Plotting an imshow() image in 3d in Matplotlib

To plot an imshow() image in 3D in Matplotlib, you can display 2D data as both a traditional image and as a 3D surface plot. This technique is useful for visualizing data from different perspectives.

Step-by-Step Approach

Here's the process to create 3D visualizations of imshow data ?

• Create xx and yy coordinate grids using numpy

• Generate 2D data using mathematical functions

• Create a figure with multiple subplots

• Display data as a 2D image using imshow()

• Display the same data as a 3D contour plot

• Show the combined visualization

Basic Example

This example creates a 2D function and displays it both as an image and 3D plot ?

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

# Set figure size
plt.rcParams["figure.figsize"] = [10.0, 5.0]
plt.rcParams["figure.autolayout"] = True

# Create coordinate grids
xx, yy = np.meshgrid(np.linspace(0, 1, 50), np.linspace(0, 1, 50))

# Generate 2D data
data = np.cos(4 * np.pi * xx) * np.sin(4 * np.pi * yy)

# Create figure with subplots
fig = plt.figure()

# 2D imshow plot
ax1 = fig.add_subplot(121)
im = ax1.imshow(data, cmap="viridis", interpolation='bilinear', 
                origin='lower', extent=[0, 1, 0, 1])
ax1.set_title('2D Image View')
ax1.set_xlabel('X')
ax1.set_ylabel('Y')

# 3D contour plot
ax2 = fig.add_subplot(122, projection='3d')
ax2.contourf(xx, yy, data, levels=50, zdir='z', offset=-1, cmap="viridis")
ax2.set_title('3D Contour View')
ax2.set_xlabel('X')
ax2.set_ylabel('Y')
ax2.set_zlabel('Z')

plt.tight_layout()
plt.show()

[Displays a side-by-side comparison of 2D imshow and 3D contour plots]

3D Surface Plot

You can also create a true 3D surface instead of contours ?

import matplotlib.pyplot as plt
import numpy as np

# Create data
x = np.linspace(-2, 2, 30)
y = np.linspace(-2, 2, 30)
X, Y = np.meshgrid(x, y)
Z = np.exp(-(X**2 + Y**2))

fig = plt.figure(figsize=(12, 5))

# 2D imshow
ax1 = fig.add_subplot(131)
im1 = ax1.imshow(Z, cmap='plasma', extent=[-2, 2, -2, 2])
ax1.set_title('2D Image')
plt.colorbar(im1, ax=ax1)

# 3D surface
ax2 = fig.add_subplot(132, projection='3d')
surf = ax2.plot_surface(X, Y, Z, cmap='plasma', alpha=0.8)
ax2.set_title('3D Surface')

# 3D wireframe
ax3 = fig.add_subplot(133, projection='3d')
ax3.plot_wireframe(X, Y, Z, color='black', alpha=0.6)
ax3.set_title('3D Wireframe')

plt.tight_layout()
plt.show()

[Displays three different visualizations of the same 2D data]

Key Parameters

Conclusion

Combining imshow() with 3D plotting provides powerful visualization capabilities. Use contourf() for filled contours or plot_surface() for true 3D surfaces to display your 2D data from multiple perspectives.