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

import numpy as np
import xarray as xr

from pathlib import Path
import pandas as pd
from logging import debug, info




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
        
    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
    nlevel = domain.nlev

    # Time profiles
    Pdir = tracer.dir_profils
    f = Path(Pdir, 'timeprofiles-hour-in-day_GNFR.csv')
    coef_dict = {}
    key = 'hour'

    coef = pd.read_csv(f, sep=';', comment='#')
    l = list(coef.columns)
    l2 = l[l.index('GNFR_Category_Name') + 1:]
    coef_dict[key] = coef[l2].values

    cat_list = tracer.cat_select
    # corresponding CM : TNO categories
    # 0: Agriculture;
    # 1: Energy;
    # 2: Industrial;
    # 3: Transportation;
    # 4: Residential, Commercial, Other;
    # 5: Solvents production and application;
    # 6: Waste;
    # 7: International Shipping"
    TNO_cat = {0: 15, 1: 1, 2: 2, 3: 6, 4: 3, 5: 5, 6: 13, 7: 10}

    # Loop over dates/files and import data
    data = []
    outdates = []
    nc = None
    opened_file = ""
    emis = 0
    for dd, ff in zip(dates, files):
        dd_CET = dd[0]
        ddi = dd_CET.day
        houri = dd_CET.hour
        if ff != opened_file or nc is None:
            info('Reading of {} in {} for {}'.format([varnames], ff, dd))
            opened_file = ff
            nc = xr.open_dataset(ff, decode_times=False)
            emis = nc[varnames].values

        emis_array = np.zeros((nlevel, nlat, nlon))
        for cat in cat_list:
            local_hour_coef = coef_dict['hour'][TNO_cat[cat] - 1]
            emis_array[0, :, :] += \
                emis[ddi - 1, cat, :, :] * local_hour_coef[houri % 24]

        data.append(emis_array)
        outdates.append(dd_CET)

    return xr.DataArray(
        data,
        coords={"time": outdates},
        dims=("time", "lev", "lat", "lon"),
    )