Aggregate several AxesSubplot after multiprocessing to draw a matplotlib figure

问题

I'm trying to create a subplot of several pcolormesh graphs that would be like this one:

I achieve to do this by computing on one process:

import matplotlib.pyplot as plt
import numpy as np

def create_spectrogram(data, x, y, ax):
    ax.pcolormesh(x, y, data)


def do_it_simple():
    size = 10
    data = np.arange(size * size).reshape((size, size))

    y = np.arange(0, 10)
    x = np.arange(0, 10)

    fig, axes = plt.subplots(nrows=2, ncols=5)
    axes_list = [item for sublist in axes for item in sublist]

    for ax in axes_list:
        create_spectrogram(data, x, y, ax=ax)

    plt.savefig("test_simple.png")

def main():
    do_it_simple()

Assuming my data array is really big, there are two very slow steps:

ax.pcolormesh(x, y, data)
plt.savefig()

I was wondering if I could use multiprocessing to parallelize the pcolormesh process. Now I have:

import matplotlib.pyplot as plt
import numpy as np
from multiprocessing import Pool
from functools import partial

def multiprocesser_handler(data_set, ax):
    (data, x, y) = data_set
    ax.pcolormesh(x, y, data)
    return ax


def do_it_multiprocessed():
    size = 10
    data = np.arange(size * size).reshape((size, size))

    y = np.arange(0, 10)
    x = np.arange(0, 10)

    fig, axes = plt.subplots(nrows=2, ncols=5)
    axes_list = [item for sublist in axes for item in sublist]

    data_set = (data, x, y)

    func = partial(multiprocesser_handler, data_set)

    with Pool(3) as p:
        axes_list = p.map(func, axes_list)

    print("DEBUG1:", axes_list)
    for ax in axes_list:
        print("DEBUG2:",ax.get_children())

    plt.savefig("test_multiprocess.png")

def main():
    do_it_multiprocessed()

The results are:

DEBUG1 [<matplotlib.axes._subplots.AxesSubplot object at 0x7fa6335fc2e8>, <matplotlib.axes._subplots.AxesSubplot object at 0x7fa633524080>, ....

DEBUG2 [<matplotlib.collections.QuadMesh object at 0x7fa63129c278>, <matplotlib.spines.Spine object at 0x7fa6312f8dd8>, <matplotlib.spines.Spine object at 0x7fa63130d4e0>, <matplotlib.spines.Spine object at 0x7fa63130d5f8>, <matplotlib.spines.Spine object at 0x7fa631295fd0>, <matplotlib.axis.XAxis object at 0x7fa63130d668>, <matplotlib.axis.YAxis object at 0x7fa6312f8e48>, Text(0.5, 1.0, '2005'), Text(0.0, 1.0, ''), Text(1.0, 1.0, ''), <matplotlib.patches.Rectangle object at 0x7fa63129cb38>]
DEBUG2 [<matplotlib.collections.QuadMesh object at 0x7fa6334fdb70>, <matplotlib.spines.Spine object at 0x7fa633260438>, <matplotlib.spines.Spine object at 0x7fa63336f128>, <matplotlib.spines.Spine object at 0x7fa63336f1d0>, <matplotlib.spines.Spine object at 0x7fa6334fd908>, <matplotlib.axis.XAxis object at 0x7fa63336f550>, <matplotlib.axis.YAxis object at 0x7fa633260b00>, Text(0.5, 1.0, '2006'), Text(0.0, 1.0, ''), Text(1.0, 1.0, ''), <matplotlib.patches.Rectangle object at 0x7fa63352f470>]
...

It seems like I achieved to parallelize correctly the pcolormesh computation process (the result contains matplotlib.collections.QuadMesh objects). But when I open the result figure I get this:

I guess that I do not merge all the AxesSubplot correctly, any idea to do this ?

I also tried to use imshow() that is known to be more efficient than pcolormesh, but I had some issues that are described here: Change y log scale imshow()

Thanks in advance !

来源：https://stackoverflow.com/questions/57269797/aggregate-several-axessubplot-after-multiprocessing-to-draw-a-matplotlib-figure

标签

python

matplotlib

python-multiprocessing

subplot