Matplotlib 的快速实时绘图/PyPlot

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最佳答案

首先，问题中提交的代码在我的机器上运行，速度为7 fps，后端是 QT4Agg。

现在，正如在许多文章中所建议的，像给你或给你，使用 blit可能是一种选择。虽然这篇文章提到 blit 会导致很强的内存泄漏，但是我没有注意到这一点。

我已经修改了你的代码一点点，并比较了帧速率和没有使用位点。下面的代码给出

没有闪光灯的情况下，每秒28格
每秒175格

密码:

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




def live_update_demo(blit = False):
x = np.linspace(0,50., num=100)
X,Y = np.meshgrid(x,x)
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)


img = ax1.imshow(X, vmin=-1, vmax=1, interpolation="None", cmap="RdBu")




line, = ax2.plot([], lw=3)
text = ax2.text(0.8,0.5, "")


ax2.set_xlim(x.min(), x.max())
ax2.set_ylim([-1.1, 1.1])


fig.canvas.draw()   # note that the first draw comes before setting data




if blit:
# cache the background
axbackground = fig.canvas.copy_from_bbox(ax1.bbox)
ax2background = fig.canvas.copy_from_bbox(ax2.bbox)


plt.show(block=False)




t_start = time.time()
k=0.


for i in np.arange(1000):
img.set_data(np.sin(X/3.+k)*np.cos(Y/3.+k))
line.set_data(x, np.sin(x/3.+k))
tx = 'Mean Frame Rate:\n {fps:.3f}FPS'.format(fps= ((i+1) / (time.time() - t_start)) )
text.set_text(tx)
#print tx
k+=0.11
if blit:
# restore background
fig.canvas.restore_region(axbackground)
fig.canvas.restore_region(ax2background)


# redraw just the points
ax1.draw_artist(img)
ax2.draw_artist(line)
ax2.draw_artist(text)


# fill in the axes rectangle
fig.canvas.blit(ax1.bbox)
fig.canvas.blit(ax2.bbox)


# in this post http://bastibe.de/2013-05-30-speeding-up-matplotlib.html
# it is mentionned that blit causes strong memory leakage.
# however, I did not observe that.


else:
# redraw everything
fig.canvas.draw()


fig.canvas.flush_events()
#alternatively you could use
#plt.pause(0.000000000001)
# however plt.pause calls canvas.draw(), as can be read here:
#http://bastibe.de/2013-05-30-speeding-up-matplotlib.html




live_update_demo(True)   # 175 fps
#live_update_demo(False) # 28 fps

更新:
为了更快地绘图，可以考虑使用图表。
正如 Pyqtgraph 文档所说: “对于绘图，pyqtgraph 不像 matplotlib 那样完整/成熟，但运行速度要快得多。”

我将上面的示例移植到 pyqtgraph。虽然它看起来有点丑，但是它在我的机器上可以运行250fps。

总结一下,

Matplotlib (无 blitting) : 28 fps
Matplotlib (带 blitting) : 175 fps
图片来源: 250帧/秒

Pyqtgraph 代码:

import sys
import time
from pyqtgraph.Qt import QtCore, QtGui
import numpy as np
import pyqtgraph as pg




class App(QtGui.QMainWindow):
def __init__(self, parent=None):
super(App, self).__init__(parent)


#### Create Gui Elements ###########
self.mainbox = QtGui.QWidget()
self.setCentralWidget(self.mainbox)
self.mainbox.setLayout(QtGui.QVBoxLayout())


self.canvas = pg.GraphicsLayoutWidget()
self.mainbox.layout().addWidget(self.canvas)


self.label = QtGui.QLabel()
self.mainbox.layout().addWidget(self.label)


self.view = self.canvas.addViewBox()
self.view.setAspectLocked(True)
self.view.setRange(QtCore.QRectF(0,0, 100, 100))


#  image plot
self.img = pg.ImageItem(border='w')
self.view.addItem(self.img)


self.canvas.nextRow()
#  line plot
self.otherplot = self.canvas.addPlot()
self.h2 = self.otherplot.plot(pen='y')




#### Set Data  #####################


self.x = np.linspace(0,50., num=100)
self.X,self.Y = np.meshgrid(self.x,self.x)


self.counter = 0
self.fps = 0.
self.lastupdate = time.time()


#### Start  #####################
self._update()


def _update(self):


self.data = np.sin(self.X/3.+self.counter/9.)*np.cos(self.Y/3.+self.counter/9.)
self.ydata = np.sin(self.x/3.+ self.counter/9.)


self.img.setImage(self.data)
self.h2.setData(self.ydata)


now = time.time()
dt = (now-self.lastupdate)
if dt <= 0:
dt = 0.000000000001
fps2 = 1.0 / dt
self.lastupdate = now
self.fps = self.fps * 0.9 + fps2 * 0.1
tx = 'Mean Frame Rate:  {fps:.3f} FPS'.format(fps=self.fps )
self.label.setText(tx)
QtCore.QTimer.singleShot(1, self._update)
self.counter += 1




if __name__ == '__main__':


app = QtGui.QApplication(sys.argv)
thisapp = App()
thisapp.show()
sys.exit(app.exec_())

Matplotlib 的快速实时绘图/PyPlot

结论