Source code for pycif.plugins.models.TM5.io.native2inputs

import logging

import pandas as pd

from .....utils import path
from .inputs.make_chemistry import make_chemistry
from .inputs.make_fluxes import make_fluxes
from .inputs.make_inicond import make_inicond
from .inputs.make_meteo import make_meteo
from .inputs.params import make_config

MODULE_NAME = __name__[__name__.index('TM5') :] if 'TM5' in __name__ else __name__
logger = logging.getLogger(MODULE_NAME)




def native2inputs(
    self,
    datastore,
    input_type,
    datei,
    datef,
    runsubdir,
    mode="fwd",
    onlyinit=False,
    do_simu=True,
    check_transforms=False,
    **kwargs,
):
    """Converts data at the model data resolution to model compatible input
    files. Input types should be consistent with the ones specified in the
    mapper.
    Calls sub-functions dealing with individual input types

    The datastore provided to this function is a pyCIF Plugin, whose main
    interesting attribute is "datastore.datastore".
    This attribute is a dictionary of which each key is a tuple (input_type, parameter).
    Associated values are the following:
        - spec: an xarray.Dataset including the corresponding input data
        - incr: same for increments
        - dirorig: directory where to find original files for that input
        - fileorig: file format for that input


    Args:
        self: the model Plugin
        input_type (str): one of 'fluxes'
        datastore (Plugin): data to convert
        datei, datef: date interval of the sub-simulation
        mode (str): running mode: one of 'fwd', 'adj' and 'tl'
        runsubdir (str): sub-directory for the current simulation
        workdir (str): the directory of the whole pyCIF simulation

    VERSION HISTORY
    1.2 02-02-2021 by J.C.A. van Peet.
        Updated the path to the TM5 executable.
    1.1 18-01-2021 by J.C.A. van Peet.
        Renamed tm5.exe to tm5.x
    1.0 ??-??-???? by A. Berchet.

    """

    logger.debug("NATIVE2INPUTS => Start info")
    logger.debug(f"   input_type = {input_type}")
    logger.debug(f"   datei      = {datei}")
    logger.debug(f"   datef      = {datef}")
    logger.debug(f"   runsubdir  = {runsubdir}")

    ddi = min(datei, datef)
    ddf = max(datei, datef)

    # Hour steps of the sub-run
    hour_dates = pd.date_range(ddi, ddf, freq="1h")

    if datastore is None:
        datastore = {}

    sdc = ddi.strftime("%Y%m%d%H")

    # If mode is "fwd" or "tl" but onlyinit is True,
    # it means that we are initializing an adjoint by running backward
    # the adjoint pipeline
    if mode in ["tl", "fwd"] and onlyinit:
        mode = "adj"

    if not do_simu:
        return

    # Deals with other inputs
    if input_type in "flux":
        make_fluxes(self, datastore, runsubdir, ddi, mode)

    if input_type == "inicond":
        make_inicond(self, datastore, runsubdir, mode, ddi, ddf)

    if input_type == "chemistry":
        make_chemistry(self, datastore, runsubdir, mode, ddi, ddf)

    if input_type == "meteo":
        make_meteo(self, datastore, runsubdir, mode, ddi, ddf)