import pandas as pd
from imod.msw.fixed_format import VariableMetaData
from imod.msw.pkgbase import MetaSwapPackage
from imod.msw.timeutil import to_metaswap_timeformat
# I did not use long variable names here (e.g. "precipitation"), as MetaSWAP
# uses these 2 to 4 character names to print its output to. This also has the
# benefit that the user is able to set additional variable names via kwargs
# (there are more than 130 possible variable names to choose from in MetaSWAP)
[docs]
class VariableOutputControl(MetaSwapPackage):
"""
Control which variables will be created as output. The variable names used
in this class provide a condensed water balance. You can use additional
keyword arguments to set more variables by using their specific name, e.g.
`vcr = True` for the water balance error. For all possibilities see the
SIMGRO Input and Output description.
All budgets will be written in m unit to in `.idf` files and to mm unit in
`.csv` files.
Parameters
----------
Pm: bool
Write measured precipitation
Psgw: bool
Write sprinkling precipitation, from groundwater
Pssw: bool
Write sprinkling precipitation, from surface water
qrun: bool
Write runon
qdr: bool
Write net infiltration of surface water
qspgw: bool
Groundwater extraction for sprinkling from layer
qmodf: bool
Sum of all MODFLOW stresses on groundwater
ETact: bool
Write total actual evapotranspiration, which is the sum of the
sprinkling evaporation (Esp), interception evaporation (Eic), ponding
evaporation (Epd) bare soil evaporation (Ebs), and actual transpiration
(Tact).
**kwargs: bool
Additional variables to let MetaSWAP write
"""
_file_name = "sel_key_svat_per.inp"
_settings: dict = {}
_metadata_dict = {
"variable": VariableMetaData(10, None, None, str),
"option": VariableMetaData(10, 0, 3, int),
}
[docs]
def __init__(
self,
Pm=True,
Psgw=True,
Pssw=True,
qrun=True,
qdr=True,
qspgw=True,
qmodf=True,
ETact=True,
**kwargs,
):
super().__init__()
# Convert to integer, as MetaSWAP expects its values as integers.
self.dataset["Pm"] = int(Pm)
self.dataset["Psgw"] = int(Psgw)
self.dataset["Pssw"] = int(Pssw)
self.dataset["qrun"] = int(qrun)
self.dataset["qdr"] = int(qdr)
self.dataset["qspgw"] = int(qspgw)
self.dataset["qmodf"] = int(qmodf)
self.dataset["ETact"] = int(ETact)
# Set additional settings
for key, value in kwargs.items():
self.dataset[key] = int(value)
def _render(self, file, *args):
variable, option = zip(
*[(var, self.dataset[var].values) for var in self.dataset.data_vars]
)
dataframe = pd.DataFrame(data={"variable": variable, "option": option})
self._check_range(dataframe)
return self.write_dataframe_fixed_width(file, dataframe)
[docs]
class TimeOutputControl(MetaSwapPackage):
"""
Specify the accumulation periods which will be used to write output. For
example, say the model computes on a daily timestep, but timesteps two days
apart are specified, the summed fluxes of each two days are written by
MetaSWAP.
Parameters
----------
time: xr.DataArray
Timesteps at which to write output.
"""
_file_name = "tiop_sim.inp"
_settings: dict = {}
_metadata_dict = {
"time_since_start_year": VariableMetaData(15, 0.0, 366.0, float),
"year": VariableMetaData(6, 1, 9999, int),
"option": VariableMetaData(6, 1, 7, int),
}
[docs]
def __init__(self, time):
super().__init__()
self.dataset["times"] = time
def _render(self, file, *args):
year, time_since_start_year = to_metaswap_timeformat(self.dataset["times"])
dataframe = pd.DataFrame(
data={"time_since_start_year": time_since_start_year, "year": year}
)
dataframe["time_since_start_year"] += 1
dataframe["option"] = 7
self._check_range(dataframe)
return self.write_dataframe_fixed_width(file, dataframe)
