Source code for imod.couplers.metamod.wel_svat_mapping
import numpy as np
import pandas as pd
import xarray as xr
from imod import mf6
from imod.couplers.metamod.mappingbase import MetaModMapping
from imod.msw.fixed_format import VariableMetaData
[docs]class WellSvatMapping(MetaModMapping):
"""
This contains the data to connect MODFLOW 6 well cells to MetaSWAP svats.
This class is responsible for the file `wellindex2svat.dxc`.
Parameters
----------
svat: array of floats (xr.DataArray)
SVAT units. This array must have a subunit coordinate to describe
different land uses.
well: mf6.Well
Modflow 6 Well package to connect to.
"""
# TODO: Do we always want to couple to identical grids?
_file_name = "wellindex2svat.dxc"
_metadata_dict = {
"wel_id": VariableMetaData(10, 1, 9999999, int),
"free": VariableMetaData(2, None, None, str),
"svat": VariableMetaData(10, 1, 9999999, int),
"layer": VariableMetaData(5, 0, 9999, int),
}
_with_subunit = ("wel_id", "svat", "layer")
_to_fill = ("free",)
[docs] def __init__(self, svat: xr.DataArray, well: mf6.WellDisStructured):
super().__init__()
self.well = well
well_mod_id, well_svat, layer = self._create_well_id(svat)
self.dataset["wel_id"] = well_mod_id
self.dataset["svat"] = well_svat
self.dataset["layer"] = layer
def _create_well_id(self, svat):
"""
Get modflow indices, svats, and layer number for the wells
"""
# Convert to Python's 0-based index
well_row = self.well["row"] - 1
well_column = self.well["column"] - 1
well_layer = self.well["layer"]
n_subunit = svat["subunit"].size
well_svat = svat.values[:, well_row, well_column]
well_active = well_svat != 0
well_svat_1d = well_svat[well_active]
# Tile well_layers for each subunit
layer = np.tile(well_layer, (n_subunit, 1))
layer_1d = layer[well_active]
well_id = self.well.dataset.coords["index"] + 1
well_id_1d = np.tile(well_id, (n_subunit, 1))[well_active]
return (well_id_1d, well_svat_1d, layer_1d)
def _render(self, file, *args):
data_dict = {}
data_dict["svat"] = self.dataset["svat"].values
data_dict["layer"] = self.dataset["layer"].values
data_dict["wel_id"] = self.dataset["wel_id"].values
for var in self._to_fill:
data_dict[var] = ""
dataframe = pd.DataFrame(
data=data_dict, columns=list(self._metadata_dict.keys())
)
self._check_range(dataframe)
return self.write_dataframe_fixed_width(file, dataframe)
