def as_dict(): """Return metadata as a dictionary. This is a static method. You can use it to get the metadata in dictionary format for an impact function. :returns: A dictionary representing all the metadata for the concrete impact function. :rtype: dict """ dict_meta = { "id": "EarthquakeBuildingFunction", "name": tr("Earthquake on buildings"), "impact": tr("Be affected"), "title": tr("Be affected"), "function_type": "old-style", "author": "N/A", "date_implemented": "N/A", "overview": tr( "This impact function will calculate the impact of an " "earthquake on buildings, reporting how many are expected " "to be damaged." ), "detailed_description": "", "hazard_input": "", "exposure_input": "", "output": "", "actions": "", "limitations": [], "citations": [], "categories": { "hazard": { "definition": hazard_definition, "subcategories": [hazard_earthquake], "units": [unit_mmi], "layer_constraints": [layer_raster_continuous], }, "exposure": { "definition": exposure_definition, "subcategories": [exposure_structure], "units": [unit_building_type_type, unit_building_generic], "layer_constraints": [layer_vector_polygon, layer_vector_point], }, }, "parameters": OrderedDict( [ ("low_threshold", 6), ("medium_threshold", 7), ("high_threshold", 8), ( "postprocessors", OrderedDict([("AggregationCategorical", aggregation_categorical_postprocessor())]), ), ] ), } return dict_meta
def as_dict(): """Return metadata as a dictionary. This is a static method. You can use it to get the metadata in dictionary format for an impact function. :returns: A dictionary representing all the metadata for the concrete impact function. :rtype: dict """ dict_meta = { 'id': 'EarthquakeBuildingFunction', 'name': tr('Earthquake on buildings'), 'impact': tr('Be affected'), 'title': tr('Be affected'), 'function_type': 'old-style', 'author': 'N/A', 'date_implemented': 'N/A', 'overview': tr('This impact function will calculate the impact of an ' 'earthquake on buildings, reporting how many are expected ' 'to be damaged.'), 'detailed_description': '', 'hazard_input': '', 'exposure_input': '', 'output': '', 'actions': '', 'limitations': [], 'citations': [], 'categories': { 'hazard': { 'definition': hazard_definition, 'subcategories': [hazard_earthquake], 'units': [unit_mmi], 'layer_constraints': [layer_raster_continuous], }, 'exposure': { 'definition': exposure_definition, 'subcategories': [exposure_structure], 'units': [unit_building_type_type, unit_building_generic], 'layer_constraints': [layer_vector_polygon, layer_vector_point] } }, 'parameters': OrderedDict([ ('low_threshold', 6), ('medium_threshold', 7), ('high_threshold', 8), ('postprocessors', OrderedDict([('AggregationCategorical', aggregation_categorical_postprocessor())])) ]) } return dict_meta
def as_dict(): """Return metadata as a dictionary. This is a static method. You can use it to get the metadata in dictionary format for an impact function. :returns: A dictionary representing all the metadata for the concrete impact function. :rtype: dict """ dict_meta = { 'id': 'EarthquakeBuildingFunction', 'name': tr('Earthquake on buildings'), 'impact': tr('Be affected'), 'title': tr('Be affected'), 'function_type': 'old-style', 'author': 'N/A', 'date_implemented': 'N/A', 'overview': tr( 'This impact function will calculate the impact of an ' 'earthquake on buildings, reporting how many are expected ' 'to be damaged.'), 'detailed_description': '', 'hazard_input': '', 'exposure_input': '', 'output': '', 'actions': '', 'limitations': [], 'citations': [], 'layer_requirements': { 'hazard': { 'layer_mode': layer_mode_continuous, 'layer_geometries': [layer_geometry_raster], 'hazard_categories': [ hazard_category_single_event, hazard_category_multiple_event ], 'hazard_types': [hazard_earthquake], 'continuous_hazard_units': [unit_mmi], 'vector_hazard_classifications': [], 'raster_hazard_classifications': [], 'additional_keywords': [] }, 'exposure': { 'layer_mode': layer_mode_classified, 'layer_geometries': [ layer_geometry_point, layer_geometry_polygon ], 'exposure_types': [exposure_structure], 'exposure_units': [], 'exposure_class_fields': [structure_class_field], 'additional_keywords': [] } }, 'parameters': OrderedDict( [('low_threshold', low_threshold()), ('medium_threshold', medium_threshold()), ('high_threshold', high_threshold()), ('postprocessors', OrderedDict([ ('AggregationCategorical', aggregation_categorical_postprocessor())]))] ) } return dict_meta