多年来,我一直在努力在matplotlib中进行有效的现场策划,直到今天我仍然不满意。
我想要一个redraw_figure
函数来更新数字“实时”(代码运行),并且如果我在断点处停止,将显示最新的图。
以下是一些演示代码:
import time
from matplotlib import pyplot as plt
import numpy as np
def live_update_demo():
plt.subplot(2, 1, 1)
h1 = plt.imshow(np.random.randn(30, 30))
redraw_figure()
plt.subplot(2, 1, 2)
h2, = plt.plot(np.random.randn(50))
redraw_figure()
t_start = time.time()
for i in xrange(1000):
h1.set_data(np.random.randn(30, 30))
redraw_figure()
h2.set_ydata(np.random.randn(50))
redraw_figure()
print 'Mean Frame Rate: %.3gFPS' % ((i+1) / (time.time() - t_start))
def redraw_figure():
plt.draw()
plt.pause(0.00001)
live_update_demo()
在运行代码时,Plots应该会更新,我们应该在redraw_figure()
之后的任何断点处停止时看到最新的数据。问题是如何最好地实施redraw_figure()
在上面的实现(plt.draw(); plt.pause(0.00001)
)中,它可以工作,但速度非常慢(~3.7FPS)
我可以将其实现为:
def redraw_figure():
plt.gcf().canvas.flush_events()
plt.show(block=False)
并且运行速度更快(~11FPS),但是当您在断点处停止时(例如,如果我在t_start = ...
线上放置断点,第二个图未出现),则图不是最新的。 / p>
奇怪的是,实际上工作的是两次调出节目:
def redraw_figure():
plt.gcf().canvas.flush_events()
plt.show(block=False)
plt.show(block=False)
如果您在任何一条线上中断,那么它可以提供~11FPS并且可以将图表保持为数据。
现在我听说它已经弃用了“block”关键字。并且两次调用相同的函数似乎是一个奇怪的,可能是非便携式的hack。
那么我能在这个以合理的帧速率绘制的函数中放入什么,不是一个巨大的kludge,最好能在后端和系统中工作?
一些注意事项:
TkAgg
后端,但欢迎任何后端/系统上的解决方案blog建议实施:
def redraw_figure(): fig = plt.gcf() fig.canvas.draw() fig.canvas.flush_events()
但至少在我的系统上,根本没有重绘图。
所以,如果有人有答案,你会直接让我和其他成千上万的人非常开心。他们的幸福可能会流向他们的朋友和亲戚,他们的朋友和亲戚等等,这样你就有可能改善数十亿人的生活。
ImportanceOfBeingErnest展示了如何使用blit进行更快的绘图,但这并不像在redraw_figure
函数中添加不同内容那么简单(您需要跟踪要重绘的内容)。
答案 0 :(得分:36)
首先,问题中发布的代码在我的机器上以7 fps运行,QT4Agg作为后端。
现在,正如许多帖子(如here或here中所建议的那样),使用blit
可能是一种选择。虽然this article提到那个blit导致强烈的内存泄漏,但我无法观察到。
我稍微修改了你的代码并比较了使用和不使用blit的帧速率。下面的代码给出了
代码:
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)
fig.canvas.draw() # note that the first draw comes before setting data
h1 = ax1.imshow(X, vmin=-1, vmax=1, interpolation="None", cmap="RdBu")
h2, = ax2.plot(x, lw=3)
text = ax2.text(0.8,1.5, "")
ax2.set_ylim([-1,1])
if blit:
# cache the background
axbackground = fig.canvas.copy_from_bbox(ax1.bbox)
ax2background = fig.canvas.copy_from_bbox(ax2.bbox)
t_start = time.time()
k=0.
for i in np.arange(1000):
h1.set_data(np.sin(X/3.+k)*np.cos(Y/3.+k))
h2.set_ydata(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(h1)
ax2.draw_artist(h2)
# 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()
plt.pause(0.000000000001)
#plt.pause calls canvas.draw(), as can be read here:
#http://bastibe.de/2013-05-30-speeding-up-matplotlib.html
#however with Qt4 (and TkAgg??) this is needed. It seems,using a different backend,
#one can avoid plt.pause() and gain even more speed.
live_update_demo(True) # 28 fps
#live_update_demo(False) # 18 fps
<强>更新强>
为了更快地绘图,可以考虑使用pyqtgraph
正如pyqtgraph documentation所说的那样:&#34;对于绘图,pyqtgraph几乎不像matplotlib那样完整/成熟,但运行得更快。&#34;
我将上面的例子移植到pyqtgraph。虽然它看起来有点难看,但它在我的机器上以250 fps运行。
总结一下,
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_())