Merge pull request #1040 from pp-mo/colours_eg

Added a custom colour plotting example.

docs/iris/example_code/graphics/anomaly_log_colouring.py

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+"""
+Colouring anomaly data with logarithmic scaling
+===============================================
+
+In this example, we need to plot anomaly data where the values have a
+"logarithmic" significance  -- i.e. we want to give approximately equal ranges
+of colour between data values of, say, 1 and 10 as between 10 and 100.
+
+As the data range also contains zero, that obviously does not suit a simple
+logarithmic interpretation.  However, values of less than a certain absolute
+magnitude may be considered "not significant", so we put these into a separate
+"zero band" which is plotted in white.
+
+To do this, we create a custom value mapping function (normalization) using
+the matplotlib Norm class `matplotlib.colours.SymLogNorm
+<http://matplotlib.org/api/colors_api.html#matplotlib.colors.BoundaryNorm>`_.
+We use this to make a cell-filled pseudocolour plot with a colorbar.
+
+NOTE: By "pseudocolour", we mean that each data point is drawn as a "cell"
+region on the plot, coloured according to its data value.
+This is provided in Iris by the functions :meth:`iris.plot.pcolor` and
+:meth:`iris.plot.pcolormesh`, which call the underlying matplotlib
+functions of the same names (i.e. `matplotlib.pyplot.pcolor
+<http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.pcolor>`_
+and  `matplotlib.pyplot.pcolormesh
+<http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.pcolormesh>`_).
+See also: http://en.wikipedia.org/wiki/False_color#Pseudocolor.
+
+"""
+import cartopy.crs as ccrs
+import iris
+import iris.coord_categorisation
+import iris.plot as iplt
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcols
+import matplotlib.ticker as mticks
+
+
+def main():
+    # Load a sample air temperatures sequence.
+    file_path = iris.sample_data_path('E1_north_america.nc')
+    temperatures = iris.load_cube(file_path)
+
+    # Create a year-number coordinate from the time information.
+    iris.coord_categorisation.add_year(temperatures, 'time')
+
+    # Create a sample anomaly field for one chosen year, by extracting that
+    # year and subtracting the time mean.
+    sample_year = 1982
+    year_temperature = temperatures.extract(iris.Constraint(year=sample_year))
+    time_mean = temperatures.collapsed('time', iris.analysis.MEAN)
+    anomaly = year_temperature - time_mean
+
+    # Construct a plot title string explaining which years are involved.
+    years = temperatures.coord('year').points
+    plot_title = 'Temperature anomaly'
+    plot_title += '\n{} differences from {}-{} average.'.format(
+        sample_year, years[0], years[-1])
+
+    # Define scaling levels for the logarithmic colouring.
+    minimum_log_level = 0.1
+    maximum_scale_level = 3.0
+
+    # Use a standard colour map which varies blue-white-red.
+    # For suitable options, see the 'Diverging colormaps' section in:
+    # http://matplotlib.org/examples/color/colormaps_reference.html
+    anom_cmap = 'bwr'
+
+    # Create a 'logarithmic' data normalization.
+    anom_norm = mcols.SymLogNorm(linthresh=minimum_log_level,
+                                 linscale=0,
+                                 vmin=-maximum_scale_level,
+                                 vmax=maximum_scale_level)
+    # Setting "linthresh=minimum_log_level" makes its non-logarithmic
+    # data range equal to our 'zero band'.
+    # Setting "linscale=0" maps the whole zero band to the middle colour value
+    # (i.e. 0.5), which is the neutral point of a "diverging" style colormap.
+
+    # Create an Axes, specifying the map projection.
+    plt.axes(projection=ccrs.LambertConformal())
+
+    # Make a pseudocolour plot using this colour scheme.
+    mesh = iplt.pcolormesh(anomaly, cmap=anom_cmap, norm=anom_norm)
+
+    # Add a colourbar, with extensions to show handling of out-of-range values.
+    bar = plt.colorbar(mesh, orientation='horizontal', extend='both')
+
+    # Set some suitable fixed "logarithmic" colourbar tick positions.
+    tick_levels = [-3, -1, -0.3, 0.0, 0.3, 1, 3]
+    bar.set_ticks(tick_levels)
+
+    # Modify the tick labels so that the centre one shows "+/-<minumum-level>".
+    tick_levels[3] = r'$\pm${:g}'.format(minimum_log_level)
+    bar.set_ticklabels(tick_levels)
+
+    # Label the colourbar to show the units.
+    bar.set_label('[{}, log scale]'.format(anomaly.units))
+
+    # Add coastlines and a title.
+    plt.gca().coastlines()
+    plt.title(plot_title)
+
+    # Display the result.
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()

docs/iris/example_tests/test_anomaly_log_colouring.py

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+# (C) British Crown Copyright 2014, Met Office
+#
+# This file is part of Iris.
+#
+# Iris is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Lesser General Public License as published by the
+# Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# Iris is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with Iris.  If not, see <http://www.gnu.org/licenses/>.
+
+
+# Import Iris tests first so that some things can be initialised before
+# importing anything else.
+import iris.tests as tests
+
+import extest_util
+
+with extest_util.add_examples_to_path():
+    import anomaly_log_colouring
+
+
+class TestAnomalyLogColouring(tests.GraphicsTest):
+    """Test the anomaly colouring example code."""
+    def test_anomaly_log_colouring(self):
+        with extest_util.show_replaced_by_check_graphic(self):
+            anomaly_log_colouring.main()
+
+
+if __name__ == '__main__':
+    tests.main()