rename generic setup methods #475

docs/api.rst

@@ -273,10 +273,10 @@ Setup methods
    GridModel.setup_maps_from_rasterdataset
    GridModel.setup_maps_from_raster_reclass
    GridModel.setup_grid
-   GridModel.setup_grid_from_constant
-   GridModel.setup_grid_from_rasterdataset
-   GridModel.setup_grid_from_raster_reclass
-   GridModel.setup_grid_from_geodataframe
+   GridModel.setup_grid_data_from_constant
+   GridModel.setup_grid_data_from_rasterdataset
+   GridModel.setup_grid_data_from_raster_reclass
+   GridModel.setup_grid_data_from_geodataframe
 
 
 .. _lumped_model_api:
@@ -369,8 +369,8 @@ Setup methods
    MeshModel.setup_config
    MeshModel.setup_region
    MeshModel.setup_mesh2d
-   MeshModel.setup_mesh2d_from_rasterdataset
-   MeshModel.setup_mesh2d_from_raster_reclass
+   MeshModel.setup_mesh2d_data_from_rasterdataset
+   MeshModel.setup_mesh2d_data_from_raster_reclass
    MeshModel.setup_maps_from_rasterdataset
    MeshModel.setup_maps_from_raster_reclass
 

docs/dev/plugin-quickstart.rst

@@ -59,14 +59,14 @@ Using HydroMT generic methods, for your user, the build configuration file would
 
 .. code-block:: yaml
 
-  setup_grid_from_rasterdataset:
+  setup_grid_data_from_rasterdataset:
     raster_fn: vito
     fill_method: nearest
     reproject_method: mode
     rename:
       vito: landuse
 
-  setup_grid_from_raster_reclass:
+  setup_grid_data_from_raster_reclass:
     raster_fn: vito
     reclass_table_fn: vito_reclass
     reclass_variables:

examples/grid_model_build.yaml

@@ -10,13 +10,13 @@ setup_grid:
   basin_index_fn: merit_hydro_index
   hydrography_fn: merit_hydro
 
-setup_grid_from_constant:
+setup_grid_data_from_constant:
   constant: 0.01
   name: c1
   dtype: float32
   nodata: -99.0
 
-setup_grid_from_rasterdataset:
+setup_grid_data_from_rasterdataset:
   raster_fn: merit_hydro_1k
   variables:
     - elevtn
@@ -25,22 +25,22 @@ setup_grid_from_rasterdataset:
     - average
     - mode
 
-setup_grid_from_rasterdataset2:
+setup_grid_data_from_rasterdataset2:
   raster_fn: vito
   fill_method: nearest
   reproject_method: mode
   rename:
     vito: landuse
 
-setup_grid_from_raster_reclass:
+setup_grid_data_from_raster_reclass:
   raster_fn: vito
   reclass_table_fn: vito_reclass
   reclass_variables:
     - manning
   reproject_method:
     - average
 
-setup_grid_from_geodataframe:
+setup_grid_data_from_geodataframe:
   vector_fn: hydro_lakes
   variables:
     - waterbody_id
@@ -53,7 +53,7 @@ setup_grid_from_geodataframe:
     waterbody_id: lake_id
     Detph_avg: lake_depth
 
-setup_grid_from_geodataframe2:
+setup_grid_data_from_geodataframe2:
   vector_fn: hydro_lakes
   rasterize_method: fraction
   rename:

examples/working_with_models.ipynb

@@ -267,7 +267,7 @@
    "source": [
     "We can setup maps data using the ``setup_maps_from_raster`` and ``setup_maps_from_raster_reclass`` methods. Both methods add data to the **maps** component based on input raster data (RasterDataset type), but the second method additionally reclassifies the input data based on a reclassification table. The **maps** component gathers any raster input data without any requirements for a specific grid (CRS and resolution). It can contain, for example, direct model input data for models like Delft3D-FM that will interpolate input data on the fly to the model mesh, or auxiliary data that are not used by the model kernel but can be used by HydroMT to build the model (e.g. a gridded DEM), etc.\n",
     "\n",
-    "For models that require all their input data to be resampled to the exact computation grid (all raster at the same resolution and projection), then the input data would go into the **grid** component. The corresponding ``setup_grid_from_raster`` and ``setup_grid_from_raster_reclass`` functions for the **grid** components are also available.\n",
+    "For models that require all their input data to be resampled to the exact computation grid (all raster at the same resolution and projection), then the input data would go into the **grid** component. The corresponding ``setup_grid_data_from_raster`` and ``setup_grid_data_from_raster_reclass`` functions for the **grid** components are also available.\n",
     "\n",
     "But back to our example, let's add both a DEM map from the data source *merit_hydro_1k* and a manning roughness map based on reclassified landuse data from the *vito* dataset to our model grid object."
    ]
@@ -278,14 +278,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mod.setup_grid_from_rasterdataset(\n",
+    "mod.setup_grid_data_from_rasterdataset(\n",
     "    raster_fn=\"merit_hydro_1k\",\n",
     "    variables=\"elevtn\",\n",
     "    fill_method=None,\n",
     "    reproject_method=\"bilinear\",\n",
     "    rename={\"elevtn\": \"DEM\"},\n",
     ")\n",
-    "mod.setup_grid_from_raster_reclass(\n",
+    "mod.setup_grid_data_from_raster_reclass(\n",
     "    raster_fn=\"vito\",\n",
     "    fill_method=\"nearest\",\n",
     "    reclass_table_fn=\"vito_reclass\",  # Note: from local data catalog\n",

examples/working_with_models_basics.ipynb

@@ -167,10 +167,10 @@
    "source": [
     "In this configuration, you see that we will prepare quite a lot of data for our grid model using some of the generic model methods for grid. We will prepare:\n",
     "\n",
-    "- A grid with constant values 0.01 using [setup_grid_from_constant](https://deltares.github.io/hydromt/latest/_generated/hydromt.GridModel.setup_grid_from_constant.html)\n",
-    "- A couple of grid based on reprojection of MERIT Hydro IHU (elevation, basins) and VITO (landuse) using [setup_grid_from_rasterdataset](https://deltares.github.io/hydromt/latest/_generated/hydromt.GridModel.setup_grid_from_rasterdataset.html). Note that to use the same method twice within the same configuration file, you can end the method name with a number.\n",
-    "- A grid generated by mapping roughness values to the land use classes in VITO using [setup_grid_from_raster_reclass](https://deltares.github.io/hydromt/latest/_generated/hydromt.GridModel.setup_grid_from_raster_reclass.html)\n",
-    "- A couple of lake properties including the fraction of the grid cells covered by the lake geometry using [setup_grid_from_geodataframe](https://deltares.github.io/hydromt/latest/_generated/hydromt.GridModel.setup_grid_from_geodataframe.html)"
+    "- A grid with constant values 0.01 using [setup_grid_data_from_constant](https://deltares.github.io/hydromt/latest/_generated/hydromt.GridModel.setup_grid_data_from_constant.html)\n",
+    "- A couple of grid based on reprojection of MERIT Hydro IHU (elevation, basins) and VITO (landuse) using [setup_grid_data_from_rasterdataset](https://deltares.github.io/hydromt/latest/_generated/hydromt.GridModel.setup_grid_data_from_rasterdataset.html). Note that to use the same method twice within the same configuration file, you can end the method name with a number.\n",
+    "- A grid generated by mapping roughness values to the land use classes in VITO using [setup_grid_data_from_raster_reclass](https://deltares.github.io/hydromt/latest/_generated/hydromt.GridModel.setup_grid_data_from_raster_reclass.html)\n",
+    "- A couple of lake properties including the fraction of the grid cells covered by the lake geometry using [setup_grid_data_from_geodataframe](https://deltares.github.io/hydromt/latest/_generated/hydromt.GridModel.setup_grid_data_from_geodataframe.html)"
    ]
   },
   {
@@ -297,7 +297,7 @@
    "outputs": [],
    "source": [
     "%%writefile ./tmp_grid_model1/grid_model_update.yaml\n",
-    "setup_grid_from_rasterdataset:\n",
+    "setup_grid_data_from_rasterdataset:\n",
     "    raster_fn: merit_hydro_1k\n",
     "    variables: uparea\n",
     "    reproject_method: max\n",

hydromt/models/model_grid.py

@@ -31,7 +31,7 @@ def __init__(self, *args, **kwargs) -> None:
         self._grid = None  # xr.Dataset()
 
     # generic grid methods
-    def setup_grid_from_constant(
+    def setup_grid_data_from_constant(
         self,
         constant: Union[int, float],
         name: str,
@@ -73,7 +73,7 @@ def setup_grid_from_constant(
 
         return [name]
 
-    def setup_grid_from_rasterdataset(
+    def setup_grid_data_from_rasterdataset(
         self,
         raster_fn: Union[str, Path, xr.DataArray, xr.Dataset],
         variables: Optional[List] = None,
@@ -143,7 +143,7 @@ def setup_grid_from_rasterdataset(
 
         return list(ds_out.data_vars.keys())
 
-    def setup_grid_from_raster_reclass(
+    def setup_grid_data_from_raster_reclass(
         self,
         raster_fn: Union[str, Path, xr.DataArray],
         reclass_table_fn: Union[str, Path, pd.DataFrame],
@@ -231,7 +231,7 @@ def setup_grid_from_raster_reclass(
 
         return list(ds_vars.data_vars.keys())
 
-    def setup_grid_from_geodataframe(
+    def setup_grid_data_from_geodataframe(
         self,
         vector_fn: Union[str, Path, gpd.GeoDataFrame],
         variables: Optional[Union[List, str]] = None,
@@ -291,7 +291,7 @@ def setup_grid_from_geodataframe(
         if gdf.empty:
             self.logger.warning(
                 f"No shapes of {vector_fn} found within region,"
-                " skipping setup_grid_from_vector."
+                " skipping setup_grid_data_from_geodataframe."
             )
             return
         # Data resampling

hydromt/models/model_mesh.py

@@ -33,7 +33,7 @@ def __init__(self, *args, **kwargs) -> None:
         self._mesh = None
 
     ## general setup methods
-    def setup_mesh2d_from_rasterdataset(
+    def setup_mesh2d_data_from_rasterdataset(
         self,
         raster_fn: Union[str, Path, xr.DataArray, xr.Dataset],
         grid_name: Optional[str] = "mesh2d",
@@ -112,7 +112,7 @@ def setup_mesh2d_from_rasterdataset(
 
         return list(ds_sample.data_vars.keys())
 
-    def setup_mesh2d_from_raster_reclass(
+    def setup_mesh2d_data_from_raster_reclass(
         self,
         raster_fn: Union[str, Path, xr.DataArray],
         reclass_table_fn: Union[str, Path, pd.DataFrame],

tests/test_model.py

@@ -524,44 +524,44 @@ def test_gridmodel_setup(tmpdir):
         add_mask=True,
     )
     # Add data with setup_* methods
-    mod.setup_grid_from_constant(
+    mod.setup_grid_data_from_constant(
         constant=0.01,
         name="c1",
         nodata=-99.0,
     )
-    mod.setup_grid_from_constant(
+    mod.setup_grid_data_from_constant(
         constant=2,
         name="c2",
         dtype=np.int8,
         nodata=-1,
     )
-    mod.setup_grid_from_rasterdataset(
+    mod.setup_grid_data_from_rasterdataset(
         raster_fn="merit_hydro",
         variables=["elevtn", "basins"],
         reproject_method=["average", "mode"],
         mask_name="mask",
     )
-    mod.setup_grid_from_rasterdataset(
+    mod.setup_grid_data_from_rasterdataset(
         raster_fn="vito",
         fill_method="nearest",
         reproject_method="mode",
         rename={"vito": "landuse"},
     )
-    mod.setup_grid_from_raster_reclass(
+    mod.setup_grid_data_from_raster_reclass(
         raster_fn="vito",
         fill_method="nearest",
         reclass_table_fn="vito_mapping",
         reclass_variables=["roughness_manning"],
         reproject_method=["average"],
     )
-    mod.setup_grid_from_geodataframe(
+    mod.setup_grid_data_from_geodataframe(
         vector_fn="hydro_lakes",
         variables=["waterbody_id", "Depth_avg"],
         nodata=[-1, -999.0],
         rasterize_method="value",
         rename={"waterbody_id": "lake_id", "Depth_avg": "lake_depth"},
     )
-    mod.setup_grid_from_geodataframe(
+    mod.setup_grid_data_from_geodataframe(
         vector_fn="hydro_lakes",
         rasterize_method="fraction",
         rename={"hydro_lakes": "water_frac"},
@@ -712,9 +712,9 @@ def test_meshmodel_setup(griduda, world):
     region = {"mesh": griduda}
     mod1 = MeshModel(data_libs=["artifact_data", dc_param_fn])
     mod1.setup_mesh2d(region, grid_name="mesh2d")
-    mod1.setup_mesh2d_from_rasterdataset("vito", grid_name="mesh2d")
+    mod1.setup_mesh2d_data_from_rasterdataset("vito", grid_name="mesh2d")
     assert "vito" in mod1.mesh.data_vars
-    mod1.setup_mesh2d_from_raster_reclass(
+    mod1.setup_mesh2d_data_from_raster_reclass(
         raster_fn="vito",
         reclass_table_fn="vito_mapping",
         reclass_variables=["roughness_manning"],