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# Source code for safe.impact_functions.generic.categorised_hazard_population

    import numpy
    from safe.impact_functions.core import FunctionProvider
    from safe.impact_functions.core import get_hazard_layer, get_exposure_layer
    from safe.impact_functions.core import get_question, get_function_title
    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

    class CategorisedHazardPopulationImpactFunction(FunctionProvider):
        """Plugin for impact of population as derived by categorised hazard

        :author AIFDR
        :rating 2
        :param requires category=='hazard' and \
                        subcategory=='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')

        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 '),
                                    '%i' % total_impact],
                                   header=True),
                          TableRow([tr('People in high hazard area '),
                                    '%i' % high],
                                   header=True),
                          TableRow([tr('People in medium hazard area '),
                                    '%i' % medium],
                                   header=True),
                          TableRow([tr('People in low hazard area'),
                                    '%i' % 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: %i') % 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

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