Creating Custom Colorbar Tickels in Matplotlib

Introduction

In data visualization, colorbars are used to represent the range of values of a dataset using color. Matplotlib is a Python library for creating a variety of visualizations, including colorbars. In this lab, we will learn how to customize the tick labels on a colorbar in Matplotlib.

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Import necessary libraries and fix random state

First, we need to import the necessary libraries and fix the random state for reproducibility. We will use numpy to generate some random data, matplotlib.pyplot for creating visualizations, and cm from matplotlib for defining the color maps.

import matplotlib.pyplot as plt
import numpy as np
from numpy.random import randn

from matplotlib import cm

## Fixing random state for reproducibility
np.random.seed(19680801)

Create a plot with a vertical colorbar

We will start by creating a plot with a vertical colorbar. We will generate some random data using randn from numpy and clip the values to the range of -1 to 1. We will then create an AxesImage object using imshow and the coolwarm colormap. Finally, we will add a title to the plot.

## Make plot with vertical (default) colorbar
fig, ax = plt.subplots()

data = np.clip(randn(250, 250), -1, 1)

cax = ax.imshow(data, cmap=cm.coolwarm)
ax.set_title('Gaussian noise with vertical colorbar')

Customize tick labels on the vertical colorbar

Next, we will customize the tick labels on the vertical colorbar. We will create a colorbar using colorbar and specify the tick locations using the ticks parameter. We will then set the tick labels using set_yticklabels on the ax attribute of the colorbar object.

## Add colorbar, make sure to specify tick locations to match desired ticklabels
cbar = fig.colorbar(cax, ticks=[-1, 0, 1])
cbar.ax.set_yticklabels(['< -1', '0', '> 1'])  ## vertically oriented colorbar

Create a plot with a horizontal colorbar

We will now create a plot with a horizontal colorbar. We will follow the same steps as in Step 2, but this time we will use the afmhot colormap and set the orientation of the colorbar to horizontal.

## Make plot with horizontal colorbar
fig, ax = plt.subplots()

data = np.clip(randn(250, 250), -1, 1)

cax = ax.imshow(data, cmap=cm.afmhot)
ax.set_title('Gaussian noise with horizontal colorbar')

cbar = fig.colorbar(cax, ticks=[-1, 0, 1], orientation='horizontal')
cbar.ax.set_xticklabels(['Low', 'Medium', 'High'])  ## horizontal colorbar

Display the plot

Finally, we will display the plot using plt.show().

plt.show()

Summary

In this lab, we learned how to customize the tick labels on a colorbar in Matplotlib. We first created a plot with a vertical colorbar and customized the tick labels using set_yticklabels. We then created a plot with a horizontal colorbar and customized the tick labels using set_xticklabels. Customizing the tick labels on a colorbar can help make your visualizations more informative and easier to read.