Source code for pycif.plugins.obsoperators.standard.transforms.init_mainpipe

import itertools
import copy

from .utils import add_default
from .....utils.classes.transforms import Transform




def init_mainpipe(self, all_transforms,
                  backup_comps, mapper):
    """Initialize the core of the transform_pipe depending on the list of
    transformations specified in obsoper.transform_pipe.
    
    .. warning::
        
        If no transform_pipe is specified, the CTM model specified in the Yaml is run by
        default.
        
        On the opposite, if ``transform_pipe`` is specified in the observation operator,
        only transforms explicitly specified will be used.
        Thus, if custom transforms need to be run on top of the model, one should not
        forget to include the transform ``run_model`` in the ``transform_pipe``.
        Another option (recommended for most applications) is to use the ``controlvect``
        and ``obsvect`` transform_pipes to define transforms related to the control
        vector and to the observation vector respectively.
        
    """
    
    # Initializes the overall transform pipe
    # If no pipe is specified, assume that the model should be run
    if not hasattr(self, "transform_pipe"):
        if not self.ignore_model:
            yml_dict = {"transform": {
                "run_model": {
                    "plugin": {
                        "name": "run_model",
                        "version": "std",
                        "type": "transform",
                    }
                }
            }}
        else:
            yml_dict = {"transform": {}}

        self.transform_pipe = Transform.load_from_dict(yml_dict).transform

    main_transforms = self.transform_pipe
    transfs_ids = getattr(main_transforms, "attributes", [])

    # Loops backwards on available transformations and update inputs/out
    # According to model outputs
    for transform in transfs_ids:
        # Initializes mapper if not already done
        if transform not in mapper:
            # Replacing the transform by a transform class
            # if not already initialized
            transf = getattr(main_transforms, transform)
            if transf is None:
                transf = Transform.from_dict({}, orig_name=transform)
        
            setattr(all_transforms, transform, transf)
            all_transforms.attributes.append(transform)
            
            # Updating the general mapper, and creates a local one
            transf_mapper = transf.ini_mapper(
                general_mapper=mapper,
                backup_comps=backup_comps,
                transform_name=transform,
                all_transforms=all_transforms
            )
            transf.mapper = transf_mapper
            mapper[transform] = transf_mapper

        else:
            transf_mapper = mapper[transform]
    
    # Keep in memory the transformations in the main pipe
    all_transforms.mainpipe = \
        [tr for tr in all_transforms.attributes
         if tr not in all_transforms.controlpipe]