Source code for pycif.plugins.models.lmdz_ico.io.outputs2native

from __future__ import annotations

import datetime
from os import PathLike
from typing import Any, Literal

from .....utils.datastores.empty import init_empty
from .outputs import fetch_end, read_sim

sim_outputs = ("concs", "pressure", "dpressure", "airm", "hlay")




def outputs2native(
    self,
    data2dump: dict[tuple[str, str], dict[str | datetime.datetime, Any]],
    input_type: str,
    datei: datetime.datetime,
    datef: datetime.datetime,
    runsubdir: str | PathLike,
    mode: Literal["fwd", "tl", "adj"] = "fwd",
    onlyinit: bool = False,
    check_transforms: bool = False,
    **kwargs,
) -> dict[tuple[str, str], dict[str | datetime.datetime, Any]]:
    """Reads outputs to pycif objects.

    If the mode is 'fwd' or 'tl', only observation-like outputs are extracted.
    For the 'adj' mode, all outputs relative to model sensitivity are extracted.

    Dumps to a NetCDF file with output concentrations if needed

    Args:
        self (pycif.utils.classes.models.Model): Model object
        runsubdir (str): current sub-sumilation directory
        mode (str): running mode; one of: 'fwd', 'tl', 'adj'; default is 'fwd'

        dump (bool): dumping outputs or not; default is True
    Return:
        dict

    """
    # Switching datei and datef if adjoint
    ddi = min(datei, datef)
    ddf = max(datei, datef)

    if not hasattr(self, "dataobs"):
        self.dataobs = {spec: init_empty() for spec in self.chemistry.active_species}

    # If no data to extract, pass
    if not data2dump:
        return data2dump

    # Read simulations if input_type is "concs"
    elif input_type in sim_outputs and not onlyinit:
        return read_sim(self, data2dump, ddi, runsubdir, mode)

    # Fetch end concentration if input_type is endconcs
    elif input_type == "endconcs":
        return fetch_end(
            self, data2dump, ddi, runsubdir, mode, onlyinit, check_transforms
        )

    else:
        return data2dump