Getter Docstrings

[pauladkisson]

Added Returns section to all getter docstrings.

Fixes #1097

Cline (Claude 3.5 Sonnet) helped. It was so close to just getting it right after the first couple attempts, but then took a lot of coaching to get those last few...

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 89.58%. Comparing base (96dfdff) to head (857436a).
Report is 26 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1185      +/-   ##
==========================================
- Coverage   90.69%   89.58%   -1.11%     
==========================================
  Files         129      129              
  Lines        8189     8357     +168     
==========================================
+ Hits         7427     7487      +60     
- Misses        762      870     +108     

Flag	Coverage Δ
unittests	89.58% <100.00%> (-1.11%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
src/neuroconv/basedatainterface.py	80.21% <ø> (-15.13%)	⬇️
src/neuroconv/baseextractorinterface.py	100.00% <ø> (ø)
...oconv/datainterfaces/behavior/video/video_utils.py	97.14% <ø> (ø)
...atainterfaces/behavior/video/videodatainterface.py	93.54% <ø> (ø)
...roconv/datainterfaces/ecephys/axona/axona_utils.py	57.94% <ø> (ø)
...erfaces/ecephys/baserecordingextractorinterface.py	93.87% <ø> (+0.06%)	⬆️
...nterfaces/ecephys/basesortingextractorinterface.py	80.19% <ø> (ø)
...ainterfaces/ecephys/neuroscope/neuroscope_utils.py	100.00% <ø> (ø)
...aces/ecephys/neuroscope/neuroscopedatainterface.py	93.65% <ø> (-2.39%)	⬇️
.../ecephys/openephys/openephysbinarydatainterface.py	97.29% <ø> (ø)
... and 29 more

... and 10 files with indirect coverage changes

[pauladkisson]

After the meeting today, I think this kind of task would be a really good benchmark for AI agents / LLMs / prompts.

Some quantifiable questions:

• how many true positive? (docstrings that needed to be edited that were CORRECTLY edited)
• how many false positives? (docstrings that DIDNT need to be edited that were INCORRECTLY edited)
• how many false negatives? (docstrings that needed to be edited that were MISSED or edited INCORRECTLY)
• how many attempts before it completes the task? (In this case I had to re-prompt cline with different instructions 7 times until it finally got all of the docstrings I wanted)
• Total cost
• Total runtime

With that in mind, it might be worthwhile to branch off from neuroconv with a dedicated ai_docstrings_benchmark branch before merging these changes.