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# Source code for safe.impact_functions.earthquake.padang_building_impact_model

    """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 assumed to fall the 9 classes below as described in
    the Geoscience Australia/ITB 2009 Padang earthquake
    survey (http://trove.nla.gov.au/work/38470066).

    Class Building Type                              Median (MMI)  Beta (MMI)
    -------------------------------------------------------------------------
    1     URM with river rock walls                        7.5     0.11
    2     URM with Metal Roof                              8.3     0.1
    3     Timber frame with masonry in-fill                8.8     0.11
    4     RC medium rise Frame with Masonry in-fill walls  8.4     0.05
    5     Timber frame with stucco in-fill                 9.2     0.11
    6     Concrete Shear wall  high rise* Hazus C2H        9.7     0.15
    7     RC low rise Frame with Masonry in-fill walls     9       0.08
    8     Confined Masonry                                 8.9     0.07
    9     Timber frame residential                        10.5     0.15
    """

    from safe.impact_functions.core import (FunctionProvider,
                                            get_hazard_layer,
                                            get_exposure_layer,
                                            get_question,
                                            format_int)
    from safe.storage.vector import Vector
    from safe.common.utilities import ugettext as tr
    from safe.common.numerics import lognormal_cdf
    from safe.common.tables import Table, TableRow
    from safe.impact_functions.mappings import osm2padang, sigab2padang
    from safe.engine.interpolation import assign_hazard_values_to_exposure_data

    # Damage curves for each of the nine classes derived from the Padang survey
    damage_curves = {1: dict(median=7.5, beta=0.11),
                     2: dict(median=8.3, beta=0.1),
                     3: dict(median=8.8, beta=0.11),
                     4: dict(median=8.4, beta=0.05),
                     5: dict(median=9.2, beta=0.11),
                     6: dict(median=9.7, beta=0.15),
                     7: dict(median=9.0, beta=0.08),
                     8: dict(median=8.9, beta=0.07),
                     9: dict(median=10.5, beta=0.15)}

    class PadangEarthquakeBuildingDamageFunction(FunctionProvider):
        """Risk plugin for Padang earthquake damage to buildings

        :param requires category=='hazard' and \
                        subcategory=='earthquake' and \
                        layertype=='raster' and \
                        unit=='MMI'

        :param requires category=='exposure' and \
                        subcategory=='structure' and \
                        layertype=='vector' and \
                        datatype in ['osm', 'itb', 'sigab']
        """

        title = tr('Be damaged depending on building type')

        def run(self, layers):
            """Risk plugin for Padang building survey
            """

            # Extract data
            H = get_hazard_layer(layers)    # Ground shaking
            E = get_exposure_layer(layers)  # Building locations

            question = get_question(H.get_name(),
                                    E.get_name(),
                                    self)

            # Map from different kinds of datasets to Padang vulnerability classes
            datatype = E.get_keywords()['datatype']
            vclass_tag = 'VCLASS'
            if datatype.lower() == 'osm':
                # Map from OSM attributes
                Emap = osm2padang(E)
            elif datatype.lower() == 'sigab':
                # Map from SIGAB attributes
                Emap = sigab2padang(E)
            else:
                Emap = E

            # Interpolate hazard level to building locations
            I = assign_hazard_values_to_exposure_data(H, Emap,
                                                      attribute_name='MMI')

            # Extract relevant numerical data
            attributes = I.get_data()
            N = len(I)

            # Calculate building damage
            count_high = count_medium = count_low = count_none = 0
            for i in range(N):
                mmi = float(attributes[i]['MMI'])

                building_type = Emap.get_data(vclass_tag, i)
                damage_params = damage_curves[building_type]
                beta = damage_params['beta']
                median = damage_params['median']
                percent_damage = lognormal_cdf(mmi,
                                               median=median,
                                               sigma=beta) * 100

                # Add calculated impact to existing attributes
                attributes[i][self.target_field] = percent_damage

                # Calculate statistics
                if percent_damage < 10:
                    count_none += 1

                if 10 <= percent_damage < 33:
                    count_low += 1

                if 33 <= percent_damage < 66:
                    count_medium += 1

                if 66 <= percent_damage:
                    count_high += 1

            # Generate impact report
            table_body = [question,
                          TableRow([tr('Buildings'), tr('Total')],
                                   header=True),
                          TableRow([tr('All'), N]),
                          TableRow([tr('No damage'), format_int(count_none)]),
                          TableRow([tr('Low damage'), format_int(count_low)]),
                          TableRow([tr('Medium damage'),
                                    format_int(count_medium)]),
                          TableRow([tr('High damage'), format_int(count_high)])]

            table_body.append(TableRow(tr('Notes'), header=True))
            table_body.append(tr('Levels of impact are defined by post 2009 '
                                'Padang earthquake survey conducted by Geoscience '
                                'Australia and Institute of Teknologi Bandung.'))
            table_body.append(tr('Unreinforced masonry is assumed where no '
                                'structural information is available.'))

            impact_summary = Table(table_body).toNewlineFreeString()
            impact_table = impact_summary
            map_title = tr('Earthquake damage to buildings')

            # Create style
            style_classes = [dict(label=tr('No damage'), min=0, max=10,
                                  colour='#00ff00', transparency=0),
                             dict(label=tr('Low damage'), min=10, max=33,
                                  colour='#ffff00', transparency=0),
                             dict(label=tr('Medium damage'), min=33, max=66,
                                  colour='#ffaa00', transparency=0),
                             dict(label=tr('High damage'), min=66, max=100,
                                  colour='#ff0000', transparency=0)]
            style_info = dict(target_field=self.target_field,
                              style_classes=style_classes)

            # Create vector layer and return
            V = Vector(data=attributes,
                       projection=E.get_projection(),
                       geometry=E.get_geometry(),
                       name='Estimated pct damage',
                       keywords={'impact_summary': impact_summary,
                                 'impact_table': impact_table,
                                 'map_title': map_title,
                                 'target_field': self.target_field},
                       style_info=style_info)
            return V

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