pyplot: really slow creating heatmaps
I have a loop that executes the body about 200 times. In each loop iteration, it does a sophisticated calculation, and then as debugging, I wish to produce a heatmap of a NxM matrix. But, generating this heatmap is unbearably slow and significantly slow downs an already slow algorithm.
My code is along the lines:
import numpy
import matplotlib.pyplot as plt
for i in range(200):
matrix = complex_calculation()
plt.set_cmap("gray")
plt.imshow(matrix)
开发者_如何转开发 plt.savefig("frame{0}.png".format(i))
The matrix, from numpy, is not huge --- 300 x 600 of doubles. Even if I do not save the figure and instead update an on-screen plot, it's even slower.
Surely I must be abusing pyplot. (Matlab can do this, no problem.) How do I speed this up?
Try putting plt.clf()
in the loop to clear the current figure:
for i in range(200):
matrix = complex_calculation()
plt.set_cmap("gray")
plt.imshow(matrix)
plt.savefig("frame{0}.png".format(i))
plt.clf()
If you don't do this, the loop slows down as the machine struggles to allocate more and more memory for the figure.
I think this is a bit faster:
import matplotlib.pyplot as plt
from matplotlib import cm
fig = plt.figure()
ax = fig.add_axes([0.1,0.1,0.8,0.8])
for i in range(200):
matrix = complex_calculation()
ax.imshow(matrix, cmap=cm.gray)
fig.savefig("frame{0}.png".format(i))
plt.imshow
calls gca
which calls gcf
which checks to see if there is a figure; if not, it creates one. By manually instantiating the figure first, you do not need to do all that.
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