Source code for pycif.plugins.datastreams.fluxes.lmdz_bin.read

import numpy as np
import pandas as pd
import xarray as xr

from scipy.io import FortranFile

from logging import info




def read(
    self,
    name,
    tracdir,
    tracfile,
    varnames,
    dates,
    interpol_flx=False,
    **kwargs
):
    """Read LMDZ-DISPERSION fluxes from Fortran unformatted binary files.

    For each requested date, opens the corresponding binary file and reads
    it record by record until exhaustion; each record holds a pair of
    forward and tangent-linear real values per grid cell, reshaped to
    ``(ndays_in_file, nlat, nlon)`` using the domain's horizontal size.
    Timestamps are attributed to each daily record starting at `dd`.

    Args:
        self: the model Plugin, providing ``domain`` (for ``nlon``/``nlat``).
        name: the name of the component
        tracdir, tracfile: flux directory and file format (a ``strftime``
            pattern applied to each date in `dates`)
        varnames: Unused, kept for interface consistency with other flux
            plugins.
        dates: list of dates to extract
        interpol_flx (bool): if True, interpolates fluxes at time t from
        values of surrounding available files

    Returns:
        xr.DataArray: the forward-flux data (from the first requested
        file), with dimensions ``(time, lat, lon)``, indexed by the daily
        timestamps derived from `dates`.
    """

    list_file_flx = [dd.strftime(tracfile) for dd in dates]

    trcr_dates = []
    trcr_flx = []
    trcr_flx_tl = []
    for dd, file_flx in zip(dates, list_file_flx):
        # Read binary file
        data = []

        with FortranFile(f"{tracdir}/{file_flx}") as f:
            while True:
                try:
                    data.append(f.read_reals())

                except BaseException:
                    info(f"End of file {file_flx}")
                    break

        # Reshape file
        nlon = self.domain.nlon
        nlat = self.domain.nlat

        data = np.array(data)

        flx = data[:, 0].reshape((-1, nlat, nlon))
        flx_tl = data[:, 1].reshape((-1, nlat, nlon))

        trcr_flx.append(flx)
        trcr_flx_tl.append(flx_tl)
        trcr_dates.extend(list(pd.date_range(dd, freq="D", periods=len(flx))))

    xmod = xr.DataArray(
        trcr_flx[0], coords={"time": trcr_dates}, dims=("time", "lat", "lon")
    )

    return xmod