pax_global_header00006660000000000000000000000064146505530060014516gustar00rootroot0000000000000052 comment=666accc8e1cae3ddeae96edd51677df953e6851f mapclassify-2.8.0/000077500000000000000000000000001465055300600140405ustar00rootroot00000000000000mapclassify-2.8.0/.git-blame-ignore-revs000066400000000000000000000001031465055300600201320ustar00rootroot00000000000000# black-ification of code 71bfea486c64d3e87d0677f824ee6b17d576d028 mapclassify-2.8.0/.gitattributes000066400000000000000000000000451465055300600167320ustar00rootroot00000000000000mapclassify/_version.py export-subst mapclassify-2.8.0/.github/000077500000000000000000000000001465055300600154005ustar00rootroot00000000000000mapclassify-2.8.0/.github/dependabot.yml000066400000000000000000000011251465055300600202270ustar00rootroot00000000000000# To get started with Dependabot version updates, you'll need to specify which # package ecosystems to update and where the package manifests are located. # Please see the documentation for all configuration options: # https://help.github.com/github/administering-a-repository/configuration-options-for-dependency-updates version: 2 updates: - package-ecosystem: "github-actions" directory: "/" schedule: interval: "daily" reviewers: - "jGaboardi" - package-ecosystem: "pip" directory: "/" schedule: interval: "daily" reviewers: - "jGaboardi" mapclassify-2.8.0/.github/release.yml000066400000000000000000000004511465055300600175430ustar00rootroot00000000000000changelog: exclude: labels: - ignore-for-release authors: - dependabot - pre-commit-ci categories: - title: Bug Fixes labels: - bug - title: Enhancements labels: - enhancement - title: Other Changes labels: - "*" mapclassify-2.8.0/.github/workflows/000077500000000000000000000000001465055300600174355ustar00rootroot00000000000000mapclassify-2.8.0/.github/workflows/build_docs.yml000066400000000000000000000035501465055300600222720ustar00rootroot00000000000000 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 package run: pip install . - name: Make Docs run: cd docs; make html - name: Commit Docs run: | git clone https://github.com/ammaraskar/sphinx-action-test.git --branch gh-pages --single-branch gh-pages cp -r docs/_build/html/* gh-pages/ cd gh-pages git config --local user.email "action@github.com" git config --local user.name "GitHub Action" git add . git commit -m "Update documentation" -a || true # The above command will fail if no changes were present, # so we ignore the return code. - name: push to gh-pages uses: ad-m/github-push-action@master with: branch: gh-pages directory: gh-pages github_token: ${{ secrets.GITHUB_TOKEN }} force: true mapclassify-2.8.0/.github/workflows/release.yml000066400000000000000000000022611465055300600216010ustar00rootroot00000000000000name: Release Package on: push: # Sequence of patterns matched against refs/tags tags: - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10 workflow_dispatch: inputs: version: description: Manual Release default: test required: false jobs: build: runs-on: ubuntu-latest steps: - name: Checkout repo uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: '3.x' - name: Install Dependencies run: | python -m pip install --upgrade pip build twine python -m build twine check --strict dist/* - name: Create Release Notes uses: actions/github-script@v7 with: github-token: ${{secrets.GITHUB_TOKEN}} script: | await github.request(`POST /repos/${{ github.repository }}/releases`, { tag_name: "${{ github.ref }}", generate_release_notes: true }); - name: Publish distribution 📦 to PyPI uses: pypa/gh-action-pypi-publish@release/v1 with: user: __token__ password: ${{ secrets.PYPI_PASSWORD }} mapclassify-2.8.0/.github/workflows/testing.yml000066400000000000000000000054001465055300600216340ustar00rootroot00000000000000name: Continuous Integration on: push: branches: - "*" pull_request: branches: - "*" schedule: - cron: "59 21 * * *" jobs: testing: name: (${{ matrix.os }}, ${{ matrix.environment-file }}) runs-on: ${{ matrix.os }} defaults: run: shell: bash -l {0} strategy: matrix: os: ["ubuntu-latest"] environment-file: [ ci/39-oldest.yaml, ci/39-latest.yaml, ci/39-numba-latest.yaml, ci/310-oldest.yaml, ci/310-latest.yaml, ci/310-numba-latest.yaml, ci/311-latest.yaml, ci/311-numba-latest.yaml, ci/312-latest.yaml, ci/312-numba-latest.yaml, ci/312-dev.yaml, ] include: - environment-file: ci/312-latest.yaml os: macos-13 # Intel - environment-file: ci/312-numba-latest.yaml os: macos-13 # Intel - environment-file: ci/312-latest.yaml os: macos-14 # Apple Silicon - environment-file: ci/312-numba-latest.yaml os: macos-14 # Apple Silicon - environment-file: ci/312-latest.yaml os: windows-latest - environment-file: ci/312-numba-latest.yaml os: windows-latest fail-fast: false steps: - name: checkout repo uses: actions/checkout@v4 with: fetch-depth: 0 # Fetch all history for all branches and tags. - name: setup micromamba uses: mamba-org/setup-micromamba@v1 with: environment-file: ${{ matrix.environment-file }} micromamba-version: 'latest' - name: environment info run: | micromamba info micromamba list - name: spatial versions run: | python -c "import geopandas; geopandas.show_versions();" - name: Run pytest run: | pytest \ mapclassify \ -r a \ -v \ -n auto \ --color yes \ --cov-append \ --cov mapclassify \ --cov-report xml \ --cov-report term-missing - name: run docstring tests if: contains(matrix.environment-file, '312-numba-latest') && contains(matrix.os, 'ubuntu') run: | pytest \ -v \ -r a \ -n auto \ --color yes \ --cov-append \ --cov mapclassify \ --cov-report xml \ --doctest-only \ --mpl mapclassify - name: codecov (${{ matrix.os }}, ${{ matrix.environment-file }}) uses: codecov/codecov-action@v4 with: token: ${{ secrets.CODECOV_TOKEN }} file: ./coverage.xml name: mapclassify-codecov mapclassify-2.8.0/.gitignore000066400000000000000000000004511465055300600160300ustar00rootroot00000000000000*.pyc .CHANGELOG.md.swp .ropeproject/ dist/ examples/notebooks/.ipynb_checkpoints/ examples/python/ mapclassify.egg-info/ mapclassify/.ropeproject/ mapclassify/datasets/calemp/.ropeproject/ mapclassify/tests/.ropeproject/ .DS_Store .vscode/settings.json __pycache__ /notebooks/.ipynb_checkpoints/ mapclassify-2.8.0/.pre-commit-config.yaml000066400000000000000000000003261465055300600203220ustar00rootroot00000000000000files: 'mapclassify\/' repos: - repo: https://github.com/astral-sh/ruff-pre-commit rev: "v0.5.1" hooks: - id: ruff - id: ruff-format ci: autofix_prs: false autoupdate_schedule: quarterly mapclassify-2.8.0/CHANGELOG.md000066400000000000000000000234501465055300600156550ustar00rootroot00000000000000# Version 2.4.1 (2020-12-20) This is a bug-fix release. We closed a total of 9 issues (enhancements and bug fixes) through 3 pull requests, since our last release on 2020-12-13. ## Issues Closed - BUG: support series in sampled classifiers (#99) - BUG: FisherJenksSampled returns ValueError if Series is passed as y (#98) - REGR: fix invariant array regression (#101) - REGR: UserDefined classifier returns ValueError("Minimum and maximum of input data are equal, cannot create bins.") (#100) - [DOC] add example nb for new classify API (#91) - 2.4.0 Release (#97) ## Pull Requests - BUG: support series in sampled classifiers (#99) - REGR: fix invariant array regression (#101) - 2.4.0 Release (#97) The following individuals contributed to this release: - Serge Rey - Martin Fleischmann - Stefanie Lumnitz # Version 2.4.0 (2020-12-13) We closed a total of 39 issues (enhancements and bug fixes) through 15 pull requests, since our last release on 2020-06-13. Issues Closed - Remove timeout on tests. (#96) - BUG: HeadTailBreaks RecursionError due to floating point issue (#92) - Handle recursion error for head tails. (#95) - Add streamlined API (#72) - [API] add high-level API mapclassify.classify() (#90) - BUG: Fix mapclassify #88 (#89) - exclude Python 3.6 for Windows (#94) - CI: update conda action (#93) - EqualInterval unclear error when max_y - min_y = 0 (#88) - BUG: fix unordered series in greedy (#87) - BUG: greedy(strategy='balanced') does not return correct labels (#86) - Extra files in PyPI sdist (#56) - MAINT: fix repos name (#85) - DOC: content type for long description (#84) - MAINT: update gitcount notebook (#83) - Update documentations to include tutorial (#63) - build binder for notebooks (#71) - current version of mapclassify in docs? (#70) - 404 for notebook/tutorials links in docs (#79) - DOC: figs (#82) - DOCS: new images for tutorial (#81) - DOC: missing figs (#80) - DOCS: update documentation pages (#78) - Make networkx optional, remove xfail from greedy (#77) ## Pull Requests - Remove timeout on tests. (#96) - Handle recursion error for head tails. (#95) - [API] add high-level API mapclassify.classify() (#90) - BUG: Fix mapclassify #88 (#89) - exclude Python 3.6 for Windows (#94) - CI: update conda action (#93) - BUG: fix unordered series in greedy (#87) - MAINT: fix repos name (#85) - DOC: content type for long description (#84) - MAINT: update gitcount notebook (#83) - DOC: figs (#82) - DOCS: new images for tutorial (#81) - DOC: missing figs (#80) - DOCS: update documentation pages (#78) - Make networkx optional, remove xfail from greedy (#77) The following individuals contributed to this release: Serge Rey Stefanie Lumnitz James Gaboardi Martin Fleischmann # Version 2.3.0 (2020-06-13) ## Key Enhancements - Topological coloring to ensure no two adjacent polygons share the same color. - Pooled classification allows for the use of the same class intervals across maps. ## Details We closed a total of 30 issues (enhancements and bug fixes) through 10 pull requests, since our last release on 2020-01-04. ## Issues Closed - Make networkx optional, remove xfail from greedy (#77) - BINDER: point to upstream (#76) - add binder badge (#75) - Binder (#74) - sys import missing from setup.py (#73) - [WIP] DOC: Updating tutorial (#66) - chorobrewer branch has begun (#27) - Is mapclassify code black? (#68) - Code format and README (#69) - Move testing over to github actions (#64) - Add pinning in pooled example documentation (#67) - Migrate to GHA (#65) - Add a Pooled classifier (#51) - Backwards compatability (#48) - Difference between Natural Breaks and Fisher Jenks schemes (#62) - ENH: add greedy (topological) coloring (#61) - Error while running mapclassify (#60) - Pooled (#59) - Invalid escape sequences in strings (#57) - 3.8, appveyor, deprecation fixes (#58) ## Pull Requests - Make networkx optional, remove xfail from greedy (#77) - BINDER: point to upstream (#76) - add binder badge (#75) - Binder (#74) - [WIP] DOC: Updating tutorial (#66) - Code format and README (#69) - Migrate to GHA (#65) - ENH: add greedy (topological) coloring (#61) - Pooled (#59) - 3.8, appveyor, deprecation fixes (#58) ## Acknowledgements The following individuals contributed to this release: - Serge Rey - James Gaboardi - Eli Knaap - Martin Fleischmann # Version 2.2.0 (2019-12-21) This releases brings new functionality for [formatting of legend classes](https://github.com/sjsrey/geopandas/blob/legendkwds/examples/choro_legends.ipynb). We closed a total of 21 issues (enhancements and bug fixes) through 9 pull requests, since our last release on 2019-06-28. ## Issues Closed - 2.2 (#54) - 2.2 (#53) - conda-forge UnsatisfiableError on windows and python 3.7 (#52) - [MAINT] updating supported Python versions in setup.py (#49) - BUG: RecursiveError in HeadTailBreaks (#46) - BUG: HeadTailBreaks raise RecursionError (#45) - BUG: UserDefined accepts only list if max not in bins (#47) - BUG: avoid deprecation warning in HeadTailBreaks (#44) - remove docs badge (#42) - Remove doc badge (#43) - Docs: moving to project pages on github and off rtd (#41) - BUG: Fix for downstream breakage in geopandas (#40) ## Pull Requests - 2.2 (#54) - 2.2 (#53) - [MAINT] updating supported Python versions in setup.py (#49) - BUG: RecursiveError in HeadTailBreaks (#46) - BUG: UserDefined accepts only list if max not in bins (#47) - BUG: avoid deprecation warning in HeadTailBreaks (#44) - Remove doc badge (#43) - Docs: moving to project pages on github and off rtd (#41) - BUG: Fix for downstream breakage in geopandas (#40) The following individuals contributed to this release: - Serge Rey - James Gaboardi - Wei Kang - Martin Fleischmann # Version 2.1.0 (2019-06-26) We closed a total of 36 issues (enhancements and bug fixes) through 16 pull requests, since our last release on 2018-10-28. ## Issues Closed - ENH: dropping 3.5 support and adding 3.7 (#38) - ENH: plot method added to Mapclassify (#36) - ENH: keeping init keyword argument to avoid API breakage. (#35) - mapclassify.Natural_Break() does not return the specified k classes (#16) - Fix for #16 (#32) - Mixed usage of brewer2mpl and palettable.colorbrewer in color.py (#33) - Chorobrewer (#34) - conda-forge recipe needs some love (#14) - generating images for color selector (#31) - doc: bump version and dev setup docs (#30) - environment.yml (#29) - add color import and chorobrewer notebook (#28) - Chorobrewer (#26) - chorobrewer init (#25) - add badges for pypi, zenodo and docs (#24) - add geopandas and libpysal to test requirement (#23) - adjust changelog and delete tools/github_stats.py (#22) - add requirements_docs.txt to MANIFEST.in (#21) - gadf and K_classifiers not in __ini__.py (#18) - rel: 2.0.1 (#20) ## Pull Requests - ENH: dropping 3.5 support and adding 3.7 (#38) - ENH: plot method added to Mapclassify (#36) - ENH: keeping init keyword argument to avoid API breakage. (#35) - Fix for #16 (#32) - Chorobrewer (#34) - generating images for color selector (#31) - doc: bump version and dev setup docs (#30) - environment.yml (#29) - add color import and chorobrewer notebook (#28) - Chorobrewer (#26) - chorobrewer init (#25) - add badges for pypi, zenodo and docs (#24) - add geopandas and libpysal to test requirement (#23) - adjust changelog and delete tools/github_stats.py (#22) - add requirements_docs.txt to MANIFEST.in (#21) - rel: 2.0.1 (#20) The following individuals contributed to this release: - Serge Rey - Wei Kang # Version 2.0.1 (2018-10-28) We closed a total of 12 issues (enhancements and bug fixes) through 5 pull requests, since our last release on 2018-08-10. ## Issues Closed - gadf and K_classifiers not in __ini__.py (#18) - rel: 2.0.1 (#20) - fix doctests (interactive examples in inline docstrings) (#19) - complete readthedocs configuration & add Slocum 2009 reference (#17) - prepping for a doc based release (#15) - new release on pypi (#10) - prepare for release 2.0.0 (#13) ## Pull Requests - rel: 2.0.1 (#20) - fix doctests (interactive examples in inline docstrings) (#19) - complete readthedocs configuration & add Slocum 2009 reference (#17) - prepping for a doc based release (#15) - prepare for release 2.0.0 (#13) The following individuals contributed to this release: - Serge Rey - Wei Kang # Version 2.0.0 (2018-08-10) Starting from this release, mapclassify supports python 3+ only (currently 3.5 and 3.6). This release also features a first stable version of mapclassify in the process of pysal refactoring. There is a big change in the api in that we no longer provide an api module (`from mapclassify.api import Quantiles`). Instead, users will directly `from mapclassify import Quantiles`. GitHub stats for 2017/08/18 - 2018/08/10 These lists are automatically generated, and may be incomplete or contain duplicates. We closed a total of 8 issues, 4 pull requests and 4 regular issues; this is the full list (generated with the script :file:`tools/github_stats.py`): Pull Requests (4): * :ghpull:`12`: b'Clean up for next pypi release' * :ghpull:`11`: b'move notebooks outside of the package' * :ghpull:`9`: b'ENH: move classifiers up into init' * :ghpull:`8`: b'Moving to python 3+' Issues (4): * :ghissue:`12`: b'Clean up for next pypi release' * :ghissue:`11`: b'move notebooks outside of the package' * :ghissue:`9`: b'ENH: move classifiers up into init' * :ghissue:`8`: b'Moving to python 3+' # Version 1.0.1 (2017-08-17) - Warnings added when duplicate values make quantiles ill-defined - Faster digitize in place of list comprehension - Bug fix for consistent treatment of intervals (closed on the right, open on the left) v<1.0.0dev> 2017-04-21 - alpha release mapclassify-2.8.0/LICENSE.txt000066400000000000000000000027021465055300600156640ustar00rootroot00000000000000Copyright 2018 PySAL-mapclassify Developers Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. mapclassify-2.8.0/README.md000066400000000000000000000225561465055300600153310ustar00rootroot00000000000000# mapclassify: Classification Schemes for Choropleth Maps [![Continuous Integration](https://github.com/pysal/mapclassify/actions/workflows/testing.yml/badge.svg)](https://github.com/pysal/mapclassify/actions/workflows/testing.yml) [![codecov](https://codecov.io/gh/pysal/mapclassify/branch/main/graph/badge.svg)](https://codecov.io/gh/pysal/mapclassify) [![PyPI version](https://badge.fury.io/py/mapclassify.svg)](https://badge.fury.io/py/mapclassify) [![DOI](https://zenodo.org/badge/88918063.svg)](https://zenodo.org/badge/latestdoi/88918063) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pysal/mapclassify/main) `mapclassify` implements a family of classification schemes for choropleth maps. Its focus is on the determination of the number of classes, and the assignment of observations to those classes. It is intended for use with upstream mapping and geovisualization packages (see [geopandas](https://geopandas.org/mapping.html)) that handle the rendering of the maps. For further theoretical background see [Rey, S.J., D. Arribas-Bel, and L.J. Wolf (2020) "Geographic Data Science with PySAL and the PyData Stack”](https://geographicdata.science/book/notebooks/05_choropleth.html). ## Using `mapclassify` Load built-in example data reporting employment density in 58 California counties: ```python >>> import mapclassify >>> y = mapclassify.load_example() >>> y.mean() 125.92810344827588 >>> y.min(), y.max() (0.13, 4111.4499999999998) ``` ## Map Classifiers Supported ### BoxPlot ```python >>> mapclassify.BoxPlot(y) BoxPlot Interval Count -------------------------- ( -inf, -52.88] | 0 ( -52.88, 2.57] | 15 ( 2.57, 9.36] | 14 ( 9.36, 39.53] | 14 ( 39.53, 94.97] | 6 ( 94.97, 4111.45] | 9 ``` ### EqualInterval ```python >>> mapclassify.EqualInterval(y) EqualInterval Interval Count -------------------------- [ 0.13, 822.39] | 57 ( 822.39, 1644.66] | 0 (1644.66, 2466.92] | 0 (2466.92, 3289.19] | 0 (3289.19, 4111.45] | 1 ``` ### FisherJenks ```python >>> import numpy as np >>> np.random.seed(123456) >>> mapclassify.FisherJenks(y, k=5) FisherJenks Interval Count -------------------------- [ 0.13, 75.29] | 49 ( 75.29, 192.05] | 3 ( 192.05, 370.50] | 4 ( 370.50, 722.85] | 1 ( 722.85, 4111.45] | 1 ``` ### FisherJenksSampled ```python >>> np.random.seed(123456) >>> x = np.random.exponential(size=(10000,)) >>> mapclassify.FisherJenks(x, k=5) FisherJenks Interval Count ---------------------- [ 0.00, 0.64] | 4694 ( 0.64, 1.45] | 2922 ( 1.45, 2.53] | 1584 ( 2.53, 4.14] | 636 ( 4.14, 10.61] | 164 >>> mapclassify.FisherJenksSampled(x, k=5) FisherJenksSampled Interval Count ---------------------- [ 0.00, 0.70] | 5020 ( 0.70, 1.63] | 2952 ( 1.63, 2.88] | 1454 ( 2.88, 5.32] | 522 ( 5.32, 10.61] | 52 ``` ### HeadTailBreaks ```python >>> mapclassify.HeadTailBreaks(y) HeadTailBreaks Interval Count -------------------------- [ 0.13, 125.93] | 50 ( 125.93, 811.26] | 7 ( 811.26, 4111.45] | 1 ``` ### JenksCaspall ```python >>> mapclassify.JenksCaspall(y, k=5) JenksCaspall Interval Count -------------------------- [ 0.13, 1.81] | 14 ( 1.81, 7.60] | 13 ( 7.60, 29.82] | 14 ( 29.82, 181.27] | 10 ( 181.27, 4111.45] | 7 ``` ### JenksCaspallForced ```python >>> mapclassify.JenksCaspallForced(y, k=5) JenksCaspallForced Interval Count -------------------------- [ 0.13, 1.34] | 12 ( 1.34, 5.90] | 12 ( 5.90, 16.70] | 13 ( 16.70, 50.65] | 9 ( 50.65, 4111.45] | 12 ``` ### JenksCaspallSampled ```python >>> mapclassify.JenksCaspallSampled(y, k=5) JenksCaspallSampled Interval Count -------------------------- [ 0.13, 12.02] | 33 ( 12.02, 29.82] | 8 ( 29.82, 75.29] | 8 ( 75.29, 192.05] | 3 ( 192.05, 4111.45] | 6 ``` ### MaxP ```python >>> mapclassify.MaxP(y) MaxP Interval Count -------------------------- [ 0.13, 8.70] | 29 ( 8.70, 16.70] | 8 ( 16.70, 20.47] | 1 ( 20.47, 66.26] | 10 ( 66.26, 4111.45] | 10 ``` ### [MaximumBreaks](notebooks/maximum_breaks.ipynb) ```python >>> mapclassify.MaximumBreaks(y, k=5) MaximumBreaks Interval Count -------------------------- [ 0.13, 146.00] | 50 ( 146.00, 228.49] | 2 ( 228.49, 546.67] | 4 ( 546.67, 2417.15] | 1 (2417.15, 4111.45] | 1 ``` ### NaturalBreaks ```python >>> mapclassify.NaturalBreaks(y, k=5) NaturalBreaks Interval Count -------------------------- [ 0.13, 75.29] | 49 ( 75.29, 192.05] | 3 ( 192.05, 370.50] | 4 ( 370.50, 722.85] | 1 ( 722.85, 4111.45] | 1 ``` ### Quantiles ```python >>> mapclassify.Quantiles(y, k=5) Quantiles Interval Count -------------------------- [ 0.13, 1.46] | 12 ( 1.46, 5.80] | 11 ( 5.80, 13.28] | 12 ( 13.28, 54.62] | 11 ( 54.62, 4111.45] | 12 ``` ### Percentiles ```python >>> mapclassify.Percentiles(y, pct=[33, 66, 100]) Percentiles Interval Count -------------------------- [ 0.13, 3.36] | 19 ( 3.36, 22.86] | 19 ( 22.86, 4111.45] | 20 ``` ### PrettyBreaks ```python >>> np.random.seed(123456) >>> x = np.random.randint(0, 10000, (100,1)) >>> mapclassify.PrettyBreaks(x) Pretty Interval Count ---------------------------- [ 300.00, 2000.00] | 23 ( 2000.00, 4000.00] | 15 ( 4000.00, 6000.00] | 18 ( 6000.00, 8000.00] | 24 ( 8000.00, 10000.00] | 20 ``` ### StdMean ```python >>> mapclassify.StdMean(y) StdMean Interval Count -------------------------- ( -inf, -967.36] | 0 (-967.36, -420.72] | 0 (-420.72, 672.57] | 56 ( 672.57, 1219.22] | 1 (1219.22, 4111.45] | 1 ``` ### UserDefined ```python >>> mapclassify.UserDefined(y, bins=[22, 674, 4112]) UserDefined Interval Count -------------------------- [ 0.13, 22.00] | 38 ( 22.00, 674.00] | 18 ( 674.00, 4112.00] | 2 ``` ## Alternative API As of version 2.4.0 the API has been extended. A `classify` function is now available for a streamlined interface: ```python >>> classify(y, 'boxplot') BoxPlot Interval Count -------------------------- ( -inf, -52.88] | 0 ( -52.88, 2.57] | 15 ( 2.57, 9.36] | 14 ( 9.36, 39.53] | 14 ( 39.53, 94.97] | 6 ( 94.97, 4111.45] | 9 ``` ## Use Cases ### Creating and using a classification instance ```python >>> bp = mapclassify.BoxPlot(y) >>> bp BoxPlot Interval Count -------------------------- ( -inf, -52.88] | 0 ( -52.88, 2.57] | 15 ( 2.57, 9.36] | 14 ( 9.36, 39.53] | 14 ( 39.53, 94.97] | 6 ( 94.97, 4111.45] | 9 >>> bp.bins array([ -5.28762500e+01, 2.56750000e+00, 9.36500000e+00, 3.95300000e+01, 9.49737500e+01, 4.11145000e+03]) >>> bp.counts array([ 0, 15, 14, 14, 6, 9]) >>> bp.yb array([5, 1, 2, 3, 2, 1, 5, 1, 3, 3, 1, 2, 2, 1, 2, 2, 2, 1, 5, 2, 4, 1, 2, 2, 1, 1, 3, 3, 3, 5, 3, 1, 3, 5, 2, 3, 5, 5, 4, 3, 5, 3, 5, 4, 2, 1, 1, 4, 4, 3, 3, 1, 1, 2, 1, 4, 3, 2]) ``` ### Binning new data ```python >>> bp = mapclassify.BoxPlot(y) >>> bp BoxPlot Interval Count -------------------------- ( -inf, -52.88] | 0 ( -52.88, 2.57] | 15 ( 2.57, 9.36] | 14 ( 9.36, 39.53] | 14 ( 39.53, 94.97] | 6 ( 94.97, 4111.45] | 9 >>> bp.find_bin([0, 7, 3000, 48]) array([1, 2, 5, 4]) ``` Note that `find_bin` does not recalibrate the classifier: ```python >>> bp BoxPlot Interval Count -------------------------- ( -inf, -52.88] | 0 ( -52.88, 2.57] | 15 ( 2.57, 9.36] | 14 ( 9.36, 39.53] | 14 ( 39.53, 94.97] | 6 ( 94.97, 4111.45] | 9 ``` ### Apply ```python >>> import mapclassify >>> import pandas >>> from numpy import linspace as lsp >>> data = [lsp(3,8,num=10), lsp(10, 0, num=10), lsp(-5, 15, num=10)] >>> data = pandas.DataFrame(data).T >>> data 0 1 2 0 3.000000 10.000000 -5.000000 1 3.555556 8.888889 -2.777778 2 4.111111 7.777778 -0.555556 3 4.666667 6.666667 1.666667 4 5.222222 5.555556 3.888889 5 5.777778 4.444444 6.111111 6 6.333333 3.333333 8.333333 7 6.888889 2.222222 10.555556 8 7.444444 1.111111 12.777778 9 8.000000 0.000000 15.000000 >>> data.apply(mapclassify.Quantiles.make(rolling=True)) 0 1 2 0 0 4 0 1 0 4 0 2 1 4 0 3 1 3 0 4 2 2 1 5 2 1 2 6 3 0 4 7 3 0 4 8 4 0 4 9 4 0 4 ``` ## Development Notes Because we use `geopandas` in development, and geopandas has stable `mapclassify` as a dependency, setting up a local development installation involves creating a conda environment, then replacing the stable `mapclassify` with the development version of `mapclassify` in the development environment. This can be accomplished with the following steps: ``` conda-env create -f environment.yml conda activate mapclassify conda remove -n mapclassify mapclassify pip install -e . ``` mapclassify-2.8.0/ci/000077500000000000000000000000001465055300600144335ustar00rootroot00000000000000mapclassify-2.8.0/ci/310-latest.yaml000066400000000000000000000004111465055300600171100ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.10 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - pytest-mpl - codecov - matplotlib mapclassify-2.8.0/ci/310-numba-latest.yaml000066400000000000000000000004401465055300600202120ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.10 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - pytest-mpl - codecov - matplotlib # optional - numba mapclassify-2.8.0/ci/310-oldest.yaml000066400000000000000000000004251465055300600171130ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.10 # required - networkx=2.7 - numpy=1.23 - pandas=1.4 - scikit-learn=1.0 - scipy=1.8 # testing - geopandas<1 - libpysal - pytest - pytest-cov - pytest-xdist - codecov - matplotlib=3.5 mapclassify-2.8.0/ci/311-latest.yaml000066400000000000000000000004111465055300600171110ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - pytest-mpl - codecov - matplotlib mapclassify-2.8.0/ci/311-numba-latest.yaml000066400000000000000000000004401465055300600202130ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.11 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - pytest-mpl - codecov - matplotlib # optional - numba mapclassify-2.8.0/ci/312-dev.yaml000066400000000000000000000010531465055300600163770ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 # testing - pytest - pytest-cov - pytest-xdist - pytest-mpl - codecov # optional - pyproj - pip - pip: - --pre --index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple --extra-index-url https://pypi.org/simple - scipy - scikit-learn - pandas - networkx - matplotlib - shapely - fiona - git+https://github.com/pysal/libpysal.git@main - git+https://github.com/geopandas/geopandas.git@main mapclassify-2.8.0/ci/312-latest.yaml000066400000000000000000000005761465055300600171260ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - pytest-mpl - codecov - matplotlib # docs - nbsphinx - numpydoc - sphinx - sphinx-gallery - sphinxcontrib-bibtex - sphinx_bootstrap_theme mapclassify-2.8.0/ci/312-numba-latest.yaml000066400000000000000000000004671465055300600202250ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.12 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas - libpysal - pytest - pytest-cov - pytest-xdist - pytest-doctestplus - pytest-mpl - codecov - matplotlib # optional - numba mapclassify-2.8.0/ci/39-latest.yaml000066400000000000000000000003731465055300600170470ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.9 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas<1 - libpysal - pytest - pytest-cov - pytest-xdist - codecov - matplotlib mapclassify-2.8.0/ci/39-numba-latest.yaml000066400000000000000000000004221465055300600201420ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.9 # required - networkx - numpy - pandas - scikit-learn - scipy # testing - geopandas<1 - libpysal - pytest - pytest-cov - pytest-xdist - codecov - matplotlib # optional - numba mapclassify-2.8.0/ci/39-oldest.yaml000066400000000000000000000004241465055300600170420ustar00rootroot00000000000000name: test channels: - conda-forge dependencies: - python=3.9 # required - networkx=2.7 - numpy=1.23 - pandas=1.4 - scikit-learn=1.0 - scipy=1.8 # testing - geopandas<1 - libpysal - pytest - pytest-cov - pytest-xdist - codecov - matplotlib=3.5 mapclassify-2.8.0/codecov.yml000066400000000000000000000005441465055300600162100ustar00rootroot00000000000000codecov: notify: after_n_builds: 10 coverage: range: 50..95 round: nearest precision: 1 status: project: default: threshold: 2% patch: default: threshold: 2% target: 80% ignore: - "tests/*" comment: layout: "reach, diff, files" behavior: once after_n_builds: 10 require_changes: true mapclassify-2.8.0/docs/000077500000000000000000000000001465055300600147705ustar00rootroot00000000000000mapclassify-2.8.0/docs/Makefile000066400000000000000000000015361465055300600164350ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. 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k.>shsM$$$j痔?)ߏ \v aaaN;C:E.++C}}=z-̘1gv:k"""Ᏻƞ38Zau8lP𗋡VHIKKI'".X{'$-3}^_$ i&\.on2`\$@cc#D"+~!Mn _h"$R)Uxu!IDATS]mqqq2re"""Xwq@) ?j*jjW \ BB ~VוWkjw{f?5K.GEii)|Mj*L&߿-[/wCqq1fˑ==2ݕdR ^`aw7[RX/DV.ufWLvQHQA&.2vW]]@k1118y$*++1sL <ܹ.… pBlڴ :6lz 'Ot+Z())_|˗/u/&""# D*K"xe(gO{ [class*='col-'] { display: flex; flex-direction: row, column; } .row.equal-height.row:after, .row.equal-height.row:before { display: flex; } .row.equal-height > [class*='col-'] > .thumbnail, .row.equal-height > [class*='col-'] > .thumbnail > .caption { display: flex; flex: .9 .1 auto; flex-direction: column; } .row.equal-height > [class*='col-'] > .thumbnail > .caption > .flex-text { flex-grow: 1; } .row.equal-height > [class*='col-'] > .thumbnail > img { width: 350px; height: 200%; /* force image's height */ /* force image fit inside it's "box" */ -webkit-object-fit: cover; -moz-object-fit: cover; -ms-object-fit: cover; -o-object-fit: cover; object-fit: cover; } } .row.extra-bottom-padding{ margin-bottom: 20px; } .topnavicons { margin-left: 10% !important; } .topnavicons li { margin-left: 0px !important; min-width: 100px; text-align: center; } .topnavicons .thumbnail { margin-right: 10px; border: none; box-shadow: none; text-align: center; font-size: 85%; font-weight: bold; line-height: 10px; height: 100px; } .topnavicons .thumbnail img { display: block; margin-left: auto; margin-right: auto; } /* Table with a scrollbar */ .bodycontainer { max-height: 800px; width: 100%; margin: 0; padding: 0; overflow-y: auto; } .table-scrollable { margin: 0; padding: 0; } .label { color: #E74C3C; font-size: 100%; font-weight: bold; width: 100px; text-align: left; vertical-align: middle; } div.body { max-width: 1080px; } table.longtable.align-default{ text-align: left; }mapclassify-2.8.0/docs/_static/references.bib000066400000000000000000000026171465055300600212230ustar00rootroot00000000000000%% This BibTeX bibliography file was created using BibDesk. %% http://bibdesk.sourceforge.net/ %% Created for Wei Kang at 2018-10-25 22:16:36 -0700 %% Saved with string encoding Unicode (UTF-8) @article{Jiang_2013, Author = {Jiang, Bin}, Doi = {10.1080/00330124.2012.700499}, Issn = {1467-9272}, Journal = {The Professional Geographer}, Month = {Aug}, Number = 3, Pages = {482--494}, Publisher = {Informa UK Limited}, Title = {Head/Tail Breaks: A New Classification Scheme for Data with a Heavy-Tailed Distribution}, Url = {http://dx.doi.org/10.1080/00330124.2012.700499}, Volume = 65, Year = 2013, Bdsk-Url-1 = {http://dx.doi.org/10.1080/00330124.2012.700499}} @article{Rey_2016, Author = {Rey, Sergio J. and Stephens, Philip and Laura, Jason}, Doi = {10.1111/tgis.12236}, Issn = {1361-1682}, Journal = {Transactions in GIS}, Month = {Oct}, Number = 4, Pages = {796--810}, Publisher = {Wiley}, Title = {An evaluation of sampling and full enumeration strategies for {Fisher Jenks} classification in big data settings}, Url = {http://dx.doi.org/10.1111/tgis.12236}, Volume = 21, Year = 2016, Bdsk-Url-1 = {http://dx.doi.org/10.1111/tgis.12236}} @book{Slocum_2009, Author = {Slocum, Terry A. and McMaster, Robert B. and Kessler, Fritz C. and Howard, Hugh H.}, Publisher = {Pearson Prentice Hall, Upper Saddle River}, Title = {Thematic cartography and geovisualization}, Year = {2009}} mapclassify-2.8.0/docs/api.rst000066400000000000000000000015221465055300600162730ustar00rootroot00000000000000.. _api_ref: .. currentmodule:: mapclassify API reference ============= .. _classifiers_api: Classifiers ----------- .. autosummary:: :toctree: generated/ mapclassify.BoxPlot mapclassify.EqualInterval mapclassify.FisherJenks mapclassify.FisherJenksSampled mapclassify.greedy mapclassify.HeadTailBreaks mapclassify.JenksCaspall mapclassify.JenksCaspallForced mapclassify.JenksCaspallSampled mapclassify.MaxP mapclassify.MaximumBreaks mapclassify.NaturalBreaks mapclassify.Percentiles mapclassify.PrettyBreaks mapclassify.Quantiles mapclassify.StdMean mapclassify.UserDefined Utilities --------- .. autosummary:: :toctree: generated/ mapclassify.KClassifiers mapclassify.Pooled mapclassify.classify mapclassify.gadf mapclassify.util.get_color_array mapclassify-2.8.0/docs/conf.py000066400000000000000000000245201465055300600162720ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # giddy documentation build configuration file, created by # sphinx-quickstart on Wed Jun 6 15:54:22 2018. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import sys import sphinx_bootstrap_theme sys.path.insert(0, os.path.abspath("../")) # import your package to obtain the version info to display on the docs website import mapclassify # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ #'sphinx_gallery.gen_gallery', "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.viewcode", "sphinxcontrib.bibtex", "sphinx.ext.mathjax", "sphinx.ext.doctest", "sphinx.ext.intersphinx", "numpydoc", "matplotlib.sphinxext.plot_directive", "nbsphinx", ] bibtex_bibfiles = ["_static/references.bib"] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = ".rst" # The master toctree document. master_doc = "index" # General information about the project. project = "mapclassify" # string of your project name, for example, 'giddy' copyright = "2018-, pysal developers" author = "pysal developers" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The full version. version = mapclassify.__version__ # should replace it with your PACKAGE_NAME release = mapclassify.__version__ # should replace it with your PACKAGE_NAME # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "tests/*"] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # # html_theme = 'alabaster' html_theme = "bootstrap" html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() html_title = "%s v%s Manual" % (project, version) # (Optional) Logo of your package. Should be small enough to fit the navbar (ideally 24x24). # Path should be relative to the ``_static`` files directory. # html_logo = "_static/images/package_logo.jpg" # (Optional) PySAL favicon html_favicon = "_static/images/pysal_favicon.ico" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = { # Navigation bar title. (Default: ``project`` value) "navbar_title": project, # string of your project name, for example, 'giddy' # Render the next and previous page links in navbar. (Default: true) "navbar_sidebarrel": False, # Render the current pages TOC in the navbar. (Default: true) #'navbar_pagenav': True, #'navbar_pagenav': False, # No sidebar "nosidebar": True, # Tab name for the current pages TOC. (Default: "Page") #'navbar_pagenav_name': "Page", # Global TOC depth for "site" navbar tab. (Default: 1) # Switching to -1 shows all levels. "globaltoc_depth": 2, # Include hidden TOCs in Site navbar? # # Note: If this is "false", you cannot have mixed ``:hidden:`` and # non-hidden ``toctree`` directives in the same page, or else the build # will break. # # Values: "true" (default) or "false" "globaltoc_includehidden": "true", # HTML navbar class (Default: "navbar") to attach to

element. # For black navbar, do "navbar navbar-inverse" #'navbar_class': "navbar navbar-inverse", # Fix navigation bar to top of page? # Values: "true" (default) or "false" "navbar_fixed_top": "true", # Location of link to source. # Options are "nav" (default), "footer" or anything else to exclude. "source_link_position": "footer", # Bootswatch (http://bootswatch.com/) theme. # # Options are nothing (default) or the name of a valid theme # such as "amelia" or "cosmo", "yeti", "flatly". "bootswatch_theme": "yeti", # Choose Bootstrap version. # Values: "3" (default) or "2" (in quotes) "bootstrap_version": "3", # Navigation bar menu "navbar_links": [ ("Installation", "installation"), ("Tutorial", "tutorial"), ("API", "api"), ("References", "references"), ], } # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # html_sidebars = {'sidebar': ['localtoc.html', 'sourcelink.html', 'searchbox.html']} # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = project + "doc" # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ( master_doc, f"{project}.tex", f"{project} Documentation", "pysal developers", "manual", ), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, project, f"{project} Documentation", [author], 1)] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, project, f"{project} Documentation", author, "PySAL Developers", "map classification schemes.", "Miscellaneous", ), ] # ----------------------------------------------------------------------------- # Autosummary # ----------------------------------------------------------------------------- # Generate the API documentation when building autosummary_generate = True # avoid showing members twice numpydoc_show_class_members = False numpydoc_use_plots = True class_members_toctree = True numpydoc_show_inherited_class_members = True numpydoc_xref_param_type = True # automatically document class members autodoc_default_options = {"members": True, "undoc-members": True} # display the source code for Plot directive plot_include_source = True def setup(app): app.add_css_file("pysal-styles.css") # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { "geopandas": ("https://geopandas.org/en/latest/", None), "libpysal": ("https://pysal.org/libpysal/", None), "matplotlib": ("https://matplotlib.org/stable/", None), "networkx": ("https://networkx.org/documentation/stable/", None), "numpy": ("https://numpy.org/doc/stable/", None), "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), "python": ("https://docs.python.org/3.11/", None), "scipy": ("https://docs.scipy.org/doc/scipy/", None), } # This is processed by Jinja2 and inserted before each notebook nbsphinx_prolog = r""" {% set docname = env.doc2path(env.docname, base=None) %} .. only:: html .. role:: raw-html(raw) :format: html .. nbinfo:: This page was generated from `{{ docname }}`__. Interactive online version: :raw-html:`Binder badge` __ https://github.com/pysal/mapclassify/blob/main/{{ docname }} .. raw:: latex \nbsphinxstartnotebook{\scriptsize\noindent\strut \textcolor{gray}{The following section was generated from \sphinxcode{\sphinxupquote{\strut {{ docname | escape_latex }}}} \dotfill}} """ # This is processed by Jinja2 and inserted after each notebook nbsphinx_epilog = r""" .. raw:: latex \nbsphinxstopnotebook{\scriptsize\noindent\strut \textcolor{gray}{\dotfill\ \sphinxcode{\sphinxupquote{\strut {{ env.doc2path(env.docname, base='doc') | escape_latex }}}} ends here.}} """ # List of arguments to be passed to the kernel that executes the notebooks: nbsphinx_execute_arguments = [ "--InlineBackend.figure_formats={'svg', 'pdf'}", "--InlineBackend.rc={'figure.dpi': 96}", ] mathjax3_config = { "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}}, } mapclassify-2.8.0/docs/index.rst000066400000000000000000000037251465055300600166400ustar00rootroot00000000000000.. documentation master file mapclassify =========== mapclassify is an open-source python library for Choropleth map classification. It is part of `PySAL`_ the Python Spatial Analysis Library. .. raw:: html .. toctree:: :hidden: :maxdepth: 3 :caption: Contents: Installation Tutorial API References .. _PySAL: https://github.com/pysal/pysal mapclassify-2.8.0/docs/installation.rst000066400000000000000000000034151465055300600202260ustar00rootroot00000000000000.. Installation Installation ============ mapclassify supports python `3.9`_, `3.10`_, `3.11`_, and `3.12`_. Please make sure that you are operating in a python 3 environment. Installing released version --------------------------- mapclassify is available in on `conda`_ via the `conda-forge`_ channel:: conda install -c conda-forge mapclassify mapclassify is also available on the `Python Package Index`_. Therefore, you can either install directly with `pip` from the command line:: pip install -U mapclassify or download the source distribution (.tar.gz) and decompress it to your selected destination. Open a command shell and navigate to the decompressed folder. Type:: pip install . Installing development version ------------------------------ Potentially, you might want to use the newest features in the development version of mapclassify on github - `pysal/mapclassify`_ while have not been incorporated in the Pypi released version. You can achieve that by installing `pysal/mapclassify`_ by running the following from a command shell:: pip install git+https://github.com/pysal/mapclassify.git You can also `fork`_ the `pysal/mapclassify`_ repo and create a local clone of your fork. By making changes to your local clone and submitting a pull request to `pysal/mapclassify`_, you can contribute to mapclassify development. .. _3.9: https://docs.python.org/3.9/ .. _3.10: https://docs.python.org/3.10/ .. _3.11: https://docs.python.org/3.11/ .. _3.12: https://docs.python.org/3.12/ .. _conda: https://docs.conda.io/en/latest/ .. _conda-forge: https://anaconda.org/conda-forge/mapclassify .. _Python Package Index: https://pypi.org/project/mapclassify/ .. _pysal/mapclassify: https://github.com/pysal/mapclassify .. _fork: https://help.github.com/articles/fork-a-repo/ mapclassify-2.8.0/docs/references.rst000066400000000000000000000001441465055300600176420ustar00rootroot00000000000000.. reference for the docs References ========== .. bibliography:: _static/references.bib :all: mapclassify-2.8.0/docs/tutorial.rst000066400000000000000000000004221465055300600173630ustar00rootroot00000000000000Tutorial ======== .. toctree:: :maxdepth: 1 :caption: Contents: notebooks/01_maximum_breaks.ipynb notebooks/02_legends.ipynb notebooks/03_choropleth.ipynb notebooks/04_pooled.ipynb notebooks/05_Greedy_coloring.ipynb notebooks/06_api.ipynb mapclassify-2.8.0/environment.yml000066400000000000000000000005471465055300600171350ustar00rootroot00000000000000# Run `conda-env create -f environment.yml` name: mapclassify channels: - conda-forge dependencies: - python - geopandas - git - ipywidgets - jupyter - jupyterlab - libpysal - matplotlib - nbconvert - networkx - numba - palettable - pip - scikit-learn - seaborn - pip: - git+https://github.com/pysal/mapclassify.git@main mapclassify-2.8.0/mapclassify/000077500000000000000000000000001465055300600163535ustar00rootroot00000000000000mapclassify-2.8.0/mapclassify/__init__.py000066400000000000000000000012241465055300600204630ustar00rootroot00000000000000import contextlib from importlib.metadata import PackageNotFoundError, version from . import util from ._classify_API import classify from .classifiers import ( CLASSIFIERS, BoxPlot, EqualInterval, FisherJenks, FisherJenksSampled, HeadTailBreaks, JenksCaspall, JenksCaspallForced, JenksCaspallSampled, KClassifiers, MaximumBreaks, MaxP, NaturalBreaks, Percentiles, PrettyBreaks, Quantiles, StdMean, UserDefined, gadf, load_example, ) from .greedy import greedy from .pooling import Pooled with contextlib.suppress(PackageNotFoundError): __version__ = version("mapclassify") mapclassify-2.8.0/mapclassify/_classify_API.py000066400000000000000000000137321465055300600214000ustar00rootroot00000000000000from .classifiers import ( BoxPlot, EqualInterval, FisherJenks, FisherJenksSampled, HeadTailBreaks, JenksCaspall, JenksCaspallForced, JenksCaspallSampled, MaximumBreaks, MaxP, NaturalBreaks, Percentiles, PrettyBreaks, Quantiles, StdMean, UserDefined, ) __author__ = "Stefanie Lumnitz " _classifiers = { "boxplot": BoxPlot, "equalinterval": EqualInterval, "fisherjenks": FisherJenks, "fisherjenkssampled": FisherJenksSampled, "headtailbreaks": HeadTailBreaks, "jenkscaspall": JenksCaspall, "jenkscaspallforced": JenksCaspallForced, "jenkscaspallsampled": JenksCaspallSampled, "maxp": MaxP, "maximumbreaks": MaximumBreaks, "naturalbreaks": NaturalBreaks, "quantiles": Quantiles, "percentiles": Percentiles, "prettybreaks": PrettyBreaks, "stdmean": StdMean, "userdefined": UserDefined, } def classify( y, scheme, k=5, pct=[1, 10, 50, 90, 99, 100], pct_sampled=0.10, truncate=True, hinge=1.5, multiples=[-2, -1, 1, 2], mindiff=0, initial=100, bins=None, lowest=None, anchor=False, ): """ Classify your data with ``mapclassify.classify``. Input parameters are dependent on classifier used. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. scheme : str ``pysal.mapclassify`` classification scheme. k : int (default 5) The number of classes. pct : numpy.array (default [1, 10, 50, 90, 99, 100]) Percentiles used for classification with ``percentiles``. pct_sampled : float default (0.10) The percentage of n that should form the sample (``JenksCaspallSampled``, ``FisherJenksSampled``) If ``pct`` is specified such that ``n*pct > 1000``, then ``pct=1000``. truncate : bool (default True) Truncate ``pct_sampled`` in cases where ``pct * n > 1000``. hinge : float (default 1.5) Multiplier for *IQR* when ``BoxPlot`` classifier used. multiples : numpy.array (default [-2,-1,1,2]) The multiples of the standard deviation to add/subtract from the sample mean to define the bins using ``std_mean``. mindiff : float (default is 0) The minimum difference between class breaks if using ``maximum_breaks`` classifier. initial : int (default 100) Number of initial solutions to generate or number of runs when using ``natural_breaks`` or ``max_p_classifier``. Setting initial to ``0`` will result in the quickest calculation of bins. bins : numpy.array (default None) :math:`(k,1)`, upper bounds of classes (have to be monotically increasing) if using ``user_defined`` classifier. Default is ``None``. For example: ``[20, max(y)]``. lowest : float (default None) Scalar minimum value of lowest class. Default is to set the minimum to ``-inf`` if ``y.min()`` > first upper bound (which will override the default), otherwise minimum is set to ``y.min()``. anchor : bool (default False) Anchor upper bound of one class to the sample mean. Returns ------- classifier : mapclassify.classifiers.MapClassifier Object containing bin ids for each observation (``.yb``), upper bounds of each class (``.bins``), number of classes (``.k``) and number of observations falling in each class (``.counts``). Notes ----- Supported classifiers include: * ``quantiles`` * ``boxplot`` * ``equalinterval`` * ``fisherjenks`` * ``fisherjenkssampled`` * ``headtailbreaks`` * ``jenkscaspall`` * ``jenkscaspallsampled`` * ``jenks_caspallforced`` * ``maxp`` * ``maximumbreaks`` * ``naturalbreaks`` * ``percentiles`` * ``prettybreaks`` * ``stdmean`` * ``userdefined`` Examples -------- >>> import libpysal >>> import geopandas >>> from mapclassify import classify Load example data. >>> link_to_data = libpysal.examples.get_path("columbus.shp") >>> gdf = geopandas.read_file(link_to_data) >>> x = gdf['HOVAL'].values Classify values by quantiles. >>> quantiles = classify(x, "quantiles") Classify values by box_plot and set hinge to ``2``. >>> box_plot = classify(x, 'box_plot', hinge=2) >>> box_plot BoxPlot Interval Count ---------------------- ( -inf, -9.50] | 0 (-9.50, 25.70] | 13 (25.70, 33.50] | 12 (33.50, 43.30] | 12 (43.30, 78.50] | 9 (78.50, 96.40] | 3 """ # reformat scheme_lower = scheme.lower() scheme = scheme_lower.replace("_", "") # check if scheme is a valid scheme if scheme not in _classifiers: raise ValueError( f"Invalid scheme: '{scheme}'\n" f"Scheme must be in the set: {_classifiers.keys()}" ) elif scheme == "boxplot": classifier = _classifiers[scheme](y, hinge) elif scheme == "fisherjenkssampled": classifier = _classifiers[scheme](y, k, pct_sampled, truncate) elif scheme == "headtailbreaks": classifier = _classifiers[scheme](y) elif scheme == "percentiles": classifier = _classifiers[scheme](y, pct) elif scheme == "stdmean": classifier = _classifiers[scheme](y, multiples, anchor) elif scheme == "jenkscaspallsampled": classifier = _classifiers[scheme](y, k, pct_sampled) elif scheme == "maximumbreaks": classifier = _classifiers[scheme](y, k, mindiff) elif scheme in ["naturalbreaks", "maxp"]: classifier = _classifiers[scheme](y, k, initial) elif scheme == "userdefined": classifier = _classifiers[scheme](y, bins, lowest) elif scheme in [ "equalinterval", "fisherjenks", "jenkscaspall", "jenkscaspallforced", "quantiles", "prettybreaks", ]: classifier = _classifiers[scheme](y, k) return classifier mapclassify-2.8.0/mapclassify/classifiers.py000066400000000000000000002435711465055300600212500ustar00rootroot00000000000000""" A module of classification schemes for choropleth mapping. """ import copy import functools import warnings import numpy as np import scipy.stats as stats from sklearn.cluster import KMeans __author__ = "Sergio J. Rey" __all__ = [ "MapClassifier", "quantile", "BoxPlot", "EqualInterval", "FisherJenks", "FisherJenksSampled", "JenksCaspall", "JenksCaspallForced", "JenksCaspallSampled", "HeadTailBreaks", "MaxP", "MaximumBreaks", "NaturalBreaks", "Quantiles", "Percentiles", "PrettyBreaks", "StdMean", "UserDefined", "gadf", "KClassifiers", "CLASSIFIERS", ] CLASSIFIERS = ( "BoxPlot", "EqualInterval", "FisherJenks", "FisherJenksSampled", "HeadTailBreaks", "JenksCaspall", "JenksCaspallForced", "JenksCaspallSampled", "MaxP", "MaximumBreaks", "NaturalBreaks", "Quantiles", "Percentiles", "PrettyBreaks", "StdMean", "UserDefined", ) K = 5 # default number of classes in any map scheme with this as an argument SEEDRANGE = 1000000 # range for drawing random ints from for Natural Breaks FMT = "{:.2f}" try: from numba import njit HAS_NUMBA = True except ImportError: HAS_NUMBA = False def njit(_type): # noqa ARG001 def decorator_njit(func): @functools.wraps(func) def wrapper_decorator(*args, **kwargs): return func(*args, **kwargs) return wrapper_decorator return decorator_njit def _format_intervals(mc, fmt="{:.0f}"): """ Helper methods to format legend intervals. Parameters ---------- mc: MapClassifier fmt: str (default '{:.0f}') Specification of formatting for legend. Returns ------- tuple: edges : list :math:`k` strings for class intervals. max_width : int Length of largest interval string. lower_open : bool True: lower bound of first interval is open. False: lower bound of first interval is closed. Notes ----- For some classifiers, it is possible that the upper bound of the first interval is less than the minimum value of the attribute that is being classified. In these cases ``lower_open=True`` and the lower bound of the interval is set to ``numpy.NINF``. """ lowest = mc.y.min() if hasattr(mc, "lowest") and mc.lowest is not None: lowest = mc.lowest lower_open = False if lowest > mc.bins[0]: lowest = -np.inf lower_open = True edges = [lowest] edges.extend(mc.bins) edges = [fmt.format(edge) for edge in edges] max_width = max([len(edge) for edge in edges]) return edges, max_width, lower_open def _get_mpl_labels(mc, fmt="{:.1f}"): """ Helper method to format legend intervals for matplotlib (and geopandas). Parameters ---------- mc : MapClassifier fmt : str (default '{:.1f}') Specification of formatting for legend. Returns ------- intervals : list :math:`k` strings for class intervals. """ edges, max_width, lower_open = _format_intervals(mc, fmt) k = len(edges) - 1 left = ["["] if lower_open: left = ["("] left.extend("(" * k) right = "]" * (k + 1) lower = ["{:>{width}}".format(edges[i], width=max_width) for i in range(k)] upper = ["{:>{width}}".format(edges[i], width=max_width) for i in range(1, k + 1)] lower = [_l + r for _l, r in zip(left, lower)] upper = [_l + r for _l, r in zip(upper, right)] intervals = [_l + ", " + r for _l, r in zip(lower, upper)] return intervals def _get_table(mc, fmt="{:.2f}"): """ Helper function to generate tabular classification report. Parameters ---------- mc: MapClassifier fmt: str (default '{:.2f}') specification of formatting for legend. Returns ------- table : str Formatted table of classification results. """ intervals = _get_mpl_labels(mc, fmt) interval_width = len(intervals[0]) counts = list(map(str, mc.counts)) count_width = max([len(count) for count in counts]) count_width = max(count_width, len("count")) interval_width = max(interval_width, len("interval")) header = f"{'Interval' : ^{interval_width}}" header += " " * 3 + f"{'Count' : >{count_width}}" title = mc.name header += "\n" + "-" * len(header) table = [title, "", header] for i, interval in enumerate(intervals): row = f"{interval} | {counts[i] : >{count_width}}" table.append(row) return "\n".join(table) def head_tail_breaks(values, cuts): """Head tail breaks helper function.""" values = np.array(values) mean = np.mean(values) if len(cuts) > 0 and cuts[-1] == mean: # this fixes floating point from GH#117 return cuts cuts.append(mean) if len(set(values)) > 1: return head_tail_breaks(values[values > mean], cuts) return cuts def quantile(y, k=4): """ Calculates the quantiles for an array. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 4) Number of quantiles. Returns ------- q : numpy.array :math:`(n,1)`, quantile values. Notes ----- If there are enough ties that the quantile values repeat, we collapse to pseudo quantiles in which case the number of classes will be less than ``k``. Examples -------- >>> import mapclassify >>> import numpy >>> x = numpy.arange(1000) >>> mapclassify.classifiers.quantile(x) array([249.75, 499.5 , 749.25, 999. ]) >>> mapclassify.classifiers.quantile(x, k=3) array([333., 666., 999.]) """ w = 100.0 / k p = np.arange(w, 100 + w, w) if p[-1] > 100.0: p[-1] = 100.0 q = np.array([stats.scoreatpercentile(y, pct) for pct in p]) q = np.unique(q) k_q = len(q) if k_q < k: warnings.warn( f"Not enough unique values in array to form {k} classes. " f"Setting k to {k_q}.", UserWarning, stacklevel=2, ) return q def binC(y, bins): # noqa N802 """ Bin categorical/qualitative data. Parameters ---------- y : numpy.array :math:`(n,q)`, categorical values. bins : numpy.array :math:`(k,1)`, unique values associated with each bin. Return ------ b : numpy.array :math:`(n,q)` bin membership, values between ``0`` and ``k-1``. Examples -------- >>> import numpy >>> import mapclassify >>> numpy.random.seed(1) >>> x = numpy.random.randint(2, 8, (10, 3)) >>> bins = list(range(2, 8)) >>> x array([[7, 5, 6], [2, 3, 5], [7, 2, 2], [3, 6, 7], [6, 3, 4], [6, 7, 4], [6, 5, 6], [4, 6, 7], [4, 6, 3], [3, 2, 7]]) >>> y = mapclassify.classifiers.binC(x, bins) >>> y array([[5, 3, 4], [0, 1, 3], [5, 0, 0], [1, 4, 5], [4, 1, 2], [4, 5, 2], [4, 3, 4], [2, 4, 5], [2, 4, 1], [1, 0, 5]]) """ # TODO: consider renaming ``binC`` to ``bin_c`` to resolve N802 (gh#185) if np.ndim(y) == 1: k = 1 n = np.shape(y)[0] else: n, k = np.shape(y) b = np.zeros((n, k), dtype="int") for i, _bin in enumerate(bins): b[np.nonzero(y == _bin)] = i # check for non-binned items and warn if needed vals = set(y.flatten()) for val in vals: if val not in bins: warnings.warn( f"\nValue not in bin: {val}\nBins: {bins}", UserWarning, stacklevel=2 ) return b def bin(y, bins): # noqa A001 """ Bin interval/ratio data. Parameters ---------- y : numpy.array :math:`(n,q)`, values to bin. bins : numpy.array :math:`(k,1)`, upper bounds of each bin (monotonic). Returns ------- b : numpy.array :math:`(n,q)`, values of values between ``0`` and ``k-1``. Examples -------- >>> import numpy >>> import mapclassify >>> numpy.random.seed(1) >>> x = numpy.random.randint(2, 20, (10, 3)) >>> bins = [10, 15, 20] >>> b = mapclassify.classifiers.bin(x, bins) >>> x array([[ 7, 13, 14], [10, 11, 13], [ 7, 17, 2], [18, 3, 14], [ 9, 15, 8], [ 7, 13, 12], [16, 6, 11], [19, 2, 15], [11, 11, 9], [ 3, 2, 19]]) >>> b array([[0, 1, 1], [0, 1, 1], [0, 2, 0], [2, 0, 1], [0, 1, 0], [0, 1, 1], [2, 0, 1], [2, 0, 1], [1, 1, 0], [0, 0, 2]]) """ # TODO: consider renaming ``bin`` to ``bin_int`` to resolve A001 (gh#185) if np.ndim(y) == 1: k = 1 n = np.shape(y)[0] else: n, k = np.shape(y) b = np.zeros((n, k), dtype="int") i = len(bins) if not isinstance(bins, list): bins = bins.tolist() binsc = copy.copy(bins) while binsc: i -= 1 c = binsc.pop(-1) b[np.nonzero(y <= c)] = i return b def bin1d(x, bins): """ Place values of a 1-d array into bins and determine counts of values in each bin. Parameters ---------- x : numpy.array :math:`(n, 1)`, values to bin. bins : numpy.array :math:`(k,1)`, upper bounds of each bin (monotonic). Returns ------- binIds : numpy.array 1-d array of integer bin IDs. counts : int Number of elements of ``x`` falling in each bin. Examples -------- >>> import numpy >>> import mapclassify >>> x = numpy.arange(100, dtype = "float") >>> bins = [25, 74, 100] >>> binIds, counts = mapclassify.classifiers.bin1d(x, bins) >>> binIds array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) >>> counts.tolist() [26, 49, 25] """ left = [-float("inf")] left.extend(bins[0:-1]) right = bins cuts = list(zip(left, right)) k = len(bins) binIds = np.zeros(x.shape, dtype="int") while cuts: k -= 1 _l, r = cuts.pop(-1) binIds += (x > _l) * (x <= r) * k counts = np.bincount(binIds, minlength=len(bins)) return (binIds, counts) def _pretty_number(x, rounded=True): exp = np.floor(np.log10(x)) f = x / 10**exp if rounded: if f < 1.5: nf = 1.0 elif f < 3.0: nf = 2.0 elif f < 7.0: nf = 5.0 else: nf = 10.0 else: if f <= 1.0: nf = 1.0 elif f <= 2.0: nf = 2.0 elif f <= 5.0: nf = 5.0 else: nf = 10.0 return nf * 10.0**exp def _pretty(y, k=5): low = y.min() high = y.max() rg = _pretty_number(high - low, False) d = _pretty_number(rg / (k - 1), True) miny = np.floor(low / d) * d maxy = np.ceil(high / d) * d return np.arange(miny, maxy + 0.5 * d, d) def load_example(): """ Helper function for doc tests """ from .datasets import calemp return calemp.load() def _kmeans(y, k=5, n_init=10): """ Helper function to do k-means in one dimension. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int, (default 5) Number of classes to form. n_init : int, (default 10) Number of initial solutions. Best of initial results is returned. """ y = y * 1.0 # sklearn.cluster.KMeans needs float or double dtype y.shape = (-1, 1) result = KMeans(n_clusters=k, init="k-means++", n_init=n_init).fit(y) class_ids = result.labels_ centroids = result.cluster_centers_ binning = [] for c in range(k): values = y[class_ids == c] binning.append([values.max(), len(values)]) binning = np.array(binning) binning = binning[binning[:, 0].argsort()] cuts = binning[:, 0] y_cent = np.zeros_like(y) for c in range(k): y_cent[class_ids == c] = centroids[c] diffs = y - y_cent diffs *= diffs return class_ids, cuts, diffs.sum(), centroids def natural_breaks(values, k=5, init=10): """ Natural breaks helper function. Jenks natural breaks is k-means in one dimension. Parameters ---------- values : numpy.array :math:`(n, 1)` values to bin. k : int, (default 5) Number of classes. init: int, (default 10) Number of different solutions to obtain using different centroids. Best solution is returned. """ values = np.array(values) uv = np.unique(values) uvk = len(uv) if uvk < k: warnings.warn( f"Not enough unique values in array to form {k} classes. " f"Setting k to {uvk}.", UserWarning, stacklevel=2, ) k = uvk kres = _kmeans(values, k, n_init=init) sids = kres[-1] # centroids fit = kres[-2] class_ids = kres[0] cuts = kres[1] return (sids, class_ids, fit, cuts) @njit("f8[:](f8[:], u2)") def _fisher_jenks_means(values, classes=5): """ Jenks Optimal (Natural Breaks) algorithm implemented in Python. Notes ----- The original Python code comes from here: http://danieljlewis.org/2010/06/07/jenks-natural-breaks-algorithm-in-python/ and is based on a JAVA and Fortran code available here: https://stat.ethz.ch/pipermail/r-sig-geo/2006-March/000811.html Returns class breaks such that classes are internally homogeneous while assuring heterogeneity among classes. """ n_data = len(values) mat1 = np.zeros((n_data + 1, classes + 1), dtype=np.int32) mat2 = np.zeros((n_data + 1, classes + 1), dtype=np.float32) mat1[1, 1:] = 1 mat2[2:, 1:] = np.inf v = np.float32(0) for _l in range(2, len(values) + 1): s1 = np.float32(0) s2 = np.float32(0) w = np.float32(0) for m in range(1, _l + 1): i3 = _l - m + 1 val = np.float32(values[i3 - 1]) s2 += val * val s1 += val w += np.float32(1) v = s2 - (s1 * s1) / w i4 = i3 - 1 if i4 != 0: for j in range(2, classes + 1): if mat2[_l, j] >= (v + mat2[i4, j - 1]): mat1[_l, j] = i3 mat2[_l, j] = v + mat2[i4, j - 1] mat1[_l, 1] = 1 mat2[_l, 1] = v k = len(values) kclass = np.zeros(classes + 1, dtype=values.dtype) kclass[classes] = values[len(values) - 1] kclass[0] = values[0] for countNum in range(classes, 1, -1): pivot = mat1[k, countNum] _id = int(pivot - 2) kclass[countNum - 1] = values[_id] k = int(pivot - 1) return np.delete(kclass, 0) class MapClassifier: r""" Abstract class for all map classifications :cite:`Slocum_2009` For an array :math:`y` of :math:`n` values, a map classifier places each value :math:`y_i` into one of :math:`k` mutually exclusive and exhaustive classes. Each classifer defines the classes based on different criteria, but in all cases the following hold for the classifiers in PySAL: .. math:: C_j^l < y_i \le C_j^u \ \forall i \in C_j where :math:`C_j` denotes class :math:`j` which has lower bound :math:`C_j^l` and upper bound :math:`C_j^u`. Map Classifiers Supported * :class:`mapclassify.classifiers.BoxPlot` * :class:`mapclassify.classifiers.EqualInterval` * :class:`mapclassify.classifiers.FisherJenks` * :class:`mapclassify.classifiers.FisherJenksSampled` * :class:`mapclassify.classifiers.HeadTailBreaks` * :class:`mapclassify.classifiers.JenksCaspall` * :class:`mapclassify.classifiers.JenksCaspallForced` * :class:`mapclassify.classifiers.JenksCaspallSampled` * :class:`mapclassify.classifiers.MaxP` * :class:`mapclassify.classifiers.MaximumBreaks` * :class:`mapclassify.classifiers.NaturalBreaks` * :class:`mapclassify.classifiers.Quantiles` * :class:`mapclassify.classifiers.Percentiles` * :class:`mapclassify.classifiers.StdMean` * :class:`mapclassify.classifiers.UserDefined` In addition to the classifiers, there are several utility functions that can be used to evaluate the properties of a specific classifier, or for automatic selection of a classifier and number of classes. * :func:`mapclassify.classifiers.gadf` * :class:`mapclassify.classifiers.K_classifiers` """ def __init__(self, y): y = np.asarray(y).flatten() self.name = "Map Classifier" self.fmt = FMT self.y = y self._classify() self._summary() def get_fmt(self): return self._fmt def set_fmt(self, fmt): self._fmt = fmt fmt = property(get_fmt, set_fmt) def _summary(self): yb = self.yb self.classes = [np.nonzero(yb == c)[0].tolist() for c in range(self.k)] self.tss = self.get_tss() self.adcm = self.get_adcm() self.gadf = self.get_gadf() def _classify(self): self._set_bins() self.yb, self.counts = bin1d(self.y, self.bins) def _update(self, data, *args, **kwargs): """ The only thing that *should* happen in this function is 1. input sanitization for pandas 2. classification/reclassification. Using their ``__init__`` methods, all classifiers can re-classify given different input parameters or additional data. If you've got a cleverer updating equation other than the intial estimation equation, remove the call to ``self.__init__`` below and replace it with the updating function. """ if data is not None: data = np.asarray(data).flatten() data = np.append(data.flatten(), self.y) else: data = self.y self.__init__(data, *args, **kwargs) @classmethod def make(cls, *args, **kwargs): """ Configure and create a classifier that will consume data and produce classifications, given the configuration options specified by this function. Note that this implements a *partial application* of the relevant class constructor. ``make`` creates a function that returns classifications; it does not actually do the classification. If you want to classify data directly, use the appropriate class constructor, like ``Quantiles``, ``Max_Breaks``, etc. If you *have* a classifier object, but want to find which bins new data falls into, use ``find_bin``. Parameters ---------- *args : required positional arguments All positional arguments required by the classifier, **excluding** the input data. rolling : bool A boolean configuring the outputted classifier to use a rolling classifier rather than a new classifier for each input. If ``rolling``, this adds the current data to all of the previous data in the classifier, and rebalances the bins, like a running median computation. return_object : bool Return the classifier object (or not). return_bins : bool Return the bins/breaks (or not). return_counts : bool Return the histogram of objects falling into each bin (or not). Returns ------- A function that consumes data and returns their bins (and object, bins/breaks, or counts, if requested). Notes ----- This is most useful when you want to run a classifier many times with a given configuration, such as when classifying many columns of an array or dataframe using the same configuration. Examples -------- >>> import libpysal >>> import mapclassify >>> import geopandas >>> import numpy >>> import pandas >>> df = geopandas.read_file(libpysal.examples.get_path("columbus.dbf")) >>> classifier = mapclassify.Quantiles.make(k=9) >>> cl = df[["HOVAL", "CRIME", "INC"]].apply(classifier) >>> cl["HOVAL"].values[:10] array([8, 7, 2, 4, 1, 3, 8, 5, 7, 8]) >>> cl["CRIME"].values[:10] array([0, 1, 3, 4, 6, 2, 0, 5, 3, 4]) >>> cl["INC"].values[:10] array([7, 8, 5, 0, 3, 5, 0, 3, 6, 4]) >>> data = [ ... numpy.linspace(3,8,num=10), ... numpy.linspace(10, 0, num=10), ... numpy.linspace(-5, 15, num=10) ... ] >>> data = pandas.DataFrame(data).T >>> data 0 1 2 0 3.000000 10.000000 -5.000000 1 3.555556 8.888889 -2.777778 2 4.111111 7.777778 -0.555556 3 4.666667 6.666667 1.666667 4 5.222222 5.555556 3.888889 5 5.777778 4.444444 6.111111 6 6.333333 3.333333 8.333333 7 6.888889 2.222222 10.555556 8 7.444444 1.111111 12.777778 9 8.000000 0.000000 15.000000 >>> data.apply(mapclassify.Quantiles.make(rolling=True)) 0 1 2 0 0 4 0 1 0 4 0 2 1 4 0 3 1 3 0 4 2 2 1 5 2 1 2 6 3 1 4 7 3 0 4 8 4 0 4 9 4 0 4 >>> dbf = libpysal.io.open(libpysal.examples.get_path("baltim.dbf")) >>> data = dbf.by_col_array("PRICE", "LOTSZ", "SQFT") >>> my_bins = [1, 10, 20, 40, 80] >>> cl = [mapclassify.UserDefined.make(bins=my_bins)(a) for a in data.T] >>> len(cl) 3 >>> cl[0][:10] array([4, 5, 5, 5, 4, 4, 5, 4, 4, 5]) """ # only flag overrides return flag to_annotate = copy.deepcopy(kwargs) return_object = kwargs.pop("return_object", False) return_bins = kwargs.pop("return_bins", False) return_counts = kwargs.pop("return_counts", False) rolling = kwargs.pop("rolling", False) if rolling: # just initialize a fake classifier data = list(range(10)) cls_instance = cls(data, *args, **kwargs) # and empty it, since we'll be using the update cls_instance.y = np.array([]) else: cls_instance = None # wrap init in a closure to make a consumer. # Qc Na: "Objects/Closures are poor man's Closures/Objects" def classifier(data, cls_instance=cls_instance): if rolling: cls_instance.update(data, inplace=True, **kwargs) yb = cls_instance.find_bin(data) else: cls_instance = cls(data, *args, **kwargs) yb = cls_instance.yb outs = [yb, None, None, None] outs[1] = cls_instance if return_object else None outs[2] = cls_instance.bins if return_bins else None outs[3] = cls_instance.counts if return_counts else None outs = [a for a in outs if a is not None] if len(outs) == 1: return outs[0] else: return outs # for debugging/jic, keep around the kwargs. # in future, we might want to make this a thin class, so that we can # set a custom repr. Call the class `Binner` or something, that's a # pre-configured Classifier that just consumes data, bins it, & # possibly updates the bins. classifier._options = to_annotate return classifier def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"k": kwargs.pop("k", self.k)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new def __str__(self): return self.table() def __repr__(self): return self.table() def table(self): fmt = self.fmt return _get_table(self, fmt=fmt) def __call__(self, *args): """ This will allow the classifier to be called like it's a function. Whether or not we want to make this be ``find_bin`` or ``update`` is a design decision. I like this as ``find_bin``, since a classifier's job should be to classify the data given to it using the rules estimated from the ``_classify()``. function. """ return self.find_bin(*args) def get_tss(self): """Returns sum of squares over all class means.""" tss = 0 for class_def in self.classes: if len(class_def) > 0: yc = self.y[class_def] css = yc - yc.mean() css *= css tss += sum(css) return tss def _set_bins(self): pass def get_adcm(self): """ Absolute deviation around class median (*ADCM*). Calculates the absolute deviations of each observation about its class median as a measure of fit for the classification method. Returns sum of *ADCM* over all classes. """ adcm = 0 for class_def in self.classes: if len(class_def) > 0: yc = self.y[class_def] yc_med = np.median(yc) ycd = np.abs(yc - yc_med) adcm += sum(ycd) return adcm def get_gadf(self): """Goodness of absolute deviation of fit.""" adam = (np.abs(self.y - np.median(self.y))).sum() # return 1 if array is invariant gadf = 1 if adam == 0 else 1 - self.adcm / adam return gadf def find_bin(self, x): """ Sort input or inputs according to the current bin estimate. Parameters ---------- x : numpy.array, int, float A value or array of values to fit within the estimated bins. Returns ------- right : numpy.array, int A bin index or array of bin indices that classify the input into one of the classifiers' bins. Notes ----- This differs from similar functionality in ``numpy.digitize(x, classi.bins, right=True)``. This will always provide the closest bin, so data "outside" the classifier, above and below the max/min breaks, will be classified into the nearest bin. ``numpy.digitize`` returns :math:`k+1` for data greater than the greatest bin, but retains 0 for data below the lowest bin. """ x = np.asarray(x).flatten() right = np.digitize(x, self.bins, right=True) if right.max() == len(self.bins): right[right == len(self.bins)] = len(self.bins) - 1 return right def get_legend_classes(self, fmt=FMT): """ Format the strings for the classes on the legend. Parameters ---------- fmt : str (default '{:.2f}') Formatting specification. Returns ------- classes : list :math:`k` strings with class interval definitions. """ return _get_mpl_labels(self, fmt) def plot( self, gdf, border_color="lightgray", border_width=0.10, title=None, legend=False, cmap="YlGnBu", axis_on=True, legend_kwds={"loc": "lower right", "fmt": FMT}, file_name=None, dpi=600, ax=None, ): """ Plot a mapclassifier object. Parameters ---------- gdf : geopandas.GeoDataFrame Contains the geometry column for the choropleth map. border_color : str (default 'lightgray') Matplotlib color string to use for polygon border. border_width : float (default 0.10) Width of polygon border. title : str (default None) Title of map. cmap : str (default 'YlGnBu') Matplotlib color string for color map to fill polygons. axis_on : bool (default True) Show coordinate axes. legend_kwds : dict (default {'loc': 'lower right', 'fmt':FMT}) Options for ``ax.legend()``. file_name : str (default None) Name of file to save figure to. dpi : int (default 600) Dots per inch for saved figure. ax : matplotlib.Axis (default None) Axis on which to plot the choropleth. Default is ``None``, which plots on the current figure. Returns ------- f, ax : tuple Matplotlib figure and axis on which the plot is made. Examples -------- >>> import libpysal >>> import geopandas >>> import mapclassify >>> gdf = geopandas.read_file(libpysal.examples.get_path("columbus.shp")) >>> q5 = mapclassify.Quantiles(gdf.CRIME) >>> q5.plot(gdf) # doctest: +SKIP Notes ----- Requires ``matplotlib``, and implicitly requires a ``geopandas.GeoDataFrame`` as input. """ try: import matplotlib.pyplot as plt except ImportError: raise ImportError( "Mapclassify.plot depends on matplotlib.pyplot, and this was" "not able to be imported. \nInstall matplotlib to" "plot spatial classifier." ) from None if ax is None: f = plt.figure() ax = plt.gca() else: f = plt.gcf() ax = gdf.assign(_cl=self.y).plot( column="_cl", ax=ax, cmap=cmap, edgecolor=border_color, linewidth=border_width, scheme=self.name, legend=legend, legend_kwds=legend_kwds, ) if not axis_on: ax.axis("off") if title: f.suptitle(title) if file_name: plt.savefig(file_name, dpi=dpi) return f, ax def plot_histogram( self, color="dodgerblue", linecolor="black", linewidth=None, ax=None, despine=True, **kwargs, ): """Plot histogram of `y` with bin values superimposed Parameters ---------- color : str, optional hue to color bars of the histogram, by default "dodgerblue". linecolor : str, optional color of the lines demarcating each class bin, by default "black" linewidth : int, optional change the linewidth demarcating each class bin ax : matplotlib.Axes, optional axes object to plot onto, by default None despine : bool, optional If True, to use seaborn's despine function to remove top and right axes, default is True kwargs : dict, optional additional keyword arguments passed to matplotlib.axes.Axes.hist, by default None Returns ------- matplotlib.Axes an Axes object with histogram and class bins Raises ------ ImportError depends matplotlib and rasies if not installed """ try: import matplotlib.pyplot as plt if ax is None: _, ax = plt.subplots() except ImportError as e: raise ImportError from e( "You must have matplotlib available to use this function" ) # plot `y` as a histogram ax.hist(self.y, color=color, **kwargs) # get the top of the ax so we know how high to raise each class bar lim = ax.get_ylim()[1] # plot upper limit of each bin for i in self.bins: ax.vlines(i, 0, lim, color=linecolor, linewidth=linewidth) # despine if specified if despine: ax.spines["right"].set_visible(False) ax.spines["top"].set_visible(False) return ax class HeadTailBreaks(MapClassifier): """ Head/tail Breaks Map Classification for Heavy-tailed Distributions. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> import numpy >>> numpy.random.seed(10) >>> cal = mapclassify.load_example() >>> htb = mapclassify.HeadTailBreaks(cal) >>> htb.k 3 >>> htb.counts.tolist() [50, 7, 1] >>> htb.bins array([ 125.92810345, 811.26 , 4111.45 ]) >>> numpy.random.seed(123456) >>> x = numpy.random.lognormal(3, 1, 1000) >>> htb = mapclassify.HeadTailBreaks(x) >>> htb.bins array([ 32.26204423, 72.50205622, 128.07150107, 190.2899093 , 264.82847377, 457.88157946, 576.76046949]) >>> htb.counts.tolist() [695, 209, 62, 22, 10, 1, 1] Notes ----- Head/tail Breaks is a relatively new classification method developed for data with a heavy-tailed distribution. Implementation based on contributions by Alessandra Sozzi . For theoretical details see :cite:`Jiang_2013`. """ def __init__(self, y): MapClassifier.__init__(self, y) self.name = "HeadTailBreaks" def _set_bins(self): x = self.y.copy() bins = [] bins = head_tail_breaks(x, bins) self.bins = np.array(bins) self.k = len(self.bins) class EqualInterval(MapClassifier): """ Equal Interval Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. Each value is the ID of the class the observation belongs to :math:`yb[i] = j` for :math:`j>=1` if :math:`bins[j-1] < y[i] <= bins[j]`, otherwise :math:`yb[i] = 0`. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> ei = mapclassify.EqualInterval(cal, k=5) >>> ei.k 5 >>> ei.counts.tolist() [57, 0, 0, 0, 1] >>> ei.bins array([ 822.394, 1644.658, 2466.922, 3289.186, 4111.45 ]) Notes ----- Intervals defined to have equal width: .. math:: bins_j = min(y)+w*(j+1) with :math:`w=\\frac{max(y)-min(j)}{k}` """ def __init__(self, y, k=K): """ see class docstring """ if min(y) == max(y): raise ValueError( f"Not enough unique values in array to form {k} classes. " "All values in `y` are equal." ) self.k = k MapClassifier.__init__(self, y) self.name = "EqualInterval" def _set_bins(self): y = self.y k = self.k max_y = max(y) min_y = min(y) rg = max_y - min_y width = rg * 1.0 / k cuts = np.arange(min_y + width, max_y + width, width) if len(cuts) > self.k: # handle overshooting cuts = cuts[0:k] cuts[-1] = max_y bins = cuts.copy() self.bins = bins class Percentiles(MapClassifier): """ Percentiles Map Classification Parameters ---------- y : numpy.array Attribute to classify. pct : numpy.array (default [1, 10, 50, 90, 99, 100]) Percentiles. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> p = mapclassify.Percentiles(cal) >>> p.bins array([1.357000e-01, 5.530000e-01, 9.365000e+00, 2.139140e+02, 2.179948e+03, 4.111450e+03]) >>> p.counts.tolist() [1, 5, 23, 23, 5, 1] >>> p2 = mapclassify.Percentiles(cal, pct = [50, 100]) >>> p2.bins array([ 9.365, 4111.45 ]) >>> p2.counts.tolist() [29, 29] >>> p2.k 2 """ def __init__(self, y, pct=[1, 10, 50, 90, 99, 100]): self.pct = pct MapClassifier.__init__(self, y) self.name = "Percentiles" def _set_bins(self): y = self.y pct = self.pct self.bins = np.array([stats.scoreatpercentile(y, p) for p in pct]) self.k = len(self.bins) def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"pct": kwargs.pop("pct", self.pct)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class PrettyBreaks(MapClassifier): def __init__(self, y, k=5): """ Pretty breakpoints Computes breaks that are equally spaced round values which cover the range of values in `y`. The breaks are chosen so that they are 1, 2, or 5 times a power of 10. Parameters ---------- y : array (n,1) attribute to classify k : int The number of desired classes Notes ----- The number of classes may be different from the specified `k`, as the rounding of the upper bounds takes precedent. The lower bound of the first interval will be equal to the minimum of the data. """ self.k = k MapClassifier.__init__(self, y) self.name = "Pretty" def _set_bins(self): bins = _pretty(self.y, self.k) self.bins = bins[1:] class BoxPlot(MapClassifier): """ BoxPlot Map Classification. Parameters ---------- y : numpy.array Attribute to classify hinge : float (default 1.5) Multiplier for *IQR*. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin ids for observations. bins : array :math:`(n,1)`, the upper bounds of each class (monotonic). k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. low_outlier_ids : numpy.array Indices of observations that are low outliers. high_outlier_ids : numpy.array Indices of observations that are high outliers. Notes ----- The bins are set as follows:: bins[0] = q[0]-hinge*IQR bins[1] = q[0] bins[2] = q[1] bins[3] = q[2] bins[4] = q[2]+hinge*IQR bins[5] = inf (see Notes) where :math:`q` is an array of the first three quartiles of :math:`y` and :math:`IQR=q[2]-q[0]`. If :math:`q[2]+hinge*IQR > max(y)` there will only be 5 classes and no high outliers, otherwise, there will be 6 classes and at least one high outlier. Examples -------- >>> import mapclassify >>> import numpy >>> cal = mapclassify.load_example() >>> bp = mapclassify.BoxPlot(cal) >>> bp.bins array([-5.287625e+01, 2.567500e+00, 9.365000e+00, 3.953000e+01, 9.497375e+01, 4.111450e+03]) >>> bp.counts.tolist() [0, 15, 14, 14, 6, 9] >>> bp.high_outlier_ids.tolist() [0, 6, 18, 29, 33, 36, 37, 40, 42] >>> cal[bp.high_outlier_ids].values array([ 329.92, 181.27, 370.5 , 722.85, 192.05, 110.74, 4111.45, 317.11, 264.93]) >>> bx = mapclassify.BoxPlot(numpy.arange(100)) >>> bx.bins array([-49.5 , 24.75, 49.5 , 74.25, 148.5 ]) """ def __init__(self, y, hinge=1.5): """ Parameters ---------- y : numpy.array :math:`(n,1)`, attribute to classify hinge : float (default 1.5) Multiple of inter-quartile range. """ self.hinge = hinge MapClassifier.__init__(self, y) self.name = "BoxPlot" def _set_bins(self): y = self.y pct = [25, 50, 75, 100] bins = [stats.scoreatpercentile(y, p) for p in pct] iqr = bins[-2] - bins[0] self.iqr = iqr pivot = self.hinge * iqr left_fence = bins[0] - pivot right_fence = bins[-2] + pivot if right_fence < bins[-1]: bins.insert(-1, right_fence) else: bins[-1] = right_fence bins.insert(0, left_fence) self.bins = np.array(bins) self.k = len(bins) def _classify(self): MapClassifier._classify(self) self.low_outlier_ids = np.nonzero(self.yb == 0)[0] self.high_outlier_ids = np.nonzero(self.yb == 5)[0] def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"hinge": kwargs.pop("hinge", self.hinge)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class Quantiles(MapClassifier): """ Quantile Map Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. Each value is the ID of the class the observation belongs to :math:`yb[i] = j` for :math:`j>=1` if :math:`bins[j-1] < y[i] <= bins[j]`, otherwise :math:`yb[i] = 0`. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> q = mapclassify.Quantiles(cal, k=5) >>> q.bins array([1.46400e+00, 5.79800e+00, 1.32780e+01, 5.46160e+01, 4.11145e+03]) >>> q.counts.tolist() [12, 11, 12, 11, 12] """ def __init__(self, y, k=K): self.k = k MapClassifier.__init__(self, y) self.name = "Quantiles" def _set_bins(self): y = self.y k = self.k self.bins = quantile(y, k=k) class StdMean(MapClassifier): """ Standard Deviation and Mean Map Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify multiples : numpy.array (default [-2, -1, 1, 2]) The multiples of the standard deviation to add/subtract from the sample mean to define the bins. anchor : bool (default False) Anchor upper bound of one class to the sample mean. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Notes ----- If anchor is True, one of the intervals will have its closed upper bound equal to the mean of y. Intermediate intervals will have widths equal to the standard deviation of y. The first interval will be closed on the minimum value of y, and the last interval will be closed on the maximum of y. The first and last intervals may have widths different from the intermediate intervals. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> st = mapclassify.StdMean(cal) >>> st.k 5 >>> st.bins array([-967.36235382, -420.71712519, 672.57333208, 1219.21856072, 4111.45 ]) >>> st.counts.tolist() [0, 0, 56, 1, 1] >>> st3 = mapclassify.StdMean(cal, multiples = [-3, -1.5, 1.5, 3]) >>> st3.bins array([-1514.00758246, -694.03973951, 945.8959464 , 1765.86378936, 4111.45 ]) >>> st3.counts.tolist() [0, 0, 57, 0, 1] >>> stda = mapclassify.StdMean(cal, anchor=True) >>> stda.k 9 >>> stda.bins array([ 125.92810345, 672.57333208, 1219.21856072, 1765.86378936, 2312.50901799, 2859.15424663, 3405.79947527, 3952.4447039 , 4111.45 ]) >>> float(cal.mean()), float(cal.std()), float(cal.min()), float(cal.max()) (125.92810344827588, 546.6452286365233, 0.13, 4111.45) """ def __init__(self, y, multiples=[-2, -1, 1, 2], anchor=False): self.multiples = multiples self.anchor = anchor MapClassifier.__init__(self, y) self.name = "StdMean" def _set_bins(self): y = self.y s = y.std(ddof=1) m = y.mean() if self.anchor: min_z = int((y.min() - m) / s) max_z = int((y.max() - m) / s) + 1 self.multiples = list(range(min_z, max_z)) cuts = [m + s * w for w in self.multiples] y_max = y.max() if cuts[-1] < y_max: cuts.append(y_max) self.bins = np.array(cuts) self.k = len(cuts) def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"multiples": kwargs.pop("multiples", self.multiples)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class MaximumBreaks(MapClassifier): """ Maximum Breaks Map Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. mindiff : float (default 0) The minimum difference between class breaks. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> mb = mapclassify.MaximumBreaks(cal, k=5) >>> mb.k 5 >>> mb.bins array([ 146.005, 228.49 , 546.675, 2417.15 , 4111.45 ]) >>> mb.counts.tolist() [50, 2, 4, 1, 1] """ def __init__(self, y, k=5, mindiff=0): if min(y) == max(y): raise ValueError( f"Not enough unique values in array to form {k} classes. " "All values in `y` are equal." ) self.k = k self.mindiff = mindiff MapClassifier.__init__(self, y) self.name = "MaximumBreaks" def _set_bins(self): xs = self.y.copy() k = self.k xs.sort() diffs = xs[1:] - xs[:-1] idxs = np.argsort(diffs) k1 = k - 1 ud = np.unique(diffs) if len(ud) < k1: warnings.warn( "Insufficient number of unique diffs. Breaks are random.", UserWarning, stacklevel=3, ) mp = [] for c in range(1, k): idx = idxs[-c] cp = (xs[idx] + xs[idx + 1]) / 2.0 mp.append(cp) mp.append(xs[-1]) mp.sort() self.bins = np.array(mp) def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"k": kwargs.pop("k", self.k)}) kwargs.update({"mindiff": kwargs.pop("mindiff", self.mindiff)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class NaturalBreaks(MapClassifier): """ Natural Breaks Map Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. initial : int (default 10) The number of initial solutions generated with different centroids. The best of initial results are returned. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> import numpy >>> numpy.random.seed(123456) >>> cal = mapclassify.load_example() >>> nb = mapclassify.NaturalBreaks(cal, k=5) >>> nb.k 5 >>> nb.counts.tolist() [49, 3, 4, 1, 1] >>> nb.bins array([ 75.29, 192.05, 370.5 , 722.85, 4111.45]) """ def __init__(self, y, k=K, initial=10): self.k = k self.init = initial MapClassifier.__init__(self, y) self.name = "NaturalBreaks" def _set_bins(self): x = self.y.copy() k = self.k values = np.array(x) uv = np.unique(values) uvk = len(uv) if uvk < k: warnings.warn( f"Not enough unique values in array to form {k} classes. " f"Setting k to {uvk}.", UserWarning, stacklevel=3, ) k = uvk uv.sort() # we set the bins equal to the sorted unique values and ramp k # downwards. no need to call kmeans. self.bins = uv self.k = k else: res0 = natural_breaks(x, k, init=self.init) self.bins = np.array(res0[-1]) self.k = len(self.bins) def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"k": kwargs.pop("k", self.k)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class FisherJenks(MapClassifier): """ Fisher Jenks optimal classifier - mean based. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> fj = mapclassify.FisherJenks(cal) >>> float(fj.adcm) 799.24 >>> fj.bins.tolist() [75.29, 192.05, 370.5, 722.85, 4111.45] >>> fj.counts.tolist() [49, 3, 4, 1, 1] """ def __init__(self, y, k=K): if not HAS_NUMBA: warnings.warn( "Numba not installed. Using slow pure python version.", UserWarning, stacklevel=3, ) nu = len(np.unique(y)) if nu < k: raise ValueError( f"Fewer unique values ({nu}) than specified classes ({k})." ) self.k = k MapClassifier.__init__(self, y) self.name = "FisherJenks" def _set_bins(self): x = np.sort(self.y).astype("f8") self.bins = _fisher_jenks_means(x, classes=self.k) class FisherJenksSampled(MapClassifier): """ Fisher Jenks optimal classifier - mean based using random sample. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. pct : float (default 0.10) The percentage of :math:`n` that should form the sample. If ``pct`` is specified such that :math:`n*pct > 1000`, then :math:`pct = 1000./n`, unless truncate is ``False``. truncate : bool (default True) Truncate ``pct`` in cases where :math:`pct * n > 1000.`. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Notes ----- For theoretical details see :cite:`Rey_2016`. """ def __init__(self, y, k=K, pct=0.10, truncate=True): self.k = k n = y.size if (pct * n > 1000) and truncate: pct = 1000.0 / n ids = np.random.randint(0, n, int(n * pct)) y = np.asarray(y) yr = y[ids] yr[-1] = max(y) # make sure we have the upper bound yr[0] = min(y) # make sure we have the min self.original_y = y self.pct = pct self._truncated = truncate self.yr = yr self.yr_n = yr.size MapClassifier.__init__(self, yr) self.yb, self.counts = bin1d(y, self.bins) self.name = "FisherJenksSampled" self.y = y self._summary() # have to recalculate summary stats def _set_bins(self): fj = FisherJenks(self.y, self.k) self.bins = fj.bins def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"k": kwargs.pop("k", self.k)}) kwargs.update({"pct": kwargs.pop("pct", self.pct)}) kwargs.update({"truncate": kwargs.pop("truncate", self._truncated)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class JenksCaspall(MapClassifier): """ Jenks Caspall Map Classification. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> jc = mapclassify.JenksCaspall(cal, k=5) >>> jc.bins array([1.81000e+00, 7.60000e+00, 2.98200e+01, 1.81270e+02, 4.11145e+03]) >>> jc.counts.tolist() [14, 13, 14, 10, 7] """ def __init__(self, y, k=K): self.k = k MapClassifier.__init__(self, y) self.name = "JenksCaspall" def _set_bins(self): x = self.y.copy() k = self.k # start with quantiles q = quantile(x, k) solving = True xb, cnts = bin1d(x, q) # class means if x.ndim == 1: x.shape = (x.size, 1) n, k = x.shape xm = [np.median(x[xb == i]) for i in np.unique(xb)] xb0 = xb.copy() q = xm it = 0 rk = list(range(self.k)) while solving: xb = np.zeros(xb0.shape, int) d = abs(x - q) xb = d.argmin(axis=1) if (xb0 == xb).all(): solving = False else: xb0 = xb it += 1 q = np.array([np.median(x[xb == i]) for i in rk]) cuts = np.array([max(x[xb == i]) for i in np.unique(xb)]) cuts.shape = (len(cuts),) self.bins = cuts self.iterations = it class JenksCaspallSampled(MapClassifier): """ Jenks Caspall Map Classification using a random sample. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. pct : float (default 0.10) The percentage of :math:`n` that should form the sample. If ``pct`` is specified such that :math:`n*pct > 1000`, then :math:`pct = 1000./n`. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> import numpy >>> cal = mapclassify.load_example() >>> numpy.random.seed(0) >>> x = numpy.random.random(100000) >>> jc = mapclassify.JenksCaspall(x) >>> jcs = mapclassify.JenksCaspallSampled(x) >>> jc.bins array([0.20108144, 0.4025151 , 0.60396127, 0.80302249, 0.99997795]) >>> jcs.bins array([0.19978245, 0.40793025, 0.59253555, 0.78241472, 0.99997795]) >>> jc.counts.tolist() [20286, 19951, 20310, 19708, 19745] >>> jcs.counts.tolist() [20147, 20633, 18591, 18857, 21772] # not for testing since we get different times on different hardware # just included for documentation of likely speed gains #>>> t1 = time.time(); jc = Jenks_Caspall(x); t2 = time.time() #>>> t1s = time.time(); jcs = Jenks_Caspall_Sampled(x); t2s = time.time() #>>> t2 - t1; t2s - t1s #1.8292930126190186 #0.061631917953491211 Notes ----- This is intended for large :math:`n` problems. The logic is to apply ``Jenks_Caspall`` to a random subset of the :math:`y` space and then bin the complete vector :math:`y` on the bins obtained from the subset. This would trade off some "accuracy" for a gain in speed. """ def __init__(self, y, k=K, pct=0.10): self.k = k n = y.size if pct * n > 1000: pct = 1000.0 / n ids = np.random.randint(0, n, int(n * pct)) y = np.asarray(y) yr = y[ids] yr[0] = max(y) # make sure we have the upper bound self.original_y = y self.pct = pct self.yr = yr self.yr_n = yr.size MapClassifier.__init__(self, yr) self.yb, self.counts = bin1d(y, self.bins) self.name = "JenksCaspallSampled" self.y = y self._summary() # have to recalculate summary stats def _set_bins(self): jc = JenksCaspall(self.y, self.k) self.bins = jc.bins self.iterations = jc.iterations def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ kwargs.update({"k": kwargs.pop("k", self.k)}) kwargs.update({"pct": kwargs.pop("pct", self.pct)}) if inplace: self._update(y, **kwargs) else: new = copy.deepcopy(self) new._update(y, **kwargs) return new class JenksCaspallForced(MapClassifier): """ Jenks Caspall Map Classification with forced movements. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default 5) The number of classes required. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> jcf = mapclassify.JenksCaspallForced(cal, k=5) >>> jcf.k 5 >>> jcf.bins array([1.34000e+00, 5.90000e+00, 1.67000e+01, 5.06500e+01, 4.11145e+03]) >>> jcf.counts.tolist() [12, 12, 13, 9, 12] >>> jcf4 = mapclassify.JenksCaspallForced(cal, k=4) >>> jcf4.k 4 >>> jcf4.bins array([2.51000e+00, 8.70000e+00, 3.66800e+01, 4.11145e+03]) >>> jcf4.counts.tolist() [15, 14, 14, 15] """ def __init__(self, y, k=K): if min(y) == max(y): raise ValueError( f"Not enough unique values in array to form {k} classes. " "All values in `y` are equal." ) self.k = k MapClassifier.__init__(self, y) self.name = "JenksCaspallForced" def _set_bins(self): x = self.y.copy() k = self.k q = quantile(x, k) solving = True xb, cnt = bin1d(x, q) # class means if x.ndim == 1: x.shape = (x.size, 1) n, tmp = x.shape xm = [x[xb == i].mean() for i in np.unique(xb)] q = xm xbar = np.array([xm[xbi] for xbi in xb]) xbar.shape = (n, 1) ss = x - xbar ss *= ss ss = sum(ss) down_moves = up_moves = 0 solving = True it = 0 while solving: # try upward moves first moving_up = True while moving_up: class_ids = np.unique(xb) nk = [sum(xb == j) for j in class_ids] candidates = nk[:-1] i = 0 up_moves = 0 while candidates: nki = candidates.pop(0) if nki > 1: ids = np.nonzero(xb == class_ids[i]) mover = max(ids[0]) tmp = xb.copy() tmp[mover] = xb[mover] + 1 tm = [x[tmp == j].mean() for j in np.unique(tmp)] txbar = np.array([tm[xbi] for xbi in tmp]) txbar.shape = (n, 1) tss = x - txbar tss *= tss tss = sum(tss) if tss < ss: xb = tmp ss = tss candidates = [] up_moves += 1 i += 1 if not up_moves: moving_up = False moving_down = True while moving_down: class_ids = np.unique(xb) nk = [sum(xb == j) for j in class_ids] candidates = nk[1:] i = 1 down_moves = 0 while candidates: nki = candidates.pop(0) if nki > 1: ids = np.nonzero(xb == class_ids[i]) mover = min(ids[0]) mover_class = xb[mover] target_class = mover_class - 1 tmp = xb.copy() tmp[mover] = target_class tm = [x[tmp == j].mean() for j in np.unique(tmp)] txbar = np.array([tm[xbi] for xbi in tmp]) txbar.shape = (n, 1) tss = x - txbar tss *= tss tss = sum(tss) if tss < ss: xb = tmp ss = tss candidates = [] down_moves += 1 i += 1 if not down_moves: moving_down = False if not up_moves and not down_moves: solving = False it += 1 cuts = [max(x[xb == c]) for c in np.unique(xb)] cuts = np.reshape(np.array(cuts), (k,)) self.bins = cuts self.iterations = it class UserDefined(MapClassifier): """ User Specified Binning. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. bins : numpy.array :math:`(k,1)`, upper bounds of classes (have to be monotically increasing). lowest : float (default None) Scalar minimum value of lowest class. Default is to set the minimum to ``-inf`` if ``y.min()`` > first upper bound (which will override the default), otherwise minimum is set to ``y.min()``. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> bins = [20, max(cal)] >>> bins [20, 4111.45] >>> ud = mapclassify.UserDefined(cal, bins) >>> ud.bins.tolist() [20.0, 4111.45] >>> ud.counts.tolist() [37, 21] >>> bins = [20, 30] >>> ud = mapclassify.UserDefined(cal, bins) >>> ud.bins.tolist() [20.0, 30.0, 4111.45] >>> ud.counts.tolist() [37, 4, 17] Notes ----- If upper bound of user bins does not exceed ``max(y)`` we append an additional bin. """ def __init__(self, y, bins, lowest=None): if bins[-1] < max(y): bins = np.append(bins, max(y)) self.lowest = lowest self.k = len(bins) self.bins = np.array(bins) self.y = y MapClassifier.__init__(self, y) self.name = "UserDefined" def _set_bins(self): pass def _update(self, y=None, bins=None): if y is not None: if hasattr(y, "values"): y = y.values y = np.append(y.flatten(), self.y) else: y = self.y if bins is None: bins = self.bins self.__init__(y, bins) def update(self, y=None, inplace=False, **kwargs): """ Add data or change classification parameters. Parameters ---------- y : numpy.array (default None) :math:`(n,1)`, array of data to classify. inplace : bool (default False) Whether to conduct the update in place or to return a copy estimated from the additional specifications. **kwargs : dict Additional parameters that are passed to the ``__init__`` function of the class. For documentation, check the class constructor. """ bins = kwargs.pop("bins", self.bins) if inplace: self._update(y=y, bins=bins, **kwargs) else: new = copy.deepcopy(self) new._update(y, bins, **kwargs) return new # We have to override the plot method for additional kwargs for UserDefined def plot( self, gdf, border_color="lightgray", border_width=0.10, title=None, legend=False, cmap="YlGnBu", axis_on=True, legend_kwds={"loc": "lower right", "fmt": FMT}, file_name=None, dpi=600, ax=None, ): try: import matplotlib.pyplot as plt except ImportError: raise ImportError( "Mapclassify.plot depends on matplotlib.pyplot, and this was" "not able to be imported. \nInstall matplotlib to" "plot spatial classifier." ) from None if ax is None: f = plt.figure() ax = plt.gca() else: f = plt.gcf() if "fmt" in legend_kwds: legend_kwds.pop("fmt") ax = gdf.assign(_cl=self.y).plot( column="_cl", ax=ax, cmap=cmap, edgecolor=border_color, linewidth=border_width, scheme=self.name, legend=legend, legend_kwds=legend_kwds, classification_kwds={"bins": self.bins}, # for UserDefined ) if not axis_on: ax.axis("off") if title: f.suptitle(title) if file_name: plt.savefig(file_name, dpi=dpi) return f, ax class MaxP(MapClassifier): """ MaxP Map Classification. Based on Max-p regionalization algorithm. Parameters ---------- y : numpy.array :math:`(n,1)`, values to classify. k : int (default K==5) Number of classes required. initial : int (default 1000) Number of initial solutions to use prior to swapping. seed1 : int (default 0) Random state for initial building process. seed2 : int (default 1) Random state for swapping process. Attributes ---------- yb : numpy.array :math:`(n,1)`, bin IDs for observations. bins : numpy.array :math:`(k,1)`, the upper bounds of each class. k : int The number of classes. counts : numpy.array :math:`(k,1)`, the number of observations falling in each class. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> mp = mapclassify.MaxP(cal) >>> mp.bins array([3.16000e+00, 1.26300e+01, 1.67000e+01, 2.04700e+01, 4.11145e+03]) >>> mp.counts.tolist() [18, 16, 3, 1, 20] """ def __init__(self, y, k=K, initial=1000, seed1=0, seed2=1): if min(y) == max(y): raise ValueError( f"Not enough unique values in array to form {k} classes. " "All values in `y` are equal." ) self.k = k self.initial = initial self.seed1 = seed1 self.seed2 = seed2 MapClassifier.__init__(self, y) self.name = "MaxP" def _set_bins(self): x = self.y.copy() k = self.k q = quantile(x, k) if x.ndim == 1: x.shape = (x.size, 1) n, tmp = x.shape x.sort(axis=0) # find best of initial solutions solution = 0 best_tss = x.var() * x.shape[0] tss_all = np.zeros((self.initial, 1)) while solution < self.initial: remaining = list(range(n)) seeds = [ np.nonzero(di == min(di))[0][0] for di in [np.abs(x - qi) for qi in q] ] np.random.seed(self.seed1) rseeds = np.random.permutation(list(range(k))).tolist() [remaining.remove(seed) for seed in seeds] self.classes = classes = [] [classes.append([seed]) for seed in seeds] while rseeds: seed_id = rseeds.pop() current = classes[seed_id] growing = True while growing: current = classes[seed_id] low = current[0] high = current[-1] left = low - 1 right = high + 1 move_made = False if left in remaining: current.insert(0, left) remaining.remove(left) move_made = True if right in remaining: current.append(right) remaining.remove(right) move_made = True if move_made: classes[seed_id] = current else: growing = False tss = _fit(self.y, classes) tss_all[solution] = tss if tss < best_tss: best_solution = classes best_it = solution best_tss = tss solution += 1 classes = best_solution self.best_it = best_it self.tss = best_tss self.a2c = a2c = {} self.tss_all = tss_all for r, cl in enumerate(classes): for a in cl: a2c[a] = r swapping = True while swapping: np.random.seed(self.seed2) rseeds = np.random.permutation(list(range(k))).tolist() total_moves = 0 while rseeds: _id = rseeds.pop() growing = True total_moves = 0 while growing: target = classes[_id] left = target[0] - 1 right = target[-1] + 1 n_moves = 0 if left in a2c: left_class = classes[a2c[left]] if len(left_class) > 1: a = left_class[-1] if self._swap(left_class, target, a): target.insert(0, a) left_class.remove(a) a2c[a] = _id n_moves += 1 if right in a2c: right_class = classes[a2c[right]] if len(right_class) > 1: a = right_class[0] if self._swap(right_class, target, a): target.append(a) right_class.remove(a) n_moves += 1 a2c[a] = _id if not n_moves: growing = False total_moves += n_moves if not total_moves: swapping = False xs = self.y.copy() xs.sort() self.bins = np.array([xs[cl][-1] for cl in classes]) def _ss(self, class_def): """Calculates sum of squares for a class.""" yc = self.y[class_def] css = yc - yc.mean() css *= css return sum(css) def _swap(self, class1, class2, a): """Evaluate cost of moving ``a`` from ``class1`` to ``class2``.""" ss1 = self._ss(class1) ss2 = self._ss(class2) tss1 = ss1 + ss2 class1c = copy.copy(class1) class2c = copy.copy(class2) class1c.remove(a) class2c.append(a) ss1 = self._ss(class1c) ss2 = self._ss(class2c) tss2 = ss1 + ss2 return False if tss1 < tss2 else True # noqa SIM211 def _fit(y, classes): """Calculate the total sum of squares for a vector :math:`y` classified into classes. Parameters ---------- y : numpy.array :math:`(n,1)`, variable to be classified. classes : array :math:`(k,1)`, integer values denoting class membership. """ tss = 0 for class_def in classes: yc = y[class_def] css = yc - yc.mean() css *= css tss += sum(css) return tss kmethods = {} kmethods["Quantiles"] = Quantiles kmethods["FisherJenks"] = FisherJenks kmethods["NaturalBreaks"] = NaturalBreaks kmethods["MaximumBreaks"] = MaximumBreaks def gadf(y, method="Quantiles", maxk=15, pct=0.8): r""" Evaluate the Goodness of Absolute Deviation Fit (*GADF*) of a classifier and find the minimum value of :math:`k` for which ``gadf > pct``. Parameters ---------- y : numpy.array :math:`(n, 1)`, values to be classified. method : str (default 'Quantiles') The classification method in: ``{'Quantiles', 'Fisher_Jenks', 'Maximum_Breaks', 'Natrual_Breaks'}``. maxk : int (default 15) Maximum value of :math:`k` to evaluate. pct : float (default 0.8) The percentage of *GADF* to exceed. Returns ------- k : int Number of classes. cl : object Instance of the classifier at :math:`k`. gadf : float Goodness of absolute deviation fit (*GADF*). Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> qgadf = mapclassify.classifiers.gadf(cal) >>> qgadf[0] 15 >>> float(qgadf[-1]) 0.3740257590909283 Quantiles fail to exceed 0.80 before 15 classes. If we lower the bar to 0.2 we see quintiles as a result >>> qgadf2 = mapclassify.classifiers.gadf(cal, pct = 0.2) >>> qgadf2[0] 5 >>> float(qgadf2[-1]) 0.21710231966462412 Notes ----- The *GADF* is defined as: .. math:: GADF = 1 - \sum_c \sum_{i \in c} |y_i - y_{c,med}| / \sum_i |y_i - y_{med}| where :math:`y_{med}` is the global median and :math:`y_{c,med}` is the median for class :math:`c`. See Also -------- KClassifiers """ y = np.array(y) adam = (np.abs(y - np.median(y))).sum() for k in range(2, maxk + 1): cl = kmethods[method](y, k) gadf = 1 - cl.adcm / adam if gadf > pct: break return (k, cl, gadf) class KClassifiers: """ Evaluate all :math:`k`-classifers and pick optimal based on :math:`k` and *GADF*. Parameters ---------- y : numpy.array :math:`(n,1)`, values to be classified. pct : float (default 0.8) The percentage of *GADF* to exceed. Attributes ---------- best : MapClassifier Instance of the optimal ``MapClassifier``. results : dict Keys are classifier names, values are the ``MapClassifier`` instances with the best ``pct`` for each classifier. Examples -------- >>> import mapclassify >>> cal = mapclassify.load_example() >>> ks = mapclassify.classifiers.KClassifiers(cal) >>> ks.best.name 'FisherJenks' >>> ks.best.k 4 >>> float(ks.best.gadf) 0.8481032719908105 Notes ----- This can be used to suggest a classification scheme. See Also -------- gadf """ def __init__(self, y, pct=0.8): results = {} best = gadf(y, "FisherJenks", maxk=len(y) - 1, pct=pct) pct0 = best[0] k0 = best[-1] keys = list(kmethods.keys()) keys.remove("FisherJenks") results["FisherJenks"] = best for method in keys: results[method] = gadf(y, method, maxk=len(y) - 1, pct=pct) k1 = results[method][0] pct1 = results[method][-1] if (k1 < k0) or (k1 == k0 and pct0 < pct1): best = results[method] k0 = k1 pct0 = pct1 self.results = results self.best = best[1] mapclassify-2.8.0/mapclassify/datasets/000077500000000000000000000000001465055300600201635ustar00rootroot00000000000000mapclassify-2.8.0/mapclassify/datasets/__init__.py000066400000000000000000000000561465055300600222750ustar00rootroot00000000000000""" Datasets module """ from . import calemp mapclassify-2.8.0/mapclassify/datasets/calemp/000077500000000000000000000000001465055300600214245ustar00rootroot00000000000000mapclassify-2.8.0/mapclassify/datasets/calemp/README.md000066400000000000000000000004601465055300600227030ustar00rootroot00000000000000calemp ====== Employment density for California counties ------------------------------------------ * calempdensity.csv: data on employment and employment density in California counties. Polygon data, n=58, k=11. Source: Anselin, L. and S.J. Rey (in progress) Spatial Econometrics: Foundations. mapclassify-2.8.0/mapclassify/datasets/calemp/__init__.py000066400000000000000000000000241465055300600235310ustar00rootroot00000000000000from .data import * mapclassify-2.8.0/mapclassify/datasets/calemp/calempdensity.csv000066400000000000000000000156271465055300600250150ustar00rootroot00000000000000"Geographic Area","Geographic Area","Geographic Name","GEONAME","GEOCOMP","STATE","Number of Employees for All Sectors","Number of employees","Class Number","sq. km","emp/sq km" "05000US06001","06001","Alameda County, California","Alameda County, California","00","06",630171,630171,5,1910.1,329.92 "05000US06003","06003","Alpine County, California","Alpine County, California","00","06",813,813,1,1913.1,0.42 "05000US06005","06005","Amador County, California","Amador County, California","00","06",9061,9061,2,1534.7,5.9 "05000US06007","06007","Butte County, California","Butte County, California","00","06",59578,59578,3,4246.6,14.03 "05000US06009","06009","Calaveras County, California","Calaveras County, California","00","06",7344,7344,2,2642.3,2.78 "05000US06011","06011","Colusa County, California","Colusa County, California","00","06",4000,4000,1,2980.5,1.34 "05000US06013","06013","Contra Costa County, California","Contra Costa County, California","00","06",338156,338156,5,1865.5,181.27 "05000US06015","06015","Del Norte County, California","Del Norte County, California","00","06",4303,4303,1,2610.4,1.65 "05000US06017","06017","El Dorado County, California","El Dorado County, California","00","06",44477,44477,3,4432.8,10.03 "05000US06019","06019","Fresno County, California","Fresno County, California","00","06",257975,257975,4,15444.7,16.7 "05000US06021","06021","Glenn County, California","Glenn County, California","00","06",4487,4487,1,3405.5,1.32 "05000US06023","06023","Humboldt County, California","Humboldt County, California","00","06",36962,36962,3,9253.5,3.99 "05000US06025","06025","Imperial County, California","Imperial County, California","00","06",34156,34156,3,10813.4,3.16 "05000US06027","06027","Inyo County, California","Inyo County, California","00","06",5820,5820,1,26397.5,0.22 "05000US06029","06029","Kern County, California","Kern County, California","00","06",183412,183412,4,21086.8,8.7 "05000US06031","06031","Kings County, California","Kings County, California","00","06",23610,23610,2,3598.8,6.56 "05000US06033","06033","Lake County, California","Lake County, California","00","06",10648,10648,2,3259.4,3.27 "05000US06035","06035","Lassen County, California","Lassen County, California","00","06",3860,3860,1,11803.9,0.33 "05000US06037","06037","Los Angeles County, California","Los Angeles County, California","00","06",3895886,3895886,5,10515.3,370.5 "05000US06039","06039","Madera County, California","Madera County, California","00","06",24957,24957,2,5538.5,4.51 "05000US06041","06041","Marin County, California","Marin County, California","00","06",101358,101358,4,1346.2,75.29 "05000US06043","06043","Mariposa County, California","Mariposa County, California","00","06",3739,3739,1,3758.6,0.99 "05000US06045","06045","Mendocino County, California","Mendocino County, California","00","06",24898,24898,2,9089,2.74 "05000US06047","06047","Merced County, California","Merced County, California","00","06",43369,43369,3,4995.8,8.68 "05000US06049","06049","Modoc County, California","Modoc County, California","00","06",1467,1467,1,10215.9,0.14 "05000US06051","06051","Mono County, California","Mono County, California","00","06",7289,7289,1,7885.2,0.92 "05000US06053","06053","Monterey County, California","Monterey County, California","00","06",108660,108660,4,8603.8,12.63 "05000US06055","06055","Napa County, California","Napa County, California","00","06",56029,56029,3,1952.5,28.7 "05000US06057","06057","Nevada County, California","Nevada County, California","00","06",29805,29805,3,2480.3,12.02 "05000US06059","06059","Orange County, California","Orange County, California","00","06",1478452,1478452,5,2045.3,722.85 "05000US06061","06061","Placer County, California","Placer County, California","00","06",133427,133427,4,3637.4,36.68 "05000US06063","06063","Plumas County, California","Plumas County, California","00","06",4863,4863,1,6614.8,0.74 "05000US06065","06065","Riverside County, California","Riverside County, California","00","06",556789,556789,5,18669.1,29.82 "05000US06067","06067","Sacramento County, California","Sacramento County, California","00","06",480346,480346,5,2501.1,192.05 "05000US06069","06069","San Benito County, California","San Benito County, California","00","06",12163,12163,2,3597.9,3.38 "05000US06071","06071","San Bernardino County, California","San Bernardino County, California","00","06",579135,579135,5,51961.2,11.15 "05000US06073","06073","San Diego County, California","San Diego County, California","00","06",1205862,1205862,5,10889.6,110.74 "05000US06075","06075","San Francisco County, California","San Francisco County, California","00","06",497485,497485,5,121,4111.45 "05000US06077","06077","San Joaquin County, California","San Joaquin County, California","00","06",179276,179276,4,3624.1,49.47 "05000US06079","06079","San Luis Obispo County, California","San Luis Obispo County, California","00","06",88413,88413,3,8558.7,10.33 "05000US06081","06081","San Mateo County, California","San Mateo County, California","00","06",368859,368859,5,1163.2,317.11 "05000US06083","06083","Santa Barbara County, California","Santa Barbara County, California","00","06",145202,145202,4,7092.6,20.47 "05000US06085","06085","Santa Clara County, California","Santa Clara County, California","00","06",886011,886011,5,3344.3,264.93 "05000US06087","06087","Santa Cruz County, California","Santa Cruz County, California","00","06",76488,76488,3,1154.3,66.26 "05000US06089","06089","Shasta County, California","Shasta County, California","00","06",52804,52804,3,9804.8,5.39 "05000US06091","06091","Sierra County, California","Sierra County, California","00","06",324,324,1,2469.4,0.13 "05000US06093","06093","Siskiyou County, California","Siskiyou County, California","00","06",9992,9992,2,16284,0.61 "05000US06095","06095","Solano County, California","Solano County, California","00","06",108653,108653,4,2145,50.65 "05000US06097","06097","Sonoma County, California","Sonoma County, California","00","06",165261,165261,4,4082.4,40.48 "05000US06099","06099","Stanislaus County, California","Stanislaus County, California","00","06",141928,141928,4,3870.9,36.67 "05000US06101","06101","Sutter County, California","Sutter County, California","00","06",20430,20430,2,1561,13.09 "05000US06103","06103","Tehama County, California","Tehama County, California","00","06",13809,13809,2,7643.2,1.81 "05000US06105","06105","Trinity County, California","Trinity County, California","00","06",1668,1668,1,8233.3,0.2 "05000US06107","06107","Tulare County, California","Tulare County, California","00","06",94949,94949,4,12495,7.6 "05000US06109","06109","Tuolumne County, California","Tuolumne County, California","00","06",14519,14519,2,5790.3,2.51 "05000US06111","06111","Ventura County, California","Ventura County, California","00","06",273745,273745,5,4781,57.26 "05000US06113","06113","Yolo County, California","Yolo County, California","00","06",63769,63769,3,2622.2,24.32 "05000US06115","06115","Yuba County, California","Yuba County, California","00","06",11374,11374,2,1632.9,6.97 mapclassify-2.8.0/mapclassify/datasets/calemp/data.py000066400000000000000000000005161465055300600227110ustar00rootroot00000000000000from os.path import abspath, dirname import pandas as pd def load(): """ Load the data and return a DataSeries instance. """ df = _get_data() return df["emp/sq km"] def _get_data(): filepath = dirname(abspath(__file__)) filepath += "/calempdensity.csv" df = pd.read_csv(filepath) return df mapclassify-2.8.0/mapclassify/greedy.py000066400000000000000000000253711465055300600202140ustar00rootroot00000000000000""" greedy - Greedy (topological) coloring for GeoPandas Copyright (C) 2019 Martin Fleischmann, 2017 Nyall Dawson """ import operator __all__ = ["greedy"] def _balanced(features, sw, balance="count", min_colors=4): """ Strategy to color features in a way which is visually balanced. Algorithm ported from QGIS to be used with GeoDataFrames and libpysal weights objects. Original algorithm: Date : February 2017 Copyright : (C) 2017 by Nyall Dawson Email : nyall dot dawson at gmail dot com Parameters ---------- features : geopandas.GeoDataFrame GeoDataFrame. sw : libpysal.weights.W Spatial weights object denoting adjacency of features. balance : str (default 'count') The method of color balancing. min_colors : int (default 4) The minimal number of colors to be used. Returns ------- feature_colors : dict Dictionary with assigned color codes. """ feature_colors = {} # start with minimum number of colors in pool color_pool = set(range(min_colors)) # calculate count of neighbours neighbour_count = sw.cardinalities # sort features by neighbour count - handle those with more neighbours first sorted_by_count = sorted( neighbour_count.items(), key=operator.itemgetter(1), reverse=True ) # counts for each color already assigned color_counts = {} color_areas = {} for c in color_pool: color_counts[c] = 0 color_areas[c] = 0 if balance == "centroid": features = features.copy() features.geometry = features.geometry.centroid balance = "distance" for feature_id, _ in sorted_by_count: # first work out which already assigned colors are adjacent to this feature adjacent_colors = set() for neighbour in sw.neighbors[feature_id]: if neighbour in feature_colors: adjacent_colors.add(feature_colors[neighbour]) # from the existing colors, work out which are available (ie non-adjacent) available_colors = color_pool.difference(adjacent_colors) feature_color = -1 if len(available_colors) == 0: # no existing colors available for this feature; add new color and repeat min_colors += 1 return _balanced(features, sw, balance, min_colors) else: if balance == "count": # choose least used available color counts = [ (c, v) for c, v in color_counts.items() if c in available_colors ] feature_color = sorted(counts, key=operator.itemgetter(1))[0][0] color_counts[feature_color] += 1 elif balance == "area": areas = [ (c, v) for c, v in color_areas.items() if c in available_colors ] feature_color = sorted(areas, key=operator.itemgetter(1))[0][0] color_areas[feature_color] += features.loc[feature_id].geometry.area elif balance == "distance": min_distances = {c: float("inf") for c in available_colors} this_feature = features.loc[feature_id].geometry # find features for all available colors other_features = { f_id: c for (f_id, c) in feature_colors.items() if c in available_colors } distances = features.loc[other_features.keys()].distance(this_feature) # calculate the min distance from this feature to the nearest # feature with each assigned color for other_feature_id, c in other_features.items(): distance = distances.loc[other_feature_id] if distance < min_distances[c]: min_distances[c] = distance # choose color such that min distance is maximised! # - ie we want MAXIMAL separation between features with the same color feature_color = sorted( min_distances, key=min_distances.__getitem__, reverse=True )[0] feature_colors[feature_id] = feature_color return feature_colors def greedy( gdf, strategy="balanced", balance="count", min_colors=4, sw="queen", min_distance=None, silence_warnings=True, interchange=False, ): """ Color GeoDataFrame using various strategies of greedy (topological) colouring. Attempts to color a GeoDataFrame using as few colors as possible, where no neighbours can have same color as the feature itself. Offers various strategies ported from QGIS or implemented within NetworkX for greedy graph coloring. ``greedy`` will return ``pandas.Series`` representing assigned color codes. Parameters ---------- gdf : GeoDataFrame GeoDataFrame strategy : str (default 'balanced') Determine coloring strategy. Options are ``'balanced'`` for algorithm based on QGIS Topological coloring. It is aiming for a visual balance, defined by the balance parameter. Other options are those supported by ``networkx.greedy_color``: * ``'largest_first'`` * ``'random_sequential'`` * ``'smallest_last'`` * ``'independent_set'`` * ``'connected_sequential_bfs'`` * ``'connected_sequential_dfs'`` * ``'connected_sequential'`` (alias for the previous strategy) * ``'saturation_largest_first'`` * ``'DSATUR'`` (alias for the previous strategy) For details see https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html balance : str (default 'count') If strategy is ``'balanced'``, determine the method of color balancing. * ``'count'`` attempts to balance the number of features per each color. * ``'area'`` attempts to balance the area covered by each color. * ``'centroid'`` attempts to balance the distance between colors based on the distance between centroids. * ``'distance'`` attempts to balance the distance between colors based on the distance between geometries. Slower than ``'centroid'``, but more precise. Both ``'centroid'`` and ``'distance'`` are significantly slower than other especially for larger GeoDataFrames. Apart from ``'count'``, all require CRS to be projected (not in degrees) to ensure metric values are correct. min_colors: int (default 4) If strategy is ``'balanced'``, define the minimal number of colors to be used. sw : 'queen', 'rook' or libpysal.weights.W (default 'queen') If min_distance is None, one can pass ``'libpysal.weights.W'`` object denoting neighbors or let greedy generate one based on ``'queen'`` or ``'rook'`` contiguity. min_distance : float (default None) Set minimal distance between colors. If ``min_distance`` is not ``None``, slower algorithm for generating spatial weghts is used based on intersection between geometries. ``'min_distance'`` is then used as a tolerance of intersection. silence_warnings : bool (default True) Silence libpysal warnings when creating spatial weights. interchange : bool (default False) Use the color interchange algorithm (applicable for NetworkX strategies). For details see https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html Returns ------- color : pandas.Series ``pandas.Series`` representing assinged color codes. Examples -------- >>> from mapclassify import greedy >>> import geopandas >>> world = geopandas.read_file( ... "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip" ... ) >>> africa = world.loc[world.CONTINENT == "Africa"].copy() >>> africa = africa.to_crs("ESRI:102022").reset_index(drop=True) Default: >>> africa["greedy_colors"] = greedy(africa) >>> africa["greedy_colors"].head() 0 1 1 0 2 0 3 1 4 4 Name: greedy_colors, dtype: int64 Balanced by area: >>> africa["balanced_area"] = greedy(africa, strategy="balanced", balance="area") >>> africa["balanced_area"].head() 0 1 1 2 2 0 3 1 4 3 Name: balanced_area, dtype: int64 Using rook adjacency: >>> africa["rook_adjacency"] = greedy(africa, sw="rook") >>> africa["rook_adjacency"].tail() 46 3 47 0 48 2 49 3 50 1 Name: rook_adjacency, dtype: int64 Adding minimal distance between colors: >>> africa["min_distance"] = greedy(africa, min_distance=1000000) >>> africa["min_distance"].head() 0 1 1 9 2 0 3 7 4 4 Name: min_distance, dtype: int64 Using different coloring strategy: >>> africa["smallest_last"] = greedy(africa, strategy="smallest_last") >>> africa["smallest_last"].head() 0 3 1 1 2 1 3 3 4 1 Name: smallest_last, dtype: int64 """ # noqa if strategy != "balanced": try: import networkx as nx STRATEGIES = nx.algorithms.coloring.greedy_coloring.STRATEGIES.keys() except ImportError: raise ImportError("The 'networkx' package is required.") from None try: import pandas as pd except ImportError: raise ImportError("The 'pandas' package is required.") from None try: from libpysal.weights import Queen, Rook, W, fuzzy_contiguity except ImportError: raise ImportError("The 'libpysal' package is required.") from None if min_distance is not None: sw = fuzzy_contiguity( gdf, tolerance=0.0, buffering=True, buffer=min_distance / 2.0, silence_warnings=silence_warnings, ) if not isinstance(sw, W): if sw == "queen": sw = Queen.from_dataframe( gdf, silence_warnings=silence_warnings, use_index=False ) elif sw == "rook": sw = Rook.from_dataframe( gdf, silence_warnings=silence_warnings, use_index=False ) if strategy == "balanced": color = pd.Series(_balanced(gdf, sw, balance=balance, min_colors=min_colors)) elif strategy in STRATEGIES: color = nx.greedy_color( sw.to_networkx(), strategy=strategy, interchange=interchange ) else: raise ValueError(f"'{strategy}' is not a valid strategy.") color = pd.Series(color).sort_index() color.index = gdf.index return color mapclassify-2.8.0/mapclassify/pooling.py000066400000000000000000000063611465055300600204020ustar00rootroot00000000000000import numpy from .classifiers import ( BoxPlot, EqualInterval, FisherJenks, FisherJenksSampled, MaximumBreaks, Quantiles, StdMean, UserDefined, ) __all__ = ["Pooled"] dispatcher = { "boxplot": BoxPlot, "equalinterval": EqualInterval, "fisherjenks": FisherJenks, "fisherjenkssampled": FisherJenksSampled, "quantiles": Quantiles, "maximumbreaks": MaximumBreaks, "stdmean": StdMean, "userdefined": UserDefined, } class Pooled: """Applying global binning across columns. Parameters ---------- Y : numpy.array :math:`(n, m)`, values to classify, with :math:`m>1`. classifier : str (default 'Quantiles') Name of ``mapclassify.classifier`` to apply. **kwargs : dict Additional keyword arguments for classifier. Attributes ---------- global_classifier : mapclassify.classifiers.MapClassifier Instance of the pooled classifier defined as the classifier applied to the union of the columns. col_classifier : list Elements are ``MapClassifier`` instances with the pooled classifier applied to the associated column of ``Y``. Examples -------- >>> import mapclassify >>> import numpy >>> n = 20 >>> data = numpy.array([numpy.arange(n)+i*n for i in range(1,4)]).T >>> res = mapclassify.Pooled(data) >>> res.col_classifiers[0].counts.tolist() [12, 8, 0, 0, 0] >>> res.col_classifiers[1].counts.tolist() [0, 4, 12, 4, 0] >>> res.col_classifiers[2].counts.tolist() [0, 0, 0, 8, 12] >>> res.global_classifier.counts.tolist() [12, 12, 12, 12, 12] >>> res.global_classifier.bins == res.col_classifiers[0].bins array([ True, True, True, True, True]) >>> res.global_classifier.bins array([31.8, 43.6, 55.4, 67.2, 79. ]) """ def __init__(self, Y, classifier="Quantiles", **kwargs): method = classifier.lower() valid_methods = list(dispatcher.keys()) if method not in valid_methods: raise ValueError( f"'{classifier}' not a valid classifier. " f"Currently supported classifiers: {valid_methods}" ) self.__dict__.update(kwargs) Y = numpy.asarray(Y) n, cols = Y.shape y = numpy.reshape(Y, (-1, 1), order="f") ymin = y.min() global_classifier = dispatcher[method](y, **kwargs) # self.k = global_classifier.k col_classifiers = [] name = f"Pooled {classifier}" for c in range(cols): res = UserDefined(Y[:, c], bins=global_classifier.bins, lowest=ymin) res.name = name col_classifiers.append(res) self.col_classifiers = col_classifiers self.global_classifier = global_classifier self._summary() def _summary(self): self.classes = self.global_classifier.classes self.tss = self.global_classifier.tss self.adcm = self.global_classifier.adcm self.gadf = self.global_classifier.gadf def __str__(self): s = "Pooled Classifier" rows = [s] for c in self.col_classifiers: rows.append(c.table()) return "\n\n".join(rows) def __repr__(self): return self.__str__() mapclassify-2.8.0/mapclassify/tests/000077500000000000000000000000001465055300600175155ustar00rootroot00000000000000mapclassify-2.8.0/mapclassify/tests/__init__.py000066400000000000000000000000001465055300600216140ustar00rootroot00000000000000mapclassify-2.8.0/mapclassify/tests/baseline/000077500000000000000000000000001465055300600212775ustar00rootroot00000000000000mapclassify-2.8.0/mapclassify/tests/baseline/test_histogram_plot.png000066400000000000000000000210261465055300600261000ustar00rootroot00000000000000PNG  IHDR Xvp pHYsaa?i!IDATx{yb.:h 3QFzkNPU3x4tLKlQ Vѕ@b;0bji8(13ZGE?|yg9{RE :=p @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ 5ѣW^ѵk8c̙ei({?W^yeɟI<#?׿u٣@i*EQeqy7N/1o޼%X5`hmmɓ'= Rww+!_}l۶4^U_ѹs:uj~ M'|r˿KT*t~vovL6-LÇC=4&MO?t| _(yJ%X5ЧO9rdQFEDڵk;wڵkcΝJ4hP8{>!Ʃc9&.䒘?~X" v|6l؇>Ǩ5^zyyl]qWbat>e6?ySc͂~\oٟ>{ޛ|_s_;򓟴HZj??"@jsύcwwaÆ?3f̈1c?}|CǡG4mcÏ#"៮ϹSeC^/Ϲv?}7(2v=9S{~F-Z&MǨQn),YRhPπH׮]c1{G S;~/oS˦Oe7~7H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4V^Je=\@i@vƈ#ڝtI%M 5tǰaꆗ`iH M0!{1rxg J%@jG1qx%Νz >m#G,q:(g@3ƌ/RڵqITEDDT*o[˜عnQn'Wqhf^sss477w8VQlo $7oe˖K.zo7G)'M 5/}) rJ7~ӟƜ9sbÆ =FAbѢEĎ;w>}8SJ#@j`ʔ)1eʔ]4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4H#@4$ɂ RDSSS٣@iH7x#n߿٣@H믿><8sJ%@jl…3ļyJ'@jhƍ1iҤ={vuQe 5t 7 'Ǐ/{ epַK.uER){ v&L|+ѿزeKDDe˖ܹs4668% 5iӦذaC̙3'̙z^ .㷵̉;vr5Vk1.{ΫjT/[8hiip>{xgbŊѷo||7G)rDB6H t%C9d`]4$?;v({ (                                 xϏD׮]wq…  JP-[Gj5|x1ck +Q߾}App ޽;v7oŋʕ+oo J#@jn|0""=׾]w]S@yH ~q5ƍcҥq7Fkkkr-e 54`0`@DD=:""Nƍ;$i<@ Zuڝu;CΫjT/[$QKKKt)>O|}G\4{"4jC^ݻwN;-?شiS,^8-Zz_p 5p??Ė-[) }Ѹ+J#@jફ1M@ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @RV:qG\/B٣@H ~8qb,_<Ν7naÆŪUJP~;;㎋{7\MMMq'믿^DPH[ڵk3L٣@iH &Dkkkqe3 xC=3 56cƌ5kVs=q7=3 54cƌ>}zL>=nqZuڝu;C{D>@sss477w8VQlo 9sfL>=Θ6mG\^} 50gΜ8Ϗ{aÆ4KҥK#";N|;p(.Xzu#@].X@ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @R#۷on-=8âR J%@j7ߌ;^xa@]h({17oJ6m =NHR){;^ @ @?^̉;vr5VKΫjT/[uqs4rycԄFmZbE#"_%KDDѣ[neH ?>~_yűx∈xc-i2(ח= @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ @ 5cǎ4iR?t'xT ep?A̞=;>O?j5v_җJX|y/_vS~57; 5OFSSS\zίK}sl/_vS~ }#@jG?Q|ӟp4hPu8 x7w?{7G @jR58yӧx뭷""HDĮ]""WW뮗lq|*Zl9"žŞzQR֞9kڵk?ޭ[Ν;[np*EQeqk96o@x≨V}/>uxc=W\qE^x!N>H 5bŊ=zt<qe5*^z/rHmڴ)V\{lt5sd$@jsύ>/2;hnnp¸J!@jdǎqw7x뭷b1uXhP6@hժUqW1<ȸ ^pڵk _B455EϞ=/W_}/b1`ڵk;N?Xpa{| ,JMMMOիWGRs=^)dz>G^zE׮]㏏3gnr~ϯ)Ǻu /GnbqwΝ;g?{G$c1qX|y̝;76nÆ UV+wߍo~o|#~goJ \[l>:X|y<#qƕW^fjno-p~sƚ5k}tIm?xuYѣGxGb1yﯾ|wuW5kD߾}#SO=5",/r|sIJe_b}QV}PfÆ ζo^~gvv饗}-nv~smݖ2+>vS1ccǎ-ƍW466f?ZZZ(/^M_WEccc1~nի(ζ3)wQDD^[DD[oEa?3 ᬩ)N6/}mevS 3<p~ݔc'O{~&LFGd&Lָ;""7ߌ޽{wwQEl޼9uƃ 7;w~M7_뮋) 7'pB?~=z'ƃ>---1wxcrʈsθK.[o5y=ztEa7u9vgvf79cc͚5(>OFcر1nܸ;wnDϾj({]w=Xw}1tv*>îonkbƍtҸ5n営{&߷Xti[wl? 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geopandas.read_file(link_to_data) self.x = gdf["HOVAL"].values def test_box_plot(self): a = mapclassify.classify(self.x, "box_plot") b = mapclassify.BoxPlot(self.x) _assertions(a, b) def test_equal_interval(self): a = mapclassify.classify(self.x, "EqualInterval", k=3) b = mapclassify.EqualInterval(self.x, k=3) _assertions(a, b) def test_fisher_jenks(self): a = mapclassify.classify(self.x, "FisherJenks", k=3) b = mapclassify.FisherJenks(self.x, k=3) _assertions(a, b) def test_fisher_jenks_sampled(self): a = mapclassify.classify( self.x, "FisherJenksSampled", k=3, pct_sampled=0.5, truncate=False ) b = mapclassify.FisherJenksSampled(self.x, k=3, pct=0.5, truncate=False) _assertions(a, b) def test_headtail_breaks(self): a = mapclassify.classify(self.x, "headtail_breaks") b = mapclassify.HeadTailBreaks(self.x) _assertions(a, b) def test_quantiles(self): a = mapclassify.classify(self.x, "quantiles", k=3) b = mapclassify.Quantiles(self.x, k=3) _assertions(a, b) def test_percentiles(self): a = mapclassify.classify(self.x, "percentiles", pct=[25, 50, 75, 100]) b = mapclassify.Percentiles(self.x, pct=[25, 50, 75, 100]) _assertions(a, b) a = mapclassify.classify(self.x, "prettybreaks") b = mapclassify.PrettyBreaks(self.x) _assertions(a, b) def test_jenks_caspall(self): a = mapclassify.classify(self.x, "JenksCaspall", k=3) b = mapclassify.JenksCaspall(self.x, k=3) _assertions(a, b) def test_jenks_caspall_forced(self): a = mapclassify.classify(self.x, "JenksCaspallForced", k=3) b = mapclassify.JenksCaspallForced(self.x, k=3) _assertions(a, b) def test_jenks_caspall_sampled(self): a = mapclassify.classify(self.x, "JenksCaspallSampled", pct_sampled=0.5) b = mapclassify.JenksCaspallSampled(self.x, pct=0.5) _assertions(a, b) def test_natural_breaks(self): a = mapclassify.classify(self.x, "natural_breaks") b = mapclassify.NaturalBreaks(self.x) _assertions(a, b) def test_max_p_classifier(self): a = mapclassify.classify(self.x, "max_p", k=3, initial=50) b = mapclassify.MaxP(self.x, k=3, initial=50) _assertions(a, b) def test_std_mean(self): a = mapclassify.classify(self.x, "std_mean", multiples=[-1, -0.5, 0.5, 1]) b = mapclassify.StdMean(self.x, multiples=[-1, -0.5, 0.5, 1]) _assertions(a, b) def test_user_defined(self): a = mapclassify.classify(self.x, "user_defined", bins=[20, max(self.x)]) b = mapclassify.UserDefined(self.x, bins=[20, max(self.x)]) _assertions(a, b) def test_bad_classifier(self): classifier = "George_Costanza" with pytest.raises(ValueError, match="Invalid scheme: 'georgecostanza'"): mapclassify.classify(self.x, classifier) mapclassify-2.8.0/mapclassify/tests/test_greedy.py000066400000000000000000000140031465055300600224030ustar00rootroot00000000000000import sys import geopandas import libpysal import pytest from ..greedy import greedy PY39 = sys.version_info.major == 3 and sys.version_info.minor == 9 world = geopandas.read_file( "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip" ) sw = libpysal.weights.Queen.from_dataframe( world, ids=world.index.to_list(), silence_warnings=True ) def _check_correctess(colors): assert len(colors) == len(world) for i, neighbors in sw.neighbors.items(): if len(neighbors) > 1: assert (colors[neighbors] != colors[i]).all() @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS.") class TestGreedy: def test_default(self): colors = greedy(world) assert set(colors) == set([0, 1, 2, 3, 4]) assert colors.value_counts().to_list() == [36, 36, 35, 35, 35] assert (colors.index == world.index).all() _check_correctess(colors) def test_rook(self): colors = greedy(world, sw="rook") assert set(colors) == set([0, 1, 2, 3, 4]) assert colors.value_counts().to_list() == [36, 36, 35, 35, 35] _check_correctess(colors) def test_sw(self): colors = greedy(world, sw=sw) assert set(colors) == set([0, 1, 2, 3, 4]) assert colors.value_counts().to_list() == [36, 36, 35, 35, 35] _check_correctess(colors) def test_min_distance(self): europe = world.loc[world.CONTINENT == "Europe"].to_crs(epsg=3035) colors = greedy(europe, min_distance=500000) assert set(colors) == set(range(13)) assert colors.value_counts().to_list() == [3] * 13 def test_invalid_strategy(self): strategy = "spice melange" with pytest.raises(ValueError, match=f"'{strategy}' is not a valid strategy."): greedy(world, strategy=strategy) @pytest.mark.filterwarnings("ignore:Geometry is in a geographic CRS.") @pytest.mark.parametrize("pysal_geos", [None, 0]) class TestGreedyParams: def test_count(self, pysal_geos): colors = greedy( world, strategy="balanced", balance="count", min_distance=pysal_geos ) assert set(colors) == set([0, 1, 2, 3, 4]) assert colors.value_counts().to_list() == [36, 36, 35, 35, 35] _check_correctess(colors) def test_area(self, pysal_geos): colors = greedy( world, strategy="balanced", balance="area", min_distance=pysal_geos ) assert set(colors) == set([0, 1, 2, 3, 4]) assert colors.value_counts().to_list() == [55, 49, 39, 32, 2] _check_correctess(colors) def test_centroid(self, pysal_geos): colors = greedy( world, strategy="balanced", balance="centroid", min_distance=pysal_geos ) assert set(colors) == set([0, 1, 2, 3, 4]) assert colors.value_counts().to_list() == [39, 36, 36, 34, 32] _check_correctess(colors) def test_distance(self, pysal_geos): colors = greedy( world, strategy="balanced", balance="distance", min_distance=pysal_geos ) assert set(colors) == set([0, 1, 2, 3, 4]) assert colors.value_counts().to_list() == [38, 36, 35, 34, 34] _check_correctess(colors) def test_largest_first(self, pysal_geos): colors = greedy(world, strategy="largest_first", min_distance=pysal_geos) assert set(colors) == set([0, 1, 2, 3, 4]) assert colors.value_counts().to_list() == [64, 49, 42, 21, 1] _check_correctess(colors) def test_random_sequential(self, pysal_geos): """based on random, no consistent results to be tested""" colors = greedy(world, strategy="random_sequential", min_distance=pysal_geos) _check_correctess(colors) def test_smallest_last(self, pysal_geos): colors = greedy(world, strategy="smallest_last", min_distance=pysal_geos) assert set(colors) == set([0, 1, 2, 3]) # skip pn Python 3.9 due to networkx/networkx#3993 if not PY39: assert colors.value_counts().to_list() == [71, 52, 39, 15] _check_correctess(colors) def test_independent_set(self, pysal_geos): colors = greedy(world, strategy="independent_set", min_distance=pysal_geos) assert set(colors) == set([0, 1, 2, 3, 4]) assert colors.value_counts().to_list() == [91, 42, 26, 13, 5] _check_correctess(colors) def test_connected_sequential_bfs(self, pysal_geos): colors = greedy( world, strategy="connected_sequential_bfs", min_distance=pysal_geos ) assert set(colors) == set([0, 1, 2, 3, 4]) counts = colors.value_counts().to_list() _check_correctess(colors) def test_connected_sequential_dfs(self, pysal_geos): colors = greedy( world, strategy="connected_sequential_dfs", min_distance=pysal_geos ) assert set(colors) == set([0, 1, 2, 3, 4]) _check_correctess(colors) def test_connected_sequential(self, pysal_geos): colors = greedy(world, strategy="connected_sequential", min_distance=pysal_geos) assert set(colors) == set([0, 1, 2, 3, 4]) _check_correctess(colors) def test_saturation_largest_first(self, pysal_geos): colors = greedy( world, strategy="saturation_largest_first", min_distance=pysal_geos ) assert set(colors) == set([0, 1, 2, 3]) assert colors.value_counts().to_list() == [71, 47, 42, 17] _check_correctess(colors) def test_DSATUR(self, pysal_geos): colors = greedy(world, strategy="DSATUR", min_distance=pysal_geos) assert set(colors) == set([0, 1, 2, 3]) assert colors.value_counts().to_list() == [71, 47, 42, 17] _check_correctess(colors) def test_index(self, pysal_geos): world["ten"] = world.index * 10 reindexed = world.set_index("ten") colors = greedy(reindexed, min_distance=pysal_geos) assert len(colors) == len(world) assert set(colors) == set([0, 1, 2, 3, 4]) assert colors.value_counts().to_list() == [36, 36, 35, 35, 35] mapclassify-2.8.0/mapclassify/tests/test_mapclassify.py000066400000000000000000000530031465055300600234420ustar00rootroot00000000000000import types import numpy import pytest from ..classifiers import * from ..classifiers import bin, bin1d, binC, load_example from ..pooling import Pooled RTOL = 0.0001 class TestQuantile: def test_quantile(self): y = numpy.arange(1000) expected = numpy.array([333.0, 666.0, 999.0]) numpy.testing.assert_almost_equal(expected, quantile(y, k=3)) def test_quantile_k4(self): x = numpy.arange(1000) qx = quantile(x, k=4) expected = numpy.array([249.75, 499.5, 749.25, 999.0]) numpy.testing.assert_array_almost_equal(expected, qx) def test_quantile_k(self): y = numpy.random.random(1000) for k in range(5, 10): numpy.testing.assert_almost_equal(k, len(quantile(y, k))) assert k == len(quantile(y, k)) class TestUpdate: def setup_method(self): numpy.random.seed(4414) self.data = numpy.random.normal(0, 10, size=10) self.new_data = numpy.random.normal(0, 10, size=4) def test_update(self): # Quantiles quants = Quantiles(self.data, k=3) known_yb = numpy.array([0, 1, 0, 1, 0, 2, 0, 2, 1, 2]) numpy.testing.assert_allclose(quants.yb, known_yb, rtol=RTOL) new_yb = quants.update(self.new_data, k=4).yb known_new_yb = numpy.array([0, 3, 1, 0, 1, 2, 0, 2, 1, 3, 0, 3, 2, 3]) numpy.testing.assert_allclose(new_yb, known_new_yb, rtol=RTOL) # User-Defined ud = UserDefined(self.data, [-20, 0, 5, 20]) known_yb = numpy.array([1, 2, 1, 1, 1, 2, 0, 2, 1, 3]) numpy.testing.assert_allclose(ud.yb, known_yb, rtol=RTOL) new_yb = ud.update(self.new_data).yb known_new_yb = numpy.array([1, 3, 1, 1, 1, 2, 1, 1, 1, 2, 0, 2, 1, 3]) numpy.testing.assert_allclose(new_yb, known_new_yb, rtol=RTOL) # Fisher-Jenks Sampled fjs = FisherJenksSampled(self.data, k=3, pct=70) known_yb = numpy.array([1, 2, 0, 1, 1, 2, 0, 2, 1, 2]) numpy.testing.assert_allclose(known_yb, fjs.yb, rtol=RTOL) new_yb = fjs.update(self.new_data, k=2).yb known_new_yb = numpy.array([0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1]) numpy.testing.assert_allclose(known_new_yb, new_yb, rtol=RTOL) class TestFindBin: def setup_method(self): self.V = load_example() def test_find_bin(self): toclass = [0, 1, 3, 5, 50, 70, 101, 202, 390, 505, 800, 5000, 5001] mc = FisherJenks(self.V, k=5) known = [0, 0, 0, 0, 0, 0, 1, 2, 3, 3, 4, 4, 4] numpy.testing.assert_array_equal(known, mc.find_bin(toclass)) mc2 = FisherJenks(self.V, k=9) known = [0, 0, 0, 0, 2, 2, 3, 5, 7, 7, 8, 8, 8] numpy.testing.assert_array_equal(known, mc2.find_bin(toclass)) class TestMake: def setup_method(self): self.data = [ numpy.linspace(-5, 5, num=5), numpy.linspace(-10, 10, num=5), numpy.linspace(-20, 20, num=5), ] self.ei = EqualInterval.make() self.q5r = Quantiles.make(k=5, rolling=True) def test_make(self): assert isinstance(self.ei, types.FunctionType) assert isinstance(self.q5r, types.FunctionType) assert hasattr(self.ei, "_options") assert self.ei._options == dict() assert hasattr(self.q5r, "_options") assert self.q5r._options == {"k": 5, "rolling": True} def test_apply(self): ei_classes = [self.ei(d) for d in self.data] known = [numpy.arange(0, 5, 1)] * 3 numpy.testing.assert_allclose(known, ei_classes) q5r_classes = [self.q5r(d) for d in self.data] known = [[0, 1, 2, 3, 4], [0, 0, 2, 3, 4], [0, 0, 2, 4, 4]] accreted_data = set(self.q5r.__defaults__[0].y) all_data = set(numpy.asarray(self.data).flatten()) assert accreted_data == all_data numpy.testing.assert_allclose(known, q5r_classes) class TestBinC: def test_bin_c(self): bins = list(range(2, 8)) y = numpy.array( [ [7, 5, 6], [2, 3, 5], [7, 2, 2], [3, 6, 7], [6, 3, 4], [6, 7, 4], [6, 5, 6], [4, 6, 7], [4, 6, 3], [3, 2, 7], ] ) expected = numpy.array( [ [5, 3, 4], [0, 1, 3], [5, 0, 0], [1, 4, 5], [4, 1, 2], [4, 5, 2], [4, 3, 4], [2, 4, 5], [2, 4, 1], [1, 0, 5], ] ) numpy.testing.assert_array_equal(expected, binC(y, bins)) class TestBin: def test_bin(self): y = numpy.array( [ [7, 13, 14], [10, 11, 13], [7, 17, 2], [18, 3, 14], [9, 15, 8], [7, 13, 12], [16, 6, 11], [19, 2, 15], [11, 11, 9], [3, 2, 19], ] ) bins = [10, 15, 20] expected = numpy.array( [ [0, 1, 1], [0, 1, 1], [0, 2, 0], [2, 0, 1], [0, 1, 0], [0, 1, 1], [2, 0, 1], [2, 0, 1], [1, 1, 0], [0, 0, 2], ] ) numpy.testing.assert_array_equal(expected, bin(y, bins)) class TestBin1d: def test_bin1d(self): y = numpy.arange(100, dtype="float") bins = [25, 74, 100] binIds = numpy.array( [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ] ) counts = numpy.array([26, 49, 25]) numpy.testing.assert_array_equal(binIds, bin1d(y, bins)[0]) numpy.testing.assert_array_equal(counts, bin1d(y, bins)[1]) class TestNaturalBreaks: def setup_method(self): self.V = load_example() def test_natural_breaks(self): # assert expected, natural_breaks(values, k, itmax)) assert True # TODO: implement your test here def test_NaturalBreaks(self): nb = NaturalBreaks(self.V, 5) assert nb.k == 5 assert len(nb.counts) == 5 numpy.testing.assert_array_almost_equal( nb.counts, numpy.array([49, 3, 4, 1, 1]) ) def test_NaturalBreaks_stability(self): for i in range(10): nb = NaturalBreaks(self.V, 5) assert nb.k == 5 assert len(nb.counts) == 5 def test_NaturalBreaks_randomData(self): for i in range(10): V = numpy.random.random(50) * (i + 1) nb = NaturalBreaks(V, 5) assert nb.k == 5 assert len(nb.counts) == 5 class TestHeadTailBreaks: def setup_method(self): x = list(range(1, 1000)) y = [] for i in x: y.append(i ** (-2)) self.V = numpy.array(y) def test_HeadTailBreaks(self): htb = HeadTailBreaks(self.V) assert htb.k == 4 assert len(htb.counts) == 4 numpy.testing.assert_array_almost_equal( htb.counts, numpy.array([975, 21, 2, 1]) ) def test_HeadTailBreaks_doublemax(self): V = numpy.append(self.V, self.V.max()) htb = HeadTailBreaks(V) assert htb.k == 4 assert len(htb.counts) == 4 numpy.testing.assert_array_almost_equal( htb.counts, numpy.array([980, 17, 1, 2]) ) def test_HeadTailBreaks_float(self): V = numpy.array([1 + 2**-52, 1, 1]) htb = HeadTailBreaks(V) assert htb.k == 2 assert len(htb.counts) == 2 numpy.testing.assert_array_almost_equal(htb.counts, numpy.array([2, 1])) class TestPrettyBreaks: def setup_method(self): self.V = load_example() def test_pretty(self): res = PrettyBreaks(self.V) assert res.k == 5 numpy.testing.assert_array_equal(res.counts, [57, 0, 0, 0, 1]) numpy.testing.assert_array_equal(res.bins, list(range(1000, 6000, 1000))) class TestMapClassifier: def test_Map_Classifier(self): # map__classifier = Map_Classifier(y) assert True # TODO: implement your test here def test___repr__(self): # map__classifier = Map_Classifier(y) # assert expected, map__classifier.__repr__()) assert True # TODO: implement your test here def test___str__(self): # map__classifier = Map_Classifier(y) # assert expected, map__classifier.__str__()) assert True # TODO: implement your test here def test_get_adcm(self): # map__classifier = Map_Classifier(y) # assert expected, map__classifier.get_adcm()) assert True # TODO: implement your test here def test_get_gadf(self): # map__classifier = Map_Classifier(y) # assert expected, map__classifier.get_gadf()) assert True # TODO: implement your test here def test_get_tss(self): # map__classifier = Map_Classifier(y) # assert expected, map__classifier.get_tss()) assert True # TODO: implement your test here class TestEqualInterval: def setup_method(self): self.V = load_example() def test_EqualInterval(self): ei = EqualInterval(self.V) numpy.testing.assert_array_almost_equal( ei.counts, numpy.array([57, 0, 0, 0, 1]) ) numpy.testing.assert_array_almost_equal( ei.bins, numpy.array([822.394, 1644.658, 2466.922, 3289.186, 4111.45]) ) with pytest.raises( ValueError, match="Not enough unique values in array to form 5 classes." ): EqualInterval(numpy.array([1, 1, 1, 1])) class TestPercentiles: def setup_method(self): self.V = load_example() def test_Percentiles(self): pc = Percentiles(self.V) numpy.testing.assert_array_almost_equal( pc.bins, numpy.array( [ 1.35700000e-01, 5.53000000e-01, 9.36500000e00, 2.13914000e02, 2.17994800e03, 4.11145000e03, ] ), ) numpy.testing.assert_array_almost_equal( pc.counts, numpy.array([1, 5, 23, 23, 5, 1]) ) class TestBoxPlot: def setup_method(self): self.V = load_example() def test_BoxPlot(self): bp = BoxPlot(self.V) bins = numpy.array( [ -5.28762500e01, 2.56750000e00, 9.36500000e00, 3.95300000e01, 9.49737500e01, 4.11145000e03, ] ) numpy.testing.assert_array_almost_equal(bp.bins, bins) class TestQuantiles: def setup_method(self): self.V = load_example() def test_Quantiles(self): q = Quantiles(self.V, k=5) numpy.testing.assert_array_almost_equal( q.bins, numpy.array( [ 1.46400000e00, 5.79800000e00, 1.32780000e01, 5.46160000e01, 4.11145000e03, ] ), ) numpy.testing.assert_array_almost_equal( q.counts, numpy.array([12, 11, 12, 11, 12]) ) class TestStdMean: def setup_method(self): self.V = load_example() def test_StdMean(self): std_mean = StdMean(self.V) numpy.testing.assert_array_almost_equal( std_mean.bins, numpy.array( [-967.36235382, -420.71712519, 672.57333208, 1219.21856072, 4111.45] ), ) numpy.testing.assert_array_almost_equal( std_mean.counts, numpy.array([0, 0, 56, 1, 1]) ) class TestMaximumBreaks: def setup_method(self): self.V = load_example() def test_MaximumBreaks(self): mb = MaximumBreaks(self.V, k=5) assert mb.k == 5 numpy.testing.assert_array_almost_equal( mb.bins, numpy.array([146.005, 228.49, 546.675, 2417.15, 4111.45]) ) numpy.testing.assert_array_almost_equal( mb.counts, numpy.array([50, 2, 4, 1, 1]) ) with pytest.raises( ValueError, match="Not enough unique values in array to form 5 classes." ): MaximumBreaks(numpy.array([1, 1, 1, 1])) class TestFisherJenks: def setup_method(self): self.V = load_example() def test_FisherJenks(self): fj = FisherJenks(self.V) assert fj.adcm == 799.24000000000001 numpy.testing.assert_array_almost_equal( fj.bins, numpy.array([75.29, 192.05, 370.5, 722.85, 4111.45]) ) numpy.testing.assert_array_almost_equal( fj.counts, numpy.array([49, 3, 4, 1, 1]) ) class TestJenksCaspall: def setup_method(self): self.V = load_example() def test_JenksCaspall(self): numpy.random.seed(10) jc = JenksCaspall(self.V, k=5) numpy.testing.assert_array_almost_equal( jc.counts, numpy.array([14, 13, 14, 10, 7]) ) numpy.testing.assert_array_almost_equal( jc.bins, numpy.array( [ 1.81000000e00, 7.60000000e00, 2.98200000e01, 1.81270000e02, 4.11145000e03, ] ), ) class TestJenksCaspallSampled: def setup_method(self): self.V = load_example() def test_JenksCaspallSampled(self): numpy.random.seed(100) x = numpy.random.random(100000) jc = JenksCaspall(x) jcs = JenksCaspallSampled(x) numpy.testing.assert_array_almost_equal( jc.bins, numpy.array([0.19718393, 0.39655886, 0.59648522, 0.79780763, 0.99997979]), ) numpy.testing.assert_array_almost_equal( jcs.bins, numpy.array([0.20856569, 0.41513931, 0.62457691, 0.82561423, 0.99997979]), ) class TestJenksCaspallForced: def setup_method(self): self.V = load_example() def test_JenksCaspallForced(self): numpy.random.seed(100) jcf = JenksCaspallForced(self.V, k=5) numpy.testing.assert_array_almost_equal( jcf.bins, numpy.array( [ 1.34000000e00, 5.90000000e00, 1.67000000e01, 5.06500000e01, 4.11145000e03, ] ), ) numpy.testing.assert_array_almost_equal( jcf.counts, numpy.array([12, 12, 13, 9, 12]) ) with pytest.raises( ValueError, match="Not enough unique values in array to form 5 classes." ): JenksCaspallForced(numpy.array([1, 1, 1, 1])) class TestUserDefined: def setup_method(self): self.V = load_example() def test_UserDefined(self): bins = [20, max(self.V)] ud = UserDefined(self.V, bins) numpy.testing.assert_array_almost_equal(ud.bins, numpy.array([20.0, 4111.45])) numpy.testing.assert_array_almost_equal(ud.counts, numpy.array([37, 21])) def test_UserDefined_max(self): bins = numpy.array([20, 30]) ud = UserDefined(self.V, bins) numpy.testing.assert_array_almost_equal( ud.bins, numpy.array([20.0, 30.0, 4111.45]) ) numpy.testing.assert_array_almost_equal(ud.counts, numpy.array([37, 4, 17])) def test_UserDefined_invariant(self): bins = [10, 20, 30, 40] ud = UserDefined(numpy.array([12, 12, 12]), bins) numpy.testing.assert_array_almost_equal(ud.bins, numpy.array([10, 20, 30, 40])) numpy.testing.assert_array_almost_equal(ud.counts, numpy.array([0, 3, 0, 0])) def test_UserDefined_lowest(self): bins = [20, max(self.V)] ud = UserDefined(self.V, bins, lowest=-1.0) numpy.testing.assert_array_almost_equal(ud.bins, numpy.array([20.0, 4111.45])) numpy.testing.assert_array_almost_equal(ud.counts, numpy.array([37, 21])) classes = ["[ -1.00, 20.00]", "( 20.00, 4111.45]"] assert ud.get_legend_classes() == classes class TestStdMeanAnchor: def setup_method(self): self.V = load_example() def test_StdMeanAnchor(self): sm = StdMean(self.V, anchor=True) bins = numpy.array( [ 125.92810345, 672.57333208, 1219.21856072, 1765.86378936, 2312.50901799, 2859.15424663, 3405.79947527, 3952.4447039, 4111.45, ] ) counts = numpy.array([50, 6, 1, 0, 0, 0, 0, 0, 1]) numpy.testing.assert_array_almost_equal(sm.bins, bins) numpy.testing.assert_array_almost_equal(sm.counts, counts) class TestMaxP: def setup_method(self): self.V = load_example() def test_MaxP(self): numpy.random.seed(100) mp = MaxP(self.V) numpy.testing.assert_array_almost_equal( mp.bins, numpy.array([3.16000e00, 1.26300e01, 1.67000e01, 2.04700e01, 4.11145e03]), ) numpy.testing.assert_array_almost_equal( mp.counts, numpy.array([18, 16, 3, 1, 20]) ) with pytest.raises( ValueError, match="Not enough unique values in array to form 5 classes." ): MaxP(numpy.array([1, 1, 1, 1])) class TestGadf: def setup_method(self): self.V = load_example() def test_gadf(self): qgadf = gadf(self.V) assert qgadf[0] == 15 assert qgadf[-1] == 0.37402575909092828 class TestKClassifiers: def setup_method(self): self.V = load_example() def test_K_classifiers(self): numpy.random.seed(100) ks = KClassifiers(self.V) assert ks.best.name == "FisherJenks" assert ks.best.gadf == 0.84810327199081048 assert ks.best.k == 4 class TestPooled: def setup_method(self): n = 20 self.data = numpy.array([numpy.arange(n) + i * n for i in range(1, 4)]).T def test_pooled(self): res = Pooled(self.data, k=4) assert res.k == 4 numpy.testing.assert_array_almost_equal( res.col_classifiers[0].counts, numpy.array([15, 5, 0, 0]) ) numpy.testing.assert_array_almost_equal( res.col_classifiers[-1].counts, numpy.array([0, 0, 5, 15]) ) numpy.testing.assert_array_almost_equal( res.global_classifier.counts, numpy.array([15, 15, 15, 15]) ) res = Pooled(self.data, classifier="BoxPlot", hinge=1.5) numpy.testing.assert_array_almost_equal( res.col_classifiers[0].bins, numpy.array([-9.5, 34.75, 49.5, 64.25, 108.5]) ) def test_pooled_bad_classifier(self): classifier = "Larry David" message = f"'{classifier}' not a valid classifier." with pytest.raises(ValueError, match=message): Pooled(self.data, classifier=classifier, k=4) class TestPlots: def setup_method(self): n = 20 self.data = numpy.array([numpy.arange(n) + i * n for i in range(1, 4)]).T @pytest.mark.mpl_image_compare def test_histogram_plot(self): ax = Quantiles(self.data).plot_histogram() return ax.get_figure() @pytest.mark.mpl_image_compare def test_histogram_plot_despine(self): ax = Quantiles(self.data).plot_histogram(despine=False) return ax.get_figure() @pytest.mark.mpl_image_compare def test_histogram_plot_linewidth(self): ax = Quantiles(self.data).plot_histogram( linewidth=3, linecolor="red", color="yellow" ) return ax.get_figure() mapclassify-2.8.0/mapclassify/tests/test_rgba.py000066400000000000000000000010141465055300600220350ustar00rootroot00000000000000import geopandas import numpy as np from numpy.testing import assert_array_equal from mapclassify.util import get_color_array world = geopandas.read_file( "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip" ) def test_rgba(): colors = get_color_array(world.area, cmap="viridis")[0] assert_array_equal(colors, np.array([68, 1, 84, 255])) def test_rgba(): colors = get_color_array(world.area, cmap="viridis", as_hex=True)[0] assert_array_equal(colors,'#440154') mapclassify-2.8.0/mapclassify/util.py000066400000000000000000000057651465055300600177170ustar00rootroot00000000000000import numpy as np from ._classify_API import classify as _classify def get_color_array( values, scheme="quantiles", cmap="viridis", alpha=1, nan_color=[255, 255, 255, 255], as_hex=False, **kwargs, ): """Convert array of values into RGBA or hex colors using a colormap and classifier. This function is useful for visualization libraries that require users to provide an array of colors for each object (like pydeck or lonboard) but can also be used to create a manual column of colors passed to matplotlib. Parameters ---------- values : list-like array of input values scheme : str, optional string description of a mapclassify classifier, by default `"quantiles"` cmap : str, optional name of matplotlib colormap to use, by default "viridis" alpha : float alpha parameter that defines transparency. Should be in the range [0,1] nan_color : list, optional RGBA color to fill NaN values, by default [255, 255, 255, 255] as_hex: bool, optional if True, return a (n,1)-dimensional array of hexcolors instead of a (n,4) dimensional array of RGBA values. kwargs : dict additional keyword arguments are passed to `mapclassify.classify` Returns ------- numpy.array numpy array (aligned with the input array) defining a color for each row. If `as_hex` is False, the array is :math:`(n,4)` holding an array of RGBA values in each row. If `as_hex` is True, the array is :math:`(n,1)` holding a hexcolor in each row. """ try: import pandas as pd from matplotlib import colormaps from matplotlib.colors import Normalize, to_hex except ImportError as e: raise ImportError("This function requires pandas and matplotlib") from e if not (alpha <= 1) and (alpha >= 0): raise ValueError("alpha must be in the range [0,1]") if not pd.api.types.is_list_like(nan_color) and not len(nan_color) == 4: raise ValueError("`nan_color` must be list-like of 4 values: (R,G,B,A)") # only operate on non-NaN values v = pd.Series(values, dtype=object) legit_indices = v[~v.isna()].index.values legit_vals = v.dropna().values # transform (non-NaN) values into class bins bins = _classify(legit_vals, scheme=scheme, **kwargs).yb # normalize using the data's range (not strictly 1-k if classifier is degenerate) norm = Normalize(min(bins), max(bins)) normalized_vals = norm(bins) # generate RBGA array and convert to series rgbas = colormaps[cmap](normalized_vals, bytes=True, alpha=alpha) colors = pd.Series(list(rgbas), index=legit_indices).apply(np.array) # put colors in their correct places and fill empty with designated color v.update(colors) v = v.fillna(f"{nan_color}").apply(np.array) # convert to hexcolors if preferred if as_hex: colors = colors.apply(lambda x: to_hex(x / 255.0)) return colors.values return np.stack(v.values) mapclassify-2.8.0/notebooks/000077500000000000000000000000001465055300600160435ustar00rootroot00000000000000mapclassify-2.8.0/notebooks/01_maximum_breaks.ipynb000066400000000000000000000332741465055300600224230ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to mapclassify\n", "\n", "`mapclassify` implements a family of classification schemes for choropleth maps. \n", "Its focus is on the determination of the number of classes, and the assignment of observations to those classes.\n", "It is intended for use with upstream mapping and geovisualization packages (see [geopandas](https://geopandas.org/mapping.html) and [geoplot](https://residentmario.github.io/geoplot/user_guide/Customizing_Plots.html) for examples) that handle the rendering of the maps.\n", "\n", "In this notebook, the basic functionality of mapclassify is presented." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.127728Z", "start_time": "2022-11-04T16:51:54.017906Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.4.2+78.gc62d2d7.dirty'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mapclassify as mc\n", "\n", "mc.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example data\n", "`mapclassify` contains a built-in dataset for employment density for the 58 California counties." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.397263Z", "start_time": "2022-11-04T16:51:55.130764Z" } }, "outputs": [], "source": [ "y = mc.load_example()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Functionality\n", "All classifiers in `mapclassify` have a common interface and afford similar functionality. We illustrate these using the `MaximumBreaks` classifier.\n", "\n", "`MaximumBreaks` requires that the user specify the number of classes `k`. Given this, the logic of the classifier is to sort the observations in ascending order and find the difference between rank adjacent values. The class boundaries are defined as the $k-1$ largest rank-adjacent breaks in the sorted values." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.407290Z", "start_time": "2022-11-04T16:51:55.401874Z" } }, "outputs": [ { "data": { "text/plain": [ "MaximumBreaks\n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.13, 228.49] | 52\n", "( 228.49, 546.67] | 4\n", "( 546.67, 2417.15] | 1\n", "(2417.15, 4111.45] | 1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc.MaximumBreaks(y, k=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The classifier returns an instance of `MaximumBreaks` that reports the resulting intervals and counts. The first class has closed lower and upper bounds:\n", "\n", "```\n", "[ 0.13, 228.49]\n", "```\n", "\n", "with `0.13` being the minimum value in the dataset:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.413265Z", "start_time": "2022-11-04T16:51:55.408990Z" } }, "outputs": [ { "data": { "text/plain": [ "0.13" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Subsequent intervals are open on the lower bound and closed on the upper bound. The fourth class has the maximum value as its closed upper bound:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.419714Z", "start_time": "2022-11-04T16:51:55.415775Z" } }, "outputs": [ { "data": { "text/plain": [ "4111.45" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assigning the classifier to an object let's us inspect other aspects of the classifier:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.426490Z", "start_time": "2022-11-04T16:51:55.421539Z" } }, "outputs": [ { "data": { "text/plain": [ "MaximumBreaks\n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.13, 228.49] | 52\n", "( 228.49, 546.67] | 4\n", "( 546.67, 2417.15] | 1\n", "(2417.15, 4111.45] | 1" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb4 = mc.MaximumBreaks(y, k=4)\n", "mb4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `bins` attribute has the upper bounds of the intervals:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.433994Z", "start_time": "2022-11-04T16:51:55.429143Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ 228.49 , 546.675, 2417.15 , 4111.45 ])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb4.bins" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and `counts` reports the number of values falling in each bin:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.441325Z", "start_time": "2022-11-04T16:51:55.437014Z" } }, "outputs": [ { "data": { "text/plain": [ "array([52, 4, 1, 1])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb4.counts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The specific bin (i.e. label) for each observation can be found in the `yb` attribute:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.447878Z", "start_time": "2022-11-04T16:51:55.443824Z" } }, "outputs": [ { "data": { "text/plain": [ "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0, 1, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb4.yb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Changing the number of classes\n", "\n", "Staying with the the same classifier, the user can apply the same classification rule, but for a different number of classes:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.454514Z", "start_time": "2022-11-04T16:51:55.449706Z" } }, "outputs": [ { "data": { "text/plain": [ "MaximumBreaks\n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.13, 146.00] | 50\n", "( 146.00, 228.49] | 2\n", "( 228.49, 291.02] | 1\n", "( 291.02, 350.21] | 2\n", "( 350.21, 546.67] | 1\n", "( 546.67, 2417.15] | 1\n", "(2417.15, 4111.45] | 1" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb7 = mc.MaximumBreaks(y, k=7)\n", "mb7" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.461787Z", "start_time": "2022-11-04T16:51:55.456906Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ 146.005, 228.49 , 291.02 , 350.21 , 546.675, 2417.15 ,\n", " 4111.45 ])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb7.bins" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.471152Z", "start_time": "2022-11-04T16:51:55.466248Z" } }, "outputs": [ { "data": { "text/plain": [ "array([50, 2, 1, 2, 1, 1, 1])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb7.counts" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.477524Z", "start_time": "2022-11-04T16:51:55.473430Z" } }, "outputs": [ { "data": { "text/plain": [ "array([3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 1, 0, 0, 0, 6, 0, 0, 3, 0, 2, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb7.yb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One additional attribute to mention here is the `adcm` attribute:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.483597Z", "start_time": "2022-11-04T16:51:55.479867Z" } }, "outputs": [ { "data": { "text/plain": [ "727.3200000000002" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb7.adcm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`adcm` is a measure of fit, defined as the mean absolute deviation around the class median. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.489640Z", "start_time": "2022-11-04T16:51:55.485845Z" } }, "outputs": [ { "data": { "text/plain": [ "1181.4900000000002" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb4.adcm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `adcm` can be expected to decrease as $k$ increases for a given classifier. Thus, if using as a measure of fit, the `adcm` should only be used to compare classifiers defined on the same number of classes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Next Steps\n", "`MaximumBreaks` is but one of many classifiers in `mapclassify`:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.496548Z", "start_time": "2022-11-04T16:51:55.492318Z" } }, "outputs": [ { "data": { "text/plain": [ "('BoxPlot',\n", " 'EqualInterval',\n", " 'FisherJenks',\n", " 'FisherJenksSampled',\n", " 'HeadTailBreaks',\n", " 'JenksCaspall',\n", " 'JenksCaspallForced',\n", " 'JenksCaspallSampled',\n", " 'MaxP',\n", " 'MaximumBreaks',\n", " 'NaturalBreaks',\n", " 'Quantiles',\n", " 'Percentiles',\n", " 'StdMean',\n", " 'UserDefined')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc.classifiers.CLASSIFIERS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To learn more about an individual classifier, introspection is available:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T16:51:55.537870Z", "start_time": "2022-11-04T16:51:55.499084Z" } }, "outputs": [], "source": [ "mc.MaximumBreaks?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------------\n", "\n", "For more comprehensive appliciations of `mapclassify` the interested reader is directed to the chapter on [choropleth mapping](https://geographicdata.science/book/notebooks/05_choropleth.html) in [Rey, Arribas-Bel, and Wolf (2020) \"Geographic Data Science with PySAL and the PyData Stack”](https://geographicdata.science/book).\n", "\n", "-------------------------" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py310_mapclassify]", "language": "python", "name": "conda-env-py310_mapclassify-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.8.0/notebooks/02_legends.ipynb000066400000000000000000000132021465055300600210260ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Legends in mapclassify\n", "\n", "`mapclassify` allows for user defined formatting of legends" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:55.559087Z", "start_time": "2022-11-04T18:03:53.594867Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.4.2+78.gc62d2d7.dirty'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mapclassify\n", "\n", "mapclassify.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.030661Z", "start_time": "2022-11-04T18:03:55.564369Z" } }, "outputs": [], "source": [ "cal = mapclassify.load_example()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.041172Z", "start_time": "2022-11-04T18:03:56.034966Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.13, 1.16] | 10\n", "( 1.16, 3.38] | 10\n", "( 3.38, 9.36] | 9\n", "( 9.36, 24.32] | 10\n", "( 24.32, 70.78] | 9\n", "( 70.78, 4111.45] | 10" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q6 = mapclassify.Quantiles(cal, k=6)\n", "q6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default is to use two decimal places for this dataset.\n", "\n", "If the user desires a list of strings with these values, the `get_legend_classes` method can be called\n", "which will return the strings with the default format:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.047765Z", "start_time": "2022-11-04T18:03:56.042764Z" } }, "outputs": [ { "data": { "text/plain": [ "['[ 0.13, 1.16]',\n", " '( 1.16, 3.38]',\n", " '( 3.38, 9.36]',\n", " '( 9.36, 24.32]',\n", " '( 24.32, 70.78]',\n", " '( 70.78, 4111.45]']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q6.get_legend_classes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To set the legends to integers, an option can be passed into the method:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.055615Z", "start_time": "2022-11-04T18:03:56.050635Z" } }, "outputs": [ { "data": { "text/plain": [ "['[ 0, 1]',\n", " '( 1, 3]',\n", " '( 3, 9]',\n", " '( 9, 24]',\n", " '( 24, 71]',\n", " '( 71, 4111]']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q6.get_legend_classes(fmt=\"{:.0f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this does not change the original object:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.064112Z", "start_time": "2022-11-04T18:03:56.058884Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.13, 1.16] | 10\n", "( 1.16, 3.38] | 10\n", "( 3.38, 9.36] | 9\n", "( 9.36, 24.32] | 10\n", "( 24.32, 70.78] | 9\n", "( 70.78, 4111.45] | 10" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The format can be changed on the object by calling the `set_fmt` method:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T18:03:56.073242Z", "start_time": "2022-11-04T18:03:56.067329Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "--------------------\n", "[ 0, 1] | 10\n", "( 1, 3] | 10\n", "( 3, 9] | 9\n", "( 9, 24] | 10\n", "( 24, 71] | 9\n", "( 71, 4111] | 10" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q6.set_fmt(fmt=\"{:.0f}\")\n", "q6" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:py310_mapclassify]", "language": "python", "name": "conda-env-py310_mapclassify-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.8.0/notebooks/03_choropleth.ipynb000066400000000000000000033421751465055300600215760ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Choropleth\n", "\n", "`mapclassify` is intended to be used with visualization packages to handle the actual rendering of the choropleth maps defined on its classifiers. In this notebook, we explore some examples of how this is done. The notebook also includes an example that combines `mapclassify` with [ipywidgets](https://ipywidgets.readthedocs.io/en/latest/) to allow for the interactive exploration of the choice of:\n", "\n", "- classification method\n", "- number of classes\n", "- colormap" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:43.535547Z", "start_time": "2022-11-05T01:00:40.391594Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.4.2+78.gc62d2d7.dirty'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import libpysal\n", "import geopandas\n", "import mapclassify\n", "import matplotlib.pyplot as plt\n", "\n", "mapclassify.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example in this notebook uses data on sudden death infant syndrome for counties in North Carolina. It is a built-in dataset available through `libpysal`. We use `libpysal` to obtain the path to the shapefile and then use `geopandas` to create a geodataframe from the shapefile:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:43.545340Z", "start_time": "2022-11-05T01:00:43.539863Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sids2\n", "=====\n", "\n", "North Carolina county SIDS death counts and rates\n", "-------------------------------------------------\n", "\n", "* sids2.dbf: attribute data. (k=18)\n", "* sids2.html: metadata.\n", "* sids2.shp: Polygon shapefile. (n=100)\n", "* sids2.shx: spatial index.\n", "* sids2.gal: spatial weights in GAL format.\n", "\n", "Source: Cressie, Noel (1993). Statistics for Spatial Data. New York, Wiley, pp. 386-389. Rates computed.\n", "Updated URL: https://geodacenter.github.io/data-and-lab/sids2/\n", "\n" ] } ], "source": [ "libpysal.examples.explain(\"sids2\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:43.733714Z", "start_time": "2022-11-05T01:00:43.549872Z" } }, "outputs": [ { "data": { "text/html": [ "
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AREAPERIMETERCNTY_CNTY_IDNAMEFIPSFIPSNOCRESS_IDBIR74SID74NWBIR74BIR79SID79NWBIR79SIDR74SIDR79NWR74NWR79geometry
00.1141.44218251825Ashe370093700951091.01.010.01364.00.019.00.9165900.0000009.16590313.929619POLYGON ((-81.47276 36.23436, -81.54084 36.272...
10.0611.23118271827Alleghany37005370053487.00.010.0542.03.012.00.0000005.53505520.53388122.140221POLYGON ((-81.23989 36.36536, -81.24069 36.379...
20.1431.63018281828Surry3717137171863188.05.0208.03616.06.0260.01.5683811.65929265.24466871.902655POLYGON ((-80.45634 36.24256, -80.47639 36.254...
30.0702.96818311831Currituck370533705327508.01.0123.0830.02.0145.01.9685042.409639242.125984174.698795MULTIPOLYGON (((-76.00897 36.31960, -76.01735 ...
40.1532.20618321832Northampton3713137131661421.09.01066.01606.03.01197.06.3335681.867995750.175932745.330012POLYGON ((-77.21767 36.24098, -77.23461 36.214...
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" ], "text/plain": [ " AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID \\\n", "0 0.114 1.442 1825 1825 Ashe 37009 37009 5 \n", "1 0.061 1.231 1827 1827 Alleghany 37005 37005 3 \n", "2 0.143 1.630 1828 1828 Surry 37171 37171 86 \n", "3 0.070 2.968 1831 1831 Currituck 37053 37053 27 \n", "4 0.153 2.206 1832 1832 Northampton 37131 37131 66 \n", "\n", " BIR74 SID74 NWBIR74 BIR79 SID79 NWBIR79 SIDR74 SIDR79 \\\n", "0 1091.0 1.0 10.0 1364.0 0.0 19.0 0.916590 0.000000 \n", "1 487.0 0.0 10.0 542.0 3.0 12.0 0.000000 5.535055 \n", "2 3188.0 5.0 208.0 3616.0 6.0 260.0 1.568381 1.659292 \n", "3 508.0 1.0 123.0 830.0 2.0 145.0 1.968504 2.409639 \n", "4 1421.0 9.0 1066.0 1606.0 3.0 1197.0 6.333568 1.867995 \n", "\n", " NWR74 NWR79 geometry \n", "0 9.165903 13.929619 POLYGON ((-81.47276 36.23436, -81.54084 36.272... \n", "1 20.533881 22.140221 POLYGON ((-81.23989 36.36536, -81.24069 36.379... \n", "2 65.244668 71.902655 POLYGON ((-80.45634 36.24256, -80.47639 36.254... \n", "3 242.125984 174.698795 MULTIPOLYGON (((-76.00897 36.31960, -76.01735 ... \n", "4 750.175932 745.330012 POLYGON ((-77.21767 36.24098, -77.23461 36.214... " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pth = libpysal.examples.get_path(\"sids2.shp\")\n", "gdf = geopandas.read_file(pth)\n", "gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once created, the geodataframe has a `plot` method that can be called to create our first choropleth map. We will specify the column to classify and plot as `SIDR79`: SIDS death rate per 1,000 births (1979-84). The classification scheme is set to `Quantiles`, and the number of classes set to `k=10` (declies):" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.072165Z", "start_time": "2022-11-05T01:00:43.736051Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 398, "width": 1260 } }, "output_type": "display_data" } ], "source": [ "ax = gdf.plot(column=\"SIDR79\", scheme=\"Quantiles\", k=10, figsize=(16, 9))\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can peak under the hood a bit and recreate the classification object that was used in the previous choropleth:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.081602Z", "start_time": "2022-11-05T01:00:44.075185Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "--------------------\n", "[0.00, 0.56] | 10\n", "(0.56, 1.15] | 10\n", "(1.15, 1.40] | 10\n", "(1.40, 1.79] | 10\n", "(1.79, 2.08] | 10\n", "(2.08, 2.18] | 10\n", "(2.18, 2.38] | 10\n", "(2.38, 2.81] | 10\n", "(2.81, 3.40] | 10\n", "(3.40, 6.11] | 10" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q10 = mapclassify.Quantiles(gdf.SIDR79, k=10)\n", "q10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For quick, exploratory work, the classifier object has its own `plot` method that takes a geodataframe as an argument:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.264108Z", "start_time": "2022-11-05T01:00:44.084642Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 195, "width": 543 } }, "output_type": "display_data" } ], "source": [ "q10.plot(gdf);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Back to working directly with the dataframe, we can toggle on the `legend`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.637932Z", "start_time": "2022-11-05T01:00:44.266642Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 398, "width": 1260 } }, "output_type": "display_data" } ], "source": [ "ax = gdf.assign(cl=q10.yb).plot(\n", " figsize=(16, 9),\n", " column=\"cl\",\n", " categorical=True,\n", " k=10,\n", " cmap=\"OrRd\",\n", " linewidth=0.1,\n", " edgecolor=\"white\",\n", " legend=True,\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we see the 10 classes, but without more specific information on the legend, the user has to know that 0 is the first decile and 9 the 10th. We also do not know the values that define these classes. \n", "\n", "We can rectify this as follows:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.646109Z", "start_time": "2022-11-05T01:00:44.640625Z" } }, "outputs": [ { "data": { "text/plain": [ "['[0.00, 0.56]',\n", " '(0.56, 1.15]',\n", " '(1.15, 1.40]',\n", " '(1.40, 1.79]',\n", " '(1.79, 2.08]',\n", " '(2.08, 2.18]',\n", " '(2.18, 2.38]',\n", " '(2.38, 2.81]',\n", " '(2.81, 3.40]',\n", " '(3.40, 6.11]']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q10.get_legend_classes()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.653754Z", "start_time": "2022-11-05T01:00:44.648642Z" } }, "outputs": [ { "data": { "text/plain": [ "{0: '[0.00, 0.56]',\n", " 1: '(0.56, 1.15]',\n", " 2: '(1.15, 1.40]',\n", " 3: '(1.40, 1.79]',\n", " 4: '(1.79, 2.08]',\n", " 5: '(2.08, 2.18]',\n", " 6: '(2.18, 2.38]',\n", " 7: '(2.38, 2.81]',\n", " 8: '(2.81, 3.40]',\n", " 9: '(3.40, 6.11]'}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapping = dict([(i, s) for i, s in enumerate(q10.get_legend_classes())])\n", "mapping" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:44.659702Z", "start_time": "2022-11-05T01:00:44.656459Z" } }, "outputs": [], "source": [ "def replace_legend_items(legend, mapping):\n", " for txt in legend.texts:\n", " for k, v in mapping.items():\n", " if txt.get_text() == str(k):\n", " txt.set_text(v)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.026943Z", "start_time": "2022-11-05T01:00:44.662743Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 398, "width": 1260 } }, "output_type": "display_data" } ], "source": [ "ax = gdf.assign(cl=q10.yb).plot(\n", " figsize=(16, 9),\n", " column=\"cl\",\n", " categorical=True,\n", " k=10,\n", " cmap=\"OrRd\",\n", " linewidth=0.1,\n", " edgecolor=\"white\",\n", " legend=True,\n", " legend_kwds={\"loc\": \"lower right\"},\n", ")\n", "ax.set_axis_off()\n", "replace_legend_items(ax.get_legend(), mapping)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interactive Exploration of Choropleth Classification\n", "\n", "Next, we develop a small application that relies on `mapclassify` together with [palettable](https://jiffyclub.github.io/palettable/) and [ipywidgets](https://ipywidgets.readthedocs.io/en/latest/) to explore the choice of:\n", "\n", "- classification method\n", "- number of classes\n", "- colormap\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.128715Z", "start_time": "2022-11-05T01:00:45.033912Z" } }, "outputs": [], "source": [ "from ipywidgets import (\n", " interact,\n", " Button,\n", " Dropdown,\n", " FloatSlider,\n", " HBox,\n", " IntSlider,\n", " Label,\n", " Output,\n", " RadioButtons,\n", " Tab,\n", " VBox, \n", ")\n", "\n", "from palettable import colorbrewer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Declare that our application shall have 3 options for color scheme..." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.139393Z", "start_time": "2022-11-05T01:00:45.131309Z" } }, "outputs": [], "source": [ "data_type = RadioButtons(options=[\"Sequential\", \"Diverging\", \"Qualitative\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and define thise 3 colormaps based on [colorbrewer](https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.146386Z", "start_time": "2022-11-05T01:00:45.142688Z" } }, "outputs": [], "source": [ "sequential = colorbrewer.COLOR_MAPS[\"Sequential\"]\n", "diverging = colorbrewer.COLOR_MAPS[\"Diverging\"]\n", "qualitative = colorbrewer.COLOR_MAPS[\"Qualitative\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, create value and name options to bind the radio button to the dropdown menus..." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.162086Z", "start_time": "2022-11-05T01:00:45.148913Z" } }, "outputs": [], "source": [ "bindings = {\n", " \"Sequential\": range(3, 9 + 1),\n", " \"Diverging\": range(3, 11 + 1),\n", " \"Qualitative\": range(3, 12 + 1),\n", "}\n", "cmap_bindings = {\n", " \"Sequential\": list(sequential.keys()),\n", " \"Diverging\": list(diverging.keys()),\n", " \"Qualitative\": list(qualitative.keys()),\n", "}\n", "class_val = Dropdown(options=bindings[data_type.value], value=5)\n", "cmap_val = Dropdown(options=cmap_bindings[data_type.value])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and define 7 `mapclassify` objects data classification. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.168640Z", "start_time": "2022-11-05T01:00:45.165083Z" } }, "outputs": [], "source": [ "k_classifiers = {\n", " \"equal_interval\": mapclassify.EqualInterval,\n", " \"fisher_jenks\": mapclassify.FisherJenks,\n", " \"jenks_caspall\": mapclassify.JenksCaspall,\n", " \"jenks_caspall_forced\": mapclassify.JenksCaspallForced,\n", " \"maximum_breaks\": mapclassify.MaximumBreaks,\n", " \"natural_breaks\": mapclassify.NaturalBreaks,\n", " \"quantiles\": mapclassify.Quantiles,\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it all together with helper functions for visualizing & updating plot options." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.175681Z", "start_time": "2022-11-05T01:00:45.170573Z" } }, "outputs": [], "source": [ "def k_values(ctype, cmap):\n", " k = list(colorbrewer.COLOR_MAPS[ctype][cmap].keys())\n", " return list(map(int, k))\n", "\n", "def update_map(method=\"quantiles\", k=5, cmap=\"Blues\"):\n", " classifier = k_classifiers[method](gdf.SIDR79, k=k)\n", " mapping = dict([(i, s) for i, s in enumerate(classifier.get_legend_classes())])\n", " plt_kws = dict(figsize=(16, 9), column=\"cl\", categorical=True, legend=True, lw=0.1)\n", " ax = gdf.assign(cl=classifier.yb).plot(\n", " k=k, cmap=cmap, ec=\"grey\", legend_kwds={\"loc\": \"lower left\"}, **plt_kws\n", " )\n", " ax.set_axis_off()\n", " ax.set_title(\"SIDR79\")\n", " replace_legend_items(ax.get_legend(), mapping)\n", "\n", "def type_change(change):\n", " class_val.options = bindings[change[\"new\"]]\n", " cmap_val.options = cmap_bindings[change[\"new\"]]\n", "\n", "def cmap_change(change):\n", " cmap = change[\"new\"]\n", " ctype = data_type.value\n", " k = k_values(ctype, cmap)\n", " class_val.options = k" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's play with the interactive plot!\n", "* *the plot will not render in the docs, but may be accessed directly by running this notebook locally or as a binder.*" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T01:00:45.475549Z", "start_time": "2022-11-05T01:00:45.177737Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "af6a5214390f44b6a5e043384769a369", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='method', index=6, options=('equal_interval', 'fisher_jenks', 'jenk…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "657f0b12b1d540628cd77d50457b41f1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(RadioButtons(options=('Sequential', 'Diverging', 'Qualitative'), value='Sequential'), Output())…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_type.observe(type_change, names=[\"value\"])\n", "cmap_val.observe(cmap_change, names=[\"value\"])\n", "out = Output()\n", "Tab().children = [out]\n", "interact(update_map, method=list(k_classifiers.keys()), cmap=cmap_val, k=class_val)\n", "display(VBox([data_type, out]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Changing the type of colormap (sequential, diverging, qualitative) will update the options for the available color maps (`cmap`). Changing any of the values using the dropdowns will update the classification and the resulting choropleth map.\n", "\n", "It is important to note that the example variable is best portrayed with the sequential colormaps. The other two types of colormaps are included for demonstration purposes only." ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:py310_mapclassify]", "language": "python", "name": "conda-env-py310_mapclassify-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 2 } mapclassify-2.8.0/notebooks/04_pooled.ipynb000066400000000000000000000745451465055300600207120ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pooled Classification\n", "\n", "A common workflow with longitudinal spatial data is to apply the same classification scheme to an attribute over different time periods. More specifically, one would like to keep the class breaks the same over each period and examine how the mass of the distribution changes over these classes in the different periods.\n", "\n", "The `Pooled` classifier supports this workflow." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.941529Z", "start_time": "2022-11-05T19:18:40.603589Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.4.2+78.gc62d2d7.dirty'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mapclassify\n", "import numpy\n", "\n", "mapclassify.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sample Data\n", "We construct a synthetic dataset composed of 20 cross-sectional units at three time points. Here the mean of the series is increasing over time." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.949728Z", "start_time": "2022-11-05T19:18:41.945010Z" } }, "outputs": [ { "data": { "text/plain": [ "(20, 3)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = 20\n", "data = numpy.array([numpy.arange(n) + i * n for i in range(1, 4)]).T\n", "data.shape" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.955945Z", "start_time": "2022-11-05T19:18:41.951635Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[20, 40, 60],\n", " [21, 41, 61],\n", " [22, 42, 62],\n", " [23, 43, 63],\n", " [24, 44, 64],\n", " [25, 45, 65],\n", " [26, 46, 66],\n", " [27, 47, 67],\n", " [28, 48, 68],\n", " [29, 49, 69],\n", " [30, 50, 70],\n", " [31, 51, 71],\n", " [32, 52, 72],\n", " [33, 53, 73],\n", " [34, 54, 74],\n", " [35, 55, 75],\n", " [36, 56, 76],\n", " [37, 57, 77],\n", " [38, 58, 78],\n", " [39, 59, 79]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Default: Quintiles\n", "The default is to apply a [vec](https://en.wikipedia.org/wiki/Vectorization_(mathematics)) operator to the data matrix and treat the observations as a single collection. Here the quantiles of the pooled data are obtained." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.965023Z", "start_time": "2022-11-05T19:18:41.957991Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.80] | 12\n", "(31.80, 43.60] | 8\n", "(43.60, 55.40] | 0\n", "(55.40, 67.20] | 0\n", "(67.20, 79.00] | 0\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.80] | 0\n", "(31.80, 43.60] | 4\n", "(43.60, 55.40] | 12\n", "(55.40, 67.20] | 4\n", "(67.20, 79.00] | 0\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.80] | 0\n", "(31.80, 43.60] | 0\n", "(43.60, 55.40] | 0\n", "(55.40, 67.20] | 8\n", "(67.20, 79.00] | 12" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = mapclassify.Pooled(data)\n", "res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the class definitions are constant across the periods." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.971895Z", "start_time": "2022-11-05T19:18:41.967042Z" } }, "outputs": [], "source": [ "res = mapclassify.Pooled(data, k=4)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.978331Z", "start_time": "2022-11-05T19:18:41.974160Z" } }, "outputs": [ { "data": { "text/plain": [ "array([15, 5, 0, 0])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.col_classifiers[0].counts" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.984738Z", "start_time": "2022-11-05T19:18:41.980393Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 0, 5, 15])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.col_classifiers[-1].counts" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:41.990334Z", "start_time": "2022-11-05T19:18:41.986702Z" } }, "outputs": [ { "data": { "text/plain": [ "array([15, 15, 15, 15])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.global_classifier.counts" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.001119Z", "start_time": "2022-11-05T19:18:41.997311Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 34.75] | 15\n", "(34.75, 49.50] | 5\n", "(49.50, 64.25] | 0\n", "(64.25, 79.00] | 0\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 34.75] | 0\n", "(34.75, 49.50] | 10\n", "(49.50, 64.25] | 10\n", "(64.25, 79.00] | 0\n", "\n", "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 34.75] | 0\n", "(34.75, 49.50] | 0\n", "(49.50, 64.25] | 5\n", "(64.25, 79.00] | 15" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract the pooled classification objects for each column." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.007476Z", "start_time": "2022-11-05T19:18:42.003714Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 34.75] | 15\n", "(34.75, 49.50] | 5\n", "(49.50, 64.25] | 0\n", "(64.25, 79.00] | 0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0, c1, c2 = res.col_classifiers\n", "c0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare to the unrestricted classifier for the first column..." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.016243Z", "start_time": "2022-11-05T19:18:42.010510Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 24.75] | 5\n", "(24.75, 29.50] | 5\n", "(29.50, 34.25] | 5\n", "(34.25, 39.00] | 5" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.Quantiles(c0.y, k=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and the last column comparisions." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.024230Z", "start_time": "2022-11-05T19:18:42.018980Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 34.75] | 0\n", "(34.75, 49.50] | 0\n", "(49.50, 64.25] | 5\n", "(64.25, 79.00] | 15" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c2" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.034362Z", "start_time": "2022-11-05T19:18:42.027227Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[60.00, 64.75] | 5\n", "(64.75, 69.50] | 5\n", "(69.50, 74.25] | 5\n", "(74.25, 79.00] | 5" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.Quantiles(c2.y, k=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Non-default classifier: BoxPlot" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.046090Z", "start_time": "2022-11-05T19:18:42.037414Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled BoxPlot\n", "\n", " Interval Count\n", "------------------------\n", "( -inf, -9.50] | 0\n", "( -9.50, 34.75] | 15\n", "( 34.75, 49.50] | 5\n", "( 49.50, 64.25] | 0\n", "( 64.25, 108.50] | 0\n", "\n", "Pooled BoxPlot\n", "\n", " Interval Count\n", "------------------------\n", "( -inf, -9.50] | 0\n", "( -9.50, 34.75] | 0\n", "( 34.75, 49.50] | 10\n", "( 49.50, 64.25] | 10\n", "( 64.25, 108.50] | 0\n", "\n", "Pooled BoxPlot\n", "\n", " Interval Count\n", "------------------------\n", "( -inf, -9.50] | 0\n", "( -9.50, 34.75] | 0\n", "( 34.75, 49.50] | 0\n", "( 49.50, 64.25] | 5\n", "( 64.25, 108.50] | 15" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = mapclassify.Pooled(data, classifier=\"BoxPlot\", hinge=1.5)\n", "res" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.054064Z", "start_time": "2022-11-05T19:18:42.048325Z" } }, "outputs": [ { "data": { "text/plain": [ "array([ -9.5 , 34.75, 49.5 , 64.25, 108.5 ])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.col_classifiers[0].bins" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.060428Z", "start_time": "2022-11-05T19:18:42.056889Z" } }, "outputs": [], "source": [ "c0, c1, c2 = res.col_classifiers" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.067574Z", "start_time": "2022-11-05T19:18:42.062869Z" } }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0.yb" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.076215Z", "start_time": "2022-11-05T19:18:42.070735Z" } }, "outputs": [], "source": [ "c00 = mapclassify.BoxPlot(c0.y, hinge=3)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.085022Z", "start_time": "2022-11-05T19:18:42.078938Z" } }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c00.yb" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.093521Z", "start_time": "2022-11-05T19:18:42.088235Z" } }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -3.75] | 0\n", "(-3.75, 24.75] | 5\n", "(24.75, 29.50] | 5\n", "(29.50, 34.25] | 5\n", "(34.25, 62.75] | 5" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c00" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.100363Z", "start_time": "2022-11-05T19:18:42.095608Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled BoxPlot\n", "\n", " Interval Count\n", "------------------------\n", "( -inf, -9.50] | 0\n", "( -9.50, 34.75] | 15\n", "( 34.75, 49.50] | 5\n", "( 49.50, 64.25] | 0\n", "( 64.25, 108.50] | 0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Non-default classifier: FisherJenks" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.111872Z", "start_time": "2022-11-05T19:18:42.103537Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled FisherJenks\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.00] | 12\n", "(31.00, 43.00] | 8\n", "(43.00, 55.00] | 0\n", "(55.00, 67.00] | 0\n", "(67.00, 79.00] | 0\n", "\n", "Pooled FisherJenks\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.00] | 0\n", "(31.00, 43.00] | 4\n", "(43.00, 55.00] | 12\n", "(55.00, 67.00] | 4\n", "(67.00, 79.00] | 0\n", "\n", "Pooled FisherJenks\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 31.00] | 0\n", "(31.00, 43.00] | 0\n", "(43.00, 55.00] | 0\n", "(55.00, 67.00] | 8\n", "(67.00, 79.00] | 12" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = mapclassify.Pooled(data, classifier=\"FisherJenks\", k=5)\n", "res" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.119626Z", "start_time": "2022-11-05T19:18:42.113905Z" } }, "outputs": [ { "data": { "text/plain": [ "FisherJenks\n", "\n", " Interval Count\n", "----------------------\n", "[20.00, 23.00] | 4\n", "(23.00, 27.00] | 4\n", "(27.00, 31.00] | 4\n", "(31.00, 35.00] | 4\n", "(35.00, 39.00] | 4" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0, c1, c2 = res.col_classifiers\n", "mapclassify.FisherJenks(c0.y, k=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Non-default classifier: MaximumBreaks\n", "\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.127177Z", "start_time": "2022-11-05T19:18:42.121621Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[20, 40, 60],\n", " [10, 10, 10],\n", " [22, 42, 62],\n", " [23, 43, 63],\n", " [24, 44, 64],\n", " [25, 45, 65],\n", " [26, 46, 66],\n", " [27, 47, 67],\n", " [28, 48, 68],\n", " [29, 49, 10],\n", " [30, 50, 70],\n", " [31, 51, 71],\n", " [32, 52, 72],\n", " [33, 53, 73],\n", " [34, 54, 74],\n", " [35, 55, 75],\n", " [36, 56, 76],\n", " [37, 57, 77],\n", " [38, 58, 78],\n", " [39, 59, 79]])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[1, 0] = 10\n", "data[1, 1] = 10\n", "data[1, 2] = 10\n", "data[9, 2] = 10\n", "data" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.136098Z", "start_time": "2022-11-05T19:18:42.128885Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled MaximumBreaks\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 15.00] | 1\n", "(15.00, 21.00] | 1\n", "(21.00, 41.00] | 18\n", "(41.00, 61.00] | 0\n", "(61.00, 79.00] | 0\n", "\n", "Pooled MaximumBreaks\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 15.00] | 1\n", "(15.00, 21.00] | 0\n", "(21.00, 41.00] | 1\n", "(41.00, 61.00] | 18\n", "(61.00, 79.00] | 0\n", "\n", "Pooled MaximumBreaks\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 15.00] | 2\n", "(15.00, 21.00] | 0\n", "(21.00, 41.00] | 0\n", "(41.00, 61.00] | 1\n", "(61.00, 79.00] | 17" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = mapclassify.Pooled(data, classifier=\"MaximumBreaks\", k=5)\n", "res" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.143133Z", "start_time": "2022-11-05T19:18:42.138961Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled MaximumBreaks\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 15.00] | 1\n", "(15.00, 21.00] | 1\n", "(21.00, 41.00] | 18\n", "(41.00, 61.00] | 0\n", "(61.00, 79.00] | 0" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0, c1, c2 = res.col_classifiers\n", "c0" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.149335Z", "start_time": "2022-11-05T19:18:42.144915Z" } }, "outputs": [ { "data": { "text/plain": [ "array([20, 10, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n", " 37, 38, 39])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c0.y" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.154525Z", "start_time": "2022-11-05T19:18:42.151983Z" } }, "outputs": [], "source": [ "import warnings" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.163717Z", "start_time": "2022-11-05T19:18:42.156794Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Insufficient number of unique diffs. Breaks are random.\n" ] } ], "source": [ "with warnings.catch_warnings():\n", " warnings.filterwarnings(\"error\")\n", " try:\n", " mapclassify.MaximumBreaks(c0.y, k=5)\n", " except UserWarning as e:\n", " print(e)\n", " with warnings.catch_warnings():\n", " warnings.filterwarnings(\"ignore\")\n", " mapclassify.MaximumBreaks(c0.y, k=5)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.174401Z", "start_time": "2022-11-05T19:18:42.166025Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 20\n", "(62.80, 66.60] | 0\n", "(66.60, 71.40] | 0\n", "(71.40, 75.20] | 0\n", "(75.20, 79.00] | 0\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 20\n", "(62.80, 66.60] | 0\n", "(66.60, 71.40] | 0\n", "(71.40, 75.20] | 0\n", "(75.20, 79.00] | 0\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 4\n", "(62.80, 66.60] | 4\n", "(66.60, 71.40] | 4\n", "(71.40, 75.20] | 4\n", "(75.20, 79.00] | 4" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = mapclassify.Pooled(\n", " data, classifier=\"UserDefined\", bins=mapclassify.Quantiles(data[:, -1]).bins\n", ")\n", "res" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.181788Z", "start_time": "2022-11-05T19:18:42.176573Z" } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 4\n", "(62.80, 66.60] | 4\n", "(66.60, 71.40] | 4\n", "(71.40, 75.20] | 4\n", "(75.20, 79.00] | 4" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.Quantiles(data[:, -1])" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.188254Z", "start_time": "2022-11-05T19:18:42.183388Z" } }, "outputs": [ { "data": { "text/plain": [ "array([60, 10, 62, 63, 64, 65, 66, 67, 68, 10, 70, 71, 72, 73, 74, 75, 76,\n", " 77, 78, 79])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[:, -1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pinning the pooling\n", "\n", "Another option is to specify a specific subperiod as the definition for the classes in the pooling.\n", "\n", "### Pinning to the last period\n", "\n", "As an example, we can use the quintles from the third period to defined the pooled classifier:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.198019Z", "start_time": "2022-11-05T19:18:42.190998Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 20\n", "(62.80, 66.60] | 0\n", "(66.60, 71.40] | 0\n", "(71.40, 75.20] | 0\n", "(75.20, 79.00] | 0\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 20\n", "(62.80, 66.60] | 0\n", "(66.60, 71.40] | 0\n", "(71.40, 75.20] | 0\n", "(75.20, 79.00] | 0\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 4\n", "(62.80, 66.60] | 4\n", "(66.60, 71.40] | 4\n", "(71.40, 75.20] | 4\n", "(75.20, 79.00] | 4" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pinned = mapclassify.Pooled(\n", " data, classifier=\"UserDefined\", bins=mapclassify.Quantiles(data[:, -1]).bins\n", ")\n", "pinned" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.206186Z", "start_time": "2022-11-05T19:18:42.200535Z" } }, "outputs": [ { "data": { "text/plain": [ "UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 62.80] | 44\n", "(62.80, 66.60] | 4\n", "(66.60, 71.40] | 4\n", "(71.40, 75.20] | 4\n", "(75.20, 79.00] | 4" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pinned.global_classifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pinning to the first period" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T19:18:42.215909Z", "start_time": "2022-11-05T19:18:42.207832Z" } }, "outputs": [ { "data": { "text/plain": [ "Pooled Classifier\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 23.80] | 4\n", "(23.80, 27.60] | 4\n", "(27.60, 31.40] | 4\n", "(31.40, 35.20] | 4\n", "(35.20, 39.00] | 4\n", "(39.00, 79.00] | 0\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 23.80] | 1\n", "(23.80, 27.60] | 0\n", "(27.60, 31.40] | 0\n", "(31.40, 35.20] | 0\n", "(35.20, 39.00] | 0\n", "(39.00, 79.00] | 19\n", "\n", "Pooled UserDefined\n", "\n", " Interval Count\n", "----------------------\n", "[10.00, 23.80] | 2\n", "(23.80, 27.60] | 0\n", "(27.60, 31.40] | 0\n", "(31.40, 35.20] | 0\n", "(35.20, 39.00] | 0\n", "(39.00, 79.00] | 18" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pinned = mapclassify.Pooled(\n", " data, classifier=\"UserDefined\", bins=mapclassify.Quantiles(data[:, 0]).bins\n", ")\n", "pinned" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the quintiles for the first period, by definition, contain all the values from that period, they do not bound the larger values in subsequent periods. Following the [mapclassify policy](https://github.com/pysal/mapclassify/blob/a7770fb98bf945dad3c62ccf2c0f8b53abb1774a/mapclassify/classifiers.py#L589), an additional class is added to contain all values in the pooled series." ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:py310_mapclassify]", "language": "python", "name": "conda-env-py310_mapclassify-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.8.0/notebooks/05_Greedy_coloring.ipynb000066400000000000000000134402671465055300600225460ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started with mapclassify.greedy\n", "\n", "Greedy or topological coloring (or sequential coloring) is a cartographic method of assigning colors to polygons (or other geometries, `mapclassify.greedy` supports all geometry types) in such a way, that no two adjacent polygons share the same color.\n", "\n", "`greedy` is a small toolkit within `mapclassify` providing such a functionality on top of GeoPandas GeoDataFrames. `mapclassify.greedy()` is all we need." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:49.035556Z", "start_time": "2022-11-04T20:25:46.646113Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.6.0+5.g2788475.dirty'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from mapclassify import __version__, greedy\n", "\n", "__version__" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:50.439227Z", "start_time": "2022-11-04T20:25:49.038378Z" } }, "outputs": [], "source": [ "import geopandas\n", "import seaborn\n", "\n", "seaborn.set()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Few of `greedy`'s methods of coloring require either measuring of areas or distances. To obtain proper values, our GeoDataFrame needs to be in a projected CRS. Let's use Africa and reproject it to 'ESRI:102022':" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:50.942770Z", "start_time": "2022-11-04T20:25:50.444949Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "world = geopandas.read_file(\"https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip\")\n", "africa = world.loc[world.CONTINENT == \"Africa\"]\n", "africa = africa.to_crs(\"ESRI:102022\")\n", "ax = africa.plot(figsize=(8, 12), edgecolor=\"w\")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Default\n", "\n", "The default usage of `greedy` is extremely simple. Greedy returns a Series with color codes, so we can assign it directly as a new column of our GeoDataFrame:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:51.398283Z", "start_time": "2022-11-04T20:25:50.947778Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "data": { "text/plain": [ "1 1\n", "2 0\n", "11 0\n", "12 1\n", "13 4\n", "Name: greedy_default, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "africa[\"greedy_default\"] = greedy(africa)\n", "africa[\"greedy_default\"].head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using resulting color codes as plotting categories gives us following the plot:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:51.740911Z", "start_time": "2022-11-04T20:25:51.400845Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = africa.plot(\n", " figsize=(8, 12),\n", " column=\"greedy_default\",\n", " categorical=True,\n", " cmap=\"Set3\",\n", " legend=True,\n", " edgecolor=\"w\",\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Strategies\n", "\n", "### Balanced\n", "\n", "Greedy offers several strategies of coloring. The default strategy is `balanced` based on `count` attempting to balance the number of features per each color. Other balanced modes are `area` (balance the area covered by each color), `distance` and `centroid` (both attemtps to balance the distance between colors). Each of them attempt to balance the color assignment according to different conditions and hence can result in a different number of colors." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:51.747414Z", "start_time": "2022-11-04T20:25:51.743068Z" } }, "outputs": [], "source": [ "africa = africa.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:52.098809Z", "start_time": "2022-11-04T20:25:51.750210Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "africa[\"greedy_area\"] = greedy(africa, strategy=\"balanced\", balance=\"area\")\n", "ax = africa.plot(\n", " \"greedy_area\",\n", " categorical=True,\n", " figsize=(8, 12),\n", " cmap=\"Set3\",\n", " legend=True,\n", " edgecolor=\"w\",\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Different modes of balancing within the `balanced` strategy can be set using the `balance` keyword." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:52.491906Z", "start_time": "2022-11-04T20:25:52.101125Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "africa[\"greedy_distance\"] = greedy(africa, strategy=\"balanced\", balance=\"distance\")\n", "ax = africa.plot(\n", " \"greedy_distance\",\n", " categorical=True,\n", " figsize=(8, 12),\n", " cmap=\"Set3\",\n", " legend=True,\n", " edgecolor=\"w\",\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NetworkX strategies\n", "\n", "On top of four modes of balanced coloring strategy, `greedy` offers all `networkx.greedy_coloring()` strategies, like `largest_first`:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.134096Z", "start_time": "2022-11-04T20:25:52.494317Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "africa[\"greedy_largest_first\"] = greedy(africa, strategy=\"largest_first\")\n", "ax = africa.plot(\n", " \"greedy_largest_first\",\n", " categorical=True,\n", " figsize=(8, 12),\n", " cmap=\"Set3\",\n", " legend=True,\n", " edgecolor=\"w\",\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another strategy provided by NetworkX is `smallest_last`. All strategies provide different results. Check [Comparison of strategies](#Comparison-of-strategies) below for details." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.487667Z", "start_time": "2022-11-04T20:25:53.136233Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "africa[\"greedy_s\"] = greedy(africa, strategy=\"smallest_last\")\n", "ax = africa.plot(\n", " \"greedy_s\",\n", " categorical=True,\n", " figsize=(8, 12),\n", " cmap=\"Set3\",\n", " legend=True,\n", " edgecolor=\"w\",\n", ")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Greedy is variable in a way how to define adjacency and which coloring strategy to use. All options are described in this documentation together with comparison of their performance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining adjacency\n", "\n", "The key in toplogical coloring is the definition of adjacency, to understand which features are neighboring and could not share the same color. `mapclassify.greedy` comes with several methods of defining it. Binary spatial weights denoting adjacency are then stored as `libpysal` weights objects." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.498536Z", "start_time": "2022-11-04T20:25:53.495171Z" } }, "outputs": [], "source": [ "from shapely.geometry import Point" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For illustration purposes, let's generate a 10x10 mesh of square polygons:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.505998Z", "start_time": "2022-11-04T20:25:53.502015Z" } }, "outputs": [], "source": [ "def poly_lattice_gdf(dim, plot=False):\n", " polys = []\n", " for x in range(dim):\n", " for y in range(dim):\n", " polys.append(Point(x, y).buffer(0.5, cap_style=3))\n", " _gdf = geopandas.GeoDataFrame(geometry=polys)\n", " if plot:\n", " ax = _gdf.plot(edgecolor=\"w\")\n", " ax.set_axis_off()\n", " return _gdf" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.676696Z", "start_time": "2022-11-04T20:25:53.509107Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf = poly_lattice_gdf(10, plot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### libpysal adjacency\n", "\n", "The most performant way of generating spatial weights is using libpysal contiguity weights. As they are based on the shared nodes or edges, the dataset needs to be topologically correct. Neighboring polygons needs to share vertices and edges, otherwise their relationship will not be captured. There are two ways to define contiguity weights - `\"rook\"` and `\"queen\"`.\n", "\n", "#### Rook\n", "\n", "Rook identifies two objects as neighboring only if they share at least on edge - line between two shared points. Use rook if you do not mind two polygons touching by their corners having the same color:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:53.853264Z", "start_time": "2022-11-04T20:25:53.679119Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf[\"rook\"] = greedy(gdf, sw=\"rook\", min_colors=2)\n", "ax = gdf.plot(\"rook\", edgecolor=\"w\", categorical=True, cmap=\"Set3\")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Queen\n", "\n", "The default option in `greedy` is `\"queen\"` adjacency. Queen adjaceny identifies two objects as neighboring if they share at least one point. It ensures that even polygons sharing only one corner will not share a color:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.036657Z", "start_time": "2022-11-04T20:25:53.855749Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf[\"queen\"] = greedy(gdf, sw=\"queen\", min_colors=2)\n", "ax = gdf.plot(\"queen\", edgecolor=\"w\", categorical=True, cmap=\"Set3\")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Intersection-based adjacency\n", "\n", "As noted above, if the topology of the dataset is not ideal, libpysal might not identify two visually neighboring features as neighbors. `greedy` can utilize an intersection-based algorithm using GEOS intersection to identify if two features intersects in any way. They do not have to share any points. Naturally, such an approach is significantly slower ([details below](#Performance)), but it can provide correct adjacency when libpysal fails.\n", "\n", "To make `greedy` use this algorithm, one just needs to define `min_distance`. If it is set to 0, it behaves similarly to `queen` contiguity, just capturing all intersections:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.432862Z", "start_time": "2022-11-04T20:25:54.039221Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf[\"geos\"] = greedy(gdf, min_distance=0, min_colors=2)\n", "ax = gdf.plot(\"geos\", edgecolor=\"w\", categorical=True, cmap=\"Set3\")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`min_distance` also sets the minimal distance between colors. To do that, all features within such a distance are identified as neighbors, hence no two features within the set distance can share the same color:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.854327Z", "start_time": "2022-11-04T20:25:54.436079Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf[\"dist1\"] = greedy(gdf, min_distance=1, min_colors=2)\n", "ax = gdf.plot(\"dist1\", edgecolor=\"w\", categorical=True, cmap=\"Set3\")\n", "ax.set_axis_off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reusing spatial weights\n", "\n", "Passing `libpysal.weights.W` object to `sw`, will skip generating spatial weights and use the passed object instead. That will improve the performance if one intends to repeat the coloring multiple times. In that case, weights should be denoted using the GeodataFrame's index.\n", "\n", "### Performance\n", "\n", "The difference in performance of libpysal and GEOS-based method is large, so it is recommended to use libpysal if possible. Details of comparison between all methods are below:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.860153Z", "start_time": "2022-11-04T20:25:54.857221Z" } }, "outputs": [], "source": [ "import numpy\n", "import pandas\n", "import time" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.867962Z", "start_time": "2022-11-04T20:25:54.862767Z" } }, "outputs": [], "source": [ "def run_greedy(_gdf, greedy_kws, min_colors=4, runs=5):\n", " timer = []\n", " for run in range(runs):\n", " s = time.time()\n", " colors = greedy(_gdf, min_colors=min_colors, **greedy_kws)\n", " e = time.time() - s\n", " timer.append(e)\n", " _mean_time = round(numpy.mean(timer), 4)\n", " return _mean_time, colors\n", "\n", "def printer(m, t, c):\n", " print(f\"\\t{m}:\\t\", t, \"s;\\t\", c + 1, \"colors\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:25:54.875087Z", "start_time": "2022-11-04T20:25:54.871547Z" } }, "outputs": [], "source": [ "params = {\n", " \"rook\": dict(sw=\"rook\"),\n", " \"queen\": dict(sw=\"queen\"),\n", " \"geos\": dict(min_distance=0),\n", " \"dist1\": dict(min_distance=1)\n", "}" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.324334Z", "start_time": "2022-11-04T20:25:54.878330Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n", "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "10 ----------------------------------------------\n", "\trook:\t 0.0031 s;\t 2 colors\n", "\tqueen:\t 0.0028 s;\t 4 colors\n", "\tgeos:\t 0.0026 s;\t 4 colors\n", "\tdist1:\t 0.0057 s;\t 10 colors\n", "20 ----------------------------------------------\n", "\trook:\t 0.0107 s;\t 2 colors\n", "\tqueen:\t 0.0099 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tgeos:\t 0.0061 s;\t 4 colors\n", "\tdist1:\t 0.0188 s;\t 10 colors\n", "30 ----------------------------------------------\n", "\trook:\t 0.0239 s;\t 2 colors\n", "\tqueen:\t 0.0224 s;\t 4 colors\n", "\tgeos:\t 0.0131 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tdist1:\t 0.069 s;\t 10 colors\n", "40 ----------------------------------------------\n", "\trook:\t 0.0462 s;\t 2 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tqueen:\t 0.0471 s;\t 4 colors\n", "\tgeos:\t 0.0261 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tdist1:\t 0.0846 s;\t 10 colors\n", "50 ----------------------------------------------\n", "\trook:\t 0.0698 s;\t 2 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tqueen:\t 0.0683 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tgeos:\t 0.048 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tdist1:\t 0.1344 s;\t 10 colors\n", "60 ----------------------------------------------\n", "\trook:\t 0.1067 s;\t 2 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tqueen:\t 0.1031 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tgeos:\t 0.0729 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tdist1:\t 0.2205 s;\t 10 colors\n", "70 ----------------------------------------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\trook:\t 0.1493 s;\t 2 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tqueen:\t 0.1396 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tgeos:\t 0.1082 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tdist1:\t 0.3423 s;\t 10 colors\n", "80 ----------------------------------------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\trook:\t 0.1976 s;\t 2 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tqueen:\t 0.1864 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tgeos:\t 0.1639 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tdist1:\t 0.4713 s;\t 10 colors\n", "90 ----------------------------------------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\trook:\t 0.27 s;\t 2 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tqueen:\t 0.2479 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tgeos:\t 0.2187 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tdist1:\t 0.646 s;\t 10 colors\n", "100 ----------------------------------------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:308: 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", " sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\trook:\t 0.3191 s;\t 2 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/mapclassify/mapclassify/greedy.py:306: 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", " sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tqueen:\t 0.2963 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tgeos:\t 0.3076 s;\t 4 colors\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n", "/Users/martin/Git/libpysal/libpysal/weights/util.py:1658: FutureWarning: The `query_bulk()` method is deprecated and will be removed in GeoPandas 1.0. You can use the `query()` method instead.\n", " inp, res = gdf.sindex.query_bulk(gdf.geometry, predicate=predicate)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tdist1:\t 0.8972 s;\t 10 colors\n" ] } ], "source": [ "times = pandas.DataFrame(index=params.keys())\n", "steps = range(10, 110, 10)\n", "for step in steps:\n", " print(step, \"----------------------------------------------\")\n", " gdf = poly_lattice_gdf(step, plot=False)\n", " for method, kwargs in params.items():\n", " mean_time, colors = run_greedy(gdf, kwargs, min_colors=2)\n", " printer(method, mean_time, numpy.max(colors))\n", " times.loc[method, step] = mean_time" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.347774Z", "start_time": "2022-11-04T20:36:29.327020Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 10 20 30 40 50 60 70 80 90 \\\n", "rook 0.0031 0.0107 0.0239 0.0462 0.0698 0.1067 0.1493 0.1976 0.2700 \n", "queen 0.0028 0.0099 0.0224 0.0471 0.0683 0.1031 0.1396 0.1864 0.2479 \n", "geos 0.0026 0.0061 0.0131 0.0261 0.0480 0.0729 0.1082 0.1639 0.2187 \n", "dist1 0.0057 0.0188 0.0690 0.0846 0.1344 0.2205 0.3423 0.4713 0.6460 \n", "\n", " 100 \n", "rook 0.3191 \n", "queen 0.2963 \n", "geos 0.3076 \n", "dist1 0.8972 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "times" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.352569Z", "start_time": "2022-11-04T20:36:29.350031Z" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.660070Z", "start_time": "2022-11-04T20:36:29.354841Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = times.T.plot()\n", "ax.set_ylabel(\"time (s)\")\n", "ax.set_xlabel(\"# of polygons\")\n", "locs, labels = plt.xticks()\n", "plt.xticks(ticks=locs, labels=(times.columns ** 2)[:7], rotation=\"vertical\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting without the GEOS methods, the difference between `queen` and `rook` is minimal:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.906521Z", "start_time": "2022-11-04T20:36:29.662613Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = times.loc[[\"rook\", \"queen\"]].T.plot()\n", "ax.set_ylabel(\"time (s)\")\n", "ax.set_xlabel(\"# of polygons\")\n", "locs, labels = plt.xticks()\n", "plt.xticks(ticks=locs, labels=(times.columns ** 2)[:7], rotation=\"vertical\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison of strategies\n", "\n", "Different coloring strategies lead to different results, but also have different performance. It all depends on the prefered goal. \n", "\n", "If one want visually balanced result, `'balanced'` strategy could be the right choice. It comes with four different modes of balancing - `'count'`, `'area'`, `'distance'`, and `'centroid'`. The first one attempts to balance the number of features per each color, the second balances the area covered by each color, and the two last are based on the distance between features (either represented by the geometry itself or its centroid, which is a bit faster).\n", "\n", "Other strategies might be helpful if one wants to minimize the number of colors as not all strategies use the same amount in the end. Or they just might look better on your map." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.912141Z", "start_time": "2022-11-04T20:36:29.908900Z" } }, "outputs": [], "source": [ "strategies = [\n", " \"balanced\",\n", " \"largest_first\",\n", " \"random_sequential\",\n", " \"smallest_last\",\n", " \"independent_set\",\n", " \"connected_sequential_bfs\",\n", " \"connected_sequential_dfs\",\n", " \"saturation_largest_first\",\n", "]\n", "balanced_modes = [\"count\", \"area\", \"centroid\", \"distance\"]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.917560Z", "start_time": "2022-11-04T20:36:29.914999Z" } }, "outputs": [], "source": [ "import libpysal\n", "import warnings" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:36:29.979308Z", "start_time": "2022-11-04T20:36:29.920269Z" } }, "outputs": [], "source": [ "sw = libpysal.weights.Queen.from_dataframe(\n", " world, ids=world.index.to_list(), silence_warnings=True\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is a comparison of performance and the result of each of the strategies supported by `greedy`.\n", "\n", "When using the `'balanced'` strategy with `'area'`, `'distance'`, or `'centroid'` modes, keep in mind that your data needs to be in a projected CRS to obtain correct results. For the simplicity of this comparison, let's pretend that dataset below is (even though it is not).\n", "\n", "Strategies used in `mapclassify.greedy` have two origins - `'balanced'` is ported from QGIS while the rest comes from NetworkX. The nippet below generates each option 20x and returns the mean time elapsed together with the number of colors used." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:39:15.844559Z", "start_time": "2022-11-04T20:36:29.982473Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "balanced_count\n", "\ttime:\t 0.0005 s;\t 5 colors\n", "balanced_area\n", "\ttime:\t 0.0129 s;\t 5 colors\n", "balanced_centroid\n", "\ttime:\t 2.1403 s;\t 5 colors\n", "balanced_distance\n", "\ttime:\t 2.0867 s;\t 5 colors\n", "largest_first\n", "\ttime:\t 0.0056 s;\t 5 colors\n", "random_sequential\n", "\ttime:\t 0.0025 s;\t 5 colors\n", "smallest_last\n", "\ttime:\t 0.0047 s;\t 4 colors\n", "independent_set\n", "\ttime:\t 0.0652 s;\t 5 colors\n", "connected_sequential_bfs\n", "\ttime:\t 0.0043 s;\t 5 colors\n", "connected_sequential_dfs\n", "\ttime:\t 0.0045 s;\t 5 colors\n", "saturation_largest_first\n", "\ttime:\t 0.0405 s;\t 4 colors\n" ] } ], "source": [ "times = {}\n", "with warnings.catch_warnings():\n", " warnings.filterwarnings(\n", " \"ignore\", message=\"Geometry is in a geographic CRS.\", category=UserWarning\n", " )\n", " for strategy in strategies:\n", " if strategy == \"balanced\":\n", " for mode in balanced_modes:\n", " stgy_mode = strategy + \"_\" + mode\n", " print(stgy_mode)\n", " kwargs = dict(strategy=strategy, balance=mode, sw=sw)\n", " mean_time, colors = run_greedy(world, kwargs, runs=20)\n", " printer(\"time\", mean_time, numpy.max(colors))\n", " world[stgy_mode], times[stgy_mode] = colors, mean_time\n", " else:\n", " print(strategy)\n", " kwargs = dict(strategy=strategy, sw=sw)\n", " mean_time, colors = run_greedy(world, kwargs, runs=20)\n", " printer(\"time\", mean_time, numpy.max(colors))\n", " world[strategy], times[strategy] = colors, mean_time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, `smallest_last` and `saturation_largest_first` were able, for this particular dataset, to generate greedy coloring using only 4 colors. If one wants to use a higher number than the minimal, the `'balanced'` strategy allows the setting of `min_colors`." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:39:16.475775Z", "start_time": "2022-11-04T20:39:15.847142Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "times = pandas.Series(times)\n", "ax = times.plot(kind=\"bar\")\n", "ax.set_yscale(\"log\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot above shows the performance of each strategy. Note that the vertical axis is using log scale.\n", "\n", "Below are all results plotted on the map." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2022-11-04T20:39:22.246880Z", "start_time": "2022-11-04T20:39:16.478530Z" } }, "outputs": [ { "data": { "image/png": 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c0lpjaY2tNZa20Th3Lw2lUEphoFCK7OeZ9u6aemxa6/x+hBBCCCGEKBQ03awPliz1MBadBOyEEOIcp7UGbUO+AkJBUEQVfF0QMNF2dnnDgNz/FaAMlDH9BuPOvnXBvjX5SBCq4LMEa6Zia41GY6qZN3dP2xYnh/o5PtRPZyJGxraxskG5/GdbY0+jqG8hA4WhnCCgqRQKhaGyH9mgXu7/hsoG/gqXUQqv4aLI4yXi9lLk8VLs9RF2eTALfsckoCeEEEIIIc5HErATQohzgLYslGmOfJ0YhugQDMfR8Tgk4pBMQDKBzmSyQTgbbA225QTUbCv7tQ0+H6qmHtwuSCRG1k0MO/9PFHytNZgulMsFuQ/TBS43uEzns+k8PrLMyGO5z9rtRrnc4HY73/d4wOsDj3fCIKG2LCfQN4MA4nKnsxluueCU1prBdIrO4RjdyTjdiTjRdAogH/TKZc6NzYBTKOKZFK3x6IyDcdNho7E1oDXped520OXOB/EiHi9Fbi9FHh/FHh8htwejIHBnaRuY/8w/IYQQQgghlpIE7IQQYhnSthOEmCwYpS0LDCMf1KGjDevBX6N7e2CgD7JBnTmNYQGWn3XYyO0Bnw+8fvAHUCWlqLIKqKxBbdrmbFvrZRWwmc14WuJDdCVidCXidCeG6UnGSWd/F5a73PTZkay6bOZd9nPh44Yysll3Rv4xpXAy8CAfcAQYSqeIptO0DUdRONuqDYRZGynBY5iYykBnMxAVy+fnL4QQQgghxFxIwE4IIZaA1tlMNvS4aaY6k0afPok+cQR98ii0t4BhOhlnbg+qtBzVuB7VtBFdXol+/AHsB36d3d45yrbBMFElpVBVg6qsQdWsgtLykWW0DcqcfBu5xXR2+mfBDOGZBHqMCYJwdnbKr5ENoA6kkgylk5R6/QTdnvxylrbz00LHSmQytMaHODbYR2cihrUCmrhfUFLJjXVrl2TfhU3uCwN8QgghhBBCnAskYCeEEAtMZ9IwOADxGDqdhkwa0mkYjjmPxaIQj0Es6vy/uwMsa8xWMpBKOtvr60EfOwS//fniP5mFUlKGWt2EKi2DYBgCQVQ4AqEw+IMoz0jQS9vOa6MMJziXm0aKUowN16Usi5Sd/bAskrZFysqQsu1Rj+cCbprc5wLZL9ymwepgMXXB8KiAWzSdojU+RPtwlI7hGB3DMVL2yM/PbRiUePyUen2Uev2Uev2U+QL4TRduw8CdfR4+l4tLKuq4pKJu1HN4vLOZxzubZ/vKLqiT0X5SloXHnDpQamtNOvt6p20br2GOCmSOXbZQ4Wsdy6QwUPhd7pFSiLPgZORlg35jGmnkauZBLhA4dbMOIYQQQgghFoIE7IQQYgHZ+/eiX3wOfeoYJIaXejjLjlq7AfOt75zZOkZBrb7stNO2+BAno/0MpVPZKZRJoukUmXnOUnuqqxW3YVAfLEJrTftwlGErM+U6adumMxGjMxGb+PkAbsMk7PZQ7guwsaiMdZHS/Pcvr1zFgf5u+lOJ+Xwq82IoneKzB56e0za8hknY4yXs9hBxO5/Dbg8uZdCbStCbHM5/pG2bTUVl3LyqaVwDjlygLdfsYixbaxJWhuFMmngmTSyTJmFliGfSDFsZElYGlzLwmS68ppn/7DVd+E0XPtOF3+XGm/39kyCeEEIIIYRYSBKwE0KIWdC5DLiCLqfON3R+eqvWGrVlO8YFu9C2jX5hL/bDv4XuziUZ83Kk4xMHsSar4Zebzqrydc+c1z1j2zzV1bqwg81K2zbHh/rmbXsaSNkWPclhepLDHBroGfV9n2mSGJdxee5I2hbJhNNQYypew+T92y4b97hl2wymk/Qkh+lPJZwAXCZD3EqTyH4ezmRGZT1Ol0sZlPn8lHsDlPsCVPoC1IeKZrwdIYQQjpGs+JFyFBPdZBFCiEIqOoh5+hgqlUR7vFgNTehQZKmHteAkYCeEENMwtgsr7S3YB/Y7nVUtC6wMuNwYOy+G6rqR5bPrKMOArTsxL7gQffwwNJ9Ct7eg21qcJhHnA9MFFVWoympUVY3TMKJmFZCdmqjtfPacMpxGApa285lUKduiMzvlNJpOZbOinCypnnM4e/FcDtbNVHcijqVtXujroi+ZoC81TDSdmnMPXAWUeH35wJwTnAsSdnvyjV1y3XuFEA472+wFzagbKEKA8/tRWFIgYWWIplMMZTPgo5k0GdumLhimPhjBbZjj1hFCCKOjBc8zD+E6cQhVUK9bGwaZxo2kdl+NXVU3xRZWNqX1CqhqLYQQSyAXRCKTQe99Et3dCcPDkEqijx5yvjeRugaMi69EbduZD/JpbTuNE3LT+LQe+V4y4QTuUkknwGe6UKbhBLgMEwwj+7jprG8YTh28ZBKdHIZEApIJZ/3hOPYTD0M8uhgv0fSUVWC87DrU9otGnrOVcZpIZE/Kc5lzueBcOhucaxuO0jkco304tiynhIqVx0DhMU08hkmVP8iO0irqguH8795UjUHE8pQ/vqKYqJEPjOmsbdvZ47fKPyYmFs+k84G5sNs7rvt1IpPB55L7/2KkNqihFGnb4kx0kFOxAZpjg/Qmh6dspGSgqPIHqQ9FWB0spjYYwshm38nfpxArUyKR4Nix45SXV+PxeGe1Ddexl/Dd+0PUFOVntOki8Yo7yDRtnu1QZ+RrX/sKTz/9JF/4wpcnXSaVStLd3U5T01p8Pt+c9ifvsEIIMQFtWaA1+tHfYT/2QL7hw7S0nMZu+S78/AdQUooqLoXiUqfDaXEpqqQciorBHwBAeX3Q0DjuAnM6xp7G6uE4vPT88gjYVdVgXHUDassOsO1RGYrKdN5+cllLScviwEAX7fEY7cNR+lOJOWdNifNTscfHH2/YOe3lx2bOja2NJ5aHsYEinU47x7noEHpwAGJDEB0ClxsqqqC6FiLFToAulUQfPYg+csBp+BMKZ5vaRCBSBJFi5zGvL78vbGvUTYXz1c9OHyZlZSj2+ri+pnFcsxiPaWLZNiiFgQRXzke5v81ck572eJT7Wo7RnZx+5ruNpm04SttwlKe6WvGZLraVVLCrtJqwx4utbQw5NgtxXjE6Ws4arANQVgbfvT8gfvufLHim3V13fYcvf/kL7Nx54YLup5AE7IQQYiKGAe2t2HufmlmwrlAmDV0d6K4OgPEBKJfLuaAsKcN45evRxSVOZohS6NYz6OZTzjgMYySzzjDB60WVVUBxCUSj6JNH0adPoE+fyNbHW8xQlwK/HwJBCIRQgSAEg6hNF2Cs3+xMDVYqPzV4rFygxOdysb2kisZwir7kMP2pJAOpBN2JOKdjg4v4fMRK5zGm7lg7lmTSLQ5tOzdBMMxRtT7HLTcmMFfwDXQ28K97u9Gnjjvby2bX6Z4u9JMPj17H5XaOk709TgCucHMT7Twb7FNVNaimjRjbds3uyZ4Dct2a37R266jHCssUQPbvR/6GzmujAulaU+kP8sa1W7n75EHahmd38zBhZXimu4093W2sCRezq7SaNeFiKU0gxHnE88xDZw3W5Sgrg+eZh0nc+qYFGUtnZyef+MTH2bdvLw0NqxdkH5ORKbFCCDGJ3BQqWk5jH9zvZGcMxxnVZEIpZzrqXGuo+fwYb/gfqDVN+Ww0PTSIPnIAfewg+viRxe0ya5rOxavbPfLZ7QGXG+Xzo9ZtRG3aBsHwhI0hxmbUzUThtBpLa75w4JlZNQwQApyMuxtqG2kIFcnF3iLQtj3qmKCH49DXi+7pgv4edF8vqqYO4+KXTbi+vfdJ9L49zvHXyoCVQYUiqKaNqA2bUeVVBVNaIZdnrEwT++ghGBpwjsnJBDqZGCkZkPvassDtQXk8I8c1j8f57Pag3B6oWYVau86J6kltNiFmLBfc/dWZoxyP9ucDwHNR7PGxu7yG7aVV8zBCIcRCm8uUWBUdJPj1f0VNVn5oAtowiP2P9y9II4qHH36Q3/zm17zzne/mq1/9Mm1trYs2JVYCdkKIFUGn0yNZWot8AaW1nS2LNPl0jMzHPjD3HZkujJtfg9p1KSpbEyjXvEKn01j//gmILly2mdq4DfNNfzStZcc14ZgHucL+SqlssM7m56ePcGweO7KK88v6SCmvWNWEqZRMp1okuQw5e+9T2Pf+ZPIM5ZpVTvOZbIAfrdEdbdDVPvUOwkWoNU2o+jWohrVORpxh5DtLO1l3TgfKiWrZTTbm/Hpo5z1mhpmaQojRcjdILNumfThKS3yIlvgQ7fEow9PMmhlrfaSUVzdsGLcfnd2XZuQYJDdnhFhacwnYuV7ai/+3P57xPodvvI3M5oXNjv/4x/9+UQN2MiVWCLEiKLc7/3890IeOx8HnA38A5fOPfC+XeTGPtYeUMsYXi8vtr6fLuSidi3ARxkWXoS66HBUKOxlm2ZpAkH1OrWcgtrB16XSL07lWVZ+9/sNsg3W54uWFU6osbRNNp+hLJuhPJRhIJRlMJ+kYjjGYnuV0ZHHeu6JyFZdVrpKsukWW66pr7LoEhgawH/39xEG7tmZ0W/PMdzA0gN7/LHr/s87XHi+qrgHVuA61YzcqUgwUBOGmOWaZ1inE/Modd03DoDYQpjoQ4hLlnF9kbJt4Js1QOslQOkU0k3I6x6bTRDMp0raF13ThNUw8ppn/vDZckp+WnQvMnYoO0J2IM5hOYipFxO2l2OujLhDGa7ryNwPNWdQJFkIsDTXLckSzXW85k4CdEGLejZ0SNS/bLJhmqYpK0P4A1je/4ASy3B6nuUNJGZSUOZ9Ly6GsEiJFToaatp3A20TbjsfyXViVy51/DoWdXMetY1mQSqGPvIQe7J/FM1Kopg2oi69AbdhC9raw8x2lnDH1djt1mXq70E8/NnlX2vkSHcL64r+C24Nxy20Yuy5B29aMMk0K73QXBkzjmTQDqQR92YDcQLZG3UAqSTSTWohnI85j6yOlXFa5CpAadUshF7RTV92Aedk16GefwH7qEejrmb+dGCbG1TdAXQOqstrJvMvd5LCsbJacXKALsRwopTAL7ny6DIOIx0vE482fN0yVFZfrJA9OYyBba9K2xb3Nx6bMwi/2+KgNhKkNhFgViFDi9ckUdyFWAD3LrrKzXW85k4CdEGLeLcRF0tjGBcrjxfyT/+nUJrJtyGTQXe3QegZ94ij6sQdgoM+5aNuyA+OVt6F9gQnHpp96BPvB+5wvTJfTybV2FWr9Zli/BTVBKrMyTafZwsVX4rrsGidoF485mX/DcUgOQ2IYnUw4AS93Qb0kt8fJCCkucS4sUSgje6HZegZ94gi6qxN9+MVszbxFVFSCqq4FNDqTyU/NnUhh8fGElWEglcg3ixhMJRlIO4G5oXQSS6oviEXklumMSy5/UezxwMUvw7z0KvThl9BPPIQ+eXTO2zduehXq4isnLJGQu0mjrQygRt14mermjRBi8U2ncUku4Kezwb2W2BC/aj5CNJMGwKUMfKYLn2lmP7vwZj/7TBdaQ2ciho2mzOuXoJ0Qy5zV0IQ2DJQ9sxp2Vn3TAo5qaUjATggxb3JNGiY7Ecpl3k3aBXCGlGGAPzDyQCQCjesxshdnOjEMg/3oeBzd3oqxdsOE2zGuvRm16xLnYrKtBVVUDCXlTidWQ01Zry33uIoUOx1fGcnOI/88dbZ4OeQbVig1av389mrrUbX1zvjTafSex7AffwhmlcU3DTWrMHZchKpZBVW1KK8TnJxOjTpTGSQyGY4N9dEaH6I7EacrESez0JmAQpyFJb+Dy0r+WLJuE8bGrej+XvTpk+j2ZmhrQbe3TL+pjmGgLrgQ49Krp96nMtCJGPYTDzu1P4cGIRTGePUb0Cp7jJ7i/UoIsbzka+JpG5dhcEfjFnymC49pjiqzMXYdnc3MUyintKX8zQux7OlQhEzjRtzHDkx7nUzjpgVpOLHUJGAnhJizXABOmSb2sUPYh1+C3m4nmBYpRq1ajVq7HpVNU16okyWlDCiIMSmfH3z+fBBtysBbUQlceBmGaWanU3HWqaCTBR5nkmGYK5SuTx5FlZRDUXF+feV2oy67BuOyawDI/MvH5q/phNuDumAXxiv/wPl6zIXrdGvUeU2TTUVlbCkuz0+DG0yn6ErEiGfSJC2LpJ1xPlvZz3b2/9nP6RncPRNiOiz5nVqW8jc4ikudKazbduaPd9Z996Aff2DydZs2Ylxzk9OswuWatPTCqOOyMqC7A93XA/E4yrbR+5/F2Hnx5OsIIZYtS2sMFDWB0LSWN5Ry+towUnJECLEypHZfjevkEdQ0mtRo00Vq91WLMKrFJwE7Ic5TZ8ug0rYN8Sh4vOByTRm8ygVqsG2Mpo3QtHHUBZC2LVjiGZHTCaLlLyZzGXoLUItvsnHZe5/CfmGvMyW3rBxVXoWqqIK6Boz1m51lL9iFfvzB2e3IdKG2X+gET+sboaxiXrIdlVKYhYE+pSjyeAm7PbPq1ParM0c5MNA96/EIAdAajzKQShBxL+xNAjF7zrRV541Bt7c6JQAm4/Vh/MFbnBswuezkSY7NI+87Nvj8mG8c3fl6ops38vshxPI3nXMJO3tMyS2bti26E8N0J+KE3R7WhItHLSsZd0IsX3ZVHYlX3IHv3h9MGbTTpovEK+7Arjp707yVSGkthYWEOF/kLlJ0Kok+9CL64H50a/PIFE1lOJ8TwxAbAq0x/+Jvnamh55GpGi3kMuLmEsjLBTdhJDhonziK/a0vTLFWdmrtLKmtOzFvf+v4cWTn6s7nCWuuI9tEwbq0bZGyLJRSuJSByzBGLfOdo/vpSMTmbSzi/KFwCoxX+YMUeXx4DZMtJRUEXG65MFumcp1clWGg4zGnLMGxQ+hjh0bqd5omxg23oi65SppICCHGsbWNoQwGUkla44P5AF13Ms5QeqSp1e1rNtMQKhq3vmTYCrEwEokEx44dp7y8Gs8cmkEYHS14nnkY14mDo2raacMg07iJ1O6rll2wLpVK0t3dTlPTWnwT1EKfCQnYCXGOywfpYlH0gefRB19AnzgKtjV+YZcLvH7welG19RhXXOvUNluAMaGU0/VUGUt6ETY2iy534qZPHcc+egC6OsAwndfG5YZgENW0EdWw1rnInGzq3VQdZjta0W0tMNCH7u9zPrc1T7+G02yYLozb/tCZilZcigqOTCeZ70xCS+t81l08k2ZPdxsH+rvyxaGFmC8lHh+7yqqp8oeo8AVw5aZXantcfHts52Kx/OTfr7QGK+NkBsvPTCywXAfSieqg2QWXSdJxenk6HR3gme5WTkYHplyu1OvjwrIaoukUhlL5hhQ+00Wp10/I7cn/jHOda5ngR64KHpQbQUJMbr4CdjkqOoh55hgqlUR7vFj1Tcu2Zp0E7IQQZ5W78LGPHkI/8SD62GHGXcGWVaIa1qDqG1FrmlAlZaO3URDI0bbtnJnY2W3Msli3TibR+55G9/c6jRoqqpxptAuksBGGTqVgOOZ0anW5s1N9R57fqOeKk0WnU0mIx5yOr/E4JOJOZ1qtwedHt5yGzPg0bVVVi7pgF8rtGT2eXIDPspygqWU561sZp8tsdMjZXzzqdJh1e8Drc6Yme33g8zm1+bw+58PlgngcPdgHA/0wNIAeHBj5HIuiSstR9WugvhFVvxrl8eaz/KZbq262cp1ko+kUhwd6OBUd4ExscMaNKRTZgAsq+9kJDEqDi/Pb5uJybq5rkgtpIcSUtHZaD+SCMEY2szxtW7THY7QOD9E1HGPYyjBsZUhaGVKWhdd0saGolKurVy/1Uzgv5TL2xwXOtHNOsKe7jYc6Ts95P2G3hz9evxPTMEhaGU5HBxi2MtndqnwTW5V/xNl/mS9AmdePodS46bhCnO/mO2C3kkjATggxLfbjD6APvwQeH4TDEIqgQmEoKkHVr0H5/GcN3Ghtg63Rp46jjx0Ew3S6qBaXOgG+SDHK7R5Z3rZhkkwWbVuQTGH//Afol/blHzfe8g5U47qzNnk4G21ZkEk7mWqmC9IpZ9zNJ9FnTjrZchMc8tT6zRiv+0NUIHj2fWRfD9Ao04WOx7CffBj9zGNOoK1wuzsuxnzdm2b2HPLTZXNtZXPjVVN34NXayVjUesKsxVxW41JlMxZmMFjapnM4hqV1flqsSxmYhsJUBqZSGMrAgGnVwLO0jWVrLG2T0TaxTJrW2BBtw1Ha4lEG08nFeZJiSXgMk9WhIhrDxawNl+SnwMpFkxDnvomCJJZtk7It0rZN0sqQsEeaHqWyTZCG0ina4kP0pRKE3V6KPT6KPbnPPkp9fiJu76iMKzmmzFw+UOp8NeqURqHQ6HzwtHD5XACsMxGjOTbIcCbjVG4puGmXtCz29baTmadL2Sp/EK2hKxGbUQESlzKo8gep9oeoCYSoC4QJuj3yOzMLuXNFdLY+duFPYswPZaY1ksXik4CdBOyEEJNwgj6WE1wrOAnKB4IKHp90G9nAm37s99gP/RZSUwQ9/AEnCFhcApU1GFt3oiqrJwzeaW2jlIH90j7sX/zICXJ5fZhv/jMnCwzQ6bTTDdXlBo8H5Z3egU7bNvroQeyH70d5PE5WmseD7umClqnvwBpv+B+ojVtnHNDK1WCisw3ri/86+ps1qzCufyWqth7lDzjLn6XZx4z3m/uAglqE5/fUv9wdeTP7sxzOpGmND9Eaj9I+7HxIZ9pzV4UvwMtr1lAbCGFMMMVNCLEyjQ3ORdMp2oejdAzH6ByO0ZWIM2ylsSa4tAm63JR6/dkPH6VePyWe0dMgc+8dMn1+9goDdAZwKjpA0rbI2M4NtVGfbZug201jqJgyX4C0bdEWj9IcG6QlPkRbPLpis+jDbg87SqvYVVaNqQwJLE3DcCZNXypBb3KYaDpF0rIwskG5XJA2939TKfymmzJfgGKPF3f2hv9UNZTF4pOAnQTshBALRNsWxOPYP/o2+uTR2W2ktBy1dQfGtgtRldUT7yc6hPXZTzlTTE0TSsqc4FNv90gQyuvDfP9HwO05e5BxksLBursT63P/b/IVi0ow/+JvJqyXNFFm2mRBt8w/f9Rp1jGRSDFqVQNq24WoDVvO3qF3TA08nU7DQJ8zNTeVhGQS0qmRD2WAx+NM93V7UT4f1Naj3O55CxKuZIUXepa2+daR5+lLJZZ4VGKh7Cyt4uU1a+SiW4gVamxduYFUgrZ4lM6EE5zrTMRJTNA1MOL2UukLUJINzpX7/JR4/XgKL+jHZHWJ6Rn7M0nbNhnbIpX9SFrORzr79ZHBXk6dpa5cjs90kbIsJ7vqHFHs8fGq+vWU+wISPJqC1pq9Pe080H5q1tsIutyUeHwUe32UePyUeH2Uef1EPF5MZYzLdszXuS2YHi/mnwTs5idg55qnMQkhziHKMJ0gz2VXozZtyxf/xuVyPpvmyNcuN8rlcgJFVTWjAlvWz/4b6wv/5ATvyqtGAkq5z9rJwCMUcR7LZdSVlDlfe30YV17v7GMab6aTLuMPoLZf5GTa9feB2w0+v1MLzudHXfIyZxppYSagZTlTag+/hG5vhfYWdEcrlFViXHQZbNs5auqptm2Ma27E/uXdE49hsB/9Uj/6peed5+4PjDSycLudWnU+X77pB26PU4eutwf6uiEWnfbPL8/tcbIGd16MWsA6gStB4YmaqQy2llSwr7djVAc5sbLV+EO8umEDAZdbLo6EWGFG3VSxbdqGR2dapSZqlJVV7Q/RFC5hfVEppV5/fnt6gkYSSinMiToJCCAXlKMgc5/8a9ibHM7XoW2ODZKc4mcyUxMFX1eaEo+P6kCIVYEwa8IlhN0e6UB7FrnXp8IX4IKSSo4O9jI8i9+FWCZNLJOmOT5y09xUilfVr2dtuGTcOYGpDFCQtDIMpJJoIOByETDd+dkZObngnmTeiqUiATshxIRUUTFEIs402lw5Nec7I1Mvmbo7lm45BT6/E5CLFKEqq1FVtVBeCR6vE+i74VVTjkNb1tzrrvn8mLfdOfk+stN0R752stt0NOFkryWGnSBfRRUkk9gP/QbD7cHYsj2/jjIM1MUvQ9XWo/t6oL8PPdCb/dznBApzwaFcVlzhGGb95Arr3I3h8aIHB1DdHdC0UU4cs2ytubi8lksq6uhOxDky2MvTXa0rduqNcAymk8QyKUJykSTEimMoxaGBHp7tbqMjERvVnXUsl1LUB4toipSwLlKar1upxmxvwhafYlK21qRti6ODfU5tWK3J2Dbt2eDpbAIp56qgy021P5SvW1ftD+HJzmSwbDsf9JH3oanlXp+6YIRVwQg31DbSHBvk0EAPPclhijy+fOZcqddPyOUeqYmYn36t8+URNdqpFIPGbZgEJrnhn8sW9ZouKv0u4pk07fEoCcupleg2TDyGSbHHR8TjBcW4Y4wQi0WmxAoh5oXOpKG12clC09rpQFtdgwqGne9PUFdvucvX/Zug2cNUAQFnSqs97rnqxDBEhyAWRceGYDjuTHFNp4HsiUv+wwDDcLLtPD7welF+fzYDz5evz6dMEz0cd7Y52A/JBKqsAkrK8h1qZUrs5HL1droSMe4+eVAuSFYwQyk8hsElFXXsLq/F0hpzhRxrhDif5aarvdTXxb0txyZcxmOYrIuU0hQuoTFcjMsw8l3IxdzY2gYUz/W080RXMwlr/jLnziV1gTDbSipZEyoimD2/srQtUyrnWS4wlntNLdtelJp0YztJF3YElp/v7MiUWJkSK8R5a9Jabem00yU1lXKaRKSSzrTLUDgfOBvZRjZzLh+zL4jdzyCopm0r25lUQV09qrY+2+9+9DaUUs402hXEGfPEga6pXh8nI3D8RURuCi7lldnMRTvfCWvCLDnNtBpJKH/AmWJbVuHUvhuTkSjBusmpbCHjCl+QNzddwA9PHqBfatutGGVeP29s3IrXHH/MMpWSTDshlqnCbqBdiRgv9HVxsL97wmXXhku4qW4tftOVXweQYN28cWq79qcSEqybgKkU79t66STfk9/B+TZu+upcZ9lMU+58EDlnWLaS9gD96cNYJDHxUuzegNcoWuphLbiVdfUshACyrc5tG2UY2EcPYf/8BzDYXxB8m4DbA6VlTvaVz+8EzwwjW5POQBXUplP1a9B1DaP2M+lYjJURDMolE8/m4j0X3FSGgY5FYaAfnUo4zz33GhqGM202FHE61ObWnaCBBEwe1JuLfIaemDFDKUJuD3c2bePHJw/SNjyLmoFi0fWnEjzX287W4grCE9y9lWCdEMtT2rZ5tqeNA/3dkzYA8pkurqtZw6bi8nznR/mLnlq+Bh2gsvVMcq9Z7ng4KpMo+7jbMLmuthGlFHt72hd72MuarTUv9HWyLlLKUDpJyOXB73Lnv2/Zdj7IlGvKMbbJgZh/0kDm/DGUOUNz8nf0pV9i5AgHatigxL2FVd7rCLvqF3QMAwMD/Od/fpZHHnmYWCzGunXrefe738vOnbsWdL8gU2KFOCdYv/0F+tHfze9G/UHUhs0Yl1yFql3lZNKp8VNDF8qkWYQznN6ZW163tTjNMyprRgXURi2bycCYTB1tW2Db2A/+Bn34Reicxomsz49a04Rx6+2oUHjSoGd+mnBB84pxy0y6rg32+EBg4euTf+7DcSeo6J6klodlTTjt93xka43Wml+cOcLRob6lHo4o4M1m7eZ+Sw2lCLjchN0ewm4vm4rKqQuGJatOiGWqMIiRsW0+f+CZSWuHboiUcn3tWrymKYGPabBsm2gmxUAqSTSdymciGkphKoXC+WwqhY3T5CFpZUhZFgnb+Zy0LE5E+8+JBhALzaUMyrx+ynwByr1+yn0BlFK0x6NEMymnSzmM6qqbk7HtfI21qcz2RrOtbYwJMv9yAa5xEzpWQKfUwteiP5kgbqWJZ9IkLYtyX4DK7Osv0+SXl/mYEtuT2s+h+HfRTH5cUrjYGHgzZZ5tsx3qWf3P//lu+vp6+cAH/oaSkhJ++MP/5mc/+wnf/OZ3WLOmcdzyMiVWCJG/KLX3PYN+6pH527Dbg1q7HrVuk/NRXJr9hnKmcZrmggV4Jqr9puMx7Ed+B90dEIpgvPI2YAZZfckEme99DZpPjjxWVIIqLUenkk4duUwGtW0XxpXXgeEblaWmDJPMV/4d2pqntz/TRF18BcbVNzp16GBcwC0fVItF0S/tQ/f3OR1hhwZgaBDVtBHzla93mmEUdKFVhuGctAz0ozvboKsdu6sT3dUO3Z1QXon5qtuhZlW+BoceHMB+8Nfofc+AZWU78Tr17/B4UaEwasNm1JadEI6c98E7I3sx8+qGDTzQfkoyDZaJTUVlvLJ+/aTfz12ISLBOiOWvNT40aaOfiNvL9bVraAyXyN/zNCQyGb5+5Dmpv7rIMtqmIxGjIxGbdJnraxtpjQ6xv6+DaCZNLJ0ilkmP6njsNgw82QYHHtMc+b9hUh+KsLW4YsZj0xqe623nya4W/C43AdOV/ezG7xp/6e8yDNaGSyj1+sfVj1sOxh4Hgm43btMgYLpJ2hksbdOXSlDq9Uuw7hwzlDlz1mAdgCbDofh3uMB494Jk2p05c5qnnnqCL33p62zfvgOA97//r3n88Ue57757ecc73jXv+ywkATshViBt22DbWL/8EXrvU3PfYHkVav0m1MZtqPo1TmAom20GoFNJ9JmT6NPHoa8X1dCIsfuK0WPKNWjImiwLTmfroxR+X2sNVgZ99KDTlKGsAqpqUIEQeH0YN96KPrAfVV0HM5iCq7VGBYK4/uS9WD//IXrP4843BrKdW7OM17wBY9elk18c9PVMvpPiUtTWHajyKlRVjVOfLjtVYrLOVPr0cewnHkIfOegEKMe64MLsf7JBS9sCy8b6wbfQRw9AZpI3rtYzWF/+DGrXpRg3vho8bgiHMV51B1z7CvS+p9FdHU7AsrgUwhHnZ+xyo1vPQHmlM2WayTP7zgdGtvbZy2vWUOsP8UD7KWKZ9FIP67x2fKifZ7vb2FlWPWG2jVIKUybLCbHs5KdfojnY38PennY6JwhyGEpxUVkNl1euwsj+KS+noEFO7uaAOaazfC5zyZii3uxC8JimBOuWoX29HZyM9jOQSk65XNq2Sds2McafYxwZ7KXU66fM68eljGnXcjMNg6FscHC65y4PtZ+m1OtjfaSMjUVllPsCyyZgPnYMbsN0shMlinHOa07+7qzBuhxNhpbk79jkevu8j6O4uIR//dd/Z9OmzfnHcvXHBwcH5n1/Y8mvuhArTC5YZ3/7S+hTE3dTm5ZQGONl16G2bEdFip3tkq3T1t+LPnnMCdKdOQldHRTm0Kt1m0dtSkcHoacb3duF7u9FVa+CDVucum65hhRag7bRzz8LgSCs25TP5FJKgcuN/eQj6JNHRzYcKUZt341xyctQG7cB4xsqTPo6pdMo90iNEePW12PlAnZj2A/+Bt3Xi7FhC7q23nkNctNJbQvjVbdj//C/xq2ndl2CccttznTTCerUjRuTbaOPH8b+7lcnDtTllnvwPqxYFOOW25xXfTiO9e0vQ3vLNJ64Rj/7BNaB/Rg3vwZjx27n4VAYdfm1GLkMSchnEuaz+DKZ/O/B+S53gri+qIzGcAnfObZ/0jpLYmGU+wJcXrGKgMtN2/AQrfEox4cOsK2kMlvTauJpP0KI5eXxzmb293YStyYOHqwKhLmxbi3FHmfa0HIIEkxlf28nJ6MDxNIpgm43QZeHkNtD0OUm5PIQ9ngIu7z4JshmAmdqcNq2MJWRn7Y6W4ZSlHv9dCeHZ70NsTDOFqw7m4y2uev4i6MecymFyzBwKQOXYWY/GwWPKUxlcHKof8b7600meLKrhSe7WthdXsPV1avnNP75UBggj2ezFH0uF17ThWeSG/i21ujsNYti4bvLioWRtAfoTb80o3V60y+RtAfmvRFFOBzmiiuuHPXY/ff/hubmZi699PJ53ddEJGAnxAqSa2Bgf//rsw/WuVyoy6/BuOpGp9mEMRLA0U8+jPXYAxAbmnIT9kP3oQ88j+7ugL5epzZc4TjBqeO2dQfGjouhtBz9zGPYTz8KsWwxf38A46bXwI6LUMpA2zbG6+/E+tw/QjIbGBnsRz/yW6xHfovash3zjrPfNclnhg0NYh96AX3yKLq70+mYO5mBPvTDv8V6+LfOuLdfhHnLbYAzJVZt3Yk+dRx9JJvdZpoYt7wOY+O2ad+B1JYFPV3YP/jWlMG6/PLPPIbV0Yqx+wrs+3/pNBWZieGYU3MvG7BTSqENA21l8jXz9EA/9lMPo48cQFXVOtmVq5ugoioftMRQqPM8KBK30lLTZ5FV+gK8Zd32/Nd1BV2uE1aG4UzaOVmX83Ahlr0nuya/2RRwubmjcUu2Kfry/oPWWvNEVwuPdxaUyJjkPo7bMHj35t0TTtHLZeH97PRhXrt6AxMdyHL1VPNUYfsI8tMWba25o3ELPzhxgO5kfJbPTKwUGa3JWBZgwQRZefOlczi25NNj7ezv/3M9HRwe6BnXDMxA4TVNfKYLv8uFz3ThM934co+ZbnwuF/7slGC/6cJrmpPWDiz8m1NzDKSLuetPHwZmlkSgsRnIHKHSs3thBpW1b99z/MM/fIyrr76Wq666ZkH3BRKwE2LFyAWirN/9En300Ky2odZtwnjNGyAUhmyBWW1b0N+L9d2vQG/39DbU04Xu6Zp6mcQwes8TWHuemPj7w3Hse36AWVKGrl/tdFwNFznBsWfHr6Nfeh77hedQWy6YsjNtPgOvpBR1yZUYl1+DjsecAOOhF9HHDjuNHqYYNwP9o/etbcxXvn70Y9ltTPtExlDYe5+cOnA41pmT2GdOTn/5sYrL8v/VQwPork7o6UT3dKFbTkPL6XxnYd3VgX5hr7Owx4uqa4D6NRjrN8Oq1U6wWHFeBe+01jTHBrnn9GGSU/3OiHnXkxzm4fbTlHh9lPsClHsDuLJ/2z7TlT+RF0IsX9F0imNnad4znEmT1vak2TLLha017cNRnug8ez1bUylurF07YbAuN0XYRBHLpPJBOEvb+cL/CStDc2yQgVQSS9vYWpPJfrZsjcc0WRsuodofxFAKv8vNG9du5e6TB6TDuZiz+mCEP1jjzKRZyiB6LmA2mE5O+Httoxm2MgxbGfpSM9uuE9xzgnm+wg/XyGMBl5vaQHjUWMTisZhdhmpGL+xsmIceeoCPfOR/sW3bBXz8459Y0H3lSMBOiBXDufNjXvdK9MuuQ58+iT5zHE6fdOqPpc/+bmW8+g4IRUYaOtg2pDPYv7lnJKttMdkW1ve/jvmOv4LiUuwXn0M/v2fyxe/9Mea6jWiv76wnEUqpfA0+FQjCzkswLroc3d+Lfd896APPj17BH0Q1rEE1NKI2bhvdjVZrp45fXw8qHAZfwJkuq22YbmctjbPdJx46+7LzRD/xEJnDL0J/7+R17yaSSqJPHIETR7Ae+o1T827bToztF0FlzcINeJmJW2me7Gqh1Oun2Ouj2ONjIJXkyGAPaZk6vCDchoGBImlbPN3dmn9cAVX+IBeV1bChyAlEywm0EMvXPacPcyo6MKrA/kQ00BIbZHWoeFn+Tec627bFh/hF89FxDTbH8psuXrt6I9X+0KjHc90r+1MJ9vd1cqC/my3ZhgK21tha87u2k7TEB+mdxvnYk10tBFxumsIlNEVKWB0s4pqa1eOmUAoxUz2JYVrjQ9QFI9OaRVLY/XmqLq25DLapstdyN+NymX1aa66pXk3HcIzW+NSzf6bL1pp4xukyezbV/hCvrF9HxO1dlsenc5nJ7LrKutTcOrJO5Qc/uItPf/qfufba6/joR/8vHo9nwfZVSGktt6lz+pIJMtrCY5hE3N6RoMYyKbq5lAoL6kqa8PIwquacbaOfewr7l3c7nUABAiFUdS1U1aKqalC19U5TgYKf3diOoDqZhIFeJ9PNHyhYLoP1f/+W8b3g50lxKaqhccpgXY668nqMl79i1g0R8t1WE8NOl9bokNMpNddsoeA10baFMkzsvU9j//YeiGcLZSvlvEZbtmNcdg2qqHhaTRq0baOfeNB5GbVThts+sB9az8zquSwF873/y8leLPw9yqQhMYwKRc6ZZhW5LIjCY10uAyKjbQ729/Bif9e8nUAKx3U1a9hWUsn+vk72dLcxmB5/h7XY4+WislouKK2U9yIhlqHC8+bhTJreZILe5DCnYwMcGhjfxGl9pJRX1q9DZfPNlsM5d65G5pnoAI93NtM8zWP9dTVr2FlWnf8691r0JYe5t/nYqEyht6/bQWnBDch7m4/yUv80ZzqM4TYMTGVI+QYxb7YWV3BtzWrchjllgK0/leAnpw5S6QuyJlzM2nAJAddIDWnLdrq4difiDKVTRDxeyr0Bij3efCON3N9Ja3yI/lSCgOkm5Pbkp7o+093Gox1Lc67sUgaXV65ic3E5IbdnyacKrySJRIJjx45TXl6NxzO9AJzGCdgm7QH2DH4CPYNpsQqDiyL/a95r2AH86Ec/4J/+6ZO84Q1/yF/+5QcwznKtk0ol6e5up6lpLT7f3IKIErAr8NvWEyjAY5iUef1sKZl5K+1zTe6EZSid4tBAN2go9voo8fopcntxGcaoOyti6Wjbho5WdDyGqq5DBUMjj0+nIcIUgWn7+GHsb38pP31ySfkDmO//ezDNBX+z1IlhrLu+BqeOT76QUqhNFzjZix7vlK+ztixn+YITFPvXP0U/+fB8D33hNKxF1dVDfx+6vxcG+vKBTHXBhRiveJ1TB/AcCNpNx2AqyQt9nbzU3z1hcEnMTH0wwh2NWwDnQuDQQA9PdbXQM0FB9aurG7iwrEbef8Sc5JvwOF+N+a6auuN5wQ0vMbnCwvE/OnmAltgQmTG1XP2mi+2lVewqqybgci/ZuWVuvyeH+nmiq5nW+MymmIZcbq6oqmdrcUX+pk9um6ejA/y29QT92QZGG4vKWBMqptIfoMTj58RQP/ecObwAz0qI2fGbLq6tWc2monKUUqOmblvaZjiT4bvHXiCaGT3Lp9wbIOR205tMMJROTni7XwFFHi+lXj9ht5dT0YH838ZyVeb1szZcwtpwCTWBEEb2dZgsqzBHZxthjCpLqbLvOEvQXXqxzCRgl+tRaGlNyrLwu1wciH2T3vQL095fmXsbm4Lz3yX29OlT3HnnHbzsZVfxwQ9+aNT3vF4voVB43DoSsFtAuawKW2vMc/SPp1BhFkmua5XHcAIhw5k0Bwd6ONjfPWlNjPpghBtr11LsdX4RE1YGU6l85yuxuOZyAZEL2OnONnR7K7q9FYayrardbnC5we1xOq+6nf+TSqL7+5ymDdnP05maO1dq606MK66FmlXOA7Z91oDkbOiW01hf+bfpLVxUgnnnnzpZjFMEq/Kvc3+vEwzsaJun0S4TXp+TAbllOyo8codrpTewmO7F409PHTprzSYxMZcy2Fri3NEfuSBw3osPD/TwfG8HHsMk5PYQdnup8AVYEy5e6mGLFSqXDWyfOoY+ctC5CWSaTtdv0wTDQMeiTsOfwQH0QL/znmgYqFWrnfIJq5ugrh5lukZlvYuJjT2OWrZNRttkbJt09rOlbaoKppMu9iwXS9ucig7wk1OzqxWcs6O0iutq1owau61tNPCrM8c4PDg601CxYHMYhJgzr2FSF4zQEIywOlRMmc9PIpPhe8dfoG+ZB9kWitcwWRMupjGbVegzXVi545ltk7YtUrZF0nI+O187j6dtZzmPYRJwOU0xgi6n07Tf5cJtmOdEQsxsMuwKDWXOsD/6eTRnzxw2cLEt9G7CrvrZDHVK3/jGV/nP//zchN975StfzUc+8rFxj0vATsyYztbHKJzOmrYtWuNRWmKDtMSHaItHyWgbBXhNk6RlTevkwVSKTUXl9CSHaS8I7CmgMVzCjXWNeIzJu/IUsrR9Xrfgzk/XXKIC/6Pqts2B9cu70U8/Og8jOotACLV+E2rDFtS6TSiP12kGoeae9aC1Rh8+gH3XV6e/ktuD8QdvRm3YOuX+nZ+vcm4nxWMQHUQPDkB0yPl/bw+6txv6up3H5lu4yGmusdDBVY/XCWBWVKEqqqCiGlVVA5Hi/K20lXBTxNaa3uQwd588mM8KCbrcFHl8FHm8lHv9XFBaBcBTXS08skTTNlYqBVxaUceF5TX4TNe0L9BlWoqYjfz7bFcH9n0/Qx89OI21DGhaD92dzo2pQqaJ2rgN44ZbUSVlE68uZmypy9HcffIgJ6P9s17fpQzeselCfOb4cuH3t55gX2/HHEYnxNIKmG7S2pJ6vgVyyS9zpYBNReVcXrmKIo93RZ/jzDVgB9CTeoFD8e9MGbRTuNgYeDNlnm2zHeq8k4CdmLbcjzdhWRwe7GEwlWQonaQ3OUxXIr6od/O8hkk4mxkBEM2kiGXSaK0p9vgo9fop8foo8fgo8wUoco/UNhjLzqYWK9S4Aqa5g+VyC/pprZ3upMbIVM7CAJnWGqwM9t3fgdJyjEuudAIrTHxButDTcSbLFtCZtBMQmyKwl/nqf0DzyQUZ16QME1Y1YDSuRzVthLoG56LsLEHI/MXb2Hp+tg3xGPZTD6NfeA76xtfdmYjatgvjdX8445+N8/uRfc0LxqvTaejvRXd3wNCg0303kXACbtkPnSz8OjG6C67HC/4A+AOoQBC1/SKMHbvRyQR6z+PY+55xgoKJ4fz+F1wwjHHplahLrgK3e0VkpNxz+jBHBnvPulyxx0ddIMyxoV4SlnSWnY6w28Nb123Ha8xsmvtSX9CL5SV/DM3VILUykEw62XKmC+VyAid6aBD7d79E73tm8jIPgSBq9xUYa9ejK6rA78dQpnMjJxGHE0exH38o/z5nvPZNGDsvXqRnKhaarTUJK8PXDz83pw7hf77polG1vGytOTrYy8/PHJmPYQohzmFbiyu4qW7tkp/n5JJptNagyM+AmI75CNiBk2nXkvwdvemXRtW0UxiUurdQ571uQTLr5kICdmJabK2JplM83d3Ki32dZFbgj9ptGPnsPI9h4DZMvKZJkcdHudePz3QzlE4ymP1IWRblvgBV/iA1/vCoOxP5qYiLdJGnte006TAM9NAgur0FujvR3Z3onk7IZDBvejWqYa2zfF8v9oO/di4iwLlrf9HlGNfeDF5fPhspF4DSzadQq1aP3+8ERf9nmjmXD2IN9mM/9Bvo63UywAb7IZWr06XAZTpTZVPJxQv2TKW03Kmf5vVBKIzasBVj284JF9Va5zPc7AfvQxWXOll6VTXZ79mgDKf+n2GgW89gP/Br9JEDk+7eePu7MdY0LchTc2oRZl9jpVBTZKzqTNrpCuvxTvm7kGuqkf9ey2msX/0YWk7P/xOYiMeL8Ya3oxrXL+ugndaaPT1tPNnZMu7iTeF0EVsbKWF9pJRSrx+QqbFzYaAwlHOjwm+6WRcpYWNRGTUBp0bIdOrFiPND/v3QykBHG7rlNLqt2emc3tUx/n3JdDk3NKY6H3J5MD749xhuHykrRl/iOH3J4/QnTxFwV1Ad2EG5fyOGMrGffRL7nv92squvuh51xbWobEbV2AzQc2F603I09pxuOp0op+tbR56nOxmf1bpew+Q9W0aCuJa2GUgl+c6x/ZKVJIQ4q6uqGthdXrOkATtba/qTTmfrgMud/XARcnsImG78LveUx9mxAbvcO+9sn1HSHmAgc4SMTuBSPopc6xekwcR8kICdOKtcgd+vHNpLbBptq89VbsOg1OMn4vGyNlzC1pIKJxspHoNI8ejstmxg5mxyd/GVaeazsPD78yfpkA2uNJ/CPvQC+vAB6J586oNqXI8eGpx8GY8XddnVGC+7DuXxYJ88hv37X8GZk5gf+iS4XKNPVp9+FPvpx8DjQXm8TqbA6rWo9ZtRxaX5YNRUAZ/88wTobMf61d3O61NcCi6XU9+uo3WkI+1CcXucYKXWEJt6aqhqXI/5tj8f9/jYAGYuSKXTafSTD2E/fH9BEJKRabY7L8ZYs27UNGVlGNjHDmHf+xNnatQYxnW3QE09qrjE6SLrde4mzTVIrPt7sb7xefB6wetD+fwQjEBJMSpSApEiVFUtKhiaU5fW3MWvvfdp7Pt/DrGZFdueFdOF8aY/Rq1dvkG7XPMdrTUdwzGaY4P4XC7KvH7KfYF8rZHCi/MvHHiGYenWNysB001juJgNRaU0hIowlTQ3EiPyN1tsG733Sexnn4TOtpHgnMuFccOrYeMWOPgi9m9+NqMbSubb341e3cjjbZ+mL3lswmXWRm5gc+ltWD/7PrS1OPXsGtbmbxBpy+LMcJRDAz2cGOqnLhhma3EFq0NF+Q54S501cS7Ivbc2xwZ5qqsFn+lcSAZdnmytS+cjYLrzMzamMwsjV995MJ0knknTlYhzf+uJGY2txh/iD5tGT8+SqbBCiOmq8ge5uLyWVcHIqEzdnOFMmmgmjZ0te6W1xsL5HHA5XXYnmpI/kcnOsWytGUgl+enpg/QmJ65V6DVM/NlgHjg3J5z6pBqXZbEhaVJcWonH4zSrdCkj/zm3y7kG8pYjCdiJSeUyD2KZNI92nOaFvq6lHtKyUeb18/b1O8j89Pvw3FOAgnAYistQ1bWojVtRa9Zl79aPz0jLB3p6utCHXkSfOII+fWIk2OPxQjDkTD/s7XamGM4nfxAikVFNCtT2i6Co2MmA6+uB3h4Yjk2+jeJSVNNG1OomVFU1lJajsgfYfAbXmGlpY1+LfKaiZTkZg6ePYz/w63yn0Blze6Cy2vkZVNWiquugosoJTBWOI5mEnk50Zzu6u9P5/+CAE1SKRcGyMG59PcZFl08ZIBsVcB3sR7/4nJONVlwGPh+0NaPPnHQyGDdtw3j1G5zXOtfZNdvp1f7pXejn90z93Dxe1PrNmLe/ddzrOZPAmm5vwfriv55lKeUEZnfsRm3b5WRoziEAprs6sD7/j7Ne/6yUATV1qMZ1qHWbMdY0zSnYuFhyN0Mm6+qVq3f3raPPL9EIV6Zqf5DGcAlN4RIq/cFRDZGEgILan4mEc7Pl6UdHve+o9/0dBMMol4lRkIFpWynYvw/7iYecG0A+H/gCzk2gYwWNBUrKMF79BtSatRzp/xWH+38x4ThM5eG6+v+LxwiOZGBrm3QmQ3c6yaloPy/1dTEwQY3QoMvNpqJythSXU+Tx4Zkg830+M8TOB7nLmGgmxf2tJzg+1D/hcj7TRaUvSG0gRG0gTG0wjGeCmy2Teb63g8c6molb07sJvq2kgpvqRjLuba1JWhla41ESVoZoJsUzXa1zmnIrhDg/RNxe6gJhlFL0JYfpTQ5P69hhKpVvZhF0u7FsTdLOkLQskrZFyrJwGwYXl9eyo6xqwjryWmsy2uaHJw5M2oRyMn4bdtpeikorMSYIOhpK5QN4bsPAa7jy3XNX+rufBOwW0EqeapO2LQ4P9HKgv4szsUHpNjXG+IDdBDxe1LqNTtOANesgGBwJaDWfwn7wvmkWqF4pFBQVo8orneYA5ZWoyhooKYNgaNLgST7olKsZNNiP9aNvQybtBNq8PieAmb04Ul4f+PzZr/0of8D52h9AhSPONsfU+MtnUeSaE4wJmI2b6plO5TMnYXrZCyN147K9xJUxquOsTiac5zHB9qbdWKO+Edcf/8VIoLPlNPbvfuVkPF78MmfbU9XY0xp98hj2t75w9n3llJbjeu+Hzr7cWdhHD0JnO7qrHd3ZDm0tI9NyZ8s0MW57s/P8PZ5zqrNirrnPj04eoDm+AM1CziFew2R1yOmu1hQuwedySRMJMaH8zbL+PuzHfo9ODDs3xXo6nZqdAGvW4Xr7uzg00IPfdFEbCOIyzp5ZYNuW876RTKBKy7GxODX4MAd6f4xm4ouhSv8FXFL9LjrjMbqTcdqHoxzo7yI5i2mOplJOd0DTyU5wOgU6XQLLvH4agkWYhrGiz01zxma2zTTzPHd8yJlo3bZ4lO8df2Ha26z2h1gXKWFDURnFHt+Ux6BcFsue7jYe6zxz1nPsukCY21ZvwmUYoy6Ac5ddSim+eWQfPcl5vrkrhBCzEHJ5uLSijgtKK/OP5Y6H8Uyae5uPcjI6MKNtni1gNxGPYeIzXfhd08sOXK7mM2C3sl+JeXZv81EagkU0RUrwZlszL6cTpNzJzeGBHp7oasGybecEAp0vkGtJ/HVSBadLky+USqJfeh79UkF2TG5aZnRwAUe3VDQM9KEH+uDYodGvjGE404aLS8Dldi6SEsOo0grUrothw9bckRwixbj+9H3jt56rBZf7vcwGxdQEJ6+kkk5WYiqFTqfQ8Rj09zoXUw1roaExP66JTqaV2wNFnhk9e6UUOrut3BRhrVQ+00t5xx9gtdbO78lzT09vJ7nnNxzHuu9n6H17AI0+fhiefBjzD96Mrls96YWLUgp7z+PTf1LlVZh3vA2t7Tl3GVZNG6FxHUY2pV4nhtEH9qMP7kcfOwyznfJpKOcDnNdnhQbrCrPAbK3pGI7yaEezBOtwXpOwy0PY4yXs9hBxe4m4PUQ8XorcPiIeL4ZSo95nJZtIFMpnd7e3YT1yP/rgfozb34q5ZUd+GVtrJ6CXPcze33o83+xla3EFF5ZVU+bzYSjn+H6k/1464vuxdAqvGaHE20ipt4lgpJLm/l9xcvBBUvbUGQTF3tVobfOz04cYzMyt07aVrTUcnaRjt0sZNIQirA2XsC5SSsDlnrfA9kJn8hUG6FK2RUtskLRt0xQuGbe/3FjGyo0tZVucGurnZHSAgMvNxeW1+UBYbj97e9p5orN5RmNsH47SPhzlkY4zlHr9TvAuUjZhlq+hlNPRurKOQwPddJ8l0NYSH+Irh/dyWUUdO8uqR7aRvSH5THerBOuEEMtGNJPi/rYTPN3dysXltfhdLs7EBjkdHaAvNfF02IWQsi3StrXiA3bzSTLsCnz2padJ2RYGirpgmHWREtZHygi5Pcuifk7hSVratmiNRzk51M8LfZ2SUj8N5V4/b1u/g8xP74LpBlvEaG4PavMFqO0XoRrXOwE7PT/ZUfaTD2M/+fDk3Vi9PtSlV2Jceb3T8a+wLt0ca8TZTz+KPvwSOpVE1TWg6lY700tD4VEXESobzNOP/R77/l9Ob+NKobbudJpVTFD/wfyLv4WSsklfQ/vB+5wpx9NRUYX5jveDMXVTiqmMmwKdnYY2trOxTqfRB/c79fxmMx3adDl1n9ZtRK3fgqqoGjVdebmztaYvOcyRwV7OxAZpi0fJzDX7cIUJuz1sKa5gXaQEl2Hmb4r4TBd+c3RtTSvbhGeiacRCFMp17NaHX0I/8zhq527w+gENa9dzdKjfKYBtOplouULY0XSKxyYI2HgMg2ur17CxuAS34WYo1cbxgftpjj4xqtvcdK0KXcb28jejUHQnhnmo/TSnYjPLOpitSl+QpkgJ68IlVMxy6njufLZjOEZLbJCw20tRNrDuz2ZAzOacd1SAzrI4ExukOTbImdggXYkYQZebP914IUY2YGVnOw6ayuBUtJ+exDA22ilPmL0ZnbFtWuJDtMejTimCLJ/p4pLyWnaWVdMSH+T3bScnra80GyG3h3XhEtYXlTnT0HBu9Vra5ldnjnJ0hg2Frq5u4KKy0cXjH24/zdPdrfM2ZiGEWG5mk2EHzuVlpT+4cANbBDIldoHkAnZjVfqCbC4uZ2txRXbajj2qPspSyf3oMlrzQl8He7rbGUwnz7LW+avcG+Bt67eT+cn3INeJVcyI8do3Yewc6Xp2tkCZ1jbYmlw1gpHupPaEATedyWD968dgeIqubIEQxjU3ojZtc2oZxWNO5l91rdMUg4m74o7L9suGF5RpYt39HfT+Z8c/3zf9MWrDFmdsJ485nQi1xv7J9+anIUN5Fa73fHDCb2mt0S89j/3D/2LKrNBC/gDm298NFVWzCqJqy4KuDuxH7kf3dDl1AbfuQF153aimKqOWH+zH+vrnYGiOF6yhiFNfsWkjqnYVFJWgsnfXZtIUZjHk6tR97/gL52W3vxp/iCurG6gPRmQaq5g32radRhLPPYX9+IPQ34/xl38HoTApywIFGdvmBydemvXd/gvLqtldXkPI7eW5rm/SHH1yVtvxGGFWR66iMXItHjNEPJPi52eO0HyW5kjzKeRyO3UfIyWsDhblA2zTLQeR1jafe+np/LuL2zAo8fhYFynlsspVZw3aWdrGwAnAJ6wMZ6KDNMedIF13Ij7hu1ZjqJiIx4vfdOULlO/v66QrMbtOrIvBZ7pYGy6m2h9ib0/7jH/3avwh3rB2S/61gpFznt+0HGd/3/gGVkIIcS6QgJ1MiV00nYkYne0xHu44zdpQMVtLKmkMF+ezCZbqQiW3X7dS7Cit5oKSKh5oP8Xz0oFKLBD7tz9HH3nJCaYUlUBRCZSWQbjIqQWXTjlTW5NJp0lEKpl/TEcHoeUMurcb4+KXwaVXZevTOdNRdXcn9oP3TR2sA4hHsX/1Y/jVj8d/L1KMWtPk1B9ctxEVLkKnkhAdQg/2w0C/k+WmlDMN0zDRgG45PfG+sifV9ov7sH/4rTm+ehPo6cR+7Peoy68dKV5uOzX69CO/w37gPqYdrANn2u1XPoPxqjtQO3bPeDjKNMn88FvQk21WoxS6ozU/JXai5XWkGONVt2N/76sz3t8o0UH0vqfR+3LZrwrCESgtQ1XWYFx9IzoQXPKgXa78wN2nDp6XwbqLymq4qroh//VSZ56LlS/fifvJh7EfuT+bsWtgvPeDEArz01OHOBHtn5d9PdvTzrM97fzV1kswlXfW20nZQxzp/yXHB37LFTUfoMhbz6ai8kUN2EUzafb3dbK/rxOXMri4opZLKmpB67OWc1FK8fuWk/l3F0Mp3rHxQrxTdBQsnL7en0zQHB+kJTZEa3xo2kGs+fo5LqaEleGl/m5e6u+e8bqFx8tRpUBwuo9LGRshhJi+VDTF0OkBrJSF6TEJNxThCc2sHNJKJAG7GbC15uhQH0eH+gi43GwuKueC0kpKvX4yto1rCS8kc7U1bqhtZH2klP29nZyM9k+YMXjeyp0ryfnR7MWi+fp+c3kZ7ft+Bs88hnH9K8EfxH78AfSRg3PcKjDYj35+z0j3VpfL6QA7W8kEuqsd+6d3zW1ck9Ea+zc/Rx0+gPH6N0OkCAYHse7+Npw5Obtt+oNgWbOaJqy1xrjl9ZBKOFOQyypRRcUTZizmGQbGhi3Y4aK5Z9mNHo2zvaEB9KnjWM/vwbjhVtTuK/JF6JeCoRSPdpyZtObUucprmLxiVRNNkdI5T0EXIkdbFtg21o/+a1TtWONP34sqKedXzUcXJMjjTCOd2zHEbQS5qPLPiHhWcaC/m9+2npifwc1CRts83tnMkcFe3rZu+5TL2lqT+5djoMYF68YG401lcG/zMY4P9ZGYbf3S80TA5eaWVU2sDhVPeLw8NNDNIx1nGDrP3keEEGI2Yh1R2p9pZeBEf3bmVpahKGospnp3LcGq0JKNb6FJwG6W4pk0e3ra2NPTxpvWbqU2EAbOXvdjIafT5k4IVgXDrA4VYWvN6egAL/V3cXBgkrpgQiyV3m7sHyxA1lqhuQTrAPvnP8CZh5Wen/FMQp86hvX5f0Rt2Y5+4TknK3EmvD7U5u2oHbtRq9dmp/3Orim6amwa1xhksmCdtizQNtYvfjTPwboJJBPYv/gR9v5nMV/7Jigtd8ZQ2E14irHOF0trbqxbS9jtmbBe1rmoLhDmllXrCLmdu5gSrBPzQdsWRAexvvsV6GzPP2688Y8w6hr4fevJBTx30SjmdqzYVPJayv0beKarlYc6JsnSHsNtGNT4w5T5/HgNk57kMD3JYfqTiVE12mYrbZ39Jq1TQw5esWodq0NF/Kr5GJa2SVoZ3NlalIV/47nadL3JYV7q75rzGM91a0JF3LJqXT4AOva13NvTzoPtp5ZqeEIIsaL0H+vlxL1H0dYE75G2ZuBYH4Mn+2l8xTqKm0oXbBy9vb38+7//K0888RjJZJJduy7ive/9Sxob1y7YPnMkYDcPoulU/g7ak10tPNnZgsc08BguPIaJxzTxGiY+l4sLSipZFYzMawdanS3Oa+RqcjHS0aohVMSacDFrwyX8pvX4eTmFK0cuMcWMzTHgNyPJBHrvU6Mfq63HfP2b0WdOoo8eRB89mO3KW4SKFENRMWrtBtTGrWCY+Sm8zDKg4qw7vYtYbdvQ34v1/W9AV/tZl5+Uyw2hMIQjqFAE3d8LbVMEwk6fwPr8PzkdbH0+p4uzx4Nx5XXg889+HNNkZgume1dAY4y5qPGHWF9UysaiMsJu77JovLSQrGzDEJV9B5Wg5MLSWqNPHnNu2iRGOmUat9yGsWkbT3W1sLd3DseVs49gzl20Twz+jprgTnaUVfJiXxc9qbN3/Ly5rokNRWXOCAoyr2yt6Usl6BqO0Z2M05MYpicZZyCVnFYYr8zr55KKWjYWlU/rbzXXqbQxXIJLGWS0zdcOP8f20ip2lVUTcLlJ2xYDqSS9yWH6UwkJ1p2FoRRXVtWzu7w2/zMobApiaZuW2BBPdrUs9VCFEGJFiHVEJw/WFdCW5sS9R9lw+5YFy7T767/+SwzD4NOf/iw+n48vfekLvPe9f84Pf/hTfAt8/SEBuwIKKPJ4qfGH8LvcPNfTPq0TpSc6W8jYNhuKyijz+rHRJCyLxAR3Og/0d1PjD3FZZR2N4ZJZZdzZ2nYuKpTCsm06EjFaY0MMppPUByOsz54MwsiUhk3F5VT5g/zg5IHzbirXOFIzRKwESmG+6g6ng2xx6ahmHznjuqouYpBBv7AX++c/nHk2YAHjptdgXH7NuMft557G/s09k3eftTLowy/mv1RNG1GBxUuF18Az3W2Ltr/F4DFMKnwB1kecIF3Q7cm/18DS16qbKAiRC3hMFaDINcXQ6FHvtbkbXaYyiGfSnBjqB5wMKLdh0BguWaincl7T2kYpA/34A9i//aXTCChLXXsz6uKX8WJfF490nFnQcWRsTX3ock4PPULanl3Dg2i6nUfb/oUrat7Pm9dt5XvHXqQrOXXQ7sH2U3gMk/pgxAnWZX+HDaUo8/op8fjYQFn+9zlj2/SlhukcjtOTDeQlrAylXj9lPj9l3gAVvgCh7N+rURAANJQinkkzmEo6nZtdLrymK98sQgM/O3Uo39162MrwZFcLT3e34jVMhmXa64xsKS5nd3kt4BxvcrO2nupq4WS0n47hmNSsE0KIGWh/pvWswbocbWk6nmll7a0b5n0cAwP91NbW8Ud/9KesXdsEwB//8Z/x1re+iePHj7Nly9Z532ch6RJbYGydic8feHrCoNtkXMpAo6f9hrw6VMSr6zfgOUumRmEXvsFUkub4IG3xKG3xKN2J+KhpFBeUVHJd7Rq0BhvtXJRkPzLa5t7mY7TEF68o8nJS4Qvw1nXbydz9Xdi/Z6mHI8TkXG6M29+C2rB12WX6aNtGv7gP++5vz3lb5l/+b6d5ybh9WJBKY//2HvSeJ5m0tqHbg3HjqzAuftmi1bXLvU/s6+3gofZTKyprWQHFHh/lPuciv9IXpMofJOBy53/P5jP7e77YWpO2LR5oO8VgOkkskyKWTpPRNhsiZVxUXkOlP5gf+0iQDk5G+2mODVLhC7IqECbscRoNRNMpDg50c2Sgl7bh8R2fX1W/nnWR0iUPVJ5LtG2BrbF/ehf6hb2jvme85g2onZdwbLCPn585Mi/TQ3MUsCZcTIUvkA9wlXh8KKXR2HTE9tMae4bO4Rex9czLHwRdFVxe835cRpBvHt3PQCp51nV8psn20iouKqvB73KftS6krW1A5X8fC2dWFK6X+xtojQ/xbHc7R4d681NaFVAfjLC1pIKmcCm/azsxq0YKYmKGUqwKhCny+CjyeAm5PRzo7+ZUdIHLRQghxDI1ly6xxZabF76+d2blzQ3Ftv+xc8EbUfT09PAf//Fpnn12D9///t34/eMz7KRL7AIpbLee0TabispJ2RZht5dwtnZPT3KY3uQwHcPRccG8jJ74wq0+GKHCF8BnOnc3vaaJ33TTEIqgUJOeqOUeT1kWj3c1c2ywj8H01CeCuY5hYioSoxbLWCCE+eY/heq6bBfd7HFpCRstFFKGASXzUCPCMCAUmfB5KcNEew3MV92B3nUp1n33wOkTFP7tqjXrMF73h04n2ew6iyF3rL6gpJK14WLubT7Gmdjgoux7poIuNxeUVFKWDdAVebz5YJyVzZ6bqLD8cqKzmUIJK8PqUBFD6RTRdIqhdJLe1DAHBro5MNBNbSDEhWU1rIuU0jkc48X+Lg4P9IzLEgq43ARMF91nyYRK21Z+SttyC5qvRNq2YGgI666vQfvoKYHGnX+GsX4Tz/d2cH/riXl9hw67Pbxy1TrqgpF84Gr077xJdXAHtaELsewUxwZ+y+H+n89oH7FMF+3x51gdvppSj29aAbuEZfFUVyt7utu4tX49jeFizCkKd4ydiaGUyi+fu+9uac2B/i6e6+2gKzE+a1ADp2ODnI4NAsem/wTFtNhaO6/tMn0/EEKIlWTo9MDML9ltzdCZAco2VyzImAA++cn/w09/+mM8Hg//9E+fnjBYN98kYDdG7uTchcF1tY3ASG0bIH+BM5xJ87XDz5GcRhfW1zRswG2Y+ZMqpaZXIyc31cfncrGrrJqMbfNCX6eEm4Q4V4XCmH/yPyFchDIMtNZYv/4purUZ823vzB+fpuzaOkPatp0g3EyUlIFpwgwykMexbaxvfB7zFa+FuoZx48gfH6vrcP3Re9CDA+j9e7AP7MfYeTHG7itmN/Z5YihF0OXhjsYtPNPdykPt0ys6v1jWhkt4xaomPNlA5nIPzE0m93tQ5PERdnvRWmMW/Mz/88Ae4laa1niU1viRfGbdZOKZNPFpNJH5dctxTkYHeHnNGnymSzLtZil/zDpyEPsn3xtVrw4MjHe8D6NmFU90Ns97E5f1kVJuqmvCbUw9rTvXLdY0PKwvfgXHB35LRiemvZ+a4IWsiVzDkYFeTswwm8rSmvbhKI3h4hmtV0gDKdviW0f2EV3gBklCCCHEYrBSs7vGmO160/WmN72Z2277A370ox/ywQ9+gC9+8ats2rR5QfcpAbsChXVwCoNpE13YeE0Xr1u9kWNDfbTFo3QMxybNsHMbprPdWZzw58ZT5PZyQ20jDcEIv2o+NqvpIiG3J5/pp1Ac6O+S4J8Qy4VSGK9+gxOsywXjrAz62SchlcT+1U9QJaXYbS2onRfD2vWgDKcG0iyDVlprUGDd/0uIDjoZfbY90nU1nUYnhp2L7GQi+znJvGWpNp/E+sq/obbswLjp1VBUMi4Ymf9/OIK67BpcL7vOaXgBSxasy8kdn3eX1/JkZ8u0buAshqurGthdUXtONYqwtZ4w281jmsStkSDFfL6nHR7oocjj5cqqhnPqtVwsub9T67c/Rz/2wOhvGgbGe/8WVVTK/a0n2NfbMa/7LvcFeHXDhmlnSNrawlAmGhu3GSCTmV7Azm0E2Fn+NoYzKe45c3hWY01kMvmmYePHpUfVpYPxgUdDKTyGyY11a/npqcPzOp1YCCGEWEyebKPOuGd29WVNz8LOuMl1hf3Qh/43L7zwPD/84ff53//7owu6TwnYFZjJybihFDWBMDWBMEY2E641PsQz3a0czxavns12J5M74dxQVMbaSAm9yWG6EnF6k8MMpJJ4DBO/y4XfdON3uRhKpzg51I+pFBeV11IbcBppFDoTG2DoPGpAIZdaYtmqX4N56x+gqmrzD2nbRh85CNnpVXrP4/nLMP3icxApRm3citp8AaxucjLyZph5p5TCfvh+9CP3T72gx4v5P/8XpFPYTz6Mful5GOyf2XOcgn5pH9aB/ag1TaitO2DrTpTPP+r5KKWcrD6WPlA3kRKvn/YJaqEtNrdhsLvC+T06VwJMTqfF0Zl1uRp0QZeb/tT0s6Gmqz4Y4fqaRkq8vvy0XDF92rIgEcf6729mp7OPsWo1qqiUlvgQz89zsA4gmk5OK1iXC9T1JA7TEn2KjvgLpO1Jmt1MIGMn6EkcocK/hVfVr+fnZ47MeKxJ25p0nGnb4jvHXsBvuqgPRWgIFlEXCGMao2s1KqAxXEKJ10fPWaZ7CyGEEEut2ONDqdwNWecxr+kkOWnAbCgCQ5Hv4DMdhiJcXzTvY+3t7eWZZ57i+utvxMxeixiGQWPjWrq6Fr4UmQTs5qDwBN5QitpAmNet3sRgKslgOknatom4vfO6T6UUbmVS5Q9R7guMqkGUy0BAOVN3L69clX987MXGg22nzqtg3SjSZ0UsJbcHPB7weMHnx7j8GowLLnRqPBXSGjpaJ9/OYD/66UfRB18Ay0I1NKKaNsCGLU6W3jQCDNq2MK66AeobIToEPh/K5wefH7w+Z5xuz6gAmXHjq1E3vxbdegZ7/7PoF/fB0DwU1dY2+sQR9Ikj8Mu7UY3rMW64FV1VuyJqiFX6AssiYLdSprrORGG9rpygy83u8lp2l9dyX8sxXujrmtM+ij0+VgXD1AbCNASLiHi8TkBkBfzuLSe5aer69HHsH30bYmP+JiLF4HJDfx/09bCqtJwyr/+sNQVnImC6ubiidsplbG2hMGiP7ePowL0MpmY3HVdj81TH59lcehsbim7gLR4f3z62f0bbiGbPxSbKoHMbJq9fvYmvHXmOtuEoT3W1YmZvGDcEIzSEirC05vhgHyeiffQm5z94LYQQQsw3jcZruAq+HkmuUYAn5KGosZiBY33T3mZxY/GCNJzo7u7iIx/5X5SWlrJ79yUAZDJpDh06yFVXXT3v+xtLAnbzKHeSFfF4iWQ70S1kE96xF2aTTbsdG6zTWnNReQ3P9rTJxAkhFkMwhHH1jaiLLkOZow+7OlsHblzjBdPE7myffJtFJRjXvgK14yJoPYP1/a+jD+6Hxx/E9d4PjVw4T5F1l9unamjMPqDOXlszF7yrqcOornOCd6dPYO/f42TexaeRnaIMmKSEAAC2jT52COv4Ecw/eS+6um7eavYtBFtrrqpu4GR04KyNgRba2vD4rrvnmsKbUAOpBLaGGn+IoUyKWDp11vc1hdM1vC4YoS4Qpj4YyXfqtNH591bJqpu+fD3Jni6s+3+BPvTiuGWMV92BuvDSUceYPd1t8xas85suLi6vZWdZNcYkxzJbO8fb5uiTHBu4j1h6Pu6Maw703k2Zbz1l3lUzXrslPsT3jr1AfSjCqkCE2mAYj2HmM+isMcdKS2uaY4M0xwbnve6fEEIIsRgGUkmGjQwRjxczW99/rOrdtQye7EdbZ49YKFNRtXvqm3WztX79Bi677HL+6Z8+xYc+9L8JhyN84xtfZWhokDe96c0Lss9CSi9kRElMyNI2Bs40WtTiZ0TkLna+deR5upOzmx++ElX6Arxl3XYyP/o2vLB3qYcjzhNq2y6M17wRTGNanUwLA2z28cPok0fRDxdMWa2owrjoMtTulznbN00n6JdKoo8egt4u1IYt6N5u6OtBXXIlyuNd0G6Xhc0frLu/g97/7OQLK4XrI//sPL99z2A/9Qi0npl8+fJKzD///8AwlnW2k61tepMJ7j55YMkKv1f4Avzh2m3Oic8yfq3mQ2FNu8LAmtaaeCbNYDrFYDrJUDrJUDpFIpOh1OunJhCiJhDKN4LSSGBuLrS2AQUD/di/+yX6hb0TZ7E3rMX4H+/myGAvB/u7MZQiY9uciPbPeQwht4cLS6vzgbqJfp62ttDYnBp8mOMD95Owpn/HfjqqAzvZXfUOnulq5aGOuTWgUUCVP8SGSCnri0p5vreTp7unyLYWQgghliG/DTttL0WllRhjSnMVKvP6MY2Jq7n2H+vlxL1HpwzaKVPR+Ip1FDeVzsOoJxaNDvH5z3+Whx76PUNDUXbu3Mn73vcB1q5tmnD5VCpJd3c7TU1r8fl8c9q3BOyWkcIfxXxdbE00HfZUdIDneto5NjS/J6zLnQTsxFIw//R9UFs/vSmquaYPaCdbVjlBKusH34JwBGPnxajqOrRtTRj809mGEYXZaNq2p5U5N1e54In92APYv78XpghaGdfd4kzFza0bi6JfeBZ94AX06RPjsu+Ma29GXX3jsg9C2VqTti1+3XyMo4t4fI24vVxSUcu2kkpAAlA5Y4N6uZtly/33aCXI3VjQ0SHsB+5F730qe+yagGFg/PXHiRkG3zz6PKl5as5S5QtyUXkNG4rKnN1M8HPNnVf1JY+xt+sbDGd652XfYzVGXs7Wsjuw7AzHhgb4bcsJEnZmQfYlhBBCrATTDdiZSlHmDUzanzPWEaXjmVb6T/SPrmlnKIobi6naXUuwKjS/g58jCdidQ+ar81xh9kzh/wdTSX7XdoKT0QFCLg82Ol8v5XxT6QvylnUXSMBOLCyPF1W/xqnVFCnCuPwalHf2B+pccE5ng1hqGdcoy0/DTSbRzz2FPvgC+swJsCa4QPf6MK5/JcbFLxu9jeG4s97p4+jmU9DdhfH6O1FbdizrabE5uWP6vt4OHmw7NWn38PlQ7PFySUUdW4orAAnUiYWVD9KlUujDL6IP7EcffhEyUwem1JXXY17/Sn5w4iXOxAbnNAaFM+17d3kNdcEItrYxJjkmOqe3mgO9P+H44P3Mbw/h8SKeVTQV3Uht8CI0mpZ4jJ+eOkhqskCmEEIIcQ6bbsAOwGe68iXFJjubTUVTDJ0ZwEpZmB6TcH3RgtSsmw/zGbCTGnZLbOxUntnc+c9PxTp1kHgmjakU1f4QIbeHIwM9ZLIx2aWurbRsSIxaLBC1YQvGa96ACoYLsuXmuM1cnbkJLkq1bYOVcS6YXW6Ue+o3w4WWmxarvF7YfQXGpVehM2n0yWPoY4fQxw5DV7YuXzKB/cu7sZ99EvNVt6PqGpxjoD8A2y9C7bwYpRQ6mQSPe1kHKgvljukXlFTSEIzw8zNH6ErMf+mBi8treVlV/ah9CjGfcscwZZpOIP3AfuwDzzuNYSYKwk8mG2jvnYdadX+wZjMNoaKCBg2THxeUUuzr+g5noo/Neb/TMZhqZm/X1znY9zO2l99JfXAz20uqeaZHprMKIYRYWEGXG5/pyn90DEeXrETL2SjAle12bmXfzxNWhnTCotTrnzQe4gl5KNtcsYgjXR4kYLeMzC5Yp0laFr84c2RUp7G53sUWQsyAz4/xitdh7NjtBNHI/j0vUEZYLrivjxzA/v29qA2bMS6/Fu1yAXry4J7W42rBaW07M3ALOsFqy3Km0RoTbEdrZ8qqZsqMt9z3lMsNazeg1m5A3Ww401+PH0Z3d0BPN7qnC+tb/4navB3jplehfYFR21Xe+e20vVgMpSjy+LizaRs/P31k3ksQBF1uFPNXPkGIQtqyIJ1C73sa68B+OH1y6kYxU0k4gTr3BMeTmWgMF9MQKgLOHqTWWpO247TEnprTPmfD7yqhyNNALJ2UYJ0QQogFd1VVw7ju6Pt6Ori/7cQSjWg8t2HgMUy8pgvXmHp1ucCdpW0mbj9xfpOA3QqVu2A/PtTHb1qOM2xJrRQhlsSqNZhv+iPwBwAmDHLNt1yQxti4FWPj1jF16iao42RZMBxHt57B2LBlzLYM7KcexnrqEfD6UOWVqPJKKK+CqhqIFI/O3OvuRB96ASqqYN1mMAwmCxLm91HwmqhgCLbscIJNhbX2YlEYHEAFllcNirkwlEJruHlVE984so/4PN7pfLq7lR2lVZgSsBPzKH8z4MQR7J/cBbGhuW8z7mSYuqfRdGcqV1Y1TLuMiFIKtxFgdfhqTgz+bk77nS63EWRz6W00hK8gaaX5/onxnXKFEEKI+dQYLh4XrNNas6+3fYlG5Cjx+Gj0hQgMJCj1+PF6vWgmnu6aaxjlYmXMpllsErBboZRSPNx+WjqHzYBc1op5V1uP+dZ3gsu1KIG6yZxt307dqSQqUjSqCy04mXe6rwd6u52v25rHV3pyeyAUdqb4DhRkivkDqO0XYVzxcogUjeoWe7bxjHssGEIHguPGt9IppfAYJjfVruUnpw/N23ZjmTRtw1FqA2GZEivmlf3kw9j3/mT+Njg0ADi152Y7PXx9pJQKX2DG620ouZVTQw9j64WcFqSoD13O5tLbcBk+Xurr4t6WYwu4PyGEEOcCv+miyOOjyON1Ptw+ij0+Ih4vT3W1sL+vc8r1q/xBblm1btTNLFtr9vd10j0PZSimy20YbCmuoMofpNjjo9TrJ+ByMzw8zPGhE/lmEmc7W5Wz2YlJwG4FsrXG1pqW+Mqc9how3VT4A5yKDizNAKSGnZgPVTWYb/1zcJlLGqybtuLScR1kAeexutVoHp583XQK+nrGPz4cRz/5MNbTj6K27XI6v5ZXznqICzmNeCkZSrE2UsKVVfU80dkyL40oavwhVgUj8zA6IQrYNiQT87vNk0exW87wsrp6Sjw+7ms5jj3DBhBXVtXPuEmXUgq38mMq14IF7Io8DVxQ/ocUe1fTnxzmp6dfoGcRL5KEEEKsLOXeANfVrqHKHxyVeW5ly83k3ueuq11DS3xoXP1XBayLlHJReQ21gTBWwXuj1pqMbfNYx5lFeS4B082usmp2llXhMUxsNAYqPxNIyrbMDwnYrUC5P8o3rd3G3u42ft9+aolHND0VvgAXltWwtcQpFvlkZwuPdk59QFGAxzBJ2jMocC3EQiuvwnz7u8HtzjeFWO4mCyoq04SGNXPbuG2jn9+D9fyzGK++HbXrkoKGGwqUKpiye37SWnNxeS1biit4qP0URwf7AD0qbKEhX0z/bK6unv70QCGmTSkoKZ/3zdpf+Qzc+adsWb+ZZ7pbZ3Tn31AKV3bq/Uzvv8fSnaTt+Q+guY0gm0peQ0P4SjI6w/2tJ9jX2zHv+xFCCHFuUMDFFbVcUVkP6HFNk0xljH6L03DLqia+e+wFNOAzTS4oqWJXWTUhtyd/vmgWBOsAnuhqXvBSWSUeHxeVO9f0Klt1TimFKTlyC0ICditU7iJta0nlsg/YFXt83FS3dlw2iM+c+NevPhhhQ1EZ1f4gZd4AplK0xId4qb+LIwO9ErwTS0spzD/8Y/B6V0yw7mxUUQkEQxCLznFLGvuXd6M62lChMHg84PY6gc2GRnQock5Nd52JXLAy4HLzyvr1ky4XTad4uOM0B/q7J11mTaiYOsmuEwtAGQaUL0wHNuXxkrAyM85As7Xmv0+8xBsbtxJwuabsDDt6PYtEph+3ESRtx2Yz5AkoGsIvY3PpbZjKw5HBXn555ghzz5kVQghxrirz+rll1bp8aYep6j7nmEpR6QtydfXq/JRTQ420ZDDGBOrSts3ennae7Zl97TqPYZLR9oQ3jxXQGC7hwrLqfLd2uWm8OCRgt8K5DIOrqho40N9Nd3J2tWEWUqnXzxsbt+SDcyeH+tnf18np6MCkgbfLKlexKhAGRi5yawNh6gJhrq9t5NTQAC3xIVrig3QMx/LtoKfL2LQNOxSG4WE4tB9SqTk8Q3G+UVt3okrnPwNlKWnbRl10Ofqh38x9Y5aFfuqRcRPejD94K2rL9rlvf4U728lN0OXmllXr2F1ewwNtpybs+H1V9cynBwoxbSVlC7JZu6aOk0P9M5wM6xhIJbnr+Iu8ce0Wgi7PNH/3FaW+9dzY8Cm6hw9xqO8eBlJT3+D0u8oo9q6m2LOaoLsSl+HDZfgwlReX4cHvKgUgZWX4r6Mv0JOS6a9CCCEmpoDd5bVcUbXKyUSbxXnbReU1WNp2MvAK5M4DB9NJnulu46X+LtL2zG8flXh8NEVKWBcppcYfIm6leaqrlf29nWS0jQIuLKvhwrJqwh5vPpi3FOegSg/g5hiKBBofaZrQqmjRx7HYJGC3wljaxrI1HtPE1hqF84d8cUUthwd6eLjjNAOpJOAUsizx+klYmXHz3xdDQzDCrfUb8JomSinStsXPzxwhNUWGnEsZ9CTi1I0ppJ77v4liTbiYNeFiDKWwtE3HcIzm2CC9yQQew8BruvCaJi5l0JdK0B6P0pWIk7AyzsHtggsxLrgQyE4/iw3B8aPYv/4pxOeaYSTOdcY1N027ucKKoRTG5ddgPfEQZI8f8840YZZv7rnX+1xrSDGR3MlcmTfAHY1bODHUR1s8itswcRsGIbeHCl9wiUcpzmXK5wevb35r2a3fjOH2cDLaP+tNDKaTfPPI81xWUceF5TVo9LgLmEK5TDyFSbl/E2W+dTzV8QV6EofxmcUE3OUEXGUEXOUUe1dT4m3EbTrZD7bOoDAnvLjSWuMxXbyyYR0Pt5+Z03MSQghxbir1+rllVROV2XO22QTrcusUvtflAnXtw1Ge7mrl+FDflDfCIm4vu8qqKfX6+H3bSQZTKeqCYdaGS1gXKaHI48vHFJRSBEw311av5rKKVTzT3UrI7WFnaVV+e0sRqDN1Mz4exM1BVEFOu8YgrTeR4BostWrRx7VYJGC3wqQtm9OxAWoCIcJuLzAy3X1dpJR1kVJ6EnGKPD48BRe2B/q7eLjjDNH0wmeTNQSLuKJqFbWBcP6gYmvNvp6OccG6Gn+I1aEiKnwBqvwhwm4PSql8eu9ECg8UpjKo8Yeo8gcxlYHW2ilmrUeWzW2vN5ng6GAv/akEPYlhhtJJagJh1oaLqblgF2rrDnj4t+gH75t4xx4f6oprnAy9555BP/HgnF8rscK4XKg5NFVYrpRS4POjtl+EfuaxhdlJZxts3AJqZgE3bVmQSWM9eB/GtTejlTq3gqWTyB3nGkJFNISK8sc0ssez87keoFgEpeXQ1jwvm1Ivuw51/S30pRIcG+w7+wpTSNkWD3WcJq1tLq+c/sm5oQw0Li6rfi8ajVFwHHKCc8aoKUqGmvz0OHdOUeELsrOsSgJ2Qghxjgq7PYRcHmKZNLFMatqzujYXl3NT3VqYZVbdRHLXxrF0ivvbTnJ8aPT76aUVdewqqyaaTjGYThJNpwi63KyLlOZPId/S5Mx08ZgmlrYxslGEwmvr3Hh9psmVVfVLfr7p1i8S5L9RjK/Lp7Dx8BJuDhPTbySttizKmE6fPsXb334nH/jA3/CqV71mwfcnAbsVxudysS5SOmFJx9wfW4V/fPbFpqJyAi43Pzp5cMHGtioQ5urq1VQHQuPSZQ2lOBMfxFSKcl+A+mCEC0oqKfH6sbUNqAkPFtNRWOQy/381fpkyn59irzffvcbWmubYII9kA5k31DWy+tqbsbdfhP3V/xjJtmtcj/HqO6C4FEMpEpkM3pteje33o39/7+xfMLHyeLxLPYIFoS0Lkgn0qeMLtg/7+BFc19w0o3W0ZUEsivXVf4fBfuz2Voy3vOO8Cljl76qeH09XLANaa1R1HXoWATu1pgm1dSf2A/dBbAjjtjejLtjFyWg/vzhzdMoM++mqC4S5tKJuxscBlc+4G81QLrRto+0MKCN/Q0BbltMsp+AGQW6f/akkT3Q1c3CKWpNCCCGWnt90EXC58ZsuMlqTtDKkbIukZZHR46eQGijWRorZXlLF6lDRqPeZpJUhnknzcMfpbPOwiZV7A/lEkvlga41G81RXK093tZAZs12/6eLSijpchkHA5abCF8h3Y1cFde/chjFh1t5ElsN5tqmbJw3WFVJkCPJ9hvSfLXimXSaT5u///u8YHl682YsSsFuBZpuKWnhn21CKxlAxPck4/fM0Be762kbKssU0x47R0ppb6tbhMU2MMRl00y0gPR/MUXfQFauCEd64ditnogM82tnMgf5urq9txPirD6N//VOMnRejauuJZdLs62zmyGAP/akkt65az7qrbkCv3YD9va/JVNrzxTkYsNOWBYP9WN/6T+jvXbgdNZ9Cp9Mot3t647JtsDJY3/4iDPY7j504gv2T72Hcdud5FbQTYlFpjdqyA733yemvEynGuOk1GFt3oLXGXLcJ+8V9qAt2sa+3g9+3nZxV7bqxwm4Pr2nYAMztYqJwer3u70W3NsNAH3qgzznemNls6rIKp6afZUE4giopQ2vNN448Ny/PRwghxOz5TJM1oWKCLg8Bl5uAy03Q5Sbkdr72ma4pr5u11qRt2wng2RZJK0Oxx0fA5XamiY5Z12u68BgmVf7QhAE7v+ki7PbSNjxEx3CMKn8QW9uzvtbNzVQ7Ge3n920n82WvChlK8fKaNeMSXybq2LrSzpt9PHjWYF2OIoOPh4hx54KO6ctf/k8CgcCC7mMsCdidJ5RSeEyTKn+QDZEytpVU4ne5GEwlGUonGbYy/Lr5GDaaLcUVtMejeEyT+mCENaFiUrbFD08emHIfPz51iJvrmqgPRcZdTJtKYbpGft2WywEjd3CrC0Z409qtnI4O8LvWk1xUXkP5rX9AxrZ5srOZPd2to+5m/PzMYS6pqOOy2nrU+z+C2v8s9i/uhow0sDin5TIvbBu0DcboaVQrjbZtaG/B+s5XYHi+uihOwrbQJ4/Cuk1gW2BMXB8KRtL+7bu+Dl0do7+3/1lspTBe94cStBNiASjDgLXrweeHxFnuILtcqCuuxbjqBshlsCmFDhfB5dfQkxyec7BuVSDMmnAxdYEwVf4QhlJzr6FjWVjf/zq6+fSkx77CMas16zDe+EdYVoafnTkiwTohhFhi20oquLp6NT7T5WSgae3UZGb615m562OPaRIa872p3mcaghGKVq3Da7oIuFz4TSdQaBqja81Ztj3r81Q7G0z8TctxDg/2TLhMwOXmtQ0bqPaHzrnzYafBxMxmBro5gNIDC9aIYu/ePfz4x3fzX//1PV772lcuyD4mIgG784Stba6sauDKKvKFJQFCbg9htwcNvHXddpQiXxuv0BOd05sa4zHNFXkRnTsorwqGaQgV0RobonM4xiMdpzkZHRi3vAae7GrhYH83V1evZv3Oi9EXXOgE7n561yKPXiya3m6s730NyitQkWKIFENVDRSXrsy6akqBP4CqqnGCaQvMvucHqA2bUZU1qPo16Oq6UccKnZ0ej21j/+z76BNHJtyOfn4Ptm1jvD6XabcCX3shJpDPPrdtCgoX5m8WLNp7q1IYd7wN/fSj6MMHnCD72EU2bsO45TaIRBhbp0eZJkprBlKJOQW3PIbJ7Y1bnLpz81gLSHk8UF4Fp09ARdW4GwNjGdfdgu1x8/XD+xiUG3NCCLFkKn0BbqhdS3UglH/PNJSadWOzmdJoKn1BqvyhfKOGicx2TLnn1BIb5FfNx4hO8p5T5Q/yuoaN+F3uFXfdPR1ON9iZdb1V2Lg5RooL5308Q0NDfOxjH+YDH/ggVVXV8779qUjA7jxhjJkKOvb/Cgi53Yyt7mJrzZGBHh6bImDnUgYbi8q4tmb1qLnxK1HudaoOOAfh16/ZTMLK0DUcx9I2drapha01lrZJWBY9yTjNrYPUBsNs3HkxhCPY3/7S0j4RsWD04RfhcEH2hc+P+f6PgOFZymHNilIKXVyK+fZ3YT+/J9speQEz7YYG0HueyFXVQF1zI+a1N4903e1sx97zBHr/s2fN7NEv7HWCdn/wFrQ+x7r2ivOWUgr78QfR0SFUOOwE7qrqMJo2OBmxi/T+qpSC1U0YazegE8Po5/egu9qdYHtVLVTVoLw+5293koC5oRRrwyWUef30zLJTfU0glD1Pmd/nrbXGuPFVqJteDUDmXz8GQ4OTL3/8MEZdPXF7elNzhBBCzC+vYfKyqnp2lFahC+qzLTZDGQtWVzhXA/7h9tPs6WmbdLlyr583rd2KYh4yzpcpxew61Svmp9TXWP/4j59g27bt3HzzLQuy/alIwE7kTTS/3lCKx7taJly+yh9kW3Elm0vK8Rhmfp69pW20Jn/XYSUeSArH7DNd1Ici+a9zdz40IwWoNZpvHNlHTyLOFU0b4ZWvx/7l3Ys9bLEUEsPopx6Fy69ZkUGj3JjVtp2YNXVYX/43WIRu0qDRD96H1XoG1dCI9eI+aJ/4WDPpFl7a59QGueNtKzKzV4ix7Ad+jZ3tVK4B1bge45KrRoLaiyhX4035/HDR5c7+x0xnP9uYbDRXVjXw09OHZjUGp9v87Ov/TGbsscJ4/VtQVTUwOIA+8Dz2s0+MCuDZB/bjuuYmdpVW83R367yORQghxNS2FJdzTfVqvKYr20Th3DjfyzWTyNVY708l+MWZI3Ql4lOuV+zxnbVpxEqn8c1yvfmvN/6rX/2c557by3e+89/zvu3pkICdmFLKsugtuDO+KhhhQ6SU9ZFSgm4PltaY2Y6rhlIkrQzHh/qIptMYSnFBSSUuw1iRQbvJqIKsxFy2g601V1ev5p7Th4l4vGzdfQWqtwf9xINLN1CxaOzHH8S85GVotXLT0pVhossqMV77Ruwf/tei7VcfOYA+MnV9zCnXP7Af+2f/jfnaN0nQTqxY2rbRe5/MB+sAqK7DuPNPUS7XvHWam61c8A5z6tNGJwsQp3GFYWIqg6ZICbWBMK3xoRnvtzYQnvOFmdYajY2hTCcbt+AiR2sNtkW6rpxophlfaQmBq2/AvPpG9Ev70M2nnOdSvwbIzUQQQgixGMq9AW6oa8zevNHnzPWkrTUJK8OpaD99yQT9qQR9qQRdiXg+y24qHYkYvclhSr3+/Pammp67EqVpyhbDmP60WI1BmqZ5H8s99/yM3t5eXvva0dl1//iPn+A73/kW3/veD+d9n4UkYCfGcU5unT/+RzvP5B9fEyri9Ws2Y2k7H9XPBev6Uwke72zm6GAvltZU+0NcVVWPxzSndeBZ6QxlsD5SSmO4mN+2nCDk8tBw06vRg/3w0r6lHp5YaLEh7Pt/iXHza0c9rLUNth652F3mlGGgtu5EN59CP/HQUg9n2vRzT2P5g5jZ6W1CrDTKMLAeH/83pw+9iB7sR/f1wEC/0+ymtBzzFa9b9DFOJR8sb2sGfwCKS/Lfs7Xmprq1fPvofjJ6+ifeCqgNzL2QdsLq48zQEyStAbaWvREKgnYamxNDv+f/Z++t4yPJznP/7znVzOoWM4yGGXZnlpm8ttfMFMd2bIc595fcJPc6uYmTm+QmsQOOMaZds9f2GtfLszTMrNGIWa2WGuuc3x8laUYjjWikEUx9Px/tjgpPtbqrq5563+c51vud0eXdMsy6/LdSvHodcu2mkSNEaYWplv/1jI2Njc1C45IGuwrL2RK76BW2XMS6EY72dfJMW+Os1h3IZvjCqQO4pEGBx8f9ZXVE3LOrSFusaBEmq1fj4ui018myZl4CJ/7iLz5BOj22Rfctb3mED33o17jnnvvmfH+XYwt2NmMYUehf7WphT1crSTNH1O1hZSjGpmgRSutxJbgC68SqNWyMFrExr5CYxzcq1C23E+yVUFrzSOUqDvS089PmMzxStZr8N70bNdAPFxoWeng284x++XnYvAOKy9BmDvp60V3tkEpBRTUimm8tZ5rD6bKL83OhtUZuvRFzCQl2AHr3Uyi/H3nzXQs9FBubGaG1ho5W6O4YO6OtGfXNL41bXtx6z4K0yF7OqCWEUjCYwPzxdyCdRr7rV7nU4EcKQcTl4c6SKn7Wcm7a2y/3h3DK2T/s0FrRMriXfZ2fG52WzPWwvegjoMXw2Ay6UmPbddOqn70dnxkzzW2E2Fb4IdbnVfBM++xusGxsbGxspmZlKMpdpTV4DMeyu4cc+d5sTPTzSufV2ytklEnz0MC1sre95qS4HScnEUztH6txkOK2eRlHYWHhhNPz8qKUlJTOyz4vxRbsbABGy4ybBuO82NFEPJthc6yYVeEYUbd30lJbIQQ+h5OHK+vHtO0st5PsVIwc78ZoESvDMXZ3NLEjvxT/ez+K/sn34NUXFniENvOKVpif/xR4vBDvh8uzEX1+REU1orIGUVWHLi0fNqmau+TDuUAIgQ7lTb3gIkRn0nZbrM3SQykwFThdU/pHivo1yNvvmzfD6+milUIfP4z5/UchnWbkfCduuYvLE2PB+n7cEC2icTDOif7uKbcvEdxVUn1VLVDJXC8Hu74yZlpH8gjPtXySqGcFSmfJqRSdyalb8tNmnPahw6zKqyHsdNOfnR9TaxsbG5vrlbDLzd0lNVQHI8uq/fVSFJpnWs6zr6dtTrfrdSxPuwZTlDOo34afRycV7TQOBnkbpii/hqO7dgi90MYoNguK0lbi6ZG+TvZ1t9GTTuI1HLy/fpNl7Mny6oe/Vox80RzqaacyECbs8mCaOcT5s6gnn4Bm+wn9dU8ghNi0Hbn1RkQ0f5y30kKhlQmJAcx//N8LPZSZ4fNj/O6fL+rqRRubK6GVQp87NWnCuKhfg3zbB0CKRXGuyP3dn8NQYuxEfxDH7/8FwDjxfCRd/fMn95PIZcesVuDxUeT1k8zlGMplqQqE2VVYPuvPstaa7tRJGgeeI9+7hqi7DtCYOkNOZzBVGlNncEo/XkceLiPA+fgzNCdeJZFtQ2OO26ZLBri74hP0ZXJ8q+E4idy1COexsbGxWd4YQrA9v5SdhWXLOvXU1IojvZ38fAaV5pPhczgJO924DIM3Va+Zk23ONalUijNnzpKfX4zLNfswCEM34eEZnBwb42lnedatIcVti06sy2TSdHW1UVdXi8dzde3KdoXddciIR50Azif6eKLpDCkzhyEEK4J57Cgoxb0My5CvJVIItNZUBiJ89uQ+yv0h1kUKWFVdh/HB30Snkqgffw8OvrrQQ7VZKBJx9PNPYj7/pCXcPfgGtMO5oIKT1ho0mI99cUH2f1WkUug9uxFbbkQ7htMtF4GoYWMzHYSUiLpVKJfbqri7TNCivGpYrFtEgrR52dNuaSBf+5bRX0fGaQU7KKRhgBasCuezp7t1dLmIy8Pba9eNa3+9mufJGk2+dxX53lWoXBrR02O9rg43GAFwGGgpIZND9A1C0M2KyP2siNxPKhfnVN+PuDDwAoocTumlxL+V8sCNGNJFntvBh1ZtIWeadGdSfLvhOH6nk950cgbW2DY2NjY2Ff4Q95TWEhkWcxbN99s8YAg5rQrz6RByunl//SYcC2yNca0wRTmDvBOh+3FyBkEajdsKppgHz7rFhi3YXQeMXPRmtWIgk2YolyXk8hBwOEmbJtvzS3BLB6sjMdyGA1MrW6ybA4QQhF1uCjw+mgbjNA3GebL1HPWhKLcVV+G97W5MW7CzAfSBVzHPnETeejeiegU6vxAhpVXtJq7NDfqIJ5b5o28vzQpQZaKe+A48+QRi8w7kztsgEl0UXl82NtPFeO+vQawA9ejn0Q1nLs7IZtAnjyJ8frTPjygoWvj2b4cTRkyYpYF86/sQ9WOf8mszBy1N6MazUFGDqKhmfV7BqGAnEbymoh7jCnYbs0Ug0Kkk5hc+De3T9AkqKoHKWly7bmN97G3URx4gmesl4q4CBNlkhqZXm2k90Io/30+kMkLxhiI+unobQgqUqUiaOdpSg/ys5QxDuak9d2xsbGyuR3yGk9tLKlkTKbBsl66D+86hXJamwficbOvGgrLr8l5dizAZti70MK45dkvsMmbkT5syc7zS1UJ/Jk1tMI8VoTzchgOlFWBVgiEYFyZhc/WMtByfivdwtK+T84l+AN5as5aynIn5T59Y4BHaLEpcbkRpBaK2HrF1J8IfQJvmvKXNaqUg3o/6wTfQZ05MvcKSQCDWbkQ+9Ebw+mzRzmZJoJUCIaCjDfPf//6Ky4ltuzAefvM1HNl4zO98FX1wjzWeLTdivO6t45f52eNW4rRSiB03Yzz0RgD60ikcUuKUEpc05u1mLffpT0Jn+8xXrFuN8a4PorWm/VAHjbsvkGhPjFuscF0hgaIAibYBfPl+8qoi5NXkcbK/mx82nZ6DI7BZLIRdbrbFSqgKhBnIZohn08QzaeLZNAPZDK1DiRmlINvYXK9szCvk1uIqnFJeN6KTqRUHutt5qu38VW8r7HTzgZWbl8RrN1ctsUsRuyXW5opcmszak07RmRpEo9lZUI7LMDC1GhXm5IhAtwQ+8EsVKSwvhpXhKGsi+Zwb6CWeyVDuD6FOLxdhxGbOyaTRDafRDafhqZ8g6tcituyASNQypnc4rOoWhwOcrjGtZ9O98bXaX60f/fyTqGd/DsuqIkSjjx7APHMCee/DiG27rKlagdKLq7XQ5rrhciNtrUzEcCuo1npUWDZ/9vik29F7dqOq6xBrNy2IGK21Qj78ZpTHi25vGW3hvfQcpLVC3vMwbNuFevk55L0Pj86PuK/u4nUylFaI4VQOx8f+EJ3NQGc7+uwp1L6XoKdr8g3ECpB1KyGdQTicHP3ulUMpOo500HFkJN23kwbgpt/aRYHHNyfHYrMwSAQhl5s8l4eI20O5L8iKUBSFZijbToHHQ7E3jCGcGMOfv8O9Hfy0+ewCj9zGZvFS4PFxb2ktxb7AwleILwBzVRjjMi65ZrjOXsPrFbvCbplw6U1ATzpJbzpJ0Omm0OsfI9LZLCwjfyd19iTqv/9joYdjsxwQEvx+5B33IzbfMGkV3kiVntYaWi6gThxBH9k/9Q3scsAfRBQWQ0ERIr8QUVgCpeVgOBZdUq/N8kRpTTKXxe90oV58BnXkAHL7LuSm7WMqaLXWVqtpdye6q8P62bMbkkNjN+hyY3zkdyGcN2/Vt5OhTXOc8G0qE+NyL7rhtvSrqRJW2kRg7UtrhUYhMMY9rEhkM1wY7KdpcIB4Jk2JL0CxN0DU7SXi9qD7eyevbPcFMH77T0FK4m2DnPnFGXrP9U57nNG6KFvevZlT/T08fuHkrI7V5triEIKV4XwKPD7yXB5iHi9Bp3v0mlppjakytCcPcrj7UXJq7OfQIX3sKv4t3I4S/u3Yq5fnw9vY2AArQzFeU7Hiur7W6kun+Nyp/bNe3yUNqgJhaoIRVoZjuOS1/96fKRcr7IpwuebvQd1iJJNJ0dXVblfY2Vzk0if2eS4P4UsuNmyxbvEwEkahvvyZhR6KzXJBK0gMQCA0ZbWsMAz0QBzzC5+6PkS6SxkcQJ8bgHOnLt5QuT3Inbch77h/IUdms8wxhyu+9nS18nJnMx9btRWUCU0NqKYG1CvPY7z319BCWOETQoDHC2WVUFxm/b55B+YX/w0G+i9uOJPGfPQLGO/5MARC1/xpu5KC/d2to548EZeX20uqRo9ZYgnhIxWAWkrUJdMnwwrHUkhhkFVJOpPH6EoeYyjbRcBVQqF3PYW+taPiHViZEqAp9PgJuTygrVOiwLIG0VpDNouoX4M+c9L6G1yKNJA33wGGwQv/+iKp3tSMX5O+xj76m/qpK41QH4pyKt4z423YzD2GEPgcTvwOF36Hc/jf1u/14Shew4GpTXJqiLTZQetgO33pC/SkTtOfaYRJ4kRyaoiG+DNsKngXK0JRpBCU+YI4pcGFwTiNif5pJQpX+EPcW1ZLxOXh+40nOB2fvlBsY7PY6c0kOdzbgXf48+dzOPEZThzXUVtsxO3B73AyeHmo1BTke3zcWVxFmT+EFGJJFeI4nU6EgHQ6fd0Jdul0GiGs1+BqsSvsbGzmkFH/IRhuN7R+F5c8BdFaQ1c75g++CY1zE+1tY2P8ym9AedXUN8LKhJ5uzE99EuxaAADk238FsWL1glQp2SxfRiqqzw308lTreXozKbbGirmjpNqqmuvpRJ89hSgoQg63bF8JbZowmMD87D9DvG/8ArEC5MNvRlTUXLP3sdaajDL5rxP7SA+LX36Hk1JfkA15hVQHI2OEu87UIBcScVaGYwScrnE3HZdW0fWnL9A+dJCOocP0ZRoZf64S1ITuwCl9gMAQTpyGH48RwuOI4JIBNBrQw36+GimcuEUAYRioE0dQu58CrREuN2L1esS6zQiPl3hLnFc+M/tAKMNtsO39W/EX+mlLDfLNhmPklO1tdi0IO91sihURcLgIOK0fn+EcbSEbQWuN0gqlTVJmL4e7v0536mpsSiQPVv/z6Ps5p3JorXBIJ0II+tIp9vW0sa+7bdx41+YVsD6vgKDzor/TF08doDudvIrx2NgsfpxS8q7aDUTc7os2TUzc6nm5ncRELIUW0R9eODXjpNjXV66kJpi3ZIXNlpYWenv7CAYjuN1uYGkex/TRpNNpBgb6yMuLUFpaetVbtAU7G5s5ZKTlR+1/BQwHIr8QikrGeQyN+BapA6+ifvY4DI43s7axmQnitnuQt98/bT+r3Gf/GZqu3vx2WVBejeODv2EnytrMGVprErksP2s+S0Oib3R6uT/I/WV1ALiFxG04rHbRad5omN97FL3/5YlnFpVgfOT35v2GZWSsSmvSZo4vnT44rmKg1BfktuJKetJJOpNDtCUTtCYTY+ZvihayJlKAqUykkHQmj9E6uJeO5BHSZv/lu50z1kffRlXotjGvk8rliLcmaHyhkc7jV1997PA42Pi2DeRV55E2cxzq6eCZ9iWYvr2EqAqEeW3FSgwpyKkkpkqSUYOkzQFSuV6Gct0MZjtJZNpI5NqZrGpuNlQGb8HnyKdp4CUSOSsF2ecooCp4K9Wh2+jN5PjKmUO4DQfVgQjr8wosP2OtGMx2cK7/SfzOQqrDd6I07O5oYm9XG8p+sGazjIm4PLy1Zi0Bp2u4stoS537ecpYLg3Hc0sBjOLi5qIJSX3DC77elINSBVXl+uLeTX7RMv1jDIQQfW7MDxxK+NtVa09raSl9fH9eL6iQERCIRSkpK5uS9aQt2NjZziNbaqoL43L9Abzfy3R9B1Ky4ogiglQktTVbVhI3N1VBYguOjvz+tRbVpol9+DvXT78/zoJYOYsfNiLpViJJyRCi80MOxWeJorUmaOf7rxF5yk1xmeaSDj63dPn59ZQ5XaWNd+Q23yuY+/XfQ2TZ+Q8PIN7zTqhSbgyq7EY86tIZcjpQUdKWGcEqDYl+A84l+nmg6zdAM23su5caCMm4uquBE7w851ffDqx7zdHAIDw9U/wPdZ7s5/3wjWmn6Gvrmfj9uBzV3VBNbEcOf76c7NcQXTx+c8/3YWNxVUs3mWDFn+5/kaM83F3o4Y7in4m/xOIKjv1vVqUO0JvZwvO/7Y3zxPDLC9uKPEnaV05tJ8d3zJ+jLzLw922Z5E3F5WBmKWt6Lbi8BhxMHVu2SGI7dyaLpzaZpGRrgzEAvTYMDCzzqK5Pn8lDmDxJz+zjS10lXaqxXpABuKapkR0HpmGo7U2sMIehIDlLo9S/AyGfGTH3saoIR3lC1ev4GdA0xTZNsdvbXC0sJp9OJMYfdDraHnY3NHCKEQHt9GB/7Q9RPvoeoqpm0YkdIA11aYSV/Zqf2OLGxuSIdrejebsuAfqoncVIi1m8GW7AbRb/yPPqV5wGQD70Rsf2mJfHE1mZxIoTAazjYGC1ib/eVBbaUynFhME6ZLzim3UVIw2rZFALcXvB40MnkpGIdgHryCYy1m2ZVcaCHbRyENNCJAfTxQ+iONjAMjPtfj8xm+EbDMSSCUl+ApqGrv/k70d/NrsJyin0brplgl9Mp2gcPUVC9luOPnyDVN/diSKw+xtrXr0ErjZk10Upb3sLMdV2XzQhPt50n6HRTG7oTpbMc7/3eQg9plBO936fAt5asmSCjBukYOkxveuJE2ZTq47mW/0OxbwubC97Pm6vX8JUzh0iayynF3Wa2OJA8XFlPTSA8GuZDfy+66zwMDVpmnsoEpXCFIhSXV1KSX8q2/FK0adKdSfN8xwXODCwuj8TeTIreSYRpDTzb3kjz0AAPlq/AIQUSQevQAM+0NdKWTPCm6jVUDPu8LVam8rGrCoTJd/uIebyj/19KnnWTYRjGnIpY1xO2YGdjM8eMpHAar3kTAForxCQnWiEl4sZb0K/uhpTtWWIze9SPv4vxjg9OuZwQAoJhy9S+2W7Tuhx1cA+OHTcv9DBslgE78ksnFezWRQqo8IfGTdeZDOrJJyA3w5v0/l7Uj7+D8fBbLAFOjSS5Tn2xL4RAvfA05pED0No0Zp6qXgG19da/0XMi1gGsieQjhaAndWZOtjddzsafpMi/gVhdlOY9LXO+/UChH5ffxWA2Q8KhaE308pOms7ZYN0+EnG5uLqqgxBdACEFF8OZFJdhdSDzPhcTzM1qnbWgfL7X1s7Pkd3hj9WoePXuUnLbfQdczBW4v76xZhzQk+uXnMF96Dvp6mLLP0BdAlJQhqmqJbd3J66tWkcym+fypQ6TU0hKCzw708ujZw7y3fhMAL3U20zZst3C0r5OqwOLukNBaUxOMcLi3c9y8+lCU11auRGmNRk8roMnm+mDpy7U2NosQIYQVQAEIIUf/PRFamci7HsL4g/+FfNeHEZt3wDJ4kmJz7dEnj6IO77Pa6aZaVilE3cprMKolSNIWzm3mhpOTpISW+4LcW1bLhM4kDgfyHR8Ex8yfq+o9L5L7z39EPf4Y+qVnoafbqsKYbB3TRB3Zj/r5D8eJdUiJqKmnNT008cpXwSudLTQm+qkK3Y5Ljhcu54Mbi36DXSW/hdaKRMfgvOyjcfcFhnqGyCnFV84c5tk268FIocfH6nCMbbES3EvYk2ixsTIcZU0kH5c0OdbzXX7Z9BcLPaQ5oTd9lgOdX6TA4+fhivplb9VuMzleh8tK3hYSMhno7Z5arAMYSqDPnEA9+QTmP/wvzG99GY80eKRqaV4D9mXSo/9OX1J5OpJYvpjRQE0gb8J5dcE8K6hJCAwhbbHOZhS7ws7GZp641Eh8MlPx0QRZIaC2HrliFWZiAH36+DUesc1yQD3xXYwVq9Fuz6Rf9kJK9NnT13BkSwcRjiz0EGyWOEprOpKDPN02NtjFJQ3qQnmsDMWoDlqVAFf6nMralehV69FH9s98AK1N6NYmtGFglFVBXuyKi1qVeAr108cnnC9WrEa4XBzrmPtq3JxW/LjpDL+6agsb89/Bqx3/Mef7uByn4UVrxYufepmh7rkXIct3lJNXE8ET9uAzJB+s30zY7Rm33K6CUr5y9sikbWA2kxNwuLihoJSN0SIAjnR/gwuJ3Qs8qrmlZfBVfI58VuW9ljtLqnmytWGhh3TNua2okhPxbtqT8yOwLxUaB/v51xN7+NDKLbgrqme3EWWiD+9DxQoouf0+aoIRzg30zeUw5x3vJQ+yUpc8jFrMrbAjSCGoDoYxhMC8RGwVQG0ob1m0vtrMPbZgZ2Mzj1x6IzadJyUj3mO6s33exmSzzBlKoL71ZeSb34N2Oi8KwpegTRN95gQ0NVz78S1mSsqRd9yPXLl2NPHZxmamjBhid6WHWB8pIJHL4DYcoyKdIeQY0+zL0emUdUN18hj61NHZDyQvhvGej0AkOuX3j3r6pxDvGzNNvubNiLJKiMZIZTMc67v69NSJSOQy9GVS+J1F87L9yznY9VVuK/sfFG0sQiCouKEUw2lYD83AEi9NhZnTDHUn6T3fR/uhdgY7pxYLtrxnM9HaKDqXheQgWilCUqJ2P215jA4NohMDCI8H59s+wPtXbORLZw7RnV76Vb1hl5uY24tEIIX1I4SgJ52cN6HlDVWrKPD6UTrHs83/QH+mYV72s9Cc7v8xfmcBm2O7GMhmeKVr7tu4Fyvvr99I1O1jW34JKTPLyXgvr3Q0E89dn77PtxRW4BESfZW+1/q5J2HTdh4qreXfTuxdMu36hhC8brht1KpEE5fMWxpil1MalPmCNA5XBLqkwa3FlXgMW5axmRj7nWFjsxhxuRZ6BDZLGH36OOa//A3ywUcQ6zZb7a8jYrDWCMPA/MWPFniUi4RgCFFehdhyI7J+DVqpUbFuNsb9NjYCMLViTTifdZGC0ffQpSLdFcU6rcHpQqxYgygsQW/biYjkoV56Fr33pZkNxO2ByMStN2P219+LPn0cseUGRKwQte9l6O5ArFiNGQrTk07y0wsnZ7bvWeAyglMvNAckstYDsZpbqy37isZzqJNHYdgEXHh9CJ8fRyhCuLyKSGU1NbdWW9YWWoPWJOMZvGE3Qz1JOk924/Q5kUKQVxVG7X0J9fhjk45BA7qtGVm9gkKvf0kLdkGni12F5WPe65czmM1wKt7DqXgPzYMDKKbRxjcNnmg6zbb8UlZHYtxc+gcMZjs40ft92ob2zcn2FxMHuv4bryOfW4vrGcimOd7fjVNKirwBmgfjc/SKLi7eX7+RPJeHA51fRqOoCOxiU7SeTdEilDbJKcVgLkdbcpAz8R5Ox3uWjPA0G95Rs44SfxB1/BDqx1fp0WjmUN9/DNd7P8I7a9fz5bOH52aQ88wdJdUUePxIIVBacUtRJd9rPAFY7bFL4bpNac36vEIaB+PUBCLcW1aLz+Fc6GHZLGKEntA8xcbGZqHQpgktFzA/96+wLC/BbK4lYuVa5MNvAX/AaoNNDqGe/qnlbXU9EitErFqLKKtCVNYgApZIMCJq6lQKffIIcuO2JXHhZ3N9oHM5zL/6o5mt5HJjvPvDiMtap5Q2kWKCytvhlFiURv3sccSK1aSq6/j3E3uvYuRTc0thBRG3h5XhGC2JPezt/Oy87g/ghqKPk+9Zhf7KZ9FnpxAjQxGr0jAYAsOBCIcRK9ehWy4gVm8YrcQdOV+Y3/4K+tDUr5l8/dsRG7ehlImpNU+3X+BQ33gj8sWK13BwQ0EZm2NWZWTH0AFO9v4QU6cxVQ4TSwCtCOykPHAjfmcpDmmQNnOcifdyOt5DQ6J/wiCFmNuL3+FECDFqvC6A1mSCoQnSFb2Ggw3RQjZHiwk4XTTGn+dg91fm9fgXijvK/hKfM58fNJ7kxsIyirwBEtkM+7rbONzbMSZN1i0N8j0+NJrWocSSuqIcEev2d36R5sFXRqd7HVHy3DV4Hfn4nTHCriqCrlKkMFDa5PHGU5xZYi2e0+H+slrW5RVi/uR76BefmbPtiu27MF7zZl7saOKFjqapV1hAaoIR3lC1etz07zQc51yiD4A3Vq2mMhBCLoFquzPxHupC0Ukr7m1swBbsbGwWLeqV59FHD6CbL8BVlr7bXOe43IgbboF4H/rIATCXVirYnGAYyFvvQdx6z+ikMVWHQmD+8gn0S89hvOtDUFpht8TaLAijl2XNjai9L6HPn4GeLnC5rSqwS0OMAkFEfhFEYyDlaAUYbg/y5rvA6xt9n1vbtlLLB7MdNCdeQQgHXkce/ekmmhMvo8ixq/i3CbvLAVC5HP90/NU5P8bV4Rgbo0UUun24LgvWSGTaaR86xEC2lZbEKyjm/nzldxZye9n/hDMnUV/5zOw35PZYrbTptPX6FxRBR+vYv9GViEQRG7YiPF5EbT0UFPO1hqO0LXKfLpc02JZfwvb8Egwh6EmdZG/HF8ioqQ3fS/3bqQreSshdjVM6ySqT0/FeTvR30zI0QH0oyqZoEYVe/4TrK605He/hQE87rUMD3FhQjtfhIKcUptZ4HQ7W5xVysPOrNCaem+tDXxRIXNxd+Ve4DT+mVpyP/5JC73p8zkI0mtPxXpxCUuT143de7NZI5rI82drAif7ueRtbqTfIplgR/ZkUL3e1kJvO52ACbiwo5eaiSo71fJcz/T+dcnkpnIRdFdxQ/HHaklm+ce4qrAQWIdtiJdxWXIl+9QXUj74959uX7/k1dGUNnzq5d9Z/s2vBzoIybiwsG9P6qrSmJ53kS6cPAlAbjPDIBKLeYsPUCjFsH2BjMxW2YGdjs0jQyhz1G9OmCcIKpNBKQVc7+twpdGMD+sRhmCLxz8bG5hJKKzDe8A6rum6CiyOttZWoue9l5MNvsVoD7QRHmwXASngW6Gd/jnrmZ8PCj0Dsug1592sgOYQ+cwKRXwj5hYjhMINLL+WEEMPVctqqGjVN9MkjqJ/9wKoIu+dBhJBk1RAvt32Kvsz5ceMo8W9la8EH0crkn47NnWC3JpzPjoJS8j0+cqkc3We6ad3fRjqRJh1PU7imgLp76nB6rPagntQZOpPH8RhhelJnaB3cMycCnhQOthV+mAL3StQn/uSqt3fVeP04/vB/obRmb1cr+7rbGBj26Mpzecgok8EJKsuuBUVePzml6E4n8RlO3lu/EY/hoD99nn2dn2coN7uqwHzPKurC9xHx1OGUlrCktSZtDnAh8TwdQ0dQWqHJoVQWQ7qoizxAoXc9DukkY5o4pCSnMsMVeBIQJLKtPNfy13P4Ciw+3DLMjSW/zsneH422//ocBayLvZmoeyWmTpPMddGXbqQreRyXEWRl3kO4jQiPnTtKy9DAnI1lRHwv8nhxGs7RhwJKm/RlMiSH2xSV1uzvbuP0QO+U23RJyUdWbyWZa+fZ5r9BM71r3u2FHybmXc9TLec50td5xfZYn+Eg3+Mn4nLTm0nSmhxctEJV2OXmV1ZsRJ87Yz1cmKAi9aopLMHx0d/nQHcbv1jEoSZ3FFexKVqEcdn1WSKb4T+HK8EF8Ksrt+B3umwxzGbZYAt2NjaLhJGWPPP7jyIiUSivRlRUI5zWjctIkh8D/agffNMKDbCxWWaILTciNmxB+Pzg8V30c7z0qyqThng/Ot4HiTh6YAAScUgMWMsHgohACAJBq51txSpQesKKOa0U+sWnLTFjw1aMN77r2hyojc1laKWsoILGBqviyuGAeBzMLCK/6KIopxRIOaN27dxn/2VsyEysAPmh3wKXixO93+dM/8+51ILBKb3cVPL7OI0Ynz529S2xNcEIj1SuQghBLp3jzJNnaXql6YquDw6Pg9v/6DaAMa3piUwHz7V+kpyafbprVfA21sXejBQOVMsF1Gf+adbbmkvErXcjN92AiOWjtSZn5jAQSIcDbZq82N3C7o7mazqmqNvLu+s2YAjB4d4OOlJD3F1aw4tt/0JX8tic7afQu54S/xYa4k/Tn5k6jbg6dDvVwds5P/AC5+I/n7NxLGckLu6p/Guk8HB2oJezA30c6+ucVZusS0ruLKlmVTiKQzrIqRRtQwdpHzxAR/IoPkeMssAOinybcEg3Aokh3TiEi3MD/Xy/8cSkXnMRl4d31K7F63DxdNMnGMhOL2Qj37OKrYW/isvwo7RJPJPh/GCcgMNJvtuHz2lgCDnOEsASFE3ak0P8pPnsnCY3F7i93FNaS8jloi+b4XBPO0dmEOCzPlLAfeV15P7r/0Hz3Cd1j2C8/+OkS8v59DxbIFwND5TVsTqSP06I68uk+NzJ/aO/rw7HeKii/hqPzsZm/rAFOxubRYRWJqTT6JYL0N6CPnsKPRBHhCMQjiDveAA8XoRhoE4dQz35BLRd2wt4G5t5QQjk/a9D3njbpN5xYypRlRoWL8SYNFxL3LYqlSYTNrRSEO/D/NQnIZqP46O/P3YbWk2YsmtjM9eMvOe1UqNppVfrnzgantLajPmf/zB+AZcH+Z4PI8oqGcp1IYWDZK6HE72PW62LoVtQSvO5k/uuOpGx1Bfk7bXr6DrVxcGvH0KrqS89XX4nxRuL8Rf4OfWz01TdVEX1LVUAKKU4G/8Fx3u/M6NxrIy8hpV5r0F1tKKe/hkcPQSLzaY+nIeoW4WoqrUeSLQ0ITZsRa5aRzqXtUIvlEnrUILTA72cmSejfUMI3l23gbDLSWfyCIW+jRhCYmrFEw2/Pg97tJlvPEYeWwreR9BViVO6OdHfzdNt5/E7nAScLvwOF42D/fRn0hOuH3F5uLu0hgp/AIGkfeggDfGn6U6dRE/xLpQ4WJX3WmrD9wxXASs0GqUVyZyiN5OiPZlAa9hWUITSGfZ3/jftQwdmeJSCiLuaIt8Gin0bCbpKMXWWeLqZ/sx5hrLdpMw+UmY/GTOB2wjhd+YTdJZRGbwJIRx0JAf54YVT9F+FHU2pN8jdpdXke7yYOkNv6hwhdzluI0h3aoiedAqvw4HXcOAxHBzu7WRPVyspZVUQV/rD3FFcRcztAdPE/PQnoa9n1uOZCrFpB/L1b+PxxlOcHpi//VwNb6leQ7k/NO67sSs1NNoSO8I7a9dT6PXbVXY2ywJbsLOxWYSMVtMJgfrml9DHD1sVRvmFGO//OHi8AJZwd/wQ6pc/sXxzbOaXUBgSg6CuQw+4+SS/EHnvaxH1a64srl1BxBsROGYrbphf/DS64Qxi0w6MR96O+cR3oL8P4+0fsEMnbBYtFy/dNOISP59RkS6VQh87gD5yAH3u1OSeatt2IW++EzE0iC4oRLo8Y4IpftB4kpPxq7+B+9DKLYjeLHu+uJdccubnUGEIIpURXAEXVbsqCZYEiaebUeQ41vNdulMnCDiKEMJgINtC2FVJvnc1rYN7GMp1sz72NqpDt6NOHUd99Sp86xYIseNmS8RTGlFQCIUlVsuzUqRyWb545hBDc+hPemdJNZuiRext/0/akgfwOWJsKfhVcirJS+3/PGf7sVkYVue9gbph8exShnJZvnLmEAPDYlXY5aYmEGFDXiH5Hi8aRePAC5zr/wWDs2iHDrsqCbnKMYQTQ7rwGBFCrnJCrjKchg+AruRJ9nd+npTZf9XH6ZR+smqI6YS4OaWfmtCd1IbvBgy+f/4UDYNTj8HvcFLiDVLo9RF1eyn0+Ai7PGTVEGf7f05D/GlyOgUIqkO3szrvdQgMcjqFS/gt+wLDssAZzGURgM/pguQQ6qVn0K/shvn2tnS6MH7nzxBeH9o0SSuTvlyG9uQgJ/q6aJrDNurZ4BCCj6/ZMa4dFqAtmeCrZ8am3JZ4A7yjbv21Gp6NzbxiC3Y2NouY0ZuvgTh6z27U87+EUBjjfR+zEutGfIoMA3XkAOrpn0Bn+0IPe1lifOi3EaUVmMkh9Cf/bKGHs/QJ5yHWb0Zu3IYoLBltCZ+KEX8crTWk0wiP5+K8aW5jZFm97yXUD745fmZJOcYb34XIL7yYHptJI1xuSyAE2+POZlGgEwMXk47TKfTRg+gj+9HnTg9Xmc4ccddDiJtuRxoOtNa0Jwc53NtBfzZNfybNQDaNOYtLxxsLSrmpoAJlKs4+dY7GF2bX3lW2rZT6++sxnAZaa7JKDXuZJXFKL0IIlFajKYFKa9JmP15HBN3ZblWqLAccTigtR27chty2i8cbT3JqDoRVgHy3j/fWbySRaeep5r+ck23aLD5K/NsIucpIZNqIZ6xujVtK/5is1gitcDsuhlZkzEHOxZ+kIf4MWTU/4pFLBq0QnMwFpiOwzRdeR4wbi34dnzOfzCWe0WmlSOSy9KdTDOYyVPjDxDxunNI5uozSOYay3ZwfeJbGgecw9ZWr9Dbnv4/y4I2YX/p3dLwPsXo9cu0mMAzUS8+iD+69tiFhvoCVKl5ciigqRZSWI8J5aK3pSA7y3cYTC+alWRUI86bqNeOmm1pxrK+Lnzafpcjr57biKjqTg7QmE2zMK6TMH7Kr7GyWPLZgZ2OzBBgxENcnj6K+93XI5RAbtiBvvBVRVGqJCFqDlKhc7mJaoM2cIQ1j9El07nP/AhcaFnZASxix42bkA48M/8JohdCIQA3D7/lMGlIp8PnBkOPaU7VWw8b6M2tb1aYJyUHMf/1bSE/iVZMXQ6xcC0OD6MP7IZaPvOdhxMq1duWdzaLA/NK/W76Npok+e2rWIt2ElFcjVqyEnXcgXa7R9/yIiPfVs4en2MB48lweHixfQbEvQOfJLk4+cZJUXwpP2EP9A/XkVUYYaE/QsqeZ7rM9OD1Okn1JAoUBHG4H+avzqdpVSdY0+frZIwyaWbLK5NaiSqJuL8f7u0ibJhGXh550kt5MitpghNuKq0bHkPvL35uzl2jBcTgxPvTbqFg+/35iL+k5Ms43hOBXV27B6zD4UcNvzMk2bRY/AUcJt5f+iXX9OPzdu7frcyQyrSSyHdMOf1gOOISHlXkP4zaCw+2+Aq8jit+Rj9sIIYQkbcbpGDpCd+okiWwHyVw3aXOA6YiNee46bir+bfSBV1Hff2zej2fWuD2I7Tchb78PpORAXydPLkAwxe3FVWyOFY1JiB3he+dPcGagl0cqV1EdjKDREy5nY7NUsQU7G5slhPVx1dDajD51DHX6BCIcwXjze8YsN5TLcrR3dslty5W5ONG1DA1wf3kdLiEhnUKk0xDvQ3e0oZoa4MRRSM3eDP16QN5xP/L2+8Z7duVy6MP7UPtesqpE06mLorMvgHztW5Cr1191m6rWGpobMb/539A/dVrd5Rgf/X0oKLb97WzmFW2aY/wX9dCgdeNkXPRvFFKinvkZ6pc/nv8BSQeUlSM3bEXuuJkz8V6+1zj74KO3VK+hzBdECEH36R4ilWGkIRAtjeiCYqTXO+F6Wmv6M2m+fOYgmQnEqXy3j/V5BUghOJ/op3GwH6c0rHbcbAb96m70z38w8wHnF1n/71pcFewjQTk/aTo9IyP7qdgcLeKu0hpO9/1sxj6BNkuLkKucmKeegLOYCv9ORCqN+eX/RBSVYDzyDvZ1fIHmwZcXepiLCoGBy/CTNuOzWl/i4L6Kv8EYzFgVv1fwDFxUhCKWz/DaTTQPxnn03NFpr+pAUuoP0pYcmPC8PR0+UL+ZiMs94fXfvx/fg1NIfmXlZvthqs2yxLHQA7CxsZk+1heRQJeUQ3EZ4rZ7kMOahs7lEA4HSmvimTTPtM9fmtT1zBdPHWBzrJiw003A6SKvpAx/ZQ1y+y6U1lZa6YkjqI5WZFkVIi+G7u5EHT8Ep49f9JKqXoHcdTsiHMF85QXYs3thD2y+EQL54BuQO262flcKDAM621GvPo8+tO+K1W5i5RpE3aoxFXizQWttmTc//o1ZiXVICcGIZfzscqN9fvvi0GZ+kBJ9aK91buhssz4bQkIkDxErgPxCRKzA8qe7FqgcXGhAv/1XGMxm+EnzmVltJuBwcl9ZHRWBMKrhNLqvl+jqjTDYj/ripyHeZ+2ufg2ydiU6OYS4436SuSw/bjpDV3poTEuWIQTF3gDl/hBuabAxWoQhQKPZHCtGaU3KzFmf09Ym9HNPTm+g9WuQ67dY//davlpaa8ue4uXn0GdPQmvTrF6DuUQ3nQeg1BeaM8HOazi4vaSKVC5ui3XLnICjiFtL/+Tig4G2Fsyvfw76e9Hdneib72JzwfuoCt7Cyx2fJqfmLj11KaMxZy3WAewo/iiG4cH81meXhlgHEO9DfeNLcM/DlO66Hb/DecX22C2xYir9YWJuDwHDgSENhJQc6+viiabTM971mnA+eW7PFed7DQfr8grQgH1FZrMcsSvsbGyWOAPZNEGnG7C8HEbKwD97Yh/92SVyIbDEcUpJnstLZSDEylCMYl8AsG7whnJZfA7nqK+SzmTAcGA4HJhKkTRzBJwuzFwOcfYk6mc/WHRVHFeNNJBveCdy/ebRCjl14FXUS89OedMrbrwV44FH5iQAYqQqyfzOV9EH98xyKwLQyHteg9h5+1UJiDY2V0KbOfSZk6infmIlgS+GSzWfH+P3/5Kft5zjUG8HQYeLtMrNqGKi0h/mzTVrUGdOoL78X0wnoVX+4f9GuT282tXCwZ4O0ipHnsvL/eW1FHj8o8uZSjGU62R36z+SUXFinlVUBW8h5K7AVBnC7vJhe4nhB1zpIevBQSaD+eQTEAgiV6xG5EUhv3i4XV+glebMQC8Ng/1sjRUTdVvVfyqVtCwqjs+8NfiqyS9E+IPg8yMfeTvN2TSPzaDiZTIE8EB5HWsiBfSmGtjd+g8o7KCl5UixdxPbiz+C+dgX0KeOw+UCjGEgbrkbees9gGbQ7KYreZzzA88ykG1ZkDEvdSQOHqz+f1Yr7C9/DP4A9HRZD2UcDsSWG5E7bka3t6D37EY3zO7hyLwRDGP89p9yNtHHz1rOjk42kOwqLGdNKIrhcKCzWehsQ7c2IapXQF6UL54+RE9mZqLv+rwC7i2tBa6cnP798yd4oHwFLvt6zGaZYgt2NjZLFK01Oa347Mn9SARl/iB3lVTjMRwIIebUgNpmZgQcTtyGg75MClNrnFJS7A1Q7A0QdXvJKcX5wT4aBvrJaUWFP8SGvELqw1EkAh3vs6o4XniG6dzQLmq8PuRb3oesWQFYrX7qB99A739lWquLbbuQr3nT1Yt1I0Lh0QPoxnPWjfrIj9aWD5jbg3B70P296IbTFwNcnC7kfa9DlFeifv5D9JkTEIli/Maf2OETNvPCiG+pkBKdTqPPn0afO40+8Cokr3HbfSAE1XWIgmKM2+6hMzlITmuKvT40lgXDhcE4Z+O99GZSpE2TQq+frDLJKkWBx0eFP4TP4SSrFdWBCOrVF1A//NakuxW334dxx/1orcipDIZ0jzEP11rTkzrNhcSLNCVe4krnyurQHawOvwGHw0lmMEPTq804vQ58UR9CCnwxH56wVb1hZk1S/Sn6zvdx8senMFwG6964jmhNHt+/cIIzA33ku71E3V5uL6nCj0T99R/P1Ss9NaUVGA++AVF+0ZNPmyaPNhynJTm3KY5bYsXcXlyF1gqNZcdxpPvrXEgs82rw64iKwM1sKngXuU//nVXJeyVihcjtOxF1qxEFVnu4mUtzrP+7NMSfvkajXT7cUfbn+J2FY65r9GDCquL3eEnl+nBLP9JwoTvaMB/7InR3LOCIxyLf/B7kus3jpmut0SePoF54ChobAA1FpRgf+R1Ox/t4/MLJGe1nS7SYO0urp3xgu7+7jc2x4hlt28ZmKWELdjY2SxilNc+2NbKnuxWADXmF3FtmPYl6qbOZ59svLOTwbGaIx3CwJpLPpmgRUbfXqro7fRz1k+9ZbZhLjaISjHf8KiIcAYYv5tDo3c8gS8vB58f8/mPQPEn7diiC43fmJpVXm+Zwv8TwhZ8QF1uBRoQ7NEgrYEQPDaJ2P4XctAOi+SAE+uAe1He/BoDx/o9DRbUt2tnMOyPpxCSHUN/9Gvr08Wu2b+ODv4UorwQgq1KYKgUIulInGMi0UuRbT9RTh6mzGMI5bn2tNTqdQqSS6EAI6XBg7n4K/dPHJ92v2HUHxn2vJafSvNT2LyRzvdSE7sQQLvoy5+lKHidlTt7a7ncUcXvpnzHQOsC5ZxroOdODVmMve4UUFK4tJJvM0nNm/HlWOiQ3fHgH7jwPXzp7iL7hCpHXVNRTH4qijx1Ef+NLk45jrjB+53+i/H7OJvrpy6Q52NNGf/bKKZRXS5kvSF0oj5yyhNZCr499nZ+jdXDvvO3TZu7wyAjbiz9CKtdPX7qBzuRx+jMNo/N3Ff8OMW89uX/4XzDQP72N+gOImnrkDbdAWQUvd/wbncm5qe68XvAYeWzKfxd9mQskMm1E3FWEXGVI4eR4z3foTltWBzWhO1kTeT1Cgfru19FHDyzwyIfx+hE1dYxpQBUC3dpkVQuOkF+I8b6PYXo8fPr4PnIzeAC9I7+UW4srp9Vd8b3zJyj1BdlRUDrDA7GxWRrYgp2NzRJGa01POsUXT1tf4gL4yOpt+BxOu8JuiVPmC7IpWsTKcMya0NOJ+vkPF6b9ahaIrTsxXvuW0d9HLrr6MynCLg9pM4fSGrfhQH37K3B43xW3ZXzsD60WsAXyixvxztNaox7/BnrfSxDNx/j1P7Y97GyuKaNhE7ufRv30+/O+P/nQGxHbd5FRgxzufoyOoUOYerxAdF/lJ3EZAVRPF+TF0L/8MZgmcuuNaLcH7fFCYgD9qU/C1hvg5eem3vf7P4asquPnjX9CypymmDB2C9xd/lc4cj5e/NRLZAZnL2x5o162f3A7wiX44umDxHMZvIaDO0qqWRWOof7hLyExtxVugPXwYNM2RP0aRDgPUVoxZvZXzxymLZmY+/1OgEsavKVmLfkeL79o/GMyaux+PUaYssBOwq5yulMn6UgeIZnrQQonDuEhp5MobbfWXkt2FH6UQu86ME2E0xLTtTIxVQYlFC7DP/vgGrcH41d/C52Xx9Mtf81gbplZeSwSXDLIrSV/iNcVw/zZ4+gXnlroIU0b48O/gyoo5svnjtCdTk57vRsKSrmlaHpiHcCXTx+kIzXEXSXVbIoW2ddlNssOO3TCxmYJI4Qg5vEScXnoy6TQwKNnjxB1ezkzMAtTfZtFQ/PQAM1DA/yytYH1eYVsiRXje+v7UZ/5p0Vhdj4ppRUTinU/bT7Dsb4uQk43fZkUTmnwsTXbkavXoyYR7PSR/Yjb7kUPJ7OOiBaTMZ1lrrjuJReJWutRnzohBGLtRnA6ESvXXgzOsLG5Roy8p+Wu28EwUE/MXyiAeM+HkbWruDDwIoe6v4bSExuMA5zo/QH1kQfwRPMtL9W7HgQgpxStQwMMDPSyNq8A9bt/hujpQhcUoc6chNMnITfstbpqPfLGWxBONzgdUFCCMhVSzO5S9aaS38PrDHPgmwevSqwDSPYk2fvFvWz7wDbeWrOW/zq1n6SZ4/n2C6wMRZG/+T+sc3N3J6JuFUTzrcCh3m70S89Of0cVNYjqOoTHY7Xo169FhMJorbjcTr0x0U//DP2groaMMtnb1cqDFStwSDcZlcBjhIl66ikP7KTAuxqwEtlL/FutKmWtEMO+uj2ps7zQ+vfXbLw2UOBZZaWvf+drVmBNQREUFCMLijCiMcyXn0cf2T+7jadTmF/+T4zf/B+sjb2RV9r/bU7Hfr0Rda9gZd5DeB1RHMKLA+fwuU9g6ox1LRKzghWWBKEIoqScg92tMxLrgk4XNxVaDyamK7wN5rLsyC8lZeY40d/NqnDMFu1slhW2YGdjswzIc3tG23R6Myl6r+FFvM38kjRzvNLVwvH+Lj60aivyw7+D1hrzK5+BMycWengT09GK+a0vo9tbwOPF+MCv80zbeQ73dgKMvj8zyrQuQqfYnNrzIsZt9wICncsiHMOVAmaOYWf4MeKc1iPtrbPj0gu9yy/6RG29dUOulB04YbOgyBtuAWWifjI/lXaiooqMmcApPUjhuIJgJ7mr/H8jhUEy1w1IPI4Qe7paUVrzalcLSdOqqmoeGqAmGKGosIhgWSVy+00AKNPykpROB7lUjmwqizdiBTsIBHeU/jkXBl/kUPfXmY6nZ8xdz8q8h4l6ajj545N0nZyb9NTBjkHaDrRSvLWUsMvNO2vXY2pNVis8The8/Veg4TRy687RFmahFKq/F93ZDj3doIfH73COMfgXG7Yib74TUVRqte6P5B2Ontes9v3BbIafNp/lwmA/uQVokPEPV2ndXvbnZFUCjyMCgNLmqDB36RlzZBpA1FOLSwbJqHmoRLQZR03oLqThIrdnN6Chrwfd1wOnjs2d6BMIIqSkK3ntWvSXG3nuWnYUfgSXI4hOp6G9DR1vg8EB1GDCqlSOxtDhPNSeFxd6uNNGrFqH1opXOltntN4N+WUz3tctRZVWSqzWJLIZW6yzWXbYgp2NzRJHa03EeeW4c5vlwUA2w2PnjrI1VsyKUBT5zg+i43H0Z/4RhgYXenhjyeXQwxVz8r0fJacVB3quol0mEUcf2ovYuA0uuQEUxvivsBGXB/XYF5EPv8ny25FzJ6yNbssW62wWAXLn7ZDJzK6lbSpyJg63hyLfBnYW/yYvt32KjEpQ6F3PpoL3IjDIqgF8zjzOJ/qJuctImia7OxrY1zPewP5QbweHei3jdJc0+ED9JvxOF2efOofL72KgJU77kQ6idVE2v3MTKptD9HVDvI+qulso823ndPwnNAw8TU6l8DkKWBG+nyLfRpyGf7QKzZASrTXnnm3gwktzW43s9DrRQrM6nI/X4bTa5HM5MIDeHuTWncDFSkgNGG/7gPVv04TebpASEc23wm2OH4Kcibz5zosi3wTnlpEbUL/ThRAsiFgHsK+7jfV5hUTdXgwZGZ0uxfTOhxF3NR3JQ/M0OptLqQ3ehe7uhMZz87YPuXYj2sxxzg6emBXF3k1sK/ggJAYwf/k19JED45N6YelU1V2C3LCVVC5HIjf96uZdheVsihXNaD9K61GxTgiB3+ma6VBtbBY9tmBnY7PEUVoTdrkXehg214CmwThNg3Hy3V4eqVpNKJKHfu+vYf7Xv8AMLoquGSVlUFXL8b5OsmqSyphpPA1VP/gmovEccttOKK24GBIhJSiTkWoUIQTm499EnzyC+egAxns/Ci5bXLNZvogdN8M8CHbqk38BEsTbPkBk5VruqfxbTJXFIZ2klYnSGk2EVzqbeXaaAUcOIVgTKSDP7cFnOEn2Jml8oRF/gZ9kb5LYihj1969AZXOov/6ji2OpqMF46A2sLn49q/Jei2lmkMKJNAyaBuOcG7iAIQSrwjHy3F5a97Vw9smzc/6a+PJ95NDsLChDdXeg/vVvATBXrEa+7QPjWvHH/NswIL8QrRRaa7pdLqLbbkI6HOOWnQitNWcHemlIzMbPb/Y4hGRFKI98j498t4+o2zvrbVlVmLMch/TgwENK9c16G9cLAUcRHmcE9cr8+lyKdVtI5DpZ8mn21xRJsW8T5YEbKPKuh84OzP/+DxhcPpWnYuuNiIpq9k3ze8EQgvvKalkTKZjF3jRKM5ogLu3qOptliC3Y2dgscaQQhGzB7rqiK53k6+eO8EBZHZVFpYg/+QTi5DHUo59f6KFZlFYiN2yFbTtJmjmem+SirTudJH/1Bti6E/YOt3s4nOAPWFVs0gBDguFAnzqKufdFy4Nn6w1QVGZd5A4OoAcTkEiguzqgpwv5wOsR22++RgdsY7OAJIfmacMKFOhTx2DlWgCO9PXQnU5yuLcDc4ZVXjsLytiaX4JbGqjhmhFvnpe7/uxOAMyMieEy0KaJ+aNvXbKmhOYLmP/xj1BciqioRpZVgtsNqzdwYTDOK10tALzS1cJrKuqp2VxCf3Oc1r0za8eais7jXdSV1qK0RuRyyN/5M7jQgFi9YVxr/qWMVH8orWkeGuCFjiaah6wb9B35pdxSdNGzaaRKWHPx5lNpTcNAH49fOIW6htV1DiF5U/VqyvwhTK2QUxoYXBmlTRLZ8ZWXVyLgKKI8uJN872oCRgGGtDoJBnMdHO75Bl3JY7Mey3Il4CihMnQTlf6bwFToA6/O7w7NHD5HlLCrekz6rM1Y3DLM2tgbibrr8MgQwnCgTRN94qiVOp9JL/QQZ09+Efj80N1pXY/lxZAPPEJPaogXO5unXN1jOHh95UpKfMFZ7V4M2wXY2Cxn7JRYG5sljqk1J/q6+HHzmYUeis0CEHV72ZFfyrq8AsyfPo7e/dTc78ThQtxwE3h8wwZFlm/cRJVxsqoOSssRQtA2lODnLWfpSF1ZUPAYBq+vXEWJL4jo7wWvD+GeuMVbd7ZjfvqT42cYBqKiBlG3ErFiNRSVgtazDp2wsVkqaK3RDWdQX5pHw/fKWuT7PkpnOslXzsyundEpJb+x9oaxgS7D6cuXogfiqAOvIDZsB48HhES6rNZTnckiEn0wlARDonu7obgMGSvgOw3HOZfoAyDP5eHNNWsIOt2kkxme++TUibTTRUhB7V21VO6swMyYpPpTBIuDU4bcjBx3MpflRH83B3s76LrkvOgznNxdWkN9ODpuHVMpTsS7+Vnz2RmLpFeDIQSPVK2mwh+aUdXKSNCE0jnS5gAeI4wQkv50I8+2/M0V1/M6oqyKPEyeuxavjCAdVmubHoijG86gmxrA7UbeeCv4AqRz/Zzoe5wLid1Xe6hLEp+jgFL/NmKeeoLOEtzCf9Hf9UID6qmfoM+enN9BFBRj/OpvIlxutFIolSFDkoyZIGX2k8x2M5jr4EJ8NzmWr7dyvmcNq6OvJ5XrpTd1ltah/QzlOnFID5vz30eRZ711vdTciD53Cn3utPV+zi3R1GQhEOs2I2+4BVFRPTpZZzOWJYrLxX+e2s/QFMcXcXl4Y/VqQk73rCvjlNbI4QcdtnBns1yxBTsbmyWOqRUvd7awu2ORJ4fazCsfW7Md94UGzC/O5Y27RL7l3bB6A3IG4lfr0ACPN56atneJWxq8Z8VG/E4nhhi/H22a6AOvop77heUBBVYK46p1iLpViKpahMNpeUQNt8Xa2FwPaKXQh/ejvvOVeduHfM9HkLUr+dzJffTNshJEAL+17sYxN2UjItelwp35vUfRSuF4wzvIJDLksibnnzuPdEiCxQFCpSEcHgdaabx5F1szv3L6EO2psV6eW2LF3FlSzdHHj9F9upvC1YW0vNqCmqw9f5oUbyhi7RvWkkvlcHqd015v5KbyaF8nP246g0NIVkdibI2VkO/xAQxX0GmkkCitMLXm6bZGDvd0jFYmzjdSCF5XuZLqQGRWN9KHux/jfPwZNFbKb8BZTMYcIGVeuZ335pI/IOKuho5W9Pmz6MZz6AsNEO8bu6BhIDZuQ95yNyKaTzY3yNmBpzjV9wTXS2tmoXcjO4o+YlVkplPo5gvQ0ohuaUK3XID+3ms3mEAQUVphfSfHCiBagAhHIBBCuK3uD21maR7aw8Gur6JYoiLVFfA5Crij9E8R2Rw4HBdF01x2+MGmRO97CfX0T2EgvsCjnRvE2k0Yb3kv6WyWQ32dNA/FKfYGKfD4CDpd7O5o5vRAz6TbKPMFeaRqFQ5pYMzyms3UisM9nfRmktxRUm2LdjbLFrsl1sZmiWMIOZoQa3P90jw4QE1R6dxtsLwa8c4PIr0+DvV0sL+njUQ2w0iz1mSPejLKnNZtpddwsClaNNomN7KO1tryp1Mm+tXdqN1PWzdtUiLWbkTsuAVZXXfRpH1YTLRTW22uO5SCxPzeBKr9ryBrVxJyuscIdhJBkdfPQDYzpThf4PGPE37EcDjEyOdWmyby3odRTz6BMhXpwQz7/3s/mcGJt+2Nebnp13cBTLj/o72dbIkVs/a1a0Zv5MpvKOPFf31pRsc/EZHqPIQQMxLr4GJ4hEsa3FpUycZoIa5Lzn0w0gY74sckEWjuKa3h1qIKTsV7ONXfw/nB/nltjV2fV0htMG/G6yltIoXBYLYDPSyeKZ0jnpn6gaIQBvR2Y/77/518QdNE73sZc98riFVrcdx6D6vKXsOK4D00JJ7lWO+3ZzzupUZf+qyVEH3iKOobX1zYwSQG0CePAhOEIzhdECtA3nwX5et3UurbRtPgy5wfeIb+zPT8zRY7txT/PiKTxfzPf4S+XojGECXliNJycLlRLz5jtYsuI3Sr9Xk+0NvBcx3W3/HMQN+0118djnF/eR0CcVWec4aQnIx3c2EwTiKb4cGKFQht+9jZLD9swc7GZhnQbwt21z3NQ3FqiipBOkBd3RNs+fq3waYdDOWyPHHuKBcG504Q8BgO6kNRVoVjlPtDgHVrKoS46I6kTKvN79tfhaGEtUzdKuTr3ooIRdDKtKbZLa821ztSoBPzbFZ+aC/m69/Gg+UrODvQx7PtjdQG87izpAq34RgOQ+jjUG8HFwb7JwyY6UkP0ZdJjWt9urQaQhgG+PwYD7+Z3KOfx//m97H+zevY+6V9E8YkJruTJPuSuMJuqgIRjvaNvSlOK5MvnDzAmkg+W2LFFHr9eELTS1SXDklsRZTYynxCpUEMh8G5ZxpoO2h5sOVVR6a1nStRE8yjNjjBuW8CRl4jt+FgTSSf9XmFJLIZftx0hsbB+QmgON3fw/b8EkJOF3KCqucr0Zs6S3+mkd7ULAI/tJpWANElK6BPHME8cQQqa5C33kPdinsIuop5uf3TM9//EiKjErSmDlGyZhPEChavIJTNQFsz6lv/jXr258i7HqRy1c1Uhm5GmVmeb/v7JS/cGcIFqcSwl6iGni50Txf6yP6FHtr80duN7uqgPhwdFeymy86CMm4qqpiTajitNW1J6xrxZLyHwXPHuLu0hnyPD1Or0Y6Ni76gekbnMxubxYLdEmtjswz4j+N7GJwgCt7m+qHUF+DttevJfevLcHjfrLcj3/khZP1qDvd28FTreTLD4tjVUOT1UxvMoyYQocjrB8Yaql+ONnPovS+hfvRt8PqQ978euWn7lD5RNjbXI+ajX0Afn5233LRZuxHjvtchwnm0DA1Q6guOueG61EfombZG9nSPD3so8wV5U/UapLhyVcWI/5n5o2+D4UDe91q6T3Vz+NtHMNPjz0XSJbnp13fhDLj45yMvX7FldHO0iLtKawDIpTKgIdmfZqB1gMxgFjObI5c2MdMmJZuKiVSGRysA6e22PJvyYmQSafou9FO4pnC2r+ScMPJ6H+xp55m2xjk5T19OwOniHbXr8Duc07rJVdpEIHmm+a8YyLbMeH83l/wBkXQE858+MZvhAiDvuB95+330JE/zQts/jE53SB8+RxSXDOI0fLikH6f0kcx10zz4yqz3t5A4pIf7yz+JPnYI9a0vL/Rwpk84D1FchnzkHcRFJ8+2/J+FHtFVke9ZxY1Fv47e/TTq5z9c6OFcM+Qb30Vu9Qb+9cSeaS0vgHtKa9kQnbtzZ08qyRdOHxg3vdwfYnO0iJpgBKc0yCqT1qEEFf6Q3TJrsySxK+xsbJY4GdO0xTob2pOD1k3cqnWoyQS7lWuRr3sr+AJXvGl+saOJF+bIE3FHfim3FldaqYpcrBaZ9JJJSMTmGzBq6sEftNIgsSvqbJYuIwLLCFrrCVsaxSRi1uVopdAnjqCPH776AUaiEI5YLV3xPsb1vMfjo9VPxd7A6FhHGBmzEILt+SUTCnbNQwN87tR+biosZ12kYMJqByEskcx46I2ofS+hD+0ltm4Luz6+k+M/PEHXia6LC0vY+JYNuINuulNDk/q79Q+38upMBnnmKChNoLyS4JbxNgI6l0O/9Azm8cPQ1mIlOA6brDtvu5fCNUUL7pU08nqvzytkdTgfrRXxbIbWZIKmwTjtyUF6r7LyPpHN8Ni5o7yzdj0eY+okRikMlDZZmfcQezr+a8b708y0wm486qmfoNNpove9lvsr/x6hBYZwIowr3+6syryOVzv/Y1ptu4uJnErRPPQq5et3op79BXTMbSLyvNHfi+7vRe3+JaHb7yfoLGMgO3Wa6GIk37OKDbF3gpBQWrHQw7mmiHAeyWk+KHBKyYPlK6ibRZv9lTC1oiU5cXV502CcpsE4hhCEnG6SZpaY20dl7bo527+NzbXEFuxsbJYwSutxbUA21ycV/jBSCMzWyW865JYbkf4gx/q6aBqMoy+7yU3lcpwemBvD6jWRfG4trkRfJlZMhZASpIT8ha1isbG5WkaEnYFsmhc6mmhM9BNxechze4i4PDilwaWekF6Hg3yPjzyXF8ewQJ1TCoVGDvv9jHyWtBCWwfxsggiKShErViHKqxGVNQif/+KYTRPifeiuDqu6zOVGbt4x2oo+1WfZ73RR7PXTlhwcNy+RzfDT5rPs7WrlHXXrEYwXvkZF/Y3bR/3tXH4XG9+2gUPfOEznsU6kQ3Lz7+7C5bXE/Dy3mwfK6ni2vZF31KzDAH7Sco6G4ZbRhkQfh3s7WJ9XiE6lUD/4hrUzwwEuFzic4HRa/0/EYeiysWuNPnIAbrkbrUyEXBx+mVIIHFIihUGBw0mB18/GaBEwLOgqk6zWdGRSvNTRPOMW2v5MmqFcFq9jel59UhiU+LdS7NtE+9BhNNOv/NNaWcLHVaJ3P4U5lMDYuA36+1D9vZYQPZRAZ9KQyQz/pBE1K/A+8HpuLfkjWpMH2d/x+XkLRVgRfoCq0O24ZJC06qdz6Ajn+n9BItc+620e732cMv8NyDvvRz36hbkb7DVAv/Qc3HQnNxZ9nCeb/ueSC6PYGHsXFYFdMJhA/eK76L1X7425pMiLkjCzBJ0ubi6sIOr28kTT6TEPCoJOF1uixWyMFuGYg0CwSx+UGELSNDi5HYRLGgSdLmqDeZT4Ale1bxubhcRuibWxWeJ86dQButLJhR6GzQLiEJJ31q0nT0j03/x/ky+8ci2Od3yQ3R1N85osnO/28a4V65FMXZlhY7Mc0VqTVYqn2ho42ts144RPj+Egq0zMCS7TnFLyuspVVAXCmGYO0dcD586gDrwKTQ1WaExVLfqlZyB32Y2w4cD447+yRHGuXLmqh4NfQEwa6KK1An1xO0prjvd38eOmM5Me35ZYMXcUV015fhi9TNWAgK6TXZgZk+INxbzc9mkS2XY25r+TfO+qMTd0WinOJPr4fuNJAB4qX8GqcAy950XUD7856T4nQmzajvHIO2a83rVg5DUabVF+8Wl0T7eV1hmJIerXIFwusrks54cGeKatkb5MijyXZ8pKvI+v2Y57kgq1yxkJnsipNJ3Jo7QM7qF1cO+U6+0q/m2iZgnm//2LKy9Uu9J6f2eml0A+LTxe5F0PIrbfRF/6HM+3/v2sNuOQPsoDN1DgXQMaOlNHaUnsJaMGiHlWsavkt+hMDXI+0U+FP0ShxxLJcyqNEFZC+sVPuiXiazRamyhyKJ0mp9I0DjzLufgvWR97O1WBWxBSova9jPr+o3PyclxLxKp1yLe+n2Sul2N938XAiZQOlMoRzzYRz7SwmJJ/HdJDdfAOqoK34HVGUfteQv3oO3AddLnI174VYgXog3uQG7dBZQ2m1lYQ7nDPhKkVL3Y0k1OKUn+Q+lDUWncOrgGV1pyO93Cgp52MaZJWuUlTy+8vq2NdXsHoutj+dTZLGLvCzsZmCTOUy9pinQ0PVawg6vaivvnfEy8QCCHvfgi9ZgOG20PazNE8h0ESE1EVCCNssc7mOsdlGKRNc8ZiHUDKvHLFSVYpvnP+OHXBPMr9ISr9IWLbdyG378I0TYSUVsXtXQ8im85j/vwHEO+HknJEzQqEY+rLPyGEVYE2CTqXAzOHcF8Mc5BCsCacj0sa/OjCaXJ64hvu/d1trM8rJOb2TnpDNyrADfvbxVbEENKa1pE8jMeIEHKVWanRSqF2P20Z3D/0RlZs3sFHV23lB02nWR3Jt4SNWYh1APL2+xatj+al51mtFHLn7Zjf/dpFTy2HA1G/FmPjVurq11JXvwmUiTAcaNNk0MzRNDTAj5pOj9v2nq5WbiqafrufFJa465BuinwbKfFv4eW2T9GRPDLpehoF8grvg0gU+b6PIiNRlKkQ7S2YT/0YTh2b9riuSCqJ+tG3McqrcMb8Uy8/IZK7yv8Kl+Emq0wEgiL/BtZF30pO50DnUFrz9bNHRkNZfIaT6mCE6kCYpJmjO51EYv0tDSEQCJxS4jEcoz9hV4S10TdT5r8RtxGAdIrcf/wD9M9NVfy1Rp84gvrGl/C+5b1sK/zg+Plag5nDJEeWFBlzEIf04BJeDJxWpatSZEmTNPvoSB7mVO+PZl2t55A+Sv1bibrrMKQLKZxIYSBx4HcU4DZClrdlfy/mk19FH5yef9tSR2y5Abn1RuvcUlU7Ot0x7rwtuaWoAksem1lnxWQorUmbOX7ecpaUaVXtlvmCvKtuA6fiPezrbqMzNTS6/K1FlayN5F8c1SXJ2zY2SxFbsLOxWcL4HE4q/KE5TfG0WVpsjRVTF8xDPf1TODpsvrtqPcatd6MjUbTHgxxu32pM9HO4vZEzA70TVu3MJbZOZ3O9I4TA1IoNeYVU+sOkzBzPzzBRbzKU1pyK93Aq3gNYFXllviDl/hBpM0dDoo+1kQLWllXi+pXfGLPunCT0mSZkM5j//NcYv/1niGGvSbCOvTaYx7vq1vODC6fonuDBkga+ce4o95XVsiIUnXJMI0KZNORwwIHB5vz3EXFX4TICKGUiBOhUEgqKrIRKwONw8paatdY+EzP8rpQSNm5H+HxWy+wSYCQsQ+66A/PAq9bEXA597CD62EEIRZB3vwYG+lCtzYjCYnyVtayursMlDb7beGLM9l7sbMbrcLI5WjTj94wUBlor6iMPUhrYQZ67Fq8RQStNb/YMR7q/ORpQYVUJXrZ96UDc9QDixttQpubkD47jjfko3VyC852/ikqmUN/8Ipw9OduX6yI+P1nVN+PVyv07WRG5H5fh5qWOZl4Y/ozH3D6KfX6KvQFKfUE6Ul1jEpSHzCxH+zpnbGuyPb+EmworcEiJTvYtWbFuBH38EOY//zX4/GCaVlWvw4kIhiAUQYTCyFAEdziCJxiGZALdfwEG+lEDAwifD0eskFBRCeHoA6wI3kNX5hRHu781ZfiJx8ijPvIAeZ46/DKKNNwIISzxf2Qs5vBPVw/qxPPoE0ega/ZtzEsCjxdRW4+oW41YuQYRCDGY6aRtaD91kXut10eMfyA76mV6yX/nAq01J+M9o2IdwC1FFTilwdrh5OymwTh7u1oJOF3sKBjvTWpjs5SxW2JtbJYwplac6u+Z8Km4zfXBr6/ZgTOVxPz7P4cbbkHeeg8yEGQol6V1aIDedIqeTJKGgX4SuTlsI5qCPJeH99Vvuui3tcAm7TY2C4lSCiklP7xwCq01VYEIOW3SMNDPuUTfvO7bISS1wTxAM5DNEHF5eLBixVVvV5sm+vhh1Le+jPH7fzHGB28Ec7hd9qm28xzoufJN7sZoEXcWV1mCGyMeRdM/X7zc2czLnc28u24DEbfXGp/WaJVlyOzD1Cl8jnwc0oP5ra/Akf2Tb3DXHRg33ooKhjAuqai7PDxkpmitUWir+nh42uVJu3NJ7l//Fro7prWsvOc1yJvvQmlF61CCw72dHLlETHqwfAVrLqlauRq6TnaRV5OHNCTHer/D2fjPuaHo4xTIWsy//VMrFOnN74GKGqTDoPtMD8e+d4z0gNUCJwxBwap8Vj+8GkdfO+Z//uNVj8n4o0+Ay0XGTPCzpj+ZcvnK4G2sibwOp8M3Ou14X9e8Xo/VBCPsyC+l1ONDGg7U/ldQ3/v6vO1vyVFchtxxE2LjNjAcpHP95PTFlu+0OcCejs/idUTYEHs7YVelJXB3d6Ibz6GbG9HNjVaAh1o8rbjzitNl+QUP+x+L2+5F3n4fQkrMXJoBs522wX1I4WJl3oPDwrpGXMP2Uq015xP9fPv8cQAq/KHRhzAjXHr+tK83bZYbdoWdjc0SxhCSsMsz9YI2y5bmoTg1wTzEn/0dhpT0pJK81HSaE/3dE6ZQXgsiLjcRl4eWoQHK/aEFGYONzWJAKRORTqP+4X+j//AveU1FvTXdNNFoNkaL+PzJA8SzV/biuVpyWnEy3g1YN/x3lFTNiTgkDANWrUXccueEYh0w7Mulubu0hpWhKE+2NkxYbXewp53mwThbYyVIIUa9h6aD1orqQITn2i/QPJggbDhJqE6ebf4/Y1rjpHDyQNU/INdsQI0IdptvwNi8HXXqGPr5X148trsfQhgGBvD1s4cZyuXIc3u4u6SGoNM1o5vBkddaa01XOkljop94Jo3CSgrWw35lhV4/6/IKcA57mc2FeCd33QbpNGiN2vcSdF+5mkv9/Efo7i6M172VMn+IUl+Q6kCYHw4LUKlcdriyUczqZn3kJlorzVDPEEe+fZT1b17H6tpHSJo9Y1Ji5W33ImtW0HqwjQsvXmCgday5vDY1HUc7KdlcSrR4br5j1I++jahdiXvzDvI9q+hKnZhwuWLfFjZE34rbGUa3t6JDILw+jvV18bPms3Mylsu5q6Sa9eEYDocTnUmjD+7FPLIfffbUvOxvydLWjHr8G/DTxxGbtuNetxn3JZWx/vxq7i3/hFU5m8min38S8+XnIDF5eMGyxeXGeM9HEOVVqEN70Yf3I8oqAXi2+ZP0ZxoAKA/cyOaC9wEjDxiurRgmhKDIG+CO4iqkEFQGwiitxnjSXXq+tMU6m+WGXWFnY7PE6UoN8aXTBxd6GDYLhADqgnkUev20DiXmvVrnUtzSoC6UR57LS8zjxSkkBV4/vssSBe2nnTbXK1qZ0NaC+Zl/Ao8HSiutyo2Gs7BjF46H3sj+7jaebG2Yl/0LIORyE3P7qAvmsSFaOKeVXNo0EYYxreRUpRUCwdG+LrrTQySyGU7Heyf0uHtT9RqqAuFpjVVpzcGedp5sbeCdteuJuuGnjX84Ot8pvfidRfgdBWwp/IBVGZjJgNOFdBiYWRPDaaBSKURvF/gCEAoDkMhl+MyJfaPbWhHM4+7SGvxO15TntZGx96STvNB+gYZEPxk1eWqqQ0hWR2JsihZT5PVjajXj4J5RgVCZaKWHvaQkSIE+sBf1giVMilAYwhFEfhFi205obUbtfRG583ZESRnxdDMhdxmdyUFMNMXeAK2De3FIH/melVddYXN+dyPnnj7HtvdtxV/oQ4kcBi6rpdlwIN0uDn/7CO2HrlyZueK+FVTeWA7NF8DMWtVChmGJDy88NfNBudwYf/QJ+jINNA++iqmymDpN2+B+FDk2xN5BVehWdG836hc/Qjedx/it/w+F5oX2JqQQo95Ze7rbONrbeUUPx8lwS0nM7Sfq9lAbymNFKIo6cQS97yX06RMwib+lzSTkxZC33YvuaEXveREmCS1Y9jicGO/5MJRX0ZU6Qb5n1eg5XGvNk01/RjJn2S1sKXg/Jf7tCxraYF5W8SguSUy3sVnu2IKdjc0SpyeV5AunDyz0MGyuI8IuNx9cuWWhh2Fjs2TIfeqTY32P8oswPvYH9KSTPNF0mo5LDLPnArc0eFP1Ggo8vtGWzvlouRwRrWaybXNYuJNC0J5M8K2G4+MCNgRQHYiwMVpEbTAyEhB7ReHqRxdOcby/m+2xEm4rqeJc/1P0ZRoIOkuoDt2OQ1qV6GY2R9epHlRWkUvn6D3Xg8ppqm+rJlIRHj0mM2NiuAx+2drA/p52SrwBbi6qwOdw4pIGIZd7wnFcymA2w3PtFzja1zmLyBEo9FgVd7XBCGGXx2rxHX4d1PDrPlK5N9JmK4WgMdHPnq7WMQ9vXFJyb2kt9cE85CWJv6NJwNIArcaIrolMO+cHnmF13iMY0smZ/p9zrOfbSOFkZ/FvEnFXjwZMzJbOE10cf/wYax9ZizQkmaGMJTIqKzzj/PONDHVf+bPh8Diou6uWovVFaGX93RACb8SDOnoA9Y0vzXhMxgd+HVFZM2ZaMtvDi23/wh1lf4o+chD1g8egagWyZgWidhUUFgGWLyO93WA4EKEwGWWyv7udlzqbpyXcOaTk1+o343K6xkxXe19CPf7YjI/FZpkTCCE2bYeWC+hz06y2lAZi0zbkbfdCKMKh7q/TmHgOiQufIw+nEcDUaeKZptFVNua/m/LADUixcI15I3JFXyZFe3KQYm+AkMtti3Y21wW2YGdjs4QxleJoXxc/a5mfNgwbm8u5qbCcnYXlCz0MG5slRe6//h80N16cUL8Wxzs/yNfOHKY1mZjz/W2OFnFXac3UC84Sra12zpahAY73d3NrUSUuw5jxzZMa3k7jYD/7uts4n+gft0zA6WJjXiGbokW4DceE+3j07BGah6y2tjdUraY6EEIIiVI5hrpSNDzbAELQfaKLXMYkVBpk3ZvW4Yta/mPKtMQUacjR4xvIZvjS6YNsyy/hxoIya/4l+x4J/fjRhVO4h1M8vYYDt+HAIQQNif5ZVVdNRNDpoioQpioQwWc46cuk6M+miGfSOKWB3+HEKQ2O9XXRlb6ywOWWBr+ycjMyqTj42CESbQl8MR83fGQHQgiOdH8Ll+HHLYOc7P0hKdUHCDxGmJTZz7AkhcfI4/byP8Uh3DOqtFOmshJ+NQgp6G3oZd+X9jPXtyJrXreG4o2FqL//n5BKTb3CpRgGBELW/w0DUVOP8eAbUCqLyJrolguI6roxx62yadS//V9IJmFYfBd3PoC89R6EEHyn4fiE1e8rglEqAyF60knakgl2FpZTEwijfvht6OlCDw5Y7ZpDg1fzctgsN0rKrffWqnVWG7lSqMe+gD55dNLVxNYbkbffjwiFSWZ7Odz9KO3JK3foeIwwmws+QL53JUqbVy3QXy0/az7LoV7Lk3N9XgH3ltba3Rs21wW2h52NzRLGkJILg+NvcGxs5oOg0zUq1l1aJWNjY3NldDYLHW1jpolYDAA1q9qrydkYLeLOkup5qai7lIFchm80HAOgO53k7bXrZrwNOVwlVh2IUOkP86XTB+nLXBRYHELwxqrV+BxOulJJKgKWX5mpFcYlgkngkoqkkNMFCBqea+DMLy4+zCrdWsqtf3grx75/nHhzHIfHMVohOCLUjaCBnnSSd9atJ+8Sn9jL22Cfa2tEAykzR8rM0TfjV2B6DGQzHO7t5HDvzBJFLyetTDLKxJlRxJusxNxEe4L+C/1EKiP4nQUc7fkmSl9a8ahJmX1jtpMyeznQ+d9sL/rwtPd96LFDBIoDhMvD9DfHaTvQNmn13NXQ8GwDJZuLka95M+pbX57ZyqY5JnlVd7ajAkGIFaLOnUI+9AaaB1/lwsDzDGRaubfybxCpNPL+16HPnUb39SLveQgRK6Qvk+aptoYxYl2ey8P9ZbWUeHwIY/xtmHrpWfSe3bM9dJvljNuDvPshxPabQKnR5GwtJfKt77fSduN949dzuZFveAdy9QaGst0cafv3SYU6gKhnBVsLfgWXEQRYcLGuN50cFesATvR3c2dJNc4FHpeNzbXAFuxsbJYwSmu25ZdwdqBvSm8cG5urZSCboSs1RMztHXOzbGNjMzHaNNEnDkN2bEKz2Hk7yVyW3vQMq3+moD4U5Z7SGvQ8i3VCCELOi22hfVd5HFIINJr3128arVrTwK7CCqJuL1IIPH7HqAh56fnH1Ir7y+oIOd20JRPEPD4adzeOEesAitcXIQ3J2kfW0PRyE10nuindUjKhF50UgupgBKUUQgiO93XRMjSAqTWmVpha05Uaon8ew0LmC6U1vpiP2/+/2+k51c2hxw6z54t7ufk3b6I6fBvtQwfpTE5eqQPgdxZOf5+mIloXpeNYJ2d/ee5qhj8tkr1J2g+3U7hmI2zYBof2XNX21JNPACA/+gconaPAu45i31ZyatCq5AwEkKvWw6r1AKTNHM+3NnCwp2NUlA84XLy2op5irx/MHPqVF1Anj6Jbm6zWxkgeBEPow/uvaqw2yxOxbjPyoTeAx2edr4yxQpU+fRwS8bEref2I8krk/a+HvCin+p7gRO/jV9yH1xFlVd7rKPKux2n4FkVV3Qiey8TtrFIc7+tibV6BfT1qs+yxBTsbmyWMFIICj5+Hylfw3caJE81sbOYKhxC4pzCWt7GxuQQh0E3nL/4uJfKN74JgmNN9XXP+oOWmwvJ5r6wbQQqBwKpGCzidUy0+JSM3XStDUZqjRZzo72ZbfsloHuHIMY2EV4yIbCNJtLcWW+mGmWSWM0+Nt4noPNFJpDoCQNl2q8VVK221aE6A1hopJc+2NfJKV8tVH99i4YunDlIRCLE5WkTdmkLu/LPb6TnXh+GWaK3wOmIYwo2pL4qRXkcMtxGiL93ASFtsvmcVWqtptcQKKSjdWorhctBzpmeejmwsZ548S6AogP8N70DffCfqa58dUzk3LaIFiLUb0UMJ5OoNyMJijnZ/izXRNzKQzRByWb6HUki+euYwaTNH0OmiPTlI+rLP9v3ldZT4g6hXX7AEwOQl1YWpJLrryuEaNtc34sZbMR545IqfN/3i06if/QCEQGzbiaiuR1RWI0IRa75pMpBrJZnrJeAsIZFtHbeNoLOUW0r/CCHkqEi3WMQ6AO+wf+il35mNg3E2RIsWcFQ2NtcGW7CzsVnijESc29jMNX6Hk4+s3saR3k6ebjvPg+V1+J0u2zPExmYaaNOEeN/YiplAELF2E2llcry/a073VxOIEPP45nSbU+GQEpc0KPEF53S7txZXcmaglxc6LnBDfhkOKUcFu4mSCoUQmFqBgpc+9SIqM947ru1gO9W3VeNwO8a1wF7OSNXdz5vPcvCSNqzlgELjMRxUByKWuCsN8uuGW7S1ycb8d7A2+kaebf5rTJ1jQ+ztFPk3ANAQf4bD3V9HIMnz1Ezbv27kO6Pj2LV7LVN9KV7+91cov6GMurvqkL/xP+CV51E/+e70NuByIT/2u0jjYrv12f5f0J06ZVWYXhY88taatTQPxWlMxIm6vXgdTryGA6/Did/hpMwbQOdyqOd/OVass7G5HK/P8lF0uxGVNRj3vnb4nDTx500d3Asl5RivfxsUFI8LkBGGQUCWsDH/nWiteLr5EySyY20aYt6VGPLqH7zMJxGXe0xAk2Ffi9pcJ9iCnY3NMsAhJSGnm/gSbM+xWXy8sWo1Fwbj7OmynsKuyytgXV7BhK1jNjY2EyMMg9zn/xUGBy5OjPejz5zEXbdyXDLq1RJyua/5Z/TWokrW5xXikHLO9i2EwEByX1kd32o4xrmBPt6zYuOU6xlCoqWm6pZqTv1kfGJiNpnl5BOnWP+mdVOOdST5dmdhOS3JBF1znOK70PgMJ4aU5D77z/DuDyHdXrRWSGHQcbyTwtUFrMx7mGLfJgQS9crziHWbqQju4mjPt6gJ3TmavDsTVr9mFYFCP+dfaERl5yaQYzK01lx4qYn2Ix2svL+eop23Qv0a1Bf+1QpzmAT5wCNIw0XH0FGaEy8Rz7YwkGkG4NX2zwCQzHWTzPUS89ZT5r+BIk8NFf4KBFarttYmigymSoNW6IN7oO/aVBjaLE3E+i3IR96BuCzNebLzleMjv2sFt2irhZ8JKuNGquWEkGg9vrI737NqWvtaCLTWZJSiLzP2HmdNJJ+sMnHanR82yxxbsLOxWQZorbm5qJwnms4s9FBsljgxt5fqYITqYIRXulp4of0CNxVVACy6izgbm8WMzqRhID5uunr5ORwrVhFxeeicQyHocG8HW2MlhF2uCavQ5hqtNZtjxaMJn3N9fijzBbm3tJYN0cJp30QKIajcWUE6nqJx94Vx8/Nq8qY9VikEAaeLlaHoshPszg/2ASCqV6A+/Xfwa7+H9PoBKFxdAECpfzu6oxX15c9ATT2OHTdzpveHrAjfz8q8h6bdDnspTp+T2jtqSfamaDvYNvUKc0QmkeHwt47Qdaqb1Q+vQv72/4Tzp1Hff+yKbbLq1RehvIr82EoKvKt5pvn/jM5rG9o3ZtnWwb20Du4d/k0C48XIW0v/B6EVq6C4DDpaQc2/YGmztBAbtmK88V3jUpOnOl+Nnh+naGHVWtGXPs9gbnx4zeHuxxjKdVETunPSar6F4pWu5jHtsNtiJVQFIgs3IBuba4gt2NnYLAOEEKwO55PM5Xiq7fzUK9jYXIHgJWmLAC92NlMXilI0fDNnY2MzPYTLjdh1B/rYwTFVNfLO+0nlcpwdmKGf1hRsyCskfFmb3nwychM5H0K+FIJDvR1sGvYnmsk+tNasuHcFQz1Juk6MbTs++eOTOD0OCtdOLQJqrUmZudFK4+VETzrFYDaDb80GzOd+gfqnT8AdDyDqV6N9fshmUb/8MRx41VqhrQmVy1ATvgtDuGYl1sHFv2M6vjDdAG0H2+g730flTZWUbV2B8Zv/A93eitqzG/a8xBihraXREjNdLuQff4Ji/0YG+prHbdPvLGRD7B0M5To51/80A9nxywC0Dx0gnPcaqxoqOYTe+yLqFz8CPfdJ0TZLFJ91nTXTc+p0lxdCMpgd35bukkGinjpSuX56UmeIeeuvmRfqVGitSSuTfd0XBf61kXxuL6lalNWANjbzgdCXy/g2NjZLmlc6W3iuvRH7g20zUx6uqGdlODb6++HeDs4N9PFwRb19UWRjM0O0Uggp0bks5t/8KQy3wMqP/yHECvnBhZOcjs+NaFfsDfDOuvWL/gbmStV4SuvRsYvh+X3pFCGXe1Y3jVprMgMZnvvH58fNkw5J2bZS6u6qQxhiUj+7fd2t/LJ1eT4Ee3fdBvKVRv39n09vhVAE+ZHfQfoCKGUir6IN7fl/eoFU/8Vk4WBpkECBn2w6R+nmEkKlIbJDWYZ6hjj7y3MMdg7Oel9XwulzUnFjBWXbSnH5XaicCd0dqFdfgFdfGLOs/L0/p8/RyQut/3fM9ICzmFtK/wgwrFZuYfBEw++MCey4ZCtsiL2dRLaD2tAdeJ1Rcn/35zCUmPNjs1mqCOQ7P4ioWznGg24u0FqRMvv4xYU/wyX9ZJT1vgs6y7il9I8xJtjfYvg+0VrzbHsjrw4/OIm5vbxnxcbR7wkbm+sBu8LOxmaZsT2/hHyPj++cP77QQ7FZYrzc2TxGsFufV8j6vEKU1tiXRTY2M0NISwgSDidE86HTqhBQ338M4wO/Tok3OGeC3ZpIPkqra9IKezXktKJ1KIHf4cTrcKK1ZiCbIZ5Nk8hmqAyEibq9CJi1WAfWjZw75MYb9ZLsSY5OD5YEueHDOzAzJgPtCcJloStuQ2vNllgJUkieam3AXEbPt0NON4VeP+rsyemvFO9D/d+/RG++AeO1b7mq/acHLgpaqx5eRdmWktHPizZNdONZXMEw/pVRClblM9Q9xMmfnJ7ThNnsUJazvzzL2V+epWhdIbEVMaJ1UdyveRO5owdgaFgkjETB7yc+sH90XUO4cUg3lcFbkMLBZ07s57WVKyn1BVkbfROHur86wR7V6HSvI0xt+O45Oxab5YJGffsrGO/8VXRZpfXAR5njxDutNSg1xudu6i1rvI4oD9d8Cq0Vps4hhltoUzmTxxuOkchmGcplyWnF1lgxd5RUz+XBzYiRCr/9PW3svaS67mq+F2xsliq2YGdjs8wQQlATjOCWBmk13ljWxuZKdKSG+IfDL3JXSTWbY8Wj0+2LIxubq0MEQ+jONgiFEe/9KIlclj3dV9dq6TEc3FZcSW0wD9+w+LWY0VojEPRn0uzuaKJ5aKzpv1sabIwWXZIGe3XnHa01hWsLOf/cxQo56bBEIcNlECqdPNl2pHpjQ14hWWXyTFvjVY1noZBCUOUP43E4SGQzDGQzJLJpjvZ2srpmBZRWQss0j00p9N4X0Zt3QFklcFGYngnhijDePC+VuyoIFAZQ+17CfOk5cLqgp/OiWOb2ILbfhG/X7Wx+1yZUOksmZZLsSXLke0fJxDMz3vflSIdk5YMrMZwGmaGsNe32+1HtLbD/VeRb3ovWJqf6fkTIVYFGcXPJ742GbnQmBxnMZfl2wzE+vmbHqLn/CAXetdSE7iKrhkib/Sht4jbCAIjqWvTRg1d9DDbLiFQS83P/Ak4X8qY7ELffN2a2Nk1Ao48fgZVrwTBAiCmrzS5/XxrCiRCCRDbDtxqO0Z1Ojpm/t7uN6kCEykD4ml8DKq0ZymV5ouk0Fwbj4+bZ2Fxv2IKdjc0yRGnN9oJSnm8fb7ptYzMVWdsM28ZmTtHJIZAG1NRjOBy09ncTdrrJKTXGSHu6OKXkTdWrKfD4R2+mFqo9yNQKY7iyb7IWKiEEDiFYm5fPhmghA9k0x/u6aR4a4MJgP1KIOb8xrNxVwYXdF1CmIloXZcu7NwOgTDVpK+ylSCHYGivhcG8nPZfd1C4WBFDuD1HqC6K0xiklEZeHqNtLntszLkVRac2R3g6kkOi3vQ/9j/97RvszH/sCYuN2RHkVVNWC1zft9582Tba+d7NVPZTNYv7o2+hXxrcuA5BOoZ9/EvPFpxEbtiIqa/HECvBUlHPzr+/k0LeOjvMpnC6lW0sp2VxCqDSIkILeTIpoxItSCnnDzVZ0xGvehJSSnEpxc8nv43Pmo7XG1CYvdjRhas0rnZZnXUYplDZJmX2AIOAspip4CzXhO8mYOZRWOORFMVqbJvK2ezHPnxubJG1jA5DNoI4cwNhyA4TzRivtdFODFZbS0wUeL2LrTutzGI1BQfG0BPQR/0mlNS5pYOqJr/meaDrNr67aOm+C3Yj4NrL9kaq6o32dPNV6fsLvxsX+YMrGZj6wPexsbJYpSmu+cOoAfZnU1Avb2FzCjvxSbi2uXBT+JTY2ywWdzUImTc7jwWlYz0tNrfl2w7FxVQQCyPf40Iz1d5NYwtdtxZVU+Oeu8kFrjUYhEAghUdpECoOsOUTKjAMagSTgKkJpEzHcJJ9VmuP9XRzr68LUiofK6wm73NM6b2itUWgMIUkPhzvsKCgdJy5d7XE1vdzE6V+cQRqSW3/vltEqu5lgakXL4AC/aD2HxBIW+zLpcTeULmkQdllCrKk1STM76QMQKQSbo0WkTZPziX4SuQzGsI9fiS9IgcdHY6KfMn+ISn+IQq+f84l+zif60cOvXYU/xMpwDI/hQCmFxsox0DmF0+1AKZP9nV+iI3WEsKuCkKuMYt9Goh7Lm1Rrjfrqf6FPz9JGQ0jk7fcibrsHlAYpJ/z7a9O0KoGkRD3/JOrIAWhvhdl0AkTzMd7+AYgV0rSnhZM/mkFrL+D0OrntD28llcvSNDTAkd4Ozgz0AeAQEikEUbeX6kCYbflFuA0X8UyKo31d1IeiPN9+gdMThMb8xtptmGqIrBok6CpFa8VANsuBnnZ8DidBp4uqQBj38Odfmybksqiffh+996WZvw421wECKmuQ6zahW5vQ+1+5OKu4DFFSjigpQ1SvQBQUzWoPzYNxHj13dMJ595XVsiaSP/pQZq5pG0pQ7AugtCZt5vhJ89lJA5luK65ka6zE7vywua6wBTsbm2WK0orGRJxv2152NjPEISS3FFWwJVZsC3Y2NvOI0pr2ZIKvnT0yOq06EOaOkmqibu8V15uumG5d4ukxiZ5aazIqQdYcIquS5HSKnEqSU2n8znzy3LX0pc9zLv5L2gb3o8iNrpvnrqXAu4bK4M1I4effj+8ld8ll5GsrVlIRCFni0QxSBi3BkDk3Eh+5xM0ms5x/vpGidYUEigLTrq6bjIxp8mJnE/u62zC1xiEE76/fTOiSpN6RVtoDPe3j1vcYDl4/7HsG1nGnzRxuw0FPOslQLku5PzR6HNl0jkw8gzfmxbhk/DnTZLAlQdOrzbQdbAMBxRuKqb61Cm/Uy8vtn6IrdWzc/r2OKOlcnHsr/g+O3gHMf/v7q0ssraxBbr4BIlFEXgyCIYRhoM0cdHeh25qttvCWJvRMvPOuhMOJfPjNyE3bySYzmBlFOpHm1f/aM+WqBasL2Pi2DXz1zCHaklMHWrilJD2NyvNCj5931q0FxrYgKq0wtYnSGYay7Zzu+yntyYP4HAXcUPQxAq4izO9+HX3glStt2sZmDPKRdyA3bR/2szNBGjNO09bAuYE+jvd3caK/e8Ll6kNRXlu5co5GfRFTKzqSg3zt7BHWhPMp9QV5oeMCSTN3xXU8hsGHVm2d04c6NjZLAVuws7FZ5vyg8SQn43Nn1GxzfZDv8fHeFRsXehg2NtcFr3S2sLujiZ2FZdxQUDYjsWsitFYIIelPX6B1aB+JTBsps49Uro+0GUdzZfFBICedD3Bf5d/RkTJ5bIKqjBsLythVWL6oKiAuTafVSiPk1Y9tZJuJXIYLiTjFvgB5Ls+Ym+YRYbVpMM65gT68Dgcew/op8QbwOpwTvk7Pt19gY7QQT05y7pkGWg+2khu6eCPryfOABq006fjYRNKi9UWsf9M60tkBjvZ+k+bByUWg8sAuNhe8B/O7X0MfePVqXpLLEODzQ3IIrtByNyd72XwDYuUaRCiCKKtkzxf30tfQd8XlYyui1N+/EnfEzT8fnzuBzGs4eLhiJeX+IMlcN32ZRrqSx2kbPEhGxSdd976Kv8XZ1oP52X+es/HYLFOERN71IPKWu+Zkc184tZ+e9JU7cWqDeTxStWpO9nUpWmu+cuYQHamhaa+zJpzPgxUr5nwsNjaLHdvDzsZmGaO05sHyFQydP07T4OQXjDY2l9Jvt1Lb2FwzdhSUsqOgdPT32YpdVruq5Fz8l5ztf5KUOfMU2qnEOoDBbAcBR8mE84xFJNSNcKmINhdi3aXb9DtcrIrEJmwZG1mmxBeg1Be0RD4BkiubxGutubGgDAEc+fZhOo+P92hL9V75/JxNWsEJr3b+B73ps1MeR1NiN6sir8Hz4BtRkSj65edhGlVnU6NhKDEH25liL/tfRu9/GfILcXz8jwgU+McJdiVbSijbUkqgOIDhNEhnc/y45cycjmNdXgEVgRCn+37K8d7vzmjdC4mXqCu/B/nAI+hzp9Dnz0Jqcfol2iwQQiDWbUbe9aBVwToHKK3YHC3mydaGSZaa+7oepTWHeztmJNYBtCbn/3xiY7MYsQU7G5tljBQCBbyhahXfPHfM/rKzmTZZpUiZOTyG/TVhY3MtuTTEYSpGvOa0VgzleohnmjjT/1P60g3zOsZ4pplSf/mE8470drI6kk/Q6Zo336O5YK48Oi1xdfLtjL4OE3m7XTYOrTU6p9j7hX0MtM48jGCk4i7srpiWYAewu+3/sb3wQwRvuxe23Ij5TzMLoVgUDCfLlt9QTjaZo/N4Jyqn8MV8rHl4NSmV43wyTkNnH/snaFG+Wo70drKrsJw8d82M1z3R+wPyvasJbduJvPFWS9jtbEefO4l64WmI9835eG2WEC43xvs/higpR89hKJgUkjWR/EkFO7ec+TWg1ho14rt6ybnNVAopBFmleG4WoXhFXv+M17GxWQ7Yd2I2Nssc68tS8qbqNTx67gidM3yiZXN9si1WYot1NjbXAKUVJ/p7+GXrOXwOJ/eV1Y36mo1waUCDtY4l1HWnTnKi93Hi6aYxXnPzP+bcFcWu/mya84l+1kcKptKxFozptMWqnJpVQMUV93kFgfDyaVJKDjx6cFZiHUCwxHrvdCdPT3udoVwnz7T8NavzHmFF5D7wB2BwaT3gE5W1ADgjbta/aR25jIk2FU6vE2Uqvnj6AEO5+fuMJM0cZ+K91AYrZryuIsOzLX8NWD6RlcGbKC/YhYgVYKxaj/lf/2/J/T1s5g75mjdBkVWBPZ0U2OmitaY/k77ifJ/h5NbiymlZNIwsk8xl6c2k6Ekn6Ukn6U2nSOQyRF1eCrw+Cj1+DvV2kJrEq24iijx+7iurtcPQbK5L7LsxG5vrACkEDinZWVDG4xdOLfRwbBYxUghWh/O5zU6JtbGZdywfNMHujgukTJOUafLo2SOsCEWRQpAyc2SVIub2Wm2V3iBRjxcpDE73/Yzjvd9ZkHEX+dbTn8lccX7M7V1UHnaXI+R4L7vLf59LsQ4YTWSdVljIVVTRFKwuIJtLMZBtntF6AUcRFf4brV+W4MMasWYjuVyWfz2+hwp/kBsLyhFC0NbZwdHernkV68Cq/qkMhIHJU29dMkBWDV2x9TyRbcXnLAQ0JxO91AfzMN7za5if/1eYxGvMZnki1m5Ebtw2L9tWaBoH+8dMK/EG2BQtoswfJOzyTFusS5s5vtVw7Iptru3JQY71TzjringNB6sj+azPK6DA4x+t2rOxud5Yet/INjY2s0IKQbk/TKHHR38mTVpNflFpc32xJpJPfShKVSCMUxpXbXpvY2MzPV7saKLvkioHDZy6LCioZWiAQ70dgOV/9t76jVQGb1kQwS7mWYnPmc+h3vGCkCEEtxVXjaabLmbGiHV6boIoroTW2qqQSg5BQdHFfV7hHLvygZU0vdpM86szE92kQxJbEaU7Mz4VdipuLvl9HFmBhqGwxwABAABJREFU+e3PL70WTMNArF5Py7BYcGFwgAuDM38NroY3Vq3GIRTPt/zdFZYQrM57HSsi96O0SXK4hT2R/f/Ze+/4OJLzTv+p6smDmQEGOQMEATDnuDknbdAqrcIqR8uWHM6+8/l+sn13vvPZ0tnnKEu2cljtStqcc+QG7nKZc0LOOU3qqt8fA4AEEQiQg0Cynv3sB0R3dVX1YKan+tvv+36bidkD+ByZ+JxZpLvLcEofLzTWsLurhcWBDO4oWoz8xBdQP/s+zDAyyXCBk7DR8RhYjpRG141gCUmm20vQ6WZNZi7lgYwx67/piHWDiTgPnNhP93DtYwH4nS5CTjdBl3v057HeLo71ja2r6rUcXJlXQsjpxiktHFIm/xcSn8M5JkjbrEkNlypGsDMYLiHcluTeYefPhFIc7+vi+cbjRGwj3l2qSAQ3Fi5ieUb2jBZpBoPh/FBa0x2N8E5748yOQ7OtpZ7bSyoJuUroidXO0gzHk+dbzbqcLzKUiLG9fbyYdFdJNaVpoTmbT6qY7agNIQTan5Z0TZ1izBERz5/uoPrWKtoOthHrnzyS8XQyKzMpv6oM6ZAc73hpRvPzO3JxWD7UE/ejD+6d0bELAbF4CcLl4v2m6dXsSzWWEDilRWfkEP2JpgnbLM34IItCN1Db38NgIk6m20e6ezk53pVIIUlom5it6YnFebX5ELXDRmFH+7p4obmG64vLkbd8EPXEb+by1AzzjD68D/vv/wp5+TWw6cqkOJ0i4U4iWJuZx9rMPCBZngGmt/5TWqPR7Opo4e22BobsBAW+NFZm5FIdysQh5WjNOoVGIvBajjGCXY7HzwdLq/FN4pZtMBiSGMHOYLiEkKcVAHdIyeJgmKpQJlE7wctNNezrbpvH2RnmkrDbQ2laOouDYYqG62WZBZPBMDcorRHAMw3HUHrmLnxdsaSDZJozf84EO4f0si7nSwzE4/zk6G5iE6RtSiFIJvkazkQIMaHpxJlt1O73oGoFAnAH3FMKdkIIclfkUHZlGf5sP/HEEIe6HqV9aGbRZeXBa5Oi4tGDMzpuoSDWbCKRiHOsr3vOxlybmce6zDwSWuMYLq5f2/vGpO2lcKBRPFx7iMQMU553d7WyJacIX0b4fKdtuBAZGkA9/wS89SrWN/4MXK6UdDuufuYMTIKkEOzsaGHITrAuK5/KYJgMlweFRgx/A5zs7ybd5SXT4wUYUxd5aXoWNxUsGmdMYTAYxmMEO4PhEmbkS9IlLa4vKKcimMG2lnrao8aY4mJmbTiPq/JLEAg0piaIwTBXjESxdkSHeKnp5Dk7d/fEoiityfEtp2Hg7RTPcmKUTiCFxfH+9gnFOoD93W3DdbwwafUzRNs2NNbB0tVE++Ps/fl7kxpPSIekYG0+pZeX4gl5iMT72NP+K2r6Xj2nsfviyShPsWIt+q1z62PecHsQVcuIK5t0l2c0LW+20VoTcnmI2f3E7H4Od71D89CuSdvX9r1OeehaLssu4tWWmYvsHgR0d569oeHipb8PvWs7rNuCsKx5mcLpBkhrMvOSkXbD13ohBPs723BbFlE7westdUgh+HzlGlyWRaE/yK1FFUTsBGsz802dZINhmhjBzmAwIITAIQSLAhmUpoV4uOYQdcPpGIaLi8pgmGsLyk5bKJnFksEw24yIVz2xCG+3NXCgu52Zx9WdIqZsavt7yPctS9kcz4bScWL2wGhE7kQc6G6nJxalwBfgyrySOZvbxYCwLCguQwB7f7uT3obx38FCCgo3FFJ+dRlOr5OhRCfvtvyM5sH3z2vsmr5XKU7bSuiG27FPHoPmmdXOm1cScfSRA3irl3NtfhkP1cxNlGDDYB9xZaOBlxv+x1nb98Wb6IocZ2W4+JwEOyEEZGSS/M4+n6uH4UJGvfcWjo2Xz+2YwxHhAEd7u3ii/gjVwUxuKCzHEhJrOEV3Z0czb7c1cHNhBVWhTKpCmQwm4jhOS+GtDmWN9mXEOoNheqS+eqXBYLhgkUJgCcmHy5ZSOMVNmeHCQwArM3K4qXARSiuzUDIY5gitNS1D/Txcc5AfHdnF/vMU60Y43NuBS3pJc+SmoLfpcbz3BTI9Pr5avZZ0l2fcfk1SyNje3sjhng60Usn/zyHt91JipHYUQKw/NqFYl7Msh63f2ELVLZU4PU6O9zzPi/XfOm+xboS3mv4BpeNY93wOUbmUC+Zhjq0Q4SzsRIJnG47NyZCbsgv49OJVyWjZyJFpH3ek+xnclpMNmfkzHvOtjkZEeSVi69UzPtZwEdHSiDq8PxmRO0dIIdjb1cb3Du3gsbrDKK050NPOT4/upis6NFrWoXGwj605RaNR1k5pEXJ5xkRaj0TiGQyG6WMi7AwGwxikENhacXluMfUDvXgdTnyWE6/Dgddy4LYc+CwHzzUeZ193+3xP1zAN8r1pXF9QTo7Xb1IQDIY5RGvNkd5Onqg7kvKYmGO9XVyXr9la8Me8VPstEsx+KuDR7qfpjdazNvtzfHbxCr57cMek6bGP1x3hG9XrcHS2QyyGLipBnFYjSSsFWs9Kape2bZBy9Fqn7QQIOSsui6lACkn70CBhtwdXmotgYXBUtEsvTafypsUEC4Ko/j7s+3+MvOMjpHvKUzqHBBHeaf0uG3O+huOTX0J3tKG2vYTe9d7Cdia1LMgI0xgdZCARn/Xh8r1pXJZTTFfkJG82/T8U0zMFAWgd2kN3tIbLcguJ2An2TrNu8JW5xQgEcaVwlC5Cv/nyOc7ecDGgHv811u/+KVqKMdfUWRtPa4r8gXGBnT2xKInhhw1KK24rrhyz35REMBhSg9DmsafBYDgLOh6HoUEY7AdpIXKSjlKtQwPs7Wpld2cLMyuhbJgrNmTlc2VuCRqzeDIY5hJbKxoG+niw5uA5GUtMhUtaBF1uNmUVUB3K5MX6bzGUmLv6VjneFWzK+zodkUHuP76fiBov6GS6vXxm0Qr0m6+gXngCvD5EySJ0fy8IgcgvQhSXIapXIFwutG2ft3inlUJIiTq0F33sMLS3QigdkZkDmdmIsgpwuxFyfuo/TYeRhypDXUMMdQ8RLg+jolH0S0+h334NAHnnPYg1G3m+7r8StVNfvqIkcBVVoVtwO0LQ3Yn9b/8XYtGUj5Mq5J33wKp1/OOBd2d9LfLR8mXkedw8V/snKGYuZKY581ib/XlC7mI6IoPcd3zvpKL3CH+4bNOo2Kx2vI167IFzmrvh4kGs3oj1wY/P2XgjrrB7Olt5uakGhSbL7eMzlavmbA4Gw6WKEewMBsOkqG0vo158Es4MvS8uR65Yg1i2GpEWQNs2nfEoh3o62NHRdNbFp2FuuL6gnNXhXBNVZzDME4/UHOJYX1dK+vJaDm4urKDQH8B9mtteZ+QE25q+nZIxZsKi0A0sybgTW2u+e/D9Mc6Xl+cUsykrH6JR7F98HxqmqNllORCLqxHL1yCWrgLLOqfrlVY2DA6iHnsAfXj/xI3yCrG+9E2E5UiJQJgKkmKuQmuFJZ0k/ud/Rmy5ErFuC/iDsPNt1DOPAQqQsGQ5cuPlUF6BBqKJLrpjtTQPvE/jwLspnVuedzXrc76E3rkd9divU9p3SikswfGl3+elppO839E8q0N9pGwpWW6bF+v/v3Puw+vI5IqC/4zbCvBk3REO9nRM2f53qtfhaW5CvfQUurUZBiY2IzFcWsgPfgKxct2cRw5/98C7DNkJrskrZU1m7ozcZQ0Gw8wxKbEGg2FCtNaIVevghSfH7vB4sW65C33yKPaP/hmRV4AoLCG8bDWX5RazNbuQ/kSc431dvNveSE/87Oki1+SVsiKUOW57QmueajhOzUBPqk7rkmFDVj6rw8naVkasMxjmnriyU2LeUx3KJNfrZ0koC6/DQWfkEP2xFnpjdXRFTtKfaErBbGfO8Z7n6YnWsjX/D1gazGRPdxsOKbm1cDGLg+nog/tQj94PkaGpO7IT6EP70If2ITbXIm++6xxnJLB/8e9TGyY0N2D/898gsnIQqzdAySJICyCknDcBLxn5bIEYHlvZ6G0vo7e9TFKgW4b86L1QVAppQaSU2FpR199Le3SQAl+AXO9qCvxrWWl/ipq+VznY9QikINaseWgXDYPvUbRuC3rvTvSJ6ddrm1Oa6tFKkevxp7zrEn+QLI+PDJeXgUSMHI8fpc/9c53jXc66nC8hsHilqeasYh1Ac2SAsrwCdM0xMA9EDcOoRx9ABkJQVjEnop3Wmq5YhCE7gSUEyzOyjVhnMMwBRrAzGAwTIoRA+wOI9VvQ2984tX3VesgvQuQV4rjsWgDUob3YP/93kAJRvYK0patYXVDEqnAu0USc5uggthp2mRIjpaxPiUhF/gCOoSH0kQNj5uAsWcSHSqp4saWWXZ0ts37OFwt53rRkGqyJrDMY5gWlNXu72oip8ysMHnC6+EBxJQllE1O9bGv8Hj2xmTtMzhadkaPYKkZFKExcK24uKEdKiX73TdTTD89YXNDvvgmXX4tOC8782pWIQ+s0oqu6O9Hdneijw26iQiKqlmJ9/AsTNlfaRoqphTytNRqF4FTdPKUTw79PfkN7+jVaKxvqasbsl9/4U2Q4E6U1TYN91LU3UjfQS9NgH4nTEmQsISj2h9iYXcDi9BspC15D08B29rQ/MKMaaxOxu+2XFPo3Qk4eLFTBTino7WZpKJPFgXSO9/fwZP3Rc+6uNC2E13JQ5A+yKpyL0hpbJ7CEg5jdz/aWfz33voNXoXHww8O76E9M729zsLuD8uIM5I13oF54MvleNxiUjbr/R1j/5a/mZjg0jYN9CGBrTtGYSG+DwTB7mE+awWCYEnndbdh730/WsANwuZKFwk97micWL8WqXIb9w39Cv/4C9usvQFoQUb0c15KVlBaXghDDBWuHbzJOz8aPxdB73kM98+jYwX1+rI9/geuLy1kbzuWBE/sZXMjFrxcIi4MZKDSWefJpMMwLUoiUpOZ5hm+I9rT/jIaBd867v1SjUXRFT5DrKUOHsrAsB4nv/d3UUW5TYSdQj/06WZMsLelUPt3IN+FyI0oXzTwKTCvkFdej7Dhvt/4rcTWA1jYaTcCZT7ZvKTnelXgcIbRWwFiXQ6VtQHCy92X6481E7V4s4SbDXU55KPlQa0TQO134U1ohgL2dbSxJD2Mp0K1joyX1gd3oy67hUE8HT9cfndS4xNaak/3dnOzvJt+blhTugpeR79/Evo77qet/Y5Ijz06+f23yfBdA+vBU2D//PqJyKc5lq6kuLOHJsx8yIZfnFrM5u3D0975YM680/BWpiFiE5PslbtvTFusADvS0U52eSfmmK7CWrEA98iv0yblxxDUscGLROUuJFQiWp2dT6AuQ7vKYh8IGwxxhBDuDwTApQgi0y4W85mbUUw8BoA/sQVz/gbHthhfyIisHPVKrqL8X/d6b6PfePPcJDA5g//CfEes2k3HLXXy5cg1PNBznaN/cFVe/ECnwBZCYRZTBMJ+cj9FEwOmiOpTJsvRsAAYTZ0+bmy86IkeoTF9M81A/FcEMiJ6fW60+cgD7//4lBEKI4lJEeSUsWZmsl6pUMkp7socR/rRzGzSUQX+ijY7IoTGb++PNNA2+D0DAmU+ObyV5vtWku8sQQqC0TdPADo72PEtfbKxI2TjwLiF3CSF3Cfs7fkuubyUZnkU4pReAuBrEbaWhgX87uIPfXbIB3Te2/IN+/nG05WDJ5iuwteK5huNndRtuGurn0drDZLq93FxYwaqsT1KYtp63mv+ZcxGdXJYPAOvGO0i8+SroBZqS2dGWdLbt7cH66GcIudz0zNAoY2NWAZuzC2ke2M3O9p8ikcRUf8qm6HfmkumpZDAx88jbh2sOUeoPcXtRBe7Pfh11YA/q5WegdX5S4g3zj6hajrz6pjkbTwqB1pqQy2OEOoNhDjGCncFgmBIhJWy4DLZvg/aW5KI4kUA4xl4+tG0jFi9F70pt0WvQ6B1vYZ88ivWxz3FHUQU/ONJP7wyeTi80kvVwPPgcTgTQG4/RG4siRPJGPeB045IWR3s76YrN/ObXuYAdEA2GS4Vcr5/e+MwEA0sI7iyppjyQjtKaqN3D0e7X6Iou3GiazshRpLDYklWATsShOzUmG/T1oPfvRu/fDU/8FvIKEVVLkas2QGb2qCPsSJSH2v4Geu/OcxpKNzfgKy+dejrxJvp6mjjW8yxuK0jYXUFH5MiUgs5bTf+AEBa2jlLT9yoAAolDeoirQTbnfZPK4CJ64/nJczm4d1wf6pmHsfIKWFFWQU8syttt04te7IgO8avj+9iSU8jm7GpuKvk221v+ha7o8WkdP8KJ3pdIc+ZTGrwCPN6kW2x6BnS0zaifOcHnR15zE9q2iZ6DKLYqnMNAvJV3W/8t9VNzZHFZ3h8ihIsHa/adUx81Az38y6Ed3FBQzoqqpTiWrkTt34V6+Vnw+ZIRpgf2QJspIXIpIG//MKQF53RMIYR5HGwwzDFGsDMYDGdHa+TNd6F+8f3k773dEM4a00RYFixfBS+EoXsWIuA627F//n2sb/wpd5ct4SdHd6d+jFnGYzn4+KLlhN3JCIsRk+4zn1Qm06fgyrwS6gZ6earuCP3DNWvSnC6qg5m0RgYmLWhfN9BDlseLZZZVBsO8YGtFrtfPkd7pXwslgmvyyyhLC1Hb+wYHux4jps7ftGK26YqeQCuFHOhHPfng7EVgNTegmxuwX30BUb0MeeUNUFgCsSj2y8+g33r13PpNCyAqqmkd2jXtQ6J272jk3VQoEqDHlnHQKOIqWWKirm8b63KWsCYjF93dObHQUliCKi4lbieon6GJiUKzrbWemv4ebiuuZGv+H1HT+wr7Oqfn+Op1ZJLlqSaS6EYrG+vzXwdbIfIKUIf2offvQpRVoI8dRu/bOaO5pRyfH+tzX4dwNg/XHyGiZl4+wyktBuMpEpxPw+sIszX/j7Ckj18c3Uf3OTyIO53nG0/wcmMN1xeWsbR6OY5lqwHQWiGuvRXdWId6+ZlxdYENFxf6/XcQV1yfLDljMBguWoxgZzAYzoqwLMTianTVMvTh/aid7yCvvXV8SLzWyFvvRt33g9mZSH8v6qWnCd90J3leP81DA2N2VwbDrMvMxxJJZ4v325s50NOe8mkIINebRo7XT8DpImbbHOvrojM6hEMIQi4PETvBwBmFoZXWhN3e0YiQyVIKTn+CWegL8PFFK3i56SSVoTDVoSzEcJujvZ08Vnt4XIpUTX8PG7IKUn3aBoNhmggE6S7PlG2cUpLh8hJ2eyhJC1EZDOO2HNT3b2d3xy/maKbnj9JxInYPnqEI+tD4CLHUo9GH9mEf2pc0QujpPq80XJGTjG5rGdiZshmejt+Rw5LwB9nX8Wsi9lgxqHlwFwkVwedwJSMJT5/XJ7+EqKhOusIqxQPH9tAePYvj7iQ0DPbx0yO7uL6gnCXp15LjXcG2pr8jqiZ3YHdKP5fl/ye8jvRTG7PzTs1v8RJk9fJkpOOyNdjHD5+qdTvXCIH1md+BcDavttZzoq97Rofne9OoDIbxWA76oqmN3hdINuR8DZdM477j++mIndvf8EwSKGoHelkSCmOrOLV92zjU+QiLM26hJOcyXJ/8Emrv+6inHobB1KX1GhYO6q3XsBYvgYLi0Yhjg8Fw8SG0Po8iKwaD4ZJBKwWDA6jnHkPv3Yn80CcRy1ZPKDrZT/wW/e622ZmI04X1n/6CqLR4tO4w9YN9QHLB/fGypcl0ncgQOF2ItADd0SEeqTmckkVymtPFlbklLAqk47YcaK1RaAQCKQRROzHqmpVQipeaTrKnq3X0eAF8sHQJZWmhGdX/UFojhRj9OYLWmgPd7TzdMDZdTgBfXbIer+UwdUYMhjkm6Sip+M2JAzQNjb1RlkKwJJTFhqx8sjy+0e0JlaA3VsOJ3pdoGtgx11M+b6oz7qQy/RYS3/329JxaFxKWhfXH/4NuGnmj6Tsp774kcDmrsj5F88Au3m393rj9KzI/TkngcvQTD43WfBXXfwB5+bXs627jSG8nrUMD4x4AnStL07O4oaAcgWZfx33U9U9UZ1awKffrZHmXoH78XfCl4bjnc5P2qZVCv/M66plHUjLHmSOwvvz7kFuAsCz64lGO9nZyrLebuLIJu71kuD3YWtMbi7IuK4+4UvTHYxT5g/gcTmytGIy1sLP9x/TE6lI2s/LgtSwLf4RnG46xrzu1DxC/uWwDA/Em3m39HkOJsdG8K8IfpzTtcojFUNteQh8/DI2pO6/UIBAr1iRrHhsTjXNG3nkPYtX6aZnzGAyGCw8j2BkMhmkzWjOotRn7F/+OvP42xMp1Y0QhrTXYNvbf/Q84IwIuVYjFS5Iugv40+hMx9nd3sC6cg2NgAPt7/zf5lF8IxNrNyBs+QMLp5LuH3yehzj1Vq8CXxl0l1bgtCzkN99WRKLqa/h7aIgNIBMszskcFvVShtWZHRzOvNNeM2X55TjEbswvGCHwGg2F2GRHrfnvyAI2nRbW4pMWqcA7rs/LxO1xEEr20DO6iK3KC9sihcZFXFxoSF7eW/h3q2UfRb78239OZMfK2DyHWbeKJ2t9Ped9O6eXm0v8LwAt1/98YYUXi4MrC/4rfmYMUFkprtG1jORwc6enk8brxEdSpIOR084HiSnK9fqJ2D7aOYes4topj6ygO4SbdU4Y6eRT10tPQUIf13/7PlA+AtFLY//x/oGueDFK8PqwvfAMyMum32/E5srBOq+eaUDZCCCwhiasYMbsXp/QxmGintm8btX2vkyon2BE8Vohri/47ndE4Pzu2J6V9r8/M5+r8UrY1/R2dkaMTtklz5LIx93fwObIQUqJeeTZpVDEfFBQjb7wjaU525AC69gRyw1ZEYQk6Hsf+3t9BR+vZ+zGMQ378C4jKpSbCzmC4SDGCncFgmDHatqG/F/sn30VuuQq56YpxbezHf41+763Zm4TThdiwFblmIyInH52IY//HP0JL49h2+UVYX/wmLdFBfnl85oWeBbAiI4frC8oBZiyAKa3QOtnR6c6tqY58u+/Y3jHRPAGni89XrsGaIvXWYDCkDqU1cWXzUM3BMWLdusw8Ls8txhKSwXgr+zt/S+vQXKSOzi23Ff8/ePsN1POPz/dUZozYsBV524d54uTvzkr/63O+Qr5/DTW9r7Gn477R7Xm+NWzI/QpKax6tPUyGy0O6y0PzUD/7umfX1EEAG7MLKPaHcEmJU1q4pIVDSpzDv4+g3noVUVKeNP6YRBTQtp0smfHAj2d13lOSHsb6+n+mT7XxauNfkedN1nZrjx4hMVw3MOQqoy9Wn6wvOItI4WRT7u8S9lTwg0O76EuxUdaXq9bgkIO8UPctOIusK3GwJf+bZLgXoV56Gn30EDQ3wFzcAoYykNffhly5jkQiQUc8QpbTjeVwkkgkeLujkS2ZBciuDuzvfmfhuhAvWATWH/8l4lwdsg0Gw4LH1LAzGAwzRlgWOi2I9cVvJp+op4eRVctG92utkbd9GDU4kHQsmw3iMfSbr2C/+UqyjpEQ0NI0vl1TPer5x8m7+S5WZeSyu2t67mkuabE8I5v1mfkEXe7RiLmZIoVktr0flFZsySnioZqDo9v64jG2tdZxZW7J7A5uMFzijFwb3u9o5u22BiL2WCFgXWYeWg/xWuM/0BefnsPnhYZD+hCWEzUbhkMXASd6XyTfv4bS4JUc6X56NKIyx7cMpRMc7e3heN/cRllq4J22Rt5pa5xwv0MI0pwurs0vo3TTFRCLTVncXlgWYulK9LJV4+rxzRndnajnHiN424e4ufg7nOh7icPdT3F65FxP7OSsT0MKJxtzf4dMz2Jebq5NuViXHEPgdYS5rui/U9P3BrV9rxNXE2c1KBK81fSPXF3wLXzX3IK47jZ0fx/qpafQ72+fNZFMrNmUdDJFsK+rlecajo/+JTJcHnpiESSSDeE8XMEQOJ3JsiaQfK+ZmJKzU1hsxDqD4SLHCHYGg+GcEJYF/jQIhFC/+Rnyz/761D4hkqLdRz6DeuwB9M7tszuZs9RM0m+9hl65jmtyi9nb1XLWpJfytHQ+UFyJ87RIgoUdpSZwyfG1S3a0N7MpuxBPitNwDQZDEqU1Gs1TtUc53DtxKqAGBuLtF61YB1DgS5ZG0A218z2VBUln5CgdQ0fI9FayJe8bvNH0bWwdJ8+3BoHFYCKOQwgSC0igSGhNdyzKS001XF9QhtKaAqcDl7Qm/T7UWiHvuAe77iT0zY/Dsd7+BnZvN44rrqeq6AMsClzH83X/jQTn58w6XZJi3dfI8lTxSnMt73fMTk3H7x16nzXhXNZl5rEk4w6q0m/lWM/zHO95noQef66KBC81/gUSF0WBLSwO3YDvjo+hL7sWtftdaKxHN9ZPbFCRmQ29PZBIIJavRm69GuJx1P5d6JpjEIkkzV9GIv2ERN50O3LL1XRGBnngxH4Gz3iQ0TXslFseCOFyOJPpyv/lr5LjuFzg8aLu+wH66EEME+D2INZtQS5ZgbZtU7/OYLiIMSmxBoPhnNBKgbKxv/OXSYe+zGwcv/enY9sMR56oY4ehsRb16vOQosLZM6agGOuL36Q3HuUnR/aQmES2W5GRzY0Fi9DMPP11vlBa81pzLe91jI8w/ELlGkIu9wIXHA2GC4MzI2374zEerztC47D5zUR8oXIN0MLrjX8z+xOcJ5ZmfIiK9BtIfPvPYXB2apfOJrOdEgvgdWRyffH/BKAnWkfb0H4qQjeNvp86IkP84tgeEgs4JXBjVgFX5BZPXcvOtqGxDvXCk+i6E3AetWPPF7FmE9Zd97Ct8e/ojE5c5+28+keyPPMeCtM2kFAR4moQSzjxObJ4pbmWHbMk1p1JusvDrYUV5Pn8JHSUEz0vUtP3GlF7atG0wL+BZRkfwi0Do4KPHuiHgT50fx8M9CMKihGZ2ajXnofsXOSSlcQSfWitcTnSEMM1fXV9DWr7G8mMi8VLoKiUwz0dPFF/9tc9z+un2B8i1+unKpRJQkVBKazeAex/+VtQ9vm/SBcZcthFGq1BSrPGMxguYkzYhcFgmBGj1vFao+77YVKsA+hoSy7OC0pGF34jCwixqBIWVSJ6u9HvTuRINwc01qF+81OCH/kMn69azb8ffn9ck4DTdc616uabyeZ7tLeTdVn5s52VazBc9Kjh55uvN9dyrK+LvniU+FnEiEy3l3S3h57oxV0MvDc2HD0YyrggBbu5YCjRwcHOR1kSvpOQu5iQu5jTn5lnuD1ck1/K840n5nGWU7O7s4UtOYVYyEm/c4RloQuKsT73dXQ0ij6wB/Xco/PyvtDtSRMDh/SkvG+Bxdrsz5PvX0vzUD8aBy6ZgVNKXmyqYVfn9MpvpILuWIT7Tuwj2+Pj5sIKFqffyuL0W2gceI+TPS/RHauZ8LjGgXdpHHgXgLB7MXn+1YRcxTjT03CGs/HICuJaY9k2csvV4HBwpPtpDnU9CoDHyiDbuwS/M49F+Vdj3f1JtG0TUzZvNtdMW7BsHhqgeWiAu0qq0VrTOriXxv53WZ/7Fayv/hH2w/dBU31qXqyLALFmI7Jy6XxPw2AwzBFGsDMYDDNipOC0rjmGrjs5Zp99/4+xPv1VdFbumPB8IQRaKeTqjdjzJdhB8sbhoV8S+PC9LE/PYl93+5j9q8O5CC48kwYBbMjKZ1dnC7EznkTv7mplQ3bB/EzMYLiIkELwcM1Bjvd1T6u93+Gk0BcAIOQuJs2ZR398biJu5poMd1nyH90XttvtbHO89wXKQ9fikmmIMwyBpBCsCudSP9DLwZ55clo9C1Fl8+DJg1yZV0LB8Ht7IkYf2rndsHIt1uIq7Ad+AmesGcZhWZBXCAP9kIp6iMP10NZmfY4D3Q8PO8GeP1I42ZDzZbK9y3inrZE3WutS0u/50hYZ5OfH9hBwuLi2oIyytHUUpW0ioSJE7T4idg+RRDddkWOc7HtlzLGd0aPjohBvK/tH6vp7yfel4XO6qO97a1SsA4jYXdT1J9d0h7seZ032Z8n3r+UHh94noqZn6uGxHKzLzMMpLUIuN1G7nx1tPwBgV/vPWZX1CeTVN6F+9cPzeWkuHoLpyFvvPue6ygaD4cLDCHYGg+GcEOWVWH/839G7tqNefQ6G0yfsh36J9eU/QCsbcVpdNSElFJUib/8I6smH5i3FQe/bhf7Ah1kRzh0n2HktRzLi4QJbBAkhcFsOLs8t4qWmsU/Su2MRavt7KPIHJ42IsJXCmsT5z2AwJKPrDvW0jxPrXNJifVY+6S4P6S43ffEY9QO9ZHp8rA7nnna8zVWF/40j3U9ytPtZNBdXilfAVZBMpRsy0XVToXScY93PsjT8oQn3a625sXARLUMDozW+FhoNg33cf3wfnxsut3C2aHRhWWhfGtbnfhf1whPo7dsgfpoJgxCIZauTbvOFxYjhmqvq6KGkSGOfh5trazP2w/fhuOJ6lqV/MGWC3Yacr5DtXcrrLfVsb5/YtGM+6UvEeLT2MBLYlF1InjcNn9OL1wqQ6SmhMG0DIXcJu9t/gZ6iqm9/rIUMd5ieWAyfw4UlXWR6KhmIt5PprcQpvMRUPzE1iEAQU/2AnpZY5xCS9Vn5bMwqwCEFSmsc0iJmn3rf1/e/SVX6rXg8qY+QvFCRd3wMLIcR6wyGSwgj2BkMhnNCCJEsDLx+K46Nl6NOHkM9ej+0NGH/8J+xPvFFtNc3rhCuWLcZq6AY9fIz6MMHGC1SPFdohd63k4I1m0hzuOg/zb1NCDHrjq6zhRSCNeE89nW10RoZHLPvSG8nxf7g6O9xZeM8TUwdEevME1uDYTxaa+JK8UrTeEOFLTmFrMvMh2gEMThATiBIZTCMRqMO70ft34W46x52tDdT4AtQlX47Bf4N7Gr76aRpahciQ4lO8FeAtEy9qbNQ0/c6lem34pDecddbIQQSyZ0lVcP17BZmmWkN/PrEfq4rKGNxMHzW7w4hJVprrBvvQF93G7r+JProIYgMIi+7FpGRearcxjBycTW6cin64Hk4zYczEelhcHuwpPvc+5kAISRVwTC7OpuJzWOdvqlQwFtt481urssvY3V4M/n+tQwmOhmIt9Abq6cnWkdPrI6o3QPAYKKNdHc2D508wJV5pSwKrCLfv260H63VaA27EWL22esUV4cyuS6/DLfloDdax/ttP2Ig0ULYvRhbj3XUVdpGOF3ncPYXH2LZKuTi6vmehsFgmGOMYGcwGM6LkQW2KCnD+vqfJAtNv/0a9vf/Duv3/9v49kKic/OxPvFF7Id/hd41yw6yE6BeeQ5r1QY+u3glOztbebO1DgVEbTt5J3KBalaaZHTGL4/tHSODSiHGnJZ1xgJbaYUUpmixwXAmSmtsrXi24RiDZ9yIOoRkZUYuoqEO+wf/MLp9nFx158eIK8X9J/ZTFQxzU2E5lxf8CSd6X+JQ12PjblAvPCRhz+Jk8XPLCHZnw9ZRjvU8T3XG7Uz0ZWMJQdjtZXU4b0IjoYVC/3AU140F5SxLz8Y6W6TdSE1by4KSckRx+bCQlxS7RsttDAt32rYRK9eek2Anlq9BXnYNoqAYrWwGEu0c63hkxv1Mxjst/0p58FqWhT/E9fnlPNVwLGV9zwUvNp2kcbCPpenZBJzppLuzyPWtQorkgzylbQTJNYGtFF+oWkvb0AA/O7qXskA6fsvJ3u42umMRfA4HIacHWysiicSYh6CTsTm7EIe0ebPpH+iKHh/dPpExiBAWxOfJrGwh4XIjb/vQOGHbYDBc/BjBzmAwpAQhLbSQyJvuhGWrsH/0r8kIuqpl46PsRqK7ervnfqIAfT2oB36M67pb2ZxfxKbMPDqGC8hfyKmhUkhyvWmsCueOKXjtsRzo0yS7M1OYpLhwz9lgmE32d7fxWnMtQ2ek5WV7fHywpBqXlGiHA/mJLyaFKqXAHv6p7OS/T/u8He7t5GhvJ3eWVFMevJZ8/1p2tf2M9sihuT61lLE66158zkzsR+4fm+pomJSTvS+zOP0mLCZ38N6cU8iertZxdUkXGqfPXyuVTHE9q3gnR7XK0yO0ThcjhGVB1XJwuUdr0U0LXxryQ58iroao73mBw91PkVCDZz9uRmhO9L5IaeBKsrxpKe57bjjY0zGuVmK+N42KQAYhlxtbaxCwNBCG5kays7L53OKVDNoJ2iJDdA/X7RtMJBhM9M9o7IRSxOyBMWLdZFjCiY4uzPTwuUReewt4/UasMxguQYxgZzAYUsboIr24HApL0LvfQy5dOfkB85juo48exD56EHILkKs3kLlsFSKUMW/zSRVaa64vKCfs9vJS00kg6VQpzhI2qHVS0rvQ3HENhtlAaU1HdIjnGo5PmLS/MasAv9NFXNmorBxEdg5SCAQCKcS4z1HfaUKWAh6uPUSxP8DtxZVsyf996vreZH/nb4mnXFiYfWr73qDIvzEZDbXrXdALMz1wIZHQEfZ3/pZVWZ+acL8QApe0+GBpNY/WHiJiL1zRziWtU9FzUqIO7UNUL59xP0rb49IrhcOB9dX/hG5vgd5udGcHurEWGusnFYfFkhUgBG80fYeBxOw6tfbFm8jyrMAtJdEFmhY7E5qG+mkaOiW+3VSwCGFZ2G+8iD5xFLn1KnyLqigrLMHncDCYmHl9wQJfGrleP00DZ39IsSbrs7itILpl7jMxFhRFpYjNV4z7fBgMhksDI9gZDIaUo5WNKKtAb3sZ3dYM4exxUXbatrE++zvJOk+vPQ/181TPqaUR9eyj8OyjyJvvQm65an7mkSJGbpzWZuZxsLud7liExcHw2QuDi7NJegbDpYMUgnfbGycU61zSonL4M/VgzUEaByePLpFCIGHCWmR1A3189+AObi5cxNL0TeT6VrKn/T6aBt9P3YnMAV3RY+zveoRl5Xcjr74R9fIz8z2lC4LavjfI968j01M1mop4OlIICnwBLssp5sXhhy8Lkd2drVQGwyilEHUnUU8/jJyhYKe0TU+0lvahgyxOv3mMMCHCmZARBpUU9ISUyUi+znZ0U31SzGtrTf7saEcsW03CHmAo0YHXEcZthXBID8Ny+vB3ncQhPbitdLyO0PDPDNxWkLq+bRzufmJa8z7R+xI53uV8qXoNPzuyl95ppINeSLzX0URVMAPnh+9Fvfgk6sWn4PABHF/8BlfllnKwu53GoT4SSuGQEkUyem4yLCG4uXAxcRVlR9uPphx7a94fkumtRL3zOuqlp1N8ZhcQvjSsez6XfMBtFmkGwyWJEewMBkPqGcm+VDb2g7/E+sofjGsyIuCJimocVcuwH7kfvfOdOZ3m2AkJxJKVExZRvhCxteITFSuIT5FONVIo3NZqXF07g+FSZ2iS6JGq0CkB/Gwyt9JTeTBCaVqI9sgQ+7raWZ6RxfrcL9M8sIs9Hb8aLfx+IXCi93nKglfhW7wEjGA3bfZ2PMA1hd+adL8UglXhXN5qbRhXQ3GhUDvQwyvNNVyTX4aKR8HjndHxWms6Ioc51v0cZcGrJ/z+FUKAdeqWRUgJWTlJUwl9aj0xkpJrobmt/B/POrbSNqCHhbzkuFUZH8CSbg50PnjW4zsjR3iz+e/ZkvdNbi+p4pfH907zrC8MOqJDbO9o4rKcYuQ1t2Bv3wbtLejBAZZlZLMsI3tMe6018USCQ31dPNc4Pt11RUYO6S4321u+C1NcGbfk/j6Z3krs5x5Hb3sp1ad14SAk8qOfGU6FHS/qK62RQoz+NBgMFydGsDMYDKlHCERWLtrpguYG9PZtsOGyCWtvCMtCa428/cPYzQ3QPN7RbE7ILUCkX/gpsSOMCHDOCRZ5I2igPx4l4Eyte57BcKETV4qmwb5x230OJ+sz89GJOMLpOu+bpBsKygk63Sit0RpsbZPjW8G13r9gX8dvqOvfdl79zyUD8RZ8udWQkQmJOAz0J2v5GSZlIN7C0Z5nWRy6edK6b1IIgi43g0MLU7AD2N3ZwqbsQrwVS5AOVzLKforvntOp7XudvR33c0PR/8btTLqZT9ex/MwxRuvfTTMUaaLIRoCK0A0A0xLtuqMnaR7YRY5v3VnbXmhsyirg8twSdO1x7Cd+O1pL0P72n0MgiAhnQ3o4+YDWtsHtxnn97SwNhicU7DJcHhI6QevQ1MJmyFWE2rfz0hbrAHndrYjSRRN+FpRWRGybZxqOcXfpknmYncFgmCuMYGcwGFKPEIiV67AWV6Me+zX68H7kpiumaC7QCKzPfA3165+iTxyZw8kOz6F00SXnviWFoHlwAHzgd5y/+GAwXOiM1HLc09lC9Izo1AJfgDtLqvBIC/36C3DVjdMWBiZjMBEn2N+P/qe/PjWHUAby019hdfa9FKZtYnf7zxlMtJ/XOHPBzrafckPRX2F95Q8RHi+6sx315IPoYxeuocZccLjrcdLdZWR6KicUkJRWbMkp5LHaw0kjgAVIQmserzvCHcWVeMsqsJViKrluRJA71PUYR7qfAsApPKjtb6AH+pFX3zQ3E5+CitAN1Pa+zkCiFQBLuKgI3YhT+sa19TmzcEoHHukgomZe122hsjErH51IoLZvg7Yz6gH29aL7eqHmlEOuKK1AeDzsam8c15dDCKpCmcSmETls4YTurvOe/wWLy4286Q7k+q0T7lZaMZCI88CJ/YScnjmenMFgmGsunTtTg8EwZ4gRlziPD+uezyOvumF0n56keLawLHB7kJ/+CuLy6+ZqqqfGzy+aVxOM+aIimEGawzXsImswXLro4c//O20NvN5SN2bf2sw8Pla+DE8igfq3v0Mf2g+APE+NuzceQ3vPEAB6ulD//DfYzz1G2FXO1UXfYlHwehZ6AaOY6mN/94NEHBFqe98gEfRi3fsV5Ic+xUKf+3yiUexo/Q8iie7hFM2xSCFZFMjga0vWsyW7cMG+kvUDvfzH4fd5su7IqNu60jZaK7RWKG0P/59ACMHBzkdHxboM9yKkw4UoW4x+7XlobUbPszuu0jbloWuApFi3Ke/3qEy/laLAFeP+D7hKiC7QlOXz4fXWeqJaYX34XsSKtZM3TAsiP/xp5Ec/QzwR55XmWiDppn1DQTlLQplszC7E73Cyp/0XZx1XSAf4/IhNVyBvvRtRUZ2qU1rwiPJKrN/9L4i1mydvg+CRmkP0xKJUhcLYxujHYLioMRF2BoNh1hiNVisqRbe3Yj/+a6y7P4VOC4wzoRhpr7XGuuEDqMJi1G9+NncpVR4vXELRdSMIhusDXYJipcFwOkIIXmg8wa7OU5EkAritaDHV6VmohjrUj/8ZEgnIzR/ef37ySV88ivaHJtynt72M3rkdee+XWZr3IQrSNrKr7af0xcdHrywUTva+wsneVwDY3fELVmZ+ktKVV6AbatFvvzbPs1u4xNUgx3ueZ0XWPZO2cVsOtuYUketN4/G6hRltF1eKo72doyYEjf3v0hOrQ+kElnBiSReWcNMXa6Bh4JTz5+ac3wVAvfcmKIX9k+8mhZqVa5N16dDTTrFNFVJYlAWvIce7koQaIs1VwAuNJ9nd1Tqn85hPdnW2YAnBNfll6DMj7E5D3ng7YtlKYlpzpKdzdPuiQAarwrmsCucC0BttpD0ydcStxAVCINduGv7bgygoxr7YI3WdLuSNtyM3Xj5ltofSmt2dLbRGBnEIwZL0LFOD2GC4yDGCncFgmHWEtNAZmVgf+QzqkV8hP/oZtBAT17QbKea+ZAXy9o+iHr1/bubo8U6rZs7FhhgpWLxg4zYMhtlHac2ertYxYh0kTSGq07OSToVPPQT5RYjKJYjcAoDzTiPvi8eSdTwnazA4gPr+/4M1mwh+4G6uLPyvHO1+mqPdz6BY+Kl3ezp+SZanGt+Nt6PTw+hjh9A1xyE+hZtmWhCxdhM01KKPH567yc4zHkc6StuT1lWD5PV6USCdNeE83utomsPZTZ+E1hzq6aA6lMHO9p+ctb3EgSWcqDdfOSXqDg2gHvw5avsbiNJyRE4BVFQhfP45L13hdYTRKCJ2gpahgTkbd6GQ601L/qNnkhTV9DBi5TraIoNke3ysCOfQE4/ydlsDDilJqARvNn2borQtHO1+9qzjKWJsb/kutorSET3G2uzPUlCwHlzu0Rp654uoqEa3t05+TnOMWLoKeevd4E++1lO9vxNK8cZwBHhFMIxrjoVsg8Ew9xjBzmAwzAnCstBeH6J6OfrAHuTqDVO3FzJ509bbjZoL18EZOtstNLTWJLRCkkxHnomQYGrXGS51tNa82Vo/bnuhL4itFPqph6CgBPmlb45+XgYTcXpikfMatz8eS7r8ZWRCV8fkDXe+g9q/E+sTX6Ky9DYK/BvY2f4TuqMnz2v82SToKmJZ+MP4XdnYykZsvAxry1Vo20bXnUQf2Y9++3Wwh4VHlxt5+bWIy64By5GsbdpYh3r1ueEU5IUXUZZKEnr6YsTmnEJ2d7UQX6CmHvu621iekc3y8Mc40v0UMTXewGWETblfByFRe3aM31l3Al13YvgvLxDLV2N95NOzNe0JEUIgsPBYkk9UrOD1llrebV+YYuls8HZrPUuCYcSGrejXXhjfoL8PGmrJLixJfmbjMZalZ/F2WwNOIdFoemJ19HTWjT92Ek43pajre4vCtI3Im+9C7XwH6k6e+8mkh5Ef+AhycTU6GkE9cj/6yP5k1PR8EM5C3vZhZEXVtIVohR6tr7oiI8c4xBoMlwBGsDMYDHOGsCzEhsuA6bvAyatvQnd3oXe+M7uT840vJL2QGSmOP7JQE0JgK01bdJDBRBynlAgEXoeTbI8PpfWp9FeDwTCK0prjfd0MJsbXoCryBxB9vcnP2u0fJq5sfn5sD33xGCoFKYl98WGRJr9oasEOIBbD/sm/wpIV+O7+BJfn/wknel/kUNdj2HqKiLU5xmOFqM64k6K0Ldja5q3WerYNi6Gl/hArwzkUFhThKylHVyxB/foniDUbkVfdCG4PPfW97P3tPvJW5VF2WTGOj38B3daSFO727bxo0/fbhw5RlX7btKLsXNJiXWY+b7fNk6v6Wagf6KU9Mkh56BrKQ9eQUDYJNcBQopPeWD2dkaO0De0npvpJd5WiD+2DpvGC+Vg0et9OVHYu4qob5/y7TAqB1pqr8kop9AV4tPbwRS4hJ+mMReiIRsjcchX2ay8yKpwHgsib7gQNuqsTWVSKbmlCH9xD+hXX45EOKoIZJNTQeY3fHjlA19Bx0levx7FuM/ZP/21qYzKHE1G9PBmRhwaXGxFKh1AGomoZIKh9q5bc5Tm4P/bZZNptRytq53b0tpfPa65TIiWibDGE0pMOu+mZiFXrR3dPN2rUYznwO5wIISjxB82azmC4BDCCncFgmBemu8jQWiNv+xB2zbGz39CeK24PIjBxHamFiNaagUScPV2tZHt8LA6GgeRCrsAX4NXmmjERAOkuD0vTs1iXmYdDSiwhxwl+BsOlzESfA0sI8rxpcGg42iOUTt1ALz0pSsuCZEosgMjOnf7N/8G9qL/5FvJjn6O86lry/evY1fbTs9aGmm0s4aYidCMV6TcikBzq6eDZ+uMkOBUFVjPQQ81A0iVybTiXa8oqsP74v4/WD23Y0cihx5PnUfN6DTWv11C0sZDyq8twffhe9HW3ol57Hr3rPZhnU4JU0x09wWuNf8PGnK/hcaRPLdoBG7IWrmAH8Itje8hy+8j0eMl0+8jyeMn2FFAaLKM0eAVaa2xt45AOVPVycDimFemkXn4WmVeYjNa37Qnr4c4WI+uWRYEMNmQVsH0CN9SLkQM97VyZVwLhTOgcdqwOhZErhmsMDovoavvr6LqTOK6+iesKygi5POzrePy8x3+j+Tusyf4chb4N6JaJoxtFaQWiaili7RaE91TGhFYKnbCx44r+hn72PrCX2GCcI88cJXdlLlmLM8lZnoNctho7xYKdWLUeuekK1MvPIK+9BVFQnDQ3GqnLeA7vXaU1W3OKUMNrOLOCMxgufoxgZ5h2pJPBMB8IIdBSIm+8A/XAj2dnEGldMJ8DpTXtkUF6YhGqQ5lkuDyj+0bOoTwtnYDTTV88Sm8sRu1AD2+21nOwu527SqtxSYueWBRbK0rSQhfMuRsMs4GACYWP1eFcLClJ7H0/2W5ggFAgkNKxBxPxZPRrZvbMonWUQv3qh1BWgftjn2VL/u9T2/cmBzp/Q/w8I1pmjqA4bStLwnfhkn6aBvt5vO4o/Ympo/7e70ymdK7NyuOVplruLKnCG/KMa1e/vYH67Q3krcyl4oYKPHfeg772FtTrL6J3vJXSdLaAMz9lfZ0TWvF+249Zk/1p/M6cSZsJIXAs8ELztta0RAZoiYyt++aUkkx3UsTL9HjJcHmoCIbhns+jfvHv0+hZox74MeKL34S8wtmZ/FSjD39feqxL5xbqWG8nV+aVIBYvSX7mcvIRLhdwKjJMa428+YPY//I36M52ykPpAFO+j2dCrmc51NfAYP+4fWLJSqx7Poe2bZCS/Q/vp2lX81n7bNnTQsueFnKXZ09pqjFjHE7E5ddiXXNz0kjtU19Ozo1h0fc8RGYpBKvCuSYV1mC4hLh0vm0MkzLyhMbctBsWKsKyYFEVOF1TFys/V4YGkgXOC4rntJj1TBj5fEohyPR4yfL4xqW4jvy7yB+kwBcYbT+USPBueyPH+7r48ZFdY/pdHMjg5qIKHNLCMp9/wyXEyA3PttZ6mofG3gSWp6VzTX4ZqqEO9u8GQHd3EQpnpXQOmqRo5w9lnFsHJ4+h/vbP4a6PU7R6E7m+Fexpv4/mwZ2pnOakZHuXsiz8EQKufLqjQzxYv5+mofE31JOxt7uNvd1tAHRGhwiGJ68l2rynheY9LWRVZVF182I8t3wQrr4Jte0l9PZtKSlIf3XRt867j1SitUJMIswl9MKsX3c24krRPDRA82kGDjcWlLN8UdX0O8nIhNyCefm+1sBgPHZJRNeFnG78TlfyHsG2sW69G269G4Z/18oede8VQqAtidywFV13EldgNV3xExzuejIlc1HaRpSUIz/7dfQrz6BPHkeUL0YsXz0qtj1ad4QPFFVScnkpLftaUYnpfUb6WwcJrNkIykbv3YmuOwmJeNINPBabXnaHEIiyxYhV6xHLVyOcw4LmSNmSFEaCpqIcg8FguHAQWptPvcFgWPhopdC73kM9+qtZ6V8sXYX1sc/OSt8zYSrhXGuNQmPNMLJiZHEnhWAgHiOuFQL4zYkD9MSjBJwuPlBcSb43zYj2hksGpTURO8FbrfU4pMQh5OjP8kA6IWmhvvud4fSlZD1NuXYT/7J/+2jR71Tw8UXLyY9Esf/pr8+vo/xC5Ce+iAyEaBp4n70d9xO1e1MzyTPwWOmsyvokOb4VRBIxXmqu5UB3+3n1eVPhIqr9YV7961en1T6jLIPq26rwZXohHkO/9SrqrVchMvMIQ7FhK/K2D/Ncw/EZHzubXFtQhnMKF8h/3r+d2EWQGvyx8mUUIFHf+YtptBbIe7+cFEfmMB32dJTW9MQi/PrEgSkjSd3SSum1YrbxOZxckVtMjsdP2O3FcZogeubaZKK1ysg23dcLPi9P1P5+SudXnXEni9KuxXK40dEowu0GQL33JnL9Vl5pqqEnFuGO4ipOvnaS4y+fmHbfKz66gpzqzKRBmm3D0CAiLRlRrQf60SeOomuPo/fvgoH+pJv14mpEOAvC2YiyCoQ/bc7TtA0Gw8WPEewMUzLy9jA38YaFQuKv/ywlkRQTIe/9yqzfBJy+mNN9vcmIQYczmSLR0wXBEHj9s77gU1qzo6OJV5trgWSU7dacIjZnF45G3ZrPveFiZiTCbqSeo0afMiIVTCqM/+jwTrrO0x32dG4rWkyl24f+29REd4mb7kBsvgKbBPs6HqC+/62U9DtCYdomVmZ+HCEc7Ghr4bXW6bs/TsWGrHyuyC2h5tWZ3WgHCgIsvX0Jabl+sBPod95AvfkKDEzuTHomI4Ld3+97+1ymPmvcWrSYqlB40vfiQzUHOdHXPbeTSjE+h5OvVq9D79yOevT+qRu7PciPfBpRUT3v309KawYTcR44sZ/u064HLmmxND2LtZl5hFxufnB4J/2zkRkwCywOZHBnaXVK+lJ2jCdr/yAlfZ3JouAN5PvX4pBe0qxsiMcQHi/7u9p4uuEYn1y0nCyHlzf+fhuJyPRT5j3pHtbeuwZfpm+8QDkcTah2vI167AGs3/vTZCkDOwFCLtjsDIPBcOFjUmINU3L6l9XpUToGw1yjtQJbgT17T6v1vp2ImaTlzLT/oSH07ndRJ4+i62ugf/wNpbzjo4jVG2ZtDqPjCMGacB7NgwM0DPYymIizrbWe2v4e1mbm4XM4KfQHZ30eBsN8obUGIXi09vCY7WLcP0AM/xJTdkrFOiB5M5+WnrL+9LOPobdvw7r3K6zJ/gxFaZvZ1f5zhhLnZ9rjkgFWZX2SPP9quqND/ObEbnrPUqduJuzpbKXYH6TsqjJaDrQy0DJw9oOAvsY+3vn+dnxZPpbeuYTQ1quxNl+J3vEW6o2XoLc7ZXOcayJ2fFKxTmnNDQXl/OTI7gs6yq5y2DRJvfrcWdvK629DLKqad7EOkt+hPoeTTyxazu7OVjpjQxR4AyzLyBqtLyiEoMCbxuF45zzPdnpE7NTVg3y79bsp6+tMGge2U5C2njRnDn3xOG7LQiTio6UNnmk4zmcqVrH4hgraDrUPP4jR9DX3E+uf4JoloHhTERXXVyDkcArrGe+xkdRfNVLDLzM7uf0SqmVoMBjmB3OVMUwbI9QZ5hMhJOq9NyCFC8oz0SeOzsqNgLZt9IHdqIfvm1pwLF2EWL1x1DFxtpFCcHtJJQBRO8FrzbXs7mqlfjApJN5aVMHS9OSi1ETbGi42Rr7TKoIZPDuPqZB98RjSskip5NLVgfqnv0ZfcT3ha27kmsI/552Wf6YjcuScusv3rWVl1idxSA/b2xp4rSU1UXWnE1U2LzfV8PmqDJbfvYx3v/8eSk2/Tttg+yDv/XAHnpCHpXcuIWP9VqwNl6F3vYt6/YVT7pYXEJXBzEn3SSHwO1xck186r+/f86U6lIkeHIDuyUUtUV4JoQzE+q0LKpJJCoHbcrAhOx9LSGytxgistlbkedM43HthCHb5vtSZ6gzEz276cG4I1ud8hYCzkKcbjk+Yit8RHaJhsJei9YUUrj9lTNLf2s/b331nXPsVH1lBztJhAe4saxwRDCFyC8bU7zMYDIbZxAh2hhljbtwNc81oXZS22VoADtPdidr1LmLlupTeFAjLQh09NLVYl5GJ9ckvJQsXz9Fn63QR3iUtbihcRNNQP22RQQCebTjO+x3NuKRFgS/ApuxCHOZzb7hIGPmcrcjI4VBPBzX9PfMyj75ENDmXcDZ0tqW0b/36C+id7yB/77+wLPxR3mj6W5Se/kMPp/SxIvMeCtM20heL8Mtje+hMcYTh6XTFIrzUdJJr88tYcmc1+x8+MOM+Ij0R3v/ZTlw+J0vuXELWyvVYazag9+1CvfY8tM7y90gK6YlF8Duck34nSCFYkZGD13Kyvb2RxsHppwEvBHwOJ4W+AHrX9knbiJXrsD70KYBRp82FRPJ7NPn3OTMaUiLI8vjmYVYzZ0VGNlfmlaTEgE5pm015v8cbjd/B1qkpYSKQlAWvZlHoejxWBq2RAY71dk3a/oGTB8hweZDD5ltLQplszCkkoyyDrpOnjqu8aTE5S7Ondc7atpF3fRwcDljgLs0Gg+HiwQh2hhkz8qVmhDvDXDH6nmtrnfWx1KMPIAMhKKtI7ZP8/qmLv8vNV4DlmB/Xu+EFetROsCm7EI+0sLWmNx4lrhQeyyLgdBsXWcNFidKaFRk58yfYjdS3yi9MuWAHQH8f6oUnCd76IW4o/t8c732R+r63idiT3+wC5HiXszr7Mzilj50dzbzYdDL1c5sInbwmNew4PxfO2GCc3b/ag8PloPr2anKXrsSxYi3q0D7Ua89j3fJBGHH9dTg4VcBw4dAVi5DnS8Ni6mtveSCdskCIR2sPXxA17VzSoiKYwcqMHADUq89P2lZeft3od9SFVsxfzOEDuHNBAFWhTDZnF5LlGV+37VyRwiLgzGdD7lc53PUEPbFalI6fY19OvI4MVmfdS4a7At3VgW4/QHblUjZmFfDGFDU0Ty9f0BEZZF04n5KtxaOCXfHmIkq2lsxoPsLtGf23VmpBRXwaDIaLEyPYGc6Jp+uP4pQW1+WXpewL3mCYCq1srI9+Gvvf/m/SoWu2UDbq/h9hff1P0MEQIkVPUXX7FGKjEIjVm+btZkQDSikcQlIZDI/eGqrTPInkDG48dDwGgwPgDwyn9+pkUWZznTAsQDSa3lkyspkOI4KdyMlD75ulQbZvw+5ox3HzXVRn386SjDvZ3f4LavveGNNMYJHlraYobTOFaRsZTMT49bF9tA5H3c4FpWkhYkNxempTI6AmYgn2PbiPAw5J5S2VFKyqxlG9HIC+eJTOaAQdi9OxANMWOyJDo/UTp0IKgdJwV0k1j9QeWpCi3YhIVx3MpDQQSqaQRiPot1+DrknqK3p9iNz8uZ1oyll4QrBEsCIjm03ZhQRd7tHv+lR+RwshyfJUkV2whIF4Gy/VT8cB+BQO4eHKwj/D70yK6kolsJ96CLYnr1nyv/wVBb40iv1B2iODDE1RLmVDZj6X5xRjWRJlJ8/VG/ZSeXPlWe9hRjM8errQxw6hThxFt7ciV65FXnbtjM7JYDAYzgUj2BlmjB521ksohRACpdW0FpQGw/kgpIX2+rE+9WXsR+6HlvOLvpiSWBT19mvIG27nfN/aWil0zfGkA+ykjYBYBO1yzcvTWikETLBgPVtE3Yi7ZuNgHw/VHGRFejbXFpSjHvgJ+uhB8HgR67YgN14GwfTRIv9GuDMYTjGYiKO0RoSzZvfW/vhh1He/DWlB5B/8GQFnIS4ZwG0FSHPmkudfQ65vFQ7pJqESo46Lc02u189Qc+oFQpVQHHr8EIeePETVzZUUbSziRF83zzdO35F2rjnZ383VonRabUccj28rWsy/HXwPW8+/UOSSFhWBDKpCmZSdJtKJg/tIvP4iNNZO/Z6PRNB24oIu7L8A/gxjEMBtxYupDIZHX/vZqlEthERrRVzN/PNcFNiCz5GJev9tdE83euf2seuorg6K84soTgvRMNDH/ScmftqR6/VzRW4xvQ19HH7qMH1NybTxnGU5w88Sz1KzTgjs559Av/HiqY3ZeYh1W00dO4PBMCdcuN+AhnlDAzcVViT/rTXS1HEwzBHCstA5+Ti+9p9Qe99HvfjU5E/mzxO9423YcjU6Le2cF2Ra2RBPoF5++uyNhZxQNFvICJGU6sNuL27LwYHuDq4tKIe04cLVkSH0tpew33sT+YGPIFeuBTBRuYYFhURQ6E9dsfVzYSARIy2UMTeD9feCsikPXUN56JrRzXE7TtPgIDs7ajjSNz/RZl7Lgd/porZ+FuvMKTj63DGKNxWPiSJeiHREh+iNRQk4XdO6ZophE4TStHSO902d8pxK3NIi5HITcnlGf4ZdHgr8gZmLdMOIimrEkhUXtFgHyTp9C4kbCxclI+nF3DxqF0IScpUQ9iymM3J0ukdRHrwW3duNevSBCVuo7/89KpSB+L0/pSs2NGlPXukADcGCAAXrCqh5vYZIT4TMivC0Hsjqnq6xYh0gr7kJnE4j1hkMhjnhwv4WNMwLydQLPaMUOYMhVYykjYqlq7CWrUY9+Av0vp2pHygawb7vB1hf/AZaSFBqWimr2rZBJtM/9eH9qCcenLx+nc8PRWUIKRGBYIpPYHaYSGzzWA4+sWg5g/FkjRrdfEb0YzSCevDnqDdeRG6+AlFcjg5nmdovhgWBEIKQyz2vc+iLx0ibQ9FQPf0YlJSje7uhoBiroprvHdpJTM1vUf9sjx+A9iOz8yBmBK0unBq8R3o7WZOZe9Y6diPYWrEsPWvWBDuPZbEyI5ccr5+MYYHOfZqoprTCVgkc0onWisQP/xHqa6YfPer1Ie+6B1m9IumwbtsXXO2608ny+HAISUJP3/F4trguv4zl6dMzWEglGsXa7M/xcv3/PKsJhUBSGrwKvzObxBsPTt2xbSMta0qzlZMDPXz/8A5uKqqgbG0+BWvzOfnaSdoPt5NRNo2HJGcIrqJyKWLJSrN+MRgMc4YR7AznxGyFzxsM00VYFlor5Ic+hfKnofftgoEUO+Q1N2D/0/9BLKpClFVARTX4/KMLtdPFOQDd34s+cRR94gj62GHo7Z68b5cL64/+HGE5Rg1c5pozxTetNQo9zuluhBGhfigRx3vaIlYKQcDpxieGb6o62hCXX4vceAXq3W2I0kWI/CKwE2A50If3IbNyxow/0vdE4xkMs43PciKYv2pTvbEoeT7/3A244y3UjrcAEDfdkby2LQByvD6U1nSdmN3osJFrrrwAynns625jVTgHwfSuh5aQVIUyKepopj6FrrFey8H6rHzWZuZhCUlcDRK3++iJ1tAfb6YnWkdX9DiDiaRxSshVwpWFfwrL1qDqa6Y9jth4GaJyWfLfF7BQN4IUgnxfGnUDUxtPzTZX5ZawJjNvXsaWwsJjpbM0fDd7O341ZdvV2Z+hKG0TqqdztF7dZIiV6xBC0DDB2i/fm0Z1KJNBO05vLMqjNYfxWBZ3llaz6JpF7PrVbjqPd5Jemo60JhffhD8N68t/iK47DrmFyLIKtJpf8fX0tZFZJxkMFz9GsDMYDBcsI/VR5C0fhFs+CA21SbGspxt9aC/0p+BmpbcbvfMd9M53wOFAfvLLiPLF6FgMfWhvcrzOdujsgL4JiqQHQ6f+rTQoG3HlDcjNV57muGwD1jw89U5mhGxrqaNlaICw28um7AK8DjlhJJ1gWOSb5CbXciS/UuRVNyCvuD657frbxjipaa0RqzeOc1eTQtA02MeQncAlLYr8QbMINcwZQgj8Dif9iXNzMjxf+uIxdCB9XsZeSOR4/CSikxePTxnDyuxZylctCNojg/zoyC5uKlhEWSB9WiUFlNbcVFjBT47uSkktO5e0uHfxSvwOF93RE+xu+wX9iaYpj+mJ1dI+dIjwhs3w7CNTD+BwQGEJAoFcsvK857uQUFpT4AvMm2DnEIKbCyuoTs+al/FHEEJSFryKI91PErUnfy26oycp9G9EPX2W9wyAJ+nYGlM2S9Oz2JxdSMtQP22RQdaE80hzONGAJSXlgTaeqj/Gr47v46tV61h211J2/OR91n12LcItpq5ll1cwxvxkvqPrpBC82lxDbX8v1aFMVodzcVmWKTdiMFykGMHOYDBc0Jy+cNKFxYj8wmRdkStvwP7F96GtJXWDJRKoX3wflVuQNL2wp04fs770+4jCknHblbYRQnC85wWK0rbisnypm+MMGElvvyy3mIidQGk9Gjk30aJvZJvHMfVXh7zi+jGC3Ol/IzGJwYXSihzPqQgjWyksk3JimEMiZ/k8zyZ98ShSWszLDIb1nIVwmxd2e4n3x+ZkLK0mf/iw0OiPx3irrYGStABSnD3qTA6ned9duoSn64+OCtHpLjcR2yYyhaPmRFyTX4rf4eTNpr+jKzp9I5Ij3U+xNf8P0FfdgH71+Ykb5eRjffzziIxMgHEPcy4GCn3zUyMz6HRzR0nlaKr5QiDDXU7z4K5J99f0vsai4HV4bv8I6uDeqTsbLsPxsfJlZLi9qP4+QsEw1cFMtLJRv/wPOHYIddOdLNlyFVHb5sWmk/z65H4+U7GKiusXse/B/az51Ooph1lo78eEUuzubCWmbFojA2xvb+TrSzcYsc5guEgxgp3BYLhoEELCcGqDTgtgfe2P0e9uQ738DAylyHXQtqGx7uztVqyFzGw6IoNsa61Pzo+kYHVzYRm1fW9wqOtxFoWuT828zpGRKDZPilJztdag9YwXuFLIhaEYGC5JhhLxea0x1RePJW+2snKhPYUPGS4wnFJix+YmylFfAKlkDim5KreEIn+QDLebSKKbhI6Q5sw7q3AnhKDIH+Texav4j0Pv45CSeytWodHU9vcQcnmQQmAJgdJwoq+LrliEgUQMW2u01tQO9LIokM6KjBzq+t6ckVgH0BE5THe0huDWqycV7OSWK+E0w5WFJo6cLyMpsXPNmnAuV+WVIoVYMO9zpRNkuBdNKdhpbPZ3PsiG3K/Ah+9F/fbnk7cdzmoICUniyd/C9m3DfZwx7rOPIn1+1qzeQEd0iF2dLbzd0cDWymLaD3dQ93YdRRuLzuoYuxBQWlPT3z2m3mjETtAWGSTT7V0wf2uDwZA6jGBnMBguSkZr32y4DGvNJvSu7ajt26BtFt0HR5ASx4fvBaCnr4sjvWMdF28uLMNWUZzSM/tzmQGpeDo7WQSdwbBQ0VrTHZu6EPps0x8fjirLK7jEBTuLRGKO/hZ6YdfjdUnJ5ytX43e6GYi30dC/kyPdTxFw5rEp73en1YcUAp/DSZE/QKEviENKBuPtlKYFiNndKGy0VrgtL2szc5FCjvkeONDdTnkgnUiil13tPzun8zjS/RQbc7+GWrcFdrwFvjTkpsuT9UyjEcTSVcxf9ci5wW05SHd56I5FZn0sl7S4u7SaQn9wAaZICjyO9En3SuHEbQVoHtzJ8Z4XKV9+LaK9Ff3KsxMfsOtdEvEY7N8LTP3ART18HyxZQb4vjV2dLbzZ2sDiQJiqmyt59wfvEl4Uxhv2TlnPbiGgtWZggtINb7XWc0dJ1TzMyGAwzDZGsDMYDBc1QkpwuWDdFhwbL0fXnkimWbQ0ohtqIToLC2ilsF9+Buuam8n1+CnwBca5mNk6jhTOSTowGAxzhRCCPV2nRLJ0lwev5SCqEnTOxvVhAvqGBTuRkzfn0oXIzgVALQDRxCEk0djcJAYv9Ai7zdmF+J1u3m7+J9qGDoxuH0p0JKPWXEXTSo9VWvOB4kpq+3tR2ublhr+YorUk4MzHIT2szPw41aECBuNtbG/57jmfR8vgHgbibXhvuA3yCxGrN4JlgVbJ8hVcGG6950ue1z8ngl1JWpBCf9J1fqG9rgJJZ+TohPvSnHlsyPkqXmeYl+r+gv2dv8XnyCT36huxa47ByUmiO/fvnv74sRhueerW99cnDvCVqrUs++Ay9j20nw1fXL8ARc7xTJTSHp9nh2+DwTB7GMHOYDBcEoxG3BWVIotKEVKiE3H09m2obS+lxqDiNPQrz5KoOYb341/k7tJqfnPiAC2RASCZ/hlXgxSmbUzpmAaD4dyoCmZS6AsSdnvIP63e1IHudl5vqR0V1GaLQTuOrRUie44Fuw2XIRYvYVdHC/F5dD60hOCqvFI8DgdtHSkqX3AWFnoNu2yvn7gaGiPWjVDb9zorMz85rX6kEDilxeJgBuqsad+KvngDAK82/i9AcrbIpakIeypZmnE3fmc2ymEj1m4+zfl1YUcypRJbKfJ9aRzs6ZjVcUr8ITZlFS5Y0UkIQdvQ/jO3Upy2lRWZH0MMC9CV6beyp+M+3m/7EdcU/QXuj34a9e2/PP8JxGMEvd7RXyMqwVONx7i9qJKKaxdx5NmjVN1SiVIKuUBTs4UQRCeot7o4GMbWCksszHkbDIZzxwh2BoPhkmKMAYLDCZuvwNp0OXrP++jjh9EnjkL/WAczUVqB2HIVIr8QvD70yWPoA7vRB/dCZGjcGPLD9yKycpK/xCK43SFK0kK0RAZwSIkUFpXpt+GyfAt2YW0wXEqUpAVIqASaBEe7n6U7coIc3wqqQltZEsrkZH83b7TU0zosus8GffEYweplICXMhXjmcCBvvZuGwT5eaj45++NNQrrLwx0llWS5fbTsa+Hw00fmZmC9sF1iM11e+mONE+5rGdzLqqwZTF5r4jrKtsa/neEszud9KFib/Tk8VtIpXQoLzh4QeFGSrGOXeuMJh5AEXS4WBTJYHc4l5PKgFuiaQmtNTPUxlOga3ZbmzGNV1qcIeyrGrIWKA5dztOdZhhId7O24n425X0NvvhL99mvnNQe1byfZV97AVXklvNpcC8CR3k5ea6njyopiHB4Hu365m+rbqvCEPAuypp0UYkyEnVtabM4uZEVGzoKOGDYYDOeOEewMBsMlzUhKDivXIdckI95UzTH07h3gdiOXr0EUlqBt+1RkwOJqROVSuPku1DOPoHduP9Xf9R9ArlhLV3QIrUGjUZEB6geSIqBvOB3jdGdYbdvDdd80DNcQ0so+NbfT0LYNUi7IBbnBcKGhtcbWcV5p+J8MJcZGvzQP7eJg16MsC99NsX89Hy1fys+P7aFnlurdtQwNEJDW3Ih1AMXlSCl5s7UelQLDmXOhMhjmlqIKhIK9v91H677WWR3P4XWQUZpBfDBZA2qhXkcdSPxOB0e69024P2r30BOtJeAqnFZarBCCvmg9/Ym5q48Y9lTgdWScveElgBCCHI+fHI+P1khqIkgzXB4+VbESl2UlRbrh7QtZtHFbQW4s+RvahvaTUBGKA5eN7jv9syiFJNNTSX1/Bw6RrPWr28//2qBffAoVzmLD8jVE7ATvtCUF8Xc7Ghmy49xYUE7RxkLe+u7bLLmtmrzVeQvuGhFJJGge6h/9/ZaiCsoDGQv6724wGM4PI9gZDAYDp6XMAqK4HFlagT7txnnM/mEhTbvdWHd9HFVcjnr81+D2wtarOdHXxUM1hyYcpzcR463WerI9fiqCGUlxrrsDfWAveL0QzgKHE93bgzqwG93anIzs6+sFIRCVSxEbLkMLMWZOBoPhXNAc7Xl6nFg3Qkz1sbP9p/i6n+Cqwr/g7tIl/PLYXmLKRiJYGc7BJS2O9nbSFYvgEIIsj4+w28fy9GyEGHaHRoy6II/+zil/FoEg7PYi60+S0kpEUsLazYhQOsKXBj4fwpeGTg+j05IRP11zUFdrItzS4gPFlcR6o2z/j3eJ9c9u2rEQgvWfXUda7inHTrt3/tKAp2JlOBspLFoH907aZmfbT9ic901cVtpZRTuNojdWn+ppTojEQX7aeipCN6C1Srq3GwC4pWgxPz+2JyUC+apwLo7hjIELQawZEb5clp98/zpAI5j44aPSCfzOZJZCeeha1GA/HJt4TTVT1G9+hvalcUX5Yur6e2kaFr/2dbdR7A+ytDILh8vBgccO4svyEcgPLCgjigdO7KM9eiqzo8gfvCD+/gaD4dwxgp3BYDCcwUjarDhLDZORGxGxdhPS5QKlkZbFtpapb4yah/pZmZGD0hr9+ovoF5+csr0+zdlWHz8M772F9ZF7IbfApNQaDOeI0jZDiQ6O97xw1raDiQ7ea/03NuZ+nTtLqnio5iCLAhlcX1CO0por80poGeon7PbiHBH0IxEYGhg2wDztBn30Zl2f2qeB3l7spx+Z+YnkFyE3XgbBdNSj90NvDzickB5GfuaryEBotGlCKaJ2gs7oEK1drTQO9p1yqJ1jFgWTUSG7798962IdQNGmQvw5fl5uOklHZIiA08WR3tmtKXauLEnPJmb30xOrG90mcaA4lQrXF2/i9ca/ZVPe1wk486cUxqSwSHeXztp8pXCS5akmx7ecAv8GXJbfiHVnIIUg0+1lc3Yhb7aen3gqhWBFRvYFK9ScPSpUEHQVsSn390h3l2K//ExKx9fPPQZf+UPc1tjb4Ddb61iankX20mwa3m1gzwN72fw7mxAesWDWWZke36hgF3S6x52DwWC4+DCfcoPBYDhPhBCwfPUpAW+KdZ1E8IHiShzxOPaP/hnqTk5vkOIy5JIVqIN7EflF6M4ORG7BgllEGgwXElorBIIdrT9C6fi0jmkb2s/+zt+wLPxR7iypwtYaOxFHf/sv4fpbyV6yEppPkNj9HiKUjn7zZYjNohC18XLkldcj0pKOkBob6w++NfxvhRQWtlI8VnuY+oFeosqet9TXiagKZhKLxulr6j974/PE4XFQcV0F7ZFBdnQ0n/2AeSbL46Z54F1GhF6PlcH1Rf+DSKKHN5q+g9sRIMe7kiM9T/N6w99QlfEBKkI3jf7dJ8LtCE24/VxxCA+FaZvI9a0ky1uNFA6UtkfHN2LdxGzOLuRIbyft55EaawlxUQs1Uliku0pwWWno9hb0K8+mdoDha2bsDGfVoZHacMPXyWhflObdzRRuKERY87/WUlqzKauAgXiMiLKJTWA+YTAYLj4u3qu9wWAwzCGn35xMVeOqwB/AKS1U44npi3VZuTi+8A0A5GXXorVG2TaDiTg+h/N8pm0wXHKMRP6c7H2VnljNjI492fsyDuGmOuPOZF97dqBjEdRTD8FTD50aI6UzPgOHC/lH/x/S66c/1sLxjsdpGngfh/SS7V1KWfBqPFYOT9UfpSM6RPc8pbxOhUtalAVCdByamwi3grX5SIfkyZNH52S880UA2d5l5HhX0D50kOWZHwEEHvxcX/w/R2udLg7diK3jJHSU2r5t5PiW4rHSJxTLfI5MfI5MBidJ/54JAWcBG3N/B68jPEYknE49vUsZIQRaK67PL+P+E2e6pU6fuFIMJGL4Ha4Uzm5h4bT8tAzuJie8AnILoGViA5azkhaAcDYilA69PeiOVuSWK9Fa0x+Pken2cktRBcf7uhlKxJP1HltOPUToqummeHNxak7qPJFCkOnx8bFFywF48ORBemIRgk63eXhrMFzEGMHOYDAYUoitFZ9evIrnGo5zor97zL6VGTncWLgo+UtZOWLLlei3pnA9CwTBtrHuugeAZ+uPkeZ0sbOjhYhKUBXM5PaSylk6E4Ph4kQIidaKsuBV1PW9MSbtcDoc7XmGiuBNWMdOoB765SzNchIys5Gf+jLC42V7y7/RMrh7dFdcDVLb9zq5vpU4ZTbH+rqm6Gh+yXR7sYTE5Z+bBw65y3OxtaIjNt7VeyEStzVeZ4hNeV8fraWq9+9CvfgU8orr0U310NeDWLYah2XhzC2gNOty7ESUuBrEKf3jbuC11mR5l1Db98Y5zUkKJ0onKPCvZ3XWpxHDoqG4VK1fzxEpJAW+AG5pEVXnHiHVERnC53detEKNFBatQ/tJd5fj+sLvov7+f8F0oxIzMrE+9CnIzUc4x4uaWmtaIwPYWnFHUSXZHh/ZHv9oivFAyyk38O6a7lScTso4PQ06oRVHe7tYn5WPPRw1PrL3Yn1fGAyXIkawMxgMhhRiCYnP4eTusiUMJuJE7AQD8Rheh5Msj4+4ivBuy79RnXEn4Zs/CDd/EKXisG8P6sFfgGUhKpchlqxArt4AJJ1hX246yd7utjFjbc0pRGl9wdaxMRjmCyEkStssCX+Qt5v/aXirZEveN/A7c2gZ3MPejl9RFryaqN1P08B7Y45PEMHh9c7pnOXdn4KVq7F1nH3tPx8j1p1O2+B+crzLqQqGOdzbOa2+l6dnUeQPEnC6SXM4iSib7W0NHOvrTuEZnKJpqJ/XW2q5oriERdct4viLx2dlHAAEKFthSUmGyzNvJhszwS0E6r030XveT4oOWTmot1+Hrg7UYw+MttOHTrnIisVLkFffjCwooi/eTMA51uFSoyhK2zpjwc7vzGV99hcJuApHo+lMfbrzQwhBcVqQo73nLqp3RIco9AewuDi//5VOEHAW8E7Lv3J5/h8jv/qHqH/4X2c/0OHA+vgX0JmZtEUP0jvQQFfkON3RGkLuYsKeSnK8K8j2FPCV6nUA7Gr/OW2D+9mY9zXS3aV4Qm4G2pPiYHwwzkD7AL5M34IRwWytONzTSfNgH5FEgoDThd/h4nhfF0pr1mXlEXC6zfrQYLhIMIKdwWAwpJiRBZLP4cTncJLu8oxuO9HzIl5HBu80/zNVGbfjtgIEXIWkrVgND/4CUbkU657PAdAbi2IBD9YcpC06NjIk4HSR6fHN5WkZDBcVUlhkuKu5pfSfGHEstIaNZsqCV1ESuAIpJLZW9EUb6U80jR7bETlKYdF68KXB4CzVYPP4kFffiDpxBLlkBXLVOmr7tnGg8yHiamDSw+r636Q4cBm3Fi2i53iUlsjkbQHuLqmmPJiB1pqYGiCS6CTbHeau0iXE7Dj7uzt4senkjKef702jJxZh0D5llJDmcPGhsmr8DifPNST79IY8M+57JnjTvaSXpHOgu/2CEOvSHC6kw4lddxJdcwxqjk0rxVofPYh98hjWl/+AQFbOaPrliLAmhUXYswi3FSJq90zYh8DCZflxSA9+Ry65vhUUBbaMunmORNMZse78sLUiz5t2XoJdTyxyWjzVxYfAIt+/loNdj7Cv89esyvokKjsX2lqSKbIuV7JgcFcn9J16P8vbPgxZOexo/Xeah3aN6bNtaD9tQ/s51PUIXkeYVZmfIqr6qO9/E4C3Gv+Bm0q+Q+7KXI6/dGL0uK4TXXgzvAuijh0kXcWXpmexND1rdJvWmr1drUgheKnpJALBNXml+J0uI9oZDBc4RrAzGAyGWWZksaS1YnH6TQgsVmRGaeh/l55oLenusmS7W+6GYLIY8k+O7KIjOnn6VoZrdm9yDYZLAcewQNcfj5HmHLskkiMmMgh8zswxgt2Rricp9K3H+tSXsO/7IfT3pnhiDuQf/BnS7UVuuQqApoGd7G7/BWerkGfrGO+0/AtXFvwpHyyt4nuH3p+0rQRKA0FO9r7Kvo5fo0mm6Akscn0rKA5czprMFWR5fNT0d7MsPZuQy0VvLMabbQ0c6G4f7asikM5NhYuQIvnaOWXy9UyoBE1DgzQM9LIxKw+NQuk4txYlHXbzVuWRuzKXw88cof7t83PPnIj4YBytNc6zuH4vFK7ILUZrjT5xDvX2EnHsH/wj8vaPwIq12DqOhXOMwDaRyYpAsi7ni+T6Vo2pQ3e6iYQhdUgEud608+ojYtsXtRAjhMBlBViT/Wn2dybrg4qlq9DuIzi++I3Rdrq1Gfu7307+kpGJXLuJhv53x4l1ZzKU6OTtln8asy1BBFvHcAfHrq96G/so2rgwrh9nRs1prUcj/24uqgDAVoqfHdvNoJ0gbYKUYIPBcGFhBDuDwWCYI4SQCJKLPofwUBzYAqNVRwRi8xUADCbiU4p1cEpMMBgM587IzU6a04WtbHpjNezteIDitMsIuYtIqCh7On7FYGJsOvpAooWd7T9jTe69yCuuQz39cGontmIt0u1le8v3sHWMmN1P7wxq7UXtXhr6t1MWuo414VyO9HYykBgv1KwM5yKFRUP/O6NiHSQdZ5sHd9E8uIuq9NupyriNIn+QqN3Hyd5t5PvXcWvRYq7LL6V+oA+Pw0mB189AvI226DEEktbBPQhhkeVdQqF/A8X+IF2RE+xo+yEu6eeKgv9CW2QQDeR6/bPm1FF2VSlaa95ua5idAVJMVSAdfeQA9HafWwexKOqhX2KlZ2AVFDOQaMPnzBoV3jLcFbQO7QHAEm4CzjzW5X4Zr5UxLuXPiHWzgxAi+Z4/D5RWKZrNwkUKSb5/HYe7ngBAeH0QPOV2rLVGd5+W9t/flzTl0ufuzi1xEOsbaxxmOeUYYWw+GRHrXmuuxSklW3KKgBFDE528jArBh8uW0h+PL4g5GwyG88MIdgaDwTBPSDH+Eny4p4Mn646c9diF6PxoMFxoCCGo6X2dNGcuUjgIOPLYkvsN9Oh/iq15v48QFgk1RPvQIQ53P0XQVYitIkT1AO78opTPS1YtR+kErYN7xwhpM6EreoIyFNcVlHNdQTkJFWdbSyPvdiQjBV1ScnVeMb2xRrqjJyft53D345zofRGH9BC1e1E6wf7OB8n1raQ8eC3lgQpsHaem71X2dz40LoKrceBd9nU8QNBVTHf0BBrFEB2c7H2FsuBVo9FfoeIg9e+c06lOScnWEoQQVAczaRmaOj14vtmQWYDD4cTefm7GEKNojf3gL7G+8of4XGG0VigkoFmd9Sl6YnW4rQAhdwmQjKQzN/Zzi8dykOv1n/N7cml6FrZWWJfAw7u4iqC0DekZ0N56SjxTClFchvX1/wzxGOrt16C7k5C/9JzHktIieoZg5/Q50UovmJRYrTWX5RZxpKdzTMRdMm09id/hIuB0z98kDQZDyjCCncFgMCwQlFaUB9JZPI1i8UX+wBzNavqM1EsyBckNFwo6HqM0eAU6MgRaow8eBjsBlgMsK2kCU7oIHE76lZOy0NWUBK8am5KU70V++F7UjrfhRFJsF1uvgSuvT/Z53w+h/uT0J7ViLSxZRuvQvnMW6wCaB3fy9Mk/JM2ZR8hdzMrMT7A5pxCf08nJvh58DgcO6WBvx/1opo7WiatB4up0h0ZNy+DuSY0vzsTWMbqix8ZsO9T1KAX+DbgdydTA3KXZnMjyMdg+TSfIaVL3dh1ZlVmsDefxVls9MTX3kUkuKflg6RKits17HY3UD/SNa3NdfhmrM3LQdSfRRw+d/6DdneiuDmRBMZCM3PY5nLgdQbKtZWOamki6uUdpzVW5pfz65P4ZH5vh8lAeyJiFWS08tNZE7G6aB3aSV7kSmhpAqeS12bLA6wOvD60U1t2fBMCXmPk1xCWDrM7+FEIKon1jI/Scvrlxs54uQgiEhiXpWWg9cWjyyHfUiKAXVzYJpfBYDiPOGwwXGEawMxgMhgVCMs1Vc3tJFQ+c2E/9wOR1sSqDmQsmRQMYXTTu7XiA0sBV+J05o2m7WinQOrm4NhgWElZyGaQe+iX68MQ3ztaf/HcGhOCnR3cTcLqoDIaxhKRlqJ9bixbjd7oQK9bC8tVw9DAUlyI9Xtojg3gcTryf+Sr6f//X6c2nahny7k/QG2tgZ+uPz/v0NIq+eCN98UZWZN6D23KxNiOXDVkFNA8lzTIG4+1n6WV2SOgIL9Z/iysK/jMBVz7CsijeXMyhJ1IgVp3GkWeO0n6og3WfXUueN0DtwMSGC7NByOXmU4tW4nGcWm6H3R5+dGRsfa3Lc4pZk5mH2vku6vFfk4r8YHH1TciCYl5trmF/dzuVwTDXF5Qn983C94bSNknzFmvBfC8tZOSwU2x5Wjon+rtndOzKcM4l4wCqdAKB4ETvSxSkrYdrb0muKc5AyFPrDUu68VgZROyzm3oEnAWszPoE6a5yBIKmnU10HOkY08bpdbHQ/D2kENN6D4y0A3jgxH4+XLYUn8NhyqoYDBcQ5tNqMBgMC4iRxdUdxVXkT1GU2udYOM9blLaxdZSYGmBF5scIuPLGinWRQSPWGRYkQiZrE8kb74C8QhgxJgiEEEtWIO/5HMKXRkQlI9364jF2dDSzvb2R2oFefnp0N4/WHOJ7B9/jaG8XdkUlQw6LB08e4KdHd/Nk/VEspwv56a+efTJSIu/5DAOJFt5u/kcSOjVp74X+jazN/gIO6UEd2Iv+qz9FHd5PridZQyumZsnldhrYOspgoh2dSKBtG09odlK47Hjy71fgO79C/zNFKT0q1h147CDxoTh98bHROw4p2RDORR07hHrkvmSE5/kSTEdcdSPH+7p4t72JDxRXcn1B+eiNeypJ1gyzidq9SOE4a7Sm4RRKa64rKJuxIYrXcs5WyccFhxQOSgJXEFMDow8GxRSvl5ASLWBx+k1T9uuWIa4u/BZXFfw3QlYZdW/V88Y/bGP/IwdQ9qn3sLQk/izfghShzxTrpoq2s4Tk8txi3mtvYsGpjwaDYUoWzh2fwWAwGIDk4sptWXysfBkvNdfQOtRP82l1boJON1ke3zzOMMmIg2B9/5sIbVEc3Dq+kRAgJOrIAWTlUiAp4k214DYY5hIhBDqcheOrf4Rua0E9+yjy7k8ifH6UnaB5sJ8n6yd27ByyExztS0ZxPD5ce1JwKj6qfqCXt1rr2bKoCn3dregXn5p8Ii4PUjo50fvSGemn506+by1rcz6PSkSxD+5BP/BjANR9P0B+6fex83MndA2dK8qC15DjWYY+dhiRW4DDPTvL0v7mfnobe9maX0RLZIATfd2zMs6Z9CViHO3tZJE/ndZ9rSy9YwmdHWMNhW4prEBaEvuZR1I3cCgdKSV7O1sB2NnRTKF3uIzCedyrK1shLXnqp07QHa2lK3KMIz1Psz77S2R5l6TgBC4NpBAEnG6uyy/nmYZjZz/gEqUidAN5/tVJMVjLs4pnUliUBK7gaPfTROyJI2pz/asJuPJpO9TO/of2k4iOF8rdQTer7llJWm7avAt2U2VUjOzrjUdxSQuvY3wKrxSCxcEw7ZHBSyIy02C4mDCCncFgMCxApBBo4IbhFKau6BAxZSMQHOhun/NC0yPiHEBcDSGQROwe9rT/Ep8jk1XhT4y21VonU2ClhK4O1Htvot95A126CCwHYsNWWLxk3hfABsMIowJyOAvrU19G2za/ObGfxoF+EjOMGDozxmHks6pbm6c8Tn7i8yht0zE0selMUdpm0t2lSOFA4kz+FA6UThBTAxT41xFXQwwm2lE6gdaKTG8VamgA9bd/Pr7DpnochSV4HWGGElPXzJwNqjPuZHHwJnTNMdRvfob1h9/CjqW2fh2AK83F6k+swp/rGzWfmCvBDsBWSXFLOiV23B5XCL7MH0Qf3AdtLakbtO7kcPRWOVepUtLdHgC6a7sJFYUQcnrX3uS1HIQUaKVp2ddK1uJMLJfF9pZ/o33oIPZpjpx+Z465rs8QKQTL0rN4o6WO/sT03E0Fl06MlEbhtkL4nFnJ32cQJbos/FF2tP3HmG0uGWBR6AY8jnSUtgkvysDhcYwT7HKW5bDk9moslzXtz8tsMZ3U19r+Hn5z8gDX5pexNjMPGCvydUWHyHB7R11lDQbDhYMR7AwGg2GBo44dIpSVg3Y4kZYkKy/p7DcXNexGxmga2EFX5DhRu5emwfdH9xenXc6qrE9AQx2JB3+B45t/hhAC9far2DveHnMTqo8la1PpIweQd3wE1mw65/mPiILDd5MIIUgohWOkjs0Cqu9nuHAQlpV871gWYbeX2inqSE6XgNMFgLz2FtTe98c3yM2HlibIy6dpYAcDidYJ+1kW/igO4YJEHJQGrRC2RlsSvE5ob8eVW4DXkUUkEU9GWfT3Yt/3gwn7Uy89jVi/hQL/eo71PHfe5zkT8nxrWRy6GX1gD+rBn4NtQyKBN+xN6TgZizJY/YmVaGlT17+NfP9a8uYwLXZLdiFVwUxaD7YR648x2D5IQXYaLilJd3n4WOlSXA4n9v5dZ+9spux+D39eAbjcKKeb5t3NHHnmCJf/wWVYzvEihNYarTQIkMPX0cH2QZp2N9Nd081gxyDxwTglW4upuL5iQsMRc809NzSwLCOLd9oa53sqCw4pLBAWStlIOf36iFJYFKStI6Y+RuvgPuJqiCUZd5HhSka0aqVJRBPEB+JYrlMlO4KFQapuqSRUFEo6w86zWAfjU1/PRJNMrf9Q6RJK00Kj20deK6U1Ge7UXlsNBsPcYQQ7g8FgWKCMLrZeeRbqTgJguzzID3wIFcpAli6akzkc7HyUoz1PT7BXsir8Maivxf7xv4L31IJQ7Xl/8ogRrVBvvYZj7ebxuyJDgACXa8K0WW3bUF+DjkRA2cilK0/NRkDETnCgu5014dyZnqrBAAynyGrNqnAuOzvPP+rppaaT5PvSCKSHkbd/FPXsIxCLIe/5PFQuRVoW6uBehHRQmLaRA50PT1gs3ZJOOHgANZzWOiE334nYdCV7u9vZlF2AOrQ/6ao4EYuqEELOU3Td7TDQh/rtz5KOj4B6+Wl8t3+U/HX5NO1oOu8xHB4Hqz+5kojq5J3Gf2Ug3spAvIXlmR/ljuJKHqs7FckogesLFlHgSyPN4eL9zma2tdZPe6wNmfn0xqNj3L3L/CG2ZhfRfqSdvb/eC8DJ12tY/qFlfKVqHb2JKE47gX3fD9AnJo6qPB/Uw/cBIG++C7nlKlr2NJOIJNj5852suXcNljMpUggpULaiu7aHgbZ+or1RIr1R+pv7GWgbmGqIMQRchXis9JSfx6WAAFZm5JxVsHMIybqsvDkVnRcKoq8PnRaYUT1crTWlgasoC14zmsbd8F4DJ1+vIdI9tkaosARLbl9CwZr80Rp2C0Gsm050nQAKfAFsnawgGUnE8DuSD4r08PEjDzFtreY8Q8NgMJwfRrAzGAyGhU4sirjyBkQoHfXUw6iHfpncfsPtyMuvnfXha/ten3B7hrsMYTmxX30OtEJ++N7RRaFcugrVWDd5p61NJL795+B0JevcKQUDfclIG7cnWUOsenkykk6pZORT3Unsn30PTivaLr7yh5BbgJASKSQeS7I4mIFCY10ySUOGVCOEIMvj4/LcYt5payB+hithttvLrcWVeIZdZjujQ/zm5IEJ+xqyExzu6WRjdgGs34Jcv2X0c9JT30OoKIRcsoJ4JI4lba4t/kteb/hb+uINOKUfryODqN1L3B7EnRaYct7quSewShaxNiePE31dlK7fgujpQr/+wvhzzMkHoCc2fWEqFTikjzRHLuqNZ8HrR152Dbq9Bbx+dCJO9U2LUyLYrf30GoSEd5qSYh3Aid6X8FjpVKbfyIbBft7tSI5zW3ElVaFM+lv7iRNhU2YBJ/q6aRo6uyHH16rW4XO50FqzZqCXB08mI4lvL65kqHuIvb/Zl4xcA1r3tzLUNcSGL6wn0+VF73p3VsS601EvPAFrN1N1axXvfG87PfW9vPP97WRXZ4EQ9Df3013bjUqcn1lEWeBqNNpcdc8BIQQhl4c8r39MvdozuTy3mLWZeZfc66y1Aq9vxuZVyYeeyVdKWkmRKr0kHSFrx7aTgpUfXUFWZdaYtvPN6WLdVFkDI9ut4Z/He7tYGc4lruyk4QTJEivJV+NSeucYDBcHRrAzGAyGBYy2bRxf++NTv3d3ol9/EQD1/ONJcWvDBGYPKWIo0Tmpi2SOb3lyTq3NiLLFyPLK0X3yiutQO96Cro7JOx8cACa4OYlGUL/6Eaq4DJGTh8jMRtWeQB/cM66p/eufYn3p98HnTzogaj2uRpTBcC5ordmYVUBFIIMHTuwnMuze+fnK1WS4vcTVEJ2RwwgkJWnLuKO4isfqDk/YV5rTiVKK7f/xLsH8IEKAHbNp2ddKID+AL9NHy94WnD4nl/3BFjbkfpWOyEGK0y4fjvhTKGx01lmWbcpGnzyGzMnjkdrD3FlcRdl1t6IP7oX206MFJWLtRuJqiLg9/SiqVFAWuDLp5Lh/N/KqGxAbL0/eQmqVTPUVGumQ5yUgFazLJ5Af4EDnQ6Ni3QgHuh6iNHgVhf4g73Y04ZEOFgcyaHivgYOPH8Lpc7LpKxv5aOlSfnJsF5XBLDLcHqJ2guN9XdQP9gFQEcjgyrwSfC4X+x85gDvoZtHV5Xx96XrQYEnJrt/uG3cefU199Db2kl6Sjjp68JzPcdokEqgHf47vni9QdWsVBx87yFDnELVvTvFA5RzI9i4drXNqmDlKa6qCmZMKdpluL2sz84YFnKSbvJ5G9NXFgD6wF7lsVUr68mX5WHR1Ofse2g8kxa4VH15OVmXWgoioG+HMyLrppAJrrdnR0cwrzTVkenzkedNG+xj5uXDO0GAwTBcj2BkMBsMC5swnytb1H0BXr0C98AT65DHU7nenLdid/oR2pHDzVItArTWD8ckFt2L/VnRnO/R2o2NRdDyOcCbdyXQiDn3nU/9LQ90JdN2JcUX8x9DVgf2dvwS/H9KCyE2Xw9rNxonWcN4IkYxFCLu9fKRsKfcd38v6zHwy3F4OdD5Mff/bRIcdCJeHP0pF8CrSXR66Y5FxfTUPDrAklIUn4KFxx9i0t96GXnobkp+VWH+MvQ/sZ8mdVRSnXUbniS5adjeTUZ5B9rIshHv6YrTSmtdbalkUzECVlI0R7OTnv47wB9jR8i+TCvKzgUP6qArdiu5og/YWxMp1JIbivPXdd4gNxpihv8c4pJQsvmUxhevz6InVcrx3fGSh1xHGEi7648m043VZeUgpRwWs+GCc93+2kw1f2sAXqtaOuUauz8qnLx5Dowm5PNhxm2MvHqNpZzJSr+NIB+VXlxHrj9F6oI3exr4J5zkiDOia4+d3wtPl8H70zncoXLeZaE+EE6+ePOeuLNf4WwevI4zPmXkeEzQIoDqUyasttRPuX5OZN05skUJMK2XyQkbHY6jnHkuZYCeEoO1gGwBOn5MVH1pOxqKMBVV/cbqRdWciht8PJf4gBb6Jo7FHIu2UVkiTGmswXBAYwc5gMBguNAqKsD77dewnfpssQA+c7OumLJA+rqnWGk1yYX+op4P26CBrwnnUDfSQ500jw+0dtyAc+b07eoITvS9NOIWQqwSPMx0tuxDrtqB3vIXe/jpsuTrZx7FDo3ObdbSC/j7o70M9+gD6wB6sT35pbsY2XPRIIcj2+LinfDk5Xi9dkRMc63l2TJvD3U9SHrqWzdkFPNMwXoTZ1dXClpxCyq4qo/1w+5TjtR9u5/XvjG3TtKuZnMM5rPzoClRmNnS0TXq8HhocvRHriUeJ2gkc196C3vF28nzu/hSypJz9HQ/SNrR/Wq9BqqgI3YC0XCQe+AkA6umHcXzwE6z/7Fq2/+A9EpHEWXo4RVZ1FuVXlyEti0Q0jkooQiVBLMtB6+Be9nT8ivGevZDlSTpUvz1cL2xxIMxQ9xCDHaccagc7Bnnn394he0kWfc399NT14A64WXxDBa60ZG2oTnuQI88dpb/llODZ19TH7l+NjwQ+k5FU6LkMd1GPPQChdBZdW03m4kwadzbRuq91nDvmVHgzvJReVsKQPfZBTrZ3uTH6OU+EEARcbnI9floip6LsHEJQEQyT5faOeX2lEDxcc4jLcorI8vguStFO2zb6yEHo6UYn4giH87zfZ1ppOo51kl6azsqPrsDhdSyo9+3pQtq51JvbmF3AmszcKYXco72dBJwusj3+i/J9YzBcbBjBzmAwGC4whEw6Wcorb8D+5b8DkOZ0EbUTuIdratlaIRFE7ATvtjdxoq+Lu8uWsCQ9WaMl7PbySO0hCn1BbigoH13cjSyG97bfz8m+Vyadw0Cilfahg6QHy7D+f/buOj6SLDvw/e9GJKOYWSpm6mpmmu4e6mFyjz1gz+zaXtvP8Jb37drv7a6ZxjP2DnqYoaenmbm6i7lUUolZSqWSM+59f6SkkqrESmHd7+dTM1JmRNwrtTIz4sS559zzENbh15GvPIdRUAyREeTrLy79L2Ia6vxpVCyKcHtWbA7a+iKEoNjtYTjZzmtdf3vV82kZw1Ip8hxTd+KTSnF+eICtxQULnkO4K5OtJTZtQ73y3PQbhgYRponHtBG10hzu7+JAQenl57dspTNymIvDTy14LgtV5tmLGuyHnkxGmjr2FlIp3O/5CLf+4U20vtHO+ccvzOlYW965CYfXyXCyDafhwSZc9MZPcX7oUULJ6Zd7DiczTTg+UreNmJUm3+mm9cjV28dDcVpfb5v0/YkfnpzPjzutoUshqq4HzOU9DZf/+iXUnQ/g33uQzQ9tYtMDG+k720fn0S76L/SP19qbimEz2PGB7WAqXmn7y0nP1QZu53LujrZQUiluLK7kZy1nsUaz4O8tr2dzTgGWmpx+GkunuBgepCs6wic37sJhmOsu+CJME3n+NCiJ9YU/R+TmIw7ekmmYM896dhPt/uguglXBzHnUKsrEzyxxvtzpfiHNIcb2u/JvYby+sBB0RMP0xaM8XLMlK/PWNG1p6YCdpmnamqQQgSDG1l0AhJJxvtd0ClMI6vy5FLm8DCRinBrqpcjt5e7yOnyjXcMACl1eHqjYwC9az/Fkx0XuLa8fParkSM83aI+8MePoaRnnta6/pdJ3A7sKP4G46Q7US88gv/1/lu5HngfV2ozYuDXztV4eq2WBEAbNw89hqcRVzykk7SNvUuW/kd/ctAe3YePnbedpDA8BUODyUOkNYLOZeIu9RLrnXzcuNhjDSlmIqroZA3aqOxMMu6O0hkfbLmSKjI/FYQqLMewuugaPznv8xbDh4qay/wuvowjrhUcnz/f421itzRjv/CAVe2vnFLCrOFCBzW1jJNnFC+1/Nq+5hJKXONr7DXYWfAy/w0lsMMbFZ5vmdYyFMh0mRVuLyKvLywTHElf/LS019cwvUc/8EorLMO64n4KGDRRtLSIVS9F1vJuuY13jS7Qn2vqeLfiKfbzd92USMjT+eL5rA35H6VXba/NnCEG1L8i7qjbys5Zz5DhcbApmlhpPDN5YStI0+t4StVI809HEA5Ubpjrkmify8lGGCQN9qIE+VDSCbcMiAk0CglVBgFUVrIPJJUoWmvU31uUcmHQjVirFl88dJpZOk1YSn82elTlrmrb0dMBO0zRtDRLCQFkWxq33AFAfyOOzvhzaIsOcGurl6c7MBei+/FJuK61GKnnVcpoit5d7y+v5ecs5VJmiN3aKt3u/TFrG5jyP1pFXqQncQeCWu7FeepaplqCtBPnz76MqqzEeeB/C559TzT5Nm4lUafyOsmmfz3HWAOBJpREeJw+U1/OFM4dJI9mVW0yu003zS5cWFKwDQEF8KI6noHDm7Xo6kSeO0LB5OzBabHz07x9vpq5R3Bpc2BwWaE/xb+CzF2P97Huow69fvcHQAOqVZzE/voHy/WW0H+q4eptR2967leIdxQwmmjjS+7UFzad15FVi6QF2F/w67twAOz+0g5M/PkkysrTL+Le8azPF24pRUiEbz0I8OvtOS6W7A/mdLwMgN+/AdvOdlO8tp/K6CqIDMTqPdhJqzQTm8jfkU7ytmAtDT9AZeWvSYWoDdyCVpRtOZEkmaJfDe6o34bU5kMCVv1lTGFwauRw0PRPqZ19B6bpc4mjccjcUlSK//7VMF/nONlRrM5RXLehG3Go9B8jGkvKx85yRVJK3+ju5raQagN54lNd72wmnkuPbjqRTk1ZlaJq2eulXqaZp2ho1tiRk7ETPZhhU+4JU+YL47U7SSuK2Zd7mpysufHywhyK3l954lKCzel7BujGhxCWCgQpwOiFxdcH9FWGzId7zYZTdzsn+7wGwKfedmDj0haW2IAKTCt/1nB96jJScHGgp9uzAby9F9nYj//HPobQM+2d/j9vKqhlIxCj3+LGkpPHpxgWP7yvx4S30It8+MfvGfj8xaQFj3QFHA3axzLxNsbydlPMd9agzJ6cO1o1SjedQ/b3U3lpD+6EOincUU7CxgLOPnsVb6KXy+kpya4I4PE6aQs9ycuAHLOYGQV/8LE+1/QkNwfvZWPMQBz93kDf/+RDx0NK8h+VU52SCdSNhrMd/CicOL8k4C3LmONaZ42AYqBtuw7X3eupuq53UNbMndpozgz+ZtJvXXkyxZ9eqDYKsVYYQVHmDKKavQ3blEtk3ejt4Z9XG5ZjeshMbtmB84BHk974KUqIazyLKKld6Wlm12NeQVJKRVIo3+jo4NdjLpmA+HdEwz3Y20zvNjYGBRIwSt0+/fjVtldMBO03TtDXuymUUUkluKakC4AunD3FdYfmU+1lKcrCwnDxnpu5W28jMy2Cnk+uqR6VSIBfZ4jGLjDvfAXY7L3X8L4ZH61l1R49xU+kf4jB9OminzVsmKO7i+pLf4dXOvyatMoEdv72MvYWfQoVDmWAdEoYGACh1+9iVVwxAMr647K2622qRqTTy0R/NvKFpIsqr6YpmljWaEzPsRrMp1GLbsc5DfeAebDY31rG3ZtlSofp6cGws4Pp/cxB3vgtDmBRtK8QQBlJZ9MfP0977Om0j0wf+5utC6Ff0xs5wS/kfUbCpgLY32mbfaSFUppmFM+DBfPhjyOJS1NO/XJqxFkpK1MvPol5+FlwexM13YN50J5FkL290/d1VmzcE70MhEVflgGmLlelSPXUgRSpFwDE56H5heIC4lV5XtezGbkYKw4CNWzDe8xHkj76F6mjFWEQNu/XIEAZPtDdS6vFzQ1EF+wtKEULwjooGftZylqHk1cvvBxJxitw+/erVtFVOB+w0TdPWmYm1bj6zaS8w9XILUxjkOlxIpTja+7VZ69ZNpz9+Dn/gVsyPfxbrF98Htxfh8YLLjRrsg442mLAUY1mYJoYwcdtyxgN2sfQAr3b9NbeU/bEO2GkLYggTv6Ocm8v+iBP936Uvfpb6nHsQSiC/+JcwFgiLx1Cd7RSVXg6WX3j8/ILHdXjtFG4uRB49BHKWrp5eP8Jmo3e00+SkJbEuFwCWWprXY5F7OwFHBZ2Rw0TS3UBmyaTqbEednSUzsLgUsXELSTlC2j9ER6STtvBrFHt2MpC4QG/01HiQNNtCyWbiqWE23r8B025w6eWWrI8x1DLEq3//GsIQbHvvVopuuhMZCaNeW7kGPTMRldUYN9xOJNnLs+3/7arn3bZ8KnzXIRZQGF9bHKUUAfvkgJ0CemMRyr2BlZnUEph8M9JA7NibaSp1/jQqEQe7HWHoz3KpFClpcXd5HQG7E4UabwGT73RzQ1Elj7VlaoPahMF7qjcRSacIpxLoZjGatvrpgJ2mado6Zhut8TLdkgcxejHvtuUteIwT/d8hkupma+X7sX3+j656XqXTyG//H9TFcwseY1627gRHJjDRELyf7ujx8aciqW7CyS5yXTXLMxdt3TGEicdeyPWlv0tf7Ax5rgZEaytEJ9SmUwrrK3+PqKpFhYexfe7/IlAepPNI14LGDFbmACDfeHn2jcMhVDpNkcs3Ot8pAnYy+7XaduZ/jEr/jQgh2BR8kIvhZ8l11eI0A6hzs98MMN79ISyV5Nm2/zppaX5f/EzW5zqVp9r+PTeU/B4NdzfgzvNw5udLM66SihM/OsmBPDe+29+BOn8G+nuXZKwFq67D+OAnSVjDPN/+32GKjMyG4L0olL7UXwGGEFdl2AF47Y51k103FSUtxN7rUccPY339nzAf+TzKJtZ8UympMjdhDLHwy3KnacM+nl05+W/grb7O8a8fqtpAlS+IVIruWGTaLE5N01YPHbDTNE3T2JT7EC3hl0jKkQXt3zT8LJFULy4zh6jVTzTVRzwdosi9hZ0FH8X+wUewvvRXMNCX5ZlfoW4jtg88giWTdEeOc3Lge1dt0hE5hN1w43MUZ6XQs3btGcvQzHNlOjPKqYLRqWSm1tKOTJbr0KWFN3oIVgWRaQs65pD5pRSq8SzV9Zl6ViOpJMLuQNkc44FsmcUMuyL3dhpy7iXP1YA89ArWq89jvOO91DfcjUqlUK88h3z52ZkPUliKKCnn4tBjC6qjmR2SV7v+gu35H6Z6zy2koqlF1RyckYILTzWy68M7MT//x8gnfop6fZVk2pVWYH70M6RUnOfa/x8kV2d0+uwlVPlv0tl1K0QIQYU3gMdmJ5rOBN9twiBoX97alMtNGCaiph7+8/+EVAp56ghGbgGqtCKTbbcGP8ulsoinQ/THz1Hpv2FBxxgL0k4VrFUwmkkHTsOkxpcDZEJ6pR7fgsbTNG156YCdpmnaNU6M3pENOqvojZ1a8HF6YlcveeuKHWWoo4U7y/8L5kc/hTpzAtzeTDaSYUBZBURGkM8/Ab3di/gpRvkzy4Fe6/o7BhNTX2w3DT9D0/Az1AbuYFv+B3TQTlswQ5gopTBuvw9pGMjnHr+czTYmNw+lFJUHK0kn0vSfH5j3ODmVQRgZnvP26o2XsG3axv78UtoiYa4vMuDjn0EUZurp3Vj6B8SsQTJVskykSnFi4PsMxOe3bNdh+DhQ+JsgQL76PPKJnwEgv/kvqOo6VE8XxGbpiuvxYnzmt0mrBE3Dz8xr/KVwov87+OzFVN2wgZZXW0hFl6Zz7GDTIC/99cvs/ugu/Hc+gHrz5ZWvA1pYjPlrv4VlWDzX/t+nXYK8Pf9DOrtuhdmEwa3FVbzR106VN8je/NJr5nPMECY4TNh9HTIaRv71f8e4/z2wY++a+x0YwuRI39cYiF8gYQ3TkHNfVo8vgL35pbzc00pCWpwfHqAhkJvJrNPnPpq2JuhbY5qmaRpSWQQcFUty7Lg1yBu9X4TcfMT1t6J27oTrb4brbiRZXoDYshPzwfdnZSzjwYdJWGHCqY5Zt20afpa3e76MQiHTEjUh0CKty99P/FrTrjR2wSNuuRvzU78Do0GxMerFp1GvPk+gwMGuD++gdHfpvMeIDsTAFwCHY07bq6EBVCzKLUUVvL92SyYoXVkNrkyDGbvpIeAox+8ow+coxu8o4/qS36bIvX1e89pZ8HEQAutv/9/xYN3oDFCXGmcP1gFs2Ylhd/Jm9z+RWrHsusmO9X0rs7T3gU04fHP7nS9EOpbm3K/OYzgcGA+8b8nGmZOcPMxHPoe0GTzf+ackZXjKzYo9uyhwb9J1QFeYIQRbcwv55Ibd3FFaQ8DhXNfLYSdSStIbO83hnq9iePwY978XkVsw/l6s1OppgDUTpRRNoWcZiGfqy3VE3hp/PJsOFpXzaw072RDI42J4EFMYGELoYJ2mrRE6w07TNE1DYGDJq7uIZUtf7DSPt/4haTk5Y8NmeLiv8n8imxZekH+S0BD2/AKCjkr647PXzOuIHGJH3kdJDlokRpL4irw4vA6GWkL0nuklHUvhK/GRV5eHr8iDMEyUZSF0hzrtCkIIVEk55m/+AfLZxzKdNgGUQj75c3j+CcyPf5YtD22k80jnzAe7QvMLzZTsKMZ45weRP/zXqTdyuhBbd2HsuQ5RWYOSFogJNSxnCLAIYYCCA8W/xevd/0Bf7PSsc7IZHopdW1FH3oDQwpf7ivwCAMLJJerOugDRdC+tI69QseUGCjcVcPG5i0vSiAJguH2YntM9FOw6AIdehq7ZbzZknT+A+cnPo1xOXuz4n8TSU2eBGsLO9vwPopTUy2FXkUxH2WuHQpG0RuiIHGKn/BjmzkzZgXODv2Qw0ciB4s/DKv4bHcvqbx5+npMDPxh/fDjZxumBH1MXvAunGUAqa9GB8bGgXJ7TzTurNiKVQip1zQR3NW090AE7TdM0DSEE7ZFDSzrGlcE6gINFnwcF6thbWRlDfvEvMX7vP3Gg+HO0hF9Cobgw9NgMmTsGNsNB85GLtLySuSA3HSZW0rq8yfFutr7Hgb/Ej/XUo4iyCtiwFWw2fYdam0SYmSWy5t0PIYtKkT/7HlijNcCSCeTRQ5iVNTh8DpIjV9eRc/odePI9uHLcGKZAScVQS4hof5SeUz0UbpwiA840ETfdiXHLXWDaxpfkzrd7ohAGSkm25L6bF+cQsNua+14wTORLi1vGKlweACw1S/fbJSFwmQHiVginGaDKfxMeWwGx9AD9iQsMJS+xJfe9NNzdwMDFQcKdU2edLYZhGhh2E2Ea2H7zD5CnjyO/99WsjzMttxfzkc+Bz8fLXX/FSHr6YPKGnPtxmTn6fU/LOjUaSBJCzCGYpIinh1BIXu36KwxhYzjZPl7/8lD3l7iu5HNLP+kFGAvWNQ49xenBH131fGPoSS6GnqbYs5MK30GKPJn3/MUG7sZ+p4Lpm5BpmrY66YCdpmmaBkCus46e2PHZN8yioL0S9darMNifnQOm08gv/DnG7/7f1PhvAyHw2Yt5s/sLV21aE7iNhsA7EIZBuOPyhfikYB3gynFRsr0IeeRN1MvPoABsNoxb70HccjdKyjXfpU7LnvElstv3YOYVYH3/6zA8lHmsohqZSo8H6wybQfH2Ysr3leEr9mHaL1+UjS2LEkKQCCcwbAYYAnHgJtSZ4+D1Y2zdidi5HwIBYHSJ0yIuxoQwCDqryHdtoH+Wenalnl2ZZa+LfO3KpgsYe64j4KhgKNE0r32Djiq25X8AQ5g0D79AR+QtpJq95pwh7FT4rqMueA8+exF9sbO4zCAeeyFpaWEaBuaEjo3Skmx7eCtvfPFNZDp7y+0Mm8HOD+8krzaX13ra2ZJbQLC0PGvHv4rbi3HPOzNZjXY72Ozg84PDyeu9/0go2TztrgXuLTQE79cX+1rWPNfZTMtICL/dScDhJGh3EnQ4aQhkutZP290eg0r/TTSGnmQo0XzV8z2x41wafpFK/42raun2xMy2jsjb026nkHjthXjsBUiVwhTZW5avX7+atvbogJ2maZqGVBZ5rvplDtgZCNOG7OvJ3iGFQBSVIixFQkkQYDPcU266Le+DCCE49ZNTDDZPv6SvYGMBwjSxXnvh8oPpNPKZx1AdbRgPfxSlTL1MVptEGAaqtALzd/496thbyFeehUCQZDRNoDxA6e4SSnaUYDpMUCCMyRdSEy+snP5M90clJeId70U88PDo95llr9m8CJPKYk/hr/Nix/9Hwso0uhCYuGw5gKLUs4dy30Fsphd5bPqLzrkSgSAAprDPa79K343sLPgYaZkmpRS7C3+Nbfkf4NzgozQPP4dicmCt1LOHbfkfIJruw2cvxW54GEklODXYS32gFgU83tbE6VCmk7XLsOG12bGbBl6bnXdVbmLj/Rs484uzmA4TYQiEISjZUUxiOEHP6d55zd+wG+z+6C5yqnJ4vvsSb/d3Ue71E/T6EdX1qKEBSCbnVgdwLkrKMT/yKfD6iMpBlEpjqRRSdXG+91czLoN2mTnsLfwNMj0n9QW/tnhSKW4qrqQjOkLTyNCk57bnFnJvef20+wphYDOc1Afv4fTgj6fc5szgTxbcdTXbxrLqjvR3MZSMc2dZLZFk14z7BB1V+O1lOsCmaZoO2GmapmkgECStkWUd02a4Ml/MpTNiIAfiMUhOUWfPZoPyKoytuxDbdiO8PpKpFF9vPM7H6reS72pgU+67ODt4uSi+25a5g3/2l2fpPDrzifNIT+b3IjxeriwFrc4cx/rSX2P++r9BuT365FqbZDyIu3Mftj3XAeACDnx6P9KSGOZoZuYc/2yuzOSc77LXuTCEicP0c7Dkd+iOHkMpi0r/DeOvmUxB90yHQeOG27BOHIbU1ct750odfgN5293sLfoUL81QP22ioKOSHQUfYSAR4xsXjiGBCq+fO0tr2Zr3PmoDt9MXP0s01cdQsoVQooXtBR9G4MFuuBlMpHi++xRtkdHM2varx4jLNPHk5WW6p0K9bNtXjr8sgDffg+kwxy/EZVoS+ttXSYTnVgfU5rKx6yM7CVQEeaarmaMDmQ7ZT7Q18kjDDuyf/HzmdyMl6pXnMt2HrVmWDPv8iO17UKePX64pWFWLqKpFuD2I627BIs2rXX8xYybdlQQG+4o+jc1wrdqaYNrak8k0M3hfzWa+e/EUfYno+HMnBnvx2hzcVFw57f6CTDbwdFIyRtIaGb3RsPJ6YxF8dgcNgTwsJakJ3j5p/lKlCac6CCc78NjyyXHVruBsNU1bTXTATtM0TVuRC7G0jGJZMYyGzZllsdMQ192M+Y73AqCGQ6jmRtTZ45C2MG6/F4rLEIaBtNL0JOMcbWvkxFAm2+WLZ4/wkbpt1AXvoin0DEmZCb7VBe5GCEHfudmX8w23DWcym3bsRU3VHKOvG6QF6RTYl66jpLZ2TZV9OR6sW4UMYeKzF+ML3g1kLo7HjL9XCIEqKMK48XbkC0/BQjszRkeQ//x32H/r31EXuIuTA9+fdZe64N1Ipfj2xRPjeXRtkTBfv3CMBn8et5ZUUeK5DpthYAhzfHnx95snBOnm6fH2iyQsi/q8XHrTMdqHwzgMk5ODvXywZgv7fn0vx757nJHuqW982Jw2EOAr8rLtfdtx+hwMpmLjwTqA4XSSL507zLbcIgwE1f4cqm68HXPzdqwffRM6p2jMkVeAcdOdiF37MzUUb7kb+drzCNOGuPmuTDBYSsJWN690/cWUtURnsqPgI+Q4a3SwTss6QwjshskHarfwnYsnGUxe/tt8vbcdt2ljb0HpeGB8IiEEAUc5mT/wqbuqSmVN+fhyUkoynGynwFVBgctDNN3PYHyATbkPkZKKlMzM0RRQ6t0zvoQ309hF3wDUNE0H7DRN0zQySza25j9M28hr40Gt5dCfaKRo41ZE3UbUxam7uqqWy3WtBhx2glt3YBvtChdPpWgaHqApPMiZ0NTBN9dosMSYsOSuO3acmsBtlO4qoemF5hnnKNOSrmPdlO65DnXi8NXzrK5D+AKcD/VT4Q3gMnUzCm3tm1vtJ4Vx+32Ifddj/cvfjtfqm7feTujvozzvAOeGfklKXl4GWuTezu6CT2KpJEPJJkKJVnKcNYRTKZJTZOdeCA9wIXw5S6/en8OWnEJiVnrBwboxz3Vd4rmuS1c9/sOW07yncjMHPrOfvnN9BMoDyLSi63gXNqeNwk0FuHMvL81PpeO0jLxMlf8mduQUcnzo8nLahJS83Z/J+j3U30m9P5cHyuuwffp3UW++jBoOwcgw6vxpcLowf/P3UaaN5ugwh/o6eKi8Addt9wFgqRRPtPwRkoU19KgP3keV/6YF7atpc2EIgcM0+Vj9Dn7VdoEL4cvlKZ7rukTUSnFzcdWUQTuH6WNT7kOcHfz5Vce1GW489vwln/9MpLIIJVp4pfMv2ZT7Lkxh5+TA9yj3HSTftYFvNx6nP3m5IVadP4f3VG8GVuYmqqZpq5NQY7cdNU3TtGuWUpJL4Rc50f/dZR3XwMF9Vf8TIzSC9Xd/Nu12YtN2zA//Ol89d4SBZJwyj59Kb4A3e9uZKa8nYHPw6c17OdH/PZqHn5v03A0lf0Cuo5bOo100PXeRRHj6ZX0On4Obf/8m5FO/QL0y4Tg2G+bn/4iUL8Dfn32Lm4oqOVBYNocud5q2fijLQp09gfz+1xd+kOJSjM/+O/ri53ir55+xVIIq/81sz/sw8cE4yUiSQHlgPDOxIxLmO00ns/QTLJ7DMPh4/U5ynC7SVpK4NYTXVoBUimi6h7742dGacUkuDD2OQnJz2Z9gMwr54tnDMx7bhsG7azZR5fYBlzM2lZQoJfmX88cYSY82MhGCD9duo9Dl5vn2/4doen619caUevexr+hTUwZKNC3bxv7ODvV18EJXy6TntgQLuLeiDsHUHWSP9H6dtpHXJj0mMLm/+i8wjZXJepfKIpYe4OWO/33VTdB9RZ8hz7Wdfzj91vhjdsPg1zfsxmOz6/MHTdMm0Rl2mqZp1zipLKKpXk4N/GjZxy717sYQ9kyB9Zk4M0X3o+lMpkhHNExHdPZsmfpgpu5WX+zMVc+92fMFbin9E8r2lBLpjdD6Wuu0x6m9tQYhBOr45Atrsfd6yMnjF82Z458b7udg0RJ2edS0VUiYJmLrLmRZJXRM/zqaUXcn6tknKLzzfm4r/48c7ftXtud9mOG2YY5+6xjpRBphCFxBFzf+zg0MTshMWQ2SUvJKTysPVG7gla6/YDjZChgwwy2FruhhNuQ8MMtWkEbyw+bLTSGqvAFqfDkEHE6ODnSNB+sAbi2uotjt5Wjfvy44WJfrrGNP4SOjy/J0po+29MaCwvsLyuiIhrkwfDnT7nSoj/5EjHdVbcRnd0wKaCml2Jz77qsCdgqLULKVXGfdsgaclVIoJCkZ5bWuv7kqWFfg3kKxZweXRiY/XuL24dMlNTRNm4L+FNY0TbvGCQSHe7+CVKllH3t3wSfAspA//96M26mBzHLXGn9wXscv9/iRyiKSuroTbVpGebb9P5OyYhRsmHrpjGHPfEwGq3JQQ4MQDk1+/rqbGUkmaI5kHu+NRwmnEujkde1aJJyuRe2vXnoa6+tfxG3L4/qS30GmJEe/kwnWASipcAUzY/TGozMdakW4bZll99Z4nbiZ6/r1RE9gCJMDhfML8rdEhnmhu4VftJ6ndcIy33KPn70FpXRG3qZtZPq6oDMpdG/lYMm/RWDoYJ227KRS3Flai/2KBjs98Qj/2nic4VQCOaFephACly1IiWfXVceKpHpR09S3yxal1GgjHkhZUfrj52gefo5XOv7iqgY6XnsR1xV/nlja4rHWC5Oea40Mcy7Uj9TnDpqmXUFn2Gmapl2DMgElRSTVS9PwM4SSC8yKWaRL4Zeo9t+M+fBHsb7yj9MXru9sQ6XTvKO8nvtKa4hJi69eODZlDauJOqJhNgbzKXRvpSd2YsptWkdeob7ubkp2FNN9qocbf+dGDDNzR97hdRAPxXH6HajDr2Pc9SCqvwd15E3E/hsR+YUc6myedLyzoX725pcg5tr6U9PWiy07ELUNYFmZf14vIpgLoSHk2RNQVIpRU4/q7UaNDEMiCck4tDRBdLRuXfOFTDarUjQ930Q6Nrn+2oZ7G0im0+N13lYTt5k5rXaYASJzyG4LJVsJJzvZmVvI671TtKqdp6Ajk4l8pPcbC9q/PngPm3PfAygdrNNWhCEEHpud+8rreaL9Ikl5uXFE3Erzw+bTfLRuO07z8vJYpST7i3+T1vBrnBz4PmmZyb69GHqKQvdmHKZ/jjU5508IQfPwC1wYepy4NTTjtpW+GwD4yvmjU567PN7eSKHLS9DhwNCvP03TRumAnaZp2jVJcSH0+JTFmpfTmcGf4neUkVfRgNixB3Xsrak3tNLIb/4zxh33Y1bV4sNOvT+P06G+GY//dn8XNxSVUx24ZZqAnaAzepgK30E2PbQJKyVxBZwkrDR9iRjDgyHq/LkgBGLPQcToXX+15yCiqpae6AiHrwgcnA8NsL+gbCG/Dk1b08wDkxsUKKWQKExhYFx/6+UntkzeT0mVCeCdPYF8+VlUNEJS2mh9Y3JXVH+JD3+pn5euqHG1WvTGo0gluansD3i+7b8TTnXOuk9L+CW25r2PHIeLoeT8OrheaaG5OTbDzc78j1Lm2zdaS0wHC7SVYwhBQyCPUrePX7ZdoH1C+YtQMsGPL53hg7XbJuwhQCnKfQcodG/laN/X6I2dJpzq4PWuf+C2iv+wpPOdS7AOBBW+6+mPx6e90ZiSkmc7m3i4ZsuUzy8HqZSuoadpq4wO2Gmapl2TBJZcniWwdsNDXfBuDGFDKYlCji4hURR5tpHjrEFZFmLzjukDdgA+P6KqllQ6xbnw4KzBujGDiQR5zlqcZoCENTz+uNdWyNa891Ps3QGAtCQ7P5T5uice5ftNp8a3rfUFOVhUwes9bdxRUoNRUkbnUB+Ptk1e1gLQGRthJJXEY7MjQBds19Y9qSw6Ioc42f89BCZCGJR597Mt//383YnXcZl2itxeRlJJPly3jVjnCCd/fArTZmA4THKqcijeVkTgwE2I/TcCMHyuD2VNDkEVbi5EWpK3+1Zfdh1kasuBojd2mpFU95z26Y2dRgiDnblFvNCdnUCkgTHLYtzLitzb2FXwCeymF9DvV9rqYAiB1+7gg7Vb+UXrec4PX15e2hWL8ONLZ9iWU4ghBEIIqrwBnKYNp+njYMlvc27wUc4N/ZJwqp2+2FnyXA2LyrIbC2Rlzl1GM/uQJKzQHIJ1kO/aiMsW5IWuxhm3K3H7VqzRiw7WadrqpAN2mqZp15ixk8G++NllGS/PVc+GnPtJyzRjOSBi/IQXmsND1PhzoGEzmDawJi+Bw+5AbNiMces9pNIp/u7MDEG9Kbze2847qxq4tfw/cKL/ewQcZZR59+O1FyKVxaHeDqLpNAG7A4ki1+niuc5Lk47RNBKiaSRTp67pwtFZx/zOxZPszitmf6HOtNPWP0OYtI+8SUpebgSRkpHxr6NWiuaRIQBaIiFqS3Oou60WZ9BJ39k+Wl5poeWVFpx+JwUb88lvyKf75NV1JwPlAaIyTXrO4ajl82BFA5tyCmgbeYPjfd9GzXGOlf4bkcri6EAWg5CzdbEA7IabrXkfoNJ/vW4uoa1KhhBIpbi5uHJSwA4yNd9aI5dvwPntDt5TvYl8pwcBbMh5gBxnLcf6vkl39BgF7k0LnkemTp1CoRhOtuO25ZKwwvTFztI68sr4dtX+Wyjx7MZly6U/fobu6AmGk23kOmupC95NWqY5MTTzUvnG8CB78ktwmrasBs/GzvtmCgYaQpCw0jhNm+4OrWmriA7YaZqmXUMm3kE9WPw7nB78MS3hF5Z0zLGCzC2RMEcHumkKD121ze9vvx5ht2Pcejfy2V9lHgzkYDzwXkT9ZoTNRjKV5PkFLIVrDA/yjQsn+FDtFvYVfQqAeDrNkf4uXu5uITFLHbyFGE4lODLQrQN22jUjz9VAnqth/PUecGT+9g3DQE54jb3U3UJtfQ7FO4pJSIuGqhyUUrS+1kYinKD9rQ7a3+qYcgxPgZdQKjnlcyupwZ/HxmAeTaHnODkwcwOdidy2PGoCt9ERjWTn5xpPSJw5+Fbo3sLuwkewG2NZdTpYp61OhhDkOt00BHIndY69UjiV5NuNJ3mocgM1/hwMIShwb+Luqj8FWFQASgjB420XuK2kCq+9kLd6/oXe2CmcZgCnGcRpBtiU+06q/DcRSSVJWBaVvpupCdw+foy0TPFW3+xZt73xKN9oPM57R4OPUwXtxn4WqdScsvgnnvcpmLa6bigZ52vnj1Hg8ox35NU0beXpgJ2mado1YOyELZJO8lznJSLpFHeW1rCz4MNsyXsvF4ee5Hzol0sydjjVScqKUufPpdYX5K9OvnHVNod6O9iWW4T71nsgmAvRCKJuI6qgiIuRYd7u75jUDXG++hMx/vHM25S6feQ4XJwN9S15js7+glJ9l1q7JkhlUR+8B6nk6MVg5n8TVor0FQHxgUScfzxziLSU1PtzeVf1JmKDs9duE0LgCjhpDi38fWCp3FVWTTTdx+nBH815H4HBvsJPgxL8svXqpfULMZeOmLqxhLbWSKXYlVcyY8AOIK0kj7Vd4JENu/DY7JOWwC72c7h5JERr4wk+Xr+d64o/z3CynaCzcvx5pSRHB7p5uqNp/LF6fw4V3iDnQv10xkbmNM6WnAI2BfIzdT+nmPNYF9nnOprx2O1szynEe0Vg7cpA3mAiRsDhxBAG5jS/B6UU/YkYaSXpio0wnErogJ2mrRI6YKdpmnYNMITgsbYLnAv1Y42e8P1r43G25hRwa0k1DTnvQJKmL3aGoLMKh+HjQuhXWRl7Z/7HsJseYPoLyhe6W3ihu4UHKxrYuH03UilSSvFCZzPHZ1lCMh+dsZE5nzgvxvWF5ezOL1nycTRtNTCEyf85e5hQKjGn7VOjQbxtuYWkkmn6zs5ej3JsWdpqC4AXOt147U6O9j6BVOnZdxi1KfddBJ3VPNF+kZH00mcNGsLOroKPU+47oBtLaGuKIQSV3gBu00bsypIZV0hIi8faLvCB2q1Ze79ISYv46Lj/fPYwD1dvJtdZyNGBbrqiYXIdbrpiES6EJy/bbQwP0TjFioLp+OwO7iuvnzZrTiqFpSTPdjYTSaVIKYvXe9vx253kOl3kOFw4DJNQMk5bNMxIKkme002hy0Ou0z3jElshBKVuHw7DpMDlxmOzz3nemqYtLR2w0zRNuwYopWgcHhwP1o05NdRHRzTMR+q2syXvPZOeqw7cyqHuLxBKti5q7FxXHX3xKC92XSKlZs5re7TtAo8uarSVV+PL4cbiytk31LR1QClFOJWcc7BujNu0UevPpff03ALywhAoS+Gyra5T1wJ3ZlnpUPLSLFtelufaQH3wHppGhjiZxRsSY+/uxhVLYg1h42DxvyXPVQ/oxhLa2iOAHXlFvNE79XL5iVojwzze1sjtpdXYDXPRteAmnjdJ4AeXzizqeNM5UFAKTP/6NITAECb3ltePP5aprTd6M1Rl9g04nFT6guMrK+baTMJts3N/RT0Ngbys/DyapmXH6jrr0TRN07JOKsWxgW6S0pry+aFkgn868xYBh5M8pxsU2AyDu8tquansT0hZI/THz3G8/9ukZHTe4wsEI6nkeNOG9W4gESOaTuGaULhZd17T1iuF4txw/7z3G8v4SEXn1q26YGM+psPkWOfcuq8ul8DosrGUNff3xhLPTqSy+MmlpWr8IyZ9vafwk+S56nVWnbam3VxcRdKyODIw+3vAyaFeLoYH+eSGXYtu4OAybbhM23iW3VKp8gannefEunUTtxFCXC5DcMWuY9vN52dvCOStykxmTbuW6U9uTdO0dS6tJK/0tM24jQJCyQRN4SGaRoY4PzzA/zl3BEtJ7KaXEu9ubiv/T+Q4a+Y9/kC8kWpfgFuLqxb2A6wxw6kE32w8zqs9bRwb7OH88ADtkWGGknGUmr3GlKatJYYwOB8amH3DK3REw5wN9VO2r4z8DbNndLhz3SipuBCeuY7VctueU0jSipCwhmffeIK51Jubr8tvL5cvtrfnf4ASzx4drNPWtLEOp3eW1XLdHJs5Fbo8uG32rNwwK3C6F32M2aRmaIC1HGcO1ugKiIWMJZVCzrKCQtO0hdGf3pqmaeuYpSQXhwcXdGc4KS1e6W4DBPLL/4hDOtlf9JvYjPmduL7Z8wW6o8fZW1A87zmsVeFUktd723m2s5lHW8/z3aZTfPnckfH6O2OFo5VS419r2lpjKUX3IupCPt7WSCydou6O+hm3c/gclO4uXfIMl/lyGTaCTjfnh36JYuoMZsg0mLAJF04zgMdWgM9ewvS9GhdvLOemIXg/NYHbdbaMti6MBe1uLq5iwxyWbe7JL8nKTTKpJPsLVrbjuyEEl2ZZpTDxXGIh5xXmaFB/rgFOOXr+IpXi6EA3Z0MDRNOZjGk1Wm9P07TF00tiNU3T1impJKYwOB2avaD7tMcg022MoQHk176I49P/lhtLfo/j/d9lMNE4x3mkuTj8DCXeXWwO5nMmNP/lc+vFWMfMUDLOE+0XKXR5qPPnUukNYBoGUkkMnQmjrRGmELze077g/dNK0h2LUO73TLtNXl0e29+/DdNh8njnxQWPtRTsRua1Gk8PsTn33eS7NhJ0ViEQKBQKiYE5ZXabJZPcUFTBm70dpLN0YTucynTb3Zb/fs4NPcrmvHfp5W3aujK2LPTWkioaw4MzBqaODHRT7PbhsdmRSmEzFvbZagiDukDmc7o1Mr9M2rnw2Ry8o6KB4tF6mFdSStETj/KTS2d4d9UmqnzBSbXplFIMJeP47c5FZxOOBTine88YG3MoGac7FqE3HuHC8CADidj4NrkOF1W+ILcUV2GaUx5G07R50AE7TdO0dUogeLm7laZ5dCmbyECwv6AUFRqEkWEYGUb+9Hv4HngvN5b+Hi+0/ynhVOecjhVN9QCQ43AtaC7rxXAqQSiV4GeXzpKQFu3RMEcGurEJQa0/l605hdT6c8Zzb/SFtraaSaXYEMxf1DLV4VSCymBgyudya3PZ/bFdxNIpvtl4jIFkfMHjLIWGQC5KKvYUfhrDyLxWZXgY1XwBDANhGKh0GpVMopJxSCQgEUNJibH7ANeXVLAzr4gXu1o5lYXmE12xCIf7u9iVtxefoxSpLAyhr5i19cUQgoDdyc7cohnr2V0aCfGV80fYlVfMrSXViwpeS6V4sHIDL3S1ZOW1OqbKG+CdVRuxG+a0c1PA2VAfllL8tOUs767aRI0/B6kUv2q7wIXhAXKcbj5Uuw1zkXVzZ/v9jB23NxahZSREV2xkUrAOYDAZZ3AgTrHby5acgvHMPU3TFkYH7DRN09YpqRRem31B+wrgvoo6fDYH1uPfuvzE0UPIk8cw/uS/sTX//bze9XdzOp7d9AEQSSUXNJ/14gfNp6fMCEgrxfnhAc4PD+A2bWwOFrAtt5Ciae64a9pqYAjBlpwCjg500xENL+gYfrsDmbx6Oak7z82uD+8klk7xL+eOkGb1La+6rbiaZCTJYPMQ7W+1U7KjmLI9ZVjPPQEDM1/Uyzdehqpa3O/9GPdX1LOvoIRnOy/RtsgMnkN9HWzPLcJnL9XNbrR17YaiSk4N9U3bUAsydeEO9XXiNGwcKCwdXy4+X4YQuEwb91fUk7DSNGaplua+gjIcMwTrxsYee18YC9odLCznwvAgLtPGu6s3Ue3LwVJy2W7y1QVyaQjkIYTgcH8Xz3Y2X7VNTyzCtpzCOR3PUnK8eJ5u1KVpk+mQt6Zp2jplCJHp+gq4zbnfnzGE4KHKjWwOFqBefxHOnJi8QTqJeuVFCt1bKHRvHX/YZeaS79pIJrfPwBCXg4WZmk3QEs3+cpK1ZC51ZWJWmsMDXfxr43GODnTrRhXaqiaV4q6y2lkvg8s8Pn5/+/U0BHLHHzOFoMzjJz4Qu2r7mptrwBR8vfHYqgzWOQwDwzS48OQFTv7oJEOXhug51YMwBPh8cztISxPyb/4H6Z9/n3xh44O1W3l31cYFZyJvCubzSMMuTH3Bq61zQgicpsn+gtI5bX8hPLDochNjy1DzXdlrQGE3jFmDbEop+uKX3yMtpXilp41oOsn7a7dQ6Q0CLGsmmykuz3tPfgnbcgop8/jH/5W4vQwkYrP+bGM18M4O9XNkoJsjA910jt780fV9NS1DZ9hpmqatU0II0kqyI7eIe8rr+Omls7PeFTaF4J1VG6n15SBfehr1zGNTbqee+SXywA3sK/oMg4kmUlaEEu9uDGGSsqKYhhOlJOeGfsHF0NP47SVYKk0omViKH3Xderazma05BdgnLGvTNam01cQQggKnm1uKq3ihu+Wq5312B7W+HDYF8wF4Z+VGjg50E02nyHd5cJk2zr3QPGkfh89Byc5imiIhoqus0cSYe8rqAIiHEmz/wHbcOS6cPmcmwN41t1IB495+Dfn2a3Dfe6g5cCO1G3ZxpL+L13rbiFvTZw+NEUCpx8e95XXYxOwBAE1bDwRwXWE550ID9CWiM27bHYtwfKCHbbmFCBZebkKh8NucC9p3KrZZgmxSKfri0SnrXI6kU5we6mVTsCBr81kIqRT3VVzdOOiFrksz7CMRCE4P9fFqTxvDqcnnhnlOF7vyStiRW4Shb0Bo1zgdsNM0TVvHan051PlzUUqxv6B02oBdscvLhmAem4MF+OwO5DOPoV56esZjy6//E+b97yE/txScTjh3hnTjWWzbd6OGhhCFRWwueQ+VvhtIyihJa/Vlyax2eQ4XduNysC6UjCMV5DpdKJUpa6+bVGgrTQjB/sIyuuMRzk5oKlPnz+HByg3YhIFlSfob+zGdNraXFCKMzOK03jO99J2f3IimfH85CMEzHU3L/JPMXZnHT7Q/iq/YS/HWItIyiYkdee4kLLDWnnz8J/D8rxAPf4LdDZvYllvIKz1tHB3onjHb5I7SGnbnlyB1MF+7hmS6xkrur6jnW40nkMyckfV81yViVoo8p5syjx+nacOc5+vFQOC3OxYz7Um89pnLlhhC8MyE5aY2YbA1t5CduUW82tPGUx1NlHn8WWk4sVBjjS+ufO9JS0ksncJ9RWkWqRRpqfhu0wl641MHWgcScZ7tbOa1njb2FZSyMZBP0OFECIElJYYQy/5eN/YzKikRC2xgomkLoQN2mqZp69jEE5qJSyrGVHoD3F9ej9/hREoJ/b3IF56EE4dnP3hnO9ZX/uGqh603Xx7/2vzU7+Atr8InBL3xyMJ+iGvYSDrJ232d9MajtERChEdrAPpsDqp9Qap9QWr8ObimWfI81tFN05aaUor7yusZTibojI0AsCGQj00YvP7FN4h0z/317y3wkLDShNOrt+al27Rhy3ey6YFNxFKDvNDxP7i78v9FlJTNEjaYRTyO/NY/Q2Ex9g88wu0l1ezNL+GxtsZp6wRWejNNO/RrXbvWGMKg0OXhvoo6HmubuXN9Ulq81N0KZBpgfaJhx2j5jrm/boQQVPuDfLx+B4PJOOZobbs3eztoGhkCwGPaKfP4aIsOz5gh67c78Nunz9ZTStGfiE163f/ahp0E7ZnAVdDhJBWW/LzlHB+p345Si29UNV0Gf+YG4fTvMUIIXuxq4ehA96SagoUuDzvyiiedixhC8Gjb+WmDdRPFrDQvdbfyUncrNiHYGCzg/gnZfMqyEMvVijbzCx4P1ilpIQzd1Edbejpgp2mats5JpRhOJXihe/LyBLdp46HKDTjTaazHf4Z642WQWVx+VlkD5ZVcCr9Ilf8mBqYIGGozi1sWz02xrGQkneTkUC8nR7vVFbm85DicWEqN/svcgd6XXzreTU5fzGtLSQiBAby/dgs/aj5DezTM673tbMkpYMO9GzjyjSNzPpaVlBgLLA6/XI70d7O/qIyuyFFODfyIlIzRGHqKDTn3Z2eA3m7kP/4v2LQd//s+xm0lVXz74smrNvPZHeQ6s1dTS9PWGiEEm4MFxK00z3ZOvwxzoqFknBODPezMK4Z5vteYwqDI7aXA5Rnfs7hqA4f6Oqn15VDs9iKEIJZO8WpPG6FkgqFknMErMm8rvFN3x574cxW4PGwK5tMeGWYkncJt2sYDarHRcgE98SiH+7vYX1A2r59jovHsMWAgHuNieBC7YWA3TGyGQZ7ThcMwp83kk0pxsLCcpvAgfYkYgkwPiSc7mmiJDHNbSfV4E7TXettpCg/Ne45ppegevRmU/ul3oacTY9/1sGMf2Gyg1JJmvo0H6kZ/V2PBOl2mRFtqOmCnaZq2jinLwjBNDvV1kpKTl6TeW16H0zCRX/4b6J1nzaU5MD/+GYQwaB95g5rAreNZN1r29cQj9EyRwXhpJES1L8jtJdXkuzz6xFJbUpkLOYP31Wzhx5fOkOd0o1B48z3zOo4rZ2FNF5bTCz0t7CsspSd2kmi6lxxHNQ0596JiWc4kPnsC0d5KbkU1DsOclL0igHdXbczueJq2Bgkh2JNfykgqxZt9HXPaJ9/pWdSNgYmBKwODg4Xlk+rjOU0bd5bVjm/zw+bTXBoJ4bHZ2ZZTyC0lVVhKztgsQirFg5UbCCcT/PO5wyQsC+doRr3HvLzUtNjtm9eNOakkSSm5ODxInT8X54QstRynk33O0kkNr8aWoKrRJhFXjmMIgcM0KfH4sBkGH6zdxumhPl7sbuFsqJ/G4UH2F5Tittl5tadtTnOckSGgoxXZ0QqP/wyxfQ9i226oqUcYxpJm3o399x3LsNPnVNpS0wE7TdO0dUpJiTBNlFLkXLHsos6fS30gD+vFp5ckWAegjMyp8J7CTwJwKRxaknG0mV0aCfH1C8fYnlvEbSXV2Iz5LQHStPkY+9t6f80WhBCEu8O8/eU5LLEHhCHY9OAm8mpzeS0bF3VLTCGxGS7shpt9xZ+FZAr5N3+W9XGst1/HUV3HIxt28svWC7SPLpErcfsods+xI62mrXNKKW4qrqQlEqJ7DoHzsUy4bJiqFt7Ez1k5WjKgMzpCfSB3PEw4W2fXsWP4HU4chknMShHAiVSK20qrcZomr/S08XZfJ5XVM2fsTT6ugdMQbM4pmFT7MjPe6OymWhrL9MtipVJsCuazL78UQwi25RayIZjHc52XODXUy2u97XOe33TGQ4gTf2/JBOrt11BvvwZOF2LDFsTmHbBxC8LuQCmJWIJav8IwJ9Wz0zdEtaWiKyZqmqatQ8qyoO0SKjSEJHM3tNoXHH++IZCLlU6hnvll9gd3uRF7r0f0ZwrJu235SGXRn9RLYleKAo4P9vDtiyeIplMzFrDXtMUau6CzkhZH/vUo6WRm6VbB5svdDA3TwHRczk4wHSZ7fm03ZXtKOTbQzStrIWCnFF5bEQeLfxunGUB+818guQR1946/hfzK3+O1JB+s3cotxVWYQrAhmKdfy5o2auy95N1Vm8brOk4naHeOZ6otB0MIPDY7dYGcSdlq85HndBNLp8ePB3B9UQW78oppDA9yqHdumYVjxGj3Vds8lpHOdLMvc56ZQ47TNd7Z1WmY3F9Rz4dqt1Lq9uFYZM238d/ZdPNIxFEnDiN/8HWs//kfsX70TUimMufEWTRxHmps9Yp+L9aWiM6w0zRNW4eEaZJ+8ueYdz2IEQiyO6+YvQWlHOnv4sRgDw2BPIzuLrJ3CiMQe67DuPF2REERAEkrzUg8RsDh4NKIzq5bDfoTMb7ZeJyHa7aQ73TrTDttyQghEKZgxwd3cPbRs+z7jd3YHA4ifRHO/eo8Oz+0A9NukggnePXvX2P3x3YRqAjwTGczRwe6V3r6c2IpRXXgZqSykD/+DrQ2L91grc3I//2fMT70G+zfsJkDhZl6VfO96Ne09WwsMPaB2q2cGOzhha4W4tbVtXl9Wez0Op+5TayXN99sLL/dQVrJqzK57IbBzcWVbM/NnHutdM3aiVmDY/Ms9fj4SP12AFLSomUkxFv9XbRFhhc2yFx+PMtCHX8bq7UZ84OPoIrLslrjTr72PKKiBsqrMlPSnWO1JSKU/qTXNE1bd1QyifW//hNYaXC4IBnHeO/HMHbuzWRkpFLIr/4DdGYhi6WsEvPB9yPKKggn41wcCXF6qG/ajobayrMbBu+s3Ei1LzjjEhdNWyylFCgLSCAM7/jjsVSK9liYhkAeiZEEDo+Dpzoucny0kcpa8N7qTVQ7vcgv/iUMLN+8xf4b4R3vxdQXiJo2LakUCSvNUx1NnB8emPRcpTfAB2q3rtDMFuaLZ97i9tIaNgTyxj+zx4JzY0G8o/3d7MovXuGZzm6sdl9vPMpbfR2cDfVjzSEkkeNw8Rsbd5N+7MfwxktzH9DuwPjAryEaNmelky6AfOYxjE3bEBXV44/pJbHaUtCf9JqmaeuR3Y7Yd33m69HOZPLH38S6cDaTqfEX/yUrwTrjrgcxP/27WEXFPNXexD+fO8LTHU06WLfKpaTkJ5fO8lRH01W1fvR9PC2bMt30bIh0C0opWkZCNA4P8rXGo/ys5Ryv97RjOQ1e7G5ZU8E6gLZIGOw2GOhf3oHdbn0Cr2mzMITAZdp4Z9VGduVNDmLZlqCm2VJRStEVHSGSTmFd0TxsLHCngJFUkpd7WldghvM3loWX73Rzf0UDn920lxuKKnDOumR2gYGxVBL5nS9DdHFNgZSUICXyuV9B2yVERfX4fK6ck1JyqkNo2rzpJbGapmnrlHHrPVhvvwbpy8tB1De/RNbCMaYNcdMd9MQjfLvxBPrUZG2RKI4P9nB8sIcch5OA3YXDMKj251x1caNpi6IkOLYQSyf5QfPpSU+93NO6Zi4yrzSYjGEIA1lYvGTNe6Zi7Dk4fQ0nTdPGjdWKu6usFgEcGV1uv5ayyhUQcDi5qbiSMq8fhWKqNaEu08bH63esquYHSqkZs/jHHneZNg4WllPu8V/1GTHpeGNfLOTHkxJ14Qzs2AtTBNhgNCAnBEgLDBNGm7eNPwfIZx/D2LoLbr57vFPs1CZ3k9W0hdIBO03TtHVICAFeH+TkQ9/S1IMSm7cjhOBIf7cO1q1xQ8kEQ8kEABfCg1R6A+Q6XKvmpF9bH+yGyburNlLs9uI0LmeGTDTx++5YlB80n1m2+c1XjsOFVBJ6l7HmXkU1Ijd/+cbTtDVuLGh3Z1kt23ILEWQy79aKsUzB/QWlCMSUwa+xx3x2x6r63BZCzCm2NrZdlS9InT+Hi+GhKbcbXwAgFhYAk0/+HCMSRuw6AF4fyrLGA3JwuQ6d6ulGDQ9BOIRsu4TIzUfFY4hADubdD03qDjvTz5QZVKHU5HE0bT7WzruVpmmaNmdKShjog9Dgko1h3PsuIskEJ9fYMjZtds3hIYL5xZgLuo2taVcQBqg0dsNGnd8PVgci1TGaJTb2Nzbx/wUYuVR6a/HbHITTS9B5NQvE2FxdDojHl2fMYO6yjKNp68lY8KTY7Rt/TCrFQCJGWkocponLtOGx2Ve8acNUrmxYMf02q4dSiuFUAoEg4HDOaR+pFLeX1tAUPjLzapCF/qiREeSTv4Cnfomo34jYfQA2bwdhXA7WWRaitBw8HlR3ByiFSqcxbroD4QvMKVg3RkkLwsMQCKyqzEdtbdEBO03TtPVICMgrwPzdf4/86XdR56dfYrBgyQRxhz37x9VW3KVIiL0FpSs9DW09ETaIPoaIPz7H7YOI3P/GdUXlPN3RtLRzW6DzwwPcUlIF974b+bPvLsuYKqw7bmvaYkmlGErG+VbjCdITao1VeYPcUFROuXfmAIscTfVabUGy1SbocM1re0MIchwuit0+umIjVz2vxsJ4i/29q8zyWNXXgzhzAvN9Hx9/ajwTLpCDuPkuDMMYrUeXGXM+3WCFYUJuHqq5ESqqUULoTDtt3tZOxU1N0zRtzjKF3g3weDE+8inEDbdn7+D5RRif+E1EQRFzW+ygrTVtkeHxCxJNywqlwH0PmOVz3D6ESl1kSzBvTptvDubzjooG7iytWbaT26FknLNDfYjdBxB3P7Q8g7ZeQh5+E8hkgmiaNj9qNIMulEywt6AE94TlsS2REN9tOsX/OXeYSDqFVApLSiwlkUqON2UyhKBlJERy9DUoldKfmVcYC3bOt5GVVIoKr3+2oy9wVhOOsHMftt/9D+PBuivnOX4eDQhhLDg7TimFqKmHoQEwLh9D/71oc6Uz7DRN09YxIQyUUpj3vhOZm4f85Y8WfUxj70FETQNv9XXwYldLFmaprTYpKemKjVDq9uklHFp2CAFKgOtuiHxtbrvEn8Lh/yx78ks43N815TYfrN1CscuN3XSMP+YwzPGlWI3hAbpii+sMOJPH2hpRwKYbb8fq6YJjh5ZsLACURP7sO6gzxzA/8qmlHUvT1impFFXeANW+IAVOD79suzDp+VAywfebTrExmI/TMLGbJg4j889pmnTHRni+qwUBFLm9VHmDVPmCVHoDmYXy+nMTYMHLi8s9AQ5xdSOfyzXsFv/7VVd0916q/2ZjNRTJzUdM6E6sszO1udIBO03TtHVu7CTEOHAT8tCr0LOIbob+IGLzdtKWxfM6WLeutUfCFLu9uo6dlj3CBMc2iJjAHLLDUqdQ6VZuLa7g9GAfcZme9PTGQB4V3iAkj0GyD1QEPO9ka27h+DY7cgv4p7OHs/yDXCZRPNZ2gVKPD/8d9yGXOmA3SpRWzKuWkqZpGeONEEbPjTbnFFDu8dMWHaYjOkJHNExfPMpwKkHj8ADhVJKEnPr9SgHdsQjdsQhv9nVwfWE5NxRVLNvPstotNCiV65xuKa0aO/DcDuRygy8wdfO1tkuo8DB4fUv+PiqEgCuWwo4tuda17bTZ6ICdpmnaNUJZFsbBm5E///7CD5Kbh8gr4MxAT/Ympq1K+U63XvKsZZ9wgH0DpObW/VWMfAMj+Id8uG4rX71wjDK3n+sKy0hJi3KvHyVDiJGvwliv6sTrZJZLKfA8jM22fYl+kMsU8HZ/F7eXVENxGXR3LPmYYt8NWcky0TQN/A4nm+z5bA4WIIQgJS0EAttoICdhpWkeCXEu1E9TeGhS3buJDvV1sjWnkJwJASdrtP6ZqV+vM1JKYSnFkYGuaTOqLy8ineF3aRiIbbsR2/dmGkuYJmpoEHXibeTJo9DVPn4064ffwPzgIyine9lry40F6SYuHR77ejU2PtFWjg7YaZqmXSOEacLu6xCnjqEazy7sIC1NqO4ONuQV8GR2p6etMuVevz5h1LJPWWDfPueAHbIHEfkBeb6P8Hvbrhtd5p8GjMzyoshPGQ/WAagJhcrlIHbDxGPaiFrpK4+cFU7DoMTtx2PaEEJgPvAw1lf+fknGmiSV0lkZmpZFxoTlinZjcvDGadpoCOSyKZhPWkqaR4boio3QG4vSn4gRs1KkpCStJF85fwSf3UHA7iTgcBKwOynz+Kj15yKVnDTOVBaTcbXWs7Ve6LrEkYFuXKbJgxUbsJB0xyL0xCJ0RMOXA3bT/IxiwxaM+9+DyCtASSvT9AEgmIO44XZsN9+F6u7A+vLfQzIBly5i/eP/xnjvx6Buw4r+7iZm2xlTBPG0a5dQ860EqWmapq1ZSkqwLKx//F+ZArgLILbtxnz/J3i6o4mjA1MsM9DWvAKnm1/bsGulp6GtVzIMQ/+ZifkSs3LsB+EGOQDpi5nAn1kMVuv0+xj5kPOfONLfxTOdzYud9bgtOQXcVlKJ0zQxxeR731Kl4ejbmUxmOXUWTjaYf/TfEW7Pkh1f07TpTdUlViqVycwTAhNBWknOhPo5PdRLR3SEco+fh6o24LU5pg3EXHnciZlWMz1nKTWvDD4l5XhBuNXQtXTs9zGcTADgNE3sholCYSAQQtAeGebx9ov8xsbdWM8/gXru6o7j5n/8nyCMGZe4KilRrz6HfOrRCY8KjA98ArFp+6r4fVwZ2B37b60DeNcmnWGnaZp2DRFGpgmFcfOdyF/8YEHHUKeOoVqbubOsilAiTnMklOVZaiut3BvQJ4ba0lAKhBewA8m575ecojbcTME6AOEDMjWmssUA7i2rIZrupTH8NtF0P9F0P7F0P07DT33OvZTuvg527EF0diLPHEe9/hKk5/GzzkUsinK6dA07TVsBU2WfG0LgnNBx1sRge24hu/KKGUklcdtsmMKYcrmjVIrWyDApaXExPEiDP48Sjw+PzT4p6yqSTnIu1I/TsFHrz8Fts2fGmsNn9VjGmYpGUM2NiEAQUVG9yN9EdoydawQcziufGf+q1OPn4ZrNmW+me98LDSHyCmYeyzDg+tvg8BvQ3zv6qEI+9hPMhi0oY+EdYbPlyizMsdkoZlwMrK1TOsNO0zTtGqQsC+uv/weMDC/sAC435m/8NiqvgL89/SZLl0eirYQP1GyhzBvQNXe0pRH9KcSfXfpxnNeD98P87ak3SGcp2+22kir2FZTxQvufMZxsm3KbgKOSmsAtFHt24TT9SCsFx95GvvA0DPVPuc98ia07MT/wSFaOpWnayhmr3ZaUFp7RAJxUCoXCvCLLCq4OFlpKIhDTlrCYePPN+sUPQFoYu69DVNWiLGtVZJTN1ViwU7ZegotnUakU9HSiWpoQW3Zg3P0QeLyzBtyUZUFfD9bXvgATbuiIg7dg3Pfuy9/rcyBtFdAZdpqmadciITBuuA355M8Xtn88hgqHEAVF3FpSzRu97VfViMpzuCj3+Dk+1DvNQbTV6LrCMip9wZWehraepc4tzzgyU88uaHPSn4xl5ZCFLi+WTBFN9U27zXCylWN93wK+RcBRwcacBynafR22PQexLAsRCSN//C1oblzwPNSpY6jOdigu1Vl2mraGCSGwicsNLmAsKHc5WKSmCdYBk4J6E40F6iLpFBeGB9idX4L50PszxxptmrGWgnXA5WWhZeVQVp7p+Duh3qCSck5BNmGaqMIizF//N1hf/wKMhDP7v/EyqrAEUVaBKF0d3X4tJTGY3KBCu7boT3hN07RrkDAMxIGbMi3vF0opsNLsLSjlkfqdk566vrCcR+p3cHdZ7bwO6bc52BjI49biKt5dtYn3VG1a+Py0eWsI5HJTUSU6+V5bMsoC58HlGcvKdBq8t6Iua4d8vusShrBR5jswp+2Hk20c6vkiXZG3ADBNk4jbjfjop8HhWNRc5Osv6GCdpq1jY5/FCcuiM5oJKsl5fD6/1tPGv5w9jM/uGD/WlUGutUYIgTBtmX9X/BzzeT8Uhgl5BZif/X0oKc88qCTyF9/H+vn3M3X+VpBUioSVZmRCg6Gx//bZyhjX1gb9Ka9pmnatMk3EwVsWvLv81y9h/emfYH3zn3E7HPzW5n18smEnn964hxuLKzMduOZYVN5j2vjkhp18ZvNeHqrayP7CMur8QeoCubgMnQy+HDYE8niocmOmRoq+i6stFWFmlqpyZa2iJSD7IPpTSj1+6v25WTlkJJ1ECIElE/Pa7+3eL9MfO0dKWvy85TzYbBif/DeLmos6cQQVi+kAu6atU0IIpJK4bDbSSvJaTxsKNWvQzlKSztgIxwd7uKOshnp/rv5cn4IwTfB6MT/125nz4fKqzI3szjasv/xvqNGVIyvxHmsIgd0wCY7W9RurYyiVmpSNqa1/+ipI0zTtWiUExvW3Yr3yHKQWXhBdXTiD9fhPcW/dmSmAbBhYb7+OcHsQN981474Ow+CO0ho2B/MAycn+7zMYv0g03U914FYacu4nLtMzHkNbvI2BfB6obECgg3XacrCD68blqWOXOp8ZMUsXOFXezHLxkdT8O2Q3DT/P/uKNOAyTl7tbuaWkCvIKYWCBZQOsNPLFpzDvfaduEqNp69RYA4IKb5BqXw4we5adKQzKPH4+s2mvfm+YhTBMlBCY979n/DEVj8FgP6qlCZwu8PogmDveuGO5TNfcRP83vbbogJ2mado1SgiBcjgRN9yKeuGpRR1LvfYC1msvTD7+PQ8hZ8iwu6uslu25BRgYdEePcnrgx0TSly9cnaZfZ44sg03BfB6oaAB0sE5bRu6HIHURrEtLO47ILDuNpi8H/n02ByML7Npa4s50no2ke+a971C8CaUUxW4v54f7ubWkGrF1J+qlpxc0FwqKETfeAejXrqatdxObQE3XYGLMxIDOtfDesNgAlriyK6vLDaUVICUIAaGhsQ0XMcvsGftZdeDu2qADdpqmadcyITBuugvrzVcndcrKCrsDwzC5r7yOMo+foM1OXEqiVhpTCHIcLjojb3N26OdEUldf/A7EG6kJ3Ea+w521gvHaZBsDeTpYpy0/ITKr5Z37IbrUAbvMcqJ3VW0kaaXx2E0MYUMpCQjOhfp5tO3CnA+X7/KQtCKk5fzfk7bmvw8FnA71MZJKkrQsbLUN8wzYGXDdjRj7b4C8IqyExbFvHWXnh3ZgmHqZlKZp19bn+Vjn2KUwXhMvJ1NS4crA3kq7lv47X8t0wE7TNO0aJoRA2WwYt9yFfOJnWT22ajqP2LiVbblFqP5eyCvAY9pIq16UkrSEmzne/x2mr3M3WmxZWlmdl5bhMm3cU14P6JM+bSUIMAJLP4wcRskodtmBXShEvBWsdoTzINjq2RjMY0NwP2mpiKYtHm09T3d8+psXAbuDWHphS1hzXXV0RsOMjJYg6IqNUFFUBtt2I3LyUBfPQmf7VfuJm+5A3HIPIMA0MWwm8VCczldaaHu9jWQkqYN1mqYtu4nBMqnUipTVGErGGUrEqfYHp+2Yq2lrmQ7YaZqmXeOEYcB1N8PrL0JoMGvHVaePY50+Dg4nJBMY7/kIbN/Na51/S1KGZ93faQZRShGwOxe8fE2b3vWF5dgNQwfrtBWiQKWWfhirHTH0768e3bEbpSQXQk8iSeM2cynx7ub9tZv4ZuMphpJx8p1u7iuvJ5JO8tOWcwC4TJPBRN+CpuIwgrRGOsa/T0kLIxDEeP8nRh95EJlKQ3gI0mnIyQchEDaTUEuIkZ4RrLSk93QvodbQ+HEMm75I1TRt+RlXLNNNWhY2Q4zX3VtqlpK0RoYZSsSp8ecsy5iattx0wE7TNE0DwLjtPuTPvpP9Aycz3RTls7/C3L6HPUWf5PWuv5t1t67oEWoDd/DBus0819nCkYH5F3nXppbrcLE7v2TJlpFo2pzIhQW+ssLwk5RRzg39YvyhtpHXuaH09/iNjbuxVBqDsYtOD/lON/2JGEJArrOWQvdWemOn5jmompQBUu0JEhuMcfwHJ0jH0vhKfORUBcmtzsXh99N7rJt0PE08lKDjrY5pa3oKQ7+ONU1bWT2xCD9rOce7qjZS5PZiKYkpjGnrrGVjKaspDM6G+jEndFDV5zXaeqMDdpqmaVqmtf3uffD689DduTSDhAZRb75MwXU34bEVEp1lWVksPcCLHf8f+4s+yx2l9ZS4ffyqvXFp5nYNMRDcU1630tPQrnXChHTLyg2fPI7T/SA2w0VaxgEYTFzk2bb/Qo6zBr+9DKlStEcOcVv5f+TXGnaSVmmiaQun4eVgyb+lL3YOn72Yo33fmFPwLmGFqPD6AdgczMdmMzn+6FnCHZmM49hgjN7T819uayUt0ok0NufynNaPdajUF8aadm2ylAQy5xNy9EaEVIrhVIJvXTxBjS9IvT+XOn8uCWmR63ChyLxnDCcTOE0bTtNcVIBNKUUomaAtMgzAkf4uduQVAfp9SVtfdMBO0zRNy5AK810fxvqXv4Yl6s4qX3wKc9/17Cr4OK92/dWs26dkhNe6/oZt+R9ka+6tbMnJJ60s4mnJpZEQT3RcXJJ5rmd3ldVS7vHrpbDaylEWJI9Bar4ZalmcglmCUunxYN2YWHqAWHqATt4ef+zF9j+jwL2ZoLOaKv+NtI2EaI/2sje/FrtpJ9dZO6eAnRAGQYcLyHSqBYgPxWfaZc6i/VECZUtfE1ApRcJKk7As/A6HrhmladeQsWy57liEnliEApeHvniUztgIl8KZZfpSKS6Gh7gYHgKaAMhxuNieW0h7JEzTyBAAeU4Xn9ywe+FzAS6EB8a/vxgeYnd+yYKPp2mrlQ7YaZqmaUAmy06VliMO3oJ67YWlGSSQAzY78URo1k3HKCQn+r9DR+Qtgo4KvPYi/I4ytudtoMDl5smOJnrj0aWZ7zritdnZlVc8egda01aIsjJLYaPfXZ7hPB8EI4/M5V3mn0AhHNuRMjGnY0TSvUTCvRB+kUiqly1576YtGubvTr/Fb2/dh93wzHqMHfkfwW3L4Zet5wE4G+rn1tJqAuUBov2Lf/+Kh+L4S5cnEN8WGeaVnjY+0bBzycfSNG31EELwbGczh/u75rXfUDLOS92tkx7bl1+66CWsZR7/+NfNI0OcHuplU7BAZ/9q64oO2GmapmmTGHc9gHXmBAwNzL7xPInKGgAO93513vsOxM8zED8//n1t4E625j3MJxp2krRSPNfZwomhhXVvvBZ8qHYbOU7XtPVkNG3JKQtUFMJfAJWdzLKZmQjXjaRlgpSMIjI9DAEQ6TDDydaZd59CY+hxcp21XFe4jU3BfExh4jB9s+5X7NlNW2SYM6F+AMLpJOm0RbAiQNex+V38TkWmVSYeucQvbUWmc3d/IsaxgW525BXpLDtNuwYopWgMD847WHelSm+AjcF8duQVL+o4hhC8ckUQ8K2+TrbkFC7quJq22uiAnaZpmjZOCIESBsb970F+58vZH8DtBmkBctGHahp+hraR1ylyb6U2eCf3lNdxV1k1AIPJJI+3N9Idiyx6nPWg3OMnx5lZiqeDddqKESaEvwJyaJkGzASSWsOvcnLge1k76tG+r7Or4OM4zRzCSYP+CTcSpuK25eE0fZwLNU96PGwlKdxSSDqRpun5ZmR64e+LypIo1GhQcmkopbCU5M3eTKfbE4O9egmapl0jhBBZCc4/WLkBj82+6JuHSinao+FJj4VTycVOT9NWHR2w0zRN0662RDXshNOFUosP1o1JyQjtkTfpiLxFpf9GXGYQUzio8t/ER+u28d2m03RccUJ3LdocLBjv2KZpK0JZkDoH6eWsO5n5e1dZuEEwUUpGOdTzpTlvX+LZixCCLTkFDCbiXIpkSgIcH+jhYGE51TdWk1udy9tfP7zgoJ2Uoxl2S2DswloIwS9bLjCYzGRH9sQjxNIp3Db70gysadqSk0plco9nCZ4dH+jmxe75ZyVfaSgRx2XaFr1sNWalsa44V7Ub5qKOqWmrkQ7YaZqmaZMJEIHg0hzbNFmKq0qFpCX80vj3zeHnuavyf1DjC+qAHeAwzSXNvNG0WQkTkm8s85iZgJ1U1vKOO6rIvZ1K/w3kOupRUlHq8fNw9Wbe6u/k7f4uDvV3cqi/k+05hdxTUUfVDZU0v3hpQWMtJjtvNmMX8j9uPjNeMH7MYDJz8a0zdzVt7RgLwqekRWtkmEpvABNjyiCapSQ9sQhPdjQtetwqb4Ayr3/2DefAY7PjNEwS8vL7+3AqwVAiTtDhzKwY0SVAtHVAB+w0TdO0SYRhQlklomEz6sKZ7B7c40ctVRrIBPH0EFJZVPqC0NO25OOtdmlpjf7e9YmrtkJUEpInlnnQsQy75QnYeW3F1ARupdC9FY9ZgGGaSEsy1BKirbGRwaZBqm6oYv/2MvYXlhFLpfjRpTOcGOplR24RNbfU0Hmki0R4bs0wJooNxpbswlQqxflQ/1XBOoDBRJxitw+d16Jpa4cQgksjQ/zk0lkspfDZHNxVVkudP+eq9xFTGDydhWCd0zC5v6Jh0Y0mJnLbbCSSk9/f3+zr4O6yWpTKnPWoLI6naStBB+w0TdO0qyhpYbz3o1jf/Sq0ZGcJm9i4FWPHHrqjx7NyvJkoJE3Dz1IXuHPJx1oL0kouS0F6TZuSUpBuAVLLPPDyZNi5bXncWvofsdsydSIjfRFaz7cz0DjA0KWhSdlvJ354kr5zfdg9dmpvreWByga+cv4oP289z2c27qFoWxGtr81/2dnAhX7EfRuy9jONsZQkLSXPd7VM+XwoGddvK5q2BoyVxWiPDPNURxOhZGJ8SelIOslPW85yX3k9W3MKrgraOc3Fhwz2FZTisdmzGjxLyaszi08N9VLvz6UtOsy50AAfqd+Gy7Rj6qAdkLkBA+gg5hqiA3aapmnaVYRholxuzEc+h3z8p6g3Xpp9p9mOuf9GpEzxZvcXsjDDmbnMHOqDdwOZS/alWyy2Nkx1Uqtpy0eC1bEC445m2C1xwM5l5mC3uWh++RKtr7WSHJm58HnX8W4A8ury8NZmyg+MpJNIKbF7FlYPLn9DPkoqhJG9izClFH3xKD9vOc9Ieuqf6fzwADcUVWRtTE3Tsk8phYHgW40n6IqNTLvdE+2NSKWo8gUwhMBt2rEZBiVuH62R4UXNIZpOZT24X+4JcG64f9JjllL8pOXs+PffvHCC99duIehw6jq+gEKN1i009HLhNUIH7DRN07QpCcNAKYX5jveitu7C+sUPoK974QfsaMXYsAWb4SIt49mb6JQydxATVvqaD9YBpKXU2XXayhEmJN5cmXEBqdJLNoTblo/LzEEpRXwoPmuwbozD5yC/IZ/GkcHxx6RS2F3zPzUXhqDyYGXWX+NHBrp5vuvSeEbGlbbmFFLs9tIRDVPm8euLP01bhcbquD3b0TxjsA4yZ05PdkxeVeG12YmkF58dfWywh4Ddyb6CUhTZyfDqjUdm3WYknSScSpDrcC16vHVBQTSdJuhwrvRMtDnSATtN0zRtWuMXYBXVmJ/7v1CvPId84UlIze2idNKxdu4nkQ4vQ7AO4laIwXgTdrNsycdaCyyldNMJbWUoCbIPrMV3F5y/zN/8UtSwswkX2/I/QKX/BiDT9KFgY8H4833n+kgMT1+Lzp3jQhiCU4N9449ZKGyu+WfYle8rwxlwZjVg9mpPG6/OUP/TYZjcX1GftfE0TVsaQgh+1dbIqaHeBe2fjWAdZG5IvNDdwsWRId5RUY/f7lxwPTupFN2xkfGO1bNJWpauCjLKEEIH69YYHbCbQHeS0TRNm5owR0uK33g75s59yF/+EHX25LyOoZobcezcg83wkJbRJZjlZB2Rt9iaV83mYD7RdJq0kqSlRVpK4laaqLV0WTerTa7TpZtOaCtEQfLoCo2ded8yxMKWmU7HEHZuKf+/cdvykMfeQr70LMb7P0bBhlLyG/IA2PSOjbS81sqFJy9MeYxEOHPTI9/l4mIYPtmwC6fNNu9urw6fg7o76hb184xdNI+kkvTFozSNDHG4v2vGfSwlx2tiaZq2ellKcnqBwbql0BYZ5v+cO0KDP489+cWUewPzvgYXwOmhvlm3uzxmmIZA3gJmu/5M/D2v5thHNpuTrHU6YDfBav2D1TRNWy2EYaB8fswP/way8SzymcegY26ZM/KFJzB37WNPwSO82bP0dew6I4fZmvcwD1ReXYhdKcWv2hvndcK3lhW5vPrCWlsZwgRr5uDPkpH9KKuPTTkP0B5+g7gcWvQhg45qNuTcj9deSPr7X4dTmWCk/MKfIw0DpASPD+O9H6X6xk2U7ixhqGWIC081EhuMjR8nEU6QjCS5Lr+cXIebHJebs4+do/2t9jnPxRlwsu+TezEd5oLPYS0laR0Z5uRQL+dDA8g5dvG2lKIjEqbCG9Dnz5q2ilwZhBlKJOb4ql4+UinODfdzbrifDYE87imvw2GYcw7QCCFoi4TnPF5rZFi/T01BCLHqgnZjgTodrLtMB+w0TdO0eRFGJvAjahqwfebfIc+dQj77K+ia5UIzHIJIGJ+zeBlmCXFrkGfb/isO048hTAxMhDApcG2iPuceQonpl6utJwLId7pXehratczqmfCNCUuwRHWagRHhL0Hw97ml/I95svU/sJgWNAWuzRwo/hxCgTx3ajxYN26suUt0BPnNL6Fuvgv7hi0UbKyiYFMBR799jIHGAQCUVLz91bfZ+8m9bM0tJNQWou2N6ZegTmSYBkXbiqi/qw6H14Fhzj8YbynF+VA/v2ybOgNwLqJWWuftatoqMjErqXUkxPNdLXMOwq+U88MDdMZGeE/VJgpcnlkDNVIpwqkE/Ym5r9ToS0SJpJJ47Y7FTnfdWU3BOtDda6eiA3aapmnagowtkxX1m7Bt3Io8cxz57OPQ0zn19gdvBV+Ak8vQJXZMNN1HND05iy7oqEQqSUds7ndn17IchwvT0Nl12gqybwbXTSj7NoThRykJVidi5Gsge2bffzFkD2Lkqzh8n+WO8v/M8+3/A8n8l8MXe3ayr+gzEI0i//5/QXz2i0X10tNYLz0NHi/G7/0X8urzxgN2AJG+KG/+yyG2PbyNi89enOFIGc6Ak4oDFZTvK8Puti+qK6xBZmnaQtX6cmjw5+qLK01bJaSS9MVj2AyDPKebSl8Ql2nSssjursthJJXkOxdPcndZLQ2BPBymOe2SSAG81dc57zDkW/2d3FpSnZX5aksn08F29QUSV5IO2GmapmmLMh6427AV2+YdyDdfQf7yh5M38vkxbr+X4WQbPbETKzDLywYTzRjC4JGGnTzWdoGeOVx4r2UFLs9KT0G7himlEJ4HUVYSurqRnSfB4UBs2Y4K/jFY7WSy3owJ/0Tm/4WY/JgY/X+rDxH5Lsg5dq1OnUFE/hWP9+PcUfFfuRR+iSLPVnz2EpJWhHCqg3ODvyScmjpLuMi9g/1Fn0UNhzLBuvQ8m+5EIyDllJlo8aE4b335rRl3z6kKUnl9JYWbC5mY0rbQYJ1UilAyzslF1LXaW1Cig3WatkoopUhYFj+5dJYKr58HKjeQWGN1etNK8qv2RmhvpMob5KHKDTjNq5f7SxRnQvMvZ3Kor5Mch4ttuYW6RMgqM3FZrv5cuZoO2GmapmlZMRa4Mw7ciHzucYiOjD9n3PkOMEze7vrySk1vXH/8LKf6f8SG3Hfwkbqt/MOpt0kvYpncapfvdOvi8NrKkTKTffuDb0x+3OPDfOS3wJsLiUSmm6xSIFXm/7EyS0wnPja2TXkFKviHiNiTYHWCioOKTf5/0iB8YKsBWw3KVgco3PY8NuY+CCMjiO4e7D4/nrydlHr30Dz8AiPJTuJWiLg1RCjRgt9Rxr6iT6FGhpF/+2eXl70uA2EItj28leJtxUhLZi5osnAtI4DD/V1YauFL5Tw2u86A0LRVQgGXRkKMpJOcCfUTs9J0RMOklvH9KptaIiGe7LjInaU1eO0OLDV2w0PwSncbcWthZRUuDA+yM295yrJoM5NKkbDSmMIgrSRu06Y/U6ahA3aapmlaVikpMf/tHyGfehR1+A0oKUfsPgBAdeBmTg38cJYjLL2Lw0/RGzvNbRX/gYNF5bzcM7fGGWuNKQQNgTyErjKlrRiVyTC7UnQE6wt/vrBDulyYv/Y5VMn9057gK2UhxGh3a2UhxjP2gNdeQnV3Iu57F8LtQQDSsqjy34jAQIwGt9MyjsBEpC3kP/7F4oN183gZGjaDHR/cTn59fub7BdSpm45E4Rjr/L1AHjO7nXc1TVs4hSIhLwexLo2EVnA22XF+eIDzwwNUePxszilAKsWhvk6GUwuvP9wXn+KzSFsRCsWXzx0hJSWbcwq4v6J+pae0aumAnaZpmpZVwjBQLjfmOz+IuvUeCOQgLYvmaJj64F10jBxiKHlppadJONVOJNVHfSB33QbsDhaWz6mIs6YtGWEgdu2HN16CvizVq4vHsb70V2DYIBCAQBB8AfD5EQXFmAduvBysA5QSWIkUI31RhBAEb7gNgNhAlK5DzXSf7CYRSnDz792E6Gkl/aNvQlUtxs59YEnk4z+ZU826afn8CLuN2EBs9m0B026y66M7yanKWfCy1xmpxQXc8p1uXbxd01YRgWA4uT4babVFw7RFs1NzOFc34Fo1TGHwW1v2kZISl2nTK0FmoAN2mqZpWtaNZagQCNIRHeHHl06TlJJ/t+0A+e6NqyJgJzBw23LojK3+gswLVeEN6Nw6bUUJw0CZNsyPfSYTZItlsWakTMPQQObfKFVRDQduZLB5kEhflOG2EN0ne5Dpy9lxmx7cRKQvQtvrk7uytr7RRvWNVdCwCd58BXn0UFamKa67GSEEfef7Z93WX+Zn84Ob8Jf4lyZYR6ZGULnXv+D9Nwbzpy0Ir2na8lJK0RYZ5ujAHGt6XsMGE3H93rWKmMLAHM0e18G66enfjKZpmrZkpIKu2AjJ0aVkKWmR52pY4VllBJ3VGMJG4/DgSk9lyeQ4XLomiLbihGlCIIjxoU+ONo5YysEyf+/NL13i7KNn6TzaNSlYB3D20bNXBesAml9oJtQWwrj33eDIXgaZKKkgnUgTH4rPuF31TVVc95kD+Ip9Sxasg0z3vWK3D69tYVl2m4MF+kaApq0CUinODw/wo+YzJOXC6rpdS0bSSU4N9SIXUb9T05abDthpmqZpS8YQglyHa/z7xuEQxZ4d7Cv6NIZYuhpIQUcVDcH72F/0m9xQ8ntcX/I7HCz+t1T5bx7fZkvuu7FkmrNDs2e9rEWGEAu+INe0bBOGiVFdj9iwZakHAjJZJ/NlpSzO/PwMwjQxPvBI9qbkcpGOz9yxseqGShrubkApldV6ddORSrEnv2Te+xW4POQ69Y0ATVtpY+9xz3VeQqIDUHP1ak8bfaMlDnTgTlsL9JJYTdM0bckIIch3Xa4Z8qv2RqRSbM/bS9vIm3RHj2ZlHJvh4YaS38VjFmIaDgwjc8FrpSzS8TTCEBg2g8KCrWwIPsiZwZ+Q797IsYHuddshNldn12mrjLIsxPY9qHMnl2yM8b/5Bb6sI31RLr3cQs3Nm+H9n7i6u+1CKIXNacO0m1ipyVkwNpeNuttrqTxYiVJq2V6zAtiSU8hL3fOr37klWKCXlGnaCpNKIYDnOpsZSSdXejprSjiV5F8bj1Pjy+GGonJKPX79nqatajpgp2mapi0pv9056XvTMJDKYiB+Pmtj7C/6NH5bOd0nuon0Ron0jjDSE7lqCVrx9mIa7mlgd+GvAeBep50ODQT3ltfpk1BtVRGmCVt3wK+8U3eOzcogmb93uYiOro1PNyJMQfUNu8G0Ib/7lUVNyXrzZWzv+ziBigCDTZOX4G97eBv59XkAyxpgF0IQneeFvgC25ujlsJq2EpRSKDLZ80lp8VjrBZpGhlZ6WmtW88gQzSND1PhyuLe8Do/Nrs+XtFVJB+w0TdO0JWUIgU0I0qNLD2p8Afrj50jJ7BSf99mKyXdupOX1Ni48cWHGbbtPdNN7upcDn9mPr9jHcGp9dlW7taSKErdPZ9hpq48wEPuuR7349BIdf3EZdmMuPHEBlZbU3LIdPvGbmaBdcmGZLEbdBgCifVe/5/lLl7Ze3XSkUvTF59a1dkyFN6C7w2raMhtKxlFKkZAWzeFMkKkzOqIXwWZJ88gQXz1/lNtLq9meW7Tmb3SOZWpLJTF0I4d1QQfsNE3TtCVnM0zSVqaGk9M06Yp0Ze3YuwsfwUpJml9sntP20pL0XejHU+hZl13VNgby2VtQutLT0LSpCYFx8Fasl5+DpSiSnoUMuzGNz1xEWpK62zci/vjPkK89j3ry5/M7iMMBOw8Q7gqTCF99g8DhWf4AWCZTR/FGb/u89tscLMBSUnfz07QlMjGLLpJKcnKod97L1rX5S0qLJ9ovcmaon/fXblnW8gTZpshkQ/cnYhS6vGv6Z9Ey9CeupmmatuTsxuWPm55YlEr/DdiEa4Y95s5ly2OwaZB0bOai7hO1vNwCCm4prszKHFaLHIeT+yrqFlRwX9OWgxAC4fUhtu9emgHG3muy9Bpoer6Z17/wOulEGqOyZt77i1vuwTANTv5w6rp9A00DSGv562i+3tPOYHLmrrUTGQg2BvN0sE7Tsmis6cHY/6ek5M2+Dr545i2+ePZtHaxbZi2REOFUYk1nLxpCIJXCkornOpuRKN1cY43Tn7qapmnaknMY5vjXT3Y0YQoH1YFbs3JsE+esHRivlIqlUHL9ncDU+zMX1PpuqraaKSkx7n0nOLMTtJ9kPMMue6/vkZ4Ig02DqJKKywHBuXA4MPbfSLgrTGSK5bAAZ35xFtTCutoulBCCM6H5dceu8PpxmnphjqZlw1gApXF4kG81HudvTr7O184f5Utn3+bl7lYi6dQKz/Da9aPmM6SkXNNBLkMIitxeNgTy+HHzWWJWak3/PNc6HbDTNE3TltzErIzeeJT+RJz64D0Yi6zMEHRUYbc5Gbg4ML8dBZh2k+RSLMlbQfkuN2pN3xvWrgXCMMDtxbjzHUtw8LEadtl9HVx6pQXDbsP8vf+E8ZFPw9ZdM+9gGBif/2OU3cG5x85Nu1l8KM6lVy6xHC9bqRQpaXF0oJvh5Pzqd24I5GOp9dlRW9OWk1SKuJXmm43H+XnrObpiERSZJYzr7ZxkLepPxPjppTNruo4dZIJ2pR4/95TX8ljrBXpiEb36Yo3St8o0TdO0JRe5ohvhqz1tvLNqI3muevriZ8cfd9vy2JjzIAlrmOFkG6FkG5FUD2NXs25bPjbhwFIpLJViQ847kJak91zfvOZj2jMZf3FrfZ0cF7m8GLqHo7YGCMOAAzchLjWiTh1b3MFy8zHuuB8cTkROLgDZji0Ntw9z7vHzFG0pxF+7EXPDZlTrLchv/BOkr87wNT73hwh/kGPfPcZQS2jGY3ce7aL21trsTngar3S38VZ/57z2EaCXw2raAo0FScZqizWHh3i6s4lwamFNbLSl1xYN0xOLUOjyrOkVC4YQ+O0O3lO9mdd72+mMhdmTX6rr2q0xOmCnaZqmLam0lFct7zg/PIAl0xR6to0H7ByGnxtK/h0uMxeQGIZ9dP84b/X8M9F0P7eX/2fEhItGJRX9jf1YifkF3sYCdjtzi6j0BuiLR6nwBHAZJs3REC90XmI4vXZOpis8fm4tqabI7V3pqWjaPCiMhz+ONfA30DW/BggTidoGjB17SYQTKEuRaAsR7c9OF+qJWl9rpfW1VoQQbH7nJsr21MIf/DfEyDDqzAmw2cDlRmzcivB4OfnjU/Sdm33paWxgft1aF8oQgo5oeN77lXn8uG32JZiRpq1vEzuOnh7s5ZWetnXbnX69OTXUy20l1eMB17Ua4DKEgUBxY1EFA4kYZ0P9bAzkrfluuNcSHbDTNE3TllRompPTUCpFsXsHp/kRNuHi+pLfxmXmIL/2BWhpQubmIzZtx7jlTvYVfYaB+EWUkli/+AHC4QCPD3HjnXgLvbhz3cQG537Rm4wkOff4eYIVQQJlfgpzCoiH4oS6htmwsYAGXy5fbzzGwDyKsq+UrTmF3F9Rr+uTaGuOEAYKC2PzduQiAnaQyWJ56S9fztLMpuf0O6m9rYbSXZlOzH3Kwubzk3vDbUDmBoVpmHQe7qDr2Ny7YQ+1hsipDC7JnCcyF3CBtiGQp7vDatoCjAVEhpJxnuy4iKU/p9eMs6F+bi2pJiUtnKYNqRSCtRm4G5tzrtNNvssDLG/dVG1xdMBO0zRNWzJSKQbiUwfSLg4Psr+wjD2Fv06uqx6XGUR+7+vQ0pTZYLAf9drzqFNHMH77TyjybEWePg5vvz5e7kldOIPz47/FgU/v5/A3jxDumHv2SOtrrbSS6cBm2AxkOrOGzhlwcv3nD3JPeT3fbZq6s+NKsgkDn90x/v3+gszyBn2nVFuTDAPKq1Z6FrMShqDuzjqqrs90lr44Msiv2i6QkFevvf30xj0EygLzOn7n4Y5lCdgFHS7a5pllV+fP1UvtNW0BUtLi0kiIQ32dOli3xkTSKb52/ijhVJL9BaXcWFw5HrRbqyaeJ+pzxrVDB+w0TdO0JaOUYmiaLLW3+7vYnV9EoXs3NsOGkhacOXH1hsMh5Le+DPc+hPzZ9yY/19KE/Kc/x/zMv2PfJ/dy/Hsn6L8wv+6HwHiwDiAxnCDcGcZX5p73cZaCwzAp8/ip8Pqp9AYpdnv1iZa2bgghwOlc6WnMyO62s/NDOwhWBWmNDPNY24UZuzieDfVxoLgcd557zstdDbu55HWFpFJsySng5FDvnPfx2x3kLEU3X027BtgNk2c7m3W9ujVqcPT89bXedlJSckvJ6r+5pK0/Ordd0zRNWzKGENMG7EbSSf721CH+/vRb9MQiiGhk+gM1nUd+8a8gPkVdqoFe5N/8KSIeYddHdlK6u3TR844PJ3AKc9HHWQy3aeP+8nr+zZb9PFyzmX0FpZToYJ22Hhkr+1qbibfAw4HP7idQEeDpjiZ+0Hx6xmAdwBu97UipKN5ePOdx8upyUVnubHslQwiqfEEKnHO/GVHjy9FLpzRtgaRSxKZoSqOtPQOJmD7/0laEDthpmqZpS0bMELAb47c7KPH44PTxhQ8UjyL/+k9RfV1sffcW6m6vxel3zL7fdIcLxXEYJjYMDhaW0eDPXfjc5qnA5WF/QSm/vnE3m3MKxjNuTGGsydopmjYrc3UG7IQh2PWxXdh9Dr7XfJpjgz1z2i8hJcOpBCU75h6wc+d6WI61VpaSbAzmz3n7Gl8QHa7TtPmTStEaGSad7ZbV2orojIWJpJO6XrC27PSSWE3TNG1JDSamD9jlOFzcUlyFVAr54tOLG0imkV/4c/jYZ6m9bRO1t9WSiqXoPdtH41ONJCNzX5Iy0DhA7S01/PbWAwgjcxV9PtTPz1vPL26O08hxuLixqIIafw6u0eLGoGuMaNcIz+rsblyyswR3jpufXTpHR2x+dd9ODvVyU3El/hIf4a6RWbfvOd1D7S01C5zpPCjwzrHjq8MwqfXn6vchTVsAQwie6ri40tPQsiRuWXyz8QTvq95MrtOt3xe1ZaMDdpqmadqSSUmLkfTVgbJit5frC8up8+dmllsdPQQjw1kZU37zS8iyKsTmbdiqainZUUvR1kIan75I+5vtc1reNXRpiGPfPYY7183QpSE23LeB0lJ/VuZ3pT35JdxaXIUQYvwEUJ8IatcSEcjJBO1mWha/zBxeBw131RNOJrgQHpj3/m/2tnNDQTnFO0sId12YdjvDZlB5sILaW2sWMdu5M4TAZc4tYLc5mL+grrKapkEklSSUTKz0NLQsGkkl+fbFkzxUuYFqX1CvetCWhQ7YaZqmaUtmuuWw763ejEsI1NE3kY/9FGZZNjtvHS2ojhYsgJw8jI98io33b6B8bxmNzzTSf35g1sBd75m+8a+lpVBZXhgWsDu5v6KeCm9gyYvNa9pqJ8qrUOdPL+oYht1Apha3/Mxf6mfTAxvxFnoxbAbfbZqiEc4cSMCSEptj6uW+7lw3FQfKKdtbhs1pW7b3ACEElb4AdsMgNUWH24l25hWjWJaVupq27njtDur8uVwMD670VLQsSkqLH186w20l1ewtWHzNZE2bjQ7YaZqmaUtCKkVffOoOiQYCmhqRP/3u0k9kaAD5hf8NO/fjecfD7PrILpKRJB1HOml7o43E8Ox3wAOlfjoS2cv+yXE4+Wj9DhyjxfZ1sE67linLQlRULyJgJxBCcOAzB3jji2+grIUH1+vuqMNX6qctFubNjg56p2p0M0c20yTSN3n/YGWQqhurKNxUgJIKw8yUk17O9wCnYXJ/eQMv97QykJj8Hu212akP5FHvz6HIvTqXKmvaWiCVYndesQ7YrUMKeK7rEhLF/oKylZ6Ots7pgJ2maZq2JBSKwcTUATuJWv5C88cOIY8dQm7fg+2mO6m6vpLCjQW89o+vz7ibv8SHzWWjqTM7J90u0+Th6i3YDVMvfdU0AMOAyppFHUIphbfAQ/WN1TS/2DzlNvkN+dTcUo3NaaPndC8tr7VgJazx5+0eO/n1eZwd7ueXbdMvY52LMo8fYQgivZlAv6/Iy5Z3bSFQHkBaEiEEwlyZ178QgvpADhuCeTSFBznU18lwKsGBgjK25xYhQDea0LRFMoSgxp9DjS9I80hopaejLYEXulqwCYNdecX6xqu2ZHTATtM0TVsSpjCuyt4YI5VCmCvUqPzEYeSJwxj3vQfv9bfgCrqIh6Zfklu0vRhpSW4uqWIomaBxEXfLDSF4V9UmAg6nDtZp2ighBFTWgsMJi6j51BkNU3tbDd0nuokNTn7vKdhUwK4P7ySRShOTaWpvraHiugr6z/eRTqSxOWwUbi0C4PWe9gXPodDp5r6KBoL2TJfq5EiSnOocdn9s13iAzlip974JDJGZQ5UvSO1oF2yp1Pj7kn530rTFGSu7cU9ZHV85f1R3i12nnulspsaXQ9Dh1EE7bUms/BmDpmmatm5NV3BZKgVimTPsrrRzH/FQfNbusQUN+YBCxOM8WLmBkkUsE9tfUEqZx6+DdZp2BWGzITZtW9QxfnLpHAqovrl60uPOgJMN9zQQS6X4h7OH+PL5I3zr4gnCRoq8rQWU7CmlYHsRnakI328+RX9y6hsNc/FQ1UYKDRNHJJNZV3l9JaW7SjBMA8NYfafdprg8J/2+pGnZI0Rmqb7f4aTWn7PS09GW0KmhXp2VrC0ZnWGnaZqmLZnIFB1ibUJgX+ELV+PdH8LweDj9jcPI9PR3vYUh8BR44Mwx5C++j/H7/4Wbiqv4YfPCam1VeoM6c0XTpqCkhdi0HXX87QUfIy7TSKWQ6cvLXIu3F7PlXZvBEDw6YZlrdzzC1y4cW9Scr7Qzt4gchwv51KOoV57DeORzFG6ooPHZJsr2lOnmMpp2jbjyte6zOVZwNtpSuxge4sbiypWehrZO6YCdpmmatmSi6fRVj23JKcRts5M+fmgFZgQUlyF27KfjcCcDF2de3hqsCGKYBulLjRCPIyIRDNvCg43Fbq++YNe0KQjDhOq6RR/HFAJhCPylfgo2FVB3Wy1DiRjfazzNyBQ3ELJpX0EpDPajXn0eUKhzp7BV1ZKKpZZ0XE3TVpeJn/OHejtoGhlaucloSy5uXX2uq2nZogN2mqZp2pKIW+lMc4krNIUHSUkL86Y7kW++suzzMj76aVKJNOefOD/jdnn1eez84A5kMglH3waXB9xu0snp693NxpISVnglsKatVsLnh0AODA8t+BgdsTAV+yuo2F+BUoq2kRDfW2BG7HzFrDQ5gRxE/UbUhTMItxuUYscHdgC6G7SmXYte6G5Z6SloS8zU7+3aEtIBO03TNG1JRNNXZ5UI4PqiCuyGiexceGH3BatpwAgEIZqk/s46Bi4OMtIzQnwojpKZ4KLdbadkVwkb7m1AjYwgv/QXsGsf4t53IQ2Dk92tCx7+YniQrbmFk+pGaZqWoZRCbNmBev3FBR/jR81nua6ojMFEnNbI8JJn1U304+bTfLJhF56PfArVfAFRUUPESvNa1yXuLl989qCmaWvPnvwSDvd3rfQ0tCXks+slz9rS0QE7TdM0LeuUUoykrr5QfqCigY3BfOTh15E/+97yT6z5AtZzj2PfupOy3SVUHKgAQElFciSJAlwBJwCyoxX5o29h/NrnMAqL6YiGebytkcFFZNjZDAOhq9hp2rSM2+7FOvzGgrvFppG80tOW5VnNTUJK/vncYT5Uu41ARTWDqThPtzdxoLAMS0kdqNe0a4xSiv35pTpgt84NpxKTumxrWjbpgJ2maZqWdQpFfyI66bFtOYVsyilAvvo88omfrdDMQD3/BNbzTwAgSysQ1fWIkjIceYUgBPJUM/L0cUT9RozP/yFppXixo4mjA92LHrvY5dMndJo2DSEEyunCuOch5KM/zDxo2mAN1QeSwLebTk56rNDt0cE6TbsGCSHw2R3YhCCtdB/R9SqUTHBsoJudecX6HE/LOh2w0zRN07JOICbdUQ7andxZVoPs71nRYN1VOttQnW1XVdozPvhJxObtXAwP8mR7E1Fr8UXjbcIgx+la9HE0bT0ThoHYfyMkE8hzpzA/9hnUxfPIn30XopGVnt6C5Dj0617TrlVCCDbnFHBisHelp6ItoVd72tiWW4ghdKFiLbv07T5N0zQtqyylOBPqZ2jCkrZ7yuswlEJ+9R9XcGZzYNgwfusPMLbs4FBfJz9tOZeVYB3AttxCvRhW0+bIuPEOzF/7LVLCQDRsxnjnB6be0OVG1G9ctnkFbA625xTOefsaXxC7oS/gNO1apZTi3vJ6NgXzV3oq2hKKWWkSlrXS09DWIZ1hp2mapmWVKQRv9F5uKJHrcFHlCyJffwk1El7Bmc3C4cL47T8Gr58n2y9yfLAna4feEMjj1pKqrB1P064Ngp+0nGFXXjEbN23H+OTn4dJF5LlTkE4jauoxbr8PHE5OD/Ut6UwchsH7qrdQ4vYiDIObiir41sWThGdoalHs9nJveb2ubaRp1zAhBFIpNgbyORvqX+npaEvIYeqbM1r26YCdpmmallXRdIr+RGz8+x25RUgpkc88uoKzmoXNgfHbf4LyePnppbM0jwxl7dA3FVVysKgcqRRCX7Rr2pwpAQeLyvl5yzkcpvn/t3efQZJl53nnn3Nd+sryrqva93SPn8E4DDAwQwEESAAEQYBBEZS0oiSuVYT2k0Ifd/fzKjZipVituLIURS4JQpQogisRAAkSAIEBBpgBxk9Pe1fepTf3nv2QZqq6qrqrukxmVf9/ER1VddPc09nVmfc+95z31eDYhNITx+V99JON263VYqWs//zea5qvlu7ybPduIpXRL02elWuM7He+qWh2WsnPfkl/5/Rj+sqVd3SztP5CxAcGRvXR0WOSRFgH3OccY3S6p09D8aRmy8W7PwAHTsL1FDCbGnuAwA4AsKvi7tqPlrO9A9LslFTdfCZKR3leI6xLpfQfr7yjK/nlXXvqk5k+PTd8RBIn7cB2OcbRsXSvxpIZ/eGVdxrbJD3UXJJ6rbii5XvsJrtV2SCmLx09K5NbUfj//mtp+qYkKSqX5P7ab+jx/mHdvLE2sPvZIyf1SN+wLCE9gCYrq58ZO67fu/Rmp4eCPXCud5D3fOwJatgBAHaVY4wSq0K7yFqZbq3r4Xhy/v4/kjI9+qMr7+5qWOc7jj4xfkIRneGAexZZq4+NHmvXf4wkvb40q9eXZvc8rJOkv3HiYZlqVeG//WftsE6SnI98QmG9rm/eurTm/k8PjumRvmFJ4sQNQJtjHGVpQHNoPdDTv66BGbAbCOwAALsu5fnt7/O1qpRMdXA0m3P/4f8mJ9snxxjVot0NFT88PKmk5zOzDtgBxxgNxpPtEGw/pTxfgetJC/NSIS9JMo88KedX/rbM0RP6/vwtVaOoff/eIK4XRo7KEtID2EB1l48z0B3irqexZIbjPewJAjsAwK6KrFWu9v7y19BaWT/o4Ig28ewLMrGYJKkeVfWlEw/p6cGxXXnqkURKTw6McvAG7AJrrT46enTdcvu9VqjX9I2bl6SxI3J+9e/IPPNhuV/8G6qfOqfzywt6aVVzHUn62OgxSSyJArCx3iCuGHXODp3JVA/He9gz1LADAOyayFpdXFlUpXkVeTLV0+gQ+/L3Ojyy9dzHn1YlzOnrV/+RHLn64Oj/rI+OntR4MqMLK4uaKuXXNM/YKiPpZ4+ckm1+D2BnjDHyHVcvjEw2ArR99NrSrFzH0YvHTsk5cUb5Slm/ef7Vdff7wMCoTvX07evYABwsjjF6IDuwq13o0Xmewxwo7B0COwDArnGM0fncQvv7vzZ+QlG5rOhrf9Dhka1nR0Y0VXhJklWkuv5q6n/X2b7P6VT2Uzrd06/IWv1o7pa+N3NddRvd9flaXhw7rsFYglk2wC5yjNFj/SO6sLKoS7vYxXkrXl2Y1kypqKTn673m+9tqR1M9+ujoMQqOA7ijyFo9PzyhN5dmFbJ0/tBwuDyLPURgBwDYNZG17YYTD/cOqS+IK/yDf9fhUW3g6Ak5bqCZ0htrNr+z+J91fvG/ynE8PTX0d/TU4IM61zugr9+4pMt3CQlcY/TpidN6oKefk3ZgD0TW6tMTp/Rvzv9EpbC+r/u+WcptuP1kpk+fmzwjiSYTAO7MMUYpz9dj/SN6ZX6q08PZkrjrKeF6ClxXgdP847oqh3Vdzi3RaEHSRKpHoY3kGmbaYfcR2AEAdo21Vn2xhKRGgBz/9nsAAEdLSURBVCVJuvxeB0e0MefJZ2VtpPny+XW3Raoqiqp6afqfaiB2Rh8Y+Q390vFzenV+Sn85dXXD2XZx19PnJs/oSKqHk3ZgjzjGKOZ6+ujoUf3XGxc7PRw93DukTx45KSPCOgBb99zQka4N7FxjdCTZo+OZrE6k+zQQT2x634VKSd+dvqbzK+tnHt8vHGN0MtNLWIc9Q2AHANg1jjEaaAZ2VwvLMsbIfOCDst/5ZodHdptjp7RSva56dOcadfOV8/r61X+oJ4d+XY/1P61j6ay+evltrdQq7fucSPfq0xOnFHM9ig4De6zxHpPs9DD09OAYy2ABbJsxRknPV48fW3Mssd/Snq9yGKpuI2X8QCfSvTrZ06ejqaw8x1Foo7su9ewN4vrc0QdUrNdUDusq1mt6bWFGby/P3Tcz7x7qHVTC8zs9DBxiBHYAgF1jVgV2C5WyCrWqkuceVdhtgV0ioVztwpbv/srsv9b1/Et6avi/1y8dP6ffufC6qlGoU5k+ff7YWUXWEtYB+6STBb6NGnUqnxgYJawDcM96g/i+B3Yxx9UTA6N6rG9YmSAmSe2lnNZaWal9LLOVGWOt+yY9X0nPV28Q10SqRx8emdQP5m7qjcWZQ12rz6gxW5LPAuwl5m4CAHbV6oOWy/llRcMjHRzNJqyV2WaR4NnSm3p55p+pN4jps82aVR8emSSsA/bZbLnQkf3GXU9fOHZOj/c33tM4QQNwr/YrrHOM0ZFkRh8dOarfOPcBPT88obQftG9vBXPGmB0fy7Qen/YDfWL8hP7uA0/KO6RLRROup89MnlE2iPNZgD3FDDsAwK4JbaTLuaX2z1fzy3q4b0j1kTFp+lbnBrah7R9gFWrTimyowXhSJzN9Gox3fmkecL95bXF23/f5aN+wPjp6VL7jcnIGYEdCG2m5Wt7z/Tw9OKYPDU/KcxxF1u5bvU3HGFlrlfYD9cXimi0X73j/Y+msPnXklH7v0htarnZumfBWHU316LNHH1DguJ0eCu4DhzPyBgB0hGucNd1UL+eXVI1Cmb/3DySvO64Rmec+IhNPaqV6Y9uPfWbkf5Tk6us3LurFsWOKDvFSD6AbRdbqmcGxfdlX2vP1zOC4fv3M4/rkkZMKHJfZtAB2zJHZ8xpvnnH0/PBEu4SAY8y+XmwwxiiyVl8+9YiOprKb3u9IMqPPHz2rtB9osAvqk96NI6NPTZxSjM8D7JPuOHsCABwal/PL7e9LYV3fuHFRPz95RvWePmlh/2fG3M6cfVi1qKgLy3+67cculM6rJzuuLxw/p8hGHKwB+8wxRr1BfEfP0ePHVLeRivVae1vS9VW3kbJBTE8NjKk3FtdYIi2r9+fiMrMOwE606sVdXLUS4V7FHFe1KFK0SfT3oZGJji9HdYxRZKVfOPqAfvfi65qvrG30NZZI65eOn2vPyMusWqrbrc5mB5TxY50eBu4jBHYAgF1hrdVcpbjmJFiSalHUoRFtzGSyqkdlOXIVqb6tx15Y/oYmMi/IMY6cQ1qXBeh2fbHEljssDsWTeqRvSOeygwqtle84irmNw99yWNdsqVEPbyLVI0mqN0+oW0vHiOgA7BZrpa9dP6/3Vha29bgeP6bnhsaV9AKlPF+9sbjirqcwijRbKWqmVFDC9dQbSyhwHLnGWVOnrpMcY+Q5jr54/EH9q3dfVd1GSnq+Pjw8qUf6htqNLqy1d10622mecfShkQlqF2NfEdgBAHbNW0tz67bF3GaNj3B74dhesbWaEt6gnh39n/Ta3O/KMa4qYU7VKH/Hxxk5enTwV+UYcaAGdNjfO/uk/t17P9VCpbSmC2GjS2FGQ/GkJlNZDcaTzdmw6wP2uOtpItXTmEXX/D/tG2oSAdgbxkgDsYRueL7ytepd7+8Zo/5YUl88fk4x11tXg851HI3EUxqKJXelacRecYxR2g+U9n0tVSv6/NEHNJJIty+KWGv1/dkbKoV1ucZ0bWfZD49MKuPHuvZ1xuFEYAcA2BWhtXp9cWbNNscYPTc0oahckpYXOzSytUxYl6xVf/y0Xpz8XyRJkQ11Pf99XVj+uoq1OSW8AZXri4pUl5GrycwHdab35xV3e1kWB3SByFr9zdOPKV+r6j9ceVv5WlUfGp5od3BdPQPiTrNhmUUHYL84Mnp26IieHhzX92au60fztzashRtzXD03fEQfGBhrLivdfEaXMUbuATkueXJgVNcLOY0lM2u2W0nXCyv6W6cf04/mbunb01c7M8A78B1Hj/ePENat0vq9tNZybLyHjLVdGmEDAA6MqBnWfePmpTXbPzAwqo+NHlP4+/9Wevu1Do3uNgNDcr/wZWl8UtL7V6sjG8rIUaS6XOMrtDUtlN9Txh9TzM1KsjIsgwW6yuoTWZYpATgIWqffS9WKfjB7Q9UolJVVNYyU9Dx9fOy44q536N7PWu/Rt79vz5WLChxXvbG4crWK/p93XunwSNc7lx3Qz0+e6fQwusrqwE6izuteYYYdAGDHHGP0yvzUmm2ecfSh4UnZmanuCeskaX5W4b/8JzIf+ricFz/dWA7nunKaS+Fc+Y2vxtdg/Kyk1kEIByJAt1l9QnvYTm4BHE6tYCMbxPSpiVPrbj+sFx/en/XcCusa5QqGE6n2TMOMH1PWj2l5CzVK99Oj/SOH9t/lXrVeixvFnMaTGcnadk1C7B6mCgAAdiSyVlfzy+u6f/mOo8B1Zd99o0MjuwMbyX73zxT+5v8h425es8oYh1l1AABg120WbNwvgcfqggStmXflsK5sEFPK8zs4srWeGRzXZKrnvvl32Q5rrcaTGX3t2nl9/eZFrdQqGy7zxr3jLAQAsCOOMXp1YWrd9lbRYNNFB13rdFkHWwAAgPvBRkso466nL514SP/duaf0/PBEB0Z1+3hcfXhkUlQR21jr3/BTR05pqljQj+ZusR5llxHYAQB2pBzWdTG3tG77kwOjkiRbLu7ziLYhFpMkWYI7AACAjrl9BtvzwxP6mbHjnRlM0+me/nXdebGWY4w8x9GXTjyo5WqZ12qXEdgBAO5ZZK3eXJxdM/3dkdHPHjmpD49MKrz0nuxffqODI7yLG1cV/vZvSqWibBh2ejQAAABoeqx/RD1+rGP7P9PTL+bW3Z1jjOKup88dfUDL1TLLYncRgR0A4J45xuj1pdk1285k+/VI37Cil78n+1v/rEMj2zp74R1F3/mmxBVBAACArvJw31DH9j2WzFC7boscY+QZR9Olgiphvb09tKxi2QkCOwDAPbHWarZc0NxtS14Dx5W1VtHX/qBDI9smP5Dz9IckrgYCAAB0DSNpMtXTkX2nPF9x1+vIvg+yU5k+fX/mRvtnl+ZtO8KrBwC4JzeLeX3r1pUNbztI9Sucz3xR6hu4Y7dYAAAA7C9jjMaSafUG8X3f90Asue/7POiMMTJGGk6k9NrCtL4/c12FWrXTwzrQiIwBAPfktcUZXSusrNtejZq14AZHpLnpfR7V9pjHn5Hz+NOdHgYAAAA2YGT0qycf1u9felPzldK2HtsbxGWt1XKtcsf7HUtndbqnX4HjyDWOYq6ro6msImtZErtNjnE0GE/o3194XZKUcD09PjCq0Fq5q17L0EbMvtsCAjsAwLZF1iqxyTKBq/llWWvlfPjjiv7T7+3zyLbOnDgj57NflLX2QM0IBAAAuF84xijmevryqUf03sqCLqws6Up+SZVo82ZhI4mUPjl+UsOJlK7ll/WVy29teL+He4f0zNC4+mMJhTaSkVHriNCY97/H9qxeSvzNW5f17sqCHu8f0eme/nYAOl8u6VtTV/TFY+fkOgR3myGwAwBsm5VV2g82vK0U1nWrlNfo6XP7PKot6B+U89hTMo8/I9PbJxuGhHUAAABdzDFGjnH1QHZAD/Y2mlCU6jXNV0q6nF/SpdySZps1lc9mB/TpI6dkjGlflB2KJ9u3S5JnHP385Gmd7ulvdzRlttfusNZqubp2RuO1woquFVbkGqO0Hyjp+pouFTScSBLW3QWBHQBg2xwZncj06i+m1tewO93Tp2wQ37+uqxPHZHr7pXxONp+T6jUp3SPT0yNlsjKZbOPr8IjM6BHZKGqPjbp1AAAAB8PqUC3h+TriehpPZvTCyFHlahVNFQs6k+1vB3WRtRpPZvQ3Tz+ma/kVfXv6iurW6rOTZ9p18VjyuruspJvF3Ia3hc0wb1mNQO+B7ADLju+CwA4AsG3GGPXHEsr4gXKrismeyPTqF46eVZRfUfSV39qXsbg/9wWZ8ckNb7NRJNlIMs77IR1X8gAAAA681ctW016gkxm/vV1aG8aNp9L68qlH2zPqCIl2l7VWVo2yOe+tLGx6v9FESkPxlKysnuwf5d/hLgjsAAD3bDyZ0TvL8+2fH+gZUFivyf7j/3XfxmDPvyWNHtkwiGtsI6ADAAA4zIwxa5oa3K41O4+AaG8YY/TD2Rv60dwtlcL6hvc5kszol0881P43CG20n0M8kAjsAADbFlmrShjqan65vc1IOpXpk3PrujYvA7z77MKcHGbNAQAAAB1zLjuo0Fq9uTi7YWfeMz39a36mbuDd8QoBALbNMUZfv3FhzRW0oXhKcc9T+NqP9nUs5sQZ2XA/I0IAAAAAq2X8QM8NHdHfPfukzmYH1t3u0Hl32wjsAADblqtW9F5ucc22I8mMrLXST17e17GY8UmJGXYAAABAxxhj5DS78/7M2HEFzvvN3UYTaT3WP9LB0R1MnOEAALYlslbT5cK67eOpjKJqRapWN3jUDhhH6und9GZ7/bIUUQMDAAAA2A+Rte0GHrczxijh+ZpI9UiSfMfRZyZPt2/D1lHDDgCwLVZWs+Xiuu0jiZSchfndrV/X2y/3V/62zOgR2Zlbin78UqPJRLpHpm9A5vgpmcefJrADAADApkIbramZdnu32MhaGlJsgbVWxhhNlfIaT2bWbZcar+VUKa9LzdU4L44dV8aP8freAwI7AMC2uMbRbGn9DLtivaaeILar+3Kefl4aHmv8MDQi51Ofl/n0L7Zvt2HYODhw3Y2fAAAAAPelahjqX51/VYOxhM71DupsdkC+46oahrpeWNG1wooWKiWNJdP64PBEp4d7IFhJr8zd0remrugzE6f1QHZAxhjVokie48ioEYJ+f+a6HGP09OC4Hukb7vSwDywCOwDAtm00w26xUtZIKrPBve+dzefaxWnNBp2kDEEdAAAANuA5jj44dER/duuyrhZW9Gc3LykbxLVQKWn1Ys66jfTBjo3y4LDNWYhvLs1Jkr49fU0Jz9cbS7O6Wczp7z7wpGxzqeznjj6gahgq6flrZt9he6hhBwDYlloUarlWWdflablalvxgd3e2MCdDQwkAAABsk2OMnhgY1URz6WbdWs3fFtZJUiWs7//gDhhrrayk705f00yzlvVKraI/uPyW3lqaU18Ql/R+4wnXOEp6vowxhHU7wAw7AMCWWWt1q5jXBwZG9cLIUb2zPK9X56eU9Hw9OTAqY4ysHEm7U1POTt2UDRtV8YzrNpbAMqsOAAAAWxBZq6eHxnX9yjub3udUT/8+jujg+vbUVf1o/ta67b1BTJ+ZPLOmDiD16nYH0xYAAFsWyepmMafTPf1yjdGDvYP64okHNZHqUdz1FP2rf6rdCuskSStLCv/5P5b9ycuytZo0c0u2XGqHeAAAAMBmHGM0nti8ZMtgLKnnqV93R5G1WqqW9cr81LrbEq6nLx5/UJ7jEtLtAQI7AMCWucbRbLmosWS6McVd0ly5qOlSvjHdfYNmFDs2O63olZek2SmZsQkpiEkOBwQAAAC4u7jn6XSmb932sURazw6Nd2BEB0dorapRqD+59p6i2xYT+46jXzp+Thk/Jpewbk+wJBYAsC2TqR65zQYQVtLV/LKmm0GdefAx2e98c1f3Z559Qc6nPv/+z9S0AwAAwBZF1upTE6c0df4nytdrkqSjqay+cOysXI4rNxVZq3ytoq9efktL1cqa2/qCuH7x2Fllgzgz6/YQgR0AYMtCG+mJgdF2jQrHGF0rrGi5VlE5rCt47iOyL39P2qCL7L1wfvZzcp7/ON2lAAAAcE8cY+Q7rn7x2DldzS/LdRw91jfcPrbkOHOtyEYyMvrJwpS+O31d1Wh9KZpPT5xSbxDnddtjxMkAgC1rzaxrXUkLo0hTpbwk6b9cf09RMiXz9//Rru3PPPZU4ysHAwAAALhHjjEaiCf05MCoHu8faXczlTjObIlsY8nrVKmgf3/hdf35rSvrwrrRREovjh1T4Li8bvuAGXYAgC1b3f3J2kYDirD54Z6rVWWMZIr52ypc3CM/kEltXiQYAAAA2CrXOBIZ0xrWWlk1As2lalnfnrqqC7nFdfebTPXohZFJjSUzCm2jwdzq8wLsDQI7AMCWWGtVDUO5TmNZgTFGVwsr7dtTnt8oajc4LD32tPTTl3e2w/7BnT0eAAAAwIYia2Vl9cPZm3p3ZUFzm5S08Yyjzx89K69Z76+14gZ7j8AOALAlkbV6fWlG352+pqPprCZTPXpjabZ9++X8sr5y+U196fhDcj/4EYU7DOychx6TjSKaTAAAAAC7KLJWxXpNf3T1HU01m8dt5mx2QL7jsAS2AwjsAABb4jqOpksFhdbqUm5Jl3JLa283Rr98/CEZSdHF89t+fnPijJyf+4LUk21s8AOJAwMAAABg11hrdaOQ0x9fe1elsH7H+zoyem7oiKxYTdwJBHYAgC2bbjaY2Ehoreo2kn/louw3/njrT+oHcj71C3Keep4ZdQAAAMAesdbqjaVZff3GxS3VnH6sf1jZIMbsug7hrAgAsCXVMNRStXLH+xTqNZl0z7ae1/nwizJPPidJhHUAAADAHois1XylpG/evLSlsC7muPrwyOSejwubY4YdAOCurLV6fXHmjvdJuJ76YwlFV1/d+hMbI/OBD7L0FQAAANhDjjF6bXFGKS9QOayrGoWb3vdUpk8fHzvWbjSHziCwAwDc1SvzU/rW1JU73udYulF7LvrBd7f+xEeOyWS2NyMPAAAAwPaENtKLY8f14thxSVIlrOvluVt6ZX6qHd4NxpJ6ceyYJtNZRdbKIazrKAI7AMAdFes1rdQqmkj1aLZUUGWTq3HH0r0Ka1Vp9tbWn3zqhqK3X5Nz7lFZa7mCBwAAAOwB16wtPRM4rp4fntDzwxMKbaTIWgWOK9tcMEtY13kEdgCAO4q7rj46eqz9of3Nm5f0k4Xpdfc53dMn59YNbT65fgP1mqLf+zeyz74g52d/QdYY6tgBAAAAe8wY0+786hj3/e30g+0aBHYAgDtymlfjrLVarJZ1Jb+87j4vjByV7zgK//gr97QP+4PvKLx2Wc4nPitNHJMJAjrGAgAAALhvGWvtVhqEAADuU636FT9dmNa3bl1R3UZrbh9LpPWrpx5R9Porir7627uwRyPnl/+mzNlHZFz37ncHAAAAgEOGGXYAgDtyjNGfXDuvt5fnN7x9Mt2jyFpFf/g7u7ND35c58yBhHQAAAID7FoEdAGBTkbW6UchtGtZJ0ly5JMcYRRPHpKuX7n1nrifzxNNyPvIJyePjCQAAAMD9izMiAMCmHGP0o/mbd7zPqUyfoiiSZrbRHfY25unn5Xz8U1IyLVkrY6hdBwAAAOD+RWAHANhUZK0u5pY2vM1IerR/RA/3DUnvvCGVy/e0D3PijNzPfEnWWhljJFrIAwC6VKuuKwAAe43ADgCwKccYecZZ12hiKJ7Uz0+cVn8sIbswq+gPf/ce92DkfOrzdIQFAHQ1a61+9+IberB3QOPJjLJBXHGXUykAwN7hUwYAcEcJz1OuVl2z7cHeQfXF4gr/8Hek1358z89tHvuAzMjYTocIAMCeqkShivWafrowo7eW5pUNYvrM5JlODwsAcIgR2AEA7ijhrg/symFd1urewzrHkXnocTk/+wuyNqJmHQCgq8VdT3/v7JNrtoU2ksvnFwBgjxDYAQDuKO7667aV6nU5xih0HCmKNnjU5syzL8j5yCdk0pnGUlhOdgAABxBhHQBgL/EpAwDYlLVWx9LZddtztYqMMXI++8vSyQekh5/Y0vOZ5z4i9+e+IKXSjZ+pWwcAAAAA6zDDDgCwKWOMnh4c09XCsq7kl9vbL+eX9frijB558lk5Tz4rSar7gfTqDzZ/smSq0WCi1Q0WAAAAALAhpjYAAO7ISvrIyNF1279+46K+evkt/Zfr7zU2+OuXzq5RLknVSsfDutBGstZ2dAwAAAAAcCcEdgCAu7pWWFm3zUq6kl/WdKkgSTKx+J2fJIpk33lDNgz3YIQb7G5VKGetbf88UyqobqM1twMAAABAN2FJLADgjhxjdH5lYdPbFytlzZWL6vvYJ6VXfyjl14d7Lfbt1+U89tReDLMtslaOMVqqlPXTxWn1BnEV6zX1BnFdK6zo/MqCTvf069MTp9r3BQAAAIBuYizrggAAdxBZq3eW5/X/tZa+bmAgltCvnXpUnuO8P5vNRlIUyYShVK83lsLmczIjY/sy7h/N3dITAyNyjaNKWNf5lQWNJzLqi8VljFForVzCOgAAAABdiMAOALAlM6WCfjB7U+dX5rXRB8dALKHheEqB6ypwXMVcVzHH05lsv5LeXerb7bLWctfW7LnIWllr5dKVFgAAAMABQGAHANiS1vLRWhTq+zM39PLczQ2DO0nyHUdP9I/q8f4R9QQxhTaSa94Py+pRKM9x92fgAAAAAHDAENgBALalFdzNlAr6xs1Lmirl193nhZFJPT04LsnKWun1xVn9aP6W8rWqfuXkwxqMJ+RYyTDjDQAAAADWoekEAGBbWstMB+MJ/erJh/Xa4ox+MHtTK7VK+z6jibSMJGMcffXKW7qSX27fNlMqaDCeIKwDAAAAgE0Q2AEAtiWyVqGNdDm3pGPpXj3SN6zH+ke0WCnpQm5RN4s5DSdSkhrh3Oqwrq1aU/TTl2Weep7gDgAAAABuQ2AHANiWxgw7Ryd7+vTThWlVw1BnswPqDeJ6cmBUTw+Oy1qrSli/Y2fZ6Ot/LPfMg7I9vZIxjS6yAAAAAAACOwDA9jVCO6PH+0dVjyL9YO6G3lqa02girZOZPsVcV39267LytermT1KrKvy//7HMU8/L+dDHZZMpyVpm3AEAAAC479F0AgCwI62PkZqN9NOFab0yP6XcHYK6T46f1EOZXtl/+X82NkSRVK3KnHpAzodelBkYajxvFDUCPJdusgAAAADuLwR2AIBdEzU/Us6vLOh6YUVL1bKWqmWtVCtqfdi8OHZcTw6Mrn9sFMr+4e/KXjovMzIuc+4ROc98WDYKZRxCOwAAAAD3DwI7AMCuC20kR+/XpcvXqnp1YUpvLs2pHkUaS6QlSb2xuB7qHdJIIiUbRTKOI1utSJ7f+D4MmWEHAAAA4L5DYAcA2HPWWlk1at/VokjFelWhteqPJRRZKyPRdAIAAAAAmgjsAAAAAAAAgC5CKz4AAAAAAACgixDYAQAAAAAAAF2EwA4AAAAAAADoIgR2AAAAAAAAQBchsAMAAAAAAAC6CIEdAAAAAAAA0EUI7AAAAAAAAIAuQmAHAAAAAAAAdBECOwAAAAAAAKCLENgBAAAAAAAAXYTADgAAAAAAAOgiBHYAAAAAAABAFyGwAwAAAAAAALoIgR0AAAAAAADQRQjsAAAAAAAAgC5CYAcAAAAAAAB0EQI7AAAAAAAAoIsQ2AEAAAAAAABdhMAOAAAAAAAA6CIEdgAAAAAAAEAXIbADAAAAAAAAugiBHQAAAAAAANBFCOwAAPcla62stZ0eBgAAAACsQ2AHALhvRDaStVa5WkVXC8sqh/VODwkAAAAA1vE6PQAAAPaStVZWkmOM3ltZ1EuzN5T1YzqR6dVIIq3IRnIM168AAAAAdA9jWQ8EADgkVodzt3t1fkpGRo/0D8k1jkIbySWoAwAAANCFCOwAAAeetVbmtpBudXgXrfqo2yjMAwAAAIBuQmAHADhQWkGckVQK60p6fvu2yFoCOQAAAAAHHoEdAOBAaH1clcNQr8zf0uuLMzqW7tWnJk4R1AEAAAA4VGg6AQA4EIwxemnmhl6avaG6jSRJj/QNE9YBAAB0qVZzL47XgO2j2jYAoGtZa9v1574/c13fnbnWDuskyXUMB38AcIC03tdZ5IPd1jpeqEVh+2d+z7rD5fySVqqVNTWFAdwdM+wAAF2ndRV2pVbRa4szemtpTrladd393lqa02gizVVbAOgyrRPz1ntzNQqVr1WVr1VVrNfkGqOE5yvhekr5geKu136ckdY1EgI20wrlFislfePmJd0o5jQQS+hkpk+P9A2pL5bYs+OEyFpZWbWK6xo1LiSWw7oCx202vmrMMLt/Gf35zcsajCf1uaMPdHowwIFCDTsAQNdoHVBPFfN6afaGLuQW19w+HE/piYERpTxfKS9QbxBX4LoKbST3vj4YBoDOstYqkpVrHJXDuq4XVnS9sKLZclELlZIK9dodH+8ao75YQg/09Oux/hHFXY8LMR3S6rze7RfDbHtGXaTvTF/VTxamtdGJ7WSqRx8dPaqRXbzA1wrhFitlXc0vq24j1aNI5bCua4VlzZSL8h1H48mMnh08osl0T9e/nnfTnrHYDCZv/5vcHrJH1qoc1vWjuVv64dxNGUm/eOysjqV7D/TrAOwnAjsAQMe1PooK9Zq+deuK3l2ZX3N7wvX0/PCEHu8fUSQrR4bZFwDQRSJr9er8lN5dWdDNYm5Hz/WBgVF9bPSYJGbadcpf3Lqis70DGo6nui5caR0zLFcrenVhSm8uzakc1u/6uJOZXn1waEKjye0Fd60wWpJc46geRbqwsqDXl2Z1Jb+8ped4oKdfzw4d0XAite62nQR5rcdWwrqK9ZqK9ZoSnq+sH5PrvH8hsxXCtoRRs7yI0ZoLnrf/XSUptJFWqhXNV0parlZUCevtgPL9IiWN5yzUq8rVqirUaxv+mwSOq186fk5jiXRj9132uwV0GwI7AEBHRdaqbiN9f+a6Xp2fUn3Vx5Ijo6eHxvTc0BG5xum6kwYAuN+srUFlGzNtmk2BvjtzbVf24RqjJwdG9WjfcHM54/2+pHD3WWtbqzhljGn/7BijH8/d0remrihwXH3p+IMaTaYV2mjTi2W3h0Eb3S5tP5zZ6HFRswbid6av6pX5qQ1n1N3NZKpHzw0d0dF09o73awRQNS1XK1qulbVcrWiuXNSV5oy6ezGezKgviKsWRarbUDHX04PZQR1LZ5uzGiNJd6/P2wrqbhRW9NLsDV2+LTg0kjJ+TCnPV9oPlPR8VaNQ8+WSFiqlNeMPHFcZP1DGD5T2A6W9QL7jaq5c1HS5oMVK6Z5e5824xui5oSM6lenTQDy54d+1NZvPSqrbSNUwVLkZFK5mrVVorUIbycjIcxx5xpHnOAocVzHXVeC4B2LGKLARAjsAQMdE1qoS1vWVy29prlxcc1tfENdnJs9oKJ6UxFVYAOiU1omzJF3MLaoWRXKMUWgjXcuv6HJ+6a5LXu/VWCKtJwdG9UB2QE4zWLp9BtBWHfYT9juFai31KFKuVtFytaJSWJfvOIq7njzH0dX8st5YnNVitdy+v2uMjqayOpLK6Egyo54gprjryXdchVGkfL2q5WpFRlLS85XwfHmOI6cZOlXDUPOVourWKul6SvuBEp6/rsbh7cphXQuVkqy16g3iSvmBwijSTxam9cO5m7vy+5byfPmOK9esDcislfL16pZm7e2WuOtpNJHWaCKlsWRGQ/GkEq63ZpZcY2yNZaZvL8/rtYVpzVVK+zbGveAZo2wQb4dytShcc+F2t2T9mJ4bPqJH+oZ3/bmBvURgBwDoGGutcrWq/sW7r6zZbiT9xtkPKOH5cg/xyRUAdIvVNeik94vpu8ZRrlrRTxdn9NrijIp7FMzdTdoLNJRIKu0FSnm+skFcD2T75RmnPTNsM6tnIyU9X32xhKT3/85mCzOaullrpvrFlUVdyS/rVimnlWpFCc9X2gsUuG674Uel2UF1p1xjFN7jaeRALKFj6axc42ihUtJitazqqnHVwnDdOFuhWi26t5ltB5VnHCU8T55xVArrqoT1XZ3tdj95sn9UL44fv+uMUKCbENgBADruDy+/rUv5pfbPI/GUfu30o50bEADcB1Z3ZF2olHQpt6RrhWW5xlE2iCnp+bqcW9bVwtbqdO0333F0LjuoB3sHdSSZWbO0U1J7FuCFlUW9NHtDs82Z3HHX00gipawfUyaIKevHlA1iygZxJT1/0/11y4n+6nA1slY/nr+l781cv+/CLGA7PGP0aP+InhkcV9oP7jrLE+gGBHYAgI6y1urb01f18tyt9jbXGP3yiYc0mkhzIAUAu8A2a385prFkshaFupJf1qXcki7nl5SrVTs9xB1Jer6OJDOKu55irisjo1ulvKaKuW0tsXOMUcL1lHB9JTyv3QlzItWjDw5P7M3gt6DVDd1aq9lyUVcLy7pZyOlGMafSPi7dBA46I2k0kdZYMq2xZGOpd9oPJG1tWTmwnwjsAAAd05oJ8S/ffWXdyWLc9fS5yTOaTGcPfd0hAPeH24voN2ZKadeX/t/epKEc1jVfLmmuXNR8paiZckFTxUK7Fhy25ucmTutss5befml9/l3OLemnC9O6VljZtWWtABrSfqDxRFrjyYyGE6lG3UTPX/teba1kdNdAL2o2wLjbUn1gKwjsAAC7LmoGcW6z49lG3f1aHz//5foFvbU8t+lznenp1+P9I5pI9ci5w/MBQDdrdbd8afaG3l6eU28QV18Q19PN5Vk7ObFbvUSyHkW6lFvUhdyS5stFLVXLBDy7xDOOvnzqEfXF4ttueHEvImtVi0L90dV3da2wsuf7A/A+1xj1NJfLZ/xYs5NuTL1BTP2xhBIbLJ+31uqtpTndKOZ0MtOn45msHMI77ACBHQBg17Tq+1zJL+lGIaeFSkmP9g/rWLp3zf1axcz/9PrFO4Z1q/mOo2PprB7rG9HxTC+z7gAcGJG1Cm2kP7j0lm6V8mtuS7q+PjZ2TCczvYq53gaP3fwiRSsEvFnMabpU0M1iTlfyy6pbapntlRdGJvXUwNi67p27LbJW1lr94ZW3dZWwDug6McdVNojLc4wc48iRtFKraKlaad/Hdxw9mB3UEwOjGown2ytLOH7FVhHYAQB21V9OXVlTj06SPjN5Rmd6+tccoPzHK+/oYm7xnvbRF8T1S8fPKePHOOgB0JVWz3pbqVb0tWvn14V1qxlJw/GU+mJxlcNQ5bAm33H17NARHUtnFVorR43ltK1i6VZWv3/xzTs+L3ZH3PV0NNWjj4weVY8f29UaV63ldsa83602V6voP115RzPNRhkADrb+WELHUlkdTWc1GE8o4foKXHfNfW7v1g0Q2AEAdtX/9dYPVQ7XLr9KuJ6+fOoRZfygPVPkX7zzilZqlY2eYkvSXqBff+BxecahODCwD1ozY3dakPv2Om533N+qrpxmg8esvk+nwvvbOw1Ww1CFelX5WlXzlZLeWZ7XjWJuR/sYiad0rndQ53oH5TtOu9nAxdxiu/Mp9sbpTJ+eGTqisWRa0vvNH7Zjo+6yYRSpbiOV6nUt18paqTZm5ixWSlqolrVUKVNjEDjkHBklPE9Jz1faC5T2A53NDmgy1cOxLSQR2AEAdtk/f/tHKtRr67Zn/EC/evIRJT1fRtJfzVzXS7M3drSvJ/pH9DPjJ3b0HADurLWEpxzW9e7yvE739Lc76tWjSKV6TZkgdsdl6quXdc6XS1qolBS4jnzHVdzx1B9PtPcjSbPloparZeVrVRXqNdWb4UbdRrLWyjGOXGPkOU77JCfjB0q4ngLXVeC4irnehkHJTl+LVojiGkeRtbpeWNHl/JJmSgXNlot07DwkevyYvnj8nPpiiR2VYGidan1n+pou5ha1XK2wZBnApoyk54aO6KHeIfXG4pIaF4Y2umiFw4/ADgCwq/7Jmz9QLdr4ZGQ0kdJfP/mIjCQr6auX39pxIe3PNpfbShzIADvVmkXXmkEU2ki3inldyi3p1YWp9v/t/lhc5XqoYtgI589mB/TXxk8ocNx1wUZkrSphXX8xdUVX8ssbBvpPDYzpueEjemNxVq/MT+1o9m3LueyAPjF+Un6z1tjqpaTbDV/CKGrWJiorV6sqV6tqsVLWlfwSDR0OqdFESl8+9eiuPNefXDuvt5fnd+W5ANw/AsfVSCKlo+msnugfUeC41MC7zxDYAQB2RWStFiol/dZ7P73j/Z4bOqIPDU/ISqpGoX7nwutaqpbveb9G0tOD43phZLLxMwcxOAB2e+bXvew/slYyaodzy9WybhbzWqyUtFQta6la0Wy5oHCLh4oJ19PHRo/pob6h9rZyWNdUMa9v3Ly0KyHcdsUcV4/1j+ix/mFVo0hz5aIWKiWt1CrtWXthtH7hYdh8fSJrVazXlK9X933s6LxfOfGQBuJJRdYq5roy2vrS69asvB/M3tB3pq/t8UgBHHa+4+jh3iGNJzPqiyXUF8TX1cDbjm4oKYG7I7ADAGxL60TfMY06Vqs7Xn318lu6kl++4+NdY/Q/nHtagesqslb5WlX//sJrO15G9sGhI3p+eILADl0vslbL1YpK9ZpSvq+UF8gx2rQT6OrH2WbI5sjI6v3ldq3/j6ttVLw6jCLl6zUtVctaqZa1XKtovlzSzWJu15ZyjsRTClxXcywPxSFgJD3b/HzZyklt6/Px4sqiXpq9QUMQAHvGM46sbHN2fOPYIHAdBY4n33Has+ZDa+Uao5jjKeZ6iruuUn6gHj+mbBDTYCzZXn4b2mhbFyewt9b3jgcA4DarQ7nzKwtarla0XC0rtFae48gzjlZqlbuGdVJj5spKraIBJyHHGKX9QJ+dfEBfufzmjsZ4fmVBH2rOsgP2U+sEfbm5XDLmuApctxnErT3ojazV1cKy/ujKO6o3w7aBWEK/dupROZscG5fqNS1XK8rVKirUayrUa6qEdfmOK99xlPFjerhvaE1AVw7rulXMaa5c0ly5qMVqWSu1ioobLEfdbdPlwp7vA9gPnnH0mckzOpnp3dL9I2u1VCnr6zcv7rjRCADcze31MCPZZpfxzUo1bD7TPea4GktmNJ5Mazie0kgipZQfbLlRFPYGgR2AQ2WjZWarZ6VwxejeRLL6wexNvbU0t+nyVW+LXfOOprIajCfbPzvGaDLdo7FEekczEZaq5Y4vM8T9J7JWs+WCvnXryroT9LQX6BNHTuhoKiu3OQPOMUbvLs+3wzpJmq+U9Jvv/FhpP1DgOM06cI0QfLla3rQm5Go3iit6rH9E1/IrupBb1K1ijv6SwA599ugZncz0bem+UfNi1O9deoOZpQAOnEoU6nJ+SZfzS+1tSc/Xg9lBfXD4iALH5Ri7AwjsABwqobXyjNHbS3O6WcypEoVKur7SfqBYu3Ogq7jbaKHemgGzWi0K5RqHYG8V1zg6ksxoqVLWUDzZnkKf8QP1BnENNAO4P7t5Sa8uTEtqTMvvi8U1GE8qG8TU4zf+jCczG3bcG4wndxTYhc0aen2xBP922HOt3+FbxZz+w5W3NwzV8vWq/uOVd9o/O80LBht1iCyHdZV3cJL/+uKsXl+cvefHA1jvwspiY5aJF6xbJtYqD9EK4t9bWdDXb1ykCQmAQ6NYr6luI3mOQ1jXIdSwA3DorJ66HdpIK9WKHGPkO64848hr1nQIbSRr1SgkveoAvLX0k9laa60O2VqzFm+vm1VqLtdzjVE2iLfvHzYDCkcb19mqRZH+xbuv7CiwkKTheEpfPvUIgR32TGgjucbRUqWsv5q5pneW55nJBhxijoxO9vRqMtWjhOsr5flK+oGKtZoWKiUtVEuaLRd1fYcdzwGgmzjG6DOTZ3Smp59zog4isANwaLTCtjVva81lsEZq3Ca7YWi0X6LmFXpjzIazzO4nkbUykgr1mr5+86Iu5ZZ25Xm/fOoRjcRTHFhgV0SrmjqUw7reW1nQhZVFXcwtEtQBAIBDx5HRZ4+e0alMH8fTHUZgB+BAC63VfLmoH87dVNxtdEQKHFee48haqRqFqkWhalGkuOspG8TUGzSWaSY9f83J+E7d7epTZCMV63V99fJbGkmk9dHRo0p6/pquq+3AcVXQuNMPyk4Gg5u1jC/Wa7pRWNGVworeWJxRuIsfRacyffr8sbO79ny4f9z+fpCvVXWzmNOtYl43izlNlfKEdAAA4NByjdHPT5zR6R7Cum5ADTvgAAmjSK6ztcL+0ubLFm93kKc5u8ZoOJHSUrWs6dL2OhMOxBI6kenVQ71DGogl7voatFqju6uaK4RRpGJYU6FWUzmsaySRUsLzN3z8QqWsr15+S4V6TfOVkt5dntdoIqW+WEL9sYR6g5hCa1UK6yrVa7KSHu0bVtoP2rPRtvvv1CqCnXR9Ba7bfp7bZyLe7XdkI6s7Uq7eVonC9tLYYvNPrlZVvlZVrl7VUrWsfK26rX1tx4Xcokr12qb/DsDtWstcbxZzuphb1EypqNlygcLxAADgvmAk9fgxfWrilMaTmQN7bnjYMMMO6FK7EaJNFfNaqpaV8nxlg7hSvr8mXNnpvjYKsDohspGWqxX98bXzmi0Xt/14xxh9fPSYzvUOyjfOmlA0jCIV6jXlahWt1Bph01KlrMVqWUvV8oY113qDmMYSGclI1TBUJQpVCeuar5TaM3i2M7YHs4M6numVa4x8x5FnHAWuq5QXKO566wpgt2bqGUlvL8/pGzcvyVqrMz0DerB3UMZIpXq9XeQ+4fkaT2Y00GzWsNnvw+rZR9ZazZSLuppf1my5oFyt2gjl6tVt/x132zOD4/rI6NGOjgEHR2Qj1a3V166e16VVndEAAAAOK884enpwTMfSvcoGMSU9Xw4le7oOgR1wAG0lYFv9ZmutVc1GqkWh4q7XDthCa+WuDntkm4swtWEQt3pGVS2KdHFlQXVr9WDvoKTdWVZ6ryIbSTK6UVhRKayrGoUqh3Vdzi3p6jYLQRtJnuPIkTkQ3d4Srtde5jsUT2oontJ8paiX525pqVre8vN4xtHZ7IA+OHxE2SDe3l6PIpXDuqZLeU2VCpoq5XWrmFe1y16b3iCunxk7ruOZ3gM9axT7a/V75WKlrK9de1cz9xD8AwAAdCujxrHyQDyhgVhSj/ePKOn5u1J+B3uHwA7YhrDZMGB1MNWaZWZkNqzV1bp9s+6YUbMi0urbW4+Rdq+O2e1WL7HM16qar5S0XC1ruVpRZK0G4gkNxVPtWVe5WkXz5ZIWq2XdKKzocn5J9ebbR48f0xePP6hsEFsX2nXiKs3q1881jn48d0t/NXO96wKmbmUk9cUSqoahymGt/e/cTQZjCQ0nUkp5gTJ+48+JTJ+kzgbHOLhaTWt++73XNFchsAMAAAefI6MXx4/rkb6h9mSM3azhjb1FYAdsoBWktUK0MIq0UClpulzQXLmohUpZjjFKuJ5Svq+MF1PgOu2aXa2vgeMqG8SUDeLqDeKKu67CZl25yFrVbaR8raZ8vVHfqxzWFTiu4q6nuOcp4XqKuZ7ibuP7VlOF0FqFNmp8jSLJvB/4Oc1A0aza1qpZFtpI9ajxuHytqkv5JV3KLd11FpYj0w4WN5P0fL04ekxnewfbwWU1DHWjmNNos65baKOOdGi11qoahfrezHX9eH5qX/eN3ffM4LheGJlsd9rdKCgHtqpVv64eRfrzW5f12uJMp4cEAACwY31BXJ8YP6GJVA+z6A4oAjvcF1q/5naDbbef6FfDUHOVomZKBc2UC5ouFTRfLt01sELDYCyp45lsu7Niq5baWDKtU5l+TaZ7lPYCJVyvXStuo+YFq62uzbbRh03r9tXB5Yb3kdU/f/vHG9adQ/ebTPXoo6PHNJJIseQVO9J6P6mGod5dmdd7Kwu6ml/uytmkAAAA25ENYnp6cFyP9g3LSu0SSDh4COxwoIRRtOGS042WoLbCn0pY11SpoGoYysq2b68263KVm8X3C/Wq5sol5et7170Sa/mOo7QXaDyZ0ZFURpOpHmX8WHsmYD2KVLWRliuNBg9L1bKWaxXJNqZwu8aR6xgFjquU5yvlBUr7gXqCmHr8QL7jtvdVDuv6oyvv6Hox18G/MbZjMJbURPP3YjKdVdz1KISLe7K6BEA9inS9sKI3lmZ1oVmHEwAA4KDrDeL62Ogxncz0yoolr4cBgR0ODGutruSXdTm/pOVqRUvVsvpijTelbBBXJaxrpVbRUqUR6syVi7rZ7JKK+1Pc9ZT1Y8oEgW4V8yrUa50eEm6T9WMaiCc1EEsoG8TU48eUDWJKNwPXVl0xDjiwVbfP2M3XqrpeWGnP+p0tF5kxDQAADpXeIK6/fvJhxV2P4+ZDhMAOB4a1Vu+uLOhr186v2W4k+Y5LQwGgi/UGMQ3EkuqPJTQQS2gw3vjec94vfrtZcxZgI6s7W7cOTEv1mqZKhTUdjYsE9QAA4BDzjKO/deYx9fjrGwDiYPM6PQBgq4wxOpsd0F/curJm2aqVCOuALjWWSOuFkUlNprOSWl2prJzb6hU2Di44wLifrW4gcqfO2NZalcO6lqplzZVLWqg0/syUC8yiBQAA950nB0YJ6w4pAjt0pc2WwbU6ogLYXcPxlJ4ZGl/XkMNRYwZr4LgKXFcx15VnHEXWNrsVW0U2Ur1ZI8xr1hV0jSPPcdQbxBXZ6P3nI5i7L7XCuNsbx9SjSIVml+yVWlW5WkXFek31ZgfsqNkRu9q8X7HZgZulAQAAAA2TqR6Org8pAjtsKGqulN6vlH51zSFrraZKeV3ILWqhUlIlDFUO66qEoUphTbUouvsTAtiWgXhCZ7MD7Z9bS1RlddcZT3dz+2w63D9Ca+Uao+lSXjeLeS03G8esVCvK1arMjgYAANihlOdTUuaQIrDDhhxjdDm/pIlkjzzHUWijxtpTIxltfPJurV3TwfFOy5miVTMtIms1XynpRrMo+OX88rpZPgD21ltLc5pI9ujR/mFJjfcAa9WsEXbvYR3uL6sv9tSjSO8uz+vH81OaKRc6PDIAAIDD6UYxp6FEql1aBIcHTSewqT+++q4u5pZ0qqdPR1NZhTZSOayrGoWqhKEqYV2lsN5eolQO63KNoycHRvXs0BHFXHfN80XNukNz5aLmK0XNl0uar5Q0XSqobpk1B3SDuOsp4wfNP42urWPJtMYSabmOsyaUB1YHdNUo1I1CTtcLK7peWNF0udC+HQAAAHvnyYFRfXT0KA3cDhkCO2wojCJdyC3qT669p+geqgXFHFcD8UQz2AtVieosZQUOANcYZfyY6jZSNQzbSxZdY3Qi3auPjR1TNogT3O2S1tLjVjmAqHlltJte21bJgtUHgKGNlKtWNVsu6HqxEdLNlYvUlgMAAOiQZwbH9cLIJIHdIUJgd4iFUbTj2lOFWlU/np/ShdyCFirl3R0ggI7rC+I6k+3XYCypkURK2SC+LiyqRaFqUaSE68kYo9BGcqlLt22rQ85qFGq5WtZ8uaSlallL1bJc46gviKs3Fld/LKFsEJNrHEU22tc6gKv3l69Vm7OiG51YW2PN16qEcwAAAF3EkdFHRo/qqcGxdsMvSeuafuHgILA7xN5YnJVjpKTnK+UFSnieEq4vp1k3bitBXuvXwxijcr2ua4VlXSus6M2lOYqFA4fAl44/qIlUj6T9azJzv2m9j85VSvrT6xc0vcV6bp5xdDLTq3O9gzqR6d2XJQ6hjbRQKekvbl3RrVKemdEAAAAHzESqRyPxlBKep7jrKeF6Snq+huIpuY7hwvsBQmB3CNWjSJ7jaL5c1Neuv6e5cnHN7YPxpM709OtMT78G48ltzZZp1SOqhHV9d/qabhbzqkaNZXO1KFTIrxNwoPy3Zz+gtB90ehiHVmSt6lGk785c00/mp++pxIDUKDPwhePnNJpI72mwGlmrv5i6olfmp/ZsHwAAANg9jow8x1E2iKk3iLf/ZIOY0n6ghNsI7phld/AQ2B1irXDttYUZvbsyr5vF3LpAbTSR0tOD4zrT07+t/8CbdaCJrFWt2ZSi1YyiFNZVCmuqhKHqUaTQRgqtVd1GCqNIdWu1XC1roVJiiRWwz/7Bw89SnHaPWGt1rbCir107r9IudL7u8WP6b848Jt9x737nHfjzW5c3DexijquE5zcO/JqzthOuJ99x5JjGFdvG10Ydvku5JV3ILe7peAEAAO4H57IDemHkqOKuJ6d5rLXRhdxWjWSO8Q8+Arv7QKtuUj2KdL2wohvFnFxjFG8m7QnP01giI99xdv0/dKNYuSRrJSMZbV5Trx5FmikXNFXMa7r5dbFK3TxgLz01MKYHewc1nEhJEvXpdkGr5MCP5m7pO9PX7nlW3UZ+buK0Hsj279m/UetizEaHBpt9PrQOChtP8P52xxhdL+T0lctv7sVQAQAA7gsJ19Mnj5zU6Z7+TSfO4HAisLvPtGbdra5Nt5OmFLut1SXRdRono9Uw1K1SXtcKy3p59taunvgCeF/S9XU806tPjp84FIVpWxcqtlqvczeE1so1RsvVsv7r9Qu6Xszt+j6eHRrXh4YnD0y9QWutvj19VT+au8W7NwAAwD34G6ce1WA8eWCO/7B7COzQ9Vq/oq8vzujrNy91eDTA4fZo37BeGJlUwvP3NezaLZGNZGT04/kp/XRhWud6B/VE/8iu1e1oXVQwty1BqEeR3ltZ0OuLM7paWNnxfiQp7noajCU0nEhpNJHWWDKtbBA/UFdWW2NdqpT1vZnremt5rtNDAgAAODCOJDP6lZMPd3oY6BACOxwIreDgN9/5sQr1WqeHAxx6I4mUTmb6dCrTp/5YQp6zfglmK7za6xl5rX1sxdX8sr41daXdbMczRr9+5gml/GDHVyUja7VYKetmMaflWlnL1YpWqhUtVysqhvf2vhR3XfX4MfUEMWX9uPpjCQ3EE+qPJRR3vfZ+pYPZxff20Pe33vvpukZIAAAAWO9Upk8/P3m6XSMY9x8CO3SlRu07K9c4iqzVO8vzenV+SrdK+U4PDbgvucY0O0z577eI93z1BjH1B42QKePH2gcTYRTtKMhbPYtssVLS5fySFiplBY7brL/pKu76zcYHnlxjtFSt6FJuUeWwrkoYqhzWdTY7oCcHRnctULTWaqZc1NX8sq4XV1QJ6wqtVRhZhTZSZG27sY7vuEp5vlKer6QftL9Peb56g7gyQUzBqgYSkbWy+xCA7lRrnJLWzTRcfZ9craLZclHz5ZLmK0XNlYuaq5T2e7gAAAAHimuMPjZ6TE8MjG7rwjUOHwI7bMntS7BW/9q0trdCNllJRmu60txphkjrcUamXXNqrlzU9cKKbhYb9et2o8MigL3lGKOsH9NQPKnRZFpHkj0aTiTlGmdHs/Fa7xGOTLsOmm2916x22/vOXll9QWG7j5H2Z4zbdafxVaNQlbCuchiq3Oz83f45rL/fDXzV10oUduqvAgAAcKCNJ9P66ycfkURDuPsdgR12bKFSUr5WXXMCV26f0NVVjSL1x+I6kuzRRCqjhOe3H1sNQ81XSporFzVfKWqmVNBUqaC6jTr4NwKwW1xjNBRPajCe1GCs8XUonlzzPiCt6jS6QeCPrbm9ac9W1KJQ+VpVK7WqcrWKcht8rUW8HwMAAOynHj+miVSPJlM9OpbOKu0HB6qOMXYHgR12pDVz7nJ+SbPlohaaS58WKuVNQ7feIKaMH9NipaQ89eiA+1LC9dQXiyvueoq5nmLNpa4x11PMdRVzPKV8Xxk/UMpbW3+utSRzs+WY3aS9fNSoHUZuqHmb0fY6d7dmLdajSDPlgm4Wc5orlxTaSNY2ZyKqEeZZNX6uR1b5elX5WlVVZsIBAAB0PerZ3Z8I7LArWielrZkd1lpNlwr6/UtvMlsOwI6lPF9pP1DGD5TxY8r4gfqCuIYTKWX8mKT9a4Jxu9bswNWzAmtRqMVKWfOVonK1avvihtXa8MzaxizEwHUVc1wFa8JLVzG3UaPP3B5YNpfkXssv6y+nrmqmXFi3QhgAAACHR18Q13PDR3QuOyhJ27rIi4OJwA57xlqrb926olcWpjo9FACHmO84GoglNRhPaDCW1FA8peFEUrFd7LK60XMU6zUtVytarpa1Umt0jF2slrVQKe1qN2sjNcPKmHpWBZbTpYLeWJrdtf0AAACg+yU9X4/2Det0T5+G4ykZYxRG0bbKouBgILDDnvrWrcv68TyBHYD9l/J8DcUbAd5DvUMaiCfu2mnr9ll61lotVyuaLhc0Vy42lv5XSsrVKgr5+AQAAEAHxV1PR5t17iZSPeqLJSQ1mlVQE/rgI7DDnnl57qb+cupqp4cBAJIatT8+ODyh4XhSUmMJQWRtezlBOaxrtlTUbLmguUpRc+Wi5sollvUDAADgQIi7rkYTGY0n0xpLZjSWSCtwXUmEeAcRgR12nbVWbyzN6k9vXOz0UABgnWcGx/XCyKRCa3W1sKyLK4u6nF/WSq3S6aEBAAAAu6o3iGs0kdJIIq2+IK7+WEIZP1hTfz5q1kdGdyGww554e2lOK7WKivWaSmFdpXpdpbCmUr2uYr2qOr92ADoo6fmqhnXeiwAAAHBfSvuBeoO4eoOYxhIZPdo/LGstM/C6CIHdPmp19pOVZCQj066ltBtF0btFK6GXbSwzu/3vFFmrt5fn9PLsTc1VSh0aJQAAAAAAkKSnBsb0sbFja7bdfm5PZ9r9RWB3m8hGcppTQTcK0UIbrZsqensRc2utbPNxxXpN+VpV5bCuclhXJQxViRpfq2GoclRXLQzVF0toMtWjI6mMfMc9VAHeRlqv82y5qEKtqlJYV7k5A6/1WpWaX8v1uipR2H5dG6+NVaT3X2sAAAAAAHDvEq6nlB8o4XqKu54SrqeE57e/j3ueBmJJZYPYumwkslbWWslow1p5oY0agR919LaMwK6pNfXzRmFFfzl1VQnP12A8ocFYUsOJlHqDuKpRqIsri7qQW9SV/LJcY3QsndWJTJ9OZnoVcz1J0nK1rLeX5vXuyrxmy8VtjcNIGkmkNZHKaCyR0ZFURknPl9T4Bd9s1tpB1grd7vSfe8vP0Uz/K1Go377wmvK16p6MGQAAAACA+9F4MqPH+oZ1MtOnSlRXvlZTvl5VsV5VoV5TsV5Todb4aoxRbxBTNoirN4irL9b42so5pPdXI9IUYy0Cu1W+PXVVP5y7ueFtjoyaC1o31Ara6lG468s8M36g8WSm/Uud9HylPV8pP1DS9dtdX1paybZj7s9fdmutqlGo33znx6pFdHcEAAAAAKCbeMZZE+T1xuIajqc0lkx3emhdg8BulX/65g9VjcJOD2PbXGPaQV6PH2sXjuyLJdQXxJXyg3WPub2e3p2S7LvVpNvsMatnvEmSWmvetbN176tn5G00nnoU6S+mrugnC9P3vA8AAAAAALB/AsfV33/omU4Po2t4nR4Adi60VrlaVblaVdOlwrrbPWPUE8SV8nzFHFcx11Pguoq7ngLHVaz5fcxprEmPO64C15WVVKhVla9Xla/VVGhOb21Mba0qX68ptJF8x5XvOAqaX33H3fR733GUcH31xmJKeUE7bLPWKmoGcLaxYd0MwTCKlK9X23/XQr2qQnPqbWNcja8HMXQFAAAAAABoIbC7D9St1UKlpIUu68hqJGX8mLJB409rdmDUCiDrVeVqFeWbAV05rHd6yAAAAAAAAHuOwA4dYyWt1CpaqVV0bf3EQAAAAAAAgPuSc/e7AAAAAAAAANgvBHYAAAAAAABAFyGwAwAAAAAAALoIgR0AAAAAAADQRQjsAAAAAAAAgC5CYAcAAAAAAAB0EQI7AAAAAAAAoIsQ2AEAAAAAAABdhMAOAAAAAAAA6CIEdgAAAAAAAEAXIbADAAAAAAAAuoix1tpOD6JbVMJ6p4cAAAAAAABwX4q5XqeH0DUI7AAAAAAAAIAuwpJYAAAAAAAAoIsQ2AEAAAAAAABdhMAOAAAAAAAA6CIEdgAAAAAAAEAXIbADAAAAAAAAugiBHQAAAAAAANBFCOwAAAAAAACALkJgBwAAAAAAAHQRAjsAAAAAAACgixDYAQAAAAAAAF2EwA4AAAAAAADoIgR2AAAAAAAAQBchsAMAAAAAAAC6CIEdAAAAAAAA0EUI7AAAAAAAAIAuQmAHAAAAAAAAdBECOwAAAAAAAKCLENgBAAAAAAAAXYTADgAAAAAAAOgiBHYAAAAAAABAFyGwAwAAAAAAALoIgR0AAAAAAADQRQjsAAAAAAAAgC5CYAcAAAAAAAB0EQI7AAAAAAAAoIsQ2AEAAAAAAABdhMAOAAAAAAAA6CIEdgAAAAAAAEAXIbADAAAAAAAAugiBHQAAAAAAANBFCOwAAAAAAACALkJgBwAAAAAAAHQRAjsAAAAAAACgixDYAQAAAAAAAF2EwA4AAAAAAADoIgR2AAAAAAAAQBf5/wHNFcR4H7aS0gAAAABJRU5ErkJggg==", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt_kws = dict(categorical=True, figsize=(16, 12), cmap=\"Set3\", legend=True)\n", "for strategy in times.index:\n", " ax = world.plot(strategy, **plt_kws)\n", " ax.set_axis_off()\n", " ax.set_title(strategy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "------------------------------" ] } ], "metadata": { "kernelspec": { "display_name": "pysal_dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.8.0/notebooks/06_api.ipynb000066400000000000000000000411751465055300600201740ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview of the mapclassify API\n", "\n", "There are a number of ways to access the functionality in `mapclassify`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first load the example dataset that we have seen earlier." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.167785Z", "start_time": "2022-11-05T15:10:14.404320Z" }, "tags": [] }, "outputs": [], "source": [ "import geopandas\n", "import libpysal\n", "import mapclassify" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Current `mapclassify` version." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.182165Z", "start_time": "2022-11-05T15:10:19.171353Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'2.4.2+107.gb97c316a.dirty'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.586837Z", "start_time": "2022-11-05T15:10:19.187232Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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DISCBD X Y NSA NSB \\\n", "0 19.531 15.725980 2.850747 ... 5.03 38.799999 44.070000 1.0 1.0 \n", "1 21.232 18.801754 5.296720 ... 4.27 35.619999 42.380001 1.0 1.0 \n", "2 15.956 30.626781 4.534649 ... 3.89 39.820000 41.180000 1.0 1.0 \n", "3 4.477 32.387760 0.394427 ... 3.70 36.500000 40.520000 1.0 1.0 \n", "4 11.252 50.731510 0.405664 ... 2.83 40.009998 38.000000 1.0 1.0 \n", "\n", " EW CP THOUS NEIGNO geometry \n", "0 1.0 0.0 1000.0 1005.0 POLYGON ((8.62413 14.23698, 8.55970 14.74245, ... \n", "1 0.0 0.0 1000.0 1001.0 POLYGON ((8.25279 14.23694, 8.28276 14.22994, ... \n", "2 1.0 0.0 1000.0 1006.0 POLYGON ((8.65331 14.00809, 8.81814 14.00205, ... \n", "3 0.0 0.0 1000.0 1002.0 POLYGON ((8.45950 13.82035, 8.47341 13.83227, ... \n", "4 1.0 0.0 1000.0 1007.0 POLYGON ((8.68527 13.63952, 8.67758 13.72221, ... \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pth = libpysal.examples.get_path(\"columbus.shp\")\n", "gdf = geopandas.read_file(pth)\n", "y = gdf.HOVAL\n", "gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Original API (< 2.4.0)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.595711Z", "start_time": "2022-11-05T15:10:19.589037Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -0.70] | 0\n", "(-0.70, 25.70] | 13\n", "(25.70, 33.50] | 12\n", "(33.50, 43.30] | 12\n", "(43.30, 69.70] | 7\n", "(69.70, 96.40] | 5" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp = mapclassify.BoxPlot(y)\n", "bp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extended API (>= 2.40)\n", "\n", "Note the original API is still available so this extension keeps backwards compatibility." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.603460Z", "start_time": "2022-11-05T15:10:19.598526Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -0.70] | 0\n", "(-0.70, 25.70] | 13\n", "(25.70, 33.50] | 12\n", "(33.50, 43.30] | 12\n", "(43.30, 69.70] | 7\n", "(69.70, 96.40] | 5" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp = mapclassify.classify(y, \"box_plot\")\n", "bp" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.611996Z", "start_time": "2022-11-05T15:10:19.608075Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "mapclassify.classifiers.BoxPlot" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(bp)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.619168Z", "start_time": "2022-11-05T15:10:19.614412Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "----------------------\n", "[17.90, 23.08] | 10\n", "(23.08, 30.48] | 10\n", "(30.48, 39.10] | 9\n", "(39.10, 45.83] | 10\n", "(45.83, 96.40] | 10" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "q5 = mapclassify.classify(y, \"quantiles\", k=5)\n", "q5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Robustness of the `scheme` argument" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.627988Z", "start_time": "2022-11-05T15:10:19.621853Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -0.70] | 0\n", "(-0.70, 25.70] | 13\n", "(25.70, 33.50] | 12\n", "(33.50, 43.30] | 12\n", "(43.30, 69.70] | 7\n", "(69.70, 96.40] | 5" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.classify(y, \"boxPlot\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.634396Z", "start_time": "2022-11-05T15:10:19.629847Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -0.70] | 0\n", "(-0.70, 25.70] | 13\n", "(25.70, 33.50] | 12\n", "(33.50, 43.30] | 12\n", "(43.30, 69.70] | 7\n", "(69.70, 96.40] | 5" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.classify(y, \"Boxplot\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.641115Z", "start_time": "2022-11-05T15:10:19.636017Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "BoxPlot\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, -0.70] | 0\n", "(-0.70, 25.70] | 13\n", "(25.70, 33.50] | 12\n", "(33.50, 43.30] | 12\n", "(43.30, 69.70] | 7\n", "(69.70, 96.40] | 5" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.classify(y, \"Box_plot\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2022-11-05T15:10:19.691302Z", "start_time": "2022-11-05T15:10:19.645124Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "StdMean\n", "\n", " Interval Count\n", "----------------------\n", "( -inf, 1.50] | 0\n", "( 1.50, 19.97] | 5\n", "(19.97, 56.90] | 37\n", "(56.90, 75.37] | 3\n", "(75.37, 96.40] | 4" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.classify(y, 'Std_Mean')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2022-10-26T03:01:45.977181Z", "start_time": "2022-10-26T03:01:45.931234Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "StdMean\n", "\n", " Interval Count\n", "----------------------\n", "[17.90, 19.97] | 5\n", "(19.97, 38.44] | 24\n", "(38.44, 56.90] | 13\n", "(56.90, 75.37] | 3\n", "(75.37, 93.83] | 3\n", "(93.83, 96.40] | 1" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.classify(y, 'Std_Mean', anchor=True)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(38.43622446938775, 18.466069465206047, 17.9, 96.400002)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.mean(), y.std(), y.min(), y.max()" ] }, { "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": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.8.0/notebooks/07_std_anchor.ipynb000066400000000000000000005205621465055300600215520ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to mapclassify\n", "\n", "`mapclassify` implementsbins = [ ybar + trim * ystd for trim in range(-2, 2+1) ] a family of classification schemes for choropleth maps. \n", "Its focus is on the determination of the number of classes, and the assignment of observations to those classes.\n", "It is intended for use with upstream mapping and geovisualization packages (see [geopandas](https://geopandas.org/mapping.html) and [geoplot](https://residentmario.github.io/geoplot/user_guide/Customizing_Plots.html) for examples) that handle the rendering of the maps.\n", "\n", "In this notebook, the basic functionality of mapclassify is presented." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2022-10-26T02:53:25.104870Z", "start_time": "2022-10-26T02:53:23.858480Z" } }, "outputs": [ { "data": { "text/plain": [ "'2.4.2+55.g0155c6e6.dirty'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mapclassify as mc\n", "\n", "mc.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import geopandas as gpd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "gdf = gpd.read_file(\"data/nyc/nyc.shp\")" ] }, { "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": {}, "output_type": "display_data" } ], "source": [ "gdf.plot()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf.plot(column='rent2008')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "StdMean \n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.00, 68.04] | 3\n", "( 68.04, 662.61] | 0\n", "( 662.61, 1851.75] | 45\n", "(1851.75, 2446.32] | 2\n", "(2446.32, 2900.00] | 5" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "std = mc.StdMean(gdf.rent2008)\n", "std" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "StdMean \n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.00, 68.04] | 3\n", "( 68.04, 662.61] | 0\n", "( 662.61, 1257.18] | 33\n", "(1257.18, 1851.75] | 12\n", "(1851.75, 2446.32] | 2\n", "(2446.32, 2900.00] | 5" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stda = mc.StdMean(gdf.rent2008, anchor=True)\n", "stda" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1257.1818181818182" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = gdf.rent2008\n", "y.mean()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "y1 = y.values\n", "y1[0] = 5000" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "StdMean \n", "\n", " Interval Count\n", "--------------------------\n", "( -inf, -227.42] | 0\n", "(-227.42, 551.24] | 3\n", "( 551.24, 2108.58] | 45\n", "(2108.58, 2887.24] | 5\n", "(2887.24, 5000.00] | 2" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc.StdMean(y1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "StdMean \n", "\n", " Interval Count\n", "--------------------------\n", "[ 0.00, 551.24] | 3\n", "( 551.24, 1329.91] | 35\n", "(1329.91, 2108.58] | 10\n", "(2108.58, 2887.24] | 5\n", "(2887.24, 3665.91] | 1\n", "(3665.91, 4444.57] | 0\n", "(4444.57, 5000.00] | 1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc.StdMean(y1, anchor=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1329.909090909091" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y1.mean()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2.1144409159637743" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z.min()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "68.04306411189191" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.mean() - 2 * y.std()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.0000007194253047" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(2446.321 - y.mean()) / y.std()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-2.1144409159637743, 2.7630386718042783)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "min(z), max(z)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1257.1818181818182" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.mean()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[-2, 2]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(map(int, (min(z), max(z))))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "ybar = y.mean()\n", "ystd = y.std()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "bins = [ ybar + trim * ystd for trim in range(-2, 2+1) ]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[68.04306411189191,\n", " 662.6124411468551,\n", " 1257.1818181818182,\n", " 1851.7511952167815,\n", " 2446.3205722517446]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "int(-2.1)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "int(2.1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 4 } mapclassify-2.8.0/notebooks/08_manual_coloring.ipynb000066400000000000000000244031361465055300600226030ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1d5cebc1-08a5-4f67-94b0-3bdb849b820d", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:02.598126Z", "iopub.status.busy": "2024-07-24T18:19:02.594152Z", "iopub.status.idle": "2024-07-24T18:19:04.550638Z", "shell.execute_reply": "2024-07-24T18:19:04.550229Z", "shell.execute_reply.started": "2024-07-24T18:19:02.598020Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Author: eli knaap\n", "\n", "pandas : 2.1.4\n", "geopandas : 0.14.2\n", "mapclassify: 2.7.1.dev0+gaf62513092fd.d20240723\n", "geodatasets: 2024.7.0\n", "\n" ] } ], "source": [ "import geodatasets\n", "import geopandas as gpd\n", "import mapclassify\n", "import pandas as pd\n", "from mapclassify.util import get_color_array\n", "\n", "%load_ext watermark\n", "%watermark -a 'eli knaap' -iv" ] }, { "cell_type": "code", "execution_count": 2, "id": "8aa8876b-aafe-456b-895a-b34f7311746f", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:04.551852Z", "iopub.status.busy": "2024-07-24T18:19:04.551641Z", "iopub.status.idle": "2024-07-24T18:19:05.358837Z", "shell.execute_reply": "2024-07-24T18:19:05.358497Z", "shell.execute_reply.started": "2024-07-24T18:19:04.551836Z" } }, "outputs": [], "source": [ "df = gpd.read_file(geodatasets.get_path(\"geoda cincinnati\")).to_crs(4326)" ] }, { "cell_type": "code", "execution_count": 3, "id": "baa23e00-83ea-4962-90c5-695893d17f03", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:05.359301Z", "iopub.status.busy": "2024-07-24T18:19:05.359213Z", "iopub.status.idle": "2024-07-24T18:19:05.749711Z", "shell.execute_reply": "2024-07-24T18:19:05.749359Z", "shell.execute_reply.started": "2024-07-24T18:19:05.359292Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 413, "width": 529 } }, "output_type": "display_data" } ], "source": [ "# use mapclassify under the hood\n", "df.plot(\"DENSITY\", scheme=\"quantiles\", cmap=\"YlOrBr\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "bb6b3e10-4053-4b54-845e-d1fdea1fdc95", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:05.750781Z", "iopub.status.busy": "2024-07-24T18:19:05.750623Z", "iopub.status.idle": "2024-07-24T18:19:05.760801Z", "shell.execute_reply": "2024-07-24T18:19:05.760541Z", "shell.execute_reply.started": "2024-07-24T18:19:05.750771Z" } }, "outputs": [], "source": [ "# get colors directly and pass them to geopandas\n", "colors = get_color_array(\n", " df.DENSITY.values, scheme=\"quantiles\", cmap=\"YlOrBr\", as_hex=True\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "id": "545ce6dd-2b2a-42ad-989f-4efae3f46277", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:05.761339Z", "iopub.status.busy": "2024-07-24T18:19:05.761243Z", "iopub.status.idle": "2024-07-24T18:19:05.765723Z", "shell.execute_reply": "2024-07-24T18:19:05.765458Z", "shell.execute_reply.started": "2024-07-24T18:19:05.761330Z" } }, "outputs": [ { "data": { "text/plain": [ "array(['#fee290', '#fd9828', '#fee290', '#662505', '#fee290', '#fd9828',\n", " '#662505', '#fd9828', '#fd9828', '#ca4b02', '#fd9828', '#662505',\n", " '#662505', '#fd9828', '#ffffe5', '#662505', '#662505', '#ca4b02',\n", " '#662505', '#662505', '#fee290', '#662505', '#ca4b02', '#662505',\n", " '#ffffe5', '#fd9828', '#fee290', '#ca4b02', '#fd9828', '#fd9828',\n", " '#ffffe5', '#ca4b02', '#fd9828', '#ca4b02', '#ffffe5', '#662505',\n", " '#662505', '#ca4b02', '#fd9828', '#ca4b02', '#fee290', '#ca4b02',\n", " '#662505', '#ffffe5', '#fd9828', '#fd9828', '#fd9828', '#fee290',\n", " '#fee290', '#fee290', '#662505', '#ffffe5', '#ffffe5', '#fee290',\n", " '#ffffe5', '#ca4b02', '#fd9828', '#fd9828', '#ffffe5', '#ca4b02',\n", " '#fd9828', '#fee290', '#ca4b02', '#ca4b02', '#ca4b02', '#ca4b02',\n", " '#662505', '#fd9828', '#fee290', '#fee290', '#fee290', '#fee290',\n", " '#fee290', '#ffffe5', '#fd9828', '#ffffe5', '#ffffe5', '#fee290',\n", " '#fd9828', '#ffffe5', '#ffffe5', '#ffffe5', '#ffffe5', '#ffffe5',\n", " '#ffffe5', '#fee290', '#ffffe5', '#fee290', '#ca4b02', '#ffffe5',\n", " '#ffffe5', '#ffffe5', '#ca4b02', '#fee290', '#ffffe5', '#662505',\n", " '#662505', '#fee290', '#fd9828', '#ffffe5', '#ca4b02', '#ffffe5',\n", " '#fd9828', '#fd9828', '#662505', '#662505', '#fd9828', '#fee290',\n", " '#fd9828', '#662505', '#fd9828', '#ffffe5', '#ca4b02', '#fd9828',\n", " '#662505', '#ca4b02', '#662505', '#fd9828', '#fee290', '#ca4b02',\n", " '#fee290', '#ffffe5', '#ffffe5', '#ffffe5', '#fd9828', '#662505',\n", " '#ca4b02', '#ffffe5', '#fd9828', '#fee290', '#ffffe5', '#ca4b02',\n", " '#ffffe5', '#fee290', '#fee290', '#ffffe5', '#662505', '#ffffe5',\n", " '#fd9828', '#662505', '#fd9828', '#662505', '#662505', '#ffffe5',\n", " '#662505', '#ffffe5', '#662505', '#ffffe5', '#fd9828', '#ffffe5',\n", " '#ffffe5', '#ca4b02', '#662505', '#662505', '#ca4b02', '#662505',\n", " '#ffffe5', '#662505', '#ca4b02', '#fee290', '#fd9828', '#ca4b02',\n", " '#662505', '#ffffe5', '#fd9828', '#fd9828', '#ca4b02', '#ca4b02',\n", " '#662505', '#ffffe5', '#fee290', '#fee290', '#662505', '#ca4b02',\n", " '#ffffe5', '#662505', '#fd9828', '#fd9828', '#ffffe5', '#ffffe5',\n", " '#fee290', '#fee290', '#fee290', '#ffffe5', '#ffffe5', '#fee290',\n", " '#ffffe5', '#ffffe5', '#ffffe5', '#ffffe5', '#ca4b02', '#ffffe5',\n", " '#fee290', '#ffffe5', '#662505', '#fee290', '#fee290', '#662505',\n", " '#fd9828', '#fd9828', '#ca4b02', '#ca4b02', '#ffffe5', '#fee290',\n", " '#fee290', '#662505', '#ffffe5', '#fd9828', '#ffffe5', '#ffffe5',\n", " '#fee290', '#fd9828', '#fd9828', '#fee290', '#ca4b02', '#ca4b02',\n", " '#fd9828', '#fee290', '#fee290', '#fee290', '#fee290', '#fee290',\n", " '#fee290', '#ca4b02', '#fee290', '#662505', '#fee290', '#662505',\n", " '#ca4b02', '#662505', '#662505', '#fd9828', '#ffffe5', '#ffffe5',\n", " '#ffffe5', '#ffffe5', '#ca4b02', '#662505', '#ca4b02', '#662505',\n", " '#662505', '#ffffe5', '#ffffe5', '#ca4b02', '#ca4b02', '#ca4b02',\n", " '#ca4b02', '#ca4b02', '#662505', '#ffffe5', '#fee290', '#fee290',\n", " '#fee290', '#ffffe5', '#fee290', '#ffffe5', '#ffffe5', '#fee290',\n", " '#fee290', '#fee290', '#ca4b02', '#662505', '#fd9828', '#fd9828',\n", " '#662505', '#fd9828', '#ca4b02', '#ffffe5', '#662505', '#fd9828',\n", " '#fee290', '#ffffe5', '#fee290', '#ca4b02', '#ca4b02', '#ca4b02',\n", " '#ca4b02', '#fd9828', '#fd9828', '#662505', '#ca4b02', '#ca4b02',\n", " '#ca4b02', '#ca4b02', '#ca4b02', '#fd9828', '#fd9828', '#ca4b02',\n", " '#662505', '#ca4b02', '#662505', '#ca4b02', '#ca4b02', '#ca4b02',\n", " '#fee290', '#ffffe5', '#ffffe5', '#662505', '#ca4b02', '#fee290',\n", " '#fee290', '#ffffe5', '#fd9828', '#ca4b02', '#fd9828', '#fd9828',\n", " '#fee290', '#fee290', '#662505', '#fd9828', '#ca4b02', '#ffffe5',\n", " '#fee290', '#fee290', '#662505', '#662505', '#ffffe5', '#662505',\n", " '#662505', '#ca4b02', '#fee290', '#fd9828', '#fee290', '#662505',\n", " '#ffffe5', '#662505', '#ffffe5', '#ffffe5', '#ffffe5', '#fee290',\n", " '#ffffe5', '#ffffe5', '#fee290', '#ffffe5', '#fd9828', '#fee290',\n", " '#662505', '#ffffe5', '#fee290', '#ffffe5', '#662505', '#ffffe5',\n", " '#ffffe5', '#fee290', '#ca4b02', '#fd9828', '#fee290', '#fd9828',\n", " '#fee290', '#662505', '#fee290', '#662505', '#fd9828', '#662505',\n", " '#662505', '#fd9828', '#fee290', '#fd9828', '#662505', '#662505',\n", " '#fee290', '#fd9828', '#ca4b02', '#fd9828', '#ffffe5', '#fee290',\n", " '#ca4b02', '#ca4b02', '#fee290', '#ffffe5', '#fd9828', '#fee290',\n", " '#fd9828', '#ffffe5', '#662505', '#fee290', '#fd9828', '#fee290',\n", " '#ca4b02', '#ffffe5', '#fd9828', '#662505', '#fd9828', '#ca4b02',\n", " '#662505', '#662505', '#fd9828', '#662505', '#662505', '#662505',\n", " '#ca4b02', '#fd9828', '#fee290', '#fee290', '#ca4b02', '#ffffe5',\n", " '#662505', '#fd9828', '#ca4b02', '#662505', '#ca4b02', '#ffffe5',\n", " '#ca4b02', '#662505', '#ca4b02', '#662505', '#662505', '#ca4b02',\n", " '#ca4b02', '#fd9828', '#662505', '#fd9828', '#fee290', '#662505',\n", " '#662505', '#662505', '#fee290', '#fd9828', '#ca4b02', '#fee290',\n", " '#ca4b02', '#662505', '#fd9828', '#fd9828', '#ca4b02', '#662505',\n", " '#fd9828', '#fd9828', '#fee290', '#ca4b02', '#fd9828', '#ca4b02',\n", " '#ca4b02', '#ca4b02', '#fd9828', '#ca4b02', '#fd9828', '#ca4b02',\n", " '#ca4b02', '#ca4b02', '#fd9828', '#fd9828', '#fd9828', '#662505',\n", " '#fee290', '#ca4b02', '#fd9828', '#fee290', '#662505', '#662505',\n", " '#fd9828', '#662505', '#fd9828', '#fd9828', '#ffffe5', '#ca4b02',\n", " '#fee290'], dtype=object)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colors" ] }, { "cell_type": "code", "execution_count": 6, "id": "e45fcd62-a96a-4601-a355-783cd0323d32", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:05.766178Z", "iopub.status.busy": "2024-07-24T18:19:05.766104Z", "iopub.status.idle": "2024-07-24T18:19:05.881828Z", "shell.execute_reply": "2024-07-24T18:19:05.881469Z", "shell.execute_reply.started": "2024-07-24T18:19:05.766169Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 413, "width": 529 } }, "output_type": "display_data" } ], "source": [ "df.plot(color=colors)" ] }, { "cell_type": "markdown", "id": "a76cea95-34e0-4f3a-a945-1b1a4477fec2", "metadata": {}, "source": [ "geopandas `explore` method can also use mapclassify under the hood or take a list of colors" ] }, { "cell_type": "code", "execution_count": 7, "id": "adcc5cbd-fa95-40c9-9532-7ad9a4898ce4", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:07.092626Z", "iopub.status.busy": "2024-07-24T18:19:07.091691Z", "iopub.status.idle": "2024-07-24T18:19:07.716706Z", "shell.execute_reply": "2024-07-24T18:19:07.716400Z", "shell.execute_reply.started": "2024-07-24T18:19:07.092565Z" } }, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# json doesnt like numpy arrays\n", "df.explore(color=list(colors), tiles=\"CartoDB Positron\")" ] }, { "cell_type": "markdown", "id": "533b18dc-33aa-42d1-b160-dc693272eb0b", "metadata": {}, "source": [ "For some visualization libraries, you *need* to pass the colors explicitly. Their examples usually punt on classification schemes, but rather use linear or logarithmic scalers " ] }, { "cell_type": "code", "execution_count": 8, "id": "151d7f77-042f-40b2-b0d0-a1aa90268ed2", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:08.671281Z", "iopub.status.busy": "2024-07-24T18:19:08.669078Z", "iopub.status.idle": "2024-07-24T18:19:08.897162Z", "shell.execute_reply": "2024-07-24T18:19:08.896824Z", "shell.execute_reply.started": "2024-07-24T18:19:08.671246Z" } }, "outputs": [], "source": [ "from lonboard import Map, PolygonLayer" ] }, { "cell_type": "markdown", "id": "da5f4fee-3487-4fd8-bd8a-b1cf4bf3deb4", "metadata": {}, "source": [ "lonboard requires a 2-dimensional array of integers" ] }, { "cell_type": "code", "execution_count": 9, "id": "f4eed567-9259-47b6-a251-4a5266dbb59f", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:09.673900Z", "iopub.status.busy": "2024-07-24T18:19:09.672155Z", "iopub.status.idle": "2024-07-24T18:19:09.697348Z", "shell.execute_reply": "2024-07-24T18:19:09.696833Z", "shell.execute_reply.started": "2024-07-24T18:19:09.673846Z" } }, "outputs": [], "source": [ "colors = get_color_array(df.DENSITY.values, scheme=\"quantiles\", cmap=\"YlOrBr\", alpha=0.6)" ] }, { "cell_type": "code", "execution_count": 10, "id": "f1d9986f-d9b7-4116-bd29-a0f61869085c", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:10.132077Z", "iopub.status.busy": "2024-07-24T18:19:10.131659Z", "iopub.status.idle": "2024-07-24T18:19:10.137252Z", "shell.execute_reply": "2024-07-24T18:19:10.136625Z", "shell.execute_reply.started": "2024-07-24T18:19:10.132052Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[254, 226, 144, 153],\n", " [253, 152, 40, 153],\n", " [254, 226, 144, 153],\n", " ...,\n", " [255, 255, 229, 153],\n", " [202, 75, 2, 153],\n", " [254, 226, 144, 153]], dtype=uint8)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colors" ] }, { "cell_type": "code", "execution_count": 11, "id": "b52bf815-aa3d-41c6-bc1d-d9fdcb730c68", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:10.666185Z", "iopub.status.busy": "2024-07-24T18:19:10.664272Z", "iopub.status.idle": "2024-07-24T18:19:10.817881Z", "shell.execute_reply": "2024-07-24T18:19:10.815869Z", "shell.execute_reply.started": "2024-07-24T18:19:10.666136Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "74b162341db149db9df27fd66ce7837a", "version_major": 2, "version_minor": 1 }, "text/plain": [ "Map(layers=[PolygonLayer(get_fill_color=\n", "[\n", " [\n", " 254,\n", "…" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get RGBA instead of hex\n", "layer = PolygonLayer.from_geopandas(\n", " df,\n", " get_fill_color=colors,\n", ")\n", "m = Map(layers=[layer], _height=800)\n", "m" ] }, { "cell_type": "code", "execution_count": 12, "id": "03c6550e-1797-4d05-8e3d-118400c0e033", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:11.363932Z", "iopub.status.busy": "2024-07-24T18:19:11.363595Z", "iopub.status.idle": "2024-07-24T18:19:11.385160Z", "shell.execute_reply": "2024-07-24T18:19:11.384267Z", "shell.execute_reply.started": "2024-07-24T18:19:11.363910Z" } }, "outputs": [], "source": [ "import pydeck as pdk" ] }, { "cell_type": "markdown", "id": "c3af84ec-7f59-4ff8-a4d4-8a77eb3aa0e8", "metadata": {}, "source": [ "pydeck requires a list of RGBA colors, but because JSON cant serialize uint8, it needs lists of floats" ] }, { "cell_type": "code", "execution_count": 13, "id": "d13dcfd4-a3e1-4d5a-b7bd-35134fa83fab", "metadata": { "execution": { "iopub.execute_input": "2024-07-24T18:19:12.514055Z", "iopub.status.busy": "2024-07-24T18:19:12.512244Z", "iopub.status.idle": "2024-07-24T18:19:12.545455Z", "shell.execute_reply": "2024-07-24T18:19:12.544857Z", "shell.execute_reply.started": 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ct=ct.concat(mu({key:at.key,value:mt,valueSpec:at.valueSpec,validateSpec:at.validateSpec,style:at.style,styleSpec:at.styleSpec,arrayElementValidator:X})),qr(mt)===\"array\"&&mt.length===0&&ct.push(new ve(at.key,mt,\"array must have at least one stop\")),ct},default:function(at){return at.validateSpec({key:at.key,value:at.value,valueSpec:a,validateSpec:at.validateSpec,style:at.style,styleSpec:at.styleSpec})}}});return h===\"identity\"&&D&&q.push(new ve(u.key,u.value,'missing required property \"property\"')),h===\"identity\"||u.value.stops||q.push(new ve(u.key,u.value,'missing required property \"stops\"')),h===\"exponential\"&&u.valueSpec.expression&&!Md(u.valueSpec)&&q.push(new ve(u.key,u.value,\"exponential functions not supported\")),u.styleSpec.$version>=8&&(F&&!_f(u.valueSpec)?q.push(new ve(u.key,u.value,\"property functions not supported\")):D&&!h_(u.valueSpec)&&q.push(new ve(u.key,u.value,\"zoom functions not supported\"))),h!==\"categorical\"&&!V||u.value.property!==void 0||q.push(new ve(u.key,u.value,'\"property\" property is required')),q;function X(at){let ct=[],mt=at.value,bt=at.key;if(qr(mt)!==\"array\")return[new ve(bt,mt,`array expected, ${qr(mt)} found`)];if(mt.length!==2)return[new ve(bt,mt,`array length 2 expected, length ${mt.length} found`)];if(V){if(qr(mt[0])!==\"object\")return[new ve(bt,mt,`object expected, ${qr(mt[0])} found`)];if(mt[0].zoom===void 0)return[new ve(bt,mt,\"object stop key must have zoom\")];if(mt[0].value===void 0)return[new ve(bt,mt,\"object stop key must have value\")];if(E&&E>Cn(mt[0].zoom))return[new ve(bt,mt[0].zoom,\"stop zoom values must appear in ascending order\")];Cn(mt[0].zoom)!==E&&(E=Cn(mt[0].zoom),x=void 0,P={}),ct=ct.concat(fi({key:`${bt}[0]`,value:mt[0],valueSpec:{zoom:{}},validateSpec:at.validateSpec,style:at.style,styleSpec:at.styleSpec,objectElementValidators:{zoom:vf,value:rt}}))}else 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h!==\"categorical\"||mt!==\"number\"||isFinite(bt)&&Math.floor(bt)===bt?h!==\"categorical\"&&mt===\"number\"&&x!==void 0&&btnew ve(`${u.key}${A.key}`,u.value,A.message));let h=a.value.expression||a.value._styleExpression.expression;if(u.expressionContext===\"property\"&&u.propertyKey===\"text-font\"&&!h.outputDefined())return[new ve(u.key,u.value,`Invalid data expression for \"${u.propertyKey}\". 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a=u.value,h=u.key,A=u.styleSpec,x=u.style,E=u.validateSpec;if(!a.type)return[new ve(h,a,'\"type\" is required')];let P=Cn(a.type),D;switch(P){case\"vector\":case\"raster\":return D=fi({key:h,value:a,valueSpec:A[`source_${P.replace(\"-\",\"_\")}`],style:u.style,styleSpec:A,objectElementValidators:uh,validateSpec:E}),D;case\"raster-dem\":return D=function(F){var V;let q=(V=F.sourceName)!==null&&V!==void 0?V:\"\",X=F.value,rt=F.styleSpec,at=rt.source_raster_dem,ct=F.style,mt=[],bt=qr(X);if(X===void 0)return mt;if(bt!==\"object\")return mt.push(new ve(\"source_raster_dem\",X,`object expected, ${bt} found`)),mt;let Pt=Cn(X.encoding)===\"custom\",jt=[\"redFactor\",\"greenFactor\",\"blueFactor\",\"baseShift\"],Rt=F.value.encoding?`\"${F.value.encoding}\"`:\"Default\";for(let Gt in X)!Pt&&jt.includes(Gt)?mt.push(new ve(Gt,X[Gt],`In \"${q}\": \"${Gt}\" is only valid when \"encoding\" is set to \"custom\". ${Rt} encoding 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a===\"visibility\"?this.visibility:this._unevaluatedLayout.getValue(a)}setLayoutProperty(a,h,A={}){h!=null&&this._validate(s0,`layers.${this.id}.layout.${a}`,a,h,A)||(a!==\"visibility\"?this._unevaluatedLayout.setValue(a,h):this.visibility=h)}getPaintProperty(a){return a.endsWith(uo)?this._transitionablePaint.getTransition(a.slice(0,-11)):this._transitionablePaint.getValue(a)}setPaintProperty(a,h,A={}){if(h!=null&&this._validate(rA,`layers.${this.id}.paint.${a}`,a,h,A))return!1;if(a.endsWith(uo))return this._transitionablePaint.setTransition(a.slice(0,-11),h||void 0),!1;{let x=this._transitionablePaint._values[a],E=x.property.specification[\"property-type\"]===\"cross-faded-data-driven\",P=x.value.isDataDriven(),D=x.value;this._transitionablePaint.setValue(a,h),this._handleSpecialPaintPropertyUpdate(a);let F=this._transitionablePaint._values[a].value;return F.isDataDriven()||P||E||this._handleOverridablePaintPropertyUpdate(a,D,F)}}_handleSpecialPaintPropertyUpdate(a){}_handleOverridablePaintPropertyUpdate(a,h,A){return!1}isHidden(a){return!!(this.minzoom&&a=this.maxzoom)||this.visibility===\"none\"}updateTransitions(a){this._transitioningPaint=this._transitionablePaint.transitioned(a,this._transitioningPaint)}hasTransition(){return this._transitioningPaint.hasTransition()}recalculate(a,h){a.getCrossfadeParameters&&(this._crossfadeParameters=a.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(a,void 0,h)),this.paint=this._transitioningPaint.possiblyEvaluate(a,void 0,h)}serialize(){let a={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(a.layout=a.layout||{},a.layout.visibility=this.visibility),le(a,(h,A)=>!(h===void 0||A===\"layout\"&&!Object.keys(h).length||A===\"paint\"&&!Object.keys(h).length))}_validate(a,h,A,x,E={}){return(!E||E.validate!==!1)&&fh(this,a.call(za,{key:h,layerType:this.type,objectKey:A,value:x,styleSpec:ee,style:{glyphs:!0,sprite:!0}}))}is3D(){return!1}isTileClipped(){return!1}hasOffscreenPass(){return!1}resize(){}isStateDependent(){for(let a in this.paint._values){let h=this.paint.get(a);if(h instanceof Mo&&_f(h.property.specification)&&(h.value.kind===\"source\"||h.value.kind===\"composite\")&&h.value.isStateDependent)return!0}return!1}}let w_={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array};class mh{constructor(a,h){this._structArray=a,this._pos1=h*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8}}class kn{constructor(){this.isTransferred=!1,this.capacity=-1,this.resize(0)}static serialize(a,h){return a._trim(),h&&(a.isTransferred=!0,h.push(a.arrayBuffer)),{length:a.length,arrayBuffer:a.arrayBuffer}}static deserialize(a){let h=Object.create(this.prototype);return h.arrayBuffer=a.arrayBuffer,h.length=a.length,h.capacity=a.arrayBuffer.byteLength/h.bytesPerElement,h._refreshViews(),h}_trim(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())}clear(){this.length=0}resize(a){this.reserve(a),this.length=a}reserve(a){if(a>this.capacity){this.capacity=Math.max(a,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);let h=this.uint8;this._refreshViews(),h&&this.uint8.set(h)}}_refreshViews(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")}}function wn(u,a=1){let h=0,A=0;return{members:u.map(x=>{let E=w_[x.type].BYTES_PER_ELEMENT,P=h=Sf(h,Math.max(a,E)),D=x.components||1;return A=Math.max(A,E),h+=E*D,{name:x.name,type:x.type,components:D,offset:P}}),size:Sf(h,Math.max(A,a)),alignment:a}}function Sf(u,a){return Math.ceil(u/a)*a}class Es extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h){let A=this.length;return this.resize(A+1),this.emplace(A,a,h)}emplace(a,h,A){let x=2*a;return this.int16[x+0]=h,this.int16[x+1]=A,a}}Es.prototype.bytesPerElement=4,Ge(\"StructArrayLayout2i4\",Es);class gh extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A){let x=this.length;return this.resize(x+1),this.emplace(x,a,h,A)}emplace(a,h,A,x){let E=3*a;return this.int16[E+0]=h,this.int16[E+1]=A,this.int16[E+2]=x,a}}gh.prototype.bytesPerElement=6,Ge(\"StructArrayLayout3i6\",gh);class Wo extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A,x){let E=this.length;return this.resize(E+1),this.emplace(E,a,h,A,x)}emplace(a,h,A,x,E){let P=4*a;return this.int16[P+0]=h,this.int16[P+1]=A,this.int16[P+2]=x,this.int16[P+3]=E,a}}Wo.prototype.bytesPerElement=8,Ge(\"StructArrayLayout4i8\",Wo);class p0 extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P){let D=this.length;return this.resize(D+1),this.emplace(D,a,h,A,x,E,P)}emplace(a,h,A,x,E,P,D){let F=6*a;return this.int16[F+0]=h,this.int16[F+1]=A,this.int16[F+2]=x,this.int16[F+3]=E,this.int16[F+4]=P,this.int16[F+5]=D,a}}p0.prototype.bytesPerElement=12,Ge(\"StructArrayLayout2i4i12\",p0);class Fd extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P){let D=this.length;return this.resize(D+1),this.emplace(D,a,h,A,x,E,P)}emplace(a,h,A,x,E,P,D){let F=4*a,V=8*a;return this.int16[F+0]=h,this.int16[F+1]=A,this.uint8[V+4]=x,this.uint8[V+5]=E,this.uint8[V+6]=P,this.uint8[V+7]=D,a}}Fd.prototype.bytesPerElement=8,Ge(\"StructArrayLayout2i4ub8\",Fd);class Tf extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h){let A=this.length;return this.resize(A+1),this.emplace(A,a,h)}emplace(a,h,A){let x=2*a;return this.float32[x+0]=h,this.float32[x+1]=A,a}}Tf.prototype.bytesPerElement=8,Ge(\"StructArrayLayout2f8\",Tf);class Ho extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P,D,F,V,q){let X=this.length;return this.resize(X+1),this.emplace(X,a,h,A,x,E,P,D,F,V,q)}emplace(a,h,A,x,E,P,D,F,V,q,X){let rt=10*a;return this.uint16[rt+0]=h,this.uint16[rt+1]=A,this.uint16[rt+2]=x,this.uint16[rt+3]=E,this.uint16[rt+4]=P,this.uint16[rt+5]=D,this.uint16[rt+6]=F,this.uint16[rt+7]=V,this.uint16[rt+8]=q,this.uint16[rt+9]=X,a}}Ho.prototype.bytesPerElement=20,Ge(\"StructArrayLayout10ui20\",Ho);class lA extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P,D,F,V,q,X,rt){let at=this.length;return this.resize(at+1),this.emplace(at,a,h,A,x,E,P,D,F,V,q,X,rt)}emplace(a,h,A,x,E,P,D,F,V,q,X,rt,at){let ct=12*a;return this.int16[ct+0]=h,this.int16[ct+1]=A,this.int16[ct+2]=x,this.int16[ct+3]=E,this.uint16[ct+4]=P,this.uint16[ct+5]=D,this.uint16[ct+6]=F,this.uint16[ct+7]=V,this.int16[ct+8]=q,this.int16[ct+9]=X,this.int16[ct+10]=rt,this.int16[ct+11]=at,a}}lA.prototype.bytesPerElement=24,Ge(\"StructArrayLayout4i4ui4i24\",lA);class bi extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A){let x=this.length;return this.resize(x+1),this.emplace(x,a,h,A)}emplace(a,h,A,x){let E=3*a;return this.float32[E+0]=h,this.float32[E+1]=A,this.float32[E+2]=x,a}}bi.prototype.bytesPerElement=12,Ge(\"StructArrayLayout3f12\",bi);class T extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)}emplaceBack(a){let h=this.length;return this.resize(h+1),this.emplace(h,a)}emplace(a,h){return this.uint32[1*a+0]=h,a}}T.prototype.bytesPerElement=4,Ge(\"StructArrayLayout1ul4\",T);class l extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P,D,F,V){let q=this.length;return this.resize(q+1),this.emplace(q,a,h,A,x,E,P,D,F,V)}emplace(a,h,A,x,E,P,D,F,V,q){let X=10*a,rt=5*a;return this.int16[X+0]=h,this.int16[X+1]=A,this.int16[X+2]=x,this.int16[X+3]=E,this.int16[X+4]=P,this.int16[X+5]=D,this.uint32[rt+3]=F,this.uint16[X+8]=V,this.uint16[X+9]=q,a}}l.prototype.bytesPerElement=20,Ge(\"StructArrayLayout6i1ul2ui20\",l);class d extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P){let D=this.length;return this.resize(D+1),this.emplace(D,a,h,A,x,E,P)}emplace(a,h,A,x,E,P,D){let F=6*a;return this.int16[F+0]=h,this.int16[F+1]=A,this.int16[F+2]=x,this.int16[F+3]=E,this.int16[F+4]=P,this.int16[F+5]=D,a}}d.prototype.bytesPerElement=12,Ge(\"StructArrayLayout2i2i2i12\",d);class v extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E){let P=this.length;return this.resize(P+1),this.emplace(P,a,h,A,x,E)}emplace(a,h,A,x,E,P){let D=4*a,F=8*a;return this.float32[D+0]=h,this.float32[D+1]=A,this.float32[D+2]=x,this.int16[F+6]=E,this.int16[F+7]=P,a}}v.prototype.bytesPerElement=16,Ge(\"StructArrayLayout2f1f2i16\",v);class b extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A,x){let E=this.length;return this.resize(E+1),this.emplace(E,a,h,A,x)}emplace(a,h,A,x,E){let P=12*a,D=3*a;return this.uint8[P+0]=h,this.uint8[P+1]=A,this.float32[D+1]=x,this.float32[D+2]=E,a}}b.prototype.bytesPerElement=12,Ge(\"StructArrayLayout2ub2f12\",b);class M extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h,A){let x=this.length;return this.resize(x+1),this.emplace(x,a,h,A)}emplace(a,h,A,x){let E=3*a;return this.uint16[E+0]=h,this.uint16[E+1]=A,this.uint16[E+2]=x,a}}M.prototype.bytesPerElement=6,Ge(\"StructArrayLayout3ui6\",M);class O extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt){let jt=this.length;return this.resize(jt+1),this.emplace(jt,a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt)}emplace(a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt,jt){let Rt=24*a,Gt=12*a,Yt=48*a;return this.int16[Rt+0]=h,this.int16[Rt+1]=A,this.uint16[Rt+2]=x,this.uint16[Rt+3]=E,this.uint32[Gt+2]=P,this.uint32[Gt+3]=D,this.uint32[Gt+4]=F,this.uint16[Rt+10]=V,this.uint16[Rt+11]=q,this.uint16[Rt+12]=X,this.float32[Gt+7]=rt,this.float32[Gt+8]=at,this.uint8[Yt+36]=ct,this.uint8[Yt+37]=mt,this.uint8[Yt+38]=bt,this.uint32[Gt+10]=Pt,this.int16[Rt+22]=jt,a}}O.prototype.bytesPerElement=48,Ge(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",O);class B extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt,jt,Rt,Gt,Yt,ce,Ne,ir,Fe,Re,Me,Ye){let Ie=this.length;return this.resize(Ie+1),this.emplace(Ie,a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt,jt,Rt,Gt,Yt,ce,Ne,ir,Fe,Re,Me,Ye)}emplace(a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt,bt,Pt,jt,Rt,Gt,Yt,ce,Ne,ir,Fe,Re,Me,Ye,Ie){let Ae=32*a,hr=16*a;return this.int16[Ae+0]=h,this.int16[Ae+1]=A,this.int16[Ae+2]=x,this.int16[Ae+3]=E,this.int16[Ae+4]=P,this.int16[Ae+5]=D,this.int16[Ae+6]=F,this.int16[Ae+7]=V,this.uint16[Ae+8]=q,this.uint16[Ae+9]=X,this.uint16[Ae+10]=rt,this.uint16[Ae+11]=at,this.uint16[Ae+12]=ct,this.uint16[Ae+13]=mt,this.uint16[Ae+14]=bt,this.uint16[Ae+15]=Pt,this.uint16[Ae+16]=jt,this.uint16[Ae+17]=Rt,this.uint16[Ae+18]=Gt,this.uint16[Ae+19]=Yt,this.uint16[Ae+20]=ce,this.uint16[Ae+21]=Ne,this.uint16[Ae+22]=ir,this.uint32[hr+12]=Fe,this.float32[hr+13]=Re,this.float32[hr+14]=Me,this.uint16[Ae+30]=Ye,this.uint16[Ae+31]=Ie,a}}B.prototype.bytesPerElement=64,Ge(\"StructArrayLayout8i15ui1ul2f2ui64\",B);class U extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a){let h=this.length;return this.resize(h+1),this.emplace(h,a)}emplace(a,h){return this.float32[1*a+0]=h,a}}U.prototype.bytesPerElement=4,Ge(\"StructArrayLayout1f4\",U);class W extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A){let x=this.length;return this.resize(x+1),this.emplace(x,a,h,A)}emplace(a,h,A,x){let E=3*a;return this.uint16[6*a+0]=h,this.float32[E+1]=A,this.float32[E+2]=x,a}}W.prototype.bytesPerElement=12,Ge(\"StructArrayLayout1ui2f12\",W);class Z extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h,A){let x=this.length;return this.resize(x+1),this.emplace(x,a,h,A)}emplace(a,h,A,x){let E=4*a;return this.uint32[2*a+0]=h,this.uint16[E+2]=A,this.uint16[E+3]=x,a}}Z.prototype.bytesPerElement=8,Ge(\"StructArrayLayout1ul2ui8\",Z);class $ extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a,h){let A=this.length;return this.resize(A+1),this.emplace(A,a,h)}emplace(a,h,A){let x=2*a;return this.uint16[x+0]=h,this.uint16[x+1]=A,a}}$.prototype.bytesPerElement=4,Ge(\"StructArrayLayout2ui4\",$);class st extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(a){let h=this.length;return this.resize(h+1),this.emplace(h,a)}emplace(a,h){return this.uint16[1*a+0]=h,a}}st.prototype.bytesPerElement=2,Ge(\"StructArrayLayout1ui2\",st);class At extends kn{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(a,h,A,x){let E=this.length;return this.resize(E+1),this.emplace(E,a,h,A,x)}emplace(a,h,A,x,E){let P=4*a;return this.float32[P+0]=h,this.float32[P+1]=A,this.float32[P+2]=x,this.float32[P+3]=E,a}}At.prototype.bytesPerElement=16,Ge(\"StructArrayLayout4f16\",At);class pt extends mh{get anchorPointX(){return this._structArray.int16[this._pos2+0]}get anchorPointY(){return this._structArray.int16[this._pos2+1]}get x1(){return this._structArray.int16[this._pos2+2]}get y1(){return this._structArray.int16[this._pos2+3]}get x2(){return this._structArray.int16[this._pos2+4]}get y2(){return this._structArray.int16[this._pos2+5]}get featureIndex(){return this._structArray.uint32[this._pos4+3]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+8]}get bucketIndex(){return this._structArray.uint16[this._pos2+9]}get anchorPoint(){return new w(this.anchorPointX,this.anchorPointY)}}pt.prototype.size=20;class yt extends l{get(a){return new pt(this,a)}}Ge(\"CollisionBoxArray\",yt);class dt extends mh{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get glyphStartIndex(){return this._structArray.uint16[this._pos2+2]}get numGlyphs(){return this._structArray.uint16[this._pos2+3]}get vertexStartIndex(){return this._structArray.uint32[this._pos4+2]}get lineStartIndex(){return this._structArray.uint32[this._pos4+3]}get lineLength(){return this._structArray.uint32[this._pos4+4]}get segment(){return this._structArray.uint16[this._pos2+10]}get lowerSize(){return this._structArray.uint16[this._pos2+11]}get upperSize(){return this._structArray.uint16[this._pos2+12]}get lineOffsetX(){return this._structArray.float32[this._pos4+7]}get lineOffsetY(){return this._structArray.float32[this._pos4+8]}get writingMode(){return this._structArray.uint8[this._pos1+36]}get placedOrientation(){return this._structArray.uint8[this._pos1+37]}set placedOrientation(a){this._structArray.uint8[this._pos1+37]=a}get hidden(){return this._structArray.uint8[this._pos1+38]}set hidden(a){this._structArray.uint8[this._pos1+38]=a}get crossTileID(){return this._structArray.uint32[this._pos4+10]}set crossTileID(a){this._structArray.uint32[this._pos4+10]=a}get associatedIconIndex(){return this._structArray.int16[this._pos2+22]}}dt.prototype.size=48;class Ft extends O{get(a){return new dt(this,a)}}Ge(\"PlacedSymbolArray\",Ft);class Ht extends mh{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get rightJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+2]}get centerJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+3]}get leftJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+4]}get verticalPlacedTextSymbolIndex(){return this._structArray.int16[this._pos2+5]}get placedIconSymbolIndex(){return this._structArray.int16[this._pos2+6]}get verticalPlacedIconSymbolIndex(){return this._structArray.int16[this._pos2+7]}get key(){return this._structArray.uint16[this._pos2+8]}get textBoxStartIndex(){return this._structArray.uint16[this._pos2+9]}get textBoxEndIndex(){return this._structArray.uint16[this._pos2+10]}get verticalTextBoxStartIndex(){return this._structArray.uint16[this._pos2+11]}get verticalTextBoxEndIndex(){return this._structArray.uint16[this._pos2+12]}get iconBoxStartIndex(){return this._structArray.uint16[this._pos2+13]}get iconBoxEndIndex(){return this._structArray.uint16[this._pos2+14]}get verticalIconBoxStartIndex(){return this._structArray.uint16[this._pos2+15]}get verticalIconBoxEndIndex(){return this._structArray.uint16[this._pos2+16]}get featureIndex(){return this._structArray.uint16[this._pos2+17]}get numHorizontalGlyphVertices(){return this._structArray.uint16[this._pos2+18]}get numVerticalGlyphVertices(){return this._structArray.uint16[this._pos2+19]}get numIconVertices(){return this._structArray.uint16[this._pos2+20]}get numVerticalIconVertices(){return this._structArray.uint16[this._pos2+21]}get useRuntimeCollisionCircles(){return this._structArray.uint16[this._pos2+22]}get crossTileID(){return this._structArray.uint32[this._pos4+12]}set crossTileID(a){this._structArray.uint32[this._pos4+12]=a}get textBoxScale(){return this._structArray.float32[this._pos4+13]}get collisionCircleDiameter(){return this._structArray.float32[this._pos4+14]}get textAnchorOffsetStartIndex(){return this._structArray.uint16[this._pos2+30]}get textAnchorOffsetEndIndex(){return this._structArray.uint16[this._pos2+31]}}Ht.prototype.size=64;class St extends B{get(a){return new Ht(this,a)}}Ge(\"SymbolInstanceArray\",St);class Bt extends U{getoffsetX(a){return this.float32[1*a+0]}}Ge(\"GlyphOffsetArray\",Bt);class Qt extends gh{getx(a){return this.int16[3*a+0]}gety(a){return this.int16[3*a+1]}gettileUnitDistanceFromAnchor(a){return this.int16[3*a+2]}}Ge(\"SymbolLineVertexArray\",Qt);class $t extends mh{get textAnchor(){return 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jr{constructor(a=[]){this.segments=a}prepareSegment(a,h,A,x){let E=this.segments[this.segments.length-1];return a>jr.MAX_VERTEX_ARRAY_LENGTH&&Ke(`Max vertices per segment is ${jr.MAX_VERTEX_ARRAY_LENGTH}: bucket requested ${a}`),(!E||E.vertexLength+a>jr.MAX_VERTEX_ARRAY_LENGTH||E.sortKey!==x)&&(E={vertexOffset:h.length,primitiveOffset:A.length,vertexLength:0,primitiveLength:0},x!==void 0&&(E.sortKey=x),this.segments.push(E)),E}get(){return this.segments}destroy(){for(let a of this.segments)for(let h in a.vaos)a.vaos[h].destroy()}static simpleSegment(a,h,A,x){return new jr([{vertexOffset:a,primitiveOffset:h,vertexLength:A,primitiveLength:x,vaos:{},sortKey:0}])}}function ql(u,a){return 256*(u=ut(Math.floor(u),0,255))+ut(Math.floor(a),0,255)}jr.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,Ge(\"SegmentVector\",jr);let 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q=[],X=[],rt=V.vertexLength;for(let ct of D)if(ct.length!==0){ct!==D[0]&&X.push(q.length/2);for(let mt=0;mten)||u.y===a.y&&(u.y<0||u.y>en)}function yK(u){return u.every(a=>a.x<0)||u.every(a=>a.x>en)||u.every(a=>a.y<0)||u.every(a=>a.y>en)}let JF;Ge(\"FillExtrusionBucket\",wC,{omit:[\"layers\",\"features\"]});var vK={get paint(){return JF=JF||new Hn({\"fill-extrusion-opacity\":new nr(ee[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new dr(ee[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new nr(ee[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new nr(ee[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new wf(ee[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new dr(ee[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new 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x=this.layers[0].layout.get(\"line-sort-key\"),E=!x.isConstant(),P=[];for(let{feature:D,id:F,index:V,sourceLayerIndex:q}of a){let X=this.layers[0]._featureFilter.needGeometry,rt=S(D,X);if(!this.layers[0]._featureFilter.filter(new un(this.zoom),rt,A))continue;let at=E?x.evaluate(rt,{},A):void 0,ct={id:F,properties:D.properties,type:D.type,sourceLayerIndex:q,index:V,geometry:X?rt.geometry:y(D),patterns:{},sortKey:at};P.push(ct)}E&&P.sort((D,F)=>D.sortKey-F.sortKey);for(let D of P){let{geometry:F,index:V,sourceLayerIndex:q}=D;if(this.hasPattern){let X=vC(\"line\",this.layers,D,this.zoom,h);this.patternFeatures.push(X)}else this.addFeature(D,F,V,A,{});h.featureIndex.insert(a[V].feature,F,V,q,this.index)}}update(a,h,A){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(a,h,this.stateDependentLayers,A)}addFeatures(a,h,A){for(let x of this.patternFeatures)this.addFeature(x,x.geometry,x.index,h,A)}isEmpty(){return this.layoutVertexArray.length===0}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(a){this.uploaded||(this.layoutVertexArray2.length!==0&&(this.layoutVertexBuffer2=a.createVertexBuffer(this.layoutVertexArray2,TK)),this.layoutVertexBuffer=a.createVertexBuffer(this.layoutVertexArray,wK),this.indexBuffer=a.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(a),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())}lineFeatureClips(a){if(a.properties&&Object.prototype.hasOwnProperty.call(a.properties,\"mapbox_clip_start\")&&Object.prototype.hasOwnProperty.call(a.properties,\"mapbox_clip_end\"))return{start:+a.properties.mapbox_clip_start,end:+a.properties.mapbox_clip_end}}addFeature(a,h,A,x,E){let P=this.layers[0].layout,D=P.get(\"line-join\").evaluate(a,{}),F=P.get(\"line-cap\"),V=P.get(\"line-miter-limit\"),q=P.get(\"line-round-limit\");this.lineClips=this.lineFeatureClips(a);for(let X of h)this.addLine(X,a,D,F,V,q);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,a,A,E,x)}addLine(a,h,A,x,E,P){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(let Pt=0;Pt=2&&a[F-1].equals(a[F-2]);)F--;let V=0;for(;V0;if(Ne&&Pt>V){let Me=rt.dist(at);if(Me>2*q){let Ye=rt.sub(rt.sub(at)._mult(q/Me)._round());this.updateDistance(at,Ye),this.addCurrentVertex(Ye,mt,0,0,X),at=Ye}}let Fe=at&&ct,Re=Fe?A:D?\"butt\":x;if(Fe&&Re===\"round\"&&(YtE&&(Re=\"bevel\"),Re===\"bevel\"&&(Yt>2&&(Re=\"flipbevel\"),Yt100)jt=bt.mult(-1);else{let Me=Yt*mt.add(bt).mag()/mt.sub(bt).mag();jt._perp()._mult(Me*(ir?-1:1))}this.addCurrentVertex(rt,jt,0,0,X),this.addCurrentVertex(rt,jt.mult(-1),0,0,X)}else if(Re===\"bevel\"||Re===\"fakeround\"){let Me=-Math.sqrt(Yt*Yt-1),Ye=ir?Me:0,Ie=ir?0:Me;if(at&&this.addCurrentVertex(rt,mt,Ye,Ie,X),Re===\"fakeround\"){let Ae=Math.round(180*ce/Math.PI/20);for(let hr=1;hr2*q){let Ye=rt.add(ct.sub(rt)._mult(q/Me)._round());this.updateDistance(rt,Ye),this.addCurrentVertex(Ye,bt,0,0,X),rt=Ye}}}}addCurrentVertex(a,h,A,x,E,P=!1){let D=h.y*x-h.x,F=-h.y-h.x*x;this.addHalfVertex(a,h.x+h.y*A,h.y-h.x*A,P,!1,A,E),this.addHalfVertex(a,D,F,P,!0,-x,E),this.distance>e6/2&&this.totalDistance===0&&(this.distance=0,this.updateScaledDistance(),this.addCurrentVertex(a,h,A,x,E,P))}addHalfVertex({x:a,y:h},A,x,E,P,D,F){let V=.5*(this.lineClips?this.scaledDistance*(e6-1):this.scaledDistance);this.layoutVertexArray.emplaceBack((a<<1)+(E?1:0),(h<<1)+(P?1:0),Math.round(63*A)+128,Math.round(63*x)+128,1+(D===0?0:D<0?-1:1)|(63&V)<<2,V>>6),this.lineClips&&this.layoutVertexArray2.emplaceBack((this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start),this.lineClipsArray.length);let q=F.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,q),F.primitiveLength++),P?this.e2=q:this.e1=q}updateScaledDistance(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance}updateDistance(a,h){this.distance+=a.dist(h),this.updateScaledDistance()}}let r6,i6;Ge(\"LineBucket\",SC,{omit:[\"layers\",\"patternFeatures\"]});var n6={get paint(){return i6=i6||new Hn({\"line-opacity\":new dr(ee.paint_line[\"line-opacity\"]),\"line-color\":new dr(ee.paint_line[\"line-color\"]),\"line-translate\":new nr(ee.paint_line[\"line-translate\"]),\"line-translate-anchor\":new nr(ee.paint_line[\"line-translate-anchor\"]),\"line-width\":new dr(ee.paint_line[\"line-width\"]),\"line-gap-width\":new dr(ee.paint_line[\"line-gap-width\"]),\"line-offset\":new dr(ee.paint_line[\"line-offset\"]),\"line-blur\":new dr(ee.paint_line[\"line-blur\"]),\"line-dasharray\":new aA(ee.paint_line[\"line-dasharray\"]),\"line-pattern\":new wf(ee.paint_line[\"line-pattern\"]),\"line-gradient\":new Bd(ee.paint_line[\"line-gradient\"])})},get layout(){return r6=r6||new Hn({\"line-cap\":new nr(ee.layout_line[\"line-cap\"]),\"line-join\":new dr(ee.layout_line[\"line-join\"]),\"line-miter-limit\":new nr(ee.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new nr(ee.layout_line[\"line-round-limit\"]),\"line-sort-key\":new dr(ee.layout_line[\"line-sort-key\"])})}};class PK extends dr{possiblyEvaluate(a,h){return h=new un(Math.floor(h.zoom),{now:h.now,fadeDuration:h.fadeDuration,zoomHistory:h.zoomHistory,transition:h.transition}),super.possiblyEvaluate(a,h)}evaluate(a,h,A,x){return h=kt({},h,{zoom:Math.floor(h.zoom)}),super.evaluate(a,h,A,x)}}let rT;class IK extends ji{constructor(a){super(a,n6),this.gradientVersion=0,rT||(rT=new PK(n6.paint.properties[\"line-width\"].specification),rT.useIntegerZoom=!0)}_handleSpecialPaintPropertyUpdate(a){if(a===\"line-gradient\"){let h=this.gradientExpression();this.stepInterpolant=!!function(A){return A._styleExpression!==void 0}(h)&&h._styleExpression.expression instanceof sh,this.gradientVersion=(this.gradientVersion+1)%Number.MAX_SAFE_INTEGER}}gradientExpression(){return this._transitionablePaint._values[\"line-gradient\"].value.expression}recalculate(a,h){super.recalculate(a,h),this.paint._values[\"line-floorwidth\"]=rT.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,a)}createBucket(a){return new SC(a)}queryRadius(a){let h=a,A=s6(We(\"line-width\",this,h),We(\"line-gap-width\",this,h)),x=We(\"line-offset\",this,h);return A/2+Math.abs(x)+te(this.paint.get(\"line-translate\"))}queryIntersectsFeature(a,h,A,x,E,P,D){let F=_e(a,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),P.angle,D),V=D/2*s6(this.paint.get(\"line-width\").evaluate(h,A),this.paint.get(\"line-gap-width\").evaluate(h,A)),q=this.paint.get(\"line-offset\").evaluate(h,A);return q&&(x=function(X,rt){let at=[];for(let ct=0;ct=3){for(let bt=0;bt0?a+2*u:u}let CK=wn([{name:\"a_pos_offset\",components:4,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint16\"},{name:\"a_pixeloffset\",components:4,type:\"Int16\"}],4),LK=wn([{name:\"a_projected_pos\",components:3,type:\"Float32\"}],4);wn([{name:\"a_fade_opacity\",components:1,type:\"Uint32\"}],4);let 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Hs=24,a6=rn,l6=function(u,a,h,A,x){var E,P,D=8*x-A-1,F=(1<>1,q=-7,X=h?x-1:0,rt=h?-1:1,at=u[a+X];for(X+=rt,E=at&(1<<-q)-1,at>>=-q,q+=D;q>0;E=256*E+u[a+X],X+=rt,q-=8);for(P=E&(1<<-q)-1,E>>=-q,q+=A;q>0;P=256*P+u[a+X],X+=rt,q-=8);if(E===0)E=1-V;else{if(E===F)return P?NaN:1/0*(at?-1:1);P+=Math.pow(2,A),E-=V}return(at?-1:1)*P*Math.pow(2,E-A)},c6=function(u,a,h,A,x,E){var P,D,F,V=8*E-x-1,q=(1<>1,rt=x===23?Math.pow(2,-24)-Math.pow(2,-77):0,at=A?0:E-1,ct=A?1:-1,mt=a<0||a===0&&1/a<0?1:0;for(a=Math.abs(a),isNaN(a)||a===1/0?(D=isNaN(a)?1:0,P=q):(P=Math.floor(Math.log(a)/Math.LN2),a*(F=Math.pow(2,-P))<1&&(P--,F*=2),(a+=P+X>=1?rt/F:rt*Math.pow(2,1-X))*F>=2&&(P++,F/=2),P+X>=q?(D=0,P=q):P+X>=1?(D=(a*F-1)*Math.pow(2,x),P+=X):(D=a*Math.pow(2,X-1)*Math.pow(2,x),P=0));x>=8;u[h+at]=255&D,at+=ct,D/=256,x-=8);for(P=P<0;u[h+at]=255&P,at+=ct,P/=256,V-=8);u[h+at-ct]|=128*mt};function rn(u){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(u)?u:new 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h=0;h=128&&f6(h,A,this),this.pos=h-1,this.writeVarint(A),this.pos+=A},writeMessage:function(u,a,h){this.writeTag(u,rn.Bytes),this.writeRawMessage(a,h)},writePackedVarint:function(u,a){a.length&&this.writeMessage(u,OK,a)},writePackedSVarint:function(u,a){a.length&&this.writeMessage(u,BK,a)},writePackedBoolean:function(u,a){a.length&&this.writeMessage(u,NK,a)},writePackedFloat:function(u,a){a.length&&this.writeMessage(u,FK,a)},writePackedDouble:function(u,a){a.length&&this.writeMessage(u,zK,a)},writePackedFixed32:function(u,a){a.length&&this.writeMessage(u,UK,a)},writePackedSFixed32:function(u,a){a.length&&this.writeMessage(u,VK,a)},writePackedFixed64:function(u,a){a.length&&this.writeMessage(u,jK,a)},writePackedSFixed64:function(u,a){a.length&&this.writeMessage(u,GK,a)},writeBytesField:function(u,a){this.writeTag(u,rn.Bytes),this.writeBytes(a)},writeFixed32Field:function(u,a){this.writeTag(u,rn.Fixed32),this.writeFixed32(a)},writeSFixed32Field:function(u,a){this.writeTag(u,rn.Fixed32),this.writeSFixed32(a)},writeFixed64Field:function(u,a){this.writeTag(u,rn.Fixed64),this.writeFixed64(a)},writeSFixed64Field:function(u,a){this.writeTag(u,rn.Fixed64),this.writeSFixed64(a)},writeVarintField:function(u,a){this.writeTag(u,rn.Varint),this.writeVarint(a)},writeSVarintField:function(u,a){this.writeTag(u,rn.Varint),this.writeSVarint(a)},writeStringField:function(u,a){this.writeTag(u,rn.Bytes),this.writeString(a)},writeFloatField:function(u,a){this.writeTag(u,rn.Fixed32),this.writeFloat(a)},writeDoubleField:function(u,a){this.writeTag(u,rn.Fixed64),this.writeDouble(a)},writeBooleanField:function(u,a){this.writeVarintField(u,!!a)}};var MC=c(a6);let EC=3;function WK(u,a,h){u===1&&h.readMessage(HK,a)}function HK(u,a,h){if(u===3){let{id:A,bitmap:x,width:E,height:P,left:D,top:F,advance:V}=h.readMessage(qK,{});a.push({id:A,bitmap:new Vx({width:E+2*EC,height:P+2*EC},x),metrics:{width:E,height:P,left:D,top:F,advance:V}})}}function qK(u,a,h){u===1?a.id=h.readVarint():u===2?a.bitmap=h.readBytes():u===3?a.width=h.readVarint():u===4?a.height=h.readVarint():u===5?a.left=h.readSVarint():u===6?a.top=h.readSVarint():u===7&&(a.advance=h.readVarint())}let p6=EC;function A6(u){let a=0,h=0;for(let P of u)a+=P.w*P.h,h=Math.max(h,P.w);u.sort((P,D)=>D.h-P.h);let A=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(a/.95)),h),h:1/0}],x=0,E=0;for(let P of u)for(let D=A.length-1;D>=0;D--){let F=A[D];if(!(P.w>F.w||P.h>F.h)){if(P.x=F.x,P.y=F.y,E=Math.max(E,P.y+P.h),x=Math.max(x,P.x+P.w),P.w===F.w&&P.h===F.h){let V=A.pop();D=0&&A>=a&&sT[this.text.charCodeAt(A)];A--)h--;this.text=this.text.substring(a,h),this.sectionIndex=this.sectionIndex.slice(a,h)}substring(a,h){let A=new I_;return A.text=this.text.substring(a,h),A.sectionIndex=this.sectionIndex.slice(a,h),A.sections=this.sections,A}toString(){return this.text}getMaxScale(){return this.sectionIndex.reduce((a,h)=>Math.max(a,this.sections[h].scale),0)}addTextSection(a,h){this.text+=a.text,this.sections.push($x.forText(a.scale,a.fontStack||h));let A=this.sections.length-1;for(let x=0;x=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)}}function nT(u,a,h,A,x,E,P,D,F,V,q,X,rt,at,ct,mt){let bt=I_.fromFeature(u,x),Pt;X===n.ai.vertical&&bt.verticalizePunctuation();let{processBidirectionalText:jt,processStyledBidirectionalText:Rt}=ua;if(jt&&bt.sections.length===1){Pt=[];let ce=jt(bt.toString(),IC(bt,V,E,a,A,at,ct));for(let Ne of ce){let ir=new I_;ir.text=Ne,ir.sections=bt.sections;for(let 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this.symbolInstances.length===0&&!this.hasRTLText}uploadPending(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload}upload(a){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(a),this.iconCollisionBox.upload(a)),this.text.upload(a,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(a,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0}destroyDebugData(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()}destroy(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()}addToLineVertexArray(a,h){let A=this.lineVertexArray.length;if(a.segment!==void 0){let x=a.dist(h[a.segment+1]),E=a.dist(h[a.segment]),P={};for(let D=a.segment+1;D=0;D--)P[D]={x:h[D].x,y:h[D].y,tileUnitDistanceFromAnchor:E},D>0&&(E+=h[D-1].dist(h[D]));for(let D=0;D0}hasIconData(){return 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A=this.symbolInstances.get(h);this.featureSortOrder.push(A.featureIndex),[A.rightJustifiedTextSymbolIndex,A.centerJustifiedTextSymbolIndex,A.leftJustifiedTextSymbolIndex].forEach((x,E,P)=>{x>=0&&P.indexOf(x)===E&&this.addIndicesForPlacedSymbol(this.text,x)}),A.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,A.verticalPlacedTextSymbolIndex),A.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,A.placedIconSymbolIndex),A.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,A.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}}}let w6,S6;Ge(\"SymbolBucket\",C_,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),C_.MAX_GLYPHS=65535,C_.addDynamicAttributes=kC;var OC={get paint(){return S6=S6||new Hn({\"icon-opacity\":new dr(ee.paint_symbol[\"icon-opacity\"]),\"icon-color\":new dr(ee.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new dr(ee.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new dr(ee.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new dr(ee.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new nr(ee.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new nr(ee.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new dr(ee.paint_symbol[\"text-opacity\"]),\"text-color\":new dr(ee.paint_symbol[\"text-color\"],{runtimeType:Us,getOverride:u=>u.textColor,hasOverride:u=>!!u.textColor}),\"text-halo-color\":new dr(ee.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new dr(ee.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new dr(ee.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new nr(ee.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new nr(ee.paint_symbol[\"text-translate-anchor\"])})},get layout(){return w6=w6||new Hn({\"symbol-placement\":new nr(ee.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new nr(ee.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new nr(ee.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new dr(ee.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new nr(ee.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new nr(ee.layout_symbol[\"icon-allow-overlap\"]),\"icon-overlap\":new nr(ee.layout_symbol[\"icon-overlap\"]),\"icon-ignore-placement\":new nr(ee.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new nr(ee.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new nr(ee.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new dr(ee.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new nr(ee.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new nr(ee.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new dr(ee.layout_symbol[\"icon-image\"]),\"icon-rotate\":new dr(ee.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new dr(ee.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new nr(ee.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new dr(ee.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new dr(ee.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new nr(ee.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new nr(ee.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new nr(ee.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new dr(ee.layout_symbol[\"text-field\"]),\"text-font\":new dr(ee.layout_symbol[\"text-font\"]),\"text-size\":new dr(ee.layout_symbol[\"text-size\"]),\"text-max-width\":new dr(ee.layout_symbol[\"text-max-width\"]),\"text-line-height\":new nr(ee.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new dr(ee.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new dr(ee.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new dr(ee.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new nr(ee.layout_symbol[\"text-variable-anchor\"]),\"text-variable-anchor-offset\":new dr(ee.layout_symbol[\"text-variable-anchor-offset\"]),\"text-anchor\":new dr(ee.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new nr(ee.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new nr(ee.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new dr(ee.layout_symbol[\"text-rotate\"]),\"text-padding\":new nr(ee.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new nr(ee.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new dr(ee.layout_symbol[\"text-transform\"]),\"text-offset\":new dr(ee.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new nr(ee.layout_symbol[\"text-allow-overlap\"]),\"text-overlap\":new nr(ee.layout_symbol[\"text-overlap\"]),\"text-ignore-placement\":new nr(ee.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new nr(ee.layout_symbol[\"text-optional\"])})}};class T6{constructor(a){if(a.property.overrides===void 0)throw new Error(\"overrides must be provided to instantiate FormatSectionOverride class\");this.type=a.property.overrides?a.property.overrides.runtimeType:Ca,this.defaultValue=a}evaluate(a){if(a.formattedSection){let h=this.defaultValue.property.overrides;if(h&&h.hasOverride(a.formattedSection))return h.getOverride(a.formattedSection)}return a.feature&&a.featureState?this.defaultValue.evaluate(a.feature,a.featureState):this.defaultValue.property.specification.default}eachChild(a){this.defaultValue.isConstant()||a(this.defaultValue.value._styleExpression.expression)}outputDefined(){return!1}serialize(){return null}}Ge(\"FormatSectionOverride\",T6,{omit:[\"defaultValue\"]});class aT extends ji{constructor(a){super(a,OC)}recalculate(a,h){if(super.recalculate(a,h),this.layout.get(\"icon-rotation-alignment\")===\"auto\"&&(this.layout._values[\"icon-rotation-alignment\"]=this.layout.get(\"symbol-placement\")!==\"point\"?\"map\":\"viewport\"),this.layout.get(\"text-rotation-alignment\")===\"auto\"&&(this.layout._values[\"text-rotation-alignment\"]=this.layout.get(\"symbol-placement\")!==\"point\"?\"map\":\"viewport\"),this.layout.get(\"text-pitch-alignment\")===\"auto\"&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")===\"map\"?\"map\":\"viewport\"),this.layout.get(\"icon-pitch-alignment\")===\"auto\"&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),this.layout.get(\"symbol-placement\")===\"point\"){let A=this.layout.get(\"text-writing-mode\");if(A){let x=[];for(let E of A)x.indexOf(E)<0&&x.push(E);this.layout._values[\"text-writing-mode\"]=x}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()}getValueAndResolveTokens(a,h,A,x){let E=this.layout.get(a).evaluate(h,{},A,x),P=this._unevaluatedLayout._values[a];return P.isDataDriven()||Yp(P.value)||!E?E:function(D,F){return F.replace(/{([^{}]+)}/g,(V,q)=>D&&q in D?String(D[q]):\"\")}(h.properties,E)}createBucket(a){return new C_(a)}queryRadius(){return 0}queryIntersectsFeature(){throw new Error(\"Should take a different path in FeatureIndex\")}_setPaintOverrides(){for(let a of OC.paint.overridableProperties){if(!aT.hasPaintOverride(this.layout,a))continue;let h=this.paint.get(a),A=new T6(h),x=new Jm(A,h.property.specification),E=null;E=h.value.kind===\"constant\"||h.value.kind===\"source\"?new t0(\"source\",x):new wt(\"composite\",x,h.value.zoomStops),this.paint._values[a]=new Mo(h.property,E,h.parameters)}}_handleOverridablePaintPropertyUpdate(a,h,A){return!(!this.layout||h.isDataDriven()||A.isDataDriven())&&aT.hasPaintOverride(this.layout,a)}static hasPaintOverride(a,h){let A=a.get(\"text-field\"),x=OC.paint.properties[h],E=!1,P=D=>{for(let F of D)if(x.overrides&&x.overrides.hasOverride(F))return void(E=!0)};if(A.value.kind===\"constant\"&&A.value.value instanceof ln)P(A.value.value.sections);else if(A.value.kind===\"source\"){let D=V=>{E||(V instanceof Gl&&Ki(V.value)===gt?P(V.value.sections):V instanceof Ti?P(V.sections):V.eachChild(D))},F=A.value;F._styleExpression&&D(F._styleExpression.expression)}return E}}let M6;var tJ={get paint(){return M6=M6||new Hn({\"background-color\":new nr(ee.paint_background[\"background-color\"]),\"background-pattern\":new aA(ee.paint_background[\"background-pattern\"]),\"background-opacity\":new nr(ee.paint_background[\"background-opacity\"])})}};class eJ extends ji{constructor(a){super(a,tJ)}}let E6;var rJ={get paint(){return E6=E6||new Hn({\"raster-opacity\":new nr(ee.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new nr(ee.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new nr(ee.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new nr(ee.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new nr(ee.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new nr(ee.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new nr(ee.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new nr(ee.paint_raster[\"raster-fade-duration\"])})}};class iJ extends ji{constructor(a){super(a,rJ)}}class nJ extends ji{constructor(a){super(a,{}),this.onAdd=h=>{this.implementation.onAdd&&this.implementation.onAdd(h,h.painter.context.gl)},this.onRemove=h=>{this.implementation.onRemove&&this.implementation.onRemove(h,h.painter.context.gl)},this.implementation=a}is3D(){return this.implementation.renderingMode===\"3d\"}hasOffscreenPass(){return this.implementation.prerender!==void 0}recalculate(){}updateTransitions(){}hasTransition(){return!1}serialize(){throw new Error(\"Custom layers cannot be serialized\")}}class sJ{constructor(a){this._callback=a,this._triggered=!1,typeof MessageChannel<\"u\"&&(this._channel=new MessageChannel,this._channel.port2.onmessage=()=>{this._triggered=!1,this._callback()})}trigger(){this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout(()=>{this._triggered=!1,this._callback()},0))}remove(){delete this._channel,this._callback=()=>{}}}let BC=63710088e-1;class dA{constructor(a,h){if(isNaN(a)||isNaN(h))throw new Error(`Invalid LngLat object: (${a}, ${h})`);if(this.lng=+a,this.lat=+h,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}wrap(){return new dA(Et(this.lng,-180,180),this.lat)}toArray(){return[this.lng,this.lat]}toString(){return`LngLat(${this.lng}, ${this.lat})`}distanceTo(a){let h=Math.PI/180,A=this.lat*h,x=a.lat*h,E=Math.sin(A)*Math.sin(x)+Math.cos(A)*Math.cos(x)*Math.cos((a.lng-this.lng)*h);return BC*Math.acos(Math.min(E,1))}static convert(a){if(a instanceof dA)return a;if(Array.isArray(a)&&(a.length===2||a.length===3))return new dA(Number(a[0]),Number(a[1]));if(!Array.isArray(a)&&typeof a==\"object\"&&a!==null)return new dA(Number(\"lng\"in a?a.lng:a.lon),Number(a.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: , lat: }, an object {lon: , lat: }, or an array of [, ]\")}}let P6=2*Math.PI*BC;function I6(u){return P6*Math.cos(u*Math.PI/180)}function C6(u){return(180+u)/360}function L6(u){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+u*Math.PI/360)))/360}function k6(u,a){return u/I6(a)}function R6(u){return 360*u-180}function FC(u){return 360/Math.PI*Math.atan(Math.exp((180-360*u)*Math.PI/180))-90}class lT{constructor(a,h,A=0){this.x=+a,this.y=+h,this.z=+A}static fromLngLat(a,h=0){let A=dA.convert(a);return new lT(C6(A.lng),L6(A.lat),k6(h,A.lat))}toLngLat(){return new dA(R6(this.x),FC(this.y))}toAltitude(){return this.z*I6(FC(this.y))}meterInMercatorCoordinateUnits(){return 1/P6*(a=FC(this.y),1/Math.cos(a*Math.PI/180));var a}}function D6(u,a,h){var A=2*Math.PI*6378137/256/Math.pow(2,h);return[u*A-2*Math.PI*6378137/2,a*A-2*Math.PI*6378137/2]}class zC{constructor(a,h,A){if(a<0||a>25||A<0||A>=Math.pow(2,a)||h<0||h>=Math.pow(2,a))throw new Error(`x=${h}, y=${A}, z=${a} outside of bounds. 0<=x<${Math.pow(2,a)}, 0<=y<${Math.pow(2,a)} 0<=z<=25 `);this.z=a,this.x=h,this.y=A,this.key=Kx(0,a,a,h,A)}equals(a){return this.z===a.z&&this.x===a.x&&this.y===a.y}url(a,h,A){let x=(P=this.y,D=this.z,F=D6(256*(E=this.x),256*(P=Math.pow(2,D)-P-1),D),V=D6(256*(E+1),256*(P+1),D),F[0]+\",\"+F[1]+\",\"+V[0]+\",\"+V[1]);var E,P,D,F,V;let q=function(X,rt,at){let ct,mt=\"\";for(let bt=X;bt>0;bt--)ct=1<1?\"@2x\":\"\").replace(/{quadkey}/g,q).replace(/{bbox-epsg-3857}/g,x)}isChildOf(a){let h=this.z-a.z;return h>0&&a.x===this.x>>h&&a.y===this.y>>h}getTilePoint(a){let h=Math.pow(2,this.z);return new w((a.x*h-this.x)*en,(a.y*h-this.y)*en)}toString(){return`${this.z}/${this.x}/${this.y}`}}class O6{constructor(a,h){this.wrap=a,this.canonical=h,this.key=Kx(a,h.z,h.z,h.x,h.y)}}class Nc{constructor(a,h,A,x,E){if(a= z; overscaledZ = ${a}; z = ${A}`);this.overscaledZ=a,this.wrap=h,this.canonical=new zC(A,+x,+E),this.key=Kx(h,a,A,x,E)}clone(){return new Nc(this.overscaledZ,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)}equals(a){return this.overscaledZ===a.overscaledZ&&this.wrap===a.wrap&&this.canonical.equals(a.canonical)}scaledTo(a){if(a>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${a}; overscaledZ = ${this.overscaledZ}`);let h=this.canonical.z-a;return a>this.canonical.z?new Nc(a,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Nc(a,this.wrap,a,this.canonical.x>>h,this.canonical.y>>h)}calculateScaledKey(a,h){if(a>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${a}; overscaledZ = ${this.overscaledZ}`);let A=this.canonical.z-a;return a>this.canonical.z?Kx(this.wrap*+h,a,this.canonical.z,this.canonical.x,this.canonical.y):Kx(this.wrap*+h,a,a,this.canonical.x>>A,this.canonical.y>>A)}isChildOf(a){if(a.wrap!==this.wrap)return!1;let h=this.canonical.z-a.canonical.z;return a.overscaledZ===0||a.overscaledZ>h&&a.canonical.y===this.canonical.y>>h}children(a){if(this.overscaledZ>=a)return[new Nc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];let h=this.canonical.z+1,A=2*this.canonical.x,x=2*this.canonical.y;return[new Nc(h,this.wrap,h,A,x),new Nc(h,this.wrap,h,A+1,x),new Nc(h,this.wrap,h,A,x+1),new Nc(h,this.wrap,h,A+1,x+1)]}isLessThan(a){return this.wrapa.wrap)&&(this.overscaledZa.overscaledZ)&&(this.canonical.xa.canonical.x)&&this.canonical.ythis.max&&(this.max=X),X=this.dim+1||h<-1||h>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(h+1)*this.stride+(a+1)}unpack(a,h,A){return a*this.redFactor+h*this.greenFactor+A*this.blueFactor-this.baseShift}getPixels(){return new zc({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))}backfillBorder(a,h,A){if(this.dim!==a.dim)throw new Error(\"dem dimension mismatch\");let x=h*this.dim,E=h*this.dim+this.dim,P=A*this.dim,D=A*this.dim+this.dim;switch(h){case-1:x=E-1;break;case 1:E=x+1}switch(A){case-1:P=D-1;break;case 1:D=P+1}let F=-h*this.dim,V=-A*this.dim;for(let q=P;q=this._numberToString.length)throw new Error(`Out of bounds. Index requested n=${a} can't be >= this._numberToString.length ${this._numberToString.length}`);return this._numberToString[a]}}class z6{constructor(a,h,A,x,E){this.type=\"Feature\",this._vectorTileFeature=a,a._z=h,a._x=A,a._y=x,this.properties=a.properties,this.id=E}get geometry(){return this._geometry===void 0&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry}set geometry(a){this._geometry=a}toJSON(){let a={geometry:this.geometry};for(let h in this)h!==\"_geometry\"&&h!==\"_vectorTileFeature\"&&(a[h]=this[h]);return a}}class N6{constructor(a,h){this.tileID=a,this.x=a.canonical.x,this.y=a.canonical.y,this.z=a.canonical.z,this.grid=new Na(en,16,0),this.grid3D=new Na(en,16,0),this.featureIndexArray=new he,this.promoteId=h}insert(a,h,A,x,E,P){let D=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(A,x,E);let F=P?this.grid3D:this.grid;for(let V=0;V=0&&X[3]>=0&&F.insert(D,X[0],X[1],X[2],X[3])}}loadVTLayers(){return this.vtLayers||(this.vtLayers=new uA.VectorTile(new MC(this.rawTileData)).layers,this.sourceLayerCoder=new F6(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers}query(a,h,A,x){this.loadVTLayers();let E=a.params||{},P=en/a.tileSize/a.scale,D=r0(E.filter),F=a.queryGeometry,V=a.queryPadding*P,q=V6(F),X=this.grid.query(q.minX-V,q.minY-V,q.maxX+V,q.maxY+V),rt=V6(a.cameraQueryGeometry),at=this.grid3D.query(rt.minX-V,rt.minY-V,rt.maxX+V,rt.maxY+V,(bt,Pt,jt,Rt)=>function(Gt,Yt,ce,Ne,ir){for(let Re of Gt)if(Yt<=Re.x&&ce<=Re.y&&Ne>=Re.x&&ir>=Re.y)return!0;let Fe=[new w(Yt,ce),new w(Yt,ir),new w(Ne,ir),new w(Ne,ce)];if(Gt.length>2){for(let Re of Fe)if(ne(Gt,Re))return!0}for(let Re=0;Re(Rt||(Rt=y(Gt)),Yt.queryIntersectsFeature(F,Gt,ce,Rt,this.z,a.transform,P,a.pixelPosMatrix)))}return ct}loadMatchingFeature(a,h,A,x,E,P,D,F,V,q,X){let rt=this.bucketLayerIDs[h];if(P&&!function(bt,Pt){for(let jt=0;jt=0)return!0;return!1}(P,rt))return;let at=this.sourceLayerCoder.decode(A),ct=this.vtLayers[at].feature(x);if(E.needGeometry){let bt=S(ct,!0);if(!E.filter(new un(this.tileID.overscaledZ),bt,this.tileID.canonical))return}else if(!E.filter(new un(this.tileID.overscaledZ),ct))return;let mt=this.getId(ct,at);for(let bt=0;bt{let D=a instanceof oA?a.get(P):null;return D&&D.evaluate?D.evaluate(h,A,x):D})}function V6(u){let a=1/0,h=1/0,A=-1/0,x=-1/0;for(let E of u)a=Math.min(a,E.x),h=Math.min(h,E.y),A=Math.max(A,E.x),x=Math.max(x,E.y);return{minX:a,minY:h,maxX:A,maxY:x}}function oJ(u,a){return a-u}function j6(u,a,h,A,x){let E=[];for(let P=0;P=A&&X.x>=A||(q.x>=A?q=new w(A,q.y+(A-q.x)/(X.x-q.x)*(X.y-q.y))._round():X.x>=A&&(X=new w(A,q.y+(A-q.x)/(X.x-q.x)*(X.y-q.y))._round()),q.y>=x&&X.y>=x||(q.y>=x?q=new w(q.x+(x-q.y)/(X.y-q.y)*(X.x-q.x),x)._round():X.y>=x&&(X=new w(q.x+(x-q.y)/(X.y-q.y)*(X.x-q.x),x)._round()),F&&q.equals(F[F.length-1])||(F=[q],E.push(F)),F.push(X)))))}}return E}Ge(\"FeatureIndex\",N6,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});class pA extends w{constructor(a,h,A,x){super(a,h),this.angle=A,x!==void 0&&(this.segment=x)}clone(){return new pA(this.x,this.y,this.angle,this.segment)}}function G6(u,a,h,A,x){if(a.segment===void 0||h===0)return!0;let E=a,P=a.segment+1,D=0;for(;D>-h/2;){if(P--,P<0)return!1;D-=u[P].dist(E),E=u[P]}D+=u[P].dist(u[P+1]),P++;let F=[],V=0;for(;DA;)V-=F.shift().angleDelta;if(V>x)return!1;P++,D+=q.dist(X)}return!0}function W6(u){let a=0;for(let h=0;hV){let ct=(V-F)/at,mt=Da.number(X.x,rt.x,ct),bt=Da.number(X.y,rt.y,ct),Pt=new pA(mt,bt,rt.angleTo(X),q);return Pt._round(),!P||G6(u,Pt,D,P,a)?Pt:void 0}F+=at}}function lJ(u,a,h,A,x,E,P,D,F){let V=H6(A,E,P),q=q6(A,x),X=q*P,rt=u[0].x===0||u[0].x===F||u[0].y===0||u[0].y===F;return a-X=0&&Gt=0&&Yt=0&&rt+V<=q){let ce=new pA(Gt,Yt,jt,ct);ce._round(),A&&!G6(u,ce,E,A,x)||at.push(ce)}}X+=Pt}return D||at.length||P||(at=Z6(u,X/2,h,A,x,E,P,!0,F)),at}Ge(\"Anchor\",pA);let L_=gl;function 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xt=this._layers[vt.id]=n.aC(vt);xt._featureFilter=n.a6(xt.filter),this.keyCache[vt.id]&&delete this.keyCache[vt.id]}for(let vt of nt)delete this.keyCache[vt],delete this._layerConfigs[vt],delete this._layers[vt];this.familiesBySource={};let ht=n.bl(Object.values(this._layerConfigs),this.keyCache);for(let vt of ht){let xt=vt.map(se=>this._layers[se.id]),_t=xt[0];if(_t.visibility===\"none\")continue;let Dt=_t.source||\"\",Mt=this.familiesBySource[Dt];Mt||(Mt=this.familiesBySource[Dt]={});let Vt=_t.sourceLayer||\"_geojsonTileLayer\",ie=Mt[Vt];ie||(ie=Mt[Vt]=[]),ie.push(xt)}}}class c{constructor(tt){let nt={},ht=[];for(let Dt in tt){let Mt=tt[Dt],Vt=nt[Dt]={};for(let ie in Mt){let se=Mt[+ie];if(!se||se.bitmap.width===0||se.bitmap.height===0)continue;let ae={x:0,y:0,w:se.bitmap.width+2,h:se.bitmap.height+2};ht.push(ae),Vt[ie]={rect:ae,metrics:se.metrics}}}let{w:vt,h:xt}=n.p(ht),_t=new n.q({width:vt||1,height:xt||1});for(let Dt in tt){let Mt=tt[Dt];for(let Vt in Mt){let ie=Mt[+Vt];if(!ie||ie.bitmap.width===0||ie.bitmap.height===0)continue;let se=nt[Dt][Vt].rect;n.q.copy(ie.bitmap,_t,{x:0,y:0},{x:se.x+1,y:se.y+1},ie.bitmap)}}this.image=_t,this.positions=nt}}n.bm(\"GlyphAtlas\",c);class f{constructor(tt){this.tileID=new n.O(tt.tileID.overscaledZ,tt.tileID.wrap,tt.tileID.canonical.z,tt.tileID.canonical.x,tt.tileID.canonical.y),this.uid=tt.uid,this.zoom=tt.zoom,this.pixelRatio=tt.pixelRatio,this.tileSize=tt.tileSize,this.source=tt.source,this.overscaling=this.tileID.overscaleFactor(),this.showCollisionBoxes=tt.showCollisionBoxes,this.collectResourceTiming=!!tt.collectResourceTiming,this.returnDependencies=!!tt.returnDependencies,this.promoteId=tt.promoteId,this.inFlightDependencies=[],this.dependencySentinel=-1}parse(tt,nt,ht,vt,xt){this.status=\"parsing\",this.data=tt,this.collisionBoxArray=new n.a3;let _t=new n.bn(Object.keys(tt.layers).sort()),Dt=new n.bo(this.tileID,this.promoteId);Dt.bucketLayerIDs=[];let Mt={},Vt={featureIndex:Dt,iconDependencies:{},patternDependencies:{},glyphDependencies:{},availableImages:ht},ie=nt.familiesBySource[this.source];for(let ni in ie){let Hr=tt.layers[ni];if(!Hr)continue;Hr.version===1&&n.w(`Vector tile source \"${this.source}\" layer \"${ni}\" does not use vector tile spec v2 and therefore may have some rendering errors.`);let jn=_t.encode(ni),Bi=[];for(let xn=0;xn=es.maxzoom||es.visibility!==\"none\"&&(_(xn,this.zoom,ht),(Mt[es.id]=es.createBucket({index:Dt.bucketLayerIDs.length,layers:xn,zoom:this.zoom,pixelRatio:this.pixelRatio,overscaling:this.overscaling,collisionBoxArray:this.collisionBoxArray,sourceLayerIndex:jn,sourceID:this.source})).populate(Bi,Vt,this.tileID.canonical),Dt.bucketLayerIDs.push(xn.map(oa=>oa.id)))}}let se,ae,lr,vr,Xe=n.aH(Vt.glyphDependencies,ni=>Object.keys(ni).map(Number));this.inFlightDependencies.forEach(ni=>ni?.cancel()),this.inFlightDependencies=[];let cr=++this.dependencySentinel;Object.keys(Xe).length?this.inFlightDependencies.push(vt.send(\"getGlyphs\",{uid:this.uid,stacks:Xe,source:this.source,tileID:this.tileID,type:\"glyphs\"},(ni,Hr)=>{cr===this.dependencySentinel&&(se||(se=ni,ae=Hr,zi.call(this)))})):ae={};let wr=Object.keys(Vt.iconDependencies);wr.length?this.inFlightDependencies.push(vt.send(\"getImages\",{icons:wr,source:this.source,tileID:this.tileID,type:\"icons\"},(ni,Hr)=>{cr===this.dependencySentinel&&(se||(se=ni,lr=Hr,zi.call(this)))})):lr={};let xi=Object.keys(Vt.patternDependencies);function zi(){if(se)return xt(se);if(ae&&lr&&vr){let ni=new c(ae),Hr=new n.bp(lr,vr);for(let jn in Mt){let Bi=Mt[jn];Bi instanceof n.a4?(_(Bi.layers,this.zoom,ht),n.bq({bucket:Bi,glyphMap:ae,glyphPositions:ni.positions,imageMap:lr,imagePositions:Hr.iconPositions,showCollisionBoxes:this.showCollisionBoxes,canonical:this.tileID.canonical})):Bi.hasPattern&&(Bi instanceof n.br||Bi instanceof n.bs||Bi instanceof n.bt)&&(_(Bi.layers,this.zoom,ht),Bi.addFeatures(Vt,this.tileID.canonical,Hr.patternPositions))}this.status=\"done\",xt(null,{buckets:Object.values(Mt).filter(jn=>!jn.isEmpty()),featureIndex:Dt,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:ni.image,imageAtlas:Hr,glyphMap:this.returnDependencies?ae:null,iconMap:this.returnDependencies?lr:null,glyphPositions:this.returnDependencies?ni.positions:null})}}xi.length?this.inFlightDependencies.push(vt.send(\"getImages\",{icons:xi,source:this.source,tileID:this.tileID,type:\"patterns\"},(ni,Hr)=>{cr===this.dependencySentinel&&(se||(se=ni,vr=Hr,zi.call(this)))})):vr={},zi.call(this)}}function _(gt,tt,nt){let ht=new n.a8(tt);for(let vt of gt)vt.recalculate(ht,nt)}function w(gt,tt){let nt=n.l(gt.request,(ht,vt,xt,_t)=>{if(ht)tt(ht);else if(vt)try{let Dt=new n.bw.VectorTile(new n.bv(vt));tt(null,{vectorTile:Dt,rawData:vt,cacheControl:xt,expires:_t})}catch(Dt){let Mt=new Uint8Array(vt),Vt=`Unable to parse the tile at ${gt.request.url}, `;Vt+=Mt[0]===31&&Mt[1]===139?\"please make sure the data is not gzipped and that you have configured the relevant header in the server\":`got error: ${Dt.messge}`,tt(new Error(Vt))}});return()=>{nt.cancel(),tt()}}class I{constructor(tt,nt,ht,vt){this.actor=tt,this.layerIndex=nt,this.availableImages=ht,this.loadVectorData=vt||w,this.fetching={},this.loading={},this.loaded={}}loadTile(tt,nt){let ht=tt.uid;this.loading||(this.loading={});let vt=!!(tt&&tt.request&&tt.request.collectResourceTiming)&&new n.bu(tt.request),xt=this.loading[ht]=new f(tt);xt.abort=this.loadVectorData(tt,(_t,Dt)=>{if(delete this.loading[ht],_t||!Dt)return xt.status=\"done\",this.loaded[ht]=xt,nt(_t);let Mt=Dt.rawData,Vt={};Dt.expires&&(Vt.expires=Dt.expires),Dt.cacheControl&&(Vt.cacheControl=Dt.cacheControl);let ie={};if(vt){let se=vt.finish();se&&(ie.resourceTiming=JSON.parse(JSON.stringify(se)))}xt.vectorTile=Dt.vectorTile,xt.parse(Dt.vectorTile,this.layerIndex,this.availableImages,this.actor,(se,ae)=>{if(delete this.fetching[ht],se||!ae)return nt(se);nt(null,n.e({rawTileData:Mt.slice(0)},ae,Vt,ie))}),this.loaded=this.loaded||{},this.loaded[ht]=xt,this.fetching[ht]={rawTileData:Mt,cacheControl:Vt,resourceTiming:ie}})}reloadTile(tt,nt){let ht=this.loaded,vt=tt.uid;if(ht&&ht[vt]){let xt=ht[vt];xt.showCollisionBoxes=tt.showCollisionBoxes,xt.status===\"parsing\"?xt.parse(xt.vectorTile,this.layerIndex,this.availableImages,this.actor,(_t,Dt)=>{if(_t||!Dt)return nt(_t,Dt);let Mt;if(this.fetching[vt]){let{rawTileData:Vt,cacheControl:ie,resourceTiming:se}=this.fetching[vt];delete this.fetching[vt],Mt=n.e({rawTileData:Vt.slice(0)},Dt,ie,se)}else Mt=Dt;nt(null,Mt)}):xt.status===\"done\"&&(xt.vectorTile?xt.parse(xt.vectorTile,this.layerIndex,this.availableImages,this.actor,nt):nt())}}abortTile(tt,nt){let ht=this.loading,vt=tt.uid;ht&&ht[vt]&&ht[vt].abort&&(ht[vt].abort(),delete ht[vt]),nt()}removeTile(tt,nt){let ht=this.loaded,vt=tt.uid;ht&&ht[vt]&&delete ht[vt],nt()}}class R{constructor(){this.loaded={}}loadTile(tt,nt){return n._(this,void 0,void 0,function*(){let{uid:ht,encoding:vt,rawImageData:xt,redFactor:_t,greenFactor:Dt,blueFactor:Mt,baseShift:Vt}=tt,ie=xt.width+2,se=xt.height+2,ae=n.a(xt)?new n.R({width:ie,height:se},yield n.bx(xt,-1,-1,ie,se)):xt,lr=new n.by(ht,ae,vt,_t,Dt,Mt,Vt);this.loaded=this.loaded||{},this.loaded[ht]=lr,nt(null,lr)})}removeTile(tt){let nt=this.loaded,ht=tt.uid;nt&&nt[ht]&&delete nt[ht]}}function N(gt,tt){if(gt.length!==0){j(gt[0],tt);for(var nt=1;nt=Math.abs(Dt)?nt-Mt+Dt:Dt-Mt+nt,nt=Mt}nt+ht>=0!=!!tt&>.reverse()}var Q=n.bz(function gt(tt,nt){var ht,vt=tt&&tt.type;if(vt===\"FeatureCollection\")for(ht=0;ht>31}function Li(gt,tt){for(var nt=gt.loadGeometry(),ht=gt.type,vt=0,xt=0,_t=nt.length,Dt=0;Dt<_t;Dt++){var Mt=nt[Dt],Vt=1;ht===1&&(Vt=Mt.length),tt.writeVarint(rr(1,Vt));for(var ie=ht===3?Mt.length-1:Mt.length,se=0;segt},ih=Math.fround||(Uo=new Float32Array(1),gt=>(Uo[0]=+gt,Uo[0]));var Uo;let Si=3,Ns=5,ll=6;class kc{constructor(tt){this.options=Object.assign(Object.create(No),tt),this.trees=new Array(this.options.maxZoom+1),this.stride=this.options.reduce?7:6,this.clusterProps=[]}load(tt){let{log:nt,minZoom:ht,maxZoom:vt}=this.options;nt&&console.time(\"total time\");let xt=`prepare ${tt.length} points`;nt&&console.time(xt),this.points=tt;let _t=[];for(let Mt=0;Mt=ht;Mt--){let Vt=+Date.now();Dt=this.trees[Mt]=this._createTree(this._cluster(Dt,Mt)),nt&&console.log(\"z%d: %d clusters in %dms\",Mt,Dt.numItems,+Date.now()-Vt)}return nt&&console.timeEnd(\"total time\"),this}getClusters(tt,nt){let ht=((tt[0]+180)%360+360)%360-180,vt=Math.max(-90,Math.min(90,tt[1])),xt=tt[2]===180?180:((tt[2]+180)%360+360)%360-180,_t=Math.max(-90,Math.min(90,tt[3]));if(tt[2]-tt[0]>=360)ht=-180,xt=180;else if(ht>xt){let se=this.getClusters([ht,vt,180,_t],nt),ae=this.getClusters([-180,vt,xt,_t],nt);return se.concat(ae)}let Dt=this.trees[this._limitZoom(nt)],Mt=Dt.range(Jn(ht),ki(_t),Jn(xt),ki(vt)),Vt=Dt.data,ie=[];for(let se of Mt){let ae=this.stride*se;ie.push(Vt[ae+Ns]>1?Rc(Vt,ae,this.clusterProps):this.points[Vt[ae+Si]])}return ie}getChildren(tt){let nt=this._getOriginId(tt),ht=this._getOriginZoom(tt),vt=\"No cluster with the specified id.\",xt=this.trees[ht];if(!xt)throw new Error(vt);let _t=xt.data;if(nt*this.stride>=_t.length)throw new Error(vt);let Dt=this.options.radius/(this.options.extent*Math.pow(2,ht-1)),Mt=xt.within(_t[nt*this.stride],_t[nt*this.stride+1],Dt),Vt=[];for(let ie of Mt){let se=ie*this.stride;_t[se+4]===tt&&Vt.push(_t[se+Ns]>1?Rc(_t,se,this.clusterProps):this.points[_t[se+Si]])}if(Vt.length===0)throw new Error(vt);return Vt}getLeaves(tt,nt,ht){let vt=[];return this._appendLeaves(vt,tt,nt=nt||10,ht=ht||0,0),vt}getTile(tt,nt,ht){let vt=this.trees[this._limitZoom(tt)],xt=Math.pow(2,tt),{extent:_t,radius:Dt}=this.options,Mt=Dt/_t,Vt=(ht-Mt)/xt,ie=(ht+1+Mt)/xt,se={features:[]};return this._addTileFeatures(vt.range((nt-Mt)/xt,Vt,(nt+1+Mt)/xt,ie),vt.data,nt,ht,xt,se),nt===0&&this._addTileFeatures(vt.range(1-Mt/xt,Vt,1,ie),vt.data,xt,ht,xt,se),nt===xt-1&&this._addTileFeatures(vt.range(0,Vt,Mt/xt,ie),vt.data,-1,ht,xt,se),se.features.length?se:null}getClusterExpansionZoom(tt){let nt=this._getOriginZoom(tt)-1;for(;nt<=this.options.maxZoom;){let ht=this.getChildren(tt);if(nt++,ht.length!==1)break;tt=ht[0].properties.cluster_id}return nt}_appendLeaves(tt,nt,ht,vt,xt){let _t=this.getChildren(nt);for(let Dt of _t){let Mt=Dt.properties;if(Mt&&Mt.cluster?xt+Mt.point_count<=vt?xt+=Mt.point_count:xt=this._appendLeaves(tt,Mt.cluster_id,ht,vt,xt):xt1,ie,se,ae;if(Vt)ie=Xi(nt,Mt,this.clusterProps),se=nt[Mt],ae=nt[Mt+1];else{let Xe=this.points[nt[Mt+Si]];ie=Xe.properties;let[cr,wr]=Xe.geometry.coordinates;se=Jn(cr),ae=ki(wr)}let lr={type:1,geometry:[[Math.round(this.options.extent*(se*xt-ht)),Math.round(this.options.extent*(ae*xt-vt))]],tags:ie},vr;vr=Vt||this.options.generateId?nt[Mt+Si]:this.points[nt[Mt+Si]].id,vr!==void 0&&(lr.id=vr),_t.features.push(lr)}}_limitZoom(tt){return Math.max(this.options.minZoom,Math.min(Math.floor(+tt),this.options.maxZoom+1))}_cluster(tt,nt){let{radius:ht,extent:vt,reduce:xt,minPoints:_t}=this.options,Dt=ht/(vt*Math.pow(2,nt)),Mt=tt.data,Vt=[],ie=this.stride;for(let se=0;sent&&(cr+=Mt[xi+Ns])}if(cr>Xe&&cr>=_t){let wr,xi=ae*Xe,zi=lr*Xe,ni=-1,Hr=((se/ie|0)<<5)+(nt+1)+this.points.length;for(let jn of vr){let Bi=jn*ie;if(Mt[Bi+2]<=nt)continue;Mt[Bi+2]=nt;let xn=Mt[Bi+Ns];xi+=Mt[Bi]*xn,zi+=Mt[Bi+1]*xn,Mt[Bi+4]=Hr,xt&&(wr||(wr=this._map(Mt,se,!0),ni=this.clusterProps.length,this.clusterProps.push(wr)),xt(wr,this._map(Mt,Bi)))}Mt[se+4]=Hr,Vt.push(xi/cr,zi/cr,1/0,Hr,-1,cr),xt&&Vt.push(ni)}else{for(let wr=0;wr1)for(let wr of vr){let xi=wr*ie;if(!(Mt[xi+2]<=nt)){Mt[xi+2]=nt;for(let zi=0;zi>5}_getOriginZoom(tt){return(tt-this.points.length)%32}_map(tt,nt,ht){if(tt[nt+Ns]>1){let _t=this.clusterProps[tt[nt+ll]];return ht?Object.assign({},_t):_t}let vt=this.points[tt[nt+Si]].properties,xt=this.options.map(vt);return ht&&xt===vt?Object.assign({},xt):xt}}function Rc(gt,tt,nt){return{type:\"Feature\",id:gt[tt+Si],properties:Xi(gt,tt,nt),geometry:{type:\"Point\",coordinates:[(ht=gt[tt],360*(ht-.5)),ts(gt[tt+1])]}};var ht}function Xi(gt,tt,nt){let ht=gt[tt+Ns],vt=ht>=1e4?`${Math.round(ht/1e3)}k`:ht>=1e3?Math.round(ht/100)/10+\"k\":ht,xt=gt[tt+ll],_t=xt===-1?{}:Object.assign({},nt[xt]);return Object.assign(_t,{cluster:!0,cluster_id:gt[tt+Si],point_count:ht,point_count_abbreviated:vt})}function Jn(gt){return gt/360+.5}function ki(gt){let tt=Math.sin(gt*Math.PI/180),nt=.5-.25*Math.log((1+tt)/(1-tt))/Math.PI;return nt<0?0:nt>1?1:nt}function ts(gt){let tt=(180-360*gt)*Math.PI/180;return 360*Math.atan(Math.exp(tt))/Math.PI-90}function Vo(gt,tt,nt,ht){for(var vt,xt=ht,_t=nt-tt>>1,Dt=nt-tt,Mt=gt[tt],Vt=gt[tt+1],ie=gt[nt],se=gt[nt+1],ae=tt+3;aext)vt=ae,xt=lr;else if(lr===xt){var vr=Math.abs(ae-_t);vrht&&(vt-tt>3&&Vo(gt,tt,vt,ht),gt[vt+2]=xt,nt-vt>3&&Vo(gt,vt,nt,ht))}function cl(gt,tt,nt,ht,vt,xt){var _t=vt-nt,Dt=xt-ht;if(_t!==0||Dt!==0){var Mt=((gt-nt)*_t+(tt-ht)*Dt)/(_t*_t+Dt*Dt);Mt>1?(nt=vt,ht=xt):Mt>0&&(nt+=_t*Mt,ht+=Dt*Mt)}return(_t=gt-nt)*_t+(Dt=tt-ht)*Dt}function xo(gt,tt,nt,ht){var vt={id:gt===void 0?null:gt,type:tt,geometry:nt,tags:ht,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};return function(xt){var _t=xt.geometry,Dt=xt.type;if(Dt===\"Point\"||Dt===\"MultiPoint\"||Dt===\"LineString\")Pa(xt,_t);else if(Dt===\"Polygon\"||Dt===\"MultiLineString\")for(var Mt=0;Mt<_t.length;Mt++)Pa(xt,_t[Mt]);else if(Dt===\"MultiPolygon\")for(Mt=0;Mt<_t.length;Mt++)for(var Vt=0;Vt<_t[Mt].length;Vt++)Pa(xt,_t[Mt][Vt])}(vt),vt}function Pa(gt,tt){for(var nt=0;nt0&&(_t+=ht?(vt*Vt-Mt*xt)/2:Math.sqrt(Math.pow(Mt-vt,2)+Math.pow(Vt-xt,2))),vt=Mt,xt=Vt}var ie=tt.length-3;tt[2]=1,Vo(tt,0,ie,nt),tt[ie+2]=1,tt.size=Math.abs(_t),tt.start=0,tt.end=tt.size}function Nl(gt,tt,nt,ht){for(var vt=0;vt1?1:nt}function mn(gt,tt,nt,ht,vt,xt,_t,Dt){if(ht/=tt,xt>=(nt/=tt)&&_t=ht)return null;for(var Mt=[],Vt=0;Vt=nt&&vr=ht)){var Xe=[];if(ae===\"Point\"||ae===\"MultiPoint\")gi(se,Xe,nt,ht,vt);else if(ae===\"LineString\")oi(se,Xe,nt,ht,vt,!1,Dt.lineMetrics);else if(ae===\"MultiLineString\")du(se,Xe,nt,ht,vt,!1);else if(ae===\"Polygon\")du(se,Xe,nt,ht,vt,!0);else if(ae===\"MultiPolygon\")for(var cr=0;cr=nt&&_t<=ht&&(tt.push(gt[xt]),tt.push(gt[xt+1]),tt.push(gt[xt+2]))}}function oi(gt,tt,nt,ht,vt,xt,_t){for(var Dt,Mt,Vt=lo(gt),ie=vt===0?bo:hl,se=gt.start,ae=0;aent&&(Mt=ie(Vt,lr,vr,cr,wr,nt),_t&&(Vt.start=se+Dt*Mt)):xi>ht?zi=nt&&(Mt=ie(Vt,lr,vr,cr,wr,nt),ni=!0),zi>ht&&xi<=ht&&(Mt=ie(Vt,lr,vr,cr,wr,ht),ni=!0),!xt&&ni&&(_t&&(Vt.end=se+Dt*Mt),tt.push(Vt),Vt=lo(gt)),_t&&(se+=Dt)}var Hr=gt.length-3;lr=gt[Hr],vr=gt[Hr+1],Xe=gt[Hr+2],(xi=vt===0?lr:vr)>=nt&&xi<=ht&&ul(Vt,lr,vr,Xe),Hr=Vt.length-3,xt&&Hr>=3&&(Vt[Hr]!==Vt[0]||Vt[Hr+1]!==Vt[1])&&ul(Vt,Vt[0],Vt[1],Vt[2]),Vt.length&&tt.push(Vt)}function lo(gt){var tt=[];return tt.size=gt.size,tt.start=gt.start,tt.end=gt.end,tt}function du(gt,tt,nt,ht,vt,xt){for(var _t=0;_t_t.maxX&&(_t.maxX=ie),se>_t.maxY&&(_t.maxY=se)}return _t}function Ul(gt,tt,nt,ht){var vt=tt.geometry,xt=tt.type,_t=[];if(xt===\"Point\"||xt===\"MultiPoint\")for(var Dt=0;Dt0&&tt.size<(vt?_t:ht))nt.numPoints+=tt.length/3;else{for(var Dt=[],Mt=0;Mt_t)&&(nt.numSimplified++,Dt.push(tt[Mt]),Dt.push(tt[Mt+1])),nt.numPoints++;vt&&function(Vt,ie){for(var se=0,ae=0,lr=Vt.length,vr=lr-2;ae0===ie)for(ae=0,lr=Vt.length;ae24)throw new Error(\"maxZoom should be in the 0-24 range\");if(tt.promoteId&&tt.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var ht=function(vt,xt){var _t=[];if(vt.type===\"FeatureCollection\")for(var Dt=0;Dt1&&console.time(\"creation\"),ae=this.tiles[se]=gn(gt,tt,nt,ht,Mt),this.tileCoords.push({z:tt,x:nt,y:ht}),Vt)){Vt>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",tt,nt,ht,ae.numFeatures,ae.numPoints,ae.numSimplified),console.timeEnd(\"creation\"));var lr=\"z\"+tt;this.stats[lr]=(this.stats[lr]||0)+1,this.total++}if(ae.source=gt,vt){if(tt===Mt.maxZoom||tt===vt)continue;var vr=1<1&&console.time(\"clipping\");var Xe,cr,wr,xi,zi,ni,Hr=.5*Mt.buffer/Mt.extent,jn=.5-Hr,Bi=.5+Hr,xn=1+Hr;Xe=cr=wr=xi=null,zi=mn(gt,ie,nt-Hr,nt+Bi,0,ae.minX,ae.maxX,Mt),ni=mn(gt,ie,nt+jn,nt+xn,0,ae.minX,ae.maxX,Mt),gt=null,zi&&(Xe=mn(zi,ie,ht-Hr,ht+Bi,1,ae.minY,ae.maxY,Mt),cr=mn(zi,ie,ht+jn,ht+xn,1,ae.minY,ae.maxY,Mt),zi=null),ni&&(wr=mn(ni,ie,ht-Hr,ht+Bi,1,ae.minY,ae.maxY,Mt),xi=mn(ni,ie,ht+jn,ht+xn,1,ae.minY,ae.maxY,Mt),ni=null),Vt>1&&console.timeEnd(\"clipping\"),Dt.push(Xe||[],tt+1,2*nt,2*ht),Dt.push(cr||[],tt+1,2*nt,2*ht+1),Dt.push(wr||[],tt+1,2*nt+1,2*ht),Dt.push(xi||[],tt+1,2*nt+1,2*ht+1)}}},Te.prototype.getTile=function(gt,tt,nt){var ht=this.options,vt=ht.extent,xt=ht.debug;if(gt<0||gt>24)return null;var _t=1<1&&console.log(\"drilling down to z%d-%d-%d\",gt,tt,nt);for(var Mt,Vt=gt,ie=tt,se=nt;!Mt&&Vt>0;)Vt--,ie=Math.floor(ie/2),se=Math.floor(se/2),Mt=this.tiles[Dr(Vt,ie,se)];return Mt&&Mt.source?(xt>1&&console.log(\"found parent tile z%d-%d-%d\",Vt,ie,se),xt>1&&console.time(\"drilling down\"),this.splitTile(Mt.source,Vt,ie,se,gt,tt,nt),xt>1&&console.timeEnd(\"drilling down\"),this.tiles[Dt]?ve(this.tiles[Dt],vt):null):null};class Mr extends I{constructor(tt,nt,ht,vt){super(tt,nt,ht),this._dataUpdateable=new Map,this.loadGeoJSON=(xt,_t)=>{let{promoteId:Dt}=xt;if(xt.request)return n.f(xt.request,(Mt,Vt,ie,se)=>{this._dataUpdateable=Us(Vt,Dt)?La(Vt,Dt):void 0,_t(Mt,Vt,ie,se)});if(typeof xt.data==\"string\")try{let Mt=JSON.parse(xt.data);this._dataUpdateable=Us(Mt,Dt)?La(Mt,Dt):void 0,_t(null,Mt)}catch{_t(new Error(`Input data given to '${xt.source}' is not a valid GeoJSON object.`))}else xt.dataDiff?this._dataUpdateable?(function(Mt,Vt,ie){var se,ae,lr,vr;if(Vt.removeAll&&Mt.clear(),Vt.remove)for(let Xe of Vt.remove)Mt.delete(Xe);if(Vt.add)for(let Xe of Vt.add){let cr=gr(Xe,ie);cr!=null&&Mt.set(cr,Xe)}if(Vt.update)for(let Xe of Vt.update){let cr=Mt.get(Xe.id);if(cr==null)continue;let wr=!Xe.removeAllProperties&&(((se=Xe.removeProperties)===null||se===void 0?void 0:se.length)>0||((ae=Xe.addOrUpdateProperties)===null||ae===void 0?void 0:ae.length)>0);if((Xe.newGeometry||Xe.removeAllProperties||wr)&&(cr=Object.assign({},cr),Mt.set(Xe.id,cr),wr&&(cr.properties=Object.assign({},cr.properties))),Xe.newGeometry&&(cr.geometry=Xe.newGeometry),Xe.removeAllProperties)cr.properties={};else if(((lr=Xe.removeProperties)===null||lr===void 0?void 0:lr.length)>0)for(let xi of Xe.removeProperties)Object.prototype.hasOwnProperty.call(cr.properties,xi)&&delete cr.properties[xi];if(((vr=Xe.addOrUpdateProperties)===null||vr===void 0?void 0:vr.length)>0)for(let{key:xi,value:zi}of Xe.addOrUpdateProperties)cr.properties[xi]=zi}}(this._dataUpdateable,xt.dataDiff,Dt),_t(null,{type:\"FeatureCollection\",features:Array.from(this._dataUpdateable.values())})):_t(new Error(`Cannot update existing geojson data in ${xt.source}`)):_t(new Error(`Input data given to '${xt.source}' is not a valid GeoJSON object.`));return{cancel:()=>{}}},this.loadVectorData=this.loadGeoJSONTile,vt&&(this.loadGeoJSON=vt)}loadGeoJSONTile(tt,nt){let ht=tt.tileID.canonical;if(!this._geoJSONIndex)return nt(null,null);let vt=this._geoJSONIndex.getTile(ht.z,ht.x,ht.y);if(!vt)return nt(null,null);let xt=new class{constructor(Dt){this.layers={_geojsonTileLayer:this},this.name=\"_geojsonTileLayer\",this.extent=n.N,this.length=Dt.length,this._features=Dt}feature(Dt){return new class{constructor(Mt){this._feature=Mt,this.extent=n.N,this.type=Mt.type,this.properties=Mt.tags,\"id\"in Mt&&!isNaN(Mt.id)&&(this.id=parseInt(Mt.id,10))}loadGeometry(){if(this._feature.type===1){let Mt=[];for(let Vt of this._feature.geometry)Mt.push([new n.P(Vt[0],Vt[1])]);return Mt}{let Mt=[];for(let Vt of this._feature.geometry){let ie=[];for(let se of Vt)ie.push(new n.P(se[0],se[1]));Mt.push(ie)}return Mt}}toGeoJSON(Mt,Vt,ie){return et.call(this,Mt,Vt,ie)}}(this._features[Dt])}}(vt.features),_t=zl(xt);_t.byteOffset===0&&_t.byteLength===_t.buffer.byteLength||(_t=new Uint8Array(_t)),nt(null,{vectorTile:xt,rawData:_t.buffer})}loadData(tt,nt){var ht;(ht=this._pendingRequest)===null||ht===void 0||ht.cancel(),this._pendingCallback&&this._pendingCallback(null,{abandoned:!0});let vt=!!(tt&&tt.request&&tt.request.collectResourceTiming)&&new n.bu(tt.request);this._pendingCallback=nt,this._pendingRequest=this.loadGeoJSON(tt,(xt,_t)=>{if(delete this._pendingCallback,delete this._pendingRequest,xt||!_t)return nt(xt);if(typeof _t!=\"object\")return nt(new Error(`Input data given to '${tt.source}' is not a valid GeoJSON object.`));{Q(_t,!0);try{if(tt.filter){let Mt=n.bC(tt.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(Mt.result===\"error\")throw new Error(Mt.value.map(ie=>`${ie.key}: ${ie.message}`).join(\", \"));_t={type:\"FeatureCollection\",features:_t.features.filter(ie=>Mt.value.evaluate({zoom:0},ie))}}this._geoJSONIndex=tt.cluster?new kc(function({superclusterOptions:Mt,clusterProperties:Vt}){if(!Vt||!Mt)return Mt;let ie={},se={},ae={accumulated:null,zoom:0},lr={properties:null},vr=Object.keys(Vt);for(let Xe of vr){let[cr,wr]=Vt[Xe],xi=n.bC(wr),zi=n.bC(typeof cr==\"string\"?[cr,[\"accumulated\"],[\"get\",Xe]]:cr);ie[Xe]=xi.value,se[Xe]=zi.value}return Mt.map=Xe=>{lr.properties=Xe;let cr={};for(let wr of vr)cr[wr]=ie[wr].evaluate(ae,lr);return cr},Mt.reduce=(Xe,cr)=>{lr.properties=cr;for(let wr of vr)ae.accumulated=Xe[wr],Xe[wr]=se[wr].evaluate(ae,lr)},Mt}(tt)).load(_t.features):function(Mt,Vt){return new Te(Mt,Vt)}(_t,tt.geojsonVtOptions)}catch(Mt){return nt(Mt)}this.loaded={};let Dt={};if(vt){let Mt=vt.finish();Mt&&(Dt.resourceTiming={},Dt.resourceTiming[tt.source]=JSON.parse(JSON.stringify(Mt)))}nt(null,Dt)}})}reloadTile(tt,nt){let ht=this.loaded;return ht&&ht[tt.uid]?super.reloadTile(tt,nt):this.loadTile(tt,nt)}removeSource(tt,nt){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),nt()}getClusterExpansionZoom(tt,nt){try{nt(null,this._geoJSONIndex.getClusterExpansionZoom(tt.clusterId))}catch(ht){nt(ht)}}getClusterChildren(tt,nt){try{nt(null,this._geoJSONIndex.getChildren(tt.clusterId))}catch(ht){nt(ht)}}getClusterLeaves(tt,nt){try{nt(null,this._geoJSONIndex.getLeaves(tt.clusterId,tt.limit,tt.offset))}catch(ht){nt(ht)}}}class sa{constructor(tt){this.self=tt,this.actor=new n.C(tt,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:I,geojson:Mr},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=(nt,ht)=>{if(this.workerSourceTypes[nt])throw new Error(`Worker source with name \"${nt}\" already registered.`);this.workerSourceTypes[nt]=ht},this.self.registerRTLTextPlugin=nt=>{if(n.bD.isParsed())throw new Error(\"RTL text plugin already registered.\");n.bD.applyArabicShaping=nt.applyArabicShaping,n.bD.processBidirectionalText=nt.processBidirectionalText,n.bD.processStyledBidirectionalText=nt.processStyledBidirectionalText}}setReferrer(tt,nt){this.referrer=nt}setImages(tt,nt,ht){this.availableImages[tt]=nt;for(let vt in this.workerSources[tt]){let xt=this.workerSources[tt][vt];for(let _t in xt)xt[_t].availableImages=nt}ht()}setLayers(tt,nt,ht){this.getLayerIndex(tt).replace(nt),ht()}updateLayers(tt,nt,ht){this.getLayerIndex(tt).update(nt.layers,nt.removedIds),ht()}loadTile(tt,nt,ht){this.getWorkerSource(tt,nt.type,nt.source).loadTile(nt,ht)}loadDEMTile(tt,nt,ht){this.getDEMWorkerSource(tt,nt.source).loadTile(nt,ht)}reloadTile(tt,nt,ht){this.getWorkerSource(tt,nt.type,nt.source).reloadTile(nt,ht)}abortTile(tt,nt,ht){this.getWorkerSource(tt,nt.type,nt.source).abortTile(nt,ht)}removeTile(tt,nt,ht){this.getWorkerSource(tt,nt.type,nt.source).removeTile(nt,ht)}removeDEMTile(tt,nt){this.getDEMWorkerSource(tt,nt.source).removeTile(nt)}removeSource(tt,nt,ht){if(!this.workerSources[tt]||!this.workerSources[tt][nt.type]||!this.workerSources[tt][nt.type][nt.source])return;let vt=this.workerSources[tt][nt.type][nt.source];delete this.workerSources[tt][nt.type][nt.source],vt.removeSource!==void 0?vt.removeSource(nt,ht):ht()}loadWorkerSource(tt,nt,ht){try{this.self.importScripts(nt.url),ht()}catch(vt){ht(vt.toString())}}syncRTLPluginState(tt,nt,ht){try{n.bD.setState(nt);let vt=n.bD.getPluginURL();if(n.bD.isLoaded()&&!n.bD.isParsed()&&vt!=null){this.self.importScripts(vt);let xt=n.bD.isParsed();ht(xt?void 0:new Error(`RTL Text Plugin failed to import scripts from ${vt}`),xt)}}catch(vt){ht(vt.toString())}}getAvailableImages(tt){let nt=this.availableImages[tt];return nt||(nt=[]),nt}getLayerIndex(tt){let nt=this.layerIndexes[tt];return nt||(nt=this.layerIndexes[tt]=new o),nt}getWorkerSource(tt,nt,ht){return this.workerSources[tt]||(this.workerSources[tt]={}),this.workerSources[tt][nt]||(this.workerSources[tt][nt]={}),this.workerSources[tt][nt][ht]||(this.workerSources[tt][nt][ht]=new this.workerSourceTypes[nt]({send:(vt,xt,_t)=>{this.actor.send(vt,xt,_t,tt)}},this.getLayerIndex(tt),this.getAvailableImages(tt))),this.workerSources[tt][nt][ht]}getDEMWorkerSource(tt,nt){return this.demWorkerSources[tt]||(this.demWorkerSources[tt]={}),this.demWorkerSources[tt][nt]||(this.demWorkerSources[tt][nt]=new R),this.demWorkerSources[tt][nt]}}return n.i()&&(self.worker=new sa(self)),sa}),i([\"./shared\"],function(n){\"use strict\";var o=\"3.6.2\";class c{static testProp(l){if(!c.docStyle)return l[0];for(let d=0;d{window.removeEventListener(\"click\",c.suppressClickInternal,!0)},0)}static mousePos(l,d){let v=l.getBoundingClientRect();return new n.P(d.clientX-v.left-l.clientLeft,d.clientY-v.top-l.clientTop)}static touchPos(l,d){let v=l.getBoundingClientRect(),b=[];for(let M=0;M{l=[],d=0,v=0,b={}},T.addThrottleControl=W=>{let Z=v++;return b[Z]=W,Z},T.removeThrottleControl=W=>{delete b[W],B()},T.getImage=(W,Z,$=!0)=>{f.supported&&(W.headers||(W.headers={}),W.headers.accept=\"image/webp,*/*\");let 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$=l.shift();if($.cancelled){Z--;continue}let st=M($);d++,$.innerRequest=st}},U=(W,Z)=>{let $=new Image,st=W.url,At=!1,pt=W.credentials;return pt&&pt===\"include\"?$.crossOrigin=\"use-credentials\":(pt&&pt===\"same-origin\"||!n.s(st))&&($.crossOrigin=\"anonymous\"),$.fetchPriority=\"high\",$.onload=()=>{Z(null,$),$.onerror=$.onload=null},$.onerror=()=>{At||Z(new Error(\"Could not load image. 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l[0]=T[0],l[1]=T[1],l[2]=T[2],l}var J,ut=function(T,l,d){return T[0]=l[0]-d[0],T[1]=l[1]-d[1],T[2]=l[2]-d[2],T};J=new n.A(3),n.A!=Float32Array&&(J[0]=0,J[1]=0,J[2]=0);var Et=function(T){var l=T[0],d=T[1];return l*l+d*d};function kt(T){let l=[];if(typeof T==\"string\")l.push({id:\"default\",url:T});else if(T&&T.length>0){let d=[];for(let{id:v,url:b}of T){let M=`${v}${b}`;d.indexOf(M)===-1&&(d.push(M),l.push({id:v,url:b}))}}return l}function Xt(T,l,d,v,b){if(v)return void T(v);if(b!==Object.values(l).length||b!==Object.values(d).length)return;let M={};for(let O in l){M[O]={};let B=n.h.getImageCanvasContext(d[O]),U=l[O];for(let W in U){let{width:Z,height:$,x:st,y:At,sdf:pt,pixelRatio:yt,stretchX:dt,stretchY:Ft,content:Ht}=U[W];M[O][W]={data:null,pixelRatio:yt,sdf:pt,stretchX:dt,stretchY:Ft,content:Ht,spriteData:{width:Z,height:$,x:st,y:At,context:B}}}}T(null,M)}(function(){var T=new n.A(2);n.A!=Float32Array&&(T[0]=0,T[1]=0)})();class qt{constructor(l,d,v,b){this.context=l,this.format=v,this.texture=l.gl.createTexture(),this.update(d,b)}update(l,d,v){let{width:b,height:M}=l,O=!(this.size&&this.size[0]===b&&this.size[1]===M||v),{context:B}=this,{gl:U}=B;if(this.useMipmap=!!(d&&d.useMipmap),U.bindTexture(U.TEXTURE_2D,this.texture),B.pixelStoreUnpackFlipY.set(!1),B.pixelStoreUnpack.set(1),B.pixelStoreUnpackPremultiplyAlpha.set(this.format===U.RGBA&&(!d||d.premultiply!==!1)),O)this.size=[b,M],l instanceof HTMLImageElement||l instanceof HTMLCanvasElement||l instanceof HTMLVideoElement||l instanceof ImageData||n.a(l)?U.texImage2D(U.TEXTURE_2D,0,this.format,this.format,U.UNSIGNED_BYTE,l):U.texImage2D(U.TEXTURE_2D,0,this.format,b,M,0,this.format,U.UNSIGNED_BYTE,l.data);else{let{x:W,y:Z}=v||{x:0,y:0};l instanceof HTMLImageElement||l instanceof HTMLCanvasElement||l instanceof HTMLVideoElement||l instanceof 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n.E{constructor(){super(),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new n.R({width:1,height:1}),this.dirty=!0}isLoaded(){return this.loaded}setLoaded(l){if(this.loaded!==l&&(this.loaded=l,l)){for(let{ids:d,callback:v}of this.requestors)this._notify(d,v);this.requestors=[]}}getImage(l){let d=this.images[l];if(d&&!d.data&&d.spriteData){let v=d.spriteData;d.data=new n.R({width:v.width,height:v.height},v.context.getImageData(v.x,v.y,v.width,v.height).data),d.spriteData=null}return d}addImage(l,d){if(this.images[l])throw new Error(`Image id ${l} already exist, use updateImage instead`);this._validate(l,d)&&(this.images[l]=d)}_validate(l,d){let v=!0,b=d.data||d.spriteData;return this._validateStretch(d.stretchX,b&&b.width)||(this.fire(new n.j(new Error(`Image \"${l}\" has invalid \"stretchX\" value`))),v=!1),this._validateStretch(d.stretchY,b&&b.height)||(this.fire(new n.j(new Error(`Image \"${l}\" has invalid \"stretchY\" value`))),v=!1),this._validateContent(d.content,d)||(this.fire(new n.j(new Error(`Image \"${l}\" has invalid \"content\" value`))),v=!1),v}_validateStretch(l,d){if(!l)return!0;let v=0;for(let b of l){if(b[0]-1);U++,M[U]=B,O[U]=W,O[U+1]=De}for(let B=0,U=0;B{let B=this.entries[b];B||(B=this.entries[b]={glyphs:{},requests:{},ranges:{}});let U=B.glyphs[M];if(U!==void 0)return void O(null,{stack:b,id:M,glyph:U});if(U=this._tinySDF(B,b,M),U)return B.glyphs[M]=U,void O(null,{stack:b,id:M,glyph:U});let W=Math.floor(M/256);if(256*W>65535)return void O(new Error(\"glyphs > 65535 not supported\"));if(B.ranges[W])return void O(null,{stack:b,id:M,glyph:U});if(!this.url)return void O(new Error(\"glyphsUrl is not set\"));let Z=B.requests[W];Z||(Z=B.requests[W]=[],Sr.loadGlyphRange(b,W,this.url,this.requestManager,($,st)=>{if(st){for(let At in st)this._doesCharSupportLocalGlyph(+At)||(B.glyphs[+At]=st[+At]);B.ranges[W]=!0}for(let At of Z)At($,st);delete B.requests[W]})),Z.push(($,st)=>{$?O($):st&&O(null,{stack:b,id:M,glyph:st[M]||null})})},(b,M)=>{if(b)d(b);else if(M){let O={};for(let{stack:B,id:U,glyph:W}of M)(O[B]||(O[B]={}))[U]=W&&{id:W.id,bitmap:W.bitmap.clone(),metrics:W.metrics};d(null,O)}})}_doesCharSupportLocalGlyph(l){return!!this.localIdeographFontFamily&&(n.u[\"CJK Unified Ideographs\"](l)||n.u[\"Hangul Syllables\"](l)||n.u.Hiragana(l)||n.u.Katakana(l))}_tinySDF(l,d,v){let b=this.localIdeographFontFamily;if(!b||!this._doesCharSupportLocalGlyph(v))return;let M=l.tinySDF;if(!M){let B=\"400\";/bold/i.test(d)?B=\"900\":/medium/i.test(d)?B=\"500\":/light/i.test(d)&&(B=\"200\"),M=l.tinySDF=new Sr.TinySDF({fontSize:48,buffer:6,radius:16,cutoff:.25,fontFamily:b,fontWeight:B})}let O=M.draw(String.fromCharCode(v));return{id:v,bitmap:new n.q({width:O.width||60,height:O.height||60},O.data),metrics:{width:O.glyphWidth/2||24,height:O.glyphHeight/2||24,left:O.glyphLeft/2+.5||0,top:O.glyphTop/2-27.5||-8,advance:O.glyphAdvance/2||24,isDoubleResolution:!0}}}}Sr.loadGlyphRange=function(T,l,d,v,b){let M=256*l,O=M+255,B=v.transformRequest(d.replace(\"{fontstack}\",T).replace(\"{range}\",`${M}-${O}`),Q.Glyphs);n.l(B,(U,W)=>{if(U)b(U);else if(W){let Z={};for(let $ of n.n(W))Z[$.id]=$;b(null,Z)}})},Sr.TinySDF=class{constructor({fontSize:T=24,buffer:l=3,radius:d=8,cutoff:v=.25,fontFamily:b=\"sans-serif\",fontWeight:M=\"normal\",fontStyle:O=\"normal\"}={}){this.buffer=l,this.cutoff=v,this.radius=d;let B=this.size=T+4*l,U=this._createCanvas(B),W=this.ctx=U.getContext(\"2d\",{willReadFrequently:!0});W.font=`${O} ${M} ${T}px ${b}`,W.textBaseline=\"alphabetic\",W.textAlign=\"left\",W.fillStyle=\"black\",this.gridOuter=new Float64Array(B*B),this.gridInner=new Float64Array(B*B),this.f=new Float64Array(B),this.z=new Float64Array(B+1),this.v=new Uint16Array(B)}_createCanvas(T){let l=document.createElement(\"canvas\");return l.width=l.height=T,l}draw(T){let{width:l,actualBoundingBoxAscent:d,actualBoundingBoxDescent:v,actualBoundingBoxLeft:b,actualBoundingBoxRight:M}=this.ctx.measureText(T),O=Math.ceil(d),B=Math.max(0,Math.min(this.size-this.buffer,Math.ceil(M-b))),U=Math.min(this.size-this.buffer,O+Math.ceil(v)),W=B+2*this.buffer,Z=U+2*this.buffer,$=Math.max(W*Z,0),st=new Uint8ClampedArray($),At={data:st,width:W,height:Z,glyphWidth:B,glyphHeight:U,glyphTop:O,glyphLeft:0,glyphAdvance:l};if(B===0||U===0)return At;let{ctx:pt,buffer:yt,gridInner:dt,gridOuter:Ft}=this;pt.clearRect(yt,yt,B,U),pt.fillText(T,yt,yt+O);let Ht=pt.getImageData(yt,yt,B,U);Ft.fill(De,0,$),dt.fill(0,0,$);for(let St=0;St0?oe*oe:0,dt[$t]=oe<0?oe*oe:0}}Ke(Ft,0,0,W,Z,W,this.f,this.v,this.z),Ke(dt,yt,yt,B,U,W,this.f,this.v,this.z);for(let St=0;St<$;St++){let Bt=Math.sqrt(Ft[St])-Math.sqrt(dt[St]);st[St]=Math.round(255-255*(Bt/this.radius+this.cutoff))}return At}};class Li{constructor(){this.specification=n.v.light.position}possiblyEvaluate(l,d){return n.z(l.expression.evaluate(d))}interpolate(l,d,v){return{x:n.B.number(l.x,d.x,v),y:n.B.number(l.y,d.y,v),z:n.B.number(l.z,d.z,v)}}}let oo;class zl extends n.E{constructor(l){super(),oo=oo||new n.r({anchor:new n.D(n.v.light.anchor),position:new Li,color:new n.D(n.v.light.color),intensity:new n.D(n.v.light.intensity)}),this._transitionable=new n.T(oo),this.setLight(l),this._transitioning=this._transitionable.untransitioned()}getLight(){return this._transitionable.serialize()}setLight(l,d={}){if(!this._validate(n.t,l,d))for(let v in l){let b=l[v];v.endsWith(\"-transition\")?this._transitionable.setTransition(v.slice(0,-11),b):this._transitionable.setValue(v,b)}}updateTransitions(l){this._transitioning=this._transitionable.transitioned(l,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(l){this.properties=this._transitioning.possiblyEvaluate(l)}_validate(l,d,v){return(!v||v.validate!==!1)&&n.x(this,l.call(n.y,n.e({value:d,style:{glyphs:!0,sprite:!0},styleSpec:n.v})))}}class No{constructor(l,d){this.width=l,this.height=d,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}}getDash(l,d){let v=l.join(\",\")+String(d);return this.dashEntry[v]||(this.dashEntry[v]=this.addDash(l,d)),this.dashEntry[v]}getDashRanges(l,d,v){let b=[],M=l.length%2==1?-l[l.length-1]*v:0,O=l[0]*v,B=!0;b.push({left:M,right:O,isDash:B,zeroLength:l[0]===0});let U=l[0];for(let W=1;W1&&(U=l[++B]);let Z=Math.abs(W-U.left),$=Math.abs(W-U.right),st=Math.min(Z,$),At,pt=M/v*(b+1);if(U.isDash){let yt=b-Math.abs(pt);At=Math.sqrt(st*st+yt*yt)}else At=b-Math.sqrt(st*st+pt*pt);this.data[O+W]=Math.max(0,Math.min(255,At+128))}}}addRegularDash(l){for(let B=l.length-1;B>=0;--B){let U=l[B],W=l[B+1];U.zeroLength?l.splice(B,1):W&&W.isDash===U.isDash&&(W.left=U.left,l.splice(B,1))}let d=l[0],v=l[l.length-1];d.isDash===v.isDash&&(d.left=v.left-this.width,v.right=d.right+this.width);let b=this.width*this.nextRow,M=0,O=l[M];for(let B=0;B1&&(O=l[++M]);let U=Math.abs(B-O.left),W=Math.abs(B-O.right),Z=Math.min(U,W);this.data[b+B]=Math.max(0,Math.min(255,(O.isDash?Z:-Z)+128))}}addDash(l,d){let v=d?7:0,b=2*v+1;if(this.nextRow+b>this.height)return n.w(\"LineAtlas out of space\"),null;let M=0;for(let B=0;B{b.send(l,d,M)},v=v||function(){})}getActor(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]}remove(l=!0){this.actors.forEach(d=>{d.remove()}),this.actors=[],l&&this.workerPool.release(this.id)}}function Uo(T,l,d){let v=function(b,M){if(b)return d(b);if(M){let O=n.F(n.e(M,T),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);M.vector_layers&&(O.vectorLayers=M.vector_layers,O.vectorLayerIds=O.vectorLayers.map(B=>B.id)),d(null,O)}};return T.url?n.f(l.transformRequest(T.url,Q.Source),v):n.h.frame(()=>v(null,T))}class Si{constructor(l,d){l&&(d?this.setSouthWest(l).setNorthEast(d):Array.isArray(l)&&(l.length===4?this.setSouthWest([l[0],l[1]]).setNorthEast([l[2],l[3]]):this.setSouthWest(l[0]).setNorthEast(l[1])))}setNorthEast(l){return this._ne=l instanceof n.L?new n.L(l.lng,l.lat):n.L.convert(l),this}setSouthWest(l){return this._sw=l instanceof n.L?new n.L(l.lng,l.lat):n.L.convert(l),this}extend(l){let d=this._sw,v=this._ne,b,M;if(l instanceof n.L)b=l,M=l;else{if(!(l instanceof Si))return Array.isArray(l)?l.length===4||l.every(Array.isArray)?this.extend(Si.convert(l)):this.extend(n.L.convert(l)):l&&(\"lng\"in l||\"lon\"in l)&&\"lat\"in l?this.extend(n.L.convert(l)):this;if(b=l._sw,M=l._ne,!b||!M)return this}return d||v?(d.lng=Math.min(b.lng,d.lng),d.lat=Math.min(b.lat,d.lat),v.lng=Math.max(M.lng,v.lng),v.lat=Math.max(M.lat,v.lat)):(this._sw=new n.L(b.lng,b.lat),this._ne=new n.L(M.lng,M.lat)),this}getCenter(){return new n.L((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)}getSouthWest(){return this._sw}getNorthEast(){return this._ne}getNorthWest(){return new n.L(this.getWest(),this.getNorth())}getSouthEast(){return new n.L(this.getEast(),this.getSouth())}getWest(){return this._sw.lng}getSouth(){return this._sw.lat}getEast(){return this._ne.lng}getNorth(){return this._ne.lat}toArray(){return[this._sw.toArray(),this._ne.toArray()]}toString(){return`LngLatBounds(${this._sw.toString()}, ${this._ne.toString()})`}isEmpty(){return!(this._sw&&this._ne)}contains(l){let{lng:d,lat:v}=n.L.convert(l),b=this._sw.lng<=d&&d<=this._ne.lng;return this._sw.lng>this._ne.lng&&(b=this._sw.lng>=d&&d>=this._ne.lng),this._sw.lat<=v&&v<=this._ne.lat&&b}static convert(l){return l instanceof Si?l:l&&new Si(l)}static fromLngLat(l,d=0){let v=360*d/40075017,b=v/Math.cos(Math.PI/180*l.lat);return new Si(new n.L(l.lng-b,l.lat-v),new n.L(l.lng+b,l.lat+v))}}class Ns{constructor(l,d,v){this.bounds=Si.convert(this.validateBounds(l)),this.minzoom=d||0,this.maxzoom=v||24}validateBounds(l){return Array.isArray(l)&&l.length===4?[Math.max(-180,l[0]),Math.max(-90,l[1]),Math.min(180,l[2]),Math.min(90,l[3])]:[-180,-90,180,90]}contains(l){let d=Math.pow(2,l.z),v=Math.floor(n.G(this.bounds.getWest())*d),b=Math.floor(n.H(this.bounds.getNorth())*d),M=Math.ceil(n.G(this.bounds.getEast())*d),O=Math.ceil(n.H(this.bounds.getSouth())*d);return l.x>=v&&l.x=b&&l.y{this._loaded=!1,this.fire(new n.k(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=Uo(this._options,this.map._requestManager,(M,O)=>{this._tileJSONRequest=null,this._loaded=!0,this.map.style.sourceCaches[this.id].clearTiles(),M?this.fire(new n.j(M)):O&&(n.e(this,O),O.bounds&&(this.tileBounds=new Ns(O.bounds,this.minzoom,this.maxzoom)),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})},this.serialize=()=>n.e({},this._options),this.id=l,this.dispatcher=v,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,n.e(this,n.F(d,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=n.e({type:\"vector\"},d),this._collectResourceTiming=d.collectResourceTiming,this.tileSize!==512)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(b)}loaded(){return this._loaded}hasTile(l){return!this.tileBounds||this.tileBounds.contains(l.canonical)}onAdd(l){this.map=l,this.load()}setSourceProperty(l){this._tileJSONRequest&&this._tileJSONRequest.cancel(),l(),this.load()}setTiles(l){return this.setSourceProperty(()=>{this._options.tiles=l}),this}setUrl(l){return this.setSourceProperty(()=>{this.url=l,this._options.url=l}),this}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)}loadTile(l,d){let v=l.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),b={request:this.map._requestManager.transformRequest(v,Q.Tile),uid:l.uid,tileID:l.tileID,zoom:l.tileID.overscaledZ,tileSize:this.tileSize*l.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function M(O,B){return delete l.request,l.aborted?d(null):O&&O.status!==404?d(O):(B&&B.resourceTiming&&(l.resourceTiming=B.resourceTiming),this.map._refreshExpiredTiles&&B&&l.setExpiryData(B),l.loadVectorData(B,this.map.painter),d(null),void(l.reloadCallback&&(this.loadTile(l,l.reloadCallback),l.reloadCallback=null)))}b.request.collectResourceTiming=this._collectResourceTiming,l.actor&&l.state!==\"expired\"?l.state===\"loading\"?l.reloadCallback=d:l.request=l.actor.send(\"reloadTile\",b,M.bind(this)):(l.actor=this.dispatcher.getActor(),l.request=l.actor.send(\"loadTile\",b,M.bind(this)))}abortTile(l){l.request&&(l.request.cancel(),delete l.request),l.actor&&l.actor.send(\"abortTile\",{uid:l.uid,type:this.type,source:this.id},void 0)}unloadTile(l){l.unloadVectorData(),l.actor&&l.actor.send(\"removeTile\",{uid:l.uid,type:this.type,source:this.id},void 0)}hasTransition(){return!1}}class kc extends n.E{constructor(l,d,v,b){super(),this.id=l,this.dispatcher=v,this.setEventedParent(b),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=n.e({type:\"raster\"},d),n.e(this,n.F(d,[\"url\",\"scheme\",\"tileSize\"]))}load(){this._loaded=!1,this.fire(new n.k(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=Uo(this._options,this.map._requestManager,(l,d)=>{this._tileJSONRequest=null,this._loaded=!0,l?this.fire(new n.j(l)):d&&(n.e(this,d),d.bounds&&(this.tileBounds=new Ns(d.bounds,this.minzoom,this.maxzoom)),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))})}loaded(){return this._loaded}onAdd(l){this.map=l,this.load()}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)}setSourceProperty(l){this._tileJSONRequest&&this._tileJSONRequest.cancel(),l(),this.load()}setTiles(l){return this.setSourceProperty(()=>{this._options.tiles=l}),this}serialize(){return n.e({},this._options)}hasTile(l){return!this.tileBounds||this.tileBounds.contains(l.canonical)}loadTile(l,d){let v=l.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme);l.request=j.getImage(this.map._requestManager.transformRequest(v,Q.Tile),(b,M,O)=>{if(delete l.request,l.aborted)l.state=\"unloaded\",d(null);else if(b)l.state=\"errored\",d(b);else if(M){this.map._refreshExpiredTiles&&O&&l.setExpiryData(O);let B=this.map.painter.context,U=B.gl;l.texture=this.map.painter.getTileTexture(M.width),l.texture?l.texture.update(M,{useMipmap:!0}):(l.texture=new qt(B,M,U.RGBA,{useMipmap:!0}),l.texture.bind(U.LINEAR,U.CLAMP_TO_EDGE,U.LINEAR_MIPMAP_NEAREST),B.extTextureFilterAnisotropic&&U.texParameterf(U.TEXTURE_2D,B.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,B.extTextureFilterAnisotropicMax)),l.state=\"loaded\",d(null)}},this.map._refreshExpiredTiles)}abortTile(l,d){l.request&&(l.request.cancel(),delete l.request),d()}unloadTile(l,d){l.texture&&this.map.painter.saveTileTexture(l.texture),d()}hasTransition(){return!1}}class Rc extends kc{constructor(l,d,v,b){super(l,d,v,b),this.type=\"raster-dem\",this.maxzoom=22,this._options=n.e({type:\"raster-dem\"},d),this.encoding=d.encoding||\"mapbox\",this.redFactor=d.redFactor,this.greenFactor=d.greenFactor,this.blueFactor=d.blueFactor,this.baseShift=d.baseShift}loadTile(l,d){let v=l.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),b=this.map._requestManager.transformRequest(v,Q.Tile);function M(O,B){O&&(l.state=\"errored\",d(O)),B&&(l.dem=B,l.needsHillshadePrepare=!0,l.needsTerrainPrepare=!0,l.state=\"loaded\",d(null))}l.neighboringTiles=this._getNeighboringTiles(l.tileID),l.request=j.getImage(b,(O,B,U)=>n._(this,void 0,void 0,function*(){if(delete l.request,l.aborted)l.state=\"unloaded\",d(null);else if(O)l.state=\"errored\",d(O);else if(B){this.map._refreshExpiredTiles&&l.setExpiryData(U);let W=n.a(B)&&n.J()?B:yield function($){return n._(this,void 0,void 0,function*(){if(typeof VideoFrame<\"u\"&&n.K()){let st=$.width+2,At=$.height+2;try{return new n.R({width:st,height:At},yield n.M($,-1,-1,st,At))}catch{}}return n.h.getImageData($,1)})}(B),Z={uid:l.uid,coord:l.tileID,source:this.id,rawImageData:W,encoding:this.encoding,redFactor:this.redFactor,greenFactor:this.greenFactor,blueFactor:this.blueFactor,baseShift:this.baseShift};l.actor&&l.state!==\"expired\"||(l.actor=this.dispatcher.getActor(),l.actor.send(\"loadDEMTile\",Z,M))}}),this.map._refreshExpiredTiles)}_getNeighboringTiles(l){let d=l.canonical,v=Math.pow(2,d.z),b=(d.x-1+v)%v,M=d.x===0?l.wrap-1:l.wrap,O=(d.x+1+v)%v,B=d.x+1===v?l.wrap+1:l.wrap,U={};return U[new n.O(l.overscaledZ,M,d.z,b,d.y).key]={backfilled:!1},U[new n.O(l.overscaledZ,B,d.z,O,d.y).key]={backfilled:!1},d.y>0&&(U[new n.O(l.overscaledZ,M,d.z,b,d.y-1).key]={backfilled:!1},U[new n.O(l.overscaledZ,l.wrap,d.z,d.x,d.y-1).key]={backfilled:!1},U[new n.O(l.overscaledZ,B,d.z,O,d.y-1).key]={backfilled:!1}),d.y+1{this._updateWorkerData()},this.serialize=()=>n.e({},this._options,{type:this.type,data:this._data}),this.id=l,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._pendingLoads=0,this.actor=v.getActor(),this.setEventedParent(b),this._data=d.data,this._options=n.e({},d),this._collectResourceTiming=d.collectResourceTiming,d.maxzoom!==void 0&&(this.maxzoom=d.maxzoom),d.type&&(this.type=d.type),d.attribution&&(this.attribution=d.attribution),this.promoteId=d.promoteId;let M=n.N/this.tileSize;this.workerOptions=n.e({source:this.id,cluster:d.cluster||!1,geojsonVtOptions:{buffer:(d.buffer!==void 0?d.buffer:128)*M,tolerance:(d.tolerance!==void 0?d.tolerance:.375)*M,extent:n.N,maxZoom:this.maxzoom,lineMetrics:d.lineMetrics||!1,generateId:d.generateId||!1},superclusterOptions:{maxZoom:d.clusterMaxZoom!==void 0?d.clusterMaxZoom:this.maxzoom-1,minPoints:Math.max(2,d.clusterMinPoints||2),extent:n.N,radius:(d.clusterRadius||50)*M,log:!1,generateId:d.generateId||!1},clusterProperties:d.clusterProperties,filter:d.filter},d.workerOptions),typeof this.promoteId==\"string\"&&(this.workerOptions.promoteId=this.promoteId)}onAdd(l){this.map=l,this.load()}setData(l){return this._data=l,this._updateWorkerData(),this}updateData(l){return this._updateWorkerData(l),this}setClusterOptions(l){return this.workerOptions.cluster=l.cluster,l&&(l.clusterRadius!==void 0&&(this.workerOptions.superclusterOptions.radius=l.clusterRadius),l.clusterMaxZoom!==void 0&&(this.workerOptions.superclusterOptions.maxZoom=l.clusterMaxZoom)),this._updateWorkerData(),this}getClusterExpansionZoom(l,d){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:l,source:this.id},d),this}getClusterChildren(l,d){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:l,source:this.id},d),this}getClusterLeaves(l,d,v,b){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:l,limit:d,offset:v},b),this}_updateWorkerData(l){let d=n.e({},this.workerOptions);l?d.dataDiff=l:typeof this._data==\"string\"?(d.request=this.map._requestManager.transformRequest(n.h.resolveURL(this._data),Q.Source),d.request.collectResourceTiming=this._collectResourceTiming):d.data=JSON.stringify(this._data),this._pendingLoads++,this.fire(new n.k(\"dataloading\",{dataType:\"source\"})),this.actor.send(`${this.type}.loadData`,d,(v,b)=>{if(this._pendingLoads--,this._removed||b&&b.abandoned)return void this.fire(new n.k(\"dataabort\",{dataType:\"source\"}));let M=null;if(b&&b.resourceTiming&&b.resourceTiming[this.id]&&(M=b.resourceTiming[this.id].slice(0)),v)return void this.fire(new n.j(v));let O={dataType:\"source\"};this._collectResourceTiming&&M&&M.length>0&&n.e(O,{resourceTiming:M}),this.fire(new n.k(\"data\",Object.assign(Object.assign({},O),{sourceDataType:\"metadata\"}))),this.fire(new n.k(\"data\",Object.assign(Object.assign({},O),{sourceDataType:\"content\"})))})}loaded(){return this._pendingLoads===0}loadTile(l,d){let v=l.actor?\"reloadTile\":\"loadTile\";l.actor=this.actor;let b={type:this.type,uid:l.uid,tileID:l.tileID,zoom:l.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};l.request=this.actor.send(v,b,(M,O)=>(delete l.request,l.unloadVectorData(),l.aborted?d(null):M?d(M):(l.loadVectorData(O,this.map.painter,v===\"reloadTile\"),d(null))))}abortTile(l){l.request&&(l.request.cancel(),delete l.request),l.aborted=!0}unloadTile(l){l.unloadVectorData(),this.actor.send(\"removeTile\",{uid:l.uid,type:this.type,source:this.id})}onRemove(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})}hasTransition(){return!1}}var Jn=n.Q([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]);class ki extends n.E{constructor(l,d,v,b){super(),this.load=(M,O)=>{this._loaded=!1,this.fire(new n.k(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,this._request=j.getImage(this.map._requestManager.transformRequest(this.url,Q.Image),(B,U)=>{this._request=null,this._loaded=!0,B?this.fire(new n.j(B)):U&&(this.image=U,M&&(this.coordinates=M),O&&O(),this._finishLoading())})},this.prepare=()=>{if(Object.keys(this.tiles).length===0||!this.image)return;let M=this.map.painter.context,O=M.gl;this.boundsBuffer||(this.boundsBuffer=M.createVertexBuffer(this._boundsArray,Jn.members)),this.boundsSegments||(this.boundsSegments=n.S.simpleSegment(0,0,4,2)),this.texture||(this.texture=new qt(M,this.image,O.RGBA),this.texture.bind(O.LINEAR,O.CLAMP_TO_EDGE));let B=!1;for(let U in this.tiles){let W=this.tiles[U];W.state!==\"loaded\"&&(W.state=\"loaded\",W.texture=this.texture,B=!0)}B&&this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))},this.serialize=()=>({type:\"image\",url:this.options.url,coordinates:this.coordinates}),this.id=l,this.dispatcher=v,this.coordinates=d.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(b),this.options=d}loaded(){return this._loaded}updateImage(l){return l.url?(this._request&&(this._request.cancel(),this._request=null),this.options.url=l.url,this.load(l.coordinates,()=>{this.texture=null}),this):this}_finishLoading(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))}onAdd(l){this.map=l,this.load()}onRemove(){this._request&&(this._request.cancel(),this._request=null)}setCoordinates(l){this.coordinates=l;let d=l.map(n.U.fromLngLat);this.tileID=function(b){let M=1/0,O=1/0,B=-1/0,U=-1/0;for(let st of b)M=Math.min(M,st.x),O=Math.min(O,st.y),B=Math.max(B,st.x),U=Math.max(U,st.y);let W=Math.max(B-M,U-O),Z=Math.max(0,Math.floor(-Math.log(W)/Math.LN2)),$=Math.pow(2,Z);return new n.W(Z,Math.floor((M+B)/2*$),Math.floor((O+U)/2*$))}(d),this.minzoom=this.maxzoom=this.tileID.z;let v=d.map(b=>this.tileID.getTilePoint(b)._round());return this._boundsArray=new n.V,this._boundsArray.emplaceBack(v[0].x,v[0].y,0,0),this._boundsArray.emplaceBack(v[1].x,v[1].y,n.N,0),this._boundsArray.emplaceBack(v[3].x,v[3].y,0,n.N),this._boundsArray.emplaceBack(v[2].x,v[2].y,n.N,n.N),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this}loadTile(l,d){this.tileID&&this.tileID.equals(l.tileID.canonical)?(this.tiles[String(l.tileID.wrap)]=l,l.buckets={},d(null)):(l.state=\"errored\",d(null))}hasTransition(){return!1}}class ts extends ki{constructor(l,d,v,b){super(l,d,v,b),this.load=()=>{this._loaded=!1;let M=this.options;this.urls=[];for(let O of M.urls)this.urls.push(this.map._requestManager.transformRequest(O,Q.Source).url);n.X(this.urls,(O,B)=>{this._loaded=!0,O?this.fire(new n.j(O)):B&&(this.video=B,this.video.loop=!0,this.video.addEventListener(\"playing\",()=>{this.map.triggerRepaint()}),this.map&&this.video.play(),this._finishLoading())})},this.prepare=()=>{if(Object.keys(this.tiles).length===0||this.video.readyState<2)return;let M=this.map.painter.context,O=M.gl;this.boundsBuffer||(this.boundsBuffer=M.createVertexBuffer(this._boundsArray,Jn.members)),this.boundsSegments||(this.boundsSegments=n.S.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(O.LINEAR,O.CLAMP_TO_EDGE),O.texSubImage2D(O.TEXTURE_2D,0,0,0,O.RGBA,O.UNSIGNED_BYTE,this.video)):(this.texture=new qt(M,this.video,O.RGBA),this.texture.bind(O.LINEAR,O.CLAMP_TO_EDGE));let B=!1;for(let U in this.tiles){let W=this.tiles[U];W.state!==\"loaded\"&&(W.state=\"loaded\",W.texture=this.texture,B=!0)}B&&this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))},this.serialize=()=>({type:\"video\",urls:this.urls,coordinates:this.coordinates}),this.roundZoom=!0,this.type=\"video\",this.options=d}pause(){this.video&&this.video.pause()}play(){this.video&&this.video.play()}seek(l){if(this.video){let d=this.video.seekable;ld.end(0)?this.fire(new n.j(new n.Y(`sources.${this.id}`,null,`Playback for this video can be set only between the ${d.start(0)} and ${d.end(0)}-second mark.`))):this.video.currentTime=l}}getVideo(){return this.video}onAdd(l){this.map||(this.map=l,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))}hasTransition(){return this.video&&!this.video.paused}}class Vo extends ki{constructor(l,d,v,b){super(l,d,v,b),this.load=()=>{this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof HTMLCanvasElement?this.options.canvas:document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new n.j(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},this.prepare=()=>{let M=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,M=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,M=!0),this._hasInvalidDimensions()||Object.keys(this.tiles).length===0)return;let O=this.map.painter.context,B=O.gl;this.boundsBuffer||(this.boundsBuffer=O.createVertexBuffer(this._boundsArray,Jn.members)),this.boundsSegments||(this.boundsSegments=n.S.simpleSegment(0,0,4,2)),this.texture?(M||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new qt(O,this.canvas,B.RGBA,{premultiply:!0});let U=!1;for(let W in this.tiles){let Z=this.tiles[W];Z.state!==\"loaded\"&&(Z.state=\"loaded\",Z.texture=this.texture,U=!0)}U&&this.fire(new n.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))},this.serialize=()=>({type:\"canvas\",coordinates:this.coordinates}),d.coordinates?Array.isArray(d.coordinates)&&d.coordinates.length===4&&!d.coordinates.some(M=>!Array.isArray(M)||M.length!==2||M.some(O=>typeof O!=\"number\"))||this.fire(new n.j(new n.Y(`sources.${l}`,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new n.j(new n.Y(`sources.${l}`,null,'missing required property \"coordinates\"'))),d.animate&&typeof d.animate!=\"boolean\"&&this.fire(new n.j(new n.Y(`sources.${l}`,null,'optional \"animate\" property must be a boolean value'))),d.canvas?typeof d.canvas==\"string\"||d.canvas instanceof HTMLCanvasElement||this.fire(new n.j(new n.Y(`sources.${l}`,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new n.j(new n.Y(`sources.${l}`,null,'missing required property \"canvas\"'))),this.options=d,this.animate=d.animate===void 0||d.animate}getCanvas(){return this.canvas}onAdd(l){this.map=l,this.load(),this.canvas&&this.animate&&this.play()}onRemove(){this.pause()}hasTransition(){return this._playing}_hasInvalidDimensions(){for(let l of[this.canvas.width,this.canvas.height])if(isNaN(l)||l<=0)return!0;return!1}}let cl={},xo=T=>{switch(T){case\"geojson\":return Xi;case\"image\":return ki;case\"raster\":return kc;case\"raster-dem\":return Rc;case\"vector\":return ll;case\"video\":return ts;case\"canvas\":return Vo}return cl[T]};function Pa(T,l){let d=n.Z();return n.$(d,d,[1,1,0]),n.a0(d,d,[.5*T.width,.5*T.height,1]),n.a1(d,d,T.calculatePosMatrix(l.toUnwrapped()))}function na(T,l,d,v,b,M){let O=function($,st,At){if($)for(let pt of $){let yt=st[pt];if(yt&&yt.source===At&&yt.type===\"fill-extrusion\")return!0}else for(let pt in st){let yt=st[pt];if(yt.source===At&&yt.type===\"fill-extrusion\")return!0}return!1}(b&&b.layers,l,T.id),B=M.maxPitchScaleFactor(),U=T.tilesIn(v,B,O);U.sort(as);let W=[];for(let $ of U)W.push({wrappedTileID:$.tileID.wrapped().key,queryResults:$.tile.queryRenderedFeatures(l,d,T._state,$.queryGeometry,$.cameraQueryGeometry,$.scale,b,M,B,Pa(T.transform,$.tileID))});let Z=function($){let st={},At={};for(let pt of $){let yt=pt.queryResults,dt=pt.wrappedTileID,Ft=At[dt]=At[dt]||{};for(let Ht in yt){let St=yt[Ht],Bt=Ft[Ht]=Ft[Ht]||{},Qt=st[Ht]=st[Ht]||[];for(let $t of St)Bt[$t.featureIndex]||(Bt[$t.featureIndex]=!0,Qt.push($t))}}return st}(W);for(let $ in Z)Z[$].forEach(st=>{let At=st.feature,pt=T.getFeatureState(At.layer[\"source-layer\"],At.id);At.source=At.layer.source,At.layer[\"source-layer\"]&&(At.sourceLayer=At.layer[\"source-layer\"]),At.state=pt});return Z}function as(T,l){let d=T.tileID,v=l.tileID;return d.overscaledZ-v.overscaledZ||d.canonical.y-v.canonical.y||d.wrap-v.wrap||d.canonical.x-v.canonical.x}class ao{constructor(l,d){this.timeAdded=0,this.fadeEndTime=0,this.tileID=l,this.uid=n.a2(),this.uses=0,this.tileSize=d,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.rtt=[],this.rttCoords={},this.expiredRequestCount=0,this.state=\"loading\"}registerFadeDuration(l){let d=l+this.timeAdded;dM.getLayer(W)).filter(Boolean);if(U.length!==0){B.layers=U,B.stateDependentLayerIds&&(B.stateDependentLayers=B.stateDependentLayerIds.map(W=>U.filter(Z=>Z.id===W)[0]));for(let W of U)O[W.id]=B}}return O}(l.buckets,d.style),this.hasSymbolBuckets=!1;for(let b in this.buckets){let M=this.buckets[b];if(M instanceof n.a4){if(this.hasSymbolBuckets=!0,!v)break;M.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(let b in this.buckets){let M=this.buckets[b];if(M instanceof n.a4&&M.hasRTLText){this.hasRTLText=!0,n.a5();break}}this.queryPadding=0;for(let b in this.buckets){let M=this.buckets[b];this.queryPadding=Math.max(this.queryPadding,d.style.getLayer(b).queryRadius(M))}l.imageAtlas&&(this.imageAtlas=l.imageAtlas),l.glyphAtlasImage&&(this.glyphAtlasImage=l.glyphAtlasImage)}else this.collisionBoxArray=new n.a3}unloadVectorData(){for(let l in this.buckets)this.buckets[l].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"}getBucket(l){return this.buckets[l.id]}upload(l){for(let v in this.buckets){let b=this.buckets[v];b.uploadPending()&&b.upload(l)}let d=l.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new qt(l,this.imageAtlas.image,d.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new qt(l,this.glyphAtlasImage,d.ALPHA),this.glyphAtlasImage=null)}prepare(l){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(l,this.imageAtlasTexture)}queryRenderedFeatures(l,d,v,b,M,O,B,U,W,Z){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:b,cameraQueryGeometry:M,scale:O,tileSize:this.tileSize,pixelPosMatrix:Z,transform:U,params:B,queryPadding:this.queryPadding*W},l,d,v):{}}querySourceFeatures(l,d){let v=this.latestFeatureIndex;if(!v||!v.rawTileData)return;let b=v.loadVTLayers(),M=d&&d.sourceLayer?d.sourceLayer:\"\",O=b._geojsonTileLayer||b[M];if(!O)return;let B=n.a6(d&&d.filter),{z:U,x:W,y:Z}=this.tileID.canonical,$={z:U,x:W,y:Z};for(let st=0;stv)b=!1;else if(d)if(this.expirationTime{this.remove(l,M)},v)),this.data[b].push(M),this.order.push(b),this.order.length>this.max){let O=this._getAndRemoveByKey(this.order[0]);O&&this.onRemove(O)}return this}has(l){return l.wrapped().key in this.data}getAndRemove(l){return this.has(l)?this._getAndRemoveByKey(l.wrapped().key):null}_getAndRemoveByKey(l){let d=this.data[l].shift();return d.timeout&&clearTimeout(d.timeout),this.data[l].length===0&&delete this.data[l],this.order.splice(this.order.indexOf(l),1),d.value}getByKey(l){let d=this.data[l];return d?d[0].value:null}get(l){return this.has(l)?this.data[l.wrapped().key][0].value:null}remove(l,d){if(!this.has(l))return this;let v=l.wrapped().key,b=d===void 0?0:this.data[v].indexOf(d),M=this.data[v][b];return this.data[v].splice(b,1),M.timeout&&clearTimeout(M.timeout),this.data[v].length===0&&delete this.data[v],this.onRemove(M.value),this.order.splice(this.order.indexOf(v),1),this}setMaxSize(l){for(this.max=l;this.order.length>this.max;){let d=this._getAndRemoveByKey(this.order[0]);d&&this.onRemove(d)}return this}filter(l){let d=[];for(let v in this.data)for(let b of this.data[v])l(b.value)||d.push(b);for(let v of d)this.remove(v.value.tileID,v)}}class ee{constructor(){this.state={},this.stateChanges={},this.deletedStates={}}updateState(l,d,v){let b=String(d);if(this.stateChanges[l]=this.stateChanges[l]||{},this.stateChanges[l][b]=this.stateChanges[l][b]||{},n.e(this.stateChanges[l][b],v),this.deletedStates[l]===null){this.deletedStates[l]={};for(let M in this.state[l])M!==b&&(this.deletedStates[l][M]=null)}else if(this.deletedStates[l]&&this.deletedStates[l][b]===null){this.deletedStates[l][b]={};for(let M in this.state[l][b])v[M]||(this.deletedStates[l][b][M]=null)}else for(let M in v)this.deletedStates[l]&&this.deletedStates[l][b]&&this.deletedStates[l][b][M]===null&&delete this.deletedStates[l][b][M]}removeFeatureState(l,d,v){if(this.deletedStates[l]===null)return;let b=String(d);if(this.deletedStates[l]=this.deletedStates[l]||{},v&&d!==void 0)this.deletedStates[l][b]!==null&&(this.deletedStates[l][b]=this.deletedStates[l][b]||{},this.deletedStates[l][b][v]=null);else if(d!==void 0)if(this.stateChanges[l]&&this.stateChanges[l][b])for(v in this.deletedStates[l][b]={},this.stateChanges[l][b])this.deletedStates[l][b][v]=null;else this.deletedStates[l][b]=null;else this.deletedStates[l]=null}getState(l,d){let v=String(d),b=n.e({},(this.state[l]||{})[v],(this.stateChanges[l]||{})[v]);if(this.deletedStates[l]===null)return{};if(this.deletedStates[l]){let M=this.deletedStates[l][d];if(M===null)return{};for(let O in M)delete b[O]}return b}initializeTileState(l,d){l.setFeatureState(this.state,d)}coalesceChanges(l,d){let v={};for(let b in this.stateChanges){this.state[b]=this.state[b]||{};let M={};for(let O in this.stateChanges[b])this.state[b][O]||(this.state[b][O]={}),n.e(this.state[b][O],this.stateChanges[b][O]),M[O]=this.state[b][O];v[b]=M}for(let b in this.deletedStates){this.state[b]=this.state[b]||{};let M={};if(this.deletedStates[b]===null)for(let O in this.state[b])M[O]={},this.state[b][O]={};else for(let O in this.deletedStates[b]){if(this.deletedStates[b][O]===null)this.state[b][O]={};else for(let B of Object.keys(this.deletedStates[b][O]))delete this.state[b][O][B];M[O]=this.state[b][O]}v[b]=v[b]||{},n.e(v[b],M)}if(this.stateChanges={},this.deletedStates={},Object.keys(v).length!==0)for(let b in l)l[b].setFeatureState(v,d)}}class ls extends n.E{constructor(l,d,v){super(),this.id=l,this.dispatcher=v,this.on(\"data\",b=>{b.dataType===\"source\"&&b.sourceDataType===\"metadata\"&&(this._sourceLoaded=!0),this._sourceLoaded&&!this._paused&&b.dataType===\"source\"&&b.sourceDataType===\"content\"&&(this.reload(),this.transform&&this.update(this.transform,this.terrain),this._didEmitContent=!0)}),this.on(\"dataloading\",()=>{this._sourceErrored=!1}),this.on(\"error\",()=>{this._sourceErrored=this._source.loaded()}),this._source=((b,M,O,B)=>{let U=new(xo(M.type))(b,M,O,B);if(U.id!==b)throw new Error(`Expected Source id to be ${b} instead of ${U.id}`);return U})(l,d,v,this),this._tiles={},this._cache=new Nl(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._maxTileCacheZoomLevels=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new ee,this._didEmitContent=!1,this._updated=!1}onAdd(l){this.map=l,this._maxTileCacheSize=l?l._maxTileCacheSize:null,this._maxTileCacheZoomLevels=l?l._maxTileCacheZoomLevels:null,this._source&&this._source.onAdd&&this._source.onAdd(l)}onRemove(l){this.clearTiles(),this._source&&this._source.onRemove&&this._source.onRemove(l)}loaded(){if(this._sourceErrored)return!0;if(!this._sourceLoaded||!this._source.loaded())return!1;if(!(this.used===void 0&&this.usedForTerrain===void 0||this.used||this.usedForTerrain))return!0;if(!this._updated)return!1;for(let l in this._tiles){let d=this._tiles[l];if(d.state!==\"loaded\"&&d.state!==\"errored\")return!1}return!0}getSource(){return this._source}pause(){this._paused=!0}resume(){if(!this._paused)return;let l=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,l&&this.reload(),this.transform&&this.update(this.transform,this.terrain)}_loadTile(l,d){return this._source.loadTile(l,d)}_unloadTile(l){if(this._source.unloadTile)return this._source.unloadTile(l,()=>{})}_abortTile(l){this._source.abortTile&&this._source.abortTile(l,()=>{}),this._source.fire(new n.k(\"dataabort\",{tile:l,coord:l.tileID,dataType:\"source\"}))}serialize(){return this._source.serialize()}prepare(l){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(let d in this._tiles){let v=this._tiles[d];v.upload(l),v.prepare(this.map.style.imageManager)}}getIds(){return Object.values(this._tiles).map(l=>l.tileID).sort(mn).map(l=>l.key)}getRenderableIds(l){let d=[];for(let v in this._tiles)this._isIdRenderable(v,l)&&d.push(this._tiles[v]);return l?d.sort((v,b)=>{let M=v.tileID,O=b.tileID,B=new n.P(M.canonical.x,M.canonical.y)._rotate(this.transform.angle),U=new n.P(O.canonical.x,O.canonical.y)._rotate(this.transform.angle);return M.overscaledZ-O.overscaledZ||U.y-B.y||U.x-B.x}).map(v=>v.tileID.key):d.map(v=>v.tileID).sort(mn).map(v=>v.key)}hasRenderableParent(l){let d=this.findLoadedParent(l,0);return!!d&&this._isIdRenderable(d.tileID.key)}_isIdRenderable(l,d){return this._tiles[l]&&this._tiles[l].hasData()&&!this._coveredTiles[l]&&(d||!this._tiles[l].holdingForFade())}reload(){if(this._paused)this._shouldReloadOnResume=!0;else{this._cache.reset();for(let l in this._tiles)this._tiles[l].state!==\"errored\"&&this._reloadTile(l,\"reloading\")}}_reloadTile(l,d){let v=this._tiles[l];v&&(v.state!==\"loading\"&&(v.state=d),this._loadTile(v,this._tileLoaded.bind(this,v,l,d)))}_tileLoaded(l,d,v,b){if(b)return l.state=\"errored\",void(b.status!==404?this._source.fire(new n.j(b,{tile:l})):this.update(this.transform,this.terrain));l.timeAdded=n.h.now(),v===\"expired\"&&(l.refreshedUponExpiration=!0),this._setTileReloadTimer(d,l),this.getSource().type===\"raster-dem\"&&l.dem&&this._backfillDEM(l),this._state.initializeTileState(l,this.map?this.map.painter:null),l.aborted||this._source.fire(new n.k(\"data\",{dataType:\"source\",tile:l,coord:l.tileID}))}_backfillDEM(l){let d=this.getRenderableIds();for(let b=0;b1||(Math.abs(O)>1&&(Math.abs(O+U)===1?O+=U:Math.abs(O-U)===1&&(O-=U)),M.dem&&b.dem&&(b.dem.backfillBorder(M.dem,O,B),b.neighboringTiles&&b.neighboringTiles[W]&&(b.neighboringTiles[W].backfilled=!0)))}}getTile(l){return this.getTileByID(l.key)}getTileByID(l){return this._tiles[l]}_retainLoadedChildren(l,d,v,b){for(let M in this._tiles){let O=this._tiles[M];if(b[M]||!O.hasData()||O.tileID.overscaledZ<=d||O.tileID.overscaledZ>v)continue;let B=O.tileID;for(;O&&O.tileID.overscaledZ>d+1;){let W=O.tileID.scaledTo(O.tileID.overscaledZ-1);O=this._tiles[W.key],O&&O.hasData()&&(B=W)}let U=B;for(;U.overscaledZ>d;)if(U=U.scaledTo(U.overscaledZ-1),l[U.key]){b[B.key]=B;break}}}findLoadedParent(l,d){if(l.key in this._loadedParentTiles){let v=this._loadedParentTiles[l.key];return v&&v.tileID.overscaledZ>=d?v:null}for(let v=l.overscaledZ-1;v>=d;v--){let b=l.scaledTo(v),M=this._getLoadedTile(b);if(M)return M}}_getLoadedTile(l){let d=this._tiles[l.key];return d&&d.hasData()?d:this._cache.getByKey(l.wrapped().key)}updateCacheSize(l){let d=Math.ceil(l.width/this._source.tileSize)+1,v=Math.ceil(l.height/this._source.tileSize)+1,b=Math.floor(d*v*(this._maxTileCacheZoomLevels===null?n.c.MAX_TILE_CACHE_ZOOM_LEVELS:this._maxTileCacheZoomLevels)),M=typeof this._maxTileCacheSize==\"number\"?Math.min(this._maxTileCacheSize,b):b;this._cache.setMaxSize(M)}handleWrapJump(l){let d=Math.round((l-(this._prevLng===void 0?l:this._prevLng))/360);if(this._prevLng=l,d){let v={};for(let b in this._tiles){let M=this._tiles[b];M.tileID=M.tileID.unwrapTo(M.tileID.wrap+d),v[M.tileID.key]=M}this._tiles=v;for(let b in this._timers)clearTimeout(this._timers[b]),delete this._timers[b];for(let b in this._tiles)this._setTileReloadTimer(b,this._tiles[b])}}update(l,d){if(this.transform=l,this.terrain=d,!this._sourceLoaded||this._paused)return;let v;this.updateCacheSize(l),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used||this.usedForTerrain?this._source.tileID?v=l.getVisibleUnwrappedCoordinates(this._source.tileID).map(Z=>new n.O(Z.canonical.z,Z.wrap,Z.canonical.z,Z.canonical.x,Z.canonical.y)):(v=l.coveringTiles({tileSize:this.usedForTerrain?this.tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:!this.usedForTerrain&&this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled,terrain:d}),this._source.hasTile&&(v=v.filter(Z=>this._source.hasTile(Z)))):v=[];let b=l.coveringZoomLevel(this._source),M=Math.max(b-ls.maxOverzooming,this._source.minzoom),O=Math.max(b+ls.maxUnderzooming,this._source.minzoom);if(this.usedForTerrain){let Z={};for(let $ of v)if($.canonical.z>this._source.minzoom){let st=$.scaledTo($.canonical.z-1);Z[st.key]=st;let At=$.scaledTo(Math.max(this._source.minzoom,Math.min($.canonical.z,5)));Z[At.key]=At}v=v.concat(Object.values(Z))}let B=v.length===0&&!this._updated&&this._didEmitContent;this._updated=!0,B&&this.fire(new n.k(\"data\",{sourceDataType:\"idle\",dataType:\"source\",sourceId:this.id}));let U=this._updateRetainedTiles(v,b);if(gi(this._source.type)){let Z={},$={},st=Object.keys(U),At=n.h.now();for(let pt of st){let yt=U[pt],dt=this._tiles[pt];if(!dt||dt.fadeEndTime!==0&&dt.fadeEndTime<=At)continue;let Ft=this.findLoadedParent(yt,M);Ft&&(this._addTile(Ft.tileID),Z[Ft.tileID.key]=Ft.tileID),$[pt]=yt}this._retainLoadedChildren($,b,O,U);for(let pt in Z)U[pt]||(this._coveredTiles[pt]=!0,U[pt]=Z[pt]);if(d){let pt={},yt={};for(let dt of v)this._tiles[dt.key].hasData()?pt[dt.key]=dt:yt[dt.key]=dt;for(let dt in yt){let Ft=yt[dt].children(this._source.maxzoom);this._tiles[Ft[0].key]&&this._tiles[Ft[1].key]&&this._tiles[Ft[2].key]&&this._tiles[Ft[3].key]&&(pt[Ft[0].key]=U[Ft[0].key]=Ft[0],pt[Ft[1].key]=U[Ft[1].key]=Ft[1],pt[Ft[2].key]=U[Ft[2].key]=Ft[2],pt[Ft[3].key]=U[Ft[3].key]=Ft[3],delete yt[dt])}for(let dt in yt){let Ft=this.findLoadedParent(yt[dt],this._source.minzoom);if(Ft){pt[Ft.tileID.key]=U[Ft.tileID.key]=Ft.tileID;for(let Ht in pt)pt[Ht].isChildOf(Ft.tileID)&&delete pt[Ht]}}for(let dt in this._tiles)pt[dt]||(this._coveredTiles[dt]=!0)}}for(let Z in U)this._tiles[Z].clearFadeHold();let W=n.ab(this._tiles,U);for(let Z of W){let $=this._tiles[Z];$.hasSymbolBuckets&&!$.holdingForFade()?$.setHoldDuration(this.map._fadeDuration):$.hasSymbolBuckets&&!$.symbolFadeFinished()||this._removeTile(Z)}this._updateLoadedParentTileCache()}releaseSymbolFadeTiles(){for(let l in this._tiles)this._tiles[l].holdingForFade()&&this._removeTile(l)}_updateRetainedTiles(l,d){let v={},b={},M=Math.max(d-ls.maxOverzooming,this._source.minzoom),O=Math.max(d+ls.maxUnderzooming,this._source.minzoom),B={};for(let U of l){let W=this._addTile(U);v[U.key]=U,W.hasData()||dthis._source.maxzoom){let $=U.children(this._source.maxzoom)[0],st=this.getTile($);if(st&&st.hasData()){v[$.key]=$;continue}}else{let $=U.children(this._source.maxzoom);if(v[$[0].key]&&v[$[1].key]&&v[$[2].key]&&v[$[3].key])continue}let Z=W.wasRequested();for(let $=U.overscaledZ-1;$>=M;--$){let st=U.scaledTo($);if(b[st.key])break;if(b[st.key]=!0,W=this.getTile(st),!W&&Z&&(W=this._addTile(st)),W){let At=W.hasData();if((Z||At)&&(v[st.key]=st),Z=W.wasRequested(),At)break}}}return v}_updateLoadedParentTileCache(){this._loadedParentTiles={};for(let l in this._tiles){let d=[],v,b=this._tiles[l].tileID;for(;b.overscaledZ>0;){if(b.key in this._loadedParentTiles){v=this._loadedParentTiles[b.key];break}d.push(b.key);let M=b.scaledTo(b.overscaledZ-1);if(v=this._getLoadedTile(M),v)break;b=M}for(let M of d)this._loadedParentTiles[M]=v}}_addTile(l){let d=this._tiles[l.key];if(d)return d;d=this._cache.getAndRemove(l),d&&(this._setTileReloadTimer(l.key,d),d.tileID=l,this._state.initializeTileState(d,this.map?this.map.painter:null),this._cacheTimers[l.key]&&(clearTimeout(this._cacheTimers[l.key]),delete this._cacheTimers[l.key],this._setTileReloadTimer(l.key,d)));let v=d;return d||(d=new ao(l,this._source.tileSize*l.overscaleFactor()),this._loadTile(d,this._tileLoaded.bind(this,d,l.key,d.state))),d.uses++,this._tiles[l.key]=d,v||this._source.fire(new n.k(\"dataloading\",{tile:d,coord:d.tileID,dataType:\"source\"})),d}_setTileReloadTimer(l,d){l in this._timers&&(clearTimeout(this._timers[l]),delete this._timers[l]);let v=d.getExpiryTimeout();v&&(this._timers[l]=setTimeout(()=>{this._reloadTile(l,\"expired\"),delete this._timers[l]},v))}_removeTile(l){let d=this._tiles[l];d&&(d.uses--,delete this._tiles[l],this._timers[l]&&(clearTimeout(this._timers[l]),delete this._timers[l]),d.uses>0||(d.hasData()&&d.state!==\"reloading\"?this._cache.add(d.tileID,d,d.getExpiryTimeout()):(d.aborted=!0,this._abortTile(d),this._unloadTile(d))))}clearTiles(){this._shouldReloadOnResume=!1,this._paused=!1;for(let l in this._tiles)this._removeTile(l);this._cache.reset()}tilesIn(l,d,v){let b=[],M=this.transform;if(!M)return b;let O=v?M.getCameraQueryGeometry(l):l,B=l.map(pt=>M.pointCoordinate(pt,this.terrain)),U=O.map(pt=>M.pointCoordinate(pt,this.terrain)),W=this.getIds(),Z=1/0,$=1/0,st=-1/0,At=-1/0;for(let pt of U)Z=Math.min(Z,pt.x),$=Math.min($,pt.y),st=Math.max(st,pt.x),At=Math.max(At,pt.y);for(let pt=0;pt=0&&St[1].y+Ht>=0){let Bt=B.map($t=>dt.getTilePoint($t)),Qt=U.map($t=>dt.getTilePoint($t));b.push({tile:yt,tileID:dt,queryGeometry:Bt,cameraQueryGeometry:Qt,scale:Ft})}}return b}getVisibleCoordinates(l){let d=this.getRenderableIds(l).map(v=>this._tiles[v].tileID);for(let v of d)v.posMatrix=this.transform.calculatePosMatrix(v.toUnwrapped());return d}hasTransition(){if(this._source.hasTransition())return!0;if(gi(this._source.type)){let l=n.h.now();for(let d in this._tiles)if(this._tiles[d].fadeEndTime>=l)return!0}return!1}setFeatureState(l,d,v){this._state.updateState(l=l||\"_geojsonTileLayer\",d,v)}removeFeatureState(l,d,v){this._state.removeFeatureState(l=l||\"_geojsonTileLayer\",d,v)}getFeatureState(l,d){return this._state.getState(l=l||\"_geojsonTileLayer\",d)}setDependencies(l,d,v){let b=this._tiles[l];b&&b.setDependencies(d,v)}reloadTilesForDependencies(l,d){for(let v in this._tiles)this._tiles[v].hasDependency(l,d)&&this._reloadTile(v,\"reloading\");this._cache.filter(v=>!v.hasDependency(l,d))}}function mn(T,l){let d=Math.abs(2*T.wrap)-+(T.wrap<0),v=Math.abs(2*l.wrap)-+(l.wrap<0);return T.overscaledZ-l.overscaledZ||v-d||l.canonical.y-T.canonical.y||l.canonical.x-T.canonical.x}function gi(T){return T===\"raster\"||T===\"image\"||T===\"video\"}ls.maxOverzooming=10,ls.maxUnderzooming=3;let oi=\"mapboxgl_preloaded_worker_pool\";class lo{constructor(){this.active={}}acquire(l){if(!this.workers)for(this.workers=[];this.workers.length{d.terminate()}),this.workers=null)}isPreloaded(){return!!this.active[oi]}numActive(){return Object.keys(this.active).length}}let du=Math.floor(n.h.hardwareConcurrency/2),ul;function bo(){return ul||(ul=new lo),ul}lo.workerCount=n.ac(globalThis)?Math.max(Math.min(du,3),1):1;class hl{constructor(l,d){this.reset(l,d)}reset(l,d){this.points=l||[],this._distances=[0];for(let v=1;v0?(b-O)/B:0;return this.points[M].mult(1-U).add(this.points[d].mult(U))}}function Ia(T,l){let d=!0;return T===\"always\"||T!==\"never\"&&l!==\"never\"||(d=!1),d}class wo{constructor(l,d,v){let b=this.boxCells=[],M=this.circleCells=[];this.xCellCount=Math.ceil(l/v),this.yCellCount=Math.ceil(d/v);for(let O=0;Othis.width||b<0||d>this.height)return[];let U=[];if(l<=0&&d<=0&&this.width<=v&&this.height<=b){if(M)return[{key:null,x1:l,y1:d,x2:v,y2:b}];for(let W=0;W0}hitTestCircle(l,d,v,b,M){let O=l-v,B=l+v,U=d-v,W=d+v;if(B<0||O>this.width||W<0||U>this.height)return!1;let Z=[];return this._forEachCell(O,U,B,W,this._queryCellCircle,Z,{hitTest:!0,overlapMode:b,circle:{x:l,y:d,radius:v},seenUids:{box:{},circle:{}}},M),Z.length>0}_queryCell(l,d,v,b,M,O,B,U){let{seenUids:W,hitTest:Z,overlapMode:$}=B,st=this.boxCells[M];if(st!==null){let pt=this.bboxes;for(let yt of st)if(!W.box[yt]){W.box[yt]=!0;let dt=4*yt,Ft=this.boxKeys[yt];if(l<=pt[dt+2]&&d<=pt[dt+3]&&v>=pt[dt+0]&&b>=pt[dt+1]&&(!U||U(Ft))&&(!Z||!Ia($,Ft.overlapMode))&&(O.push({key:Ft,x1:pt[dt],y1:pt[dt+1],x2:pt[dt+2],y2:pt[dt+3]}),Z))return!0}}let At=this.circleCells[M];if(At!==null){let pt=this.circles;for(let yt of At)if(!W.circle[yt]){W.circle[yt]=!0;let dt=3*yt,Ft=this.circleKeys[yt];if(this._circleAndRectCollide(pt[dt],pt[dt+1],pt[dt+2],l,d,v,b)&&(!U||U(Ft))&&(!Z||!Ia($,Ft.overlapMode))){let Ht=pt[dt],St=pt[dt+1],Bt=pt[dt+2];if(O.push({key:Ft,x1:Ht-Bt,y1:St-Bt,x2:Ht+Bt,y2:St+Bt}),Z)return!0}}}return!1}_queryCellCircle(l,d,v,b,M,O,B,U){let{circle:W,seenUids:Z,overlapMode:$}=B,st=this.boxCells[M];if(st!==null){let pt=this.bboxes;for(let yt of st)if(!Z.box[yt]){Z.box[yt]=!0;let dt=4*yt,Ft=this.boxKeys[yt];if(this._circleAndRectCollide(W.x,W.y,W.radius,pt[dt+0],pt[dt+1],pt[dt+2],pt[dt+3])&&(!U||U(Ft))&&!Ia($,Ft.overlapMode))return O.push(!0),!0}}let At=this.circleCells[M];if(At!==null){let pt=this.circles;for(let yt of At)if(!Z.circle[yt]){Z.circle[yt]=!0;let dt=3*yt,Ft=this.circleKeys[yt];if(this._circlesCollide(pt[dt],pt[dt+1],pt[dt+2],W.x,W.y,W.radius)&&(!U||U(Ft))&&!Ia($,Ft.overlapMode))return O.push(!0),!0}}}_forEachCell(l,d,v,b,M,O,B,U){let W=this._convertToXCellCoord(l),Z=this._convertToYCellCoord(d),$=this._convertToXCellCoord(v),st=this._convertToYCellCoord(b);for(let At=W;At<=$;At++)for(let pt=Z;pt<=st;pt++)if(M.call(this,l,d,v,b,this.xCellCount*pt+At,O,B,U))return}_convertToXCellCoord(l){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(l*this.xScale)))}_convertToYCellCoord(l){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(l*this.yScale)))}_circlesCollide(l,d,v,b,M,O){let B=b-l,U=M-d,W=v+O;return W*W>B*B+U*U}_circleAndRectCollide(l,d,v,b,M,O,B){let U=(O-b)/2,W=Math.abs(l-(b+U));if(W>U+v)return!1;let Z=(B-M)/2,$=Math.abs(d-(M+Z));if($>Z+v)return!1;if(W<=U||$<=Z)return!0;let st=W-U,At=$-Z;return st*st+At*At<=v*v}}function ve(T,l,d,v,b){let M=n.Z();return l?(n.a0(M,M,[1/b,1/b,1]),d||n.ae(M,M,v.angle)):n.a1(M,v.labelPlaneMatrix,T),M}function jo(T,l,d,v,b){if(l){let M=n.af(T);return n.a0(M,M,[b,b,1]),d||n.ae(M,M,-v.angle),M}return v.glCoordMatrix}function gn(T,l,d){let v;d?(v=[T.x,T.y,d(T.x,T.y),1],n.ag(v,v,l)):(v=[T.x,T.y,0,1],vt(v,v,l));let b=v[3];return{point:new n.P(v[0]/b,v[1]/b),signedDistanceFromCamera:b}}function Ul(T,l){return .5+T/l*.5}function Ca(T,l){let d=T[0]/T[3],v=T[1]/T[3];return d>=-l[0]&&d<=l[0]&&v>=-l[1]&&v<=l[1]}function Te(T,l,d,v,b,M,O,B,U,W){let Z=v?T.textSizeData:T.iconSizeData,$=n.ah(Z,d.transform.zoom),st=[256/d.width*2+1,256/d.height*2+1],At=v?T.text.dynamicLayoutVertexArray:T.icon.dynamicLayoutVertexArray;At.clear();let pt=T.lineVertexArray,yt=v?T.text.placedSymbolArray:T.icon.placedSymbolArray,dt=d.transform.width/d.transform.height,Ft=!1;for(let Ht=0;HtMath.abs(d.x-l.x)*v?{useVertical:!0}:(T===n.ai.vertical?l.yd.x)?{needsFlipping:!0}:null}function Us(T,l,d,v,b,M,O,B,U,W,Z,$,st,At,pt,yt){let dt=l/24,Ft=T.lineOffsetX*dt,Ht=T.lineOffsetY*dt,St;if(T.numGlyphs>1){let Bt=T.glyphStartIndex+T.numGlyphs,Qt=T.lineStartIndex,$t=T.lineStartIndex+T.lineLength,oe=Dr(dt,B,Ft,Ht,d,Z,$,T,U,M,st,pt,yt);if(!oe)return{notEnoughRoom:!0};let pe=gn(oe.first.point,O,yt).point,he=gn(oe.last.point,O,yt).point;if(v&&!d){let be=gr(T.writingMode,pe,he,At);if(be)return be}St=[oe.first];for(let be=T.glyphStartIndex+1;be0?pe.point:La($,oe,Qt,1,b,yt),be=gr(T.writingMode,Qt,he,At);if(be)return be}let Bt=tt(dt*B.getoffsetX(T.glyphStartIndex),Ft,Ht,d,Z,$,T.segment,T.lineStartIndex,T.lineStartIndex+T.lineLength,U,M,st,pt,yt);if(!Bt)return{notEnoughRoom:!0};St=[Bt]}for(let Bt of St)n.ak(W,Bt.point,Bt.angle);return{}}function La(T,l,d,v,b,M){let O=gn(T.add(T.sub(l)._unit()),b,M).point,B=d.sub(O);return d.add(B._mult(v/B.mag()))}function Mr(T,l){let{projectionCache:d,lineVertexArray:v,labelPlaneMatrix:b,tileAnchorPoint:M,distanceFromAnchor:O,getElevation:B,previousVertex:U,direction:W,absOffsetX:Z}=l;if(d.projections[T])return d.projections[T];let $=new n.P(v.getx(T),v.gety(T)),st=gn($,b,B);if(st.signedDistanceFromCamera>0)return d.projections[T]=st.point,st.point;let At=T-W;return La(O===0?M:new n.P(v.getx(At),v.gety(At)),$,U,Z-O+1,b,B)}function sa(T,l,d){return T._unit()._perp()._mult(l*d)}function gt(T,l,d,v,b,M,O,B){let{projectionCache:U,direction:W}=B;if(U.offsets[T])return U.offsets[T];let Z=d.add(l);if(T+W=b)return U.offsets[T]=Z,Z;let $=Mr(T+W,B),st=sa($.sub(d),O,W),At=d.add(st),pt=$.add(st);return U.offsets[T]=n.al(M,Z,At,pt)||Z,U.offsets[T]}function tt(T,l,d,v,b,M,O,B,U,W,Z,$,st,At){let pt=v?T-l:T+l,yt=pt>0?1:-1,dt=0;v&&(yt*=-1,dt=Math.PI),yt<0&&(dt+=Math.PI);let Ft,Ht,St=yt>0?B+O:B+O+1,Bt=b,Qt=b,$t=0,oe=0,pe=Math.abs(pt),he=[],be;for(;$t+oe<=pe;){if(St+=yt,St=U)return null;$t+=oe,Qt=Bt,Ht=Ft;let Ee={projectionCache:$,lineVertexArray:W,labelPlaneMatrix:Z,tileAnchorPoint:M,distanceFromAnchor:$t,getElevation:At,previousVertex:Qt,direction:yt,absOffsetX:pe};if(Bt=Mr(St,Ee),d===0)he.push(Qt),be=Bt.sub(Qt);else{let pr,tr=Bt.sub(Qt);pr=tr.mag()===0?sa(Mr(St+yt,Ee).sub(Bt),d,yt):sa(tr,d,yt),Ht||(Ht=Qt.add(pr)),Ft=gt(St,pr,Bt,B,U,Ht,d,Ee),he.push(Ht),be=Ft.sub(Ht)}oe=be.mag()}let Ze=be._mult((pe-$t)/oe)._add(Ht||Qt),Kr=dt+Math.atan2(Bt.y-Qt.y,Bt.x-Qt.x);return he.push(Ze),{point:Ze,angle:st?Kr:0,path:he}}let nt=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ht(T,l){for(let d=0;d=1;Vr--)tr.push(Ee.path[Vr]);for(let Vr=1;Vrgn(ei,U,pt));tr=Vr.some(ei=>ei.signedDistanceFromCamera<=0)?[]:Vr.map(ei=>ei.point)}let Jr=[];if(tr.length>0){let Vr=tr[0].clone(),ei=tr[0].clone();for(let On=1;On=be.x&&ei.x<=Ze.x&&Vr.y>=be.y&&ei.y<=Ze.y?[tr]:ei.xZe.x||ei.yZe.y?[]:n.am([tr],be.x,be.y,Ze.x,Ze.y)}for(let Vr of Jr){Kr.reset(Vr,.25*he);let ei=0;ei=Kr.length<=.5*he?1:Math.ceil(Kr.paddedLength/Gi)+1;for(let On=0;On=this.screenRightBoundary||bthis.screenBottomBoundary}isInsideGrid(l,d,v,b){return v>=0&&l=0&&dv.collisionGroupID===d}}return this.collisionGroups[l]}}function vr(T,l,d,v,b){let{horizontalAlign:M,verticalAlign:O}=n.au(T);return new n.P(-(M-.5)*l+v[0]*b,-(O-.5)*d+v[1]*b)}function Xe(T,l,d,v,b,M){let{x1:O,x2:B,y1:U,y2:W,anchorPointX:Z,anchorPointY:$}=T,st=new n.P(l,d);return v&&st._rotate(b?M:-M),{x1:O+st.x,y1:U+st.y,x2:B+st.x,y2:W+st.y,anchorPointX:Z,anchorPointY:$}}class cr{constructor(l,d,v,b,M){this.transform=l.clone(),this.terrain=d,this.collisionIndex=new _t(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=v,this.retainedQueryData={},this.collisionGroups=new lr(b),this.collisionCircleArrays={},this.prevPlacement=M,M&&(M.prevPlacement=void 0),this.placedOrientations={}}getBucketParts(l,d,v,b){let M=v.getBucket(d),O=v.latestFeatureIndex;if(!M||!O||d.id!==M.layerIds[0])return;let B=v.collisionBoxArray,U=M.layers[0].layout,W=Math.pow(2,this.transform.zoom-v.tileID.overscaledZ),Z=v.tileSize/n.N,$=this.transform.calculatePosMatrix(v.tileID.toUnwrapped()),st=U.get(\"text-pitch-alignment\")===\"map\",At=U.get(\"text-rotation-alignment\")===\"map\",pt=Dt(v,1,this.transform.zoom),yt=ve($,st,At,this.transform,pt),dt=null;if(st){let Ht=jo($,st,At,this.transform,pt);dt=n.a1([],this.transform.labelPlaneMatrix,Ht)}this.retainedQueryData[M.bucketInstanceId]=new ae(M.bucketInstanceId,O,M.sourceLayerIndex,M.index,v.tileID);let Ft={bucket:M,layout:U,posMatrix:$,textLabelPlaneMatrix:yt,labelToScreenMatrix:dt,scale:W,textPixelRatio:Z,holdingForFade:v.holdingForFade(),collisionBoxArray:B,partiallyEvaluatedTextSize:n.ah(M.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(M.sourceID)};if(b)for(let Ht of M.sortKeyRanges){let{sortKey:St,symbolInstanceStart:Bt,symbolInstanceEnd:Qt}=Ht;l.push({sortKey:St,symbolInstanceStart:Bt,symbolInstanceEnd:Qt,parameters:Ft})}else l.push({symbolInstanceStart:0,symbolInstanceEnd:M.symbolInstances.length,parameters:Ft})}attemptAnchorPlacement(l,d,v,b,M,O,B,U,W,Z,$,st,At,pt,yt,dt){let Ft=n.aq[l.textAnchor],Ht=[l.textOffset0,l.textOffset1],St=vr(Ft,v,b,Ht,M),Bt=this.collisionIndex.placeCollisionBox(Xe(d,St.x,St.y,O,B,this.transform.angle),$,U,W,Z.predicate,dt);if((!yt||this.collisionIndex.placeCollisionBox(Xe(yt,St.x,St.y,O,B,this.transform.angle),$,U,W,Z.predicate,dt).box.length!==0)&&Bt.box.length>0){let Qt;if(this.prevPlacement&&this.prevPlacement.variableOffsets[st.crossTileID]&&this.prevPlacement.placements[st.crossTileID]&&this.prevPlacement.placements[st.crossTileID].text&&(Qt=this.prevPlacement.variableOffsets[st.crossTileID].anchor),st.crossTileID===0)throw new Error(\"symbolInstance.crossTileID can't be 0\");return this.variableOffsets[st.crossTileID]={textOffset:Ht,width:v,height:b,anchor:Ft,textBoxScale:M,prevAnchor:Qt},this.markUsedJustification(At,Ft,st,pt),At.allowVerticalPlacement&&(this.markUsedOrientation(At,pt,st),this.placedOrientations[st.crossTileID]=pt),{shift:St,placedGlyphBoxes:Bt}}}placeLayerBucketPart(l,d,v){let{bucket:b,layout:M,posMatrix:O,textLabelPlaneMatrix:B,labelToScreenMatrix:U,textPixelRatio:W,holdingForFade:Z,collisionBoxArray:$,partiallyEvaluatedTextSize:st,collisionGroup:At}=l.parameters,pt=M.get(\"text-optional\"),yt=M.get(\"icon-optional\"),dt=n.ar(M,\"text-overlap\",\"text-allow-overlap\"),Ft=dt===\"always\",Ht=n.ar(M,\"icon-overlap\",\"icon-allow-overlap\"),St=Ht===\"always\",Bt=M.get(\"text-rotation-alignment\")===\"map\",Qt=M.get(\"text-pitch-alignment\")===\"map\",$t=M.get(\"icon-text-fit\")!==\"none\",oe=M.get(\"symbol-z-order\")===\"viewport-y\",pe=Ft&&(St||!b.hasIconData()||yt),he=St&&(Ft||!b.hasTextData()||pt);!b.collisionArrays&&$&&b.deserializeCollisionBoxes($);let be=this.retainedQueryData[b.bucketInstanceId].tileID,Ze=this.terrain?(Ee,pr)=>this.terrain.getElevation(be,Ee,pr):null,Kr=(Ee,pr)=>{var tr,Gi;if(d[Ee.crossTileID])return;if(Z)return void(this.placements[Ee.crossTileID]=new ie(!1,!1,!1));let Jr=!1,Vr=!1,ei=!0,On=null,tn={box:null,offscreen:null},Gs={box:null,offscreen:null},hs=null,Bn=null,qo=null,jr=0,ql=0,Zl=0;pr.textFeatureIndex?jr=pr.textFeatureIndex:Ee.useRuntimeCollisionCircles&&(jr=Ee.featureIndex),pr.verticalTextFeatureIndex&&(ql=pr.verticalTextFeatureIndex);let yu=pr.textBox;if(yu){let Ws=Fn=>{let fs=n.ai.horizontal;if(b.allowVerticalPlacement&&!Fn&&this.prevPlacement){let Zo=this.prevPlacement.placedOrientations[Ee.crossTileID];Zo&&(this.placedOrientations[Ee.crossTileID]=Zo,fs=Zo,this.markUsedOrientation(b,fs,Ee))}return fs},Ps=(Fn,fs)=>{if(b.allowVerticalPlacement&&Ee.numVerticalGlyphVertices>0&&pr.verticalTextBox){for(let Zo of b.writingModes)if(Zo===n.ai.vertical?(tn=fs(),Gs=tn):tn=Fn(),tn&&tn.box&&tn.box.length)break}else tn=Fn()},Eo=Ee.textAnchorOffsetStartIndex,yh=Ee.textAnchorOffsetEndIndex;if(yh===Eo){let Fn=(fs,Zo)=>{let _n=this.collisionIndex.placeCollisionBox(fs,dt,W,O,At.predicate,Ze);return _n&&_n.box&&_n.box.length&&(this.markUsedOrientation(b,Zo,Ee),this.placedOrientations[Ee.crossTileID]=Zo),_n};Ps(()=>Fn(yu,n.ai.horizontal),()=>{let fs=pr.verticalTextBox;return b.allowVerticalPlacement&&Ee.numVerticalGlyphVertices>0&&fs?Fn(fs,n.ai.vertical):{box:null,offscreen:null}}),Ws(tn&&tn.box&&tn.box.length)}else{let Fn=n.aq[(Gi=(tr=this.prevPlacement)===null||tr===void 0?void 0:tr.variableOffsets[Ee.crossTileID])===null||Gi===void 0?void 0:Gi.anchor],fs=(_n,ho,Gr)=>{let Ua=_n.x2-_n.x1,S_=_n.y2-_n.y1,zd=Ee.textBoxScale,cA=$t&&Ht===\"never\"?ho:null,Yl={box:[],offscreen:!1},Yo=dt===\"never\"?1:2,me=\"never\";Fn&&Yo++;for(let ke=0;kefs(yu,pr.iconBox,n.ai.horizontal),()=>{let _n=pr.verticalTextBox;return b.allowVerticalPlacement&&!(tn&&tn.box&&tn.box.length)&&Ee.numVerticalGlyphVertices>0&&_n?fs(_n,pr.verticalIconBox,n.ai.vertical):{box:null,offscreen:null}}),tn&&(Jr=tn.box,ei=tn.offscreen);let Zo=Ws(tn&&tn.box);if(!Jr&&this.prevPlacement){let _n=this.prevPlacement.variableOffsets[Ee.crossTileID];_n&&(this.variableOffsets[Ee.crossTileID]=_n,this.markUsedJustification(b,_n.anchor,Ee,Zo))}}}if(hs=tn,Jr=hs&&hs.box&&hs.box.length>0,ei=hs&&hs.offscreen,Ee.useRuntimeCollisionCircles){let Ws=b.text.placedSymbolArray.get(Ee.centerJustifiedTextSymbolIndex),Ps=n.aj(b.textSizeData,st,Ws),Eo=M.get(\"text-padding\");Bn=this.collisionIndex.placeCollisionCircles(dt,Ws,b.lineVertexArray,b.glyphOffsetArray,Ps,O,B,U,v,Qt,At.predicate,Ee.collisionCircleDiameter,Eo,Ze),Bn.circles.length&&Bn.collisionDetected&&!v&&n.w(\"Collisions detected, but collision boxes are not shown\"),Jr=Ft||Bn.circles.length>0&&!Bn.collisionDetected,ei=ei&&Bn.offscreen}if(pr.iconFeatureIndex&&(Zl=pr.iconFeatureIndex),pr.iconBox){let Ws=Ps=>{let Eo=$t&&On?Xe(Ps,On.x,On.y,Bt,Qt,this.transform.angle):Ps;return this.collisionIndex.placeCollisionBox(Eo,Ht,W,O,At.predicate,Ze)};Gs&&Gs.box&&Gs.box.length&&pr.verticalIconBox?(qo=Ws(pr.verticalIconBox),Vr=qo.box.length>0):(qo=Ws(pr.iconBox),Vr=qo.box.length>0),ei=ei&&qo.offscreen}let vu=pt||Ee.numHorizontalGlyphVertices===0&&Ee.numVerticalGlyphVertices===0,_h=yt||Ee.numIconVertices===0;if(vu||_h?_h?vu||(Vr=Vr&&Jr):Jr=Vr&&Jr:Vr=Jr=Vr&&Jr,Jr&&hs&&hs.box&&this.collisionIndex.insertCollisionBox(hs.box,dt,M.get(\"text-ignore-placement\"),b.bucketInstanceId,Gs&&Gs.box&&ql?ql:jr,At.ID),Vr&&qo&&this.collisionIndex.insertCollisionBox(qo.box,Ht,M.get(\"icon-ignore-placement\"),b.bucketInstanceId,Zl,At.ID),Bn&&(Jr&&this.collisionIndex.insertCollisionCircles(Bn.circles,dt,M.get(\"text-ignore-placement\"),b.bucketInstanceId,jr,At.ID),v)){let Ws=b.bucketInstanceId,Ps=this.collisionCircleArrays[Ws];Ps===void 0&&(Ps=this.collisionCircleArrays[Ws]=new se);for(let Eo=0;Eo=0;--pr){let tr=Ee[pr];Kr(b.symbolInstances.get(tr),b.collisionArrays[tr])}}else for(let Ee=l.symbolInstanceStart;Ee=0&&(l.text.placedSymbolArray.get(B).crossTileID=M>=0&&B!==M?0:v.crossTileID)}markUsedOrientation(l,d,v){let b=d===n.ai.horizontal||d===n.ai.horizontalOnly?d:0,M=d===n.ai.vertical?d:0,O=[v.leftJustifiedTextSymbolIndex,v.centerJustifiedTextSymbolIndex,v.rightJustifiedTextSymbolIndex];for(let B of O)l.text.placedSymbolArray.get(B).placedOrientation=b;v.verticalPlacedTextSymbolIndex&&(l.text.placedSymbolArray.get(v.verticalPlacedTextSymbolIndex).placedOrientation=M)}commit(l){this.commitTime=l,this.zoomAtLastRecencyCheck=this.transform.zoom;let d=this.prevPlacement,v=!1;this.prevZoomAdjustment=d?d.zoomAdjustment(this.transform.zoom):0;let b=d?d.symbolFadeChange(l):1,M=d?d.opacities:{},O=d?d.variableOffsets:{},B=d?d.placedOrientations:{};for(let U in this.placements){let W=this.placements[U],Z=M[U];Z?(this.opacities[U]=new Vt(Z,b,W.text,W.icon),v=v||W.text!==Z.text.placed||W.icon!==Z.icon.placed):(this.opacities[U]=new Vt(null,b,W.text,W.icon,W.skipFade),v=v||W.text||W.icon)}for(let U in M){let W=M[U];if(!this.opacities[U]){let Z=new Vt(W,b,!1,!1);Z.isHidden()||(this.opacities[U]=Z,v=v||W.text.placed||W.icon.placed)}}for(let U in O)this.variableOffsets[U]||!this.opacities[U]||this.opacities[U].isHidden()||(this.variableOffsets[U]=O[U]);for(let U in B)this.placedOrientations[U]||!this.opacities[U]||this.opacities[U].isHidden()||(this.placedOrientations[U]=B[U]);if(d&&d.lastPlacementChangeTime===void 0)throw new Error(\"Last placement time for previous placement is not defined\");v?this.lastPlacementChangeTime=l:typeof this.lastPlacementChangeTime!=\"number\"&&(this.lastPlacementChangeTime=d?d.lastPlacementChangeTime:l)}updateLayerOpacities(l,d){let v={};for(let b of d){let M=b.getBucket(l);M&&b.latestFeatureIndex&&l.id===M.layerIds[0]&&this.updateBucketOpacities(M,v,b.collisionBoxArray)}}updateBucketOpacities(l,d,v){l.hasTextData()&&(l.text.opacityVertexArray.clear(),l.text.hasVisibleVertices=!1),l.hasIconData()&&(l.icon.opacityVertexArray.clear(),l.icon.hasVisibleVertices=!1),l.hasIconCollisionBoxData()&&l.iconCollisionBox.collisionVertexArray.clear(),l.hasTextCollisionBoxData()&&l.textCollisionBox.collisionVertexArray.clear();let b=l.layers[0],M=b.layout,O=new Vt(null,0,!1,!1,!0),B=M.get(\"text-allow-overlap\"),U=M.get(\"icon-allow-overlap\"),W=b._unevaluatedLayout.hasValue(\"text-variable-anchor\")||b._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"),Z=M.get(\"text-rotation-alignment\")===\"map\",$=M.get(\"text-pitch-alignment\")===\"map\",st=M.get(\"icon-text-fit\")!==\"none\",At=new Vt(null,0,B&&(U||!l.hasIconData()||M.get(\"icon-optional\")),U&&(B||!l.hasTextData()||M.get(\"text-optional\")),!0);!l.collisionArrays&&v&&(l.hasIconCollisionBoxData()||l.hasTextCollisionBoxData())&&l.deserializeCollisionBoxes(v);let pt=(yt,dt,Ft)=>{for(let Ht=0;Ht
0,$t=this.placedOrientations[dt.crossTileID],oe=$t===n.ai.vertical,pe=$t===n.ai.horizontal||$t===n.ai.horizontalOnly;if(Ft>0||Ht>0){let he=es(Bt.text);pt(l.text,Ft,oe?oa:he),pt(l.text,Ht,pe?oa:he);let be=Bt.text.isHidden();[dt.rightJustifiedTextSymbolIndex,dt.centerJustifiedTextSymbolIndex,dt.leftJustifiedTextSymbolIndex].forEach(Ee=>{Ee>=0&&(l.text.placedSymbolArray.get(Ee).hidden=be||oe?1:0)}),dt.verticalPlacedTextSymbolIndex>=0&&(l.text.placedSymbolArray.get(dt.verticalPlacedTextSymbolIndex).hidden=be||pe?1:0);let Ze=this.variableOffsets[dt.crossTileID];Ze&&this.markUsedJustification(l,Ze.anchor,dt,$t);let Kr=this.placedOrientations[dt.crossTileID];Kr&&(this.markUsedJustification(l,\"left\",dt,Kr),this.markUsedOrientation(l,Kr,dt))}if(Qt){let he=es(Bt.icon),be=!(st&&dt.verticalPlacedIconSymbolIndex&&oe);dt.placedIconSymbolIndex>=0&&(pt(l.icon,dt.numIconVertices,be?he:oa),l.icon.placedSymbolArray.get(dt.placedIconSymbolIndex).hidden=Bt.icon.isHidden()),dt.verticalPlacedIconSymbolIndex>=0&&(pt(l.icon,dt.numVerticalIconVertices,be?oa:he),l.icon.placedSymbolArray.get(dt.verticalPlacedIconSymbolIndex).hidden=Bt.icon.isHidden())}if(l.hasIconCollisionBoxData()||l.hasTextCollisionBoxData()){let he=l.collisionArrays[yt];if(he){let be=new n.P(0,0);if(he.textBox||he.verticalTextBox){let Kr=!0;if(W){let Ee=this.variableOffsets[St];Ee?(be=vr(Ee.anchor,Ee.width,Ee.height,Ee.textOffset,Ee.textBoxScale),Z&&be._rotate($?this.transform.angle:-this.transform.angle)):Kr=!1}he.textBox&&wr(l.textCollisionBox.collisionVertexArray,Bt.text.placed,!Kr||oe,be.x,be.y),he.verticalTextBox&&wr(l.textCollisionBox.collisionVertexArray,Bt.text.placed,!Kr||pe,be.x,be.y)}let Ze=!!(!pe&&he.verticalIconBox);he.iconBox&&wr(l.iconCollisionBox.collisionVertexArray,Bt.icon.placed,Ze,st?be.x:0,st?be.y:0),he.verticalIconBox&&wr(l.iconCollisionBox.collisionVertexArray,Bt.icon.placed,!Ze,st?be.x:0,st?be.y:0)}}}if(l.sortFeatures(this.transform.angle),this.retainedQueryData[l.bucketInstanceId]&&(this.retainedQueryData[l.bucketInstanceId].featureSortOrder=l.featureSortOrder),l.hasTextData()&&l.text.opacityVertexBuffer&&l.text.opacityVertexBuffer.updateData(l.text.opacityVertexArray),l.hasIconData()&&l.icon.opacityVertexBuffer&&l.icon.opacityVertexBuffer.updateData(l.icon.opacityVertexArray),l.hasIconCollisionBoxData()&&l.iconCollisionBox.collisionVertexBuffer&&l.iconCollisionBox.collisionVertexBuffer.updateData(l.iconCollisionBox.collisionVertexArray),l.hasTextCollisionBoxData()&&l.textCollisionBox.collisionVertexBuffer&&l.textCollisionBox.collisionVertexBuffer.updateData(l.textCollisionBox.collisionVertexArray),l.text.opacityVertexArray.length!==l.text.layoutVertexArray.length/4)throw new Error(`bucket.text.opacityVertexArray.length (= ${l.text.opacityVertexArray.length}) !== bucket.text.layoutVertexArray.length (= ${l.text.layoutVertexArray.length}) / 4`);if(l.icon.opacityVertexArray.length!==l.icon.layoutVertexArray.length/4)throw new Error(`bucket.icon.opacityVertexArray.length (= ${l.icon.opacityVertexArray.length}) !== bucket.icon.layoutVertexArray.length (= ${l.icon.layoutVertexArray.length}) / 4`);if(l.bucketInstanceId in this.collisionCircleArrays){let yt=this.collisionCircleArrays[l.bucketInstanceId];l.placementInvProjMatrix=yt.invProjMatrix,l.placementViewportMatrix=yt.viewportMatrix,l.collisionCircleArray=yt.circles,delete this.collisionCircleArrays[l.bucketInstanceId]}}symbolFadeChange(l){return this.fadeDuration===0?1:(l-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment}zoomAdjustment(l){return Math.max(0,(this.transform.zoom-l)/1.5)}hasTransitions(l){return this.stale||l-this.lastPlacementChangeTimel}setStale(){this.stale=!0}}function wr(T,l,d,v,b){T.emplaceBack(l?1:0,d?1:0,v||0,b||0),T.emplaceBack(l?1:0,d?1:0,v||0,b||0),T.emplaceBack(l?1:0,d?1:0,v||0,b||0),T.emplaceBack(l?1:0,d?1:0,v||0,b||0)}let xi=Math.pow(2,25),zi=Math.pow(2,24),ni=Math.pow(2,17),Hr=Math.pow(2,16),jn=Math.pow(2,9),Bi=Math.pow(2,8),xn=Math.pow(2,1);function es(T){if(T.opacity===0&&!T.placed)return 0;if(T.opacity===1&&T.placed)return 4294967295;let l=T.placed?1:0,d=Math.floor(127*T.opacity);return d*xi+l*zi+d*ni+l*Hr+d*jn+l*Bi+d*xn+l}let oa=0;class Um{constructor(l){this._sortAcrossTiles=l.layout.get(\"symbol-z-order\")!==\"viewport-y\"&&!l.layout.get(\"symbol-sort-key\").isConstant(),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]}continuePlacement(l,d,v,b,M){let O=this._bucketParts;for(;this._currentTileIndexB.sortKey-U.sortKey));this._currentPartIndex!this._forceFullPlacement&&n.h.now()-b>2;for(;this._currentPlacementIndex>=0;){let O=d[l[this._currentPlacementIndex]],B=this.placement.collisionIndex.transform.zoom;if(O.type===\"symbol\"&&(!O.minzoom||O.minzoom<=B)&&(!O.maxzoom||O.maxzoom>B)){if(this._inProgressLayer||(this._inProgressLayer=new Um(O)),this._inProgressLayer.continuePlacement(v[O.source],this.placement,this._showCollisionBoxes,O,M))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0}commit(l){return this.placement.commit(l),this.placement}}let Ss=512/n.N/2;class nh{constructor(l,d,v){this.tileID=l,this.bucketInstanceId=v,this._symbolsByKey={};let b=new Map;for(let M=0;M({x:Math.floor(U.anchorX*Ss),y:Math.floor(U.anchorY*Ss)})),crossTileIDs:O.map(U=>U.crossTileID)};if(B.positions.length>128){let U=new n.av(B.positions.length,16,Uint16Array);for(let{x:W,y:Z}of B.positions)U.add(W,Z);U.finish(),delete B.positions,B.index=U}this._symbolsByKey[M]=B}}getScaledCoordinates(l,d){let{x:v,y:b,z:M}=this.tileID.canonical,{x:O,y:B,z:U}=d.canonical,W=Ss/Math.pow(2,U-M),Z=(B*n.N+l.anchorY)*W,$=b*n.N*Ss;return{x:Math.floor((O*n.N+l.anchorX)*W-v*n.N*Ss),y:Math.floor(Z-$)}}findMatches(l,d,v){let b=this.tileID.canonical.zl)}}class ai{constructor(){this.maxCrossTileID=0}generate(){return++this.maxCrossTileID}}class ka{constructor(){this.indexes={},this.usedCrossTileIDs={},this.lng=0}handleWrapJump(l){let d=Math.round((l-this.lng)/360);if(d!==0)for(let v in this.indexes){let b=this.indexes[v],M={};for(let O in b){let B=b[O];B.tileID=B.tileID.unwrapTo(B.tileID.wrap+d),M[B.tileID.key]=B}this.indexes[v]=M}this.lng=l}addBucket(l,d,v){if(this.indexes[l.overscaledZ]&&this.indexes[l.overscaledZ][l.key]){if(this.indexes[l.overscaledZ][l.key].bucketInstanceId===d.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(l.overscaledZ,this.indexes[l.overscaledZ][l.key])}for(let M=0;Ml.overscaledZ)for(let B in O){let U=O[B];U.tileID.isChildOf(l)&&U.findMatches(d.symbolInstances,l,b)}else{let B=O[l.scaledTo(Number(M)).key];B&&B.findMatches(d.symbolInstances,l,b)}}for(let M=0;M{d[v]=!0});for(let v in this.layerIndexes)d[v]||delete this.layerIndexes[v]}}let ln=(T,l)=>n.x(T,l&&l.filter(d=>d.identifier!==\"source.canvas\")),Dn=n.F(n.ax,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\",\"setGlyphs\",\"setSprite\"]),Vm=n.F(n.ax,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),Go=n.aw();class Gn extends n.E{constructor(l,d={}){super(),this.map=l,this.dispatcher=new ih(bo(),this,l._getMapId()),this.imageManager=new ue,this.imageManager.setEventedParent(this),this.glyphManager=new Sr(l._requestManager,d.localIdeographFontFamily),this.lineAtlas=new No(256,512),this.crossTileSymbolIndex=new Dc,this._spritesImagesIds={},this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new n.ay,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",n.az());let v=this;this._rtlTextPluginCallback=Gn.registerForPluginStateChange(b=>{v.dispatcher.broadcast(\"syncRTLPluginState\",{pluginStatus:b.pluginStatus,pluginURL:b.pluginURL},(M,O)=>{if(n.aA(M),O&&O.every(B=>B))for(let B in v.sourceCaches){let U=v.sourceCaches[B].getSource().type;U!==\"vector\"&&U!==\"geojson\"||v.sourceCaches[B].reload()}})}),this.on(\"data\",b=>{if(b.dataType!==\"source\"||b.sourceDataType!==\"metadata\")return;let M=this.sourceCaches[b.sourceId];if(!M)return;let O=M.getSource();if(O&&O.vectorLayerIds)for(let B in this._layers){let U=this._layers[B];U.source===O.id&&this._validateLayer(U)}})}loadURL(l,d={},v){this.fire(new n.k(\"dataloading\",{dataType:\"style\"})),d.validate=typeof d.validate!=\"boolean\"||d.validate;let b=this.map._requestManager.transformRequest(l,Q.Style);this._request=n.f(b,(M,O)=>{this._request=null,M?this.fire(new n.j(M)):O&&this._load(O,d,v)})}loadJSON(l,d={},v){this.fire(new n.k(\"dataloading\",{dataType:\"style\"})),this._request=n.h.frame(()=>{this._request=null,d.validate=d.validate!==!1,this._load(l,d,v)})}loadEmpty(){this.fire(new n.k(\"dataloading\",{dataType:\"style\"})),this._load(Go,{validate:!1})}_load(l,d,v){var b;let M=d.transformStyle?d.transformStyle(v,l):l;if(!d.validate||!ln(this,n.y(M))){this._loaded=!0,this.stylesheet=M;for(let O in M.sources)this.addSource(O,M.sources[O],{validate:!1});M.sprite?this._loadSprite(M.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(M.glyphs),this._createLayers(),this.light=new zl(this.stylesheet.light),this.map.setTerrain((b=this.stylesheet.terrain)!==null&&b!==void 0?b:null),this.fire(new n.k(\"data\",{dataType:\"style\"})),this.fire(new n.k(\"style.load\"))}}_createLayers(){let l=n.aB(this.stylesheet.layers);this.dispatcher.broadcast(\"setLayers\",l),this._order=l.map(d=>d.id),this._layers={},this._serializedLayers=null;for(let d of l){let v=n.aC(d);v.setEventedParent(this,{layer:{id:d.id}}),this._layers[d.id]=v}}_loadSprite(l,d=!1,v=void 0){this.imageManager.setLoaded(!1),this._spriteRequest=function(b,M,O,B){let U=kt(b),W=U.length,Z=O>1?\"@2x\":\"\",$={},st={},At={};for(let{id:pt,url:yt}of U){let dt=M.transformRequest(M.normalizeSpriteURL(yt,Z,\".json\"),Q.SpriteJSON),Ft=`${pt}_${dt.url}`;$[Ft]=n.f(dt,(Bt,Qt)=>{delete $[Ft],st[pt]=Qt,Xt(B,st,At,Bt,W)});let Ht=M.transformRequest(M.normalizeSpriteURL(yt,Z,\".png\"),Q.SpriteImage),St=`${pt}_${Ht.url}`;$[St]=j.getImage(Ht,(Bt,Qt)=>{delete $[St],At[pt]=Qt,Xt(B,st,At,Bt,W)})}return{cancel(){for(let pt of Object.values($))pt.cancel()}}}(l,this.map._requestManager,this.map.getPixelRatio(),(b,M)=>{if(this._spriteRequest=null,b)this.fire(new n.j(b));else if(M)for(let O in M){this._spritesImagesIds[O]=[];let B=this._spritesImagesIds[O]?this._spritesImagesIds[O].filter(U=>!(U in M)):[];for(let U of B)this.imageManager.removeImage(U),this._changedImages[U]=!0;for(let U in M[O]){let W=O===\"default\"?U:`${O}:${U}`;this._spritesImagesIds[O].push(W),W in this.imageManager.images?this.imageManager.updateImage(W,M[O][U],!1):this.imageManager.addImage(W,M[O][U]),d&&(this._changedImages[W]=!0)}}this.imageManager.setLoaded(!0),this._availableImages=this.imageManager.listImages(),d&&(this._changed=!0),this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new n.k(\"data\",{dataType:\"style\"})),v&&v(b)})}_unloadSprite(){for(let l of Object.values(this._spritesImagesIds).flat())this.imageManager.removeImage(l),this._changedImages[l]=!0;this._spritesImagesIds={},this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new n.k(\"data\",{dataType:\"style\"}))}_validateLayer(l){let d=this.sourceCaches[l.source];if(!d)return;let v=l.sourceLayer;if(!v)return;let b=d.getSource();(b.type===\"geojson\"||b.vectorLayerIds&&b.vectorLayerIds.indexOf(v)===-1)&&this.fire(new n.j(new Error(`Source layer \"${v}\" does not exist on source \"${b.id}\" as specified by style layer \"${l.id}\".`)))}loaded(){if(!this._loaded||Object.keys(this._updatedSources).length)return!1;for(let l in this.sourceCaches)if(!this.sourceCaches[l].loaded())return!1;return!!this.imageManager.isLoaded()}_serializeByIds(l){let d=this._serializedAllLayers();if(!l||l.length===0)return Object.values(d);let v=[];for(let b of l)d[b]&&v.push(d[b]);return v}_serializedAllLayers(){let l=this._serializedLayers;if(l)return l;l=this._serializedLayers={};let d=Object.keys(this._layers);for(let v of d){let b=this._layers[v];b.type!==\"custom\"&&(l[v]=b.serialize())}return l}hasTransitions(){if(this.light&&this.light.hasTransition())return!0;for(let l in this.sourceCaches)if(this.sourceCaches[l].hasTransition())return!0;for(let l in this._layers)if(this._layers[l].hasTransition())return!0;return!1}_checkLoaded(){if(!this._loaded)throw new Error(\"Style is not done loading.\")}update(l){if(!this._loaded)return;let d=this._changed;if(this._changed){let b=Object.keys(this._updatedLayers),M=Object.keys(this._removedLayers);(b.length||M.length)&&this._updateWorkerLayers(b,M);for(let O in this._updatedSources){let B=this._updatedSources[O];if(B===\"reload\")this._reloadSource(O);else{if(B!==\"clear\")throw new Error(`Invalid action ${B}`);this._clearSource(O)}}this._updateTilesForChangedImages(),this._updateTilesForChangedGlyphs();for(let O in this._updatedPaintProps)this._layers[O].updateTransitions(l);this.light.updateTransitions(l),this._resetUpdates()}let v={};for(let b in this.sourceCaches){let M=this.sourceCaches[b];v[b]=M.used,M.used=!1}for(let b of this._order){let M=this._layers[b];M.recalculate(l,this._availableImages),!M.isHidden(l.zoom)&&M.source&&(this.sourceCaches[M.source].used=!0)}for(let b in v){let M=this.sourceCaches[b];v[b]!==M.used&&M.fire(new n.k(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:b}))}this.light.recalculate(l),this.z=l.zoom,d&&this.fire(new n.k(\"data\",{dataType:\"style\"}))}_updateTilesForChangedImages(){let l=Object.keys(this._changedImages);if(l.length){for(let d in this.sourceCaches)this.sourceCaches[d].reloadTilesForDependencies([\"icons\",\"patterns\"],l);this._changedImages={}}}_updateTilesForChangedGlyphs(){if(this._glyphsDidChange){for(let l in this.sourceCaches)this.sourceCaches[l].reloadTilesForDependencies([\"glyphs\"],[\"\"]);this._glyphsDidChange=!1}}_updateWorkerLayers(l,d){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeByIds(l),removedIds:d})}_resetUpdates(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={},this._glyphsDidChange=!1}setState(l,d={}){this._checkLoaded();let v=this.serialize();if(l=d.transformStyle?d.transformStyle(v,l):l,ln(this,n.y(l)))return!1;(l=n.aD(l)).layers=n.aB(l.layers);let b=n.aE(v,l).filter(O=>!(O.command in Vm));if(b.length===0)return!1;let M=b.filter(O=>!(O.command in Dn));if(M.length>0)throw new Error(`Unimplemented: ${M.map(O=>O.command).join(\", \")}.`);for(let O of b)O.command!==\"setTransition\"&&this[O.command].apply(this,O.args);return this.stylesheet=l,this._serializedLayers=null,!0}addImage(l,d){if(this.getImage(l))return this.fire(new n.j(new Error(`An image named \"${l}\" already exists.`)));this.imageManager.addImage(l,d),this._afterImageUpdated(l)}updateImage(l,d){this.imageManager.updateImage(l,d)}getImage(l){return this.imageManager.getImage(l)}removeImage(l){if(!this.getImage(l))return this.fire(new n.j(new Error(`An image named \"${l}\" does not exist.`)));this.imageManager.removeImage(l),this._afterImageUpdated(l)}_afterImageUpdated(l){this._availableImages=this.imageManager.listImages(),this._changedImages[l]=!0,this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new n.k(\"data\",{dataType:\"style\"}))}listImages(){return this._checkLoaded(),this.imageManager.listImages()}addSource(l,d,v={}){if(this._checkLoaded(),this.sourceCaches[l]!==void 0)throw new Error(`Source \"${l}\" already exists.`);if(!d.type)throw new Error(`The type property must be defined, but only the following properties were given: ${Object.keys(d).join(\", \")}.`);if([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(d.type)>=0&&this._validate(n.y.source,`sources.${l}`,d,null,v))return;this.map&&this.map._collectResourceTiming&&(d.collectResourceTiming=!0);let b=this.sourceCaches[l]=new ls(l,d,this.dispatcher);b.style=this,b.setEventedParent(this,()=>({isSourceLoaded:b.loaded(),source:b.serialize(),sourceId:l})),b.onAdd(this.map),this._changed=!0}removeSource(l){if(this._checkLoaded(),this.sourceCaches[l]===void 0)throw new Error(\"There is no source with this ID\");for(let v in this._layers)if(this._layers[v].source===l)return this.fire(new n.j(new Error(`Source \"${l}\" cannot be removed while layer \"${v}\" is using it.`)));let d=this.sourceCaches[l];delete this.sourceCaches[l],delete this._updatedSources[l],d.fire(new n.k(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:l})),d.setEventedParent(null),d.onRemove(this.map),this._changed=!0}setGeoJSONSourceData(l,d){if(this._checkLoaded(),this.sourceCaches[l]===void 0)throw new Error(`There is no source with this ID=${l}`);let v=this.sourceCaches[l].getSource();if(v.type!==\"geojson\")throw new Error(`geojsonSource.type is ${v.type}, which is !== 'geojson`);v.setData(d),this._changed=!0}getSource(l){return this.sourceCaches[l]&&this.sourceCaches[l].getSource()}addLayer(l,d,v={}){this._checkLoaded();let b=l.id;if(this.getLayer(b))return void this.fire(new n.j(new Error(`Layer \"${b}\" already exists on this map.`)));let M;if(l.type===\"custom\"){if(ln(this,n.aF(l)))return;M=n.aC(l)}else{if(\"source\"in l&&typeof l.source==\"object\"&&(this.addSource(b,l.source),l=n.aD(l),l=n.e(l,{source:b})),this._validate(n.y.layer,`layers.${b}`,l,{arrayIndex:-1},v))return;M=n.aC(l),this._validateLayer(M),M.setEventedParent(this,{layer:{id:b}})}let O=d?this._order.indexOf(d):this._order.length;if(d&&O===-1)this.fire(new n.j(new Error(`Cannot add layer \"${b}\" before non-existing layer \"${d}\".`)));else{if(this._order.splice(O,0,b),this._layerOrderChanged=!0,this._layers[b]=M,this._removedLayers[b]&&M.source&&M.type!==\"custom\"){let B=this._removedLayers[b];delete this._removedLayers[b],B.type!==M.type?this._updatedSources[M.source]=\"clear\":(this._updatedSources[M.source]=\"reload\",this.sourceCaches[M.source].pause())}this._updateLayer(M),M.onAdd&&M.onAdd(this.map)}}moveLayer(l,d){if(this._checkLoaded(),this._changed=!0,!this._layers[l])return void this.fire(new n.j(new Error(`The layer '${l}' does not exist in the map's style and cannot be moved.`)));if(l===d)return;let v=this._order.indexOf(l);this._order.splice(v,1);let b=d?this._order.indexOf(d):this._order.length;d&&b===-1?this.fire(new n.j(new Error(`Cannot move layer \"${l}\" before non-existing layer \"${d}\".`))):(this._order.splice(b,0,l),this._layerOrderChanged=!0)}removeLayer(l){this._checkLoaded();let d=this._layers[l];if(!d)return void this.fire(new n.j(new Error(`Cannot remove non-existing layer \"${l}\".`)));d.setEventedParent(null);let v=this._order.indexOf(l);this._order.splice(v,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[l]=d,delete this._layers[l],this._serializedLayers&&delete this._serializedLayers[l],delete this._updatedLayers[l],delete this._updatedPaintProps[l],d.onRemove&&d.onRemove(this.map)}getLayer(l){return this._layers[l]}getLayersOrder(){return[...this._order]}hasLayer(l){return l in this._layers}setLayerZoomRange(l,d,v){this._checkLoaded();let b=this.getLayer(l);b?b.minzoom===d&&b.maxzoom===v||(d!=null&&(b.minzoom=d),v!=null&&(b.maxzoom=v),this._updateLayer(b)):this.fire(new n.j(new Error(`Cannot set the zoom range of non-existing layer \"${l}\".`)))}setFilter(l,d,v={}){this._checkLoaded();let b=this.getLayer(l);if(b){if(!n.aG(b.filter,d))return d==null?(b.filter=void 0,void this._updateLayer(b)):void(this._validate(n.y.filter,`layers.${b.id}.filter`,d,null,v)||(b.filter=n.aD(d),this._updateLayer(b)))}else this.fire(new n.j(new Error(`Cannot filter non-existing layer \"${l}\".`)))}getFilter(l){return n.aD(this.getLayer(l).filter)}setLayoutProperty(l,d,v,b={}){this._checkLoaded();let M=this.getLayer(l);M?n.aG(M.getLayoutProperty(d),v)||(M.setLayoutProperty(d,v,b),this._updateLayer(M)):this.fire(new n.j(new Error(`Cannot style non-existing layer \"${l}\".`)))}getLayoutProperty(l,d){let v=this.getLayer(l);if(v)return v.getLayoutProperty(d);this.fire(new n.j(new Error(`Cannot get style of non-existing layer \"${l}\".`)))}setPaintProperty(l,d,v,b={}){this._checkLoaded();let M=this.getLayer(l);M?n.aG(M.getPaintProperty(d),v)||(M.setPaintProperty(d,v,b)&&this._updateLayer(M),this._changed=!0,this._updatedPaintProps[l]=!0):this.fire(new n.j(new Error(`Cannot style non-existing layer \"${l}\".`)))}getPaintProperty(l,d){return this.getLayer(l).getPaintProperty(d)}setFeatureState(l,d){this._checkLoaded();let v=l.source,b=l.sourceLayer,M=this.sourceCaches[v];if(M===void 0)return void this.fire(new n.j(new Error(`The source '${v}' does not exist in the map's style.`)));let O=M.getSource().type;O===\"geojson\"&&b?this.fire(new n.j(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):O!==\"vector\"||b?(l.id===void 0&&this.fire(new n.j(new Error(\"The feature id parameter must be provided.\"))),M.setFeatureState(b,l.id,d)):this.fire(new n.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}removeFeatureState(l,d){this._checkLoaded();let v=l.source,b=this.sourceCaches[v];if(b===void 0)return void this.fire(new n.j(new Error(`The source '${v}' does not exist in the map's style.`)));let M=b.getSource().type,O=M===\"vector\"?l.sourceLayer:void 0;M!==\"vector\"||O?d&&typeof l.id!=\"string\"&&typeof l.id!=\"number\"?this.fire(new n.j(new Error(\"A feature id is required to remove its specific state property.\"))):b.removeFeatureState(O,l.id,d):this.fire(new n.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}getFeatureState(l){this._checkLoaded();let d=l.source,v=l.sourceLayer,b=this.sourceCaches[d];if(b!==void 0)return b.getSource().type!==\"vector\"||v?(l.id===void 0&&this.fire(new n.j(new Error(\"The feature id parameter must be provided.\"))),b.getFeatureState(v,l.id)):void this.fire(new n.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));this.fire(new n.j(new Error(`The source '${d}' does not exist in the map's style.`)))}getTransition(){return n.e({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)}serialize(){if(!this._loaded)return;let l=n.aH(this.sourceCaches,M=>M.serialize()),d=this._serializeByIds(this._order),v=this.map.getTerrain()||void 0,b=this.stylesheet;return n.aI({version:b.version,name:b.name,metadata:b.metadata,light:b.light,center:b.center,zoom:b.zoom,bearing:b.bearing,pitch:b.pitch,sprite:b.sprite,glyphs:b.glyphs,transition:b.transition,sources:l,layers:d,terrain:v},M=>M!==void 0)}_updateLayer(l){this._updatedLayers[l.id]=!0,l.source&&!this._updatedSources[l.source]&&this.sourceCaches[l.source].getSource().type!==\"raster\"&&(this._updatedSources[l.source]=\"reload\",this.sourceCaches[l.source].pause()),this._serializedLayers=null,this._changed=!0}_flattenAndSortRenderedFeatures(l){let d=O=>this._layers[O].type===\"fill-extrusion\",v={},b=[];for(let O=this._order.length-1;O>=0;O--){let B=this._order[O];if(d(B)){v[B]=O;for(let U of l){let W=U[B];if(W)for(let Z of W)b.push(Z)}}}b.sort((O,B)=>B.intersectionZ-O.intersectionZ);let M=[];for(let O=this._order.length-1;O>=0;O--){let B=this._order[O];if(d(B))for(let U=b.length-1;U>=0;U--){let W=b[U].feature;if(v[W.layer.id]{let pe=Ft.featureSortOrder;if(pe){let he=pe.indexOf($t.featureIndex);return pe.indexOf(oe.featureIndex)-he}return oe.featureIndex-$t.featureIndex});for(let $t of Qt)Bt.push($t)}}for(let Ft in pt)pt[Ft].forEach(Ht=>{let St=Ht.feature,Bt=W[B[Ft].source].getFeatureState(St.layer[\"source-layer\"],St.id);St.source=St.layer.source,St.layer[\"source-layer\"]&&(St.sourceLayer=St.layer[\"source-layer\"]),St.state=Bt});return pt}(this._layers,O,this.sourceCaches,l,d,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(M)}querySourceFeatures(l,d){d&&d.filter&&this._validate(n.y.filter,\"querySourceFeatures.filter\",d.filter,null,d);let v=this.sourceCaches[l];return v?function(b,M){let O=b.getRenderableIds().map(W=>b.getTileByID(W)),B=[],U={};for(let W=0;W{cl[b]=M})(l,d),d.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:l,url:d.workerSourceURL},v):v(null,null))}getLight(){return this.light.getLight()}setLight(l,d={}){this._checkLoaded();let v=this.light.getLight(),b=!1;for(let O in l)if(!n.aG(l[O],v[O])){b=!0;break}if(!b)return;let M={now:n.h.now(),transition:n.e({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(l,d),this.light.updateTransitions(M)}_validate(l,d,v,b,M={}){return(!M||M.validate!==!1)&&ln(this,l.call(n.y,n.e({key:d,style:this.serialize(),value:v,styleSpec:n.v},b)))}_remove(l=!0){this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),n.aJ.off(\"pluginStateChange\",this._rtlTextPluginCallback);for(let d in this._layers)this._layers[d].setEventedParent(null);for(let d in this.sourceCaches){let v=this.sourceCaches[d];v.setEventedParent(null),v.onRemove(this.map)}this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove(l)}_clearSource(l){this.sourceCaches[l].clearTiles()}_reloadSource(l){this.sourceCaches[l].resume(),this.sourceCaches[l].reload()}_updateSources(l){for(let d in this.sourceCaches)this.sourceCaches[d].update(l,this.map.terrain)}_generateCollisionBoxes(){for(let l in this.sourceCaches)this._reloadSource(l)}_updatePlacement(l,d,v,b,M=!1){let O=!1,B=!1,U={};for(let W of this._order){let Z=this._layers[W];if(Z.type!==\"symbol\")continue;if(!U[Z.source]){let st=this.sourceCaches[Z.source];U[Z.source]=st.getRenderableIds(!0).map(At=>st.getTileByID(At)).sort((At,pt)=>pt.tileID.overscaledZ-At.tileID.overscaledZ||(At.tileID.isLessThan(pt.tileID)?-1:1))}let $=this.crossTileSymbolIndex.addLayer(Z,U[Z.source],l.center.lng);O=O||$}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((M=M||this._layerOrderChanged||v===0)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(n.h.now(),l.zoom))&&(this.pauseablePlacement=new Vl(l,this.map.terrain,this._order,M,d,v,b,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,U),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(n.h.now()),B=!0),O&&this.pauseablePlacement.placement.setStale()),B||O)for(let W of this._order){let Z=this._layers[W];Z.type===\"symbol\"&&this.placement.updateLayerOpacities(Z,U[Z.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(n.h.now())}_releaseSymbolFadeTiles(){for(let l in this.sourceCaches)this.sourceCaches[l].releaseSymbolFadeTiles()}getImages(l,d,v){this.imageManager.getImages(d.icons,v),this._updateTilesForChangedImages();let b=this.sourceCaches[d.source];b&&b.setDependencies(d.tileID.key,d.type,d.icons)}getGlyphs(l,d,v){this.glyphManager.getGlyphs(d.stacks,v);let b=this.sourceCaches[d.source];b&&b.setDependencies(d.tileID.key,d.type,[\"\"])}getResource(l,d,v){return n.m(d,v)}getGlyphsUrl(){return this.stylesheet.glyphs||null}setGlyphs(l,d={}){this._checkLoaded(),l&&this._validate(n.y.glyphs,\"glyphs\",l,null,d)||(this._glyphsDidChange=!0,this.stylesheet.glyphs=l,this.glyphManager.entries={},this.glyphManager.setURL(l))}addSprite(l,d,v={},b){this._checkLoaded();let M=[{id:l,url:d}],O=[...kt(this.stylesheet.sprite),...M];this._validate(n.y.sprite,\"sprite\",O,null,v)||(this.stylesheet.sprite=O,this._loadSprite(M,!0,b))}removeSprite(l){this._checkLoaded();let d=kt(this.stylesheet.sprite);if(d.find(v=>v.id===l)){if(this._spritesImagesIds[l])for(let v of this._spritesImagesIds[l])this.imageManager.removeImage(v),this._changedImages[v]=!0;d.splice(d.findIndex(v=>v.id===l),1),this.stylesheet.sprite=d.length>0?d:void 0,delete this._spritesImagesIds[l],this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new n.k(\"data\",{dataType:\"style\"}))}else this.fire(new n.j(new Error(`Sprite \"${l}\" doesn't exists on this map.`)))}getSprite(){return kt(this.stylesheet.sprite)}setSprite(l,d={},v){this._checkLoaded(),l&&this._validate(n.y.sprite,\"sprite\",l,null,d)||(this.stylesheet.sprite=l,l?this._loadSprite(l,!0,v):(this._unloadSprite(),v&&v(null)))}}Gn.registerForPluginStateChange=n.aK;var So=n.Q([{name:\"a_pos\",type:\"Int16\",components:2}]),jl=\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;varying float v_depth;void main() {float extent=8192.0;float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/extent;gl_Position=u_matrix*vec4(a_pos3d.xy,get_elevation(a_pos3d.xy)-ele_delta,1.0);v_depth=gl_Position.z/gl_Position.w;}\";let Ki={prelude:_i(`#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\n`,`#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\n#ifdef TERRAIN3D\nuniform sampler2D u_terrain;uniform float u_terrain_dim;uniform mat4 u_terrain_matrix;uniform vec4 u_terrain_unpack;uniform float u_terrain_exaggeration;uniform highp sampler2D u_depth;\n#endif\nconst highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitShifts=vec4(1.)/bitSh;highp float unpack(highp vec4 color) {return dot(color,bitShifts);}highp float depthOpacity(vec3 frag) {\n#ifdef TERRAIN3D\nhighp float d=unpack(texture2D(u_depth,frag.xy*0.5+0.5))+0.0001-frag.z;return 1.0-max(0.0,min(1.0,-d*500.0));\n#else\nreturn 1.0;\n#endif\n}float calculate_visibility(vec4 pos) {\n#ifdef TERRAIN3D\nvec3 frag=pos.xyz/pos.w;highp float d=depthOpacity(frag);if (d > 0.95) return 1.0;return (d+depthOpacity(frag+vec3(0.0,0.01,0.0)))/2.0;\n#else\nreturn 1.0;\n#endif\n}float ele(vec2 pos) {\n#ifdef TERRAIN3D\nvec4 rgb=(texture2D(u_terrain,pos)*255.0)*u_terrain_unpack;return rgb.r+rgb.g+rgb.b-u_terrain_unpack.a;\n#else\nreturn 0.0;\n#endif\n}float get_elevation(vec2 pos) {\n#ifdef TERRAIN3D\nvec2 coord=(u_terrain_matrix*vec4(pos,0.0,1.0)).xy*u_terrain_dim+1.0;vec2 f=fract(coord);vec2 c=(floor(coord)+0.5)/(u_terrain_dim+2.0);float d=1.0/(u_terrain_dim+2.0);float tl=ele(c);float tr=ele(c+vec2(d,0.0));float bl=ele(c+vec2(0.0,d));float br=ele(c+vec2(d,d));float elevation=mix(mix(tl,tr,f.x),mix(bl,br,f.x),f.y);return elevation*u_terrain_exaggeration;\n#else\nreturn 0.0;\n#endif\n}`),background:_i(`uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),backgroundPattern:_i(`uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),circle:_i(`varying vec3 v_data;varying float v_visibility;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=v_visibility*opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;varying float v_visibility;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);float ele=get_elevation(circle_center);v_visibility=calculate_visibility(u_matrix*vec4(circle_center,ele,1.0));if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,ele,1);} else {gl_Position=u_matrix*vec4(circle_center,ele,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}`),clippingMask:_i(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),heatmap:_i(`uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}`),heatmapTexture:_i(`uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}`,\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),collisionBox:_i(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,get_elevation(a_pos),1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),collisionCircle:_i(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),debug:_i(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,get_elevation(a_pos),1);}\"),fill:_i(`#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}`),fillOutline:_i(`varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),fillOutlinePattern:_i(`uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}`),fillPattern:_i(`#ifdef GL_ES\nprecision highp float;\n#endif\nuniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}`),fillExtrusion:_i(`varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\n#ifdef TERRAIN3D\nattribute vec2 a_centroid;\n#endif\nvarying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;\n#ifdef TERRAIN3D\nfloat height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\n#else\nfloat height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\n#endif\nbase=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}`),fillExtrusionPattern:_i(`uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\n#ifdef TERRAIN3D\nattribute vec2 a_centroid;\n#endif\nvarying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;\n#ifdef TERRAIN3D\nfloat height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\n#else\nfloat height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\n#endif\nbase=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}`),hillshadePrepare:_i(`#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hillshade:_i(`uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),line:_i(`uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_width2=vec2(outset,inset);}`),lineGradient:_i(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_width2=vec2(outset,inset);}`),linePattern:_i(`#ifdef GL_ES\nprecision highp float;\n#endif\nuniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}`),lineSDF:_i(`uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}`),raster:_i(`uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),symbolIcon:_i(`uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,ele,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),ele,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,ele,1.0);float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),z,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float visibility=calculate_visibility(projectedPoint);v_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));}`),symbolSDF:_i(`#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float inner_edge=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);inner_edge=inner_edge+gamma*gamma_scale;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(inner_edge-gamma_scaled,inner_edge+gamma_scaled,dist);if (u_is_halo) {lowp float halo_edge=(6.0-halo_width/fontScale)/SDF_PX;alpha=min(smoothstep(halo_edge-gamma_scaled,halo_edge+gamma_scaled,dist),1.0-alpha);}gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,ele,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),ele,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,ele,1.0);float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),z,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}`),symbolTextAndIcon:_i(`#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}`,`const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,ele,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),ele,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,ele,1.0);float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),z,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}`),terrain:_i(\"uniform sampler2D u_texture;varying vec2 v_texture_pos;void main() {gl_FragColor=texture2D(u_texture,v_texture_pos);}\",jl),terrainDepth:_i(\"varying float v_depth;const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitMsk=vec4(0.,vec3(1./256.0));highp vec4 pack(highp float value) {highp vec4 comp=fract(value*bitSh);comp-=comp.xxyz*bitMsk;return comp;}void main() {gl_FragColor=pack(v_depth);}\",jl),terrainCoords:_i(\"precision mediump float;uniform sampler2D u_texture;uniform float u_terrain_coords_id;varying vec2 v_texture_pos;void main() {vec4 rgba=texture2D(u_texture,v_texture_pos);gl_FragColor=vec4(rgba.r,rgba.g,rgba.b,u_terrain_coords_id);}\",jl)};function _i(T,l){let d=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,v=l.match(/attribute ([\\w]+) ([\\w]+)/g),b=T.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),M=l.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),O=M?M.concat(b):b,B={};return{fragmentSource:T=T.replace(d,(U,W,Z,$,st)=>(B[st]=!0,W===\"define\"?`\n#ifndef HAS_UNIFORM_u_${st}\nvarying ${Z} ${$} ${st};\n#else\nuniform ${Z} ${$} u_${st};\n#endif\n`:`\n#ifdef HAS_UNIFORM_u_${st}\n ${Z} ${$} ${st} = u_${st};\n#endif\n`)),vertexSource:l=l.replace(d,(U,W,Z,$,st)=>{let At=$===\"float\"?\"vec2\":\"vec4\",pt=st.match(/color/)?\"color\":At;return B[st]?W===\"define\"?`\n#ifndef HAS_UNIFORM_u_${st}\nuniform lowp float u_${st}_t;\nattribute ${Z} ${At} a_${st};\nvarying ${Z} ${$} ${st};\n#else\nuniform ${Z} ${$} u_${st};\n#endif\n`:pt===\"vec4\"?`\n#ifndef HAS_UNIFORM_u_${st}\n ${st} = a_${st};\n#else\n ${Z} ${$} ${st} = u_${st};\n#endif\n`:`\n#ifndef HAS_UNIFORM_u_${st}\n ${st} = unpack_mix_${pt}(a_${st}, u_${st}_t);\n#else\n ${Z} ${$} ${st} = u_${st};\n#endif\n`:W===\"define\"?`\n#ifndef HAS_UNIFORM_u_${st}\nuniform lowp float u_${st}_t;\nattribute ${Z} ${At} a_${st};\n#else\nuniform ${Z} ${$} u_${st};\n#endif\n`:pt===\"vec4\"?`\n#ifndef HAS_UNIFORM_u_${st}\n ${Z} ${$} ${st} = a_${st};\n#else\n ${Z} ${$} ${st} = u_${st};\n#endif\n`:`\n#ifndef HAS_UNIFORM_u_${st}\n ${Z} ${$} ${st} = unpack_mix_${pt}(a_${st}, u_${st}_t);\n#else\n ${Z} ${$} ${st} = u_${st};\n#endif\n`}),staticAttributes:v,staticUniforms:O}}class Gl{constructor(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null}bind(l,d,v,b,M,O,B,U,W){this.context=l;let Z=this.boundPaintVertexBuffers.length!==b.length;for(let $=0;!Z&&$({u_depth:new n.aL($t,oe.u_depth),u_terrain:new n.aL($t,oe.u_terrain),u_terrain_dim:new n.aM($t,oe.u_terrain_dim),u_terrain_matrix:new n.aN($t,oe.u_terrain_matrix),u_terrain_unpack:new n.aO($t,oe.u_terrain_unpack),u_terrain_exaggeration:new n.aM($t,oe.u_terrain_exaggeration)}))(l,Qt),this.binderUniforms=v?v.getUniforms(l,Qt):[]}draw(l,d,v,b,M,O,B,U,W,Z,$,st,At,pt,yt,dt,Ft,Ht){let St=l.gl;if(this.failedToCreate)return;if(l.program.set(this.program),l.setDepthMode(v),l.setStencilMode(b),l.setColorMode(M),l.setCullFace(O),U){l.activeTexture.set(St.TEXTURE2),St.bindTexture(St.TEXTURE_2D,U.depthTexture),l.activeTexture.set(St.TEXTURE3),St.bindTexture(St.TEXTURE_2D,U.texture);for(let Qt in this.terrainUniforms)this.terrainUniforms[Qt].set(U[Qt])}for(let Qt in this.fixedUniforms)this.fixedUniforms[Qt].set(B[Qt]);yt&&yt.setUniforms(l,this.binderUniforms,At,{zoom:pt});let Bt=0;switch(d){case St.LINES:Bt=2;break;case St.TRIANGLES:Bt=3;break;case St.LINE_STRIP:Bt=1}for(let Qt of st.get()){let $t=Qt.vaos||(Qt.vaos={});($t[W]||($t[W]=new Gl)).bind(l,this,Z,yt?yt.getPaintVertexBuffers():[],$,Qt.vertexOffset,dt,Ft,Ht),St.drawElements(d,Qt.primitiveLength*Bt,St.UNSIGNED_SHORT,Qt.primitiveOffset*Bt*2)}}}function rs(T,l,d){let v=1/Dt(d,1,l.transform.tileZoom),b=Math.pow(2,d.tileID.overscaledZ),M=d.tileSize*Math.pow(2,l.transform.tileZoom)/b,O=M*(d.tileID.canonical.x+d.tileID.wrap*b),B=M*d.tileID.canonical.y;return{u_image:0,u_texsize:d.imageAtlasTexture.size,u_scale:[v,T.fromScale,T.toScale],u_fade:T.t,u_pixel_coord_upper:[O>>16,B>>16],u_pixel_coord_lower:[65535&O,65535&B]}}let Gp=(T,l,d,v)=>{let b=l.style.light,M=b.properties.get(\"position\"),O=[M.x,M.y,M.z],B=function(){var W=new n.A(9);return n.A!=Float32Array&&(W[1]=0,W[2]=0,W[3]=0,W[5]=0,W[6]=0,W[7]=0),W[0]=1,W[4]=1,W[8]=1,W}();b.properties.get(\"anchor\")===\"viewport\"&&function(W,Z){var $=Math.sin(Z),st=Math.cos(Z);W[0]=st,W[1]=$,W[2]=0,W[3]=-$,W[4]=st,W[5]=0,W[6]=0,W[7]=0,W[8]=1}(B,-l.transform.angle),function(W,Z,$){var st=Z[0],At=Z[1],pt=Z[2];W[0]=st*$[0]+At*$[3]+pt*$[6],W[1]=st*$[1]+At*$[4]+pt*$[7],W[2]=st*$[2]+At*$[5]+pt*$[8]}(O,O,B);let U=b.properties.get(\"color\");return{u_matrix:T,u_lightpos:O,u_lightintensity:b.properties.get(\"intensity\"),u_lightcolor:[U.r,U.g,U.b],u_vertical_gradient:+d,u_opacity:v}},Wl=(T,l,d,v,b,M,O)=>n.e(Gp(T,l,d,v),rs(M,l,O),{u_height_factor:-Math.pow(2,b.overscaledZ)/O.tileSize/8}),_d=T=>({u_matrix:T}),yd=(T,l,d,v)=>n.e(_d(T),rs(d,l,v)),vd=(T,l)=>({u_matrix:T,u_world:l}),xd=(T,l,d,v,b)=>n.e(yd(T,l,d,v),{u_world:b}),lt=(T,l,d,v)=>{let b=T.transform,M,O;if(v.paint.get(\"circle-pitch-alignment\")===\"map\"){let B=Dt(d,1,b.zoom);M=!0,O=[B,B]}else M=!1,O=b.pixelsToGLUnits;return{u_camera_to_center_distance:b.cameraToCenterDistance,u_scale_with_map:+(v.paint.get(\"circle-pitch-scale\")===\"map\"),u_matrix:T.translatePosMatrix(l.posMatrix,d,v.paint.get(\"circle-translate\"),v.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+M,u_device_pixel_ratio:T.pixelRatio,u_extrude_scale:O}},ft=(T,l,d)=>{let v=Dt(d,1,l.zoom),b=Math.pow(2,l.zoom-d.tileID.overscaledZ),M=d.tileID.overscaleFactor();return{u_matrix:T,u_camera_to_center_distance:l.cameraToCenterDistance,u_pixels_to_tile_units:v,u_extrude_scale:[l.pixelsToGLUnits[0]/(v*b),l.pixelsToGLUnits[1]/(v*b)],u_overscale_factor:M}},Lt=(T,l,d=1)=>({u_matrix:T,u_color:l,u_overlay:0,u_overlay_scale:d}),Kt=T=>({u_matrix:T}),ge=(T,l,d,v)=>({u_matrix:T,u_extrude_scale:Dt(l,1,d),u_intensity:v});function Qe(T,l){let d=Math.pow(2,l.canonical.z),v=l.canonical.y;return[new n.U(0,v/d).toLngLat().lat,new n.U(0,(v+1)/d).toLngLat().lat]}let ti=(T,l,d,v)=>{let b=T.transform;return{u_matrix:jm(T,l,d,v),u_ratio:1/Dt(l,1,b.zoom),u_device_pixel_ratio:T.pixelRatio,u_units_to_pixels:[1/b.pixelsToGLUnits[0],1/b.pixelsToGLUnits[1]]}},is=(T,l,d,v,b)=>n.e(ti(T,l,d,b),{u_image:0,u_image_height:v}),Ts=(T,l,d,v,b)=>{let M=T.transform,O=Ra(l,M);return{u_matrix:jm(T,l,d,b),u_texsize:l.imageAtlasTexture.size,u_ratio:1/Dt(l,1,M.zoom),u_device_pixel_ratio:T.pixelRatio,u_image:0,u_scale:[O,v.fromScale,v.toScale],u_fade:v.t,u_units_to_pixels:[1/M.pixelsToGLUnits[0],1/M.pixelsToGLUnits[1]]}},Vs=(T,l,d,v,b,M)=>{let O=T.lineAtlas,B=Ra(l,T.transform),U=d.layout.get(\"line-cap\")===\"round\",W=O.getDash(v.from,U),Z=O.getDash(v.to,U),$=W.width*b.fromScale,st=Z.width*b.toScale;return n.e(ti(T,l,d,M),{u_patternscale_a:[B/$,-W.height/2],u_patternscale_b:[B/st,-Z.height/2],u_sdfgamma:O.width/(256*Math.min($,st)*T.pixelRatio)/2,u_image:0,u_tex_y_a:W.y,u_tex_y_b:Z.y,u_mix:b.t})};function Ra(T,l){return 1/Dt(T,1,l.tileZoom)}function jm(T,l,d,v){return T.translatePosMatrix(v?v.posMatrix:l.tileID.posMatrix,l,d.paint.get(\"line-translate\"),d.paint.get(\"line-translate-anchor\"))}let Ox=(T,l,d,v,b)=>{return{u_matrix:T,u_tl_parent:l,u_scale_parent:d,u_buffer_scale:1,u_fade_t:v.mix,u_opacity:v.opacity*b.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:b.paint.get(\"raster-brightness-min\"),u_brightness_high:b.paint.get(\"raster-brightness-max\"),u_saturation_factor:(O=b.paint.get(\"raster-saturation\"),O>0?1-1/(1.001-O):-O),u_contrast_factor:(M=b.paint.get(\"raster-contrast\"),M>0?1/(1-M):1+M),u_spin_weights:Bx(b.paint.get(\"raster-hue-rotate\"))};var M,O};function Bx(T){T*=Math.PI/180;let l=Math.sin(T),d=Math.cos(T);return[(2*d+1)/3,(-Math.sqrt(3)*l-d+1)/3,(Math.sqrt(3)*l-d+1)/3]}let l_=(T,l,d,v,b,M,O,B,U,W)=>{let Z=b.transform;return{u_is_size_zoom_constant:+(T===\"constant\"||T===\"source\"),u_is_size_feature_constant:+(T===\"constant\"||T===\"camera\"),u_size_t:l?l.uSizeT:0,u_size:l?l.uSize:0,u_camera_to_center_distance:Z.cameraToCenterDistance,u_pitch:Z.pitch/360*2*Math.PI,u_rotate_symbol:+d,u_aspect_ratio:Z.width/Z.height,u_fade_change:b.options.fadeDuration?b.symbolFadeChange:1,u_matrix:M,u_label_plane_matrix:O,u_coord_matrix:B,u_is_text:+U,u_pitch_with_map:+v,u_texsize:W,u_texture:0}},c_=(T,l,d,v,b,M,O,B,U,W,Z)=>{let $=b.transform;return n.e(l_(T,l,d,v,b,M,O,B,U,W),{u_gamma_scale:v?Math.cos($._pitch)*$.cameraToCenterDistance:1,u_device_pixel_ratio:b.pixelRatio,u_is_halo:+Z})},gf=(T,l,d,v,b,M,O,B,U,W)=>n.e(c_(T,l,d,v,b,M,O,B,!0,U,!0),{u_texsize_icon:W,u_texture_icon:1}),Gm=(T,l,d)=>({u_matrix:T,u_opacity:l,u_color:d}),fl=(T,l,d,v,b,M)=>n.e(function(O,B,U,W){let Z=U.imageManager.getPattern(O.from.toString()),$=U.imageManager.getPattern(O.to.toString()),{width:st,height:At}=U.imageManager.getPixelSize(),pt=Math.pow(2,W.tileID.overscaledZ),yt=W.tileSize*Math.pow(2,U.transform.tileZoom)/pt,dt=yt*(W.tileID.canonical.x+W.tileID.wrap*pt),Ft=yt*W.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:Z.tl,u_pattern_br_a:Z.br,u_pattern_tl_b:$.tl,u_pattern_br_b:$.br,u_texsize:[st,At],u_mix:B.t,u_pattern_size_a:Z.displaySize,u_pattern_size_b:$.displaySize,u_scale_a:B.fromScale,u_scale_b:B.toScale,u_tile_units_to_pixels:1/Dt(W,1,U.transform.tileZoom),u_pixel_coord_upper:[dt>>16,Ft>>16],u_pixel_coord_lower:[65535&dt,65535&Ft]}}(v,M,d,b),{u_matrix:T,u_opacity:l}),Wm={fillExtrusion:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_lightpos:new n.aP(T,l.u_lightpos),u_lightintensity:new n.aM(T,l.u_lightintensity),u_lightcolor:new n.aP(T,l.u_lightcolor),u_vertical_gradient:new n.aM(T,l.u_vertical_gradient),u_opacity:new n.aM(T,l.u_opacity)}),fillExtrusionPattern:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_lightpos:new n.aP(T,l.u_lightpos),u_lightintensity:new n.aM(T,l.u_lightintensity),u_lightcolor:new n.aP(T,l.u_lightcolor),u_vertical_gradient:new n.aM(T,l.u_vertical_gradient),u_height_factor:new n.aM(T,l.u_height_factor),u_image:new n.aL(T,l.u_image),u_texsize:new n.aQ(T,l.u_texsize),u_pixel_coord_upper:new n.aQ(T,l.u_pixel_coord_upper),u_pixel_coord_lower:new n.aQ(T,l.u_pixel_coord_lower),u_scale:new n.aP(T,l.u_scale),u_fade:new n.aM(T,l.u_fade),u_opacity:new n.aM(T,l.u_opacity)}),fill:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix)}),fillPattern:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_image:new n.aL(T,l.u_image),u_texsize:new n.aQ(T,l.u_texsize),u_pixel_coord_upper:new n.aQ(T,l.u_pixel_coord_upper),u_pixel_coord_lower:new n.aQ(T,l.u_pixel_coord_lower),u_scale:new n.aP(T,l.u_scale),u_fade:new n.aM(T,l.u_fade)}),fillOutline:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_world:new 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n.aR(T,l.u_accent)}),hillshadePrepare:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_image:new n.aL(T,l.u_image),u_dimension:new n.aQ(T,l.u_dimension),u_zoom:new n.aM(T,l.u_zoom),u_unpack:new n.aO(T,l.u_unpack)}),line:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_ratio:new n.aM(T,l.u_ratio),u_device_pixel_ratio:new n.aM(T,l.u_device_pixel_ratio),u_units_to_pixels:new n.aQ(T,l.u_units_to_pixels)}),lineGradient:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_ratio:new n.aM(T,l.u_ratio),u_device_pixel_ratio:new n.aM(T,l.u_device_pixel_ratio),u_units_to_pixels:new n.aQ(T,l.u_units_to_pixels),u_image:new n.aL(T,l.u_image),u_image_height:new n.aM(T,l.u_image_height)}),linePattern:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_texsize:new n.aQ(T,l.u_texsize),u_ratio:new n.aM(T,l.u_ratio),u_device_pixel_ratio:new n.aM(T,l.u_device_pixel_ratio),u_image:new n.aL(T,l.u_image),u_units_to_pixels:new n.aQ(T,l.u_units_to_pixels),u_scale:new n.aP(T,l.u_scale),u_fade:new n.aM(T,l.u_fade)}),lineSDF:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_ratio:new n.aM(T,l.u_ratio),u_device_pixel_ratio:new n.aM(T,l.u_device_pixel_ratio),u_units_to_pixels:new n.aQ(T,l.u_units_to_pixels),u_patternscale_a:new n.aQ(T,l.u_patternscale_a),u_patternscale_b:new n.aQ(T,l.u_patternscale_b),u_sdfgamma:new n.aM(T,l.u_sdfgamma),u_image:new n.aL(T,l.u_image),u_tex_y_a:new n.aM(T,l.u_tex_y_a),u_tex_y_b:new n.aM(T,l.u_tex_y_b),u_mix:new n.aM(T,l.u_mix)}),raster:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_tl_parent:new n.aQ(T,l.u_tl_parent),u_scale_parent:new n.aM(T,l.u_scale_parent),u_buffer_scale:new n.aM(T,l.u_buffer_scale),u_fade_t:new n.aM(T,l.u_fade_t),u_opacity:new n.aM(T,l.u_opacity),u_image0:new n.aL(T,l.u_image0),u_image1:new n.aL(T,l.u_image1),u_brightness_low:new n.aM(T,l.u_brightness_low),u_brightness_high:new n.aM(T,l.u_brightness_high),u_saturation_factor:new n.aM(T,l.u_saturation_factor),u_contrast_factor:new n.aM(T,l.u_contrast_factor),u_spin_weights:new n.aP(T,l.u_spin_weights)}),symbolIcon:(T,l)=>({u_is_size_zoom_constant:new n.aL(T,l.u_is_size_zoom_constant),u_is_size_feature_constant:new n.aL(T,l.u_is_size_feature_constant),u_size_t:new n.aM(T,l.u_size_t),u_size:new n.aM(T,l.u_size),u_camera_to_center_distance:new n.aM(T,l.u_camera_to_center_distance),u_pitch:new n.aM(T,l.u_pitch),u_rotate_symbol:new n.aL(T,l.u_rotate_symbol),u_aspect_ratio:new n.aM(T,l.u_aspect_ratio),u_fade_change:new n.aM(T,l.u_fade_change),u_matrix:new n.aN(T,l.u_matrix),u_label_plane_matrix:new n.aN(T,l.u_label_plane_matrix),u_coord_matrix:new n.aN(T,l.u_coord_matrix),u_is_text:new n.aL(T,l.u_is_text),u_pitch_with_map:new n.aL(T,l.u_pitch_with_map),u_texsize:new n.aQ(T,l.u_texsize),u_texture:new n.aL(T,l.u_texture)}),symbolSDF:(T,l)=>({u_is_size_zoom_constant:new n.aL(T,l.u_is_size_zoom_constant),u_is_size_feature_constant:new n.aL(T,l.u_is_size_feature_constant),u_size_t:new n.aM(T,l.u_size_t),u_size:new n.aM(T,l.u_size),u_camera_to_center_distance:new 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n.aM(T,l.u_aspect_ratio),u_fade_change:new n.aM(T,l.u_fade_change),u_matrix:new n.aN(T,l.u_matrix),u_label_plane_matrix:new n.aN(T,l.u_label_plane_matrix),u_coord_matrix:new n.aN(T,l.u_coord_matrix),u_is_text:new n.aL(T,l.u_is_text),u_pitch_with_map:new n.aL(T,l.u_pitch_with_map),u_texsize:new n.aQ(T,l.u_texsize),u_texsize_icon:new n.aQ(T,l.u_texsize_icon),u_texture:new n.aL(T,l.u_texture),u_texture_icon:new n.aL(T,l.u_texture_icon),u_gamma_scale:new n.aM(T,l.u_gamma_scale),u_device_pixel_ratio:new n.aM(T,l.u_device_pixel_ratio),u_is_halo:new n.aL(T,l.u_is_halo)}),background:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_opacity:new n.aM(T,l.u_opacity),u_color:new n.aR(T,l.u_color)}),backgroundPattern:(T,l)=>({u_matrix:new n.aN(T,l.u_matrix),u_opacity:new n.aM(T,l.u_opacity),u_image:new n.aL(T,l.u_image),u_pattern_tl_a:new n.aQ(T,l.u_pattern_tl_a),u_pattern_br_a:new n.aQ(T,l.u_pattern_br_a),u_pattern_tl_b:new n.aQ(T,l.u_pattern_tl_b),u_pattern_br_b:new 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M=l.gl;this.buffer=M.createBuffer(),l.bindVertexBuffer.set(this.buffer),M.bufferData(M.ARRAY_BUFFER,d.arrayBuffer,this.dynamicDraw?M.DYNAMIC_DRAW:M.STATIC_DRAW),this.dynamicDraw||delete d.arrayBuffer}bind(){this.context.bindVertexBuffer.set(this.buffer)}updateData(l){if(l.length!==this.length)throw new Error(`Length of new data is ${l.length}, which doesn't match current length of ${this.length}`);let d=this.context.gl;this.bind(),d.bufferSubData(d.ARRAY_BUFFER,0,l.arrayBuffer)}enableAttributes(l,d){for(let v=0;v0){let 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vu=T.translatePosMatrix(pe.posMatrix,he,M,O),_h=Ft||b&&$t||yu?Jm:jr,Ws=T.translatePosMatrix(ql,he,M,O,!0),Ps=Ee&&d.paint.get(b?\"text-halo-width\":\"icon-halo-width\").constantOr(1)!==0,Eo;Eo=Ee?be.iconsInText?gf(pr.kind,Jr,Ht,dt,T,vu,_h,Ws,ei,hs):c_(pr.kind,Jr,Ht,dt,T,vu,_h,Ws,b,ei,!0):l_(pr.kind,Jr,Ht,dt,T,vu,_h,Ws,b,ei);let yh={program:Gi,buffers:Ze,uniformValues:Eo,atlasTexture:On,atlasTextureIcon:Bn,atlasInterpolation:tn,atlasInterpolationIcon:Gs,isSDF:Ee,hasHalo:Ps};if(St&&be.canOverlap){Bt=!0;let Fn=Ze.segments.get();for(let fs of Fn)oe.push({segments:new n.S([fs]),sortKey:fs.sortKey,state:yh,terrainData:Vr})}else oe.push({segments:Ze.segments,sortKey:0,state:yh,terrainData:Vr})}Bt&&oe.sort((pe,he)=>pe.sortKey-he.sortKey);for(let pe of oe){let he=pe.state;if(st.activeTexture.set(At.TEXTURE0),he.atlasTexture.bind(he.atlasInterpolation,At.CLAMP_TO_EDGE),he.atlasTextureIcon&&(st.activeTexture.set(At.TEXTURE1),he.atlasTextureIcon&&he.atlasTextureIcon.bind(he.atlasInterpolationIcon,At.CLAMP_TO_EDGE)),he.isSDF){let be=he.uniformValues;he.hasHalo&&(be.u_is_halo=1,e0(he.buffers,pe.segments,d,T,he.program,Qt,Z,$,be,pe.terrainData)),be.u_is_halo=0}e0(he.buffers,pe.segments,d,T,he.program,Qt,Z,$,he.uniformValues,pe.terrainData)}}function e0(T,l,d,v,b,M,O,B,U,W){let Z=v.context;b.draw(Z,Z.gl.TRIANGLES,M,O,B,It.disabled,U,W,d.id,T.layoutVertexBuffer,T.indexBuffer,l,d.paint,v.transform.zoom,T.programConfigurations.get(d.id),T.dynamicLayoutVertexBuffer,T.opacityVertexBuffer)}function Ed(T,l,d,v,b){if(!d||!v||!v.imageAtlas)return;let M=v.imageAtlas.patternPositions,O=M[d.to.toString()],B=M[d.from.toString()];if(!O&&B&&(O=B),!B&&O&&(B=O),!O||!B){let U=b.getPaintProperty(l);O=M[U],B=M[U]}O&&B&&T.setConstantPatternPositions(O,B)}function Pd(T,l,d,v,b,M,O){let B=T.context.gl,U=\"fill-pattern\",W=d.paint.get(U),Z=W&&W.constantOr(1),$=d.getCrossfadeParameters(),st,At,pt,yt,dt;O?(At=Z&&!d.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",st=B.LINES):(At=Z?\"fillPattern\":\"fill\",st=B.TRIANGLES);let Ft=W.constantOr(null);for(let Ht of v){let St=l.getTile(Ht);if(Z&&!St.patternsLoaded())continue;let Bt=St.getBucket(d);if(!Bt)continue;let Qt=Bt.programConfigurations.get(d.id),$t=T.useProgram(At,Qt),oe=T.style.map.terrain&&T.style.map.terrain.getTerrainData(Ht);Z&&(T.context.activeTexture.set(B.TEXTURE0),St.imageAtlasTexture.bind(B.LINEAR,B.CLAMP_TO_EDGE),Qt.updatePaintBuffers($)),Ed(Qt,U,Ft,St,d);let pe=oe?Ht:null,he=T.translatePosMatrix(pe?pe.posMatrix:Ht.posMatrix,St,d.paint.get(\"fill-translate\"),d.paint.get(\"fill-translate-anchor\"));if(O){yt=Bt.indexBuffer2,dt=Bt.segments2;let be=[B.drawingBufferWidth,B.drawingBufferHeight];pt=At===\"fillOutlinePattern\"&&Z?xd(he,T,$,St,be):vd(he,be)}else yt=Bt.indexBuffer,dt=Bt.segments,pt=Z?yd(he,T,$,St):_d(he);$t.draw(T.context,st,b,T.stencilModeForClipping(Ht),M,It.disabled,pt,oe,d.id,Bt.layoutVertexBuffer,yt,dt,d.paint,T.transform.zoom,Qt)}}function Id(T,l,d,v,b,M,O){let B=T.context,U=B.gl,W=\"fill-extrusion-pattern\",Z=d.paint.get(W),$=Z.constantOr(1),st=d.getCrossfadeParameters(),At=d.paint.get(\"fill-extrusion-opacity\"),pt=Z.constantOr(null);for(let yt of v){let dt=l.getTile(yt),Ft=dt.getBucket(d);if(!Ft)continue;let Ht=T.style.map.terrain&&T.style.map.terrain.getTerrainData(yt),St=Ft.programConfigurations.get(d.id),Bt=T.useProgram($?\"fillExtrusionPattern\":\"fillExtrusion\",St);$&&(T.context.activeTexture.set(U.TEXTURE0),dt.imageAtlasTexture.bind(U.LINEAR,U.CLAMP_TO_EDGE),St.updatePaintBuffers(st)),Ed(St,W,pt,dt,d);let Qt=T.translatePosMatrix(yt.posMatrix,dt,d.paint.get(\"fill-extrusion-translate\"),d.paint.get(\"fill-extrusion-translate-anchor\")),$t=d.paint.get(\"fill-extrusion-vertical-gradient\"),oe=$?Wl(Qt,T,$t,At,yt,st,dt):Gp(Qt,T,$t,At);Bt.draw(B,B.gl.TRIANGLES,b,M,O,It.backCCW,oe,Ht,d.id,Ft.layoutVertexBuffer,Ft.indexBuffer,Ft.segments,d.paint,T.transform.zoom,St,T.style.map.terrain&&Ft.centroidVertexBuffer)}}function Fx(T,l,d,v,b,M,O){let B=T.context,U=B.gl,W=d.fbo;if(!W)return;let Z=T.useProgram(\"hillshade\"),$=T.style.map.terrain&&T.style.map.terrain.getTerrainData(l);B.activeTexture.set(U.TEXTURE0),U.bindTexture(U.TEXTURE_2D,W.colorAttachment.get()),Z.draw(B,U.TRIANGLES,b,M,O,It.disabled,((st,At,pt,yt)=>{let dt=pt.paint.get(\"hillshade-shadow-color\"),Ft=pt.paint.get(\"hillshade-highlight-color\"),Ht=pt.paint.get(\"hillshade-accent-color\"),St=pt.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);pt.paint.get(\"hillshade-illumination-anchor\")===\"viewport\"&&(St-=st.transform.angle);let Bt=!st.options.moving;return{u_matrix:yt?yt.posMatrix:st.transform.calculatePosMatrix(At.tileID.toUnwrapped(),Bt),u_image:0,u_latrange:Qe(0,At.tileID),u_light:[pt.paint.get(\"hillshade-exaggeration\"),St],u_shadow:dt,u_highlight:Ft,u_accent:Ht}})(T,d,v,$?l:null),$,v.id,T.rasterBoundsBuffer,T.quadTriangleIndexBuffer,T.rasterBoundsSegments)}function r0(T,l,d,v,b,M){let O=T.context,B=O.gl,U=l.dem;if(U&&U.data){let W=U.dim,Z=U.stride,$=U.getPixels();if(O.activeTexture.set(B.TEXTURE1),O.pixelStoreUnpackPremultiplyAlpha.set(!1),l.demTexture=l.demTexture||T.getTileTexture(Z),l.demTexture){let At=l.demTexture;At.update($,{premultiply:!1}),At.bind(B.NEAREST,B.CLAMP_TO_EDGE)}else l.demTexture=new qt(O,$,B.RGBA,{premultiply:!1}),l.demTexture.bind(B.NEAREST,B.CLAMP_TO_EDGE);O.activeTexture.set(B.TEXTURE0);let st=l.fbo;if(!st){let At=new qt(O,{width:W,height:W,data:null},B.RGBA);At.bind(B.LINEAR,B.CLAMP_TO_EDGE),st=l.fbo=O.createFramebuffer(W,W,!0,!1),st.colorAttachment.set(At.texture)}O.bindFramebuffer.set(st.framebuffer),O.viewport.set([0,0,W,W]),T.useProgram(\"hillshadePrepare\").draw(O,B.TRIANGLES,v,b,M,It.disabled,((At,pt)=>{let yt=pt.stride,dt=n.Z();return n.aS(dt,0,n.N,-n.N,0,0,1),n.$(dt,dt,[0,-n.N,0]),{u_matrix:dt,u_image:1,u_dimension:[yt,yt],u_zoom:At.overscaledZ,u_unpack:pt.getUnpackVector()}})(l.tileID,U),null,d.id,T.rasterBoundsBuffer,T.quadTriangleIndexBuffer,T.rasterBoundsSegments),l.needsHillshadePrepare=!1}}function f_(T,l,d,v,b,M){let O=v.paint.get(\"raster-fade-duration\");if(!M&&O>0){let B=n.h.now(),U=(B-T.timeAdded)/O,W=l?(B-l.timeAdded)/O:-1,Z=d.getSource(),$=b.coveringZoomLevel({tileSize:Z.tileSize,roundZoom:Z.roundZoom}),st=!l||Math.abs(l.tileID.overscaledZ-$)>Math.abs(T.tileID.overscaledZ-$),At=st&&T.refreshedUponExpiration?1:n.ad(st?U:1-W,0,1);return T.refreshedUponExpiration&&U>=1&&(T.refreshedUponExpiration=!1),l?{opacity:1,mix:1-At}:{opacity:At,mix:0}}return{opacity:1,mix:0}}let d_=new n.aT(1,0,0,1),yf=new n.aT(0,1,0,1),Ba=new n.aT(0,0,1,1),Wn=new n.aT(1,0,1,1),p_=new n.aT(0,1,1,1);function Cd(T,l,d,v){Xp(T,0,l+d/2,T.transform.width,d,v)}function $p(T,l,d,v){Xp(T,l-d/2,0,d,T.transform.height,v)}function Xp(T,l,d,v,b,M){let O=T.context,B=O.gl;B.enable(B.SCISSOR_TEST),B.scissor(l*T.pixelRatio,d*T.pixelRatio,v*T.pixelRatio,b*T.pixelRatio),O.clear({color:M}),B.disable(B.SCISSOR_TEST)}function i0(T,l,d){let v=T.context,b=v.gl,M=d.posMatrix,O=T.useProgram(\"debug\"),B=ci.disabled,U=Je.disabled,W=T.colorModeForRenderPass(),Z=\"$debug\",$=T.style.map.terrain&&T.style.map.terrain.getTerrainData(d);v.activeTexture.set(b.TEXTURE0);let st=l.getTileByID(d.key).latestRawTileData,At=Math.floor((st&&st.byteLength||0)/1024),pt=l.getTile(d).tileSize,yt=512/Math.min(pt,512)*(d.overscaledZ/T.transform.zoom)*.5,dt=d.canonical.toString();d.overscaledZ!==d.canonical.z&&(dt+=` => ${d.overscaledZ}`),function(Ft,Ht){Ft.initDebugOverlayCanvas();let St=Ft.debugOverlayCanvas,Bt=Ft.context.gl,Qt=Ft.debugOverlayCanvas.getContext(\"2d\");Qt.clearRect(0,0,St.width,St.height),Qt.shadowColor=\"white\",Qt.shadowBlur=2,Qt.lineWidth=1.5,Qt.strokeStyle=\"white\",Qt.textBaseline=\"top\",Qt.font=\"bold 36px Open Sans, sans-serif\",Qt.fillText(Ht,5,5),Qt.strokeText(Ht,5,5),Ft.debugOverlayTexture.update(St),Ft.debugOverlayTexture.bind(Bt.LINEAR,Bt.CLAMP_TO_EDGE)}(T,`${dt} ${At}kB`),O.draw(v,b.TRIANGLES,B,U,Ji.alphaBlended,It.disabled,Lt(M,n.aT.transparent,yt),null,Z,T.debugBuffer,T.quadTriangleIndexBuffer,T.debugSegments),O.draw(v,b.LINE_STRIP,B,U,W,It.disabled,Lt(M,n.aT.red),$,Z,T.debugBuffer,T.tileBorderIndexBuffer,T.debugSegments)}function Cn(T,l,d){let v=T.context,b=v.gl,M=T.colorModeForRenderPass(),O=new ci(b.LEQUAL,ci.ReadWrite,T.depthRangeFor3D),B=T.useProgram(\"terrain\"),U=l.getTerrainMesh();v.bindFramebuffer.set(null),v.viewport.set([0,0,T.width,T.height]);for(let W of d){let Z=T.renderToTexture.getTexture(W),$=l.getTerrainData(W.tileID);v.activeTexture.set(b.TEXTURE0),b.bindTexture(b.TEXTURE_2D,Z.texture);let st={u_matrix:T.transform.calculatePosMatrix(W.tileID.toUnwrapped()),u_texture:0,u_ele_delta:l.getMeshFrameDelta(T.transform.zoom)};B.draw(v,b.TRIANGLES,O,Je.disabled,M,It.backCCW,st,$,\"terrain\",U.vertexBuffer,U.indexBuffer,U.segments)}}class ah{constructor(l,d){this.context=new Oc(l),this.transform=d,this._tileTextures={},this.terrainFacilitator={dirty:!0,matrix:n.Z(),renderTime:0},this.setup(),this.numSublayers=ls.maxUnderzooming+ls.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Dc}resize(l,d,v){if(this.width=Math.floor(l*v),this.height=Math.floor(d*v),this.pixelRatio=v,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(let b of this.style._order)this.style._layers[b].resize()}setup(){let l=this.context,d=new n.a_;d.emplaceBack(0,0),d.emplaceBack(n.N,0),d.emplaceBack(0,n.N),d.emplaceBack(n.N,n.N),this.tileExtentBuffer=l.createVertexBuffer(d,So.members),this.tileExtentSegments=n.S.simpleSegment(0,0,4,2);let v=new n.a_;v.emplaceBack(0,0),v.emplaceBack(n.N,0),v.emplaceBack(0,n.N),v.emplaceBack(n.N,n.N),this.debugBuffer=l.createVertexBuffer(v,So.members),this.debugSegments=n.S.simpleSegment(0,0,4,5);let b=new n.V;b.emplaceBack(0,0,0,0),b.emplaceBack(n.N,0,n.N,0),b.emplaceBack(0,n.N,0,n.N),b.emplaceBack(n.N,n.N,n.N,n.N),this.rasterBoundsBuffer=l.createVertexBuffer(b,Jn.members),this.rasterBoundsSegments=n.S.simpleSegment(0,0,4,2);let M=new n.a_;M.emplaceBack(0,0),M.emplaceBack(1,0),M.emplaceBack(0,1),M.emplaceBack(1,1),this.viewportBuffer=l.createVertexBuffer(M,So.members),this.viewportSegments=n.S.simpleSegment(0,0,4,2);let O=new n.a$;O.emplaceBack(0),O.emplaceBack(1),O.emplaceBack(3),O.emplaceBack(2),O.emplaceBack(0),this.tileBorderIndexBuffer=l.createIndexBuffer(O);let B=new n.b0;B.emplaceBack(0,1,2),B.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=l.createIndexBuffer(B);let U=this.context.gl;this.stencilClearMode=new Je({func:U.ALWAYS,mask:0},0,255,U.ZERO,U.ZERO,U.ZERO)}clearStencil(){let l=this.context,d=l.gl;this.nextStencilID=1,this.currentStencilSource=void 0;let v=n.Z();n.aS(v,0,this.width,this.height,0,0,1),n.a0(v,v,[d.drawingBufferWidth,d.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(l,d.TRIANGLES,ci.disabled,this.stencilClearMode,Ji.disabled,It.disabled,Kt(v),null,\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)}_renderTileClippingMasks(l,d){if(this.currentStencilSource===l.source||!l.isTileClipped()||!d||!d.length)return;this.currentStencilSource=l.source;let v=this.context,b=v.gl;this.nextStencilID+d.length>256&&this.clearStencil(),v.setColorMode(Ji.disabled),v.setDepthMode(ci.disabled);let M=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(let O of d){let B=this._tileClippingMaskIDs[O.key]=this.nextStencilID++,U=this.style.map.terrain&&this.style.map.terrain.getTerrainData(O);M.draw(v,b.TRIANGLES,ci.disabled,new Je({func:b.ALWAYS,mask:0},B,255,b.KEEP,b.KEEP,b.REPLACE),Ji.disabled,It.disabled,Kt(O.posMatrix),U,\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}stencilModeFor3D(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();let l=this.nextStencilID++,d=this.context.gl;return new Je({func:d.NOTEQUAL,mask:255},l,255,d.KEEP,d.KEEP,d.REPLACE)}stencilModeForClipping(l){let d=this.context.gl;return new Je({func:d.EQUAL,mask:255},this._tileClippingMaskIDs[l.key],0,d.KEEP,d.KEEP,d.REPLACE)}stencilConfigForOverlap(l){let d=this.context.gl,v=l.sort((O,B)=>B.overscaledZ-O.overscaledZ),b=v[v.length-1].overscaledZ,M=v[0].overscaledZ-b+1;if(M>1){this.currentStencilSource=void 0,this.nextStencilID+M>256&&this.clearStencil();let O={};for(let B=0;B=0;this.currentLayer--){let U=this.style._layers[v[this.currentLayer]],W=b[U.source],Z=M[U.source];this._renderTileClippingMasks(U,Z),this.renderLayer(this,W,U,Z)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayerdt.source&&!dt.isHidden(Z)?[W.sourceCaches[dt.source]]:[]),At=st.filter(dt=>dt.getSource().type===\"vector\"),pt=st.filter(dt=>dt.getSource().type!==\"vector\"),yt=dt=>{(!$||$.getSource().maxzoomyt(dt)),$||pt.forEach(dt=>yt(dt)),$}(this.style,this.transform.zoom);U&&function(W,Z,$){for(let st=0;st<$.length;st++)i0(W,Z,$[st])}(this,U,U.getVisibleCoordinates())}this.options.showPadding&&function(U){let W=U.transform.padding;Cd(U,U.transform.height-(W.top||0),3,d_),Cd(U,W.bottom||0,3,yf),$p(U,W.left||0,3,Ba),$p(U,U.transform.width-(W.right||0),3,Wn);let Z=U.transform.centerPoint;(function($,st,At,pt){Xp($,st-1,At-10,2,20,pt),Xp($,st-10,At-1,20,2,pt)})(U,Z.x,U.transform.height-Z.y,p_)}(this),this.context.setDefault()}renderLayer(l,d,v,b){if(!v.isHidden(this.transform.zoom)&&(v.type===\"background\"||v.type===\"custom\"||(b||[]).length))switch(this.id=v.id,v.type){case\"symbol\":(function(M,O,B,U,W){if(M.renderPass!==\"translucent\")return;let Z=Je.disabled,$=M.colorModeForRenderPass();(B._unevaluatedLayout.hasValue(\"text-variable-anchor\")||B._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"))&&function(st,At,pt,yt,dt,Ft,Ht){let St=At.transform,Bt=dt===\"map\",Qt=Ft===\"map\";for(let $t of st){let oe=yt.getTile($t),pe=oe.getBucket(pt);if(!pe||!pe.text||!pe.text.segments.get().length)continue;let he=n.ah(pe.textSizeData,St.zoom),be=Dt(oe,1,At.transform.zoom),Ze=ve($t.posMatrix,Qt,Bt,At.transform,be),Kr=pt.layout.get(\"icon-text-fit\")!==\"none\"&&pe.hasIconData();if(he){let Ee=Math.pow(2,St.zoom-oe.tileID.overscaledZ);Qp(pe,Bt,Qt,Ht,St,Ze,$t.posMatrix,Ee,he,Kr,At.style.map.terrain?(pr,tr)=>At.style.map.terrain.getElevation($t,pr,tr):null)}}}(U,M,B,O,B.layout.get(\"text-rotation-alignment\"),B.layout.get(\"text-pitch-alignment\"),W),B.paint.get(\"icon-opacity\").constantOr(1)!==0&&wt(M,O,B,U,!1,B.paint.get(\"icon-translate\"),B.paint.get(\"icon-translate-anchor\"),B.layout.get(\"icon-rotation-alignment\"),B.layout.get(\"icon-pitch-alignment\"),B.layout.get(\"icon-keep-upright\"),Z,$),B.paint.get(\"text-opacity\").constantOr(1)!==0&&wt(M,O,B,U,!0,B.paint.get(\"text-translate\"),B.paint.get(\"text-translate-anchor\"),B.layout.get(\"text-rotation-alignment\"),B.layout.get(\"text-pitch-alignment\"),B.layout.get(\"text-keep-upright\"),Z,$),O.map.showCollisionBoxes&&(Km(M,O,B,U,B.paint.get(\"text-translate\"),B.paint.get(\"text-translate-anchor\"),!0),Km(M,O,B,U,B.paint.get(\"icon-translate\"),B.paint.get(\"icon-translate-anchor\"),!1))})(l,d,v,b,this.style.placement.variableOffsets);break;case\"circle\":(function(M,O,B,U){if(M.renderPass!==\"translucent\")return;let W=B.paint.get(\"circle-opacity\"),Z=B.paint.get(\"circle-stroke-width\"),$=B.paint.get(\"circle-stroke-opacity\"),st=!B.layout.get(\"circle-sort-key\").isConstant();if(W.constantOr(1)===0&&(Z.constantOr(1)===0||$.constantOr(1)===0))return;let At=M.context,pt=At.gl,yt=M.depthModeForSublayer(0,ci.ReadOnly),dt=Je.disabled,Ft=M.colorModeForRenderPass(),Ht=[];for(let St=0;StSt.sortKey-Bt.sortKey);for(let St of Ht){let{programConfiguration:Bt,program:Qt,layoutVertexBuffer:$t,indexBuffer:oe,uniformValues:pe,terrainData:he}=St.state;Qt.draw(At,pt.TRIANGLES,yt,dt,Ft,It.disabled,pe,he,B.id,$t,oe,St.segments,B.paint,M.transform.zoom,Bt)}})(l,d,v,b);break;case\"heatmap\":(function(M,O,B,U){if(B.paint.get(\"heatmap-opacity\")!==0)if(M.renderPass===\"offscreen\"){let W=M.context,Z=W.gl,$=Je.disabled,st=new Ji([Z.ONE,Z.ONE],n.aT.transparent,[!0,!0,!0,!0]);(function(At,pt,yt){let dt=At.gl;At.activeTexture.set(dt.TEXTURE1),At.viewport.set([0,0,pt.width/4,pt.height/4]);let Ft=yt.heatmapFbo;if(Ft)dt.bindTexture(dt.TEXTURE_2D,Ft.colorAttachment.get()),At.bindFramebuffer.set(Ft.framebuffer);else{let Ht=dt.createTexture();dt.bindTexture(dt.TEXTURE_2D,Ht),dt.texParameteri(dt.TEXTURE_2D,dt.TEXTURE_WRAP_S,dt.CLAMP_TO_EDGE),dt.texParameteri(dt.TEXTURE_2D,dt.TEXTURE_WRAP_T,dt.CLAMP_TO_EDGE),dt.texParameteri(dt.TEXTURE_2D,dt.TEXTURE_MIN_FILTER,dt.LINEAR),dt.texParameteri(dt.TEXTURE_2D,dt.TEXTURE_MAG_FILTER,dt.LINEAR),Ft=yt.heatmapFbo=At.createFramebuffer(pt.width/4,pt.height/4,!1,!1),function(St,Bt,Qt,$t){var oe,pe;let he=St.gl,be=(oe=St.HALF_FLOAT)!==null&&oe!==void 0?oe:he.UNSIGNED_BYTE,Ze=(pe=St.RGBA16F)!==null&&pe!==void 0?pe:he.RGBA;he.texImage2D(he.TEXTURE_2D,0,Ze,Bt.width/4,Bt.height/4,0,he.RGBA,be,null),$t.colorAttachment.set(Qt)}(At,pt,Ht,Ft)}})(W,M,B),W.clear({color:n.aT.transparent});for(let At=0;At{let St=n.Z();n.aS(St,0,yt.width,yt.height,0,0,1);let Bt=yt.context.gl;return{u_matrix:St,u_world:[Bt.drawingBufferWidth,Bt.drawingBufferHeight],u_image:0,u_color_ramp:1,u_opacity:dt.paint.get(\"heatmap-opacity\")}})(W,Z),null,Z.id,W.viewportBuffer,W.quadTriangleIndexBuffer,W.viewportSegments,Z.paint,W.transform.zoom)}(M,B))})(l,d,v,b);break;case\"line\":(function(M,O,B,U){if(M.renderPass!==\"translucent\")return;let W=B.paint.get(\"line-opacity\"),Z=B.paint.get(\"line-width\");if(W.constantOr(1)===0||Z.constantOr(1)===0)return;let $=M.depthModeForSublayer(0,ci.ReadOnly),st=M.colorModeForRenderPass(),At=B.paint.get(\"line-dasharray\"),pt=B.paint.get(\"line-pattern\"),yt=pt.constantOr(1),dt=B.paint.get(\"line-gradient\"),Ft=B.getCrossfadeParameters(),Ht=yt?\"linePattern\":At?\"lineSDF\":dt?\"lineGradient\":\"line\",St=M.context,Bt=St.gl,Qt=!0;for(let $t of U){let oe=O.getTile($t);if(yt&&!oe.patternsLoaded())continue;let pe=oe.getBucket(B);if(!pe)continue;let he=pe.programConfigurations.get(B.id),be=M.context.program.get(),Ze=M.useProgram(Ht,he),Kr=Qt||Ze.program!==be,Ee=M.style.map.terrain&&M.style.map.terrain.getTerrainData($t),pr=pt.constantOr(null);if(pr&&oe.imageAtlas){let Jr=oe.imageAtlas,Vr=Jr.patternPositions[pr.to.toString()],ei=Jr.patternPositions[pr.from.toString()];Vr&&ei&&he.setConstantPatternPositions(Vr,ei)}let tr=Ee?$t:null,Gi=yt?Ts(M,oe,B,Ft,tr):At?Vs(M,oe,B,At,Ft,tr):dt?is(M,oe,B,pe.lineClipsArray.length,tr):ti(M,oe,B,tr);if(yt)St.activeTexture.set(Bt.TEXTURE0),oe.imageAtlasTexture.bind(Bt.LINEAR,Bt.CLAMP_TO_EDGE),he.updatePaintBuffers(Ft);else if(At&&(Kr||M.lineAtlas.dirty))St.activeTexture.set(Bt.TEXTURE0),M.lineAtlas.bind(St);else if(dt){let Jr=pe.gradients[B.id],Vr=Jr.texture;if(B.gradientVersion!==Jr.version){let ei=256;if(B.stepInterpolant){let On=O.getSource().maxzoom,tn=$t.canonical.z===On?Math.ceil(1<0?d.pop():null}isPatternMissing(l){if(!l)return!1;if(!l.from||!l.to)return!0;let d=this.imageManager.getPattern(l.from.toString()),v=this.imageManager.getPattern(l.to.toString());return!d||!v}useProgram(l,d){this.cache=this.cache||{};let v=l+(d?d.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\")+(this.style.map.terrain?\"/terrain\":\"\");return this.cache[v]||(this.cache[v]=new pu(this.context,Ki[l],d,Wm[l],this._showOverdrawInspector,this.style.map.terrain)),this.cache[v]}setCustomLayerDefaults(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()}setBaseState(){let l=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(l.FUNC_ADD)}initDebugOverlayCanvas(){this.debugOverlayCanvas==null&&(this.debugOverlayCanvas=document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new qt(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))}destroy(){this.debugOverlayTexture&&this.debugOverlayTexture.destroy()}overLimit(){let{drawingBufferWidth:l,drawingBufferHeight:d}=this.context.gl;return this.width!==l||this.height!==d}}class fi{constructor(l,d){this.points=l,this.planes=d}static fromInvProjectionMatrix(l,d,v){let b=Math.pow(2,v),M=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map(B=>{let U=1/(B=n.ag([],B,l))[3]/d*b;return n.b3(B,B,[U,U,1/B[3],U])}),O=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map(B=>{let U=function(st,At){var pt=At[0],yt=At[1],dt=At[2],Ft=pt*pt+yt*yt+dt*dt;return Ft>0&&(Ft=1/Math.sqrt(Ft)),st[0]=At[0]*Ft,st[1]=At[1]*Ft,st[2]=At[2]*Ft,st}([],function(st,At,pt){var yt=At[0],dt=At[1],Ft=At[2],Ht=pt[0],St=pt[1],Bt=pt[2];return st[0]=dt*Bt-Ft*St,st[1]=Ft*Ht-yt*Bt,st[2]=yt*St-dt*Ht,st}([],ut([],M[B[0]],M[B[1]]),ut([],M[B[2]],M[B[1]]))),W=-((Z=U)[0]*($=M[B[1]])[0]+Z[1]*$[1]+Z[2]*$[2]);var Z,$;return U.concat(W)});return new fi(M,O)}}class mu{constructor(l,d){this.min=l,this.max=d,this.center=function(v,b,M){return v[0]=.5*b[0],v[1]=.5*b[1],v[2]=.5*b[2],v}([],function(v,b,M){return v[0]=b[0]+M[0],v[1]=b[1]+M[1],v[2]=b[2]+M[2],v}([],this.min,this.max))}quadrant(l){let d=[l%2==0,l<2],v=K(this.min),b=K(this.max);for(let M=0;M=0&&O++;if(O===0)return 0;O!==d.length&&(v=!1)}if(v)return 2;for(let b=0;b<3;b++){let M=Number.MAX_VALUE,O=-Number.MAX_VALUE;for(let B=0;Bthis.max[b]-this.min[b])return 0}return 1}}class vf{constructor(l=0,d=0,v=0,b=0){if(isNaN(l)||l<0||isNaN(d)||d<0||isNaN(v)||v<0||isNaN(b)||b<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=l,this.bottom=d,this.left=v,this.right=b}interpolate(l,d,v){return d.top!=null&&l.top!=null&&(this.top=n.B.number(l.top,d.top,v)),d.bottom!=null&&l.bottom!=null&&(this.bottom=n.B.number(l.bottom,d.bottom,v)),d.left!=null&&l.left!=null&&(this.left=n.B.number(l.left,d.left,v)),d.right!=null&&l.right!=null&&(this.right=n.B.number(l.right,d.right,v)),this}getCenter(l,d){let v=n.ad((this.left+l-this.right)/2,0,l),b=n.ad((this.top+d-this.bottom)/2,0,d);return new n.P(v,b)}equals(l){return this.top===l.top&&this.bottom===l.bottom&&this.left===l.left&&this.right===l.right}clone(){return new vf(this.top,this.bottom,this.left,this.right)}toJSON(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}}}class Kp{constructor(l,d,v,b,M){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=M===void 0||!!M,this._minZoom=l||0,this._maxZoom=d||22,this._minPitch=v??0,this._maxPitch=b??60,this.setMaxBounds(),this.width=0,this.height=0,this._center=new n.L(0,0),this._elevation=0,this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new vf,this._posMatrixCache={},this._alignedPosMatrixCache={},this._minEleveationForCurrentTile=0}clone(){let l=new Kp(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return l.apply(this),l}apply(l){this.tileSize=l.tileSize,this.latRange=l.latRange,this.width=l.width,this.height=l.height,this._center=l._center,this._elevation=l._elevation,this._minEleveationForCurrentTile=l._minEleveationForCurrentTile,this.zoom=l.zoom,this.angle=l.angle,this._fov=l._fov,this._pitch=l._pitch,this._unmodified=l._unmodified,this._edgeInsets=l._edgeInsets.clone(),this._calcMatrices()}get minZoom(){return this._minZoom}set minZoom(l){this._minZoom!==l&&(this._minZoom=l,this.zoom=Math.max(this.zoom,l))}get maxZoom(){return this._maxZoom}set maxZoom(l){this._maxZoom!==l&&(this._maxZoom=l,this.zoom=Math.min(this.zoom,l))}get minPitch(){return this._minPitch}set minPitch(l){this._minPitch!==l&&(this._minPitch=l,this.pitch=Math.max(this.pitch,l))}get maxPitch(){return this._maxPitch}set maxPitch(l){this._maxPitch!==l&&(this._maxPitch=l,this.pitch=Math.min(this.pitch,l))}get renderWorldCopies(){return this._renderWorldCopies}set renderWorldCopies(l){l===void 0?l=!0:l===null&&(l=!1),this._renderWorldCopies=l}get worldSize(){return this.tileSize*this.scale}get centerOffset(){return this.centerPoint._sub(this.size._div(2))}get size(){return new n.P(this.width,this.height)}get bearing(){return-this.angle/Math.PI*180}set bearing(l){let d=-n.b5(l,-180,180)*Math.PI/180;this.angle!==d&&(this._unmodified=!1,this.angle=d,this._calcMatrices(),this.rotationMatrix=function(){var v=new n.A(4);return n.A!=Float32Array&&(v[1]=0,v[2]=0),v[0]=1,v[3]=1,v}(),function(v,b,M){var O=b[0],B=b[1],U=b[2],W=b[3],Z=Math.sin(M),$=Math.cos(M);v[0]=O*$+U*Z,v[1]=B*$+W*Z,v[2]=O*-Z+U*$,v[3]=B*-Z+W*$}(this.rotationMatrix,this.rotationMatrix,this.angle))}get pitch(){return this._pitch/Math.PI*180}set pitch(l){let d=n.ad(l,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==d&&(this._unmodified=!1,this._pitch=d,this._calcMatrices())}get fov(){return this._fov/Math.PI*180}set fov(l){l=Math.max(.01,Math.min(60,l)),this._fov!==l&&(this._unmodified=!1,this._fov=l/180*Math.PI,this._calcMatrices())}get zoom(){return this._zoom}set zoom(l){let d=Math.min(Math.max(l,this.minZoom),this.maxZoom);this._zoom!==d&&(this._unmodified=!1,this._zoom=d,this.tileZoom=Math.max(0,Math.floor(d)),this.scale=this.zoomScale(d),this._constrain(),this._calcMatrices())}get center(){return this._center}set center(l){l.lat===this._center.lat&&l.lng===this._center.lng||(this._unmodified=!1,this._center=l,this._constrain(),this._calcMatrices())}get elevation(){return this._elevation}set elevation(l){l!==this._elevation&&(this._elevation=l,this._constrain(),this._calcMatrices())}get padding(){return this._edgeInsets.toJSON()}set padding(l){this._edgeInsets.equals(l)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,l,1),this._calcMatrices())}get centerPoint(){return this._edgeInsets.getCenter(this.width,this.height)}isPaddingEqual(l){return this._edgeInsets.equals(l)}interpolatePadding(l,d,v){this._unmodified=!1,this._edgeInsets.interpolate(l,d,v),this._constrain(),this._calcMatrices()}coveringZoomLevel(l){let d=(l.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/l.tileSize));return Math.max(0,d)}getVisibleUnwrappedCoordinates(l){let d=[new n.b6(0,l)];if(this._renderWorldCopies){let v=this.pointCoordinate(new n.P(0,0)),b=this.pointCoordinate(new n.P(this.width,0)),M=this.pointCoordinate(new n.P(this.width,this.height)),O=this.pointCoordinate(new n.P(0,this.height)),B=Math.floor(Math.min(v.x,b.x,M.x,O.x)),U=Math.floor(Math.max(v.x,b.x,M.x,O.x)),W=1;for(let Z=B-W;Z<=U+W;Z++)Z!==0&&d.push(new n.b6(Z,l))}return d}coveringTiles(l){var d,v;let b=this.coveringZoomLevel(l),M=b;if(l.minzoom!==void 0&&bl.maxzoom&&(b=l.maxzoom);let O=this.pointCoordinate(this.getCameraPoint()),B=n.U.fromLngLat(this.center),U=Math.pow(2,b),W=[U*O.x,U*O.y,0],Z=[U*B.x,U*B.y,0],$=fi.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,b),st=l.minzoom||0;!l.terrain&&this.pitch<=60&&this._edgeInsets.top<.1&&(st=b);let At=l.terrain?2/Math.min(this.tileSize,l.tileSize)*this.tileSize:3,pt=St=>({aabb:new mu([St*U,0,0],[(St+1)*U,U,0]),zoom:0,x:0,y:0,wrap:St,fullyVisible:!1}),yt=[],dt=[],Ft=b,Ht=l.reparseOverscaled?M:b;if(this._renderWorldCopies)for(let St=1;St<=3;St++)yt.push(pt(-St)),yt.push(pt(St));for(yt.push(pt(0));yt.length>0;){let St=yt.pop(),Bt=St.x,Qt=St.y,$t=St.fullyVisible;if(!$t){let Ze=St.aabb.intersects($);if(Ze===0)continue;$t=Ze===2}let oe=l.terrain?W:Z,pe=St.aabb.distanceX(oe),he=St.aabb.distanceY(oe),be=Math.max(Math.abs(pe),Math.abs(he));if(St.zoom===Ft||be>At+(1<=st){let Ze=Ft-St.zoom,Kr=W[0]-.5-(Bt<>1),pr=St.zoom+1,tr=St.aabb.quadrant(Ze);if(l.terrain){let Gi=new n.O(pr,St.wrap,pr,Kr,Ee),Jr=l.terrain.getMinMaxElevation(Gi),Vr=(d=Jr.minElevation)!==null&&d!==void 0?d:this.elevation,ei=(v=Jr.maxElevation)!==null&&v!==void 0?v:this.elevation;tr=new mu([tr.min[0],tr.min[1],Vr],[tr.max[0],tr.max[1],ei])}yt.push({aabb:tr,zoom:pr,x:Kr,y:Ee,wrap:St.wrap,fullyVisible:$t})}}return dt.sort((St,Bt)=>St.distanceSq-Bt.distanceSq).map(St=>St.tileID)}resize(l,d){this.width=l,this.height=d,this.pixelsToGLUnits=[2/l,-2/d],this._constrain(),this._calcMatrices()}get unmodified(){return this._unmodified}zoomScale(l){return Math.pow(2,l)}scaleZoom(l){return Math.log(l)/Math.LN2}project(l){let d=n.ad(l.lat,-this.maxValidLatitude,this.maxValidLatitude);return new n.P(n.G(l.lng)*this.worldSize,n.H(d)*this.worldSize)}unproject(l){return new n.U(l.x/this.worldSize,l.y/this.worldSize).toLngLat()}get point(){return this.project(this.center)}getCameraPosition(){return{lngLat:this.pointLocation(this.getCameraPoint()),altitude:Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter+this.elevation}}recalculateZoom(l){let d=this.pointLocation(this.centerPoint,l),v=l.getElevationForLngLatZoom(d,this.tileZoom);if(!(this.elevation-v))return;let b=this.getCameraPosition(),M=n.U.fromLngLat(b.lngLat,b.altitude),O=n.U.fromLngLat(d,v),B=M.x-O.x,U=M.y-O.y,W=M.z-O.z,Z=Math.sqrt(B*B+U*U+W*W),$=this.scaleZoom(this.cameraToCenterDistance/Z/this.tileSize);this._elevation=v,this._center=d,this.zoom=$}setLocationAtPoint(l,d){let v=this.pointCoordinate(d),b=this.pointCoordinate(this.centerPoint),M=this.locationCoordinate(l),O=new n.U(M.x-(v.x-b.x),M.y-(v.y-b.y));this.center=this.coordinateLocation(O),this._renderWorldCopies&&(this.center=this.center.wrap())}locationPoint(l,d){return d?this.coordinatePoint(this.locationCoordinate(l),d.getElevationForLngLatZoom(l,this.tileZoom),this.pixelMatrix3D):this.coordinatePoint(this.locationCoordinate(l))}pointLocation(l,d){return this.coordinateLocation(this.pointCoordinate(l,d))}locationCoordinate(l){return n.U.fromLngLat(l)}coordinateLocation(l){return l&&l.toLngLat()}pointCoordinate(l,d){if(d){let st=d.pointCoordinate(l);if(st!=null)return st}let v=[l.x,l.y,0,1],b=[l.x,l.y,1,1];n.ag(v,v,this.pixelMatrixInverse),n.ag(b,b,this.pixelMatrixInverse);let M=v[3],O=b[3],B=v[1]/M,U=b[1]/O,W=v[2]/M,Z=b[2]/O,$=W===Z?0:(0-W)/(Z-W);return new n.U(n.B.number(v[0]/M,b[0]/O,$)/this.worldSize,n.B.number(B,U,$)/this.worldSize)}coordinatePoint(l,d=0,v=this.pixelMatrix){let b=[l.x*this.worldSize,l.y*this.worldSize,d,1];return n.ag(b,b,v),new n.P(b[0]/b[3],b[1]/b[3])}getBounds(){let l=Math.max(0,this.height/2-this.getHorizon());return new Si().extend(this.pointLocation(new n.P(0,l))).extend(this.pointLocation(new n.P(this.width,l))).extend(this.pointLocation(new n.P(this.width,this.height))).extend(this.pointLocation(new n.P(0,this.height)))}getMaxBounds(){return this.latRange&&this.latRange.length===2&&this.lngRange&&this.lngRange.length===2?new Si([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null}getHorizon(){return Math.tan(Math.PI/2-this._pitch)*this.cameraToCenterDistance*.85}setMaxBounds(l){l?(this.lngRange=[l.getWest(),l.getEast()],this.latRange=[l.getSouth(),l.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])}calculatePosMatrix(l,d=!1){let v=l.key,b=d?this._alignedPosMatrixCache:this._posMatrixCache;if(b[v])return b[v];let M=l.canonical,O=this.worldSize/this.zoomScale(M.z),B=M.x+Math.pow(2,M.z)*l.wrap,U=n.ao(new Float64Array(16));return n.$(U,U,[B*O,M.y*O,0]),n.a0(U,U,[O/n.N,O/n.N,1]),n.a1(U,d?this.alignedProjMatrix:this.projMatrix,U),b[v]=new Float32Array(U),b[v]}customLayerMatrix(){return this.mercatorMatrix.slice()}_constrain(){if(!this.center||!this.width||!this.height||this._constraining)return;this._constraining=!0;let l,d,v,b,M=-90,O=90,B=-180,U=180,W=this.size,Z=this._unmodified;if(this.latRange){let At=this.latRange;M=n.H(At[1])*this.worldSize,O=n.H(At[0])*this.worldSize,l=O-MO&&(b=O-pt)}if(this.lngRange){let At=(B+U)/2,pt=n.b5($.x,At-this.worldSize/2,At+this.worldSize/2),yt=W.x/2;pt-ytU&&(v=U-yt)}v===void 0&&b===void 0||(this.center=this.unproject(new n.P(v!==void 0?v:$.x,b!==void 0?b:$.y)).wrap()),this._unmodified=Z,this._constraining=!1}_calcMatrices(){if(!this.height)return;let l=this.centerOffset,d=this.point.x,v=this.point.y;this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height,this._pixelPerMeter=n.b7(1,this.center.lat)*this.worldSize;let b=n.ao(new Float64Array(16));n.a0(b,b,[this.width/2,-this.height/2,1]),n.$(b,b,[1,-1,0]),this.labelPlaneMatrix=b,b=n.ao(new Float64Array(16)),n.a0(b,b,[1,-1,1]),n.$(b,b,[-1,-1,0]),n.a0(b,b,[2/this.width,2/this.height,1]),this.glCoordMatrix=b;let M=this.cameraToCenterDistance+this._elevation*this._pixelPerMeter/Math.cos(this._pitch),O=Math.min(this.elevation,this._minEleveationForCurrentTile),B=M-O*this._pixelPerMeter/Math.cos(this._pitch),U=O<0?B:M,W=Math.PI/2+this._pitch,Z=this._fov*(.5+l.y/this.height),$=Math.sin(Z)*U/Math.sin(n.ad(Math.PI-W-Z,.01,Math.PI-.01)),st=this.getHorizon(),At=2*Math.atan(st/this.cameraToCenterDistance)*(.5+l.y/(2*st)),pt=Math.sin(At)*U/Math.sin(n.ad(Math.PI-W-At,.01,Math.PI-.01)),yt=Math.min($,pt),dt=1.01*(Math.cos(Math.PI/2-this._pitch)*yt+U),Ft=this.height/50;b=new Float64Array(16),n.b8(b,this._fov,this.width/this.height,Ft,dt),b[8]=2*-l.x/this.width,b[9]=2*l.y/this.height,n.a0(b,b,[1,-1,1]),n.$(b,b,[0,0,-this.cameraToCenterDistance]),n.b9(b,b,this._pitch),n.ae(b,b,this.angle),n.$(b,b,[-d,-v,0]),this.mercatorMatrix=n.a0([],b,[this.worldSize,this.worldSize,this.worldSize]),n.a0(b,b,[1,1,this._pixelPerMeter]),this.pixelMatrix=n.a1(new Float64Array(16),this.labelPlaneMatrix,b),n.$(b,b,[0,0,-this.elevation]),this.projMatrix=b,this.invProjMatrix=n.as([],b),this.pixelMatrix3D=n.a1(new Float64Array(16),this.labelPlaneMatrix,b);let Ht=this.width%2/2,St=this.height%2/2,Bt=Math.cos(this.angle),Qt=Math.sin(this.angle),$t=d-Math.round(d)+Bt*Ht+Qt*St,oe=v-Math.round(v)+Bt*St+Qt*Ht,pe=new Float64Array(b);if(n.$(pe,pe,[$t>.5?$t-1:$t,oe>.5?oe-1:oe,0]),this.alignedProjMatrix=pe,b=n.as(new Float64Array(16),this.pixelMatrix),!b)throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=b,this._posMatrixCache={},this._alignedPosMatrixCache={}}maxPitchScaleFactor(){if(!this.pixelMatrixInverse)return 1;let l=this.pointCoordinate(new n.P(0,0)),d=[l.x*this.worldSize,l.y*this.worldSize,0,1];return n.ag(d,d,this.pixelMatrix)[3]/this.cameraToCenterDistance}getCameraPoint(){let l=Math.tan(this._pitch)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new n.P(0,l))}getCameraQueryGeometry(l){let d=this.getCameraPoint();if(l.length===1)return[l[0],d];{let v=d.x,b=d.y,M=d.x,O=d.y;for(let B of l)v=Math.min(v,B.x),b=Math.min(b,B.y),M=Math.max(M,B.x),O=Math.max(O,B.y);return[new n.P(v,b),new n.P(M,b),new n.P(M,O),new n.P(v,O),new n.P(v,b)]}}}function lh(T,l){let d,v=!1,b=null,M=null,O=()=>{b=null,v&&(T.apply(M,d),b=setTimeout(O,l),v=!1)};return(...B)=>(v=!0,M=this,d=B,b||O(),b)}class Ld{constructor(l){this._getCurrentHash=()=>{let d=window.location.hash.replace(\"#\",\"\");if(this._hashName){let v;return d.split(\"&\").map(b=>b.split(\"=\")).forEach(b=>{b[0]===this._hashName&&(v=b)}),(v&&v[1]||\"\").split(\"/\")}return d.split(\"/\")},this._onHashChange=()=>{let d=this._getCurrentHash();if(d.length>=3&&!d.some(v=>isNaN(v))){let v=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(d[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+d[2],+d[1]],zoom:+d[0],bearing:v,pitch:+(d[4]||0)}),!0}return!1},this._updateHashUnthrottled=()=>{let d=window.location.href.replace(/(#.+)?$/,this.getHashString());try{window.history.replaceState(window.history.state,null,d)}catch{}},this._updateHash=lh(this._updateHashUnthrottled,300),this._hashName=l&&encodeURIComponent(l)}addTo(l){return this._map=l,addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this}remove(){return removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this}getHashString(l){let d=this._map.getCenter(),v=Math.round(100*this._map.getZoom())/100,b=Math.ceil((v*Math.LN2+Math.log(512/360/.5))/Math.LN10),M=Math.pow(10,b),O=Math.round(d.lng*M)/M,B=Math.round(d.lat*M)/M,U=this._map.getBearing(),W=this._map.getPitch(),Z=\"\";if(Z+=l?`/${O}/${B}/${v}`:`${v}/${B}/${O}`,(U||W)&&(Z+=\"/\"+Math.round(10*U)/10),W&&(Z+=`/${Math.round(W)}`),this._hashName){let $=this._hashName,st=!1,At=window.location.hash.slice(1).split(\"&\").map(pt=>{let yt=pt.split(\"=\")[0];return yt===$?(st=!0,`${yt}=${Z}`):pt}).filter(pt=>pt);return st||At.push(`${$}=${Z}`),`#${At.join(\"&\")}`}return`#${Z}`}}let ch={linearity:.3,easing:n.ba(0,0,.3,1)},Jp=n.e({deceleration:2500,maxSpeed:1400},ch),tA=n.e({deceleration:20,maxSpeed:1400},ch),A_=n.e({deceleration:1e3,maxSpeed:360},ch),m_=n.e({deceleration:1e3,maxSpeed:90},ch);class n0{constructor(l){this._map=l,this.clear()}clear(){this._inertiaBuffer=[]}record(l){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:n.h.now(),settings:l})}_drainInertiaBuffer(){let l=this._inertiaBuffer,d=n.h.now();for(;l.length>0&&d-l[0].time>160;)l.shift()}_onMoveEnd(l){if(this._drainInertiaBuffer(),this._inertiaBuffer.length<2)return;let d={zoom:0,bearing:0,pitch:0,pan:new n.P(0,0),pinchAround:void 0,around:void 0};for(let{settings:M}of this._inertiaBuffer)d.zoom+=M.zoomDelta||0,d.bearing+=M.bearingDelta||0,d.pitch+=M.pitchDelta||0,M.panDelta&&d.pan._add(M.panDelta),M.around&&(d.around=M.around),M.pinchAround&&(d.pinchAround=M.pinchAround);let v=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,b={};if(d.pan.mag()){let M=uh(d.pan.mag(),v,n.e({},Jp,l||{}));b.offset=d.pan.mult(M.amount/d.pan.mag()),b.center=this._map.transform.center,pl(b,M)}if(d.zoom){let M=uh(d.zoom,v,tA);b.zoom=this._map.transform.zoom+M.amount,pl(b,M)}if(d.bearing){let M=uh(d.bearing,v,A_);b.bearing=this._map.transform.bearing+n.ad(M.amount,-179,179),pl(b,M)}if(d.pitch){let M=uh(d.pitch,v,m_);b.pitch=this._map.transform.pitch+M.amount,pl(b,M)}if(b.zoom||b.bearing){let M=d.pinchAround===void 0?d.around:d.pinchAround;b.around=M?this._map.unproject(M):this._map.getCenter()}return this.clear(),n.e(b,{noMoveStart:!0})}}function pl(T,l){(!T.duration||T.durationd.unproject(U)),B=M.reduce((U,W,Z,$)=>U.add(W.div($.length)),new n.P(0,0));super(l,{points:M,point:B,lngLats:O,lngLat:d.unproject(B),originalEvent:v}),this._defaultPrevented=!1}}class g_ extends n.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(l,d,v){super(l,{originalEvent:v}),this._defaultPrevented=!1}}class js{constructor(l,d){this._map=l,this._clickTolerance=d.clickTolerance}reset(){delete this._mousedownPos}wheel(l){return this._firePreventable(new g_(l.type,this._map,l))}mousedown(l,d){return this._mousedownPos=d,this._firePreventable(new la(l.type,this._map,l))}mouseup(l){this._map.fire(new la(l.type,this._map,l))}click(l,d){this._mousedownPos&&this._mousedownPos.dist(d)>=this._clickTolerance||this._map.fire(new la(l.type,this._map,l))}dblclick(l){return this._firePreventable(new la(l.type,this._map,l))}mouseover(l){this._map.fire(new la(l.type,this._map,l))}mouseout(l){this._map.fire(new la(l.type,this._map,l))}touchstart(l){return this._firePreventable(new kd(l.type,this._map,l))}touchmove(l){this._map.fire(new kd(l.type,this._map,l))}touchend(l){this._map.fire(new kd(l.type,this._map,l))}touchcancel(l){this._map.fire(new kd(l.type,this._map,l))}_firePreventable(l){if(this._map.fire(l),l.defaultPrevented)return{}}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class gu{constructor(l){this._map=l}reset(){this._delayContextMenu=!1,this._ignoreContextMenu=!0,delete this._contextMenuEvent}mousemove(l){this._map.fire(new la(l.type,this._map,l))}mousedown(){this._delayContextMenu=!0,this._ignoreContextMenu=!1}mouseup(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new la(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)}contextmenu(l){this._delayContextMenu?this._contextMenuEvent=l:this._ignoreContextMenu||this._map.fire(new la(l.type,this._map,l)),this._map.listens(\"contextmenu\")&&l.preventDefault()}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class Ln{constructor(l){this._map=l}get transform(){return this._map._requestedCameraState||this._map.transform}get center(){return{lng:this.transform.center.lng,lat:this.transform.center.lat}}get zoom(){return this.transform.zoom}get pitch(){return this.transform.pitch}get bearing(){return this.transform.bearing}unproject(l){return this.transform.pointLocation(n.P.convert(l),this._map.terrain)}}class eA{constructor(l,d){this._map=l,this._tr=new Ln(l),this._el=l.getCanvasContainer(),this._container=l.getContainer(),this._clickTolerance=d.clickTolerance||1}isEnabled(){return!!this._enabled}isActive(){return!!this._active}enable(){this.isEnabled()||(this._enabled=!0)}disable(){this.isEnabled()&&(this._enabled=!1)}mousedown(l,d){this.isEnabled()&&l.shiftKey&&l.button===0&&(c.disableDrag(),this._startPos=this._lastPos=d,this._active=!0)}mousemoveWindow(l,d){if(!this._active)return;let v=d;if(this._lastPos.equals(v)||!this._box&&v.dist(this._startPos)M.fitScreenCoordinates(v,b,this._tr.bearing,{linear:!0})};this._fireEvent(\"boxzoomcancel\",l)}keydown(l){this._active&&l.keyCode===27&&(this.reset(),this._fireEvent(\"boxzoomcancel\",l))}reset(){this._active=!1,this._container.classList.remove(\"maplibregl-crosshair\"),this._box&&(c.remove(this._box),this._box=null),c.enableDrag(),delete this._startPos,delete this._lastPos}_fireEvent(l,d){return this._map.fire(new n.k(l,{originalEvent:d}))}}function ca(T,l){if(T.length!==l.length)throw new Error(`The number of touches and points are not equal - touches ${T.length}, points ${l.length}`);let d={};for(let v=0;vthis.numTouches)&&(this.aborted=!0),this.aborted||(this.startTime===void 0&&(this.startTime=l.timeStamp),v.length===this.numTouches&&(this.centroid=function(b){let M=new n.P(0,0);for(let O of b)M._add(O);return M.div(b.length)}(d),this.touches=ca(v,d)))}touchmove(l,d,v){if(this.aborted||!this.centroid)return;let b=ca(v,d);for(let M in this.touches){let O=b[M];(!O||O.dist(this.touches[M])>30)&&(this.aborted=!0)}}touchend(l,d,v){if((!this.centroid||l.timeStamp-this.startTime>500)&&(this.aborted=!0),v.length===0){let b=!this.aborted&&this.centroid;if(this.reset(),b)return b}}}class Rd{constructor(l){this.singleTap=new Fa(l),this.numTaps=l.numTaps,this.reset()}reset(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()}touchstart(l,d,v){this.singleTap.touchstart(l,d,v)}touchmove(l,d,v){this.singleTap.touchmove(l,d,v)}touchend(l,d,v){let b=this.singleTap.touchend(l,d,v);if(b){let M=l.timeStamp-this.lastTime<500,O=!this.lastTap||this.lastTap.dist(b)<30;if(M&&O||this.reset(),this.count++,this.lastTime=l.timeStamp,this.lastTap=b,this.count===this.numTaps)return this.reset(),b}}}class Al{constructor(l){this._tr=new Ln(l),this._zoomIn=new Rd({numTouches:1,numTaps:2}),this._zoomOut=new Rd({numTouches:2,numTaps:1}),this.reset()}reset(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()}touchstart(l,d,v){this._zoomIn.touchstart(l,d,v),this._zoomOut.touchstart(l,d,v)}touchmove(l,d,v){this._zoomIn.touchmove(l,d,v),this._zoomOut.touchmove(l,d,v)}touchend(l,d,v){let b=this._zoomIn.touchend(l,d,v),M=this._zoomOut.touchend(l,d,v),O=this._tr;return b?(this._active=!0,l.preventDefault(),setTimeout(()=>this.reset(),0),{cameraAnimation:B=>B.easeTo({duration:300,zoom:O.zoom+1,around:O.unproject(b)},{originalEvent:l})}):M?(this._active=!0,l.preventDefault(),setTimeout(()=>this.reset(),0),{cameraAnimation:B=>B.easeTo({duration:300,zoom:O.zoom-1,around:O.unproject(M)},{originalEvent:l})}):void 0}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class za{constructor(l){this._enabled=!!l.enable,this._moveStateManager=l.moveStateManager,this._clickTolerance=l.clickTolerance||1,this._moveFunction=l.move,this._activateOnStart=!!l.activateOnStart,l.assignEvents(this),this.reset()}reset(l){this._active=!1,this._moved=!1,delete this._lastPoint,this._moveStateManager.endMove(l)}_move(...l){let d=this._moveFunction(...l);if(d.bearingDelta||d.pitchDelta||d.around||d.panDelta)return this._active=!0,d}dragStart(l,d){this.isEnabled()&&!this._lastPoint&&this._moveStateManager.isValidStartEvent(l)&&(this._moveStateManager.startMove(l),this._lastPoint=d.length?d[0]:d,this._activateOnStart&&this._lastPoint&&(this._active=!0))}dragMove(l,d){if(!this.isEnabled())return;let v=this._lastPoint;if(!v)return;if(l.preventDefault(),!this._moveStateManager.isValidMoveEvent(l))return void this.reset(l);let b=d.length?d[0]:d;return!this._moved&&b.dist(v){T.mousedown=T.dragStart,T.mousemoveWindow=T.dragMove,T.mouseup=T.dragEnd,T.contextmenu=function(l){l.preventDefault()}},Na=({enable:T,clickTolerance:l,bearingDegreesPerPixelMoved:d=.8})=>{let v=new rA({checkCorrectEvent:b=>c.mouseButton(b)===0&&b.ctrlKey||c.mouseButton(b)===2});return new za({clickTolerance:l,move:(b,M)=>({bearingDelta:(M.x-b.x)*d}),moveStateManager:v,enable:T,assignEvents:fh})},co=({enable:T,clickTolerance:l,pitchDegreesPerPixelMoved:d=-.5})=>{let v=new rA({checkCorrectEvent:b=>c.mouseButton(b)===0&&b.ctrlKey||c.mouseButton(b)===2});return new za({clickTolerance:l,move:(b,M)=>({pitchDelta:(M.y-b.y)*d}),moveStateManager:v,enable:T,assignEvents:fh})};class Ge{constructor(l,d){this._minTouches=l.cooperativeGestures?2:1,this._clickTolerance=l.clickTolerance||1,this._map=d,this.reset()}reset(){this._active=!1,this._touches={},this._sum=new n.P(0,0),setTimeout(()=>{this._cancelCooperativeMessage=!1},200)}touchstart(l,d,v){return this._calculateTransform(l,d,v)}touchmove(l,d,v){if(this._map._cooperativeGestures&&(this._minTouches===2&&v.length<2&&!this._cancelCooperativeMessage?this._map._onCooperativeGesture(l,!1,v.length):this._cancelCooperativeMessage||(this._cancelCooperativeMessage=!0)),this._active&&!(v.length0&&(this._active=!0);let b=ca(v,d),M=new n.P(0,0),O=new n.P(0,0),B=0;for(let W in b){let Z=b[W],$=this._touches[W];$&&(M._add(Z),O._add(Z.sub($)),B++,b[W]=Z)}if(this._touches=b,BMath.abs(T.x)}class zx extends Dd{constructor(l){super(),this._map=l}reset(){super.reset(),this._valid=void 0,delete this._firstMove,delete this._lastPoints}touchstart(l,d,v){super.touchstart(l,d,v),this._currentTouchCount=v.length}_start(l){this._lastPoints=l,a0(l[0].sub(l[1]))&&(this._valid=!1)}_move(l,d,v){if(this._map._cooperativeGestures&&this._currentTouchCount<3)return;let b=l[0].sub(this._lastPoints[0]),M=l[1].sub(this._lastPoints[1]);return this._valid=this.gestureBeginsVertically(b,M,v.timeStamp),this._valid?(this._lastPoints=l,this._active=!0,{pitchDelta:(b.y+M.y)/2*-.5}):void 0}gestureBeginsVertically(l,d,v){if(this._valid!==void 0)return this._valid;let b=l.mag()>=2,M=d.mag()>=2;if(!b&&!M)return;if(!b||!M)return this._firstMove===void 0&&(this._firstMove=v),v-this._firstMove<100&&void 0;let O=l.y>0==d.y>0;return a0(l)&&a0(d)&&O}}let dh={panStep:100,bearingStep:15,pitchStep:10};class y_{constructor(l){this._tr=new Ln(l);let d=dh;this._panStep=d.panStep,this._bearingStep=d.bearingStep,this._pitchStep=d.pitchStep,this._rotationDisabled=!1}reset(){this._active=!1}keydown(l){if(l.altKey||l.ctrlKey||l.metaKey)return;let d=0,v=0,b=0,M=0,O=0;switch(l.keyCode){case 61:case 107:case 171:case 187:d=1;break;case 189:case 109:case 173:d=-1;break;case 37:l.shiftKey?v=-1:(l.preventDefault(),M=-1);break;case 39:l.shiftKey?v=1:(l.preventDefault(),M=1);break;case 38:l.shiftKey?b=1:(l.preventDefault(),O=-1);break;case 40:l.shiftKey?b=-1:(l.preventDefault(),O=1);break;default:return}return this._rotationDisabled&&(v=0,b=0),{cameraAnimation:B=>{let U=this._tr;B.easeTo({duration:300,easeId:\"keyboardHandler\",easing:l0,zoom:d?Math.round(U.zoom)+d*(l.shiftKey?2:1):U.zoom,bearing:U.bearing+v*this._bearingStep,pitch:U.pitch+b*this._pitchStep,offset:[-M*this._panStep,-O*this._panStep],center:U.center},{originalEvent:l})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}disableRotation(){this._rotationDisabled=!0}enableRotation(){this._rotationDisabled=!1}}function l0(T){return T*(2-T)}let c0=4.000244140625;class bf{constructor(l,d){this._onTimeout=v=>{this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(v)},this._map=l,this._tr=new Ln(l),this._el=l.getCanvasContainer(),this._triggerRenderFrame=d,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222}setZoomRate(l){this._defaultZoomRate=l}setWheelZoomRate(l){this._wheelZoomRate=l}isEnabled(){return!!this._enabled}isActive(){return!!this._active||this._finishTimeout!==void 0}isZooming(){return!!this._zooming}enable(l){this.isEnabled()||(this._enabled=!0,this._aroundCenter=!!l&&l.around===\"center\")}disable(){this.isEnabled()&&(this._enabled=!1)}wheel(l){if(!this.isEnabled())return;if(this._map._cooperativeGestures){if(!l[this._map._metaKey])return;l.preventDefault()}let d=l.deltaMode===WheelEvent.DOM_DELTA_LINE?40*l.deltaY:l.deltaY,v=n.h.now(),b=v-(this._lastWheelEventTime||0);this._lastWheelEventTime=v,d!==0&&d%c0==0?this._type=\"wheel\":d!==0&&Math.abs(d)<4?this._type=\"trackpad\":b>400?(this._type=null,this._lastValue=d,this._timeout=setTimeout(this._onTimeout,40,l)):this._type||(this._type=Math.abs(b*d)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,d+=this._lastValue)),l.shiftKey&&d&&(d/=4),this._type&&(this._lastWheelEvent=l,this._delta-=d,this._active||this._start(l)),l.preventDefault()}_start(l){if(!this._delta)return;this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);let d=c.mousePos(this._el,l),v=this._tr;this._around=n.L.convert(this._aroundCenter?v.center:v.unproject(d)),this._aroundPoint=v.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._triggerRenderFrame())}renderFrame(){if(!this._frameId||(this._frameId=null,!this.isActive()))return;let l=this._tr.transform;if(this._delta!==0){let B=this._type===\"wheel\"&&Math.abs(this._delta)>c0?this._wheelZoomRate:this._defaultZoomRate,U=2/(1+Math.exp(-Math.abs(this._delta*B)));this._delta<0&&U!==0&&(U=1/U);let W=typeof this._targetZoom==\"number\"?l.zoomScale(this._targetZoom):l.scale;this._targetZoom=Math.min(l.maxZoom,Math.max(l.minZoom,l.scaleZoom(W*U))),this._type===\"wheel\"&&(this._startZoom=l.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}let d=typeof this._targetZoom==\"number\"?this._targetZoom:l.zoom,v=this._startZoom,b=this._easing,M,O=!1;if(this._type===\"wheel\"&&v&&b){let B=Math.min((n.h.now()-this._lastWheelEventTime)/200,1),U=b(B);M=n.B.number(v,d,U),B<1?this._frameId||(this._frameId=!0):O=!0}else M=d,O=!0;return this._active=!0,O&&(this._active=!1,this._finishTimeout=setTimeout(()=>{this._zooming=!1,this._triggerRenderFrame(),delete this._targetZoom,delete this._finishTimeout},200)),{noInertia:!0,needsRenderFrame:!O,zoomDelta:M-l.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}_smoothOutEasing(l){let d=n.bb;if(this._prevEase){let v=this._prevEase,b=(n.h.now()-v.start)/v.duration,M=v.easing(b+.01)-v.easing(b),O=.27/Math.sqrt(M*M+1e-4)*.01,B=Math.sqrt(.0729-O*O);d=n.ba(O,B,.25,1)}return this._prevEase={start:n.h.now(),duration:l,easing:d},d}reset(){this._active=!1,this._zooming=!1,delete this._targetZoom,this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout)}}class u0{constructor(l,d){this._clickZoom=l,this._tapZoom=d}enable(){this._clickZoom.enable(),this._tapZoom.enable()}disable(){this._clickZoom.disable(),this._tapZoom.disable()}isEnabled(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()}isActive(){return this._clickZoom.isActive()||this._tapZoom.isActive()}}class iA{constructor(l){this._tr=new Ln(l),this.reset()}reset(){this._active=!1}dblclick(l,d){return l.preventDefault(),{cameraAnimation:v=>{v.easeTo({duration:300,zoom:this._tr.zoom+(l.shiftKey?-1:1),around:this._tr.unproject(d)},{originalEvent:l})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class nA{constructor(){this._tap=new Rd({numTouches:1,numTaps:1}),this.reset()}reset(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,delete this._tapPoint,this._tap.reset()}touchstart(l,d,v){if(!this._swipePoint)if(this._tapTime){let b=d[0],M=l.timeStamp-this._tapTime<500,O=this._tapPoint.dist(b)<30;M&&O?v.length>0&&(this._swipePoint=b,this._swipeTouch=v[0].identifier):this.reset()}else this._tap.touchstart(l,d,v)}touchmove(l,d,v){if(this._tapTime){if(this._swipePoint){if(v[0].identifier!==this._swipeTouch)return;let b=d[0],M=b.y-this._swipePoint.y;return this._swipePoint=b,l.preventDefault(),this._active=!0,{zoomDelta:M/128}}}else this._tap.touchmove(l,d,v)}touchend(l,d,v){if(this._tapTime)this._swipePoint&&v.length===0&&this.reset();else{let b=this._tap.touchend(l,d,v);b&&(this._tapTime=l.timeStamp,this._tapPoint=b)}}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class ph{constructor(l,d,v){this._el=l,this._mousePan=d,this._touchPan=v}enable(l){this._inertiaOptions=l||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"maplibregl-touch-drag-pan\")}disable(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"maplibregl-touch-drag-pan\")}isEnabled(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()}isActive(){return this._mousePan.isActive()||this._touchPan.isActive()}}class us{constructor(l,d,v){this._pitchWithRotate=l.pitchWithRotate,this._mouseRotate=d,this._mousePitch=v}enable(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()}disable(){this._mouseRotate.disable(),this._mousePitch.disable()}isEnabled(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())}isActive(){return this._mouseRotate.isActive()||this._mousePitch.isActive()}}class _u{constructor(l,d,v,b){this._el=l,this._touchZoom=d,this._touchRotate=v,this._tapDragZoom=b,this._rotationDisabled=!1,this._enabled=!0}enable(l){this._touchZoom.enable(l),this._rotationDisabled||this._touchRotate.enable(l),this._tapDragZoom.enable(),this._el.classList.add(\"maplibregl-touch-zoom-rotate\")}disable(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"maplibregl-touch-zoom-rotate\")}isEnabled(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()}isActive(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()}disableRotation(){this._rotationDisabled=!0,this._touchRotate.disable()}enableRotation(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()}}let Bc=T=>T.zoom||T.drag||T.pitch||T.rotate;class h0 extends n.k{}function Od(T){return T.panDelta&&T.panDelta.mag()||T.zoomDelta||T.bearingDelta||T.pitchDelta}class f0{constructor(l,d){this.handleWindowEvent=b=>{this.handleEvent(b,`${b.type}Window`)},this.handleEvent=(b,M)=>{if(b.type===\"blur\")return void this.stop(!0);this._updatingCamera=!0;let O=b.type===\"renderFrame\"?void 0:b,B={needsRenderFrame:!1},U={},W={},Z=b.touches,$=Z?this._getMapTouches(Z):void 0,st=$?c.touchPos(this._el,$):c.mousePos(this._el,b);for(let{handlerName:yt,handler:dt,allowed:Ft}of this._handlers){if(!dt.isEnabled())continue;let Ht;this._blockedByActive(W,Ft,yt)?dt.reset():dt[M||b.type]&&(Ht=dt[M||b.type](b,st,$),this.mergeHandlerResult(B,U,Ht,yt,O),Ht&&Ht.needsRenderFrame&&this._triggerRenderFrame()),(Ht||dt.isActive())&&(W[yt]=dt)}let At={};for(let yt in this._previousActiveHandlers)W[yt]||(At[yt]=O);this._previousActiveHandlers=W,(Object.keys(At).length||Od(B))&&(this._changes.push([B,U,At]),this._triggerRenderFrame()),(Object.keys(W).length||Od(B))&&this._map._stop(!0),this._updatingCamera=!1;let{cameraAnimation:pt}=B;pt&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],pt(this._map))},this._map=l,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new n0(l),this._bearingSnap=d.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(d);let v=this._el;this._listeners=[[v,\"touchstart\",{passive:!0}],[v,\"touchmove\",{passive:!1}],[v,\"touchend\",void 0],[v,\"touchcancel\",void 0],[v,\"mousedown\",void 0],[v,\"mousemove\",void 0],[v,\"mouseup\",void 0],[document,\"mousemove\",{capture:!0}],[document,\"mouseup\",void 0],[v,\"mouseover\",void 0],[v,\"mouseout\",void 0],[v,\"dblclick\",void 0],[v,\"click\",void 0],[v,\"keydown\",{capture:!1}],[v,\"keyup\",void 0],[v,\"wheel\",{passive:!1}],[v,\"contextmenu\",void 0],[window,\"blur\",void 0]];for(let[b,M,O]of this._listeners)c.addEventListener(b,M,b===document?this.handleWindowEvent:this.handleEvent,O)}destroy(){for(let[l,d,v]of this._listeners)c.removeEventListener(l,d,l===document?this.handleWindowEvent:this.handleEvent,v)}_addDefaultHandlers(l){let d=this._map,v=d.getCanvasContainer();this._add(\"mapEvent\",new js(d,l));let b=d.boxZoom=new eA(d,l);this._add(\"boxZoom\",b),l.interactive&&l.boxZoom&&b.enable();let M=new Al(d),O=new iA(d);d.doubleClickZoom=new u0(O,M),this._add(\"tapZoom\",M),this._add(\"clickZoom\",O),l.interactive&&l.doubleClickZoom&&d.doubleClickZoom.enable();let B=new nA;this._add(\"tapDragZoom\",B);let U=d.touchPitch=new zx(d);this._add(\"touchPitch\",U),l.interactive&&l.touchPitch&&d.touchPitch.enable(l.touchPitch);let W=Na(l),Z=co(l);d.dragRotate=new us(l,W,Z),this._add(\"mouseRotate\",W,[\"mousePitch\"]),this._add(\"mousePitch\",Z,[\"mouseRotate\"]),l.interactive&&l.dragRotate&&d.dragRotate.enable();let $=(({enable:Ft,clickTolerance:Ht})=>{let St=new rA({checkCorrectEvent:Bt=>c.mouseButton(Bt)===0&&!Bt.ctrlKey});return new za({clickTolerance:Ht,move:(Bt,Qt)=>({around:Qt,panDelta:Qt.sub(Bt)}),activateOnStart:!0,moveStateManager:St,enable:Ft,assignEvents:fh})})(l),st=new Ge(l,d);d.dragPan=new ph(v,$,st),this._add(\"mousePan\",$),this._add(\"touchPan\",st,[\"touchZoom\",\"touchRotate\"]),l.interactive&&l.dragPan&&d.dragPan.enable(l.dragPan);let At=new o0,pt=new __;d.touchZoomRotate=new _u(v,pt,At,B),this._add(\"touchRotate\",At,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",pt,[\"touchPan\",\"touchRotate\"]),l.interactive&&l.touchZoomRotate&&d.touchZoomRotate.enable(l.touchZoomRotate);let yt=d.scrollZoom=new bf(d,()=>this._triggerRenderFrame());this._add(\"scrollZoom\",yt,[\"mousePan\"]),l.interactive&&l.scrollZoom&&d.scrollZoom.enable(l.scrollZoom);let dt=d.keyboard=new y_(d);this._add(\"keyboard\",dt),l.interactive&&l.keyboard&&d.keyboard.enable(),this._add(\"blockableMapEvent\",new gu(d))}_add(l,d,v){this._handlers.push({handlerName:l,handler:d,allowed:v}),this._handlersById[l]=d}stop(l){if(!this._updatingCamera){for(let{handler:d}of this._handlers)d.reset();this._inertia.clear(),this._fireEvents({},{},l),this._changes=[]}}isActive(){for(let{handler:l}of this._handlers)if(l.isActive())return!0;return!1}isZooming(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()}isRotating(){return!!this._eventsInProgress.rotate}isMoving(){return!!Bc(this._eventsInProgress)||this.isZooming()}_blockedByActive(l,d,v){for(let b in l)if(b!==v&&(!d||d.indexOf(b)<0))return!0;return!1}_getMapTouches(l){let d=[];for(let v of l)this._el.contains(v.target)&&d.push(v);return d}mergeHandlerResult(l,d,v,b,M){if(!v)return;n.e(l,v);let O={handlerName:b,originalEvent:v.originalEvent||M};v.zoomDelta!==void 0&&(d.zoom=O),v.panDelta!==void 0&&(d.drag=O),v.pitchDelta!==void 0&&(d.pitch=O),v.bearingDelta!==void 0&&(d.rotate=O)}_applyChanges(){let l={},d={},v={};for(let[b,M,O]of this._changes)b.panDelta&&(l.panDelta=(l.panDelta||new n.P(0,0))._add(b.panDelta)),b.zoomDelta&&(l.zoomDelta=(l.zoomDelta||0)+b.zoomDelta),b.bearingDelta&&(l.bearingDelta=(l.bearingDelta||0)+b.bearingDelta),b.pitchDelta&&(l.pitchDelta=(l.pitchDelta||0)+b.pitchDelta),b.around!==void 0&&(l.around=b.around),b.pinchAround!==void 0&&(l.pinchAround=b.pinchAround),b.noInertia&&(l.noInertia=b.noInertia),n.e(d,M),n.e(v,O);this._updateMapTransform(l,d,v),this._changes=[]}_updateMapTransform(l,d,v){let b=this._map,M=b._getTransformForUpdate(),O=b.terrain;if(!(Od(l)||O&&this._terrainMovement))return this._fireEvents(d,v,!0);let{panDelta:B,zoomDelta:U,bearingDelta:W,pitchDelta:Z,around:$,pinchAround:st}=l;st!==void 0&&($=st),b._stop(!0),$=$||b.transform.centerPoint;let At=M.pointLocation(B?$.sub(B):$);W&&(M.bearing+=W),Z&&(M.pitch+=Z),U&&(M.zoom+=U),O?this._terrainMovement||!d.drag&&!d.zoom?d.drag&&this._terrainMovement?M.center=M.pointLocation(M.centerPoint.sub(B)):M.setLocationAtPoint(At,$):(this._terrainMovement=!0,this._map._elevationFreeze=!0,M.setLocationAtPoint(At,$),this._map.once(\"moveend\",()=>{this._map._elevationFreeze=!1,this._terrainMovement=!1,M.recalculateZoom(b.terrain)})):M.setLocationAtPoint(At,$),b._applyUpdatedTransform(M),this._map._update(),l.noInertia||this._inertia.record(l),this._fireEvents(d,v,!0)}_fireEvents(l,d,v){let b=Bc(this._eventsInProgress),M=Bc(l),O={};for(let Z in l){let{originalEvent:$}=l[Z];this._eventsInProgress[Z]||(O[`${Z}start`]=$),this._eventsInProgress[Z]=l[Z]}!b&&M&&this._fireEvent(\"movestart\",M.originalEvent);for(let Z in O)this._fireEvent(Z,O[Z]);M&&this._fireEvent(\"move\",M.originalEvent);for(let Z in l){let{originalEvent:$}=l[Z];this._fireEvent(Z,$)}let B={},U;for(let Z in this._eventsInProgress){let{handlerName:$,originalEvent:st}=this._eventsInProgress[Z];this._handlersById[$].isActive()||(delete this._eventsInProgress[Z],U=d[$]||st,B[`${Z}end`]=U)}for(let Z in B)this._fireEvent(Z,B[Z]);let W=Bc(this._eventsInProgress);if(v&&(b||M)&&!W){this._updatingCamera=!0;let Z=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),$=st=>st!==0&&-this._bearingSnap{delete this._frameId,this.handleEvent(new h0(\"renderFrame\",{timeStamp:l})),this._applyChanges()})}_triggerRenderFrame(){this._frameId===void 0&&(this._frameId=this._requestFrame())}}class v_ extends n.E{constructor(l,d){super(),this._renderFrameCallback=()=>{let v=Math.min((n.h.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(v)),v<1&&this._easeFrameId?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},this._moving=!1,this._zooming=!1,this.transform=l,this._bearingSnap=d.bearingSnap,this.on(\"moveend\",()=>{delete this._requestedCameraState})}getCenter(){return new n.L(this.transform.center.lng,this.transform.center.lat)}setCenter(l,d){return this.jumpTo({center:l},d)}panBy(l,d,v){return l=n.P.convert(l).mult(-1),this.panTo(this.transform.center,n.e({offset:l},d),v)}panTo(l,d,v){return this.easeTo(n.e({center:l},d),v)}getZoom(){return this.transform.zoom}setZoom(l,d){return this.jumpTo({zoom:l},d),this}zoomTo(l,d,v){return this.easeTo(n.e({zoom:l},d),v)}zoomIn(l,d){return this.zoomTo(this.getZoom()+1,l,d),this}zoomOut(l,d){return this.zoomTo(this.getZoom()-1,l,d),this}getBearing(){return this.transform.bearing}setBearing(l,d){return this.jumpTo({bearing:l},d),this}getPadding(){return this.transform.padding}setPadding(l,d){return this.jumpTo({padding:l},d),this}rotateTo(l,d,v){return this.easeTo(n.e({bearing:l},d),v)}resetNorth(l,d){return this.rotateTo(0,n.e({duration:1e3},l),d),this}resetNorthPitch(l,d){return this.easeTo(n.e({bearing:0,pitch:0,duration:1e3},l),d),this}snapToNorth(l,d){return Math.abs(this.getBearing()){if(this._zooming&&(v.zoom=n.B.number(b,U,$t)),this._rotating&&(v.bearing=n.B.number(M,W,$t)),this._pitching&&(v.pitch=n.B.number(O,Z,$t)),this._padding&&(v.interpolatePadding(B,$,$t),At=v.centerPoint.add(st)),this.terrain&&!l.freezeElevation&&this._updateElevation($t),St)v.setLocationAtPoint(St,Bt);else{let oe=v.zoomScale(v.zoom-b),pe=U>b?Math.min(2,Ht):Math.max(.5,Ht),he=Math.pow(pe,1-$t),be=v.unproject(dt.add(Ft.mult($t*he)).mult(oe));v.setLocationAtPoint(v.renderWorldCopies?be.wrap():be,At)}this._applyUpdatedTransform(v),this._fireMoveEvents(d)},$t=>{this.terrain&&this._finalizeElevation(),this._afterEase(d,$t)},l),this}_prepareEase(l,d,v={}){this._moving=!0,d||v.moving||this.fire(new n.k(\"movestart\",l)),this._zooming&&!v.zooming&&this.fire(new n.k(\"zoomstart\",l)),this._rotating&&!v.rotating&&this.fire(new n.k(\"rotatestart\",l)),this._pitching&&!v.pitching&&this.fire(new n.k(\"pitchstart\",l))}_prepareElevation(l){this._elevationCenter=l,this._elevationStart=this.transform.elevation,this._elevationTarget=this.terrain.getElevationForLngLatZoom(l,this.transform.tileZoom),this._elevationFreeze=!0}_updateElevation(l){this.transform._minEleveationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);let d=this.terrain.getElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);if(l<1&&d!==this._elevationTarget){let v=this._elevationTarget-this._elevationStart;this._elevationStart+=l*(v-(d-(v*l+this._elevationStart))/(1-l)),this._elevationTarget=d}this.transform.elevation=n.B.number(this._elevationStart,this._elevationTarget,l)}_finalizeElevation(){this._elevationFreeze=!1,this.transform.recalculateZoom(this.terrain)}_getTransformForUpdate(){return this.transformCameraUpdate?(this._requestedCameraState||(this._requestedCameraState=this.transform.clone()),this._requestedCameraState):this.transform}_applyUpdatedTransform(l){if(!this.transformCameraUpdate)return;let d=l.clone(),{center:v,zoom:b,pitch:M,bearing:O,elevation:B}=this.transformCameraUpdate(d);v&&(d.center=v),b!==void 0&&(d.zoom=b),M!==void 0&&(d.pitch=M),O!==void 0&&(d.bearing=O),B!==void 0&&(d.elevation=B),this.transform.apply(d)}_fireMoveEvents(l){this.fire(new n.k(\"move\",l)),this._zooming&&this.fire(new n.k(\"zoom\",l)),this._rotating&&this.fire(new n.k(\"rotate\",l)),this._pitching&&this.fire(new n.k(\"pitch\",l))}_afterEase(l,d){if(this._easeId&&d&&this._easeId===d)return;delete this._easeId;let v=this._zooming,b=this._rotating,M=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,v&&this.fire(new n.k(\"zoomend\",l)),b&&this.fire(new n.k(\"rotateend\",l)),M&&this.fire(new n.k(\"pitchend\",l)),this.fire(new n.k(\"moveend\",l))}flyTo(l,d){if(!l.essential&&n.h.prefersReducedMotion){let tr=n.F(l,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(tr,d)}this.stop(),l=n.e({offset:[0,0],speed:1.2,curve:1.42,easing:n.bb},l);let v=this._getTransformForUpdate(),b=this.getZoom(),M=this.getBearing(),O=this.getPitch(),B=this.getPadding(),U=\"zoom\"in l?n.ad(+l.zoom,v.minZoom,v.maxZoom):b,W=\"bearing\"in l?this._normalizeBearing(l.bearing,M):M,Z=\"pitch\"in l?+l.pitch:O,$=\"padding\"in l?l.padding:v.padding,st=v.zoomScale(U-b),At=n.P.convert(l.offset),pt=v.centerPoint.add(At),yt=v.pointLocation(pt),dt=n.L.convert(l.center||yt);this._normalizeCenter(dt);let Ft=v.project(yt),Ht=v.project(dt).sub(Ft),St=l.curve,Bt=Math.max(v.width,v.height),Qt=Bt/st,$t=Ht.mag();if(\"minZoom\"in l){let tr=n.ad(Math.min(l.minZoom,b,U),v.minZoom,v.maxZoom),Gi=Bt/v.zoomScale(tr-b);St=Math.sqrt(Gi/$t*2)}let oe=St*St;function pe(tr){let Gi=(Qt*Qt-Bt*Bt+(tr?-1:1)*oe*oe*$t*$t)/(2*(tr?Qt:Bt)*oe*$t);return Math.log(Math.sqrt(Gi*Gi+1)-Gi)}function he(tr){return(Math.exp(tr)-Math.exp(-tr))/2}function be(tr){return(Math.exp(tr)+Math.exp(-tr))/2}let Ze=pe(!1),Kr=function(tr){return be(Ze)/be(Ze+St*tr)},Ee=function(tr){return Bt*((be(Ze)*(he(Gi=Ze+St*tr)/be(Gi))-he(Ze))/oe)/$t;var Gi},pr=(pe(!0)-Ze)/St;if(Math.abs($t)<1e-6||!isFinite(pr)){if(Math.abs(Bt-Qt)<1e-6)return this.easeTo(l,d);let tr=Qtl.maxDuration&&(l.duration=0),this._zooming=!0,this._rotating=M!==W,this._pitching=Z!==O,this._padding=!v.isPaddingEqual($),this._prepareEase(d,!1),this.terrain&&this._prepareElevation(dt),this._ease(tr=>{let Gi=tr*pr,Jr=1/Kr(Gi);v.zoom=tr===1?U:b+v.scaleZoom(Jr),this._rotating&&(v.bearing=n.B.number(M,W,tr)),this._pitching&&(v.pitch=n.B.number(O,Z,tr)),this._padding&&(v.interpolatePadding(B,$,tr),pt=v.centerPoint.add(At)),this.terrain&&!l.freezeElevation&&this._updateElevation(tr);let Vr=tr===1?dt:v.unproject(Ft.add(Ht.mult(Ee(Gi))).mult(Jr));v.setLocationAtPoint(v.renderWorldCopies?Vr.wrap():Vr,pt),this._applyUpdatedTransform(v),this._fireMoveEvents(d)},()=>{this.terrain&&this._finalizeElevation(),this._afterEase(d)},l),this}isEasing(){return!!this._easeFrameId}stop(){return this._stop()}_stop(l,d){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){let v=this._onEaseEnd;delete this._onEaseEnd,v.call(this,d)}if(!l){let v=this.handlers;v&&v.stop(!1)}return this}_ease(l,d,v){v.animate===!1||v.duration===0?(l(1),d()):(this._easeStart=n.h.now(),this._easeOptions=v,this._onEaseFrame=l,this._onEaseEnd=d,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))}_normalizeBearing(l,d){l=n.b5(l,-180,180);let v=Math.abs(l-d);return Math.abs(l-360-d)180?-360:v<-180?360:0}queryTerrainElevation(l){return this.terrain?this.terrain.getElevationForLngLatZoom(n.L.convert(l),this.transform.tileZoom)-this.transform.elevation:null}}class ua{constructor(l={}){this._toggleAttribution=()=>{this._container.classList.contains(\"maplibregl-compact\")&&(this._container.classList.contains(\"maplibregl-compact-show\")?(this._container.setAttribute(\"open\",\"\"),this._container.classList.remove(\"maplibregl-compact-show\")):(this._container.classList.add(\"maplibregl-compact-show\"),this._container.removeAttribute(\"open\")))},this._updateData=d=>{!d||d.sourceDataType!==\"metadata\"&&d.sourceDataType!==\"visibility\"&&d.dataType!==\"style\"&&d.type!==\"terrain\"||this._updateAttributions()},this._updateCompact=()=>{this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact===!1?this._container.setAttribute(\"open\",\"\"):this._container.classList.contains(\"maplibregl-compact\")||this._container.classList.contains(\"maplibregl-attrib-empty\")||(this._container.setAttribute(\"open\",\"\"),this._container.classList.add(\"maplibregl-compact\",\"maplibregl-compact-show\")):(this._container.setAttribute(\"open\",\"\"),this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.remove(\"maplibregl-compact\",\"maplibregl-compact-show\"))},this._updateCompactMinimize=()=>{this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.contains(\"maplibregl-compact-show\")&&this._container.classList.remove(\"maplibregl-compact-show\")},this.options=l}getDefaultPosition(){return\"bottom-right\"}onAdd(l){return this._map=l,this._compact=this.options&&this.options.compact,this._container=c.create(\"details\",\"maplibregl-ctrl maplibregl-ctrl-attrib\"),this._compactButton=c.create(\"summary\",\"maplibregl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=c.create(\"div\",\"maplibregl-ctrl-attrib-inner\",this._container),this._updateAttributions(),this._updateCompact(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"terrain\",this._updateData),this._map.on(\"resize\",this._updateCompact),this._map.on(\"drag\",this._updateCompactMinimize),this._container}onRemove(){c.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"terrain\",this._updateData),this._map.off(\"resize\",this._updateCompact),this._map.off(\"drag\",this._updateCompactMinimize),this._map=void 0,this._compact=void 0,this._attribHTML=void 0}_setElementTitle(l,d){let v=this._map._getUIString(`AttributionControl.${d}`);l.title=v,l.setAttribute(\"aria-label\",v)}_updateAttributions(){if(!this._map.style)return;let l=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?l=l.concat(this.options.customAttribution.map(b=>typeof b!=\"string\"?\"\":b)):typeof this.options.customAttribution==\"string\"&&l.push(this.options.customAttribution)),this._map.style.stylesheet){let b=this._map.style.stylesheet;this.styleOwner=b.owner,this.styleId=b.id}let d=this._map.style.sourceCaches;for(let b in d){let M=d[b];if(M.used||M.usedForTerrain){let O=M.getSource();O.attribution&&l.indexOf(O.attribution)<0&&l.push(O.attribution)}}l=l.filter(b=>String(b).trim()),l.sort((b,M)=>b.length-M.length),l=l.filter((b,M)=>{for(let O=M+1;O=0)return!1;return!0});let v=l.join(\" | \");v!==this._attribHTML&&(this._attribHTML=v,l.length?(this._innerContainer.innerHTML=v,this._container.classList.remove(\"maplibregl-attrib-empty\")):this._container.classList.add(\"maplibregl-attrib-empty\"),this._updateCompact(),this._editLink=null)}}class un{constructor(l={}){this._updateCompact=()=>{let d=this._container.children;if(d.length){let v=d[0];this._map.getCanvasContainer().offsetWidth<=640||this._compact?this._compact!==!1&&v.classList.add(\"maplibregl-compact\"):v.classList.remove(\"maplibregl-compact\")}},this.options=l}getDefaultPosition(){return\"bottom-left\"}onAdd(l){this._map=l,this._compact=this.options&&this.options.compact,this._container=c.create(\"div\",\"maplibregl-ctrl\");let d=c.create(\"a\",\"maplibregl-ctrl-logo\");return d.target=\"_blank\",d.rel=\"noopener nofollow\",d.href=\"https://maplibre.org/\",d.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),d.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(d),this._container.style.display=\"block\",this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container}onRemove(){c.remove(this._container),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._compact=void 0}}class sA{constructor(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1}add(l){let d=++this._id;return this._queue.push({callback:l,id:d,cancelled:!1}),d}remove(l){let d=this._currentlyRunning,v=d?this._queue.concat(d):this._queue;for(let b of v)if(b.id===l)return void(b.cancelled=!0)}run(l=0){if(this._currentlyRunning)throw new Error(\"Attempting to run(), but is already running.\");let d=this._currentlyRunning=this._queue;this._queue=[];for(let v of d)if(!v.cancelled&&(v.callback(l),this._cleared))break;this._cleared=!1,this._currentlyRunning=!1}clear(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]}}let d0={\"AttributionControl.ToggleAttribution\":\"Toggle attribution\",\"AttributionControl.MapFeedback\":\"Map feedback\",\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\",\"TerrainControl.enableTerrain\":\"Enable terrain\",\"TerrainControl.disableTerrain\":\"Disable terrain\"};var Ah=n.Q([{name:\"a_pos3d\",type:\"Int16\",components:3}]);class x_ extends n.E{constructor(l){super(),this.sourceCache=l,this._tiles={},this._renderableTilesKeys=[],this._sourceTileCache={},this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.deltaZoom=1,l.usedForTerrain=!0,l.tileSize=this.tileSize*2**this.deltaZoom}destruct(){this.sourceCache.usedForTerrain=!1,this.sourceCache.tileSize=null}update(l,d){this.sourceCache.update(l,d),this._renderableTilesKeys=[];let v={};for(let b of l.coveringTiles({tileSize:this.tileSize,minzoom:this.minzoom,maxzoom:this.maxzoom,reparseOverscaled:!1,terrain:d}))v[b.key]=!0,this._renderableTilesKeys.push(b.key),this._tiles[b.key]||(b.posMatrix=new Float64Array(16),n.aS(b.posMatrix,0,n.N,0,n.N,0,1),this._tiles[b.key]=new ao(b,this.tileSize));for(let b in this._tiles)v[b]||delete this._tiles[b]}freeRtt(l){for(let d in this._tiles){let v=this._tiles[d];(!l||v.tileID.equals(l)||v.tileID.isChildOf(l)||l.isChildOf(v.tileID))&&(v.rtt=[])}}getRenderableTiles(){return this._renderableTilesKeys.map(l=>this.getTileByID(l))}getTileByID(l){return this._tiles[l]}getTerrainCoords(l){let d={};for(let v of this._renderableTilesKeys){let b=this._tiles[v].tileID;if(b.canonical.equals(l.canonical)){let M=l.clone();M.posMatrix=new Float64Array(16),n.aS(M.posMatrix,0,n.N,0,n.N,0,1),d[v]=M}else if(b.canonical.isChildOf(l.canonical)){let M=l.clone();M.posMatrix=new Float64Array(16);let O=b.canonical.z-l.canonical.z,B=b.canonical.x-(b.canonical.x>>O<>O<>O;n.aS(M.posMatrix,0,W,0,W,0,1),n.$(M.posMatrix,M.posMatrix,[-B*W,-U*W,0]),d[v]=M}else if(l.canonical.isChildOf(b.canonical)){let M=l.clone();M.posMatrix=new Float64Array(16);let O=l.canonical.z-b.canonical.z,B=l.canonical.x-(l.canonical.x>>O<>O<>O;n.aS(M.posMatrix,0,n.N,0,n.N,0,1),n.$(M.posMatrix,M.posMatrix,[B*W,U*W,0]),n.a0(M.posMatrix,M.posMatrix,[1/2**O,1/2**O,0]),d[v]=M}}return d}getSourceTile(l,d){let v=this.sourceCache._source,b=l.overscaledZ-this.deltaZoom;if(b>v.maxzoom&&(b=v.maxzoom),b=v.minzoom&&(!M||!M.dem);)M=this.sourceCache.getTileByID(l.scaledTo(b--).key);return M}tilesAfterTime(l=Date.now()){return Object.values(this._tiles).filter(d=>d.timeAdded>=l)}}class b_{constructor(l,d,v){this.painter=l,this.sourceCache=new x_(d),this.options=v,this.exaggeration=typeof v.exaggeration==\"number\"?v.exaggeration:1,this.qualityFactor=2,this.meshSize=128,this._demMatrixCache={},this.coordsIndex=[],this._coordsTextureSize=1024}getDEMElevation(l,d,v,b=n.N){var M;if(!(d>=0&&d=0&&vl.canonical.z&&(l.canonical.z>=b?M=l.canonical.z-b:n.w(\"cannot calculate elevation if elevation maxzoom > source.maxzoom\"));let O=l.canonical.x-(l.canonical.x>>M<>M<>8<<4|M>>8,d[O+3]=0;let v=new n.R({width:this._coordsTextureSize,height:this._coordsTextureSize},new Uint8Array(d.buffer)),b=new qt(l,v,l.gl.RGBA,{premultiply:!1});return b.bind(l.gl.NEAREST,l.gl.CLAMP_TO_EDGE),this._coordsTexture=b,b}pointCoordinate(l){let d=new Uint8Array(4),v=this.painter.context,b=v.gl;v.bindFramebuffer.set(this.getFramebuffer(\"coords\").framebuffer),b.readPixels(l.x,this.painter.height/devicePixelRatio-l.y-1,1,1,b.RGBA,b.UNSIGNED_BYTE,d),v.bindFramebuffer.set(null);let M=d[0]+(d[2]>>4<<8),O=d[1]+((15&d[2])<<8),B=this.coordsIndex[255-d[3]],U=B&&this.sourceCache.getTileByID(B);if(!U)return null;let W=this._coordsTextureSize,Z=(1<0&&Math.sign(M)<0||!v&&Math.sign(b)<0&&Math.sign(M)>0?(b=360*Math.sign(M)+b,n.G(b)):d}}class Nx{constructor(l,d,v){this._context=l,this._size=d,this._tileSize=v,this._objects=[],this._recentlyUsed=[],this._stamp=0}destruct(){for(let l of this._objects)l.texture.destroy(),l.fbo.destroy()}_createObject(l){let d=this._context.createFramebuffer(this._tileSize,this._tileSize,!0,!0),v=new qt(this._context,{width:this._tileSize,height:this._tileSize,data:null},this._context.gl.RGBA);return v.bind(this._context.gl.LINEAR,this._context.gl.CLAMP_TO_EDGE),d.depthAttachment.set(this._context.createRenderbuffer(this._context.gl.DEPTH_STENCIL,this._tileSize,this._tileSize)),d.colorAttachment.set(v.texture),{id:l,fbo:d,texture:v,stamp:-1,inUse:!1}}getObjectForId(l){return this._objects[l]}useObject(l){l.inUse=!0,this._recentlyUsed=this._recentlyUsed.filter(d=>l.id!==d),this._recentlyUsed.push(l.id)}stampObject(l){l.stamp=++this._stamp}getOrCreateFreeObject(){for(let d of this._recentlyUsed)if(!this._objects[d].inUse)return this._objects[d];if(this._objects.length>=this._size)throw new Error(\"No free RenderPool available, call freeAllObjects() required!\");let l=this._createObject(this._objects.length);return this._objects.push(l),l}freeObject(l){l.inUse=!1}freeAllObjects(){for(let l of this._objects)this.freeObject(l)}isFull(){return!(this._objects.length!l.inUse)===!1}}let Mo={background:!0,fill:!0,line:!0,raster:!0,hillshade:!0};class oA{constructor(l,d){this.painter=l,this.terrain=d,this.pool=new Nx(l.context,30,d.sourceCache.tileSize*d.qualityFactor)}destruct(){this.pool.destruct()}getTexture(l){return this.pool.getObjectForId(l.rtt[this._stacks.length-1].id).texture}prepareForRender(l,d){this._stacks=[],this._prevType=null,this._rttTiles=[],this._renderableTiles=this.terrain.sourceCache.getRenderableTiles(),this._renderableLayerIds=l._order.filter(v=>!l._layers[v].isHidden(d)),this._coordsDescendingInv={};for(let v in l.sourceCaches){this._coordsDescendingInv[v]={};let b=l.sourceCaches[v].getVisibleCoordinates();for(let M of b){let O=this.terrain.sourceCache.getTerrainCoords(M);for(let B in O)this._coordsDescendingInv[v][B]||(this._coordsDescendingInv[v][B]=[]),this._coordsDescendingInv[v][B].push(O[B])}}this._coordsDescendingInvStr={};for(let v of l._order){let b=l._layers[v],M=b.source;if(Mo[b.type]&&!this._coordsDescendingInvStr[M]){this._coordsDescendingInvStr[M]={};for(let O in this._coordsDescendingInv[M])this._coordsDescendingInvStr[M][O]=this._coordsDescendingInv[M][O].map(B=>B.key).sort().join()}}for(let v of this._renderableTiles)for(let b in this._coordsDescendingInvStr){let M=this._coordsDescendingInvStr[b][v.tileID.key];M&&M!==v.rttCoords[b]&&(v.rtt=[])}}renderLayer(l){if(l.isHidden(this.painter.transform.zoom))return!1;let d=l.type,v=this.painter,b=this._renderableLayerIds[this._renderableLayerIds.length-1]===l.id;if(Mo[d]&&(this._prevType&&Mo[this._prevType]||this._stacks.push([]),this._prevType=d,this._stacks[this._stacks.length-1].push(l.id),!b))return!0;if(Mo[this._prevType]||Mo[d]&&b){this._prevType=d;let M=this._stacks.length-1,O=this._stacks[M]||[];for(let B of this._renderableTiles){if(this.pool.isFull()&&(Cn(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects()),this._rttTiles.push(B),B.rtt[M]){let W=this.pool.getObjectForId(B.rtt[M].id);if(W.stamp===B.rtt[M].stamp){this.pool.useObject(W);continue}}let U=this.pool.getOrCreateFreeObject();this.pool.useObject(U),this.pool.stampObject(U),B.rtt[M]={id:U.id,stamp:U.stamp},v.context.bindFramebuffer.set(U.fbo.framebuffer),v.context.clear({color:n.aT.transparent,stencil:0}),v.currentStencilSource=void 0;for(let W=0;W{T.touchstart=T.dragStart,T.touchmoveWindow=T.dragMove,T.touchend=T.dragEnd},aA={showCompass:!0,showZoom:!0,visualizePitch:!1};class Bd{constructor(l,d,v=!1){this.mousedown=O=>{this.startMouse(n.e({},O,{ctrlKey:!0,preventDefault:()=>O.preventDefault()}),c.mousePos(this.element,O)),c.addEventListener(window,\"mousemove\",this.mousemove),c.addEventListener(window,\"mouseup\",this.mouseup)},this.mousemove=O=>{this.moveMouse(O,c.mousePos(this.element,O))},this.mouseup=O=>{this.mouseRotate.dragEnd(O),this.mousePitch&&this.mousePitch.dragEnd(O),this.offTemp()},this.touchstart=O=>{O.targetTouches.length!==1?this.reset():(this._startPos=this._lastPos=c.touchPos(this.element,O.targetTouches)[0],this.startTouch(O,this._startPos),c.addEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.addEventListener(window,\"touchend\",this.touchend))},this.touchmove=O=>{O.targetTouches.length!==1?this.reset():(this._lastPos=c.touchPos(this.element,O.targetTouches)[0],this.moveTouch(O,this._lastPos))},this.touchend=O=>{O.targetTouches.length===0&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),this.touchRotate.reset(),this.touchPitch&&this.touchPitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()},this._clickTolerance=10;let b=l.dragRotate._mouseRotate.getClickTolerance(),M=l.dragRotate._mousePitch.getClickTolerance();this.element=d,this.mouseRotate=Na({clickTolerance:b,enable:!0}),this.touchRotate=(({enable:O,clickTolerance:B,bearingDegreesPerPixelMoved:U=.8})=>{let W=new s0;return new za({clickTolerance:B,move:(Z,$)=>({bearingDelta:($.x-Z.x)*U}),moveStateManager:W,enable:O,assignEvents:wf})})({clickTolerance:b,enable:!0}),this.map=l,v&&(this.mousePitch=co({clickTolerance:M,enable:!0}),this.touchPitch=(({enable:O,clickTolerance:B,pitchDegreesPerPixelMoved:U=-.5})=>{let W=new s0;return new za({clickTolerance:B,move:(Z,$)=>({pitchDelta:($.y-Z.y)*U}),moveStateManager:W,enable:O,assignEvents:wf})})({clickTolerance:M,enable:!0})),c.addEventListener(d,\"mousedown\",this.mousedown),c.addEventListener(d,\"touchstart\",this.touchstart,{passive:!1}),c.addEventListener(d,\"touchcancel\",this.reset)}startMouse(l,d){this.mouseRotate.dragStart(l,d),this.mousePitch&&this.mousePitch.dragStart(l,d),c.disableDrag()}startTouch(l,d){this.touchRotate.dragStart(l,d),this.touchPitch&&this.touchPitch.dragStart(l,d),c.disableDrag()}moveMouse(l,d){let v=this.map,{bearingDelta:b}=this.mouseRotate.dragMove(l,d)||{};if(b&&v.setBearing(v.getBearing()+b),this.mousePitch){let{pitchDelta:M}=this.mousePitch.dragMove(l,d)||{};M&&v.setPitch(v.getPitch()+M)}}moveTouch(l,d){let v=this.map,{bearingDelta:b}=this.touchRotate.dragMove(l,d)||{};if(b&&v.setBearing(v.getBearing()+b),this.touchPitch){let{pitchDelta:M}=this.touchPitch.dragMove(l,d)||{};M&&v.setPitch(v.getPitch()+M)}}off(){let l=this.element;c.removeEventListener(l,\"mousedown\",this.mousedown),c.removeEventListener(l,\"touchstart\",this.touchstart,{passive:!1}),c.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.removeEventListener(window,\"touchend\",this.touchend),c.removeEventListener(l,\"touchcancel\",this.reset),this.offTemp()}offTemp(){c.enableDrag(),c.removeEventListener(window,\"mousemove\",this.mousemove),c.removeEventListener(window,\"mouseup\",this.mouseup),c.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),c.removeEventListener(window,\"touchend\",this.touchend)}}let Hn;function uo(T,l,d){if(T=new n.L(T.lng,T.lat),l){let v=new n.L(T.lng-360,T.lat),b=new n.L(T.lng+360,T.lat),M=d.locationPoint(T).distSqr(l);d.locationPoint(v).distSqr(l)180;){let v=d.locationPoint(T);if(v.x>=0&&v.y>=0&&v.x<=d.width&&v.y<=d.height)break;T.lng>d.center.lng?T.lng-=360:T.lng+=360}return T}let ji={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function w_(T,l,d){let v=T.classList;for(let b in ji)v.remove(`maplibregl-${d}-anchor-${b}`);v.add(`maplibregl-${d}-anchor-${l}`)}class mh extends n.E{constructor(l){if(super(),this._onKeyPress=d=>{let v=d.code,b=d.charCode||d.keyCode;v!==\"Space\"&&v!==\"Enter\"&&b!==32&&b!==13||this.togglePopup()},this._onMapClick=d=>{let v=d.originalEvent.target,b=this._element;this._popup&&(v===b||b.contains(v))&&this.togglePopup()},this._update=d=>{if(!this._map)return;let v=this._map.loaded()&&!this._map.isMoving();(d?.type===\"terrain\"||d?.type===\"render\"&&!v)&&this._map.once(\"render\",this._update),this._map.transform.renderWorldCopies&&(this._lngLat=uo(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);let b=\"\";this._rotationAlignment===\"viewport\"||this._rotationAlignment===\"auto\"?b=`rotateZ(${this._rotation}deg)`:this._rotationAlignment===\"map\"&&(b=`rotateZ(${this._rotation-this._map.getBearing()}deg)`);let M=\"\";this._pitchAlignment===\"viewport\"||this._pitchAlignment===\"auto\"?M=\"rotateX(0deg)\":this._pitchAlignment===\"map\"&&(M=`rotateX(${this._map.getPitch()}deg)`),d&&d.type!==\"moveend\"||(this._pos=this._pos.round()),c.setTransform(this._element,`${ji[this._anchor]} translate(${this._pos.x}px, ${this._pos.y}px) ${M} ${b}`),this._map.terrain&&!this._opacityTimeout&&(this._opacityTimeout=setTimeout(()=>{let O=this._map.unproject(this._pos),B=40075016686e-3*Math.abs(Math.cos(this._lngLat.lat*Math.PI/180))/Math.pow(2,this._map.transform.tileZoom+8);this._element.style.opacity=O.distanceTo(this._lngLat)>20*B?\"0.2\":\"1.0\",this._opacityTimeout=null},100))},this._onMove=d=>{if(!this._isDragging){let v=this._clickTolerance||this._map._clickTolerance;this._isDragging=d.point.dist(this._pointerdownPos)>=v}this._isDragging&&(this._pos=d.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",this._state===\"pending\"&&(this._state=\"active\",this.fire(new n.k(\"dragstart\"))),this.fire(new n.k(\"drag\")))},this._onUp=()=>{this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),this._state===\"active\"&&this.fire(new n.k(\"dragend\")),this._state=\"inactive\"},this._addDragHandler=d=>{this._element.contains(d.originalEvent.target)&&(d.preventDefault(),this._positionDelta=d.point.sub(this._pos).add(this._offset),this._pointerdownPos=d.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},this._anchor=l&&l.anchor||\"center\",this._color=l&&l.color||\"#3FB1CE\",this._scale=l&&l.scale||1,this._draggable=l&&l.draggable||!1,this._clickTolerance=l&&l.clickTolerance||0,this._isDragging=!1,this._state=\"inactive\",this._rotation=l&&l.rotation||0,this._rotationAlignment=l&&l.rotationAlignment||\"auto\",this._pitchAlignment=l&&l.pitchAlignment&&l.pitchAlignment!==\"auto\"?l.pitchAlignment:this._rotationAlignment,l&&l.element)this._element=l.element,this._offset=n.P.convert(l&&l.offset||[0,0]);else{this._defaultMarker=!0,this._element=c.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");let d=c.createNS(\"http://www.w3.org/2000/svg\",\"svg\"),v=41,b=27;d.setAttributeNS(null,\"display\",\"block\"),d.setAttributeNS(null,\"height\",`${v}px`),d.setAttributeNS(null,\"width\",`${b}px`),d.setAttributeNS(null,\"viewBox\",`0 0 ${b} ${v}`);let M=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");M.setAttributeNS(null,\"stroke\",\"none\"),M.setAttributeNS(null,\"stroke-width\",\"1\"),M.setAttributeNS(null,\"fill\",\"none\"),M.setAttributeNS(null,\"fill-rule\",\"evenodd\");let O=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");O.setAttributeNS(null,\"fill-rule\",\"nonzero\");let B=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");B.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),B.setAttributeNS(null,\"fill\",\"#000000\");let U=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];for(let Ft of U){let Ht=c.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");Ht.setAttributeNS(null,\"opacity\",\"0.04\"),Ht.setAttributeNS(null,\"cx\",\"10.5\"),Ht.setAttributeNS(null,\"cy\",\"5.80029008\"),Ht.setAttributeNS(null,\"rx\",Ft.rx),Ht.setAttributeNS(null,\"ry\",Ft.ry),B.appendChild(Ht)}let W=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");W.setAttributeNS(null,\"fill\",this._color);let Z=c.createNS(\"http://www.w3.org/2000/svg\",\"path\");Z.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),W.appendChild(Z);let $=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");$.setAttributeNS(null,\"opacity\",\"0.25\"),$.setAttributeNS(null,\"fill\",\"#000000\");let st=c.createNS(\"http://www.w3.org/2000/svg\",\"path\");st.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),$.appendChild(st);let At=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");At.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),At.setAttributeNS(null,\"fill\",\"#FFFFFF\");let pt=c.createNS(\"http://www.w3.org/2000/svg\",\"g\");pt.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");let yt=c.createNS(\"http://www.w3.org/2000/svg\",\"circle\");yt.setAttributeNS(null,\"fill\",\"#000000\"),yt.setAttributeNS(null,\"opacity\",\"0.25\"),yt.setAttributeNS(null,\"cx\",\"5.5\"),yt.setAttributeNS(null,\"cy\",\"5.5\"),yt.setAttributeNS(null,\"r\",\"5.4999962\");let dt=c.createNS(\"http://www.w3.org/2000/svg\",\"circle\");dt.setAttributeNS(null,\"fill\",\"#FFFFFF\"),dt.setAttributeNS(null,\"cx\",\"5.5\"),dt.setAttributeNS(null,\"cy\",\"5.5\"),dt.setAttributeNS(null,\"r\",\"5.4999962\"),pt.appendChild(yt),pt.appendChild(dt),O.appendChild(B),O.appendChild(W),O.appendChild($),O.appendChild(At),O.appendChild(pt),d.appendChild(O),d.setAttributeNS(null,\"height\",v*this._scale+\"px\"),d.setAttributeNS(null,\"width\",b*this._scale+\"px\"),this._element.appendChild(d),this._offset=n.P.convert(l&&l.offset||[0,-14])}if(this._element.classList.add(\"maplibregl-marker\"),this._element.addEventListener(\"dragstart\",d=>{d.preventDefault()}),this._element.addEventListener(\"mousedown\",d=>{d.preventDefault()}),w_(this._element,this._anchor,\"marker\"),l&&l.className)for(let d of l.className.split(\" \"))this._element.classList.add(d);this._popup=null}addTo(l){return this.remove(),this._map=l,l.getCanvasContainer().appendChild(this._element),l.on(\"move\",this._update),l.on(\"moveend\",this._update),l.on(\"terrain\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this}remove(){return this._opacityTimeout&&(clearTimeout(this._opacityTimeout),delete this._opacityTimeout),this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),c.remove(this._element),this._popup&&this._popup.remove(),this}getLngLat(){return this._lngLat}setLngLat(l){return this._lngLat=n.L.convert(l),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this}getElement(){return this._element}setPopup(l){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),l){if(!(\"offset\"in l.options)){let b=Math.abs(13.5)/Math.SQRT2;l.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[b,-1*(38.1-13.5+b)],\"bottom-right\":[-b,-1*(38.1-13.5+b)],left:[13.5,-1*(38.1-13.5)],right:[-13.5,-1*(38.1-13.5)]}:this._offset}this._popup=l,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this}getPopup(){return this._popup}togglePopup(){let l=this._popup;return l?(l.isOpen()?l.remove():l.addTo(this._map),this):this}getOffset(){return this._offset}setOffset(l){return this._offset=n.P.convert(l),this._update(),this}addClassName(l){this._element.classList.add(l)}removeClassName(l){this._element.classList.remove(l)}toggleClassName(l){return this._element.classList.toggle(l)}setDraggable(l){return this._draggable=!!l,this._map&&(l?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this}isDraggable(){return this._draggable}setRotation(l){return this._rotation=l||0,this._update(),this}getRotation(){return this._rotation}setRotationAlignment(l){return this._rotationAlignment=l||\"auto\",this._update(),this}getRotationAlignment(){return this._rotationAlignment}setPitchAlignment(l){return this._pitchAlignment=l&&l!==\"auto\"?l:this._rotationAlignment,this._update(),this}getPitchAlignment(){return this._pitchAlignment}}let kn={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0},wn=0,Sf=!1,Es={maxWidth:100,unit:\"metric\"};function gh(T,l,d){let v=d&&d.maxWidth||100,b=T._container.clientHeight/2,M=T.unproject([0,b]),O=T.unproject([v,b]),B=M.distanceTo(O);if(d&&d.unit===\"imperial\"){let U=3.2808*B;U>5280?Wo(l,v,U/5280,T._getUIString(\"ScaleControl.Miles\")):Wo(l,v,U,T._getUIString(\"ScaleControl.Feet\"))}else d&&d.unit===\"nautical\"?Wo(l,v,B/1852,T._getUIString(\"ScaleControl.NauticalMiles\")):B>=1e3?Wo(l,v,B/1e3,T._getUIString(\"ScaleControl.Kilometers\")):Wo(l,v,B,T._getUIString(\"ScaleControl.Meters\"))}function Wo(T,l,d,v){let b=function(M){let O=Math.pow(10,`${Math.floor(M)}`.length-1),B=M/O;return B=B>=10?10:B>=5?5:B>=3?3:B>=2?2:B>=1?1:function(U){let W=Math.pow(10,Math.ceil(-Math.log(U)/Math.LN10));return Math.round(U*W)/W}(B),O*B}(d);T.style.width=l*(b/d)+\"px\",T.innerHTML=`${b} ${v}`}let p0={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\"},Fd=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \");function Tf(T){if(T){if(typeof T==\"number\"){let l=Math.round(Math.abs(T)/Math.SQRT2);return{center:new n.P(0,0),top:new n.P(0,T),\"top-left\":new n.P(l,l),\"top-right\":new n.P(-l,l),bottom:new n.P(0,-T),\"bottom-left\":new n.P(l,-l),\"bottom-right\":new n.P(-l,-l),left:new n.P(T,0),right:new n.P(-T,0)}}if(T instanceof n.P||Array.isArray(T)){let l=n.P.convert(T);return{center:l,top:l,\"top-left\":l,\"top-right\":l,bottom:l,\"bottom-left\":l,\"bottom-right\":l,left:l,right:l}}return{center:n.P.convert(T.center||[0,0]),top:n.P.convert(T.top||[0,0]),\"top-left\":n.P.convert(T[\"top-left\"]||[0,0]),\"top-right\":n.P.convert(T[\"top-right\"]||[0,0]),bottom:n.P.convert(T.bottom||[0,0]),\"bottom-left\":n.P.convert(T[\"bottom-left\"]||[0,0]),\"bottom-right\":n.P.convert(T[\"bottom-right\"]||[0,0]),left:n.P.convert(T.left||[0,0]),right:n.P.convert(T.right||[0,0])}}return Tf(new n.P(0,0))}let Ho={extend:(T,...l)=>n.e(T,...l),run(T){T()},logToElement(T,l=!1,d=\"log\"){let v=window.document.getElementById(d);v&&(l&&(v.innerHTML=\"\"),v.innerHTML+=`
${T}`)}},lA=o;class bi{static get version(){return lA}static get workerCount(){return lo.workerCount}static set workerCount(l){lo.workerCount=l}static get maxParallelImageRequests(){return n.c.MAX_PARALLEL_IMAGE_REQUESTS}static set maxParallelImageRequests(l){n.c.MAX_PARALLEL_IMAGE_REQUESTS=l}static get workerUrl(){return n.c.WORKER_URL}static set workerUrl(l){n.c.WORKER_URL=l}static addProtocol(l,d){n.c.REGISTERED_PROTOCOLS[l]=d}static removeProtocol(l){delete n.c.REGISTERED_PROTOCOLS[l]}}return bi.Map=class extends v_{constructor(T){if(n.bg.mark(n.bh.create),(T=n.e({},dr,T)).minZoom!=null&&T.maxZoom!=null&&T.minZoom>T.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(T.minPitch!=null&&T.maxPitch!=null&&T.minPitch>T.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(T.minPitch!=null&&T.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(T.maxPitch!=null&&T.maxPitch>85)throw new Error(\"maxPitch must be less than or equal to 85\");if(super(new Kp(T.minZoom,T.maxZoom,T.minPitch,T.maxPitch,T.renderWorldCopies),{bearingSnap:T.bearingSnap}),this._cooperativeGesturesOnWheel=l=>{this._onCooperativeGesture(l,l[this._metaKey],1)},this._contextLost=l=>{l.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new n.k(\"webglcontextlost\",{originalEvent:l}))},this._contextRestored=l=>{this._setupPainter(),this.resize(),this._update(),this.fire(new n.k(\"webglcontextrestored\",{originalEvent:l}))},this._onMapScroll=l=>{if(l.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},this._onWindowOnline=()=>{this._update()},this._interactive=T.interactive,this._cooperativeGestures=T.cooperativeGestures,this._metaKey=navigator.platform.indexOf(\"Mac\")===0?\"metaKey\":\"ctrlKey\",this._maxTileCacheSize=T.maxTileCacheSize,this._maxTileCacheZoomLevels=T.maxTileCacheZoomLevels,this._failIfMajorPerformanceCaveat=T.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=T.preserveDrawingBuffer,this._antialias=T.antialias,this._trackResize=T.trackResize,this._bearingSnap=T.bearingSnap,this._refreshExpiredTiles=T.refreshExpiredTiles,this._fadeDuration=T.fadeDuration,this._crossSourceCollisions=T.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=T.collectResourceTiming,this._renderTaskQueue=new sA,this._controls=[],this._mapId=n.a2(),this._locale=n.e({},d0,T.locale),this._clickTolerance=T.clickTolerance,this._overridePixelRatio=T.pixelRatio,this._maxCanvasSize=T.maxCanvasSize,this.transformCameraUpdate=T.transformCameraUpdate,this._imageQueueHandle=j.addThrottleControl(()=>this.isMoving()),this._requestManager=new et(T.transformRequest),typeof T.container==\"string\"){if(this._container=document.getElementById(T.container),!this._container)throw new Error(`Container '${T.container}' not found.`)}else{if(!(T.container instanceof HTMLElement))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=T.container}if(T.maxBounds&&this.setMaxBounds(T.maxBounds),this._setupContainer(),this._setupPainter(),this.on(\"move\",()=>this._update(!1)),this.on(\"moveend\",()=>this._update(!1)),this.on(\"zoom\",()=>this._update(!0)),this.on(\"terrain\",()=>{this.painter.terrainFacilitator.dirty=!0,this._update(!0)}),this.once(\"idle\",()=>{this._idleTriggered=!0}),typeof window<\"u\"){addEventListener(\"online\",this._onWindowOnline,!1);let l=!1,d=lh(v=>{this._trackResize&&!this._removed&&this.resize(v)._update()},50);this._resizeObserver=new ResizeObserver(v=>{l?d(v):l=!0}),this._resizeObserver.observe(this._container)}this.handlers=new f0(this,T),this._cooperativeGestures&&this._setupCooperativeGestures(),this._hash=T.hash&&new Ld(typeof T.hash==\"string\"&&T.hash||void 0).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:T.center,zoom:T.zoom,bearing:T.bearing,pitch:T.pitch}),T.bounds&&(this.resize(),this.fitBounds(T.bounds,n.e({},T.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=T.localIdeographFontFamily,this._validateStyle=T.validateStyle,T.style&&this.setStyle(T.style,{localIdeographFontFamily:T.localIdeographFontFamily}),T.attributionControl&&this.addControl(new ua({customAttribution:T.customAttribution})),T.maplibreLogo&&this.addControl(new un,T.logoPosition),this.on(\"style.load\",()=>{this.transform.unmodified&&this.jumpTo(this.style.stylesheet)}),this.on(\"data\",l=>{this._update(l.dataType===\"style\"),this.fire(new n.k(`${l.dataType}data`,l))}),this.on(\"dataloading\",l=>{this.fire(new n.k(`${l.dataType}dataloading`,l))}),this.on(\"dataabort\",l=>{this.fire(new n.k(\"sourcedataabort\",l))})}_getMapId(){return this._mapId}addControl(T,l){if(l===void 0&&(l=T.getDefaultPosition?T.getDefaultPosition():\"top-right\"),!T||!T.onAdd)return this.fire(new n.j(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));let d=T.onAdd(this);this._controls.push(T);let v=this._controlPositions[l];return l.indexOf(\"bottom\")!==-1?v.insertBefore(d,v.firstChild):v.appendChild(d),this}removeControl(T){if(!T||!T.onRemove)return this.fire(new n.j(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));let l=this._controls.indexOf(T);return l>-1&&this._controls.splice(l,1),T.onRemove(this),this}hasControl(T){return this._controls.indexOf(T)>-1}calculateCameraOptionsFromTo(T,l,d,v){return v==null&&this.terrain&&(v=this.terrain.getElevationForLngLatZoom(d,this.transform.tileZoom)),super.calculateCameraOptionsFromTo(T,l,d,v)}resize(T){var l;let d=this._containerDimensions(),v=d[0],b=d[1],M=this._getClampedPixelRatio(v,b);if(this._resizeCanvas(v,b,M),this.painter.resize(v,b,M),this.painter.overLimit()){let B=this.painter.context.gl;this._maxCanvasSize=[B.drawingBufferWidth,B.drawingBufferHeight];let U=this._getClampedPixelRatio(v,b);this._resizeCanvas(v,b,U),this.painter.resize(v,b,U)}this.transform.resize(v,b),(l=this._requestedCameraState)===null||l===void 0||l.resize(v,b);let O=!this._moving;return O&&(this.stop(),this.fire(new n.k(\"movestart\",T)).fire(new n.k(\"move\",T))),this.fire(new n.k(\"resize\",T)),O&&this.fire(new n.k(\"moveend\",T)),this}_getClampedPixelRatio(T,l){let{0:d,1:v}=this._maxCanvasSize,b=this.getPixelRatio(),M=T*b,O=l*b;return Math.min(M>d?d/M:1,O>v?v/O:1)*b}getPixelRatio(){var T;return(T=this._overridePixelRatio)!==null&&T!==void 0?T:devicePixelRatio}setPixelRatio(T){this._overridePixelRatio=T,this.resize()}getBounds(){return this.transform.getBounds()}getMaxBounds(){return this.transform.getMaxBounds()}setMaxBounds(T){return this.transform.setMaxBounds(Si.convert(T)),this._update()}setMinZoom(T){if((T=T??-2)>=-2&&T<=this.transform.maxZoom)return this.transform.minZoom=T,this._update(),this.getZoom()=this.transform.minZoom)return this.transform.maxZoom=T,this._update(),this.getZoom()>T&&this.setZoom(T),this;throw new Error(\"maxZoom must be greater than the current minZoom\")}getMaxZoom(){return this.transform.maxZoom}setMinPitch(T){if((T=T??0)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(T>=0&&T<=this.transform.maxPitch)return this.transform.minPitch=T,this._update(),this.getPitch()85)throw new Error(\"maxPitch must be less than or equal to 85\");if(T>=this.transform.minPitch)return this.transform.maxPitch=T,this._update(),this.getPitch()>T&&this.setPitch(T),this;throw new Error(\"maxPitch must be greater than the current minPitch\")}getMaxPitch(){return this.transform.maxPitch}getRenderWorldCopies(){return this.transform.renderWorldCopies}setRenderWorldCopies(T){return this.transform.renderWorldCopies=T,this._update()}getCooperativeGestures(){return this._cooperativeGestures}setCooperativeGestures(T){return this._cooperativeGestures=T,this._cooperativeGestures?this._setupCooperativeGestures():this._destroyCooperativeGestures(),this}project(T){return this.transform.locationPoint(n.L.convert(T),this.style&&this.terrain)}unproject(T){return this.transform.pointLocation(n.P.convert(T),this.terrain)}isMoving(){var T;return this._moving||((T=this.handlers)===null||T===void 0?void 0:T.isMoving())}isZooming(){var T;return this._zooming||((T=this.handlers)===null||T===void 0?void 0:T.isZooming())}isRotating(){var T;return this._rotating||((T=this.handlers)===null||T===void 0?void 0:T.isRotating())}_createDelegatedListener(T,l,d){if(T===\"mouseenter\"||T===\"mouseover\"){let v=!1;return{layer:l,listener:d,delegates:{mousemove:M=>{let O=this.getLayer(l)?this.queryRenderedFeatures(M.point,{layers:[l]}):[];O.length?v||(v=!0,d.call(this,new la(T,this,M.originalEvent,{features:O}))):v=!1},mouseout:()=>{v=!1}}}}if(T===\"mouseleave\"||T===\"mouseout\"){let v=!1;return{layer:l,listener:d,delegates:{mousemove:O=>{(this.getLayer(l)?this.queryRenderedFeatures(O.point,{layers:[l]}):[]).length?v=!0:v&&(v=!1,d.call(this,new la(T,this,O.originalEvent)))},mouseout:O=>{v&&(v=!1,d.call(this,new la(T,this,O.originalEvent)))}}}}{let v=b=>{let M=this.getLayer(l)?this.queryRenderedFeatures(b.point,{layers:[l]}):[];M.length&&(b.features=M,d.call(this,b),delete b.features)};return{layer:l,listener:d,delegates:{[T]:v}}}}on(T,l,d){if(d===void 0)return super.on(T,l);let v=this._createDelegatedListener(T,l,d);this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[T]=this._delegatedListeners[T]||[],this._delegatedListeners[T].push(v);for(let b in v.delegates)this.on(b,v.delegates[b]);return this}once(T,l,d){if(d===void 0)return super.once(T,l);let v=this._createDelegatedListener(T,l,d);for(let b in v.delegates)this.once(b,v.delegates[b]);return this}off(T,l,d){return d===void 0?super.off(T,l):(this._delegatedListeners&&this._delegatedListeners[T]&&(v=>{let b=this._delegatedListeners[T];for(let M=0;Mthis._updateStyle(T,l));let d=this.style&&l.transformStyle?this.style.serialize():void 0;return this.style&&(this.style.setEventedParent(null),this.style._remove(!T)),T?(this.style=new Gn(this,l||{}),this.style.setEventedParent(this,{style:this.style}),typeof T==\"string\"?this.style.loadURL(T,l,d):this.style.loadJSON(T,l,d),this):(delete this.style,this)}_lazyInitEmptyStyle(){this.style||(this.style=new Gn(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())}_diffStyle(T,l){if(typeof T==\"string\"){let d=this._requestManager.transformRequest(T,Q.Style);n.f(d,(v,b)=>{v?this.fire(new n.j(v)):b&&this._updateDiff(b,l)})}else typeof T==\"object\"&&this._updateDiff(T,l)}_updateDiff(T,l){try{this.style.setState(T,l)&&this._update(!0)}catch(d){n.w(`Unable to perform style diff: ${d.message||d.error||d}. Rebuilding the style from scratch.`),this._updateStyle(T,l)}}getStyle(){if(this.style)return this.style.serialize()}isStyleLoaded(){return this.style?this.style.loaded():n.w(\"There is no style added to the map.\")}addSource(T,l){return this._lazyInitEmptyStyle(),this.style.addSource(T,l),this._update(!0)}isSourceLoaded(T){let l=this.style&&this.style.sourceCaches[T];if(l!==void 0)return l.loaded();this.fire(new n.j(new Error(`There is no source with ID '${T}'`)))}setTerrain(T){if(this.style._checkLoaded(),this._terrainDataCallback&&this.style.off(\"data\",this._terrainDataCallback),T){let l=this.style.sourceCaches[T.source];if(!l)throw new Error(`cannot load terrain, because there exists no source with ID: ${T.source}`);for(let d in this.style._layers){let v=this.style._layers[d];v.type===\"hillshade\"&&v.source===T.source&&n.w(\"You are using the same source for a hillshade layer and for 3D terrain. Please consider using two separate sources to improve rendering quality.\")}this.terrain=new b_(this.painter,l,T),this.painter.renderToTexture=new oA(this.painter,this.terrain),this.transform._minEleveationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._terrainDataCallback=d=>{d.dataType===\"style\"?this.terrain.sourceCache.freeRtt():d.dataType===\"source\"&&d.tile&&(d.sourceId!==T.source||this._elevationFreeze||(this.transform._minEleveationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this.terrain.sourceCache.freeRtt(d.tile.tileID))},this.style.on(\"data\",this._terrainDataCallback)}else this.terrain&&this.terrain.sourceCache.destruct(),this.terrain=null,this.painter.renderToTexture&&this.painter.renderToTexture.destruct(),this.painter.renderToTexture=null,this.transform._minEleveationForCurrentTile=0,this.transform.elevation=0;return this.fire(new n.k(\"terrain\",{terrain:T})),this}getTerrain(){var T,l;return(l=(T=this.terrain)===null||T===void 0?void 0:T.options)!==null&&l!==void 0?l:null}areTilesLoaded(){let T=this.style&&this.style.sourceCaches;for(let l in T){let d=T[l]._tiles;for(let v in d){let b=d[v];if(b.state!==\"loaded\"&&b.state!==\"errored\")return!1}}return!0}addSourceType(T,l,d){return this._lazyInitEmptyStyle(),this.style.addSourceType(T,l,d)}removeSource(T){return this.style.removeSource(T),this._update(!0)}getSource(T){return this.style.getSource(T)}addImage(T,l,d={}){let{pixelRatio:v=1,sdf:b=!1,stretchX:M,stretchY:O,content:B}=d;if(this._lazyInitEmptyStyle(),!(l instanceof HTMLImageElement||n.a(l))){if(l.width===void 0||l.height===void 0)return this.fire(new n.j(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));{let{width:U,height:W,data:Z}=l,$=l;return this.style.addImage(T,{data:new n.R({width:U,height:W},new Uint8Array(Z)),pixelRatio:v,stretchX:M,stretchY:O,content:B,sdf:b,version:0,userImage:$}),$.onAdd&&$.onAdd(this,T),this}}{let{width:U,height:W,data:Z}=n.h.getImageData(l);this.style.addImage(T,{data:new n.R({width:U,height:W},Z),pixelRatio:v,stretchX:M,stretchY:O,content:B,sdf:b,version:0})}}updateImage(T,l){let d=this.style.getImage(T);if(!d)return this.fire(new n.j(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));let v=l instanceof HTMLImageElement||n.a(l)?n.h.getImageData(l):l,{width:b,height:M,data:O}=v;if(b===void 0||M===void 0)return this.fire(new n.j(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(b!==d.data.width||M!==d.data.height)return this.fire(new n.j(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));let B=!(l instanceof HTMLImageElement||n.a(l));return d.data.replace(O,B),this.style.updateImage(T,d),this}getImage(T){return this.style.getImage(T)}hasImage(T){return T?!!this.style.getImage(T):(this.fire(new n.j(new Error(\"Missing required image id\"))),!1)}removeImage(T){this.style.removeImage(T)}loadImage(T,l){j.getImage(this._requestManager.transformRequest(T,Q.Image),l)}listImages(){return this.style.listImages()}addLayer(T,l){return this._lazyInitEmptyStyle(),this.style.addLayer(T,l),this._update(!0)}moveLayer(T,l){return this.style.moveLayer(T,l),this._update(!0)}removeLayer(T){return this.style.removeLayer(T),this._update(!0)}getLayer(T){return this.style.getLayer(T)}getLayersOrder(){return this.style.getLayersOrder()}setLayerZoomRange(T,l,d){return this.style.setLayerZoomRange(T,l,d),this._update(!0)}setFilter(T,l,d={}){return this.style.setFilter(T,l,d),this._update(!0)}getFilter(T){return this.style.getFilter(T)}setPaintProperty(T,l,d,v={}){return this.style.setPaintProperty(T,l,d,v),this._update(!0)}getPaintProperty(T,l){return this.style.getPaintProperty(T,l)}setLayoutProperty(T,l,d,v={}){return this.style.setLayoutProperty(T,l,d,v),this._update(!0)}getLayoutProperty(T,l){return this.style.getLayoutProperty(T,l)}setGlyphs(T,l={}){return this._lazyInitEmptyStyle(),this.style.setGlyphs(T,l),this._update(!0)}getGlyphs(){return this.style.getGlyphsUrl()}addSprite(T,l,d={}){return this._lazyInitEmptyStyle(),this.style.addSprite(T,l,d,v=>{v||this._update(!0)}),this}removeSprite(T){return this._lazyInitEmptyStyle(),this.style.removeSprite(T),this._update(!0)}getSprite(){return this.style.getSprite()}setSprite(T,l={}){return this._lazyInitEmptyStyle(),this.style.setSprite(T,l,d=>{d||this._update(!0)}),this}setLight(T,l={}){return this._lazyInitEmptyStyle(),this.style.setLight(T,l),this._update(!0)}getLight(){return this.style.getLight()}setFeatureState(T,l){return this.style.setFeatureState(T,l),this._update()}removeFeatureState(T,l){return this.style.removeFeatureState(T,l),this._update()}getFeatureState(T){return this.style.getFeatureState(T)}getContainer(){return this._container}getCanvasContainer(){return this._canvasContainer}getCanvas(){return this._canvas}_containerDimensions(){let T=0,l=0;return this._container&&(T=this._container.clientWidth||400,l=this._container.clientHeight||300),[T,l]}_setupContainer(){let T=this._container;T.classList.add(\"maplibregl-map\");let l=this._canvasContainer=c.create(\"div\",\"maplibregl-canvas-container\",T);this._interactive&&l.classList.add(\"maplibregl-interactive\"),this._canvas=c.create(\"canvas\",\"maplibregl-canvas\",l),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\"),this._canvas.setAttribute(\"role\",\"region\");let d=this._containerDimensions(),v=this._getClampedPixelRatio(d[0],d[1]);this._resizeCanvas(d[0],d[1],v);let b=this._controlContainer=c.create(\"div\",\"maplibregl-control-container\",T),M=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach(O=>{M[O]=c.create(\"div\",`maplibregl-ctrl-${O} `,b)}),this._container.addEventListener(\"scroll\",this._onMapScroll,!1)}_setupCooperativeGestures(){this._cooperativeGesturesScreen=c.create(\"div\",\"maplibregl-cooperative-gesture-screen\",this._container);let T=typeof this._cooperativeGestures!=\"boolean\"&&this._cooperativeGestures.windowsHelpText?this._cooperativeGestures.windowsHelpText:\"Use Ctrl + scroll to zoom the map\";navigator.platform.indexOf(\"Mac\")===0&&(T=typeof this._cooperativeGestures!=\"boolean\"&&this._cooperativeGestures.macHelpText?this._cooperativeGestures.macHelpText:\"Use \\u2318 + scroll to zoom the map\"),this._cooperativeGesturesScreen.innerHTML=`\n
${T}
\n
${typeof this._cooperativeGestures!=\"boolean\"&&this._cooperativeGestures.mobileHelpText?this._cooperativeGestures.mobileHelpText:\"Use two fingers to move the map\"}
\n `,this._cooperativeGesturesScreen.setAttribute(\"aria-hidden\",\"true\"),this._canvasContainer.addEventListener(\"wheel\",this._cooperativeGesturesOnWheel,!1),this._canvasContainer.classList.add(\"maplibregl-cooperative-gestures\")}_destroyCooperativeGestures(){c.remove(this._cooperativeGesturesScreen),this._canvasContainer.removeEventListener(\"wheel\",this._cooperativeGesturesOnWheel,!1),this._canvasContainer.classList.remove(\"maplibregl-cooperative-gestures\")}_resizeCanvas(T,l,d){this._canvas.width=Math.floor(d*T),this._canvas.height=Math.floor(d*l),this._canvas.style.width=`${T}px`,this._canvas.style.height=`${l}px`}_setupPainter(){let T={alpha:!0,stencil:!0,depth:!0,failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1},l=null;this._canvas.addEventListener(\"webglcontextcreationerror\",v=>{l={requestedAttributes:T},v&&(l.statusMessage=v.statusMessage,l.type=v.type)},{once:!0});let d=this._canvas.getContext(\"webgl2\",T)||this._canvas.getContext(\"webgl\",T);if(!d){let v=\"Failed to initialize WebGL\";throw l?(l.message=v,new Error(JSON.stringify(l))):new Error(v)}this.painter=new ah(d,this.transform),f.testSupport(d)}_onCooperativeGesture(T,l,d){return!l&&d<2&&(this._cooperativeGesturesScreen.classList.add(\"maplibregl-show\"),setTimeout(()=>{this._cooperativeGesturesScreen.classList.remove(\"maplibregl-show\")},100)),!1}loaded(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()}_update(T){return this.style&&this.style._loaded?(this._styleDirty=this._styleDirty||T,this._sourcesDirty=!0,this.triggerRepaint(),this):this}_requestRenderFrame(T){return this._update(),this._renderTaskQueue.add(T)}_cancelRenderFrame(T){this._renderTaskQueue.remove(T)}_render(T){let l=this._idleTriggered?this._fadeDuration:0;if(this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(T),this._removed)return;let d=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;let b=this.transform.zoom,M=n.h.now();this.style.zoomHistory.update(b,M);let O=new n.a8(b,{now:M,fadeDuration:l,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),B=O.crossFadingFactor();B===1&&B===this._crossFadingFactor||(d=!0,this._crossFadingFactor=B),this.style.update(O)}this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.terrain?(this.terrain.sourceCache.update(this.transform,this.terrain),this.transform._minEleveationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._elevationFreeze||(this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom))):(this.transform._minEleveationForCurrentTile=0,this.transform.elevation=0),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,l,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:l,showPadding:this.showPadding}),this.fire(new n.k(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,n.bg.mark(n.bh.load),this.fire(new n.k(\"load\"))),this.style&&(this.style.hasTransitions()||d)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles();let v=this._sourcesDirty||this._styleDirty||this._placementDirty;return v||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new n.k(\"idle\")),!this._loaded||this._fullyLoaded||v||(this._fullyLoaded=!0,n.bg.mark(n.bh.fullLoad)),this}redraw(){return this.style&&(this._frame&&(this._frame.cancel(),this._frame=null),this._render(0)),this}remove(){var T;this._hash&&this._hash.remove();for(let d of this._controls)d.onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),typeof window<\"u\"&&removeEventListener(\"online\",this._onWindowOnline,!1),j.removeThrottleControl(this._imageQueueHandle),(T=this._resizeObserver)===null||T===void 0||T.disconnect();let l=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");l&&l.loseContext(),this._canvas.removeEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.removeEventListener(\"webglcontextlost\",this._contextLost,!1),c.remove(this._canvasContainer),c.remove(this._controlContainer),this._cooperativeGestures&&this._destroyCooperativeGestures(),this._container.classList.remove(\"maplibregl-map\"),n.bg.clearMetrics(),this._removed=!0,this.fire(new n.k(\"remove\"))}triggerRepaint(){this.style&&!this._frame&&(this._frame=n.h.frame(T=>{n.bg.frame(T),this._frame=null,this._render(T)}))}get showTileBoundaries(){return!!this._showTileBoundaries}set showTileBoundaries(T){this._showTileBoundaries!==T&&(this._showTileBoundaries=T,this._update())}get showPadding(){return!!this._showPadding}set showPadding(T){this._showPadding!==T&&(this._showPadding=T,this._update())}get showCollisionBoxes(){return!!this._showCollisionBoxes}set showCollisionBoxes(T){this._showCollisionBoxes!==T&&(this._showCollisionBoxes=T,T?this.style._generateCollisionBoxes():this._update())}get showOverdrawInspector(){return!!this._showOverdrawInspector}set showOverdrawInspector(T){this._showOverdrawInspector!==T&&(this._showOverdrawInspector=T,this._update())}get repaint(){return!!this._repaint}set repaint(T){this._repaint!==T&&(this._repaint=T,this.triggerRepaint())}get vertices(){return!!this._vertices}set vertices(T){this._vertices=T,this._update()}get version(){return nr}getCameraTargetElevation(){return this.transform.elevation}},bi.NavigationControl=class{constructor(T){this._updateZoomButtons=()=>{let l=this._map.getZoom(),d=l===this._map.getMaxZoom(),v=l===this._map.getMinZoom();this._zoomInButton.disabled=d,this._zoomOutButton.disabled=v,this._zoomInButton.setAttribute(\"aria-disabled\",d.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",v.toString())},this._rotateCompassArrow=()=>{let l=this.options.visualizePitch?`scale(${1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)}) rotateX(${this._map.transform.pitch}deg) rotateZ(${this._map.transform.angle*(180/Math.PI)}deg)`:`rotate(${this._map.transform.angle*(180/Math.PI)}deg)`;this._compassIcon.style.transform=l},this._setButtonTitle=(l,d)=>{let v=this._map._getUIString(`NavigationControl.${d}`);l.title=v,l.setAttribute(\"aria-label\",v)},this.options=n.e({},aA,T),this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",l=>l.preventDefault()),this.options.showZoom&&(this._zoomInButton=this._createButton(\"maplibregl-ctrl-zoom-in\",l=>this._map.zoomIn({},{originalEvent:l})),c.create(\"span\",\"maplibregl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",\"true\"),this._zoomOutButton=this._createButton(\"maplibregl-ctrl-zoom-out\",l=>this._map.zoomOut({},{originalEvent:l})),c.create(\"span\",\"maplibregl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",\"true\")),this.options.showCompass&&(this._compass=this._createButton(\"maplibregl-ctrl-compass\",l=>{this.options.visualizePitch?this._map.resetNorthPitch({},{originalEvent:l}):this._map.resetNorth({},{originalEvent:l})}),this._compassIcon=c.create(\"span\",\"maplibregl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",\"true\"))}onAdd(T){return this._map=T,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Bd(this._map,this._compass,this.options.visualizePitch)),this._container}onRemove(){c.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map}_createButton(T,l){let d=c.create(\"button\",T,this._container);return d.type=\"button\",d.addEventListener(\"click\",l),d}},bi.GeolocateControl=class extends n.E{constructor(T){super(),this._onSuccess=l=>{if(this._map){if(this._isOutOfMapMaxBounds(l))return this._setErrorState(),this.fire(new n.k(\"outofmaxbounds\",l)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=l,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\");break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}this.options.showUserLocation&&this._watchState!==\"OFF\"&&this._updateMarker(l),this.options.trackUserLocation&&this._watchState!==\"ACTIVE_LOCK\"||this._updateCamera(l),this.options.showUserLocation&&this._dotElement.classList.remove(\"maplibregl-user-location-dot-stale\"),this.fire(new n.k(\"geolocate\",l)),this._finish()}},this._updateCamera=l=>{let d=new n.L(l.coords.longitude,l.coords.latitude),v=l.coords.accuracy,b=this._map.getBearing(),M=n.e({bearing:b},this.options.fitBoundsOptions),O=Si.fromLngLat(d,v);this._map.fitBounds(O,M,{geolocateSource:!0})},this._updateMarker=l=>{if(l){let d=new n.L(l.coords.longitude,l.coords.latitude);this._accuracyCircleMarker.setLngLat(d).addTo(this._map),this._userLocationDotMarker.setLngLat(d).addTo(this._map),this._accuracy=l.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},this._onZoom=()=>{this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},this._onError=l=>{if(this._map){if(this.options.trackUserLocation)if(l.code===1){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;let d=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=d,this._geolocateButton.setAttribute(\"aria-label\",d),this._geolocationWatchID!==void 0&&this._clearWatch()}else{if(l.code===3&&Sf)return;this._setErrorState()}this._watchState!==\"OFF\"&&this.options.showUserLocation&&this._dotElement.classList.add(\"maplibregl-user-location-dot-stale\"),this.fire(new n.k(\"error\",l)),this._finish()}},this._finish=()=>{this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},this._setupUI=l=>{if(this._map){if(this._container.addEventListener(\"contextmenu\",d=>d.preventDefault()),this._geolocateButton=c.create(\"button\",\"maplibregl-ctrl-geolocate\",this._container),c.create(\"span\",\"maplibregl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",\"true\"),this._geolocateButton.type=\"button\",l===!1){n.w(\"Geolocation support is not available so the GeolocateControl will be disabled.\");let d=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=d,this._geolocateButton.setAttribute(\"aria-label\",d)}else{let d=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=d,this._geolocateButton.setAttribute(\"aria-label\",d)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=c.create(\"div\",\"maplibregl-user-location-dot\"),this._userLocationDotMarker=new mh({element:this._dotElement}),this._circleElement=c.create(\"div\",\"maplibregl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new mh({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",d=>{d.geolocateSource||this._watchState!==\"ACTIVE_LOCK\"||d.originalEvent&&d.originalEvent.type===\"resize\"||(this._watchState=\"BACKGROUND\",this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this.fire(new n.k(\"trackuserlocationend\")))})}},this.options=n.e({},kn,T)}onAdd(T){return this._map=T,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),function(l,d=!1){Hn===void 0||d?window.navigator.permissions!==void 0?window.navigator.permissions.query({name:\"geolocation\"}).then(v=>{Hn=v.state!==\"denied\",l(Hn)}).catch(()=>{Hn=!!window.navigator.geolocation,l(Hn)}):(Hn=!!window.navigator.geolocation,l(Hn)):l(Hn)}(this._setupUI),this._container}onRemove(){this._geolocationWatchID!==void 0&&(window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),c.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,wn=0,Sf=!1}_isOutOfMapMaxBounds(T){let l=this._map.getMaxBounds(),d=T.coords;return l&&(d.longitudel.getEast()||d.latitudel.getNorth())}_setErrorState(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"ACTIVE_ERROR\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}}_updateCircleRadius(){let T=this._map.getBounds(),l=T.getSouthEast(),d=T.getNorthEast(),v=l.distanceTo(d),b=Math.ceil(this._accuracy/(v/this._map._container.clientHeight)*2);this._circleElement.style.width=`${b}px`,this._circleElement.style.height=`${b}px`}trigger(){if(!this._setup)return n.w(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new n.k(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":wn--,Sf=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this.fire(new n.k(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new n.k(\"trackuserlocationstart\"));break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"OFF\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}if(this._watchState===\"OFF\"&&this._geolocationWatchID!==void 0)this._clearWatch();else if(this._geolocationWatchID===void 0){let T;this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),wn++,wn>1?(T={maximumAge:6e5,timeout:0},Sf=!0):(T=this.options.positionOptions,Sf=!1),this._geolocationWatchID=window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,T)}}else window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0}_clearWatch(){window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)}},bi.AttributionControl=ua,bi.LogoControl=un,bi.ScaleControl=class{constructor(T){this._onMove=()=>{gh(this._map,this._container,this.options)},this.setUnit=l=>{this.options.unit=l,gh(this._map,this._container,this.options)},this.options=n.e({},Es,T)}getDefaultPosition(){return\"bottom-left\"}onAdd(T){return this._map=T,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-scale\",T.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container}onRemove(){c.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0}},bi.FullscreenControl=class extends n.E{constructor(T={}){super(),this._onFullscreenChange=()=>{(window.document.fullscreenElement||window.document.mozFullScreenElement||window.document.webkitFullscreenElement||window.document.msFullscreenElement)===this._container!==this._fullscreen&&this._handleFullscreenChange()},this._onClickFullscreen=()=>{this._isFullscreen()?this._exitFullscreen():this._requestFullscreen()},this._fullscreen=!1,T&&T.container&&(T.container instanceof HTMLElement?this._container=T.container:n.w(\"Full screen control 'container' must be a DOM element.\")),\"onfullscreenchange\"in document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in document&&(this._fullscreenchange=\"MSFullscreenChange\")}onAdd(T){return this._map=T,this._container||(this._container=this._map.getContainer()),this._controlContainer=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),this._controlContainer}onRemove(){c.remove(this._controlContainer),this._map=null,window.document.removeEventListener(this._fullscreenchange,this._onFullscreenChange)}_setupUI(){let T=this._fullscreenButton=c.create(\"button\",\"maplibregl-ctrl-fullscreen\",this._controlContainer);c.create(\"span\",\"maplibregl-ctrl-icon\",T).setAttribute(\"aria-hidden\",\"true\"),T.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),window.document.addEventListener(this._fullscreenchange,this._onFullscreenChange)}_updateTitle(){let T=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",T),this._fullscreenButton.title=T}_getTitle(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")}_isFullscreen(){return this._fullscreen}_handleFullscreenChange(){this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-fullscreen\"),this._updateTitle(),this._fullscreen?(this.fire(new n.k(\"fullscreenstart\")),this._map._cooperativeGestures&&(this._prevCooperativeGestures=this._map._cooperativeGestures,this._map.setCooperativeGestures())):(this.fire(new n.k(\"fullscreenend\")),this._prevCooperativeGestures&&(this._map.setCooperativeGestures(this._prevCooperativeGestures),delete this._prevCooperativeGestures))}_exitFullscreen(){window.document.exitFullscreen?window.document.exitFullscreen():window.document.mozCancelFullScreen?window.document.mozCancelFullScreen():window.document.msExitFullscreen?window.document.msExitFullscreen():window.document.webkitCancelFullScreen?window.document.webkitCancelFullScreen():this._togglePseudoFullScreen()}_requestFullscreen(){this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen?this._container.webkitRequestFullscreen():this._togglePseudoFullScreen()}_togglePseudoFullScreen(){this._container.classList.toggle(\"maplibregl-pseudo-fullscreen\"),this._handleFullscreenChange(),this._map.resize()}},bi.TerrainControl=class{constructor(T){this._toggleTerrain=()=>{this._map.getTerrain()?this._map.setTerrain(null):this._map.setTerrain(this.options),this._updateTerrainIcon()},this._updateTerrainIcon=()=>{this._terrainButton.classList.remove(\"maplibregl-ctrl-terrain\"),this._terrainButton.classList.remove(\"maplibregl-ctrl-terrain-enabled\"),this._map.terrain?(this._terrainButton.classList.add(\"maplibregl-ctrl-terrain-enabled\"),this._terrainButton.title=this._map._getUIString(\"TerrainControl.disableTerrain\")):(this._terrainButton.classList.add(\"maplibregl-ctrl-terrain\"),this._terrainButton.title=this._map._getUIString(\"TerrainControl.enableTerrain\"))},this.options=T}onAdd(T){return this._map=T,this._container=c.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._terrainButton=c.create(\"button\",\"maplibregl-ctrl-terrain\",this._container),c.create(\"span\",\"maplibregl-ctrl-icon\",this._terrainButton).setAttribute(\"aria-hidden\",\"true\"),this._terrainButton.type=\"button\",this._terrainButton.addEventListener(\"click\",this._toggleTerrain),this._updateTerrainIcon(),this._map.on(\"terrain\",this._updateTerrainIcon),this._container}onRemove(){c.remove(this._container),this._map.off(\"terrain\",this._updateTerrainIcon),this._map=void 0}},bi.Popup=class extends n.E{constructor(T){super(),this.remove=()=>(this._content&&c.remove(this._content),this._container&&(c.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new n.k(\"close\")),this),this._onMouseUp=l=>{this._update(l.point)},this._onMouseMove=l=>{this._update(l.point)},this._onDrag=l=>{this._update(l.point)},this._update=l=>{if(!this._map||!this._lngLat&&!this._trackPointer||!this._content)return;if(!this._container){if(this._container=c.create(\"div\",\"maplibregl-popup\",this._map.getContainer()),this._tip=c.create(\"div\",\"maplibregl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className)for(let O of this.options.className.split(\" \"))this._container.classList.add(O);this._trackPointer&&this._container.classList.add(\"maplibregl-popup-track-pointer\")}if(this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=uo(this._lngLat,this._pos,this._map.transform)),this._trackPointer&&!l)return;let d=this._pos=this._trackPointer&&l?l:this._map.project(this._lngLat),v=this.options.anchor,b=Tf(this.options.offset);if(!v){let O=this._container.offsetWidth,B=this._container.offsetHeight,U;U=d.y+b.bottom.ythis._map.transform.height-B?[\"bottom\"]:[],d.xthis._map.transform.width-O/2&&U.push(\"right\"),v=U.length===0?\"bottom\":U.join(\"-\")}let M=d.add(b[v]).round();c.setTransform(this._container,`${ji[v]} translate(${M.x}px,${M.y}px)`),w_(this._container,v,\"popup\")},this._onClose=()=>{this.remove()},this.options=n.e(Object.create(p0),T)}addTo(T){return this._map&&this.remove(),this._map=T,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new n.k(\"open\")),this}isOpen(){return!!this._map}getLngLat(){return this._lngLat}setLngLat(T){return this._lngLat=n.L.convert(T),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\")),this}trackPointer(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")),this}getElement(){return this._container}setText(T){return this.setDOMContent(document.createTextNode(T))}setHTML(T){let l=document.createDocumentFragment(),d=document.createElement(\"body\"),v;for(d.innerHTML=T;v=d.firstChild,v;)l.appendChild(v);return this.setDOMContent(l)}getMaxWidth(){var T;return(T=this._container)===null||T===void 0?void 0:T.style.maxWidth}setMaxWidth(T){return this.options.maxWidth=T,this._update(),this}setDOMContent(T){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=c.create(\"div\",\"maplibregl-popup-content\",this._container);return this._content.appendChild(T),this._createCloseButton(),this._update(),this._focusFirstElement(),this}addClassName(T){this._container&&this._container.classList.add(T)}removeClassName(T){this._container&&this._container.classList.remove(T)}setOffset(T){return this.options.offset=T,this._update(),this}toggleClassName(T){if(this._container)return this._container.classList.toggle(T)}_createCloseButton(){this.options.closeButton&&(this._closeButton=c.create(\"button\",\"maplibregl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClose))}_focusFirstElement(){if(!this.options.focusAfterOpen||!this._container)return;let T=this._container.querySelector(Fd);T&&T.focus()}},bi.Marker=mh,bi.Style=Gn,bi.LngLat=n.L,bi.LngLatBounds=Si,bi.Point=n.P,bi.MercatorCoordinate=n.U,bi.Evented=n.E,bi.AJAXError=n.bi,bi.config=n.c,bi.CanvasSource=Vo,bi.GeoJSONSource=Xi,bi.ImageSource=ki,bi.RasterDEMTileSource=Rc,bi.RasterTileSource=kc,bi.VectorTileSource=ll,bi.VideoSource=ts,bi.setRTLTextPlugin=n.bj,bi.getRTLTextPluginStatus=n.bk,bi.prewarm=function(){bo().acquire(oi)},bi.clearPrewarmedResources=function(){let T=ul;T&&(T.isPreloaded()&&T.numActive()===1?(T.release(oi),ul=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. 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this.transform(t,r)}},jE,GE;function nat(){return jE||(jE=new ss([0,0,0,0,0,0,0,0,0]),Object.freeze(jE)),jE}function sat(){return GE||(GE=new ss,Object.freeze(GE)),GE}function oat(e){return e[0]=1,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=1,e[6]=0,e[7]=0,e[8]=0,e[9]=0,e[10]=1,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,e}function s7(e,t){if(e===t){var r=t[1],i=t[2],s=t[3],n=t[6],o=t[7],c=t[11];e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=r,e[6]=t[9],e[7]=t[13],e[8]=i,e[9]=n,e[11]=t[14],e[12]=s,e[13]=o,e[14]=c}else e[0]=t[0],e[1]=t[4],e[2]=t[8],e[3]=t[12],e[4]=t[1],e[5]=t[5],e[6]=t[9],e[7]=t[13],e[8]=t[2],e[9]=t[6],e[10]=t[10],e[11]=t[14],e[12]=t[3],e[13]=t[7],e[14]=t[11],e[15]=t[15];return e}function Sb(e,t){var r=t[0],i=t[1],s=t[2],n=t[3],o=t[4],c=t[5],f=t[6],_=t[7],w=t[8],I=t[9],R=t[10],N=t[11],j=t[12],Q=t[13],et=t[14],Y=t[15],K=r*c-i*o,J=r*f-s*o,ut=r*_-n*o,Et=i*f-s*c,kt=i*_-n*c,Xt=s*_-n*f,qt=w*Q-I*j,le=w*et-R*j,ue=w*Y-N*j,De=I*et-R*Q,Ke=I*Y-N*Q,rr=R*Y-N*et,Sr=K*rr-J*Ke+ut*De+Et*ue-kt*le+Xt*qt;return 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e[0]=J*i+ut*c+Et*I+kt*Q,e[1]=J*s+ut*f+Et*R+kt*et,e[2]=J*n+ut*_+Et*N+kt*Y,e[3]=J*o+ut*w+Et*j+kt*K,J=r[4],ut=r[5],Et=r[6],kt=r[7],e[4]=J*i+ut*c+Et*I+kt*Q,e[5]=J*s+ut*f+Et*R+kt*et,e[6]=J*n+ut*_+Et*N+kt*Y,e[7]=J*o+ut*w+Et*j+kt*K,J=r[8],ut=r[9],Et=r[10],kt=r[11],e[8]=J*i+ut*c+Et*I+kt*Q,e[9]=J*s+ut*f+Et*R+kt*et,e[10]=J*n+ut*_+Et*N+kt*Y,e[11]=J*o+ut*w+Et*j+kt*K,J=r[12],ut=r[13],Et=r[14],kt=r[15],e[12]=J*i+ut*c+Et*I+kt*Q,e[13]=J*s+ut*f+Et*R+kt*et,e[14]=J*n+ut*_+Et*N+kt*Y,e[15]=J*o+ut*w+Et*j+kt*K,e}function ag(e,t,r){var i=r[0],s=r[1],n=r[2],o,c,f,_,w,I,R,N,j,Q,et,Y;return t===e?(e[12]=t[0]*i+t[4]*s+t[8]*n+t[12],e[13]=t[1]*i+t[5]*s+t[9]*n+t[13],e[14]=t[2]*i+t[6]*s+t[10]*n+t[14],e[15]=t[3]*i+t[7]*s+t[11]*n+t[15]):(o=t[0],c=t[1],f=t[2],_=t[3],w=t[4],I=t[5],R=t[6],N=t[7],j=t[8],Q=t[9],et=t[10],Y=t[11],e[0]=o,e[1]=c,e[2]=f,e[3]=_,e[4]=w,e[5]=I,e[6]=R,e[7]=N,e[8]=j,e[9]=Q,e[10]=et,e[11]=Y,e[12]=o*i+w*s+j*n+t[12],e[13]=c*i+I*s+Q*n+t[13],e[14]=f*i+R*s+et*n+t[14],e[15]=_*i+N*s+Y*n+t[15]),e}function By(e,t,r){var i=r[0],s=r[1],n=r[2];return e[0]=t[0]*i,e[1]=t[1]*i,e[2]=t[2]*i,e[3]=t[3]*i,e[4]=t[4]*s,e[5]=t[5]*s,e[6]=t[6]*s,e[7]=t[7]*s,e[8]=t[8]*n,e[9]=t[9]*n,e[10]=t[10]*n,e[11]=t[11]*n,e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}function a7(e,t,r,i){var s=i[0],n=i[1],o=i[2],c=Math.hypot(s,n,o),f,_,w,I,R,N,j,Q,et,Y,K,J,ut,Et,kt,Xt,qt,le,ue,De,Ke,rr,Sr,Li;return c0&&(o=1/Math.sqrt(o)),e[0]=r*o,e[1]=i*o,e[2]=s*o,e[3]=n*o,e}function _7(e,t){return e[0]*t[0]+e[1]*t[1]+e[2]*t[2]+e[3]*t[3]}function y7(e,t,r,i){var s=t[0],n=t[1],o=t[2],c=t[3];return e[0]=s+i*(r[0]-s),e[1]=n+i*(r[1]-n),e[2]=o+i*(r[2]-o),e[3]=c+i*(r[3]-c),e}function Nh(e,t,r){var i=t[0],s=t[1],n=t[2],o=t[3];return e[0]=r[0]*i+r[4]*s+r[8]*n+r[12]*o,e[1]=r[1]*i+r[5]*s+r[9]*n+r[13]*o,e[2]=r[2]*i+r[6]*s+r[10]*n+r[14]*o,e[3]=r[3]*i+r[7]*s+r[11]*n+r[15]*o,e}function v7(e,t,r){var i=t[0],s=t[1],n=t[2],o=r[0],c=r[1],f=r[2],_=r[3],w=_*i+c*n-f*s,I=_*s+f*i-o*n,R=_*n+o*s-c*i,N=-o*i-c*s-f*n;return e[0]=w*_+N*-o+I*-f-R*-c,e[1]=I*_+N*-c+R*-o-w*-f,e[2]=R*_+N*-f+w*-c-I*-o,e[3]=t[3],e}var b4t=function(){var e=cat();return function(t,r,i,s,n,o){var c,f;for(r||(r=4),i||(i=0),s?f=Math.min(s*r+i,t.length):f=t.length,c=i;cMath.PI*2)throw Error(\"expected radians\")}function Aat(e,t,r,i,s,n){let o=2*n/(r-t),c=2*n/(s-i),f=(r+t)/(r-t),_=(s+i)/(s-i),w=-1,I=-1,R=-2*n;return e[0]=o,e[1]=0,e[2]=0,e[3]=0,e[4]=0,e[5]=c,e[6]=0,e[7]=0,e[8]=f,e[9]=_,e[10]=w,e[11]=I,e[12]=0,e[13]=0,e[14]=R,e[15]=0,e}function b7(){var e=new ya(4);return ya!=Float32Array&&(e[0]=0,e[1]=0,e[2]=0),e[3]=1,e}function w7(e){return e[0]=0,e[1]=0,e[2]=0,e[3]=1,e}function iD(e,t,r){r=r*.5;var i=Math.sin(r);return e[0]=i*t[0],e[1]=i*t[1],e[2]=i*t[2],e[3]=Math.cos(r),e}function nD(e,t,r){var i=t[0],s=t[1],n=t[2],o=t[3],c=r[0],f=r[1],_=r[2],w=r[3];return e[0]=i*w+o*c+s*_-n*f,e[1]=s*w+o*f+n*c-i*_,e[2]=n*w+o*_+i*f-s*c,e[3]=o*w-i*c-s*f-n*_,e}function S7(e,t,r){r*=.5;var i=t[0],s=t[1],n=t[2],o=t[3],c=Math.sin(r),f=Math.cos(r);return e[0]=i*f+o*c,e[1]=s*f+n*c,e[2]=n*f-s*c,e[3]=o*f-i*c,e}function T7(e,t,r){r*=.5;var i=t[0],s=t[1],n=t[2],o=t[3],c=Math.sin(r),f=Math.cos(r);return e[0]=i*f-n*c,e[1]=s*f+o*c,e[2]=n*f+i*c,e[3]=o*f-s*c,e}function M7(e,t,r){r*=.5;var i=t[0],s=t[1],n=t[2],o=t[3],c=Math.sin(r),f=Math.cos(r);return e[0]=i*f+s*c,e[1]=s*f-i*c,e[2]=n*f+o*c,e[3]=o*f-n*c,e}function E7(e,t){var r=t[0],i=t[1],s=t[2];return e[0]=r,e[1]=i,e[2]=s,e[3]=Math.sqrt(Math.abs(1-r*r-i*i-s*s)),e}function Mb(e,t,r,i){var s=t[0],n=t[1],o=t[2],c=t[3],f=r[0],_=r[1],w=r[2],I=r[3],R,N,j,Q,et;return N=s*f+n*_+o*w+c*I,N<0&&(N=-N,f=-f,_=-_,w=-w,I=-I),1-N>zh?(R=Math.acos(N),j=Math.sin(R),Q=Math.sin((1-i)*R)/j,et=Math.sin(i*R)/j):(Q=1-i,et=i),e[0]=Q*s+et*f,e[1]=Q*n+et*_,e[2]=Q*o+et*w,e[3]=Q*c+et*I,e}function P7(e,t){var r=t[0],i=t[1],s=t[2],n=t[3],o=r*r+i*i+s*s+n*n,c=o?1/o:0;return e[0]=-r*c,e[1]=-i*c,e[2]=-s*c,e[3]=n*c,e}function I7(e,t){return e[0]=-t[0],e[1]=-t[1],e[2]=-t[2],e[3]=t[3],e}function sD(e,t){var r=t[0]+t[4]+t[8],i;if(r>0)i=Math.sqrt(r+1),e[3]=.5*i,i=.5/i,e[0]=(t[5]-t[7])*i,e[1]=(t[6]-t[2])*i,e[2]=(t[1]-t[3])*i;else{var s=0;t[4]>t[0]&&(s=1),t[8]>t[s*3+s]&&(s=2);var n=(s+1)%3,o=(s+2)%3;i=Math.sqrt(t[s*3+s]-t[n*3+n]-t[o*3+o]+1),e[s]=.5*i,i=.5/i,e[3]=(t[n*3+o]-t[o*3+n])*i,e[n]=(t[n*3+s]+t[s*3+n])*i,e[o]=(t[o*3+s]+t[s*3+o])*i}return e}var C7=p7;var L7=Fy,k7=_7,R7=y7,D7=A7;var O7=m7;var B7=g7;var F7=function(){var e=qR(),t=ZR(1,0,0),r=ZR(0,1,0);return function(i,s,n){var o=YR(s,n);return o<-.999999?(Dy(e,t,s),zE(e)<1e-6&&Dy(e,r,s),Wj(e,e),iD(i,e,Math.PI),i):o>.999999?(i[0]=0,i[1]=0,i[2]=0,i[3]=1,i):(Dy(e,s,n),i[0]=e[0],i[1]=e[1],i[2]=e[2],i[3]=1+o,B7(i,i))}}(),R4t=function(){var e=b7(),t=b7();return function(r,i,s,n,o,c){return Mb(e,i,o,c),Mb(t,s,n,c),Mb(r,e,t,2*c*(1-c)),r}}(),D4t=function(){var e=Xj();return function(t,r,i,s){return e[0]=i[0],e[3]=i[1],e[6]=i[2],e[1]=s[0],e[4]=s[1],e[7]=s[2],e[2]=-r[0],e[5]=-r[1],e[8]=-r[2],B7(t,sD(t,e))}}();var gat=[0,0,0,1],lg=class extends np{constructor(t=0,r=0,i=0,s=1){super(-0,-0,-0,-0),Array.isArray(t)&&arguments.length===1?this.copy(t):this.set(t,r,i,s)}copy(t){return this[0]=t[0],this[1]=t[1],this[2]=t[2],this[3]=t[3],this.check()}set(t,r,i,s){return this[0]=t,this[1]=r,this[2]=i,this[3]=s,this.check()}fromObject(t){return this[0]=t.x,this[1]=t.y,this[2]=t.z,this[3]=t.w,this.check()}fromMatrix3(t){return sD(this,t),this.check()}fromAxisRotation(t,r){return iD(this,t,r),this.check()}identity(){return w7(this),this.check()}setAxisAngle(t,r){return this.fromAxisRotation(t,r)}get ELEMENTS(){return 4}get x(){return this[0]}set x(t){this[0]=Qi(t)}get y(){return this[1]}set y(t){this[1]=Qi(t)}get z(){return this[2]}set z(t){this[2]=Qi(t)}get w(){return this[3]}set w(t){this[3]=Qi(t)}len(){return D7(this)}lengthSquared(){return O7(this)}dot(t){return k7(this,t)}rotationTo(t,r){return F7(this,t,r),this.check()}add(t){return C7(this,this,t),this.check()}calculateW(){return E7(this,this),this.check()}conjugate(){return I7(this,this),this.check()}invert(){return P7(this,this),this.check()}lerp(t,r,i){return i===void 0?this.lerp(this,t,r):(R7(this,t,r,i),this.check())}multiplyRight(t){return nD(this,this,t),this.check()}multiplyLeft(t){return nD(this,t,this),this.check()}normalize(){let t=this.len(),r=t>0?1/t:0;return this[0]=this[0]*r,this[1]=this[1]*r,this[2]=this[2]*r,this[3]=this[3]*r,t===0&&(this[3]=1),this.check()}rotateX(t){return S7(this,this,t),this.check()}rotateY(t){return T7(this,this,t),this.check()}rotateZ(t){return M7(this,this,t),this.check()}scale(t){return L7(this,this,t),this.check()}slerp(t,r,i){let s,n,o;switch(arguments.length){case 1:({start:s=gat,target:n,ratio:o}=t);break;case 2:s=this,n=t,o=r;break;default:s=t,n=r,o=i}return Mb(this,s,n,o),this.check()}transformVector4(t,r=new wb){return v7(r,t,this),QA(r,4)}lengthSq(){return this.lengthSquared()}setFromAxisAngle(t,r){return this.setAxisAngle(t,r)}premultiply(t){return this.multiplyLeft(t)}multiply(t){return this.multiplyRight(t)}};var YE={EPSILON1:.1,EPSILON2:.01,EPSILON3:.001,EPSILON4:1e-4,EPSILON5:1e-5,EPSILON6:1e-6,EPSILON7:1e-7,EPSILON8:1e-8,EPSILON9:1e-9,EPSILON10:1e-10,EPSILON11:1e-11,EPSILON12:1e-12,EPSILON13:1e-13,EPSILON14:1e-14,EPSILON15:1e-15,EPSILON16:1e-16,EPSILON17:1e-17,EPSILON18:1e-18,EPSILON19:1e-19,EPSILON20:1e-20,PI_OVER_TWO:Math.PI/2,PI_OVER_FOUR:Math.PI/4,PI_OVER_SIX:Math.PI/6,TWO_PI:Math.PI*2};var oD=`#if (defined(SHADER_TYPE_FRAGMENT) && defined(LIGHTING_FRAGMENT)) || (defined(SHADER_TYPE_VERTEX) && defined(LIGHTING_VERTEX))\n\nstruct AmbientLight {\n vec3 color;\n};\n\nstruct PointLight {\n vec3 color;\n vec3 position;\n vec3 attenuation;\n};\n\nstruct DirectionalLight {\n vec3 color;\n vec3 direction;\n};\n\nuniform AmbientLight lighting_uAmbientLight;\nuniform PointLight lighting_uPointLight[MAX_LIGHTS];\nuniform DirectionalLight lighting_uDirectionalLight[MAX_LIGHTS];\nuniform int lighting_uPointLightCount;\nuniform int lighting_uDirectionalLightCount;\n\nuniform bool lighting_uEnabled;\n\nfloat getPointLightAttenuation(PointLight pointLight, float distance) {\n return pointLight.attenuation.x\n + pointLight.attenuation.y * distance\n + pointLight.attenuation.z * distance * distance;\n}\n\n#endif\n`;var _at={lightSources:{}};function aD(){let{color:e=[0,0,0],intensity:t=1}=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};return e.map(r=>r*t/255)}function yat(e){let{ambientLight:t,pointLights:r=[],directionalLights:i=[]}=e,s={};return t?s[\"lighting_uAmbientLight.color\"]=aD(t):s[\"lighting_uAmbientLight.color\"]=[0,0,0],r.forEach((n,o)=>{s[\"lighting_uPointLight[\".concat(o,\"].color\")]=aD(n),s[\"lighting_uPointLight[\".concat(o,\"].position\")]=n.position,s[\"lighting_uPointLight[\".concat(o,\"].attenuation\")]=n.attenuation||[1,0,0]}),s.lighting_uPointLightCount=r.length,i.forEach((n,o)=>{s[\"lighting_uDirectionalLight[\".concat(o,\"].color\")]=aD(n),s[\"lighting_uDirectionalLight[\".concat(o,\"].direction\")]=n.direction}),s.lighting_uDirectionalLightCount=i.length,s}function z7(){let e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:_at;if(\"lightSources\"in e){let{ambientLight:t,pointLights:r,directionalLights:i}=e.lightSources||{};return t||r&&r.length>0||i&&i.length>0?Object.assign({},yat({ambientLight:t,pointLights:r,directionalLights:i}),{lighting_uEnabled:!0}):{lighting_uEnabled:!1}}if(\"lights\"in e){let t={pointLights:[],directionalLights:[]};for(let r of e.lights||[])switch(r.type){case\"ambient\":t.ambientLight=r;break;case\"directional\":t.directionalLights.push(r);break;case\"point\":t.pointLights.push(r);break;default:}return z7({lightSources:t})}return{}}var lD={name:\"lights\",vs:oD,fs:oD,getUniforms:z7,defines:{MAX_LIGHTS:3}};var vat=new Uint8Array([0,255,255,255]),xat={pickingSelectedColor:null,pickingHighlightColor:vat,pickingActive:!1,pickingAttribute:!1};function bat(){let e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:xat,t={};if(e.pickingSelectedColor!==void 0)if(!e.pickingSelectedColor)t.picking_uSelectedColorValid=0;else{let r=e.pickingSelectedColor.slice(0,3);t.picking_uSelectedColorValid=1,t.picking_uSelectedColor=r}if(e.pickingHighlightColor){let r=Array.from(e.pickingHighlightColor,i=>i/255);Number.isFinite(r[3])||(r[3]=1),t.picking_uHighlightColor=r}return e.pickingActive!==void 0&&(t.picking_uActive=!!e.pickingActive,t.picking_uAttribute=!!e.pickingAttribute),t}var wat=`uniform bool picking_uActive;\nuniform bool picking_uAttribute;\nuniform vec3 picking_uSelectedColor;\nuniform bool picking_uSelectedColorValid;\n\nout vec4 picking_vRGBcolor_Avalid;\n\nconst float COLOR_SCALE = 1. / 255.;\n\nbool picking_isColorValid(vec3 color) {\n return dot(color, vec3(1.0)) > 0.001;\n}\n\nbool isVertexPicked(vec3 vertexColor) {\n return\n picking_uSelectedColorValid &&\n !picking_isColorValid(abs(vertexColor - picking_uSelectedColor));\n}\n\nvoid picking_setPickingColor(vec3 pickingColor) {\n if (picking_uActive) {\n picking_vRGBcolor_Avalid.a = float(picking_isColorValid(pickingColor));\n\n if (!picking_uAttribute) {\n picking_vRGBcolor_Avalid.rgb = pickingColor * COLOR_SCALE;\n }\n } else {\n picking_vRGBcolor_Avalid.a = float(isVertexPicked(pickingColor));\n }\n}\n\nvoid picking_setPickingAttribute(float value) {\n if (picking_uAttribute) {\n picking_vRGBcolor_Avalid.r = value;\n }\n}\nvoid picking_setPickingAttribute(vec2 value) {\n if (picking_uAttribute) {\n picking_vRGBcolor_Avalid.rg = value;\n }\n}\nvoid picking_setPickingAttribute(vec3 value) {\n if (picking_uAttribute) {\n picking_vRGBcolor_Avalid.rgb = value;\n }\n}\n`,Sat=`uniform bool picking_uActive;\nuniform vec3 picking_uSelectedColor;\nuniform vec4 picking_uHighlightColor;\n\nin vec4 picking_vRGBcolor_Avalid;\nvec4 picking_filterHighlightColor(vec4 color) {\n if (picking_uActive) {\n return color;\n }\n bool selected = bool(picking_vRGBcolor_Avalid.a);\n\n if (selected) {\n float highLightAlpha = picking_uHighlightColor.a;\n float blendedAlpha = highLightAlpha + color.a * (1.0 - highLightAlpha);\n float highLightRatio = highLightAlpha / blendedAlpha;\n\n vec3 blendedRGB = mix(color.rgb, picking_uHighlightColor.rgb, highLightRatio);\n return vec4(blendedRGB, blendedAlpha);\n } else {\n return color;\n }\n}\nvec4 picking_filterPickingColor(vec4 color) {\n if (picking_uActive) {\n if (picking_vRGBcolor_Avalid.a == 0.0) {\n discard;\n }\n return picking_vRGBcolor_Avalid;\n }\n return color;\n}\nvec4 picking_filterColor(vec4 color) {\n vec4 highightColor = picking_filterHighlightColor(color);\n return picking_filterPickingColor(highightColor);\n}\n\n`,QE={name:\"picking\",vs:wat,fs:Sat,getUniforms:bat};var cD=`\nuniform float lighting_uAmbient;\nuniform float lighting_uDiffuse;\nuniform float lighting_uShininess;\nuniform vec3 lighting_uSpecularColor;\n\nvec3 lighting_getLightColor(vec3 surfaceColor, vec3 light_direction, vec3 view_direction, vec3 normal_worldspace, vec3 color) {\n vec3 halfway_direction = normalize(light_direction + view_direction);\n float lambertian = dot(light_direction, normal_worldspace);\n float specular = 0.0;\n if (lambertian > 0.0) {\n float specular_angle = max(dot(normal_worldspace, halfway_direction), 0.0);\n specular = pow(specular_angle, lighting_uShininess);\n }\n lambertian = max(lambertian, 0.0);\n return (lambertian * lighting_uDiffuse * surfaceColor + specular * lighting_uSpecularColor) * color;\n}\n\nvec3 lighting_getLightColor(vec3 surfaceColor, vec3 cameraPosition, vec3 position_worldspace, vec3 normal_worldspace) {\n vec3 lightColor = surfaceColor;\n\n if (lighting_uEnabled) {\n vec3 view_direction = normalize(cameraPosition - position_worldspace);\n lightColor = lighting_uAmbient * surfaceColor * lighting_uAmbientLight.color;\n\n for (int i = 0; i < MAX_LIGHTS; i++) {\n if (i >= lighting_uPointLightCount) {\n break;\n }\n PointLight pointLight = lighting_uPointLight[i];\n vec3 light_position_worldspace = pointLight.position;\n vec3 light_direction = normalize(light_position_worldspace - position_worldspace);\n lightColor += lighting_getLightColor(surfaceColor, light_direction, view_direction, normal_worldspace, pointLight.color);\n }\n\n for (int i = 0; i < MAX_LIGHTS; i++) {\n if (i >= lighting_uDirectionalLightCount) {\n break;\n }\n DirectionalLight directionalLight = lighting_uDirectionalLight[i];\n lightColor += lighting_getLightColor(surfaceColor, -directionalLight.direction, view_direction, normal_worldspace, directionalLight.color);\n }\n }\n return lightColor;\n}\n\nvec3 lighting_getSpecularLightColor(vec3 cameraPosition, vec3 position_worldspace, vec3 normal_worldspace) {\n vec3 lightColor = vec3(0, 0, 0);\n vec3 surfaceColor = vec3(0, 0, 0);\n\n if (lighting_uEnabled) {\n vec3 view_direction = normalize(cameraPosition - position_worldspace);\n\n for (int i = 0; i < MAX_LIGHTS; i++) {\n if (i >= lighting_uPointLightCount) {\n break;\n }\n PointLight pointLight = lighting_uPointLight[i];\n vec3 light_position_worldspace = pointLight.position;\n vec3 light_direction = normalize(light_position_worldspace - position_worldspace);\n lightColor += lighting_getLightColor(surfaceColor, light_direction, view_direction, normal_worldspace, pointLight.color);\n }\n\n for (int i = 0; i < MAX_LIGHTS; i++) {\n if (i >= lighting_uDirectionalLightCount) {\n break;\n }\n DirectionalLight directionalLight = lighting_uDirectionalLight[i];\n lightColor += lighting_getLightColor(surfaceColor, -directionalLight.direction, view_direction, normal_worldspace, directionalLight.color);\n }\n }\n return lightColor;\n}\n`;var Tat={};function Mat(e){let{ambient:t=.35,diffuse:r=.6,shininess:i=32,specularColor:s=[30,30,30]}=e;return{lighting_uAmbient:t,lighting_uDiffuse:r,lighting_uShininess:i,lighting_uSpecularColor:s.map(n=>n/255)}}function N7(){let e=arguments.length>0&&arguments[0]!==void 0?arguments[0]:Tat;if(!(\"material\"in e))return{};let{material:t}=e;return t?Mat(t):{lighting_uEnabled:!1}}var Zf={name:\"gouraud-lighting\",dependencies:[lD],vs:cD,defines:{LIGHTING_VERTEX:1},getUniforms:N7},Ny={name:\"phong-lighting\",dependencies:[lD],fs:cD,defines:{LIGHTING_FRAGMENT:1},getUniforms:N7};var Eat=`attribute float transform_elementID;\nvec2 transform_getPixelSizeHalf(vec2 size) {\n return vec2(1.) / (2. * size);\n}\n\nvec2 transform_getPixelIndices(vec2 texSize, vec2 pixelSizeHalf) {\n float yIndex = floor((transform_elementID / texSize[0]) + 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j)et.push(this._getHash(J)),et.push(this._getHash(s[J]));for(let J of Q)Y.push(this._getHash(J)),Y.push(this._getHash(n[J]));let K=\"\".concat(w,\"/\").concat(I,\"D\").concat(et.join(\"/\"),\"M\").concat(R.join(\"/\"),\"I\").concat(Y.join(\"/\"),\"V\").concat(N.join(\"/\"),\"H\").concat(this.stateHash,\"B\").concat(c).concat(f?\"T\":\"\");if(!this._programCache[K]){let J=VR(this.gl,{vs:r,fs:i,modules:_,inject:n,defines:s,hookFunctions:this._hookFunctions,transpileToGLSL100:f});this._programCache[K]=new rp(this.gl,{hash:K,vs:J.vs,fs:J.fs,varyings:o,bufferMode:c}),this._getUniforms[K]=J.getUniforms||(ut=>{}),this._useCounts[K]=0}return this._useCounts[K]++,this._programCache[K]}getUniforms(t){return this._getUniforms[t.hash]||null}release(t){let r=t.hash;this._useCounts[r]--,this._useCounts[r]===0&&(this._programCache[r].delete(),delete this._programCache[r],delete this._getUniforms[r],delete this._useCounts[r])}_getHash(t){return this._hashes[t]===void 0&&(this._hashes[t]=this._hashCounter++),this._hashes[t]}_getModuleList(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:[],r=new Array(this._defaultModules.length+t.length),i={},s=0;for(let n=0,o=this._defaultModules.length;n{},Rat={},fn=class{constructor(t){let r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},{id:i=ta(\"model\")}=r;ye(Jd(t)),this.id=i,this.gl=t,this.id=r.id||ta(\"Model\"),this.lastLogTime=0,this.animated=!1,this.initialize(r)}initialize(t){this.props={},this.programManager=t.programManager||Uh.getDefaultProgramManager(this.gl),this._programManagerState=-1,this._managedProgram=!1;let{program:r=null,vs:i,fs:s,modules:n,defines:o,inject:c,varyings:f,bufferMode:_,transpileToGLSL100:w}=t;this.programProps={program:r,vs:i,fs:s,modules:n,defines:o,inject:c,varyings:f,bufferMode:_,transpileToGLSL100:w},this.program=null,this.vertexArray=null,this._programDirty=!0,this.userData={},this.needsRedraw=!0,this._attributes={},this.attributes={},this.uniforms={},this.pickable=!0,this._checkProgram(),this.setUniforms(Object.assign({},this.getModuleUniforms(t.moduleSettings))),this.drawMode=t.drawMode!==void 0?t.drawMode:4,this.vertexCount=t.vertexCount||0,this.geometryBuffers={},this.isInstanced=t.isInstanced||t.instanced||t.instanceCount>0,this._setModelProps(t),this.geometry={},ye(this.drawMode!==void 0&&Number.isFinite(this.vertexCount),kat)}setProps(t){this._setModelProps(t)}delete(){for(let t in this._attributes)this._attributes[t]!==this.attributes[t]&&this._attributes[t].delete();this._managedProgram&&(this.programManager.release(this.program),this._managedProgram=!1),this.vertexArray.delete(),this._deleteGeometryBuffers()}getDrawMode(){return this.drawMode}getVertexCount(){return this.vertexCount}getInstanceCount(){return this.instanceCount}getAttributes(){return this.attributes}getProgram(){return this.program}setProgram(t){let{program:r,vs:i,fs:s,modules:n,defines:o,inject:c,varyings:f,bufferMode:_,transpileToGLSL100:w}=t;this.programProps={program:r,vs:i,fs:s,modules:n,defines:o,inject:c,varyings:f,bufferMode:_,transpileToGLSL100:w},this._programDirty=!0}getUniforms(){return this.uniforms}setDrawMode(t){return this.drawMode=t,this}setVertexCount(t){return ye(Number.isFinite(t)),this.vertexCount=t,this}setInstanceCount(t){return ye(Number.isFinite(t)),this.instanceCount=t,this}setGeometry(t){return this.drawMode=t.drawMode,this.vertexCount=t.getVertexCount(),this._deleteGeometryBuffers(),this.geometryBuffers=U7(this.gl,t),this.vertexArray.setAttributes(this.geometryBuffers),this}setAttributes(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};if(Wf(t))return this;let r={};for(let i in t){let s=t[i];r[i]=s.getValue?s.getValue():s}return this.vertexArray.setAttributes(r),this}setUniforms(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};return Object.assign(this.uniforms,t),this}getModuleUniforms(t){this._checkProgram();let r=this.programManager.getUniforms(this.program);return r?r(t):{}}updateModuleSettings(t){let r=this.getModuleUniforms(t||{});return this.setUniforms(r)}clear(t){return Hf(this.program.gl,t),this}draw(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this._checkProgram();let{moduleSettings:r=null,framebuffer:i,uniforms:s={},attributes:n={},transformFeedback:o=this.transformFeedback,parameters:c={},vertexArray:f=this.vertexArray}=t;this.setAttributes(n),this.updateModuleSettings(r),this.setUniforms(s);let _;He.priority>=Uy&&(_=this._logDrawCallStart(Uy));let w=this.vertexArray.getDrawParams(),{isIndexed:I=w.isIndexed,indexType:R=w.indexType,indexOffset:N=w.indexOffset,vertexArrayInstanced:j=w.isInstanced}=this.props;j&&!this.isInstanced&&He.warn(\"Found instanced attributes on non-instanced model\",this.id)();let{isInstanced:Q,instanceCount:et}=this,{onBeforeRender:Y=V7,onAfterRender:K=V7}=this.props;Y(),this.program.setUniforms(this.uniforms);let J=this.program.draw(Object.assign(Rat,t,{logPriority:_,uniforms:null,framebuffer:i,parameters:c,drawMode:this.getDrawMode(),vertexCount:this.getVertexCount(),vertexArray:f,transformFeedback:o,isIndexed:I,indexType:R,isInstanced:Q,instanceCount:et,offset:I?N:0}));return K(),He.priority>=Uy&&this._logDrawCallEnd(_,f,i),J}transform(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{discard:r=!0,feedbackBuffers:i,unbindModels:s=[]}=t,{parameters:n}=t;i&&this._setFeedbackBuffers(i),r&&(n=Object.assign({},n,{35977:r})),s.forEach(o=>o.vertexArray.unbindBuffers());try{this.draw(Object.assign({},t,{parameters:n}))}finally{s.forEach(o=>o.vertexArray.bindBuffers())}return this}render(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};return He.warn(\"Model.render() is deprecated. Use Model.setUniforms() and Model.draw()\")(),this.setUniforms(t).draw()}_setModelProps(t){Object.assign(this.props,t),\"uniforms\"in t&&this.setUniforms(t.uniforms),\"pickable\"in t&&(this.pickable=t.pickable),\"instanceCount\"in t&&(this.instanceCount=t.instanceCount),\"geometry\"in t&&this.setGeometry(t.geometry),\"attributes\"in t&&this.setAttributes(t.attributes),\"_feedbackBuffers\"in t&&this._setFeedbackBuffers(t._feedbackBuffers)}_checkProgram(){if(!(this._programDirty||this.programManager.stateHash!==this._programManagerState))return;let{program:r}=this.programProps;if(r)this._managedProgram=!1;else{let{vs:i,fs:s,modules:n,inject:o,defines:c,varyings:f,bufferMode:_,transpileToGLSL100:w}=this.programProps;r=this.programManager.get({vs:i,fs:s,modules:n,inject:o,defines:c,varyings:f,bufferMode:_,transpileToGLSL100:w}),this.program&&this._managedProgram&&this.programManager.release(this.program),this._programManagerState=this.programManager.stateHash,this._managedProgram=!0}ye(r instanceof rp,\"Model needs a program\"),this._programDirty=!1,r!==this.program&&(this.program=r,this.vertexArray?this.vertexArray.setProps({program:this.program,attributes:this.vertexArray.attributes}):this.vertexArray=new Iy(this.gl,{program:this.program}),this.setUniforms(Object.assign({},this.getModuleUniforms())))}_deleteGeometryBuffers(){for(let t in this.geometryBuffers){let r=this.geometryBuffers[t][0]||this.geometryBuffers[t];r instanceof Fr&&r.delete()}}_setAnimationProps(t){this.animated&&ye(t,\"Model.draw(): animated uniforms but no animationProps\")}_setFeedbackBuffers(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};if(Wf(t))return this;let{gl:r}=this.program;return this.transformFeedback=this.transformFeedback||new ip(r,{program:this.program}),this.transformFeedback.setBuffers(t),this}_logDrawCallStart(t){let r=t>3?0:Lat;if(!(Date.now()-this.lastLogTime>> DRAWING MODEL \".concat(this.id),{collapsed:He.level<=2})(),t}_logDrawCallEnd(t,r,i,s){if(t===void 0)return;let n=CR({vertexArray:r,header:\"\".concat(this.id,\" attributes\"),attributes:this._attributes}),{table:o,unusedTable:c,unusedCount:f}=ME({header:\"\".concat(this.id,\" uniforms\"),program:this.program,uniforms:Object.assign({},this.program.uniforms,i)}),{table:_,count:w}=ME({header:\"\".concat(this.id,\" uniforms\"),program:this.program,uniforms:Object.assign({},this.program.uniforms,i),undefinedOnly:!0});w>0&&He.log(\"MISSING UNIFORMS\",Object.keys(_))(),f>0&&He.log(\"UNUSED UNIFORMS\",Object.keys(c))();let I=LR(this.vertexArray.configuration);He.table(t,n)(),He.table(t,o)(),He.table(t+1,I)(),s&&s.log({logLevel:Uy,message:\"Rendered to \".concat(s.id)}),He.groupEnd(Uy)()}};var Eb=class{constructor(t){let r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.gl=t,this.currentIndex=0,this.feedbackMap={},this.varyings=null,this.bindings=[],this.resources={},this._initialize(r),Object.seal(this)}setupResources(t){for(let r of this.bindings)this._setupTransformFeedback(r,t)}updateModelProps(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{varyings:r}=this;return r.length>0&&(t=Object.assign({},t,{varyings:r})),t}getDrawOptions(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},r=this.bindings[this.currentIndex],{sourceBuffers:i,transformFeedback:s}=r;return{attributes:Object.assign({},i,t.attributes),transformFeedback:s}}swap(){return this.feedbackMap?(this.currentIndex=this._getNextIndex(),!0):!1}update(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this._setupBuffers(t)}getBuffer(t){let{feedbackBuffers:r}=this.bindings[this.currentIndex],i=t?r[t]:null;return i?i instanceof Fr?i:i.buffer:null}getData(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{varyingName:r}=t,i=this.getBuffer(r);return i?i.getData():null}delete(){for(let t in this.resources)this.resources[t].delete()}_initialize(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};this._setupBuffers(t),this.varyings=t.varyings||Object.keys(this.bindings[this.currentIndex].feedbackBuffers),this.varyings.length>0&&ye(fr(this.gl))}_getFeedbackBuffers(t){let{sourceBuffers:r={}}=t,i={};if(this.bindings[this.currentIndex]&&Object.assign(i,this.bindings[this.currentIndex].feedbackBuffers),this.feedbackMap)for(let s in this.feedbackMap){let n=this.feedbackMap[s];s in r&&(i[n]=s)}Object.assign(i,t.feedbackBuffers);for(let s in i){let n=i[s];if(typeof n==\"string\"){let o=r[n],{byteLength:c,usage:f,accessor:_}=o;i[s]=this._createNewBuffer(s,{byteLength:c,usage:f,accessor:_})}}return i}_setupBuffers(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{sourceBuffers:r=null}=t;Object.assign(this.feedbackMap,t.feedbackMap);let i=this._getFeedbackBuffers(t);this._updateBindings({sourceBuffers:r,feedbackBuffers:i})}_setupTransformFeedback(t,r){let{model:i}=r,{program:s}=i;t.transformFeedback=new ip(this.gl,{program:s,buffers:t.feedbackBuffers})}_updateBindings(t){if(this.bindings[this.currentIndex]=this._updateBinding(this.bindings[this.currentIndex],t),this.feedbackMap){let{sourceBuffers:r,feedbackBuffers:i}=this._swapBuffers(this.bindings[this.currentIndex]),s=this._getNextIndex();this.bindings[s]=this._updateBinding(this.bindings[s],{sourceBuffers:r,feedbackBuffers:i})}}_updateBinding(t,r){return t?(Object.assign(t.sourceBuffers,r.sourceBuffers),Object.assign(t.feedbackBuffers,r.feedbackBuffers),t.transformFeedback&&t.transformFeedback.setBuffers(t.feedbackBuffers),t):{sourceBuffers:Object.assign({},r.sourceBuffers),feedbackBuffers:Object.assign({},r.feedbackBuffers)}}_swapBuffers(t){if(!this.feedbackMap)return null;let r=Object.assign({},t.sourceBuffers),i=Object.assign({},t.feedbackBuffers);for(let s in this.feedbackMap){let n=this.feedbackMap[s];r[s]=t.feedbackBuffers[n],i[n]=t.sourceBuffers[s],ye(i[n]instanceof 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Fr(this.gl,{data:r,accessor:{size:1}}),this.elementCount=t}_updateBindings(t){if(this.bindings[this.currentIndex]=this._updateBinding(this.bindings[this.currentIndex],t),this._swapTexture){let{sourceTextures:r,targetTexture:i}=this._swapTextures(this.bindings[this.currentIndex]),s=this._getNextIndex();this.bindings[s]=this._updateBinding(this.bindings[s],{sourceTextures:r,targetTexture:i})}}_updateBinding(t,r){let{sourceBuffers:i,sourceTextures:s,targetTexture:n}=r;if(t||(t={sourceBuffers:{},sourceTextures:{},targetTexture:null}),Object.assign(t.sourceTextures,s),Object.assign(t.sourceBuffers,i),n){t.targetTexture=n;let{width:o,height:c}=n,{framebuffer:f}=t;f?(f.update({attachments:{36064:n},resizeAttachments:!1}),f.resize({width:o,height:c})):t.framebuffer=new yi(this.gl,{id:\"transform-framebuffer\",width:o,height:c,attachments:{36064:n}})}return t}_setSourceTextureParameters(){let t=this.currentIndex,{sourceTextures:r}=this.bindings[t];for(let i in r)r[i].setParameters(Nat)}_swapTextures(t){if(!this._swapTexture)return null;let r=Object.assign({},t.sourceTextures);r[this._swapTexture]=t.targetTexture;let i=t.sourceTextures[this._swapTexture];return{sourceTextures:r,targetTexture:i}}_createNewTexture(t){let r=yE(t,{parameters:{10241:9728,10240:9728,10242:33071,10243:33071},pixelStore:{37440:!1}});return this.ownTexture&&this.ownTexture.delete(),this.ownTexture=r,r}_getNextIndex(){return(this.currentIndex+1)%2}_processVertexShader(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{sourceTextures:r,targetTexture:i}=this.bindings[this.currentIndex],{vs:s,uniforms:n,targetTextureType:o,inject:c,samplerTextureMap:f}=G7({vs:t.vs,sourceTextureMap:r,targetTextureVarying:this.targetTextureVarying,targetTexture:i}),_=Ly([t.inject||{},c]);this.targetTextureType=o,this.samplerTextureMap=f;let w=t._fs||bb({version:Py(s),input:this.targetTextureVarying,inputType:o,output:Uat}),I=this.hasSourceTextures||this.targetTextureVarying?[uD].concat(t.modules||[]):t.modules;return{vs:s,fs:w,modules:I,uniforms:n,inject:_}}};var nc=class{static isSupported(t){return fr(t)}constructor(t){let r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};this.gl=t,this.model=null,this.elementCount=0,this.bufferTransform=null,this.textureTransform=null,this.elementIDBuffer=null,this._initialize(r),Object.seal(this)}delete(){let{model:t,bufferTransform:r,textureTransform:i}=this;t&&t.delete(),r&&r.delete(),i&&i.delete()}run(){let t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},{clearRenderTarget:r=!0}=t,i=this._updateDrawOptions(t);r&&i.framebuffer&&i.framebuffer.clear({color:!0}),this.model.transform(i)}swap(){let t=!1,r=[this.bufferTransform,this.textureTransform].filter(Boolean);for(let i of r)t=t||i.swap();ye(t,\"Nothing to swap\")}getBuffer(){let 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vec3(0.0)\n);\n`),elt=`\n`.concat(Z7,`\n\nstruct FragmentGeometry {\n vec2 uv;\n} geometry;\n\nfloat smoothedge(float edge, float x) {\n return smoothstep(edge - SMOOTH_EDGE_RADIUS, edge + SMOOTH_EDGE_RADIUS, x);\n}\n`),Y7={name:\"geometry\",vs:tlt,fs:elt};var rlt=Object.keys(Yr).map(e=>\"const int COORDINATE_SYSTEM_\".concat(e,\" = \").concat(Yr[e],\";\")).join(\"\"),ilt=Object.keys(Ja).map(e=>\"const int PROJECTION_MODE_\".concat(e,\" = \").concat(Ja[e],\";\")).join(\"\"),nlt=Object.keys(po).map(e=>\"const int UNIT_\".concat(e.toUpperCase(),\" = \").concat(po[e],\";\")).join(\"\"),Q7=\"\".concat(rlt,`\n`).concat(ilt,`\n`).concat(nlt,`\n\nuniform int project_uCoordinateSystem;\nuniform int project_uProjectionMode;\nuniform float project_uScale;\nuniform bool project_uWrapLongitude;\nuniform vec3 project_uCommonUnitsPerMeter;\nuniform vec3 project_uCommonUnitsPerWorldUnit;\nuniform vec3 project_uCommonUnitsPerWorldUnit2;\nuniform vec4 project_uCenter;\nuniform mat4 project_uModelMatrix;\nuniform mat4 project_uViewProjectionMatrix;\nuniform vec2 project_uViewportSize;\nuniform float project_uDevicePixelRatio;\nuniform float project_uFocalDistance;\nuniform vec3 project_uCameraPosition;\nuniform vec3 project_uCoordinateOrigin;\nuniform vec3 project_uCommonOrigin;\nuniform bool project_uPseudoMeters;\n\nconst float TILE_SIZE = 512.0;\nconst float PI = 3.1415926536;\nconst float WORLD_SCALE = TILE_SIZE / (PI * 2.0);\nconst vec3 ZERO_64_LOW = vec3(0.0);\nconst float EARTH_RADIUS = 6370972.0;\nconst float GLOBE_RADIUS = 256.0;\nfloat project_size_at_latitude(float lat) {\n float y = clamp(lat, -89.9, 89.9);\n return 1.0 / cos(radians(y));\n}\n\nfloat project_size() {\n if (project_uProjectionMode == PROJECTION_MODE_WEB_MERCATOR &&\n project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT &&\n project_uPseudoMeters == false) {\n \n if (geometry.position.w == 0.0) {\n return project_size_at_latitude(geometry.worldPosition.y);\n }\n \n float y = geometry.position.y / TILE_SIZE * 2.0 - 1.0;\n float y2 = y * y;\n float y4 = y2 * y2;\n float y6 = y4 * y2;\n return 1.0 + 4.9348 * y2 + 4.0587 * y4 + 1.5642 * y6;\n }\n return 1.0;\n}\n\nfloat project_size_at_latitude(float meters, float lat) {\n return meters * project_uCommonUnitsPerMeter.z * project_size_at_latitude(lat);\n}\nfloat project_size(float meters) {\n return meters * project_uCommonUnitsPerMeter.z * project_size();\n}\n\nvec2 project_size(vec2 meters) {\n return meters * project_uCommonUnitsPerMeter.xy * project_size();\n}\n\nvec3 project_size(vec3 meters) {\n return meters * project_uCommonUnitsPerMeter * project_size();\n}\n\nvec4 project_size(vec4 meters) {\n return vec4(meters.xyz * project_uCommonUnitsPerMeter, meters.w);\n}\nmat3 project_get_orientation_matrix(vec3 up) {\n vec3 uz = normalize(up);\n vec3 ux = abs(uz.z) == 1.0 ? vec3(1.0, 0.0, 0.0) : normalize(vec3(uz.y, -uz.x, 0));\n vec3 uy = cross(uz, ux);\n return mat3(ux, uy, uz);\n}\n\nbool project_needs_rotation(vec3 commonPosition, out mat3 transform) {\n if (project_uProjectionMode == PROJECTION_MODE_GLOBE) {\n transform = project_get_orientation_matrix(commonPosition);\n return true;\n }\n return false;\n}\nvec3 project_normal(vec3 vector) {\n vec4 normal_modelspace = project_uModelMatrix * vec4(vector, 0.0);\n vec3 n = normalize(normal_modelspace.xyz * project_uCommonUnitsPerMeter);\n mat3 rotation;\n if (project_needs_rotation(geometry.position.xyz, rotation)) {\n n = rotation * n;\n }\n return n;\n}\n\nvec4 project_offset_(vec4 offset) {\n float dy = offset.y;\n vec3 commonUnitsPerWorldUnit = project_uCommonUnitsPerWorldUnit + project_uCommonUnitsPerWorldUnit2 * dy;\n return vec4(offset.xyz * commonUnitsPerWorldUnit, offset.w);\n}\nvec2 project_mercator_(vec2 lnglat) {\n float x = lnglat.x;\n if (project_uWrapLongitude) {\n x = mod(x + 180., 360.0) - 180.;\n }\n float y = clamp(lnglat.y, -89.9, 89.9);\n return vec2(\n radians(x) + PI,\n PI + log(tan_fp32(PI * 0.25 + radians(y) * 0.5))\n ) * WORLD_SCALE;\n}\n\nvec3 project_globe_(vec3 lnglatz) {\n float lambda = radians(lnglatz.x);\n float phi = radians(lnglatz.y);\n float cosPhi = cos(phi);\n float D = (lnglatz.z / EARTH_RADIUS + 1.0) * GLOBE_RADIUS;\n\n return vec3(\n sin(lambda) * cosPhi,\n -cos(lambda) * cosPhi,\n sin(phi)\n ) * D;\n}\nvec4 project_position(vec4 position, vec3 position64Low) {\n vec4 position_world = project_uModelMatrix * position;\n if (project_uProjectionMode == PROJECTION_MODE_WEB_MERCATOR) {\n if (project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT) {\n return vec4(\n project_mercator_(position_world.xy),\n project_size_at_latitude(position_world.z, position_world.y),\n position_world.w\n );\n }\n if (project_uCoordinateSystem == COORDINATE_SYSTEM_CARTESIAN) {\n position_world.xyz += project_uCoordinateOrigin;\n }\n }\n if (project_uProjectionMode == PROJECTION_MODE_GLOBE) {\n if (project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT) {\n return vec4(\n project_globe_(position_world.xyz),\n position_world.w\n );\n }\n }\n if (project_uProjectionMode == PROJECTION_MODE_WEB_MERCATOR_AUTO_OFFSET) {\n if (project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT) {\n if (abs(position_world.y - project_uCoordinateOrigin.y) > 0.25) {\n return vec4(\n project_mercator_(position_world.xy) - project_uCommonOrigin.xy,\n project_size(position_world.z),\n position_world.w\n );\n }\n }\n }\n if (project_uProjectionMode == PROJECTION_MODE_IDENTITY ||\n (project_uProjectionMode == PROJECTION_MODE_WEB_MERCATOR_AUTO_OFFSET &&\n (project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT ||\n project_uCoordinateSystem == COORDINATE_SYSTEM_CARTESIAN))) {\n position_world.xyz -= project_uCoordinateOrigin;\n }\n return project_offset_(position_world) + project_offset_(project_uModelMatrix * vec4(position64Low, 0.0));\n}\n\nvec4 project_position(vec4 position) {\n return project_position(position, ZERO_64_LOW);\n}\n\nvec3 project_position(vec3 position, vec3 position64Low) {\n vec4 projected_position = project_position(vec4(position, 1.0), position64Low);\n return projected_position.xyz;\n}\n\nvec3 project_position(vec3 position) {\n vec4 projected_position = project_position(vec4(position, 1.0), ZERO_64_LOW);\n return projected_position.xyz;\n}\n\nvec2 project_position(vec2 position) {\n vec4 projected_position = project_position(vec4(position, 0.0, 1.0), ZERO_64_LOW);\n return projected_position.xy;\n}\n\nvec4 project_common_position_to_clipspace(vec4 position, mat4 viewProjectionMatrix, vec4 center) {\n return viewProjectionMatrix * position + center;\n}\nvec4 project_common_position_to_clipspace(vec4 position) {\n return project_common_position_to_clipspace(position, project_uViewProjectionMatrix, project_uCenter);\n}\nvec2 project_pixel_size_to_clipspace(vec2 pixels) {\n vec2 offset = pixels / project_uViewportSize * project_uDevicePixelRatio * 2.0;\n return offset * project_uFocalDistance;\n}\n\nfloat project_size_to_pixel(float meters) {\n return project_size(meters) * project_uScale;\n}\nfloat project_size_to_pixel(float size, int unit) {\n if (unit == UNIT_METERS) return project_size_to_pixel(size);\n if (unit == UNIT_COMMON) return size * project_uScale;\n return size;\n}\nfloat project_pixel_size(float pixels) {\n return pixels / project_uScale;\n}\nvec2 project_pixel_size(vec2 pixels) {\n return pixels / project_uScale;\n}\n`);function slt(e,t){if(e===t)return!0;if(Array.isArray(e)){let r=e.length;if(!t||t.length!==r)return!1;for(let i=0;i{for(let s in i)if(!slt(i[s],t[s])){r=e(i),t=i;break}return r}}var $7=[0,0,0,0],olt=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0],X7=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],alt=[0,0,0],K7=[0,0,0],llt=Yf(ult);function hD(e,t,r=K7){r.length<3&&(r=[r[0],r[1],0]);let i=r,s,n=!0;switch(t===Yr.LNGLAT_OFFSETS||t===Yr.METER_OFFSETS?s=r:s=e.isGeospatial?[Math.fround(e.longitude),Math.fround(e.latitude),0]:null,e.projectionMode){case Ja.WEB_MERCATOR:(t===Yr.LNGLAT||t===Yr.CARTESIAN)&&(s=[0,0,0],n=!1);break;case Ja.WEB_MERCATOR_AUTO_OFFSET:t===Yr.LNGLAT?i=s:t===Yr.CARTESIAN&&(i=[Math.fround(e.center[0]),Math.fround(e.center[1]),0],s=e.unprojectPosition(i),i[0]-=r[0],i[1]-=r[1],i[2]-=r[2]);break;case Ja.IDENTITY:i=e.position.map(Math.fround),i[2]=i[2]||0;break;case Ja.GLOBE:n=!1,s=null;break;default:n=!1}return{geospatialOrigin:s,shaderCoordinateOrigin:i,offsetMode:n}}function clt(e,t,r){let{viewMatrixUncentered:i,projectionMatrix:s}=e,{viewMatrix:n,viewProjectionMatrix:o}=e,c=$7,f=$7,_=e.cameraPosition,{geospatialOrigin:w,shaderCoordinateOrigin:I,offsetMode:R}=hD(e,t,r);return R&&(f=e.projectPosition(w||I),_=[_[0]-f[0],_[1]-f[1],_[2]-f[2]],f[3]=1,c=Nh([],f,o),n=i||n,o=qf([],s,n),o=qf([],o,olt)),{viewMatrix:n,viewProjectionMatrix:o,projectionCenter:c,originCommon:f,cameraPosCommon:_,shaderCoordinateOrigin:I,geospatialOrigin:w}}function J7({viewport:e,devicePixelRatio:t=1,modelMatrix:r=null,coordinateSystem:i=Yr.DEFAULT,coordinateOrigin:s=K7,autoWrapLongitude:n=!1}){i===Yr.DEFAULT&&(i=e.isGeospatial?Yr.LNGLAT:Yr.CARTESIAN);let o=llt({viewport:e,devicePixelRatio:t,coordinateSystem:i,coordinateOrigin:s});return o.project_uWrapLongitude=n,o.project_uModelMatrix=r||X7,o}function ult({viewport:e,devicePixelRatio:t,coordinateSystem:r,coordinateOrigin:i}){let{projectionCenter:s,viewProjectionMatrix:n,originCommon:o,cameraPosCommon:c,shaderCoordinateOrigin:f,geospatialOrigin:_}=clt(e,r,i),w=e.getDistanceScales(),I=[e.width*t,e.height*t],R=Nh([],[0,0,-e.focalDistance,1],e.projectionMatrix)[3]||1,N={project_uCoordinateSystem:r,project_uProjectionMode:e.projectionMode,project_uCoordinateOrigin:f,project_uCommonOrigin:o.slice(0,3),project_uCenter:s,project_uPseudoMeters:!!e._pseudoMeters,project_uViewportSize:I,project_uDevicePixelRatio:t,project_uFocalDistance:R,project_uCommonUnitsPerMeter:w.unitsPerMeter,project_uCommonUnitsPerWorldUnit:w.unitsPerMeter,project_uCommonUnitsPerWorldUnit2:alt,project_uScale:e.scale,project_uWrapLongitude:!1,project_uViewProjectionMatrix:n,project_uModelMatrix:X7,project_uCameraPosition:c};if(_){let j=e.getDistanceScales(_);switch(r){case Yr.METER_OFFSETS:N.project_uCommonUnitsPerWorldUnit=j.unitsPerMeter,N.project_uCommonUnitsPerWorldUnit2=j.unitsPerMeter2;break;case Yr.LNGLAT:case Yr.LNGLAT_OFFSETS:e._pseudoMeters||(N.project_uCommonUnitsPerMeter=j.unitsPerMeter),N.project_uCommonUnitsPerWorldUnit=j.unitsPerDegree,N.project_uCommonUnitsPerWorldUnit2=j.unitsPerDegree2;break;case Yr.CARTESIAN:N.project_uCommonUnitsPerWorldUnit=[1,1,j.unitsPerMeter[2]],N.project_uCommonUnitsPerWorldUnit2=[0,0,j.unitsPerMeter2[2]];break;default:break}}return N}var hlt={};function flt(e=hlt){return\"viewport\"in e?J7(e):{}}var Vh={name:\"project\",dependencies:[CE,Y7],vs:Q7,getUniforms:flt};function fD(){return[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]}function JA(e,t){let r=Nh([],t,e);return Fy(r,r,1/r[3]),r}function dD(e,t){let r=e%t;return r<0?t+r:r}function tG(e,t,r){return r*t+(1-r)*e}function kb(e,t,r){return er?r:e}function dlt(e){return Math.log(e)*Math.LOG2E}var Vy=Math.log2||dlt;function Bu(e,t){if(!e)throw new Error(t||\"@math.gl/web-mercator: assertion failed.\")}var jh=Math.PI,eG=jh/4,Fu=jh/180,pD=180/jh,jy=512,KE=4003e4,Gy=85.051129,rG=1.5;function Rb(e){return Math.pow(2,e)}function JE(e){return Vy(e)}function va(e){let[t,r]=e;Bu(Number.isFinite(t)),Bu(Number.isFinite(r)&&r>=-90&&r<=90,\"invalid latitude\");let i=t*Fu,s=r*Fu,n=jy*(i+jh)/(2*jh),o=jy*(jh+Math.log(Math.tan(eG+s*.5)))/(2*jh);return[n,o]}function oc(e){let[t,r]=e,i=t/jy*(2*jh)-jh,s=2*(Math.atan(Math.exp(r/jy*(2*jh)-jh))-eG);return[i*pD,s*pD]}function AD(e){let{latitude:t}=e;Bu(Number.isFinite(t));let r=Math.cos(t*Fu);return JE(KE*r)-9}function Db(e){let t=Math.cos(e*Fu);return jy/KE/t}function Wy(e){let{latitude:t,longitude:r,highPrecision:i=!1}=e;Bu(Number.isFinite(t)&&Number.isFinite(r));let s=jy,n=Math.cos(t*Fu),o=s/360,c=o/n,f=s/KE/n,_={unitsPerMeter:[f,f,f],metersPerUnit:[1/f,1/f,1/f],unitsPerDegree:[o,c,f],degreesPerUnit:[1/o,1/c,1/f]};if(i){let w=Fu*Math.tan(t*Fu)/n,I=o*w/2,R=s/KE*w,N=R/c*f;_.unitsPerDegree2=[0,I,R],_.unitsPerMeter2=[N,0,N]}return _}function Ob(e,t){let[r,i,s]=e,[n,o,c]=t,{unitsPerMeter:f,unitsPerMeter2:_}=Wy({longitude:r,latitude:i,highPrecision:!0}),w=va(e);w[0]+=n*(f[0]+_[0]*o),w[1]+=o*(f[1]+_[1]*o);let I=oc(w),R=(s||0)+(c||0);return Number.isFinite(s)||Number.isFinite(c)?[I[0],I[1],R]:I}function tP(e){let{height:t,pitch:r,bearing:i,altitude:s,scale:n,center:o}=e,c=fD();ag(c,c,[0,0,-s]),WE(c,c,-r*Fu),HE(c,c,i*Fu);let f=n/t;return By(c,c,[f,f,f]),o&&ag(c,c,Gj([],o)),c}function mD(e){let{width:t,height:r,altitude:i,pitch:s=0,offset:n,center:o,scale:c,nearZMultiplier:f=1,farZMultiplier:_=1}=e,{fovy:w=cg(rG)}=e;i!==void 0&&(w=cg(i));let I=w*Fu,R=s*Fu,N=Bb(w),j=N;o&&(j+=o[2]*c/Math.cos(R)/r);let Q=I*(.5+(n?n[1]:0)/r),et=Math.sin(Q)*j/Math.sin(kb(Math.PI/2-R-Q,.01,Math.PI-.01)),Y=Math.sin(R)*et+j,K=j*10,J=Math.min(Y*_,K);return{fov:I,aspect:t/r,focalDistance:N,near:f,far:J}}function cg(e){return 2*Math.atan(.5/e)*pD}function Bb(e){return .5/Math.tan(.5*e*Fu)}function Hy(e,t){let[r,i,s=0]=e;return Bu(Number.isFinite(r)&&Number.isFinite(i)&&Number.isFinite(s)),JA(t,[r,i,s,1])}function Qf(e,t,r=0){let[i,s,n]=e;if(Bu(Number.isFinite(i)&&Number.isFinite(s),\"invalid pixel coordinate\"),Number.isFinite(n))return JA(t,[i,s,n,1]);let o=JA(t,[i,s,0,1]),c=JA(t,[i,s,1,1]),f=o[2],_=c[2],w=f===_?0:((r||0)-f)/(_-f);return kE([],o,c,w)}function Fb(e){let{width:t,height:r,bounds:i,minExtent:s=0,maxZoom:n=24,offset:o=[0,0]}=e,[[c,f],[_,w]]=i,I=plt(e.padding),R=va([c,kb(w,-Gy,Gy)]),N=va([_,kb(f,-Gy,Gy)]),j=[Math.max(Math.abs(N[0]-R[0]),s),Math.max(Math.abs(N[1]-R[1]),s)],Q=[t-I.left-I.right-Math.abs(o[0])*2,r-I.top-I.bottom-Math.abs(o[1])*2];Bu(Q[0]>0&&Q[1]>0);let et=Q[0]/j[0],Y=Q[1]/j[1],K=(I.right-I.left)/2/et,J=(I.top-I.bottom)/2/Y,ut=[(N[0]+R[0])/2+K,(N[1]+R[1])/2+J],Et=oc(ut),kt=Math.min(n,Vy(Math.abs(Math.min(et,Y))));return Bu(Number.isFinite(kt)),{longitude:Et[0],latitude:Et[1],zoom:kt}}function plt(e=0){return typeof e==\"number\"?{top:e,bottom:e,left:e,right:e}:(Bu(Number.isFinite(e.top)&&Number.isFinite(e.bottom)&&Number.isFinite(e.left)&&Number.isFinite(e.right)),e)}var iG=Math.PI/180;function zb(e,t=0){let{width:r,height:i,unproject:s}=e,n={targetZ:t},o=s([0,i],n),c=s([r,i],n),f,_,w=e.fovy?.5*e.fovy*iG:Math.atan(.5/e.altitude),I=(90-e.pitch)*iG;return w>I-.01?(f=nG(e,0,t),_=nG(e,r,t)):(f=s([0,0],n),_=s([r,0],n)),[o,c,_,f]}function nG(e,t,r){let{pixelUnprojectionMatrix:i}=e,s=JA(i,[t,0,1,1]),n=JA(i,[t,e.height,1,1]),c=(r*e.distanceScales.unitsPerMeter[2]-s[2])/(n[2]-s[2]),f=kE([],s,n,c),_=oc(f);return _.push(r),_}var sG=512;function eP(e){let{width:t,height:r,pitch:i=0}=e,{longitude:s,latitude:n,zoom:o,bearing:c=0}=e;(s<-180||s>180)&&(s=dD(s+180,360)-180),(c<-180||c>180)&&(c=dD(c+180,360)-180);let f=Vy(r/sG);if(o<=f)o=f,n=0;else{let _=r/2/Math.pow(2,o),w=oc([0,_])[1];if(nI&&(n=I)}}return{width:t,height:r,longitude:s,latitude:n,zoom:o,pitch:i,bearing:c}}var oG=.01,mlt=[\"longitude\",\"latitude\",\"zoom\"],aG={curve:1.414,speed:1.2};function rP(e,t,r,i){let{startZoom:s,startCenterXY:n,uDelta:o,w0:c,u1:f,S:_,rho:w,rho2:I,r0:R}=lG(e,t,i);if(fo?0:w}function lG(e,t,r){r=Object.assign({},aG,r);let i=r.curve,s=e.zoom,n=[e.longitude,e.latitude],o=Rb(s),c=t.zoom,f=[t.longitude,t.latitude],_=Rb(c-s),w=va(n),I=va(f),R=Nj([],I,w),N=Math.max(e.width,e.height),j=N/_,Q=Bj(R)*o,et=Math.max(Q,oG),Y=i*i,K=(j*j-N*N+Y*Y*et*et)/(2*N*Y*et),J=(j*j-N*N-Y*Y*et*et)/(2*j*Y*et),ut=Math.log(Math.sqrt(K*K+1)-K),Et=Math.log(Math.sqrt(J*J+1)-J),kt=(Et-ut)/i;return{startZoom:s,startCenterXY:w,uDelta:R,w0:N,u1:Q,S:kt,rho:i,rho2:Y,r0:ut,r1:Et}}var _lt=`\nconst int max_lights = 2;\nuniform mat4 shadow_uViewProjectionMatrices[max_lights];\nuniform vec4 shadow_uProjectCenters[max_lights];\nuniform bool shadow_uDrawShadowMap;\nuniform bool shadow_uUseShadowMap;\nuniform int shadow_uLightId;\nuniform float shadow_uLightCount;\n\nvarying vec3 shadow_vPosition[max_lights];\n\nvec4 shadow_setVertexPosition(vec4 position_commonspace) {\n if (shadow_uDrawShadowMap) {\n return project_common_position_to_clipspace(position_commonspace, shadow_uViewProjectionMatrices[shadow_uLightId], shadow_uProjectCenters[shadow_uLightId]);\n }\n if (shadow_uUseShadowMap) {\n for (int i = 0; i < max_lights; i++) {\n if(i < int(shadow_uLightCount)) {\n vec4 shadowMap_position = project_common_position_to_clipspace(position_commonspace, shadow_uViewProjectionMatrices[i], shadow_uProjectCenters[i]);\n shadow_vPosition[i] = (shadowMap_position.xyz / shadowMap_position.w + 1.0) / 2.0;\n }\n }\n }\n return gl_Position;\n}\n`,ylt=`\nconst int max_lights = 2;\nuniform bool shadow_uDrawShadowMap;\nuniform bool shadow_uUseShadowMap;\nuniform sampler2D shadow_uShadowMap0;\nuniform sampler2D shadow_uShadowMap1;\nuniform vec4 shadow_uColor;\nuniform float shadow_uLightCount;\n\nvarying vec3 shadow_vPosition[max_lights];\n\nconst vec4 bitPackShift = vec4(1.0, 255.0, 65025.0, 16581375.0);\nconst vec4 bitUnpackShift = 1.0 / bitPackShift;\nconst vec4 bitMask = vec4(1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0, 0.0);\n\nfloat shadow_getShadowWeight(vec3 position, sampler2D shadowMap) {\n vec4 rgbaDepth = texture2D(shadowMap, position.xy);\n\n float z = dot(rgbaDepth, bitUnpackShift);\n return smoothstep(0.001, 0.01, position.z - z);\n}\n\nvec4 shadow_filterShadowColor(vec4 color) {\n if (shadow_uDrawShadowMap) {\n vec4 rgbaDepth = fract(gl_FragCoord.z * bitPackShift);\n rgbaDepth -= rgbaDepth.gbaa * bitMask;\n return rgbaDepth;\n }\n if (shadow_uUseShadowMap) {\n float shadowAlpha = 0.0;\n shadowAlpha += shadow_getShadowWeight(shadow_vPosition[0], shadow_uShadowMap0);\n if(shadow_uLightCount > 1.0) {\n shadowAlpha += shadow_getShadowWeight(shadow_vPosition[1], shadow_uShadowMap1);\n }\n shadowAlpha *= shadow_uColor.a / shadow_uLightCount;\n float blendedAlpha = shadowAlpha + color.a * (1.0 - shadowAlpha);\n\n return vec4(\n mix(color.rgb, shadow_uColor.rgb, shadowAlpha / blendedAlpha),\n blendedAlpha\n );\n }\n return color;\n}\n`,vlt=Yf(Tlt),xlt=Yf(Mlt),blt=[0,0,0,1],wlt=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0];function Slt(e,t){let[r,i,s]=e,n=Qf([r,i,s],t);return Number.isFinite(s)?n:[n[0],n[1],0]}function Tlt({viewport:e,center:t}){return new En(e.viewProjectionMatrix).invert().transform(t)}function Mlt({viewport:e,shadowMatrices:t}){let r=[],i=e.pixelUnprojectionMatrix,s=e.isGeospatial?void 0:1,n=[[0,0,s],[e.width,0,s],[0,e.height,s],[e.width,e.height,s],[0,0,-1],[e.width,0,-1],[0,e.height,-1],[e.width,e.height,-1]].map(o=>Slt(o,i));for(let o of t){let c=o.clone().translate(new Ve(e.center).negate()),f=n.map(w=>c.transform(w)),_=new En().ortho({left:Math.min(...f.map(w=>w[0])),right:Math.max(...f.map(w=>w[0])),bottom:Math.min(...f.map(w=>w[1])),top:Math.max(...f.map(w=>w[1])),near:Math.min(...f.map(w=>-w[2])),far:Math.max(...f.map(w=>-w[2]))});r.push(_.multiplyRight(o))}return r}function Elt(e,t){let{shadowEnabled:r=!0}=e;if(!r||!e.shadowMatrices||!e.shadowMatrices.length)return{shadow_uDrawShadowMap:!1,shadow_uUseShadowMap:!1};let i={shadow_uDrawShadowMap:!!e.drawToShadowMap,shadow_uUseShadowMap:e.shadowMaps?e.shadowMaps.length>0:!1,shadow_uColor:e.shadowColor||blt,shadow_uLightId:e.shadowLightId||0,shadow_uLightCount:e.shadowMatrices.length},s=vlt({viewport:e.viewport,center:t.project_uCenter}),n=[],o=xlt({shadowMatrices:e.shadowMatrices,viewport:e.viewport}).slice();for(let c=0;c0?i[\"shadow_uShadowMap\".concat(c)]=e.shadowMaps[c]:i[\"shadow_uShadowMap\".concat(c)]=e.dummyShadowMap;return i}var Nb={name:\"shadow\",dependencies:[Vh],vs:_lt,fs:ylt,inject:{\"vs:DECKGL_FILTER_GL_POSITION\":`\n position = shadow_setVertexPosition(geometry.position);\n `,\"fs:DECKGL_FILTER_COLOR\":`\n color = shadow_filterShadowColor(color);\n `},getUniforms:(e={},t={})=>\"viewport\"in e&&(e.drawToShadowMap||e.shadowMaps&&e.shadowMaps.length>0)?Elt(e,t):{}};var Plt={color:[255,255,255],intensity:1},cG=[{color:[255,255,255],intensity:1,direction:[-1,3,-1]},{color:[255,255,255],intensity:.9,direction:[1,-8,-2.5]}],Ilt=[0,0,0,200/255],qy=class{constructor(t={}){G(this,\"id\",\"lighting-effect\"),G(this,\"props\",void 0),G(this,\"shadowColor\",Ilt),G(this,\"shadow\",void 0),G(this,\"ambientLight\",void 0),G(this,\"directionalLights\",void 0),G(this,\"pointLights\",void 0),G(this,\"shadowPasses\",[]),G(this,\"shadowMaps\",[]),G(this,\"dummyShadowMap\",null),G(this,\"programManager\",void 0),G(this,\"shadowMatrices\",void 0),this.setProps(t)}setProps(t){this.ambientLight=null,this.directionalLights=[],this.pointLights=[];for(let r in t){let i=t[r];switch(i.type){case\"ambient\":this.ambientLight=i;break;case\"directional\":this.directionalLights.push(i);break;case\"point\":this.pointLights.push(i);break;default:}}this._applyDefaultLights(),this.shadow=this.directionalLights.some(r=>r.shadow),this.props=t}preRender(t,{layers:r,layerFilter:i,viewports:s,onViewportActive:n,views:o}){if(this.shadow){this.shadowMatrices=this._calculateMatrices(),this.shadowPasses.length===0&&this._createShadowPasses(t),this.programManager||(this.programManager=Uh.getDefaultProgramManager(t),Nb&&this.programManager.addDefaultModule(Nb)),this.dummyShadowMap||(this.dummyShadowMap=new pi(t,{width:1,height:1}));for(let c=0;ci.getProjectedLight({layer:t})),pointLights:this.pointLights.map(i=>i.getProjectedLight({layer:t}))},r}cleanup(){for(let t of this.shadowPasses)t.delete();this.shadowPasses.length=0,this.shadowMaps.length=0,this.dummyShadowMap&&(this.dummyShadowMap.delete(),this.dummyShadowMap=null),this.shadow&&this.programManager&&(this.programManager.removeDefaultModule(Nb),this.programManager=null)}_calculateMatrices(){let t=[];for(let r of this.directionalLights){let i=new En().lookAt({eye:new Ve(r.direction).negate()});t.push(i)}return t}_createShadowPasses(t){for(let r=0;rs&&(n=s);let o=this._pool,c=t.BYTES_PER_ELEMENT*n,f=o.findIndex(_=>_.byteLength>=c);if(f>=0){let _=new t(o.splice(f,1)[0],0,n);return i&&_.fill(0),_}return new t(n)}_release(t){if(!ArrayBuffer.isView(t))return;let r=this._pool,{buffer:i}=t,{byteLength:s}=i,n=r.findIndex(o=>o.byteLength>=s);n<0?r.push(i):(n>0||r.lengththis.opts.poolSize&&r.shift()}},Gh=new _D;function Yy(){return[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]}function hG(e){return[e[12],e[13],e[14]]}function fG(e){return{left:Zy(e[3]+e[0],e[7]+e[4],e[11]+e[8],e[15]+e[12]),right:Zy(e[3]-e[0],e[7]-e[4],e[11]-e[8],e[15]-e[12]),bottom:Zy(e[3]+e[1],e[7]+e[5],e[11]+e[9],e[15]+e[13]),top:Zy(e[3]-e[1],e[7]-e[5],e[11]-e[9],e[15]-e[13]),near:Zy(e[3]+e[2],e[7]+e[6],e[11]+e[10],e[15]+e[14]),far:Zy(e[3]-e[2],e[7]-e[6],e[11]-e[10],e[15]-e[14])}}var uG=new Ve;function Zy(e,t,r,i){uG.set(e,t,r);let s=uG.len();return{distance:i/s,normal:new Ve(-e/s,-t/s,-r/s)}}function Clt(e){return e-Math.fround(e)}var Ub;function iP(e,t){let{size:r=1,startIndex:i=0}=t,s=t.endIndex!==void 0?t.endIndex:e.length,n=(s-i)/r;Ub=Gh.allocate(Ub,n,{type:Float32Array,size:r*2});let o=i,c=0;for(;osuper.render({target:o,layers:t,layerFilter:r,views:i,viewports:s,onViewportActive:n,cullRect:I,effects:R?.filter(ut=>ut.useInPicking),pass:N,isPicking:!0,moduleParameters:Q}));return this._colorEncoderState=null,{decodePickingColor:Y&&Flt.bind(null,Y),stats:K}}shouldDrawLayer(t){let{pickable:r,operation:i}=t.props;return r&&i.includes(\"draw\")||i.includes(\"terrain\")||i.includes(\"mask\")}getModuleParameters(){return{pickingActive:1,pickingAttribute:this.pickZ,lightSources:{}}}getLayerParameters(t,r,i){let s={...t.props.parameters},{pickable:n,operation:o}=t.props;return this._colorEncoderState?n&&o.includes(\"draw\")&&(Object.assign(s,gG),s.blend=!0,s.blendColor=Blt(this._colorEncoderState,t,i)):s.blend=!1,o.includes(\"terrain\")&&(s.blend=!1),s}_resetColorEncoder(t){return this._colorEncoderState=t?null:{byLayer:new Map,byAlpha:[]},this._colorEncoderState}};function Blt(e,t,r){let{byLayer:i,byAlpha:s}=e,n,o=i.get(t);return o?(o.viewports.push(r),n=o.a):(n=i.size+1,n<=255?(o={a:n,layer:t,viewports:[r]},i.set(t,o),s[n]=o):(or.warn(\"Too many pickable layers, only picking the first 255\")(),n=0)),[0,0,0,n/255]}function Flt(e,t){let r=e.byAlpha[t[3]];return r&&{pickedLayer:r.layer,pickedViewports:r.viewports,pickedObjectIndex:r.layer.decodePickingColor(t)}}var tm={NO_STATE:\"Awaiting 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State transferred from previous layer\",INITIALIZED:\"Initialized\",AWAITING_GC:\"Discarded. Awaiting garbage collection\",AWAITING_FINALIZATION:\"No longer matched. Awaiting garbage collection\",FINALIZED:\"Finalized! Awaiting garbage collection\"},Qy=Symbol.for(\"component\"),zu=Symbol.for(\"propTypes\"),nP=Symbol.for(\"deprecatedProps\"),sp=Symbol.for(\"asyncPropDefaults\"),$f=Symbol.for(\"asyncPropOriginal\"),Wh=Symbol.for(\"asyncPropResolved\");function op(e,t=()=>!0){return Array.isArray(e)?_G(e,t,[]):t(e)?[e]:[]}function _G(e,t,r){let i=-1;for(;++i0}delete(){}getData(){return this.isLoaded?this._error?Promise.reject(this._error):this._content:this._loader.then(()=>this.getData())}setData(t,r){if(t===this._data&&!r)return;this._data=t;let i=++this._loadCount,s=t;typeof t==\"string\"&&(s=jA(t)),s instanceof Promise?(this.isLoaded=!1,this._loader=s.then(n=>{this._loadCount===i&&(this.isLoaded=!0,this._error=void 0,this._content=n)}).catch(n=>{this._loadCount===i&&(this.isLoaded=!0,this._error=n||!0)})):(this.isLoaded=!0,this._error=void 0,this._content=t);for(let n of this._subscribers)n.onChange(this.getData())}};var jb=class{constructor({gl:t,protocol:r}){G(this,\"protocol\",void 0),G(this,\"_context\",void 0),G(this,\"_resources\",void 0),G(this,\"_consumers\",void 0),G(this,\"_pruneRequest\",void 0),this.protocol=r||\"resource://\",this._context={gl:t,resourceManager:this},this._resources={},this._consumers={},this._pruneRequest=null}contains(t){return t.startsWith(this.protocol)?!0:t in this._resources}add({resourceId:t,data:r,forceUpdate:i=!1,persistent:s=!0}){let n=this._resources[t];n?n.setData(r,i):(n=new Vb(t,r,this._context),this._resources[t]=n),n.persistent=s}remove(t){let r=this._resources[t];r&&(r.delete(),delete this._resources[t])}unsubscribe({consumerId:t}){let r=this._consumers[t];if(r){for(let i in r){let s=r[i],n=this._resources[s.resourceId];n&&n.unsubscribe(s)}delete this._consumers[t],this.prune()}}subscribe({resourceId:t,onChange:r,consumerId:i,requestId:s=\"default\"}){let{_resources:n,protocol:o}=this;t.startsWith(o)&&(t=t.replace(o,\"\"),n[t]||this.add({resourceId:t,data:null,persistent:!1}));let c=n[t];if(this._track(i,s,c,r),c)return c.getData()}prune(){this._pruneRequest||(this._pruneRequest=setTimeout(()=>this._prune(),0))}finalize(){for(let t in this._resources)this._resources[t].delete()}_track(t,r,i,s){let n=this._consumers,o=n[t]=n[t]||{},c=o[r]||{},f=c.resourceId&&this._resources[c.resourceId];f&&(f.unsubscribe(c),this.prune()),i&&(o[r]=c,c.onChange=s,c.resourceId=i.id,i.subscribe(c))}_prune(){this._pruneRequest=null;for(let t of Object.keys(this._resources)){let r=this._resources[t];!r.persistent&&!r.inUse()&&(r.delete(),delete this._resources[t])}}};var zlt=`\nvec4 project_position_to_clipspace(\n vec3 position, vec3 position64Low, vec3 offset, out vec4 commonPosition\n) {\n vec3 projectedPosition = project_position(position, position64Low);\n mat3 rotation;\n if (project_needs_rotation(projectedPosition, rotation)) {\n // offset is specified as ENU\n // when in globe projection, rotate offset so that the ground alighs with the surface of the globe\n offset = rotation * offset;\n }\n commonPosition = vec4(projectedPosition + offset, 1.0);\n return project_common_position_to_clipspace(commonPosition);\n}\n\nvec4 project_position_to_clipspace(\n vec3 position, vec3 position64Low, vec3 offset\n) {\n vec4 commonPosition;\n return project_position_to_clipspace(position, position64Low, offset, commonPosition);\n}\n`,Rs={name:\"project32\",dependencies:[Vh],vs:zlt};var Ao={inject:{\"vs:DECKGL_FILTER_GL_POSITION\":`\n // for picking depth values\n picking_setPickingAttribute(position.z / position.w);\n `,\"vs:DECKGL_FILTER_COLOR\":`\n picking_setPickingColor(geometry.pickingColor);\n `,\"fs:#decl\":`\nuniform bool picking_uAttribute;\n `,\"fs:DECKGL_FILTER_COLOR\":{order:99,injection:`\n // use highlight color if this fragment belongs to the selected object.\n color = picking_filterHighlightColor(color);\n\n // use picking color if rendering to picking FBO.\n color = picking_filterPickingColor(color);\n `}},...QE};var Nlt=[Vh],Ult=[\"vs:DECKGL_FILTER_SIZE(inout vec3 size, VertexGeometry 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Gf({id:\"deck.gl\"}),viewport:s||new ac({id:\"DEFAULT-INITIAL-VIEWPORT\"}),timeline:n||new KA,resourceManager:this.resourceManager,onError:void 0},Object.seal(this)}finalize(){this.resourceManager.finalize();for(let t of this.layers)this._finalizeLayer(t)}needsRedraw(t={clearRedrawFlags:!1}){let r=this._needsRedraw;t.clearRedrawFlags&&(this._needsRedraw=!1);for(let i of this.layers){let s=i.getNeedsRedraw(t);r=r||s}return r}needsUpdate(){return this._nextLayers&&this._nextLayers!==this._lastRenderedLayers?\"layers changed\":this._needsUpdate}setNeedsRedraw(t){this._needsRedraw=this._needsRedraw||t}setNeedsUpdate(t){this._needsUpdate=this._needsUpdate||t}getLayers({layerIds:t}={}){return t?this.layers.filter(r=>t.find(i=>r.id.indexOf(i)===0)):this.layers}setProps(t){\"debug\"in t&&(this._debug=t.debug),\"userData\"in t&&(this.context.userData=t.userData),\"layers\"in t&&(this._nextLayers=t.layers),\"onError\"in t&&(this.context.onError=t.onError)}setLayers(t,r){Ls(Vlt,this,r,t),this._lastRenderedLayers=t;let i=op(t,Boolean);for(let s of i)s.context=this.context;this._updateLayers(this.layers,i)}updateLayers(){let t=this.needsUpdate();t&&(this.setNeedsRedraw(\"updating layers: \".concat(t)),this.setLayers(this._nextLayers||this._lastRenderedLayers,t)),this._nextLayers=null}_handleError(t,r,i){i.raiseError(r,\"\".concat(t,\" of \").concat(i))}_updateLayers(t,r){let i={};for(let o of t)i[o.id]?or.warn(\"Multiple old layers with same id \".concat(o.id))():i[o.id]=o;let s=[];this._updateSublayersRecursively(r,i,s),this._finalizeOldLayers(i);let n=!1;for(let o of s)if(o.hasUniformTransition()){n=\"Uniform transition in \".concat(o);break}this._needsUpdate=n,this.layers=s}_updateSublayersRecursively(t,r,i){for(let s of t){s.context=this.context;let n=r[s.id];n===null&&or.warn(\"Multiple new layers with same id \".concat(s.id))(),r[s.id]=null;let o=null;try{this._debug&&n!==s&&s.validateProps(),n?(this._transferLayerState(n,s),this._updateLayer(s)):this._initializeLayer(s),i.push(s),o=s.isComposite?s.getSubLayers():null}catch(c){this._handleError(\"matching\",c,s)}o&&this._updateSublayersRecursively(o,r,i)}}_finalizeOldLayers(t){for(let r in t){let i=t[r];i&&this._finalizeLayer(i)}}_initializeLayer(t){try{t._initialize(),t.lifecycle=tm.INITIALIZED}catch(r){this._handleError(\"initialization\",r,t)}}_transferLayerState(t,r){r._transferState(t),r.lifecycle=tm.MATCHED,r!==t&&(t.lifecycle=tm.AWAITING_GC)}_updateLayer(t){try{t._update()}catch(r){this._handleError(\"update\",r,t)}}_finalizeLayer(t){this._needsRedraw=this._needsRedraw||\"finalized \".concat(t),t.lifecycle=tm.AWAITING_FINALIZATION;try{t._finalize(),t.lifecycle=tm.FINALIZED}catch(r){this._handleError(\"finalization\",r,t)}}};function mo(e,t,r){if(e===t)return!0;if(!r||!e||!t)return!1;if(Array.isArray(e)){if(!Array.isArray(t)||e.length!==t.length)return!1;for(let i=0;ir.containsPixel(t)):this._viewports}getViews(){let t={};return this.views.forEach(r=>{t[r.id]=r}),t}getView(t){return this.views.find(r=>r.id===t)}getViewState(t){let r=typeof t==\"string\"?this.getView(t):t,i=r&&this.viewState[r.getViewStateId()]||this.viewState;return r?r.filterViewState(i):i}getViewport(t){return this._viewportMap[t]}unproject(t,r){let i=this.getViewports(),s={x:t[0],y:t[1]};for(let n=i.length-1;n>=0;--n){let o=i[n];if(o.containsPixel(s)){let c=t.slice();return c[0]-=o.x,c[1]-=o.y,o.unproject(c,r)}}return null}setProps(t){t.views&&this._setViews(t.views),t.viewState&&this._setViewState(t.viewState),(\"width\"in t||\"height\"in t)&&this._setSize(t.width,t.height),this._isUpdating||this._update()}_update(){this._isUpdating=!0,this._needsUpdate&&(this._needsUpdate=!1,this._rebuildViewports()),this._needsUpdate&&(this._needsUpdate=!1,this._rebuildViewports()),this._isUpdating=!1}_setSize(t,r){(t!==this.width||r!==this.height)&&(this.width=t,this.height=r,this.setNeedsUpdate(\"Size changed\"))}_setViews(t){t=op(t,Boolean),this._diffViews(t,this.views)&&this.setNeedsUpdate(\"views changed\"),this.views=t}_setViewState(t){t?(!mo(t,this.viewState,3)&&this.setNeedsUpdate(\"viewState changed\"),this.viewState=t):or.warn(\"missing `viewState` or `initialViewState`\")()}_onViewStateChange(t,r){this._eventCallbacks.onViewStateChange&&this._eventCallbacks.onViewStateChange({...r,viewId:t})}_createController(t,r){let i=r.type;return new i({timeline:this.timeline,eventManager:this._eventManager,onViewStateChange:this._onViewStateChange.bind(this,r.id),onStateChange:this._eventCallbacks.onInteractionStateChange,makeViewport:n=>{var o;return(o=this.getView(t.id))===null||o===void 0?void 0:o.makeViewport({viewState:n,width:this.width,height:this.height})}})}_updateController(t,r,i,s){let n=t.controller;if(n&&i){let o={...r,...n,id:t.id,x:i.x,y:i.y,width:i.width,height:i.height};return(!s||s.constructor!==n.type)&&(s=this._createController(t,o)),s&&s.setProps(o),s}return null}_rebuildViewports(){let{views:t}=this,r=this.controllers;this._viewports=[],this.controllers={};let i=!1;for(let s=t.length;s--;){let n=t[s],o=this.getViewState(n),c=n.makeViewport({viewState:o,width:this.width,height:this.height}),f=r[n.id],_=!!n.controller;_&&!f&&(i=!0),(i||!_)&&f&&(f.finalize(),f=null),this.controllers[n.id]=this._updateController(n,o,c,f),c&&this._viewports.unshift(c)}for(let s in r){let n=r[s];n&&!this.controllers[s]&&n.finalize()}this._buildViewportMap()}_buildViewportMap(){this._viewportMap={},this._viewports.forEach(t=>{t.id&&(this._viewportMap[t.id]=this._viewportMap[t.id]||t)})}_diffViews(t,r){return t.length!==r.length?!0:t.some((i,s)=>!t[s].equals(r[s]))}};var Glt=/([0-9]+\\.?[0-9]*)(%|px)/;function ap(e){switch(typeof e){case\"number\":return{position:e,relative:!1};case\"string\":let t=Glt.exec(e);if(t&&t.length>=3){let r=t[2]===\"%\",i=parseFloat(t[1]);return{position:r?i/100:i,relative:r}}default:throw new Error(\"Could not parse position string \".concat(e))}}function lp(e,t){return e.relative?Math.round(e.position*t):e.position}function _r(e,t){if(!e)throw new Error(t||\"deck.gl: assertion failed.\")}var Xc=class{constructor(t){G(this,\"id\",void 0),G(this,\"viewportInstance\",void 0),G(this,\"_x\",void 0),G(this,\"_y\",void 0),G(this,\"_width\",void 0),G(this,\"_height\",void 0),G(this,\"_padding\",void 0),G(this,\"props\",void 0);let{id:r,x:i=0,y:s=0,width:n=\"100%\",height:o=\"100%\",padding:c=null,viewportInstance:f}=t||{};_r(!f||f instanceof ac),this.viewportInstance=f,this.id=r||this.constructor.displayName||\"view\",this.props={...t,id:this.id},this._x=ap(i),this._y=ap(s),this._width=ap(n),this._height=ap(o),this._padding=c&&{left:ap(c.left||0),right:ap(c.right||0),top:ap(c.top||0),bottom:ap(c.bottom||0)},this.equals=this.equals.bind(this),Object.seal(this)}equals(t){return this===t?!0:this.viewportInstance?t.viewportInstance?this.viewportInstance.equals(t.viewportInstance):!1:this.ViewportType===t.ViewportType&&mo(this.props,t.props,2)}makeViewport({width:t,height:r,viewState:i}){if(this.viewportInstance)return this.viewportInstance;i=this.filterViewState(i);let s=this.getDimensions({width:t,height:r});return!s.height||!s.width?null:new this.ViewportType({...i,...this.props,...s})}getViewStateId(){let{viewState:t}=this.props;return typeof t==\"string\"?t:t?.id||this.id}filterViewState(t){if(this.props.viewState&&typeof this.props.viewState==\"object\"){if(!this.props.viewState.id)return this.props.viewState;let r={...t};for(let i in this.props.viewState)i!==\"id\"&&(r[i]=this.props.viewState[i]);return r}return t}getDimensions({width:t,height:r}){let i={x:lp(this._x,t),y:lp(this._y,r),width:lp(this._width,t),height:lp(this._height,r)};return this._padding&&(i.padding={left:lp(this._padding.left,t),top:lp(this._padding.top,r),right:lp(this._padding.right,t),bottom:lp(this._padding.bottom,r)}),i}get controller(){let t=this.props.controller;return t?t===!0?{type:this.ControllerType}:typeof t==\"function\"?{type:t}:{type:this.ControllerType,...t}:null}};var Kc=class{constructor(t){G(this,\"_inProgress\",void 0),G(this,\"_handle\",void 0),G(this,\"_timeline\",void 0),G(this,\"time\",void 0),G(this,\"settings\",void 0),this._inProgress=!1,this._handle=null,this._timeline=t,this.time=0,this.settings={duration:0}}get inProgress(){return this._inProgress}start(t){var r,i;this.cancel(),this.settings=t,this._inProgress=!0,(r=(i=this.settings).onStart)===null||r===void 0||r.call(i,this)}end(){if(this._inProgress){var t,r;this._timeline.removeChannel(this._handle),this._handle=null,this._inProgress=!1,(t=(r=this.settings).onEnd)===null||t===void 0||t.call(r,this)}}cancel(){if(this._inProgress){var t,r;(t=(r=this.settings).onInterrupt)===null||t===void 0||t.call(r,this),this._timeline.removeChannel(this._handle),this._handle=null,this._inProgress=!1}}update(){var t,r;if(!this._inProgress)return!1;if(this._handle===null){let{_timeline:i,settings:s}=this;this._handle=i.addChannel({delay:i.getTime(),duration:s.duration})}return this.time=this._timeline.getTime(this._handle),this._onUpdate(),(t=(r=this.settings).onUpdate)===null||t===void 0||t.call(r,this),this._timeline.isFinished(this._handle)&&this.end(),!0}_onUpdate(){}};var vG=()=>{},bD={BREAK:1,SNAP_TO_END:2,IGNORE:3},Wlt=e=>e,Hlt=bD.BREAK,Hb=class{constructor(t){G(this,\"getControllerState\",void 0),G(this,\"props\",void 0),G(this,\"propsInTransition\",void 0),G(this,\"transition\",void 0),G(this,\"onViewStateChange\",void 0),G(this,\"onStateChange\",void 0),G(this,\"_onTransitionUpdate\",r=>{let{time:i,settings:{interpolator:s,startProps:n,endProps:o,duration:c,easing:f}}=r,_=f(i/c),w=s.interpolateProps(n,o,_);this.propsInTransition=this.getControllerState({...this.props,...w}).getViewportProps(),this.onViewStateChange({viewState:this.propsInTransition,oldViewState:this.props})}),this.getControllerState=t.getControllerState,this.propsInTransition=null,this.transition=new Kc(t.timeline),this.onViewStateChange=t.onViewStateChange||vG,this.onStateChange=t.onStateChange||vG}finalize(){this.transition.cancel()}getViewportInTransition(){return this.propsInTransition}processViewStateChange(t){let r=!1,i=this.props;if(this.props=t,!i||this._shouldIgnoreViewportChange(i,t))return!1;if(this._isTransitionEnabled(t)){let s=i;if(this.transition.inProgress){let{interruption:n,endProps:o}=this.transition.settings;s={...i,...n===bD.SNAP_TO_END?o:this.propsInTransition||i}}this._triggerTransition(s,t),r=!0}else this.transition.cancel();return r}updateTransition(){this.transition.update()}_isTransitionEnabled(t){let{transitionDuration:r,transitionInterpolator:i}=t;return(r>0||r===\"auto\")&&!!i}_isUpdateDueToCurrentTransition(t){return this.transition.inProgress&&this.propsInTransition?this.transition.settings.interpolator.arePropsEqual(t,this.propsInTransition):!1}_shouldIgnoreViewportChange(t,r){return this.transition.inProgress?this.transition.settings.interruption===bD.IGNORE||this._isUpdateDueToCurrentTransition(r):this._isTransitionEnabled(r)?r.transitionInterpolator.arePropsEqual(t,r):!0}_triggerTransition(t,r){let i=this.getControllerState(t),s=this.getControllerState(r).shortestPathFrom(i),n=r.transitionInterpolator,o=n.getDuration?n.getDuration(t,r):r.transitionDuration;if(o===0)return;let c=n.initializeProps(t,s);this.propsInTransition={};let f={duration:o,easing:r.transitionEasing||Wlt,interpolator:n,interruption:r.transitionInterruption||Hlt,startProps:c.start,endProps:c.end,onStart:r.onTransitionStart,onUpdate:this._onTransitionUpdate,onInterrupt:this._onTransitionEnd(r.onTransitionInterrupt),onEnd:this._onTransitionEnd(r.onTransitionEnd)};this.transition.start(f),this.onStateChange({inTransition:!0}),this.updateTransition()}_onTransitionEnd(t){return r=>{this.propsInTransition=null,this.onStateChange({inTransition:!1,isZooming:!1,isPanning:!1,isRotating:!1}),t?.(r)}}};var hg=class{constructor(t){G(this,\"_propsToCompare\",void 0),G(this,\"_propsToExtract\",void 0),G(this,\"_requiredProps\",void 0);let{compare:r,extract:i,required:s}=t;this._propsToCompare=r,this._propsToExtract=i||r,this._requiredProps=s}arePropsEqual(t,r){for(let i of this._propsToCompare)if(!(i in t)||!(i in r)||!Ro(t[i],r[i]))return!1;return!0}initializeProps(t,r){let i={},s={};for(let n of this._propsToExtract)(n in t||n in r)&&(i[n]=t[n],s[n]=r[n]);return this._checkRequiredProps(i),this._checkRequiredProps(s),{start:i,end:s}}getDuration(t,r){return r.transitionDuration}_checkRequiredProps(t){this._requiredProps&&this._requiredProps.forEach(r=>{let i=t[r];_r(Number.isFinite(i)||Array.isArray(i),\"\".concat(r,\" is required for transition\"))})}};var qlt=[\"longitude\",\"latitude\",\"zoom\",\"bearing\",\"pitch\"],Zlt=[\"longitude\",\"latitude\",\"zoom\"],fg=class extends hg{constructor(t={}){let r=Array.isArray(t)?t:t.transitionProps,i=Array.isArray(t)?{}:t;i.transitionProps=Array.isArray(r)?{compare:r,required:r}:r||{compare:qlt,required:Zlt},super(i.transitionProps),G(this,\"opts\",void 0),this.opts=i}initializeProps(t,r){let i=super.initializeProps(t,r),{makeViewport:s,around:n}=this.opts;if(s&&n){let o=s(t),c=s(r),f=o.unproject(n);i.start.around=n,Object.assign(i.end,{around:c.project(f),aroundPosition:f,width:r.width,height:r.height})}return i}interpolateProps(t,r,i){let s={};for(let n of this._propsToExtract)s[n]=il(t[n]||0,r[n]||0,i);if(r.aroundPosition&&this.opts.makeViewport){let n=this.opts.makeViewport({...r,...s});Object.assign(s,n.panByPosition(r.aroundPosition,il(t.around,r.around,i)))}return s}};var em={transitionDuration:0},Ylt=300,sP=e=>1-(1-e)*(1-e),$y={WHEEL:[\"wheel\"],PAN:[\"panstart\",\"panmove\",\"panend\"],PINCH:[\"pinchstart\",\"pinchmove\",\"pinchend\"],TRIPLE_PAN:[\"tripanstart\",\"tripanmove\",\"tripanend\"],DOUBLE_TAP:[\"doubletap\"],KEYBOARD:[\"keydown\"]},dg={},qb=class{constructor(t){G(this,\"props\",void 0),G(this,\"state\",{}),G(this,\"transitionManager\",void 0),G(this,\"eventManager\",void 0),G(this,\"onViewStateChange\",void 0),G(this,\"onStateChange\",void 0),G(this,\"makeViewport\",void 0),G(this,\"_controllerState\",void 0),G(this,\"_events\",{}),G(this,\"_interactionState\",{isDragging:!1}),G(this,\"_customEvents\",[]),G(this,\"_eventStartBlocked\",null),G(this,\"_panMove\",!1),G(this,\"invertPan\",!1),G(this,\"dragMode\",\"rotate\"),G(this,\"inertia\",0),G(this,\"scrollZoom\",!0),G(this,\"dragPan\",!0),G(this,\"dragRotate\",!0),G(this,\"doubleClickZoom\",!0),G(this,\"touchZoom\",!0),G(this,\"touchRotate\",!1),G(this,\"keyboard\",!0),this.transitionManager=new Hb({...t,getControllerState:r=>new this.ControllerState(r),onViewStateChange:this._onTransition.bind(this),onStateChange:this._setInteractionState.bind(this)}),this.handleEvent=this.handleEvent.bind(this),this.eventManager=t.eventManager,this.onViewStateChange=t.onViewStateChange||(()=>{}),this.onStateChange=t.onStateChange||(()=>{}),this.makeViewport=t.makeViewport}set events(t){this.toggleEvents(this._customEvents,!1),this.toggleEvents(t,!0),this._customEvents=t,this.props&&this.setProps(this.props)}finalize(){for(let r in this._events)if(this._events[r]){var t;(t=this.eventManager)===null||t===void 0||t.off(r,this.handleEvent)}this.transitionManager.finalize()}handleEvent(t){this._controllerState=void 0;let r=this._eventStartBlocked;switch(t.type){case\"panstart\":return r?!1:this._onPanStart(t);case\"panmove\":return this._onPan(t);case\"panend\":return this._onPanEnd(t);case\"pinchstart\":return r?!1:this._onPinchStart(t);case\"pinchmove\":return this._onPinch(t);case\"pinchend\":return this._onPinchEnd(t);case\"tripanstart\":return r?!1:this._onTriplePanStart(t);case\"tripanmove\":return this._onTriplePan(t);case\"tripanend\":return this._onTriplePanEnd(t);case\"doubletap\":return this._onDoubleTap(t);case\"wheel\":return this._onWheel(t);case\"keydown\":return this._onKeyDown(t);default:return!1}}get controllerState(){return this._controllerState=this._controllerState||new this.ControllerState({makeViewport:this.makeViewport,...this.props,...this.state}),this._controllerState}getCenter(t){let{x:r,y:i}=this.props,{offsetCenter:s}=t;return[s.x-r,s.y-i]}isPointInBounds(t,r){let{width:i,height:s}=this.props;if(r&&r.handled)return!1;let n=t[0]>=0&&t[0]<=i&&t[1]>=0&&t[1]<=s;return n&&r&&r.stopPropagation(),n}isFunctionKeyPressed(t){let{srcEvent:r}=t;return!!(r.metaKey||r.altKey||r.ctrlKey||r.shiftKey)}isDragging(){return this._interactionState.isDragging||!1}blockEvents(t){let r=setTimeout(()=>{this._eventStartBlocked===r&&(this._eventStartBlocked=null)},t);this._eventStartBlocked=r}setProps(t){t.dragMode&&(this.dragMode=t.dragMode),this.props=t,\"transitionInterpolator\"in t||(t.transitionInterpolator=this._getTransitionProps().transitionInterpolator),this.transitionManager.processViewStateChange(t);let{inertia:r}=t;this.inertia=Number.isFinite(r)?r:r===!0?Ylt:0;let{scrollZoom:i=!0,dragPan:s=!0,dragRotate:n=!0,doubleClickZoom:o=!0,touchZoom:c=!0,touchRotate:f=!1,keyboard:_=!0}=t,w=!!this.onViewStateChange;this.toggleEvents($y.WHEEL,w&&i),this.toggleEvents($y.PAN,w),this.toggleEvents($y.PINCH,w&&(c||f)),this.toggleEvents($y.TRIPLE_PAN,w&&f),this.toggleEvents($y.DOUBLE_TAP,w&&o),this.toggleEvents($y.KEYBOARD,w&&_),this.scrollZoom=i,this.dragPan=s,this.dragRotate=n,this.doubleClickZoom=o,this.touchZoom=c,this.touchRotate=f,this.keyboard=_}updateTransition(){this.transitionManager.updateTransition()}toggleEvents(t,r){this.eventManager&&t.forEach(i=>{this._events[i]!==r&&(this._events[i]=r,r?this.eventManager.on(i,this.handleEvent):this.eventManager.off(i,this.handleEvent))})}updateViewport(t,r=null,i={}){let s={...t.getViewportProps(),...r},n=this.controllerState!==t;if(this.state=t.getState(),this._setInteractionState(i),n){let o=this.controllerState&&this.controllerState.getViewportProps();this.onViewStateChange&&this.onViewStateChange({viewState:s,interactionState:this._interactionState,oldViewState:o})}}_onTransition(t){this.onViewStateChange({...t,interactionState:this._interactionState})}_setInteractionState(t){Object.assign(this._interactionState,t),this.onStateChange(this._interactionState)}_onPanStart(t){let r=this.getCenter(t);if(!this.isPointInBounds(r,t))return!1;let i=this.isFunctionKeyPressed(t)||t.rightButton||!1;(this.invertPan||this.dragMode===\"pan\")&&(i=!i);let s=this.controllerState[i?\"panStart\":\"rotateStart\"]({pos:r});return this._panMove=i,this.updateViewport(s,em,{isDragging:!0}),!0}_onPan(t){return this.isDragging()?this._panMove?this._onPanMove(t):this._onPanRotate(t):!1}_onPanEnd(t){return this.isDragging()?this._panMove?this._onPanMoveEnd(t):this._onPanRotateEnd(t):!1}_onPanMove(t){if(!this.dragPan)return!1;let r=this.getCenter(t),i=this.controllerState.pan({pos:r});return this.updateViewport(i,em,{isDragging:!0,isPanning:!0}),!0}_onPanMoveEnd(t){let{inertia:r}=this;if(this.dragPan&&r&&t.velocity){let i=this.getCenter(t),s=[i[0]+t.velocityX*r/2,i[1]+t.velocityY*r/2],n=this.controllerState.pan({pos:s}).panEnd();this.updateViewport(n,{...this._getTransitionProps(),transitionDuration:r,transitionEasing:sP},{isDragging:!1,isPanning:!0})}else{let i=this.controllerState.panEnd();this.updateViewport(i,null,{isDragging:!1,isPanning:!1})}return!0}_onPanRotate(t){if(!this.dragRotate)return!1;let r=this.getCenter(t),i=this.controllerState.rotate({pos:r});return this.updateViewport(i,em,{isDragging:!0,isRotating:!0}),!0}_onPanRotateEnd(t){let{inertia:r}=this;if(this.dragRotate&&r&&t.velocity){let i=this.getCenter(t),s=[i[0]+t.velocityX*r/2,i[1]+t.velocityY*r/2],n=this.controllerState.rotate({pos:s}).rotateEnd();this.updateViewport(n,{...this._getTransitionProps(),transitionDuration:r,transitionEasing:sP},{isDragging:!1,isRotating:!0})}else{let i=this.controllerState.rotateEnd();this.updateViewport(i,null,{isDragging:!1,isRotating:!1})}return!0}_onWheel(t){if(!this.scrollZoom)return!1;let r=this.getCenter(t);if(!this.isPointInBounds(r,t))return!1;t.srcEvent.preventDefault();let{speed:i=.01,smooth:s=!1}=this.scrollZoom===!0?{}:this.scrollZoom,{delta:n}=t,o=2/(1+Math.exp(-Math.abs(n*i)));n<0&&o!==0&&(o=1/o);let c=this.controllerState.zoom({pos:r,scale:o});return this.updateViewport(c,{...this._getTransitionProps({around:r}),transitionDuration:s?250:1},{isZooming:!0,isPanning:!0}),!0}_onTriplePanStart(t){let r=this.getCenter(t);if(!this.isPointInBounds(r,t))return!1;let i=this.controllerState.rotateStart({pos:r});return this.updateViewport(i,em,{isDragging:!0}),!0}_onTriplePan(t){if(!this.touchRotate||!this.isDragging())return!1;let r=this.getCenter(t);r[0]-=t.deltaX;let i=this.controllerState.rotate({pos:r});return this.updateViewport(i,em,{isDragging:!0,isRotating:!0}),!0}_onTriplePanEnd(t){if(!this.isDragging())return!1;let{inertia:r}=this;if(this.touchRotate&&r&&t.velocityY){let i=this.getCenter(t),s=[i[0],i[1]+=t.velocityY*r/2],n=this.controllerState.rotate({pos:s});this.updateViewport(n,{...this._getTransitionProps(),transitionDuration:r,transitionEasing:sP},{isDragging:!1,isRotating:!0}),this.blockEvents(r)}else{let i=this.controllerState.rotateEnd();this.updateViewport(i,null,{isDragging:!1,isRotating:!1})}return!0}_onPinchStart(t){let r=this.getCenter(t);if(!this.isPointInBounds(r,t))return!1;let i=this.controllerState.zoomStart({pos:r}).rotateStart({pos:r});return dg._startPinchRotation=t.rotation,dg._lastPinchEvent=t,this.updateViewport(i,em,{isDragging:!0}),!0}_onPinch(t){if(!this.touchZoom&&!this.touchRotate||!this.isDragging())return!1;let r=this.controllerState;if(this.touchZoom){let{scale:i}=t,s=this.getCenter(t);r=r.zoom({pos:s,scale:i})}if(this.touchRotate){let{rotation:i}=t;r=r.rotate({deltaAngleX:dg._startPinchRotation-i})}return this.updateViewport(r,em,{isDragging:!0,isPanning:this.touchZoom,isZooming:this.touchZoom,isRotating:this.touchRotate}),dg._lastPinchEvent=t,!0}_onPinchEnd(t){if(!this.isDragging())return!1;let{inertia:r}=this,{_lastPinchEvent:i}=dg;if(this.touchZoom&&r&&i&&t.scale!==i.scale){let s=this.getCenter(t),n=this.controllerState.rotateEnd(),o=Math.log2(t.scale),c=(o-Math.log2(i.scale))/(t.deltaTime-i.deltaTime),f=Math.pow(2,o+c*r/2);n=n.zoom({pos:s,scale:f}).zoomEnd(),this.updateViewport(n,{...this._getTransitionProps({around:s}),transitionDuration:r,transitionEasing:sP},{isDragging:!1,isPanning:this.touchZoom,isZooming:this.touchZoom,isRotating:!1}),this.blockEvents(r)}else{let s=this.controllerState.zoomEnd().rotateEnd();this.updateViewport(s,null,{isDragging:!1,isPanning:!1,isZooming:!1,isRotating:!1})}return dg._startPinchRotation=null,dg._lastPinchEvent=null,!0}_onDoubleTap(t){if(!this.doubleClickZoom)return!1;let r=this.getCenter(t);if(!this.isPointInBounds(r,t))return!1;let i=this.isFunctionKeyPressed(t),s=this.controllerState.zoom({pos:r,scale:i?.5:2});return this.updateViewport(s,this._getTransitionProps({around:r}),{isZooming:!0,isPanning:!0}),this.blockEvents(100),!0}_onKeyDown(t){if(!this.keyboard)return!1;let r=this.isFunctionKeyPressed(t),{zoomSpeed:i,moveSpeed:s,rotateSpeedX:n,rotateSpeedY:o}=this.keyboard===!0?{}:this.keyboard,{controllerState:c}=this,f,_={};switch(t.srcEvent.code){case\"Minus\":f=r?c.zoomOut(i).zoomOut(i):c.zoomOut(i),_.isZooming=!0;break;case\"Equal\":f=r?c.zoomIn(i).zoomIn(i):c.zoomIn(i),_.isZooming=!0;break;case\"ArrowLeft\":r?(f=c.rotateLeft(n),_.isRotating=!0):(f=c.moveLeft(s),_.isPanning=!0);break;case\"ArrowRight\":r?(f=c.rotateRight(n),_.isRotating=!0):(f=c.moveRight(s),_.isPanning=!0);break;case\"ArrowUp\":r?(f=c.rotateUp(o),_.isRotating=!0):(f=c.moveUp(s),_.isPanning=!0);break;case\"ArrowDown\":r?(f=c.rotateDown(o),_.isRotating=!0):(f=c.moveDown(s),_.isPanning=!0);break;default:return!1}return this.updateViewport(f,this._getTransitionProps(),_),!0}_getTransitionProps(t){let{transition:r}=this;return!r||!r.transitionInterpolator?em:t?{...r,transitionInterpolator:new fg({...t,...r.transitionInterpolator.opts,makeViewport:this.controllerState.makeViewport})}:r}};var Zb=class{constructor(t,r){G(this,\"_viewportProps\",void 0),G(this,\"_state\",void 0),this._viewportProps=this.applyConstraints(t),this._state=r}getViewportProps(){return this._viewportProps}getState(){return this._state}};var xG=5,Qlt=1.2,wD=class extends Zb{constructor(t){let{width:r,height:i,latitude:s,longitude:n,zoom:o,bearing:c=0,pitch:f=0,altitude:_=1.5,position:w=[0,0,0],maxZoom:I=20,minZoom:R=0,maxPitch:N=60,minPitch:j=0,startPanLngLat:Q,startZoomLngLat:et,startRotatePos:Y,startBearing:K,startPitch:J,startZoom:ut,normalize:Et=!0}=t;_r(Number.isFinite(n)),_r(Number.isFinite(s)),_r(Number.isFinite(o)),super({width:r,height:i,latitude:s,longitude:n,zoom:o,bearing:c,pitch:f,altitude:_,maxZoom:I,minZoom:R,maxPitch:N,minPitch:j,normalize:Et,position:w},{startPanLngLat:Q,startZoomLngLat:et,startRotatePos:Y,startBearing:K,startPitch:J,startZoom:ut}),G(this,\"makeViewport\",void 0),this.makeViewport=t.makeViewport}panStart({pos:t}){return this._getUpdatedState({startPanLngLat:this._unproject(t)})}pan({pos:t,startPos:r}){let i=this.getState().startPanLngLat||this._unproject(r);if(!i)return this;let n=this.makeViewport(this.getViewportProps()).panByPosition(i,t);return this._getUpdatedState(n)}panEnd(){return this._getUpdatedState({startPanLngLat:null})}rotateStart({pos:t}){return this._getUpdatedState({startRotatePos:t,startBearing:this.getViewportProps().bearing,startPitch:this.getViewportProps().pitch})}rotate({pos:t,deltaAngleX:r=0,deltaAngleY:i=0}){let{startRotatePos:s,startBearing:n,startPitch:o}=this.getState();if(!s||n===void 0||o===void 0)return this;let c;return t?c=this._getNewRotation(t,s,o,n):c={bearing:n+r,pitch:o+i},this._getUpdatedState(c)}rotateEnd(){return this._getUpdatedState({startBearing:null,startPitch:null})}zoomStart({pos:t}){return this._getUpdatedState({startZoomLngLat:this._unproject(t),startZoom:this.getViewportProps().zoom})}zoom({pos:t,startPos:r,scale:i}){let{startZoom:s,startZoomLngLat:n}=this.getState();if(n||(s=this.getViewportProps().zoom,n=this._unproject(r)||this._unproject(t)),!n)return this;let{maxZoom:o,minZoom:c}=this.getViewportProps(),f=s+Math.log2(i);f=Il(f,c,o);let _=this.makeViewport({...this.getViewportProps(),zoom:f});return this._getUpdatedState({zoom:f,..._.panByPosition(n,t)})}zoomEnd(){return this._getUpdatedState({startZoomLngLat:null,startZoom:null})}zoomIn(t=2){return this._zoomFromCenter(t)}zoomOut(t=2){return this._zoomFromCenter(1/t)}moveLeft(t=100){return this._panFromCenter([t,0])}moveRight(t=100){return this._panFromCenter([-t,0])}moveUp(t=100){return this._panFromCenter([0,t])}moveDown(t=100){return this._panFromCenter([0,-t])}rotateLeft(t=15){return this._getUpdatedState({bearing:this.getViewportProps().bearing-t})}rotateRight(t=15){return this._getUpdatedState({bearing:this.getViewportProps().bearing+t})}rotateUp(t=10){return this._getUpdatedState({pitch:this.getViewportProps().pitch+t})}rotateDown(t=10){return this._getUpdatedState({pitch:this.getViewportProps().pitch-t})}shortestPathFrom(t){let r=t.getViewportProps(),i={...this.getViewportProps()},{bearing:s,longitude:n}=i;return Math.abs(s-r.bearing)>180&&(i.bearing=s<0?s+360:s-360),Math.abs(n-r.longitude)>180&&(i.longitude=n<0?n+360:n-360),i}applyConstraints(t){let{maxZoom:r,minZoom:i,zoom:s}=t;t.zoom=Il(s,i,r);let{maxPitch:n,minPitch:o,pitch:c}=t;t.pitch=Il(c,o,n);let{normalize:f=!0}=t;return f&&Object.assign(t,eP(t)),t}_zoomFromCenter(t){let{width:r,height:i}=this.getViewportProps();return this.zoom({pos:[r/2,i/2],scale:t})}_panFromCenter(t){let{width:r,height:i}=this.getViewportProps();return this.pan({startPos:[r/2,i/2],pos:[r/2+t[0],i/2+t[1]]})}_getUpdatedState(t){return new this.constructor({makeViewport:this.makeViewport,...this.getViewportProps(),...this.getState(),...t})}_unproject(t){let r=this.makeViewport(this.getViewportProps());return t&&r.unproject(t)}_getNewRotation(t,r,i,s){let n=t[0]-r[0],o=t[1]-r[1],c=t[1],f=r[1],{width:_,height:w}=this.getViewportProps(),I=n/_,R=0;o>0?Math.abs(w-f)>xG&&(R=o/(f-w)*Qlt):o<0&&f>xG&&(R=1-c/f),R=Il(R,-1,1);let{minPitch:N,maxPitch:j}=this.getViewportProps(),Q=s+180*I,et=i;return R>0?et=i+R*(j-i):R<0&&(et=i-R*(N-i)),{pitch:et,bearing:Q}}},Yb=class extends qb{constructor(...t){super(...t),G(this,\"ControllerState\",wD),G(this,\"transition\",{transitionDuration:300,transitionInterpolator:new fg({transitionProps:{compare:[\"longitude\",\"latitude\",\"zoom\",\"bearing\",\"pitch\",\"position\"],required:[\"longitude\",\"latitude\",\"zoom\"]}})}),G(this,\"dragMode\",\"pan\")}setProps(t){t.position=t.position||[0,0,0];let r=this.props;super.setProps(t),(!r||r.height!==t.height)&&this.updateViewport(new this.ControllerState({makeViewport:this.makeViewport,...t,...this.state}))}};var Xy=class extends Xc{get ViewportType(){return lc}get ControllerType(){return Yb}};G(Xy,\"displayName\",\"MapView\");var $lt=new qy;function Xlt(e,t){var r,i;let s=(r=e.order)!==null&&r!==void 0?r:1/0,n=(i=t.order)!==null&&i!==void 0?i:1/0;return s-n}var Qb=class{constructor(){G(this,\"effects\",void 0),G(this,\"_resolvedEffects\",[]),G(this,\"_defaultEffects\",[]),G(this,\"_needsRedraw\",void 0),this.effects=[],this._needsRedraw=\"Initial render\",this._setEffects([])}addDefaultEffect(t){let r=this._defaultEffects;if(!r.find(i=>i.id===t.id)){let i=r.findIndex(s=>Xlt(s,t)>0);i<0?r.push(t):r.splice(i,0,t),this._setEffects(this.effects)}}setProps(t){\"effects\"in t&&(mo(t.effects,this.effects,1)||this._setEffects(t.effects))}needsRedraw(t={clearRedrawFlags:!1}){let r=this._needsRedraw;return t.clearRedrawFlags&&(this._needsRedraw=!1),r}getEffects(){return this._resolvedEffects}_setEffects(t){let r={};for(let s of this.effects)r[s.id]=s;let i=[];for(let s of t){let n=r[s.id];n&&n!==s?n.setProps?(n.setProps(s.props),i.push(n)):(n.cleanup(),i.push(s)):i.push(s),delete r[s.id]}for(let s in r)r[s].cleanup();this.effects=i,this._resolvedEffects=i.concat(this._defaultEffects),t.some(s=>s instanceof qy)||this._resolvedEffects.push($lt),this._needsRedraw=\"effects changed\"}finalize(){for(let t of this._resolvedEffects)t.cleanup();this.effects.length=0,this._resolvedEffects.length=0,this._defaultEffects.length=0}};var $b=class extends sc{shouldDrawLayer(t){let{operation:r}=t.props;return r.includes(\"draw\")||r.includes(\"terrain\")}};var Klt=\"deckRenderer.renderLayers\",Xb=class{constructor(t){G(this,\"gl\",void 0),G(this,\"layerFilter\",void 0),G(this,\"drawPickingColors\",void 0),G(this,\"drawLayersPass\",void 0),G(this,\"pickLayersPass\",void 0),G(this,\"renderCount\",void 0),G(this,\"_needsRedraw\",void 0),G(this,\"renderBuffers\",void 0),G(this,\"lastPostProcessEffect\",void 0),this.gl=t,this.layerFilter=null,this.drawPickingColors=!1,this.drawLayersPass=new $b(t),this.pickLayersPass=new ug(t),this.renderCount=0,this._needsRedraw=\"Initial render\",this.renderBuffers=[],this.lastPostProcessEffect=null}setProps(t){this.layerFilter!==t.layerFilter&&(this.layerFilter=t.layerFilter,this._needsRedraw=\"layerFilter changed\"),this.drawPickingColors!==t.drawPickingColors&&(this.drawPickingColors=t.drawPickingColors,this._needsRedraw=\"drawPickingColors changed\")}renderLayers(t){if(!t.viewports.length)return;let r=this.drawPickingColors?this.pickLayersPass:this.drawLayersPass,i={layerFilter:this.layerFilter,isPicking:this.drawPickingColors,...t,target:t.target||yi.getDefaultFramebuffer(this.gl)};i.effects&&this._preRender(i.effects,i);let s=this.lastPostProcessEffect?this.renderBuffers[0]:i.target,n=r.render({...i,target:s});i.effects&&this._postRender(i.effects,i),this.renderCount++,Ls(Klt,this,n,t)}needsRedraw(t={clearRedrawFlags:!1}){let r=this._needsRedraw;return t.clearRedrawFlags&&(this._needsRedraw=!1),r}finalize(){let{renderBuffers:t}=this;for(let r of t)r.delete();t.length=0}_preRender(t,r){this.lastPostProcessEffect=null,r.preRenderStats=r.preRenderStats||{};for(let i of t)r.preRenderStats[i.id]=i.preRender(this.gl,r),i.postRender&&(this.lastPostProcessEffect=i.id);this.lastPostProcessEffect&&this._resizeRenderBuffers()}_resizeRenderBuffers(){let{renderBuffers:t}=this;t.length===0&&t.push(new yi(this.gl),new yi(this.gl));for(let r of t)r.resize()}_postRender(t,r){let{renderBuffers:i}=this,s={...r,inputBuffer:i[0],swapBuffer:i[1],target:null};for(let n of t)if(n.postRender){if(n.id===this.lastPostProcessEffect){s.target=r.target,n.postRender(this.gl,s);break}let o=n.postRender(this.gl,s);s.inputBuffer=o,s.swapBuffer=o===i[0]?i[1]:i[0]}}};var Jlt={pickedColor:null,pickedObjectIndex:-1};function bG({pickedColors:e,decodePickingColor:t,deviceX:r,deviceY:i,deviceRadius:s,deviceRect:n}){let{x:o,y:c,width:f,height:_}=n,w=s*s,I=-1,R=0;for(let N=0;N<_;N++){let j=N+c-i,Q=j*j;if(Q>w)R+=4*f;else for(let et=0;et=0){let K=et+o-r,J=K*K+Q;J<=w&&(w=J,I=R)}R+=4}}if(I>=0){let N=e.slice(I,I+4),j=t(N);if(j){let Q=Math.floor(I/4/f),et=I/4-Q*f;return{...j,pickedColor:N,pickedX:o+et,pickedY:c+Q}}or.error(\"Picked non-existent layer. Is picking buffer corrupt?\")()}return Jlt}function wG({pickedColors:e,decodePickingColor:t}){let r=new Map;if(e){for(let i=0;i=0){let n=e.slice(i,i+4),o=n.join(\",\");if(!r.has(o)){let c=t(n);c?r.set(o,{...c,color:n}):or.error(\"Picked non-existent layer. Is picking buffer corrupt?\")()}}}return Array.from(r.values())}function SD({pickInfo:e,viewports:t,pixelRatio:r,x:i,y:s,z:n}){let o=t[0];t.length>1&&(o=tct(e?.pickedViewports||t,{x:i,y:s}));let c;if(o){let f=[i-o.x,s-o.y];n!==void 0&&(f[2]=n),c=o.unproject(f)}return{color:null,layer:null,viewport:o,index:-1,picked:!1,x:i,y:s,pixel:[i,s],coordinate:c,devicePixel:e&&\"pickedX\"in e?[e.pickedX,e.pickedY]:void 0,pixelRatio:r}}function SG(e){let{pickInfo:t,lastPickedInfo:r,mode:i,layers:s}=e,{pickedColor:n,pickedLayer:o,pickedObjectIndex:c}=t,f=o?[o]:[];if(i===\"hover\"){let I=r.index,R=r.layerId,N=o?o.props.id:null;if(N!==R||c!==I){if(N!==R){let j=s.find(Q=>Q.props.id===R);j&&f.unshift(j)}r.layerId=N,r.index=c,r.info=null}}let _=SD(e),w=new Map;return w.set(null,_),f.forEach(I=>{let R={..._};I===o&&(R.color=n,R.index=c,R.picked=!0),R=TD({layer:I,info:R,mode:i});let N=R.layer;I===o&&i===\"hover\"&&(r.info=R),w.set(N.id,R),i===\"hover\"&&N.updateAutoHighlight(R)}),w}function TD({layer:e,info:t,mode:r}){for(;e&&t;){let i=t.layer||null;t.sourceLayer=i,t.layer=e,t=e.getPickingInfo({info:t,mode:r,sourceLayer:i}),e=e.parent}return t}function tct(e,t){for(let r=e.length-1;r>=0;r--){let i=e[r];if(i.containsPixel(t))return i}return e[0]}var Kb=class{constructor(t){G(this,\"gl\",void 0),G(this,\"pickingFBO\",void 0),G(this,\"depthFBO\",void 0),G(this,\"pickLayersPass\",void 0),G(this,\"layerFilter\",void 0),G(this,\"lastPickedInfo\",void 0),G(this,\"_pickable\",!0),this.gl=t,this.pickLayersPass=new ug(t),this.lastPickedInfo={index:-1,layerId:null,info:null}}setProps(t){\"layerFilter\"in t&&(this.layerFilter=t.layerFilter),\"_pickable\"in t&&(this._pickable=t._pickable)}finalize(){this.pickingFBO&&this.pickingFBO.delete(),this.depthFBO&&(this.depthFBO.color.delete(),this.depthFBO.delete())}pickObject(t){return this._pickClosestObject(t)}pickObjects(t){return this._pickVisibleObjects(t)}getLastPickedObject({x:t,y:r,layers:i,viewports:s},n=this.lastPickedInfo.info){let o=n&&n.layer&&n.layer.id,c=n&&n.viewport&&n.viewport.id,f=o?i.find(R=>R.id===o):null,_=c&&s.find(R=>R.id===c)||s[0],w=_&&_.unproject([t-_.x,r-_.y]);return{...n,...{x:t,y:r,viewport:_,coordinate:w,layer:f}}}_resizeBuffer(){var t,r;let{gl:i}=this;if(!this.pickingFBO&&(this.pickingFBO=new yi(i),yi.isSupported(i,{colorBufferFloat:!0}))){let s=new yi(i);s.attach({36064:new pi(i,{format:fr(i)?34836:6408,type:5126})}),this.depthFBO=s}(t=this.pickingFBO)===null||t===void 0||t.resize({width:i.canvas.width,height:i.canvas.height}),(r=this.depthFBO)===null||r===void 0||r.resize({width:i.canvas.width,height:i.canvas.height})}_getPickable(t){if(this._pickable===!1)return null;let r=t.filter(i=>this.pickLayersPass.shouldDrawLayer(i)&&!i.isComposite);return r.length?r:null}_pickClosestObject({layers:t,views:r,viewports:i,x:s,y:n,radius:o=0,depth:c=1,mode:f=\"query\",unproject3D:_,onViewportActive:w,effects:I}){let R=this._getPickable(t),N=El(this.gl);if(!R)return{result:[],emptyInfo:SD({viewports:i,x:s,y:n,pixelRatio:N})};this._resizeBuffer();let j=Sy(this.gl,[s,n],!0),Q=[j.x+Math.floor(j.width/2),j.y+Math.floor(j.height/2)],et=Math.round(o*N),{width:Y,height:K}=this.pickingFBO,J=this._getPickingRect({deviceX:Q[0],deviceY:Q[1],deviceRadius:et,deviceWidth:Y,deviceHeight:K}),ut={x:s-o,y:n-o,width:o*2+1,height:o*2+1},Et,kt=[],Xt=new Set;for(let qt=0;qt=_)break;let De=kt[ue],Ke={color:De.pickedColor,layer:null,index:De.pickedObjectIndex,picked:!0,x:s,y:n,pixelRatio:N};Ke=TD({layer:De.pickedLayer,info:Ke,mode:f});let rr=(le=Ke.object)!==null&&le!==void 0?le:\"\".concat(Ke.layer.id,\"[\").concat(Ke.index,\"]\");Xt.has(rr)||Xt.set(rr,Ke)}return Array.from(Xt.values())}_drawAndSample({layers:t,views:r,viewports:i,onViewportActive:s,deviceRect:n,cullRect:o,effects:c,pass:f},_=!1){let w=_?this.depthFBO:this.pickingFBO,I={layers:t,layerFilter:this.layerFilter,views:r,viewports:i,onViewportActive:s,pickingFBO:w,deviceRect:n,cullRect:o,effects:c,pass:f,pickZ:_,preRenderStats:{}};for(let K of c)K.useInPicking&&(I.preRenderStats[K.id]=K.preRender(this.gl,I));let{decodePickingColor:R}=this.pickLayersPass.render(I),{x:N,y:j,width:Q,height:et}=n,Y=new(_?Float32Array:Uint8Array)(Q*et*4);return Dh(w,{sourceX:N,sourceY:j,sourceWidth:Q,sourceHeight:et,target:Y}),{pickedColors:Y,decodePickingColor:R}}_getPickingRect({deviceX:t,deviceY:r,deviceRadius:i,deviceWidth:s,deviceHeight:n}){let o=Math.max(0,t-i),c=Math.max(0,r-i),f=Math.min(s,t+i+1)-o,_=Math.min(n,r+i+1)-c;return f<=0||_<=0?null:{x:o,y:c,width:f,height:_}}};var ect={zIndex:\"1\",position:\"absolute\",pointerEvents:\"none\",color:\"#a0a7b4\",backgroundColor:\"#29323c\",padding:\"10px\",top:\"0\",left:\"0\",display:\"none\"},Jb=class{constructor(t){G(this,\"el\",null),G(this,\"isVisible\",!1);let r=t.parentElement;r&&(this.el=document.createElement(\"div\"),this.el.className=\"deck-tooltip\",Object.assign(this.el.style,ect),r.appendChild(this.el))}setTooltip(t,r,i){let s=this.el;if(s){if(typeof t==\"string\")s.innerText=t;else if(t)t.text&&(s.innerText=t.text),t.html&&(s.innerHTML=t.html),t.className&&(s.className=t.className);else{this.isVisible=!1,s.style.display=\"none\";return}this.isVisible=!0,s.style.display=\"block\",s.style.transform=\"translate(\".concat(r,\"px, \").concat(i,\"px)\"),t&&typeof t==\"object\"&&\"style\"in t&&Object.assign(s.style,t.style)}}remove(){this.el&&(this.el.remove(),this.el=null)}};var pg=Ri(TG());var rct={mousedown:1,mousemove:2,mouseup:4};function ict(e,t){for(let r=0;r0&&i.type===\"pointerdown\"&&(ict(s,n=>n.pointerId===i.pointerId)||s.push(i)),t.call(this,i)}}function EG(e){e.prototype.handler=function(r){let i=rct[r.type];i&1&&r.button>=0&&(this.pressed=!0),i&2&&r.which===0&&(i=4),this.pressed&&(i&4&&(this.pressed=!1),this.callback(this.manager,i,{pointers:[r],changedPointers:[r],pointerType:\"mouse\",srcEvent:r}))}}MG(pg.PointerEventInput);EG(pg.MouseInput);var PG=pg.Manager,Hh=pg;var qh=class{constructor(t,r,i){this.element=t,this.callback=r,this.options={enable:!0,...i}}};var IG=Hh?[[Hh.Pan,{event:\"tripan\",pointers:3,threshold:0,enable:!1}],[Hh.Rotate,{enable:!1}],[Hh.Pinch,{enable:!1}],[Hh.Swipe,{enable:!1}],[Hh.Pan,{threshold:0,enable:!1}],[Hh.Press,{enable:!1}],[Hh.Tap,{event:\"doubletap\",taps:2,enable:!1}],[Hh.Tap,{event:\"anytap\",enable:!1}],[Hh.Tap,{enable:!1}]]:null,MD={tripan:[\"rotate\",\"pinch\",\"pan\"],rotate:[\"pinch\"],pinch:[\"pan\"],pan:[\"press\",\"doubletap\",\"anytap\",\"tap\"],doubletap:[\"anytap\"],anytap:[\"tap\"]},CG={doubletap:[\"tap\"]},LG={pointerdown:\"pointerdown\",pointermove:\"pointermove\",pointerup:\"pointerup\",touchstart:\"pointerdown\",touchmove:\"pointermove\",touchend:\"pointerup\",mousedown:\"pointerdown\",mousemove:\"pointermove\",mouseup:\"pointerup\"},Ky={KEY_EVENTS:[\"keydown\",\"keyup\"],MOUSE_EVENTS:[\"mousedown\",\"mousemove\",\"mouseup\",\"mouseover\",\"mouseout\",\"mouseleave\"],WHEEL_EVENTS:[\"wheel\",\"mousewheel\"]},kG={tap:\"tap\",anytap:\"anytap\",doubletap:\"doubletap\",press:\"press\",pinch:\"pinch\",pinchin:\"pinch\",pinchout:\"pinch\",pinchstart:\"pinch\",pinchmove:\"pinch\",pinchend:\"pinch\",pinchcancel:\"pinch\",rotate:\"rotate\",rotatestart:\"rotate\",rotatemove:\"rotate\",rotateend:\"rotate\",rotatecancel:\"rotate\",tripan:\"tripan\",tripanstart:\"tripan\",tripanmove:\"tripan\",tripanup:\"tripan\",tripandown:\"tripan\",tripanleft:\"tripan\",tripanright:\"tripan\",tripanend:\"tripan\",tripancancel:\"tripan\",pan:\"pan\",panstart:\"pan\",panmove:\"pan\",panup:\"pan\",pandown:\"pan\",panleft:\"pan\",panright:\"pan\",panend:\"pan\",pancancel:\"pan\",swipe:\"swipe\",swipeleft:\"swipe\",swiperight:\"swipe\",swipeup:\"swipe\",swipedown:\"swipe\"},ED={click:\"tap\",anyclick:\"anytap\",dblclick:\"doubletap\",mousedown:\"pointerdown\",mousemove:\"pointermove\",mouseup:\"pointerup\",mouseover:\"pointerover\",mouseout:\"pointerout\",mouseleave:\"pointerleave\"};var RG=typeof navigator<\"u\"&&navigator.userAgent?navigator.userAgent.toLowerCase():\"\",Ag=typeof window<\"u\"?window:global;var aP=!1;try{let e={get passive(){return aP=!0,!0}};Ag.addEventListener(\"test\",null,e),Ag.removeEventListener(\"test\",null)}catch{aP=!1}var nct=RG.indexOf(\"firefox\")!==-1,{WHEEL_EVENTS:sct}=Ky,DG=\"wheel\",OG=4.000244140625,oct=40,act=.25,tw=class extends qh{constructor(t,r,i){super(t,r,i),this.handleEvent=s=>{if(!this.options.enable)return;let n=s.deltaY;Ag.WheelEvent&&(nct&&s.deltaMode===Ag.WheelEvent.DOM_DELTA_PIXEL&&(n/=Ag.devicePixelRatio),s.deltaMode===Ag.WheelEvent.DOM_DELTA_LINE&&(n*=oct)),n!==0&&n%OG===0&&(n=Math.floor(n/OG)),s.shiftKey&&n&&(n=n*act),this.callback({type:DG,center:{x:s.clientX,y:s.clientY},delta:-n,srcEvent:s,pointerType:\"mouse\",target:s.target})},this.events=(this.options.events||[]).concat(sct),this.events.forEach(s=>t.addEventListener(s,this.handleEvent,aP?{passive:!1}:!1))}destroy(){this.events.forEach(t=>this.element.removeEventListener(t,this.handleEvent))}enableEventType(t,r){t===DG&&(this.options.enable=r)}};var{MOUSE_EVENTS:lct}=Ky,BG=\"pointermove\",FG=\"pointerover\",zG=\"pointerout\",NG=\"pointerenter\",UG=\"pointerleave\",ew=class extends qh{constructor(t,r,i){super(t,r,i),this.handleEvent=n=>{this.handleOverEvent(n),this.handleOutEvent(n),this.handleEnterEvent(n),this.handleLeaveEvent(n),this.handleMoveEvent(n)},this.pressed=!1;let{enable:s}=this.options;this.enableMoveEvent=s,this.enableLeaveEvent=s,this.enableEnterEvent=s,this.enableOutEvent=s,this.enableOverEvent=s,this.events=(this.options.events||[]).concat(lct),this.events.forEach(n=>t.addEventListener(n,this.handleEvent))}destroy(){this.events.forEach(t=>this.element.removeEventListener(t,this.handleEvent))}enableEventType(t,r){t===BG&&(this.enableMoveEvent=r),t===FG&&(this.enableOverEvent=r),t===zG&&(this.enableOutEvent=r),t===NG&&(this.enableEnterEvent=r),t===UG&&(this.enableLeaveEvent=r)}handleOverEvent(t){this.enableOverEvent&&t.type===\"mouseover\"&&this._emit(FG,t)}handleOutEvent(t){this.enableOutEvent&&t.type===\"mouseout\"&&this._emit(zG,t)}handleEnterEvent(t){this.enableEnterEvent&&t.type===\"mouseenter\"&&this._emit(NG,t)}handleLeaveEvent(t){this.enableLeaveEvent&&t.type===\"mouseleave\"&&this._emit(UG,t)}handleMoveEvent(t){if(this.enableMoveEvent)switch(t.type){case\"mousedown\":t.button>=0&&(this.pressed=!0);break;case\"mousemove\":t.which===0&&(this.pressed=!1),this.pressed||this._emit(BG,t);break;case\"mouseup\":this.pressed=!1;break;default:}}_emit(t,r){this.callback({type:t,center:{x:r.clientX,y:r.clientY},srcEvent:r,pointerType:\"mouse\",target:r.target})}};var{KEY_EVENTS:cct}=Ky,VG=\"keydown\",jG=\"keyup\",rw=class extends qh{constructor(t,r,i){super(t,r,i),this.handleEvent=s=>{let n=s.target||s.srcElement;n.tagName===\"INPUT\"&&n.type===\"text\"||n.tagName===\"TEXTAREA\"||(this.enableDownEvent&&s.type===\"keydown\"&&this.callback({type:VG,srcEvent:s,key:s.key,target:s.target}),this.enableUpEvent&&s.type===\"keyup\"&&this.callback({type:jG,srcEvent:s,key:s.key,target:s.target}))},this.enableDownEvent=this.options.enable,this.enableUpEvent=this.options.enable,this.events=(this.options.events||[]).concat(cct),t.tabIndex=this.options.tabIndex||0,t.style.outline=\"none\",this.events.forEach(s=>t.addEventListener(s,this.handleEvent))}destroy(){this.events.forEach(t=>this.element.removeEventListener(t,this.handleEvent))}enableEventType(t,r){t===VG&&(this.enableDownEvent=r),t===jG&&(this.enableUpEvent=r)}};var GG=\"contextmenu\",iw=class extends qh{constructor(t,r,i){super(t,r,i),this.handleEvent=s=>{this.options.enable&&this.callback({type:GG,center:{x:s.clientX,y:s.clientY},srcEvent:s,pointerType:\"mouse\",target:s.target})},t.addEventListener(\"contextmenu\",this.handleEvent)}destroy(){this.element.removeEventListener(\"contextmenu\",this.handleEvent)}enableEventType(t,r){t===GG&&(this.options.enable=r)}};var uct={pointerdown:1,pointermove:2,pointerup:4,mousedown:1,mousemove:2,mouseup:4},hct=1,fct=2,dct=3,pct=0,Act=1,mct=2,gct=1,_ct=2,yct=4;function WG(e){let t=uct[e.srcEvent.type];if(!t)return null;let{buttons:r,button:i,which:s}=e.srcEvent,n=!1,o=!1,c=!1;return t===4||t===2&&!Number.isFinite(r)?(n=s===hct,o=s===fct,c=s===dct):t===2?(n=!!(r&gct),o=!!(r&yct),c=!!(r&_ct)):t===1&&(n=i===pct,o=i===Act,c=i===mct),{leftButton:n,middleButton:o,rightButton:c}}function HG(e,t){let r=e.center;if(!r)return null;let i=t.getBoundingClientRect(),s=i.width/t.offsetWidth||1,n=i.height/t.offsetHeight||1,o={x:(r.x-i.left-t.clientLeft)/s,y:(r.y-i.top-t.clientTop)/n};return{center:r,offsetCenter:o}}var PD={srcElement:\"root\",priority:0},nw=class{constructor(t){this.handleEvent=r=>{if(this.isEmpty())return;let i=this._normalizeEvent(r),s=r.srcEvent.target;for(;s&&s!==i.rootElement;){if(this._emit(i,s),i.handled)return;s=s.parentNode}this._emit(i,\"root\")},this.eventManager=t,this.handlers=[],this.handlersByElement=new Map,this._active=!1}isEmpty(){return!this._active}add(t,r,i,s=!1,n=!1){let{handlers:o,handlersByElement:c}=this,f=PD;typeof i==\"string\"||i&&i.addEventListener?f={...PD,srcElement:i}:i&&(f={...PD,...i});let _=c.get(f.srcElement);_||(_=[],c.set(f.srcElement,_));let w={type:t,handler:r,srcElement:f.srcElement,priority:f.priority};s&&(w.once=!0),n&&(w.passive=!0),o.push(w),this._active=this._active||!w.passive;let I=_.length-1;for(;I>=0&&!(_[I].priority>=w.priority);)I--;_.splice(I+1,0,w)}remove(t,r){let{handlers:i,handlersByElement:s}=this;for(let n=i.length-1;n>=0;n--){let o=i[n];if(o.type===t&&o.handler===r){i.splice(n,1);let c=s.get(o.srcElement);c.splice(c.indexOf(o),1),c.length===0&&s.delete(o.srcElement)}}this._active=i.some(n=>!n.passive)}_emit(t,r){let i=this.handlersByElement.get(r);if(i){let s=!1,n=()=>{t.handled=!0},o=()=>{t.handled=!0,s=!0},c=[];for(let f=0;f{t.srcEvent.preventDefault()},stopImmediatePropagation:null,stopPropagation:null,handled:!1,rootElement:r}}};var vct={events:null,recognizers:null,recognizerOptions:{},Manager:PG,touchAction:\"none\",tabIndex:0},Jy=class{constructor(t=null,r){this._onBasicInput=s=>{let{srcEvent:n}=s,o=LG[n.type];o&&this.manager.emit(o,s)},this._onOtherEvent=s=>{this.manager.emit(s.type,s)},this.options={...vct,...r},this.events=new Map,this.setElement(t);let{events:i}=this.options;i&&this.on(i)}getElement(){return this.element}setElement(t){if(this.element&&this.destroy(),this.element=t,!t)return;let{options:r}=this,i=r.Manager;this.manager=new i(t,{touchAction:r.touchAction,recognizers:r.recognizers||IG}).on(\"hammer.input\",this._onBasicInput),r.recognizers||Object.keys(MD).forEach(s=>{let n=this.manager.get(s);n&&MD[s].forEach(o=>{n.recognizeWith(o)})});for(let s in r.recognizerOptions){let n=this.manager.get(s);if(n){let o=r.recognizerOptions[s];delete o.enable,n.set(o)}}this.wheelInput=new tw(t,this._onOtherEvent,{enable:!1}),this.moveInput=new ew(t,this._onOtherEvent,{enable:!1}),this.keyInput=new rw(t,this._onOtherEvent,{enable:!1,tabIndex:r.tabIndex}),this.contextmenuInput=new iw(t,this._onOtherEvent,{enable:!1});for(let[s,n]of this.events)n.isEmpty()||(this._toggleRecognizer(n.recognizerName,!0),this.manager.on(s,n.handleEvent))}destroy(){this.element&&(this.wheelInput.destroy(),this.moveInput.destroy(),this.keyInput.destroy(),this.contextmenuInput.destroy(),this.manager.destroy(),this.wheelInput=null,this.moveInput=null,this.keyInput=null,this.contextmenuInput=null,this.manager=null,this.element=null)}on(t,r,i){this._addEventHandler(t,r,i,!1)}once(t,r,i){this._addEventHandler(t,r,i,!0)}watch(t,r,i){this._addEventHandler(t,r,i,!1,!0)}off(t,r){this._removeEventHandler(t,r)}_toggleRecognizer(t,r){let{manager:i}=this;if(!i)return;let s=i.get(t);if(s&&s.options.enable!==r){s.set({enable:r});let n=CG[t];n&&!this.options.recognizers&&n.forEach(o=>{let c=i.get(o);r?(c.requireFailure(t),s.dropRequireFailure(o)):c.dropRequireFailure(t)})}this.wheelInput.enableEventType(t,r),this.moveInput.enableEventType(t,r),this.keyInput.enableEventType(t,r),this.contextmenuInput.enableEventType(t,r)}_addEventHandler(t,r,i,s,n){if(typeof t!=\"string\"){i=r;for(let w in t)this._addEventHandler(w,t[w],i,s,n);return}let{manager:o,events:c}=this,f=ED[t]||t,_=c.get(f);_||(_=new nw(this),c.set(f,_),_.recognizerName=kG[f]||f,o&&o.on(f,_.handleEvent)),_.add(t,r,i,s,n),_.isEmpty()||this._toggleRecognizer(_.recognizerName,!0)}_removeEventHandler(t,r){if(typeof t!=\"string\"){for(let o in t)this._removeEventHandler(o,t[o]);return}let{events:i}=this,s=ED[t]||t,n=i.get(s);if(n&&(n.remove(t,r),n.isEmpty())){let{recognizerName:o}=n,c=!1;for(let f of i.values())if(f.recognizerName===o&&!f.isEmpty()){c=!0;break}c||this._toggleRecognizer(o,!1)}}};function mg(){}var xct=({isDragging:e})=>e?\"grabbing\":\"grab\",qG={id:\"\",width:\"100%\",height:\"100%\",style:null,viewState:null,initialViewState:null,pickingRadius:0,layerFilter:null,glOptions:{},parameters:{},parent:null,gl:null,canvas:null,layers:[],effects:[],views:null,controller:null,useDevicePixels:!0,touchAction:\"none\",eventRecognizerOptions:{},_framebuffer:null,_animate:!1,_pickable:!0,_typedArrayManagerProps:{},_customRender:null,onWebGLInitialized:mg,onResize:mg,onViewStateChange:mg,onInteractionStateChange:mg,onBeforeRender:mg,onAfterRender:mg,onLoad:mg,onError:e=>or.error(e.message,e.cause)(),onHover:null,onClick:null,onDragStart:null,onDrag:null,onDragEnd:null,_onMetrics:null,getCursor:xct,getTooltip:null,debug:!1,drawPickingColors:!1},cp=class{constructor(t){G(this,\"props\",void 0),G(this,\"width\",0),G(this,\"height\",0),G(this,\"userData\",{}),G(this,\"canvas\",null),G(this,\"viewManager\",null),G(this,\"layerManager\",null),G(this,\"effectManager\",null),G(this,\"deckRenderer\",null),G(this,\"deckPicker\",null),G(this,\"eventManager\",null),G(this,\"tooltip\",null),G(this,\"metrics\",void 0),G(this,\"animationLoop\",void 0),G(this,\"stats\",void 0),G(this,\"viewState\",void 0),G(this,\"cursorState\",void 0),G(this,\"_needsRedraw\",void 0),G(this,\"_pickRequest\",void 0),G(this,\"_lastPointerDownInfo\",null),G(this,\"_metricsCounter\",void 0),G(this,\"_onPointerMove\",r=>{let{_pickRequest:i}=this;if(r.type===\"pointerleave\")i.x=-1,i.y=-1,i.radius=0;else{if(r.leftButton||r.rightButton)return;{let s=r.offsetCenter;if(!s)return;i.x=s.x,i.y=s.y,i.radius=this.props.pickingRadius}}this.layerManager&&(this.layerManager.context.mousePosition={x:i.x,y:i.y}),i.event=r}),G(this,\"_onEvent\",r=>{let i=nR[r.type],s=r.offsetCenter;if(!i||!s||!this.layerManager)return;let n=this.layerManager.getLayers(),o=this.deckPicker.getLastPickedObject({x:s.x,y:s.y,layers:n,viewports:this.getViewports(s)},this._lastPointerDownInfo),{layer:c}=o,f=c&&(c[i.handler]||c.props[i.handler]),_=this.props[i.handler],w=!1;f&&(w=f.call(c,o,r)),!w&&_&&_(o,r)}),G(this,\"_onPointerDown\",r=>{let i=r.offsetCenter,s=this._pick(\"pickObject\",\"pickObject Time\",{x:i.x,y:i.y,radius:this.props.pickingRadius});this._lastPointerDownInfo=s.result[0]||s.emptyInfo}),this.props={...qG,...t},t=this.props,this._needsRedraw=\"Initial render\",this._pickRequest={mode:\"hover\",x:-1,y:-1,radius:0,event:null},this.cursorState={isHovering:!1,isDragging:!1},t.viewState&&t.initialViewState&&or.warn(\"View state tracking is disabled. Use either `initialViewState` for auto update or `viewState` for manual update.\")(),vy()===\"IE\"&&or.warn(\"IE 11 is not supported\")(),this.viewState=t.initialViewState,t.gl||typeof document<\"u\"&&(this.canvas=this._createCanvas(t)),this.animationLoop=this._createAnimationLoop(t),this.stats=new Gf({id:\"deck.gl\"}),this.metrics={fps:0,setPropsTime:0,updateAttributesTime:0,framesRedrawn:0,pickTime:0,pickCount:0,gpuTime:0,gpuTimePerFrame:0,cpuTime:0,cpuTimePerFrame:0,bufferMemory:0,textureMemory:0,renderbufferMemory:0,gpuMemory:0},this._metricsCounter=0,this.setProps(t),t._typedArrayManagerProps&&Gh.setOptions(t._typedArrayManagerProps),this.animationLoop.start()}finalize(){var t,r,i,s,n,o,c,f;if((t=this.animationLoop)===null||t===void 0||t.stop(),this.animationLoop=null,this._lastPointerDownInfo=null,(r=this.layerManager)===null||r===void 0||r.finalize(),this.layerManager=null,(i=this.viewManager)===null||i===void 0||i.finalize(),this.viewManager=null,(s=this.effectManager)===null||s===void 0||s.finalize(),this.effectManager=null,(n=this.deckRenderer)===null||n===void 0||n.finalize(),this.deckRenderer=null,(o=this.deckPicker)===null||o===void 0||o.finalize(),this.deckPicker=null,(c=this.eventManager)===null||c===void 0||c.destroy(),this.eventManager=null,(f=this.tooltip)===null||f===void 0||f.remove(),this.tooltip=null,!this.props.canvas&&!this.props.gl&&this.canvas){var _;(_=this.canvas.parentElement)===null||_===void 0||_.removeChild(this.canvas),this.canvas=null}}setProps(t){this.stats.get(\"setProps Time\").timeStart(),\"onLayerHover\"in t&&or.removed(\"onLayerHover\",\"onHover\")(),\"onLayerClick\"in t&&or.removed(\"onLayerClick\",\"onClick\")(),t.initialViewState&&!mo(this.props.initialViewState,t.initialViewState,3)&&(this.viewState=t.initialViewState),Object.assign(this.props,t),this._setCanvasSize(this.props);let r=Object.create(this.props);Object.assign(r,{views:this._getViews(),width:this.width,height:this.height,viewState:this._getViewState()}),this.animationLoop.setProps(r),this.layerManager&&(this.viewManager.setProps(r),this.layerManager.activateViewport(this.getViewports()[0]),this.layerManager.setProps(r),this.effectManager.setProps(r),this.deckRenderer.setProps(r),this.deckPicker.setProps(r)),this.stats.get(\"setProps Time\").timeEnd()}needsRedraw(t={clearRedrawFlags:!1}){if(!this.layerManager)return!1;if(this.props._animate)return\"Deck._animate\";let r=this._needsRedraw;t.clearRedrawFlags&&(this._needsRedraw=!1);let i=this.viewManager.needsRedraw(t),s=this.layerManager.needsRedraw(t),n=this.effectManager.needsRedraw(t),o=this.deckRenderer.needsRedraw(t);return r=r||i||s||n||o,r}redraw(t){if(!this.layerManager)return;let r=this.needsRedraw({clearRedrawFlags:!0});r=t||r,r&&(this.stats.get(\"Redraw Count\").incrementCount(),this.props._customRender?this.props._customRender(r):this._drawLayers(r))}get isInitialized(){return this.viewManager!==null}getViews(){return _r(this.viewManager),this.viewManager.views}getViewports(t){return _r(this.viewManager),this.viewManager.getViewports(t)}getCanvas(){return this.canvas}pickObject(t){let r=this._pick(\"pickObject\",\"pickObject Time\",t).result;return r.length?r[0]:null}pickMultipleObjects(t){return t.depth=t.depth||10,this._pick(\"pickObject\",\"pickMultipleObjects Time\",t).result}pickObjects(t){return this._pick(\"pickObjects\",\"pickObjects Time\",t)}_addResources(t,r=!1){for(let i in t)this.layerManager.resourceManager.add({resourceId:i,data:t[i],forceUpdate:r})}_removeResources(t){for(let r of t)this.layerManager.resourceManager.remove(r)}_addDefaultEffect(t){this.effectManager.addDefaultEffect(t)}_pick(t,r,i){_r(this.deckPicker);let{stats:s}=this;s.get(\"Pick Count\").incrementCount(),s.get(r).timeStart();let n=this.deckPicker[t]({layers:this.layerManager.getLayers(i),views:this.viewManager.getViews(),viewports:this.getViewports(i),onViewportActive:this.layerManager.activateViewport,effects:this.effectManager.getEffects(),...i});return s.get(r).timeEnd(),n}_createCanvas(t){let r=t.canvas;return typeof r==\"string\"&&(r=document.getElementById(r),_r(r)),r||(r=document.createElement(\"canvas\"),r.id=t.id||\"deckgl-overlay\",(t.parent||document.body).appendChild(r)),Object.assign(r.style,t.style),r}_setCanvasSize(t){if(!this.canvas)return;let{width:r,height:i}=t;if(r||r===0){let n=Number.isFinite(r)?\"\".concat(r,\"px\"):r;this.canvas.style.width=n}if(i||i===0){var s;let n=Number.isFinite(i)?\"\".concat(i,\"px\"):i;this.canvas.style.position=((s=t.style)===null||s===void 0?void 0:s.position)||\"absolute\",this.canvas.style.height=n}}_updateCanvasSize(){var t,r;let{canvas:i}=this;if(!i)return;let s=(t=i.clientWidth)!==null&&t!==void 0?t:i.width,n=(r=i.clientHeight)!==null&&r!==void 0?r:i.height;if(s!==this.width||n!==this.height){var o,c;this.width=s,this.height=n,(o=this.viewManager)===null||o===void 0||o.setProps({width:s,height:n}),(c=this.layerManager)===null||c===void 0||c.activateViewport(this.getViewports()[0]),this.props.onResize({width:s,height:n})}}_createAnimationLoop(t){let{width:r,height:i,gl:s,glOptions:n,debug:o,onError:c,onBeforeRender:f,onAfterRender:_,useDevicePixels:w}=t;return new rg({width:r,height:i,useDevicePixels:w,autoResizeDrawingBuffer:!s,autoResizeViewport:!1,gl:s,onCreateContext:I=>Ty({...n,...I,canvas:this.canvas,debug:o,onContextLost:()=>this._onContextLost()}),onInitialize:I=>this._setGLContext(I.gl),onRender:this._onRenderFrame.bind(this),onBeforeRender:f,onAfterRender:_,onError:c})}_getViewState(){return this.props.viewState||this.viewState}_getViews(){let t=this.props.views||[new Xy({id:\"default-view\"})];return t=Array.isArray(t)?t:[t],t.length&&this.props.controller&&(t[0].props.controller=this.props.controller),t}_onContextLost(){let{onError:t}=this.props;this.animationLoop&&t&&t(new Error(\"WebGL context is lost\"))}_pickAndCallback(){let{_pickRequest:t}=this;if(t.event){let{result:i,emptyInfo:s}=this._pick(\"pickObject\",\"pickObject Time\",t);this.cursorState.isHovering=i.length>0;let n=s,o=!1;for(let c of i){var r;n=c,o=((r=c.layer)===null||r===void 0?void 0:r.onHover(c,t.event))||o}if(!o&&this.props.onHover&&this.props.onHover(n,t.event),this.props.getTooltip&&this.tooltip){let c=this.props.getTooltip(n);this.tooltip.setTooltip(c,n.x,n.y)}t.event=null}}_updateCursor(){let t=this.props.parent||this.canvas;t&&(t.style.cursor=this.props.getCursor(this.cursorState))}_setGLContext(t){if(this.layerManager)return;this.canvas||(this.canvas=t.canvas,q0(t,{enable:!0,copyState:!0})),this.tooltip=new Jb(this.canvas),Ml(t,{blend:!0,blendFunc:[770,771,1,771],polygonOffsetFill:!0,depthTest:!0,depthFunc:515}),this.props.onWebGLInitialized(t);let r=new KA;r.play(),this.animationLoop.attachTimeline(r),this.eventManager=new Jy(this.props.parent||t.canvas,{touchAction:this.props.touchAction,recognizerOptions:this.props.eventRecognizerOptions,events:{pointerdown:this._onPointerDown,pointermove:this._onPointerMove,pointerleave:this._onPointerMove}});for(let s in nR)this.eventManager.on(s,this._onEvent);this.viewManager=new Wb({timeline:r,eventManager:this.eventManager,onViewStateChange:this._onViewStateChange.bind(this),onInteractionStateChange:this._onInteractionStateChange.bind(this),views:this._getViews(),viewState:this._getViewState(),width:this.width,height:this.height});let i=this.viewManager.getViewports()[0];this.layerManager=new Gb(t,{deck:this,stats:this.stats,viewport:i,timeline:r}),this.effectManager=new Qb,this.deckRenderer=new Xb(t),this.deckPicker=new Kb(t),this.setProps(this.props),this._updateCanvasSize(),this.props.onLoad()}_drawLayers(t,r){let{gl:i}=this.layerManager.context;Ml(i,this.props.parameters),this.props.onBeforeRender({gl:i}),this.deckRenderer.renderLayers({target:this.props._framebuffer,layers:this.layerManager.getLayers(),viewports:this.viewManager.getViewports(),onViewportActive:this.layerManager.activateViewport,views:this.viewManager.getViews(),pass:\"screen\",effects:this.effectManager.getEffects(),...r}),this.props.onAfterRender({gl:i})}_onRenderFrame(t){this._getFrameStats(),this._metricsCounter++%60===0&&(this._getMetrics(),this.stats.reset(),or.table(4,this.metrics)(),this.props._onMetrics&&this.props._onMetrics(this.metrics)),this._updateCanvasSize(),this._updateCursor(),this.tooltip.isVisible&&this.viewManager.needsRedraw()&&this.tooltip.setTooltip(null),this.layerManager.updateLayers(),this._pickAndCallback(),this.redraw(),this.viewManager&&this.viewManager.updateViewStates()}_onViewStateChange(t){let r=this.props.onViewStateChange(t)||t.viewState;this.viewState&&(this.viewState={...this.viewState,[t.viewId]:r},this.props.viewState||this.viewManager&&this.viewManager.setProps({viewState:this.viewState}))}_onInteractionStateChange(t){this.cursorState.isDragging=t.isDragging||!1,this.props.onInteractionStateChange(t)}_getFrameStats(){let{stats:t}=this;t.get(\"frameRate\").timeEnd(),t.get(\"frameRate\").timeStart();let r=this.animationLoop.stats;t.get(\"GPU Time\").addTime(r.get(\"GPU Time\").lastTiming),t.get(\"CPU Time\").addTime(r.get(\"CPU Time\").lastTiming)}_getMetrics(){let{metrics:t,stats:r}=this;t.fps=r.get(\"frameRate\").getHz(),t.setPropsTime=r.get(\"setProps Time\").time,t.updateAttributesTime=r.get(\"Update Attributes\").time,t.framesRedrawn=r.get(\"Redraw Count\").count,t.pickTime=r.get(\"pickObject Time\").time+r.get(\"pickMultipleObjects Time\").time+r.get(\"pickObjects Time\").time,t.pickCount=r.get(\"Pick Count\").count,t.gpuTime=r.get(\"GPU Time\").time,t.cpuTime=r.get(\"CPU Time\").time,t.gpuTimePerFrame=r.get(\"GPU Time\").getAverageTime(),t.cpuTimePerFrame=r.get(\"CPU Time\").getAverageTime();let i=Du.get(\"Memory Usage\");t.bufferMemory=i.get(\"Buffer Memory\").count,t.textureMemory=i.get(\"Texture Memory\").count,t.renderbufferMemory=i.get(\"Renderbuffer Memory\").count,t.gpuMemory=i.get(\"GPU Memory\").count}};G(cp,\"defaultProps\",qG);G(cp,\"VERSION\",xV);var gg=class{constructor(t,r){G(this,\"opts\",void 0),G(this,\"source\",void 0),this.opts=r,this.source=t}get value(){return this.source.value}getValue(){let t=this.source.getBuffer(),r=this.getAccessor();if(t)return[t,r];let{value:i}=this.source,{size:s}=r,n=i;if(i&&i.length!==s){n=new Float32Array(s);let o=r.elementOffset||0;for(let c=0;c=n){let o=new Array(s).fill(1/0),c=new Array(s).fill(-1/0);for(let f=0;fc[_]&&(c[_]=w)}t=[o,c]}}return this.state.bounds=t,t}setData(t){let{state:r}=this,i;ArrayBuffer.isView(t)?i={value:t}:t instanceof Fr?i={buffer:t}:i=t;let s={...this.settings,...i};if(r.bufferAccessor=s,r.bounds=null,i.constant){let n=i.value;if(n=this._normalizeValue(n,[],0),this.settings.normalized&&(n=this.normalizeConstant(n)),!(!r.constant||!this._areValuesEqual(n,this.value)))return!1;r.externalBuffer=null,r.constant=!0,this.value=n}else if(i.buffer){let n=i.buffer;r.externalBuffer=n,r.constant=!1,this.value=i.value||null;let o=i.value instanceof Float64Array;s.type=i.type||n.accessor.type,s.bytesPerElement=n.accessor.BYTES_PER_ELEMENT*(o?2:1),s.stride=lP(s)}else if(i.value){this._checkExternalBuffer(i);let n=i.value;r.externalBuffer=null,r.constant=!1,this.value=n,s.bytesPerElement=n.BYTES_PER_ELEMENT,s.stride=lP(s);let{buffer:o,byteOffset:c}=this;this.doublePrecision&&n instanceof Float64Array&&(n=iP(n,s));let f=n.byteLength+c+s.stride*2;o.byteLength(r+128)/255*2-1);case 5122:return new Float32Array(t).map(r=>(r+32768)/65535*2-1);case 5121:return new Float32Array(t).map(r=>r/255);case 5123:return new Float32Array(t).map(r=>r/65535);default:return t}}_normalizeValue(t,r,i){let{defaultValue:s,size:n}=this.settings;if(Number.isFinite(t))return r[i]=t,r;if(!t){let o=n;for(;--o>=0;)r[i+o]=s[o];return r}switch(n){case 4:r[i+3]=Number.isFinite(t[3])?t[3]:s[3];case 3:r[i+2]=Number.isFinite(t[2])?t[2]:s[2];case 2:r[i+1]=Number.isFinite(t[1])?t[1]:s[1];case 1:r[i+0]=Number.isFinite(t[0])?t[0]:s[0];break;default:let o=n;for(;--o>=0;)r[i+o]=Number.isFinite(t[o])?t[o]:s[o]}return r}_areValuesEqual(t,r){if(!t||!r)return!1;let{size:i}=this;for(let s=0;s0&&($G.length=e.length,i=$G):i=QG,(t>0||Number.isFinite(r))&&(i=(Array.isArray(i)?i:Array.from(i)).slice(t,r),s.index=t-1),{iterable:i,objectInfo:s}}function cP(e){return e&&e[Symbol.asyncIterator]}function uP(e,t){let{size:r,stride:i,offset:s,startIndices:n,nested:o}=t,c=e.BYTES_PER_ELEMENT,f=i?i/c:r,_=s?s/c:0,w=Math.floor((e.length-_)/f);return(I,{index:R,target:N})=>{if(!n){let Y=R*f+_;for(let K=0;K=t[1]))return e;let r=[],i=e.length,s=0;for(let n=0;nt[1]?r.push(o):t=[Math.min(o[0],t[0]),Math.max(o[1],t[1])]}return r.splice(s,0,t),r}function ID(e){let{source:t,target:r,start:i=0,size:s,getData:n}=e,o=e.end||r.length,c=t.length,f=o-i;if(c>f){r.set(t.subarray(0,f),i);return}if(r.set(t,i),!n)return;let _=c;for(;_i(w+c,I)),_=Math.min(s.length,n.length);for(let w=1;w<_;w++){let I=s[w]*r,R=n[w]*r;ID({source:e.subarray(o,I),target:t,start:c,end:R,size:r,getData:f}),o=I,c=R}return ce},spring:{stiffness:.05,damping:.5}};function hP(e,t){if(!e)return null;Number.isFinite(e)&&(e={type:\"interpolation\",duration:e});let r=e.type||\"interpolation\";return{...Sct[r],...t,...e,type:r}}function fP(e,t){let r=t.getBuffer();return r?[r,{divisor:0,size:t.size,normalized:t.settings.normalized}]:t.value}function dP(e){switch(e){case 1:return\"float\";case 2:return\"vec2\";case 3:return\"vec3\";case 4:return\"vec4\";default:throw new Error('No defined attribute type for size \"'.concat(e,'\"'))}}function pP(e){e.push(e.shift())}function aw(e,t){let{doublePrecision:r,settings:i,value:s,size:n}=e,o=r&&s instanceof Float64Array?2:1;return(i.noAlloc?s.length:t*n)*o}function AP({buffer:e,numInstances:t,attribute:r,fromLength:i,fromStartIndices:s,getData:n=o=>o}){let o=r.doublePrecision&&r.value instanceof Float64Array?2:1,c=r.size*o,f=r.byteOffset,_=r.startIndices,w=s&&_,I=aw(r,t),R=r.isConstant;if(!w&&i>=I)return;let N=R?r.value:r.getBuffer().getData({srcByteOffset:f});if(r.settings.normalized&&!R){let Y=n;n=(K,J)=>r.normalizeConstant(Y(K,J))}let j=R?(Y,K)=>n(N,K):(Y,K)=>n(N.subarray(Y,Y+c),K),Q=e.getData({length:i}),et=new Float32Array(I);JG({source:Q,target:et,sourceStartIndices:s,targetStartIndices:_,size:c,getData:j}),e.byteLengtht[n])]:t[r];return hP(s,i)}setNeedsUpdate(t=this.id,r){if(this.state.needsUpdate=this.state.needsUpdate||t,this.setNeedsRedraw(t),r){let{startRow:i=0,endRow:s=1/0}=r;this.state.updateRanges=KG(this.state.updateRanges,[i,s])}else this.state.updateRanges=ow}clearNeedsUpdate(){this.state.needsUpdate=!1,this.state.updateRanges=XG}setNeedsRedraw(t=this.id){this.state.needsRedraw=this.state.needsRedraw||t}allocate(t){let{state:r,settings:i}=this;return i.noAlloc?!1:i.update?(super.allocate(t,r.updateRanges!==ow),!0):!1}updateBuffer({numInstances:t,data:r,props:i,context:s}){if(!this.needsUpdate())return!1;let{state:{updateRanges:n},settings:{update:o,noAlloc:c}}=this,f=!0;if(o){for(let[_,w]of n)o.call(s,this,{data:r,startRow:_,endRow:w,props:i,numInstances:t});if(this.value)if(this.constant||this.buffer.byteLengthw?_.set(J,Q):(t._normalizeValue(J,Y.target,0),xD({target:_,source:Y.target,start:Q,count:ut}));Q+=ut*w}else t._normalizeValue(J,_,Q),Q+=w}}_validateAttributeUpdaters(){let{settings:t}=this;if(!(t.noAlloc||typeof t.update==\"function\"))throw new Error(\"Attribute \".concat(this.id,\" missing update or accessor\"))}_checkAttributeArray(){let{value:t}=this,r=Math.min(4,this.size);if(t&&t.length>=r){let i=!0;switch(r){case 4:i=i&&Number.isFinite(t[3]);case 3:i=i&&Number.isFinite(t[2]);case 2:i=i&&Number.isFinite(t[1]);case 1:i=i&&Number.isFinite(t[0]);break;default:i=!1}if(!i)throw new Error(\"Illegal attribute generated for \".concat(this.id))}}};var lw=class{constructor({gl:t,attribute:r,timeline:i}){G(this,\"gl\",void 0),G(this,\"type\",\"interpolation\"),G(this,\"attributeInTransition\",void 0),G(this,\"settings\",void 0),G(this,\"attribute\",void 0),G(this,\"transition\",void 0),G(this,\"currentStartIndices\",void 0),G(this,\"currentLength\",void 0),G(this,\"transform\",void 0),G(this,\"buffers\",void 0),this.gl=t,this.transition=new Kc(i),this.attribute=r,this.attributeInTransition=new up(t,r.settings),this.currentStartIndices=r.startIndices,this.currentLength=0,this.transform=Mct(t,r);let s={byteLength:0,usage:35050};this.buffers=[new Fr(t,s),new Fr(t,s)]}get inProgress(){return this.transition.inProgress}start(t,r){if(t.duration<=0){this.transition.cancel();return}this.settings=t;let{gl:i,buffers:s,attribute:n}=this;pP(s);let o={numInstances:r,attribute:n,fromLength:this.currentLength,fromStartIndices:this.currentStartIndices,getData:t.enter};for(let c of s)AP({buffer:c,...o});this.currentStartIndices=n.startIndices,this.currentLength=aw(n,r),this.attributeInTransition.setData({buffer:s[1],value:n.value}),this.transition.start(t),this.transform.update({elementCount:Math.floor(this.currentLength/n.size),sourceBuffers:{aFrom:s[0],aTo:fP(i,n)},feedbackBuffers:{vCurrent:s[1]}})}update(){let t=this.transition.update();if(t){let{duration:r,easing:i}=this.settings,{time:s}=this.transition,n=s/r;i&&(n=i(n)),this.transform.run({uniforms:{time:n}})}return t}cancel(){this.transition.cancel(),this.transform.delete();for(let t of this.buffers)t.delete();this.buffers.length=0}},Tct=`\n#define SHADER_NAME interpolation-transition-vertex-shader\n\nuniform float time;\nattribute ATTRIBUTE_TYPE aFrom;\nattribute ATTRIBUTE_TYPE aTo;\nvarying ATTRIBUTE_TYPE vCurrent;\n\nvoid main(void) {\n vCurrent = mix(aFrom, aTo, time);\n gl_Position = vec4(0.0);\n}\n`;function Mct(e,t){let r=dP(t.size);return new nc(e,{vs:Tct,defines:{ATTRIBUTE_TYPE:r},varyings:[\"vCurrent\"]})}var cw=class{constructor({gl:t,attribute:r,timeline:i}){G(this,\"gl\",void 0),G(this,\"type\",\"spring\"),G(this,\"attributeInTransition\",void 0),G(this,\"settings\",void 0),G(this,\"attribute\",void 0),G(this,\"transition\",void 0),G(this,\"currentStartIndices\",void 0),G(this,\"currentLength\",void 0),G(this,\"texture\",void 0),G(this,\"framebuffer\",void 0),G(this,\"transform\",void 0),G(this,\"buffers\",void 0),this.gl=t,this.type=\"spring\",this.transition=new Kc(i),this.attribute=r,this.attributeInTransition=new up(t,{...r.settings,normalized:!1}),this.currentStartIndices=r.startIndices,this.currentLength=0,this.texture=Pct(t),this.framebuffer=Ict(t,this.texture),this.transform=Ect(t,r,this.framebuffer);let s={byteLength:0,usage:35050};this.buffers=[new Fr(t,s),new Fr(t,s),new Fr(t,s)]}get inProgress(){return this.transition.inProgress}start(t,r){let{gl:i,buffers:s,attribute:n}=this,o={numInstances:r,attribute:n,fromLength:this.currentLength,fromStartIndices:this.currentStartIndices,getData:t.enter};for(let c of s)AP({buffer:c,...o});this.settings=t,this.currentStartIndices=n.startIndices,this.currentLength=aw(n,r),this.attributeInTransition.setData({buffer:s[1],value:n.value}),this.transition.start({...t,duration:1/0}),this.transform.update({elementCount:Math.floor(this.currentLength/n.size),sourceBuffers:{aTo:fP(i,n)}})}update(){let{buffers:t,transform:r,framebuffer:i,transition:s}=this;if(!s.update())return!1;let o=this.settings;return r.update({sourceBuffers:{aPrev:t[0],aCur:t[1]},feedbackBuffers:{vNext:t[2]}}),r.run({framebuffer:i,discard:!1,clearRenderTarget:!0,uniforms:{stiffness:o.stiffness,damping:o.damping},parameters:{depthTest:!1,blend:!0,viewport:[0,0,1,1],blendFunc:[1,1],blendEquation:[32776,32776]}}),pP(t),this.attributeInTransition.setData({buffer:t[1],value:this.attribute.value}),Dh(i)[0]>0||s.end(),!0}cancel(){this.transition.cancel(),this.transform.delete();for(let t of this.buffers)t.delete();this.buffers.length=0,this.texture.delete(),this.framebuffer.delete()}};function Ect(e,t,r){let i=dP(t.size);return new nc(e,{framebuffer:r,vs:`\n#define SHADER_NAME spring-transition-vertex-shader\n\n#define EPSILON 0.00001\n\nuniform float stiffness;\nuniform float damping;\nattribute ATTRIBUTE_TYPE aPrev;\nattribute ATTRIBUTE_TYPE aCur;\nattribute ATTRIBUTE_TYPE aTo;\nvarying ATTRIBUTE_TYPE vNext;\nvarying float vIsTransitioningFlag;\n\nATTRIBUTE_TYPE getNextValue(ATTRIBUTE_TYPE cur, ATTRIBUTE_TYPE prev, ATTRIBUTE_TYPE dest) {\n ATTRIBUTE_TYPE velocity = cur - prev;\n ATTRIBUTE_TYPE delta = dest - cur;\n ATTRIBUTE_TYPE spring = delta * stiffness;\n ATTRIBUTE_TYPE damper = velocity * -1.0 * damping;\n return spring + damper + velocity + cur;\n}\n\nvoid main(void) {\n bool isTransitioning = length(aCur - aPrev) > EPSILON || length(aTo - aCur) > EPSILON;\n vIsTransitioningFlag = isTransitioning ? 1.0 : 0.0;\n\n vNext = getNextValue(aCur, aPrev, aTo);\n gl_Position = vec4(0, 0, 0, 1);\n gl_PointSize = 100.0;\n}\n`,fs:`\n#define SHADER_NAME spring-transition-is-transitioning-fragment-shader\n\nvarying float vIsTransitioningFlag;\n\nvoid main(void) {\n if (vIsTransitioningFlag == 0.0) {\n discard;\n }\n gl_FragColor = vec4(1.0);\n}`,defines:{ATTRIBUTE_TYPE:i},varyings:[\"vNext\"]})}function Pct(e){return new pi(e,{data:new Uint8Array(4),format:6408,type:5121,border:0,mipmaps:!1,dataFormat:6408,width:1,height:1})}function Ict(e,t){return new yi(e,{id:\"spring-transition-is-transitioning-framebuffer\",width:1,height:1,attachments:{36064:t}})}var Cct={interpolation:lw,spring:cw},uw=class{constructor(t,{id:r,timeline:i}){G(this,\"id\",void 0),G(this,\"isSupported\",void 0),G(this,\"gl\",void 0),G(this,\"timeline\",void 0),G(this,\"transitions\",void 0),G(this,\"needsRedraw\",void 0),G(this,\"numInstances\",void 0),this.id=r,this.gl=t,this.timeline=i,this.transitions={},this.needsRedraw=!1,this.numInstances=1,this.isSupported=nc.isSupported(t)}finalize(){for(let t in this.transitions)this._removeTransition(t)}update({attributes:t,transitions:r,numInstances:i}){this.numInstances=i||1;for(let s in t){let n=t[s],o=n.getTransitionSetting(r);o&&this._updateAttribute(s,n,o)}for(let s in this.transitions){let n=t[s];(!n||!n.getTransitionSetting(r))&&this._removeTransition(s)}}hasAttribute(t){let r=this.transitions[t];return r&&r.inProgress}getAttributes(){let t={};for(let r in this.transitions){let i=this.transitions[r];i.inProgress&&(t[r]=i.attributeInTransition)}return t}run(){if(!this.isSupported||this.numInstances===0)return!1;for(let r in this.transitions)this.transitions[r].update()&&(this.needsRedraw=!0);let t=this.needsRedraw;return this.needsRedraw=!1,t}_removeTransition(t){this.transitions[t].cancel(),delete this.transitions[t]}_updateAttribute(t,r,i){let s=this.transitions[t],n=!s||s.type!==i.type;if(n){if(!this.isSupported){or.warn(\"WebGL2 not supported by this browser. Transition for \".concat(t,\" is disabled.\"))();return}s&&this._removeTransition(t);let o=Cct[i.type];o?this.transitions[t]=new o({attribute:r,timeline:this.timeline,gl:this.gl}):(or.error(\"unsupported transition type '\".concat(i.type,\"'\"))(),n=!1)}(n||r.needsRedraw())&&(this.needsRedraw=!0,this.transitions[t].start(i,this.numInstances))}};var t9=\"attributeManager.invalidate\",Lct=\"attributeManager.updateStart\",kct=\"attributeManager.updateEnd\",Rct=\"attribute.updateStart\",Dct=\"attribute.allocate\",Oct=\"attribute.updateEnd\",Xf=class{constructor(t,{id:r=\"attribute-manager\",stats:i,timeline:s}={}){G(this,\"id\",void 0),G(this,\"gl\",void 0),G(this,\"attributes\",void 0),G(this,\"updateTriggers\",void 0),G(this,\"needsRedraw\",void 0),G(this,\"userData\",void 0),G(this,\"stats\",void 0),G(this,\"attributeTransitionManager\",void 0),G(this,\"mergeBoundsMemoized\",Yf(dG)),this.id=r,this.gl=t,this.attributes={},this.updateTriggers={},this.needsRedraw=!0,this.userData={},this.stats=i,this.attributeTransitionManager=new uw(t,{id:\"\".concat(r,\"-transitions\"),timeline:s}),Object.seal(this)}finalize(){for(let t in this.attributes)this.attributes[t].delete();this.attributeTransitionManager.finalize()}getNeedsRedraw(t={clearRedrawFlags:!1}){let r=this.needsRedraw;return this.needsRedraw=this.needsRedraw&&!t.clearRedrawFlags,r&&this.id}setNeedsRedraw(){this.needsRedraw=!0}add(t){this._add(t)}addInstanced(t){this._add(t,{instanced:1})}remove(t){for(let r of t)this.attributes[r]!==void 0&&(this.attributes[r].delete(),delete this.attributes[r])}invalidate(t,r){let i=this._invalidateTrigger(t,r);Ls(t9,this,t,i)}invalidateAll(t){for(let r in this.attributes)this.attributes[r].setNeedsUpdate(r,t);Ls(t9,this,\"all\")}update({data:t,numInstances:r,startIndices:i=null,transitions:s,props:n={},buffers:o={},context:c={}}){let f=!1;Ls(Lct,this),this.stats&&this.stats.get(\"Update Attributes\").timeStart();for(let _ in this.attributes){let w=this.attributes[_],I=w.settings.accessor;w.startIndices=i,w.numInstances=r,n[_]&&or.removed(\"props.\".concat(_),\"data.attributes.\".concat(_))(),w.setExternalBuffer(o[_])||w.setBinaryValue(typeof I==\"string\"?o[I]:void 0,t.startIndices)||typeof I==\"string\"&&!o[I]&&w.setConstantValue(n[I])||w.needsUpdate()&&(f=!0,this._updateAttribute({attribute:w,numInstances:r,data:t,props:n,context:c})),this.needsRedraw=this.needsRedraw||w.needsRedraw()}f&&Ls(kct,this,r),this.stats&&this.stats.get(\"Update Attributes\").timeEnd(),this.attributeTransitionManager.update({attributes:this.attributes,numInstances:r,transitions:s})}updateTransition(){let{attributeTransitionManager:t}=this,r=t.run();return this.needsRedraw=this.needsRedraw||r,r}getAttributes(){return this.attributes}getBounds(t){let r=t.map(i=>{var s;return(s=this.attributes[i])===null||s===void 0?void 0:s.getBounds()});return this.mergeBoundsMemoized(r)}getChangedAttributes(t={clearChangedFlags:!1}){let{attributes:r,attributeTransitionManager:i}=this,s={...i.getAttributes()};for(let n in r){let o=r[n];o.needsRedraw(t)&&!i.hasAttribute(n)&&(s[n]=o)}return s}getShaderAttributes(t,r={}){t||(t=this.getAttributes());let i={};for(let s in t)r[s]||Object.assign(i,t[s].getShaderAttributes());return i}_add(t,r={}){for(let i in t){let s=t[i];this.attributes[i]=this._createAttribute(i,s,r)}this._mapUpdateTriggersToAttributes()}_createAttribute(t,r,i){let s={...r,id:t,size:r.isIndexed&&1||r.size||1,divisor:i.instanced?1:r.divisor||0};return new up(this.gl,s)}_mapUpdateTriggersToAttributes(){let t={};for(let r in this.attributes)this.attributes[r].getUpdateTriggers().forEach(s=>{t[s]||(t[s]=[]),t[s].push(r)});this.updateTriggers=t}_invalidateTrigger(t,r){let{attributes:i,updateTriggers:s}=this,n=s[t];return n&&n.forEach(o=>{let c=i[o];c&&c.setNeedsUpdate(c.id,r)}),n}_updateAttribute(t){let{attribute:r,numInstances:i}=t;if(Ls(Rct,r),r.constant){r.setConstantValue(r.value);return}r.allocate(i)&&Ls(Dct,r,i),r.updateBuffer(t)&&(this.needsRedraw=!0,Ls(Oct,r,i))}};var hw=class extends Kc{get value(){return this._value}_onUpdate(){let{time:t,settings:{fromValue:r,toValue:i,duration:s,easing:n}}=this,o=n(t/s);this._value=il(r,i,o)}};var e9=1e-5;function r9(e,t,r,i,s){let n=t-e,c=(r-t)*s,f=-n*i;return c+f+n+t}function Bct(e,t,r,i,s){if(Array.isArray(r)){let n=[];for(let o=0;o0}add(t,r,i,s){let{transitions:n}=this;if(n.has(t)){let f=n.get(t),{value:_=f.settings.fromValue}=f;r=_,this.remove(t)}if(s=hP(s),!s)return;let o=Fct[s.type];if(!o){or.error(\"unsupported transition type '\".concat(s.type,\"'\"))();return}let c=new o(this.timeline);c.start({...s,fromValue:r,toValue:i}),n.set(t,c)}remove(t){let{transitions:r}=this;r.has(t)&&(r.get(t).cancel(),r.delete(t))}update(){let t={};for(let[r,i]of this.transitions)i.update(),t[r]=i.value,i.inProgress||this.remove(r);return t}clear(){for(let t of this.transitions.keys())this.remove(t)}};function s9(e){let t=e[zu];for(let r in t){let i=t[r],{validate:s}=i;if(s&&!s(e[r],i))throw new Error(\"Invalid prop \".concat(r,\": \").concat(e[r]))}}function o9(e,t){let r=pw({newProps:e,oldProps:t,propTypes:e[zu],ignoreProps:{data:null,updateTriggers:null,extensions:null,transitions:null}}),i=Nct(e,t),s=!1;return i||(s=Uct(e,t)),{dataChanged:i,propsChanged:r,updateTriggersChanged:s,extensionsChanged:Vct(e,t),transitionsChanged:zct(e,t)}}function zct(e,t){if(!e.transitions)return!1;let r={},i=e[zu],s=!1;for(let n in e.transitions){let o=i[n],c=o&&o.type;(c===\"number\"||c===\"color\"||c===\"array\")&&CD(e[n],t[n],o)&&(r[n]=!0,s=!0)}return s?r:!1}function pw({newProps:e,oldProps:t,ignoreProps:r={},propTypes:i={},triggerName:s=\"props\"}){if(t===e)return!1;if(typeof e!=\"object\"||e===null||typeof t!=\"object\"||t===null)return\"\".concat(s,\" changed shallowly\");for(let n of Object.keys(e))if(!(n in r)){if(!(n in t))return\"\".concat(s,\".\").concat(n,\" added\");let o=CD(e[n],t[n],i[n]);if(o)return\"\".concat(s,\".\").concat(n,\" \").concat(o)}for(let n of Object.keys(t))if(!(n in r)){if(!(n in e))return\"\".concat(s,\".\").concat(n,\" dropped\");if(!Object.hasOwnProperty.call(e,n)){let o=CD(e[n],t[n],i[n]);if(o)return\"\".concat(s,\".\").concat(n,\" \").concat(o)}}return!1}function CD(e,t,r){let i=r&&r.equal;return i&&!i(e,t,r)||!i&&(i=e&&t&&e.equals,i&&!i.call(e,t))?\"changed deeply\":!i&&t!==e?\"changed shallowly\":null}function Nct(e,t){if(t===null)return\"oldProps is null, initial diff\";let r=!1,{dataComparator:i,_dataDiff:s}=e;return i?i(e.data,t.data)||(r=\"Data comparator detected a change\"):e.data!==t.data&&(r=\"A new data container was supplied\"),r&&s&&(r=s(e.data,t.data)||r),r}function Uct(e,t){if(t===null)return{all:!0};if(\"all\"in e.updateTriggers&&n9(e,t,\"all\"))return{all:!0};let r={},i=!1;for(let s in 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n=0;n{},this.oldProps=null,this.oldAsyncProps=null}finalize(){for(let t in this.asyncProps){let r=this.asyncProps[t];r&&r.type&&r.type.release&&r.type.release(r.resolvedValue,r.type,this.component)}this.asyncProps={},this.component=null,this.resetOldProps()}getOldProps(){return this.oldAsyncProps||this.oldProps||iut}resetOldProps(){this.oldAsyncProps=null,this.oldProps=this.component?this.component.props:null}hasAsyncProp(t){return t in this.asyncProps}getAsyncProp(t){let r=this.asyncProps[t];return r&&r.resolvedValue}isAsyncPropLoading(t){if(t){let r=this.asyncProps[t];return!!(r&&r.pendingLoadCount>0&&r.pendingLoadCount!==r.resolvedLoadCount)}for(let r in this.asyncProps)if(this.isAsyncPropLoading(r))return!0;return!1}reloadAsyncProp(t,r){this._watchPromise(t,Promise.resolve(r))}setAsyncProps(t){this.component=t[Qy]||this.component;let r=t[Wh]||{},i=t[$f]||t,s=t[sp]||{};for(let n in r){let o=r[n];this._createAsyncPropData(n,s[n]),this._updateAsyncProp(n,o),r[n]=this.getAsyncProp(n)}for(let n in i){let o=i[n];this._createAsyncPropData(n,s[n]),this._updateAsyncProp(n,o)}}_fetch(t,r){return null}_onResolve(t,r){}_onError(t,r){}_updateAsyncProp(t,r){if(this._didAsyncInputValueChange(t,r)){if(typeof r==\"string\"&&(r=this._fetch(t,r)),r instanceof Promise){this._watchPromise(t,r);return}if(cP(r)){this._resolveAsyncIterable(t,r);return}this._setPropValue(t,r)}}_freezeAsyncOldProps(){if(!this.oldAsyncProps&&this.oldProps){this.oldAsyncProps=Object.create(this.oldProps);for(let t in this.asyncProps)Object.defineProperty(this.oldAsyncProps,t,{enumerable:!0,value:this.oldProps[t]})}}_didAsyncInputValueChange(t,r){let i=this.asyncProps[t];return r===i.resolvedValue||r===i.lastValue?!1:(i.lastValue=r,!0)}_setPropValue(t,r){this._freezeAsyncOldProps();let i=this.asyncProps[t];i&&(r=this._postProcessValue(i,r),i.resolvedValue=r,i.pendingLoadCount++,i.resolvedLoadCount=i.pendingLoadCount)}_setAsyncPropValue(t,r,i){let s=this.asyncProps[t];s&&i>=s.resolvedLoadCount&&r!==void 0&&(this._freezeAsyncOldProps(),s.resolvedValue=r,s.resolvedLoadCount=i,this.onAsyncPropUpdated(t,r))}_watchPromise(t,r){let i=this.asyncProps[t];if(i){i.pendingLoadCount++;let s=i.pendingLoadCount;r.then(n=>{this.component&&(n=this._postProcessValue(i,n),this._setAsyncPropValue(t,n,s),this._onResolve(t,n))}).catch(n=>{this._onError(t,n)})}}async _resolveAsyncIterable(t,r){if(t!==\"data\"){this._setPropValue(t,r);return}let i=this.asyncProps[t];if(!i)return;i.pendingLoadCount++;let s=i.pendingLoadCount,n=[],o=0;for await(let c of r){if(!this.component)return;let{dataTransform:f}=this.component.props;f?n=f(c,n):n=n.concat(c),Object.defineProperty(n,\"__diff\",{enumerable:!1,value:[{startRow:o,endRow:n.length}]}),o=n.length,this._setAsyncPropValue(t,n,s)}this._onResolve(t,n)}_postProcessValue(t,r){let i=t.type;return i&&this.component&&(i.release&&i.release(t.resolvedValue,i,this.component),i.transform)?i.transform(r,i,this.component):r}_createAsyncPropData(t,r){if(!this.asyncProps[t]){let s=this.component&&this.component.props[zu];this.asyncProps[t]={type:s&&s[t],lastValue:null,resolvedValue:r,pendingLoadCount:0,resolvedLoadCount:0}}}};var gw=class extends mw{constructor({attributeManager:t,layer:r}){super(r),G(this,\"attributeManager\",void 0),G(this,\"needsRedraw\",void 0),G(this,\"needsUpdate\",void 0),G(this,\"subLayers\",void 0),G(this,\"usesPickingColorCache\",void 0),G(this,\"hasPickingBuffer\",void 0),G(this,\"changeFlags\",void 0),G(this,\"viewport\",void 0),G(this,\"uniformTransitions\",void 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extends _g{constructor(...t){super(...t),G(this,\"internalState\",null),G(this,\"lifecycle\",tm.NO_STATE),G(this,\"context\",void 0),G(this,\"state\",void 0),G(this,\"parent\",null)}static get componentName(){return Object.prototype.hasOwnProperty.call(this,\"layerName\")?this.layerName:\"\"}get root(){let t=this;for(;t.parent;)t=t.parent;return t}toString(){let t=this.constructor.layerName||this.constructor.name;return\"\".concat(t,\"({id: '\").concat(this.props.id,\"'})\")}project(t){_r(this.internalState);let r=this.internalState.viewport||this.context.viewport,i=vD(t,{viewport:r,modelMatrix:this.props.modelMatrix,coordinateOrigin:this.props.coordinateOrigin,coordinateSystem:this.props.coordinateSystem}),[s,n,o]=Hy(i,r.pixelProjectionMatrix);return t.length===2?[s,n]:[s,n,o]}unproject(t){return _r(this.internalState),(this.internalState.viewport||this.context.viewport).unproject(t)}projectPosition(t,r){_r(this.internalState);let i=this.internalState.viewport||this.context.viewport;return mG(t,{viewport:i,modelMatrix:this.props.modelMatrix,coordinateOrigin:this.props.coordinateOrigin,coordinateSystem:this.props.coordinateSystem,...r})}get isComposite(){return!1}setState(t){this.setChangeFlags({stateChanged:!0}),Object.assign(this.state,t),this.setNeedsRedraw()}setNeedsRedraw(){this.internalState&&(this.internalState.needsRedraw=!0)}setNeedsUpdate(){this.internalState&&(this.context.layerManager.setNeedsUpdate(String(this)),this.internalState.needsUpdate=!0)}get isLoaded(){return this.internalState?!this.internalState.isAsyncPropLoading():!1}get wrapLongitude(){return this.props.wrapLongitude}isPickable(){return this.props.pickable&&this.props.visible}getModels(){return this.state&&(this.state.models||this.state.model&&[this.state.model])||[]}setModuleParameters(t){for(let r of this.getModels())r.updateModuleSettings(t)}getAttributeManager(){return this.internalState&&this.internalState.attributeManager}getCurrentLayer(){return this.internalState&&this.internalState.layer}getLoadOptions(){return this.props.loadOptions}use64bitPositions(){let{coordinateSystem:t}=this.props;return t===Yr.DEFAULT||t===Yr.LNGLAT||t===Yr.CARTESIAN}onHover(t,r){return this.props.onHover&&this.props.onHover(t,r)||!1}onClick(t,r){return this.props.onClick&&this.props.onClick(t,r)||!1}nullPickingColor(){return[0,0,0]}encodePickingColor(t,r=[]){return r[0]=t+1&255,r[1]=t+1>>8&255,r[2]=t+1>>8>>8&255,r}decodePickingColor(t){_r(t instanceof Uint8Array);let[r,i,s]=t;return r+i*256+s*65536-1}getNumInstances(){return Number.isFinite(this.props.numInstances)?this.props.numInstances:this.state&&this.state.numInstances!==void 0?this.state.numInstances:a9(this.props.data)}getStartIndices(){return this.props.startIndices?this.props.startIndices:this.state&&this.state.startIndices?this.state.startIndices:null}getBounds(){var t;return(t=this.getAttributeManager())===null||t===void 0?void 0:t.getBounds([\"positions\",\"instancePositions\"])}getShaders(t){for(let r of this.props.extensions)t=tv(t,r.getShaders.call(this,r));return t}shouldUpdateState(t){return t.changeFlags.propsOrDataChanged}updateState(t){let r=this.getAttributeManager(),{dataChanged:i}=t.changeFlags;if(i&&r)if(Array.isArray(i))for(let s of i)r.invalidateAll(s);else r.invalidateAll();if(r){let{props:s}=t,n=this.internalState.hasPickingBuffer,o=Number.isInteger(s.highlightedObjectIndex)||s.pickable||s.extensions.some(c=>c.getNeedsPickingBuffer.call(this,c));if(n!==o){this.internalState.hasPickingBuffer=o;let{pickingColors:c,instancePickingColors:f}=r.attributes,_=c||f;_&&(o&&_.constant&&(_.constant=!1,r.invalidate(_.id)),!_.value&&!o&&(_.constant=!0,_.value=[0,0,0]))}}}finalizeState(t){for(let i of this.getModels())i.delete();let r=this.getAttributeManager();r&&r.finalize(),this.context&&this.context.resourceManager.unsubscribe({consumerId:this.id}),this.internalState&&(this.internalState.uniformTransitions.clear(),this.internalState.finalize())}draw(t){for(let r of this.getModels())r.draw(t)}getPickingInfo({info:t,mode:r,sourceLayer:i}){let{index:s}=t;return s>=0&&Array.isArray(this.props.data)&&(t.object=this.props.data[s]),t}raiseError(t,r){var i,s;if(r&&(t=new Error(\"\".concat(r,\": \").concat(t.message),{cause:t})),!((i=(s=this.props).onError)!==null&&i!==void 0&&i.call(s,t))){var n,o;(n=this.context)===null||n===void 0||(o=n.onError)===null||o===void 0||o.call(n,t,this)}}getNeedsRedraw(t={clearRedrawFlags:!1}){return this._getNeedsRedraw(t)}needsUpdate(){return this.internalState?this.internalState.needsUpdate||this.hasUniformTransition()||this.shouldUpdateState(this._getUpdateParams()):!1}hasUniformTransition(){var t;return((t=this.internalState)===null||t===void 0?void 0:t.uniformTransitions.active)||!1}activateViewport(t){if(!this.internalState)return;let r=this.internalState.viewport;this.internalState.viewport=t,(!r||!uut({oldViewport:r,viewport:t}))&&(this.setChangeFlags({viewportChanged:!0}),this.isComposite?this.needsUpdate()&&this.setNeedsUpdate():this._update())}invalidateAttribute(t=\"all\"){let r=this.getAttributeManager();r&&(t===\"all\"?r.invalidateAll():r.invalidate(t))}updateAttributes(t){for(let r of this.getModels())this._setModelAttributes(r,t)}_updateAttributes(){let t=this.getAttributeManager();if(!t)return;let r=this.props,i=this.getNumInstances(),s=this.getStartIndices();t.update({data:r.data,numInstances:i,startIndices:s,props:r,transitions:r.transitions,buffers:r.data.attributes,context:this});let n=t.getChangedAttributes({clearChangedFlags:!0});this.updateAttributes(n)}_updateAttributeTransition(){let t=this.getAttributeManager();t&&t.updateTransition()}_updateUniformTransition(){let{uniformTransitions:t}=this.internalState;if(t.active){let r=t.update(),i=Object.create(this.props);for(let s in r)Object.defineProperty(i,s,{value:r[s]});return i}return this.props}calculateInstancePickingColors(t,{numInstances:r}){if(t.constant)return;let i=Math.floor(Kf.length/3);if(this.internalState.usesPickingColorCache=!0,id9&&or.warn(\"Layer has too many data objects. Picking might not be able to distinguish all objects.\")(),Kf=Gh.allocate(Kf,r,{size:3,copy:!0,maxCount:Math.max(r,d9)});let s=Math.floor(Kf.length/3),n=[];for(let o=i;o(or.deprecated(\"layer.state.attributeManager\",\"layer.getAttributeManager()\")(),t)}),this.internalState.uniformTransitions=new dw(this.context.timeline),this.internalState.onAsyncPropUpdated=this._onAsyncPropUpdated.bind(this),this.internalState.setAsyncProps(this.props),this.initializeState(this.context);for(let r of this.props.extensions)r.initializeState.call(this,this.context,r);this.setChangeFlags({dataChanged:\"init\",propsChanged:\"init\",viewportChanged:!0,extensionsChanged:!0}),this._update()}_transferState(t){Ls(lut,this,this===t);let{state:r,internalState:i}=t;this!==t&&(this.internalState=i,this.state=r,this.internalState.setAsyncProps(this.props),this._diffProps(this.props,this.internalState.getOldProps()))}_update(){let t=this.needsUpdate();if(Ls(out,this,t),!t)return;let r=this.props,i=this.context,s=this.internalState,n=i.viewport,o=this._updateUniformTransition();s.propsInTransition=o,i.viewport=s.viewport||n,this.props=o;try{let c=this._getUpdateParams(),f=this.getModels();if(i.gl)this.updateState(c);else try{this.updateState(c)}catch{}for(let w of this.props.extensions)w.updateState.call(this,c,w);let _=this.getModels()[0]!==f[0];this._postUpdate(c,_)}finally{i.viewport=n,this.props=r,this._clearChangeFlags(),s.needsUpdate=!1,s.resetOldProps()}}_finalize(){Ls(aut,this),this.finalizeState(this.context);for(let t of this.props.extensions)t.finalizeState.call(this,this.context,t)}_drawLayer({moduleParameters:t=null,uniforms:r={},parameters:i={}}){this._updateAttributeTransition();let s=this.props,n=this.context;this.props=this.internalState.propsInTransition||s;let o=this.props.opacity;r.opacity=Math.pow(o,1/2.2);try{t&&this.setModuleParameters(t);let{getPolygonOffset:c}=this.props,f=c&&c(r)||[0,0];Ml(n.gl,{polygonOffset:f}),Mn(n.gl,i,()=>{let _={moduleParameters:t,uniforms:r,parameters:i,context:n};for(let w of this.props.extensions)w.draw.call(this,_,w);this.draw(_)})}finally{this.props=s}}getChangeFlags(){var t;return(t=this.internalState)===null||t===void 0?void 0:t.changeFlags}setChangeFlags(t){if(!this.internalState)return;let{changeFlags:r}=this.internalState;for(let s in t)if(t[s]){let n=!1;switch(s){case\"dataChanged\":let o=t[s],c=r[s];o&&Array.isArray(c)&&(r.dataChanged=Array.isArray(o)?c.concat(o):o,n=!0);default:r[s]||(r[s]=t[s],n=!0)}n&&Ls(nut,this,s,t)}let i=!!(r.dataChanged||r.updateTriggersChanged||r.propsChanged||r.extensionsChanged);r.propsOrDataChanged=i,r.somethingChanged=i||r.viewportChanged||r.stateChanged}_clearChangeFlags(){this.internalState.changeFlags={dataChanged:!1,propsChanged:!1,updateTriggersChanged:!1,viewportChanged:!1,stateChanged:!1,extensionsChanged:!1,propsOrDataChanged:!1,somethingChanged:!1}}_diffProps(t,r){let i=o9(t,r);if(i.updateTriggersChanged)for(let n in i.updateTriggersChanged)i.updateTriggersChanged[n]&&this.invalidateAttribute(n);if(i.transitionsChanged)for(let n in i.transitionsChanged){var s;this.internalState.uniformTransitions.add(n,r[n],t[n],(s=t.transitions)===null||s===void 0?void 0:s[n])}return this.setChangeFlags(i)}validateProps(){s9(this.props)}updateAutoHighlight(t){this.props.autoHighlight&&!Number.isInteger(this.props.highlightedObjectIndex)&&this._updateAutoHighlight(t)}_updateAutoHighlight(t){let r={pickingSelectedColor:t.picked?t.color:null},{highlightColor:i}=this.props;t.picked&&typeof i==\"function\"&&(r.pickingHighlightColor=i(t)),this.setModuleParameters(r),this.setNeedsRedraw()}_getAttributeManager(){let t=this.context;return new Xf(t.gl,{id:this.props.id,stats:t.stats,timeline:t.timeline})}_postUpdate(t,r){let{props:i,oldProps:s}=t;this.setNeedsRedraw(),this._updateAttributes();let{model:n}=this.state;n?.setInstanceCount(this.getNumInstances());let{autoHighlight:o,highlightedObjectIndex:c,highlightColor:f}=i;if(r||s.autoHighlight!==o||s.highlightedObjectIndex!==c||s.highlightColor!==f){let _={};o||(_.pickingSelectedColor=null),Array.isArray(f)&&(_.pickingHighlightColor=f),(r||c!==s.highlightedObjectIndex)&&(_.pickingSelectedColor=Number.isFinite(c)&&c>=0?this.encodePickingColor(c):null),this.setModuleParameters(_)}}_getUpdateParams(){return{props:this.props,oldProps:this.internalState.getOldProps(),context:this.context,changeFlags:this.internalState.changeFlags}}_getNeedsRedraw(t){if(!this.internalState)return!1;let r=!1;r=r||this.internalState.needsRedraw&&this.id;let i=this.getAttributeManager(),s=i?i.getNeedsRedraw(t):!1;if(r=r||s,r)for(let n of this.props.extensions)n.onNeedsRedraw.call(this,n);return this.internalState.needsRedraw=this.internalState.needsRedraw&&!t.clearRedrawFlags,r}_onAsyncPropUpdated(){this._diffProps(this.props,this.internalState.getOldProps()),this.setNeedsUpdate()}};G(dn,\"defaultProps\",hut);G(dn,\"layerName\",\"Layer\");var fut=\"compositeLayer.renderLayers\",Ni=class extends dn{get isComposite(){return!0}get isLoaded(){return super.isLoaded&&this.getSubLayers().every(t=>t.isLoaded)}getSubLayers(){return this.internalState&&this.internalState.subLayers||[]}initializeState(t){}setState(t){super.setState(t),this.setNeedsUpdate()}getPickingInfo({info:t}){let{object:r}=t;return r&&r.__source&&r.__source.parent&&r.__source.parent.id===this.id&&(t.object=r.__source.object,t.index=r.__source.index),t}filterSubLayer(t){return!0}shouldRenderSubLayer(t,r){return r&&r.length}getSubLayerClass(t,r){let{_subLayerProps:i}=this.props;return i&&i[t]&&i[t].type||r}getSubLayerRow(t,r,i){return t.__source={parent:this,object:r,index:i},t}getSubLayerAccessor(t){if(typeof t==\"function\"){let r={index:-1,data:this.props.data,target:[]};return(i,s)=>i&&i.__source?(r.index=i.__source.index,t(i.__source.object,r)):t(i,s)}return t}getSubLayerProps(t={}){var r;let{opacity:i,pickable:s,visible:n,parameters:o,getPolygonOffset:c,highlightedObjectIndex:f,autoHighlight:_,highlightColor:w,coordinateSystem:I,coordinateOrigin:R,wrapLongitude:N,positionFormat:j,modelMatrix:Q,extensions:et,fetch:Y,operation:K,_subLayerProps:J}=this.props,ut={id:\"\",updateTriggers:{},opacity:i,pickable:s,visible:n,parameters:o,getPolygonOffset:c,highlightedObjectIndex:f,autoHighlight:_,highlightColor:w,coordinateSystem:I,coordinateOrigin:R,wrapLongitude:N,positionFormat:j,modelMatrix:Q,extensions:et,fetch:Y,operation:K},Et=J&&t.id&&J[t.id],kt=Et&&Et.updateTriggers,Xt=t.id||\"sublayer\";if(Et){let qt=this.props[zu],le=t.type?t.type._propTypes:{};for(let ue in Et){let De=le[ue]||qt[ue];De&&De.type===\"accessor\"&&(Et[ue]=this.getSubLayerAccessor(Et[ue]))}}Object.assign(ut,t,Et),ut.id=\"\".concat(this.props.id,\"-\").concat(Xt),ut.updateTriggers={all:(r=this.props.updateTriggers)===null||r===void 0?void 0:r.all,...t.updateTriggers,...kt};for(let qt of et){let le=qt.getSubLayerProps.call(this,qt);le&&Object.assign(ut,le,{updateTriggers:Object.assign(ut.updateTriggers,le.updateTriggers)})}return ut}_updateAutoHighlight(t){for(let r of this.getSubLayers())r.updateAutoHighlight(t)}_getAttributeManager(){return null}_postUpdate(t,r){let i=this.internalState.subLayers,s=!i||this.needsUpdate();if(s){let n=this.renderLayers();i=op(n,Boolean),this.internalState.subLayers=i}Ls(fut,this,s,i);for(let n of i)n.parent=this}};G(Ni,\"layerName\",\"CompositeLayer\");var gP=Math.PI/180,p9=180/Math.PI,_P=6370972,ev=256;function dut(){let e=ev/_P,t=Math.PI/180*ev;return{unitsPerMeter:[e,e,e],unitsPerMeter2:[0,0,0],metersPerUnit:[1/e,1/e,1/e],unitsPerDegree:[t,t,e],unitsPerDegree2:[0,0,0],degreesPerUnit:[1/t,1/t,1/e]}}var rv=class extends ac{constructor(t={}){let{latitude:r=0,longitude:i=0,zoom:s=0,nearZMultiplier:n=.1,farZMultiplier:o=2,resolution:c=10}=t,{height:f,altitude:_=1.5}=t;f=f||1,_=Math.max(.75,_);let w=new En().lookAt({eye:[0,-_,0],up:[0,0,1]}),I=Math.pow(2,s);w.rotateX(r*gP),w.rotateZ(-i*gP),w.scale(I/f);let R=Math.atan(.5/_),N=ev*2*I/f;super({...t,height:f,viewMatrix:w,longitude:i,latitude:r,zoom:s,distanceScales:dut(),fovyRadians:R*2,focalDistance:_,near:n,far:Math.min(2,1/N+1)*_*o}),G(this,\"longitude\",void 0),G(this,\"latitude\",void 0),G(this,\"resolution\",void 0),this.latitude=r,this.longitude=i,this.resolution=c}get projectionMode(){return Ja.GLOBE}getDistanceScales(){return this.distanceScales}getBounds(t={}){let r={targetZ:t.z||0},i=this.unproject([0,this.height/2],r),s=this.unproject([this.width/2,0],r),n=this.unproject([this.width,this.height/2],r),o=this.unproject([this.width/2,this.height],r);return n[0]this.longitude&&(i[0]-=360),[Math.min(i[0],n[0],s[0],o[0]),Math.min(i[1],n[1],s[1],o[1]),Math.max(i[0],n[0],s[0],o[0]),Math.max(i[1],n[1],s[1],o[1])]}unproject(t,{topLeft:r=!0,targetZ:i}={}){let[s,n,o]=t,c=r?n:this.height-n,{pixelUnprojectionMatrix:f}=this,_;if(Number.isFinite(o))_=OD(f,[s,c,o,1]);else{let N=OD(f,[s,c,-1,1]),j=OD(f,[s,c,1,1]),Q=((i||0)/_P+1)*ev,et=NE(FE([],N,j)),Y=NE(N),K=NE(j),ut=4*((4*Y*K-(et-Y-K)**2)/16)/et,Et=Math.sqrt(Y-ut),kt=Math.sqrt(Math.max(0,Q*Q-ut)),Xt=(Et-kt)/Math.sqrt(et);_=Hj([],N,j,Xt)}let[w,I,R]=this.unprojectPosition(_);return Number.isFinite(o)?[w,I,R]:Number.isFinite(i)?[w,I,i]:[w,I]}projectPosition(t){let[r,i,s=0]=t,n=r*gP,o=i*gP,c=Math.cos(o),f=(s/_P+1)*ev;return[Math.sin(n)*c*f,-Math.cos(n)*c*f,Math.sin(o)*f]}unprojectPosition(t){let[r,i,s]=t,n=zE(t),o=Math.asin(s/n),f=Math.atan2(r,-i)*p9,_=o*p9,w=(n/ev-1)*_P;return[f,_,w]}projectFlat(t){return t}unprojectFlat(t){return t}panByPosition(t,r){let i=this.unproject(r);return{longitude:t[0]-i[0]+this.longitude,latitude:t[1]-i[1]+this.latitude}}};function OD(e,t){let r=Nh([],t,e);return Fy(r,r,1/r[3]),r}var put=new En().lookAt({eye:[0,0,1]});function Aut({width:e,height:t,near:r,far:i,padding:s}){let n=-e/2,o=e/2,c=-t/2,f=t/2;if(s){let{left:_=0,right:w=0,top:I=0,bottom:R=0}=s,N=Il((_+e-w)/2,0,e)-e/2,j=Il((I+t-R)/2,0,t)-t/2;n-=N,o-=N,c+=j,f+=j}return new En().ortho({left:n,right:o,bottom:c,top:f,near:r,far:i})}var iv=class extends ac{constructor(t){let{width:r,height:i,near:s=.1,far:n=1e3,zoom:o=0,target:c=[0,0,0],padding:f=null,flipY:_=!0}=t,w=Array.isArray(o)?o[0]:o,I=Array.isArray(o)?o[1]:o,R=Math.min(w,I),N=Math.pow(2,R),j;if(w!==I){let Q=Math.pow(2,w),et=Math.pow(2,I);j={unitsPerMeter:[Q/N,et/N,1],metersPerUnit:[N/Q,N/et,1]}}super({...t,longitude:void 0,position:c,viewMatrix:put.clone().scale([N,N*(_?-1:1),N]),projectionMatrix:Aut({width:r||1,height:i||1,padding:f,near:s,far:n}),zoom:R,distanceScales:j})}projectFlat([t,r]){let{unitsPerMeter:i}=this.distanceScales;return[t*i[0],r*i[1]]}unprojectFlat([t,r]){let{metersPerUnit:i}=this.distanceScales;return[t*i[0],r*i[1]]}panByPosition(t,r){let i=Qf(r,this.pixelUnprojectionMatrix),s=this.projectFlat(t),n=$A([],s,LE([],i)),o=$A([],this.center,n);return{target:this.unprojectFlat(o)}}};var cc=class{static get componentName(){return Object.prototype.hasOwnProperty.call(this,\"extensionName\")?this.extensionName:\"\"}constructor(t){G(this,\"opts\",void 0),t&&(this.opts=t)}equals(t){return this===t?!0:this.constructor===t.constructor&&mo(this.opts,t.opts,1)}getShaders(t){return null}getSubLayerProps(t){let{defaultProps:r}=t.constructor,i={updateTriggers:{}};for(let s in r)if(s in this.props){let n=r[s],o=this.props[s];i[s]=o,n&&n.type===\"accessor\"&&(i.updateTriggers[s]=this.props.updateTriggers[s],typeof o==\"function\"&&(i[s]=this.getSubLayerAccessor(o)))}return i}initializeState(t,r){}updateState(t,r){}onNeedsRedraw(t){}getNeedsPickingBuffer(t){return!1}draw(t,r){}finalizeState(t,r){}};G(cc,\"defaultProps\",{});G(cc,\"extensionName\",\"LayerExtension\");var BD={bearing:0,pitch:0,position:[0,0,0]},mut={speed:1.2,curve:1.414},nv=class extends 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0);let{attributes:r={}}=t;this.typedArrayManager=Gh,this.attributes={},this._attributeDefs=r,this.opts=t,this.updateGeometry(t)}updateGeometry(t){Object.assign(this.opts,t);let{data:r,buffers:i={},getGeometry:s,geometryBuffer:n,positionFormat:o,dataChanged:c,normalize:f=!0}=this.opts;if(this.data=r,this.getGeometry=s,this.positionSize=n&&n.size||(o===\"XY\"?2:3),this.buffers=i,this.normalize=f,n&&(_r(r.startIndices),this.getGeometry=this.getGeometryFromBuffer(n),f||(i.positions=n)),this.geometryBuffer=i.positions,Array.isArray(c))for(let _ of c)this._rebuildGeometry(_);else this._rebuildGeometry()}updatePartialGeometry({startRow:t,endRow:r}){this._rebuildGeometry({startRow:t,endRow:r})}getGeometryFromBuffer(t){let r=t.value||t;return ArrayBuffer.isView(r)?uP(r,{size:this.positionSize,offset:t.offset,stride:t.stride,startIndices:this.data.startIndices}):null}_allocate(t,r){let{attributes:i,buffers:s,_attributeDefs:n,typedArrayManager:o}=this;for(let c in n)if(c in 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Uint32Array([t.buffer[1]>>>16,t.buffer[1]&65535,t.buffer[0]>>>16,t.buffer[0]&65535]),s=r[3]*i[3];this.buffer[0]=s&65535;let n=s>>>16;return s=r[2]*i[3],n+=s,s=r[3]*i[2]>>>0,n+=s,this.buffer[0]+=n<<16,this.buffer[1]=n>>>0>>16,this.buffer[1]+=r[1]*i[3]+r[2]*i[2]+r[3]*i[1],this.buffer[1]+=r[0]*i[3]+r[1]*i[2]+r[2]*i[1]+r[3]*i[0]<<16,this}_plus(t){let r=this.buffer[0]+t.buffer[0]>>>0;this.buffer[1]+=t.buffer[1],r>>0&&++this.buffer[1],this.buffer[0]=r}lessThan(t){return this.buffer[1]>>0,r[2]=this.buffer[2]+t.buffer[2]>>>0,r[1]=this.buffer[1]+t.buffer[1]>>>0,r[0]=this.buffer[0]+t.buffer[0]>>>0,r[0]>>0&&++r[1],r[1]>>0&&++r[2],r[2]>>0&&++r[3],this.buffer[3]=r[3],this.buffer[2]=r[2],this.buffer[1]=r[1],this.buffer[0]=r[0],this}hex(){return`${Tv(this.buffer[3])} ${Tv(this.buffer[2])} ${Tv(this.buffer[1])} ${Tv(this.buffer[0])}`}static multiply(t,r){return new e(new Uint32Array(t.buffer)).times(r)}static add(t,r){return new e(new Uint32Array(t.buffer)).plus(r)}static from(t,r=new Uint32Array(4)){return e.fromString(typeof t==\"string\"?t:t.toString(),r)}static fromNumber(t,r=new Uint32Array(4)){return e.fromString(t.toString(),r)}static fromString(t,r=new Uint32Array(4)){let i=t.startsWith(\"-\"),s=t.length,n=new e(r);for(let o=i?1:0;o0&&this.readData(t,i)||new Uint8Array(0)}readOffsets(t,r){return this.readData(t,r)}readTypeIds(t,r){return this.readData(t,r)}readData(t,{length:r,offset:i}=this.nextBufferRange()){return this.bytes.subarray(i,i+r)}readDictionary(t){return this.dictionaries.get(t.id)}},g3=class extends o2{constructor(t,r,i,s,n){super(new Uint8Array(0),r,i,s,n),this.sources=t}readNullBitmap(t,r,{offset:i}=this.nextBufferRange()){return r<=0?new Uint8Array(0):Sg(this.sources[i])}readOffsets(t,{offset:r}=this.nextBufferRange()){return Ai(Uint8Array,Ai(t.OffsetArrayType,this.sources[r]))}readTypeIds(t,{offset:r}=this.nextBufferRange()){return 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i=()=>{this[r]=tB(this.model.get(t))};this.model.on(`change:${t}`,i),this.callbacks.set(`change:${t}`,i)}finalize(){for(let[t,r]of Object.entries(this.callbacks))this.model.off(t,r)}};async function L3(e,t){let r=[];for(let i of t)r.push(e.get_model(i.slice(10)));return await Promise.all(r)}function Jt(e){return e!=null}function DH(e,t=20){let r;return(...s)=>{clearTimeout(r),r=setTimeout(()=>e(...s),t)}}var bdt=`\n uniform bool brushing_enabled;\n uniform int brushing_target;\n uniform vec2 brushing_mousePos;\n uniform float brushing_radius;\n\n #ifdef NON_INSTANCED_MODEL\n attribute vec2 brushingTargets;\n #else\n attribute vec2 instanceBrushingTargets;\n #endif\n\n varying float brushing_isVisible;\n\n bool brushing_isPointInRange(vec2 position) {\n if (!brushing_enabled) {\n return true;\n }\n vec2 source_commonspace = project_position(position);\n vec2 target_commonspace = project_position(brushing_mousePos);\n float distance = length((target_commonspace - source_commonspace) / 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i=this.getAttributeManager();i&&i.add({brushingTargets:{size:2,accessor:\"getBrushingTarget\",shaderAttributes:{brushingTargets:{divisor:0},instanceBrushingTargets:{divisor:1}}}}),this.state.onMouseMove=()=>{var s;(s=this.getCurrentLayer())===null||s===void 0||s.setNeedsRedraw()},t.deck&&t.deck.eventManager.on({pointermove:this.state.onMouseMove,pointerleave:this.state.onMouseMove})}finalizeState(t,r){t.deck&&t.deck.eventManager.off({pointermove:this.state.onMouseMove,pointerleave:this.state.onMouseMove})}};G(vm,\"defaultProps\",Mdt);G(vm,\"extensionName\",\"BrushingExtension\");var BH=`\nuniform DATAFILTER_TYPE filter_min;\nuniform DATAFILTER_TYPE filter_softMin;\nuniform DATAFILTER_TYPE filter_softMax;\nuniform DATAFILTER_TYPE filter_max;\nuniform bool filter_useSoftMargin;\nuniform bool filter_enabled;\nuniform bool filter_transformSize;\n\n#ifdef NON_INSTANCED_MODEL\n #define DATAFILTER_ATTRIB filterValues\n #define DATAFILTER_ATTRIB_64LOW filterValues64Low\n#else\n #define DATAFILTER_ATTRIB instanceFilterValues\n #define DATAFILTER_ATTRIB_64LOW instanceFilterValues64Low\n#endif\n\nattribute DATAFILTER_TYPE DATAFILTER_ATTRIB;\n#ifdef DATAFILTER_DOUBLE\n attribute DATAFILTER_TYPE DATAFILTER_ATTRIB_64LOW;\n\n uniform DATAFILTER_TYPE filter_min64High;\n uniform DATAFILTER_TYPE filter_max64High;\n#endif\n\nvarying float dataFilter_value;\n\nfloat dataFilter_reduceValue(float value) {\n return value;\n}\nfloat dataFilter_reduceValue(vec2 value) {\n return min(value.x, value.y);\n}\nfloat dataFilter_reduceValue(vec3 value) {\n return min(min(value.x, value.y), value.z);\n}\nfloat dataFilter_reduceValue(vec4 value) {\n return min(min(value.x, value.y), min(value.z, value.w));\n}\nvoid dataFilter_setValue(DATAFILTER_TYPE valueFromMin, DATAFILTER_TYPE valueFromMax) {\n if (filter_enabled) {\n if (filter_useSoftMargin) {\n dataFilter_value = dataFilter_reduceValue(\n smoothstep(filter_min, filter_softMin, valueFromMin) *\n (1.0 - smoothstep(filter_softMax, filter_max, valueFromMax))\n );\n } else {\n dataFilter_value = dataFilter_reduceValue(\n step(filter_min, valueFromMin) * step(valueFromMax, filter_max)\n );\n }\n } else {\n dataFilter_value = 1.0;\n }\n}\n`,FH=`\nuniform bool filter_transformColor;\nvarying float dataFilter_value;\n`;function zH(e){if(!e||!(\"extensions\"in e))return{};let{filterRange:t=[-1,1],filterEnabled:r=!0,filterTransformSize:i=!0,filterTransformColor:s=!0}=e,n=e.filterSoftRange||t;return{...Number.isFinite(t[0])?{filter_min:t[0],filter_softMin:n[0],filter_softMax:n[1],filter_max:t[1]}:{filter_min:t.map(o=>o[0]),filter_softMin:n.map(o=>o[0]),filter_softMax:n.map(o=>o[1]),filter_max:t.map(o=>o[1])},filter_enabled:r,filter_useSoftMargin:!!e.filterSoftRange,filter_transformSize:r&&i,filter_transformColor:r&&s}}function Edt(e){if(!e||!(\"extensions\"in e))return{};let t=zH(e);if(Number.isFinite(t.filter_min)){let r=Math.fround(t.filter_min);t.filter_min-=r,t.filter_softMin-=r,t.filter_min64High=r;let i=Math.fround(t.filter_max);t.filter_max-=i,t.filter_softMax-=i,t.filter_max64High=i}else{let r=t.filter_min.map(Math.fround);t.filter_min=t.filter_min.map((s,n)=>s-r[n]),t.filter_softMin=t.filter_softMin.map((s,n)=>s-r[n]),t.filter_min64High=r;let i=t.filter_max.map(Math.fround);t.filter_max=t.filter_max.map((s,n)=>s-i[n]),t.filter_softMax=t.filter_softMax.map((s,n)=>s-i[n]),t.filter_max64High=i}return t}var NH={\"vs:#main-start\":`\n #ifdef DATAFILTER_DOUBLE\n dataFilter_setValue(\n DATAFILTER_ATTRIB - filter_min64High + DATAFILTER_ATTRIB_64LOW,\n DATAFILTER_ATTRIB - filter_max64High + DATAFILTER_ATTRIB_64LOW\n );\n #else\n dataFilter_setValue(DATAFILTER_ATTRIB, DATAFILTER_ATTRIB);\n #endif\n `,\"vs:#main-end\":`\n if (dataFilter_value == 0.0) {\n gl_Position = vec4(0.);\n }\n `,\"vs:DECKGL_FILTER_SIZE\":`\n if (filter_transformSize) {\n size = size * dataFilter_value;\n }\n `,\"fs:DECKGL_FILTER_COLOR\":`\n if (dataFilter_value == 0.0) discard;\n if (filter_transformColor) {\n color.a *= dataFilter_value;\n }\n `},UH={name:\"data-filter\",vs:BH,fs:FH,inject:NH,getUniforms:zH},VH={name:\"data-filter-fp64\",vs:BH,fs:FH,inject:NH,getUniforms:Edt};var Pdt=`#define SHADER_NAME data-filter-vertex-shader\n\n#ifdef FLOAT_TARGET\n attribute float filterIndices;\n attribute float filterPrevIndices;\n#else\n attribute vec2 filterIndices;\n attribute vec2 filterPrevIndices;\n#endif\n\nvarying vec4 vColor;\nconst float component = 1.0 / 255.0;\n\nvoid main() {\n #ifdef FLOAT_TARGET\n dataFilter_value *= float(filterIndices != filterPrevIndices);\n gl_Position = vec4(0.0, 0.0, 0.0, 1.0);\n vColor = vec4(0.0, 0.0, 0.0, 1.0);\n #else\n // Float texture is not supported: pack result into 4 channels x 256 px x 64px\n dataFilter_value *= float(filterIndices.x != filterPrevIndices.x);\n float col = filterIndices.x;\n float row = filterIndices.y * 4.0;\n float channel = floor(row);\n row = fract(row);\n vColor = component * vec4(bvec4(channel == 0.0, channel == 1.0, channel == 2.0, channel == 3.0));\n gl_Position = vec4(col * 2.0 - 1.0, row * 2.0 - 1.0, 0.0, 1.0);\n #endif\n gl_PointSize = 1.0;\n}\n`,Idt=`#define SHADER_NAME data-filter-fragment-shader\nprecision highp float;\n\nvarying vec4 vColor;\n\nvoid main() {\n if (dataFilter_value < 0.5) {\n discard;\n }\n gl_FragColor = vColor;\n}\n`;function jH(e){return!!(e.getExtension(\"EXT_float_blend\")&&(e.getExtension(\"EXT_color_buffer_float\")||e.getExtension(\"WEBGL_color_buffer_float\")))}function GH(e,t){return t?new yi(e,{width:1,height:1,attachments:{36064:new pi(e,{format:fr(e)?34836:6408,type:5126,mipmaps:!1})}}):new yi(e,{width:256,height:64,depth:!1})}function WH(e,t,r){return t.defines.NON_INSTANCED_MODEL=1,r&&(t.defines.FLOAT_TARGET=1),new fn(e,{id:\"data-filter-aggregation-model\",vertexCount:1,isInstanced:!1,drawMode:0,vs:Pdt,fs:Idt,...t})}var HH={blend:!0,blendFunc:[1,1,1,1],blendEquation:[32774,32774],depthTest:!1};var Ldt={getFilterValue:{type:\"accessor\",value:0},onFilteredItemsChange:{type:\"function\",value:null,optional:!0},filterEnabled:!0,filterRange:[-1,1],filterSoftRange:null,filterTransformSize:!0,filterTransformColor:!0},qH={1:\"float\",2:\"vec2\",3:\"vec3\",4:\"vec4\"},xm=class extends cc{constructor({filterSize:t=1,fp64:r=!1,countItems:i=!1}={}){if(!qH[t])throw new Error(\"filterSize out of range\");super({filterSize:t,fp64:r,countItems:i})}getShaders(t){let{filterSize:r,fp64:i}=t.opts;return{modules:[i?VH:UH],defines:{DATAFILTER_TYPE:qH[r],DATAFILTER_DOUBLE:!!i}}}initializeState(t,r){let i=this.getAttributeManager();i&&i.add({filterValues:{size:r.opts.filterSize,type:r.opts.fp64?5130:5126,accessor:\"getFilterValue\",shaderAttributes:{filterValues:{divisor:0},instanceFilterValues:{divisor:1}}}});let{gl:s}=this.context;if(i&&r.opts.countItems){let n=jH(s);i.add({filterIndices:{size:n?1:2,vertexOffset:1,type:5121,normalized:!0,accessor:(f,{index:_})=>{let w=f&&f.__source?f.__source.index:_;return n?(w+1)%255:[(w+1)%255,Math.floor(w/255)%255]},shaderAttributes:{filterPrevIndices:{vertexOffset:0},filterIndices:{vertexOffset:1}}}});let o=GH(s,n),c=WH(s,r.getShaders.call(this,r),n);this.setState({filterFBO:o,filterModel:c})}}updateState({props:t,oldProps:r}){if(this.state.filterModel){let s=this.getAttributeManager().attributes.filterValues.needsUpdate()||t.filterEnabled!==r.filterEnabled||t.filterRange!==r.filterRange||t.filterSoftRange!==r.filterSoftRange;s&&this.setState({filterNeedsUpdate:s})}}draw(t,r){let{filterFBO:i,filterModel:s,filterNeedsUpdate:n}=this.state,{onFilteredItemsChange:o}=this.props;if(n&&o&&s){let{attributes:{filterValues:c,filterIndices:f}}=this.getAttributeManager();s.setVertexCount(this.getNumInstances());let{gl:_}=this.context;Hf(_,{framebuffer:i,color:[0,0,0,0]}),s.updateModuleSettings(t.moduleParameters).setAttributes({...c.getShaderAttributes(),...f&&f.getShaderAttributes()}).draw({framebuffer:i,parameters:{...HH,viewport:[0,0,i.width,i.height]}});let w=Dh(i),I=0;for(let R=0;R 0.0) {\n if (dashAlignMode == 0.0) {\n offset = vDashOffset;\n } else {\n unitLength = vPathLength / round(vPathLength / unitLength);\n offset = solidLength / 2.0;\n }\n\n float unitOffset = mod(vPathPosition.y + offset, unitLength);\n\n if (gapLength > 0.0 && unitOffset > solidLength) {\n if (capType <= 0.5) {\n if (!(dashGapPickable && picking_uActive)) {\n discard;\n }\n } else {\n float distToEnd = length(vec2(\n min(unitOffset - solidLength, unitLength - unitOffset),\n vPathPosition.x\n ));\n if (distToEnd > 1.0) {\n if (!(dashGapPickable && picking_uActive)) {\n discard;\n }\n }\n }\n }\n }\n`}},YH={inject:{\"vs:#decl\":`\nattribute float instanceOffsets;\n`,\"vs:DECKGL_FILTER_SIZE\":`\n float offsetWidth = abs(instanceOffsets * 2.0) + 1.0;\n size *= offsetWidth;\n`,\"vs:#main-end\":`\n float offsetWidth = abs(instanceOffsets * 2.0) + 1.0;\n float offsetDir = sign(instanceOffsets);\n vPathPosition.x = (vPathPosition.x + offsetDir) * offsetWidth - offsetDir;\n vPathPosition.y *= offsetWidth;\n vPathLength *= offsetWidth;\n`,\"fs:#main-start\":`\n float isInside;\n isInside = step(-1.0, vPathPosition.x) * step(vPathPosition.x, 1.0);\n if (isInside == 0.0) {\n discard;\n }\n`}};var kdt={getDashArray:{type:\"accessor\",value:[0,0]},getOffset:{type:\"accessor\",value:0},dashJustified:!1,dashGapPickable:!1},bm=class extends cc{constructor({dash:t=!1,offset:r=!1,highPrecisionDash:i=!1}={}){super({dash:t||i,offset:r,highPrecisionDash:i})}isEnabled(t){return\"pathTesselator\"in t.state}getShaders(t){if(!t.isEnabled(this))return null;let r={};return t.opts.dash&&(r=tv(r,ZH)),t.opts.offset&&(r=tv(r,YH)),r}initializeState(t,r){let i=this.getAttributeManager();!i||!r.isEnabled(this)||(r.opts.dash&&i.addInstanced({instanceDashArrays:{size:2,accessor:\"getDashArray\"}}),r.opts.highPrecisionDash&&i.addInstanced({instanceDashOffsets:{size:1,accessor:\"getPath\",transform:r.getDashOffsets.bind(this)}}),r.opts.offset&&i.addInstanced({instanceOffsets:{size:1,accessor:\"getOffset\"}}))}updateState(t,r){if(!r.isEnabled(this))return;let i={};r.opts.dash&&(i.dashAlignMode=this.props.dashJustified?1:0,i.dashGapPickable=!!this.props.dashGapPickable),this.state.model.setUniforms(i)}getDashOffsets(t){let r=[0],i=this.props.positionFormat===\"XY\"?2:3,s=Array.isArray(t[0]),n=s?t.length:t.length/i,o,c;for(let f=0;f0&&(r[f]=r[f-1]+$j(c,o)),c=o;return r}};G(bm,\"defaultProps\",kdt);G(bm,\"extensionName\",\"PathStyleExtension\");var Rdt=`\n#ifdef NON_INSTANCED_MODEL\nattribute float collisionPriorities;\n#else\nattribute float instanceCollisionPriorities;\n#endif\n\nuniform sampler2D collision_texture;\nuniform bool collision_sort;\nuniform bool collision_enabled;\n\nvec2 collision_getCoords(vec4 position) {\n vec4 collision_clipspace = project_common_position_to_clipspace(position);\n return (1.0 + collision_clipspace.xy / collision_clipspace.w) / 2.0;\n}\n\nfloat collision_match(vec2 tex, vec3 pickingColor) {\n vec4 collision_pickingColor = texture2D(collision_texture, tex);\n float delta = dot(abs(collision_pickingColor.rgb - pickingColor), vec3(1.0));\n float e = 0.001;\n return step(delta, e);\n}\n\nfloat collision_isVisible(vec2 texCoords, vec3 pickingColor) {\n if (!collision_enabled) {\n return 1.0;\n }\n\n // Visibility test, sample area of 5x5 pixels in order to fade in/out.\n // Due to the locality, the lookups will be cached\n // This reduces the flicker present when objects are shown/hidden\n const int N = 2;\n float accumulator = 0.0;\n vec2 step = vec2(1.0 / project_uViewportSize);\n\n const float floatN = float(N);\n vec2 delta = -floatN * step;\n for(int i = -N; i <= N; i++) {\n delta.x = -step.x * floatN;\n for(int j = -N; j <= N; j++) {\n accumulator += collision_match(texCoords + delta, pickingColor);\n delta.x += step.x;\n }\n delta.y += step.y;\n }\n\n float W = 2.0 * floatN + 1.0;\n return pow(accumulator / (W * W), 2.2);\n}\n`,Ddt={\"vs:#decl\":`\n float collision_fade = 1.0;\n`,\"vs:DECKGL_FILTER_GL_POSITION\":`\n if (collision_sort) {\n #ifdef NON_INSTANCED_MODEL\n float collisionPriority = collisionPriorities;\n #else\n float collisionPriority = instanceCollisionPriorities;\n #endif\n position.z = -0.001 * collisionPriority * position.w; // Support range -1000 -> 1000\n }\n\n if (collision_enabled) {\n vec4 collision_common_position = project_position(vec4(geometry.worldPosition, 1.0));\n vec2 collision_texCoords = collision_getCoords(collision_common_position);\n collision_fade = collision_isVisible(collision_texCoords, geometry.pickingColor / 255.0);\n if (collision_fade < 0.0001) {\n // Position outside clip space bounds to discard\n position = vec4(0.0, 0.0, 2.0, 1.0);\n }\n }\n `,\"vs:DECKGL_FILTER_COLOR\":`\n color.a *= collision_fade;\n `},Odt=(e,t)=>{if(!e||!(\"dummyCollisionMap\"in e))return{};let{collisionFBO:r,drawToCollisionMap:i,dummyCollisionMap:s}=e;return{collision_sort:!!i,collision_texture:!i&&r?r:s}},QH={name:\"collision\",dependencies:[Vh],vs:Rdt,inject:Ddt,getUniforms:Odt};var B2=class extends sc{renderCollisionMap(t,r){let i=this.gl,s=1;return Mn(i,{scissorTest:!0,scissor:[s,s,t.width-2*s,t.height-2*s],clearColor:[0,0,0,0],blend:!1,depthTest:!0,depthRange:[0,1]},()=>this.render({...r,target:t,pass:\"collision\"}))}getModuleParameters(){return{drawToCollisionMap:!0,pickingActive:1,pickingAttribute:!1,lightSources:{}}}};var F2=class extends sc{constructor(t,r){super(t,r),G(this,\"maskMap\",void 0),G(this,\"fbo\",void 0);let{mapSize:i=2048}=r;this.maskMap=new pi(t,{width:i,height:i,parameters:{10241:9729,10240:9729,10242:33071,10243:33071}}),this.fbo=new yi(t,{id:\"maskmap\",width:i,height:i,attachments:{36064:this.maskMap}})}render(t){let r=this.gl,i=[!1,!1,!1,!1];return i[t.channel]=!0,Mn(r,{clearColor:[255,255,255,255],blend:!0,blendFunc:[0,1],blendEquation:32778,colorMask:i,depthTest:!1},()=>super.render({...t,target:this.fbo,pass:\"mask\"}))}shouldDrawLayer(t){return t.props.operation.includes(\"mask\")}delete(){this.fbo.delete(),this.maskMap.delete()}};function $H(e,t){let r=[1/0,1/0,-1/0,-1/0];for(let i of e){let s=i.getBounds();if(s){let n=i.projectPosition(s[0],{viewport:t,autoOffset:!1}),o=i.projectPosition(s[1],{viewport:t,autoOffset:!1});r[0]=Math.min(r[0],n[0]),r[1]=Math.min(r[1],n[1]),r[2]=Math.max(r[2],o[0]),r[3]=Math.max(r[3],o[1])}}return Number.isFinite(r[0])?r:null}var Bdt=2048;function XH(e){let{bounds:t,viewport:r,border:i=0}=e,{isGeospatial:s}=r;if(t[2]<=t[0]||t[3]<=t[1])return null;let n=r.unprojectPosition([(t[0]+t[2])/2,(t[1]+t[3])/2,0]),{width:o,height:c,zoom:f}=e;if(f===void 0){o=o-i*2,c=c-i*2;let _=Math.min(o/(t[2]-t[0]),c/(t[3]-t[1]));f=Math.min(Math.log2(_),20)}else if(!o||!c){let _=2**f;o=Math.round(Math.abs(t[2]-t[0])*_),c=Math.round(Math.abs(t[3]-t[1])*_);let w=Bdt-i*2;if(o>w||c>w){let I=w/Math.max(o,c);o=Math.round(o*I),c=Math.round(c*I),f+=Math.log2(I)}}return s?new lc({id:r.id,x:i,y:i,width:o,height:c,longitude:n[0],latitude:n[1],zoom:f,orthographic:!0}):new iv({id:r.id,x:i,y:i,width:o,height:c,target:n,zoom:f,flipY:!1})}function Fdt(e,t){let r;if(t&&t.length===2){let[n,o]=t,c=e.getBounds({z:n}),f=e.getBounds({z:o});r=[Math.min(c[0],f[0]),Math.min(c[1],f[1]),Math.max(c[2],f[2]),Math.max(c[3],f[3])]}else r=e.getBounds();let i=e.projectPosition(r.slice(0,2)),s=e.projectPosition(r.slice(2,4));return[i[0],i[1],s[0],s[1]]}function KH(e,t,r){if(!e)return[0,0,1,1];let i=Fdt(t,r),s=zdt(i);return e[2]-e[0]<=s[2]-s[0]&&e[3]-e[1]<=s[3]-s[1]?e:[Math.max(e[0],s[0]),Math.max(e[1],s[1]),Math.min(e[2],s[2]),Math.min(e[3],s[3])]}function zdt(e){let t=e[2]-e[0],r=e[3]-e[1],i=(e[0]+e[2])/2,s=(e[1]+e[3])/2;return[i-t,s-r,i+t,s+r]}var z2=class{constructor(){G(this,\"id\",\"mask-effect\"),G(this,\"props\",null),G(this,\"useInPicking\",!0),G(this,\"order\",0),G(this,\"dummyMaskMap\",void 0),G(this,\"channels\",[]),G(this,\"masks\",null),G(this,\"maskPass\",void 0),G(this,\"maskMap\",void 0),G(this,\"lastViewport\",void 0)}preRender(t,{layers:r,layerFilter:i,viewports:s,onViewportActive:n,views:o,isPicking:c}){let f=!1;if(this.dummyMaskMap||(this.dummyMaskMap=new pi(t,{width:1,height:1})),c)return{didRender:f};let _=r.filter(N=>N.props.visible&&N.props.operation.includes(\"mask\"));if(_.length===0)return this.masks=null,this.channels.length=0,{didRender:f};this.masks={},this.maskPass||(this.maskPass=new F2(t,{id:\"default-mask\"}),this.maskMap=this.maskPass.maskMap);let w=this._sortMaskChannels(_),I=s[0],R=!this.lastViewport||!this.lastViewport.equals(I);if(I.resolution!==void 0)return or.warn(\"MaskExtension is not supported in GlobeView\")(),{didRender:f};for(let N in w){let j=this._renderChannel(w[N],{layerFilter:i,onViewportActive:n,views:o,viewport:I,viewportChanged:R});f||(f=j)}return{didRender:f}}_renderChannel(t,{layerFilter:r,onViewportActive:i,views:s,viewport:n,viewportChanged:o}){let c=!1,f=this.channels[t.index];if(!f)return c;let _=t===f||t.layers.length!==f.layers.length||t.layers.some((w,I)=>w!==f.layers[I]||w.props.transitions)||t.layerBounds.some((w,I)=>w!==f.layerBounds[I]);if(t.bounds=f.bounds,t.maskBounds=f.maskBounds,this.channels[t.index]=t,_||o){this.lastViewport=n;let w=$H(t.layers,n);if(t.bounds=w&&KH(w,n),_||!Ro(t.bounds,f.bounds)){let{maskPass:I,maskMap:R}=this,N=w&&XH({bounds:t.bounds,viewport:n,width:R.width,height:R.height,border:1});t.maskBounds=N?N.getBounds():[0,0,1,1],I.render({pass:\"mask\",channel:t.index,layers:t.layers,layerFilter:r,viewports:N?[N]:[],onViewportActive:i,views:s,moduleParameters:{devicePixelRatio:1}}),c=!0}}return this.masks[t.id]={index:t.index,bounds:t.maskBounds,coordinateOrigin:t.coordinateOrigin,coordinateSystem:t.coordinateSystem},c}_sortMaskChannels(t){let r={},i=0;for(let s of t){let{id:n}=s.root,o=r[n];if(!o){if(++i>4){or.warn(\"Too many mask layers. The max supported is 4\")();continue}o={id:n,index:this.channels.findIndex(c=>c?.id===n),layers:[],layerBounds:[],coordinateOrigin:s.root.props.coordinateOrigin,coordinateSystem:s.root.props.coordinateSystem},r[n]=o}o.layers.push(s),o.layerBounds.push(s.getBounds())}for(let s=0;s<4;s++){let n=this.channels[s];(!n||!(n.id in r))&&(this.channels[s]=null)}for(let s in r){let n=r[s];n.index<0&&(n.index=this.channels.findIndex(o=>!o),this.channels[n.index]=n)}return r}getModuleParameters(){return{maskMap:this.masks?this.maskMap:this.dummyMaskMap,maskChannels:this.masks}}cleanup(){this.dummyMaskMap&&(this.dummyMaskMap.delete(),this.dummyMaskMap=void 0),this.maskPass&&(this.maskPass.delete(),this.maskPass=void 0,this.maskMap=void 0),this.lastViewport=void 0,this.masks=null,this.channels.length=0}};var eB=2,N2=class{constructor(){G(this,\"id\",\"collision-filter-effect\"),G(this,\"props\",null),G(this,\"useInPicking\",!0),G(this,\"order\",1),G(this,\"channels\",{}),G(this,\"collisionFilterPass\",void 0),G(this,\"collisionFBOs\",{}),G(this,\"dummyCollisionMap\",void 0),G(this,\"lastViewport\",void 0)}preRender(t,{effects:r,layers:i,layerFilter:s,viewports:n,onViewportActive:o,views:c,isPicking:f,preRenderStats:_={}}){var w;if(this.dummyCollisionMap||(this.dummyCollisionMap=new pi(t,{width:1,height:1})),f)return;let I=i.filter(({props:{visible:Y,collisionEnabled:K}})=>Y&&K);if(I.length===0){this.channels={};return}this.collisionFilterPass||(this.collisionFilterPass=new B2(t,{id:\"default-collision-filter\"}));let R=r?.filter(Y=>Y.constructor===z2),N=(w=_[\"mask-effect\"])===null||w===void 0?void 0:w.didRender,j=this._groupByCollisionGroup(t,I),Q=n[0],et=!this.lastViewport||!this.lastViewport.equals(Q)||N;for(let Y in j){let K=this.collisionFBOs[Y],J=j[Y];K.resize({width:t.canvas.width/eB,height:t.canvas.height/eB}),this._render(J,{effects:R,layerFilter:s,onViewportActive:o,views:c,viewport:Q,viewportChanged:et})}}_render(t,{effects:r,layerFilter:i,onViewportActive:s,views:n,viewport:o,viewportChanged:c}){let{collisionGroup:f}=t,_=this.channels[f];if(!_)return;let w=c||t===_||!mo(_.layers,t.layers,1)||t.layerBounds.some((I,R)=>!Ro(I,_.layerBounds[R]))||t.allLayersLoaded!==_.allLayersLoaded||t.layers.some(I=>I.props.transitions);if(this.channels[f]=t,w){this.lastViewport=o;let I=this.collisionFBOs[f];this.collisionFilterPass.renderCollisionMap(I,{pass:\"collision-filter\",isPicking:!0,layers:t.layers,effects:r,layerFilter:i,viewports:o?[o]:[],onViewportActive:s,views:n,moduleParameters:{dummyCollisionMap:this.dummyCollisionMap,devicePixelRatio:El(I.gl)/eB}})}}_groupByCollisionGroup(t,r){let i={};for(let s of r){let{collisionGroup:n}=s.props,o=i[n];o||(o={collisionGroup:n,layers:[],layerBounds:[],allLayersLoaded:!0},i[n]=o),o.layers.push(s),o.layerBounds.push(s.getBounds()),s.isLoaded||(o.allLayersLoaded=!1)}for(let s of Object.keys(i))this.collisionFBOs[s]||this.createFBO(t,s),this.channels[s]||(this.channels[s]=i[s]);for(let s of Object.keys(this.collisionFBOs))i[s]||this.destroyFBO(s);return i}getModuleParameters(t){let{collisionGroup:r}=t.props,{collisionFBOs:i,dummyCollisionMap:s}=this;return{collisionFBO:i[r],dummyCollisionMap:s}}cleanup(){this.dummyCollisionMap&&(this.dummyCollisionMap.delete(),this.dummyCollisionMap=void 0),this.channels={};for(let t of Object.keys(this.collisionFBOs))this.destroyFBO(t);this.collisionFBOs={},this.lastViewport=void 0}createFBO(t,r){let{width:i,height:s}=t.canvas,n=new pi(t,{width:i,height:s,parameters:{10241:9728,10240:9728,10242:33071,10243:33071}}),o=new el(t,{format:33189,width:i,height:s});this.collisionFBOs[r]=new yi(t,{id:\"Collision-\".concat(r),width:i,height:s,attachments:{36064:n,36096:o}})}destroyFBO(t){let r=this.collisionFBOs[t];for(let i of Object.values(r.attachments))i.delete();r.delete(),delete this.collisionFBOs[t]}};var Ndt={getCollisionPriority:{type:\"accessor\",value:0},collisionEnabled:!0,collisionGroup:{type:\"string\",value:\"default\"},collisionTestProps:{}},wm=class extends cc{getShaders(){return{modules:[QH]}}draw({uniforms:t,context:r,moduleParameters:i}){let{collisionEnabled:s}=this.props,{collisionFBO:n,drawToCollisionMap:o}=i,c=s&&!!n;t.collision_enabled=c,o&&(this.props=this.clone(this.props.collisionTestProps).props)}initializeState(t,r){var i;if(this.getAttributeManager()===null)return;(i=this.context.deck)===null||i===void 0||i._addDefaultEffect(new N2),this.getAttributeManager().add({collisionPriorities:{size:1,accessor:\"getCollisionPriority\",shaderAttributes:{collisionPriorities:{divisor:0},instanceCollisionPriorities:{divisor:1}}}})}getNeedsPickingBuffer(){return this.props.collisionEnabled}};G(wm,\"defaultProps\",Ndt);G(wm,\"extensionName\",\"CollisionFilterExtension\");var Ng=class extends zg{static extensionType;constructor(t,r){super(t,r)}},U2=class extends Ng{static extensionType=\"brushing\";extensionInstance;constructor(t,r,i){super(t,i),this.extensionInstance=new vm,r.initRegularAttribute(\"brushing_enabled\",\"brushingEnabled\"),r.initRegularAttribute(\"brushing_target\",\"brushingTarget\"),r.initRegularAttribute(\"brushing_radius\",\"brushingRadius\"),r.initVectorizedAccessor(\"get_brushing_target\",\"getBrushingTarget\"),r.extensionLayerPropertyNames=[...r.extensionLayerPropertyNames,\"brushingEnabled\",\"brushingTarget\",\"brushingRadius\",\"getBrushingTarget\"]}},V2=class extends Ng{static extensionType=\"collision-filter\";extensionInstance;constructor(t,r,i){super(t,i),this.extensionInstance=new wm,r.initRegularAttribute(\"collision_enabled\",\"collisionEnabled\"),r.initRegularAttribute(\"collision_group\",\"collisionGroup\"),r.initRegularAttribute(\"collision_test_props\",\"collisionTestProps\"),r.initVectorizedAccessor(\"get_collision_priority\",\"getCollisionPriority\"),r.extensionLayerPropertyNames=[...r.extensionLayerPropertyNames,\"collisionEnabled\",\"collisionGroup\",\"collisionTestProps\",\"getCollisionPriority\"]}},k3=class extends Ng{static extensionType=\"data-filter\";extensionInstance;constructor(t,r,i){super(t,i);let s=this.model.get(\"filter_size\");this.extensionInstance=new xm({filterSize:s}),r.initRegularAttribute(\"filter_enabled\",\"filterEnabled\"),r.initRegularAttribute(\"filter_range\",\"filterRange\"),r.initRegularAttribute(\"filter_soft_range\",\"filterSoftRange\"),r.initRegularAttribute(\"filter_transform_size\",\"filterTransformSize\"),r.initRegularAttribute(\"filter_transform_color\",\"filterTransformColor\"),r.initVectorizedAccessor(\"get_filter_value\",\"getFilterValue\"),r.extensionLayerPropertyNames=[...r.extensionLayerPropertyNames,\"filterEnabled\",\"filterRange\",\"filterSoftRange\",\"filterTransformSize\",\"filterTransformColor\",\"getFilterValue\"]}},j2=class extends Ng{static extensionType=\"path-style\";extensionInstance;constructor(t,r,i){super(t,i);let s=this.model.get(\"dash\"),n=this.model.get(\"high_precision_dash\"),o=this.model.get(\"offset\");this.extensionInstance=new bm({...Jt(s)?{dash:s}:{},...Jt(n)?{highPrecisionDash:n}:{},...Jt(o)?{offset:o}:{}}),r.initRegularAttribute(\"dash_gap_pickable\",\"dashGapPickable\"),r.initRegularAttribute(\"dash_justified\",\"dashJustified\"),r.initVectorizedAccessor(\"get_dash_array\",\"getDashArray\"),r.initVectorizedAccessor(\"get_offset\",\"getOffset\"),r.extensionLayerPropertyNames=[...r.extensionLayerPropertyNames,\"dashGapPickable\",\"dashJustified\",\"getDashArray\",\"getOffset\"]}};async function rB(e,t,r){let i=e.get(\"_extension_type\"),s;switch(i){case U2.extensionType:s=new U2(e,t,r);break;case V2.extensionType:s=new V2(e,t,r);break;case k3.extensionType:s=new k3(e,t,r);break;case j2.extensionType:s=new j2(e,t,r);break;default:throw new Error(`no known model for extension type ${i}`)}return await s.loadSubModels(),s}var Ug=class extends zg{pickable;visible;opacity;autoHighlight;extensions;extensionLayerPropertyNames=[];constructor(t,r){super(t,r),this.initRegularAttribute(\"pickable\",\"pickable\"),this.initRegularAttribute(\"visible\",\"visible\"),this.initRegularAttribute(\"opacity\",\"opacity\"),this.initRegularAttribute(\"auto_highlight\",\"autoHighlight\"),this.extensions=[]}async loadSubModels(){await this.initLayerExtensions()}extensionInstances(){return this.extensions.map(t=>t.extensionInstance)}extensionProps(){let t={};for(let r of this.extensionLayerPropertyNames)Jt(this[r])&&(t[r]=this[r]);return t}onClick(t){t.index&&(this.model.set(\"selected_index\",t.index),this.model.save_changes())}baseLayerProps(){return{extensions:this.extensionInstances(),...this.extensionProps(),id:this.model.model_id,pickable:this.pickable,visible:this.visible,opacity:this.opacity,autoHighlight:this.autoHighlight,onClick:this.onClick.bind(this)}}async initLayerExtensions(){let t=async()=>{let r=this.model.get(\"extensions\");if(!r){this.extensions=[];return}let i=await L3(this.model.widget_manager,r),s=[];for(let n of i){let o=await rB(n,this,this.updateStateCallback);s.push(o)}this.extensions=s};await t(),this.model.off(\"change:extensions\"),this.model.on(\"change:extensions\",t),this.callbacks.set(\"change:extensions\",t)}};var JH=`#define SHADER_NAME arc-layer-vertex-shader\n\nattribute vec3 positions;\nattribute vec4 instanceSourceColors;\nattribute vec4 instanceTargetColors;\nattribute vec3 instanceSourcePositions;\nattribute vec3 instanceSourcePositions64Low;\nattribute vec3 instanceTargetPositions;\nattribute vec3 instanceTargetPositions64Low;\nattribute vec3 instancePickingColors;\nattribute float instanceWidths;\nattribute float instanceHeights;\nattribute float instanceTilts;\n\nuniform bool greatCircle;\nuniform bool useShortestPath;\nuniform float numSegments;\nuniform float opacity;\nuniform float widthScale;\nuniform float widthMinPixels;\nuniform float widthMaxPixels;\nuniform int widthUnits;\n\nvarying vec4 vColor;\nvarying vec2 uv;\nvarying float isValid;\n\nfloat paraboloid(float distance, float sourceZ, float targetZ, float ratio) {\n\n float deltaZ = targetZ - sourceZ;\n float dh = distance * instanceHeights;\n if (dh == 0.0) {\n return sourceZ + deltaZ * ratio;\n }\n float unitZ = deltaZ / dh;\n float p2 = unitZ * unitZ + 1.0;\n float dir = step(deltaZ, 0.0);\n float z0 = mix(sourceZ, targetZ, dir);\n float r = mix(ratio, 1.0 - ratio, dir);\n return sqrt(r * (p2 - r)) * dh + z0;\n}\nvec2 getExtrusionOffset(vec2 line_clipspace, float offset_direction, float width) {\n vec2 dir_screenspace = normalize(line_clipspace * project_uViewportSize);\n dir_screenspace = vec2(-dir_screenspace.y, dir_screenspace.x);\n\n return dir_screenspace * offset_direction * width / 2.0;\n}\n\nfloat getSegmentRatio(float index) {\n return smoothstep(0.0, 1.0, index / (numSegments - 1.0));\n}\n\nvec3 interpolateFlat(vec3 source, vec3 target, float segmentRatio) {\n float distance = length(source.xy - target.xy);\n float z = paraboloid(distance, source.z, target.z, segmentRatio);\n\n float tiltAngle = radians(instanceTilts);\n vec2 tiltDirection = normalize(target.xy - source.xy);\n vec2 tilt = vec2(-tiltDirection.y, tiltDirection.x) * z * sin(tiltAngle);\n\n return vec3(\n mix(source.xy, target.xy, segmentRatio) + tilt,\n z * cos(tiltAngle)\n );\n}\nfloat getAngularDist (vec2 source, vec2 target) {\n vec2 sourceRadians = radians(source);\n vec2 targetRadians = radians(target);\n vec2 sin_half_delta = sin((sourceRadians - targetRadians) / 2.0);\n vec2 shd_sq = sin_half_delta * sin_half_delta;\n\n float a = shd_sq.y + cos(sourceRadians.y) * cos(targetRadians.y) * shd_sq.x;\n return 2.0 * asin(sqrt(a));\n}\n\nvec3 interpolateGreatCircle(vec3 source, vec3 target, vec3 source3D, vec3 target3D, float angularDist, float t) {\n vec2 lngLat;\n if(abs(angularDist - PI) < 0.001) {\n lngLat = (1.0 - t) * source.xy + t * target.xy;\n } else {\n float a = sin((1.0 - t) * angularDist);\n float b = sin(t * angularDist);\n vec3 p = source3D.yxz * a + target3D.yxz * b;\n lngLat = degrees(vec2(atan(p.y, -p.x), atan(p.z, length(p.xy))));\n }\n\n float z = paraboloid(angularDist * EARTH_RADIUS, source.z, target.z, t);\n\n return vec3(lngLat, z);\n}\n\nvoid main(void) {\n geometry.worldPosition = instanceSourcePositions;\n geometry.worldPositionAlt = instanceTargetPositions;\n\n float segmentIndex = positions.x;\n float segmentRatio = getSegmentRatio(segmentIndex);\n float prevSegmentRatio = getSegmentRatio(max(0.0, segmentIndex - 1.0));\n float nextSegmentRatio = getSegmentRatio(min(numSegments - 1.0, segmentIndex + 1.0));\n float indexDir = mix(-1.0, 1.0, step(segmentIndex, 0.0));\n isValid = 1.0;\n\n uv = vec2(segmentRatio, positions.y);\n geometry.uv = uv;\n geometry.pickingColor = instancePickingColors;\n\n vec4 curr;\n vec4 next;\n vec3 source;\n vec3 target;\n\n if ((greatCircle || project_uProjectionMode == PROJECTION_MODE_GLOBE) && project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT) {\n source = project_globe_(vec3(instanceSourcePositions.xy, 0.0));\n target = project_globe_(vec3(instanceTargetPositions.xy, 0.0));\n float angularDist = getAngularDist(instanceSourcePositions.xy, instanceTargetPositions.xy);\n\n vec3 prevPos = interpolateGreatCircle(instanceSourcePositions, instanceTargetPositions, source, target, angularDist, prevSegmentRatio);\n vec3 currPos = interpolateGreatCircle(instanceSourcePositions, instanceTargetPositions, source, target, angularDist, segmentRatio);\n vec3 nextPos = interpolateGreatCircle(instanceSourcePositions, instanceTargetPositions, source, target, angularDist, nextSegmentRatio);\n\n if (abs(currPos.x - prevPos.x) > 180.0) {\n indexDir = -1.0;\n isValid = 0.0;\n } else if (abs(currPos.x - nextPos.x) > 180.0) {\n indexDir = 1.0;\n isValid = 0.0;\n }\n nextPos = indexDir < 0.0 ? prevPos : nextPos;\n nextSegmentRatio = indexDir < 0.0 ? prevSegmentRatio : nextSegmentRatio;\n\n if (isValid == 0.0) {\n nextPos.x += nextPos.x > 0.0 ? -360.0 : 360.0;\n float t = ((currPos.x > 0.0 ? 180.0 : -180.0) - currPos.x) / (nextPos.x - currPos.x);\n currPos = mix(currPos, nextPos, t);\n segmentRatio = mix(segmentRatio, nextSegmentRatio, t);\n }\n\n vec3 currPos64Low = mix(instanceSourcePositions64Low, instanceTargetPositions64Low, segmentRatio);\n vec3 nextPos64Low = mix(instanceSourcePositions64Low, instanceTargetPositions64Low, nextSegmentRatio);\n \n curr = project_position_to_clipspace(currPos, currPos64Low, vec3(0.0), geometry.position);\n next = project_position_to_clipspace(nextPos, nextPos64Low, vec3(0.0));\n \n } else {\n vec3 source_world = instanceSourcePositions;\n vec3 target_world = instanceTargetPositions;\n if (useShortestPath) {\n source_world.x = mod(source_world.x + 180., 360.0) - 180.;\n target_world.x = mod(target_world.x + 180., 360.0) - 180.;\n\n float deltaLng = target_world.x - source_world.x;\n if (deltaLng > 180.) target_world.x -= 360.;\n if (deltaLng < -180.) source_world.x -= 360.;\n }\n source = project_position(source_world, instanceSourcePositions64Low);\n target = project_position(target_world, instanceTargetPositions64Low);\n float antiMeridianX = 0.0;\n\n if (useShortestPath) {\n if (project_uProjectionMode == PROJECTION_MODE_WEB_MERCATOR_AUTO_OFFSET) {\n antiMeridianX = -(project_uCoordinateOrigin.x + 180.) / 360. * TILE_SIZE;\n }\n float thresholdRatio = (antiMeridianX - source.x) / (target.x - source.x);\n\n if (prevSegmentRatio <= thresholdRatio && nextSegmentRatio > thresholdRatio) {\n isValid = 0.0;\n indexDir = sign(segmentRatio - thresholdRatio);\n segmentRatio = thresholdRatio;\n }\n }\n\n nextSegmentRatio = indexDir < 0.0 ? prevSegmentRatio : nextSegmentRatio;\n vec3 currPos = interpolateFlat(source, target, segmentRatio);\n vec3 nextPos = interpolateFlat(source, target, nextSegmentRatio);\n\n if (useShortestPath) {\n if (nextPos.x < antiMeridianX) {\n currPos.x += TILE_SIZE;\n nextPos.x += TILE_SIZE;\n }\n }\n\n curr = project_common_position_to_clipspace(vec4(currPos, 1.0));\n next = project_common_position_to_clipspace(vec4(nextPos, 1.0));\n geometry.position = vec4(currPos, 1.0);\n }\n float widthPixels = clamp(\n project_size_to_pixel(instanceWidths * widthScale, widthUnits),\n widthMinPixels, widthMaxPixels\n );\n vec3 offset = vec3(\n getExtrusionOffset((next.xy - curr.xy) * indexDir, positions.y, widthPixels),\n 0.0);\n DECKGL_FILTER_SIZE(offset, geometry);\n DECKGL_FILTER_GL_POSITION(curr, geometry);\n gl_Position = curr + vec4(project_pixel_size_to_clipspace(offset.xy), 0.0, 0.0);\n\n vec4 color = mix(instanceSourceColors, instanceTargetColors, segmentRatio);\n vColor = vec4(color.rgb, color.a * opacity);\n DECKGL_FILTER_COLOR(vColor, geometry);\n}\n`;var tq=`#define SHADER_NAME arc-layer-fragment-shader\n\nprecision highp float;\n\nvarying vec4 vColor;\nvarying vec2 uv;\nvarying float isValid;\n\nvoid main(void) {\n if (isValid == 0.0) {\n discard;\n }\n\n gl_FragColor = vColor;\n geometry.uv = uv;\n\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var R3=[0,0,0,255],Udt={getSourcePosition:{type:\"accessor\",value:e=>e.sourcePosition},getTargetPosition:{type:\"accessor\",value:e=>e.targetPosition},getSourceColor:{type:\"accessor\",value:R3},getTargetColor:{type:\"accessor\",value:R3},getWidth:{type:\"accessor\",value:1},getHeight:{type:\"accessor\",value:1},getTilt:{type:\"accessor\",value:0},greatCircle:!1,numSegments:{type:\"number\",value:50,min:1},widthUnits:\"pixels\",widthScale:{type:\"number\",value:1,min:0},widthMinPixels:{type:\"number\",value:0,min:0},widthMaxPixels:{type:\"number\",value:Number.MAX_SAFE_INTEGER,min:0}},Tp=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getBounds(){var t;return(t=this.getAttributeManager())===null||t===void 0?void 0:t.getBounds([\"instanceSourcePositions\",\"instanceTargetPositions\"])}getShaders(){return super.getShaders({vs:JH,fs:tq,modules:[Rs,Ao]})}get wrapLongitude(){return!1}initializeState(){this.getAttributeManager().addInstanced({instanceSourcePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getSourcePosition\"},instanceTargetPositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getTargetPosition\"},instanceSourceColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getSourceColor\",defaultValue:R3},instanceTargetColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getTargetColor\",defaultValue:R3},instanceWidths:{size:1,transition:!0,accessor:\"getWidth\",defaultValue:1},instanceHeights:{size:1,transition:!0,accessor:\"getHeight\",defaultValue:1},instanceTilts:{size:1,transition:!0,accessor:\"getTilt\",defaultValue:0}})}updateState(t){super.updateState(t);let{props:r,oldProps:i,changeFlags:s}=t;if(s.extensionsChanged||s.propsChanged&&r.numSegments!==i.numSegments){var n;let{gl:o}=this.context;(n=this.state.model)===null||n===void 0||n.delete(),this.state.model=this._getModel(o),this.getAttributeManager().invalidateAll()}}draw({uniforms:t}){let{widthUnits:r,widthScale:i,widthMinPixels:s,widthMaxPixels:n,greatCircle:o,wrapLongitude:c}=this.props;this.state.model.setUniforms(t).setUniforms({greatCircle:o,widthUnits:po[r],widthScale:i,widthMinPixels:s,widthMaxPixels:n,useShortestPath:c}).draw()}_getModel(t){let{id:r,numSegments:i}=this.props,s=[];for(let o=0;o0&&j>0&&(c[I++]=w-n,c[I++]=w-n-1,c[I++]=w-1,c[I++]=w-n,c[I++]=w-1,c[I++]=w),w++}}return{vertexCount:o,positions:_,indices:c,texCoords:f}}function Gdt(e){let t=new Float64Array(12);for(let r=0;r 0.5) {\n vTexPos = geometry.worldPosition.xy;\n }\n\n vec4 color = vec4(0.0);\n DECKGL_FILTER_COLOR(color, geometry);\n}\n`;var Hdt=`\nvec3 packUVsIntoRGB(vec2 uv) {\n // Extract the top 8 bits. We want values to be truncated down so we can add a fraction\n vec2 uv8bit = floor(uv * 256.);\n\n // Calculate the normalized remainders of u and v parts that do not fit into 8 bits\n // Scale and clamp to 0-1 range\n vec2 uvFraction = fract(uv * 256.);\n vec2 uvFraction4bit = floor(uvFraction * 16.);\n\n // Remainder can be encoded in blue channel, encode as 4 bits for pixel coordinates\n float fractions = uvFraction4bit.x + uvFraction4bit.y * 16.;\n\n return vec3(uv8bit, fractions) / 255.;\n}\n`,rq=`\n#define SHADER_NAME bitmap-layer-fragment-shader\n\n#ifdef GL_ES\nprecision highp float;\n#endif\n\nuniform sampler2D bitmapTexture;\n\nvarying vec2 vTexCoord;\nvarying vec2 vTexPos;\n\nuniform float desaturate;\nuniform vec4 transparentColor;\nuniform vec3 tintColor;\nuniform float opacity;\n\nuniform float coordinateConversion;\nuniform vec4 bounds;\n\n/* projection utils */\nconst float TILE_SIZE = 512.0;\nconst float PI = 3.1415926536;\nconst float WORLD_SCALE = TILE_SIZE / PI / 2.0;\n\n// from degrees to Web Mercator\nvec2 lnglat_to_mercator(vec2 lnglat) {\n float x = lnglat.x;\n float y = clamp(lnglat.y, -89.9, 89.9);\n return vec2(\n radians(x) + PI,\n PI + log(tan(PI * 0.25 + radians(y) * 0.5))\n ) * WORLD_SCALE;\n}\n\n// from Web Mercator to degrees\nvec2 mercator_to_lnglat(vec2 xy) {\n xy /= WORLD_SCALE;\n return degrees(vec2(\n xy.x - PI,\n atan(exp(xy.y - PI)) * 2.0 - PI * 0.5\n ));\n}\n/* End projection utils */\n\n// apply desaturation\nvec3 color_desaturate(vec3 color) {\n float luminance = (color.r + color.g + color.b) * 0.333333333;\n return mix(color, vec3(luminance), desaturate);\n}\n\n// apply tint\nvec3 color_tint(vec3 color) {\n return color * tintColor;\n}\n\n// blend with background color\nvec4 apply_opacity(vec3 color, float alpha) {\n if (transparentColor.a == 0.0) {\n return vec4(color, alpha);\n }\n float blendedAlpha = alpha + transparentColor.a * (1.0 - alpha);\n float highLightRatio = alpha / blendedAlpha;\n vec3 blendedRGB = mix(transparentColor.rgb, color, highLightRatio);\n return vec4(blendedRGB, blendedAlpha);\n}\n\nvec2 getUV(vec2 pos) {\n return vec2(\n (pos.x - bounds[0]) / (bounds[2] - bounds[0]),\n (pos.y - bounds[3]) / (bounds[1] - bounds[3])\n );\n}\n\n`.concat(Hdt,`\n\nvoid main(void) {\n vec2 uv = vTexCoord;\n if (coordinateConversion < -0.5) {\n vec2 lnglat = mercator_to_lnglat(vTexPos);\n uv = getUV(lnglat);\n } else if (coordinateConversion > 0.5) {\n vec2 commonPos = lnglat_to_mercator(vTexPos);\n uv = getUV(commonPos);\n }\n vec4 bitmapColor = texture2D(bitmapTexture, uv);\n\n gl_FragColor = apply_opacity(color_tint(color_desaturate(bitmapColor.rgb)), bitmapColor.a * opacity);\n\n geometry.uv = uv;\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n\n if (picking_uActive && !picking_uAttribute) {\n // Since instance information is not used, we can use picking color for pixel index\n gl_FragColor.rgb = packUVsIntoRGB(uv);\n }\n}\n`);var qdt={image:{type:\"image\",value:null,async:!0},bounds:{type:\"array\",value:[1,0,0,1],compare:!0},_imageCoordinateSystem:Yr.DEFAULT,desaturate:{type:\"number\",min:0,max:1,value:0},transparentColor:{type:\"color\",value:[0,0,0,0]},tintColor:{type:\"color\",value:[255,255,255]},textureParameters:{type:\"object\",ignore:!0}},Mp=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(){return super.getShaders({vs:eq,fs:rq,modules:[Rs,Ao]})}initializeState(){let t=this.getAttributeManager();t.remove([\"instancePickingColors\"]);let r=!0;t.add({indices:{size:1,isIndexed:!0,update:i=>i.value=this.state.mesh.indices,noAlloc:r},positions:{size:3,type:5130,fp64:this.use64bitPositions(),update:i=>i.value=this.state.mesh.positions,noAlloc:r},texCoords:{size:2,update:i=>i.value=this.state.mesh.texCoords,noAlloc:r}})}updateState({props:t,oldProps:r,changeFlags:i}){let s=this.getAttributeManager();if(i.extensionsChanged){var n;let{gl:o}=this.context;(n=this.state.model)===null||n===void 0||n.delete(),this.state.model=this._getModel(o),s.invalidateAll()}if(t.bounds!==r.bounds){let o=this.state.mesh,c=this._createMesh();this.state.model.setVertexCount(c.vertexCount);for(let f in c)o&&o[f]!==c[f]&&s.invalidate(f);this.setState({mesh:c,...this._getCoordinateUniforms()})}else t._imageCoordinateSystem!==r._imageCoordinateSystem&&this.setState(this._getCoordinateUniforms())}getPickingInfo(t){let{image:r}=this.props,i=t.info;if(!i.color||!r)return i.bitmap=null,i;let{width:s,height:n}=r;i.index=0;let o=Zdt(i.color),c=[Math.floor(o[0]*s),Math.floor(o[1]*n)];return i.bitmap={size:{width:s,height:n},uv:o,pixel:c},i}disablePickingIndex(){this.setState({disablePicking:!0})}restorePickingColors(){this.setState({disablePicking:!1})}_updateAutoHighlight(t){super._updateAutoHighlight({...t,color:this.encodePickingColor(0)})}_createMesh(){let{bounds:t}=this.props,r=t;return iq(t)&&(r=[[t[0],t[1]],[t[0],t[3]],[t[2],t[3]],[t[2],t[1]]]),iB(r,this.context.viewport.resolution)}_getModel(t){return t?new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:4,vertexCount:6}),isInstanced:!1}):null}draw(t){let{uniforms:r,moduleParameters:i}=t,{model:s,coordinateConversion:n,bounds:o,disablePicking:c}=this.state,{image:f,desaturate:_,transparentColor:w,tintColor:I}=this.props;i.pickingActive&&c||f&&s&&s.setUniforms(r).setUniforms({bitmapTexture:f,desaturate:_,transparentColor:w.map(R=>R/255),tintColor:I.slice(0,3).map(R=>R/255),coordinateConversion:n,bounds:o}).draw()}_getCoordinateUniforms(){let{LNGLAT:t,CARTESIAN:r,DEFAULT:i}=Yr,{_imageCoordinateSystem:s}=this.props;if(s!==i){let{bounds:n}=this.props;if(!iq(n))throw new Error(\"_imageCoordinateSystem only supports rectangular bounds\");let o=this.context.viewport.resolution?t:r;if(s=s===t?t:r,s===t&&o===r)return{coordinateConversion:-1,bounds:n};if(s===r&&o===t){let c=va([n[0],n[1]]),f=va([n[2],n[3]]);return{coordinateConversion:1,bounds:[c[0],c[1],f[0],f[1]]}}}return{coordinateConversion:0,bounds:[0,0,0,0]}}};G(Mp,\"layerName\",\"BitmapLayer\");G(Mp,\"defaultProps\",qdt);function Zdt(e){let[t,r,i]=e,s=(i&240)/256,n=(i&15)/16;return[(t+n)/256,(r+s)/256]}function iq(e){return Number.isFinite(e[0])}var nq=`#define SHADER_NAME icon-layer-vertex-shader\n\nattribute vec2 positions;\n\nattribute vec3 instancePositions;\nattribute vec3 instancePositions64Low;\nattribute float instanceSizes;\nattribute float instanceAngles;\nattribute vec4 instanceColors;\nattribute vec3 instancePickingColors;\nattribute vec4 instanceIconFrames;\nattribute float instanceColorModes;\nattribute vec2 instanceOffsets;\nattribute vec2 instancePixelOffset;\n\nuniform float sizeScale;\nuniform vec2 iconsTextureDim;\nuniform float sizeMinPixels;\nuniform float sizeMaxPixels;\nuniform bool billboard;\nuniform int sizeUnits;\n\nvarying float vColorMode;\nvarying vec4 vColor;\nvarying vec2 vTextureCoords;\nvarying vec2 uv;\n\nvec2 rotate_by_angle(vec2 vertex, float angle) {\n float angle_radian = angle * PI / 180.0;\n float cos_angle = cos(angle_radian);\n float sin_angle = sin(angle_radian);\n mat2 rotationMatrix = mat2(cos_angle, -sin_angle, sin_angle, cos_angle);\n return rotationMatrix * vertex;\n}\n\nvoid main(void) {\n geometry.worldPosition = instancePositions;\n geometry.uv = positions;\n geometry.pickingColor = instancePickingColors;\n uv = positions;\n\n vec2 iconSize = instanceIconFrames.zw;\n float sizePixels = clamp(\n project_size_to_pixel(instanceSizes * sizeScale, sizeUnits), \n sizeMinPixels, sizeMaxPixels\n );\n float instanceScale = iconSize.y == 0.0 ? 0.0 : sizePixels / iconSize.y;\n vec2 pixelOffset = positions / 2.0 * iconSize + instanceOffsets;\n pixelOffset = rotate_by_angle(pixelOffset, instanceAngles) * instanceScale;\n pixelOffset += instancePixelOffset;\n pixelOffset.y *= -1.0;\n\n if (billboard) {\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, vec3(0.0), geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n vec3 offset = vec3(pixelOffset, 0.0);\n DECKGL_FILTER_SIZE(offset, geometry);\n gl_Position.xy += project_pixel_size_to_clipspace(offset.xy);\n\n } else {\n vec3 offset_common = vec3(project_pixel_size(pixelOffset), 0.0);\n DECKGL_FILTER_SIZE(offset_common, geometry);\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, offset_common, geometry.position); \n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n }\n\n vTextureCoords = mix(\n instanceIconFrames.xy,\n instanceIconFrames.xy + iconSize,\n (positions.xy + 1.0) / 2.0\n ) / iconsTextureDim;\n\n vColor = instanceColors;\n DECKGL_FILTER_COLOR(vColor, geometry);\n\n vColorMode = instanceColorModes;\n}\n`;var sq=`#define SHADER_NAME icon-layer-fragment-shader\n\nprecision highp float;\n\nuniform float opacity;\nuniform sampler2D iconsTexture;\nuniform float alphaCutoff;\n\nvarying float vColorMode;\nvarying vec4 vColor;\nvarying vec2 vTextureCoords;\nvarying vec2 uv;\n\nvoid main(void) {\n geometry.uv = uv;\n\n vec4 texColor = texture2D(iconsTexture, vTextureCoords);\n vec3 color = mix(texColor.rgb, vColor.rgb, vColorMode);\n float a = texColor.a * opacity * vColor.a;\n\n if (a < alphaCutoff) {\n discard;\n }\n\n gl_FragColor = vec4(color, a);\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var Ydt=1024,Qdt=4,oq=()=>{},aq={10241:9987,10240:9729,10242:33071,10243:33071};function $dt(e){return Math.pow(2,Math.ceil(Math.log2(e)))}function Xdt(e,t,r,i){let s=Math.min(r/t.width,i/t.height),n=Math.floor(t.width*s),o=Math.floor(t.height*s);return s===1?{data:t,width:n,height:o}:(e.canvas.height=o,e.canvas.width=n,e.clearRect(0,0,n,o),e.drawImage(t,0,0,t.width,t.height,0,0,n,o),{data:e.canvas,width:n,height:o})}function G2(e){return e&&(e.id||e.url)}function Kdt(e,t,r,i){let s=e.width,n=e.height,o=new pi(e.gl,{width:t,height:r,parameters:i});return gE(e,o,{targetY:0,width:s,height:n}),e.delete(),o}function lq(e,t,r){for(let i=0;io&&(lq(r,c,s),i=0,s=n+s+t,n=0,c=[]),c.push({icon:_,xOffset:i}),i=i+R+t,n=Math.max(n,I)}}return c.length>0&&lq(r,c,s),{mapping:r,rowHeight:n,xOffset:i,yOffset:s,canvasWidth:o,canvasHeight:$dt(n+s+t)}}function tpt(e,t,r){if(!e||!t)return null;r=r||{};let i={},{iterable:s,objectInfo:n}=Jc(e);for(let o of s){n.index++;let c=t(o,n),f=G2(c);if(!c)throw new Error(\"Icon is missing.\");if(!c.url)throw new Error(\"Icon url is missing.\");!i[f]&&(!r[f]||c.url!==r[f].url)&&(i[f]={...c,source:o,sourceIndex:n.index})}return i}var W2=class{constructor(t,{onUpdate:r=oq,onError:i=oq}){G(this,\"gl\",void 0),G(this,\"onUpdate\",void 0),G(this,\"onError\",void 0),G(this,\"_loadOptions\",null),G(this,\"_texture\",null),G(this,\"_externalTexture\",null),G(this,\"_mapping\",{}),G(this,\"_textureParameters\",null),G(this,\"_pendingCount\",0),G(this,\"_autoPacking\",!1),G(this,\"_xOffset\",0),G(this,\"_yOffset\",0),G(this,\"_rowHeight\",0),G(this,\"_buffer\",Qdt),G(this,\"_canvasWidth\",Ydt),G(this,\"_canvasHeight\",0),G(this,\"_canvas\",null),this.gl=t,this.onUpdate=r,this.onError=i}finalize(){var t;(t=this._texture)===null||t===void 0||t.delete()}getTexture(){return this._texture||this._externalTexture}getIconMapping(t){let r=this._autoPacking?G2(t):t;return this._mapping[r]||{}}setProps({loadOptions:t,autoPacking:r,iconAtlas:i,iconMapping:s,textureParameters:n}){if(t&&(this._loadOptions=t),r!==void 0&&(this._autoPacking=r),s&&(this._mapping=s),i){var o;(o=this._texture)===null||o===void 0||o.delete(),this._texture=null,this._externalTexture=i}n&&(this._textureParameters=n)}get isLoaded(){return this._pendingCount===0}packIcons(t,r){if(!this._autoPacking||typeof document>\"u\")return;let i=Object.values(tpt(t,r,this._mapping)||{});if(i.length>0){let{mapping:s,xOffset:n,yOffset:o,rowHeight:c,canvasHeight:f}=Jdt({icons:i,buffer:this._buffer,canvasWidth:this._canvasWidth,mapping:this._mapping,rowHeight:this._rowHeight,xOffset:this._xOffset,yOffset:this._yOffset});this._rowHeight=c,this._mapping=s,this._xOffset=n,this._yOffset=o,this._canvasHeight=f,this._texture||(this._texture=new pi(this.gl,{width:this._canvasWidth,height:this._canvasHeight,parameters:this._textureParameters||aq})),this._texture.height!==this._canvasHeight&&(this._texture=Kdt(this._texture,this._canvasWidth,this._canvasHeight,this._textureParameters||aq)),this.onUpdate(),this._canvas=this._canvas||document.createElement(\"canvas\"),this._loadIcons(i)}}_loadIcons(t){let r=this._canvas.getContext(\"2d\",{willReadFrequently:!0});for(let i of t)this._pendingCount++,jA(i.url,this._loadOptions).then(s=>{let n=G2(i),o=this._mapping[n],{x:c,y:f,width:_,height:w}=o,{data:I,width:R,height:N}=Xdt(r,s,_,w);this._texture.setSubImageData({data:I,x:c+(_-R)/2,y:f+(w-N)/2,width:R,height:N}),o.width=R,o.height=N,this._texture.generateMipmap(),this.onUpdate()}).catch(s=>{this.onError({url:i.url,source:i.source,sourceIndex:i.sourceIndex,loadOptions:this._loadOptions,error:s})}).finally(()=>{this._pendingCount--})}};var cq=[0,0,0,255],ept={iconAtlas:{type:\"image\",value:null,async:!0},iconMapping:{type:\"object\",value:{},async:!0},sizeScale:{type:\"number\",value:1,min:0},billboard:!0,sizeUnits:\"pixels\",sizeMinPixels:{type:\"number\",min:0,value:0},sizeMaxPixels:{type:\"number\",min:0,value:Number.MAX_SAFE_INTEGER},alphaCutoff:{type:\"number\",value:.05,min:0,max:1},getPosition:{type:\"accessor\",value:e=>e.position},getIcon:{type:\"accessor\",value:e=>e.icon},getColor:{type:\"accessor\",value:cq},getSize:{type:\"accessor\",value:1},getAngle:{type:\"accessor\",value:0},getPixelOffset:{type:\"accessor\",value:[0,0]},onIconError:{type:\"function\",value:null,optional:!0},textureParameters:{type:\"object\",ignore:!0}},Ep=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(){return super.getShaders({vs:nq,fs:sq,modules:[Rs,Ao]})}initializeState(){this.state={iconManager:new W2(this.context.gl,{onUpdate:this._onUpdate.bind(this),onError:this._onError.bind(this)})},this.getAttributeManager().addInstanced({instancePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getPosition\"},instanceSizes:{size:1,transition:!0,accessor:\"getSize\",defaultValue:1},instanceOffsets:{size:2,accessor:\"getIcon\",transform:this.getInstanceOffset},instanceIconFrames:{size:4,accessor:\"getIcon\",transform:this.getInstanceIconFrame},instanceColorModes:{size:1,type:5121,accessor:\"getIcon\",transform:this.getInstanceColorMode},instanceColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getColor\",defaultValue:cq},instanceAngles:{size:1,transition:!0,accessor:\"getAngle\"},instancePixelOffset:{size:2,transition:!0,accessor:\"getPixelOffset\"}})}updateState(t){super.updateState(t);let{props:r,oldProps:i,changeFlags:s}=t,n=this.getAttributeManager(),{iconAtlas:o,iconMapping:c,data:f,getIcon:_,textureParameters:w}=r,{iconManager:I}=this.state,R=o||this.internalState.isAsyncPropLoading(\"iconAtlas\");if(I.setProps({loadOptions:r.loadOptions,autoPacking:!R,iconAtlas:o,iconMapping:R?c:null,textureParameters:w}),R?i.iconMapping!==r.iconMapping&&n.invalidate(\"getIcon\"):(s.dataChanged||s.updateTriggersChanged&&(s.updateTriggersChanged.all||s.updateTriggersChanged.getIcon))&&I.packIcons(f,_),s.extensionsChanged){var N;let{gl:j}=this.context;(N=this.state.model)===null||N===void 0||N.delete(),this.state.model=this._getModel(j),n.invalidateAll()}}get isLoaded(){return super.isLoaded&&this.state.iconManager.isLoaded}finalizeState(t){super.finalizeState(t),this.state.iconManager.finalize()}draw({uniforms:t}){let{sizeScale:r,sizeMinPixels:i,sizeMaxPixels:s,sizeUnits:n,billboard:o,alphaCutoff:c}=this.props,{iconManager:f}=this.state,_=f.getTexture();_&&this.state.model.setUniforms(t).setUniforms({iconsTexture:_,iconsTextureDim:[_.width,_.height],sizeUnits:po[n],sizeScale:r,sizeMinPixels:i,sizeMaxPixels:s,billboard:o,alphaCutoff:c}).draw()}_getModel(t){let r=[-1,-1,-1,1,1,1,1,-1];return new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:6,attributes:{positions:{size:2,value:new Float32Array(r)}}}),isInstanced:!0})}_onUpdate(){this.setNeedsRedraw()}_onError(t){var r;let i=(r=this.getCurrentLayer())===null||r===void 0?void 0:r.props.onIconError;i?i(t):or.error(t.error.message)()}getInstanceOffset(t){let{width:r,height:i,anchorX:s=r/2,anchorY:n=i/2}=this.state.iconManager.getIconMapping(t);return[r/2-s,i/2-n]}getInstanceColorMode(t){return this.state.iconManager.getIconMapping(t).mask?1:0}getInstanceIconFrame(t){let{x:r,y:i,width:s,height:n}=this.state.iconManager.getIconMapping(t);return[r,i,s,n]}};G(Ep,\"defaultProps\",ept);G(Ep,\"layerName\",\"IconLayer\");var uq=`#define SHADER_NAME point-cloud-layer-vertex-shader\n\nattribute vec3 positions;\nattribute vec3 instanceNormals;\nattribute vec4 instanceColors;\nattribute vec3 instancePositions;\nattribute vec3 instancePositions64Low;\nattribute vec3 instancePickingColors;\n\nuniform float opacity;\nuniform float radiusPixels;\nuniform int sizeUnits;\n\nvarying vec4 vColor;\nvarying vec2 unitPosition;\n\nvoid main(void) {\n geometry.worldPosition = instancePositions;\n geometry.normal = project_normal(instanceNormals);\n unitPosition = positions.xy;\n geometry.uv = unitPosition;\n geometry.pickingColor = instancePickingColors;\n vec3 offset = vec3(positions.xy * project_size_to_pixel(radiusPixels, sizeUnits), 0.0);\n DECKGL_FILTER_SIZE(offset, geometry);\n\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, vec3(0.), geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n gl_Position.xy += project_pixel_size_to_clipspace(offset.xy);\n vec3 lightColor = lighting_getLightColor(instanceColors.rgb, project_uCameraPosition, geometry.position.xyz, geometry.normal);\n vColor = vec4(lightColor, instanceColors.a * opacity);\n DECKGL_FILTER_COLOR(vColor, geometry);\n}\n`;var hq=`#define SHADER_NAME point-cloud-layer-fragment-shader\n\nprecision highp float;\n\nvarying vec4 vColor;\nvarying vec2 unitPosition;\n\nvoid main(void) {\n geometry.uv = unitPosition;\n\n float distToCenter = length(unitPosition);\n\n if (distToCenter > 1.0) {\n discard;\n }\n\n gl_FragColor = vColor;\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var fq=[0,0,0,255],dq=[0,0,1],rpt={sizeUnits:\"pixels\",pointSize:{type:\"number\",min:0,value:10},getPosition:{type:\"accessor\",value:e=>e.position},getNormal:{type:\"accessor\",value:dq},getColor:{type:\"accessor\",value:fq},material:!0,radiusPixels:{deprecatedFor:\"pointSize\"}};function ipt(e){let{header:t,attributes:r}=e;!t||!r||(e.length=t.vertexCount,r.POSITION&&(r.instancePositions=r.POSITION),r.NORMAL&&(r.instanceNormals=r.NORMAL),r.COLOR_0&&(r.instanceColors=r.COLOR_0))}var Pp=class extends dn{getShaders(){return super.getShaders({vs:uq,fs:hq,modules:[Rs,Zf,Ao]})}initializeState(){this.getAttributeManager().addInstanced({instancePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getPosition\"},instanceNormals:{size:3,transition:!0,accessor:\"getNormal\",defaultValue:dq},instanceColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getColor\",defaultValue:fq}})}updateState(t){let{changeFlags:r,props:i}=t;if(super.updateState(t),r.extensionsChanged){var s;let{gl:n}=this.context;(s=this.state.model)===null||s===void 0||s.delete(),this.state.model=this._getModel(n),this.getAttributeManager().invalidateAll()}r.dataChanged&&ipt(i.data)}draw({uniforms:t}){let{pointSize:r,sizeUnits:i}=this.props;this.state.model.setUniforms(t).setUniforms({sizeUnits:po[i],radiusPixels:r}).draw()}_getModel(t){let r=[];for(let i=0;i<3;i++){let s=i/3*Math.PI*2;r.push(Math.cos(s)*2,Math.sin(s)*2,0)}return new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:4,attributes:{positions:new Float32Array(r)}}),isInstanced:!0})}};G(Pp,\"layerName\",\"PointCloudLayer\");G(Pp,\"defaultProps\",rpt);var pq=`#define SHADER_NAME scatterplot-layer-vertex-shader\n\nattribute vec3 positions;\n\nattribute vec3 instancePositions;\nattribute vec3 instancePositions64Low;\nattribute float instanceRadius;\nattribute float instanceLineWidths;\nattribute vec4 instanceFillColors;\nattribute vec4 instanceLineColors;\nattribute vec3 instancePickingColors;\n\nuniform float opacity;\nuniform float radiusScale;\nuniform float radiusMinPixels;\nuniform float radiusMaxPixels;\nuniform float lineWidthScale;\nuniform float lineWidthMinPixels;\nuniform float lineWidthMaxPixels;\nuniform float stroked;\nuniform bool filled;\nuniform bool antialiasing;\nuniform bool billboard;\nuniform int radiusUnits;\nuniform int lineWidthUnits;\n\nvarying vec4 vFillColor;\nvarying vec4 vLineColor;\nvarying vec2 unitPosition;\nvarying float innerUnitRadius;\nvarying float outerRadiusPixels;\n\n\nvoid main(void) {\n geometry.worldPosition = instancePositions;\n outerRadiusPixels = clamp(\n project_size_to_pixel(radiusScale * instanceRadius, radiusUnits),\n radiusMinPixels, radiusMaxPixels\n );\n float lineWidthPixels = clamp(\n project_size_to_pixel(lineWidthScale * instanceLineWidths, lineWidthUnits),\n lineWidthMinPixels, lineWidthMaxPixels\n );\n outerRadiusPixels += stroked * lineWidthPixels / 2.0;\n float edgePadding = antialiasing ? (outerRadiusPixels + SMOOTH_EDGE_RADIUS) / outerRadiusPixels : 1.0;\n unitPosition = edgePadding * positions.xy;\n geometry.uv = unitPosition;\n geometry.pickingColor = instancePickingColors;\n\n innerUnitRadius = 1.0 - stroked * lineWidthPixels / outerRadiusPixels;\n \n if (billboard) {\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, vec3(0.0), geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n vec3 offset = edgePadding * positions * outerRadiusPixels;\n DECKGL_FILTER_SIZE(offset, geometry);\n gl_Position.xy += project_pixel_size_to_clipspace(offset.xy);\n } else {\n vec3 offset = edgePadding * positions * project_pixel_size(outerRadiusPixels);\n DECKGL_FILTER_SIZE(offset, geometry);\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, offset, geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n }\n vFillColor = vec4(instanceFillColors.rgb, instanceFillColors.a * opacity);\n DECKGL_FILTER_COLOR(vFillColor, geometry);\n vLineColor = vec4(instanceLineColors.rgb, instanceLineColors.a * opacity);\n DECKGL_FILTER_COLOR(vLineColor, geometry);\n}\n`;var Aq=`#define SHADER_NAME scatterplot-layer-fragment-shader\n\nprecision highp float;\n\nuniform bool filled;\nuniform float stroked;\nuniform bool antialiasing;\n\nvarying vec4 vFillColor;\nvarying vec4 vLineColor;\nvarying vec2 unitPosition;\nvarying float innerUnitRadius;\nvarying float outerRadiusPixels;\n\nvoid main(void) {\n geometry.uv = unitPosition;\n\n float distToCenter = length(unitPosition) * outerRadiusPixels;\n float inCircle = antialiasing ? \n smoothedge(distToCenter, outerRadiusPixels) : \n step(distToCenter, outerRadiusPixels);\n\n if (inCircle == 0.0) {\n discard;\n }\n\n if (stroked > 0.5) {\n float isLine = antialiasing ? \n smoothedge(innerUnitRadius * outerRadiusPixels, distToCenter) :\n step(innerUnitRadius * outerRadiusPixels, distToCenter);\n\n if (filled) {\n gl_FragColor = mix(vFillColor, vLineColor, isLine);\n } else {\n if (isLine == 0.0) {\n discard;\n }\n gl_FragColor = vec4(vLineColor.rgb, vLineColor.a * isLine);\n }\n } else if (!filled) {\n discard;\n } else {\n gl_FragColor = vFillColor;\n }\n\n gl_FragColor.a *= inCircle;\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var mq=[0,0,0,255],npt={radiusUnits:\"meters\",radiusScale:{type:\"number\",min:0,value:1},radiusMinPixels:{type:\"number\",min:0,value:0},radiusMaxPixels:{type:\"number\",min:0,value:Number.MAX_SAFE_INTEGER},lineWidthUnits:\"meters\",lineWidthScale:{type:\"number\",min:0,value:1},lineWidthMinPixels:{type:\"number\",min:0,value:0},lineWidthMaxPixels:{type:\"number\",min:0,value:Number.MAX_SAFE_INTEGER},stroked:!1,filled:!0,billboard:!1,antialiasing:!0,getPosition:{type:\"accessor\",value:e=>e.position},getRadius:{type:\"accessor\",value:1},getFillColor:{type:\"accessor\",value:mq},getLineColor:{type:\"accessor\",value:mq},getLineWidth:{type:\"accessor\",value:1},strokeWidth:{deprecatedFor:\"getLineWidth\"},outline:{deprecatedFor:\"stroked\"},getColor:{deprecatedFor:[\"getFillColor\",\"getLineColor\"]}},Ku=class extends dn{getShaders(){return super.getShaders({vs:pq,fs:Aq,modules:[Rs,Ao]})}initializeState(){this.getAttributeManager().addInstanced({instancePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getPosition\"},instanceRadius:{size:1,transition:!0,accessor:\"getRadius\",defaultValue:1},instanceFillColors:{size:this.props.colorFormat.length,transition:!0,normalized:!0,type:5121,accessor:\"getFillColor\",defaultValue:[0,0,0,255]},instanceLineColors:{size:this.props.colorFormat.length,transition:!0,normalized:!0,type:5121,accessor:\"getLineColor\",defaultValue:[0,0,0,255]},instanceLineWidths:{size:1,transition:!0,accessor:\"getLineWidth\",defaultValue:1}})}updateState(t){if(super.updateState(t),t.changeFlags.extensionsChanged){var r;let{gl:i}=this.context;(r=this.state.model)===null||r===void 0||r.delete(),this.state.model=this._getModel(i),this.getAttributeManager().invalidateAll()}}draw({uniforms:t}){let{radiusUnits:r,radiusScale:i,radiusMinPixels:s,radiusMaxPixels:n,stroked:o,filled:c,billboard:f,antialiasing:_,lineWidthUnits:w,lineWidthScale:I,lineWidthMinPixels:R,lineWidthMaxPixels:N}=this.props;this.state.model.setUniforms(t).setUniforms({stroked:o?1:0,filled:c,billboard:f,antialiasing:_,radiusUnits:po[r],radiusScale:i,radiusMinPixels:s,radiusMaxPixels:n,lineWidthUnits:po[w],lineWidthScale:I,lineWidthMinPixels:R,lineWidthMaxPixels:N}).draw()}_getModel(t){let r=[-1,-1,0,1,-1,0,1,1,0,-1,1,0];return new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:6,vertexCount:4,attributes:{positions:{size:3,value:new Float32Array(r)}}}),isInstanced:!0})}};G(Ku,\"defaultProps\",npt);G(Ku,\"layerName\",\"ScatterplotLayer\");var Kv={CLOCKWISE:1,COUNTER_CLOCKWISE:-1};function Vg(e,t,r={}){return gq(e,r)!==t?(spt(e,r),!0):!1}function gq(e,t={}){return Math.sign(D3(e,t))}function D3(e,t={}){let{start:r=0,end:i=e.length}=t,s=t.size||2,n=0;for(let o=r,c=i-s;o0){let s=!0;for(let n=0;nt[2]&&(r|=2),e[1]t[3]&&(r|=8),r}function Z2(e,t){let{size:r=2,broken:i=!1,gridResolution:s=10,gridOffset:n=[0,0],startIndex:o=0,endIndex:c=e.length}=t||{},f=(c-o)/r,_=[],w=[_],I=Sm(e,0,r,o),R,N,j=vq(I,s,n,[]),Q=[];xc(_,I);for(let et=1;etr&&(_=[],w.push(_),xc(_,I)),N=q2(R,j)}xc(_,R),H2(I,R)}return i?w:w[0]}var _q=0,apt=1;function B3(e,t){for(let r=0;r=0?(xc(_,N)&&I.push(Q),ut+=j):I.length&&(I[I.length-1]=_q),H2(et,N),Y=j,K=Q;return[J?{pos:f,types:t&&w}:null,ut?{pos:_,types:t&&I}:null]}function vq(e,t,r,i){let s=Math.floor((e[0]-r[0])/t)*t+r[0],n=Math.floor((e[1]-r[1])/t)*t+r[1];return i[0]=s,i[1]=n,i[2]=s+t,i[3]=n+t,i}function lpt(e,t,r){r&8?(e[1]+=t,e[3]+=t):r&4?(e[1]-=t,e[3]-=t):r&2?(e[0]+=t,e[2]+=t):r&1&&(e[0]-=t,e[2]-=t)}function cpt(e,t,r,i){let s=1/0,n=-1/0,o=1/0,c=-1/0;for(let f=0;fn?_:n,o=wc?w:c}return i[0][0]=s,i[0][1]=o,i[1][0]=n,i[1][1]=c,i}var upt=85.051129;function nB(e,t){let{size:r=2,startIndex:i=0,endIndex:s=e.length,normalize:n=!0}=t||{},o=e.slice(i,s);xq(o,r,0,s-i);let c=Z2(o,{size:r,broken:!0,gridResolution:360,gridOffset:[-180,-180]});if(n)for(let f of c)bq(f,r);return c}function sB(e,t=null,r){let{size:i=2,normalize:s=!0,edgeTypes:n=!1}=r||{};t=t||[];let o=[],c=[],f=0,_=0;for(let I=0;I<=t.length;I++){let R=t[I]||e.length,N=_,j=hpt(e,i,f,R);for(let Q=j;Qs&&(s=c,n=o-1)}return n}function fpt(e,t,r,i,s=upt){let n=e[r],o=e[i-t];if(Math.abs(n-o)>180){let c=Sm(e,0,t,r);c[0]+=Math.round((o-n)/360)*360,xc(e,c),c[1]=Math.sign(c[1])*s,xc(e,c),c[0]=n,xc(e,c)}}function xq(e,t,r,i){let s=e[0],n;for(let o=r;o180||c<-180)&&(n-=Math.round(c/360)*360),e[o]=s=n}}function bq(e,t){let r,i=e.length/t;for(let n=0;n=i),s=s.flatMap(N=>[N[0],N[1]]),Vg(s,Kv.COUNTER_CLOCKWISE));let n=r>0,o=i+1,c=n?o*3+1:i,f=Math.PI*2/i,_=new Uint16Array(n?i*3*2:0),w=new Float32Array(c*3),I=new Float32Array(c*3),R=0;if(n){for(let N=0;N 0.0 && instanceElevations >= 0.0);\n float dotRadius = radius * coverage * shouldRender;\n\n geometry.pickingColor = instancePickingColors;\n vec3 centroidPosition = vec3(instancePositions.xy, instancePositions.z + elevation);\n vec3 centroidPosition64Low = instancePositions64Low;\n vec2 offset = (rotationMatrix * positions.xy * strokeOffsetRatio + offset) * dotRadius;\n if (radiusUnits == UNIT_METERS) {\n offset = project_size(offset);\n }\n vec3 pos = vec3(offset, 0.);\n DECKGL_FILTER_SIZE(pos, geometry);\n\n gl_Position = project_position_to_clipspace(centroidPosition, centroidPosition64Low, pos, geometry.position);\n geometry.normal = project_normal(vec3(rotationMatrix * normals.xy, normals.z));\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n if (extruded && !isStroke) {\n#ifdef FLAT_SHADING\n position_commonspace = geometry.position;\n vColor = vec4(color.rgb, color.a * opacity);\n#else\n vec3 lightColor = lighting_getLightColor(color.rgb, project_uCameraPosition, geometry.position.xyz, geometry.normal);\n vColor = vec4(lightColor, color.a * opacity);\n#endif\n } else {\n vColor = vec4(color.rgb, color.a * opacity);\n }\n DECKGL_FILTER_COLOR(vColor, geometry);\n}\n`;var Sq=`#version 300 es\n#define SHADER_NAME column-layer-fragment-shader\n\nprecision highp float;\n\nuniform vec3 project_uCameraPosition;\nuniform bool extruded;\nuniform bool isStroke;\n\nout vec4 fragColor;\n\nin vec4 vColor;\n#ifdef FLAT_SHADING\nin vec4 position_commonspace;\n#endif\n\nvoid main(void) {\n fragColor = vColor;\n#ifdef FLAT_SHADING\n if (extruded && !isStroke && !picking_uActive) {\n vec3 normal = normalize(cross(dFdx(position_commonspace.xyz), dFdy(position_commonspace.xyz)));\n fragColor.rgb = lighting_getLightColor(vColor.rgb, project_uCameraPosition, position_commonspace.xyz, normal);\n }\n#endif\n DECKGL_FILTER_COLOR(fragColor, geometry);\n}\n`;var F3=[0,0,0,255],Apt={diskResolution:{type:\"number\",min:4,value:20},vertices:null,radius:{type:\"number\",min:0,value:1e3},angle:{type:\"number\",value:0},offset:{type:\"array\",value:[0,0]},coverage:{type:\"number\",min:0,max:1,value:1},elevationScale:{type:\"number\",min:0,value:1},radiusUnits:\"meters\",lineWidthUnits:\"meters\",lineWidthScale:1,lineWidthMinPixels:0,lineWidthMaxPixels:Number.MAX_SAFE_INTEGER,extruded:!0,wireframe:!1,filled:!0,stroked:!1,getPosition:{type:\"accessor\",value:e=>e.position},getFillColor:{type:\"accessor\",value:F3},getLineColor:{type:\"accessor\",value:F3},getLineWidth:{type:\"accessor\",value:1},getElevation:{type:\"accessor\",value:1e3},material:!0,getColor:{deprecatedFor:[\"getFillColor\",\"getLineColor\"]}},af=class extends dn{getShaders(){let{gl:t}=this.context,r=!fr(t),i={},s=this.props.flatShading&&$0(t,Ii.GLSL_DERIVATIVES);return s&&(i.FLAT_SHADING=1),super.getShaders({vs:wq,fs:Sq,defines:i,transpileToGLSL100:r,modules:[Rs,s?Ny:Zf,Ao]})}initializeState(){this.getAttributeManager().addInstanced({instancePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getPosition\"},instanceElevations:{size:1,transition:!0,accessor:\"getElevation\"},instanceFillColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getFillColor\",defaultValue:F3},instanceLineColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:!0,accessor:\"getLineColor\",defaultValue:F3},instanceStrokeWidths:{size:1,accessor:\"getLineWidth\",transition:!0}})}updateState(t){super.updateState(t);let{props:r,oldProps:i,changeFlags:s}=t,n=s.extensionsChanged||r.flatShading!==i.flatShading;if(n){var o;let{gl:c}=this.context;(o=this.state.model)===null||o===void 0||o.delete(),this.state.model=this._getModel(c),this.getAttributeManager().invalidateAll()}(n||r.diskResolution!==i.diskResolution||r.vertices!==i.vertices||(r.extruded||r.stroked)!==(i.extruded||i.stroked))&&this._updateGeometry(r)}getGeometry(t,r,i){let s=new Q2({radius:1,height:i?2:0,vertices:r,nradial:t}),n=0;if(r)for(let o=0;o=t.length&&(r+=1-t.length/s);let n=r*s;return i[0]=t[n],i[1]=t[n+1],i[2]=s===3&&t[n+2]||0,i}isClosed(t){if(!this.normalize)return!!this.opts.loop;let{positionSize:r}=this,i=t.length-r;return t[0]===t[i]&&t[1]===t[i+1]&&(r===2||t[2]===t[i+2])}};function Mq(e){return Array.isArray(e[0])}var Eq=`#define SHADER_NAME path-layer-vertex-shader\n\nattribute vec2 positions;\n\nattribute float instanceTypes;\nattribute vec3 instanceStartPositions;\nattribute vec3 instanceEndPositions;\nattribute vec3 instanceLeftPositions;\nattribute vec3 instanceRightPositions;\nattribute vec3 instanceLeftPositions64Low;\nattribute vec3 instanceStartPositions64Low;\nattribute vec3 instanceEndPositions64Low;\nattribute vec3 instanceRightPositions64Low;\nattribute float instanceStrokeWidths;\nattribute vec4 instanceColors;\nattribute vec3 instancePickingColors;\n\nuniform float widthScale;\nuniform float widthMinPixels;\nuniform float widthMaxPixels;\nuniform float jointType;\nuniform float capType;\nuniform float miterLimit;\nuniform bool billboard;\nuniform int widthUnits;\n\nuniform float opacity;\n\nvarying vec4 vColor;\nvarying vec2 vCornerOffset;\nvarying float vMiterLength;\nvarying vec2 vPathPosition;\nvarying float vPathLength;\nvarying float vJointType;\n\nconst float EPSILON = 0.001;\nconst vec3 ZERO_OFFSET = vec3(0.0);\n\nfloat flipIfTrue(bool flag) {\n return -(float(flag) * 2. - 1.);\n}\nvec3 getLineJoinOffset(\n vec3 prevPoint, vec3 currPoint, vec3 nextPoint,\n vec2 width\n) {\n bool isEnd = positions.x > 0.0;\n float sideOfPath = positions.y;\n float isJoint = float(sideOfPath == 0.0);\n\n vec3 deltaA3 = (currPoint - prevPoint);\n vec3 deltaB3 = (nextPoint - currPoint);\n\n mat3 rotationMatrix;\n bool needsRotation = !billboard && project_needs_rotation(currPoint, rotationMatrix);\n if (needsRotation) {\n deltaA3 = deltaA3 * rotationMatrix;\n deltaB3 = deltaB3 * rotationMatrix;\n }\n vec2 deltaA = deltaA3.xy / width;\n vec2 deltaB = deltaB3.xy / width;\n\n float lenA = length(deltaA);\n float lenB = length(deltaB);\n\n vec2 dirA = lenA > 0. ? normalize(deltaA) : vec2(0.0, 0.0);\n vec2 dirB = lenB > 0. ? normalize(deltaB) : vec2(0.0, 0.0);\n\n vec2 perpA = vec2(-dirA.y, dirA.x);\n vec2 perpB = vec2(-dirB.y, dirB.x);\n vec2 tangent = dirA + dirB;\n tangent = length(tangent) > 0. ? normalize(tangent) : perpA;\n vec2 miterVec = vec2(-tangent.y, tangent.x);\n vec2 dir = isEnd ? dirA : dirB;\n vec2 perp = isEnd ? perpA : perpB;\n float L = isEnd ? lenA : lenB;\n float sinHalfA = abs(dot(miterVec, perp));\n float cosHalfA = abs(dot(dirA, miterVec));\n float turnDirection = flipIfTrue(dirA.x * dirB.y >= dirA.y * dirB.x);\n float cornerPosition = sideOfPath * turnDirection;\n\n float miterSize = 1.0 / max(sinHalfA, EPSILON);\n miterSize = mix(\n min(miterSize, max(lenA, lenB) / max(cosHalfA, EPSILON)),\n miterSize,\n step(0.0, cornerPosition)\n );\n\n vec2 offsetVec = mix(miterVec * miterSize, perp, step(0.5, cornerPosition))\n * (sideOfPath + isJoint * turnDirection);\n bool isStartCap = lenA == 0.0 || (!isEnd && (instanceTypes == 1.0 || instanceTypes == 3.0));\n bool isEndCap = lenB == 0.0 || (isEnd && (instanceTypes == 2.0 || instanceTypes == 3.0));\n bool isCap = isStartCap || isEndCap;\n if (isCap) {\n offsetVec = mix(perp * sideOfPath, dir * capType * 4.0 * flipIfTrue(isStartCap), isJoint);\n vJointType = capType;\n } else {\n vJointType = jointType;\n }\n vPathLength = L;\n vCornerOffset = offsetVec;\n vMiterLength = dot(vCornerOffset, miterVec * turnDirection);\n vMiterLength = isCap ? isJoint : vMiterLength;\n\n vec2 offsetFromStartOfPath = vCornerOffset + deltaA * float(isEnd);\n vPathPosition = vec2(\n dot(offsetFromStartOfPath, perp),\n dot(offsetFromStartOfPath, dir)\n );\n geometry.uv = vPathPosition;\n\n float isValid = step(instanceTypes, 3.5);\n vec3 offset = vec3(offsetVec * width * isValid, 0.0);\n\n if (needsRotation) {\n offset = rotationMatrix * offset;\n }\n return offset;\n}\nvoid clipLine(inout vec4 position, vec4 refPosition) {\n if (position.w < EPSILON) {\n float r = (EPSILON - refPosition.w) / (position.w - refPosition.w);\n position = refPosition + (position - refPosition) * r;\n }\n}\n\nvoid main() {\n geometry.pickingColor = instancePickingColors;\n\n vColor = vec4(instanceColors.rgb, instanceColors.a * opacity);\n\n float isEnd = positions.x;\n\n vec3 prevPosition = mix(instanceLeftPositions, instanceStartPositions, isEnd);\n vec3 prevPosition64Low = mix(instanceLeftPositions64Low, instanceStartPositions64Low, isEnd);\n\n vec3 currPosition = mix(instanceStartPositions, instanceEndPositions, isEnd);\n vec3 currPosition64Low = mix(instanceStartPositions64Low, instanceEndPositions64Low, isEnd);\n\n vec3 nextPosition = mix(instanceEndPositions, instanceRightPositions, isEnd);\n vec3 nextPosition64Low = mix(instanceEndPositions64Low, instanceRightPositions64Low, isEnd);\n\n geometry.worldPosition = currPosition;\n vec2 widthPixels = vec2(clamp(\n project_size_to_pixel(instanceStrokeWidths * widthScale, widthUnits),\n widthMinPixels, widthMaxPixels) / 2.0);\n vec3 width;\n\n if (billboard) {\n vec4 prevPositionScreen = project_position_to_clipspace(prevPosition, prevPosition64Low, ZERO_OFFSET);\n vec4 currPositionScreen = project_position_to_clipspace(currPosition, currPosition64Low, ZERO_OFFSET, geometry.position);\n vec4 nextPositionScreen = project_position_to_clipspace(nextPosition, nextPosition64Low, ZERO_OFFSET);\n\n clipLine(prevPositionScreen, currPositionScreen);\n clipLine(nextPositionScreen, currPositionScreen);\n clipLine(currPositionScreen, mix(nextPositionScreen, prevPositionScreen, isEnd));\n\n width = vec3(widthPixels, 0.0);\n DECKGL_FILTER_SIZE(width, geometry);\n\n vec3 offset = getLineJoinOffset(\n prevPositionScreen.xyz / prevPositionScreen.w,\n currPositionScreen.xyz / currPositionScreen.w,\n nextPositionScreen.xyz / nextPositionScreen.w,\n project_pixel_size_to_clipspace(width.xy)\n );\n\n DECKGL_FILTER_GL_POSITION(currPositionScreen, geometry);\n gl_Position = vec4(currPositionScreen.xyz + offset * currPositionScreen.w, currPositionScreen.w);\n } else {\n prevPosition = project_position(prevPosition, prevPosition64Low);\n currPosition = project_position(currPosition, currPosition64Low);\n nextPosition = project_position(nextPosition, nextPosition64Low);\n\n width = vec3(project_pixel_size(widthPixels), 0.0);\n DECKGL_FILTER_SIZE(width, geometry);\n\n vec3 offset = getLineJoinOffset(prevPosition, currPosition, nextPosition, width.xy);\n geometry.position = vec4(currPosition + offset, 1.0);\n gl_Position = project_common_position_to_clipspace(geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n }\n DECKGL_FILTER_COLOR(vColor, geometry);\n}\n`;var Pq=`#define SHADER_NAME path-layer-fragment-shader\n\nprecision highp float;\n\nuniform float miterLimit;\n\nvarying vec4 vColor;\nvarying vec2 vCornerOffset;\nvarying float vMiterLength;\nvarying vec2 vPathPosition;\nvarying float vPathLength;\nvarying float vJointType;\n\nvoid main(void) {\n geometry.uv = vPathPosition;\n\n if (vPathPosition.y < 0.0 || vPathPosition.y > vPathLength) {\n if (vJointType > 0.5 && length(vCornerOffset) > 1.0) {\n discard;\n }\n if (vJointType < 0.5 && vMiterLength > miterLimit + 1.0) {\n discard;\n }\n }\n gl_FragColor = vColor;\n\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var Iq=[0,0,0,255],_pt={widthUnits:\"meters\",widthScale:{type:\"number\",min:0,value:1},widthMinPixels:{type:\"number\",min:0,value:0},widthMaxPixels:{type:\"number\",min:0,value:Number.MAX_SAFE_INTEGER},jointRounded:!1,capRounded:!1,miterLimit:{type:\"number\",min:0,value:4},billboard:!1,_pathType:null,getPath:{type:\"accessor\",value:e=>e.path},getColor:{type:\"accessor\",value:Iq},getWidth:{type:\"accessor\",value:1},rounded:{deprecatedFor:[\"jointRounded\",\"capRounded\"]}},aB={enter:(e,t)=>t.length?t.subarray(t.length-e.length):e},bc=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(){return super.getShaders({vs:Eq,fs:Pq,modules:[Rs,Ao]})}get wrapLongitude(){return!1}initializeState(){this.getAttributeManager().addInstanced({positions:{size:3,vertexOffset:1,type:5130,fp64:this.use64bitPositions(),transition:aB,accessor:\"getPath\",update:this.calculatePositions,noAlloc:!0,shaderAttributes:{instanceLeftPositions:{vertexOffset:0},instanceStartPositions:{vertexOffset:1},instanceEndPositions:{vertexOffset:2},instanceRightPositions:{vertexOffset:3}}},instanceTypes:{size:1,type:5121,update:this.calculateSegmentTypes,noAlloc:!0},instanceStrokeWidths:{size:1,accessor:\"getWidth\",transition:aB,defaultValue:1},instanceColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,accessor:\"getColor\",transition:aB,defaultValue:Iq},instancePickingColors:{size:3,type:5121,accessor:(i,{index:s,target:n})=>this.encodePickingColor(i&&i.__source?i.__source.index:s,n)}}),this.setState({pathTesselator:new $2({fp64:this.use64bitPositions()})})}updateState(t){super.updateState(t);let{props:r,changeFlags:i}=t,s=this.getAttributeManager();if(i.dataChanged||i.updateTriggersChanged&&(i.updateTriggersChanged.all||i.updateTriggersChanged.getPath)){let{pathTesselator:c}=this.state,f=r.data.attributes||{};c.updateGeometry({data:r.data,geometryBuffer:f.getPath,buffers:f,normalize:!r._pathType,loop:r._pathType===\"loop\",getGeometry:r.getPath,positionFormat:r.positionFormat,wrapLongitude:r.wrapLongitude,resolution:this.context.viewport.resolution,dataChanged:i.dataChanged}),this.setState({numInstances:c.instanceCount,startIndices:c.vertexStarts}),i.dataChanged||s.invalidateAll()}if(i.extensionsChanged){var o;let{gl:c}=this.context;(o=this.state.model)===null||o===void 0||o.delete(),this.state.model=this._getModel(c),s.invalidateAll()}}getPickingInfo(t){let r=super.getPickingInfo(t),{index:i}=r,{data:s}=this.props;return s[0]&&s[0].__source&&(r.object=s.find(n=>n.__source.index===i)),r}disablePickingIndex(t){let{data:r}=this.props;if(r[0]&&r[0].__source)for(let i=0;i=1&&e[0].length>=2&&Number.isFinite(e[0][0])}function Fpt(e){let t=e[0],r=e[e.length-1];return t[0]===r[0]&&t[1]===r[1]&&t[2]===r[2]}function zpt(e,t,r,i){for(let s=0;sc/t));let n=tx(e),o=i&&t===3;if(r){let c=n.length;n=n.slice();let f=[];for(let _=0;_f&&c>_||(f>_?(r||(n=n.slice()),zq(n,0,2,1)):(r||(n=n.slice()),zq(n,2,0,1)))}return(0,Nq.default)(n,s,t)}var eS=class extends rm{constructor(t){let{fp64:r,IndexType:i=Uint32Array}=t;super({...t,attributes:{positions:{size:3,type:r?Float64Array:Float32Array},vertexValid:{type:Uint8ClampedArray,size:1},indices:{type:i,size:1}}})}get(t){let{attributes:r}=this;return t===\"indices\"?r.indices&&r.indices.subarray(0,this.vertexCount):r[t]}updateGeometry(t){super.updateGeometry(t);let r=this.buffers.indices;if(r)this.vertexCount=(r.value||r).length;else if(this.data&&!this.getGeometry)throw new Error(\"missing indices buffer\")}normalizeGeometry(t){if(this.normalize){let r=G3(t,this.positionSize);return this.opts.resolution?Y2(tx(r),tS(r),{size:this.positionSize,gridResolution:this.opts.resolution,edgeTypes:!0}):this.opts.wrapLongitude?sB(tx(r),tS(r),{size:this.positionSize,maxLatitude:86,edgeTypes:!0}):r}return t}getGeometrySize(t){if(jq(t)){let r=0;for(let i of t)r+=this.getGeometrySize(i);return r}return tx(t).length/this.positionSize}getGeometryFromBuffer(t){return this.normalize||!this.buffers.indices?super.getGeometryFromBuffer(t):null}updateGeometryAttributes(t,r){if(t&&jq(t))for(let i of t){let s=this.getGeometrySize(i);r.geometrySize=s,this.updateGeometryAttributes(i,r),r.vertexStart+=s,r.indexStart=this.indexStarts[r.geometryIndex+1]}else this._updateIndices(t,r),this._updatePositions(t,r),this._updateVertexValid(t,r)}_updateIndices(t,{geometryIndex:r,vertexStart:i,indexStart:s}){let{attributes:n,indexStarts:o,typedArrayManager:c}=this,f=n.indices;if(!f||!t)return;let _=s,w=Uq(t,this.positionSize,this.opts.preproject,this.opts.full3d);f=c.allocate(f,s+w.length,{copy:!0});for(let I=0;I2?o[f*n+2]:0;s[c*3]=_,s[c*3+1]=w,s[c*3+2]=I}}_updateVertexValid(t,{vertexStart:r,geometrySize:i}){let{positionSize:s}=this,n=this.attributes.vertexValid,o=t&&tS(t);if(t&&t.edgeTypes?n.set(t.edgeTypes,r):n.fill(1,r,r+i),o)for(let c=0;c0&&!Number.isFinite(e[0])}var W3=`\nattribute vec2 vertexPositions;\nattribute float vertexValid;\n\nuniform bool extruded;\nuniform bool isWireframe;\nuniform float elevationScale;\nuniform float opacity;\n\nvarying vec4 vColor;\n\nstruct PolygonProps {\n vec4 fillColors;\n vec4 lineColors;\n vec3 positions;\n vec3 nextPositions;\n vec3 pickingColors;\n vec3 positions64Low;\n vec3 nextPositions64Low;\n float elevations;\n};\n\nvec3 project_offset_normal(vec3 vector) {\n if (project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT ||\n project_uCoordinateSystem == COORDINATE_SYSTEM_LNGLAT_OFFSETS) {\n return normalize(vector * project_uCommonUnitsPerWorldUnit);\n }\n return project_normal(vector);\n}\n\nvoid calculatePosition(PolygonProps props) {\n#ifdef IS_SIDE_VERTEX\n if(vertexValid < 0.5){\n gl_Position = vec4(0.);\n return;\n }\n#endif\n\n vec3 pos;\n vec3 pos64Low;\n vec3 normal;\n vec4 colors = isWireframe ? props.lineColors : props.fillColors;\n\n geometry.worldPosition = props.positions;\n geometry.worldPositionAlt = props.nextPositions;\n geometry.pickingColor = props.pickingColors;\n\n#ifdef IS_SIDE_VERTEX\n pos = mix(props.positions, props.nextPositions, vertexPositions.x);\n pos64Low = mix(props.positions64Low, props.nextPositions64Low, vertexPositions.x);\n#else\n pos = props.positions;\n pos64Low = props.positions64Low;\n#endif\n\n if (extruded) {\n pos.z += props.elevations * vertexPositions.y * elevationScale;\n }\n gl_Position = project_position_to_clipspace(pos, pos64Low, vec3(0.), geometry.position);\n\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n\n if (extruded) {\n #ifdef IS_SIDE_VERTEX\n normal = vec3(\n props.positions.y - props.nextPositions.y + (props.positions64Low.y - props.nextPositions64Low.y),\n props.nextPositions.x - props.positions.x + (props.nextPositions64Low.x - props.positions64Low.x),\n 0.0);\n normal = project_offset_normal(normal);\n #else\n normal = project_normal(vec3(0.0, 0.0, 1.0));\n #endif\n geometry.normal = normal;\n vec3 lightColor = lighting_getLightColor(colors.rgb, project_uCameraPosition, geometry.position.xyz, normal);\n vColor = vec4(lightColor, colors.a * opacity);\n } else {\n vColor = vec4(colors.rgb, colors.a * opacity);\n }\n DECKGL_FILTER_COLOR(vColor, geometry);\n}\n`;var Gq=`#define SHADER_NAME solid-polygon-layer-vertex-shader\n\nattribute vec3 positions;\nattribute vec3 positions64Low;\nattribute float elevations;\nattribute vec4 fillColors;\nattribute vec4 lineColors;\nattribute vec3 pickingColors;\n\n`.concat(W3,`\n\nvoid main(void) {\n PolygonProps props;\n\n props.positions = positions;\n props.positions64Low = positions64Low;\n props.elevations = elevations;\n props.fillColors = fillColors;\n props.lineColors = lineColors;\n props.pickingColors = pickingColors;\n\n calculatePosition(props);\n}\n`);var Wq=`#define SHADER_NAME solid-polygon-layer-vertex-shader-side\n#define IS_SIDE_VERTEX\n\n\nattribute vec3 instancePositions;\nattribute vec3 nextPositions;\nattribute vec3 instancePositions64Low;\nattribute vec3 nextPositions64Low;\nattribute float instanceElevations;\nattribute vec4 instanceFillColors;\nattribute vec4 instanceLineColors;\nattribute vec3 instancePickingColors;\n\n`.concat(W3,`\n\nvoid main(void) {\n PolygonProps props;\n\n #if RING_WINDING_ORDER_CW == 1\n props.positions = instancePositions;\n props.positions64Low = instancePositions64Low;\n props.nextPositions = nextPositions;\n props.nextPositions64Low = nextPositions64Low;\n #else\n props.positions = nextPositions;\n props.positions64Low = nextPositions64Low;\n props.nextPositions = instancePositions;\n props.nextPositions64Low = instancePositions64Low;\n #endif\n props.elevations = instanceElevations;\n props.fillColors = instanceFillColors;\n props.lineColors = instanceLineColors;\n props.pickingColors = instancePickingColors;\n\n calculatePosition(props);\n}\n`);var Hq=`#define SHADER_NAME solid-polygon-layer-fragment-shader\n\nprecision highp float;\n\nvarying vec4 vColor;\n\nvoid main(void) {\n gl_FragColor = vColor;\n\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var q3=[0,0,0,255],Npt={filled:!0,extruded:!1,wireframe:!1,_normalize:!0,_windingOrder:\"CW\",_full3d:!1,elevationScale:{type:\"number\",min:0,value:1},getPolygon:{type:\"accessor\",value:e=>e.polygon},getElevation:{type:\"accessor\",value:1e3},getFillColor:{type:\"accessor\",value:q3},getLineColor:{type:\"accessor\",value:q3},material:!0},H3={enter:(e,t)=>t.length?t.subarray(t.length-e.length):e},wc=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(t){return super.getShaders({vs:t===\"top\"?Gq:Wq,fs:Hq,defines:{RING_WINDING_ORDER_CW:!this.props._normalize&&this.props._windingOrder===\"CCW\"?0:1},modules:[Rs,Zf,Ao]})}get wrapLongitude(){return!1}initializeState(){let{gl:t,viewport:r}=this.context,{coordinateSystem:i}=this.props,{_full3d:s}=this.props;r.isGeospatial&&i===Yr.DEFAULT&&(i=Yr.LNGLAT);let n;i===Yr.LNGLAT&&(s?n=r.projectPosition.bind(r):n=r.projectFlat.bind(r)),this.setState({numInstances:0,polygonTesselator:new eS({preproject:n,fp64:this.use64bitPositions(),IndexType:!t||Oh(t,Ii.ELEMENT_INDEX_UINT32)?Uint32Array:Uint16Array})});let o=this.getAttributeManager(),c=!0;o.remove([\"instancePickingColors\"]),o.add({indices:{size:1,isIndexed:!0,update:this.calculateIndices,noAlloc:c},positions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:H3,accessor:\"getPolygon\",update:this.calculatePositions,noAlloc:c,shaderAttributes:{positions:{vertexOffset:0,divisor:0},instancePositions:{vertexOffset:0,divisor:1},nextPositions:{vertexOffset:1,divisor:1}}},vertexValid:{size:1,divisor:1,type:5121,update:this.calculateVertexValid,noAlloc:c},elevations:{size:1,transition:H3,accessor:\"getElevation\",shaderAttributes:{elevations:{divisor:0},instanceElevations:{divisor:1}}},fillColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:H3,accessor:\"getFillColor\",defaultValue:q3,shaderAttributes:{fillColors:{divisor:0},instanceFillColors:{divisor:1}}},lineColors:{size:this.props.colorFormat.length,type:5121,normalized:!0,transition:H3,accessor:\"getLineColor\",defaultValue:q3,shaderAttributes:{lineColors:{divisor:0},instanceLineColors:{divisor:1}}},pickingColors:{size:3,type:5121,accessor:(f,{index:_,target:w})=>this.encodePickingColor(f&&f.__source?f.__source.index:_,w),shaderAttributes:{pickingColors:{divisor:0},instancePickingColors:{divisor:1}}}})}getPickingInfo(t){let r=super.getPickingInfo(t),{index:i}=r,{data:s}=this.props;return s[0]&&s[0].__source&&(r.object=s.find(n=>n.__source.index===i)),r}disablePickingIndex(t){let{data:r}=this.props;if(r[0]&&r[0].__source)for(let i=0;if.delete()),this.setState(this._getModels(this.context.gl)),n.invalidateAll()}}updateGeometry({props:t,oldProps:r,changeFlags:i}){if(i.dataChanged||i.updateTriggersChanged&&(i.updateTriggersChanged.all||i.updateTriggersChanged.getPolygon)){let{polygonTesselator:n}=this.state,o=t.data.attributes||{};n.updateGeometry({data:t.data,normalize:t._normalize,geometryBuffer:o.getPolygon,buffers:o,getGeometry:t.getPolygon,positionFormat:t.positionFormat,wrapLongitude:t.wrapLongitude,resolution:this.context.viewport.resolution,fp64:this.use64bitPositions(),dataChanged:i.dataChanged,full3d:t._full3d}),this.setState({numInstances:n.instanceCount,startIndices:n.vertexStarts}),i.dataChanged||this.getAttributeManager().invalidateAll()}}_getModels(t){let{id:r,filled:i,extruded:s}=this.props,n,o;if(i){let c=this.getShaders(\"top\");c.defines.NON_INSTANCED_MODEL=1,n=new fn(t,{...c,id:\"\".concat(r,\"-top\"),drawMode:4,attributes:{vertexPositions:new Float32Array([0,1])},uniforms:{isWireframe:!1,isSideVertex:!1},vertexCount:0,isIndexed:!0})}return s&&(o=new fn(t,{...this.getShaders(\"side\"),id:\"\".concat(r,\"-side\"),geometry:new $n({drawMode:1,vertexCount:4,attributes:{vertexPositions:{size:2,value:new Float32Array([1,0,0,0,0,1,1,1])}}}),instanceCount:0,isInstanced:1}),o.userData.excludeAttributes={indices:!0}),{models:[o,n].filter(Boolean),topModel:n,sideModel:o}}calculateIndices(t){let{polygonTesselator:r}=this.state;t.startIndices=r.indexStarts,t.value=r.get(\"indices\")}calculatePositions(t){let{polygonTesselator:r}=this.state;t.startIndices=r.vertexStarts,t.value=r.get(\"positions\")}calculateVertexValid(t){t.value=this.state.polygonTesselator.get(\"vertexValid\")}};G(wc,\"defaultProps\",Npt);G(wc,\"layerName\",\"SolidPolygonLayer\");function Z3({data:e,getIndex:t,dataRange:r,replace:i}){let{startRow:s=0,endRow:n=1/0}=r,o=e.length,c=o,f=o;for(let R=0;RR&&N>=s&&(c=R),N>=n){f=R;break}}let _=c,I=f-c!==i.length?e.slice(f):void 0;for(let R=0;Re.polygon},getFillColor:{type:\"accessor\",value:Upt},getLineColor:{type:\"accessor\",value:qq},getLineWidth:{type:\"accessor\",value:1},getElevation:{type:\"accessor\",value:1e3},material:!0},lf=class extends Ni{initializeState(){this.state={paths:[]},this.props.getLineDashArray&&or.removed(\"getLineDashArray\",\"PathStyleExtension\")()}updateState({changeFlags:t}){let r=t.dataChanged||t.updateTriggersChanged&&(t.updateTriggersChanged.all||t.updateTriggersChanged.getPolygon);if(r&&Array.isArray(t.dataChanged)){let i=this.state.paths.slice(),s=t.dataChanged.map(n=>Z3({data:i,getIndex:o=>o.__source.index,dataRange:n,replace:this._getPaths(n)}));this.setState({paths:i,pathsDiff:s})}else r&&this.setState({paths:this._getPaths(),pathsDiff:null})}_getPaths(t={}){let{data:r,getPolygon:i,positionFormat:s,_normalize:n}=this.props,o=[],c=s===\"XY\"?2:3,{startRow:f,endRow:_}=t,{iterable:w,objectInfo:I}=Jc(r,f,_);for(let R of w){I.index++;let N=i(R,I);n&&(N=G3(N,c));let{holeIndices:j}=N,Q=N.positions||N;if(j)for(let et=0;et<=j.length;et++){let Y=Q.slice(j[et-1]||0,j[et]||Q.length);o.push(this.getSubLayerRow({path:Y},R,I.index))}else o.push(this.getSubLayerRow({path:Q},R,I.index))}return o}renderLayers(){let{data:t,_dataDiff:r,stroked:i,filled:s,extruded:n,wireframe:o,_normalize:c,_windingOrder:f,elevationScale:_,transitions:w,positionFormat:I}=this.props,{lineWidthUnits:R,lineWidthScale:N,lineWidthMinPixels:j,lineWidthMaxPixels:Q,lineJointRounded:et,lineMiterLimit:Y,lineDashJustified:K}=this.props,{getFillColor:J,getLineColor:ut,getLineWidth:Et,getLineDashArray:kt,getElevation:Xt,getPolygon:qt,updateTriggers:le,material:ue}=this.props,{paths:De,pathsDiff:Ke}=this.state,rr=this.getSubLayerClass(\"fill\",wc),Sr=this.getSubLayerClass(\"stroke\",bc),Li=this.shouldRenderSubLayer(\"fill\",De)&&new rr({_dataDiff:r,extruded:n,elevationScale:_,filled:s,wireframe:o,_normalize:c,_windingOrder:f,getElevation:Xt,getFillColor:J,getLineColor:n&&o?ut:qq,material:ue,transitions:w},this.getSubLayerProps({id:\"fill\",updateTriggers:le&&{getPolygon:le.getPolygon,getElevation:le.getElevation,getFillColor:le.getFillColor,lineColors:n&&o,getLineColor:le.getLineColor}}),{data:t,positionFormat:I,getPolygon:qt}),oo=!n&&i&&this.shouldRenderSubLayer(\"stroke\",De)&&new Sr({_dataDiff:Ke&&(()=>Ke),widthUnits:R,widthScale:N,widthMinPixels:j,widthMaxPixels:Q,jointRounded:et,miterLimit:Y,dashJustified:K,_pathType:\"loop\",transitions:w&&{getWidth:w.getLineWidth,getColor:w.getLineColor,getPath:w.getPolygon},getColor:this.getSubLayerAccessor(ut),getWidth:this.getSubLayerAccessor(Et),getDashArray:this.getSubLayerAccessor(kt)},this.getSubLayerProps({id:\"stroke\",updateTriggers:le&&{getWidth:le.getLineWidth,getColor:le.getLineColor,getDashArray:le.getLineDashArray}}),{data:De,positionFormat:I,getPath:zl=>zl.path});return[!n&&Li,oo,n&&Li]}};G(lf,\"layerName\",\"PolygonLayer\");G(lf,\"defaultProps\",Vpt);function Zq(e,t){if(!e)return null;let r=\"startIndices\"in e?e.startIndices[t]:t,i=e.featureIds.value[r];return r!==-1?jpt(e,i,r):null}function jpt(e,t,r){let i={properties:{...e.properties[t]}};for(let s in e.numericProps)i.properties[s]=e.numericProps[s].value[r];return i}function Yq(e,t){let r={points:null,lines:null,polygons:null};for(let i in r){let s=e[i].globalFeatureIds.value;r[i]=new Uint8ClampedArray(s.length*3);let n=[];for(let o=0;o 0.0) {\n float inFill = alpha;\n float inBorder = smoothstep(outlineBuffer - gamma, outlineBuffer + gamma, distance);\n color = mix(outlineColor, vColor, inFill);\n alpha = inBorder;\n }\n }\n float a = alpha * color.a;\n \n if (a < alphaCutoff) {\n discard;\n }\n\n gl_FragColor = vec4(color.rgb, a * opacity);\n }\n\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var dB=192/256,$q=[],Gpt={getIconOffsets:{type:\"accessor\",value:e=>e.offsets},alphaCutoff:.001,smoothing:.1,outlineWidth:0,outlineColor:{type:\"color\",value:[0,0,0,255]}},Gg=class extends Ep{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(){return{...super.getShaders(),fs:Qq}}initializeState(){super.initializeState(),this.getAttributeManager().addInstanced({instanceOffsets:{size:2,accessor:\"getIconOffsets\"},instancePickingColors:{type:5121,size:3,accessor:(r,{index:i,target:s})=>this.encodePickingColor(i,s)}})}updateState(t){super.updateState(t);let{props:r,oldProps:i}=t,{outlineColor:s}=r;s!==i.outlineColor&&(s=s.map(n=>n/255),s[3]=Number.isFinite(s[3])?s[3]:1,this.setState({outlineColor:s})),!r.sdf&&r.outlineWidth&&or.warn(\"\".concat(this.id,\": fontSettings.sdf is required to render outline\"))()}draw(t){let{sdf:r,smoothing:i,outlineWidth:s}=this.props,{outlineColor:n}=this.state,o=s?Math.max(i,dB*(1-s)):-1;if(t.uniforms={...t.uniforms,sdfBuffer:dB,outlineBuffer:o,gamma:i,sdf:!!r,outlineColor:n},super.draw(t),r&&s){let{iconManager:c}=this.state;c.getTexture()&&this.state.model.draw({uniforms:{outlineBuffer:dB}})}}getInstanceOffset(t){return t?Array.from(t).flatMap(r=>super.getInstanceOffset(r)):$q}getInstanceColorMode(t){return 1}getInstanceIconFrame(t){return t?Array.from(t).flatMap(r=>super.getInstanceIconFrame(r)):$q}};G(Gg,\"defaultProps\",Gpt);G(Gg,\"layerName\",\"MultiIconLayer\");var rS=class{constructor({fontSize:t=24,buffer:r=3,radius:i=8,cutoff:s=.25,fontFamily:n=\"sans-serif\",fontWeight:o=\"normal\",fontStyle:c=\"normal\"}={}){this.buffer=r,this.cutoff=s,this.radius=i;let f=this.size=t+r*4,_=this._createCanvas(f),w=this.ctx=_.getContext(\"2d\",{willReadFrequently:!0});w.font=`${c} ${o} ${t}px ${n}`,w.textBaseline=\"alphabetic\",w.textAlign=\"left\",w.fillStyle=\"black\",this.gridOuter=new Float64Array(f*f),this.gridInner=new Float64Array(f*f),this.f=new Float64Array(f),this.z=new Float64Array(f+1),this.v=new Uint16Array(f)}_createCanvas(t){let r=document.createElement(\"canvas\");return r.width=r.height=t,r}draw(t){let{width:r,actualBoundingBoxAscent:i,actualBoundingBoxDescent:s,actualBoundingBoxLeft:n,actualBoundingBoxRight:o}=this.ctx.measureText(t),c=Math.ceil(i),f=0,_=Math.max(0,Math.min(this.size-this.buffer,Math.ceil(o-n))),w=Math.min(this.size-this.buffer,c+Math.ceil(s)),I=_+2*this.buffer,R=w+2*this.buffer,N=Math.max(I*R,0),j=new Uint8ClampedArray(N),Q={data:j,width:I,height:R,glyphWidth:_,glyphHeight:w,glyphTop:c,glyphLeft:f,glyphAdvance:r};if(_===0||w===0)return Q;let{ctx:et,buffer:Y,gridInner:K,gridOuter:J}=this;et.clearRect(Y,Y,_,w),et.fillText(t,Y,Y+c);let ut=et.getImageData(Y,Y,_,w);J.fill(1e20,0,N),K.fill(0,0,N);for(let Et=0;Et0?le*le:0,K[qt]=le<0?le*le:0}}Xq(J,0,0,I,R,I,this.f,this.v,this.z),Xq(K,Y,Y,_,w,I,this.f,this.v,this.z);for(let Et=0;Et-1);f++,n[f]=c,o[f]=_,o[f+1]=1e20}for(let c=0,f=0;cs&&(_=0,f++),n[I]={x:_+i,y:c+f*w+i,width:R,height:w,layoutWidth:R,layoutHeight:r},_+=R+i*2}return{mapping:n,xOffset:_,yOffset:c+f*w,canvasHeight:qpt(c+(f+1)*w)}}function tZ(e,t,r,i){let s=0;for(let o=t;oi&&(oc){let I=tZ(e,c,f,s);_+I>i&&(oi&&(I=eZ(e,c,f,i,s,n),o=n[n.length-1])),c=f,_+=I}return _}function Ypt(e,t,r,i,s=0,n){n===void 0&&(n=e.length);let o=[];return t===\"break-all\"?eZ(e,s,n,r,i,o):Zpt(e,s,n,r,i,o),o}function Qpt(e,t,r,i,s,n){let o=0,c=0;for(let f=t;f0,I=[0,0],R=[0,0],N=0,j=0,Q=0;for(let Y=0;Y<=o;Y++){let K=n[Y];if((K===`\n`||Y===o)&&(Q=Y),Q>j){let J=w?Ypt(n,r,i,s,j,Q):Hpt;for(let ut=0;ut<=J.length;ut++){let Et=ut===0?j:J[ut-1],kt=ut1||f>0){let N=e.constructor;R=new N(_);for(let j=0;j<_;j++)R[j]=e[j*c+f]}for(let N=0;N=0&&this._order.splice(r,1)}_appendOrder(t){this._order.push(t)}};function $pt(){let e=[];for(let t=32;t<128;t++)e.push(String.fromCharCode(t));return e}var Wg={fontFamily:\"Monaco, monospace\",fontWeight:\"normal\",characterSet:$pt(),fontSize:64,buffer:4,sdf:!1,cutoff:.25,radius:12,smoothing:.1},nZ=1024,sZ=.9,oZ=1.2,lZ=3,Y3=new ex(lZ);function Xpt(e,t){let r;typeof t==\"string\"?r=new Set(Array.from(t)):r=new Set(t);let i=Y3.get(e);if(!i)return r;for(let s in i.mapping)r.has(s)&&r.delete(s);return r}function Kpt(e,t){for(let r=0;r=lZ,\"Invalid cache limit\"),Y3=new ex(e)}var iS=class{constructor(){G(this,\"props\",{...Wg}),G(this,\"_key\",void 0),G(this,\"_atlas\",void 0)}get texture(){return this._atlas}get mapping(){return this._atlas&&this._atlas.mapping}get scale(){let{fontSize:t,buffer:r}=this.props;return(t*oZ+r*2)/t}setProps(t={}){Object.assign(this.props,t),this._key=this._getKey();let r=Xpt(this._key,this.props.characterSet),i=Y3.get(this._key);if(i&&r.size===0){this._atlas!==i&&(this._atlas=i);return}let s=this._generateFontAtlas(r,i);this._atlas=s,Y3.set(this._key,s)}_generateFontAtlas(t,r){let{fontFamily:i,fontWeight:s,fontSize:n,buffer:o,sdf:c,radius:f,cutoff:_}=this.props,w=r&&r.data;w||(w=document.createElement(\"canvas\"),w.width=nZ);let I=w.getContext(\"2d\",{willReadFrequently:!0});aZ(I,i,n,s);let{mapping:R,canvasHeight:N,xOffset:j,yOffset:Q}=Jq({getFontWidth:et=>I.measureText(et).width,fontHeight:n*oZ,buffer:o,characterSet:t,maxCanvasWidth:nZ,...r&&{mapping:r.mapping,xOffset:r.xOffset,yOffset:r.yOffset}});if(w.height!==N){let et=I.getImageData(0,0,w.width,w.height);w.height=N,I.putImageData(et,0,0)}if(aZ(I,i,n,s),c){let et=new rS({fontSize:n,buffer:o,radius:f,cutoff:_,fontFamily:i,fontWeight:\"\".concat(s)});for(let Y of t){let{data:K,width:J,height:ut,glyphTop:Et}=et.draw(Y);R[Y].width=J,R[Y].layoutOffsetY=n*sZ-Et;let kt=I.createImageData(J,ut);Kpt(K,kt),I.putImageData(kt,R[Y].x,R[Y].y)}}else for(let et of t)I.fillText(et,R[et].x,R[et].y+o+n*sZ);return{xOffset:j,yOffset:Q,mapping:R,data:w,width:w.width,height:w.height}}_getKey(){let{fontFamily:t,fontWeight:r,fontSize:i,buffer:s,sdf:n,radius:o,cutoff:c}=this.props;return n?\"\".concat(t,\" \").concat(r,\" \").concat(i,\" \").concat(s,\" \").concat(o,\" \").concat(c):\"\".concat(t,\" \").concat(r,\" \").concat(i,\" \").concat(s)}};var uZ=`#define SHADER_NAME text-background-layer-vertex-shader\n\nattribute vec2 positions;\n\nattribute vec3 instancePositions;\nattribute vec3 instancePositions64Low;\nattribute vec4 instanceRects;\nattribute float instanceSizes;\nattribute float instanceAngles;\nattribute vec2 instancePixelOffsets;\nattribute float instanceLineWidths;\nattribute vec4 instanceFillColors;\nattribute vec4 instanceLineColors;\nattribute vec3 instancePickingColors;\n\nuniform bool billboard;\nuniform float opacity;\nuniform float sizeScale;\nuniform float sizeMinPixels;\nuniform float sizeMaxPixels;\nuniform vec4 padding;\nuniform int sizeUnits;\n\nvarying vec4 vFillColor;\nvarying vec4 vLineColor;\nvarying float vLineWidth;\nvarying vec2 uv;\nvarying vec2 dimensions;\n\nvec2 rotate_by_angle(vec2 vertex, float angle) {\n float angle_radian = radians(angle);\n float cos_angle = cos(angle_radian);\n float sin_angle = sin(angle_radian);\n mat2 rotationMatrix = mat2(cos_angle, -sin_angle, sin_angle, cos_angle);\n return rotationMatrix * vertex;\n}\n\nvoid main(void) {\n geometry.worldPosition = instancePositions;\n geometry.uv = positions;\n geometry.pickingColor = instancePickingColors;\n uv = positions;\n vLineWidth = instanceLineWidths;\n float sizePixels = clamp(\n project_size_to_pixel(instanceSizes * sizeScale, sizeUnits),\n sizeMinPixels, sizeMaxPixels\n );\n\n dimensions = instanceRects.zw * sizePixels + padding.xy + padding.zw;\n\n vec2 pixelOffset = (positions * instanceRects.zw + instanceRects.xy) * sizePixels + mix(-padding.xy, padding.zw, positions);\n pixelOffset = rotate_by_angle(pixelOffset, instanceAngles);\n pixelOffset += instancePixelOffsets;\n pixelOffset.y *= -1.0;\n\n if (billboard) {\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, vec3(0.0), geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n vec3 offset = vec3(pixelOffset, 0.0);\n DECKGL_FILTER_SIZE(offset, geometry);\n gl_Position.xy += project_pixel_size_to_clipspace(offset.xy);\n } else {\n vec3 offset_common = vec3(project_pixel_size(pixelOffset), 0.0);\n DECKGL_FILTER_SIZE(offset_common, geometry);\n gl_Position = project_position_to_clipspace(instancePositions, instancePositions64Low, offset_common, geometry.position);\n DECKGL_FILTER_GL_POSITION(gl_Position, geometry);\n }\n vFillColor = vec4(instanceFillColors.rgb, instanceFillColors.a * opacity);\n DECKGL_FILTER_COLOR(vFillColor, geometry);\n vLineColor = vec4(instanceLineColors.rgb, instanceLineColors.a * opacity);\n DECKGL_FILTER_COLOR(vLineColor, geometry);\n}\n`;var hZ=`#define SHADER_NAME text-background-layer-fragment-shader\n\nprecision highp float;\n\nuniform bool stroked;\n\nvarying vec4 vFillColor;\nvarying vec4 vLineColor;\nvarying float vLineWidth;\nvarying vec2 uv;\nvarying vec2 dimensions;\n\nvoid main(void) {\n geometry.uv = uv;\n\n vec2 pixelPosition = uv * dimensions;\n if (stroked) {\n float distToEdge = min(\n min(pixelPosition.x, dimensions.x - pixelPosition.x),\n min(pixelPosition.y, dimensions.y - pixelPosition.y)\n );\n float isBorder = smoothedge(distToEdge, vLineWidth);\n gl_FragColor = mix(vFillColor, vLineColor, isBorder);\n } else {\n gl_FragColor = vFillColor;\n }\n\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var Jpt={billboard:!0,sizeScale:1,sizeUnits:\"pixels\",sizeMinPixels:0,sizeMaxPixels:Number.MAX_SAFE_INTEGER,padding:{type:\"array\",value:[0,0,0,0]},getPosition:{type:\"accessor\",value:e=>e.position},getSize:{type:\"accessor\",value:1},getAngle:{type:\"accessor\",value:0},getPixelOffset:{type:\"accessor\",value:[0,0]},getBoundingRect:{type:\"accessor\",value:[0,0,0,0]},getFillColor:{type:\"accessor\",value:[0,0,0,255]},getLineColor:{type:\"accessor\",value:[0,0,0,255]},getLineWidth:{type:\"accessor\",value:1}},Hg=class extends dn{constructor(...t){super(...t),G(this,\"state\",void 0)}getShaders(){return super.getShaders({vs:uZ,fs:hZ,modules:[Rs,Ao]})}initializeState(){this.getAttributeManager().addInstanced({instancePositions:{size:3,type:5130,fp64:this.use64bitPositions(),transition:!0,accessor:\"getPosition\"},instanceSizes:{size:1,transition:!0,accessor:\"getSize\",defaultValue:1},instanceAngles:{size:1,transition:!0,accessor:\"getAngle\"},instanceRects:{size:4,accessor:\"getBoundingRect\"},instancePixelOffsets:{size:2,transition:!0,accessor:\"getPixelOffset\"},instanceFillColors:{size:4,transition:!0,normalized:!0,type:5121,accessor:\"getFillColor\",defaultValue:[0,0,0,255]},instanceLineColors:{size:4,transition:!0,normalized:!0,type:5121,accessor:\"getLineColor\",defaultValue:[0,0,0,255]},instanceLineWidths:{size:1,transition:!0,accessor:\"getLineWidth\",defaultValue:1}})}updateState(t){super.updateState(t);let{changeFlags:r}=t;if(r.extensionsChanged){var i;let{gl:s}=this.context;(i=this.state.model)===null||i===void 0||i.delete(),this.state.model=this._getModel(s),this.getAttributeManager().invalidateAll()}}draw({uniforms:t}){let{billboard:r,sizeScale:i,sizeUnits:s,sizeMinPixels:n,sizeMaxPixels:o,getLineWidth:c}=this.props,{padding:f}=this.props;f.length<4&&(f=[f[0],f[1],f[0],f[1]]),this.state.model.setUniforms(t).setUniforms({billboard:r,stroked:!!c,padding:f,sizeUnits:po[s],sizeScale:i,sizeMinPixels:n,sizeMaxPixels:o}).draw()}_getModel(t){let r=[0,0,1,0,1,1,0,1];return new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:6,vertexCount:4,attributes:{positions:{size:2,value:new Float32Array(r)}}}),isInstanced:!0})}};G(Hg,\"defaultProps\",Jpt);G(Hg,\"layerName\",\"TextBackgroundLayer\");var fZ={start:1,middle:0,end:-1},dZ={top:1,center:0,bottom:-1},pB=[0,0,0,255],tAt=1,eAt={billboard:!0,sizeScale:1,sizeUnits:\"pixels\",sizeMinPixels:0,sizeMaxPixels:Number.MAX_SAFE_INTEGER,background:!1,getBackgroundColor:{type:\"accessor\",value:[255,255,255,255]},getBorderColor:{type:\"accessor\",value:pB},getBorderWidth:{type:\"accessor\",value:0},backgroundPadding:{type:\"array\",value:[0,0,0,0]},characterSet:{type:\"object\",value:Wg.characterSet},fontFamily:Wg.fontFamily,fontWeight:Wg.fontWeight,lineHeight:tAt,outlineWidth:{type:\"number\",value:0,min:0},outlineColor:{type:\"color\",value:pB},fontSettings:{type:\"object\",value:{},compare:1},wordBreak:\"break-word\",maxWidth:{type:\"number\",value:-1},getText:{type:\"accessor\",value:e=>e.text},getPosition:{type:\"accessor\",value:e=>e.position},getColor:{type:\"accessor\",value:pB},getSize:{type:\"accessor\",value:32},getAngle:{type:\"accessor\",value:0},getTextAnchor:{type:\"accessor\",value:\"middle\"},getAlignmentBaseline:{type:\"accessor\",value:\"center\"},getPixelOffset:{type:\"accessor\",value:[0,0]},backgroundColor:{deprecatedFor:[\"background\",\"getBackgroundColor\"]}},cf=class extends Ni{constructor(...t){super(...t),G(this,\"state\",void 0),G(this,\"getBoundingRect\",(r,i)=>{let{size:[s,n]}=this.transformParagraph(r,i),{fontSize:o}=this.state.fontAtlasManager.props;s/=o,n/=o;let{getTextAnchor:c,getAlignmentBaseline:f}=this.props,_=fZ[typeof c==\"function\"?c(r,i):c],w=dZ[typeof f==\"function\"?f(r,i):f];return[(_-1)*s/2,(w-1)*n/2,s,n]}),G(this,\"getIconOffsets\",(r,i)=>{let{getTextAnchor:s,getAlignmentBaseline:n}=this.props,{x:o,y:c,rowWidth:f,size:[_,w]}=this.transformParagraph(r,i),I=fZ[typeof s==\"function\"?s(r,i):s],R=dZ[typeof n==\"function\"?n(r,i):n],N=o.length,j=new Array(N*2),Q=0;for(let et=0;et0&&or.warn(\"v8.9 breaking change: TextLayer maxWidth is now relative to text size\")()}updateState(t){let{props:r,oldProps:i,changeFlags:s}=t;(s.dataChanged||s.updateTriggersChanged&&(s.updateTriggersChanged.all||s.updateTriggersChanged.getText))&&this._updateText(),(this._updateFontAtlas()||r.lineHeight!==i.lineHeight||r.wordBreak!==i.wordBreak||r.maxWidth!==i.maxWidth)&&this.setState({styleVersion:this.state.styleVersion+1})}getPickingInfo({info:t}){return t.object=t.index>=0?this.props.data[t.index]:null,t}_updateFontAtlas(){let{fontSettings:t,fontFamily:r,fontWeight:i}=this.props,{fontAtlasManager:s,characterSet:n}=this.state,o={...t,characterSet:n,fontFamily:r,fontWeight:i};if(!s.mapping)return s.setProps(o),!0;for(let c in o)if(o[c]!==s.props[c])return s.setProps(o),!0;return!1}_updateText(){var t;let{data:r,characterSet:i}=this.props,s=(t=r.attributes)===null||t===void 0?void 0:t.getText,{getText:n}=this.props,o=r.startIndices,c,f=i===\"auto\"&&new Set;if(s&&o){let{texts:_,characterCount:w}=iZ({...ArrayBuffer.isView(s)?{value:s}:s,length:r.length,startIndices:o,characterSet:f});c=w,n=(I,{index:R})=>_[R]}else{let{iterable:_,objectInfo:w}=Jc(r);o=[0],c=0;for(let I of _){w.index++;let R=Array.from(n(I,w)||\"\");f&&R.forEach(f.add,f),c+=R.length,o.push(c)}}this.setState({getText:n,startIndices:o,numInstances:c,characterSet:f||i})}transformParagraph(t,r){let{fontAtlasManager:i}=this.state,s=i.mapping,n=this.state.getText,{wordBreak:o,lineHeight:c,maxWidth:f}=this.props,_=n(t,r)||\"\";return rZ(_,c,o,f*i.props.fontSize,s)}renderLayers(){let{startIndices:t,numInstances:r,getText:i,fontAtlasManager:{scale:s,texture:n,mapping:o},styleVersion:c}=this.state,{data:f,_dataDiff:_,getPosition:w,getColor:I,getSize:R,getAngle:N,getPixelOffset:j,getBackgroundColor:Q,getBorderColor:et,getBorderWidth:Y,backgroundPadding:K,background:J,billboard:ut,fontSettings:Et,outlineWidth:kt,outlineColor:Xt,sizeScale:qt,sizeUnits:le,sizeMinPixels:ue,sizeMaxPixels:De,transitions:Ke,updateTriggers:rr}=this.props,Sr=this.getSubLayerClass(\"characters\",Gg),Li=this.getSubLayerClass(\"background\",Hg);return[J&&new Li({getFillColor:Q,getLineColor:et,getLineWidth:Y,padding:K,getPosition:w,getSize:R,getAngle:N,getPixelOffset:j,billboard:ut,sizeScale:qt,sizeUnits:le,sizeMinPixels:ue,sizeMaxPixels:De,transitions:Ke&&{getPosition:Ke.getPosition,getAngle:Ke.getAngle,getSize:Ke.getSize,getFillColor:Ke.getBackgroundColor,getLineColor:Ke.getBorderColor,getLineWidth:Ke.getBorderWidth,getPixelOffset:Ke.getPixelOffset}},this.getSubLayerProps({id:\"background\",updateTriggers:{getPosition:rr.getPosition,getAngle:rr.getAngle,getSize:rr.getSize,getFillColor:rr.getBackgroundColor,getLineColor:rr.getBorderColor,getLineWidth:rr.getBorderWidth,getPixelOffset:rr.getPixelOffset,getBoundingRect:{getText:rr.getText,getTextAnchor:rr.getTextAnchor,getAlignmentBaseline:rr.getAlignmentBaseline,styleVersion:c}}}),{data:f.attributes&&f.attributes.background?{length:f.length,attributes:f.attributes.background}:f,_dataDiff:_,autoHighlight:!1,getBoundingRect:this.getBoundingRect}),new Sr({sdf:Et.sdf,smoothing:Number.isFinite(Et.smoothing)?Et.smoothing:Wg.smoothing,outlineWidth:kt/(Et.radius||Wg.radius),outlineColor:Xt,iconAtlas:n,iconMapping:o,getPosition:w,getColor:I,getSize:R,getAngle:N,getPixelOffset:j,billboard:ut,sizeScale:qt*s,sizeUnits:le,sizeMinPixels:ue*s,sizeMaxPixels:De*s,transitions:Ke&&{getPosition:Ke.getPosition,getAngle:Ke.getAngle,getColor:Ke.getColor,getSize:Ke.getSize,getPixelOffset:Ke.getPixelOffset}},this.getSubLayerProps({id:\"characters\",updateTriggers:{all:rr.getText,getPosition:rr.getPosition,getAngle:rr.getAngle,getColor:rr.getColor,getSize:rr.getSize,getPixelOffset:rr.getPixelOffset,getIconOffsets:{getTextAnchor:rr.getTextAnchor,getAlignmentBaseline:rr.getAlignmentBaseline,styleVersion:c}}}),{data:f,_dataDiff:_,startIndices:t,numInstances:r,getIconOffsets:this.getIconOffsets,getIcon:i})]}static set fontAtlasCacheLimit(t){cZ(t)}};G(cf,\"defaultProps\",eAt);G(cf,\"layerName\",\"TextLayer\");var nS={circle:{type:Ku,props:{filled:\"filled\",stroked:\"stroked\",lineWidthMaxPixels:\"lineWidthMaxPixels\",lineWidthMinPixels:\"lineWidthMinPixels\",lineWidthScale:\"lineWidthScale\",lineWidthUnits:\"lineWidthUnits\",pointRadiusMaxPixels:\"radiusMaxPixels\",pointRadiusMinPixels:\"radiusMinPixels\",pointRadiusScale:\"radiusScale\",pointRadiusUnits:\"radiusUnits\",pointAntialiasing:\"antialiasing\",pointBillboard:\"billboard\",getFillColor:\"getFillColor\",getLineColor:\"getLineColor\",getLineWidth:\"getLineWidth\",getPointRadius:\"getRadius\"}},icon:{type:Ep,props:{iconAtlas:\"iconAtlas\",iconMapping:\"iconMapping\",iconSizeMaxPixels:\"sizeMaxPixels\",iconSizeMinPixels:\"sizeMinPixels\",iconSizeScale:\"sizeScale\",iconSizeUnits:\"sizeUnits\",iconAlphaCutoff:\"alphaCutoff\",iconBillboard:\"billboard\",getIcon:\"getIcon\",getIconAngle:\"getAngle\",getIconColor:\"getColor\",getIconPixelOffset:\"getPixelOffset\",getIconSize:\"getSize\"}},text:{type:cf,props:{textSizeMaxPixels:\"sizeMaxPixels\",textSizeMinPixels:\"sizeMinPixels\",textSizeScale:\"sizeScale\",textSizeUnits:\"sizeUnits\",textBackground:\"background\",textBackgroundPadding:\"backgroundPadding\",textFontFamily:\"fontFamily\",textFontWeight:\"fontWeight\",textLineHeight:\"lineHeight\",textMaxWidth:\"maxWidth\",textOutlineColor:\"outlineColor\",textOutlineWidth:\"outlineWidth\",textWordBreak:\"wordBreak\",textCharacterSet:\"characterSet\",textBillboard:\"billboard\",textFontSettings:\"fontSettings\",getText:\"getText\",getTextAngle:\"getAngle\",getTextColor:\"getColor\",getTextPixelOffset:\"getPixelOffset\",getTextSize:\"getSize\",getTextAnchor:\"getTextAnchor\",getTextAlignmentBaseline:\"getAlignmentBaseline\",getTextBackgroundColor:\"getBackgroundColor\",getTextBorderColor:\"getBorderColor\",getTextBorderWidth:\"getBorderWidth\"}}},sS={type:bc,props:{lineWidthUnits:\"widthUnits\",lineWidthScale:\"widthScale\",lineWidthMinPixels:\"widthMinPixels\",lineWidthMaxPixels:\"widthMaxPixels\",lineJointRounded:\"jointRounded\",lineCapRounded:\"capRounded\",lineMiterLimit:\"miterLimit\",lineBillboard:\"billboard\",getLineColor:\"getColor\",getLineWidth:\"getWidth\"}},Q3={type:wc,props:{extruded:\"extruded\",filled:\"filled\",wireframe:\"wireframe\",elevationScale:\"elevationScale\",material:\"material\",_full3d:\"_full3d\",getElevation:\"getElevation\",getFillColor:\"getFillColor\",getLineColor:\"getLineColor\"}};function rx({type:e,props:t}){let r={};for(let i in t)r[i]=e.defaultProps[t[i]];return r}function $3(e,t){let{transitions:r,updateTriggers:i}=e.props,s={updateTriggers:{},transitions:r&&{getPosition:r.geometry}};for(let n in t){let o=t[n],c=e.props[n];n.startsWith(\"get\")&&(c=e.getSubLayerAccessor(c),s.updateTriggers[o]=i[n],r&&(s.transitions[o]=r[n])),s[o]=c}return s}function AZ(e){if(Array.isArray(e))return e;switch(or.assert(e.type,\"GeoJSON does not have type\"),e.type){case\"Feature\":return[e];case\"FeatureCollection\":return or.assert(Array.isArray(e.features),\"GeoJSON does not have features array\"),e.features;default:return[{geometry:e}]}}function AB(e,t,r={}){let i={pointFeatures:[],lineFeatures:[],polygonFeatures:[],polygonOutlineFeatures:[]},{startRow:s=0,endRow:n=e.length}=r;for(let o=s;o{c.push(r({geometry:{type:\"Point\",coordinates:I}},i,s))});break;case\"LineString\":f.push(r({geometry:e},i,s));break;case\"MultiLineString\":o.forEach(I=>{f.push(r({geometry:{type:\"LineString\",coordinates:I}},i,s))});break;case\"Polygon\":_.push(r({geometry:e},i,s)),o.forEach(I=>{w.push(r({geometry:{type:\"LineString\",coordinates:I}},i,s))});break;case\"MultiPolygon\":o.forEach(I=>{_.push(r({geometry:{type:\"Polygon\",coordinates:I}},i,s)),I.forEach(R=>{w.push(r({geometry:{type:\"LineString\",coordinates:R}},i,s))})});break;default:}}var rAt={Point:1,MultiPoint:2,LineString:2,MultiLineString:3,Polygon:3,MultiPolygon:4};function iAt(e,t){let r=rAt[e];for(or.assert(r,\"Unknown GeoJSON type \".concat(e));t&&--r>0;)t=t[0];return t&&Number.isFinite(t[0])}function mZ(){return{points:{},lines:{},polygons:{},polygonsOutline:{}}}function X3(e){return e.geometry.coordinates}function gZ(e,t){let r=mZ(),{pointFeatures:i,lineFeatures:s,polygonFeatures:n,polygonOutlineFeatures:o}=e;return r.points.data=i,r.points._dataDiff=t.pointFeatures&&(()=>t.pointFeatures),r.points.getPosition=X3,r.lines.data=s,r.lines._dataDiff=t.lineFeatures&&(()=>t.lineFeatures),r.lines.getPath=X3,r.polygons.data=n,r.polygons._dataDiff=t.polygonFeatures&&(()=>t.polygonFeatures),r.polygons.getPolygon=X3,r.polygonsOutline.data=o,r.polygonsOutline._dataDiff=t.polygonOutlineFeatures&&(()=>t.polygonOutlineFeatures),r.polygonsOutline.getPath=X3,r}function _Z(e,t){let r=mZ(),{points:i,lines:s,polygons:n}=e,o=Yq(e,t);return r.points.data={length:i.positions.value.length/i.positions.size,attributes:{...i.attributes,getPosition:i.positions,instancePickingColors:{size:3,value:o.points}},properties:i.properties,numericProps:i.numericProps,featureIds:i.featureIds},r.lines.data={length:s.pathIndices.value.length-1,startIndices:s.pathIndices.value,attributes:{...s.attributes,getPath:s.positions,instancePickingColors:{size:3,value:o.lines}},properties:s.properties,numericProps:s.numericProps,featureIds:s.featureIds},r.lines._pathType=\"open\",r.polygons.data={length:n.polygonIndices.value.length-1,startIndices:n.polygonIndices.value,attributes:{...n.attributes,getPolygon:n.positions,pickingColors:{size:3,value:o.polygons}},properties:n.properties,numericProps:n.numericProps,featureIds:n.featureIds},r.polygons._normalize=!1,n.triangles&&(r.polygons.data.attributes.indices=n.triangles.value),r.polygonsOutline.data={length:n.primitivePolygonIndices.value.length-1,startIndices:n.primitivePolygonIndices.value,attributes:{...n.attributes,getPath:n.positions,instancePickingColors:{size:3,value:o.polygons}},properties:n.properties,numericProps:n.numericProps,featureIds:n.featureIds},r.polygonsOutline._pathType=\"open\",r}var nAt=[\"points\",\"linestrings\",\"polygons\"],sAt={...rx(nS.circle),...rx(nS.icon),...rx(nS.text),...rx(sS),...rx(Q3),stroked:!0,filled:!0,extruded:!1,wireframe:!1,_full3d:!1,iconAtlas:{type:\"object\",value:null},iconMapping:{type:\"object\",value:{}},getIcon:{type:\"accessor\",value:e=>e.properties.icon},getText:{type:\"accessor\",value:e=>e.properties.text},pointType:\"circle\",getRadius:{deprecatedFor:\"getPointRadius\"}},Mm=class extends Ni{initializeState(){this.state={layerProps:{},features:{}}}updateState({props:t,changeFlags:r}){if(!r.dataChanged)return;let{data:i}=this.props,s=i&&\"points\"in i&&\"polygons\"in i&&\"lines\"in i;this.setState({binary:s}),s?this._updateStateBinary({props:t,changeFlags:r}):this._updateStateJSON({props:t,changeFlags:r})}_updateStateBinary({props:t,changeFlags:r}){let i=_Z(t.data,this.encodePickingColor);this.setState({layerProps:i})}_updateStateJSON({props:t,changeFlags:r}){let 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n=Math.sin(r),o=Dl(this.e,r,n);i=this.x0+this.a*this.k0*Ce(t-this.long0),s=this.y0-this.a*this.k0*Math.log(o)}return e.x=i,e.y=s,e}function HAt(e){var t=e.x-this.x0,r=e.y-this.y0,i,s;if(this.sphere)s=de-2*Math.atan(Math.exp(-r/(this.a*this.k0)));else{var n=Math.exp(-r/(this.a*this.k0));if(s=Lp(this.e,n),s===-9999)return null}return i=Ce(this.long0+t/(this.a*this.k0)),e.x=i,e.y=s,e}var qAt=[\"Mercator\",\"Popular Visualisation Pseudo Mercator\",\"Mercator_1SP\",\"Mercator_Auxiliary_Sphere\",\"merc\"],GZ={init:GAt,forward:WAt,inverse:HAt,names:qAt};function ZAt(){}function WZ(e){return e}var YAt=[\"longlat\",\"identity\"],HZ={init:ZAt,forward:WZ,inverse:WZ,names:YAt};var QAt=[GZ,HZ],sI={},oI=[];function qZ(e,t){var r=oI.length;return e.names?(oI[r]=e,e.names.forEach(function(i){sI[i.toLowerCase()]=r}),this):(console.log(t),!0)}function $At(e){if(!e)return!1;var t=e.toLowerCase();if(typeof sI[t]<\"u\"&&oI[sI[t]])return oI[sI[t]]}function XAt(){QAt.forEach(qZ)}var 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s=1e-12,n=s*s,o=30,c,f,_,w,I,R,N,j,Q,et,Y,K,J,ut=e.x,Et=e.y,kt=e.z?e.z:0,Xt,qt,le;if(c=Math.sqrt(ut*ut+Et*Et),f=Math.sqrt(ut*ut+Et*Et+kt*kt),c/rn&&Ji.y||j>i.x||Yc&&Math.abs(f.y)>c);if(o<0)return console.log(\"Inverse grid shift iterator failed to converge.\"),i;i.x=Ce(n.x+r.ll[0]),i.y=n.y+r.ll[1]}else isNaN(n.x)||(i.x=e.x+n.x,i.y=e.y+n.y);return i}function nY(e,t){var r={x:e.x/t.del[0],y:e.y/t.del[1]},i={x:Math.floor(r.x),y:Math.floor(r.y)},s={x:r.x-1*i.x,y:r.y-1*i.y},n={x:Number.NaN,y:Number.NaN},o;if(i.x<0||i.x>=t.lim[0]||i.y<0||i.y>=t.lim[1])return n;o=i.y*t.lim[0]+i.x;var c={x:t.cvs[o][0],y:t.cvs[o][1]};o++;var f={x:t.cvs[o][0],y:t.cvs[o][1]};o+=t.lim[0];var _={x:t.cvs[o][0],y:t.cvs[o][1]};o--;var w={x:t.cvs[o][0],y:t.cvs[o][1]},I=s.x*s.y,R=s.x*(1-s.y),N=(1-s.x)*(1-s.y),j=(1-s.x)*s.y;return n.x=N*c.x+R*f.x+j*w.x+I*_.x,n.y=N*c.y+R*f.y+j*w.y+I*_.y,n}function EB(e,t,r){var i=r.x,s=r.y,n=r.z||0,o,c,f,_={};for(f=0;f<3;f++)if(!(t&&f===2&&r.z===void 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t.lat&&t.lon?[t.lon,t.lat,t.lon,t.lat]:[t.left,t.bottom,t.right,t.top]}function LB(e){var t=kB(mY(e.toUpperCase()));return t.lat&&t.lon?[t.lon,t.lat]:[(t.left+t.right)/2,(t.top+t.bottom)/2]}function IB(e){return e*(Math.PI/180)}function hY(e){return 180*(e/Math.PI)}function umt(e){var t=e.lat,r=e.lon,i=6378137,s=.00669438,n=.9996,o,c,f,_,w,I,R,N=IB(t),j=IB(r),Q,et;et=Math.floor((r+180)/6)+1,r===180&&(et=60),t>=56&&t<64&&r>=3&&r<12&&(et=32),t>=72&&t<84&&(r>=0&&r<9?et=31:r>=9&&r<21?et=33:r>=21&&r<33?et=35:r>=33&&r<42&&(et=37)),o=(et-1)*6-180+3,Q=IB(o),c=s/(1-s),f=i/Math.sqrt(1-s*Math.sin(N)*Math.sin(N)),_=Math.tan(N)*Math.tan(N),w=c*Math.cos(N)*Math.cos(N),I=Math.cos(N)*(j-Q),R=i*((1-s/4-3*s*s/64-5*s*s*s/256)*N-(3*s/8+3*s*s/32+45*s*s*s/1024)*Math.sin(2*N)+(15*s*s/256+45*s*s*s/1024)*Math.sin(4*N)-35*s*s*s/3072*Math.sin(6*N));var Y=n*f*(I+(1-_+w)*I*I*I/6+(5-18*_+_*_+72*w-58*c)*I*I*I*I*I/120)+5e5,K=n*(R+f*Math.tan(N)*(I*I/2+(5-_+9*w+4*w*w)*I*I*I*I/24+(61-58*_+_*_+600*w-330*c)*I*I*I*I*I*I/720));return t<0&&(K+=1e7),{northing:Math.round(K),easting:Math.round(Y),zoneNumber:et,zoneLetter:hmt(t)}}function kB(e){var t=e.northing,r=e.easting,i=e.zoneLetter,s=e.zoneNumber;if(s<0||s>60)return null;var n=.9996,o=6378137,c=.00669438,f,_=(1-Math.sqrt(1-c))/(1+Math.sqrt(1-c)),w,I,R,N,j,Q,et,Y,K,J=r-5e5,ut=t;i<\"N\"&&(ut-=1e7),et=(s-1)*6-180+3,f=c/(1-c),Q=ut/n,Y=Q/(o*(1-c/4-3*c*c/64-5*c*c*c/256)),K=Y+(3*_/2-27*_*_*_/32)*Math.sin(2*Y)+(21*_*_/16-55*_*_*_*_/32)*Math.sin(4*Y)+151*_*_*_/96*Math.sin(6*Y),w=o/Math.sqrt(1-c*Math.sin(K)*Math.sin(K)),I=Math.tan(K)*Math.tan(K),R=f*Math.cos(K)*Math.cos(K),N=o*(1-c)/Math.pow(1-c*Math.sin(K)*Math.sin(K),1.5),j=J/(w*n);var Et=K-w*Math.tan(K)/N*(j*j/2-(5+3*I+10*R-4*R*R-9*f)*j*j*j*j/24+(61+90*I+298*R+45*I*I-252*f-3*R*R)*j*j*j*j*j*j/720);Et=hY(Et);var kt=(j-(1+2*I+R)*j*j*j/6+(5-2*R+28*I-3*R*R+8*f+24*I*I)*j*j*j*j*j/120)/Math.cos(K);kt=et+hY(kt);var Xt;if(e.accuracy){var qt=kB({northing:e.northing+e.accuracy,easting:e.easting+e.accuracy,zoneLetter:e.zoneLetter,zoneNumber:e.zoneNumber});Xt={top:qt.lat,right:qt.lon,bottom:Et,left:kt}}else Xt={lat:Et,lon:kt};return Xt}function hmt(e){var t=\"Z\";return 84>=e&&e>=72?t=\"X\":72>e&&e>=64?t=\"W\":64>e&&e>=56?t=\"V\":56>e&&e>=48?t=\"U\":48>e&&e>=40?t=\"T\":40>e&&e>=32?t=\"S\":32>e&&e>=24?t=\"R\":24>e&&e>=16?t=\"Q\":16>e&&e>=8?t=\"P\":8>e&&e>=0?t=\"N\":0>e&&e>=-8?t=\"M\":-8>e&&e>=-16?t=\"L\":-16>e&&e>=-24?t=\"K\":-24>e&&e>=-32?t=\"J\":-32>e&&e>=-40?t=\"H\":-40>e&&e>=-48?t=\"G\":-48>e&&e>=-56?t=\"F\":-56>e&&e>=-64?t=\"E\":-64>e&&e>=-72?t=\"D\":-72>e&&e>=-80&&(t=\"C\"),t}function fmt(e,t){var r=\"00000\"+e.easting,i=\"00000\"+e.northing;return e.zoneNumber+e.zoneLetter+dmt(e.easting,e.northing,e.zoneNumber)+r.substr(r.length-5,t)+i.substr(i.length-5,t)}function dmt(e,t,r){var i=AY(r),s=Math.floor(e/1e5),n=Math.floor(t/1e5)%20;return pmt(s,n,i)}function AY(e){var t=e%uY;return t===0&&(t=uY),t}function pmt(e,t,r){var i=r-1,s=fY.charCodeAt(i),n=dY.charCodeAt(i),o=s+e-1,c=n+t,f=!1;o>mS&&(o=o-mS+ux-1,f=!0),(o===Mc||sMc||(o>Mc||sth||(o>th||smS&&(o=o-mS+ux-1),c>AS?(c=c-AS+ux-1,f=!0):f=!1,(c===Mc||nMc||(c>Mc||nth||(c>th||nAS&&(c=c-AS+ux-1);var _=String.fromCharCode(o)+String.fromCharCode(c);return _}function mY(e){if(e&&e.length===0)throw\"MGRSPoint coverting from nothing\";for(var t=e.length,r=null,i=\"\",s,n=0;!/[A-Z]/.test(s=e.charAt(n));){if(n>=2)throw\"MGRSPoint bad conversion from: \"+e;i+=s,n++}var o=parseInt(i,10);if(n===0||n+3>t)throw\"MGRSPoint bad conversion from: \"+e;var c=e.charAt(n++);if(c<=\"A\"||c===\"B\"||c===\"Y\"||c>=\"Z\"||c===\"I\"||c===\"O\")throw\"MGRSPoint zone letter \"+c+\" not handled: \"+e;r=e.substring(n,n+=2);for(var f=AY(o),_=Amt(r.charAt(0),f),w=mmt(r.charAt(1),f);w0&&(Q=1e5/Math.pow(10,R),et=e.substring(n,n+R),N=parseFloat(et)*Q,Y=e.substring(n+R),j=parseFloat(Y)*Q),K=N+_,J=j+w,{easting:K,northing:J,zoneLetter:c,zoneNumber:o,accuracy:Q}}function Amt(e,t){for(var r=fY.charCodeAt(t-1),i=1e5,s=!1;r!==e.charCodeAt(0);){if(r++,r===Mc&&r++,r===th&&r++,r>mS){if(s)throw\"Bad character: \"+e;r=ux,s=!0}i+=1e5}return i}function mmt(e,t){if(e>\"V\")throw\"MGRSPoint given invalid Northing \"+e;for(var r=dY.charCodeAt(t-1),i=0,s=!1;r!==e.charCodeAt(0);){if(r++,r===Mc&&r++,r===th&&r++,r>AS){if(s)throw\"Bad character: \"+e;r=ux,s=!0}i+=1e5}return i}function gmt(e){var t;switch(e){case\"C\":t=11e5;break;case\"D\":t=2e6;break;case\"E\":t=28e5;break;case\"F\":t=37e5;break;case\"G\":t=46e5;break;case\"H\":t=55e5;break;case\"J\":t=64e5;break;case\"K\":t=73e5;break;case\"L\":t=82e5;break;case\"M\":t=91e5;break;case\"N\":t=0;break;case\"P\":t=8e5;break;case\"Q\":t=17e5;break;case\"R\":t=26e5;break;case\"S\":t=35e5;break;case\"T\":t=44e5;break;case\"U\":t=53e5;break;case\"V\":t=62e5;break;case\"W\":t=7e6;break;case\"X\":t=79e5;break;default:t=-1}if(t>=0)return t;throw\"Invalid zone letter: \"+e}function hx(e,t,r){if(!(this instanceof hx))return new hx(e,t,r);if(Array.isArray(e))this.x=e[0],this.y=e[1],this.z=e[2]||0;else if(typeof e==\"object\")this.x=e.x,this.y=e.y,this.z=e.z||0;else if(typeof e==\"string\"&&typeof t>\"u\"){var i=e.split(\",\");this.x=parseFloat(i[0],10),this.y=parseFloat(i[1],10),this.z=parseFloat(i[2],10)||0}else this.x=e,this.y=t,this.z=r||0;console.warn(\"proj4.Point will be removed in version 3, use proj4.toPoint\")}hx.fromMGRS=function(e){return new hx(LB(e))};hx.prototype.toMGRS=function(e){return CB([this.x,this.y],e)};var gY=hx;var _mt=1,ymt=.25,_Y=.046875,yY=.01953125,vY=.01068115234375,vmt=.75,xmt=.46875,bmt=.013020833333333334,wmt=.007120768229166667,Smt=.3645833333333333,Tmt=.005696614583333333,Mmt=.3076171875;function hI(e){var t=[];t[0]=_mt-e*(ymt+e*(_Y+e*(yY+e*vY))),t[1]=e*(vmt-e*(_Y+e*(yY+e*vY)));var r=e*e;return t[2]=r*(xmt-e*(bmt+e*wmt)),r*=e,t[3]=r*(Smt-e*Tmt),t[4]=r*e*Mmt,t}function Xg(e,t,r,i){return r*=t,t*=t,i[0]*e-r*(i[1]+t*(i[2]+t*(i[3]+t*i[4])))}var Emt=20;function fI(e,t,r){for(var i=1/(1-t),s=e,n=Emt;n;--n){var o=Math.sin(s),c=1-t*o*o;if(c=(Xg(s,o,Math.cos(s),r)-e)*(c*Math.sqrt(c))*i,s-=c,Math.abs(c)Se?Math.tan(r):0,Q=Math.pow(j,2),et=Math.pow(Q,2);s=1-this.es*Math.pow(c,2),w=w/Math.sqrt(s);var Y=Xg(r,c,f,this.en);n=this.a*(this.k0*w*(1+I/6*(1-Q+R+I/20*(5-18*Q+et+14*R-58*Q*R+I/42*(61+179*et-et*Q-479*Q)))))+this.x0,o=this.a*(this.k0*(Y-this.ml0+c*i*w/2*(1+I/12*(5-Q+9*R+4*N+I/30*(61+et-58*Q+270*R-330*Q*R+I/56*(1385+543*et-et*Q-3111*Q))))))+this.y0}else{var _=f*Math.sin(i);if(Math.abs(Math.abs(_)-1)=1){if(_-1>Se)return 93;o=0}else o=Math.acos(o);r<0&&(o=-o),o=this.a*this.k0*(o-this.lat0)+this.y0}return e.x=n,e.y=o,e}function Cmt(e){var t,r,i,s,n=(e.x-this.x0)*(1/this.a),o=(e.y-this.y0)*(1/this.a);if(this.es)if(t=this.ml0+o/this.k0,r=fI(t,this.es,this.en),Math.abs(r)Se?Math.tan(r):0,j=this.ep2*Math.pow(R,2),Q=Math.pow(j,2),et=Math.pow(N,2),Y=Math.pow(et,2);t=1-this.es*Math.pow(I,2);var K=n*Math.sqrt(t)/this.k0,J=Math.pow(K,2);t=t*N,i=r-t*J/(1-this.es)*.5*(1-J/12*(5+3*et-9*j*et+j-4*Q-J/30*(61+90*et-252*j*et+45*Y+46*j-J/56*(1385+3633*et+4095*Y+1574*Y*et)))),s=Ce(this.long0+K*(1-J/6*(1+2*et+j-J/20*(5+28*et+24*Y+8*j*et+6*j-J/42*(61+662*et+1320*Y+720*Y*et))))/R)}else i=de*pd(o),s=0;else{var c=Math.exp(n/this.k0),f=.5*(c-1/c),_=this.lat0+o/this.k0,w=Math.cos(_);t=Math.sqrt((1-Math.pow(w,2))/(1+Math.pow(f,2))),i=Math.asin(t),o<0&&(i=-i),f===0&&w===0?s=0:s=Ce(Math.atan2(f,w)+this.long0)}return e.x=s,e.y=i,e}var Lmt=[\"Fast_Transverse_Mercator\",\"Fast Transverse Mercator\"],fx={init:Pmt,forward:Imt,inverse:Cmt,names:Lmt};function dI(e){var t=Math.exp(e);return t=(t-1/t)/2,t}function Ta(e,t){e=Math.abs(e),t=Math.abs(t);var r=Math.max(e,t),i=Math.min(e,t)/(r||1);return r*Math.sqrt(1+Math.pow(i,2))}function xY(e){var t=1+e,r=t-1;return r===0?e:e*Math.log(t)/r}function bY(e){var t=Math.abs(e);return t=xY(t*(1+t/(Ta(1,t)+1))),e<0?-t:t}function pI(e,t){for(var r=2*Math.cos(2*t),i=e.length-1,s=e[i],n=0,o;--i>=0;)o=-n+r*s+e[i],n=s,s=o;return t+o*Math.sin(2*t)}function wY(e,t){for(var r=2*Math.cos(t),i=e.length-1,s=e[i],n=0,o;--i>=0;)o=-n+r*s+e[i],n=s,s=o;return Math.sin(t)*o}function SY(e){var t=Math.exp(e);return t=(t+1/t)/2,t}function RB(e,t,r){for(var i=Math.sin(t),s=Math.cos(t),n=dI(r),o=SY(r),c=2*s*o,f=-2*i*n,_=e.length-1,w=e[_],I=0,R=0,N=0,j,Q;--_>=0;)j=R,Q=I,R=w,I=N,w=-j+c*R-f*I+e[_],N=-Q+f*R+c*I;return c=i*o,f=s*n,[c*w-f*N,c*N+f*w]}function kmt(){if(!this.approx&&(isNaN(this.es)||this.es<=0))throw new Error('Incorrect elliptical usage. Try using the +approx option in the proj string, or PROJECTION[\"Fast_Transverse_Mercator\"] in the WKT.');this.approx&&(fx.init.apply(this),this.forward=fx.forward,this.inverse=fx.inverse),this.x0=this.x0!==void 0?this.x0:0,this.y0=this.y0!==void 0?this.y0:0,this.long0=this.long0!==void 0?this.long0:0,this.lat0=this.lat0!==void 0?this.lat0:0,this.cgb=[],this.cbg=[],this.utg=[],this.gtu=[];var e=this.es/(1+Math.sqrt(1-this.es)),t=e/(2-e),r=t;this.cgb[0]=t*(2+t*(-2/3+t*(-2+t*(116/45+t*(26/45+t*(-2854/675)))))),this.cbg[0]=t*(-2+t*(2/3+t*(4/3+t*(-82/45+t*(32/45+t*(4642/4725)))))),r=r*t,this.cgb[1]=r*(7/3+t*(-8/5+t*(-227/45+t*(2704/315+t*(2323/945))))),this.cbg[1]=r*(5/3+t*(-16/15+t*(-13/9+t*(904/315+t*(-1522/945))))),r=r*t,this.cgb[2]=r*(56/15+t*(-136/35+t*(-1262/105+t*(73814/2835)))),this.cbg[2]=r*(-26/15+t*(34/21+t*(8/5+t*(-12686/2835)))),r=r*t,this.cgb[3]=r*(4279/630+t*(-332/35+t*(-399572/14175))),this.cbg[3]=r*(1237/630+t*(-12/5+t*(-24832/14175))),r=r*t,this.cgb[4]=r*(4174/315+t*(-144838/6237)),this.cbg[4]=r*(-734/315+t*(109598/31185)),r=r*t,this.cgb[5]=r*(601676/22275),this.cbg[5]=r*(444337/155925),r=Math.pow(t,2),this.Qn=this.k0/(1+t)*(1+r*(1/4+r*(1/64+r/256))),this.utg[0]=t*(-.5+t*(2/3+t*(-37/96+t*(1/360+t*(81/512+t*(-96199/604800)))))),this.gtu[0]=t*(.5+t*(-2/3+t*(5/16+t*(41/180+t*(-127/288+t*(7891/37800)))))),this.utg[1]=r*(-1/48+t*(-1/15+t*(437/1440+t*(-46/105+t*(1118711/3870720))))),this.gtu[1]=r*(13/48+t*(-3/5+t*(557/1440+t*(281/630+t*(-1983433/1935360))))),r=r*t,this.utg[2]=r*(-17/480+t*(37/840+t*(209/4480+t*(-5569/90720)))),this.gtu[2]=r*(61/240+t*(-103/140+t*(15061/26880+t*(167603/181440)))),r=r*t,this.utg[3]=r*(-4397/161280+t*(11/504+t*(830251/7257600))),this.gtu[3]=r*(49561/161280+t*(-179/168+t*(6601661/7257600))),r=r*t,this.utg[4]=r*(-4583/161280+t*(108847/3991680)),this.gtu[4]=r*(34729/80640+t*(-3418889/1995840)),r=r*t,this.utg[5]=r*(-20648693/638668800),this.gtu[5]=r*(212378941/319334400);var i=pI(this.cbg,this.lat0);this.Zb=-this.Qn*(i+wY(this.gtu,2*i))}function Rmt(e){var t=Ce(e.x-this.long0),r=e.y;r=pI(this.cbg,r);var i=Math.sin(r),s=Math.cos(r),n=Math.sin(t),o=Math.cos(t);r=Math.atan2(i,o*s),t=Math.atan2(n*s,Ta(i,s*o)),t=bY(Math.tan(t));var c=RB(this.gtu,2*r,2*t);r=r+c[0],t=t+c[1];var f,_;return Math.abs(t)<=2.623395162778?(f=this.a*(this.Qn*t)+this.x0,_=this.a*(this.Qn*r+this.Zb)+this.y0):(f=1/0,_=1/0),e.x=f,e.y=_,e}function Dmt(e){var t=(e.x-this.x0)*(1/this.a),r=(e.y-this.y0)*(1/this.a);r=(r-this.Zb)/this.Qn,t=t/this.Qn;var i,s;if(Math.abs(t)<=2.623395162778){var n=RB(this.utg,2*r,2*t);r=r+n[0],t=t+n[1],t=Math.atan(dI(t));var o=Math.sin(r),c=Math.cos(r),f=Math.sin(t),_=Math.cos(t);r=Math.atan2(o*_,Ta(f,_*c)),t=Math.atan2(f,_*c),i=Ce(t+this.long0),s=pI(this.cgb,r)}else i=1/0,s=1/0;return e.x=i,e.y=s,e}var Omt=[\"Extended_Transverse_Mercator\",\"Extended Transverse Mercator\",\"etmerc\",\"Transverse_Mercator\",\"Transverse Mercator\",\"Gauss Kruger\",\"Gauss_Kruger\",\"tmerc\"],dx={init:kmt,forward:Rmt,inverse:Dmt,names:Omt};function TY(e,t){if(e===void 0){if(e=Math.floor((Ce(t)+Math.PI)*30/Math.PI)+1,e<0)return 0;if(e>60)return 60}return e}var Bmt=\"etmerc\";function Fmt(){var e=TY(this.zone,this.long0);if(e===void 0)throw new Error(\"unknown utm zone\");this.lat0=0,this.long0=(6*Math.abs(e)-183)*vs,this.x0=5e5,this.y0=this.utmSouth?1e7:0,this.k0=.9996,dx.init.apply(this),this.forward=dx.forward,this.inverse=dx.inverse}var zmt=[\"Universal Transverse Mercator System\",\"utm\"],MY={init:Fmt,names:zmt,dependsOn:Bmt};function AI(e,t){return Math.pow((1-e)/(1+e),t)}var Nmt=20;function Umt(){var e=Math.sin(this.lat0),t=Math.cos(this.lat0);t*=t,this.rc=Math.sqrt(1-this.es)/(1-this.es*e*e),this.C=Math.sqrt(1+this.es*t*t/(1-this.es)),this.phic0=Math.asin(e/this.C),this.ratexp=.5*this.C*this.e,this.K=Math.tan(.5*this.phic0+Ui)/(Math.pow(Math.tan(.5*this.lat0+Ui),this.C)*AI(this.e*e,this.ratexp))}function Vmt(e){var t=e.x,r=e.y;return e.y=2*Math.atan(this.K*Math.pow(Math.tan(.5*r+Ui),this.C)*AI(this.e*Math.sin(r),this.ratexp))-de,e.x=this.C*t,e}function jmt(e){for(var t=1e-14,r=e.x/this.C,i=e.y,s=Math.pow(Math.tan(.5*i+Ui)/this.K,1/this.C),n=Nmt;n>0&&(i=2*Math.atan(s*AI(this.e*Math.sin(e.y),-.5*this.e))-de,!(Math.abs(i-e.y)0?this.con=1:this.con=-1),this.cons=Math.sqrt(Math.pow(1+this.e,1+this.e)*Math.pow(1-this.e,1-this.e)),this.k0===1&&!isNaN(this.lat_ts)&&Math.abs(this.coslat0)<=Se&&Math.abs(Math.cos(this.lat_ts))>Se&&(this.k0=.5*this.cons*ol(this.e,Math.sin(this.lat_ts),Math.cos(this.lat_ts))/Dl(this.e,this.con*this.lat_ts,this.con*Math.sin(this.lat_ts))),this.ms1=ol(this.e,this.sinlat0,this.coslat0),this.X0=2*Math.atan(this.ssfn_(this.lat0,this.sinlat0,this.e))-de,this.cosX0=Math.cos(this.X0),this.sinX0=Math.sin(this.X0))}function $mt(e){var t=e.x,r=e.y,i=Math.sin(r),s=Math.cos(r),n,o,c,f,_,w,I=Ce(t-this.long0);return Math.abs(Math.abs(t-this.long0)-Math.PI)<=Se&&Math.abs(r+this.lat0)<=Se?(e.x=NaN,e.y=NaN,e):this.sphere?(n=2*this.k0/(1+this.sinlat0*i+this.coslat0*s*Math.cos(I)),e.x=this.a*n*s*Math.sin(I)+this.x0,e.y=this.a*n*(this.coslat0*i-this.sinlat0*s*Math.cos(I))+this.y0,e):(o=2*Math.atan(this.ssfn_(r,i,this.e))-de,f=Math.cos(o),c=Math.sin(o),Math.abs(this.coslat0)<=Se?(_=Dl(this.e,r*this.con,this.con*i),w=2*this.a*this.k0*_/this.cons,e.x=this.x0+w*Math.sin(t-this.long0),e.y=this.y0-this.con*w*Math.cos(t-this.long0),e):(Math.abs(this.sinlat0)0?t=Ce(this.long0+Math.atan2(e.x,-1*e.y)):t=Ce(this.long0+Math.atan2(e.x,e.y)):t=Ce(this.long0+Math.atan2(e.x*Math.sin(c),o*this.coslat0*Math.cos(c)-e.y*this.sinlat0*Math.sin(c))),e.x=t,e.y=r,e)}else if(Math.abs(this.coslat0)<=Se){if(o<=Se)return r=this.lat0,t=this.long0,e.x=t,e.y=r,e;e.x*=this.con,e.y*=this.con,i=o*this.cons/(2*this.a*this.k0),r=this.con*Lp(this.e,i),t=this.con*Ce(this.con*this.long0+Math.atan2(e.x,-1*e.y))}else s=2*Math.atan(o*this.cosX0/(2*this.a*this.k0*this.ms1)),t=this.long0,o<=Se?n=this.X0:(n=Math.asin(Math.cos(s)*this.sinX0+e.y*Math.sin(s)*this.cosX0/o),t=Ce(this.long0+Math.atan2(e.x*Math.sin(s),o*this.cosX0*Math.cos(s)-e.y*this.sinX0*Math.sin(s)))),r=-1*Lp(this.e,Math.tan(.5*(de+n)));return e.x=t,e.y=r,e}var Kmt=[\"stere\",\"Stereographic_South_Pole\",\"Polar Stereographic (variant B)\",\"Polar_Stereographic\"],PY={init:Qmt,forward:$mt,inverse:Xmt,names:Kmt,ssfn_:Ymt};function Jmt(){var e=this.lat0;this.lambda0=this.long0;var t=Math.sin(e),r=this.a,i=this.rf,s=1/i,n=2*s-Math.pow(s,2),o=this.e=Math.sqrt(n);this.R=this.k0*r*Math.sqrt(1-n)/(1-n*Math.pow(t,2)),this.alpha=Math.sqrt(1+n/(1-n)*Math.pow(Math.cos(e),4)),this.b0=Math.asin(t/this.alpha);var c=Math.log(Math.tan(Math.PI/4+this.b0/2)),f=Math.log(Math.tan(Math.PI/4+e/2)),_=Math.log((1+o*t)/(1-o*t));this.K=c-this.alpha*f+this.alpha*o/2*_}function t0t(e){var t=Math.log(Math.tan(Math.PI/4-e.y/2)),r=this.e/2*Math.log((1+this.e*Math.sin(e.y))/(1-this.e*Math.sin(e.y))),i=-this.alpha*(t+r)+this.K,s=2*(Math.atan(Math.exp(i))-Math.PI/4),n=this.alpha*(e.x-this.lambda0),o=Math.atan(Math.sin(n)/(Math.sin(this.b0)*Math.tan(s)+Math.cos(this.b0)*Math.cos(n))),c=Math.asin(Math.cos(this.b0)*Math.sin(s)-Math.sin(this.b0)*Math.cos(s)*Math.cos(n));return e.y=this.R/2*Math.log((1+Math.sin(c))/(1-Math.sin(c)))+this.y0,e.x=this.R*o+this.x0,e}function e0t(e){for(var t=e.x-this.x0,r=e.y-this.y0,i=t/this.R,s=2*(Math.atan(Math.exp(r/this.R))-Math.PI/4),n=Math.asin(Math.cos(this.b0)*Math.sin(s)+Math.sin(this.b0)*Math.cos(s)*Math.cos(i)),o=Math.atan(Math.sin(i)/(Math.cos(this.b0)*Math.cos(i)-Math.sin(this.b0)*Math.tan(s))),c=this.lambda0+o/this.alpha,f=0,_=n,w=-1e3,I=0;Math.abs(_-w)>1e-7;){if(++I>20)return;f=1/this.alpha*(Math.log(Math.tan(Math.PI/4+n/2))-this.K)+this.e*Math.log(Math.tan(Math.PI/4+Math.asin(this.e*Math.sin(_))/2)),w=_,_=2*Math.atan(Math.exp(f))-Math.PI/2}return e.x=c,e.y=_,e}var r0t=[\"somerc\"],IY={init:Jmt,forward:t0t,inverse:e0t,names:r0t};var px=1e-7;function i0t(e){var t=[\"Hotine_Oblique_Mercator\",\"Hotine_Oblique_Mercator_Azimuth_Natural_Origin\"],r=typeof e.PROJECTION==\"object\"?Object.keys(e.PROJECTION)[0]:e.PROJECTION;return\"no_uoff\"in e||\"no_off\"in e||t.indexOf(r)!==-1}function n0t(){var e,t,r,i,s,n,o,c,f,_,w=0,I,R=0,N=0,j=0,Q=0,et=0,Y=0,K;this.no_off=i0t(this),this.no_rot=\"no_rot\"in this;var J=!1;\"alpha\"in this&&(J=!0);var ut=!1;if(\"rectified_grid_angle\"in this&&(ut=!0),J&&(Y=this.alpha),ut&&(w=this.rectified_grid_angle*vs),J||ut)R=this.longc;else if(N=this.long1,Q=this.lat1,j=this.long2,et=this.lat2,Math.abs(Q-et)<=px||(e=Math.abs(Q))<=px||Math.abs(e-de)<=px||Math.abs(Math.abs(this.lat0)-de)<=px||Math.abs(Math.abs(et)-de)<=px)throw new Error;var Et=1-this.es;t=Math.sqrt(Et),Math.abs(this.lat0)>Se?(c=Math.sin(this.lat0),r=Math.cos(this.lat0),e=1-this.es*c*c,this.B=r*r,this.B=Math.sqrt(1+this.es*this.B*this.B/Et),this.A=this.B*this.k0*t/e,i=this.B*t/(r*Math.sqrt(e)),s=i*i-1,s<=0?s=0:(s=Math.sqrt(s),this.lat0<0&&(s=-s)),this.E=s+=i,this.E*=Math.pow(Dl(this.e,this.lat0,c),this.B)):(this.B=1/t,this.A=this.k0,this.E=i=s=1),J||ut?(J?(I=Math.asin(Math.sin(Y)/i),ut||(w=Y)):(I=w,Y=Math.asin(i*Math.sin(I))),this.lam0=R-Math.asin(.5*(s-1/s)*Math.tan(I))/this.B):(n=Math.pow(Dl(this.e,Q,Math.sin(Q)),this.B),o=Math.pow(Dl(this.e,et,Math.sin(et)),this.B),s=this.E/n,f=(o-n)/(o+n),_=this.E*this.E,_=(_-o*n)/(_+o*n),e=N-j,e<-Math.pi?j-=Em:e>Math.pi&&(j+=Em),this.lam0=Ce(.5*(N+j)-Math.atan(_*Math.tan(.5*this.B*(N-j))/f)/this.B),I=Math.atan(2*Math.sin(this.B*Ce(N-this.lam0))/(s-1/s)),w=Y=Math.asin(i*Math.sin(I))),this.singam=Math.sin(I),this.cosgam=Math.cos(I),this.sinrot=Math.sin(w),this.cosrot=Math.cos(w),this.rB=1/this.B,this.ArB=this.A*this.rB,this.BrA=1/this.ArB,K=this.A*this.B,this.no_off?this.u_0=0:(this.u_0=Math.abs(this.ArB*Math.atan(Math.sqrt(i*i-1)/Math.cos(Y))),this.lat0<0&&(this.u_0=-this.u_0)),s=.5*I,this.v_pole_n=this.ArB*Math.log(Math.tan(Ui-s)),this.v_pole_s=this.ArB*Math.log(Math.tan(Ui+s))}function s0t(e){var t={},r,i,s,n,o,c,f,_;if(e.x=e.x-this.lam0,Math.abs(Math.abs(e.y)-de)>Se){if(o=this.E/Math.pow(Dl(this.e,e.y,Math.sin(e.y)),this.B),c=1/o,r=.5*(o-c),i=.5*(o+c),n=Math.sin(this.B*e.x),s=(r*this.singam-n*this.cosgam)/i,Math.abs(Math.abs(s)-1)0?this.v_pole_n:this.v_pole_s,f=this.ArB*e.y;return this.no_rot?(t.x=f,t.y=_):(f-=this.u_0,t.x=_*this.cosrot+f*this.sinrot,t.y=f*this.cosrot-_*this.sinrot),t.x=this.a*t.x+this.x0,t.y=this.a*t.y+this.y0,t}function o0t(e){var t,r,i,s,n,o,c,f={};if(e.x=(e.x-this.x0)*(1/this.a),e.y=(e.y-this.y0)*(1/this.a),this.no_rot?(r=e.y,t=e.x):(r=e.x*this.cosrot-e.y*this.sinrot,t=e.y*this.cosrot+e.x*this.sinrot+this.u_0),i=Math.exp(-this.BrA*r),s=.5*(i-1/i),n=.5*(i+1/i),o=Math.sin(this.BrA*t),c=(o*this.cosgam+s*this.singam)/n,Math.abs(Math.abs(c)-1)Se?this.ns=Math.log(i/c)/Math.log(s/f):this.ns=t,isNaN(this.ns)&&(this.ns=t),this.f0=i/(this.ns*Math.pow(s,this.ns)),this.rh=this.a*this.f0*Math.pow(_,this.ns),this.title||(this.title=\"Lambert Conformal Conic\")}}function c0t(e){var t=e.x,r=e.y;Math.abs(2*Math.abs(r)-Math.PI)<=Se&&(r=pd(r)*(de-2*Se));var i=Math.abs(Math.abs(r)-de),s,n;if(i>Se)s=Dl(this.e,r,Math.sin(r)),n=this.a*this.f0*Math.pow(s,this.ns);else{if(i=r*this.ns,i<=0)return null;n=0}var o=this.ns*Ce(t-this.long0);return e.x=this.k0*(n*Math.sin(o))+this.x0,e.y=this.k0*(this.rh-n*Math.cos(o))+this.y0,e}function u0t(e){var t,r,i,s,n,o=(e.x-this.x0)/this.k0,c=this.rh-(e.y-this.y0)/this.k0;this.ns>0?(t=Math.sqrt(o*o+c*c),r=1):(t=-Math.sqrt(o*o+c*c),r=-1);var f=0;if(t!==0&&(f=Math.atan2(r*o,r*c)),t!==0||this.ns>0){if(r=1/this.ns,i=Math.pow(t/(this.a*this.f0),r),s=Lp(this.e,i),s===-9999)return null}else s=-de;return n=Ce(f/this.ns+this.long0),e.x=n,e.y=s,e}var h0t=[\"Lambert Tangential Conformal Conic Projection\",\"Lambert_Conformal_Conic\",\"Lambert_Conformal_Conic_1SP\",\"Lambert_Conformal_Conic_2SP\",\"lcc\",\"Lambert Conic Conformal (1SP)\",\"Lambert Conic Conformal (2SP)\"],LY={init:l0t,forward:c0t,inverse:u0t,names:h0t};function f0t(){this.a=6377397155e-3,this.es=.006674372230614,this.e=Math.sqrt(this.es),this.lat0||(this.lat0=.863937979737193),this.long0||(this.long0=.7417649320975901-.308341501185665),this.k0||(this.k0=.9999),this.s45=.785398163397448,this.s90=2*this.s45,this.fi0=this.lat0,this.e2=this.es,this.e=Math.sqrt(this.e2),this.alfa=Math.sqrt(1+this.e2*Math.pow(Math.cos(this.fi0),4)/(1-this.e2)),this.uq=1.04216856380474,this.u0=Math.asin(Math.sin(this.fi0)/this.alfa),this.g=Math.pow((1+this.e*Math.sin(this.fi0))/(1-this.e*Math.sin(this.fi0)),this.alfa*this.e/2),this.k=Math.tan(this.u0/2+this.s45)/Math.pow(Math.tan(this.fi0/2+this.s45),this.alfa)*this.g,this.k1=this.k0,this.n0=this.a*Math.sqrt(1-this.e2)/(1-this.e2*Math.pow(Math.sin(this.fi0),2)),this.s0=1.37008346281555,this.n=Math.sin(this.s0),this.ro0=this.k1*this.n0/Math.tan(this.s0),this.ad=this.s90-this.uq}function d0t(e){var t,r,i,s,n,o,c,f=e.x,_=e.y,w=Ce(f-this.long0);return t=Math.pow((1+this.e*Math.sin(_))/(1-this.e*Math.sin(_)),this.alfa*this.e/2),r=2*(Math.atan(this.k*Math.pow(Math.tan(_/2+this.s45),this.alfa)/t)-this.s45),i=-w*this.alfa,s=Math.asin(Math.cos(this.ad)*Math.sin(r)+Math.sin(this.ad)*Math.cos(r)*Math.cos(i)),n=Math.asin(Math.cos(r)*Math.sin(i)/Math.cos(s)),o=this.n*n,c=this.ro0*Math.pow(Math.tan(this.s0/2+this.s45),this.n)/Math.pow(Math.tan(s/2+this.s45),this.n),e.y=c*Math.cos(o)/1,e.x=c*Math.sin(o)/1,this.czech||(e.y*=-1,e.x*=-1),e}function p0t(e){var t,r,i,s,n,o,c,f,_=e.x;e.x=e.y,e.y=_,this.czech||(e.y*=-1,e.x*=-1),o=Math.sqrt(e.x*e.x+e.y*e.y),n=Math.atan2(e.y,e.x),s=n/Math.sin(this.s0),i=2*(Math.atan(Math.pow(this.ro0/o,1/this.n)*Math.tan(this.s0/2+this.s45))-this.s45),t=Math.asin(Math.cos(this.ad)*Math.sin(i)-Math.sin(this.ad)*Math.cos(i)*Math.cos(s)),r=Math.asin(Math.cos(i)*Math.sin(s)/Math.cos(t)),e.x=this.long0-r/this.alfa,c=t,f=0;var w=0;do e.y=2*(Math.atan(Math.pow(this.k,-1/this.alfa)*Math.pow(Math.tan(t/2+this.s45),1/this.alfa)*Math.pow((1+this.e*Math.sin(c))/(1-this.e*Math.sin(c)),this.e/2))-this.s45),Math.abs(c-e.y)<1e-10&&(f=1),c=e.y,w+=1;while(f===0&&w<15);return w>=15?null:e}var A0t=[\"Krovak\",\"krovak\"],kY={init:f0t,forward:d0t,inverse:p0t,names:A0t};function zo(e,t,r,i,s){return e*s-t*Math.sin(2*s)+r*Math.sin(4*s)-i*Math.sin(6*s)}function kp(e){return 1-.25*e*(1+e/16*(3+1.25*e))}function Rp(e){return .375*e*(1+.25*e*(1+.46875*e))}function Dp(e){return .05859375*e*e*(1+.75*e)}function Op(e){return e*e*e*(35/3072)}function Bp(e,t,r){var i=t*r;return e/Math.sqrt(1-i*i)}function ff(e){return Math.abs(e)1e-7?(r=e*t,(1-e*e)*(t/(1-r*r)-.5/e*Math.log((1-r)/(1+r)))):2*t}var v0t=1,x0t=2,b0t=3,w0t=4;function S0t(){var e=Math.abs(this.lat0);if(Math.abs(e-de)0){var t;switch(this.qp=df(this.e,1),this.mmf=.5/(1-this.es),this.apa=R0t(this.es),this.mode){case this.N_POLE:this.dd=1;break;case this.S_POLE:this.dd=1;break;case this.EQUIT:this.rq=Math.sqrt(.5*this.qp),this.dd=1/this.rq,this.xmf=1,this.ymf=.5*this.qp;break;case this.OBLIQ:this.rq=Math.sqrt(.5*this.qp),t=Math.sin(this.lat0),this.sinb1=df(this.e,t)/this.qp,this.cosb1=Math.sqrt(1-this.sinb1*this.sinb1),this.dd=Math.cos(this.lat0)/(Math.sqrt(1-this.es*t*t)*this.rq*this.cosb1),this.ymf=(this.xmf=this.rq)/this.dd,this.xmf*=this.dd;break}}else this.mode===this.OBLIQ&&(this.sinph0=Math.sin(this.lat0),this.cosph0=Math.cos(this.lat0))}function T0t(e){var t,r,i,s,n,o,c,f,_,w,I=e.x,R=e.y;if(I=Ce(I-this.long0),this.sphere){if(n=Math.sin(R),w=Math.cos(R),i=Math.cos(I),this.mode===this.OBLIQ||this.mode===this.EQUIT){if(r=this.mode===this.EQUIT?1+w*i:1+this.sinph0*n+this.cosph0*w*i,r<=Se)return null;r=Math.sqrt(2/r),t=r*w*Math.sin(I),r*=this.mode===this.EQUIT?n:this.cosph0*n-this.sinph0*w*i}else if(this.mode===this.N_POLE||this.mode===this.S_POLE){if(this.mode===this.N_POLE&&(i=-i),Math.abs(R+this.lat0)=0?(t=(_=Math.sqrt(o))*s,r=i*(this.mode===this.S_POLE?_:-_)):t=r=0;break}}return e.x=this.a*t+this.x0,e.y=this.a*r+this.y0,e}function M0t(e){e.x-=this.x0,e.y-=this.y0;var t=e.x/this.a,r=e.y/this.a,i,s,n,o,c,f,_;if(this.sphere){var w=0,I,R=0;if(I=Math.sqrt(t*t+r*r),s=I*.5,s>1)return null;switch(s=2*Math.asin(s),(this.mode===this.OBLIQ||this.mode===this.EQUIT)&&(R=Math.sin(s),w=Math.cos(s)),this.mode){case this.EQUIT:s=Math.abs(I)<=Se?0:Math.asin(r*R/I),t*=R,r=w*I;break;case this.OBLIQ:s=Math.abs(I)<=Se?this.lat0:Math.asin(w*this.sinph0+r*R*this.cosph0/I),t*=R*this.cosph0,r=(w-Math.sin(s)*this.sinph0)*I;break;case this.N_POLE:r=-r,s=de-s;break;case this.S_POLE:s-=de;break}i=r===0&&(this.mode===this.EQUIT||this.mode===this.OBLIQ)?0:Math.atan2(t,r)}else{if(_=0,this.mode===this.OBLIQ||this.mode===this.EQUIT){if(t/=this.dd,r*=this.dd,f=Math.sqrt(t*t+r*r),f1&&(e=e>1?1:-1),Math.asin(e)}function B0t(){Math.abs(this.lat1+this.lat2)Se?this.ns0=(this.ms1*this.ms1-this.ms2*this.ms2)/(this.qs2-this.qs1):this.ns0=this.con,this.c=this.ms1*this.ms1+this.ns0*this.qs1,this.rh=this.a*Math.sqrt(this.c-this.ns0*this.qs0)/this.ns0)}function F0t(e){var t=e.x,r=e.y;this.sin_phi=Math.sin(r),this.cos_phi=Math.cos(r);var i=df(this.e3,this.sin_phi),s=this.a*Math.sqrt(this.c-this.ns0*i)/this.ns0,n=this.ns0*Ce(t-this.long0),o=s*Math.sin(n)+this.x0,c=this.rh-s*Math.cos(n)+this.y0;return e.x=o,e.y=c,e}function z0t(e){var t,r,i,s,n,o;return e.x-=this.x0,e.y=this.rh-e.y+this.y0,this.ns0>=0?(t=Math.sqrt(e.x*e.x+e.y*e.y),i=1):(t=-Math.sqrt(e.x*e.x+e.y*e.y),i=-1),s=0,t!==0&&(s=Math.atan2(i*e.x,i*e.y)),i=t*this.ns0/this.a,this.sphere?o=Math.asin((this.c-i*i)/(2*this.ns0)):(r=(this.c-i*i)/this.ns0,o=this.phi1z(this.e3,r)),n=Ce(s/this.ns0+this.long0),e.x=n,e.y=o,e}function N0t(e,t){var r,i,s,n,o,c=Ec(.5*t);if(e0||Math.abs(o)<=Se?(c=this.x0+this.a*n*r*Math.sin(i)/o,f=this.y0+this.a*n*(this.cos_p14*t-this.sin_p14*r*s)/o):(c=this.x0+this.infinity_dist*r*Math.sin(i),f=this.y0+this.infinity_dist*(this.cos_p14*t-this.sin_p14*r*s)),e.x=c,e.y=f,e}function G0t(e){var t,r,i,s,n,o;return e.x=(e.x-this.x0)/this.a,e.y=(e.y-this.y0)/this.a,e.x/=this.k0,e.y/=this.k0,(t=Math.sqrt(e.x*e.x+e.y*e.y))?(s=Math.atan2(t,this.rc),r=Math.sin(s),i=Math.cos(s),o=Ec(i*this.sin_p14+e.y*r*this.cos_p14/t),n=Math.atan2(e.x*r,t*this.cos_p14*i-e.y*this.sin_p14*r),n=Ce(this.long0+n)):(o=this.phic0,n=0),e.x=n,e.y=o,e}var W0t=[\"gnom\"],BY={init:V0t,forward:j0t,inverse:G0t,names:W0t};function FY(e,t){var r=1-(1-e*e)/(2*e)*Math.log((1-e)/(1+e));if(Math.abs(Math.abs(t)-r)<1e-6)return t<0?-1*de:de;for(var i=Math.asin(.5*t),s,n,o,c,f=0;f<30;f++)if(n=Math.sin(i),o=Math.cos(i),c=e*n,s=Math.pow(1-c*c,2)/(2*o)*(t/(1-e*e)-n/(1-c*c)+.5/e*Math.log((1-c)/(1+c))),i+=s,Math.abs(s)<=1e-10)return i;return NaN}function H0t(){this.sphere||(this.k0=ol(this.e,Math.sin(this.lat_ts),Math.cos(this.lat_ts)))}function q0t(e){var t=e.x,r=e.y,i,s,n=Ce(t-this.long0);if(this.sphere)i=this.x0+this.a*n*Math.cos(this.lat_ts),s=this.y0+this.a*Math.sin(r)/Math.cos(this.lat_ts);else{var o=df(this.e,Math.sin(r));i=this.x0+this.a*this.k0*n,s=this.y0+this.a*o*.5/this.k0}return e.x=i,e.y=s,e}function Z0t(e){e.x-=this.x0,e.y-=this.y0;var t,r;return this.sphere?(t=Ce(this.long0+e.x/this.a/Math.cos(this.lat_ts)),r=Math.asin(e.y/this.a*Math.cos(this.lat_ts))):(r=FY(this.e,2*e.y*this.k0/this.a),t=Ce(this.long0+e.x/(this.a*this.k0))),e.x=t,e.y=r,e}var Y0t=[\"cea\"],zY={init:H0t,forward:q0t,inverse:Z0t,names:Y0t};function Q0t(){this.x0=this.x0||0,this.y0=this.y0||0,this.lat0=this.lat0||0,this.long0=this.long0||0,this.lat_ts=this.lat_ts||0,this.title=this.title||\"Equidistant Cylindrical (Plate Carre)\",this.rc=Math.cos(this.lat_ts)}function $0t(e){var t=e.x,r=e.y,i=Ce(t-this.long0),s=ff(r-this.lat0);return e.x=this.x0+this.a*i*this.rc,e.y=this.y0+this.a*s,e}function X0t(e){var t=e.x,r=e.y;return e.x=Ce(this.long0+(t-this.x0)/(this.a*this.rc)),e.y=ff(this.lat0+(r-this.y0)/this.a),e}var K0t=[\"Equirectangular\",\"Equidistant_Cylindrical\",\"eqc\"],NY={init:Q0t,forward:$0t,inverse:X0t,names:K0t};var UY=20;function J0t(){this.temp=this.b/this.a,this.es=1-Math.pow(this.temp,2),this.e=Math.sqrt(this.es),this.e0=kp(this.es),this.e1=Rp(this.es),this.e2=Dp(this.es),this.e3=Op(this.es),this.ml0=this.a*zo(this.e0,this.e1,this.e2,this.e3,this.lat0)}function tgt(e){var t=e.x,r=e.y,i,s,n,o=Ce(t-this.long0);if(n=o*Math.sin(r),this.sphere)Math.abs(r)<=Se?(i=this.a*o,s=-1*this.a*this.lat0):(i=this.a*Math.sin(n)/Math.tan(r),s=this.a*(ff(r-this.lat0)+(1-Math.cos(n))/Math.tan(r)));else if(Math.abs(r)<=Se)i=this.a*o,s=-1*this.ml0;else{var c=Bp(this.a,this.e,Math.sin(r))/Math.tan(r);i=c*Math.sin(n),s=this.a*zo(this.e0,this.e1,this.e2,this.e3,r)-this.ml0+c*(1-Math.cos(n))}return e.x=i+this.x0,e.y=s+this.y0,e}function egt(e){var t,r,i,s,n,o,c,f,_;if(i=e.x-this.x0,s=e.y-this.y0,this.sphere)if(Math.abs(s+this.a*this.lat0)<=Se)t=Ce(i/this.a+this.long0),r=0;else{o=this.lat0+s/this.a,c=i*i/this.a/this.a+o*o,f=o;var w;for(n=UY;n;--n)if(w=Math.tan(f),_=-1*(o*(f*w+1)-f-.5*(f*f+c)*w)/((f-o)/w-1),f+=_,Math.abs(_)<=Se){r=f;break}t=Ce(this.long0+Math.asin(i*Math.tan(f)/this.a)/Math.sin(r))}else if(Math.abs(s+this.ml0)<=Se)r=0,t=Ce(this.long0+i/this.a);else{o=(this.ml0+s)/this.a,c=i*i/this.a/this.a+o*o,f=o;var I,R,N,j,Q;for(n=UY;n;--n)if(Q=this.e*Math.sin(f),I=Math.sqrt(1-Q*Q)*Math.tan(f),R=this.a*zo(this.e0,this.e1,this.e2,this.e3,f),N=this.e0-2*this.e1*Math.cos(2*f)+4*this.e2*Math.cos(4*f)-6*this.e3*Math.cos(6*f),j=R/this.a,_=(o*(I*j+1)-j-.5*I*(j*j+c))/(this.es*Math.sin(2*f)*(j*j+c-2*o*j)/(4*I)+(o-j)*(I*N-2/Math.sin(2*f))-N),f-=_,Math.abs(_)<=Se){r=f;break}I=Math.sqrt(1-this.es*Math.pow(Math.sin(r),2))*Math.tan(r),t=Ce(this.long0+Math.asin(i*I/this.a)/Math.sin(r))}return e.x=t,e.y=r,e}var rgt=[\"Polyconic\",\"poly\"],VY={init:J0t,forward:tgt,inverse:egt,names:rgt};function igt(){this.A=[],this.A[1]=.6399175073,this.A[2]=-.1358797613,this.A[3]=.063294409,this.A[4]=-.02526853,this.A[5]=.0117879,this.A[6]=-.0055161,this.A[7]=.0026906,this.A[8]=-.001333,this.A[9]=67e-5,this.A[10]=-34e-5,this.B_re=[],this.B_im=[],this.B_re[1]=.7557853228,this.B_im[1]=0,this.B_re[2]=.249204646,this.B_im[2]=.003371507,this.B_re[3]=-.001541739,this.B_im[3]=.04105856,this.B_re[4]=-.10162907,this.B_im[4]=.01727609,this.B_re[5]=-.26623489,this.B_im[5]=-.36249218,this.B_re[6]=-.6870983,this.B_im[6]=-1.1651967,this.C_re=[],this.C_im=[],this.C_re[1]=1.3231270439,this.C_im[1]=0,this.C_re[2]=-.577245789,this.C_im[2]=-.007809598,this.C_re[3]=.508307513,this.C_im[3]=-.112208952,this.C_re[4]=-.15094762,this.C_im[4]=.18200602,this.C_re[5]=1.01418179,this.C_im[5]=1.64497696,this.C_re[6]=1.9660549,this.C_im[6]=2.5127645,this.D=[],this.D[1]=1.5627014243,this.D[2]=.5185406398,this.D[3]=-.03333098,this.D[4]=-.1052906,this.D[5]=-.0368594,this.D[6]=.007317,this.D[7]=.0122,this.D[8]=.00394,this.D[9]=-.0013}function ngt(e){var t,r=e.x,i=e.y,s=i-this.lat0,n=r-this.long0,o=s/Yg*1e-5,c=n,f=1,_=0;for(t=1;t<=10;t++)f=f*o,_=_+this.A[t]*f;var w=_,I=c,R=1,N=0,j,Q,et=0,Y=0;for(t=1;t<=6;t++)j=R*w-N*I,Q=N*w+R*I,R=j,N=Q,et=et+this.B_re[t]*R-this.B_im[t]*N,Y=Y+this.B_im[t]*R+this.B_re[t]*N;return e.x=Y*this.a+this.x0,e.y=et*this.a+this.y0,e}function sgt(e){var t,r=e.x,i=e.y,s=r-this.x0,n=i-this.y0,o=n/this.a,c=s/this.a,f=1,_=0,w,I,R=0,N=0;for(t=1;t<=6;t++)w=f*o-_*c,I=_*o+f*c,f=w,_=I,R=R+this.C_re[t]*f-this.C_im[t]*_,N=N+this.C_im[t]*f+this.C_re[t]*_;for(var j=0;j.999999999999&&(r=.999999999999),t=Math.asin(r);var i=Ce(this.long0+e.x/(.900316316158*this.a*Math.cos(t)));i<-Math.PI&&(i=-Math.PI),i>Math.PI&&(i=Math.PI),r=(2*t+Math.sin(2*t))/Math.PI,Math.abs(r)>1&&(r=1);var s=Math.asin(r);return e.x=i,e.y=s,e}var ygt=[\"Mollweide\",\"moll\"],HY={init:mgt,forward:ggt,inverse:_gt,names:ygt};function vgt(){Math.abs(this.lat1+this.lat2)=0?(r=Math.sqrt(e.x*e.x+e.y*e.y),t=1):(r=-Math.sqrt(e.x*e.x+e.y*e.y),t=-1);var n=0;if(r!==0&&(n=Math.atan2(t*e.x,t*e.y)),this.sphere)return s=Ce(this.long0+n/this.ns),i=ff(this.g-r/this.a),e.x=s,e.y=i,e;var o=this.g-r/this.a;return i=Kg(o,this.e0,this.e1,this.e2,this.e3),s=Ce(this.long0+n/this.ns),e.x=s,e.y=i,e}var wgt=[\"Equidistant_Conic\",\"eqdc\"],qY={init:vgt,forward:xgt,inverse:bgt,names:wgt};function Sgt(){this.R=this.a}function Tgt(e){var t=e.x,r=e.y,i=Ce(t-this.long0),s,n;Math.abs(r)<=Se&&(s=this.x0+this.R*i,n=this.y0);var o=Ec(2*Math.abs(r/Math.PI));(Math.abs(i)<=Se||Math.abs(Math.abs(r)-de)<=Se)&&(s=this.x0,r>=0?n=this.y0+Math.PI*this.R*Math.tan(.5*o):n=this.y0+Math.PI*this.R*-Math.tan(.5*o));var c=.5*Math.abs(Math.PI/i-i/Math.PI),f=c*c,_=Math.sin(o),w=Math.cos(o),I=w/(_+w-1),R=I*I,N=I*(2/_-1),j=N*N,Q=Math.PI*this.R*(c*(I-j)+Math.sqrt(f*(I-j)*(I-j)-(j+f)*(R-j)))/(j+f);i<0&&(Q=-Q),s=this.x0+Q;var et=f+I;return Q=Math.PI*this.R*(N*et-c*Math.sqrt((j+f)*(f+1)-et*et))/(j+f),r>=0?n=this.y0+Q:n=this.y0-Q,e.x=s,e.y=n,e}function Mgt(e){var t,r,i,s,n,o,c,f,_,w,I,R,N;return e.x-=this.x0,e.y-=this.y0,I=Math.PI*this.R,i=e.x/I,s=e.y/I,n=i*i+s*s,o=-Math.abs(s)*(1+n),c=o-2*s*s+i*i,f=-2*o+1+2*s*s+n*n,N=s*s/f+(2*c*c*c/f/f/f-9*o*c/f/f)/27,_=(o-c*c/3/f)/f,w=2*Math.sqrt(-_/3),I=3*N/_/w,Math.abs(I)>1&&(I>=0?I=1:I=-1),R=Math.acos(I)/3,e.y>=0?r=(-w*Math.cos(R+Math.PI/3)-c/3/f)*Math.PI:r=-(-w*Math.cos(R+Math.PI/3)-c/3/f)*Math.PI,Math.abs(i)2*de*this.a?void 0:(r=t/this.a,i=Math.sin(r),s=Math.cos(r),n=this.long0,Math.abs(t)<=Se?o=this.lat0:(o=Ec(s*this.sin_p12+e.y*i*this.cos_p12/t),c=Math.abs(this.lat0)-de,Math.abs(c)<=Se?this.lat0>=0?n=Ce(this.long0+Math.atan2(e.x,-e.y)):n=Ce(this.long0-Math.atan2(-e.x,e.y)):n=Ce(this.long0+Math.atan2(e.x*i,t*this.cos_p12*s-e.y*this.sin_p12*i))),e.x=n,e.y=o,e)):(f=kp(this.es),_=Rp(this.es),w=Dp(this.es),I=Op(this.es),Math.abs(this.sin_p12-1)<=Se?(R=this.a*zo(f,_,w,I,de),t=Math.sqrt(e.x*e.x+e.y*e.y),N=R-t,o=Kg(N/this.a,f,_,w,I),n=Ce(this.long0+Math.atan2(e.x,-1*e.y)),e.x=n,e.y=o,e):Math.abs(this.sin_p12+1)<=Se?(R=this.a*zo(f,_,w,I,de),t=Math.sqrt(e.x*e.x+e.y*e.y),N=t-R,o=Kg(N/this.a,f,_,w,I),n=Ce(this.long0+Math.atan2(e.x,e.y)),e.x=n,e.y=o,e):(t=Math.sqrt(e.x*e.x+e.y*e.y),et=Math.atan2(e.x,e.y),j=Bp(this.a,this.e,this.sin_p12),Y=Math.cos(et),K=this.e*this.cos_p12*Y,J=-K*K/(1-this.es),ut=3*this.es*(1-J)*this.sin_p12*this.cos_p12*Y/(1-this.es),Et=t/j,kt=Et-J*(1+J)*Math.pow(Et,3)/6-ut*(1+3*J)*Math.pow(Et,4)/24,Xt=1-J*kt*kt/2-Et*kt*kt*kt/6,Q=Math.asin(this.sin_p12*Math.cos(kt)+this.cos_p12*Math.sin(kt)*Y),n=Ce(this.long0+Math.asin(Math.sin(et)*Math.sin(kt)/Math.cos(Q))),qt=Math.sin(Q),o=Math.atan2((qt-this.es*Xt*this.sin_p12)*Math.tan(Q),qt*(1-this.es)),e.x=n,e.y=o,e))}var Lgt=[\"Azimuthal_Equidistant\",\"aeqd\"],YY={init:Pgt,forward:Igt,inverse:Cgt,names:Lgt};function kgt(){this.sin_p14=Math.sin(this.lat0),this.cos_p14=Math.cos(this.lat0)}function Rgt(e){var t,r,i,s,n,o,c,f,_=e.x,w=e.y;return i=Ce(_-this.long0),t=Math.sin(w),r=Math.cos(w),s=Math.cos(i),o=this.sin_p14*t+this.cos_p14*r*s,n=1,(o>0||Math.abs(o)<=Se)&&(c=this.a*n*r*Math.sin(i),f=this.y0+this.a*n*(this.cos_p14*t-this.sin_p14*r*s)),e.x=c,e.y=f,e}function Dgt(e){var t,r,i,s,n,o,c;return e.x-=this.x0,e.y-=this.y0,t=Math.sqrt(e.x*e.x+e.y*e.y),r=Ec(t/this.a),i=Math.sin(r),s=Math.cos(r),o=this.long0,Math.abs(t)<=Se?(c=this.lat0,e.x=o,e.y=c,e):(c=Ec(s*this.sin_p14+e.y*i*this.cos_p14/t),n=Math.abs(this.lat0)-de,Math.abs(n)<=Se?(this.lat0>=0?o=Ce(this.long0+Math.atan2(e.x,-e.y)):o=Ce(this.long0-Math.atan2(-e.x,e.y)),e.x=o,e.y=c,e):(o=Ce(this.long0+Math.atan2(e.x*i,t*this.cos_p14*s-e.y*this.sin_p14*i)),e.x=o,e.y=c,e))}var Ogt=[\"ortho\"],QY={init:kgt,forward:Rgt,inverse:Dgt,names:Ogt};var bs={FRONT:1,RIGHT:2,BACK:3,LEFT:4,TOP:5,BOTTOM:6},An={AREA_0:1,AREA_1:2,AREA_2:3,AREA_3:4};function Bgt(){this.x0=this.x0||0,this.y0=this.y0||0,this.lat0=this.lat0||0,this.long0=this.long0||0,this.lat_ts=this.lat_ts||0,this.title=this.title||\"Quadrilateralized Spherical Cube\",this.lat0>=de-Ui/2?this.face=bs.TOP:this.lat0<=-(de-Ui/2)?this.face=bs.BOTTOM:Math.abs(this.long0)<=Ui?this.face=bs.FRONT:Math.abs(this.long0)<=de+Ui?this.face=this.long0>0?bs.RIGHT:bs.LEFT:this.face=bs.BACK,this.es!==0&&(this.one_minus_f=1-(this.a-this.b)/this.a,this.one_minus_f_squared=this.one_minus_f*this.one_minus_f)}function Fgt(e){var t={x:0,y:0},r,i,s,n,o,c,f={value:0};if(e.x-=this.long0,this.es!==0?r=Math.atan(this.one_minus_f_squared*Math.tan(e.y)):r=e.y,i=e.x,this.face===bs.TOP)n=de-r,i>=Ui&&i<=de+Ui?(f.value=An.AREA_0,s=i-de):i>de+Ui||i<=-(de+Ui)?(f.value=An.AREA_1,s=i>0?i-xs:i+xs):i>-(de+Ui)&&i<=-Ui?(f.value=An.AREA_2,s=i+de):(f.value=An.AREA_3,s=i);else if(this.face===bs.BOTTOM)n=de+r,i>=Ui&&i<=de+Ui?(f.value=An.AREA_0,s=-i+de):i=-Ui?(f.value=An.AREA_1,s=-i):i<-Ui&&i>=-(de+Ui)?(f.value=An.AREA_2,s=-i-de):(f.value=An.AREA_3,s=i>0?-i+xs:-i-xs);else{var _,w,I,R,N,j,Q;this.face===bs.RIGHT?i=Ax(i,+de):this.face===bs.BACK?i=Ax(i,+xs):this.face===bs.LEFT&&(i=Ax(i,-de)),R=Math.sin(r),N=Math.cos(r),j=Math.sin(i),Q=Math.cos(i),_=N*Q,w=N*j,I=R,this.face===bs.FRONT?(n=Math.acos(_),s=gI(n,I,w,f)):this.face===bs.RIGHT?(n=Math.acos(w),s=gI(n,I,-_,f)):this.face===bs.BACK?(n=Math.acos(-_),s=gI(n,I,-w,f)):this.face===bs.LEFT?(n=Math.acos(-w),s=gI(n,I,_,f)):(n=s=0,f.value=An.AREA_0)}return c=Math.atan(12/xs*(s+Math.acos(Math.sin(s)*Math.cos(Ui))-de)),o=Math.sqrt((1-Math.cos(n))/(Math.cos(c)*Math.cos(c))/(1-Math.cos(Math.atan(1/Math.cos(s))))),f.value===An.AREA_1?c+=de:f.value===An.AREA_2?c+=xs:f.value===An.AREA_3&&(c+=1.5*xs),t.x=o*Math.cos(c),t.y=o*Math.sin(c),t.x=t.x*this.a+this.x0,t.y=t.y*this.a+this.y0,e.x=t.x,e.y=t.y,e}function zgt(e){var t={lam:0,phi:0},r,i,s,n,o,c,f,_,w,I={value:0};if(e.x=(e.x-this.x0)/this.a,e.y=(e.y-this.y0)/this.a,i=Math.atan(Math.sqrt(e.x*e.x+e.y*e.y)),r=Math.atan2(e.y,e.x),e.x>=0&&e.x>=Math.abs(e.y)?I.value=An.AREA_0:e.y>=0&&e.y>=Math.abs(e.x)?(I.value=An.AREA_1,r-=de):e.x<0&&-e.x>=Math.abs(e.y)?(I.value=An.AREA_2,r=r<0?r+xs:r-xs):(I.value=An.AREA_3,r+=de),w=xs/12*Math.tan(r),o=Math.sin(w)/(Math.cos(w)-1/Math.sqrt(2)),c=Math.atan(o),s=Math.cos(r),n=Math.tan(i),f=1-s*s*n*n*(1-Math.cos(Math.atan(1/Math.cos(c)))),f<-1?f=-1:f>1&&(f=1),this.face===bs.TOP)_=Math.acos(f),t.phi=de-_,I.value===An.AREA_0?t.lam=c+de:I.value===An.AREA_1?t.lam=c<0?c+xs:c-xs:I.value===An.AREA_2?t.lam=c-de:t.lam=c;else if(this.face===bs.BOTTOM)_=Math.acos(f),t.phi=_-de,I.value===An.AREA_0?t.lam=-c+de:I.value===An.AREA_1?t.lam=-c:I.value===An.AREA_2?t.lam=-c-de:t.lam=c<0?-c-xs:-c+xs;else{var R,N,j;R=f,w=R*R,w>=1?j=0:j=Math.sqrt(1-w)*Math.sin(c),w+=j*j,w>=1?N=0:N=Math.sqrt(1-w),I.value===An.AREA_1?(w=N,N=-j,j=w):I.value===An.AREA_2?(N=-N,j=-j):I.value===An.AREA_3&&(w=N,N=j,j=-w),this.face===bs.RIGHT?(w=R,R=-N,N=w):this.face===bs.BACK?(R=-R,N=-N):this.face===bs.LEFT&&(w=R,R=N,N=-w),t.phi=Math.acos(-j)-de,t.lam=Math.atan2(N,R),this.face===bs.RIGHT?t.lam=Ax(t.lam,-de):this.face===bs.BACK?t.lam=Ax(t.lam,-xs):this.face===bs.LEFT&&(t.lam=Ax(t.lam,+de))}if(this.es!==0){var Q,et,Y;Q=t.phi<0?1:0,et=Math.tan(t.phi),Y=this.b/Math.sqrt(et*et+this.one_minus_f_squared),t.phi=Math.atan(Math.sqrt(this.a*this.a-Y*Y)/(this.one_minus_f*Y)),Q&&(t.phi=-t.phi)}return t.lam+=this.long0,e.x=t.lam,e.y=t.phi,e}function gI(e,t,r,i){var s;return eUi&&s<=de+Ui?(i.value=An.AREA_1,s-=de):s>de+Ui||s<=-(de+Ui)?(i.value=An.AREA_2,s=s>=0?s-xs:s+xs):(i.value=An.AREA_3,s+=de)),s}function Ax(e,t){var r=e+t;return r<-xs?r+=Em:r>+xs&&(r-=Em),r}var Ngt=[\"Quadrilateralized Spherical Cube\",\"Quadrilateralized_Spherical_Cube\",\"qsc\"],$Y={init:Bgt,forward:Fgt,inverse:zgt,names:Ngt};var DB=[[1,22199e-21,-715515e-10,31103e-10],[.9986,-482243e-9,-24897e-9,-13309e-10],[.9954,-83103e-8,-448605e-10,-986701e-12],[.99,-.00135364,-59661e-9,36777e-10],[.9822,-.00167442,-449547e-11,-572411e-11],[.973,-.00214868,-903571e-10,18736e-12],[.96,-.00305085,-900761e-10,164917e-11],[.9427,-.00382792,-653386e-10,-26154e-10],[.9216,-.00467746,-10457e-8,481243e-11],[.8962,-.00536223,-323831e-10,-543432e-11],[.8679,-.00609363,-113898e-9,332484e-11],[.835,-.00698325,-640253e-10,934959e-12],[.7986,-.00755338,-500009e-10,935324e-12],[.7597,-.00798324,-35971e-9,-227626e-11],[.7186,-.00851367,-701149e-10,-86303e-10],[.6732,-.00986209,-199569e-9,191974e-10],[.6213,-.010418,883923e-10,624051e-11],[.5722,-.00906601,182e-6,624051e-11],[.5322,-.00677797,275608e-9,624051e-11]],gS=[[-520417e-23,.0124,121431e-23,-845284e-16],[.062,.0124,-126793e-14,422642e-15],[.124,.0124,507171e-14,-160604e-14],[.186,.0123999,-190189e-13,600152e-14],[.248,.0124002,710039e-13,-224e-10],[.31,.0123992,-264997e-12,835986e-13],[.372,.0124029,988983e-12,-311994e-12],[.434,.0123893,-369093e-11,-435621e-12],[.4958,.0123198,-102252e-10,-345523e-12],[.5571,.0121916,-154081e-10,-582288e-12],[.6176,.0119938,-241424e-10,-525327e-12],[.6769,.011713,-320223e-10,-516405e-12],[.7346,.0113541,-397684e-10,-609052e-12],[.7903,.0109107,-489042e-10,-104739e-11],[.8435,.0103431,-64615e-9,-140374e-14],[.8936,.00969686,-64636e-9,-8547e-9],[.9394,.00840947,-192841e-9,-42106e-10],[.9761,.00616527,-256e-6,-42106e-10],[1,.00328947,-319159e-9,-42106e-10]],XY=.8487,KY=1.3523,JY=Sc/5,Ugt=1/JY,mx=18,_I=function(e,t){return e[0]+t*(e[1]+t*(e[2]+t*e[3]))},Vgt=function(e,t){return e[1]+t*(2*e[2]+t*3*e[3])};function jgt(e,t,r,i){for(var s=t;i;--i){var n=e(s);if(s-=n,Math.abs(n)=mx&&(i=mx-1),r=Sc*(r-Ugt*i);var s={x:_I(DB[i],r)*t,y:_I(gS[i],r)};return e.y<0&&(s.y=-s.y),s.x=s.x*this.a*XY+this.x0,s.y=s.y*this.a*KY+this.y0,s}function Hgt(e){var t={x:(e.x-this.x0)/(this.a*XY),y:Math.abs(e.y-this.y0)/(this.a*KY)};if(t.y>=1)t.x/=DB[mx][0],t.y=e.y<0?-de:de;else{var r=Math.floor(t.y*mx);for(r<0?r=0:r>=mx&&(r=mx-1);;)if(gS[r][0]>t.y)--r;else if(gS[r+1][0]<=t.y)++r;else break;var i=gS[r],s=5*(t.y-i[0])/(gS[r+1][0]-i[0]);s=jgt(function(n){return(_I(i,n)-t.y)/Vgt(i,n)},s,Se,100),t.x/=_I(DB[r],s),t.y=(5*r+s)*vs,e.y<0&&(t.y=-t.y)}return t.x=Ce(t.x+this.long0),t}var qgt=[\"Robinson\",\"robin\"],tQ={init:Ggt,forward:Wgt,inverse:Hgt,names:qgt};function Zgt(){this.name=\"geocent\"}function Ygt(e){var t=aI(e,this.es,this.a);return t}function Qgt(e){var t=lI(e,this.es,this.a,this.b);return t}var $gt=[\"Geocentric\",\"geocentric\",\"geocent\",\"Geocent\"],eQ={init:Zgt,forward:Ygt,inverse:Qgt,names:$gt};var al={N_POLE:0,S_POLE:1,EQUIT:2,OBLIQ:3},_S={h:{def:1e5,num:!0},azi:{def:0,num:!0,degrees:!0},tilt:{def:0,num:!0,degrees:!0},long0:{def:0,num:!0},lat0:{def:0,num:!0}};function Xgt(){if(Object.keys(_S).forEach(function(r){if(typeof this[r]>\"u\")this[r]=_S[r].def;else{if(_S[r].num&&isNaN(this[r]))throw new Error(\"Invalid parameter value, must be numeric \"+r+\" = \"+this[r]);_S[r].num&&(this[r]=parseFloat(this[r]))}_S[r].degrees&&(this[r]=this[r]*vs)}.bind(this)),Math.abs(Math.abs(this.lat0)-de)1e10)throw new Error(\"Invalid height\");this.p=1+this.pn1,this.rp=1/this.p,this.h1=1/this.pn1,this.pfact=(this.p+1)*this.h1,this.es=0;var e=this.tilt,t=this.azi;this.cg=Math.cos(t),this.sg=Math.sin(t),this.cw=Math.cos(e),this.sw=Math.sin(e)}function Kgt(e){e.x-=this.long0;var t=Math.sin(e.y),r=Math.cos(e.y),i=Math.cos(e.x),s,n;switch(this.mode){case al.OBLIQ:n=this.sinph0*t+this.cosph0*r*i;break;case al.EQUIT:n=r*i;break;case al.S_POLE:n=-t;break;case al.N_POLE:n=t;break}switch(n=this.pn1/(this.p-n),s=n*r*Math.sin(e.x),this.mode){case al.OBLIQ:n*=this.cosph0*t-this.sinph0*r*i;break;case al.EQUIT:n*=t;break;case al.N_POLE:n*=-(r*i);break;case al.S_POLE:n*=r*i;break}var o,c;return o=n*this.cg+s*this.sg,c=1/(o*this.sw*this.h1+this.cw),s=(s*this.cg-n*this.sg)*this.cw*c,n=o*c,e.x=s*this.a,e.y=n*this.a,e}function Jgt(e){e.x/=this.a,e.y/=this.a;var t={x:e.x,y:e.y},r,i,s;s=1/(this.pn1-e.y*this.sw),r=this.pn1*e.x*s,i=this.pn1*e.y*this.cw*s,e.x=r*this.cg+i*this.sg,e.y=i*this.cg-r*this.sg;var n=Ta(e.x,e.y);if(Math.abs(n)1e10)throw new Error;if(this.radius_g=1+this.radius_g_1,this.C=this.radius_g*this.radius_g-1,this.es!==0){var e=1-this.es,t=1/e;this.radius_p=Math.sqrt(e),this.radius_p2=e,this.radius_p_inv2=t,this.shape=\"ellipse\"}else this.radius_p=1,this.radius_p2=1,this.radius_p_inv2=1,this.shape=\"sphere\";this.title||(this.title=\"Geostationary Satellite View\")}function r_t(e){var t=e.x,r=e.y,i,s,n,o;if(t=t-this.long0,this.shape===\"ellipse\"){r=Math.atan(this.radius_p2*Math.tan(r));var c=this.radius_p/Ta(this.radius_p*Math.cos(r),Math.sin(r));if(s=c*Math.cos(t)*Math.cos(r),n=c*Math.sin(t)*Math.cos(r),o=c*Math.sin(r),(this.radius_g-s)*s-n*n-o*o*this.radius_p_inv2<0)return e.x=Number.NaN,e.y=Number.NaN,e;i=this.radius_g-s,this.flip_axis?(e.x=this.radius_g_1*Math.atan(n/Ta(o,i)),e.y=this.radius_g_1*Math.atan(o/i)):(e.x=this.radius_g_1*Math.atan(n/i),e.y=this.radius_g_1*Math.atan(o/Ta(n,i)))}else this.shape===\"sphere\"&&(i=Math.cos(r),s=Math.cos(t)*i,n=Math.sin(t)*i,o=Math.sin(r),i=this.radius_g-s,this.flip_axis?(e.x=this.radius_g_1*Math.atan(n/Ta(o,i)),e.y=this.radius_g_1*Math.atan(o/i)):(e.x=this.radius_g_1*Math.atan(n/i),e.y=this.radius_g_1*Math.atan(o/Ta(n,i))));return e.x=e.x*this.a,e.y=e.y*this.a,e}function i_t(e){var t=-1,r=0,i=0,s,n,o,c;if(e.x=e.x/this.a,e.y=e.y/this.a,this.shape===\"ellipse\"){this.flip_axis?(i=Math.tan(e.y/this.radius_g_1),r=Math.tan(e.x/this.radius_g_1)*Ta(1,i)):(r=Math.tan(e.x/this.radius_g_1),i=Math.tan(e.y/this.radius_g_1)*Ta(1,r));var f=i/this.radius_p;if(s=r*r+f*f+t*t,n=2*this.radius_g*t,o=n*n-4*s*this.C,o<0)return e.x=Number.NaN,e.y=Number.NaN,e;c=(-n-Math.sqrt(o))/(2*s),t=this.radius_g+c*t,r*=c,i*=c,e.x=Math.atan2(r,t),e.y=Math.atan(i*Math.cos(e.x)/t),e.y=Math.atan(this.radius_p_inv2*Math.tan(e.y))}else if(this.shape===\"sphere\"){if(this.flip_axis?(i=Math.tan(e.y/this.radius_g_1),r=Math.tan(e.x/this.radius_g_1)*Math.sqrt(1+i*i)):(r=Math.tan(e.x/this.radius_g_1),i=Math.tan(e.y/this.radius_g_1)*Math.sqrt(1+r*r)),s=r*r+i*i+t*t,n=2*this.radius_g*t,o=n*n-4*s*this.C,o<0)return e.x=Number.NaN,e.y=Number.NaN,e;c=(-n-Math.sqrt(o))/(2*s),t=this.radius_g+c*t,r*=c,i*=c,e.x=Math.atan2(r,t),e.y=Math.atan(i*Math.cos(e.x)/t)}return e.x=e.x+this.long0,e}var n_t=[\"Geostationary Satellite View\",\"Geostationary_Satellite\",\"geos\"],iQ={init:e_t,forward:r_t,inverse:i_t,names:n_t};function nQ(e){e.Proj.projections.add(fx),e.Proj.projections.add(dx),e.Proj.projections.add(MY),e.Proj.projections.add(EY),e.Proj.projections.add(PY),e.Proj.projections.add(IY),e.Proj.projections.add(CY),e.Proj.projections.add(LY),e.Proj.projections.add(kY),e.Proj.projections.add(RY),e.Proj.projections.add(DY),e.Proj.projections.add(OY),e.Proj.projections.add(BY),e.Proj.projections.add(zY),e.Proj.projections.add(NY),e.Proj.projections.add(VY),e.Proj.projections.add(jY),e.Proj.projections.add(GY),e.Proj.projections.add(WY),e.Proj.projections.add(HY),e.Proj.projections.add(qY),e.Proj.projections.add(ZY),e.Proj.projections.add(YY),e.Proj.projections.add(QY),e.Proj.projections.add($Y),e.Proj.projections.add(tQ),e.Proj.projections.add(eQ),e.Proj.projections.add(rQ),e.Proj.projections.add(iQ)}Tc.defaultDatum=\"WGS84\";Tc.Proj=Pm;Tc.WGS84=new 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bI=class{minX;minY;maxX;maxY;constructor(){this.minX=1/0,this.minY=1/0,this.maxX=-1/0,this.maxY=-1/0}updateBbox(t){t.minXthis.maxX&&(this.maxX=t.maxX),t.maxY>this.maxY&&(this.maxY=t.maxY)}updateCoord(t,r){tthis.maxX&&(this.maxX=t),r>this.maxY&&(this.maxY=r)}};function __t(e,t){switch(t.metadata.get(\"ARROW:extension:name\")){case Im.POINT:return gQ(e);case Im.LINESTRING:case Im.MULTIPOINT:return _Q(e);case Im.POLYGON:case Im.MULTILINESTRING:return yQ(e);case Im.MULTIPOLYGON:return v_t(e);default:throw new Error(\"Unknown ext type name\")}}function y_t(e){let r=xS(e).values,i=new bI;for(let s=0;svQ(r)));let t=new mm({type:new dc,nullValues:[null]});t.set(e.length-1,null);for(let r=0;rxQ(r,t));return}for(let r=0;rvS(n,t)));let r=[];for(let n of e.children)r.push(vS(n,t));let i;e.dictionary!==void 0&&(i=vS(e.dictionary,t));let s={[Oi.OFFSET]:yI(e.buffers[Oi.OFFSET],t),[Oi.DATA]:yI(e.buffers[Oi.DATA],t),[Oi.VALIDITY]:yI(e.buffers[Oi.VALIDITY],t),[Oi.TYPE]:yI(e.buffers[Oi.TYPE],t)};return new Fi(e.type,e.offset,e.length,e._nullCount,s,r,i)}function vI(e){if(\"data\"in e)return e.data.some(r=>vI(r));for(let r of e.children)if(vI(r))return!0;if(e.dictionary!==void 0&&vI(e.dictionary))return!0;let t=[Oi.OFFSET,Oi.DATA,Oi.VALIDITY,Oi.TYPE];for(let r of t)if(e.buffers[r]!==void 0&&bQ(e.buffers[r]))return!0;return!1}function bQ(e){return!(e.byteOffset===0&&e.byteLength===e.buffer.byteLength)}function yI(e,t){return e===void 0||!t&&!bQ(e)?e:e.slice()}function xI(e,t=!1){if(\"data\"in e){let i=[],s=[];for(let o of e.data){let[c,f]=xI(o);i.push(c),s.push(...f)}return[new xr(i),s]}e=vS(e,t);let r=[];for(let i=0;i1)throw new Error(\"expected 1 field\");return new sl(t[0])}case Ot.Struct:{let t=e.children.map(yS);return new pn(t)}case Ot.Union:{let t=e.children.map(yS);return new pc(e.mode,e.typeIds,t)}case Ot.FixedSizeBinary:return new Qu(e.byteWidth);case Ot.FixedSizeList:{let t=e.children.map(yS);if(t.length>1)throw new Error(\"expected 1 field\");return new Ll(e.listSize,t[0])}case Ot.Map:{let t=e.children.map(yS);if(t.length>1)throw new Error(\"expected 1 field\");let r=t[0];return new Ac(r,e.keysSorted)}case Ot.Duration:return new Yu(e.unit);default:throw new Error(`unknown type ${e}`)}}function yS(e){let t=wQ(e.type);return new si(e.name,t,e.nullable,e.metadata)}function UB(e){let t=e.children.map(s=>UB(s)),r=e.dictionary?SQ(e.dictionary):void 0,i={[Oi.OFFSET]:e.valueOffsets,[Oi.DATA]:e.values,[Oi.VALIDITY]:e.nullBitmap,[Oi.TYPE]:e.typeIds};return new Fi(wQ(e.type),e.offset,e.length,e._nullCount,i,t,r)}function SQ(e){return new xr(e.data.map(t=>UB(t)))}var VB=Object.freeze({__proto__:null,hardClone:vS,isShared:vI,preparePostMessage:xI,rehydrateData:UB,rehydrateVector:SQ});function E_t(e,t,r){let i=e.fields.findIndex(s=>s.name===r||s.metadata.get(\"ARROW:extension:name\")===t);return i!==-1?i:null}function P_t(e,t){let{index:r,data:i}=e,s=r;i.invertedGeomOffsets!==void 0&&(s=i.invertedGeomOffsets[r]);let n={data:i.data,length:i.length,attributes:i.attributes},o={index:s,data:n,target:e.target};return t(o)}function ro(e){let{props:t,propName:r,propInput:i,chunkIdx:s,geomCoordOffsets:n}=e;if(i!==void 0)if(i instanceof xr){let o=i.data[s];if(ze.isFixedSizeList(o)){_r(o.children.length===1);let c=o.children[0].values;n&&(c=EI(c,o.type.listSize,n)),t.data.attributes[r]={value:c,size:o.type.listSize,normalized:!0}}else if(ze.isFloat(o)){let c=o.values;n&&(c=EI(c,1,n)),t.data.attributes[r]={value:c,size:1}}}else typeof i==\"function\"?t[r]=(o,c)=>r===\"getPolygonOffset\"?i(o,c):P_t(c,i):t[r]=i}function EI(e,t,r){let i=r[r.length-1],s=new e.constructor(i*t);for(let n=0;n(t[i+1]=t[i]+r.length,t),new Uint32Array(e.length+1))}function no(e,t){let r=[],i=[];for(let[s,n]of Object.entries(e))s.startsWith(\"get\")&&n instanceof xr&&(r.push(n),s.endsWith(\"Color\")&&i.push(n));I_t(t,r);for(let s of i)C_t(s)}function I_t(e,t){for(let r of t)_r(e.batches.length===r.data.length);for(let r of t)for(let i=0;ithis.data):this.content}get isLoaded(){return this._isLoaded&&!this._needsReload}get isLoading(){return!!this._loader&&!this._isCancelled}get needsReload(){return this._needsReload||this._isCancelled}get byteLength(){let t=this.content?this.content.byteLength:0;return Number.isFinite(t)||console.error(\"byteLength not defined in tile data\"),t}async _loadData({getData:t,requestScheduler:r,onLoad:i,onError:s}){let{index:n,id:o,bbox:c,userData:f,zoom:_}=this,w=this._loaderId;this._abortController=new AbortController;let{signal:I}=this._abortController,R=await r.scheduleRequest(this,Q=>Q.isSelected?1:-1);if(!R){this._isCancelled=!0;return}if(this._isCancelled){R.done();return}let N=null,j;try{N=await t({index:n,id:o,bbox:c,userData:f,zoom:_,signal:I})}catch(Q){j=Q||!0}finally{R.done()}if(w===this._loaderId){if(this._loader=void 0,this.content=N,this._isCancelled&&!N){this._isLoaded=!1;return}this._isLoaded=!0,this._isCancelled=!1,j?s(j,this):i(this)}}loadData(t){return this._isLoaded=!1,this._isCancelled=!1,this._needsReload=!1,this._loaderId++,this._loader=this._loadData(t),this._loader}setNeedsReload(){this.isLoading&&(this.abort(),this._loader=void 0),this._needsReload=!0}abort(){var t;this.isLoaded||(this._isCancelled=!0,(t=this._abortController)===null||t===void 0||t.abort())}};var so={OUTSIDE:-1,INTERSECTING:0,INSIDE:1};var IQ=new Ve,O_t=new Ve,Jg=class e{constructor(t=[0,0,0],r=[0,0,0],i){G(this,\"center\",void 0),G(this,\"halfDiagonal\",void 0),G(this,\"minimum\",void 0),G(this,\"maximum\",void 0),i=i||IQ.copy(t).add(r).scale(.5),this.center=new Ve(i),this.halfDiagonal=new Ve(r).subtract(this.center),this.minimum=new Ve(t),this.maximum=new Ve(r)}clone(){return new e(this.minimum,this.maximum,this.center)}equals(t){return this===t||!!t&&this.minimum.equals(t.minimum)&&this.maximum.equals(t.maximum)}transform(t){return this.center.transformAsPoint(t),this.halfDiagonal.transform(t),this.minimum.transform(t),this.maximum.transform(t),this}intersectPlane(t){let{halfDiagonal:r}=this,i=O_t.from(t.normal),s=r.x*Math.abs(i.x)+r.y*Math.abs(i.y)+r.z*Math.abs(i.z),n=this.center.dot(i)+t.distance;return n-s>0?so.INSIDE:n+s<0?so.OUTSIDE:so.INTERSECTING}distanceTo(t){return Math.sqrt(this.distanceSquaredTo(t))}distanceSquaredTo(t){let r=IQ.from(t).subtract(this.center),{halfDiagonal:i}=this,s=0,n;return n=Math.abs(r.x)-i.x,n>0&&(s+=n*n),n=Math.abs(r.y)-i.y,n>0&&(s+=n*n),n=Math.abs(r.z)-i.z,n>0&&(s+=n*n),s}};var TS=new Ve,CQ=new Ve,t_=class e{constructor(t=[0,0,0],r=0){G(this,\"center\",void 0),G(this,\"radius\",void 0),this.radius=-0,this.center=new Ve,this.fromCenterRadius(t,r)}fromCenterRadius(t,r){return this.center.from(t),this.radius=r,this}fromCornerPoints(t,r){return r=TS.from(r),this.center=new Ve().from(t).add(r).scale(.5),this.radius=this.center.distance(r),this}equals(t){return this===t||!!t&&this.center.equals(t.center)&&this.radius===t.radius}clone(){return new e(this.center,this.radius)}union(t){let r=this.center,i=this.radius,s=t.center,n=t.radius,o=TS.copy(s).subtract(r),c=o.magnitude();if(i>=c+n)return this.clone();if(n>=c+i)return t.clone();let f=(i+c+n)*.5;return CQ.copy(o).scale((-i+f)/c).add(r),this.center.copy(CQ),this.radius=f,this}expand(t){let i=TS.from(t).subtract(this.center).magnitude();return i>this.radius&&(this.radius=i),this}transform(t){this.center.transform(t);let r=c7(TS,t);return this.radius=Math.max(r[0],Math.max(r[1],r[2]))*this.radius,this}distanceSquaredTo(t){let r=this.distanceTo(t);return r*r}distanceTo(t){let i=TS.from(t).subtract(this.center);return Math.max(0,i.len()-this.radius)}intersectPlane(t){let r=this.center,i=this.radius,n=t.normal.dot(r)+t.distance;return n<-i?so.OUTSIDE:n=f?so.INSIDE:so.INTERSECTING}distanceTo(t){return Math.sqrt(this.distanceSquaredTo(t))}distanceSquaredTo(t){let r=F_t.from(t).subtract(this.center),i=this.halfAxes,s=i.getColumn(0,II),n=i.getColumn(1,CI),o=i.getColumn(2,LI),c=s.magnitude(),f=n.magnitude(),_=o.magnitude();s.normalize(),n.normalize(),o.normalize();let w=0,I;return I=Math.abs(r.dot(s))-c,I>0&&(w+=I*I),I=Math.abs(r.dot(n))-f,I>0&&(w+=I*I),I=Math.abs(r.dot(o))-_,I>0&&(w+=I*I),w}computePlaneDistances(t,r,i=[-0,-0]){let s=Number.POSITIVE_INFINITY,n=Number.NEGATIVE_INFINITY,o=this.center,c=this.halfAxes,f=c.getColumn(0,II),_=c.getColumn(1,CI),w=c.getColumn(2,LI),I=z_t.copy(f).add(_).add(w).add(o),R=N_t.copy(I).subtract(t),N=r.dot(R);return s=Math.min(N,s),n=Math.max(N,n),I.copy(o).add(f).add(_).subtract(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),I.copy(o).add(f).subtract(_).add(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),I.copy(o).add(f).subtract(_).subtract(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),o.copy(I).subtract(f).add(_).add(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),o.copy(I).subtract(f).add(_).subtract(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),o.copy(I).subtract(f).subtract(_).add(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),o.copy(I).subtract(f).subtract(_).subtract(w),R.copy(I).subtract(t),N=r.dot(R),s=Math.min(N,s),n=Math.max(N,n),i[0]=s,i[1]=n,i}transform(t){this.center.transformAsPoint(t);let r=this.halfAxes.getColumn(0,II);r.transformAsPoint(t);let i=this.halfAxes.getColumn(1,CI);i.transformAsPoint(t);let s=this.halfAxes.getColumn(2,LI);return s.transformAsPoint(t),this.halfAxes=new ss([...r,...i,...s]),this}getTransform(){throw new Error(\"not implemented\")}};var LQ=new Ve,kQ=new Ve,Af=class e{constructor(t=[0,0,1],r=0){G(this,\"normal\",void 0),G(this,\"distance\",void 0),this.normal=new Ve,this.distance=-0,this.fromNormalDistance(t,r)}fromNormalDistance(t,r){return Bh(Number.isFinite(r)),this.normal.from(t).normalize(),this.distance=r,this}fromPointNormal(t,r){t=LQ.from(t),this.normal.from(r).normalize();let i=-this.normal.dot(t);return this.distance=i,this}fromCoefficients(t,r,i,s){return this.normal.set(t,r,i),Bh(Ro(this.normal.len(),1)),this.distance=s,this}clone(){return new e(this.normal,this.distance)}equals(t){return Ro(this.distance,t.distance)&&Ro(this.normal,t.normal)}getPointDistance(t){return this.normal.dot(t)+this.distance}transform(t){let r=kQ.copy(this.normal).transformAsVector(t).normalize(),i=this.normal.scale(-this.distance).transform(t);return this.fromPointNormal(i,r)}projectPointOntoPlane(t,r=[0,0,0]){t=LQ.from(t);let i=this.getPointDistance(t),s=kQ.copy(this.normal).scale(i);return t.subtract(s).to(r)}};var RQ=[new Ve([1,0,0]),new Ve([0,1,0]),new Ve([0,0,1])],DQ=new Ve,U_t=new Ve,Rse=new Af(new Ve(1,0,0),0),Ad=class e{constructor(t=[]){G(this,\"planes\",void 0),this.planes=t}fromBoundingSphere(t){this.planes.length=2*RQ.length;let r=t.center,i=t.radius,s=0;for(let n of RQ){let o=this.planes[s],c=this.planes[s+1];o||(o=this.planes[s]=new Af),c||(c=this.planes[s+1]=new Af);let f=DQ.copy(n).scale(-i).add(r),_=-n.dot(f);o.fromPointNormal(f,n);let w=DQ.copy(n).scale(i).add(r),I=U_t.copy(n).negate(),R=-I.dot(w);c.fromPointNormal(w,I),s+=2}return this}computeVisibility(t){let r=so.INSIDE;for(let i of this.planes)switch(t.intersectPlane(i)){case so.OUTSIDE:return so.OUTSIDE;case so.INTERSECTING:r=so.INTERSECTING;break;default:}return r}computeVisibilityWithPlaneMask(t,r){if(Bh(Number.isFinite(r),\"parentPlaneMask is required.\"),r===e.MASK_OUTSIDE||r===e.MASK_INSIDE)return r;let i=e.MASK_INSIDE,s=this.planes;for(let n=0;nf;)q_t(c,kI),OQ.copy(kI).transpose(),c.multiplyRight(kI),c.multiplyLeft(OQ),o.multiplyRight(kI),++s>2&&(++n,s=0);return t.unitary=o.toTarget(t.unitary),t.diagonal=c.toTarget(t.diagonal),t}function W_t(e){let t=0;for(let r=0;r<9;++r){let i=e[r];t+=i*i}return Math.sqrt(t)}var GB=[1,0,0],WB=[2,2,1];function H_t(e){let t=0;for(let r=0;r<3;++r){let i=e[md.getElementIndex(WB[r],GB[r])];t+=2*i*i}return Math.sqrt(t)}function q_t(e,t){let r=YE.EPSILON15,i=0,s=1;for(let _=0;_<3;++_){let w=Math.abs(e[md.getElementIndex(WB[_],GB[_])]);w>i&&(s=_,i=w)}let n=GB[s],o=WB[s],c=1,f=0;if(Math.abs(e[md.getElementIndex(o,n)])>r){let _=e[md.getElementIndex(o,o)],w=e[md.getElementIndex(n,n)],I=e[md.getElementIndex(o,n)],R=(_-w)/2/I,N;R<0?N=-1/(-R+Math.sqrt(1+R*R)):N=1/(R+Math.sqrt(1+R*R)),c=1/Math.sqrt(1+N*N),f=N*c}return ss.IDENTITY.to(t),t[md.getElementIndex(n,n)]=t[md.getElementIndex(o,o)]=c,t[md.getElementIndex(o,n)]=f,t[md.getElementIndex(n,o)]=-f,t}var Cm=new Ve,Z_t=new Ve,Y_t=new Ve,Q_t=new Ve,$_t=new Ve,X_t=new ss,K_t={diagonal:new ss,unitary:new ss};function HB(e,t=new yx){if(!e||e.length===0)return t.halfAxes=new ss([0,0,0,0,0,0,0,0,0]),t.center=new Ve,t;let r=e.length,i=new Ve(0,0,0);for(let le of e)i.add(le);let s=1/r;i.multiplyByScalar(s);let n=0,o=0,c=0,f=0,_=0,w=0;for(let le of e){let ue=Cm.copy(le).subtract(i);n+=ue.x*ue.x,o+=ue.x*ue.y,c+=ue.x*ue.z,f+=ue.y*ue.y,_+=ue.y*ue.z,w+=ue.z*ue.z}n*=s,o*=s,c*=s,f*=s,_*=s,w*=s;let I=X_t;I[0]=n,I[1]=o,I[2]=c,I[3]=o,I[4]=f,I[5]=_,I[6]=c,I[7]=_,I[8]=w;let{unitary:R}=RI(I,K_t),N=t.halfAxes.copy(R),j=N.getColumn(0,Y_t),Q=N.getColumn(1,Q_t),et=N.getColumn(2,$_t),Y=-Number.MAX_VALUE,K=-Number.MAX_VALUE,J=-Number.MAX_VALUE,ut=Number.MAX_VALUE,Et=Number.MAX_VALUE,kt=Number.MAX_VALUE;for(let le of e)Cm.copy(le),Y=Math.max(Cm.dot(j),Y),K=Math.max(Cm.dot(Q),K),J=Math.max(Cm.dot(et),J),ut=Math.min(Cm.dot(j),ut),Et=Math.min(Cm.dot(Q),Et),kt=Math.min(Cm.dot(et),kt);j=j.multiplyByScalar(.5*(ut+Y)),Q=Q.multiplyByScalar(.5*(Et+K)),et=et.multiplyByScalar(.5*(kt+J)),t.center.copy(j).add(Q).add(et);let Xt=Z_t.set(Y-ut,K-Et,J-kt).multiplyByScalar(.5),qt=new ss([Xt[0],0,0,0,Xt[1],0,0,0,Xt[2]]);return t.halfAxes.multiplyRight(qt),t}var vx=512,BQ=3,FQ=[[.5,.5],[0,0],[0,1],[1,0],[1,1]],zQ=FQ.concat([[0,.5],[.5,0],[1,.5],[.5,1]]),J_t=zQ.concat([[.25,.5],[.75,.5]]),qB=class e{constructor(t,r,i){G(this,\"x\",void 0),G(this,\"y\",void 0),G(this,\"z\",void 0),G(this,\"childVisible\",void 0),G(this,\"selected\",void 0),G(this,\"_children\",void 0),this.x=t,this.y=r,this.z=i}get children(){if(!this._children){let t=this.x*2,r=this.y*2,i=this.z+1;this._children=[new e(t,r,i),new e(t,r+1,i),new e(t+1,r,i),new e(t+1,r+1,i)]}return this._children}update(t){let{viewport:r,cullingVolume:i,elevationBounds:s,minZ:n,maxZ:o,bounds:c,offset:f,project:_}=t,w=this.getBoundingVolume(s,f,_);if(c&&!this.insideBounds(c)||i.computeVisibility(w)<0)return!1;if(!this.childVisible){let{z:R}=this;if(R=n){let N=w.distanceTo(r.cameraPosition)*r.scale/r.height;R+=Math.floor(Math.log2(N))}if(R>=o)return this.selected=!0,!0}this.selected=!1,this.childVisible=!0;for(let R of this.children)R.update(t);return!0}getSelected(t=[]){if(this.selected&&t.push(this),this._children)for(let r of this._children)r.getSelected(t);return t}insideBounds([t,r,i,s]){let n=Math.pow(2,this.z),o=vx/n;return this.x*ot&&(this.y+1)*o>r}getBoundingVolume(t,r,i){if(i){let f=this.z<1?J_t:this.z<2?zQ:FQ,_=[];for(let w of f){let I=DI(this.x+w[0],this.y+w[1],this.z);I[2]=t[0],_.push(i(I)),t[0]!==t[1]&&(I[2]=t[1],_.push(i(I)))}return HB(_)}let s=Math.pow(2,this.z),n=vx/s,o=this.x*n+r*vx,c=vx-(this.y+1)*n;return new Jg([o,c,t[0]],[o+n,c+n,t[1]])}};function NQ(e,t,r,i){let s=e instanceof rv&&e.resolution?e.projectPosition:null,n=Object.values(e.getFrustumPlanes()).map(({normal:N,distance:j})=>new Af(N.clone().negate(),j)),o=new Ad(n),c=e.distanceScales.unitsPerMeter[2],f=r&&r[0]*c||0,_=r&&r[1]*c||0,w=e instanceof lc&&e.pitch<=60?t:0;if(i){let[N,j,Q,et]=i,Y=va([N,et]),K=va([Q,j]);i=[Y[0],vx-Y[1],K[0],vx-K[1]]}let I=new qB(0,0,0),R={viewport:e,project:s,cullingVolume:o,elevationBounds:[f,_],minZ:w,maxZ:t,bounds:i,offset:0};if(I.update(R),e instanceof lc&&e.subViewports&&e.subViewports.length>1){for(R.offset=-1;I.update(R)&&!(--R.offset<-BQ););for(R.offset=1;I.update(R)&&!(++R.offset>BQ););}return I.getSelected()}var zp=512,tyt=[-1/0,-1/0,1/0,1/0],YB={type:\"object\",value:null,validate:(e,t)=>t.optional&&e===null||typeof e==\"string\"||Array.isArray(e)&&e.every(r=>typeof r==\"string\"),equal:(e,t)=>{if(e===t)return!0;if(!Array.isArray(e)||!Array.isArray(t))return!1;let r=e.length;if(r!==t.length)return!1;for(let 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jQ({viewport:e,z:t=0,cullRect:r}){return(e.subViewports||[e]).map(s=>ZB(s,t,r))}function ZB(e,t,r){if(!Array.isArray(t)){let n=r.x-e.x,o=r.y-e.y,{width:c,height:f}=r,_={targetZ:t},w=e.unproject([n,o],_),I=e.unproject([n+c,o],_),R=e.unproject([n,o+f],_),N=e.unproject([n+c,o+f],_);return[Math.min(w[0],I[0],R[0],N[0]),Math.min(w[1],I[1],R[1],N[1]),Math.max(w[0],I[0],R[0],N[0]),Math.max(w[1],I[1],R[1],N[1])]}let i=ZB(e,t[0],r),s=ZB(e,t[1],r);return[Math.min(i[0],s[0]),Math.min(i[1],s[1]),Math.max(i[2],s[2]),Math.max(i[3],s[3])]}function iyt(e,t,r){return r?VQ(e,r).map(s=>s*t/zp):e.map(i=>i*t/zp)}function $B(e,t){return Math.pow(2,e)*zp/t}function DI(e,t,r){let i=$B(r,zp),s=e/i*360-180,n=Math.PI-2*Math.PI*t/i,o=180/Math.PI*Math.atan(.5*(Math.exp(n)-Math.exp(-n)));return[s,o]}function UQ(e,t,r,i){let s=$B(r,i);return[e/s*zp,t/s*zp]}function XB(e,t,r,i,s=zp){if(e.isGeospatial){let[_,w]=DI(t,r,i),[I,R]=DI(t+1,r+1,i);return{west:_,north:w,east:I,south:R}}let[n,o]=UQ(t,r,i,s),[c,f]=UQ(t+1,r+1,i,s);return{left:n,top:o,right:c,bottom:f}}function nyt(e,t,r,i,s){let n=ryt(e,null,i),o=$B(t,r),[c,f,_,w]=iyt(n,o,s),I=[];for(let R=Math.floor(c);R<_;R++)for(let N=Math.floor(f);Nt&&(_=t);let w=s;return o&&c&&s&&!e.isGeospatial&&(w=VQ(s,o)),e.isGeospatial?NQ(e,_,i,s):nyt(e,_,n,w||tyt,c)}function GQ(e){let t={},r;return i=>{for(let s in i)if(!syt(i[s],t[s])){r=e(i),t=i;break}return r}}function syt(e,t){if(e===t)return!0;if(Array.isArray(e)){let r=e.length;if(!t||t.length!==r)return!1;for(let i=0;i{}},uyt={extent:null,tileSize:512,maxZoom:null,minZoom:null,maxCacheSize:null,maxCacheByteSize:null,refinementStrategy:\"best-available\",zRange:null,maxRequests:6,zoomOffset:0,onTileLoad:()=>{},onTileUnload:()=>{},onTileError:()=>{}},MS=class{constructor(t){G(this,\"opts\",void 0),G(this,\"_requestScheduler\",void 0),G(this,\"_cache\",void 0),G(this,\"_dirty\",void 0),G(this,\"_tiles\",void 0),G(this,\"_cacheByteSize\",void 0),G(this,\"_viewport\",void 0),G(this,\"_zRange\",void 0),G(this,\"_selectedTiles\",void 0),G(this,\"_frameNumber\",void 0),G(this,\"_modelMatrix\",void 0),G(this,\"_modelMatrixInverse\",void 0),G(this,\"_maxZoom\",void 0),G(this,\"_minZoom\",void 0),G(this,\"onTileLoad\",void 0),G(this,\"_getCullBounds\",GQ(jQ)),this.opts={...uyt,...t},this.onTileLoad=r=>{var i,s;(i=(s=this.opts).onTileLoad)===null||i===void 0||i.call(s,r),this.opts.maxCacheByteSize&&(this._cacheByteSize+=r.byteLength,this._resizeCache())},this._requestScheduler=new py({maxRequests:t.maxRequests,throttleRequests:!!(t.maxRequests&&t.maxRequests>0)}),this._cache=new Map,this._tiles=[],this._dirty=!1,this._cacheByteSize=0,this._viewport=null,this._selectedTiles=null,this._frameNumber=0,this._modelMatrix=new En,this._modelMatrixInverse=new En,this.setOptions(t)}get tiles(){return this._tiles}get selectedTiles(){return this._selectedTiles}get isLoaded(){return this._selectedTiles!==null&&this._selectedTiles.every(t=>t.isLoaded)}get needsReload(){return this._selectedTiles!==null&&this._selectedTiles.some(t=>t.needsReload)}setOptions(t){Object.assign(this.opts,t),Number.isFinite(t.maxZoom)&&(this._maxZoom=Math.floor(t.maxZoom)),Number.isFinite(t.minZoom)&&(this._minZoom=Math.ceil(t.minZoom))}finalize(){for(let t of this._cache.values())t.isLoading&&t.abort();this._cache.clear(),this._tiles=[],this._selectedTiles=null}reloadAll(){for(let t of this._cache.keys()){let r=this._cache.get(t);!this._selectedTiles||!this._selectedTiles.includes(r)?this._cache.delete(t):r.setNeedsReload()}}update(t,{zRange:r,modelMatrix:i}={}){let s=new En(i),n=!s.equals(this._modelMatrix);if(!this._viewport||!t.equals(this._viewport)||!Ro(this._zRange,r)||n){n&&(this._modelMatrixInverse=s.clone().invert(),this._modelMatrix=s),this._viewport=t,this._zRange=r;let c=this.getTileIndices({viewport:t,maxZoom:this._maxZoom,minZoom:this._minZoom,zRange:r,modelMatrix:this._modelMatrix,modelMatrixInverse:this._modelMatrixInverse});this._selectedTiles=c.map(f=>this._getTile(f,!0)),this._dirty&&this._rebuildTree()}else this.needsReload&&(this._selectedTiles=this._selectedTiles.map(c=>this._getTile(c.index,!0)));let o=this.updateTileStates();return this._pruneRequests(),this._dirty&&this._resizeCache(),o&&this._frameNumber++,this._frameNumber}isTileVisible(t,r){if(!t.isVisible)return!1;if(r&&this._viewport){let i=this._getCullBounds({viewport:this._viewport,z:this._zRange,cullRect:r}),{bbox:s}=t;for(let[n,o,c,f]of i){let _;if(\"west\"in s)_=s.westn&&s.southo;else{let w=Math.min(s.top,s.bottom),I=Math.max(s.top,s.bottom);_=s.leftn&&wo}if(_)return!0}return!1}return!0}getTileIndices({viewport:t,maxZoom:r,minZoom:i,zRange:s,modelMatrix:n,modelMatrixInverse:o}){let{tileSize:c,extent:f,zoomOffset:_}=this.opts;return KB({viewport:t,maxZoom:r,minZoom:i,zRange:s,tileSize:c,extent:f,modelMatrix:n,modelMatrixInverse:o,zoomOffset:_})}getTileId(t){return\"\".concat(t.x,\"-\").concat(t.y,\"-\").concat(t.z)}getTileZoom(t){return t.z}getTileMetadata(t){let{tileSize:r}=this.opts;return{bbox:XB(this._viewport,t.x,t.y,t.z,r)}}getParentIndex(t){let r=Math.floor(t.x/2),i=Math.floor(t.y/2),s=t.z-1;return{x:r,y:i,z:s}}updateTileStates(){let t=this.opts.refinementStrategy||ES,r=new Array(this._cache.size),i=0;for(let s of this._cache.values())r[i++]=s.isVisible,s.isSelected=!1,s.isVisible=!1;for(let s of this._selectedTiles)s.isSelected=!0,s.isVisible=!0;(typeof t==\"function\"?t:cyt[t])(Array.from(this._cache.values())),i=0;for(let s of this._cache.values())if(r[i++]!==s.isVisible)return!0;return!1}_pruneRequests(){let{maxRequests:t=0}=this.opts,r=[],i=0;for(let s of 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g=16777216,Vi=Math.max(fe,g);Vi>4,g=(Tt&15)<<4|Ms>>2,Vi=(Ms&3)<<6|cs,Be=Be+String.fromCharCode(br),Ms!==64&&(Be=Be+String.fromCharCode(g)),cs!==64&&(Be=Be+String.fromCharCode(Vi));while(li>2]=p,g[k+4>>2]=m,k=(C|0)!=0,k&&(g[C>>2]=0),ji(p,m)|0)return Nt=1,wt=Wt,Nt|0;g[Nt>>2]=0;t:do if((y|0)>=1)if(k)for(it=0,ot=1,Ct=1,L=0,k=p;;){if(!(L|it)){if(k=Wn(k,m,4,Nt)|0,m=It()|0,(k|0)==0&(m|0)==0){k=2;break t}if(ji(k,m)|0){k=1;break t}}if(k=Wn(k,m,g[16+(it<<2)>>2]|0,Nt)|0,m=It()|0,(k|0)==0&(m|0)==0){k=2;break t}if(p=S+(Ct<<3)|0,g[p>>2]=k,g[p+4>>2]=m,g[C+(Ct<<2)>>2]=ot,L=L+1|0,p=(L|0)==(ot|0),z=it+1|0,H=(z|0)==6,ji(k,m)|0){k=1;break t}if(ot=ot+(H&p&1)|0,(ot|0)>(y|0)){k=0;break}else it=p?H?0:z:it,Ct=Ct+1|0,L=p?0:L}else for(it=0,ot=1,Ct=1,L=0,k=p;;){if(!(L|it)){if(k=Wn(k,m,4,Nt)|0,m=It()|0,(k|0)==0&(m|0)==0){k=2;break t}if(ji(k,m)|0){k=1;break t}}if(k=Wn(k,m,g[16+(it<<2)>>2]|0,Nt)|0,m=It()|0,(k|0)==0&(m|0)==0){k=2;break t}if(p=S+(Ct<<3)|0,g[p>>2]=k,g[p+4>>2]=m,L=L+1|0,p=(L|0)==(ot|0),z=it+1|0,H=(z|0)==6,ji(k,m)|0){k=1;break t}if(ot=ot+(H&p&1)|0,(ot|0)>(y|0)){k=0;break}else it=p?H?0:z:it,Ct=Ct+1|0,L=p?0:L}else k=0;while(!1);return Nt=k,wt=Wt,Nt|0}function Ba(p,m,y,S,C,k,L){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0,k=k|0,L=L|0;var z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0;if(Ct=wt,wt=wt+16|0,ot=Ct,(p|0)==0&(m|0)==0){wt=Ct;return}if(z=Yo(p|0,m|0,k|0,((k|0)<0)<<31>>31|0)|0,It()|0,H=S+(z<<3)|0,Nt=H,Wt=g[Nt>>2]|0,Nt=g[Nt+4>>2]|0,it=(Wt|0)==(p|0)&(Nt|0)==(m|0),!((Wt|0)==0&(Nt|0)==0|it))do z=(z+1|0)%(k|0)|0,H=S+(z<<3)|0,Wt=H,Nt=g[Wt>>2]|0,Wt=g[Wt+4>>2]|0,it=(Nt|0)==(p|0)&(Wt|0)==(m|0);while(!((Nt|0)==0&(Wt|0)==0|it));if(z=C+(z<<2)|0,it&&(g[z>>2]|0)<=(L|0)){wt=Ct;return}if(Wt=H,g[Wt>>2]=p,g[Wt+4>>2]=m,g[z>>2]=L,(L|0)>=(y|0)){wt=Ct;return}Wt=L+1|0,g[ot>>2]=0,Nt=Wn(p,m,2,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),g[ot>>2]=0,Nt=Wn(p,m,3,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),g[ot>>2]=0,Nt=Wn(p,m,1,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),g[ot>>2]=0,Nt=Wn(p,m,5,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),g[ot>>2]=0,Nt=Wn(p,m,4,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),g[ot>>2]=0,Nt=Wn(p,m,6,ot)|0,Ba(Nt,It()|0,y,S,C,k,Wt),wt=Ct}function Wn(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0;if((g[S>>2]|0)>0){C=0;do y=Na(y)|0,C=C+1|0;while((C|0)<(g[S>>2]|0))}z=me(p|0,m|0,45)|0,It()|0,H=z&127,k=Es(p,m)|0,C=me(p|0,m|0,52)|0,It()|0,C=C&15;t:do if(!C)L=6;else for(;;){if(Ct=(15-C|0)*3|0,Nt=me(p|0,m|0,Ct|0)|0,It()|0,Nt=Nt&7,Wt=(Ho(C)|0)==0,C=C+-1|0,ot=ke(7,0,Ct|0)|0,m=m&~(It()|0),Ct=ke(g[(Wt?464:48)+(Nt*28|0)+(y<<2)>>2]|0,0,Ct|0)|0,it=It()|0,y=g[(Wt?672:256)+(Nt*28|0)+(y<<2)>>2]|0,p=Ct|p&~ot,m=it|m,!y){y=0;break t}if(!C){L=6;break}}while(!1);(L|0)==6&&(Wt=g[880+(H*28|0)+(y<<2)>>2]|0,Nt=ke(Wt|0,0,45)|0,p=Nt|p,m=It()|0|m&-1040385,y=g[4304+(H*28|0)+(y<<2)>>2]|0,(Wt&127|0)==127&&(Wt=ke(g[880+(H*28|0)+20>>2]|0,0,45)|0,m=It()|0|m&-1040385,y=g[4304+(H*28|0)+20>>2]|0,p=Wo(Wt|p,m)|0,m=It()|0,g[S>>2]=(g[S>>2]|0)+1)),L=me(p|0,m|0,45)|0,It()|0,L=L&127;t:do if(fi(L)|0){e:do if((Es(p,m)|0)==1){if((H|0)!=(L|0))if(ch(L,g[7728+(H*28|0)>>2]|0)|0){p=Fd(p,m)|0,k=1,m=It()|0;break}else{p=Wo(p,m)|0,k=1,m=It()|0;break}switch(k|0){case 5:{p=Fd(p,m)|0,m=It()|0,g[S>>2]=(g[S>>2]|0)+5,k=0;break e}case 3:{p=Wo(p,m)|0,m=It()|0,g[S>>2]=(g[S>>2]|0)+1,k=0;break e}default:return Nt=0,Wt=0,Je(Nt|0),Wt|0}}else k=0;while(!1);if((y|0)>0){C=0;do p=gh(p,m)|0,m=It()|0,C=C+1|0;while((C|0)!=(y|0))}if((H|0)!=(L|0)){if(!(mu(L)|0)){if((k|0)!=0|(Es(p,m)|0)!=5)break;g[S>>2]=(g[S>>2]|0)+1;break}switch(z&127){case 8:case 118:break t;default:}(Es(p,m)|0)!=3&&(g[S>>2]=(g[S>>2]|0)+1)}}else if((y|0)>0){C=0;do p=Wo(p,m)|0,m=It()|0,C=C+1|0;while((C|0)!=(y|0))}while(!1);return g[S>>2]=((g[S>>2]|0)+y|0)%6|0,Nt=m,Wt=p,Je(Nt|0),Wt|0}function p_(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0;if(Ct=wt,wt=wt+16|0,ot=Ct,!y)return ot=S,g[ot>>2]=p,g[ot+4>>2]=m,ot=0,wt=Ct,ot|0;g[ot>>2]=0;t:do if(ji(p,m)|0)p=1;else{if(k=(y|0)>0,k){C=0,it=p;do{if(it=Wn(it,m,4,ot)|0,m=It()|0,(it|0)==0&(m|0)==0){p=2;break t}if(C=C+1|0,ji(it,m)|0){p=1;break t}}while((C|0)<(y|0));if(H=S,g[H>>2]=it,g[H+4>>2]=m,H=y+-1|0,k){k=0,L=1,C=it,p=m;do{if(C=Wn(C,p,2,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if(z=S+(L<<3)|0,g[z>>2]=C,g[z+4>>2]=p,L=L+1|0,ji(C,p)|0){p=1;break t}k=k+1|0}while((k|0)<(y|0));z=0,k=L;do{if(C=Wn(C,p,3,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if(L=S+(k<<3)|0,g[L>>2]=C,g[L+4>>2]=p,k=k+1|0,ji(C,p)|0){p=1;break t}z=z+1|0}while((z|0)<(y|0));L=0;do{if(C=Wn(C,p,1,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if(z=S+(k<<3)|0,g[z>>2]=C,g[z+4>>2]=p,k=k+1|0,ji(C,p)|0){p=1;break t}L=L+1|0}while((L|0)<(y|0));L=0;do{if(C=Wn(C,p,5,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if(z=S+(k<<3)|0,g[z>>2]=C,g[z+4>>2]=p,k=k+1|0,ji(C,p)|0){p=1;break t}L=L+1|0}while((L|0)<(y|0));L=0;do{if(C=Wn(C,p,4,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if(z=S+(k<<3)|0,g[z>>2]=C,g[z+4>>2]=p,k=k+1|0,ji(C,p)|0){p=1;break t}L=L+1|0}while((L|0)<(y|0));for(L=0;;){if(C=Wn(C,p,6,ot)|0,p=It()|0,(C|0)==0&(p|0)==0){p=2;break t}if((L|0)!=(H|0))if(z=S+(k<<3)|0,g[z>>2]=C,g[z+4>>2]=p,!(ji(C,p)|0))k=k+1|0;else{p=1;break t}if(L=L+1|0,(L|0)>=(y|0)){L=it,k=m;break}}}else L=it,C=it,k=m,p=m}else L=S,g[L>>2]=p,g[L+4>>2]=m,L=p,C=p,k=m,p=m;p=((L|0)!=(C|0)|(k|0)!=(p|0))&1}while(!1);return ot=p,wt=Ct,ot|0}function Cd(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0;if(k=wt,wt=wt+48|0,C=k+8|0,S=k,z=p,L=g[z+4>>2]|0,y=S,g[y>>2]=g[z>>2],g[y+4>>2]=L,Ee(S,C),C=uh(C,m)|0,m=g[S>>2]|0,S=g[p+8>>2]|0,(S|0)<=0)return z=m,L=(C|0)<(z|0),z=L?z:C,z=z+12|0,wt=k,z|0;y=g[p+12>>2]|0,p=0;do m=(g[y+(p<<3)>>2]|0)+m|0,p=p+1|0;while((p|0)<(S|0));return z=(C|0)<(m|0),z=z?m:C,z=z+12|0,wt=k,z|0}function $p(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0;if(z=wt,wt=wt+48|0,S=z+8|0,C=z,!(Xp(p,m,y)|0)){wt=z;return}if(H=p,k=g[H+4>>2]|0,L=C,g[L>>2]=g[H>>2],g[L+4>>2]=k,Ee(C,S),L=uh(S,m)|0,m=g[C>>2]|0,k=g[p+8>>2]|0,(k|0)>0){C=g[p+12>>2]|0,S=0;do m=(g[C+(S<<3)>>2]|0)+m|0,S=S+1|0;while((S|0)!=(k|0))}if(m=(L|0)<(m|0)?m:L,(m|0)<=-12){wt=z;return}H=m+11|0,Fc(y|0,0,(((H|0)>0?H:0)<<3)+8|0)|0,wt=z}function Xp(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0,Zr=0,qi=0,Ei=0,hn=0,Pi=0,Sn=0,yn=0,Or=0;if(Or=wt,wt=wt+112|0,hn=Or+80|0,H=Or+72|0,Pi=Or,Sn=Or+56|0,it=p+8|0,yn=ho((g[it>>2]<<5)+32|0)|0,yn||Mi(22848,22448,800,22456),pr(p,yn),k=p,S=g[k+4>>2]|0,z=H,g[z>>2]=g[k>>2],g[z+4>>2]=S,Ee(H,hn),z=uh(hn,m)|0,S=g[H>>2]|0,k=g[it>>2]|0,(k|0)>0){L=g[p+12>>2]|0,C=0;do S=(g[L+(C<<3)>>2]|0)+S|0,C=C+1|0;while((C|0)!=(k|0))}if(z=(z|0)<(S|0)?S:z,Ei=z+12|0,C=Ua(Ei,8)|0,ot=Ua(Ei,8)|0,g[hn>>2]=0,Zr=p,qi=g[Zr+4>>2]|0,S=H,g[S>>2]=g[Zr>>2],g[S+4>>2]=qi,S=i0(H,Ei,m,hn,C,ot)|0,S|0)return Gr(C),Gr(ot),Gr(yn),yn=S,wt=Or,yn|0;t:do if((g[it>>2]|0)>0){for(k=p+12|0,S=0;L=i0((g[k>>2]|0)+(S<<3)|0,Ei,m,hn,C,ot)|0,S=S+1|0,!(L|0);)if((S|0)>=(g[it>>2]|0))break t;return Gr(C),Gr(ot),Gr(yn),yn=L,wt=Or,yn|0}while(!1);(z|0)>-12&&Fc(ot|0,0,((Ei|0)>1?Ei:1)<<3|0)|0;t:do if((g[hn>>2]|0)>0){qi=((Ei|0)<0)<<31>>31,Ut=C,$e=ot,er=C,we=C,je=ot,Zr=C,S=C,Le=C,We=ot,te=ot,_e=ot,C=ot;e:for(;;){for(ne=g[hn>>2]|0,Wt=0,re=0,k=0;;){L=Pi,z=L+56|0;do g[L>>2]=0,L=L+4|0;while((L|0)<(z|0));if(m=Ut+(Wt<<3)|0,H=g[m>>2]|0,m=g[m+4>>2]|0,yf(H,m,1,Pi,0)|0){L=Pi,z=L+56|0;do g[L>>2]=0,L=L+4|0;while((L|0)<(z|0));L=Ua(7,4)|0,L|0&&(Ba(H,m,1,Pi,L,7,0),Gr(L))}Nt=0;do{Ct=Pi+(Nt<<3)|0,ot=g[Ct>>2]|0,Ct=g[Ct+4>>2]|0;r:do if(!((ot|0)==0&(Ct|0)==0)){if(H=Yo(ot|0,Ct|0,Ei|0,qi|0)|0,It()|0,L=y+(H<<3)|0,z=L,m=g[z>>2]|0,z=g[z+4>>2]|0,!((m|0)==0&(z|0)==0))for(it=0;;){if((it|0)>(Ei|0))break e;if((m|0)==(ot|0)&(z|0)==(Ct|0))break r;if(H=(H+1|0)%(Ei|0)|0,L=y+(H<<3)|0,z=L,m=g[z>>2]|0,z=g[z+4>>2]|0,(m|0)==0&(z|0)==0)break;it=it+1|0}(ot|0)==0&(Ct|0)==0||(l(ot,Ct,Sn),tr(p,yn,Sn)|0&&(it=L,g[it>>2]=ot,g[it+4>>2]=Ct,it=$e+(k<<3)|0,g[it>>2]=ot,g[it+4>>2]=Ct,k=k+1|0))}while(!1);Nt=Nt+1|0}while(Nt>>>0<7);if(re=re+1|0,(re|0)>=(ne|0))break;Wt=Wt+1|0}if((ne|0)>0&&Fc(er|0,0,ne<<3|0)|0,g[hn>>2]=k,(k|0)>0)ot=C,Ct=_e,Nt=Zr,Wt=te,re=We,ne=$e,C=Le,_e=S,te=we,We=er,Le=ot,S=Ct,Zr=je,je=Nt,we=Wt,er=re,$e=Ut,Ut=ne;else break t}return Gr(we),Gr(je),Gr(yn),yn=-1,wt=Or,yn|0}else S=ot;while(!1);return Gr(yn),Gr(C),Gr(S),yn=0,wt=Or,yn|0}function i0(p,m,y,S,C,k){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0,k=k|0;var L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0,Zr=0,qi=0,Ei=0,hn=0;if(qi=wt,wt=wt+48|0,er=qi+32|0,we=qi+16|0,je=qi,L=g[p>>2]|0,(L|0)<=0)return Zr=0,wt=qi,Zr|0;We=p+4|0,te=er+8|0,_e=we+8|0,Ut=je+8|0,$e=((m|0)<0)<<31>>31,Le=0;t:for(;;){z=g[We>>2]|0,re=z+(Le<<4)|0,g[er>>2]=g[re>>2],g[er+4>>2]=g[re+4>>2],g[er+8>>2]=g[re+8>>2],g[er+12>>2]=g[re+12>>2],(Le|0)==(L+-1|0)?(g[we>>2]=g[z>>2],g[we+4>>2]=g[z+4>>2],g[we+8>>2]=g[z+8>>2],g[we+12>>2]=g[z+12>>2]):(re=z+(Le+1<<4)|0,g[we>>2]=g[re>>2],g[we+4>>2]=g[re+4>>2],g[we+8>>2]=g[re+8>>2],g[we+12>>2]=g[re+12>>2]),re=la(er,we,y)|0;e:do if((re|0)>0){ne=+(re|0),Wt=0;r:for(;;){hn=+(re-Wt|0),Ei=+(Wt|0),Tt[je>>3]=+Tt[er>>3]*hn/ne+ +Tt[we>>3]*Ei/ne,Tt[Ut>>3]=+Tt[te>>3]*hn/ne+ +Tt[_e>>3]*Ei/ne,Ct=lA(je,y)|0,Nt=It()|0,z=Yo(Ct|0,Nt|0,m|0,$e|0)|0,It()|0,L=k+(z<<3)|0,H=L,it=g[H>>2]|0,H=g[H+4>>2]|0;i:do if((it|0)==0&(H|0)==0)Zr=14;else for(ot=0;;){if((ot|0)>(m|0)){L=1;break i}if((it|0)==(Ct|0)&(H|0)==(Nt|0)){L=7;break i}if(z=(z+1|0)%(m|0)|0,L=k+(z<<3)|0,H=L,it=g[H>>2]|0,H=g[H+4>>2]|0,(it|0)==0&(H|0)==0){Zr=14;break}else ot=ot+1|0}while(!1);switch((Zr|0)==14&&(Zr=0,(Ct|0)==0&(Nt|0)==0?L=7:(g[L>>2]=Ct,g[L+4>>2]=Nt,L=g[S>>2]|0,ot=C+(L<<3)|0,g[ot>>2]=Ct,g[ot+4>>2]=Nt,g[S>>2]=L+1,L=0)),L&7){case 7:case 0:break;default:break r}if(Wt=Wt+1|0,(re|0)<=(Wt|0)){Zr=8;break e}}if(L|0){L=-1,Zr=20;break t}}else Zr=8;while(!1);if((Zr|0)==8&&(Zr=0),Le=Le+1|0,L=g[p>>2]|0,(Le|0)>=(L|0)){L=0,Zr=20;break}}return(Zr|0)==20?(wt=qi,L|0):0}function Cn(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0;if(it=wt,wt=wt+176|0,H=it,(m|0)<1){vu(y,0,0),wt=it;return}L=p,L=me(g[L>>2]|0,g[L+4>>2]|0,52)|0,It()|0,vu(y,(m|0)>6?m:6,L&15),L=0;do{if(S=p+(L<<3)|0,d(g[S>>2]|0,g[S+4>>2]|0,H),S=g[H>>2]|0,(S|0)>0){z=0;do k=H+8+(z<<4)|0,z=z+1|0,S=H+8+(((z|0)%(S|0)|0)<<4)|0,C=yh(y,S,k)|0,C?Ps(y,C)|0:Eo(y,k,S)|0,S=g[H>>2]|0;while((z|0)<(S|0))}L=L+1|0}while((L|0)!=(m|0));wt=it}function ah(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0;if(k=wt,wt=wt+32|0,S=k,C=k+16|0,Cn(p,m,C),g[y>>2]=0,g[y+4>>2]=0,g[y+8>>2]=0,p=Ws(C)|0,!p){Ht(y)|0,_h(C),wt=k;return}do{m=yt(y)|0;do dt(m,p)|0,L=p+16|0,g[S>>2]=g[L>>2],g[S+4>>2]=g[L+4>>2],g[S+8>>2]=g[L+8>>2],g[S+12>>2]=g[L+12>>2],Ps(C,p)|0,p=Fn(C,S)|0;while(p|0);p=Ws(C)|0}while(p|0);Ht(y)|0,_h(C),wt=k}function fi(p){return p=p|0,g[7728+(p*28|0)+16>>2]|0}function mu(p){return p=p|0,(p|0)==4|(p|0)==117|0}function vf(p){return p=p|0,g[11152+((g[p>>2]|0)*216|0)+((g[p+4>>2]|0)*72|0)+((g[p+8>>2]|0)*24|0)+(g[p+12>>2]<<3)>>2]|0}function Kp(p){return p=p|0,g[11152+((g[p>>2]|0)*216|0)+((g[p+4>>2]|0)*72|0)+((g[p+8>>2]|0)*24|0)+(g[p+12>>2]<<3)+4>>2]|0}function lh(p,m){p=p|0,m=m|0,p=7728+(p*28|0)|0,g[m>>2]=g[p>>2],g[m+4>>2]=g[p+4>>2],g[m+8>>2]=g[p+8>>2],g[m+12>>2]=g[p+12>>2]}function Ld(p,m){p=p|0,m=m|0;var y=0,S=0;if(m>>>0>20)return m=-1,m|0;do if((g[11152+(m*216|0)>>2]|0)!=(p|0))if((g[11152+(m*216|0)+8>>2]|0)!=(p|0))if((g[11152+(m*216|0)+16>>2]|0)!=(p|0))if((g[11152+(m*216|0)+24>>2]|0)!=(p|0))if((g[11152+(m*216|0)+32>>2]|0)!=(p|0))if((g[11152+(m*216|0)+40>>2]|0)!=(p|0))if((g[11152+(m*216|0)+48>>2]|0)!=(p|0))if((g[11152+(m*216|0)+56>>2]|0)!=(p|0))if((g[11152+(m*216|0)+64>>2]|0)!=(p|0))if((g[11152+(m*216|0)+72>>2]|0)!=(p|0))if((g[11152+(m*216|0)+80>>2]|0)!=(p|0))if((g[11152+(m*216|0)+88>>2]|0)!=(p|0))if((g[11152+(m*216|0)+96>>2]|0)!=(p|0))if((g[11152+(m*216|0)+104>>2]|0)!=(p|0))if((g[11152+(m*216|0)+112>>2]|0)!=(p|0))if((g[11152+(m*216|0)+120>>2]|0)!=(p|0))if((g[11152+(m*216|0)+128>>2]|0)!=(p|0))if((g[11152+(m*216|0)+136>>2]|0)==(p|0))p=2,y=1,S=2;else{if((g[11152+(m*216|0)+144>>2]|0)==(p|0)){p=0,y=2,S=0;break}if((g[11152+(m*216|0)+152>>2]|0)==(p|0)){p=0,y=2,S=1;break}if((g[11152+(m*216|0)+160>>2]|0)==(p|0)){p=0,y=2,S=2;break}if((g[11152+(m*216|0)+168>>2]|0)==(p|0)){p=1,y=2,S=0;break}if((g[11152+(m*216|0)+176>>2]|0)==(p|0)){p=1,y=2,S=1;break}if((g[11152+(m*216|0)+184>>2]|0)==(p|0)){p=1,y=2,S=2;break}if((g[11152+(m*216|0)+192>>2]|0)==(p|0)){p=2,y=2,S=0;break}if((g[11152+(m*216|0)+200>>2]|0)==(p|0)){p=2,y=2,S=1;break}if((g[11152+(m*216|0)+208>>2]|0)==(p|0)){p=2,y=2,S=2;break}else p=-1;return p|0}else p=2,y=1,S=1;else p=2,y=1,S=0;else p=1,y=1,S=2;else p=1,y=1,S=1;else p=1,y=1,S=0;else p=0,y=1,S=2;else p=0,y=1,S=1;else p=0,y=1,S=0;else p=2,y=0,S=2;else p=2,y=0,S=1;else p=2,y=0,S=0;else p=1,y=0,S=2;else p=1,y=0,S=1;else p=1,y=0,S=0;else p=0,y=0,S=2;else p=0,y=0,S=1;else p=0,y=0,S=0;while(!1);return m=g[11152+(m*216|0)+(y*72|0)+(p*24|0)+(S<<3)+4>>2]|0,m|0}function ch(p,m){return p=p|0,m=m|0,(g[7728+(p*28|0)+20>>2]|0)==(m|0)?(m=1,m|0):(m=(g[7728+(p*28|0)+24>>2]|0)==(m|0),m|0)}function Jp(p,m){return p=p|0,m=m|0,g[880+(p*28|0)+(m<<2)>>2]|0}function tA(p,m){return p=p|0,m=m|0,(g[880+(p*28|0)>>2]|0)==(m|0)?(m=0,m|0):(g[880+(p*28|0)+4>>2]|0)==(m|0)?(m=1,m|0):(g[880+(p*28|0)+8>>2]|0)==(m|0)?(m=2,m|0):(g[880+(p*28|0)+12>>2]|0)==(m|0)?(m=3,m|0):(g[880+(p*28|0)+16>>2]|0)==(m|0)?(m=4,m|0):(g[880+(p*28|0)+20>>2]|0)==(m|0)?(m=5,m|0):((g[880+(p*28|0)+24>>2]|0)==(m|0)?6:7)|0}function A_(){return 122}function m_(p){p=p|0;var m=0,y=0,S=0;m=0;do ke(m|0,0,45)|0,S=It()|0|134225919,y=p+(m<<3)|0,g[y>>2]=-1,g[y+4>>2]=S,m=m+1|0;while((m|0)!=122)}function n0(p){return p=p|0,+Tt[p+16>>3]<+Tt[p+24>>3]|0}function pl(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;return y=+Tt[m>>3],!(y>=+Tt[p+8>>3])||!(y<=+Tt[p>>3])?(m=0,m|0):(S=+Tt[p+16>>3],y=+Tt[p+24>>3],C=+Tt[m+8>>3],m=C>=y,p=C<=S&1,S>2]=0,k=k+4|0;while((k|0)<(z|0));return O(m,C),k=C,z=g[k>>2]|0,k=g[k+4>>2]|0,l(z,k,y),d(z,k,S),H=+Bc(y,S+8|0),Tt[y>>3]=+Tt[p>>3],k=y+8|0,Tt[k>>3]=+Tt[p+16>>3],Tt[S>>3]=+Tt[p+8>>3],z=S+8|0,Tt[z>>3]=+Tt[p+24>>3],it=+Bc(y,S),z=~~+Ji(+(it*it/+ml(+ +li(+((+Tt[k>>3]-+Tt[z>>3])/(+Tt[y>>3]-+Tt[S>>3]))),3)/(H*(H*2.59807621135)*.8))),wt=L,(z|0?z:1)|0}function la(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0;z=wt,wt=wt+288|0,S=z+264|0,C=z+96|0,k=z,L=k,H=L+96|0;do g[L>>2]=0,L=L+4|0;while((L|0)<(H|0));return O(y,k),H=k,L=g[H>>2]|0,H=g[H+4>>2]|0,l(L,H,S),d(L,H,C),it=+Bc(S,C+8|0),H=~~+Ji(+(+Bc(p,m)/(it*2))),wt=z,(H|0?H:1)|0}function kd(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0,g[p>>2]=m,g[p+4>>2]=y,g[p+8>>2]=S}function g_(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0;Ct=m+8|0,g[Ct>>2]=0,H=+Tt[p>>3],L=+li(+H),it=+Tt[p+8>>3],z=+li(+it)/.8660254037844386,L=L+z*.5,y=~~L,p=~~z,L=L-+(y|0),z=z-+(p|0);do if(L<.5)if(L<.3333333333333333)if(g[m>>2]=y,z<(L+1)*.5){g[m+4>>2]=p;break}else{p=p+1|0,g[m+4>>2]=p;break}else if(Nt=1-L,p=(!(z>2]=p,Nt<=z&z>2]=y;break}else{g[m>>2]=y;break}else{if(!(L<.6666666666666666))if(y=y+1|0,g[m>>2]=y,z>2]=p;break}else{p=p+1|0,g[m+4>>2]=p;break}if(z<1-L){if(g[m+4>>2]=p,L*2+-1>2]=y;break}}else p=p+1|0,g[m+4>>2]=p;y=y+1|0,g[m>>2]=y}while(!1);do if(H<0)if(p&1){ot=(p+1|0)/2|0,ot=zd(y|0,((y|0)<0)<<31>>31|0,ot|0,((ot|0)<0)<<31>>31|0)|0,y=~~(+(y|0)-((+(ot>>>0)+4294967296*+(It()|0))*2+1)),g[m>>2]=y;break}else{ot=(p|0)/2|0,ot=zd(y|0,((y|0)<0)<<31>>31|0,ot|0,((ot|0)<0)<<31>>31|0)|0,y=~~(+(y|0)-(+(ot>>>0)+4294967296*+(It()|0))*2),g[m>>2]=y;break}while(!1);ot=m+4|0,it<0&&(y=y-((p<<1|1|0)/2|0)|0,g[m>>2]=y,p=0-p|0,g[ot>>2]=p),S=p-y|0,(y|0)<0?(C=0-y|0,g[ot>>2]=S,g[Ct>>2]=C,g[m>>2]=0,p=S,y=0):C=0,(p|0)<0&&(y=y-p|0,g[m>>2]=y,C=C-p|0,g[Ct>>2]=C,g[ot>>2]=0,p=0),k=y-C|0,S=p-C|0,(C|0)<0&&(g[m>>2]=k,g[ot>>2]=S,g[Ct>>2]=0,p=S,y=k,C=0),S=(p|0)<(y|0)?p:y,S=(C|0)<(S|0)?C:S,!((S|0)<=0)&&(g[m>>2]=y-S,g[ot>>2]=p-S,g[Ct>>2]=C-S)}function js(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0;m=g[p>>2]|0,L=p+4|0,y=g[L>>2]|0,(m|0)<0&&(y=y-m|0,g[L>>2]=y,k=p+8|0,g[k>>2]=(g[k>>2]|0)-m,g[p>>2]=0,m=0),(y|0)<0?(m=m-y|0,g[p>>2]=m,k=p+8|0,C=(g[k>>2]|0)-y|0,g[k>>2]=C,g[L>>2]=0,y=0):(C=p+8|0,k=C,C=g[C>>2]|0),(C|0)<0&&(m=m-C|0,g[p>>2]=m,y=y-C|0,g[L>>2]=y,g[k>>2]=0,C=0),S=(y|0)<(m|0)?y:m,S=(C|0)<(S|0)?C:S,!((S|0)<=0)&&(g[p>>2]=m-S,g[L>>2]=y-S,g[k>>2]=C-S)}function gu(p,m){p=p|0,m=m|0;var y=0,S=0;S=g[p+8>>2]|0,y=+((g[p+4>>2]|0)-S|0),Tt[m>>3]=+((g[p>>2]|0)-S|0)-y*.5,Tt[m+8>>3]=y*.8660254037844386}function Ln(p,m,y){p=p|0,m=m|0,y=y|0,g[y>>2]=(g[m>>2]|0)+(g[p>>2]|0),g[y+4>>2]=(g[m+4>>2]|0)+(g[p+4>>2]|0),g[y+8>>2]=(g[m+8>>2]|0)+(g[p+8>>2]|0)}function eA(p,m,y){p=p|0,m=m|0,y=y|0,g[y>>2]=(g[p>>2]|0)-(g[m>>2]|0),g[y+4>>2]=(g[p+4>>2]|0)-(g[m+4>>2]|0),g[y+8>>2]=(g[p+8>>2]|0)-(g[m+8>>2]|0)}function ca(p,m){p=p|0,m=m|0;var y=0,S=0;y=Oc(g[p>>2]|0,m)|0,g[p>>2]=y,y=p+4|0,S=Oc(g[y>>2]|0,m)|0,g[y>>2]=S,p=p+8|0,m=Oc(g[p>>2]|0,m)|0,g[p>>2]=m}function Fa(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;L=g[p>>2]|0,z=(L|0)<0,S=(g[p+4>>2]|0)-(z?L:0)|0,k=(S|0)<0,C=(k?0-S|0:0)+((g[p+8>>2]|0)-(z?L:0))|0,y=(C|0)<0,p=y?0:C,m=(k?0:S)-(y?C:0)|0,C=(z?0:L)-(k?S:0)-(y?C:0)|0,y=(m|0)<(C|0)?m:C,y=(p|0)<(y|0)?p:y,S=(y|0)>0,p=p-(S?y:0)|0,m=m-(S?y:0)|0;t:do switch(C-(S?y:0)|0){case 0:switch(m|0){case 0:return z=p|0?(p|0)==1?1:7:0,z|0;case 1:return z=p|0?(p|0)==1?3:7:2,z|0;default:break t}case 1:switch(m|0){case 0:return z=p|0?(p|0)==1?5:7:4,z|0;case 1:{if(!p)p=6;else break t;return p|0}default:break t}default:}while(!1);return z=7,z|0}function Rd(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;L=p+8|0,y=g[L>>2]|0,m=(g[p>>2]|0)-y|0,z=p+4|0,y=(g[z>>2]|0)-y|0,S=_n(+((m*3|0)-y|0)/7)|0,g[p>>2]=S,m=_n(+((y<<1)+m|0)/7)|0,g[z>>2]=m,g[L>>2]=0,y=m-S|0,(S|0)<0?(k=0-S|0,g[z>>2]=y,g[L>>2]=k,g[p>>2]=0,m=y,S=0,y=k):y=0,(m|0)<0&&(S=S-m|0,g[p>>2]=S,y=y-m|0,g[L>>2]=y,g[z>>2]=0,m=0),k=S-y|0,C=m-y|0,(y|0)<0?(g[p>>2]=k,g[z>>2]=C,g[L>>2]=0,m=C,C=k,y=0):C=S,S=(m|0)<(C|0)?m:C,S=(y|0)<(S|0)?y:S,!((S|0)<=0)&&(g[p>>2]=C-S,g[z>>2]=m-S,g[L>>2]=y-S)}function Al(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;L=p+8|0,y=g[L>>2]|0,m=(g[p>>2]|0)-y|0,z=p+4|0,y=(g[z>>2]|0)-y|0,S=_n(+((m<<1)+y|0)/7)|0,g[p>>2]=S,m=_n(+((y*3|0)-m|0)/7)|0,g[z>>2]=m,g[L>>2]=0,y=m-S|0,(S|0)<0?(k=0-S|0,g[z>>2]=y,g[L>>2]=k,g[p>>2]=0,m=y,S=0,y=k):y=0,(m|0)<0&&(S=S-m|0,g[p>>2]=S,y=y-m|0,g[L>>2]=y,g[z>>2]=0,m=0),k=S-y|0,C=m-y|0,(y|0)<0?(g[p>>2]=k,g[z>>2]=C,g[L>>2]=0,m=C,C=k,y=0):C=S,S=(m|0)<(C|0)?m:C,S=(y|0)<(S|0)?y:S,!((S|0)<=0)&&(g[p>>2]=C-S,g[z>>2]=m-S,g[L>>2]=y-S)}function za(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;m=g[p>>2]|0,L=p+4|0,y=g[L>>2]|0,z=p+8|0,S=g[z>>2]|0,C=y+(m*3|0)|0,g[p>>2]=C,y=S+(y*3|0)|0,g[L>>2]=y,m=(S*3|0)+m|0,g[z>>2]=m,S=y-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=S,g[z>>2]=m,g[p>>2]=0,y=S,S=0):S=C,(y|0)<0&&(S=S-y|0,g[p>>2]=S,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=S-m|0,C=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=C,g[z>>2]=0,S=k,m=0):C=y,y=(C|0)<(S|0)?C:S,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=S-y,g[L>>2]=C-y,g[z>>2]=m-y)}function hh(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;C=g[p>>2]|0,L=p+4|0,m=g[L>>2]|0,z=p+8|0,y=g[z>>2]|0,S=(m*3|0)+C|0,C=y+(C*3|0)|0,g[p>>2]=C,g[L>>2]=S,m=(y*3|0)+m|0,g[z>>2]=m,y=S-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=y,g[z>>2]=m,g[p>>2]=0,C=0):y=S,(y|0)<0&&(C=C-y|0,g[p>>2]=C,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=C-m|0,S=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=S,g[z>>2]=0,C=k,m=0):S=y,y=(S|0)<(C|0)?S:C,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=C-y,g[L>>2]=S-y,g[z>>2]=m-y)}function rA(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0;(m+-1|0)>>>0>=6||(C=(g[15472+(m*12|0)>>2]|0)+(g[p>>2]|0)|0,g[p>>2]=C,z=p+4|0,S=(g[15472+(m*12|0)+4>>2]|0)+(g[z>>2]|0)|0,g[z>>2]=S,L=p+8|0,m=(g[15472+(m*12|0)+8>>2]|0)+(g[L>>2]|0)|0,g[L>>2]=m,y=S-C|0,(C|0)<0?(m=m-C|0,g[z>>2]=y,g[L>>2]=m,g[p>>2]=0,S=0):(y=S,S=C),(y|0)<0&&(S=S-y|0,g[p>>2]=S,m=m-y|0,g[L>>2]=m,g[z>>2]=0,y=0),k=S-m|0,C=y-m|0,(m|0)<0?(g[p>>2]=k,g[z>>2]=C,g[L>>2]=0,S=k,m=0):C=y,y=(C|0)<(S|0)?C:S,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=S-y,g[z>>2]=C-y,g[L>>2]=m-y))}function s0(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;C=g[p>>2]|0,L=p+4|0,m=g[L>>2]|0,z=p+8|0,y=g[z>>2]|0,S=m+C|0,C=y+C|0,g[p>>2]=C,g[L>>2]=S,m=y+m|0,g[z>>2]=m,y=S-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=y,g[z>>2]=m,g[p>>2]=0,S=0):(y=S,S=C),(y|0)<0&&(S=S-y|0,g[p>>2]=S,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=S-m|0,C=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=C,g[z>>2]=0,S=k,m=0):C=y,y=(C|0)<(S|0)?C:S,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=S-y,g[L>>2]=C-y,g[z>>2]=m-y)}function fh(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;m=g[p>>2]|0,L=p+4|0,S=g[L>>2]|0,z=p+8|0,y=g[z>>2]|0,C=S+m|0,g[p>>2]=C,S=y+S|0,g[L>>2]=S,m=y+m|0,g[z>>2]=m,y=S-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=y,g[z>>2]=m,g[p>>2]=0,S=0):(y=S,S=C),(y|0)<0&&(S=S-y|0,g[p>>2]=S,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=S-m|0,C=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=C,g[z>>2]=0,S=k,m=0):C=y,y=(C|0)<(S|0)?C:S,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=S-y,g[L>>2]=C-y,g[z>>2]=m-y)}function Na(p){switch(p=p|0,p|0){case 1:{p=5;break}case 5:{p=4;break}case 4:{p=6;break}case 6:{p=2;break}case 2:{p=3;break}case 3:{p=1;break}default:}return p|0}function co(p){switch(p=p|0,p|0){case 1:{p=3;break}case 3:{p=2;break}case 2:{p=6;break}case 6:{p=4;break}case 4:{p=5;break}case 5:{p=1;break}default:}return p|0}function Ge(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;m=g[p>>2]|0,L=p+4|0,y=g[L>>2]|0,z=p+8|0,S=g[z>>2]|0,C=y+(m<<1)|0,g[p>>2]=C,y=S+(y<<1)|0,g[L>>2]=y,m=(S<<1)+m|0,g[z>>2]=m,S=y-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=S,g[z>>2]=m,g[p>>2]=0,y=S,S=0):S=C,(y|0)<0&&(S=S-y|0,g[p>>2]=S,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=S-m|0,C=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=C,g[z>>2]=0,S=k,m=0):C=y,y=(C|0)<(S|0)?C:S,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=S-y,g[L>>2]=C-y,g[z>>2]=m-y)}function Dd(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;C=g[p>>2]|0,L=p+4|0,m=g[L>>2]|0,z=p+8|0,y=g[z>>2]|0,S=(m<<1)+C|0,C=y+(C<<1)|0,g[p>>2]=C,g[L>>2]=S,m=(y<<1)+m|0,g[z>>2]=m,y=S-C|0,(C|0)<0?(m=m-C|0,g[L>>2]=y,g[z>>2]=m,g[p>>2]=0,C=0):y=S,(y|0)<0&&(C=C-y|0,g[p>>2]=C,m=m-y|0,g[z>>2]=m,g[L>>2]=0,y=0),k=C-m|0,S=y-m|0,(m|0)<0?(g[p>>2]=k,g[L>>2]=S,g[z>>2]=0,C=k,m=0):S=y,y=(S|0)<(C|0)?S:C,y=(m|0)<(y|0)?m:y,!((y|0)<=0)&&(g[p>>2]=C-y,g[L>>2]=S-y,g[z>>2]=m-y)}function Hl(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0;return L=(g[p>>2]|0)-(g[m>>2]|0)|0,z=(L|0)<0,S=(g[p+4>>2]|0)-(g[m+4>>2]|0)-(z?L:0)|0,k=(S|0)<0,C=(z?0-L|0:0)+(g[p+8>>2]|0)-(g[m+8>>2]|0)+(k?0-S|0:0)|0,p=(C|0)<0,m=p?0:C,y=(k?0:S)-(p?C:0)|0,C=(z?0:L)-(k?S:0)-(p?C:0)|0,p=(y|0)<(C|0)?y:C,p=(m|0)<(p|0)?m:p,S=(p|0)>0,m=m-(S?p:0)|0,y=y-(S?p:0)|0,p=C-(S?p:0)|0,p=(p|0)>-1?p:0-p|0,y=(y|0)>-1?y:0-y|0,m=(m|0)>-1?m:0-m|0,m=(y|0)>(m|0)?y:m,((p|0)>(m|0)?p:m)|0}function xf(p,m){p=p|0,m=m|0;var y=0;y=g[p+8>>2]|0,g[m>>2]=(g[p>>2]|0)-y,g[m+4>>2]=(g[p+4>>2]|0)-y}function __(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0;S=g[p>>2]|0,g[m>>2]=S,p=g[p+4>>2]|0,L=m+4|0,g[L>>2]=p,z=m+8|0,g[z>>2]=0,y=p-S|0,(S|0)<0?(p=0-S|0,g[L>>2]=y,g[z>>2]=p,g[m>>2]=0,S=0):(y=p,p=0),(y|0)<0&&(S=S-y|0,g[m>>2]=S,p=p-y|0,g[z>>2]=p,g[L>>2]=0,y=0),k=S-p|0,C=y-p|0,(p|0)<0?(g[m>>2]=k,g[L>>2]=C,g[z>>2]=0,y=C,C=k,p=0):C=S,S=(y|0)<(C|0)?y:C,S=(p|0)<(S|0)?p:S,!((S|0)<=0)&&(g[m>>2]=C-S,g[L>>2]=y-S,g[z>>2]=p-S)}function Oe(p){p=p|0;var m=0,y=0,S=0,C=0;m=p+8|0,C=g[m>>2]|0,y=C-(g[p>>2]|0)|0,g[p>>2]=y,S=p+4|0,p=(g[S>>2]|0)-C|0,g[S>>2]=p,g[m>>2]=0-(p+y)}function o0(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;y=g[p>>2]|0,m=0-y|0,g[p>>2]=m,L=p+8|0,g[L>>2]=0,z=p+4|0,S=g[z>>2]|0,C=S+y|0,(y|0)>0?(g[z>>2]=C,g[L>>2]=y,g[p>>2]=0,m=0,S=C):y=0,(S|0)<0?(k=m-S|0,g[p>>2]=k,y=y-S|0,g[L>>2]=y,g[z>>2]=0,C=k-y|0,m=0-y|0,(y|0)<0?(g[p>>2]=C,g[z>>2]=m,g[L>>2]=0,S=m,y=0):(S=0,C=k)):C=m,m=(S|0)<(C|0)?S:C,m=(y|0)<(m|0)?y:m,!((m|0)<=0)&&(g[p>>2]=C-m,g[z>>2]=S-m,g[L>>2]=y-m)}function a0(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;S=wt,wt=wt+16|0,C=S,zx(p,m,y,C),g_(C,y+4|0),wt=S}function zx(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0;if(H=wt,wt=wt+32|0,k=H,ql(p,k),g[y>>2]=0,C=+jr(15888,k),L=+jr(15912,k),L>2]=1,C=L),L=+jr(15936,k),L>2]=2,C=L),L=+jr(15960,k),L>2]=3,C=L),L=+jr(15984,k),L>2]=4,C=L),L=+jr(16008,k),L>2]=5,C=L),L=+jr(16032,k),L>2]=6,C=L),L=+jr(16056,k),L>2]=7,C=L),L=+jr(16080,k),L>2]=8,C=L),L=+jr(16104,k),L>2]=9,C=L),L=+jr(16128,k),L>2]=10,C=L),L=+jr(16152,k),L>2]=11,C=L),L=+jr(16176,k),L>2]=12,C=L),L=+jr(16200,k),L>2]=13,C=L),L=+jr(16224,k),L>2]=14,C=L),L=+jr(16248,k),L>2]=15,C=L),L=+jr(16272,k),L>2]=16,C=L),L=+jr(16296,k),L>2]=17,C=L),L=+jr(16320,k),L>2]=18,C=L),L=+jr(16344,k),L>2]=19,C=L),L=+_f(+(1-C*.5)),L<1e-16){g[S>>2]=0,g[S+4>>2]=0,g[S+8>>2]=0,g[S+12>>2]=0,wt=H;return}if(y=g[y>>2]|0,C=+Tt[16368+(y*24|0)>>3],C=+ph(C-+ph(+Od(15568+(y<<4)|0,p))),Ho(m)|0?z=+ph(C+-.3334731722518321):z=C,C=+To(+L)/.381966011250105,(m|0)>0){k=0;do C=C*2.6457513110645907,k=k+1|0;while((k|0)!=(m|0))}L=+Ur(+z)*C,Tt[S>>3]=L,z=+hi(+z)*C,Tt[S+8>>3]=z,wt=H}function dh(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0;if(k=+hs(p),k<1e-16){m=15568+(m<<4)|0,g[C>>2]=g[m>>2],g[C+4>>2]=g[m+4>>2],g[C+8>>2]=g[m+8>>2],g[C+12>>2]=g[m+12>>2];return}if(L=+qr(+ +Tt[p+8>>3],+ +Tt[p>>3]),(y|0)>0){p=0;do k=k/2.6457513110645907,p=p+1|0;while((p|0)!=(y|0))}S?(k=k/3,y=(Ho(y)|0)==0,k=+Md(+((y?k:k/2.6457513110645907)*.381966011250105))):(k=+Md(+(k*.381966011250105)),Ho(y)|0&&(L=+ph(L+.3334731722518321))),f0(15568+(m<<4)|0,+ph(+Tt[16368+(m*24|0)>>3]-L),k,C)}function y_(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;S=wt,wt=wt+16|0,C=S,gu(p+4|0,C),dh(C,g[p>>2]|0,m,0,y),wt=S}function l0(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0,Zr=0,qi=0,Ei=0,hn=0,Pi=0,Sn=0,yn=0,Or=0;if(Sn=wt,wt=wt+272|0,k=Sn+256|0,te=Sn+240|0,Ei=Sn,hn=Sn+224|0,Pi=Sn+208|0,_e=Sn+176|0,Ut=Sn+160|0,$e=Sn+192|0,er=Sn+144|0,we=Sn+128|0,je=Sn+112|0,Zr=Sn+96|0,qi=Sn+80|0,g[k>>2]=m,g[te>>2]=g[p>>2],g[te+4>>2]=g[p+4>>2],g[te+8>>2]=g[p+8>>2],g[te+12>>2]=g[p+12>>2],c0(te,k,Ei),g[C>>2]=0,te=S+y+((S|0)==5&1)|0,(te|0)<=(y|0)){wt=Sn;return}H=g[k>>2]|0,it=hn+4|0,ot=_e+4|0,Ct=y+5|0,Nt=16848+(H<<2)|0,Wt=16928+(H<<2)|0,re=we+8|0,ne=je+8|0,Le=Zr+8|0,We=Pi+4|0,z=y;t:for(;;){L=Ei+(((z|0)%5|0)<<4)|0,g[Pi>>2]=g[L>>2],g[Pi+4>>2]=g[L+4>>2],g[Pi+8>>2]=g[L+8>>2],g[Pi+12>>2]=g[L+12>>2];do;while((bf(Pi,H,0,1)|0)==2);if((z|0)>(y|0)&(Ho(m)|0)!=0){if(g[_e>>2]=g[Pi>>2],g[_e+4>>2]=g[Pi+4>>2],g[_e+8>>2]=g[Pi+8>>2],g[_e+12>>2]=g[Pi+12>>2],gu(it,Ut),S=g[_e>>2]|0,k=g[17008+(S*80|0)+(g[hn>>2]<<2)>>2]|0,g[_e>>2]=g[18608+(S*80|0)+(k*20|0)>>2],L=g[18608+(S*80|0)+(k*20|0)+16>>2]|0,(L|0)>0){p=0;do s0(ot),p=p+1|0;while((p|0)<(L|0))}switch(L=18608+(S*80|0)+(k*20|0)+4|0,g[$e>>2]=g[L>>2],g[$e+4>>2]=g[L+4>>2],g[$e+8>>2]=g[L+8>>2],ca($e,(g[Nt>>2]|0)*3|0),Ln(ot,$e,ot),js(ot),gu(ot,er),yn=+(g[Wt>>2]|0),Tt[we>>3]=yn*3,Tt[re>>3]=0,Or=yn*-1.5,Tt[je>>3]=Or,Tt[ne>>3]=yn*2.598076211353316,Tt[Zr>>3]=Or,Tt[Le>>3]=yn*-2.598076211353316,g[17008+((g[_e>>2]|0)*80|0)+(g[Pi>>2]<<2)>>2]|0){case 1:{p=je,S=we;break}case 3:{p=Zr,S=je;break}case 2:{p=we,S=Zr;break}default:{p=12;break t}}Bn(Ut,er,S,p,qi),dh(qi,g[_e>>2]|0,H,1,C+8+(g[C>>2]<<4)|0),g[C>>2]=(g[C>>2]|0)+1}if((z|0)<(Ct|0)&&(gu(We,_e),dh(_e,g[Pi>>2]|0,H,1,C+8+(g[C>>2]<<4)|0),g[C>>2]=(g[C>>2]|0)+1),g[hn>>2]=g[Pi>>2],g[hn+4>>2]=g[Pi+4>>2],g[hn+8>>2]=g[Pi+8>>2],g[hn+12>>2]=g[Pi+12>>2],z=z+1|0,(z|0)>=(te|0)){p=3;break}}if((p|0)==3){wt=Sn;return}else(p|0)==12&&Mi(22474,22521,581,22531)}function c0(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0;H=wt,wt=wt+128|0,S=H+64|0,C=H,k=S,L=20208,z=k+60|0;do g[k>>2]=g[L>>2],k=k+4|0,L=L+4|0;while((k|0)<(z|0));k=C,L=20272,z=k+60|0;do g[k>>2]=g[L>>2],k=k+4|0,L=L+4|0;while((k|0)<(z|0));z=(Ho(g[m>>2]|0)|0)==0,S=z?S:C,C=p+4|0,Ge(C),Dd(C),Ho(g[m>>2]|0)|0&&(hh(C),g[m>>2]=(g[m>>2]|0)+1),g[y>>2]=g[p>>2],m=y+4|0,Ln(C,S,m),js(m),g[y+16>>2]=g[p>>2],m=y+20|0,Ln(C,S+12|0,m),js(m),g[y+32>>2]=g[p>>2],m=y+36|0,Ln(C,S+24|0,m),js(m),g[y+48>>2]=g[p>>2],m=y+52|0,Ln(C,S+36|0,m),js(m),g[y+64>>2]=g[p>>2],y=y+68|0,Ln(C,S+48|0,y),js(y),wt=H}function bf(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0;if(re=wt,wt=wt+32|0,Nt=re+12|0,z=re,Wt=p+4|0,Ct=g[16928+(m<<2)>>2]|0,ot=(S|0)!=0,Ct=ot?Ct*3|0:Ct,C=g[Wt>>2]|0,it=p+8|0,L=g[it>>2]|0,ot){if(k=p+12|0,S=g[k>>2]|0,C=L+C+S|0,(C|0)==(Ct|0))return Wt=1,wt=re,Wt|0;H=k}else H=p+12|0,S=g[H>>2]|0,C=L+C+S|0;if((C|0)<=(Ct|0))return Wt=0,wt=re,Wt|0;do if((S|0)>0){if(S=g[p>>2]|0,(L|0)>0){k=18608+(S*80|0)+60|0,S=p;break}S=18608+(S*80|0)+40|0,y?(kd(Nt,Ct,0,0),eA(Wt,Nt,z),fh(z),Ln(z,Nt,Wt),k=S,S=p):(k=S,S=p)}else k=18608+((g[p>>2]|0)*80|0)+20|0,S=p;while(!1);if(g[S>>2]=g[k>>2],C=k+16|0,(g[C>>2]|0)>0){S=0;do s0(Wt),S=S+1|0;while((S|0)<(g[C>>2]|0))}return p=k+4|0,g[Nt>>2]=g[p>>2],g[Nt+4>>2]=g[p+4>>2],g[Nt+8>>2]=g[p+8>>2],m=g[16848+(m<<2)>>2]|0,ca(Nt,ot?m*3|0:m),Ln(Wt,Nt,Wt),js(Wt),ot?S=((g[it>>2]|0)+(g[Wt>>2]|0)+(g[H>>2]|0)|0)==(Ct|0)?1:2:S=2,Wt=S,wt=re,Wt|0}function u0(p,m){p=p|0,m=m|0;var y=0;do y=bf(p,m,0,1)|0;while((y|0)==2);return y|0}function iA(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0,Zr=0,qi=0,Ei=0;if(Zr=wt,wt=wt+240|0,k=Zr+224|0,$e=Zr+208|0,er=Zr,we=Zr+192|0,je=Zr+176|0,Le=Zr+160|0,We=Zr+144|0,te=Zr+128|0,_e=Zr+112|0,Ut=Zr+96|0,g[k>>2]=m,g[$e>>2]=g[p>>2],g[$e+4>>2]=g[p+4>>2],g[$e+8>>2]=g[p+8>>2],g[$e+12>>2]=g[p+12>>2],nA($e,k,er),g[C>>2]=0,ne=S+y+((S|0)==6&1)|0,(ne|0)<=(y|0)){wt=Zr;return}H=g[k>>2]|0,it=y+6|0,ot=16928+(H<<2)|0,Ct=We+8|0,Nt=te+8|0,Wt=_e+8|0,re=we+4|0,L=0,z=y,S=-1;t:for(;;){if(k=(z|0)%6|0,p=er+(k<<4)|0,g[we>>2]=g[p>>2],g[we+4>>2]=g[p+4>>2],g[we+8>>2]=g[p+8>>2],g[we+12>>2]=g[p+12>>2],p=L,L=bf(we,H,0,1)|0,(z|0)>(y|0)&(Ho(m)|0)!=0&&(p|0)!=1&&(g[we>>2]|0)!=(S|0)){switch(gu(er+(((k+5|0)%6|0)<<4)+4|0,je),gu(er+(k<<4)+4|0,Le),qi=+(g[ot>>2]|0),Tt[We>>3]=qi*3,Tt[Ct>>3]=0,Ei=qi*-1.5,Tt[te>>3]=Ei,Tt[Nt>>3]=qi*2.598076211353316,Tt[_e>>3]=Ei,Tt[Wt>>3]=qi*-2.598076211353316,k=g[$e>>2]|0,g[17008+(k*80|0)+(((S|0)==(k|0)?g[we>>2]|0:S)<<2)>>2]|0){case 1:{p=te,S=We;break}case 3:{p=_e,S=te;break}case 2:{p=We,S=_e;break}default:{p=8;break t}}Bn(je,Le,S,p,Ut),!(qo(je,Ut)|0)&&!(qo(Le,Ut)|0)&&(dh(Ut,g[$e>>2]|0,H,1,C+8+(g[C>>2]<<4)|0),g[C>>2]=(g[C>>2]|0)+1)}if((z|0)<(it|0)&&(gu(re,je),dh(je,g[we>>2]|0,H,1,C+8+(g[C>>2]<<4)|0),g[C>>2]=(g[C>>2]|0)+1),z=z+1|0,(z|0)>=(ne|0)){p=3;break}else S=g[we>>2]|0}if((p|0)==3){wt=Zr;return}else(p|0)==8&&Mi(22557,22521,746,22602)}function nA(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0;H=wt,wt=wt+160|0,S=H+80|0,C=H,k=S,L=20336,z=k+72|0;do g[k>>2]=g[L>>2],k=k+4|0,L=L+4|0;while((k|0)<(z|0));k=C,L=20416,z=k+72|0;do g[k>>2]=g[L>>2],k=k+4|0,L=L+4|0;while((k|0)<(z|0));z=(Ho(g[m>>2]|0)|0)==0,S=z?S:C,C=p+4|0,Ge(C),Dd(C),Ho(g[m>>2]|0)|0&&(hh(C),g[m>>2]=(g[m>>2]|0)+1),g[y>>2]=g[p>>2],m=y+4|0,Ln(C,S,m),js(m),g[y+16>>2]=g[p>>2],m=y+20|0,Ln(C,S+12|0,m),js(m),g[y+32>>2]=g[p>>2],m=y+36|0,Ln(C,S+24|0,m),js(m),g[y+48>>2]=g[p>>2],m=y+52|0,Ln(C,S+36|0,m),js(m),g[y+64>>2]=g[p>>2],m=y+68|0,Ln(C,S+48|0,m),js(m),g[y+80>>2]=g[p>>2],y=y+84|0,Ln(C,S+60|0,y),js(y),wt=H}function ph(p){p=+p;var m=0;return m=p<0?p+6.283185307179586:p,+(p>=6.283185307179586?m+-6.283185307179586:m)}function us(p,m){return p=p|0,m=m|0,+li(+(+Tt[p>>3]-+Tt[m>>3]))<17453292519943298e-27?(m=+li(+(+Tt[p+8>>3]-+Tt[m+8>>3]))<17453292519943298e-27,m|0):(m=0,m|0)}function _u(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0;return C=+Tt[m>>3],S=+Tt[p>>3],k=+hi(+((C-S)*.5)),y=+hi(+((+Tt[m+8>>3]-+Tt[p+8>>3])*.5)),y=k*k+y*(+Ur(+C)*+Ur(+S)*y),+(+qr(+ +bn(+y),+ +bn(+(1-y)))*2)}function Bc(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0;return C=+Tt[m>>3],S=+Tt[p>>3],k=+hi(+((C-S)*.5)),y=+hi(+((+Tt[m+8>>3]-+Tt[p+8>>3])*.5)),y=k*k+y*(+Ur(+C)*+Ur(+S)*y),+(+qr(+ +bn(+y),+ +bn(+(1-y)))*2*6371.007180918475)}function h0(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0;return C=+Tt[m>>3],S=+Tt[p>>3],k=+hi(+((C-S)*.5)),y=+hi(+((+Tt[m+8>>3]-+Tt[p+8>>3])*.5)),y=k*k+y*(+Ur(+C)*+Ur(+S)*y),+(+qr(+ +bn(+y),+ +bn(+(1-y)))*2*6371.007180918475*1e3)}function Od(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0;return k=+Tt[m>>3],S=+Ur(+k),C=+Tt[m+8>>3]-+Tt[p+8>>3],L=S*+hi(+C),y=+Tt[p>>3],+ +qr(+L,+(+hi(+k)*+Ur(+y)-+Ur(+C)*(S*+hi(+y))))}function f0(p,m,y,S){p=p|0,m=+m,y=+y,S=S|0;var C=0,k=0,L=0,z=0;if(y<1e-16){g[S>>2]=g[p>>2],g[S+4>>2]=g[p+4>>2],g[S+8>>2]=g[p+8>>2],g[S+12>>2]=g[p+12>>2];return}k=m<0?m+6.283185307179586:m,k=m>=6.283185307179586?k+-6.283185307179586:k;do if(k<1e-16)m=+Tt[p>>3]+y,Tt[S>>3]=m,C=S;else{if(C=+li(+(k+-3.141592653589793))<1e-16,m=+Tt[p>>3],C){m=m-y,Tt[S>>3]=m,C=S;break}if(L=+Ur(+y),y=+hi(+y),m=L*+hi(+m)+ +Ur(+k)*(y*+Ur(+m)),m=m>1?1:m,m=+h_(+(m<-1?-1:m)),Tt[S>>3]=m,+li(+(m+-1.5707963267948966))<1e-16){Tt[S>>3]=1.5707963267948966,Tt[S+8>>3]=0;return}if(+li(+(m+1.5707963267948966))<1e-16){Tt[S>>3]=-1.5707963267948966,Tt[S+8>>3]=0;return}if(z=+Ur(+m),k=y*+hi(+k)/z,y=+Tt[p>>3],m=(L-+hi(+m)*+hi(+y))/+Ur(+y)/z,L=k>1?1:k,m=m>1?1:m,m=+Tt[p+8>>3]+ +qr(+(L<-1?-1:L),+(m<-1?-1:m)),m>3.141592653589793)do m=m+-6.283185307179586;while(m>3.141592653589793);if(m<-3.141592653589793)do m=m+6.283185307179586;while(m<-3.141592653589793);Tt[S+8>>3]=m;return}while(!1);if(+li(+(m+-1.5707963267948966))<1e-16){Tt[C>>3]=1.5707963267948966,Tt[S+8>>3]=0;return}if(+li(+(m+1.5707963267948966))<1e-16){Tt[C>>3]=-1.5707963267948966,Tt[S+8>>3]=0;return}if(m=+Tt[p+8>>3],m>3.141592653589793)do m=m+-6.283185307179586;while(m>3.141592653589793);if(m<-3.141592653589793)do m=m+6.283185307179586;while(m<-3.141592653589793);Tt[S+8>>3]=m}function v_(p){return p=p|0,+ +Tt[20496+(p<<3)>>3]}function ua(p){return p=p|0,+ +Tt[20624+(p<<3)>>3]}function un(p){return p=p|0,+ +Tt[20752+(p<<3)>>3]}function sA(p){return p=p|0,+ +Tt[20880+(p<<3)>>3]}function d0(p){p=p|0;var m=0;return m=21008+(p<<3)|0,p=g[m>>2]|0,Je(g[m+4>>2]|0),p|0}function Ah(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0;return Nt=+Tt[m>>3],ot=+Tt[p>>3],H=+hi(+((Nt-ot)*.5)),k=+Tt[m+8>>3],it=+Tt[p+8>>3],L=+hi(+((k-it)*.5)),z=+Ur(+ot),Ct=+Ur(+Nt),L=H*H+L*(Ct*z*L),L=+qr(+ +bn(+L),+ +bn(+(1-L)))*2,H=+Tt[y>>3],Nt=+hi(+((H-Nt)*.5)),S=+Tt[y+8>>3],k=+hi(+((S-k)*.5)),C=+Ur(+H),k=Nt*Nt+k*(Ct*C*k),k=+qr(+ +bn(+k),+ +bn(+(1-k)))*2,H=+hi(+((ot-H)*.5)),S=+hi(+((it-S)*.5)),S=H*H+S*(z*C*S),S=+qr(+ +bn(+S),+ +bn(+(1-S)))*2,C=(L+k+S)*.5,+(+Md(+ +bn(+(+To(+(C*.5))*+To(+((C-L)*.5))*+To(+((C-k)*.5))*+To(+((C-S)*.5)))))*4)}function x_(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0;if(k=wt,wt=wt+192|0,S=k+168|0,C=k,l(p,m,S),d(p,m,C),m=g[C>>2]|0,(m|0)<=0)return y=0,wt=k,+y;if(y=+Ah(C+8|0,C+8+(((m|0)!=1&1)<<4)|0,S)+0,(m|0)==1)return wt=k,+y;p=1;do L=p,p=p+1|0,y=y+ +Ah(C+8+(L<<4)|0,C+8+(((p|0)%(m|0)|0)<<4)|0,S);while((p|0)<(m|0));return wt=k,+y}function b_(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0;if(k=wt,wt=wt+192|0,S=k+168|0,C=k,l(p,m,S),d(p,m,C),m=g[C>>2]|0,(m|0)>0){if(y=+Ah(C+8|0,C+8+(((m|0)!=1&1)<<4)|0,S)+0,(m|0)!=1){p=1;do L=p,p=p+1|0,y=y+ +Ah(C+8+(L<<4)|0,C+8+(((p|0)%(m|0)|0)<<4)|0,S);while((p|0)<(m|0))}}else y=0;return wt=k,+(y*6371.007180918475*6371.007180918475)}function Nx(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0;if(k=wt,wt=wt+192|0,S=k+168|0,C=k,l(p,m,S),d(p,m,C),m=g[C>>2]|0,(m|0)>0){if(y=+Ah(C+8|0,C+8+(((m|0)!=1&1)<<4)|0,S)+0,(m|0)!=1){p=1;do L=p,p=p+1|0,y=y+ +Ah(C+8+(L<<4)|0,C+8+(((p|0)%(m|0)|0)<<4)|0,S);while((p|0)<(m|0))}}else y=0;return wt=k,+(y*6371.007180918475*6371.007180918475*1e3*1e3)}function Mo(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0;if(L=wt,wt=wt+176|0,k=L,pt(p,m,k),p=g[k>>2]|0,(p|0)<=1)return C=0,wt=L,+C;m=p+-1|0,p=0,y=0,S=+Tt[k+8>>3],C=+Tt[k+16>>3];do p=p+1|0,H=S,S=+Tt[k+8+(p<<4)>>3],it=+hi(+((S-H)*.5)),z=C,C=+Tt[k+8+(p<<4)+8>>3],z=+hi(+((C-z)*.5)),z=it*it+z*(+Ur(+S)*+Ur(+H)*z),y=y+ +qr(+ +bn(+z),+ +bn(+(1-z)))*2;while((p|0)<(m|0));return wt=L,+y}function oA(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0;if(L=wt,wt=wt+176|0,k=L,pt(p,m,k),p=g[k>>2]|0,(p|0)<=1)return C=0,wt=L,+C;m=p+-1|0,p=0,y=0,S=+Tt[k+8>>3],C=+Tt[k+16>>3];do p=p+1|0,H=S,S=+Tt[k+8+(p<<4)>>3],it=+hi(+((S-H)*.5)),z=C,C=+Tt[k+8+(p<<4)+8>>3],z=+hi(+((C-z)*.5)),z=it*it+z*(+Ur(+H)*+Ur(+S)*z),y=y+ +qr(+ +bn(+z),+ +bn(+(1-z)))*2;while((p|0)!=(m|0));return it=y*6371.007180918475,wt=L,+it}function nr(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0;if(L=wt,wt=wt+176|0,k=L,pt(p,m,k),p=g[k>>2]|0,(p|0)<=1)return C=0,wt=L,+C;m=p+-1|0,p=0,y=0,S=+Tt[k+8>>3],C=+Tt[k+16>>3];do p=p+1|0,H=S,S=+Tt[k+8+(p<<4)>>3],it=+hi(+((S-H)*.5)),z=C,C=+Tt[k+8+(p<<4)+8>>3],z=+hi(+((C-z)*.5)),z=it*it+z*(+Ur(+H)*+Ur(+S)*z),y=y+ +qr(+ +bn(+z),+ +bn(+(1-z)))*2;while((p|0)!=(m|0));return it=y*6371.007180918475*1e3,wt=L,+it}function dr(p,m){return p=p|0,m=m|0,m=me(p|0,m|0,52)|0,It()|0,m&15|0}function wf(p,m){return p=p|0,m=m|0,m=me(p|0,m|0,45)|0,It()|0,m&127|0}function aA(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0;if(!(!0&(m&-16777216|0)==134217728)||(L=me(p|0,m|0,45)|0,It()|0,L=L&127,L>>>0>121))return m=0,m|0;y=me(p|0,m|0,52)|0,It()|0,y=y&15;do if(y|0){for(C=1,S=0;;){if(k=me(p|0,m|0,(15-C|0)*3|0)|0,It()|0,k=k&7,(k|0)!=0&(S^1))if((k|0)==1&(fi(L)|0)!=0){z=0,S=13;break}else S=1;if((k|0)==7){z=0,S=13;break}if(C>>>0>>0)C=C+1|0;else{S=9;break}}if((S|0)==9){if((y|0)==15)z=1;else break;return z|0}else if((S|0)==13)return z|0}while(!1);for(;;){if(z=me(p|0,m|0,(14-y|0)*3|0)|0,It()|0,!((z&7|0)==7&!0)){z=0,S=13;break}if(y>>>0<14)y=y+1|0;else{z=1,S=13;break}}return(S|0)==13?z|0:0}function Bd(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;if(S=me(p|0,m|0,52)|0,It()|0,S=S&15,(S|0)>=(y|0)){if((S|0)!=(y|0))if(y>>>0<=15){if(C=ke(y|0,0,52)|0,p=C|p,m=It()|0|m&-15728641,(S|0)>(y|0))do C=ke(7,0,(14-y|0)*3|0)|0,y=y+1|0,p=C|p,m=It()|0|m;while((y|0)<(S|0))}else m=0,p=0}else m=0,p=0;return Je(m|0),p|0}function Hn(p,m,y){return p=p|0,m=m|0,y=y|0,p=me(p|0,m|0,52)|0,It()|0,p=p&15,(y|0)<16&(p|0)<=(y|0)?(y=Ze(7,y-p|0)|0,y|0):(y=0,y|0)}function uo(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0;if(L=me(p|0,m|0,52)|0,It()|0,L=L&15,!!((y|0)<16&(L|0)<=(y|0))){if((L|0)==(y|0)){y=S,g[y>>2]=p,g[y+4>>2]=m;return}if(H=Ze(7,y-L|0)|0,it=(H|0)/7|0,z=me(p|0,m|0,45)|0,It()|0,!(fi(z&127)|0))k=0;else{t:do if(!L)C=0;else for(k=1;;){if(C=me(p|0,m|0,(15-k|0)*3|0)|0,It()|0,C=C&7,C|0)break t;if(k>>>0>>0)k=k+1|0;else{C=0;break}}while(!1);k=(C|0)==0}if(ot=ke(L+1|0,0,52)|0,C=It()|0|m&-15728641,z=(14-L|0)*3|0,m=ke(7,0,z|0)|0,m=(ot|p)&~m,L=C&~(It()|0),uo(m,L,y,S),C=S+(it<<3)|0,!k){ot=ke(1,0,z|0)|0,uo(ot|m,It()|0|L,y,C),ot=C+(it<<3)|0,H=ke(2,0,z|0)|0,uo(H|m,It()|0|L,y,ot),ot=ot+(it<<3)|0,H=ke(3,0,z|0)|0,uo(H|m,It()|0|L,y,ot),ot=ot+(it<<3)|0,H=ke(4,0,z|0)|0,uo(H|m,It()|0|L,y,ot),ot=ot+(it<<3)|0,H=ke(5,0,z|0)|0,uo(H|m,It()|0|L,y,ot),H=ke(6,0,z|0)|0,uo(H|m,It()|0|L,y,ot+(it<<3)|0);return}k=C+(it<<3)|0,(H|0)>6&&(H=C+8|0,ot=(k>>>0>H>>>0?k:H)+-1+(0-C)|0,Fc(C|0,0,ot+8&-8|0)|0,C=H+(ot>>>3<<3)|0),ot=ke(2,0,z|0)|0,uo(ot|m,It()|0|L,y,C),ot=C+(it<<3)|0,H=ke(3,0,z|0)|0,uo(H|m,It()|0|L,y,ot),ot=ot+(it<<3)|0,H=ke(4,0,z|0)|0,uo(H|m,It()|0|L,y,ot),ot=ot+(it<<3)|0,H=ke(5,0,z|0)|0,uo(H|m,It()|0|L,y,ot),H=ke(6,0,z|0)|0,uo(H|m,It()|0|L,y,ot+(it<<3)|0)}}function ji(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;if(C=me(p|0,m|0,45)|0,It()|0,!(fi(C&127)|0))return C=0,C|0;C=me(p|0,m|0,52)|0,It()|0,C=C&15;t:do if(!C)y=0;else for(S=1;;){if(y=me(p|0,m|0,(15-S|0)*3|0)|0,It()|0,y=y&7,y|0)break t;if(S>>>0>>0)S=S+1|0;else{y=0;break}}while(!1);return C=(y|0)==0&1,C|0}function w_(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;if(S=me(p|0,m|0,52)|0,It()|0,S=S&15,(y|0)<16&(S|0)<=(y|0)){if((S|0)!=(y|0)&&(C=ke(y|0,0,52)|0,p=C|p,m=It()|0|m&-15728641,(S|0)<(y|0)))do C=ke(7,0,(14-S|0)*3|0)|0,S=S+1|0,p=p&~C,m=m&~(It()|0);while((S|0)<(y|0))}else m=0,p=0;return Je(m|0),p|0}function mh(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0;if(!y)return we=0,we|0;if(C=p,S=g[C>>2]|0,C=g[C+4>>2]|0,!0&(C&15728640|0)==0){if((y|0)<=0||(we=m,g[we>>2]=S,g[we+4>>2]=C,(y|0)==1))return we=0,we|0;S=1;do $e=p+(S<<3)|0,er=g[$e+4>>2]|0,we=m+(S<<3)|0,g[we>>2]=g[$e>>2],g[we+4>>2]=er,S=S+1|0;while((S|0)!=(y|0));return S=0,S|0}if($e=y<<3,er=ho($e)|0,!er)return we=-3,we|0;if(Va(er|0,p|0,$e|0)|0,Ut=Ua(y,8)|0,!Ut)return Gr(er),we=-3,we|0;S=y;t:for(;;){L=er,ot=g[L>>2]|0,L=g[L+4>>2]|0,te=me(ot|0,L|0,52)|0,It()|0,te=te&15,_e=te+-1|0,We=(S|0)>0;e:do if(We){if(Le=((S|0)<0)<<31>>31,re=ke(_e|0,0,52)|0,ne=It()|0,_e>>>0>15)for(C=0,p=ot,y=L;;){if(!((p|0)==0&(y|0)==0)){if(k=me(p|0,y|0,52)|0,It()|0,k=k&15,z=(k|0)<(_e|0),k=(k|0)==(_e|0),it=z?0:k?p:0,p=z?0:k?y:0,y=Yo(it|0,p|0,S|0,Le|0)|0,It()|0,k=Ut+(y<<3)|0,z=k,H=g[z>>2]|0,z=g[z+4>>2]|0,(H|0)==0&(z|0)==0)y=it;else for(re=0,Wt=y,Nt=z,y=it;;){if((re|0)>(S|0)){we=41;break t}if((H|0)==(y|0)&(Nt&-117440513|0)==(p|0)){it=me(H|0,Nt|0,56)|0,It()|0,it=it&7,Ct=it+1|0,ne=me(H|0,Nt|0,45)|0,It()|0;r:do if(!(fi(ne&127)|0))z=7;else{if(H=me(H|0,Nt|0,52)|0,It()|0,H=H&15,!H){z=6;break}for(z=1;;){if(ne=ke(7,0,(15-z|0)*3|0)|0,!((ne&y|0)==0&((It()|0)&p|0)==0)){z=7;break r}if(z>>>0>>0)z=z+1|0;else{z=6;break}}}while(!1);if((it+2|0)>>>0>z>>>0){we=51;break t}ne=ke(Ct|0,0,56)|0,p=It()|0|p&-117440513,z=k,g[z>>2]=0,g[z+4>>2]=0,z=Wt,y=ne|y}else z=(Wt+1|0)%(S|0)|0;if(k=Ut+(z<<3)|0,Nt=k,H=g[Nt>>2]|0,Nt=g[Nt+4>>2]|0,(H|0)==0&(Nt|0)==0)break;re=re+1|0,Wt=z}ne=k,g[ne>>2]=y,g[ne+4>>2]=p}if(C=C+1|0,(C|0)>=(S|0))break e;y=er+(C<<3)|0,p=g[y>>2]|0,y=g[y+4>>2]|0}for(C=0,p=ot,y=L;;){if(!((p|0)==0&(y|0)==0)){if(z=me(p|0,y|0,52)|0,It()|0,z=z&15,(z|0)>=(_e|0)){if((z|0)!=(_e|0)&&(p=p|re,y=y&-15728641|ne,z>>>0>=te>>>0)){k=_e;do Wt=ke(7,0,(14-k|0)*3|0)|0,k=k+1|0,p=Wt|p,y=It()|0|y;while(k>>>0>>0)}}else p=0,y=0;if(z=Yo(p|0,y|0,S|0,Le|0)|0,It()|0,k=Ut+(z<<3)|0,H=k,it=g[H>>2]|0,H=g[H+4>>2]|0,!((it|0)==0&(H|0)==0))for(Wt=0;;){if((Wt|0)>(S|0)){we=41;break t}if((it|0)==(p|0)&(H&-117440513|0)==(y|0)){Ct=me(it|0,H|0,56)|0,It()|0,Ct=Ct&7,Nt=Ct+1|0,je=me(it|0,H|0,45)|0,It()|0;r:do if(!(fi(je&127)|0))H=7;else{if(it=me(it|0,H|0,52)|0,It()|0,it=it&15,!it){H=6;break}for(H=1;;){if(je=ke(7,0,(15-H|0)*3|0)|0,!((je&p|0)==0&((It()|0)&y|0)==0)){H=7;break r}if(H>>>0>>0)H=H+1|0;else{H=6;break}}}while(!1);if((Ct+2|0)>>>0>H>>>0){we=51;break t}je=ke(Nt|0,0,56)|0,y=It()|0|y&-117440513,Nt=k,g[Nt>>2]=0,g[Nt+4>>2]=0,p=je|p}else z=(z+1|0)%(S|0)|0;if(k=Ut+(z<<3)|0,H=k,it=g[H>>2]|0,H=g[H+4>>2]|0,(it|0)==0&(H|0)==0)break;Wt=Wt+1|0}je=k,g[je>>2]=p,g[je+4>>2]=y}if(C=C+1|0,(C|0)>=(S|0))break e;y=er+(C<<3)|0,p=g[y>>2]|0,y=g[y+4>>2]|0}}while(!1);if((S+5|0)>>>0<11){we=99;break}if(ne=Ua((S|0)/6|0,8)|0,!ne){we=58;break}e:do if(We){Wt=0,Nt=0;do{if(z=Ut+(Wt<<3)|0,p=z,C=g[p>>2]|0,p=g[p+4>>2]|0,!((C|0)==0&(p|0)==0)){H=me(C|0,p|0,56)|0,It()|0,H=H&7,y=H+1|0,it=p&-117440513,je=me(C|0,p|0,45)|0,It()|0;r:do if(fi(je&127)|0){if(Ct=me(C|0,p|0,52)|0,It()|0,Ct=Ct&15,Ct|0)for(k=1;;){if(je=ke(7,0,(15-k|0)*3|0)|0,!((C&je|0)==0&(it&(It()|0)|0)==0))break r;if(k>>>0>>0)k=k+1|0;else break}p=ke(y|0,0,56)|0,C=p|C,p=It()|0|it,y=z,g[y>>2]=C,g[y+4>>2]=p,y=H+2|0}while(!1);(y|0)==7&&(je=ne+(Nt<<3)|0,g[je>>2]=C,g[je+4>>2]=p&-117440513,Nt=Nt+1|0)}Wt=Wt+1|0}while((Wt|0)!=(S|0));if(We){if(re=((S|0)<0)<<31>>31,Ct=ke(_e|0,0,52)|0,Wt=It()|0,_e>>>0>15)for(p=0,C=0;;){do if(!((ot|0)==0&(L|0)==0)){for(H=me(ot|0,L|0,52)|0,It()|0,H=H&15,k=(H|0)<(_e|0),H=(H|0)==(_e|0),z=k?0:H?ot:0,H=k?0:H?L:0,k=Yo(z|0,H|0,S|0,re|0)|0,It()|0,y=0;;){if((y|0)>(S|0)){we=98;break t}if(je=Ut+(k<<3)|0,it=g[je+4>>2]|0,(it&-117440513|0)==(H|0)&&(g[je>>2]|0)==(z|0)){we=70;break}if(k=(k+1|0)%(S|0)|0,je=Ut+(k<<3)|0,(g[je>>2]|0)==(z|0)&&(g[je+4>>2]|0)==(H|0))break;y=y+1|0}if((we|0)==70&&(we=0,!0&(it&117440512|0)==100663296))break;je=m+(C<<3)|0,g[je>>2]=ot,g[je+4>>2]=L,C=C+1|0}while(!1);if(p=p+1|0,(p|0)>=(S|0)){S=Nt;break e}L=er+(p<<3)|0,ot=g[L>>2]|0,L=g[L+4>>2]|0}for(p=0,C=0;;){do if(!((ot|0)==0&(L|0)==0)){if(H=me(ot|0,L|0,52)|0,It()|0,H=H&15,(H|0)>=(_e|0))if((H|0)!=(_e|0))if(y=ot|Ct,k=L&-15728641|Wt,H>>>0>>0)H=k;else{z=_e;do je=ke(7,0,(14-z|0)*3|0)|0,z=z+1|0,y=je|y,k=It()|0|k;while(z>>>0>>0);H=k}else y=ot,H=L;else y=0,H=0;for(z=Yo(y|0,H|0,S|0,re|0)|0,It()|0,k=0;;){if((k|0)>(S|0)){we=98;break t}if(je=Ut+(z<<3)|0,it=g[je+4>>2]|0,(it&-117440513|0)==(H|0)&&(g[je>>2]|0)==(y|0)){we=93;break}if(z=(z+1|0)%(S|0)|0,je=Ut+(z<<3)|0,(g[je>>2]|0)==(y|0)&&(g[je+4>>2]|0)==(H|0))break;k=k+1|0}if((we|0)==93&&(we=0,!0&(it&117440512|0)==100663296))break;je=m+(C<<3)|0,g[je>>2]=ot,g[je+4>>2]=L,C=C+1|0}while(!1);if(p=p+1|0,(p|0)>=(S|0)){S=Nt;break e}L=er+(p<<3)|0,ot=g[L>>2]|0,L=g[L+4>>2]|0}}else C=0,S=Nt}else C=0,S=0;while(!1);if(Fc(Ut|0,0,$e|0)|0,Va(er|0,ne|0,S<<3|0)|0,Gr(ne),S)m=m+(C<<3)|0;else break}return(we|0)==41?(Gr(er),Gr(Ut),je=-1,je|0):(we|0)==51?(Gr(er),Gr(Ut),je=-2,je|0):(we|0)==58?(Gr(er),Gr(Ut),je=-3,je|0):(we|0)==98?(Gr(ne),Gr(er),Gr(Ut),je=-1,je|0):((we|0)==99&&Va(m|0,er|0,S<<3|0)|0,Gr(er),Gr(Ut),je=0,je|0)}function kn(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0;if((m|0)<=0)return C=0,C|0;if((C|0)>=16){for(k=0;;){if(ot=p+(k<<3)|0,!((g[ot>>2]|0)==0&(g[ot+4>>2]|0)==0)){k=14;break}if(k=k+1|0,(k|0)>=(m|0)){L=0,k=16;break}}if((k|0)==14)return((S|0)>0?-2:-1)|0;if((k|0)==16)return L|0}k=0,ot=0;t:for(;;){it=p+(ot<<3)|0,z=it,L=g[z>>2]|0,z=g[z+4>>2]|0;do if(!((L|0)==0&(z|0)==0)){if((k|0)>=(S|0)){L=-1,k=16;break t}if(H=me(L|0,z|0,52)|0,It()|0,H=H&15,(H|0)>(C|0)){L=-2,k=16;break t}if((H|0)==(C|0)){it=y+(k<<3)|0,g[it>>2]=L,g[it+4>>2]=z,k=k+1|0;break}if(L=(Ze(7,C-H|0)|0)+k|0,(L|0)>(S|0)){L=-1,k=16;break t}uo(g[it>>2]|0,g[it+4>>2]|0,C,y+(k<<3)|0),k=L}while(!1);if(ot=ot+1|0,(ot|0)>=(m|0)){L=0,k=16;break}}return(k|0)==16?L|0:0}function wn(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0;if((m|0)<=0)return y=0,y|0;if((y|0)>=16){for(S=0;;){if(L=p+(S<<3)|0,!((g[L>>2]|0)==0&(g[L+4>>2]|0)==0)){S=-1,C=13;break}if(S=S+1|0,(S|0)>=(m|0)){S=0,C=13;break}}if((C|0)==13)return S|0}S=0,L=0;t:for(;;){C=p+(L<<3)|0,k=g[C>>2]|0,C=g[C+4>>2]|0;do if(!((k|0)==0&(C|0)==0)){if(C=me(k|0,C|0,52)|0,It()|0,C=C&15,(C|0)>(y|0)){S=-1,C=13;break t}if((C|0)==(y|0)){S=S+1|0;break}else{S=(Ze(7,y-C|0)|0)+S|0;break}}while(!1);if(L=L+1|0,(L|0)>=(m|0)){C=13;break}}return(C|0)==13?S|0:0}function Sf(p,m){return p=p|0,m=m|0,m=me(p|0,m|0,52)|0,It()|0,m&1|0}function Es(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;if(C=me(p|0,m|0,52)|0,It()|0,C=C&15,!C)return C=0,C|0;for(S=1;;){if(y=me(p|0,m|0,(15-S|0)*3|0)|0,It()|0,y=y&7,y|0){S=5;break}if(S>>>0>>0)S=S+1|0;else{y=0,S=5;break}}return(S|0)==5?y|0:0}function gh(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0;if(H=me(p|0,m|0,52)|0,It()|0,H=H&15,!H)return z=m,H=p,Je(z|0),H|0;for(z=1,y=0;;){k=(15-z|0)*3|0,S=ke(7,0,k|0)|0,C=It()|0,L=me(p|0,m|0,k|0)|0,It()|0,k=ke(Na(L&7)|0,0,k|0)|0,L=It()|0,p=k|p&~S,m=L|m&~C;t:do if(!y)if((k&S|0)==0&(L&C|0)==0)y=0;else if(S=me(p|0,m|0,52)|0,It()|0,S=S&15,!S)y=1;else{y=1;e:for(;;){switch(L=me(p|0,m|0,(15-y|0)*3|0)|0,It()|0,L&7){case 1:break e;case 0:break;default:{y=1;break 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0:break;default:{y=1;break t}}if(y>>>0>>0)y=y+1|0;else{y=1;break t}}for(y=1;;)if(C=(15-y|0)*3|0,k=ke(7,0,C|0)|0,L=m&~(It()|0),m=me(p|0,m|0,C|0)|0,It()|0,m=ke(co(m&7)|0,0,C|0)|0,p=p&~k|m,m=L|(It()|0),y>>>0>>0)y=y+1|0;else{y=1;break}}while(!1);if(z>>>0>>0)z=z+1|0;else break}return Je(m|0),p|0}function Fd(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0;if(S=me(p|0,m|0,52)|0,It()|0,S=S&15,!S)return y=m,S=p,Je(y|0),S|0;for(y=1;L=(15-y|0)*3|0,k=ke(7,0,L|0)|0,C=m&~(It()|0),m=me(p|0,m|0,L|0)|0,It()|0,m=ke(co(m&7)|0,0,L|0)|0,p=m|p&~k,m=It()|0|C,y>>>0>>0;)y=y+1|0;return Je(m|0),p|0}function Tf(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0;if(H=wt,wt=wt+64|0,z=H+40|0,S=H+24|0,C=H+12|0,k=H,ke(m|0,0,52)|0,y=It()|0|134225919,!m)return(g[p+4>>2]|0)>2||(g[p+8>>2]|0)>2||(g[p+12>>2]|0)>2?(L=0,z=0,Je(L|0),wt=H,z|0):(ke(vf(p)|0,0,45)|0,L=It()|0|y,z=-1,Je(L|0),wt=H,z|0);if(g[z>>2]=g[p>>2],g[z+4>>2]=g[p+4>>2],g[z+8>>2]=g[p+8>>2],g[z+12>>2]=g[p+12>>2],L=z+4|0,(m|0)>0)for(p=-1;g[S>>2]=g[L>>2],g[S+4>>2]=g[L+4>>2],g[S+8>>2]=g[L+8>>2],m&1?(Rd(L),g[C>>2]=g[L>>2],g[C+4>>2]=g[L+4>>2],g[C+8>>2]=g[L+8>>2],za(C)):(Al(L),g[C>>2]=g[L>>2],g[C+4>>2]=g[L+4>>2],g[C+8>>2]=g[L+8>>2],hh(C)),eA(S,C,k),js(k),ot=(15-m|0)*3|0,it=ke(7,0,ot|0)|0,y=y&~(It()|0),ot=ke(Fa(k)|0,0,ot|0)|0,p=ot|p&~it,y=It()|0|y,(m|0)>1;)m=m+-1|0;else p=-1;t:do if((g[L>>2]|0)<=2&&(g[z+8>>2]|0)<=2&&(g[z+12>>2]|0)<=2){if(S=vf(z)|0,m=ke(S|0,0,45)|0,m=m|p,p=It()|0|y&-1040385,k=Kp(z)|0,!(fi(S)|0)){if((k|0)<=0)break;for(C=0;;){if(S=me(m|0,p|0,52)|0,It()|0,S=S&15,S)for(y=1;ot=(15-y|0)*3|0,z=me(m|0,p|0,ot|0)|0,It()|0,it=ke(7,0,ot|0)|0,p=p&~(It()|0),ot=ke(Na(z&7)|0,0,ot|0)|0,m=m&~it|ot,p=p|(It()|0),y>>>0>>0;)y=y+1|0;if(C=C+1|0,(C|0)==(k|0))break t}}C=me(m|0,p|0,52)|0,It()|0,C=C&15;e:do if(C){y=1;r:for(;;){switch(ot=me(m|0,p|0,(15-y|0)*3|0)|0,It()|0,ot&7){case 1:break r;case 0:break;default:break e}if(y>>>0>>0)y=y+1|0;else break e}if(ch(S,g[z>>2]|0)|0)for(y=1;z=(15-y|0)*3|0,it=ke(7,0,z|0)|0,ot=p&~(It()|0),p=me(m|0,p|0,z|0)|0,It()|0,p=ke(co(p&7)|0,0,z|0)|0,m=m&~it|p,p=ot|(It()|0),y>>>0>>0;)y=y+1|0;else for(y=1;ot=(15-y|0)*3|0,z=me(m|0,p|0,ot|0)|0,It()|0,it=ke(7,0,ot|0)|0,p=p&~(It()|0),ot=ke(Na(z&7)|0,0,ot|0)|0,m=m&~it|ot,p=p|(It()|0),y>>>0>>0;)y=y+1|0}while(!1);if((k|0)>0){y=0;do m=gh(m,p)|0,p=It()|0,y=y+1|0;while((y|0)!=(k|0))}}else 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t}}if(S>>>0>>0)S=S+1|0;else{S=m;break t}}for(C=1,S=m;m=(15-C|0)*3|0,L=ke(7,0,m|0)|0,z=S&~(It()|0),S=me(p|0,S|0,m|0)|0,It()|0,S=ke(co(S&7)|0,0,m|0)|0,p=p&~L|S,S=z|(It()|0),C>>>0>>0;)C=C+1|0}else S=m;while(!1);if(z=7728+(it*28|0)|0,g[y>>2]=g[z>>2],g[y+4>>2]=g[z+4>>2],g[y+8>>2]=g[z+8>>2],g[y+12>>2]=g[z+12>>2],!(bi(p,S,y)|0)){wt=ot;return}if(L=y+4|0,g[H>>2]=g[L>>2],g[H+4>>2]=g[L+4>>2],g[H+8>>2]=g[L+8>>2],k=me(p|0,S|0,52)|0,It()|0,z=k&15,k&1?(hh(L),k=z+1|0):k=z,!(fi(it)|0))S=0;else{t:do if(!z)S=0;else for(m=1;;){if(C=me(p|0,S|0,(15-m|0)*3|0)|0,It()|0,C=C&7,C|0){S=C;break t}if(m>>>0>>0)m=m+1|0;else{S=0;break}}while(!1);S=(S|0)==4&1}if(!(bf(y,k,S,0)|0))(k|0)!=(z|0)&&(g[L>>2]=g[H>>2],g[L+4>>2]=g[H+4>>2],g[L+8>>2]=g[H+8>>2]);else{if(fi(it)|0)do;while(bf(y,k,0,0)|0);(k|0)!=(z|0)&&Al(L)}wt=ot}function l(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;S=wt,wt=wt+16|0,C=S,T(p,m,C),m=me(p|0,m|0,52)|0,It()|0,y_(C,m&15,y),wt=S}function d(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0;L=wt,wt=wt+16|0,k=L,T(p,m,k),S=me(p|0,m|0,45)|0,It()|0,S=(fi(S&127)|0)==0,C=me(p|0,m|0,52)|0,It()|0,C=C&15;t:do if(!S){if(C|0)for(S=1;;){if(z=ke(7,0,(15-S|0)*3|0)|0,!((z&p|0)==0&((It()|0)&m|0)==0))break t;if(S>>>0>>0)S=S+1|0;else break}l0(k,C,0,5,y),wt=L;return}while(!1);iA(k,C,0,6,y),wt=L}function v(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;if(S=me(p|0,m|0,45)|0,It()|0,!(fi(S&127)|0))return S=2,S|0;if(S=me(p|0,m|0,52)|0,It()|0,S=S&15,!S)return S=5,S|0;for(y=1;;){if(C=ke(7,0,(15-y|0)*3|0)|0,!((C&p|0)==0&((It()|0)&m|0)==0)){y=2,p=6;break}if(y>>>0>>0)y=y+1|0;else{y=5,p=6;break}}return(p|0)==6?y|0:0}function b(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0;Ct=wt,wt=wt+128|0,it=Ct+112|0,k=Ct+96|0,ot=Ct,C=me(p|0,m|0,52)|0,It()|0,z=C&15,g[it>>2]=z,L=me(p|0,m|0,45)|0,It()|0,L=L&127;t:do if(fi(L)|0){if(z|0)for(S=1;;){if(H=ke(7,0,(15-S|0)*3|0)|0,!((H&p|0)==0&((It()|0)&m|0)==0)){C=0;break t}if(S>>>0>>0)S=S+1|0;else break}if(C&1)C=1;else{H=ke(z+1|0,0,52)|0,ot=It()|0|m&-15728641,it=ke(7,0,(14-z|0)*3|0)|0,b((H|p)&~it,ot&~(It()|0),y),wt=Ct;return}}else C=0;while(!1);T(p,m,k),C?(c0(k,it,ot),H=5):(nA(k,it,ot),H=6);t:do if(fi(L)|0)if(!z)S=20;else for(S=1;;){if(L=ke(7,0,(15-S|0)*3|0)|0,!((L&p|0)==0&((It()|0)&m|0)==0)){S=8;break t}if(S>>>0>>0)S=S+1|0;else{S=20;break}}else S=8;while(!1);if(Fc(y|0,-1,S|0)|0,C){C=0;do{for(k=ot+(C<<4)|0,u0(k,g[it>>2]|0)|0,k=g[k>>2]|0,S=0;L=y+(S<<2)|0,z=g[L>>2]|0,!((z|0)==-1|(z|0)==(k|0));)S=S+1|0;g[L>>2]=k,C=C+1|0}while((C|0)!=(H|0))}else{C=0;do{for(k=ot+(C<<4)|0,bf(k,g[it>>2]|0,0,1)|0,k=g[k>>2]|0,S=0;L=y+(S<<2)|0,z=g[L>>2]|0,!((z|0)==-1|(z|0)==(k|0));)S=S+1|0;g[L>>2]=k,C=C+1|0}while((C|0)!=(H|0))}wt=Ct}function M(){return 12}function O(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0;if(ke(p|0,0,52)|0,z=It()|0|134225919,(p|0)<1){S=0,y=0;do fi(S)|0&&(ke(S|0,0,45)|0,L=z|(It()|0),p=m+(y<<3)|0,g[p>>2]=-1,g[p+4>>2]=L,y=y+1|0),S=S+1|0;while((S|0)!=122);return}L=0,y=0;do{if(fi(L)|0){for(ke(L|0,0,45)|0,S=1,C=-1,k=z|(It()|0);H=ke(7,0,(15-S|0)*3|0)|0,C=C&~H,k=k&~(It()|0),(S|0)!=(p|0);)S=S+1|0;H=m+(y<<3)|0,g[H>>2]=C,g[H+4>>2]=k,y=y+1|0}L=L+1|0}while((L|0)!=122)}function B(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0;if(z=wt,wt=wt+64|0,L=z,(p|0)==(y|0)&(m|0)==(S|0)|(!1|(m&2013265920|0)!=134217728|(!1|(S&2013265920|0)!=134217728))||(C=me(p|0,m|0,52)|0,It()|0,C=C&15,k=me(y|0,S|0,52)|0,It()|0,(C|0)!=(k&15|0)))return L=0,wt=z,L|0;if(k=C+-1|0,C>>>0>1&&(it=Bd(p,m,k)|0,H=It()|0,k=Bd(y,S,k)|0,(it|0)==(k|0)&(H|0)==(It()|0))&&(k=(C^15)*3|0,C=me(p|0,m|0,k|0)|0,It()|0,C=C&7,k=me(y|0,S|0,k|0)|0,It()|0,k=k&7,(C|0)==0|(k|0)==0||(g[21136+(C<<2)>>2]|0)==(k|0)||(g[21168+(C<<2)>>2]|0)==(k|0)))return it=1,wt=z,it|0;C=L,k=C+56|0;do g[C>>2]=0,C=C+4|0;while((C|0)<(k|0));return f_(p,m,1,L),it=L,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0))&&(it=L+8|0,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0)))&&(it=L+16|0,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0)))&&(it=L+24|0,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0)))&&(it=L+32|0,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0)))&&(it=L+40|0,!((g[it>>2]|0)==(y|0)&&(g[it+4>>2]|0)==(S|0)))?(C=L+48|0,C=((g[C>>2]|0)==(y|0)?(g[C+4>>2]|0)==(S|0):0)&1):C=1,it=C,wt=z,it|0}function U(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0;if(it=wt,wt=wt+16|0,L=it,!(B(p,m,y,S)|0))return z=0,H=0,Je(z|0),wt=it,H|0;for(z=m&-2130706433,C=(ji(p,m)|0)==0,C=C?1:2;g[L>>2]=0,ot=Wn(p,m,C,L)|0,k=C+1|0,!((ot|0)==(y|0)&(It()|0)==(S|0));)if(k>>>0<7)C=k;else{C=0,p=0,H=6;break}return(H|0)==6?(Je(C|0),wt=it,p|0):(ot=ke(C|0,0,56)|0,H=z|(It()|0)|268435456,ot=p|ot,Je(H|0),wt=it,ot|0)}function W(p,m){p=p|0,m=m|0;var y=0;return y=!0&(m&2013265920|0)==268435456,Je((y?m&-2130706433|134217728:0)|0),(y?p:0)|0}function Z(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;return S=wt,wt=wt+16|0,y=S,!0&(m&2013265920|0)==268435456?(C=me(p|0,m|0,56)|0,It()|0,g[y>>2]=0,y=Wn(p,m&-2130706433|134217728,C&7,y)|0,m=It()|0,Je(m|0),wt=S,y|0):(m=0,y=0,Je(m|0),wt=S,y|0)}function $(p,m){p=p|0,m=m|0;var y=0;if(!(!0&(m&2013265920|0)==268435456))return y=0,y|0;switch(y=me(p|0,m|0,56)|0,It()|0,y&7){case 0:case 7:return y=0,y|0;default:}return y=m&-2130706433|134217728,!0&(m&117440512|0)==16777216&(ji(p,y)|0)!=0?(y=0,y|0):(y=aA(p,y)|0,y|0)}function st(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0;k=wt,wt=wt+16|0,S=k,L=!0&(m&2013265920|0)==268435456,C=m&-2130706433|134217728,z=y,g[z>>2]=L?p:0,g[z+4>>2]=L?C:0,L?(m=me(p|0,m|0,56)|0,It()|0,g[S>>2]=0,p=Wn(p,C,m&7,S)|0,m=It()|0):(p=0,m=0),z=y+8|0,g[z>>2]=p,g[z+4>>2]=m,wt=k}function At(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;C=(ji(p,m)|0)==0,m=m&-2130706433,S=y,g[S>>2]=C?p:0,g[S+4>>2]=C?m|285212672:0,S=y+8|0,g[S>>2]=p,g[S+4>>2]=m|301989888,S=y+16|0,g[S>>2]=p,g[S+4>>2]=m|318767104,S=y+24|0,g[S>>2]=p,g[S+4>>2]=m|335544320,S=y+32|0,g[S>>2]=p,g[S+4>>2]=m|352321536,y=y+40|0,g[y>>2]=p,g[y+4>>2]=m|369098752}function pt(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0;if(L=wt,wt=wt+16|0,k=L,S=me(p|0,m|0,56)|0,It()|0,z=!0&(m&2013265920|0)==268435456,C=z?p:0,p=z?m&-2130706433|134217728:0,m=yu(C,p,S&7)|0,(m|0)==-1){g[y>>2]=0,wt=L;return}T(C,p,k),S=me(C|0,p|0,52)|0,It()|0,S=S&15,ji(C,p)|0?l0(k,S,m,2,y):iA(k,S,m,2,y),wt=L}function yt(p){p=p|0;var m=0,y=0,S=0;return m=Ua(1,12)|0,m||Mi(22691,22646,49,22704),y=p+4|0,S=g[y>>2]|0,S|0?(S=S+8|0,g[S>>2]=m,g[y>>2]=m,m|0):(g[p>>2]|0&&Mi(22721,22646,61,22744),S=p,g[S>>2]=m,g[y>>2]=m,m|0)}function dt(p,m){p=p|0,m=m|0;var y=0,S=0;return S=ho(24)|0,S||Mi(22758,22646,78,22772),g[S>>2]=g[m>>2],g[S+4>>2]=g[m+4>>2],g[S+8>>2]=g[m+8>>2],g[S+12>>2]=g[m+12>>2],g[S+16>>2]=0,m=p+4|0,y=g[m>>2]|0,y|0?(g[y+16>>2]=S,g[m>>2]=S,S|0):(g[p>>2]|0&&Mi(22787,22646,82,22772),g[p>>2]=S,g[m>>2]=S,S|0)}function Ft(p){p=p|0;var m=0,y=0,S=0,C=0;if(p)for(S=1;;){if(m=g[p>>2]|0,m|0)do{if(y=g[m>>2]|0,y|0)do C=y,y=g[y+16>>2]|0,Gr(C);while(y|0);C=m,m=g[m+8>>2]|0,Gr(C)}while(m|0);if(m=p,p=g[p+8>>2]|0,S||Gr(m),p)S=0;else break}}function Ht(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0,je=0,Zr=0,qi=0,Ei=0,hn=0,Pi=0,Sn=0,yn=0,Or=0;if(C=p+8|0,g[C>>2]|0)return Or=1,Or|0;if(S=g[p>>2]|0,!S)return Or=0,Or|0;m=S,y=0;do y=y+1|0,m=g[m+8>>2]|0;while(m|0);if(y>>>0<2)return Or=0,Or|0;Sn=ho(y<<2)|0,Sn||Mi(22807,22646,317,22826),Pi=ho(y<<5)|0,Pi||Mi(22848,22646,321,22826),g[p>>2]=0,er=p+4|0,g[er>>2]=0,g[C>>2]=0,y=0,hn=0,$e=0,Ct=0;t:for(;;){if(ot=g[S>>2]|0,ot){k=0,L=ot;do{if(H=+Tt[L+8>>3],m=L,L=g[L+16>>2]|0,it=(L|0)==0,C=it?ot:L,z=+Tt[C+8>>3],+li(+(H-z))>3.141592653589793){Or=14;break}k=k+(z-H)*(+Tt[m>>3]+ +Tt[C>>3])}while(!it);if((Or|0)==14){Or=0,k=0,m=ot;do Ut=+Tt[m+8>>3],Ei=m+16|0,qi=g[Ei>>2]|0,qi=qi|0?qi:ot,_e=+Tt[qi+8>>3],k=k+(+Tt[m>>3]+ +Tt[qi>>3])*((_e<0?_e+6.283185307179586:_e)-(Ut<0?Ut+6.283185307179586:Ut)),m=g[(m|0?Ei:S)>>2]|0;while(m|0)}k>0?(g[Sn+(hn<<2)>>2]=S,hn=hn+1|0,C=$e,m=Ct):Or=19}else Or=19;if((Or|0)==19){Or=0;do if(y){if(m=y+8|0,g[m>>2]|0){Or=21;break t}if(y=Ua(1,12)|0,!y){Or=23;break t}g[m>>2]=y,C=y+4|0,L=y,m=Ct}else if(Ct){C=er,L=Ct+8|0,m=S,y=p;break}else if(g[p>>2]|0){Or=27;break t}else{C=er,L=p,m=S,y=p;break}while(!1);if(g[L>>2]=S,g[C>>2]=S,L=Pi+($e<<5)|0,it=g[S>>2]|0,it){for(ot=Pi+($e<<5)+8|0,Tt[ot>>3]=17976931348623157e292,Ct=Pi+($e<<5)+24|0,Tt[Ct>>3]=17976931348623157e292,Tt[L>>3]=-17976931348623157e292,Nt=Pi+($e<<5)+16|0,Tt[Nt>>3]=-17976931348623157e292,We=17976931348623157e292,te=-17976931348623157e292,C=0,Wt=it,H=17976931348623157e292,ne=17976931348623157e292,Le=-17976931348623157e292,z=-17976931348623157e292;k=+Tt[Wt>>3],Ut=+Tt[Wt+8>>3],Wt=g[Wt+16>>2]|0,re=(Wt|0)==0,_e=+Tt[(re?it:Wt)+8>>3],k>3]=k,H=k),Ut>3]=Ut,ne=Ut),k>Le?Tt[L>>3]=k:k=Le,Ut>z&&(Tt[Nt>>3]=Ut,z=Ut),We=Ut>0&Utte?Ut:te,C=C|+li(+(Ut-_e))>3.141592653589793,!re;)Le=k;C&&(Tt[Nt>>3]=te,Tt[Ct>>3]=We)}else g[L>>2]=0,g[L+4>>2]=0,g[L+8>>2]=0,g[L+12>>2]=0,g[L+16>>2]=0,g[L+20>>2]=0,g[L+24>>2]=0,g[L+28>>2]=0;C=$e+1|0}if(Ei=S+8|0,S=g[Ei>>2]|0,g[Ei>>2]=0,S)$e=C,Ct=m;else{Or=45;break}}if((Or|0)==21)Mi(22624,22646,35,22658);else if((Or|0)==23)Mi(22678,22646,37,22658);else if((Or|0)==27)Mi(22721,22646,61,22744);else if((Or|0)==45){t:do if((hn|0)>0){for(Ei=(C|0)==0,Zr=C<<2,qi=(p|0)==0,je=0,m=0;;){if(we=g[Sn+(je<<2)>>2]|0,Ei)Or=73;else{if($e=ho(Zr)|0,!$e){Or=50;break}if(er=ho(Zr)|0,!er){Or=52;break}e:do if(qi)y=0;else{for(C=0,y=0,L=p;S=Pi+(C<<5)|0,St(g[L>>2]|0,S,g[we>>2]|0)|0?(g[$e+(y<<2)>>2]=L,g[er+(y<<2)>>2]=S,re=y+1|0):re=y,L=g[L+8>>2]|0,L;)C=C+1|0,y=re;if((re|0)>0)if(S=g[$e>>2]|0,(re|0)==1)y=S;else for(Nt=0,Wt=-1,y=S,Ct=S;;){for(it=g[Ct>>2]|0,S=0,L=0;C=g[g[$e+(L<<2)>>2]>>2]|0,(C|0)==(it|0)?ot=S:ot=S+((St(C,g[er+(L<<2)>>2]|0,g[it>>2]|0)|0)&1)|0,L=L+1|0,(L|0)!=(re|0);)S=ot;if(C=(ot|0)>(Wt|0),y=C?Ct:y,S=Nt+1|0,(S|0)==(re|0))break e;Nt=S,Wt=C?ot:Wt,Ct=g[$e+(S<<2)>>2]|0}else y=0}while(!1);if(Gr($e),Gr(er),y){if(C=y+4|0,S=g[C>>2]|0,S)y=S+8|0;else if(g[y>>2]|0){Or=70;break}g[y>>2]=we,g[C>>2]=we}else Or=73}if((Or|0)==73){if(Or=0,m=g[we>>2]|0,m|0)do er=m,m=g[m+16>>2]|0,Gr(er);while(m|0);Gr(we),m=2}if(je=je+1|0,(je|0)>=(hn|0)){yn=m;break t}}(Or|0)==50?Mi(22863,22646,249,22882):(Or|0)==52?Mi(22901,22646,252,22882):(Or|0)==70&&Mi(22721,22646,61,22744)}else yn=0;while(!1);return Gr(Sn),Gr(Pi),Or=yn,Or|0}return 0}function St(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0;if(!(pl(m,y)|0)||(m=n0(m)|0,it=+Tt[y>>3],S=+Tt[y+8>>3],S=m&S<0?S+6.283185307179586:S,p=g[p>>2]|0,!p))return p=0,p|0;if(m){m=0,y=p;t:for(;;){for(;L=+Tt[y>>3],H=+Tt[y+8>>3],y=y+16|0,ot=g[y>>2]|0,ot=ot|0?ot:p,k=+Tt[ot>>3],C=+Tt[ot+8>>3],L>k?(z=L,L=H):(z=k,k=L,L=C,C=H),!!(itz);)if(y=g[y>>2]|0,!y){y=22;break t}if(H=C<0?C+6.283185307179586:C,L=L<0?L+6.283185307179586:L,S=L==S|H==S?S+-2220446049250313e-31:S,H=H+(it-k)/(z-k)*(L-H),(H<0?H+6.283185307179586:H)>S&&(m=m^1),y=g[y>>2]|0,!y){y=22;break}}if((y|0)==22)return m|0}else{m=0,y=p;t:for(;;){for(;L=+Tt[y>>3],H=+Tt[y+8>>3],y=y+16|0,ot=g[y>>2]|0,ot=ot|0?ot:p,k=+Tt[ot>>3],C=+Tt[ot+8>>3],L>k?(z=L,L=H):(z=k,k=L,L=C,C=H),!!(itz);)if(y=g[y>>2]|0,!y){y=22;break t}if(S=L==S|C==S?S+-2220446049250313e-31:S,C+(it-k)/(z-k)*(L-C)>S&&(m=m^1),y=g[y>>2]|0,!y){y=22;break}}if((y|0)==22)return m|0}return 0}function Bt(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0;if(te=wt,wt=wt+32|0,We=te+16|0,Le=te,k=me(p|0,m|0,52)|0,It()|0,k=k&15,Wt=me(y|0,S|0,52)|0,It()|0,(k|0)!=(Wt&15|0))return We=1,wt=te,We|0;if(it=me(p|0,m|0,45)|0,It()|0,it=it&127,ot=me(y|0,S|0,45)|0,It()|0,ot=ot&127,Wt=(it|0)!=(ot|0),Wt){if(z=tA(it,ot)|0,(z|0)==7)return We=2,wt=te,We|0;H=tA(ot,it)|0,(H|0)==7?Mi(22925,22949,151,22959):(re=z,L=H)}else re=0,L=0;Ct=fi(it)|0,Nt=fi(ot)|0,g[We>>2]=0,g[We+4>>2]=0,g[We+8>>2]=0,g[We+12>>2]=0;do if(re){if(ot=g[4304+(it*28|0)+(re<<2)>>2]|0,z=(ot|0)>0,Nt)if(z){it=0,H=y,z=S;do H=p0(H,z)|0,z=It()|0,L=co(L)|0,(L|0)==1&&(L=co(1)|0),it=it+1|0;while((it|0)!=(ot|0));ot=L,it=H,H=z}else ot=L,it=y,H=S;else if(z){it=0,H=y,z=S;do H=Fd(H,z)|0,z=It()|0,L=co(L)|0,it=it+1|0;while((it|0)!=(ot|0));ot=L,it=H,H=z}else ot=L,it=y,H=S;if(bi(it,H,We)|0,Wt||Mi(22972,22949,181,22959),z=(Ct|0)!=0,L=(Nt|0)!=0,z&L&&Mi(22999,22949,182,22959),z){if(L=Es(p,m)|0,br[22032+(L*7|0)+re>>0]|0){k=3;break}H=g[21200+(L*28|0)+(re<<2)>>2]|0,it=H,ne=26}else if(L){if(L=Es(it,H)|0,br[22032+(L*7|0)+ot>>0]|0){k=4;break}it=0,H=g[21200+(ot*28|0)+(L<<2)>>2]|0,ne=26}else L=0;if((ne|0)==26)if((H|0)<=-1&&Mi(23030,22949,212,22959),(it|0)<=-1&&Mi(23053,22949,213,22959),(H|0)>0){z=We+4|0,L=0;do fh(z),L=L+1|0;while((L|0)!=(H|0));L=it}else L=it;if(g[Le>>2]=0,g[Le+4>>2]=0,g[Le+8>>2]=0,rA(Le,re),k|0)for(;Ho(k)|0?za(Le):hh(Le),(k|0)>1;)k=k+-1|0;if((L|0)>0){k=0;do fh(Le),k=k+1|0;while((k|0)!=(L|0))}ne=We+4|0,Ln(ne,Le,ne),js(ne),ne=50}else if(bi(y,S,We)|0,(Ct|0)!=0&(Nt|0)!=0)if((ot|0)!=(it|0)&&Mi(23077,22949,243,22959),L=Es(p,m)|0,k=Es(y,S)|0,br[22032+(L*7|0)+k>>0]|0)k=5;else if(L=g[21200+(L*28|0)+(k<<2)>>2]|0,(L|0)>0){z=We+4|0,k=0;do fh(z),k=k+1|0;while((k|0)!=(L|0));ne=50}else ne=50;else ne=50;while(!1);return(ne|0)==50&&(k=We+4|0,g[C>>2]=g[k>>2],g[C+4>>2]=g[k+4>>2],g[C+8>>2]=g[k+8>>2],k=0),We=k,wt=te,We|0}function Qt(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0;if(re=wt,wt=wt+48|0,L=re+36|0,z=re+24|0,H=re+12|0,it=re,k=me(p|0,m|0,52)|0,It()|0,k=k&15,Nt=me(p|0,m|0,45)|0,It()|0,Nt=Nt&127,ot=fi(Nt)|0,ke(k|0,0,52)|0,Le=It()|0|134225919,ne=S,g[ne>>2]=-1,g[ne+4>>2]=Le,!k)return(g[y>>2]|0)>1||(g[y+4>>2]|0)>1||(g[y+8>>2]|0)>1||(C=Jp(Nt,Fa(y)|0)|0,(C|0)==127)?(Le=1,wt=re,Le|0):(Wt=ke(C|0,0,45)|0,ne=It()|0,Nt=S,ne=g[Nt+4>>2]&-1040385|ne,Le=S,g[Le>>2]=g[Nt>>2]|Wt,g[Le+4>>2]=ne,Le=0,wt=re,Le|0);for(g[L>>2]=g[y>>2],g[L+4>>2]=g[y+4>>2],g[L+8>>2]=g[y+8>>2];g[z>>2]=g[L>>2],g[z+4>>2]=g[L+4>>2],g[z+8>>2]=g[L+8>>2],Ho(k)|0?(Rd(L),g[H>>2]=g[L>>2],g[H+4>>2]=g[L+4>>2],g[H+8>>2]=g[L+8>>2],za(H)):(Al(L),g[H>>2]=g[L>>2],g[H+4>>2]=g[L+4>>2],g[H+8>>2]=g[L+8>>2],hh(H)),eA(z,H,it),js(it),ne=S,We=g[ne>>2]|0,ne=g[ne+4>>2]|0,te=(15-k|0)*3|0,y=ke(7,0,te|0)|0,ne=ne&~(It()|0),te=ke(Fa(it)|0,0,te|0)|0,ne=It()|0|ne,Le=S,g[Le>>2]=te|We&~y,g[Le+4>>2]=ne,(k|0)>1;)k=k+-1|0;t:do if((g[L>>2]|0)<=1&&(g[L+4>>2]|0)<=1&&(g[L+8>>2]|0)<=1){k=Fa(L)|0,z=Jp(Nt,k)|0,(z|0)==127?it=0:it=fi(z)|0;e:do if(k){if(ot){if(L=21408+((Es(p,m)|0)*28|0)+(k<<2)|0,L=g[L>>2]|0,(L|0)>0){y=0;do k=Na(k)|0,y=y+1|0;while((y|0)!=(L|0))}if((k|0)==1){C=3;break t}y=Jp(Nt,k)|0,(y|0)==127&&Mi(23104,22949,376,23134),fi(y)|0?Mi(23147,22949,377,23134):(Wt=L,Ct=k,C=y)}else Wt=0,Ct=k,C=z;if(H=g[4304+(Nt*28|0)+(Ct<<2)>>2]|0,(H|0)<=-1&&Mi(23178,22949,384,23134),!it){if((Wt|0)<=-1&&Mi(23030,22949,417,23134),Wt|0){L=S,k=0,y=g[L>>2]|0,L=g[L+4>>2]|0;do y=Wo(y,L)|0,L=It()|0,te=S,g[te>>2]=y,g[te+4>>2]=L,k=k+1|0;while((k|0)<(Wt|0))}if((H|0)<=0){k=54;break}for(L=S,k=0,y=g[L>>2]|0,L=g[L+4>>2]|0;;)if(y=Wo(y,L)|0,L=It()|0,te=S,g[te>>2]=y,g[te+4>>2]=L,k=k+1|0,(k|0)==(H|0)){k=54;break e}}if(z=tA(C,Nt)|0,(z|0)==7&&Mi(22925,22949,393,23134),k=S,y=g[k>>2]|0,k=g[k+4>>2]|0,(H|0)>0){L=0;do y=Wo(y,k)|0,k=It()|0,te=S,g[te>>2]=y,g[te+4>>2]=k,L=L+1|0;while((L|0)!=(H|0))}if(y=Es(y,k)|0,te=mu(C)|0,y=g[(te?21824:21616)+(z*28|0)+(y<<2)>>2]|0,(y|0)<=-1&&Mi(23030,22949,412,23134),!y)k=54;else{z=S,k=0,L=g[z>>2]|0,z=g[z+4>>2]|0;do L=gh(L,z)|0,z=It()|0,te=S,g[te>>2]=L,g[te+4>>2]=z,k=k+1|0;while((k|0)<(y|0));k=54}}else if((ot|0)!=0&(it|0)!=0)if(te=Es(p,m)|0,k=S,k=21408+(te*28|0)+((Es(g[k>>2]|0,g[k+4>>2]|0)|0)<<2)|0,k=g[k>>2]|0,(k|0)<=-1&&Mi(23201,22949,433,23134),!k)C=z,k=55;else{L=S,C=0,y=g[L>>2]|0,L=g[L+4>>2]|0;do y=Wo(y,L)|0,L=It()|0,te=S,g[te>>2]=y,g[te+4>>2]=L,C=C+1|0;while((C|0)<(k|0));C=z,k=54}else C=z,k=54;while(!1);if((k|0)==54&&it&&(k=55),(k|0)==55&&(te=S,(Es(g[te>>2]|0,g[te+4>>2]|0)|0)==1)){C=4;break}te=S,Le=g[te>>2]|0,te=g[te+4>>2]&-1040385,We=ke(C|0,0,45)|0,te=te|(It()|0),C=S,g[C>>2]=Le|We,g[C+4>>2]=te,C=0}else C=2;while(!1);return te=C,wt=re,te|0}function $t(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0;return L=wt,wt=wt+16|0,k=L,p=Bt(p,m,y,S,k)|0,p||(xf(k,C),p=0),wt=L,p|0}function oe(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0;return C=wt,wt=wt+16|0,k=C,__(y,k),S=Qt(p,m,k,S)|0,wt=C,S|0}function pe(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0;return L=wt,wt=wt+32|0,C=L+12|0,k=L,!(Bt(p,m,p,m,C)|0)&&!(Bt(p,m,y,S,k)|0)?p=Hl(C,k)|0:p=-1,wt=L,p|0}function he(p,m,y,S){p=p|0,m=m|0,y=y|0,S=S|0;var C=0,k=0,L=0;return L=wt,wt=wt+32|0,C=L+12|0,k=L,!(Bt(p,m,p,m,C)|0)&&!(Bt(p,m,y,S,k)|0)?p=Hl(C,k)|0:p=-1,wt=L,(p>>>31^1)+p|0}function be(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0;if($e=wt,wt=wt+48|0,k=$e+24|0,L=$e+12|0,Ut=$e,!(Bt(p,m,p,m,k)|0)&&!(Bt(p,m,y,S,L)|0)){if(_e=Hl(k,L)|0,(_e|0)<0)return Ut=_e,wt=$e,Ut|0;for(g[k>>2]=0,g[k+4>>2]=0,g[k+8>>2]=0,g[L>>2]=0,g[L+4>>2]=0,g[L+8>>2]=0,Bt(p,m,p,m,k)|0,Bt(p,m,y,S,L)|0,Oe(k),Oe(L),_e?(ot=g[k>>2]|0,Wt=+(_e|0),Le=k+4|0,Ct=g[Le>>2]|0,We=k+8|0,Nt=g[We>>2]|0,te=k,y=ot,S=Ct,k=Nt,re=+((g[L>>2]|0)-ot|0)/Wt,ne=+((g[L+4>>2]|0)-Ct|0)/Wt,Wt=+((g[L+8>>2]|0)-Nt|0)/Wt):(S=k+4|0,Nt=k+8|0,Le=S,We=Nt,te=k,y=g[k>>2]|0,S=g[S>>2]|0,k=g[Nt>>2]|0,re=0,ne=0,Wt=0),g[Ut>>2]=y,Nt=Ut+4|0,g[Nt>>2]=S,Ct=Ut+8|0,g[Ct>>2]=k,ot=0;;){H=+(ot|0),er=re*H+ +(y|0),z=ne*H+ +(g[Le>>2]|0),H=Wt*H+ +(g[We>>2]|0),S=~~+Mf(+er),L=~~+Mf(+z),y=~~+Mf(+H),er=+li(+(+(S|0)-er)),z=+li(+(+(L|0)-z)),H=+li(+(+(y|0)-H));do if(er>z&er>H)S=0-(L+y)|0,k=L;else if(it=0-S|0,z>H){k=it-y|0;break}else{k=L,y=it-L|0;break}while(!1);if(g[Ut>>2]=S,g[Nt>>2]=k,g[Ct>>2]=y,o0(Ut),Qt(p,m,Ut,C+(ot<<3)|0)|0,(ot|0)==(_e|0))break;ot=ot+1|0,y=g[te>>2]|0}return Ut=0,wt=$e,Ut|0}return Ut=-1,wt=$e,Ut|0}function Ze(p,m){p=p|0,m=m|0;var y=0;if(!m)return y=1,y|0;y=p,p=1;do p=Oc(m&1|0?y:1,p)|0,m=m>>1,y=Oc(y,y)|0;while(m|0);return p|0}function Kr(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0;if(!(pl(m,y)|0)||(m=n0(m)|0,Nt=+Tt[y>>3],S=+Tt[y+8>>3],S=m&S<0?S+6.283185307179586:S,Ct=g[p>>2]|0,(Ct|0)<=0))return Ct=0,Ct|0;if(ot=g[p+4>>2]|0,m){m=0,y=-1,p=0;t:for(;;){for(it=p;L=+Tt[ot+(it<<4)>>3],H=+Tt[ot+(it<<4)+8>>3],p=(y+2|0)%(Ct|0)|0,k=+Tt[ot+(p<<4)>>3],C=+Tt[ot+(p<<4)+8>>3],L>k?(z=L,L=H):(z=k,k=L,L=C,C=H),!!(Ntz);)if(y=it+1|0,(y|0)<(Ct|0))p=it,it=y,y=p;else{y=22;break t}if(H=C<0?C+6.283185307179586:C,L=L<0?L+6.283185307179586:L,S=L==S|H==S?S+-2220446049250313e-31:S,H=H+(Nt-k)/(z-k)*(L-H),(H<0?H+6.283185307179586:H)>S&&(m=m^1),p=it+1|0,(p|0)>=(Ct|0)){y=22;break}else y=it}if((y|0)==22)return m|0}else{m=0,y=-1,p=0;t:for(;;){for(it=p;L=+Tt[ot+(it<<4)>>3],H=+Tt[ot+(it<<4)+8>>3],p=(y+2|0)%(Ct|0)|0,k=+Tt[ot+(p<<4)>>3],C=+Tt[ot+(p<<4)+8>>3],L>k?(z=L,L=H):(z=k,k=L,L=C,C=H),!!(Ntz);)if(y=it+1|0,(y|0)<(Ct|0))p=it,it=y,y=p;else{y=22;break t}if(S=L==S|C==S?S+-2220446049250313e-31:S,C+(Nt-k)/(z-k)*(L-C)>S&&(m=m^1),p=it+1|0,(p|0)>=(Ct|0)){y=22;break}else y=it}if((y|0)==22)return m|0}return 0}function Ee(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0;if(re=g[p>>2]|0,!re){g[m>>2]=0,g[m+4>>2]=0,g[m+8>>2]=0,g[m+12>>2]=0,g[m+16>>2]=0,g[m+20>>2]=0,g[m+24>>2]=0,g[m+28>>2]=0;return}if(ne=m+8|0,Tt[ne>>3]=17976931348623157e292,Le=m+24|0,Tt[Le>>3]=17976931348623157e292,Tt[m>>3]=-17976931348623157e292,We=m+16|0,Tt[We>>3]=-17976931348623157e292,!((re|0)<=0)){for(Nt=g[p+4>>2]|0,it=17976931348623157e292,ot=-17976931348623157e292,Ct=0,p=-1,k=17976931348623157e292,L=17976931348623157e292,H=-17976931348623157e292,S=-17976931348623157e292,Wt=0;y=+Tt[Nt+(Wt<<4)>>3],z=+Tt[Nt+(Wt<<4)+8>>3],p=p+2|0,C=+Tt[Nt+(((p|0)==(re|0)?0:p)<<4)+8>>3],y>3]=y,k=y),z>3]=z,L=z),y>H?Tt[m>>3]=y:y=H,z>S&&(Tt[We>>3]=z,S=z),it=z>0&zot?z:ot,Ct=Ct|+li(+(z-C))>3.141592653589793,p=Wt+1|0,(p|0)!=(re|0);)te=Wt,H=y,Wt=p,p=te;Ct&&(Tt[We>>3]=ot,Tt[Le>>3]=it)}}function pr(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0,er=0,we=0;if(re=g[p>>2]|0,re){if(ne=m+8|0,Tt[ne>>3]=17976931348623157e292,Le=m+24|0,Tt[Le>>3]=17976931348623157e292,Tt[m>>3]=-17976931348623157e292,We=m+16|0,Tt[We>>3]=-17976931348623157e292,(re|0)>0){for(C=g[p+4>>2]|0,Nt=17976931348623157e292,Wt=-17976931348623157e292,S=0,y=-1,H=17976931348623157e292,it=17976931348623157e292,Ct=-17976931348623157e292,L=-17976931348623157e292,te=0;k=+Tt[C+(te<<4)>>3],ot=+Tt[C+(te<<4)+8>>3],er=y+2|0,z=+Tt[C+(((er|0)==(re|0)?0:er)<<4)+8>>3],k>3]=k,H=k),ot>3]=ot,it=ot),k>Ct?Tt[m>>3]=k:k=Ct,ot>L&&(Tt[We>>3]=ot,L=ot),Nt=ot>0&otWt?ot:Wt,S=S|+li(+(ot-z))>3.141592653589793,y=te+1|0,(y|0)!=(re|0);)er=te,Ct=k,te=y,y=er;S&&(Tt[We>>3]=Wt,Tt[Le>>3]=Nt)}}else g[m>>2]=0,g[m+4>>2]=0,g[m+8>>2]=0,g[m+12>>2]=0,g[m+16>>2]=0,g[m+20>>2]=0,g[m+24>>2]=0,g[m+28>>2]=0;if(er=p+8|0,y=g[er>>2]|0,!((y|0)<=0)){$e=p+12|0,Ut=0;do if(C=g[$e>>2]|0,S=Ut,Ut=Ut+1|0,Le=m+(Ut<<5)|0,We=g[C+(S<<3)>>2]|0,We){if(te=m+(Ut<<5)+8|0,Tt[te>>3]=17976931348623157e292,p=m+(Ut<<5)+24|0,Tt[p>>3]=17976931348623157e292,Tt[Le>>3]=-17976931348623157e292,_e=m+(Ut<<5)+16|0,Tt[_e>>3]=-17976931348623157e292,(We|0)>0){for(re=g[C+(S<<3)+4>>2]|0,Nt=17976931348623157e292,Wt=-17976931348623157e292,C=0,S=-1,ne=0,H=17976931348623157e292,it=17976931348623157e292,ot=-17976931348623157e292,L=-17976931348623157e292;k=+Tt[re+(ne<<4)>>3],Ct=+Tt[re+(ne<<4)+8>>3],S=S+2|0,z=+Tt[re+(((S|0)==(We|0)?0:S)<<4)+8>>3],k>3]=k,H=k),Ct>3]=Ct,it=Ct),k>ot?Tt[Le>>3]=k:k=ot,Ct>L&&(Tt[_e>>3]=Ct,L=Ct),Nt=Ct>0&CtWt?Ct:Wt,C=C|+li(+(Ct-z))>3.141592653589793,S=ne+1|0,(S|0)!=(We|0);)we=ne,ne=S,ot=k,S=we;C&&(Tt[_e>>3]=Wt,Tt[p>>3]=Nt)}}else g[Le>>2]=0,g[Le+4>>2]=0,g[Le+8>>2]=0,g[Le+12>>2]=0,g[Le+16>>2]=0,g[Le+20>>2]=0,g[Le+24>>2]=0,g[Le+28>>2]=0,y=g[er>>2]|0;while((Ut|0)<(y|0))}}function tr(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0;if(!(Kr(p,m,y)|0))return C=0,C|0;if(C=p+8|0,(g[C>>2]|0)<=0)return C=1,C|0;for(S=p+12|0,p=0;;){if(k=p,p=p+1|0,Kr((g[S>>2]|0)+(k<<3)|0,m+(p<<5)|0,y)|0){p=0,S=6;break}if((p|0)>=(g[C>>2]|0)){p=1,S=6;break}}return(S|0)==6?p|0:0}function Gi(){return 8}function Jr(){return 16}function Vr(){return 168}function ei(){return 8}function On(){return 16}function tn(){return 12}function Gs(){return 8}function hs(p){p=p|0;var m=0,y=0;return y=+Tt[p>>3],m=+Tt[p+8>>3],+ +bn(+(y*y+m*m))}function Bn(p,m,y,S,C){p=p|0,m=m|0,y=y|0,S=S|0,C=C|0;var k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0;it=+Tt[p>>3],H=+Tt[m>>3]-it,z=+Tt[p+8>>3],L=+Tt[m+8>>3]-z,Ct=+Tt[y>>3],k=+Tt[S>>3]-Ct,Nt=+Tt[y+8>>3],ot=+Tt[S+8>>3]-Nt,k=(k*(z-Nt)-(it-Ct)*ot)/(H*ot-L*k),Tt[C>>3]=it+H*k,Tt[C+8>>3]=z+L*k}function qo(p,m){return p=p|0,m=m|0,+Tt[p>>3]!=+Tt[m>>3]?(m=0,m|0):(m=+Tt[p+8>>3]==+Tt[m+8>>3],m|0)}function jr(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;return C=+Tt[p>>3]-+Tt[m>>3],S=+Tt[p+8>>3]-+Tt[m+8>>3],y=+Tt[p+16>>3]-+Tt[m+16>>3],+(C*C+S*S+y*y)}function ql(p,m){p=p|0,m=m|0;var y=0,S=0,C=0;y=+Tt[p>>3],S=+Ur(+y),y=+hi(+y),Tt[m+16>>3]=y,y=+Tt[p+8>>3],C=S*+Ur(+y),Tt[m>>3]=C,y=S*+hi(+y),Tt[m+8>>3]=y}function Zl(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0;if(it=wt,wt=wt+32|0,C=it+16|0,k=it,T(p,m,C),L=wf(p,m)|0,H=Es(p,m)|0,lh(L,k),m=Ld(L,g[C>>2]|0)|0,!(fi(L)|0))return H=m,wt=it,H|0;do switch(L|0){case 4:{p=0,y=14;break}case 14:{p=1,y=14;break}case 24:{p=2,y=14;break}case 38:{p=3,y=14;break}case 49:{p=4,y=14;break}case 58:{p=5,y=14;break}case 63:{p=6,y=14;break}case 72:{p=7,y=14;break}case 83:{p=8,y=14;break}case 97:{p=9,y=14;break}case 107:{p=10,y=14;break}case 117:{p=11,y=14;break}default:z=0,S=0}while(!1);return(y|0)==14&&(z=g[22096+(p*24|0)+8>>2]|0,S=g[22096+(p*24|0)+16>>2]|0),p=g[C>>2]|0,(p|0)!=(g[k>>2]|0)&&(L=mu(L)|0,p=g[C>>2]|0,L|(p|0)==(S|0)&&(m=(m+1|0)%6|0)),(H|0)==3&(p|0)==(S|0)?(H=(m+5|0)%6|0,wt=it,H|0):(H|0)==5&(p|0)==(z|0)?(H=(m+1|0)%6|0,wt=it,H|0):(H=m,wt=it,H|0)}function yu(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;return S=ji(p,m)|0,(y+-1|0)>>>0>5||(C=(S|0)!=0,(y|0)==1&C)?(y=-1,y|0):(S=Zl(p,m)|0,C?(y=(5-S+(g[22384+(y<<2)>>2]|0)|0)%5|0,y|0):(y=(6-S+(g[22416+(y<<2)>>2]|0)|0)%6|0,y|0))}function vu(p,m,y){p=p|0,m=m|0,y=y|0;var S=0;(m|0)>0?(S=Ua(m,4)|0,g[p>>2]=S,S||Mi(23230,23253,40,23267)):g[p>>2]=0,g[p+4>>2]=m,g[p+8>>2]=0,g[p+12>>2]=y}function _h(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0;C=p+4|0,k=p+12|0,L=p+8|0;t:for(;;){for(y=g[C>>2]|0,m=0;;){if((m|0)>=(y|0))break t;if(S=g[p>>2]|0,z=g[S+(m<<2)>>2]|0,!z)m=m+1|0;else break}m=S+(~~(+li(+(+dl(10,+ +(15-(g[k>>2]|0)|0))*(+Tt[z>>3]+ +Tt[z+8>>3])))%+(y|0))>>>0<<2)|0,y=g[m>>2]|0;e:do if(y|0){if(S=z+32|0,(y|0)==(z|0))g[m>>2]=g[S>>2];else{if(y=y+32|0,m=g[y>>2]|0,!m)break;for(;(m|0)!=(z|0);)if(y=m+32|0,m=g[y>>2]|0,!m)break e;g[y>>2]=g[S>>2]}Gr(z),g[L>>2]=(g[L>>2]|0)+-1}while(!1)}Gr(g[p>>2]|0)}function Ws(p){p=p|0;var m=0,y=0,S=0;for(S=g[p+4>>2]|0,y=0;;){if((y|0)>=(S|0)){m=0,y=4;break}if(m=g[(g[p>>2]|0)+(y<<2)>>2]|0,!m)y=y+1|0;else{y=4;break}}return(y|0)==4?m|0:0}function Ps(p,m){p=p|0,m=m|0;var y=0,S=0,C=0,k=0;if(y=~~(+li(+(+dl(10,+ +(15-(g[p+12>>2]|0)|0))*(+Tt[m>>3]+ +Tt[m+8>>3])))%+(g[p+4>>2]|0))>>>0,y=(g[p>>2]|0)+(y<<2)|0,S=g[y>>2]|0,!S)return k=1,k|0;k=m+32|0;do if((S|0)!=(m|0)){if(y=g[S+32>>2]|0,!y)return k=1,k|0;for(C=y;;){if((C|0)==(m|0)){C=8;break}if(y=g[C+32>>2]|0,y)S=C,C=y;else{y=1,C=10;break}}if((C|0)==8){g[S+32>>2]=g[k>>2];break}else if((C|0)==10)return y|0}else g[y>>2]=g[k>>2];while(!1);return Gr(m),k=p+8|0,g[k>>2]=(g[k>>2]|0)+-1,k=0,k|0}function Eo(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0,k=0,L=0;k=ho(40)|0,k||Mi(23283,23253,98,23296),g[k>>2]=g[m>>2],g[k+4>>2]=g[m+4>>2],g[k+8>>2]=g[m+8>>2],g[k+12>>2]=g[m+12>>2],C=k+16|0,g[C>>2]=g[y>>2],g[C+4>>2]=g[y+4>>2],g[C+8>>2]=g[y+8>>2],g[C+12>>2]=g[y+12>>2],g[k+32>>2]=0,C=~~(+li(+(+dl(10,+ +(15-(g[p+12>>2]|0)|0))*(+Tt[m>>3]+ +Tt[m+8>>3])))%+(g[p+4>>2]|0))>>>0,C=(g[p>>2]|0)+(C<<2)|0,S=g[C>>2]|0;do if(!S)g[C>>2]=k;else{for(;!(us(S,m)|0&&us(S+16|0,y)|0);)if(C=g[S+32>>2]|0,S=C|0?C:S,!(g[S+32>>2]|0)){L=10;break}if((L|0)==10){g[S+32>>2]=k;break}return Gr(k),L=S,L|0}while(!1);return L=p+8|0,g[L>>2]=(g[L>>2]|0)+1,L=k,L|0}function yh(p,m,y){p=p|0,m=m|0,y=y|0;var S=0,C=0;if(C=~~(+li(+(+dl(10,+ +(15-(g[p+12>>2]|0)|0))*(+Tt[m>>3]+ +Tt[m+8>>3])))%+(g[p+4>>2]|0))>>>0,C=g[(g[p>>2]|0)+(C<<2)>>2]|0,!C)return y=0,y|0;if(!y){for(p=C;;){if(us(p,m)|0){S=10;break}if(p=g[p+32>>2]|0,!p){p=0,S=10;break}}if((S|0)==10)return p|0}for(p=C;;){if(us(p,m)|0&&us(p+16|0,y)|0){S=10;break}if(p=g[p+32>>2]|0,!p){p=0,S=10;break}}return(S|0)==10?p|0:0}function Fn(p,m){p=p|0,m=m|0;var y=0;if(y=~~(+li(+(+dl(10,+ +(15-(g[p+12>>2]|0)|0))*(+Tt[m>>3]+ +Tt[m+8>>3])))%+(g[p+4>>2]|0))>>>0,p=g[(g[p>>2]|0)+(y<<2)>>2]|0,!p)return y=0,y|0;for(;;){if(us(p,m)|0){m=5;break}if(p=g[p+32>>2]|0,!p){p=0,m=5;break}}return(m|0)==5?p|0:0}function fs(){return 23312}function Zo(p){return p=+p,+ +Ux(+p)}function _n(p){return p=+p,~~+Zo(p)|0}function ho(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0,H=0,it=0,ot=0,Ct=0,Nt=0,Wt=0,re=0,ne=0,Le=0,We=0,te=0,_e=0,Ut=0,$e=0;$e=wt,wt=wt+16|0,Nt=$e;do if(p>>>0<245){if(it=p>>>0<11?16:p+11&-8,p=it>>>3,Ct=g[5829]|0,y=Ct>>>p,y&3|0)return m=(y&1^1)+p|0,p=23356+(m<<1<<2)|0,y=p+8|0,S=g[y>>2]|0,C=S+8|0,k=g[C>>2]|0,(k|0)==(p|0)?g[5829]=Ct&~(1<>2]=p,g[y>>2]=k),Ut=m<<3,g[S+4>>2]=Ut|3,Ut=S+Ut+4|0,g[Ut>>2]=g[Ut>>2]|1,Ut=C,wt=$e,Ut|0;if(ot=g[5831]|0,it>>>0>ot>>>0){if(y|0)return m=2<>>12&16,m=m>>>z,y=m>>>5&8,m=m>>>y,k=m>>>2&4,m=m>>>k,p=m>>>1&2,m=m>>>p,S=m>>>1&1,S=(y|z|k|p|S)+(m>>>S)|0,m=23356+(S<<1<<2)|0,p=m+8|0,k=g[p>>2]|0,z=k+8|0,y=g[z>>2]|0,(y|0)==(m|0)?(p=Ct&~(1<>2]=m,g[p>>2]=y,p=Ct),Ut=S<<3,L=Ut-it|0,g[k+4>>2]=it|3,C=k+it|0,g[C+4>>2]=L|1,g[k+Ut>>2]=L,ot|0&&(S=g[5834]|0,m=ot>>>3,y=23356+(m<<1<<2)|0,m=1<>2]|0):(g[5829]=p|m,m=y,p=y+8|0),g[p>>2]=S,g[m+12>>2]=S,g[S+8>>2]=m,g[S+12>>2]=y),g[5831]=L,g[5834]=C,Ut=z,wt=$e,Ut|0;if(k=g[5830]|0,k){for(y=(k&0-k)+-1|0,C=y>>>12&16,y=y>>>C,S=y>>>5&8,y=y>>>S,L=y>>>2&4,y=y>>>L,z=y>>>1&2,y=y>>>z,H=y>>>1&1,H=g[23620+((S|C|L|z|H)+(y>>>H)<<2)>>2]|0,y=H,z=H,H=(g[H+4>>2]&-8)-it|0;p=g[y+16>>2]|0,!(!p&&(p=g[y+20>>2]|0,!p));)L=(g[p+4>>2]&-8)-it|0,C=L>>>0>>0,y=p,z=C?p:z,H=C?L:H;if(L=z+it|0,L>>>0>z>>>0){C=g[z+24>>2]|0,m=g[z+12>>2]|0;do if((m|0)==(z|0)){if(p=z+20|0,m=g[p>>2]|0,!m&&(p=z+16|0,m=g[p>>2]|0,!m)){y=0;break}for(;;)if(S=m+20|0,y=g[S>>2]|0,y)m=y,p=S;else if(S=m+16|0,y=g[S>>2]|0,y)m=y,p=S;else break;g[p>>2]=0,y=m}else y=g[z+8>>2]|0,g[y+12>>2]=m,g[m+8>>2]=y,y=m;while(!1);do if(C|0){if(m=g[z+28>>2]|0,p=23620+(m<<2)|0,(z|0)==(g[p>>2]|0)){if(g[p>>2]=y,!y){g[5830]=k&~(1<>2]|0)==(z|0)?Ut:C+20|0)>>2]=y,!y)break;g[y+24>>2]=C,m=g[z+16>>2]|0,m|0&&(g[y+16>>2]=m,g[m+24>>2]=y),m=g[z+20>>2]|0,m|0&&(g[y+20>>2]=m,g[m+24>>2]=y)}while(!1);return H>>>0<16?(Ut=H+it|0,g[z+4>>2]=Ut|3,Ut=z+Ut+4|0,g[Ut>>2]=g[Ut>>2]|1):(g[z+4>>2]=it|3,g[L+4>>2]=H|1,g[L+H>>2]=H,ot|0&&(S=g[5834]|0,m=ot>>>3,y=23356+(m<<1<<2)|0,m=1<>2]|0):(g[5829]=m|Ct,m=y,p=y+8|0),g[p>>2]=S,g[m+12>>2]=S,g[S+8>>2]=m,g[S+12>>2]=y),g[5831]=H,g[5834]=L),Ut=z+8|0,wt=$e,Ut|0}else Ct=it}else Ct=it}else Ct=it}else if(p>>>0<=4294967231)if(p=p+11|0,it=p&-8,S=g[5830]|0,S){C=0-it|0,p=p>>>8,p?it>>>0>16777215?H=31:(Ct=(p+1048320|0)>>>16&8,ne=p<>>16&4,ne=ne<>>16&2,H=14-(z|Ct|H)+(ne<>>15)|0,H=it>>>(H+7|0)&1|H<<1):H=0,y=g[23620+(H<<2)>>2]|0;t:do if(!y)y=0,p=0,ne=61;else for(p=0,z=it<<((H|0)==31?0:25-(H>>>1)|0),k=0;;){if(L=(g[y+4>>2]&-8)-it|0,L>>>0>>0)if(L)p=y,C=L;else{p=y,C=0,ne=65;break t}if(ne=g[y+20>>2]|0,y=g[y+16+(z>>>31<<2)>>2]|0,k=(ne|0)==0|(ne|0)==(y|0)?k:ne,y)z=z<<1;else{y=k,ne=61;break}}while(!1);if((ne|0)==61){if((y|0)==0&(p|0)==0){if(p=2<>>12&16,Ct=Ct>>>L,k=Ct>>>5&8,Ct=Ct>>>k,z=Ct>>>2&4,Ct=Ct>>>z,H=Ct>>>1&2,Ct=Ct>>>H,y=Ct>>>1&1,p=0,y=g[23620+((k|L|z|H|y)+(Ct>>>y)<<2)>>2]|0}y?ne=65:(z=p,L=C)}if((ne|0)==65)for(k=y;;)if(Ct=(g[k+4>>2]&-8)-it|0,y=Ct>>>0>>0,C=y?Ct:C,p=y?k:p,y=g[k+16>>2]|0,y||(y=g[k+20>>2]|0),y)k=y;else{z=p,L=C;break}if(z|0&&L>>>0<((g[5831]|0)-it|0)>>>0&&(ot=z+it|0,ot>>>0>z>>>0)){k=g[z+24>>2]|0,m=g[z+12>>2]|0;do if((m|0)==(z|0)){if(p=z+20|0,m=g[p>>2]|0,!m&&(p=z+16|0,m=g[p>>2]|0,!m)){m=0;break}for(;;)if(C=m+20|0,y=g[C>>2]|0,y)m=y,p=C;else if(C=m+16|0,y=g[C>>2]|0,y)m=y,p=C;else break;g[p>>2]=0}else Ut=g[z+8>>2]|0,g[Ut+12>>2]=m,g[m+8>>2]=Ut;while(!1);do if(k){if(p=g[z+28>>2]|0,y=23620+(p<<2)|0,(z|0)==(g[y>>2]|0)){if(g[y>>2]=m,!m){S=S&~(1<>2]|0)==(z|0)?Ut:k+20|0)>>2]=m,!m)break;g[m+24>>2]=k,p=g[z+16>>2]|0,p|0&&(g[m+16>>2]=p,g[p+24>>2]=m),p=g[z+20>>2]|0,p&&(g[m+20>>2]=p,g[p+24>>2]=m)}while(!1);t:do if(L>>>0<16)Ut=L+it|0,g[z+4>>2]=Ut|3,Ut=z+Ut+4|0,g[Ut>>2]=g[Ut>>2]|1;else{if(g[z+4>>2]=it|3,g[ot+4>>2]=L|1,g[ot+L>>2]=L,m=L>>>3,L>>>0<256){y=23356+(m<<1<<2)|0,p=g[5829]|0,m=1<>2]|0):(g[5829]=p|m,m=y,p=y+8|0),g[p>>2]=ot,g[m+12>>2]=ot,g[ot+8>>2]=m,g[ot+12>>2]=y;break}if(m=L>>>8,m?L>>>0>16777215?y=31:(_e=(m+1048320|0)>>>16&8,Ut=m<<_e,te=(Ut+520192|0)>>>16&4,Ut=Ut<>>16&2,y=14-(te|_e|y)+(Ut<>>15)|0,y=L>>>(y+7|0)&1|y<<1):y=0,m=23620+(y<<2)|0,g[ot+28>>2]=y,p=ot+16|0,g[p+4>>2]=0,g[p>>2]=0,p=1<>2]=ot,g[ot+24>>2]=m,g[ot+12>>2]=ot,g[ot+8>>2]=ot;break}m=g[m>>2]|0;e:do if((g[m+4>>2]&-8|0)!=(L|0)){for(S=L<<((y|0)==31?0:25-(y>>>1)|0);y=m+16+(S>>>31<<2)|0,p=g[y>>2]|0,!!p;)if((g[p+4>>2]&-8|0)==(L|0)){m=p;break e}else S=S<<1,m=p;g[y>>2]=ot,g[ot+24>>2]=m,g[ot+12>>2]=ot,g[ot+8>>2]=ot;break t}while(!1);_e=m+8|0,Ut=g[_e>>2]|0,g[Ut+12>>2]=ot,g[_e>>2]=ot,g[ot+8>>2]=Ut,g[ot+12>>2]=m,g[ot+24>>2]=0}while(!1);return Ut=z+8|0,wt=$e,Ut|0}else Ct=it}else Ct=it;else Ct=-1;while(!1);if(y=g[5831]|0,y>>>0>=Ct>>>0)return m=y-Ct|0,p=g[5834]|0,m>>>0>15?(Ut=p+Ct|0,g[5834]=Ut,g[5831]=m,g[Ut+4>>2]=m|1,g[p+y>>2]=m,g[p+4>>2]=Ct|3):(g[5831]=0,g[5834]=0,g[p+4>>2]=y|3,Ut=p+y+4|0,g[Ut>>2]=g[Ut>>2]|1),Ut=p+8|0,wt=$e,Ut|0;if(L=g[5832]|0,L>>>0>Ct>>>0)return te=L-Ct|0,g[5832]=te,Ut=g[5835]|0,_e=Ut+Ct|0,g[5835]=_e,g[_e+4>>2]=te|1,g[Ut+4>>2]=Ct|3,Ut=Ut+8|0,wt=$e,Ut|0;if(g[5947]|0?p=g[5949]|0:(g[5949]=4096,g[5948]=4096,g[5950]=-1,g[5951]=-1,g[5952]=0,g[5940]=0,g[5947]=Nt&-16^1431655768,p=4096),z=Ct+48|0,H=Ct+47|0,k=p+H|0,C=0-p|0,it=k&C,it>>>0<=Ct>>>0||(p=g[5939]|0,p|0&&(ot=g[5937]|0,Nt=ot+it|0,Nt>>>0<=ot>>>0|Nt>>>0>p>>>0)))return Ut=0,wt=$e,Ut|0;t:do if(g[5940]&4)m=0,ne=143;else{y=g[5835]|0;e:do if(y){for(S=23764;Nt=g[S>>2]|0,!(Nt>>>0<=y>>>0&&(Nt+(g[S+4>>2]|0)|0)>>>0>y>>>0);)if(p=g[S+8>>2]|0,p)S=p;else{ne=128;break e}if(m=k-L&C,m>>>0<2147483647)if(p=en(m|0)|0,(p|0)==((g[S>>2]|0)+(g[S+4>>2]|0)|0)){if((p|0)!=-1){L=m,k=p,ne=145;break t}}else S=p,ne=136;else m=0}else ne=128;while(!1);do if((ne|0)==128)if(y=en(0)|0,(y|0)!=-1&&(m=y,Wt=g[5948]|0,re=Wt+-1|0,m=(re&m|0?(re+m&0-Wt)-m|0:0)+it|0,Wt=g[5937]|0,re=m+Wt|0,m>>>0>Ct>>>0&m>>>0<2147483647)){if(Nt=g[5939]|0,Nt|0&&re>>>0<=Wt>>>0|re>>>0>Nt>>>0){m=0;break}if(p=en(m|0)|0,(p|0)==(y|0)){L=m,k=y,ne=145;break t}else S=p,ne=136}else m=0;while(!1);do if((ne|0)==136){if(y=0-m|0,!(z>>>0>m>>>0&(m>>>0<2147483647&(S|0)!=-1)))if((S|0)==-1){m=0;break}else{L=m,k=S,ne=145;break t}if(p=g[5949]|0,p=H-m+p&0-p,p>>>0>=2147483647){L=m,k=S,ne=145;break t}if((en(p|0)|0)==-1){en(y|0)|0,m=0;break}else{L=p+m|0,k=S,ne=145;break t}}while(!1);g[5940]=g[5940]|4,ne=143}while(!1);if((ne|0)==143&&it>>>0<2147483647&&(te=en(it|0)|0,re=en(0)|0,Le=re-te|0,We=Le>>>0>(Ct+40|0)>>>0,!((te|0)==-1|We^1|te>>>0>>0&((te|0)!=-1&(re|0)!=-1)^1))&&(L=We?Le:m,k=te,ne=145),(ne|0)==145){m=(g[5937]|0)+L|0,g[5937]=m,m>>>0>(g[5938]|0)>>>0&&(g[5938]=m),H=g[5835]|0;t:do if(H){for(m=23764;;){if(p=g[m>>2]|0,y=g[m+4>>2]|0,(k|0)==(p+y|0)){ne=154;break}if(S=g[m+8>>2]|0,S)m=S;else break}if((ne|0)==154&&(_e=m+4|0,(g[m+12>>2]&8|0)==0)&&k>>>0>H>>>0&p>>>0<=H>>>0){g[_e>>2]=y+L,Ut=(g[5832]|0)+L|0,te=H+8|0,te=te&7|0?0-te&7:0,_e=H+te|0,te=Ut-te|0,g[5835]=_e,g[5832]=te,g[_e+4>>2]=te|1,g[H+Ut+4>>2]=40,g[5836]=g[5951];break}for(k>>>0<(g[5833]|0)>>>0&&(g[5833]=k),y=k+L|0,m=23764;;){if((g[m>>2]|0)==(y|0)){ne=162;break}if(p=g[m+8>>2]|0,p)m=p;else break}if((ne|0)==162&&!(g[m+12>>2]&8|0)){g[m>>2]=k,ot=m+4|0,g[ot>>2]=(g[ot>>2]|0)+L,ot=k+8|0,ot=k+(ot&7|0?0-ot&7:0)|0,m=y+8|0,m=y+(m&7|0?0-m&7:0)|0,it=ot+Ct|0,z=m-ot-Ct|0,g[ot+4>>2]=Ct|3;e:do if((H|0)==(m|0))Ut=(g[5832]|0)+z|0,g[5832]=Ut,g[5835]=it,g[it+4>>2]=Ut|1;else{if((g[5834]|0)==(m|0)){Ut=(g[5831]|0)+z|0,g[5831]=Ut,g[5834]=it,g[it+4>>2]=Ut|1,g[it+Ut>>2]=Ut;break}if(p=g[m+4>>2]|0,(p&3|0)==1){L=p&-8,S=p>>>3;r:do if(p>>>0<256)if(p=g[m+8>>2]|0,y=g[m+12>>2]|0,(y|0)==(p|0)){g[5829]=g[5829]&~(1<>2]=y,g[y+8>>2]=p;break}else{k=g[m+24>>2]|0,p=g[m+12>>2]|0;do if((p|0)==(m|0)){if(y=m+16|0,S=y+4|0,p=g[S>>2]|0,p)y=S;else if(p=g[y>>2]|0,!p){p=0;break}for(;;)if(C=p+20|0,S=g[C>>2]|0,S)p=S,y=C;else if(C=p+16|0,S=g[C>>2]|0,S)p=S,y=C;else break;g[y>>2]=0}else Ut=g[m+8>>2]|0,g[Ut+12>>2]=p,g[p+8>>2]=Ut;while(!1);if(!k)break;y=g[m+28>>2]|0,S=23620+(y<<2)|0;do if((g[S>>2]|0)!=(m|0)){if(Ut=k+16|0,g[((g[Ut>>2]|0)==(m|0)?Ut:k+20|0)>>2]=p,!p)break r}else{if(g[S>>2]=p,p|0)break;g[5830]=g[5830]&~(1<>2]=k,y=m+16|0,S=g[y>>2]|0,S|0&&(g[p+16>>2]=S,g[S+24>>2]=p),y=g[y+4>>2]|0,!y)break;g[p+20>>2]=y,g[y+24>>2]=p}while(!1);m=m+L|0,C=L+z|0}else C=z;if(m=m+4|0,g[m>>2]=g[m>>2]&-2,g[it+4>>2]=C|1,g[it+C>>2]=C,m=C>>>3,C>>>0<256){y=23356+(m<<1<<2)|0,p=g[5829]|0,m=1<>2]|0):(g[5829]=p|m,m=y,p=y+8|0),g[p>>2]=it,g[m+12>>2]=it,g[it+8>>2]=m,g[it+12>>2]=y;break}m=C>>>8;do if(!m)S=0;else{if(C>>>0>16777215){S=31;break}_e=(m+1048320|0)>>>16&8,Ut=m<<_e,te=(Ut+520192|0)>>>16&4,Ut=Ut<>>16&2,S=14-(te|_e|S)+(Ut<>>15)|0,S=C>>>(S+7|0)&1|S<<1}while(!1);if(m=23620+(S<<2)|0,g[it+28>>2]=S,p=it+16|0,g[p+4>>2]=0,g[p>>2]=0,p=g[5830]|0,y=1<>2]=it,g[it+24>>2]=m,g[it+12>>2]=it,g[it+8>>2]=it;break}m=g[m>>2]|0;r:do if((g[m+4>>2]&-8|0)!=(C|0)){for(S=C<<((S|0)==31?0:25-(S>>>1)|0);y=m+16+(S>>>31<<2)|0,p=g[y>>2]|0,!!p;)if((g[p+4>>2]&-8|0)==(C|0)){m=p;break r}else S=S<<1,m=p;g[y>>2]=it,g[it+24>>2]=m,g[it+12>>2]=it,g[it+8>>2]=it;break e}while(!1);_e=m+8|0,Ut=g[_e>>2]|0,g[Ut+12>>2]=it,g[_e>>2]=it,g[it+8>>2]=Ut,g[it+12>>2]=m,g[it+24>>2]=0}while(!1);return Ut=ot+8|0,wt=$e,Ut|0}for(m=23764;p=g[m>>2]|0,!(p>>>0<=H>>>0&&(Ut=p+(g[m+4>>2]|0)|0,Ut>>>0>H>>>0));)m=g[m+8>>2]|0;C=Ut+-47|0,p=C+8|0,p=C+(p&7|0?0-p&7:0)|0,C=H+16|0,p=p>>>0>>0?H:p,m=p+8|0,y=L+-40|0,te=k+8|0,te=te&7|0?0-te&7:0,_e=k+te|0,te=y-te|0,g[5835]=_e,g[5832]=te,g[_e+4>>2]=te|1,g[k+y+4>>2]=40,g[5836]=g[5951],y=p+4|0,g[y>>2]=27,g[m>>2]=g[5941],g[m+4>>2]=g[5942],g[m+8>>2]=g[5943],g[m+12>>2]=g[5944],g[5941]=k,g[5942]=L,g[5944]=0,g[5943]=m,m=p+24|0;do _e=m,m=m+4|0,g[m>>2]=7;while((_e+8|0)>>>0>>0);if((p|0)!=(H|0)){if(k=p-H|0,g[y>>2]=g[y>>2]&-2,g[H+4>>2]=k|1,g[p>>2]=k,m=k>>>3,k>>>0<256){y=23356+(m<<1<<2)|0,p=g[5829]|0,m=1<>2]|0):(g[5829]=p|m,m=y,p=y+8|0),g[p>>2]=H,g[m+12>>2]=H,g[H+8>>2]=m,g[H+12>>2]=y;break}if(m=k>>>8,m?k>>>0>16777215?S=31:(_e=(m+1048320|0)>>>16&8,Ut=m<<_e,te=(Ut+520192|0)>>>16&4,Ut=Ut<>>16&2,S=14-(te|_e|S)+(Ut<>>15)|0,S=k>>>(S+7|0)&1|S<<1):S=0,y=23620+(S<<2)|0,g[H+28>>2]=S,g[H+20>>2]=0,g[C>>2]=0,m=g[5830]|0,p=1<>2]=H,g[H+24>>2]=y,g[H+12>>2]=H,g[H+8>>2]=H;break}m=g[y>>2]|0;e:do if((g[m+4>>2]&-8|0)!=(k|0)){for(S=k<<((S|0)==31?0:25-(S>>>1)|0);y=m+16+(S>>>31<<2)|0,p=g[y>>2]|0,!!p;)if((g[p+4>>2]&-8|0)==(k|0)){m=p;break e}else S=S<<1,m=p;g[y>>2]=H,g[H+24>>2]=m,g[H+12>>2]=H,g[H+8>>2]=H;break t}while(!1);_e=m+8|0,Ut=g[_e>>2]|0,g[Ut+12>>2]=H,g[_e>>2]=H,g[H+8>>2]=Ut,g[H+12>>2]=m,g[H+24>>2]=0}}else Ut=g[5833]|0,(Ut|0)==0|k>>>0>>0&&(g[5833]=k),g[5941]=k,g[5942]=L,g[5944]=0,g[5838]=g[5947],g[5837]=-1,g[5842]=23356,g[5841]=23356,g[5844]=23364,g[5843]=23364,g[5846]=23372,g[5845]=23372,g[5848]=23380,g[5847]=23380,g[5850]=23388,g[5849]=23388,g[5852]=23396,g[5851]=23396,g[5854]=23404,g[5853]=23404,g[5856]=23412,g[5855]=23412,g[5858]=23420,g[5857]=23420,g[5860]=23428,g[5859]=23428,g[5862]=23436,g[5861]=23436,g[5864]=23444,g[5863]=23444,g[5866]=23452,g[5865]=23452,g[5868]=23460,g[5867]=23460,g[5870]=23468,g[5869]=23468,g[5872]=23476,g[5871]=23476,g[5874]=23484,g[5873]=23484,g[5876]=23492,g[5875]=23492,g[5878]=23500,g[5877]=23500,g[5880]=23508,g[5879]=23508,g[5882]=23516,g[5881]=23516,g[5884]=23524,g[5883]=23524,g[5886]=23532,g[5885]=23532,g[5888]=23540,g[5887]=23540,g[5890]=23548,g[5889]=23548,g[5892]=23556,g[5891]=23556,g[5894]=23564,g[5893]=23564,g[5896]=23572,g[5895]=23572,g[5898]=23580,g[5897]=23580,g[5900]=23588,g[5899]=23588,g[5902]=23596,g[5901]=23596,g[5904]=23604,g[5903]=23604,Ut=L+-40|0,te=k+8|0,te=te&7|0?0-te&7:0,_e=k+te|0,te=Ut-te|0,g[5835]=_e,g[5832]=te,g[_e+4>>2]=te|1,g[k+Ut+4>>2]=40,g[5836]=g[5951];while(!1);if(m=g[5832]|0,m>>>0>Ct>>>0)return te=m-Ct|0,g[5832]=te,Ut=g[5835]|0,_e=Ut+Ct|0,g[5835]=_e,g[_e+4>>2]=te|1,g[Ut+4>>2]=Ct|3,Ut=Ut+8|0,wt=$e,Ut|0}return Ut=fs()|0,g[Ut>>2]=12,Ut=0,wt=$e,Ut|0}function Gr(p){p=p|0;var m=0,y=0,S=0,C=0,k=0,L=0,z=0,H=0;if(p){y=p+-8|0,C=g[5833]|0,p=g[p+-4>>2]|0,m=p&-8,H=y+m|0;do if(p&1)z=y,L=y;else{if(S=g[y>>2]|0,!(p&3)||(L=y+(0-S)|0,k=S+m|0,L>>>0>>0))return;if((g[5834]|0)==(L|0)){if(p=H+4|0,m=g[p>>2]|0,(m&3|0)!=3){z=L,m=k;break}g[5831]=k,g[p>>2]=m&-2,g[L+4>>2]=k|1,g[L+k>>2]=k;return}if(y=S>>>3,S>>>0<256)if(p=g[L+8>>2]|0,m=g[L+12>>2]|0,(m|0)==(p|0)){g[5829]=g[5829]&~(1<>2]=m,g[m+8>>2]=p,z=L,m=k;break}C=g[L+24>>2]|0,p=g[L+12>>2]|0;do if((p|0)==(L|0)){if(m=L+16|0,y=m+4|0,p=g[y>>2]|0,p)m=y;else if(p=g[m>>2]|0,!p){p=0;break}for(;;)if(S=p+20|0,y=g[S>>2]|0,y)p=y,m=S;else if(S=p+16|0,y=g[S>>2]|0,y)p=y,m=S;else break;g[m>>2]=0}else z=g[L+8>>2]|0,g[z+12>>2]=p,g[p+8>>2]=z;while(!1);if(C){if(m=g[L+28>>2]|0,y=23620+(m<<2)|0,(g[y>>2]|0)==(L|0)){if(g[y>>2]=p,!p){g[5830]=g[5830]&~(1<>2]|0)==(L|0)?z:C+20|0)>>2]=p,!p){z=L,m=k;break}g[p+24>>2]=C,m=L+16|0,y=g[m>>2]|0,y|0&&(g[p+16>>2]=y,g[y+24>>2]=p),m=g[m+4>>2]|0,m?(g[p+20>>2]=m,g[m+24>>2]=p,z=L,m=k):(z=L,m=k)}else z=L,m=k}while(!1);if(!(L>>>0>=H>>>0)&&(p=H+4|0,S=g[p>>2]|0,!!(S&1))){if(S&2)g[p>>2]=S&-2,g[z+4>>2]=m|1,g[L+m>>2]=m,C=m;else{if((g[5835]|0)==(H|0)){if(H=(g[5832]|0)+m|0,g[5832]=H,g[5835]=z,g[z+4>>2]=H|1,(z|0)!=(g[5834]|0))return;g[5834]=0,g[5831]=0;return}if((g[5834]|0)==(H|0)){H=(g[5831]|0)+m|0,g[5831]=H,g[5834]=L,g[z+4>>2]=H|1,g[L+H>>2]=H;return}C=(S&-8)+m|0,y=S>>>3;do if(S>>>0<256)if(m=g[H+8>>2]|0,p=g[H+12>>2]|0,(p|0)==(m|0)){g[5829]=g[5829]&~(1<>2]=p,g[p+8>>2]=m;break}else{k=g[H+24>>2]|0,p=g[H+12>>2]|0;do if((p|0)==(H|0)){if(m=H+16|0,y=m+4|0,p=g[y>>2]|0,p)m=y;else if(p=g[m>>2]|0,!p){y=0;break}for(;;)if(S=p+20|0,y=g[S>>2]|0,y)p=y,m=S;else if(S=p+16|0,y=g[S>>2]|0,y)p=y,m=S;else break;g[m>>2]=0,y=p}else y=g[H+8>>2]|0,g[y+12>>2]=p,g[p+8>>2]=y,y=p;while(!1);if(k|0){if(p=g[H+28>>2]|0,m=23620+(p<<2)|0,(g[m>>2]|0)==(H|0)){if(g[m>>2]=y,!y){g[5830]=g[5830]&~(1<>2]|0)==(H|0)?S:k+20|0)>>2]=y,!y)break;g[y+24>>2]=k,p=H+16|0,m=g[p>>2]|0,m|0&&(g[y+16>>2]=m,g[m+24>>2]=y),p=g[p+4>>2]|0,p|0&&(g[y+20>>2]=p,g[p+24>>2]=y)}}while(!1);if(g[z+4>>2]=C|1,g[L+C>>2]=C,(z|0)==(g[5834]|0)){g[5831]=C;return}}if(p=C>>>3,C>>>0<256){y=23356+(p<<1<<2)|0,m=g[5829]|0,p=1<>2]|0):(g[5829]=m|p,p=y,m=y+8|0),g[m>>2]=z,g[p+12>>2]=z,g[z+8>>2]=p,g[z+12>>2]=y;return}p=C>>>8,p?C>>>0>16777215?S=31:(L=(p+1048320|0)>>>16&8,H=p<>>16&4,H=H<>>16&2,S=14-(k|L|S)+(H<>>15)|0,S=C>>>(S+7|0)&1|S<<1):S=0,p=23620+(S<<2)|0,g[z+28>>2]=S,g[z+20>>2]=0,g[z+16>>2]=0,m=g[5830]|0,y=1<>2]=z,g[z+24>>2]=p,g[z+12>>2]=z,g[z+8>>2]=z;else{p=g[p>>2]|0;e:do if((g[p+4>>2]&-8|0)!=(C|0)){for(S=C<<((S|0)==31?0:25-(S>>>1)|0);y=p+16+(S>>>31<<2)|0,m=g[y>>2]|0,!!m;)if((g[m+4>>2]&-8|0)==(C|0)){p=m;break e}else S=S<<1,p=m;g[y>>2]=z,g[z+24>>2]=p,g[z+12>>2]=z,g[z+8>>2]=z;break t}while(!1);L=p+8|0,H=g[L>>2]|0,g[H+12>>2]=z,g[L>>2]=z,g[z+8>>2]=H,g[z+12>>2]=p,g[z+24>>2]=0}while(!1);if(H=(g[5837]|0)+-1|0,g[5837]=H,!(H|0)){for(p=23772;p=g[p>>2]|0,p;)p=p+8|0;g[5837]=-1}}}}function Ua(p,m){p=p|0,m=m|0;var y=0;return p?(y=Oc(m,p)|0,(m|p)>>>0>65535&&(y=((y>>>0)/(p>>>0)|0|0)==(m|0)?y:-1)):y=0,p=ho(y)|0,!p||!(g[p+-4>>2]&3)||Fc(p|0,0,y|0)|0,p|0}function S_(p,m,y,S){return p=p|0,m=m|0,y=y|0,S=S|0,y=p+y>>>0,Je(m+S+(y>>>0

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xx=16;var IS=8,sae=Ma.sizeOfH3Index(),YQ=Ma.sizeOfGeoCoord(),Ayt=Ma.sizeOfGeoBoundary(),oae=Ma.sizeOfGeoPolygon(),aae=Ma.sizeOfGeofence(),lae=Ma.sizeOfLinkedGeoPolygon(),cae=Ma.sizeOfCoordIJ(),qQ={m:\"m\",m2:\"m2\",km:\"km\",km2:\"km2\",rads:\"rads\",rads2:\"rads2\"};function myt(e){if(typeof e!=\"number\"||e<0||e>15||Math.floor(e)!==e)throw new Error(\"Invalid resolution: \"+e)}var gyt=/[^0-9a-fA-F]/;function bx(e){if(Array.isArray(e)&&e.length===2&&Number.isInteger(e[0])&&Number.isInteger(e[1]))return e;if(typeof e!=\"string\"||gyt.test(e))return[0,0];var t=parseInt(e.substring(0,e.length-8),xx),r=parseInt(e.substring(e.length-8),xx);return[r,t]}function ZQ(e){if(e>=0)return e.toString(xx);e=e&2147483647;var t=QQ(8,e.toString(xx)),r=(parseInt(t[0],xx)+8).toString(xx);return t=r+t.substring(1),t}function _yt(e,t){return ZQ(t)+QQ(8,ZQ(e))}function QQ(e,t){for(var r=e-t.length,i=\"\",s=0;s180?r[0]-=360:i<-180&&(r[0]+=360)}}function Myt(e,t,r){let[i,s]=FI(e),n=t.length;n$(t,s);let o=t[0]===t[n-1]?n-1:n;for(let c=0;ce.hexagon},extruded:!0},Np=class e extends Ni{constructor(...t){super(...t),G(this,\"state\",void 0)}initializeState(){e._checkH3Lib(),this.state={edgeLengthKM:0,resolution:-1}}shouldUpdateState({changeFlags:t}){return this._shouldUseHighPrecision()?t.propsOrDataChanged:t.somethingChanged}updateState({props:t,changeFlags:r}){if(t.highPrecision!==!0&&(r.dataChanged||r.updateTriggersChanged&&r.updateTriggersChanged.getHexagon)){let i=this._calculateH3DataProps();this.setState(i)}this._updateVertices(this.context.viewport)}_calculateH3DataProps(){let t=-1,r=!1,i=!1,{iterable:s,objectInfo:n}=Jc(this.props.data);for(let o of s){n.index++;let c=this.props.getHexagon(o,n),f=KQ(c);if(t<0){if(t=f,!this.props.highPrecision)break}else if(t!==f){i=!0;break}if(XQ(c)){r=!0;break}}return{resolution:t,edgeLengthKM:t>=0?r$(t,\"km\"):0,hasMultipleRes:i,hasPentagon:r}}_shouldUseHighPrecision(){if(this.props.highPrecision===\"auto\"){let{resolution:t,hasPentagon:r,hasMultipleRes:i}=this.state,{viewport:s}=this.context;return!!s?.resolution||i||r||t>=0&&t<=5}return this.props.highPrecision}_updateVertices(t){if(this._shouldUseHighPrecision())return;let{resolution:r,edgeLengthKM:i,centerHex:s}=this.state;if(r<0)return;let n=this.props.centerHexagon||JQ(t.latitude,t.longitude,r);if(s===n)return;if(s){let R=e$(s,n);if(R>=0&&R*i{let N=t.projectFlat(R);return[(N[0]-w)/o[0],(N[1]-I)/o[1]]}),this.setState({centerHex:n,vertices:c})}renderLayers(){return this._shouldUseHighPrecision()?this._renderPolygonLayer():this._renderColumnLayer()}_getForwardProps(){let{elevationScale:t,material:r,coverage:i,extruded:s,wireframe:n,stroked:o,filled:c,lineWidthUnits:f,lineWidthScale:_,lineWidthMinPixels:w,lineWidthMaxPixels:I,getFillColor:R,getElevation:N,getLineColor:j,getLineWidth:Q,transitions:et,updateTriggers:Y}=this.props;return{elevationScale:t,extruded:s,coverage:i,wireframe:n,stroked:o,filled:c,lineWidthUnits:f,lineWidthScale:_,lineWidthMinPixels:w,lineWidthMaxPixels:I,material:r,getElevation:N,getFillColor:R,getLineColor:j,getLineWidth:Q,transitions:et,updateTriggers:{getFillColor:Y.getFillColor,getElevation:Y.getElevation,getLineColor:Y.getLineColor,getLineWidth:Y.getLineWidth}}}_renderPolygonLayer(){let{data:t,getHexagon:r,updateTriggers:i,coverage:s}=this.props,n=this.getSubLayerClass(\"hexagon-cell-hifi\",lf),o=this._getForwardProps();return o.updateTriggers.getPolygon=Iyt(i.getHexagon,s),new n(o,this.getSubLayerProps({id:\"hexagon-cell-hifi\",updateTriggers:o.updateTriggers}),{data:t,_normalize:!1,_windingOrder:\"CCW\",positionFormat:\"XY\",getPolygon:(c,f)=>{let _=r(c,f);return Pyt(i$(_,s))}})}_renderColumnLayer(){let{data:t,getHexagon:r,updateTriggers:i}=this.props,s=this.getSubLayerClass(\"hexagon-cell\",af),n=this._getForwardProps();return n.updateTriggers.getPosition=i.getHexagon,new s(n,this.getSubLayerProps({id:\"hexagon-cell\",flatShading:!0,updateTriggers:n.updateTriggers}),{data:t,diskResolution:6,radius:1,vertices:this.state.vertices,getPosition:Eyt.bind(null,r)})}};G(Np,\"defaultProps\",Cyt);G(Np,\"layerName\",\"H3HexagonLayer\");G(Np,\"_checkH3Lib\",()=>{});var{data:Sae,getHexagon:Tae,...Lyt}=Np.defaultProps,kyt={_validate:!0},Mae={...Lyt,...kyt};var s$=[[255,255,178],[254,217,118],[254,178,76],[253,141,60],[240,59,32],[189,0,38]];function o$(e,t=!1,r=Float32Array){let i;if(Number.isFinite(e[0]))i=new r(e);else{i=new r(e.length*4);let s=0;for(let n=0;nc[0]),r=e.map(c=>c[1]),i=Math.min.apply(null,t),s=Math.max.apply(null,t),n=Math.min.apply(null,r),o=Math.max.apply(null,r);return[i,n,s,o]}function u$(e,t){return t[0]>=e[0]&&t[2]<=e[2]&&t[1]>=e[1]&&t[3]<=e[3]}var l$=new Float32Array(12);function tF(e,t=2){let r=0;for(let i of e)for(let s=0;s 0.) {\n maxValue = colorDomain[1];\n minValue = colorDomain[0];\n }\n vIntensityMax = intensity / maxValue;\n vIntensityMin = intensity / minValue;\n}\n`;var A$=`#define SHADER_NAME triangle-layer-fragment-shader\n\nprecision highp float;\n\nuniform float opacity;\nuniform sampler2D texture;\nuniform sampler2D colorTexture;\nuniform float aggregationMode;\n\nvarying vec2 vTexCoords;\nvarying float vIntensityMin;\nvarying float vIntensityMax;\n\nvec4 getLinearColor(float value) {\n float factor = clamp(value * vIntensityMax, 0., 1.);\n vec4 color = texture2D(colorTexture, vec2(factor, 0.5));\n color.a *= min(value * vIntensityMin, 1.0);\n return color;\n}\n\nvoid main(void) {\n vec4 weights = texture2D(texture, vTexCoords);\n float weight = weights.r;\n\n if (aggregationMode > 0.5) {\n weight /= max(1.0, weights.a);\n }\n if (weight <= 0.) {\n discard;\n }\n\n vec4 linearColor = getLinearColor(weight);\n linearColor.a *= opacity;\n gl_FragColor =linearColor;\n}\n`;var Sx=class extends dn{getShaders(){return{vs:p$,fs:A$,modules:[Rs]}}initializeState({gl:t}){this.getAttributeManager().add({positions:{size:3,noAlloc:!0},texCoords:{size:2,noAlloc:!0}}),this.setState({model:this._getModel(t)})}_getModel(t){let{vertexCount:r}=this.props;return new fn(t,{...this.getShaders(),id:this.props.id,geometry:new $n({drawMode:6,vertexCount:r})})}draw({uniforms:t}){let{model:r}=this.state,{texture:i,maxTexture:s,colorTexture:n,intensity:o,threshold:c,aggregationMode:f,colorDomain:_}=this.props;r.setUniforms({...t,texture:i,maxTexture:s,colorTexture:n,intensity:o,threshold:c,aggregationMode:f,colorDomain:_}).draw()}};G(Sx,\"layerName\",\"TriangleLayer\");var m$=`attribute vec3 positions;\nattribute vec3 positions64Low;\nattribute float weights;\nvarying vec4 weightsTexture;\nuniform float radiusPixels;\nuniform float textureWidth;\nuniform vec4 commonBounds;\nuniform float weightsScale;\nvoid main()\n{\n weightsTexture = vec4(weights * weightsScale, 0., 0., 1.);\n\n float radiusTexels = project_pixel_size(radiusPixels) * textureWidth / (commonBounds.z - commonBounds.x);\n gl_PointSize = radiusTexels * 2.;\n\n vec3 commonPosition = project_position(positions, positions64Low);\n gl_Position.xy = (commonPosition.xy - commonBounds.xy) / (commonBounds.zw - commonBounds.xy) ;\n gl_Position.xy = (gl_Position.xy * 2.) - (1.);\n}\n`;var g$=`varying vec4 weightsTexture;\nfloat gaussianKDE(float u){\n return pow(2.71828, -u*u/0.05555)/(1.77245385*0.166666);\n}\nvoid main()\n{\n float dist = length(gl_PointCoord - vec2(0.5, 0.5));\n if (dist > 0.5) {\n discard;\n }\n gl_FragColor = weightsTexture * gaussianKDE(2. * dist);\n DECKGL_FILTER_COLOR(gl_FragColor, geometry);\n}\n`;var _$=`attribute vec4 inTexture;\nvarying vec4 outTexture;\n\nvoid main()\n{\noutTexture = inTexture;\ngl_Position = vec4(0, 0, 0, 1.);\ngl_PointSize = 1.0;\n}\n`;var y$=`varying vec4 outTexture;\nvoid main() {\n gl_FragColor = outTexture;\n gl_FragColor.g = outTexture.r / max(1.0, outTexture.a);\n}\n`;var Dyt=2,eF={mipmaps:!1,parameters:{10240:9729,10241:9729,10242:33071,10243:33071},dataFormat:6408},v$=[0,0],Oyt={SUM:0,MEAN:1},Byt={getPosition:{type:\"accessor\",value:e=>e.position},getWeight:{type:\"accessor\",value:1},intensity:{type:\"number\",min:0,value:1},radiusPixels:{type:\"number\",min:1,max:100,value:50},colorRange:s$,threshold:{type:\"number\",min:0,max:1,value:.05},colorDomain:{type:\"array\",value:null,optional:!0},aggregation:\"SUM\",weightsTextureSize:{type:\"number\",min:128,max:2048,value:2048},debounceTimeout:{type:\"number\",min:0,max:1e3,value:500}},Fyt=[Ii.BLEND_EQUATION_MINMAX,Ii.TEXTURE_FLOAT],zyt=[Ii.COLOR_ATTACHMENT_RGBA32F,Ii.FLOAT_BLEND],Nyt={data:{props:[\"radiusPixels\"]}},Up=class extends wx{constructor(...t){super(...t),G(this,\"state\",void 0)}initializeState(){let{gl:t}=this.context;if(!Oh(t,Fyt)){this.setState({supported:!1}),or.error(\"HeatmapLayer: \".concat(this.id,\" is not supported on this browser\"))();return}super.initializeAggregationLayer(Nyt),this.setState({supported:!0,colorDomain:v$}),this._setupTextureParams(),this._setupAttributes(),this._setupResources()}shouldUpdateState({changeFlags:t}){return t.somethingChanged}updateState(t){this.state.supported&&(super.updateState(t),this._updateHeatmapState(t))}_updateHeatmapState(t){let{props:r,oldProps:i}=t,s=this._getChangeFlags(t);(s.dataChanged||s.viewportChanged)&&(s.boundsChanged=this._updateBounds(s.dataChanged),this._updateTextureRenderingBounds()),s.dataChanged||s.boundsChanged?(clearTimeout(this.state.updateTimer),this.setState({isWeightMapDirty:!0})):s.viewportZoomChanged&&this._debouncedUpdateWeightmap(),r.colorRange!==i.colorRange&&this._updateColorTexture(t),this.state.isWeightMapDirty&&this._updateWeightmap(),this.setState({zoom:t.context.viewport.zoom})}renderLayers(){if(!this.state.supported)return[];let{weightsTexture:t,triPositionBuffer:r,triTexCoordBuffer:i,maxWeightsTexture:s,colorTexture:n,colorDomain:o}=this.state,{updateTriggers:c,intensity:f,threshold:_,aggregation:w}=this.props,I=this.getSubLayerClass(\"triangle\",Sx);return new I(this.getSubLayerProps({id:\"triangle-layer\",updateTriggers:c}),{coordinateSystem:Yr.DEFAULT,data:{attributes:{positions:r,texCoords:i}},vertexCount:4,maxTexture:s,colorTexture:n,aggregationMode:Oyt[w]||0,texture:t,intensity:f,threshold:_,colorDomain:o})}finalizeState(t){super.finalizeState(t);let{weightsTransform:r,weightsTexture:i,maxWeightTransform:s,maxWeightsTexture:n,triPositionBuffer:o,triTexCoordBuffer:c,colorTexture:f,updateTimer:_}=this.state;r?.delete(),i?.delete(),s?.delete(),n?.delete(),o?.delete(),c?.delete(),f?.delete(),_&&clearTimeout(_)}_getAttributeManager(){return new Xf(this.context.gl,{id:this.props.id,stats:this.context.stats})}_getChangeFlags(t){let r={},{dimensions:i}=this.state;r.dataChanged=this.isAttributeChanged()||this.isAggregationDirty(t,{compareAll:!0,dimension:i.data}),r.viewportChanged=t.changeFlags.viewportChanged;let{zoom:s}=this.state;return(!t.context.viewport||t.context.viewport.zoom!==s)&&(r.viewportZoomChanged=!0),r}_createTextures(){let{gl:t}=this.context,{textureSize:r,format:i,type:s}=this.state;this.setState({weightsTexture:new pi(t,{width:r,height:r,format:i,type:s,...eF}),maxWeightsTexture:new pi(t,{format:i,type:s,...eF})})}_setupAttributes(){this.getAttributeManager().add({positions:{size:3,type:5130,accessor:\"getPosition\"},weights:{size:1,accessor:\"getWeight\"}}),this.setState({positionAttributeName:\"positions\"})}_setupTextureParams(){let{gl:t}=this.context,{weightsTextureSize:r}=this.props,i=Math.min(r,wy(t,3379)),s=Oh(t,zyt),{format:n,type:o}=d$({gl:t,floatTargetSupport:s}),c=s?1:1/255;this.setState({textureSize:i,format:n,type:o,weightsScale:c}),s||or.warn(\"HeatmapLayer: \".concat(this.id,\" rendering to float texture not supported, fallingback to low precession format\"))()}getShaders(t){return super.getShaders(t===\"max-weights-transform\"?{vs:_$,_fs:y$}:{vs:m$,_fs:g$})}_createWeightsTransform(t={}){var r;let{gl:i}=this.context,{weightsTransform:s}=this.state,{weightsTexture:n}=this.state;(r=s)===null||r===void 0||r.delete(),s=new nc(i,{id:\"\".concat(this.id,\"-weights-transform\"),elementCount:1,_targetTexture:n,_targetTextureVarying:\"weightsTexture\",...t}),this.setState({weightsTransform:s})}_setupResources(){let{gl:t}=this.context;this._createTextures();let{textureSize:r,weightsTexture:i,maxWeightsTexture:s}=this.state,n=this.getShaders(\"weights-transform\");this._createWeightsTransform(n);let o=this.getShaders(\"max-weights-transform\"),c=new nc(t,{id:\"\".concat(this.id,\"-max-weights-transform\"),_sourceTextures:{inTexture:i},_targetTexture:s,_targetTextureVarying:\"outTexture\",...o,elementCount:r*r});this.setState({weightsTexture:i,maxWeightsTexture:s,maxWeightTransform:c,zoom:null,triPositionBuffer:new Fr(t,{byteLength:48,accessor:{size:3}}),triTexCoordBuffer:new Fr(t,{byteLength:48,accessor:{size:2}})})}updateShaders(t){this._createWeightsTransform(t)}_updateMaxWeightValue(){let{maxWeightTransform:t}=this.state;t.run({parameters:{blend:!0,depthTest:!1,blendFunc:[1,1],blendEquation:32776}})}_updateBounds(t=!1){let{viewport:r}=this.context,i=[r.unproject([0,0]),r.unproject([r.width,0]),r.unproject([r.width,r.height]),r.unproject([0,r.height])].map(c=>c.map(Math.fround)),s=c$(i),n={visibleWorldBounds:s,viewportCorners:i},o=!1;if(t||!this.state.worldBounds||!u$(this.state.worldBounds,s)){let c=this._worldToCommonBounds(s),f=this._commonToWorldBounds(c);this.props.coordinateSystem===Yr.LNGLAT&&(f[1]=Math.max(f[1],-85.051129),f[3]=Math.min(f[3],85.051129),f[0]=Math.max(f[0],-360),f[2]=Math.min(f[2],360));let _=this._worldToCommonBounds(f);n.worldBounds=f,n.normalizedCommonBounds=_,o=!0}return this.setState(n),o}_updateTextureRenderingBounds(){let{triPositionBuffer:t,triTexCoordBuffer:r,normalizedCommonBounds:i,viewportCorners:s}=this.state,{viewport:n}=this.context;t.subData(tF(s,3));let o=s.map(c=>f$(n.projectPosition(c),i));r.subData(tF(o,2))}_updateColorTexture(t){let{colorRange:r}=t.props,{colorTexture:i}=this.state,s=o$(r,!1,Uint8Array);i?i.setImageData({data:s,width:r.length}):i=new pi(this.context.gl,{data:s,width:r.length,height:1,...eF}),this.setState({colorTexture:i})}_updateWeightmap(){let{radiusPixels:t,colorDomain:r,aggregation:i}=this.props,{weightsTransform:s,worldBounds:n,textureSize:o,weightsTexture:c,weightsScale:f}=this.state;this.state.isWeightMapDirty=!1;let _=this._worldToCommonBounds(n,{useLayerCoordinateSystem:!0});if(r&&i===\"SUM\"){let{viewport:I}=this.context,R=I.distanceScales.metersPerUnit[2]*(_[2]-_[0])/o;this.state.colorDomain=r.map(N=>N*R*f)}else this.state.colorDomain=r||v$;let w={radiusPixels:t,commonBounds:_,textureWidth:o,weightsScale:f};s.update({elementCount:this.getNumInstances()}),Mn(this.context.gl,{clearColor:[0,0,0,0]},()=>{s.run({uniforms:w,parameters:{blend:!0,depthTest:!1,blendFunc:[1,1],blendEquation:32774},clearRenderTarget:!0,attributes:this.getAttributes(),moduleSettings:this.getModuleSettings()})}),this._updateMaxWeightValue(),c.setParameters({10240:9729,10241:9729})}_debouncedUpdateWeightmap(t=!1){let{updateTimer:r}=this.state,{debounceTimeout:i}=this.props;t?(r=null,this._updateBounds(!0),this._updateTextureRenderingBounds(),this.setState({isWeightMapDirty:!0})):(this.setState({isWeightMapDirty:!1}),clearTimeout(r),r=setTimeout(this._debouncedUpdateWeightmap.bind(this,!0),i)),this.setState({updateTimer:r})}_worldToCommonBounds(t,r={}){let{useLayerCoordinateSystem:i=!1}=r,[s,n,o,c]=t,{viewport:f}=this.context,{textureSize:_}=this.state,{coordinateSystem:w}=this.props,I=i&&(w===Yr.LNGLAT_OFFSETS||w===Yr.METER_OFFSETS),R=I?f.projectPosition(this.props.coordinateOrigin):[0,0],N=_*Dyt/f.scale,j,Q;return i&&!I?(j=this.projectPosition([s,n,0]),Q=this.projectPosition([o,c,0])):(j=f.projectPosition([s,n,0]),Q=f.projectPosition([o,c,0])),h$([j[0]-R[0],j[1]-R[1],Q[0]-R[0],Q[1]-R[1]],N,N)}_commonToWorldBounds(t){let[r,i,s,n]=t,{viewport:o}=this.context,c=o.unprojectPosition([r,i]),f=o.unprojectPosition([s,n]);return c.slice(0,2).concat(f.slice(0,2))}};G(Up,\"layerName\",\"HeatmapLayer\");G(Up,\"defaultProps\",Byt);var{data:Ale,getPosition:mle,...Uyt}=Up.defaultProps,x$={_validate:!0},Vyt={...Uyt,...x$},CS=class extends Ni{static defaultProps=Vyt;static layerName=\"GeoArrowHeatmapLayer\";renderLayers(){let{data:t}=this.props,r=ws(t,Kn.POINT);if(r!==null)return this._renderLayersPoint(r);let i=this.props.getPosition;if(i!==void 0&&Ci.isPointVector(i))return this._renderLayersPoint(i);throw new Error(\"getPosition not GeoArrow point\")}_renderLayersPoint(t){let{data:r}=this.props;this.props._validate&&(_r(Ci.isPointVector(t)),no(this.props,r));let[i,s]=io(this.props,[\"getPosition\"]),n=vo(r.data),o=[];for(let c=0;cr.text()),earcutWorkerPool:null}}async initEarcutPool(){if(this.state.earcutWorkerPool)return this.state.earcutWorkerPool;let t=await this.state.earcutWorkerRequest;if(!t||window?.location?.href.startsWith(\"file://\"))return null;try{let r=RX(()=>LX(kX.fromText(t)),8);return this.state.earcutWorkerPool=r,this.state.earcutWorkerPool}catch{return null}}async finalizeState(t){await this.state?.earcutWorkerPool?.terminate(),console.log(\"terminated\")}async updateData(){let{data:t}=this.props,r=await this._updateEarcut(t),i=vo(t.data);this.setState({table:this.props.data,triangles:r,tableOffsets:i})}async _updateEarcut(t){let r=ws(t,Kn.POLYGON);if(r!==null)return this._earcutPolygonVector(r);let i=ws(t,Kn.MULTIPOLYGON);if(i!==null)return this._earcutMultiPolygonVector(i);let s=this.props.getPolygon;if(s!==void 0&&Ci.isPolygonVector(s))return this._earcutPolygonVector(s);if(s!==void 0&&Ci.isMultiPolygonVector(s))return this._earcutMultiPolygonVector(s);throw new Error(\"geometryColumn not Polygon or MultiPolygon\")}async _earcutPolygonVector(t){let r=await this.initEarcutPool();if(!r)return this._earcutPolygonVectorMainThread(t);let i=new Array(t.data.length);console.time(\"earcut\");for(let s=0;s{let _=await f(LF(o,c));i[s]=_})}return await r.completed(),console.timeEnd(\"earcut\"),i}_earcutPolygonVectorMainThread(t){let r=new Array(t.data.length);for(let i=0;i{let w=await _(LF(c,f));i[s]=w})}return await r.completed(),console.timeEnd(\"earcut\"),i}_earcutMultiPolygonVectorMainThread(t){let r=new Array(t.data.length);for(let i=0;iDX(t))):e}function OX(e){if(\"data\"in e)return new xr(e.data.map(o=>OX(o)));let t=e.valueOffsets,r=vi.getMultiPolygonChild(e),i=r.valueOffsets,s=vi.getPolygonChild(r),n=new Int32Array(t.length);for(let o=0;o{this.table=O2(this.model.get(t))};this.model.on(`change:${t}`,r),this.callbacks.set(`change:${t}`,r)}},tC=class extends mf{static layerType=\"arc\";greatCircle;numSegments;widthUnits;widthScale;widthMinPixels;widthMaxPixels;getSourcePosition;getTargetPosition;getSourceColor;getTargetColor;getWidth;getHeight;getTilt;constructor(t,r){super(t,r),this.initRegularAttribute(\"great_circle\",\"greatCircle\"),this.initRegularAttribute(\"num_segments\",\"numSegments\"),this.initRegularAttribute(\"width_units\",\"widthUnits\"),this.initRegularAttribute(\"width_scale\",\"widthScale\"),this.initRegularAttribute(\"width_min_pixels\",\"widthMinPixels\"),this.initRegularAttribute(\"width_max_pixels\",\"widthMaxPixels\"),this.initVectorizedAccessor(\"get_source_position\",\"getSourcePosition\"),this.initVectorizedAccessor(\"get_target_position\",\"getTargetPosition\"),this.initVectorizedAccessor(\"get_source_color\",\"getSourceColor\"),this.initVectorizedAccessor(\"get_target_color\",\"getTargetColor\"),this.initVectorizedAccessor(\"get_width\",\"getWidth\"),this.initVectorizedAccessor(\"get_height\",\"getHeight\"),this.initVectorizedAccessor(\"get_tilt\",\"getTilt\")}layerProps(){return{data:this.table,getSourcePosition:this.getSourcePosition,getTargetPosition:this.getTargetPosition,...Jt(this.greatCircle)&&{greatCircle:this.greatCircle},...Jt(this.numSegments)&&{numSegments:this.numSegments},...Jt(this.widthUnits)&&{widthUnits:this.widthUnits},...Jt(this.widthScale)&&{widthScale:this.widthScale},...Jt(this.widthMinPixels)&&{widthMinPixels:this.widthMinPixels},...Jt(this.widthMaxPixels)&&{widthMaxPixels:this.widthMaxPixels},...Jt(this.getSourceColor)&&{getSourceColor:this.getSourceColor},...Jt(this.getTargetColor)&&{getTargetColor:this.getTargetColor},...Jt(this.getWidth)&&{getWidth:this.getWidth},...Jt(this.getHeight)&&{getHeight:this.getHeight},...Jt(this.getTilt)&&{getTilt:this.getTilt}}}render(){return new wS({...this.baseLayerProps(),...this.layerProps()})}},eC=class extends Ug{static layerType=\"bitmap\";image;bounds;desaturate;transparentColor;tintColor;constructor(t,r){super(t,r),this.initRegularAttribute(\"image\",\"image\"),this.initRegularAttribute(\"bounds\",\"bounds\"),this.initRegularAttribute(\"desaturate\",\"desaturate\"),this.initRegularAttribute(\"transparent_color\",\"transparentColor\"),this.initRegularAttribute(\"tint_color\",\"tintColor\")}layerProps(){return{...Jt(this.image)&&{image:this.image},...Jt(this.bounds)&&{bounds:this.bounds},...Jt(this.desaturate)&&{desaturate:this.desaturate},...Jt(this.transparentColor)&&{transparentColor:this.transparentColor},...Jt(this.tintColor)&&{tintColor:this.tintColor}}}render(){return new Mp({...this.baseLayerProps(),...this.layerProps(),data:void 0,pickable:!1})}},rC=class extends Ug{static layerType=\"bitmap-tile\";data;tileSize;zoomOffset;maxZoom;minZoom;extent;maxCacheSize;maxCacheByteSize;refinementStrategy;maxRequests;desaturate;transparentColor;tintColor;constructor(t,r){super(t,r),this.initRegularAttribute(\"data\",\"data\"),this.initRegularAttribute(\"tile_size\",\"tileSize\"),this.initRegularAttribute(\"zoom_offset\",\"zoomOffset\"),this.initRegularAttribute(\"max_zoom\",\"maxZoom\"),this.initRegularAttribute(\"min_zoom\",\"minZoom\"),this.initRegularAttribute(\"extent\",\"extent\"),this.initRegularAttribute(\"max_cache_size\",\"maxCacheSize\"),this.initRegularAttribute(\"max_cache_byte_size\",\"maxCacheByteSize\"),this.initRegularAttribute(\"refinement_strategy\",\"refinementStrategy\"),this.initRegularAttribute(\"max_requests\",\"maxRequests\"),this.initRegularAttribute(\"desaturate\",\"desaturate\"),this.initRegularAttribute(\"transparent_color\",\"transparentColor\"),this.initRegularAttribute(\"tint_color\",\"tintColor\")}bitmapLayerProps(){return{...Jt(this.desaturate)&&{desaturate:this.desaturate},...Jt(this.transparentColor)&&{transparentColor:this.transparentColor},...Jt(this.tintColor)&&{tintColor:this.tintColor}}}layerProps(){return{data:this.data,...Jt(this.tileSize)&&{tileSize:this.tileSize},...Jt(this.zoomOffset)&&{zoomOffset:this.zoomOffset},...Jt(this.maxZoom)&&{maxZoom:this.maxZoom},...Jt(this.minZoom)&&{minZoom:this.minZoom},...Jt(this.extent)&&{extent:this.extent},...Jt(this.maxCacheSize)&&{maxCacheSize:this.maxCacheSize},...Jt(this.maxCacheByteSize)&&{maxCacheByteSize:this.maxCacheByteSize},...Jt(this.refinementStrategy)&&{refinementStrategy:this.refinementStrategy},...Jt(this.maxRequests)&&{maxRequests:this.maxRequests}}}render(){return new Lm({...this.baseLayerProps(),...this.layerProps(),renderSubLayers:t=>{let[r,i]=t.tile.boundingBox;return new Mp(t,{...this.bitmapLayerProps(),data:void 0,image:t.data,bounds:[r[0],r[1],i[0],i[1]]})}})}},iC=class extends mf{static layerType=\"column\";diskResolution;radius;angle;vertices;offset;coverage;elevationScale;filled;stroked;extruded;wireframe;flatShading;radiusUnits;lineWidthUnits;lineWidthScale;lineWidthMinPixels;lineWidthMaxPixels;material;getPosition;getFillColor;getLineColor;getElevation;getLineWidth;constructor(t,r){super(t,r),this.initRegularAttribute(\"disk_resolution\",\"diskResolution\"),this.initRegularAttribute(\"radius\",\"radius\"),this.initRegularAttribute(\"angle\",\"angle\"),this.initRegularAttribute(\"vertices\",\"vertices\"),this.initRegularAttribute(\"offset\",\"offset\"),this.initRegularAttribute(\"coverage\",\"coverage\"),this.initRegularAttribute(\"elevation_scale\",\"elevationScale\"),this.initRegularAttribute(\"filled\",\"filled\"),this.initRegularAttribute(\"stroked\",\"stroked\"),this.initRegularAttribute(\"extruded\",\"extruded\"),this.initRegularAttribute(\"wireframe\",\"wireframe\"),this.initRegularAttribute(\"flat_shading\",\"flatShading\"),this.initRegularAttribute(\"radius_units\",\"radiusUnits\"),this.initRegularAttribute(\"line_width_units\",\"lineWidthUnits\"),this.initRegularAttribute(\"line_width_scale\",\"lineWidthScale\"),this.initRegularAttribute(\"line_width_min_pixels\",\"lineWidthMinPixels\"),this.initRegularAttribute(\"line_width_max_pixels\",\"lineWidthMaxPixels\"),this.initRegularAttribute(\"material\",\"material\"),this.initVectorizedAccessor(\"get_position\",\"getPosition\"),this.initVectorizedAccessor(\"get_fill_color\",\"getFillColor\"),this.initVectorizedAccessor(\"get_line_color\",\"getLineColor\"),this.initVectorizedAccessor(\"get_elevation\",\"getElevation\"),this.initVectorizedAccessor(\"get_line_width\",\"getLineWidth\")}layerProps(){return{data:this.table,...Jt(this.diskResolution)&&{diskResolution:this.diskResolution},...Jt(this.radius)&&{radius:this.radius},...Jt(this.angle)&&{angle:this.angle},...Jt(this.vertices)&&this.vertices!==void 0&&{vertices:this.vertices},...Jt(this.offset)&&{offset:this.offset},...Jt(this.coverage)&&{coverage:this.coverage},...Jt(this.elevationScale)&&{elevationScale:this.elevationScale},...Jt(this.filled)&&{filled:this.filled},...Jt(this.stroked)&&{stroked:this.stroked},...Jt(this.extruded)&&{extruded:this.extruded},...Jt(this.wireframe)&&{wireframe:this.wireframe},...Jt(this.flatShading)&&{flatShading:this.flatShading},...Jt(this.radiusUnits)&&{radiusUnits:this.radiusUnits},...Jt(this.lineWidthUnits)&&{lineWidthUnits:this.lineWidthUnits},...Jt(this.lineWidthScale)&&{lineWidthScale:this.lineWidthScale},...Jt(this.lineWidthMinPixels)&&{lineWidthMinPixels:this.lineWidthMinPixels},...Jt(this.lineWidthMaxPixels)&&{lineWidthMaxPixels:this.lineWidthMaxPixels},...Jt(this.material)&&{material:this.material},...Jt(this.getPosition)&&{getPosition:this.getPosition},...Jt(this.getFillColor)&&{getFillColor:this.getFillColor},...Jt(this.getLineColor)&&{getLineColor:this.getLineColor},...Jt(this.getElevation)&&{getElevation:this.getElevation},...Jt(this.getLineWidth)&&{getLineWidth:this.getLineWidth}}}render(){return new SS({...this.baseLayerProps(),...this.layerProps()})}},nC=class extends mf{static layerType=\"heatmap\";radiusPixels;colorRange;intensity;threshold;colorDomain;aggregation;weightsTextureSize;debounceTimeout;getPosition;getWeight;constructor(t,r){super(t,r),this.initRegularAttribute(\"radius_pixels\",\"radiusPixels\"),this.initRegularAttribute(\"color_range\",\"colorRange\"),this.initRegularAttribute(\"intensity\",\"intensity\"),this.initRegularAttribute(\"threshold\",\"threshold\"),this.initRegularAttribute(\"color_domain\",\"colorDomain\"),this.initRegularAttribute(\"aggregation\",\"aggregation\"),this.initRegularAttribute(\"weights_texture_size\",\"weightsTextureSize\"),this.initRegularAttribute(\"debounce_timeout\",\"debounceTimeout\"),this.initVectorizedAccessor(\"get_position\",\"getPosition\"),this.initVectorizedAccessor(\"get_weight\",\"getWeight\")}layerProps(){return{data:this.table,...Jt(this.radiusPixels)&&{radiusPixels:this.radiusPixels},...Jt(this.colorRange)&&{colorRange:this.colorRange},...Jt(this.intensity)&&{intensity:this.intensity},...Jt(this.threshold)&&{threshold:this.threshold},...Jt(this.colorDomain)&&{colorDomain:this.colorDomain},...Jt(this.aggregation)&&{aggregation:this.aggregation},...Jt(this.weightsTextureSize)&&{weightsTextureSize:this.weightsTextureSize},...Jt(this.debounceTimeout)&&{debounceTimeout:this.debounceTimeout},...Jt(this.getPosition)&&{getPosition:this.getPosition},...Jt(this.getWeight)&&{getWeight:this.getWeight}}}render(){return new CS({...this.baseLayerProps(),...this.layerProps()})}},QS=class extends mf{static layerType=\"path\";widthUnits;widthScale;widthMinPixels;widthMaxPixels;jointRounded;capRounded;miterLimit;billboard;getColor;getWidth;constructor(t,r){super(t,r),this.initRegularAttribute(\"width_units\",\"widthUnits\"),this.initRegularAttribute(\"width_scale\",\"widthScale\"),this.initRegularAttribute(\"width_min_pixels\",\"widthMinPixels\"),this.initRegularAttribute(\"width_max_pixels\",\"widthMaxPixels\"),this.initRegularAttribute(\"joint_rounded\",\"jointRounded\"),this.initRegularAttribute(\"cap_rounded\",\"capRounded\"),this.initRegularAttribute(\"miter_limit\",\"miterLimit\"),this.initRegularAttribute(\"billboard\",\"billboard\"),this.initVectorizedAccessor(\"get_color\",\"getColor\"),this.initVectorizedAccessor(\"get_width\",\"getWidth\")}layerProps(){return{data:this.table,...Jt(this.widthUnits)&&{widthUnits:this.widthUnits},...Jt(this.widthScale)&&{widthScale:this.widthScale},...Jt(this.widthMinPixels)&&{widthMinPixels:this.widthMinPixels},...Jt(this.widthMaxPixels)&&{widthMaxPixels:this.widthMaxPixels},...Jt(this.jointRounded)&&{jointRounded:this.jointRounded},...Jt(this.capRounded)&&{capRounded:this.capRounded},...Jt(this.miterLimit)&&{miterLimit:this.miterLimit},...Jt(this.billboard)&&{billboard:this.billboard},...Jt(this.getColor)&&{getColor:this.getColor},...Jt(this.getWidth)&&{getWidth:this.getWidth}}}render(){return new e_({...this.baseLayerProps(),...this.layerProps()})}},sC=class extends mf{static layerType=\"point-cloud\";sizeUnits;pointSize;getColor;getNormal;constructor(t,r){super(t,r),this.initRegularAttribute(\"size_units\",\"sizeUnits\"),this.initRegularAttribute(\"point_size\",\"pointSize\"),this.initVectorizedAccessor(\"get_color\",\"getColor\"),this.initVectorizedAccessor(\"get_normal\",\"getNormal\")}layerProps(){return{data:this.table,...Jt(this.sizeUnits)&&{sizeUnits:this.sizeUnits},...Jt(this.pointSize)&&{pointSize:this.pointSize},...Jt(this.getColor)&&{getColor:this.getColor},...Jt(this.getNormal)&&{getNormal:this.getNormal}}}render(){return new LS({...this.baseLayerProps(),...this.layerProps()})}},oC=class extends mf{static layerType=\"polygon\";stroked;filled;extruded;wireframe;elevationScale;lineWidthUnits;lineWidthScale;lineWidthMinPixels;lineWidthMaxPixels;lineJointRounded;lineMiterLimit;getFillColor;getLineColor;getLineWidth;getElevation;constructor(t,r){super(t,r),this.initRegularAttribute(\"stroked\",\"stroked\"),this.initRegularAttribute(\"filled\",\"filled\"),this.initRegularAttribute(\"extruded\",\"extruded\"),this.initRegularAttribute(\"wireframe\",\"wireframe\"),this.initRegularAttribute(\"elevation_scale\",\"elevationScale\"),this.initRegularAttribute(\"line_width_units\",\"lineWidthUnits\"),this.initRegularAttribute(\"line_width_scale\",\"lineWidthScale\"),this.initRegularAttribute(\"line_width_min_pixels\",\"lineWidthMinPixels\"),this.initRegularAttribute(\"line_width_max_pixels\",\"lineWidthMaxPixels\"),this.initRegularAttribute(\"line_joint_rounded\",\"lineJointRounded\"),this.initRegularAttribute(\"line_miter_limit\",\"lineMiterLimit\"),this.initVectorizedAccessor(\"get_fill_color\",\"getFillColor\"),this.initVectorizedAccessor(\"get_line_color\",\"getLineColor\"),this.initVectorizedAccessor(\"get_line_width\",\"getLineWidth\"),this.initVectorizedAccessor(\"get_elevation\",\"getElevation\")}layerProps(){return{data:this.table,...Jt(this.stroked)&&{stroked:this.stroked},...Jt(this.filled)&&{filled:this.filled},...Jt(this.extruded)&&{extruded:this.extruded},...Jt(this.wireframe)&&{wireframe:this.wireframe},...Jt(this.elevationScale)&&{elevationScale:this.elevationScale},...Jt(this.lineWidthUnits)&&{lineWidthUnits:this.lineWidthUnits},...Jt(this.lineWidthScale)&&{lineWidthScale:this.lineWidthScale},...Jt(this.lineWidthMinPixels)&&{lineWidthMinPixels:this.lineWidthMinPixels},...Jt(this.lineWidthMaxPixels)&&{lineWidthMaxPixels:this.lineWidthMaxPixels},...Jt(this.lineJointRounded)&&{lineJointRounded:this.lineJointRounded},...Jt(this.lineMiterLimit)&&{lineMiterLimit:this.lineMiterLimit},...Jt(this.getFillColor)&&{getFillColor:this.getFillColor},...Jt(this.getLineColor)&&{getLineColor:this.getLineColor},...Jt(this.getLineWidth)&&{getLineWidth:this.getLineWidth},...Jt(this.getElevation)&&{getElevation:this.getElevation}}}render(){return new qS({...this.baseLayerProps(),...this.layerProps()})}},$S=class extends mf{static layerType=\"scatterplot\";radiusUnits;radiusScale;radiusMinPixels;radiusMaxPixels;lineWidthUnits;lineWidthScale;lineWidthMinPixels;lineWidthMaxPixels;stroked;filled;billboard;antialiasing;getRadius;getFillColor;getLineColor;getLineWidth;constructor(t,r){super(t,r),this.initRegularAttribute(\"radius_units\",\"radiusUnits\"),this.initRegularAttribute(\"radius_scale\",\"radiusScale\"),this.initRegularAttribute(\"radius_min_pixels\",\"radiusMinPixels\"),this.initRegularAttribute(\"radius_max_pixels\",\"radiusMaxPixels\"),this.initRegularAttribute(\"line_width_units\",\"lineWidthUnits\"),this.initRegularAttribute(\"line_width_scale\",\"lineWidthScale\"),this.initRegularAttribute(\"line_width_min_pixels\",\"lineWidthMinPixels\"),this.initRegularAttribute(\"line_width_max_pixels\",\"lineWidthMaxPixels\"),this.initRegularAttribute(\"stroked\",\"stroked\"),this.initRegularAttribute(\"filled\",\"filled\"),this.initRegularAttribute(\"billboard\",\"billboard\"),this.initRegularAttribute(\"antialiasing\",\"antialiasing\"),this.initVectorizedAccessor(\"get_radius\",\"getRadius\"),this.initVectorizedAccessor(\"get_fill_color\",\"getFillColor\"),this.initVectorizedAccessor(\"get_line_color\",\"getLineColor\"),this.initVectorizedAccessor(\"get_line_width\",\"getLineWidth\")}layerProps(){return{data:this.table,...Jt(this.radiusUnits)&&{radiusUnits:this.radiusUnits},...Jt(this.radiusScale)&&{radiusScale:this.radiusScale},...Jt(this.radiusMinPixels)&&{radiusMinPixels:this.radiusMinPixels},...Jt(this.radiusMaxPixels)&&{radiusMaxPixels:this.radiusMaxPixels},...Jt(this.lineWidthUnits)&&{lineWidthUnits:this.lineWidthUnits},...Jt(this.lineWidthScale)&&{lineWidthScale:this.lineWidthScale},...Jt(this.lineWidthMinPixels)&&{lineWidthMinPixels:this.lineWidthMinPixels},...Jt(this.lineWidthMaxPixels)&&{lineWidthMaxPixels:this.lineWidthMaxPixels},...Jt(this.stroked)&&{stroked:this.stroked},...Jt(this.filled)&&{filled:this.filled},...Jt(this.billboard)&&{billboard:this.billboard},...Jt(this.antialiasing)&&{antialiasing:this.antialiasing},...Jt(this.getRadius)&&{getRadius:this.getRadius},...Jt(this.getFillColor)&&{getFillColor:this.getFillColor},...Jt(this.getLineColor)&&{getLineColor:this.getLineColor},...Jt(this.getLineWidth)&&{getLineWidth:this.getLineWidth}}}render(){return new ZS({...this.baseLayerProps(),...this.layerProps()})}},XS=class extends mf{static layerType=\"solid-polygon\";filled;extruded;wireframe;elevationScale;getElevation;getFillColor;getLineColor;constructor(t,r){super(t,r),this.initRegularAttribute(\"filled\",\"filled\"),this.initRegularAttribute(\"extruded\",\"extruded\"),this.initRegularAttribute(\"wireframe\",\"wireframe\"),this.initRegularAttribute(\"elevation_scale\",\"elevationScale\"),this.initVectorizedAccessor(\"get_elevation\",\"getElevation\"),this.initVectorizedAccessor(\"get_fill_color\",\"getFillColor\"),this.initVectorizedAccessor(\"get_line_color\",\"getLineColor\")}layerProps(){return{data:this.table,...Jt(this.filled)&&{filled:this.filled},...Jt(this.extruded)&&{extruded:this.extruded},...Jt(this.wireframe)&&{wireframe:this.wireframe},...Jt(this.elevationScale)&&{elevationScale:this.elevationScale},...Jt(this.getElevation)&&{getElevation:this.getElevation},...Jt(this.getFillColor)&&{getFillColor:this.getFillColor},...Jt(this.getLineColor)&&{getLineColor:this.getLineColor}}}render(){return new o_({...this.baseLayerProps(),...this.layerProps()})}},aC=class extends mf{static layerType=\"text\";billboard;sizeScale;sizeUnits;sizeMinPixels;sizeMaxPixels;getBackgroundColor;getBorderColor;getBorderWidth;backgroundPadding;characterSet;fontFamily;fontWeight;lineHeight;outlineWidth;outlineColor;fontSettings;wordBreak;maxWidth;getText;getPosition;getColor;getSize;getAngle;getTextAnchor;getAlignmentBaseline;getPixelOffset;constructor(t,r){super(t,r),this.initRegularAttribute(\"billboard\",\"billboard\"),this.initRegularAttribute(\"size_scale\",\"sizeScale\"),this.initRegularAttribute(\"size_units\",\"sizeUnits\"),this.initRegularAttribute(\"size_min_pixels\",\"sizeMinPixels\"),this.initRegularAttribute(\"size_max_pixels\",\"sizeMaxPixels\"),this.initRegularAttribute(\"background_padding\",\"backgroundPadding\"),this.initRegularAttribute(\"character_set\",\"characterSet\"),this.initRegularAttribute(\"font_family\",\"fontFamily\"),this.initRegularAttribute(\"font_weight\",\"fontWeight\"),this.initRegularAttribute(\"line_height\",\"lineHeight\"),this.initRegularAttribute(\"outline_width\",\"outlineWidth\"),this.initRegularAttribute(\"outline_color\",\"outlineColor\"),this.initRegularAttribute(\"font_settings\",\"fontSettings\"),this.initRegularAttribute(\"word_break\",\"wordBreak\"),this.initRegularAttribute(\"max_width\",\"maxWidth\"),this.initVectorizedAccessor(\"get_background_color\",\"getBackgroundColor\"),this.initVectorizedAccessor(\"get_border_color\",\"getBorderColor\"),this.initVectorizedAccessor(\"get_border_width\",\"getBorderWidth\"),this.initVectorizedAccessor(\"get_text\",\"getText\"),this.initVectorizedAccessor(\"get_position\",\"getPosition\"),this.initVectorizedAccessor(\"get_color\",\"getColor\"),this.initVectorizedAccessor(\"get_size\",\"getSize\"),this.initVectorizedAccessor(\"get_angle\",\"getAngle\"),this.initVectorizedAccessor(\"get_text_anchor\",\"getTextAnchor\"),this.initVectorizedAccessor(\"get_alignment_baseline\",\"getAlignmentBaseline\"),this.initVectorizedAccessor(\"get_pixel_offset\",\"getPixelOffset\")}layerProps(){return{data:this.table,getText:this.getText,...Jt(this.billboard)&&{billboard:this.billboard},...Jt(this.sizeScale)&&{sizeScale:this.sizeScale},...Jt(this.sizeUnits)&&{sizeUnits:this.sizeUnits},...Jt(this.sizeMinPixels)&&{sizeMinPixels:this.sizeMinPixels},...Jt(this.sizeMaxPixels)&&{sizeMaxPixels:this.sizeMaxPixels},...Jt(this.backgroundPadding)&&{backgroundPadding:this.backgroundPadding},...Jt(this.characterSet)&&{characterSet:this.characterSet},...Jt(this.fontFamily)&&{fontFamily:this.fontFamily},...Jt(this.fontWeight)&&{fontWeight:this.fontWeight},...Jt(this.lineHeight)&&{lineHeight:this.lineHeight},...Jt(this.outlineWidth)&&{outlineWidth:this.outlineWidth},...Jt(this.outlineColor)&&{outlineColor:this.outlineColor},...Jt(this.fontSettings)&&{fontSettings:this.fontSettings},...Jt(this.wordBreak)&&{wordBreak:this.wordBreak},...Jt(this.maxWidth)&&{maxWidth:this.maxWidth},...Jt(this.getBackgroundColor)&&{getBackgroundColor:this.getBackgroundColor},...Jt(this.getBorderColor)&&{getBorderColor:this.getBorderColor},...Jt(this.getBorderWidth)&&{getBorderWidth:this.getBorderWidth},...Jt(this.getPosition)&&{getPosition:this.getPosition},...Jt(this.getColor)&&{getColor:this.getColor},...Jt(this.getSize)&&{getSize:this.getSize},...Jt(this.getAngle)&&{getAngle:this.getAngle},...Jt(this.getTextAnchor)&&{getTextAnchor:this.getTextAnchor},...Jt(this.getAlignmentBaseline)&&{getAlignmentBaseline:this.getAlignmentBaseline},...Jt(this.getPixelOffset)&&{getPixelOffset:this.getPixelOffset}}}render(){return 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Its focus is on the determination of the number of classes, and the assignment of observations to those classes. It is intended for use with upstream mapping and geovisualization packages (see `geopandas`_ and `geoplot`_) that handle the rendering of the maps. For further theoretical background see "`Choropleth Mapping`_" in Rey, S.J., D. Arribas-Bel, and L.J. Wolf (2020) "Geographic Data Science with PySAL and the PyData Stack”. .. _geopandas: https://geopandas.org/mapping.html .. _geoplot: https://residentmario.github.io/geoplot/user_guide/Customizing_Plots.html .. _Choropleth Mapping: https://geographicdata.science/book/notebooks/05_choropleth.html """, content-type = "text/x-rst" } classifiers = [ "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering :: GIS", ] requires-python = ">=3.9" dependencies = [ "networkx>=2.7", "numpy>=1.23", "pandas>=1.4,!=1.5.0", "scikit-learn>=1.0", "scipy>=1.8", ] [project.urls] Home = "https://pysal.org/mapclassify/" Repository = "https://github.com/pysal/mapclassify" [project.optional-dependencies] speedups = [ "numba>=0.54" ] dev = [ "ruff", "pre-commit", "watermark", ] docs = [ "nbsphinx", "numpydoc", "sphinx>=1.4.3", "sphinx-gallery", "sphinxcontrib-bibtex", "sphinx_bootstrap_theme", ] tests = [ "geopandas", "libpysal", "matplotlib", "pytest", "pytest-cov", "pytest-xdist", "pytest-doctestplus", "pytest-mpl" ] all = ["numba[speedups,dev,docs,tests]"] [tool.setuptools.packages.find] include = ["mapclassify", "mapclassify.*"] [tool.ruff] line-length = 88 lint.select = ["E", "F", "W", "I", "UP", "N", "B", "A", "C4", "SIM", "ARG"] lint.ignore = [ "B006", "B008", "B009", "B010", "C408", "E731", "F401", "F403", "F405", "N803", "N806", "N999", "UP007" ] exclude = ["mapclassify/tests/*", "docs/*"] [tool.coverage.run] source = ["./mapclassify"] [tool.coverage.report] exclude_lines = [ "if self.debug:", "pragma: no cover", "raise NotImplementedError", "except ModuleNotFoundError:", "except ImportError", ] ignore_errors = true omit = ["mapclassify/tests/*", "docs/conf.py"]