Custom Colormaps in Matplotlib’s `contourf`

If you’re looking to spice up your contour plots in Matplotlib, modifying the colormap is a great way to do this! Here’s a simple guide to help you along the way.

Using `ListedColormap`

You can create a custom colormap using `ListedColormap`. Here’s how:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

# Example data
x = np.linspace(-3.0, 3.0, 100)
y = np.linspace(-3.0, 3.0, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Create a custom colormap
custom_colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']
cmap = ListedColormap(custom_colors)

# Plot using contourf
plt.contourf(X, Y, Z, cmap=cmap)
plt.colorbar()
plt.title('Custom Colormap with `ListedColormap`')
plt.show()

Using `LinearSegmentedColormap`

If you want a gradient between colors, use `LinearSegmentedColormap`. Here’s an example:

from matplotlib.colors import LinearSegmentedColormap

# Define a linear gradient colormap
colors = ['blue', 'green', 'yellow', 'red']
cmap = LinearSegmentedColormap.from_list('my_gradient', colors)

# Plot using contourf
plt.contourf(X, Y, Z, cmap=cmap)
plt.colorbar()
plt.title('Gradient Colormap with `LinearSegmentedColormap`')
plt.show()

Tips for Choosing Colors

When choosing colors, it’s a good idea to think about:

• Color Blindness: Use color palettes that are friendly to color-blind viewers. Tools like ColorBrewer can help you find suitable options.
• Printing: Ensure that your colors are distinguishable when printed in black and white.
• Contrast: Make sure your colors have enough contrast so that differences in data can be seen clearly.

Final Tips

Don’t hesitate to play around with different colors! Visualizations are all about communication, so choose a scheme that enhances your story. And remember, seeing examples really helps—so get experimenting!