Source code for imod.mf6.wel

import numpy as np

from imod.mf6.pkgbase import BoundaryCondition

[docs]class Well(BoundaryCondition): """ WEL package. Any number of WEL Packages can be specified for a single groundwater flow model. Parameters ---------- layer: int or list of int Modellayer in which the well is located. row: int or list of int Row in which the well is located. column: int or list of int Column in which the well is located. rate: float or list of floats is the volumetric well rate. A positive value indicates well (injection) and a negative value indicates discharge (extraction) (q). print_input: ({True, False}, optional) keyword to indicate that the list of well information will be written to the listing file immediately after it is read. Default is False. print_flows: ({True, False}, optional) Indicates that the list of well flow rates will be printed to the listing file for every stress period time step in which "BUDGET PRINT"is specified in Output Control. If there is no Output Control option and PRINT FLOWS is specified, then flow rates are printed for the last time step of each stress period. Default is False. save_flows: ({True, False}, optional) Indicates that well flow terms will be written to the file specified with "BUDGET FILEOUT" in Output Control. Default is False. observations: [Not yet supported.] Default is None. """ __slots__ = ( "layer", "row", "column", "rate", "print_input", "print_flows", "save_flows", "observations", ) _pkg_id = "wel" _period_data = ("layer", "row", "column", "rate") _keyword_map = {} _template = BoundaryCondition._initialize_template(_pkg_id) def __init__( self, layer, row, column, rate, print_input=False, print_flows=False, save_flows=False, observations=None, ): super().__init__() index = np.arange(len(layer)) self["index"] = index self["layer"] = ("index", layer) self["row"] = ("index", row) self["column"] = ("index", column) self["rate"] = ("index", rate) self["print_input"] = print_input self["print_flows"] = print_flows self["save_flows"] = save_flows self["observations"] = observations
[docs] def to_sparse(self, arrdict, layer): spec = [] for key in arrdict: if key in ["layer", "row", "column"]: spec.append((key, np.int32)) else: spec.append((key, np.float64)) sparse_dtype = np.dtype(spec) nrow = next(iter(arrdict.values())).size recarr = np.empty(nrow, dtype=sparse_dtype) for key, arr in arrdict.items(): recarr[key] = arr return recarr