How to Create a Custom Colormap and Color Scale in Matplotlib?

Create a Custom Colormap and Color Scale in Matplotlib

Problem:

Design a custom colormap that transitions smoothly from red to violet to blue, mapping to values between -2 and 2. Utilize the colormap to color points in a plot and display the associated color scale.

Implementation:

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

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

# Create a custom colormap
colors = ["red", "violet", "blue"]
norm = plt.Normalize(-2, 2)
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", colors)

# Plot using custom colormap
plt.scatter(x, y, c=c, cmap=cmap, norm=norm)

# Add color scale
plt.colorbar()
plt.show()

Explanation:

• LinearSegmentedColormap: Instead of a ListedColormap that produces discrete colors, we use a LinearSegmentedColormap to create a continuous gradient.
• Normalization: The Normalize function maps the data values into a range between 0 and 1, ensuring that the colors are appropriately distributed.
• RGBA Specification: The colors are specified as strings of the desired color names.
• Scatter Plot: The data points are plotted using the custom colormap, and each point is assigned a color based on its corresponding data value.
• Color Scale: The colorbar displays the color gradient and the mapped values, enabling the user to visualize the color-value relationship.

Additional Considerations:

• Multiple Values: To create a colormap that maps more than three values to colors, specify additional tuples of normalized values and colors in the from_list method.
• Colorbar Ticks: Adjust the colorbar ticks using the set_ticks method to customize the displayed values.

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