Metadata-Version: 1.0
Name: GeobricksRasterCorrelation
Version: 0.1.13
Summary: Geobricks library to correlate two raster and create statistics and scatter charts.
Home-page: http://pypi.python.org/pypi/GeobricksRasterCorrelation/
Author: Simone Murzilli; Guido Barbaglia
Author-email: geobrickspy@gmail.com
License: LICENSE.txt
Description: Raster Correlation
        ====================
        
        The library provides an easy way correlate raster of the same size. It returns a json containing statistical outputs and frequencies information to be directly used with Highcharts JS or Matplotlib chart libraries.
        
        # Installation
        
        ## Dependencies
        
        The library has different dependencies (see also requirements.txt) click, watchdog, flask, flask-cors, numpy, scipy, pysal, brewer2mpl, rasterio, GeobricksCommon.
        
        ## On Ubuntu
        
        ```bash
        sudo add-apt-repository ppa:ubuntugis/ppa
        sudo apt-get update
        sudo apt-get install python-numpy libgdal1h gdal-bin libgdal-dev
        ```
        
        In case of compiling errors for numpy
        ```bash
        sudo apt-get install libblas3gf libc6 libgcc1 libgfortran3 liblapack3gf libstdc++6 build-essential gfortran python-all-dev libatlas-base-dev python-dev
        ```
        
        In case of compiling errors for scipy
        ```bash
        sudo apt-get install libblas-dev liblapack-dev
        ```
        
        ## Installation
        
        The library is distributed through PyPi and can be installed by typing the following commands in the console:
        ```
        pip -r https://raw.githubusercontent.com/geobricks/geobricks_raster_correlation/master/requirements.txt
        
        pip install GeobricksRasterCorrelation
        ```
        
        **N.B.** Due to a well known PyPi issue it's not possible to install scipy and pysal through setup.py or requirements.txt 
        
        In order to install pysal run the following command
        ```bash
        pip install pysal
        ```
        
        
        # Examples
        
        ## Library usage
        
        ```python
        from geobricks_raster_correlation.core.raster_correlation_core import get_correlation
        
        raster_path1 = "path_to_raster1.tif"
        raster_path2 = "path_to_raster2.tif"
        # Number of bins to be applied to the scatter chart
        bins = 300
        corr = get_correlation(raster_path1, raster_path2, bins)
        print corr
        ```
        
        ## Example with matplotlib
        
        This example generate a correlation chart with matplotlib
        
        ```python
        from geobricks_raster_correlation.core.raster_correlation_core import get_correlation
        from matplotlib import pyplot as plt
        from matplotlib.pylab import polyfit, polyval
        
        # input to your raster files
        raster_path1 = "path_to_raster1.tif"
        raster_path2 = "path_to_raster2.tif"
        
        # Number of sampling bins
        bins = 150
        
        corr = get_correlation(raster_path1, raster_path2, bins)
        x = []
        y = []
        colors = []
        # print corr['series']
        for serie in corr['series']:
            colors.append(serie['color'])
            for data in serie['data']:
                x.append(data[0])
                y.append(data[1])
        
        # Adding regression line
        (m, b) = polyfit(x, y, 1)
        yp = polyval([m, b], x)
        plt.plot(x, yp)
        
        # plotting scatter
        plt.scatter(x, y, c=colors)
        plt.show()
        ```
        
        The returned json:
         
         * corr['stats']  contains the statistics: slope, p_value, std_err, intercept, r_value
         * corr['series'] contains the output series that can be used directly as an Highcharts input or with Matplotlib.
        
Keywords: geobricks,processing,raster,gis,gdal,correlation,raster correlation,highcharts
Platform: UNKNOWN
