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

import itertools
import numpy as np
import pandas as pd
from logging import debug, info

from .....utils.classes.transforms import Transform
from .utils import add_default, propagate_parameters, \
    check_datavect, dump_transform_description
from .init_mainpipe import init_mainpipe
from .init_control_transformations import init_control_transformations
from .init_default_transformations import init_default_transformations
from .init_obsvect_transformations import init_obsvect_transformations
from .init_pipe_entry import init_pipe_entry
from .period_pipe import period_pipe
from .connect_pipes import connect_pipes
from .dump_read_inout import dump_read_inout
from .batch_computation import batch_computation

[docs]def init_transform(self): """Initialize the transform pipeline according to user choices. This includes the explicit definition of sub-pipelines (main, control vector side and observation vector side), definition based on aliases in the ``datavect``, and transforms automatically added depending on compatibility of successive input/output formats (including domain definition, dates and units).""" # Initializes the overall transform pipe all_transforms = Transform.from_dict({}) all_transforms.default_index = 0 mapper = {} backup_comps = {} info('Initializing observation operator pipe') # Initializing transformation on the control vector side init_control_transformations( self, all_transforms, self.controlvect, backup_comps, mapper) # Initializing the main pipe as defined by the observation operator init_mainpipe(self, all_transforms, backup_comps, mapper) # Initializing transformation after the observation operator pipe # i.e., on the observation side init_obsvect_transformations( self, all_transforms, self.obsvect, backup_comps, mapper) # Connect pipes, i.e., determines precursors and successors # of each transform connect_pipes(self, all_transforms, backup_comps, mapper) info('Initialize transformations with no precursors') init_pipe_entry(self, all_transforms, backup_comps, mapper) # It includes regridding, re-indexing, etc, # when successive transforms don't have same resolutions info('Add default transformations') init_default_transformations( self, all_transforms, backup_comps, mapper) # Add transforms to dump/read inputs/outputs of individual transforms dump_read_inout(self, all_transforms, backup_comps, mapper) # Distribute the order of transforms and sub-periods # Computing longer periods in the end period_order_fwd, period_order_adj = period_pipe(self, all_transforms, mapper) self.period_order_fwd = period_order_fwd self.period_order_adj = period_order_adj # Save final transform pipe to the observation observator self.transform_pipe = all_transforms self.transform_pipe.mapper = mapper # Check whether all data is available check_datavect(self, all_transforms, backup_comps, mapper) # Dump a full description of the transforms and of the transform pipe dump_transform_description(self, all_transforms, mapper) # Perturb transform pipeline for batch computation of Monte-Carlo samples if hasattr(self, "batch_computation"): batch_computation(self, all_transforms, mapper)