Reimplimentation of strict thresholding

[w-k-jones]

Fix for the bugs identified in strict thresholding in #314 and #315 . Strict thresholding is now handled in remove_parents, by removing features which do not have parents, rather than by modifying the input data.

• Strict thresholding now works for target="minimum"
• Strict thresholding produces identical results to non-stric thresholding if n_min_threshold is a fixed value
• New tests added for strict thresholding

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[codecov]

Codecov Report

Patch coverage: 60.00% and project coverage change: -0.21% ⚠️

Comparison is base (d819159) 56.04% compared to head (c21905e) 55.84%.

Additional details and impacted files

@@                Coverage Diff                 @@
##           RC_v1.5_hotfix     #316      +/-   ##
==================================================
- Coverage           56.04%   55.84%   -0.21%     
==================================================
  Files                  15       15              
  Lines                3290     3302      +12     
==================================================
  Hits                 1844     1844              
- Misses               1446     1458      +12     

Flag	Coverage Δ
unittests	55.84% <60.00%> (-0.21%)	⬇️

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Files Changed	Coverage Δ
tobac/feature_detection.py	83.94% <60.00%> (-2.53%)	⬇️

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[JuliaKukulies]

This looks all good to me and I am happy with letting remove_parents take care of the strict thresholding. Great solution @w-k-jones !

[w-k-jones]

Thanks! In future we should possibly either rename remove_parents or split it into multiple methods that handle operations involving features and their parents to reflect its wider responsibility, but this can wait until a later refactor

[harrietgilmour]

This all looks good! I also tested new implementation with own data and it appears to be working as expected. There are no longer tracks detected at a lower minimum value than the dataset.min() when using threshold='minimum' and strict_thresholding=True. It also produces the same number of tracks when using a single nminthreshold with strict_thresholding=True and strict_thresholding=False. Thanks for sorting this out :)

[w-k-jones]

This all looks good! I also tested new implementation with own data and it appears to be working as expected. There are no longer tracks detected at a lower minimum value than the dataset.min() when using threshold='minimum' and strict_thresholding=True. It also produces the same number of tracks when using a single nminthreshold with strict_thresholding=True and strict_thresholding=False. Thanks for sorting this out :)

Thanks for the review! I'll merge now