Speed up ConstructReliabilityCalibrationTables

[btrotta-bom]

Speed up ConstructReliabilityCalibrationTables. When tested on a gridded forecast with 7 reliability bins, the speedup is around 35%. The improved performance is achieved by replacing a loop iterating over the reliability bins with np.searchsorted. The numpy method uses binary search, so has time complexity log(n), where n is the number of reliability bins, whereas the old method is linear in n. So in general we should expect a speedup of around 1-log(n)/n. Also the numpy method is vectorised, whereas the old code uses a python loop, which should give a further improvement.

I have added a test for when the forecast contains nans, since the handling of this is a little trickier than in the old code.

Testing:

• [x] Ran tests and they passed OK
• [x] Added new tests for the new feature(s)

[codecov[bot]]

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 98.41%. Comparing base (96cdbef) to head (22dad9f). Report is 10 commits behind head on master.

Additional details and impacted files

```diff @@ Coverage Diff @@ ## master #1987 +/- ## ========================================== + Coverage 98.39% 98.41% +0.02% ========================================== Files 124 126 +2 Lines 12209 12403 +194 ========================================== + Hits 12013 12207 +194 Misses 196 196 ```

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[btrotta-bom]

this should remain open

[btrotta-bom]

• searchsort is mapping each forecast probability to one of the probability bins and returns an index to the associated bin,
• put_along_axis is using the bin index for each forecast probability to populate forecast_probabilities with the associated probability value (setting values outside the bin to 0) and forecast_counts with 1 (and again setting values outside the bin to 0).

Yes, correct