Metadata-Version: 2.1
Name: pylandstats
Version: 2.3.0
Summary: Open-source Python library to compute landscape metrics
Home-page: https://github.com/martibosch/pylandstats
Author: Martí Bosch
Author-email: marti.bosch@epfl.ch
License: GPL-3.0
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        # PyLandStats
        
        Open-source Pythonic library to compute landscape metrics within the PyData stack (NumPy, pandas, matplotlib...)
        
        **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)
        
        ## Features
        
        * Read GeoTiff files of land use/cover:
        
            ```python
            import pylandstats as pls
        
            ls = pls.read_geotiff('data/vaud_g100_clc00_V18_5.tif')
        
            ls.plot_landscape(legend=True)
            ```
        
            ![landscape-vaud](figures/landscape.png)
        
        * Compute pandas data frames of landscape metrics at the patch, class and landscape level:
        
            ```python
            class_metrics_df = ls.compute_class_metrics_df(metrics=['proportion_of_landscape', 'edge_density'])
            class_metrics_df
            ```
        
            | class_val | proportion_of_landscape | edge_density |
            | --------: | ----------------------: | -----------: |
            |         1 |                   7.702 |        4.459 |
            |         2 |                  92.298 |        4.459 |
        
        * Analyze the spatio-temporal evolution of landscapes:
        
            ```python
            input_fnames = [
                'data/vaud_g100_clc00_V18_5.tif',
                'data/vaud_g100_clc06_V18_5a.tif',
                'data/vaud_g100_clc12_V18_5a.tif'
            ]
        
            sta = pls.SpatioTemporalAnalysis(
                input_fnames, metrics=[
                    'proportion_of_landscape',
                    'edge_density',
                    'fractal_dimension_am',
                    'landscape_shape_index',
                    'shannon_diversity_index'
                ], classes=[1], dates=[2000, 2006, 2012], 
            )
        
            fig, axes = plt.subplots(1, 3, figsize=(15, 5))
            for metric, ax in zip(
                ['proportion_of_landscape', 'edge_density', 'fractal_dimension_am'], axes):
                sta.plot_metric(metric, class_val=1, ax=ax)
            fig.suptitle('Class-level metrics (urban)')
            ```
        
            ![spatiotemporal-analysis](figures/spatiotemporal.png)
        
        * Zonal analysis of landscapes
        
        See 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.
        
        ## Installation
        
        The easiest way to install PyLandStats is with conda:
        
            $ conda install -c conda-forge pylandstats
        
        which will install PyLandStats and all of its dependencies. Alternatively, you can install PyLandStats using pip:
        
            $ pip install pylandstats
        
        
        Nevertheless, 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:
        
            $ pip install pylandstats[geo]
            
        and you will be able to use the aforementioned features (without having to use conda).
        
        ### Development install
        
        To install a development version of PyLandStats, you can first use conda to create an environment with all the dependencies and activate it as in:
        
            $ conda create -n pylandstats -c conda-forge geopandas matplotlib-base rasterio scipy openblas
            $ conda activate pylandstats
        
        and then clone the repository and use pip to install it in development mode
        
            $ git clone https://github.com/martibosch/pylandstats.git
            $ cd pylandstats/
            $ pip install -e .
        
        ## Acknowledgments
        
        * The computation of the adjacency matrix in [transonic](https://github.com/fluiddyn/transonic) has been implemented by Pierre Augier ([paugier](https://github.com/paugier)).
        * With the support of the École Polytechnique Fédérale de Lausanne (EPFL)
        * The Corine Land Cover datasets used for the test datasets were produced with funding by the European Union
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Provides-Extra: geo
