How to make the Parula colormap in Matplotlib?

The Parula colormap is MATLAB's default colormap known for its perceptually uniform color transitions. In Matplotlib, we can create a custom Parula colormap using LinearSegmentedColormap with the official Parula color values.

Creating the Parula Colormap

We'll use the exact RGB values from MATLAB's Parula colormap to ensure authenticity −

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

# Official Parula colormap RGB values
parula_colors = [
    (0.2081, 0.1663, 0.5292),
    (0.2116, 0.1898, 0.5777),
    (0.2123, 0.2138, 0.6270),
    (0.2081, 0.2386, 0.6771),
    (0.1959, 0.2645, 0.7279),
    (0.1707, 0.2919, 0.7792),
    (0.1253, 0.3242, 0.8303),
    (0.0591, 0.3598, 0.8683),
    (0.0117, 0.3875, 0.8819),
    (0.0060, 0.4086, 0.8828),
    (0.0165, 0.4266, 0.8786),
    (0.0329, 0.4430, 0.8720),
    (0.0498, 0.4586, 0.8641),
    (0.0629, 0.4737, 0.8554),
    (0.0723, 0.4887, 0.8467),
    (0.0779, 0.5040, 0.8384),
    (0.0793, 0.5200, 0.8312),
    (0.0749, 0.5375, 0.8263),
    (0.0641, 0.5570, 0.8240),
    (0.0488, 0.5772, 0.8228),
    (0.0343, 0.5966, 0.8199),
    (0.0265, 0.6137, 0.8135),
    (0.0239, 0.6287, 0.8038),
    (0.0231, 0.6418, 0.7913),
    (0.0228, 0.6535, 0.7768),
    (0.0267, 0.6642, 0.7607),
    (0.0384, 0.6743, 0.7436),
    (0.0590, 0.6838, 0.7254),
    (0.0843, 0.6928, 0.7062),
    (0.1133, 0.7015, 0.6859),
    (0.1453, 0.7098, 0.6646),
    (0.1801, 0.7177, 0.6424),
    (0.2178, 0.7250, 0.6193),
    (0.2586, 0.7317, 0.5954),
    (0.3022, 0.7376, 0.5712),
    (0.3482, 0.7424, 0.5473),
    (0.3953, 0.7459, 0.5244),
    (0.4420, 0.7481, 0.5033),
    (0.4871, 0.7491, 0.4840),
    (0.5300, 0.7491, 0.4661),
    (0.5709, 0.7485, 0.4494),
    (0.6099, 0.7473, 0.4337),
    (0.6473, 0.7456, 0.4188),
    (0.6834, 0.7435, 0.4044),
    (0.7184, 0.7411, 0.3905),
    (0.7525, 0.7384, 0.3768),
    (0.7858, 0.7356, 0.3633),
    (0.8185, 0.7327, 0.3498),
    (0.8507, 0.7299, 0.3360),
    (0.8824, 0.7274, 0.3217),
    (0.9139, 0.7258, 0.3063),
    (0.9450, 0.7261, 0.2886),
    (0.9739, 0.7314, 0.2666),
    (0.9938, 0.7455, 0.2403),
    (0.9990, 0.7653, 0.2164),
    (0.9955, 0.7861, 0.1967),
    (0.9880, 0.8066, 0.1794),
    (0.9789, 0.8271, 0.1633),
    (0.9697, 0.8481, 0.1475),
    (0.9626, 0.8705, 0.1309),
    (0.9589, 0.8949, 0.1132),
    (0.9598, 0.9218, 0.0948),
    (0.9661, 0.9514, 0.0755),
    (0.9763, 0.9831, 0.0538)
]

# Create the colormap
parula_cmap = LinearSegmentedColormap.from_list('parula', parula_colors, N=256)

# Test the colormap
plt.figure(figsize=(8, 6))
data = np.random.rand(10, 10)
plt.imshow(data, cmap=parula_cmap)
plt.colorbar()
plt.title('Test Image with Parula Colormap')
plt.show()

Displaying Colormap Gradient

To visualize the colormap gradient itself ?

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

# Create Parula colormap (shortened version for demo)
parula_colors = [
    '#352a87', '#0f5cdd', '#1481d6', '#06a7c6', '#38b99e',
    '#92bf73', '#d9ba56', '#fcce2e', '#f9fb0e'
]

parula_cmap = LinearSegmentedColormap.from_list('parula', parula_colors)

# Display the colormap gradient
gradient = np.linspace(0, 1, 256).reshape(1, -1)
plt.figure(figsize=(10, 2))
plt.imshow(gradient, aspect='auto', cmap=parula_cmap)
plt.xlim(0, 256)
plt.yticks([])
plt.title('Parula Colormap Gradient')
plt.xlabel('Color Index')
plt.show()

Using Parula for Data Visualization

Here's a practical example using Parula for scientific data visualization ?

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

# Create simplified Parula colormap
parula_colors = ['#352a87', '#0f5cdd', '#06a7c6', '#38b99e', 
                 '#92bf73', '#d9ba56', '#fcce2e', '#f9fb0e']
parula_cmap = LinearSegmentedColormap.from_list('parula', parula_colors)

# Generate sample 2D data
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# Create visualization
plt.figure(figsize=(8, 6))
plt.contourf(X, Y, Z, levels=20, cmap=parula_cmap)
plt.colorbar(label='Value')
plt.title('2D Function Visualization with Parula Colormap')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

Key Features of Parula Colormap

• Perceptually uniform − Equal changes in data correspond to equal perceived changes in color
• Colorblind friendly − Accessible to users with color vision deficiencies
• Print friendly − Maintains clarity when printed in grayscale
• Wide dynamic range − Good contrast across the entire spectrum

Conclusion

The Parula colormap provides excellent perceptual uniformity and accessibility for scientific visualizations. Create it using LinearSegmentedColormap.from_list() with the official RGB values for authentic results in your Matplotlib plots.