How to use matplotlib cmap?

A colormap, or cmap, is a mapping from a range of values to a range of colors. In Matplotlib, cmaps are used to colorize data in plots. There are many built-in cmaps in Matplotlib, and you can also create your own.

To use a cmap in Matplotlib, you can use the plt.cm.get_cmap() function. This function takes a cmap name as an argument and returns a colormap object. You can then use the colormap object to colorize your data.

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For example, the following code creates a line plot and colors the line using the viridis cmap:

import matplotlib.pyplot as plt
import matplotlib.cm as cm

x = [1, 2, 3, 4, 5]
y = [6, 7, 8, 9, 10]

cmap = cm.viridis

plt.plot(x, y, cmap=cmap)
plt.show()

This code will create a line plot with the x-axis values in the list x and the y-axis values in the list y. The line will be colored using the viridis cmap.

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Here are some of the built-in cmaps in Matplotlib:

viridis: A perceptually uniform colormap that is well-suited for scientific plotting.
plasma: A colormap that is similar to viridis, but with a more saturated appearance.
inferno: A colormap that is designed to be used for visualizing high-temperature data.
magma: A colormap that is similar to inferno, but with a more muted appearance.
jet: A traditional colormap that is often used for visualizing data in a variety of scientific fields.

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You can find a complete list of the built-in cmaps in the Matplotlib documentation.

To create your own cmap, you can use the matplotlib.colors.LinearSegmentedColormap() class. This class allows you to specify the colors that you want to use in your cmap.

For more information on how to use cmaps in Matplotlib, you can refer to the Matplotlib documentation.