fix: handle negative model predictions in visualizations

[rmnmllr]

related to issue #388.

Tried python -m pytest, did not pass all tests even before modifying anything. No additional errors though after the fix and tested with samples mantioned in issue #388.

[rmnmllr]

@alexander-held I'm not sure why the python tests fail, pytest gives an error

>       assert "predicted yield is zero in 1 bin(s), excluded from ratio plot" in [
            rec.message for rec in caplog.records
        ]
E       AssertionError: assert 'predicted yield is zero in 1 bin(s), excluded from ratio plot' in ['predicted yield is zero in 1 bin(s),            excluded from ratio plot']

but this is not something I touched.

[alexander-held]

Thanks a lot for preparing this! I am afraid it may take me until next week to more carefully look at this, sorry for the slow feedback here.

[rmnmllr]

Thanks a lot for preparing this! I am afraid it may take me until next week to more carefully look at this, sorry for the slow feedback here.

No worries, I'm glad I can contribute in some way!
And no rush with this, I can run stuff already locally :)

[codecov]

Codecov Report

Patch coverage: 100.00% and no project coverage change.

Comparison is base (4645cb8) 100.00% compared to head (89b4170) 100.00%.

Additional details and impacted files

@@            Coverage Diff            @@
##            master      #394   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
=========================================
  Files           23        23           
  Lines         2059      2065    +6     
  Branches       324       326    +2     
=========================================
+ Hits          2059      2065    +6     

Impacted Files	Coverage Δ
src/cabinetry/visualize/plot_model.py	100.00% <100.00%> (ø)

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[alexander-held]

Hi @rmnmllr, I added tests to this to get this ready for inclusion in the next version of cabinetry that I intend to release with #390 also being completed. Thanks a lot for preparing this!

* catch negative total model prediction in visualize.plot_model.data_mc
* handle negative nominal template prediction in visualize.plot_model.templates

[rmnmllr]

Thank you @alexander-held for completing this!