Solved discrepancy in JJZ vs LW weights

[john-p-ryan]

There was a small error in the usage of the np.gradient function which was the source of the discrepancy in the different methods of estimating social welfare weights. Should be of the form np.gradient(f_array, x_grid) rather than just np.gradient(f_array), which led to the mtr_prime estimates being off by a factor of 10 in the JJZ estimates (our iot.z grid had spacing of $10 by default).

[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 49.37%. Comparing base (10985c1) to head (90e3345).
Report is 36 commits behind head on main.

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HEAD has 1 upload less than BASE

Flag	BASE (10985c1)	HEAD (90e3345)
unittests	3	2

Additional details and impacted files

@@             Coverage Diff             @@
##             main      #33       +/-   ##
===========================================
- Coverage   72.81%   49.37%   -23.45%     
===========================================
  Files           3        3               
  Lines         103      160       +57     
===========================================
+ Hits           75       79        +4     
- Misses         28       81       +53     

Flag	Coverage Δ
unittests	49.37% <100.00%> (-23.45%)	⬇️

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[jdebacker]

@john-p-ryan Great find! Merging.