Python Matplotlib creating a custom colour scale

Question

I have created a map of precipitation levels in a region based on precipitation data from NetCDF files. I would like to add a custom scale such that if precipitation is less than 800mm it would be one colour, 800-1000mm another, etc. Similar to the map found here: http://www.metmalawi.com/climate/climate.php

At the moment I am using a gradient scale but it isn't showing the detail I need. This is the code for the plot at the moment (where 'Average' is my data that I have already formatted).

    #load color palette
    colourA = mpl_cm.get_cmap('BuPu')
    
    #plot map with physical features 
    ax = plt.axes(projection=cartopy.crs.PlateCarree())
    ax.add_feature(cartopy.feature.COASTLINE)   
    ax.add_feature(cartopy.feature.BORDERS)
    ax.add_feature(cartopy.feature.LAKES, alpha=0.5)
    ax.add_feature(cartopy.feature.RIVERS)

    #set map boundary
    ax.set_extent([32.5, 36., -9, -17]) 
    
    #set axis tick marks
    ax.set_xticks([33, 34, 35]) 
    ax.set_yticks([-10, -12, -14, -16]) 
    lon_formatter = LongitudeFormatter(zero_direction_label=True)
    lat_formatter = LatitudeFormatter()
    ax.xaxis.set_major_formatter(lon_formatter)
    ax.yaxis.set_major_formatter(lat_formatter)
    
    #plot data and set colour range
    plot = iplt.contourf(Average, cmap=colourA, levels=np.arange(0,15500,500), extend='both')

    #add colour bar index and a label
    plt.colorbar(plot, label='mm per year')

    #give map a title
    plt.title('Pr 1990-2008 - Average_ERAINT ', fontsize=10)

    #save the image of the graph and include full legend
    plt.savefig('ERAINT_Average_Pr_MAP_Annual', bbox_inches='tight')

    plt.show()

Anyone know how I can do this?

Thank you!

Answer 1

This is a matplotlib question disguised as an Iris question as the issue has appeared via Iris plotting routines, but to answer this we need only a couple of matplotlib commands. As such, I'm basing this answer on this matplotlib gallery example. These are levels (containing values for the upper bound of each contour) and colors (specifying the colours to shade each contour). It's best if there are the same number of levels and colours.

To demonstrate this, I put the following example together. Given that there's no sample data provided, I made my own trigonometric data. The levels are based on the trigonometric data values, so do not reflect the levels required in the question, but could be changed to the original levels. The colours used are the hex values of the levels specified by image in the link in the question.

The code:

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(-25, 25)
y = np.arange(-20, 20)
x2d, y2d = np.meshgrid(x, y)
vals = (3 * np.cos(x2d)) + (2 * np.sin(y2d))

colours = ['#bf8046', '#df9f24', '#e0de30', '#c1de2d', '#1ebf82',
           '#23de27', '#1dbe20', '#11807f', '#24607f', '#22427e']
levels = range(-5, 6)

plt.contourf(vals, levels=levels, colors=colours)
plt.colorbar()
plt.show()

The produced image:

Colours could also be selected from a colormap (one way of doing this is shown in this StackOverflow answer). There are also other ways, including in the matplotlib gallery example linked above. Given, though, that the sample map linked in the question had specific colours I chose to use those colours directly.