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 \
    check_datavect, dump_transform_description, init_entry
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 .period_pipe import period_pipe
from .utils.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 after the observation operator pipe # i.e., on the observation side self.mainpipe = [] init_obsvect_transformations( self, all_transforms, self.obsvect, backup_comps, mapper) # Initializing the main pipe as defined by the observation operator init_mainpipe(self, all_transforms, backup_comps, mapper) # Initializing transformation on the control vector side init_control_transformations( self, all_transforms, self.controlvect, backup_comps, mapper) # Add transforms to dump/read inputs/outputs of individual transforms dump_read_inout(self, all_transforms, backup_comps, mapper) # Now initialize the pipe entry init_entry.init_entry( self, all_transforms, backup_comps, mapper ) # for transform in all_transforms.attributes: # print(transform) # print("\tPrecursors:") # for trid in mapper[transform]["precursors"]: # print(f"\t\t{trid}, {mapper[transform]['precursors'][trid]}") # print("\tSuccessors:") # for trid in mapper[transform]["successors"]: # print(f"\t\t{trid}, {mapper[transform]['successors'][trid]}") # print() # mapper["dump2inputs_std_00046"] # print(__file__) # import code # code.interact(local=dict(locals(), **globals())) # 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)