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.
Related
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
The following is a simplified example of my code. The idea behind this class is to show the figure only when the show method is executed.
# my_module.py
import matplotlib.pyplot as plt
import numpy as np
class Test:
def __init__(self):
self._fig = plt.figure()
self.ax = self._fig.add_subplot(1, 1, 1)
def show(self):
x = np.linspace(0, 10, 100)
y = np.sin(x)
self.ax.plot(x, y)
self._fig.tight_layout()
self._fig.show()
The code works as expected when it is executed from a Python shell or ipython. However, if I run this inside a Jypter Notebook:
from my_module import Test
t = Test()
At this point, an empty figure is visualized on the screen. I don't want that! Now, I tried to insert plt.close(self._fig) inside __init__, but then when I run t.show() I get UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.
I also tried to load %matplotlib widget with the previous edit plt.close(self._fig). The pictures is only shown when show is called, but it is just a picture with no interactive frame.
Another option would be to rewrite the class in such a way that the figure is created inside the show method. This is far from optimal as I would need to re-adjust my tests.
Are there any other ways to get it working correctly on all shells?
In the original post I've done two mistakes.
First, the figure was instantiated into the __init__ method, then the show method was called. In an interactive environment, once the figure is created it will be shown on the screen. We could turn off that behaviour with plt.ioff(), but then two things can happen:
If %matplotlib widget was executed, the figure will show up only once when calling t.show().
Otherwise, no plot will be shown on the screen when calling t.show().
Hence, plt.ioff() is not a valid solution. Instead, the figure must be instantiated when t.show() is executed.
The second mistake I did was to use self._fig.show(). Remember, in a interactive environment the figure is shown as soon as it is instantiated. Then, the previous command shows the figure a second time! Instead, I have to use plt.show(), which only display the figure once.
Here is the correct code example:
import matplotlib.pyplot as plt
import numpy as np
class Test:
def __init__(self):
# init some attributes
pass
def show(self):
self._fig = plt.figure()
self.ax = self._fig.add_subplot(1, 1, 1)
x = np.linspace(0, 10, 100)
y = np.sin(x)
self.ax.plot(x, y)
self._fig.tight_layout()
plt.show()
t = Test() # no figure is shown
t.show() # figure is shown
Before plotting using matplotlib, you must specify your display's DPI if you have a high DPI display, since otherwise the image is too small. I have a 4K display, so I definitely need to do this. (I think that matplotlib should automatically do this for you, but that is another topic...)
As a first attempt to specify the DPI, consider the code below. It manually specifies the display's DPI and then creates and plots a test DataFrame:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sys
# method #1: manually specify my display's DPI:
dpi = 163 # this value is valid for my Dell U2718Q 4K (3840 x 2160) display
plt.rcParams["figure.dpi"] = dpi
print("plt.matplotlib.rcParams[\"figure.dpi\"] = " + str(plt.matplotlib.rcParams["figure.dpi"]))
# define a test DataFrame (here, chose to calculate sin and cos over their range of 2 pi):
n = 100
x = (2 * np.pi / n) * np.arange(n)
df = pd.DataFrame( {
"sin(x)" : np.sin(x),
"cos(x)": np.cos(x),
}
)
# plot the DataFrame:
df.plot(figsize = (12, 8), title = "sin and cos", grid = True, color = ["red", "green"])
When I put the code above into a file and run it all at once in PyCharm, everything behaves exactly as expected: the script completes without error, the plot is generated at the correct size, and the plot remains open in a window after the script ends.
So far, so good.
But the code above is brittle: run it on a computer with a different display DPI, and the image will not be sized correctly.
Doing a web search, I found this link which has code claims to automatically determine your display's DPI. My (slight) adaptation of the code is this
# method #2: call code to determine my display's DPI (only works if the backend is Qt)
if plt.get_backend() == "Qt5Agg":
from matplotlib.backends.qt_compat import QtWidgets
qApp = QtWidgets.QApplication(sys.argv)
plt.matplotlib.rcParams["figure.dpi"] = qApp.desktop().physicalDpiX()
If I modify my file to use the code above ("method #2") instead of the manual DPI setting ("method #1"), I find that the script completes without error, but the plot only comes up for a brief instant before being automatically closed!
By successively commenting out lines in the "method #2" code, starting with the last and working backwards, I have determined that the culprit is the call to QtWidgets.QApplication(sys.argv).
In particular, if I reduce the "method #2" code to just this
if plt.get_backend() == "Qt5Agg":
from matplotlib.backends.qt_compat import QtWidgets
QtWidgets.QApplication(sys.argv)
I get this plot auto close behavior.
Another defect, is that the original "method #2" code calculates the DPI of my monitor, a Dell U2718Q, to be 160, when it really is 163: in this link go to p. 3 / 4 and look at the Pixels per inch (PPI) spec.
Does anyone know of a solution to this?
Better code to determine the DPI?
A modification of the "method #2" code which will not cause plots to auto close?
Is this a bug that needs to be reported to matplotlib or Qt?
So I have Anaconda on Windows running an environment with a bunch of stuff and:
Python 3.6.6
matplotlib 2.2.3
And when I run following code, my Python kernel crashes:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10,10))
fig = plt.figure(figsize=(10,10))
fig.add_axes([10,200,50,50])
Does anyone know why that happens or perhaps can try to reproduce so that I can know it's not just my pc dying on me?
Short answer: Use reasonable coordinates to place your axes, i.e. numbers between 0 and 1, for example
fig.add_axes([0.1 ,0.2, 0.5, 0.5])
I suppose the numbers are just too big. The figure ranges between 0 and 1. So while there are certainly cases where you may want to add an axes outside that range e.g. [-0.1,0.7,0.3,0.5], creating an axes at a coordinate 200 times the figure height is not very useful.
Well, "not useful" does not mean it should crash. So what's probably happening is that you are trying to show this figure on screen in a version which expands the figure size to the figure content. This might happen by default in a jupyter notebook, which shows the figure that is saved via fig.savefig(..., bbox_inches="tight"). So assuming a figure size of 10 by 10 inch, [10,200,50,50] leads to a figure tried to be saved of dimension 60*10=600 inch in width and 250*10=2500 inch in height. With a dpi of 72, this leads to a png image of (43200 x 180000) pixels. This seems to be to much to be handled by the renderer.
When running the code as script (adding fig.savefig(..., bbox_inches="tight")) you actually do get an error using the usual Agg-based renderer,
RuntimeError: Unknown exception in RendererAgg
which means that the renderer is not capable of producing the figure. I'm not sure why in Ipython/Juypter no such error is raised.
A more useful errormessage is shown with the "cairo" renderer,
import matplotlib
matplotlib.use("Qt4Cairo")
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10,10))
fig = plt.figure(figsize=(10,10))
fig.add_axes([10,200,50,50])
fig.savefig("hugeaxesposition.png", bbox_inches="tight")
shows
cairocffi.CairoError: cairo returned CAIRO_STATUS_INVALID_SIZE:
invalid value (typically too big) for the size of the input (surface, pattern, etc.)
I would agree that seeing such error would be more useful from a user's perspective than a crash.
While the reason for no error being shown in IPython/Jupyter might be interesting to investigate further, it sure has no high priority, since matplotlib is not designed to produce huge figures anyways.
Looking at the documentation of Figure.add_axes, the first argument is:
rect : sequence of float
The dimensions [left, bottom, width, height]
of the new axes. All quantities are in fractions of figure width and
height.
The values are fractions of the figure height and width and so should be between 0 and 1
I'm trying to create an animated histogram for work, using matplotlib.animation, but animation.FuncAnimation is not functioning properly : when using this code, that i found on the official documentation,
"""
A simple example of an animated plot
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
x = np.arange(0, 2*np.pi, 0.01)
line, = ax.plot(x, np.sin(x))
def animate(i):
print(i)
line.set_ydata(np.sin(x + i/10.0)) # update the data
return line,
# Init only required for blitting to give a clean slate.
def init():
line.set_ydata(np.ma.array(x, mask=True))
return line,
ani = animation.FuncAnimation(fig, animate, np.arange(1, 200),init_func=init,interval=25, blit=True)
plt.show()
I get as final result the graph created by init() function (an empty graph also), but no animate iterations. Furthermore, I tested other codes, which practically gave me the same result : i get the initialization, or the first frame, but not more. matplotlib and matplotlib.animation are installed, everything seems to be ok, except it doesn't work. Have someone an idea how to fix it ? (Thank you in advance :) !)
I had the same issue working with Jupyter notebook and I solved it by inserting the line
%matplotlib notebook
in the code.
It may be that IPython inside your Spyder is configured to automatically use the inline backend. This would show your plots inside the console as png images. Of course png images cannot be animated.
I would suggest not to use IPython but execute the script in a dedicated Python console. In Spyder, go to Run/Configure.. and set the option to new dedicated Python console.