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

import numpy as np
import xarray as xr
from logging import debug
import xarray as xr
from netCDF4 import Dataset
import pandas as pd




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.
    
    
    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

    """

    var2extract = varnames if varnames != "" else name

    # 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
            )
        )

        # Read the file to fetch dates
        with Dataset(ff, "r") as f:
            isvar = "time" in f.variables
            
        times = xr.open_dataset(ff)["time"].values

        # Define dates if not a variable
        if not isvar:
            times = pd.date_range(ddi, periods=len(times),
                                  freq=tracer.timeresol).values[:, np.newaxis]

        # Shift dates if in variables as the middle of periods is specified
        freq = np.unique(np.diff(times))
        if isvar and not hasattr(tracer, "interpol_resolution"):
            times -= freq / 2
        
        # Fetch correct index
        if not hasattr(tracer, "interpol_resolution"):
            dref = np.datetime64(dd[0])
            ind_time = np.where(times == dref)[0][0]
            data.append(xr.open_dataset(ff)[var2extract][ind_time].values)
        else:
            dtref = np.datetime64(dd[1]) - np.datetime64(dd[0])
            dref = np.datetime64(dd[0]) + dtref / 2
            ind_time = np.argmax(times > dref)
            d0 = times[ind_time - 1]
            d1 = times[ind_time]
            data0 = xr.open_dataset(ff)[var2extract][ind_time - 1].values
            data1 = xr.open_dataset(ff)[var2extract][ind_time].values
            data.append(
                (dref - d0) / (d1 - d0) * data1
                + (d1 - dref) / (d1 - d0) * data0)

        out_dates.append(dd[0])

    # if only one level for emissions, create the axis
    dataout = np.array(data)[:, np.newaxis]
    dataout[np.isnan(dataout)] = 0
    xmod = xr.DataArray(
        dataout,
        coords={"time": out_dates},
        dims=("time", "lev", "lat", "lon"),
    )
    return xmod