How to Create a Custom Discrete Colorbar for a Matplotlib Scatterplot?

Custom Discrete Colorbar for Matplotlib Scatterplot

In Matplotlib, creating a discrete colorbar for a scatterplot allows you to represent data points with unique colors based on their integer tag values. To achieve this, a custom discrete colormap and a BoundaryNorm can be employed.

Let's construct a scatterplot with random x and y data, and assign integer tags ranging from 0 to 20:

<code class="python">import matplotlib.pyplot as plt
import numpy as np

x = np.random.rand(20)
y = np.random.rand(20)
tag = np.random.randint(0, 20, 20)</code>

Initially, using the default settings, the colorbar displays a continuous range of colors:

<code class="python">plt.scatter(x, y, c=tag)
plt.colorbar()</code>

To create a discrete colorbar, we'll use a BoundaryNorm to define the bounds of the colormap. We also want to ensure that the tag value of 0 is represented by a gray color:

<code class="python">bounds = np.linspace(0, 20, 21)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

cmaplist = [cmap(i) for i in range(cmap.N)]
cmaplist[0] = (.5, .5, .5, 1.0)

cmap = mpl.colors.LinearSegmentedColormap.from_list(
    'Custom cmap', cmaplist, cmap.N)</code>

The updated scatterplot with the discrete colorbar looks like this:

<code class="python">scat = ax.scatter(x, y, c=tag, s=np.random.randint(100, 500, 20),
                  cmap=cmap, norm=norm)

# Add the discrete colorbar
ax2 = fig.add_axes([0.95, 0.1, 0.03, 0.8])
cb = plt.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm,
    spacing='proportional', ticks=bounds, boundaries=bounds, format='%1i')</code>

This approach provides a well-defined discrete colorbar for your scatterplot, where each tag value is represented by a unique color, including a gray color for tag value 0.

The above is the detailed content of How to Create a Custom Discrete Colorbar for a Matplotlib Scatterplot?. For more information, please follow other related articles on the PHP Chinese website!