RimEnsembleCurveFilter::applyFilter() (SUMMARY_VALUE mode) only compared
the value at the last time step inside the selected time range against
the filter's value range, discarding the result of every earlier time
step. For monotonically increasing cumulative vectors (e.g. FOPT) this
coincidentally matched user expectations, but for rate vectors (e.g.
FOPR) that oscillate at high frequency between zero and a non-zero
value, the outcome depended arbitrarily on whether the last time step
happened to land on zero or not.
The filter now excludes a realization if any time step inside the
selected time range has a value outside the selected value range, i.e.
all time steps in range must be inside the value range for the
realization to be kept. This makes the behavior consistent between
FOPT and FOPR style vectors.
Added a regression test reproducing the oscillating-rate scenario, and
updated the existing performance test to describe and exercise the new
semantics.
* Add performance unit test for RimEnsembleCurveFilter::applyFilter
* Fix O(N^2) scaling in RimEnsembleCurveFilter::applyFilter
RimEnsembleCurveSet::objectiveFunctionTimeConfig() was called once per
summary case inside the applyFilter() loop, for both the OBJECTIVE_FUNCTION
and SUMMARY_VALUE filter modes. Internally it calls allAvailableTimeSteps(),
which scans the full time series of every case in the ensemble, so
recomputing it per case turned applyFilter() into an O(caseCount^2) operation.
* Avoid redundant allAvailableTimeSteps() calls in fullTimeStepRange()
fullTimeStepRange() called allAvailableTimeSteps() three times (once for the
empty check, once for min, once for max), each re-scanning the full time
series of every case in the ensemble. Compute it once and reuse the result.
Also simplify allAvailableTimeSteps() to insert time steps directly into the
result set instead of collecting them into an intermediate vector first.
Magne Sjaastad