def interpolate_raster_vector_points(R, V, attribute_name=None):
"""Interpolate from raster layer to point data
Input
R: Raster data set (grid)
V: Vector data set (points)
attribute_name: Name for new attribute.
If None (default) the name of layer R is used
Output
I: Vector data set; points located as V with values interpolated from R
"""
msg = ('There are no data points to interpolate to. Perhaps zoom out '
'and try again')
verify(len(V) > 0, msg)
# Input checks
verify(R.is_raster)
verify(V.is_vector)
verify(V.is_point_data)
# Get raster data and corresponding x and y axes
A = R.get_data(nan=True)
longitudes, latitudes = R.get_geometry()
verify(len(longitudes) == A.shape[1])
verify(len(latitudes) == A.shape[0])
# Get vector point geometry as Nx2 array
coordinates = numpy.array(V.get_geometry(),
dtype='d',
copy=False)
# Get original attributes
attributes = V.get_data()
# Create new attribute and interpolate
N = len(V)
if attribute_name is None:
attribute_name = R.get_name()
try:
values = interpolate_raster(longitudes, latitudes, A,
coordinates, mode='linear')
except Exception, e:
msg = (_('Could not interpolate from raster layer %(raster)s to '
'vector layer %(vector)s. Error message: %(error)s')
% {'raster': R.get_name(),
'vector': V.get_name(),
'error': str(e)})
raise Exception(msg)
def get_question(hazard_title, exposure_title, func):
"""Rephrase the question asked
Input
hazard_title: string
exposure_title: string
func: impact function class
"""
function_title = get_function_title(func)
return (_('In the event of <i>%(hazard)s</i> how many '
'<i>%(exposure)s</i> might <i>%(impact)s</i>')
% {'hazard': hazard_title.lower(),
'exposure': exposure_title.lower(),
'impact': function_title.lower()})
def Xtest_Afrikaans(self):
"""Test that Afrikaans translations are working"""
# Note this has really bad side effects - lots of tests suddenly start
# breaking when this test is enabled....disabled for now, but I have
# left the test here for now as it illustrates one potential avenue
# that can be pursued if dynamically changing the language to unit test
# different locales ever becomes a requirement.
# Be sure nose tests all run cleanly before reintroducing this!
# This is part test and part demonstrator of how to reload inasafe
# Now see if the same function is delivered for the function
# Because of the way impact plugins are loaded in inasafe
# (see http://effbot.org/zone/metaclass-plugins.htm)
# lang in the context of the ugettext function in inasafe libs
# must be imported late so that i18n is set up already
from common.utilities import ugettext as _
myUntranslatedString = 'Temporarily Closed'
myExpectedString = 'Tydelik gesluit' # afrikaans
myTranslation = _(myUntranslatedString)
myMessage = '\nTranslated: %s\nGot: %s\nExpected: %s' % (
myUntranslatedString,
myTranslation,
myExpectedString)
assert myTranslation == myExpectedString, myMessage
myParent = QWidget()
myCanvas = QgsMapCanvas(myParent)
myIface = QgisInterface(myCanvas)
# reload all inasafe modules so that i18n get picked up afresh
# this is the part that produces bad side effects
for myMod in sys.modules.values():
try:
if ('storage' in str(myMod) or
'impact' in str(myMod)):
print 'Reloading:', str(myMod)
reload(myMod)
except:
pass
myPlugin = ISPlugin(myIface)
myPlugin.setupI18n('af') # afrikaans
myLang = os.environ['LANG']
assert myLang == 'af'
from impact_functions import getSafeImpactFunctions
#myFunctions = getSafeImpactFunctions()
#print myFunctions
myFunctions = getSafeImpactFunctions('Tydelik gesluit')
assert len(myFunctions) > 0
def test_ImpactFunctionI18n(self):
"""Library translations are working."""
# import this late so that i18n setup is already in place
from common.utilities import ugettext as _
myUntranslatedString = 'Temporarily Closed'
# Test indonesian too
myParent = QWidget()
myCanvas = QgsMapCanvas(myParent)
myIface = QgisInterface(myCanvas)
myPlugin = ISPlugin(myIface)
myPlugin.setupI18n('id') # indonesian
myExpectedString = 'Ditutup sementara'
myTranslation = _(myUntranslatedString)
myMessage = '\nTranslated: %s\nGot: %s\nExpected: %s' % (
myUntranslatedString,
myTranslation,
myExpectedString)
assert myTranslation == myExpectedString, myMessage
"""
# FIXME (Ole): This approach can be generalised to any strings that are not
# statically declared such as attribute values.
# So, we should merge the two dictionaries and just have one
# with strings that need to be recognised by the translation
# tools.
# Also rename this module to something more fitting, such as
# dynamic_translations.py
# See issue #168
from common.utilities import ugettext as _
names = {'title1': _('DKI buildings'), # Bangunan DKI
'title2': _('Jakarta 2007 flood'), # Banjir seperti 2007
'Jakarta 2007 flood': _('Jakarta 2007 flood'),
'A flood in Jakarta like in 2007': _('A flood in Jakarta like '
'in 2007'),
'title3': _('Jakarta flood like 2007 with pump failure at Pluit, '
'Ancol and Sunter'), # Banjir 2007 tanpa pompa di
# Pluit, Ancol dan Sunter
'Jakarta flood like 2007 with pump failure at Pluit and Ancol':
_('Jakarta flood like 2007 with pump failure at '
'Pluit and Ancol'),
'A flood in Jakarta like in 2007 but with structural improvements':
_('A flood in Jakarta like in 2007 but with structural '
'improvements'),
'title4': _('Sea wall collapse at Pluit'), # Dam Pluit Runtuh
'title5': _('Jakarta flood prone areas'), # Daerah Rawan Banjir
def run(self, layers):
"""Risk plugin for flood population evacuation
Input
layers: List of layers expected to contain
H: Raster layer of flood depth
P: Raster layer of population data on the same grid as H
Counts number of people exposed to flood levels exceeding
specified threshold.
Return
Map of population exposed to flood levels exceeding the threshold
Table with number of people evacuated and supplies required
"""
# Depth above which people are regarded affected [m]
threshold = 1.0 # Threshold [m]
# Identify hazard and exposure layers
inundation = get_hazard_layer(layers) # Flood inundation [m]
population = get_exposure_layer(layers)
question = get_question(inundation.get_name(),
population.get_name(),
self)
# Extract data as numeric arrays
D = inundation.get_data(nan=0.0) # Depth
# Calculate impact as population exposed to depths > threshold
P = population.get_data(nan=0.0, scaling=True)
I = numpy.where(D > threshold, P, 0)
M = numpy.where(D > 0.5, P, 0)
L = numpy.where(D > 0.3, P, 0)
# Count totals
total = int(numpy.sum(P))
evacuated = int(numpy.sum(I))
medium = int(numpy.sum(M)) - int(numpy.sum(I))
low = int(numpy.sum(L)) - int(numpy.sum(M))
# Don't show digits less than a 1000
if total > 1000:
total = total // 1000 * 1000
if evacuated > 1000:
evacuated = evacuated // 1000 * 1000
if medium > 1000:
medium = medium // 1000 * 1000
if low > 1000:
low = low // 1000 * 1000
# Calculate estimated needs based on BNPB Perka 7/2008 minimum bantuan
rice = evacuated * 2.8
drinking_water = evacuated * 17.5
water = evacuated * 67
family_kits = evacuated / 5
toilets = evacuated / 20
# Generate impact report for the pdf map
table_body = [question,
TableRow([_('People needing evacuation'),
'%i' % evacuated],
header=True),
TableRow(_('Map shows population density needing '
'evacuation')),
#,
## TableRow([_('People in 50cm to 1m of water '),
## '%i' % medium],
## header=True),
## TableRow([_('People in 30cm to 50cm of water'),
## '%i' % low],
## header=True)]
TableRow([_('Needs per week'), _('Total')],
header=True),
[_('Rice [kg]'), int(rice)],
[_('Drinking Water [l]'), int(drinking_water)],
[_('Clean Water [l]'), int(water)],
[_('Family Kits'), int(family_kits)],
[_('Toilets'), int(toilets)]]
impact_table = Table(table_body).toNewlineFreeString()
# Extend impact report for on-screen display
table_body.extend([TableRow(_('Notes:'), header=True),
_('Total population: %i') % total,
_('People need evacuation if flood levels '
'exceed %(eps)i m') % {'eps': threshold},
#_('People in 50cm to 1m of water: %i') % medium,
#_('People in 30cm to 50cm of water: %i') % low])
_('Minimum needs are defined in BNPB '
'regulation 7/2008')])
impact_summary = Table(table_body).toNewlineFreeString()
map_title = _('People in need of evacuation')
# Generare 8 equidistant classes across the range of flooded population
# 8 is the number of classes in the predefined flood population style
# as imported
classes = numpy.linspace(numpy.nanmin(I.flat[:]),
numpy.nanmax(I.flat[:]), 8)
# Modify labels in existing flood style to show quantities
style_classes = style_info['style_classes']
style_classes[1]['label'] = _('Low [%i people/cell]') % classes[1]
style_classes[4]['label'] = _('Medium [%i people/cell]') % classes[4]
style_classes[7]['label'] = _('High [%i people/cell]') % classes[7]
style_info['legend_title'] = _('Population Density')
# Create raster object and return
R = Raster(I,
projection=inundation.get_projection(),
geotransform=inundation.get_geotransform(),
name=_('Population which %s') % get_function_title(self),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title},
style_info=style_info)
return R
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 = H.interpolate(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 tsunami population
"""
# Extract data
H = get_hazard_layer(layers) # Depth
E = get_exposure_layer(layers) # Building locations
# Interpolate hazard level to building locations
Hi = H.interpolate(E, attribute_name='depth')
# Extract relevant numerical data
coordinates = Hi.get_geometry()
depth = Hi.get_data()
N = len(depth)
# List attributes to carry forward to result layer
attributes = E.get_attribute_names()
# Calculate building impact according to guidelines
count3 = 0
count1 = 0
count0 = 0
population_impact = []
for i in range(N):
if H.is_raster:
# Get depth
dep = float(depth[i]['depth'])
# Classify buildings according to depth
if dep >= 3:
affected = 3 # FIXME: Colour upper bound is 100 but
count3 += 1 # does not catch affected == 100
elif 1 <= dep < 3:
affected = 2
count1 += 1
else:
affected = 1
count0 += 1
elif H.is_vector:
dep = 0 # Just put something here
cat = depth[i]['Affected']
if cat is True:
affected = 3
count3 += 1
else:
affected = 1
count0 += 1
# Collect depth and calculated damage
result_dict = {self.target_field: affected,
'DEPTH': dep}
# Carry all original attributes forward
# FIXME: This should be done in interpolation. Check.
#for key in attributes:
# result_dict[key] = E.get_data(key, i)
# Record result for this feature
population_impact.append(result_dict)
# Create report
Hname = H.get_name()
Ename = E.get_name()
if H.is_raster:
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></tr>'
' <tr></tr>'
' <tr><td>%s:</td><td>%i</td></tr>'
' <tr><td>%s:</td><td>%i</td></tr>'
' <tr><td>%s:</td><td>%i</td></tr>'
'</table>' % (_('Impact'), _('Number of buildings'),
_('Low'), count0,
_('Medium'), count1,
_('High'), count3))
else:
impact_summary = ('<table border="0" width="320px">'
' <tr><th><b>%s</b></th><th><b>%s</b></th></tr>'
' <tr></tr>'
' <tr><td>%s:</td><td>%i</td></tr>'
' <tr><td>%s:</td><td>%i</td></tr>'
' <tr><td>%s:</td><td>%i</td></tr>'
'</table>' % ('Terdampak oleh tsunami',
'Jumlah gedung',
'Terdampak', count3,
'Tidak terdampak', count0,
'Semua', N))
impact_summary += '<br>' # Blank separation row
impact_summary += '<b>' + _('Assumption') + ':</b><br>'
impact_summary += ('Levels of impact are defined by BNPB\'s '
'<i>Pengkajian Risiko Bencana</i>')
impact_summary += ('<table border="0" width="320px">'
' <tr><th><b>%s</b></th><th><b>%s</b></th></tr>'
' <tr></tr>'
' <tr><td>%s:</td><td>%s:</td></tr>'
' <tr><td>%s:</td><td>%s:</td></tr>'
' <tr><td>%s:</td><td>%s:</td></tr>'
'</table>' % (_('Impact'), _('Tsunami height'),
_('Low'), '<1 m',
_('Medium'), '1-3 m',
_('High'), '>3 m'))
# Create style
style_classes = [dict(label='< 1 m', min=0, max=1,
colour='#1EFC7C', transparency=0, size=1),
dict(label='1 - 3 m', min=1, max=2,
colour='#FFA500', transparency=0, size=1),
dict(label='> 3 m', min=2, max=4,
colour='#F31A1C', transparency=0, size=1)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes)
# Create vector layer and return
if Hi.is_line_data:
name = 'Roads flooded'
elif Hi.is_point_data:
name = 'Buildings flooded'
V = Vector(data=population_impact,
projection=E.get_projection(),
geometry=coordinates,
keywords={'impact_summary': impact_summary},
geometry_type=Hi.geometry_type,
name=name,
style_info=style_info)
return V
def run(self, layers):
"""Impact plugin for hazard impact
"""
# Extract data
H = get_hazard_layer(layers) # Value
E = get_exposure_layer(layers) # Building locations
# Interpolate hazard level to building locations
H = H.interpolate(E, attribute_name='hazard_level')
# Extract relevant numerical data
coordinates = H.get_geometry()
category = H.get_data()
N = len(category)
# List attributes to carry forward to result layer
attributes = E.get_attribute_names()
# Calculate building impact according to guidelines
count2 = 0
count1 = 0
count0 = 0
building_impact = []
for i in range(N):
# Get category value
val = float(category[i]['hazard_level'])
# Classify buildings according to value
if val >= 2.0 / 3:
affected = 2
count2 += 1
elif 1.0 / 3 <= val < 2.0 / 3:
affected = 1
count1 += 1
else:
affected = 0
count0 += 1
# Collect depth and calculated damage
result_dict = {self.target_field: affected,
'CATEGORY': val}
# Record result for this feature
building_impact.append(result_dict)
# Create report
#FIXME: makes the output format the same as all other results
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:</td><td>%i</td></tr>'
' <tr><td>%s:</td><td>%i</td></tr>'
'</table>' % (_('Category'), _('Affected'),
_('Low'), count0,
_('Medium'), count1,
_('High'), count2))
# Create style
style_classes = [dict(label=_('Low'), min=0, max=0,
colour='#1EFC7C', transparency=0, size=1),
dict(label=_('Medium'), min=1, max=1,
colour='#FFA500', transparency=0, size=1),
dict(label=_('High'), min=2, max=2,
colour='#F31A1C', transparency=0, size=1)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes)
# Create vector layer and return
name = 'Buildings Affected'
V = Vector(data=building_impact,
projection=E.get_projection(),
geometry=coordinates,
keywords={'impact_summary': impact_summary},
geometry_type=H.geometry_type,
name=name,
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 = 'VCLASS'
if datatype.lower() == 'osm':
# Map from OSM attributes to the padang building classes
Emap = osm2padang(E)
elif datatype.lower() == 'sigab':
Emap = sigab2padang(E)
elif datatype.lower() == 'padang':
Emap = padang2itb(E)
else:
Emap = E
# Interpolate hazard level to building locations
Hi = H.interpolate(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(int(building_class))
damage_params = damage_curves[building_type]
beta = damage_params['beta']
median = damage_params['median']
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
# 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 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 = H.interpolate(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>%i</td></tr>'
' <tr><td>%s (10-25%%):</td><td>%i</td></tr>'
' <tr><td>%s (25-50%%):</td><td>%i</td></tr>'
' <tr><td>%s (50-100%%):</td><td>%i</td></tr>'
% (_('Buildings'), _('Total'),
_('All'), N,
_('Low damage'), count1,
_('Medium damage'), count2,
_('High damage'), count3))
impact_summary += (' <tr><td>%s (NaN):</td><td>%i</td></tr>'
% ('Unknown', count_unknown))
impact_summary += '</table>'
# Create style
style_classes = [dict(label=_('Low damage'), min=0.5, max=1.5,
colour='#fecc5c', transparency=1),
dict(label=_('Medium damage'), min=1.5, max=2.5,
colour='#fd8d3c', transparency=1),
dict(label=_('High damage'), min=2.5, max=3.5,
colour='#f31a1c', 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 damage level',
keywords={'impact_summary': impact_summary},
style_info=style_info)
return V
def run(self, layers, x=0.62275231, y=8.03314466, zeta=2.15):
"""Gender specific earthquake impact model
Input
layers: List of layers expected to contain
H: Raster layer of MMI ground shaking
P: Raster layer of population density
"""
# Define percentages of people being displaced at each mmi level
displacement_rate = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0.1, 8: 0.5, 9: 0.75, 10: 1.0}
# Extract input layers
intensity = get_hazard_layer(layers)
population = get_exposure_layer(layers)
question = get_question(intensity.get_name(), population.get_name(), self)
# Extract data grids
H = intensity.get_data() # Ground Shaking
P = population.get_data() # Population Density
# Calculate population affected by each MMI level
# FIXME (Ole): this range is 2-9. Should 10 be included?
mmi_range = range(2, 10)
number_of_exposed = {}
number_of_fatalities = {}
# Calculate fatality rates for observed Intensity values (H
# based on ITB power model
R = numpy.zeros(H.shape)
for mmi in mmi_range:
# Identify cells where MMI is in class i
mask = (H > mmi - 0.5) * (H <= mmi + 0.5)
# Count population affected by this shake level
I = numpy.where(mask, P, 0)
# Calculate expected number of fatalities per level
fatality_rate = numpy.power(10.0, x * mmi - y)
F = fatality_rate * I
# Sum up fatalities to create map
R += F
# Generate text with result for this study
# This is what is used in the real time system exposure table
number_of_exposed[mmi] = numpy.nansum(I.flat)
number_of_fatalities[mmi] = numpy.nansum(F.flat)
# Set resulting layer to zero when less than a threshold. This is to
# achieve transparency (see issue #126).
R[R < 1] = numpy.nan
# Total statistics
total = numpy.nansum(P.flat)
# Compute number of fatalities
fatalities = numpy.nansum(number_of_fatalities.values())
# Compute number of people displaced due to building collapse
displaced = 0
for mmi in mmi_range:
displaced += displacement_rate[mmi] * number_of_exposed[mmi]
displaced_women = displaced * 0.52 # Could be made province dependent
displaced_pregnant_women = displaced_women * 0.01387 # CHECK
# Generate impact report
table_body = [question]
# Add total fatality estimate
s = str(int(fatalities)).rjust(10)
table_body.append(TableRow([_("Number of fatalities"), s], header=True))
# Add total estimate of people displaced
s = str(int(displaced)).rjust(10)
table_body.append(TableRow([_("Number of people displaced"), s], header=True))
s = str(int(displaced_women)).rjust(10)
table_body.append(TableRow([_("Number of women displaced"), s], header=True))
s = str(int(displaced_pregnant_women)).rjust(10)
table_body.append(TableRow([_("Number of pregnant women displaced"), s], header=True))
table_body.append(TableRow(_("Action Checklist:"), header=True))
table_body.append(_("Are enough shelters available for %i women?") % displaced_women)
table_body.append(
_("Are enough facilities available to assist %i " "pregnant women?") % displaced_pregnant_women
)
table_body.append(TableRow(_("Notes:"), header=True))
table_body.append(_("Fatality model is from " "Institute of Teknologi Bandung 2012."))
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
map_title = _("Earthquake impact to population")
# Create new layer and return
L = Raster(
R,
projection=population.get_projection(),
geotransform=population.get_geotransform(),
keywords={
"impact_summary": impact_summary,
"total_population": total,
"total_fatalities": fatalities,
"impact_table": impact_table,
"map_title": map_title,
},
name=_("Estimated fatalities"),
style_info=style_info,
)
# Maybe return a shape file with contours instead
return L
# Create raster object with this style and return
R = Raster(I,
projection=inundation.get_projection(),
geotransform=inundation.get_geotransform(),
name='Penduduk yang %s' % (get_function_title(self)),
keywords={'impact_summary': impact_summary},
style_info=style_info)
return R
"""
from common.utilities import ugettext as _
# Flood population impact raster style
style_classes = [dict(colour='#FFFFFF', quantity=2, transparency=100),
dict(label=_('Low'), colour='#38A800', quantity=5,
transparency=0),
dict(colour='#79C900', quantity=10, transparency=0),
dict(colour='#CEED00', quantity=20, transparency=0),
dict(label=_('Medium'), colour='#FFCC00', quantity=50,
transparency=0),
dict(colour='#FF6600', quantity=100, transparency=0),
dict(colour='#FF0000', quantity=200, transparency=0),
dict(label=_('High'), colour='#7A0000', quantity=300,
transparency=0)]
flood_population_style = dict(target_field=None,
legend_title=None,
style_classes=style_classes)
# Earthquake fatality raster style
# FIXME (Ole): The styler cannot handle floats yet. Issue #126
def run(self, layers):
"""Flood impact to buildings (e.g. from Open Street Map)
"""
threshold = 1.0 # Flood threshold [m]
# Extract data
H = get_hazard_layer(layers) # Depth
E = get_exposure_layer(layers) # Building locations
question = get_question(H.get_name(),
E.get_name(),
self)
# Interpolate hazard level to building locations
if H.is_raster:
I = H.interpolate(E, attribute_name='depth')
hazard_type = 'depth'
else:
I = H.interpolate(E)
hazard_type = 'floodprone'
# Extract relevant exposure data
attribute_names = I.get_attribute_names()
attributes = I.get_data()
N = len(I)
# Calculate building impact
count = 0
buildings = {}
affected_buildings = {}
for i in range(N):
if hazard_type == 'depth':
# Get the interpolated depth
x = float(attributes[i]['depth'])
x = x > threshold
elif hazard_type == 'floodprone':
# Use interpolated polygon attribute
atts = attributes[i]
if 'FLOODPRONE' in atts:
res = atts['FLOODPRONE']
if res is None:
x = False
else:
x = res.lower() == 'yes'
else:
# If there isn't a flood prone attribute,
# assume that building is wet if inside polygon
# as flag by generic attribute AFFECTED
res = atts['Affected']
if res is None:
x = False
else:
x = res
else:
msg = (_('Unknown hazard type %s. '
'Must be either "depth" or "floodprone"')
% hazard_type)
raise Exception(msg)
# Count affected buildings by usage type if available
if 'type' in attribute_names:
usage = attributes[i]['type']
else:
usage = None
if usage is not None and usage != 0:
key = usage
else:
key = 'unknown'
if key not in buildings:
buildings[key] = 0
affected_buildings[key] = 0
# Count all buildings by type
buildings[key] += 1
if x is True:
# Count affected buildings by type
affected_buildings[key] += 1
# Count total affected buildings
count += 1
# Add calculated impact to existing attributes
attributes[i][self.target_field] = x
# Lump small entries and 'unknown' into 'other' category
for usage in buildings.keys():
x = buildings[usage]
if x < 25 or usage == 'unknown':
if 'other' not in buildings:
buildings['other'] = 0
affected_buildings['other'] = 0
buildings['other'] += x
affected_buildings['other'] += affected_buildings[usage]
del buildings[usage]
del affected_buildings[usage]
# Generate csv file of results
## fid = open('C:\dki_table_%s.csv' % H.get_name(), 'wb')
## fid.write('%s, %s, %s\n' % (_('Building type'),
## _('Temporarily closed'),
## _('Total')))
## fid.write('%s, %i, %i\n' % (_('All'), count, N))
# Generate simple impact report
table_body = [question,
TableRow([_('Building type'),
_('Temporarily closed'),
_('Total')],
header=True),
TableRow([_('All'), count, N])]
## fid.write('%s, %s, %s\n' % (_('Building type'),
## _('Temporarily closed'),
## _('Total')))
# Generate break down by building usage type is available
if 'type' in attribute_names:
# Make list of building types
building_list = []
for usage in buildings:
building_type = usage.replace('_', ' ')
# Lookup internationalised value if available
if building_type in internationalised_values:
building_type = internationalised_values[building_type]
else:
print ('WARNING: %s could not be translated'
% building_type)
building_list.append([building_type.capitalize(),
affected_buildings[usage],
buildings[usage]])
## fid.write('%s, %i, %i\n' % (building_type.capitalize(),
## affected_buildings[usage],
## buildings[usage]))
# Sort alphabetically
building_list.sort()
#table_body.append(TableRow([_('Building type'),
# _('Temporarily closed'),
# _('Total')], header=True))
table_body.append(TableRow(_('Breakdown by building type'),
header=True))
for row in building_list:
s = TableRow(row)
table_body.append(s)
## fid.close()
table_body.append(TableRow(_('Action Checklist:'), header=True))
table_body.append(TableRow(_('Are the critical facilities still '
'open?')))
table_body.append(TableRow(_('Notes:'), header=True))
assumption = _('Buildings are said to be flooded when ')
if hazard_type == 'depth':
assumption += _('flood levels exceed %.1f m') % threshold
else:
assumption += _('in areas marked as flood prone')
table_body.append(assumption)
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
map_title = _('Buildings inundated')
# Create style
style_classes = [dict(label=_('Not Flooded'), min=0, max=0,
colour='#1EFC7C', transparency=0, size=1),
dict(label=_('Flooded'), min=1, max=1,
colour='#F31A1C', transparency=0, size=1)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes)
# Create vector layer and return
V = Vector(data=attributes,
projection=I.get_projection(),
geometry=I.get_geometry(),
name=_('Estimated buildings affected'),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title},
style_info=style_info)
return V
