Contourf and Log Color Scale

Introduction

In this lab, we will learn how to use the contourf function in Matplotlib to create filled contour plots with a logarithmic color scale. We will use a dataset with positive and negative values to demonstrate this feature.

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Skills Graph

Import the necessary libraries

We need to import the following libraries:

• matplotlib.pyplot for creating plots and visualizations
• numpy for generating the dataset

import matplotlib.pyplot as plt
import numpy as np

Generate the dataset

We will generate a dataset with positive and negative values using numpy:

N = 100
x = np.linspace(-3.0, 3.0, N)
y = np.linspace(-2.0, 2.0, N)

X, Y = np.meshgrid(x, y)

## A low hump with a spike coming out.
## Needs to have z/colour axis on a log scale, so we see both hump and spike.
## A linear scale only shows the spike.
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)
z = Z1 + 50 * Z2

## Put in some negative values (lower left corner) to cause trouble with logs:
z[:5, :5] = -1

## The following is not strictly essential, but it will eliminate
## a warning.  Comment it out to see the warning.
z = ma.masked_where(z <= 0, z)

Create the plot

We will use the contourf function to create a filled contour plot with a logarithmic color scale:

fig, ax = plt.subplots()
cs = ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap=cm.PuBu_r)

cbar = fig.colorbar(cs)

plt.show()

Customize the plot

We can customize the plot by adding labels, titles, and changing the color map:

fig, ax = plt.subplots()
cs = ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap=cm.coolwarm)

ax.set_title('Contourf Plot with Log Color Scale')
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')

cbar = fig.colorbar(cs)

plt.show()

Summary

In this lab, we learned how to use the contourf function in Matplotlib to create filled contour plots with a logarithmic color scale. We also learned how to customize the plot by adding labels, titles, and changing the color map.