How to Set a Custom 0‑17 Colorbar Range in Matplotlib Contour Plots

Introduction

In a Python community, a user asked how to set the colorbar range of a plt.contour() plot to display values from 0 to 17.

First Attempt

The initial code used cbar.set_ticks(np.linspace(0, 10, 10)) and adjusted limits, but it could not achieve the required 0‑17 range.

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 10, .1)
y = np.arange(0, 10, .1)
X, Y = np.meshgrid(x, y)

data = 2 * (np.sin(X) + np.sin(3 * Y))

fig, ax = plt.subplots()
contour = ax.contour(X, Y, data)
cbar = fig.colorbar(contour, ax=ax)
cbar.set_ticks(np.linspace(0, 10, 10))  # Set 10 ticks from 0 to 10 (inclusive)
# cbar.set_ticklabels([f'{i:.1f}' for i in np.linspace(0, 10, 10)])  # Set tick labels
cbar.ax.set_ylim(0, 10)  # Set the limits of the colorbar
plt.show()

Even after setting the limits to 10, the user needed a colorbar that spanned 0‑17 while the data’s maximum value was 1.

Working Solution – Custom Colorbar

A second approach creates a separate ScalarMappable, sets its clim to (0, 17), defines specific tick levels, and places the colorbar on the right side.

import matplotlib.pyplot as plt
import matplotlib.cm as cm

fig, ax = plt.subplots(1, 1, figsize=(10, 6))

# Create a ScalarMappable object for the colorbar
sm = cm.ScalarMappable(cmap=plt.get_cmap('viridis'))
sm.set_clim(0, 17)

# Define tick positions (levels)
levels = [0, 4, 8, 12, 17]

# Create the colorbar with custom ticks and label
cbar = fig.colorbar(sm, ax=ax, orientation='vertical',
                    label='Data Value', ticks=levels)

plt.show()

The resulting plot shows a colorbar with the desired range, although the visual appearance may differ from the original contour plot.

Conclusion

The discussion demonstrates how to customize Matplotlib colorbars for contour plots, providing a practical solution for setting specific value ranges.