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By: a guest on May 6th, 2012  |  syntax: None  |  size: 2.92 KB  |  hits: 21  |  expires: Never
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  1. why is plotting with Matplotlib so slow?
  2. from pylab import *
  3. import time
  4.  
  5. ion()
  6. fig = figure()
  7. ax1 = fig.add_subplot(611)
  8. ax2 = fig.add_subplot(612)
  9. ax3 = fig.add_subplot(613)
  10. ax4 = fig.add_subplot(614)
  11. ax5 = fig.add_subplot(615)
  12. ax6 = fig.add_subplot(616)
  13.  
  14. x = arange(0,2*pi,0.01)
  15. y = sin(x)
  16. line1, = ax1.plot(x, y, 'r-')
  17. line2, = ax2.plot(x, y, 'g-')
  18. line3, = ax3.plot(x, y, 'y-')
  19. line4, = ax4.plot(x, y, 'm-')
  20. line5, = ax5.plot(x, y, 'k-')
  21. line6, = ax6.plot(x, y, 'p-')
  22.  
  23. # turn off interactive plotting - speeds things up by 1 Frame / second
  24. plt.ioff()
  25.  
  26.  
  27. tstart = time.time()               # for profiling
  28. for i in arange(1, 200):
  29.     line1.set_ydata(sin(x+i/10.0))  # update the data
  30.     line2.set_ydata(sin(2*x+i/10.0))
  31.     line3.set_ydata(sin(3*x+i/10.0))
  32.     line4.set_ydata(sin(4*x+i/10.0))
  33.     line5.set_ydata(sin(5*x+i/10.0))
  34.     line6.set_ydata(sin(6*x+i/10.0))
  35.     draw()                         # redraw the canvas
  36.  
  37. print 'FPS:' , 200/(time.time()-tstart)
  38.        
  39. import matplotlib.pyplot as plt
  40. import numpy as np
  41. import time
  42.  
  43. x = np.arange(0, 2*np.pi, 0.01)
  44. y = np.sin(x)
  45.  
  46. fig, axes = plt.subplots(nrows=6)
  47. styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'c-']
  48. lines = [ax.plot(x, y, style)[0] for ax, style in zip(axes, styles)]
  49.  
  50. fig.show()
  51.  
  52. tstart = time.time()
  53. for i in xrange(1, 20):
  54.     for j, line in enumerate(lines, start=1):
  55.         line.set_ydata(np.sin(j*x + i/10.0))
  56.     fig.canvas.draw()
  57.  
  58. print 'FPS:' , 20/(time.time()-tstart)
  59.        
  60. import matplotlib.pyplot as plt
  61. import numpy as np
  62. import time
  63.  
  64. x = np.arange(0, 2*np.pi, 0.1)
  65. y = np.sin(x)
  66.  
  67. fig, axes = plt.subplots(nrows=6)
  68.  
  69. styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'c-']
  70. def plot(ax, style):
  71.     return ax.plot(x, y, style, animated=True)[0]
  72. lines = [plot(ax, style) for ax, style in zip(axes, styles)]
  73.  
  74. # Let's capture the background of the figure
  75. backgrounds = [fig.canvas.copy_from_bbox(ax.bbox) for ax in axes]
  76.  
  77. fig.show()
  78.  
  79. # We need to draw the canvas before we start animating...
  80. fig.canvas.draw()
  81.  
  82. tstart = time.time()
  83. for i in xrange(1, 2000):
  84.     items = enumerate(zip(lines, axes, backgrounds), start=1)
  85.     for j, (line, ax, background) in items:
  86.         fig.canvas.restore_region(background)
  87.         line.set_ydata(np.sin(j*x + i/10.0))
  88.         ax.draw_artist(line)
  89.         fig.canvas.blit(ax.bbox)
  90.  
  91. print 'FPS:' , 2000/(time.time()-tstart)
  92.        
  93. import matplotlib.pyplot as plt
  94. import matplotlib.animation as animation
  95. import numpy as np
  96.  
  97. x = np.arange(0, 2*np.pi, 0.1)
  98. y = np.sin(x)
  99.  
  100. fig, axes = plt.subplots(nrows=6)
  101.  
  102. styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'c-']
  103. def plot(ax, style):
  104.     return ax.plot(x, y, style, animated=True)[0]
  105. lines = [plot(ax, style) for ax, style in zip(axes, styles)]
  106.  
  107. def animate(i):
  108.     for j, line in enumerate(lines, start=1):
  109.         line.set_ydata(np.sin(j*x + i/10.0))
  110.     return lines
  111.  
  112. # We'd normally specify a reasonable "interval" here...
  113. ani = animation.FuncAnimation(fig, animate, xrange(1, 200),
  114.                               interval=0, blit=True)
  115. plt.show()
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