import numpy as np
import xarray as xr
from imod.msw.fixed_format import VariableMetaData
from imod.msw.pkgbase import MetaSwapPackage
from imod.util.spatial import spatial_reference
[docs]
class GridData(MetaSwapPackage):
"""
This contains the grid data of MetaSWAP.
This class is responsible for the file `area_svat.inp`
Parameters
----------
area: array of floats (xr.DataArray)
Describes the area of SVAT units. This array must have a subunit coordinate
to describe different landuses.
landuse: array of integers (xr.DataArray)
Describes the landuse type of SVAT units.
This array must have a subunit coordinate.
rootzone_depth: array of floats (xr.DataArray)
Describes the rootzone depth of SVAT units.
This array must have a subunit coordinate to describe different landuses.
surface_elevation: array of floats (xr.DataArray)
Describes the surface elevation of SVAT units.
This array must not have a subunit coordinate.
soil_physical_unit: array of integers (xr.DataArray)
Describes the physical parameters of SVAT units.
These parameters will be looked up in a table according to the given integers.
This array must not have a subunit coordinate.
active: array of bools (xr.DataArray)
Describes whether SVAT units are active or not.
This array must not have a subunit coordinate.
"""
_file_name = "area_svat.inp"
_metadata_dict = {
"svat": VariableMetaData(10, 1, 99999999, int),
"area": VariableMetaData(10, 0.0, 999999.0, float),
"surface_elevation": VariableMetaData(8, -9999.0, 9999.0, float),
"temp": VariableMetaData(8, None, None, str),
"soil_physical_unit": VariableMetaData(6, 1, 999999, int),
"soil_physical_unit_string": VariableMetaData(16, None, None, str),
"landuse": VariableMetaData(6, 1, 999999, int),
"rootzone_depth": VariableMetaData(8, 0.0, 10.0, float),
}
_with_subunit = ("area", "landuse", "rootzone_depth")
_without_subunit = ("surface_elevation", "soil_physical_unit")
_to_fill = ("soil_physical_unit_string", "temp")
[docs]
def __init__(
self,
area: xr.DataArray,
landuse: xr.DataArray,
rootzone_depth: xr.DataArray,
surface_elevation: xr.DataArray,
soil_physical_unit: xr.DataArray,
active: xr.DataArray,
):
super().__init__()
self.dataset["area"] = area
self.dataset["landuse"] = landuse
self.dataset["rootzone_depth"] = rootzone_depth
self.dataset["surface_elevation"] = surface_elevation
self.dataset["soil_physical_unit"] = soil_physical_unit
self.dataset["active"] = active
self._pkgcheck()
def generate_index_array(self):
"""
Generate index arrays to be used on other packages
"""
area = self.dataset["area"]
active = self.dataset["active"]
isactive = area.where(active).notnull()
index = isactive.values.ravel()
svat = xr.full_like(area, fill_value=0, dtype=np.int64).rename("svat")
svat.values[isactive.values] = np.arange(1, index.sum() + 1)
return index, svat
def _pkgcheck(self):
super()._pkgcheck()
dx, _, _, dy, _, _ = spatial_reference(self.dataset)
if (not np.isscalar(dx)) or (not np.isscalar(dy)):
raise ValueError("MetaSWAP only supports equidistant grids")
active = self.dataset["active"]
cell_area = active.astype(float) * dx * abs(dy)
total_area = self.dataset["area"].sum(dim="subunit")
# Apparently all regional models intentionally provided area grids
# smaller than cell area, to allow surface waters as workaround.
unequal_area = (total_area > cell_area).values[active.values]
if np.any(unequal_area):
raise ValueError(
"Provided area grid with total areas larger than cell area"
)
