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
datatype = E.get_keywords()["datatype"]
vclass_tag = "ITB_Class"
if datatype.lower() == "osm":
# Map from OSM attributes to the ITB building classes
# Emap = osm2itb(E)
print "osm2itb has not been implemented"
elif datatype.lower() == "sigab":
# Emap = sigabitb(E)
print "sigab2itb has not been implemented"
elif datatype.lower() == "itb":
Emap = E
# Interpolate hazard level to building locations
Hi = assign_hazard_values_to_exposure_data(H, Emap, attribute_name="MMI")
# Extract relevant numerical data
coordinates = Emap.get_geometry()
shaking = Hi.get_data()
N = len(shaking)
# List attributes to carry forward to result layer
attributes = Emap.get_attribute_names()
# Calculate building damage
count50 = 0
count25 = 0
count10 = 0
count0 = 0
building_damage = []
for i in range(N):
mmi = float(shaking[i]["MMI"])
building_class = Emap.get_data(vclass_tag, i)
building_type = str(building_class)
damage_params = vul_curves[building_type]
beta = damage_params["beta"]
median = damage_params["median"]
msg = "Invalid parameter value for " + building_type
verify(beta + median > 0.0, msg)
percent_damage = lognormal_cdf(mmi, median=median, sigma=beta) * 100
# Collect shake level and calculated damage
result_dict = {self.target_field: percent_damage, "MMI": mmi}
# Carry all orginal attributes forward
for key in attributes:
result_dict[key] = Emap.get_data(key, i)
# Record result for this feature
building_damage.append(result_dict)
# Debugging
# if percent_damage > 0.01:
# print mmi, percent_damage
# Calculate statistics
if percent_damage < 10:
count0 += 1
if 10 <= percent_damage < 33:
count10 += 1
if 33 <= percent_damage < 66:
count25 += 1
if 66 <= percent_damage:
count50 += 1
# fid.close()
# Create report
Hname = H.get_name()
Ename = E.get_name()
impact_summary = '<b>In case of "%s" the estimated impact to ' '"%s" ' "is:</b><br><br><p>" % (Hname, Ename)
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>%i</td></tr>"
" <tr><td>%s (<10%%):</td><td>%i</td></tr>"
" <tr><td>%s (10-33%%):</td><td>%i</td></tr>"
" <tr><td>%s (33-66%%):</td><td>%i</td></tr>"
" <tr><td>%s (66-100%%):</td><td>%i</td></tr>"
"</table></font>"
% (
tr("Buildings"),
tr("Total"),
tr("All"),
N,
tr("No damage"),
count0,
tr("Low damage"),
count10,
tr("Medium damage"),
count25,
tr("High damage"),
count50,
)
)
impact_summary += "<br>" # Blank separation row
impact_summary += "<b>" + tr("Assumption") + ":</b><br>"
# This is the proper text:
# tr('Levels of impact are defined by post 2009 '
# 'Padang earthquake survey conducted by Geoscience '
# 'Australia and Institute of Teknologi Bandung.'))
# tr('Unreinforced masonry is assumed where no '
# 'structural information is available.'))
impact_summary += tr(
"Levels of impact are defined by post 2009 "
"Padang earthquake survey conducted by Geoscience "
"Australia and Institute of Teknologi Bandung."
)
impact_summary += tr("Unreinforced masonry is assumed where no " "structural information is available.")
# 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=building_damage,
projection=E.get_projection(),
geometry=coordinates,
name="Estimated pct damage",
keywords={"impact_summary": impact_summary},
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 = 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
datatype = E.get_keywords()['datatype']
vclass_tag = 'ITB_Class'
if datatype.lower() == 'osm':
# Map from OSM attributes to the ITB building classes
# Emap = osm2itb(E)
print 'osm2itb has not been implemented'
elif datatype.lower() == 'sigab':
# Emap = sigabitb(E)
print 'sigab2itb has not been implemented'
elif datatype.lower() == 'itb':
Emap = E
# Interpolate hazard level to building locations
Hi = assign_hazard_values_to_exposure_data(H,
Emap,
attribute_name='MMI')
# Extract relevant numerical data
coordinates = Emap.get_geometry()
shaking = Hi.get_data()
N = len(shaking)
# List attributes to carry forward to result layer
attributes = Emap.get_attribute_names()
# Calculate building damage
count50 = 0
count25 = 0
count10 = 0
count0 = 0
building_damage = []
for i in range(N):
mmi = float(shaking[i]['MMI'])
building_class = Emap.get_data(vclass_tag, i)
building_type = str(building_class)
damage_params = vul_curves[building_type]
beta = damage_params['beta']
median = damage_params['median']
msg = 'Invalid parameter value for ' + building_type
verify(beta + median > 0.0, msg)
percent_damage = lognormal_cdf(mmi, median=median,
sigma=beta) * 100
# Collect shake level and calculated damage
result_dict = {self.target_field: percent_damage, 'MMI': mmi}
# Carry all orginal attributes forward
for key in attributes:
result_dict[key] = Emap.get_data(key, i)
# Record result for this feature
building_damage.append(result_dict)
# Debugging
#if percent_damage > 0.01:
# print mmi, percent_damage
# Calculate statistics
if percent_damage < 10:
count0 += 1
if 10 <= percent_damage < 33:
count10 += 1
if 33 <= percent_damage < 66:
count25 += 1
if 66 <= percent_damage:
count50 += 1
# fid.close()
# Create report
Hname = H.get_name()
Ename = E.get_name()
impact_summary = ('<b>In case of "%s" the estimated impact to '
'"%s" '
'is:</b><br><br><p>' % (Hname, Ename))
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>%i</td></tr>'
' <tr><td>%s (<10%%):</td><td>%i</td></tr>'
' <tr><td>%s (10-33%%):</td><td>%i</td></tr>'
' <tr><td>%s (33-66%%):</td><td>%i</td></tr>'
' <tr><td>%s (66-100%%):</td><td>%i</td></tr>'
'</table></font>' %
(_('Buildings'), _('Total'), _('All'), N, _('No damage'), count0,
_('Low damage'), count10, _('Medium damage'), count25,
_('High damage'), count50))
impact_summary += '<br>' # Blank separation row
impact_summary += '<b>' + _('Assumption') + ':</b><br>'
# This is the proper text:
#_('Levels of impact are defined by post 2009 '
# 'Padang earthquake survey conducted by Geoscience '
# 'Australia and Institute of Teknologi Bandung.'))
#_('Unreinforced masonry is assumed where no '
# 'structural information is available.'))
impact_summary += _('Levels of impact are defined by post 2009 '
'Padang earthquake survey conducted by Geoscience '
'Australia and Institute of Teknologi Bandung.')
impact_summary += _('Unreinforced masonry is assumed where no '
'structural information is available.')
# 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=building_damage,
projection=E.get_projection(),
geometry=coordinates,
name='Estimated pct damage',
keywords={'impact_summary': impact_summary},
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 = 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([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
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
datatype = E.get_keywords()['datatype']
vclass_tag = 'ITB_Class'
if datatype.lower() == 'osm':
# Map from OSM attributes to the ITB building classes
# Emap = osm2itb(E)
print 'osm2itb has not been implemented'
elif datatype.lower() == 'sigab':
# Emap = sigabitb(E)
print 'sigab2itb has not been implemented'
elif datatype.lower() == 'itb':
Emap = E
# Interpolate hazard level to building locations
Hi = assign_hazard_values_to_exposure_data(H, Emap,
attribute_name='MMI')
# Extract relevant numerical data
coordinates = Emap.get_geometry()
shaking = Hi.get_data()
N = len(shaking)
# List attributes to carry forward to result layer
attributes = Emap.get_attribute_names()
# Calculate building damage
count50 = 0
count25 = 0
count10 = 0
count0 = 0
building_damage = []
for i in range(N):
mmi = float(shaking[i]['MMI'])
building_class = Emap.get_data(vclass_tag, i)
building_type = str(building_class)
damage_params = vul_curves[building_type]
beta = damage_params['beta']
median = damage_params['median']
msg = 'Invalid parameter value for ' + building_type
verify(beta + median > 0.0, msg)
percent_damage = lognormal_cdf(mmi,
median=median,
sigma=beta) * 100
# Collect shake level and calculated damage
result_dict = {self.target_field: percent_damage,
'MMI': mmi}
# Carry all orginal attributes forward
for key in attributes:
result_dict[key] = Emap.get_data(key, i)
# Record result for this feature
building_damage.append(result_dict)
# Debugging
#if percent_damage > 0.01:
# print mmi, percent_damage
# Calculate statistics
if percent_damage < 10:
count0 += 1
if 10 <= percent_damage < 33:
count10 += 1
if 33 <= percent_damage < 66:
count25 += 1
if 66 <= percent_damage:
count50 += 1
# fid.close()
# Create report
Hname = H.get_name()
Ename = E.get_name()
impact_summary = ('<b>In case of "%s" the estimated impact to '
'"%s" '
'is:</b><br><br><p>' % (Hname, Ename))
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>%i</td></tr>'
' <tr><td>%s (<10%%):</td><td>%i</td></tr>'
' <tr><td>%s (10-33%%):</td><td>%i</td></tr>'
' <tr><td>%s (33-66%%):</td><td>%i</td></tr>'
' <tr><td>%s (66-100%%):</td><td>%i</td></tr>'
'</table></font>' % (_('Buildings'), _('Total'),
_('All'), N,
_('No damage'), count0,
_('Low damage'), count10,
_('Medium damage'), count25,
_('High damage'), count50))
impact_summary += '<br>' # Blank separation row
impact_summary += '<b>' + _('Assumption') + ':</b><br>'
# This is the proper text:
#_('Levels of impact are defined by post 2009 '
# 'Padang earthquake survey conducted by Geoscience '
# 'Australia and Institute of Teknologi Bandung.'))
#_('Unreinforced masonry is assumed where no '
# 'structural information is available.'))
impact_summary += _('Levels of impact are defined by post 2009 '
'Padang earthquake survey conducted by Geoscience '
'Australia and Institute of Teknologi Bandung.')
impact_summary += _('Unreinforced masonry is assumed where no '
'structural information is available.')
# 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=building_damage,
projection=E.get_projection(),
geometry=coordinates,
name='Estimated pct damage',
keywords={'impact_summary': impact_summary},
style_info=style_info)
return V
