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:
It's your bottleneck. In your case, you don't need to re-draw things like the axes boundaries, tick labels, etc. 2) In your case, there are a lot of subplots with a lot of tick labels. These take a long time to draw.
Short answer is no, there is currently no backend to matplotlib that supports gpu rendering. HOWEVER there are other plotting packages that do and may suit your needs.
Plotly has several advantages over matplotlib. One of the main advantages is that only a few lines of codes are necessary to create aesthetically pleasing, interactive plots. The interactivity also offers a number of advantages over static matplotlib plots: Saves time when initially exploring your dataset.
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.
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