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Live plot through tkinter .after - Combining pyplot event loop with tkinter event loop [duplicate]

I am having problems trying to make matplotlib plot a function without blocking execution.
I have tried using show(block=False) as some people suggest, but all I get is a frozen window. If I simply call show(), the result is plotted properly but execution is blocked until the window is closed. From other threads I've read, I suspect that whether show(block=False) works or not depends on the backend. Is this correct? My backend is Qt4Agg. Could you have a look at my code and tell me if you see something wrong? Here is my code.
from math import *
from matplotlib import pyplot as plt
print(plt.get_backend())
def main():
x = range(-50, 51, 1)
for pow in range(1,5): # plot x^1, x^2, ..., x^4
y = [Xi**pow for Xi in x]
print(y)
plt.plot(x, y)
plt.draw()
#plt.show() #this plots correctly, but blocks execution.
plt.show(block=False) #this creates an empty frozen window.
_ = raw_input("Press [enter] to continue.")
if __name__ == '__main__':
main()
PS. I forgot to say that I would like to update the existing window every time I plot something, instead of creating a new one.

I spent a long time looking for solutions, and found this answer.
It looks like, in order to get what you (and I) want, you need the combination of plt.ion(), plt.show() (not with block=False) and, most importantly, plt.pause(.001) (or whatever time you want). The pause is needed because the GUI events happen while the main code is sleeping, including drawing. It's possible that this is implemented by picking up time from a sleeping thread, so maybe IDEs mess with that—I don't know.
Here's an implementation that works for me on python 3.5:
import numpy as np
from matplotlib import pyplot as plt
def main():
plt.axis([-50,50,0,10000])
plt.ion()
plt.show()
x = np.arange(-50, 51)
for pow in range(1,5): # plot x^1, x^2, ..., x^4
y = [Xi**pow for Xi in x]
plt.plot(x, y)
plt.draw()
plt.pause(0.001)
input("Press [enter] to continue.")
if __name__ == '__main__':
main()

A simple trick that works for me is the following:
Use the block = False argument inside show: plt.show(block = False)
Use another plt.show() at the end of the .py script.
Example:
import matplotlib.pyplot as plt
plt.imshow(add_something)
plt.xlabel("x")
plt.ylabel("y")
plt.show(block=False)
#more code here (e.g. do calculations and use print to see them on the screen
plt.show()
Note: plt.show() is the last line of my script.

You can avoid blocking execution by writing the plot to an array, then displaying the array in a different thread. Here is an example of generating and displaying plots simultaneously using pf.screen from pyformulas 0.2.8:
import pyformulas as pf
import matplotlib.pyplot as plt
import numpy as np
import time
fig = plt.figure()
canvas = np.zeros((480,640))
screen = pf.screen(canvas, 'Sinusoid')
start = time.time()
while True:
now = time.time() - start
x = np.linspace(now-2, now, 100)
y = np.sin(2*np.pi*x) + np.sin(3*np.pi*x)
plt.xlim(now-2,now+1)
plt.ylim(-3,3)
plt.plot(x, y, c='black')
# If we haven't already shown or saved the plot, then we need to draw the figure first...
fig.canvas.draw()
image = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))
screen.update(image)
#screen.close()
Result:
Disclaimer: I'm the maintainer for pyformulas.
Reference: Matplotlib: save plot to numpy array

Live Plotting
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2 * np.pi, 100)
# plt.axis([x[0], x[-1], -1, 1]) # disable autoscaling
for point in x:
plt.plot(point, np.sin(2 * point), '.', color='b')
plt.draw()
plt.pause(0.01)
# plt.clf() # clear the current figure
if the amount of data is too much you can lower the update rate with a simple counter
cnt += 1
if (cnt == 10): # update plot each 10 points
plt.draw()
plt.pause(0.01)
cnt = 0
Holding Plot after Program Exit
This was my actual problem that couldn't find satisfactory answer for, I wanted plotting that didn't close after the script was finished (like MATLAB),
If you think about it, after the script is finished, the program is terminated and there is no logical way to hold the plot this way, so there are two options
block the script from exiting (that's plt.show() and not what I want)
run the plot on a separate thread (too complicated)
this wasn't satisfactory for me so I found another solution outside of the box
SaveToFile and View in external viewer
For this the saving and viewing should be both fast and the viewer shouldn't lock the file and should update the content automatically
Selecting Format for Saving
vector based formats are both small and fast
SVG is good but coudn't find good viewer for it except the web browser which by default needs manual refresh
PDF can support vector formats and there are lightweight viewers which support live updating
Fast Lightweight Viewer with Live Update
For PDF there are several good options
On Windows I use SumatraPDF which is free, fast and light (only uses 1.8MB RAM for my case)
On Linux there are several options such as Evince (GNOME) and Ocular (KDE)
Sample Code & Results
Sample code for outputing plot to a file
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(2 * x)
plt.plot(x, y)
plt.savefig("fig.pdf")
after first run, open the output file in one of the viewers mentioned above and enjoy.
Here is a screenshot of VSCode alongside SumatraPDF, also the process is fast enough to get semi-live update rate (I can get near 10Hz on my setup just use time.sleep() between intervals)

A lot of these answers are super inflated and from what I can find, the answer isn't all that difficult to understand.
You can use plt.ion() if you want, but I found using plt.draw() just as effective
For my specific project I'm plotting images, but you can use plot() or scatter() or whatever instead of figimage(), it doesn't matter.
plt.figimage(image_to_show)
plt.draw()
plt.pause(0.001)
Or
fig = plt.figure()
...
fig.figimage(image_to_show)
fig.canvas.draw()
plt.pause(0.001)
If you're using an actual figure.
I used #krs013, and #Default Picture's answers to figure this out
Hopefully this saves someone from having launch every single figure on a separate thread, or from having to read these novels just to figure this out

I figured out that the plt.pause(0.001) command is the only thing needed and nothing else.
plt.show() and plt.draw() are unnecessary and / or blocking in one way or the other. So here is a code that draws and updates a figure and keeps going. Essentially plt.pause(0.001) seems to be the closest equivalent to matlab's drawnow.
Unfortunately those plots will not be interactive (they freeze), except you insert an input() command, but then the code will stop.
The documentation of the plt.pause(interval) command states:
If there is an active figure, it will be updated and displayed before the pause......
This can be used for crude animation.
and this is pretty much exactly what we want. Try this code:
import numpy as np
from matplotlib import pyplot as plt
x = np.arange(0, 51) # x coordinates
for z in range(10, 50):
y = np.power(x, z/10) # y coordinates of plot for animation
plt.cla() # delete previous plot
plt.axis([-50, 50, 0, 10000]) # set axis limits, to avoid rescaling
plt.plot(x, y) # generate new plot
plt.pause(0.1) # pause 0.1 sec, to force a plot redraw

Iggy's answer was the easiest for me to follow, but I got the following error when doing a subsequent subplot command that was not there when I was just doing show:
MatplotlibDeprecationWarning: Adding an axes using the same arguments
as a previous axes currently reuses the earlier instance. In a future
version, a new instance will always be created and returned.
Meanwhile, this warning can be suppressed, and the future behavior
ensured, by passing a unique label to each axes instance.
In order to avoid this error, it helps to close (or clear) the plot after the user hits enter.
Here's the code that worked for me:
def plt_show():
'''Text-blocking version of plt.show()
Use this instead of plt.show()'''
plt.draw()
plt.pause(0.001)
input("Press enter to continue...")
plt.close()

The Python package drawnow allows to update a plot in real time in a non blocking way.
It also works with a webcam and OpenCV for example to plot measures for each frame.
See the original post.

Substitute the backend of matplotlib can solve my problem.
Write the bellow command before import matplotlib.pyplot as plt.
Substitute backend command should run first.
import matplotlib
matplotlib.use('TkAgg')
My answer come from Pycharm does not show plot

Cancelling displaying a Matplotlib plot [duplicate]

Matplotlib offers these functions:
cla() # Clear axis
clf() # Clear figure
close() # Close a figure window
When should I use each function and what exactly does it do?

They all do different things, since matplotlib uses a hierarchical order in which a figure window contains a figure which may consist of many axes. Additionally, there are functions from the pyplot interface and there are methods on the Figure class. I will discuss both cases below.
pyplot interface
pyplot is a module that collects a couple of functions that allow matplotlib to be used in a functional manner. I here assume that pyplot has been imported as import matplotlib.pyplot as plt.
In this case, there are three different commands that remove stuff:
See matplotlib.pyplot Functions:
plt.cla() clears an axis, i.e. the currently active axis in the current figure. It leaves the other axes untouched.
plt.clf() clears the entire current figure with all its axes, but leaves the window opened, such that it may be reused for other plots.
plt.close() closes a window, which will be the current window, if not specified otherwise.
Which functions suits you best depends thus on your use-case.
The close() function furthermore allows one to specify which window should be closed. The argument can either be a number or name given to a window when it was created using figure(number_or_name) or it can be a figure instance fig obtained, i.e., usingfig = figure(). If no argument is given to close(), the currently active window will be closed. Furthermore, there is the syntax close('all'), which closes all figures.
methods of the Figure class
Additionally, the Figure class provides methods for clearing figures.
I'll assume in the following that fig is an instance of a Figure:
fig.clf() clears the entire figure. This call is equivalent to plt.clf() only if fig is the current figure.
fig.clear() is a synonym for fig.clf()
Note that even del fig will not close the associated figure window. As far as I know the only way to close a figure window is using plt.close(fig) as described above.

There is just a caveat that I discovered today.
If you have a function that is calling a plot a lot of times you better use plt.close(fig) instead of fig.clf() somehow the first does not accumulate in memory. In short if memory is a concern use plt.close(fig) (Although it seems that there are better ways, go to the end of this comment for relevant links).
So the the following script will produce an empty list:
for i in range(5):
fig = plot_figure()
plt.close(fig)
# This returns a list with all figure numbers available
print(plt.get_fignums())
Whereas this one will produce a list with five figures on it.
for i in range(5):
fig = plot_figure()
fig.clf()
# This returns a list with all figure numbers available
print(plt.get_fignums())
From the documentation above is not clear to me what is the difference between closing a figure and closing a window. Maybe that will clarify.
If you want to try a complete script there you have:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(1000)
y = np.sin(x)
for i in range(5):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(x, y)
plt.close(fig)
print(plt.get_fignums())
for i in range(5):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(x, y)
fig.clf()
print(plt.get_fignums())
If memory is a concern somebody already posted a work-around in SO see:
Create a figure that is reference counted

plt.cla() means clear current axis
plt.clf() means clear current figure
also, there's plt.gca() (get current axis) and plt.gcf() (get current figure)
Read more here: Matplotlib, Pyplot, Pylab etc: What's the difference between these and when to use each?

How come pyplot from Matplotlib doesn't allow you to save an image after you show it?

If I put pyplot.show() before I try to save an image, the file doesn't actually contain the image.
At first I thought it was a bug but then I switched pyplot.show() and pyplot.savefig('foo5.png') and it worked.
Here is an example code piece.
def plot(embeddings, labels):
assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'
pyplot.figure(figsize=(20, 20)) # in inches
for i, label in enumerate(labels):
x, y = embeddings[i,:]
pyplot.scatter(x, y)
pyplot.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',
ha='right', va='bottom')
pyplot.savefig('foo4.png')
pyplot.show()
pyplot.savefig('foo5.png')
books = [bookDictionary[i] for i in range(1, num_points2+1)]
plot(two_d_embeddings, books)
print( os.listdir() )
foo4.png is just fine, but foo5.png is blank.

As you found out yourself, using pyplot you need to save the figure before it is shown.
This is in fact only true for non-interactive mode (ioff), but that is the default and probably most common use case.
What happens is that once pyplot.show() is called, the figure is shown and an event loop is started, taking over the python even loop. Hence any command after pyplot.show() is delayed until the figure is closed. This means that pyplot.savefig(), which comes after show is not executed until the figure is closed. Once the figure is closed, there is no figure to save present inside the pyplot state machine anymore.
You may however save a particular figure, in case that is desired. E.g.
import matplotlib.pyplot as plt
plt.plot([1,2,3])
fig = plt.gcf()
plt.show()
fig.savefig("foo5.png")
Here, we call the savefig method of a particular figure (in this case the only one present) for which we need to obtain a handle (fig) to that figure.
Note that in such cases, where you need fine control over how matplotlib works it's always useful to not use pyplot, but rely mostly on the object oriented interface.
Hence,
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1,2,3])
plt.show()
fig.savefig("foo5.png")
would be the more natural way to save a figure after having shown it.

Matplotlib figure not updating on data change

I'm implementing an image viewer using matplotlib. The idea is that changes being made to the image (such as filter application) will update automatically.
I create a Figure to show the inital image and have added a button using pyQt to update the data. The data does change, I have checked, but the Figure does not. However, if after I've pressed the filter application button, I move the image using matplotlib's standard tool bar, the image is then updated.
I assume I'm doing something wrong when updating the image, but since the fact of moving it actually forces the update, it then shows the data change. I would like for this to happen when I press the button, though.
Below is some of the code. This is the initial figure initialization, which shows the original image:
self.observableFig = Figure((4.0, 4.0), dpi=100)
self.canvas = FigureCanvas(self.observableFig)
self.canvas.setParent(self.observableWindow)
self.canvas.setFocusPolicy(Qt.StrongFocus)
self.canvas.setFocus()
self.canvas.mpl_connect('button_press_event', self.on_click)
# Showing initial data on Window
self.observableFig.clear()
self.observableAxes = self.observableFig.add_subplot(1, 1, 1)
min, max = self.min, self.max
self.observableAxes.imshow(
self.data,
vmin=min,
vmax=max,
origin='lower'
)
And this is the event for when the button that changes the data is pressed:
self.observableAxes.imshow(self.data/2, origin='lower')
# plt.clf()
# plt.draw()
# plt.show()
I have tried draw(), show(), basically anything I've found on pyplot about this. I have also tried both with and without plt.ion() at the beginning, but it hasn't made a difference in this.
Thanks in advance.

The reason that nothing is updating is that you're trying to use pyplot methods for a figure that's not a part of the pyplot state machine. plt.draw() won't draw this figure, as plt doesn't know the figure exists.
Use fig.canvas.draw() instead.
Regardless, it's better to use fig.canvas.draw() that plt.draw(), as it's clear which figure you're drawing (the former draws one, the latter draws all, but only if they're tracked by pyplot).
Try something along these lines:
import numpy as np
import matplotlib.pyplot as plt
data = np.random.random((10,10))
# To make a standalone example, I'm skipping initializing the
# `Figure` and `FigureCanvas` and using `plt.figure()` instead...
# `plt.draw()` would work for this figure, but the rest is identical.
fig, ax = plt.subplots()
ax.set(title='Click to update the data')
im = ax.imshow(data)
def update(event):
im.set_data(np.random.random((10,10)))
fig.canvas.draw()
fig.canvas.mpl_connect('button_press_event', update)
plt.show()

How can I show figures separately in matplotlib?

Say that I have two figures in matplotlib, with one plot per figure:
import matplotlib.pyplot as plt
f1 = plt.figure()
plt.plot(range(0,10))
f2 = plt.figure()
plt.plot(range(10,20))
Then I show both in one shot
plt.show()
Is there a way to show them separately, i.e. to show just f1?
Or better: how can I manage the figures separately like in the following 'wishful' code (that doesn't work):
f1 = plt.figure()
f1.plot(range(0,10))
f1.show()

Sure. Add an Axes using add_subplot. (Edited import.) (Edited show.)
import matplotlib.pyplot as plt
f1 = plt.figure()
f2 = plt.figure()
ax1 = f1.add_subplot(111)
ax1.plot(range(0,10))
ax2 = f2.add_subplot(111)
ax2.plot(range(10,20))
plt.show()
Alternatively, use add_axes.
ax1 = f1.add_axes([0.1,0.1,0.8,0.8])
ax1.plot(range(0,10))
ax2 = f2.add_axes([0.1,0.1,0.8,0.8])
ax2.plot(range(10,20))

With Matplotlib prior to version 1.0.1, show() should only be called once per program, even if it seems to work within certain environments (some backends, on some platforms, etc.).
The relevant drawing function is actually draw():
import matplotlib.pyplot as plt
plt.plot(range(10)) # Creates the plot. No need to save the current figure.
plt.draw() # Draws, but does not block
raw_input() # This shows the first figure "separately" (by waiting for "enter").
plt.figure() # New window, if needed. No need to save it, as pyplot uses the concept of current figure
plt.plot(range(10, 20))
plt.draw()
# raw_input() # If you need to wait here too...
# (...)
# Only at the end of your program:
plt.show() # blocks
It is important to recognize that show() is an infinite loop, designed to handle events in the various figures (resize, etc.). Note that in principle, the calls to draw() are optional if you call matplotlib.ion() at the beginning of your script (I have seen this fail on some platforms and backends, though).
I don't think that Matplotlib offers a mechanism for creating a figure and optionally displaying it; this means that all figures created with figure() will be displayed. If you only need to sequentially display separate figures (either in the same window or not), you can do like in the above code.
Now, the above solution might be sufficient in simple cases, and for some Matplotlib backends. Some backends are nice enough to let you interact with the first figure even though you have not called show(). But, as far as I understand, they do not have to be nice. The most robust approach would be to launch each figure drawing in a separate thread, with a final show() in each thread. I believe that this is essentially what IPython does.
The above code should be sufficient most of the time.
PS: now, with Matplotlib version 1.0.1+, show() can be called multiple times (with most backends).

I think I am a bit late to the party but...
In my opinion, what you need is the object oriented API of matplotlib. In matplotlib 1.4.2 and using IPython 2.4.1 with Qt4Agg backend, I can do the following:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1) # Creates figure fig and add an axes, ax.
fig2, ax2 = plt.subplots(1) # Another figure
ax.plot(range(20)) #Add a straight line to the axes of the first figure.
ax2.plot(range(100)) #Add a straight line to the axes of the first figure.
fig.show() #Only shows figure 1 and removes it from the "current" stack.
fig2.show() #Only shows figure 2 and removes it from the "current" stack.
plt.show() #Does not show anything, because there is nothing in the "current" stack.
fig.show() # Shows figure 1 again. You can show it as many times as you want.
In this case plt.show() shows anything in the "current" stack. You can specify figure.show() ONLY if you are using a GUI backend (e.g. Qt4Agg). Otherwise, I think you will need to really dig down into the guts of matplotlib to monkeypatch a solution.
Remember that most (all?) plt.* functions are just shortcuts and aliases for figure and axes methods. They are very useful for sequential programing, but you will find blocking walls very soon if you plan to use them in a more complex way.

Perhaps you need to read about interactive usage of Matplotlib. However, if you are going to build an app, you should be using the API and embedding the figures in the windows of your chosen GUI toolkit (see examples/embedding_in_tk.py, etc).

None of the above solutions seems to work in my case, with matplotlib 3.1.0 and Python 3.7.3. Either both the figures show up on calling show() or none show up in different answers posted above.
Building upon #Ivan's answer, and taking hint from here, the following seemed to work well for me:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1) # Creates figure fig and add an axes, ax.
fig2, ax2 = plt.subplots(1) # Another figure
ax.plot(range(20)) #Add a straight line to the axes of the first figure.
ax2.plot(range(100)) #Add a straight line to the axes of the first figure.
# plt.close(fig) # For not showing fig
plt.close(fig2) # For not showing fig2
plt.show()

As #arpanmangal, the solutions above do not work for me (matplotlib 3.0.3, python 3.5.2).
It seems that using .show() in a figure, e.g., figure.show(), is not recommended, because this method does not manage a GUI event loop and therefore the figure is just shown briefly. (See figure.show() documentation). However, I do not find any another way to show only a figure.
In my solution I get to prevent the figure for instantly closing by using click events. We do not have to close the figure — closing the figure deletes it.
I present two options:
- waitforbuttonpress(timeout=-1) will close the figure window when clicking on the figure, so we cannot use some window functions like zooming.
- ginput(n=-1,show_clicks=False) will wait until we close the window, but it releases an error :-.
Example:
import matplotlib.pyplot as plt
fig1, ax1 = plt.subplots(1) # Creates figure fig1 and add an axes, ax1
fig2, ax2 = plt.subplots(1) # Another figure fig2 and add an axes, ax2
ax1.plot(range(20),c='red') #Add a red straight line to the axes of fig1.
ax2.plot(range(100),c='blue') #Add a blue straight line to the axes of fig2.
#Option1: This command will hold the window of fig2 open until you click on the figure
fig2.waitforbuttonpress(timeout=-1) #Alternatively, use fig1
#Option2: This command will hold the window open until you close the window, but
#it releases an error.
#fig2.ginput(n=-1,show_clicks=False) #Alternatively, use fig1
#We show only fig2
fig2.show() #Alternatively, use fig1

As of November 2020, in order to show one figure at a time, the following works:
import matplotlib.pyplot as plt
f1, ax1 = plt.subplots()
ax1.plot(range(0,10))
f1.show()
input("Close the figure and press a key to continue")
f2, ax2 = plt.subplots()
ax2.plot(range(10,20))
f2.show()
input("Close the figure and press a key to continue")
The call to input() prevents the figure from opening and closing immediately.