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
