.. _graphics-rotated_pole_mapping:


Rotated pole mapping 
=====================

This example uses several visualisation methods to achieve an array of differing images, including:

 * Visualisation of point based data
 * Contouring of point based data
 * Block plot of contiguous bounded data
 * Blue marble image underlay



.. plot:: /net/home/h06/ecamp/nounpack_iris/docs/iris/example_code/graphics/rotated_pole_mapping.py

::

    """
    Rotated pole mapping
    =====================
    
    This example uses several visualisation methods to achieve an array of differing images, including:
    
     * Visualisation of point based data
     * Contouring of point based data
     * Block plot of contiguous bounded data
     * Blue marble image underlay
    
    """
    
    import matplotlib.pyplot as plt
    
    import iris
    import iris.plot as iplt
    import iris.quickplot as qplt
    import iris.analysis.cartography
    
    
    def main():
        fname = iris.sample_data_path('rotated_pole.nc')
        temperature = iris.load_strict(fname)
    
        # Calculate the lat lon range and buffer it by 10 degrees
        lat_range, lon_range = iris.analysis.cartography.lat_lon_range(temperature)
        lat_range = lat_range[0] - 10, lat_range[1] + 10
        lon_range = lon_range[0] - 10, lon_range[1] + 10
    
    
        # Plot #1: Point plot showing data values & a colorbar
        plt.figure()
        iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
        points = qplt.points(temperature, c=temperature.data)
        cb = plt.colorbar(points, orientation='horizontal')
        cb.set_label(temperature.units)
        iplt.gcm().drawcoastlines()
        plt.show()
    
    
        # Plot #2: Contourf of the point based data
        plt.figure()
        iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
        qplt.contourf(temperature, 15)
        iplt.gcm().drawcoastlines()
        plt.show()
    
    
        # Plot #3: Contourf overlayed by coloured point data
        plt.figure()
        iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
        qplt.contourf(temperature)
        iplt.points(temperature, c=temperature.data)
        iplt.gcm().drawcoastlines()
        plt.show()
    
    
    
        # For the purposes of this example, add some bounds to the latitude and longitude
        temperature.coord('grid_latitude').guess_bounds()
        temperature.coord('grid_longitude').guess_bounds()
    
    
        # Plot #4: Block plot
        plt.figure()
        iplt.map_setup(temperature, lat_range=lat_range, lon_range=lon_range)
        iplt.pcolormesh(temperature)
        iplt.gcm().bluemarble()
        iplt.gcm().drawcoastlines()
        plt.show()
    
    
    if __name__ == '__main__':
        main()