We need an evaluation of the workflow we use to perform calculations on the joined timeseries. Can we generalize it to be more generic and more extensible?
Pre "group by and aggregate" (i.e. attributes and user defined fields), but this could also be a step in the processing if there was/is a need.
Filter (remove rows from the population)
Group by (determine the populations to be evaluated)
Aggregate (reduce the rows to those created by the groups and add any aggregates, mean, bias, kge, etc),
This will be a Python function and could include an additional filter or sub-aggregates (i.e. calculation of mean daily)
This can return a single value of a struct or list. If struct or list it may require unpacking.
Finalize (A few things can happen here. Multiple aggregated values can be combined, an aggregate the creates a Struct can be unpacked, what else?
We need an evaluation of the workflow we use to perform calculations on the joined timeseries. Can we generalize it to be more generic and more extensible?