[FIX] Remove requirement for equal {participants x sessions} across pipelines/assessments

[alyssadai]

• Fixes [BUG] extra requirements for same subjects and sessions across pipelines seems too restrictive #61

Changes proposed in this pull request:

• Removed unnecessary check for equal {participants x sessions} across pipelines (for imaging CSVs) or assessments (for phenotypic CSVs)
  • When pivoting the long-format input to wide, pandas will simply create empty cells for any combinations that do not exist in the original data, which we think is fine for now
• Switched to using a method for reshaping the long input data to wide (pd.pivot -> pd.pivot_table) that has finer control over the aggregation method when there are duplicates, and values used to fill missing values in the resulting pivoted table
  • EDIT: Sticking with pd.pivot due to known problems with pd.pivot_table silently dropping NaNs
• Added input check for duplicate entries for a given participant-session and pipeline/assessment combination (i.e., when there is >1 value for a participant-session for a specific pipeline or phenotypic measure)
  • When this happens, error out for now (pivot_table could theoretically handle this by keeping only the first occurrence in the resulting pivoted table, but this is not intuitive and may mean relevant data is lost - rather, we probably want to flag and tell users to fix duplicate observations)

Checklist

This section is for the PR reviewer

• PR has an interpretable title with a prefix ([ENH], [FIX], [REF], [TST], [CI], [MNT], [INF], [MODEL], [DOC]) (see our Contributing Guidelines for more info)
• PR links to GitHub issue with mention Closes #XXXX
• Tests pass
• Checks pass

For new features:

• Tests have been added

For bug fixes:

• There is at least one test that would fail under the original bug conditions.

[surchs]

Thanks @alyssadai! I have not tested this locally, but the code makes sense to me! If you'd like me to test it, could you provide some files to try the new functionality out with?

I left some comments, otherwise looks 🧑‍🍳

[alyssadai]

Thanks for your comments @surchs!

I've made some additional changes including reverting back to using pivot instead of pivot_table due to some (documented) issues w/ the latter dropping NaNs silently that I didn't notice before. Tests and comments have also been updated to prevent this from coming back, and to capture some of the current peculiarities in the long-to-wide tabular data transform.

Let me know if the changes/comments make sense 🙂

[surchs]

looks great, thanks @alyssadai !

🧑‍🍳