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
):
    """Get fluxes from pre-computed fluxes and load them into a pycif
    variables

    Args:
        self: the model Plugin
        name: the name of the component
        tracdir, tracfile: flux directory and file format
        dates: list of dates to extract
        interpol_flx (bool): if True, interpolates fluxes at time t from
        values of surrounding available files

    """

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

                except BaseException:
                    info("End of file {}".format(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