def test_classification_value_maps(self):
"""Test for classification_value_maps method."""
value_maps = default_classification_value_maps(flood_hazard_classes)
wet_class = flood_hazard_classes['classes'][0]
dry_class = flood_hazard_classes['classes'][1]
expected = {
'dry': dry_class['string_defaults'],
'wet': wet_class['string_defaults']
}
self.assertDictEqual(value_maps, expected)
value_maps = default_classification_value_maps(
cyclone_au_bom_hazard_classes)
category_5_class = cyclone_au_bom_hazard_classes['classes'][0]
category_4_class = cyclone_au_bom_hazard_classes['classes'][1]
category_3_class = cyclone_au_bom_hazard_classes['classes'][2]
category_2_class = cyclone_au_bom_hazard_classes['classes'][3]
category_1_class = cyclone_au_bom_hazard_classes['classes'][4]
tropical_depression_class = cyclone_au_bom_hazard_classes['classes'][5]
expected = {
'category_1':
category_1_class.get('string_defaults', []),
'category_2':
category_2_class.get('string_defaults', []),
'category_3':
category_3_class.get('string_defaults', []),
'category_4':
category_4_class.get('string_defaults', []),
'category_5':
category_5_class.get('string_defaults', []),
'tropical_depression':
tropical_depression_class.get('string_defaults', [])
}
self.assertDictEqual(value_maps, expected)
def test_classification_value_maps(self):
"""Test for classification_value_maps method."""
value_maps = default_classification_value_maps(flood_hazard_classes)
wet_class = flood_hazard_classes['classes'][0]
dry_class = flood_hazard_classes['classes'][1]
expected = {
'dry': dry_class['string_defaults'],
'wet': wet_class['string_defaults']
}
self.assertDictEqual(value_maps, expected)
value_maps = default_classification_value_maps(
cyclone_au_bom_hazard_classes)
category_5_class = cyclone_au_bom_hazard_classes['classes'][0]
category_4_class = cyclone_au_bom_hazard_classes['classes'][1]
category_3_class = cyclone_au_bom_hazard_classes['classes'][2]
category_2_class = cyclone_au_bom_hazard_classes['classes'][3]
category_1_class = cyclone_au_bom_hazard_classes['classes'][4]
tropical_depression_class = cyclone_au_bom_hazard_classes['classes'][5]
expected = {
'category_1': category_1_class.get('string_defaults', []),
'category_2': category_2_class.get('string_defaults', []),
'category_3': category_3_class.get('string_defaults', []),
'category_4': category_4_class.get('string_defaults', []),
'category_5': category_5_class.get('string_defaults', []),
'tropical_depression': tropical_depression_class.get(
'string_defaults', [])
}
self.assertDictEqual(value_maps, expected)
def activate_classification(self, exposure, classification=None):
"""Set active to True for classification for the exposure.
If classification = None, all classification set active = False.
:param exposure: Exposure definition.
:type exposure: dict
:param classification: Classification definition.
:type classification: dict
"""
if self.layer_mode == layer_mode_continuous:
selected_unit = self.parent.step_kw_unit.selected_unit()['key']
target = self.thresholds.get(exposure['key'])
if target is None:
self.thresholds[exposure['key']] = {}
target = self.thresholds.get(exposure['key'])
else:
selected_unit = None
target = self.value_maps.get(exposure['key'])
if target is None:
self.value_maps[exposure['key']] = {}
target = self.value_maps.get(exposure['key'])
if classification is not None:
if classification['key'] not in target:
if self.layer_mode == layer_mode_continuous:
default_classes = default_classification_thresholds(
classification, selected_unit)
target[classification['key']] = {
'classes': default_classes,
'active': True
}
else:
default_classes = default_classification_value_maps(
classification)
target[classification['key']] = {
'classes': default_classes,
'active': True
}
return
for classification_key, value in target.items():
if classification is None:
value['active'] = False
continue
if classification_key == classification['key']:
value['active'] = True
else:
value['active'] = False
def activate_classification(self, exposure, classification=None):
"""Set active to True for classification for the exposure.
If classification = None, all classification set active = False.
:param exposure: Exposure definition.
:type exposure: dict
:param classification: Classification definition.
:type classification: dict
"""
if self.layer_mode == layer_mode_continuous:
selected_unit = self.parent.step_kw_unit.selected_unit()['key']
target = self.thresholds.get(exposure['key'])
if target is None:
self.thresholds[exposure['key']] = {}
target = self.thresholds.get(exposure['key'])
else:
selected_unit = None
target = self.value_maps.get(exposure['key'])
if target is None:
self.value_maps[exposure['key']] = {}
target = self.value_maps.get(exposure['key'])
if classification is not None:
if classification['key'] not in target:
if self.layer_mode == layer_mode_continuous:
default_classes = default_classification_thresholds(
classification, selected_unit)
target[classification['key']] = {
'classes': default_classes,
'active': True
}
else:
default_classes = default_classification_value_maps(
classification)
target[classification['key']] = {
'classes': default_classes,
'active': True
}
return
for classification_key, value in target.items():
if classification is None:
value['active'] = False
continue
if classification_key == classification['key']:
value['active'] = True
else:
value['active'] = False
