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': 'ClassifiedRasterHazardBuildingFunction', 'name': tr('Classified raster hazard on buildings'), 'impact': tr('Be impacted'), 'title': tr('Be impacted in each hazard class'), 'function_type': 'old-style', 'author': 'Dianne Bencito', 'date_implemented': 'N/A', 'overview': tr( 'To assess the impacts of a classified hazard in raster ' 'format on a buildings vector layer.'), 'detailed_description': tr( 'This function will treat the values in the hazard raster ' 'layer as classes representing low, medium and high ' 'impact. You need to ensure that the keywords for the hazard ' 'layer have been set appropriately to define these classes.' 'The number of buildings that will be impacted will be ' 'calculated for each class. The report will show the total ' 'number of buildings that will be affected for each ' 'hazard class.'), 'hazard_input': tr( 'A hazard raster layer where each cell represents the ' 'class of the hazard. There should be 3 classes: e.g. ' '1, 2, and 3.'), 'exposure_input': tr( 'A vector polygon layer which can be extracted from OSM ' 'where each polygon represents the footprint of a ' 'building.'), 'output': tr( 'The impact layer will contain all structures that were ' 'exposed to the highest class (3) and a summary table ' 'containing the number of structures in each class.'), 'actions': tr( 'Provide details about the number of buildings that are ' 'within each hazard class.'), 'limitations': [tr('The number of classes is three.')], 'citations': [], 'legend_notes': '', 'map_title': tr('Buildings affected'), 'legend_units': tr('(Low, Medium, High)'), 'legend_title': tr('Structure inundated status'), 'layer_name': tr('Estimated buildings affected'), 'layer_requirements': { 'hazard': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_raster], 'hazard_categories': [ hazard_category_single_event, hazard_category_multiple_event ], 'hazard_types': hazard_all, 'continuous_hazard_units': [], 'vector_hazard_classifications': [], 'raster_hazard_classifications': [ generic_raster_hazard_classes ], '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 'parameters': OrderedDict([ ('Categorical hazards', categorical_hazards()), ('postprocessors', OrderedDict([ ('BuildingType', building_type_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': 'ClassifiedRasterHazardPopulationFunction', 'name': tr('Classified raster hazard on population'), 'impact': tr('Be affected in each class'), 'title': tr('Be affected in each hazard class'), 'function_type': 'old-style', 'author': 'Dianne Bencito', 'date_implemented': 'N/A', 'overview': tr( 'To assess the impacts of classified hazards in raster ' 'format on a population raster layer.'), 'detailed_description': tr( 'This function will treat the values in the hazard raster ' 'layer as classes representing low, medium and high ' 'impact. You need to ensure that the keywords for the hazard ' 'layer have been set appropriately to define these classes.' 'The number of people that will be affected will be ' 'calculated for each class. The report will show the total ' 'number of people that will be affected for each ' 'hazard class.'), 'hazard_input': tr( 'A hazard raster layer where each cell represents the ' 'class of the hazard. There should be three classes: e.g. ' '1, 2, and 3.'), 'exposure_input': tr( 'An exposure raster layer where each cell represents the' 'population count for that cell.'), 'output': tr( 'Map of population exposed to the highest class and a table ' 'with the number of people in each class'), 'actions': tr( 'Provide details about how many people would likely be ' 'affected for each hazard class.'), 'limitations': [tr('The number of classes is three.')], 'citations': [], 'layer_requirements': { 'hazard': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_raster], 'hazard_categories': [ hazard_category_single_event, hazard_category_multiple_event ], 'hazard_types': hazard_all, 'continuous_hazard_units': [], 'vector_hazard_classifications': [], 'raster_hazard_classifications': [ generic_raster_hazard_classes ], 'additional_keywords': [] }, 'exposure': { 'layer_mode': layer_mode_continuous, 'layer_geometries': [layer_geometry_raster], 'exposure_types': [exposure_population], 'exposure_units': [ count_exposure_unit, density_exposure_unit], 'exposure_class_fields': [], 'additional_keywords': [] } }, 'parameters': OrderedDict([ ('Categorical hazards', categorical_hazards()), ('postprocessors', OrderedDict([ ('Gender', default_gender_postprocessor()), ('Age', age_postprocessor()), ('MinimumNeeds', minimum_needs_selector()), ])), ('minimum needs', default_minimum_needs()) ]) } 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': 'ClassifiedRasterHazardPopulationFunction', 'name': tr('Classified raster hazard on population'), 'impact': tr('Be affected'), 'title': tr('Be affected'), 'function_type': 'old-style', 'author': 'Dianne Bencito', 'date_implemented': 'N/A', 'overview': tr('To assess the impacts of classified hazards in raster ' 'format on a population raster layer.'), 'detailed_description': tr('This function will treat the values in the hazard raster ' 'layer as classes representing low, medium and high ' 'impact. You need to ensure that the keywords for the hazard ' 'layer have been set appropriately to define these classes.' 'The number of people that will be affected will be ' 'calculated for each class. The report will show the total ' 'number of people that will be affected for each ' 'hazard class.'), 'hazard_input': tr('A hazard raster layer where each cell represents the ' 'class of the hazard. There should be three classes: e.g. ' '1, 2, and 3.'), 'exposure_input': tr('An exposure raster layer where each cell represents the ' 'population count for that cell.'), 'output': tr('Map of population exposed to the highest class and a table ' 'with the number of people in each class'), 'actions': tr('Provide details about how many people would likely be ' 'affected for each hazard class.'), 'limitations': [tr('The number of classes is three.')], 'citations': [{ 'text': None, 'link': None }], 'legend_title': tr('Number of People'), 'legend_units': tr('(people per cell)'), 'legend_notes': tr('Thousand separator is represented by %s' % get_thousand_separator()), 'layer_requirements': { 'hazard': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_raster], 'hazard_categories': [ hazard_category_single_event, hazard_category_multiple_event ], 'hazard_types': hazard_all, 'continuous_hazard_units': [], 'vector_hazard_classifications': [], 'raster_hazard_classifications': [generic_raster_hazard_classes], 'additional_keywords': [] }, 'exposure': { 'layer_mode': layer_mode_continuous, 'layer_geometries': [layer_geometry_raster], 'exposure_types': [exposure_population], 'exposure_units': [count_exposure_unit, density_exposure_unit], 'exposure_class_fields': [], 'additional_keywords': [] } }, 'parameters': OrderedDict([('Categorical hazards', categorical_hazards()), ('postprocessors', OrderedDict([ ('Gender', default_gender_postprocessor()), ('Age', age_postprocessor()), ('MinimumNeeds', minimum_needs_selector()), ])), ('minimum needs', default_minimum_needs())]) } 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': 'ClassifiedRasterHazardBuildingFunction', 'name': tr('Classified raster hazard on buildings'), 'impact': tr('Be impacted'), 'title': tr('Be impacted in each hazard class'), 'function_type': 'old-style', 'author': 'Dianne Bencito', 'date_implemented': 'N/A', 'overview': tr('To assess the impacts of a classified hazard in raster ' 'format on a buildings vector layer.'), 'detailed_description': tr('This function will treat the values in the hazard raster ' 'layer as classes representing low, medium and high ' 'impact. You need to ensure that the keywords for the hazard ' 'layer have been set appropriately to define these classes.' 'The number of buildings that will be impacted will be ' 'calculated for each class. The report will show the total ' 'number of buildings that will be affected for each ' 'hazard class.'), 'hazard_input': tr('A hazard raster layer where each cell represents the ' 'class of the hazard. There should be 3 classes: e.g. ' '1, 2, and 3.'), 'exposure_input': tr('A vector polygon layer which can be extracted from OSM ' 'where each polygon represents the footprint of a ' 'building.'), 'output': tr('The impact layer will contain all structures that were ' 'exposed to the highest class (3) and a summary table ' 'containing the number of structures in each class.'), 'actions': tr('Provide details about the number of buildings that are ' 'within each hazard class.'), 'limitations': [tr('The number of classes is three.')], 'citations': [], 'layer_requirements': { 'hazard': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_raster], 'hazard_categories': [ hazard_category_single_event, hazard_category_multiple_event ], 'hazard_types': hazard_all, 'continuous_hazard_units': [], 'vector_hazard_classifications': [], 'raster_hazard_classifications': [generic_raster_hazard_classes], '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 'parameters': OrderedDict([('Categorical hazards', categorical_hazards()), ('postprocessors', OrderedDict([('BuildingType', building_type_postprocessor())]))]) } return dict_meta