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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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