link 有一个使用 matplotlib 进行实时绘图的示例。我认为主要的收获是你不需要在每次调用 plot 时都使用 plt.show() 或 plt.draw() 。该示例改为使用 set_ydata。同样 set_xdata 可用于更新您的 x_axis 变量。代码如下
import matplotlib.pyplot as plt
import numpy as np
# use ggplot style for more sophisticated visuals
plt.style.use('ggplot')
def live_plotter(x_vec,y1_data,line1,identifier='',pause_time=0.1):
if line1==[]:
# this is the call to matplotlib that allows dynamic plotting
plt.ion()
fig = plt.figure(figsize=(13,6))
ax = fig.add_subplot(111)
# create a variable for the line so we can later update it
line1, = ax.plot(x_vec,y1_data,'-o',alpha=0.8)
#update plot label/title
plt.ylabel('Y Label')
plt.title('Title: {}'.format(identifier))
plt.show()
# after the figure, axis, and line are created, we only need to update the y-data
line1.set_ydata(y1_data)
# adjust limits if new data goes beyond bounds
if np.min(y1_data)<=line1.axes.get_ylim()[0] or np.max(y1_data)>=line1.axes.get_ylim()[1]:
plt.ylim([np.min(y1_data)-np.std(y1_data),np.max(y1_data)+np.std(y1_data)])
# this pauses the data so the figure/axis can catch up - the amount of pause can be altered above
plt.pause(pause_time)
# return line so we can update it again in the next iteration
return line1
当我在下面的示例中运行此功能时,我在计算机上使用其他应用程序没有任何问题
size = 100
x_vec = np.linspace(0,1,size+1)[0:-1]
y_vec = np.random.randn(len(x_vec))
line1 = []
i=0
while i<1000:
i=+1
rand_val = np.random.randn(1)
y_vec[-1] = rand_val
line1 = live_plotter(x_vec,y_vec,line1)
y_vec = np.append(y_vec[1:],0.0)