import numpy as np
import xarray as xr
from imod.msw.fixed_format import VariableMetaData
from imod.msw.pkgbase import MetaSwapPackage
[docs]
class AnnualCropFactors(MetaSwapPackage):
"""
For each vegetation type specify a yearly trend in vegetation factors and
interception characteristics. These are used if WOFOST is not used.
This class is responsible for the file `fact_svat.inp`.
Parameters
----------
soil_cover: array of floats (xr.DataArray)
Soil cover in m2/m2. Must have a "vegetation_index" and "day_of_year" a
coordinates.
leaf_area_index: array of floats (xr.DataArray)
Leaf area index in m2/m2. Must have a "vegetation_index" and
"day_of_year" a coordinates.
interception_capacity: array of floats (xr.DataArray)
Interception capacity in m3/m2. Must have a "vegetation_index" and
"day_of_year" a coordinates.
vegetation_factor: array of floats (xr.DataArray)
Vegetation factor. Must have a "vegetation_index" and "day_of_year" a
coordinates.
interception_factor: array of floats (xr.DataArray)
Interception evaporation factor. Must have a "vegetation_index" and
"day_of_year" a coordinates.
bare_soil_factor: array of floats (xr.DataArray)
Bare soil evaporation factor. Must have a "vegetation_index" and
"day_of_year" a coordinates.
ponding_factor: array of floats (xr.DataArray)
Ponding factor. Must have a "vegetation_index" and "day_of_year" a
coordinates.
"""
_file_name = "fact_svat.inp"
_metadata_dict = {
"vegetation_index": VariableMetaData(6, 0, 999, int),
"day_of_year": VariableMetaData(6, 1, 366, int),
"soil_cover": VariableMetaData(8, 0.0, 1.0, float),
"leaf_area_index": VariableMetaData(8, 0.0, 10.0, float),
"interception_capacity": VariableMetaData(8, 0.0, 0.1, float),
# io manual: min value vegetation_factor = 0.1, but example file has 0.
# and works
"vegetation_factor": VariableMetaData(8, 0.0, 10.0, float),
"interception_factor": VariableMetaData(8, 0.01, 10.0, float),
"bare_soil_factor": VariableMetaData(8, 0.01, 10.0, float),
"ponding_factor": VariableMetaData(8, 0.01, 10.0, float),
}
[docs]
def __init__(
self,
soil_cover: xr.DataArray,
leaf_area_index: xr.DataArray,
interception_capacity: xr.DataArray,
vegetation_factor: xr.DataArray,
interception_factor: xr.DataArray,
bare_soil_factor: xr.DataArray,
ponding_factor: xr.DataArray,
):
super().__init__()
self.dataset["soil_cover"] = soil_cover
self.dataset["leaf_area_index"] = leaf_area_index
self.dataset["interception_capacity"] = interception_capacity
self.dataset["vegetation_factor"] = vegetation_factor
self.dataset["interception_factor"] = interception_factor
self.dataset["bare_soil_factor"] = bare_soil_factor
self.dataset["ponding_factor"] = ponding_factor
self._pkgcheck()
def _render(self, file, *args):
dataframe = self.dataset.to_dataframe(
dim_order=("vegetation_index", "day_of_year")
).reset_index()
self._check_range(dataframe)
return self.write_dataframe_fixed_width(file, dataframe)
def _pkgcheck(self):
dims = self.dataset.dims
dims_expected = ("day_of_year", "vegetation_index")
if len(set(dims) - set(dims_expected)) > 0:
raise ValueError(
f"Please provide DataArrays with dimensions {dims_expected}"
)
day_of_year = self.dataset.coords["day_of_year"].values
if not np.all(day_of_year == np.arange(1, 367)):
raise ValueError(r"Not all days of the year included in data.")
