Source code for pycif.plugins.models.template.ini_mapper

import datetime
import numpy as np
from logging import info

from ....utils.classes.setup import Setup
from ....utils.classes.domains import Domain

from ....utils.check.errclass import PluginError




def ini_mapper(model, general_mapper={}, backup_comps={},
               transforms_order=[], ref_transform="",
               transform_name="", all_transforms=None, **kwargs):
    """Build the data-flow mapper for the template model.

    Constructs a minimal mapper controlled by ``input_arguments`` flags:

    * ``empty_mapper=True`` — returns an empty mapper (no inputs/outputs).
    * ``only_outputs=True`` — returns a mapper with concentration outputs only.
    * Otherwise — returns a mapper with both flux inputs and concentration outputs.

    Args:
        model: template plugin instance.
        general_mapper (dict): unused.
        backup_comps (dict): unused.
        transforms_order (list): unused.
        ref_transform (str): unused.
        transform_name (str): unused.
        all_transforms: unused.
        **kwargs: unused.

    Returns:
        dict: mapper with ``inputs``, ``outputs``, and optionally
        ``outputs2inputs``.
    """
    if model.empty_mapper:
        mapper = {"inputs": {},
                  "outputs": {}}

    if model.only_outputs:
        output_intervals = {
            ddi: np.append(
                model.tstep_dates[ddi][:-1, np.newaxis],
                model.tstep_dates[ddi][1:, np.newaxis],
                axis=1)
            for ddi in model.tstep_dates}

        mapper = {
            "inputs": {},
            "outputs": {
                ("concs", spec): {
                    "force_loadout": True,
                    "input_dates": output_intervals,
                    "domain": model.domain,
                    "sampled": True,
                    "sparse_data": False,
                }
                for spec in model.chemistry.acspecies.attributes
            }}

        # Update the transform so it can start the pipeline
        getattr(all_transforms, transform_name).start_pipe = True

    if model.include_fluxes:
        input_intervals = {
            ddi: np.append(
                model.input_dates[ddi][:, np.newaxis],
                model.input_dates[ddi][:, np.newaxis]
                + datetime.timedelta(hours=1),
                axis=1)
            for ddi in model.input_dates}

        mapper["inputs"] = {
            ("flux", spec): {
                "input_dates": input_intervals, "force_dump": True,
                "sparse_data": False,
                "sampled": False
            }
            for spec in model.chemistry.emis_species.attributes
        }

        # Update the transform so it can start the pipeline
        getattr(all_transforms, transform_name).start_pipe = True

    info("The model was initialized with the following mapper:")
    info(mapper)

    return mapper