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Resources","sub_category":"Soil and Land","monthly_downloads":1821,"total_dependent_repos":2,"total_dependent_packages":0,"readme":"[![PyPI version fury.io](https://badge.fury.io/py/pylandstats.svg)](https://pypi.python.org/pypi/pylandstats/)\n[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pylandstats.svg)](https://anaconda.org/conda-forge/pylandstats)\n[![Documentation Status](https://readthedocs.org/projects/pylandstats/badge/?version=latest)](https://pylandstats.readthedocs.io/en/latest/?badge=latest)\n[![tests](https://github.com/martibosch/pylandstats/actions/workflows/tests.yml/badge.svg)](https://github.com/martibosch/pylandstats/blob/main/.github/workflows/tests.yml)\n[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/martibosch/pylandstats/main.svg)](https://results.pre-commit.ci/latest/github/martibosch/pylandstats/main)\n[![codecov](https://codecov.io/github/martibosch/pylandstats/branch/main/graph/badge.svg?token=fqjSTzixmC)](https://codecov.io/github/martibosch/pylandstats)\n[![GitHub license](https://img.shields.io/github/license/martibosch/pylandstats.svg)](https://github.com/martibosch/pylandstats/blob/master/LICENSE)\n\n# PyLandStats\n\nOpen-source library to compute landscape metrics in the Python ecosystem (NumPy, pandas, matplotlib...)\n\n**Citation**: Bosch M. 2019. \"PyLandStats: An open-source Pythonic library to compute landscape metrics\". *PLOS ONE, 14(12), 1-19*. [doi.org/10.1371/journal.pone.0225734](https://doi.org/10.1371/journal.pone.0225734)\n\n## Features\n\n- Read GeoTiff files of land use/cover:\n\n  ```python\n  import pylandstats as pls\n\n  ls = pls.Landscape(\"../data/processed/veveyse-AS18_4.tif\")\n  ls.plot_landscape(legend=True)\n  ```\n\n  ![landscape-veveyse](figures/landscape.png)\n\n- Compute pandas data frames of landscape metrics at the patch, class and landscape level:\n\n  ```python\n  class_metrics_df = ls.compute_class_metrics_df(\n      metrics=[\"proportion_of_landscape\", \"edge_density\", \"euclidean_nearest_neighbor_mn\"]\n  )\n  class_metrics_df\n  ```\n\n  | class_val | proportion_of_landscape | edge_density | euclidean_nearest_neighbor_mn |\n  | --------: | ----------------------: | -----------: | ----------------------------: |\n  |         1 |                7.749572 |    19.102211 |                    309.244705 |\n  |         2 |               56.271868 |    50.599270 |                    229.079970 |\n  |         3 |               33.946252 |    38.167200 |                    253.299859 |\n  |         4 |                2.032308 |     3.722177 |                    552.835154 |\n\n- Analyze the spatio-temporal evolution of landscapes:\n\n  ```python\n  import matplotlib.pyplot as plt\n\n  input_filepaths = [\n      \"../data/processed/veveyse-AS97R_4.tif\",\n      \"../data/processed/veveyse-AS09R_4.tif\",\n      \"../data/processed/veveyse-AS18_4.tif\",\n  ]\n\n  sta = pls.SpatioTemporalAnalysis(input_filepaths, dates=[\"1992\", \"2004\", \"2012\"])\n  sta.plot_metric(\"contagion\")\n  ```\n\n  ![spatiotemporal-analysis](figures/spatiotemporal.png)\n\n- Zonal analysis of landscapes\n\nSee the [documentation](https://pylandstats.readthedocs.io/en/latest/?badge=latest) and the [pylandstats-notebooks](https://github.com/martibosch/pylandstats-notebooks) repository for a more complete overview.\n\n## Installation\n\nThe easiest way to install PyLandStats is with conda:\n\n```\n$ conda install -c conda-forge pylandstats\n```\n\nwhich will install PyLandStats and all of its dependencies. Alternatively, you can install PyLandStats using pip:\n\n```\n$ pip install pylandstats\n```\n\nNevertheless, note that in order to define zones by vector geometries in `ZonalAnalysis`, or in order to use the the `BufferAnalysis` and `SpatioTemporalBufferAnalysis` classes, PyLandStats requires [geopandas](https://github.com/geopandas/geopandas), which cannot be installed with pip. If you already have [the dependencies for geopandas](https://geopandas.readthedocs.io/en/latest/install.html#dependencies) installed in your system, you might then install PyLandStats with the `geo` extras as in:\n\n```\n$ pip install pylandstats[geo]\n```\n\nand you will be able to use the aforementioned features (without having to use conda).\n\n### Development install\n\nTo install a development version of PyLandStats, you can first use conda to create an environment with all the dependencies and activate it as in:\n\n```\n$ conda create -n pylandstats -c conda-forge geopandas matplotlib-base rasterio scipy openblas\n$ conda activate pylandstats\n```\n\nand then clone the repository and use pip to install it in development mode\n\n```\n$ git clone https://github.com/martibosch/pylandstats.git\n$ cd pylandstats/\n$ pip install -e .\n```\n\n## Acknowledgments\n\n- The computation of the adjacency matrix in [transonic](https://github.com/fluiddyn/transonic) has been implemented by Pierre Augier ([paugier](https://github.com/paugier))\n- Several information theory-based metrics from Nowosad and Stepinski [1] were added by [achennu](https://github.com/achennu)\n- With the support of the École Polytechnique Fédérale de Lausanne (EPFL)\n- The Corine Land Cover datasets used for the test datasets were produced with funding by the European Union\n\n## References\n\n1. 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