import os
from collections import defaultdict
from pathlib import Path
from typing import Any, Dict, Union
import numpy as np
from imod.logging import init_log_decorator
from imod.mf6.interfaces.iregridpackage import IRegridPackage
from imod.mf6.package import Package
from imod.mf6.utilities.dataset import is_dataarray_none
from imod.mf6.write_context import WriteContext
from imod.schemata import DTypeSchema
OUTPUT_EXT_MAPPING = {
"head": "hds",
"concentration": "ucn",
"budget": "cbc",
}
[docs]
class OutputControl(Package, IRegridPackage):
"""
The Output Control Option determines how and when heads, budgets and/or
concentrations are printed to the listing file and/or written to a separate
binary output file.
https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.4.2.pdf#page=53
Currently the settings "first", "last", "all", and "frequency"
are supported, the "steps" setting is not supported, because of
its ragged nature. Furthermore, only one setting per stress period
can be specified in imod-python.
Parameters
----------
save_head : {string, integer}, or xr.DataArray of {string, integer}, optional
String or integer indicating output control for head file (.hds)
If string, should be one of ["first", "last", "all"].
If integer, interpreted as frequency.
save_budget : {string, integer}, or xr.DataArray of {string, integer}, optional
String or integer indicating output control for cell budgets (.cbc)
If string, should be one of ["first", "last", "all"].
If integer, interpreted as frequency.
save_concentration : {string, integer}, or xr.DataArray of {string, integer}, optional
String or integer indicating output control for concentration file (.ucn)
If string, should be one of ["first", "last", "all"].
If integer, interpreted as frequency.
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.
Examples
--------
To specify a mix of both 'frequency' and 'first' setting,
we need to specify an array with both integers and strings.
For this we need to create a numpy object array first,
otherwise xarray converts all to strings automatically.
>>> time = [np.datetime64("2000-01-01"), np.datetime64("2000-01-02")]
>>> data = np.array(["last", 5], dtype="object")
>>> save_head = xr.DataArray(data, coords={"time": time}, dims=("time"))
>>> oc = imod.mf6.OutputControl(save_head=save_head, save_budget=None, save_concentration=None)
"""
_pkg_id = "oc"
_keyword_map = {}
_template = Package._initialize_template(_pkg_id)
_init_schemata = {
"save_head": [
DTypeSchema(np.integer) | DTypeSchema(str) | DTypeSchema(object),
],
"save_budget": [
DTypeSchema(np.integer) | DTypeSchema(str) | DTypeSchema(object),
],
"save_concentration": [
DTypeSchema(np.integer) | DTypeSchema(str) | DTypeSchema(object),
],
}
_write_schemata = {}
[docs]
@init_log_decorator()
def __init__(
self,
save_head=None,
save_budget=None,
save_concentration=None,
head_file=None,
budget_file=None,
concentration_file=None,
validate: bool = True,
):
save_concentration = (
None if is_dataarray_none(save_concentration) else save_concentration
)
save_head = None if is_dataarray_none(save_head) else save_head
save_budget = None if is_dataarray_none(save_budget) else save_budget
if save_head is not None and save_concentration is not None:
raise ValueError("save_head and save_concentration cannot both be defined.")
dict_dataset = {
"save_head": save_head,
"save_concentration": save_concentration,
"save_budget": save_budget,
"head_file": head_file,
"budget_file": budget_file,
"concentration_file": concentration_file,
}
super().__init__(dict_dataset)
self._validate_init_schemata(validate)
def _get_ocsetting(self, setting):
"""Get oc setting based on its type. If integers return f'frequency {setting}', if"""
if isinstance(setting, (int, np.integer)) and not isinstance(setting, bool):
return f"frequency {setting}"
elif isinstance(setting, str):
if setting.lower() in ["first", "last", "all"]:
return setting.lower()
else:
raise ValueError(
f"Output Control received wrong string. String should be one of ['first', 'last', 'all'], instead got {setting}"
)
else:
raise TypeError(
f"Output Control setting should be either integer or string in ['first', 'last', 'all'], instead got {setting}"
)
def _get_output_filepath(self, directory: Path, output_variable: str) -> Path:
varname = f"{output_variable}_file"
ext = OUTPUT_EXT_MAPPING[output_variable]
modelname = directory.stem
filepath = self.dataset[varname].values[()]
if filepath is None:
filepath = directory / f"{modelname}.{ext}"
else:
if not isinstance(filepath, str | Path):
raise ValueError(
f"{varname} should be of type str or Path. However it is of type {type(filepath)}"
)
filepath = Path(filepath)
if filepath.is_absolute():
path = filepath
else:
# Get path relative to the simulation name file.
sim_directory = directory.parent
path = Path(os.path.relpath(filepath, sim_directory))
return path
def render(self, directory, pkgname, globaltimes, binary):
d: dict[str, Any] = {}
for output_variable in OUTPUT_EXT_MAPPING.keys():
save = self.dataset[f"save_{output_variable}"].values[()]
if save is not None:
varname = f"{output_variable}_file"
output_path = self._get_output_filepath(directory, output_variable)
d[varname] = output_path.as_posix()
periods: defaultdict[int, Dict[str, str]] = defaultdict(dict)
for datavar in ("save_head", "save_concentration", "save_budget"):
if self.dataset[datavar].values[()] is None:
continue
key = datavar.replace("_", " ")
if "time" in self.dataset[datavar].coords:
package_times = self.dataset[datavar].coords["time"].values
starts = np.searchsorted(globaltimes, package_times) + 1
for i, s in enumerate(starts):
setting = self.dataset[datavar].isel(time=i).item()
periods[s][key] = self._get_ocsetting(setting)
else:
setting = self.dataset[datavar].item()
periods[1][key] = self._get_ocsetting(setting)
d["periods"] = periods
return self._template.render(d)
def write(
self,
pkgname: str,
globaltimes: Union[list[np.datetime64], np.ndarray],
write_context: WriteContext,
):
# We need to overload the write here to ensure the output directory is
# created in advance for MODFLOW6.
super().write(pkgname, globaltimes, write_context)
for datavar in ("head_file", "concentration_file", "budget_file"):
path = self.dataset[datavar].values[()]
if path is not None:
if not isinstance(path, str):
raise ValueError(
f"{path} should be of type str. However it is of type {type(path)}"
)
filepath = Path(path)
filepath.parent.mkdir(parents=True, exist_ok=True)
return
@property
def is_budget_output(self) -> bool:
return self.dataset["save_budget"].values[()] is not None
