Source code for pycif.plugins.transforms.basic.vertical_interpolation.forward

import copy
from logging import warning
import numpy as np
import pandas as pd
import xarray as xr
from .....utils.classes.domains import Domain
try:
    import cPickle as pickle
except ImportError:
    import pickle

from .utils.array.forward import array_forward
from .utils.sparse.forward import sparse_forward




def forward(
    transf,
    inout_datastore,
    controlvect,
    obsvect,
    mapper,
    di,
    df,
    mode,
    runsubdir,
    workdir,
    onlyinit=False,
    save_debug=False,
    **kwargs
):
    ddi = min(di, df)

    for trid in mapper["outputs"]:
        is_sparse_out = mapper["outputs"][trid].get("sparse_data", False)
        is_sampled_out = mapper["outputs"][trid]["sampled"]
        # is_sparse_in = mapper["inputs"][trid].get("sparse_data", False)
        # is_sampled_in = mapper["inputs"][trid]["sampled"]

        # WARNING: for sparse data as targets, do nothing at the moment
        if is_sparse_out or is_sampled_out:
            sparse_forward(transf, mapper, inout_datastore,
                           trid, ddi, onlyinit, mode, **kwargs)
            continue

        # Deal with full data
        inout_datastore["outputs"][trid] = {ddi: {}}
        array_forward(transf, mapper, inout_datastore,
                      trid, ddi, mode, onlyinit, **kwargs)