[Bug]: matplotlib==3.5.2 breaks ipywidgets #23229
Description
Bug summary
After updating matplotlib to 3.5.2 (from 3.5.1) the display/updating of widgets created with ipywidgets is broken, at least for certain types of plots.
Update of print statements in response to moving the widgets works fine but the plot is not modified.
To reproduce:
- dump the following into
requirements.txt:
ipykernel
jupyterlab-widgets
ipywidgets
numpy
ipympl
matplotlib==3.5.2
conda create python=3.7 --name myenv & conda activate myenv & pip install -r requirements.txt- Run the code below in jupyter
Code for reproduction
from IPython.display import display
from ipywidgets import (
FloatSlider,
HBox,
VBox,
Layout,
fixed,
interactive_output,
)
class MiniRaster:
def __init__(
self,
):
self._trains = [
np.linspace(0, 10, 100),
np.linspace(0, 10, 1000)
]
def _interact(self, ax, linewidth, xmax):
if ax is None:
fig, ax = plt.subplots()
ax.cla()
ax.eventplot(
self._trains,
linewidth=linewidth
)
plt.xlim(0, xmax)
print('linewidth = ', linewidth)
print('xmax = ', xmax)
return ax
def interact(self):
fig, ax = plt.subplots()
linewidth_slider = FloatSlider(
min=0,
max=10,
step=1,
value=1,
description="width=",
continuous_update=False,
layout=Layout(width="95%"),
)
xmax_slider = FloatSlider(
min=1,
max=20,
step=1,
value=10,
description="xmax=",
continuous_update=False,
layout=Layout(width="95%"),
)
ui = VBox([linewidth_slider, xmax_slider])
out = interactive_output(
# return interactive_output(
self._interact,
{
"ax": fixed(ax),
"linewidth": linewidth_slider,
"xmax": xmax_slider
},
)
display(ui, out)
raster = MiniRaster()
%matplotlib widget
raster.interact()Actual outcome
Print statements follow the widgets, not the figure
Expected outcome
When pinning matplotlib version to 3.5.1, both the print statement and the figure follow the widgets
Additional information
This doesn't happen for all types of figures/widgets.
I'm at a loss as to why, but this snippet works fine for me for both versions
import ipywidgets as widgets
m = widgets.FloatSlider(min=-5,max=5,step=0.5, description="Slope")
c = widgets.FloatSlider(min=-5,max=5,step=0.5, description="Intercept")
# An HBox lays out its children horizontally
ui = widgets.HBox([m, c])
def plot(m, c):
x = np.random.rand(10)
y = m *x + c
plt.cla()
plt.plot(x,y)
plt.show()
out = widgets.interactive_output(plot, {'m': m, 'c': c})
display(out, ui)
I had the same behavior on python3.7 and 3.10, and on VSCode jupyter and jupyter notebook.
conda == 4.11.0
Operating system
Ubuntu
Matplotlib Version
3.5.2
Matplotlib Backend
module://ipympl.backend_nbagg
Python version
3.10.4
Jupyter version
6.4.12
Installation
pip
Activity
tacaswell commentedon Jun 9, 2022
If you add a
plt.ion()in just after%matplotlib widgetdoes it work again?Can you try editing your post, the layout markup is not quite right.
TomBugnon commentedon Jun 9, 2022
Yes it works again with
plt.ion()..!Is it a bug or should I have been better reading the doc? :)
ianhi commentedon Jun 9, 2022
@TomBugnon in general I would try to avoid mixing
interactive_outputwith%matplotlib widget, instead you shoulddisplay(fig.canvas)see more about this here: https://matplotlib.org/ipympl/examples/full-example.html#interacting-with-other-widgets. There is some complex behavior ininteractive_outputthat is tailored for theinlinebackend which expects a new figure to be created everytime, whereas here you are updating the same figureIn your case you can just include the
fig.canvasin the VBox like so:ui = VBox([linewidth_slider, xmax_slider, fig.canvas])tacaswell commentedon Jun 9, 2022
xref matplotlib/ipympl#469 and this should be fixed by #23057
Erotemic commentedon Jul 18, 2022
I think I'm experiencing the same issue. A downgrade to 3.5.1 resolves the issue for me, but I'm not using "widget", I'm using the "qt" backend.
I've been using matplotlib + terminal IPython for whats coming close to a decade, so I'm not doing any fancy new Jupyter notebook stuff. I just open my terminal and start making figures. The following code when pasted into IPython breaks for me on matplotlib 3.5.2:
Hopefully the patch fixes this case too, but I figure I'd comment because this MWE is a bit simpler than the previous and might be easier to integrate into a regression test.
Tested on CPython 3.10.5 with IPython 8.4.0, and CPython 3.9.9 with IPython 8.3.0 in Ubuntu 22.04, all installed via pyenv and pip.
jklymak commentedon Jul 18, 2022
@Erotemic please open a new issue explaining exactly what has broken for you.