Source code for safe.impact_functions.inundation.flood_raster_osm_building_impact.impact_function

# coding=utf-8
"""InaSAFE Disaster risk tool by Australian Aid - Flood Raster Impact on OSM

Contact : [email protected]

.. note:: This program is free software; you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
     the Free Software Foundation; either version 2 of the License, or
     (at your option) any later version.


__author__ = 'lucernae'

import logging
from collections import OrderedDict

from safe.impact_functions.inundation\
    .flood_raster_osm_building_impact.metadata_definitions import \
from safe.impact_functions.bases.continuous_rh_classified_ve import \
from import Vector
from safe.utilities.i18n import tr
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 (
import safe.messaging as m
from safe.messaging import styles
from safe.common.exceptions import KeywordNotFoundError
LOGGER = logging.getLogger('InaSAFE')

[docs]class FloodRasterBuildingFunction( ContinuousRHClassifiedVE, BuildingExposureReportMixin): # noinspection PyUnresolvedReferences """Inundation raster impact on building data.""" _metadata = FloodRasterBuildingMetadata() def __init__(self): """Constructor (calls ctor of base class).""" super(FloodRasterBuildingFunction, self).__init__()
[docs] def notes(self): """Return the notes section of the report. :return: The notes that should be attached to this impact report. :rtype: safe.messaging.Message """ message = m.Message(style_class='container') threshold = self.parameters['threshold'].value message.add( m.Heading(tr('Notes and assumptions'), **styles.INFO_STYLE)) checklist = m.BulletedList() checklist.add(tr( 'Buildings are flooded when flood levels ' 'exceed %.1f m') % threshold) checklist.add(tr( 'Buildings are wet when flood levels ' 'are greater than 0 m but less than %.1f m') % threshold) checklist.add(tr( 'Buildings are dry when flood levels are 0 m.')) checklist.add(tr( 'Buildings are closed if they are flooded or wet.')) checklist.add(tr('Buildings are open if they are dry.')) message.add(checklist) return message
@property def _affected_categories(self): """Overwriting the affected categories, since 'unaffected' are counted. :returns: The categories that equal effected. :rtype: list """ return [tr('Flooded'), tr('Wet')]
[docs] def run(self): """Flood impact to buildings (e.g. from Open Street Map).""" self.validate() self.prepare() threshold = self.parameters['threshold'].value # Flood threshold [m] verify(isinstance(threshold, float), 'Expected thresholds to be a float. Got %s' % str(threshold)) # Determine attribute name for hazard levels hazard_attribute = 'depth' # Interpolate hazard level to building locations interpolated_layer = assign_hazard_values_to_exposure_data( self.hazard.layer, self.exposure.layer, attribute_name=hazard_attribute) # Extract relevant exposure data attribute_names = interpolated_layer.get_attribute_names() features = interpolated_layer.get_data() total_features = len(interpolated_layer) # but use the old get_osm_building_usage try: structure_class_field = self.exposure.keyword( 'structure_class_field') except KeywordNotFoundError: structure_class_field = None # Building breakdown self.buildings = {} # Impacted building breakdown self.affected_buildings = OrderedDict([ (tr('Flooded'), {}), (tr('Wet'), {}), (tr('Dry'), {}) ]) for i in range(total_features): # Get the interpolated depth water_depth = float(features[i]['depth']) if water_depth <= 0: inundated_status = 0 # dry elif water_depth >= threshold: inundated_status = 1 # inundated else: inundated_status = 2 # wet # Count affected buildings by usage type if available if (structure_class_field in attribute_names and structure_class_field): usage = features[i].get(structure_class_field, None) else: usage = get_osm_building_usage( attribute_names, features[i]) if usage is None or usage == 0: usage = 'unknown' 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 self.buildings[usage] += 1 # Add calculated impact to existing attributes features[i][self.target_field] = inundated_status category = [ tr('Dry'), tr('Flooded'), tr('Wet')][inundated_status] self.affected_buildings[category][usage][ tr('Buildings Affected')] += 1 # Lump small entries and 'unknown' into 'other' category self._consolidate_to_other() # Generate simple impact report impact_table = impact_summary = self.html_report() # For printing map purpose map_title = tr('Flooded buildings') legend_title = tr('Flooded structure status') legend_units = tr('(flooded, wet, or dry)') style_classes = [ dict( label=tr('Dry (<= 0 m)'), value=0, colour='#1EFC7C', transparency=0, size=1 ), dict( label=tr('Wet (0 m - %.1f m)') % threshold, value=2, colour='#FF9900', transparency=0, size=1 ), dict( label=tr('Flooded (>= %.1f m)') % threshold, value=1, colour='#F31A1C', transparency=0, size=1 )] style_info = dict( target_field=self.target_field, style_classes=style_classes, style_type='categorizedSymbol') vector_layer = Vector( data=features, projection=interpolated_layer.get_projection(), geometry=interpolated_layer.get_geometry(), name=tr('Estimated buildings affected'), keywords={ 'impact_summary': impact_summary, 'impact_table': impact_table, 'target_field': self.target_field, 'map_title': map_title, 'legend_title': legend_title, 'legend_units': legend_units, 'buildings_total': total_features, 'buildings_affected': self.total_affected_buildings}, style_info=style_info) # Create vector layer and return self._impact = vector_layer return vector_layer