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Updated flood raster osm IF to use report mixin
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Christian Christelis committed Apr 1, 2015
1 parent eb9e587 commit dd88894
Showing 1 changed file with 72 additions and 153 deletions.
225 changes: 72 additions & 153 deletions safe/impact_functions/inundation/flood_raster_osm_building_impact/impact_function.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,22 +13,24 @@
__author__ = 'lucernae'

import logging
from collections import OrderedDict

from safe.impact_functions.base import ImpactFunction
from safe.impact_functions.inundation\
.flood_raster_osm_building_impact.metadata_definitions import \
FloodRasterBuildingMetadata
from safe.storage.vector import Vector
from safe.utilities.i18n import tr
from safe.common.utilities import format_int, get_osm_building_usage, verify
from safe.common.tables import Table, TableRow
from safe.common.utilities import get_osm_building_usage, verify
from safe.engine.interpolation import assign_hazard_values_to_exposure_data
from safe.impact_reports.building_exposure_report_mixin import (
BuildingExposureReportMixin)


LOGGER = logging.getLogger('InaSAFE')


class FloodRasterBuildingFunction(ImpactFunction):
class FloodRasterBuildingFunction(ImpactFunction, BuildingExposureReportMixin):
# noinspection PyUnresolvedReferences
"""Inundation raster impact on building data."""
_metadata = FloodRasterBuildingMetadata()
Expand All @@ -38,102 +40,48 @@ def __init__(self):
super(FloodRasterBuildingFunction, self).__init__()
self.target_field = 'INUNDATED'

def _tabulate(self, attribute_names, buildings, dry_buildings, dry_count,
inundated_buildings, inundated_count, question, threshold,
total_features, wet_buildings, wet_count):
table_body = [
question,
TableRow([tr('Building type'),
tr('Number Inundated'),
tr('Number of Wet Buildings'),
tr('Number of Dry Buildings'),
tr('Total')], header=True),
TableRow(
[tr('All'),
format_int(inundated_count),
format_int(wet_count),
format_int(dry_count),
format_int(total_features)])]
school_closed = 0
hospital_closed = 0
# Generate break down by building usage type if available
list_type_attribute = [
'TYPE', 'type', 'amenity', 'building_t', 'office',
'tourism', 'leisure', 'building']
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)
building_list.append([
building_type.capitalize(),
format_int(inundated_buildings[usage]),
format_int(wet_buildings[usage]),
format_int(dry_buildings[usage]),
format_int(buildings[usage])])

if usage.lower() == 'school':
school_closed = 0
school_closed += inundated_buildings[usage]
school_closed += wet_buildings[usage]
if usage.lower() == 'hospital':
hospital_closed = 0
hospital_closed += inundated_buildings[usage]
hospital_closed += wet_buildings[usage]

# Sort alphabetically
building_list.sort()

table_body.append(TableRow(tr('Breakdown by building type'),
header=True))
for row in building_list:
s = TableRow(row)
table_body.append(s)

# Action Checklist Section
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(TableRow(
tr('Are the critical facilities still open?')))
table_body.append(TableRow(
tr('Which structures have warning capacity (eg. sirens, speakers, '
'etc.)?')))
table_body.append(TableRow(
tr('Which buildings will be evacuation centres?')))
table_body.append(TableRow(
tr('Where will we locate the operations centre?')))
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?') % format_int(school_closed)))
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?') %
format_int(hospital_closed)))

# Notes Section
table_body.append(TableRow(tr('Notes'), header=True))
table_body.append(TableRow(
tr('Buildings are said to be inundated when flood levels '
'exceed %.1f m') % threshold))
table_body.append(TableRow(
tr('Buildings are said to be wet when flood levels '
'are greater than 0 m but less than %.1f m') % threshold))
table_body.append(TableRow(
tr('Buildings are said to be dry when flood levels '
'are less than 0 m')))
table_body.append(TableRow(
tr('Buildings are said to be closed if they are inundated or '
'wet')))
table_body.append(TableRow(
tr('Buildings are said to be open if they are dry')))
return table_body
def notes(self):
"""Return the notes section of the report.
:return: The notes that should be attached to this impact report.
:rtype: list
"""
threshold = self.parameters['threshold [m]']
return [
{
'content': tr('Notes'),
'header': True
},
{
'content': tr(
'Buildings are said to be inundated when flood levels '
'exceed %.1f m') % threshold
},
{
'content': tr(
'Buildings are said to be wet when flood levels '
'are greater than 0 m but less than %.1f m') % threshold
},
{
'content': tr(
'Buildings are said to be dry when flood levels '
'are 0 m or less.')
},
{
'content': tr(
'Buildings are said to be closed if they are '
'inundated or wet.')
},
{
'content': tr(
'Buildings are said to be open if they are dry.')
}]
@property
def _affected_categories(self):
return [tr('Number Inundated'), tr('Number of Wet Buildings')]

def _tabulate(self):
return self.generate_html_report()

def run(self, layers=None):
"""Flood impact to buildings (e.g. from Open Street Map).
Expand Down Expand Up @@ -166,7 +114,6 @@ def run(self, layers=None):
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
total_features = len(interpolated_layer)
buildings = {}

# The number of affected buildings

Expand All @@ -178,6 +125,14 @@ def run(self, layers=None):
wet_buildings = {}
dry_buildings = {}

# Building breakdown
self.buildings = {}
# Impacted building breakdown
self.affected_buildings = OrderedDict([
(tr('Number Inundated'), {}),
(tr('Number of Wet Buildings'), {}),
(tr('Number of Dry Buildings'), {})
])
for i in range(total_features):
# Get the interpolated depth
water_depth = float(features[i]['depth'])
Expand All @@ -190,67 +145,31 @@ def run(self, layers=None):

# Count affected buildings by usage type if available
usage = get_osm_building_usage(attribute_names, features[i])
if usage is not None and usage != 0:
key = usage
else:
if usage is None or usage == 0:
key = 'unknown'

if key not in buildings:
buildings[key] = 0
inundated_buildings[key] = 0
wet_buildings[key] = 0
dry_buildings[key] = 0
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)])

# Count all buildings by type
buildings[key] += 1
if inundated_status is 0:
# Count dry buildings by type
dry_buildings[key] += 1
# Count total dry buildings
dry_count += 1
if inundated_status is 1:
# Count inundated buildings by type
inundated_buildings[key] += 1
# Count total dry buildings
inundated_count += 1
if inundated_status is 2:
# Count wet buildings by type
wet_buildings[key] += 1
# Count total wet buildings
wet_count += 1
self.buildings[usage] += 1
# Add calculated impact to existing attributes
features[i][self.target_field] = inundated_status

affected_count = inundated_count + wet_count

category = [
tr('Number of Dry Buildings'),
tr('Number of Wet Buildings'),
tr('Number Inundated')][inundated_status]
self.affected_buildings[category][usage][
tr('Buildings Affected')] += 1

affected_count = self.total_affected_buildings
# 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
inundated_buildings['other'] = 0
wet_buildings['other'] = 0
dry_buildings['other'] = 0

buildings['other'] += x
inundated_buildings['other'] += inundated_buildings[usage]
wet_buildings['other'] += wet_buildings[usage]
dry_buildings['other'] += dry_buildings[usage]
del buildings[usage]
del inundated_buildings[usage]
del wet_buildings[usage]
del dry_buildings[usage]

self._consolidate_to_other()
# Generate simple impact report
table_body = self._tabulate(attribute_names, buildings, dry_buildings,
dry_count, inundated_buildings,
inundated_count, self.question, threshold,
total_features, wet_buildings, wet_count)

# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
impact_table = impact_summary = self._tabulate()

# Prepare impact layer
map_title = tr('Buildings inundated')
Expand Down

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