rank_histogram tied ranks

[aaronspring]

Description

Closes #335

Type of change

• New feature (non-breaking change which adds functionality)

How Has This Been Tested?

• pytest

Checklist (while developing)

• I have added docstrings to all new functions.
• I have commented my code, particularly in hard-to-understand areas
• Tests added for pytest, if necessary.

Pre-Merge Checklist (final steps)

• I have rebased onto main or develop (wherever I am merging) and dealt with any conflicts.
• I have squashed commits to a reasonable amount, and force-pushed the squashed commits.

References

Hamill, T. M. (2001). Interpretation of Rank Histograms for Verifying Ensemble Forecasts. Monthly Weather Review, 129(3), 550–560. doi: 10/dkkvh3

[pep8speaks]

Hello @aaronspring! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2021-11-26 01:01:38 UTC

[codecov]

Codecov Report

Merging #364 (df38314) into main (b52cc3f) will decrease coverage by 0.13%.
The diff coverage is 88.09%.

@@            Coverage Diff             @@
##             main     #364      +/-   ##
==========================================
- Coverage   94.58%   94.45%   -0.14%     
==========================================
  Files          24       24              
  Lines        2661     2688      +27     
==========================================
+ Hits         2517     2539      +22     
- Misses        144      149       +5     

Impacted Files	Coverage Δ
xskillscore/core/resampling.py	100.00% <ø> (ø)
xskillscore/core/np_deterministic.py	98.49% <66.66%> (-0.75%)	⬇️
xskillscore/core/probabilistic.py	89.85% <86.36%> (-0.50%)	⬇️
xskillscore/core/np_probabilistic.py	100.00% <100.00%> (ø)
xskillscore/tests/test_probabilistic.py	96.76% <100.00%> (+0.04%)	⬆️

---

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[aaronspring]

#258 handled for rank_histogram

[aaronspring]

@raspstephan interested in reviewing/trying out?

[benowen-bom]

I think this is a good fix to #335. The changes LGTM and I've tested the function out on some sample distributions with tied ranks and the behaviour seems correct.

I have provided a suggestion that might speed things up faster, but overall LGTM.

[aaronspring]

thanks for the review @benowen-bom implemented your suggestion and it is great to rely on apply_on_axis which is a loop.

[dougiesquire]

Looks great to me - thanks @aaronspring and @benowen-bom! One very minor comment

[raspstephan]

Awesome, thanks @aaronspring for implementing this!