API Reference

Core Module

`exposure(disable_progress=False, **kwargs)`

Run the exposure analysis.

Identifies which features are affected by the hazard footprint, depending on the type of input data (raster or vector).

Parameters:

Name	Type	Description	Default
`disable_progress`	`bool`	If True, disables progress bars. Defaults to False.	`False`
`**kwargs`		Optional keyword arguments like `asset_type` (only for vector).	`{}`

Returns:

Type	Description
	geopandas.GeoDataFrame \| xarray.DataArray: Affected assets or raster overlay result.

`calculate(disable_progress=False, save_output=False, **kwargs)`

Perform a damage calculation using the provided inputs.

Applies vulnerability curves and maximum damage values to the exposed features or raster grid to calculate expected damage.

Parameters:

Name	Type	Description	Default
`disable_progress`	`bool`	If True, disables progress bars. Defaults to False.	`False`
`save_output`	`bool`	Not implemented. Placeholder for output saving option.	`False`
`**kwargs`		asset_type (str, optional): Infrastructure class to evaluate. multi_curves (dict, optional): Mapping of asset types to curve sets. subtypes (list, optional): Used for subtype analysis.	`{}`

Returns:

Type	Description
	pd.DataFrame \| xr.DataArray: Estimated damages for each asset or grid cell.

`risk(hazard_dict, **kwargs)`

Perform a risk assessment across multiple hazard return periods.

Integrates damages from each return period and computes expected annual damages. Supports both single and multi-curve inputs for infrastructure types.

Parameters:

Name	Type	Description	Default
`hazard_dict`	`dict`	Dictionary mapping return periods to hazard raster paths.	required
`**kwargs`		asset_type (str, optional): Infrastructure class to evaluate. multi_curves (dict, optional): Mapping of asset types to curve sets.	`{}`

Returns:

Type	Description
	pd.DataFrame \| None: A GeoDataFrame with risk values for each asset, or None if no results.

Vector-based Specific Functions

`VectorExposure(hazard_file, feature_file, asset_type='roads', object_col='object_type', disable_progress=False)`

Load and overlay vector or raster hazard with vector exposure data.

Parameters:

Name	Type	Description	Default
`hazard_file`	`Path \| Dataset \| DatasetReader`	Hazard input.	required
`feature_file`	`Path \| GeoDataFrame \| DataFrame`	Exposure input.	required
`asset_type`	`str`	Infrastructure category (only for OSM).	`'roads'`
`object_col`	`str`	Name of the object type column.	`'object_type'`
`disable_progress`	`bool`	Whether to suppress progress bars.	`False`

Returns:

Name	Type	Description
`tuple`		(features, object_col, hazard_crs, cell_area_m2)

`VectorScanner(hazard_file, feature_file, curve_path, maxdam_path, asset_type='roads', multi_curves=dict(), object_col='object_type', disable_progress=False, **kwargs)`

Perform vector-based direct damage assessment using hazard and exposure layers.

Parameters:

Name	Type	Description	Default
`hazard_file`	`Path \| Dataset \| GeoDataFrame`	Hazard input.	required
`feature_file`	`Path \| GeoDataFrame`	Exposure input.	required
`curve_path`	`Path \| DataFrame`	Vulnerability curve(s).	required
`maxdam_path`	`Path \| DataFrame \| dict`	Maximum damage values.	required
`asset_type`	`str`	Infrastructure class (only for OSM).	`'roads'`
`multi_curves`	`dict`	Multiple curve sets.	`dict()`
`object_col`	`str`	Column name with object type.	`'object_type'`
`disable_progress`	`bool`	Whether to suppress progress bars.	`False`

Returns:

Type	Description
	gpd.GeoDataFrame: Exposure data with calculated damages.

Raster-based Specific Functions

`match_and_load_rasters(raster_in1, raster_in2)`

Match and clip two raster files to their common spatial extent and resolution.

Code adapted from http://sciience.tumblr.com/post/101722591382/finding-the-georeferenced-intersection-between-two

Parameters:

Name	Type	Description	Default
`raster_in1`	`str or Path`	Path to the first raster file.	required
`raster_in2`	`str or Path`	Path to the second raster file.	required

Returns:

Name	Type	Description
`tuple`		np.ndarray: Clipped raster array from the first file. np.ndarray: Clipped raster array from the second file. Affine: Affine transform of the intersecting region.

`RasterScanner(exposure_file, hazard_file, curve_path, maxdam_path, lu_crs=28992, haz_crs=4326, hazard_col='FX', dtype=np.int32, save=False, **kwargs)`

Run a raster-based direct damage assessment using hazard and exposure layers.

Parameters:

Name	Type	Description	Default
`exposure_file`	`Path \| ndarray`	Path to land-use GeoTIFF or numpy array.	required
`hazard_file`	`Path \| ndarray \| Dataset`	Path to hazard raster or dataset.	required
`curve_path`	`Path \| DataFrame \| ndarray`	Vulnerability curve(s).	required
`maxdam_path`	`Path \| DataFrame \| ndarray`	Maximum damage values.	required
`lu_crs`	`int`	CRS of the land-use file (default EPSG:28992).	`28992`
`haz_crs`	`int`	CRS of the hazard file (default EPSG:4326).	`4326`
`hazard_col`	`str`	Column containing hazard intensity (default "FX").	`'FX'`
`dtype`	`type`	Output dtype for damage raster.	`int32`
`save`	`bool`	If True, saves damage results to file.	`False`

Other Parameters:

Name	Type	Description
`nan_value`	`float`	Replace this value in the hazard raster with 0.
`cellsize`	`float`	Cell size (m²) if exposure and hazard are arrays.
`resolution`	`float`	Resolution in target projection (used for reprojection).
`output_path`	`str or Path`	Output directory for saving results.
`scenario_name`	`str`	Scenario name used for filenames.
`in_millions`	`bool`	Convert results to millions.
`crs`	`CRS`	CRS for saving output raster (optional).
`transform`	`Affine`	Affine transform for saving raster (optional).

Raises:

Type	Description
`ValueError`	If cell size is not provided when required.
`ValueError`	If vulnerability or max damage file has invalid structure.

Returns:

Name	Type	Description
`tuple`		pd.DataFrame: Damage per land-use category. np.ndarray: Damage map (grid with estimated damages). np.ndarray: Reprojected land-use map. np.ndarray: Reprojected hazard map.