hvplot using groupby with active legend is flickering

[mosc9575]

I am running a hvplot() on a xarray in a Jupyter Notebook in VSCode and the figure is flickering if it has a legend.

The changing size of the figure with the legend is unwanted and makes is hard to focus on the shown data. To me it looks like the figure is updated twice, once without the legend and then the total available space is used, and a second time with the legend.

If this is right, there is somewhere in the process a unneeded calculation.

All software version info

Python, HvPlot, Panel, Bokeh

1. Python version : 3.12.4 (tags/v3.12.4:8e8a4ba, Jun 6 2024, 19:30:16) [MSC v.1940 64 bit (AMD64)]
2. IPython version : 8.25.0
3. Tornado version : 6.4.1
4. Bokeh version : 3.4.2
5. BokehJS static path : ~\Envs\fbpinn\Lib\site-packages\bokeh\server\static
6. node.js version : (not installed)
7. npm version : (not installed)
8. jupyter_bokeh version : 4.0.5
9. hvplot version : 0.10.0
10. panel version : 1.4.4
11. Operating system : Windows-10-10.0.19045-SP0

VSCode

1. Version: 1.91.1 (user setup)
2. Commit: f1e16e1e6214d7c44d078b1f0607b2388f29d729
3. Date: 2024-07-09T22:06:49.809Z
4. Electron: 29.4.0
5. ElectronBuildId: 9728852
6. Chromium: 122.0.6261.156
7. Node.js: 20.9.0
8. V8: 12.2.281.27-electron.0
9. OS: Windows_NT x64 10.0.19045

import hvplot.xarray  # noqa
import numpy as np
from xarray import Dataset

x = np.arange(-np.pi,np.pi,0.1)
t = np.arange(0,10,0.1)
u = np.array([k*np.sin(x) for k in t]).T
v = np.array([k*np.cos(x) for k in t]).T

ds = Dataset({"u": (["x", "time"], u), "v": (["x", "time"], v)}, coords={"x": x, "time": t})

a = ds[["u", "v"]].hvplot(groupby="time", legend=False).opts(ylim=(-10.2,10.2))
b = ds[["u", "v"]].hvplot(groupby="time").opts(ylim=(-10.2,10.2))

a+b

Comment

I was trying to reproduce this in a browser in JupyterLab and JupyterNotebook but there was no flickering. I don't know why. I guess the update frequency can be different, which could explain the different visual observations.

• I may be interested in making a pull request to address this

[ahuang11]

I can reproduce, but not sure why; perhaps something wrong with jupyter-bokeh; maybe you can submit an issue there too https://github.com/bokeh/jupyter_bokeh