Source code for imod.wq.btn

import jinja2
import numpy as np
import scipy.ndimage

from imod.wq.pkgbase import Package

[docs]class BasicTransport(Package): """ Handles basic tasks that are required by the entire transport model. Among these tasks are definition of the problem, specification of the boundary and initial conditions, determination of the stepsize, preparation of mass balance information, and printout of the simulation results. Parameters ---------- icbund: xr.DataArray of int is an integer array specifying the boundary condition type (inactive, constant-concentration, or active) for every model cell. For multi-species simulation, ICBUND defines the boundary condition type shared by all species. Note that different species are allowed to have different constant-concentration conditions through an option in the Source and Sink Mixing Package. ICBUND=0, the cell is an inactive concentration cell for all species. Note that no-flow or "dry" cells are automatically converted into inactive concentration cells. Furthermore, active cells in terms of flow can be treated as inactive concentration cells to minimize the area needed for transport simulation, as long as the solute transport is insignificant near those cells. ICBUND<0, the cell is a constant-concentration cell for all species. The starting concentration of each species remains the same at the cell throughout the simulation. (To define different constantconcentration conditions for different species at the same cell location, refer to the Sink/Source Mixing Package.) Also note that unless explicitly defined as a constant-concentration cell, a constant-head cell in the flow model is not treated as a constantconcentration cell. If ICBUND>0, the cell is an active (variable) concentration cell where the concentration value will be calculated. starting_concentration: float or xr.DataArray of floats is the starting concentration (initial condition) at the beginning of the simulation (unit: ML-3) (SCONC). For multispecies simulation, the starting concentration must be specified for all species, one species at a time. porosity: float, optional is the "effective" porosity of the porous medium in a single porosity system (PRSITY). Default value is 0.35. n_species: int, optional is the total number of chemical species included in the current simulation (NCOMP). For single-species simulation, set n_species = 1. Default value is 1. inactive_concentration: float, optional is the value for indicating an inactive concentration cell (ICBUND=0) (CINACT). Even if it is not anticipated to have inactive cells in the model, a value for inactive_concentration still must be submitted. Default value is 1.0e30 minimum_active_thickness: float, optional is the minimum saturated thickness in a cell (THKMIN), expressed as the decimal fraction of the model layer thickness, below which the cell is considered inactive. Default value is 0.01 (i.e., 1% of the model layer thickness). """ _pkg_id = "btn" _mapping = (("icbund", "icbund"), ("dz", "thickness"), ("prsity", "porosity")) _template = jinja2.Template( "[btn]\n" " ncomp = {{n_species}}\n" # Number of components " mcomp = {{n_species}}\n" # Number of mobile components " thkmin = {{minimum_active_thickness}}\n" " cinact = {{inactive_concentration}}\n" " {%- for species, layerdict in starting_concentration.items() %}\n" " {%- for layer, value in layerdict.items() %}\n" " sconc_t{{species}}_l{{layer}} = {{value}}\n" " {%- endfor -%}\n" " {%- endfor -%}\n" " {%- for name, dictname in mapping -%}\n" " {%- for layer, value in dicts[dictname].items() %}\n" " {{name}}_l{{layer}} = {{value}}\n" " {%- endfor -%}\n" " {%- endfor -%}\n" )
[docs] def __init__( self, icbund, starting_concentration, porosity=0.35, n_species=1, inactive_concentration=1.0e30, minimum_active_thickness=0.01, ): super().__init__() self["icbund"] = icbund self["starting_concentration"] = starting_concentration self["porosity"] = porosity self["n_species"] = n_species self["inactive_concentration"] = inactive_concentration self["minimum_active_thickness"] = minimum_active_thickness
def _render(self, directory, nlayer): """ Renders part of [btn] section that does not depend on time, and can be inferred without checking the BoundaryConditions. Parameters ---------- directory : str thickness : xr.DataArray Taken from BasicFlow Returns ------- rendered : str """ d = {} dicts = {} d["mapping"] = self._mapping # Starting concentration also includes a species, and can't be written # in the same way as the other variables; _T? in the runfile if "species" in self.dataset["starting_concentration"].coords: starting_concentration = {} for i, species in enumerate( self.dataset["starting_concentration"]["species"].values ): da = self.dataset["starting_concentration"].sel(species=species) starting_concentration[i + 1] = self._compose_values_layer( "starting_concentration", directory, nlayer=nlayer, da=da ) d["starting_concentration"] = starting_concentration else: d["starting_concentration"] = { 1: self._compose_values_layer( "starting_concentration", directory, nlayer=nlayer ) } # Collect which entries are complex (multi-dim) data_vars = [t[1] for t in self._mapping] for varname in self.dataset.data_vars.keys(): if varname == "starting_concentration": continue # skip it, as mentioned above if varname in data_vars: # multi-dim entry dicts[varname] = self._compose_values_layer( varname, directory, nlayer=nlayer ) else: # simple entry, just get the scalar value d[varname] = self.dataset[varname].values # Add these from the outside, thickness from BasicFlow # layer_type from LayerPropertyFlow dicts["thickness"] = self._compose_values_layer( "thickness", directory, nlayer=nlayer, da=self.dataset.thickness ) d["dicts"] = dicts return self._template.render(d) def _pkgcheck(self, ibound=None): to_check = [ "starting_concentration", "porosity", "n_species", "minimum_active_thickness", ] self._check_positive(to_check) active_cells = self.dataset["icbund"] != 0 if (active_cells & np.isnan(self.dataset["starting_concentration"])).any(): raise ValueError( f"Active cells in icbund may not have a nan value in starting_concentration in {self}" ) _, nlabels = scipy.ndimage.label(active_cells.values)
# if nlabels > 1: # raise ValueError( # f"{nlabels} disconnected model domain detected in the icbund in {self}" # )