import numpy as np
import pandas as pd
import xarray as xr
from imod.msw.fixed_format import VariableMetaData
from imod.msw.pkgbase import MetaSwapPackage
from imod.prepare import common
class MeteoMapping(MetaSwapPackage):
"""
This class provides common methods for creating mappings between
meteorological data and MetaSWAP grids. It should not be instantiated
by the user but rather be inherited from within imod-python to create
new packages.
"""
def __init__(self):
super().__init__()
def _render(self, file, index, svat):
data_dict = {"svat": svat.values.ravel()[index]}
row, column = self.grid_mapping(svat, self.meteo)
data_dict["row"] = row[index]
data_dict["column"] = column[index]
dataframe = pd.DataFrame(
data=data_dict, columns=list(self._metadata_dict.keys())
)
self._check_range(dataframe)
return self.write_dataframe_fixed_width(file, dataframe)
@staticmethod
def grid_mapping(svat: xr.DataArray, meteo_grid: xr.DataArray) -> pd.DataFrame:
flip_meteo_x = meteo_grid.indexes["x"].is_monotonic_decreasing
flip_meteo_y = meteo_grid.indexes["y"].is_monotonic_decreasing
nrow = meteo_grid["y"].size
ncol = meteo_grid["x"].size
# Convert to cell boundaries for the meteo grid
# Method always returns monotonic increasing edges
meteo_x = common._coord(meteo_grid, "x")
meteo_y = common._coord(meteo_grid, "y")
# Create the SVAT grid
svat_grid_y, svat_grid_x = np.meshgrid(svat.y, svat.x, indexing="ij")
svat_grid_y = svat_grid_y.ravel()
svat_grid_x = svat_grid_x.ravel()
# Determine where the svats fit in within the cell boundaries of the meteo grid
row = np.searchsorted(meteo_y, svat_grid_y)
column = np.searchsorted(meteo_x, svat_grid_x)
# Find out of bounds members
if (column == 0).any() or (column > ncol).any():
raise ValueError("Some values are out of bounds for column")
if (row == 0).any() or (row > nrow).any():
raise ValueError("Some values are out of bounds for row")
# Flip axis when meteofile bound are flipped, relative to the coords
if flip_meteo_y:
row = (nrow + 1) - row
if flip_meteo_x:
column = (ncol + 1) - column
n_subunit = svat["subunit"].size
return np.tile(row, n_subunit), np.tile(column, n_subunit)
[docs]
class PrecipitationMapping(MeteoMapping):
"""
This contains the data to connect precipitation grid cells to MetaSWAP
svats. The precipitation grid does not have to be equal to the metaswap
grid: connections between the precipitation cells to svats will be
established using a nearest neighbour lookup.
This class is responsible for the file `svat2precgrid.inp`.
Parameters
----------
precipitation: array of floats (xr.DataArray)
Describes the precipitation data. The extend of the grid must be larger
than the MetaSvap grid. The data must also be coarser than the MetaSvap
grid.
"""
_file_name = "svat2precgrid.inp"
_metadata_dict = {
"svat": VariableMetaData(10, None, None, int),
"row": VariableMetaData(10, None, None, int),
"column": VariableMetaData(10, None, None, int),
}
[docs]
def __init__(
self,
precipitation: xr.DataArray,
):
super().__init__()
self.meteo = precipitation
[docs]
class EvapotranspirationMapping(MeteoMapping):
"""
This contains the data to connect evapotranspiration grid cells to MetaSWAP
svats. The evapotranspiration grid does not have to be equal to the metaswap
grid: connections between the evapotranspiration cells to svats will be
established using a nearest neighbour lookup.
This class is responsible for the file `svat2etrefgrid.inp`.
Parameters
----------
evapotransporation: array of floats (xr.DataArray)
Describes the evapotransporation data. The extend of the grid must be
larger than the MetaSvap grid. The data must also be coarser than the
MetaSvap grid.
"""
_file_name = "svat2etrefgrid.inp"
_metadata_dict = {
"svat": VariableMetaData(10, None, None, int),
"row": VariableMetaData(10, None, None, int),
"column": VariableMetaData(10, None, None, int),
}
[docs]
def __init__(
self,
evapotranspiration: xr.DataArray,
):
super().__init__()
self.meteo = evapotranspiration
