Metadata-Version: 2.1
Name: spopt
Version: 0.2.1
Summary: Spatial Optimization in PySAL
Home-page: https://github.com/pysal/spopt
Maintainer: PySAL Developers
Maintainer-email: xin.feng@ucr.edu, jgaboardi@gmail.com
License: 3-Clause BSD
Download-URL: https://pypi.org/project/spopt
Keywords: spatial optimization
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: geopandas (>=0.7)
Requires-Dist: libpysal
Requires-Dist: networkx
Requires-Dist: numpy (>=1.3)
Requires-Dist: pandas (>=1)
Requires-Dist: pulp
Requires-Dist: scikit-learn (>=0.22)
Requires-Dist: scipy (>=0.11)
Requires-Dist: spaghetti
Provides-Extra: dev
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Requires-Dist: pytest ; extra == 'tests'
Requires-Dist: pytest-cov ; extra == 'tests'


<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)
[![unittests](https://github.com/pysal/spopt/workflows/.github/workflows/unittests.yml/badge.svg)](https://github.com/pysal/spopt/actions?query=workflow%3A.github%2Fworkflows%2Funittests.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)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![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)

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.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.coverage import MCLP
from spopt.locate.util import simulated_geo_points
import numpy
import geopandas
import pulp
import spaghetti

solver = pulp.PULP_CBC_CMD(msg=False)
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(0.2).unary_union),
    crs=street.crs,
    columns=["geometry"],
)
client_points = simulated_geo_points(street_buffered, needed=CLIENT_COUNT, seed=CLIENT_SEED)
facility_points = simulated_geo_points(
    street_buffered, needed=FACILITY_COUNT, seed=FACILITY_SEED
)
ntw.snapobservations(client_points, "clients", attribute=True)
clients_snapped = spaghetti.element_as_gdf(
    ntw, pp_name="clients", snapped=True
)

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"],
)
mclp_from_cost_matrix = MCLP.from_cost_matrix(cost_matrix, ai, MAX_COVERAGE, p_facilities=P_FACILITIES)
mclp_from_cost_matrix = mclp_from_cost_matrix.solve(solver)
```
<p align="center">
<img src="docs/_static/images/mclp.svg" 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/)
- [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
```

## 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 the [gitter room](https://gitter.im/pysal/spopt).

## 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 citation:

```
@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}
}
```

## 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)

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