def test_humanize_class3(self):
     """Test humanize class 3
     First class interval < 1
     Interval < 1
     """
     my_array = [0.1, 0.5, 0.9]
     my_result_class = humanize_class(my_array)
     my_expected_class = [('0', '0.1'),
                          ('0.1', '0.5'),
                          ('0.5', '0.9')]
     my_msg = 'got: %s expect: %s' % (my_result_class, my_expected_class)
     # print_class(my_array, my_result_class, my_expected_class)
     assert my_result_class == my_expected_class, my_msg

 def test_humanize_class2(self):
     """Test humanize class 2
     First class interval > 1
     Interval > 1
     """
     my_array = [1.1, 5754.1, 11507.1]
     my_result_class = humanize_class(my_array)
     my_expected_class = [('0', '1'),
                          ('1', '5,754'),
                          ('5,754', '11,507')]
     # print_class(my_array, my_result_class, my_expected_class)
     my_msg = 'got: %s expect: %s' % (my_result_class, my_expected_class)
     assert my_result_class == my_expected_class, my_msg

 def test_humanize_class4(self):
     """Test humanize class 4
     First class interval > 1
     Interval < 1
     """
     my_array = [7.1, 7.5, 7.9]
     my_result_class = humanize_class(my_array)
     my_expected_class = [('0', '7.1'),
                          ('7.1', '7.5'),
                          ('7.5', '7.9')]
     my_msg = 'got: ' + str(my_result_class)
     my_msg += ' expect: ' + str(my_expected_class)
     print_class(my_array, my_result_class, my_expected_class)
     assert my_result_class == my_expected_class, my_msg

 def test_humanize_class6(self):
     """Test humanize class 5
         First class interval < 1
         Interval > 1
         """
     my_array = [0, 6.1, 7.2, 8.3, 9.4, 10.5]
     my_result_class = humanize_class(my_array)
     my_expected_class = [('0', '6'),
                          ('6', '7'),
                          ('7', '8'),
                          ('8', '9'),
                          ('9', '11')]
     # print_class(my_array, my_result_class, my_expected_class)
     my_msg = 'got: %s expect: %s' % (my_result_class, my_expected_class)
     assert my_result_class == my_expected_class, my_msg

 def test_humanize_class(self):
     """Test humanize class
     First class interval < 1
     Interval > 1
     """
     my_array = [0.1, 1.2, 2.3, 3.4, 4.5]
     my_result_class = humanize_class(my_array)
     my_expected_class = [('0', '0.1'),
                          ('0.1', '1.2'),
                          ('1.2', '2.3'),
                          ('2.3', '3.4'),
                          ('3.4', '4.5')]
     print_class(my_array, my_result_class, my_expected_class)
     my_msg = 'got: ' + str(my_result_class)
     my_msg += ' expect: ' + str(my_expected_class)
     assert my_result_class == my_expected_class, my_msg

 def test_humanize_class(self):
     """Test humanize class
     First class interval < 1
     Interval > 1
     """
     array = [0.1, 1.2, 2.3, 3.4, 4.5]
     result_class = humanize_class(array)
     expected_class = [
         ('0', '0.1'),
         ('0.1', '1.2'),
         ('1.2', '2.3'),
         ('2.3', '3.4'),
         ('3.4', '4.5')
     ]
     # print_class(array, result_class, expected_class)
     self.assertEqual(result_class, expected_class)

    def run(self):
        """Plugin for impact of population as derived by classified hazard.

        Counts number of people exposed to each class of the hazard

        Return
          Map of population exposed to high class
          Table with number of people in each class
        """
        self.validate()
        self.prepare()

        # The 3 classes
        # TODO (3.2): shouldnt these be defined in keywords rather? TS
        categorical_hazards = self.parameters['Categorical hazards'].value
        low_class = categorical_hazards[0].value
        medium_class = categorical_hazards[1].value
        high_class = categorical_hazards[2].value

        # The classes must be different to each other
        unique_classes_flag = all(
            x != y for x, y in list(
                itertools.combinations(
                    [low_class, medium_class, high_class], 2)))
        if not unique_classes_flag:
            raise FunctionParametersError(
                'There is hazard class that has the same value with other '
                'class. Please check the parameters.')

        # Extract data as numeric arrays
        hazard_data = self.hazard.layer.get_data(nan=True)  # Class
        if has_no_data(hazard_data):
            self.no_data_warning = True

        # Calculate impact as population exposed to each class
        population = self.exposure.layer.get_data(scaling=True)

        # Get all population data that falls in each hazard class
        high_hazard_population = numpy.where(
            hazard_data == high_class, population, 0)
        medium_hazard_population = numpy.where(
            hazard_data == medium_class, population, 0)
        low_hazard_population = numpy.where(
            hazard_data == low_class, population, 0)
        affected_population = (
            high_hazard_population + medium_hazard_population +
            low_hazard_population)

        # Carry the no data values forward to the impact layer.
        affected_population = numpy.where(
            numpy.isnan(population),
            numpy.nan,
            affected_population)
        affected_population = numpy.where(
            numpy.isnan(hazard_data),
            numpy.nan,
            affected_population)

        # Count totals
        self.total_population = int(numpy.nansum(population))
        self.affected_population[
            tr('Population in High hazard class areas')] = int(
                numpy.nansum(high_hazard_population))
        self.affected_population[
            tr('Population in Medium hazard class areas')] = int(
                numpy.nansum(medium_hazard_population))
        self.affected_population[
            tr('Population in Low hazard class areas')] = int(
                numpy.nansum(low_hazard_population))
        self.unaffected_population = (
            self.total_population - self.total_affected_population)

        # check for zero impact
        if self.total_affected_population == 0:
            message = no_population_impact_message(self.question)
            raise ZeroImpactException(message)

        self.minimum_needs = [
            parameter.serialize() for parameter in
            self.parameters['minimum needs']
        ]

        total_needs = self.total_needs
        impact_table = impact_summary = self.html_report()

        # Create style
        colours = [
            '#FFFFFF', '#38A800', '#79C900', '#CEED00',
            '#FFCC00', '#FF6600', '#FF0000', '#7A0000']
        classes = create_classes(affected_population.flat[:], len(colours))
        interval_classes = humanize_class(classes)
        style_classes = []

        for i in xrange(len(colours)):
            style_class = dict()
            if i == 1:
                label = create_label(
                    interval_classes[i],
                    tr('Low Population [%i people/cell]' % classes[i]))
            elif i == 4:
#.........这里部分代码省略.........

    def run(self):
        """Risk plugin for tsunami population evacuation.

        Counts number of people exposed to tsunami levels exceeding
        specified threshold.

        :returns: Map of population exposed to tsunami levels exceeding the
            threshold. Table with number of people evacuated and supplies
            required.
        :rtype: tuple
        """
        self.validate()
        self.prepare()

        # Determine depths above which people are regarded affected [m]
        # Use thresholds from inundation layer if specified
        thresholds = self.parameters['thresholds'].value

        verify(
            isinstance(thresholds, list),
            'Expected thresholds to be a list. Got %s' % str(thresholds))

        # Extract data as numeric arrays
        data = self.hazard.layer.get_data(nan=True)  # Depth
        if has_no_data(data):
            self.no_data_warning = True

        # Calculate impact as population exposed to depths > max threshold
        population = self.exposure.layer.get_data(nan=True, scaling=True)
        if has_no_data(population):
            self.no_data_warning = True

        # merely initialize
        impact = None
        for i, lo in enumerate(thresholds):
            if i == len(thresholds) - 1:
                # The last threshold
                thresholds_name = tr(
                    'People in >= %.1f m of water') % lo
                impact = medium = numpy.where(data >= lo, population, 0)
                self.impact_category_ordering.append(thresholds_name)
                self._evacuation_category = thresholds_name
            else:
                # Intermediate thresholds
                hi = thresholds[i + 1]
                thresholds_name = tr(
                    'People in %.1f m to %.1f m of water' % (lo, hi))
                medium = numpy.where((data >= lo) * (data < hi), population, 0)

            # Count
            val = int(numpy.nansum(medium))
            self.affected_population[thresholds_name] = val

        # Carry the no data values forward to the impact layer.
        impact = numpy.where(numpy.isnan(population), numpy.nan, impact)
        impact = numpy.where(numpy.isnan(data), numpy.nan, impact)

        # Count totals
        self.total_population = int(numpy.nansum(population))
        self.unaffected_population = (
            self.total_population - self.total_affected_population)

        self.minimum_needs = [
            parameter.serialize() for parameter in
            filter_needs_parameters(self.parameters['minimum needs'])
        ]

        impact_table = impact_summary = self.html_report()

        # check for zero impact
        if numpy.nanmax(impact) == 0 == numpy.nanmin(impact):
            message = m.Message()
            message.add(self.question)
            message.add(tr('No people in %.1f m of water') % thresholds[-1])
            message = message.to_html(suppress_newlines=True)
            raise ZeroImpactException(message)

        # Create style
        colours = [
            '#FFFFFF', '#38A800', '#79C900', '#CEED00',
            '#FFCC00', '#FF6600', '#FF0000', '#7A0000']
        classes = create_classes(impact.flat[:], len(colours))
        interval_classes = humanize_class(classes)
        style_classes = []

        for i in xrange(len(colours)):
            style_class = dict()
            if i == 1:
                label = create_label(interval_classes[i], 'Low')
            elif i == 4:
                label = create_label(interval_classes[i], 'Medium')
            elif i == 7:
                label = create_label(interval_classes[i], 'High')
            else:
                label = create_label(interval_classes[i])
            style_class['label'] = label
            style_class['quantity'] = classes[i]
            if i == 0:
                transparency = 100
            else:
#.........这里部分代码省略.........

#.........这里部分代码省略.........

                # Update building count for each category
                category = new_data_table[poly_id][category_title]
                categories[category] += 1

        # Count totals
        total = len(exposure_layer)

        # Generate simple impact report
        blank_cell = ''
        table_body = [question,
                      TableRow([tr('Volcanoes considered'),
                                '%s' % volcano_names, blank_cell],
                               header=True),
                      TableRow([tr('Distance [km]'), tr('Total'),
                                tr('Cumulative')],
                               header=True)]

        cumulative = 0
        for name in category_names:
            # prevent key error
            count = categories.get(name, 0)
            cumulative += count
            if is_point_data:
                name = int(name) / 1000
            table_body.append(TableRow([name, format_int(count),
                                        format_int(cumulative)]))

        table_body.append(TableRow(tr('Map shows buildings affected in '
                                      'each of volcano hazard polygons.')))
        impact_table = Table(table_body).toNewlineFreeString()

        # Extend impact report for on-screen display
        table_body.extend([TableRow(tr('Notes'), header=True),
                           tr('Total number of buildings %s in the viewable '
                              'area') % format_int(total),
                           tr('Only buildings available in OpenStreetMap '
                              'are considered.')])

        impact_summary = Table(table_body).toNewlineFreeString()
        building_counts = [x[self.target_field] for x in new_data_table]

        if max(building_counts) == 0 == min(building_counts):
            table_body = [
                question,
                TableRow([tr('Number of buildings affected'),
                          '%s' % format_int(cumulative), blank_cell],
                         header=True)]
            my_message = Table(table_body).toNewlineFreeString()
            raise ZeroImpactException(my_message)

        # Create style
        colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
                   '#FFCC00', '#FF6600', '#FF0000', '#7A0000']

        # Create Classes
        classes = create_classes(building_counts, len(colours))
        # Create Interval Classes
        interval_classes = humanize_class(classes)

        style_classes = []
        for i in xrange(len(colours)):
            style_class = dict()
            style_class['label'] = create_label(interval_classes[i])
            if i == 0:
                style_class['min'] = 0
            else:
                style_class['min'] = classes[i - 1]
            style_class['transparency'] = 30
            style_class['colour'] = colours[i]
            style_class['max'] = classes[i]
            style_classes.append(style_class)

        # Override style info with new classes and name
        style_info = dict(target_field=self.target_field,
                          style_classes=style_classes,
                          style_type='graduatedSymbol')

        # For printing map purpose
        map_title = tr('Buildings affected by volcanic hazard zone')
        legend_notes = tr('Thousand separator is represented by %s' %
                          get_thousand_separator())
        legend_units = tr('(building)')
        legend_title = tr('Building count')

        # Create vector layer and return
        impact_layer = Vector(
            data=new_data_table,
            projection=hazard_layer.get_projection(),
            geometry=hazard_layer.get_geometry(as_geometry_objects=True),
            name=tr('Buildings affected by volcanic hazard zone'),
            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},
            style_info=style_info)
        return impact_layer

#.........这里部分代码省略.........
        categories = {}
        for attr in new_attributes:
            attr[self.target_field] = 0
            cat = attr[category_title]
            categories[cat] = 0

        # Count impacted building per polygon and total
        for attr in P.get_data():

            # Update building count for associated polygon
            poly_id = attr['polygon_id']
            if poly_id is not None:
                new_attributes[poly_id][self.target_field] += 1

                # Update building count for each category
                cat = new_attributes[poly_id][category_title]
                categories[cat] += 1

        # Count totals
        total = len(my_exposure)

        # Generate simple impact report
        blank_cell = ''
        table_body = [question,
                      TableRow([tr('Volcanos considered'),
                                '%s' % volcano_names, blank_cell],
                               header=True),
                      TableRow([tr('Distance [km]'), tr('Total'),
                                tr('Cumulative')],
                               header=True)]

        cum = 0
        for name in category_names:
            # prevent key error
            count = categories.get(name, 0)
            cum += count
            if is_point_data:
                name = int(name) / 1000
            table_body.append(TableRow([name, format_int(count),
                                        format_int(cum)]))

        table_body.append(TableRow(tr('Map shows buildings affected in '
                                      'each of volcano hazard polygons.')))
        impact_table = Table(table_body).toNewlineFreeString()

        # Extend impact report for on-screen display
        table_body.extend([TableRow(tr('Notes'), header=True),
                           tr('Total number of buildings %s in the viewable '
                              'area') % format_int(total),
                           tr('Only buildings available in OpenStreetMap '
                              'are considered.')])
        impact_summary = Table(table_body).toNewlineFreeString()
        map_title = tr('Buildings affected by volcanic hazard zone')

        # Create style
        colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
                   '#FFCC00', '#FF6600', '#FF0000', '#7A0000']
        building_counts = [x[self.target_field] for x in new_attributes]
        classes = create_classes(building_counts, len(colours))
        interval_classes = humanize_class(classes)
        style_classes = []
        for i in xrange(len(colours)):
            style_class = dict()
            style_class['label'] = create_label(interval_classes[i])
            if i == 0:
                transparency = 100
                style_class['min'] = 0
            else:
                transparency = 30
                style_class['min'] = classes[i - 1]
            style_class['transparency'] = transparency
            style_class['colour'] = colours[i]
            style_class['max'] = classes[i]
            style_classes.append(style_class)

        # Override style info with new classes and name
        style_info = dict(target_field=self.target_field,
                          style_classes=style_classes,
                          style_type='graduatedSymbol')

        # For printing map purpose
        map_title = tr('Building affected by volcanic hazard zone')
        legend_notes = tr('Thousand separator is represented by \'.\'')
        legend_units = tr('(building)')
        legend_title = tr('Building count')

        # Create vector layer and return
        V = Vector(data=new_attributes,
                   projection=my_hazard.get_projection(),
                   geometry=my_hazard.get_geometry(as_geometry_objects=True),
                   name=tr('Buildings affected by volcanic hazard zone'),
                   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},
                   style_info=style_info)
        return V

    def run(self):
        """Indonesian Earthquake Fatality Model."""
        self.validate()
        self.prepare()

        displacement_rate = self.hardcoded_parameters['displacement_rate']

        # Extract data grids
        hazard = self.hazard.layer.get_data()   # Ground Shaking
        # Population Density
        exposure = self.exposure.layer.get_data(scaling=True)

        # Calculate people affected by each MMI level
        # FIXME (Ole): this range is 2-9. Should 10 be included?
        mmi_range = self.hardcoded_parameters['mmi_range']
        number_of_exposed = {}
        number_of_displaced = {}
        number_of_fatalities = {}

        # Calculate fatality rates for observed Intensity values (hazard
        # based on ITB power model
        mask = numpy.zeros(hazard.shape)
        for mmi in mmi_range:
            # Identify cells where MMI is in class i and
            # count people affected by this shake level
            step = self.hardcoded_parameters['step']
            mmi_matches = numpy.where(
                (hazard > mmi - step) * (
                    hazard <= mmi + step),
                exposure, 0)

            # Calculate expected number of fatalities per level
            exposed = numpy.nansum(mmi_matches)
            fatalities = self.fatality_rate(mmi) * exposed

            # Calculate expected number of displaced people per level
            displacements = displacement_rate[mmi] * (exposed - fatalities)

            # Adjust displaced people to disregard fatalities.
            # Set to zero if there are more fatalities than displaced.
            # displacements = numpy.where(
            #    displacements > fatalities, displacements - fatalities, 0)

            # Sum up numbers for map
            # We need to use matrices here and not just numbers #2235
            mask += mmi_matches * (1 - self.fatality_rate(mmi))   # Displaced

            # Generate text with result for this study
            # This is what is used in the real time system exposure table
            number_of_exposed[mmi] = exposed
            number_of_displaced[mmi] = displacements
            # noinspection PyUnresolvedReferences
            number_of_fatalities[mmi] = fatalities

        # Total statistics
        self.total_population = numpy.nansum(number_of_exposed.values())
        self.total_fatalities = numpy.nansum(number_of_fatalities.values())
        total_displaced = numpy.nansum(number_of_displaced.values())

        # As per email discussion with Ole, Trevor, Hadi, total fatalities < 50
        # will be rounded down to 0 - Tim
        # Needs to revisit but keep it alive for the time being - Hyeuk, Jono
        if self.total_fatalities < 50:
            self.total_fatalities = 0

        affected_population = self.affected_population
        affected_population[tr('Number of fatalities')] = self.total_fatalities
        affected_population[
            tr('Number of people displaced')] = total_displaced
        self.unaffected_population = (
            self.total_population - total_displaced - self.total_fatalities)
        self._evacuation_category = tr('Number of people displaced')

        self.minimum_needs = [
            parameter.serialize() for parameter in
            filter_needs_parameters(self.parameters['minimum needs'])
        ]
        total_needs = self.total_needs

        # Result
        impact_summary = self.generate_html_report()
        impact_table = impact_summary

        # Create style
        colours = ['#EEFFEE', '#FFFF7F', '#E15500', '#E4001B', '#730000']
        classes = create_classes(mask.flat[:], len(colours))
        interval_classes = humanize_class(classes)
        style_classes = []
        for i in xrange(len(interval_classes)):
            style_class = dict()
            style_class['label'] = create_label(interval_classes[i])
            style_class['quantity'] = classes[i]
            if i == 0:
                transparency = 100
            else:
                transparency = 30
            style_class['transparency'] = transparency
            style_class['colour'] = colours[i]
            style_classes.append(style_class)

#.........这里部分代码省略.........

    def run(self):
        """Run classified population evacuation Impact Function.

        Counts number of people exposed to each hazard zones.

        :returns: Map of population exposed to each 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
        """
        self.validate()
        self.prepare()

        # Value from layer's keywords
        self.hazard_class_attribute = self.hazard.keyword('field')

        # Input checks
        msg = ('Input hazard must be a polygon layer. I got %s with '
               'layer type %s' % (
                   self.hazard.name, self.hazard.layer.get_geometry_name()))
        if not self.hazard.layer.is_polygon_data:
            raise Exception(msg)

        # Check if hazard_class_attribute exists in hazard_layer
        if (self.hazard_class_attribute not in
                self.hazard.layer.get_attribute_names()):
            msg = ('Hazard data %s does not contain expected hazard '
                   'zone attribute "%s". Please change it in the option. ' %
                   (self.hazard.name, self.hazard_class_attribute))
            # noinspection PyExceptionInherit
            raise InaSAFEError(msg)

        # Get unique hazard zones from the layer attribute
        self.hazard_zones = list(
            set(self.hazard.layer.get_data(self.hazard_class_attribute)))

        # Interpolated layer represents grid cell that lies in the polygon
        interpolated_layer, covered_exposure_layer = \
            assign_hazard_values_to_exposure_data(
                self.hazard.layer,
                self.exposure.layer,
                attribute_name=self.target_field
            )

        # Initialise total population affected by each hazard zone
        for hazard_zone in self.hazard_zones:
            self.affected_population[hazard_zone] = 0

        # Count total affected population per hazard zone
        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 hazard zone
                hazard_zone = row[self.hazard_class_attribute]
                self.affected_population[hazard_zone] += population

        # Count total population from exposure layer
        self.total_population = int(
            numpy.nansum(self.exposure.layer.get_data()))

        # Count total affected population
        total_affected_population = self.total_affected_population
        self.unaffected_population = (
            self.total_population - total_affected_population)

        self.minimum_needs = [
            parameter.serialize() for parameter in
            filter_needs_parameters(self.parameters['minimum needs'])
        ]

        # check for zero impact
        if total_affected_population == 0:
            table_body = [
                self.question,
                TableRow(
                    [tr('People impacted'),
                     '%s' % format_int(total_affected_population)],
                    header=True)]
            message = Table(table_body).toNewlineFreeString()
            raise ZeroImpactException(message)

        impact_table = impact_summary = self.generate_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:
#.........这里部分代码省略.........

#.........这里部分代码省略.........
                                '%s' % format_int(evacuated),
                                blank_cell],
                               header=True),
                      TableRow([category_header,
                                tr('Total'), tr('Cumulative')],
                               header=True)]

        for name in category_names:
            table_body.append(
                TableRow([name,
                          format_int(all_categories_population[name]),
                          format_int(all_categories_cumulative[name])]))

        table_body.extend([
            TableRow(tr(
                'Map shows the number of people affected in each of volcano '
                'hazard polygons.')),
            TableRow(
                [tr('Needs per week'), tr('Total'), blank_cell], header=True),
            [tr('Rice [kg]'), format_int(total_needs['rice']), blank_cell], [
                tr('Drinking Water [l]'),
                format_int(total_needs['drinking_water']),
                blank_cell],
            [tr('Clean Water [l]'), format_int(total_needs['water']),
                blank_cell],
            [tr('Family Kits'), format_int(total_needs['family_kits']),
                blank_cell],
            [tr('Toilets'), format_int(total_needs['toilets']), blank_cell]])
        impact_table = Table(table_body).toNewlineFreeString()

        # Extend impact report for on-screen display
        table_body.extend(
            [TableRow(tr('Notes'), header=True),
             tr('Total population %s in the exposure layer') % format_int(
                 total),
             tr('People need evacuation if they are within the '
                'volcanic hazard zones.')])

        population_counts = [x[self.target_field] for x in new_data_table]
        impact_summary = Table(table_body).toNewlineFreeString()

        # check for zero impact
        if numpy.nanmax(population_counts) == 0 == numpy.nanmin(
                population_counts):
            table_body = [
                question,
                TableRow([tr('People needing evacuation'),
                          '%s' % format_int(evacuated),
                          blank_cell], header=True)]
            my_message = Table(table_body).toNewlineFreeString()
            raise ZeroImpactException(my_message)

        # Create style
        colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
                   '#FFCC00', '#FF6600', '#FF0000', '#7A0000']
        classes = create_classes(population_counts, 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 == 0:
                transparency = 100
                style_class['min'] = 0
            else:
                transparency = 30
                style_class['min'] = classes[i - 1]
            style_class['transparency'] = transparency
            style_class['colour'] = colours[i]
            style_class['max'] = classes[i]
            style_classes.append(style_class)

        # Override style info with new classes and name
        style_info = dict(target_field=self.target_field,
                          style_classes=style_classes,
                          style_type='graduatedSymbol')

        # For printing map purpose
        map_title = tr('People affected by volcanic hazard zone')
        legend_notes = tr('Thousand separator is represented by  %s' %
                          get_thousand_separator())
        legend_units = tr('(people)')
        legend_title = tr('Population count')

        # Create vector layer and return
        impact_layer = Vector(
            data=new_data_table,
            projection=hazard_layer.get_projection(),
            geometry=hazard_layer.get_geometry(as_geometry_objects=True),
            name=tr('People affected by volcanic hazard zone'),
            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},
            style_info=style_info)
        return impact_layer

    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.name, 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)):
#.........这里部分代码省略.........

    def run(self):
        """Indonesian Earthquake Fatality Model."""
        displacement_rate = self.hardcoded_parameters['displacement_rate']
        fatality_rate = self.compute_fatality_rate()

        # Extract data grids
        hazard = self.hazard.layer.get_data()   # Ground Shaking
        # Population Density
        exposure = self.exposure.layer.get_data(scaling=True)

        # Calculate people affected by each MMI level
        mmi_range = self.hardcoded_parameters['mmi_range']
        number_of_exposed = {}
        number_of_displaced = {}
        number_of_fatalities = {}
        # Calculate fatality rates for observed Intensity values (hazard
        # based on ITB power model
        mask = numpy.zeros(hazard.shape)
        for mmi in mmi_range:
            # Identify cells where MMI is in class i and
            # count people affected by this shake level
            step = self.hardcoded_parameters['step']
            mmi_matches = numpy.where(
                (hazard > mmi - step) * (hazard <= mmi + step), exposure, 0)

            # Calculate expected number of fatalities per level
            exposed = numpy.nansum(mmi_matches)
            fatalities = fatality_rate[mmi] * exposed

            # Calculate expected number of displaced people per level
            displacements = displacement_rate[mmi] * (
                exposed - numpy.median(fatalities))

            # Adjust displaced people to disregard fatalities.
            # Set to zero if there are more fatalities than displaced.
            # displacements = numpy.where(
            #    displacements > fatalities, displacements - fatalities, 0)

            # Sum up numbers for map
            # We need to use matrices here and not just numbers #2235
            # filter out NaN to avoid overflow additions
            mmi_matches = numpy.nan_to_num(mmi_matches)
            mask += mmi_matches   # Displaced

            # Generate text with result for this study
            # This is what is used in the real time system exposure table
            number_of_exposed[mmi] = exposed
            number_of_displaced[mmi] = displacements
            # noinspection PyUnresolvedReferences
            number_of_fatalities[mmi] = fatalities

        # Total statistics
        total_fatalities_raw = numpy.nansum(
            number_of_fatalities.values(), axis=0)

        # Compute probability of fatality in each magnitude bin
        if (self.__class__.__name__ == 'PAGFatalityFunction') or (
                self.__class__.__name__ == 'ITBBayesianFatalityFunction'):
            prob_fatality_mag = self.compute_probability(total_fatalities_raw)
        else:
            prob_fatality_mag = None

        # Compute number of fatalities
        self.total_population = numpy.nansum(number_of_exposed.values())
        self.total_fatalities = numpy.median(total_fatalities_raw)
        total_displaced = numpy.nansum(number_of_displaced.values())

        # As per email discussion with Ole, Trevor, Hadi, total fatalities < 50
        # will be rounded down to 0 - Tim
        # Needs to revisit but keep it alive for the time being - Hyeuk, Jono
        if self.total_fatalities < 50:
            self.total_fatalities = 0

        affected_population = self.affected_population
        affected_population[tr('Number of fatalities')] = self.total_fatalities
        affected_population[
            tr('Number of people displaced')] = total_displaced
        self.unaffected_population = (
            self.total_population - total_displaced - self.total_fatalities)
        self._evacuation_category = tr('Number of people displaced')

        self.minimum_needs = [
            parameter.serialize() for parameter in
            filter_needs_parameters(self.parameters['minimum needs'])
        ]
        total_needs = self.total_needs

        # Create style
        colours = ['#EEFFEE', '#FFFF7F', '#E15500', '#E4001B', '#730000']
        classes = create_classes(mask.flat[:], len(colours))
        interval_classes = humanize_class(classes)
        style_classes = []
        for i in xrange(len(interval_classes)):
            style_class = dict()
            style_class['label'] = create_label(interval_classes[i])
            style_class['quantity'] = classes[i]
            style_class['transparency'] = 30
            style_class['colour'] = colours[i]
            style_classes.append(style_class)

#.........这里部分代码省略.........

#.........这里部分代码省略.........
        total_not_affected = total_population - total_affected

        # check for zero impact
        if total_affected == 0:
            table_body = [
                self.question,
                TableRow(
                    [tr('People affected'),
                     '%s' % format_int(total_affected)], header=True)]
            message = Ta