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

from logging import debug
import numpy as np




def outputs2native(
        self, data2dump, input_type, di, df, runsubdir, mode="fwd",
        onlyinit=False, check_transforms=False, **kwargs
):
    """Reads outputs to pyCIF objects.

    Args:
        self: the model itself
        data2dump (dict): a dictionary with output data structure to be filled
            with correct data for every 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

    Return:
        dict: a dictionary with structure the components/tracers to be extracted

    Note:

        The input data ``data2dump`` has a dictionary structure with two levels:
        component/tracer and date.
        This reads as:

        .. code-block:: python

            data2dump = {
                (comp1, tracer1): {
                    dd0: pd.DataFrame
                    dd1: pd.DataFrame
                    [...]
                }
            }

        In the output, the date level should be removed and only the outputs
        corresponding to the present simulation (``di``) should be included

    """

    debug("Filling output concentrations for the template model")
    debug("One has to fill the following data structure (data2dump):")
    debug(data2dump)

    dataout = {}
    if mode in ["tl", "fwd"]:
        # Loop over species in data2dump
        for trid in data2dump:
            dataout[trid] = data2dump[trid][di]
            dataout[trid].loc[:, ("maindata", "spec")] = \
                np.random.uniform(400, 410, len(dataout[trid]))

            if mode == "tl":
                dataout[trid].loc[:, ("maindata", "incr")] = \
                    np.random.uniform(0, 10, len(dataout[trid]))

    return dataout