Source code for pycif.plugins.datastreams.fields.lmdz_prodloss3d.read

import xarray as xr
import numpy as np
from netCDF4 import Dataset
from .....utils.hdf5 import _hdf5_lock




def read(
    self,
    name,
    tracdir,
    tracfile,
    varnames,
    dates,
    interpol_flx=False,
    comp_type=None,
    model=None,
    **kwargs
):
    """Get pre-computed production/loss fields and load them into a pyCIF
    variable.

    Opens the monthly file matching each requested date, appends a
    duplicated first-longitude column to close the cyclic LMDZ grid, and
    picks the time slice matching the requested day of month (falling
    back to the first day in the file if no exact match is found).

    Args:
        self: the fluxes Plugin
        name: the name of the component
        tracdir, tracfile: flux directory and file format
        dates: list of dates to extract
        interpol_flx (bool): unused, accepted for interface compatibility
        comp_type: unused, accepted for interface compatibility
        model: unused, accepted for interface compatibility

    Returns:
        xarray.DataArray with dims ``(time, lev, lat, lon)``.

    """
    list_file_prodloss = [dd.strftime(tracfile) for dd in dates]
    trcr_prodloss = []
    for dd, file_prodloss in zip(dates, list_file_prodloss):
        with _hdf5_lock:
            ds = xr.open_dataset(f"{tracdir}/{file_prodloss}", decode_times=True)

            data = ds[f'{name}_prod'].values

            # Loops zonally for consistency with other call to gridded values
            data = np.append(data, data[..., np.newaxis, 0], axis=3)

            # Searching for the right day
            day = dd.day
            ds_days = ds["time_counter.day"]
            if day in ds_days:
                data = data[day - 1]
            else:
                data = data[0]

        trcr_prodloss.append(data)

    xmod = xr.DataArray(
        np.array(trcr_prodloss),
        coords={"time": dates},
        dims=("time", "lev", "lat", "lon")
    )

    return xmod