Source code for pycif.plugins.transforms.system.dump2inputs.forward

def forward(
        transform,
        inout_datastore,
        controlvect,
        obsvect,
        mapper,
        di,
        df,
        mode,
        runsubdir,
        workdir,
        do_simu=True,
        onlyinit=False,
        check_transforms=False,
        **kwargs
):
    """Write CIF-internal arrays to model-native input files.

    Groups input tracers by reference name (stripping any ``__sample#N``
    suffix for ensemble tracers), then calls ``transform.native2inputs``
    for each group to write the data to the format expected by the model
    executable.

    After writing, the ``'spec'`` and ``'incr'`` fields are removed from
    the datastore to free memory; the written files become the canonical
    source for subsequent model reads.  The output datastore is updated
    with the data dict returned by ``native2inputs``.

    Args:
        transform (Plugin): dump2inputs instance (carries
            ``native2inputs``).
        inout_datastore (dict): mutable datastore.
        controlvect: unused.
        obsvect: unused.
        mapper (dict): transform mapper.
        di (datetime): sub-simulation start date.
        df (datetime): sub-simulation end date.
        mode (str): ``'fwd'`` or ``'tl'``.
        runsubdir (str): passed to ``native2inputs``.
        workdir (str): unused.
        do_simu (bool): passed to ``native2inputs``.
        onlyinit (bool): passed to ``native2inputs``.
        check_transforms (bool): passed to ``native2inputs``.
        **kwargs: unused.
    """
    datastore = inout_datastore["inputs"]
    trids_in = list(mapper["inputs"].keys())

    # Retrieve reference configuration if batch sampling
    trids_in_ref = list(set(
        [(trid[0], trid[1].split("__sample#")[0]) for trid in trids_in]))

    for trid in trids_in_ref:
        input_type = trid[0]

        trids2dump = [
            tr for tr in trids_in
            if tr[1].split("__sample#")[0] == trid[1]
        ]

        # Create new data to extract
        data2dump = {
            tr: {"data": datastore.get(tr, {di: {}}),
                 **mapper["inputs"][tr]}
            for tr in trids2dump
        }

        # If the model does not need to compute a simulation,
        # just skip this step
        transform.native2inputs(
            data2dump, input_type, di, df, runsubdir, mode, onlyinit, do_simu,
            check_transforms=check_transforms
        )

        # Dumping data from the datastore
        for t in data2dump:
            if t not in datastore:
                continue
            if "spec" in datastore[t][di]:
                del datastore[t][di]["spec"]
            if "incr" in datastore[t]:
                del datastore[t][di]["incr"]

        # Update outputs
        for t in trids2dump:
            inout_datastore["outputs"][t].update(data2dump[t]["data"])