from __future__ import annotations
import abc
import itertools
import textwrap
import warnings
from datetime import datetime
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
import imod.mf6.utilities
from imod.logging import init_log_decorator, logger
from imod.logging.logging_decorators import standard_log_decorator
from imod.logging.loglevel import LogLevel
from imod.mf6 import StructuredDiscretization, VerticesDiscretization
from imod.mf6.boundary_condition import (
BoundaryCondition,
DisStructuredBoundaryCondition,
DisVerticesBoundaryCondition,
)
from imod.mf6.interfaces.ipointdatapackage import IPointDataPackage
from imod.mf6.mf6_wel_adapter import Mf6Wel, concat_indices_to_cellid
from imod.mf6.package import Package
from imod.mf6.utilities.dataset import remove_inactive
from imod.mf6.utilities.grid import broadcast_to_full_domain
from imod.mf6.utilities.imod5_converter import well_from_imod5_cap_data
from imod.mf6.validation import validation_pkg_error_message
from imod.mf6.validation_context import ValidationContext
from imod.mf6.write_context import WriteContext
from imod.prepare import assign_wells
from imod.prepare.cleanup import cleanup_wel
from imod.prepare.layer import create_layered_top
from imod.schemata import (
AllValueSchema,
AnyNoDataSchema,
DTypeSchema,
EmptyIndexesSchema,
ValidationError,
)
from imod.select.points import points_indices, points_values
from imod.typing import GridDataArray, Imod5DataDict
from imod.typing.grid import is_spatial_grid, ones_like
from imod.util.expand_repetitions import resample_timeseries
from imod.util.structured import values_within_range
ABSTRACT_METH_ERROR_MSG = "Method in abstract base class called"
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: GridAgnosticWell, domain_2d: GridDataArray) -> GridAgnosticWell:
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)
return cls._from_dataset(selection)
def _df_groups_to_da_rates(
unique_well_groups: pd.api.typing.DataFrameGroupBy,
) -> xr.DataArray:
# Convert dataframes all groups to DataArrays
is_steady_state = "time" not in unique_well_groups[0].columns
if is_steady_state:
da_groups = [
xr.DataArray(df_group["rate"].iloc[0]) for df_group in unique_well_groups
]
else:
da_groups = [
xr.DataArray(
df_group["rate"], dims=("time"), coords={"time": df_group["time"]}
)
for df_group in unique_well_groups
]
# Assign index coordinates
da_groups = [
da_group.expand_dims(dim="index").assign_coords(index=[i])
for i, da_group in enumerate(da_groups)
]
# Concatenate datarrays along index dimension
return xr.concat(da_groups, dim="index")
def _prepare_well_rates_from_groups(
pkg_data: dict,
unique_well_groups: pd.api.typing.DataFrameGroupBy,
times: list[datetime],
) -> xr.DataArray:
"""
Prepare well rates from dataframe groups, grouped by unique well locations.
Resample timeseries if ipf with associated text files.
"""
has_associated = pkg_data["has_associated"]
start_times = times[:-1] # Starts stress periods.
if has_associated:
# Resample times per group
unique_well_groups = [
resample_timeseries(df_group, start_times)
for df_group in unique_well_groups
]
return _df_groups_to_da_rates(unique_well_groups)
def _prepare_df_ipf_associated(
pkg_data: dict, start_times: list[datetime], all_well_times: list[datetime]
) -> pd.DataFrame:
"""Prepare dataframe for an ipf with associated timeseries in a textfile."""
# Validate if associated wells are assigned multiple layers, factors,
# and additions.
for entry in ["layer", "factor", "addition"]:
uniques = set(pkg_data[entry])
if len(uniques) > 1:
raise ValueError(
f"IPF with associated textfiles assigned multiple {entry}s: {uniques}"
)
# Validate if associated wells are defined only on first timestep or all
# timesteps
is_defined_all = len(set(all_well_times) - set(pkg_data["time"])) == 0
is_defined_first = (len(pkg_data["time"]) == 1) & (
pkg_data["time"][0] == all_well_times[0]
)
if not is_defined_all and not is_defined_first:
raise ValueError(
"IPF with associated textfiles assigned to wrong times. "
"Should be assigned to all times or only first time. "
f"PRJ times: {all_well_times}, package times: {pkg_data['time']}"
)
df = pkg_data["dataframe"][0]
df["layer"] = pkg_data["layer"][0]
return df
def _prepare_df_ipf_unassociated(
pkg_data: dict, start_times: list[datetime]
) -> pd.DataFrame:
"""Prepare dataframe for an ipf with no associated timeseries."""
is_steady_state = any(t is None for t in pkg_data["time"])
if is_steady_state:
index_dicts = [{"layer": lay} for lay in pkg_data["layer"]]
else:
index_dicts = [
{"time": t, "layer": lay}
for t, lay in zip(pkg_data["time"], pkg_data["layer"])
]
# Concatenate dataframes, assign layer and times
iter_dfs_dims = zip(pkg_data["dataframe"], index_dicts)
df = pd.concat([df.assign(**index_dict) for df, index_dict in iter_dfs_dims])
# Prepare multi-index dataframe to convert to a multi-dimensional DataArray
# later.
dimnames = list(index_dicts[0].keys())
df_multi = df.set_index(dimnames + [df.index])
df_multi.index = df_multi.index.set_names(dimnames + ["ipf_row"])
# Temporarily convert to DataArray with 2 dimensions, as it allows for
# multi-dimensional ffilling, instead pandas' ffilling the last value in a
# column of the flattened table.
ipf_row_index = pkg_data["dataframe"][0].index
# Forward fill location columns, only reindex layer, filt_top and filt_bot
# if present.
cols_ffill_if_present = {"x", "y", "filt_top", "filt_bot"}
cols_ffill = cols_ffill_if_present & set(df.columns)
da_multi = df_multi.to_xarray()
indexers = {"ipf_row": ipf_row_index}
if not is_steady_state:
indexers["time"] = start_times
# Multi-dimensional reindex, forward fill well locations, fill well rates
# with 0.0.
df_ffilled = da_multi[cols_ffill].reindex(indexers, method="ffill").to_dataframe()
df_fill_zero = da_multi["rate"].reindex(indexers, fill_value=0.0).to_dataframe()
# Combine columns and reset dataframe back into a simple long table with
# single index.
df_out = pd.concat([df_ffilled, df_fill_zero], axis="columns")
return df_out.reset_index().drop(columns="ipf_row")
def _unpack_package_data(
pkg_data: dict, times: list[datetime], all_well_times: list[datetime]
) -> pd.DataFrame:
"""Unpack package data to dataframe"""
start_times = times[:-1] # Starts stress periods.
has_associated = pkg_data["has_associated"]
if has_associated:
return _prepare_df_ipf_associated(pkg_data, start_times, all_well_times)
else:
return _prepare_df_ipf_unassociated(pkg_data, start_times)
def get_all_imod5_prj_well_times(imod5_data: dict) -> list[datetime]:
"""Get all times a well data is defined on in a prj file"""
wel_keys = [key for key in imod5_data.keys() if key.startswith("wel")]
wel_times_per_pkg = [imod5_data[wel_key]["time"] for wel_key in wel_keys]
# Flatten list
wel_times_flat = itertools.chain.from_iterable(wel_times_per_pkg)
# Get unique times by converting to set and sorting. ``sorted`` also
# transforms set to a list again.
return sorted(set(wel_times_flat))
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]
dim_cellid_coords = ["layer"] + cell2d_coords
cellid = concat_indices_to_cellid(cellid_ls, dim_cellid_coords)
# Assign extra coordinate names.
xy_coords = {
"x": ("ncellid", x),
"y": ("ncellid", y),
}
cellid = cellid.assign_coords(coords=xy_coords)
return cellid.astype(int)
class GridAgnosticWell(BoundaryCondition, IPointDataPackage, abc.ABC):
"""
Abstract base class for grid agnostic wells
"""
_imod5_depth_colnames: list[str] = []
_depth_colnames: list[tuple[str, type]] = []
@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
@classmethod
def is_grid_agnostic_package(cls) -> bool:
return True
def _create_cellid(
self, assigned_wells: pd.DataFrame, active: xr.DataArray
) -> GridDataArray:
like = ones_like(active)
# Groupby index and select first, to unset any duplicate records
# introduced by the multi-indexed "time" dimension.
unique_assigned_wells = assigned_wells.groupby("index").first()
d_for_cellid = unique_assigned_wells[["x", "y", "layer"]].to_dict("list")
return derive_cellid_from_points(like, **d_for_cellid)
def _create_dataset_vars(
self, assigned_wells: pd.DataFrame, cellid: xr.DataArray
) -> xr.Dataset:
"""
Create dataset with all variables (rate, concentration), with a similar shape as the cellids.
"""
data_vars = ["id", "rate"]
if "concentration" in assigned_wells.columns:
data_vars.append("concentration")
ds_vars = assigned_wells[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 = assigned_wells.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 render(self, directory, pkgname, globaltimes, binary):
raise NotImplementedError(
textwrap.dedent(
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 _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 mask(self, domain: GridDataArray) -> GridAgnosticWell:
"""
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)
def to_mf6_pkg(
self,
active: GridDataArray,
top: GridDataArray,
bottom: GridDataArray,
k: GridDataArray,
validate: bool = False,
strict_well_validation: bool = True,
) -> 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.
validate: bool, default True
Run validation before converting
strict_well_validation: bool, default True
Set well validation strict:
Throw error if well is removed entirely during its assignment to
layers.
Returns
-------
Mf6Wel
Object with wells as list based input.
"""
validation_context = ValidationContext(
validate=validate, strict_well_validation=strict_well_validation
)
return self._to_mf6_pkg(active, top, bottom, k, validation_context)
def _to_mf6_pkg(
self,
active: GridDataArray,
top: GridDataArray,
bottom: GridDataArray,
k: GridDataArray,
validation_context: ValidationContext,
) -> Mf6Wel:
if validation_context.validate:
errors = self._validate(self._write_schemata)
if len(errors) > 0:
message = validation_pkg_error_message(errors)
raise ValidationError(message)
wells_df = self._create_wells_df()
nwells_df = len(wells_df["id"].unique())
if nwells_df == 0:
raise ValidationError(
"No wells were assigned in package. None were present."
)
assigned_wells = self._assign_wells_to_layers(wells_df, active, top, bottom, k)
filtered_assigned_well_ids = self.gather_filtered_well_ids(
assigned_wells, wells_df
)
message_assign = self.to_mf6_package_information(
filtered_assigned_well_ids, reason_text="permeability/thickness constraints"
)
error_on_well_removal = validation_context.strict_well_validation
if error_on_well_removal and len(filtered_assigned_well_ids) > 0:
logger.log(loglevel=LogLevel.ERROR, message=message_assign)
raise ValidationError(message_assign)
ds = xr.Dataset()
ds["cellid"] = self._create_cellid(assigned_wells, active)
ds_vars = self._create_dataset_vars(assigned_wells, 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[()]
filtered_final_well_ids = self.gather_filtered_well_ids(ds, wells_df)
if len(filtered_final_well_ids) > 0:
reason_text = "inactive cells or permeability/thickness constraints"
message_end = self.to_mf6_package_information(
filtered_final_well_ids, reason_text=reason_text
)
logger.log(loglevel=LogLevel.WARNING, message=message_end)
ds = ds.drop_vars("id")
return Mf6Wel(**ds.data_vars)
def gather_filtered_well_ids(
self, well_data_filtered: pd.DataFrame | xr.Dataset, well_data: pd.DataFrame
) -> list[str]:
filtered_well_ids = [
id
for id in well_data["id"].unique()
if id not in well_data_filtered["id"].values
]
return filtered_well_ids
def to_mf6_package_information(
self, filtered_wells: list[str], reason_text: str
) -> str:
message = textwrap.dedent(
f"""Some wells were not placed in the MF6 well package. This can be
due to {reason_text}.\n"""
)
if len(filtered_wells) < 10:
message += "The filtered wells are: \n"
else:
message += " The first 10 unplaced wells are: \n"
for i in range(min(10, len(filtered_wells))):
ids = filtered_wells[i]
x = self.dataset["x"][int(filtered_wells[i])].values[()]
y = self.dataset["y"][int(filtered_wells[i])].values[()]
message += f" id = {ids} x = {x} y = {y} \n"
return message
def _create_wells_df(self) -> pd.DataFrame:
raise NotImplementedError(ABSTRACT_METH_ERROR_MSG)
def _assign_wells_to_layers(
self,
wells_df: pd.DataFrame,
active: GridDataArray,
top: GridDataArray,
bottom: GridDataArray,
k: GridDataArray,
) -> pd.DataFrame:
raise NotImplementedError(ABSTRACT_METH_ERROR_MSG)
@classmethod
def _validate_imod5_depth_information(
cls, key: str, pkg_data: dict, df: pd.DataFrame
) -> None:
raise NotImplementedError(ABSTRACT_METH_ERROR_MSG)
@classmethod
def from_imod5_data(
cls,
key: str,
imod5_data: dict[str, dict[str, GridDataArray]],
times: list[datetime],
minimum_k: float = 0.1,
minimum_thickness: float = 0.05,
) -> "GridAgnosticWell":
"""
Convert wells to imod5 data, loaded with
:func:`imod.formats.prj.open_projectfile_data`, to a Well object. As
iMOD5 handles wells differently than iMOD Python normally does, some
data transformations are made, which are outlined further.
iMOD5 stores well information in IPF files and it supports two ways to
specify injection/extraction rates:
1. A timeseries of well rates, in an associated text file. We will
call these "associated wells" further in this text.
2. Constant rates in an IPF file, without an associated text file.
We will call these "unassociated wells" further in this text.
Depending on this, iMOD5 does different things, which we need to mimic
in this method.
*Associated wells*
Wells with timeseries in an associated textfile are processed as
follows:
- Wells are validated if the following requirements are met
* Associated well entries in projectfile are defined on either all
timestamps or just the first
* Multiplication and addition factors need to remain constant through time
* Same associated well cannot be assigned to multiple layers
- The dataframe of the first projectfile timestamp is selected
- Rate timeseries are resampled with a time weighted mean to the
simulation times.
- When simulation times fall outside well timeseries range, the last
rate is forward filled.
- Projectfile timestamps are not used. Even if assigned to a
"steady-state" timestamp, the resulting dataset still uses simulation
times.
*Unassociated wells*
Wells without associated textfiles are processed as follows:
- When a unassociated well disappears from the next time entry in the
projectfile, the well is deactivated by setting its rate to 0.0. This
is to prevent the well being activated again in case of any potential
forward filling at a later stage by
:meth:`imod.mf6.Modflow6Simulation.create_time_discretization`
- Wells assigned to a "steady-state" entry in the projectfile will have
no "time" dimension in the resulting dataset.
- Times beyond the year 2261 are out of bounds for pandas. In associated
timeseries these are ignored, instead the last stage is forward
filled.
.. note::
In case you are wondering why is this so complicated? There are two
main reasons:
- iMOD5 is inconsistent in how it treats timeseries for grid data,
compared to point data. Whereas grids are forward filled when
there is no entry specified for a time entry, unassociated wells
are deactivated. Associated wells, however, are forward filled.
- Normally there are two levels in which times are defined: The
simulation times, which are the requested times for the
simulation, and projectfile times, on which data is defined. With
associated ipfs, times are defined in three
levels: There are simulation times (in iMOD5 in the ini
file), there are projectfile times, and there are times
defined in the associated textfiles on which data is defined.
Parameters
----------
key: str
Name of the well system in the imod5 data
imod5_data: dict
iMOD5 data loaded from a projectfile with
:func:`imod.formats.prj.open_projectfile_data`
times: list
Simulation times
minimum_k: float, optional
On creating point wells, no point wells will be placed in cells with
a lower horizontal conductivity than this. Wells are placed when
``to_mf6_pkg`` is called.
minimum_thickness: float, optional
On creating point wells, no point wells will be placed in cells with
a lower thickness than this. Wells are placed when ``to_mf6_pkg`` is
called.
"""
pkg_data = imod5_data[key]
all_well_times = get_all_imod5_prj_well_times(imod5_data)
df = _unpack_package_data(pkg_data, times, all_well_times)
cls._validate_imod5_depth_information(key, pkg_data, df)
# Groupby unique wells, to get dataframes per time.
colnames_group = ["x", "y"] + cls._imod5_depth_colnames
# Associated wells need additional grouping by id
if pkg_data["has_associated"]:
colnames_group.append("id")
wel_index, unique_well_groups = zip(*df.groupby(colnames_group))
# Unpack wel indices by zipping
varnames = [("x", float), ("y", float)] + cls._depth_colnames
index_values = zip(*wel_index)
cls_input: dict[str, Any] = {
var: np.array(value, dtype=dtype)
for (var, dtype), value in zip(varnames, index_values)
}
cls_input["rate"] = _prepare_well_rates_from_groups(
pkg_data, unique_well_groups, times
)
cls_input["minimum_k"] = minimum_k
cls_input["minimum_thickness"] = minimum_thickness
return cls(**cls_input)
[docs]
class Well(GridAgnosticWell):
"""
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: list of floats or np.array of floats
is the y location of the well.
x: list of floats or np.array of floats
is the x location of the well.
screen_top: list of floats or np.array of floats
is the top of the well screen.
screen_bottom: list of floats or np.array of floats
is the bottom of the well screen.
rate: 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)
"""
_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(),
AllValueSchema("<=", "screen_top"),
],
"y": [AnyNoDataSchema(), EmptyIndexesSchema()],
"x": [AnyNoDataSchema(), EmptyIndexesSchema()],
"rate": [AnyNoDataSchema(), EmptyIndexesSchema()],
"concentration": [AnyNoDataSchema(), EmptyIndexesSchema()],
}
_imod5_depth_colnames: list[str] = ["filt_top", "filt_bot"]
_depth_colnames: list[tuple[str, type]] = [
("screen_top", float),
("screen_bottom", float),
]
[docs]
@init_log_decorator()
def __init__(
self,
x: np.ndarray | list[float],
y: np.ndarray | list[float],
screen_top: np.ndarray | list[float],
screen_bottom: np.ndarray | 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 = 0.05,
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)
[docs]
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
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 _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
@standard_log_decorator()
def _validate(self, schemata: dict, **kwargs) -> dict[str, list[ValidationError]]:
kwargs["screen_top"] = self.dataset["screen_top"]
return Package._validate(self, schemata, **kwargs)
def _assign_wells_to_layers(
self,
wells_df: pd.DataFrame,
active: GridDataArray,
top: GridDataArray,
bottom: GridDataArray,
k: GridDataArray,
) -> pd.DataFrame:
# 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
minimum_k = self.dataset["minimum_k"].item()
minimum_thickness = self.dataset["minimum_thickness"].item()
# 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()
assigned_wells = assign_wells(
wells_df, top_3d, bottom, k, minimum_thickness, minimum_k, True
)
# Set multi-index again
assigned_wells = assigned_wells.set_index(index_names).sort_index()
return assigned_wells
@classmethod
def _validate_imod5_depth_information(
cls, key: str, pkg_data: dict, df: pd.DataFrame
) -> None:
if "layer" in pkg_data.keys() and (np.any(np.array(pkg_data["layer"]) != 0)):
log_msg = textwrap.dedent(
f"""
In well {key} a layer was assigned, but this is not
supported for imod.mf6.Well. Assignment will be done based on
filter_top and filter_bottom, and the chosen layer
({pkg_data["layer"]}) will be ignored. To specify by layer, use
imod.mf6.LayeredWell.
"""
)
logger.log(loglevel=LogLevel.WARNING, message=log_msg, additional_depth=2)
if "filt_top" not in df.columns or "filt_bot" not in df.columns:
log_msg = textwrap.dedent(
f"""
In well {key} the 'filt_top' and 'filt_bot' columns were
not both found; this is not supported for import. To specify by
layer, use imod.mf6.LayeredWell.
"""
)
logger.log(loglevel=LogLevel.ERROR, message=log_msg, additional_depth=2)
raise ValueError(log_msg)
[docs]
@standard_log_decorator()
def cleanup(self, dis: StructuredDiscretization | VerticesDiscretization):
"""
Clean up package inplace. This method calls
:func:`imod.prepare.cleanup_wel`, see documentation of that
function for details on cleanup.
dis: imod.mf6.StructuredDiscretization | imod.mf6.VerticesDiscretization
Model discretization package.
"""
# Top and bottom should be forced to grids with a x, y coordinates
top, bottom = broadcast_to_full_domain(**dict(dis.dataset.data_vars))
# Collect point variable datanames
point_varnames = list(self._write_schemata.keys())
if "concentration" not in self.dataset.keys():
point_varnames.remove("concentration")
point_varnames.append("id")
# Create dataset with purely point locations
point_ds = self.dataset[point_varnames]
# Take first item of irrelevant dimensions
point_ds = point_ds.isel(time=0, species=0, drop=True, missing_dims="ignore")
# Cleanup well dataframe
wells = point_ds.to_dataframe()
minimum_thickness = float(self.dataset["minimum_thickness"])
cleaned_wells = cleanup_wel(wells, top.isel(layer=0), bottom, minimum_thickness)
# Select with ids in cleaned dataframe to drop points outside grid.
well_ids = cleaned_wells.index
dataset_cleaned = self.dataset.swap_dims({"index": "id"}).sel(id=well_ids)
# Assign adjusted screen top and bottom
dataset_cleaned["screen_top"] = cleaned_wells["screen_top"]
dataset_cleaned["screen_bottom"] = cleaned_wells["screen_bottom"]
# Ensure dtype of id is preserved
id_type = self.dataset["id"].dtype
dataset_cleaned = dataset_cleaned.swap_dims({"id": "index"}).reset_coords("id")
dataset_cleaned["id"] = dataset_cleaned["id"].astype(id_type)
# Override dataset
self.dataset = dataset_cleaned
[docs]
class LayeredWell(GridAgnosticWell):
"""
Agnostic WEL package, which accepts x, y and layers.
This package can be written to any provided model grid, given that it has
enough layers. 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: list of floats or np.array of floats
is the y location of the well.
x: list of floats or np.array of floats
is the x location of the well.
layer: list of ints or np.array of ints
is the layer of the well.
rate: 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
---------
>>> layer = [1, 2]
>>> y = [83.0, 77.0]
>>> x = [81.0, 82.0]
>>> rate = [1.0, 1.0]
>>> imod.mf6.LayeredWell(x, y, layer, 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.LayeredWell(x, y, layer, rate_transient)
"""
_pkg_id = "wel"
_auxiliary_data = {"concentration": "species"}
_init_schemata = {
"layer": [DTypeSchema(np.integer)],
"y": [DTypeSchema(np.floating)],
"x": [DTypeSchema(np.floating)],
"rate": [DTypeSchema(np.floating)],
"concentration": [DTypeSchema(np.floating)],
}
_write_schemata = {
"layer": [AnyNoDataSchema(), EmptyIndexesSchema()],
"y": [AnyNoDataSchema(), EmptyIndexesSchema()],
"x": [AnyNoDataSchema(), EmptyIndexesSchema()],
"rate": [AnyNoDataSchema(), EmptyIndexesSchema()],
"concentration": [AnyNoDataSchema(), EmptyIndexesSchema()],
}
_imod5_depth_colnames: list[str] = ["layer"]
_depth_colnames: list[tuple[str, type]] = [("layer", int)]
[docs]
@init_log_decorator()
def __init__(
self,
x: np.ndarray | list[float],
y: np.ndarray | list[float],
layer: np.ndarray | list[int],
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 = {
"layer": _assign_dims(layer),
"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)
[docs]
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
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["layer"], layer_min, layer_max)
# Replace dataset with reduced dataset based on booleans
new.dataset = ds.loc[{"index": in_bounds}]
return new
def _create_wells_df(self) -> pd.DataFrame:
return self.dataset.to_dataframe()
def _assign_wells_to_layers(
self,
wells_df: pd.DataFrame,
active: GridDataArray,
top: GridDataArray,
bottom: GridDataArray,
k: GridDataArray,
) -> pd.DataFrame:
return wells_df
@classmethod
def _validate_imod5_depth_information(
cls, key: str, pkg_data: dict, df: pd.DataFrame
) -> None:
if np.any(np.array(pkg_data["layer"]) == 0):
log_msg = textwrap.dedent(
f"""
Well {key} in projectfile is assigned to layer 0, but should be >
0 for LayeredWell
"""
)
logger.log(loglevel=LogLevel.ERROR, message=log_msg, additional_depth=2)
raise ValueError(log_msg)
if "layer" not in df.columns:
log_msg = textwrap.dedent(
f"""
IPF file {key} has no layer assigned, but this is required
for LayeredWell.
"""
)
logger.log(loglevel=LogLevel.ERROR, message=log_msg, additional_depth=2)
raise ValueError(log_msg)
[docs]
@classmethod
def from_imod5_cap_data(cls, imod5_data: Imod5DataDict):
"""
Create LayeredWell from imod5_data in "cap" package. Abstraction data
for sprinkling is defined in iMOD5 either with grids (IDF) or points
(IPF) combined with a grid. Depending on the type, the function does
different conversions
- grids (IDF)
The ``"artifical_recharge_layer"`` variable was defined as grid
(IDF), this grid defines in which layer a groundwater abstraction
well should be placed. The ``"artificial_recharge"`` grid contains
types which point to the type of abstraction:
* 0: no abstraction
* 1: groundwater abstraction
* 2: surfacewater abstraction
The ``"artificial_recharge_capacity"`` grid/constant defines the
capacity of each groundwater or surfacewater abstraction. This is an
``1:1`` mapping: Each grid cell maps to a separate well.
- points with grid (IPF & IDF)
The ``"artifical_recharge_layer"`` variable was defined as point
data (IPF), this table contains wellids with an abstraction capacity
and layer. The ``"artificial_recharge"`` grid contains a mapping of
grid cells to wellids in the point data. The
``"artificial_recharge_capacity"`` is ignored as the abstraction
capacity is already defined in the point data. This is an ``n:1``
mapping: multiple grid cells can map to one well.
Parameters
----------
imod5_data: dict[str, dict[str, GridDataArray]]
dictionary containing the arrays mentioned in the project file as
xarray datasets, under the key of the package type to which it
belongs, as returned by
:func:`imod.formats.prj.open_projectfile_data`.
"""
data = well_from_imod5_cap_data(imod5_data)
return cls(**data) # type: ignore
[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
