.. _graphics-anomaly_log_colouring: 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 `_. 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 `_ and `matplotlib.pyplot.pcolormesh `_). See also: http://en.wikipedia.org/wiki/False_color#Pseudocolor. .. plot:: /data/local/ecamp/iris/docs/iris/example_code/graphics/anomaly_log_colouring.py :: """ 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 `_. 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 `_ and `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 "+/-". 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()