Matplotlib Colormap Image Generation

Introduction

This tutorial will guide you through the process of making a set of images with a single colormap, norm, and colorbar in Python's Matplotlib library. You will learn how to generate data, set color scales, and update images to respond to changes in the norm of other images.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

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Skills Graph

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Import Libraries

First, we need to import the necessary libraries: numpy and matplotlib. We will also set a random seed to ensure reproducibility.

import matplotlib.pyplot as plt
import numpy as np

Generate Data and Create Subplots

Next, we will generate data for our images. We will create a 3x2 grid of subplots, with each subplot containing a randomly generated array of values.

np.random.seed(19680801)
Nr = 3
Nc = 2

fig, axs = plt.subplots(Nr, Nc)
fig.suptitle('Multiple images')

images = []
for i in range(Nr):
    for j in range(Nc):
        ## Generate data with a range that varies from one plot to the next.
        data = ((1 + i + j) / 10) * np.random.rand(10, 20)
        images.append(axs[i, j].imshow(data))
        axs[i, j].label_outer()

Set Color Scale and Create Colorbar

Now, we will set the color scale for our images and create a colorbar to show the range of values. We will find the minimum and maximum values for all images and normalize the color scale accordingly.

vmin = min(image.get_array().min() for image in images)
vmax = max(image.get_array().max() for image in images)
norm = colors.Normalize(vmin=vmin, vmax=vmax)
for im in images:
    im.set_norm(norm)

fig.colorbar(images[0], ax=axs, orientation='horizontal', fraction=.1)

Update Images

Finally, we will update the images to respond to changes in the norm of other images. This will allow us to change the colormap and color scale of one image and have all others update accordingly.

def update(changed_image):
    for im in images:
        if (changed_image.get_cmap() != im.get_cmap()
                or changed_image.get_clim() != im.get_clim()):
            im.set_cmap(changed_image.get_cmap())
            im.set_clim(changed_image.get_clim())

for im in images:
    im.callbacks.connect('changed', update)

Summary

In this lab, we learned how to create a set of images with a single colormap, norm, and colorbar in Python's Matplotlib library. We generated data, set color scales, and updated images to respond to changes in the norm of other images. This is a useful technique for visualizing multiple sets of data with the same color scale and colormap.