Iris 1.7

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Iris developer guide

# Rotated pole mappingΒΆ

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
"""
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

"""
import cartopy.crs as ccrs
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')

# 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()
plt.show()

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

if __name__ == '__main__':
main()