create a road disruption detection function

scripts/disrupted_road_detection.py

@@ -0,0 +1,58 @@
+# %%
+from pathlib import Path
+from nird.utils import load_config
+import geopandas as gpd
+from snail import intersection, io
+import warnings
+
+warnings.simplefilter("ignore")
+
+base_path = Path(load_config()["paths"]["SRE_base_path"])
+
+month = "May"  # update month: ["May", "June", "July"]
+scenario = "FLRF"  # update scenario: ["FLSW", "FLRF"]
+
+# %%
+floodPath = (
+    base_path
+    / "inputs"
+    / "incoming_data"
+    / "12-14_2007 Summer_UK Floods"
+    / f"{month}"
+    / f"UK_2007_{month}_{scenario}_RD_5m_4326.tif"
+)
+
+roads = gpd.read_file(
+    base_path / "inputs" / "processed_data" / f"{month}" / f"{scenario}_links.shp",
+    engine="pyogrio",
+)
+# project roads to 4326
+roads_prj = roads.to_crs("epsg:4326")
+
+# read the raster data
+flood_data = io.read_raster_band_data(floodPath)  # RD: depth (meter)
+
+# run the intersection analysis
+grid, bands = io.read_raster_metadata(floodPath)
+prepared = intersection.prepare_linestrings(roads_prj)
+flood_intersections = intersection.split_linestrings(prepared, grid)
+flood_intersections = intersection.apply_indices(flood_intersections, grid)
+
+flood_intersections["level_of_flood"] = intersection.get_raster_values_for_splits(
+    flood_intersections, flood_data
+)
+# attach the maximum flood depth to each road link
+# in case one road link intersects with multiple rasters
+roads_flooddepth = flood_intersections.groupby(by=["id"], as_index=False).agg(
+    {"level_of_flood": "max"}
+)
+
+# %%
+roads_flooddepth.to_csv(
+    base_path
+    / "inputs"
+    / "processed_data"
+    / f"{month}"
+    / f"{scenario}_flooded_links.csv",
+    index=False,
+)