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

import xarray as xr
from netCDF4 import Dataset
from .....utils.check.errclass import CifError
from .....utils.hdf5 import _hdf5_lock




def read(
    self,
    name,
    tracdir,
    tracfile,
    varnames,
    dates,
    interpol_flx=False,
    comp_type=None,
    model=None,
    tracer=None,
    **kwargs
):
    """Get the initial concentration field from the legacy LMDZ4 restart
    file and load it into a pyCIF variable.

    All entries of ``tracfile`` must resolve to the same file (a single
    restart file is expected); the variable is looked up by ``varnames``
    first, falling back to ``q{restart_id:02d}`` from the chemical
    scheme if ``varnames`` is not found in the file.

    Args:
        self: the IC Plugin
        name: the name of the component
        tracdir, tracfile: restart file directory and file format
        varnames: name of the variable to read, or a name absent from
            the file to trigger the ``restart_id``-based fallback
        dates: list of dates to extract; only the earliest date is used
        interpol_flx (bool): unused, accepted for interface compatibility
        comp_type: unused, accepted for interface compatibility
        model: the model Plugin, used to resolve the fallback variable
            name via ``model.chemistry.acspecies``
        tracer: unused, accepted for interface compatibility

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

    Raises:
        CifError: if more than one distinct file is given in
            ``tracfile``.

    """
    
    if len(set(tracfile)) > 1:
      raise CifError(
            'More than one file for initial conditions: is not logical'
        )
    
    ic_file = min(dates).strftime(list(set(tracfile))[0])
    with _hdf5_lock:
        with Dataset(ic_file, "r") as f:
            try:
                data = f.variables[varnames][:]
            except KeyError:
                spec_id = f"q{getattr(model.chemistry.acspecies, name).restart_id:02d}"
                data = f.variables[spec_id][:]
        
    xmod = xr.DataArray(
        data, coords={"time": [min(dates)]}, dims=("time", "lev", "lat", "lon")
    )

    return xmod