Source code for pycif.plugins.transforms.basic.unit_conversion.adjoint

import copy




def adjoint(
        transf,
        inout_datastore,
        controlvect,
        obsvect,
        mapper,
        di,
        df,
        mode,
        runsubdir,
        workdir,
        onlyinit=False,
        **kwargs
):
    ddi = min(di, df)
    for trid in mapper["inputs"]:
        if onlyinit:
            inout_datastore["inputs"][trid][ddi] = \
                inout_datastore["outputs"][trid][ddi]
            continue

        # Compute scaling with/without areas
        scaling = transf.scale
        if transf.grid_to_surface:
            domain = mapper["inputs"][trid]["domain"]
            if not hasattr(domain, "areas"):
                domain.calc_areas()

            scaling /= domain.areas

        # Apply scaling for sparse data
        if mapper["inputs"][trid].get("sparse_data", False):

            xmod_out = inout_datastore["outputs"][trid][ddi]
            inout_datastore["inputs"][trid][ddi] = copy.deepcopy(
                xmod_out
            )
            xmod_in = inout_datastore["inputs"][trid][ddi]
            xmod_in.loc[:, ("maindata", "adj_out")] *= scaling

            continue

        # Initialize input data if cleaned by memory
        if ddi not in inout_datastore["inputs"][trid]:
            inout_datastore["inputs"][trid][ddi] = {}

        # Propagate backward
        inout_datastore["inputs"][trid][ddi]["adj_out"] \
            = scaling * inout_datastore["outputs"][trid][ddi]["adj_out"]