How to speed up matplotlib when plotting and saving lots of figures?

Question

I am processing observing data from many antenna baseline. Currently what I am working is to plot ~ 40 figures, each of which has 4x5 subplot region. I found it slow when pl

Answer 1

I've modified your code to make it runnable:

import numpy as np
import matplotlib.pyplot as plt
import time

PLT_PAGE_NUM = 39 # default is 39
SUB_PLT_NUM = 20 # default is 20
TIME_LENGTH = 1000

xcor_real_arr = np.random.random((SUB_PLT_NUM*PLT_PAGE_NUM,TIME_LENGTH))
xcor_imag_arr = np.random.random((SUB_PLT_NUM*PLT_PAGE_NUM,TIME_LENGTH))
for pp in xrange(0,PLT_PAGE_NUM):

    plt.figure(figsize=(20,12))

    start_time = time.time() 
    for kk in xrange(0,SUB_PLT_NUM):
        plt.subplot(5,4,kk+1)
        plt.plot(np.arange(0,TIME_LENGTH), xcor_real_arr[SUB_PLT_NUM*pp+kk,0:],'r-',
                 range(0,TIME_LENGTH), xcor_imag_arr[SUB_PLT_NUM*pp+kk,0:],'b-')
        plt.title('XCOR of '+ str(SUB_PLT_NUM*pp+kk), color='k') 

    plt.savefig('test_imag_real'+str(pp)+'.png',format='png',dpi=100)
    print 'Fig-'+str(pp)+' has been saved'
    print "Excution time:", time.time()-start_time

On my machine, each figure takes about 3 seconds:

Fig-0 has been saved
Excution time: 3.01798415184
Fig-1 has been saved
Excution time: 3.08960294724
Fig-2 has been saved
Excution time: 2.9629740715

Using ideas from the Matplotlib Animations Cookbook (and also demonstrated by Joe Kington, here), we can speed this up by about 33% (1 second per figure) by reusing the same axes and simply redefining the y-data for each plot:

import numpy as np
import matplotlib.pyplot as plt
import time

PLT_PAGE_NUM = 39 # default is 39
SUB_PLT_NUM = 20 # default is 20
TIME_LENGTH = 1000

xcor_real_arr = np.random.random((SUB_PLT_NUM*PLT_PAGE_NUM,TIME_LENGTH))
xcor_imag_arr = np.random.random((SUB_PLT_NUM*PLT_PAGE_NUM,TIME_LENGTH))
plt.figure(figsize=(20,12))

ax = {}
line1 = {}
line2 = {}

for pp in xrange(0,PLT_PAGE_NUM):
    start_time = time.time() 
    for kk in xrange(0,SUB_PLT_NUM):
        if pp == 0:
            ax[kk] = plt.subplot(5,4,kk+1)
            line1[kk], line2[kk] = ax[kk].plot(np.arange(0,TIME_LENGTH),
                                   xcor_real_arr[SUB_PLT_NUM*pp+kk,0:],'r-',
                                   range(0,TIME_LENGTH),
                                   xcor_imag_arr[SUB_PLT_NUM*pp+kk,0:],'b-')
        else:
            line1[kk].set_ydata(xcor_real_arr[SUB_PLT_NUM*pp+kk,0:])
            line2[kk].set_ydata(xcor_imag_arr[SUB_PLT_NUM*pp+kk,0:])
        plt.title('XCOR of '+ str(SUB_PLT_NUM*pp+kk), color='k') 

    plt.savefig('test_imag_real'+str(pp)+'.png',format='png',dpi=100)
    print 'Fig-'+str(pp)+' has been saved'
    print "Excution time:", time.time()-start_time

which yields these execution times:

Fig-0 has been saved
Excution time: 3.0408449173
Fig-1 has been saved
Excution time: 2.05084013939
Fig-2 has been saved
Excution time: 2.01951694489

(The first figure still takes 3 seconds to set up the initial plots. It is on subsequent figures where we can save some time.)