Source code for pycif.plugins.obsoperators.standard.transforms.utils.init_default_transformations

import copy
from logging import debug

from . import init_regrid, init_reindex, init_sparse




def init_default_transformations(
        self, all_transforms,
        backup_comps, mapper,
        transform,
        do_pipe_entry=False,
        trid_to_check=None
):
    """Initialize default transformations based on compatibility of input/output
    formats of successive transforms.
    """

    debug(f"Initialize default transformations for {transform}")

    # Add default transformations:
    # reprojections, sampling of full data to sparse data, etc.
    transf_plg = getattr(all_transforms, transform)
    transf_mapper = mapper[transform]
    precursors = transf_mapper["precursors"]
    successors = transf_mapper["successors"]
    ref_outputs = transf_mapper["outputs"]

    # Loop only on certaint trid if specified
    list_trid_successors = copy.deepcopy(list(successors.keys()))
    if trid_to_check != None:
        list_trid_successors = trid_to_check

    # Loop on successors and add intermediate transforms for reprojections
    for trid in list_trid_successors:
        trid_dict = ref_outputs[trid]
        ref_successors = copy.deepcopy(successors[trid])
        for tr in ref_successors:
            # Skip if not in successors anymore
            # Means that it has been replaced during initialization of other transforms
            if tr not in successors[trid]:
                continue

            tr_plg = getattr(all_transforms, tr)
            tr_mapper = mapper[tr]
            tmp_dict = tr_mapper["inputs"][trid]

            # Fetch info from yml
            prm = trid[1]
            cmp = trid[0]
            components = self.datavect.components
            comps = components.attributes

            # Use backup_components if defined
            # Skip if no backup is defined
            cmp_in = cmp if cmp in comps else backup_comps.get(cmp, "")
            cmp_plg = getattr(components, cmp_in, None)

            param = ref_outputs[trid].get("tracer", None)

            # Converts full data to sparse data (obs typically)
            # or conversely
            # Includes horizontal, vertical and temporal sampling
            do_sparse = (trid_dict.get("sparse_data", False) is not None
                         and tmp_dict.get("sparse_data", False) is not None)
            if (trid_dict.get("sparse_data", False)
                    or tmp_dict.get("sparse_data", False)) \
                    and do_sparse:
                successor_id = init_sparse.init_sparse(
                    self,
                    trid,
                    trid_dict, tmp_dict,
                    transform, tr,
                    param,
                    all_transforms,
                    mapper,
                    backup_comps,
                    precursors,
                    do_pipe_entry=do_pipe_entry
                )
                continue

            # Otherwise, do reprojections if necessary
            do_reproj = (
                not trid_dict.get("domain", None) is None
                and not tmp_dict.get("domain", None) is None
            )
            do_reindex = (
                not trid_dict.get("input_dates", None) is None
                and not tmp_dict.get("input_dates", None) is None
            )

            domain_ref = trid_dict.get("domain", None)
            domain_precurs = tmp_dict.get("domain", None)
            same_domain = domain_ref == domain_precurs if domain_ref is not None else False

            # Horizontal and vertical reprojection if needed
            successor_id = tr
            precursor_id = transform
            trid_dict = mapper[precursor_id]["outputs"][trid]
            tmp_dict = mapper[successor_id]["inputs"][trid]

            temp_first = getattr(getattr(
                param, "time_interpolation", None
            ), "first", False)
            if temp_first and do_reindex:
                precursor_id = init_reindex.init_reindex(
                    self,
                    trid,
                    trid_dict, tmp_dict,
                    precursor_id, successor_id,
                    param,
                    all_transforms,
                    mapper,
                    backup_comps,
                    precursors,
                    do_pipe_entry=do_pipe_entry
                )
                continue

            trid_dict = mapper[precursor_id]["outputs"][trid]
            tmp_dict = mapper[successor_id]["inputs"][trid]

            if not same_domain and do_reproj:
                regrid_pipe, did_nothing = init_regrid.init_regrid(
                    self,
                    trid,
                    trid_dict, tmp_dict,
                    precursor_id, successor_id,
                    param,
                    all_transforms,
                    mapper,
                    backup_comps,
                    precursors,
                    do_pipe_entry=do_pipe_entry
                )
                precursor_id = regrid_pipe[-2]

                # Stop here as recursive init_default
                # will take over for intermediate transforms
                # Carry on if did_nothing in regrid, in case,
                # there is a need for time_interpolation
                if not did_nothing:
                    continue

            trid_dict = mapper[precursor_id]["outputs"][trid]
            tmp_dict = mapper[successor_id]["inputs"][trid]

            # Do temporal re-indexing
            if not temp_first and do_reindex:
                successor_id = init_reindex.init_reindex(
                    self,
                    trid,
                    trid_dict, tmp_dict,
                    precursor_id, successor_id,
                    param,
                    all_transforms,
                    mapper,
                    backup_comps,
                    precursors,
                    do_pipe_entry=do_pipe_entry
                )