Temporal data mapping happens element-wise for every timestamp in a simulation. This becomes prohibitively slow when working with large (gridded) datasets. Since the entire dataset (multidimensional) shares a single date-time array, mapping should only be done of the source and target date-time arrays are not equal. Even then, not all data needs to be mapped...maybe numpy index-based copying of data?
Temporal data mapping happens element-wise for every timestamp in a simulation. This becomes prohibitively slow when working with large (gridded) datasets. Since the entire dataset (multidimensional) shares a single date-time array, mapping should only be done of the source and target date-time arrays are not equal. Even then, not all data needs to be mapped...maybe numpy index-based copying of data?