Soiling algorithm updates

[martin-springer]

• Code changes are covered by tests
• Code changes have been evaluated for compatibility/integration with TrendAnalysis
• New functions added to __init__.py
• API.rst is up to date, along with other sphinx docs pages
• Example notebooks are rerun and differences in results scrutinized
• Updated changelog

[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 96.03%. Comparing base (596f584) to head (9b1fd4c).

Additional details and impacted files

@@               Coverage Diff               @@
##           development     #435      +/-   ##
===============================================
- Coverage        96.19%   96.03%   -0.17%     
===============================================
  Files               11       11              
  Lines             2209     2370     +161     
===============================================
+ Hits              2125     2276     +151     
- Misses              84       94      +10     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[martin-springer]

Seems like the pytest.PytestRemovedIn8Warning: in the degradation_and_soiling_example notebook might be related to nbval < 0.10 computationalmodelling/nbval#180

However, nbval greater than 0.9.6 has a bug with semicolon computationalmodelling/nbval#194

@mdeceglie - I believe you removed most of the semicolons from the notebooks? If so, we could try using a newer version of nbval.

[mdeceglie]

There are still a few semicolons, I'm fine with attempting to remove them and updating nbval

[martin-springer]

@mdeceglie - This change from .mean() to .median() seems to cause the RuntimeWarning:Mean of empty slice.
Seems counterintuitive but changing it back to mean get's rid of the warning...

[martin-springer]

We could add a check whether pi_norm is empty.
Also, I'm not sure why "inferred_start_loss" and "inferred_end_loss" are the same here?

[martin-springer]

I'm adding the following condition for now. If there's a better way , we can change it:

"inferred_start_loss": np.nan if run.pi_norm.isna().any() else run.pi_norm.median(), # changed from mean/Matt "inferred_end_loss": np.nan if run.pi_norm.isna().any() else run.pi_norm.median(), # changed from mean/Matt