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

import datetime
import calendar
import os
import pandas as pd
import numpy as np
import xarray as xr
from netCDF4 import Dataset

from .....utils.netcdf import readnc




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

    Args:
        self: the BC Plugin
        name: the name of the component
        tracdir, tracfile: raw data directory and file format
        dates: list of dates to extract
        comp_type: type of boundary conditions to generate

    """

    if ddi is None:
        raise Exception("CAMS netCDF read function was called "
                        "without specifying ddi")

    # Variable name to extract
    var2extract = name
    if varnames != "":
        var2extract = varnames

    # Reading fields for periods within the simulation window
    xout = []
    opened_file = ""
    for dd, dd_file in zip(dates, files):
        # Avoid opening the file for all dates
        if dd_file != opened_file:
            ds = xr.open_dataset(dd_file)
            opened_file = dd_file

        ntimes = ds.dims["time"]

        freq = pd.DatetimeIndex([dd[0]]).days_in_month[0] * 24 / ntimes
        date_end = ds["time"].to_pandas().dt.to_pydatetime()[-1]
        date_index = \
            ntimes - 1 - int((date_end - dd[0]) /
                             datetime.timedelta(hours=freq))

        # bottom of the atmosphere = at the beginning of the table
        lat = ds['latitude']
        conc = ds[var2extract].values[date_index]

        if lat[1] < lat[0] and conc.ndim == 4:
            conc = conc[:, :, ::-1, :]
        elif lat[1] < lat[0] and conc.ndim == 3:
            conc = conc[:, ::-1, :]

        # Swap levels if required to
        if getattr(tracer, "flip_level", False):
            if conc.ndim == 4:
                conc = conc[:, ::-1]
            elif conc.ndim == 3:
                conc = conc[::-1]

        # Append to datastore
        xout.append(conc)

    xmod = xr.DataArray(
        np.array(xout),
        coords={"time": np.array(dates)[:, 0]},
        dims=("time", "lev", "lat", "lon"),
    )

    return xmod