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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

How to stop display resolution from affecting axes in pyqtgraph plots

I am using pyqtgraph to plot some data and noticed that when I move the plot from my laptop screen to a second monitor, the scaling on the plot is affected:
laptop monitor:
external monitor:
notice that the axes got "compressed", and the plot is no longer scaled properly on the second monitor.
I found others reporting similar issues on the web, but could not find any real solution. One solution suggested was to make the monitors' resolutions the same. I don't like this solution because I'd have to sacrifice laptop resolution to accommodate my lower resolution external monitor.
The other solution I found was to add the line app.setAttribute(QtCore.Qt.AA_Use96Dpi) to the main loop, prior to instantiating the Qapplication as shown below, to allegedly have Qt ignore the OS's DPI settings:
def main():
import sys
app = QtWidgets.QApplication(sys.argv)
app.setAttribute(QtCore.Qt.AA_Use96Dpi)
MainWindow =GraphWindow()
MainWindow.show()
sys.exit(app.exec_())
This seems at first to work, because the plotted data is scaled properly on the axes. However, it doesn't seem to really work -- the addition of this line affected the scaling of the axes on the laptop as shown below (same data is now plotted on axes that span 0 to 7000 on the x-axis, and -2 to -26dB on the Yaxis):
,
but did "fix" the issue when moving the plot onto the second monitor to look like the first "original" laptop plot shown above.
This is particularly worrisome, because in the case of the laptop output after the app.setAttribute(QtCore.Qt.AA_Use96Dpi) instruction "looks" right, but misrepresents the actual data. I could have easily missed this had included this instruction when I first plotted the data.
What is the right way to have the plot accurately display regardless of the OS's DPI setting and monitor resolutions? It is very strange that the plotted data seems disassociated with the axis values.
Here is a mininimal reproducible sample:
from PyQt5 import QtWidgets, QtCore
from pyqtgraph import PlotWidget, plot
import pyqtgraph as pg
import sys # We need sys so that we can pass argv to QApplication
import os
from numpy.random import seed
from numpy.random import randint
class MainWindow(QtWidgets.QMainWindow):
def __init__(self, *args, **kwargs):
super(MainWindow, self).__init__(*args, **kwargs)
self.graphWidget = pg.PlotWidget()
self.setCentralWidget(self.graphWidget)
x = [1,2,3,4,5,6,7,8,9,10]
seed(1)
y = randint(5,35,10)
# plot data: x, y values
self.graphWidget.plot(x, y)
def main():
app = QtWidgets.QApplication(sys.argv)
app.setAttribute(QtCore.Qt.AA_Use96Dpi)
main = MainWindow()
main.show()
sys.exit(app.exec_())
if __name__ == '__main__':
main()

The setAttribute solution never worked for me in that way and the windll manipulation makes the gui blurry...
adding following two lines before app = QApplication(sys.argv) solved my problem:
QApplication.setHighDpiScaleFactorRoundingPolicy(Qt.HighDpiScaleFactorRoundingPolicy.PassThrough)
QtCore.QCoreApplication.setAttribute(QtCore.Qt.AA_EnableHighDpiScaling, True)

Answers can be found here: https://github.com/pyqtgraph/pyqtgraph/issues/756
Quick Summary of this issue:
There are essentially two ways to solve this problem.
Make your app DPI-aware (by Androwei)
import ctypes
import platform
def make_dpi_aware():
if int(platform.release()) >= 8:
ctypes.windll.shcore.SetProcessDpiAwareness(True)
# add this code before "app = QtWidgets.QApplication(sys.argv)"
make_dpi_aware()
set Qt.HighDpiScaleFactorRoundingPolicy to PassThrough (by andybarry)
# add this code before "app = QtWidgets.QApplication(sys.argv)"
QtWidgets.QApplication.setAttribute(QtCore.Qt.HighDpiScaleFactorRoundingPolicy.PassThrough)
I have tried both, and they both work perfectly! Thanks to these contributors. Hope you can find this useful as well.

Matplotlib: how to make the background transparent on Windows

I need the whole plot window to be transparent so that a chrome window, for example, on my desktop could be seen through the plot, so that I can add points to it while seeing what's behind it.
https://stackoverflow.com/a/45505906/13650485
The answer I've listed above is EXACTLY what I want to do, except my interactive system doesn't work with TK. I'd like to use Qt5Agg. When I run the code above, the system won't accept it -- it says QT5 is currently running. If I run it without QT already loaded, it creates a blank transparent window (yay!) but if I move it or click on the icon it turns opaque black without any plot. If I change tk to Qt5 it complains on lift. If I remove the "win" code, it has no transparency(obviously). I've tried adding everything I can think of to make the canvas transparent and I can change the color but not make it transparent.
import matplotlib
# make sure Tk backend is used
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
# create a figure and some subplots
fig, ax = plt.subplots(figsize=(4,2))
ax.plot([2,3,5,1])
fig.tight_layout()
win = plt.gcf().canvas.manager.window
win.lift()
win.attributes("-topmost", True)
win.attributes("-transparentcolor", "white")
plt.show()
When I made the changes suggested by: eyllanesc
I found within a vanilla Spyder 4.1.3 | Python 3.7.7 64-bit | Qt 5.9.6 | PyQt5 5.9.2 | Windows 10
In order to import QtCore I had to first
conda install pyqt
not enough, so then conda install pyqt5
and also conda update --all
When I did that, the code ran without errors. This is a better first result!, but I still only get the frozen mpl.fig window. This time, however, it is white. . . The console returns, but the mpl window hangs. Run again, a new frozen window. Restart and run again: same result.
I hope that this is a simple error; please teach this newby.
#eyllanesc
Revised: Python screen tracing application – needs a mostly transparent plot window.
I need the whole plot window to be transparent so that a chrome window, for example, on my desktop could be seen through the plot, so that I can add plot (x, y) points to it while seeing what's behind it.
Adding the command win.setWindowFlags(QtCore.Qt.FramelessWindowHint) did indeed make the window transparent, but it made the tool bar transparent, got rid of the title bar, and removed the ability to move or resize the window. It also made it so that the graph area was not sensitive to the mouse unless I was over the line. I added the facecolor attribute to the subplots command so I could see what was going on. As long as I put a non-zero value for either the fig-alpha or the ax-alpha, the graph is sensitive to the mouse over the whole area.
I need to be able to move and resize the window and would like to have the toolbar be opaque or at least sensitive to the mouse over the whole toolbar. Can you help with this? Thanks for past help!
## Python Code Fragment by Helen for Windows 10
## to test sequence creating plot with transparent
## background (to be used to trace and record xy pairs)
from PyQt5 import QtCore
import matplotlib
matplotlib.use("Qt5Agg") #define backend, must be before pyplot is imported
import matplotlib.pyplot as plt
# create a figure and a subplot
fig,ax = plt.subplots(figsize=(4, 2),facecolor=(1.,1.,0.,0.1)) #facecolor of figure
fig.patch.set_alpha(0.1)
ax.patch.set_alpha(0.1)
# plot some fixed points
ax.plot([2, 3, 5, 1])
fig.tight_layout()
#make window transparent to the desktop
win = plt.gcf().canvas.manager.window
win.setAttribute(QtCore.Qt.WA_NoSystemBackground, True)
win.setAttribute(QtCore.Qt.WA_TranslucentBackground, True)
win.setStyleSheet("background:transparent")
win.setWindowFlags(QtCore.Qt.FramelessWindowHint)
win.setWindowTitle("My App")
plt.show()

You have to use the Qt flags, tested on Linux:
from PyQt5 import QtCore
import matplotlib
# make sure Tk backend is used
matplotlib.use("Qt5Agg")
import matplotlib.pyplot as plt
# create a figure and some subplots
fig, ax = plt.subplots(figsize=(4, 2))
fig.patch.set_alpha(0.0)
ax.patch.set_alpha(0.0)
ax.plot([2, 3, 5, 1])
fig.tight_layout()
win = plt.gcf().canvas.manager.window
win.setAttribute(QtCore.Qt.WA_NoSystemBackground, True)
win.setAttribute(QtCore.Qt.WA_TranslucentBackground, True)
win.setStyleSheet("background:transparent")
plt.show()

Flickering and jumping output in jupyter using interact widget

I'm trying to make use of Interact in a Jupyter Notebook. A bit down on that page, it's stated that
On occasion, you may notice interact output flickering and jumping, causing the notebook scroll position to change as the output is updated. The interactive control has a layout, so we can set its height to an appropriate value (currently chosen manually) so that it will not change size as it is updated.
And I'm experiencing exactly that problem when I'm trying to replicate the example.
The following snippet...
%matplotlib inline
from ipywidgets import interactive
import matplotlib.pyplot as plt
import numpy as np
def f(m, b):
plt.figure(2)
x = np.linspace(-10, 10, num=1000)
plt.plot(x, m * x + b)
plt.ylim(-5, 5)
plt.show()
interactive_plot = interactive(f, m=(-2.0, 2.0), b=(-3, 3, 0.5))
output = interactive_plot.children[-1]
output.layout.height = '350px'
interactive_plot
... should produce this output:
But I'm only getting this:
To be precise, the sliders do pop up for half a second, but seem to be overwritten by the graph. I'm guessing that this is exactly the problem they are adressing. So I thought I could remedy the problem using different values than 350 in output.layout.height = '350px', but with absolutley no success so far. I've tried 100, 200, 250, 300, 750 and 1400.
So could there be something else causing the problems?
Thank you for any suggestions!

I just checked that the solution proposed here:
Jupyter Notebook: interactive plot with widgets
works with recent classic Jupyter.
To avoid flickering you could switch to using
https://github.com/AaronWatters/jp_doodle -- look at the examples
under "Features" named interactive*. The jp_doodle dual_canvas
has a context manager which suppresses intermediate updates.

Mayavi colorbar in TraitsUI creating blank window

I'm trying to create a GUI in TraitsUI that includes two Mayavi figures. I have implemented these figures as per the multiple engines example in the Mayavi documentation.
However, when I add a colorbar to one of the figures and run the GUI script it sometimes opens a blank Mayavi Scene Editor window in addition to the desired TraitsUI window. This blank window doesn't always appear, never on the first run after restarting the python kernel, and sometimes only after running the script a few times in succession and closing the windows that appear each time.
Running the much-reduced code below produces the same behaviour, and removing the mlab.colorbar(s) line stops the problem. How can I get a colorbar without opening blank windows? There doesn't seem to be an obvious way to assign a colorbar to a specific figure as for the surface plot. I am running Python 3.5 on Windows 7 (but get the same issues on Ubuntu).
from traits.api import HasTraits, Instance, on_trait_change
from traitsui.api import View, Item
import numpy as np
from mayavi.core.api import Engine
from mayavi.core.ui.api import SceneEditor, MlabSceneModel
from mayavi import mlab
#Generate a test surface to display
def test_surf():
x, y = np.mgrid[-7.:7.05:0.1, -5.:5.05:0.05]
z = np.sin(x + y) + np.sin(2 * x - y) + np.cos(3 * x + 4 * y)
return x, y, z
class MyApp(HasTraits):
#Create a mayavi scene with a specified engine
engine = Instance(Engine, ())
scene = Instance(MlabSceneModel)
def _scene_default(self):
self.engine.start()
return MlabSceneModel(engine=self.engine)
#Plot the surface when the scene is activated
#on_trait_change('scene.activated')
def populate_scene(self):
s = mlab.surf(*test_surf(), figure=self.scene.mayavi_scene)
mlab.colorbar(s)
view = View(Item('scene', editor=SceneEditor()))
if __name__ == '__main__':
MyApp().configure_traits()

You may add something that closes/quits the windows you invoke.
For example you could close the figure self.scene.mayavi_scene using function mayavi.mlab.close.