import abc
import pathlib
import numpy as np
from imod.mf6.pkgbase import Package
from imod.mf6.validation import PKG_DIMS_SCHEMA
from imod.schemata import (
AllValueSchema,
DimsSchema,
DTypeSchema,
IdentityNoDataSchema,
IndexesSchema,
)
class Storage(Package):
_pkg_id = "sto_deprecated"
def __init__(*args, **kwargs):
raise NotImplementedError(
r"Storage package has been removed. Use SpecificStorage or StorageCoefficient instead."
)
class StorageBase(Package, abc.ABC):
def get_options(self, d):
# Skip both variables in grid_data and "transient".
not_options = list(self._grid_data.keys())
not_options += "transient"
for varname in self.dataset.data_vars.keys(): # pylint:disable=no-member
if varname in not_options:
continue
v = self.dataset[varname].values[()]
if self._valid(v): # skip None and False
d[varname] = v
return d
def _render_dict(self, directory, pkgname, globaltimes, binary):
d = {}
stodirectory = pathlib.Path(directory.stem) / pkgname
for varname in self._grid_data:
key = self._keyword_map.get(varname, varname)
layered, value = self._compose_values(
self[varname], stodirectory, key, binary=binary
)
if self._valid(value): # skip False or None
d[f"{key}_layered"], d[key] = layered, value
periods = {}
if "time" in self.dataset["transient"].coords:
package_times = self.dataset["transient"].coords["time"].values
starts = np.searchsorted(globaltimes, package_times) + 1
for i, s in enumerate(starts):
periods[s] = self.dataset["transient"].isel(time=i).values[()]
else:
periods[1] = self.dataset["transient"].values[()]
d["periods"] = periods
d = self.get_options(d)
return d
[docs]class SpecificStorage(StorageBase):
"""
Storage Package with specific storage.
From wikipedia (https://en.wikipedia.org/wiki/Specific_storage):
"The specific storage is the amount of water that a portion of an aquifer
releases from storage, per unit mass or volume of aquifer, per unit change
in hydraulic head, while remaining fully saturated."
If the STO Package is not included for a model, then storage changes will
not be calculated, and thus, the model will be steady state. Only one STO
Package can be specified for a GWF model.
Parameters
----------
specific_storage: array of floats (xr.DataArray)
Is specific storage. Specific storage values must be greater than
or equal to 0. (ss)
specific_yield: array of floats (xr.DataArray)
Is specific yield. Specific yield values must be greater than or
equal to 0. Specific yield does not have to be specified if there are no
convertible cells (convertible=0 in every cell). (sy)
transient: ({True, False})
Boolean to indicate if the model is transient or steady-state.
convertible: array of int (xr.DataArray)
Is a flag for each cell that specifies whether or not a cell is
convertible for the storage calculation. 0 indicates confined storage is
used. >0 indicates confined storage is used when head is above cell top
and a mixed formulation of unconfined and confined storage is used when
head is below cell top. (iconvert)
save_flows: ({True, False}, optional)
Indicates that storage flow terms will be written to the file specified
with "BUDGET FILEOUT" in Output Control. Default is False.
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 = "sto"
_grid_data = {
"convertible": np.int32,
"specific_storage": np.float64,
"specific_yield": np.float64,
}
_keyword_map = {
"specific_storage": "ss",
"specific_yield": "sy",
"convertible": "iconvert",
}
_init_schemata = {
"convertible": (
DTypeSchema(np.integer),
IndexesSchema(),
PKG_DIMS_SCHEMA,
),
"specific_storage": (
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
),
"specific_yield": (
DTypeSchema(np.floating),
IndexesSchema(),
PKG_DIMS_SCHEMA,
),
"save_flows": (DTypeSchema(np.bool_), DimsSchema()),
}
_write_schemata = {
"convertible": (
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
# No need to check coords: dataset ensures they align with idomain.
),
"specific_storage": (
AllValueSchema(">=", 0.0),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
),
"specific_yield": (
AllValueSchema(">=", 0.0),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
),
}
_template = Package._initialize_template(_pkg_id)
[docs] def __init__(
self,
specific_storage,
specific_yield,
transient,
convertible,
save_flows: bool = False,
validate: bool = True,
):
super().__init__(locals())
self.dataset["specific_storage"] = specific_storage
self.dataset["specific_yield"] = specific_yield
self.dataset["convertible"] = convertible
self.dataset["transient"] = transient
self.dataset["save_flows"] = save_flows
self._validate_init_schemata(validate)
def render(self, directory, pkgname, globaltimes, binary):
d = self._render_dict(directory, pkgname, globaltimes, binary)
return self._template.render(d)
[docs]class StorageCoefficient(StorageBase):
"""
Storage Package with a storage coefficient. Be careful,
this is not the same as the specific storage.
From wikipedia (https://en.wikipedia.org/wiki/Specific_storage):
"Storativity or the storage coefficient is the volume of water released
from storage per unit decline in hydraulic head in the aquifer, per
unit area of the aquifer. Storativity is a dimensionless quantity, and
is always greater than 0.
Under confined conditions:
S = Ss * b, where S is the storage coefficient,
Ss the specific storage, and b the aquifer thickness.
Under unconfined conditions:
S = Sy, where Sy is the specific yield"
If the STO Package is not included for a model, then storage changes will
not be calculated, and thus, the model will be steady state. Only one STO
Package can be specified for a GWF model.
Parameters
----------
storage_coefficient: array of floats (xr.DataArray)
Is storage coefficient. Storage coefficient values must be greater than
or equal to 0. (ss)
specific_yield: array of floats (xr.DataArray)
Is specific yield. Specific yield values must be greater than or
equal to 0. Specific yield does not have to be specified if there are no
convertible cells (convertible=0 in every cell). (sy)
transient: ({True, False})
Boolean to indicate if the model is transient or steady-state.
convertible: array of int (xr.DataArray)
Is a flag for each cell that specifies whether or not a cell is
convertible for the storage calculation. 0 indicates confined storage is
used. >0 indicates confined storage is used when head is above cell top
and a mixed formulation of unconfined and confined storage is used when
head is below cell top. (iconvert)
save_flows: ({True, False}, optional)
Indicates that storage flow terms will be written to the file specified
with "BUDGET FILEOUT" in Output Control. Default is False.
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 = "sto"
_grid_data = {
"convertible": np.int32,
"storage_coefficient": np.float64,
"specific_yield": np.float64,
}
_keyword_map = {
"storage_coefficient": "ss",
"specific_yield": "sy",
"convertible": "iconvert",
}
_init_schemata = {
"convertible": (
DTypeSchema(np.integer),
PKG_DIMS_SCHEMA,
),
"storage_coefficient": (
DTypeSchema(np.floating),
PKG_DIMS_SCHEMA,
),
"specific_yield": (
DTypeSchema(np.floating),
PKG_DIMS_SCHEMA,
),
"save_flows": (DTypeSchema(np.bool_), DimsSchema()),
}
_write_schemata = {
"convertible": (
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
# No need to check coords: dataset ensures they align with idomain.
),
"storage_coefficient": (
AllValueSchema(">=", 0.0),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
),
"specific_yield": (
AllValueSchema(">=", 0.0),
IdentityNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
),
}
_template = Package._initialize_template(_pkg_id)
[docs] def __init__(
self,
storage_coefficient,
specific_yield,
transient,
convertible,
save_flows: bool = False,
validate: bool = True,
):
super().__init__(locals())
self.dataset["storage_coefficient"] = storage_coefficient
self.dataset["specific_yield"] = specific_yield
self.dataset["convertible"] = convertible
self.dataset["transient"] = transient
self.dataset["save_flows"] = save_flows
self._validate_init_schemata(validate)
def render(self, directory, pkgname, globaltimes, binary):
d = self._render_dict(directory, pkgname, globaltimes, binary)
d["storagecoefficient"] = True
return self._template.render(d)
