Creating Gaussian Noise Plots with Vertical and Horizontal Colorbars Using Python Matplotlib

The article introduces a method for plotting Gaussian noise images with matplotlib in Python, showing how to add customized colorbars for visual reference.

First, the required libraries are imported:

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

To create a plot with a default vertical colorbar, the following code is used:

fig, ax = plt.subplots()
data = np.clip(randn(250, 250), -1, 1)
cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
ax.set_title('www.linuxidc.com')
cbar = fig.colorbar(cax, ticks=[-1, 0, 1])
cbar.ax.set_yticklabels(['< -1', '0', '> 1'])

The resulting figure displays the Gaussian noise with a vertical colorbar indicating low, zero, and high values.

For a plot with a horizontal colorbar, the code changes the colormap and orientation:

fig, ax = plt.subplots()
data = np.clip(randn(250, 250), -1, 1)
cax = ax.imshow(data, interpolation='nearest', cmap=cm.afmhot)
ax.set_title('www.linuxidc.com')
cbar = fig.colorbar(cax, ticks=[-1, 0, 1], orientation='horizontal')
cbar.ax.set_xticklabels(['Low', 'Medium', 'High'])

The resulting image shows the Gaussian noise with a horizontal colorbar labeled "Low", "Medium", and "High".

Both examples include images illustrating the visual output of the plots.

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