicnJyRERaG9jQ0qgoKA7duhvnjxv/bG27ZREb6rV6navbJljZLMunePAHfHjvRclA7Ey2SXkkR4OT/7ZZn3vHn0rzIPE6DIldIOHKB/ly/H88xMVG3diu1jx4pzWrRgaNGCGHiAnEdQvt7N3bvFyMhIZtyrl9B01y4C0zW9swGQowuQs1dZSc539+4Eqn/7jYgBxuiaDQzI8WKM+txbWWHfoUOim0SCPgEBjIuIYAaVlVBxc+MwZw76BAZyQqNG4BYuRPGkSSgLDua4jRtxeNo0cfz330uSHBxwxccHUzdvBj98OC926ABWXk5gtkMHyn1XVyelzZEjBDTPnCFnbv16AqBubuQsduz4yuPS0NDA8OHDsXfvXkgkEty+fRtSqRSpjx4hoWtXUfH0KTtx4oQokUigpaWFnJwcBgAXL15k1dXVFGGrzyorKVLZsSNF9YcNezWSqqFBvy8qIvVH//4kOT1yhMDFL78Q4fJX7cwZIiMZg1Lh9La2gk+fPsW2bdsQEBoK44IClGhp4ZS2Ntq0aYM8Y2NIrawgjY3F1C++4A9WVQkOe/cyu/Hj+XaTJkF38WJyQA8eJMBkYkJOa1YWzZ9OnYj0SUmhOTxrFjnX+/a9syVdy5Yt+ZCQEHHYsGGcsu3V7NmzudDQULFqwABWWFKCvXPnCi8yMznF0qXofOECXNLTofrw4ZvpKjX3HdO0qZipULDB+/cjb+RIpDo6supnz5iDg4PYxsODJXTsiMKEBDE/PR0KhYI9f/4cxkoHXKEggPz4MeXhLlpEX87OBCxeXr9lZZS2UFRESgZRJFJn+PBa5UZRURFKSkoklu9RXtnZ2UFTIoHxyJFItraG6Zo1qK8SS3l5ORqZm6NjZCQq4uKwZPp0dF+wAC1/+AHc3LkEdtXUiBApKSHCYPJkIsNeuvb+/fvzHMcJN2/eZC1atGCYObNeYqZeoK+0gAAirl7rb+7k5ASn1ashxsaiLCgIDaRSxMnluH//vvirm5vYtVMnzuzuXejk5lJkfvhwmjOqqpS+0Lo1vZG9fa0yoWDyZJgWFTG+qAglCxdCe+tWAk4ASYabNYNsyBAkXr2KuPR0RWpqKieEh8Pb3Z3zVqbdqKi8UXA0OjoajDFoK6P1yjout24BcXGYMHQoLykrQ5Kjo6h24wZre+cOeCsrBoWCrq9DB+C773D3xAkhOTmZiykuFu1NTFgHDQ0azEaNCPC3bo3qX39Fpa4uYtLSEGNgIBgbGMB8924OQUG01/v60j5rZUWgt6CA7nH3bmDtWnB5eei9axef3by5YK+vz6FhQ8phHzQIaNUKzWNiuJM9eqBDfj6sxo2j59+7N0rj4tBn2zYmrlkD5udH87iigkDyunXAhg1I9/MT7zo7s6aXL5O6LSyMCJU+fajbxNChQFkZ5LGxnHzCBKg0aULrrWtX2oeTksgHKSuDSmIicO2amKepyVwvX4ZqeTkeDhiAhqWl0PP3Bysrg31cHLQjIgg4rllD5PjWraR227uX1k8NuBdF8c3AzJEjQHg4snkeDaZPx8VRo4QYfX1u2LBhaJyTw2HkSEqvWbuWiNDPPiPgX10NqKhAbdEiTK6sREhIiJCYmMgGDhzItLt2RcH06dB/vcDgu0xd/dW2n28ziYSIrdBQ2jM+xHJygGvXcL8mtatRo0aQSCTo378/27LltXR2VVU6RxYsgFFREQxq9jNNTU0BAIeGDXHPzg7NysuRY2MjaquqMq0XL2AikyHr8WMBs2Zx2L8f+PVXCFIpzquosJmrV0NaVUXP192dyIe7d2k/MTQk/0Zbm+bJ2LE0FtOn0/cA+aXdugE3bqDE2Rn3goIw+PZtaKenv0J4yuVy7Nmzp0wmk30XFBR09sMG53/DPuXsf7L/GgsODuZVVVUv+fj4tPL19f2PEFXx8fE4cuQIdGraY5WUlKCyshLnz59HixYtEPB6flZyMh0QI0e+vxr5P8EKCsjhunGD5Nr/ZktJScG+ffswePBg2NnZKfNj5TzPMw8PD7610ll7n8nldAhevfqG7Ppvtf37UfT559j62Wco09SEyHHEdv8VUzo3Li4Ebt7XUm/mTFJdxMb+tc/7CNu0aZNgZGTEDRw48I3fVVRUYMOGDaJEIhF69erFf7DCpqKCDnGlrVxJ39vbEwnw+DE5p6qqCAkJQWJiIqZMmQKdj015mTAB8uxs/Nq5s1Can88peB6O+vrikL59GczNaQybNiUS7qefSMo9diytVScnklMDBL4rKijC/prl5eVh/fr1YIzB2NgYGjduoJOmJvY0aiTKZDLWunVrsX379iwzMxMnT54UAwIC2NmzZ8WMjAw2ZcqUOkCotKtXKSq5cSONS027rreaKNL1rl9PUTV1dYpM1kc2fohVVRGx4eqKF2VlCA0NRUZGBnr06FGX5vCSxcbG4uTJkxhpbg6bsjLsvH0bKTUELM/zmD95MrhRo4Djx3Hz9m2UfvUVWlVVQaNlS6oN0r//myk0W7fS1+7dVJegspLIu7FjyVk/fZpAxVvqSFRXV2Pjxo3gOE6YNm0aec3R0QQe7OyQpKqKpy9eQEtLC02bNkVRZiau/v674om+Pt+xY0exZcuWTEluCIKATZs2yYsKCiSzQkPBKxRQMTUlB1tp27aR0x0ZCblcjl9++QUlJSWY6OMD06ZNwXr2JILm5Emq5fC+bidFRXWKjIwMUnf07ElkB4D169crzMzM0L9//w9qJXJu925khYWh9+LF0K9nPq1ZtUqwuHqV87t6FSGjRgG2tsKQVas4/f37ifQpKqIInxKATJ1K+9SPP9J8Bej/HIdjx44JcXFxXFBQEKUVeXgQcfmS7d+/X3z8+DH77rvvXk3VSkujSPqBAwT6+/enKPTL9uefpHgqKwM2b4ZCocDddu1wzscH+i9eQLC2hl9YGPjSUiT36KGQaWqKnVatkqgmJADduqH6+++RqK4OfXV16HTujCe2tjg2YACsnz5F99BQCEOGwHTECMDLC9XV1di5ZIk4fOVKduObb9B80iQYqqhQjZQ1ayi9xNX1DWC2Zs0aRZs2bXhPT8+6ejBpaTRuW7cSiVdQQCRrdDTJkutRDmVlZSE1NRUvXrzAw4cPRV9fXwYABhcuQCcnB4qmTZG9cyfOdOoEhUQCCAIcRVFw6taNufbtyzB+PAEnZ2c6K06eJDK5eXOA41BeXIzfd+xQ2F2+zLv17w9TKysCUpWVdD1Xr0IYMQJnOA6mXbrAY9s2IlJiY3H0yRPx7t27TENDA7169ULTpk2JzL14EVi5EqcOHhQsjh1jNrNnM50WLQBVVRSXlqLg4UOYOjpCNSIC+PxzyOVyLFmyBAsWLAD/9Cmtq6Qkat1pZ4dL/v6Kuw8fMq+oKMS4uHAmdnZITEwU+epq5tawoVB66xbyFArOLjERFhkZaDJoEJEKokgkR1ER3f+yZRRoefIECAxEQnKyWLVhA3MdNAjYtg1XQ0MFHuKfWwAAIABJREFU1x07OBW5HBWqqki3sUHcsGHCyJ9/5riDB0lBlZ5OrzcxIQVEbCyBzw4diCCtWdeiKCIsLAwxN2/CJTYWGDIEfZX1aT7ESkpIWfIhOf5PnhAZfPnyW/fD1y07OxsRs2ahWlUVdhMn1qakZmRkYMeOHVAoFPDy8hK6d+9eN7F/+QUZy5fjwNChqNDTw5AhQ3D69GlFYUEBr1dYCGl1NZxFUfTduJGVlpZiW3Awupw9C37/ftg7OoItXIg8mQzRZmbCo9xczsrBAbanT+PZ2LFonZoK/cJCIlI6dqSaD5cv05nMGD1HO7u6G1i+HOLDh9jq4yPoFReLg3ieh4EBKYNq7NSpU1V37949V11d3TsoKOgTuH3J+EWLFv2nr+GTfbL3WnBwsIaKispvDRo06BQQEKD6t1Vt/0gzMTFBw4YN0bFjR5iamsLc3Bw6OjqIiopCTk4OtLW1YW5uXveCwYMpatWjx3/kej/a1NUpb7xxY8oN+zfb9u3bFc2bN+datGgBuVyO+Ph4MTc3FwYGBrwoioK5uTmrrq6uv5DLy+buTuz1Sxv/32qHDpGjN3gwSvv2xeOqKpRXVMDb21uwtbX98Ml47x6Bl8BAimS6uxPAfC2iVa/5+VExK4WCoii+vn/9ft5jDRs2ZKdPn0Z0dLTo7OzMVFVV8fz5cxQUFGDv3r2iQqEQZ82axX+U8qJ5c3J+lbI7Hx+KoFdWkrRz4kTA2holmzbhlKUlhnToALOXD/sPsYQEYOBAcIMHw6ttW9bU1RUigCfPnrE2PXtSBEmhIDmslRVqW6EtWkQRm8BAimwAtcWxMGQIzp8/rzhy5AhnbW2Na9euKY4ePcppa2tDIpHA1NRU0fzqVWZlaMiSbG1FBwcHwd/fn+N5Hnp6evDy8mK6urpwc3Nj9+7dEyMjI5mdnR2RGIJAgGrdOopkjBtXJ9uvzyorKcK9YQNJT/X1CRhpahL4t7X96AKNoiiiaO1aSIcPR7itrRASFsZ4nhcVCgVMTExq+4QDBILT0tIQGRmJ0tJSNGrTBmYJCbC9cweOPXsiizGhW7duzKSmhR+iomB28iTu6umJprGxTHPePMqPr68w4cqVdH+tWkEoLUUux+G+rq6YpavLDL7/Hmk+PpAsXIgHpqbIKC1FTk4O7t27h7179+LGjRu4cuUKJBKJ0KNHD87Q0JDGtn9/ctKHDYOBqSkaN24MCwsLSKurofPrr3BeupQzNDTEhQsXhKioKKanp8fCw8Nx9OhR6AgCm3b2LJOuXw/+228pIrl5MwHTsWMpstekCeDiAi4zEw7PnkEtKUk0WrKE3SguFhtv3MjYzJkE+D+ky8miRSRxbd+eiBDltY8YgXg9PdxNT+dG1xSheqdFRgK9eiHU3Byjf/sN2suXvxFpl1dXg589m3W8fh3aGRlo2asXWrZsydSnT6e6IC1bEoBLTq5rvefhQetrzpy6oqA1znaCTCZYentzTZo0IbDSvfsbIERXV5fdvn0bjo6OtTJsAARwHj4klYe5OX1u9+6vXvOVK0RmdesGODiAYwymK1fC57ff4D5yJPiiImTo60MuihB8fbkCS0vutJUVUq9dE8yuXcMmXV3WZ9IkXC4tFR64uLAWS5eiY34+3LKz8dDAQDzcpAmLTE6Gp6cnHjx4gMfZ2aLvt9+yxr17Q2P9etqf582j5+7mRs/9pRSLZ8+e4eGff3K9EhLA+/rSOkxOptxqU1NSXrm5EQD196e1kZRE0fZ79+gMqLnfmnmtePbsGSsqKmIlJSWKJ0+e4GlRkWh3+DDb16KFUG1hIUwJC+NUBg5Eo5MnxY6hoVxhRATLHjFCMFqwgHErVqA2Ir1oEQr37MGdlStxrbpaYengwF2/fp1zHjFCdOzenSEhge7H25uKOFpZIapJE+G6TMb6JidDMmYMzU1zczR1c2N+fn5ITk4Wr1y5wvz8/OjZ9OoFWUgI1LduZUbnzzN9Dw8iMxYsQO7hw+JuMzOWGBYG+zVrcMTcXIyOiQGXns58TEwo4m9rS36HtTWweDFCjh9nDg4OnMemTUywsYFjnz5ISUkReTU11jswkJm3acNa9+sHg7Q0GKWnQ7J0KaXeuLqS3xIXRyrKyEiaj127Ara2CCsrE03c3ZlBq1bYm5wsPOU45jRsGJOmpkJ16lRYfPklWvTsyVjDhkRw/PgjPS9nZyKbqqvpZ2vXkq80fHjtHHj06BHOnj2LLhcvorRNG7HK0JC5KOtLfIhlZ9NZ8LL67m1mYEBjZWDwTrXTy3b16lWx8f79zKNRIzhOnQqA8vW3bdsGZdDX3d2dlZeXY8uWLWjatCmempvjSmYmzNXUYPr0KcIzM1FZWcm1j4hACx8ftPX2hv3KlYx98QWkBQVw27QJ16ZPFy9eucIaN24M3awsaFhYwH7yZKZtaoqC0lKFrFUrlpWVJUYWFKAt1VshX+aHH+hehg6lc9jHh1JElGeaoyOuFRUJyUVFGBcby7PQUNqPa0i3hw8fIiIiolAmk3UKCgqq+PCB/9+wTzL+T/aPtuDgYEcVFZUZKioqw62trSUDBgzQ+Nsq4/9Fez2FQE9PDwsWLMCPP/6I+/fvw8PDgySDy5ZRXur/QYT8b7XgYMoNTkykgjr/RrO1teWio6MRHR0NANDQ0BCHDBnCnz9/XoiJieFu3boFnucxZcoU6L0NBAkCgZ9/g3QfWVkUjYmOJrBqawsjOztM9vbGDz/8AHd39w+bjGvWkLKjd29yEgSBpOQfY2pqBI6vXiWp4tdf1xUb/JutQYMG8PDwQGxsLEtISEBiYqKQlpbGSSQSwdjYuLYI3kdZRET9hdacnQnAKhSAry9u29mhbU6OwrZNGx4ZGeRgGRi8v5hifj4RIseOAV5e4Gruo3PnzoiNiUHuunVo4OdHDkJGBgHrl/Mj9fUJ3MfFUZpBr14oKimBWFiI69ev8zY2NuKOHTuYKIp8x44dcf36daFVq1Zcey0tHtu3AyYmGM/YW8eFMYYvvviCHTp0SDx44ABmFRQwnD9PRNK8eW+P0AgCAavt2ynqvWIFERPu7uToVFbSa+3tyXEODv4ocjE+Ph5XExPh27SpeD8/XwwMDMTFixdFnudZs2bNav+usrISO3bsEJ49e8YBQGBgIOxsbYHx46FlawutsjJMnjyZgygSACgpAW7dAjdhAqwbN2Z7LCwwU5mTWt+c/eknQFUV+QcOQL5uHfb26YNSfX3Wb/9+JDVvjiQjI7GRpaWosXw5i+3RQyg0NIQoimLTpk15AwMDFhkZiQkTJnDampoUTVUqfer7rIcPSaa+ZAlJxZ2c+MjISBw7dkysrq5mTKHA+NhYxgcG1qXGcByNK89TpP7AAVJgxMcD48bBoFEjdNi9m2XMmYNrf/zBUkJDxYkTJ344EWhn90ruNLS0qGaBnx9Kfv1VVHVxYZs3bxZUVFTQu3dvzsLCov5ipra2wNix4JOTUWhkhNCtW0V1dXVWUlIilJSUQCGXcw63b6NVUZEo+/13JnkZeA8fTuB09WoiNQYNIhDo6ko/nzCBop2bN1OOdEUFFEOHojo1lfkGBVGUd/Rocs5fy2dXFkm8dOmSMGTIEFon585RnQJlGlGHDrSPe3pS1FSpgPH2ptSVgwdpjjOGrPPnUZWXB3HsWFRlZAjuM2dyKUFBiE1OhlhYKEzetIkr8fPjDIqKMLBBA8h8fNB7wQIOgkCAYtw4oH17eC5ezFwPHMCyZcvwc03uuUwm454YGsKW4yhibGFByiPGqHCeREI1VbS0gN69kWVpCQ89PUElMZFDSQmRIkoQNmIEEXKTJxP4HjeOQLWdHd3Pzz9T7Zc9e4ApU5CQkKDIysrijY2N4eLigoCAAF4ikdD+mJuLLydM4KCnB+zZg2arVmFX+/asTcOGsGrUCFslEvH06tWYIgg4GxWl8Dp3jo+NiREfN2jAOmRkQBIZyYXcuQOZmRnOnz/PdIuL4fj770QszZ8P3LqFShMThFlZcf1cXKB64gTlTo8fTyRtTVQ+JSWFNW/eXIFbt3g8fw506IAqBweEFhcjkOexc+dORYWnJ9poavL5Dx/iy+3bwd+7h6gRIyDftInpVFWh/Z07uFBYKKZOnix6njjBmgQGMtUJEyAXBJSVlbFOnTpBmpICX4UC+OknTJk3j1u5cqW4evVqplJVhe5XrkBn/HhYK2t+xMfTM5oyhVJhPDxobLW0qHBtkybIbdCA3bx1C3/m5wt6enqYEBPDVPT1qWCmjw+dzS4uRByEhNAaUNafWLaMyJkLF2hM9u9/ZX7n5ORAR0tL9M7JYcnu7mzPrVtITU398HbOurof58OUlRH5dPLkW/8kNTUVOTk5SEpKQlpaGgvYvRvGLi6kCPv8cxQUFLzy96EvKZc2b94MjuPEKhsbZv70Kfr++ScMVqzA+dRUmJeVoYm5OZFyiYm030dFQeroiP5Dh7Kna9fK8/PzJQ2/+IL2SgAuLi5wcXHhAUAul7Nly5Yh4sIF+FlZ0X6Rmkpn2sSJRJDK5aTmsrAABg7E47w8WCxbxo2fNw/cqVO0D9X4Ey9evMCxY8cqZDJZz6CgoBcfPoj/O/YJ7H+yf6QFBwfbq6qqrlFVVe3g5eUlbd68Of/WqtD/AIuOjhYAUGQDINBw7x5FZv5DKoR/yTQ0CDT9m8F+QEAA69SpEzRq+hWjpkbCyJEjuZycHBgZGWHDhg2KzMxMvl6wrzx031fU5mNNEGqjjJg2jQDWS8ZxHMzMzBQnTpxgw4YN49RflqcrraKCQNzPP5PzwBhFbZV5aH/V2rShXO3kZIrA3b37b+mgYG5ujtjYWISFhcHCwgLTp0+HlpbWX2Pavv6aiia+qzoxzwN796LwxAlI9+zhhREjwJmYkANkYkISvzNnKErzOuEnihQtPnKkrj8vAOTkQC0kBAaCILLgYJZnYADDdeve3vHB0REoLoYoirgvkyH72jVEyeXQMjJSDB8+nAdQW5CzXbt2BBzs7Skq8VKU513WPD2dKWJikGVpCfNNm8iZqc/y8siRu3qVlDaWllTIUOO1TqOVlaQgKimhuZaVRcX6Rox4596jUCiwZ88eZN+/j8kXL0InMpI5N2jAh4aGioWFhdzkyZNficAeP35cIZPJ8O23376a97pnT921KR3PZcsI3JSVASYmaNSkCYrU1SF+/z3Y9OmUrvCybd4M3L2LiwMHyi8XFkqadO+O3oaGMO/UCceTkzHM3ByuX37J4O/P4OYGxxs3+NrcZBARERkZibLMTGhraBAxNHLk24kwT08idV6ydu3awdvbmx3cuBFea9bgzODBCo/u3Xnzl/+oYUMCPrt2EYFnZERR36io2jlpaW0NGxsbJCcns/Lycmi8/rxesoqKClRUVEBLSwuVAwdC/fFjZKenIyMjA/n5+Yr8/HwUqqlxVR4ebPru3XgxfDh3tkkTcceOHVBRUYGjo6M8ICCgzo87dozWwuzZwLRpOOnvD4lEAiMjI6Fhw4aclZUVGmzbBvXz58EdPcrQvv2rFzRiRJ2yxMiISLjBgwmUmprWFT5Tdm1RV8dBTU1Fhq4u67Z3L82B336j8di2jdaiiQnA89DR0YGWlpb46NEjThRFMIBImfnzaZ9VmokJgfurVykyDhDpIZXS/RUVAZs3Q1i3DhrV1ci2tRWMtLRYg27dYLRiBfQ8PSExMuKutmqF5u3bQxIRARsNDVIGLFxIkdroaAIYogj07Ak1NTUEBQUhIyMD586dQ1paGs1xDQ1K6xEEWqc5OURMxMaSND8nB+jeHbdycxVtxozhlX3WXzG5nFqRGRrSeiwpIXBkZwdx0CDENG4M/u5dNIyMREVeHjTKyzlTURRLNDXFrKwsrkGDBmjXrh3tjw4OFPU8cADYuBGa4eEYvmEDHs+dC6e5czGbMf5GaCiiL13Cg+RkPvHpU/TdtYu1qKrC3nHjYHf/PvO9dAkPOncWH+vrs+zlyyHt2xc27dtTgOKPP8DHxsL32DFRtmQJKw4Ph46ubp16CEBxzf7Ypk0bHt99V3v2lDCG5nFxiB4yBLn+/nzf5GQxQyoVnnTvDh+eZy/y82E+bBi8srORNnAg9E+fRtqTJ+j7ww/cNT093ABgdvo0NDQ0IJFIRKlUSptXbi6wbRskkydj9uzZLOHRIzT+5RfkZmWJ186eZQaZmdCvribCPzyciJbRo+nMzs6mOi1LlwLr18PA2JjZ3LolqkycyNx9fJhKSgrtBQApq2xsiOiaNIlIA+X+eecO1aqorqZ5uGEDAd0OHYCxY1E2YABiYmLQQktLxL17zIbn0VoqFXbu3MmJoog5c+a8qmapzyQSWlcfal26kDJNSfa+ZBUVFXjw4AFOnz4NdXV1saysjA0YMAAuLi40H5cvB7y80Pil9qC9evWCpaUljh49Kjg7O3Pt2rVDdXU127NnD9IBbJk4EQGPH6N/aCjsrlyp2ydUVWkdt25N4wLAwMAAly5dElvcuMHY99/XdbyovVUJmjVrBsmPP4pIS2PYsIEKdI4dW3e+SSR05gOQtWqFqupqqAYGQreigkizGnWCQqHA/v37yxQKxcKgoKDoDx/A/y37BPY/2T/KgoODOYlEMl9FRWV+mzZtVFu1avX+FlX/AEtLS2Pq6upi69atGZYuJenYjBlvbRn1j7dZswg0HD36bwf89R2CHMfVpkNUV1ezzMxMXLhwQezWrRur7bEuihQledlR/DssKYmAwrffEqh+S1HFMWPG8OvWrRPPnDkj9O/fvw793LtHzl1AAEWucnJe6cX8t5mNDTl95uYENqdNe7cE/CNMFEU8rKl+O2HCBJiZmf1rcpqPaAXXrVs3LL97F97TpkG3qIicid27CXiMG0fzMi6OAJ27O73o++/J0btyhebFqVMk5wwLAy5dgsc337ANPA8kJcHn3DmxS5cu9aPgzp0hc3BA1NKliATEL9LTmdiqFVqPG1e7kF8BuuXlJO/8kLoFGRlAVRXst29HXKtW2GVkJM5t1uzV66iuplSH5csJVK1fT/vIu+Sgeno0vioqRKjk5NBcePjwrfOupKQE169fx9OnT9HHygo6KSkov3oV65OSBMYYGz169BvrMi0tjR86dOibBa5u3CCQf+YMkSWbN0Nx8SLOhocLoqYmGoWEsIYeHkxFRQXnVVTQJDsbxseP42R1tWBvb8+lp6crdC9c4CoLC9mNy5clDg4O6DVnDrSOHoVi3DhkDhpEMuidO0niXVhIgOfQIaCiAkWtW2Pr1q2iqa6uYNq3L4/Ro0kl8DaTywl43b37Rm0ESXk5huflQfb554izseF2/PEH7O3tFf379+d5nicQGxJCc7JHD/peX5/A4Evj0r59eyQnJ2PFihXo2rWr2KJFC/bkyRMUFhYKhYWFnJaWFrKyspCSkgJBEKBQKMAEAV8uX45jM2eKqtbWgp6eHm9paQlPT09YWVlBfexYWDx+jLFPnzJh2jQ8yszEoUOHJL169aorpHj1Kjn//fqhaVoaNCoqRO+tWxlA2Bq7dtE8nDQJbwB9unDKm1aaqSmtrSFDaOy1tCjV4PhxYOxYKAQBCQkJvLe3N7QcHAiM9u9PEVZtbVICPHlC4Do1Ff369WO7du3C2rVrxane3kwlOrr+zhHr1tHeNmcOtQUFiGj59lua5337opwxWC9YAKP8fE5S05KL+/xzOHz5JWBmBsPHjyFOn06vqWlXh8mTX011adeOOg3UBBIsLS2RlZWFgIAANDYwoP1ECSKVr4mNJYLixx8BiQSPe/ZEzuHD9dYuKS4uhuLPP/Fs40Y8z8iAJCcHmt9+C1dRBMvNRZFUitOnT8PU1FQRFRAAURTh9uefbHBCAqf29dfsytKlQnXLlkQqxsTQ3paeTs+wQQNwGhoo09PDo4oK0anmGbc2NkaplRXcpk7F+vXrcbpHDwSMHIlxDRogJiZGIevTh+v2/DlruXIlyj08sK+gAD5378J2+nRkJyZCRyZDuy1b2Plu3eChTNNq0QJFwcHYcfu2orq6mjczMxON1NUZRo0CmjZFZWUldu7cKXR3cIC7iQnXdfZswMWF2W7ezBpraSF5+XJBdfVqzqCoCDhyBLbt2wMyGbqsXcvE4GA0NjWFRm6umJOTo4iNjZVoamqKtXPWzIyI7YwMSL/9Fi6XLgEWFmjs4sJOeHoqIoyNuR7u7izqyBGoNW+ORufPQwHAfMcOAuuJiQQM169HxcaNsMnJYdrjxon56uo4OW6c4G9uzjUESO2xZg0RlS+DToD2ZCsrUmOMHo1aUmfoUJSamGDf/PmwsLSUd/nhBwmaNQPatEHHjh05PT09RERE4PLly+jxPrWVKFJ0+0NNU5P294MHUT10KIqLi/HixQtERkYKOTk5HMdxYqtWrZitrS0LCQkRXFxcaIOSSOh+KiuhdeoUvLy8wHEc3NzcwHEcJk2aVLuR3blzB+np6QCA1h07Il8uh/0ff0D09CSFg5UVjbGREZ3FRUWAujpGjhwpWbx4MYoHDYJuPfddOWECdB8/huG6dQxSKREsn39O7/eayeVy7OzTRzRTU0MPZ2eGoUOJeJs/HwBw6dIleXFx8S2FQrH2wwfvf88+gf1P9o+x4OBgE1VV1X36+vpeQ4cOVdf9K+2j/kOWm5vLKioqIFy/Du7AAXJy6ov2/reYMgIxbRpFz/5DqgpBEFBRUcHdvHkTgiCw4uJi+kV6Osn0Hj8mJ+7vsPx8yhueNo2K33Tr9s7quFKpFEOGDGG///47CwgIAH/6NLHthw+TQzhwIEW6/p3WpQs590ePkuPati0d5v+CVVRU4NixY0hKSoK5uTmUlc3/sj19SqD1Pek3crm89l9RFKGurk5RFGWrw2+/rcup//57cv63b6eft2tH83TtWlp7s2fTz2u+fAD4dOqEtWvXiteuXWNt2rSpN+Kal5eH+Fmz0PzSJTSMjWXa/fvD38rqzWg6QKC6WTPK7XyfnTlDzsmaNWB378Lm9m08OHmSPXnyBLa2tkQwXblCYM3KiqKPGzZ8eIpGUBCBprlzCaDt2EFzr29fep8a4iw7Oxt5eXk4cuQIACJWXHkeyM1FZm4uyu/e5QIDA9GgnnodPM+LxcXFb5IkjRvTnKtp/Vjl5AR5gwZImjkTFl26cFpff42jPA+ZgQHiEhOFRFNTzvOHH5DYtSuXmZkpN+J5zs7fn5U1a4YZ9vZ16TojR6K0eXN4z5xJOcXK9AUTE4oYl5YCe/fi4bFjgk9ZGfMePZrHoUMkOX+XcRxFnV8vWvfiBbWu7NcPKgMHYgBjrLi4GL/88gv/+NIlWP3xBzRbtADXrh0VzpPJ6OvQIcpp3rGjNtWk4Uut5y5fviyGhYUpx43T0dERpFIp09DQYH5+fmjdujUKCgpgaGgIoWdPTG/enEFV9U2WWEeH5vzEieDOnoVTSAhUVVXF/Px81qBBAwIKL+XnP9fQAGdmxtwqK3H79m0ULF2K1leuoLJnT5xq3Fho8+AB5/S6bHjHDgIQmZl1P/PzI2XOxIkUzdbRIfLNxAR8z55wdXWtbUOppqZGEVCFglQmJ08SME1IAMaNg82cOZhga4trR48yfuXKd++PbdvSM3/xgtJ4+vYlkkJVFXBxQSM7O9zp2VPUl0pZEwcHeg5DhgDdu0O0skJ2YSHYli1Qb9YManPn0n5+9CilH6Sl0ZzV0alTRZWVAbduIeDkScFy716utiZDYSGNSb9+VFzv1CmS8teo+M6cOaPw9fXlVFRUXlkbjx8/xtlNmzB+yxaEzZ0rqJmYMIVCIT5//pzDkSPgJBJcGD1aVFdXFz///PO65z1lCv0bF4fG0dHshb4+kca//UZfLVrQ89m3D6lt2mDvZ59h5q1bDKtXEzkSHQ0tY2NoGRlh4sSJ2LJlC4oiImC/bx/6REXxACDbvRsix8Haxwf+9vYIj46G3W+/IaKqSvQ/cYKd6doVL5o1ozHR1AS0tKB28CBKp0zhBVVVyOVydnLUKNgmJODw4MHgeR62trai69df80zZIeTgQdydP198IpEww/x8zvTnn6Hj5ET7hYYGgdvvvgPbtQvOZWVwHjeO4fJlyVNBQOmDBwxXr1INnkWLgOJiWq+XLtHaO34c4HkMvnOHf5SdjRs6Onjy9KmoePZMwbKz+dNHjrC59vZQKyigNJwdO4BRo1DctStCWrYULA0M4PHgARuwdi23LzMTzu3awejGDRjExIgyT088jomBgYEBc3Z2pjltZkZE89ixVCi3xlL8/XHo0CH0jIiAtbq6BAcO1BYI5nkenp6eUFNTQ3h4uKJHjx7vjvxoaJDy5GOM44D163GY44TEJ084VVVVsWHDhtyYMWNgYWHBACAtLQ2VlZXcyZMn0atXr7rXhoWBmz0b3VNT36oA8/LygqOjI2JjY3G5Jsp+Z/Ro2N+5A19/f2jt30/r6Mcf6Qzr0oW6T6ioQCKRiA/T0ljrP/+kZ8hxRGB7eKDaxQWZcjkenTuncExP59mAAXUKi9csNDRUUaqlhW6BgTy2bSNCoaa7UHJyMm7cuFEik8mGfCrI9277BPY/2X/cgoODmYqKSpBEIvna3d2d79Spk/RtLZ/+qaapqQmzq1dx4PFjNF6+HC319P77F5ejI0XtCgr+Y2Cf4ziMGzcO+vr6iIyMFMPCwpidnR10LSzIEfyYAnFvM1Ekh3TvXopCaWmRpO8dlp2djQMHDijKSkp482fPKLI2eTLJkf+vi56qqdEBC9AzGzv24+sB1Jgoili+fDk4joO/vz+8vb3/9etr354qi3/33Vv/RBAErFq1SpTL5UwqlYrGxsaiVCp9lR344w/6NzqaCICTJ8nxXraMAHJkJCkxAgMJXNRjvXv3Zrt27UJubu4buZQhISHyhw8fSix79hTb7drFDNTUiEy4epWqWb9ujFEE+V3tNMPCKId+506SQteoUqQuL6VzAAAgAElEQVRSKVSqqvDnqlWYmpFBUtWlS4m0+NDuBi+bpeWrUk5NTXKefH1p3FesQHZlJbZs2QKe56GtrS1OmjSJaVRW0tgtXAj7b75Bu3btcPr0aXHatGns9VQHOzs74c6dO5yLi0vdL6qqaL6PG0dOeIcOCDlyRDD18OA+nzePkw4bhufa2qLHuXMsZdAgzJkzhztz5gwqi4rQr7BQbPrjjxIsXEgR8tcrsANQWFoi19gY1T/8AOmVK/SMO3WicZfLAVtbWJiYMKPERIZp094P9AFy/F9rp4jCQqoI7uBAJF3NveuoqMAvM1OhMW4cH966NWS6usLgXr04xhhEqRTlvr5ItbERNAHWKCWFMUNDwNISCoUCANC6dWt07dqVk8vlSEpKgq6ubr0qGWWKGpebS8TY7Nn1X7tUSnPy2TPA3x/OjRsLqampfIMGDSilZ82a2gJfbcrLxROGhmzZsmVo/uAB2lpboyA/XzzYsCFTKSrirl27Jjo5Ob36kKdMqQObL1uXLpTXrMzlX76crkUQ0LZtW8THx+P69evo0KEDgZ2a+wfHEUGhbIkqk8F4yhR0jIhA9ooVsNi9m1RU9dXjsLCgz5w8mda0jQ0RskZGQGAg1HJy0EJNjYWvWoUmABE1Eglw8ybYrFlw3LIF4VZWSIqJQa+0NJJda2qSQqiykvaPBQsIID94QPUBLl6EtbY2F9Gli9Bz9mwOTZrUAaGqKqq5Mm8eEcyjRytbn4lGRkZMEATk5OQgLy8Pt2/fFlJTUzl9S0tRIzmZzTAxUT5zBgDlY8Yg/+lTtGKM6Roa1o+0mjdHlZ6e2CAigsHFhQoCWlhQhN/dHRg9GveLi9FYTU2hERjI1xb4BEhdAUCZXmbVsycpokQRuH0bhQcP4s8+fTDJxQWemzfDc+ZMIDERc7W1WZG1NU40a4b2ERHI790b8r17YWhkhPKLF8EOHYKrq6toYGAgehobc3kBARBTU9GoUSMMHjyYZ4wR2Xr3LoRx41CVnc38k5MhiY+HmjKtJzyc9riEBIjnz+Nx+/bCi/JyNDIw4CyGD0fKo0dCkaMj17RbN7rfiopacglPntDc4nng8WOYtW0Ls5QUoHt3tAcYwsMlivv3cYYxqKqqEkiPiiJStnNnTDczA5ydOdXbt2mcSkvRfcECaMydC628PNwMDGSZLVtCkpSEB9XVij///JNvYmmp6DNvHp8+aRLsKipQpauLguxs3LlzR3H79m2+bdu2uGtlBZ1Fi0TNCxcYli2jvaRm3jg4OODEiRP8s2fP3k2ccxylghUVvZUYVxbSq+2i1rIl7o0ahez4eO6Lr7+GYT1zycTEBO7u7kJsbCwniiJ69+5NvwgIoLS6pCQild9SW0BHRwcdOnRQng3QXbUKke3aocTaWhxSXMwwciSdw23bksqspu6Ik5MTCwsPR8tNmyCZNYt+N3w4sHYtdGbOxMCqKtzr1o2/Zm4u3n/6FNnBwaxHjx6ip6dn7T3cuXNHfHz/PjdTRYVxbdvWFc2dOhWV1O6wXC6XDw0KCsqu9+I/Wa391+ORT/bfb1KpdIWOjs7kwMBA9bcWYfuH2/A+fcAvWoToKVPEsGvXWNi1awAAd3f3us31v9HMzUlC/PXXBNj+A6arq4vjx4+LmZmZoiAITKNdO3I4/67rCQig/OirVz+4vsKFEydQ8ewZP0Aigf3BgxQ9/Zh8u3+X7d1LBMipU0Ra1CfVfYeVl5cDAGbMmPHxLe/eZg8evHVcCwoKcOrUKSErK4tpamrC2dlZcfnyZb68vJytW7dOcHFx4Tq8rtzw8qJCSQoFSS/lcpJ5CgJF9rS1KXqnlFu/ZDY2NpBKpWJcXBwzMjICx3FQVVXF3r17FWlpaZKZM2dCS0uL4cgRAgJr19afihMWRuC9PhIAoOu5c4cUKFOmkHOrHIObN+H64AHs09MR9fQpFN99B97P719L+Zk8mZz5l40xYPZsRBw+rHBv1oy/4eMD+4AAcejQoYzjOLoYmYxysn18AABt27ZFVFQUEhIS4PBSPicAtGrViv/999+RmZkJC2WdgYoKIjB+/x3Q0kJhdTUSnz/nuPHjKec2OBjGhYVMOzwc3/74I/Dll+jeoQMB03XrGOLjqWq3UrHzmqmrqyPHz09c6+CAL8ePZyzp/7H33mFRndv3+HrPmRl6r4LSmyggiNhAY48SY2+xl6DGHqOJJtFYYmJLjC3GxNiIXVTEXhAVQVSaoiCIhQ7S28DMnPP9Y1MV1Hvv55b8fq7n4VHgzDDnPW/Ze+21906hsXdwAIqKoNLXR4GaGjgXF1hevdpsdKgRtm0j57LWuC0qqi+YVttGUxCo4NfixfB1dOSFhw9hLAjYtm0bW7lyJYyNjQVDQ0OuqnVrGJuYcPe0tTHo2DHR4+JFxvbtw+Mag7dDzeeRSCRwaSqf+1VkZpIz9yYwRuqNuXPR8ddf+ZDTp8UO3t6MPXnSSBLfKjOTTfz+e6QfPw6bZ8+g/vw5DG/dYp+VlmLTpk1oWHyxDqJIayc4uLFCSCajOVYb2fb3B3r3Bj75BIU1kczq6mq6tm9fWpOvgucBnodEFPGiVSuo6enB8uRJIpJzcuizT5zYeK9gjBy1X34hxZSeHl377BkweTKeqKkJ1idPcjh5kvKXo6KI+IyJASstRcy2behSVkZ/Q6UiR9TNjZz90FBycnr0oFSwUaOAvXtxNiQESqVSxCvzH3FxdG15OakI5s+H4OaGkpISydGjR8HzPCQSiaihoaHS1tbmRmprw3zVKlY5cyZe1fhpWltD89gxtAoPpyj4q89gwgRg2TIk9+8vyjQ0YNamDa2zDh3orLp2DUhNhe+DB9jfoQOfn5gIo9oaFm3b1pFZCQkJMDExEcwcHTn8+COd4/b20B03Di/j43H2xQthwNy5HG7eJPVAnz7Q27cPy3V0EBsWhkerVuH65s1QaWhg4IkTGObsLDqsWMH4Bw+YcPMm9tjbw8jISDVw4ECeY4wc1fx8QKUCl5SEqs8/x7ljxyBcuSKMOnWKAwDx66/x+KuvxIeFhUJpXBxvfP8+S2/XjoXfuAEHBwdVRnU15+DqSufYkiWkoFiwgJ7btm14fu8ehC++gDhjBlrcvo3QO3eQkZEhmpiYMN8LFxArkYiiKNaTle7u1AHExASnBg9Gv9JSqL14QQSUtjZshw8ntUS7dvhg8mRaf1pawNWrfH5+PsLOnOGem5sLqoMHuZULFoBbvx4ymUzU0tJCQEAAjI2NUZKVhXyFguX5+cFEKiVi6N49oGtXyGQyODo6qq5du8aNGjWqWQMj7MYNlRfA//zddxD/gbOgQ2QkPi4rQ3M1rdTV1eHv78/du3cP0dHRje1RxkgtY21Naqc3QCKR4KNOnaB8+BBRAwYg08hIhFzOwPNk+7i6kvrG1RXYsAFdunRBTEwMLq9di35t2iDlwAHE//KLqkqhEKt27+Zcr17litTVEe3kxKprcvpr1QgAkPv8OSQTJ7LZ2tpQ37GD9uv584GYGIiiiJCQELlSqTy2bNmyi+88WP8/xntn/z3+q/j+++/namlpzZw8ebLmmwoZ/U8jNhaaZ88CiYnw09ZmXVQqJCUlITc3F7du3YKPj8+/LoX+b4HjKKKqq1vPqP8HkZKSgsOHD0MQBNauXTvWp08fSFu0eHOht3dBVRU5GTY2JNFsGMF5A4SKCkTGxuLDb77BYxcXOIaGgv9fal9am8O+aRMZxX5+dF/vSGLIZDJoampi06ZN0NTUFObPn//2Vl9vwrBhlBIxfHijHyuVSmzdulVVVlbGOzo6it27d+c8PT0hk8l4Nzc3pKamorq6mrtx4wY0NTXRsWFdhqoqIjW2biV5vJoapXR4eNB93rwJPH1KDtsXX1BEsEG0smvXriw0NBSxsbHgOA4ymQzV1dV8QEBAfZ56795EKPTpQ2vg1bmfktK02kWhIHn5hx/SvdfmjhcUUFTq888pMvnFF1Dv0wdJISHC7Xv32BArK2Zvb/96Pvy7oqSkzgFuSHBkZGQgLCGBzx47Fj0lEpjWFr2qRa00tYYUkslkYIwhKCgIAQEBjQxIc3NzeHh4qIKCgjB16lRec8IEkjbv3Ut90KuqoO/hgU9lMhw4cABXr15FzxqnQ/3FC5oH2toU+bW0pOjj6tX1Fa6bgIaGBoYPH862b9+Oex4e8C4uBiQSVN+6hVtpaYLU0pI99PcXph06xOPkSVIhrV795lSWe/caj9vVq+RY1Mpzo6PJASsooAi2hwc4ANoAvvzyS1ZQUID79+9zt27dEqttbZlTfLw4Y948tiM0lCU4O8MiJESF9HSeMzTEP9SaEiCp/Pjxr9UAaBIDB+KWUqnqNW8ejw0baBxroVAAa9dC+/FjuOjokKP0xRfgOK5OdRAREcF69uyJRuub4yh9q6TkddWUmRlFtlevBlxcoFq6FFkhITiUlobu3bujW20r0IAASt2pkf2+iuqOHXFdVxdDfXxojQC0X128SO3pfvmFnNJacuSzz2i/nz6dugNs3EjpORYWUN+wgeOnT4eYlQWWlITK779HXs+esPL3R3Z6OmwePACTy1GQnQ3DP/8kJ7+sjOZtTX4xfvkFDQvrPXv2TPjoo49eP+h8fMjRB6geyL59KPD0RFdra5FfuJC5e3jA0NCQodauTkrCsdu34VdaCo2mbJshQ0jR0xDXr5PDXl4ObN8ONQMD2J89S0Uwf/uNVCllZURYrV0LvQcP0LqqCmnr10OZkiKa+fkx/PEH7f1du8Ln2jVYP3/OFWpqwuDnnyltQhShdvAg/O7eRed16zhUVVFOe0YGqg4eROC9ezD28xM5iUSM6dqVm/Xbb5Bu2YKqmTNholCQc7d/P7hWrSCVSjFo0CBej+dpfTNGJOivv0JYuhQG33wjtgkNZSe7d2elhYXYvWEDPvntN5z/9FO4GxryXrm5MHd1ZZJz5xC5cSOysrP58txcqLKzUfnZZyibMQMa48dDOzCQ0qDu3EH44cOq0du380dKSsTHLVsymUyG1q1bIy8vT3iQmsolOjkxANi4caOooaGBvLw8Nik2FlpyOR4FBOB5WRkm+voiYdQolPr5ofP69WD29jCcOBHc+fOkGlu6FPjsMxj9+COGPn7M0KcPKxo1Css6doRKLodEImEA6uaI7qNHOLJggfAyKor7YvFiSG7fprXy9Cmgp4f27dvzx48fF1Cj7Dh79ixKS0upDahMhrt374pFRUV822++wdI5c4hcQ+P6MM2dDT8tXYpOd+4QSdTEOa9QKLBr1y4BANeyZUtVw88NgNYdz9N9N0UANgBnagpZXh76xMbi8e+/izhwgEiwR4/ozAsMhGrpUkQnJeFmZKToHRPDHP76Cw/MzXHi2jXY2Nnxurq64E+fBnJyEN++PQaPHAmVSoXjx4/j4cOHokV+PlOtWoU7WlqihZ8ftJctYzAwoNSbDRsAdXXcvXNHSE5Ozqqurm5ChvQeTeG9s/8e/zWsWLHCT01N7ceJEydq/G0dfYAMeH//ul7KPM/XtnNCcXGxuGPHDvZaBeu/Ezp0oE02NpbkdP9BREVFwcLCQkxLS2NGW7cKL5Ys4e59/bVSeeYM09DQgJmZGefj48PUmurb3RyCg+meSkvpQK4t+PcmCAJFaR0dcXXxYuiHhKCTj0/zVd3/29i0if79/nuKcN+7904Ov1Qqxbx581BUVISdO3dye/fuFaZOnfrPT9zWrVFtZYX79+4hLi5OdHBwYN26dUN8fDxEUeQWL14MmUzWyPgwNjaGsbExKioqcP369cZEWVgYFVyKjCSppEWDWukPHpCTExlJ0f2cHHL8y8pw5cABsf3ixUz/+XN069YNhYWFKCgoUPXq1YuvqqqCtbU1GhUC1dWlufHDDyT73b+foj8AGeTdu79eNO/JE5Kif/wxrRUNDWoXlpZGUTM7O2pN1qCn9uTJk7kzZ86ojhw5wmtrawvdu3fn3N3d//G9Qk+Popo1jltpaSkuXryoSkxM5Dt37oy+ffvWt2o6c4ak4HI5RcnLyiiaWwNra2v2+PFj/P777+JXtX2Qa9C3b19+/fr1WL9uHT6pqoKjtzfy8/NhZGBA6Qrt2sFizRp4enoiLi5O7NmzJ71+0CCK/mRlUUS1sJCKWB45Qrmqs2ZRtFoU6fm1bk1qiIoKmKSkYEhBgVisqckwcSJgawteTQ0pY8dyLkuXYmqXLjybPp0ImLFjyVH9+efXVB0AyMGysiIHr7SUnEZtbVonaWkoXLkSMbm5Qqm+PlLathXFS5eYcOECY4yJoijC0NBQGDlypCQiIkJUV1eHUqmE1dOnMIuIwOTJk5Geno6CCxc47wMHkDJsWF3nhn8IdnZEZr2lFokgCHiUmMh5h4SAbd5M0eYuXSgVISGBao/Y25MRHxtLhQRBlbINDQ0FGxubpom8Gzfq/ptb44yqqalBEARkSCQQevSA3pw5ONGxI1pnZIiTVCpm/cEH9a/fubNxC8FaKBSAnx8kgYEoyslBQkICWtUW5KolWrKzaR4UFFB0W1+f9jJzczL2DQwoCpmTA6Snw27YMFRYWuLChx+Cd3BA3smTsH7+HNmrV4ued++yjyUS3O3cGY+rq6E+ahTaLVpU50hhwQKaky4uRIqeO4eMjAwoFAruVVULAJqv69fXqXmepafj1KBBGJyTw6yLiurGFwDllD97BrXx48WdO3eyESNGvK7ssLOj/aI24v7oEaki/PzImf/iC/A+Prg9ezZaDRxIZMOCBfQaIyN63t9+i14ADvE8okpKxIBPP2UwMqJ57ugI6YIFiD5xQnxZXi5OvnyZY998Q3toixaI7d8fDwYORJ/kZDgtXQosXIjsyZPRc+FCcMePs0PjxjFzKytRvmgRMyovJxLqjz9o3+jbF+jcGapffgEfGEhEa8+ewJgxuL17t5LLy+Mrvv4are7eZTGLFqEwPZ1xLVuim7Mz/vrqK+GzefM46cOHRLRt3Ajk5qLrwoVQ+fri0YkTYGZm2D13rrJCqeTKt27lbIuLRcN+/QTLBQvYC21tvuzoUYz28WGipSXAGBhjDEolQ3w8Pli9GkWCgKioKNy/cIH5V1RAPH0aWQ8fYkpgIG6NGYMntrZoefOmiM2bxeDx48WM7Gx+7NOnVD/F3JxImG3bSHZ+5gywahX0PT2BceMgeXU9v3wJDB+OcbGx3Nrdu/HkyRM4d+1KKp3SUsDNDbZBQVAqldyKFSsgk8lEmUzGzM3NhdDQUM7Q0FBlZGTEZsyYwbjevakuyTvYw0qlEqGhoShVU0PhgAEwPHKE1ClNXJeTk8NNnjwZVlZWr5NYPE9KnC5daG9+kzqqY0dg0iQ4TZyI4keP+IqJE6GprU1BBnd3IC4OgU+fqvQzM7mAmBimKZejcto0xGto4Kvp0yGTyVB64wbuvHwJ73Xr0MbFpY5gb/v4MfD77wzz5uGBjo7w3NVV7L9gAQ+Oo/PB3h74/HM8ffoUly5dKlMoFP2WL19e/taBeg8A75399/gvYeXKlcOkUunuYcOGaRg0PCT/Tnj5kqqgHznSbN5up06dWGxsLJ4/fw5bW9v/8Af8P8SQISRNboY9/nfByckJZ86cYVKpFF1WruRSw8Kgo6MjkclkKC8vR0xMjHDnzh3Mnj2bvbVrg1wOREQQax8YSA7Pu2D8eDq4r1zBpS1b0EpDQ+XasePfo6jEnDl0CBcXU4vCGTPe+hKZTAZTU1O0adNGfP78Oc6ePSvq6uoyxhiioqKE1q1b48MPP3y7BxMejuu+vgg9dw6amppwcXHBzZs3ERcXp6qoqODd3Nzq2ys1QG5uLi5fvqzKzc3l9PX1BRsbGx7PnlHuZe09TJvW9N+USgE/PyTs2oWHd+6gS4cOsGzdGiVHjzJWXEwO4fz58La3xx8mJvydO3dUQ4cO5ZskbSoqyLjduJFyPwGa/59+SvLSWmc/O5uM9YULabw7d6aK5V9+SXL1SZNI3thEipJUKsXgwYP5fv36ISoqCpcvX8b58+ehrq4uAGAcx6Fnz56sTZs2byeWHj4kh2TECJw5c0b1/PlzvqZQE/1eS4vmfUwM5XVPmEAO1Cvrpri4WADAVVVVsaysLLSovXfQ3Pi8ogKJZ88ieNw4iKdPCxUVFZxNfr7wye3bnKSmPZeFhQULDw+vf1MtLdo39uyhsbp8meoYqKtTNHf+fPq9IFBOvlRKn0suR66BAe6HhrKWhoZUm0BNDZy+PuTt2ommhw4xDqB8URcXIhXOnKH3W7v29VzwRYvIuC0tJbKoRQu6dvt24Pp1ZDk4ILl1a9Zn8GDmTk5ErbPOJBIJgoODsX37drRo0UKYOHEi7QFffcVQVgYrfX0qzNelC0sfPBj6mzbhYd++QtsLF7h/SBG1fXtd8bc34dGjR7BKT4dlcHD9PB08mBz7iAga206d6Dm/csZWV1ezZtMK2rdHdf/+OOvtrYqLi+MB1BJholKpZOrq6uikry+MjI7mLL75hnJ2GypfIiPprPj008bvW9OuT2Jnh5YtW+Lu3bv48MMPG19jbl7fQaKqilIpLl2igpzffUckwG+/0Xlrbw/cvAlNd3f43r8PzbNnIe/RA+qGhihwc2OxEybAdfRodNHVxZ49e5Ceno6qmBjUKIjob7RsSWP3+DFw8CBCi4pEe3t7geO41x9YZWVdJD4/Px8HDx6EVF9fPCAIbMnBgzTuK1bQtRERgFKJgfPmsYqKCsTFxalcXFzq37Oigsbs6FEqyvb110QE7thBc9/BAQgORtHp0xBLSoD79yH8+Se42raiXboQoRAUBAwdCiMjI5SWlopgjMjGc+fqVF4DJk5k69atY8/Ly2FTUgIcP46S2Fi4nTyJGx98gOO7d6MnY+gQFATrdu0gDB6MkNWrxa4REfBNTGRYvryeEMzKos994wbg4QEPpVIl27ePx6FDwIABUKlUiEtIkHRJT0fa1KmCr48Ps/viC3gVFUF1/jxMnZzQY98+TnLjBrVRPHuWSMjcXCA7G8KGDYjv1k38ZNcu1qbGP6k6eBAlN26w+L59efvRo2E5ciS0Bw8GOnQAmz27vk5Bbi7ZZLq60AfQt29fZrhzp2CXksIZbt9ORGh+PkaNGEEKlqIihg0bmOPYsVgJ4OWpU7AfOZLm7qxZROhevIi6MeU4IoCVysZ7ppEREBsLdSsrGBkZqQoLC+k5q6vTdR99BM7YGFMdHJDn5AQzS0umr6+PuLg4lpmZKcyaNat+XnTu/Np+3BTS09Nx4MABiKIoenl5MXuJhEijJpz9mqCIkJ6ezjUsHNoINWMDNTU6E14tXlqLBQsAb29oHjgAiYeHcNrEhBsF0Npfvx7CgwcY8eOPvJqFBfiaOaypUqGTXE73lZcHnZAQlFlYCNGiyDqoqzPs2UOE6+3bQN++iNfQwFkHBzbr0085juNo3T1+DHz9NUpLS3H48OFKpVI5dPny5clvHaj3qMN7Z/89/qNYsWKFtkwm26Ojo9N/2LBhms1uPn8H7NhBh8sbiteZmprC2tpaDAwMZO3atfv75u/b25MhM2ECOcn/YsX3d4W3tzdaVlTA5NNPgc8/h13HjrBrfAm3ceNGVUJCAu/m5oZmJee1VdsTEqjo09s6JahU5AhcvUqGWI2jFpWXhwEDBvx9JBq1EeqoKDIGxo+nqME7EDZ9+/Zl27dvZ3fu3Gn4Yy4pKUl4zUhvAiWDBkHu54ehK1eibdu2YIwxPz8/xMfHc/fv3xfbtGnT5DgeP35cUFNT49u3b4+OPj48duwgo2v6dIqyv9JTuCmcPHkSBgYG+MPYGK4mJnjs6gomCHh24IAwJTKSk6akwMrXF/1//JEXSkrASySvj4mDAxl+x46RUT1zJjn2EREkca6qoihZVhY5H0lJZMTPmUNEQHAwydXfARoaGujevTvn5+eHHTt2iDzPc76+vnj8+DFOnz4tnjt3jgUEBOCNHUpqpZwjRqC6upqTy+WvR5VlMooQdutGBqy9/WsV0cvKymBnZyfq6+sL+/btYw4ODpx/TS9yAFB3cgLHmEpbW5vz9vbm3N3dcejgQfYbY2gBqHJ+/ZXPy8uDRCJBUVFRfXX9GTPobz94QGqTuDhytqZOpZ/Vtvh8xdG9nJoqKnR0WLddu4hAmTQJrH17eLVpIyp27GBnMjPBJyeLH8bEMGzeTGSenR09u8BAKrwGUOS0ooIcpblzyQGeMoWM+l69gJ07kRsdDWlqqmBnZ9ekdz59+nTu8OHDyM3NrZ8sUind148/1snSW3p6IsPGBu3CwzkhJgZcmzbv3p3Fw4Pqh4wc2eSvo6OjkZSUhJSUFLhlZLDStDToAkQ2TZ1K5FJYGBEsPj6UivIKqqqqWFJSEkpKSqCpqQlDQ0OYmZmRWmDGDPHKkydMLScH8+bNQ4M6OvX3XF3NYe9ecuzPnCHHrVaSHxdHSqKGzn5kJK2bY8cAAL169cKRI0fePA49etCXSkUGv50dkXxFRfSVmUnrzNER2k5OwMSJ0HR2BhiDMYCG9PvUqVMRFBQknj9/nl26dAljx46tJ99HjwZiYlD9wQfooafHdB48eO3ZV1RUQGlvjytTpkB+8KCQmprKWVpaCrq6utz98nIotmyB9OpVUpT4+xOhZWgICAK0SkshLyxkWLWKiNfgYFL8nDlDkVSA0gL8/GgfaRA4kEgkzGvnTqQWFoqBw4ezXgoFunbtSnv4lCnkIGVlIT4+XujSpQstdi0tUh8sXQowhuPHj6uUSiVf8PIlbJ4+BZyd8SItDa0TE+G3cyeefP21WJ2YyLJ378bZYcPE/Jp6CjEdOzIPa2volJcTIaFQ0HtqaBCJ6e8P24AAfn9AgDDUyIjTKyqCXC6Hrbe3qIqMZAozM/bM2xv2ffvCwMgI2LEDWbq6SPvhB5W7jw+P4GAiHWUyqpUxcSKSXF3Ra/Bg6rtek3qllq3e+GMAACAASURBVJgIk4QE9Jo8GQe2bYORgYHA8TyHzZvJQa0NQJw9W6/0SkwEfvwREV27QvrNNzBkjPaVGzco5Sg8nFK89PSAXbvga2cH1/Pn6d5MTUkZFhBAZNOECUSsuLsTWdi+PSpMTcFxHNQFgZQ0sbEAgPz8fN7c3Lx+4nAcqcNUKpgtWQKz+fOBOXPw/PlzXLx4kQ0bNqzxoZOYSORHE+2Ia5GcnIwDBw7AzMwMM2bMoNerVDTfcnPp878COzs7REVF1c+RpqCmRoGrGTNoXF89D1NSSBXVsiUUq1cj0d+fs2+gulOuWIFL1dVC1ZgxbPDo0QwPHtB8ycig10ZEkELOygr2I0Zw6StXomThQugGBtL8Wr8e+fn5CPntN3z88cdMV1eX7OuJE4ETJyBqaeHE/v0VgiBsXrZs2ZVm7+M9msR7Z/89/mNYsWKFRCaThdjb23ccMmSIuvRd20r9r0EUqSLvxIkkAX+D48QYw9ixY1l4eDjCwsKQmJgoTpgwgf0tc/iNjIhhjYl5t0JY/0cwd3Mjw7cZJ69Tp078hQsXxDNnzrCWLVuqfH19eQcHB/rlyZMU8ezThw7st7Vz3LWLHITQUDLcnJwaMe0SiQS2trb/o9r9N8DHh/IHc3LIaL5x463RQy0tLSxatAgA5f0plUoEBwerlEplI4NBqVTi7t274DgO3t7euH//PuLi4oSMBQu4efPnQ7Mm2gsA+vr66NatG6vL720CRUVFnLm5ucpPEHj06EGGwE8/NVstuCmIoojx48fj6dOnOHHiBNp37qzq/tVX/Pbt27lfZs2Ct4WFyiY7m0m1tTk+LIwcJD8/kk8rlfUt71JTySD19iZnpl07cqq1tSmK8vIlkRDh4TRPhg2jn/+TbTc5joOjo6N469Yt5uzsjDZt2kAQBLZ582ZVeHg43ti+acMGCIKAkOBgVXp6Om9vb98oKl8Hxsgp/P13cnJfgbq6OpeamoqFCxfyDg4OiIyMFDZv3owpDg6c8apVYHfvwoPneQ8QMbBnzx4hLy+Pm3XlCtQKCviwP/8Uhw0bxqKiooS9e/eKc+bM4Tm5nJ6jUklR9SdPUKKjgwIfHzGzc2f4bN/OJLXOfkPcuAHvbduQtHAh2Lx5lDc+ahRgZIQu7u6c/N498M+e4fZPPzGkpNBrVqyg6PC5c1ShfdUqmuvBwfVOqY0NkTUrVlA+dM1+UVfluhkwxuDg4IDMzMzGF/78M62rBhg2bRr+ZAzDg4PRZskSMnQbOgLN4f59IjWacfZv3Lih0tbW5vvb26PIy4vmbS1ycmh8CwrIaejdu8n3UCgUyMjIEF68eIGqqipWUlLCZDKZqFQqmX5hIRvo4QHbWuVCU5DJSEWxbh3tlTt3korA0rLpTgK//95I2WJhYQG5XP5uaQ48T3UCPviA5lBAAP3c0rJOOozVq+vy0dG6dZNvM3ToUNalSxcEBgZi3759GDRoENrVpuZ4euLYyJGib7t2THfFCiJIa1ITSktLsW3bNtjdu4feV67g4vbt3KxZs6Cvr8+tW7dObN26tSg1M+PQvz8RLsuWAW3akAx82DD4VVYiee1aDomJ5Mj/8AOdQzIZzdFLl0gC/+235Gw9eUL3zBgMUlK4G+3aIVNLi/Xr1w+XLl2CnZ0dpTZ5eIALDUXud9+hysqKa1urNHJ1Bc6dQ8nz59gTHFwXaS5ITqbPJZHg1L17cP/1Vww8eBAuZmbs2cyZKJ0xA3KAVVVVwdDQUCguLmbntLUx0seHiJusLCLJSkqIPO7VC9qmpijZs4fbs2dP3ThrlZSwbmpqSExMZElJSVhmZ0fqiZs3Efnllxhw8SIPd3faCyoqSDWQkgLo60Ny544YO3w465eVRc9TW5tqAYSHAy9forCoSOXk7Ezz0tcXOHyY9ue//qLUoL596UOkpgJyOcoqKznTWlurupqc7wEDaJ44ORFZ5OoKszt3xAxTU9ht2cJkV64QWbZ4MdXBGD+eFGUAsHs3zty7h7sPHoDneZiIosq3bVsu7c4dVlvfJCcn57VOL7XdAwAAI0fipVIpuIwb93q6SH4+jW8z9uGDBw9w/PhxuLi4YMSIEY3fPySExnPjxtde165dOy4iIgIrVqyArq6uOGfOHNZkYGTkSBrDyko6JxqeY7t301qfOxe7Ro+Gy4cfQk9PD8q8PMDAAKlBQSLv5cW6//QTg1JJZ3ZGBpEle/fSHpGSAowciTaBgdDJzxcPXLqEUUePMgMDAyiVShw4cEB0dnZGXceXQ4doLLS1EX7zpjIjI+O5QqFY3uTgvMcbwX/3v1Rc6j3+P41bt259b25uPnTMmDEa/1LRr/82bt2ijWvWrHfKreJ5HjY2NpBIJHj8+DG7e/cuOnXq1HwU+n8VtU5CTAwZK29gn/9PIIpkcLVpQ7nGzcDKygq+vr7Mw8MDBQUF3JUrV5B5/74qLzERBrt2MZmzM9jw4Y3zu1/FgAFEZri7k1Fam4PWQH4rl8tx8+ZN9O7d++9Zf4Exembm5uQg7N1LZNU7yAZ5nodUKoVCoeAiIiKYrq5unSMZEhKCmzdv4uXLl+K1a9fYkydP8MnGjazzxx9D658ghSxFESW7d3P2t29T8cNevZqUwL8Jd+/eFTQ1NZmXlxd8fX3h7OzMqampwc3NDe5eXvDs0YOz7dGD8YsWUd/nxERykKqqKC9xxAgaL0tLMsRnzSLDx8mJCgNu2kRzJiuLxnT7doroWVrWEwX/JOzs7Fh0dDRSUlLEdu3asRopORcREcF16tQJCQkJUKlU0NDQaDwPKyog6OvjpK0tJ3AclEqlysfHh2tS/n/vHjm8n3/eiKyMjo5GbGwsPDw8RDc3N2ZiYoJ27doxpUIh3rxwgVUzJqSYmzMzMzNERUXhyJEjsLCwEMaNG8fpmptDkp8Ph2++YVra2rCzs2OxsbEsZ9cuwXnXLg4KBTnVhYXA8OE4eemSaHX1Kov08WHPysqQeOeOEJGeztTU1JCfkQFjhQJ4/Bjljx7hnoWF0KF/f7rZAwfIcHd3h0QiQUxMjJgpkcBn2za6kU2bKFrfvz858ydO0BwaNIgM4thYisY5OVFEsYEy69mzZyguLhY9PT2bXeChoaEqS0tL3qkhWeboSFLzTp3qDGQ9PT2UlZWJiRwHLzMzhlatyJivVRo0BycnUoc0s8cUFRVxpaWlqo9++omzA6BWW6y0tJSKZE2dSvfavTuRVAUFjc4puVyOW7du4fPPP2c+Pj6sc+fOzN3dHe7u7iwrK0vwDA1lrS5dwl03N7x8+bJpwgigCLKREa0dPz9q4WhgQOvj22/JSQIoij1hAilJauYaz/MIDw+Hh4dHnWKkDtXVlHIVHEyFN2u7YrRpQ47fnTtE4vTrR/UI0tLI8fv1VyJ0dXWpBoOODjk/PF93/9ra2ujSpQuKi4uFsLAwplKpYFdD0oRnZqrS797lHA4fFrnkZMZNmABRFHHy5ElBKpWKY+fMYZpdu6LtkCFQV1dHWVkZrl+/zjp27MjMTE3B1Sp7Tp+ms2vOHGDhQuwxMhLKtbREtwULGKysaNwkEop67txJqqGsLGDPHrw4fRoat26BnzkT4HkYfP89rnl4QNPCQhw6dChLSkoSwsLC2I0bNxAZGSma9O/Pio4dEzXMzeHWr1/dQn45dy4eBgaKuW3bsmnTpjETExPxwbVropeTE2M+PkhKShJMzc1hFx3NEBQEvVWrUCwIqLSxESwdHYUxY8bwz58+FWRlZWhz9iyDvj4RxFFRlGqzbh1gZwd9fX04OTkhKSkJUqlU0NTUFKuqqph5djY6L1mChw8fotPkyZDo6AC//YZSdXWcsLCA35IlpOoZN47W5rVrwLRpSLp1S/S6epWpGxvT3/j+e6q7cuMG4OiI69evo3379qwu9dPIiEi04cPpsxkYACNGIGfuXFw0MFBphodzFdra0FyzBmrz56OkvBwaYWG0tnR1gZEjIeTkIDY7m5mYmCBdLmfBWloqk/R0Tnj2DBqnTtF5eeAAzfENG3C9uFjoNmgQ+/ijj2B99SqX/PHHQn5JiZCUlMTkcjnr27cvtBoQ3HXgOChUKty8eVNIU6lY9969mVZVVeNCmNbWtJc0UW8kMzMTgYGBMDAwEKdOnfpaa1TY2pKCpgnV3bNnz/Dw4UMAgCiKzM/Pr/m0MHV1eiahoY0L6/bqRZ/thx8Q2batmFNUxB5ERaHr8OE4kJMjpg0fLn789declpYWzfERI+iMXLKE5rlMRmoWpRLYtAl606ax54WFuHDhAjMxMUFkZKSqoKAAkyZNojPr6lVat/PmIa+wECdOnKhUKBSdli9fXtj0B3+PN+Fv5m28x98VK1ascFdTU5s3cuRIDf4/XNH9/xQ7d5Kxdu3aO8mJG6JLly64du0aJBLJ38/RrwXHkUEVGEis+r8Tokj5vO/SsgpkXA/o3x8dHRyg4evLPxgwAL/16iU62toKQ1+tQAuQdHjZMjK2OnWi5+ri0uzfS05Oho6OjlhTiffvi/Hj6cDduJEO01Gj3rkOg4eHB27duoUbN26IXl5eTBAEpKamClKplJs9ezbLycmBoaEh1HV0yBj/RyCXAw8ewG7RIlQ4Ows/ODhwmhERgi/Pc506dfqH3kpfX5979OiRysfHh2+41vT09BpL4Ws7Ffz+O32fm0vEklJJhpeFBRmbZmY0P4YPp9ZMQ4aQMzV79r/s3L8KxhgGDBiAw4cPM7lcjtzcXISGhorV1dVsTW0+cw1sbW1VLVq04IyNjZmxsTEyR4wQTU1NWY/+/fHXX3/xJ0+eFIcOHfr6w120iCTAEyY0+nFycjLU1NTg5ORUZ0yy4mJ8MGMG53bwIB5VVLDYO3eE8PBwTiKRYNSoUbC3t6e11a0bjWNODmBuDklFBQIePmQVBw7wRT4+0D9zhv7IuXPA7dt48eyZ2GHfPvZJ27a4/tlnolN8PDtkYICjR49iwr59yNTXR9H+/YgdO1bMS0nh5XI5OYbm5o0cdBsbGxYVFYXffvtNNXjwYN4sOJh+cfYskYWdOpGhXlRE30+YQNJtHZ1G915dXY30d2ifmZGRwXt6er760MgJvXmzUaeQZ8+eic7Ozhw+/ZTkw998Q+ohD4/m/wDHEXE0ejSpx16Br68vNv38M59y+DDs7e1JW5+XR3NSLqfo2erVdPHu3aQSaFD1vbCwsK7rQi1quwZ4enqK0aKoSuA4CI8eITMzk3dxcUGzBXTbt6dIdFgYKShOnKBuC7VrrKCAHLoLF16r7i+TyYSioiJOT0+PnOTQULpvc3OS7y9eTGoJpZLUWQC9z7RpVI9h6FAiVtavp+gwx5Fqw82NXmNiQlH0S5eoRsTWrfRsunbFwP79uaKiIsTFxYm9evViADBjxgyJfNIknDMxEcTCQq5nhw6ssH17pFpbc9OnTwenUDSad9XV1dDQ0BDPnD7NHJYvh35uLhXvfPaMntvTp8CxY7Bt25ZLS0tTvTZ2XbrQHhIXB0yciL0cp3p2+jTPq1SYbW8PfQ8PaAweDHNHR5GpqwuMMX7SpElcYmIiHB0dERERwYJOnxZ7T5jAnJcvR/XYsZDVEFAvunZFeXAw++STT2BgYABPT092f/NmFh8aCo0+fdC+fXvu2rVrYk8dHbCjR8FmzoR9XBzs58zh4OICzJmDMfv2cfsXLFAhLY3Hw4dEuOjqkgM3ZgzVG9DUhIWFBcaPH4/du3ezRYsWMVRVkbrC2Rk6OjpixRdfMNmoUVCNHYsO588jLCwMhSEhMBg+nPaM8+eB5GQIYWEItbPjTJcsgZ5USs9qyBBKnZo0CcjIgKGjI2exeTMRsv7+tIZPnyYy9uVLeo2jI8o3bkSaqSk/PSgIF6ysxPNt26qcs7I4jVOnOM34eKhZWFBKib8/xPR05A4ahH5r1jDF/PmQxMSwolu3cMbXF58UFUG/Y0cIAQF4Eh4Oo8uXgd69uYqKCmimpkJz715YrF7Nv60mR0VFBSIjI8VHjx6JCgcHMSAggNNcvZpsqYZpVN9+S/O0VnHSAPn5+QCAwsJCdu/ePbRv377xBba2NO7Bwa91K3J0dISpqamYm5vL1NTUxLrWq83h6FEi2CsraY1VVFDw48ABYN06tElIEFquW8cfHT8eIevWiR8MHsysrKzqCYiyMkq5c3EhOy4hgcjXBw8aqSuHDRvGoqOjcezYMahUKl5DQ0PYvHmzUsYYRq1bJ4keNgx5paXIysqqVqlUi5cvX572xs/9Hs3ib+pxvMffDRKJZJKPj4+a9r87GvzvREkJGRaHD//Djj4AZGdnQ6VSYerUqX9fZx+gQ7+sjKrT/7tqLqxaRYZrbVX5d0FyMjBlCox++gm4fRs+trZ4cvAgUygUja/bsoWct549KddNEMjpbwLl5eVISEiAmpoa4uLiUFxczA4fPqzq1asXb9xMUUaADve8vDwYGBigqKgIurq6DXNf//uQSOjgZYyM875963uMvwGlpaUoKCiArq4uVqxYAYlEAqVSyS1duhQ8z8PCwoIKrw0b1qwUsUncvElSTzc3IDgYbc3MOJuyMly9epW7fPkybt++rezZs6fEzc3ttZeePn1ajI+PZ+rq6oKpqSkrKCgQKisr+X4NIl3vDFNTcliVSprn589TYTBBICe2oIAMl39zCpKLiws4jkN+fj5KSkogl8sZANja2mL06NEQBAHPnj3Dixcv+KysLDExMVEll8uZhrY210EqhZ2dHXr37o27d+8KaIro2riRDOVXosdeXl5ITExERESE4OrqykEU6Zrhw2Hg7o7EPXtUFRUVEi8vL6Fr165cI+JEIiG5bVwcOZe9eoGfPRsHx4xBrqkpPIKDxZYtWzJDNTVUfP21aNipE2fbrx9YYSE+3LGDYfdufHX+PCoGDkTFqVPYGRQEtTNnBCsrK27ChAn1EeDycopi18DKygq9evVCdnY2++OPP7BkyRJSPAwYQF8ArW9BIEXD0qVkTDs7k1w7MhIYORK3N28WLRUKpj17NhGADg71aiBDw7qxsrOzU126dAmurq6NxzUiguZIA1hYWIgFBQX0zYcfkrNYXU1zvbkikwCtx2aKZGlLpfhy40b8WVAgHjc3Z1Xl5fjq2DHIhg8nIqM2hQkg4qpPHxqv4GBg7FhoaGhAqVRCFMXXonvt27fn21tY0Pq9dQsrV658+1k1ciQpFl6+JNnuBx+Qoy2K5CRERjZ5LzKZTJBt3Mhh4kRaU9On0/X+/vSZO3Z8rZ4EUlLo/jZsIAXEvXtE4nToUE80+fsTudO/PzknKhWpSdq1o/dfsQJcaCjsJ08WHI4f586npKg+3LKFf/DoEUJDQ/HZmjV8zNq1QFCQmKmjA15fn4VduyYMyc7msGkTpbIBMFRTw+InT9ije/fErGXLmP6AAbQGxowhpysqCjhyBI9VKrGsrIw/c+YM+vTpQ8UBFy4k0rVdOyA2FkVVVchmjJ+zdSsivvxSVXX+PH9XW1vwjo3lhjg5se1Pn/JpaWlo1aoV3Gvao/Xo0QOVlZXChZgY3sfVFalffSXe79hRZBKJqFQque4AM6g5+ziOwydt2yJdLsf+Q4fg4OAAq1u3mHLiREi7dCFlweHDFE03NwcCApD88ceoiI+nCO3IkZS6qK9PX35+RCJt2QKAao5UVVWx/Px8atdZ02u+hYmJoBEezl+fMgXG+flI27sXEEVUenrCYNUqIjs4Dhg/HoU//ACzqiqqpSCRkPKoplI/li1DYUQE8kNCUFxeDs3r16Ell4Pt3Elz9cQJmgf9+gEeHtB68kQsMzdnT+/cwaDWrRk2bJCIWVl4rFSC19auJwh++gmhjx8LbUpLgaAgTrp1K9zmzuVEMzOEtGiB38+fR1cvL6EkJoYzvXQJiRs2qLq6uPCOrVtTDYPalIu3IDg4WJWZmcl5enpyfn5+tKbWrKF96dw5+v/ly5Tm0cz7ubm5wcnJCevWrUNiYmK9s19ZSQqXjh3p359+ovmnVBKZ36oVVcEvLWW1z+qtMDOjfcrEhEg4OzuaF999B6xfjw8GDuRTtm8X27ZpIw786COO3bxJz3z9+vpaFMeO0RhfuUJFNWfNajKN0tPTE6dPn665lUpOoVBwnkVFSF27FtKWLZEcFgae58sFQdjx9g/+Hs3hb+xxvMffCYyxqsrKSgWAf6BH2v8Qrl+nCEFCwjvJnpuCSY18MzIyEkOayk/9u0BLi8Zj4ULqSf1PEB9vhbn5uztU+flk1PXpQ8a9l1fdgVlZWanMysqSvExLg/HWrVRE69kzMgCNjYlZbwbV1dXYsGEDtLW1BZlMJigUCubq6sorFApu586dGDlyJBwaGtY1yMnJwe7du8HzvFBVVcWpqakJ1dXVnLu7u2rgwIH/O7KWWkN/zRpy/O7eJQO9Gen98+fPsW/fPqipqQkVFRWse/fusLKygpaWFhrV3/j+exrbWkfrTXj2jIidvn2pzVFtziVIcvvxxx+jS5cuePLkieTkyZPIyMhA37596+TrxcXFiI+PZ61btxbNzc253NxclY2NDevYsSNkMtnbcy0EgYy2I0eIwDI1JYJpyBByWpOSqN7BmDH1jt+XX1LVZH//d0rj+Wdw6tQpURAEpqGhAUtLS7i6ukKpVDZqD+ji4lLbzqu+5/OECWRsBQTA3t4eoaGh/C+//KL69NNP+bro7KFDJDGOjHzt7547d05lb2/PDx06lIMgkFH/2WfA998jKjISGRkZkpqiba+PrakpGf8nTlA0ztYWrFs3dNDTg0qlwtmzZ9mDBw+gUCjwga4uY5WVKNy4EYYApQaVlIDLzIR2x47QdHWF6vhxSKVSTkNDAw0LuRZkZEAiCNAFkU9//PGHWFlZyRQKBaejoyMAaPzZsrMp6r5/PznkN29SxLd3b3rmDg5Q2tigSCZjvnZ2MLC357FpEz1vUaS9bu5ckqNqa2PAzz/zievWIauiAi1at6ao+oABdDaMGkXGbU0ldC8vL/6vv/7Cs2fPKI+3QweSve/YQcSWj0/TqpqAADLcm4JMBumWLfh07Fh278oVmE2ZAl5Tk86lJvYjtGxJBRFXrAA++QT6+vrgOA7FxcVNE5B6enWFJRlj2LRpk8BxHPr06cN5NKdImDaNCiCuW0ekwvnzNGZ799I6aojQUOCXX6D50Ue87smT5KwsW0YkgVxOz6kpCALtUYMH0341cyZd+/vvdBZt20bOc0wMOXOrV1MXkqlTKZ1h1ix6n1GjgIIC+HIcV/biBeLu3OHP+/vDJTERJp064eajR3Dv2RPaYWHMdt8+uBw+jJwLFxhiYmgtKJXA5s2kGLGwwCUDA+ZtbFxftHbYMHJ0uneHEBEBgwkT2JRLl3BKT0/YGB/PFi1YwCTXriHO2xsXL10Spv70EzuQn8/07O1FcdAg5t+jB59lYIAz6emcTVAQjA0N8am5OQru3xdx+nTdZGGMwd/fn+/evTuysrKgN2cOsywtZbKa9AP76GiqUVFT40BmYwO7jh1hnZqKF8+f47Nbt1AyfDgMRREsI4McPH9/2tvMzaEREoIJP/zAIyGBSDwbG3ovR0eat6dOARs2oGrOHGzevBn29vYqIyMj2ofGjgWqqmCnrc3vCgjAy4QE6A0ciH5//CH6PH3KUoOD8cDYGJkmJmJbjmMmJiYotbeH1/79KMvJgX5tYdOePUnh8eOPSPX3R7W+Pg5YWiqLi4sl4/z8YD9uHKVeffcdkQYbNgCMwUxHh/kbGiJ69Wo42ttDUlGBkl9/hWL2bEj696c5YmmJOFHE3fJybm7XrqS6adEC0NAA09dH2+RkxNvaQn3pUs5r926oZ2fDa+1aHgsX0rP29aW95S1ISUlBcnIyP23atNdTYtTVaa/o1o3e082N7CuASAAnJ0pNmDkTqsBApE6dKg6Kj2eSQ4dov/3hByItBwygYFR0NAU9oqNJfZmQAOzaBfmsWeipqQmjXr1gHR/PcOcO7f9qaqRWe/qUnq+hYf1+JJPRNW3b0tru04fIvEGDwBISUKmmhq4nT3JMJqMzf80aWlvjxtHeeeFC/X0uW0b7ma8vpVHVpClUVVUhsKad8/Lly5GVlYXULVtgc/So+JeaGjNVKgWe55Uqlarj8uXLm+jn+R7vivfO/nv8R6BQKPY/evRotr+//9/T2V++nAz+f9LRB1DnoCQnJwvFxcXcGytr/6+jf3+Kcr6loNU/jKQkau8SEtJszmojFBbSQZOcTAbnkiWNfj2la1fJpS1bhF9/+437OigI3IIFTRawaQpyuRwAMG7cOM7MzKzhh2F3797FkSNHMHz4cDg5OaG8vBznzp1TlZWVIScnh3d3d29YtZ8rLi7G1q1beS0tLRQUFKg6duzI1/WY/m+je3f694svyJgODW3kgBQVFSE7OxsnT54UBUFgALjevXuLXl5eeC0lRxTrCxG9CXI5OYWXL5MBMHhw033RARgbG8PY2BgWFhb4888/cffuXVhbW2PUqFEIDw+HVCrFkCFDaiWEzZMpCgVFOIOCyKiJj6fP8Msv9LOOHcnwMTcnh9jfn1QPkyaRIfbhh1QIKjaWnBhRpCjf6NFNO1lvgSiKEAQBT548QXp6OiQSCRwcHHDmzBlkZmYya2vrOnk1x3F4a2tJAPjzzzqiy9TUFPPmzcPRo0fZ/v37VdOnT6df1HYQaWL+OTg48E+fPhU0NTXJ2R8woC4dw9nZGTdu3BCuXbuGQYMGNa4FUFlJbfW++oqMznv3aG2amsKrdWuoFArIZDI4Ojri5cuXuCqVYsQvvyBaR0fsnpnJ4O9PTuKpU4CLC+7fvw+AyJyYmBikpKSIdnZ2LDs7W9AsLmbVZWXMPjQUiYmJora2tjh9+nR248YN9OnTh3utnsaSJWTEm5lR8bKDB4HSUhz69ltVYWWlKKqpsbKYGK7S25v1XryYZKtNteXs2hUoKICmkRGkDWfHRAAAIABJREFUffsiPDNTNbxlS5I4t2tHBm1REa2nkhIgKws2NjawtLQULl68yE2bNo3OABcXSgMLCyNy5o8/Xp/7aWkkxc3NbUwGvHhBOez79oGTy9H2u+/wTFOT1u0raQmNMGwYfUVHAytXQqNbN1Vubi7fpLOvqUlRXkHAnDlzUFFRwaWkpODKlSsqDw+PpteXVErO0uTJRGY8ekREXp8+tO5kMnJkvvqK1GB378Jw1Cj2olMnoXWXLhxatSLn5k24f5/2eUtLWnu3b1Okf/p0ci6OHqV7tLSkNTlsGDm7eXn0TKZNq09bqVlX2j//DKe8PMRERyP1yROkp6aiVUQESk+fxvkBA8SeISHQKClh6qNGMXz+OUVyZ84Erl7FXRMTXJBIoNTQaFyQLTQUsLZGRUUF9u3bh2ofH1FrxAg2pmdP7tLQoahs1Qrhq1YhJi4O3bp1Y1mbNrGhXbuihZUVY2FhwGefocXmzbAUReHQ4cPi7Nmz+YKTJ3Fl/35munYtdBmjNJyaeaGtrQ1HR0fgr79g2r07zXctLSK2G+7PT58CRkYYM2YMLm/ZgqMBAUJGUhI33t4eie3bo9OdOzC8cgXYvRsqOzvEqVSCU8uWnHqfPqiKigKXnw8hPx9p+floY25O+3ZAAGSdO0NTU1O0tbWt/2M5OUBZGezOnQNLSBAtjxxhBvr6UL9/n8HfHwYxMUjz8sLTqirx2rVroiAIqKys5LxatIBnSAg9U4DqNCiVwIEDKO7SBRYWFuLkyZMlP/74o6inp8fw8CE57nfukPNraEgO7pYtMExOFodcvcokLVoAqanQvHABUd7euGJiIiq3bRNcXFx4/Q0bxMlubkzTz4+e28yZRNZt3Qq9rVvFgenprN3SpTRvhg+n+iweHqRu2LLlrQGJ4uJi/PXXX+jWrdvrjn6t7RQSUtdqEatX01dlJUn6J04kEjw7G3t+/12U8DzrPGwYnFxdaf+wtqb1WlJC7+XlRfPcwIAILwCx0dHiJX9/1r1LF9g6OtL5ZWxMe8zLl6QC+PFH2hvT02ndbthANq+zM63Xr76iKP/s2US6PXkChZMTrshkEMrLVT2Dg3nTJroANELr1qQSkUgQFhYm3r9/H/n5+YzjOAyoCQy00NREi1GjgMGD2RQ7O2zbto3jOO7X9232/nW8d/bf49+KFStWcAAcAXR7a57Q/yLS08m4qi388y+A53l8++23CAoKwqFDh+qN778rRo8m1nfvXmL6/y+gVNKh/TZHXxTrI8enT7/eCvDSJYoU7d6Nnk+ecLc6dYLw6BG4fyB94vbt24K5ubloZmb22nPy9vYGx3E4evQoWrVqpcrLy+N0dXWZvb0917ZtW3h7eze6Xk9PD56enmJiYqKgr6/P79+/H0uXLn3nz/IfwYYNNK579lCULCoKFXI5tm3bBqVSCU9PT7F///6M53k0u5YtLUmy96YWk7m55Mh4eFB+4jtWrm/VqhWWLVuGtLQ07N69G2vXroUgCDA1NRVfq1ZUXU3GzNmzFNVbu5aKPF29SpHdAQOoddVnn9F8++ADctRiYuja3r3pmoZpR0ZGZJjVSqPT06n91549ZGwNHUoRmmbmbllZGQIDA4XS0lImkUggl8tZdXV13e81NTWFsLAwDgCsra0xadKkdxqXRhAEyqO+cweQyaClpYWRI0dyP//8M86fPw81mQwWyclwrvl9LZ4+fYrbt2/X5YVi5sx6B74GBgYGGDp0KBcYGIjU1FRxzJgxrEWLFrTWoqPJwK+sJAPY3R3o1w+ioyOuu7qqOn35Jf904kShNDOTOcXEsLLFiwVpURHnu2ULw5o1NJaamsB336F6zRqcBJELPM/j5cuXUFdXR2JiIpRKJTcwN1do0bYtOxgfr2rVqhX/8ccfM4lEgn5N1YhQKsnJDAigOfHFF/RZg4LQbs0a3rqoCImBgVBoa8PY2PjNEldLy7qId1a3buKLBw+ox7yDA0UWJ04kp9PNjeTiLi7A/v3o5+XFHQwOxpkzZ+pbr2prU9TywAEinRwcyECvRS0h8CrS0iiNoaQECAhA8cuXqPbzE3kdnXc7W2UywMgI2jo6LDcnB07NdeQwMwPWroX+lCnQ19eHqakpIiIi+L1796J9+/aoq/zeEKamNA5ffknkyubNRK716UNzw8WFSKCavHkTfX3c6tdPbP2G4quNEB5e7wQyRnn8mZn0/cCB5HxNmkRntaYmzcMRI2hP09Gh18+aRcTMyJF1kmITExP0rZk7nSoqUF5eju3bt8PdyYmZjRkDqFSwW7SIyAZBIHLw9GnkX7wIZWoqJBIJfv/9d0ilUlEqlYof377N2S9ejN/CwwWpVMo++eQTBmNjsMxMdI2NBYuMRNyiReKYCROYjY0Nw7hxlFL1/Dk5dn/8Ady4gQlz53Lr16/Hzp07kZeXB2WLFijQ0YHu5ctEtBcWNiYZjYyI8Bg0iKKqurq0DlNTiUwqLwdMTKAmkcD/6FFg1SpO6NULil9+QVZ6Orbs2oXlX30FeHigiueR6e/PCiZPRnhFhSrr5595y7Q0eJSU4HxQEJydnXEqIkJMd3BgAzZvRteBA9mNiAiha9eutPkVFgJyOVjbtkjgeXi3aEHy8rIywNgYbMMGWDk6YnoNM7dt2zZVZWUlHnt5odXevWLK/ftCvpsbLCwseE8/P2ipVCi7elUw7tyZq66uhkKhoCJ9u3aRSmbNGgrGAEQQ/PorMsePR5mjo+gaH88AQDp8OAbs3AndggJWvGABn7N+vaijri6aGRvT2rl1i0joixeB6mr45uWx5JISlIWEQDsujlQrCgWt7a1b6esNKC4uxq5du0QrKyuxxwcfcKiqorPRw4P2u8WL6Ty6fZsc6tGj6R4iIui6AwfqO+aEhSF9xQqm7uoKpy+/pJ+92nVixgz6kstpLfTpAzAGXX19JtfVhcTamuy12rW7bl39a2s7eiiVdD7r6ADz59P3tWeCREIkoKcn8PIlPD/8kGloaiKuspL7448/8NFHH8HNza35wn9SKaq+/hoPJ08WTKOiOMyejYCAgMYkyKJFNG83bEDeo0eQSqUZCoVi8RsH+j3eCe+d/ff4t2HlypVjpVLpTzKZTMvExETl6+vbRInS/3Hs2kVSq3/R0a8Fx3GQSqWilpbW37Cc+yvgeXJ8Cv8PiqMKAknA1q1r5GC8hupqYrzbtye22cur3tEXBIootWlDUrI9e4Aff8TVS5egl5BQV1ivNldVoVDg+vXrQnR0NDM1NRUGDhzIGzYoIiWXyyGTyZolZLy8vODk5ITLly/zrVq1Qo8ePd5ocA8YMIAB4AsLC7Ft27Ymc2abQ0lJCbZu3Qqe54UxY8ZwVv+uWgmMkbGoUABKJUr37oVUKsUXX3wBmUzWdGX3hti1i4prNYW0NKqYnZpKz+YdCy82/ngMVlZWmDt3LjZv3gwA8HZzY7h9myIyI0aQMyGTkdOemkrO/oYN5MDo6Lw+v6qrKZoxdixFq86ebdph37q1rp8yAJJHz59P8+7gQUpHCAggR2zYsLrooVKpRFBQkOrx48e8TCZjI0aMYGVlZdDT04OxsTHU1dVrVT//+p4gk9G4lpXV/X1NTU0MHToUwcHBaF1UpHLYupWPqKpSddi6lT916pT46NEjxhhDmzZtVGpqaqxTx44MLVqQw/YK7O3t8fXXX+PixYts1/btWFpWBi4ujtZdVla9aubyZYgch+DevVWPnz5lxuHhcOR5ThYfD4lCgY+KijjR1BRCRQVJ0GvGu7BFC1Tu3Qv9SZPEjz76iDWo8cIEQcDu3btRWVzMTHV0MG/evDeTpaJIqoTVq+tJm169qKbE9OnI3L8fjtXV8LS0pFzTmvn0NqhUKqSmpjLbFy9E9OlDVfgXLCCCaObM+guzswGeR4v58zElKAh/fvklqh89gqzWSNfQIKXIqVP0+r17AXv7+teHhJAjMGUKfZ+YSLmz+/eTUxsbiyvffguJmproVPn/2PvusKiu7e13nxmGNkMHBSkCIkWKglhQEbBj773XGDUaE/UaEzUaTWIsMYktlsREjZrYKypgL4AIKh2V3otIZ+ac74/FMFTF3OT+bu7n+zw+KE45Z5+9117rXe9au4xpaGi82Z44OyNrwwZYfPYZ575iBWX2GntPSEidYwLFYjFGjx6Nhw8f8mfOnOEcHBwar+X39KRse1ERBTSTJ5NNyMuj8dHQoDV49CiMY2IQEh3d/DlfWlr3mMGQEDoS7ulTuoeFCykQWbmS9ghlMz3G6H09elBJwa5dtDYePyaJfy2Fi5aWFq5evaowNjbG8PHjVfNr7VoiChij+1m7Fv369YODgwN++uknzJ49G1lZWSwvL49J9u3DiW+/BdepE5s7dy5TljiVamqi6P59HNuxA/O2bmU6Xl4knRaLSVm0bh0RP4MHAwoFkm/cAGMM2dnZ8PX15bW1tdlvly5hwcGDTHrrFs2LO3cou6t8hg4O1PhvyxYKnHx8aB5aWZHK4f33SfY+eTKQmwvuww+hHhiI8Bs30PvoUVJOHDmC8o8+wozvvmO7Vq+GZ69eookdOuDlzZvQGzAAF548wddffw1NTU0U6esjIjUVHVavRuWUKVxN4ziJBMjKguT6dbxo144urn9/srXLllGZQy0yq6SkhE2fPh0ymQwVzs6s5b17osCyMjx58gSlubmK9levcr1evOCyOnRAWloaJK9eIX7YMEXaBx+IXNu3h66BAWqoS0EAvvsO1klJCJw5kzkNHw6Ym4MXBOSdOQORnp5gumwZM9XXZ+jUiSEjgzL1pqakzpFIABsbFI0Zg4Tr12H9889UlnLiBCkm0tPpOb0uIVFQgOiFCwVjGxtMzs7mYGJC6+HECSIE+/al52RtTU2flVi5kj53xw4izv74g0hBAD4+PvyDBw+AxvaJxEQisZVNd0+doj1AJoONjQ2kUqmQmprKjI2N8VpFoVisKlcbNIj8scpKItYcHEjGn5kJcBy4yEg4pabCSVubZRcXIyApSUgAUOLiwqtVE6eampqcpqYmU1dXh0QiwZ07dwRNQ0M2bu5cLFiwoO53l5WRemDFClRVVeHcuXOlVVVVk1avXl3Z4Drf4a3xLth/h78F69atm6ylpbVrzJgxWn9bYPJ3gueJJV28+E8FJa9DcXExeJ4XQLW2/2wsW0aS5x9/JMfrz0IpsX5dZu3GDaoXjIoiqZqdHf2+tJQ2+OPH6Xqys+s0ykpLS4NEImFPnjzBvXv3+LS0NE5dXR1yuRxSqVQYNGgQFxYWxnbs2IERI0ZAIpFAJBIhKSmJq6qqalgDXAtSqRTDhg17q1vV1dWFmpoaf+XKFdarVy/W1OkU1TJQRWlpKTiOY1VVVVyPHj1w6NAhYcmSJazBkVVvgaysLCgUCpiZmYHneeTl5UEmk0FDQwNpZWU4/PIlP+TMGa7NqlUQquXHbwwkVq0iB7T+dZWVUaZCV5cc8NWrm5TsvxbK+vqLF6F/7x7e9/MDmz8fSadPI3/ECBgUF5OjduIEBU0SSaPHENVBUBAF8X37Utai3lnpdaCjQxLZn3+um1XhOCIKJk4kJ/DYMXLgcnKAmTNxPCKCT05O5kaMGIG2bds2fr7xX4kDB2jMa8HR0RGOjo5AdrboVnm5ECiTia5u3AiZTIbx48fD2NgYOjo6Ihw7RlnPqKgmT2gQcxzYyZP8h8eOcWzOHApYlWfcK9/Trh2SNDT42Ph4bt6ECUznyRMiWebPBxwdYTBqFAVkBQUQlixB3PTpuP3woSIjI0PkPmIEv9DXl+PqNXMtKytDamoqRi5ezPCa5pg1SKlu3Fy7B4WlJQXWc+fiTo8e6Pr999B87z1yjOPi6Bm/bg4AKLh5E5Znz0K9vJy7ZGHBx967xxcWFYk9Kyvhv2MHEZFr16r6OezcCfnHH0Pz55/BPDyIlDIzI8dWS4vItbw8emZ371LNNEB2rXbDv88+oyDawYGyd+vXw8/bG4cPH8bmzZshFovRu3dvwcPDo+GxXLVw4cIFJLdoAens2Xx3gM5/r7/HJSZStnnQoJpf2djYwMbGhtuwYQMKCgpqetAAINs9eDBlpjdupMA1IYGCpA4diBhWZuWrYWZmhpKSEtYs0rOkhMgOZd09QMHS7NmqMgGASi1CQ4k4vnKl4Rx2cyPCMT2d7nHHDnoGI0eSjBlAZGSkaE51nXsNBg8mssLXl57J1q3A0qWwsLCAmpoaKisroTyhQYiOhk5+PqRSKavdyyR10CBByM1lhRMm4Om0afCytCRV2rp1ZE8OH66xnXdGjIDj9evQWrNGmDBhAjMxMeEA4NmzZ4r9+/djwYIFIu7hQ+o94+ZGa97Dg+53yRJSH9y+TcHjgwcUyJqbU1Dr50eE0enT9L4ZM2DDmKJQT08UYGOD/NOneROZjJN16iQs+vBDJqoeW+2QEMDYGIsXLwYAyGQyplAoIJfL8fLUKXjt2YMQY2MK9q2tgdBQqKemQty2LfhOncCdPElk2IkTRHL07EkKMldXyOVyrqaJ7ZgxQFwcfDIzcVBXVxi6fr1Io2NHoH172Dg4gOc4DLS1hcnp06K4GzeEA3Z2bGSbNrAFyA84fBgID4fOo0dM08cHsLBAZWUl9u/fz78cM4abOnUqg1RKiYpNm8huv3xJiYNp04iM+vpraG3bhjIvLzC5nJQSS5fSfJkxg/o1KJGWRuTFzJnU60UQgG+/hdG9e8ywf3+ap3Pm0J5XW63T2BGcvXqRnP+DD4iUycwk+7B6Nbp27coFBwfjt99+48eMGaMqVyotpRKYsDDVvurgQPZi61YAQEVFBQsPD0dERAR0dHQUgiBg3rx5otf6EDk5RDb06kXEw/79pCSJjCTFQXVfEmRlweThQ0wwNGQFn3wC7RMnRNGTJkGSloZ0a2uk6ekJhTIZXyUIQmlpqZi3tVWoz58vgr8/le/NmaMqtQkKAmQy3Lh2rUqhUFxdvXp1cNMX+A5vA9GaNWv+r6/hHf7HsHbt2pYikejqjBkztMxed7b5fzNu3iRDt3DhX955W19fnwsKCmIdOnRoeMbwPxEJCZQVmzWr2Ue41cHmzZQV3LixrmxaCYVClf0aNowaPxkakpMnElEmPzubNuMPP2zAuMvlcjx+/Bjx8fGClZUVN2HCBDg5OaFbt27w8fHhjI2N4erqypKTk/k7d+6wxMREPjo6WigvL0fnzp05qya6Yv9ZMMZga2vLrl27xsfGxqJDhw6NDtqTJ0/w8OFDbsSIEZyxsTFr0aIF7+vry924cYO1b9++eV11G0FUVBR+/fVXREZG4t69e/z169dZWFgY7ty5gxcvXvBhYWFCSUkJ96y4GEHu7ujo5qZoO2wYh169SKrbGASBMkmDB9d9zdWr5HwVFZETN2BAw5KLxqA8a/vAAQqA4uKIfOM4cjbEYmiNGoVgbW3FAwcHdP3gA6Y2YAA5s8bGb+6QfOMGBQpdu5LjNmpUXRl1YxCJyBaYmTU9DspTHjw8UBUfj+KlS/EiP5+N7NCBWfn4gPubu/gDoHX488+qc85rw80Nph4ezG7OHPj4+KBnz57MwMAA6urqRKaoq5NDV93xuwFevEDO+++Dj4hgki5dwKysIPH3b9htXSbDgadP2bzoaCZbvJgyqV27UvA4cCBgbIzi4mLcCw8X5N99x148fSqod+tGa9PUlLFNm8hxrvUc1dTUEBYWJlhGRDBxQQEED4+a7HJeXh7y8vJUxyqWlVEQ9dNPdfusWFrioUKBBzdvIrOyEj0GDoTYzY3m6YABRPpoajbepDIjA0hNhdbEiZB5e+OknR1KbWyYr58fFxMTg/T0dPj4+ZETXI/g1tLXR1ZJCR/WvTtzHTSIntH336tq3Pv2pTm+aBHJzM3NKbB0cKBsoiCQ0x8YSKRUWBjg6gqZTAYvLy/m7e0NXV1dXLx4kenp6aHFa07CEIvFiI6NxbjPPmOic+dICbNsWV3bvX07rbN6R3gBwPXr1xEaGorbt2+j4skTwWbDBoaRIykI3rOHMuhyOQUJ331H675z5wZ7g7q6Om7cuIHg4GD06NEDDXot1MKru3eRe/o0fiwsRGxsLLKzs1FZWQkjmQzs8mVVGRlj9F3OzkTUDxjQeJ8dmYyIAV9fKutYuZLI45QUhOfnC2pqaqyB3b9yBbh4EakFBUJkeroQdfWq8LCykhUUFMDR0bGmvwbr1w9amZkQ9+pV89aqqipcfPKEmcyciR7DhqHNkCEQGRmR1PrAASJLfHzAFxXh+59/5p/q6rKOO3bAz9+f1T6zvU2bNlxoaChLT0+Ho6cng0xG+6O1NQWkhoZkgwSB1ExOTpSdHT6cCC1jY8r6RkYS+eLsTHZVJGJX3NxYJcfxE7/+mnu1ciVc4+OZ2MZGpXzYtAnw94eGkxOUKhKRSAQ1NTX8GhgotH74kPWxsIBaz56klGrTBqL583E/IgJu6elQ//RTIkvHj0fVjh0IHjwY0jVr8OT2bSSpq8OhfXvoKBWU3bpBumMH0mQy4ZWODrO6coV8g7lzwXbuhMmWLdBcuhT206axXE1NtJswAeoSCZVArFkD7NuHLUZGaNWxI6ysrPDTTz/xjDE2Y8YMZmhoSPvI0KFE9Pj50Th5e6tKH9LSIL53D5aJiRDFxUFcVUXzJDOTbMpHH5GNyMigveroUVIReXpCPmECdl29Kty3s2O9p06FesuWb95blBg4kPwZExMigJQqNBMTiKRSWLm6IuzhQ+Tk5MDe3p4W1KefkrplzBjV5xga0lgvWABwHLS0tHie5wWxWCyYmpqiqqqKS0lJYe3atWv8OnbvJgXMxYtERLZvT+MyaxbZemW5FkDPxc4OrFUraE2ZAvFHH8G0bVuYyGSwdXCA844drOODB1znsWO5LllZyE5Nxb2kJKF927YMnp60n964QbZ+xAjk5eXh1KlTFVVVVf19fHxeNX6B7/C2eJfZf4e/HGKxeJmLiwv3xoYd/6346ivKDF650rzA5C2Qn5+PK1euCAAaHgn3T8WwYeQ0/fEHBU1vixcvmu5qvns3OS1hYSTbVJIBiYm0QRcVkdRVuZk24jB27NgR7u7u4Hm+Jquq00hZxqRJk2oa6r39TbwdTE1N8cEHH4i+/vprFBYWNuiKHRUVhTNnzsDGxkaws7NjSpm3IAgQi8UICAgQRo0a9VZZ4uzsbPz++++KnJwc0YABAwQXFxcWExPDtWnTBjKZDPn5+QgMDBQkEglmz54NTU1N5ObmwtjYWFRzzOLFi/Ss65NU5eXkDClRVUWOQWkprafXZUtLS8nZPX6cpMP37pGUft8+Yvq9vckJ6tevQZAdXVEhUtfQEJo8B7w+YmIoYIqPJye3V6/mNYJUYtAgKhV4g20oKC/H9pcvgYkTMc7ICIZ379IY5eZSBujvJEE3baJgqz5KSoC8PKhZWzeUcqakUJAUHk7Z2fooL6fnsmoVDEJDcWviRMUNV1cUFxeLOl25Itjb27MaBVf1UUsmLVogLS8Pdnv3ghs0CDh+HIJEguSkJNy9e1eRkJAg0tfX5/X37BH1FYkYBIHsgLs7BS8ZGXVIBMYYBgwYgEcbN0LIyUHsN9+gVatWiqlTp4r279/Pl5aWcoMHD4a7uzvNnZSUhgoSxpAfECC0KyuDbPlySCQSBg8Panbl708BwKBB1JCrd2/6WV5OWdJ16yg4j4qCCccB69bB3t4erq6uSElJUeTk5HBo25ahiSM2Y2NjOZlMRk7tH3/QvE9IIBIkNJSaZu3bR470+vVEXFpbU+awXTvK3CoUREw0Qhq5uLjg/Pnzgo6OzmsZV2VQ/ccff/C9e/fmjFNTqV/Fd9+pjiB9zZGny5Ytgzw4GLG3buFFbCyPjAwRhg6lNaE8otLJiUqpJk2iN/XuTcHCli11PsvOzg6xsbH46quv4OzsjD59+jQgMWNiYviCFSu4DGtrSCQSlJaWCiEhIezevXtwTk7GiJAQsNoZf8ZoDnl6kpLk55+bJqFFIhrnJUtIhbB/P8a2acPu/vijAC8vVmeNm5oisW1bXHBxYXP37mWFgiA8iYlR2A4cyNna2qq+YP78Bs0vK/z8AEdH2PbvjxpbpVBQ1vf8eSIpX75E5fDhmJGezr3cvx9GW7eSaiYoiAg4qRRqamqYMGEC27NnD54+fYp27dpRQkIQSE0SFETZcmUPkthYUlwlJZFaR12d5tjAgfRMvv0WePAA5ZmZrMX+/cKszz/nuNmz0cHUlIgeT0/6zBYt6O/dujU6jPotWwrJkyezjiYmVPMeE0MkvrMzhuvr8zv69WNLFi9m4Y8eISUlBSaMQX7rFi4tWaLwCgripgcFMWm/ftQTgzFAJALbvRt+np5cmIMD3U9sLJH6OTlk3wQB+Zcv48nvv2OIVEplL+fOAQ4OiImLQ3l5OV68eIGNGzeC4ziO53kEBAQII0eOrDsZzMzIJgOk8FDis8/w6zffoPOVK3DX0CA/Z+VKem4aGrRGu3UjInP0aACAvGVLHD16VCgoKGDa2tqKsLAwUefOnWuXar0eW7eqyons7EgF9+GHNJ8mTYK1pSXGbN3KDhw4gKKiIvRp2xYtZsxAA5tjYUEKlgcPgK5d4e7uzrlXZ+IrKipw5swZoaye+qsGJSW0/65dS88iLU1VStSyJc2vjRupLKExcBz5z8qSJH9/Igzy8iBOSEB/HR0uZetWFFhZQd/UlPbgOXOAzZshCALOnTtXIgjC+tWrV6e9ecDeobl4F+y/w1+KtWvXdldTU3vPx8fnn9l1v6CAnIOTJ//SQL+yshIXLlxQPH36VNSiRQssWrQI+s1le/8J4HnKDtnZkaywOUhMpA1p+/aGzlhAADmNlpbE1iuD8+paQ1y8SPWX6urNkoRzHNe8zfY/CI7jwPN8o13WlRuxo6Mjq33djDFMnDgRR44cYXv27FHMnz+/WU0eeZ7H4cNFsHv8AAAgAElEQVSH+ZYtW3JSqZR3c3Pj1NXVa6SnAGBgYIBRo0bV+bwawm7pUnIoP/yQNuYlS1QvEgTKGJ0/TwHjwYOUFdi5kzICtZ9PSQmRO+fPUwbwX/+i2tngYKr7tram71qzhogdpaS5EVRUVKCkpATe3t5vlpNkZNCfiRMpyK+WNzYXlZWVUFNTAzM0BF9cjJKbN6Hl7Q2RSIScnBzExcUJqampGDx4MCsrK8PRo0fBGMOqVatU8y4nh+byqFEkR7a1bTTj+W/DyIiydjt31rVhT5+SYql+czWFghzWzz4jp74+7tyhLPSZM0B0NEQtW2K4mpoIoMZ+165dEyKuX2dzOA5SQcArhQJFjo7oP3MmDp8+LcRqabH+8fHAsmU4kJEhFHAcWrduzb3//vvQ19en+XbyJH3/gweUWZ89m9b69et1xsfJyYk59egBKBQonjwZv/zyC9u8eTNfUVHBde/eHVevXuUdjI05rS5d6ma6aiHK3V3onJnJtfH1VX02YyTF/fZbul9lE62vvqL5/OuvtC9Ukw8vCwrAcRy8vb1RXl6OR48eiXop5a7t21Mdfz0UFxfXzbhraVG36+xsWiN+fjTnt2yh8fDxoe/mOGr6FxhIr21C0ZOTk4OKigpWu+9IY2jXrh1kMhl+/fVXbvfu3Vi4cCF0lSdTfPopjcXt26QyCAur++boaEhatIDa1q1wCApC9tKlImzfTsQKYxSc/PBDw+Bj40aai5GRtA569UJSUhISEhLQrVs3ZGRkIDw8HOHh4ZgyZQqdtQ6yW0d/+40bw3HovHEjOBp/BgAZGRnYs3s3uO7dMbywsO53MkZ25M4dWm+//PL6YzIZIxVAv35okZqKtkOHsrIOHaB5+DDZJKkU8jZtcN3Ghvfs14+TuLjA5PJl5vfqlQh6enXXsIND3X9XVYHT00Omnh42bdqETz75BOLhw6lM4vlzes0nn4A/cADbZ8wQDLS1hVldunAoLKSmbZs3Exm0ezewdy+MunfH8M6dEXDokGCxYgXT0dGh7ztxgtby7NlEZp49S3vytWs0r9LTaV91d6e1ERhIpIyxMWw1NdF6715WaGoKg2XV/dC0tYn0mT+fFBtPnzZ5zK5cLuel1tYc9PXJturr02urqmC7dy8nX7EC38bF8eWRkRzP88jv0QO9z5xBv6lTRUoCDVu2EHE/fDitixYtULFwIQx37MCxIUPkY27eFGP6dMqi6+sD7u7InjYN+np6PPvlFw7z5lGG2NMTpQ8fCgBYXl4ePD090bZtWxw6dAjVp8k0jfXrEcVxuJmejhm7d6Nq6VK0mj6d7iUxkVQRRkbUjHPdupq+KErk5eUhISGBjRkzBuXl5aK7d+/yN2/e5Bhj0NTU5AcPHszZ29s3/f3nz9ftHWJtDWzfDsXYsXg4dSocevSA6W+/YXpGBn6srESXNWuQ1asXnH74oWEwV1BAQXutPfT06dPyR48eiUUiEVMoFDh8+DCfm5vLnJ2dmaenJ2SXLlGzw7AwsjulpbSPzJ+v+ty5c4koUijerKBTwtCQ/rRtCwmA7D59hLvnz/OT2rQRoaKCSn0AhIWFITk5med5fsvrP/Ad3hbvgv13+EtQ3XV/jJqa2r6xY8dqNJY5/a/HuXPkXD5+3Hwj1gzI5XJs3rwZlZWVolmzZqFVq1b//Fr9+tDXp4yTujqx7s0hSp4+pQCvtmNUVESbyKefUmfayZPp3y1b0ka+fLnKqfuLeyn8p1FQUACe5xEVFQV1dXWIRCJUVFQgISFBERUVJQKAxs61Njc3h5+fH86dOyfasmWLYtGiRaLGMvzPnz/HoUOHYGFhIWRmZsLQ0BAjR45kampqf27+MUbPDCBn0d6eHB7GyDlISqKA6NEjImO0tUmCf+UKZUj9/ek5rlpFjoiGBjnhLVtSpvKHH97qcu7fv68QBEHk4eHR9IsqKihj9+OPVMcbHt6kw9oYIiIicP36dRQUFIAxBkEQoN2zJ4wPHkTyrVvw9vYWgoODmVQq5UtLS0WbNm0CQESOubl53VpkY2MiN1asoPv+4gsiH0pKaDxfd3Ta24DjKKhPT68rJx89miTN8bVOMaqqosB23ToiQWojN5dkoFVVlNnp27dBxtI6LQ2zrl/nLltbC88uX2ah7u7Ia91akMvlbDIALy8vFhAQgMePH0P7X/9SdLewEHls2wZ24UJNN3QA5OC3aUPzxdeXakI1NSno7N697nVVVQEVFZBKpZg9ezaXkJAAXV1dtGjRAs+fP2cvZs4UnGxtGXbubDA0eXl5KBaLOd7KioKSDz9U/ae2NmXuVq+mPiQ2NjRXCgooK11rTwgNDeWNjY0FTU1NUXx8vLJUSNF+0yaRRF8fHChQrU8w5ubmNuwBoiTDAgNJEvzwIWX8U1IoezhtGpWaREe/tqdJSEgIAFKPvWn/tbS0xIoVK7B+/Xrs3LkTK1asoLVx/z4RUSdO1JXwv3pFdc2enoCJCRIDAhC1ZAl85s6lOVb7NJZWrRoqV5RlEevWkdy8Vy8Uv3oFbW1tRe/evWsGdu3atTh48CAWLVqEsLAwREdHC8ZyuWDfqxfH1SuNMDU1hUxHB623b0f53bvQqN3oDKB14OVFtmjLFlp3b9qXOA7M0hLpX3/Nhz56JEz95RcRIiOROWsWQs+d40efP89pf/klfXa7dtQTJD2dbJeSvN+wgfasQ4dIFbJlC7ROnsTMwkKcWrECtw4cgM/48USgCQLZz5UrkTFlCjTT0tiYL75g0NVV9ThYtoyCbWdnyoJWVMAxMhLGp07hfnIy35sxjllaUp13URERVnfv0nwpLqbrysggxc7WrbS+du3C87lzoaOjA+34eNy5coWPW7ZMmL9sWV3Hp08fsvlbttAaaIKY1NXV5XJycngMHMihqIiIifR0Iu/KyqC9Zw9evnzJ9ejRAzExMTxEIpZgasoC58yBzYQJ8PPzo/f8+iupXnR1gVmzYLF0Kcru3UPfLVtEMDcnIm35ckou9OmDOF1dtD5/nqssKYFk+3agQwcUFhaivLycAbQGS0pKBDMzM+bu7s4/fvyY27RpE9/Z05Nrcfo0rmtp8R3lcs79xx+BvDxUvXiBuNxcZLm5Ie7ePcxydCQlxuef0/w/cYIavnboQFL+33+vMyYmJibQ1NREcXExPD090aFDBw4g8j4iIoL9/vvv0NbWVnTp0kXUpXbdvxLK0yWqIZfLcSgwkC/p25cbvnYtfh4xAobFxXCLjhYMfX2RZm/Pot3c+IubN7Nu3bqx7rVt5cCBpLCorAQkEuTm5iIiIkI8YcIE6OvrIzIyEunp6eB5Xrh9+zYrTk5WDImNFeH771VqN7GY9qrae6ZYTHu/hwcRlNV73tvAwsqK3dDV5aCtTbauVSvI5XIEBATIBUGY8a4p31+PdzX77/BvY+3atWYSieS+oaHhpFGjRmkrWfl/FASBnJyBA5uuV/2TKCkpwa1bt/Dxxx/DUNkh+H8R+vokg33+vKaDbJP4+GNyYmsfu5SURNneli1JUhoVRc73lCn02d27kyP5TySSGoGWlhZyc3OF2NhY/tmzZ3xiYiKfkpIiaGhoiEpKSgS5XM5evXqlcHBwaCBJMDExgYODAxITE9m9e/eE+Ph4lpqaiqqqKhgZGeHixYu4ePEiBEGAhYWF4O7uzg0cOLDJZoDNBmP0R1OTspxqajXdo/Hdd3Qk1Lx5JN999ozWVXk5ZRcmT6Y62rZtKTNgakpO3Z+4ptLSUhw6dIjT0dGBl5dX4y/au5eyXOPHU6Dv7f1Wap2rV68KV65cYRzHYfLkyTAyMkJOTg4Gdu8OvyNH8MjJiU9MTub69u2L0aNHcz169IBUKkXPnj1hZ2eHkJAQISYmRjAwMGB1VDyMUYA9fjwFelevkhICoOzjGzKzzcKCBXWDaYDW0McfqxpD8Tz9VFen8VE+B0GgQGXbNhovjiOSon17+v+SEso+HTlC/2duDrMpU9iLjh3hM348evfuzXJzc4XLly+zuLg4GBoaCmPHjmV9+/XjzGxtwe7fpyxqRUVd1YeBAdmDsjIKHr29SUJc3ykWi8kOmJuD4zgYGRlBJpOBMYbrly/DxtSUmSxfTsF7PezatUthaWnJPJydGfvqqwZN41BZSQ0W792judy/P5FToaEUdPn7IzU3FxcvXmTDhw/n9PT0YGhoCA8PD0RGRqJ40yYWcv06TsXE4NatW9DV1UXL6q72bdq0wd27d5m9vT1kTRE7IhHV6/ftSz/XrqVrcnYmyXD1kYbYt48I0WnTyObyPKzd3NDiiy+EU/n5rE1aGrTv30eliwvKDxyAxMiIxu3JEyqFUShQJZfj5q1bcHFx4R0cHFjNXMjNpWMkW7em+bhrFzXPcnIiIqRnTxQaGeFybi46eXs37B2ydy8RAI2V7vTsSc/45Enoz5yJYEdHrqOnJ5SN7DQ0NJCYmIj79+8jMzNT0NTUxBxXV06ck0PzoR4cHBxwKTYWKYWFMPHxgXb9Z84YKSSUTfWqe4aUlZXhl19+EdLT03kzMzMuJiYG6urqNT10TExM2NW7d7lwIyPEGxkhISQEvmFh0PHxYaJevWicjIxoHmZmUpKgTx96fr6+ZBPV1YnY2LoV+ePHY8+PP2L0H3+gbXAwxN99R83qqgPFE+fOCZcYY3OCgqBbVlZXLq8kRD09aQ16eABdu0Jt9mx2KTkZjDG0kskY1NRov3z0iNQTd++S9F0mI6L8xAm6zqNHkbJwIQ4dPozoq1dhtno1DEJDhc7Ll1PDtitX6D2xsZQAGTWKSrISE2ktpqeT7H3oUCIYjh1DcefOzHvKFCaxtiZy5dAhIojGjwdr2RJt7Oxgb2+P27dvK9zc3LgBAwYw7dJSVD5+jII2bagcgePo/lq3JoJh504gNBSZVlZQj4pi0g0bSM330Uc0zlOmQOfyZbTfvRsH/fwQ7+go3A8J4QMDA7mEhAQ4OjoiJycHEydMYLLISNh7e7NuN26g3dmz7FrLlrznDz+wF4aGrKh9e7T79FOgZUtcYAyPAEycNAkOzs40L5Unx0yfTnbB3p7UbT160Fr09a2x2Tdu3EBCQgI8PT3r+HpqamowNzdn7u7ukEql3KVLl6Cjo6Ns1ksvSksjtVe1sqKoqAhnzpxBTk4OFBoaLMXTU5hz9iyzWLcO+Z06sQGrVrHW2dnouHs3M2zVigUFBQlXrlxhJiYm1DxTIgEuXEBFVBT2hIfzN2/eZIIgYMSIEdDS0oK1tTVcXV1Zly5dWN6xY4peO3aIynfuhIaLCwA6tYhfvx5cdDSRTPXRsSOVPaipvbU67d69ezwrKIDbe+8xTJwIGBggJCRESEpKuvHJJ58sf6sPe4dm4V1m/x3+LVQH+je6dOli5ePjI27uUWL/VUhIIAN79myzz/x+W4jF4saPKvpfw65dlEXg+aZroQsKSK720UfkVMTHk0xs1y6S6H36qSpbVFFB71HW1P2Pobp+sLFol506dQrFxcWNvk8sFsPMzAwzZsxg9+7dQ3l5uZCXl6c4d+6c+MSJE9DV1RXGjx/PjI2Noa+v3/BBKAMtZRf1Nm0ogJs8GUhOpszmxo2URbGxIefG1pYyf8eP07OKjKypNUWHDkTwyGQUmFy61Hi34b8AoaGhQkxMDGxtbVliYiIqKytVpRCCQM7myZOqkoLmlpUAiI6ORlBQEK+lpcVSU1NZz5494ePjAwCwsLBQEQuTJmHxuHFc7R4CHMfBs1Zjt48++ohdvXqVHTlyBJaWlopx48bVVWAwRsTHV1+RGubLLykg+fxzIk8GDPjzCqOPPqKszrlz9O/t20nd8Pix6jUjR5K0cu9e1e/Ky2kOXL5M2fzatdshIZRhHDuWmslNnVoTlGgB6FHrqLYRI0aw1NRUvnXr1tyQIUNUm4KGBnV1TkkhafqDB6qzpNXU6BrLyymgMDOj46R69FB1fgZIml1URM+3Hgb/+ivTnju3ybknlUpFIpFIwbp1E+G77yj4dXam9TBkCAXYGzbQcygtpT3hX/+ie9XWhsDziPj8c9550CCudevWNZ8rk8kwd+5cjr9/H7yrK0pnzMCTJ0/4c+fOcZcvX+Z79uzJOVaf4MArSZb6ePCAlAw2NtRUbeFCeo5JSUScmZuTjNjOjoJJpRy4c2egdWuIRSK0a9uWvfL2xpPt29EiNxd/ZGZi/g8/4PGjR3z7AQM4zalT6dmNGoWK5GSwSZMwZPJkDvv2kdR7wQIiWQcPprmjtBFSKQV92tqAiwusBQEAcPXqVYyurleuQVgYKTWaAmMo6tIFoV26CIwxJjpypMa+d+nSBV26dEFKSgpatWpFJUxz5lAg3Qj09fUxaOVKPJ83D8fWrIGel5cwZsyYOl3wIRLRXA8MBE6fhmLwYOzbt08oLCxkOTk53MOHD8HzPJycnPjRo0dzyuc5dOhQnDp1CpyhoTBwyhRmJJczbNhAhEWnTkTEjBpFa0i5P23fTuTd06f0/Dw98eL0aZR7eaG9l5fCLDRUhPHjIVcocOr334WYmBimra2NoqIiNm3aNOjNnEm2NSlJ1a/i4EGa7+HhNXJnAFCTSDBuyhT2448/QjZ8OJ220bs32ZIHDyjYnzePrk15VvqOHQAAw8BA+F27BufHjxH3669w+9e/uBcffIDHQ4ag2/nzYMuXU7CvbND28iXt20VFRHA4OdH+PnIkwPPQ4jg87dSJ7/TwIYeqKirJyc+nZ9epE4ymTIGRiwsEQYBEImGampqwmDIFakeO4FhCAiIiIlQqNgsLIhEePwYGDYKRpqZwceRI1nflSnDl5UKSrS1cPviAPd+2DaUaGjj18cfI1tKCXkaG0Lp1a1GvLl3ApFKYffstrkdHQ/7iBSVxHjwA17cv9F1d8f6wYdwzf3+8OHQISydPRjHPQ0MuR2JioqJPnz6iNm3aqObywYOkOtDTo3mtrEU3MyPC9scfgTVrUKJQIDg4GJaWlrBTnhRUD1KpFO7u7nj58qXixo0b3NmzZ5mmpiZvbW2NUX37cs8HDsTdw4eFyspK5ObmstLSUowaNUpwcnJiABimT4dev37Q++ADIoumTweysuC4Zg2i/f35xyUlopMnTwrR0dH8kCFDRJW9eqFi2TKUTZmCFStWNF7KmJQE77ZtRRf69+fjDh7kJBIJdHR0+JKSEuYZGMjaDx0K3YbvItKpoIDmf0REA9VXU1AoFAgPD+dGeHuT0sfWFlVVVQgODi6vqKj48M2f8A5/Bv8fRB/v8Hfh888/H6CmpnbUy8tLw9vb+58Z6APULdzW9m8L9GUyGVq0aMEfOXKETZ48mf231Y7/pbC3p+C9f3+qD6yPn34iRjgqihoInThBm4WNDfDJJxRkurnVOJWoZpn/f8SzZ88U/fr1U0V7aWnkdGtoUPZx8mSo37qFnikpDNOmAXPniqsmTQJvYAD1adMYFi2iutvcXJrjxsaUEVQoyEkpLyenSkeHsrRbt5Ijm5dHmzdA2WCplALT8ePpuwcOVAUaV66QZPzD/9weff78eQYQ4eHm5iZIJBIyPHfvkmz1m29Istunz2szDnK5HCKRqEZqn5iYiGPHjsHGxoarqqrCtGnTYG5u3vibvbwoMDp2rMnPl0gk8Pf3h5+fH3bt2sWOHj3Kjx8/nmt0/YvFlEFetoyc+p9+omDLx4eyR2/b32P8eHLElAgOJiJEicpKCgSUTmlFBc2R996j4H/PHvrOykpSbISGUra3fXtav717v/ESSktLOT09vUbl7LCwIPtgY0PdyKdOpeDBwYGygosXE+EwZUqDYwRrrr/+92Vlga+sxDWxGN2eP0d9hZlcLkdJSQnfunVrBsZojt+/T+OyYAFdg78/OfVyOWUYHz4klcq5c4CBAbIWLYLXhQuc1N+/UUKT27MHHGPQEYng5eXFdenSBVFRUdyFCxcQHBwscBxXV+Xx7Bndf6dOFDS0bk3PZfBgWp/KzFl+PimbvvmG1mBt27hyperzjhxBFwA3dXTwkuMwx8YG4kWL8Oi333ArOhqtd+3i7VJSOLOjR6GoqICwbx8UwcEQ29nROK9ZQ9/XoQOpKkaMIJJh6tQ698kYg6GhITIyMho+mx49VPahEQiCgF9OnhQq3N2xpF8/qPv6EtFSi6CpaSBZWUlKsaFDm/w8CwsLWMhkcFFTw67kZGzYsAE+1Vn+3NxcIS8vj8/Pz2et+vYVvIKCRK8OHxaK3N1ZvwEDcO3aNcyYMQOBgYFCRUUFS01NhYmJCSQSCdq1a4dnz57xGRkZgrW1tQiJiURivnhB+9Rvv9EcmjqVCKLAQCrNMTOjbH/1nBZ99x3SzczgFhTE5T16BMPLl3HhzBkhOjqade3aFXFxcQIAZqw8inH5clJCbdigKqX44gtaD48e1bl3Y2Nj+Pv7C6dPn4aFhQWTSqVkS7y86A9AdnHIEJprISHA4cPQ8vYGbt8WinV02KXoaARMny6YmpoiNTWVFS1fDn9/f7KdkZGU1Z00iQgMZZ+LzZvps62sgH37IKbOsaQ0MTUlorFzZ2p2GREBBAUh+bPPoNOqlch6/Hh6r44OZC4uaB8fL5w+fZrVKVkbP5660UdFofirrzDk6FG88vBAy2vXGHr3htzdHU47dwIXLsCzZ0/wK1aA8/fnkJNDc7eoCDA3hyI/n7+bmioMKixswJpaWFhAU1MTZ8+eRUxMjLJMS+SgLA+Mi6Px2rFDZSd/+okCfuX8XLSI/J2uXVG+ezcAYGq9tdIYfH19Rb6+vkhMTERGRgZ3584dISw8HJEaGtBRV+elUimXlJSE+fPnw9jYWGVkTEzIVq1eTURkmzZAURHyc3KQGREh+nDqVDBbW3bgwAF8+eWXYIKADs7OfFcDg8aTTeHhwMyZMDp4EGM/+IArKipCeno6cnNzOalUioSnT/nMfv24RoN9gPaIXbvIRjQToaGhgllWFm/30Uci3LwJALh//z4vCELQ6tWrH73h7e/wJ/FOxv8Ofwrr1q2bJJFIfp00aZK2q6ur6B8Z6FdWUgOchQtpc/kb78HKyoo9evRICAkJ4Tt16vQ/HO2DNqRr18hxrt81eulSCh5dXIj1P3SIpIGTJ1PGeMIEci5atfq/ufa/CmVlFAi8fEnZAUtLanCWnU0OkrLb99WrxG6PGEFOREYGBVQGBgjv0YPvePIkJ/3oIwrKXV0pCHFxoazvsmVUM3j/Pr3/2DGIOnSA2NqaHJCBAynT7upKQZWfH0lBXVyIWGGMasaVJygsXkxyUWtr+j1AAYayrEUpX9XTU0l0jx2j2lVlJuQ/AHV1dSExMZFNmzYNnTt3Znj+nBzhH34gR++LL4i8q7WeCwoKkJaWBm1tbYSGhiI0NFTx+++/c5GRkUJGRga0tLTYwYMHoaamhgULFqBDhw6vr3vW0aGMr7J502sgFovRvn17dv78eebm5vb6IxOVMu5BgyjQ37ePnH93d5LmNreLv6kpOXIODqoGil99RXPyu+8oa791K8lPz54loiEri9bs3LkUYH3+OdXHR0TQfBg4kILSZvY80NDQEAIDA1liYiLf6PGSrVoRITFuHAWIyjXv7EwElZ0d/b1fPwpWlL065HKSUNfOnlVVoXDSJPzm7Y0qqRRhYWG4desWUlNTFS4uLhwAHDp0SAGADR06lBMXFND4VFXR+IwZU/feOI5+7+hI3+XoCPj64m5cHK+fmMj0SkupJMLIqC4h6e9Pa6+6jIkxBhMTE7i7u+PFixessKAAndu3h/pnn1Fg3aULBRV+frQPDRigksUuXkyB/Pz5RER7e9Ozs7SkefCa/crKygqWlpaQyWTQ1NSEh4cHKyoqQk5ODouMjBTu37/PeACZmZkQt2oFq7ZtiWB1cyM7fOIE1ZSbmTVJuN66dQsvX75EgyNkf/mF7qOJeZKfn4+bN2+ypUuXMvWWLVWy7DZtaDxqBw6PHtEz8vVt8l4BAAMHQrNrVzzLzBSKiopYYWEhn5qayufn57MWLVpwTk5OLCsri93lecHm/n3mOWAAc+jTB7wgCOfOncOIESPYo0eP+JCQEBYZGQkDAwOWn58Pe3t7dv36da5jx45Qa9GCrlUsJhvt6UnPqrKS1paBAdWbe3nRc/P1BZYvh2779pAtW4aiS5dworKSxWZkIDY2lg0ZMgReXl7w9PRkt27dgq2tLZ3Koq5OdiwtjeyMvj7Z3tmzaU7WI5hatmzJsrKy+Js3bwqenp5cA1/s4kUi8WbMIBJ+wgTg3j2YHjnCJPPmwahlS6G0tJQfN24cl5KSgqQnTwQvY2OGr74i0mnAACoJkslIJTBqFJFghw4B+/ej0tMTR8rLmcW8eaxV+/ZEXq1YQYHw0KGAqysqCgsRmJyMwQYGMFA2CZTJIOnQAWbff89u2tqivKJCsDE3Z08fP8btq1fx0skJRh07wqRPH6Y5cCD0fvsN4levID1xAup2dmCDB4N98QWwcCHY9u1EMPXpQ+tSUxPo2hWPFApWVlYmKG1AbVQ3V1U8efKEAwAvLy+0aNFC4erqyrHKSupx0rFjXVVJTg5du5GR6neGhoAgQPPVK0Tn5QmadEzu6+drNQwMDFBZWYmHDx8y+dWrGH/+PFz27+fs7e1Zz549G5alALTvT5xICqm2bQFra+zIzYWTkxMcJ0+GpHVrdJ45k3N1dUUPb2+0S0tjFkVFrEHvk/h48k/c3YnkBx2HaWRkBEtLS8gzM2H76afsJ21tRDx+rFBXV+dMTEzQYH61a0fKh4ULqdzjNeB5HkePHkU/bW1OX0cHGDAAr169wvHjx8srKytH+vj45DZr4N7hrfEus/8Ob4W1a9dyampq6zU0NBZPnTpV8x97vB5AksnSUnIy/maywtDQEFOmTOE2b96Mffv28dOnT288w/e/AD09kr7Nn09OkKEhyRJv3qSgzN+f2PL792njSkqiDNaFC/+3152fT52xLV4AACAASURBVHWGVlYkl3V3J4f1m29IVrt7NwUnK1ZQcPLVV5QlnzqVsiYDB1Kd3MmT5KRdu0YdvVesIGn8oUMUoPToQRLJkhJyoJSOwfDhtHlraAA//QT13FxR/IQJaKHszvvihepaCwvp5/Ja5W2//KL6u7LZXe0NvmPHv3zIcPw4yVn/g4iPj2cAsH/vXvg/eCBYxsWxzDVr4Pzbb+CqzyTPyspCWFgYEhISBJ7nUVRUxJQN9gBqcjhp0iQ8ffqUPXv2jI+IiGAAsHjx4uZdhFRK2fKoKFUG7TV49uwZNDQ0BD09veYbGqmUnmNxMc2lo0cpGLO3Jwf8dadQMEbOvaMjBUyTJ5Otk8vJgXd1pYD6t98oa52QQEHn9u10xJSyt4KPT93jqN4CHTt2ZKWlpUJkZGTTLzI0JGKhrExFbtjaUlDl7k4kxLhxpCBROpLx8USaDRig+pxLl6Cbnw8LNzdFr+qGbw8ePEBycnLNSziOY/LSUqZ4/pzKEzIzKWAzNm68T8LQoVRGoFQIaGlBrUcP7phYLCy2t2d4+JD2kOJimgPOztT3pT4ho1BAMzUV3YuK4Pvzz9BRyqBHjaLgtkePxsdm+3Z6Rko4O5NdHTiQgsGNG5t9bCTHcZStJbCjR48iPDwcYrEYsbGxcHNzg66uLpU1/PYbkXhGRhQ49e1bN7iphrq6Ol69eoXo6GjUNBsTBArcX0OUKQOYuLg4ODk50VzV0CBb1q4d7QPdu9NnpKXVJXWagoYGqlq3hmLMGO6DL76Ajo5Og4Hx8PAgKbSSpHv2DD1XreJiYmL4sLAw9v7774sqKytx/Phx4ciRIwCAfv36wcDAgA8PD+e6ubrSXFA+E5GISDlBIIL7jz9oXX35JQXXly5RNvyDD2BUWAj94GDmduuWUF5eDqlUKtTuxaKhocEXFxerrrlrV9pLJk+moM7enux/+/Y0JvV6PmhrayM/P18UHx+POt3eIyMpK+/iQut48WJS7owdC1HfvhB5ecFlwwbm4uIiAoDylBRhzqFDDAoFcPp0/QdH0vr9+2l+jBmDooMHsX37dsgMDHgPDw+6/vnzVb14qs9gFxYtgo6REaSff07j1KsX7akdOkBt7Fi837o19j56xFxmzkS5qSkiBw/Gk+xs3P7kE3Szt+e7XLvGYcoUlcKkd28ivwYNon+fOaO6zlp2sXv37ti1a5eovLy8LiFVjQEDBog6duwIIyMjqNP7RKiqoiaHmzY1LBWKiWn8+MH33gO7dQsj163DhaoqvjFyoSkYGxvDyclJ6DZlClPbsKHm940m0I4cIT/E1pbGYPt2xHp7CxUVFczEwYEITIkEmDIF+lOn0jgPG0Z7Ve3u+c+e0f6wYgUlChrBk6goaA8fLixasoSFhIQgMDBQCAgIEAYMGMC51CcABw1SHYv8GsTGxsIwLU2w6dGDKXsTBAQEVAiCsG/16tUxzRqwd/hTeBfsv0OzsXbt2hYSieSQoaFhlwkTJmhKm7G4/2uhbDh17tzfHugroa2tjcWLF2Pbtm1cVlYWTE1N/yPf+38CiYSckkuXiIU+dowc7Oxscj5WrSIn+fvv/9znl5eTjFZLizIXfn7UQOjOHQoKNm4kB6dvX3Kmr1+nQOLcOXq9mxtdx5QplAWtqqKN7+FDkiyvWkVzRNnN+euvKVBSnl/eowc5wPr6qrOpf/hBpWSoqFA54spzy48eVV3//fv0s2VLVeOpKVNU/z9yJIS9e6HQ1Gx4jNV/CyorSXrcHGf8L0BhYSHS09ORHBeHTqGh6JmVhedz5rDbHCfEpKSwU9XSUsYYRCIRpFKpok2bNiIjIyPo6enBwsICJ0+ehIODAzw8PEQAYEMKBS42NhbHjx9HRkYGbJU1mW9CUhLNiwsX3mhD1NTUUFlZyf6UAkoqVZ3/nplJQdGyZdS5umXLuh33ayM/n7KQCgXJbhMTaa4lJtL8Gz+e5vD+/UQU7dtHJRwffkjZrP793/5a60FdXZ0VFBS8/qZFIgok7OwocCopoX9/8QWRYqtXU1Zz+HAiB6ys6gY7JSUAx0Fy7hzG6+mJ5HI5goKC+NTUVFZVVUWvEQSMHjKEK7a1RXZAgGB97hyDSET24sMPaa3XR0EBqW4mT675la2tLa5fv84uaGjA//ZtGruffiLJ9YULZA9kMhrzGzfoWv39AR0dvPrkE9zv1w+zpk2DuL7iqT4eP6YO6AcO1P29mhoFS0eOUDAyb17DRozNwNixY1FaWorg4GA8efJE2LZtG+ujri54nTnDcPYsjTNARMzRo0Ri1EJAQAByc3NhY2PDd+nSRRXYZGeTrX0NCaGhoYEhQ4bg999/x+LFi1UKmvfeo5+rVlFg+sknZHtrjX9TkCsUiJ88GUwkEnR0dN68yHbuBBYsQN7ZsygsKOCUEnKJRIKJEycynufxxx9/ICUlRcHzPFNXV6dscWO9CBgjVUinThQMKu351q30nLy8AA8PiEQi9OzZU3ltNdf4yy+/KCorK0UNssEeHhQ4Z2UR8WFjQ0RMPb+rsLAQ4eHhonHjxtUN9AWB9sCsLCKap0yhuTpiBNmuBw/o5/XrQEYG5AEBaFVWxh5/+CG8lM8CoPV16hSVdQweTOv14kU6ueDVKygUCkyaNImSFzxPBNqlS6qySD09SP74AynffMPn7N7NtViyhGzPli1kd+ztYRQfj2kTJyIuI4Nvn57OecybB7i6IqdVKwTk53NdAgJoLgYG0roKCaF5oexr0ASMq0tDcnNzGy3JkkgkaFVfRbhyJZUBLF3a8AMfP6b/awzdu0MxYwZDXBxDfHyz90VdXV2MHj2a4fZtUll9+WXjL3z2jGzixYv07/btUT5lCgqXL2f9J09W9TwQBCKlnj6la7CyogSEpSXZ9aQkIqyWLiXisB7S09MREBAgmJ4/D7m+PvT09NCnTx9R7969cfv2bRYQEKBQkkM1MDcnf6p/f1JENNFj6ebNm/ywmzc56OgAI0YgPz8fMTExcrlcvqpZg/UOfxrvgv13eCPWrl3LGGMTxGLxzo4dO2r4+vqq/aObzeXlUaZo/vz/WKCvhFLC21yZ1z8aZ89SZmH4cNqktm2juuDQ0Jp6PJiYUEO+qVMp4F69mogBpVzyyy9VjaliYykzkZNDmQ97e3J+xowhqXF4OB39NmsWbWgtWpBjYmVFG2DnzipH6YcfaEM0NiZniOOoTlmJJ09Uf1c2Navt8NZi4DFtGv2s1bCruRm3pvDkyRNkZWWxnj17QhAEPH78GHfu3OF5nkfv3r25tsqmZv+XCAoiqfft23/r11RVVeHevXsIunIFbtHRQjddXdZKEKBx/DjatWmDdtWOc3x8PBQKBQoLC+Hq6gotLa0GdZoTleUJ9WBvbw8/Pz8cPXoUCxcubLpbem307ElOp1zesFylHu7fv09HOP07EIlI6v7rr+SsHT1KSpnx42mu+/jUnXd795LDFxBAc9vWloJ+Ozt6z7hxNUdjwcKCMkB/sVKL4zgIglC3AVdjYEzVnK9VK1rHgwZR4KzsFXDgANmFlBQiLJQZqS+/pKznwIEoKSnBpUuXhMTERObo6Mi8vLxEiI4GZs2CZOVKxG3ahOCMDPYxqrtidu1KxGBYGAVXtdGlCznWtTJiylryxMREAf7+1Bdj6lSyTzNmkA3q3p1s3vnzRMoEBQGmpnDjeZx59AhBwcHo00TDuRrk5TXalwCASk79+edEAl279qcaOWppacHf3x/9+/dnOxctAsLCGH74QRXoA2Qn612H8nm2b98eQ4cOrWvommic2BgEQUB2dnbDcpmwMPo5YgSN/8aNdf47MTERwcHBfLt27TgjIyOEhoYKiYmJTEcmE4bVVj+9Dhoa4HfvRl67dujr7IwOtRVSoHlbrQjicnNzWWpqKmQyGSxGjYLW65rP2tpSjXxmJhFFw4cT8atUc9SzAbt371aUlZVxCxYsaLxsaOJEyuRu3kyqDn9/IkKWLQM6d0ZmZiZ2794NfX19hZ2dXd1JsH07lX0IAql7ysuJ1HJ3JwVCt25ERCxdChw7hvLevRFjbo6uynWalETKtFmziEzfvBnlYjE4ExPkaGig1bhxKCoqgkQiUdnLsjIiJeqdOsSZmUHX1JSJv/8eChcXiHr3pnvIzCSV365daHnxIlp+/z2HGzdIhbRqFbR37IBYR4eHpSWH2FgiEjp2pOD12rXGu8TXwtPqo2KbnZg6coTKhjp1anxNrV9Pvks97NmzR8jPz2dyuRzD7OyIhPvhh7c7Hjg7m9RVjUEQaG0dPVrTLyAzMxOHwsKE1p068cNSUkS4c4eeJ2Mqtd3QoSTVnz6dSEJ3dyJJxo5tQOABQHl5Ofbv348OHToIndPTOUktFQNjDPr6+hAEoWmneeDAhqenVCMyMhIV8fGc3okTRFIDCAwMLAOwbfXq1S+bMULv8G/gHxyxvcPfjbVr1zIAdhKJZLOWlpbv6NGjtc2aWzP634rDhymrFRLyHw/0AarfVVdXR0xMDEkY/6ngedp8IiLIcQgMJHnt7Nn0Z+BAes2iReRkhIWRU7FiBQUox49T5szHR3VsUcuWquPAOnZUbbYbNpADYW+vkqKHh6uu5dUr+mljo6o1Vx5lBqiaqHXsqHp/bXn739Q1/t9BcnIyxGIxbt68KZw6dYrxPC9IpVIuLy8PYWFhiuTkZDx//hz9+vUTWTaV2f27IRKR0/A34/mzZ0jatQteEgnvIxJx4vfeaxiYAU12QG4uvLy8cPPmTeHly5esWcE+x1G2ZNkyyuK9BhYWFnjx4gW2bdsmGBkZ8cOGDRMVFBRAT09PeUa74OzszAyae+yekRE5a9OmUaC7Zg0Ff9u20TrQ0SEC4PlzCozHjiUHNiGBHHGOI0XLqlXk9DclJf830aFDB8jlcpw6dQpqampvtnkaGmQfHB2pH0X//uT4+/qSUqG8nBxfhYJe/+oVOefVDvDFixcVqampbOrUqVyLlBSyOevXU9ZpwABYZWWBHTwobNiwgSm74vd5/lzoqqbG2I8/1r0Wxihgs7OjLLPqnvgXL14IUJ6ioaurau7n7U1jP38+NdKq83G038TExLw52Pf0JNvYFEQicuijoqic4Ztv/vSRsVxMDPxu3MDtTp3g0a4d6hSHyGR0/+HhNUQQz/MoLS2Fb2N19BzXrOaNTk5OCA4OxsWLF7Fw4cK6/8kYPeP8fJoPEREoMjfH/ZgYxD5/LuTn5zNnZ2d2+fJlSCQSWFlZ8bNmzRK1MDBgsLQkRVkzer6cv3BBkThuHLeoqorh8mWaR7V8gqysLEVBQYFIJpMhKSlJ/vz5czZv5UrRkW++UQybMUPUoPdGVRVl0vfsoXmwdi1di6UlKd38/GhuHD0KaGjgxMWLQk5OjmjJkiWN12YrMXs2zfuoKArWzMxqCBgDAwNoaGigd+/eogZlgdOn0/1kZxPx5OxM16N8nVxO2fWICCA4GNrJyZg7fjzOJyXh4qZNgl1GBtrMnMlw6xbkurqIi4vDzZs3hcLFi1lFbCyGP36MZ8+eCSYmJoJEIqEPzcsjoq4RMqTP7Nns6MuXvHNoKLwKCznm6krk/vr1lACYOpV6RaSlEfF46hQyFAq4/fwzqU2kUhVZb2lJe3ynTg1KGmrD0dER6urqQlJSEtN7k0IuMBCFP/wgXBs2jGdFRfD29hYZGhoiJiYGz549U/Tp00ekNm0acOIEqmxsUFVVhfT0dMjlcujq6goFBQVs1KhRcHBwoHKUnBzVnGgOhg9v+hSLPXuI7Ll1CwqFAlevXlXcu3dP5ObmhiFLloi4a9dITcFxdYPtEydoD7hzh4jfy5cpudKE/VGSXP369ePETk4NSpLMzc1RXl7OyeXyxhv+LVxI861LF7z84w+cunoVMpkMr169QtLz55h55AiupafzpSNGcHp6eoiNja2Uy+VfNW+A3uHfwbtg//9DrF27tgWAIWKx2EUulz8HEAfgGYBsANoA/DQ0NMaKxeJuampqnJubm9jPz09d7U3Sw/92CALJxv4PMvpKMMZgYGAghISEsP+qYF+hoOZwZmbEmhsbk3O3cycxwZcuqc6fNjWlrNuwYZRx2LKFsu5JSZS56N6dgrFWrSgrfvs2BfkGBlTnvmMHyeirqoAlSyjDHhVFjt2KFZRpHzdOdW215e2v6fL8v4T+/fsjOzsbSUlJrHPnzujfvz9bv349zM3NYWhoKLpz5w7s7Oxw+vRpYf78+UxUTYzwPI/s7GzI5XJIpVK8evUKqampYIzBycnp9U3n3hZRUa/tkv3v4tmzZ8hPSIDo00/RQ6HgrQ4d4tC27d+6dnV0dPjIyEhmbm7ePGmGiws5rFu2vPa6evbsCTMzM5SXl7OgoCC2efNmiMViKBQKiMViaGlpCUFBQWzOnDlvV96jrU2O5bFjlIXbvp3W3NKlVHOvrU1rdcYMKmE5d46c++nTa7IrfycKCwsRUH0qR0hISPMIzo4dKRD57DMiBAMCSGofE0NBjru7qo5+0SLKni1fjlevXuHFixecj7k5axEXRw6uqSnZsWpZsqmpKT7++GOWk5ODBw8eIDw8HLctLJheRQWcKispKKuNPn2o3KgaZWVliI2N5aysrBqen2dsTJk1B4fXKnvatWv3+vt/8oTsZ2npmzP2Tk5kM2fOpIDubZUZKSlAcDBM5s5FWm4usrOzVZ3wAbLns2bRc6j+7OzsbAA0Fg3sSXIyPac3QCwWw9LSEunp6TyAhoNVUkIk1PnzOHDsmLz7nDliJ5FISP34Y37ChAkiAwMDZm9vDxsbG2hqaqoGKSamWaoquVyOiIgI0XsLF4LT0iKly8OHdU42mDVrliglJUUpjRcDQL6TE57fvy9KSUlBA3VVZSXtlamp9Fyiour+/9WrNCdv3wa/di0EKys2f80aaL9J8cNx1KB15kz6+65dNZJ2NV9fVFRU4MmTJ1AoFLC2tkZQUBDUfvqJb52RAYNevTiT9etpTc2bRwE/QGqBbdsoO/7BB4BYDKamBnGrVvB8+P/Yu+6wKM7t/X4zS1+qgjQRsYOAHURFxZIbe2+ILbZcNWp6NIkxxfiLudeCJWqMJZYYuwJ2LCBGxEIxKCoICkgVlrIsuzPz++OwLEi1JDG5vs/Dswq7M7MzXznnPe855zqMDAxYkJ8fHAMCYGBggJ82bxbz8/NhaWmJeYsXM3WnTrh48yZutmvHBg8ezPLz82FsbAzWrx/SevXCqc6dxb59+3J2dnbaXHhYWFig/ZAhXPQPP6DTqlUwmDSJ6iU1bkwO+//9n47w37MHaNoUTteuQYyPZ4mPHsGlYm2UXr3oPoeG1rgHiaKII0eOQJIk8HXNo99/hyY/H796eDArZ2e+uKhIXLduHaytraWCggLwPM/Fx8djtFKJAz/9hCJTUzDGYGRkJAiCwExMTJggCLo8+yFDKKDx8ceUQlFR9VcTFi+mQNSZM5V/r1LRfVq7FmAMly5dkq5cucKPGDEC7u7udMJ+/WjNPH6c1gxtC1ieJ2LYzIwUJ0ZG1XdJKoO+vj4MDAykzMhIZj9+fOU6QaDOUqIo1tw+FCA7r3VrRFy5ggcPHsDNzU2wsbFh/Xv14hpYW8OxWzcuLj5eCA8P5xljXy1ZsqSg7pvzGi+K187+/xi+/PLLN2Qy2YFmzZrB0dHRJD8/vzQjI0OZl5fHKZVKfZ7nRScnJ8HV1VXetGlTKtrzT0BMDBmMwcG1F7b6E9CkSRPpzp07Iqrvr/5yIEm0+MfFkZGWnU0bwaxZFM0zNKRo11tvkREQHU1OfnQ0OQzdu9Pf9fVpgxgwgKS0hoaUO2ZpSYaHtsdyxY1Y24ZtyxaKeGo32saNSYavLfyjUpE6IDycIgKPHtG/w8Mpsmdrq4tGdOxI/zY3ryKF/CeC4ziMGjUKGzduRHx8vPSvf/2LeXh4SImJiWJycjLXu3dv1rVrV6xZs0YMDAzkTE1NRYVCwRUVFTE9PT2J4zhJo9FwPM+LZmZmUklJCbt8+TKzsbER8/PzmY+PD2vXrt3ze82iSPnKw4a9xG+tw73oaDyZPh2ODx8iNTAQTYYP5/AnpA717duX3717N/71r39V35P4abi5kWEaE0MEVi3QKg/c3d257OxsWFlZ4e7duygsLETHjh25n3/+Wdq6dSuztrYWhw4dyj1T8dPUVIqErlhBjn5aGkXMLl6kv8+aRc5/8+Z0zRcuUPqLNh/4Jc8pjUaDx48fY8uWLQCA3r17w1eby1wfyGR0TzmOCtk5OlIU//59Wtdu3SIJviAQkQEgNjwcqtxc1mLZMpxv1w4OGzdWq/bgOA6NGjXCkydPRA8PD8nGxobP+fhjiN99B+7Tp1JHJ0ygPGdRhAggIiICgiBIY8aMqX5wfPZZVcKgDNrikBpt3Y+a0LYtKTDqK8338yNC1dubSI3p0+skxERRhCYjAxkLFuCJvT2yhg2D8fXrVfOXAbq/v/xSXgy0pKSk/B5WQXo6kbj1gJeXFzZv3szl5OSgQUXZd2QkEbwxMYC+PkRR5E7NnSv17NCBTS4t5TlPT4CciKoHvXyZHKynWtRVhCRJOHHihGhsbCw1aNCAbvLmzVTkbdeucnWYsbFxpRz44uJiPPj8c7jPmiU0a9as8sP5+Weq2bBrF5FDFdtcaiGXUw0ZAHf19PDwxAlYBASQPXLsGI31p+TvlbBuHaXmXL1K57l/H8zPDwMHDpSioqKkU6dOscLCQmZubi6Nd3TkTC5dwk19fcRNnSr0unKF53geiI8nlZ1GQ+Pa3JyOt20bUFoKw02b4OLqCnz4IdoC4p6PPkJps2ZcZn4+98knn+iiuZ98gjaWloiNiZGOHTtWPthMR44EJ5cLJpLE7969GxqNBmZmZpK/vz+ztrZG4r17+Nfp03hgagp1cTFcBQFc165ka4gijTOASP2cHOiHhiLc318Iu3CBC3ByYpXy7gcOJDVAo0bVSsf37dsnpqens4EDB7K2WpKjGgjZ2WALFuBm69ZSQbNm4sxRo/jExEQuMTER9vb2rH///jA0NMSlS5egr1Ri6owZ2HPwoJCXl8czxtChQwfut99+gyAI+OWXX7B48WK6T6NHk80TEUH2TTVqtEoYM6Z6hdX06YC3N7LHjsU6kuczY2PjqkUA33yTxt3+/bR+urmRimT1aiKjDx8mhUBhYY3F9BQKBTQaDTPg+WrXEY7jYGdnJx46dIgbW6bqCwwMFAoKCnhDQ0OhpKSEU6vVzKB1a7Q4fBhv3r+PLkuW8FAqy+sfudvawtzKik9KSspSq9WBtd+U13hZeO3s/w/hq6++CtDT09vo7+9vVEH6q1/288/G4cPEBv/Fjj4A5OfnS5V6p9YXRUW0iN+5Q8bgG2+QdLdrV/r96tUk55w/n/5+4QIZq2+/TY5zTAxFi9zdyVlv0YKi7K6uJBXUVnC/cEF3zhUrdP/u0IFeazNIKl7rokWUU6dltcPDiWX++WeqJt22LUXDKkrXtHm4ubkUJRJFclx27iSZ6o0bFEGxsSFCwNOT3uPsTBvr37EFZA2Qy+UYOnQodu3axVQqFYYMGcLwFEG0YMECPioqCsXFxbyNjQ0cHR1RVqBKeyM4gIz7Q4cOSTzP840bN0ZISAjs7Oyev3aERkOqjpedQpCTA3HDBiiuX5ce29uz9mfOwO5PJBybNWsGExMT8cSJExgwYED95mhkJP389FO9z9OwrLp5RWciICCAPXjwAMcOHWKbVq+Go4OD0CY+HnazZvGmN2/C4uZNsO+/J0n7mDFkPPbrR47NV19RVDMigoz5Pn0ot1fr7M+fTyqawkKSzH7xBUn7+/Sh+f/NN0TwHT5Mx/vlF/rs7NnkTPbuTY7G7Nk0N7UtmypWeC5DQUEBVq5cWe7cTp48Gc71iWw9Da1j0bQpXefMmURaeHvTOSdPJqm0tTVw7Rq83nkHFrNmYdtHH0FRWoqZtahYSktL8fDhQ27mzJmIj4+XHnfuDC48vOriwRiwZg1iVCpcUKul3Nxc1rVr15oXmQ0baG39+OMqf+I4DhzHITo6Wurfv3/Nxxg3jgjXZ4Aok+HW8uXQW7EC8ZcviymurpKDszPn7u7OrK2tERsbi+joaKG0tJQZGBhIyuxs3iUuDpb5+Qj38KC1GVScq0ohM1NTiia//TZgZobY2FjI5XIJFYrMlUPb470esLW1RevWrcWNGzeyRYsW6Y51+zYpNspIE39/f+706dMIDg+XMtq2lfosX86B52kOLF5cmWTr3ZvSCKoZl1qEhYWJ0dHRXEDFwn/W1lR8bsECkiEvXFjlc5mZmWiSmIj2Pj48e/rY2q4+DRoQaV1DbRCAxt7+qCgMmDRJ4r7/nuH336lTzfLlRNRpZeBPk5uGhjT++/envT8wECCikHXs2JGJoog7d+6gpSgy/sYNYPFieC9Zgvi4OG7vnj3i+H/9i8PQoeTEzZ9P9sKXX9I8mjaN7Ajt/rl2LbxEkWvr5YWcs2exfdQo7Nu3Txw/fjytiQEBaH7oED728GCFvr4wNjaG7NgxWlsWLSq/OaIoYvfu3dLOdetYn2PHYCeXI2jQILjdvYsGYWEoSkwEt20bRAcHGKhU0Od52lsWLaJn8csvaOfpyZ8tLUVhBYUNAJpnM2ZQQMPTEzAyQmFhIeLj43H9+nUxKyuLkyQJJ0+elMLCwiS5XC6ampoyc3NzXi6Xw8TEBJJKhbvffINiGxs8dnKSAiZO5MsUAZKHhwcbVoHM7tGjB/Dhh3g4ejSysrL4YcOGgTHGnzlzRjKiczMAyM/P15FX3t40HiZPpjlWWyqBgUHVgoMqFY2tkSPx5MmTimOIy83NRZW0rwEDyOGfMIHuY0gIKWS0rXKVSrIDa5ijarUakiTBKjW1nJh6Gj169OD27duHwMBAwczMjHvy5Ak/fvx4cBzH79y5s+yyVYC+Plpqz3v/Pq3jZWqyM2fOPqn+oAAAIABJREFUFGk0msVLliypoTDJa7xsvHb2/wewdOlSpqen94WRkdH7kyZN+nu3y3tWFBeTM6ntw/oKIC8vD61ataJd9cEDWpyNjamAip8fyQkPHKD6Ap06USGwbt1oA1yzhmReUVGUz5qdTUZNq1a0YTs5EZtrYkLsbXy87sRjxtDr7Nm639VQTOWFUFpKG01GRuXfazeyQ4eoEE0tbDusrHTtsLQkA0BGXF4eOSSZmeR4nDlDaQbaNjzOzhQF7NiRHBlz8+eqWP0qQBsFVKlU5XLIiuA4Dl26dKnzOBzHYeTIkeUGdUxMjHDy5Ene3NxcM2TIEFl+fj40Gk25E1onNmyg6GodBZIAXUSTMYaCggKUlJSUV0kuh1IJrF+PAhsbRJ84gd+6dmX/XroUsj9ZxcFxHCZPnsz9+OOPklwuF3x9fesOr44bR3NQkmomm5RKcmLatyd5pzaHd+xYqugfFQV89RWc79/HvDVrmNrLC1dcXflWe/ZglyTBJiMDA42MYAxQFKxTJ3IKoqNpzlckGo4e1aUWBARQpFWtpvdmZJAhqO0QUVbAChoNSXqbNSPnTpuPbGpK15qbS9HHceOIIDx7luagXE5Fv3r2pNSA8HColy/HkMhIqd3lywyzZxMxkJpKRVH9/SmX1dCw+nZ31eH77+neNm9Ocz8vj45z5gwRGyNHAmfOgP/1V7i2b4/gFSugp6cnhYSESCNGjOCqU6elpaVBT08P1tbWSEhIYNnNmgno2VNX5KoCilauRMiJE4CFBSwsLKQePXrU7KiXOR01YdCgQTh69ChLSUlBtbU2lEpKh6pnB46srCwcOXJEys3NBc/zaP3OO2KfLVt44fx5hIwaJRw7dowrKipi+vr66NmzJ9+wYUMU5Oai5d690Le1hcGBA+ip0eDUqVO4evVquRJjyJAhaN++PZ3EyopyfQUBarUacXFx8PLyqv4e5OTUO5WAMYbRo0dzX3/9NRQKBaUEzJ5NDkuF1C1DQ0MMHjwYLVq0YAcPHmSuU6fCThRpTFtYkKNsakrKMwMDXXHEIUOqPe+FCxe40aNHV73/NjZEbl+5QsTWUz3DnZyc8N1770kT5XLWsKQE165dg5mZGdw3baJc5ZYtST5dW/49gKKiImg0Gri6ulJHCHd3+undm/a3YcNo/zp6lPbSiuoUPT2K6kdE0JyfN49SLPT1wXEc2rRpQ6S3QgFMmADZunUwDQ9nnWfOREFcHExPnCAlUseOVPhw7NgaK9pzHAfTyEhE7Ngh9vzpJ67pyZMMI0fqlCunT4PT14eZdt998oTsrYrHYAwTi4u5+PbtIY+MhJiZCSaK6N6kCbI++wyBJ06g34oVuN26NZrfuYPiS5dE7/PnWeS4cSjZtYt55OTAdvlyNOjTB0baOh0VMXgw1DNmIO3+fZxycREyMjJ4MzMzoW3btvykSZOgVquRk5PDcnJy2JMnTziFQoFHjx5BqVQKKpUKvvv2cR0MDVnSnDmY0Ls3x3Eczpw5IwqCgMGDB1cd4wMGoHGLFmjcuDGuXr0qBQQEMA8PDwYAOTk5yMrKqqxSAUiZ5OVF6/ygQTWTYdqiemVtH1FURGv9li2AlRVa2NpiyZIlUCgU2LdvnxQYGMgAoEuXLnjjjTfAJSQQadupEwVNtCktWocboPVp/nw6RzVrTIMGDcAYg2bDBuhNm1Ztsc3WrVtj6NChKCws5JOSkiRJkiBJEpo1a4bFixfj+++/l3r16sXK23HOm0dj5sABAEBSUhIeP35cKEnStupvxGv8EXjt7P/DsXTpUjcDA4NAU1NTr0mTJhnXq/DUPwWSRBu3ldWf0x5MFMlofvKEjNAePSgirVKR4VHWcqbV/v28T0wMXZufHzkAn31GxqurKzHWWsnXypV07Y6OZGgzRhEILcqMMwB/Wgu0OvHNN/S979+v/u8HD9LrxIn0XQOfQcnF8xQ9qehkao26wkJi0dVqilCePUtkyvnzpBLo2ZNyeF1dyYhydSXD6DkqWf8ZEEURISEhopeXF6tXO6lngImJCUtKSgIAWUJCAtRqNdRqdf2jsAUFdcrWBUHAzz//LCUnJzOe56Gvrw+lUgkA8PPzg0ajkWJv3mSmCQnwiY6GS8eOSGzVCmfLige9cPX654S1tTXGjx/PduzYwbdt27Zq9KQiBIGMpl9/JSIuMpKi7I8fk8NgZUXOsZ4eRaGyssjptbAgss7RkRzmvn11TtLdu9CTyZAXFCQe/e9/peykJP6NBQtg3Lw5/b1iW6PqnlVyMjlAGg3Nr927Sb4ZFETjf/ZsOt+oUbrPyGS6QlJa5wOorOyJiKDXshaHAMipEEVa49asARo2RKKZmaho2hQQBIbHj+k9WVnkXIwZo4t67t9P5MKOHfT5pUup3sD775OBOmkSOfqzZ9Oc/uILcqjee49k1++9R6TBp59SZLaMQJo7dy7S09NZVFSUuHbtWlhZWQl+fn58RRVFbGwsGjRoIALgbGxsUFhYyITHj8EfOlTFGC/My8OwQ4fgEBXFfv75Z/HEiRPi8OHDeUBHZGVmZuLs2bMCO32atzQ1FW2bNuUSExOF/v378xUrgbdv3x4nT57E9u3bsXjx4qqpIpJEUvR6Ijc3F6mpqWzQoEHw8PCAnp4ejzffBIKD4X/hAo+FC1HaqBF4nqe8ZVEko9vQsPzZymQyDBgwAL6+vggMDERpaSmOHz+uc/a1z/7AAaj27IFarca1a9fQpzqir0GD8mKJdeH333/Hvn37ABABbqZSEen19dfVvr9169YwMzOTdu3axYYMGYIWBw9SjvTXXxOhtHcvkVL37lF+czXOfnJyMjiO07bcrAoHB3KWFi3SpbqVgeM4zFm5kh1PSZGSmzZlMplMZFlZrOmRI8xk2zawM2dqJBgqwtLSEjKZDIWFhZUJXEtLeg0Pp/UjPJzG/4cfUjDAx4dSbuzt6RoDAugZVkwb0UZzw8Npn/3ySzgNGIAkNzcp/uJFdLGwYDA0pDFWH1uQMfTx9+eum5hIyRs2sEYPH8Lw0iWam+vX0/qXnU2KvVGjylNqANDvw8OBdevQZvBgoE8fPDp+XGzZpw/DqFGsEcdhUc+ewOrV6HzyJESlEhkLF3J8XBws3NykW/n5wu6sLF41ejQ6REZCGjKE1rA2bVBcXIzbt2/jxo0bQq6NDR+wcydcAgN5f3//Sl1YjIyMYGZmhqbaGh868Dh6FPjgA6BPHziXzdF79+4hMjKSe+utt6oWoFOpqEbAZ59h0KBB+PHHH7Fv3z5x3LhxnEwmQ4MGDao6+lo0bkyO88yZFMypLtXnp590BYoBIthKS3XjogxmZmbw9/dn9+7dg0KhkJIDA1nxl19Cvn49neOtt4hwvHCB1BEbNtA+A9D66elJ9SSqIR1CQ0NhZGgoSStWMNTSgtajrBhox44d2fLly7Fnzx58/vnnuHv3LlQqFStPnyoooPoQPXsCjGnrKBRpNJq3lyxZUr98n9d4KXjt7P+D8eWXXw7Q09Pb7+vra9ClSxfub90u71khSVSIr2dPHVP6PCgupqiUszMdp0ULMox//JEW0lWrKMK1fz9FoKdMoSj71q3kiKekkKFvYUERi44dkZ6TIwQNGsQNc3RkSEzUnUvrBAOUfwXUq7LxK4elS+ne14Xp06nKcFnBpxdu+yWX61rdVKyAO2sWRQMTEiglQEsGLFpExX2ys2lDtbSkZ9eqFSkBrK3/krSA1NRUnD9/HikpKSgtLeW6VWh/87Lg7+/PxcXFQalUQl9fHx07dsSWLVtw6tQpaerUqazOYpzNmulSLkBOT3Z2Nsk5yzpO7N27V0hPT+c6dOgAT09PpKamQiaTITk5GefPn4epIGD4zp0oZQy/jhkDmZkZ9BISBBcXFzZw4MAX6134gtDT06MIhza/uqSEopZGRhTJfustMqaWLKGxFRlJTm1yMqlwCgspqnLhAhlYRka6+hYVWzxW/HeZQfZ7QgJu3LiBlJQUTq1WQyaTobnW0a8N2dkU5Q4KIqdJC19fGvsdO+p6MCckkOrH0FBX7O75bhS9GhiUK2tuOjvDol07qrGgrc8BUPoRQK3oADJso6KIgMjKovWUMZp3ZmYU2Tp/vjxyj/XrqeOHttUjY9UW5zIyMoKLiwtcXFz4J0+eIDo6mjtw4AD69euHzmWFq5KSkjQ9e/aUAYCLiwtsbGywMikJ7S0sxPYpKZxVhajvgatXMaKwEKZyOcaPH8+tX78eKpVKtLS0RH5+PouPj2c8z6NJkybcwAYNkJ6YyIJPnRKVSiXfoUOHKm2/3nvvPSxbtgxBQUEY8rRzaGdH5Ec9C1+2aNECMpkMLi4uKJ+zMhl9PjcXGDQI+r/9pnMuVq/W5Wg/ZQ/I5XJ88skn2LRpE9LT0yufaNAg4Nw5mBgbg+M4KJVKVFuRe/t2kuDXA9rI+syZM2GXn08F2iIjay2wN2nSJHb69Gns27cPXbp0kfr27avronDqFK1J2dlEiKtUVdL2cnJyIEkSSkpKoF9DbQU4ORFhd+UK7eNTp5b/yeTDD9G4cWPWvHlztDMx4RRnzuDHhQsF8/h4vujqVbH5kyfw8PDgautaVFhYCI1Gg7y8vJqdQ1tbcp4HD6a1Z9w4IrV++onmhbc3EQEqFUn+t22j+W1lRXMyNJSe+U8/gTt+HE1mzOBuXLiAS4Dk7unJvBlDPZvRQZIkdBoxgq1//Fi0iIjgPJYtI1vH2Zme2YEDtOa0bUvOHc9TkGLZMiJBZTIigRYuRKKtLe7Fx7Owr77CJ598Qs9g/nwgIQHc8eOw+/RTIDER7Rlj7ctS1nJycrBWFGHh4YGSyEioFy4Ug7y8OGMrK02bNm1k48ePh/HEibCdO1dnN9WFY8coNWXTpvL8dYVCgf3798PPz6/69LbiYrIfANjY2OCtt95iu3btkv773//C19cXrVq1guVTjnklDB1K9tzw4UQyPN1t4//+j+xLf38KlIgiqSCrsUEMGUPbS5cApZKJJSXSKTs7Zl9cDO+KHUB69qR7P348rcNa0vqLL0iBEhZWJbp/7do1sZ+dHdOfMKFqgclqYGBggKlTpyIqKkqSJIk1KVOJpP/6KxpcuULjctu28oLL0dHRKCkpuS1J0uE6D/4aLxX/Q97f/xaWLl3qraenty8gIMCocT2Z9n8UsrLImP3yy8q/F0XaPK9fp+hRYiIxqJ9+StJWT09d79lPPyUH/Nw5ihBv3EiLVrduZJAaGZHRr+0NnZKik3BqJYAVewTPmgUA8DE05H/++WcMe8Fe7K8k2rUjo13be742aDe72bPJ6K/ooLxMMEaOfEVJmtYoUKloA1coyDGLjiby4ehRUmf8+99kNDVvTlETT09i6f+g2g/FxcX48ccf4ebmJvr4+HDNmzevX8/3Z4S+vj46VEyPADB+/HisXLmSHTlyRBo5ciRj1RgZarUaEYcPix6ffspibW0lkTFERkZylpaWSCszhLTgeZ6fOnVqedEvrXHf2dycVBdGRgy7d6PE3R0eZ87A2dkZ7u7uf63MQpIgXrqEk8ePC/1tbZmNvz+H7dvJiZDJSB5cXExO8vTpNP/19Wm9KSkhJ3XSJJ0ksw71w9O4efMmgoOD0bJlS8Hf3583MTGpu5K0FoaG5Cw+PTYdHUlKuXQpkRPanuIrVpDzl5hIDtZLIoNbtmzJzp07h5ycHM2sWbNqPijH0bwCSPqsXTMr5rtr64d4eFDhz/Bwimq++SaRAGq1jnCoBpaWlujVqxczNTWVQkJCWEhICJycnESlUslrDXOe5zFlyhQuKysLeVOmsOjp01E4bx7efPNNaDQaZOnrg9u7F3j4EJZOTujUqZOYnp6OlJQUKTs7m7m7u8PLywsODg4MXl6w0miYm6sr++qrr5CcnAyNRoOCggJkZmZKlpaWTOtIlClrKiM2tkq7q9oQFBQkmZmZwdzcvOpknTqVnPS+fem5K5XkjK1dW2MRQQAwNzdHeno6IiIiIJPJ4OrqCnnDhsDo0WAXLuDDDz/E8uXLERYWVrX9noFBvSP7crkcFhYWUnp6OrNbv54KrdWxJ5qZmcHHxwcZGRliZGQk5+TkpKt70b8/OUn6+uTENGhABJOeHv1wHNq3b48zZ84gIyOj9s4kTZvSev/uu+TUln1PzscHXVu0oEi2vz8sjh7FgkWL+LzvvsPdu3e5W7duSdevX8fo0aNrbAUql8vh7u4u7d27l02YMAFOTk41FwM1MKCf48fp+YWEkCO9aBHtWyYm5Pzn51MUeMQIem///pQn3rw5MHcuZJ06wbNnTxRFRODu3bvi5cuXOZlMJpmYmIhDhgzha1JzKZVK/Pe//4WhoSFKSkq4RqNHk52Ul0ck5nffUQpdo0Y0V3/+mWymjh2JhNDXp300MxM4dQrdunXjWq9ejYf29oj4z3+kHv/5D+PmzQNTqeiEkZG0X69eTc6xszNCQ0MlmUzGQnkeDVNShEEaDb+wUSMYDhwoK09B0NY52LqVoue1IS6O7umXX5YTrKIo4pdffhHt7e3h7e1d/cMwNqbjl6FRo0ZYsGABd+HCBYSFhYlnz57lBg4cKNVa+NbEhGyK//s/qpFQca0uKCB7BCDCzNu7svoKoOe8fj2tf8eOAe+/j+4LFrCzZ8/iQni41LxVK1YpFa9bN0rneuMN2nPNzGifGDuWFJWffQaA0gW3b98uAOA9nJyeKb3TyckJTk5ODABKL15Eo/R0lNraErGsrR8AUvqdPXu2WKVSzV+yZEk1FSxf448Ek6qrGvoaf2ssXbrURiaTJYwZM8b8RXtP/+2g0VDk7cgRkpVmZBB7u3s3Sb/lcpKAvv02saslJbQIrl1LDKSbGxmTKSnEshoavvRLLC0txcqVK9G9e3f8EVHbvxR79tB9ftYofUEBETAnTlQmSP5KKBQ0Dh4+JCN0+3babH18KDKqr08pGPb2NFbkcooKvYAaQKPR4JtvvoGvr2/1faz/YMTGxiIoKEgyNTXF2LFjmbW1NZRKJQwNDXH+/Hnx2rVrnKlKJbROTuYvu7hIHMdJtra2XGZmpti8eXPGcRzLzc2FnZ0d3NzcKhf7SkmhIlQDB1K6xbRptTpqfzgUCko3GTSIIlA7dgDR0Si1t8fFDh3Qa8UKyI4cIeNMLq853eOdd8ixWLKE/v/NN0RgJSRUKkpUHyxfvlzq3r07unfvXv9BVFBA69aZM2SA1/Rd3dyIzKqYllBYSJHeuXNpzayPgqAOpKen49ChQ8jNzcWnT1e3f15IEhnHAEUM/fwoAqbNba4HioqKkJSUhMOHD8PExERasGBBVUIrKgpF336L7X36SABENzc3LiIiAp+kpVF+9apVtZ/khx+IGF6+HMHBwfj9999FnuclPT09ydzcXJaVlSUWFxdz2tZVS7RjBqBWg92711gY62mcO3cOly9fxvTp01FrHZ7ff6f0CWdnIqzr6EGvUCiwc+dOCIKgAcAKCwt5V1dXsc2ZM6xlaioT9+/H+vXrkZOTgwEDBpSrJQDQ/Zk0CXkch1u3bqGwsBCJiYlSSUkJFAoFMzc3l9RqNTM0NBTNzMzw4MEDbjoAh7FjaexW4/Rev34dCQkJMDExQVJSklBQUMC7ubkJt2/f5lu0aCGMHDlSNzFLS4mA27uXiCCNhvb6sDAgPh7S+PHYbmyMHu+9h2ZmZjp5c01ITqY9rWVLcqQ9PMhucHAg9VhuLqmcKhBl169fx8mTJzFnzpwaCQVRFPHDDz8IWVlZPAAsWrQI9W5tLIo07v/9b1Ix9OpF+dk7dxLJMX06yeunTqU5Xw1UKhVSU1Nx79498erVq9y0adNQWFiI0NBQoaymANPT05NkMhnLy8vjeJ5HyxYtMGrECNrfJIkUjebmtO4dP05ryPr1tC+++y6NuV69UPD111BGR8Nk5058P2MG/M6exSNHR2h69ZJabt+OTr/9xoTISOgrlbSPNmpUXlMgU6NBwooVyP/gA/To0YPupyQR0dG/PwVQtEqS/HxaGwICaq4HlJFB6VT+/uTwliEuLg7Hjx8X33vvPa5G4uXePRoDMTHV/vnGjRs4evQoWrZsKQwfPpw3rM12LCmh77h6tU69qVbTOIqPp3Hbpo1uf0xKIiLk/n3696efVqq1UFhYiJCQEPH+/ftsyJAhrGKnipycHNzavh09du8GO3+e9jKFgtaCiRMBOzucPn0acXFx0syZM5nJzZt07LrmRkXk5wPm5lD36IErkoSz/fohICCgUrpMTEwMQkJCoj7++OPOtRzpNf4gvI7s/wNhYGCwrUOHDkb/U46+JNECumgRFbDJz6cou5eXTpIdGUnMpkxWOYo8eTK9VpQfVpSBv2To6+tj7Nix2L17Nx49eoROnTqhWS35UX8LKBS0ye7f/3wOnKkpPTNtWkMt1ZT/NJiZkdGgNRzeeINeBYFyKjMyKEqQlETff/t2cqY+/5ycKHt7Mr5atqTNs47CTQDlz7q5uYkXL17kvLy8/vS8dXd3d5iYmLBdu3bhl19+QZ8+fbBv3z7o6+ujtLSU69+/P7qeO8fDzw89x42rUvW/WuTlkRzx9m0y5nr0oHv7Z0BbnC4xkbpNrFxJZJSxMeUy7tpFksY33iDjCsDub78VTU1NJZmrK4/69IX38KBInxaLF9PrkiVE/vz4Y70uVesA6uvr19/RV6vJeFuwoHZH3cyMyJbk5MrOvlxOzsIHH9AaOXs25QjXlNNcD9jZ2aFPnz4IDg4W8DLaiwoCOXGtW9O99vOj32/cSGt6YiKRKnWQbCYmJmjbti2aNWsGRqj6pk6dYDJsGN6ysWFr4+L48+fP0+/nzCHHqi6UlpIjBmDgwIGoJh2FKykpwZEjR3D79m2EhoaKfn5+9J7k5GdSgkRERCAgIKB2Rx+gdnizZ9NY/+EHWp9qWaPNzMzwb0rDkgHA3bt3sXv3bu53uRwfL1sGThDQrVs36ejRo6yKQxMejtLJk7Fz505JpVKBMSbp6+szT09PBgAymYw1btwY6enpXFpaGhwVCqnhkSMM06dXcvRLS0sRHByMBw8eCGq1mm/Tpo1YUFAgdenShWvXrh1iY2P5W7duoUOHDjS+ioqo8OWmTRSxXblSp87atKn8mTC5HLYuLsLtTZuYy+bNHCsqon2/USOat6GhtD5p70+TJiSlnzyZns2xY3Su9u3JttB2u6iADh06IDIyUrhy5Qrr169ftesix3EYP348r1Vy3UtIQOvWrcGUStpDunUjaby+PqXhzJlD13fnDkVqT5+m8ejqSte+aBEdWE+P7KATJ4i8GzGCouqJibS/uroCTZvCICQELo8ewWXiRK5NbKxUuG0bs8nOxqA2bTiZmxszjYqCUVwcMoYMgVhcDCE3Fw1jY2mPa9+eUhzCw8mhj4ujOkVffUWRYq1EfsAAKO3sELl9O9JathQd2rdHW47jUj098S8HB1j+8APTBAUheP58qW+vXizhwAG0Pn2aBt3165AkCbHz5knNCgtZ94EDSY6+YAHddwcH+o7JyUTmzJxJ19W5M43v/furEkclJURQjB9fydEHiOAyNzevvd2qnl7NZCqoHodGo0FISAgfGxtbmQR7GoaGRIydOkVrmo0NpUZMmEDE86ZNdD6tCmHAAPpZtaradU4ul2P06NHclStXcPjwYURERAgjR47k9fX1sWXLFkmpVLLU7t3xry5dYHT2LB6rVEiNiJCML19mzbZtQ0ZGhmRrayuamJjw+Ooruqf1dfaVSlKUXboEvbAwtMjIwNkffsDVq1dFFxcXDqBUkHPnzhWqVKqXxP6+xrPitbP/D8OXX345wszMrKefn98/v52eFoJADtnAgbTh/Pe/ZHQpFBQ9PHiQFs5XqD+7s7MzJk6ciKioKOHXX3/ljY2NBQcHB75379415/G9ysjMJDb6RSK1Q4bQz65dZLwkJdUp6/xLwPNkbDg4VO4UMGkSGYIZGXTtpaVkdAgCRVjCwigqM2oUjcXmzem1RYtyYuPOnTuIj4/nGjRoIMlksr+kj6CLiwvee+89bN68Wdy3bx/HGEPv3r3Rrl07GBoaksz63XfrPpBKRRH8QYOoRdx//vPHRfIFgdoytm5NEfqoKJIp2tiQ8d++Pa0HJSWk7LG1JadXm/8NlBN8mZmZOieiLty8SeRBdXnvp04RCfnNN5R7fvp0jYcpLCzEhg0bRD09PVZbvm+V72xrS8ZhfZ6HJJGhvGFDpXoLYIzSbkpKKEXh7Fkau87Oz6VsUqvVsLKyQkFBAV9cXPzihJU23adZs8rfUy4neXX79mQIL1tWr8MZ1VItHwAgCDDYvh0DPvsMR44cwcyZMykN6NNPqZ7H050kKmL0aCK3aoGhoSHGjh2LFStWICwsjPPz86N7v23bM6mCOI6DSV0E4m+/kaptxgx6xmvWkNR6xIh6V/xv0aIFHB0d8ejRI0jz54ONG4ejKSkMIHJQC1VBAaT4eOwPCpIEQZAWLlzIcRxX7Rdq2rQpkJuLoLAwKea771jnCmqD0tJSrFu3TjQ1NYWvry9fVniw0kYQHh4u+Pr6ck2dnRmOHyen7+ZNcnCtrYls/eILSim4dElXjG7zZvgoFPzKlSvx6PvvhVkAD09PuheiSGvVvXtU0+XYMYpY371LufKff05zrkcPIhEqpitoc9UlCTh9GgP69OHDly1DQVwcjObNgzhxIvJGjgTfoAFM3n4bu+fOFTtt2cKZFBcDEyaglbs7Hn71FZzc3Mhmyc6mvcPcnMZ/ejrNSTs7ncT6/fdpfXNzo+eqUFCKzs2btPbY2tL+WbFLiPZVm2bEGBqXKXwZx8FckhgYK3+fbdl7du7cKejp6XFjx46tfoB++ind51mziFQYPBiws0Na9+5QP3kiThw1imPHjtF32bcPCAyElJ6OjN9/x6DNm9nNjz5CaGysqDx9mjk1bsxsJk5EQkICrjo6onOxv871AAAgAElEQVRoKH0HgEjLvXtJbXH4MO3B1O6PyL9hw+i9hw9XXuMkie5NUVG15GtRUZFkZGRUu7Ghr6/r+FMDOnfujLi4OCQmJgqdO3eufQ/p14/Wrh49iJxauZLGoZ0d2QVBQWTPLlpE6pw69k7GGLy9veHu7o7g4GCsX78eACRXV1fR09OTf/DgAY4XF6Ozry8ODxuGJn36wPbqVenHZctYgZERGzBgAF3vpElV6wlUh8OHidC+eZOI/LI0PW2a0u3bt7m1a9eKTZs2lQRB4JVKZTKAU3Uf+DX+CLx29v9BWLp0qbOent7WkSNHGv9PFOO7c4cY2n37qBhely46OZ2BAS3sjNEmXFpaa57iX4GyXCdepVIhLi6Ov3//vrBx40Z+xIgRaK0tNPd3wNWrZEwFB7+c440dS5FStZrY+3oWq3olYGJCUVFtZFRbG0CSyIhLSiIJYloabeCrVtE4nTMHyMyEmVKJVpmZGL1gAWNFRbTB/wVFAo2NjTF//nzu8uXLcHd31xUZKy2lwlAVK3U/DVEkA+Drr8n5vnSp/m3W6oOHD8lxnjaNjC8PDzKcpk2jtaBhQ4rSm5gQCaV1iLQR4RqcnNLSUmzevFngeZ5rVd82nZ98QsZZxdZ3FcEYRZJatqTxvHt3uZKoLC9ays3NFfPy8liTJk0wevTo6iPOT+PRIzJ0jxwh6Xd9wHFEONVUkM/QkIgSgO5rfj5FLuu4HkmSUFpaCqVSieDgYPHevXvlRnN8fDw6ajuLPCu0PaZnzSKypGJnAC0YI4dMoaAoWG3tPOuL0aOBJk2gevwYcrlcsLKyIiM4LY1SjbQKn+oQHEzr4caNdZ6mVatWuHHjBrZu3YrJ27aBa9SIiJt6wsjISEhPT+drJIfj46kGwJgxOgfxk08oQjxqFDmzdREfZXj06BEAgJs4kUjdpyCKIvZv3Ci56+mxEpVKmjJlSs1yaC1mzUKznBzE+PhIHUWRXbt2DU+ePEFcXJxobW2NCRMmcDXVq1AoFLw8JYUIvZgYIjIqtpQ1NibHvGFDcsQr1D25efOmyPM818DamgZ2xe4W2tZx3t66e/PllySZHzOGnMm9e4kQ+L//o+d17x6t9zNm0FyfOxdOkZFwKymRkrZvZ4cLCjD43j3Enzsn5lpZMQ8XF8m+SROu9bZt0Od5jACwQpLg3r695DRgAIO2t/u2bbrrCgnR/btdO3rt14/UOqtXU6R9xQpalx8/JjIiN5fuSz0I1tpmeGFhIZKTk/n58+fX/KZFi2idDw+n19WrgaIiXO/USYyPj+eunD9Pz+DcORg6OmJAnz4iHxPDnT5xAm++8w6uvvOOiKIidsrXl0GSIP/+e+QrlbC1tZXKO9FoCy3n5xNpKUk03wMDqcjf6NG0P33zDT0bDw+d2ik8nFQNFVo6VkRBQQFFtWvD/fvkgNdRj8jR0RF37typX5SiYUNSQSQn0zM8c4YCCNqo/7lzzxykMjExwZgxY/iytAI2bNgwnuM4NGvWDGLv3tAcO4Z3d+8Gv3Ahw5Mn6KxSIaxrV6lFixYMsbG01mpbs1aHdevounv2pLWZsXJHX4tp06bB0tISv//+O3fnzh08fPgQarV6yetc/b8O/wMe4f8ODAwM9vn6+pr84wvyHT5MrYDef58WpaZNq2875+xMUr6MDHpPYuIz5c/+WTAwMEDHjh3RsWNHfv/+/eK1a9e4v5Wzv349bcAVuwm8CGQyMqivXKH8w/79622UvrJgjPK6nzbMJ04kAyUrC6E//SQWPXwIu8RETtyyBbyLCxmTPE8F1tRqkpVaW5Nx+QcVCayIrto8aS0uX6YoTlhY9R+4eJHIgOnT6Zqft+6AINA9O3SIit8NG0Zy1kWLyAncvFknx2zThupsaAuedeqkO049UicAcvRXr14tNmrUiE2fPp0Z1PfehoRU6/xUgpb8OX8eJR98gM15eYJaEKBUKnlra2tJJpMxe3t7adiwYXy9HH2A8j2XLavsqNQHLVtSvqqnJ8n1a8KxY7Rurl1LBu7+/ZUcJq2DHxgYiKKiIgDUwcDExET64IMPcPv2bRw/flwKCQlhjx8/hp+fn7bug1BUVARTU1POx8en9q4Pw4ZRxFutpmh0TZWu7exoffD0pAjni5KDJiaQDh6EJi1NtB0/XucAnDtHjlRtcHOrNzk3aNAgaDQaxMbGYnm3bvh3QADqF2sniKJYc/HGR49oHvbuXZmcYIzGzZUr5BwvXKhzHmuBkZGRpFQq2dL799E9PBwyb29YOThIKPMTb926hcJHj9C6e3d4TJ9et6MTHQ188AGsbG2521u34ttvv4VcLpcsLS1FDw8P1rt37xodfYgi/MLCpCZhYQyff06O+NPBDY6jLjpffUXkxpo15X/Kz89HkyZNMGrUqJqvs0MHnWrr+nV6zcykgEHPnhRRnz9f12ouK0v32TJixHPPHgYAHgDw+edor0t1Kh8giYmJOBcUJMjMzDgfH59nZ3WPHiVCfMECeq5BQaTmSE2lSHZkJJEA5ubPfGgt0tPTYWRkJMjl8pqdYWNjIl3u36dIe1ERRCsrPJ4zh5v27ruwt7cvT1NKSEjAbQcHznnuXMxLSgJbtQoKY2Ou1NMTYWFhGPXjjxAYw96AABgYGKCgoKBykVpvb/qRJJKbt2tHxHJiIqkvfvuNric6mtbdQ4doHdu3r0Y7QqPRICMjo3w8VwtnZyJ86kBqaqpoamLCUFREY0atpj0+L48CVA0a0P+fPKGihi1akJJj/Xra60NCKF3i7l2qU6JQPFfaW5s2bRAcHFwpNYHjOOgPHUp76LJlwNy5MJozB/2XLGHQ06N7lZFR/QHPnycVwt27dP22tjV2XdL6IF26dAHP80hNTY1YsmTJgWf+Eq/x0vDa2f+HYOnSpU319fXbent7v5pNw18GgoNpwb1/nzZzG5vaDVYtGjUiJ8TWlhb9mTNfuSi/FiqVSlIqlbVvOq8SFIpKFWpfKry8aOPJyCCj9cSJektP/1bQ18ePx48LGRzHd58yBY2bNAGvrY48YwYVeisoIGf23j3qFGFsTNG5xEQyYHr3JoNASwb8UfUOjIwq9Z4uR3w8ycM9PXVVwOuTgqHNp2/Zkgw3Pz/6blOmkHFx9So5fA0bUkShWzf6e1lnCwQEvPBXEkURW7duFRo2bMgCAgK4ejvcEyfS2lKx73xt6NULe1esEEru3eMXrlmDkogIyN3cni1PJSKC1BJxcbXLyWtDv341O85aMEbr5cSJROSV9blXf/ghUtLTcezYMSk/P58BQN++faFQKPAmqVh4gPKWO3TowEJDQxEVFSVev36dAwCO4/hmzZrh2rVrCA8Ph4GBAWbNmlXZmFcoaKyvXUtrvqlp3e0BLSxI7dGwIUU1n1dNUIasIUMgLl3KVWqNFxVFY7SGAl0AKIpaWlqvc3AchxEjRsCvuBhRO3Yg2M1N8m/Tpt7rvkajqZ6Uys0lkrBfv/KWV1Xg5UX32d+f5lkdY2nixIlsx44dUKlUaJmZidzbt/G7nl75taampopGZmZM/6kIX7WIjaXzBgWhkZMTPvzwQ6jVapRFcGteuEQROHkS4scfI6NjR7DBg9Hd17fm83TqRGunk1MlKbubmxu3d+9eSRRFVqf6QIvISAouGBrStZua0k91LdrqAVEUsWXLFiErK4v39vZm3bp1qz/BqMW+fbROzplD3y0igtJMAFL97NhBJISPD6U4fvvtc+0L6enpYo1tCiti1iwq0qfRIPrePUTMnSu1HTSIOZYpSbjBgwEArq6ucJ0/nyLtM2YAubnoZGICLF6Mrl98ge2lpaK9pyc3z9cXW7dulQIDAzF48GC4ublVzqlnjAhlgJzOrl1p/5DLyU7UKlvc3WmNrlhXRRRpfypb2wYMGMDvX7QI29auxcRx4yCLjKTUIC2p3aMHEUeJiaRSKyqiNSori+aOJNFe9eABultacjlZWcAvv9Ac69uXHPycHNrHu3alfdTenjqKGBuTGsLdnQim4GBS/XXoQN/B05MI5c8+o8+vWkVkwMCBlb9TBTx8+BA3b96URFGsfj0ZM4ZULNu2kT08bx4pDPr0qdoBQK2m+zV0KCkPailSqtFocO7cORQVFYl6enoSQO321Gp1PQz11/gj8drZ/+fA28nJSc1x3MsvH/9X484dcmLmzCGW+r33nv0YnTqREbZiBS3cz9gS68/CwIED+R9//FEKDQ2Fn1Z2/KpCoyGH4MIFKozzR4DjKCrh5kYbZG7uy5WEvwIQRRHZ2dmcv78/qrRAMjXVOS7aPLpp0yjynZtLxseDB2TUZGfT5r9qFTmE06ZR7qqtLeU7OzpWis4+F0JDqa6CFqmpZPTMn0+G26RJNctGb9ygiIK9PUX+P/uMDNZff6WcVJ6n6+vZk6IHlpZUvV+Lfv1e7NprQEhICBQKBT937lxU5+gfPnxYksvlrK+2arIW48Y98/0MCAjgV6xYIT3+/HPm6OZG0ZwRI+qvOHr7bTLGylomPRemTKE0i19/rTtSVZarfnDNGqnT1q3sYkoKFLa2omuvXlzfvn1rL2gFwM/PD9oCdPfu3YMkSWjRogVSUlKQlpaG+Ph4YfXq1byrq6uglZvigw9oPPv5ESlbz77taNmSvtewYeTAvkDR0xRLS6hsbUX98+e58ur43t70rGorHpqURJLht9+u97ksiorgZmKCG2lpz0TwGhoaSg8fPiwvggWAiLEPPqB7UZOjr0W/fkRgvPEGpZZMm1ajKsHe3h4fa1sijh2L0KNHAaUSKpUKBgYGMDc355CRISItjZW3UKwOSiU90xUryCED1VCos45CXBw5XmfP4uRbbyFbXx9vPD0fn8aGDbQmKpVUqb6MGHRxcUFpaSn77bff4OPjU/sxtDh1ihxoT8/yQp4vglu3biEvL49/9913YWho+OyFafLyaIxduqRz8JctI+d15Ehy4DiOyIjz50mZs3IlRbqHD3+m1LDs7GyptLSUCw0NRa9evWqe83I5jaN//xtPJkyQ9Nq0oY4yBw/S+paURHuDoyOtX2vWULAmJISc2OPHwSZPhteoUdzj998XzXJyuIULF/JRUVEICgqSrl69Kk2bNq36kzdoQE51aSmN/48+IpsxPJyk/HfvklO+dCmle3p50TyeMgXIzoY8LAyTdu3Cr/36Ic/ODg3nzCEVz5YtdM09elAKVoMGur3KxISIJDs7+u79+gEWFrh96RIe5+eLXjNn1v1cHz+m5/jmm5SOFB1Nzn6HDuRcM0ZzGqDfadNMPv+c9vgTJyg15+FDSjdp1QoYOxbR+/YJ8aWlfKdOnWoOHE2ZQikQt26ReuXuXSLqv/hC1yFg/34icbKzSYlQx3ofEhKCGzduwMLCgmvUqBFSU1Oh0WieLFmy5FKtH3yNPxyvnf1/DvJUKpX4V1/ES4U2Z3v6dDLikpJeLH9ZX59yoySJNu1vvyX29hWChYUFRo8ezXbu3AlfX1+80rUXZLJKhVn+MBgb06b75AkVRYqMrLGl0N8N2hZMcrmcc3qW+8jzZLxYW5PhUhFz5tBzKS0loiwpiar7chxFxm/dorkwYQL9rkkTcsDryu2UJCqeOGIERSxiY0kiO2QIOQ3alkxqNZ3Px4fIgIULyVB//30y9FavJuLCxYX+rZXYrlunO9efXEzT3t5eMDIyquLBhYSESPHx8Uyj0eDx48cYNWoUFSk8e5aMvGeMIHMcB0dHR+x69AhTMjLQaPNmciIGD67dkDpwgJ71jRsvp2hlRARF/uohS7148SJinzxhsVOnwsLUVJwfGEgXUM8WcVo0r9AtoKxeCby9vXmFQoGNGzeyiwcPij2Liji2Zg2ptxYsqHdbvXK0a0fRJwMDGufPuU4oFArILCw4bNqk+548T3nAZ8/W/N179dI5X/WBKAJvvQV++HAUb9iAlJQU1LQOpKenA6ACfyYmJujVqxcfFBQEa2tr6OnpgWk0aLZ/P5ivb/0VL0ZGRPosXEjkSLduda8DdnYY9fXXWDVvHo4dOyaMGjWK12g0EsvKYnWS6PPn09qzdm39ri87mxzaTZsoZWf/fiSuWye5NGlSWQ1SHRo1ov3+4EGK8E+YAPA8MspkynZ2dnWfX5IoajxrFu13jx6RNPsFoVAoYGpqKj6Xo5+aSqRpWlplhaKBAREoT38va2td+8FFi2itt7Gpd8eN/v3781euXJFiYmKkmzdvin369OE9a3rOHToAs2fDYuRIVlJSogEgQ2Ag/W3aNJrXFy7QXr50qU4ZYWpKipnCQrT28kKJuTlLjotDUwCdOnWCi4sLW7duHRMEQZe6olDQ2HjzTSK2nJ3pmBs2UHqToSEFBjZvpvdr247q69MeBZBsvQzXTpyQUs6dk6yGD+cwciT9smLdhKNHaY+rpXjdnTt3EHP/PiZPnlz3c713j0ivd94hZd4PP1C60vHjRKpPmkR7olbCP3y47rPJyfTapo2O1FQoSHGgVmPA++/zjufPo11UFMPGjVTbZdMmSoWrmCI6bx6pHrp2pXvRuTP9e/58eiaffkrPq0LRxuqgUCgQHR2NGzduoE2bNhgzZgxEUcTKlSuLJEkaXuMHX+NPwyvsSbzGMyIhKytLT5KkaqNTfztMmkQbw9mztCi+aESyIrTVp729ySmqob/vX4UmTZrAyMhIOnLkiDRy5MhX58IqYu9e4LvvSDL7Z8HSkjbsZs2IxZ4//4VyEf9qlJSU4Ndff5UEQeCmT59ef0lpXTAw0ClXtIqLefPIcNVKpOPiyGBISSHj59tvdRK+rCwyijp1okhGw4Y0Z1QqMn6uXiXyZfZsGgM7dtAxV6wggyglhWSGdnZ0/lWr6Hhnz+quUdsq6hXA/fv3BR8fnyqO/tmzZ3Ht2jU2efJkCIKA48ePS1u2bBEDAgJ4+YYNVFDtGZz94uJi7N+/X0xLS2OCIGDTpk1os3ixOGLwYI7r04fGdU1t+g4cICl7bbLlZ4HWgXn8uFZVQUlJCc6dOwcbGxu4u7tLv/32G5E8T55QBE2lIsLmBfYcMzMzTJw4kYubNUvKjouDpacnZFOm0Dh7nu4NrVrR+Pv8c3LynqO2hYmJCWLc3QXfjh35cqkuQLLaa9dqdvavXaMI6smT9TvRpk3A0qWwSU+Hqakptm7diunTp0NfXx+ZmZlwc3NDfn4+fv75Z6mgoIAxxiRBEJhGo4Genh54nheDgoKYTCYTux0+zOcbGEjtjh1j/LOsJba2VPxs8mSKgK9eXXutC3NzZLVrh2YGBkhNTeUkSYKpqSkeGBszZZcuqDFGr1LRuWor9KaFJJEM+vhxUh7s2lUxhUu0sLCoW49+6hRFLgsKyKFJTgZcXJCfnw8TExOxadOmdd+k+Hgil5ctozHv4vJS6se4u7vjwoUL3N27d/HMLZKnTiVnfefOyr9ftYrUdsnJ1atPevSgdMbgYJobb79NKpg65q5cLkefPn2Yr68vrl27xp08eRLnzp0T5s6dy1cJRpibAydPwmrPHhTzPKfRaHQBi59+Kk/FwLBhtN799htdryjS+mZiAmnIEJySJNapQwc0Levfjvx8NExPh0KhgOXgwaQM4HkiENLTqX6LjQ3N9eXLKSq+di3tOxER5KDX0eXk/PnzzMfHp/p9OCaGxmQdNWhCQkKEXr16cQ4ODrXf1Lw8Kii4bJnumOPHU7FVMzNKg4uPp3353XdrTrMxMyPnHKAaHaB6Ksu+/BL/btuWCA/tZ7dto/uhUND9KC6m/Rug9wQG0qsgUKHHBg3o87UUPS0uLkZqaioOHz6M4uJiuLq6YmQZURIbGwu1Wn1nyZIlF2q9F6/xp+C1s//PQaIgCKV5eXnGlnXlY76qUCiIoZ0xg5wTW1tabJ6j/VOdWLiQXr28KAfviy9e/jleAOPGjWM7tJWxX0X4+pJE/M9G8+YUsT54kJzTbt3+/Gt4AaSkpODIkSOCKIqcUqmEsbEx/P39WZ0y1pcBxshwateualGud9+ldAClkgiwpCSKBJWUkFF2+TIZVTExVATq1i16Bt98Q4aLJFEUYOlSMsIiI3XHfoULhl64cEHMz8/nKxrcGo0Ghw4dwr179zBlypTyYkPTpk1jO3bsYCv/8x/w7dph8KBBeDq+JYoioqOjERcXB41GI3l5eTFnZ2fs2rVLTEtL45o0aQJvb28mk8mQkpIiJiQkcEFBQdKQn35i0GhIwXT+PEW1AYpKZWaShPRlIyqKlE2pqZXW2NOnT0vR0dGSj48Pd/78eVhbW0vjxo1j6enpDIBYnq88fDg5VA8ekAxUGw17VqSlwe7rr2G9bx9bu26dOGbGDGb33XeMPeXoPxORPXkyGdGRkTR+69vOsAwcx6FUJiOSxcaGpLIAPReVqnI7s4owNHw2Z3DGjHJj/91338XXX3+NO3fu4OrVqygpKUF8fLz46NEjZmVlJc2ePZtp23GKoqiVU5Nn8p//8OJbb2FtWpqUHBIijhgx4tmTs3/6iYi7desoit6kSY1vdd61C9YhIfj+8WMWExOD4OBg9qa+PoyUyuo/cPcujbWoqLoJ2qgoau85cybJvJ9yNjiOYzExMVLXrl0rPQBBEMAY08nMp09HeUrBnj10rP37ERoaKjo7O7O0tDTU2uYyLIzWu8hIWu+iosheKCvA9yIwMzNDv379sH//fixcuJDUQvVBYiJJq6tTPi1dSuv1kSPk2Gk7wVQEY+RItm1L64qPDzmU9Wi1pqenB29vb9a5c2ds3ryZ7dy5U5o0aVJV59jSEo6nTsGsQwcuODgYQysWzNSqy6yt6fyXLtF+Eh5OBHSjRoj395eaKJWS39ixHIyMgIwMPJw2TRqXnMws168nJZmTE0W1y9Qu5UU5BYH2JbmcxtutW0QaffhhnaRG06ZNpdu3b0vdu3ev6u3v2UPPv5bUyp07d4pqtZpvV1fBy99/p7F96FB5KgsAmhdTp5LT7eBAc+DCBVrLli2rVyFNgLonMMZgZWVFKQtdutAfIiLoVdumFqBxJElkb3/2GY3t8HC6r1u3kuJMSwr16EH3QduNgOdx//59HDx4EPb29sJ7773Ha8eCKIoIDQ0tUqlUr3P1XxG8dvb/IViyZIn03XffJefl5Vn87Zz9Bw+oVcrKlbSA+/g8d+GbZ0ZMDLHB335LG4i//59z3jpgY2MDsawd0XO3rvqjsGQJLfzPWgn8ZUFfn5wiSSKZ7hdfEEv+N8Cvv/4qNm/enOc4TnJ3d2fOzs6vhhJHT0/X0cLDo/LflEoyMnv1IoOpcWOKxmgN1KcjTH8w8vPzwfO8rh3gM0Ababp06RKuXLkilJaW8jzPY8OGDZKrqytzdXXF2bNnpcLCQubv74+KnU0MDQ0xc+ZMThw8GFkyGTYDCD13TpAkCTzPgzHGFAoFp6+vj3bt2kkqlUoKCgpiKpUKLVq0kObPnw8LC4tyQ1KSJC46OhrHjx9nfn5+9H22bKGq7wsWEKmllZv+EfDyosjpU5Hv69evs5KSEnb69Gm4u7tLI0aMYABw+fLlyv3OfXzoJzyc6qh06UIG67NWjk5IABISION5/PvyZW6fp6f0ODZWchUElpmZKXTq1IlPSEgQ4+LiOAMDA1hZWQkuLi7w8/PjASCvrK+9xdPFO52cSG1y82bN3SNqgJ2dHfLy8vitHCe2vHgRSY0bszZt2rCOHTuSo7Rnj66dXUV4eBBxVh9o2wpu317+KwcHB4SFhcHBwUEYOnQov2fPHsna2pobP348e7qqdjl++AFISQE3eTIcL17kiouLny+dj+fJ2di8mVJ1QkNrdsw1GhiuWgVu1CgEBwfDw8ND6iBJrMZnHx9Px67N0c/Kohoe586R8qd372odtNGjR3Pr1q3DzZs3AQBRUVGCQqHgioqKGGMMjo6OQteuXXlnZ2cYeHuTo/fBB1D17g11djaKioq47OxsxMfHQy6XC40bN+aHDRtWOWVOkoB334U0ezbi79xB0507YTRuHBHcL4mY7dy5My5fvixERUVx3bt3r3sTCA2lfe7Ro6rdBwBgyhRI+flInz0b9woKpPS9e8WSkhLI5XIIgoDu3bvz5eSGszOROYsWUWeG27eJvKuH3cXzPCZMmMBt27ZN2rJlC/T09KBWq8XWrVtzNjY2sLGxQd7w4eASE5GWliZC14WAIJeTc+7gQHO/f38ac5KExzt24FRYGBs/ahRjp08DDx4gMyYGh3v1Yh999BF9vqaizBs2UFX+VatIDaKnRwq3H36gWgZ1FPflOE7ka2oB8e23NCZqQGZmJpKTk7l33nkHxrWloGVmUkDr558rO/pa5OTo2qpqUwYKC+nZ5+bWSjboDpEDAwODqvddC57XpRrNnUsE0apVpMITBCKJXFx0doAkEUmTnk5pe5MnkwLw4UO4X7sG+zfewMUhQ1hR584wDQwEtmzBrVu3oFKpEgGE1nnBr/Gn4LWz/8/C3ytnPz6epHFZWfQjCPWvbP2yoN24FQr60WhoYfuLZf0ymQwDBw6UgoKCmEKhoEI3rwoePKjqEP4VYIw2/m7dSFrcpk31RtArgtTUVKhUKm7QoEHQRuj+FmCMHMLMTIpqAlQAacWKP7WrRWFhIYKDg6W7d+8yxhgaNmwo9urVi2tVS4705cuX8ejRIxQVFUnZ2dlSUVERZ2BgIKnVajZw4EBeJpPBwcEBycnJ7Pz589KdO3eYra2tOG7cOL6K81gG7vPP0cjAAO84OyM5OZnneR4FBQUQBAFOTk5wdHQEqCgSGzhwIIqLi1Fd2yrGGDw9PREZGSmdPHmSjRw5kgi0t94i5VFgIN3vP6qzAkDGcLt2VCTLwQEpKSkoKSlBu3btxKFDh3KoUNzJ0dERMTExqCTNBUh6ev8+reEtW5I0vWIRx5qQlkYR5JAQyrPfvx/6Bgbw37CBXY6KQlhYmChJEr9//34A4Pr27YsGDRogMeJ8S+0AACAASURBVDGRDwsLQ3Z2ttCqVSv+8OHDkMlkMDExkdzd3Vnr1q1hb29PJNqaNZT3vXMnRanqmfbj6OiIefPmIT8/n1N+9BGSg4NxKTdX7NixI1dtqzct4uOJuK5PukVyMilo/p+97w6L6uq+XufemWHoIIggVYqiooICdrEkInajsWB/1fyM6TFNjRqSaKrRqMlrYo1Go7H3GmxYwQrYsICA0jsDzMy95/tjg4AUQbHk/VzPwzMKc/u55+yy9tpl1poRI0YgJSUFDg4OoiAIePfdd6t/+OvX0/iYOhWwtkZcXBwaNGjwZHPLpEkkNlYyFivLEDdqBPG33zDT378k6MewbRsphD+Mzz6jOuGqSne0Wpq/J0+mDOLff1ebhbW2toafnx/279/PBUHg3t7eYsOGDWFqaorQ0FApLi5OTEpKgomJCZ/Srh0T/P1x78MPcfDqVXgOGIDmM2fKzZo1E2xsbHD8+HHxZHHGc8iQIeCcI+bqVcjz5vE7774rs/r1xTMbN2JkfDx3f/NNhqtX67R0rFWrVmJUVJT8SGc/O5uES8PDH9gsnHPk5eUhLS0NGo0Gmps3kbt5M7/i6orXtmxB9o8/ihYWFsjPz4csy3zVqlVo1aqV1LlzZ9HMzIzucb9+9MxmzKDgyj//kMP/CPvHzMwMw4cPZ3v27JEcHBwEQRCE0FDy6xhjsDI2lkesWSNsGD++8lKF996jcStJFOCMiwM++gjb4+NlxzZtBFsvLwoqb9qEep06wX3kyOrZDykptC8PDwrClQSdGKPSqGbNStmcVcDV1VXYt29fxS4Nq1eTM1zSgrGK+yFJUvVCk8ePU5Y8LKzqwMOhQxUZk337UlLqu+9ovnhEkiU9PR2GhoY162cfH0+lEDNn0jNZvZrW9u+/L63tX7Kk9PslrUe3bHkgFmj8ySdgCgUO/fyzNGjHDpH//juOHj2aV1RUNG327Nk1O4+XeOp4cS3jl6g1OOfGoig+yHQUFBTAysoKNWqb8iyRnk41gf/5Dwm6/PLLk/dGflJ88w19BgeTcXjgwPM9HwCtWrViVlZWWLlyJdzc3KoUbnqm2LWrXCbquWPsWPp89VVaFL/77vmeTzU4ffo0XF1dJYVC8e9qj/njj0QLvXmTDPusLKKa379fLdW3LlBYWIiIiAicPXtWysvLExUKBXvzzTeh0+kQFRUlbN68GZMnTybKYjH0ej3u3buH2NhYHD9+HI0bN5asra2Zj4+P4OnpiezsbKZWq2FWJgtpZWWF1q1blxjcVT+fDRvIkAwKghmo9rY6CIJQLQOhOBOJtLS00l9yTlRJE5PSutTvvnv8VnvVwcCAWDrFQRxdMZOgV69eFaz9Fi1a4MCBA4iMjISPj0/5P4oilV398w/VhU6YQM5DZW0aAbpGWSaKvbExZXNPniTDUqlE+/bt0b59e6GwsBAJCQlQqVQP5j9PT080a9YMGzduFK5evQqAnOSTJ0+yqKgo6ezZs4KtrS0fO3asIAgC1Z6uWUPU1VoEk83MzGBiYoJ9soyusbGwXbuW7sno0bQ++PpW3MjJicqLagIPDzL8y0CtVtd8nj9+nOqvP/vsgUhqcSb3yQOJJWJn48ZRLXWTJhUd8Js3qZvE+vX0f4WioraORkMOzKRJlR/n9GkKxFhYEP28huUWvXv3Ru/evRmKg1FarRa//PILt7KyEj08PPidO3dYTk4OW+7tzbXbtnExJkbobm+PRvv3Y3V0NL906RLGjBmD06dPw9bWFjdu3EBeXh4uX74s537/PWsZE8OyAgLE7NhYybigQERWFovOyYHdqVPQWlribHq6ZGFhIfj4+LBHCgVWA39/f4SFhQmhoaGoX78+dDodMjMzkZaWJqWnp7PCwkImMsbHzZ0rXO/bV77Tq5eQfegQcnNzuUajYYIgwMDAQFYqldz17l32yokTQtfVqyGcP4+Gr7xSloXAMjMzsXbtWly8eBH16tWTR48eLZiYmJBj/803NI6mT6cEzLffViu8K0kSsrKy4O/vL0qShAsXLshqtZp17tyZ+fv7Q6FQCHJeHtpkZ/MDBw7IHh4e5efUrVvJOf/iCzrmnj3AG28g38FB6Nq1K9X1jx0LtGmDnN690XP3bspAV+bwL11KZTYLF5LexMNlY4MHU4nAI9CqVSu2Y8cOHD16tHxypVOnRwZc1Wo1zM3NpR9++EFs166d1K1bt/IbXL5MwfGtW6tnGHz9Nel+7NpV/vctW1LrvzNn6J7NmlVlQCYlJUU2Nzd/tI1x8yaVCfTvT8HJL7+kgEZNyjMFgdYnAOr33kPH778XMk6eRM6NG8hKTERubm4OgL2P3tFLPCu8dPb/hyBJUv34+HgcOnQIoihCoVBwzjkbO3Zs9bVpzwqyTPWhrVtTDWpY2NPNWj0OFi8mJyY2libommSoniIcHBzQuXNnac2aNWKTJk3kIUOGPD/KQVwclTkkJNStYGJdoKQG7euvyTh9QcoxSiDLMm7fvv3oDM6LhsxMqsUv2+7SwoKEEpOSiNJ86FDNW8c9AlqtFv/88w+ys7ORmZmJlJQUGBsbw8fHBwEBAeUyyra2tkhISJC2bt0qduzYEWfPnpWTk5NRVFQkKBQKWFhYyIGBgfD19S03ydjY2Dz+CR48SDXclWU7HwOyLCM8PJw1a9aMfrFiBY3h27cp667RUI2nKBLzqLYU+Zpg8WJg/Xrs2b9fikhNFRs2bCgbGBhUmGcYY2jXrh0OHz4s+/j4VD4PNW9e2u3k3j0yspXK8gr1qalEGT18mIJIBQVUtvD77w8MyBKo1epyKv4lcHFxwdSpU1lqaipyc3Ph6uoKV1IXF4tVoPmqVat4QEAA02g0MJw/H+52dsBPP9GxasjckmUZ593d0WX0aAhJSeSMarVkbAcHV8zwM0Zr3KOg0ZDgaGxsRfX0muDcOXrvpk59uOMAd3FxqZs5pnlzci4GDKDAxvTp5QUTO3QgB70Enp7l+37fvUsBg1OnKgotxsaS89O6NWWWe/V6IpHHK1euQK/X8zFjxjAArLCwEMeOHUPrDz5gorU1Y15esIiIAP75BxMSE8U9Gg3WrFkDWZbRvXt3bNmyBX/++SdvmZXFM1q2hN3y5WyElRUAiFfWrkXWpUv8xJEjUtsTJ8QsAwOWZGIiJiYmSsePHxdbtWole3t7C3l5ebh9+7Z85coVptPpmCzLEEURSqWSGxsbo0mTJiwnJwdpaWnczc2NCYKA8+fPSwYGBuL169elqKgoXhwcVNSrV0/09fWFubk54mJi2Oa+fRHv6Cjg2jVYWlqiT58+zMnJqYQyToO5pBe7gQEJpSYllQvGWlpa4u233xZlWcbq1auxdOlSPmHCBPYg6GluTmyiVatoPV26lLK8xe8k5xzx8fG4cOGCdOXKFVGhUMiMMSZJElxdXYV33nmnHIVdmD0bLSdMYGdjYysaeQEBNJ8BkK2scN7DA+rTp9F9+3acVathEhsL++IAkebHHxE/bhzqu7uTzVhMf9cVFeHeX38hkTH4NG0KQzOzci2VJUkCAIiBgRSc02iq7fKi1+sBoDwLobCQKPQ1KFl87733xIsXL2L79u2iv78/jEtELtevpyDmli2Pbhs8Zgy9E5XB1ZXW3nPnaE3+6qtK7efk5GRub29f/XGOH6d1ZtasUt0jV1c6vx496Bg1KR/lHLlHjiBz+XKEjhmDSebm2LV/f75er/929uzZ/y6m8f84Xjr7/yMICQkxAmB57Ngx+Pn54ZVXXoFKpWK7d+/Gpk2b+OTJk9lzzfD/8QdNULdu0UTyImSpK0O9evQzfz5l8Z6zsw8AXbt2FT08PLBs2TLB19e3Yi/2Z4GiImqblpX1ZO0PnxZKxnZREVH6dDoyxF+Qc42NjUVhYaHQrrI63xcVGg05NxcvVt5OzMaGjHRTU3LYnrCWNTQ0FMePHy8RF+I5OTksKCgI/iQwVGlU0M/Pj4WGhsp79uxh9vb2fNiwYaKlpWVJW666DYwVFJABXIdjijEGe3t7XLlyBVuWLeMDhw9nQtm50ciIsp8AOYUzZ5KSfh1iz549vOm0aUzdqJEYMHs2AgICqrxv7dq1Y6GhoSw8PBx+JZ0eHgZj1E4KIPrrDz9Q8EIQKJtnbk7tIZ2d6T19910K/tayTZ4gCGjQoAEaPFRnLAgCRo8eLSxduhR/ltGTMFco5BG//CKEXbjArzRuzIyMjCS1Wi3k5ORAEAQwxriRkRG3trYWLS0tERgYiPv370NSKnFp7lx07N2b6M5OTsC2bZWPg6ys0udVHQwNSfjtcRz9+/cpC/vZZxVEuwwNDeW0tDSGuhr7gkDr4MyZwM6dxNYo0QTy9KTAyaVLQIsWJOj11lul2/72GzmeZR39oiKyBSIiKGM6dGidCPAmJCRwKyurB9esVqvRs2dPEoAzNCzVpbh6FfjiC/Tetw+CIECpVMLFxQVarRZCVhZv/dtvYnKvXjLI0QcANDt6FBg9mvkGByvg7Ez7CwwEADE5ORk7duzgV65ckZVKJbewsBDat2/PkpOT4evri5SUFBw5cgSpqalMo9HA3t5eatSokXjt2jUZANq2bSv4+vpCqVRWnvX4/ns4r1yJ74YPR8+ePdG+RHm9MogilbLFxBAde/58+vdDEAQB48aNE9atWycvX74c7777LntQqi4IxCRKSSEnd+1awMMD+W3bYs2aNXJWVhazt7cXRo4cCScnJyErKwv//e9/wRjjKpWqwgth4OyMjocOYdGiRRAEQerVq5eYl5cHo7t3IR45guPp6UhISIAkSTBr3Ji3tLPjvVauZNmcs/nu7pLu4EGhSZMmLMbXF61u3MC5MWNwvHNnNPf3h/zXX+ixdy+iOnTAZmdnFCUnS/e//lr08/OTY2JiWHp6OlMoFJgxYwYlKkJDif1XBXJycqBQKErKsAinTlHGu0TosRqkpqZi7969vG3btrKRkRHd0E2bqESlRAn/UXB1pTKWPn0qLxWqV49EcUNDibHw228VdBbS09OFasfJjh00Vj76qLzAcYcO9HnoELBxI7S7d+PixYsl629FyDLuv/cez9u/n/0VHIz+ffogLy8Pd+7cYbIsr3r0xb7EswTj1YhOvMS/ByEhIT0B7Hd2dpbGjRv3YOEo6eOdmpoqtm3bVurVq9ezS6VzTgIgTk7kNKelEWX034Q9e0iQ7uzZ5+44Llq0SPbz83s+DuPo0WSwnzjx7I/9OBg+nDIHe/Y87zMBAKxatYqbmJjw58rMqA30ejIIEhIeraafmEg1kZcuVS469AgUFhZi7969uHr1KhwdHTFkyJDqax+fF3r2JMNqzZo63a0sy7j5yy9w+vhjfPfpp5gdElL5F+/eJeGm+fMpCFAHTr9er8fcuXMxsG9fFBUVoZWHB1Rls7MPITU1Fb/++ivefvttWJVxiKpFQQHNnxMnkhOxa1fpOjBjBjG+5s594vlVq9VCoVBg27ZtiIqKgqenp9S7d29xzZo13M7Ojpmbm8seDg6CkJICs9BQ3B08GKmpqZxzzps2bSpotVpERETg8uXLAABDQ0MUFBTAxMQETRnjvY2NGf7v/+hgc+bQejZ/fsVrvXaNWldVh48/pvtRjd5EpUhOpmzrW2+VttUsg/Pnz+PAgQOYMGEC6td12ceMGRUdl6+/Ls3Se3rS+QEUvGnWjH5UKrIFEhNJ+K9zZ6Ii1xE7TJZlLFq0iLdo0YJ1f1jALDaWzveLL8hBDwigTPWIEQ/KYhISEvDXqlVoEBeHQmNj2Hbvzvv378+Kd04lYhs30jWvWEGBg0qYY8nJyVhSXN9cv359np6ezkRRhJ+fn9yxY0ehWuG2ysA5Cdjdv4+LFhbYvn07AgIC0LU65fyBA6lG3cSEgkKNGlV7iHnz5skdOnRgD3c2AID8/HzcO3sWlh9/jDhRROQbb0hjxo8XH1bfT05OxpYtW7goivIbb7xR3r7MzETm0aOIyMmRi+rVY+fOnWOWlpay05Ur8D1xgp2cOZMLgsC6d+/OHpRiTZmCoqgo3AsMxBa1GnnFtHKjvDwM/+svFKlUSGnUiKu++IL5/vIL9I0b4+QrryA7Oxs3b96EsbGxnJ2djaKiIoExBhcXFz7Sy4vB3Lza9Sk5ORnLli3DtGnTygtgVtV5oxg6nQ537tzB4cOHYWxsLI8aNYo2/v576gbw3Xe1Y77Z21NgraoMf8k5LVlCNfxTpjy4rujoaL5p0yb26aefVq5xsHEjlc189VVF3aV33qHr/OEHyMeOYVFUFM/KyWHe3t4IDAwsvz+9HtixA+eWLOGxwcFs8LhxSElJwaZNm+TMzMxfZ8yY8U7NL/glngVeZvb/R6BSqaZ17NiRd+nSpdxkKwgCpkyZIu7YsQNXr14VAgMDn776d1ERGQHvvFPar7tp06d7zKcFb2/KPjBGrAQ3t+d2Ks7Ozvzo0aP8zJkzcoMGDYQhQ4awCn1unxaWLSNa7r8FCxfS+d66RUZmXfUnf0zk5+fLTZs2fcFqVqpBr15koNREad/enlTdnZyoBjAgoMaHycrKwi+//AK9Xg9nZ2dp4MCB4gvp6ANEM38KEMLD0fjtt5Ho6wscOICNGzeiYcOGKCgoQL169dC6dWtq6ZeejgYKBUwyMhB77RpOLV4sN7axEe4WFPA2bdowr+IWZXfv3gVACtGtW7dGhdZYINrzrVu3EBkZCc45PL28oHrrLTIeDx2q8lxL1o5aPSNDQxoTFhbkDP/5JwWS4uLI4AwMfCJHf9u2bfzmzZs8Pz9fMDMz4zk5OaxNmzZy3759RQB48803S3ZON0KtBlauRLMxY4BmzRgAptPpcOnSJVy9ehUuLi7o1asXcnJyYGxsXFICx/Dee5SpGzKklOL+MJKTKSv3qLkyLKy0hVVNIUnkZI4bV6mjDwDe3t4IDQ3lv/76Kxs1ahTc3NwgSVIJc6F2x3sYc+YQHfrHH8lp9/WlgEVCAq35H39M37t3j5zr7dvJ0Y+Lo/Xj8mXqYlDHa2hhYSGysrJYpUHwzEzKZH7xBekGNG5MGdNBg4jKzBju3buH3vv2cSe1mmWuWAEnJ6fSG1XSMrDEEa1Xr2JJQjEOHTrEbW1t0b17d+bu7s6ysrJgbGwMlUpV+wBvfj69G/v2AV27ol7xO/1IfYCvvqJ3Sa0mPQfGyrMtHsKAAQOEDRs2IC0tTXZzcxNsi53Sc+fOyeHh4YKhoaFkOWmS0MPVlY2bPl1Ebi4lcMrYHQ0aNMC4cePYL7/8Ihw4cIAYFSWwtITlmTN4VRQFfP01AgMDqaNHXh4QG4uhXl7lB2VODnD0KAy2b0ejBQvQJyGBnxk0iI0YMQJxcXFwbN0a+OADuN+8yXD2LPDaa1AMHowu5enswr59+xAfHy8PHjxYWL58OTtTWIi2ixYRS60KWFtbw9TUlK9bt46PGjVKQGYmjZe4uHL0f1mWkZCQgMOHD8v3799nOp2OMcbg6uoqBQYG0onMnUvMtx9+oM/a4OrVR5drMUZif2fOAFOmIO/NN/F3aqp07949sU+fPhUdfc5pbLRuTWyryoJAEyYAggC9KOLs6tX81Rs3mGLNGuzbt4//8MMPzNDQUB4wYIDg4eoK/tZbKMzKwoEuXZiVVov58+dLOTk5ImiO/aV2F/wSzwIvnf3/AXz55Zf9DA0N23bo0KHK1bx379748ccfcfv2bbg9LYdVoyGRJScnMhj/8x+aYP7NaNiQjJjTp6mWKSurysX+aaNv375ikyZNkJWVJZ46dYrPnz9ftrOzEwYOHPhYbchqBM4p4LFgwYN+0P8K2NjQz9y51Cv73LnndiqnT59GWlqaWOeZtqeJ5ctr9/0OHUjZt39/yv49okzn/v372LFjh5yRkcHc3Nx4r169BAsLixc3GLJ4MRlgY8bU7X6jo4FXXgGSk9GwXTt0KSxEVFQUT0pKgrGxsXzixAlx586dAChwK8syFMbGaNCundzjxAnBevt2xC9YgM2bN2P37t0oLCwEQE455xw3btxAcHAwMjIyHogY/vbbbzwpKYmJoghjY2O8//77JOL61VeUea8G1tbWMDEx4ZGRkaxt27Y1v86iIqKo7t1Ldfz79hGdOziYAku1hCzLyM3NxZEjR3D58mXWr18/1rBhQ5w8eVJWqVQICgqqeiw5OgLXr5NmwMGDSB0wAJs2beK5ubm8ZcuW6Nu3rwCgQnkA2rQhiu2QIZRBVSgq1gEbGz9atFKSiB5cG0gSsas+/7zaYFqxgj/buXMn/vzzT3Ts2BEnT56ElZWV3LJlS+bl5cWeqDVviSM3fjyJqDVsSOt8z54U6NBoaK4tadv3009EDbe1JYbcUwhOlwSecnNzK7Y98/EpVVEfPJie38CBpLsQEwM0boycU6eknL59xeaTJ8P0YfG06Ojy5UlXr5KjVUnXAc45jI2Nubu7O2OM4YnusyzT83ZzQ3p6Ov766y+0a9eu+ja8OTnUqs3bm4QyCwooaFAN3N3doVAo+LVr19itW7f0BQUFCs45LC0teXBwMFxcXErfIxcXEmTcsoUCvGUo4IaGhggODmYrV65Eq1atyr87JSrvkgRlie2UnU3vfUJC+RPavp1U893dgblzkfrpp7znihVMNXQoPGxtqazgp59IkG/ZMnJ2ix39nJwcREdHIycnB+fOncOrr74q1KtXD0OHDsXa1avho1RClZ1dZTcFURQxduxYtmjRInbp0iW0cnNDwccf4+atW7h//z5SU1ORlZUl5eTkiMXOPR83bpyQkJCAQ4cO8eDgYDqR6dMp8/3mm6UlL7XBokW0lpZVwa8Kbdsi8803EbF8OdydnNjwb7+t+A5IEt23q1fpnKqyQ65fRzZjWHbwoOzs6gq/vDymbNwYjRs3ZjqdDhcuXGBb165Fh1OneLKpKbvZqhVMzM1hZWUl+fn5iWlpaTh37tz2zz777FrtL/olnjZeOvv/coSEhDRXKpXrR4wYYVhdllehUMDf359t2rQJH374YemkWxfQ62lRmTmTaN7h4bU3Zl50tGtHgjeyTBTA5curFXt5GhAEASVtxvz8/Ni5c+dYaGgofvrpJ9jb2+snTJjwdN7noUOrzCS98Jg+nQyD7dtp8dyz55mWY6SmpiI0NBTDhw8vERB7sXHmDGW97t6tvWHu6EgGvyRRHfZPP1W5j+PHjyMvL0/o1KkT79ixo1BZ9vmFwuMKqVWHhQspS5aeDqhUYAC6deuGbt26PegKIMsy8vPzkZ2dDTs7uwdZesaYgIkTgcxMjCkqYnm9e+OnPn1gbG4Of39/dO7cGefPn8euXbsQUlwWYG1tzfV6Pc/KyhLGjRsH54edUltbcoD696dsaCVISUlBQUEBq6DGXx30eqKaLl5MAm6+vqX0VlNTmluGD6d7UYMsWGxsLP4o7gjSsGFDedCgQUJJV4RBgwbVLGCkVJKTMG8eNubn84zcXPbZZ59Vz5QaMYLO/+5dCmb98AM5t0OHln7H3Jzq2KtDkyaURZs2rUanCoDuX5s2xFB6xPylUqlgXVyKce7cOT5ixAiWmJgoREdHy8ePH2cDBgxA81rqI5RDz56UFZ84kYQqO3Qg0UVTU7r2vDwSNisRZ5w4sc4EPCtDse6FfO7cOaF3794Vv+DlRc8qKIjYhiU06W3bAK0W/t9/L4bPmlW5SvrZsxSMK0GDBqQzUQl69OjBfv/9d6bVamFQohHwOJg6FahXD3kffYSIY8f4qVOnWP369RFIOgFV4+23yeGPjKT/f/wxMRu02ipbpJ44cQKCILCPPvoIjLGyg7/ie+ThQWUr27Y9aA0qu7pi85EjsrW1ta5bt24GrVu3ljZv3owpU6aUbm9rS2UFn31GzwEgh9PWtjxFnnNi4g0aRP83M0P6K69wlUoFuz59qJOGLAMxMdBnZiJNocDeL76Qk6ytmYFaLWs0GtHKykpWKpW8X79+YgnTydnZGe06dpTX37vHmvz6K2s8eXKVgZiUlBQolUrs3bsXseHhuN6sGVT//MMtLS1lGxsb1rhxY9HFxQXW1tZgjIkACR/u2bOH5WVlwWTmTCpTCQp6/DKVFi1qrGMRFxeHv6Kj0TYoiHc5c0bAkiXAhx+WfiEvj5gdfftWa7NqNBqkLVqEREFA088+44GBgaJ48SIF7bp3h1KphL+fH2s2fTruWVmhzbJlMCy9vhJR1PyioqIfHu+iX+Jp46Wz/y9ESEgIA+DGGOujVCpn9OrVy7CcqEgV6N69O6KioqRly5axAQMGCE+s0F/SPsnDgzI0P/74TPtuP3OYmlLmqyQaLUnPrZuAIAjw8/ODr68v0tPTsXTpUkVkZOQjW4HVCklJpKb83//W3T6fBxgjA7ukBi4trbxq9FPE2bNnoVKpUF0v+BcKPj7khD1uBk6tpvt77Bg5AJU4yNnZ2bh7967s6urKO3fu/OJm80uQkUEU5icx4B9GairVdA4ZUm27MUEQYGpqWjV919ISuHsX+XZ2UJua4qPevalOGkTpViqVqF+/PlJTU7F//35Wv359dOvWraKjXwI3N5rDZbmCYr1Wq8XGjRu5h4cHKhPjqhJhYeTcW1nRmvH552R0ltCLe/SgEomZM2m8vPIKOZCVBIA0Gg3Wr1/PFQoFe+utt2BhYfH4UaIePXB48WK9+7ffKup16MAVCkX116RUUjD73Dk61++/J8exLAoKaB0cNqzq/Rw8WHNHgHPgk0+A7t3JCaphUKxjx46wsLBAixYtmCAI8PDwQNeuXYWoqChs374dGRkZcqdOnYTHpvZbWFDwZuJEcuJWraIxM24cXf/hwyQA9owCnIIgICMjo/I/fvBBqfhjUhLdw/x8yjZ7eODC6NFygb19xRubm0uO7Zw5pb9zc6vS2T9w4IDcvHlzVNbJolawsECirS3WLF4Mc3NzHhgYyFpXUr+t0Wjw999/o1FKCg9YvJhh5kzKCpubU6nMKvLT5gAAIABJREFUqFFA27YUBCgRzCyD1NRUHD16FK1atZIZYzU/54EDKeDzzz8o+OAD6Bo1Eo41bmzg4uICLy8v8dy5c9BoNOUzzH37UrnO3Ln0HqlUlPHXaIgNA5A+QUbGg1pynU6H23FxgnmnThR8HDIEGD0ahTduYMn48Wju4MCDlywRtCNH4v6QIaKDgwOMjIwqvY5u3boJCeHhKNq+Xf5FkgS1Wi03aNCAeXt7s+bNm+Ps2bM4efKkpNVqRX9/f+7h7Mwcvv0WWLAAzNGRoZp2rAYGBjBUKlHw2WcwqV8f6NfvycRq+/Sh9bMayLKMI0eOyKdPnxY6d+6Mzp07MwwcSEG4d9+ldzA7m8rqLC1p7qhkTZdlGadOncKxY8e4/cSJ8quvvCK2d3Cga42JIUbn6dO0r4kTYRISgsbt27OH56GrV69Cp9Pdmj179r9E1On/P7x09v9FCAkJcVMqlVNUKtUoQRBM3d3d4ePjY9joESIsZfH222+LS5cuxdKlSzF16lSoVCo8lkp/ZCTR4nbtomxpkyY1NkT+1bCxoQm1oIAm0bCwikInzxCMMVhbWyMoKIjv2LGDZWZmoktd1afHxFAv1v8FeHqS0bZ7N9W9pqY+9XKM9PR0REREwN/fX49/w1zbuTNlBUaPrtVmixYtkj09PQVRFNGlSxcorK2pNjI+nrJqR4+irLr1pk2buKGhoTBw4MC6voKngyFDKKP3119Pvi9ZJgr0vHkVKayPiVOJiTjg749OXl6UNT97FvDygiiKaFk8N9nZ2ZX8u3rvrmFDchTWrSNnoQz2798v63Q6vP766zWf6PPz6efwYVof9u+n+XLEiNLvqNVkoOp0VBNerJiOkSMhjxqFQp0Ox48fR3h4OCRJgru7O3/ttdfYk2o75OTk4NSZM4rXmzSBx5UrNfN6x48nWrgsU2byzTcpS1wCI6PqO7isWkXOWEn28lFYtowcoj59ahVIVygUaFWmDVlhYSFyc3Ph6ekJS0tL/PnnnywuLk4eOnSo8Nhdemxtaf2fNInai2m1tDZu3kzO9TMMhDdv3lw4fvy4hMqcskGDKMMN0Jo2cCC1syzuNHCva1fZvDJq0ZkzNBeWvY7YWBrPZbP9xdDpdIJCoXj8dmN37wLvv4+omTOxY+9euLm5YdiwYZW+a+Hh4di/fz+E/Hy4HTvGsg0MIAYGwsTcnK519GgaZ9u2VQi4ajQaJCYmYsuWLdzJyQn+/v61N9yMjIB+/RCXkQHDgwePjF29usWe5GSr1GL6fgWGjJMTlVD88Uepsv3KlRSALwnE/PMP2ZHFAai0tDQUpqczYx8fEklMSQH27oX6ww+huXkTjfz9mcGqVTC4fh2molgt05IxBsdJk4CmTYXPgoIQGxcnXL9+XTp48KCwdetWZmpqygMCAsSWLVtCqVTSCWRm1owFmJ+PIQcP8hP16jHzQYPQ7Uk1Zxij+fHy5SqZTtu3b5diYmLY+PHjYVfyfBs0IH2JyEjg//6PGGMjR5LDXgnOnDmDY8eOyaIoskGDBjHPmTNFJCVR8A4AXn+dnk1uLq0FJSyeSu7J5cuX84qKiio/0Eu8EHjxDdCXQEhIiKhQKD5XKpWftmnTRtGqVStlgwYNHktwRxAE9O/fH2vWrOHz5s1jarUawcHBcHyU4nYJVq+m7MbcuTQpeHg8d5X65wJDQ6KGt2hBTsCgQXXSQuhx4e3tzQwNDbFp0yYUFBQ8mvL3KERE0LUdPFg3J/iioE8fWkSLiqjH7Jw5Ty1Idfv2bSgUCgQFBdXJPKvVah8vMFdTBAVRJqgWOHz4MDIyMoTLly9znU7HoqOj4eHhgYiICJgZGWFcx47ITEsDUlJgZG2NFStW8MLCQubo6Ci/+Nz9YmzZQs5dXUCnI4chJ6fO2CWHDx/mvr6+vHtQkICMDJqH+vYlfYGyFPOaQpaJjt2+/QMxtbi4OERGRgpTpkypVPCvSnz1FYn+9elDztPvv1NGvDKDWKmk73EOfbt2SPrmG+T/9BOiPTyQ7OXFO3brxry8vGBtbf34Geli3L59G3/99ReMjY1l+++/F6BSkUE7dWr1Svru7tSKS5ap/3xkZPm6fVGsvvXehQsU+KqJs79yJZXFfPnlg17ntYVer8e33377oOc4naIIlUrFb926JWzatEkODg5+vPfw1i3q0LJgAQUlAAqGjBtHzvAnn1BQ68MP6f4YGj6VuZZzjosXL8pmZmaVt5b64AM617AwGs89e1IGuWdPYPlyiG3agBVT+PV6PSIiIhAVFcX7FxQwm4fL1/LyaFuQyOW1a9fg7e0NV1dXZGZmyu3bt3/8C0xIQEZWFnbs3QsvLy9ER0fzXbt28d69e1eYKo8ePco9IiPZ6zt34sz48dIyU1PR7fx5DHRyoiREfj7d86wsyuyvWAGoVLh+/TrWr18PAwMD+Pj48B49eghPIvKbYGcnRbu5hXU8cGD/q6Gh/znj5+cR5+hY+Tplbk7BroEDae4bO7a0flyjoWz0lSsPvm5nYID3Fy1C1OnTHEOGMHh4YHtiouzyww9Cd4UCBX36UGBz504qb5k3j8qBqkK9esDSpVAoFHDv3Rvu7u5inz59UFRUBJVKxcrNKZ6eVLIQFFT9DcjOBpYuRaNOndiNdu1wJzGRd3tUQPVREAQSAq2C/ZOamorLly+Lr732WqmjX4LiIAwOHaLAShVt806cOIGjR4+iX79+QvPmzWlODwggQcISiCIF7hYvpoB9FaU/ubm5uHPnjghg/+Nc7ks8G7x09l9whISEGBsYGOy0trZu+/rrrxuaVyEuUhvY2dnho48+Yvfv38eVK1ewZs0atG/fXg4ICKjc/uacnPyWLWlSdnUlg+U//3nic/lXo1s3ujfTp9PEXE0P12eBxo0b4/XXX8fGjRvh6OiIZsV03sfC1KkUZX9KCuTPFU5OxFjYuZMCVmZmdR6wkmUZUVFRMDEx4ajh4i/L8gNHSq/X4969e3BwcIBGo8GqVauk9PR0cdq0aXXv8O/YAWzdSs5FLXHjxg04OzvzcePGsaSkJISFhUlXr14V1Go1y8zNxWJnZ27622/sjV9/xS/vvINWPXrI/v7+opmZ2b/D0Q8JofHxwQdPth/OyZiaM4cMpzrC/PnzuU6nY4GBgWSslgQc/fxovr5+nYzs2tRMGxlRB4sydOVr167BwMBAqpWIYk4OBRzMzKi04+5dqlWvQiBWq9Xi7NmzSExMxO3bt7mBry+6DRqEgTduMEGtZggNpX09odClLMs4dOiQLAiC8P7775eOQysrejaP0iN45RUypocPJ2cjNbVUlE8QKACg01VkDkkSOcY1mWvOnydRrZUry7FiaotNmzZBoVBg4sSJsLCwgE6nw/379wFAuHjxIq91O7gS1f05c4Cff6Z59NVXydGKj6dgyJ07JMA4ejQJMk6cSO0CDxwgNku3bkThbtuWGB9DhjxR+R/nHKmpqUKVQYtVq0rvuUJBDCYfHwoA3LsH84QEdluSsHjxYik7O1s0MzOTCzQaQRceTtdWFq6uKFSrsWTBAqmwsFC0s7OT16xZIzg4OKCoqEh4bF2WOXNw3tlZ3v/qq8KwoUPh5uaGLl26sF9//ZU1JpG00u9qtfiPhQUL02hQ1K0b2n/+uVhw9aocHh7O9u3bx7p27Qq1kRF0Dg6QMzKQa22No/PnQ+fsjNu3byMoKIj7+/szlHSneAKYmJgIsii+99+33nJWiGJg0ObNHt3/+QdrnZz0GTpd0fDhw40fCNM6OVHwLymJnP2YGHpvbGyISfTJJ6Xz1/XrgLU1Yvz8uEVREUNQEODigls//YScN97ACIUCiu+/J8FAX1/a7vp1+qmuZK5FC2qNWUbboVJ9hbfeKu07XxXS0qgkqXVr4MMPoTp6VE5NTWWRkZEocaCvX7+OmJgYBAYGQqFQ1DxBd/Mm/VTSsUOtVkMUReTk5FTc7uRJejffeYfu7dKlwIgR0DZpgl9//VUqLCwURFHksiwLI0eOLF/O1bZtxXXi5EmgRQvcs7SENjYWzs7OFa4hMjKSC4Kwbfbs2f+idk3//+Gls/8CIyQkRG1gYHDQw8PDZ+DAgWqxDmlxgiDA3t4e9vb28PLywrp163Dx4kXu5+fHO3XqVLoInDoFtGpF2ZjvvgMmT66zc/ifAGNk3ACUKRgzpgL99dmdCkPjxo3h4OAg37p1S6ips5+SkoLMzMzSunKdjozeuspmvohwdycBqaQkagt55syj+8nXAtu2beOJiYls0KBBj3T2ExIS8Oeff3KtVsssLCx406ZN2fnz57kkSawkAKDT6USAggB17uyr1Y8loHXhwgWkp6dj9OjRDABsbW0xZMgQEQAOHjyIkydPomvXrszFxQXKwYPxtq8vVKdPiw/aWP0boFJVLtxVG5SIUPn711nbsYyMDKxYsULOz88Xxo4dW5E2O3s2fb7+OgVod++u3QEMDMho/uILxPfogTNnzqB37961W4AmTKCgw9SpZMibmxObBiSyt2XLFolzznQ6HQNo/lKpVLC3t5cHDBggenp6lrIIsrJILOr334ku/uabJFhXyzVRq9Xi559/hkajEVq3bl3+3fz5Z6IK9+xJlOOqnvvIkeQ03rkDrFlDtcZlW1Ru2VL5eb35Js05J09Wf5Lh4cC33xJj7AkCGzk5Obh16xYCAwNR0lJNrVY/0H9o3LgxQ02zkIsXU7Bm7lxi9iUnk8AkQAGJjh0pIRATQ/XG339PmfSYGJpb+valDD/n5EA7O9PfZs8mxyswkNgLf/9N42bWLHIIs7LonanGSRIEAaIolmMvlENGBjEOduwgscjt2+lTqQTmzUPT118XEgYMkFq0aSM2adIE5ubmwrI5c+R6kZFCBccxPx+XT5zglgEBbMSIEVCpVMLFixelixcvIiAgAIaGhrU20rhOh7xly3Cxd282eto0lOgvWVhYoEGDBvKNGzeYo6Pjg7KVqJkz4fLLL3AaPVo2nD5dgJ0dujZoINSvXx+nT5+W582bJ9SvXx/KPn3g2LIlDFq25PWysliKRiONGjVKLNda8AlhaWnJOOemgiD8lwOhewYPbmaekmLjt2qVwquwMG11XJzxyI8+ejD+4OFRmnm+d49KdgICyLEt7nQi5+WB+/khq1Ej3HJ3Z9eaNUOnu3dhp9PBwMBA0HAuK8aPF2BuTt0HQkPpHXz3XVL4X7u2akd95kwKbFeH7dtpvFaXWLt/v7Td7KRJAGPo0qWLoFQq5S1btrCsrCzEx8fLMTExAgCcL+4IIYoilEollEolV6lUXKVScbVaDbVazdVqNTM0NBQMDAxYo7AwGOTlIa1tWxgYGEClUpX7dHR05KdOnWIdy3REwNq1xJ6ZNauUode8OTBjBu527AgYGAjjx49n+fn5zNHRsZxI97lz57j1hAmoP3o0MyppoQlAu307fv7uO+hWrYJOp4OxsTGmTp36wOHnnOPs2bP5RUVF/4NZof8tvHT2X1CEhIQwlUq13sXFxXvQoEHqp8l4tbOzwwcffCCEh4fj4MGDzMrKCiaMwfTkSVjMmkWU7ujop3b8/xkEBlKUOTGR6qeeQpuhmkBFdD3ep08f9qhxc+zYMRw+fBgAMGPGDChyc6lu9+bNiuJT/4uwtSUDoGFDot9W1qu5lkhNTcWVK1fYoEGD0Lx580e+uHv27JGaNGnCevTowbZs2cJPnz7NZFlms2bNQlJSEtLS0nDt2jWelJTEap2Jqw6yTFm3efNKW2rVECkpKdizZw+GDBlSaQlQjx49cPLkSTRp0gRWVlZAw4ZQ3bxJ1OcbN2rfe/h5IC6Oah+fNDjh40P7+fHHujkvkLOcn58vfPrppxV7KpfFxo30nNetoyxYWFjND7J2LeDujssREbIoioK3t3fNt5UkwMEBmpEjcXPtWrBLl6BcsACexX/eu3cvb9iwodCoUSNmb2+PyMhI2cHBQWjWrBnEyqLaFhbU233gQApcfPUVzbc2NuQs1oDmzjnH5cuXwRiT33//fcHc3Lyi01O/Pj3v6OhyrcXKQRQpO3j9OjmRD7f17NOH/v6w4vcPP5DDXB1ycmifT+joZ2dnY8mSJdzDw4O3bt269sYD53Qu3t40ZszN6V4rFBWvd/t2csoPHCDmxYwZFAS5fp3YD8uXU4ZxyBDAwYGU+r29yYGPiaF9rF1LtcE6Hd1frZbuwaxZ5Kx36ULP+403gN9+o+4EJiaASgW9Xg9JkmBWVW9yExPah1ZLY+WTT0pZTIaGcFIq2QRra7Es5dnh0iWWHxwMw4eCDDlqNe5oNKx58+asJOjq7e0t1urdKHubr1/HhRUr5IOTJ7Px48czm4fmxR49egh///23fOHCBdY4O1vudvGicM7eHlbNmvFWH30klASoBUFAixYt0KJFCyE5ORlHjhyRXSdOZL5KJWNt2jBYWADp6U+cLdLr9bh8+TKysrJw+/ZtJCYm0nVwXl+WZXMANhlWVgjr3v3Lqd98o49p1Wq84tdfG2HWLBo7Dg6kaREbS7ocokjUfc6Bpk1xa/58XD99Wla3bSvobW15lLs7s3dwQHh4OC8sLGQqlQpdunRhYIw0o/z9aWwsWkTvzbFjFESIiaHAwsNQqShw1blz1cH9r76iwEMlooYA6Nx//JH0aKZPf/BrURTRqVMnISYmRg4NDRUaNGgAR0dHWafTCa6urpBlGX5+fsjLy0N+fj7Lz89nGo0GhYWFKCgogEajQWZmJrRaLY9q00YW8vN5wf79kGWZFQf+Sz4hSRJTKBSQJAmiIFDA448/iDlUNsnTsiWkL75A8sKFvK0so8FDnUKSkpJw7Ngx+datW6xxUBDS8/L4eJ2Opaenw7RDBxwMCIDGxQVvTpyIM2fO4Pz58zh27BgPCAhgAJCcnIyCggINgOoVBV/iuYNxXnmZ00s8X8yZM+dLS0vLqZMmTTKq0zZ5j8DBgwfh/OGH3DA7m62ZMAGNXFykoaNGVWp/vUQV8PenhWDFimd+6PPnz0v79u0T9Xo9zM3NpUmTJolVOYhLly6VMzMzhUGDBuH48eOyVquVJ0+erMDFi2SMPQYKCwuhUqlqV9f7IkCjIYd/716qVX5MHD16FCdOnIC/v7/co0ePR9YWZ2ZmYvHixXj//fcfZNwKCwuRn59PTnIxbt++jfXr12PChAkV+3/XAnfv3sWuXbvkbt26CU0dHIiSvHt3revH//zzTxgaGkqDBw+ucmJYsmSJPj09XTF58uTSaykqop8ZMyiT+iKPk+7dSdxq7drH276wkIzLrVtJcf5JGQJlkJycjCVLluDzzz9HjebmO3dobE+ZQudTU4G4uXOhMzDAGktLOTs7G//3f/8nPDLgJMvAwIHInTMHW3/+Gb0OHMC5BQv4+atXmUqlkouKigRJkjB8+PAn61Jx7hxlCO/epfH74YfVZuNOnjzJDx8+zDp06IBu3bpVv+9TpyiLvW1b5Vn62Fhy6Hv0oLE8aVKpc9G1K21X9nlfvQps2EAOTlVITqaM4rZtTxxoXbBgARdFkb311ls1n4uTkihIPW4c1eIfO0YBihLHujI83JFGr6fPsoFujYacp3v3qAQgJIQEPL/+mhy1mgjK7thB87OFBZ3f6tXkaIWHY+2XX8odvvhCcF64EELr1lRaERRUMdh+9y5ts2oVlRVYWdH/Y2IoWFFcn3/v3j3cCA5G608/hdlDNdv6hQsRuWEDPz5sGIKDg5n1Y+puZGdnIzIykht+/jnMUlNhHRbGqmoHBwD6oiKc/vFHNFyxAvdsbWHz1Vdo3L17zQ7WvTvRugcOfKJytby8PPz8889Qq9XcwsJCliSJ3b9/XxAEYZ8sy73KfHXp7Nmz3wgJCWnRICfn7ISkJLXSx4eO7+5OQZeePSn7XlhItpIsA2PHQmthgQRbWzhv3Aixkg4ElSI5mcbE5MkUaOvQoZTNVFnXkdWr6Z5U18GqbEvAsrh+nYJfkkSBp0ogSRLS09NRv359JCUl4ffiUkiFQoEZM2bU7JpycmgOyMysNGmk0+mwcOFC2dXFhQ+8eFFkdnbE4nooQKjT6fD7779zRXIyb3PggGBkb88Lpk9nRkZGSE9Px9GjR2Fvb88HDhzILBYtwl8ZGdLNevVEQRDQKSpKvtG6tXBPo8G0adOgUCjw7bffonv37rxdu3YMANasWVMQHx//6/Tp0z+q2YW9xPPCy8z+C4gvv/xyhJGR0dQxY8Y8O0c/Kwvo2xevvvMOsGEDQ8OGeJNzLFy4UNRqtXhS5eP/r3DkCDkx69aRav+ECc/ksLdu3cLevXtFHx8fvPLKK9i6dSsWL14sjx8/XqhfZhHIycnBihUrJM65MGXKFJiYmMDOzk44NmyYkLN5M8weQ5QvKioKYWFhSE5ORtu2bdGrV69Hb/SCICUlBefPn0ev+/eJvjxiBBmBxW0MZVlGTk4Ojh8/DicnJ7Ro0QIZGRkwMTEpl1WNjo7GiRMnMGzYMLi5uVWwsPV6PRISEpCdnf1AKTsvLw+yLJej1BXT+spt6+rqirZt28rr1q3jH3zwwWNF3kr6k1taWgpRX3+NKDs7KfDgQbHKjFg1yMjI0Hfq1Kna9WPy5MmKJUuWyJGRkULXrl3plwYGlGULC6PPZ9QC8bGwf3+VbbZqhA4dKDu8aFHdnVMxQkNDOQCm0+lq5uw3akROW1QUzUe9e9eslaCZGZS5uRg7dqywadMmafHixfLEiROFetWxHaKigNRU7Dp6VPZLTWU269axoE6dmMfNm0hOTha8vLxgaGj45OUobdrQT2YmZZLbtKF6VS+vcmJSGo0Ge/bskaOjo4XXXnutZu1JW7YkIzs1lRzghw1/FxcSMczOpszkoUOlzv748RXr9a9fr164T6+neefrr+uEUcU5ZxkZGcjPz6+6bSNATn1aGunwODsT62bGjFIdk08+qf5AnTvTfV+0iN5nW1sSN7t8ufQ7RkYkOJaRQeUJFhZ0Py5dolKRmBiqfd6woerjlO1wUMJOWbcOUkYGbi5aJPTq3h2CjQ3pJbz1FjEzBg0ih37FCnKC3NxKe9A3aVIawGjUiBz9ZctQ2Lw51q5Ygb62ttys2KEpC4WdHVoFBbE7Dg78999/R79+/Wrc7raoqAhXrlxBRESEnJKSIrRKSOCquXMFNz8/CJXYeWlpadiyZQuahIXxzmFhLGH8eLlRQYFwuU0bjK9NQDo0lALY8+c/sqVbVZBlGStXrpQcHR0xZswYEYB48eJF7N69O1mv149njE3knC+cPXv2g0JylUr1SdP+/ZXKLl0o0NOvH5VwfPcdvZ+pqfTOzJlDz6pXL2h+/hmbr19HD4C3rmmJSYMGpL/zn//Q9X35JQWprlyhsfcwQ8bJib5TmR7Ra6/Ru1AZC+vqVTr37t2rtelEUUQJQ8POzg6zZ89Geno6lixZgrS0NNQoQGRmRoFBrbZSZ1+pVGLSmDHCrmnTePqNG9x6xw5WWTA5Pj4eWVlZbNoXX7DcSZNw8eefYTt5Mj80ZIhUqFCwoKAgsXXr1nSf9+9H0KhR4j9WVhh08CCEFSuEgGK7/9tvv+VFRUUMAHx9fRlAdtPdu3e1er1+5qMv6CWeN146+y8YQkJCrBQKxe8jR440MnlMBd5a4coVmvj++IOi4UFBNNEAMNZqIYoi5Cpqt7VaLTIyMvBwZwDOORhj0Ol0yM7OhqWlZc0M0uJtdTrd01Udf9ooyXzFxZGBU8KeeUpdC7RaLWJjY/H333+jffv26NGjBwBg6NCh4qFDh+SlS5fC3t6e5+fny3l5eUyr1QrNmjVD7969WYlTaWJigsajRuHE+vXIXr8e+fn58quvvio4OTmVO1ZJX9ZmzZrB0tIS6enpSElJwbZt2+Du7i4lJyeLWVlZT+U66xparRaHDh1CeHg4AMDJyQnNPD0pyyBJgFaLS1evYu/evVyr1TJLS0t+48YN7Ny5k0mSBMYYGGNQKBRcFEUuSZLQu3dv2c3NTbh79y62bt0qGRsbs2KKHpMkiUmSBFmWERYWJpuamrL79+/DwcGBq9XqR6bf2rdvL5w+fRoZGRmo1tkqA1mWodFokJ2djQ0bNnA/Pz8EBQUx6dQp3Llxg82fPx/m5uZyOaGyGty3/Px8hb4ki1cN2rRpIxw4cAAODg5wd3enX9rZkSr5zZuUBT1xovr6yOeBd94hR6G6TGxVSE2ld/7vv59aKUxiYiIfMWIEq5bCXxm8vGhO0uuJxrptGzlrVeHtt4GkJIhhYRg6dKi4Y8cOed26ddLbb79d+YSu0UAOC8PBTz5Biy++EBw++ADo1AkA4O7uXjoG6hKWlkS5nTyZWBjz55MOh7s70L8/IiIiEB0dLQQHB8OjMmpvZTA2JgbEwoWUha6MpTV0KLELNm4kCnoJVqygbHXZlrj9+1NmszLk5pKT8c03te6GURUmTJiAZcuW8QULFrCZMx+yxfPzKbv+zTeUaVepiDKfkVHa87ym+OMPSIxBBMAtLXFl5EhccnRE62vXEB8fL9vY2AgPWgDWq0e1/CdOUKlbcjLVGGdmljJ8goOJ+l9d+8ISCAJi0tKgUKlgumhRqchfSanEkiUUbN+xgz6jo0sztm+8QfNPYSFplrz9NpCZiSNHjkj2BQVoKopihTIMAHB2hqBS4bUBA1h0dDR27NjBb926Jffp00esLjETExODrVu3QqFQSC1atBBHd+oEdceOAmbPrhAY2rt3L4+MjOQF+fmCW0EBLjk4MI2HB9pv2CAU7dqFkW5utUu+hIeXBpp27qRrdXAAPvqIxm6PHlSDnppK79Dff9Pc4O9P9mGrVgg/fBjWcXHCkM8/Z7h/H7CwQExMjF6SpOuzZ89OAvB12UOGhIRMADDKz8+P7re9PQXELl6kco4+fei5R0RQkGv5cmDwYFiMGwfNnDnYuXMna968eeXieZVBoaD3bulSusZly+iofmShAAAgAElEQVRdcnWljkJlA16NGlHWX5YrMssmT35g/5bDuXPEjBo+nHQBagkrKyvY29tL58+fF3vWtGTO25uCb5WxLLOyYPbZZwhq0ID9t1EjvJ6aCo8yzj7nHFu2bJGuXbsmmpqackEQmLmjIwK++YZhwQI0uXNHgW++Kb/uHj4MC4UCgxMTKehYPC71ej1KHP0JEyY80Ic5e/ZsEYD/zp49u6DWN+QlnjleOvsvHrra29vLFVpq1DVOnqRFX5Ypws05RfTLQKVSwdTUVIqMjBTaPRTljouLw4YNG7QFBQWqQYMGPejnfODAAe2pU6dUjDEOQFIqlZmSJFmMGTNGWdZxzMnJQUZGBmxsbGBoaAjGGG7fvo0///yTc86Zvb29VFBQkM85h62trejq6mrctGlTGNfWGHmemDaNPmfMoCxhRMQT7/LevXuIiorieXl5ckpKCsvKyhKKW7Lxbt26lRNsYYzh1VdfFTw9PXH+/HnZ0dFRrFevHlxdXWFqaiqWo5h/9RXcX38dzN8fp06dkurXry+sWbMGPXv2RFpaGmJiYvQ6nY7p9XpBkiR29OhRMMa4TqdjgiAgMDBQ8vPzE+fOnYt2dVD3/rSh1+uxcOFC2cjIiAUHB7PIyEi+ceNGZmpqKtn06yfKly9jQPfuSO3QAT2//BI+Pj5gjDHOOeLj41GvXj0YGxtDq9VCo9Gw/Px8tm7dOuzfv58dP35cys/PF3x9fUVZlmXGGDw9PZlSqYStrS1SU1Pxzz//8JLevg4ODjWKAhkZGcHExIQnJiaymjj79+/fx9q1a+X8/HyBMYYmTZrI3bt2FdmWLVD89Rc8AOG9rCwsXrxYSElJQVpaGkRRhIeHR5XU35ycHPzxxx+StbW1UCzwVS38/PyQkpKCvXv38ilTprByQT8nJ8owGxqWb1/2IsDb+/FZB6NHkwOxbVvdnhPoma5evZrr9XrBuTKKak2hUNC85ONDBn/nzlWLNB44AMyaBRYbi549ewrz58/Hjh07wDlHXl4emjVrhlatWiEpKQnJb7wBg7g4FHTtKrt4egomY8c+/jnWFioVZdVHj0buyZNImj+fW3zwAbsVEACvrl25h4dH7aOtPXtSllqrragYHxxMDnJ4ODks8fHkPIwcWV7n4cIFcv5zcioGfHU6MqyDgqpsk/U4MDMzg5OTE4suq7Xz/fdEuZ84Edi1ixz9zZtL/17N2pqeng5jY2Pk5OQgLCxMsrGxEa22beN38/L4aTs7wcHBQdZoNEy0tGQtw8JwPzycn+/alRUWFsLBwaFcSdIDLQTOyf745hsKdsgyUfVtbSkbPWvWIzUm8vLyoFarZZVKVXHC2riRnNytW8lBO3iwVJgPoGfy9deUEf7Pf6Dt1QvXvb3FCQCJEleGpCQq3xgwAM2bN4ejoyNbuXIllixZwoODg5nVQ50TJEnCvn37pEuXLolt27ZFjx49RMTH070u+XwIUVFR3MvLS+h87BhMt2wBP3MGhW3bQvfnnzCrKbUdoFKFNWuAPXuIyWBnR5nvU6doPBsaApaWyDMzw+lr12AvilAmJKCRXg8xM5NKVY4dw/mLF7nhnj2svygyZf/+xGipVw9NjI1Fv0uXuvDZs8cxIBiAGsCHHJjSz8fHWd+jh9bop59U6N+fSisYozKVTp3ovHJyyIm2tQWOH6fgHIDevXvj8OHDNXf0S6BQkAjmli0UiFu0iBz/3buJOVCyP2dn+n1CAq1BJYiIoHvysP1y8ybZch99VGttm7JwcnISYmNja9yd50H52MOBxnv3KHDVoAEsv/oKgRcvYtOmTZgwYQJsbGyQm5uLiIgIxMTECLIsY+zYsaXHUyrpOs6epYDaokXEYgJIb2r6dGKBXLr0YBNBEFCvXj05JydH2LZtm+zj48OaNGnCLl++zPV6/ZLHviEv8Uzxsmb/BcOcOXMWd+nSZUrnzp2fThr4zh0S2unTh6hrP/xQ7devXLmC7du34913333gaKelpeG3334r0uv1hwD0cXZ21nTq1MnI2NgYy5Yt08my3BBANgB59uzZUkhIyFAAG8zNzXPd3d0N0tLSChMTE5UKhSJOp9O5ODk58ZEjRxru3r1be+nSpZ8EQUgURdG7qKjoNwASAG8DA4P+jLFeEydONFAqlVWL8byIyMmhydPHh1oN9etXq80557hx4wZOnTolJyYmCvb29mCMoWHDhmjSpAmUSmXFfqu1RZcupIxczAoAgLNnz/KwsDCYmZlxX19fwdTUFCqVCra2tkhLS4NGo0GjRo3KtYubM2cOunfvjs6dOz/Z+VSC6OhoMMbg7u7+xMyPVatWcb1ez8eOHSsolUpwzpGZmYmoqCg5IyODm5iYCI5ZWcylUycYREQQpf8RzpVOp0NGRgaWLFkCQRDw+eef17zVTg2xbt06SZIkNnr06Goz8enp6fj9999569ateUBAgFBYWAgLCwvqsjBsGGXWih3vffv2SREREaJSqYQoirywsJAplUquVqt58+bNBU9PT9jY2GDDhg08NjaWubi4SMOGDRNr+gxkWca8efNkJycnDBgwQKiQjY6OJsr77dtP1GasznDhAmU8attGKy6OrqF9ezI861igMzo6Gjt37uQODg7o168fq4s2rAAoAxYcTO3QqkJ+Po0XtRqXL1+WIyIiuKmpKYyNjdmVK1dYQUEBM9Jo4GBhwXsaGzPLffvIoH5KbKbqkJmZiVWrVvH8/HzmZ2+P5hs3wt7cHGz4cHLiXFxqv9MhQ6jFVhlBLgCUIS8qIsd12jTKCo4cSdTkkuPo9dTt42HBP0kiw7tpU3LA61i/YsGCBXIbW1uh86xZNKZ//ZUCam+9VeN9FBQUYPPmzfKtW7cET09PqX79+sLx48cZAAw5doyrnZxYg0WLcPToURgZGSHgzh0IM2eSc718OZYuXcrNzMz4sGHDKr84zskZ3byZxDt1OnL8r18nJ336dGKefPopOXAPIT4+HqtXr8a4ceNgX8Ki2b2bsrdJSZQ9Hjy49FhqNc1/q1fT3ywsHozRaz168AIbG/iYmjLMnFm5gNuWLfQsv/vuwa9kWcbWrVv59evXWf/+/eHl5fXgbxs2bJDi4+PZmDFjhAfie97etM7Om1dh9xqNBn98/DHesLeH+PHH5Ay/8QYFPWpi82Rnk8PbuDGxGcLCqA2iQkGMij59KNHTpg20Wi3+/vtvOS4uTmCMwcbm/7F33WFRXdt3nXtnBhw60hVsiIgFFSuoqMFeYuwtsUSjJj59ecYkGvMmY/QZW4zGHms09hqNJQpWFAui2EBEqdJ7nXbv74/tCMLQFEt+n+v7+GJgyr3nnnvuWXuvvbadLisriysoKGAWFhY6lUrF1Go1J4oiJkyYUGqPkZWainVLlqBReLjLwCNHTABYHxkwQCg0Nr5SWKMGun/wAZz03S02b6ZykcmTqZRDraZjKyggkrlkCa2fnTohQqfDkSNHxK+++urlF4+YGOqKNGUKfae3N2Xm9ffYnDk0F551BwFA6qCYmBeDtIGBZAg5Zw6Vp7wC7t+/j1OnTukqXYaXlUUBiuLPy8hIWjN69aLzeoY///xTvH//PnN0dNTFxcXxPM+LNjY2LC8vT5gxY4bhe+/ECQqGde9OAZiFC+k7d+2ia1ECWq0WQUFBuH37ti4jI4OXSCR3vv322+ZVG4X3eFt4T/bfISiVSk+pVHp58uTJ8pIR4leGWk1SLS8vinh36VLpjdiGDRt0JiYmbMiQIZxMJsPmzZu1cXFxMQB8OY6bIghCPM/zSziO0/A8v+6bb76ZbeDcagNwBNAFQBSAcwqFIkWpVLYFcPVZD9JUjUbjoVAoUgwdx08//XRQpVJ9xHGc8PXXX3NVjvy+bfzxB6BQQIyIQFpaWqmabz1EUURycjJMTU3x9OlTnD59WszMzGQODg66YcOG8dVe3nHtWrVklURRxLx58+Dh4YGhQ4dWw4EVQR9IAAATExPhs88+41424BMXF4fNmzdj2rRplZPD9+pFDrc//1ypzz9z5ozWysqKeXl5VburZUxMDLZv344hQ4aUaW6Wn5+P5cuXw8PDQ/vRRx8VMc7QUJJxM1bq3s/JyYGxsTGkUilyc3Px9OlTxMfH49GjR7rU1FReq9XCzs5OGDp0aPn12mXgmSJAUKlUzNjYWGSMwcPDQ+jatateE0hr08WLtDa9TfTpQ9mmqhps/uc/VBP8Ep4XFSE/Px9LliyBo6OjMGrUKO61lHiNGkVka9++0n978oSuT1xcKQWGKIp4/PgxnGbNgnG9emDnzgG7dxt2w37NSElJwZo1a+Dg4CCOHz/+uWM6oqOJpJ05Q9nUadOq1gbR358kyDNmvBjEiYujAI9OB+TmUiC3TRuSkOvLI776ijp+FA/OiCKN84kTRCaqq2wtK4u+p39/hMXEiKFff41hYWEMc+dWzqOhBCIjI7Fjxw40btwYWVlZQps2bbhjx44BKhWGjh6NRiUJkCAUrS05OcjlOPz666/i6NGjWcmSsFI4e5aI4KxZpPTp358+a+NGcuI/dYrWYL17O2PPfUh69uyJ9l5elLn08gLGjjXspj5rFv2tSRMqn+jfHzh7FhGRkTi6Y4f4L2dnJg0JIfNQQ8G6c+eAmzfpXi+BO3fu4NixY6KHh4fQt29f/vHjx9i1axdmzpyJ5/drYiL9187OYHBHEASc6N8fvSQS8Fu2UGBi4UIixOXh0SMqx5w9m1QiXbqQESFAvzt3jjL6u3YBubkQhg3Dis2bddbW1qx3795c8S4A2dnZuHfvnmhmZsacnZ1hZGRkcJ9y6dIlnDt37vbcuXOf68yVSuVwALtHjhwJNzc3+mV6On2/XE7X18IC+PJLulZyOY1zjRoUQLl3DzlDhuDOgQPQ9u6t6/TxxzxzcXm5oGF6Osnuvb3p/qxZk+5fxiiIcvs2qTqKo7gxX1QUBaC2by/Kfr8C9AH42bNnV+5kYmPpWaT3mdCXv0ilpQxWBUHApk2bdHZ2dryvry8sLS2xYcMGsXbt2ujTp0/Z3xcWRn4ZvXrRM69DB1onKni+rF69Ojc1NXWCQqEw8LB4j3cR72X87wCUSiXP8/x0qVS6YMCAAcbVTvTXraPa09hYMsMpx/XVEEaNGsWvXLlS/Omnn/S/kgCor1Ao4gDMBQClUrlep9OJc+bMMRg9evbaOADXS/wpjjF2ThCExoIgdC6L6AOASqUaDaCukZHR7tjY2Oavpf6zmiCKIuLi4qDT6XDlyhVdSkoKy8vL42rOmiVIv/qK9dq9my2fMgXenTtDFEWYmJggKytLvHbtGpPL5WJ+fj4TBAEymUxo0qQJN27cOMjl8upviRAYSJuDzMxXziwxxlC3bl0hPT292i3WDx48CACYPn06Dh8+zJYvXw4fHx/4+flV+bOMjIyej3mlcPIkbQK++IJq/4ruA4Pw8/N7beuqi4sL3NzcxLNnz4qNGjUyOM6BgYGws7MTXiD6AEXwlyx53s+4OIqbeJmamsLNzQ1ubm7o2rUrLwgCUlJSYGdnV2F3gbJgbm6OqVOnclevXoVMJmPXrl3DhQsXOF9fX1KGtG1LGf6BA4k8vWq7u1fBX3/R5rCyuHuXstg//1zkz1GNyM7Oxvbt2wEAw4cPfz1EH6BsdHIyyUQPHCDfAj3q1QNWrzboTM8YQ4N69YiQHDtGZmzF69XfEFQqFTZu3Ah3d3dx+PDhL07UOnXop3dvIosjRlCmfto0yqxXpML44APqB96kCcnhP/yQfl+7NmUPa9cmA6/+/amtmz6rn5tLmcL581/8vK1bKcO6YUNpM7+q4skTCuaLIikXUlIAhQI2Mhl7cOgQAnv1gs9LBsb1bTWbNGkCf39/MSAgQOfp6cl3XbIEpqGh1HavODiOxjYyEigshOmdO7CyshLDwsJQYX/3rl2JpD5+TKTs9Gki559+SgRs0KCiUhN7e2DVKtw3MoK9ra3QvnFjDg4OFDy5caNscrhkCbBjBwVk4uMBExPo0tNx7NgxsWWXLky6YEFRi0FD0GiKOg6UQLNmzWBsbMx27tzJ165dG+Hh4YKbmxszNTWlg9m+nQwP4+MNP2vXrEHetm240acP2nbuDNsPPqB1pTzX+HPnaI5dukREfuxYIqnF8dlnwMiRSElJwYNatdChZ0/Er1wJYdw4bsyYMaykn5K5uTk6dOhQ4UIfEhKi0el0fwOAUqnsCcAXgCUAJNy+Dbfr16nmPCSE7rFhwygIaG9P995///siaf3tNwCAWUwMWlpYYOfly3zLzp1hJpPR2IWGUrlR48YVHRrB2poCRFOnkjfD8uW0Vm/cSAGhxYvpfrW1JW+OPXtoPwRQcO+//6XgczWpSK2srKDRaFhSUlLlOuo4OFDphVZL4/jtt6QwKNEhAiCp/aRJk55fyMzMTCQkJLD69esLKK9soGZNmpM3bpACZuDA8o0yQcZ8WVlZWgCHKj6J93hX8J7sv2UolcrmMplsl42NTZ1BgwbVqDaiL4pUO1qvHtUx+vrSpqKKRB+gzb+np6f2xo0bhTzPb+U4rrtWq51a/DUKhcKwi18FUCgUTwFU0Afp+WsLADz46aefdu7cudPDzMys0Nvb26hdu3ZvrjdhJbFv3z7to0ePJFKpVLC3t2e+vr6cra0twsPDOUcPD9gD+HDwYAQdPqwT7Ox4tVqt5XmeDRw4kM/Ly2Oenp56qfrr602m05G0tLhB0ivC2tqae/jwoYBqPu7U1FQBAJeRkYEhQ4awK1eu4MaNG8jKyoKfnx+qImkODQ2FVCp9bjRTKTBGZLmwkMoyOK5Svb1fB6KioljTpk1LPcCfPHmChIQE3Lx5U+zdu3fR+Gu1lMWKjn5RElhJcBz3Su3+9JBIJPDx8YEgCDh+/DgAvFjm0KQJmXbl5RHRfBut+UaPps1oJVUcAGgTevky/fs1yNY3bdokZmdnsw8//FA02Bu+ulCvHv3s3UsqpH/960UTq+HDSc7+yy8v1rqKIpHhxo1JsvsWiD4A7N+/X2CMsVJEvzjMzKhs4do1IlIHDxI57tiRNv7lBQAlEsqempgUGbsB9Hy9e5fKoASBuh7o28qZmFDWtTju3SNDsr/+enmin5FBsvzvvqM526IFBWMSEoictG4NGwA4dAjh4eHwKVlCUAk8k6cLUqmU1alTh02fPr2IFXbqRCoCQxg0iM7rWUlYZmYmq6wvCRgjxcWff9I5DhlCn3XgAO1d+vWj1508CTRqhFoffih0v3SJw5QpNKaVUai5uNBaY2UFHDuGe/7+EARB8PX15bFlC3l1lNV6zcamTPK9detWXWxsLO/r6ys0aNCAO3HiBPeFvmQiP5/OpU2b0muaINC5ennhwvnzgo+zM2dz5AgFlQ19lyhSAKldOwqs5ObS+j5vXmmFSGgoBQEWLsSWxYtFjUbD8hYsEFpv2MCNHjKkFNGvChwcHDTp6elRAMADy41ycxu3CglB7ZgYPGzeXBuh1YoNJ0+W4ocfXhxLjYbaxPXsafiDXVxQ49NPkZOTI57s1QtDfXwYUlMpWfXnn6SUcXIi74CcHCLuZd1HjAFr11JtukpFpRmLFxPB5TgqIRk7lo5FX6Zw7x5luw8cqDaiD9Bz9JnJL6vU81QqpXt87Vo6z23bqLRMEGgPYmJC5S75+aSiOnyYgn0ZGXj8v/8J3uPGwW/nTg5HjtA5zppFpQ137tAaHxREr+/YkcZQraa1pAIEBwerRVH8TaFQVOzQ+x7vDN6T/bcEpVIplUql82Qy2YyePXsat2zZklVLfW9ODtW3zZ1LG4AuXapee1oChYWF8Pb2lt64cUMqiiLmzJlTydDq68G33367SKlULs3Ozv7A39//aLtqcjA2BFEUERQUJMbHxwsDBgwot05Zq9UiLS0NERERePjwoWT69OkwNzd/4cnu5ORE/+jUCR7R0fCYN49HYiJgafnm78WePSl6vGNHtX2kpaUlcnNzufXr14t9+vRhzobqHl8CU6ZM4S5duoQ9e/Zg2rRp6NGjB5o1a4ZDhw4Jq1at4kaPHo26lazFTU5ORp06dQSe56vGJPVu2pMmkZQzOLiKZ1E96N27N44fPy60a9eOCwgIwNOnT7UqlYpTq9WctbW10K5dOzRr1qxoMZk9m4jF/ftv5XiLIzk5Gb/99hscHByEjz/+uLRSwMiICH9gIK1lb9qlf/ToygdxgoKI1B09ShL414QePXqw/fv3w1CA57Vg2DD6efSIniFJSbSx1JOUmJgXyX5UFGUrBw+me+MNQ6vV4saNG3j06BH38ccfV+5NjJFB1YABRIgiIkjCOnQoSYzLmgN6vws3N8r+NWxI5x0aSvXQ+pad+qC6jQ1trPVeKJcuEUm5cuXliL5SSd/drh1t/r/8ku4V/X1UTBFTWFgIAHBzc3veIaeyEAQBR48eFaKjo/H555+zF9Qk27cTOTUkkwdoDPWBlLAwuDRvLh48eJBdu3ZN6NevH1ey9VhhYSEeP36MrKws1KhRA3Xr1oWFhQVYzZpUdnHpEhH5K1dofD09SVYdHQ3TL77g4h4/RtqVK2j98cf0LPPxIeKif9aWROfOFOzp3RtISUHDR48Qt2sXPQyuXSMFyJkzFNwtiSdPaC0tcb8nJCQgPj6enzlzJuRyOZeVlQVBEBATEwMruZwM8jZsMJiVxX/+Q3Ppzh0UNG/ONVm+HGz9epKfF0deHmV4AwNJqTl0KM3bhAS6/ob2JnfvQhUSglXLlunUajX/fE/CcXRMei+Ol0BmYqKRa0TE6hRb28y5qamhINXm6VwTk/A/Bw7MvGliEm4fHZ37YWKi6fN6f39/yrBXzs9D1Ol0TGdvD97JqUhKP24czYn8fAqI5OTQWKjVFAyqVevFkhXGaK7Wq0cBubAwCtQtXkyKxqwsmlvjxtH8Wb2ajAxfQ9trY2NjgTFWNOCCQNdAo6H9hIsLKYSuXCHVUdu2dK/9+itJ+nv3prVl+3ZSBykUlLTZuJECIcbGgKkpGGPQarUifHzo9a6uFBhs2pTWq7lzKaufkUHHcegQfX4FflJarRa3bt0S3hvz/fPwnuy/BSiVykYymeyQk5NTnUGDBtUotw9uZZGSQgtUy5a0SRMEioC+8sdSDaSVlVWBRCKBVqtd+eoH++p4Zvxn4+joqAJQLQWPGo0GoaGhiI2NFQoLC0U7Ozs+JiZGSEpKgkwmYxs3bhT79u3L6tSpg+zsbKSkpMDe3h6xsbE4ffq0mJGRwWQymWhubi707Nmz4v7ldepQtNrCgrJB//sfDPVKfW3Yvp0eNNWI1q1bw8HBAdeuXcPWrVthZmYmDBw4kKssES8LHMehc+fOiIyMFPbv34+xY8dyjo6O+Pzzz7kzZ86IO3bsYDKZTBw0aBCzsbGBubm5QUf5oKAgPHnyBIMGDXr5lPH69SR3vnmTXKOr4T6rCho3boyjR49yq1atgrOzs9C5c2eJpaUlHB0dYWRk9OJ5aTRUC2mgxvRtIC4uDlqtFhMmTODKbFXl4kJje+8eZRAvX34zKorTp0nCW1F9rB6WlpR5e80mdOfPnxcsLS25KilRqgOuriSLNjGhAPKXXxKBu32bCIajI60fP/9MY9Gv30vVhb8sUlNTcfHiRSE0NJSTyWTo1q0b6r9MYLt5c/rp1o18GsaPJzLdvTv9vuT1rVWLavBlMiIaZmZEmCwsqAa5c+ei+bpv3/PWg0hKonHcv79qROLePcrIBQdTFrewkAL4Dx+W+zZjY2N4enqK/v7+zMrKCk2aNKn0V966dQvh4eEYP348Z1nymZSYSCSpLOhb2332GcAYRo0axT0zg8OGDRswZcoUREZG6q5evcqysrI4nU4HExMTwcjISMzMzOR1z0poevXqJbZr147B1xfPSUt+Ps0zUQQSE9EgOBihDRrgzPHjYovYWCYxM6Pn6JYtVA5w+jSVBxi6d8LCgOHDseejj4T6pqYcHjygGm6ZjOTfhsi+mZlBdWRSUhJMTExEuVzOAMDCwgIffviheOzYMRbz5An6r1pVOpP95AkFYL//nojYkydwePAAwQ0bQla3Lp6HKtLTaUw7dKCMbmgo3W8eHnQ8ZewfBZ0Ol01McKFzZzRzc2N+fn5FLfs+/7woIFMVf53UVODiRagzMzFo3jw+tWbNGJO8vAgA30EUIwHAFMAsAEql0jgpKWnU77//vnbmzJnGEomE5q+he8oABg0axG3fvh0nTpzQ9evXr4gg29sXmS4ePkzPuCtXqKQhKorut2bN6LmXnk5qCjMzIrKNG1NZSHAw/e3PP0muP38+yfkDAmhcXoXoCwKNk6kpBYRTU4m0r1+PBqmpzOn2bSLpS5aQ4tbZGVi1igj90KH0fmNjWktHjqTARmxskYpj0qQiY8EzZ4q+91CRqv5mcjIgCAyfflr092Ilj+np6VAnJcHBwYECmN7elfLxevDgATiOC1UoFI9ffoDe423gPdl/g1AqlbWlUuk3Uqn0Uz8/P6PWrVu/dA1sKcyfT7VR589Tq6Rqwq1bt7QSiWRdRkbGdQCRCoXiUYVvekMwNjYe7+npWQ2REto47ty5U9RoNKK9vT0zMzPjnjx5Ipqbm3NffvklJBIJDh06JP7xxx/w8fERr1+/zkRRFFQqFQcAXbt2ZfXq1YOTkxMDUPlQua0tbWCuXqWNs4XF63ewFgR6+PzxB0X3qxE1atRAw4YN0bBhQ6ZWq3Hq1Cls27YNjRs3xjADbspVha2tLRccHIzExMTnKgk/Pz/m7u6Ox48fY/fu3RAEATzPo2bNmoKjoyNr27YtMzExwcqVKyGVStG7d2+xUaNGLz/IHEeKiLNnaTP51Vdlyz5fA3iex8yZM5GYmAhnZ+ey15DMTCIm9+9X2EngdSMmJgbHjx8Xk5KSmImJCcrrSf0cDRpQtsHYmDY5r5vw//Yb3Y8Vkf0bN2hT9vAh+aG8ZhQUFKzfPNwAACAASURBVLyW7haVQqdOtF78/TeRYCsrksA6O1M2adEi2hxfvGjYvfw1IT8/H6tXr4axsTFzdnZGdna2+ModbKysgJkzaa79+ScFz5csofr8koRo0iSSAM+aRZnWyZOpZVdcHI2ViQllgL29iTyGhdFm/eTJslsc6lFQQIZlvr40F7//nsojdLoKu+eUhIWFBTM2NkbjytY5P0NcXBysrKxga2v74h/y8qieviJDwYcPSQKtVgOxsZA5O2PMmDHczp07hWPHjolPnjzhmzZtiuHDh0MURURERHD+/v4obhj97FlKkEhIijxgAI1tnTr078BANPP2xtGjR1lEQgIaW1rSeM2ZQ2vyyJEUXLGxIS8Kfes0jqNgQG4uRri7c/cDAgRMmcLBxoa+R+9IXnLddHIqnXEHYGdnh4KCghfmn6enJ5MrFBC3bTMcmFm2jMhp376klpk3D959+mBRdLTYKD2dOcnllOX9+OPnRoIYO5bGdMyYcod/+/btOt7fnx+4fz9qhYaiXv36LwaBZTKSqXt6ElkuT5mUn0+qhJMnqRQhPBwPLC11f3/++YF8jhuhUCjK8mlSA9i6cOHCxZmZmcY269ZRjb7e76ICuLi4wNTUVFdQUMCVq0yRSinA1rkz/X94ON1vsbFEgG/dovWpaVPy19i8mVR6u3aR4sbHh8Z161ZSyxhqAfssuAStlsbrwgUKvvz1F6lB9C0cmzaljPnx4+SBsmULvb59e8DYGPnm5shs1YoIdqNGFAjQB0i7dSv9vYsWkbpqwQI6r02bgLQ0mvsTJlBArYQyIygoCHFxcdy4ceNe+H1qaiouXLigS0xMZCkpKRwAjB40CPUzMoCnT8FVwoTw6tWrOYWFhaXbSLzHO4/3ZP8NQKlUehgZGX0vkUgGtmzZkvP29pZVW9skPZYvr/b61idPnuD69etqrVa7SqFQhFfrh1cDtFrt2aCgIO9Hjx4hPj5eaNeunbxFixacvIr9ulUqFbZs2YK6deti8ODBXLGM8AtPl8GDB7OYmBjs3LkTFhYW2qlTp1bP/SOXU6RZ32d427ZX6udaIXQ6ina/5tpamUyG/v37cxYWFjh79iyCg4Ph9cyhOjc3F3K5vMx+7mUhLi4OAF4g+wBQu3Zt1K5dm3Xo0AEcx+nLKbgHDx4IGzduZDKZTLSwsMAXX3zBOI6rHlY+ciT97N1LsriwsDdWYy6TyVCuu7UoUvbn7Nm3TvQBMg1MTk5mnTp1Qs2aNSvXa9jYmGSaN25QViI6+vXJ+nU6uo4VGexpNLSh07tJl4AoitDpdFXzgygDgiBg8+bNKCgo4MrqvPBGwHEUSAZogzpwIG0+4+Np3Zo69Y0S/dzcXCxbtgxyuVz48ssvuXv37uH06dMQBKHK64lBmJoS+dGvk4cPU6u2CRMoS6zPdPfqReReFClzuHIlbfz1vat//bVI/bF0KZG7soh+dDTNp4cPSaqbk0PZO3d3+oxird6qgoiICMHd3R1cFQYmKioKISEh8PPzK/2eL74gAnr1avkf8ugRlSu4u9MceVbyNHDgQO6XX34BALi6uj4PJuzZs0eoVasWN2zYMDDGUMqE8vffKeDi50cZyObNifhMmQL2xx+ok5qKR48eiY0bN6Z1RU+AUlPpvwsXkkqlRw/696efkorn558hy8iAmJ3N5T98CHnXrvTe8HDyYShpxpqebrAcytzcHFqtFlevXhXbtGnDOI5DVFQUrtrYoHPx9m4AKcPu36f5wRgFIQIDgbZtUThwIMQlS5jtokUkeb98mUqLTp0iJU0lEjn5+fmIjo7mJ3/3HWp89hnqlaV2sbSkcfz9d3qO6cm0Tkf3e1QUkVZrawqwzJlDpFUmQ9D69Xn5iYlbyiL6eixYsGCsIAjWljVq0PeUcJGvCB999BG/Y8cOMSgoCB06dKjcm8zM6L4FKEgiihQ0u3SJzuvnn4lY16hB89jOjvwOfHxI6eDmRnOrRw/KnPv7U2CkVy8KwI0YQUm1evXovR070n9/+okC1HZ2wI8/0vf36lV0XAoFEn77TbSsU6dI7VMRTE1pvQ0JoXP6+WfaJ86fTyqPr7+mubpsGWBvD9HCAqdOnQIAbNu2DTzPQyKRQBAE6HQ6uLi4cI0bN2afffYZbq9bh5gZM7DT1xcTfvsN111d0augoEj9UQLp6elISkoCgCMGX/Ae7zTek/1XhFKptATQFkABgDC9m7xSqTQG0MfY2Hi6kZFR2w4dOsjatm3Ll3UjvTKqmWSIoohDhw7lazSaie8i0QcArVb7U1paWlpKSgoDcOfChQtzAgICujk5OWl8fX3NnJycoFarKzRvO3z4sE4ul2Po0KEVZuRdXFzw7bffMryOe4fjKILepQs91Dt2NBxlfhU8ekRZq7Vrq/dzy4G3tzcKCwuFU6dOcefPn9dxHIfs7GxeKpWKarWaDRw4EJ6enuV+RlRUFA4fPizm5eWx3r17o0EZbbP0GWM7OzvY2dnBx8eHy8zMxOPHj5mbm1v1kIGS6NePzH8EgWR7BjI/bxz6doHLl7/tIwFAXhWPHj1CN8peVC3Y0ro1KShMTCh40bVSfp5Vw0cfERHbsKHs10RFEdGPjS1Vr5yfn4/g4GCEhoYKqamp3PTp02H1EmaoxbF3717Ex8dj7NixVTKgfK04eZKypMOHU0a/Y0cKfGg0r6XGtSTy8/Oxd+9eAQA3YcIETiKRwNraGiqVqvrbCPM8kZv27al0Yds2ygYDlJ10daXM2uzZRMq++opkwoGBRU7giYkUDPn886K6fT2Sk4lETJ1KkvGRIyloGBVFmeySr38J5OTksFq1alXpfvv777/FmjVrCj4+PqWfh7/9RoS3ItSrR+R0xYoX5oVcLsfEiRNx5swZNGvW7PnveZ6Hk5MTSpU0arWUWf72WyKmxe+7Tz8ltcm5c/A9fhzxublUxmjI1G72bPoRBMrsDhtGAYiaNcHFxSF9+XKdTKnk8c039PrJkyl4W9yIESBCXMyYUBAE3LlzB9euXRNr1qzJTp48yVQqldjU3p6ZduwI161b4dK3b9H79fdJnTr0WcnJVH/v7g6MGQN5377ozRjCBg7U2Z8/zwubNyNv1ixwX3+N9NhY5OXlwb0S/d6ZRgPLuXPBnnXxKBOHD1PQ4eOP6Ucmo3N3cqIA1fTplP0vkVXPy8vjADRSKpXB5XVQ4jiuFp+Tw/OnThEpraL6zcXFBfXr10dkZKSuQ4cOL2cuwNiLmf+ePWkf1KQJ3XsAzbNLl2iOtWtHzxwPDzKu+/prKi2Ljy/6zAEDSn9PJZ77HMdBEKroZe3rW9SykT6kqCTkww+pA4qREdCsGdjAgfh2xgzsOXVKSFWp8Mknn3AqlQoymQxmZmYwNjZ+fgG8kpIAd3d0++QTZDx+jMjMTHH16tXo2rWrYKhtcHBwsIYxtk2hUKiqdgLv8S7gPdl/Bfz444/DJBLJFltbW61OpxPT09NrzJ8/XyeRSDJ5nrdycHDQeHl5mTVt2rRystV3BFqtFsePH1erVKqHAA687eMpC886AKwv9qu+SqXSIjY2ts+ePXvWCIJgotPppLNnz4YhYz2dToekpCSEhYXxX3755Rs77nKhN0iZPp0MWqZNq97Pv3qVovVvsIZbIpGgR48eXMeOHRETE8Pn5+fD1dUVGRkZLCQkRAwODhY9PT3LZeGZmZnIyspiAHD69OnnCoHKwNLSEq1atXrFsygHcjltlK5cIdl5QgJlDd4mfvjhzZvblYHTp0/j2rVr6Nu378t3aWjThjbngwaR5LU6fE6K47//LV+aHBtL7dROnjRYs3v9+nWcO3cO3t7eHM/z4po1a1jt2rV17dq149zd3V9KSfKsFEWsW7fum6kPMQRBoI2xkxNlj9LSaDP8rBUmbtygDFOzZpQRW7qUMpELF1K2escOui9Gj6bAQJ8+RC6++ILuk9BQkoUfPEif4ehI2fL69YnwFgvOiaKI27dv4+nTp9yYMWNQs2ZNaLVa7Nq1S/Dx8UGVDTerAk9Pyqo9fUqBpxUriITOmkWy8ePHiRisXElZ2zNnaJxataKMYXHi/t13lKGWSCgYN3nyi0SoGjpf6GFlZcUSExMrp6R5hoSEBGZos499+yiYuawSKl4rK6pDTkkhQnnvHmW0QYHYUSVk4z179uT27NkDU1NT0cfHh3EcR4G9wYOJZMXHGyaKHIcLEonu8ogR/PiBAxn8/GierV9v2HiO48jUDqBrdvs2MHo0/BYs4DUcB+HOHXB161I9/ebNpNQYP75IkWFs/AL537Bhgy4tLY338PBAVlaWzsvLi9na2nL7du0Su3p7o33fvkUHPWEC3RP+/vT/6elFHRm2bQP694emXz80nj0bJ0eM4M+sXInw+Hgx9eefGc/z0AfHJ0yYgFq1apU59KmpqbBISwN7+LDicguep+M5eZKeFwMGkES9glKTbt261Th58uRSlUr1CwzMLaVSyQMYCcC4c1AQyegHDSr/WMqAhYUFu3btGr9x40Zh+PDhnFQqBWMMRi/jD5KdTc8Pb2+S96ek0I+TExn2/f03PQsaNnypY60IHMdBW0brxjJha0uBVUPmeTJZ0bjeuQM8fQqj/fvx8Y4d3K4+fcSETZvQbMaM0l14NmygPaClJRAYCCtfX3w1YQI7efIkzpw5w5fcX+l0OgQHB2s1Gs3qqh38e7wreE/2XwE8z680NTU1Tk9PVwmCwDk4OBRyHCd1c3NzbNmyJeRyedX7XL0DuHv3Lu7cufNUq9V2fVZ39Y+BQqHIArBLqVSeBfCBTCbbLJVKZXoZk1Qq1ctjhYSEBE4mk4m1atXSmZubv1v3woMHtLkZP56izPoWPq+Ce/dIglaJ9iqvA3K5/IWsxDMTPbZz584K39uiRQvk5+fjzJkzcHNzE3mef3sEqCx06ECZmpwcyhTv3//m2/Ndu0YZr9DQN+YhUB6ePn2Ka9euYfz48XBycno1MublRRmOrCwiTCtWVM857tpFG90+fQz/PSeHiGxoaCn5pSiKuHXrFiKeEYju3bujU6dO7OLFiygoKOD379+PGjVqiA4ODhg1alSVOq6Ym5vj/v37LCQkBC1btnzp06s0UlKIvLdtSxLsJk2I7O/bRwT9xg0iBufOkXIkKorIuY0NkTGtliSzBw5QJky/iZbJaNwaNaLrpdPRz717tLn+178oODVsGGXW/vMfIlp6H5rHjyG6u+NG+/ZitEzGZjx4ALNhw4A5cxB986Zo3q0b892/n0EioesYEEDEKiSEgm6ennSszs606X2VOePkRPW9I0eSQmr8eAr0NWhA62pcHJG3deuKDFC//ZbIjkJBxxYXR/Jyve/Da4SzszPu3LnDMjMzUcporwx06tQJFy9eRF5envjhhx8yYz1R0Gqrpt744Qd6js2aVWF5TIMGDTBo0CDs2bOHtUxMhGlYGCklDh+uMHAaHh7OajdsCIvGjYEbN6C7eRP5I0YgT6eDzcqVkBTP9Ofk0Bqdm0vzY9Cg5/XqEkFA9syZsOzeneb8+PEUsFixggJednZEhgsKgEaNEPb99/BZt453NzODdOpUhkGDeN2ECYidM0cckJ8Px/PnGZYto+/s04c8DP7zHwrsnDlDgSF9+YdKBcyZA+nevQiXSBCamAiWlIRWrVqxMWPGgDEGc3Nztn37dt0ff/zBDx482KC6Ta1WY/v27WjRurVOsmpVxZlwUSQ1BM/TMZbVDq8EWrRowQcHB6vj4uJ0ZbykP4Dt1qmpuOLrK2S4uhZYnztnUqdOHdSrYvlgz5490aFDBxw8eJCtXbsWKpUKPM/Dy8tL8Pb25io0uFapKLA2dCitU1euFF1PoKiDxRdf0DM8P5+CdCdPUoD3FZGVlYXk5GQ4ODiA53lRp9MhOzsbKpWqtCeGIbRsSQHB//63fANUS8vnxo1s4kS0evCAGY8bh6xTp2ChVJJK5YMPKGCpUFB5AGNkhvssuNOtWzdcu3atVEnUw4cPwRgLVygUYa86Hu/xdsD/8MMPb/sY/rG4ePGiTqvVpqpUqiGCICzNzs6+kpWVdTE+Pr7J/fv3ZRYWFrKaNWtWqeXN28ajR49w4MABLcdx33///fcX3/bxvCy6dOmSe/78+Uc8z3+Un59vc/ToUU1gYCBSUlLUZ86cYVlZWdy///1v1rVrV9aqVas33My7EtDPmagokvjZ2FBW4hX64qJDB9p86uVs7wDMzMwQGBjIatasWe6DLzw8HH/++Sf8/PzQq1evd/eG4nkyUDp2jLJSOt0bkTc/h7ExbW7egWssCAK2bt0qmpubi127dq2eayaRUEb4l1+IXFWUuaoMVq6kFkSGDJJOnSKSaqAuXaVSYcOGDQgJCUFhYSHq1Kkjenp6MolEggYNGqBRo0Zo3rw55HI5u379Ojt//jyCg4PRoEGD0jXJBuDi4oJLly4hPDwc7du3rxYfAAC0wQ8IoAxnWhptJBkjYnPoEJl/xcdTuUTPnsDu3URQbW0p4x4TQ7L1adPod4cPE5nnebo+9vY0D62tqeyBMZLCN2xI5lV9+9JGu21buoaM0fh27kxjPX06yZxHjwb+9S9EJiTgcH4+4uvWFYfPmMFM6tUDWraEkJ2NGw8fot2ECczq2jV6b1oaBW/69KGAUGAgBYl69aLA28GDZDL26aeU3btwgdbXIUNI/n3lCrW3GzSIZN8ZGbSB3raNzC6fPqWMYMOGNPemTqX7/fx5ymY7O9Oafe1aUY21Wk0B25gYqgP+6CMKpLwBODs748GDB8K5c+dY/fr1UWGHGACOjo4wMTFBWFgYS0xMhIeHBxHWevUqbM31Anx8aA309KRgWVhYUS9zA5Cr1bgZGAhfUQQXG0sS5Qr8RgRBwOnTp1lWVhYuXbqEwOvXcSk6GvdsbQWjR49w5/p12C9bxoz/+ovmQYsWRPL79QOysyF27ozEwYORZWSEWy1aoN6RI5C0akVkv107Iujjx9Mc+fZboFkzPE1NxUE3N11IXh7nOWwYbPr0AerWhcgYzqSlCRYhIcy5XTvG+vQhNdL27fSdKhUFs/bto5ru1FSaU4MH0/hYWACtW8OufXvEx8eLNjY2wqBBgzhjY+PnWWxPT09Oq9Xi2LFjcHV1fX49k5KScPbsWZw/f16sKZOJQ2bO5NnEiVT6VBYmTyZTunXr6H6Miqp0JxKVSoXjx4/zAGp36dIlt+Tfz58/H26ak3P38zVreplnZiov2tsfiImJuXvv3r1m5ubmRg4ODpV+HjDG9J0lmImJCTp37gwPDw/cuHFDOHPmDJecnCw0adKk9OdpNHT/1qlD1/Ljj6mcRio1PC6mpnRfiyIFhAYMoLKau3dp/XoJnD9/Xr13715dWFjY48uXL5tlZGRI09PTxQsXLrDr16+jRYsWMC6RdRcEAdnZ2UW/l0iok0duLh7LZFCr1ZBIJNi7dy8SExMNlzTK5bCpUwdPu3fH1qwsZJ85Izr8/jsT7ewg3bCB1DlubvTaSZPImNTKCjzPIzAwEC1atHhBOfHXX3/lpqWlze3SpUvoSw3Ee7x1VH+d23tAqVRyjLEPpVLpMlNTU9tu3bqZVqXtzdtEUFAQAgIC/p4zZ07lQrzvOJRKZVMAd6RS6VKdTpcnCEIqz/MjdTqdd9OmTQsHDx78z1Bf9OtHmZWTJ1/u/YJA7y8hjX0XcOHCBfHmzZviv//97zIPLDY2Flu2bIGFhYXo6+vLGjRoULq+811DZiZt/oOC3szmfuBAyoZV1vznNSM5ORlr167FF198gZK9tasFwcGUwQ0Kevmyifx8eq+hgKxWSxmPgwdfCJ5kZmYiLi4OBw4cAGMMTZs2xaAKJKqZmZk4cuQI4uPjRY1Gw/r27Su2atWKMcbKDQbHxcVh06ZNkMlkaNSoEQoKCsShQ4cyQ2VJpaDv4bx3L2WoIiJI3rxpE2XumzUj062gIKoL1V8jtZpUKXPmFJmELVtGRK9zZ8rWTp9OY2djQ5+9Ywdlw6oBoiji0aNHUKvVsLS0xPbt2yGXyzFt2rQXsk3btm3TqdVqNnHixMp3tRFFGhMTEyKfUimdw/Hj1HkgJobWWKWSghleXvTz3Xck1z92jAIid+5QoGPkSAoafP01BaAWLKBggH78Dx4kolCO7Pp1QxAELFq0SGzcuDEbMGBApX1Ltm3bpuN5nh8zZgyd++7dRRL4ymLSJCJZf/xB5Q0Xy84f5Do64mGzZmKrv/+uNBEURRFLliyBjYWF2K9HD2YUEACj06dhvHAhxI4dkdShAxIfP0YTFxfkODvjrL29Llou52rXri3Y29vzt2/fFpsfPYpHnTvrCszN8fHKlbyluTnD2LE0x3fuBPbsAY4eRVpaGk4pFELrs2e5tI0bxZYtW7LiRC3e3x+Jc+agcUAA5CYmdM4qFc0RY2MKUi5aRCT0/n3K2OpVMjNnFt1/APz9/fHgwQNh2rRpBi/W1q1bRa1Wiw4dOjALCwts375ddHJyEjMyMtjIwYOZfWQkef8Ygk5Hz6bgYApQtWlD90XTphTcNOR5UALXr1+Hv7//X99++22/Ml/EmCkAZ4jiA/2v5s2bt7dZs2Yf9evXT/Kqpa0RERHYuXMn2rVrJz5PAIgi3XdKJRkCRkWRguhln78rVlDAp18/CvasX2+4jaMBqFQqLF68WC0IQl2FQpGgVCodAHwBQArgoEwmm926deu+3bt3f2EgVq5cqcvIyOD1XkaCICB63TrEnzolBHh5cXrOZmRkBFEUIQgCWrZsCZ7n4ebmBmdnZ4SEhCAjIwNhYWG67OxsvnXr1nj8+LHQd9EizlitFhM8PCC6uzPnrCxYcxy4bduePwOXLFkijBw5kqv9bB5kZWVh1apV+Vqt1kahUBS83EC+x9vGe7L/GqFUKjkA3aVS6c4RI0ZYv1QP4DeMO3fu4CDVY5opFIpSEdt/IpRKpZHeVGThwoVz1Gr1AgAYPnx4pcxu3gkUFpL0MDycNpNVkfWHh9MDPSnp7deSG0B2djaWL1+O77//vsyNqFarRWFhIfz9/cXHjx8L2dnZ/IwZMyotTX1ruH6dsiXr1wPjxpWunasuiCIwcSJlIl5zl4XKorCwEIsXL8awYcPQqFGj6lc45ecTufrxRyJwLxP86dGDslo7drz4+2XLaJPu4ABwHAoLC7F//34xMjLy+UnopcdV7f5x+/ZtnDx5UiwsLGQAoFAoSr1GEATcv38f9+/fx4MHtFeWyWSQyWTQ6XTClClTuOdZWkEokifv3EmkfPFiIq137lA2a/RoWgP09fUlN6xaLZGA7t2JdCxcSJ+3dy9tmJcvJ5f48+dJyq5HfDwFD69fpyCTXhL7CoiIiMC+fftgZGSkU6vVHM/z4ujRo7mSdcrLli0TunXrxr2REofyIIo0/nl59OPtTTX5lShPehM4cuQI7ty5A51OBy8vL6Ffv36VYvs3b97E2bNnhZkzZ3IQRZo7VfUBmTiRiFL//jRPSq4BubmkINm0CeuPHRPqtW7N9aioE42+XGTtWkCjQY6LC3QzZiCwTRuY1qgh+DZtymHmTLovTEywYcMGbVpSkqSjvz+ax8UhbfFiPEhKwlOJRGzi5sY6jB0Ldu4cBa3y8oiQL1pEJNHFhcpX6tXD8ZAQ5Pj7i8MiIhj7889Sh5WyejXS168XG4WGMogieVHY2BCRv3KFArGffUb3ob4MxtSU1CEhIfSaZ8+/ZcuWCe7u7lynTp0MqjGys7Nx4MABISMjQ8zJyeEtLS0xY8YM+uOUKRR8Kmu/OXIkKXVKBl7WriVvkv/9r9Rbisu6dTod5s+fD4lE8ut33303vdSLAYCxfwOYBFF8gWXPnz8/TqfT1ZJKperPPvtM9jJB4MzMTAQEBCAsLAzdu3cX27RpQ+Ot1dI5z51L6iGgUoGLSuH2bTIi9fcH1qyhz69A4p+amooNGzakz5kzp6ahvyuVyvZyudx/+vTp8uJZ9J9++kl0dXVlkZGRorW1tZCUlMTbFBSIHyQni7VXrOAkUikkEgkYY8jPz8fFixfFtLQ0XUREhASg54RcLhfNzMyEZs2a8a6urmQWGxkJrakpEgsLEZ+YiMyzZ3Vtli7lI+vXx43x43UdO3bkmzVrhhUrVmi7d+8u8fDwAAAEBATorl69unX27NkTq2cw3+Nt4N2qU/5/hmcGcqcWLlx4IDk5edI/gezXrPl8XaoL4O7bO5LqQ3H3UI7jWgGARCJJi46ONnF3d/9nZPb1xkC7d9ND+osvKt/b3c2NMkzvINEH8Nyw5vbt25BKpfDw8MDDhw+RmJgIrVaLe/fu6XJycnidTgcjIyOYmprypqamMClPoviuoE0bypQuXUoSwWpw2C6FM2co+7ppU/V/9ivA2NgY7du3x4EDB9CuXTudn5/fK9SgGIBcTmT//HmSRsfFVb17xW+/lf6dKAK7d0NwdcW+wkIhLCxMT46Yu7s7OnXq9ELbx6rC09MTzZo1Y+vXr0dycjK2bNmia9OmDd+oUaPnRq6rV6/WFRQU8PXq1RMaNmyIIUOGcDKZDLqnT3H82DEc/9e/0ObxY7HB3r0MTZtSQOm//yVpMM9TG6i1aylzd/Ro2Qej0dD8mTmT5s/s2URWT56krGS9evRZmZlEEkqed24ubYJdXSkD+scfLz0uegQFBenc3Ny4IUOG6OdLqUXur7/+QmFhIefq6vrK3/fKYIzucXd3kmg3bEjBlvbtaWzftG9HMWRnZ+Phw4di165dkZeXx0JDQ7l+/cpOxhaHsbEx9b0/dQrYuJHOrarYuJEy2jxPQbm7d2meaLX035EjSVXi4gLR2poFBQWhW7duRSUrgkBBvd9/p7kXHQ2sXk3y87AwoEEDmPXtC/TsiRY5Odi2bRvnO2fOC4cwceJESUhICBp9+SVM5XJYbNiA+lu2AL/+ymBhQaRbDxMTUiOsWUMmZn37AufPozAh1AGz0gAAIABJREFUAbecneHXvj2YXv5cHEuXQujeHUfy8sSvx45lMDKiAO+lS3Rf8jzdSzt2kAJLKqW1ijGS0ael0ffa2wM//oi+ffty+/fvR0xMjDB16tRSwRlzc3OMHz+eA4CEhARs2LCBCLkg0HfOm1f6GFNSKPC/dKnhOTl8OPDjj8hKSUGBTgdRFGFsbIzExETs3bsX1tbWQr169cSEhAReKpWmaDSa7wxec4rqngBQqj+jTqdzASBjjJ2JjY31qSrZT0hIwJYtW0RHR0dx2LBhnGuDBgx79pAKIy6OxtfHp9LZ90rD05NKnwAqy7C1LWqbWEZLwJiYGHAcd72sj1QoFEELFiyIOnHihOvAgQOfS7W0Wi3r3bs3Vq9ezczNzTFs2DCYm5kxNnAgQ3r6CwEMuVyOnj17MgCS4OBg8eHDh/Dz82M2NjaMMVb0vL10CRg0CJJHj1Db3h61TU2BAwd4BAVBYm2NvNBQ7vDhw/j7778FU1NTFhUVBXd3dwiCgGvXrqnVanUlXDnf413Gu6Xn/X8KnuebVqZW7l2Ao6MjWrVqpeZ5/tIzZcL/K3zzzTdDFAoF02q1rYODg5GQkPC2D6lqmDaNNkmbNlFrmIqwcCFF+f38Xv+xvSSsra0xYMAA8c8//8Rff/2FhQsX4siRIwgPDxfCwsLE1q1b85aWlkK3bt0wdOhQptVqxcGDB/9zOlzIZCR/7daNCICBrNArISaGNjrvIHr06AGe50V7e/vqJfrF4etLDukSCTl4VxarVpGMtnhd8PTp1I/7+nXcdnFBZGQk16dPH7i7u2Ps2LEYPnz4KxF9PTiOw+jRo+Hs7AxjY2Pu+PHjwsaNG8WCAlJJFmZmckOcnDC0a1duVHg4JxsxArh5E3ynTuiflcX5duiAWLmcrVi3Ttw1b56gPnqUNrm//kob07p1DXYNAEAESqOhjGuXLrSObNlCm9bmzanO/fffSdY7fTrN2+vXDRMIR0dSBPzvf7Q23bv30mOSn5+P48eP66KionhPT88yo5gBAQG4desWPvnkk7dfyqPveW1mRmN07x7VQteqRaZ9Dx5U/BmvEefOnYOVlZXYvn171qJFCxQWFuLBgweVknPevXtXZ2JiQkHmlw2qZGfTe7Ozaa4NGULz5cIFKhMpLCTyaWmJcePGMQu1WkxdvJhMw6ZNI9VNRgbd33pJ/OXLFJBas4aCVHI5BFNTnD59WjA2Ni7V14zjOHh5eZFPBsdR5vvoUVJjeHjQ3C3eDs3ZGbhxA4X79kHVvz+W5uUhNCQEdevW1XnWr88QXqITcW4u8OuvkObkQJabyzBwIAUJZs2iY/3jD7q3Nm2i9cbICFiy5EX/nZo1gW++IfPRe/fgXqMG/Pz8kJ2dXeEezN7eHhKJhPYyaWlkJKo3nyuO2bPJNLFWLcMKDWtrCHI5jn3zDX7//Xdh+/btwpo1a3DkyBHRy8sLLVq04KKiopCQkACNRmMLIFupVPZSKpXivHnzyLiNiP4VAE4QxSsGDtcMQIFarfaRyWRISEioUiu6PXv26Nq2bSuOHz+ecx0zhuZQ587kGyKV0rOguol+SQQFkZno3r2k1AAM3ufJyckaQRDKbVut1WpH3L59W/bwmVGnVquFIAiQy+X4+uuvMXz4cN7CwgKM4yhYdPp0mZ/l5eXFRo4cyWxtbV9U0eXmUjA4IIBKEgAKRiYlAfXqwdzaGl26dGGtWrVCbm4u16pVK/7u3bvCokWLsHbtWhHATYVC8XYXsvd4ZbzP7L9mKJXKFkZGRi3K6gv+riE8PBy3bt2SSCSS43Pnzq1iQ9B/DhQKRdSPP/44ccuWLWvat28v79at2z/rXhg+nCSCOh0RnLLIfP36ZW/63yG0bNmStWjRAgCQk5MDuVwOiUTyPHORmZmJc+fOwdzcXBwyZAirXV3yvDcF/cN3yBCqH4yOpk3lq/onKJW04Zkw4dWP8TVArVZDpVKx177+eXlR9kLfqqsyKpbo6NItpjQagDEIgvC8F3ibNm3Qpk2baj9kc3NzTJgwAYiKYjq1mv3y+LGY3KYNLLy80Dgzkzn/9RfNETc3yhZ7elLQiOPgCMBh8mTUjoxkAQEBWLFihdCpUyeubdu2L5TChISEICcnB2ZmZrCzskKtxETg88+RPHkyrjVqJDp88AFrXacOBTwOHKAs4zff0OZZIiEy1qMH/Rg+CSJOT59StnDMGKprr2KNekJCArZv3w4zMzNMnDgRjmUYuQmCgCtXrqB9+/ZwLmGY+FYQHU2BkUmT6DotW0aqK8aI5G3dSmZ91dFN5SWQm5uLwsJCxvM87Ozs0L59exw6dIiZmJjAxcWl3PcyxjgznmdwcKCg8cvA3JxMCo2NyRNhyhSqfY6MJCK8dy+Vh4waBeOsLNi1by+K588D7dszTJ9OXgmOjuSTUAZ0Oh22bdsmJCcns+nTp1euVsjengjxiROUpfX2pmDEvHlISEvD9dBQ5LZvj+F79qCXubnO4/PP+bYJCTy8vIg86XH3LgUyoqJg3q4dPlCpGKys6Nzu36e6eFdXOkd9CZe/v+H7w82NVAubNyNXqcSVQYPg3qVLhW0Tc3NzodPpSKrt4UHXauTIohdcv14kP6+ICHfsiBqbNmH69OlcMT+C59/fqVMnXhAE3L17F8ePHxeMjIz2Z2dnA8BmpVLJhjZuLPd48OAagKAyvqERAHAcF3j06FEXURQlPM9bT5061cjMzAzp6elISUlBamqqePbsWSaTyfLlcrm2adOmcqlUKrGMiOA/WLqUAhpz55Lxnq1tucaPrw2zZlFpRk4OPX+CgynrbmoKMIaIiIgCjUZTbi2PQqG4s2DBgr27du0a9vXXX0On06FMH5dJk2g9qQpEkfaGffuS70Z6OhlOrlhBJSoch6SkJOzatUvUtzZ+9rzjoqKisG3bNgZgQ9W+9D3eRfyzCM4/EEZGRks7d+5s9FI9QV8C+sWisiY8xd934cIF7eXLlwEgSK1Wj30tB/gOQRCEeEEQdly+fHmyr68v+Fdxun/TMDUlt+KAAGqnl5xcmjguXUo1k+96Xfsz6B9wxVUwKSkp2LBhA6RSKScIAjIzM1m1uZK/DXz1Ff3X3Z2u36JFL/9ZaWlENKZMqdbe3NWJQ4cOwdraWpTL5a+/g0LHjtSaLzmZMme//FJ2mUtaGmVk9evyJ58Q4V27FgCQk5WF/Px8dOrUqXqOTRTp5+hR2nC1aEEmeQMGAKmp4G/fRruJE9nt27cRY2oK6549ddJRo8pdkBhjcHV1Rf369dmFCxfEwMBA8fTp08zZ2Vno3r07BwDHjh6FrbW10HHnTlbj4UO29auvdG6DB/PnUlOhMTdn9VJT0ToqirJ+48cTWW3enL5gzBhaO1atKv/ctm6lsYyJoQ1wTk7lS4wAREZGYs+ePWjdujV69OhR7jlHRERAq9XC29u7Up/92vDgAdWh37hR1D4vOZkUFcUVV46OJOP+/PO30gozKytLqFev3vMHw7NsMS5cuKAbM2ZMuWNtZmYmWh44IEKh4PAqCjiplIh0+/Z036WnU9a+WTPKVLZtS6qRunURs2MHix83jjmWcIVPSUmBpaVlKTVXVlYWduzYgczMTO6zzz6rvH9GcjK1YvP3JwK8cyewezfuzZ2LpBs38KBtW7gNGybyP/zAmjZpwmPAALrmfftSXb8eCgWR/fnzof35Zzh8+CEFd77/nsi1VEoByOJYt47WfkPdPwBgwgSomjSB48qVaLtxI4OfX7mu+idPnoSrq6sgl8s5/P47BS6KIyCASh5kMmg0GmhVKty6dQuxsbFalUrF2draMgcHB8YYQ4N27eA7dizSIyLg1KyZwe/jOA7NmzdH8+bNOQAmZ86c0QYGBi5yefJkkcrIKB+iWF593Q0Adb///vtoAFAqlVIA6pycHMTFxWHfvn1aqVT6SK1WuwNAQUGBZ0FBgb3j1KlH8oyNa7b99VcwHx9KYFSyHOW1gjFS9WRk0LOkc2cKPhw4gMz0dFMA8RV9hFarncDzvMuvv/7qWVBQUAOg/Xip/aiLCwWaCgoqX5IpCBRc8/Ojf+/fT+Uwz4g+QIFWPdGXSqXPg0s2Njbgeb5Qp9PtrfR4vMc7i3/wrvmfAcaYcWxsrCYzM9OoLDMxrVYLlUqFvLw8SKVSitC+BARBwC+//FIolUqFSZMmyWsUWxD0RoyGIoYZGRnYv39/fmpqaphWq/0UwB2FQlFW/9T/NzAyMpqlUqn69O7dGydPntR269ZNUuMdrWsvE9260cYlLY2Iw+nTFAgQBKo57Nat2lyy3wY4joNEIoG3tzfat29fqk3NPxY3bpCMc80aIpl6Q6HK4tw5kglHRr6Ww9NqtdBqta803sHBwYiMjMTEiRPfHMuRyUgKe/UqyX7LOv6hQymzt3s3EdNevV6Q8+tJhUqlMvz+8pCTQxupdeso05OTQwZ3K1ZQ3bOlJXkMKBTk6WBmhtzcXFxYuRKali3Rs2dPoV27dpWOPHIchy5dunC+vr5ISUnBxYsXxe2bN8M0KQkz/P1hPn06h8WLUWBnB5uHDxGZkaEb1KYNf/3qVV2d1FQes2dToKRbt6LMn1ZLktjKdHYYP75o/o4aRSS4SxeSWJcDURRx9uxZ4fLly5yfnx/aV6K9VcOGDVGjRg3h/v37zMvL6+204IyIIEXIp5++GEi9epU248WDkT17EgH4/nsqp3qD5XxBQUFIS0vjfH19X/i9s7MzTp06xefm5pbbAtLV1ZXb06wZulc1m1gS27aR+VxUFLB5M/DXXxQA+eknIh4eHsCoURDmzoUsIYHVPnwYaN0aiUFBeBAdLcap1Sw6OhqiKKJx48a6wYMH8/p9THh4OFJTUzFmzJjK9SzXIyODVDP6a1W/PjBnDkLmz0dLUYSPTCa2uXWLoWtXKsvYto3UBQ8eFF3zCxeozWO3bsC0aTAC8NDRETXr1gXXvDnd/4Zw4kSFgR+rNm2Q6O4u8leuMNy9S/OmcWODrzU1NcX169e56HHj4DRsGKT6c9qzhwJxx4/jz6NHtXcWLJBotVpIJBKYm5vrnJ2dJTqdDvfu3RNCQkKgVquZlZWV2NzDg7U+fJiCMRVAEATcuXNHA0DikJqa6fbwYdkSDDz3sdIT/TYA/gDIzPXEiRP5oij2nz17dsC8efNWmGZmTp++cuUxiU7Xr1Amm/z7+PF7hnh48O9Kt5kXoA8anzlDc+vSJXz100/cin//uweAzeW9VaFQ5CmVSu+CgoLWAOrIZLI1T548sS3lR2JuTs+omBhqM1oRTp2ioFp4OO0Pu3enPeGkSS/Mv9u3bwsAOD9Shz7/w82bN3USieTw3LlzCys3CO/xLuM92X/NKCws7B8REfFDamrqp5MmTTIp2TIpJSUFv/32m1oURQ3P81mCIFiNHDmyhq2tLfQLc8kHsiiKePDgAZ48eaLu2LGjzNzcHPHx8bh37x7UanWKWq0+7e/vP6pfv37GAGVOdu/erXFxcdGMHj1aXjLrf/LkybzExMT1giB8o1AotK95SN4ZqFSq0UZGRjv//vvvNlqtNovn+dq9evV6MxKM6gTH0abF3Z1Mf9LTifDoM07/YNSsWRMfffQRDh8+jJycHF2/fv34and1fxvQ39Ph4VQ716dP1TJ/8+dTTfUvv1TbId29exdHjx4Fz/OCRqPhnhkiijzPi4IgwMjISHR3d+f17X04jitTQZSdnQ1/f3+xQ4cOzM5Q/ejrRJMmVFcZGEibnaCgos2YHocOUUBs3DgKlh0//sKfGWOQyWS4cOGCOHjwYGbwPPPyyBBPraaNfe/etLk+cYIkvNeuUQalf3/aaLm4vFhuUyyzd//+fWg0Gjxrt/RStR2MMdhZWWFwaCgvbtyIyEOHYNqjB30/Y6gBoJ+bGwURNBrUGDGCkzJGMt/iBHX1ajJWu3mzcnNSKiWp9qNHZJi4YEFREKWc9/v7+4uBgYFcjx49KkX0AQps2NnZcWlpaW+njdChQ7RZjo8nNUTJv330Uen3GBtTcO/332k+vgGEh4cjICAAo0aNQklj4DZt2uDEiRPIzMwsl+yL169j7Lp10H37LV5a8xYWRufcoAGpG3Q6mo+ffEKlDRoNvS43FxzHoZWJiYiNG9lSS0sMWbUKzra2uNS3L+YuWICs8+dxdskSPu+HH2B69y6EadNgZGQEczs7ocHatRyUSrofk5MpiCYIhsukCgrIrK5EB46goCBEAbD55hv0at2aQaGgY542jYIBPXpQ8GvECJLp9+hBAfYOHYBffwXz98eRgAA4TJ6MMle8sDAKoiUllTtsN27cQJYgsIzly2Gn9ypQKAzOrz59+sDW1haqceOwT6UCy8pCG09PuJibQ+vjg12bNwtxcXGSESNGQKPRwNzcHC4uLsUvKScIAnJzc7Fjxw7hWtu2fJfYWLpWFagd09LSUFhYmP29UvkfThRPQBSjy31DMUil0hYajaYhYyxw7969DiqVimq9GJv9Hcc5b/7000YSnW4wgNhFc+Z0BcAfP368IC4uDnXq1BG7du0qdyhZhvW2IZORys7WFnfmzdPo1OpWYGw6gJ8hir+X9TaFQiECuA7g+vz58zs/ffr0XwbNRzmOEjqVIfv29lTqkJtLXVk6d6Y9Q4k12dXVlcvIyND5+Pg8v9gajQaXL19Wq1SqBZU99fd4t/Ge7L9mKBSKDKVS+e+cnBy7pUuXDmzZsiXXsWNHmampKRhjOH36dJ4gCP+bO3fu/wBg3rx5fXfu3LkbABhjGkEQjE1NTdnUqVON9X01Dx48WPDw4cMEjUYTn5WV1SktLS0vNzc3WxCEvVqtdjuA5NDQ0JGdOnWChYUFLl68mK3Vav+Pve+Oj6Lsvj/PzG42nUBCCgkhlBB6aIYO0psUKRY6CqIIFnhVVGDfBRUVAUVBEBAEFBSp0rv0lgYJIZQUQiA9pG+beX5/3CzZkN004it8fzmfTz6BzcxOfWaec++5534VExPzWX5+frEXfEZGBmJiYkRZlpf8/0T0AUCtVj8EMAigNig3b9489EySfYBkbevXUxbRw4Ne1O+8Q7XcJ09Sza+NDblqd+hAgYAaNWgSlpVFLbNUKpq4P2VkulmzZkhMTMSFCxfEkJAQNGrUSBo7duwzVHNRCr77jn5PmEAvZWp7aR2yTG2ajhyp8uu0d+9ertfr2ZAhQwQ3NzfIsozMzEymUqmYra0tEhMT+Y0bN+TQ0FBBp9NBqVSidu3a3GAwSIGBgYouXboAADZt2iTdvXtXbNCggWw+gfifIzCQiIVSSePCZOb23/9SlmTSJJoMaUsmLuzs7DBlyhRs3LgRp06exPMeHjR+oqOJtH32GdUgt2hBMneVipQCy5fTdysUlFkrJ4KCgnDy5El+4sQJ1rRpUzweFC4VRiMRnFGjKKgweDDYuHFo5O9fcllJAtatw80//8SFwEDmNmoU6pgTfb2elEBffVWx+8vWltYFqAxAEKjzxOHDFuuEs7KyEBcXBw8PD3Sy4mZtDV5eXkhISJCBynPQCuPWLcqKLVpE98DjwSNTgNWSQocxCgQkJtK47dv3H9/dCxcuwNfXV2rQoEGJc2Q0GsE5t9oK06QCzAVwv0ED7F61io8bN47VqGjbvcREIqlLl5KR2bBhdB4+/pgc9s3l5oWmpT0+/5ylz5qFdlevouDAATRt2pTN4xwYOxYufn4wtG8vnXNyEp7LzGRpmZk8SpJYvZYtaUzOnUvBtl276Bnp6Ult7aZMoZ729+6RkmrPHsqOjhpF+9ewIbBrFw7HxcFRktA9KIjehytW0HiZMYN8cRo3pmv/yy/0PvX1pW1FRNB4EQS4hITwkJAQNmDAAMvnRKGgfSoFer0eR48e5S4uLuR1olDQe0Gno9Kar74qUfP/XP36QHAweHw8Uhcu5G7Tp7NF774LJggQHjwQTO1PrUEQBDg7O2P69OmiVqulwML582Uqe/R6PWwMBqPA+TwAFXBHBT755JM1ANYAQLKnZw3nrKzjWltbDiBMlOWYqT/9dBM//URmERrNFQDy3bt3IwwGwyu3bt16MTY29rPXXnvN9n9B+I1GI0RRLH/7WEFA0zffVJ5YufK1682aLW52/foJMDYWwChwbiEiSNBoNKJSqeyvVCplWDJQHzaMlFRlBQ0HDSIlUd++pC767DOrJYNRUVFy/fr1iz0nrl69CsbYJbVa/X+iI1c1qsn+/wSFUbtXNRpN/ZCQkFlXrlx5k3MuOjg4FOTn5ytlWV5hWnb+/Pn7QI6lAIDPPvvsPQCLTJmliIgI3Lx584Fer2/BGOsbGxvbzGg0zgSwtXA7AIDPP//8qz/++OPDHj162N+9e9cBQLgoirqTJ08KsixLAJCfny9HR0c7qFSqfZ9++un9/83ZePqg0WicFArFGEmSVLIs4/bt27hz546hVatWysd7Oz/1cHKiycyECTQZ4ZwykBkZVM8cG0vBgK+/JpIweTKRElM21FTL/OmnNOGZPJkmOt27k9O0TkcBhF276Htat6aJcNu2ZNIFkFlNQgI5DNesSRMmEwGqBEJDQ1HoJQEvLy/cvn1bXLJkiTxt2jShtMzUMwWNhiag9+9TdspUN/049u8HJk6kZaoYbdq0QXh4OM/KyuJt27YVAMDPrJdwgwYNWLdu3RjnHLIsIzU1FTdu3GAGg0Fx6tQpeHl54dixY3JOTo4wbdo0uLm5/bsBGUdHqgU+cAAYP57Ih0pF9Y6HDpGkOOKxuUxWFslzW7ZE7Q0b8Oa2bWzr0KHS89u3i3jtNXK879WLzDETE4vuaQu9qSuKGTNmsMWLF2P9+vV82rRpZc8qjUZqHfj555SFnzWL2npZG2dRUUBSEqS9e3GwSRPkeXmhr3ltdHg4lTOYS5XLi1dfJWVDZiaN+WbN6PyHhlKW1Qycc3xLihQWFBRUse2A1D43b97830UkZZkMwaKiKIjhaqFt9rlzFES15pVjZ0f3y+uv03P0H+4i8PDhQ2NQUJDFG0GhUMDNzU2Kj48XvL29i53H/Px8LF68GCpRhG9cHHf9+GOmjYzEpUuXHvlAlAtaLWXQ//tfoGdP+kyS6P3TujWZaW7bRuU0j8HV1RU9TesAFCwpDFyN+Ogjcc2aNTxk9Wr0nTWLRe/di3kTJgiYNImWVavpByByb/LK+O47GvsNG9I2J0+m35mZKIiIAH/3XfD33sP4/fthf+wY7Z+rK70bX3mFiO+779L1S02lIOJPP9G9LggU6KL+5ywkJAT9+vWzrHq6f58Ieym4efMmDAYDy87ORn5+PnnYNGhAKog6dail31tvFTele+UVoGFDBMyejYCvvmKYPh3vt2gBe3t7LF26FJcvX+ZNmzYt15ixtbUl9UFSUpnLOnAO14SE2gAcwHnFk0WMjQAwwQMYAeCknVZ7A5yXMIhQq9WhGo1GaTAYTKUASxYsWBC4e/ful6ZNm/aPJmhyc3OxbNky2dXVVd+pUydbNzc3eHt7l+mLlZSUBKVSKex49VVds7lzE8DYTQDnwZgAYCeAdywoIVRGo9E/KCjI8rUyte+9f79kG1QTtFqab9WsSc/jl16y2vLXYDAgMTFRCAwMfPQZ5xynTp3K02q1/y31AKvxTKGa7P8PoVarYwHM1Gg0/wEg5+bmPgdAVqvVWdbWUSgUQQ0aNLC9desWfHx8sG/fPq1erx+jVqsLAOwBYLFRqdFo/Cw9Pb3OH3/8MR7AJLVavU+j0XQKDg7uCcBUiKoFsGvOnDkZVXqgzxgUCsU3RqPxjezsbCxatMggimKCTqdL5Zy38fb2rkCK7SnBwIE0IUlKoglocDCRE3McPFj077Fji/5tNNLkqF07+rdSSS8YT0+qcUxJIUVA7dqU6czMpAmcyTE5PZ2yfN9+Sy8cb2+SsL7yCrnGq1Qkef7xR5p4ubuTo/XYsaRMcHCgLNCxY4/UCPViYuCUlYXGkoTunTohqVUrHPjtNyEzNhaOjRrRPj7Lpn0AtdWpX59I/7ZtJUkoQNfzhReob/U/oL7o378/8/X1xfbt25mnpyeaWqkRZYxBFEV4enrC09MTnHOkpKTImzZtEurUqYM33niDPVVBmIEDidzLMkl327WjifulS5SJHz+ezmvt2jSZ/+EHMprr0gW6Ro2QnpAgFCuJqSrTvsdgb2+Pjh074sqVK2z16tV88uTJrESG32gkEjVwIAXo3nqLghnu7pSptARZpuNfvhx86lSc/c9/kHniBNTmMnStlsb44sWVN/Q0+SD8+isRoN27iXjpdMUyhJxzCIIAWZYREhKCTp06wZqfjSXY2dmhoKBAKKvm/HFotVps376dA+CvvPKKUC5D1m+/paDQ1asljdbMkZNDz9rS0KMHBQWuXiVTugq2Ds3Pzy+3AZ1erxdKMwWuW7euePXqVd6hQ4diRmB2dnYQBAHDa9ZEwLJljK1ZA0mWWXx8PC9NDVAMnNMzXBTpmW7CkSOk9AgIoKDl3bsWyX5pUCgUeOutt9jSpUulgoICsfBYLXuLmI8d0zvu+nXaj7ffJu8AACf278fl995D79694TxnTpFqY9cuCkykplIwa+xYCqzduUPLpKcT8c7PJxXd5s0YptMhSa/HjT175GZz5wrYtYsCO+PGUbDo66+BESNIjm8KpDo7A4whOzsbUVFROFj4bmaMYdmyZfD19eUdOnTgzZo1E/D11xRYMKmWvv6avmPFCnr/9upF59XbGyZ3CMYY0tLSKvbC6NKFSo769y81MOW8ZAl6Hz6s1Pz3vzXVQKqlZQpbOA8QBKHuvHnzVoP6v28GsBFAOoBgABycl2ryUUjyH4Fz/lFaWtrLFTquSiAqKgpKpfJgWlpasz179vjZ2Ng8kCSptp2dXYGLiwsaNGjgpNfrpYyMDK1WqzU6Ozsr8/LypISEBFmSpJmc818Ld/gygMtgzBaUtc8GY+MA5INzk6QfLavMAAAgAElEQVSvgDFmMBqNNhafT6JIJP7aNctk/9gxeh988w09r2bPBt5/3+qxKZVKPPfcc9L+/fvF8+fPSy1bthR9fX2h0+kyAfz9ZGeuGk8TmEmyVY2nE1988cWbgiB8whiLNRgMHQEUzJ0799mwV39GoNFouqpUqmV6vf4DzvltAIlKpXK1t7f3xBdffNHGyckJBQUF5Xf6fVogyzQZmDOHsu+WZL3/S+h0JFVXqSjrYmNDxOXePSIuhw8T6ejWjYIRPj5Aairk27ex38kJTf78E5AkeH78MVKWL0eiszO8k5LgLYpQffklHWfNmjTRPHqU1AhRUbSNKVOotZiHB5G96Gj6nZhIxPm558hAytWVfgwGIj3/RlmD0Qj8/Tdlbbdsoe1zTkqLdetosvgPIjw8HPv27UO3bt3kbt26lSubxzmHJEmoVKcEvZ6Oz8aGJuNNmlBQ6e5dIlAHDhQFQ77+mmTAp07RsjNmkFzxhRfoHnrlFZrML19ONeTLl9N98PHH9H1vvknbnDGD7ruEBJrUL11KapimTSkDWTjWs7OzsWzZMnz66aeVO7YKorBuFsnJyZg1a1ZRH3mjsUgCvX8/BYP69y/VpRsAEcuPPqJJX6NG2BUWJkVFRQmdO3dmj4zbsrNJ5rxuHRnrVRbJyXTezMnB2rXkdH78eInjvHPnDnbt2oUmTZrg5ZfLP2eXZRlLlizhHh4e8oQJE8qtHgkODsb+/fvBOUePHj3QsWNHWCXE2dlATAwFLu/ds97aFKD7t18/crp+PKhqCf360X1mKuEpB44ePYqzZ8+aPB3KXH7JkiVy586dBWslEtnZ2Vi+fDm6dOnCe/bsWewBt2zZMt6vXz/WvEkTQBSh1+uxbNky3qpVK2ngwIFlD4IHD+j5PXp0kTcJQGO8VSsi2qZxWI66cEs4fvw4Ll26BJ1OhxYtWsgjR44sn+ogPJzIutkzNC0tDStWrMBbb70Fq/4iskyKN87pHbZiBV33OXOofMDWFuHBwbh46hSU2dnceP8+C+zbF0GSRM+y1q2B06dpnXv3KDg0ciSwcCG94z78EBGbNuGBuzv8RZH7Nm/ODIMHI2H7dtxMT4euSRN5RO/eAvPzo/Ol11MpXng4BR1iYqiUISenhPLk22+/lRs2bIghQ4ZUzAtk+nQK0o0ZY/nv9+8DNWti759/FgTHxNgJgnBbluUNgiBMFwThmNFonAGgkY2NzR96vb6h5/378rSfftoDYCSA5QA2gPMrFdonM2g0muaOjo4XZs+e/Y9GltevX59z9+7d1wEcAmADClA0A2AAECgIQmtZlnWMsRTO+V0APgAyAJxWq9WlmzMw9mXhv/4LYPLnGg1cXF0XT58+3cFqYG3vXkqyjB9f8m9ffUVzm/ffp/efJQ8RCygoKMC1a9cQGhoqJyUlCaIoLpk7d+5/yrVyNZ4JPOPpsP/7+OSTT1YBWAUAGo3GHYD+392j/3tQq9VnABTTmmo0mvcTExNzVq5cOd3b21uMiYlRvvHGG1b7Pz+VEASajOTn0yQrJMSqo+//BCpVUdakSZOiz5s3p9/mveLNnIAFAK3u3sUme3s0btxYHj58uNDg9dexSaMBAIx59VX4N2xINaAGA2X5mzenTG3jxpSZqVWLnGxtbKis4cED+vzQIZLEOztTFs/JiRQMV65QpvSjj2iff/oJ+P57klPWqUNqhvHjKetnZ0cZ1iNHKKBia0tlE127UgS+Rg2SNaek0D6pVLSPtraWAwkKBQUaTDWyOTm0bFRUyb7wFUVeHn2/QkH7FhhIE8WMDAp4/PEHAoOC4DZkCG6++66wZepU6RUbG5FnZkKYMoXMyWbOpPXnzKGSkblzwRiDYuFCus82bqTz+5//kGR5yJAi+am9PRnCHT1Kk+fUVFqnY0f6e5s2pPTYtIl+rl2jbNqwYeSKvWYNEdfoaJLWzphBk86sLLrfbWxoQu7tTb8Bql+sV48CBx98QMR58WK6RwSBll23jiZGGzfSfmdlAX36wDkwEKratSG1bg3F339T0OzsWdqH4GBatwprRh0dHdG9e3ds27YNq1euxH/ef5/238GBJnJbttA93bhx6V+k09H5y8ujjGTv3gBjyD93DrIssw6mLLROR+fs008pKPIk8PCg++PVV4vMB6dMIRn3Tz9REM7sOE1dZzjnlutTrUAQBDRv3pzFxMRUiLykpaXBw8ODu7q6spMnT+LkyZPw8vLCG2b79QgzZ9IYP3Wq+LPKEi5epOtTHqIPkPIhK4vG3WPmedYQGRnJAbD8/PxyLV+vXj3h5s2bUqdOnSwyaWdnZ7Rv3x7nz59nzz//fLGMvSIhgTUeNIiOH4CNjQ2aNm3KIiIiFAMHDix9w3/9RUTj2rWS7cEYoyCAmxuR57feomfaqVPlOiZz9OrVC9euXZN0Op0YEREhtGjRotSadAAUJDMYSgRLU1JSIIoirHoScE7vichIqtd/8026bpGRgEoFqVMnXHvxRewufN44ODhwp7Zt2cHr1xE0f37R9zg7k7qtUFEAAHjpJSTfu4cta9fyGv36sZcnTYJ9fDxDRgYULi5o7OMDX3t7HLt8Wci7cweOXbtSYNJgINXMjz9S8MLOjkoOHguarVu3zpiVlaVwd3eHLMsVa8k8dy4FF199teR7KjcXaN8e+O03DBo71q7zw4eIiopqmJOTo7a3t1dGRka+kp6e3o4xVu+12FgHZatW2JecfB9ElJXgvCqcKrv4+PhUwdcQDAYDRFEscY4SEhLsARxSq9XZZh9HFv6+CWBbpTfK+RwAAGOBkiDMUwA1xtSrZ89K6yQTFERznXHjil+XL7+kMq6XXqL5y+TJ5d4NOzs7BAUFoUWLFsI333wjS5L0BD2Bq/E0oprsP0NQq9Up//Y+/P8CtVqdB2D2F198ce/+/fvDVSpVjaysrMBniuybYG9P0X97e5qofPQRZUmfIezZs0f28/Njo0ePFgAgOTkZKpUKLi4u8DcRH/OsjKnvvPlxmhsjmSat/fsXfWaJ7EyeXESQmzShTL9WS4qAxo3JAKegoMjc0GAguf3t2zQhXLuWPps8mYha06ZUx/zgAbXjmj2b1Ahvv03Khm7dyLG5oIDI0YgRJIdWqeizadNIBvvuuxRYuHCBMtPr1hHp6NiR9umDDyiwkJRE2aOdO2ny7u9PhGzaNCK/ubkkWw8JITXB558DH30E7w4d4BkXhyUJCezCqVNgaWk4m5XFp8XFMceCAsoembJ25sRz1CjarpsbkWaA2myZVDEnTgCNGlEJh6l29caNovVNJm8ffkg/ANXPmnD3btG1NF1Pc1dt00TaXHI9h+ZTWL2aJq8ff0wT+Bs3KNCTk0MBHldXCoyZnOHfeQdo2BD6P/9Emrs7vB0c6LqePk3X7bXX6Pq88w4dr15PE64rV+h8T5tGx9i6NRmHzZxJBpm2ttYncgCaNW6Md5o2hX7mTKQFBMDt3XdJVlurltV1ikGrpeBI3bpEDMzavY0ZM0ZctWqVvGbNGsHJ0RG9Fy7kcd7e7OakSXi9IkTAGpRKyoqbo2ZNIvstWhQzZXMrJMcVJfsABQsyMzPZgQMH0KRJE/j5+ZVqOJecnIzU1FSuVCoxcuRIDBs2DOvXr8d9k8+ICatW0f6vWlXShM8azpyhYEl5Ubs2PT9GjSKia632thAnT57kpj7Yzz3mf2ANPj4+OHPmTKkp8zZt2uDixYvIycmhuvBCCE5OPGPECOZhdj9kZGTIPj4+HKWZIj58SM+En36y3gfc25uUDX360PiuRFbfhKZNm4rnz58HQIS9XGRflmlsmOHy5cto3LixrFKpLN+DW7cSkd6xg4KD/frR2BowAPj6azCDAbZ5eRgeEMAD161jcHcX9u/fz5OSktg333wj9+jRQ2jTpg0UqaklJPH5+flYv3kzb9qmDR86dChjjBUPyLdujYRbtxCxYwcG/Oc/dL6mTqX7Zs0aeq989x0FAi9fpmOsXRvo1g05RiMexMYqoFTi4MGDEEUR7c09OsqClxeN3ZAQUsGZw8aG3mc9ekAAUKtWLXTp0oUBUAJAfmys2HnVqmZJGo3R48EDpAK4X6+eFpxPKf8OWIZGo/ECkABAbG5KFJQDmZmZOH78eMGdO3dkg8GgdHZ21ur1ekGv1ys458xoNCrs7e31HTt2VDVr1kyoVfi8VSgUktFovKjRaL4DsNrcG6uC+10TQG21Wl2yTRLn4Z9pNB/WcHZeU2PWLHq3zJhBwdjHPULc3ekddeVKkR9KYiIlK3x8KCDVpk1ldhFRUVFQqVSHPvroI4slGdV4dlFN9qtRjVLwySefLNNoNMsFQfgrMjIyoEmTJs9mo3d7e5roREcTKbHWlugphSRJ8PT0fDSb37Nnj6zT6YTk5GQcPHgQVt2PnxSiWESWzFvhmCaWEyYUfdasWcn1d+0q+vfjNdWSRLJpvZ6206YNvdgDA0kF4O5OpFSppL/LMhHT+vXp2o0dS9e1Z8+iCcGyZTRJc3Sk+mBnZyLwhSoImJMbUw/5ZcuKPgsPLzr0mBj479wpHC4ogI2NDYdez87Mni0P6NCBbpzffit5DswzWabMgjlBMa9rLoXwVjk4pzKOhQuJkO/ZQ+fHFLwzeSTo9XTuOCe3/oUL4avTIYsxeNvYEHk3BSlM58o0rkSRSIwp+KHV0t8SEkhJMHMmnZPkZNp2nToUBKhZkwIjx4/T+cnORs0jR3C+Wzf5THi4MOWddx5lwUuFTkfX8q+/aCJet65F5UivXr1YaEiI5KpSMfb++8JtoxGJiYlPdHofYeVKOl7ztnu1ahFhSk4mQlhYn3/v3j0wxvDiiy9WeB7SvXt3eHl54ejRo1J4eLhQs2ZN3r9/f8HcUNJUAx0WFianpKQIbm5u3BQslGUZDx48QO3atTkABs4pKFcoTbZKVh+HJFGG3nwMlAetWwNLltA985iUPSYmBiEhIUhPT5cfPnwoGI1G1rZtW4SFhUEQBGi1Wss16ma4ffs2Lzw2qw/5Bw/IB+3x8rSm9+/zqyNHMvOeAdnZ2bxr167WmXluLpHfBQuIDJeGt9+msd+wIbBzJ1J//RX3Bg3ibdq0qVC9VJs2bZCfn4+UlBScO3eOd+vWzfr6WVlU0vPYO+/bb7+Vs7KyhEmTJlk+T9euUZBw376iddu1I2Lt4AA0bw7hyhWEvfGG1Pann8TcwEDYHz6M/r17syZNmmDHjh3C/v37sX//fjSQJNmmb19hcG4ujh49Kt++fRt5eXmCIAhs8ODBrDQ/BK1WC8Y5BYR/+IEy+MOHkw+EyUh38mS6lyIigLw82K1ejXd37sT1V17hrq6u7OzGjWjTqhXE8nb6YIyeZ4mJxcn+0qXkK1MYaCmGzZuBggLU9/ER7GVZqhcYqMDw4bDPy4Nh2TJvjUYjPF57XwnkgvwBPMtL9pOTk7F+/foCo9G4WJKkjQAyMzIy/EFy+1xQf/mUvLy8XmfOnHn91KlT/d3d3RWdO3d2YIwpOedNAPwIyuQft74ly9BoNCKADMZYAQBr9aBb8vLy+h/67rvRA/v3V+GbbyiQk5hIz3bz4OPLLxe9y6OjSSHn5ETvr0oSfQAIDQ3N0Wq1ayv9BdV4alFN9qtRjTKgUqkuiqLYrFmzZs9mWz4TBIEyqwARxvnzKyT1+jchSRI3N/EaPXq0cO/ePej1evz111/Izc2VR40a9exEL4Aisx0TTDJgX18iHlu2UMZw5Upye9+6lbLK8+bRhG72bCKm/v5FfgzmNbqmCVp5iYsFvPjii2jUqBHs7e3Z5s2bcenSJaFZs2bw9fWt9Hf+K7hxg+71556j89y2reXlWremH4Cy/rIM3y+/lGqlpYkQBDq/vXvTNTh3joI1glAUCOrQoSig8csvRd9rUiTs3Fn02S+/0H4cP04Tth07iKA3aADUq4eOR44I4XXqIObtt+V2x48LSEqikohOnUid0LcvqT7Cw0nZ0acP7fP27aWWFjRu3Jg1Xr1axLlzNGFfv57z7GyWmZlZvqBCaeCcglW//168xVyjRkQS8vOBDRtw7tw56cSJE2Lfvn3LJK7W4O/vD39/f9FoNOLAgQNsy5Yt3N3dnfv4+DAnJyd26tQpODo6SvXq1RMnT54MGxubR88HU/u5GjVqIDY2FvVff52eievWVWwn7t6l9SrTtWXYMODVV5HKOda1bs2VSiVv1KgRDwsLEwGgXr168PPzg4+PDw8KCmJhYWH46quvuMFgYPb29vL48eMFD5OCyQxxcXG4c+cO69WrV6nk+dKlS1wQBHb58mW5U6dOdG6io9H511+Fcy+9VGzZWrVqiXfu3JHatm1bkvDLMpGNQYNK9zYwP+49e6DfsgX3Tp7kqWFh7KDRyBo2bFhMYVAWateujWHDhuHLL78EiKxZR9++NG7MfBJu3bqFnJwcYfr06ahdu3bJdY4fJ4XUuXPFiZazM5XyzJhB//7uO7zSo4dY4O+PM76+6LpoEezc3NDg7bcxa9Ys6PV63L59G3VHjRLOd+vGv0tKYkajUejfvz/q16+PmjVrWvUDiYiIwI5t29D45k0KDB44QIqm/v3puTN3Lql+TBBFGn8AFJ07w2ntWnS4do3JV67g1l9/gdetSzLvV18lcmgqZbKGdu3oudmnT5E6KyioqPQOIMWZWk1KjbAwwMkJAVOnAgMHPrpXHBwcYGNjwwsKChoDuIFKQqPRONvY2CzV6/Weo0ePLpdhJOcc27dvz9Xr9e/Mnz/fzDESFy0sfhjAYY1Go0hMTBy/d+/eNzjnfqIoZkmStBKAhQjHo32rC8CoVqtLdBMA0AkAbG1trbayU6vVkkaj2RsXFzccgqDChx/StdZqKaBz+jS9Lxijd9jRo4+eIVAo6D61dB+XE9nZ2UhKSlIA2F/pL6nGU4tqsl+NapSNrHr16pW7dc0zgbVraZLw9980OaisA/f/AMnJycjJyRGvX78ut2vXTgAAFxeXRw7e+fn5OG8py/AsY/58kk/u2EEZpOnTadJ1+zZNPAcPLuq/nJlZ/nrhSqBloX/CvHnzsH37dr5hwwbWt2/fIoLwLGDnTlI/CAJlFLVaciz+TykeRIKAm7dv44wsi6mffIKXgSJZfFwcSfijo6nOv6CAAgDmGe3SIElE1KdPJxn4+vWUGTVzJ486exZpW7fC87XXBAQEkKrD05OIhiSRlPeHH2gf1q+nANAPP5TtIZCZSaqQQpM0E8EKDg5Gn/KQtdLAGHU5MMuwP8LixcCJE8iKiMDff/8tvvDCC+UymysLCoUCQ4YMQf/+/dnevXvZ7du3ZVmW5d69e4vPPfecxWy0vb09Ro8ejcvff8/2XLqEdxYuBKuMn8mBA0+28198gZD33oO+oIDpdDp29epVCIKAl19+GY0bNzaNLwYAr732GhhjzNnZGfv27cPOnTvlN998s9gY3L59O4+IiGA9evTgVtt3FWLMmDFs3bp1OH78uNCxY0ciTgEBOLB5szHiwgXFldBQPnHiRObh4QGtVst9fHwsZ/Y/+YRUG+bO+2Xhxg2k//EH3/HKK9y5bVvukZvLNm/ezKdPn16hZ0pqair0ej2GDx9e+oJbtxZ5oBTizJkzaNq0qVy7du2S27x5k5QA27eXLOfw8iK/kU6dKMDx6qtAjRqwy8lB8JAhaP/HH7Dr0gU4cgTCzp2wfftttGjRApg/HwNeeIEFGY3IyspC/TJK6Yz5+bg1bx6aCwL6GwwQ6ten/THHtm2U4bcGpRJo2xZyq1a49OABEnv3hktICOzXreOu164xl9xc5NatCykoiDedMIE5NWpU/Pnl5UVE89w5CnL26UPjuGtXKhsLCaEs84kTVLb0zTdWd6VFixaK0NDQiQA+trpQITQajQKAqFardYX/9xAE4QelUtnJycnJc9CgQWhQTr+Le/fuISsrK5dz/kvZSxPUarURwPrCn7L2tYMoih8DGAaAazSatmq1OuyxxZROTk7agoKCFhqNxvmx+n9zRBYUFBSVCZjKPkJDKaA/YgQFXdauJcXe5cv0zpk584mIPgAEBwcbRVH8Y+7cudon+qJqPJWoJvvVqEYZ0Ol030RFRe1ftWpVliiK8PX1VfXr18+2XG2InlaY+q7OmkXEccGCf3d/SsGGDRu4jY0Ns0QuOee4c+eO7O7u/gxfDDOkp1M2/6OPaKJlyno2a0Y9q1esoGu2bx8RvjNnaMKZnl5kvPcPQRAEjB49mh06dAhHjx4tIghPOySJpJBTpxZ9Fh9PAYBSyL7BYMCWLVvg7e2N0SYSbq6cyMyk387ORS7d7u408XV0JDWBuXeAJJGZ1qRJ5HDdsyeVdtjbk7lj8+bUOmnVKuDiRQT07o0Bvr6I9/eH144dECZNoon97dt0ncPDiVSrVGSKFhVFTt9//EGZf0s1/nv3UnvB+PhH95aPjw+LiIiApSxxZcBr14Z+wADcWroULVq1KvqDgwPg7w9jz57wnzNHDgwMrNJgkY2NDUaQ+Vq5vrdZ06bw3L4dCQMHckb1xhXH2rV0viuJB7a2CG/SBB/8/DNWjh6NgF695MGDBwuWxlUds9r+wYMHC6tWreKrVq2CIAiyLMvc1dVVvHHjBhs6dCjKI4l3dHTE66+/jqVLl2LDhg3y2IEDBTRtivyvvlIEBgYiNDSURUZGwsPDA82bN2enTp2CjY0NBEEAY4x+jEbUTUhA6siRMISEgDEGSxn6+/fvIy4uDqdOnYJCoYBCqeS6IUPYG336sJqLF8MYG4svBw1ikZGRqEgddmhoKFxdXaXAwEDrJQaTJhEhN/dnAZCWliZbJIzBwaSa2rDBctAKILVVbi6VBM2YASQmIvLll+X3liwR+MqVpPaws6Ng0HffERF7+BBwdkYtUI27VRQUACEhyPrkE/jq9Wi8ciUcW7cuGURMSqJnWDmCVAqFAk2aNJESEhKE52bPZomJiazuwoXIT0+HfvVqGE6eZOn5+XCKjSUz0/Hj6berK5U+qdWkWKpVCzh5kn4fPkwmhW+9RaSzDLRv394mNDT0bY1Gs1StVherCddoNPaCIExTqVTDOOc1GWPNOefiggULJs6fP38jgMmyLI+SZRlGo1FrNfBkAeHh4Tqj0fhDFZQPlIBGo1Eolcq1Xl5ezTt06CDn5+cLBw8ePK/RaFqo1eo7ZotGFhQUwMfHhyUkJHwEoITJh0ajYYyx1zw9PUs+v0xlg0uXkoomMZHeGYcOkf/PEwb7JUnCpUuX9Hq93nq0phrPNKrJfjWqUTZOAXgjOTk5HoAqPT39s1u3bjWcOHGig1MpPWifCVy+TNnO2bNJijpr1r+9RyUgSRJeeuklNDKvmTf7W3x8vAAAy5cvl/r27Sta6w//1INzqg/V6UjO/Thxf+45ygaHhZHUXBSpZvPuXSJ8Xl4UEDC1tfoHEBcXh7t37z6a7D8TWLeOMiTmmY9Bg+gnO7uYgZ051q5dy21tbfHKK6+wUl2szd3cr1wpkoNv3kxkv1cvmpClp9PEPzKSsmBduxKZWLOGrmleXjF/A/HuXdw/dIiHhoayFI0Gw3x9werVK9pW06aUVb16lUh8y5Z0POHhpAJZtIiWMV2ntDQKVuzcWcwv4eDBg7Czs3uk4KgodDod7t27h4SEBB4TEyMn3b8vjr11C/s3b0bzr74qdp+cz8iQMtu3F4d37y6UWwVR1ZBlykZOmoTNc+bwhgEBqJS+4MYN6uDQsGGldyUtLQ0F9vZ40L07Zo4YAWWXLhaJ/uNwcHDApEmTWHBwMHd0dBS0Wi1Onz6Nli1bSm3atCk3EXJ0dMR7772HlStXslW//IKGnTvjXmYmXAWBd+rUifUq7KrQqVMnZGRk8IiICJlzDs45XOPi0Gf9evGPjz82Gh88YHjwgOfm5op16tSRx48fLwJULnHo0CEpJCRErFWrlqTX60VHR0feuXNnFnj2LMTx44EjR6BwcECHCxfw559/4sCBAxg3bhw8y1CoGI1GXLhwAWPGjCn9eCWpyLDVDAqFgjs6OhZf9+ZNCt4sWGCd6NMJofE8bBjg5YWU33/HnsaNhY9kGcK5c8DEiRTM8/Ehv5Rx4+gZMGsWZdstISeHnuWvvw74++OXXr14/VateLs2bSw/fFavpjH9/felHr4JL7/88qNjfaSoqVMHjX74Ad99953RtXdvBZycyKfg118pcNi1K6k2rl8Hhg6lbgpt25K8f968cm3XBHd3d3h7eyvi4+PHAvjW9LlGo2muVCoP16tXz6VJkyb2zs7OqF+/PiIiInD48OGXAWwE8BWAZQDE7OzshJMnTwr9+vUrl/nA3bt3tbIsn67QzpYTKpVqrZ2dnf+YMWOYSqViAJCXl6c4d+7cdo1G089kqq1Wq1O+/vrrmLZt2zZLTk5+T6PRHFOr1Y/X/rfknL/fs2dP6xs0taBNTaVAzMyZVaLqi4qKAoAbarXaaplBNZ5tVJP9alSjDBQ6868x/V+j0eyVZTkhNTX12Sf7JhLj50cTIq32H88QVwIsLCxMbtiwYYmJsEKhwLx585Cfn4/t27eLx44dk5o2bSpmZ2cjISEBKpUKDRo0qFjLoX8DMTFEQHbtIuM2SxN+NzeacF25Qu3qTMdkMuc7c4b+/dlnRP4uXKjy3dy5c6ecnZ0teHp6ypzzchGTfx2bNlFG5HHs308Z9szMEuebc46srCw2cOBAODpWoI2zyaRxxgz6AaiLQ2gokfoePagfuyiSDNYE8z70LVrQb5UKQ4cOZQ0aNMCuXbtQr149tHncfGno0OJdJJydKeCj05Fip2ZN8gbIzKQJ+pEjxRzxeWG7sMrI6e/cuYO9e/fy7OxsZmdnJ7u4uMDPz08cPHgwbKZPh2LRIsiyDLHQfM5oNOLy5ctC05kzofjmG9qf99+v8HafCFlZFPhxdYVcrx7yrl5lsbGxlXLXxpUrpOR4gjGQk5MDAMJX37oAACAASURBVLg3bRoaREdTMG/x4nKt6+rqin79+j3aeIcOHWBvb19he3tnZ2c0aNBAdtu6lfl/+60wmPw/ShzU4MGDGUxu/JyTQef8+Zj29tuPXhZxcXHYvHmz+Oeff8opKSl4+PChYGdnxyZNmoT09HRx//79/I033mA2Njb0DGvVit47a9agb3o6Go4fjwMHDmDbtm185syZpZ7Yw4cPS3Z2dkLDhg2tL3fuHI07C74QTk5OYmJi4qPSMNy5Q9LosWPLbkM5bhxlVUUR6aNG4fb8+QhasACCRkM+Ho0aUeDg4UMa7+++S14srVtTidaoUUWmjFotXfctW2g8b9yIGIUCOZs2MT8/P5adnQ1bW1vYPG6s17gxjfEnhCzL0Gq1gpOzM+1jvXrACy/QM8MUKA0NpYUVCgpWVhJdunSxu3///scajeY3AG5KpXKmUqmc2K9fP1X79u0fvaSPHDliOHfunFIQhIcAUOiAr9NoNE0457Xc3d3LPWYzMjIcADwuq68SMMZqde7cWaUyK/Xo1q2bwmAwNLt48WK0RqPprVarQxYsWDCQc96sfv36GDFihP0ff/zxu0ajaaRWq7NM66lUqh8CAwN1Xl5eZXtDRUZS556Py6yIKBeuXr2aq9Vqyxc1qsYziadqRl+NajwLUKvV/Msvv7y9Y8eOWhMnTrTTarX4+++/8+Lj423Gjx+vfObMywCKEANUM5yeXpx8/IuQZRm+vr6IjIwUOnfuXEzKagJjDCqVCrGxsWCMiV9++SWXZZnZ2tpynU7HRFGU69atK+Tl5cmcc3nq1KlP13MvK4uI+fDhZRt9DRhAslQvr5ITPZPyYcoUyvLm55OZ0q5dxTsJPAFME866desKer0eqvK2J/u3cPUqKR0stZwaMIDOuwWydu/ePRiNRlSJSmTGDDLFev55+r+5cV050KJFC4SFhfFLly7xNuZZvtGjSV5rahVoDpWKyMiBA9R60MuLaqpbtgTnHDdv3sS+ffsgSRIAVOg4ZVnG9u3bpevXr4vdu3dnXbp0KWZ+BwC3fvkFb33/PfjnnwOiCFmW8eOPP3JJkuQOHTqIqFGDMpP/S2i1NA7WrqVSCVmGfvt2DB8+vHJsPSeHxtcToH379jhy5AgSEhI42rVj2LoVMBorFWx1cHCo9H681K+fiBkzirpTlAZJooz2rFlEjM3g5+eHqVOnYu/evfDz8xMCAgJQv359QRAEpKSkwGAwsEdmdAoFBaN69CCZvcGABg0aoHnz5vj777/LvCbXr18Xnn/+eeuqG0miFquHDlkcI7m5uVJQUBAx7gcPSJbevXuZ3QSSk5Nx68QJNFi6FBtTUqArKEBPhQJdDAZqSadW03YPHKDzlJlJpGzXLvps3jwgI4Oy5n/+SUGTjRuBr79+1Mnj2BrKLezZswcAlVDNM8+kP3hAgbtXXy3rNJWA0WjEpk2bpLS0NGY0Gpler2e1a9dmderUITf3LVuolCAtjWryExNJFfXLL+QXcv48lZlVAo0aNUKnTp1qnj17Nk4QBKF9+/Zihw4dFDXM/BTCw8Nx8eJFnSAI5xhjIabPNRpNTxQ64Z87dy7/4sWLRi8vL1WDBg1sAwICoLSgmJBlGZIkiQByKrXDZUCr1S4+efJk75YtW9qbjEYFQUCfPn2UXl5eLnv27DmzYMGCFZzz/wBAWFgYunbtihYtWjhev379J4CsYBYsWPCio6Nju969e5f9Qk1Lo/aWv/5aJccgyzJiY2OVAA5WyRdW46nE0zXprUY1nhHMmTPn+YULF8759ddfF+bl5UlGo3EWALuDBw8ufOONN57ddP8vv9BEJCqKpMWVmExUBMePH8e1a9ckQRC4k5OT0K9fPyExMRFubm7w8PDAypUreX5+PuvVq9ejvtyWoFAoMHPmTGRkZODhw4esZs2aaNiwIZNlGWfOnOEpKSmyn58fu3DhguLs2bPoYu5g/G/i/Hlq23X0KGWZy4IgUDb0119pYmpJWeLpSZNMg4Hc203yfhcX4L33nmh3x48fL1y6dAlnz56Fn58fmllqN/g0Yc8eOn5L2VdBoMm5aWJuhvT0dDg5OUk2NjaVbwRuwqlTFNAxkf1KYOjQoWzZsmXs3Llz6Ny5M03Ks7Mps2wNokjH9t57RKpat0b2779j9d270Ov1kGUZffv2Rb169eBVSPIuX74sRUdHY/To0aK1QM7x48fl2NhYNnv2bKuqB/8JE7AmOJjf/+ILNnLUKNy7dw9arZZ/8MEHdD6DgkiFMmQIlRX800qilStpnB0+DDkwEBs3bJDS09MBQLSrTLeKzExqc3ij0sbiAIgYKJVKXrduXY7WrRkOHKCykPnzS5eRVzVq1KB2n+VBbCxJ0bt2tfhnDw8PvP766yUY+KlTp7idnR0EQSgajI0b0705YQJ9Z1ISVCoVRFGEXq8vmc02Q15eHjMYDNb3UxCoPVlhECQ7Oxv37t1DUlISUlNTkZubKzZs2LDoWtapQ/XnpeD69et827ZtTGAMkr8/r1unDnP38kL3Hj1I/t6/P/1otaRYaNeOnu3JycDPP1OpzooVNCY/+4wCdnv3FtVkF6Jjx47YsWMHBgwYgKZNm2L58uXYs2cPGGMQRRG+Bw7A9fZtRBw5AkEQHvkoCIIAURTBGIO/v3+JLgOhoaE4duyY7ODgIAwZMoQlJibygoICPsjZmQkTJ1JgcMsWUi5MmEBlSLNmUdmCry89y3buJKWCKFovSbACxhh69uypbNWqldLBwaFEJ4579+5h3759WZIkdVer1VcfWz1MFMUUURRj09LSfuCc309KSmoZFRU1evfu3e3btm3LunXrZmP+TJJlGYwx2aRgqmqo1erTixYt2rJ79+4xL730kp250q158+bw8PCwW7FixSNjmGPHjqFr165o2bKlbVhY2GiNRrNVrVbvVKlUEzt06GBf2v3+CIsWkYKsip6ZCQkJUCgU9z799NOkKvnCajyVqCb71ahGJSHL8o/5+fm+jLFQtVq9RqPR1ExNTf0iJiam3E6xTx3s7elnyRKqz/sHyb5er8fZs2fRt29f0dbWFtevX5fXrFkDpVIJzjlkWYa9vT2bNWtWuaTUtWrVKmF8JAgCunfvLpovc/DgQURFRcHJyQn+/v7w8/Mr3TDpn0JICL2wFy8m6WR50aYNTRBXrKDJmTUolTQxAGhCbWNDtX4aDU1uKzhRA0jy26dPH8TGxvIbN26wpk2bPr21+zodBTyGDrW+jKcnkQK9ns5PIerWrYv8/HzxwIEDGDhw4JPth6Njxa6vBTg7O8PW1hZpaWmU1du3jzKWpcFgIJXOhg1A587IPHIEyQsXokdgIG+9fDmzMZV/mOHixYtieno69u/fjxdffLHE33U6HaKjo1mLFi1YqWOSMYwbNIiFLliA7YzBwcFBHjlyZHECWL8+7eOZM08UCCkTmZlkpNajB9CmDcJCQhAfHy92794dzZo1g6uF81Amfv+diNoTlgdlZ2dDlmX2qEe8QkGZ/Z07/3clDno9BY3Cw8u+T3/8kQwizVtIlgOccxiNRlZQUICQkBC0NbW+tLUlp/ytW+mZlpGBDseO4e+//+YXL15k3bp1s/qd7u7uPCEhwfLDp6AAcr16+O2DD6QHANPr9YIsy3B0dOQuLi6ym5sbGzZsmODAGPDXX1T+UkYgND4+Hn/99RdzdHTEW2+9BfuZMxlq1yalDud0bjp0IBWBjQ3dHxs2UMebQYNIbfPNN9RBxcuLxu+ZM9RWdeJE8vYoxPnz52UvLy8hMDAQCoUCrVq1krVarcw5Z7Is8xhfX1xv0oRpHzzghZ89emfKsozc3FwhIiKCv/HGG49u0OzsbOzZswft2rVjA3r3ZgqdDk2++IJBECg4/Nxz5DFw8CAFJydOpMDTpUuk9Fu9mgLMFy6QSuHjj6lMopztOk0tCLVaLRQKBVxcXODh4VFMHZaamgoAsRaIPtRqdSaAx80XjgP4TqPR+ISFhX0aEhIyqW3btkLPnj1tbG1toVAoIIqi0Wg0ugFIffw7qwJ6vX5mTExM/+joaJ8mTZoU+5ubmxveffdd3LlzB1lZWbxmzZps7dq1uYmJiY4KhSLbaDQWAIBWqw3LzMwcjLI42Y0b9BwrZVxUFJcuXSowGAwV7DlajWcN1WS/GtWoJArrraab/T9To9G8uWnTpo1z5859VKv6TGL2bPr5/Xeqd75oqSVtxREdHY3o6GhTloX7+fnJHTt2FAGgdevWgk6ng0qlQkZGBuLj4xEYGFil9fZt2rSBo6MjYmJieFpaGj916hTbv38/UyqVXBAErlAo0KNHD6GttT7sVYUDB0gKuX17MVO2cuODD2hiOWlS2a3WAMrEANSeLSSEMt3r1lEWysenwpt//vnn2W+//YbU1FS4urpKvXv3Fp+4R3tVY+tWCm6U1pLI3p5IxuXLxSTZrq6ueO211/Dzzz8DwJMR/lOnis7/E0ChUEhubm4iduwgZceUKaWv8OqrVMqxfz/1mk5I4MoZMzCxWzeGbt2ovGD69GKriKLIAbCrV68iKCgI3o+Vlezfv1+SZZmZ14pbg51Khc6enmg1ezbs7e2FEuNYECho8eOPFBCxVGrxJEhNJQK1dy91HygMSrVo0QJ//fUXQkNDUaoZVmnIzKTs5xPClMn7/vvv+bhx41itWrWo3OLGDfo9efITb6NMiCIpH8oi+pwTMS6tXaUVMMYwefJkrFixAo0LperFtv/JJ1S33rYtBEFA8+bN+fHjx1mHDh2sZvdbtGjBrl69KsHkI1AIWZZxYO9eqNq1g1PjxujRtq1Qs2ZNODg4gFFkkpY3GMhpPieHgp9l4PLly1ySJPbBBx/QO8nDg3wbBg6ke2vWLOCrr4jMM0ZSa7WaMuKOjjReBwwg47s9e2ibLVpQVj8xkUw7NRrA0xP5+fno0qXLo8z30KFDBZi6TBQUkNfB+fNWzdn27duH8PBw7N+/Hzk5OcjIyJCyMzOFVm5ufFBCgiC0b0+lA23bUjs389LD9HQKIg8fTj8ZGUU+IiYMHUp+BDY2NL5eeMHqeZMkCaGhoThz5ky+Tqe7BuAeY8yBc95Ar9f71a5dWzd8+HAnT09PRERE6GRZrmH1y6xArVbfA/CWRqNZGBYWtigiImL0yJEj7Ro0aIC6desaYmNjB4KM/qocarW6QKPRTD148OCfAQEBDubBb51Oh+3bt+clJSVJsiwfkGX5ZQC/AVjw6aefJpp9zbWUlJR8AJbdYgEaf8uWUbC/it61siwjOjpaIUnST1XyhdV4alFN9qtRjSqCRqNhAH5xc3PjxaSKzzK6dYOcno7M9HQYo6Nx4u5dnpeXx/v27StkZWXh1q1baNq0Kfz9/aGwIivT6/W4fv06EhIScPXqVfj4+EgODg5s5MiRQqNGjYpN1ExRfktZ+qoAYwyNGzdG48aNGQpNqLKzsxETE8NcXV1ZZGQkDh06hL///lt66aWXxMfJTpXg558pQ7JlC/XOrQxsbanH8uTJZDRX3ux6ixaUjTE5/wM0AU1LAypg0ubv74+2bdvKCQkJPC4uTly+fDnmzZv3dBkhrl1r2ZjvcWRkFHU1MAsMeHh4YMKECdi8eTM8PT1LmuOVF3fuWC63qAD0ej0UCoWgjYyUMHWq+DhJL4G4OJIJF3pc/Pzzz3JaWhp7+eWXGerXJ0Jy+jQF9MaMAdq1Q3JyMlJSUhgAuLm58bVr17Ju3brxRo0aMW9vb0iShJs3b4pDhw61OtaLoWdPoGtXOObmWs+AiyIRhnfeofuyqpCQQGUCAweSD4aF8TFkyJDKfffDh3R+q8CbRafTQZIkZGZmFknSRZHO17x5RKoqozyoCL78suwOHrGxwNtvU3CyMmUPwKPgt729ffE/2NhQcEMUSeWUlYXBX3wh3Lx5U166dKnQp08f3q5dO/a4gsjT0xOnT58WHzx48KgMJTIyEvv37JGfP3mS1Vm9mnn7+VmOuMsyPX85L7chYmRkJGvUqFHRu33mTFI5mNCxI3Vm2Lq1SBHn7U1qjexsGmfnz1OHjOBgCviaMvsFBST9nz4dp5o141miKAQ8Ju1/hIwMUg2UUtaWn58Pg8HA7t+7JzVJS0OjVq3EVl99BcXzzzP24YcUBAsIoDIDc6Sn03MjKIj2NyODzGNff734coJAgebISGppeuaMxa4UGRkZ2LhxY75Wq43S6XRfANhZaLYHANBoNKrk5OS3fv7556/ffvttZVxcnCjLcq8SX1ROqNXq+wAmLliw4LetW7f+FhgY6ODv7+/44MGDF/EPkf1CHNJqtfeio6MDzLP7oaGhSElJOWM0Gkeo1ep8jUYzRa1W51pY38vV1bV0Df/9+3SOH78WT4Dw8HAoFIqwuXPnplfZl1bjqQT7p2pZqlGN/x+h0Wj6KpXKLU2aNHGytbUVkpKS9CNGjLB3cXH5t3etBLKyshAVFYVWrVqVmIBlZWXhyJEjSEtLk7KysgSfiAg2cts27Fy3TnKpVYtdvnxZKCTOUmxsrKjT6WBra8udnJxkW1tbITc3V9bpdEyWZSZJErO1teX29vZ80KBBwtNuYGg0GrF7924pMjJSHDduXNWWZOzbR22dtm17crLAOdX2ens/Wbu9xYspC3XrFmVAKyg53717N8LCwuDi4oJ3qyCDXSUIDibPiddeK18gRKu16NgNAOfPn+fnz5+XZ82aVXGpjsFAJlwvvVTkvl1BFGbouIe7O580b54gqNWUBbSGH38kAhcTA63BgODgYJw4cQJz5swpTtI5J2Ly+efApk3IdnXFsvXr4ejoiNmzZ+PixYuIiIiQ09PTYTAYBJVKxW1tbeUZM2aU/0CmTKFgx4kT1pfhnJYJDqaOFE+K4GAiMzExJYhycHAwjh49yl1cXPi0adMqF5mKiCAZ89y5T7yr165dw44dO9CvXz/eqVOn4jdqdjb1NO/Rg2rq/wkYjWRaeO7co8CQRXz7LRH+776r9KYSExOxdu1a9O/fHx0fN8zjnNqNjh1L5R2Fz7Pw8HDs2rULU6dOLWHOyjnH0aNH5ZCQEPbmm2+yrVu3ShkZGeKQevV48//+l7HoaMtjn3PKwN+6RYqGcpqMbtiwgcfHx7N27dphwIABUNy5Q+qYI0eKFoqOpgz/zp1FZUEREXTejhwhyX9AAAW3fHwo+LpnDy1rYwPk5eHhsGFIun8fnocPw8WS6ur77ymwYE0RxjlOfvYZbPR6uXN+voBLl0jFpVAApb3LTK1B3dwokMEYBUz37KEfaygoIKXLK6+QErAw8GI0GrFx40bt/fv3V0iS9IE5yX8cX3zxxW0/P7+Gt27dAoAgtVp92foGyweNRlNTpVKt1ul0o0CB/bcBbFKr1f+IWZ9Go5kRGBj49fDhwx9Fw/bs2aMNDQ2dp1arS+1fv2jRos3PP//82E6dOllewGAg9cTcuVUm4eecY8WKFTnp6ekvq9XqA2WvUY1nGdWZ/WpUowqhVquPaDSaNteuXRulVCrbSpLU+erVq37du3d/alKesizj6NGjuHz5MhwcHPixY8eYnZ2dZGdnh4KCAmi1WtFoNKJx48ZS8+bNBV9fX+br6wusXIlXRVHEW2+h54oVMHAOJycnkXMOnU6HzMxMdvfuXbGgoIA7OjqK7u7usLGxgdFohLe3NyuRmnlKoVAoMHLkSDE+Ph7BwcFVQ/YliSZ4U6ZQRqcCqgVZlnHixAnEx8fLer2e5ebmckEQmIuLCxs3cSJstmyhOu7HnLRlWca1a9cQEBBQwgipGD74gDK82dlk/HP2LLWLK20dMwwbNgxhYWHIzbWUsPiX8NdfZHBW3ltOECj4cuZMiSBM+/bt2fHjx8W4uDj4VdQ0zUQoKuF9kZycjF27dvH09HQ2dOhQ1tzfn7HBg0s35bt5k7Y1cCAgivh982YeHx/Phg0bVjIbzxgt+9JLwJIlcP77b3Tq0kVO9fBgAFiHDh3QoUMHAQBSUlIQGxvLnnvuuYpFLBYtokBKaWCMzCOXLCHCWdnJbGYmkbjPP6eM42NEX5ZlHD58mAcEBLBKO/ADwPLlQO/elV7dhIcPH2LHjh2oUaMGOnbsWHJ/nJ2JPP3+e5U5b5cAY6RSKA2zZ9M98gSBPJ1Oh23btgEgVZDF/fjxR8p616xJpDkgAC1btsSxY8ek9evXixMmTECNGjUgyzIKCgpMKjCm1WrZt99+i5YtW2Lc2LFwMBpZqWanv/5KhH/JknITfQCYNGkSu3v3LtavXw9ZljH0hRfomWswFPmfBAQAL75I0vYRI+izc+dIfdWtG6mH5s6l98GMGRSsbdcO/MYNhBw4gJTNm6XI7t2F2nfusEFjxlB2ffz4RyaD4Jw6SVgaI9evU+nHlClos2YNYvv0oWdPeZ7jGRnUotDBoYjoAyTjHzSo9HXt7ChQ0awZGRAmJkL28sLOnTt1ycnJlyRJ+qQ0og8AkiTNunXr1m5BEHbJsnyl7B0uG4U1/i9pNJouAM4AWAFghUajAYA2arW6qtvxnb5z545s/kH9+vVtw8PD5wAolezr9frrDx8+NMIaJ7t+nZ5nVkwxK4P79+8jJyenAEAZ5i/V+L+AarJfjWpUMdRqdQKAZQCg0WganD59+tKJEydcS3Ov/qchyzLCw8Nhb2+PvXv3SgqFQhg/fjyrW7cuy8rKQkpKivjw4UPY2Nigfv36cHR0hPi46YCTE2XgYmJga2sL20JpLmMMtra28PLyMskpnwlSXxY6dOiAU6dOcb1ez8rlkmsNskwE+vZtyqKVQ9ItyzLu37+PixcvIjY2Vs7LyxMCAgLg4+ODnJwcoWbNmoiKiuJL//wT/RIS0HrVKibQJOYRVq9eLaekpAg9e/ZE97L6RgsCkYukJKovff55yvBs304TzFJIs16vB0CZnLy8vCdqAVYlyMujTGgpdaQlYGNDk28L10apVKJHjx78zz//5LNmzSpZe14akpMrreCIj49HUlIS6927N1qkp5Ms/sED65L469cpCxwcDPj5ITU1FXFxcWzKlCklau+LQRSBDz+EoVMnZP7wgxD4xx8k4zWrrXZ3d4d7aUEGa6hdm0zJ6tUj6bE1uLkR2WCMgk7O1ktXLYJzckM/eZLIl4WMaGJiIvR6PWvevHnly020WjIoK0eNd1kwqamGDRtm3eRy7Voqsblypeo9DTin875vH9C5s+Vlbt4Ejh0jglrJWO2dO3dw4sQJ5OTk4IMPPigp4zdhyBCq3T99mq7/vn0QBAGzZs0SDxw4wDds2MBkWX7kPC9JEtzd3eHk5CTn5OQIfn5+osPevRSUSEqyvL/r1tH1++wzCjBVEL6+vhg2bBh2796NF154AYJJkm+uVGjfntrpDRxIRDghgX4vXEiS93HjyJ9g/nxa/uxZXJ42jZ8+fJi9u3Gj2PbECdg3agSn0FBqHWoy9OvTh8bG778DzZvTullZpMyysyOizjng4oJTK1YgLS0N/pKEMp/GGRkUJDAai6tVjEagdWsy6CsLokhBiOxsoFEjPJg3D1GZmQrO+TC1Wq0va3VZlu8U/p5fVmCgolCr1WcBMI1G0xDA+6AMf0FVbqMQVwsKCsT8/PxH93idOnUgy7KrRqNpo1arQ62tqFQqm9WoUcMyH8vIIPPIgwcrPQYt4cKFCwVGo3GZWq2Wy166Gs86qsl+NarxD0KtVsd8+eWXK4xG43xLfWD/KciyjL179+LatWsQRZEbjUZWmAnhLVu2RL9+/R71J3ZxcUG5ywwaNqTJWEoKkYGbN0vPND7D6NixI27cuMF/+OEH3q1bN+G5yhjp5eQAo0aRqVU5X9ZHjhyRQ0JCmF6vZz4+PnKTJk14//79oVQqizGUnj17sri4OOzZsgUsNBRtrl0DWrYEAERERCAjI0Nwc3NDTEyMZN6RoFSYyO6+fUQyzpyh/Y+NtVqra2Njg27duuH06dP45ptv4OXlJY8dO1b410j/7t2U2S2lptUiPviAJLJTppBxnxk6d+7Mbty4wZctWyZPmjRJKLeDuyQBU6ciPz8fa9as4UFBQcyqVPMxBAUFITMzE8eOHcNz06dDtXGj9XZLd+6QkuH06UfBhYsXL8q1a9dm3t7e5Zoh5jRvjuiAANSSJLQ4fpzcwqdOLbfCwyqSk8s3SQ0MJPm20Ugkt7zYtYuu3Y0bROSs4OHDhwCAHTt28I8//rhys+bwcJp4V8G9bVJa5OSUoiq2syO3/JdfJrVKVbe63LbNYg96AETyr12jQMMTtPk6duwYHjx4gDFjxlgn+iZkZwODB5fo4z5w4EDWt+//Y++8w6I4uzZ+P7PL0gQURJoFe4UoaixYsBt710Q0mmKvidFY4oao0WjU+BqNSey9xRZ7EkSMYldQwQYiIkXpnd2deb4/Dr3uwmrUb3/X5ZUAs7Oz03bOOfe5T7ecJE1KSgrUajVsbGwYALZjxw4cP34c7335JWSNGhV9vh0+TMmaefPIXK+MWFpa5iZnUlOpcp93HzZtSq07166RN8t331ECIiGBxvC1bUtB9cqVdHxNTBAxcCCrd+WKKLe0lNnVqUPfq+npVJmvWZMUA/fukQojIwNwd6d71aZNpGjJdtPPwt3dHWvXrhXWrFmDefPmFf9h4uNpHenpucmHbOLigO7dS27vKLxzwC9fxvmzZ6UPjh173PLGDQsolQlavHJM1n/vav9muqFUKoMBTMn69yrWz5cvX/7k5cuXDWtktcLZ2NigXr16GSEhISu9vLy6FhVYe3l52clksgHVqlUresWrVpGSQwcVSmkkJSXh/v37kCRJhxutgbcZ2bfffvtfb4MBA+80Fy9e/KVq1aqVXV1dX5lDvyRJWL9+vfjXX38JN2/eFH18fFhGRgZGjBjBWrRowdq3b49OnTrB3d2d1alTRyi3ot7cnCSXzZtTUFCnjn4+yBtElis0yxoRyB49esTr1q2rfZU/gEadEAAAIABJREFUNpacl0NDaX6zFtXEa9eu4cKFC6x///5s8ODBaNasGatXr55Q1HnDGEOlSpVgYW0tBV66xMQ9e6SKgwaxKzdu4PTp0+jUqZMUFxfHTU1NmYuLi24HXKGgypejIx1bV1eatWxmlpNQyEvNmjVRv359ZGRkICQkhFlYWKBqGVz+9cKsWfRP1/dnjCSzLi6FzKYYY2jatClLTk6WTpw4wapUqcIqa5NM2LkTALDx+nUeGxvLnj59inbt2mk9rrBWrVqwnDULpnFxMBs3ruiFMjIowKhZM2eEnSiKOHnyJNzc3Ji2HhmmpqYIDQuTNE2bssb9+lEguHYtyXhLC9JKont3qkTKZKX7Fnh4ULXS2bl0IziNhoLRWrXoeJVgMLl//37xwoULAgAwxphKpSpbe86xY6QaKehOXgYYY7h48SIEQUCjkoJ4a2sKJrPHRJZHZZSXBQtILVJU4opzUmJ066aTcWde1Go1Nm/ezCMjI5mnpyfqaPMd0asX9e5//TUlHPOoc7Ir+owxGBsb50scVKpUCeLhw5J6+XKmHjMGVgU9Dk6fJin90KGFZtrrQkBAAPbt24datWppmjZtKqBGDfJMKTgRxcGBgufBg2mE4nffAX//jYv162OfQsHb7tnD2K1bwMCBSEpJQWhoKIJSUoR2u3dTIv3nn8lHJXsU4/z59PPmzZQQGTGCEgmtWtE+KqDckclkuHjxIubMmVP8M0d8PCUTRJHMIAvy119k3qrFtJfIyEhcu3aNzHgjIzX3goIe9j9w4LlcFBm8vK7j22+LrdZ7eXkJyJKSK5XKb0t9szeYCxcutLO3t2+S11+iQYMG8uDgYLuMjIwR3t7eRzw8PPL1u50/f/6zqlWr9vTw8Ch8oO7fp2TfmDHlT7rm4cSJE+mxsbHrvvnmm6N6W6mBNxpDZd+AgVeMSqVaGxoauu7x48do2LDhK3mPtWvXiomJibKRI0ciJSVFZmtrW8jQSO/0759bdfL1pYDwHcPY2BhdunRhbm5uOHLkCF+7di2rX78+HzBgACtRChwWRg+WX39NAZMWbN++XXr27JkwfPhw7R6Ms2jcuLFQ49df8bxXL2HnrFl4XrUqXFxc0LRpU8Hb27t8pnlyeW7v6ccf06imgwepmpoVyGbj4OBAc+CBVzJJQSuuXKGAIc8YPZ14+JAerotoXRAEAT179pTZ2dnxP/74A61bt0bnPLOxi0KqUwfHgoPxknP24Ycf4uTJk3zfvn38ww8/1FpHnlSxIoyKk3DHxdHD//nzeGZiggu7d4smJiZISkpiGo0Gbdu21SnJ8/777wunTp0SUamSDD//TOPrBg6kcU9LlpR9qkCzZhT0TJ1a8nIVKlA/fNu2JMkvKdD47juSNN+/jxJ7tAGEhIQIXbt2hZubG2JjY7F9+3bUq1cPOpmFShJ5MJRnDGMBGGOIjo4ufcFWrUhxkpQE7N+vnzc/c6ZoL4mkJDJ4/Pvvco34unjxIo+IiGCjR49GzZo1tX/hsmVUFS8qAC2G6tWro3qfPsKTtDRs374dZmZmSEpKwsSJE1Hl8WM6bh9/XO4kTWBgoFitWjXZqFGj6Nk5NZXUL9cK+Mk1bEjJt8OHgVGjIHXogPN37/LLvr5MJUnszsGDaDBgAG4PGcJPvfceA2OkrjM3p0SBJFEwbmGRO+pw5Urye3FwKNUzQqFQwNjYmMfExDCHAl4uAEhlsGIFJa5mzwZABQMAuS0uP/5IKogCyZ6YmBgEBgYiNDSUx8XFiWlpaXIAsLOzEy9fvixTq9VyACONMzNvZWU1r4OxfeB8eTGbOyzrv9NK/FBvAZmZmdeio6MHAcjJyBkZGeGTTz4x8/HxqXfp0qWH33333aiFCxceyfMyjZGRkSrva3JYuZK+c/Vo0BkdHY2goCCNRqNZrLeVGnjjMQT7Bgy8Yr755pv133//vdvJkydHW1lZGTk6OkIURQAoV6U/IyMDJ0+exNOnT8XU1FTZjBkzYKlrr2t5USgo4BAEegj+7ruyzY1/w6lUqRLGjBkjhIeH4+jRo1i1apU0ZcoUoUjju3v3aL9MmkSBkhb4+voiKipKmDBhArSWiOehQoUKqH/yJOq6uUHj4wOFoyNWrFgBCwsLydLSUj/mkNmO/9l96JJEo/+WLcsxB+zWrRt27tyJc+fOFZ6n/To4dozMBcujXKlTB5g7t1jH+2bNmrEqVapg+/btMDIyQvsSDOXSv/8eId27Y/S0aXB2doatrS3bsGEDu3jxItzd3UveDkmC8OWXuNWxI78fEcHb3L4tVKhQAaamprC3t0dUVBSqDBoEoyZNcGPSJH7mwAFWs2ZNITo6Wnr58qXQoUMHSZfe9Bs3bkjnz59nnPPcnWdrS0Hhpk3A1q0UjI8erftkgePHtVdaODvTefXsGcmtCx7LU6dIrj9vHv3T4ljb2dnh/v37kru7u1C1alW4urryAwcOYPr06UyrMYIAcOsWyfh1CVxLwcrKiqtUKu1O1lWr6LOnppa/jSAzk+T5RfG//1FwXI4JMr/99psUGRkp1KpVi9esWVO3i/GHHyjhkJJCybtWrUp/zZMnQLNmqDl4MIY8eIB9+/YBAA5//z0+j4uD4OlZfLuCDiQlJTErKyuObF+ahg1JWaNSFVZcLF0K9O6NzB9/xLErV8Sa/v6ySYsXY/25cwiJjMTpkSMxbcMGZm1khMq//ZarRrCwoFakkBBS7KxeTW0qT5+Sr0Xt2qTKcHUl48RiMDU1FaOjo+WFgv2kJECppPVMnQqJc/hdugRfX1/OOWcVKlQQ5YyxPjNmCFbvv49APz+EhIQgJiZGk5aWJpMkidna2oo1atRgzZs3lzs6OqJixYpgjMnOnz8PPz+/u19//TX1p3POwdhMAABjzQGEgPP4vJsjk8mWZD0PvQuz3u9FRkamo0DgzhhDp06dFHXr1lXs3Llzz/fff79NrVYfBAX6s1xcXAqbOQUHU9Jz5Ei9buCpU6dSJUn6RqlUJup1xQbeaAzBvgEDrwG1Wv2lWq3+9Pfff8fMmTOxOsvgaeLEiWUyvtq+fbv4/PlzwdzcHB4eHrK6dev+Z+Z/EAQK/OztSU6YllY+2e8bCmMM1apVw+TJk9nGjRv56dOnMWDAgPwLhYSQ0mHpUqoklUJgYCB8fHyk2NhYYejQoToH+hqNBrGxsTA3N0daWhps586FsGgRTvXrJ6WlpQmdOnXS/xSIjh3pX0oKKRgkiSr9VlawbtoUANC2OMOvV0lSEgVi5a287tlTqkrFyckJI0eOxK5duxAfH4+ePXuiYHtH5NOnUERGosb770vOzs4CQEmjFi1a8KCgILi7u5ccBIWEAGfOYNqNG+wfX1/m6+ur0Wg0TK1WC6rUVFY9Lo4ndujAnN3dcef0aTZ48GA0aNCAAZCtXLmSX7t2TejQoYNWZnQvXrzAyZMnhWbNmsHDwyP/dpmZUUU+MJCkyZUrU3tHwRndJdGwIRmTrVunXRA5Zw71OV+6lN8FXqMBgoJI6q+DrHXw4MFs9erV7OXLl7C1tUWrVq3YjRs3EB4erv2EhZMnaZSjHrGwsOAqlYoDKP0gWVqSaqVBA0qelFFeD4Ck8mvWFK7sb99OCcovvihXwiwuLk6oUqWKNGrUKN3vP5aWpB6aP5/aOvbsKf018+aRP8rx46hfvz4WLlyI6MuXcX/2bJxt2VLq2b17ue+DYWFhiIyMFEaMGJH7SyMjCpxDQui4FPgccX37ImHkSN7OzEyoYmsLWXAwRFGEkZERuK0tTK5fR51u3WhCwOTJ+V9fqxaZ3nXtCnh70/ds5cpk9qrR0P23BKytreWRkZFS06ZNcz57+KNHqPTNN8h47z2cc3DAi19+EZOTkwW5XI6BAwcye3t7PHr0SBa7YAHioqOx/f592NjYSNWrV+cuLi5yR0dH2NjYgDFWKNsniiKuXr2alpmZOSnfHzj3BQAw9gcAMwA5N2gvLy9nALUUCsWfc+fOzSzxA70dBMbExCgyM4v+KLa2tvj0009Nrl+//tnDhw9HCYIg1atXT9GwYcMco1sAgEYD2VdfQRo9GhygZJKWlDRO/enTp4iMjEySJOkXrVdo4J3AEOwbMPAaUCqViV5eXi1kMtnfP/30kyXo4S78r7/+sh84cKC8NOOijIwMrFu3TqpUqZIQFxcnZWZmyrp27Yr69etrb673KhEEcvQFKOCfN6/ww8s7AmMMjRo1Ery9vaFWq9GhQwfY2tpCOHKEKpG7dhX7IC5JEv755x/cuXNHVKlUAuecVa1aFYMGDYK9Fr2ReXn27Bl2kpQ+pzpoV6kSb/X8OVL37WMfzZ+P2gV6z/VKhQo589NfzJyJsJAQnO7ZE3VDQuDv78919gkoLydOkJz2s8/Kt542bahy5uFBD9rFUL16dYwdOxYHDhzgK1asYDVr1uQDBgxgZmZmUKlU2Ld5M7fr1o31KBBouLm5MT8/v5K34ckT6qUNDIQcQI8ePdCjR4+c7+vMWbNgfPEi89+yBX8ePw6FQsGzAn0AZGh24sQJacuWLRgzZkyRng/ZqFQq7Nixg7/33nvo06dP8cesUSPq4798mQKcHj1IvaJNlZ8x8q8ICSFZqjYMG0ZJs2nT6PVjxpCq5JTuI6EtLS1hZGSEoKAg2Nra4sWLFzm/1wrOqaWlY0ed37s4njx5gidPngiltYLkw8IC8PKic0OStPIBKRIfn0K+FHj0CPj+e9q/5UzWWlhYICkpqewBdufO5J+xvDjldx44p3tu3gArIQF2y5bB+rPP8P2TJ4LdrVuQy+XgnKNu3bowLc0PogiioqI4AJYtd8/h5k1qdykQ7F++fBnXY2LQr00b2C9YwFi9ekBKCroFBvLTp08zJycnjgYNGDZuBD79lF6U9Z0piiKSL1yA1ciRYI8fA+fPU1tFRgYdu6NH6XP36EHftQXOy/T0dMjlcjx79izndyeOHuU1589nNxs2RIBGg4aCIDZr1kzm5OQER0fHHMPIli1b4mXTppDb2WHejBnQdvyIv78/JEm6rVQqLxSzyBAAFmBsNgAjcL4EAJPJZFEqlWqlNu/xFhAuiqLxihUrCv2hQBAuAyU+cPXqVVwtMPGgYlwc3MPCcCIggHxJyklejxhRFCdrMyHBwLuFIdg3YOA1oVQqb3h5eVkDsAEgAeBPnz7dsGrVqkGTJ0+WVyqhP/Lo0aNcJpMJL1++RMuWLYU2bdqU6YHlteDtTQ+S+/bRg31Rc5Xfctq0aQMLCwvcvXtX/PXXX2UVU1Px8ZYtCFEq4TJ+PFR5xu9kI0kSNmzYIKlUKta+fXuZJElwcXGBmZlZmR6KNRoNNBoNRowYwW7cuIHmzZvjwoUL0sP69THw9m2ZomrVsgcDOnDnzh0catwYaNwYQ2vVQoP165H2008M9+/TxIbXsA0A6AG4tJ5wbXnxAoiIKHUxe3t7TJ06lb18+RJHjhzB77//Lk6dOlW2YcMG7hgdzXpLEswL+BeEhITwrMkcxQfWs2dT9e7w4cJ/8/GBsVIJfPMN3rOygoOjIzZt2pRvXY0aNUK9evWEFStW8JCQENStWxeccyQlJYFznj1aE8HBwfDx8eFGRkZSv379So/aGaNkyLZtJCtu1owSIyXIiXM4f556hbXF3R349VeqNH/3HSUWypHYrFatmvT48WPeoUMHWUZGBiwsLCRra2vtTs7QUAqG9Wg6+ccff/CKFSsynVUwn3xCRm0rVmhX9S7I7Nm0T/O6ewcH02z70nwStCAzMxMxMTHQuj2iKJydaRSmuzu1DJX0OQcOJAPIJUvo5+RkUoZMnAijHj3Q5uxZHDt2LGdxuVyOcePGwdbWVqdNatmyJbt37x7fuXMnnzJlSu5507cvJV6ykCQJBw8e5MHBwWzY+PGovngxw4wZdJ14euK9oCB2+vRpdOzYka7ZHj2odeGnnwBra6T07QvfL77g15ycWJWZM1Hj9m3pg7t3BaZW07IzZtAbMUaf29YWoaGhOHfunCiXyzFs2DDZ2bNn8eDBAwAQfvjhB0gaDQbv2sWMx45Fz2nT0CE9HRUrViz6etdoYFuvHp1nWqo7OOfw8fFJzcjI+LrEhYAkMHYfQA8wVkEJPAfnRZgKvJ0olUq+bNmyl59//rldWdrxANA90sMD8PWFmx7bMi9duiT5+vp6L1iwoIgvFQPvOoZg34CB10jWDNmY7J+9vLxGAlDHx8ejUqVK4Jzj4sWLuHLlimb8+PFyAPD29sajR4/Y559/DrtyjAx6bWS7HW/eTBXKr4v//n9bEQQBrq6ucHV1lfHFi5Hq6IiAHTtw+d49/uf33zPGGBQKBZfL5dzOzg7m5ubCo0ePJFNTU/b5558zfYylq1mzJvr06cMPHDjARo0ahWrVqqFu3br0AHfwIFVhR48u9/uURmxsLACS7jfq1g3w9EQFxkjmPXkyPZzKZCR5fVVcukTnnb7aB377jYKG+HitTMpsbW1RuXJlHhMTI/zwww/czMwMvTt3hnlwcKFlHz58yIyNjSUUF+wnJFBwk+XrkQ9fXzKju3sXsLZGWFgYdu/eDSsrKw0KfJ/L5XK0adOG7d27F6ampjnqD8YY1Go1AMDY2JhnZGSwTp066daEb2ND/37/nVo4YmPJdb8kJUlMDAVu0dHaG/3Z25N6ZO5cUm6UgbCwMBw8eFAEVREFgAzM1Gq19sqTR4/Iy0FPc67v3LmD1NRU1qtXr7L5tnTrRpXezEzdR3I9eJC/Cs459QUPHJjP/b4saDQabNmyRTI2Nmb9+vUr387inFpHli4tebnRo3PGTYJz8nvo3JnORwDdu3eHh4dHTvJh37592Lp1K8aMGaNTwM8Yg0wm46Io5v9clSuTSZ+PD9LS0rBp0yYRgDBhwgRUqlSJPGzWrKH70yefwMTYOOe6E0URoigitGVL2LRrB/OFCxHk7S113rVL6BAaiq3794vXrl2TaTQaqW+fPgJr0QIYPx6RtWsjVqFAox9/xKPbt1G5QwfYK5WyEEEQly1bBmNjY96tWzf2/vvvIzI8HOYff4zzzZqh38yZkMnlUJR0znh7Azt25KoNtODJkydQqVQvAPxb6sKcHwNwDIx5ARgCxlzBeRE3u7cTQRDiUlNTyx7sb9lCijI9BvoxMTHw8fHJUKvVesqGG3jbYCX1dxgwYODVs3jx4v+Joji1SpUqqZxzHhMTo+CcKxhj4JzD1tZWql+/vtSlS5e3Mzk3ejTJ24uQtr3VcA5ERdF4pe3bgTp1wDmHRqMB5xzx8fFISEjA3r17UaVKFd62bVvm4uKiVQ+1LmzevBkZGRnSpEmTclccEUFS61mzyjVqSluOHDnC/f392ezZs3MVJ6JI/5YupcTP06dFutzrha++IjMrfZoZtWsH1KhBEuFSSEhIwNq1a9GoUSPcvXsXs2fPhunOnaRqyRqHB5A8d/Hixfjggw/wflETA1JSqHpc1HSLf/+l9pD0dKBKFURGRmLr1q1wd3dHhw4dit22jIwMPH36FBYWFjkTOiRJgkajQXp6On766Sd069atfD4Lq1dTFf7YMdpnxQUTfn4U/GhT9Z0+HWjRAujXj0anNWyo88QPjUaDFStW8AYNGsDR0ZHVqFED9vb2iI6OxoYNGwAATZs25f379y/5pOzcmSqvepo4snLlSrFhw4ayLl26wLis87MliQL0BQu0N0VNTKQgIvsa5Jzc/Zs3p0RNOa/NHTt2SCEhIcL8+fPLV9nPRq0mdYulJY2BK8ivv1L1v0kT2h8TJlDCok+fEhVFR44c4UFBQWz48OFwdnbW+p4cEhKCPXv2YO7cubmvSU8HatTAs4sXsfvQIV6jRg1p0KBBshwPD7WajpGzM/1zcMD6S5fw8uXLfOuWZ2bi899+Q4K1NRw3bECFzp0hSRIWLVoEIyMjfPTRR3B2dkZ6hw4Qb97Euc6dEdCqFTjn+DAoiNf+/XcGU1MEh4TA1taWWlQyM4HRo3G9Rg08cHXVjPT0LP2gREZSkl6H+8HevXvTHj169PU333yj3egZIFtb3gpAAoBW4Hyb1q99g1m+fLlvv3792jco6OGgDY8ekUJt0iS9+R5xzrFt27bU8PBw5YIFC96VdgkDOvJ2Bg8GDLxDiKI4E8DuFy9eVAWQCsBHLpeHazQa66FDh/JGjRoJ0MbA6U3lww/JTCsujn7+r8ay6RONhnrDq1cnw6Ssh2TGGLIk2rCzs4OdnR2USiVQkmS7nKjVal6hQoX854ejI83QvnWLpPSvIsDOQ58+fZi/vz9iY2NRNVvqnD1X/ZtvyKwwO5D18aHAXF8kJJCcVY8j0QCQCZqWppcWFhYQBAF3795Fq1atuKmpKcPt27kVxyzCwsIAAKdPn8b58+el6tWrC9HR0RpTU1OhZcuWQtP33iP3+4JBZWIiBXaHDwPt2uHhw4c4duwYqlevXmKgDwAmJiaoXyDhIwgCFAoFEhPJkDkmJibXYbwszJxJ18PPPwO3b1MlvqhjbGNDMvJVq4pfV2YmnTeiSFMerKxIYTF+PCk4dDiXBUGAXC7naWlpaNGiBcuuotva2qJLly786tWr7Pbt2+zevXt83rx5Ra84PJzuWY0aaf2+JZGSkoL09HRZixYtyh7oAxTMNmpE9x9tg/333gPGjaM+b4ASsKdOUTKlnPeIzMxMhISECB999JF+An2ATDdHjaIxpkUF+5s3U2tHkyaU8Ms26CwleB8wYACzt7fHnj17wBiDp6enVmMYq1atCo1Ggx07dkgff/wxvYmpKe4uXox/Nm2Ce58+3N3dXZa3RxpbttA1nZ5OI0EVCgxYtAj79u2TevbsKdSuXRsKhQJSZCTYuXOobGMDYc4cSsLUqAHGGCpXroy9e/eiffv2iKhZE6Zubvhg1iy02LYNlTw9YeLszBAQAPTsidrBwYCpKU1tmDoVoY0a4axMhkEtWmh3UIYOpckbWpKamorg4GBBkqSdpS+dB6o0XgZjHgAWgrGDANJKdJh7O1AV8nXQlqVLKcGpR4PjgIAAHhkZ+UIUxf/pbaUG3joMwb4BA/8xSqVSBHA57++8vLwmmZiYbKtevbqxRqPR38PTf0F2EObpSb2v/5au9HujycigynnFirnmYf8R4eHhiIqKYp06dSr8xxEjqJ/U3j5fdflVoNFoAABFznQWBKpoAcAff1BgPnIkBXHr15f/zU+eJJOsMWPKv668VKxI6ojk5JKDU9AIzdmzZyM9PR0WFhZ0QnTtWijgrVq1Kj799FPY29vjwIEDPDk5WWrVqpU8LS2Nv/Ty4mH+/tD8/TdzlqTcymFQEO3D+/cBGxsEBATg+PHjaNu2bamBfmnY2tqif//+OHr0KOvTp0/5VCcWFhTkHz0KPH9OVddvvyVVTzYyGcmEi1N4SBJV8KdOpcRBNuPHkwnZX3/lyLO1QRAETJ06VVixYgWCgoLQJGvOuiAIaNeuHatevTp27NgBtVrNNmzYoJkwYULhG+2jR+RJoKd7cEBAAExMTHTuGS+SpUvJwOubb4BFi0pf/vr13CkGfn7AoEEUTJcj6aDRaBAfH4+0tDQA9Pnq6sunxcYG+PNPqjIXNCRMTKTRfJzTderiQsdJy3ah1q1bo3Xr1jh58iQOHTrEp0+fzlgp9/IzZ86IxsbGgru7uwCQQubAgQO81tatbETfvrBr167wBdS9O10DCgUF/cuXw1GhwMyZM2nZiAigY0cIfn6Anx9l3AYMAL74AsJvv4Exht69e+PWrVu4ffu22CEiQmjQrBmTOznBwdeXpt8sWUIJDy8vCvQTEwGlElKLFtiflCT1/uADQatKc1ISmQ1Wq6bVPgQAf39/LpPJTsyfPz++9KWLgHMfMFYHQHsAq8BYd3AeV6Z1vRlkZH8f6sS9e6RiKiqpVUaSkpJw4sSJDLVaPVCpVKr1tmIDbx1vcQRhwMC7i1Kp3LdkyZIfVq5cWQMAxowZgxo1avzXm1U+tm+naoOfHz20L1z4X2+R7qSn0xeyqyvJev9jLl+mHFGRknBBoGDr55/pYbngHGg9YmJiArlcjoSEhJLHB3bpQv+dNIkq/SEhlAT6+++yVzMuXaJxh68CF5dSR1xlY2RklKPqgChS73B0dKFlspUPH374Yd5mbSaZm+Ph0aPSob17GeccZmZmosAY91y+XP7Q1VV69NFHrFmzZuzEiRPo1asXmupJHZE9Jkpv7SX9+9M+u3OHkjnNm+fKqmvXJrVJSkrhvv0DB0guv2tX4dnqjFFv9OjRFMy0b6/15sTHx0MQhCID0OrVq+Orr75CbGwsNm/eLA8NDS08iu/CBb3MaM/Gzc0N3t7eiI+Ph7U+VE52dsDevcDEiaToKY5Zs+g+MGgQXXfjx5PnQp06ZX7r5ORkrF27Nqd1yc7Ojrdv316/2c+uXSlh6eIC/PMP/U6S6FzauZOusZcvSV1ShntI165dcevWLbZv3z5pyJAhQkmJ9aioKObs7Iw6derk68/vPWECKhR3f1UoKCHRqhW1uVStSklvMzMaqWZvT+q3vOfCoUN0zg0cCFmXLjA1NUUf8lOQoVkzWidjpMiQJErq9uxJHinr15NyY/58eNepA9MHD5irtu0nT55QQkLL5A/nHFeuXEnNzMxco90blLAixi4D+BNAOhhzA+c3y7XO/whRFF9kZGTo+iKahDFokE4jRUuCc47jx4+nA/hZqVT662WlBt5a3l5psAED7ziMsbVyudwLwPd//vmndhHHm4wg0AN+tvPz20ZkJFWQP/+c3JP/YyRJQkJCAjc1NeUmxT0gtGxJD8mvwS/ByMhICggIgFYSRnd3cpY2NaXqt6kpVXO9vXV703//pYdeHYI/nRgyhMzQfH11e93z54CtrfYV00mTIMjlaLBkiTB37lxMmTIFH7RsKettZCS/smKFeLZNG+Hly5fs+PHjsLKyEvUV6AM0eq9MJnElUaEnjXSoAAAgAElEQVQCGXN++SU59/ftm2sK9+OPhYPnzEwK1m7dIrf/ohIPtrZkdGZjQ2oLLYiJicHjx4+h0WgQHh5e5DIKhQIODg7o2LEj37VrF+Li8hQVRZECTH0ZP4ISY8bGxlJx26MzVaqQ+uOvvyiIL47MTFInJCTQ/vv118JJFR1JTEyEWq3GF198gZkzZ2LChAmsSpUq5VpnIQSB9v/48fl/d/IkmT4+ekRJzTJOalAoFBg9ejSCg4OFJUuW4PHjx8Uu6+bmJoSHh/PHjx9j7dq1vEqVKhg/fjyr0KkTtVMUxcWLpHixtqbERZ06uRX0ihUpWfHdd/nPeUEA/v0XCampGLhvH0ILGn1m+U2AMVLLDB9ORqKPHtF6BwwAZszAnZs3xU6dOpWqWMhh0SI6j7QkLi4O6enpKmhjzFcanKvAuReAagBOgTGXcq/zP0Cj0USlp6fr9qKICGpZGjRIb9vh4+Ojfvr0abBarVbqbaUG3loMwb4BA28o8+bNWzl//vxvjY2NW9ra2lZYvXp12uLFizWHDx9Wpaam/tebV3Y8Pal6t21buapKr5XssUd//61XSW95CAgIwPPnz9mQIUNKfpIbN47k8/oKLorBw8ND8PX1LTQzuEQcHKgSxRgFICoV9Xyv0bJQtG8fBYevkv/9L7fHWVs0GmCtll5VkkQS/azEAGMMVlZWaLB9O+ocP45eI0fK5s6dC1dXV0mlUmHQoEF6i8x9fX2lc+fOoUqVKq/GDdvSkir2P/5I1f3Fi6mKuX07/T0tjRI/9+8DYWEUDJVE27a0X7/6Squ337lzp+jj45OruCiBdu3aMZlMho0bN/KcytzBg/SeWno3aINKpUJmZqag19GpcjkpKaZPL/rvUVFkoti3L6kjNm7Uy3WjUtG47rVr1/J169bxFC1VMDpz5Aglgn7/na6X7t0p0N+2jZKv5UwwVKtWDfPnz0fDhg2xp4Qxfw0bNkRqaqqwa9cuMMbYsGHDyIivYkVSGKmLUEoPGABkKbDQrRvg5ETTNmxtSTVSVOsTACgUODhsmFgxIQEuXl65vzc1pffKi6cnKWp69yYn/c2bkfTRRxi6fr2soF9HiQwZolPAGR0dDblcHpA1ZUg/cP4QQBMAYWBsHxh7q0x+JEmKTUtL014yn5hIzxZffaW3lsDHjx/Dz88vWaVSdVUqlTpmHgy8ixiCfQMG3nBEUTS+f/8+UlJSVkmS9GFAQIDixo0bb7uJDVUj/vc/enjToZrw2rl0iUaxHTpEvaFvCNmy5B07duDgwYPFL2hpSQ+ACxbkmwetb7KlokFBQWU7N7duJSnqvXvUpwtQUJgVUBQiLo4k4r16lenttGbpUnoo18U3ys9PO2+KtDQ6Nv/8Q14GAB2jbdsoOPvzT8THxyM8PBxXr14VPDw8YF/OOejZXL58GefOnRNat26NcePG6bm0nwdBoD78PXvIaO/yZTIfW7OG+qubNiUHf23bCJYto+W1qJ4pFApmZ2fH582bh9oljQXM4rPPPgNjjHlnK0xu36bWAj2yZ88e0cbGhmuzPTqxfDkF8cePF/5bz540tz4wkFpoSvGg0BYnJyf07t0bI0eOZJaWlli5ciWWLFmS016kV/bupWA/NZUSg+vXU9JYT+1tISEhePLkCa9du3aRiS9JknDx4kUJoKBfkiTkVMwtLEjtleVbkI87d3KnhFhaUpLiiy/oeDRtWvSIzSxa9uol2z98OKTExFxPEldX2g95X5eaSkaAvXsDZ84gaedOnFEo+IvVqyWjxERKPpbG6dPAjRuknNGShIQEaDSaQK1foC2cvwSQAWo1rgfGyj+r9vURn5aWVsyXVhEcP05munoaq5yamoo//vgjXa1WD1EqldGlv8LA/wcMwb4BA284Go1mgkwmWylJ0kqFQjECAKpXr/7fucLpCxMTCtTOnaNq3ysMRMuMKNI4JxOTN06FYG5ujnlZFedSzb4aNqR+3p26GSbrgm+W1L1JkyYsKCioRDlsiYwcSQqK1FSSv965Azx8SPLjvJw6RYmBMsp3tUYQSPKsi3Te3Jycz0vj2DEKXvNy6BAlGAQBt+/e5evWrcOuXbvg4eGBjh076rbtxRAQEIAzZ87A1dVV6q6D4V25qFyZZMIODpTImDEDuHoVWLdOt2NoZUWBT9OmVLEugT59+giRkZHs0aNHWm5iZfTr1w/Xrl3DvRs3SHrdvLn221YKly5dwvPnz2UfffQR0/cITggCVQa/+IKSTXm5coVcvocPJ6NDLU3sSsPY2BgtWrRA9erVMXnyZDZw4EDeqFEjnDlzBsuWLcORI0e0a+vRhps3gfPnSb6fnEzJDT21DEiShIMHD3I7OzsMHz68UOJLrVZj586dkr+/PyZMmIAhQ4YgMzMTXl5eyFHZbdtG125BjIzyt/NMnkwJpAYNKCFYvz7dB65eBfzzt1Y/evQICdbWkC9fTomnL78k2f706dRfD9B9ctMm+nn1aqB2bQTt3y91DAqC26BBAnr0oPcsjYsXS0w8FEV6ejpXq9WROr1IWzjPBOeDAfgDCAZjQ1/J++if+LS0NO12ZHAwcO0aFT30AOccf/zxR5ooiuuVSuU5vazUwDvBf69FNWDAQIkolcogALMAwMvL6ytBEDJ9fHwGdu3a1TQ0NBTx8fGZDRs2NK7zhgWjWtOlC/DiBVVqBw+mIE6Po2fKzNat9DB27Vq53KpfJdny5FKDfUEg+e7p01RZqlxZ79vy/vvv4+rVqzh58iQUCgVXqVTs66+/RmZmJoKDg+Hg4ABbW1vt+8PNzan3HaCxcykpwNmzVB3O7k8dOFDvn6NI+vUr2fysIBcukCS2JCIjafuHDs2Vb+7cSdW5Xr2QKQg4d+4c3N3d0bFjR70Y6CUnJ+PevXs4c+YM2rdvj86dO7/6hP/69fSZrlwBPvmEjqOHByV0PvuMAhxdzSMdHGj/BgWRwVkxVK9eHXK5HLt378bChQuhTe9yvXr1YGRkhMdHjqBhdDSEMpjoRUdH48CBA6IkSVySJKjVaqFevXpCYGAghgwZAisrK53XqRVVqlAwbGxMveB2djTq0NycJPw7d5IM/BXh6urKXF1d0bRpU5w/f577+/szf39/yOVytG3bFkVODclGFCmIl8mo5UilovaEJ0/IHd7PjxJEmZnkTVCzpt62W5IkZGZmssTERH79+nWpVatWOddFZmYmduzYIaWlpfFp06aRbB9k7Oft7Y2cdozWrYHY2MIrb9gw/2SJQYPy3xvOnqWK//Ll1DZy9Sr5V0ydCve2bREYGAhV06YwXbqUTGG//ZbaXSSJlATr11Of/o8/5qzyfIcOcPrwQ4b582nfrVlDSdPGjYtX0PTrp12CMv9+kwCUwXpeBzhPB2O9AESBsVEAToLzInb0G0N8WlqadjKwRYtoHKOePFMuXLigef78+RO1Wj1XLys08M5gqOwbMPAWoVQqn0iSNCUqKipo586dT319fTfcvHlz/v79+2MPHTqkyp6b/dYhCPTw4uREVXRd3Wz1TWoqcOIEBWJvaKAPANlyY63GKjVoQA/K27a9km2pWLEiWrZsyWvVqoU5c+YwAFi3bh22bt2KY8eOYdu2bXzp0qX45ZdfpJMnT0ohISHI61ocHh6OjRs34s8//5TOnj2Lc+fOIT09HbGxsbi1cCFS9u+nB+GqVantIyhIr8ZpJeLoSA/tixdrt/zjx6X3eXfuTCOzsh/0oqLIMT02FpGJifjll1+4XC6X9BXoA8CdO3f4mTNnoFAoeGc9y9MBUMAmSdR+kR3QbN5MVdn+/Sng6NgR2L2belTnz6epBT/8oHNVEUuWUDLul19KXMzc3FwEgL1793Jvb2/ExMRotXqTa9fg8+IFj4/XfaLY9evXER8fL/Pw8JB3795d3rVrVyEiIkLy8PCQ6tWrp/P6dKJOHVJLjBhBrSdmZiTxjo/XTZ2iDZxTO0VyMgXo/v5ARARqBgVhjIkJm9q+PXo+e4bad+5As3QpYj/9lFpWRoyg1oKff6YE79dfU6DZsyclJGfOpDFyV65Q4uzxY7o+suXoe/fSPVpPyOVyTJ8+Hba2tuzs2bNC9qj39PR0bN68matUKj5p0qScQB+gey5jDIsWLcKGDRukiA4dkF6UYsLfn1QV2fTuDXz6ae7PtWpR8nX5ckpiCALd42JiYL9sGSZs2oTfVqwAt7Kia+rAAfrsDx7Qa5KSSB2UlchSqVRQq9WCVcWKlOD18KDj06ULtQwVRVAQLfsGeNEUCTnzRwEYC2Dmf7w1pZGsUqlKzyreukVKm7Fj9fKmgYGB+PfffxNVKlV3w5g9AwV5Q69sAwYMFIdSqYwHkE9bumjRIvWdO3fW1K5dG+/pmJ1/Y6hShYKAuDgK+oODdaum6gulktzXz73ZKjhJknDhwgVYWVlpHwxOmEAP0U+e6LUyBgApKSm4c+cO2rRpA0EQIJPJkJzlmj558mRUrlyZJSUl4d69e0JgYCD8/f25JEmsSZMmPDY2lj979kwwMjKCXC4Xnj59CiC3NUChUPCLFhbo3bs3q/nkCfjChdCcPo3QyZMRPmQIf/78ueTh4SGLiIhAixYt9DdGLi9xcSTTXbCg5OU4Bz76CChp1rgoUkUv25zrxAmgenXg6VPA2Bhntm6VOOfCxIkTZfr6LHFxcahcuTKjtxf11wZ07RqpRbp3pwry+fMUcGYbwF2/nrtsWhoFMhYW1F/u6EjX/IoVtFyNGiVW6gvRqRMlSMaPL7ZiOW3aNNmGDRvw8OFD9vDhQ3DOeZcuXYr9/IwxzJ0zB/yvv/Bb584sYNs2PmXKFFbSSLaCSJIEMzMz5B151qxZs9dXXFm8mO5hly+TQVyNGrljL2kDKQAUBFLPaDR03oaFUfD5zz90rDp0IKVV9qi3J08oIJ8zhxKyXl50/BwcKCmakUGB/I4dgEIB63Hj0KpBA7SqXRvXr1yR/CIihJ7VqkE+fjydA7VrU6W7YkVqXclWXgwtoNiWJEoqjBxJn23kSHqP06fputEDlpaW4JzD0tISjDGkpKRg8+bN3MzMTPrkk08KXYc2NjaYPXs2oqOjcfToUb591y6MPXqUm06ZwvJVauvXzz+mdenSktUV1ta0DADWoQPCKlWCyDmpYjw8qKq/dSuZFk6cSMciz7ZdvnwZlSpVkiwsLAQ0aEBJ3h9/pOPj6Un3nYKtOwoFKUB0vNcoFAoZY0x/7pUlwbkExroAkIGxYwDOgfPVr+W9dcNCoVCUXNkXRWDlSlJT6EFpEx0djSNHjqSp1eruSqUyotwrNPDOYQj2DRh4B2CMedWvXx9az9N9k7G2pgd/R0dy3h4//pXOiM+Bc3IFt7LKHW30BnMxa9TTyGzzJ22wsqIxdcuWUfVPT5Wc9PR07N+/X7KysoK7uzsDgPHjx2P9+vUAqBcaoAfqNm3aoA0Fgiw0NBQ+Pj7M1taWDRs2DBXyVMMzMjJw4cIF1K1bF/b29mzPnj3Yvn073nNykqxSUvBg+HDBok4dXuOXX3gXPz/ZpvHjIQgCwsLCpN69e+vX7RygMWU3b1IlzdKy+OWiouhhffjwov9+/z7Qrh0FVnI5BTLTp9P4LReaNtWlSxdh+/btyMjIyLdPysqzZ8+wefPmnJ9LbfsoCc4p8dGjB/X5bt9O8uUPPqBqY7ZhWkHVxZgxVMXatYt+Xr2aKranT9M6/vc/6jffsYMCTm1o1476nb/8khIGRZzPgiBg0qRJ2LZtmxQaGipER0dLMTExssoltLKwW7fA3Nww7LPPsHPnTuzYsQOjRo2CtgF/3bp1ERiof98yrZHL6Tzt0oWUI336UELmyhWq7m/cSAH2/Pmk9LG0pOMVFkbHNzycfmdmRokbOzv6l5ZGAXq29NzaOn+VOpu+fXP/v0MHAIBpvXrCjYMHce/wYZ6t/AFALTmlsW0bfQ9cvEhBv0pF55mnJ51HevJV6NmzJzZs2AA/Pz/4+fnxypUrc09Pz2ITbgqFAtWqVcOUKVNkd+/exc2AANYzMxMsqw3tzp07/PzJk+geEYEa/foxY2Nj2rctW2q3QRUqwNfKSmzRooWMKZWUlLl8mdQsyck0GWbqVKBePerL5xw3btwQ27dvn18XPmsWLXvpEgWYt26RUimb33/PNQAsQFJSEp48eYKkpCQIggAHBwfUrFkTjDEYGxtDLpdr7+hXXkhyoQFj2wAowJgzgEhwnvnatqF0rM3MzEpOpj5/DjRqVDipVQZUKhX27NmTJoriBKVSebPcKzTwTmII9g0YeDf4/PHjxxv37dtnNHz4cDOt5+q+qTRuTA90K1eS7Pd1JDE+/ZQqV97eehuB8ypxdHQEYwwvXryAlZUVFNomRAYMoGrctWt6Gb8VEBDAjx8/zmxsbPjYsWNzHjJtbW1hbm4upaamCr6+vrxDhw6FdqqzszPGFPOQaWJigm7duuX8PHbsWDx79gzhS5cKVUQRDby84ODgwDBiBMP581gwcCDE99/Hnt69+aoHD9C7d2/etGlT/R7I69fpfExMLD5REhVF1evicHKiwNbMjCS+9++TS3rW8eOc49mzZ5IoioLWx7QU8t4PHB0d8fHHH2v/4oQEcoz29KTg2smJAnYXFxo1lnfEYHHO6JmZtF+qVcv9XXQ0PeyePUt/nzaNAk4/Pwr458/XLhllY0NGjQcOkNFnMYwaNUoICgrC4cOHZevXr0evXr3QIq+8Oi8vXgAdO8La2hojR45kmzZtwpIlS9C0aVP07t271KBfrVbrz5hOF+LjqdI7dmyu2ZuTE1XeXVxIBm5nR/u2uCo6QMmBbIq695bBGK9x48ZITU3FqVOnWEJCAipqa8qYnEwtMWvXkhw+MxNwdib/gQkTaJzdzz/rZSSqjY0NOnTogLNnz8LW1hajR4/WutTdpEkTJCUk4ODo0Yj28MDEiRNx8uRJ1tDMDM4bNmClvT06duzI20yezNiPP2oV6Pn6+iIlJUUWExMjApBBLqd7dqtWpLCoXZuUA+bmwJUrSO/bF/aDB8veq1GDWmbySsSrV6dEQ8OGdE5s3EieGSoVtdrMmJHvvTnn8PPzE8+dO6eSy+XearX6viAIZoIg9HR0dLQbMWKEmampKWQymZPWO1hfcP4HAICxgwBsAejHuVQ/WJubmxffhJ+cTF4t+/aV+zmDc45jx45lpKenH/3mm2+K6dEwYMDQs2/AwDvBggULDoqi2Ozhw4cmmZlvUpK7HCgUQGgoPWw2a/bqxvMlJ1MP6Mcf08i3tyDQB4DatWujTZs24uHDh7Fq1Sr+PNvMrjQYo0rZuHFU/SsHKSkpOHbsGGvZsiXGjx8vKxicjhs3TgCAc+fO6WWnVqtaFW3q10ftmTPhkC2Bd3Ag6bAgQNa9Ozxnz5YNCwvDy3nz2LNnz/Txtrm0aEHV/ZKCirQ0ktcWhVJJQctHH9HPy5ZRu0jWfrtx44a0du1afuHCBda3b1/tEzil4JjVDsMYQ48ePUpeb/aIwZEjqWr++DEFkNkjAbdto8+/ZYt28+e3bKGe/NOn81fsN26ktoXQUGpjAGj/du1KgaqvLxChhSJVJqMH5ypVKGlSDIIgoHHjxujcuTM45ziR/Z5Fff7Dh3MSEzY2Nvjyyy/RvXt3hISE8OXLl2PTpk0lmpL5+/tDpVKB6zKusTxs2UK97zIZJaJsbKjdZPduMuxbu5YC4kGDKEGjqz+CnnByorhQ69YUUaTre/duSgQxRp4u2cnKDRvoc+3ZQ8qOyPIbw5uZmcHIyIhPmDBB53tWYw8PuGk0iI2NxfLlyyGKIvpNmQJFcjL69++Pq1evSismT8Z1Z2culnIMIiMjce7cOTRs2JC3aNGCgkeNhkzdTp2i5BtjpAr67DPEN26M9RMnose338IoJAQ4epTUHHXrAn/8QdeZmRndv3bsoHtUVBSdL/7+hdpnHjx4gPPnz7/QaDQN5syZ02fBggWz5s2bNykzM7Pe8+fPj+/cuTPVhsb0NSy08a+PYQBGgrHhYKyYm+5rp5KZmVnxRj9+fmTKpwdDZX9/f/7o0aNolUr1eblXZuCdxhDsGzDw7tC3bt26KhMTk/96O/TPsGEkfXz6VP8j+saOpQej9u21C17eILp16yabNWsWMjMz2YMHD7R/oZkZSTvnzCnze1+9epWvWbMGVapU0eStwOfF0tIScrk8px+23Fy9SsFLq1aF/2ZkRA/8FSuibuPGMGKMh925QxUrlfZjj0vF0pLM9Yo7D69epQfrojAzo+p3QgIZzG3dmmMwFxcXh9OnTwsuLi5s5syZrFmzZnrbZFXW5582bRqqF+xx5pzO/6Qk4LffqOoHUCWwQgUKwJ8/p37e2rUp2NKFO3dI7l3U7/v0oQpnRkZuksHOjoITSaIkjo9P6e9RqRKZjI0bV+r9oW3btjkjDIusvkdF5crds5DJZGjTpg1mzJjBPD098fz58xJLyNnmh6+0uh8TQ1XtQ4fonGzfnvrg9+2jUZWOjhQcajSUnDl+nP79/judv/fu5e7zcnDixAnu5eWFDRs2iEuXLsWyZcvg6+srpaWlFfr8Dg4OsLCwwI0bN0pfsSTRObdmDW1vVosLALq+unShZMBHH1G1e/VqYNUquv7KQbbqoCxeGVYzZ6L2zJkYOnQonJycMGrUKEr+NWmCRo0aYca0abIpGzfiwt9/86VLl6I488fExERs2rQJzZs3FwcPHsxyrllvbzre6emUOMuzf/38/GBTr55oXbUq3duPHKFWjf/9j8wPs3v1U1LomhoyhFz8O3emlq4C+Pj4JKlUqilKpTIs7++VSqVGpVLNiYyMNLKxsYFKpapW6MWvC84lcB4OgAHoC8bkYEw/1vZlRCaT2Zqbmxd9f3j6lBJURexvXXn58iVOnjyZrlKpeiuVSv25VRp4JzHI+A0YeHc49eTJkx+Sk5NhYWHxX2+LfpmbNUnG1ZWCty++KP8679+nL93VqynAeRWmbq+BwMBAGBkZcQ8PD90qUUOHUsDg7U0PfDqQlJSEs2fPMk9PTzg7Oxf7PaJSqVCjRg0EBwdDrVaXv1L9+++kwCiNadMQV7Uqe37yJJqdOwczzqn61759yRJ7bbC1JdlsSkrRvfvZPc4FWbSIpOpWViSbPX2azussJcmff/4p1qpVS+jUqZPepSXGWRMloqOjc+XT27dTRW/lSmDSJNqmIUOoTQHIMQkrM8+ekcv65s1FT7T47DMK4L76CnB3p/32/vv0N8aowq9QULVaqSTpeUnnz+TJlAS6fj13PcXAOYexsXHRAd2xY8C8eUUqfBhjcHR0hLGxMX755RexcuXKsmfPnokWFhaCXC5narVa6tatm3D9+nXdjDO1RZIoePvtN+q7bteOzumCHgxdu9L5/tNPFGxmm7Y6OVEg2LQpfb6WLWnf5u2x1xE3Nzd2/fp1uLi4yBQKBUxMTODt7c3PnTuH9957jw8YMCBnRzLGIJfLeYUKFUo/x0eNokq9XF7YRb5WrdwxfQAdL09PSgysWkVJpJEjdVJp+fv780uXLiEmJoYxxtiTJ09QU1cTU86BefPQ6N9/0ahRI/pdZmauGaoowqxOHcycO1dYvHgxilPhnThxglerVo336dMnN3CNjaX1XL1KCbcCyoDAwEDpgw8+yB/oyuXkpQHQ9TVuHG3je++RnH/uXODuXVJK5CE1NRXR0dGWAIqTRtWytLTMNDExUXDOFV5eXgqlUqnHjKqOcL4XwF4w9iWAj8BYG3D+n2yPXC53KtYvZtEiur+W876gVquxZ8+eVFEUpyuVynvlWpmB/xe8nU+3BgwYKIRSqXwE4IctW7akPX/+vNgHibeagAAK9leupCC1rHBOY4sqVKDg7y0N9AH64ler1ezUqVO6lenMzMjYassWeiDVgbS0NADUc18cN2/exNKlSxEcHIw6depI5Q70Y2KAJk1ohJsWDBo0CBVbtcKKYcOwaPlyxCiVXLN3L/Vj371b9u2QyykgDAoq+u/Z/fgFt33jRqqyLlxIn8HHJ+e8E0URz549k/Xq1euV9JCwp09hGxWF04cOUfU3JIQChuztfP6cDPesrakHWB/cvUuBZnGjK1u1ypWyTp5MSYGCdOhAbuJhYeTCnp5e/PsxRsHQuHE0kq8YUlJScPXqVVhbWxd9vdy/T4ZnxSCXyzF58mTUrVuXRUVF8Xbt2skiIiJYXFwcOOfCrl27EBgYCHNzc+jFOyUjgwwRGzakqqC7O51jxsYUqBcM9Ddtontb586kGinKx6BrV1rfwoUk6//6a2rPKIMSwcHBAdbW1hJjDC1btoSLiwumT58u+/jjjxEYGMju5rnWEhISEB8fz+qWNKkim5Ur6Rzp3r3o5Fl0NPXuZ6t2qlendq+AAODGDXq9DqNoz5w5w5KTk9mkSZPg7u4u7tq1C7t27dJth1SpQtdP1hQSAHScVq/OVVHs3YsXL16Ac44sGTxUKlWO+iYmJgbBwcGsdevW+b+Uxo8nE89sZc3MmaSOAXDnzh1IkiRoNYKVMTreSUmUHBkyJL9qAjQG1dTU9KpSqbxWzFpi09PT5YwxmJqapgF4/X37RfMzgOUAKoKxouVmrxhBEBpZF6VkunaNkmqfl19xf+LEiYzU1NTTkiQVcdM0YKAwhsq+AQPvEGq12is5Odlo48aN8wRBkGbNmqV/V/L/kuyqZEAABeplmRV+4QJVt7y9qUL7luPm5gZTU1P8+eefLDAwkI8dO5aV5DSeDw8PGnF4+rTWQTQAnDhxQqpF/dfFZkn8/PwAAAsXLgRjrPzZlOPHSf6uwzEbNWoUYmJikJiYiHWSxPp16YJm+/aR3D8sjHrCyzLeMSODzr2AAJK2Z8M5Bc7ZfgIABWqxsSQ9fvQIfO9ePOrTB85Nm0KRVZmMjY2FXC7nVlZW+gv2IyKoL/fIEWSMH492ERG4Mm0ax5EjDDVqUHV02CMTaroAACAASURBVDC9vV0+pk4lFcPhw8UvM3BgbjA2dCidhw8fFg60bWwogH3+nIK5JUuoP70oKlemIM/CghINBZMuAHx8fJCRkVH0iNL4eGoJKBD8FKRChQro2rWr0LVrVwBA8+bNIZPJ8PfffyMqKgrm5ub4KNuXoSxkZtK/tWupOh8RQf4OvXpRu0pJqNW5ycs5cyiJM2pU4eUYI2d2gM7b7dvJGPXJEwoAdUhUNG/enF29elVq27ZtznXu7OyMzp0749ChQwgPD0eDBg2Q3WL222+/Yfbs2UWv7PFjUiscOULtFP/8U/Ryjo7UgpW3FcHTk1o/pk8n5/7wcFpGi3G0vXv3lo4dOyaEhoaiU6dOMhcXF6xbt07QyUyQdgapS/J+N7VpQ8mJP/8EZsyA9dOnqFChgrhs2TKZpaUlT05OZqIoomrVqpKNjY0gSRLq5b0O0tPpvM/bVx8SQklEAH5+flKLFi2YTCbT7qDt2UPj+I4eBQ4eLPTn6OhoqFSqkvohLFQqlUIURVhaWmrS0tKcATzR6r1fJeTKvw+MdQKwA4w1AOcJr+vtvby8mFwur13o+1eSKOHTv3+R9yRdCAgIQFBQUIxKpRqrVCpfkymIgbedt7ecZcCAgUIolUqu0Wh+NTIyirK3t8/QZTb0W0X2KKYBA4odGVQkCQk0fmjChHci0AcAIyMjuLq64pNPPkFaWhrTeeRXly7UB1pcn3kBUlJSEB4eLrRp06bE74/WrVsDAJYtW4bU1HK2FHJOD0zZwYkOVK5cGbVr14aHhwdOnDiBiMGD6bOmpZF51cWLuvf0m5pSEJ830AeA1FTg22/z96jPnQvpk0+g+fFH3LtyBSs+/RT7z57Fxo0b+bNnz+Dt7S3t3r2bM8bK/uCmUpHxE0CB0ldf0UNlVtCYvncvDg8ZAicnJ5Yzju1VERtL5noljScE6BrODviMjCio37On6GUFgQzzNm+mPuPduynhUhRdutC6v/yyyD/36tULCoUCp0+fZv7+/vn3+eXLtP2lBdQFkGXtz+yHfGNjY8lc1/uLJJGqIDmZ1EbbtwOjR5NCwsiIAoXStuvSJZKvL1hAP9vaaudDMngwBX4qFbB+PRkmXryodaW/efPmLDExUchW/GTTunVruLq6irdu3eLbtm3Dr7/+mvVRpeLPdWdnuoa+/ZYq2SUlHebOpf2UF7mcAn4rK1IGHD5Mzv2l0LhxY8Hd3Z1fvnxZBOhYVq1aVfrjjz90uy5TUuj8z8u2bXTsuncHjh+HXC7H9OnTZRMmTED79u3ZjBkzMHfuXIiiyPz9/dG3b1+eowrRaAA3N0oWWFnlrvPoUaBTJwBAXFwca9CgQemB/qlT5MExZAjtW7W6SLPRiIiIFFEUi6vqA8AlURRZREQEbGxs5ACcS33v1wnn50DbVB2M/QPGSrkZ6Q0nQRCMCrVRPn5M8v1yjtqLiYnB8ePH01UqVR+lUplc+isMGCAMwb4BA+8eteRyufWnn35qZqTjQ+tbx5Il1L8fFla8rDqbPXvooWnKlPwjid4RKmXNqy5JWl8kzs70UFyKsdWzZ8/wyy+/SGvWrIGxsTEvUqoIICMjA0ePHsXTp08hCIJ+XMmvXyczOze3Mq+iY8eOsLKyEk+dOgWRcwqGIyLI5btfP937lhUKqo7nbQm4fx/Yvx8JiYkICgrCrX//xS9Nmkgr2rdH/PLl8L92TXJv1w7z5s2Do6Mj27p1K65fvw5XV1fWq1cv3b+PJ04kd+1jx3KVGUuWkMS3YkVSbJiYIFvdU9wx0xvbttF0AX//oqXXeRk1CvDyyv150CA6F0uSXrdtC3zyCQUt/fsXbzA3bx7JndXqQn/KPiez/j//CnbsINO7MlIhK7AWRVFQaZtAevCAjLu+/Zbk9cbG1I8/ZQolOErbj3n55JMcw0cAlBBt21b717dtS5X01q0pWfLZZ6R2KOX6zU4qv3jxotDfBgwYIJs7dy5TKpVo27YtBEFAgwYNil5hnz4UmNvakvKjtG3PzKSAPzY2/+/bt6f7xf79dC4+fkyeD6U44Ds5ObG4uDhZnp+F8PBw3cxPhw/PP7oQAG7fpm0NCCCFCug8tLW1hZubGypUqACFQoH27dszY2NjuLm55QbuGRm5Yy/zMnIk8PHHCA4OBgDmkFdNVBCNhiT/kkRtLsnJpD46frzIxaOjoyUAxfY6KZVKCYBgZGQEGxsbM8aYjuYGrwHOMwA8BBAKwAiMldOsRSvcHBwcMvO176SmUnHh/ffL1S4oiiL27t2bKknSLKVS6a+HbTXw/whDsG/AwLvHc7VaHfP9999LDx8+/K+35dXSuDGZ9i1bRg8xxXH1KgU/u3a9MxX9gly5ckWSyWSF3da1YeJE6t8sJuB/8eIFdu/eDScnJ2H06NH4+uuvmVXeKlMWx44dww8//IDbt29n95His88+ywmCysyWLRR4lJMRI0bIwsPDERUVRb+wsqLK4cGDZOx16xY9BCdrUTRhjOTqeXum09MRU6kS1q1bh7MnToj1P/gAnf79V/iqdm1o/P0x4qefBHd3dwiCgAEDBmDOnDmYNWuW0LlzZ7iUIh3HvXtUeV2zJjfpkZhIwdiQIeRFAFCfe57WhIcPH+LHH38EkDv67JWxezepZ7RBowEOHMj92d6e+lpLkv4DlGTZvp2C2r59i1YDVK5M1e1mzagiWoChWRW2fO0liYm07p49tdv+IqhTpw48PT2RmJiIFStWFL9gUhKN+0xLox78s2dJch8WRttQFhNJSaLE01df5f5u4cL8+1gbBIH238WL5JMwaRIlQIpxjgcAX19f0dzcXCot0ditWzcwxlCpUiUhLCys8AIeHuTLceoUyeFLC44sLEjKXlwSa906CryzZ9IPGVLsOMfY2FjEx8fDzMwsJyPQpUsXVKxYEQcOHNB+soJMRj4JeRMkq1aREujo0cJmg3lITU2FQqHIfeGdO6RSmDu38L6YNg3hgwfj0KFDUsuWLaUS/SHGjqVe8V696Hty1iy6fop4TVpaGhITE80A3C/pYyoUivP//vtvpqWlJTM2Ni7/HLlXAecZ4PxTABYAboKxFq/y7eRy+fuVK1fOP64kMJDO6XJOV7l8+bKYkpJyWxTFX0pf2oCB/BiCfQMG3jGUSuUjjUZTVRTFCXv27EFGcXLXd4n164Hz56lSUVDW//PP9IDTpg39e0dJS0tjoigiKSmp0N845+Cc48WLF7h69So2bNgg7d27V7x9+zYSExPpoe+nn6jSmuchlXMOf39//P7776hdu7bUr18/VKtW/KSl0NBQDgCff/45Fi5ciIULF5Y/wHz5koyvhg8v33oA2NrawsbGhp89e1YCSIWw8f/YO++oqM6tjT/vmWEoUkWQomJDsLdYsULsGkvsRhNjicbExGtyY2KZO9HYElM0xsQSu9GoaFTsBUTB3kGUjggq0tvU835/bBCQNsAQTb75rcUSmZkzp82Zs/d+9rM3buQJGRkk6W/ZsqDn28eHqoNlMWcOBad5vbNibCzOqdUYOHAgPvnsM4mFry88dToIGRlwdnEp5s4uk8nKdmzfvZvM2HQ6ck6/eZOCl5Ur6fFdu2g9yyAuLg46nQ7Tpk0r89hViehoMos7dkz/pMzz57R9hZk+nUYrlgdjpKpYsICC2Vu3ipv32dtTpTy/vSGPsLAw7MvrUy7Si33pEpmrVbH1qVGjRjAzM4NWqy36AOdUab52ja5XK1fSdjx5QoFYjRqVr/zl5lLAGx1dNIB7990XUu8KI5FQ0mjrVuqBDwwkBczNm8WeGhsbC3d3d71WvkePHrh3755ux44duHDhAkXQFy9SUPvZZ/R5b9uWgn19yMqipF0JSR24uABTplC/9J49pHSYObOIgePjx4+xY8cO/Pzzz/Dz8ytyTpiYmGDs2LEVM1p0daXWicJJr4wMSuB89x2tRym4u7sjKyurQElw/z4lH186Ly5evIgf/fz42WPH0L59e/Tu3bvkfb9lCyVsVq6kbWaMEkwrVlDSsAQePnwImUx2VS6X55T4hDzUavWg8PDwp2FhYeCvyPlebziPAeAFIASMfVtdsn6JRDK5Xbt2BY6k8fGUEP7++yotNy0tDQEBAWqVSvWusU/fSGUwBvtGjPwLyftCCJFKpVqVSoWwsDCk6Vtx+6ciCNRL7ehI/8/KokDfx4cCiIqYLP0D6dGjBwOAH374Ab/++qsYGBiI27dv4/r161i9ejX/+uuvsW7dOvj7+4sNGzYURFGUnDt3TrdmzRp888032BMVpbvh6ipGTZnC09LSyNRu7Vp+/Phx/tZbb2HkyJHlfl+MHz+eAcDevXvBGDOMG/mhQxSAlebqXkGsra1ZrVq1GACEh4fj8ePHbNu2bdi/f79u1U8/cV8zM/HKlStcM3QojSg7cYJmeZfGunUvZsFnXLgAmU4ntn30iF575AgFeKX0jxchK4tGjSUnk3Q7I4Mkv3FxFHhlZ1O7RaNGFMTqSffu3WFmZvbCMLFaOHSIAu6KBKtvvEEmY4Vp1YqSGi8nAUqjc2fym9i0iWT9L1eff/yR9unmzQCArVu3avfs2QPOOYYPHy7WKVxBT0+ndTIAzZs35wCwYcMG8fmVK1S1V6moT/rhQ/r/jRt0vTKEr4qZGQXlL7vcP39O51FVkMlIQTFwICWacnOpUlzIOC87O1uw0NN4rEePHpg1a5akd+/eOHPmjMA5p0SIvT2pU7Zvr5i6wtKSgto8Z/sS2bWLAt7nzylBt3YttMuX487t29ixYwcSEhL4xIkTIZfLMWXKlCKGFikpKZBKpVzvMYqCQK00hZULTZvScX/7bZrKUQpWVlYwMTGhYO7cOUocbNhQ5Dm3b99GQEAAxspkbOKJE/D29haKrZtOR8f9/Hm6ljg7F5xnw4bRNakUNURmZiZ0Ol3ZPV0A5HJ5tlqtXhoREQG1Wh1V3vNfOZyHA2AA2gPoAcYMalyiUCi66XQ6B6fCJorff09qkip8xjnn+Ouvv3I45yvlcnmkAVbVyP9D/qXuXUaMGAHwTKvVSjdu3KjKzc19ZmtrW3PWrFk1DBKAva74+BQYdH34ITl7jx1LstR/ORcvXhQBCFOnTsXNmzeFkJAQMScnhwNAkyZNJG5ubrCyskL9+vUL3xlKRFFEeHg4wsLCJI87d0bt5cuxY/FipNnZwc3NTfzwww8l+t7o3rp1C0AlfANKQxQpkKmEMV9pvPHGGzhw4ACLiYkRU1NThd69eyMnJ4enp6dLevfujZCQEBYYGKg7p9VKJru4wDEzE3BwgFajgfTjj6n3t3CF/PZturkWRSS3aIFniYkcdetSUJ6cXHYAHBdHVccffiAJc7NmdO7K5bTdhZMElfzc7t+/X9RqtULbKspIS0QUqXL40Ufkvl8ROKdgNymJlBT5dOhAN8ljxui/zatXA6dOUdXUwoKM7fJp0YIquhMn4unTp5LGjRvz4cOHMwsLi4IDo9VSMOjnV7FtKIWB/fqxTn5+uJ6YyAKOH4d3SgrstNqqjXwsjfh4qryX4KqOw4epTcAQiiaplJQXWi21W+zdS7/XqIGcnBxWJMjRg5YtW8Lf35/HDhwIt61bGVu7lrbBw4NUGxVh0CDybvnxx5I/bxIJeKtWiFq0SLSJjBRufPmlKNm6VXDav583/fxzNnjkSFbaNS41NRUqlYolJye/GJVXLjdvUttN/hSA7t0pKeHlVeZoy5ycHKjVaubm5kZmbh99RK/JIyYmBn5+fhg+fDicmjYtMGIsjE5HXgedOhUfZ8k59f6/806p6xATE5Oh1WqD9NtQ7ALwK+e8Gh0/DQjnOQC8wZgJgAgwNg+cly61qACCIEyyt7cvSLxcuUIJ3yqa8gUGBmoSEhJitVrtUgOsppH/pxiDfSNG/qXI5fKIJUuWfJCdna3jnG9NSUnJunjxorZ58+bS/fv3Z9eqVUsYPHiw+b/SsX/iRApEcnIoeJg6lYKHBg2q14n8FRIcHCyYm5vD1dU1XzqvV4QuCAI8PDzgkX8T2qIFZu3fD90XX0BqYlKhneXt7Y2LFy8iL8nANBoNJBJJ2VL1srh2Dfjtt+Lu1lWgWbNmsLKyQkJCgtC6dev8cWAvosp21A8v2bBhA9+8eTNr06YNf/bGGzxOoRCmnD0LybvvwiE0lCqe+RLpVq2AcePQcP16pNarJ6SdOQPbo0eLV8+ePKHe9F696Hzs14/ktTodycjzP4sffmiQbU1MTER4eLgwceJENKxoAKUPERHUP//JJxVPRjBGkuqXX9ejB+2n0FDy5NB3WX37kgHaBx/Q8XB3J2l89+6Avz9y//c/aGQyNmzYMBSrQl+8SNXPipjhlcS1a4BcDmHfPjg8f45uI0eyVWFhsO3WDT5V9a0ojcRECnBL2v/r1xtMEfMCqZSONwDs2gX+yScw7dMHDSqoHLO0tMSM999nOWvX4szhw6J3nz6CoFCQQqSimJjQKNWYmFITBVFRUdjZvr3g0ru32OW335jp8OFo7OrKmq9YQedcKcmKFi1a4OLFi1i7di2mTJmiX1tS//7Uo5/PRx+RsmzYsKKJwpe4ceMGHBwcRNPISEHn748siQTPIyOh1WqRnJyMM2fOQBRFajXQamm8n79/QYJj925KcC9bVtzQDyADR7m8TBVEngJQrzF6crk8U0Emm630ef5rA+caMDYTQDwY6wHgKjjPLe9lZSGTyRp37dpVkrd8Gu86YgQlNCvJ06dPERgYqNVqtX3lcvnr3Sph5LXGKOM3YuRfzIIFC9YvWrRok1wu13LOB164cEGzadOm3MTExG9DQ0Pj71VHpel1QBBIajprFt08WluTlHPWLDJtCg0t12H6n8T9vEkE77//ftUX1q4dmFYLaSlOzWUhCALs7Ozw8OFDtm/fPr506VKsXLmy8m78vr4lzwivInXr1kWnTp1ezP0uiSlTpjBvb288evSIm5qaCu/NnIlTCxfyX06exOONG/FkyRKkJSWRzH/LFmDyZLCcHNSLjwd7+rRg9Nz8+STH//lnqjLTwingb9iQqqQSiWHk3C+R7whfko9Dldm3j5JpDx8WHQlWEQYPLj5CTxDobzt3Vnx5rVpR8uTGDWD06IIJHTVrQnfuHNrdu8dLHIl35w6ZoFUGUaQ2j1WraH80a0bXlr174atSia6urqJPOb4KlSYmhgK30kz4Pv6YWhyqi/HjEXnwIMy0WpjnyeP1Nmg8dQp2YWHA5cu4lpTEsj74gIxWCyVjr169Ku7Zs0e3bt06vnr1ajEgIEBb4rIEgVQTZTjS37hxQ3R1deVTP/1UaD5+PGt8+zYFY3v2UFvMqVMlfidYW1tj2rRpcHd35xs3bsTzPH+OMnF1pYkQ+YwYAVy+TD4Ed++W+rI6depAFxkp5Hbvjl+//RY//vgj/vzzT/7XX3/xc+fOwcLCQqxTpw7funUrduzerUtTq5GblkbnYGYmXWtSUigJIJMVXXh8PCUUy3LtB5CbmysBoMdGFqFjBZ//6uH8OIAQAMsBrKrKohQKxWDGWKcXCfO7d+n6UwWfGa1Wi127duXodLoP5HJ5fFXWz4gRY7BvxMj/E+Ry+VmVSjU0Ozt74sKFCxUajca92kdxvQ7Y2NBN76JFNNe7WTOqcAwZQlWRaL2KGK81Z8+e5Q4ODi/mfFcJqZSClydPyh6DVgrDhg0DQJJTb29vqNVqbNmyRQwICKhY0PnsGfWxGyKBUQkEQUCHDh0wdepUYfTo0XB1dcW4ceOYq6uruL97d76hVy/sWrQImrffhujuDvF//0M25/zIgAHMfPp06s8FqPc2IoIqzvmO+RMnUh9vNePm5oY333xT99dff+GoPsZ3+iKKFFhERlZpnBS++qpI7/cLRo0i13R9x9eBKpInTpygYPH99+lGOyWFeqQlEoi//444Ozume3lEpyhSUqDQBINy4ZyuHc2b03t4epKCoEcPquhZWIBzjujoaKFx48bVd5+1fHlxQ9LCNG1aZiXZEBw/fZprWrTQSc6fp+Pm5UVu9OW51/v6Atu2wdXVFb1ycliImRnXFkqKREdH4+jRo4JWq5XUrl2b1axZU7h27Zpk165d4q+//ioW86DRaklJc+dOsbfKysrC/fv3BR8fH5I/zJhBPg7du5OfS3AwJeN++63ESRy2trbo06cPA1BcFVISjo70Wc8fCbh9O/lBBAdTQqoUGjZsiFoeHvyvMWO415gxWLhwIebNm8fc3NzEGjVqiHPmzBGmTJnCevTogRo1aki2jhunW79qFVStWwNBQUB4eMl+HqJIrQX37pWr9DA3N9cBKDsjUJTTACrw4XmNoCx0NwCfg7HVYOyDii5CoVA0AHB4yJAhFqamppTwW7CgRGPFiuDv769RqVQXFy1aVPr4BiNG9ORfqN81YsRIacjl8lP5v5uamt738/NraGdnxywtLXmfPn1MTQ0t+XzdaNiQfoKDSXJ79y6ZFVlaUi/lmDH0+z+M5s2bs6Agfdss9aB9e6rc/vEH3RhXgHr16kEulwN50vhmzZph7dq1QlxcHPz9/TFs2DDUq1cPdnZ24JxDFEVISmqtOHCAArDXqO1CKpVi6tSpL+7gnj59iu/d3Ljt2rWYunEjswDYcB8fyFxdgfwRiIY8LpXAy8tLEh4eLmZmZha58wwMDERERASePn3Ku3Tpwnr27KnfAqOiqB81JKTqaoTff6cq6Mvky+8XLIC4fDlCQkKQmJiI9PR0+Pj4QKvV4sSJEzAxMYGXlxeePXuGY8eOQafTQalUYsiQIRAmTQLCw6FbuhTn/f3FwIYNhaEajSiZNUvAyZMF6/74MUmsy/OZ0GrpXMz3VvjkE3KPt7WlROJLMMZgb2+PwMBA9OrVq2r7qSQ4B379FdBoSn/OW29VqzGpVqtFWloa69ChgwQWFuSXcPEiqVWWL6dk0NdfFz3Gokgj6NblTRDTaNAxOhq7XV35wz17+NixYwVTU1Ps3bsX1tbWfMKECQwAVqxYwZVKJYuIiGAuLi78p59+wuzZs2FnZ0fLkUpJ0eHpWWw9w8LCYG5uLhbxK2GMfBru3KGEzYEDFKTNnUv/vjTC9PDhw6KJiQmzsLAov19FEOj6mT8h4uzZgpGRpbjgAwB++QXj9uxhCAh48aeTJ0+KsbGxbPbs2S/6wbt27UoPOjtL7r35Jq7Vq8dbd+7MLEsLLrdupfcdNKjcVbe0tERKSkqpWWOFQmELQJDL5SkAYGJiotRoNFAoFHZyubz0+YyvK5yLALLB2F0ADmCsJoC0vL/rwzhzc3Pu6elJ50VsLCm4Smqj0JPY2FhcuXIlR6PRlG6uYMRIBTAG+0aM/D9FpVINe/bs2ZvJyckeAMYkJyc7dO/eXaiW3t7XDcaol79DBzLzioujys4bb9CNnr09fVkXNg57TcnOzsbFixchkUg4CvWeV5nPPiPTtbffLjpLvoLY29tj0aJF+OGHH8SMjAzh3LlzuqysLIm7uzsSEhJ4VlYW8/Dw0PXv319inS99F0VSZPToYaCNqR5qCwLmODiwG46OiPXw4A4uLsxu5Eh68DUa82hqalpEn6xUKnH+/HnUq1ePe3h4MH9/f8TGxqJPnz5wLkfmi5UrKTgaO7bqK3b2LGBnV7Jh2JAhyFq4ED+ZmEBbSJIcGhpa5Gn5Y8oGDx4MZ2dn7Nq1C6tWreJ16tRhaWlpuuzOnQWH1FRh3uHDkG3YICApidQ8+c71Bw4UlzznwzlNQQgMpAr6tWvk5t6pE1XuSggsC9OiRQsEBATg/v37aGpoJUfbtrTfPvus9Od8+y1Vkj//3LDvnUd++1CRkY62tkCfPrSPvv+eJiQsWEA/jRpRW9W0aRR4ymTA6dMQvLwwbNIkYe3ateLNmzdRv3595ObmYsaMGS+uZ9bW1lwmk/GJEycKfn5+cHZ25lZWVkWvd56epNjatu2FX0ZWVhZO7t8PU4lEQHIysH8/TW44eZLUGRs2UJJi3DhKQJw6RQH/7NlA9+5IT0+Hr6+vGBcXJ0wqbPxYHgcOUIX/008p8HdwoPcsi9q1yWgwj2vXrvHr16+zadOmsWItRxcvAoyhyUcfYfu9e/z8L7+wUaNGoXHjl0bea7XURtCzZ0UqzaV+8THG3uOc/6BQKEQTExM/c3Nzbycnp5xHjx4NBvDPrUJzTmMPGNsJoA6AcrOfCoWipomJySfDhw9njDFSI40YQUmnSqJUKrF3794crVb7jlwuf1bpBRkxUghW6V5KI0aM/GtQKBTtAUwD8MFXX30FExOTV71Krwalkm6igoNJxq5UkgN127ZkAvUa8ueff+qSkpKEiRMnshfBsqE4dIj6gbdurZpcG1QFVKvVsLCwwI0bN+Dv7y927dpVsLGxwbFjx+Ds7MzHjBlDrtiXL1O19PjxSrvQVytaLY22WruWevYDAqjqK4p0zug5huzv4sKFC+KZM2eEBg0a6EaPHi3ZvXs3B8AnTZokMMZw6tQp3L9/n6elpbG5c+fCsiR1iyhSJf6ddyhIq+L5AICCKisr4H//K/ZQWFgYLi1fDveaNXmnlSuZRqOBmZkZ7t27hyZNmiBfhSSKItRq9Qv/hezsbISFhSEyMlJnb28vNGjQgNWpUwcyX19qC8l/v02bClzLd+woOrYuIYGC+ZYtqUr3009khNemTYU2Lzk5GT///DOcnZ359OnTDXsinztHwW1ZyZmAAAoymzUz6FvnExwczK9evSrOnj27dPlNRgbNl7e0pOTJ4MG0PoxRIuCrr8iQsmVLnDlzBhcuXICHhweSkpL4xx9/XOI+O3LkCDKTk3XjOnSQwNOTrlFmZpSs7diRkhxJScDKldj0v/+J4+bMETBoECy+/JLM8tasIYXKjRs0Jm/XLvq/ry/J+jMygE8/hWriRKyLi+MWdnbc29tbKBZIl8Xx48CjR5TY0If//pcSIHkKm7CwMPj6+mLs1xIkDQAAIABJREFU2LFFzTUjI8lwdu9eUiUIAtCrF65cuYJTp06hRo0afOzYsQXTERYsIPn+wYN6rYa/vz+/ePHi2vnz539c2nMUCsUsAD8DgL29fa69vb15XFyc3xdffDFYv419jWHMFIAnKOHhCM59S3qaQqGQmJqanmvTpk2n/v37U7Zw+XJS2ixcWOm337dvX254ePieL7/8cnKlF2LEyEsYg30jRowAABQKxQjG2N63336bNW/e/DWMsP5mcnLoxm/TpoIqf9euBpvFbSh++OEH7uLiwsZUwQyoVESR5gR/+mm1VtlTU1Oxbt06aDQaDB48GO1PnaKK7wcVbqH8e8ifB79xY9FkxIgR5HNQUh/6K+bZs2fYs2cPT0lJYQAwdOhQtCkUvHLOceDAAd2DBw+EcePGsWLjE8+epcTX7dskszcEOh0lR15a3oEDB3Dnzh3UTkzE+8nJkO3aZZhkW2oqMGECBZ2LFpF65NAhCrTS06nH+v59MlAMDqYb98aNK92uEBMTg61bt2L69OnlKyYqwvjxwIoV5ffj//47ydFL6uM2ADt37tTFxMRIRo8eDffCyZLSsLamoPzqVQpSw8JINfH11/Q459jw6ac8XSZjrRo1Qt+kJPJbWbWKgvfFi4EOHRDy1lvIePZM7OLvLyAkhK5PLi50HFeuBAYOBNzckBYSgp+uXMGc//wH1vqYSCYkkO/AW28Bkybh6Zw5eJiRIXZft06Am1vFdk5KCpk+/vZbwci96GhK0ryMRkPfKzt3Ai1aIDQ0FAcPHsSgQYPQOn98H0DJg8hIOme/+44MZ9PTKVkBSqj6+fnxkJAQ5uXlpevZs6cEu3fTOZAv/S93FyRg69atiWq12lUul5cZICgUipoAfADsAcDkcvm/576BsYkAPgPQFgB/2WV2yZIlcicnp/++//77FoIgkOrnzBlSZlTyWvXgwQP4+vomqNXqJnK5PLvqG2HECGE06DNixEg+BznnC/bt21d59/R/ExYWFOgeO0bVzFq1aARcmzZ0A/fs9VDY1alTh4WFhVXPwgWhYO57Jcz69MXOzg6f5cmRbxw/zlPPnkWGIWTihuTZM6oaBgVRBXDDhuKqg19/pX7k1xBHR0fMmDGDff7555g7d26RQB+gHvPhw4dLPDw8+MmTJ8Xz58/j4sWL9ODx41ThvnfPcIE+AKxeTZXeQiQnJ+POnTvo2bMnRi9dClmnThTkGAI7O5JXr1wJvPtuQQuPRgN07gwsWQL4+FBQ1qgRVc4rGeiLoohTp05BEATDBvpKJe0PfYLXU6fIgb2a8PHxkWi1Wvj7+5f9hSGKlEhJSqKA+soVGpU4YAAFxZMnA25ugE6HqTt2sPdMTdG3WTNS+GRnkxKgc2c6frt3QztmDE43by7kRkdTy9X27ZT8sLen6urgwUB4OO5aW4tSmQyW+rZjubiQtD8nB9ixA5IhQ5AJCNfmzBFLDNLLwsaGWhbS0ykJ2K9fyWajokjKkitXwJs3x82bN3Hw4EEMGTKkaKD/7beUdO7aldojBIHaDvICfYA8RYYOHcomT54Mf39/iahQ0D7RM9AHAGdnZ0gkEhsA5coY8vr2Axljj/V+g38KnG8H0AbARAABYOzFDD2FQjGKc75w5MiRFOhzTteOunUrHeir1WocPnw4R61WTzIG+kYMjTHYN2LECABALpeLANQAWEJCwqtendcLNzeqps2ZQ3LIR48oYPjsMzKxS0p6ZavWsGFD2NjY6KrtDaysqBpagtTakMhkMnz55ZfoGBfH7ggCP/jXX9W3TRUhOhr44guqSvbvD7RrR9XekmTsjo5001eFns3qxMTEBBYWFiXL9EEBv4+Pj/Ds2TPh3LlzuHLligilkgLje/eoKmtIBgygKnohNm3axAHA3d0dNWvWBFq3pp5zQyUgTU1Jnt+nD3D9Oo1NjIujwGzLFkryGSChoVQqkZCQAKlUarjMqSjSuRUYWDDasSx+/52q3dWEk5MTZs+ejYSEBFbmGNclS0gJY2pKyZPhw8n13sqKDFLfeYe27ZtvwPbvRy1PT0qqHjlC5n7jxpFnglQKdO6M1l5esLCwEOPjS5hIxhi1XPTpg4iICObg4AChIi0nJiaAQgF064Zahw+j9/DhiJDJhMwdO+j80Ol5WZJIKPGg1dK51qABjcR7mUOHwL/9FpExMfjll1/EkydP8mHDhqFly5b0eHAw7b9x4+h8LewvERhIy34JCwsLgHOo//ijwucyYwzm5uYaAHo5OzLG7nPO65iamn5XoTf6J0BVjwMAjgEQbrZpM2DZsmW/mpqabh05cqRgm29+eeUKqZ6qoK47evSoUqPRHJHL5a+fLMzIPx5jsG/EiJHCbASw4ujRo1mvekUqi0qlqj5lAmPk2r1kCVWSxo6lm6lu3ahqc+MGSYX/Rpo2bYr09HSJugJjyirM6NEU4FZjlRAAZFIpWnfrBuspU1hMTIyk2Hitv5PUVBplFRQEhIbSsf/66/ID3ogICh7/odjY2GD+/PkAAPPnzxnu3iXpcEmBSlVxcio2bSHf1Toufx96e1NyzVAjMr/6ihJ0b79NwePSpdSaoFQaZvl5WFhYYNCgQVCr1ezs2bOGWeiZM2RAp+/1bcoUYNkyw7x3KdjZ2cHS0hIXLlwo2b08v4c5fypFVha1Sly8SJ+x+/cp4O/Xj4L+pCT6f3w8BcgzZtCxP3KkyFi8/MkLJWJpCd61K7TXrzOrypqsdukCbN2K5JwcNL1/H3GWlqRKWLAAeK7nGPp9+0h1MGsWqUhe7uXOzERCw4bYOW2a+KevL2/cuLHw+eefs2b5HgtKJVXunz+npMfLo1Vr1y7RR8LGxgb9TUzwx0cfiRWp6udjb28vAaCXoyTnvAMA93nz5lWPC+QrRKFQCF8rFH1XLF/ecs/kyXc9wsOPNrO3f3fGjBnmTZs2JVmXUkmJfwuLSk+OCQsLQ2hoaJpardbT4MGIkYphDPaNGDHyArlcng5gbUJCgqVO3wrGK0Sj0SA1L7gWRRHHjx/ny5cvx7Vr16r/zSUS6rN86y0KBocPp37ML7+kG+wjR6iqU808ffoUpqamXFaao7ghsLamvs/t26t3my5fBnx9Ud/HB5xzbNiwATk5OdX3fiUhivTTpw8F9xMmAIcPlzuf+gX79lFA9g+GMYZ58+ah/7597P4nn/DY5GRcvnwZBk8oJSYWqTzn5OS8qMLWyx99JpFQRXXTpsq/j1pd0BcOUHW2a1cgJoaSdH/8QaZnBqZdu3YAgKioqKovjHM6JxMT9Tet9PIic9FqZuDAgXj69KkQ/XJCJjub3PkfPqTEDgBMnEhBb34PvL099dzb29NxataMetKbNgX8/ChIjooCfvmloKVq9GiY5OQw5dmz4CpVieuU06EDVABcXFzwrAItV0qlEiqVCmq1GhkqFVxnz0bY4MFIzMjguHOH5PnffkvXqvLo1IkC9U2byMhz9eoXD2VmZiKqXz8xdeJE1KhfX/j8889Zv379ClQI48eTf8CaNcCPP5Z8zJs0oUDz5eRPXBw6rluH5Oho4ebNm3pvez7u7u6WpqamepntyeXyCLlcHlHhN3mNUSgUDRcvXvyZTCZ7XKtWrc0+Pj7jOixY0EAWFYWh/fqZ2X7wQUFyMD6eVCuVHK+ZkZGBgwcP5mo0mhFyuTzDcFthxEgBxtF7RowYeZlEU1PT82vWrOkwdepU89Ikv6+S6OhonD9/PvP58+dmWVlZJg0aNODp6emq9PT0LABmR48etZTJZGLr1q3/noSmRFIQ4KWl0azdb78F5s2jyqGTE82ur4aZ8VFRUVCpVNVvjDR4MBlqXbz4wjHa4Ny5A7z1FqysreHh4cEfPHjAzp49Kw4ePPjvOY7JydQb/O23lLipTFUwJ4fGfj1+TAHMPxFRhMm5c6h55gx+3rKFabZsAWMMgiCgQ4cOhnufJk2KGJYdO3YM+XLwItLrevWABw8o6KvIaFClkhJxzZqRpH3KFPo8pqZSf+2AAXScTp+mz+3EiWRm9+67Btm8R3leA96GUEV8+ilVwcsb31aYLl0M67FQCh4eHrCxscG+ffv43Llz2Ys+5ho1qAfew4OeePkyTbB4+XPRtCkppfbupZaNoCCaMlB4ioCPD/3bsCFw+zZ6W1iwmvPmIfXmTdRs357OjwULgNxcXIyPFy+6ugoO9+/j3qVLusDAQEmTJk340KFDmWkZSbvNmzeLcXFxgiAIEEURjDG4ubmJMTKZULt9e7zp4EDnUd++wO7dpAiZPr30HTN8OCVIjx6l/bBwIX0PALgcHMyTW7cWhsybh+aFzf9u3CA1xPjxZARaHlIpfS4KL8PeHuzECXRJS8Px48d5eno679mzp8D0TBK5u7vj5MmTAxUKRY3/b/3jCoXCAkCkh4dHTpcuXSzq1KmDIvstI4OSWAkJlDQcPLjSijeVSoVt27Zl63S6b+RyebBhtsCIkeIYK/tGjBgpglwu16pUql45OTlR1Wb8VgVSUlKwfft2XWxs7JKsrKyPAXjEx8efS01NXabT6ZzkcrkVgLkHDx4UXonRoK0t9Rnv2EEO5hoNVXv/8x9g/nySpBpwvfKrh0H5MtnqgjGaT71mDY0lNDRPnwJ79gDvvQepVIqxY8eyDh064M6dO9X/PRUaCgwbRuPBFi+mG7jKyn8tLCh4tNWr5fW1g3OOY2PGIGXCBPyyeTN0nEMmk3FHR0fx5cp+eHg49/f3F0WxZAV3uUgkVLlNTgZA6hwA6NWrV1FTO0tLCtRjY/VbrlJJCpSvv6bA3cyMqviurvR4Tg75b6xYUTBRwc6OVDpXr9L5XcltunDhgrhx40bx4cOH2LVrFzw9PYuOTqssn35K14+K8Ouv9JmqZgRBwJQpUyAIAjt+/Dj9cfBgkuDnJ4e2bKG+c3v70lUyo0aRq/nDhyTrf1yC79sbbwBTpqDNuHG4s3YtdrZrxzUdO9KIQVEEeveG03vvCc5hYZi0dy9myWSS6e+8g/v377Ply5cjPc9oNC0tDceOHRM3btwoBgQE4Pvvv+dxcXHC6NGj8cknn8DHxweffPIJGGOscePGGD9pEsOYMeTZcvkyteukpNC0gNIUL6amlBS4cIFagrLyuuNEEV1nz2a5gsA1ha8TOh21TAUH0/7LV7eURVJS0UDf35+SJE2bwsvLC++99x67fPkyCw4O1vtLx9bWFvXq1eOMsXf0fc0/HYVC4bF48eJFgiAk29jYKEePHm1Rt25dFEuQWFuTas/cnJIxb71F144KIooidu/enZOZmblfq9UuN9BmGDFSIsbKvhEjRoohl8u5QqHwTU5O9gRg+HJ0Jbl7967O19dXYmpqen3evHkrCz3k89JTTwEo/kX9d+PoSL3Bb79NAc2ePeTwP3Ei3fR260Z9qVUgXzpboRnQlaVJEzKo8/cnvwJDsns3VVULHbPIyEidRqORhIWF4fHjx7xWrVpo3bp1iQc1JyeHjKkqQng4yTBlMup/BQyzXY6OJNmdM6fqy6pmOOfw8/PTNW3aVOLq6oqQTZv4NQ8P9uiNN3ivnj2ZqakpHBwc2I4dO5CdnY3Y2FgkJycjODhYTE9PFzQaDWvUqBHqljcGrjQYoyDI3h4uLi4IDQ1FnTp1ij+vZUsyaWvXrnwnend3YPZs8tZYsqT445s2UfCsUgH795PRJkDB5qhR5J6uUtFNfQWvIWfOnBEAwNfXl+t0OtalS5cKvb5E5HJqO+jXr2Kvmz6dEiV/A5aWlnB0dOTPnj2jHbZkScEkg5s3ab/27Vt+O0ytWnRdfOMNcrK/dYtG6ZVwHPr3749169aJ6y5fFhq0a8fbA4JLTAxubtsGzY0bXPLhhwzdu8Nx2DB8+uQJNo0bhxpr1iCyVSv8ee8e7GrWhL29vXD79m1dixYtJG+++eYLRUm3bt0AAJMmTSr6xubmdK06ehTYvJnWd8QIYP16cvMvjExGCY5x44om/9LSYO7lBdvevfm2bdv4x9OmCRg7lpKpISH6twwBpIZo164gqVKjBiUs87bD2dkZ/fr1Y6dPn+YdO3aEVM/pEt27d7d89OjR1wqF4k+5XP73GtH8jSgUChOZTLZKJpNNbdWqlaRt27YyR0fH8l9oaUkKt0oaYJ45c0adkJBwS61WTy1vxKERI1XFGOwbMWKkGAqFoqmJicnsxo0bvzaBvlKphK+vrwSAr0qlWlTO0yMBYN26dapp06aZ6nuDU63Y2wMffgjMnEk3sMHBVP0PDKR58v37V0pye+nSJe7m5sb0ukExBF9+SYFHs2b6yUz1QRSpIj5sWJE/q9VqBgCHDh0S7e3tWXBwMHN0dISjoyM459DpdAgNDcXjx49x/fp1WFlZYcyYMXDNr+CWhkpF6oqPP6bAfNs26m82FDExZKz1Ggf7nHM8ePAAly5dEmNjYyU3b96ERXY2Plyzhrns3w/n/v1fBDk7d+6ESqViwcHBuH37tmhmZiY6OztLmzVrhvPnz8Pl5SCnIvj6vlBR5Pfpl5g4qFOHzrl790o+VvfvU9/so0c0ItPNreSJCWFhFJjNn08V/J9+Kgj289mxg9pVjh6linHHjnptyuO8SnTNmjX5xx9/bLhMY3R05T5rt2/TdcfT02CrUhqxsbGIj4/HjA4dSLYfGkrKjceP6dp28iQpnvQh3zgxMZESo9HRZHL3UsAvlUoxc+ZMSVBQEMLCwtimTZswdOhQbuPszELs7BjkcgqEP/8c6iZNYOXrK4acOCFEX7+OiZmZqJOTI2DPHuDwYQnq1i35fCkJQaDKe+vWZPjYsSO1cS1dWjwhk5xM5+W9eyQBT0wEVq0C27gR/ZRKYfO8ebhz/Tpa1a8PDBpUsUAfIH8DiYSC/QMHyEzzpfO5VatWOHXqFP/mm2/YF198gRs3bojnz58XOOcQRVHj7u4u1q9f39Tc3BwuLi4wMTGBm5sb3NzcakZGRj5SKBQOcrk8t2Ir9vqjUCjMZDLZH7Vr1+47ZswY8xr6fv/u3Enf4fkqlgoSERGBq1evZmk0mmFyuVxTqYUYMVIBXoM7YCNGjLxumJiYzO3YsaNVo0aNXvWqvEAmk6Fhw4aauLg4j/nz54eU9Vy5XJ6jUCjMk5OTn0RHR5u6u7v/XatZPoyRaVbbtiQ17tePgo+LF6li2bkzVQzKcX3XaDS4evUqUlNT2YQJE/6mlQet/08/AZ98ggrPni6NS5doWS+ZpM2dO1dISUmBra2tIAgCTp06hU2bNkGn06Fu3bqiubm5EBERAVtbWxGAkJmZCc451Go1GGN49uwZXF1dkZWVhTVr1qBXr168S8uWDE2akIni0aP63+BXhL596ec1RaVS4cKFC7hy5Qpv2LAha9myJc95+JA7engI5pGRMM83U8tj+PDhuHnzJpo3bw5bW1sBhVoAg4ODER8fD7fCUuKKMGwYMHUqlBMmYNeuXRwAS09Ph4ODQ/HnyuVURT17tsD/4ttvgWfPSJK/dCkFimWpZRwdSSYukZBSJa+vvggWFmSGt24dKQBCQoqOPCuFY8eOidbW1sLkyZMNF+hfvw5s3VphhQEASnq4uBRLolUHWq0WJiYm3K5TJ4YpU2j/xsVR8HrpUonHRKvVFqk0h4eH49KlSxBFkZuZmbEnT55oPRYskPTv0YOhc2cKYkeNKrIMqVSKHj16oEePHjh79iwOHDhQdEd98gnQvTsc6tXDhObNhdOnT6NThw5wtrOjkWn5vgL37wPNm5OaY9EiIDeX9l1Z3x1161JP/sGD5DeybBn13H/+eYGqoVYt+pkzh86rEyfofGUM5s+eYcrOnfBVKrnVvHmsQWUStn5+RX8vYT8LgoA5c+YIy5Ytw4YNG3hmZqag0WjAGDvJOZeHhYV1i4qKas4Yqy+KYitRFE10Ol0NqVSaLQjCTd0/wa23gigUClOZTHa6fv367UaOHGluYmKi/4uvXqVrRyXIzMzEvn378g35Xt3MXiP/r2CvpKfViBEjry0KhaKNVCq9NHv2bNNKjy2qJmJjY7FlyxYAEPSRvn3zzTdzLC0tl4waNcqiStXHv4PsbOrtTEqimfYjRgDTptGov7wbEc45wsPDkZGRAT8/P8hkMj5w4EDWWt+KmSHZvp3W9T//qfqy/viDqvt6JC2USiWePXuGbdu2QSqV8qFDh7KmTZvi2LFj/MqVK8Uioj59+uDhw4dceeUKG3rgABK3bUOEvz8fsmABMzc3r/q6l8bUqWQEt39/9b1HBcnOzsbOnTvFlJQUQaVSoUePHujduzc92KYNJShWrix7IYWIiYnBjh070LdvX7Rv3x6SyhhQHjkCeHgg0dIS69evR+PGjfmECRNKj2xnzKBAPCCAPienTlHv9MyZ5b9XWhoFb2FhBSZx7dpR8qp795Jfk5sLfP89Ba5r1xYEcYVITk7GiRMndOHh4ZJJkyahQRVbc16gVlM/8KVL1MZQCM45cnNzYWJigpycHFhbW4NzXtTYMN+pvqLV4gqSm5uLCxcu8Mb/+Q8a/Pgjg48PKWeaNiVPhq++Kvaa48eP4+rVq7C0tNRNnjxZYmtri++//17n6OgocXJy4leuXGGenp4IDw9HzZo10fXxY3ja20PSti2pHKytS1yX7du386ioKPb5558XtPXs2EFqpPK8E9LS6Lzq0IFUPxERwIYN9JmYMoUCPGtrUnu8zJMnVOldsYLMBY8epYRtaipdK21s6DNmbk6GrR98AKxYAaVKhbPh4brbt29LGGPcyspK1Ol0HAAsLS2lMpkMgwcPhm1pHiAKBZ3Pn31Gy3VxKTUxlJqaitWrV0MqlUZptdq35HJ5iUlzhULBAJjL5fK/eRSKYVAoFJZyubzU8cEKhcJCIpH4Ozs7t5w8ebKZoG/CNyODFCZr15Z6/pWFKIrYvHlzzpMnT1bNnz+/PHWiESMGw1jZN2LEyMv4NGrUSHzdAn0AsC9wcR4JYG95z9dqtT9mZGSoN2zY8LMgCNza2jqrY8eOFp07d5a88n7+l6lRo0ACOnw4ycC3baPRTa1bQ9miBU48f45bt28DAJydnfn06dNf3Ub06kVVsxEjKCFRWZKSqCKm54goMzMz1KtXD19RAMHyb9QGDBjA6tSpAxMTExprFRUFqVSK0E2b4PjsGUtv3x73mzZF4IULgEzGnK9dQ8eOHVGWO3eV+OCDEgPDvxOlUkkjxDIyUKdOHWRnZyMxMVEYNGgQnJ2dyQRPp6Oq5uHDJJWvADt37oROp8OxY8cgiiI6d+5c8ZW0tASePEGNvITVkydPSj+nlUoKevN7ue/fB8aM0f+9NBrgm2+KusG//XbBWLiSMDcnKfnixRTQmZkVm3e+Z88eJCUlScaOHWuQQJ9zjvT0dFhZWkKSkgI1YxC0Wjx9+hQJCQl4/vy5+PDhQ5aens4YY2CMQSaTQSKR4MMPP4SZmRn5lcyaReu6vPr8v54+fYoNGzZAp9OxmpaWvEGDBshKSYFFXBye/f47bNu2hRlIARIQEMAtLS1FpVLJGGPs/fffZ9evX2fr168XZ8yYIeh0OtayZUu0bt2aPXz4UFejRg1MmTJFcvPmTX4mNxdXLC352NWrhdzr13Fn40Z09fLCgwcPULduXfj7+yM2NlbXuHFj9ujRIxYQEIC+ffvi/PnzaLt0KaznzIFQXrBva0t980BBki4lhST6NWuSD8fu3ZR8+eorOnf69KFzwsmJPCVcXUmJ0agRtS40aEDP1emAxo0podm//4upHWaWlhjo7i4ZMGAAHj9+zBISEiQymQyCICA1NRWPHz/m69ev5zNmzBCsSwowe/akNo0JE0j5Mm5ciZum0+lw4MCBHJlMtru8PvG8x/6pgX5zAPdWrFhxUalU7gVwGcAjAE8AWAMYKpPJltvY2NQcN26cid6BPlBg3lmJeyPOOY4ePapKSkq6o9VqFRVegBEjVcBY2TdixEgRli5d+lunTp2m+vj4vJbTOnbu3MkjIiLyKw9KfV6zePHit0RRrA3gOQDfr776ChWS7b1CtJGROLFhA2+ybx+TaTR4NmuW2OHttwW8Di0Wp08Dd+9WrTd91Sqqni5YYLj1AkgqKwh4MnMmnsTEcOnmzaxhw4YwMzPD4sWLAQC1a9fm77//PpPpIdGuFHv30qxtfVy1K4lOp8OtW7eQmJgoPnjwQOjRowekUimuXbsmJiQkCABJnWvVqiUmJycLderU0U2aNKmgBL9wIfX63r1bYal4XFwcgoKC+IMHD9ibb76J8PBwsW/fvkKFVDQffUQVs23bkJSUhN9++w0DBgxA+7wRZQAoyM/JoSBr2jSq0s6b92KMmV6IIlVrFy+mwC2fpCR6LN+gsSw2baLz9dy5F89Xq9VYtmwZnJ2dMb2sMWx6wjlHQECAGLFzpzBu926c2rqV3757lzHGYGZmJlpYWPCaNWtKGjZsiDfeeANqtRpKpRIREREICgrSZWZmSkxMTCCTyXizGzfQtX9/Zv2O4UzVRVHElStX4OrqivT0dPz111/wEUWIp0/jlLc3Ro0aBdU778AuJQV/fPABRFGElZWVmJ2dLQwZMgTZ2dmwtLSEp6cnJBIJOOf4/fffxYSEBKFBgwbimDFjBBMTExw8eBC5ubm6cePGSYCCpAJjDE2srETH/fsFC7WaB40YgbScHObq6io6OzsLoaGhYt++fYWDBw9CKpVCFEXY2dlxjVotzpo1SyKranJPqwWUSogbNuCxUgkXqRSSzZtp8sG5cyTtHzaMzrNff6Wq/uHDlAiIiSHPk5AQvcdycs5x6NAh3cOHD9nMmTOFYqNwdTqaUpGdDbRoUepnOCAgQBsUFBSkVqu95XL5v06Wn49CoXCTSqXhQ4cONYmMjFQ+evRIlZWVJVOpVGYSiURbt25dVY8ePSwrnJT76ScaZdmhQ6Vaam7cuMFPnDgRq1ar28rl8rQKL8CIkSpgrOwbMWLZXgumAAAgAElEQVSkCIIgmGdmZqoBlN00/oro0qULi4iIAAAnADH6vGbhwoWHAEChUNQQBEGdmZkpq1n4hv81JjQ7G9fMzVnHoCDYP30Kt0uXBKxaRQGLoyPJZF/VmLfu3Wms4KlTVOGqKKJIvb1TphhunTinCm7z5sDixXDauxdOQJG7s2HDhkGpVCIgIIB/9913rFGjRrp+/fpJdDodbGxsIJFIDDPJYelS6vk20Nz2kjhy5Iju1q1bEuT10Z89e5arVCpmY2PDv/jiC0RFRaFhw4a4cOGC0KtXL3h4eBQE+g8ekPz3448rdQNbr1497Nq1C+3bt8eTJ094QkKCsG3bNsyePVv/yQgrVrzwTXBwcIBOp8ORI0fQpk0bSBijx3r1oqrojh1k+LZzJxmdVYTYWGoZWL266N8//JDOl4MHy1/GlCl0XsXHA3/+CXz8MeLi4gCQqWB8fHzJkwReIiMjAxYWFpBKpUhMTERaWhqUSiWio6N10dHRgkajYRPnzkWEvT2iY2PF6dOnS+zt7SGTyYolYKVSKSwsLNCxY0d07NhRIooi4uPjkZOTw2KysrjvtWuwMjfX9e3bV1IVtVZ6ejoEQcCZM2d0d+/elYiiCJlMxgcPHsxah4Tg7smTXCKRsMu//MLbLlsGhzZt2DxHR6SkpCAiIkLw9PSETQlTFBhjGDVqlBASEoKOHTsK+a0gLVu2xL59+16cq7Vr18ann34KqVQKqVQqnAB0nmfOCB1GjmS6jAxIWrcWMjIyEBERwfz8/NC0aVNERUXhyy+/BOecpdStK4m7eRONt29HfHw8fH19dVqtFiqVSujYsSNr1aoVBEEA5xwqlap0k0+pFJfu3cN5jUbMVauFHp07o/eMGaTiOXu2IJA/d44k9u+9R+0U27aRWmDzZvKJiI6m9oqsLLqWP3hAvhCurnSO63SARAKWk4O3XFwkt44eFS9NmsS9PvyQmcfH07qo1aRwWbeOEgmlfIbVajUuXLig02q1U/7NgX4ecYIgpNjb29du0aKFGfLuY0RRhCAIJgAqnuXPzaXrRo8elbpOpqSk4Pjx40qNRjPEGOgbeRUYK/tGjBgpwrJly1bXq1fv/QkTJlTcGr6auXbtGvz8/CCVSpfMnz9/YWWWsXz58k2MsQmiKGLs2LGmBuuvrSaCgoIQFBQkfvbZZwU3+jod9RBv20Y/CxZQ4O/l9fcH/gEBJKeeOLHi0wQCA4Hff6cfQwTXt2+T5PbCBVoXPapncXFxOHz4ME9NTWX5PlTdu3fXeXt7SwCqrEVGRsLBwaHEYOVVc+vWLRw9ehQajQZjx46Fh4eHfi+8f5+SNTdvUjWykmzdulUXFxcnkclkfNy4cWzbtm2wsrICY4z36dMHTZs2LfvA/vUXyZ59fQEAv/zyi5iUlCQM6dMH7fr1o2DG3p56ZPMltzodBd3+/mVL8AsTHU3b+XJrRVoaLbciPbihodQ+4OuL73x9katWQxRFAIC7u7vo7OwsuLi4wMnJCZGRkQgLC4OtrS3u3bsnNm7cWLh79y5q1Kghenh4CHfv3oWpqSnPyspipqam6Nu3L9o8fgwhJITUC1Xh448Rn5GBw507c0tLS3HixIllmio8efIEhw4d4hqNhnft2hVOTk6CIAi4c+eO7tKlSy8C/FmzZrGIiAh4enrCYvFiQKFAhlKJW998I3bZskWQPnwIVsXPyoMHD7Bnzx4sXLiw7MTbsWM0ZvGvv2haA4CHDx/ijz/+gLu7uzh+/HgBAEI2bhSPxsYKGnNzzjlnb7zxhuji4iJoNBocz3NV55xDq9WCMYbevXuj+0s+Drdv38bp06dFAKxv374sMzOTX716Vfzkk0+K79eoKAoS33sPuHaN/jZyJF2fgoJIzdK0KSlWmjal879uXcDbm8z+dDo677VawNMT/Px5hERF8fs1a/JhrVoJJhYWBSaVrq6Az8vTZwuIjIzE/v377/73v/810PiU15tvvvnmu86dO3/q4+NT9WlCYWH0fTJ5csH+rgCiKGLDhg05SUlJ8gULFnxX5fUxYqQSGCv7RowYKQJjrG+XLl1eWaDPOS9uNAVysfUj5+E358+ff6ayy1epVNMAbJJKpbuysrIqaSFe/SiVSuzdu1cXExMj8fb2LrozJBLqBZXLaYRYQABJnAMDqSo0bRq5+leXPL0wPXtSH6uFBakMKkJKCo2wqmqgf+QIcOYM7Y+lSyskm69Xrx5mzpzJ8gIw3Lx5E4GBgZKgoCDY2dnxtLQ0ptVqIQgC+vXrJ3bs2FH/9pbsbKpIR0bS/jEAycnJuHnzpmhra4v27dsLp0+fhlQqFdu0aSPoPXXiyRMaTRcYWKVAHwDeffddiVqthlQqZQC1FaSlpQEACwgIgJOTE7Zv3847d+7MatWqhYYv9007OBSR0M+wtBSivv8ekkmTKABq0KD4xASJhIKo336jY14eaWkkwb19u7hc/+lTmoaxapX+G92sGXDrFnKPH8fotWtx5oMPxDhRFLy9vfHkyRMeFRWlu379upCbm8usrKy4m5sbDw8PZ2ZmZkJMTAwHwJydnZGamqobO3asRCKRsC1btmDAgAFo3bo1fZbzFANVYuZM1JFKMb52bbZmzRpJbm4uSjKlzM3NhVKpxN27d8XExETBxMSE+fn5gTEGURRhbm7O2rRpAxcXFzRq1IhZW1ujXbt29Pk9cACYMwfWSUnosXChgA8/JDO6SpKamorz588jJCQE3t7eImOs7M/bgAGU9ElOBt58E/j9dzRp0gTjxo2Di4vLi9c2nzpV8Fy+HEn16zOboUNhbm7+4rF27doBoO+e5ORkJCYmwtfXF7du3RJ79eolmJmZ4ejRozqlUinx9vZm7dq1YxKJBGq1mp05c0aSnJxMfjKiSMmphAQ6p/Il/T//TOP58q9zJV0nC/9t+PBiD7NBg9BMFNndPXvENYmJuo+mTZPo23707Nkz6HS6G3o9+V+AVqs9+vDhw2k+Pj4Vd9F7mSVLqEf/pUkx+hIQEKBNTU29p9Ppvq/yuhgxUkmMwb4RI0ZeoFAo2gPwqDbTMj348ccfdRkZGZL//ve/OHr0aG5mZibee+8980LBf5WuW3K5XAQQtHz58vjMzEw3gEaRyWQyw0i3DcSaNWuQk5MjmTNnDko0ZspHKqWqjo8PVYnOnaNxZK1aUWDTuXOl+wz1ZvFiGok1cqT+QW1KCjB3LlVOKsvt22SgFhNDEwtsbIBK9CcLgoD8iQYNGzZEr169kJycjBs3brAWLVrA1NQUT548wbFjx4Rjx46hT58+utq1a0scHR1RpjS6Rg2SyOdVfauKKIpYv349TExMmEqlYidPnoQgCHz27NmC3rJ5UaSK/qxZ1GJgAAoHHYMHD8aRI0cAUJ/1apLNs2PHjgEA5s6dC0tLSyQnJyMwMFCnzMhgb0+ZIoizZoEPGYIbGg3PaNyYvVm3btkj9GbNogA9I6P8qjxjFHCV1JeflQUEB1dwiwFIJDDt1w9xnp5o37Kl8K6TE4SOHQHgRfmPcw5GFxUGAGlpadiyZQucnJzEkSNHCvnX2YMHD/I6deqgdevWDGFhwPvvlzt6Uy8CAwFTU9i89x5q1Kihe/jwoSQpKYk3bNiQ5Sddnj9/jvXr1wMAnJycYGtryxs0aMD69+8PQRDyf4oH3CdOFEw2SE2l68+ePZT8qyRXrlzB6dOnYW9vz0eNGsXc3d31S6y5utKx7dSJkiSRkWjSs2exJJEkNBRO9vZ0zSgBxhhq1aqFWrVqoX79+jh58iT38/ODKIro1q0b69y5M2Qy2YsLqUwmQ4MGDXRB27YJQ3r2ZJg5k6T569YBP/xA1yILC1ovAyAIAkaPHi3ZunWr+Pvvv4vTp08v8dC8TG5uLrRa7T+jb80wBD9//txMpVJV3oBVFElx9ssvlTLkA0hhEhwcnKHRaIbl3XcYMfJKMAb7RowYKUwoAOTkvDoj3szMTAEAVq5cCUEQckRRtP/jjz9yo6KizEA3zUGGeB+1Wi0/derU6UuXLuVmZmaa29vb544aNcrcwcGhmKrg7yY3Nxc5OTl49913yw70X8bCAhg0iH7i4+lm//p1qvRPnEjVIwcHwwf+tWqR8dT779MYPX2Wv20bMH585Rzrs7Pphn3wYOC//6WA2kAIggAHBwc4ODjA09Pzxd9FUUT37t1x7NgxnDp1SiIIAmQyGZo1a4bY2Fg+YsQIVqIx3UcfAdev48+nT3WMMWHIkCHMrJKBXExMDNRqNTw8PNjAgQMRGBiIFi1asAoF+k+fknFgq+pR9LZv3x7t27fHqlWrxKysLAGgymn9+vXh6+uLnJwcREZG4uDBgxA0GonXxYuQhIXhtosLolNSEOrpyeqOHl1k/nqJWFlRr/3+/SSxLQ1RpF77tWtLW2GSVVcCQSqFet48PDh/Xmy1e7eAn3+m6nIeLycPbW1t8emnn74I/gHqp759+zYbMWIE/WHUKKpWV2AEYqncv/9i7J6FhQWuXr2Kx48fs8uXL8PExAQ2NjZicnKy0KxZM3Hw4MHCn3/+CY1GI3p7e5dfNf7pJ3KB//JL6km/cIFULFXg9OnTaNGiBQYMGMAqPNpdKqVJC2lpJIUfOrS46mPbNjpncnNLDfjzsbKywttvvy3RaDTgnBf3S3j6FNi/H4M5lySvWwedqSkke/dSNd/Pj6T4Z84YTNGTj0Qiwbhx44SNGzfy48ePY+DAgeW+xsrKCiYmJtkGXZHXGLlcnrtixYoLoaGh3m3btq3cQvz9KWk+Zkylvi9TU1Oxf//+3Lw+/QoajBgxYliMwb4RI0ZeIJfLc5ctWxZnampaffbhZSCKIjjnDEArAB1EUTwOgIWHh49gjNkD2CCXyzMN8V6LFi06o1AoGmZmZjYFEJWRkbHl119/7dS1a1dtnz59Xum18bfffkONGjV4/fr1Kx+V16lTMIbprbeoSvH113Tz36ED9fdXUcJdBG9vcis/fbp8sz5RpPFplQnSQ0KA3r1JwRAd/beNtxMEAfb29hg7diwCAgLQtGlThISEICwsTKdWqyUHDhzg48ePZ3Z2dkVepzlwAOJ//4v7H34oAYDQ0FCYmJjAysqKSyQSbmNjw4cMGSIpL6mj0Wiwfft2AEB+y0G//FGN+jJjBu2/ixcr9rpKMGXKFCEkJAQymQytW7eGTCbD1atXxXXr1gmCRoOaaWng1tbocO0azgwZgqBCXgOPHj3Kr4qX/SajR9P25JmZlUhiIvXYvzQurwi1a9OM9FICg7LWJTk5WSe0bCnB5MkUFEydCvz4I40ULAfOObZu3SoCEGxsbMhc8vr14m0LlWXJkhe/enp6SoLzFAwffPABcnJyEBERIfTr1w/169cXACC/v71MRJHOn6NH6f/515Iff6zy6vbs2ROBgYH81q1bjHOOPn36iF27dq3YzrC1peRNejowcCAwdmxRiXynTsAbb5DMXg+KTG3JziZFgyhSYqFHD1h//jlWP32KeVOnQpKfINm7l1o9DBzo52Nubo7WrVvnG9WWS17CQq/JNf8WlEqlX0JCQre2bdtWvJft8mVSrVy9Wqmqvk6nw549e7JFUfxaLpcbpDhhxEhVMAb7RowYKQJjLDEqKsqpXr16f0PDd1GCgoK0ADiAULlcfrfQQ2uq4/3kcnk0gGgAUCgUXQF0DAoKCvb09ERdQwbCFeDRo0dIT0/Hl19+abjyu6srjVjjnAzZnjyhvtB69ajnv359vUdBlYpUStW+OXOoP7Wsvt3AQOprdXDQf/l79pCT9eHD9NO8edXWt5JIpVL45Jlhubi4oE+fPhJRFPHnn3/ydevWsSFDhqBly5YASCK9PTmZa+fO5W96eQleXl6Ii4uDv78/tFotq1u3LouOjhZ/+OEH2NvbY+rUqXi56p/vDl442LS0tBQ7dOhQsSDo2TPgiy/0CkINga2tLby8vIr87b3x44XkzEykvfWWaJ6YKNjeu4fDLVvq6qxfLzGrWxdKCwvUq1cPKpUKSUlJcHR0LPtNWrcmEztHxyIV9SI8fUrtHmWZay1fTh4GLyGKIu7evYvjx4+/8BLp1q0b7O3tUbNmTTg5OcHGxobFx8eLcHMTkJNDbQWHDgEjRpQrxRdFEQkJCcKgQYNQz8WFPqdHjgB5PeRV5quvaLt/+AHdu3eHUqmEubk5auUlPupVZiTk0aOkpEhMJOXAH38A5c2v1xMvLy94eXkxrVaLrVu38qioKHTp0qXi7VVmZvQzeTIlg27fpnW0sqJrhz5jFvPhnAL8vXspoRkQQMc2LykjADCztERCQgLqnzpFVX19JjtUETs7O6SkpIiiKJYr5Tc1NYUgCJUfxfAPhDFmb2lpWfF7GLWa2tG++46q+pXg3LlzmrS0tOtardZoyGfktcAY7BsxYqQIOp1OmT+f++8iMjISO3bs4ACkgiCsXrhw4d8+Hkgul4sKheIygJ937tw5Y/bs2VK95dEGJDAwUNekSRO9zZcqBGMFgUTfvnTjevo0OUe7u1PwP2JE5avlTk5khPbjj2Ubp+XmktpAHw4epNFU1tZA//60bgbqgTUUgiBg7NixwqVLl3Do0CGcPXuWm5qa8oyMDMHKyorNfPyY4cABwMsL9erV+z/2zjwsqvr74+977zDAgOyKAgJuuYuiGO577ntq5ZJpZlrZ9m11mUYtM/OXlZm5hFu55Ja5LygmgqKCgSiigoqAguzMfu/9/XFAEUFmhhmXuq/nmUec5XMX7gxzznmf98GEBw262DVr1uD69etYuHAhevTogVatWpXOTUdubi4A6g92c3MTXnvtNdbFxcW89+fff9Pv9do1i/tPrQHr64uay5ah5t9/s2BZgGUxePBgjp86FZcaNkStjh2NQUFBsh07dvArV67kxo8fj5o1a+L06dNCfHw8OI7DxIkT2VKTOaPRCNnUqTT+saJgPzeXWj1iYx8d4HXoQPLvknGc2dnZSEpKQlRUlCiKoti5c2e2du3aOH/+vBgREcGUuNJj0qRJEEURHMdRNKpQUFLq7l2SuC9bRtXlSiguJmV1bGws3y44mMMXXwCtW1t2ciuiV697EuQSg8nqrXf9Op3PzExKEh44AJw7Z/W2IJlMhr59+zKrV69mNmzYAE9PT7Rr167q5E95Ro2i29tvkxLo/HlKqJQmOKdMqfy1yck0XpFlyWCzc2dqDRg79qGnyuVysSgri4FWS2qTx0CzZs1w9OhR5tixY+jZs+cjn1ty3qyUQXo2YBiGMztJpNXSNRITY/qUj3LcuHEDp0+fLjYYDKOkPn2JpwVp9J6EhMQ9VCoVx7Jsdu/evZ07dOjw2JKBO3bsMCQmJkYZjcZvAJxQKpX5j2vb5VGpVK8A+G3y5Mkmzcy2NtHR0UJ4eDhbr149YcyYMSYZMFWbwkIKBFeupMrdN9/Ql+KQEPMD/7t3KeDp3LnivvDsbKr8l+knrpCbN+kLV5cu1Ns/Y4Z5+/GEyMrKQnp6OoqKisAwDEJDQ8Fu3Uq9whUECmU5fPgwIstJ7Js1awa1Wo3U1FQ4OjqKBoOBmTlzpuk7lJtL5oUJCWTW+Lg5cQJ49VUKni5epDFjZa7pixcv4o+NGyFy3AOjAw8dOoSYmBjwPA93d3c+JCSEi4yMFAoLC9m3334b165dE/fu3csMHDBAbLtzJ8MMG4bb/v4QRRG1S7+oazTAyZPQde6MyMhIBAUFkWt6efr2BXx8gLAwnDt3Tti/fz/r6urKBwcHcx06dHjgqXq9HgCwb98+MSEhgTEajejcufM9tcc99u+nBFVuLq1f7n3E8zwWLVoEnU4HF4YR3k9JYbFqlUXjvSolKooq0x07Vn8tUQTc3WlMYmAgfT4wTPUVQRVw9uxZ7N+/HzVq1ICbmxvPMAxu3LjBNW3aVOzcuTNjdtAPkInn3r10DdasSUqOV1998DkGA1V0t28HvviC2j+GD6/SiyBh4kTB79Ah1i0tzbZGqOUICwvja9WqxQ0cOPCRzzMYDPj666+NgiA4KZVK/WPavSfKN998c2zAgAHdWrRoYfqLJk+mvzsHD1q0TaPRiKVLlxYXFBRMnDNnzlaLFpGQsAFSZV9CQqIsLWUymWNoaKjNPxtKZyF7eXnpsrOz7QG8q1Qq42y93UehUqkYAL9169ZN8PPzeyIufaGhoWxAQAA2btzIREVFPSSFtgk1apAkeulS6nU9eJBGau3YQV/yJ02iSqUpX2Q9PalCumEDyaPLJyu2b6cqc2WBvihS4qF9e5qhHR1d/eN7jJSa+z3A6NEUbBQWPrKy3rt3b/Ts2RM3btyAn5/fPZO6u3fvYunSpdBoNIxcLhdRxuDtkajVpOT4v/+rcJyXTfniCzrWd96h1g7godYLtVqNLVu24KXNm6Hr1UsICAi4d7H06dMHvXr1Qm5uLjw9PTkAaNu2Lbty5Up+2bJlnCAITO3atXH4yBEY8vKEpkuXssvr14eLiwvef/99JCYkwGvUKLF4xQomds8ePikpiT1z5gzz7rvvPuzQvXv3vWD88uXLoqOjozBt2jSuokRbqeJm6NChTP/+/ZGXl1dxxblfP0o29OpFCpoSwz29Xo8zZ87g1KlTop2dnTh9+nSWP3OGxeHD1g30AXqv8Xz1g32jkda5fp2C5X79qAJqSdBtAuHh4ULXrl2ZLl26MCiZbpCamoqDBw+KK1euZFiWFR0cHAR3d3euWbNmqFOnDhwdHXHlyhWcO3dO0Gg0YskUAaZRo0bsgAEDKEExbBipXD76iAxMb94kNUZyMvXw29vT5+Dy5fS+qSKIBgBcuQKvQYPY7c7O4kRRZNjHFOzn5+cjPT2dG2OC1NzOzg4uLi6avLy8NgBO2X7vnjxGozGotjnV+YQE8rioRpvT4cOH9Vqt9m9RFLdZvIiEhA2Qgn0JCYl7MAwzulmzZrLHMYLOzc0NAJCdnf0bgB+USuV5m2+0CpRKpahSqQx37txhjEYjW6UjuI2oU6cOOnfujCNHjoiCIDBdunR5fBt3db0vf717lwKhiRPpi7KvLwUOVblujx4NnDlDM6fL9h/zPAULX3xR8es2bgTmzKE+24QEoE4dax3Vk6dzZ3Iuf+utRz6NZVkEBgY+cF9Zk7CGpjqe8zwZif344yOl5FbFaCRfgAULKOmjUAByOcmoKyA7OxsAkNW8OTqPHMmW73EvNUUsRSaTYcqUKVxaWho8PDzg7OyMoqIiZseaNUJOVJTgrtOxuQUFWLt2rXgnPp55VRSZ344eBS+K3HvvvYf169fzK1euZD09PYUXXniBu7f2yZPkB7FmDYYPH84tXLgQKSkpaNCgwSMPVy6XP1pa7uhIrt65uWSWOW0a8ps2xaFDh1C3bl1MmjSJvSf5P3686vNrLtOnU/LMGuvExQG//Qa0aEEBs40CfbVaDYPBwD733HMP3B8YGIg33niDFQQBmZmZTGpqKvfPP/+IUVFRKC4uZniehyAIaN++PSMIAnvmzBkAQExMDErHCMLHhz5j/v6bTD7d3KjKHxZG/dk9e5IKxlQWLADCwuB94QJ0WVlYtWoVP27cOO5xtH+lp6dDoVDwCoXCpAxRkyZNHGJiYgbjPxDsq1QqO5lMJpo80eHMGWrRsNCQD0BpoqnIYDCMUyqVkmRa4qlCCvYlJCTKYgAgoMysaGvx+++/F2dmZjIcxwksyzI5OTlOdnZ26QaD4WulUpls7e1Vg0YXL15M/fLLL1G/fn1Nv379HBUKBXQ6HbKzs9GoUSPzDaMsICQkhMnMzERkZKRYUuF6/Hh6ktT11VepkhcZST30Bw7QDOkXX6RZ8uVhWeqHfest6nst6YVGRAQZIJU37/vpJ1qnbVsadySX/7sCfYD6fi2Y+ZyTk4Pk5GS8/PLL2LRpExITExm9Xo8qPR1KJzFs2WLBzpqJVksBc/fuwKFDpASpYtJCfn4+wsLCAABn/P3xnFwOU8JHmUz2QDLExcUFr86YweHuXcDLCymDBuHEiRMIUSiEWmfPsr3j46HVauHq6oqJEydy4eHhyMvLY5YvX46AgAC+V69eXB25HOB5iKKI2NhYODo6CvXq1bOOskcuJ7+ANm2APXtQs2VLuLm54ebNm0xCQgJarFpFCYE4G4ia9u6l91upssISRJESRleu0O/3xx9JmWMjVq5cydvZ2XGlyeDysCwLHx8f+Pj4oGPHjg+MMBQEAXq9nlm1apUYHBzM9O/f//4IR1Gkz7DJk0m51LgxmfhFRJhfzTUaSTUxYQIwaRIYOztMnDiR2b59O5YsWYKAgADhhRdeYB9S+FiR5ORk3tvb2+S/040aNbKLi4sbDmCWzXbqKUClUtnZ29tH+Pr62pvU7nHnDiWv9+6lZJAFaLXa0jF7o5RK5V2LFpGQsCFSsC8hIXEPURRT0tLS9ADMKG9UTX5+PpKTk50AfAggDuS472gwGA4olcrHbsb3KJRK5XWVStUMQP2UlJQly5Yta6hQKDRqtdoBAPP666/D19fX5vvBMAz69euH2NhYJicnBx6lAfOTIiiIbjxPo/VWraLg39eXDM46dnzwS3PjxtQjvmsXKQMACjxGjrz/nOhoGlEVE0Ovb9mSbv9GDAaqHP79t1mj1Xbv3i2kpKSwTk5OaNu2LS5cuCA8NPO7PGo1VWPLVUetjlZL6oG1a4HFiykg/OefKl+2b98+8fTp0/cCtZfz81Fz2TLyZ7CUiROBL75AvbffRj1XVwYtWjCYPh2hZXwKnJ2dMYSMIdmCggLs3buX/fXXX+Hs7CwOmjmTOb1pE5+SksJ16NDB+l4ZH39MAWe7duhgZ4dTY8eiWbNmwHff0e/LFmRnUyuBpdy6RR4LcXGUjAsPp/e1jSgoKEBhYSE3cuTIh1stquDatWviyZMnhVu3bnF+fn7iwIEDGTYrixItHTqQeuKLL0h1xLJkLGhnZ9mYwx9/BNavpwRXiRrF0dERY3F1CkkAACAASURBVMeO5e7cuYOIiAhxxYoV8Pf3F8aOHWsT35W7d+8ioIIJEpXh7+8Pnufrq1Sq+kql8prVd+jp4SUPD4+W48aNU1SZlOd5MqodO5ZaOyzk0KFDOkEQNiuVynCLF5GQsCFSsC8hIXEPlmUbBwQEPHpelJmUzAdXcxz3/axZs/7PmmvbCqVSeRHARZVKdZFhmE/UavU2AEfs7e2jjx8/3jIkJMTe3d29YrMvKyKXy9GgQQNh586dJPl9GuA4Cvp//JECiRMnSO4fGkrztv/3P3L1t7cH5s2jL1Nt29KX7g8/pMBep7vvlL5jB7BmzZM+Ktvj7EzS5zKu76aQl5fH1KxZEzzP48yZM2jatOmjJaI7d5JsPjXV8qkKptKmDbUILF4MTJtm0kuOHz+OBwL9l1+Gt709UFRUvX2pV48C0+XLSVWye/cjHbVdXFwgCALc3NxE71q1ENiiBf5WqcQPPvzwofGHVoNhkLliBZK/+w6dc3MFtksXFgMH0og8W/DOO5SMsRRfX1KGTJlCSpsNG6y3bxUQFRWF2rVr802bNjW5Yi2KIqKjo4Xjx4+z3t7e3Pvdu8O5Zk0WO3fS6L2rV4GFC8kvwtOT2kz+9z8K9sPNjM3y8yHOno0tLVoIRRMnig7btnE9evSAT5mKcK1atTBq1CguMzMTK1asYNVqNZxtMO5So9Ew5rQLyGQyhIaGyk6fPv0VgJesvkNPASqVyo/juKUdOnRwNkl9l5tLSrIqphk8irS0NMTHx2sNBsMHFi8iIWFjpGBfQkLiHizLNnFzc6u2hP/GjRs4ePCgOjc3l1Wr1Q4cx93gef6Zkw+WVECmlv5fpVL1u3bt2orLly+PqFWrlnratGk2b850d3dHVlbW0xHol8fRkar8AI3S27mTKv4FBdSr36QJ9eC/9x7JZ3v3piDsrbeAGzeocmiBtP2ZZft2ICfHpKceOHDAeO7cOZler2dkMhlmzpyJEvl+5e9PnY7Oe1iY7QL9O3doTGNKCkmgzezdTk9PFwCw/fr1Q/v27aklJiGBJjjMm1e9fevbl66tn3+mJFIVXL9+HYMHD2ZatGgBeHpiYseOsvK+AdZm4/HjEIODhbE3brC4c8f0EZSW8M039F5ctsz81/bsSe/XIUOAX34h93obc/bsWQwcONCsvz/Z2dk4tWULO/DKFSiWLIFzv37UdvThh0BGBvlGdO364IsmTqxyMkZ5Nm3aJMr/+gvBMTG47OrK9uzXD4cPH8atW7fEjz/++KHI8vLly6KHh4fg7Oxs9ZY4URRRWFjImmVAB6BDhw6y6OjooSqV6jmlUnnZ2vv1JFCpVJ4AGnEc1x3AgkaNGhlbmqIO+/JLSmTFxVk8QcFgMGDr1q1qo9E4TalU5lq0iITEY0AK9iUkJO4hCELh+fPnjZ07d7b4s8FgMGDjxo1arVa7EMAJABzP81H/hpmzJf14I7/66qvvfH1933wc2/T29mb/+ecf0x3YnxQKBY3Ie+UVcrpOSSF3/6QkCsLeeYd6XDt3Bg4fpkTBf43Vq8mkLyurwoc1Gg1Onz4tCoLAxMTEyPr06YPnnnsO7u7uAPDoPv28PBp1uHPn/QSMNVm6lNouNmwgJYaFbSU9evRgk5KSEBcXJ7Zr147hOA5ITwf++qv6wX7r1tS+sGoVVforIT8/H7/88gv0ej3TrFkzulOvp2s1KKh6+1AFHMcJnTt3ZrFhA/2uwsKo4vzZZ9bfWJ8+lsv433kHOHWK+vMvXrT+pIAKkMvlgsmad1EEJk+GV58+6FCjhuCYmMhuWr0a/TdvRpvgR4yU37OH2k3efdfk/YrauROdPv2UuThvHoo+/RRv1KoFb29vpKWlCZcuXWJPnjyJjuUmHhQVFQnOzs42SdImJiZCFEXR39/frL8Jjo6O6Ny5s93Jkye/BmA74wUbo1KpmtjZ2X0miuIImUwmd3Nz07q6urIDBgyAh4dH1d9diotposTQodUalbh//36dRqPZN2fOnI0WLyIh8RiQgn0JCYl7CIKwIDs7e7xGo4GjicEYz/MwGAz3pK9bt241CIJwUKlUzrXlvj4pVCpVAMdxr7dq1cq2JcASfHx8oNfrGaPRiCc1HcBs/Pzo1qULjdFbs4Yq2r/9Rj356ek2D6qeSiZMeNCzADSbOT8/H6dOnRLi4uJYmUwGV1dXwdPTUwwJCalw/NtDGI1kBDdr1oPTD6zBW28BM2aQC3sp1Rjjd+nSJQBAZmYmo9fr6XPmhReoilxdGAaIjyevgkrOW2pqKtatWwdRFNG/f3/cO7/ffkttKDa4LpOTk+Hl5YVLly4JeXl5rKDXA0ePkuJl6FBg5UqqQnt4WFfp4uBgvoz/2DFg9mxqh+jRg4L+xxDoA0BAQACbnJzMt2zZ8uENiiJd5/v2UdX+wgXAxQVMnTp4/uWXWVGlwsB//hH27NnDMiyL1q1bV7yRM2cqTbZViF6P1OPHhbotWzIvjBvHlA0Oe/fuzV66dAmHDh1CaGgoyr5Xg4KCuPXr14uCIMCaPft6vR47d+5Er169GEvWbd++PRcZGdlPpVIFPckJOHPnzu0ok8kGff656T0sKpXqeXt7+3VyuTzg+eeft2vbti3r4uIChmGqcCstQ3o6jXU9cYLGMVrIjRs3EB8frzEYDK9bvIiExGPiGfnmKCEh8ThQKpUX58+ff+3cuXP1OnXqxGRlZSEzMxM1a9ZEWcmgKIpIS0tDYWEh9u3bpy0qKnKYOnUqzp49i8uXL9sB+Df/AWzH87zz4cOHjZMnT7b5mEJvb28ANJLKxcXFptuyCfXrA3PnUhBz8yZJihMTqZ+2Z0/qmezRg8yybCyhfuLI5ZT4MBqB6dNx69Yt7NmzR8zIyGAAsJ6ennj99dcZBwcH8y6qYcMoibJggXX2U6sFNm0iuXNmJvX/9+1LbuzVpPR6btu2rejo6EjHWVQEeHnRdqtDXh5VykeMoJ8rcHQ/duwYXFxcyCugZF8AkNrEBoiiiN9//x0AYG9vz7z44otoxnFAbCw9wd+fkmIffUQBSHi49VQv+/aRn4Y5Sg9/f7qWevYEoqLo/fuYyM3NRZ06de5f+4JAqoImTaiN4I03gJdeos8TmQxYsuTeUxmGQVBQEKvRaMQDBw6gTp06zAO/XwAoLKTEVYlSpkquXkVxx47IGTeOtVu16qEqsKenJ6ZOnYoVK1bg4MGDQr9+/e5F3z4+PrC3t8fZs2cREhJiwdmoGK1WC7lcjvDwcMTFxfFBQUFcSEiIyYlgBwcHdO/e3T4iIuJHAF2rfIENUKlUHIBIg8EAlUo1s6pRdSqVSmFnZ7dALpdPGThwoGPz5s0tS6AYjXTdqFTVCvSNRiO2b99ebDQapyiVyjyLF5KQeExwX1Q271hCQuI/SURExNC0tLT6bm5u2Lhxo+bq1auRZ8+e9YiMjERxcTGMRiN78+ZNbN++PTc5OTlFq9XOYVk2PyYmJuj27duXRFEco1QqLzzp47AV3bt3vxgREbFbrVaPcXFxcaxdu7ZNR/GxLIvz58+LTk5OjI+Fo4GeCrp2Jef9X38FBg6kEW3x8TTf+tAhoH9/Ms5avZqCHRuOrXqiHDoEFBaiMDgYq1atQmFhIdOyZUsxKyuL6dWrF+rWrWveejxPQfLLLz880tBctFqqfN26RcmY99+n4Kphw+qtW4Zjx44hKysL3bt3Z+4ZXNrZUbX1hRcsc0cvxc6OriejkVQkAwY89JScnBxcvnwZ9evXxwOj0X7/nYKA0aMt334FMAwDJycnMTk5menfvz8TpNWSguDjjx881l69KKDNzr6vTqguTZvStky9Ll58EWjQgHreR460/TSHMuTk5ODo0aPo+8ILjOuRIxSUrVxJypL336eExahRlBRq2bJS+bWPjw9z9uxZxmAwoFGjRg8++P775Ofw6qtV71BaGv4+cwZJd++i98KFeChxUEJUVJRw8+ZNpl69ekz9MokRhmFgNBoRGxsrhISEWK20b29vj06dOqFp06YAwMTGxuLYsWNgWZbx9/c3aQ0fHx8mLi6u5pEjR5zCw8OPdrdCEs8cIiIiXvL29u7HcZwgCIJzeHh4ePfu3SsM+FUq1XNyuTy8fv36vSdMmODo5+dn+d/bESOA27fNauGoiOPHjxtTU1MjjUbjZ4/73ElIWMLTafokISHxxBBF8RdRFHfv2bMnVRCEqZ988kkPo9Hoqdfru546dWr2jh07bv71118wGAwjP/nkk+azZ89eNWvWrAkA2FmzZjX9L4yfUSqVZ3me77tnz578uXPnori42Kbb8/b2xqFDh0ShOs7aTwOBgSTXXriQbrVq0RfwnBz6Yi+TkVHb/v001m/gQHJMftaPu4SLFy/iTO/eONWrF35csgQKhUIYN24chg8fzsyePRvB5krwV68G2rUj80NzkwRlKT2/w4eTq36rVhT022BkWOm4sI0bN6KwsJDuZBgK9KvjyC+KNBauuJgC1b17yZyuHG3btoVMJkN2dvaDDwQG0nFbGZ7nER4ezgBAUlIS8PzzdG7LV2I57v45ePttqsiLjx68UCXbtgErVpi6o+Rb8NprlPigYNLm7N+/X/zqq6+w83//w/hz5wT/OnXIMT8+nuT6pecqKMik61EQBOTn51ccnH/6KX3OVMXly0BoKK6eOCF4zZwpVhboA0BRyTVbUQDaqlUrJjc3l1PbYLSil5cXunbtyrzzzjvM6NGjmb///hvr1q0TjEZjla/lOA4vv/yywmAwfA5AUKlUpmUJrIS9vX3/4OBgpylTpjh4enq+bW9vf0KlUnVQqVQPnMS5c+cOt7Ozi+3Zs2fjMWPGODg5OVm+UZ6nqTCmJHoeQXp6OiIjI3U6ne61qhQJEhJPC4xY3T8mEhIS/ylKJHg1JPkaoFKpBgLY/dlnnz3aPK2aqNVqLFq0CK1bt8bQoUNttp3HxurVNG9+zRqS9vfuTZX8L78kQ7F+/cit/9tvaaxb69bUZ/nDDyQt7t3bJoGordDr9di2bRufnJzMOTs7C1PmzmVvT5qEhv9XjUmUgkDnIiuLZPzVWcfVlRIsbdvavJXi0qVL2Lx5Mzp16oSePXvel+O2bAn83/9Zbi5YWEhB4tKldG2cPEl93VOmPPC05cuXC66urnjxxRdZOzu7+w8IAikaqpM0qQBRFBERESFERUWxnY4fF7oaDGyVLQM6HfDHH2Tgt24dmV9awg8/UBvGV189+nm7dpFnQJ8+gFr9oD+DDTh//jyuXLmCW5cv8wPDwrjCiRPh6+KCmqdOAWvXVuu9/euvvwqZmZnMsGHD7psvArRucjIwf/5Dr8nPz8fZs2eRkZGBFjk58B80CHbXrmFJVBS8vb2Fvn37sn5+fhVKx2NjY4Vjx44xBQUFjLOzs6BQKISpU6fKSp+7atUqwdfXl+3fv7/Fx2QKBQUF2LJli3D37l1m5MiRTMMyahxRFKFWq5Geno6srCzcuHGjKCUlhWNZNkKn08WIojhPqVQabLqDZfjmm29iRowY0a5hw4alI0XFyMhItV6vvyuK4kG9Xp/q4OAwkGXZ1mPHjnWstqJt2zZqF4uKqpYhX3FxMZYtW6bWaDQT5syZs616OyUh8fiQevYlJCTMQqlU8gD+84F+CS1cXV2Ncrncpp+lCoUCTk5Oz2bPfkW0aUM9y6JIwVVSEsnb162jXvH9+0ny/8MP9PyjRymYu3YNeP11Gt937BhVa95//4keSlUYjUYsX75cFASB+eCDD+Ds7Myif3/UqE4v9J07QGgoJUw6dbJsjT//pN7ns2fp/LZrZ/n+mEhiYiL++OMPAEBwcPCDwdOmTdUb77Z5MxnLla5Zpw4lD155BXBywtGjR3Hu3DlBp9Oxw4YNwwOBPgAYDNSukJ9v1YQHwzDo3r0727RpU+y4fZsNbt0aVU5dt7cnlcWJE1Thv337kdMFKuWllyhxUBUJCZSA27WL3OptwK1bt7D1t9/44sJCLiQ6Gn3On0fqwYNcQHY2ZGPGUPvA229XaxtGoxE3b95kQ0JCkJubiwdMTXNyyDOjHDExMTh48CA8PDyEJgoFGs6axW5KTES6jw8UCgWfnp7OhYWFoV27duLAgQMfihTbtGnDtmrVCgsWLICfnx8uXbokmzdvHurVqydOmDCBCQ0NZffv3y/079/fptlJFxcXTJ48mT1+/Lj422+/YejQoSgsLOTj4+OL8vLyHARBEORy+QWj0RhrMBhOADipVCqv2HKfKkMUxVrOzvQu4DgOzz//PNO+fXunGzduOGVkZLxeVFQk1KxZk23RogU4a5lDTplSrUCf53ls3LhRbTAYfpICfYlnDSnYl5CQkLCc7Pz8fJkoijbt2y9BfMC86lkmOJhur7xCfcIjRlBVsVcv6suNiCCDsHXrqBe9Vq3789xv3KB/jx6l/uZbt2ic36ZNJMV2dASeoqRIdnY2ioqKmE8//fS+e3ZAAPkT/Pij+QuKIlWhJ00CfH3Nf/3HH5Mkum9fmg4APJZAHwACAwMhl8tFvV7PpKWlwaPs+L7t2ynBU25agUkUFVFVf9Cg+/fVq0cz4q9cAYKCEBkZiYCAAHbkyJFQVFQpt7enBFT5JICV8N62DbV4Xohr2JDtbMoLnJzIEf/wYQqCt20Dmjc3b6PLl9P7ZdWqih8XBOC77yixMGYMYObcdpPIzwcSEnAiPh7TVCpO/d13EAcOhAvDoFXr1qTasRIymQzDhg1DdHS0cOHCBTY7O5uUUFlZNPWgggRbeHi42L17d6bTuXMsnn8euHoVk728oNFokJaWxvE8j61bt6KoqKjSz16O4zBz5kyIosiePn0aBw4cQEpKClNUVISmTZtiz5497KVLl9CkSROrHWtFHD9+3PD333/zLMs6/Pnnn7C3t9+m0+l+BnBRqVTetunGzUAQBPvyyTaGYRAQEFDa5mOdxIhOR39HwsKq7T+xf/9+XVZW1mmDwWCDGZkSErZFCvYlJCQkLEClUjkCWDVo0CCReQyRvqOjo5iYmMjY+gvjY6VZM5INl8KyJOHv0YMC2XnzSJbt5vZwxbHUZMloJIlm/fok0d22jVoDvviC+jMfo5t4RchkMhgMBiQmJqJFqTza3h44eJCqjebMqxdFOj9jx9KYPVPR6ahFonQqAsuS0Vk1jarMRaFQ4MMPP2QWLFgAXfmKc3q65QaDDENBfXmX9QYNgC++wPF334UoinjxxRcfPVL08GHq+beie3opwokTcC4qYk01UbtH797Axo30e5szh65rU2XuL7xA11hlpKSQxP+774CrV6039k+rpakbI0YA8+fj9oEDuPT668g4fBgBHTpYZxuVEBQUhKCgIDYtLQ3r1q1Dnz59oPj4Y1LDVKBaEAQBgc7O5OzfvTu9L0Az6UsN/maZ8F5jGAYMwyA0NBShoaFYs2aNsGTJEtbR0VHQ6XRsdHS01YN9QRCQk5MDvV6P69evi5GRkXd5nm8N4A4AfPrpp09ln64oinKZTIasrCzExcUhJSVFyM/PhyiKGD9+PFunTh3rbCgjgxKrDRpUa5m4uDjx/Pnz2QaDYXiJslFC4plCCvYlJCQkLMMXAFJTUw0Mw8jbtGlj0+r+8OHD2VWrVqFTp06VOkM/c8yaRdX5/v2pR7lE2gl7ezIKmzCBTLXi4qg683//R7LzsnJcmYzUAQDw/ffAN99Q3//ff1MP+qlTNAFg3z6S/VtzjnkV5OfnIzY2lre3t2dr1ap1/+LgOGpdMNczRxCoKm/qTHqtlozOWrYkd/o336QEyhNi27ZtYkJCAuPp6Sm2atXqwTfL99+TQZy5iCL1mG/dCrRti4KCAixZsgTOzs5CSEgI20WrxaW//hL7jBrFPDLQB4ADB+gatHawf+0a7i5ZgpiVK9Hdkup5mzYUuBw9SuqXTp0eNvirCJ6vXMZ/7BhQowaNVbxzxzrvi7VrgfPn6X29bh3+zMkxXm3alCusW5fp3r27zQP9svj5+SEgIIBfv349pn73HVfheRBF9N+5E8YaNaif34o+IBMnTmQzMzORl5fH/vXXX/AqSSJUF0EQkJCQgGPHjhXl5eUpZDJZPsuyRQzDZBoMhvFPUwW/MhiGsfv5559FnueZOnXq8E2aNGEDAgKY8PBw4ejRo3jllVeqv5E5cwAfHzKbrAY3b97E3r171QaD4QXJp0jiWUUK9iUkJCQsQKlUXlGpVH2Sk5NfS0hIeOXs2bOQyWSFCoXCoU+fPnYe5lRsTcDHxwdt2rQRNm7cyMyYMYOxaM7w04iHBwUuGRlA+VFZHEdV+1u3qGLP8xRQXL1KAX5F8vPSoCW8ZCjExYtUseM4wNubqtmTJwNnzpDs24KeUFEUcevWLSQnJ4t37twRXFxc0L9//3sLaTQabNu2Tbx69SoDgBsxYgRqlbYhlLJhAwVFqammbXTJEjKd27Kl6udqtXRO586laub583TOnjBubm4MAEyePJmxLx9czp9P+/nnn+YtqtEAEydSQAwaVSmKIgoLC9nw8HB4Tp2KkLlzGZfp06teyxb96oIAtGoFjz17EBAQwH/77bdckyZNhBEjRpj3Bq5Th9pbStoSsHlz1UZ6R4/S84cPf/ixadNIARMZSetZAs+TAeDw4XR96vWAqyuyBQEn585FXGysrHu7dmJ2drbYpEmTx96CNGLECO7E8OEoOnECzuvXP/igICAjORnOeXmMoUkTmxh+chyHzMxMUavVPjCWz1IEQcD69evVGRkZV3U63f8AhH/++edV2+8/ZbAse7tPnz4NWrduDZZl731u+vv7MxcuXBBQXRm/KJIPRbdu1VomMzMTGzZs0BiNxheVSmVitRaTkHiCSMG+hISEhIUolcrDAA4vWLAg4fbt26N4nt8M4Gtvb2+he/fuVv/22KtXL/by5cvYsGGDMGHChH9HtM+ywF9/UWCydClVeMvj6wvMmEEKgLNnSSLs4kIu5Tk51LNfGU2bAjNn0s9Xr5Ls//x54J136MvgF1/QyLVJk6DX62EwGGBvb3/P2EsQBNy6dQtpaWlITEzkMzMzOZ7nIZfLRS8vL7FWrVpcTEwM+vbtC4ZhkJiYiN27d0Ov1zPNmzdH27ZtUa8ic7UBA8zrua9Rw/R+9oYNybhw/vyqndgfI6WKlHPnzomdOnV6MPgbOfJewG4WP/4IvPoqtHo91q1bZ8zLy5M5OjrCy8tLzMzMxLZz55gxgoDAPBOKcgsXAsePWzfoZ1kgLQ2cmxvqRkSIqampuHbtmuVrPfcczZ7neTLwe9S1P336w2oJoxGIjqYgPy3N/HGDt26R1H3KFFKNREeTXL9FC6BjR8TFxeHPn36Cl5eX8PzzzwvdunV7Yt8zHR0dUSMwUPg7M5PJIdM6hmVZxERFoe706WJyYCCT+sEHeKNXL6tv+9KlS9i+fTs8PT2FkSNHcg9MBrCQU6dO8RkZGck6na6dUql85oL8MpwTRbFB+YR1p06dmJMnTzKLFy8W3N3dGXd3d6Zx48Yw69zFx9MEl61bq5XAycrKwpo1azRGo3HCnDlz9lu8kITEU4AU7EtISEhUk88++2wBgAUAMG/evMERERGdunfvbvXtKBQKjBo1CmFhYawgCBWOgnpmcXWlUWk8X3m1vVEjurVpQ0H6kSMkQf7nHwpC/PwevQ03N/q3V6/7Rn9eXjh37pyYnpQkdF6xgvt94kQIPA+5r68gq1lTvHPnDscwjOji4iI0aNCAGzZsGJycnODg4MAAYAAgLi4OixYtQu3atYXU1FQ2KChIGDZs2KN/OR4e1Gt+4ACZ5VVGSgoFwseP329zqIiLFyl5kZZGCgBze8NtiNFoxIULF7Bz5074+/sL/v7+D5+b2rWBS5fMW1ijAb9wIXYoFOLlwkLG1dVVptFoMGrUKDRo0IBJTk7Gtm3bwL/zDrj0dGrreBQ9e1ZvIkB5rl4l/4kSVUVOTg7DMAzGjx9fvTfu9Onk0P/KK8AHH5BSpSJ++43O6U8/3b9vxQpSt8yeTVJnU7hwgbZXuzYlF/bvBz7/nN5P9vbUanPvqRcEmUzGvvXWWyysZbRmKampCJ0yhb1RsyZOnjzJL168mOMMBjhxHDxat2biAgKgzcy0evuVKIrYsWMH+vXrh+DgYKvYyfM8j4iICL1OpxvzjAf60Gq1h5OTkwe0bdvWqez9Dg4OePPNN5GRkcHm5OSIERER+Oeff+Dj4yMqFArGxcUFoaGhqFmzZuWLnzhBJpvV+NuYk5ODsLAwtcFgmDZ79uytFi8kIfGUIAX7EhISElaEZdlwQRA6bdq0yTB48GA7Jyenql9kBkajEXK5XGRZ9t/hzF9KcDAZpM2fTw7dZZ3Vy9O4MZmW7dpFcvgffiB3+4wMwNPTrBFLd6dNw19LlzKdO3TgmDZtMH3YMIgvvYSCw4fZuG+/xYsnTsDlyy8Z1KlT6Zf29957DxkZGbhy5QrbuXNnNGjQwLRvmhs3kuT0UcF+YSHQtWvlgf7XX1PC49tvydNALn+qAn0ACAsLQ3p6OgBg/PjxrKyifvPLl0mBMWZMlevp9Xro9Xpci4rCrunTEeDmJr46YgTjW04p0aJFCzJFzMkhU8OQkEc7zgcFVWs810N4eZHCosR5PDAwkLt+/Trv7e1d/QDQ25sSQKVtDCrVw4kKN7cHDSCzs+lai4ysegLD3btk6vj553RzdgbWr6dkUkXTDAAUFRUhJSWFbd++ffWOzVrMng1oNPDfuhX+/v7c1eRk1Bw8WKwxZAhzc9EiaMPCMHjwYKtvVhAEGI1GHDlyRNi9ezfr7e3NT5kyhatOcjYpKQkMw1xUKpVJVtzVJ8X2q1ev/qDX6yEvNw6xZs2apcE8ExwcjJSUFBQWFjJFRUVCdna2uGLFCq5x48bisGHDmIc+Rz75hHxJpk2zeMeKiooQFham1uv1/5s9e/Y6ixeSkHiK+BeVhSQkJCSePDNn+F2ivQAAIABJREFUzpwDYHRSUpLdxYsXrb5+YmKi8K8x6KsIjYYq06YwZAgZMDVpQoZ+331H48mUSpM3d+7cOcHDw4Pv9cILcH3pJcDBAczOnXCNj0e3tm3hkpFBVc3Zs2l7AI0TK4OLiwsaN26MgQMHooE5zs+//EKV1ooQRUp4FBZSP3R5pk2jILZhQzp+lgXGjTN9248JtVp9L9CXyWSoMNAHgOefv++z8Aj0ej0WLVqExYsWwW/MGDTIz8f48ePZ8oH+A3h4kKndlSrGiufk0PPMNU6siJgYYM0aCvZL4HkeWq3Wet+7PD2pl9/JiRJHZSdbAKRgGT+efjYYKNExfDjQvn3llc/PPqNbbi5JotVq8lH47Td6TSWBfm5uLhYvXgyZTIbephpI2hJRJGPOX3+l/9+5gwZGI1x++olh5s9H7ZKkz927d62+aY7jMHDgQOG5554T33//fRQUFHBHjhyp1pqnT58u1Gq1FXwQPHsolcpsmUx2Njk5+ZHPc3FxQVBQEDp37ox+/fqx48aN46ZMmYKUlBRmwYIF9z5XAADXr9N16uaGjIwMJCYmIi8vD4IgmLxfBoMB69evL9Zqtf83a9asny09PgmJpw2psi8hISFhRVQqlTuALb1790aQpcZXjyAtLQ3PPffcv6uqX5Yvv6SAf/p0ctZ/lHQdICO6vn2BLl1Izr9tGwXjW7aQfDkiotKX5uXl4fz582zHjh0ffrDU0G/fPvq/INAIp6IiahdYtozUCPHxwOjRlstGg4KoIlXegVqtBhwdScVQilZL/eQjR5I8Oynp/iSCpxSFQoEOHTogKioKoaGhlZtvyWSU3Dh3rlKn+YiICBw7dgzOzs5o0rQpzvXogfYzZpi2IzNmAB07kudDZc7ztWtTYscanD1Llfcy4w05joNOp2PS09Ph4+Njne1wHF3nej2NsvzgA3rvABSgx8dTwJuaShMqyl5PAL3XLl+mvvv9+8lHomZNSiIdOmTybsTFxQEARo8e/XS0F82cSa06GzZQ4P/CC9RS8d13AAA5gCFDhuC5as5fr4zg4GA2ODgYOp0OarUa7uXHQpqBwWBAWlqaA4Dt1tvDJ4tWq/3jypUrwc2bN3cw53W1atXC5MmTcfbsWXHNmjVMs2bNxCEODgxrMACJibhy7Ro2b94MBwcHUaPRMKIoQi6Xi3Z2doK9vT2cnJxYd3d3xtPTE97e3qhTpw6cnZ2h0+mwadMmTV5e3gGj0Whif4uExLOBFOxLSEhIWBGlUpk7f/78m4cPH6574sQJbePGjTF06FAHa/SFGgwG3L59mx1pqlHbs4q9PZnoxcZSEG8KCgUQGkoV1V9/BVavpqC8sJCCn/nzcaGoCLdv30ZeXh5/8+ZNtrCwkAkMDBRCQ0Orjk6Cg+kGUEAoisCOHSSjHz0aGDaM1AVDhpgnBf/kk4dN1hYupJFpf/xB/9dqKckQEwO89x4F+0ePmr6NJ4xer4evr6/Qo0ePys8zywIODjTn3tW1wqcUFRUBoFnqvffvp8RQ+QkOleHiQj35v/9Ov6fKmDWLRj5WR4qekUF99G+++cDddevWBQD8/vvv+N///mf5+hUhl5NDf1YWJai6dqUAt1Urur7i4ugxZ2cgKoqUFKGhlNBat47OZf36wNtvm73poqIiREdHi56eng+1UjwxOnSgto2YGAr69+x5yBCzjSWGkGZib2+PDh06ICYmhm/Xrp1F7RtqtRosyxbNmjWr2Nr79wQ5npKSYgBgVrAPAB4eHujTpw/TsmVLbN++Xbz91VcMJk9GboMG2LlzJ/r37y8GBwczoihCp9MhPz+fKSgo4PLz85Gfny/m5OQYExMTmVOnTrFqtZoRBAEcx4kcx/2h1+tfVyqVVpD2SEg8PUjBvoSEhISV4Xk+AEBdrVbrl5CQsM3BwcGtX79+Zn+pKc+1a9egUCiEmjVrPgWlMxvCstRXnJxM1f2PPzb9tTVqUDV16FCqeK5aBcTE4Nbduzj/ww9wql8ffNOmXLdu3VCvXj24urqafy5LpczjxtFNFGmWvYsLBU6ffw4kJtLtuedIbl0Zo0ZR0qBsn/2dOxRsCgKdi27dSKq/di2NS3uGOHz4sBgfH48uXbpUXfHdto2C1kro0qULzpw5g+iICPTevJmq2Obw8cfApk33z2tF3LkDmOLc/ygGD6bEULkWDS8vLwwfPhy7d++GTQw227ala3HwYLru33+fzPU2bgTCwkhtoNEAkyYBp09TcqBuXVJSvPSSRZs8c+YM9u7diyZNmgijR4+2ihldtYmNJVVM795Uze/UyfRJFjagWbNmiImJ4SrqUTeFu3fvQiaTpVp/z54o54uKiuzUajUUlbSGVEVtZ2e8CbDnNm0SDx4/zhj++AMjRoxAy5YtGQBgGAYODg5wcHBAmdY3BmViH41Gg2XLluk1Gs1BvV4/UQr0Jf6NSMG+hISEhJUp+cJwA8CNefPm7Th16tS00tFs1cHR0REGg4HVarVwcKh27uDpR6sFVq6kMXmOjua9NjAQWLQISEiANjsbOW+9hT7FxahpZ0fV26tXK60gmw3DAPPm0c88T3J/V1fgww/p57lzaV/mzXs48DcaqQrcpw+Zn82eTUGvwUDJg/PnSU7t4lLlbuj1eshksqdDRl1CcnKyoNfrudDQ0Kov/sGDacrCsGEVPuzi4gInJyfR/sYNBlevPjIxUCG+vpSAOXaMqvwVsWVL9Xv2jx+v9KGWLVti165dKCgogFvpdAhrwjA0yjIriyrbN26QYmL3biAzk1Quu3dTgF9URNecpyddh46OpKox4XPqwoUL2LqVjMo7derE9+7d++kI9AEy7ExOBg4epFuJQeKTIjc3F0ajEfHx8Whb1USICtBqtQCQa/Ude4IolUr+m2++uZiRkdHGLJ+TsvzyC9idO9HuzTeZZsHByMrKQoAZ0zS0Wi3WrFmj1ul0a3mef0sK9CX+rUjBvoSEhIQNEQRBCWCaTqerdoDu7+8PPz8/fuvWrcy4ceOenojOVrRsSf3EJcZLMHOcodFoxAWexx4nJzQaMYJvERfHIT2dAro33qDefkGofNSfJXDcfVn+yZMUOF67RhV5nqceexcXSmLcvg34+FCrAUBqhtxcoF8/CvB376ZkQQXcuXMHycnJUCgUKCgoQHx8PJ+Tk8PJZDI0bNgQAwYMgHNVfgc2otSNXC6Xw9XVlc3PzzctAfHFF1WOxzPodMxrGzZAP3gw5H36mLdjDENKipJ2gAqZPp2M/A4eNG9tgH7XzZtTRb98a0YJxcXF4HkeojVMACtDpyNTwtLxkn/+ST34rq6kbCgoIJn7Dz9Qr//06XS8okgmhQ4OpIxZtoyeO3s2EB1NvfxZWcjRavEPz2Pw9etoEhgIx9atOXz7LalT0tPp9W3b0jhIliUvgexsutZzciip4OdH7z8HB3pv8zwZDcpklXo2mITBQKqGd94h1cITDvQB3AtA1eUNFE2k5O+GDTJDTxZRFJPy8vIs66XYu5facaZNA1gWCoXCrEBfp9Nh7dq1xXl5eZsMBoMU6Ev8q5GCfQkJCQnbUgBA0Ov1rDWq8YMHD+Z+/vln3LhxA/5P2Yg1m8Aw1GN85YpZwX56ejrCwsIgl8uFIUOGsC1atOAgCMCpU1Rp/+ADWjM0lHr8hw613f43aEDVVoCk5Pn5QEoKSfW3b6dK85dfkjv/8uVAqXN3Bcer1+uxa9cuMSkpifHy8hL0ej0cHR3FoKAgtm3btsjJyUFERAS/dOlSdvr06YxLiSJAr9cjISEBsbGxQm4uFQnt7OxEZ2dntmbNmsygQYOspghYvHixoFarWaVSifz8fLFt27YwaVSkXA7cukVBWiWwgHikd2+me6tWqGXJzvXrB7RoQW0RFZmzvf32w672piKKZLT4iLF2p06dEmQyGetqLVVJedasAT76iCr727aRoqRJEwr4e/QgE0Q/P7ouS536AWDq1PvHYDDQz6NG0blwcqLkhVwO5OYiautWcC4uYvC4cQwKCmgigFoNuLuTekCjoVtqKiULeJ7ea/XqUQLLwYG8LXbsoORW8+bkc9C6NalffH1JFfPXX5SMa9iQ1u/Shd6r3t50nqOjqW3HyYn2uV07Gt1oMNC6TwmOjo7gOA4ajcai17u5ucFoNAZad6+ePFqtNqmgoEAESetNp9QT4/BhunbMRK/XY926dcU5OTnb9Xr9G1KgL/FvRwr2JSQkJGwMy7KCXC63SiTl7u6ODh06CNu2bRPff//9p0c6a0sWLiSZ8fffA1OmVDr+qyxXrlyBl5eXMHXq1PvnnWWporlrF/Uw79xJAU3z5hSEZ2TQLHFbUtb4LSOD9mn1ajq+b74h1UFiIgVd5eTUt27dwvr160UPDw9x6tSpjJeX10PXlEKhwNixY7ldu3bxy5cvZ9zd3ZGfny9qNBquRo0aYvPmzdGzZ0+WZVkUFBQgMzNTPHnyJGrVqoXQ0NBqH96vv/4qqNVqFgDmzp0LURTZESNGmPbijRspYKxkPwoKCtD3jz+YWvPno5al4ycZhlQd69ffb70oS926NAKwqjn05TEaaWTdV19VWk02GAw4c+YM27t3b+u3WgweTNexSkVBfXExBUQ7dlClf+VKCvhXrKDgfNeuiivoDHO/PcLL6/79ZdpPUqOjwXEcMHDg/cdLr+uyaouuXR9ef9Kk+z9X5BPw6ad0LktVMIJAwXthISkAGjakx318SCEgk9Hx5edTYiEri4L/p4i0tDTwPI+GDRta9PoSJ38nlUrlq1Qqb1l1554s+RqNxgAajmAaGRmkDvnnH1KamIlGoykN9HeW9OibPptPQuIZRQr2JSQkJGyLXhAE2ZUrV9CiRQurLNilSxc2NjZWjIiIQLdu3ayy5lMPw1DVslYt4OWXq3x6UlKS4OnpWXFEZWdHAV9BAVXWf/6ZgoX+/SlIevFFCpJs7YtQOgLuuecoyOzenb7IpqcDDAN1cDDON24snAoOFmV6PXPXaGQ7duyIPn36VBkpDh48mAsICEBBQQF8fHzg6+sLBwcHBuWqaKIoMvb29mK7du2sMs4xJyeHqVevntC1a1d269atKC4uNr03ffnyR8quz585g0YZGXBp0qR6Ozl9OgXmmZk0bq8sKSnkxp+fb96aly+TtHjhwkqfcv36dej1erRs2dKCna6AwkLyN9i9mwL7pk3pmgoIoGuob1+a3hAXR9e5QgEsWUIV/cWLqTK6evWD5pAm4OTkZLx586YsPz8fNlEolEr5KxqRWDbJUzqJIS6Ozvv8+aQo4Dg6Jy1b0rl4gqjVaqxbtw4AUL9+fYvWYBgGjRo14i9evDgCwI9W3L0nTaFWqzUv2H/xRWoR+eEH8zdWWIiwsDB1UVFRmMFgmCFV9CX+K/z7ez4lJCQkniAlXyje3LZtG9asWVNw9OhR8cyZM4iOjsbVq1eh0+nMXlMmk2HIkCHMyZMnxRLzpn8/HEdjtPr3p/FpVSCKouhdVfXXxQWYOJF6kps3J3n/oUOUWLC3p97fa9ess/+PolcvmoF+7RowcCAS3nwTixYtEg40b4784GBxqLc3N12lYj977z30yc9ncOJElUsyDIOgoCB06dIFDRo0qNAvgud57N69G4MGDWJk1emTLuHatWvQarXM0KFD2cDAQLz00kt4/fXXYV/ZXPvy/PHHI8fiteY4hE2ahNvV9Viws6Oq4MaNDz/WsqX5bvxaLSUNLlyo3OUfgI+PDwRBwOnTp83c4XJcukTXppMT9eEXFFDQ37jx/eckJ1OV/NVX6f/9+lEyIjYWWLCAKuxjxlDlvGdP8pcwkYkTJ8q8vb35VatWVe84rEF2Nsn+a9Sg42nQgDwZvv+epnE8YTZt2iQAQOOyv5sK4HkeglB5kTk4OFghl8s/UKlUT96EwHoUabVa0yvrsbH02b9kidkbysnJwYoVK9RFRUXfSIG+xH8NqbIvISEhYXtWAXC9fv165s2bN5vI5fLGPM+7GgyGXgAgl8uFHj168KGhoSZ/kWvYsCH8/f2FzZs3Mz4+PoarV6/yWVlZjgqFwjhp0iS7EunnvwuZjGSc771HQf8jjtHd3Z25aeqYOjc3GlM2aRLJ+X19ySwvLIySDC4uVEWtV89KB1KGwkIymTp5EqhVCzp3d+w7eRJ9R45kW3z4IViWpch2xAjInZ1Jiu3tTaZngwbR/728THJQL09eXh5YlhVbtGhRraq+0WhETEwMDh48CJZl71V7/fz8zFvIz6/yfn1BADNmDFqOGycEBgZWv1AxahT1fGs0D056YBiqiH/9NWDqHPYvvyTju+TkRz5NoVBAoVBYXg2/epWM9yIiqN3j9depYl8RX3xBiZPkZJK529tTMmvYMEpMDBhAfe++vnRfRgZVS+vWBYYPr3JX+vXrx61duxb//PMPWrRo8WQmQPz0E1XzU1OBrVvp37lzKQFy4AA9Z/VqOg9PYP9K57y3adMGPXr0gCiKD01kKS4uxp49ezRJSUlyAKKbm5va19dX5unpqbCzswPDMOB5HjzPQ6fTBQLQz5s375PZs2d/89gPyPrI7ezsTPvs+fVXapFJTDT7d5mamopNmzZpjEbjR7NmzVpmyY5KSDzLSMG+hISEhI1RKpU8gAq/nKlUqmC9Xj/swIEDsz08PPBcRaZhlTBo0CDup59+Qmpqqj2A1wHYFRcXz0pISPDt0qWLdXb+aaNpUxoXdvkyfbmvICAzGo1ISkpix40bZ97arq5kkhcdTbfmzam/eflymnFfNnCyFnfuUI+1kxOwejUOvvaa8PqxY6z7gQNkTFhKqbN+qadAXh4Zzbm4ULCm0ZCj+oEDVKk1wYU8Pz8fHMeZb5BVhujoaERFRaGgoAAA0Lt3bx6AZaX3Tp0q7cONjIzE9SFD0P2DD9jqjrAEANSvD8yZQ7/L8jPYAwPNCyhUKmDGDJOe6uzsLF67do1pY2oiAaCedTs76od/801KSJUa6lXGd99Rf7tKRaaH9etTL35oKJnt1a9P9x86REkDhYLc95ctA1q1IkO9vn0rXb527dpwd3fn//zzTy4mJkZ87bXXmMcW8B87Rj3bb71Fsu7XX6ce/x9+oPaM0v3Qaun4GzeudDqCLYmMjBRyc3PZ7Ozs4vj4eIFlWbuQkBCuadOmdqtWrULHjh2RmJioLioq2igIwgcA+JycnBY5OTltGIapK5PJXAFAEASdKIo6AHcByARBWPfYD8Y21LC3t686DklNpQRU9+5mjdsURRGnT58Wjhw5Umw0GkfOmTPnkOW7KiHx7CIF+xISEhJPEKVSeQ7Aufnz54/auHFjkwkTJqCeiRVkV1dXhISEiDExMdrPP/98NQB8+eWX8vDw8O/9/PxMXueZQy4nCfbBgyTtL0N6ejp+/fVXODo68v7+/pYFnaGhwPPP07z0+HjgzBnq4c/Pp6r6lStUhbYG+/YBpQZ29vZQ5OQwLjExVJl9FG5uZLYGUCIiPZ38BsaOpX7l7Gzg9GmqdFZCZGQkX7duXYsjtH379t2TpNepUweTJk2CTCazXGOflETB25UrDz1Ue+pU0f3ddxkfHx+Ll3+ISZPo91o+2P/xx/uO9FXx8suUgJozx6Sne3l5iZcuXTI9W5GSQutnZVGCy5R2i/37qRK6ZQtdw7XKzS3YtYuuZZ4Hvv2WguSdO8nLYPp0es2nn1IipFYtUpGUQy6X4+233+bUajUWLVrE3LhxA4GBgSYflsWIIgX6Z8+S6qbUmb9RI1Li7NlDyQCAPDdSU8nJf+NGk7w+rMXdu3dx/Phxluf5CEEQ+s6ePVunUqnanDx5MioyMhIAEB0dfV0QhNkANpSRlZ8quf3rsbOza+bh4fHozGlCAn0m/PwzJUVNxGg0YteuXdqkpKR0g8HwglKpvFrd/ZWQeFaRevYlJCQkngJ4nn8ewFvr1q3D4cOHRVN7+fPy8oxGo/Fe4/7MmTN/4Dhu/bp16xATE/Pv7Uv88kuSMx84AE1uLrKzs6HX65GdnQ1nZ2f+ww8/5KpVaWQYoFs3kti/8w5VUrdsoUDaz4+qnipV9Y9j58571aq7d+8iqkMHxjhmDPXxv/wyVXGrws2NAjInJwryQ0MpmMvMpOCoUSNKKqjV92bMp6amIi0tjR02bJjFZXKNRgNvb29hzpw5eOONN1Dtvv8mTWhUXDkuXbgARqdjvK1dne3ZkyraBw8+eP9LL5FJnykMHWpWENK+fXvWaDRW/cR588hhv149SmjVqGH6/PncXFKkANTHvnv3g497eJB8v1s3Gmf3+efkW3GqJMbs2pUSRcHBVFGdMYOUIxWgUCjg7u7OHz582PafNaNHU/vFjBmU4OJ5mq5Rvz6pGF59lZQ5ZWFZ4OJFav2xsr+J0WiESqXC77///kBm6Pbt21i5cqWG5/mPZs+e3V2pVOoAQKlUxs6ZM8cBQEOlUsnMnj07UKlUrv+v9o9zHNerbt26lX/+RESQQmX2bLPeY/n5+VixYkVxUlLSIb1e30oK9CX+60jBvoSEhMRTgFKpLADwM4D+Z86cCf/666+xdu3a4uzsbOTk5FT4msTERCQmJtqJotiz7P08z78KYNz58+cLbb/njw+tVouEhAQcPnwYW7Zs4XcfOSJkT56MqDFjsGrVKvGbb77Bjh07YGeChN1kOI4C7xMnKFj+/nvqk371VQrGzp4lGb0lqNXAJ58AISGIj4/HTz/9hNq+voL8q68oYK9fn+THogWxwNix9yv/s2bRDPM1awA/P/A8j4sffoguHh5MRcZ9pmI0GsXbt29bR1YPUCX2+PEHgrLU1FScWbJELPjpJ9HTgpnaVdKpE7CunCr6++9NMwFbuJACYzNaZjiOg1wur9xY85VXgPPngRdeoDGTAJkGmkO9evenAnTo8MDYvHvI5XRNDx9OrRPdu5NZ5e3b9LidHbWIJCQAH31EFf/SkX7lGDlyJHfr1i3GZgF/RARw4wZJ9Bcvvn9/bi55DJS63P/yCykgyhMSQp4Eqam0loUYjUYYyig+7t69CwDIz8+XiSXv0Zs3byIsLEyn1+vfmz179rcVrSMFn4BKpVLo9fr6FSp1eB5YupRMS996iz7LTCQ1NRU///yzJjc39yu9Xj9UqVQ+fMFKSPzHkGT8EhISEk8JJRWe/QD2q1Qqx9TUVPVPJY7SXbt2RadOnSCXyyEIAsLCwoxpaWkyuVz+/WeffRZXfh2VSuWclZXlnJOTAw8PjydwNNbl/Pnz2LNnD+zt7XknJyfW3d2dy8vPR3JYGNq3bYueCQlMUXAw9Ho93NzcqmnXXgEMA7z7Lo0t27ePpMK5uUBQEM38Bkgy/+abD8umK2PbNuCvv4A+fbB//35RoVAw48ePZxl7e3JP79yZ+rSbNKFKZiXz56vc71JH9unTYRw7FuFHjqBZXBzy6tSB4dw52L37Ls2Wl8lMMvpLT0/Hhg0bRI1GwzRq1MjyHv2KWLsWePttoEEDGI1GrF+7Fh/+9RejeOUVq23iAfr2JZPEa9fuB42iSMaH77xT+etEkZInffrc//2bgK+vL/R6PXP+/Hk8//zzdGdhIUnQZ8ygILyw0PIec4OBFCGnTlHyZMKEyvuc69WjlokuXaiSHxsLjBtHPhiffkqJLjs7CqiXLaPrPjaWVAeLFlFvf8kx2dnZITIykundu7dl+/0oPvoIGDKEklalJCbSe2fLlvv3RURU2HIAgCr869dTMunvv83avCiKOHv2rHjgwAEDwzBC3bp1Df7+/k5XrlwxsixbmJeXl7N79+66/fr1c/jjjz/UOp1uKoDfzD/Q/xQhXl5eGrvymdm8PGq5SEykViZTP0sBXLp0Cdu3by8q6c8/WPUrJCT+G0jBvoSEhMRTiFKp1KhUqoYAMgGMi4yM/OT48eP1xo0bhzp16iAtLU0GYMhnn332VyVL/AZg8LJly3r4+PiIo0ePdnIuNXl7hkhLS8OOHTvEwsJC9OnThwkJCXk4sExJAYYPh3NcXOVu7tbCw4MqTV270nzvt94CPviAjPtK+4Jv36YvqVWN/rtzh1zRAQwYMIA5cOCAsHjxYrZ169Z8t7AwzunFF6lCO3VqpcZ15nIlIwMxMTGQrVyJxMREMXrLFoxv0oS5fuUKAocOhdCxI7gff4TDyZNUXa4g+A8PDxc1Gg3z7rvvwtXV1bqJlezsewZrLMuCYVnsnTZNfLFHDyvJB8phb0/V4M2bqXIMUAV46dJHB/t37pA83EyysrLAcRzNXFerqdVCrSa58vTplECoDkYjuZaXBkm//krTBr76quLnN2xIZpReXsDNm1Qd/+QTCohbt6YWEQB6jsOFevVwLjparCuTMYY//xS6Ll7MOo0eDXbgQLRu3Rox5fwzqoUoUhJi1qz/Z++8w6I4uzZ+P7PL0ougSBEEURExdrGXWFBj1xQbSqxJNJZ0k+hmYxKNJtEkxo4lKrGhRrGixh4EVCwYFRABpQgqvWyZ5/vjgIIssCAkb77M77q4DLtTnpkdCOec+9yHEhfPP4dBQTTqsCSHD1dsTPn11zRqcO9eUnQYGEhGR0fj6NGjj7Va7RAAaXfv3m2XmJg4UqPRvM4Ym65Wq1MuX758jnOuLSws/EOpVG6t2sX+9xAEobu7u7tpqRf/+otUNW3b0r8GKrREUcTp06e1f/75Z3ZRf35EbaxZQuLfCuPVkQdKSEhISPytqFQqBmAxgA/kcjnXarUyAM5KpTKpkv1M5HL5Gi8vr9dGjhxpWtG2/2ukpaVh3bp1aNu2LXx9fSse71UkqUVm5rMK7d9BQgJJvgsLyUiqZ0/q+c7Lo17pnJxnTvol4Zx6URcsKFV5vX//Pg4ePKh79OiR7K3Tp3kdDw/GVq6kxIKfH/Vvv4D0/sKFC7h+/bpu+vTpMrVajR07dohxcXGCubk5N83MhCYnh8kBTF2zBkd+/pmbHDjAjRUKaKdNE0xOzE65AAAgAElEQVRNTXHq1Cmu0WgYAEydOhU1apgHUEX7nXdIzg4gvUULXOjalQ9ds6Z2gn2Agn2lkozqDOmLv3WLqt+PH5ce22cAGzduFAVRZBMnT2YYPJieg4MHq7lwPaxdSyMpX3uNvr90iQLcDh0q3m/zZurdv3ePgqz584HLl4GAAGjr1sUPP/wgAhDq168vMsaYmYkJt123TjDPzUWij4/OQiaTXbSywrx586CogmO6XuLiSHWwcCEpExo2LP3+pUv0vo3NM+f9tDTaLje3cnVK587UgvPpp5UuRavV4syZMzh79ixkMlnY559/3hF4+vt4PoBdAF4B8J2xsXFoYWHhCKVSmVLla/6PsWTJkrNDhgzp5uXlRS/s3w/Ex1PSqQpGipmZmdi5c2fuo0ePbhQWFo6s7P+HEhL/RaRgX0JCQuJfhEqlagHAEYAVgN+VSmWlbl9ff/31dTMzsyZz586twZlxtcuhQ4d0kZGRsmbNmokjR440zF9m0SKqZN65U6258y/EmTM097tvX6BpU6r8nz9PMvGsLJJEl+TaNZJKHzqk93BRUVE4vWULb5aXJ3aPjJQZHT9OVc4vviBTuWpw48YNHDlyBC1atMCAAQOevl48/7v474HHjx/jwIEDOo1Gw0bGxAg5aWn8ULNmfOzXXwt/fvklr9u8ObNxcoJH8+Zl5oa/MN9+S73hPj5ITU5Gav/+0H3/va5Nv34135pRksWLKXAsNkT09CSZeHmjMJOSqiTfByhw/HrhQsxfvhxCUBBVrmtyjCNAcvf+/UlxAlCF/uJF4IMPKt83MZGSVLa2pCQ5fBiIjcXp5GR+u2FDPnXq1FL+DJxzPHr0CI/Xr4fD8uXYOG4cnADe8+OPmX0V5NelUKspWXHihP62FZ2Ogvrlyym5VkxGBrVCzJ1b+Tl0Ovr9sGXLsxYXPdy4cYMHBwcXMsbu6HS6MxqNZrm+fnuVSmXPGDvFOe+sVCozDbrO/zAqlUqQyWTZc+bMMbMwNyen/b/+os+ifXuDjxMVFYX9+/fni6L4lVarXaxUKsVaXLaExL8WKdiXkJCQ+H+MSqUyAqD28vJCx44d4eTkVLMGdrVAYWEhFi9ejCFDhqBt27ZV2zk5mar7Hh4Gy0BrDM6pl//336n67u9Pld+GDUkuvXjxswAlMZH+wPX1Lfdwly5dQuGMGfC+e5dbx8UxmJtTr/SlS8D27VVaWmJiIjZs2AAXFxdx0qRJVTbnFdVqhL3zDq61aCFOvXVLYCdPUlJl6VKqxNXUKMLbtynobtIEBUFBWHv2LGZ8/z1kzydLaprYWAqUr12jxMznn5PC4PmAfvNmChKPH6/a8UNCoH3/fSx54w182q8fBTU1PZdep6MKt739s2MHBgKrV1MyyhD8/am6XzS+Li0oCLqZM2EUFAS7zp3LT6KJInKvX0eOry9ud++ObmvW4GZyMiwtLWFsbAwLCwuYm5uXnxzKySFDwk2byKDR2lr/dk+e0LbPt+vcukVKiy5dDLvOrCxK6Bw7ptcAsUhVlKfRaPoCCP2vOubXBiqVqoW5ufmfH0yfboEPP6SkzoABgIODQfuLoojg4OCCqKioR2q1ergk25eQqBjZF5XN8pWQkJCQ+NfSq1cv8ezZs23S0tKaRUZGIi4urqBt27b/qF/L/v37tWfOnOGZmZmCTqeDQqEAY+xpQHf16lUkJCTwYcOGsSoHeZaWFEBHR9MfkH8njJG8uE8fkqPOmUPr6NOHghNfX1IebNpEQZiPDyUB9BAYGIjQ0FCgTRuxmbm5YGxsTNfm6EjBThUN3K5du8YfPHiAWbNmVSvCZDIZbHr3xukLFxDfooXY/PvvBUEupwpyt26UgPj4Ywr8Oa++smL+fFJEDB4MWf/+uOXkhAIHB7j8HV4M7u5k1NekCRm9GRmVVVEwRiMOfXwMO+7ixXTMXr2QFBeHSyYm6DluXO0oT/74g8bTlfQa8PQERo0yvN1g6FBg0CBqF3F0xNrjx3UKf394HT/OEBJCihV9rQ6MQeHggNtdu+LPzEzuOn06Y3v34leNBlE3b/KzZ8+yixcv4s6dO7rbt2/zvLw8lpKSwqysrCCIIgTOqcI7ciRV9vVx8iSNS1ywoKxS5osvSA1Q3L5QGcbGpAIwNaU2iueM/Q4cOJCblpa2UKlUbu/Vq5dhx5QwiHPnzr3tY2/f0z0wUI6mTYFJk8pP7jyHRqPB9u3b82NiYiLUanUXpVJ5r3ZXKyHx70cavSchISHx/5z58+cPVyqVDMD0+/fvV7/huwa4ceMGj4yMlLu7u8uioqKwa9cu/tNPP2Hx4sU4fPgwwsPDcf78ed6iRQtW7d7fkBBApaIg65/AyAho14569rt1IxO+rCxySm/QAHBzo5Fmu3bpnV9++fJlxMTEwNHRUfR7/33B6s03gSlTSDHQujXJ/wcMKDsyrhw457hx4wZcXFxeSOZqYWGBOXPmsLi4OFlSRgYFXFevktlZgwZkrAVQhW7FCpJWnzxZtdGBH35IXxoNEnbtQry7OzjnMGg2/YtiZgbs3k3rHT/+maS/mN27KdFSLJEvD1Gklg5RJGf93FwkFRQgoG5dDBk6tPbW7+AAfPZZ6deio8lfwFAEgST8c+cidvJkABC6Dhgg4NNPyTzxwgUyFSyHdl26YNaCBcwmIgIeGzdivoMDPj5/ng1v1Yo7OztzZ2dnmUKhkF24cEE8duwY3/7ee9DZ2OBuYiKpD/SNCSzGy4tUFfoUO99+SwqGqiCT0RSCd98lY8MSPHjwgHPOqyjfkKgMlUol87xzZ1an334zxuuv04QTA3/P5+fnY+PGjXmJiYlH1Gp1X6VS+f9qtKyERG0hufFLSEhI/EcQBGGmo6Mjj4+PZ6mpqbCzs8O+ffsKtFqt0fTp02U2Rc7btUVycjJ+//13NmrUKHh7e6NoTBcDgLCwMERGRoqxsbHczs5O6N+/f/VPZGdHcub27ana2apVzVxAVTEzI7O+vn3JbX3bNnqtd28KjhctouDqxAmgQwdozcywevVqMSsrS2jdujUfOnQoJeR9fMjZf/hwkirb2lIF1sCe8by8PDx8+JD5+fm9sBbexMQERkZGPD09Ha6urs/K0x06PDOB+/NPMk+7eJGC5pQUSr60aUNz3Svi4UNK1hw6BJfZs+Ho6Kg7fvy4LD4+Xhw7dmztFih69iRn+gcPyJ3/eSPEPXuoJaNrV/37iyKZyzk7k/t9nz7kAA/g0fXrkMlkaFOVwLuq/PEHGc+VxMWFvAGqAmPIDA7G7nXr4G9pyeRyOalKAgLIrX/ZMgqui83ViggPDxcfP37MfX19ZRYODoCDA4R27ZCRnIyTp0+zgYcPI3XSJN7/o48YMjJk2LsX4kcfYZ+VFSwTE9GoWTO9y0lNTYVm2jSkMMZjx4xh7WJi4OrqWtoIcMIESs4UjzM0lH79qKXm+nVArYa6dWvcvHkTOTk5FgAiVCrVRaVSWY2ZlxJlYIyNadp09ZOmTc1NFy2qUn/+48ePsWXLlrzc3NzNGo1mptSfLyFhOFKwLyEhIfEfQS6Xxzx48OClLVu25DHG7mm12uaCIDwRRdHxypUrePnll2v1/BcvXtS5uLgwb2/vMkGbj48PfHx8ai6Yq1ePgk53dwogq2sYVhPUrUtBX0wMjQybMYMCwshICvY//BAYPhwFM2YgOzlZ0CgU6Nu377NA2tKSeqgnTqTe5Lg46id/8oQSGceOVTjmz9zcHE5OTmJQUBAmTpz4wvd4xIgRLCgoCBERETpLS0s8ePBAZmVlxfv06cMaNWoEVjwNoX9/SroA5KOQmUnzswcOpN74ggJKzJSUhScmUj98164Q+vfHtDp1ZPfv38fGjRuFqKgoeHl5VTyV4UVgDHB1Bb78ksYd7t0LfPUVvffoEbVelAfnFAQvWwbcv08JgxKEhoZyc3Pz2nONFEVyrx84sPTrVlZAr15UuTZk0kARgfv367wbNUL9Tz+VoVmzZ20j06dTouqvv0idUkI2f/bsWZadnS1wztGqVSuYmZkhMzMT2y0s4NKoEWSZmci/cIFpt26F/M4dICgIgr8/XN54A0ePHkVSUpI4YMAAwdzcHAkJCUhISICxsTHOnz+P3tnZUAwdCplMJu7YsUMAgBkzZuBpgrKwUP/UC0OQyYC1ayHGxWHtoEF5OTk51wVBiBBF8SKAC9U7qEQpGGMcWJllZzeyzgcfGLMqBPrJycnYvHlzvlar/fDzzz9fWYurlJD4f4lk0CchISHxH6FoXFRzAAlKpTJbpVIZA1AD8LO3t185efJk8xcem1UOGo0GixcvRqtWrTC0NqXMz7NmDfVNx8SU7fP9pwgIoOA9Lo4M34qD48WLIf7yC76fPVt0dnbGoEGDBOviXlaNhqq2/v6kFihm1izqlXd2rvCUt2/fxt69e/knn3xSIwFnWloabt++jSdPnsDMzAw3b97U5ebmCoWFhaxv377oWl71OzeXFA5Tp1LlWy4n1/eFC4H336fExq+/kgqghHHaL7/8wtPT01nPnj15r169ai9o1mioVeKLL4D164GjRymQr1OH1qXv2W3XjiYILF5MJnl6HPYvXbqE4OBg9OnTB92q6LdgEMXmfPpMzl56ifrSXV0NOtSlS5cQEhKC2bNnw1StpkTChQvUy19MZCR9Xp99hofNm2N/cLCYkpIiNGnSBLdv34aRkRHX6XRMp9OhVatWfPjw4aygoACnR45En+hoyE1N6XNPSAA6d8bevXsRFRUFQRCg0+lgZmbG69evL8ZGR8tGnjsH9/XrYeHu/vT0wcHBups3b7KJEycK9e3syJjzBX0dwkNDefqSJX+9fOhQJ5OCAkkiXlMwZg/gt9BOnc5fef31uW/NmWNh6BSPlJQUbNy4MV+r1frNnz8/qHYXKiHx/xMp2JeQkJD4j6NSqQRjY+NdhYWFI7t06aLp1auXUU069mdnZ2PLli08Pz+fjxgxQmhUHNz+HYgi9ZW7ulLAVltVYUNJTyejvogISkDMmUPV+YkTgWbNgLQ05DEGmasr1k+fjoZ9+ugGDx5MWYpLl6j3f/x4kpsXBzcnTgBLlgBHjpRr/JaZmYmffvoJ8+fPr7VLE0URa9euhbOzszhkyBDDbnRBAfXB9+hB8v05c6g3ftUqclbPzQXat0d6RgaC9uwRU1JShNatW+t69+4ts7S0rJ0LuXiR7mnJOeyJiaWDybQ0qnZHRFCVu0WLCkci5ufnY8mSJWCMYcGCBTW/5k8/pTWtW1f2vUOHqEXB3LzSw6jVavzwww988ODBrEWLFvRiWBgweDD1/5c0UnvyBNi7F7c3beLhY8ey3kOHwqm81pKMDDy5exeB69fDt359NFmwgBII77wDfPMNNl65Ij4yMRHmFo3OKzbm5DExwODBYFevlkqicM4RHBzML1++zD7r1QtyPz9KHLwAa9esyZowe/Yjk8LCpeB81QsdTIJgrAmA1TpB+G7JwoW/jBw1yt3T09OgXVNSUrBp06Z8tVo9ccGCBbtqd6ESEv9/kWT8EhISEv9xivofR6lUqpcuXLhwrWHDhmha3nzxKhIfH48dO3bAwcGBv/XWW0KtSbDLQxCoStyzJznfBwT8ved/nvPnKfEgCDTD/eBBGqO3ezeZ9Y0bB7PmzYHAQPi9/DKi+/SRaa9dg/zTT6mC3Lcv9fyXbEvw8qJecp2uXKk25xyiKOL+/ftoUFNj8vSQnZ0tOjo6Gv4hm5hQEPf++2TUFRlJr3t7U9/8778Dhw+jbsuWmD5smJAzYACSvv6a7Tx8GBZPnkBRWIi8YcOQHxfHG3TsyPv4+govnKhyc6NkioUFudIPHw5cuULvHT5Mwe+CBfS6TGaQM39qaioAwN/f/8XWVtGay5NGHzlC7RIG9LPv3LlTdHBwgLe397OskY8PqVBiYigxU6Ta4DY2uNi4MbhazV61s4OJWl3+gf39kX/vHtJHjMApJyfehDGGrl0psaLVYuz06cJ1Bwew2bMhFH9+6elgKSmUTHkuicUYg7zoWb+YkYHOq1a9kON0RkYG0tLTZTtGj35p4ubNBWBsHDjf9gKH/G9Dpfs3AUwGMPJrpXJAvTp16hn6/5XU1NTiQN9fCvQlJF4MKdiXkJCQkCjmBgBERkbqGjduLKsoMM/OzkZqaioyMzNhb28PGxsbPF9pDQ0NFU+ePCl06tQJvXv3/mdL6r/9BuTlUd+4gWOeagU7O+DHH599zxiNqysoAPbtIxO4NWsAlQpW1tZIdXXlqba2zPn+fer7nz+f+t1//JGSFyNHklHf2rVkUGZhQYHocxT3Nh8/frzWAs6MjAwUFhYKbYtd+SsjP59M5fr3J7NCV1fqeV+4kEzlund/anCHe/eAwkJY5OSg6eTJgnPnzihYvx7Zf/yB03FxfPTGjezu0aO4uWuX2CoxUcAPP9C9tLCgSQbh4STPr1ev8rF39esDkydTEsXSkoL6kBCaB/7oEfXkM0YmdQYSFRWlq1+/PlxdXWu+l0QUyaF++HD979+8SZMpKgn2Y2NjkZCQILzzzjsoI7M2NwdOngQPCEDOhQvIzMpCaGioGBUVJXi99x46m5pSlf7zz0vPuv/oI6BVK+Rt3Ih1y5djwoQJcHNze3bwIhPEbZ98wq2ePEH7Dz9kiI4m1/0PPiD1QDltD126dGEmJiZI2b6d75XLeTtvb8HBwQEmzxsrGsCdO3cgl8tDJm7alIvNm+sBWAbGzoLzF5ML/BdhzBTAbACWAEaqvviiqZFcvnL48OFmhsj3Y2NjsXPnznyNRjNpwYIFO2t7uRIS/9+Rgn0JCQkJCQCAUqnkKpXKKSYm5tTJkyfd+/bta5Sbm4vo6GgcO3ZMY2JigsaNGxvFx8fnPXz48KlmWS6XP9JqtXbW1ta5Y8aMMbe3t0d4eDiOHz8ujBkzBh4eHv/kZRFOTiQJb9iQjNeq6lBeRTIzM7F3717Rw8ND8PHxgXGxBPmrryhIbNiw9A4mJtSLn5VFQWrXrsCSJbCaPJltvnoVw4KDefO4OMZ69iSpdlwcMHZs6WO0a1fGGK4kTk5OePjwIUfRBISa5sqVK7C3t9cJglBxQJuYSFX8gwfpfsTGAnfuAJ07Az/9RMH/8xQnaOztgenTYQ7A/KefYAfADWD48kuYRUez/Rs2sFbjx9P9tbCghEJ8PLU5DB1KiZIzZ6hCP3MmBfDjxpEpoK8vSfGNjQEPD0pAiCKwdCkpDT74AHjzTWqjqAJpaWmIiIiQjS7ptVCTREQAP/xAa9PH7t2VJjhEUcTevXvFnj17MhsbG/0bz52LY40aiVmTJwvJTk4Q3d0xa9Ys1KlTh943MaGfsXXr6B6ZmJDyQS7HxSIzSiMjI4ii+FSmn5SUhF27dukyMjJkADBq4UKwrVvJ/O/hQ/JOKAdra2u8/PLL0P3yC4svLOSBgYHQ6XTo1asXulfx5zs2NjansLDwKACA8zQw5gjABowNBufBVTrYfxnGjADsBhAKYKHqiy/MjIyM9r/++utmjo6OFe7KOceff/6pO3XqVI5GoxmqVCrP/B1LlpD4/47Usy8hISEhUQqVSuVqZGR0ycjIyLagoEAjl8sfazSa/ZzzbLlc7qPVatcA2KdUKvNK7NNQJpO9r9Pp3gVIxeni4sLHjBnDqlNpqzVOn34mdzagh7m67N69W5eYmCgTRVHMyckRxo4diyYuLhQw/vxz5dVltZqqoklJuD9sGHYlJIhyCws2wc+PWTdtSms/c4ZGhr3yyrP9dDoKWr/9toys+/bt29i+fTvmz59fK472P/30k65Tp04yn/Jk7cnJgKMjKRLGjqVxfMX3IT0deO89ChTHjKEAtQpr1Gq1WLZsmdiqVSvu6+tbfrJBFCkhUr8+tU9YW9OaVqyg8+7YQcmgGzeeGdrl55cdw2cAnHMcO3ZMl5iYKEtKSqqdXn0AyM4miX15Y/1mzKDr/Oabcg+xf/9+JCQk8HfeeYeV92wUFhbi559/xvjgYNRv3Bhs/fqyG8XEkGFkSAgpNGbPBgAkJCRg165dYk5OjtCkSRNx7NixwoMHD7B+/Xp06NBB17dvX5mRkREpCtRqoEkT6Lp3B/z8ILt8mZIyFU0L0ekAmQznz59HWFiYbu7cuQYrKHQ6HZYuXZpfWFjYQqlU3n36BmOvAvgOgDukP5Yrh7HmAAIAzAEQBs754sWLg1q2bDn4lVdeqdD5NS8vD3v37s1PSEhIUKvV/ZVKZfzfsmYJif8AUmVfQkJCQqIUSqUyQaVSddJoNBMAfD9v3rwsA/aJV6lUswGsB1BfEIQWCQkJP3z33Xeajh07yrp27SrI5XLIZDIIglBWJvx30bMnyeXfe4/kzbWQiMjLy0NUVJSsR48eePnll4UlS5aI4eHhzPboUWbr7W3YtSsUwPLlwM2baBASgtlxccI5MzNx06ZNePv+faaIjqbq//37JH0vCqogk5H7ulZb6nAajQbbt2+Hk5MTFwShxm6+KIq4cuUKwsPDRY1GI3h7e5fdKD//WeU+KYnue8mpD9evU5C/eTMF/9eu0fqrMBnixo0bEASB9e3bt+IMgSA8M9qbMOHZ67/+Sv8OHAhs3Eiu/Obm1HaxZQtND6giwcHBumvXrskcHR3xSsmETE0zfTowaVL57w8YQNdTDmlpabhx4wbefPPNcgN9gO5xbm4ujPfswY27dyFMnIjIbt24hb09GzJkCCWQGjem57Zx41IKiISEBOTk5Ah169blbdu2FQCgePJH//79ZbISkzI0oohbU6fyPRoNM7lyBf1DQ7l3aCgzqlePPAM6PTf2vmlTIDgYaNoUzZs3xx9//CHbuXMnf/XVVyu8nmKK/BQelgr0AYDz3WBsL4AOYCwXnEdVerD/IvQLrTWAjQDGFd+nhQsXTrSwsBjQr1+/Cn+Q4+LisGvXrjytVrteo9F8pFQqC/+GVUtI/GeQgn0JCQkJiTIolcpYAMoq7sMBXCv6NkSlUi3X6XQtIyIigkJDQ10BQBRFOQAIgqCTyWTFX2Jubq5F69atMWzYsBq9Dr0MHUrBJGNU6a3BKjfnHHv27OEmJiZo164dA4CJEycKISEhPGXdOtzs1Ensjip4iTVvDjRvDiE8HN2uXxcaLlqEeGtrNOndG3j8mGTnu3eTJF0Q6JqWLyeZf+/ewNGjiE9KQmBgIGxsbHQTJ06s0Z7xlStX8qysLNa2bVt0796dmRerJYrv6/DhQEYG9eYnJQHFc9FLEhxMQVxxEqRlyyoH+7m5uTA1Na2ZRAZjNG4vO5sCS8aoSv3555UrMkpw9+5dmUKh4JMmTardzFZSEikVysPDg5It5bB9+3ZdmzZt4OjoWOGzkZKSwgGwn1esgKlcjgl//AEvR0d+ICmJabVajBo1ijZ0cSEPihL3ys7ODgDg6urKmzVrxgDA1NQUAPDXX3+h2Pn/QWAgjN5/Hyc+/JAP6dWLxcXF4WKdOjj8+DHeOHqUN1q9muHAATp2sat7x45PR0/WqVMH/v7+CAgIYGFhYej0fGJADw8fPgRjLFLvm5zrwNgc0M9sLfVh/Iuh/vyZAPoCaA/OtQCgUqmGC4IQMGrUKFl5hpk6nQ4nTpxQR0RE5Gq12jcWLFgQ8vctXELiv4MU7EtISEhI1ApFwf9VAI1Lvq5SqeSiKJqIomis0WiMAZgAWBIZGTlq4MCBTyt+tYYgAK++CrzxBhmbbd1aY4dOTk5GbGwsmzZtGqysrAAA9evXx/gxY1iGpSUORkQI1XIL6NABq8PCtEY9e8rHr1xJa16+nAIdb2+SxO/fD1y+DDCGFABiQQE/tGwZTy4sFDp37oy+ffvWaKB/7949ZGdnszlz5sDMzOxZAiMnhyTwR45QD769PQVn+gL9kyfpc3Bze/aavX0ZZUJlxMfHw0HfjPnqEBtL6oj0dOoZL57gsHUr4Odn0CFu3LjBMzIy2KhRo2o30E9LozGFXl7lb3PlCrUs6DHwO3v2LAoLC4U+ffpUus4uXbowLy8vNGjQgH5GP/4Y9Z88EYzffZcfpiTPs2M8fkxtBXfuACYm8PLygqenJ09OTn66ydWrVwEAbm5uyMvLw6FDh3R5Z87IugwbhjnvvScAQJHZI/vxxx9xxtqaN7pxgyE9nST9U6eSOmPOnFItOQ0aNIClpSVu377NO3XqVOl1ZWRkcLVafbOCTcYBEMDY6wD2gvPyZRL/JRhzBt0bawCDiwP9L7/8sr+RkdE2Z2dnFhgYyAcOHMhatWpVatfk5GTs3bs3Nysr66JGoxmtVCrT/v4LkJD4byAF+xISEhISfytKpVILIKfoq5hXFy9eHB4dHd1erxS8NvjqK5rNrdWWO7Kuqpw6dQo2Njbc0dGxdJARHg7r776DZsAAhIWFcR8fnyoFgWlpaXj85In8TZUKJp98QlMFAgOBL78E6tSh4Pqrr8i9/PRpHMvO5vcGDGDDMzNZ8/R0yPv2rZHrK0lKSgrkcjnUajXMzMwosA8IAK5eBYKCyDOgItWERgP4+wOrVwONGj173c6uShV0ALC3t8fdu3cr39AQGjUibwczMwoqw8OBIUPI6O+dd+g6K3lebt26BSsrK96iRYvaDfbXrwcuXSJ1R3kMGAAUVc5LkpeXh3PnzvFXX32VGZJgq1OnzjMzPoBaRkQRnn/+yY45OODJkyfP3rezo2C8RKv7w4cPRR8fn6cJJy8vL5w8eRI7duzgKSkprHdkJGs9ejRMixUCJWCM6dzc3GQwNqYq/pUr9DPQvj1QWEgeCyXGUY4ePRqbNm1i+/fvx9ChQyu8rsePH+eLohhX7gacczDGAXwFQAbgt4rv1L8LlUplpVAogkRRbPbZZ5+5GLQTY64A1gD4HcBScM5VKhWTyWRzFArF12PHjjV1dXXFjRs3cODAAVy+fFkcN26coFAoEB4eLoaEhOTqdLo5oihuLEoKS0hI1BL/7JCcWIwAACAASURBVCgkCQkJCQmJIjQaTUBQUJB44MABPHnypPZP2KQJ0KMHBXchVVeQhoaGitu2bRPT0tJw9epVLF++nMfGxmLIkCFlA7ysLLBhwzBu3DgcP36cnTx5kqsrmktegqCgIB4QEMD7yWQ657feAtu0iar4wcEUyNWrB/TqRT38ly8DM2aguYcHs7Cw0LUcMADyKlbJDcXHxwfmZma8sH17qhy/8go51wMUJFfWHpGQQHPWn+9nDwoidUAV8Pb2RnJyspCUlARNBf3plXLhAqk+OnSg701MgAYNgLNnyfjw0SPg6FEykSuHLVu28KioKDZixIjaN6YYOBD45JOKt8nOBubOLfNyYGCgrlGjRmKTJk2qf357e+DOHbgoFDzMzw8PSk6D+OQTMjwsQhAEITk5WVf8va2tLYyMjPj9+/eZl5cXOj98KJiWk0SpU6cOTp8+jWXLlvEVP//ML2/erMXkyc8mJIwcSYaV+fkAaPLEm2++ibi4OP7NN99g69atOlEU9R778ePHGgAVG8JxLgLwArC7qML//4KiQP9PLy+v7lqttoFKpapY/cMYA2NDAHwPYB44X6364gsjlUo10tjYOKJOnToLp0+fbupaZG7ZokULzJw5E6IoYvXq1bq4uDiEhIRkajSa1vPnz98gBfoSErWPFOxLSEhISPxPMH/+/NWc805XrlxJ/umnn3D27Fle6ybYRkbAd9+RyVc5wcDzaDQaHD16FCEhIUJMTIywcuVKnDhxQteqVSs2a9YsNCpZpS7mzBng9dfh5uaG8ePHIywsDEFBQbqyGz4jNzcXa9as4Tdv3mQjhg5lPgqFDD16UEUVoMrz2rWAqSlVnz08qKoeHw/z9HSM+/ZbWYGHB1Xb33mHguiaIiICQs+eGDxoELvUpAmiLCw4GjemgNgQgoOBPn2oAvw8DRsa/FkUU9z/vW7dOnzzzTc4cOAAUlJSqnQMAKTyKOr/fsrAgVRF1mqB336j0YHTpgF5eXoPYW1tzQCSk9cqokhTF5o1q3g7Y2MycizBjRs3kJaWJhs0aNALt3bIFQoM6d2btQkPR8CaNVi9erUYEhICMTOTjDAzMyGKIh49esQ8PT1Lna9JkyZgjGFIYSFw7BhQjmeHn5+f7LPPPkM/V1fmef06q7tkifzmyJGcz5pF92D3buDUKZoEcOIEwDkcHR0xa9YsNm3aNMTFxcnWrFmj95dJVlaWAOC+vvdKwbkOgBOAlWCsblXv0/8aKpVKrlAoQry9vT18fX2NBUGoOPvImAmAAQDeBTAXnEd+9dVX/kZGRqlOTk6bBg4c2Pbtt982L6X+AGBpaYkJEyYICoWCBQYGcq1WO6WMGaKEhEStIcn4JSQkJCT+Z1AqleEqlaoFgKknT55cfPbs2UJPT0957969Zc//EVljvP46EBYG9OtHwXJR4Pg8nHPs378fkZHk5dWhQwf07dsX2dnZsLOzKz9oys2lQKaoAuvq6opZs2ax5cuXy1asWMEHDRrE3N3dS+2Sn5+PX375BUZGRmxWy5awHj68/LUNHw7s2gVMnkwjyN54A838/HCxWTMoEhLQ9vFjqv7XhFpi/nwyYHvlFaBdO7g2aIBTY8Ygr6BA9CaJc+VotUC7dqQG0Ccf79evym0VNjY2UCrJT/LkyZMIDQ3lly9fZk5OTjwtLY29/fbbsLGxqXgSwo0bgIMDyfRL4ugIpKSQ+mPUKGDKFNo2PJz60ou8GYqxtbVF3bp1RblcXrsFlcREUhxUNkLS0ZGejyLTRK1Wi+DgYN67d29YWFjUiPrAuH9/aC5dQpu33kLe7dvCLY2GR0ZG8q7BwYL5vXvQFqlLLC0tS+03atQoNqphQwryhw6lxIQ+Hj2C/Nw5tNi6FS0GD0bopEk4evQom5KUBGdnZ/rcFiygpNqtWzRB4cMPwby9UbduXbz22mvYsWMHO3/+PLp27YqYmBicO3eOZ2RkiAUFBZYADHOA5zwejNUHUA+M9QXnx1/gtv3TjLGzs/MePHiwsU6nA8hzwRSl26sI6s9/C9SfP0D1xReC0TffLDMzM5s2evRoMycnpwpPZGRkhIYNG+qePHlyW6vV7q3xK5GQkCgXJo0OlZCQkJD4X0WlUr0E4BwAqxEjRvCWLVvWjjRaFIHFi8HnzMHW3bvFQrUaTZs2ZR4eHkwQBGg0GmzcuBEA0LlzZ/Tq1ctwI8G0NBon16dPqZfz8vKwdOlS9O/fv5SRWGpqKn799Vfk5eXBb8gQNHryhAI7f3/9x9fpqJopikBWFpm1LV2KMzqdaNWwodB65Up639MTGD0a2LQJqFuFwmRWFo32W7UKWLSIFAQlxtatW7eOm5mZYdy4cYZ9NhMnUsLgq6/0vz9rFlWES5r2VYPQ0FDExsYiJiYGgiCgQYMG4pAhQ4S6desWtWE/t9wJE8hYLji47MGio2mU3MWLz15bupTaJlasAOzswDnHvXv3EBwczD08PFitjtsDqKXA3Nyw8ZGNG5MPgbMzduzYwWNjY5mxsTGfMmUKs7a2rrElJc+dizoJCTDauROhoaH82tGj8P/uO7ZuwQJu5eDAR48eLZiUXG9eHjn3A4Ctrf6DbtoEHDgAjB1LozPr1sWqVat02dnZsg8//LDs56jVAocPA3v2kOph5kzA3BxHjhzBxYsXUa9ePajVap2xsbHM3d0dYWFhhQsWLKjaDE7GpgF4H0Az/Av/kFapVDKFQpEwevRop+JE47Zt23Lj4uJW6XS6T5RK5TPVEWM+ADoBEFRffPETgL7GxsZL7e3tG48ePdrMzMys0vNduXKFHz58+KFGo3lJMuOTkPh7kWT8EhISEhL/syiVyusg6eyso0ePFtRaL78gAJ9+ivAhQ9Dy++8FFxcX4a+//hK3bdsmrl+/HluLHPvffvtt+Pr6Vm1iwNKlQGhomZf37t0rAkCxWV9BQQHS0tKwZs0aFBYWYl7Xrmg0dCiNoisv0AdI1l9YSFX3mTPJnf/XX5Hj7i7IIyOppzkvj2ax5+YCNFe8cs6do5nzALmqZ2WR63/J+fQATE1N2cOHDw1Pwri5lTlGKRITK+yJN5ROnTph3LhxmDNnDoqDwpUrV2LTpk3il19+iYyMjGcbiyLw66/A77/rP1iTJtTLX9II7/33ySDu6lUgORnp6en49ddfIYqi2KtXrxdef6W8+WZZFUJ5TJoEmJoiMTERMTExbPr06XB2duYBAQE8v6jPvSZwXLYMJrt3Q/buu+hqbc3e/vJLZrpqFWZ98AHz9/cvHegD1A7h768/0P/zTwruzcwoMTRq1NMklampqZCfn48NGzaU7feQy8lQceVKSoR16QJcu4YB/frB1NQUaWlpyMzMlHXp0gUWFhbcyMhoc5UvlPO1ALwBdANjblXe/5/nVVtbWyu3Egm1IUOGmNvb27+tUCgSFy5cOEOlUpmDsb4AZqfWr5/+1cKF5gqFIsnW1na3r69vyzfffNOgQP/+/fs4fPhwjkaj6SEF+hISfz+SjF9CQkJC4n8apVKZC+DnhQsX2m3cuPGT9957rxyt74tx4cIF8WqTJsLLnp7o7+sLMFYzo+qSkihQeQ6dTicCEE6dOiU2aNBA+O2338AYg1wu59ZJScyoSRNgwwaS4FfG4MEkiXd3BzhHYWEhwo2MYPPppyQzX7iQWhU2bqS59xMnApv1xDic04i5QYNIth4bS0Hl+fPlnloulyMrKws5OTmwsLAof41qNQXM69ZVPBfe1rbKPfsVUVy59vf3F8LCwpCamiokJCTgxx9/RLdu3cSXX35ZEFq1out8773yD9StG1WZR42iaQGCQAH/Dz8AS5ag7qZNMDMzQ//+/WWGBEEvjK8vfe6GYGsLMT0du4KDxa5du8LOzk4YPXq0sHbtWl1AQIAwYsQI5vy8V0F1YYxMAS9epCkAI0cCixcDn39eejtRBD76qOzzffcu9d/v2UNKks6dy0xnmDBhAtu3bx/++usvITk5GQ4ODmUr/KamwKefUjLhs88Ae3u8O3IkomQy2Nvbw9HREYGBgdlqtfpYta6Tcy0Y+xjAAwDTq3WMfwiFQrEoJSXFIjo6Gk2bNgUAWFlZYerUqeaJiYnm50+e/NY+MHD5pS5dHkZ26pSdYmv7qnfz5qKPj4+Jk5NTxe0wJdDpdAgKCsrVaDRTlUrlndq8JgkJCf1IlX0JCQkJiX8Foiguy87ONk5ISKjxY2u1WoSEhAjOr7yCZr/8AnTqBBw69OIHTk2lYKVjxzJvTZgwQT5x4kRERUXx3377DU2aNNGNHDkSdePi2NSgIDC12nDDO8ao6unvD1ha4sKWLVoAcGnYkJQB27ZR4uCTTyiQP3WK1ADFFBQAx4+TBPrzz6larVJR4F8JxZXhLVu2VLzhrVukDigxIk0vDg5Uka0FfHx8YGRkBM45BEHAuXPnhHv37pEUf9y4infu2JGMHP/6q/Tr770HzJqFrF27YBofD5msZnJEFZKYSP8a2upw4ACur18PmUzGunfv/vRvvylTpsgaNmzINm/ejPPnz9dchmXLFmDECPLDyM4mI8mSExZ0OuCllygB5OhIrz15Qs/glCmkRAkOpqq8nsBSEASMHDkS1tbWurVr12LZsmV4/Pix/rU4OdGz37EjTM+eRfu9e+EK6iN/+PChDMC1F7jSoQDeBmOvGxwB/8OoVCpPtVrtDuCpl0IxjDG4KhQYU1Bg3qNpU7nNihVOL8+Y4fnRRx8pRowYYeLs7GzwZXLOcfDgQXV+fn4ogJ01fiESEhIGIQX7EhISEhL/CpRKZSaAyTt27Mg3dGydIeTk5CA8PJwDZLoHxoAxY8oYr1WL48eB69fLfdvNzQ3vvvuu7K233sLo0aNlRmfOILtuXRgdPkzS8arQrh3QuzfUa9bggbGxHACeVmsVCvIMOHWKpNN+fiSR9vMD0tOp4j9tGkmg4+NpdJ6BvPHGGwDImK5cbt2ir1On9AZvpXBw0G/c9wIcOXIEq1ev5iqVCuHh4fD19YWbm5uuS5cuOjelEnB1rVhtAFAlv25d/UmgV16BtYUFXtuzB/JyXPprlN27gSNHDN488+uvcdzEBCNHjmQlkxGCIGDIkCHw8/PD2bNn2R9//FGTkgoK4NPTacxiSSPB3FyactC2LalJdu8mr4bERGDnTmpHMYCZM2fKfHx8kJ2djZ9//hkFRf3/nHOkpKTg0qVLuHHjBjIyM0lh8M479HO1YgXw3XcozMszBlCNsQ1F0Eg+GYClIKf6/3kYY7MAwNbWttDT07P0m2FhwJdfAsbGMPrpJ3i0aYNGjRrByMioyuc5cuSIOioq6lZhYeEoacSehMQ/h2TQJyEhISHxr0GlUlkByJw5cybs9I1tqyLx8fHYunUrrKysdJ06dZJ1KJ6vDgBxccBrr1HvuiEmaPq4epWqlJ07V75tTAx0PXti08iR3KpHD/G1116reom4oAA5rVohXhBQ8MEHaDd5ctltOCd3+caNaW3jxlElthqFyZs3b2Lfvn1cFEU2e/bsMm7rTxkxgkbqLV9e+UHfeIMMAbt0qfJ69LF7924xNjZWeOmll7ilpSXz8PDAU/dwjYYSG/v2GWZamJtLkwiOHCkzGaGwsBBrPvoIb4sijMaNI3VIbVFQQBVzQ1o8AFzy9RXN6tWD17Zt5RZ5UlNTERAQAGdnZ+7n58cEoYbqQRERwDffkGneo0fAzZvA+vXA6tVkGqhU0uft4UEqlGoQHR2NwMBA1KtXD25ubggPD4dcLoe5ubmOc468vDxZnTp1RHd3d2ZhYcGa16sHu127kLJmjXjfwaFX+0uXzr2Q0R5jclDQPwic76n2cWoRlUrFAPgACAWAWbNmodSEky1b6JlydTW8PaQcLly4oDt9+nSiWq1uq1Qqa8loRUJCwhCknn0JCQkJiX8TuQqF4q9jx4659+jRw+RF+4xv3rzJnZ2dmb+/f9nA2sWFRqtlZVUv2BdF6lE/c6bybTdtAjp0gOzyZbS4d48dPXpUptcxvjJMTJDp7Q3byEju2Ljxs521Wgrs4+Mp8ZCaCuzYAcybB7z1VrUCfQA4cuSIrkWLFrIBAwaUb1p47RopByobEVfiGmrCoA8ARFFEbGwse+ONN+Dm5lb2Iq9fp2SOoZibU6vEl19SP3kJZDIZ8p2ccDQhQTd40iQZIiLIXK428PQE/vjDoGD/4sWLyDE2Flq+9FKF21lbW0OhUIj37t0TsrKyYGNjUzNrdXICMjOBvXvpfkRGUtC/ZAlw+zbdy+7dq/0MAoBx0ci+tLQ0pKWRB1yHDh3g6+srAwC1Wo3w8HAhNDQUOTk5+NPUFB/Nn48jQM7rixYtA3AEjK0B54nVWgD173sCWAvGjoHzsuPr/kFUKpWdsbHx1sLCwgEAYGNjI9apU4eyOVot8PPPwL179LvAy6va5+GcIyIigp86dSqjyJBPCvQlJP5hJBm/hISEhMS/BqVSqVOr1d3u3bu3f/369UhOTq7WcTQaDYok3ax79+76N5LLyUzu0CFg6tSqnyQhgeToxT3J5ZGeDnzxBVVr69d/2lN++/btqp8TwMXXX9cl9evHYGxMkmgA6NABmDGD/pA/cYKC1sGDqcKang5Mr56/mE6nY+np6bzcouijR+SMHhMDGCoF7t4dqAHVBgCcPXsWjDHWsGHDsm9evQr06FH1xMLQoWQ+l5lZ6mW5XI4pU6bghouLbM+8eZwPHQocPPgCqy+HvDz6PA3o1y8oKMDJkye5y8cfw2jQoHK3u3r1KpYuXQpLS0v22Wef1VygD1Cwf+IEJX3c3QFLS5ruYGJCz1+PHi8U6AOAq6srpk2bhokTJ2LSpEmYO3cuevfu/fR9hUKBrl27YuDAgQCe+UwoPDzYdx9++AOABFDv/UwwVkE/SgVwHgXAAYANGCvnl8rfj0qlclQoFBEtW7bs/frrrwMA7O3t6QY8fEi/2+rUoaTLCwT6BQUF+O233/KOHz8erdFouiqVyuolTiQkJGoUKdiXkJCQkPhXoVQqH8+bN+8NuVy+9Pz58zpD1becc0RHR+PatWvILArUOnToAA8Pj4p3bN6c+rWrqvJ98ICq5xWxaBFJme/coZ57AG3btkWrVq34jh07oNFoqnZOAFZXrwqKmzcpmB83joLDEyfIfV8uL20WGBRECY07d6rlgD99+nQhOTmZnT17Vv/NUSiA336jEXWGkp5eJpCuLmFhYWL79u1FvQqJVq2AtLSq+wPY2VEF9NQpPW/ZYdq0aUh8/Bj72rXT8WXLKOFRk9y+TSaKFcjsw8LCsGjRIv7999/D2dlZbBobS6MZ9ZCVlYUjR45wxhhee+01JpfXoOjz6lWqGqemAqtWUfV49mzg7Fnqna9B7w1HR0e4ubnBxcUFVlZW0HcdzZo1Q7t27WBtbc0fPHgAR0dHC7mxca+iUXo/ADABMB2MTaqW4R7nWgB+AAyciVi7qFSqBgqFIqxLly4NXnnlFYW7uzsA4M6dO+ZZ587RWNBOnWgUZtHUiuqQmpqKVatW5cXHx+9Qq9UtlUpl9TKVEhISNY4k45eQkJCQ+Fei1WpToqKiZHXr1tX16tWr3P72wsJCHDlyRHf37l1BrVbD2NhYzMnJkdna2uq6d+9eeV+8jw99TZhAsvyhQw1bYEAA9Xe3aKH//YIC6hVfsaJMwDl8+HB269YtfvDgQT58+PDKE/PJyZQ4+PFHtD15ksWamYn47TcBzZqRdLo8OfnHH1OQX68emaS5uwPt2iE5ORk7duzQabVaJooi69OnD2tXlIwo5tChQ/zy5ctMp9PBxsambGC0cSOwbBkFfFUhO7v0pIBqEh0djfz8fKFbt25l31yzhqrK4eHVO3jjxtTjPGgQJVBKYGtri6lTp7KtW7eyRebmmPLSSzD66CPUmTOn3MMVFBTg3Llz6NKlCxQKhd5A9SmLF9Mz1bq13uOEhITg2rVr6NmzJ3N0dESDBg1kePAAMNY/sXLDhg06BwcH9tprr7EXGhm4bx95IDg60nM1cSIlke7epbGGs2bRfd+8GbCwAMaOBU6epETA4cPAjz9W/9wGIggCLCwskJmZydavXw97e3smk8l6AAA4TwfwHRhrDGACgH1gbDY4v1elk3C+CIwtBWO9AUSB89QavgyDUKlUJgqF4kz37t0du3XrJgPwdFJEi+vXkRkSorP6/nuZvkkhVeHatWs8ODg4X6fTvT1//vxfX3zlEhISNYkU7EtISEhI/FvZCMD99OnTM21sbNBaT/ADUFB68+ZNWb9+/dC+fXsIglAc4FfNAM/dHShvvJc+vL2BElLiUnz0EcmYL14sd3dPT0+WmZlZsZzgo4+AZs2Avn2BP/8ECgqwd9o0nY2NjdDh1i0atSeTkcu2PszNSVFw9SoFr+3bA+3a4c6dO8jMzJQVjQbUHTx4UHbs2DHu7u7ONBoN12g0LCkpiY0aNQppaWlo3rx52WO7uVHlsKoFUs6BIpl1dYmOjsaOHTvQp08frlAoyi5gwACSLleXtm3JZyEmhu7/c5iZmWHq1KlCVlYWDnMutj9zRqjTq1eZAF2r1UIURRw4cAA3b97E+fPnAQDGxsa8X79+zMvLC6IowszMDLGxsbCyskLu+PGIycxE/Lp1Ym5uLre1tWV9+vQRHB0dERAQwDUaDRs/fjxKtS6Ym1Ov/OjRpc6fmpqKnJwc2fDhw2FQoF/82QQFAc7OdP0rVlDiZN06koF/8gn147dv/yzB8Pbb5IS/ePEzZcmTonbu339/NmrRwwP46itKqMXFlZ8oewHatGmDxMRE2NnZ4fr161yj0TR97hpjwJgSgAeA3WDsCICl4NxwuQn18CtBZngf1+DyDcbIyGi+m5ubQ9euXZ/+njOSyzEnIwMXCgr4/latZJ3kcrFdNVW+BQUFOHjwYMHt27cfaTSaV5RK5YuMMJSQkKglpGBfQkJCQuLfSgGAmQCNsNPH6dOndTdv3pQNHToUL1ViUFYpKhVJsocOJWl6RYZzZ87QPHZ94+gKCijQmTatwtO1adMG27ZtE5KTk+FYsu//5k1yrL9yhSr2pqbkoB0ejvz8fOTl5Ql169ZlmD6dkgkVzY83MaGRc48eUdDFOXDoEB5kZPCWLVuKbm5uMjc3N1n37t0RFhbGr1+/DmNjY6SlpaFly5bw8vKCl74+3wkTaJLBkCEVXqNe6tc3vL9fD1qtFvv27RN79+7NunTpUjbQDwyk4xf1L1ebwYPJSb6cVg3GGKytrdF40CC2XaPB5FGjuOOUKQzz5gEAMjIysHr1aq7RaJgoiujYsaOuW7dusvDwcJ6QkIBz587x4OBgVnwsxhjs0tMx5OBBpH/5pa5Zs2aCpaWlcPfuXd369eshl8uh1WrZ4MGDUcajoKCAntnFi0u9HB4ejrp163JXV9ey96l4asPeveSjsGoVsGsXPXe//kqVe19fmprQvHlpf4KSkw20WurXT0rSP5Vi2DD6Asihv3Vr4NgxYMoUei6/+oqSK6+8UtknYhDW1tbw8/MDAHTo0IEFBASUNf6g3qAYMNYZwNugiv8fAPaA8wIDT9UHgA6MvQZg9wu5/VcRlUplJZfLZ/fv39/0aTeCRgNMngxrT0/0+vBDdvXXX8Xg4GChSZMmsKrimNGEhATs2LEjT6vV7tBoNLOUSuX/lCGhhITEM6RgX0JCQkLi3woHgJEjR+o1FMvLy8P58+dlw4cPh7e3d82c0caG+ozv3ydH9PI4dky/Md+771LQExRU6alcXV0hk8l4enIyc3R0BPr1o6Bq4UJKOHBOxn4luHLlCjIzM1m/fv2oNWDePEoqHD1a/ti7rVspEACApCRoX38dD+fORdt+/Z5WBK2srNC3b1+hb9++AIAzZ87gjz/+wLBhw1BmRJtGQ0Givmq/ITg4kIlbFRBFEXv27OHx8fEi55yZm5uzjh076pcUXLpE9+a116q3vmL69AG+/hq4caPCCnT79u1ZamqqeNzamvk1aABs2waMHYsVK1bAxsaG+fv7Qy6Xw8TERAYAL7/88tN1azQaPHz4sNgpH4pz54CkJIwdO/bpZ9O6dWuZlZUVzp8/j9atW6Nt27ZlF+HiApw/T89MCaVFjx49sGbNGr5QpWLTuneHY0QEMHkyBfHGxsB331F/fatW9Ox+/DHQoAEQEmLYPTpyhKr258+T18Crr1a8fb9+9O+IEcDw4fTfCQk0tjE+ntppHjygtpV69ao/ErOIIuf+8uU6nGsA/ATG3AHMAlAHjN0DcKjS4J2q+6ag/v37AP58ocVWAYVCscjT01OwLU42RkcDfn6kuOjaFaYyGSZPniysXLkSy5Ytg1KpNOi4nHNcvHhRPHnyZK5Wqx2zYMGCWnCglJCQqEmkYF9CQkJC4l+JUqksUKlUW/bt2zfexcWFlQz4Hz16hA0bNsDW1lbn5eVV9Xn15SGTUQBz9ixJl5cu1b/dyy9TgFQM59Sb3LIlyZkrIzcX1y5cgEtyMlp07UpKgI8/JlMzKysKMp/jwoUL/OzZs6xp06bPJNleXlRZPXaMevP1mbotWkTz4zdsQODp09r4efPk7Vq04O2dnMrV3/v4+ODUqVNITU2Fo6MjCgsLUVhYCCtBoJnqwcFVN74r5tYtul9duhi0+YEDB3DlyhVYW1ujT58+MrVajTZt2jztTy7F/fv0mdXEDHmZjIKnPXsqlZunpKTwhm3aCPDxoWD62jU4eXqKiYmJQn5+PuqVM0LPyMgIpcZL2tmRF8Jz9O7dGxcvXsS1a9cwVJ+nhCDQsxcVBdjbk2FjaCisJk7EpB9/ZPGurqjj6EhBYUEBsGEDSeqtrAB9ngeGcvgw+S8MGkSy/Ph4w/ctTkqsXUv/ZmfTsyWXk4M8YzQpY8YM+nmoRlvGnTt38gsLC1dVuiHncQDmgrGOAF4F0AeM/QjOK74gzvPBmAsABRgbDM6Dq7zIKqJSqdooR79xAAAAIABJREFUFIo3+/fvbwoAuHCBkjbffkuTD4qoV68eXn31VURERGhhQDygVqvx+++/F8TExNzXaDT9lUrl3Vq7CAkJiRpDCvYlJCQkJP7NTBdF0S80NBQDBgwAANy/fx8BAQEwNTXl06ZNk5WpPNcEJibUB6/TUdBXksePqW+55Pz2WbNo+wp69KHVUgV+0CCgfXtYurnBaPx4kc2YIYOJCfXll0NiYiJCQkJY27ZtMajkiDVBIBO0776jYEvf5IF3333qRSCKouDUoIHou327gAcPqB1B7+WboGHDhnzt2rXMwcFBfPz4saBWq9EpNpZ3unKFGX32GcyqG+wXFgIZGQZteuvWLVy+fBn+/v5wcXFhlX7WnToBH3wAVGCWVyW8vMhwLiGBWin0cO/ePaSkpMiGDh1KwfOGDcDlyxgWGCiscnBAQYGhqnBQe8SqVWUSIYIgYNKkSQgICEBsbCwaN25cer/wcHpmu3QBFiwgV/zkZMDeHofHjhVN2rZlbUePFvDWW7S9i0sVboIeRJE8DUqa7j169GKVeEtLSpQAzxQD+fmUeFMoSOVy4wYZTT56ZND4xsTERA2ASwavgfOLYOwSgJYAtoOxYwAWFrnwl7ePFox1ALARjDUA5y/uPlkOKpXKWKFQbO3fv7+JuZkZtW2cP0+Jyfr1y2zv7e0Nb2/vSmOBx48fY9u2bXk5OTkH1Wq1v1KpzKuVC5CQkKhxpGBfQkJCQuJfi1KpzFepVIiIiEDTpk1x4sQJMSkpSbC3txenT58u1EqgD9Cc89OnqbLbqhXQv/+z9548oYDd2JiCnkuXyHn888/1H2vHDgquBAHw96fq8+XLOLZpE29Sv74MTZvq36+Iu3fvYufOnfDw8MAQfT3y5uZULff0BGJicPT2bVy6dAkKhQIuLi6wMTGB2w8/YHffvmAymaDRaJDz88+wsLSkYMrUVO95/fz82NWrV5Gamio0atQInerVw+nTp9mWVq3ER999J8jlcnh6eordunUTHBwcDLyxIKm2hYVBm96+fRv29vZiw4YNDfugr117oRFjZbCyIsO5mzfLDfYfP34MuVzO69SpQ6VqS0vcsbFB7uXLmOLtzR1cXAxzMOScEjOdOul9287ODpxz2Jb0iXjyhFQb48bRvPtJk+gZKyHl7/nuu7KNGzeCc16taXN6uXSJRv2NH/9M4fHwISp7lquETEaeFdev0/eDBpEpJuf087R377OWAz3JHa1Wi4yMDDMAVRsXQYH9ZTDWH8B4AHvA2C8AjoNzXTn7/AnGHAE0BGO24LyaYyAqRiaTvefk5OTextOTYc4cMhXdtMmgxIc+RFFEWFiYePLkyUJRFD/W6XS/KJXKv817QEJC4sWppb+CJCQkJCQk/j5EUcSWLVvAGBOGDx+OWg30S5KeTpLokuzcCfTqRf89bx7NZPfxKV1Zi4ujwAsgF/xz5yiIS0ujJIGpKbKzs7khxlnbt2+HQqHA2LFjy99o4UJSDdSti9DQULRv3x6Ojo7io0ePeORff8E9MhJ9X3oJnp6eaNKkic7I0pKSD/XqlatGEAQBbdq0wYABA9CvXz9YTp+OwU+eYOb77wt+fn4YMWIE8vLy2Jo1a3D3bhUUv5mZz5zaK6CgoADXr1/H4MGDK/+gtVpKdjx6VFaJ8aL07EkV7Dz9xc6WLVvC1taWL1myBIGBgVyr1eLEhQu6jK++gsM77zC88QaQY4C/2YED5bdiAHjy5AnkcjkF+5wDERGUhDp1ipIRERE0hSAhodR+Li4uMDIy4tHR0VW9cv2cOQO89BIlrUqqO7Kzyx8BWRN06EA+DIyRUqVfP0oEHDhA7/v6Atu30z1Uq5Geng6FQpGsVCqrN/qB8yxwvhLAJwCmAJhQVMEvb3styOxvWbXOVwkqlcpNEIRPB/bsafp/7J13eFTF98bfuVvSQwqhBUKvIRA6qEAApYQuX5qIoIKKFBVUUMr1UgQFURRpKqBICSC9hdB7CySEEgiRQAghPaRsdveW+f0xCXVTKaK/+TxPniS7c+fO7C48eeec8x7y2musS8SoUSUW+nfv3sUvv/ySfeDAgXOyLDeaNGnSfC70OZx/Hzyyz+FwOJx/O14Gg+Eve3v7Fv369bMrrrP0E/Httyz6/cknrG7Y0ZG52r/2GkvvHjMG+PLL+wLzvfdYJLhDB5ZKbbWy+nYbNGvWTNizZw+qVq1qs6abUop9+/ZpmqYJw4YNe9wo70Fq1ACmTwd96y3UCghA848+gpub2/0LPvsMLVQVLZg4u6+Gjx9nwu0RY7fHSE5m+61ZEwBQrVo1AEC9evXIsmXL6NmzZ2m1atWKdvpC6X3DwAI4duwY3N3dtUqVKhU+r6oCr79uu4zhSalShR3k7N/PosuPoNfrMXz4cCEuLg7Lly8nK1asQHp6uuDl7c3KACpWBBYsYNHngkoftm8vMCvhjz/+0KxWq4Bjx4DPPmOt8BYtYk72eUyYwMokNm9+6FpfX1+yfft26uPjQ+yf0PQOgwaxfwtvvfXw45UqsdfqeZC3hwED7rcb7NyZRf5DQoDevZF88iQanD59F4Q0AaVFT+V/FEovAegLQroBeCfXwX89KL1tY+ynIESfmxVwoljt/ApAkiS9nZ3dxi5ubg5lundnpQwFGYgWQnR0NNatW5ejqurXiqLMEkVRexrr5HA4zx8e2edwOBzOvxpRFJOtVmu7nJycmQsWLMiJjo5+vgswGlld7JkzrNZ85kxWR7xwITMNi4piIlhVmdgpX55937evQHFXr149qKqK5ORkm89rmoYjR44IgYGBRWudJYogDRog08dHu3r16sPPjRrFvh7Fzw/4+Wf2PT9u3mTPu7nZjJp369aNREZGCosWLdISEhIKX2dWFqsnL4Tz589r/v7+heedW63Ad98xAfqssj2++Qb46af7/eJt4O3tjb59+yIjIwOUUpKWlsZer9mz2QFQ9+4ssyM/pk1jBnX50Lp8eaHJ6dMwXbzI2tbVrfuw0AeYud/atSy6/eC1rVuDEKJ98803OHz4cMmEXVYWM+CLiXlc6APAb78VfGD0DElNTUX6O+9A8/Vl2Q4xMbh0+bK58eHDKoAeIMQBhISDEPsS1zIw871PAUQCWAxCxuUzTgEwA0ARnDqLhpGQr/yTkmo3WL1ah23bSiz0ZVnGtm3bLEFBQWkWi6XLxIkTv+ZCn8P5d8Mj+xwOh8P515P7B6kkSdLBoKCgDS1atHBu166d4bmk8ut0LNX977+ZyLp0idXzr17NovqSBIwYwcZ++WWRpz1w4AA8PT1pnTp1bIqP+Ph42NnZ0caNGxdNnFStCrz1FtpNm0YcfX1ZaUEeAwawdnm2GDiQXWsLSlnZwQ8/sJZ5NvDy8kL//v2xfv16EhoaisDC+qV7ebE68wLIbTEoNGuWf9b0A4NZ3XJuf/tnQpkyQJMmrOtBly75DqtVqxaqV6+OQ4cOYd++fahevToqVKjAXNIPHmQlIe3aPW6mduMGS1O3dQhisQApKag/YwYiy5al9u+8Q/ItVXByYoc3q1YBR4+CUort27eroaGhOkKIDgBKlSolKIoCnU5XPN07ZQqwZw/zRXgUSlnpSoUKRZ+vhJhMJkRHR+Pu3btIS0uzxsXF5SQkJJQCgFq1ask9evQw6FxdERUVhcsffthFFMV4EOIG1hrPAmAbCBFAaRcQMgQsSl/wBzIPNm43CLkAIBCE7AfwGYDQR1r1tQSggpDXQemGJ9nvvM8++6jDyZOfNypd2kD27mXdCkpAYmIi1qxZY8rOzg6WZXmYKIr5tyTkcDj/GrjY53A4HM5/BlEUD0iSVPf06dN//f333/79+/d3ei5p/YSwmuStW1kkOTycCeB69Vjq9dixxZ4yICAAixYtIhaLBbZSq93c3GC1WomiKNAX8Q98c6tW8ImOJhkhIcw8LY9WrVj/d1t4eACBgcDLLwOffw707Hn/ublzmQO6jXZwD1KzZk0IgkCSk5MpgIIVpJdXvqaA9/aR62AfHx+PypUr5z9QllnWwaOZDE8bQoBu3dj7Xgg6nQ4JCQmqt7e3rkKe+CWEieXt24EhQ1haf24pxL3nv/ji8cyJ2FjWu75LF5ydPx/nL1+mTXW6gl/fXr2QnJGBpMuXcezYMZqcnCyULl36XgbJvn376MaNG4ler8eYMWPg4uJS+P6Tklj2RD6+BcjKYiUtT1oiYANKKeLi4nDlyhX13LlzOWazWW80Go/JshyhKEosgAgA5wGUiYmJGTt//vx+DRo0cDAYDOcnTpwYnztJOgDWioCQPgDKgBA9gNkADoCQngDagNIPirio2wB+BSFnAMzL/fk4KL2W+7wCQkqBZQBcBqWXS7L3LT16vN4yOXmOb/fuesOnn5ZI6FNKERYWRnfu3JmjquoYTdOW8tp8Due/Axf7HA6Hw/lPIYpigiRJbRITEycvWLBgfJ8+fRxq5taSP1P8/VnK/po1LFXaYGCCrWdP9r2YWQYpKSkwGAw2hT7AWroZDAaqaVqRw6+7wsKQ9PnnGOrszARYnuu9Xs+yDgIDbbboAsBq3h9N55dlJnKLgKIocHNzK3ytiYmFpvGHhIQAQL796e8xZQqwaRNwuURaqng0bQr8+CPQvn2hrvPVqlXTHT16VMWD/ggAq/mPi2NZIhYLS8UHmIHja6/dH3flCjB+PPOKmD0bpx0csH/HDrz88ssaCivR9PbGusxMNBg2DNn9+2sfffSRLu8zZrVacenSJVK2bFn8+eefdNWqVcTb21v19/fXlS5d2vZn8fZt9vm+eZNlONgiKYml9z9lEhISsG7duuzMzMy7mqatVRRlGYDLn3/+uS3ThzsAhk6dOnXV+fPnJ1ssluk2J6XUDCDPxZBtiJD6AG7n/pwEYDiAwwAagtJ9+S6Q0jAAbUHIcABfgZBNYK796aD0LgipAMAehHQCpcHF2fvujh07N710aS3Gj9c5jR5dnEvvYTabsXXr1pxr167dkWW5myiKl0o0EYfDeWHhYp/D4XA4/zkeSOvfv27duo1NmzZ17tChg1H3tJ3YH8XHh0W/AdbuLjgYCAtj0drWrZkJm5dXkRzh9+7dq1arVo0gH/GWkJCAMmXKwFiMfvbVq1dHZGgo5DlzqP7nnwnJc2YXBObaXpBz97hxLIrfuzdrazZ+POsoUMT6YEpp0bwFMjKAa9fyfVrTNFBKMWTIEDgW5u4uScwU8XlgMLA+9sePFyr2jUYjzcjI0CUmJqLMowL5vfeYgV6ukIefHzB9OvD990xMr17NMkZeeol9GQwgZ87AycmJvvrqq0X6u66ct7da/9o13cvvvad7MNpuNBrh7+8PAAgMDCTBwcFaSkqKbvny5aCUwt3dXUtJSRHefvtt+Pj4AGYz86CIispf6APMxNLJqShLKxImkwlHjhyxnjlzRlZVdbSmacuLGo2eMmXKbgC7i3VDSrcD2J7726sArgHoDmAmgKog5AcAe0Hp1nyu/yVX6HcE8BMICQalf4JSGYS8CmApCKmYb+u+ByFESCtV6p0aXl4LMpcv19V+9dVibYUthyI8PBy7du3KoZSutlqto0VRzCctg8Ph/JvhYp/D4XA4/1lEUTwkSVKd0NDQv2JiYhr379/fqdTT7LNeEHXqsC+A1XOfPQtMmsTE7NChTPy88opN4W+1WpGRkaEbOnSozalNJhNOnDiBbt26FctMzM/PD97e3gi6dYv069ULTg+67G/ezKLJkyfnP4GHBxt/5w5z/B4/vsj3NhgM1GKxAIWl8derV2Aavyk3VTx3rvz55BMmQJ9lrf6jjBgB1K8P9OjBzBltQCnFkSNHULly5ceFfh49e7KU91u3WBvC3btZ+8ARIwBXV2D48HuR/mPHjmH//v15Uf1CT5EopTBUrkx+Gz9eHXvzpi6/gwlfX1/4+vreO2hKSEhAVFSUsHfvXixbtgy+vr5a1wkTBMPrr8M0aRJcKM2/xv/q1aeSwm82m3H06FH55MmTKiFkjSzLU0RRjH3iiYsDpXm1GmtyvwB2IKeCkIYA9gMoDaAugNugNC33uiQAK0FIGIDmICQYwMegdCcI8QFQG4QQUHox33sT4nqrQoVBVienb2/MmkXblUDoZ2RkYO3atdlJSUk3rVbrEFEUTxd7Eg6H86+Bi30Oh8Ph/KcRRTFJkqSApKSkiQsXLvzi9ddfd6hVSOT1qVO1Kvvq04dFQe/eZTXOH3/MXNbd3Zkje27N7bVr12Bvb09dXFxsqqfQ0FAoioKGDRsWeylbtmxR79SooXOYOJEdPkREsCfc3Ji4LIgKFYAVK5gHwcKFTPwXEVVVyc1HerzbxGxmXQ3yIS+aX6j5Ys2a+ZckPCv0evaezp+f76GJoihITU0l7xWWcdCpE2sT99ZbQEICc+tfvfqhDg5WqxVHjhyhfn5+JCAgwKbQN5vNiImJQWZmJjIyMmhsbCyNjY0Vhg0axNz6DxxgJSiFULZsWZQtWxaNGzfGkSNHcOvmTazs1UtLsbMTzN9/j06dOtGWLVvaVvuZmazF4BOQlJSE5cuX5yiKslmW5S9EUYx5ogmfJpSOAQAQYg/gTVCqgZDvAKSBkEEAggC8A0ozQelFEHIJQBSAJSDkVwA7AYwDUB6AbQdLQqplOjtPulWt2gB11ixjwEsvFStNSdM0nD9/Hrt27cpRVfVbRVFmiKJYeCYBh8P5V8PFPofD4XD+8+Sm9U+TJOnA+vXrNzZp0sTl1VdfffZp/bbI8w9YvZpFbTdvBtatY6I7MhLo0QPJhazL3d0djo6Ohddn28DBwUHQGQwgU6awPuyqyrIL3n33vvAvCEliRoQ1agAdOxbpnpqmQZbl/CPZD2I0PtYa7kHu3mWtyTMyMvKfY8MGoH//gssSnhW9ejHHe5MJsFFmYDAY4O3trS1ZsoS+9957ugLLMF57jWVbmM1MMN+4wV6b3NKJ7777jlqtVlK1alVcuHABiqJAURTIsgxFURAXF6dGR0frHBwcVHt7e+ro6Kj39PQkLVu2RPlq1YDQ0IeNAIuAo6MjOt66BcycKWhRUYiKikJYWBiOHz9OmjdvbvsQpkyZ+/4QJeDGjRtYtWpVjqIoH0yePPmPEk/0rGH1/jtyf+4MACDEBUzE54CQnwBUAKV9cmv/2wCYCGAZmBngGRASCFYScD91hZCAxNKl+5xs02ZguQkTjC2bNSvWv/v4+Hhs3rw5Oz09PcpisYwWRfHIE++Vw+H8K+Bin8PhcDj/bxBF8bAkSXXOnj277vr1680GDhz4/NL6beHuzlL6hw5lwn/jRuCXX2BMSqK9KlcmCA9naeEPiH9ZlhEcHAxnZ+diiX1FUXD79m1cu3aNuLu7U/TtS3DsGEsNX7KEicjOnZn5W36vSVwcM/KbMIFlAixbBrz9dpHuLwgC3NzcCh+Ynl6gq727uzsIIUgqqCf9Z58B33zDnOqfN+XKsfcrJOThzgUPMHjwYGH+/PnYvn07evfunf9cmgZ8+inbh07HzAZ/+IEdEN24AUedDsTODnv37lUEQUDel06nI3q9XihVqpTugw8+gKenp+3To+rVWZeFzz5jhxRFpX9/wNsbgiCgdu3aqF69OubOnUtXr16tDRo06PF7HTzI7lMCIiMjsWHDBpOiKL1z6+3/XVCaCeAVAAAhiwCUza13OAegA4B9ANwANAPwPwA9wLwAFuaO65Tq7i7tev31Bk0/+siuXr16RS7dSUlJwZ49e3Kio6Otqqp+oWna4tyDTw6H8/8E8nDbTw6Hw+Fw/vtIkiTo9foJgiBM6tOnz/NP6y8ATdPw47hxeLdyZbhUrcoM/9q3B6ZMQQqAPYcOITIyEpMnTy48lT2XiIgIbNq0CZRSNG7cWOvQoYPg4OAAbNvGDPeio5m54Pr1TPDbisIqCnOcHz2aZQHcuQM0aABcvMhMBwth4cKFNCkpiQwcOBAeHh7wzC/qfvgw69cuSfnONX/+fKSkpGDcuHFwfnStsszS6YvTI/5pc+wYcPIk8NFH+XZh2Lhxo3L+/Hm9vb097dChA2natOnjg2bOZOUeiYlAly4sU2DVKhbp79oVSZ6e2FOvnjJw2DA9Stpx4ocfmHeErfs/isXCzAe3bAEeKSHJysrCTz/9ROvUqaP17NlT99Bns3179n62bl2speXk5GDu3LkWRVFeEkXxbLEuftG5b3DQH0BvUNofhEQCOADAEcBFChiia9QI2DVgwEv/GznSoVy5ckWaOjMzE/v37zdHRESoAGYrivKdKIpZz2IbHA7nxYaLfQ6Hw+H8v0WSpJcNBsOmxo0bu7722mv/TFq/DWbOnEnbtGlDqlativIAyKFDoBERuLRpEy7XrYvarVrBb8wY5gBfCJqmYebMmejZsyetX7/+4wr4xAkm9gcNYpFjStkBwKNYrexwoFevhwVsejqrKS/Eld9qteKXX35BcnIynJyc8Omnn9oeeOECcOYMy3bIh5iYGPz++++PH3hkZDB3+MhIoFKlAtfzzGnfHpgzh9XF54OqqtiyZQtNT08nb9vKkFi1CtixA/jzT+Zor9ezDJDccoj1f/6peu/YQVodPizg+HHg9GlW21/cfus7djDDyLZtCx5HKTBvHjBmjM1DjIyMDCxcuJD6+/trnTp1uv+P6auv2DXF8HgAWPr+mjVrrowfP75OsS78t0LIu2AGf/Mo0O1848Z/Hx88uGz/IUOc3PMxfHyQnJwcHD582Hr69GmVEPKLLMtTRVFMeebr5nA4Lyw8jZ/D4XA4/28RRfGoJEl1wsLC1l6/fr3FgAEDivRH9bOmbNmydM+ePUSn00FVVYwaNQoe/fsjxGzGm927o/T8+cwEbskSJrpeeQWws7M5V3JyMhRFga+vr+1Qt6MjS8VPT2dp+tevPy72w8PZY5GRj4u80aOZsd/+/QXuyWg04v3338cPP/xAs7OzSUxMDKpUqfL4wOxs4FLB7b59fHyg0+kQHx8Pb2/v+0+4uAC7dv3zQh9gnQpmzQLWrs13iE6ng7u7O0lKSnq8JMNiYbX/eR0PHBxY1kK1akBwME7odNqVGzd0ry1cyJzuk5KAuXPZ4U3v3izbonr1oq11wwYm5AsS+yNHsvKOr7/Od4irqyveeecdsmTJEl2LFi1Y2Qal7DP1yOfTarVix44dSE9Ph4ODA1xdXeHu7o769evfy9bw8PCAoiiVJEmyF0XRXLTN/Ks5HFWjRoVLDRsmlU5IyLo5YkT5Yf36OegLObyxWq04efKkeuTIESuAdYqifCmKYtzzWTKHw3mR4WKfw+FwOP+vEUUxRZKkV1NTU8ctWrRoaq9evRzq1q37j65p6NChgslkgqOjIxYuXEhXrVoFR0dH7a6rq27Z6dN0xPLlxFlRWC10cDAzWrt2DejbF2jX7qHI7tWrV+Hp6akRQmznkzdowKK1zZoBjRoxw75HMZuBUaMecoK/x/Ll7HtKSqGGeHq9Hp9++ilZunSptmrVKuH9999/PJ3fZCqwZh9g9f/u7u7qsmXLdKNHj0apUqWAmzeBDz4Atm8v8NrnRvv2wKFDzPTQz++xpy0WCw4dOqSdO3dOeOWVVx5/b3buZBkOD2YGGAzswKViRfz966+0fPnyuOc5UbEiK4GwWln6/+LFwLlzTPx3755vOQEA4JdfmChPTc0/+v7KK0Uy2fPy8kKZMmW0efPmCR4eHni9Wzd4G42AkxNiYmJw4sSJPP8IzcPDA9WrVxeys7PV1NRUGhMTI+zZs0dwdnZWO3TooPP19UXp0qWF5OTk0WAGdv8qJEkiAJwe+NIDyAKQAIACaA6gLNG0DAD+I8qU+SLD3d2dvvmmUqVZM/uXKlTIv50hWBvFiIgI7Nq1K0fTtL1Wq3WcKIpXn/nGOBzOvwaexs/hcDgcTi6SJDU3GAxbGzRoUKpz5852hUXUngdpaWk4cOAAXFxccObMGWqxWIijoyM+++yz+4NMJhadXb+eibKMDFZ737w5Dh07hmPHjmHChAn53+T2bVazPWoUi9wmJt53kp8zh4n4goz4FIVFfffsAVq1KnRPmqZh2rRpqFKlCoYMGfLwk1FRLBo+cWKBc8THx+O3337DiBEj2IFBeDgwezZLeX9RWL2aOejbeO3PnTuHXbt20SZNmpCOtroa3LjB2uI9+voAwPr1sLzzDhZPm4ahb78NV1fXx8fIMivPeO89ZhTYuDFQuXL+7vszZgBr1jzekSE6mh2i7Nr1kFFkQWRkZGDHjh1ISEighr//JsOOHcO2L77QIiMjBV9fX81oNMLb21vw8/N7TMyazWaEhYXRQ4cOEbP5XjD/+ylTpowtyr0lSXID8LcgCCs0TVsiimL+feufAZIk+QAIdHBwGGC1WlsCEHQ6nWIwGFRCCJVlWW+1Wo2EEM3FxcXs4eJCe3/+uXPksGGq1quXoV79+nBxcSn0PnFxcdi6dWt2enp6rMVieVcUxWPPfHMcDudfxz//VwyHw+FwOC8IoiiekiSpdkRExOobN260HjhwoJNHMeuMnzbu7u73HNsbNmxIFixYAJPJhKioKNTMM2VzdATefJN9JSQAe/cCiYlQq1SBU/nyeH3mzHxbwQEAKlRgUX2TiaXlWyz3x4aEsBTugtDrgatXAW9vJvwLOSQRBAGBgYHYvXs3bt26hYoP9mC3Wtn9i8jJkyeVQD8/PQyGF0voA6x13q+/Ppb1YDabERISgooVK5KHyhDysFhYxsUHH9ie9/XXYa5QAenBwVi9ahV9/4MPHg//GgxAnTos+0NRgHHjWMvEo0eBs2eBwMCHo/2ffMJMAB+FUva+FsPPwtXVFf/73/+wYcMGknXxIi7Y2dHIyMi8TI4CXSXt7e3RsmVL0rJlS1gslrze8B9KkjSliCZzpQG4a5o2Rq/X5wAo4JTryZAkSa/X63cAuKHX68uqqtrcaDSWqlGjhla3bl3HatWqQa8k8MrtAAAgAElEQVTXw2AwGB481FAUBWpams5u2jQD5s4FXFzQrFcvwWbmzCMkJSVh//79OdeuXbMoijKOUrqcO+xzOJz8KHZ/Xg6Hw+Fw/suIophutVoD09LSvly0aFHOhQsX/uklAQDCw8OxYMGCe78nJyfbHli2LPDGG7jZuDHmDBqEm5UqoealS8Bbb7E6/5AQlpb/KNu2sci8kxMQE8MeW7SIRdl79Ch8gd7ewI8/FmrUl0ezZs1Qrlw5hIaGPlw34OhYJGFZvnx5vPXWWzh79qz+1vjxtCBDv3+M0qWZF0JIyEMPWywW5OTk4M6dO9i+fTvmzZunWq3W+wOyslgkvkMH2/MKAu5WrIhBK1bgfzt2FNx2gBAm/H/8kWVN3L7NvAT+/JOVPFy/zsY5OrJxr756/9qRI9nhS16pRjFYvHixmpqaqjVr3RqlevYkY8aMyb8Dgw2ys7Mxa9Ys7NixA5qm2QEoUm9AURSv6fX6HwBAUZSHPkiSJDlIkuQhSVLhqrqIUErLKooyzGw2d5dluSyllNjb2xvPnz9vPXDgAGbOnImtW7daH7xGn5ICu+xs4Phx9l7362e7ROYBMjMzsWnTJvOSJUuyrl69+rUsyz5TpkxZyoU+h8MpCJ7Gz+FwOBxOPkiS1NhgMGz19fX1DAwMtDMUwf3+WaEoCmbMmIHmzZvDw8MDzZo1y7f13t27d/Hzzz9DlmVMmTKFpUonJTEn9wkTgLAwZu6Xk8Nq/PNqsdu2ZY7uvXoBY8cyEXLhQv4ZAY9iMgHnzwMtWxZp+IYNGxAbG0s/+uij+4I1IoI58YeGFmmOGdOnQ1FVfPrxx3DKq19/kYiOZiUQhw499HBOTg7s7OygaRp++ukn6uXlhTfffJO9Dhs2sJKEw4fzzZKIj4/HxmnTkOPsDN927bTOXboUPYBDKaBp7HW+cAH46y/gyhVWAjJkCCs/MBqBjh3ZOvz9i7XlgwcP4syZM3T06NHEuH49yzb55JNizQEAISEhOHbsGABkA6gmimJiUa+VJOkbAJ8D6EYIcbWzs5tqtVqrCIKgqaqq1+v1d3U6XQaA8xaLZSOldK0oitnFXiS7lxNYu7xkADUEQdivadq9lI2BAwfiXnvP6dPZv72bN4s0d0pKCvbu3ZsTFRUFQRD+sFqtn4uimFGSdXI4nP9/cLHP4XA4HE4BSJLkamdnt8LJyenVgQMHOpYuXfofW8v333+PjIwMfPHFFzDaiARGRERg48aN0Ov1qFChAn3jjTeIrXHIyGCp3Nu2sVTurCyW1t26NRAUBLi5sciypydQku4EjRqxw4LBgwscZjKZMHv2bIwaNep+1DcmBhBF4Pffi3Sru02aINrNjQa/+ipxdHSkVapUod26dRNelDaKAJgPgre37fp7AAkJCVi2bBmaN2+O9gEB7PXv0AHYuLHAaRVFwcHgYLTs2xdnZs/W2o4cWfyMTVVlhwoTJrDXPS0NOHmSvf/5rLcwFi5cqDZq1EjXsmVL5gVQqhTzgygBv//+e2ZMTMz7oiiuLu61kiRRAHBzc8vu3r27k4+PD/R6PSilSE9PR05ODhISEnD27Nms+Ph4HSHkG0VRZomiWPQ6Etv3dQKQ1bBhQ/To0YMdyo0ezbJuPvqIHbQUcDBFKcXff/+NsLAwc2RkpApgtqIoc0VRzHySdXE4nP9/6L766qt/eg0cDofD4bywBAQEWPbu3RtksVhSzp07197V1VVfrly5glOnnxFNmjTBuXPn6I0bN9CgQYN7a9i3bx+CgoLo5cuXSZ06dai/vz/p2LGjbaEPsDZoVaqwyG27dkz8BwWxyPySJcBPPzGDuIJM+QrCwYEZBDo5FTgsODhYS0hIIHfu3EFGRoa2c+dO6mQ0EnerFRdcXBAfHw8PDw/cuXMHsbGx0DQNTk5O0DTtXlaDfcOGKN+nD7Hz9oajoyMJDQ0lPj4++Ke9Fh7C2ZkdXvTpw9LqH3vaGU5OTjh+/Lj60ssvC/D3Z++Lj0+B0wqCgGq1auGKyYTdZjMpW748in0YJQjss/DuuyyLYPx4FulPTgbeead4c+USFhYGNzc3VKlSheDMGeDll5kvRBG5e/cuIiMjIQgC7ty5oyYmJh4ICAgoWqpHLl9//fV+g8FQvlmzZrr+/fsbPT09731mCCFwcHCAi4sLypcvj8aNGxv9/PwMCQkJzbOzs9/at2/f/oCAgCJnETxKQECAfPjwYdc6deq0qnr1KhP5N28Cvr5A/fqsVaINrFYrzp07h3Xr1mWdP38+NiEhYZ6qqgMnT54cHBAQYLV5EYfD4RQAN+jjcDgcDqcQRFGkABZJknRsx44d26Kjo726detmn6+YfkYYjUaoqkqio6NhtVphNpsRFRWFw4cPo1WrVmjevDnc3NyKdxCh1wODBrGvtDQWVR42rFgmeY8xZAhr+/a//zFHeRvlBlevXkVoaKgAAAkJCTQtLY1WqlRJt3v7dliCg7FL06her6dbt24VCCFwdnZWs7OzdV5eXoiPj0dAQIDWZsMGgQwbBtSpg+a58yYkJGgrV64Uxo4de69f+z+Ovz9LkT97FmjSxOYQHx8fZGdn6+40b45yOh3w7bdFnr6uJMEwcyZ1bduW7F+1Cu3aty/62sxmdrDz008sol+mDMvyKKQbQkH4+PgIN2/eVAHokJRUrGvv3LmDpUuXmnU63V5VVVvLsuwK4FZx1yDLckCzZs3QqVOnIo13d3fHm2++6Xj27NlqISEhJ7/55pujZrN5kiiKJ4t776lTpwYaBOG9apUqse4Uf/wBjBmT73hFUXDq1Cnt0KFDFkLIMbPZPB3Awdz/dzgcDqfEcLHP4XA4HE4REUXxvCRJ9a5evfrbggULug0cONCxbNmyz3UNFosFRqMRM2fOBMB617/88st49dVXnzzbwN0d6N8f6NuX1fNv3MiM3fbvL/5cVasyN3gbUEqxb98+rWnTpoK9vT3atWtHBEHQAUB2o0YwbN4M/wkTCABiMpmgKApcXV11QUFBNDIyktSoUQPHjx0jVbdtg6ltW9SsVw95aftDhw4Vpk+fnq+fwT8CIUxE796dr9j39PREv379cDUkBOTKFVjKl0c5qxV6vb7Qvej1etQZOZLclmUcPXYM9Xx9UeDnklLmiUApSyv382MGjlFRrANAYCAwcybQrBkz7SsGZrMZWVlZMJlM7PNYunSxovrXmWHgmvHjx78tSVItvV4/TlGU4GItgtEtLCxsY2BgYJE3QAhBkyZNSIMGDRzOnTvX4eDBgy/NmjVrj8ViGSqKYlph10uS5GVnZ7eg0t27Xd9autRBN3IkEB9v87ALYC0ow8LCsGfPHpOmaccsFstYURQjbA7mcDicEsBr9jkcDofDKQHTpk0bqtPpfu7UqZND48aNyaP9wp8FN2/exLJly9ChQwfUqVMHK1euRHp6Onr27En9/f2f/gIiI1ld/6efMtO2gQOLP8esWUCtWsDrr997KCIiAjt37tTGjh0r6B81oMvMZFHQZcsem0rTNJhMJjg7O0MJD8exu3dx4sQJLScnR6hXr57q7e0tXLx4kd65c0d49913UaEYIvNpYzabkZqaCovFgrS0NDSsXRu6N94AVqwAXF0fGkspxd27dxG2erWSvX27nuh0CG3aFJqmQafTwc3NTW3atKmuefPmDwn/5OTkx9L2b7duTS85ORGHyZPRqlWrhw8KEhKAy5eZE39CAjBlCjuEcHAAunUD5s5lGQgASzefM4eVYxSR6OhorFq1Cm5ublpgYKBQvXp1wMODZQ4UoXc8wDI+Nm3adOLzzz9vVeQb20CSpAaOjo7HP/74Y8eSGmsqioIdO3aYIyIiVEEQVlqt1u9FUYy0cS9XvV7/WfnY2HGtMzIMlX/5RW8MCWFGlzaglOLixYvYvXt3ttVqvWyxWMaIoni8RIvkcDicAuBin8PhcDicEiJJUh2j0bijevXq5Xr06OFgn08t7tPgypUr2Lx5M/Xx8dH69eunEwQBOTk5UFX12aer37jBDNuuX2eirTgHG2PHAtWrszZuYIJwy5YttHr16qSHrZZ+WVnAZ58BCxfmP2diIqtnv3ULqYKAFStWUFmWqZ2dHU1PT9dpmoYGDRpoOTk5WqlSpRAYGKgvzmGMxWLBmjVrVL1eT5o3by5UrFgRgiDAarXCJVe0Ukpx4sQJGhsbq/n4+OhSUlLUuLg4UEpJo0aNhJCQEOh0OioIApVlWahRo4baLz1dR6Kj76XoU0px/vx5nDx5ksbHx5M31qxBZaMRxnHjcLdnTyQlJUGn0yEmJganT58GAFquXDnY2dmRxMREmpqaSgwGA+zs7FRFUYQKFSpog61WXayDA9bExcHT01MbOmiQIAQHswj78OFMvE+YwMwXBYHVknftylos1q17/0VITWVdGIrxmT5x4gROnjxJx4wZww6/MjNZK78TJ4r8mbl79y7mz5+frSiKF4BORqOxmyzLK6dMmVKs9BJJkoidnd1KV1fXHm+88YaTm5tbcS5/iJSUFERERKjHjx+XCSHpgiBcoZTGEUKcAFRXs7Jq1qlcWXvNbHZwPniQvZY2oJTi2rVrCA4OzsrKyrplsVjGANjD0/U5HM6zgot9DofD4XCeAEmSHA0Gw2pBEDp369bNUL9+/WcS4v/xxx/VqlWr6rp164bnkUVgE0qZq/yiRYAtoZ4fsgx15kwca9eOHj58mPj7+6Nz586209PNZuCLL4Dvvy94zqys+y0DHyApKQmnTp1CRkYGYmNjkZOTg8GDB8PR0RF6vR6enp6IjIxEfHy89tJLLwkWiwVhYWH0woULNNfNX3fq1CmUKlVKlWVZl5qaCgD3ygSqVKmiKYqCW7duCXq9nrq6utLs7GxUrFhRqFChAuLj4+n169fRpk0b8vLLL+duyYwffviBvtKwIV6ZPJkkr14Nq5MTcnJysHLlStSsWZN27dqVOFIK/YYNzIn/EXM+VVVx8uRJJCcna6qqUlVV0bFjR50sy0hPT4eqqti6dSusVist6+hI+//8s3DE1xd1TSb4lC/P0vVr1nz4NbtyhfkqbN3KTPoeJDmZif/Y2CILfk3T8N1332mtWrUir7zyCkFCAmsjOGJEka7PY/Xq1aarV69qAJybNGmC8+fPm2RZ7iSK4pHizCNJEtHpdOP1ev2UNm3a2Ldo0YI8SZcGVVWRmpqK5ORkmEwm6PV6lCpVCj59+kCoX7/ADhKxsbHYuXNnVkpKSqosy59QSjdykc/hcJ41XOxzOBwOh/OESJIkABgP4GtRFIt8HaW0yML9+++/V7t27aq716/7n2LnThatXboUKF++SKL//L59mk+fPsKqjz7SOgwcKNSuXTv/wVYr8w7IzqfleUgIa2MW+Vg29WPMnTtXy8zMFHQ6HYxGI5VlmdjZ2dHs7GwCMDd7nU4HQRDQpk0bZGdn4/r165qvr6/w8ssvIzIyElu2bNEaN24seHl5oW7duli2bJmiaRpeeuklfe3atVFQNoemadi6dSuuXLmimc1mAQDaZWZqyTExwvn69QEAzZs3R5cuXYDly4FVq4C4OODCheJlT+SiJicjY9kyXBcE1J04EWeaNkX62LHo/kAJxT2OH2cHAJs3s/fRFhMnsjFlyhR5DTExMVi1ahXatWuntbKzE7BiReEHN48gyzIWLVokp6amGurVq5fTqFEjh7Vr12bKstxFFMWjxZoMgCRJzYxG43cVKlRo0r9/f8enkoFjtbJDmW+/ZZkrpUvbrM3PyspCcHBwzpUrV8yKooyjlP4hiqL65AvgcDicwuFin8PhcDicp4AkSU6CINzRNM25UaNGaufOnXX5ufVnZWVh7dq15tjYWHsA6NSpE1q2bFng/HPmzNFee+01oWHDhk9/8SVh9GgmEj//nAnzR/qGq6qKQ4cOadHR0YiLixP69u2Leu7uLG28RYv856WUOZgfP25b8MbHMx+B4cMLXaKmaVAUBYIgQK/XQ9M0hIeHo3Tp0khNTUX58uVx4MABtG7dGuXzE7zF5MqVK/Dy8oLJZMLatWs1o9FIunTpQipWrIjTp08j4+RJtLlwAdaZM3E7ORn1c0U/duxg7Q/1euC334p+Q1VlBoonTrD3wWwG3n0Xt1xdse+rr9Dg8mUIixejQYMG96/Zvh1YsAD49df8hT4A3LoF7NrFujMUg9jYWPz555+0f+nSpFpkJPDdd8W6HmD/Rn766SfF399f6dKli/21a9ewbt26HFVVp6qqOru4glmSJIPBYPjD29u725AhQ0pe96Jp7H0aMICVm3z8MVCxoo1hGk6fPq3t27fPAmCJ1WqdLIpiZonvy+FwOCWAi30Oh8PhcJ4SkiQ5AuhqMBi+lGXZv0ePHmjUqNFDYy5evIj169cDgArgbULIFEppDQ8PDzpy5EhiK7XdarXi+++/p4GBgcTPz+95bKVIJCUlIWfyZFTauRPkxo17j1NKcfjwYRw9epT6+fmRypUrw8/PD3jvPVb3HxJS8MRTprCvR8379u5lAnTIkGewm6KjKAqioqJw69Yt2NnZISIiQs3MzMx744gsyyCE4JVXXtFat24tPJY6LknMvC3v4EZRWMZETAzQpQtQo0bhi4iOZrXhDRoAs2ezLgoffHDvgMRsNmPlyJFoERpK/+rdm9SuXVvr1LGj4L55M2uH+M03QLlyBd8jJgZo2pRlG9jZFeMVAkJCQmBYu1YLaNlSwLvvFuva/EhLS8OGDRuyExISzJTSpYqirARwoajCX5IkNwBpNWrUMPXp06f4EX5NA86fZ+9RZORjB1x5xMbGYtOmTdnZ2dkXLRbL26IoXirejTgcDufpwMU+h8PhcDhPGUmSCICJAKZNmTIFhBDcuXMHt27dwvbt2wFgBIDFoijS3LHTcscDALy9va2lSpUydujQAYcOHUJ4eDicnZ3xySefvFAt5VauXIlr166hLKXo0qMHfN57D6dmz8buI0eg0+kQGBgIf3//+xfk/c3x998s9Tk/+vdnvckfFZgzZgBXrxZYG/2siY+Pxx9//EEJIaRs2bJqdnY28fX1Rd26dQVN0+Dl5QVCyL0vm4SEsLT9P/9k4vyvv5hpXoMGwPz5+UfbrVYm8PfuZSI8LY11SHjktaSUYs2aNWp6ejpGjBihs06ejG3Z2arzhQu6Gq6uMI8ciboBAUUrIUlOZqaMxRT7u3fvhssff2itunUTMGBAsa4tjISEBJw7d06+dOmSxWQy6Q0Gwymz2fy1KIqFtuiTJKmGwWCYbG9v3/fDDz8suqnm7NksE+LKFfY5tvHaybKMkJAQa1hYmEmW5REAgnhdPofD+SfhYp/D4XA4nGeAJEllACTUr1+f+vr6kqCgIACAwWBY/OWXX35gY7wrgIYAegNIByDlPde1a1c0adLknzPmy4c5c+YgO7e23tVqRZtdu7Cte3e0MJnQ9NNPUdpWrbeiAG5uLJLdurXtif38WBr/g2ZyiYnMQf4JDNaeFJPJhIULF2r169dHx44dhRK/H5QCbdsCP//M9qqqrCXejh2sLOJRzp5lrQ8bNgSCg4E+fZhXQj4HP6GhoTQkJARjxowhjo6OwODBQEoKkmrVwq4qVbS/794VevXqhSKVhPzxBxO5hw4Va4s//fST2slk0tXq0IEdTDwjLl26hKioKERGRubIsvzBpEmT/ijKdbNmzfqzSpUqvQcMGOBY4MD584EKFdgeEhKAZs1sDrt9+zaCgoJMZrM52Gq1DhdFMaX4u+FwOJyni77wIRwOh8PhcIqLKIqJU6dODbhw4cLeCxcu6AwGw7ovv/yyXwHjMwAcBnA4V/jfE/s1a9Z84YQ+ANSoUUOLiIgQvvjiC+j1esSPGoX3KEX5hg2ZiV/Hjo9fpNezunsXF8BkYu3dHqVOHSaAH6R9ezbf3LnPZjOFsH//fhw/fhyVK1dG+/btSy70ARYVnjePmRx+8w0T8YMGARbL/TEWC/DDD8wpf+xYoFo14LXXgDffLHDqpKQkBAcHkz59+sDR0ZEdLLi4AE2bwis2FoNGjRKmzZiBCxcuwM7ODpqmoW7duvl/vjp3ZoaBxUDTNGRkZOiqnjkDFOJF8SSkpKRg/fr1CqVUP3jwYIcVK1b8Pn36dIdJkyYtLuxai8UyIjo6umdiYiLK2DqUun2bGROGhbHffXwe65AAsL0ePnxYOXLkiFlV1femTJmy+ok3xuFwOE+JFycXkMPhcDic/xhTpkw5CMANwEBZlvsX41IfAHj99ddhNBrpkSPF6jj23OjWrZvg6OiIAwcOAADKly+P8hUqsAhox45MnH7xxeMXurgAP/7IRL0typRhGQAPcuQIMG3a091AEYiLi8PPP/+snj59Gv369cMbb7whGAyGJ5+4dm0m6ENDAX9/4NgxoE0b4PBhZj44fz4z3Js7F+jdm7WvK1u2wCllWcbq1atpvXr1aO3atdlrOHQo0KgRS/ffuhVCQgLq1Kmj3b59W9u4cSNdt24dVqxYQU+ePIlly5bR9PT0hyctUwbo2RPYt6/IW7t06RLs7OyoIS7Opnnd0yAqKgrHjx/XKKX3/pZt27YtVFVdNHXq1IGFXS+KYialdN65c+fkx560WtlnMziYZTWMGmVzjrS0NPz666/Zx48fP6soSj0u9DkczosGT+PncDgcDucFZNq0aWvt7Ox65eTkGABg4sSJ0D9qWPcPEh4ejuTkZBw9ehTvvPMOKtoSdUePAq6u7Odjx4D337//nKIwk7latR6vfxZF1vLNw4P93qwZi3Ln9q1/XkRERGDbtm20QYMGtH379oKDg8PTvcH69cyDoH9/4N13WaeCpUtZnXy3bkXub5/H5s2b1ZiYGIwePVonmEzs9e7ShTnH5312bt5kJRL92dnT9evXERQURC0Wy703oVSpUvD29qaCIKBChQq01alTAtatYwcuhaAoCubNm6c1adIEAYcPC/jss2LvoyAOHToknzhxQsvJybHT6XRRqqrWzHtOp9Mlq6paGoBZFMVC3yxJknpXqVJl+ZAhQ9iH9OpVoF8/duBisbB2evlw8eJFbN68OYdS+pWiKHNEUdSewvY4HA7nqfLi/NXA4XA4HA7nHpMnT+43ffr0XwRB6KZpWrkZM2agXLlymr+/v9CoUSPk19bvWXLq1CkaGxurtWzZUrdp0yY4Oztrbdu2FWwKfeC+OF+8GNi0iYnPnBzAwYGJz9q1gbp1WfuyBw8CtmwB3nmHiX1KWW3/c245GB4eju3bt6Nz585o1KjRk6XtA8C5c2w/Hh7AW28xoX/sGOtB/9tvLEX8wAGbqeJF4dKlS7h06ZLw4YcfEiE1FRg3jqXgv/HGw7X9mzcDK1feE/tVq1bFhAkTCADcvXsXQUFBSE1NxaVLlwgAXLlyhZTp2RPVhwwBZBkoIKshLCwMe/bs0by8vNCmaVMBixcX29jvUWJjY7Ft27bMevXquej1euzfv98AYCaAdZMmTTonSdI3AD4HAL1eH0spHUwIKbS1niRJRK/XdylXrpwDkpPZwVRAAMtGcXJi2Sc2UFUVu3btsoSHh6fLstxVFMXQJ9ogh8PhPEN4ZJ/D4XA4nBecXMf+fgA6Axjq4uKijR079rmW4oWHh2PTpk33fq9YsSLeLW5LtfBwoFUrID0dyDus2L6dpa8/KK66dmVp7FWrsjT2UaPuj39OfPvtt7Ru3bqke/fuBQ/Mc2Y/cIAJ25Yt2X7++ov1Y583D7h2DWjcGOjenZU1dOzI+tcfPQqMHAm89BITmjodM9MrJunp6Vi4cCG6dOkCfw8PYOpU5pnQv79N13gArCNAkyb3sydscOXKFaxZswb+/v5az6AgAdWqAdOn2xy7b98+nDp1Cu3bt6dNmjQhuuRk4NNPgRUrir2fPFRVxZo1a8zXrl3bYWdn56MoSrggCLIsy1+Ioniv3kCSpEUA3gcAnU4XNmnSpEa25pMkyQ5AF6PR2JUQ0pgQUv+D4cONpX74Adizhx2+FIDZbMaqVatMCQkJJ6xWa58H18DhcDgvIjyyz+FwOBzOC05u+64gAEGSJE3IzMy8M2vWLHXMmDE6R1sGd0/AtWvX4OnpCXd3dwCsjRsh5F4JwfDhw2Fvbw/XvPT84tCwITM8MxpZHfjMmUzYnzrF0tjDwpjg7dCBjYmJAebMAT788CnusHAyMjJgtVpJUlISRVoagbs7sHs3kJrKUuJbtWKHEImJLHKenQ0sWMBa5rVqBVSqxAwG+/W7n91w9uz9G+Q527u4sEjy5ctMdL/3HtC3L4ug5xNZfhRVVbF69WqtSpUq8Le3FzB3LjtM6JevFyTj00+BIUOY+V8+ODg4gBACk8lEtUGDIKSl5Ts2ISEB9evXV5s3b87aJcTEPFFUX1EUbNy4Ub1x40Y0gOETJkxIBQBJkgYAiJg1a9Zlq9V6iBBiD+DewlRV9Z81a9aMCRMmTHx0Tjs7u/3u7u5+9evXd/Lw8CC13n8fuqgo4Jdf2AFJAaSnp+P33383mUymP6xW6yhRFNUCL+BwOJwXAC72ORwOh8P5FyGKYoIkSZ4WiyVl/fr18ltvvWVQVRXZ2dklE+APcOPGDaxcuRKCIKBRo0Zaeno6uXHjBilTpox2584doXTp0qqbm9uTHTDUqsWEsCCwmuiLF9ljvXvfTzePi2MC2tubuaI/KzSNHTQ0b84i3adPA5MmIathQ/j5+aHBiBEEpUsz8X34MOsiMGAA0KkT4OXFjO+uX2dzrV17f95Vq+7/XJCp3smTQIsW7JAjMBC4c4dlCpQrxwRo376FbmHv3r2ayWTC+02bCli6lLX069278L2HhbGo/9Gj+Xoh+Pj4YOzYsfjxxx91UX36QP71V+pZpw4pb6OVntVq1R4qdUhPB6pUKXwd+XDo0CHtypUrN1VVfVUUxVQAmDp1agCA1Z6enrRu3boV09LSXvH09LTPzs6WExMTM6pVq/yoFAUAACAASURBVObq7e2NjRs3jp0xY0bqxIkTv3twTkqpT4eAAOcac+cCEyeyw6a6dfPPfsglLi4Of/75Z44sy5MmTZr0fYk3xeFwOM8ZnsbP4XA4HM6/EEmS+gJYSwi550huNBoxcuTIEon+zMxMzJ07Fw4ODnTw4MFkx44dmtlspm3atNHFxsaiZcuW8Cgg5bvENGnCouHz5wMTJgA1a7KU6jp1WK3/k4r9oCCWPXD2LHNW/+MPoF075oA/cybg5sZM64KDgX37MM/fX6146JCuyfDhcGvRgonvR/rZOzo6Ph3PhI8+YvsvV+7hNoWxseyxsWOZkeHChTYvj46ORlBQEEbUqwf3c+fYoUXXrkW//82bQL16wI0bgKdnvsOCgoLUK1eu6AatX0/jPT3JtSFDKACtd+/eulKlSgEAvvvuO7VXr1666tWrs4uOHQPS0oq3nge4ePEitmzZEv3FF1/UyHts5syZZ+rXr9+oe/fuBZaw3L17F0uWLMkxmUyBoigeyHt8xeDBn6pt2nw1dPVqJ3z/fZF8IC5fvoyNGzeaFEV5Y8qUKZtLtBkOh8P5h+CRfQ6Hw+Fw/oWIorhOkqQylFIHAIMARFmt1nWxsbHw9fUt9nwnT56ETqfDxx9/TIxGI9599917gsrPz+8prvwRTp9mEfZZs4ANG4BJk4BSpVh9e/8CuhVarSzVf+FCFmnPzmbt6Y4cYdcqCrBzJxPU1auzlPI8R/qvv2Zi2t4eMJvZY4MHw9y3LzLnzNFdaNAAV8+dY6Z6NhAEAUOHDkXZQlrhFYimsQONS5fYzw9SqRL7/vrrrHQgJQWYMYOVNOQePGRlZWH9+vXoZWcH99BQdoDRvn3x1uDjw+anlGUoVK1qc1j//v1Zav7IkUS7dQsXT5yger1eWLRoEdq0aUP9/f0JIQSq+kBm++nTzIixhBiNRhBC7r0wU6dODaCU1s99LF+xn5aWhkOHDplNJpMDgIYADgAACGn2JvDVrIoVnVLWrYNnAYcbeZw5c0bdvXt3hizLHUVRPFPizXA4HM4/BBf7HA6Hw+H8SxFFMSn3x5mSJOkBwMvLq0RzVa5cGSdPnnz+Lv+CwL569mQit0cPYPRoICmJmdzNmcMi1vXqsSh4dDSr7z99mgnlpUuBChXYmLxI7YIFQF6pwZ079+/VogX73qrVY8uIjo7Ghg0b4OHhoX3wwQeCIOQfPN65cyddtmwZGT58eJFEo01iY4HlywE/v/xbCrZrx76fOnXfPC48HLRBA6xdu1ZreusW6pUqJaBHD7b/kmA0Muf+4GDgwoWCx3p4oGbjxqi5dq2AFi0QHh6OgwcPavv27dNpmqZLSkpCrVq12Nhz55gfQQmpUKECZFn2kSSJiKJIjUbjL02aNLFr27btY2NTU1Nx5swZa3h4uGy1WlVCyO8A/gJwGoQMA9AHQCABKlrt7VMSExOFwt63qKgo7N69O0uW5WaiKEaXeCMcDofzD/JcnXw5HA6Hw+E8G0RRVABmLlcSEhMToSjKU11TsahbFxg0CAgNBTIyWJQfYD3h//4bcHUF3n6bRaEXL74vTE+fZgcF5csDP//MHqtUqcC09Ee5cOEC/vzzT2iaRgsT+gDQpUsXUqdOHbp48WIEBwdrSUlJiIuLw82bN1Hk8sizZ4EyZdjPNWsWPLZ5c+DECZYB0LYtIubM0Tz27UOAIAgYMKDkQj+P2bPZ/AkJj2cZPAghwJgx99rvNWzYEGPGjNENGzYMOp0OaWlp9z9DjRuzzgQlZM+ePRa9Xn+vhkNV1XWhoaE0KSkJZrMZ169fR2RkJDZu3GheuHBh1tmzZ1eYTKZmiqK0lWV5dOuDB1/pt2bNS8dbtKAZzs4h0ldfAZSm6/X61YmJiQVskgn9devWmWRZ7sKFPofD+TfDI/scDofD4fxHEAQhZuXKlVWaNWsmBwYG5t8Q/REopdizZ8/9qOw/SUAAM5jLy1A4fpyJRlVlaf0GQ4G93osKpRQXLlxASkoKTp06hTZt2qBdu3YFO7U9QK9evUjjxo2xY8cOeubMGQiCQAVBgNVqJdWrV9c8PT1hMBgEnU6Hq1evaiaTiVSvXl0rV66crn79+rC3WFgmgr190Ret1yP2zBlEf/ml0P3IEeheegmoX78k238YQQCcnVlZwWefsdKH/PjwQ2DJEibmcylbtiw6duyIPXv2UIvFovXp00eH6Gh2MFMCNE1DWFiYHVh5yvuSJGkAHBVFIcuXL0/VNM3ZaDRGE0JSZVneryjKdxMnTkyXJKmyATggA6h344Z0zceH7g0I0O/u0gUA4gGsVhQlNCMj438AbLYKiIiIwNatWzNlWe4kiuLxEm2Aw+FwXhC42OdwOBwO5z/C5MmTq3799ddHT58+/VKbNm3g7OxcpOuys7MBAIX2lH9ebNhwXyhevcrE6PbtrDVdaiqwcSOLZnt7l/gW27ZtUy9evCjY29vT2rVrC+3yUuaLgY+PDz744ANd7q8EYM7tR44cEZKSkiDLMhRFoZUrVxa8vLxw6dIlXWRkpHbo0CHhfydOUH3lyuRGlSo0Z98+UrlyZdwzt8uHHJMJYZMno/ndu9Bv3Mh8CCIimM/Bxo2PGQkWm9OnmZdBVhYT/7awswO+/565/vv7A2Bt8qxWK/R6PXXIq9NX1fsHNsXkem6HA4PBsLhSpUo1nZ2dtfPnzzsCwMSJEx9K2cgtX/H7+uuv+9lR+tHYefMcyB9/ILRjR12Lpk1hHx6O7du3A8BvAFYbjcYWZcqUsSn0z5w5o+3evTtdluU2oiheLNHiORwO5wWCu/FzOBwOh/MfQpIkewA5AwYMQO3atYt0jSzL+PrrrzFo0CDUqFGj8AueNQcOAMOGAdeuPfy4pjFB26QJq9vv2ZPV58+YUazps7Oz8cMPP6BDhw5o+QSp5iXBbDbj4IEDKPPdd7RcZCTZN24cUnU6pKam4rXXXkOpUqVQvnz5xzofUEXBiVGjNM9z50itkBCCvI4L4eHAjz8Cv/3GPADeeIPV4ZeUK1dY1P7OHcDF5d7DGRkZyMzMhF6vh0N6OlyrVQMMBiQkJGDp0qXUxcWFNmrUiLRs2ZLoLBZWlhETU2hbO1usWrVKiYqK0rdt21Zp3bq1XqfTISMjAz/++KNJVdUyAHIANNLpdK8KgvCFl9UqdNyzxyHh22/1luBg8wFBMGq2S1V9DAbDlTFjxjg8ehAWFhZGd+zYkSrLcgueus/hcP4r8Mg+h8PhcDj/IURRNM+cOfOXNWvWDDcajUrv3r31derUKfCaPBf19PT057HEwvHzA9555/HH8yLXoaHs+969wMGD7OcxY1ibt06dCpzabDYjJCQEjo6OasuWLXUFDn4G2Nvbo5OPD5CURFCvHgZ9/DEAICQkRDtz5oxGKUVWVpbe19dX7dq1q85gMABWK65Pn05dTp0SfA4cAB5srdiwIRP6ZjPrHe/nxzwAStB+EQBQuzZw8iSL7CsKoNcjLi4Ov//+O1UUhej1epRKTMRbK1bgwtatCD9/ntatW5f26tXrvrhOSmImiCUQ+gDQrVs3vU6ng5OTkx5gfhJbtmzJUlXVGcBXRqOxo9ForFGuTBnaqWZNJ6uqwrJihXZkz54LmYLwC4AwAEYAIQBUAK/lPtarRo0a6qNCPyIigu7YsSNdluWXudDncDj/JbjY53A4HA7nP4bVan0fwM+yLPcJCgqaXKtWLevVq1eNvr6+/8fencdFVfV/AP/cWVkERcQt0TTN3Upz30vLVi217NEytbSefNoXS+V6Sfu12/pom7mkmamZZWpuaT6KqLjgjogLIoggCAwzd/39ccVcUBBmGJTP+/WaF8NdzvkO9Xo533vO+R6jcePGQuPGjS+oum87uyVdSYv7eV14uFmhPjMTuGiE+wJ33mm+AMDlMh8GxMYCL70E/O9/F1xqGAa++uorIy0tTahcubL24IMPlnmif058vLkE4aWXzh3q3bu3pXfv3hYAOHXqFGbMmCEsWbLEuLd7d8Hx44/IW7FCyP70UwRcLokPCACOHzfft21rruX//vuSxdeiBTB6NLBtG2I++girVq1Chw4djDvuuEMQBAF5OTnIPHjQ2LVxo6EHBqJbt24XjqLrOtCnT8n6BhB69jMuW7ZM3bNnj5KTkxMIoJIgCFsFQXixQ4cO6NGjh0149VWo0dF4f9gwjzp06GNRUVG/AMDEiROfczqd77tcLthsts/Hjh27BgAmTZp0S61atYLP72v37t347bffziiK0lUUxf0lDpqIqByyTpgwwd8xEBERkRf16NEDPXr0SOvevfuatWvX7svIyNgkCMLpU6dOnTxw4ICwefPm0HXr1unJycmeFi1a2K1WK8LDw7Fy5Urs3r1bbdu2rf936+nbFwgOBm6/vXjXP/ggcNNNQE6Oueb8jjvMdf26DqN1a+zatQtbtmwROnbsiMGDB1suniZfpo4eNavxd+sG1Kt3yemgoCA0btxYWPvrr7jhjz+E0KwsfN+6Nbp27YrKlSsX3f6//mVu27d8uTlDYsSIqx9lb90aK3Td+Gv3buGRRx9F27ZtBeFsGw6nE5U7dhTauFxC26FDhXPr9AvExZkPXXr1uro+z7Np0yZj7dq1VlmWfwBwGwCEhYVVGzlypL2pJFmEPXuAceMwp2rV3Mzs7AlRUVHfAYAkSc1sNtuCm2++2ZGVlbVfUZRBPXr0UCZOnPiQIAjRnTp1coaFhQEAdu7caZxN9LuLohhf4mCJiMopjuwTERFdx0RR/On83yVJClVVtT+Amw8cODAmKysLVapUQcuWLZGQkKBnZGT4P9EHgD//BM4mZVelSRPg7bfN92PGwLjlFvwyZAju/vVXVP/wQ/2udu38//l+/92cLn+FBxnhmoZ7du4UsgTBCPnqK0H76ivUrl27eO1XqmS+2rQxdzAAzAr7EyYU+2+6JzMTsXl5wthp02Br2tSM93wnTwIvvgg8/PClhQGPHStenFcQHh5e8HRimMPh8AwbNsxZc+lSB6xWczbHbbdBsduRlJzs1HX9GwCQJKmS3W5f2LZtW1tMTIyu63ovURRdkiRZHA7HVw8//HBIgwYNAADbtm0zli5dmqUoShdRFPeUOmAionKIyT4REVEFIoriGQDfS5JkdzqdXadMmdJKUZSQ7t274+DBg5ZepRiN9apt24BffwWmTCl5Gw8/jH179yK+USPkPPmk8eyzz1oQGWlW9X/tNWDPngu2kCsThmHuaV+jBnDxiHiBEycASUL9nj3xUU4O7jpyxLBYLMKGDRvQrVu34vdVr56ZkOfkmLUN3G5g925zTf9FRfyysrKwc+dOOBwObN++XcvIyLD27dcPthYtCh+hb9/eXNtfWKFnq9WcWVAKDRs2hCiKUBQFFk1zWg0DiIoyH+Y8/TQAIPXYMdhstrQ333wzKzo6+nGHw/Fuw4YNq9StW9cWFxcX/8Ybb6Scbe7foaGhgQVbS+7atQtLly49rShKR1EUD5QqUCKicsz/T7eJiIiozImiqHg8nhmKooQAQExMjBEYGKi2Luvk93LsdsALNQT++OMPCFYrHpw40RwpTkgAXnkFWLHinyR2/nyzcnxZ2L3bnFJ/uUKCqanmXva9esH5/PN45NFHhSVLlgiCIBjVqlUrWZ8hIcD27UCtWsDjj5u1As5L0lVVxdSpU43du3frcXFx+k033SQ8//zzaNGiBdCvn7krQv36gMdzYbsLFgD9+xf+GXS9ZLFexB4XB2uNGuZWfkePmoX/zrLZbDAMI/Cdd94ZFxISMnXQoEG1Bw4cGHTkyBHV4/HMBwBJkprYbLYP77nnnkqCICApKQmLFy/OVRSlBxN9IrrecWSfiIiognI4HF0rVaqkDBo0yB4RESGgPH0vuPNOs9J8wXZ7JXDs2DHk5uaiZ8+eKFinjYAA8+cDDwAZGeb7jz8GHnsMGDAA+OAD83df2bvXLEA4bNil506cMKfdT5xorueHOcLdvn17dOnSRbi4inyJbNliVtn/4AOo06bhq//8R8vMzLTWrl3bGDFiROF/6IKCfRdv6derl5nYXyw8HIiMLF2cS5eaWzBOnGi+L+Szp6amQlGU8MDAwLeeeuqpwJCzWwXqug7DMNwA4HA4ojt27Ghr0KAB0tLSMHfu3HxFUR7kGn0iqgjKzz/qREREVKYEQfAEBwer1apVs/s7lkI1a2ZO5T9vNLe4DMPAtGnTEBQUdPmp7wVF6zZsMH/+/fc/2/o995w5+v7ggyUI/AoUxXydt4c9ACA52dw6cNYsoFWrC071KUVl+4vpALbu2IG/3W69RseOljrVqmHkvHmw/PTT5Z+o2GzmbIjx480HEt9+ax5v1sz8G23ZcmH9gc8+A2bPLlmA6enmrINTp8xCi3Y70KlToZc2b94cbrcb9evXP5foA4BhGDoASJL0CoCBbdu2RXp6OqZPn56vKMowURTXlCw4IqJrC5N9IiKiCsrj8TyXlpbWKyYmpl7Hjh1Ltim6L82efWlhuKsUeTUjzF27mmvbAXM2QUCAWVl+5Ehg8+YS7xt/gdmzzV0Czp+tcOAA8NBDwJIlwI03lr6PK5g6darmdrst3e+7T2jdujWEnBwrfvoJqFIF+OUX88FKzZqF39ytm5mMn+/PP82tBBcv/udYYCBQp07JAuzRA7jrLmDyZHPJwRU4HA50LORBkN1utwGoB+DfVqsVhw4dwh9//JEvy/IzUVFRP11yAxHRdYpr9omIiCooURRlWZZ7r169Ou/UqVP+DudSlSsD69eX6NbDZ9fgW63WkvX9+edm0hkWZk75FwRz//rPPjNHnkuyJl1VgVWrgNtu++fY1q3mEoKVK32e6GdlZSEzM9M6evRooU2bNuZWeqGhwMKF5oONDz4Apk0D8vMBWb60gd69zVh79jRrAADAmDHAjBn//D1k2azQHxpa/MDy84GWLc2Cf5s2mYl+KQQGBlocDkclAM00TcPChQuhKMrI8ePHzyxVw0RE1xgm+0RERBWY0+l8W1XVSuvWrdM0TStRG5IkITo6GtOnT9cTEhK8F9zvvwP//W+Jbl2yZInRrFkz9C+sgNzVqF8fEEXz/dtvm1Ptf/8dqFrVTPpPnix+WwkJ5gOMrl3N35ctA15/3fxZq1bp4iyEpmk4dOgQ/v77b0ybNi3nyy+/NKpXr646Ll57X2DDBuCtt8zPe9FSgnMEwUzMg4PN30NCgCeeMB+OAObI/44dxZsF4XYDX3xhPmh45BGz0r4X6hLUq1cPFoulsyiKewG0BRAxbty4H0rdMBHRNYbT+ImIiCowWZYXWK3W1F27dj3XvXt3a3h4eJH3JCcn48iRI9i1a5eWkZFhBYA77rgDOTk5mDdvHkJDQ7VRo0ZZL5tUFpcklWjq/JkzZ5CRkSGcTfpKF8P5CtbO169vFo8TBHMLusGDgXHjzFH6zp0vf/+aNWZRu9q1zen8S5YAP/4IRER4JbyMjAzExMQoTZs2tVssFvz888/5mqal6Lq+XlGU5QDmPPjgg0V/93v3XXNHgBMnzKn7mzaZDzcKfPaZufRg4EDg55/NooJOp3nu9Gmzcn5RZNl8KPDJJ2axwvHjS/ahL6IoCnJzc+F2u28CAFEUt3ilYSKiaxCTfSIiogosKipqfnR0tCswMPCZqlWrXrFQn67rmDlzppacnGyNiIjQ6tWrJwwYMACVK1eGzWYDAEv37t0xc+ZM4bvvvsONN96Ipk2bom7duiVLujXNXPt96BAQFFTk5YZhIDY2FifPjrZ379796vssDosFuPVW831Cgllwb906c4r7qVPATz+ZU/Uvrjdw4IB57+TJQFKSmehWr16iENLS0jB16lSEhYW5b7vttoBjx44phw8ftimKcjg+Pj7A4/FEAnhKFMU5ACBJUjsACC3O9HqLxVxS4HabDzLCwsyq+A88YO6QAJj/PfLzzc8+ZAjw/ffmVoknT15+VkCBN980Cy/u2WNu6+cFiqJg7dq1SmxsrG61Wvfb7falXmmYiOgaxmSfiIioghMEoanL5QrYsGGD0rlz50sSfl3XERsbi7Vr1xqhoaGWZ599FuHh4YUuhg8KCkL//v0ta9as0ZKTk4XY2FhLzZo1MWrUqKsPzGYzp3d7PEUm+0lJSdi0aZOekJBgCQ4O1tq0aWMJDQ31fdFBm8183XWXmegLAvDVV0DfvmbRuwkTzKUIggAsWmRuY5eTYx4vxiyKwrhcLkydOhUAkJWVtWD16tWDLRbLOl3Xfwbw+5gxY45LkmQXRVEBAEmSAh0Ox4IHH3wQQcV4aHJOQIAZJ2DWTmja1JyFYLGYD2F+/x2YOdNczjBjhlk9v2bNy0/FHzvWfFgwejRQkv8fLiMlJQXz5s1zud3utYqiPPPWW28d9VrjRETXMCb7REREFZyu6x9bLJaWO3fufKJSpUraLbfcckEiv2zZMsTFxeH+++8XWrVqVeQofUREBB555BErAGzatElftmyZZd68eWjYsCFkWYbH47ngpyzLUBQFiqJAVVVNVVVD0zTouo6QkBBB/egjIbtyZaiqKgwfPlyoUaMGAEBVVcTGxiIpKUk7dOiQNSgoyNKvXz+0bNmyhFX5SqlgycGaszu7xcYC+/aZx4cMAY4cATp0AJ5/3hzR1jQz4bdYrmq5QmBgIFq3bo24uDgAaA+g2vjx4zPOv6Yg0QcAh8PxYYMGDcKbN29e8s+2bJn584UXzCUMO3aYSX5iojklf84cc6r/+vXmTIDz7d5tznLIzjYL+d1wQ8njOI+maVi3bp26YcMGt6ZpI6Oion70SsNERNcJwTAMf8dAREREfnZ2mvemhg0b5g0ePDi44Ljb7cYnn3xidO/eXShsm7PiWL16tb5nzx7BMAzDbrcbdrsdDocDBT+dTiecTqfF4XAIdrsdBS+bzYbIMWMg2O3I/uwzbN682UhMTDRefvlli8ViwaFDhzB79my0bNkSbdq0ubpt9sqKYZgj+q+8Yib04eHA338DjRoB99xjbjU3ZoxZqG7BAvMBwebNwP33m8sCWrQA5s4FXnoJ2L4dZ44fx5rq1d25v/9uS61e3eZ2OlH7+PE2w2bM2IZCvtRJktTE4XDEvfDCC4FXNap/JR6PuSShe3fg6FGzWN/YsWYl//vvN48XbJvncgE1agB//PFPYUIvyMzMxNy5c/POnDmz2ePxDBFF8bjXGiciuk4w2SciIiJIkhQhCEJKrVq1tLp16xp33313AAAsWLAAubm5+hNPPGERvLHP/NVKTQWsViAiArqu46OPPkJERIT25JNPWteuXYs9e/YYzz77rB8CK4aZM4GPPzYT+ZCQf9a7X8zjMT9jSor5uu02s4Bfixbm8TlzgNGjETN8OIJOncLKu+7C019/fWxjp06RAIy7/vwzBcD9AL4FkA9gBIBvAPx3Wd++r7dyuW6t/eOPFsyYYRYH7N3bnH3QubO5BMHlAiIjzRkGxaXrZoHBBx4wl1ps22bOWjh50kz8K1UyC/cVzAAIDi66zWJKS0vD999/n6+q6lhN0z4RRZFfZomICsFp/ERERARRFNOjo6PvPHHixGvp6el3d+/eHQEBAUhKSjLatWvnn0QfMBPFF14A5syBxWLBqFGjMHnyZGtWVhZq1aqFmJgY/8R1OYZhTnnfts2c3j51qpkEX0lBJfu6dc0XAAwf/s/5Nm2QnZ2N5T16nDv08auvFkxjEDZ26tRAnDBBAfAAgAAApwB89lePHnmnatVqXj0szAK73Vw6kJtrbnH35Zfmg4SYGLO4XkKCOb3+3nvN9fTjxgHR0UB8PLB2LTB/PvDyy+aOBE2bmsfuu88c4Zdlc1ZCYqK5lWB8vLlsYdQoIDCwRDsqFP6nNZCQkICFCxe6ZVl+Mioqap5XGiYiuk5xZJ+IiIjOkSTJ7nA4ZoeFhd370EMPBU+dOhUvvvgiKleu7J+AzpwB2rUzK7efHXmePXu2lpiYaHU4HPB4PLjzzjvRpUsX/8R3vr/+AlQVmDQJeO01M3H2EsMwkJ6ejoULF8qnTp3aZrVaK8my3NxisUSPHz9eLOyeSZMmTW7Tps1zffr0ueIuC+ecOWOO2APmUoJbbgH27jXfP/+8+dCla1ezCN9HH5lbI376qbn0YN0682GBppnbC65Y4bXPDpif/7fffvPs3LlTNgxj1Pjx47k+n4ioCEz2iYiI6AKSJFkAaADQoEED7fHHH/dPwbsCmma+HA4A5u4AO3fuRK1atZCTk4Off/4Zd9xxB9q3b++f+JKSgIULgY0bzYS4SxevjWYX0DQN3377rSszM3OLLMt3A3AAqCmK4oHCrpckyWm321NHjBhRpaCgoc8tXQocPw489ZRXm9V1HQsXLnQnJCTslmX5DlEUz3i1AyKi6xSn8RMREdEFRFHUo6Ojf6pbt+6jfk/0ATN5vu8+c2o5AIvFglvP7nNfo0YN9OjRAxs2bNDat29ftrGqqjlVfetWc9/4//zn3AMJb4uPj0dmZmbi2WRXA+AGcKWk98Fq1apZyyzR/9//gA8+MNfxe5GmaZg3b17+4cOHt8qyfLcoii6vdkBEdB1jsk9ERESXsFqtR2vVqqUB8H+y/847ZkX3y3A6nbDb7WU3VfHkSbPo3v79wNNPm1PZL7e3vJfoug6LxXLqbKJ/RZIkCQEBAWM6dOgQ4tOgCrjd5g4Dr79urtH3EkVR8OOPP7qOHz/+P1mWHxBF0eO1xomIKgAm+0RERHQJVVWPpqWlKSgPyX6TJubIcbNmhZ62XE0V+dLIyDCT/FtuMQvxTZli7i1fBhISElyapm0s5uXtBUFo0rx5c5/GdM7nn5uF//r08VqTmqZh9uzZrhMnTqyQZXmgKIqK1xonIqogmOwTERFRYRYdO3bsnQMHDgTcfPPN/o1k/35g9GhgwIBCT6ekpGhBQUG+eyiRl2duf5eRYdYOaNcO6NfPZ91dkIjgzwAAIABJREFU2HUeVq9ejX379gUB+Lg49zidzim9e/cOtFrL4DlNVhYwYwawfr3XmjQMA4sWLXKfOHFioyzL/Yszm4GIiC5VRo/CiYiI6FoiimKyqqoD5s6dqy5dujTfr8F06wYcO3bZ0xaLRVAUxfvT+GXZ3K6ua1cgLs584BAdbe5VX0Y2b96sx8XFwWKx/CGKYkZR10uSVFfTtKatWrXy/V6JmgY88QSweDFQpYrXml27dq1y4MCBQ7Is92WiT0RUckz2iYiIqFCiKP5pGMatmzdvDvBrIBaLuY3dZbZz69Spk+XUqVOW48ePe6c/TQN27TJnEnz6KbB6tTll38fr8gtTr149CwAYhvF9MW8Z0KJFC5TJqP6UKUCjRkD9+l5r8vDhw9iwYUOeLMt3iqKY57WGiYgqICb7REREdCUpVqs16/vvv89NT0/3XxRt2lw22Q4NDUXnzp21GTNmYM2aNaXrZ9UqoH9/YM8e4LXXzPXoXhy1vlqHDx/W7Xb7rKioqPnFuT4wMPCe+vXrO30dFxITAUUBXn3Va9sMulwuzJs3L19V1cdEUUz1SqNERBUYk30iIiK6LFEUT6uqGpmSknLo6NGj/gvkrbeAmjUve7pHjx7WIUOGYNOmTfjoo490VVWvrv3//Q945BFzDfrIkcDAgeb0fT+SZRkxMTGyoij/V5zrJUkSZFnuVN+LI+2FMgzgvfeAypWBWrW81KSBX375xaVp2rSoqKhlXmmUiKiCY7JPRERElyVJUoDT6Vyhqmqrxo0b+y+QGTMuW6CvQN26dfHaa68hICBAWLx4cfHa3bkTWLTI3N6vf3+z8N6993pttLo0fvnlF8UwjO2iKO4t5i0RFovFGhLi4x33NmwwZ1o88YTXmty8ebN+9OjRY7Isv+y1RomIKjgm+0RERHQlIR6Pp2N4eLhWZlvcFebxx4Hp04u8zGKxoGrVqjh16pR+xQsTE4E1a4BnnwVOnwaWLAEefRQoi7XuxRAbG4t9+/bZFUX56ipuC3c4HKpheL9W4TlpacBnnwHt2wM272zqlJaWhpUrV7plWX5QFEXZK40SERGTfSIiIro8URTTATTOyso6efDgQf8FUqUK8N//ApmZV7xMVVUcOHBAqFatWuFD82438MknZmV9QQCWLweGDfNFxCUmyzKWLl1a8Ov3kiQVdw3+AUVRUmNiYnxXwX7nTqBtW+DWW73SnKZpmDdvXp6maaNFUTzglUaJiAgAk30iIiIqgiiKB2w22/rU1FT/jrpu2AAcOnTFS+x2O0aMGIE9e/YIixYt0txut3lC0wBJAlq2BO64A5g1C+jRwy8V9ouSl5cHu92eBSDcYrGsAdBOkqTAou4TRVGTZfnONWvWZO/dW9yZ/1dh/XogKgp45RWvNRkTE6Pl5eVt13V9utcaJSIiAIDg06leREREdF2QJKmWzWbbO3DgwMo333yz/wIxjGKtpz98+DBmzJiBAF3HUEFAhKrCGh5urvuPjCyDQEvOMAx88803eSdPnpyiadoRAJ8DeAbAYgBGUZXqJUm63eFwrHv++ecDg4ODvRWU+bd79VWgY0evNJmdnY0vv/wyX1GUVqIo+nHaCBHR9Ykj+0RERFQcqQA2p6Wl+S+C1183C+gVw4033IA3IyIwIDzcOPHbb5ianY3PbTY9oWCkvxwTBAGPPfZYcI0aNf4NM9EHgBlWq3UCgBOSJBmSJN0tSVKhTz1EUdxiGMb0VatWee/DvvmmWcDQS4k+APz+++8uwzA+ZKJPROQbTPaJiIioSDab7emQkJBO7du3918QAwcCI0Zc+RpdBzZtAl55BY6dO3FTnz5Cq9hYDP2//4Pb7bbMmTMHixcvNjZv3ozk5GQsXboUK1aswOTJk7Wvv/7aiImJga5fubZfWQgJCcETTzwRZLfbZUEQeomi6NY07T/nXbIMgP7OO+9MkSTpxovvVxRlXHx8vJqa6oXt6vfvN6fw9+lT+rbOOnjwII4ePZqtquokrzVKREQX4DR+IiIiKtL777+/+t577+3ZokUL/wWhKOY2eQMHXnrOMIDsbODhh80q8fPnA6GhF1xy5swZ7N27F5s2bTJcLpegqirq1Kmj6bqOunXrWnRdF3bu3Gl4PB4hIiICrVq1QocOHcrowxUuKSkJM2fOBABYrdZ5mqY90qJFCzRr1gyVKlXC3r17ta1btx598803G1x879tvv/10tWrVJo8aNSq4xDspyLI5df+FF4CbbirVZymg6zq++OKLvKysrCFRUVGLvNIoERFdwjt7phAREdF1Tdf1BocOHTIqV64s1KlTB4I/9qG3WIDhw4GePYFq1f45vm8f8PHHgNMJfPghcNttha7rDw0NRfv27dG+ffvzT16w116nTp2ELVu2YNeuXcaKFSuE8PBwCIKAkJAQhISEID8/H+Hh4b76hJeoX78+br31Vi07O9tSr169Rxo0aIDI82oOBAYGWmNjY2sUdq+u699lZ2c/s2nTpls7duxYsmz/88+B4GCgwSXPEkps+/bthsvlOmAYxq9ea5SIiC7BkX0iIiIqkiRJHa1W60NWq3VIRERE6KBBg4Ir+aOSfUYGULWqmczv2gV88IFZOC4jAxgyxGt7vwPAqlWr9A0bNlhsNht0XT/3eumllxB60awBf3C73fj6669dOTk548aOHTu5sGskSbrJbrdvf/LJJyvVrl376jo4cACIjQW6d/daUUNZljF58uR8t9vdTRTFLV5plIiICsVkn4iIiIpNkiSrzWabfMMNNwx78sknyz7bF0Vzyn5goPkzIgJ4/HEgIMDrXem6jiNHjiAyMhK6rmPRokXG0aNHjVdffdXvNY88Hg9mzJjhysjImC3L8ihRFC/7hS46OvrJqlWrfjFy5Mhgh8NRvA50HXj5ZaBZM2DkSG+FjTVr1qibNm36fcyYMQ95rVEiIiqU3/+xIiIiomuHKIqaqqpbrVZr0Rf7gmEAn3xi/nz9deDpp32S6AOAxWJB/fr1YbPZ4HA4kJOTA4/HY5k2bRr27Nnjkz6LIycnB9OnT3dlZGQskGX5mSsl+gBgGMaMnJyclWvWrJGL3cnevUBQUNEFEa+Cy+XCxo0bVY/H86rXGiUiostisk9ERERXxWKxBKelpdnS09PLvvMHHgAqVTK3gvPilP3iGDp0qHD77bfrhmFg+fLlmq7rSEhIQFxcXJnFkJ6ejilTpuSfOnVqsizLT4qiWOTWAaIoGrIsj9yyZYtSrP9mqanmQ5RRowAvPtRZv369YrFYfhJFMdFrjRIR0WVxGj8RERFdtbfffntEYGDgZ4888khQZGRk2RbsMwxzmrmfZhfk5+fjs88+g6ZpEAQBsixj0KBBaNy4sU/7TUpKwty5c/NVVR01fvz4WVd7/8SJE1+tW7fuhCeeeCL4ihfOmAEcPw689VaJY72Yy+XC5MmT3aqqNhJFMdlrDRMR0WVZJ0yY4O8YiIiI6BrTvXv3bWvWrDkTHx9/u8ViCahbt27ZzRbcsQNo0QJ45RWzQn8Zs9vtaNmyJRo0aID77rsPISEhxh9//IGcnBzB6XSicuXKXu1PlmWsWbNGWbZsmUtRlAeioqJKVMV+zZo1W10u17O1a9cOqVq1auEX/fkn8PPPwKeflibkS8TExGjJycm/jBs3bppXGyYiosviNH4iIiIqkbFjx36hKMqovXv35pfpTMFWrYBvv/VLol+gcuXKuOmmm2C1WtGmTRuhT58+iI2NxfTp0+F2u0vdvqIoSExMxOLFiz0ffvihZ8uWLUtVVW0iiuKakrYpiqJHluUXVqxYkVvoBbpu1kEYM6bEcRdG0zRs3LjR4/F43vVqw0REdEWcxk9EREQlJklSsMPh2BoYGHhD165dg9q0aVM2GfipU8CECcAXX5RJd8WhaRrmzJmja5pmefLJJ694rWEYyMnJwenTp5GTkwOXywVN03D69Gn50KFD7tOnTwc5HI79siz/qOv6DG9NfZckyWa321MGDx4cUa9evQtPjh8P9OplbrXnRfHx8ViyZMnWMWPG3O7VhomI6IrKtrINERERXVdEUcyTJKmpLMvdly9f/ktYWFiVBg0a+L7jgABg9WogN9cs2FcOWK1WdOnSxfLjjz9ecs4wDCQnJyMxMVE/ePBgbnp6ukPXddVutx8BkKzr+gld1/NVVT1qGMbfALa+8cYbpZ8icBFRFFVJkl5ZuXLllyNGjAg5d2L9eiAxEXjVu4XyDcPAmjVrcj0ez3ivNkxEREVisk9ERESlcnbrt7+io6MH/vjjj79ZrVbh5ptv1h5++OEgn3VaqRKwZ485wl9Okn0AqFGjBlRVha7rsFgsyMjIQGxsrLxjxw4NQLqmab+oqroCQByA1LFjx/pjiuX81NTU/2ZmZqJq1aqA2w3ExQGvvQZ4ud7AgQMH4HK5UgEs82rDRERUJE7jJyIiIq+RJKkRgP8A+I/D4dBlWbaEhoYaiqLkAzBq1qypZWdnWwIDA4UBAwYEV6lSpeSdnT4N1KoFHD4M1KzpnQ9QSgkJCZgzZw6GDx+O7du3u3fu3KkIgvCtoihTRVE84O/4CkyaNOmDW2655T/333+/E59+Chw9Cnz0kVf7MAwDX331VW5aWtoIURTnebVxIiIqEkf2iYiIyGtEUUwA8LwkSW9omrYbQP0zZ85oANoDyE9KSmoNwGWxWH44ePAgbr+9FMu4w8KAXbvKTaIPAHXr1kX16tUxa9YsCIJwQFXVrqIonvF3XBdTVXXyjh07Rvdq1AgBhmHubOBlx44dw+nTp3MALPB640REVCQm+0REROR1oijmAyhs8X4iALzzzjs7cnNzuwEQStVRZCTQpQuwbFm5mM6fk5OD3NxcQ9M0Qdf1vuUx0QcAURRT3p80aVnW2LF9a95/v4Datb3eR2xsbL6qqh+Loqh5vXEiIioSk30iIiIqc7qur0pPT+8AwFmqhpxOoHZtIDPT78l+bm4uvvvuu3yPx/OfqKio7/waTDHU27Fjqycl5X4MHer174P5+fnYv3+/Rdf16d5um4iIisd/G9QSERFRhaVp2pQ9e/Y48/PzS9/YvHnAiRPmPvF+tGvXLsMwjN+uhUQfglDjoUWLnvpzwIA8CKWbXFGY+Ph4w2q1rhBF8ZTXGyciomJhsk9ERET+kOF0OmPmz5/v0kubpOs6cPfdwMaN3omshPbv35/j8XiulUJ0r5+uUuW7bF23erthwzAQExOT5/F4Jnu7bSIiKj4m+0RERFTmRFE0PB5Pt+PHj+/duHFj6dZ0WyxASgrQubOXoiuZrKwsADji1yCKQxAGAkiZPnz41kqVKnl9OsTx48eRl5eXC+Avb7dNRETFx2SfiIiI/EIURcXj8Qxcu3at5/Tp06VrLDAQaNYM2LTJO8FdpczMTOTl5QHATr8EUFyCEALgBQArtdDQe5o2ber1QgebN292a5r2uSiK/l1XQURUwTHZJyIiIr8RRTFJ1/Uv/v77b3nfvn3QtBIO8gsCMGKE37bhy8rKgs1mOyCKouyXAIrvSQBvwjB2WCyWvg0bNvTqd0FFUbBnzx5B07TyX7eAiOg6x2SfiIiI/ErTtOXbtm1z/PTTT/jrr78uyPZVVYWiKMVr6JVXgHXrgFNlXxPO5XIBQHqZd3w1BOEOAG0AbJckqYkgCOG1vbzlXmJiImw22z5RFNO82jAREV01JvtERETkb+sFQRgHYPSWLVvkEydOAABOnjyJd999V/3kk088Z5Ppor33HrB8ue8ivQyXywVN01LLvOPiEoQgANUBTINh5Fit1kEtW7a0CV6uxL9t27Y8t9v9X682SkREJeL1fVWJiIiIrsbZqe+TACA6Ovrk999//33r1q0deXl5sFqt3ymK4po7d+6o4cOHB+/fvx+6rqNp06aFNxYfDxiG+fLBlnKXk5mZqSiKklBmHV69YQCawTCeAwCbzTa0RYsWDm924Ha7cejQIRuAn73ZLhERlQxH9omIiKjciIqK+llRlOZxcXFfHzx4MFaW5bGKoryRmpqa88MPP+QvWLDg1C+//HLyt99+86iqemkDggC0agV88kmZxn3o0CG3YRixZdppcQlCFQCtAUgAIElSfQA1IiMjvdpNfHw8bDbbKlEUS1ltkYiIvIEj+0RERFSuiKJ4BMDo849JktQuMTHxLgDLAOTv3r172t69e+96+OGHAxs2bAgA0HUdx44dQ+4zz0Bp1AgtVBU2m++/6uzevRtZWVl5ANb5vLOrJQg2AN8BmArDOHn2aNs6deqo3pzCbxgGYmJict1ud9k+ZSEiostisk9ERETlniiKx2AmrQX6SZLUY968eb8+8sgjoQ0bNsS8efNchw8fPmkYxl9d3n23Z9yOHbXbvf663ZtxyLKMzMxMCIIAXdeRmJior1u3zqUoyj2iKBazkmCZ6gJABbCy4IDNZutQt25dr265l5qaitzc3HwAq7zZLhERlRyTfSIiIromiaL4lyRJD8+fP/+3bt26Be7fvz8IQGNRFOXT77+/cm+NGtUBeCXZP3ToEFasWJFz8uTJALvdnioIggFAMwxji6IoUaIo7vNGP14lCHUAvAXgXhiGUXDYZrPdFxkZ6dWCBrt371Z1XZ8liqLuzXaJiKjkmOwTERHRNUsUxVWSJN33999/P2e1Wn8bN26cDACfvfjiiABgb834eNRv0QKlmbIeExOjrF69OkdV1VGGYfw2ZswYj9c+gG99CGAGDONccQNJkroGBwffUK9ePa92tGfPnnxVVRd4tVEiIioV4bwHvURERETXjbygoN2HGzWqt2LYMKNt27bBNWrUEEJCQhAREQGL5Z8axW63GykpKcjOzoau66hVqxZq1aoFQRCgqiree+89j6qqTUVRTPLjx7k6gjAAZiHmhQXJviRJAoB8q9XqbNKkCVq3bo0bb7zxgr/F1dB1HZqmIS8vD59//rlH1/WQcrqUgYioQuLIPhEREV2XgvPz77rx4MGs7OzsNuvWrRtqtVpb6LoeabPZqgwZMiTQYrFg/fr1+Xv37rXY7fZ9hmEcBKAbhtHJZrOF3XXXXUGRkZHQNM1+jSX61QEMAvD2+aP6giD0DQwMtHbo0AHJycnazz//bNU0DVar1QBwbqa/YRjCee/P/SzsvSAIMAwDDocj880332SiT0RUjjDZJyIiouuTYRwPFoRx4oQJLhjGiILDkiS9On/+/Ek5OTmaqqqTdF3/duzYsWnn3ypJUpc//vjjG4/H08Rms+0u++BLpTuAWTCMHQUHJEkKcDgcU/v3729r0KABAFgNw0BmZiZUVRUsFgssFgsEQSj0Z2HvC17Lly9XYmNjp/rt0xIRUaGY7BMREdH1LBvAxUXjvszJyWmsKMr8qKio5YXdJIriekmSmtvt9rd0XU/wfZheIgg9ATwGYMj5h2022+t169YNOZvon71UQHh4eKm7PHjwYL6u63+VuiEiIvIqrtknIiKi65sghAOoAsNI9HcoPiUIlWAm+okwjNUFhyVJusFmsx3497//HRQWFubVLj0eD95//31Z1/XKoii6vdo4ERGVCkf2iYiI6Hr3KYAaAHr7OxAfGwHgRhjGN+cfdDqdn99+++12byf6AJCUlASHwxH/xhtvMNEnIipnmOwTERHR9e4pANd38bh/RvV7nX9YkqSWTqezT7du3ey+6DYhIcHj8Xjm+qJtIiIqnZLttUJERER0rTAMN4DXIAg/+TsUnxAEG4DFAB6HYeSef8rpdE7q0qWL0+Fw+KTr5ORkj2EYW33SOBERlQpH9omIiKgiWAcgxd9B+MhwALsAHDz/oCRJjR0OR6+2bdv6ZHDH7XYjIyMjAECML9onIqLS4cg+ERERXf8MYwOAvyEID/g7FK8ShMYAbgQQhYuqLjudzo87derkcDqdPun6yJEjcDgcO0RRzPdJB0REVCpM9omIiKiieBjAWH8H4TWCIACIArALhpF1/ilJku5zOp3dO3XqZPVV98nJyZrH4yl060IiIvI/JvtERERUUXwMoCMEwWcJcBm7G+Za/QtqEUiSZHM4HP+97777gu12n9TlAwAkJSXl6bq+0WcdEBFRqXDNPhEREVUMhmFAEEYCeBFAM282LUmSYLPZvldVdaIoigeLvqOUBKE2gCEA3oNhaOefslgsIyIiIsIbNWrks+51XUdaWloAgE0+64SIiEqFyT4RERFVJPMBHPFBu11UVR0KYCuAz33Q/sVaAPgLhhF//kFJkiLsdvt79957b7A5y983kpKSYLVaj4wdOzbDZ50QEVGpcBo/ERERVRyGkQngKARB9FaTkiR1gVntHwB+9la7lyUIPQGMAzDt4lNOp/PrNm3aBNWuXdunIWzZssUly/JnPu2EiIhKhck+ERERVTSVAdzjjYaio6N7W63WFWd/bSaKYqo32r0sQbAD6A9gDAxDP/+UJEkNdF3v06NHD98t1Ie5GiIpKUkwDGOxL/shIqLSYbJPREREFYthxMAwOkAQwkrTTHR09AC73f5r+/btAwICAraJorjXWyFegQRgz9mtBC/gcDjeatu2rdVXW+0VSElJgWEYp0VRPOrTjoiIqFSY7BMREVHFIwi9ACThvIXtkiQ5JUmKLM7t0dHR99lsthkDBgwIDA4OhqZpsT6LtYAg1APQB8DMi09JkhSh6/rgDh06+HRUHwDi4+NlXden+7ofIiIqHSb7REREVBGtAtAehmEAgCRJYXa7/YDdbk/4v//7v4WSJDkud+M777wzPjAwcN7gwYODGjVqhLp16wLAAEmSKvssWkFwAvgAwH0wjNzzT0mSJDidzlmtW7cWQkJCfBYCYFbh37lzp6qq6myfdkRERKXGZJ+IiIgqHjPJ1yEIS87uS7+pTZs2NV5++WWnLMsPAfAUdpskSd2tVuubzzzzTFC9evUAAHXq1EHLli0rORyO73wY8csA9sIwTlx8wmq1vhwaGtqld+/evp2/D2Dfvn3QdT1RFMU9vu6LiIhKh8k+ERERVVQpAODweJ6MiIiofddddzkDAgJw0003FYz2Vzv/YkmSQu12+9x+/foFXjyC3qdPH2dAQMA90dHRw70epSA0B5AKYMrFpyRJamixWN5+7LHHgm023++ovG7duhyPxyP5vCMiIio1JvtERERUMRlGHoAHbjh9OuqOO+44ty/94MGDhc6dOyt2u31PdHR0X0mSBEmS6jgcjq233nprWOPGjS9pym63Y8iQIUEBAQFfTJo06XmvxSgIVgD/AaDAMC6p9O90Ot/v1KmTPSysVLUGiyUzMxMZGRkGgF993hkREZUak30iIiKqsPbffHOjx779NrJmpUrnjgmCgF69etkHDRoUERoaOtvpdCbb7fZ9Xbt2vfGee+657FT5iIgIjBw5MtDhcPzfxIkTn/JSiG0A7AUw5+ITkiTdBqBPx44dfT+kD2D37t26xWKZL4qiWhb9ERFR6ZTJPw5ERERE5dHcf/3rnuY33ugeUL16wMXnGjRogBdeeCE4NTU1ODAwEFWqVCmyvSpVqmD48OFB33777WcTJ060jxs37pKp98UmCHUAiABehGHo55+SJMnhcDh+7tOnT6Cvt9orsH379jxZln8ok86IiKjUOLJPREREFZbFYqlePSzMifr1gdzcS84LgoBatWoVK9EvEB4ejqeffjqwUqVKH77zzjv/lSTJetWBmWsK2gNYBcNIuPi03W6X6tSpU+uWW2656qZLIjU1FTk5OTKAdWXSIRERlRqTfSIiIqqwnE7nnRGNGgn4178AXS/6hmKqWrUqRo0aFVSzZs2hDodjpSRJt0uSJFxFE3cCeAqG8fHFJyRJ6iQIwgv9+vULKqgz4GtxcXGyYRjfiKKolUmHRERUakz2iYiIqEKSJKmKx+Npc9NNNwGSBPz0k1cT/sDAQAwdOjSobdu2nYODg/9yOp0LJEkq+ruXmcG/DeCVQmKubrfbl/Tv3/+SHQF8RZZlbN++XVdV9Zsy6ZCIiLyCyT4RERFVVL2rV6/ucjgc5m9jxwIHDni1A6vVil69etlffPHF4KpVq95lt9unFGNa/8cAxsMwLtjLXpKk6g6HY/ntt98edPPNN3s1ziuJi4szLBbLX6IoHiqzTomIqNSY7BMREVFF1bRhw4bm8LjNBqSlAT5Kom02Gx5//PHg6tWrD3E6ncskSSq8CIAgdARQH8Cm8w9HR0f3tdvt+9q1a9e8d+/eDp8EWQhN07B+/fp8j8czoaz6JCIi72A1fiIiIqqoDh89ejQPQDAAwDCAatWAVauA227zemeBgYEYNmxY0NKlS7vu3LnzUHR09DDDMBaLomgAAAQhCMA9AF6FYeQAgCRJIU6nc3pISEifhx56KOjGG2/0elxXEhcXZ6iquk0UxU1FX01EROUJk30iIiKqkGw2201Hjx4NPnfAYgGmTwcaNfJZn1arFffff7+zefPmzl9//XW22+0+Hh0d/Y1hGDGvBQT0DnK7IU2YkAhJamGxWPrabLbXmzRpYr/vvvsC7Xa7z+IqjNvtxurVq/M9Hs/zZdoxERF5BZN9IiIiqpB0XW/sdDoNAP+UtH/gAeDFF831+9Wr+6zv+vXr4/nnnw8+dOjQzbt3737btWWLsvqeewIP3nyz22azveR0OtGoUSNbhw4dAmrUqOGzOK5k5cqVHl3X54uiGOeXAIiIqFSY7BMREVGFpOv6lJCQkHsAhJ47KAjAtm3A7t0+TfYBwGKxoGHDhmh4440B+PPPAKNfP+T061cJAEJDQ4u63adSUlKwc+dOj6IoL/k1ECIiKjEm+0RERFRRxZw+fdrpdrsREBDwz9F164CsLHMNf1nsY79vH2C3Q3j8cYSWRX9F0HUdixYtylNV9UVRFDP9HQ8REZUMq/ETERFRhSSKosdutx9JT0+/9GSzZsA3ZbCt/LFjwKBBgCiWzYOFYti6datx5syZ/YZhTPd3LEREVHLWgCKtAAAabUlEQVQc2SciIqIKy2KxnHK73Zfut7duHVCvnm87Nwxg5kzgmWeAsDDf9lVMubm5WLlypVuW5aHndgkgIqJrEkf2iYiIqEIzjEJy2oYNgaeeAlas8F3H8+YB2dnAc8/5ro+rtHr1ardhGNNEUdzl71iIiKh0OLJPREREFZIkSRaHw9GwSpUqhV9QtSqgKL7p3O0GJk40E/5yMn0/IyMD8fHxmqqqUf6OhYiISo8j+0RERFQhWa3W58LCwoKrVatW+AWTJwNNmwKFrekvrXffBb77zmy/nFi5cqULwAcsykdEdH1gsk9EREQVjiRJVQRBeHfAgAHBFssVvg717w+89553O1+2DDh6FGjRwrvtlsKJEyeQmJgoq6r6ob9jISIi7+A0fiIiIqpwHA7Hx02aNLFcdlS/wN9/A0FB3us4NxdISTHX6Xuz3VJavnx5nqZp40VRzPN3LERE5B0c2SciIqIKRZKkWwRBGHTPPfcEFHlxcDAwdCgwYYJ3Op86Fdi7F2jTxjvtecGxY8eQkpKSr+v61/6OhYiIvIcj+0RERFRhSJLkcDgc03v27BkQEFB0rg8AuPtuoEaN0nd+7BiQlQWMGVP6trxo1apVuZqmjRNFUfZ3LERE5D0c2SciIqIKw263v1OnTp2b27VrV/wS+IMHA1WqAJs2lbxjRTGT/HbtgPDwkrfjZWdH9d26rn/v71iIiMi7OLJPREREFYIkSbc4HI5/P/TQQ4HC1W5399FHQEAA0L59yTrftg2wWIAHHijZ/T6ycuXKPI7qExFdn5jsExER0XVPkqRaDofj9z59+gRUqlTp6huYMwcQBEDXzaT9aiQnA6NGAZs3m22UE8eOHcOJEyfyOapPRHR94jR+IiIiuuZIklRZkqTgYl7b2OFwxHbu3LnmbbfdVrJsWxDMhP3BB6/+3jfeAF5/HbCVrzGWVatWFVTg56g+EdF1qHz9q0NERERUBEmSGgPYZ7fbNwNod6Vr33nnnddtNpvUu3dv5+233166YfXnngNU9erumTkTuOsu4JFHStW1t6WmpiIlJUXhqD4R0fWLI/tERER0zZAkKQTAXgBQFKXRla6Njo7u73A4xNGjRweUOtEHgFatgMxM4Kuvind9Whowe7ZZlM9qLXX33rRu3TqXruvvi6Lo8XcsRETkG0z2iYiI6JphtVpfBCA4HI7lAC67H54kSVVsNtu3jz76aFDlypW9F8CuXcC6dcW79tdfzSn8TZt6r38vOHnyJBISEnRN0/7r71iIiMh3mOwTERHRNUGSpBBBEF7v27cvDMNoiysk+w6HY2Lz5s0DIyMjvRvEiy8CP/wAZGdf+bo//gD+/hvo1Mm7/ZeSYRhYsmRJnmEYUaIoFvEhiIjoWsZkn4iIiK4JNpttfMOGDS233norevbsWdlut8e//fbb/5EkKej86yRJqqzr+pPdunVz+iSQCROADh0ufz4725zu/8wz5nZ95cju3buRmpp6QtO0z/0dCxER+ZZgGIa/YyAiIiICAEiS1AiASxTF4xcd7+V0OhePHj06sGDrvOPHj2P16tV5R44csdpstj88Hs8nAA7YbDaxSZMmw/r37++bTPvMGTOhv9ysgY8+Ao4fBz7+2Cfdl5Tb7cann36a73a77xBFMcbf8RARkW+xGj8RERGVC5Ik1QJwwGKxfANg5HnHe9rt9l8fffTRc4k+ANxwww14/PHHg/Py8rB9+/Z+mzdvvsvlctmrVKmi9unTx3dD6qGhwMaNgCQB33574bnsbHNN/6xZPuu+pFasWOHRdX0eE30iooqByT4RERGVC1ar9U9N02AYxk0FxyRJqma32+cOHDgwqH79+oXeFxwcjM6dO1s6d+5c8CTAN9P3z1e1KqAoFx7zeIB+/YBPPzUfCJQjSUlJiI+PdymK8pK/YyEiorLBNftERETkd5Ik1dA0rQWAZJvNduDssQ4Oh2N7u3btwho1uuIue2WvbVvgm2+A+Ph/jv3yC9CoEdCypf/iKoTb7cb8+fNdqqoOFkXxtL/jISKissGRfSIiIvI7QRA+P1tHqPlbb711RpKk6oIgrO/bt6+1WbNm/g6vcL/8AoweDaSnA3v3AsuXA1OnAoLg78jOMQwDixcvzldVdV5UVNRSf8dDRERlhyP7RERE5HeGYQwEAFEUzwCA1WodUatWrfxym+gDwCOPAElJgGEAY8YA998POH2/guBqbNq0SU9MTEyWZfnf/o6FiIjKFqvxExERkV9JklQZQNbZX3vabLYHAgMDnxk2bFhQWFiYP0Mr2oYNwODBZuX9vn0BS/kZRzl69Ch++OGHHEVRbhVF8ZC/4yEiorLFafxERETkb32rVKkiZ2VlOQCsqVu3rqtfv35BISEh/o6raJmZ5sh+kyblKtHPysrCjz/+mK+q6iNM9ImIKiYm+0RERORXAQEBI3r27Olo1apVwaEgf8ZzVX78EXj1VaBpU39Hco7H48HMmTNdqqpGRUVFLfN3PERE5B9M9omIiMhvJEmqYbPZ2jdu3NjfoVyd3Fzg2WeByZOB6tX9Hc05uq5j3rx5rry8vPmqqn7s73iIiMh/ys98MyIiIqpwrFbryObNmxvOclbYrki//QacOQNUq+bvSC7w559/ysnJyTtlWX5KFEUWZiIiqsCY7BMREZHfOByOQa1atQrwdxxX5cMPgdq1gQULytU6/bi4OCMuLi5dluX7RFFU/B0PERH5V/n5F4qIiIgqFEmSImRZbhgZGenvUIrv5Engq6+AyEjAVn5WQyYlJWHZsmW5iqLcIYpipr/jISIi/ys//0oRERFRhWK1Wkc1b95ct9vt/g6leLZsAdatA3btAsrRsoPjx49j7ty5LlVV+4qieMDf8RARUfnAkX0iIiIqc5IkhVosllc6dux47Uzhf/llczS/HCX66enpmDVrVr6iKIOioqLW+DseIiIqPziyT0RERGXO4XC827Rp04CaNWv6O5Si5eUB770HLFxYrgrynTlzBtOnT3cpivLvqKio3/wdDxERlS8c2SciIqIyJUlSVV3Xn7zzzjuvjVH9r74C4uKAsDB/R3KOpmmYPXt2nizL744fP36Gv+MhIqLyh8k+ERERlbU7IyMj1ZCQEH/HUbTp04E+fYCffgKsVn9HAwBQVRVz5sxxZWdn/62q6kR/x0NEROUTk30iIiIqa1WCg4PLR+Z8JYmJwKRJQEAAEBzs72gAAIqiYNasWa7k5ORVHo/nQVEUDX/HRERE5RPX7BMREVFZ25Oamqr6O4gr2rsXSEgANm4sN+v0ZVnGzJkzXenp6X/IsvyYKIrl+29IRER+xZF9IiIiKmu7Tp8+HWgY5XRQ2jCAf/3LHNkvJ4l+Xl4epk2b5kpPT18oy/KjTPSJiKgoTPaJiIioTImimG2z2dJSUlL8HcqlFMVcp794MfDSS/6OBgCQlpaGKVOmuDIzM7+QZXmoKIq6v2MiIqLyj9P4iYiIqMxpmjYrPj7+lRtuuMHh71guMGECsH078Pjj/o4EALB//34sWLAgX1XVkVFRUbP9HQ8REV07OLJPREREZU5V1T/2798vl6up/L//Djz1FDB7NmDz73iIYRhYvXq1umDBgkxFUXoy0ScioqvFZJ+IiIj8YWNeXp6alZXl7zhM27cDb7wBqCpQpYpfQ3G5XJg1a5YrNjZ2p6IozURR3OTXgIiI6JrEZJ+IiIjKnCiKms1m23r8+HF/hwIcOgQIArBwIdCokV9DOXLkCL788ktXcnLytx6Pp4Moiml+DYiIiK5ZXLNPREREfuF2u1cnJyd3a9Gihd1vQSgKMHCgWX3/lVf8Foau61i3bp26YcOGPFVVH4uKilrqt2CIiOi6wGSfiIiI/MIwjE1HjhxxAajslwA0Dfjf/4BvvgFat/ZLCIA5bf+nn35ypaWl7VYUpZ8oiuVwmwIiIrrWMNknIiIif9manp4epOs6LBY/rCx8/XVg507gzz/Lvu+z0tLS8MMPP7g8Hs+3iqK8Ioqi6rdgiIjouiKUqyq4REREVKG8++67SUOGDLmxTp06ZdtxbCwQGAjUrg2Eh5dt32ft2bMHixYtcnFbPSIi8gUW6CMiIiK/0TRt8aFDh8p25CEmBhg2DKhUyS+Jvq7rWLlypbJo0aJ0RVG6MtEnIiJfYLJPREREfqOq6qYjR47klVmHx44BNWoA330H1K9fZt0WcLvd+OGHH1xbtmzZoShKC1EU48o8CCIiqhCY7BMREZE/LTl69KjN5XL5vieXC3j0UeD334EOHXzf30VOnTqFKVOmuI4fPz7b4/F0EkXxZJkHQUREFQbX7BMREZFfvfvuu7/07Nmzb/v27QWfdaLrwIED+P/27j826jrP4/jr+512inRLgYOuFHcXw11qQOAWEBVRyaFE3fgDOV3ulrBBFpbE0+uZvTtcgc/Ongt7BGNWN0iAdQ/DjwqFBouw4YdrQAhLNUKLhQJXLRXttgXabjvTme93vt/7AzHu8UOlMx369flISJr58f585r95Mt8fqqmRHnpIstK31KXU1NRo8+bN0WQy+a/z589f1a2LAwC+kbgaPwAAyKh4PL7s3XffnXTrrbfmpW2Rn/1MOnxY2r07bUtciu/72rNnj7tv3742x3F+YIw50K0bAAB8Y3EYPwAAyLQ/trS02G1tbemZXlcnTZ4srV+fnvmX4bquSktLY/v37z/22fn5hD4AoNsQ+wAAIKOMMW52dvbhhoaG1A/fv1+aNEn6/velgoLUz7+MaDSqV199NXry5MndiURinDHm025bHAAAEfsAAOAa4Hne/7a2tqZ2aHOz1Lev9Nvfnr8Cfzdpbm7W8uXLo83NzcsTicTDxphYty0OAMBnOGcfAABknO/7sWQymbqB0aj0yCPSnDnSjBmpm3sFvu+roqLC27VrV6fneU/Pnz//d92yMAAAl0DsAwCAjAuFQjf07t07NcN8XzpzRnr8cWn69NTM/BKu62rTpk2x2trajxzHmWKMqemWhQEAuAxiHwAAZFwymRw7aNCg1AybN0+qrpbKy1Mz70t0dHSopKQk2tjY+HYikZhqjOnsloUBALgCYh8AAGRUJBK5Pjs7O3/AgAFdH9bWJn3nO9KTT3Z91ldQXV2tN954I+b7/vJEIvEfxpgUnosAAMDVI/YBAECmjSooKOi0LCunS1P275d+9COpslLKy0vR1i7N8zzt27cvuXfv3nOO4/zAGHMwrQsCAPA1EfsAACDT+ubm5nbtDkGxmBSPS4sXpz30Ozs7VVZW1llXV3fUcZyHjTH1aV0QAICrwK33AABAxkQikYGS/iEnJyf7qod0dkqTJ58P/mnTUre5S2hvb9fLL78c/+ijjzbF4/E7CH0AwLWKX/YBAEBGRCKR3pIaJWnw4MFXN8T3pT//WbrtNum++1K4u4vV1tZq48aNUc/zlj777LMmrYsBANBFxD4AAMiUH174Y+jQoVc34ec/P3+bvRUrUrWni3iep7fffts5cOBAh+M4/2iM2Z22xQAASBFiHwAApNXzzz8/1fO8vyxcuHDHFx+3LOt7Q4YM8ceMGWNd1ZX4XVfq6JCKi1O11Yu0t7erpKQk2tzcXOk4zhRjTEPaFgMAIIU4Zx8AAKRNJBKxk8lkqe/72yORiPXF57Kzs2+/6aabrOHDh3/9wfv3S6NGSUuXSsOGpWq7f6WpqUnLly+PNjY2vhyPxycQ+gCAnoRf9gEAQDrlSVK/fv06Y7HY0UWLFsUcx3lPUlk4HJ548803f/2JnicdPy49/bQUDqd4u+fV1dVp3bp1Mdd1/2XBggW/T8siAACkEb/sAwCAdBpfUFDwl7lz5/Z+9NFHixzH+XtJsyRtTSQS4UQi8fWmxePSpEnS8OHST3+ajv3q6NGjWrt2bYfjOA8R+gCAnorYBwAA6XQ2FotZ4XBYtbW1yc8e+2fbtp+xbXvN5s2bOy68sKamRi+++KJfVVUl3/cvnuT70smTUmGhNGZMyjfq+77eeeed5ObNm9scx7lr4cKFu1K+CAAA3YTD+AEAQDpVdHR05CQSCY0YMSJ04MAB2bY92fO8XaFQ6Gxra6slSR988IG2bNmioqIi68033/QPHTrkjRgxInTjjTcqPz///KRf/OL8Yftr16Z8k/F4XBs2bIh+/PHHJ13XfdAYcyrliwAA0I2IfQAAkE5/m5WVlQyFQtmFhYV66qmnVFVV9eNPPvlkal5eXnjChAk5Bw8e9Hfu3GlNmTJFw4YNU2dnp7V+/Xpry5YtysvL0zPPPHP+yvsVFdLixSnfYFtbm1avXh1tb28vTSQSPzHGOClfBACAbkbsAwCAdBo9cODAZCgUkiT1799fd999tyUpr7OzU2VlZcm6ujp72rRpGjp0qCSpV69emjlzpr1s2TIvHo/bHTt3KveFF6Rt2yQ7tWcgNjY2as2aNdFYLLbYdd1fGWMucf4AAAA9D7EPAABSLhKJFEi6XdKhlpaWiwI6Go1q1apVfnZ2toqLi61evXpdNGPGjBl2SUlJ8vCvfx269YEHFEpx6FdVVam8vDyaTCafXLBgwf+kdDgAABlG7AMAgHSYJWlRKBRaMnjwYOuLT0SjUb322mtebm6uZs2aFbrcgG+Fw/rJ1q2h340b553+7nc1xXXtrKyuf3VxXVfbt2+PV1VVnXEc535jTGWXhwIAcI3havwAACDlsrKyRkuSZVn/dv/99+deePzMmTN66aWXZNu2P3PmzCt/D/nTnyTX1Q/nzbOrq6vt+vr6Lu+rsbFRK1as6Dhy5MgfHccZRugDAIKK2AcAACnnuu6/S9K9996b3bdv388fr66u9vPy8pJz5swJ2Vc6LH/JEunUKam8XN/Kz9fQoUOTr7/+umpqaq5qP8lkUjt27EisXLmy/ezZs/MSicQDxpjWqxoGAEAPYF3yPrYAAABdFIlE/ClTpmjkyJEX7mHv7d271546daqKioou/8ZoVBo3Tlq3Tho58vOHKyoqtHPnTo0dO9a755577Cv+Z8EXtLS0qKSkpKOlpaUiHo8/boxp6upnAwDgWsc5+wAAIOUikcjtkjRkyBBJ0u7du7333nvPmjFjhm644YbLv/HgQWnNGqmy8qIr799yyy0aMmSIVq9erdOnT3vTpk2zr7vuuivu48iRIyovL495nvdL13WXGmO8rn0yAAB6Bg7jBwAAKWdZ1n9JUp8+fXT8+HFVVFRYTzzxhHXF0Jek3/xGKiy87C32Bg4cqOLiYtt1XX/ZsmV+Q0PDJV+XTCa1ffv2RFlZWUcikbjvueeeW0LoAwC+SYh9AACQcr7vn7jw9969e71Ro0ZZAwcO/PI3rl4tzZt3xZdkZWVp9uzZoaKiImvVqlX6/xfuO3funFauXBk9fPjwO57nfc8Ys+fqPgUAAD0XsQ8AANJhlXT+PPuGhgb7jjvu+Grv+oq31jtx4oSqq6u966+/3uvXr58kyfd9vf/++/4rr7wSO3v27K/i8fg9xpgzV7d9AAB6Ns7ZBwAA6fChJG3btk2FhYXJ/Pz8UCqGuq6r0tJSv7a21po0aZI1btw4y7IsnTt3Tps2bYo2NTU1OI7zsDHmSCrWAwCgpyL2AQBAOnz7wh99+vRxd+zY4efk5GTl5OQoJydH4XD4r/4NGjToSwd++OGHKi0t9fr06aO5c+da/fv3t3zfV2VlpbZu3RrzPO+/k8nkYmOMk9ZPBgBAD0DsAwCAdKiVNFfSgGPHjp2TlGfbdn5WVtbf2Lbdz7KsfMuy8qLR6O2SVFxcrPz8/EsO8jxPmzZt8k+cOGFNnDjRuu222yzbttXa2qry8vJofX19k+M4jxhjDnXfxwMA4Npm+b6f6T0AAIBvqEgk8m1JDQ8++KBGjx590fOnTp3Shg0bvNzcXD322GP2gAED5HmeDh486L311ltx3/dfcF33eWNMvPt3DwDAtYvYBwAAGbVo0aLjEydO/Lvx48d//pjnedqyZYtfXV1t3Xnnnd6ECRNs27bV1tamjRs3Rpuammri8fg/GWNqMrh1AACuWRzGDwAAMspxnJIjR4785/jx48OSdPr0aZWUlHg5OTnW7NmzVVBQYEtSTU2NysrKYp7nLXUc55fGmGRmdw4AwLWL2AcAAJn2h08//XSB67ratm2bX1VVZY0fP1533XWXFQqFFI/HtWHDhlh9fX3HZ1fa35/pDQMAcK3jMH4AAJBRkUikt6Rm27avC4fDmj59ugoKClRXV6fm5mZvz549juu6f3Ac58fGmNZM7xcAgJ6A2AcAABkXiUTCtm3v8zxvrCRlZWW5lmWdCYVCxzo7O5dI2m6M4UsLAABfEbEPAACuGZFIJCwpV9JQSZXGmESGtwQAQI9E7AMAAAAAEDB2pjcAAAAAAABSi9gHAAAAACBgiH0AAAAAAAKG2AcAAAAAIGCIfQAAAAAAAobYBwAAAAAgYIh9AAAAAAAChtgHAAAAACBgiH0AAAAAAAKG2AcAAAAAIGCIfQAAAAAAAobYBwAAAAAgYIh9AAAAAAAChtgHAAAAACBgiH0AAAAAAAKG2AcAAAAAIGCIfQAAAAAAAobYBwAAAAAgYIh9AAAAAAAChtgHAAAAACBgiH0AAAAAAAKG2AcAAAAAIGCIfQAAAAAAAobYBwAAAAAgYIh9AAAAAAAChtgHAAAAACBgiH0AAAAAAAKG2AcAAAAAIGCIfQAAAAAAAobYBwAAAAAgYIh9AAAAAAAChtgHAAAAACBgiH0AAAAAAAKG2AcAAAAAIGCIfQAAAAAAAobYBwAAAAAgYIh9AAAAAAAChtgHAAAAACBgiH0AAAAAAAKG2AcAAAAAIGCIfQAAAAAAAobYBwAAAAAgYIh9AAAAAAAChtgHAAAAACBgiH0AAAAAAAKG2AcAAAAAIGCIfQAAAAAAAobYBwAAAAAgYIh9AAAAAAAChtgHAAAAACBgiH0AAAAAAAKG2AcAAAAAIGCIfQAAAAAAAobYBwAAAAAgYIh9AAAAAAAChtgHAAAAACBgiH0AAAAAAAKG2AcAAAAAIGCIfQAAAAAAAub/AFuCKhK2u2s2AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "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-4.12.1/pyproject.toml000066400000000000000000000064741466413560300163550ustar00rootroot00000000000000[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", "scikit-learn>=1.1", ] [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", "sqlalchemy>=2.0", "xarray>=2022.3", "zstd", ] dev = [ "pre-commit", "ruff", "watermark", ] docs = [ "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.ruff] line-length = 88 lint.select = ["E", "F", "W", "I", "UP", "N", "B", "A", "C4", "SIM", "ARG"] target-version = "py310" exclude = ["libpysal/tests/*", "docs/*"] [tool.ruff.lint.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 ] markers = [ "network: marks tests that require a network connection" ] libpysal-4.12.1/scripts/000077500000000000000000000000001466413560300151155ustar00rootroot00000000000000libpysal-4.12.1/scripts/w_g_comparison.py000066400000000000000000000133551466413560300205040ustar00rootroot00000000000000#!/usr/bin/env python """ Build the documentation for the member comparison of W and Graph """ import inspect import pandas as pd import geopandas as gpd import numpy as np from libpysal.io import open as psopen from libpysal import weights from libpysal import graph from libpysal import examples examples.explain('sids2') # Read the file in gdf = gpd.read_file(examples.get_path('sids2.shp')) gdf = gdf.set_crs('epsg:4326') # Make weights and graph w_queen = weights.Queen.from_dataframe(gdf) g_queen = graph.Graph.build_contiguity(gdf, rook=False) g_members = set(dir(g_queen)) w_members = set(dir(w_queen)) # filter out private members g_members = {attr for attr in g_members if not attr.startswith('_')} w_members = {attr for attr in w_members if not attr.startswith('_')} compat = [] changed = [] for member in g_members & w_members: g_member = getattr(g_queen, member) w_member = getattr(w_queen, member) print(member, type(g_member), type(w_member), w_member.__class__.__name__) if type(g_member) == type(w_member): compat.append(member) else: changed.append(member) changed.sort() compat.sort() changed_content = [] header = "Member, W Type, Graph Type" changed_content.append(header) for member in changed: line = [member] line.append(getattr(w_queen, member).__class__.__name__) line.append(getattr(g_queen, member).__class__.__name__) changed_content.append(",".join(line)) changed_content = [line.split(",") for line in changed_content] def create_rst_table(data): if not data or not all(isinstance(row, list) for row in data): raise ValueError("Input should be a list of lists") # Determine the width of each column col_widths = [max(len(str(item)) for item in column) for column in zip(*data)] # Function to create a row separator def create_separator(char): return "+" + "+".join(char * (width + 2) for width in col_widths) + "+" # Function to create a row def create_row(row): return "|" + "|".join(f" {str(item).ljust(width)} " for item, width in zip(row, col_widths)) + "|" # Create the table table = [] table.append(create_separator('-')) table.append(create_row(data[0])) table.append(create_separator('=')) for row in data[1:]: table.append(create_row(row)) table.append(create_separator('-')) return "\n".join(table) changed_table = create_rst_table(changed_content) content = """ W to Graph Member Comparisions ============================== Overview -------- This guide compares the members (attributes and methods) from the `W` class and the `Graph` class. It is intended for developers. Users interested in migrating to the new Graph class from W should see the `migration guide `_. Members common to W and Graph ----------------------------- """ common_content = [] header = "Member, Typee" common_content.append(header) for member in compat: line = [ f"`{member} `_"] label = ":attr" ga = getattr(g_queen, member) class_type = type(ga) gat = f"{class_type.__module__}.{class_type.__name__}" if inspect.ismethod(ga): label = ":meth" gs = f" {gat}" line.append(gs) common_content.append(",".join(line)) common_content = [line.split(",") for line in common_content] content = f"{content}\n\n{create_rst_table(common_content)}" head = """ Members common to W and Graph with different types -------------------------------------------------- """ changed_content = [] header = "Member, Queen Type, Graph Type" changed_content.append(header) for member in changed: ms = f"{member}" line = [ms] ga = getattr(g_queen, member) class_type = type(ga) gat = f"{class_type.__module__}.{class_type.__name__}" gat = f"`{gat} `_" gs = f"{gat}" wa = getattr(w_queen, member) class_type = type(wa) wat = f"{class_type.__module__}.{class_type.__name__}" wat = f"`{wat} `_" ws = f"{wat}" line.append(ws) line.append(gs) changed_content.append(",".join(line)) changed_content = [line.split(",") for line in changed_content] content = f"{content}\n\n{head}\n\n{create_rst_table(changed_content)}" head = """ Members unique to W ------------------- """ content = f"{content}\n\n{head}" w_only = [member for member in w_members - g_members] w_only.sort() w_content = [] header = "Member, Type" w_content.append(header) for member in w_only: line = [ f"`{member} `_"] wa = getattr(w_queen, member) class_type = type(wa) wat = f"{class_type.__module__}.{class_type.__name__}" ws = f"{wat}" line.append(ws) w_content.append(",".join(line)) w_content = [line.split(",") for line in w_content] content = f"{content}\n\n{create_rst_table(w_content)}" head = """ Members unique to Graph ----------------------- """ content = f"{content}\n\n{head}" g_only = [member for member in g_members - w_members] g_only.sort() g_content = [] header = "Member, Type" g_content.append(header) for member in g_only: line = [ f"`{member} `_"] ga = getattr(g_queen, member) class_type = type(ga) gat = f"{class_type.__module__}.{class_type.__name__}" gs = f"{gat}" line.append(gs) g_content.append(",".join(line)) g_content = [line.split(",") for line in g_content] content = f"{content}\n\n{create_rst_table(g_content)}" with open("../docs/migration.rst", 'w') as guide: guide.write(content)