Source code for safe.engine.impact_functions_for_testing.BNPB_earthquake_guidelines

"""Impact function based on Padang 2009 post earthquake survey

This impact function estimates percentual damage to buildings as a
function of ground shaking measured in MMI.
Buildings are currently assumed to be represented in OpenStreetMap with
attributes collected as during the July 2011 Indonesian mapping competition.

This impact function maps the OSM buildings into 2 classes:
Unreinforced masonry (URM) and reinforced masonry (RM) according to
the guidelines.
"""

import numpy

from safe.impact_functions.core import (FunctionProvider,
                                        get_hazard_layer,
                                        get_exposure_layer,
                                        format_int)
from safe.storage.vector import Vector
from safe.common.utilities import ugettext as tr
from safe.impact_functions.mappings import osm2bnpb
from safe.impact_functions.mappings import unspecific2bnpb
from safe.impact_functions.mappings import sigab2bnpb
from safe.engine.interpolation import assign_hazard_values_to_exposure_data


# Damage 'curves' for the two vulnerability classes
damage_parameters = {'URM': [6, 7],
                     'RM': [6, 8]}


[docs]class EarthquakeGuidelinesFunction(FunctionProvider): """Risk plugin for BNPB guidelines for earthquake damage to buildings :param requires category=='hazard' and \ subcategory.startswith('earthquake') and \ layertype=='raster' and \ unit=='MMI' :param requires category=='exposure' and \ subcategory=='structure' and \ layertype=='vector' """ vclass_tag = 'VCLASS' target_field = 'DMGLEVEL'
[docs] 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 = assign_hazard_values_to_exposure_data(H, 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&#58;</td><td>%s</td></tr>' ' <tr><td>%s (10-25%%)&#58;</td><td>%s</td></tr>' ' <tr><td>%s (25-50%%)&#58;</td><td>%s</td></tr>' ' <tr><td>%s (50-100%%)&#58;</td><td>%s</td></tr>' % (tr('Buildings'), tr('Total'), tr('All'), format_int(N), tr('Low damage'), format_int(count1), tr('Medium damage'), format_int(count2), tr('High damage'), format_int(count3))) impact_summary += (' <tr><td>%s (NaN)&#58;</td><td>%s</td></tr>' % ('Unknown', format_int(count_unknown))) impact_summary += '</table>' # Create style style_classes = [dict(label=tr('Low damage'), min=0.5, max=1.5, colour='#fecc5c', transparency=0), dict(label=tr('Medium damage'), min=1.5, max=2.5, colour='#fd8d3c', transparency=0), dict(label=tr('High damage'), min=2.5, max=3.5, colour='#f31a1c', 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 damage level', keywords={'impact_summary': impact_summary}, style_info=style_info) return V