def run(self, layers):
"""Risk plugin for Padang building survey
"""
# Extract data
H = get_hazard_layer(layers) # Ground shaking
E = get_exposure_layer(layers) # Building locations
question = get_question(H.get_name(),
E.get_name(),
self)
# Map from different kinds of datasets to Padang vulnerability classes
datatype = E.get_keywords()['datatype']
vclass_tag = 'VCLASS'
if datatype.lower() == 'osm':
# Map from OSM attributes
Emap = osm2padang(E)
elif datatype.lower() == 'sigab':
# Map from SIGAB attributes
Emap = sigab2padang(E)
else:
Emap = E
# Interpolate hazard level to building locations
I = assign_hazard_values_to_exposure_data(H, Emap,
attribute_name='MMI')
# Extract relevant numerical data
attributes = I.get_data()
N = len(I)
# Calculate building damage
count_high = count_medium = count_low = count_none = 0
for i in range(N):
mmi = float(attributes[i]['MMI'])
building_type = Emap.get_data(vclass_tag, i)
damage_params = damage_curves[building_type]
beta = damage_params['beta']
median = damage_params['median']
percent_damage = lognormal_cdf(mmi,
median=median,
sigma=beta) * 100
# Add calculated impact to existing attributes
attributes[i][self.target_field] = percent_damage
# Calculate statistics
if percent_damage < 10:
count_none += 1
if 10 <= percent_damage < 33:
count_low += 1
if 33 <= percent_damage < 66:
count_medium += 1
if 66 <= percent_damage:
count_high += 1
# Generate impact report
table_body = [question,
TableRow([_('Buildings'), _('Total')],
header=True),
TableRow([_('All'), N]),
TableRow([_('No damage'), count_none]),
TableRow([_('Low damage'), count_low]),
TableRow([_('Medium damage'), count_medium]),
TableRow([_('High damage'), count_high])]
table_body.append(TableRow(_('Notes'), header=True))
table_body.append(_('Levels of impact are defined by post 2009 '
'Padang earthquake survey conducted by Geoscience '
'Australia and Institute of Teknologi Bandung.'))
table_body.append(_('Unreinforced masonry is assumed where no '
'structural information is available.'))
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
map_title = _('Earthquake damage to buildings')
# Create style
style_classes = [dict(label=_('No damage'), min=0, max=10,
colour='#00ff00', transparency=1),
dict(label=_('Low damage'), min=10, max=33,
colour='#ffff00', transparency=1),
dict(label=_('Medium damage'), min=33, max=66,
colour='#ffaa00', transparency=1),
dict(label=_('High damage'), min=66, max=100,
colour='#ff0000', transparency=1)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes)
# Create vector layer and return
V = Vector(data=attributes,
projection=E.get_projection(),
geometry=E.get_geometry(),
name='Estimated pct damage',
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title},
style_info=style_info)
return V
def run(self, layers):
"""Risk plugin for Padang building survey
"""
# Extract data
H = get_hazard_layer(layers) # Ground shaking
E = get_exposure_layer(layers) # Building locations
question = get_question(H.get_name(), E.get_name(), self)
# Map from different kinds of datasets to Padang vulnerability classes
datatype = E.get_keywords()['datatype']
vclass_tag = 'VCLASS'
if datatype.lower() == 'osm':
# Map from OSM attributes
Emap = osm2padang(E)
elif datatype.lower() == 'sigab':
# Map from SIGAB attributes
Emap = sigab2padang(E)
else:
Emap = E
# Interpolate hazard level to building locations
I = assign_hazard_values_to_exposure_data(H,
Emap,
attribute_name='MMI')
# Extract relevant numerical data
attributes = I.get_data()
N = len(I)
# Calculate building damage
count_high = count_medium = count_low = count_none = 0
for i in range(N):
mmi = float(attributes[i]['MMI'])
building_type = Emap.get_data(vclass_tag, i)
damage_params = damage_curves[building_type]
beta = damage_params['beta']
median = damage_params['median']
percent_damage = log_normal_cdf(mmi, median=median,
sigma=beta) * 100
# Add calculated impact to existing attributes
attributes[i][self.target_field] = percent_damage
# Calculate statistics
if percent_damage < 10:
count_none += 1
if 10 <= percent_damage < 33:
count_low += 1
if 33 <= percent_damage < 66:
count_medium += 1
if 66 <= percent_damage:
count_high += 1
# Generate impact report
table_body = [
question,
TableRow([tr('Buildings'), tr('Total')], header=True),
TableRow([tr('All'), N]),
TableRow([tr('No damage'), format_int(count_none)]),
TableRow([tr('Low damage'),
format_int(count_low)]),
TableRow([tr('Medium damage'),
format_int(count_medium)]),
TableRow([tr('High damage'),
format_int(count_high)])
]
table_body.append(TableRow(tr('Notes'), header=True))
table_body.append(
tr('Levels of impact are defined by post 2009 '
'Padang earthquake survey conducted by Geoscience '
'Australia and Institute of Teknologi Bandung.'))
table_body.append(
tr('Unreinforced masonry is assumed where no '
'structural information is available.'))
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
map_title = tr('Earthquake damage to buildings')
# Create style
style_classes = [
dict(label=tr('No damage'),
min=0,
max=10,
colour='#00ff00',
transparency=0),
dict(label=tr('Low damage'),
min=10,
max=33,
colour='#ffff00',
transparency=0),
dict(label=tr('Medium damage'),
min=33,
max=66,
colour='#ffaa00',
transparency=0),
dict(label=tr('High damage'),
min=66,
max=100,
colour='#ff0000',
transparency=0)
]
style_info = dict(target_field=self.target_field,
style_classes=style_classes)
# Create vector layer and return
V = Vector(data=attributes,
projection=E.get_projection(),
geometry=E.get_geometry(),
name='Estimated pct damage',
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'target_field': self.target_field
},
style_info=style_info)
return V