Metadata-Version: 2.1
Name: pylandstats
Version: 0.1.0
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
        ===============================
        
        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
