Fix caching bug in signals.deterministic_signals.Deterministic

[vallis]

This bug will cause the wrong delays to be returned when signals.deterministic_signals.Deterministic.get_delay() is called repeatedly with the same parameters (e.g., when computing finite-difference partial derivatives). It wouldn't be a problem when every set of parameters is new, as in MCMC sampling.

Reproducible symptom: when issuing the sequence

xxret = deterministic_instance.get_delay(xx)
yyret = deterministic_instance.get_delay(yy)
xxret2 = deterministic_instance.get_delay(xx)

one finds that yyret != xxret (correct) but xxret2 = yyret (wrong!).

Cause: that signals.deterministic_signals.Deterministic computes the delay and stores it in an instance variable, then the caching mechanism (signal.cache_call) saves a reference to the instance variable. So the second call (get_delay(yy)) ends up modifying the cached value for xx.

Cure: The fix is for get_delay to return a new array whenever it's called (this won't prevent caching to function correctly).
In general, any Signal that uses caching must return new numpy arrays if these are functions of the parameters. It's only OK to return references to instance variables for arrays that never change.

[codecov]

Codecov Report

Merging #325 (50a12b2) into master (b4a651a) will increase coverage by 0.00%.
The diff coverage is 100.00%.

Additional details and impacted files

@@           Coverage Diff           @@
##           master     #325   +/-   ##
=======================================
  Coverage   88.37%   88.37%           
=======================================
  Files          13       13           
  Lines        3011     3012    +1     
=======================================
+ Hits         2661     2662    +1     
  Misses        350      350           

Impacted Files	Coverage Δ
enterprise/signals/deterministic_signals.py	100.00% <100.00%> (ø)

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