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

import copy




def adjoint(
        transform,
        inout_datastore,
        controlvect,
        obsvect,
        mapper,
        di,
        df,
        mode,
        runsubdir,
        workdir,
        onlyinit=False,
        **kwargs
):
    """Broadcast the output sensitivity to all input tracers.

    The adjoint of summation: each input tracer gets a copy of the output
    sensitivity ``adj_out`` (since :math:`\\partial(\\sum x_i)/\\partial x_j = 1`
    for all :math:`j`).  For sparse data, the ``parameter`` metadata field
    is updated to the input tracer name.

    Args:
        transform (Plugin): families instance.
        inout_datastore (dict): mutable datastore.
        controlvect: unused.
        obsvect: unused.
        mapper (dict): transform mapper.
        di (datetime): sub-simulation start date.
        df (datetime): unused.
        mode (str): ``'adj'``.
        runsubdir (str): unused.
        workdir (str): unused.
        onlyinit (bool): if ``True`` (non-sparse), return immediately.
        **kwargs: unused.
    """

    trid_out = list(mapper["outputs"].keys())[0]
    xmod = inout_datastore["outputs"]

    # If sparse data, different treatment
    if mapper["outputs"][trid_out].get("sparse_data", False):
        for trid in mapper["inputs"]:
            inout_datastore["inputs"][trid][di] = \
                copy.deepcopy(xmod[trid_out][di])

            inout_datastore["inputs"][trid][di].loc[
                :, ("metadata", "parameter")
            ] = trid[1].lower()

        return

    if onlyinit:
        return

    for trid in mapper["inputs"]:
        inout_datastore["inputs"][trid][di] = \
            {k: xmod[trid_out][k] for k in xmod[trid_out]}

        if not onlyinit:
            inout_datastore["inputs"][trid][di]["adj_out"] = \
                copy.deepcopy(xmod[trid_out][di]["adj_out"])