Exemplo n.º 1
0
    def create(self, validated_data):

        with transaction.atomic():
            # prepare ValidationRun model
            new_val_run = ValidationRun(start_time=timezone.now())
            new_val_run.interval_from = validated_data.get('validation_period').get('interval_from', None)
            new_val_run.interval_to = validated_data.get('validation_period').get('interval_to', None)
            new_val_run.anomalies = validated_data.get('anomalies').get('method')
            new_val_run.anomalies_from = validated_data.get('anomalies').get('anomalies_from', None)
            new_val_run.anomalies_to = validated_data.get('anomalies').get('anomalies_to', None)
            new_val_run.min_lat = validated_data.get('spatial_subsetting').get('min_lat', None)
            new_val_run.min_lon = validated_data.get('spatial_subsetting').get('min_lon', None)
            new_val_run.max_lat = validated_data.get('spatial_subsetting').get('max_lat', None)
            new_val_run.max_lon = validated_data.get('spatial_subsetting').get('max_lon', None)
            new_val_run.scaling_method = validated_data.get('scaling').get('method', None)

            for metric in validated_data.get('metrics'):
                if metric.get('id') == 'tcol':
                    new_val_run.tcol = metric.get('value')

            new_val_run.save()

            # prepare DatasetConfiguration models
            reference_config = None
            dataset_config_models = []
            configs_to_save = [validated_data.get('reference_config')]
            print('Reference config:')
            print(configs_to_save)
            configs_to_save.extend(validated_data.get('dataset_configs'))
            for config in configs_to_save:
                config_model = DatasetConfiguration.objects.create(validation=new_val_run,
                                                                   dataset_id=config.get('dataset_id'),
                                                                   version_id=config.get('version_id'),
                                                                   variable_id=config.get('variable_id'))
                config_model.save()
                filter_models = []
                for filter_id in config.get('basic_filters'):
                    filter_models.append(DataFilter.objects.get(id=filter_id))

                for filter_model in filter_models:
                    config_model.filters.add(filter_model)
                config_model.save()
                dataset_config_models.append(config_model)

            new_val_run.reference_configuration = dataset_config_models[0]
            new_val_run.save()

        return new_val_run
Exemplo n.º 2
0
    def test_validation_run_clean(self):
        run = ValidationRun()

        ## default object should be valid
        run.clean()

        ## object with just a from date should be invalid
        run.interval_from = datetime(2000, 1, 1)
        with pytest.raises(ValidationError):
            run.clean()

        ## object with just a to start date should be invalid
        run.interval_from = None
        run.interval_to = datetime(2000, 1, 1)
        with pytest.raises(ValidationError):
            run.clean()

        ## object with from date after to date should be invalid
        run.interval_from = datetime(2001, 1, 1)
        with pytest.raises(ValidationError):
            run.clean()

        ## object with from date before to date should be valid
        run.interval_to = datetime(2005, 1, 1)
        run.clean()

        ## object with no spatial subsetting should be valid
        run.min_lat = None
        run.max_lat = None
        run.min_lon = None
        run.max_lon = None
        run.clean()

        ## spatial subsetting with only two coords should be invalid
        run.min_lat = -45.0
        run.max_lat = +45.0
        with pytest.raises(ValidationError):
            run.clean()

        ## spatial subsetting with four coords should be valid
        run.min_lon = -120.0
        run.max_lon = +120.0
        run.clean()

        ## climatology with moving average should be valid without time period
        run.anomalies = ValidationRun.MOVING_AVG_35_D
        run.clean()

        ## climatology without anomalies time period should be invalid
        run.anomalies = ValidationRun.CLIMATOLOGY
        with pytest.raises(ValidationError):
            run.clean()

        ## climatology with broken time period should be invalid
        run.anomalies_from = datetime(2005, 1, 1)
        run.anomalies_to = datetime(2000, 1, 1)
        with pytest.raises(ValidationError):
            run.clean()

        ## climatology with correct time period should be invalid
        run.anomalies_from = datetime(2000, 1, 1)
        run.anomalies_to = datetime(2005, 1, 1)
        run.clean()

        ## climatology with moving average should be invalid with time period
        run.anomalies = ValidationRun.MOVING_AVG_35_D
        with pytest.raises(ValidationError):
            run.clean()