How to Speed Up Matplotlib Plotting to Enhance Performance?

Why is Matplotlib So Slow?

When evaluating Python plotting libraries, it's important to consider performance. Matplotlib, a widely used library, can seem sluggish, raising questions about speeding it up or exploring alternative options. Let's dive into the issue and explore possible solutions.

The provided example showcases a plot with multiple subplots and data updates. With Matplotlib, this process involves redrawing everything, including axes boundaries and tick labels, resulting in slow performance.

Understanding the Bottlenecks

Two key factors contribute to the slowness:

1. Excessive Redrawing: Matplotlib's fig.canvas.draw() function redraws the entire figure, even when only a small portion needs updating.
2. Abundant Tick Labels: A large number of tick labels and subplots can significantly burden the drawing process.

Optimizing with Blitting

To address these bottlenecks, consider using blitting. Blitting involves updating only specific parts of the figure, reducing the rendering time. However, backend-specific code is needed for efficient implementation, which may require embedding Matplotlib plots within a GUI toolkit.

GUI-Neutral Blitting

A GUI-neutral blitting technique can provide reasonable performance without backend dependency:

1. Capture Background: Before animation, capture the background of each subplot to restore later.
2. Update and Draw: For each frame, update the data and artist of the lines, restoring the background and blitting the updated portion.
3. Avoid Redraw: Use fig.canvas.blit(ax.bbox) instead of fig.canvas.draw() to update only the necessary area.

Example Implementation:

<code class="python">import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 2*np.pi, 0.1)
y = np.sin(x)

fig, axes = plt.subplots(nrows=6)

styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'c-']
def plot(ax, style):
    return ax.plot(x, y, style, animated=True)[0]

lines = [plot(ax, style) for ax, style in zip(axes, styles)]

# Capture Background
backgrounds = [fig.canvas.copy_from_bbox(ax.bbox) for ax in axes]

for i in xrange(1, 2000):
    for j, (line, ax, background) in enumerate(zip(lines, axes, backgrounds), start=1):
        fig.canvas.restore_region(background)
        line.set_ydata(np.sin(j*x + i/10.0))
        ax.draw_artist(line)
        fig.canvas.blit(ax.bbox)</code>

Animation Module

Recent Matplotlib versions include an animations module, which simplifies blitting:

<code class="python">import matplotlib.pyplot as plt
import matplotlib.animation as animation

def animate(i):
    for j, line in enumerate(lines, start=1):
        line.set_ydata(np.sin(j*x + i/10.0))

ani = animation.FuncAnimation(fig, animate, xrange(1, 200), interval=0, blit=True)</code>

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