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Improving Plot Performance with Matplotlib

Matplotlib is a powerful plotting library for Python, but when dealing with large datasets or complex visualizations, performance can become an issue. Here are some strategies to improve plot performance in Matplotlib.

Using Agg Backend

The Agg backend is a non-interactive backend that renders plots to a raster format, which can be faster than interactive backends.


import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

x = range(10000)
y = [i**2 for i in x]
plt.plot(x, y)
plt.savefig('plot.png')

By using the 'Agg' backend, the plot is rendered to a file without opening a window, which can significantly reduce rendering time.

Reducing Data Points

When plotting very large datasets, consider reducing the number of data points to speed up rendering.


import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100000)
y = np.sin(x)

# Reduce the number of points
x_reduced = x[::100]
y_reduced = y[::100]

plt.plot(x_reduced, y_reduced)
plt.show()

By slicing the arrays, we reduce the number of points plotted, improving performance while maintaining the overall trend.

Using Line Collections

Line collections can be used to efficiently plot many lines at once, which is faster than plotting them individually.


import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np

x = np.arange(1000)
y = np.random.rand(1000)

segments = [((i, y[i]), (i+1, y[i+1])) for i in range(len(x)-1)]
lc = LineCollection(segments, color='blue')

fig, ax = plt.subplots()
ax.add_collection(lc)
ax.set_xlim(x.min(), x.max())
ax.set_ylim(y.min(), y.max())
plt.show()

Using LineCollection allows for efficient rendering of multiple line segments, which is beneficial for performance.

Avoiding Repeated Plotting

To improve performance, avoid plotting the same data repeatedly within loops or functions.


import matplotlib.pyplot as plt

x = range(100)
y = [i**2 for i in x]

# Plot once outside the loop
plt.plot(x, y)
for _ in range(10):
    plt.pause(0.1)  # Simulate some work

plt.show()

By plotting once and using the pause method, you can update the plot without redrawing it completely, saving time.

Using Blitting

Blitting is a technique that can be used to update only a portion of the plot, making animations and updates more efficient.


import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)

fig, ax = plt.subplots()
line, = ax.plot(x, y)

plt.ion()  # Turn on interactive mode
plt.show()

for phase in np.linspace(0, 2*np.pi, 100):
    line.set_ydata(np.sin(x + phase))
    plt.draw()
    plt.pause(0.1)

Blitting updates only the parts of the figure that have changed, which reduces the time it takes to update the plot.