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

import numpy as np
import xarray as xr
from logging import debug




def read(
        self,
        name,
        varnames,
        dates,
        files,
        interpol_flx=False,
        tracer=None,
        model=None,
        ddi=None,
        **kwargs
):
    """Get fluxes from raw files and load them into a pyCIF
    variables.

    The list of date intervals and corresponding files is directly provided,
    coming from what is returned by the :bash:`fetch` function.
    One should loop on dates and files and extract the corresponding temporal slice of data

    Warning:
         Make sure to optimize the opening of files. There is high chances that
         the same file has to be open and closed over and over again to loop on the dates.
         If this is the case, make sure not to close it between each date.

    Args:
        name (str): name of the component
        varnames (list[str]): original names of variables to read; use `name`
            if `varnames` is empty
        dates (list): list of the date intervals to extract
        files (list): list of the files matching dates
        
    Return:
        xr.DataArray: the actual data with dimension:
            time, levels, latitudes, longitudes

    """

    # Get domain dimensions for random generation
    domain = tracer.domain
    nlon = domain.nlon
    nlat = domain.nlat
    nlev = domain.nlev

    # Loop over dates/files and import data
    data = []
    out_dates = []
    for dd, ff in zip(dates, files):
        debug(
            "Reading the file {} for the date interval {}".format(
                ff, dd
            )
        )

        # Generate random values instead of reading
        data.append(
            np.random.normal(
                tracer.average_value, tracer.std_value,
                (nlev, nlat, nlon)))
        out_dates.append(dd[0])

    # if only one level for emissions, create the axis:
    xmod = xr.DataArray(
        np.array(data),
        coords={"time": out_dates},
        dims=("time", "lev", "lat", "lon"),
    )

    return xmod