How to Create Custom Colormaps and Color Scales with Matplotlib?

Creating Custom Colormaps and Color Scales with Matplotlib:

Creating a custom colormap in matplotlib involves a straightforward process. To establish a continuous (smooth) color scale, consider leveraging the LinearSegmentedColormap instead of the ListedColormap.

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

# Defining random data points
x, y, c = zip(*np.random.rand(30, 3)*4 - 2)

# Establishing normalization parameters
norm = plt.Normalize(-2, 2)

# Generating a linear segmented colormap from a list
colormap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["red", "violet", "blue"])

# Plotting the points with the custom colormap
plt.scatter(x, y, c=c, cmap=colormap, norm=norm)

# Adding a color scale to the plot
plt.colorbar()

plt.show()

This method ensures a seamless color transition between the specified values.

Further customization is possible by supplying tuples of normalized values and corresponding colors to the from_list method.

# Custom values and colors
custom_values = [-2, -1, 2]
custom_colors = ["red", "violet", "blue"]

# Generating a segmented colormap from custom tuples
colormap = matplotlib.colors.LinearSegmentedColormap.from_list("", list(zip(map(norm, custom_values), custom_colors)))

# Applying the colormap to the plot
plt.scatter(x, y, c=c, cmap=colormap, norm=norm)
plt.colorbar()

plt.show()

By utilizing this technique, you can create personalized colormaps that precisely represent your data.

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