Rasterization for complex plots in Matplotlib

Introduction

Rasterization helps make complex plots faster and smaller by turning detailed parts into images.

When your plot has many points or lines and is slow to draw.

When saving plots as vector files that become very large.

When you want to keep text sharp but simplify complex shapes.

When sharing plots that need to load quickly on websites.

When combining detailed images with simple graphics in one plot.

Syntax

Matplotlib

plot_object.set_rasterized(True)

You apply rasterization to parts of the plot like lines or collections.

This works well when saving to vector formats like PDF or SVG.

Examples

This example rasterizes the line plot to speed up saving as PDF.

Matplotlib

import matplotlib.pyplot as plt
x = range(1000)
y = [i**0.5 for i in x]
plt.plot(x, y, rasterized=True)
plt.savefig('plot.pdf')

Rasterizes only the scatter points in the SVG file.

Matplotlib

fig, ax = plt.subplots()
scatter = ax.scatter(x, y)
scatter.set_rasterized(True)
plt.savefig('scatter.svg')

Sample Program

This code creates a noisy sine wave with many points and rasterizes the line to make saving faster and file smaller.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 10000)
y = np.sin(x) + np.random.normal(0, 0.1, x.size)

fig, ax = plt.subplots()
line, = ax.plot(x, y, label='Noisy sine wave')
line.set_rasterized(True)

ax.set_title('Rasterized Line Plot Example')
ax.legend()
plt.savefig('rasterized_plot.pdf')
print('Plot saved as rasterized_plot.pdf')

Important Notes

Rasterization only affects vector output formats like PDF and SVG, not PNG or JPG.

Use rasterization on complex parts to keep text and labels sharp.

Too much rasterization can reduce quality, so use it wisely.

Summary

Rasterization turns complex plot parts into images to improve speed and file size.

Apply rasterization to lines, scatter points, or collections in matplotlib.

Best used when saving vector files with many details.