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
