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

import pandas as pd
import numpy as np
import itertools
from ....utils.dates import date_range




def ini_periods(self, **kwargs):
    """Compute temporal discretisation for the Lagrangian model.

    Splits the full simulation window into sub-simulation periods and
    defines the footprint input date windows that extend backward by
    ``self.backward_trajdays`` from each period start.

    Sets the following attributes on *self*:

    * ``subsimu_dates`` — boundary dates of sub-simulation windows
      (length = n_periods + 1).
    * ``tstep_dates`` — dict mapping each period start to its 1-hourly
      time-step array.
    * ``input_dates`` — dict mapping each period start to the backward-
      shifted flux input dates needed by the footprints.
    * ``input_background`` — same but shifted for background concentration
      read windows.
    * ``tstep_all`` — sorted unique merge of all time steps.
    * ``iniobs`` / ``reset_obs`` — per-period flags controlling obs dumping.

    Args:
        self: Lagrangian model plugin instance with ``datei``, ``datef``,
            ``period``, ``subperiod``, and ``backward_trajdays`` set.
        **kwargs: unused.
    """
    # Hardcoded 1h for now
    datei = self.datei
    datef = self.datef
    period = self.period
    subperiod = self.subperiod

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

    # Time steps per day
    freq = f'{3600:.0f}s'

    # List of time steps
    self.tstep_dates = {
        ddi: date_range(ddi, ddf, period=freq)
        for ddi, ddf in zip(self.subsimu_dates[:-1],
                            self.subsimu_dates[1:])
    }

    # List of input dates
    self.input_dates = {
        ddi: date_range(ddi - pd.to_timedelta(self.backward_trajdays), ddf,
                        period=period, subperiod=subperiod)
        for ddi, ddf in zip(self.subsimu_dates[:-1],
                            self.subsimu_dates[1:])
    }
    self.input_background = {
        ddi: date_range(ddi - pd.to_timedelta(self.backward_trajdays),
                        ddf - pd.to_timedelta(self.backward_trajdays),
                        period=period, subperiod=subperiod)
        for ddi, ddf in zip(self.subsimu_dates[:-1],
                            self.subsimu_dates[1:])
    }

    # Merged list of time steps
    self.tstep_all = np.sort(np.unique(
        np.concatenate(list(self.tstep_dates.values()))))

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