floodresilience.flood_model.flooded_buildings
This script detects if buildings are flooded for a given scenario.
Functions
Append the details of which buildings are flooded for a given flood_model_id to the database. |
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Create a building DataFrame with attribute "is_flooded", |
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Identify all buildings in building_polygons that intersect with areas in flooded_polygons. |
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Retrieve building outlines for an area of interest from the database. |
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Take a flood depth raster and apply depth thresholding on it so that only areas |
Module Contents
- floodresilience.flood_model.flooded_buildings.store_flooded_buildings_in_database(engine: sqlalchemy.engine.Engine, buildings: pandas.DataFrame, flood_model_id: int) None
Append the details of which buildings are flooded for a given flood_model_id to the database.
- Parameters:
engine (Engine) – The sqlalchemy database connection engine
buildings (pd.DataFrame) – DataFrame containing a mapping of building_ids to their flood status for the current model run
flood_model_id (int) – The id of the current flood model run, to associate with the building flood data.
- floodresilience.flood_model.flooded_buildings.find_flooded_buildings(engine: sqlalchemy.engine.Engine, area_of_interest: geopandas.GeoDataFrame, flood_model_output_path: pathlib.Path, flood_depth_threshold: float) pandas.DataFrame
Create a building DataFrame with attribute “is_flooded”, depending on if the area for each building is flooded to a depth greater than or equal to flood_depth_threshold. the index, building_outline_id, matches building_outline_id from nz_building_outline table.
- Parameters:
engine (Engine) – The sqlalchemy database connection engine
area_of_interest (gpd.GeoDataFrame) – A GeoDataFrame with a polygon specifying the area to get buildings for.
flood_model_output_path (pathlib.Path) – Path to the flood model output file to be read.
flood_depth_threshold (float) – The minimum depth required to designate a pixel in the raster as flooded.
- Returns:
A pd.DataFrame specifying if each building is flooded or not.
- Return type:
pd.DataFrame
- floodresilience.flood_model.flooded_buildings.categorise_buildings_as_flooded(building_polygons: geopandas.GeoDataFrame, flood_polygons: geopandas.GeoDataFrame) geopandas.GeoDataFrame
Identify all buildings in building_polygons that intersect with areas in flooded_polygons.
- Parameters:
building_polygons (gpd.GeoDataFrame) – A GeoDataFrame with each polygon representing a building outline
flood_polygons (gpd.GeoDataFrame) – A GeoDataFrame with each polygon representing a flooded area
- Returns:
A copy of building_polygons with an additional boolean attribute “is_flooded”
- Return type:
gpd.GeoDataFrame
- floodresilience.flood_model.flooded_buildings.retrieve_building_outlines(engine: sqlalchemy.engine.Engine, area_of_interest: geopandas.GeoDataFrame) geopandas.GeoDataFrame
Retrieve building outlines for an area of interest from the database.
- Parameters:
engine (Engine) – The sqlalchemy database connection engine
area_of_interest (gpd.GeoDataFrame) – A GeoDataFrame polygon specifying the area of interest to retrieve buildings in.
- Returns:
A GeoDataFrame containing all of the building outlines in the area
- Return type:
gpd.GeoDataFrame
- floodresilience.flood_model.flooded_buildings.polygonize_flooded_area(flood_raster: xarray.DataArray, flood_depth_threshold: float) geopandas.GeoDataFrame
Take a flood depth raster and apply depth thresholding on it so that only areas flooded deeper than or equal to flood_depth_threshold are represented. Return the data in a collection of polygons.
- Parameters:
flood_raster (xarray.DataArray) – Raster with each pixel representing flood depth at the point
flood_depth_threshold (float) – The minimum depth specified to consider a pixel in the raster flooded
- Returns:
A GeoDataFrame containing polygons of the flooded areas
- Return type:
gpd.GeoDataFrame