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Description
Bug summary
For relatively simple plotting examples the plt.clf() command can take over 30% of the calculation time. One would expect that a clear command is relatively simple and takes a limited amount of time
Code for reproduction
import numpy as np
import time
import matplotlib
import matplotlib.pyplot as plt
print(matplotlib.__version__)
def profile_expression(expression):
import cProfile
import tempfile
import os
import subprocess
tmpdir = tempfile.mkdtemp()
statsfile = os.path.join(tmpdir, "profile_expression_stats")
print("profile_expression: running")
t0 = time.perf_counter()
cProfile.run(expression, statsfile)
dt = time.perf_counter() - t0
print(f"profiling: {dt:.2f} [s]")
r = subprocess.Popen(["snakeviz", statsfile])
return r
x=np.linspace(0, 10, 1000)
y=np.linspace(0, 20, 1000)**0.1
def go():
for ii in range(5):
plt.figure(100);
plt.clf()
plt.plot(x, y, '.', label='n')
plt.plot(x, y+x, '.-', label='m')
plt.xlabel('dsf')
plt.title('sdfs')
plt.legend()
plt.draw()
if __name__=='__main__':
profile_expression('go()')Actual outcome
The output of the minimal example (which requires https://github.com/jiffyclub/snakeviz to be installed) is a profiling graph of the code executed in the minimal example:
Expected outcome
The plt.clf() should be very fast compared to the actual plotting. Going into the details of the profiling results shows a large amount of time being spend in axis.reset_ticks.
Additional information
No response
Operating system
Windows
Matplotlib Version
3.6.0.dev3058+g87197156eb
Matplotlib Backend
Qt5Agg
Python version
3.10.0
Jupyter version
No response
Installation
git checkout