def run(self, layers):
"""Risk plugin for earthquake school damage
"""
# Extract data
H = get_hazard_layer(layers) # Ground shaking
E = get_exposure_layer(layers) # Building locations
keywords = E.get_keywords()
if 'datatype' in keywords:
datatype = keywords['datatype']
if datatype.lower() == 'osm':
# Map from OSM attributes to the guideline classes (URM and RM)
E = osm2bnpb(E, target_attribute=self.vclass_tag)
elif datatype.lower() == 'sigab':
# Map from SIGAB attributes to the guideline classes
# (URM and RM)
E = sigab2bnpb(E)
else:
E = unspecific2bnpb(E, target_attribute=self.vclass_tag)
else:
E = unspecific2bnpb(E, target_attribute=self.vclass_tag)
# Interpolate hazard level to building locations
H = assign_hazard_values_to_exposure_data(H, E,
attribute_name='MMI')
# Extract relevant numerical data
coordinates = E.get_geometry()
shaking = H.get_data()
N = len(shaking)
# List attributes to carry forward to result layer
attributes = E.get_attribute_names()
# Calculate building damage
count3 = 0
count2 = 0
count1 = 0
count_unknown = 0
building_damage = []
for i in range(N):
mmi = float(shaking[i]['MMI'])
building_class = E.get_data(self.vclass_tag, i)
lo, hi = damage_parameters[building_class]
if numpy.isnan(mmi):
# If we don't know the shake level assign Not-a-Number
damage = numpy.nan
count_unknown += 1
elif mmi < lo:
damage = 1 # Low
count1 += 1
elif lo <= mmi < hi:
damage = 2 # Medium
count2 += 1
elif mmi >= hi:
damage = 3 # High
count3 += 1
else:
msg = 'Undefined shakelevel %s' % str(mmi)
raise Exception(msg)
# Collect shake level and calculated damage
result_dict = {self.target_field: damage,
'MMI': mmi}
# Carry all orginal attributes forward
for key in attributes:
result_dict[key] = E.get_data(key, i)
# Record result for this feature
building_damage.append(result_dict)
# Create report
impact_summary = ('<table border="0" width="320px">'
' <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
' <tr></tr>'
' <tr><td>%s:</td><td>%s</td></tr>'
' <tr><td>%s (10-25%%):</td><td>%s</td></tr>'
' <tr><td>%s (25-50%%):</td><td>%s</td></tr>'
' <tr><td>%s (50-100%%):</td><td>%s</td></tr>'
% (tr('Buildings'), tr('Total'),
tr('All'), format_int(N),
tr('Low damage'), format_int(count1),
tr('Medium damage'), format_int(count2),
tr('High damage'), format_int(count3)))
impact_summary += (' <tr><td>%s (NaN):</td><td>%s</td></tr>'
% ('Unknown', format_int(count_unknown)))
impact_summary += '</table>'
# Create style
style_classes = [dict(label=tr('Low damage'), min=0.5, max=1.5,
colour='#fecc5c', transparency=0),
dict(label=tr('Medium damage'), min=1.5, max=2.5,
colour='#fd8d3c', transparency=0),
dict(label=tr('High damage'), min=2.5, max=3.5,
colour='#f31a1c', transparency=0)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes)
# Create vector layer and return
V = Vector(data=building_damage,
projection=E.get_projection(),
geometry=coordinates,
name='Estimated damage level',
keywords={'impact_summary': impact_summary},
style_info=style_info)
return V
def run(self, layers):
"""Risk plugin for earthquake school damage
"""
# Extract data
H = get_hazard_layer(layers) # Ground shaking
E = get_exposure_layer(layers) # Building locations
keywords = E.get_keywords()
if 'datatype' in keywords:
datatype = keywords['datatype']
if datatype.lower() == 'osm':
# Map from OSM attributes to the guideline classes (URM and RM)
E = osm2bnpb(E, target_attribute=self.vclass_tag)
elif datatype.lower() == 'sigab':
# Map from SIGAB attributes to the guideline classes
# (URM and RM)
E = sigab2bnpb(E)
else:
E = unspecific2bnpb(E, target_attribute=self.vclass_tag)
else:
E = unspecific2bnpb(E, target_attribute=self.vclass_tag)
# Interpolate hazard level to building locations
H = assign_hazard_values_to_exposure_data(H, E, attribute_name='MMI')
# Extract relevant numerical data
coordinates = E.get_geometry()
shaking = H.get_data()
N = len(shaking)
# List attributes to carry forward to result layer
attributes = E.get_attribute_names()
# Calculate building damage
count3 = 0
count2 = 0
count1 = 0
count_unknown = 0
building_damage = []
for i in range(N):
mmi = float(shaking[i]['MMI'])
building_class = E.get_data(self.vclass_tag, i)
lo, hi = damage_parameters[building_class]
if numpy.isnan(mmi):
# If we don't know the shake level assign Not-a-Number
damage = numpy.nan
count_unknown += 1
elif mmi < lo:
damage = 1 # Low
count1 += 1
elif lo <= mmi < hi:
damage = 2 # Medium
count2 += 1
elif mmi >= hi:
damage = 3 # High
count3 += 1
else:
msg = 'Undefined shakelevel %s' % str(mmi)
raise Exception(msg)
# Collect shake level and calculated damage
result_dict = {self.target_field: damage, 'MMI': mmi}
# Carry all orginal attributes forward
for key in attributes:
result_dict[key] = E.get_data(key, i)
# Record result for this feature
building_damage.append(result_dict)
# Create report
impact_summary = (
'<table border="0" width="320px">'
' <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
' <tr></tr>'
' <tr><td>%s:</td><td>%s</td></tr>'
' <tr><td>%s (10-25%%):</td><td>%s</td></tr>'
' <tr><td>%s (25-50%%):</td><td>%s</td></tr>'
' <tr><td>%s (50-100%%):</td><td>%s</td></tr>' %
(tr('Buildings'), tr('Total'), tr('All'), format_int(N),
tr('Low damage'), format_int(count1), tr('Medium damage'),
format_int(count2), tr('High damage'), format_int(count3)))
impact_summary += (' <tr><td>%s (NaN):</td><td>%s</td></tr>' %
('Unknown', format_int(count_unknown)))
impact_summary += '</table>'
# Create style
style_classes = [
dict(label=tr('Low damage'),
min=0.5,
max=1.5,
colour='#fecc5c',
transparency=0),
dict(label=tr('Medium damage'),
min=1.5,
max=2.5,
colour='#fd8d3c',
transparency=0),
dict(label=tr('High damage'),
min=2.5,
max=3.5,
colour='#f31a1c',
transparency=0)
]
style_info = dict(target_field=self.target_field,
style_classes=style_classes)
# Create vector layer and return
V = Vector(data=building_damage,
projection=E.get_projection(),
geometry=coordinates,
name='Estimated damage level',
keywords={'impact_summary': impact_summary},
style_info=style_info)
return V