Questions to ask when choosing a color map

Different color maps can perform better or worse at conveying metrics, letting the viewer know the value of pixels on the screen, and conveying form—showing how those pixels relate to one another. Whether or not form or metrics or both are important for a given image is really key to deciding what kind of color map to use for that image.

The different questions that can be asked when choosing a color map are as follows:

• Do we need to think whether or not the shape, the value, or-again-both are important for that field?
• Are there critical values or transitions in the data?
• Are there ranges of numbers that really want to pop out to the viewer?
• What is the intuitive choice of colors for the dataset?

For most people, a dataset that conveys temperature should usually have hot red colors to denote high temperatures and cool blue colors to denote cold temperatures; to reverse that would violate the intuitive sense that the viewer has and set up an initial set of stressors in their mind that's going to make interpreting the image harder than it needs to be.

Taking an example, have a look at the color map here, showing population density in the United States:

Using one kind of color map—one that's not perceptually uniform—washes out a lot of the high values. Simply changing it to another different kind of color map that has less high-valued whitish colors at the high end allows you to see more detail in the higher density, more populated eastern parts of the US.

To take a look at all of the color maps that Matplotlib provides, visit the website provided here: http://matplotlib.org/examples/color/colormaps_reference.html. This offers a little swatch of each different color map to give you an idea of what the color map can do in terms of the ranges.

Before we move ahead with the code, we will start by importing the following set of default packages, as shown here:

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

%matplotlib inline
# Set up figure size and DPI for screen demo
plt.rcParams['figure.figsize'] = (6,4)
plt.rcParams['figure.dpi'] = 150