Metadata-Version: 2.1
Name: pylandstats
Version: 0.3.1
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
        ===============================
        
        Overview
        --------
        
        Open-source Pythonic library to compute landscape metrics within the PyData stack (NumPy, pandas, matplotlib...)
        
        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 DataFrames of landscape metrics at the patch, class and landscape level
        
        ```python
        patch_metrics_df = ls.compute_patch_metrics_df()
        patch_metrics_df.head()
        ```
        
        | patch_id | class_val | area | perimeter | perimeter_area_ratio | shape_index | fractal_dimension |
        | -------: | --------: | ---: | --------: | -------------------: | ----------: | ----------------: |
        |        0 |         1 |  115 |     10600 |                92.17 |       2.409 |             1.130 |
        |        1 |         1 |   13 |      2600 |               200.00 |       1.625 |             1.100 |
        |        2 |         1 |    2 |       600 |               300.00 |       1.000 |             1.012 |
        |        3 |         1 |   69 |      6000 |                86.96 |       1.765 |             1.088 |
        |        4 |         1 |   76 |      8800 |               115.79 |       2.444 |             1.137 |
        
        ```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 |
        
        Also analyze the spatio-temporal evolution of the landscape:
        
        ```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 = sta.plot_metrics(
            class_val=1,
            metrics=['proportion_of_landscape', 'edge_density', 'fractal_dimension_am'],
            num_cols=3)
        fig.suptitle('Class-level metrics (urban)')
        ```
        
        ![spatiotemporal-analysis](figures/spatiotemporal.png)
        
        See the [pylandstats-notebooks](https://github.com/martibosch/pylandstats-notebooks) repository for a more complete overview
        
        Installation
        ------------
        
        To install use pip:
        
            $ pip install pylandstats
        
        
        Or clone the repo:
        
            $ git clone https://github.com/martibosch/pylandstats.git
            $ python setup.py install
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Provides-Extra: geo
