3D Contour Plotting with Matplotlib

Introduction

This tutorial will guide you through the process of creating a 3D contour plot using Matplotlib in Python. The contour plot represents a 3D surface using contours or level curves. We will use the contour() function to create these level curves, and the extend3d=True option to extend the curves vertically into 'ribbons'.

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Skills Graph

Import Libraries

We need to start by importing the necessary libraries for this tutorial. We will use matplotlib.pyplot for plotting, matplotlib.cm for color maps, and mpl_toolkits.mplot3d for 3D plotting.

import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import axes3d

Create Data

Next, we need to create the data we will use to generate the contour plot. We will use the get_test_data() function from the mpl_toolkits.mplot3d module to generate sample data.

X, Y, Z = axes3d.get_test_data(0.05)

Create 3D Axes

We will use the add_subplot() function to create a 3D subplot for our plot. We will also set the projection to '3d'.

ax = plt.figure().add_subplot(projection='3d')

Create Contour Plot

We will now create the contour plot using the contour() function. We will pass in the X, Y, and Z data, and set extend3d=True to extend the curves vertically into 'ribbons'. We will also set the colormap to cm.coolwarm for a nice color scheme.

ax.contour(X, Y, Z, extend3d=True, cmap=cm.coolwarm)

Display the Plot

Finally, we will use the show() function to display our plot.

plt.show()

Summary

In this tutorial, we learned how to create a 3D contour plot using Matplotlib in Python. We used the contour() function to create level curves, and the extend3d=True option to extend the curves vertically into 'ribbons'. We also used the get_test_data() function to generate sample data, and the cm.coolwarm colormap for a nice color scheme.