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from safe.impact_functions.core import (FunctionProvider,
from import Vector
from safe.common.utilities import ugettext as tr
from safe.common.tables import Table, TableRow
from safe.engine.interpolation import assign_hazard_values_to_exposure_data
import logging
LOGGER = logging.getLogger('InaSAFE')
class FloodBuildingImpactFunction(FunctionProvider):
"""Inundation impact on building data
:author Ole Nielsen, Kristy van Putten
# this rating below is only for testing a function, not the real one
:rating 0
:param requires category=='hazard' and \
subcategory in ['flood', 'tsunami']
:param requires category=='exposure' and \
subcategory=='structure' and \
# Function documentation
target_field = 'INUNDATED'
title = tr('Be flooded')
synopsis = tr('To assess the impacts of inundation on building footprints '
'originating from OpenStreetMap (OSM).')
actions = tr('Provide details about where critical response areas are')
# citations must be a list
citations = [tr('Hutchings, Field & Parks. Assessment of Flood impacts on '
'buildings. Impact. Vol 66(2). 2012')]
detailed_description = tr('This is an area for free form text where a'
'detailed description of the methodology used is given.')
permissible_hazard_input = tr('A raster layer where each cell represents '
'flood depth, or a vector polygon layer where each polygon represents '
'an inundated area. The following attributes are recognised '
'(in order): Flooded (True or False), FLOODPRONE (Yes or No) and '
'Affected (True or False).')
permissible_exposure_input = tr('vector polygon layer extracted from OSM '
'where each polygon represents the footprint of a building.')
limitation = tr('Lorem ipsum limitation')
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(),
# Determine attribute name for hazard levels
if H.is_raster:
mode = 'grid'
hazard_attribute = 'depth'
mode = 'regions'
hazard_attribute = None
# Interpolate hazard level to building locations
I = assign_hazard_values_to_exposure_data(H, E,
# 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 mode == 'grid':
# Get the interpolated depth
x = float(attributes[i]['depth'])
x = x >= threshold
elif mode == 'regions':
# Use interpolated polygon attribute
atts = attributes[i]
# FIXME (Ole): Need to agree whether to use one or the
# other as this can be very confusing!
# For now look for 'affected' first
if 'affected' in atts:
# E.g. from flood forecast
# Assume that building is wet if inside polygon
# as flagged by attribute Flooded
res = atts['affected']
if res is None:
x = False
x = bool(res)
#if x:
# print 'Got affected', x
elif 'FLOODPRONE' in atts:
res = atts['FLOODPRONE']
if res is None:
x = False
x = res.lower() == 'yes'
# Check the default attribute assigned for points
# covered by a polygon
if res is None:
x = False
x = res
# there is no flood related attribute
msg = ('No flood related attribute found in %s. '
'I was looking for either "affected", "FLOODPRONE" '
'or "inapolygon". The latter should have been '
'automatically set by call to '
'assign_hazard_values_to_exposure_data(). '
'Sorry I can\'t help more.')
raise Exception(msg)
msg = (tr('Unknown hazard type %s. '
'Must be either "depth" or "grid"')
% mode)
raise Exception(msg)
# Count affected buildings by usage type if available
if 'type' in attribute_names:
usage = attributes[i]['type']
usage = None
if 'amenity' in attribute_names and (usage is None or usage == 0):
usage = attributes[i]['amenity']
if 'building_t' in attribute_names and (usage is None
or usage == 0):
usage = attributes[i]['building_t']
if 'office' in attribute_names and (usage is None or usage == 0):
usage = attributes[i]['office']
if 'tourism' in attribute_names and (usage is None or usage == 0):
usage = attributes[i]['tourism']
if 'leisure' in attribute_names and (usage is None or usage == 0):
usage = attributes[i]['leisure']
if 'building' in attribute_names and (usage is None or usage == 0):
usage = attributes[i]['building']
if usage == 'yes':
usage = 'building'
#LOGGER.debug('usage ')
if usage is not None and usage != 0:
key = usage
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' % (tr('Building type'),
## tr('Temporarily closed'),
## tr('Total')))
## fid.write('%s, %i, %i\n' % (tr('All'), count, N))
# Generate simple impact report
table_body = [question,
TableRow([tr('Building type'),
tr('Number flooded'),
TableRow([tr('All'), format_int(count), format_int(N)])]
## fid.write('%s, %s, %s\n' % (tr('Building type'),
## tr('Temporarily closed'),
## tr('Total')))
school_closed = 0
hospital_closed = 0
# Generate break down by building usage type is available
list_type_attribute = ['type',
intersect_type = set(attribute_names) & set(list_type_attribute)
if len(intersect_type) > 0:
# Make list of building types
building_list = []
for usage in buildings:
building_type = usage.replace('_', ' ')
# Lookup internationalised value if available
building_type = tr(building_type)
# print ('WARNING: %s could not be translated'
# % building_type)
# FIXME (Sunni) : I change affected_buildings[usage] to string
# because it will be replace with &nbsp in html
if building_type == 'school':
school_closed = affected_buildings[usage]
if building_type == 'hospital':
hospital_closed = affected_buildings[usage]
## fid.write('%s, %i, %i\n' % (building_type.capitalize(),
## affected_buildings[usage],
## buildings[usage]))
# Sort alphabetically
#table_body.append(TableRow([tr('Building type'),
# tr('Temporarily closed'),
# tr('Total')], header=True))
table_body.append(TableRow(tr('Breakdown by building type'),
for row in building_list:
s = TableRow(row)
## fid.close()
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(TableRow(tr('Are the critical facilities still '
table_body.append(TableRow(tr('Which structures have warning capacity '
'(eg. sirens, speakers, etc.)?')))
table_body.append(TableRow(tr('Which buildings will be evacuation '
table_body.append(TableRow(tr('Where will we locate the operations '
table_body.append(TableRow(tr('Where will we locate warehouse and/or '
'distribution centres?')))
if school_closed > 0:
table_body.append(TableRow(tr('Where will the students from the %s'
' closed schools go to study?') %
if hospital_closed > 0:
table_body.append(TableRow(tr('Where will the patients from the %s'
' closed hospitals go for treatment '
'and how will we transport them?') %
table_body.append(TableRow(tr('Notes'), header=True))
assumption = tr('Buildings are said to be flooded when ')
if mode == 'grid':
assumption += tr('flood levels exceed %.1f m') % threshold
assumption += tr('in regions marked as affected')
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
map_title = tr('Buildings inundated')
# Create style
style_classes = [dict(label=tr('Not Flooded'), min=0, max=0,
colour='#1EFC7C', transparency=0, size=1),
dict(label=tr('Flooded'), min=1, max=1,
colour='#F31A1C', transparency=0, size=1)]
style_info = dict(target_field=self.target_field,
# Create vector layer and return
V = Vector(data=attributes,
name=tr('Estimated buildings affected'),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'target_field': self.target_field},
return V
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