from __future__ import annotations
import warnings
from copy import deepcopy
from typing import Dict, List, Union
import numpy as np
import pandas as pd
import xarray as xr
import xugrid as xu
from numpy import ndarray
from imod.mf6.auxiliary_variables import add_periodic_auxiliary_variable
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.clip import clip_by_grid
from imod.mf6.utilities.dataset import remove_inactive
from imod.mf6.write_context import WriteContext
from imod.prepare import assign_wells
from imod.schemata import AllNoDataSchema, DTypeSchema
from imod.select.points import points_indices
from imod.typing import GridDataArray
from imod.typing.grid import ones_like
from imod.util import values_within_range
def _assign_dims(arg) -> Dict:
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)
[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
----------
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.
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.
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.
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.
"""
@property
def x(self) -> ndarray[float]:
return self.dataset["x"].values
@property
def y(self) -> ndarray[float]:
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 = {
"y": [AllNoDataSchema()],
"x": [AllNoDataSchema()],
}
_regrid_method = {}
[docs] def __init__(
self,
x,
y,
screen_top,
screen_bottom,
rate,
concentration=None,
concentration_boundary_type="aux",
id=None,
minimum_k=0.1,
minimum_thickness=1.0,
print_input=False,
print_flows=False,
save_flows=False,
observations=None,
validate: bool = True,
repeat_stress=None,
):
super().__init__()
self.dataset["screen_top"] = _assign_dims(screen_top)
self.dataset["screen_bottom"] = _assign_dims(screen_bottom)
self.dataset["y"] = _assign_dims(y)
self.dataset["x"] = _assign_dims(x)
self.dataset["rate"] = _assign_dims(rate)
if id is None:
id = np.arange(self.dataset["x"].size).astype(str)
self.dataset["id"] = _assign_dims(id)
self.dataset["minimum_k"] = minimum_k
self.dataset["minimum_thickness"] = minimum_thickness
self.dataset["print_input"] = print_input
self.dataset["print_flows"] = print_flows
self.dataset["save_flows"] = save_flows
self.dataset["observations"] = observations
self.dataset["repeat_stress"] = repeat_stress
if concentration is not None:
self.dataset["concentration"] = concentration
self.dataset["concentration_boundary_type"] = concentration_boundary_type
self._validate_init_schemata(validate)
@classmethod
def is_grid_agnostic_package(cls) -> bool:
return True
def clip_box(
self,
time_min=None,
time_max=None,
z_min=None,
z_max=None,
x_min=None,
x_max=None,
y_min=None,
y_max=None,
) -> "Well":
"""
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
z_min: optional, float
z_max: optional, float
x_min: optional, float
x_max: optional, float
y_min: optional, float
y_max: optional, float
Returns
-------
sliced : Package
"""
# 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
# 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"], None, z_max)
in_bounds &= values_within_range(ds["screen_bottom"], z_min, None)
# Replace dataset with reduced dataset based on booleans
new.dataset = ds.loc[{"index": in_bounds}]
return new
def write(
self,
pkgname: str,
globaltimes: np.ndarray[np.datetime64],
validate: bool,
write_context: WriteContext,
idomain: Union[xr.DataArray, xu.UgridDataArray],
top: Union[xr.DataArray, xu.UgridDataArray],
bottom: Union[xr.DataArray, xu.UgridDataArray],
k: Union[xr.DataArray, xu.UgridDataArray],
) -> None:
if validate:
self._validate(self._write_schemata)
mf6_package = self.to_mf6_pkg(
idomain, top, bottom, k, write_context.is_partitioned
)
# TODO: make options like "save_flows" configurable. Issue gitlab #623
mf6_package.dataset["save_flows"] = True
mf6_package.write(pkgname, globaltimes, write_context)
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
)
# 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
) -> list:
"""
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 = dict((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: Union[xr.DataArray, xu.UgridDataArray],
top: Union[xr.DataArray, xu.UgridDataArray],
bottom: Union[xr.DataArray, xu.UgridDataArray],
k: Union[xr.DataArray, xu.UgridDataArray],
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
----------
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.
"""
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.")
# @TODO: reinstate this check. issue gitlab #621.
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(**dict(ds_vars.items()))
ds = remove_inactive(ds, active)
return Mf6Wel(**ds)
def regrid_like(self, target_grid: GridDataArray, *_) -> "Well":
"""
The regrid_like method is irrelevant for this package as it is
grid-agnostic, instead this method clips the package based on the grid
exterior.
"""
return clip_by_grid(self, target_grid)
def mask(self, _) -> Package:
"""
The Well package has no mask method implemented. 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.
"""
# TODO: Add docsting message to logger
# message = textwrap.dedent(self.mask.__doc__)
return deepcopy(self)
[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] 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,
):
super().__init__()
self.dataset["layer"] = _assign_dims(layer)
self.dataset["row"] = _assign_dims(row)
self.dataset["column"] = _assign_dims(column)
self.dataset["rate"] = _assign_dims(rate)
self.dataset["print_input"] = print_input
self.dataset["print_flows"] = print_flows
self.dataset["save_flows"] = save_flows
self.dataset["observations"] = observations
self.dataset["repeat_stress"] = repeat_stress
if concentration is not None:
self.dataset["concentration"] = concentration
self.dataset["concentration_boundary_type"] = concentration_boundary_type
add_periodic_auxiliary_variable(self)
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=None,
time_max=None,
layer_min=None,
layer_max=None,
x_min=None,
x_max=None,
y_min=None,
y_max=None,
) -> "WellDisStructured":
"""
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_min: optional, float
y_max: optional, float
y_max: optional, float
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] 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,
):
super().__init__()
self.dataset["layer"] = _assign_dims(layer)
self.dataset["cell2d"] = _assign_dims(cell2d)
self.dataset["rate"] = _assign_dims(rate)
self.dataset["print_input"] = print_input
self.dataset["print_flows"] = print_flows
self.dataset["save_flows"] = save_flows
self.dataset["observations"] = observations
if concentration is not None:
self.dataset["concentration"] = concentration
self.dataset["concentration_boundary_type"] = concentration_boundary_type
add_periodic_auxiliary_variable(self)
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=None,
time_max=None,
layer_min=None,
layer_max=None,
x_min=None,
x_max=None,
y_min=None,
y_max=None,
) -> "WellDisStructured":
"""
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_min: optional, float
y_max: optional, float
y_max: optional, float
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
