Python core.evacuated_population_needs函数代码示例

 def test_evacuated_population_needs(self):
     """Test evacuated_population_needs function."""
     water = ResourceParameter()
     water.name = 'Water'
     water.unit.name = 'litre'
     water.unit.abbreviation = 'l'
     water.unit.plural = 'litres'
     water.frequency = 'weekly'
     water.maximum_allowed_value = 10
     water.minimum_allowed_value = 0
     water.value = 5
     rice = ResourceParameter()
     rice.name = 'Rice'
     rice.unit.name = 'kilogram'
     rice.unit.abbreviation = 'kg'
     rice.unit.plural = 'kilograms'
     rice.frequency = 'daily'
     rice.maximum_allowed_value = 1
     rice.minimum_allowed_value = 0
     rice.value = 0.5
     total_needs = evacuated_population_needs(
         10,
         [water.serialize(), rice.serialize()]
     )
     self.assertEqual(total_needs['weekly'][0]['name'], 'Water')
     self.assertEqual(total_needs['weekly'][0]['amount'], 50)
     self.assertEqual(total_needs['weekly'][0]['table name'], 'Water [l]')
     self.assertEqual(total_needs['daily'][0]['name'], 'Rice')
     self.assertEqual(total_needs['daily'][0]['amount'], 5)
     self.assertEqual(total_needs['daily'][0]['table name'], 'Rice [kg]')

    def _tabulate(self, counts, evacuated, minimum_needs, question, rounding,
                  thresholds, total, no_data_warning):
        # noinspection PyListCreation
        table_body = [
            question,
            TableRow([(tr('People in %.1f m of water') % thresholds[-1]),
                      '%s*' % format_int(evacuated)],
                     header=True),
            TableRow(
                tr('* Number is rounded up to the nearest %s') % rounding),
            TableRow(tr('Map shows the numbers of people needing evacuation'))]
        total_needs = evacuated_population_needs(
            evacuated, minimum_needs)
        for frequency, needs in total_needs.items():
            table_body.append(TableRow(
                [
                    tr('Needs should be provided %s' % frequency),
                    tr('Total')
                ],
                header=True))
            for resource in needs:
                table_body.append(TableRow([
                    tr(resource['table name']),
                    format_int(resource['amount'])]))
        table_body.append(TableRow(tr('Action Checklist:'), header=True))
        table_body.append(TableRow(tr('How will warnings be disseminated?')))
        table_body.append(TableRow(tr('How will we reach stranded people?')))
        table_body.append(TableRow(tr('Do we have enough relief items?')))
        table_body.append(TableRow(tr('If yes, where are they located and how '
                                      'will we distribute them?')))
        table_body.append(TableRow(tr(
            'If no, where can we obtain additional relief items from and how '
            'will we transport them to here?')))
        # Extend impact report for on-screen display
        table_body.extend([
            TableRow(tr('Notes'), header=True),
            tr('Total population: %s') % format_int(total),
            tr('People need evacuation if tsunami levels exceed %(eps).1f m') %
            {'eps': thresholds[-1]},
            tr('Minimum needs are defined in BNPB regulation 7/2008'),
            tr('All values are rounded up to the nearest integer in order to '
               'avoid representing human lives as fractions.')])
        if len(counts) > 1:
            table_body.append(TableRow(tr('Detailed breakdown'), header=True))

            for i, val in enumerate(counts[:-1]):
                s = (tr('People in %(lo).1f m to %(hi).1f m of water: %(val)i')
                     % {'lo': thresholds[i],
                        'hi': thresholds[i + 1],
                        'val': format_int(val[0])})
                table_body.append(TableRow(s))
        if no_data_warning:
            table_body.extend([
                tr('The layers contained `no data`. This missing data was '
                   'carried through to the impact layer.'),
                tr('`No data` values in the impact layer were treated as 0 '
                   'when counting the affected or total population.')
            ])

        return table_body, total_needs

 def _tabulate(self, high, low, medium, minimum_needs, no_impact, question,
               total_impact):
     # Generate impact report for the pdf map
     table_body = [question,
                   TableRow([tr('Total Population Affected '),
                             '%s' % format_int(total_impact)],
                            header=True),
                   TableRow([tr('Population in High hazard class areas '),
                             '%s' % format_int(high)]),
                   TableRow([tr('Population in Medium hazard class areas '),
                             '%s' % format_int(medium)]),
                   TableRow([tr('Population in Low hazard class areas '),
                             '%s' % format_int(low)]),
                   TableRow([tr('Population Not Affected'),
                             '%s' % format_int(no_impact)]),
                   TableRow(
                       tr('Table below shows the minimum needs for all '
                          'evacuated people'))]
     total_needs = evacuated_population_needs(
         total_impact, minimum_needs)
     for frequency, needs in total_needs.items():
         table_body.append(TableRow(
             [
                 tr('Needs should be provided %s' % frequency),
                 tr('Total')
             ],
             header=True))
         for resource in needs:
             table_body.append(TableRow([
                 tr(resource['table name']),
                 format_int(resource['amount'])]))
     return table_body, total_needs

    def total_needs(self):
        """Get the total minimum needs based on the total evacuated.

        :returns: Total minimum needs.
        :rtype: dict
        """
        total_population_evacuated = self.total_evacuated
        return evacuated_population_needs(
            total_population_evacuated, self.minimum_needs)

    def test_default_needs(self):
        """default calculated needs are as expected
        """
        minimum_needs = [
            parameter.serialize() for parameter in default_minimum_needs()]
        # 20 Happens to be the smallest number at which integer rounding
        # won't make a difference to the result
        result = evacuated_population_needs(20, minimum_needs)['weekly']
        result = OrderedDict([[r['table name'], r['amount']] for r in result])

        assert (result['Rice [kg]'] == 56
                and result['Drinking Water [l]'] == 350
                and result['Clean Water [l]'] == 1340
                and result['Family Kits'] == 4)

        result = evacuated_population_needs(10, minimum_needs)['single']
        result = OrderedDict([[r['table name'], r['amount']] for r in result])
        assert result['Toilets'] == 1

    def test_arbitrary_needs(self):
        """custom need ratios calculated are as expected
        """
        minimum_needs = [
            parameter.serialize() for parameter in default_minimum_needs()]
        minimum_needs[0]['value'] = 4
        minimum_needs[1]['value'] = 3
        minimum_needs[2]['value'] = 2
        minimum_needs[3]['value'] = 1
        minimum_needs[4]['value'] = 0.2
        result = evacuated_population_needs(10, minimum_needs)['weekly']
        result = OrderedDict([[r['table name'], r['amount']] for r in result])

        assert (result['Rice [kg]'] == 40
                and result['Drinking Water [l]'] == 30
                and result['Clean Water [l]'] == 20
                and result['Family Kits'] == 10)
        result = evacuated_population_needs(10, minimum_needs)['single']
        result = OrderedDict([[r['table name'], r['amount']] for r in result])
        assert result['Toilets'] == 2

    def total_needs(self):
        """Get the total minimum needs based on the total evacuated.

        :returns: Total minimum needs.
        :rtype: dict
        """
        total_population_evacuated = sum(self.affected_population.values())
        self.minimum_needs = [
            parameter.serialize() for parameter in
            filter_needs_parameters(self.parameters['minimum needs'])
        ]

        return evacuated_population_needs(
            total_population_evacuated, self.minimum_needs)

    def _tabulate_notes(
            self,
            minimum_needs,
            table_body,
            total,
            total_impact,
            no_data_warning):
        # Extend impact report for on-screen display
        table_body.extend([
            TableRow(tr('Notes'), header=True),
            tr('Map shows population count in high, medium, and low hazard '
               'area.'),
            tr('Total population: %s') % format_int(total),
            TableRow(tr(
                'Table below shows the minimum needs for all '
                'affected people'))])
        if no_data_warning:
            table_body.extend([
                tr('The layers contained `no data`. This missing data was '
                   'carried through to the impact layer.'),
                tr('`No data` values in the impact layer were treated as 0 '
                   'when counting the affected or total population.')
            ])

        total_needs = evacuated_population_needs(
            total_impact, minimum_needs)
        for frequency, needs in total_needs.items():
            table_body.append(TableRow(
                [
                    tr('Needs should be provided %s' % frequency),
                    tr('Total')
                ],
                header=True))
            for resource in needs:
                table_body.append(TableRow([
                    tr(resource['table name']),
                    format_int(resource['amount'])]))
        return table_body, total_needs

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

        :param layers: List of layers expected to contain

            * hazard_layer: Raster layer of categorised hazard
            * exposure_layer: Raster layer of population data

        Counts number of people exposed to each category of the hazard

        :returns:
          Map of population exposed to high category
          Table with number of people in each category
        """

        # The 3 category
        high_t = self.parameters['Categorical thresholds'][2]
        medium_t = self.parameters['Categorical thresholds'][1]
        low_t = self.parameters['Categorical thresholds'][0]

        # Identify hazard and exposure layers
        hazard_layer = get_hazard_layer(layers)    # Categorised Hazard
        exposure_layer = get_exposure_layer(layers)  # Population Raster

        question = get_question(
            hazard_layer.get_name(), exposure_layer.get_name(), self)

        # Extract data as numeric arrays
        C = hazard_layer.get_data(nan=0.0)  # Category

        # Calculate impact as population exposed to each category
        P = exposure_layer.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)) - int(numpy.sum(M))
        medium = int(numpy.sum(M)) - int(numpy.sum(L))
        low = int(numpy.sum(L))
        total_impact = high + medium + low

        # Don't show digits less than a 1000
        total = population_rounding(total)
        total_impact = population_rounding(total_impact)
        high = population_rounding(high)
        medium = population_rounding(medium)
        low = population_rounding(low)

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

        # 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)]

        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 count in high or medium hazard area'),
            tr('Total population: %s') % format_int(total),
            TableRow(tr(
                'Table below shows the minimum needs for all '
                'affected people'))])

        total_needs = evacuated_population_needs(
            total_impact, minimum_needs)
        for frequency, needs in total_needs.items():
            table_body.append(TableRow(
                [
                    tr('Needs should be provided %s' % frequency),
                    tr('Total')
                ],
                header=True))
            for resource in needs:
                table_body.append(TableRow([
                    tr(resource['table name']),
                    format_int(resource['amount'])]))

        impact_summary = Table(table_body).toNewlineFreeString()
        map_title = tr('People in high hazard areas')

        # Generate 8 equidistant classes across the range of flooded population
#.........这里部分代码省略.........

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

        :param layers: List of layers expected to contain
              hazard_layer: Raster layer of tsunami depth
              exposure_layer: Raster layer of population data on the same grid
              as hazard_layer

        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
        """

        # Identify hazard and exposure layers
        hazard_layer = get_hazard_layer(layers)  # Tsunami inundation [m]
        exposure_layer = get_exposure_layer(layers)

        question = get_question(
            hazard_layer.get_name(), exposure_layer.get_name(), self)

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

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

        # Extract data as numeric arrays
        data = hazard_layer.get_data(nan=0.0)  # Depth

        # Calculate impact as population exposed to depths > max threshold
        population = exposure_layer.get_data(nan=0.0, scaling=True)

        # Calculate impact to intermediate thresholds
        counts = []
        # merely initialize
        impact = None
        for i, lo in enumerate(thresholds):
            if i == len(thresholds) - 1:
                # The last threshold
                impact = medium = numpy.where(data >= lo, population, 0)
            else:
                # Intermediate thresholds
                hi = thresholds[i + 1]
                medium = numpy.where((data >= lo) * (data < hi), population, 0)

            # Count
            val = int(numpy.sum(medium))

            # Sensible rounding
            val, rounding = population_rounding_full(val)
            counts.append([val, rounding])

        # Count totals
        evacuated, rounding = counts[-1]
        total = int(numpy.sum(population))
        # Don't show digits less than a 1000
        total = population_rounding(total)

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

        # Generate impact report for the pdf map
        # noinspection PyListCreation
        table_body = [
            question,
            TableRow([(tr('People in %.1f m of water') % thresholds[-1]),
                      '%s*' % format_int(evacuated)],
                     header=True),
            TableRow(
                tr('* Number is rounded up to the nearest %s') % rounding),
            TableRow(tr('Map shows the numbers of people needing evacuation'))]

        total_needs = evacuated_population_needs(
            evacuated, minimum_needs)
        for frequency, needs in total_needs.items():
            table_body.append(TableRow(
                [
                    tr('Needs should be provided %s' % frequency),
                    tr('Total')
                ],
                header=True))
            for resource in needs:
                table_body.append(TableRow([
                    tr(resource['table name']),
                    format_int(resource['amount'])]))

        table_body.append(TableRow(tr('Action Checklist:'), header=True))
        table_body.append(TableRow(tr('How will warnings be disseminated?')))
        table_body.append(TableRow(tr('How will we reach stranded people?')))
        table_body.append(TableRow(tr('Do we have enough relief items?')))
        table_body.append(TableRow(tr('If yes, where are they located and how '
                                      'will we distribute them?')))
#.........这里部分代码省略.........

#.........这里部分代码省略.........

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

        # Generate impact report for the pdf map
        table_body = [
            question,
            TableRow(
                [tr('People affected'), '%s*' % (
                    format_int(int(affected_population)))],
                header=True),
            TableRow(
                [TableCell(
                    tr('* Number is rounded up to the nearest %s') % (
                        rounding),
                    col_span=2)]),
            TableRow([tr('People needing evacuation'), '%s*' % (
                format_int(int(evacuated)))], header=True),
            TableRow(
                [TableCell(
                    tr('* Number is rounded up to the nearest %s') % (
                        rounding_evacuated),
                    col_span=2)]),
            TableRow([tr('Evacuation threshold'), '%s%%' % format_int(
                self.parameters['evacuation_percentage'])], header=True),
            TableRow(tr(
                'Map shows the number of people affected in each flood prone '
                'area')),
            TableRow(tr(
                'Table below shows the weekly minimum needs for all '
                'evacuated people'))]
        total_needs = evacuated_population_needs(
            evacuated, minimum_needs)
        for frequency, needs in total_needs.items():
            table_body.append(TableRow(
                [
                    tr('Needs should be provided %s' % frequency),
                    tr('Total')
                ],
                header=True))
            for resource in needs:
                table_body.append(TableRow([
                    tr(resource['table name']),
                    format_int(resource['amount'])]))

        impact_table = Table(table_body).toNewlineFreeString()

        table_body.append(TableRow(tr('Action Checklist:'), header=True))
        table_body.append(TableRow(tr('How will warnings be disseminated?')))
        table_body.append(TableRow(tr('How will we reach stranded people?')))
        table_body.append(TableRow(tr('Do we have enough relief items?')))
        table_body.append(TableRow(
            'If yes, where are they located and how will we distribute '
            'them?'))
        table_body.append(TableRow(
            'If no, where can we obtain additional relief items from and '
            'how will we transport them to here?'))

        # Extend impact report for on-screen display
        table_body.extend([
            TableRow(tr('Notes'), header=True),
            tr('Total population: %s') % format_int(total),
            tr('People need evacuation if in the area identified as '
               '"Flood Prone"'),

#.........这里部分代码省略.........
        # Use final accumulation as total number needing evacuation
        evacuated = population_rounding(cumulative)

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

        # Generate impact report for the pdf map
        blank_cell = ''
        table_body = [question,
                      TableRow([tr('Volcanoes considered'),
                                '%s' % volcano_names, blank_cell],
                               header=True),
                      TableRow([tr('People needing evacuation'),
                                '%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.'))])

        total_needs = evacuated_population_needs(
            evacuated, minimum_needs)
        for frequency, needs in total_needs.items():
            table_body.append(TableRow(
                [
                    tr('Needs should be provided %s' % frequency),
                    tr('Total')
                ],
                header=True))
            for resource in needs:
                table_body.append(TableRow([
                    tr(resource['table name']),
                    format_int(resource['amount'])]))
        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_population),
             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),

#.........这里部分代码省略.........
            TableRow(
                [tr('Volcanoes considered'),
                 '%s' % volcano_names,
                 blank_cell],
                header=True),
            TableRow(
                [tr('People needing evacuation'),
                 '%s' % format_int(
                     population_rounding(total_affected_population)),
                 blank_cell],
                header=True),
            TableRow(
                [category_header,
                 tr('Total'),
                 tr('Cumulative')],
                header=True)]

        for radius in rad_m:
            table_body.append(
                TableRow(
                    [radius,
                     format_int(
                         population_rounding(
                             affected_population[radius])),
                     format_int(
                         population_rounding(
                             cumulative_affected_population[radius]))]))

        table_body.extend([
            TableRow(tr(
                'Map shows the number of people affected in each of volcano '
                'hazard polygons.'))])

        total_needs = evacuated_population_needs(
            total_affected_population, minimum_needs)
        for frequency, needs in total_needs.items():
            table_body.append(TableRow(
                [
                    tr('Needs should be provided %s' % frequency),
                    tr('Total')
                ],
                header=True))
            for resource in needs:
                table_body.append(TableRow([
                    tr(resource['table name']),
                    format_int(resource['amount'])]))
        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_population),
             tr('People need evacuation if they are within the '
                'volcanic hazard zones.')])

        if nan_warning:
            table_body.extend([
                tr('The population layer contained `no data`. This missing '
                   'data was carried through to the impact layer.'),
                tr('`No data` values in the impact layer were treated as 0 '
                   'when counting the affected or total population.')
            ])

        impact_summary = Table(table_body).toNewlineFreeString()

#.........这里部分代码省略.........
        medium = int(numpy.sum(med))
        low = int(numpy.sum(lo))
        total_impact = int(numpy.sum(impact))

        # Perform population rounding based on number of people
        no_impact = population_rounding(total - total_impact)
        total = population_rounding(total)
        total_impact = population_rounding(total_impact)
        high = population_rounding(high)
        medium = population_rounding(medium)
        low = population_rounding(low)

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

        # Generate impact report for the pdf map
        table_body = [question,
                      TableRow([tr('Total Population Affected '),
                                '%s' % format_int(total_impact)],
                               header=True),
                      TableRow([tr('Population in High risk areas '),
                                '%s' % format_int(high)]),
                      TableRow([tr('Population in Medium risk areas '),
                                '%s' % format_int(medium)]),
                      TableRow([tr('Population in Low risk areas '),
                                '%s' % format_int(low)]),
                      TableRow([tr('Population Not Affected'),
                                '%s' % format_int(no_impact)]),
                      TableRow(tr('Table below shows the minimum '
                                  'needs for all evacuated people'))]

        total_needs = evacuated_population_needs(
            total_impact, minimum_needs)
        for frequency, needs in total_needs.items():
            table_body.append(TableRow(
                [
                    tr('Needs should be provided %s' % frequency),
                    tr('Total')
                ],
                header=True))
            for resource in needs:
                table_body.append(TableRow([
                    tr(resource['table name']),
                    format_int(resource['amount'])]))

        impact_table = Table(table_body).toNewlineFreeString()

        table_body.append(TableRow(tr('Action Checklist:'), header=True))
        table_body.append(TableRow(tr('How will warnings be disseminated?')))
        table_body.append(TableRow(tr('How will we reach stranded people?')))
        table_body.append(TableRow(tr('Do we have enough relief items?')))
        table_body.append(TableRow(tr('If yes, where are they located and how '
                                      'will we distribute them?')))
        table_body.append(TableRow(tr(
            'If no, where can we obtain additional relief items from and how '
            'will we transport them to here?')))

        # Extend impact report for on-screen display
        table_body.extend([
            TableRow(tr('Notes'), header=True),
            tr('Map shows the numbers of people in high, medium and low '
               'hazard areas'),
            tr('Total population: %s') % format_int(total)
        ])