Python ImpactFunction.run方法代码示例

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
    def test_profiling(self):
        """Test running impact function on test data."""
        hazard_layer = load_test_vector_layer(
            'gisv4', 'hazard', 'classified_vector.geojson')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'building-points.geojson')
        aggregation_layer = load_test_vector_layer(
            'gisv4', 'aggregation', 'small_grid.geojson')

        # Set up impact function
        impact_function = ImpactFunction()
        impact_function.aggregation = aggregation_layer
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        status, message = impact_function.run()
        self.assertEqual(ANALYSIS_FAILED_BAD_INPUT, status, message)
        impact_function.prepare()
        status, message = impact_function.run()
        self.assertEqual(ANALYSIS_SUCCESS, status, message)
        message = impact_function.performance_log_message().to_text()
        expected_result = get_control_text(
            'test-profiling-logs.txt')

        for line in expected_result:
            line = line.replace('\n', '')
            if line == '' or line == '-':
                continue
            self.assertIn(line, message)

        # Notes(IS): For some unknown reason I need to do this to make
        # test_provenance pass
        del hazard_layer

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
    def test_analysis_earthquake_summary(self):
        """Test we can compute summary after an EQ on population."""
        hazard = load_test_raster_layer('gisv4', 'hazard', 'earthquake.asc')
        exposure = load_test_raster_layer(
            'gisv4', 'exposure', 'raster', 'population.asc')
        aggregation = load_test_vector_layer(
            'gisv4', 'aggregation', 'small_grid.geojson')

        impact_function = ImpactFunction()
        impact_function.hazard = hazard
        impact_function.exposure = exposure
        impact_function.aggregation = aggregation
        status, message = impact_function.prepare()
        self.assertEqual(PREPARE_SUCCESS, status, message)
        status, message = impact_function.run()
        self.assertEqual(ANALYSIS_SUCCESS, status, message)

        layer = impact_function.analysis_impacted
        classification = hazard.keywords['classification']
        classes = definition(classification)['classes']
        for hazard_class in classes:
            field_name = hazard_count_field['field_name'] % hazard_class['key']
            message = '%s is not found in the EQ summary layer.' % field_name
            self.assertNotEqual(-1, layer.fieldNameIndex(field_name), message)

        check_inasafe_fields(impact_function.analysis_impacted)
        check_inasafe_fields(impact_function.aggregation_summary)

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
    def test_provenance_without_aggregation(self):
        """Test provenance of impact function without aggregation."""
        hazard_layer = load_test_vector_layer(
            'gisv4', 'hazard', 'classified_vector.geojson')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'building-points.geojson')

        hazard = definition(hazard_layer.keywords['hazard'])
        exposure = definition(exposure_layer.keywords['exposure'])
        hazard_category = definition(hazard_layer.keywords['hazard_category'])

        expected_provenance = {
            'gdal_version': gdal.__version__,
            'host_name': gethostname(),
            'map_title': get_map_title(hazard, exposure, hazard_category),
            'map_legend_title': exposure['layer_legend_title'],
            'inasafe_version': get_version(),
            'pyqt_version': PYQT_VERSION_STR,
            'qgis_version': QGis.QGIS_VERSION,
            'qt_version': QT_VERSION_STR,
            'user': getpass.getuser(),
            'os': readable_os_version(),
            'aggregation_layer': None,
            'aggregation_layer_id': None,
            'exposure_layer': exposure_layer.source(),
            'exposure_layer_id': exposure_layer.id(),
            'hazard_layer': hazard_layer.source(),
            'hazard_layer_id': hazard_layer.id(),
            'analysis_question': get_analysis_question(hazard, exposure),
            'aggregation_keywords': None,
            'exposure_keywords': deepcopy(exposure_layer.keywords),
            'hazard_keywords': deepcopy(hazard_layer.keywords),
        }

        # Set up impact function
        impact_function = ImpactFunction()
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        status, message = impact_function.prepare()
        self.assertEqual(PREPARE_SUCCESS, status, message)
        status, message = impact_function.run()
        self.assertEqual(ANALYSIS_SUCCESS, status, message)

        self.maxDiff = None

        expected_provenance.update({
            'action_checklist': impact_function.action_checklist(),
            'analysis_extent': impact_function.analysis_extent.exportToWkt(),
            'impact_function_name': impact_function.name,
            'impact_function_title': impact_function.title,
            'notes': impact_function.notes(),
            'requested_extent': impact_function.requested_extent,
            'data_store_uri': impact_function.datastore.uri_path,
            'start_datetime': impact_function.start_datetime,
            'end_datetime': impact_function.end_datetime,
            'duration': impact_function.duration
        })

        self.assertDictEqual(expected_provenance, impact_function.provenance)

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
    def test_raster_post_minimum_needs_value_generation(self):
        """Test minimum needs postprocessors on raster exposure.

        Minimum needs postprocessors is defined to only generate values
        when exposure contains population data.
        Especially important to test, since on raster exposure the population
        field is generated on the fly.
        The postprocessors need to expect generated population field exists.
        """

        # # #
        # Test with raster exposure data with population_exposure_count
        # exists.
        # # #

        hazard_layer = load_test_raster_layer(
            'hazard', 'tsunami_wgs84.tif')
        exposure_layer = load_test_raster_layer(
            'exposure', 'pop_binary_raster_20_20.asc')

        impact_function = ImpactFunction()
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.prepare()
        return_code, message = impact_function.run()

        self.assertEqual(return_code, ANALYSIS_SUCCESS, message)

        # minimum needs fields should exists in the results
        self._check_minimum_fields_exists(impact_function)

        # TODO: should include demographic postprocessor value too
        expected_value = {
            u'total_affected': 9.208200000039128,
            u'minimum_needs__rice': 25,
            u'minimum_needs__toilets': 0,
            u'minimum_needs__drinking_water': 161,
            u'minimum_needs__clean_water': 616,
            u'male': 4,
            u'female': 4,
            u'youth': 2,
            u'adult': 6,
            u'elderly': 0,
            u'total': 162.7667000000474,
            u'minimum_needs__family_kits': 1,
            u'total_not_affected': 153.55850000000828,
        }

        self._check_minimum_fields_value(expected_value, impact_function)

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
    def test_vector_post_minimum_needs_value_generation(self):
        """Test minimum needs postprocessors on vector exposure.

        Test with vector exposure data with population_count_field exists.

        Minimum needs postprocessors is defined to only generate values when
        exposure contains population data.
        """
        hazard_layer = load_test_vector_layer(
            'gisv4', 'hazard', 'tsunami_vector.geojson')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'population.geojson')
        aggregation_layer = load_test_vector_layer(
            'gisv4', 'aggregation', 'small_grid.geojson')

        impact_function = ImpactFunction()
        impact_function.aggregation = aggregation_layer
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        status, message = impact_function.prepare()
        self.assertEqual(PREPARE_SUCCESS, status, message)
        return_code, message = impact_function.run()

        self.assertEqual(return_code, ANALYSIS_SUCCESS, message)

        # minimum needs fields should exists in the results
        self._check_minimum_fields_exists(impact_function)

        expected_value = {
            u'population': 69,
            u'total': 9.0,
            u'minimum_needs__rice': 491,
            u'minimum_needs__clean_water': 11763,
            u'minimum_needs__toilets': 8,
            u'minimum_needs__drinking_water': 3072,
            u'minimum_needs__family_kits': 35,
            u'male': 34,
            u'female': 34,
            u'youth': 17,
            u'adult': 45,
            u'elderly': 6,
            u'total_affected': 6.0,
        }

        self._check_minimum_fields_value(expected_value, impact_function)

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
    def run_task(self, task_item, status_item, count=0, index=''):
        """Run a single task.

        :param task_item: Table task_item containing task name / details.
        :type task_item: QTableWidgetItem

        :param status_item: Table task_item that holds the task status.
        :type status_item: QTableWidgetItem

        :param count: Count of scenarios that have been run already.
        :type count:

        :param index: The index for the table item that will be run.
        :type index: int

        :returns: Flag indicating if the task succeeded or not.
        :rtype: bool
        """
        self.enable_busy_cursor()
        for layer_group in self.layer_group_container:
            layer_group.setItemVisibilityChecked(False)

        # set status to 'running'
        status_item.setText(self.tr('Running'))

        # .. see also:: :func:`appendRow` to understand the next 2 lines
        variant = task_item.data(QtCore.Qt.UserRole)
        value = variant[0]
        result = True

        if isinstance(value, str):
            filename = value
            # run script
            try:
                self.run_script(filename)
                # set status to 'OK'
                status_item.setText(self.tr('Script OK'))
            except Exception as e:  # pylint: disable=W0703
                # set status to 'fail'
                status_item.setText(self.tr('Script Fail'))

                LOGGER.exception(
                    'Running macro failed. The exception: ' + str(e))
                result = False
        elif isinstance(value, dict):
            # start in new project if toggle is active
            if self.start_in_new_project:
                self.iface.newProject()
            # create layer group
            group_name = value['scenario_name']
            self.layer_group = self.root.addGroup(group_name)
            self.layer_group_container.append(self.layer_group)

            # Its a dict containing files for a scenario
            success, parameters = self.prepare_task(value)
            if not success:
                # set status to 'running'
                status_item.setText(self.tr('Please update scenario'))
                self.disable_busy_cursor()
                return False

            directory = self.output_directory.text()
            if self.scenario_directory_radio.isChecked():
                directory = self.source_directory.text()

            output_directory = os.path.join(directory, group_name)
            if not os.path.exists(output_directory):
                os.makedirs(output_directory)

            # If impact function parameters loaded successfully, initiate IF.
            impact_function = ImpactFunction()
            impact_function.datastore = Folder(output_directory)
            impact_function.datastore.default_vector_format = "geojson"
            impact_function.hazard = parameters[layer_purpose_hazard['key']]
            impact_function.exposure = (
                parameters[layer_purpose_exposure['key']])
            if parameters[layer_purpose_aggregation['key']]:
                impact_function.aggregation = (
                    parameters[layer_purpose_aggregation['key']])
            elif parameters['extent']:
                impact_function.requested_extent = parameters['extent']
                impact_function.crs = parameters['crs']
            prepare_status, prepare_message = impact_function.prepare()
            if prepare_status == PREPARE_SUCCESS:
                LOGGER.info('Impact function ready')
                status, message = impact_function.run()
                if status == ANALYSIS_SUCCESS:
                    status_item.setText(self.tr('Analysis Success'))
                    impact_layer = impact_function.impact
                    if impact_layer.isValid():
                        layer_list = [
                            impact_layer,
                            impact_function.analysis_impacted,
                            parameters[layer_purpose_hazard['key']],
                            parameters[layer_purpose_exposure['key']],
                            parameters[layer_purpose_aggregation['key']]]
                        QgsProject.instance().addMapLayers(
                            layer_list, False)
                        for layer in layer_list:
                            self.layer_group.addLayer(layer)
#.........这里部分代码省略.........

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
    def test_ratios_with_raster_exposure(self):
        """Test if we can add defaults to a raster exposure.

        See ticket #3851 how to manage ratios with a raster exposure.
        """
        hazard_layer = load_test_vector_layer(
            'gisv4', 'hazard', 'tsunami_vector.geojson')
        exposure_layer = load_test_raster_layer(
            'gisv4', 'exposure', 'raster', 'population.asc')

        # Set up impact function
        impact_function = ImpactFunction()
        impact_function.debug_mode = True
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.prepare()
        status, message = impact_function.run()
        self.assertEqual(ANALYSIS_SUCCESS, status, message)

        for layer in impact_function.outputs:
            if layer.keywords['layer_purpose'] == (
                    layer_purpose_analysis_impacted['key']):
                analysis = layer
            if layer.keywords['layer_purpose'] == (
                    layer_purpose_aggregate_hazard_impacted['key']):
                impact = layer

        # We check in the impact layer if we have :
        # female default ratio with the default value
        index = impact.fieldNameIndex(female_ratio_field['field_name'])
        self.assertNotEqual(-1, index)
        unique_values = impact.uniqueValues(index)
        self.assertEqual(1, len(unique_values))
        female_ratio = unique_values[0]

        # female displaced count and youth displaced count
        self.assertNotEqual(
            -1, impact.fieldNameIndex(
                female_displaced_count_field['field_name']))
        self.assertNotEqual(
            -1, impact.fieldNameIndex(
                youth_displaced_count_field['field_name']))

        # Check that we have more than 0 female displaced in the analysis layer
        index = analysis.fieldNameIndex(
            female_displaced_count_field['field_name'])
        female_displaced = analysis.uniqueValues(index)[0]
        self.assertGreater(female_displaced, 0)

        # Let's check computation
        index = analysis.fieldNameIndex(
            displaced_field['field_name'])
        displaced_population = analysis.uniqueValues(index)[0]
        self.assertEqual(
            int(displaced_population * female_ratio), female_displaced)

        # Check that we have more than 0 youth displaced in the analysis layer
        index = analysis.fieldNameIndex(
            female_displaced_count_field['field_name'])
        value = analysis.uniqueValues(index)[0]
        self.assertGreater(value, 0)

        # Let do another test with the special aggregation layer
        hazard_layer = load_test_vector_layer(
            'gisv4', 'hazard', 'tsunami_vector.geojson')
        exposure_layer = load_test_raster_layer(
            'gisv4', 'exposure', 'raster', 'population.asc')

        aggregation_layer = load_test_vector_layer(
            'gisv4', 'aggregation', 'small_grid_ratios.geojson')
        # This aggregation layer has :
        # * a field for female ratio : 1, 0.5 and 0
        # * use global default for youth ratio
        # * do not ust for adult ratio
        # * use custom 0.75 for elderly ratio

        # Set up impact function
        impact_function = ImpactFunction()
        impact_function.debug_mode = True
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.aggregation = aggregation_layer
        status, message = impact_function.prepare()
        self.assertEqual(PREPARE_SUCCESS, status, message)
        status, message = impact_function.run()
        self.assertEqual(ANALYSIS_SUCCESS, status, message)

        impact = impact_function.impact

        # We should have a female_ratio with many values
        index = impact.fieldNameIndex(female_ratio_field['field_name'])
        self.assertNotEqual(-1, index)
        values = impact.uniqueValues(index)
        self.assertEqual(3, len(values))

        # We should have a youth_ratio with global default
        index = impact.fieldNameIndex(youth_ratio_field['field_name'])
        self.assertNotEqual(-1, index)
        values = impact.uniqueValues(index)
        self.assertEqual(1, len(values))
#.........这里部分代码省略.........

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
    def test_ratios_with_vector_exposure(self):
        """Test if we can add defaults to a vector exposure."""
        # First test, if we do not provide an aggregation,
        hazard_layer = load_test_vector_layer(
            'gisv4', 'hazard', 'classified_vector.geojson')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'population.geojson')

        # Set up impact function
        impact_function = ImpactFunction()
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.prepare()
        # Let's remove one field from keywords.
        # We monkey patch keywords for testing after `prepare` & before `run`.
        fields = impact_function.exposure.keywords['inasafe_fields']
        del fields[female_count_field['key']]
        status, message = impact_function.run()
        self.assertEqual(ANALYSIS_SUCCESS, status, message)

        impact = impact_function.impact

        # We check the field exist after the IF with only one value.
        field = impact.fieldNameIndex(
            female_ratio_field['field_name'])
        self.assertNotEqual(-1, field)
        unique_ratio = impact.uniqueValues(field)
        self.assertEqual(1, len(unique_ratio), unique_ratio)
        self.assertEqual(
            unique_ratio[0], female_ratio_default_value['default_value'])

        # Second test, if we provide an aggregation without a default ratio 0.2
        expected_ratio = 1.0
        hazard_layer = load_test_vector_layer(
            'gisv4', 'hazard', 'classified_vector.geojson')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'population.geojson')
        aggregation_layer = load_test_vector_layer(
            'gisv4', 'aggregation', 'small_grid.geojson')

        # Set up impact function
        impact_function = ImpactFunction()
        impact_function.aggregation = aggregation_layer
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.debug_mode = True
        impact_function.prepare()
        # The `prepare` reads keywords from the file.
        impact_function.aggregation.keywords['inasafe_default_values'] = {
            elderly_ratio_field['key']: expected_ratio
        }
        fields = impact_function.exposure.keywords['inasafe_fields']
        del fields[female_count_field['key']]
        status, message = impact_function.run()
        self.assertEqual(ANALYSIS_SUCCESS, status, message)
        impact = impact_function.impact

        # We check the field exist after the IF with only original values.
        field = impact.fieldNameIndex(
            female_ratio_field['field_name'])
        self.assertNotEqual(-1, field)
        unique_ratio = impact.uniqueValues(field)
        self.assertEqual(3, len(unique_ratio), unique_ratio)

        # We check the field exist after the IF with only one value.
        field = impact.fieldNameIndex(
            elderly_ratio_field['field_name'])
        self.assertNotEqual(-1, field)
        unique_ratio = impact.uniqueValues(field)
        self.assertEqual(1, len(unique_ratio), unique_ratio)
        self.assertEqual(expected_ratio, unique_ratio[0])

        # Third test, if we provide an aggregation with a ratio and the
        # exposure has a count, we should a have a ratio from the exposure
        # count.
        hazard_layer = load_test_vector_layer(
            'gisv4', 'hazard', 'classified_vector.geojson')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'population.geojson')
        aggregation_layer = load_test_vector_layer(
            'gisv4', 'aggregation', 'small_grid.geojson')

        # Set up impact function
        impact_function = ImpactFunction()
        impact_function.debug_mode = True
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.aggregation = aggregation_layer
        impact_function.prepare()
        status, message = impact_function.run()
        self.assertEqual(ANALYSIS_SUCCESS, status, message)

        impact = impact_function.impact

        # Check that we have don't have only one unique value since the ratio
        # depends on the "population / female count" and we should have at
        # least different ratios.
        field = impact.fieldNameIndex(
            female_ratio_field['field_name'])
        self.assertNotEqual(-1, field)
#.........这里部分代码省略.........

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
def run_scenario(scenario, use_debug=False):
    """Run scenario.

    :param scenario: Dictionary of hazard, exposure, and aggregation.
    :type scenario: dict

    :param use_debug: If we should use debug_mode when we run the scenario.
    :type use_debug: bool

    :returns: Tuple(status, Flow dictionary, outputs).
    :rtype: list
    """
    if os.path.exists(scenario['exposure']):
        exposure_path = scenario['exposure']
    elif os.path.exists(standard_data_path('exposure', scenario['exposure'])):
        exposure_path = standard_data_path('exposure', scenario['exposure'])
    elif os.path.exists(
            standard_data_path(*(scenario['exposure'].split('/')))):
        exposure_path = standard_data_path(*(scenario['exposure'].split('/')))
    else:
        raise IOError('No exposure file')

    if os.path.exists(scenario['hazard']):
        hazard_path = scenario['hazard']
    elif os.path.exists(standard_data_path('hazard', scenario['hazard'])):
        hazard_path = standard_data_path('hazard', scenario['hazard'])
    elif os.path.exists(standard_data_path(*(scenario['hazard'].split('/')))):
        hazard_path = standard_data_path(*(scenario['hazard'].split('/')))
    else:
        raise IOError('No hazard file')

    if not scenario['aggregation']:
        aggregation_path = None
    else:
        if os.path.exists(scenario['aggregation']):
            aggregation_path = scenario['aggregation']
        elif os.path.exists(standard_data_path(
                'aggregation', scenario['aggregation'])):
            aggregation_path = standard_data_path(
                'aggregation', scenario['aggregation'])
        elif os.path.exists(
                standard_data_path(*(scenario['aggregation'].split('/')))):
            aggregation_path = standard_data_path(
                *(scenario['aggregation'].split('/')))
        else:
            raise IOError('No aggregation file')

    impact_function = ImpactFunction()
    impact_function.debug_mode = use_debug

    layer = QgsVectorLayer(hazard_path, 'Hazard', 'ogr')
    if not layer.isValid():
        layer = QgsRasterLayer(hazard_path, 'Hazard')
    impact_function.hazard = layer

    layer = QgsVectorLayer(exposure_path, 'Exposure', 'ogr')
    if not layer.isValid():
        layer = QgsRasterLayer(exposure_path, 'Exposure')
    impact_function.exposure = layer

    if aggregation_path:
        impact_function.aggregation = QgsVectorLayer(
            aggregation_path, 'Aggregation', 'ogr')

    status, message = impact_function.prepare()
    if status != 0:
        return status, message, None

    status, message = impact_function.run()
    if status != 0:
        return status, message, None

    for layer in impact_function.outputs:
        if layer.type() == QgsMapLayer.VectorLayer:
            check_inasafe_fields(layer)

    return status, impact_function.state, impact_function.outputs

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
    def test_provenance_without_aggregation(self):
        """Test provenance of impact function without aggregation."""
        hazard_layer = load_test_vector_layer(
            'gisv4', 'hazard', 'classified_vector.geojson')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'building-points.geojson')

        hazard = definition(hazard_layer.keywords['hazard'])
        exposure = definition(exposure_layer.keywords['exposure'])
        hazard_category = definition(hazard_layer.keywords['hazard_category'])

        expected_provenance = {
            provenance_gdal_version['provenance_key']: gdal.__version__,
            provenance_host_name['provenance_key']: gethostname(),
            provenance_map_title['provenance_key']: get_map_title(
                hazard, exposure, hazard_category),
            provenance_map_legend_title['provenance_key']: exposure[
                'layer_legend_title'],
            provenance_user['provenance_key']: getpass.getuser(),
            provenance_os['provenance_key']: readable_os_version(),
            provenance_pyqt_version['provenance_key']: PYQT_VERSION_STR,
            provenance_qgis_version['provenance_key']: QGis.QGIS_VERSION,
            provenance_qt_version['provenance_key']: QT_VERSION_STR,
            provenance_inasafe_version['provenance_key']: get_version(),
            provenance_aggregation_layer['provenance_key']: None,
            provenance_aggregation_layer_id['provenance_key']: None,
            provenance_exposure_layer['provenance_key']:
                exposure_layer.source(),
            provenance_exposure_layer_id['provenance_key']:
                exposure_layer.id(),
            provenance_hazard_layer['provenance_key']: hazard_layer.source(),
            provenance_hazard_layer_id['provenance_key']: hazard_layer.id(),
            provenance_analysis_question['provenance_key']:
                get_analysis_question(hazard, exposure),
            provenance_aggregation_keywords['provenance_key']: None,
            provenance_exposure_keywords['provenance_key']:
                deepcopy(exposure_layer.keywords),
            provenance_hazard_keywords['provenance_key']: deepcopy(
                hazard_layer.keywords),
        }

        # Set up impact function
        impact_function = ImpactFunction()
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        status, message = impact_function.prepare()
        self.assertEqual(PREPARE_SUCCESS, status, message)
        status, message = impact_function.run()
        self.assertEqual(ANALYSIS_SUCCESS, status, message)

        self.maxDiff = None

        expected_provenance.update({
            provenance_action_checklist['provenance_key']:
                impact_function.action_checklist(),
            provenance_analysis_extent['provenance_key']:
                impact_function.analysis_extent.exportToWkt(),
            provenance_impact_function_name['provenance_key']:
                impact_function.name,
            provenance_impact_function_title['provenance_key']:
                impact_function.title,
            provenance_notes['provenance_key']: impact_function.notes(),
            provenance_requested_extent['provenance_key']: impact_function.
                requested_extent,
            provenance_data_store_uri['provenance_key']: impact_function.
                datastore.uri_path,
            provenance_start_datetime['provenance_key']: impact_function.
                start_datetime,
            provenance_end_datetime['provenance_key']:
                impact_function.end_datetime,
            provenance_duration['provenance_key']: impact_function.duration
        })

        self.assertDictContainsSubset(
            expected_provenance, impact_function.provenance)

        output_layer_provenance_keys = [
            provenance_layer_exposure_summary['provenance_key'],
            provenance_layer_aggregate_hazard_impacted['provenance_key'],
            provenance_layer_aggregation_summary['provenance_key'],
            provenance_layer_analysis_impacted['provenance_key'],
            provenance_layer_exposure_summary_table['provenance_key']
        ]

        for key in output_layer_provenance_keys:
            self.assertIn(key, impact_function.provenance.keys())

# 需要导入模块: from safe.impact_function.impact_function import ImpactFunction [as 别名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import run [as 别名]
    def test_earthquake_population_without_aggregation(self):
        """Testing Earthquake in Population without aggregation.

        .. versionadded:: 4.0
        """
        output_folder = self.fixtures_dir('../output/earthquake_population')

        # Classified vector with building-points
        shutil.rmtree(output_folder, ignore_errors=True)

        hazard_layer = load_test_raster_layer(
            'hazard', 'earthquake.tif')
        exposure_layer = load_test_raster_layer(
            'exposure', 'pop_binary_raster_20_20.asc')

        impact_function = ImpactFunction()
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.prepare()
        return_code, message = impact_function.run()

        self.assertEqual(return_code, ANALYSIS_SUCCESS, message)

        report_metadata = ReportMetadata(
            metadata_dict=standard_impact_report_metadata_html)

        impact_report = ImpactReport(
            IFACE,
            report_metadata,
            impact_function=impact_function)
        impact_report.output_folder = output_folder
        return_code, message = impact_report.process_components()

        self.assertEqual(
            return_code, ImpactReport.REPORT_GENERATION_SUCCESS, message)

        """Checking generated context"""
        empty_component_output_message = 'Empty component output'

        # Check Analysis Summary
        analysis_summary = impact_report.metadata.component_by_key(
            general_report_component['key'])
        """:type: safe.report.report_metadata.Jinja2ComponentsMetadata"""

        expected_context = {
            'table_header': (
                u'Estimated Number of people affected per MMI intensity'),
            'header': u'General Report',
            'summary': [
                {
                    'header_label': u'Hazard Zone',
                    'rows': [
                        {'value': 0, 'name': u'X', 'key': 'X'},
                        {'value': 0, 'name': u'IX', 'key': 'IX'},
                        {'value': '200', 'name': u'VIII', 'key': 'VIII'},
                        {'value': 0, 'name': u'VII', 'key': 'VII'},
                        {'value': 0, 'name': u'VI', 'key': 'VI'},
                        {'value': 0, 'name': u'V', 'key': 'V'},
                        {'value': 0, 'name': u'IV', 'key': 'IV'},
                        {'value': 0, 'name': u'III', 'key': 'III'},
                        {'value': 0, 'name': u'II', 'key': 'II'},
                        {'value': 0, 'name': u'I', 'key': 'I'},
                        {
                            'as_header': True,
                            'key': 'total_field',
                            'name': u'Total',
                            'value': '200'
                        }
                    ],
                    'value_label': u'Count'
                },
                {
                    'header_label': u'Population',
                    'rows': [
                        {
                            'value': '200',
                            'name': u'Affected',
                            'key': 'total_affected_field',
                        }, {
                            'key': 'total_not_affected_field',
                            'name': u'Not Affected',
                            'value': '0'
                        }, {
                            'key': 'total_not_exposed_field',
                            'name': u'Not Exposed',
                            'value': '0'},
                        {
                            'value': '200',
                            'name': u'Displaced',
                            'key': 'displaced_field'
                        }, {
                            'value': '0 - 100',
                            'name': u'Fatalities',
                            'key': 'fatalities_field'
                        }],
                    'value_label': u'Count'
                }
            ],
            'notes': [
                'Exposed People: People who are present in hazard zones and '
#.........这里部分代码省略.........