blocks|key|285972|text|问题似乎是，您期望plt.show()显示窗口，然后返回。它不会这样做的。程序将在该点停止，并仅在关闭窗口后恢复运行。您应该能够测试这一点:如果您关闭窗口，然后应弹出另一个窗口。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|285973|要解决这个问题，只需在循环后调用一次plt.show()即可。然后你就会得到完整的图。(但不是“实时绘图”)|285974|您可以尝试像这样设置关键字参数block：plt.show(block=False)一开始，然后使用.draw()进行更新。|285975|entityMap|0|LINK|mutability|MUTABLE|url|http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.draw^0|9|A|0|I|A|0|F|5|L|L|1E|7|1E|7|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@$9|S|A|T|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|X|8|@$9|Y|A|Z|B|C]|$9|10|A|11|B|C]|$9|12|A|13|B|C]]|D|@$9|14|A|15|1|16]]|E|$]]|$1|J|3|-4|5|6|7|17|8|@]|D|@]|E|$]]]|K|$L|$5|M|N|O|E|$P|Q]]]]

The problem seems to be that you expect <code>plt.show()</code> to show the window and then to return. It does not do that. The program will stop at that point and only resume once you close the window. You should be able to test that: If you close the window and then another window should pop up.

To resolve that problem just call <code>plt.show()</code> once after your loop. Then you get the complete plot. (But not a 'real-time plotting')

You can try setting the keyword-argument <code>block</code> like this: <code>plt.show(block=False)</code> once at the beginning and then use <a href="http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.draw" rel="noreferrer"><code>.draw()</code></a> to update.

blocks|key|286023|text|show可能不是最好的选择。我要做的就是使用pyplot.draw()。您可能还希望在循环中包含一个小的时间延迟(例如，time.sleep(0.05))，以便您可以看到绘图的发生。如果我对您的示例进行这些更改，它对我有效，我看到每个点一次出现一个。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|286024|entityMap^0|0|4|M|D|1O|G|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]|$9|K|A|L|B|C]|$9|M|A|N|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|O|8|@]|D|@]|E|$]]]|G|$]]

<code>show</code> is probably not the best choice for this. What I would do is use <code>pyplot.draw()</code> instead. You also might want to include a small time delay (e.g., <code>time.sleep(0.05)</code>) in the loop so that you can see the plots happening. If I make these changes to your example it works for me and I see each point appearing one at a time.

blocks|key|286113|text|以下是正在讨论的代码的工作版本(至少需要2011-11-14的Matplotlib+1.1.0版本)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|286114|import+numpy+as+np
import+matplotlib.pyplot+as+plt

plt.axis([0,+10,+0,+1])

for+i+in+range(10):
++++y+=+np.random.random()
++++plt.scatter(i,+y)
++++plt.pause(0.05)

plt.show()|code-block|syntax|javascript|286115|注意对plt.pause(0.05)的调用，它既绘制新数据又运行图形用户界面的事件循环(允许鼠标交互)。|offset|length|style|CODE|286116|entityMap^0|0|0|3|F|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

Here's the working version of the code in question (requires at least version Matplotlib 1.1.0 from 2011-11-14):
<pre><code>import numpy as np
import matplotlib.pyplot as plt

plt.axis([0, 10, 0, 1])

for i in range(10):
 y = np.random.random()
 plt.scatter(i, y)
 plt.pause(0.05)

plt.show()
</code></pre>
Note the call to <code>plt.pause(0.05)</code>, which both draws the new data and runs the GUI's event loop (allowing for mouse interaction).

blocks|key|286173|text|如果你对实时绘图感兴趣，我建议你去看看matplotlib's+animation+API。特别是，使用blit避免在每一帧上重新绘制背景可以为您带来显著的速度提升(~10倍)：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|286174|#!/usr/bin/env+python

import+numpy+as+np
import+time
import+matplotlib
matplotlib.use('GTKAgg')
from+matplotlib+import+pyplot+as+plt


def+randomwalk(dims=(256,+256),+n=20,+sigma=5,+alpha=0.95,+seed=1):
++++"""+A+simple+random+walk+with+memory+"""

++++r,+c+=+dims
++++gen+=+np.random.RandomState(seed)
++++pos+=+gen.rand(2,+n)+*+((r,),+(c,))
++++old_delta+=+gen.randn(2,+n)+*+sigma

++++while+True:
++++++++delta+=+(1.+-+alpha)+*+gen.randn(2,+n)+*+sigma+%2B+alpha+*+old_delta
++++++++pos+%2B=+delta
++++++++for+ii+in+xrange(n):
++++++++++++if+not+(0.+<=+pos[0,+ii]+<+r):
++++++++++++++++pos[0,+ii]+=+abs(pos[0,+ii]+%25+r)
++++++++++++if+not+(0.+<=+pos[1,+ii]+<+c):
++++++++++++++++pos[1,+ii]+=+abs(pos[1,+ii]+%25+c)
++++++++old_delta+=+delta
++++++++yield+pos


def+run(niter=1000,+doblit=True):
++++"""
++++Display+the+simulation+using+matplotlib,+optionally+using+blit+for+speed
++++"""

++++fig,+ax+=+plt.subplots(1,+1)
++++ax.set_aspect('equal')
++++ax.set_xlim(0,+255)
++++ax.set_ylim(0,+255)
++++ax.hold(True)
++++rw+=+randomwalk()
++++x,+y+=+rw.next()

++++plt.show(False)
++++plt.draw()

++++if+doblit:
++++++++#+cache+the+background
++++++++background+=+fig.canvas.copy_from_bbox(ax.bbox)

++++points+=+ax.plot(x,+y,+'o')[0]
++++tic+=+time.time()

++++for+ii+in+xrange(niter):

++++++++#+update+the+xy+data
++++++++x,+y+=+rw.next()
++++++++points.set_data(x,+y)

++++++++if+doblit:
++++++++++++#+restore+background
++++++++++++fig.canvas.restore_region(background)

++++++++++++#+redraw+just+the+points
++++++++++++ax.draw_artist(points)

++++++++++++#+fill+in+the+axes+rectangle
++++++++++++fig.canvas.blit(ax.bbox)

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

++++plt.close(fig)
++++print+"Blit+=+%25s,+average+FPS:+%25.2f"+%25+(
++++++++str(doblit),+niter+/+(time.time()+-+tic))

if+__name__+==+'__main__':
++++run(doblit=False)
++++run(doblit=True)|code-block|syntax|javascript|286175|输出：|286176|Blit+=+False,+average+FPS:+54.37
Blit+=+True,+average+FPS:+438.27|286177|entityMap|0|LINK|mutability|MUTABLE|url|https://matplotlib.org/stable/api/animation_api.html^0|1G|4|J|Q|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|F|3|G|5|H|7|12|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|13|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|14|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|15|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

If you're interested in realtime plotting, I'd recommend looking into <a href="https://matplotlib.org/stable/api/animation_api.html" rel="noreferrer">matplotlib's animation API</a>. In particular, using <code>blit</code> to avoid redrawing the background on every frame can give you substantial speed gains (~10x):
<pre><code>#!/usr/bin/env python

import numpy as np
import time
import matplotlib
matplotlib.use('GTKAgg')
from matplotlib import pyplot as plt


def randomwalk(dims=(256, 256), n=20, sigma=5, alpha=0.95, seed=1):
 &quot;&quot;&quot; A simple random walk with memory &quot;&quot;&quot;

 r, c = dims
 gen = np.random.RandomState(seed)
 pos = gen.rand(2, n) * ((r,), (c,))
 old_delta = gen.randn(2, n) * sigma

 while True:
 delta = (1. - alpha) * gen.randn(2, n) * sigma + alpha * old_delta
 pos += delta
 for ii in xrange(n):
 if not (0. &lt;= pos[0, ii] &lt; r):
 pos[0, ii] = abs(pos[0, ii] % r)
 if not (0. &lt;= pos[1, ii] &lt; c):
 pos[1, ii] = abs(pos[1, ii] % c)
 old_delta = delta
 yield pos


def run(niter=1000, doblit=True):
 &quot;&quot;&quot;
 Display the simulation using matplotlib, optionally using blit for speed
 &quot;&quot;&quot;

 fig, ax = plt.subplots(1, 1)
 ax.set_aspect('equal')
 ax.set_xlim(0, 255)
 ax.set_ylim(0, 255)
 ax.hold(True)
 rw = randomwalk()
 x, y = rw.next()

 plt.show(False)
 plt.draw()

 if doblit:
 # cache the background
 background = fig.canvas.copy_from_bbox(ax.bbox)

 points = ax.plot(x, y, 'o')[0]
 tic = time.time()

 for ii in xrange(niter):

 # update the xy data
 x, y = rw.next()
 points.set_data(x, y)

 if doblit:
 # restore background
 fig.canvas.restore_region(background)

 # redraw just the points
 ax.draw_artist(points)

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

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

 plt.close(fig)
 print &quot;Blit = %s, average FPS: %.2f&quot; % (
 str(doblit), niter / (time.time() - tic))

if __name__ == '__main__':
 run(doblit=False)
 run(doblit=True)
</code></pre>
Output:
<pre><code>Blit = False, average FPS: 54.37
Blit = True, average FPS: 438.27
</code></pre>

blocks|key|286225|text|如果您希望绘制，而不是在绘制更多点时冻结线程，则应该使用plt.pause()而不是time.sleep()|type|unstyled|depth|inlineStyleRanges|entityRanges|data|286226|我正在使用以下代码来绘制一系列xy坐标。|286227|import+matplotlib.pyplot+as+plt+
import+math


pi+=+3.14159

fig,+ax+=+plt.subplots()

x+=+[]
y+=+[]

def+PointsInCircum(r,n=20):
++++circle+=+[(math.cos(2*pi/n*x)*r,math.sin(2*pi/n*x)*r)+for+x+in+xrange(0,n%2B1)]
++++return+circle

circle_list+=+PointsInCircum(3,+50)

for+t+in+range(len(circle_list)):
++++if+t+==+0:
++++++++points,+=+ax.plot(x,+y,+marker='o',+linestyle='--')
++++++++ax.set_xlim(-4,+4)+
++++++++ax.set_ylim(-4,+4)+
++++else:
++++++++x_coord,+y_coord+=+circle_list.pop()
++++++++x.append(x_coord)
++++++++y.append(y_coord)
++++++++points.set_data(x,+y)
++++plt.pause(0.01)|code-block|syntax|javascript|286228|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|L|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|M|8|@]|9|@]|A|$G|H]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

If you want draw and not freeze your thread as more point are drawn you should use plt.pause() not time.sleep()

im using the following code to plot a series of xy coordinates.

<pre><code>import matplotlib.pyplot as plt 
import math


pi = 3.14159

fig, ax = plt.subplots()

x = []
y = []

def PointsInCircum(r,n=20):
 circle = [(math.cos(2*pi/n*x)*r,math.sin(2*pi/n*x)*r) for x in xrange(0,n+1)]
 return circle

circle_list = PointsInCircum(3, 50)

for t in range(len(circle_list)):
 if t == 0:
 points, = ax.plot(x, y, marker='o', linestyle='--')
 ax.set_xlim(-4, 4) 
 ax.set_ylim(-4, 4) 
 else:
 x_coord, y_coord = circle_list.pop()
 x.append(x_coord)
 y.append(y_coord)
 points.set_data(x, y)
 plt.pause(0.01)
</code></pre>

blocks|key|286262|text|这是我在我的系统上工作的一个版本。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|286263|import+matplotlib.pyplot+as+plt
from+drawnow+import+drawnow
import+numpy+as+np

def+makeFig():
++++plt.scatter(xList,yList)+#+I+think+you+meant+this

plt.ion()+#+enable+interactivity
fig=plt.figure()+#+make+a+figure

xList=list()
yList=list()

for+i+in+np.arange(50):
++++y=np.random.random()
++++xList.append(i)
++++yList.append(y)
++++drawnow(makeFig)
++++#makeFig()++++++The+drawnow(makeFig)+command+can+be+replaced
++++#plt.draw()+++++with+makeFig();+plt.draw()
++++plt.pause(0.001)|code-block|syntax|javascript|286264|drawnow(makeFig)行可以替换为makeFig()；plt.draw()序列，它仍然可以正常工作。|286265|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Here is a version that I got to work on my system.

<pre><code>import matplotlib.pyplot as plt
from drawnow import drawnow
import numpy as np

def makeFig():
 plt.scatter(xList,yList) # I think you meant this

plt.ion() # enable interactivity
fig=plt.figure() # make a figure

xList=list()
yList=list()

for i in np.arange(50):
 y=np.random.random()
 xList.append(i)
 yList.append(y)
 drawnow(makeFig)
 #makeFig() The drawnow(makeFig) command can be replaced
 #plt.draw() with makeFig(); plt.draw()
 plt.pause(0.001)
</code></pre>

The drawnow(makeFig) line can be replaced with a makeFig(); plt.draw() sequence and it still works OK.

blocks|key|503059|text|另一种选择是使用bokeh。至少对于实时绘图来说，这是一个很好的选择。以下是问题中代码的bokeh版本：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|503060|from+bokeh.plotting+import+curdoc,+figure
import+random
import+time

def+update():
++++global+i
++++temp_y+=+random.random()
++++r.data_source.stream({'x':+[i],+'y':+[temp_y]})
++++i+%2B=+1

i+=+0
p+=+figure()
r+=+p.circle([],+[])
curdoc().add_root(p)
curdoc().add_periodic_callback(update,+100)|code-block|syntax|javascript|503061|为了运行它：|503062|pip3+install+bokeh
bokeh+serve+--show+test.py|503063|bokeh通过websocket通信在web浏览器中显示结果。当数据由远程无头服务器进程生成时，它特别有用。|503064|​|503065|📷|atomic|503066|503067|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/bokeh/bokeh|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/1d3d86374049532e48503cc16fcee3be.gif|imageAlt^0|8|5|0|0|0|0|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|18|8|@]|9|@$A|19|B|1A|1|1B]]|C|$]]|$1|D|3|E|5|F|7|1C|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|1D|8|@]|9|@]|C|$]]|$1|K|3|L|5|F|7|1E|8|@]|9|@]|C|$G|H]]|$1|M|3|N|5|6|7|1F|8|@]|9|@]|C|$]]|$1|O|3|P|5|6|7|1G|8|@]|9|@]|C|$]]|$1|Q|3|R|5|S|7|1H|8|@]|9|@$A|1I|B|1J|1|1K]]|C|$]]|$1|T|3|P|5|6|7|1L|8|@]|9|@]|C|$]]|$1|U|3|-4|5|6|7|1M|8|@]|9|@]|C|$]]]|V|$W|$5|X|Y|Z|C|$10|11]]|12|$5|13|Y|14|C|$15|16|17|-4]]]]

Another option is to go with <a href="https://github.com/bokeh/bokeh" rel="noreferrer">bokeh</a>. IMO, it is a good alternative at least for real-time plots. Here is a bokeh version of the code in the question:

<pre><code>from bokeh.plotting import curdoc, figure
import random
import time

def update():
 global i
 temp_y = random.random()
 r.data_source.stream({'x': [i], 'y': [temp_y]})
 i += 1

i = 0
p = figure()
r = p.circle([], [])
curdoc().add_root(p)
curdoc().add_periodic_callback(update, 100)
</code></pre>

and for running it:

<pre><code>pip3 install bokeh
bokeh serve --show test.py
</code></pre>

bokeh shows the result in a web browser via websocket communications. It is especially useful when data is generated by remote headless server processes.

<a href="https://i.stack.imgur.com/RyGZz.gif" rel="noreferrer"><img src="https://i.stack.imgur.com/RyGZz.gif" alt="bokeh sample plot"></a>

blocks|key|503099|text|实时绘制CPU使用情况的示例用例。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|503100|import+time
import+psutil
import+matplotlib.pyplot+as+plt

fig+=+plt.figure()
ax+=+fig.add_subplot(111)

i+=+0
x,+y+=+[],+[]

while+True:
++++x.append(i)
++++y.append(psutil.cpu_percent())

++++ax.plot(x,+y,+color='b')

++++fig.canvas.draw()

++++ax.set_xlim(left=max(0,+i+-+50),+right=i+%2B+50)
++++fig.show()
++++plt.pause(0.05)
++++i+%2B=+1|code-block|syntax|javascript|503101|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

An example use-case to plot CPU usage in real-time.

<pre><code>import time
import psutil
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111)

i = 0
x, y = [], []

while True:
 x.append(i)
 y.append(psutil.cpu_percent())

 ax.plot(x, y, color='b')

 fig.canvas.draw()

 ax.set_xlim(left=max(0, i - 50), right=i + 50)
 fig.show()
 plt.pause(0.05)
 i += 1
</code></pre>

blocks|key|286482|text|这是使用while循环绘制动态实时matplot绘图动画的正确方法|type|unstyled|depth|inlineStyleRanges|entityRanges|data|286483|There+is+a+medium+article+on+that+too:|offset|length|286484|pip+install+celluloid+#这将捕获图像/动画|286485|import+matplotlib.pyplot+as+plt
import+numpy+as+np
from+celluloid+import+Camera+#+getting+the+camera
import+matplotlib.animation+as+animation
from+IPython+import+display
import+time
from+IPython.display+import+HTML

import+warnings
%25matplotlib+notebook
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')

fig+=+plt.figure()+#Empty+fig+object
ax+=+fig.add_subplot()+#Empty+axis+object
camera+=+Camera(fig)+#+Camera+object+to+capture+the+snap

def+f(x):
++++'''+function+to+create+a+sine+wave'''
++++return+np.sin(x)+%2B+np.random.normal(scale=0.1,+size=len(x))

l+=+[]

while+True:
++++value+=+np.random.randint(9)+#random+number+generator
++++l.append(value)+#+appneds+each+time+number+is+generated
++++X+=+np.linspace(10,+len(l))+#+creates+a+line+space+for+x+axis,+Equal+to+the+length+of+l

++++for+i+in+range(10):+#plots+10+such+lines
++++++++plt.plot(X,+f(X))

++++fig.show()+#shows+the+figure+object
++++fig.canvas.draw()+
++++camera.snap()+#+camera+object+to+capture+teh+animation
++++time.sleep(1)|code-block|syntax|javascript|286486|以及用于保存等：|286487|animation+=+camera.animate(interval+=+200,+repeat+=+True,+repeat_delay+=+500)
HTML(animation.to_html5_video())
animation.save('abc.mp4')+#+to+save+|286488|输出为：|286489|​|286490|📷|atomic|286491|286492|entityMap|0|LINK|mutability|MUTABLE|url|https://yogender027mae.medium.com/easy-matplot-animation-using-less-than-15-lines-of-code-24ba92f9927b|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/64677db5905f8aa31c62bb20fa57920d.gif|imageAlt^0|0|0|12|0|0|0|0|0|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|1C|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1D|8|@]|9|@$D|1E|E|1F|1|1G]]|A|$]]|$1|F|3|G|5|6|7|1H|8|@]|9|@]|A|$]]|$1|H|3|I|5|J|7|1I|8|@]|9|@]|A|$K|L]]|$1|M|3|N|5|6|7|1J|8|@]|9|@]|A|$]]|$1|O|3|P|5|J|7|1K|8|@]|9|@]|A|$K|L]]|$1|Q|3|R|5|6|7|1L|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1M|8|@]|9|@]|A|$]]|$1|U|3|V|5|W|7|1N|8|@]|9|@$D|1O|E|1P|1|1Q]]|A|$]]|$1|X|3|T|5|6|7|1R|8|@]|9|@]|A|$]]|$1|Y|3|-4|5|6|7|1S|8|@]|9|@]|A|$]]]|Z|$10|$5|11|12|13|A|$14|15]]|16|$5|17|12|18|A|$19|1A|1B|-4]]]]

This is the right way to plot Dynamic real-time matplot plots animation using while loop
<a href="https://yogender027mae.medium.com/easy-matplot-animation-using-less-than-15-lines-of-code-24ba92f9927b" rel="nofollow noreferrer">There is a medium article on that too:</a>
pip install celluloid # this will capture the image/animation
<pre><code>import matplotlib.pyplot as plt
import numpy as np
from celluloid import Camera # getting the camera
import matplotlib.animation as animation
from IPython import display
import time
from IPython.display import HTML

import warnings
%matplotlib notebook
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')

fig = plt.figure() #Empty fig object
ax = fig.add_subplot() #Empty axis object
camera = Camera(fig) # Camera object to capture the snap

def f(x):
 ''' function to create a sine wave'''
 return np.sin(x) + np.random.normal(scale=0.1, size=len(x))

l = []

while True:
 value = np.random.randint(9) #random number generator
 l.append(value) # appneds each time number is generated
 X = np.linspace(10, len(l)) # creates a line space for x axis, Equal to the length of l

 for i in range(10): #plots 10 such lines
 plt.plot(X, f(X))

 fig.show() #shows the figure object
 fig.canvas.draw() 
 camera.snap() # camera object to capture teh animation
 time.sleep(1)
</code></pre>
And for saving etc:
<pre><code>animation = camera.animate(interval = 200, repeat = True, repeat_delay = 500)
HTML(animation.to_html5_video())
animation.save('abc.mp4') # to save 

</code></pre>
output is:
<a href="https://i.stack.imgur.com/APhAE.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/APhAE.gif" alt="enter image description here" /></a>

I am trying to plot some data from a camera in real time using OpenCV. However, the real-time plotting (using matplotlib) doesn't seem to be working.

I've isolated the problem into this simple example:

<pre><code>fig = plt.figure()
plt.axis([0, 1000, 0, 1])

i = 0
x = list()
y = list()

while i &lt; 1000:
 temp_y = np.random.random()
 x.append(i)
 y.append(temp_y)
 plt.scatter(i, temp_y)
 i += 1
 plt.show()
</code></pre>

I would expect this example to plot 1000 points individually. What actually happens is that the window pops up with the first point showing (ok with that), then waits for the loop to finish before it populates the rest of the graph.

Any thoughts why I am not seeing points populated one at a time?

How do I plot in real-time in a while loop using matplotlib?

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我正在尝试使用OpenCV实时绘制来自摄像机的一些数据。然而，实时绘图(使用matplotlib)似乎不起作用。我已经将问题隔离到这个简单的例子中：fig = plt.figure()plt.axis([0, 1000, 0, 1])i = 0x = list()y = list()while i < 1000: ...

问如何使用matplotlib在while循环中实时绘图？
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