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Plotting a masked surface plot using Python, Numpy and Matplotlib
To plot a masked surface plot using Python, Numpy and Matplotlib, we can take the following steps −
- Set the figure size and adjust the padding between and around the subplots.
- Create a new figure or activate an existing figure.
- Add an 'ax' to the figure as part of a subplot arrangement.
- Return the coordinate matrices from coordinate vectors, pi and theta.
- Create x, y and z with masked data points.
- Create a surface plot with x, y, and z data points.
- To display the figure, use show() method.
Example
import matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_subplot(111, projection="3d") pi, theta = np.meshgrid( np.arange(1, 10, 2) * np.pi / 4, np.arange(1, 10, 2) * np.pi / 4) x = np.cos(pi) * np.sin(theta) y = np.sin(pi) * np.sin(theta) z = np.ma.masked_where(x >= 0.01, y) ax.plot_surface(x, y, z, color='red') plt.show()
Output
It will produce the following output
Updated on: 20-Sep-2021
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