def generate_default_validation_triple_coll():
run = ValidationRun()
run.start_time = datetime.now(tzlocal())
run.save()
data_c = DatasetConfiguration()
data_c.validation = run
data_c.dataset = Dataset.objects.get(short_name='C3S')
data_c.version = DatasetVersion.objects.get(short_name='C3S_V201912')
data_c.variable = DataVariable.objects.get(short_name='C3S_sm')
data_c.save()
other_data_c = DatasetConfiguration()
other_data_c.validation = run
other_data_c.dataset = Dataset.objects.get(short_name='SMOS')
other_data_c.version = DatasetVersion.objects.get(short_name='SMOS_105_ASC')
other_data_c.variable = DataVariable.objects.get(short_name='SMOS_sm')
other_data_c.save()
ref_c = DatasetConfiguration()
ref_c.validation = run
ref_c.dataset = Dataset.objects.get(short_name='ISMN')
ref_c.version = DatasetVersion.objects.get(short_name='ISMN_V20180712_MINI')
ref_c.variable = DataVariable.objects.get(short_name='ISMN_soil_moisture')
ref_c.save()
run.reference_configuration = ref_c
run.scaling_ref = ref_c
run.tcol = True
run.bootstrap_tcol_cis = True
run.save()
return run
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
