Source code for safe.impact_functions.volcanic.volcano_point_population.impact_function

# coding=utf-8
"""InaSAFE Disaster risk tool by Australian Aid - Volcano Point on
Population Impact Function.

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.

import numpy

from safe.impact_functions.bases.classified_vh_continuous_re import \
from safe.impact_functions.volcanic.volcano_point_population\
    .metadata_definitions import VolcanoPointPopulationFunctionMetadata
from safe.impact_functions.core import (
from safe.engine.core import buffer_points
from safe.engine.interpolation import assign_hazard_values_to_exposure_data
from import Raster
from safe.utilities.i18n import tr
from safe.common.utilities import (
from import add_needs_parameters, \
    filter_needs_parameters, get_needs_provenance_value
from safe.impact_reports.population_exposure_report_mixin import \
import safe.messaging as m
from safe.messaging import styles

[docs]class VolcanoPointPopulationFunction( ClassifiedVHContinuousRE, PopulationExposureReportMixin): """Impact Function for Volcano Point on Population.""" _metadata = VolcanoPointPopulationFunctionMetadata() def __init__(self): super(VolcanoPointPopulationFunction, self).__init__() # AG: Use the proper minimum needs, update the parameters self.parameters = add_needs_parameters(self.parameters) # TODO: alternatively to specifying the question here we should # TODO: consider changing the 'population' metadata concept to 'people' self.question = ( 'In the event of a volcano point how many people might be impacted' ) self.no_data_warning = False self.volcano_names = tr('Not specified in data')
[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 """ if get_needs_provenance_value(self.parameters) is None: needs_provenance = '' else: needs_provenance = tr(get_needs_provenance_value(self.parameters)) message = m.Message(style_class='container') message.add( m.Heading(tr('Notes and assumptions'), **styles.INFO_STYLE)) checklist = m.BulletedList() checklist.add(tr( 'Map shows buildings affected in each of the volcano buffered ' 'zones.')) checklist.add(tr( 'Total population in the analysis area: %s' ) % population_rounding(self.total_population)) checklist.add(tr( '<sup>1</sup>People need evacuation if they are within ' 'the volcanic hazard zones.')) names = tr('Volcanoes considered: %s.') % self.volcano_names checklist.add(names) checklist.add(needs_provenance) if self.no_data_warning: checklist.add(tr( 'The layers contained "no data" values. This missing data ' 'was carried through to the impact layer.')) checklist.add(tr( '"No data" values in the impact layer were treated as 0 ' 'when counting the affected or total population.')) checklist.add(tr( 'All values are rounded up to the nearest integer in ' 'order to avoid representing human lives as fractions.')) checklist.add(tr( 'Population rounding is applied to all population ' 'values, which may cause discrepancies when adding value.')) message.add(checklist) return message
[docs] def run(self): """Run volcano point population evacuation Impact Function. Counts number of people exposed to volcano event. :returns: Map of population exposed to the volcano hazard zone. The returned dict will include a table with number of people evacuated and supplies required. :rtype: dict :raises: * Exception - When hazard layer is not vector layer * RadiiException - When radii are not valid (they need to be monotonically increasing) """ self.validate() self.prepare() # Parameters radii = self.parameters['distances'].value # Get parameters from layer's keywords volcano_name_attribute = self.hazard.keyword('volcano_name_field') # Input checks if not self.hazard.layer.is_point_data: msg = ( 'Input hazard must be a polygon or point layer. I got %s with ' 'layer type %s' % (, self.hazard.layer.get_geometry_name())) raise Exception(msg) data_table = self.hazard.layer.get_data() # Use concentric circles category_title = 'Radius' centers = self.hazard.layer.get_geometry() rad_m = [x * 1000 for x in radii] # Convert to meters hazard_layer = buffer_points( centers, rad_m, category_title, data_table=data_table) # Get names of volcanoes considered if volcano_name_attribute in hazard_layer.get_attribute_names(): volcano_name_list = [] # Run through all polygons and get unique names for row in data_table: volcano_name_list.append(row[volcano_name_attribute]) volcano_names = '' for radius in volcano_name_list: volcano_names += '%s, ' % radius self.volcano_names = volcano_names[:-2] # Strip trailing ', ' # Run interpolation function for polygon2raster interpolated_layer, covered_exposure_layer = \ assign_hazard_values_to_exposure_data( hazard_layer, self.exposure.layer, attribute_name=self.target_field ) # Initialise affected population per categories for radius in rad_m: category = 'Distance %s km ' % format_int(radius) self.affected_population[category] = 0 if has_no_data(self.exposure.layer.get_data(nan=True)): self.no_data_warning = True # Count affected population per polygon and total for row in interpolated_layer.get_data(): # Get population at this location population = row[self.target_field] if not numpy.isnan(population): population = float(population) # Update population count for this category category = 'Distance %s km ' % format_int( row[category_title]) self.affected_population[category] += population # Count totals self.total_population = population_rounding( int(numpy.nansum(self.exposure.layer.get_data()))) self.minimum_needs = [ parameter.serialize() for parameter in filter_needs_parameters(self.parameters['minimum needs']) ] impact_table = impact_summary = self.html_report() # Create style colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600', '#FF0000', '#7A0000'] classes = create_classes( covered_exposure_layer.get_data().flat[:], len(colours)) interval_classes = humanize_class(classes) # Define style info for output polygons showing population counts style_classes = [] for i in xrange(len(colours)): style_class = dict() style_class['label'] = create_label(interval_classes[i]) if i == 1: label = create_label( interval_classes[i], tr('Low Population [%i people/cell]' % classes[i])) elif i == 4: label = create_label( interval_classes[i], tr('Medium Population [%i people/cell]' % classes[i])) elif i == 7: label = create_label( interval_classes[i], tr('High Population [%i people/cell]' % classes[i])) else: label = create_label(interval_classes[i]) if i == 0: transparency = 100 else: transparency = 0 style_class['label'] = label style_class['quantity'] = classes[i] style_class['colour'] = colours[i] style_class['transparency'] = transparency style_classes.append(style_class) # Override style info with new classes and name style_info = dict( target_field=None, style_classes=style_classes, style_type='rasterStyle') # For printing map purpose map_title = tr('People affected by the buffered point volcano') legend_title = tr('Population') legend_units = tr('(people per cell)') legend_notes = tr( 'Thousand separator is represented by %s' % get_thousand_separator()) # Create vector layer and return impact_layer = Raster( data=covered_exposure_layer.get_data(), projection=covered_exposure_layer.get_projection(), geotransform=covered_exposure_layer.get_geotransform(), name=tr('People affected by the buffered point volcano'), keywords={'impact_summary': impact_summary, 'impact_table': impact_table, 'target_field': self.target_field, 'map_title': map_title, 'legend_notes': legend_notes, 'legend_units': legend_units, 'legend_title': legend_title, 'total_needs': self.total_needs}, style_info=style_info) self._impact = impact_layer return impact_layer