Python ImpactFunction.hazard方法代碼示例

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    def test_pre_processors_earthquake_contour(self):
        """Test the pre_processors_earthquake_contour"""
        hazard_layer = load_test_raster_layer(
            'gisv4', 'hazard', 'earthquake.asc')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'building-points.geojson')
        impact_function = ImpactFunction()
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.crs = QgsCoordinateReferenceSystem(4326)
        status, message = impact_function.prepare()
        self.assertEqual(PREPARE_SUCCESS, status, message)

        self.assertTrue(
            pre_processor_earthquake_contour['condition'](impact_function))

        hazard_layer = load_test_raster_layer(
            'hazard', 'classified_flood_20_20.asc')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'places.geojson')
        impact_function = ImpactFunction()
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.crs = QgsCoordinateReferenceSystem(4326)
        status, message = impact_function.prepare()
        self.assertEqual(PREPARE_SUCCESS, status, message)

        # not ok, since the hazard is flood, not earthquake
        self.assertFalse(
            pre_processor_earthquake_contour['condition'](impact_function))

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    def test_pre_processors_nearby_places(self):
        """Test the pre_processors_nearby_places"""
        hazard_layer = load_test_raster_layer(
            'gisv4', 'hazard', 'earthquake.asc')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'building-points.geojson')
        impact_function = ImpactFunction()
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.crs = QgsCoordinateReferenceSystem(4326)
        status, message = impact_function.prepare()
        self.assertEqual(PREPARE_SUCCESS, status, message)

        # The exposure is not place but buildings
        self.assertFalse(
            pre_processors_nearby_places['condition'](impact_function))

        hazard_layer = load_test_raster_layer(
            'gisv4', 'hazard', 'earthquake.asc')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'places.geojson')
        impact_function = ImpactFunction()
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.crs = QgsCoordinateReferenceSystem(4326)
        status, message = impact_function.prepare()
        self.assertEqual(PREPARE_SUCCESS, status, message)

        # EQ on places, it must be OK.
        self.assertTrue(
            pre_processors_nearby_places['condition'](impact_function))

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    def test_old_fields_keywords(self):
        """The IF is not ready with we have some wrong inasafe_fields."""
        hazard_layer = load_test_vector_layer(
            'gisv4', 'hazard', 'classified_vector.geojson')
        exposure_layer = load_test_vector_layer(
            'gisv4', 'exposure', 'building-points.geojson',
            clone=True)
        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()

        # The layer should be fine.
        self.assertEqual(PREPARE_SUCCESS, status, message)

        # Now, we remove one field
        exposure_layer.startEditing()
        field = exposure_layer.keywords['inasafe_fields'].values()[0]
        index = exposure_layer.fieldNameIndex(field)
        exposure_layer.deleteAttribute(index)
        exposure_layer.commitChanges()

        # It shouldn't be fine as we removed one field which
        # was in inasafe_fields
        status, message = impact_function.prepare()
        self.assertNotEqual(PREPARE_SUCCESS, status, message)

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    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

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    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)

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    def prepare_impact_function(self):
        """Create analysis as a representation of current situation of IFCW."""

        # Impact Functions
        impact_function = ImpactFunction()
        impact_function.callback = self.progress_callback

        # Layers
        impact_function.hazard = self.parent.hazard_layer
        impact_function.exposure = self.parent.exposure_layer
        aggregation = self.parent.aggregation_layer

        if aggregation:
            impact_function.aggregation = aggregation
            impact_function.use_selected_features_only = (
                setting('useSelectedFeaturesOnly', False, bool))
        else:
            mode = setting('analysis_extents_mode')
            if self.extent.user_extent:
                # This like a hack to transform a geometry to a rectangle.
                # self.extent.user_extent is a QgsGeometry.
                # impact_function.requested_extent needs a QgsRectangle.
                wkt = self.extent.user_extent.exportToWkt()
                impact_function.requested_extent = wkt_to_rectangle(wkt)
                impact_function.requested_extent_crs = self.extent.crs

            elif mode == HAZARD_EXPOSURE_VIEW:
                impact_function.requested_extent = (
                    self.iface.mapCanvas().extent())
                impact_function.requested_extent_crs = self.extent.crs

        # We don't have any checkbox in the wizard for the debug mode.
        impact_function.debug_mode = False

        return impact_function

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    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)

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    def test_impact_function_behaviour(self):
        """Test behaviour of impact function."""
        hazard_layer = load_test_vector_layer(
            'hazard', 'flood_multipart_polygons.shp')
        exposure_layer = load_test_vector_layer('exposure', 'roads.shp')

        impact_function = ImpactFunction()
        impact_function.exposure = exposure_layer
        impact_function.hazard = hazard_layer
        impact_function.prepare()
        self.assertEqual(impact_function.name, 'Flood Polygon On Road Line')
        self.assertEqual(impact_function.title, 'be affected')

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    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)

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    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)

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
def impact_function_setup(
        command_line_arguments, hazard, exposure, aggregation=None):
    """Sets up an analysis object.

    .. versionadded:: 3.2

    :param command_line_arguments: User inputs.
    :type command_line_arguments: CommandLineArguments

    :param hazard: Hazard layer
    :type hazard: QgsLayer

    :param exposure: Exposure Layer
    :type exposure: QgsLayer

    :param aggregation: Aggregation Layer
    :type aggregation: QgsLayer

    :raises: Exception
    """
    # IF
    impact_function = ImpactFunction()

    impact_function.hazard = hazard
    impact_function.exposure = exposure
    impact_function.aggregation = aggregation
    impact_function.map_canvas = CANVAS
    # QSetting context
    settings = QSettings()
    crs = settings.value('inasafe/user_extent_crs', '', type=str)
    impact_function.requested_extent_crs = QgsCoordinateReferenceSystem(crs)
    try:
        impact_function.requested_extent = QgsRectangle(
            float(command_line_arguments.extent[0]),
            float(command_line_arguments.extent[1]),
            float(command_line_arguments.extent[2]),
            float(command_line_arguments.extent[3])
        )
    except AttributeError:
        print "No extents"
        pass
    return impact_function

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    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))
#.........這裏部分代碼省略.........

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    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)
#.........這裏部分代碼省略.........

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
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

# 需要導入模塊: from safe.impact_function.impact_function import ImpactFunction [as 別名]
# 或者: from safe.impact_function.impact_function.ImpactFunction import hazard [as 別名]
    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())