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

""" 
Reads the data in the orignal files. Is called for each key in dictionary list_dates/list_files provided by fetch.

Args:
------
- name: name of the component
- dates: list of the dates matching the files
- files: list of the files matching the dates
 -> dates and files as provided by fetch.py
- varnames: original names of variables to read
- comp_type: type of boundary conditions to generate. Useful to distinguish lateral from top from initial conditions.

Returns:
---------
- a DataArray with the actual data: 4 dimensional (time, vertical levels, latitude, longitude) with coordinates in increasing order (see also get_domain).

"""


import datetime
import os

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,
    **kwargs
):
    """Template showing how to read BCs from raw files into a pyCIF DataArray.

    Illustrates looping over the ``dates``/``files`` pairs produced by
    ``fetch`` (one pair per key), reading the requested variable(s) from
    each file, and assembling the result into a 4-dimensional DataArray.
    The body below is placeholder ``print(...)`` statements only; ``data``
    is never actually built, so this function would fail if run as-is and
    must be replaced with real logic in any plugin derived from this
    template.

    Args:
        self: The field/tracer object.
        name: Name of the component.
        varnames: Original name(s) of the variable(s) to read.
        dates: List of dates matching ``files``, as provided by ``fetch``.
        files: List of files matching ``dates``, as provided by ``fetch``.
        interpol_flx: Unused in this template.
        comp_type: Type of boundary conditions to generate; useful to
            distinguish lateral from top from initial conditions.
        **kwargs: Unused in this template.

    Returns:
        xarray.DataArray: A 4-dimensional ``(time, lev, lat, lon)`` array
        with coordinates in increasing order (see also ``get_domain``).
    """
    print('If relevant, check here the component type')
    # Example: if comp_type == "inicond": ...
    # elif comp_type in ["latcond", "topcond"]: ...

    print('Loop on the dates and files')
    for dd, ff in zip(dates, files):
        print('Here put the reading of ', [varnames],' in ',ff,' for ',dd)
        print('e.g. get a 3d array')
        print('may require some manipulations on dates to get the right index in the file')
        

    # Putting in DataArray
    # Put here the required reversing of axis to get coordinates in increasing order
    xmod = xr.DataArray(
            np.array(data)[:,:,:,:],
            coords={"time": dates},
            dims=("time", "lev", "lat", "lon")
        )

    return xmod