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': 'FloodRasterRoadsFunction', 'name': tr('Raster flood on roads'), 'impact': tr('Be flooded in given thresholds'), 'title': tr('Be flooded in given thresholds'), 'function_type': 'qgis2.0', 'author': 'Dmitry Kolesov', 'date_implemented': 'N/A', 'overview': tr('N/A'), '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_flood, hazard_tsunami], 'continuous_hazard_units': [unit_feet, unit_metres], 'vector_hazard_classifications': [], 'raster_hazard_classifications': [], 'additional_keywords': [] }, 'exposure': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_line], 'exposure_types': [exposure_road], 'exposure_units': [], 'exposure_class_fields': [road_class_field], 'additional_keywords': [] } }, 'parameters': OrderedDict([ ('min threshold', parameter_definitions.min_threshold()), ('max threshold', parameter_definitions.max_threshold()), ('postprocessors', OrderedDict([ ('RoadType', road_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': 'FloodRasterRoadsFunction', 'name': tr('Raster flood on roads'), 'impact': tr('Be flooded in given thresholds'), 'title': tr('Be flooded in given thresholds'), 'function_type': 'qgis2.0', 'author': 'Dmitry Kolesov', 'date_implemented': 'N/A', 'overview': tr('N/A'), '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_flood], 'continuous_hazard_units': [unit_feet, unit_metres], 'vector_hazard_classifications': [], 'raster_hazard_classifications': [], 'additional_keywords': [] }, 'exposure': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_line], 'exposure_types': [exposure_road], 'exposure_units': [], 'exposure_class_fields': [road_class_field], 'additional_keywords': [] } }, 'parameters': OrderedDict([ ('min threshold', parameter_definitions.min_threshold()), ('max threshold', parameter_definitions.max_threshold()), ('postprocessors', OrderedDict([ ('RoadType', road_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': 'FloodRasterRoadsQGISFunction', 'name': tr('Raster flood on roads (QGIS)'), 'impact': tr('Be flooded in given thresholds'), 'title': tr('Be flooded in given thresholds'), 'function_type': 'qgis2.0', 'author': 'Dmitry Kolesov', 'date_implemented': 'N/A', 'overview': tr('N/A'), 'detailed_description': '', 'hazard_input': '', 'exposure_input': '', 'output': '', 'actions': '', 'limitations': [], 'citations': [], 'categories': { 'hazard': { 'definition': hazard_definition, 'subcategories': [ hazard_flood, hazard_tsunami ], 'units': [ unit_metres_depth, unit_feet_depth ], 'layer_constraints': [layer_raster_continuous] }, 'exposure': { 'definition': exposure_definition, 'subcategories': [exposure_road], 'units': [unit_road_type_type], 'layer_constraints': [layer_vector_line] } }, 'parameters': OrderedDict([ # This field of the exposure layer contains # information about road types ('road_type_field', 'TYPE'), ('min threshold [m]', 1.0), ('max threshold [m]', float('inf')), ('postprocessors', OrderedDict([ ('RoadType', road_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': 'FloodVectorRoadsExperimentalFunction', 'name': tr('Polygon flood on roads'), 'impact': tr('Be flooded'), 'title': tr('Be flooded'), 'function_type': 'qgis2.0', 'author': 'Dmitry Kolesov', 'date_implemented': 'N/A', 'overview': tr('N/A'), 'detailed_description': tr('N/A'), 'hazard_input': tr(''), 'exposure_input': tr(''), 'output': tr(''), 'actions': tr(''), 'limitations': [], 'citations': [], 'categories': { 'hazard': { 'definition': hazard_definition, 'subcategories': [hazard_flood], 'units': [unit_wetdry], 'layer_constraints': [layer_vector_polygon] }, 'exposure': { 'definition': exposure_definition, 'subcategories': [exposure_road], 'units': [unit_road_type_type], 'layer_constraints': [layer_vector_line] } }, 'parameters': OrderedDict([ # This field of the exposure layer contains # information about road types ('road_type_field', 'TYPE'), # This field of the hazard layer contains information # about inundated areas ('affected_field', 'affected'), # This value in 'affected_field' of the hazard layer # marks the areas as inundated ('affected_value', '1'), ('postprocessors', OrderedDict([ ('RoadType', road_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': 'TsunamiRasterRoadFunction', 'name': tr('Raster tsunami on roads'), 'impact': tr('Be inundated'), 'title': tr('Be inundated'), 'function_type': 'qgis2.0', # should be a list, but we can do it later. 'author': 'Ole Nielsen, Kristy van Putten, and Ismail Sunni', 'date_implemented': 'N/A', 'overview': tr( 'To assess the impacts of tsunami inundation on roads in ' 'vector format with hazard in raster format.'), 'detailed_description': tr( 'The inundation status is calculated for each roads based on ' 'the tsunami threshold. The threshold can be configured in ' 'impact function options.'), 'hazard_input': tr( 'A hazard raster layer where each cell represents tsunami ' 'inundation depth (in meters).'), 'exposure_input': tr( 'Vector line where each feature represents the road.'), 'output': tr( 'Vector layer contains road is estimated to be ' 'inundated and the breakdown of the road by type.'), 'actions': tr( 'Provide details about where critical road ' 'might be inundated.'), '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_tsunami], 'continuous_hazard_units': [unit_feet, unit_metres], 'vector_hazard_classifications': [], 'raster_hazard_classifications': [], 'additional_keywords': [] }, 'exposure': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_line], 'exposure_types': [exposure_road], 'exposure_units': [], 'exposure_class_fields': [road_class_field], 'additional_keywords': [] } }, 'parameters': OrderedDict( [ ('low_threshold', low_threshold()), ('medium_threshold', medium_threshold()), ('high_threshold', high_threshold()), ('postprocessors', OrderedDict([ ('RoadType', road_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': 'FloodVectorRoadsExperimentalFunction', 'name': tr('Polygon flood on roads'), 'impact': tr('Be flooded'), 'title': tr('Be flooded'), 'function_type': 'qgis2.0', 'author': 'Dmitry Kolesov', 'date_implemented': 'N/A', 'overview': tr('N/A'), 'detailed_description': tr('N/A'), 'hazard_input': tr(''), 'exposure_input': tr(''), 'output': tr(''), 'actions': tr(''), 'limitations': [], 'citations': [ { 'text': None, 'link': None } ], 'legend_units': '', 'legend_notes': '', 'map_title': tr('Roads inundated'), 'legend_title': tr('Road inundated status'), 'layer_name': tr('Flooded roads'), 'layer_requirements': { 'hazard': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_polygon], 'hazard_categories': [ hazard_category_single_event, hazard_category_multiple_event ], 'hazard_types': [hazard_flood], 'continuous_hazard_units': [], 'vector_hazard_classifications': [ flood_vector_hazard_classes], 'raster_hazard_classifications': [], 'additional_keywords': [] }, 'exposure': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_line], 'exposure_types': [exposure_road], 'exposure_units': [], 'exposure_class_fields': [road_class_field], 'additional_keywords': [] } }, 'parameters': OrderedDict([ ('postprocessors', OrderedDict([ ('RoadType', road_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': 'TsunamiRasterRoadFunction', 'name': tr('Raster tsunami on roads'), 'impact': tr('Be inundated'), 'title': tr('Be inundated'), 'function_type': 'qgis2.0', # should be a list, but we can do it later. 'author': 'Ole Nielsen, Kristy van Putten, and Ismail Sunni', 'date_implemented': 'N/A', 'overview': tr( 'To assess the impacts of tsunami inundation on roads in ' 'vector format with hazard in raster format.'), 'detailed_description': tr( 'The inundation status is calculated for each roads based on ' 'the tsunami threshold. The threshold can be configured in ' 'impact function options.'), 'hazard_input': tr( 'A hazard raster layer where each cell represents tsunami ' 'inundation depth (in meters).'), 'exposure_input': tr( 'Vector line where each feature represents the road.'), 'output': tr( 'Vector layer contains road is estimated to be ' 'inundated and the breakdown of the road by type.'), 'actions': tr( 'Provide details about where critical road ' 'might be inundated.'), 'limitations': [], 'citations': [ { 'text': None, 'link': None } ], 'legend_units': '', 'legend_notes': '', 'legend_title': tr('Road inundated status'), '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_tsunami], 'continuous_hazard_units': [unit_feet, unit_metres], 'vector_hazard_classifications': [], 'raster_hazard_classifications': [], 'additional_keywords': [] }, 'exposure': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_line], 'exposure_types': [exposure_road], 'exposure_units': [], 'exposure_class_fields': [road_class_field], 'additional_keywords': [] } }, 'parameters': OrderedDict( [ ('low_threshold', low_threshold()), ('medium_threshold', medium_threshold()), ('high_threshold', high_threshold()), ('postprocessors', OrderedDict([ ('RoadType', road_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': 'FloodVectorRoadsExperimentalFunction', 'name': tr('Polygon flood on roads'), 'impact': tr('Be flooded'), 'title': tr('Be flooded'), 'function_type': 'qgis2.0', 'author': 'Dmitry Kolesov', 'date_implemented': 'N/A', 'overview': tr('N/A'), 'detailed_description': tr('N/A'), 'hazard_input': tr(''), 'exposure_input': tr(''), 'output': tr(''), 'actions': tr(''), 'limitations': [], 'citations': [ { 'text': None, 'link': None } ], 'legend_units': '', 'legend_notes': '', 'legend_title': tr('Road inundated status'), 'layer_requirements': { 'hazard': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_polygon], 'hazard_categories': [ hazard_category_single_event, hazard_category_multiple_event ], 'hazard_types': [hazard_flood], 'continuous_hazard_units': [], 'vector_hazard_classifications': [ flood_vector_hazard_classes], 'raster_hazard_classifications': [], 'additional_keywords': [] }, 'exposure': { 'layer_mode': layer_mode_classified, 'layer_geometries': [layer_geometry_line], 'exposure_types': [exposure_road], 'exposure_units': [], 'exposure_class_fields': [road_class_field], 'additional_keywords': [] } }, 'parameters': OrderedDict([ ('postprocessors', OrderedDict([ ('RoadType', road_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': 'FloodRasterRoadsGdalFunction', 'name': tr('Raster flood on roads (GDAL)'), 'impact': tr('Be flooded in given thresholds (GDAL)'), 'title': tr('Be flooded in given thresholds (GDAL)'), 'function_type': 'qgis2.0', 'author': 'Dmitry Kolesov', 'date_implemented': 'N/A', 'overview': tr('N/A'), 'detailed_description': '', 'hazard_input': '', 'exposure_input': '', 'output': '', 'actions': '', 'limitations': [], 'citations': [], 'categories': { 'hazard': { 'definition': hazard_definition, 'subcategories': [hazard_flood, hazard_tsunami], 'units': [unit_metres_depth, unit_feet_depth], 'layer_constraints': [layer_raster_continuous] }, 'exposure': { 'definition': exposure_definition, 'subcategories': [exposure_road], 'units': [unit_road_type_type], 'layer_constraints': [layer_vector_line] } }, 'parameters': OrderedDict([ # This field of the exposure layer contains # information about road types ('road_type_field', 'TYPE'), ('min threshold [m]', 1.0), ('max threshold [m]', float('inf')), ('postprocessors', OrderedDict([('RoadType', road_type_postprocessor())])) ]) } return dict_meta