python - matplotlib very slow. Is it normal?

Question

I am creating a couple of pdf plots with matplotlib which is composed of 400 subplots. Each one has only 5 data points. It takes 420 s on a good computer to save 5 pdf picture. Is there any way to optimize the code or it is just normal for matplotlib?

Portion of code for plotting:

plot_cnt = 1
for k in np.arange(K_min, K_max + 1):
    for l in np.arange(L_min, L_max + 1):
        ax = plt.subplot(grid[0], grid[1], plot_cnt)
        plot_cnt += 1
        plt.setp(ax, 'frame_on', False)
        ax.set_ylim([-0.1, 1.1])
        ax.set_xlabel('K={},L={}'.format(k, l), size=3)
        ax.set_xlim([-0.1, 4.1])
        ax.set_xticks([])
        ax.set_yticks([])
        ax.grid('off')
        ax.plot(np.arange(5), (data['S1']['Azimuth'][:, k - 1, l + offset_l] + \
                data['S1']['Delta Speed'][:, k - 1, l + offset_l] + \
                data['S1']['Speed'][:, k - 1, l + offset_l]) / 3,
                'r-o', ms=1, mew=0, mfc='r')
        ax.plot(np.arange(5), data['S2'][case][:, k - 1, l + offset_l],
                'b-o', ms=1, mew=0, mfc='b')
plt.savefig(os.path.join(os.getcwd(), 'plot-average.pdf'))
plt.clf()
print 'Final plot created.'

Final Picture: enter image description here

400 subplots seems a lot for me. Even if you don't have a lot data, matplotlib may no be optimized to display this kind of grids. — Simon Bergot
@Simon, is it possible to plot using a single big subplot? Then each five should be connected. Any idea? — rowman
I don't have the right environment to run the tests, but there are a few things you can do. The first step is to run the profiler on your code (see here for more information). With this, you can locate where the time is spent. The two things you could try is to find some resource to release at each step, or to create the plots in several processes using multiprocessing. If I find the time I will try this, but at this stage I cannot guarantee anything — Simon Bergot

tacaswell tacaswell · Accepted Answer · 2012-10-25T03:11:04

Building off of what @rowman said, you can do this all in one axes (as you turn off all the ticks etc). Something like:

K_max = 20
K_min = 0
L_max = 20
L_min = 0
ax = plt.subplot(111)
x_offset = 7 # tune these
y_offset = 7 # tune these
plt.setp(ax, 'frame_on', False)
ax.set_ylim([0, (K_max-K_min +1)*y_offset ])
ax.set_xlim([0, (L_max - L_min+1)*x_offset])
ax.set_xticks([])
ax.set_yticks([])
ax.grid('off')



for k in np.arange(K_min, K_max + 1):
    for l in np.arange(L_min, L_max + 1):
        ax.plot(np.arange(5) + l*x_offset, 5+rand(5) + k*y_offset,
                'r-o', ms=1, mew=0, mfc='r')
        ax.plot(np.arange(5) + l*x_offset, 3+rand(5) + k*y_offset,
                'b-o', ms=1, mew=0, mfc='b')
        ax.annotate('K={},L={}'.format(k, l), (2.5+ (k)*x_offset,l*y_offset), size=3,ha='center')
plt.savefig(os.path.join(os.getcwd(), 'plot-average.pdf'))

print 'Final plot created.'

Runs in about a second or two. I think all of the time is spent setting up the axes object which are rather complex internally. output with fake data

python - matplotlib very slow. Is it normal?

1 Answers