Source code for imod.mf6.timedis

import cftime
import numpy as np

from imod.mf6.package import Package
from imod.schemata import DimsSchema, DTypeSchema

def iso8601(datetime):
    datetype = type(datetime)
    if issubclass(datetype, np.datetime64):
        return np.datetime_as_string(datetime)
    elif issubclass(datetype, cftime.datetime):
        return datetime.isoformat()
        raise TypeError(f"Expected np.datetime64 or cftime.datetime, got {datetype}")

[docs]class TimeDiscretization(Package): """ Timing for all models of the simulation is controlled by the Temporal Discretization (TDIS) Package. Parameters ---------- timestep_duration: float is the length of a stress period. (PERLEN) n_timesteps: int, optional is the number of time steps in a stress period (nstp). Default value: 1 timestep_multiplier: float, optional is the multiplier for the length of successive time steps. The length of a time step is calculated by multiplying the length of the previous time step by timestep_multiplier (TSMULT). Default value: 1.0 validate: {True, False} Flag to indicate whether the package should be validated upon initialization. This raises a ValidationError if package input is provided in the wrong manner. Defaults to True. """ _pkg_id = "tdis" _keyword_map = {} _template = Package._initialize_template(_pkg_id) _init_schemata = { "timestep_duration": [DimsSchema("time"), DTypeSchema(np.floating)], "n_timesteps": [DimsSchema("time") | DimsSchema(), DTypeSchema(np.integer)], "timestep_multiplier": [ DimsSchema("time") | DimsSchema(), DTypeSchema(np.floating), ], } _write_schemata = {}
[docs] def __init__( self, timestep_duration, n_timesteps=1, timestep_multiplier=1.0, validate: bool = True, ): super().__init__() self.dataset["timestep_duration"] = timestep_duration self.dataset["n_timesteps"] = n_timesteps self.dataset["timestep_multiplier"] = timestep_multiplier self._validate_init_schemata(validate)
def render(self): start_date_time = iso8601(self.dataset["time"].values[0]) d = { "time_units": "days", "start_date_time": start_date_time, } timestep_duration = self.dataset["timestep_duration"] n_timesteps = self.dataset["n_timesteps"] timestep_multiplier = self.dataset["timestep_multiplier"] nper = timestep_duration.size # scalar will also have size 1 d["nper"] = nper # Broadcast everything to a 1D array so it's iterable # also fills in a scalar value everywhere broadcast_array = np.ones(nper, dtype=np.int32) timestep_duration = np.atleast_1d(timestep_duration) * broadcast_array n_timesteps = np.atleast_1d(n_timesteps) * broadcast_array timestep_multiplier = np.atleast_1d(timestep_multiplier) * broadcast_array # Zip through the arrays perioddata = [] for perlen, nstp, tsmult in zip( timestep_duration, n_timesteps, timestep_multiplier ): perioddata.append((perlen, nstp, tsmult)) d["perioddata"] = perioddata return self._template.render(d) def _structured_grid_dim_check(self, da): if da.ndim == 0: return # Scalar, no check necessary elif da.ndim == 1: if da.dims != ("time",): raise ValueError( f"1D DataArray dims can only be ('time',). " f"Instead got {da.dims} for {} in the " f"{self.__class__.__name__} package. " ) else: raise ValueError( f"Exceeded accepted amount of dimensions for " f"for {} in the " f"{self.__class__.__name__} package. " f"Got {da.dims}. Can be at max ('time', )." ) def write(self, directory, name): timedis_content = self.render() timedis_path = directory / f"{name}.tdis" with open(timedis_path, "w") as f: f.write(timedis_content)