How to Create a Custom Colormap with Matplotlib and Display a Color Scale?

Custom Colormap with Matplotlib and Color Scale Plot

Introduction

Custom colormaps allow for personalized visualization of data. This article addresses a user's query regarding creating their own colormap that transitions smoothly from red through violet to blue, mapped to values between -2 and 2. The aim is to color data points in a plot and display the accompanying color scale.

Colormap Creation

To create a continuous color scale, a LinearSegmentedColormap is employed instead of the discrete ListedColormap. A list of colors can be passed to the from_list method to generate this custom colormap:

import matplotlib.pyplot as plt
import matplotlib.colors

colors = ["red", "violet", "blue"]
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", colors)

Mapping Colors to Values

To map colors to data values, a normalization function is necessary. The Normalize function transforms values to a range suitable for the colormap:

norm = plt.Normalize(-2, 2)

Plotting with Custom Colormap

To color points in the plot using the custom colormap:

plt.scatter(x, y, c=c, cmap=cmap, norm=norm)

Color Scale Visualization

To display the color scale adjacent to the plot:

plt.colorbar()

Generalization to Arbitrary Data

The method can be generalized to map any set of values to colors:

cvals = [-2., -1, 2]
colors = ["red", "violet", "blue"]

tuples = list(zip(map(norm, cvals), colors))
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", tuples)

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