{
    "info": {
        "author": "Mart\u00ed Bosch",
        "author_email": "marti.bosch@epfl.ch",
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        "classifiers": [
            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6"
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        "description": "[![PyPI version fury.io](https://badge.fury.io/py/pylandstats.svg)](https://pypi.python.org/pypi/pylandstats/)\n[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pylandstats/badges/version.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[![Build Status](https://travis-ci.org/martibosch/pylandstats.svg?branch=master)](https://travis-ci.org/martibosch/pylandstats)\n[![Coverage Status](https://coveralls.io/repos/github/martibosch/pylandstats/badge.svg?branch=master)](https://coveralls.io/github/martibosch/pylandstats?branch=master)\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 Pythonic library to compute landscape metrics within the PyData stack (NumPy, pandas, matplotlib...)\n\n**Citation**: Bosch M. 2019. \"PyLandStats: An open-source Pythonic library to compute landscape metrics\". Preprint available at bioRxiv. https://doi.org/10.1101/715052\n\n## Features\n\nRead GeoTiff files of land use/cover\n\n```python\nimport pylandstats as pls\n\nls = pls.read_geotiff('data/vaud_g100_clc00_V18_5.tif')\n\nls.plot_landscape(legend=True)\n```\n\n![landscape-vaud](figures/landscape.png)\n\nCompute pandas DataFrames of landscape metrics at the patch, class and landscape level\n\n```python\npatch_metrics_df = ls.compute_patch_metrics_df()\npatch_metrics_df.head()\n```\n\n| patch_id | class_val | area | perimeter | perimeter_area_ratio | shape_index | fractal_dimension | euclidean_nearest_neighbor |\n| -------: | --------: | ---: | --------: | -------------------: | ----------: | ----------------: | -------------------------: |\n|        0 |         1 |  115 |     10600 |                92.17 |       2.409 |             1.130 |                   1431.782 |\n|        1 |         1 |   13 |      2600 |               200.00 |       1.625 |             1.100 |                    223.607 |\n|        2 |         1 |    2 |       600 |               300.00 |       1.000 |             1.012 |                    223.607 |\n|        3 |         1 |   69 |      6000 |                86.96 |       1.765 |             1.088 |                    316.228 |\n|        4 |         1 |   76 |      8800 |               115.79 |       2.444 |             1.137 |                    316.228 |\n\n```python\nclass_metrics_df = ls.compute_class_metrics_df(metrics=['proportion_of_landscape', 'edge_density'])\nclass_metrics_df\n```\n\n| class_val | proportion_of_landscape | edge_density |\n| --------: | ----------------------: | -----------: |\n|         1 |                   7.702 |        4.459 |\n|         2 |                  92.298 |        4.459 |\n\nAlso analyze the spatio-temporal evolution of the landscape:\n\n```python\ninput_fnames = [\n    'data/vaud_g100_clc00_V18_5.tif',\n    'data/vaud_g100_clc06_V18_5a.tif',\n    'data/vaud_g100_clc12_V18_5a.tif'\n]\n\nsta = pls.SpatioTemporalAnalysis(\n    input_fnames, metrics=[\n        'proportion_of_landscape',\n        'edge_density',\n        'fractal_dimension_am',\n        'landscape_shape_index',\n        'shannon_diversity_index'\n    ], classes=[1], dates=[2000, 2006, 2012], \n)\n\nfig, axes = sta.plot_metrics(\n    class_val=1,\n    metrics=['proportion_of_landscape', 'edge_density', 'fractal_dimension_am'],\n    num_cols=3)\nfig.suptitle('Class-level metrics (urban)')\n```\n\n![spatiotemporal-analysis](figures/spatiotemporal.png)\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    $ conda install -c conda-forge pylandstats\n\nwhich will install PyLandStats and all of its dependencies. Alternatively, you can install PyLandStats using pip:\n\n    $ pip install pylandstats\n\n\nNevertheless, note that the `BufferAnalysis` and `SpatioTemporalBufferAnalysis` classes make use of [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    $ pip install pylandstats[geo]\n    \nand you will be able to use the `BufferAnalysis` and `SpatioTemporalBufferAnalysis` classes (without having to use conda).\n\n## Acknowledgments\n\n* With the support of the \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL)\n* The Corine Land Cover datasets used for the test datasets were produced with funding by the European Union",
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