from __future__ import annotations
import warnings
from typing import Any, Optional, Tuple, Union
import cftime
import numpy as np
import numpy.typing as npt
import pandas as pd
import xarray as xr
import xugrid as xu
import imod
from imod.logging import init_log_decorator
from imod.mf6.boundary_condition import (
BoundaryCondition,
DisStructuredBoundaryCondition,
DisVerticesBoundaryCondition,
)
from imod.mf6.interfaces.ipointdatapackage import IPointDataPackage
from imod.mf6.mf6_wel_adapter import Mf6Wel
from imod.mf6.package import Package
from imod.mf6.utilities.dataset import remove_inactive
from imod.mf6.validation import validation_pkg_error_message
from imod.mf6.write_context import WriteContext
from imod.prepare import assign_wells
from imod.prepare.layer import create_layered_top
from imod.schemata import (
AnyNoDataSchema,
DTypeSchema,
EmptyIndexesSchema,
ValidationError,
)
from imod.select.points import points_indices, points_values
from imod.typing import GridDataArray
from imod.typing.grid import is_spatial_grid, ones_like
from imod.util.structured import values_within_range
def _assign_dims(arg: Any) -> Tuple | xr.DataArray:
is_da = isinstance(arg, xr.DataArray)
if is_da and "time" in arg.coords:
if arg.ndim != 2:
raise ValueError("time varying variable: must be 2d")
if arg.dims[0] != "time":
arg = arg.transpose()
da = xr.DataArray(
data=arg.values, coords={"time": arg["time"]}, dims=["time", "index"]
)
return da
elif is_da:
return "index", arg.values
else:
return "index", arg
def mask_2D(package: Well, domain_2d: GridDataArray) -> Well:
point_active = points_values(domain_2d, x=package.x, y=package.y)
is_inside_exterior = point_active == 1
selection = package.dataset.loc[{"index": is_inside_exterior}]
cls = type(package)
new = cls.__new__(cls)
new.dataset = selection
return new
[docs]
class Well(BoundaryCondition, IPointDataPackage):
"""
Agnostic WEL package, which accepts x, y and a top and bottom of the well screens.
This package can be written to any provided model grid.
Any number of WEL Packages can be specified for a single groundwater flow model.
https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=63
Parameters
----------
y: float or list of floats
is the y location of the well.
x: float or list of floats
is the x location of the well.
screen_top: float or list of floats
is the top of the well screen.
screen_bottom: float or list of floats
is the bottom of the well screen.
rate: float, list of floats or xr.DataArray
is the volumetric well rate. A positive value indicates well
(injection) and a negative value indicates discharge (extraction) (q).
If provided as DataArray, an ``"index"`` dimension is required and an
optional ``"time"`` dimension and coordinate specify transient input.
In the latter case, it is important that dimensions are in the order:
``("time", "index")``
concentration: array of floats (xr.DataArray, optional)
if this flow package is used in simulations also involving transport, then this array is used
as the concentration for inflow over this boundary.
concentration_boundary_type: ({"AUX", "AUXMIXED"}, optional)
if this flow package is used in simulations also involving transport, then this keyword specifies
how outflow over this boundary is computed.
id: list of Any, optional
assign an identifier code to each well. if not provided, one will be generated
Must be convertible to string, and unique entries.
minimum_k: float, optional
on creating point wells, no point wells will be placed in cells with a lower horizontal conductivity than this
minimum_thickness: float, optional
on creating point wells, no point wells will be placed in cells with a lower thickness than this
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.
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.
repeat_stress: Optional[xr.DataArray] of datetimes
Used to repeat data for e.g. repeating stress periods such as
seasonality without duplicating the values. The DataArray should have
dimensions ``("repeat", "repeat_items")``. The ``repeat_items``
dimension should have size 2: the first value is the "key", the second
value is the "value". For the "key" datetime, the data of the "value"
datetime will be used. Can also be set with a dictionary using the
``set_repeat_stress`` method.
Examples
---------
>>> screen_top = [0.0, 0.0]
>>> screen_bottom = [-2.0, -2.0]
>>> y = [83.0, 77.0]
>>> x = [81.0, 82.0]
>>> rate = [1.0, 1.0]
>>> imod.mf6.Well(x, y, screen_top, screen_bottom, rate)
For a transient well:
>>> weltimes = pd.date_range("2000-01-01", "2000-01-03")
>>> rate_factor_time = xr.DataArray([0.5, 1.0], coords={"time": weltimes}, dims=("time",))
>>> rate_transient = rate_factor_time * xr.DataArray(rate, dims=("index",))
>>> imod.mf6.Well(x, y, screen_top, screen_bottom, rate_transient)
"""
@property
def x(self) -> npt.NDArray[np.float64]:
return self.dataset["x"].values
@property
def y(self) -> npt.NDArray[np.float64]:
return self.dataset["y"].values
_pkg_id = "wel"
_auxiliary_data = {"concentration": "species"}
_init_schemata = {
"screen_top": [DTypeSchema(np.floating)],
"screen_bottom": [DTypeSchema(np.floating)],
"y": [DTypeSchema(np.floating)],
"x": [DTypeSchema(np.floating)],
"rate": [DTypeSchema(np.floating)],
"concentration": [DTypeSchema(np.floating)],
}
_write_schemata = {
"screen_top": [AnyNoDataSchema(), EmptyIndexesSchema()],
"screen_bottom": [AnyNoDataSchema(), EmptyIndexesSchema()],
"y": [AnyNoDataSchema(), EmptyIndexesSchema()],
"x": [AnyNoDataSchema(), EmptyIndexesSchema()],
"rate": [AnyNoDataSchema(), EmptyIndexesSchema()],
"concentration": [AnyNoDataSchema(), EmptyIndexesSchema()],
}
[docs]
@init_log_decorator()
def __init__(
self,
x: list[float],
y: list[float],
screen_top: list[float],
screen_bottom: list[float],
rate: list[float] | xr.DataArray,
concentration: Optional[list[float] | xr.DataArray] = None,
concentration_boundary_type="aux",
id: Optional[list[Any]] = None,
minimum_k: float = 0.1,
minimum_thickness: float = 1.0,
print_input: bool = False,
print_flows: bool = False,
save_flows: bool = False,
observations=None,
validate: bool = True,
repeat_stress: Optional[xr.DataArray] = None,
):
if id is None:
id = [str(i) for i in range(len(x))]
else:
set_id = set(id)
if len(id) != len(set_id):
raise ValueError("id's must be unique")
id = [str(i) for i in id]
dict_dataset = {
"screen_top": _assign_dims(screen_top),
"screen_bottom": _assign_dims(screen_bottom),
"y": _assign_dims(y),
"x": _assign_dims(x),
"rate": _assign_dims(rate),
"id": _assign_dims(id),
"minimum_k": minimum_k,
"minimum_thickness": minimum_thickness,
"print_input": print_input,
"print_flows": print_flows,
"save_flows": save_flows,
"observations": observations,
"repeat_stress": repeat_stress,
"concentration": concentration,
"concentration_boundary_type": concentration_boundary_type,
}
super().__init__(dict_dataset)
# Set index as coordinate
index_coord = np.arange(self.dataset.dims["index"])
self.dataset = self.dataset.assign_coords(index=index_coord)
self._validate_init_schemata(validate)
@classmethod
def is_grid_agnostic_package(cls) -> bool:
return True
def clip_box(
self,
time_min: Optional[cftime.datetime | np.datetime64 | str] = None,
time_max: Optional[cftime.datetime | np.datetime64 | str] = None,
layer_min: Optional[int] = None,
layer_max: Optional[int] = None,
x_min: Optional[float] = None,
x_max: Optional[float] = None,
y_min: Optional[float] = None,
y_max: Optional[float] = None,
top: Optional[GridDataArray] = None,
bottom: Optional[GridDataArray] = None,
) -> Package:
"""
Clip a package by a bounding box (time, layer, y, x).
The well package doesn't use the layer attribute to describe its depth and length.
Instead, it uses the screen_top and screen_bottom parameters which corresponds with
the z-coordinates of the top and bottom of the well. To go from a layer_min and
layer_max to z-values used for clipping the well a top and bottom array have to be
provided as well.
Slicing intervals may be half-bounded, by providing None:
* To select 500.0 <= x <= 1000.0:
``clip_box(x_min=500.0, x_max=1000.0)``.
* To select x <= 1000.0: ``clip_box(x_min=None, x_max=1000.0)``
or ``clip_box(x_max=1000.0)``.
* To select x >= 500.0: ``clip_box(x_min = 500.0, x_max=None.0)``
or ``clip_box(x_min=1000.0)``.
Parameters
----------
time_min: optional
time_max: optional
layer_min: optional, int
layer_max: optional, int
x_min: optional, float
x_max: optional, float
y_min: optional, float
y_max: optional, float
top: optional, GridDataArray
bottom: optional, GridDataArray
state_for_boundary: optional, GridDataArray
Returns
-------
sliced : Package
"""
if (layer_max or layer_min) and (top is None or bottom is None):
raise ValueError(
"When clipping by layer both the top and bottom should be defined"
)
if top is not None:
# Bug in mypy when using unions in isInstance
if not isinstance(top, GridDataArray) or "layer" not in top.coords: # type: ignore
top = create_layered_top(bottom, top)
# The super method will select in the time dimension without issues.
new = super().clip_box(time_min=time_min, time_max=time_max)
ds = new.dataset
z_max = self._find_well_value_at_layer(ds, top, layer_max)
z_min = self._find_well_value_at_layer(ds, bottom, layer_min)
if z_max is not None:
ds["screen_top"] = ds["screen_top"].clip(None, z_max)
if z_min is not None:
ds["screen_bottom"] = ds["screen_bottom"].clip(z_min, None)
# Initiate array of True with right shape to deal with case no spatial
# selection needs to be done.
in_bounds = np.full(ds.dims["index"], True)
# Select all variables along "index" dimension
in_bounds &= values_within_range(ds["x"], x_min, x_max)
in_bounds &= values_within_range(ds["y"], y_min, y_max)
in_bounds &= values_within_range(ds["screen_top"], z_min, z_max)
in_bounds &= values_within_range(ds["screen_bottom"], z_min, z_max)
# remove wells where the screen bottom and top are the same
in_bounds &= abs(ds["screen_bottom"] - ds["screen_top"]) > 1e-5
# Replace dataset with reduced dataset based on booleans
new.dataset = ds.loc[{"index": in_bounds}]
return new
@staticmethod
def _find_well_value_at_layer(
well_dataset: xr.Dataset, grid: GridDataArray, layer: Optional[int]
):
value = None if layer is None else grid.isel(layer=layer)
# if value is a grid select the values at the well locations and drop the dimensions
if (value is not None) and is_spatial_grid(value):
value = imod.select.points_values(
value,
x=well_dataset["x"].values,
y=well_dataset["y"].values,
out_of_bounds="ignore",
).drop_vars(lambda x: x.coords)
return value
def write(
self,
pkgname: str,
globaltimes: Union[list[np.datetime64], np.ndarray],
write_context: WriteContext,
):
raise NotImplementedError(
"To write a wel package first convert it to a MF6 well using to_mf6_pkg."
)
def __create_wells_df(self) -> pd.DataFrame:
wells_df = self.dataset.to_dataframe()
wells_df = wells_df.rename(
columns={
"screen_top": "top",
"screen_bottom": "bottom",
}
)
return wells_df
def __create_assigned_wells(
self,
wells_df: pd.DataFrame,
active: GridDataArray,
top: GridDataArray,
bottom: GridDataArray,
k: GridDataArray,
minimum_k: float,
minimum_thickness: float,
):
# Ensure top, bottom & k
# are broadcasted to 3d grid
like = ones_like(active)
bottom = like * bottom
top_2d = (like * top).sel(layer=1)
top_3d = bottom.shift(layer=1).fillna(top_2d)
k = like * k
index_names = wells_df.index.names
# Unset multi-index, because assign_wells cannot deal with
# multi-indices which is returned by self.dataset.to_dataframe() in
# case of a "time" and "species" coordinate.
wells_df = wells_df.reset_index()
wells_assigned = assign_wells(
wells_df, top_3d, bottom, k, minimum_thickness, minimum_k, True
)
# Set multi-index again
wells_assigned = wells_assigned.set_index(index_names).sort_index()
return wells_assigned
def __create_dataset_vars(
self, wells_assigned: pd.DataFrame, wells_df: pd.DataFrame, cellid: xr.DataArray
) -> xr.Dataset:
"""
Create dataset with all variables (rate, concentration), with a similar shape as the cellids.
"""
data_vars = ["rate"]
if "concentration" in wells_assigned.columns:
data_vars.append("concentration")
ds_vars = wells_assigned[data_vars].to_xarray()
# "rate" variable in conversion from multi-indexed DataFrame to xarray
# DataArray results in duplicated values for "rate" along dimension
# "species". Select first species to reduce this again.
index_names = wells_df.index.names
if "species" in index_names:
ds_vars["rate"] = ds_vars["rate"].isel(species=0)
# Carefully rename the dimension and set coordinates
d_rename = {"index": "ncellid"}
ds_vars = ds_vars.rename_dims(**d_rename).rename_vars(**d_rename)
ds_vars = ds_vars.assign_coords(**{"ncellid": cellid.coords["ncellid"].values})
return ds_vars
def __create_cellid(self, wells_assigned: pd.DataFrame, active: xr.DataArray):
like = ones_like(active)
# Groupby index and select first, to unset any duplicate records
# introduced by the multi-indexed "time" dimension.
df_for_cellid = wells_assigned.groupby("index").first()
d_for_cellid = df_for_cellid[["x", "y", "layer"]].to_dict("list")
return self.__derive_cellid_from_points(like, **d_for_cellid)
@staticmethod
def __derive_cellid_from_points(
dst_grid: GridDataArray,
x: list,
y: list,
layer: list,
) -> GridDataArray:
"""
Create DataArray with Modflow6 cell identifiers based on x, y coordinates
in a dataframe. For structured grid this DataArray contains 3 columns:
``layer, row, column``. For unstructured grids, this contains 2 columns:
``layer, cell2d``.
See also: https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.4.0.pdf#page=35
Note
----
The "layer" coordinate should already be provided in the dataframe.
To determine the layer coordinate based on screen depts, look at
:func:`imod.prepare.wells.assign_wells`.
Parameters
----------
dst_grid: {xr.DataArray, xu.UgridDataArray}
Destination grid to map the points to based on their x and y coordinates.
x: {list, np.array}
array-like with x-coordinates
y: {list, np.array}
array-like with y-coordinates
layer: {list, np.array}
array-like with layer-coordinates
Returns
-------
cellid : xr.DataArray
2D DataArray with a ``ncellid`` rows and 3 to 2 columns, depending
on whether on a structured or unstructured grid."""
# Find indices belonging to x, y coordinates
indices_cell2d = points_indices(dst_grid, out_of_bounds="ignore", x=x, y=y)
# Convert cell2d indices from 0-based to 1-based.
indices_cell2d = {dim: index + 1 for dim, index in indices_cell2d.items()}
# Prepare layer indices, for later concatenation
if isinstance(dst_grid, xu.UgridDataArray):
indices_layer = xr.DataArray(
layer, coords=indices_cell2d["mesh2d_nFaces"].coords
)
face_dim = dst_grid.ugrid.grid.face_dimension
indices_cell2d_dims = [face_dim]
cell2d_coords = ["cell2d"]
else:
indices_layer = xr.DataArray(layer, coords=indices_cell2d["x"].coords)
indices_cell2d_dims = ["y", "x"]
cell2d_coords = ["row", "column"]
# Prepare cellid array of the right shape.
cellid_ls = [indices_layer] + [
indices_cell2d[dim] for dim in indices_cell2d_dims
]
cellid = xr.concat(cellid_ls, dim="nmax_cellid")
# Rename generic dimension name "index" to ncellid.
cellid = cellid.rename(index="ncellid")
# Put dimensions in right order after concatenation.
cellid = cellid.transpose("ncellid", "nmax_cellid")
# Assign extra coordinate names.
coords = {
"nmax_cellid": ["layer"] + cell2d_coords,
"x": ("ncellid", x),
"y": ("ncellid", y),
}
cellid = cellid.assign_coords(**coords)
return cellid
def render(self, directory, pkgname, globaltimes, binary):
raise NotImplementedError(
f"{self.__class__.__name__} is a grid-agnostic package and does not have a render method. To render the package, first convert to a Modflow6 package by calling pkg.to_mf6_pkg()"
)
def to_mf6_pkg(
self,
active: GridDataArray,
top: GridDataArray,
bottom: GridDataArray,
k: GridDataArray,
validate: bool = False,
is_partitioned: bool = False,
) -> Mf6Wel:
"""
Write package to Modflow 6 package.
Based on the model grid and top and bottoms, cellids are determined.
When well screens hit multiple layers, groundwater extractions are
distributed based on layer transmissivities. Wells located in inactive
cells are removed.
Note
----
The well distribution based on transmissivities assumes confined
aquifers. If wells fall dry (and the rate distribution has to be
recomputed at runtime), it is better to use the Multi-Aquifer Well
package.
Parameters
----------
is_partitioned: bool
validate: bool
Run validation before converting
active: {xarry.DataArray, xugrid.UgridDataArray}
Grid with active cells.
top: {xarry.DataArray, xugrid.UgridDataArray}
Grid with top of model layers.
bottom: {xarry.DataArray, xugrid.UgridDataArray}
Grid with bottom of model layers.
k: {xarry.DataArray, xugrid.UgridDataArray}
Grid with hydraulic conductivities.
Returns
-------
Mf6Wel
Object with wells as list based input.
"""
if validate:
errors = self._validate(self._write_schemata)
if len(errors) > 0:
message = validation_pkg_error_message(errors)
raise ValidationError(message)
minimum_k = self.dataset["minimum_k"].item()
minimum_thickness = self.dataset["minimum_thickness"].item()
wells_df = self.__create_wells_df()
wells_assigned = self.__create_assigned_wells(
wells_df, active, top, bottom, k, minimum_k, minimum_thickness
)
nwells_df = len(wells_df["id"].unique())
nwells_assigned = (
0 if wells_assigned.empty else len(wells_assigned["id"].unique())
)
if nwells_df == 0:
raise ValueError("No wells were assigned in package. None were present.")
if not is_partitioned and nwells_df != nwells_assigned:
raise ValueError(
"One or more well(s) are completely invalid due to minimum conductivity and thickness constraints."
)
ds = xr.Dataset()
ds["cellid"] = self.__create_cellid(wells_assigned, active)
ds_vars = self.__create_dataset_vars(wells_assigned, wells_df, ds["cellid"])
ds = ds.assign(**ds_vars.data_vars)
ds = remove_inactive(ds, active)
ds["save_flows"] = self["save_flows"].values[()]
ds["print_flows"] = self["print_flows"].values[()]
ds["print_input"] = self["print_input"].values[()]
return Mf6Wel(**ds.data_vars)
def mask(self, domain: GridDataArray) -> Well:
"""
Mask wells based on two-dimensional domain. For three-dimensional
masking: Wells falling in inactive cells are automatically removed in
the call to write to Modflow 6 package. You can verify this by calling
the ``to_mf6_pkg`` method.
"""
# Drop layer coordinate if present, otherwise a layer coordinate is assigned
# which causes conflicts downstream when assigning wells and deriving
# cellids.
domain_2d = domain.isel(layer=0, drop=True, missing_dims="ignore").drop_vars(
"layer", errors="ignore"
)
return mask_2D(self, domain_2d)
[docs]
class WellDisStructured(DisStructuredBoundaryCondition):
"""
WEL package for structured discretization (DIS) models .
Any number of WEL Packages can be specified for a single groundwater flow model.
https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=63
.. warning::
This class is deprecated and will be deleted in a future release.
Consider changing your code to use the ``imod.mf6.Well`` package.
Parameters
----------
layer: list of int
Model layer in which the well is located.
row: list of int
Row in which the well is located.
column: 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).
concentration: array of floats (xr.DataArray, optional)
if this flow package is used in simulations also involving transport, then this array is used
as the concentration for inflow over this boundary.
concentration_boundary_type: ({"AUX", "AUXMIXED"}, optional)
if this flow package is used in simulations also involving transport, then this keyword specifies
how outflow over this boundary is computed.
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.
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.
repeat_stress: Optional[xr.DataArray] of datetimes
Used to repeat data for e.g. repeating stress periods such as
seasonality without duplicating the values. The DataArray should have
dimensions ``("repeat", "repeat_items")``. The ``repeat_items``
dimension should have size 2: the first value is the "key", the second
value is the "value". For the "key" datetime, the data of the "value"
datetime will be used. Can also be set with a dictionary using the
``set_repeat_stress`` method.
"""
_pkg_id = "wel"
_period_data = ("layer", "row", "column", "rate")
_keyword_map = {}
_template = DisStructuredBoundaryCondition._initialize_template(_pkg_id)
_auxiliary_data = {"concentration": "species"}
_init_schemata = {
"layer": [DTypeSchema(np.integer)],
"row": [DTypeSchema(np.integer)],
"column": [DTypeSchema(np.integer)],
"rate": [DTypeSchema(np.floating)],
"concentration": [DTypeSchema(np.floating)],
}
_write_schemata = {}
[docs]
@init_log_decorator()
def __init__(
self,
layer,
row,
column,
rate,
concentration=None,
concentration_boundary_type="aux",
print_input=False,
print_flows=False,
save_flows=False,
observations=None,
validate: bool = True,
repeat_stress=None,
):
dict_dataset = {
"layer": _assign_dims(layer),
"row": _assign_dims(row),
"column": _assign_dims(column),
"rate": _assign_dims(rate),
"print_input": print_input,
"print_flows": print_flows,
"save_flows": save_flows,
"observations": observations,
"repeat_stress": repeat_stress,
"concentration": concentration,
"concentration_boundary_type": concentration_boundary_type,
}
super().__init__(dict_dataset)
self._validate_init_schemata(validate)
warnings.warn(
f"{self.__class__.__name__} is deprecated and will be removed in the v1.0 release."
"Please adapt your code to use the imod.mf6.Well package",
DeprecationWarning,
)
def clip_box(
self,
time_min: Optional[cftime.datetime | np.datetime64 | str] = None,
time_max: Optional[cftime.datetime | np.datetime64 | str] = None,
layer_min: Optional[int] = None,
layer_max: Optional[int] = None,
x_min: Optional[float] = None,
x_max: Optional[float] = None,
y_min: Optional[float] = None,
y_max: Optional[float] = None,
top: Optional[GridDataArray] = None,
bottom: Optional[GridDataArray] = None,
) -> Package:
"""
Clip a package by a bounding box (time, layer, y, x).
Slicing intervals may be half-bounded, by providing None:
* To select 500.0 <= x <= 1000.0:
``clip_box(x_min=500.0, x_max=1000.0)``.
* To select x <= 1000.0: ``clip_box(x_min=None, x_max=1000.0)``
or ``clip_box(x_max=1000.0)``.
* To select x >= 500.0: ``clip_box(x_min = 500.0, x_max=None.0)``
or ``clip_box(x_min=1000.0)``.
Parameters
----------
time_min: optional
time_max: optional
layer_min: optional, int
layer_max: optional, int
x_min: optional, float
x_max: optional, float
y_min: optional, float
y_max: optional, float
top: optional, GridDataArray
bottom: optional, GridDataArray
state_for_boundary: optional, GridDataArray
Returns
-------
sliced : Package
"""
# TODO: include x and y values.
for arg in (
layer_min,
layer_max,
x_min,
x_max,
y_min,
y_max,
):
if arg is not None:
raise NotImplementedError("Can only clip_box in time for Well packages")
# The super method will select in the time dimension without issues.
new = super().clip_box(time_min=time_min, time_max=time_max)
return new
[docs]
class WellDisVertices(DisVerticesBoundaryCondition):
"""
WEL package for discretization by vertices (DISV) models. Any number of WEL
Packages can be specified for a single groundwater flow model.
https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=63
.. warning::
This class is deprecated and will be deleted in a future release.
Consider changing your code to use the ``imod.mf6.Well`` package.
Parameters
----------
layer: list of int
Modellayer in which the well is located.
cell2d: list of int
Cell 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).
concentration: array of floats (xr.DataArray, optional)
if this flow package is used in simulations also involving transport,
then this array is used as the concentration for inflow over this
boundary.
concentration_boundary_type: ({"AUX", "AUXMIXED"}, optional)
if this flow package is used in simulations also involving transport,
then this keyword specifies how outflow over this boundary is computed.
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.
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 = "wel"
_period_data = ("layer", "cell2d", "rate")
_keyword_map = {}
_template = DisVerticesBoundaryCondition._initialize_template(_pkg_id)
_auxiliary_data = {"concentration": "species"}
_init_schemata = {
"layer": [DTypeSchema(np.integer)],
"cell2d": [DTypeSchema(np.integer)],
"rate": [DTypeSchema(np.floating)],
"concentration": [DTypeSchema(np.floating)],
}
_write_schemata = {}
[docs]
@init_log_decorator()
def __init__(
self,
layer,
cell2d,
rate,
concentration=None,
concentration_boundary_type="aux",
print_input=False,
print_flows=False,
save_flows=False,
observations=None,
validate: bool = True,
):
dict_dataset = {
"layer": _assign_dims(layer),
"cell2d": _assign_dims(cell2d),
"rate": _assign_dims(rate),
"print_input": print_input,
"print_flows": print_flows,
"save_flows": save_flows,
"observations": observations,
"concentration": concentration,
"concentration_boundary_type": concentration_boundary_type,
}
super().__init__(dict_dataset)
self._validate_init_schemata(validate)
warnings.warn(
f"{self.__class__.__name__} is deprecated and will be removed in the v1.0 release."
"Please adapt your code to use the imod.mf6.Well package",
DeprecationWarning,
)
def clip_box(
self,
time_min: Optional[cftime.datetime | np.datetime64 | str] = None,
time_max: Optional[cftime.datetime | np.datetime64 | str] = None,
layer_min: Optional[int] = None,
layer_max: Optional[int] = None,
x_min: Optional[float] = None,
x_max: Optional[float] = None,
y_min: Optional[float] = None,
y_max: Optional[float] = None,
top: Optional[GridDataArray] = None,
bottom: Optional[GridDataArray] = None,
) -> Package:
"""
Clip a package by a bounding box (time, layer, y, x).
Slicing intervals may be half-bounded, by providing None:
* To select 500.0 <= x <= 1000.0:
``clip_box(x_min=500.0, x_max=1000.0)``.
* To select x <= 1000.0: ``clip_box(x_min=None, x_max=1000.0)``
or ``clip_box(x_max=1000.0)``.
* To select x >= 500.0: ``clip_box(x_min = 500.0, x_max=None.0)``
or ``clip_box(x_min=1000.0)``.
Parameters
----------
time_min: optional
time_max: optional
layer_min: optional, int
layer_max: optional, int
x_min: optional, float
x_max: optional, float
y_min: optional, float
y_max: optional, float
top: optional, GridDataArray
bottom: optional, GridDataArray
state_for_boundary: optional, GridDataArray
Returns
-------
clipped: Package
"""
# TODO: include x and y values.
for arg in (
layer_min,
layer_max,
x_min,
x_max,
y_min,
y_max,
):
if arg is not None:
raise NotImplementedError("Can only clip_box in time for Well packages")
# The super method will select in the time dimension without issues.
new = super().clip_box(time_min=time_min, time_max=time_max)
return new
