import pathlib
from copy import deepcopy
from typing import Any, List, Optional, Union
import numpy as np
import imod
from imod.common.interfaces.imaskingsettings import IMaskingSettings
from imod.common.interfaces.iregridpackage import IRegridPackage
from imod.common.utilities.grid import create_smallest_target_grid
from imod.common.utilities.regrid import (
_regrid_like,
_regrid_package_data,
)
from imod.common.utilities.regrid_method_type import RegridMethodType
from imod.logging import init_log_decorator, standard_log_decorator
from imod.mf6.disv import VerticesDiscretization
from imod.mf6.package import Package
from imod.mf6.regrid.regrid_schemes import DiscretizationRegridMethod
from imod.mf6.utilities.imod5_converter import convert_ibound_to_idomain
from imod.mf6.validation import DisBottomSchema
from imod.schemata import (
ActiveCellsConnectedSchema,
AllCoordsValueSchema,
AllValueSchema,
AnyValueSchema,
DimsSchema,
DTypeSchema,
IdentityNoDataSchema,
IndexesSchema,
ValidationError,
)
from imod.typing.grid import GridDataArray, is_unstructured
from imod.util.regrid import RegridderWeightsCache
[docs]
class StructuredDiscretization(Package, IRegridPackage, IMaskingSettings):
"""
Discretization information for structered grids is specified using the file.
(DIS6) Only one discretization input file (DISU6, DISV6 or DIS6) can be
specified for a model.
https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=35
Parameters
----------
top: array of floats (xr.DataArray)
is the top elevation for each cell in the top model layer.
bottom: array of floats (xr.DataArray)
is the bottom elevation for each cell.
idomain: array of integers (xr.DataArray)
Indicates the existence status of a cell as follows:
* If 0, the cell does not exist in the simulation. Input and output
values will be read and written for the cell, but internal to the
program, the cell is excluded from the solution.
* If >0, the cell exists in the simulation.
* If <0, the cell does not exist in the simulation. Furthermore, the
first existing cell above will be connected to the first existing cell
below. This type of cell is referred to as a "vertical pass through"
cell.
This DataArray needs contain a ``"layer"``, ``"y"`` and ``"x"``
coordinate. Horizontal discretization information will be derived from
its x and y coordinates.
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 = "dis"
_init_schemata = {
"top": [
DTypeSchema(np.floating),
DimsSchema("y", "x") | DimsSchema(),
IndexesSchema(),
],
"bottom": [
DTypeSchema(np.floating),
DimsSchema("layer", "y", "x") | DimsSchema("layer"),
IndexesSchema(),
AllCoordsValueSchema("layer", ">", 0),
],
"idomain": [
DTypeSchema(np.integer),
DimsSchema("layer", "y", "x"),
IndexesSchema(),
AllCoordsValueSchema("layer", ">", 0),
],
}
_write_schemata = {
"idomain": (
ActiveCellsConnectedSchema(is_notnull=("!=", 0)),
AnyValueSchema(">", 0),
),
"top": (
AllValueSchema(">", "bottom", ignore=("idomain", "<=", 0)),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
# No need to check coords: dataset ensures they align with idomain.
),
"bottom": (DisBottomSchema(other="idomain", is_other_notnull=(">", 0)),),
}
_grid_data = {"top": np.float64, "bottom": np.float64, "idomain": np.int32}
_keyword_map = {"bottom": "botm"}
_template = Package._initialize_template(_pkg_id)
_regrid_method = DiscretizationRegridMethod()
@property
def skip_variables(self) -> List[str]:
return ["bottom"]
[docs]
@init_log_decorator()
def __init__(self, top, bottom, idomain, validate: bool = True):
dict_dataset = {
"idomain": idomain,
"top": top,
"bottom": bottom,
}
super().__init__(dict_dataset)
self._validate_init_schemata(validate)
def _delrc(self, dx):
"""
dx means dx or dy
"""
if isinstance(dx, (int, float)):
return f"constant {dx}"
elif isinstance(dx, np.ndarray):
arrstr = str(dx)[1:-1]
return f"internal\n {arrstr}"
else:
raise ValueError(f"Unhandled type of {dx}")
def render(self, directory, pkgname, globaltimes, binary):
disdirectory = pathlib.Path(directory) / pkgname
d: dict[str, Any] = {}
x = self.dataset["idomain"].coords["x"]
y = self.dataset["idomain"].coords["y"]
dx, xmin, _ = imod.util.spatial.coord_reference(x)
dy, ymin, _ = imod.util.spatial.coord_reference(y)
d["xorigin"] = xmin
d["yorigin"] = ymin
d["nlay"] = self.dataset["idomain"].coords["layer"].size
d["nrow"] = y.size
d["ncol"] = x.size
d["delr"] = self._delrc(np.abs(dx))
d["delc"] = self._delrc(np.abs(dy))
_, d["top"] = self._compose_values(
self["top"], disdirectory, "top", binary=binary
)
d["botm_layered"], d["botm"] = self._compose_values(
self["bottom"], disdirectory, "botm", binary=binary
)
d["idomain_layered"], d["idomain"] = self._compose_values(
self["idomain"], disdirectory, "idomain", binary=binary
)
return self._template.render(d)
def _validate(self, schemata, **kwargs):
# Insert additional kwargs
kwargs["bottom"] = self["bottom"]
errors = super()._validate(schemata, **kwargs)
return errors
[docs]
@classmethod
@standard_log_decorator()
def from_imod5_data(
cls,
imod5_data: dict[str, dict[str, GridDataArray]],
regridder_types: Optional[RegridMethodType] = None,
regrid_cache: RegridderWeightsCache = RegridderWeightsCache(),
validate: bool = True,
) -> "StructuredDiscretization":
"""
Construct package from iMOD5 data, loaded with the
:func:`imod.formats.prj.open_projectfile_data` function.
Method regrids all variables to a target grid with the smallest extent
and smallest cellsize available in all the grids. Consequently it
converts iMODFLOW data to MODFLOW 6 data.
.. note::
The method expects the iMOD5 model to be fully 3D, not quasi-3D.
Parameters
----------
imod5_data: dict
Dictionary with iMOD5 data. This can be constructed from the
:func:`imod.formats.prj.open_projectfile_data` method.
regridder_types: DiscretizationRegridMethod, optional
Optional dataclass with regridder types for a specific variable.
Use this to override default regridding methods.
regrid_cache: RegridderWeightsCache, optional
stores regridder weights for different regridders. Can be used to speed up regridding,
if the same regridders are used several times for regridding different arrays.
Returns
-------
Modflow 6 StructuredDiscretization package.
"""
data = {
"idomain": imod5_data["bnd"]["ibound"].astype(np.int32),
"top": imod5_data["top"]["top"],
"bottom": imod5_data["bot"]["bottom"],
}
target_grid = create_smallest_target_grid(*data.values())
if regridder_types is None:
regridder_types = StructuredDiscretization.get_regrid_methods()
new_package_data = _regrid_package_data(
data, target_grid, regridder_types, regrid_cache
)
# Validate iMOD5 data
if validate:
if not np.all(
new_package_data["top"][1:].data == new_package_data["bottom"][:-1].data
):
raise ValidationError(
"Model discretization not fully 3D. Make sure TOP[n+1] matches BOT[n]"
)
thickness = new_package_data["top"] - new_package_data["bottom"]
new_package_data["idomain"] = convert_ibound_to_idomain(
new_package_data["idomain"], thickness
)
# TOP 3D -> TOP 2D
# Assume iMOD5 data provided as fully 3D and not Quasi-3D
new_package_data["top"] = new_package_data["top"].sel(layer=1, drop=True)
return cls(**new_package_data, validate=True)
@classmethod
def get_regrid_methods(cls) -> DiscretizationRegridMethod:
return deepcopy(cls._regrid_method)
[docs]
def regrid_like(
self,
target_grid: GridDataArray,
regrid_cache: RegridderWeightsCache,
regridder_types: Optional[RegridMethodType] = None,
) -> Union["StructuredDiscretization", VerticesDiscretization]:
"""
Regrid discretization package. Creates a StructuredDiscretization, or
VerticesDiscretization package, based on type of target_grid. It regrids
all the arrays in this package to the desired discretization, and leaves
the options unmodified.
The default regridding methods are specified in the ``_regrid_method``
attribute of the package. These defaults can be overridden using the
input parameters of this function.
Parameters
----------
target_grid: xr.DataArray or xu.UgridDataArray
a grid defined using the same discretization as the one we want to
regrid the package to.
regrid_cache: RegridderWeightsCache, optional
stores regridder weights for different regridders. Can be used to
speed up regridding, if the same regridders are used several times
for regridding different arrays.
regridder_types: RegridMethodType, optional
dictionary mapping arraynames (str) to a tuple of regrid type (a
specialization class of BaseRegridder) and function name (str) this
dictionary can be used to override the default mapping method.
Returns
-------
StructuredDiscretization or VerticesDiscretization
Depending on type of target_grid. A package with the same options as
this package, and with all the data-arrays regridded to another
discretization, similar to the one used in input argument "target_grid"
"""
as_pkg_type = None
if is_unstructured(target_grid):
as_pkg_type = VerticesDiscretization
try:
result = _regrid_like(
self, target_grid, regrid_cache, regridder_types, as_pkg_type
)
except ValueError as e:
raise e
except Exception:
raise ValueError("package could not be regridded.")
return result
