How to plot contourf and log color scale in Matplotlib?

In Matplotlib, you can create contour plots with logarithmic color scaling using contourf() combined with LogLocator(). This is particularly useful when your data spans several orders of magnitude.

Basic Setup

First, let's understand the key components needed for logarithmic contour plots ?

• Use contourf() method for filled contour plots
• Apply ticker.LogLocator() for logarithmic color scale
• Handle negative or zero values with masked arrays
• Add a colorbar to visualize the scale

Example

Here's how to create a contour plot with logarithmic color scaling ?

import matplotlib.pyplot as plt
import numpy as np
from numpy import ma
from matplotlib import ticker, cm

# Set figure properties
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True

# Create sample data
N = 100
x = np.linspace(-3.0, 3.0, N)
y = np.linspace(-2.0, 2.0, N)

X, Y = np.meshgrid(x, y)

# Generate exponential data with different scales
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)

# Combine data with large range
z = Z1 + 50 * Z2

# Mask negative/zero values for log scale
z[:5, :5] = -1
z = ma.masked_where(z <= 0, z)

# Create contour plot with log scale
fig, ax = plt.subplots()
cs = ax.contourf(X, Y, z, locator=ticker.LogLocator(), cmap=cm.PuBu_r)
cbar = fig.colorbar(cs)

plt.show()

Key Components

Understanding the important parameters ?

How It Works

The logarithmic scaling works by ?

• LogLocator() automatically determines appropriate logarithmic intervals
• Masked arrays exclude negative or zero values that can't be log-scaled
• Colorbar displays the logarithmic scale for easy interpretation
• Color mapping distributes colors evenly across log intervals

Conclusion

Use contourf() with ticker.LogLocator() to create contour plots with logarithmic color scaling. Always mask negative or zero values when using log scales, and include a colorbar for proper interpretation.