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
[docs]
def forward(
transf,
inout_datastore,
controlvect,
obsvect,
mapper,
di,
df,
mode,
runsubdir,
workdir,
onlyinit=False,
save_debug=False,
**kwargs
):
"""Vertically interpolate model fields to observation pressure/height levels.
Dispatches to either the sparse/sampled or gridded (array) implementation:
* **Sparse/sampled output** — :func:`~.utils.sparse.forward.sparse_forward`:
extracts model values at the observation vertical coordinate.
* **Array output** — :func:`~.utils.array.forward.array_forward`:
applies the configured interpolation method (``static-levels``,
``linear``, ``closest``, ``match-layer``, or ``layer-weighted``) to
the full gridded field.
Args:
transf (Plugin): vertical_interpolation instance (carries
``method``, ``coord_in``, ``coord_out``, ``ignore_level``,
and optional ``file_statlev``).
inout_datastore (dict): mutable datastore.
controlvect: unused.
obsvect: unused.
mapper (dict): transform mapper (carries ``sparse_data``,
``sampled``, and domain objects).
di (datetime): sub-simulation start date.
df (datetime): sub-simulation end date.
mode (str): ``'fwd'`` or ``'tl'``.
runsubdir (str): unused.
workdir (str): unused.
onlyinit (bool): passed to the sparse/array implementations.
save_debug (bool): if ``True``, save intermediate results.
**kwargs: forwarded to the sparse/array implementations.
"""
ddi = min(di, df)
# Fetch information from reference trid
# Valid for both ensemble and single simulations
trid_ref = list(mapper["inputs"].keys())[0]
sparse_in = mapper["inputs"][trid_ref]["sparse_data"]
sparse_out = mapper["outputs"][trid_ref]["sparse_data"]
sampled_in = mapper["inputs"][trid_ref]["sampled"]
sampled_out = mapper["outputs"][trid_ref]["sampled"]
# Deal differently sparse and array data
# Array data is parallelized along trid externally, while sparse share indexing
if sampled_out or sparse_out:
sparse_forward(transf, mapper, inout_datastore,
ddi, onlyinit, mode, **kwargs)
else:
# inout_datastore["outputs"][trid] = {ddi: {}}
array_forward(transf, mapper, inout_datastore,
ddi, mode, onlyinit, **kwargs)
# 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)
