Source code for pycif.plugins.models.template.io.outputs2native_adj

import copy
from logging import debug
from .....utils.datastores.empty import init_empty




def outputs2native_adj(
        self, data2dump, input_type, di, df, runsubdir, mode="fwd",
        onlyinit=False, do_simu=True, check_transforms=False, **kwargs
):
    """Dumps and/or save information about outputs, so the model knows where
    to extract information.

    In the present template, observations are simply saved for later use by
    ``outputs2native``.
    If the model needs information to extract concentration on-the-fly,
    the information in ``data2dump`` should be used. In particular,
    the columns ``i`` and ``j`` are the row and columns of each observation
    in the domain. The column ``tstep`` indicates on which time stamp the
    observation spans, relative to what is indicated in the variable
    ``output_intervals`` in ``ini_mapper``.

    The function is called by ``loadfromoutputs.adjoint``.

    Args:
        self: the model itself
        data2dump (dict): a dictionary with concentration data for each
            component/tracer
        input_type (str): the type of model outputs to be processed;
            this information is redundant with the components of
            the data2dump dictionary
        di (datetime.datetime): starting date of the present sub-simulation
        df (datetime.datetime): ending date of the present sub-simulation
        runsubdir (str): path to the present sub-simulation work directory
        mode (str): running mode; one of "fwd", "tl" and "adj"
        onlyinit (bool): if ``True``,  means that the function is called during
            the initialization process of the observation vector
        do_simu (bool): if ``False``, means that the observation vector
            is retrieving information from a previous existing run;
            in that case, it may not be necessary to dump files

    """

    if not (mode == "adj" and input_type == "concs"):
        return

    # Initializes data obs if not already available
    ddi = min(di, df)
    if not hasattr(self, "dataobs"):
        self.dataobs = {ddi: {spec: init_empty()
                              for spec in self.chemistry.acspecies.attributes}}

    if ddi not in self.dataobs:
        self.dataobs[ddi] = {}

    for trid in data2dump:
        ds = copy.deepcopy(data2dump[trid][ddi])

        # Saves the data to the model
        # Other models might need an input file
        # to be writen here to get info on observations
        self.dataobs[ddi][trid[1]] = ds

    debug("The following data has been save for later use:")
    debug(self.dataobs)