Source code for pycif.plugins.models.iconart.ini_periods

import datetime

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

from logging import error, info, debug

from ....utils.dates import date_range
from ....utils.netcdf import readnc




def ini_periods(self, **kwargs):
    """
    The function :bash:`ini_periods` is optional but very recommended.
    It is used to define the temporal variables ``subsimu_dates``, ``input_dates``,
    ``tstep_dates`` and ``tstep_all``.
    The function is automatically called at the initialization
    of the :bash:`model` class if available.
    If not available, the temporal variables should be defined manually
    in the :bash:`ini_data` function (not recommended).

    :bash:`ini_periods` is a class method that applies to the :bash:`model` plugin itself.
    Therefore, the only expected argument is :bash:`self`.

    .. code-block:: python

        def ini_periods(self, **kwargs):

            self.subsimu_dates = XXXX
            self.tstep_dates = XXXXX
            self.input_dates = XXXXX
            self.tstep_all = XXXXX

    """

    # Define sub-simu dates
    info("Sub-simulations are initialized as one sub-simulation "
         "covering the full simulation window")
    
    datei = self.datei
    datef = self.datef

    # List of sub-simulation windows
    self.subsimu_dates = date_range(datei, datef, period=self.periods)

    # Check that subperiods are all of the same length
    if np.unique(np.diff(self.subsimu_dates)).size > 1:
        raise Exception("Trying to run ICON-ART on a simulation window not fitting the "
                        "sub-simulation length. \nPlease check the compatibility between "
                        "the duration of your sub-simulations ({}) and the simulation "
                        "window ({} to {}): \n"
                        "Guessed sub-periods: \n".format(self.periods, datei, datef) +
                        "\n".join(["  - {}: {}".format(d0, d1 - d0)
                                   for d0, d1 in zip(self.subsimu_dates[:-1],
                                                     self.subsimu_dates[1:])])
                        )

    # Define modelling time steps for outputs
    self.tstep_dates = {}
    self.input_dates = {}
    self.lbc_input_dates = {}
    self.end_dates = {}

    for dd0, dd1 in zip(self.subsimu_dates[:-1], self.subsimu_dates[1:]):
        self.input_dates[dd0] = pd.date_range(
            dd0, dd1, freq=self.input_resolution).to_pydatetime()
        
        self.lbc_input_dates[dd0] = pd.date_range(
            dd0, dd1 + pd.Timedelta(seconds=self.lbc_timestep), 
            freq=str(self.lbc_timestep) + 's').to_pydatetime()
        
        self.tstep_dates[dd0] = pd.date_range(
            dd0, dd1, freq=self.output_resolution).to_pydatetime()

    # Initializes dictionary to keep in memory whether observations were
    # already dumped for a given period
    self.iniobs = {ddi: [] for ddi in self.subsimu_dates}
    self.reset_obs = {ddi: [] for ddi in self.subsimu_dates}

    # Dictionary to store inversion scaling factors
    self.flux_lambdas = {ddi: {} for ddi in self.subsimu_dates}
    self.lbc_lambdas = {ddi: {} for ddi in self.subsimu_dates}


    # Dictionary to move the last output of ICON to the next sub-simulation
    # self.chain_last_output = {}