#137-allow-to-use-different-names-for-the-spatial-exposure-data

docs/conf.py

@@ -203,7 +203,7 @@ def write_nested_dropdown(name, data_cat, note="", categories=[]):
     "github_url": "https://github.com",  # or your GitHub Enterprise interprise
     "github_user": "Deltares",
     "github_repo": "hydromt_fiat",
-    "github_version": "main", 
+    "github_version": "main",
     "doc_path": "docs",
     "default_mode": "light",
 }
@@ -324,4 +324,4 @@ def write_nested_dropdown(name, data_cat, note="", categories=[]):
 """
 
 # Allow errors in notebooks
-# nbsphinx_allow_errors = True
+# nbsphinx_allow_errors = True

examples/data/hydromt_fiat_catalog_global.yml

@@ -11,7 +11,7 @@ jrc_vulnerability_curves:
       paper_doi: https://dx.doi.org/10.2760/16510
 
 jrc_vulnerability_curves_linking:
-    path: damage_functions/flooding/JRC_damage_functions_linking.csv
+    path: vulnerability_linking/JRC_damage_functions_linking.csv
     data_type: DataFrame
     driver: csv
     meta:

hydromt_fiat/api/exposure_vm.py

@@ -62,9 +62,7 @@ def set_asset_locations_source(
             self.exposure_model.occupancy_type = input_source
             self.exposure_model.max_potential_damage = input_source
             self.exposure_model.ground_floor_height = 1  # TODO: make flexible
-            self.exposure_model.unit = (
-                Units.ft.value
-            )  # TODO: make flexible
+            self.exposure_model.unit = Units.ft.value  # TODO: make flexible
 
             # Download NSI from the database
             region = self.data_catalog.get_geodataframe("area_of_interest")

hydromt_fiat/data/hydromt_fiat_catalog_global.yml

@@ -0,0 +1,30 @@
+
+jrc_vulnerability_curves:
+    path: damage_functions/flooding/JRC_damage_functions.csv
+    data_type: DataFrame
+    driver: csv
+    meta:
+      category: vulnerability
+      description: JRC depth-damage functions for flooding, processed into a handy format for HydroMT-FIAT.
+      source_url: https://publications.jrc.ec.europa.eu/repository/handle/JRC105688
+      paper_ref: Huizinga, J., De Moel, H. and Szewczyk, W., Global flood depth-damage functions - Methodology and the database with guidelines, EUR 28552 EN, Publications Office of the European Union, Luxembourg, 2017, ISBN 978-92-79-67781-6, doi:10.2760/16510, JRC105688.
+      paper_doi: https://dx.doi.org/10.2760/16510
+
+jrc_vulnerability_curves_linking:
+    path: vulnerability_linking/JRC_damage_functions_linking.csv
+    data_type: DataFrame
+    driver: csv
+    meta:
+      category: vulnerability
+      description: Default linking table for the JRC damage functions (e.g., the residential damage function links to residential buildings).
+
+jrc_damage_values:
+    path: max_potential_damages/JRC_base_damage_values.csv
+    data_type: DataFrame
+    driver: csv
+    meta:
+      category: vulnerability
+      description: Base damage values from the JRC publicated Excel from the tab "MaxDamage-Data", processed into a handy format for HydroMT-FIAT.
+      source_url: https://publications.jrc.ec.europa.eu/repository/handle/JRC105688
+      paper_ref: Huizinga, J., De Moel, H. and Szewczyk, W., Global flood depth-damage functions - Methodology and the database with guidelines, EUR 28552 EN, Publications Office of the European Union, Luxembourg, 2017, ISBN 978-92-79-67781-6, doi:10.2760/16510, JRC105688.
+      paper_doi: https://dx.doi.org/10.2760/16510

hydromt_fiat/data/damage_functions/flooding/JRC_damage_functions_linking.csv → hydromt_fiat/data/vulnerability_linking/JRC_damage_functions_linking.csv

@@ -1,4 +1,4 @@
-Name,Link,Damage Type,type
+FIAT Damage Function Name,Exposure Link,Damage Type,type
 residential,residential,total,residential
 commercial,commercial,total,commercial
 industrial,industrial,total,industrial

hydromt_fiat/data_apis/open_street_maps.py

@@ -27,9 +27,7 @@ def get_assets_from_osm(polygon: Polygon):
 
 def get_landuse_from_osm(polygon: Polygon):
     tags = {"landuse": True}  # this is the tag we use to find the correct OSM data
-    landuse = ox.features.features_from_polygon(
-        polygon, tags
-    )  # then we query the data
+    landuse = ox.features.features_from_polygon(polygon, tags)  # then we query the data
 
     if landuse.empty:
         logging.warning("No land use data found from OSM")

hydromt_fiat/fiat.py

@@ -271,13 +271,13 @@ def setup_exposure_vector(
         if asset_locations == occupancy_type == max_potential_damage:
             # The source for the asset locations, occupancy type and maximum potential
             # damage is the same, use one source to create the exposure data.
-            self.exposure.setup_from_single_source(
+            self.exposure.setup_buildings_from_single_source(
                 asset_locations, ground_floor_height, extraction_method
             )
         else:
             # The source for the asset locations, occupancy type and maximum potential
             # damage is different, use three sources to create the exposure data.
-            self.exposure.setup_from_multiple_sources(
+            self.exposure.setup_buildings_from_multiple_sources(
                 asset_locations,
                 occupancy_type,
                 max_potential_damage,
@@ -306,7 +306,6 @@ def setup_exposure_vector(
         self.set_config("exposure.geom.crs", self.exposure.crs)
         self.set_config("exposure.geom.unit", unit)
 
-
     def setup_exposure_raster(self):
         """Setup raster exposure data for Delft-FIAT.
         This function will be implemented at a later stage.
@@ -574,25 +573,15 @@ def setup_social_vulnerability_index(
             # Link the SVI score to the exposure data
             exposure_data = self.exposure.get_full_gdf(self.exposure.exposure_db)
             exposure_data.sort_values("Object ID")
-            
+
             if svi.svi_data_shp.crs != exposure_data.crs:
-                svi.svi_data_shp.to_crs(crs=exposure_data.crs, inplace = True)
+                svi.svi_data_shp.to_crs(crs=exposure_data.crs, inplace=True)
 
-            svi_exp_joined = gpd.sjoin(
-                exposure_data, svi.svi_data_shp, how="left"
-            )
-            svi_exp_joined.drop(columns=['geometry'], inplace=True)
+            svi_exp_joined = gpd.sjoin(exposure_data, svi.svi_data_shp, how="left")
+            svi_exp_joined.drop(columns=["geometry"], inplace=True)
             svi_exp_joined = pd.DataFrame(svi_exp_joined)
             self.exposure.exposure_db = svi_exp_joined
 
-        # exposure opnieuw opslaan in self._tables
-
-        # TODO: geometries toevoegen aan de dataset met API
-        # we now use the shape download function by the census, the user needs to download their own shape data. They can download this from: https://www.census.gov/cgi-bin/geo/shapefiles/index.php
-        # #wfs python get request -> geometries
-
-        # this link can be used: https://github.com/datamade/census
-
     # Update functions
     def update_all(self):
         self.logger.info("Updating all data objects...")
@@ -616,12 +605,16 @@ def update_tables(self):
     def update_geoms(self):
         # Update the exposure data geoms
         if self.exposure and "exposure" in self._tables:
-            for i, geom in enumerate(self.exposure.exposure_geoms):
-                file_suffix = i if i > 0 else ""
-                self.set_geoms(geom=geom, name=f"exposure{file_suffix}")
+            for i, geom_and_name in enumerate(
+                zip(self.exposure.exposure_geoms, self.exposure.geom_names)
+            ):
+                geom = geom_and_name[0]
+                name = geom_and_name[1]
+
+                self.set_geoms(geom=geom, name=name)
                 self.set_config(
                     f"exposure.geom.file{str(i+1)}",
-                    f"./exposure/exposure{file_suffix}.gpkg",
+                    f"./exposure/{name}.gpkg",
                 )
 
         if not self.region.empty:
@@ -632,7 +625,8 @@ def update_maps(self):
 
     # I/O
     def read(self):
-        """Method to read the complete model schematization and configuration from file."""
+        """Method to read the complete model schematization and configuration from
+        file."""
         self.logger.info(f"Reading model data from {self.root}")
 
         # Read the configuration file
@@ -654,13 +648,6 @@ def _configread(self, fn):
         config = Config()
         return config.load_file(fn)
 
-    def check_path_exists(self, fn):
-        """TODO: decide to use this or another function (check_file_exist in py)"""
-        path = Path(fn)
-        self.logger.debug(f"Reading file {str(path.name)}")
-        if not fn.is_file():
-            logging.warning(f"File {fn} does not exist!")
-
     def read_tables(self):
         """Read the model tables for vulnerability and exposure data."""
         if not self._write:

hydromt_fiat/validation.py

@@ -1,13 +1,15 @@
 from pathlib import Path
 
+
 def check_dir_exist(dir, name=None):
     """ """
 
     if not isinstance(dir, Path):
         raise TypeError(
             f"The directory indicated by the '{name}' parameter does not exist."
         )
-
+
+
 def check_file_exist(root, param_lst, name=None):
     root = Path(root)
     param_lst = [Path(p) for p in param_lst]
@@ -38,12 +40,13 @@ def check_file_exist(root, param_lst, name=None):
                 else:
                     assert isinstance(param_lst[param_idx], Path)
             except AssertionError:
-                    raise TypeError(
-                        f"The file indicated by the '{name}' parameter does not"
-                        f" exist in the directory '{root}'."
-                    )
-
-#TODO: Improve this tool without calling model.get_congif(input_dir)
+                raise TypeError(
+                    f"The file indicated by the '{name}' parameter does not"
+                    f" exist in the directory '{root}'."
+                )
+
+
+# TODO: Improve this tool without calling model.get_congif(input_dir)
 # def check_file_exist(get_config, root, param_lst, name=None, input_dir=None):
 #     root = Path(root)
 #     param_lst = [Path(p) for p in param_lst]
@@ -96,24 +99,23 @@ def check_file_exist(root, param_lst, name=None):
 #                         f"'{get_config(input_dir)}'."
 #                     )
 
-def check_uniqueness(map_name_lst):
 
+def check_uniqueness(map_name_lst):
     def check_duplicates(lst):
         unique_elements = set()
         for element in lst:
             if element in unique_elements:
                 return True  # Found a duplicate
             unique_elements.add(element)
         return False  # No duplicates found
-    
+
     check = check_duplicates(map_name_lst)
 
-    if check: 
-            raise ValueError(
-            f"The filenames of the hazard maps should be unique."
-        )
+    if check:
+        raise ValueError(f"The filenames of the hazard maps should be unique.")
+
 
-#TODO: Improve this tool without calling model. Just checking the maps names
+# TODO: Improve this tool without calling model. Just checking the maps names
 # def check_uniqueness(model, *args, file_type=None, filename=None):
 #     """ """
 
@@ -136,6 +138,7 @@ def check_duplicates(lst):
 #             ):
 #                 raise ValueError(f"Each model input layers must be unique.")
 
+
 def check_param_type(param, name=None, types=None):
     """ """
 
@@ -155,6 +158,7 @@ def check_param_type(param, name=None, types=None):
                 f"{type(i)} instead."
             )
 
+
 def get_param(param_lst, map_fn_lst, file_type, filename, i, param_name):
     """ """
 
@@ -166,4 +170,4 @@ def get_param(param_lst, map_fn_lst, file_type, filename, i, param_name):
         raise IndexError(
             f"Could not derive the {param_name} parameter for {file_type} "
             f"map: {filename}."
-        )
+        )

hydromt_fiat/workflows/exposure_raster.py

@@ -8,6 +8,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 ### TO BE UPDATED ###
 class ExposureRaster(Exposure):
     def setup_buildings_value(

hydromt_fiat/workflows/exposure_vector.py

@@ -74,8 +74,8 @@ def __init__(
         )
         self.exposure_db = pd.DataFrame()
         self.exposure_geoms = list()  # A list of GeoDataFrames
-        self.source = gpd.GeoDataFrame()
         self.unit = unit
+        self._geom_names = list()  # A list of (original) names of the geometry (files)
 
     def read_table(self, fn: Union[str, Path]):
         """Read the Delft-FIAT exposure data.
@@ -102,9 +102,10 @@ def read_geoms(self, fn: Union[List[str], List[Path], str, Path]):
             fn = [fn]
 
         for f in fn:
+            self.set_geom_names(Path(f).stem)
             self.set_exposure_geoms(gpd.read_file(f, engine="pyogrio"))
 
-    def setup_from_single_source(
+    def setup_buildings_from_single_source(
         self,
         source: Union[str, Path],
         ground_floor_height: Union[int, float, str, Path, None],
@@ -178,7 +179,10 @@ def setup_from_single_source(
         # Set the geoms from the X and Y coordinates
         self.set_exposure_geoms_from_xy()
 
-    def setup_from_multiple_sources(
+        # Set the name to the geom_names
+        self.set_geom_names("buildings")
+
+    def setup_buildings_from_multiple_sources(
         self,
         asset_locations: Union[str, Path],
         occupancy_source: Union[str, Path],
@@ -235,7 +239,28 @@ def setup_asset_locations(self, asset_locations: str) -> None:
                 assets["Object ID"] = range(1, len(assets.index) + 1)
 
         # Set the asset locations to the geometry variable (self.exposure_geoms)
+        # and set the geom name
         self.set_exposure_geoms(assets)
+        self.set_geom_names("buildings")
+
+    def set_geom_names(self, name: str) -> None:
+        """Append a name to the list of geometry names `geom_names`."""
+        self.logger.info(f"Setting geometry name to {name}...")
+        self._geom_names.append(name)
+
+    @property
+    def geom_names(self) -> List[str]:
+        """Returns a list with the geom names."""
+        if len(self._geom_names) > 0 and len(self.exposure_geoms) > 0:
+            return self._geom_names
+        elif len(self._geom_names) == 0 and len(self.exposure_geoms) == 1:
+            return ["exposure"]
+        else:
+            self.logger.warning(
+                "No geometry names found, returning a list with the default names "
+                "'exposure_X'."
+            )
+            return [f"exposure_{i}" for i in range(len(self.exposure_geoms))]
 
     def set_exposure_geoms(self, gdf: gpd.GeoDataFrame) -> None:
         """Append a GeoDataFrame to the exposure geometries `exposure_geoms`."""
@@ -856,7 +881,10 @@ def link_exposure_vulnerability(
         damage_types: Optional[List[str]] = ["Structure", "Content"],
     ):
         linking_dict = dict(
-            zip(exposure_linking_table["Exposure Link"], exposure_linking_table["FIAT Damage Function Name"])
+            zip(
+                exposure_linking_table["Exposure Link"],
+                exposure_linking_table["FIAT Damage Function Name"],
+            )
         )
 
         # Find the column to link the exposure data to the vulnerability data

hydromt_fiat/workflows/hazard.py

@@ -260,6 +260,7 @@ def read_floodmaps(
 
     return da_map_fn, da_name, da_type
 
+
 def load_floodmaps(
     data_catalog: DataCatalog,
     region: gpd.GeoDataFrame,
@@ -316,6 +317,7 @@ def load_floodmaps(
 
     return da
 
+
 # def load_floodmaps(
 #     data_catalog: DataCatalog,
 #     region: gpd.GeoDataFrame,