Full Code of pysal/spopt for AI

Repository: pysal/spopt
Branch: main
Commit: 938a06485bd0
Files: 135
Total size: 39.0 MB

Directory structure:
gitextract_wl41rybo/

├── .gitattributes
├── .github/
│   ├── CONTRIBUTING.md
│   ├── dependabot.yml
│   ├── release.yml
│   └── workflows/
│       ├── build_docs.yml
│       ├── release_and_publish.yml
│       └── testing.yml
├── .gitignore
├── .pre-commit-config.yaml
├── LICENSE.txt
├── README.md
├── ci/
│   ├── 312-latest.yaml
│   ├── 312-oldest.yaml
│   ├── 313-latest.yaml
│   ├── 314-dev.yaml
│   └── 314-latest.yaml
├── codecov.yml
├── docs/
│   ├── Makefile
│   ├── _static/
│   │   ├── pysal-styles.css
│   │   └── references.bib
│   ├── api.rst
│   ├── conf.py
│   ├── index.rst
│   ├── installation.rst
│   ├── references.rst
│   └── tutorials.rst
├── environment.yml
├── notebooks/
│   ├── azp.ipynb
│   ├── component_policy.ipynb
│   ├── facloc-disperse-real-world.ipynb
│   ├── facloc-lscpb-real-world.ipynb
│   ├── facloc-real-world.ipynb
│   ├── flow.ipynb
│   ├── lscp.ipynb
│   ├── lscp_capacity.ipynb
│   ├── lscp_gis.ipynb
│   ├── lscpb.ipynb
│   ├── maxp.ipynb
│   ├── mclp.ipynb
│   ├── mclp_gis.ipynb
│   ├── p-center.ipynb
│   ├── p-dispersion.ipynb
│   ├── p-median.ipynb
│   ├── p-median_variations.ipynb
│   ├── randomregion.ipynb
│   ├── reg-k-means.ipynb
│   ├── sa3.ipynb
│   ├── skater.ipynb
│   └── ward.ipynb
├── paper/
│   ├── paper.bib
│   └── paper.md
├── pyproject.toml
└── spopt/
    ├── BaseClass.py
    ├── __init__.py
    ├── locate/
    │   ├── __init__.py
    │   ├── base.py
    │   ├── coverage.py
    │   ├── flow.py
    │   ├── p_center.py
    │   ├── p_dispersion.py
    │   ├── p_median.py
    │   └── util.py
    ├── region/
    │   ├── __init__.py
    │   ├── azp.py
    │   ├── azp_util.py
    │   ├── base.py
    │   ├── components.py
    │   ├── csgraph_utils.py
    │   ├── maxp.py
    │   ├── objective_function.py
    │   ├── random_region.py
    │   ├── region_k_means.py
    │   ├── sa3.py
    │   ├── skater.py
    │   ├── spenc.py
    │   ├── spenclib/
    │   │   ├── __init__.py
    │   │   ├── abstracts.py
    │   │   ├── scores.py
    │   │   └── utils.py
    │   ├── util.py
    │   └── ward.py
    └── tests/
        ├── __init__.py
        ├── conftest.py
        ├── test_locate/
        │   ├── data/
        │   │   ├── SF_demand_205_centroid_uniform_weight.csv
        │   │   ├── SF_network_distance_candidateStore_16_censusTract_205_new.csv
        │   │   ├── SF_store_site_16_longlat.csv
        │   │   ├── ServiceAreas_4.shp
        │   │   ├── ServiceAreas_4.shx
        │   │   ├── example_subject_schools.csv
        │   │   ├── example_subject_student_school_journeys.csv
        │   │   ├── example_subject_students.csv
        │   │   ├── flow_grid_network.pkl
        │   │   ├── lscp_cli2fac.pkl
        │   │   ├── lscp_fac2cli.pkl
        │   │   ├── lscp_geodataframe_cli2fac.pkl
        │   │   ├── lscp_geodataframe_fac2cli.pkl
        │   │   ├── lscp_preselected_loc_geodataframe_fac2cli.pkl
        │   │   ├── lscpb_cli2fac.pkl
        │   │   ├── lscpb_fac2cli.pkl
        │   │   ├── lscpb_geodataframe_cli2fac.pkl
        │   │   ├── lscpb_geodataframe_fac2cli.pkl
        │   │   ├── lscpb_preselected_loc_geodataframe_fac2cli.pkl
        │   │   ├── mclp_cli2fac.pkl
        │   │   ├── mclp_fac2cli.pkl
        │   │   ├── mclp_geodataframe_cli2fac.pkl
        │   │   ├── mclp_geodataframe_fac2cli.pkl
        │   │   ├── mclp_preselected_loc_geodataframe_fac2cli.pkl
        │   │   ├── pcenter_cli2fac.pkl
        │   │   ├── pcenter_fac2cli.pkl
        │   │   ├── pcenter_geodataframe_cli2fac.pkl
        │   │   ├── pcenter_geodataframe_fac2cli.pkl
        │   │   ├── pmedian_cli2fac.pkl
        │   │   ├── pmedian_fac2cli.pkl
        │   │   ├── pmedian_geodataframe_cli2fac.pkl
        │   │   └── pmedian_geodataframe_fac2cli.pkl
        │   ├── test_c_p_median.py
        │   ├── test_clscp-so.py
        │   ├── test_flow.py
        │   ├── test_knearest_p_median.py
        │   ├── test_locate_util.py
        │   ├── test_lscp.py
        │   ├── test_lscpb.py
        │   ├── test_mclp.py
        │   ├── test_p_center.py
        │   ├── test_p_dispersion.py
        │   └── test_p_median.py
        └── test_region/
            ├── test_azp.py
            ├── test_maxp.py
            ├── test_random_regions.py
            ├── test_region_k_means.py
            ├── test_region_util.py
            ├── test_sa3.py
            ├── test_skater.py
            ├── test_spenc.py
            └── test_ward.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitattributes
================================================
*.ipynb linguist-language=Python


================================================
FILE: .github/CONTRIBUTING.md
================================================
# Contributing Guidelines for `spopt`

Thank you for your interest in contributing! We work primarily on Github. Please review the contributing procedures [here](http://pysal.org/getting_started#for-developers) and [here](https://github.com/pysal/pysal/wiki/GitHub-Standard-Operating-Procedures) so that we can accept your contributions! Alternatively, contact someone in the [development chat channel](https://gitter.im//pysal/pysal).


## Style and format

1. At the time of this writing, Python 3.10, 3.11, and 3.12 are the officially supported versions.
2. This project implements the linting and formatting conventions of [`ruff`](https://docs.astral.sh/ruff/) on all incoming Pull Requests. To ensure a PR is properly linted and formatted prior to creating a Pull Request, [install `pre-commit`](https://pre-commit.com/#installation) in your development environment and then [set up the configuration of pre-commit hooks](https://pre-commit.com/#3-install-the-git-hook-scripts). 


================================================
FILE: .github/dependabot.yml
================================================
 # 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"
      - "xf37"

  - package-ecosystem: "pip"
    directory: "/"
    schedule:
      interval: "daily"
    reviewers:
      - "jGaboardi"
      - "xf37"


================================================
FILE: .github/release.yml
================================================
changelog:
  exclude:
    labels:
      - ignore-for-release
    authors:
      - dependabot
  categories:
    - title: Bug Fixes
      labels:
        - bug
    - title: Enhancements
      labels:
        - enhancement
    - title: Regionalization
      labels:
        - region
    - title: Facility Location
      labels:
        - locate
    - title: Transportation & Routing
      labels:
        - transport
    - title: Other Changes
      labels:
        - "*"


================================================
FILE: .github/workflows/build_docs.yml
================================================
---
name: Build Docs
on:
  push:
    tags:
      - v*
  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/314-latest.yaml
        experimental:
          - false
    defaults:
      run:
        shell: bash -l {0}
    steps:
      - name: checkout repo
        uses: actions/checkout@v6
        with:
          fetch-depth: 0
      - name: setup micromamba
        uses: mamba-org/setup-micromamba@v3
        with:
          environment-file: ${{ matrix.environment-file }}
          micromamba-version: latest
      - name: install pulp via pip
        run: pip install pulp
      - name: install package
        run: pip install .
      - name: make docs
        run: cd docs; make html
      - name: Publish to Github Pages
        uses: peaceiris/actions-gh-pages@v4
        with:
          github_token: ${{ secrets.GITHUB_TOKEN }}
          publish_dir: docs/build/html/
          keep_files: false


================================================
FILE: .github/workflows/release_and_publish.yml
================================================
---
name: Release Package
on:
  push:
    tags:
      - v*
  workflow_dispatch:
    inputs:
      version:
        description: Manual Release
        default: test
        required: false
jobs:
  build:
    name: Create release & publish to PyPI
    runs-on: ubuntu-latest
    permissions:
      id-token: write  # MANDATORY for trusted publishing to PyPI
      contents: write  # MANDATORY for the Github release action
    steps:
      - name: Checkout repo
        uses: actions/checkout@v6
        with:
          fetch-depth: 0
      - name: Set up Python
        uses: actions/setup-python@v6
        with:
          python-version: 3.x
      - name: Install Dependencies
        run: |
          python -m pip install --upgrade pip
          python -m pip install --upgrade build twine
          python -m build
          twine check --strict dist/*
      - name: Create Release Notes
        uses: actions/github-script@v8
        with:
          github-token: ${{secrets.GITHUB_TOKEN}}
          script: >
            await github.request(`POST /repos/${{ github.repository }}/releases`, {
                tag_name: "${{ github.ref }}",
                generate_release_notes: true
            });
      - name: Publish distribution 📦 to PyPI
        uses: pypa/gh-action-pypi-publish@release/v1


================================================
FILE: .github/workflows/testing.yml
================================================
---
name: Continuous Integration
on:
  push:
    branches: [main]
  pull_request:
    branches:
      - "*"
  schedule:
    - cron: 59 23 * * *
  workflow_dispatch:
    inputs:
      version:
        description: Manual CI Run
        default: test
        required: false
jobs:
  tests:
    name: ${{ matrix.os }}, ${{ matrix.environment-file }}
    runs-on: ${{ matrix.os }}
    timeout-minutes: 30
    strategy:
      matrix:
        os:
          - ubuntu-latest
        environment-file:
          - ci/312-oldest.yaml
          - ci/312-latest.yaml
          - ci/313-latest.yaml
          - ci/314-latest.yaml
          - ci/314-dev.yaml
        include:
          - environment-file: ci/314-latest.yaml
            os: macos-15-intel
          - environment-file: ci/314-latest.yaml
            os: macos-latest
          - environment-file: ci/314-latest.yaml
            os: windows-latest
      fail-fast: false
    defaults:
      run:
        shell: bash -l {0}
    steps:
      - name: checkout repo
        uses: actions/checkout@v6
        with:
          fetch-depth: 0
      - name: setup micromamba
        uses: mamba-org/setup-micromamba@v3
        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: Download test files
        run: |
          python -c '
          import libpysal
          libpysal.examples.fetch_all()
          '
      - name: run tests
        run: |
          pytest spopt \
          -v \
          -r a \
          -n logical \
          --color yes \
          --cov spopt \
          --cov-append \
          --cov-report term-missing \
          --cov-report xml .
      - name: codecov
        uses: codecov/codecov-action@v5


================================================
FILE: .gitignore
================================================
*.swp
*.pyc
.rope*
.idea/
notebooks/.ipynb_checkpoints/
.DS_Store
.ipynb_checkpoints/
*.bak
.eggs/
*.egg-info/

# Packages
*.egg
*.egg-info
dist
build
eggs
parts
bin
var
sdist
develop-eggs
.installed.cfg
lib
lib64
__pycache__

.coverage

.ruff_cache
.pytest_cache


================================================
FILE: .pre-commit-config.yaml
================================================
files: "spopt\/|notebooks\/"
repos:
  - repo: https://github.com/astral-sh/ruff-pre-commit
    rev: "v0.15.9"
    hooks:
      - id: ruff-check
      - id: ruff-format

ci:
  autofix_prs: false
  autoupdate_schedule: quarterly


================================================
FILE: LICENSE.txt
================================================
BSD 3-Clause License

Copyright 2018 pysal-spopt 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.


================================================
FILE: README.md
================================================

<p align="center">
<img src="docs/_static/images/pysal_banner.svg" width="370" height="200" />
</p>

# `spopt`: Spatial Optimization

#### Regionalization, facility location, and transportation-oriented modeling

![tag](https://img.shields.io/github/v/release/pysal/spopt?include_prereleases&sort=semver)
[![Continuous Integration](https://github.com/pysal/spopt/actions/workflows/testing.yml/badge.svg)](https://github.com/pysal/spopt/actions/workflows/testing.yml)
[![codecov](https://codecov.io/gh/pysal/spopt/branch/main/graph/badge.svg)](https://codecov.io/gh/pysal/spopt)
[![Documentation](https://img.shields.io/static/v1.svg?label=docs&message=current&color=9cf)](http://pysal.org/spopt/)
[![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)
[![status](https://joss.theoj.org/papers/1413cf2c0cf3c561386949f2e1208563/status.svg)](https://joss.theoj.org/papers/1413cf2c0cf3c561386949f2e1208563)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4444156.svg)](https://doi.org/10.5281/zenodo.4444156)
[![Discord](https://img.shields.io/badge/Discord-join%20chat-7289da?style=flat&logo=discord&logoColor=cccccc&link=https://discord.gg/BxFTEPFFZn)](https://discord.gg/BxFTEPFFZn)

Spopt is an open-source Python library for solving optimization problems with spatial data. Originating from the `region` module in [PySAL (Python Spatial Analysis Library)](http://pysal.org), it is under active development for the inclusion of newly proposed models and methods for regionalization, facility location, and transportation-oriented solutions. 

### Regionalization

```python
import spopt, libpysal, geopandas, numpy
mexico = geopandas.read_file(libpysal.examples.get_path("mexicojoin.shp"))
mexico["count"] = 1
attrs = [f"PCGDP{year}" for year in range(1950, 2010, 10)]
w = libpysal.weights.Queen.from_dataframe(mexico)
mexico["count"], threshold_name, threshold, top_n = 1, "count", 4, 2
numpy.random.seed(123456)
model = spopt.region.MaxPHeuristic(mexico, w, attrs, threshold_name, threshold, top_n)
model.solve()
mexico["maxp_new"] = model.labels_
mexico.plot(column="maxp_new", categorical=True, figsize=(12,8), ec="w");
```

<p align="center">
<img src="docs/_static/images/maxp.svg" height="350" />
</p>

### Locate

```python
from spopt.locate import MCLP
from spopt.locate.util import simulated_geo_points
import numpy, geopandas, pulp, spaghetti

solver = pulp.PULP_CBC_CMD(msg=False, warmStart=True)
lattice = spaghetti.regular_lattice((0, 0, 10, 10), 9, exterior=True)
ntw = spaghetti.Network(in_data=lattice)
street = spaghetti.element_as_gdf(ntw, arcs=True)
street_buffered = geopandas.GeoDataFrame(
    geopandas.GeoSeries(street["geometry"].buffer(0.5).unary_union),
    crs=street.crs,
    columns=["geometry"],
)
client_points = simulated_geo_points(street_buffered, needed=100, seed=5)
ntw.snapobservations(client_points, "clients", attribute=True)
clients_snapped = spaghetti.element_as_gdf(
    ntw, pp_name="clients", snapped=True
)
facility_points = simulated_geo_points(street_buffered, needed=10, seed=6)
ntw.snapobservations(facility_points, "facilities", attribute=True)
facilities_snapped = spaghetti.element_as_gdf(
    ntw, pp_name="facilities", snapped=True
)
cost_matrix = ntw.allneighbordistances(
    sourcepattern=ntw.pointpatterns["clients"],
    destpattern=ntw.pointpatterns["facilities"],
)
numpy.random.seed(0)
ai = numpy.random.randint(1, 12, 100)
mclp_from_cost_matrix = MCLP.from_cost_matrix(cost_matrix, ai, 4, p_facilities=4)
mclp_from_cost_matrix = mclp_from_cost_matrix.solve(solver)
```

*see [notebook](https://github.com/pysal/spopt/blob/main/notebooks/mclp.ipynb) for plotting code*

<p align="center">
<img src="docs/_static/images/mclp.png" height="350" />
</p>

## Examples

More examples can be found in the [Tutorials](https://pysal.org/spopt/tutorials.html) section of the documentation.
- [Max-p-regions problem](https://pysal.org/spopt/notebooks/maxp.html)
- [Skater](https://pysal.org/spopt/notebooks/skater.html)
- [Region K means](https://pysal.org/spopt/notebooks/reg-k-means.html)
- [Facility Location Real World Problem](https://pysal.org/spopt/notebooks/facloc-real-world.html)

All examples can be run interactively by launching this repository as a [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pysal/spopt/main).

## Requirements
- [scipy](http://scipy.github.io/devdocs/)
- [numpy](https://numpy.org/devdocs/)
- [pandas](https://pandas.pydata.org/docs/)
- [networkx](https://networkx.org/)
- [libpysal](https://pysal.org/libpysal/)
- [scikit-learn](https://scikit-learn.org/stable/)
- [geopandas](https://geopandas.org/)
- [pulp](https://coin-or.github.io/pulp/)
- [shapely](https://shapely.readthedocs.io/en/stable/)
- [spaghetti](https://github.com/pysal/spaghetti)

## Installation

spopt is available on the [Python Package Index](https://pypi.org/). Therefore, you can either install directly with pip from the command line:

```
$ pip install -U spopt
```

or download the source distribution (.tar.gz) and decompress it to your selected destination. Open a command shell and navigate to the decompressed folder. Type:

```
$ pip install .
```

You may also install the latest stable spopt via conda-forge channel by running:

```
$ conda install --channel conda-forge spopt
```

## Related packages

* Region
* Locate
  * [`allagash`](https://github.com/apulverizer/allagash)
  * [`maximum-coverage-location`](https://github.com/cyang-kth/maximum-coverage-location)
  * [`OR_classic_problem`](https://github.com/khamechian1987/OR_classic_problem)
  * [`p-center`](https://github.com/antoniomedrano/p-center)
  * [`pyspatialopt`](https://github.com/apulverizer/pyspatialopt)

## Contribute

PySAL-spopt is under active development and contributors are welcome.

If you have any suggestions, feature requests, or bug reports, please open new [issues](https://github.com/pysal/spopt/issues) on GitHub. To submit patches, please review [PySAL's documentation for developers](https://pysal.org/docs/devs/), the PySAL [development guidelines](https://github.com/pysal/pysal/wiki), the `spopt` [contributing guidelines](https://github.com/pysal/spopt/blob/main/.github/CONTRIBUTING.md) before  opening a [pull request](https://github.com/pysal/spopt/pulls). Once your changes get merged, you’ll automatically be added to the [Contributors List](https://github.com/pysal/spopt/graphs/contributors).

## Support

If you are having trouble, please [create an issue](https://github.com/pysal/spopt/issues), [start a discussion](https://github.com/pysal/spopt/discussions), or talk to us in [PySAL's Discord channel](https://discord.gg/BxFTEPFFZn).

## Code of Conduct

As a PySAL-federated project, `spopt` follows the [Code of Conduct](https://github.com/pysal/governance/blob/main/conduct/code_of_conduct.rst) under the [PySAL governance model](https://github.com/pysal/governance).

## License

The project is licensed under the [BSD 3-Clause license](https://github.com/pysal/spopt/blob/main/LICENSE.txt).

## Citation

If you use PySAL-spopt in a scientific publication, we would appreciate using the following citations:

```
@misc{spopt2021,
    author    = {Feng, Xin, and Gaboardi, James D. and Knaap, Elijah and
                Rey, Sergio J. and Wei, Ran},
    month     = {jan},
    year      = {2021},
    title     = {pysal/spopt},
    url       = {https://github.com/pysal/spopt},
    doi       = {10.5281/zenodo.4444156},
    keywords  = {python,regionalization,spatial-optimization,location-modeling}
}

@article{spopt2022,
    author    = {Feng, Xin and Barcelos, Germano and Gaboardi, James D. and
                Knaap, Elijah and Wei, Ran and Wolf, Levi J. and
                Zhao, Qunshan and Rey, Sergio J.},
    year      = {2022},
    title     = {spopt: a python package for solving spatial optimization problems in PySAL},
    journal   = {Journal of Open Source Software},
    publisher = {The Open Journal},
    volume    = {7},
    number    = {74},
    pages     = {3330},
    url       = {https://doi.org/10.21105/joss.03330},
    doi       = {10.21105/joss.03330},
}
```

## Funding

This project is/was partially funded through:

[<img align="middle" src="docs/_static/images/nsf_logo.png" width="75">](https://www.nsf.gov/index.jsp) National Science Foundation Award #1831615: [RIDIR: Scalable Geospatial Analytics for Social Science Research](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1831615)


================================================
FILE: ci/312-latest.yaml
================================================
name: test
channels:
  - conda-forge
dependencies:
  - python=3.12
  - fast_hdbscan # for sa3
  - geopandas
  - libpysal
  - mapclassify
  - networkx
  - numba # for sa3
  - numpy
  - pandas
  - pointpats
  - scikit-learn
  - scipy
  - shapely
  - spaghetti
  - tqdm

  # testing
  - codecov
  - coverage
  - pytest
  - pytest-cov
  - pytest-xdist

  # with pip
  - pip
  - pip:
    - pulp


================================================
FILE: ci/312-oldest.yaml
================================================
name: py312_spopt-oldest
channels:
  - conda-forge
dependencies:
  - python=3.12
  - fast_hdbscan=0.2.2 # for sa3
  - geopandas=1.1.0
  - libpysal=4.12.0
  - mapclassify=2.9.0
  - networkx=3.4
  - numba # for sa3
  - numpy=2.0.0
  - pandas=2.3.0
  - pointpats=2.5.2
  - scikit-learn=1.5.0
  - scipy=1.14.0
  - shapely=2.1.0
  - spaghetti=1.7.4
  - tqdm=4.67.0
  
  # testing
  - codecov
  - coverage
  - pytest
  - pytest-cov
  - pytest-xdist

  # with pip
  - pip
  - pip:
    - pulp==3.2


================================================
FILE: ci/313-latest.yaml
================================================
name: test
channels:
  - conda-forge
dependencies:
  - python=3.13
  - fast_hdbscan # for sa3
  - geopandas
  - libpysal
  - mapclassify
  - networkx
  - numba # for sa3
  - numpy
  - pandas
  - pointpats
  - scikit-learn
  - scipy
  - shapely
  - spaghetti
  - tqdm

  # testing
  - codecov
  - coverage
  - pytest
  - pytest-cov
  - pytest-xdist

  # with pip
  - pip
  - pip:
    - pulp



================================================
FILE: ci/314-dev.yaml
================================================
name: test
channels:
  - conda-forge
dependencies:
  - python=3.14
  - fast_hdbscan # for sa3
  - folium # for geopandas.explore()
  - matplotlib # for geopandas.explore()
  - numba # for sa3
  - tqdm

  # testing
  - codecov
  - coverage
  - pytest
  - pytest-cov
  - pytest-xdist
  
  # with pip
  - pip
  - pip:
    # dev versions of packages
    - --pre \
      --index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple  \
      --extra-index-url https://pypi.org/simple
    - networkx
    - numpy
    - pandas
    - pulp
    - scikit-learn
    - scipy
    - shapely
    - git+https://github.com/geopandas/geopandas.git
    - git+https://github.com/pysal/libpysal.git
    - git+https://github.com/pysal/mapclassify.git
    - git+https://github.com/pysal/pointpats.git
    - git+https://github.com/pysal/spaghetti.git


================================================
FILE: ci/314-latest.yaml
================================================
name: test
channels:
  - conda-forge
dependencies:
  - python=3.14
  - fast_hdbscan # for sa3
  - geopandas
  - libpysal
  - mapclassify
  - networkx
  - numba # for sa3
  - numpy
  - pandas
  - pointpats
  - scikit-learn
  - scipy
  - shapely
  - spaghetti
  - tqdm

  # testing
  - codecov
  - coverage
  - pytest
  - pytest-cov
  - pytest-xdist

  # docs - only this env
  - nbsphinx
  - numpydoc
  - sphinx
  - sphinxcontrib-bibtex
  - sphinx_bootstrap_theme

  # with pip
  - pip
  - pip:
    - pulp



================================================
FILE: codecov.yml
================================================
codecov:
  notify:
    after_n_builds: 5
coverage:
  range: 50..95
  round: nearest
  precision: 1
  status:
    project:
      default:
        threshold: 5%
    patch:
      default:
        threshold: 20%
        target: 60%
  ignore:
    - "tests/*"
comment:
  layout: "reach, diff, files"
  behavior: once
  after_n_builds: 5
  require_changes: true


================================================
FILE: docs/Makefile
================================================
# Minimal makefile for Sphinx documentation
#

# You can set these variables from the command line.
SPHINXOPTS    =
SPHINXBUILD   = sphinx-build
SPHINXPROJ    = PACKAGE_NAME
SOURCEDIR     = .
BUILDDIR      = build

# Put it first so that "make" without argument is like "make help".
help:
	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

.PHONY: help Makefile

# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
	@rsync -r --exclude '.ipynb_checkpoints/' ../notebooks/ ./notebooks/
	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

github:
	@make html

clean:
	rm -rf $(BUILDDIR)/*
	rm -rf auto_examples/


================================================
FILE: docs/_static/pysal-styles.css
================================================
/* Make thumbnails with equal heights */
@media only screen and (min-width : 500px) {
    .row.equal-height {
        display: flex;
        flex-wrap: wrap;
    }
    .row.equal-height > [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: 300px;
        height: 300px; /* force image's height */
        /* fheight: 100%; */

        /* 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: 100%;
      height: 100%;
      text-align: left;
      vertical-align: middle;
      padding: 6px;
      margin: 2px;
      overflow-x: auto;
      overflow-y: auto;
}

  div.body {
     max-width: 1080px;
 }

table.longtable.align-default{
    text-align: left;
}


================================================
FILE: docs/_static/references.bib
================================================
@article{assunccao2006efficient,
  title={{Efficient Regionalization Techniques for Socio-economic Geographical Units Using Minimum Spanning Trees}},
  author={Assunção, RM and Neves, MC and Câmara, G and da Costa Freitas, C},
  journal={{International Journal of Geographical Information Science}},
  volume={20},
  number={7},
  pages={797--811},
  year={2006},
  doi={10.1080/13658810600665111},
  publisher={Taylor \& Francis}
}


@article{calik_labbé_yaman_2015, 
  author={Calik, H and Labbé, M and Yaman, H},
  title={{p-Center Problems}}, 
  journal={Location Science},
  pages={79--92},
  year={2015},
  doi={10.1007/978-3-319-13111-5_4},
}


@article{chen_murray_jiang_2021,
  author={Chen, H and Murray, AT and Jiang, R},
  title={Open-source approaches for location cover models: capabilities and efficiency},
  journal={Journal of Geographical Systems},
  doi={10.1007/s10109-021-00350-w},
  year={2021},
  pages={361--380},
  volume={23},
  number={3},
}


@book{Church2009,
  address={Hoboken},
  author={Church, Richard L. and Murray, Alan T.},
  publisher={John Wiley {\&} Sons, Inc.},
  title={{Business Site Selection, Locational Analysis, and GIS}},
  year={2009},
  doi={10.1002/9780470432761},
}


@article{church_2018,
  author={Richard L. Church},
  title={{Tobler's Law and Spatial Optimization: Why Bakersfield?}},
  journal={International Regional Science Review},
  volume={41},
  number={3},
  pages={287--310},
  year={2018},
  doi={10.1177/0160017616650612},
}


@book{church_murray_2018, 
  author={Church, RL and Murray, AT},
  title={{Location Covering Models: History, Applications and Advancements}}, 
  publisher={Springer},
  year={2018},
  doi={10.1007/978-3-319-99846-6},
}


@article{current1988capacitated,
  title={{Capacitated Covering Models}},
  author={Current, John Richard and Storbeck, James Edward},
  journal={{Environment and Planning B: Planning and Design}},
  volume={15},
  number={2},
  pages={153--163},
  year={1988},
  publisher={SAGE Publications Sage UK: London, England},
  doi={10.1068/b150153},
}


@article{daskin_stern_edmund_1981,
  author={Daskin, MS and Stern, EH},
  title={{A Hierarchical Objective Set Covering Model for Emergency Medical Service Vehicle Deployment}},
  journal={Transportation Science},
  doi={10.1287/trsc.15.2.137},
  year={1981},
  volume={15},
  number={2},
  pages={137--152},
}


@book{daskin_2013, 
  author={Daskin, MS},
  title={{Network and Discrete Location: Models, Algorithms, and Applications}}, 
  publisher={John Wiley \& Sons, Inc.}, 
  year={2013},
  doi={10.1002/9781118537015},
}


@article{duque2011p,
  title={{The p-Regions Problem}},
  author={Duque, JC and Church, RL and Middleton, RS},
  journal={Geographical Analysis},
  volume={43},
  number={1},
  pages={104--126},
  year={2011},
  publisher={Wiley Online Library},
  doi={10.1111/j.1538-4632.2010.00810.x},
}


@article{duque2012max,
  author={Duque, JC and Anselin, L and Rey, SJ},
  title={{THE MAX-P-REGIONS PROBLEM*}},
  journal={Journal of Regional Science},
  volume={52},
  number={3},
  pages={397--419},
  doi={10.1111/j.1467-9787.2011.00743.x},
  year={2012},
}


@article{hakimi_1964, 
  author={Hakimi, SL},
  title={{Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph}}, 
  journal={Operations Research},
  volume={12}, 
  number={3},
  pages={450--459},
  year={1964},
  doi={10.1287/opre.12.3.450},
}


@article{hakimi_1965, 
  author={Hakimi, SL},
  title={{Optimum Distribution of Switching Centers in a Communication Network and Some Related Graph Theoretic Problems}}, 
  journal={Operations Research},
  number={3},
  volume={13},
  pages={462--475},
  year={1965},
  doi={10.1287/opre.13.3.462},
}


@article{kuby_1987,
  author={Kuby, MJ},
  title={{Programming Models for Facility Dispersion: The p-Dispersion and Maxisum Dispersion Problems}},
  journal={Geographical Analysis},
  doi={10.1111/j.1538-4632.1987.tb00133.x},
  year={1987},
  volume={19},
  number={4},
  pages={315--329},
}


@article{MALISZEWSKI2012331,
  author={Maliszewski, PJ and Kuby, MJ and Horner, MW},
  title={A comparison of multi-objective spatial dispersion models for managing critical assets in urban areas},
  journal={Computers, Environment and Urban Systems},
  doi={10.1016/j.compenvurbsys.2011.12.006},
  year={2012},
  volume={36},
  number={4},
  pages={331--341},
}


@article{openshaw1977geographical,
  title={{A Geographical Solution to Scale and Aggregation Problems in Region-building, Partitioning and Spatial Modelling}},
  author={Openshaw, S},
  journal={Transactions of the Institute of British Geographers},
  pages={459--472},
  year={1977},
  volume={2},
  number={4},
  publisher={JSTOR},
  doi={10.2307/622300},
}


@article{openshaw1995algorithms,
  title={{Algorithms for Reengineering 1991 Census Geography}},
  author={Openshaw, S and Rao, L},
  journal={Environment and Planning A},
  volume={27},
  number={3},
  pages={425--446},
  year={1995},
  doi={10.1068/a270425},
  publisher={SAGE Publications Sage UK: London, England}
}


@article{shi_malik_2000,
    author={Jianbo Shi and Malik, J.},
    journal={{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
    title={Normalized cuts and image segmentation},
    year={2000},
    volume={22},
    number={8},
    pages={888--905},
    doi={10.1109/34.868688}
}


@article{toregas_swain_revelle_bergman_1971, 
  author={Toregas, Constantine and Swain, Ralph and Revelle, Charles and Bergman, Lawrence},
  title={{The Location of Emergency Service Facilities}}, 
  journal={Operations Research}, 
  volume = {19},
  number = {6},
  pages={1363--1373},
  year={1971}, 
  doi={10.1287/opre.19.6.1363},
}


@article{von2007tutorial,
  title={A tutorial on spectral clustering},
  author={{von Luxburg}, Ulrike},
  journal={{Statistics and Computing}},
  volume={17},
  number={4},
  pages={395--416},
  year={2007},
  publisher={Springer},
  doi={10.1007/s11222-007-9033-z},
}


@article{wei2020efficient,
  title={Efficient regionalization for spatially explicit neighborhood delineation},
  author={Wei, R and Rey, SJ and Knaap, E},
  journal={International Journal of Geographical Information Science},
   year={2020},
  volume={35},
  number={1},
  pages={135--151},
  publisher={Taylor \& Francis},
  doi={10.1080/13658816.2020.1759806},
}


@misc{wolf2018,
  title={{ljwolf/spenc: GISRUK}},
  author={Wolf, LJ},
  year={2018},
  doi={10.5281/zenodo.1219904},
}


@article{wolf2021,
  author={Wolf, LJ},
  title={Spatially–encouraged spectral clustering: a technique for blending map typologies and regionalization},
  journal={International Journal of Geographical Information Science},
  volume={35},
  number={11},
  pages={2356--2373},
  year={2021},
  doi={10.1080/13658816.2021.1934475},
}


@inproceedings{yu_shi_2003,
  author={Yu, Stella X. and Shi, Jianbo},
  booktitle={{Proceedings Ninth IEEE International Conference on Computer Vision}}, 
  title={Multiclass spectral clustering}, 
  year={2003},
  volume={1},
  pages={313--319},
  doi={10.1109/ICCV.2003.1238361}}


@article{upchurch-kuby-lim-2009,
    author={Upchurch, Christopher and Kuby, Michael and Lim, Seow},
    title={{A Model for Location of Capacitated Alternative-Fuel Stations}},
    journal={Geographical Analysis},
    volume={41},
    number={1},
    pages={85--106},
    doi={10.1111/j.1538-4632.2009.00744.x},
    year={2009}
}


@article{hong-kuby-2016,
  title={A threshold covering flow-based location model to build a critical mass of alternative-fuel stations},
  journal={Journal of Transport Geography},
  volume={56},
  pages={128--137},
  year={2016},
  doi={10.1016/j.jtrangeo.2016.08.019},
  author={Shuyao Hong and Michael Kuby},
}


@article{KUBY2005125,
  title={The flow-refueling location problem for alternative-fuel vehicles},
  journal={Socio-Economic Planning Sciences},
  volume={39},
  number={2},
  pages={125--145},
  year={2005},
  doi={10.1016/j.seps.2004.03.001},
  author={Michael Kuby and Seow Lim},
}

%======


================================================
FILE: docs/api.rst
================================================
.. _api_ref:

.. currentmodule:: spopt

API reference
=============

.. _data_api:

Region Methods
--------------
Model based approaches for aggregating a large set of geographic units (with small footprints) into a smaller number of regions (with large footprints).

.. autosummary::
   :toctree: generated/

    region.AZP
    region.MaxPHeuristic
    region.RandomRegion
    region.RandomRegions
    region.RegionKMeansHeuristic
    region.SA3
    region.extract_clusters
    region.Skater
    region.Spenc
    region.WardSpatial

Locate Methods
--------------

Exact solution approaches to facility location modeling problems.

.. autosummary::
   :toctree: generated/

    locate.LSCP
    locate.LSCPB
    locate.MCLP
    locate.PMedian
    locate.KNearestPMedian
    locate.PCenter
    locate.PDispersion
    locate.FRLM


================================================
FILE: docs/conf.py
================================================
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.

import sphinx_bootstrap_theme

# import your package to obtain the version info to display on the docs website
import spopt

# -- 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",
    "nbsphinx",
    "numpydoc",
    "matplotlib.sphinxext.plot_directive",
]
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 = "spopt"
copyright = "2020-, pysal developers"  # noqa: A001
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 = spopt.__version__
release = spopt.__version__

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = "en"

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = ["build", "Thumbs.db", ".DS_Store", "tests/*"]

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = "sphinx"

# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False

# -- Options for HTML output ----------------------------------------------

# The theme to use for HTML and HTML Help pages.  See the documentation for
# a list of builtin themes.
#
# html_theme = 'alabaster'
html_theme = "bootstrap"
html_theme_path = sphinx_bootstrap_theme.get_html_theme_path()
html_title = f"{project} v{version} Manual"

# (Optional) Logo of your package.
# Should be small enough to fit the navbar (ideally 24x24).
# Path should be relative to the ``_static`` files directory.
# html_logo = "_static/images/package_logo.jpg"

# (Optional) PySAL favicon
html_favicon = "_static/images/pysal_favicon.ico"


# Theme options are theme-specific and customize the look and feel of a theme
# further.  For a list of options available for each theme, see the
# documentation.
#
html_theme_options = {
    # Navigation bar title. (Default: ``project`` value)
    "navbar_title": project,  # string of your project name, for example, 'spopt'
    # 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 <div> 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"),
        ("Tutorials", "tutorials"),
        ("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 = f"{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}doc.tex",
        f"{project} Documentation",
        f"{project} 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,
        project,
        "Spatial Optimization with PySAL",
        "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),
    "mapclassify": ("https://pysal.org/mapclassify/", None),
    "networkx": ("https://networkx.org/documentation/stable/", None),
    "numpy": ("https://numpy.org/doc/stable/", None),
    "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
    "pointpats": ("https://pysal.org/pointpats/", None),
    "pulp": ("https://coin-or.github.io/pulp/", None),
    "python": ("https://docs.python.org/3", None),
    "region": ("https://region.readthedocs.io/en/latest/", None),
    "scipy": ("https://docs.scipy.org/doc/scipy/", None),
    "shapely": ("https://shapely.readthedocs.io/en/stable/", None),
    "sklearn": ("https://scikit-learn.org/stable/", None),
}

numpydoc_xref_ignore = {
    "type",
    "optional",
    "default",
    "shape",
    "fitted",
    "instance",
    "cluster",
    "of",
    "or",
    "if",
    "using",
    "otherwise",
    "required",
    "from",
}


# 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:`<a href="https://mybinder.org/v2/gh/pysal/spopt/main?filepath={{ docname }}"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a>`

    __ https://github.com/pysal/spopt/blob/main/{{ docname }}

.. raw:: latex

    \nbsphinxstartnotebook{\scriptsize\noindent\strut
    \textcolor{gray}{The following section was generated from
    \sphinxcode{\sphinxupquote{\strut {{ docname | escape_latex }}}} \dotfill}}
"""  # noqa: E501

# This is processed by Jinja2 and inserted after each notebook
nbsphinx_epilog = r"""
.. raw:: latex

    \nbsphinxstopnotebook{\scriptsize\noindent\strut
    \textcolor{gray}{\dotfill\ \sphinxcode{\sphinxupquote{\strut
    {{ env.doc2path(env.docname, base='doc') | escape_latex }}}} ends here.}}
"""

# List of arguments to be passed to the kernel that executes the notebooks:
nbsphinx_execute_arguments = [
    "--InlineBackend.figure_formats={'svg', 'pdf'}",
    "--InlineBackend.rc={'figure.dpi': 96}",
]

mathjax3_config = {
    "TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": True}},
}


================================================
FILE: docs/index.rst
================================================
.. documentation master file

spopt: Spatial Optimization
===========================

Regionalization, facility location, and transportation-oriented modeling
------------------------------------------------------------------------

**spopt** is an open-source Python library for solving optimization problems with spatial data. Originating from the **region** module in `PySAL (Python Spatial Analysis Library) <http://pysal.org>`_, it is under active development for the inclusion of newly proposed models and methods for regionalization, facility location, and transportation-oriented solutions.

.. raw:: html

    <div class="container-fluid">
      <div class="row equal-height">
        <div class="col-sm-.5 col-xs-hidden">
        </div>
        <div class="col-md-4 col-xs-14">
            <a 
            href="https://pysal.org/spopt/notebooks/maxp.html" class="thumbnail">
                <img src="_static/images/notebooks_maxp_12_1.png" class="img-responsive center-block">
                <div class="caption text-center">
                <h6>Mexican State Regional Income Clustering</h6>
                </div>
            </a>
        </div>
        <div class="col-sm-4 col-xs-14">
            <a href="https://pysal.org/spopt/notebooks/skater.html" class="thumbnail">
                <img src="_static/images/notebooks_skater_12_1.png" class="img-responsive center-block">
                <div class="caption text-center">
                <h6>Airbnb Spots Clustering in Chicago</h6>
                </div>
            </a>
        </div>
        <div class="col-sm-4 col-xs-14">
            <a href="https://pysal.org/spopt/notebooks/reg-k-means.html" class="thumbnail">
                <img src="_static/images/notebooks_reg-k-means_15_1.png"
                class="img-responsive center-block">
                <div class="caption text-center">
                <h6>Grid Clustering
                </h6>
                </div>
            </a>
        </div>
        <div class="col-sm-4 col-xs-14">
            <a href="https://pysal.org/spopt/notebooks/lscp_gis.html" class="thumbnail">
                <img src="_static/images/notebooks-lscp.png"
                class="img-responsive center-block">
                <div class="caption text-center">
                <h6>Locating First Aid Stations in Toronto
                </h6>
                </div>
            </a>
        </div>
        <div class="col-sm-4 col-xs-14">
            <a href="https://pysal.org/spopt/notebooks/facloc-real-world.html" class="thumbnail">
                <img src="_static/images/notebooks-facloc-mclp.png"
                class="img-responsive center-block">
                <div class="caption text-center">
                <h6>Locating Store Sites in San Francisco
                </h6>
                </div>
            </a>
        </div>
        <div class="col-sm-4 col-xs-14">
            <a href="https://pysal.org/spopt/notebooks/lscpb.html" class="thumbnail">
                <img src="_static/images/lscpb-predef.png"
                class="img-responsive center-block">
                <div class="caption text-center">
                <h6>Backup Coverage and Predefined Locations
                </h6>
                </div>
            </a>
        </div>
        <div class="col-sm-.5 col-xs-hidden">
        </div>
      </div>
    </div>

Development
-----------

**spopt** development is hosted on github_.

Get in touch
------------

If you have a question regarding **spopt**, feel free to open an `issue`_, a new `discussion`_ on GitHub, or join a chat on PySAL's `Discord`_ channel.

Citing **spopt**
----------------

If you use **PySAL-spopt** in a scientific publication, we would appreciate citations to the following::

  @misc{spopt2021,
    author    = {Feng, Xin, and 
                 Gaboardi, James D. and 
                 Knaap, Elijah and 
                 Rey, Sergio J. and 
                 Wei, Ran},
    month     = {jan},
    year      = {2021},
    title     = {pysal/spopt},
    url       = {https://github.com/pysal/spopt},
    doi       = {10.5281/zenodo.4444156},
    keywords  = {python,regionalization,spatial-optimization,location-modeling}
  }

  @article{spopt2022,
    author    = {Feng, Xin and Barcelos, Germano and Gaboardi, James D. and
                Knaap, Elijah and Wei, Ran and Wolf, Levi J. and
                Zhao, Qunshan and Rey, Sergio J.},
    year      = {2022},
    title     = {spopt: a python package for solving spatial optimization problems in PySAL},
    journal   = {Journal of Open Source Software},
    publisher = {The Open Journal},
    volume    = {7},
    number    = {74},
    pages     = {3330},
    url       = {https://doi.org/10.21105/joss.03330},
    doi       = {10.21105/joss.03330},
  }

Funding
-------

This project is/was partially funded through:

.. figure:: _static/images/nsf_logo.png
    :target: https://www.nsf.gov/index.jsp
    :width: 100
    :align: left

    National Science Foundation Award #1831615: `RIDIR: Scalable Geospatial Analytics for Social Science Research <https://www.nsf.gov/awardsearch/showAward?AWD_ID=1831615>`_

.. raw:: html

    <img 
        src="_static/images/pysal_banner.svg" 
        class="img-responsive center-block" 
        alt="PySAL Logo" 
        width="370" 
        height="200"
    >

.. toctree::
   :hidden:
   :maxdepth: 3
   :caption: Contents:

   Installation <installation>
   Tutorials <tutorials>
   API <api>
   References <references>

.. _PySAL: https://github.com/pysal/pysal
.. _github : https://github.com/pysal/spopt
.. _issue: https://github.com/pysal/spopt/issues/new/choose
.. _discussion: https://github.com/pysal/spopt/discussions
.. _Discord: https://discord.gg/BxFTEPFFZn


================================================
FILE: docs/installation.rst
================================================
.. Installation

Installation
============

spopt supports Python >= 3.12_. Please make sure that you are operating in a Python 3 environment.

Installing released version
---------------------------

spopt is available on the `Python Package Index`_. Therefore, you can either install directly with `pip` from the command line::

  pip install -U spopt

or download the source distribution (.tar.gz) and decompress it to your selected destination. Open a command shell and navigate to the decompressed folder.
Type::

  pip install .

You may also install the latest stable spopt via conda-forge channel by running::

  conda install --channel conda-forge spopt

Installing development version
------------------------------

Potentially, you might want to use the newest features in the development version of spopt on github - `pysal/spopt`_ while have not been incorporated in the Pypi released version. You can achieve that by installing `pysal/spopt`_
 
  pip install git+https://github.com/pysal/spopt.git

You can  also `fork`_ the `pysal/spopt`_ repo and create a local clone of your fork. By making changes to your local clone and submitting a pull request to `pysal/spopt`_, you can contribute to spopt development.

.. _3.12: https://docs.python.org/3.12/
.. _Python Package Index: https://pypi.org/project/spopt/
.. _pysal/spopt: https://github.com/pysal/spopt
.. _fork: https://help.github.com/articles/fork-a-repo/ 


================================================
FILE: docs/references.rst
================================================
.. reference for the docs

References
==========

.. bibliography:: _static/references.bib
   :all:


================================================
FILE: docs/tutorials.rst
================================================
Tutorials
=========

.. toctree::
    :maxdepth: 1
    :caption: Regionalization:

    notebooks/maxp.ipynb
    notebooks/azp.ipynb
    notebooks/sa3.ipynb
    notebooks/skater.ipynb
    notebooks/ward.ipynb
    notebooks/reg-k-means.ipynb
    notebooks/randomregion.ipynb
    notebooks/component_policy.ipynb

.. toctree::
    :maxdepth: 1
    :caption: Locate:

    notebooks/lscp.ipynb
    notebooks/lscp_gis.ipynb
    notebooks/lscp_capacity.ipynb
    notebooks/lscpb.ipynb
    notebooks/mclp.ipynb
    notebooks/mclp_gis.ipynb
    notebooks/p-center.ipynb
    notebooks/p-median.ipynb
    notebooks/p-median_variations.ipynb
    notebooks/p-dispersion.ipynb
    notebooks/facloc-real-world.ipynb
    notebooks/facloc-lscpb-real-world.ipynb
    notebooks/facloc-disperse-real-world.ipynb
    notebooks/flow.ipynb


================================================
FILE: environment.yml
================================================
name: spopt
channels:
  - conda-forge
dependencies:
  - python
  - fast_hdbscan
  - geopandas
  - jupyterlab
  - libpysal
  - mapclassify
  - networkx
  - numba
  - numpy
  - pandas
  - pointpats
  - pip
  - pulp
  - scikit-learn
  - scipy
  - shapely
  - tqdm

  # notebook/binder specific
  - folium
  - glpk
  - inequality
  - matplotlib
  - matplotlib-scalebar
  - overpy
  - seaborn
  - spaghetti
  - watermark

  # bleeding edge spopt
  - pip:
    - git+https://github.com/pysal/spopt.git@main
    # (notebook/binder specific) having trouble resolving routingpy via conda-forge
    - routingpy
    - pulp


================================================
FILE: notebooks/azp.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Automatic Zoning Procedure (AZP) algorithm\n",
    "Authors: [Xin Feng](https://github.com/xf37), [James Gaboardi](https://github.com/jGaboardi)\n",
    "\n",
    "AZP can work with different types of objective functions, which are very sensitive to aggregating data from a large number of zones into a pre-designated smaller number of regions. AZP was originally formulated in [Openshaw, 1977](https://www.jstor.org/stable/622300?casa_token=QITLwNVUPj4AAAAA%3AKXYVQHrdeAtefxZKkvxxUrWA_QCedSJrZwQD36F16Jfqx7Vq47uu1G_vp9I085Iozy_roZ_Dh6qQopZ6yXjfPsy4Y2aBqso-l0wZS2YqNv9pkh1JNXc&seq=1#metadata_info_tab_contents) and then extended in [Openshaw, S. and Rao, L. (1995)](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.461.8982&rep=rep1&type=pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-29T17:23:01.190352Z",
     "start_time": "2022-10-29T17:23:01.152262Z"
    },
    "execution": {
     "iopub.execute_input": "2025-04-07T17:53:10.538497Z",
     "iopub.status.busy": "2025-04-07T17:53:10.538235Z",
     "iopub.status.idle": "2025-04-07T17:53:10.569383Z",
     "shell.execute_reply": "2025-04-07T17:53:10.569091Z",
     "shell.execute_reply.started": "2025-04-07T17:53:10.538471Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2025-04-07T13:53:10.559404-04:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.12.9\n",
      "IPython version      : 9.0.2\n",
      "\n",
      "Compiler    : Clang 18.1.8 \n",
      "OS          : Darwin\n",
      "Release     : 24.4.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 8\n",
      "Architecture: 64bit\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%config InlineBackend.figure_format = \"retina\"\n",
    "%load_ext watermark\n",
    "%watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-29T17:23:03.516249Z",
     "start_time": "2022-10-29T17:23:01.193479Z"
    },
    "execution": {
     "iopub.execute_input": "2025-04-07T17:53:10.570030Z",
     "iopub.status.busy": "2025-04-07T17:53:10.569930Z",
     "iopub.status.idle": "2025-04-07T17:53:11.974171Z",
     "shell.execute_reply": "2025-04-07T17:53:11.973969Z",
     "shell.execute_reply.started": "2025-04-07T17:53:10.570017Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Watermark: 2.5.0\n",
      "\n",
      "spopt    : 0.6.2.dev3+g13ca45e\n",
      "geopandas: 1.0.1\n",
      "libpysal : 4.12.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "import geopandas\n",
    "import libpysal\n",
    "\n",
    "import spopt\n",
    "from spopt.region import AZP\n",
    "\n",
    "%matplotlib inline\n",
    "%watermark -w\n",
    "%watermark -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mexican State Regional Income Clustering\n",
    "\n",
    "To illustrate `azp` we utilize data on regional incomes for Mexican states over the period 1940-2000, originally used in [Rey and Sastré-Gutiérrez (2010)](https://www.tandfonline.com/doi/full/10.1080/17421772.2010.493955?casa_token=r6iIZaEKaywAAAAA%3ARxjQkEOcxQ5gffa1EGyd2rByA6w0uVxg5cU3v4VaSmx2u89Nr3oSe2f_n63CV20Ysrjs07U47m98). \n",
    "\n",
    "We can first explore the data by plotting the per capital gross regional domestic product (in constant USD 2000 dollars) for each year in the sample, using a quintile classification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-29T17:23:03.662726Z",
     "start_time": "2022-10-29T17:23:03.522081Z"
    },
    "execution": {
     "iopub.execute_input": "2025-04-07T17:53:11.975233Z",
     "iopub.status.busy": "2025-04-07T17:53:11.975069Z",
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     "shell.execute_reply.started": "2025-04-07T17:53:11.975225Z"
    }
   },
   "outputs": [],
   "source": [
    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
    "mexico = geopandas.read_file(pth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-29T17:23:05.819942Z",
     "start_time": "2022-10-29T17:23:03.665789Z"
    },
    "execution": {
     "iopub.execute_input": "2025-04-07T17:53:12.014622Z",
     "iopub.status.busy": "2025-04-07T17:53:12.014473Z",
     "iopub.status.idle": "2025-04-07T17:53:12.625564Z",
     "shell.execute_reply": "2025-04-07T17:53:12.625363Z",
     "shell.execute_reply.started": "2025-04-07T17:53:12.014614Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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