Source code for pycif.plugins.transforms.system.sparse2sample.adjoint

import numpy as np
import pandas as pd
import xarray as xr
from logging import info, debug




def adjoint(
        transform,
        inout_datastore,
        controlvect,
        obsvect,
        mapper,
        di,
        df,
        mode,
        runsubdir,
        workdir,
        onlyinit=False,
        **kwargs
):
    print(__file__)
    import code
    code.interact(local=dict(locals(), **globals()))
    if onlyinit:
        return

    ddi = min(di, df)
    for trid in mapper["inputs"]:
        xmod_in = inout_datastore["inputs"][trid][ddi]
        xmod_out = inout_datastore["outputs"][trid][ddi]

        t = xmod_in["metadata"]["tstep"].astype(int).values
        lev = xmod_in["metadata"]["level"].astype(int).values
        i = xmod_in["metadata"]["i"].astype(int).values
        j = xmod_in["metadata"]["j"].astype(int).values
        
        if "adj_out" not in xmod_in["maindata"]:
            xmod_in.loc[:, ("maindata", "adj_out")] = \
                0 * xmod_in["maindata"]["spec"]
            
        xmod_in.loc[:, ("maindata", "adj_out")] = \
            xmod_out["adj_out"].values[t, lev, i, j]