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
