Source code for safe.impact_functions.generic.categorised_hazard_population

import numpy
from safe.impact_functions.core import (FunctionProvider,
                                        get_hazard_layer,
                                        get_exposure_layer,
                                        get_question,
                                        get_function_title,
                                        format_int)
from safe.impact_functions.styles import flood_population_style as style_info
from safe.storage.raster import Raster
from safe.common.utilities import ugettext as tr
from safe.common.tables import Table, TableRow


[docs]class CategorisedHazardPopulationImpactFunction(FunctionProvider): """Plugin for impact of population as derived by categorised hazard :author AIFDR :rating 2 :param requires category=='hazard' and \ unit=='normalised' and \ layertype=='raster' :param requires category=='exposure' and \ subcategory=='population' and \ layertype=='raster' :citation citation1 :citation citation2 \ next citation2 :detail The Detail """ title = tr('Be impacted')
[docs] def run(self, layers): """Plugin for impact of population as derived by categorised hazard Input layers: List of layers expected to contain H: Raster layer of categorised hazard P: Raster layer of population data Counts number of people exposed to each category of the hazard Return Map of population exposed to high category Table with number of people in each category """ # The 3 category high_t = 1 medium_t = 0.66 low_t = 0.34 # Identify hazard and exposure layers inundation = get_hazard_layer(layers) # Categorised Hazard population = get_exposure_layer(layers) # Population Raster question = get_question(inundation.get_name(), population.get_name(), self) # Extract data as numeric arrays C = inundation.get_data(nan=0.0) # Category # Calculate impact as population exposed to each category P = population.get_data(nan=0.0, scaling=True) H = numpy.where(C == high_t, P, 0) M = numpy.where(C > medium_t, P, 0) L = numpy.where(C < low_t, P, 0) # Count totals total = int(numpy.sum(P)) high = int(numpy.sum(H)) medium = int(numpy.sum(M)) - int(numpy.sum(H)) low = int(numpy.sum(L)) - int(numpy.sum(M)) total_impact = high + medium + low # Don't show digits less than a 1000 if total > 1000: total = total // 1000 * 1000 if total_impact > 1000: total_impact = total_impact // 1000 * 1000 if high > 1000: high = high // 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([tr('People impacted '), '%s' % format_int(total_impact)], header=True), TableRow([tr('People in high hazard area '), '%s' % format_int(high)], header=True), TableRow([tr('People in medium hazard area '), '%s' % format_int(medium)], header=True), TableRow([tr('People in low hazard area'), '%s' % format_int(low)], header=True)] ## TableRow([tr('Needs per week'), tr('Total')], ## header=True), ## [tr('Rice [kg]'), int(rice)], ## [tr('Drinking Water [l]'), int(drinking_water)], ## [tr('Clean Water [l]'), int(water)], ## [tr('Family Kits'), int(family_kits)], ## [tr('Toilets'), int(toilets)]] impact_table = Table(table_body).toNewlineFreeString() # Extend impact report for on-screen display table_body.extend([TableRow(tr('Notes'), header=True), tr('Map shows population density in high or medium ' 'hazard area'), tr('Total population: %s') % format_int(total)]) ## tr('Minimum needs are defined in BNPB ' ## 'regulation 7/2008')]) impact_summary = Table(table_body).toNewlineFreeString() map_title = tr('People in high hazard areas') # 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(M.flat[:]), numpy.nanmax(M.flat[:]), 8) # Modify labels in existing flood style to show quantities style_classes = style_info['style_classes'] style_classes[1]['label'] = tr('Low [%i people/cell]') % classes[1] style_classes[4]['label'] = tr('Medium [%i people/cell]') % classes[4] style_classes[7]['label'] = tr('High [%i people/cell]') % classes[7] style_info['legend_title'] = tr('Population Density') # Create raster object and return R = Raster(M, projection=inundation.get_projection(), geotransform=inundation.get_geotransform(), name=tr('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