Issue #1835 sprinkling from ipf

docs/api/msw.rst

@@ -56,6 +56,12 @@ Grid packages
     Sprinkling.from_imod5_data
     Sprinkling.get_regrid_methods
     Sprinkling.write
+    SprinklingPoints
+    SprinklingPoints.regrid_like
+    SprinklingPoints.clip_box
+    SprinklingPoints.from_imod5_data
+    SprinklingPoints.get_regrid_methods
+    SprinklingPoints.write
 
 Initial conditions
 ==================

imod/mf6/mf6_wel_adapter.py

@@ -143,6 +143,7 @@ def __init__(
         self,
         cellid,
         rate,
+        id,
         concentration=None,
         concentration_boundary_type="aux",
         save_flows: Optional[bool] = None,
@@ -153,6 +154,7 @@ def __init__(
         dict_dataset = {
             "cellid": cellid,
             "rate": rate,
+            "id": id,
             "concentration": concentration,
             "concentration_boundary_type": concentration_boundary_type,
             "save_flows": save_flows,
@@ -169,7 +171,7 @@ def _ds_to_arrdict(self, ds):
         arrdict: Dict[str, Any] = {}
 
         arrdict["data_vars"] = [
-            var_name for var_name in ds.data_vars if var_name != "cellid"
+            var_name for var_name in ds.data_vars if var_name not in ("cellid", "id")
         ]
 
         dsvar = {}

imod/mf6/utilities/imod5_converter.py

@@ -58,9 +58,16 @@ def fill_missing_layers(
 
 
 def _well_from_imod5_cap_point_data(cap_data: GridDataDict) -> dict[str, np.ndarray]:
-    raise NotImplementedError(
-        "Assigning sprinkling wells with an IPF file is not supported, please specify them as IDF."
-    )
+    df_points = cap_data["artificial_recharge_layer"]
+    data = {}
+    # Order of columns is x, y, layer, the other columns are irrelevant here.
+    data["x"] = df_points.iloc[:, 0].to_numpy().astype(float)
+    data["y"] = df_points.iloc[:, 1].to_numpy().astype(float)
+    data["layer"] = df_points.iloc[:, 2].to_numpy().astype(int)
+    data["rate"] = np.zeros_like(data["x"], dtype=float)
+    data["id"] = df_points.index.to_numpy()
+
+    return data
 
 
 def _well_from_imod5_cap_grid_data(cap_data: GridDataDict) -> dict[str, np.ndarray]:
@@ -85,7 +92,7 @@ def _well_from_imod5_cap_grid_data(cap_data: GridDataDict) -> dict[str, np.ndarr
 
 def well_from_imod5_cap_data(
     imod5_data: Imod5DataDict,
-    target_dis: IRegridPackage,
+    target_dis: Optional[IRegridPackage],
     regridder_types: DataclassType,
     regrid_cache: RegridderWeightsCache,
 ) -> dict[str, np.ndarray]:
@@ -121,6 +128,11 @@ def well_from_imod5_cap_data(
     if has_ipf_well:
         return _well_from_imod5_cap_point_data(cap_data)
     else:
+        if target_dis is None:
+            raise ValueError(
+                "target_dis must be provided when converting iMOD5 cap data "
+                "from grids (IDF)"
+            )
         cap_data_regridded = regrid_imod5_cap_data(
             imod5_data, target_dis, regridder_types, regrid_cache
         )["cap"]

imod/mf6/wel.py

@@ -61,11 +61,11 @@ def _assign_dims(arg: Any) -> tuple[Any, ...] | xr.DataArray:
         if arg.dims[0] != "time":
             arg = arg.transpose()
         da = xr.DataArray(
-            data=arg.values, coords={"time": arg["time"]}, dims=["time", "index"]
+            data=arg.to_numpy(), coords={"time": arg["time"]}, dims=["time", "index"]
         )
         return da
     elif is_da:
-        return "index", arg.values
+        return "index", arg.to_numpy()
     else:
         return "index", arg
 
@@ -347,11 +347,11 @@ class GridAgnosticWell(BoundaryCondition, IPointDataPackage, abc.ABC):
 
     @property
     def x(self) -> npt.NDArray[np.float64]:
-        return self.dataset["x"].values
+        return self.dataset["x"].to_numpy()
 
     @property
     def y(self) -> npt.NDArray[np.float64]:
-        return self.dataset["y"].values
+        return self.dataset["y"].to_numpy()
 
     @classmethod
     def _is_grid_agnostic_package(cls) -> bool:
@@ -394,7 +394,9 @@ def _create_dataset_vars(
         # Carefully rename the dimension and set coordinates
         d_rename = {"index": "ncellid"}
         ds_vars = ds_vars.rename_dims(**d_rename).rename_vars(**d_rename)
-        ds_vars = ds_vars.assign_coords(**{"ncellid": cellid.coords["ncellid"].values})
+        ds_vars = ds_vars.assign_coords(
+            **{"ncellid": cellid.coords["ncellid"].to_numpy()}
+        )
 
         return ds_vars
 
@@ -525,9 +527,9 @@ def _to_mf6_pkg(
         ds = ds.assign(**data_vars_dict)  # type: ignore[arg-type]
 
         ds = remove_inactive(ds, idomain)
-        ds["save_flows"] = self["save_flows"].values[()]
-        ds["print_flows"] = self["print_flows"].values[()]
-        ds["print_input"] = self["print_input"].values[()]
+        ds["save_flows"] = enforce_scalar(self["save_flows"])
+        ds["print_flows"] = enforce_scalar(self["print_flows"])
+        ds["print_input"] = enforce_scalar(self["print_input"])
 
         filtered_final_well_ids = self._gather_filtered_well_ids(ds, wells_df)
         if len(filtered_final_well_ids) > 0:
@@ -537,8 +539,6 @@ def _to_mf6_pkg(
             )
             logger.log(loglevel=LogLevel.WARNING, message=message_end)
 
-        ds = ds.drop_vars("id")
-
         data_vars_dict = {str(k): v for k, v in ds.data_vars.items()}
         return Mf6Wel(**data_vars_dict)  # type: ignore[arg-type]
 
@@ -1060,8 +1060,8 @@ def _find_well_value_at_layer(
         if (value is not None) and is_spatial_grid(value):
             value = imod.select.points_values(
                 value,
-                x=well_dataset["x"].values,
-                y=well_dataset["y"].values,
+                x=well_dataset["x"].to_numpy(),
+                y=well_dataset["y"].to_numpy(),
                 out_of_bounds="ignore",
             )
             in_bounds = np.full(well_dataset.sizes["index"], False)
@@ -1449,7 +1449,7 @@ def _validate_imod5_depth_information(
     def from_imod5_cap_data(
         cls,
         imod5_data: Imod5DataDict,
-        target_dis: StructuredDiscretization,
+        target_dis: Optional[StructuredDiscretization] = None,
         regridder_types: CapDataWellRegridMethod = CapDataWellRegridMethod(),
         regrid_cache: RegridderWeightsCache = RegridderWeightsCache(),
     ):
@@ -1489,6 +1489,17 @@ def from_imod5_cap_data(
             xarray datasets, under the key of the package type to which it
             belongs, as returned by
             :func:`imod.formats.prj.open_projectfile_data`.
+        target_dis: Optional[StructuredDiscretization]
+            The target discretization to which the data should be regridded.
+            Only necessary when "artificial_recharge_layer" is an IDF grid,
+            otherwise ignored.
+        regridder_types: CapDataWellRegridMethod
+            The regridder type to use for the regridding of the "artificial_recharge_layer"
+            and "artificial_recharge_capacity" grids. Only necessary when
+            "artificial_recharge_layer" is an IDF grid, otherwise ignored.
+        regrid_cache: RegridderWeightsCache
+            Cache for storing intermediate regridding results. Only necessary when
+            "artificial_recharge_layer" is an IDF grid, otherwise ignored.
         """
         data = well_from_imod5_cap_data(
             imod5_data, target_dis, regridder_types, regrid_cache

imod/msw/__init__.py

@@ -16,5 +16,5 @@
 from imod.msw.output_control import TimeOutputControl, VariableOutputControl
 from imod.msw.ponding import Ponding
 from imod.msw.scaling_factors import ScalingFactors
-from imod.msw.sprinkling import Sprinkling
+from imod.msw.sprinkling import Sprinkling, SprinklingPoints
 from imod.msw.vegetation import AnnualCropFactors

imod/msw/regrid/regrid_schemes.py

@@ -104,6 +104,28 @@ class SprinklingRegridMethod(DataclassType):
     max_abstraction_surfacewater: RegridVarType = (RegridderType.OVERLAP, "mean")
 
 
+@dataclass(config=_CONFIG)
+class SprinklingPointsRegridMethod(DataclassType):
+    """
+    Object containing regridder methods for the
+    :class:`imod.msw.Sprinkling` package. This can be provided to the
+    ``regrid_like`` method to regrid with custom settings.
+
+    Parameters
+    ----------
+    art_grid: tuple, default (RegridderType.OVERLAP, "mode")
+
+    Examples
+    --------
+    Regrid with custom settings:
+
+    >>> regrid_method = SprinklingPointsRegridMethod(art_grid=(RegridderType.OVERLAP,"min"))
+    >>> sprinking.regrid_like(target_grid, RegridderWeightsCache(), regrid_method)
+    """
+
+    art_grid: RegridVarType = (RegridderType.OVERLAP, "mode")
+
+
 @dataclass(config=_CONFIG)
 class MeteoGridRegridMethod(DataclassType):
     """