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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

  • Non native projection and a Natural Earth shaded relief image underlay

  • Air pressure at sea level
  • Air pressure at sea level
  • Air pressure at sea level
  • plot rotated pole mapping
import cartopy.crs as ccrs
import matplotlib.pyplot as plt

import iris
import iris.analysis.cartography
import iris.plot as iplt
import iris.quickplot as qplt


def main():
    # Load some test data.
    fname = iris.sample_data_path("rotated_pole.nc")
    air_pressure = iris.load_cube(fname)

    # Plot #1: Point plot showing data values & a colorbar
    plt.figure()
    points = qplt.points(air_pressure, c=air_pressure.data)
    cb = plt.colorbar(points, orientation="horizontal")
    cb.set_label(air_pressure.units)
    plt.gca().coastlines()
    iplt.show()

    # Plot #2: Contourf of the point based data
    plt.figure()
    qplt.contourf(air_pressure, 15)
    plt.gca().coastlines()
    iplt.show()

    # Plot #3: Contourf overlayed by coloured point data
    plt.figure()
    qplt.contourf(air_pressure)
    iplt.points(air_pressure, c=air_pressure.data)
    plt.gca().coastlines()
    iplt.show()

    # For the purposes of this example, add some bounds to the latitude
    # and longitude
    air_pressure.coord("grid_latitude").guess_bounds()
    air_pressure.coord("grid_longitude").guess_bounds()

    # Plot #4: Block plot
    plt.figure()
    plt.axes(projection=ccrs.PlateCarree())
    iplt.pcolormesh(air_pressure)
    plt.gca().stock_img()
    plt.gca().coastlines()
    iplt.show()


if __name__ == "__main__":
    main()

Total running time of the script: ( 0 minutes 0.955 seconds)

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