Plotting Sparsity Patterns

Introduction

In this tutorial, we will learn how to plot sparsity patterns of arrays using Matplotlib. The sparsity pattern refers to the distribution of non-zero elements in an array. We will use the spy function in Matplotlib to plot the sparsity patterns.

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Skills Graph

Importing Required Libraries

We will start by importing the required libraries which are NumPy and Matplotlib.

import matplotlib.pyplot as plt
import numpy as np

Creating Random Array

Next, we will create a random array with dimensions (20, 20) using the numpy.random.randn function. We will also set a few elements to zero to create a sparse matrix.

np.random.seed(19680801)
x = np.random.randn(20, 20)
x[5, :] = 0.
x[:, 12] = 0.

Creating Subplots

We will now create a 2x2 grid of subplots using the subplots function. This will give us four plots to visualize the sparsity pattern of the array.

fig, axs = plt.subplots(2, 2)
ax1 = axs[0, 0]
ax2 = axs[0, 1]
ax3 = axs[1, 0]
ax4 = axs[1, 1]

Plotting Sparsity Pattern

We will use the spy function to plot the sparsity pattern of the array. We will use different parameters such as markersize and precision to customize the plot.

ax1.spy(x, markersize=5)
ax2.spy(x, precision=0.1, markersize=5)
ax3.spy(x)
ax4.spy(x, precision=0.1)

Displaying the Plots

Finally, we will display the plots using the show function.

plt.show()

Summary

In this tutorial, we learned how to plot sparsity patterns of arrays using Matplotlib. We used the spy function to visualize the sparsity pattern and customized the plot using different parameters. We also learned how to create subplots and display the plots using the show function.