hydromt.GridModel.setup_grid_from_raster_reclass#
- GridModel.setup_grid_from_raster_reclass(raster_fn: str | Path | DataArray, reclass_table_fn: str | Path | DataFrame, reclass_variables: List, variable: str | None = None, fill_method: str | None = None, reproject_method: List | str | None = 'nearest', mask_name: str | None = 'mask', rename: Dict | None = None, **kwargs) List[str]#
HYDROMT CORE METHOD: Add data variable(s) to grid object by reclassifying the data in
raster_fnbased onreclass_table_fn.Adds model layers:
reclass_variables grid: reclassified raster data
- Parameters:
raster_fn (
str,Path,xr.DataArray) – Data catalog key, path to raster file or raster xarray data object. Should be a DataArray. Else use variable argument for selection.reclass_table_fn (
str,Path,pd.DataFrame) – Data catalog key, path to tabular data file or tabular pandas dataframe object for the reclassification table of raster_fn.reclass_variables (
list) – List of reclass_variables from reclass_table_fn table to add to maps. Index column should match values in raster_fn.variable (
str, optional) – Name of raster_fn dataset variable to use. This is only required when reading datasets with multiple variables. By default None.fill_method (
str, optional) – If specified, fills nodata values in raster_fn using fill_nodata method before reclassifying. Available methods are {‘linear’, ‘nearest’, ‘cubic’, ‘rio_idw’}.reproject_method (
str, optional) – See rasterio.warp.reproject for existing methods, by default “nearest”. Can provide a list corresponding toreclass_variables.mask_name (
str, optional) – Name of mask in self.grid to use for masking raster_fn. By default ‘mask’. Use None to disable masking.rename (
dict, optional) – Dictionary to rename variable names in reclass_variables before adding to grid {‘name_in_reclass_table’: ‘name_in_grid’}. By default empty.**kwargs (
dict) – Additional keyword arguments to be passed to get_rasterdataset
- Returns:
Names of added model grid layers
- Return type: