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

[source code]

[source code]

[source code]

[source code]

"""
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()