Add bokeh plots and deprecate contributions_pie

[raoulcollenteur]

Short Description

This PR adds basic plotting using Bokeh to Pastas models. To use this, bokeh plots needs to be registered as an extension, as implemented by @dbrakenhoff for plotly. I started with two basic plots, but this can grow over time.

I also added the deprecation for contributions_pie.

Checklist before PR can be merged:

• closes issue [DEVELOPMENT] Implementation of interactive Bokeh/Panel plots for Pastas models #638, Remove Contribution_pie plot #711
• is documented
• Format code with Black formatting
• type hints for functions and methods
• tests added / passed
• Example Notebook (for new features)
• Remove output for all notebooks with changes

[codacy-production]

Coverage summary from Codacy

See diff coverage on Codacy

Coverage variation	Diff coverage
✅ +0.16% (target: +0.00%)	✅ 60.00%

Coverage variation details

	Coverable lines	Covered lines	Coverage
Common ancestor commit (c7b2b58)	6114	4625	75.65%
Head commit (0fb6ce1)	6130 (+16)	4647 (+22)	75.81% (+0.16%)

Coverage variation is the difference between the coverage for the head and common ancestor commits of the pull request branch: <coverage of head commit> - <coverage of common ancestor commit>

Diff coverage details

	Coverable lines	Covered lines	Diff coverage
Pull request (#715)	5	3	60.00%

Diff coverage is the percentage of lines that are covered by tests out of the coverable lines that the pull request added or modified: <covered lines added or modified>/<coverable lines added or modified> * 100%

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[raoulcollenteur]

@dbrakenhoff shall I extend the plotly notebook example and add bokeh there as well, and rename to interactive_plots.ipynb?

[raoulcollenteur]

@dbrakenhoff this PR is ready for Review. But perhaps you can have a quick look at the Plotly plots in the notebook, somehow they don't show up on my system?

Cheers,
Raoul

[dbrakenhoff]

@raoulcollenteur, both are working for me in jupyter lab. Which versions are you on? For me. the following worked:

• plotly 5.16.1
• bokeh 3.4.0
• jupyter lab 4.1.1

FYI, there seems to be some funkiness going on in the bokeh plots:

And a note for anyone trying to view the bokeh plots in a VS Code notebook environment, the interactive figures don't show up.
bokeh/bokeh#10765

[dbrakenhoff]

See earlier comment about the funkiness in Bokeh plot. Otherwise looking good!

Setting up somewhat complicated shared axes seems a lot easier in bokeh than in plotly (not having to dive down into the low-level API). And I'm a fan of the range slider at the bottom :). Maybe we can just leave the background of that plot white, so the selected range (also gray) is clearer.

[raoulcollenteur]

@raoulcollenteur, both are working for me in jupyter lab. Which versions are you on? For me. the following worked:

• plotly 5.16.1
• bokeh 3.4.0
• jupyter lab 4.1.1

FYI, there seems to be some funkiness going on in the bokeh plots:

And a note for anyone trying to view the bokeh plots in a VS Code notebook environment, the interactive figures don't show up. bokeh/bokeh#10765

Hmm, interesting... Was this in VS code or JupyterLab? Do you have bokeh-jupyter-bokeh extension installed in JupyterLab? Perhaps that is necessary?

[dbrakenhoff]

Was this in VS code or JupyterLab? Do you have bokeh-jupyter-bokeh extension installed in JupyterLab? Perhaps that is necessary?

This was in JupyterLab. Both without and with the bokeh-jupyter-bokeh jupyterlab extension installed.
VS code does not render Bokeh plots unfortunately.

I think the data isn't sorted properly when I run ml.get_output_series():

[raoulcollenteur]

Hmm, so the problem is not in bokeh plots. If I run the code above my data is well ordered.

Python version: 3.11.3
NumPy version: 1.24.4
Pandas version: 2.1.4
SciPy version: 1.12.0
Matplotlib version: None
Numba version: 0.59.0
LMfit version: 1.2.2
Latexify version: Not Installed
Pastas version: 1.5.0b

Is this a new Pandas problem?!

[dbrakenhoff]

That could well be it, pandas 2.2 here.

[codacy-production]

Coverage summary from Codacy

See diff coverage on Codacy

Coverage variation	Diff coverage
✅ +0.06% (target: +0.00%)	✅ 66.67%

Coverage variation details

	Coverable lines	Covered lines	Coverage
Common ancestor commit (c7b2b58)	6114	4625	75.65%
Head commit (efe9f9a)	6137 (+23)	4646 (+21)	75.70% (+0.06%)

Coverage variation is the difference between the coverage for the head and common ancestor commits of the pull request branch: <coverage of head commit> - <coverage of common ancestor commit>

Diff coverage details

	Coverable lines	Covered lines	Diff coverage
Pull request (#715)	6	4	66.67%

Diff coverage is the percentage of lines that are covered by tests out of the coverable lines that the pull request added or modified: <covered lines added or modified>/<coverable lines added or modified> * 100%

See your quality gate settings    Change summary preferences

_{You may notice some variations in coverage metrics with the latest Coverage engine update. For more details, visit the documentation}

[raoulcollenteur]

I think it is related to this issue: pandas-dev/pandas#57006

Probably good to fix in ml.get_out_series anyway.. Could you check if adding sort=True in pd.concat fixes the problem?

[dbrakenhoff]

Yep, that fixes it! Committed the change. Is it still working on your end?

[raoulcollenteur]

Yes all good here! I'll go through the changes in 2.2 again to see if more might be up. Should we merge this PR now?

[raoulcollenteur]

I updated the docs (https://pastas--715.org.readthedocs.build/en/715/api/generated/generated/generated/pastas.plotting.bokeh.Bokeh.results.html#pastas.plotting.bokeh.Bokeh.results) which I think looks better now. Somehow GH requires you need to re-review the PR @dbrakenhoff.