def test_coverage_transform(self): ph = ParameterHelper(self.dataset_management, self.addCleanup) pdict_id = ph.create_parsed() stream_def_id = self.pubsub_management.create_stream_definition( 'ctd parsed', parameter_dictionary_id=pdict_id) self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id) stream_id, route = self.pubsub_management.create_stream( 'example', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id) self.addCleanup(self.pubsub_management.delete_stream, stream_id) ingestion_config_id = self.get_ingestion_config() dataset_id = self.create_dataset(pdict_id) self.ingestion_management.persist_data_stream( stream_id=stream_id, ingestion_configuration_id=ingestion_config_id, dataset_id=dataset_id) self.addCleanup(self.ingestion_management.unpersist_data_stream, stream_id, ingestion_config_id) publisher = StandaloneStreamPublisher(stream_id, route) rdt = ph.get_rdt(stream_def_id) ph.fill_parsed_rdt(rdt) dataset_monitor = DatasetMonitor(dataset_id) self.addCleanup(dataset_monitor.stop) publisher.publish(rdt.to_granule()) self.assertTrue(dataset_monitor.event.wait(30)) replay_granule = self.data_retriever.retrieve(dataset_id) rdt_out = RecordDictionaryTool.load_from_granule(replay_granule) np.testing.assert_array_almost_equal(rdt_out['time'], rdt['time']) np.testing.assert_array_almost_equal(rdt_out['temp'], rdt['temp']) np.testing.assert_array_almost_equal(rdt_out['conductivity_L1'], np.array([42.914])) np.testing.assert_array_almost_equal(rdt_out['temp_L1'], np.array([20.])) np.testing.assert_array_almost_equal(rdt_out['pressure_L1'], np.array([3.068])) np.testing.assert_array_almost_equal( rdt_out['density'], np.array([1021.7144739593881], dtype='float32')) np.testing.assert_array_almost_equal( rdt_out['salinity'], np.array([30.935132729668283], dtype='float32'))
def test_coverage_transform(self): ph = ParameterHelper(self.dataset_management, self.addCleanup) pdict_id = ph.create_parsed() stream_def_id = self.pubsub_management.create_stream_definition('ctd parsed', parameter_dictionary_id=pdict_id) self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id) stream_id, route = self.pubsub_management.create_stream('example', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id) self.addCleanup(self.pubsub_management.delete_stream, stream_id) ingestion_config_id = self.get_ingestion_config() dataset_id = self.create_dataset(pdict_id) self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingestion_config_id, dataset_id=dataset_id) self.addCleanup(self.ingestion_management.unpersist_data_stream, stream_id, ingestion_config_id) publisher = StandaloneStreamPublisher(stream_id, route) rdt = ph.get_rdt(stream_def_id) ph.fill_parsed_rdt(rdt) dataset_monitor = DatasetMonitor(dataset_id) self.addCleanup(dataset_monitor.stop) publisher.publish(rdt.to_granule()) self.assertTrue(dataset_monitor.wait()) replay_granule = self.data_retriever.retrieve(dataset_id) rdt_out = RecordDictionaryTool.load_from_granule(replay_granule) np.testing.assert_array_almost_equal(rdt_out['time'], rdt['time']) np.testing.assert_array_almost_equal(rdt_out['temp'], rdt['temp']) np.testing.assert_allclose(rdt_out['conductivity_L1'], np.array([42.914])) np.testing.assert_allclose(rdt_out['temp_L1'], np.array([20.])) np.testing.assert_allclose(rdt_out['pressure_L1'], np.array([3.068])) np.testing.assert_allclose(rdt_out['density'], np.array([1021.7144739593881], dtype='float32')) np.testing.assert_allclose(rdt_out['salinity'], np.array([30.935132729668283], dtype='float32'))
class TestTransformWorker(IonIntegrationTestCase): def setUp(self): self._start_container() self.container.start_rel_from_url('res/deploy/r2deploy.yml') # Instantiate a process to represent the test process=TransformWorkerTestProcess() self.dataset_management_client = DatasetManagementServiceClient(node=self.container.node) self.pubsub_client = PubsubManagementServiceClient(node=self.container.node) self.dataproductclient = DataProductManagementServiceClient(node=self.container.node) self.dataprocessclient = DataProcessManagementServiceClient(node=self.container.node) self.processdispatchclient = ProcessDispatcherServiceClient(node=self.container.node) self.damsclient = DataAcquisitionManagementServiceClient(node=self.container.node) self.rrclient = ResourceRegistryServiceClient(node=self.container.node) self.imsclient = InstrumentManagementServiceProcessClient(node=self.container.node, process = process) self.time_dom, self.spatial_dom = time_series_domain() self.ph = ParameterHelper(self.dataset_management_client, self.addCleanup) self.wait_time = CFG.get_safe('endpoint.receive.timeout', 10) def push_granule(self, data_product_id): ''' Publishes and monitors that the granule arrived ''' datasets, _ = self.rrclient.find_objects(data_product_id, PRED.hasDataset, id_only=True) dataset_monitor = DatasetMonitor(datasets[0]) rdt = self.ph.rdt_for_data_product(data_product_id) self.ph.fill_parsed_rdt(rdt) self.ph.publish_rdt_to_data_product(data_product_id, rdt) assert dataset_monitor.wait() dataset_monitor.stop() @attr('LOCOINT') @unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode') def test_transform_worker(self): # test that a data process (type: data-product-in / data-product-out) can be defined and launched. # verify that the output granule fields are correctly populated # test that the input and output data products are linked to facilitate provenance self.dp_list = [] self.data_process_objs = [] self._output_stream_ids = [] self.granule_verified = Event() self.worker_assigned_event_verified = Event() self.dp_created_event_verified = Event() self.heartbeat_event_verified = Event() self.parameter_dict_id = self.dataset_management_client.read_parameter_dictionary_by_name(name='ctd_parsed_param_dict', id_only=True) # create the StreamDefinition self.stream_def_id = self.pubsub_client.create_stream_definition(name='stream_def', parameter_dictionary_id=self.parameter_dict_id) self.addCleanup(self.pubsub_client.delete_stream_definition, self.stream_def_id) # create the DataProduct that is the input to the data processes input_dp_obj = IonObject( RT.DataProduct, name='input_data_product', description='input test stream', temporal_domain = self.time_dom.dump(), spatial_domain = self.spatial_dom.dump()) self.input_dp_id = self.dataproductclient.create_data_product(data_product=input_dp_obj, stream_definition_id=self.stream_def_id) # retrieve the Stream for this data product stream_ids, assoc_ids = self.rrclient.find_objects(self.input_dp_id, PRED.hasStream, RT.Stream, True) self.stream_id = stream_ids[0] self.start_event_listener() # create the DPD, DataProcess and output DataProduct dataprocessdef_id, dataprocess_id, dataproduct_id = self.create_data_process() self.dp_list.append(dataprocess_id) # validate the repository for data product algorithms persists the new resources NEW SA-1 # create_data_process call created one of each dpd_ids, _ = self.rrclient.find_resources(restype=OT.DataProcessDefinition, id_only=False) # there will be more than one becuase of the DPDs that reperesent the PFs in the data product above self.assertTrue(dpd_ids is not None) dp_ids, _ = self.rrclient.find_resources(restype=OT.DataProcess, id_only=False) # only one DP becuase the PFs that are in the code dataproduct above are not activated yet. self.assertEquals(len(dp_ids), 1) # validate the name and version label NEW SA - 2 dataprocessdef_obj = self.dataprocessclient.read_data_process_definition(dataprocessdef_id) self.assertEqual(dataprocessdef_obj.version_label, '1.0a') self.assertEqual(dataprocessdef_obj.name, 'add_arrays') # validate that the DPD has an attachment NEW SA - 21 attachment_ids, assoc_ids = self.rrclient.find_objects(dataprocessdef_id, PRED.hasAttachment, RT.Attachment, True) self.assertEqual(len(attachment_ids), 1) attachment_obj = self.rrclient.read_attachment(attachment_ids[0]) log.debug('attachment: %s', attachment_obj) # validate that the data process resource has input and output data products associated # L4-CI-SA-RQ-364 and NEW SA-3 outproduct_ids, assoc_ids = self.rrclient.find_objects(dataprocess_id, PRED.hasOutputProduct, RT.DataProduct, True) self.assertEqual(len(outproduct_ids), 1) inproduct_ids, assoc_ids = self.rrclient.find_objects(dataprocess_id, PRED.hasInputProduct, RT.DataProduct, True) self.assertEqual(len(inproduct_ids), 1) # Test for provenance. Get Data product produced by the data processes output_data_product_id,_ = self.rrclient.find_objects(subject=dataprocess_id, object_type=RT.DataProduct, predicate=PRED.hasOutputProduct, id_only=True) output_data_product_provenance = self.dataproductclient.get_data_product_provenance(output_data_product_id[0]) # Do a basic check to see if there were 3 entries in the provenance graph. Parent and Child and the # DataProcessDefinition creating the child from the parent. self.assertTrue(len(output_data_product_provenance) == 2) self.assertTrue(self.input_dp_id in output_data_product_provenance[output_data_product_id[0]]['parents']) self.assertTrue(output_data_product_provenance[output_data_product_id[0]]['parents'][self.input_dp_id]['data_process_definition_id'] == dataprocessdef_id) # NEW SA - 4 | Data processing shall include the appropriate data product algorithm name and version number in # the metadata of each output data product created by the data product algorithm. output_data_product_obj,_ = self.rrclient.find_objects(subject=dataprocess_id, object_type=RT.DataProduct, predicate=PRED.hasOutputProduct, id_only=False) self.assertTrue(output_data_product_obj[0].name != None) self.assertTrue(output_data_product_obj[0]._rev != None) # retrieve subscription from data process subscription_objs, _ = self.rrclient.find_objects(subject=dataprocess_id, predicate=PRED.hasSubscription, object_type=RT.Subscription, id_only=False) log.debug('test_transform_worker subscription_obj: %s', subscription_objs[0]) # create a queue to catch the published granules self.subscription_id = self.pubsub_client.create_subscription(name='parsed_subscription', stream_ids=[self.stream_id], exchange_name=subscription_objs[0].exchange_name) self.addCleanup(self.pubsub_client.delete_subscription, self.subscription_id) self.pubsub_client.activate_subscription(self.subscription_id) self.addCleanup(self.pubsub_client.deactivate_subscription, self.subscription_id) stream_route = self.pubsub_client.read_stream_route(self.stream_id) self.publisher = StandaloneStreamPublisher(stream_id=self.stream_id, stream_route=stream_route ) for n in range(1, 101): rdt = RecordDictionaryTool(stream_definition_id=self.stream_def_id) rdt['time'] = [0] # time should always come first rdt['conductivity'] = [1] rdt['pressure'] = [2] rdt['salinity'] = [8] self.publisher.publish(rdt.to_granule()) # validate that the output granule is received and the updated value is correct self.assertTrue(self.granule_verified.wait(self.wait_time)) # validate that the data process loaded into worker event is received (L4-CI-SA-RQ-182) self.assertTrue(self.worker_assigned_event_verified.wait(self.wait_time)) # validate that the data process create (with data product ids) event is received (NEW SA -42) self.assertTrue(self.dp_created_event_verified.wait(self.wait_time)) # validate that the data process heartbeat event is received (for every hundred granules processed) (L4-CI-SA-RQ-182) #this takes a while so set wait limit to large value self.assertTrue(self.heartbeat_event_verified.wait(200)) # validate that the code from the transform function can be retrieve via inspect_data_process_definition src = self.dataprocessclient.inspect_data_process_definition(dataprocessdef_id) self.assertIn( 'def add_arrays(a, b)', src) # now delete the DPD and DP then verify that the resources are retired so that information required for provenance are still available self.dataprocessclient.delete_data_process(dataprocess_id) self.dataprocessclient.delete_data_process_definition(dataprocessdef_id) in_dp_objs, _ = self.rrclient.find_objects(subject=dataprocess_id, predicate=PRED.hasInputProduct, object_type=RT.DataProduct, id_only=True) self.assertTrue(in_dp_objs is not None) dpd_objs, _ = self.rrclient.find_subjects(subject_type=RT.DataProcessDefinition, predicate=PRED.hasDataProcess, object=dataprocess_id, id_only=True) self.assertTrue(dpd_objs is not None) @attr('LOCOINT') @unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode') def test_transform_worker_with_instrumentdevice(self): # test that a data process (type: data-product-in / data-product-out) can be defined and launched. # verify that the output granule fields are correctly populated # test that the input and output data products are linked to facilitate provenance self.data_process_objs = [] self._output_stream_ids = [] self.event_verified = Event() # Create CTD Parsed as the initial data product # create a stream definition for the data from the ctd simulator self.parameter_dict_id = self.dataset_management_client.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True) self.stream_def_id = self.pubsub_client.create_stream_definition(name='stream_def', parameter_dictionary_id=self.parameter_dict_id) # create the DataProduct that is the input to the data processes input_dp_obj = IonObject( RT.DataProduct, name='input_data_product', description='input test stream', temporal_domain = self.time_dom.dump(), spatial_domain = self.spatial_dom.dump()) self.input_dp_id = self.dataproductclient.create_data_product(data_product=input_dp_obj, stream_definition_id=self.stream_def_id) # retrieve the Stream for this data product stream_ids, assoc_ids = self.rrclient.find_objects(self.input_dp_id, PRED.hasStream, RT.Stream, True) self.stream_id = stream_ids[0] log.debug('new ctd_parsed_data_product_id = %s' % self.input_dp_id) # only ever need one device for testing purposes. instDevice_obj,_ = self.rrclient.find_resources(restype=RT.InstrumentDevice, name='test_ctd_device') if instDevice_obj: instDevice_id = instDevice_obj[0]._id else: instDevice_obj = IonObject(RT.InstrumentDevice, name='test_ctd_device', description="test_ctd_device", serial_number="12345" ) instDevice_id = self.imsclient.create_instrument_device(instrument_device=instDevice_obj) self.damsclient.assign_data_product(input_resource_id=instDevice_id, data_product_id=self.input_dp_id) # create the DPD, DataProcess and output DataProduct dataprocessdef_id, dataprocess_id, dataproduct_id = self.create_data_process() self.addCleanup(self.dataprocessclient.delete_data_process, dataprocess_id) self.addCleanup(self.dataprocessclient.delete_data_process_definition, dataprocessdef_id) # Test for provenance. Get Data product produced by the data processes output_data_product_id,_ = self.rrclient.find_objects(subject=dataprocess_id, object_type=RT.DataProduct, predicate=PRED.hasOutputProduct, id_only=True) output_data_product_provenance = self.dataproductclient.get_data_product_provenance(output_data_product_id[0]) # Do a basic check to see if there were 3 entries in the provenance graph. Parent and Child and the # DataProcessDefinition creating the child from the parent. self.assertTrue(len(output_data_product_provenance) == 3) self.assertTrue(self.input_dp_id in output_data_product_provenance[output_data_product_id[0]]['parents']) self.assertTrue(instDevice_id in output_data_product_provenance[self.input_dp_id]['parents']) self.assertTrue(output_data_product_provenance[instDevice_id]['type'] == 'InstrumentDevice') @attr('LOCOINT') @unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode') def test_transform_worker_with_platformdevice(self): # test that a data process (type: data-product-in / data-product-out) can be defined and launched. # verify that the output granule fields are correctly populated # test that the input and output data products are linked to facilitate provenance self.data_process_objs = [] self._output_stream_ids = [] self.event_verified = Event() # Create CTD Parsed as the initial data product # create a stream definition for the data from the ctd simulator self.parameter_dict_id = self.dataset_management_client.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True) self.stream_def_id = self.pubsub_client.create_stream_definition(name='stream_def', parameter_dictionary_id=self.parameter_dict_id) # create the DataProduct that is the input to the data processes input_dp_obj = IonObject( RT.DataProduct, name='input_data_product', description='input test stream', temporal_domain = self.time_dom.dump(), spatial_domain = self.spatial_dom.dump()) self.input_dp_id = self.dataproductclient.create_data_product(data_product=input_dp_obj, stream_definition_id=self.stream_def_id) # retrieve the Stream for this data product stream_ids, assoc_ids = self.rrclient.find_objects(self.input_dp_id, PRED.hasStream, RT.Stream, True) self.stream_id = stream_ids[0] log.debug('new ctd_parsed_data_product_id = %s' % self.input_dp_id) # only ever need one device for testing purposes. platform_device_obj,_ = self.rrclient.find_resources(restype=RT.PlatformDevice, name='TestPlatform') if platform_device_obj: platform_device_id = platform_device_obj[0]._id else: platform_device_obj = IonObject(RT.PlatformDevice, name='TestPlatform', description="TestPlatform", serial_number="12345" ) platform_device_id = self.imsclient.create_platform_device(platform_device=platform_device_obj) self.damsclient.assign_data_product(input_resource_id=platform_device_id, data_product_id=self.input_dp_id) # create the DPD, DataProcess and output DataProduct dataprocessdef_id, dataprocess_id, dataproduct_id = self.create_data_process() self.addCleanup(self.dataprocessclient.delete_data_process, dataprocess_id) self.addCleanup(self.dataprocessclient.delete_data_process_definition, dataprocessdef_id) # Test for provenance. Get Data product produced by the data processes output_data_product_id,_ = self.rrclient.find_objects(subject=dataprocess_id, object_type=RT.DataProduct, predicate=PRED.hasOutputProduct, id_only=True) output_data_product_provenance = self.dataproductclient.get_data_product_provenance(output_data_product_id[0]) # Do a basic check to see if there were 3 entries in the provenance graph. Parent and Child and the # DataProcessDefinition creating the child from the parent. self.assertTrue(len(output_data_product_provenance) == 3) self.assertTrue(self.input_dp_id in output_data_product_provenance[output_data_product_id[0]]['parents']) self.assertTrue(platform_device_id in output_data_product_provenance[self.input_dp_id]['parents']) self.assertTrue(output_data_product_provenance[platform_device_id]['type'] == 'PlatformDevice') @attr('LOCOINT') @unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode') def test_event_transform_worker(self): self.data_process_objs = [] self._output_stream_ids = [] self.event_verified = Event() # test that a data process (type: data-product-in / event-out) can be defined and launched. # verify that event fields are correctly populated self.parameter_dict_id = self.dataset_management_client.read_parameter_dictionary_by_name(name='ctd_parsed_param_dict', id_only=True) # create the StreamDefinition self.stream_def_id = self.pubsub_client.create_stream_definition(name='stream_def', parameter_dictionary_id=self.parameter_dict_id) self.addCleanup(self.pubsub_client.delete_stream_definition, self.stream_def_id) # create the DataProduct input_dp_obj = IonObject( RT.DataProduct, name='input_data_product', description='input test stream', temporal_domain = self.time_dom.dump(), spatial_domain = self.spatial_dom.dump()) self.input_dp_id = self.dataproductclient.create_data_product(data_product=input_dp_obj, stream_definition_id=self.stream_def_id) # retrieve the Stream for this data product stream_ids, assoc_ids = self.rrclient.find_objects(self.input_dp_id, PRED.hasStream, RT.Stream, True) self.stream_id = stream_ids[0] # create the DPD and two DPs self.event_data_process_id = self.create_event_data_processes() # retrieve subscription from data process subscription_objs, _ = self.rrclient.find_objects(subject=self.event_data_process_id, predicate=PRED.hasSubscription, object_type=RT.Subscription, id_only=False) log.debug('test_event_transform_worker subscription_obj: %s', subscription_objs[0]) # create a queue to catch the published granules self.subscription_id = self.pubsub_client.create_subscription(name='parsed_subscription', stream_ids=[self.stream_id], exchange_name=subscription_objs[0].exchange_name) self.addCleanup(self.pubsub_client.delete_subscription, self.subscription_id) self.pubsub_client.activate_subscription(self.subscription_id) self.addCleanup(self.pubsub_client.deactivate_subscription, self.subscription_id) stream_route = self.pubsub_client.read_stream_route(self.stream_id) self.publisher = StandaloneStreamPublisher(stream_id=self.stream_id, stream_route=stream_route ) self.start_event_transform_listener() self.data_modified = Event() rdt = RecordDictionaryTool(stream_definition_id=self.stream_def_id) rdt['time'] = [0] # time should always come first rdt['conductivity'] = [1] rdt['pressure'] = [2] rdt['salinity'] = [8] self.publisher.publish(rdt.to_granule()) self.assertTrue(self.event_verified.wait(self.wait_time)) @attr('LOCOINT') @unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode') def test_bad_argument_map(self): self._output_stream_ids = [] # test that a data process (type: data-product-in / data-product-out) parameter mapping it validated during # data process creation and that the correct exception is raised for both input and output. self.parameter_dict_id = self.dataset_management_client.read_parameter_dictionary_by_name(name='ctd_parsed_param_dict', id_only=True) # create the StreamDefinition self.stream_def_id = self.pubsub_client.create_stream_definition(name='stream_def', parameter_dictionary_id=self.parameter_dict_id) self.addCleanup(self.pubsub_client.delete_stream_definition, self.stream_def_id) # create the DataProduct that is the input to the data processes input_dp_obj = IonObject( RT.DataProduct, name='input_data_product', description='input test stream', temporal_domain = self.time_dom.dump(), spatial_domain = self.spatial_dom.dump()) self.input_dp_id = self.dataproductclient.create_data_product(data_product=input_dp_obj, stream_definition_id=self.stream_def_id) # two data processes using one transform and one DPD dp1_func_output_dp_id = self.create_output_data_product() # Set up DPD and DP #2 - array add function tf_obj = IonObject(RT.TransformFunction, name='add_array_func', description='adds values in an array', function='add_arrays', module="ion_example.add_arrays", arguments=['arr1', 'arr2'], function_type=TransformFunctionType.TRANSFORM, uri='http://sddevrepo.oceanobservatories.org/releases/ion_example-0.1-py2.7.egg' ) add_array_func_id, rev = self.rrclient.create(tf_obj) dpd_obj = IonObject(RT.DataProcessDefinition, name='add_arrays', description='adds the values of two arrays', data_process_type=DataProcessTypeEnum.TRANSFORM_PROCESS ) add_array_dpd_id = self.dataprocessclient.create_data_process_definition(data_process_definition=dpd_obj, function_id=add_array_func_id) self.dataprocessclient.assign_stream_definition_to_data_process_definition(self.stream_def_id, add_array_dpd_id, binding='add_array_func' ) # create the data process with invalid argument map argument_map = {"arr1": "foo", "arr2": "bar"} output_param = "salinity" with self.assertRaises(BadRequest) as cm: dp1_data_process_id = self.dataprocessclient.create_data_process(data_process_definition_id=add_array_dpd_id, inputs=[self.input_dp_id], outputs=[dp1_func_output_dp_id], argument_map=argument_map, out_param_name=output_param) ex = cm.exception log.debug(' exception raised: %s', cm) self.assertEqual(ex.message, "Input data product does not contain the parameters defined in argument map") # create the data process with invalid output parameter name argument_map = {"arr1": "conductivity", "arr2": "pressure"} output_param = "foo" with self.assertRaises(BadRequest) as cm: dp1_data_process_id = self.dataprocessclient.create_data_process(data_process_definition_id=add_array_dpd_id, inputs=[self.input_dp_id], outputs=[dp1_func_output_dp_id], argument_map=argument_map, out_param_name=output_param) ex = cm.exception log.debug(' exception raised: %s', cm) self.assertEqual(ex.message, "Output data product does not contain the output parameter name provided") def create_event_data_processes(self): # two data processes using one transform and one DPD argument_map= {"a": "salinity"} # set up DPD and DP #2 - array add function tf_obj = IonObject(RT.TransformFunction, name='validate_salinity_array', description='validate_salinity_array', function='validate_salinity_array', module="ion.processes.data.transforms.test.test_transform_worker", arguments=['a'], function_type=TransformFunctionType.TRANSFORM ) add_array_func_id, rev = self.rrclient.create(tf_obj) dpd_obj = IonObject(RT.DataProcessDefinition, name='validate_salinity_array', description='validate_salinity_array', data_process_type=DataProcessTypeEnum.TRANSFORM_PROCESS, ) add_array_dpd_id = self.dataprocessclient.create_data_process_definition(data_process_definition=dpd_obj, function_id=add_array_func_id) self.dataprocessclient.assign_stream_definition_to_data_process_definition(self.stream_def_id, add_array_dpd_id, binding='validate_salinity_array' ) # create the data process dp1_data_process_id = self.dataprocessclient.create_data_process(data_process_definition_id=add_array_dpd_id, inputs=[self.input_dp_id], outputs=None, argument_map=argument_map) self.damsclient.register_process(dp1_data_process_id) self.addCleanup(self.dataprocessclient.delete_data_process, dp1_data_process_id) return dp1_data_process_id def create_data_process(self): # two data processes using one transform and one DPD dp1_func_output_dp_id = self.create_output_data_product() argument_map = {"arr1": "conductivity", "arr2": "pressure"} output_param = "salinity" # set up DPD and DP #2 - array add function tf_obj = IonObject(RT.TransformFunction, name='add_array_func', description='adds values in an array', function='add_arrays', module="ion_example.add_arrays", arguments=['arr1', 'arr2'], function_type=TransformFunctionType.TRANSFORM, uri='http://sddevrepo.oceanobservatories.org/releases/ion_example-0.1-py2.7.egg' ) add_array_func_id, rev = self.rrclient.create(tf_obj) dpd_obj = IonObject(RT.DataProcessDefinition, name='add_arrays', description='adds the values of two arrays', data_process_type=DataProcessTypeEnum.TRANSFORM_PROCESS, version_label='1.0a' ) add_array_dpd_id = self.dataprocessclient.create_data_process_definition(data_process_definition=dpd_obj, function_id=add_array_func_id) self.dataprocessclient.assign_stream_definition_to_data_process_definition(self.stream_def_id, add_array_dpd_id, binding='add_array_func' ) # create the data process dp1_data_process_id = self.dataprocessclient.create_data_process(data_process_definition_id=add_array_dpd_id, inputs=[self.input_dp_id], outputs=[dp1_func_output_dp_id], argument_map=argument_map, out_param_name=output_param) self.damsclient.register_process(dp1_data_process_id) #self.addCleanup(self.dataprocessclient.delete_data_process, dp1_data_process_id) # add an attachment object to this DPD to test new SA-21 import msgpack attachment_content = 'foo bar' attachment_obj = IonObject( RT.Attachment, name='test_attachment', attachment_type=AttachmentType.ASCII, content_type='text/plain', content=msgpack.packb(attachment_content)) att_id = self.rrclient.create_attachment(add_array_dpd_id, attachment_obj) self.addCleanup(self.rrclient.delete_attachment, att_id) return add_array_dpd_id, dp1_data_process_id, dp1_func_output_dp_id def create_output_data_product(self): dp1_outgoing_stream_id = self.pubsub_client.create_stream_definition(name='dp1_stream', parameter_dictionary_id=self.parameter_dict_id) dp1_output_dp_obj = IonObject( RT.DataProduct, name='data_process1_data_product', description='output of add array func', temporal_domain = self.time_dom.dump(), spatial_domain = self.spatial_dom.dump()) dp1_func_output_dp_id = self.dataproductclient.create_data_product(dp1_output_dp_obj, dp1_outgoing_stream_id) self.addCleanup(self.dataproductclient.delete_data_product, dp1_func_output_dp_id) # retrieve the id of the OUTPUT stream from the out Data Product and add to granule logger stream_ids, _ = self.rrclient.find_objects(dp1_func_output_dp_id, PRED.hasStream, None, True) self._output_stream_ids.append(stream_ids[0]) subscription_id = self.pubsub_client.create_subscription('validator', data_product_ids=[dp1_func_output_dp_id]) self.addCleanup(self.pubsub_client.delete_subscription, subscription_id) def on_granule(msg, route, stream_id): log.debug('recv_packet stream_id: %s route: %s msg: %s', stream_id, route, msg) self.validate_output_granule(msg, route, stream_id) self.granule_verified.set() validator = StandaloneStreamSubscriber('validator', callback=on_granule) validator.start() self.addCleanup(validator.stop) self.pubsub_client.activate_subscription(subscription_id) self.addCleanup(self.pubsub_client.deactivate_subscription, subscription_id) return dp1_func_output_dp_id def validate_event(self, *args, **kwargs): """ This method is a callback function for receiving DataProcessStatusEvent. """ data_process_event = args[0] log.debug("DataProcessStatusEvent: %s" , str(data_process_event.__dict__)) # if data process already created, check origin if self.dp_list: self.assertIn( data_process_event.origin, self.dp_list) # if this is a heartbeat event then 100 granules have been processed if 'data process status update.' in data_process_event.description: self.heartbeat_event_verified.set() else: # else check that this is the assign event if 'Data process assigned to transform worker' in data_process_event.description: self.worker_assigned_event_verified.set() elif 'Data process created for data product' in data_process_event.description: self.dp_created_event_verified.set() def validate_output_granule(self, msg, route, stream_id): self.assertIn( stream_id, self._output_stream_ids) rdt = RecordDictionaryTool.load_from_granule(msg) log.debug('validate_output_granule rdt: %s', rdt) sal_val = rdt['salinity'] np.testing.assert_array_equal(sal_val, np.array([3])) def start_event_listener(self): es = EventSubscriber(event_type=OT.DataProcessStatusEvent, callback=self.validate_event) es.start() self.addCleanup(es.stop) def validate_transform_event(self, *args, **kwargs): """ This method is a callback function for receiving DataProcessStatusEvent. """ status_alert_event = args[0] np.testing.assert_array_equal(status_alert_event.origin, self.stream_id ) np.testing.assert_array_equal(status_alert_event.values, np.array([self.event_data_process_id])) log.debug("DeviceStatusAlertEvent: %s" , str(status_alert_event.__dict__)) self.event_verified.set() def start_event_transform_listener(self): es = EventSubscriber(event_type=OT.DeviceStatusAlertEvent, callback=self.validate_transform_event) es.start() self.addCleanup(es.stop) def test_download(self): egg_url = 'http://sddevrepo.oceanobservatories.org/releases/ion_example-0.1-py2.7.egg' egg_path = TransformWorker.download_egg(egg_url) import pkg_resources pkg_resources.working_set.add_entry(egg_path) from ion_example.add_arrays import add_arrays a = add_arrays(1,2) self.assertEquals(a,3)
class TestTransformWorker(IonIntegrationTestCase): def setUp(self): self._start_container() self.container.start_rel_from_url('res/deploy/r2deploy.yml') # Instantiate a process to represent the test process = TransformWorkerTestProcess() self.dataset_management_client = DatasetManagementServiceClient( node=self.container.node) self.pubsub_client = PubsubManagementServiceClient( node=self.container.node) self.dataproductclient = DataProductManagementServiceClient( node=self.container.node) self.dataprocessclient = DataProcessManagementServiceClient( node=self.container.node) self.processdispatchclient = ProcessDispatcherServiceClient( node=self.container.node) self.damsclient = DataAcquisitionManagementServiceClient( node=self.container.node) self.rrclient = ResourceRegistryServiceClient(node=self.container.node) self.imsclient = InstrumentManagementServiceProcessClient( node=self.container.node, process=process) self.time_dom, self.spatial_dom = time_series_domain() self.ph = ParameterHelper(self.dataset_management_client, self.addCleanup) self.wait_time = CFG.get_safe('endpoint.receive.timeout', 10) def push_granule(self, data_product_id): ''' Publishes and monitors that the granule arrived ''' datasets, _ = self.rrclient.find_objects(data_product_id, PRED.hasDataset, id_only=True) dataset_monitor = DatasetMonitor(datasets[0]) rdt = self.ph.rdt_for_data_product(data_product_id) self.ph.fill_parsed_rdt(rdt) self.ph.publish_rdt_to_data_product(data_product_id, rdt) assert dataset_monitor.wait() dataset_monitor.stop() @attr('LOCOINT') @unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode') def test_transform_worker(self): # test that a data process (type: data-product-in / data-product-out) can be defined and launched. # verify that the output granule fields are correctly populated # test that the input and output data products are linked to facilitate provenance self.dp_list = [] self.data_process_objs = [] self._output_stream_ids = [] self.granule_verified = Event() self.worker_assigned_event_verified = Event() self.dp_created_event_verified = Event() self.heartbeat_event_verified = Event() self.parameter_dict_id = self.dataset_management_client.read_parameter_dictionary_by_name( name='ctd_parsed_param_dict', id_only=True) # create the StreamDefinition self.stream_def_id = self.pubsub_client.create_stream_definition( name='stream_def', parameter_dictionary_id=self.parameter_dict_id) self.addCleanup(self.pubsub_client.delete_stream_definition, self.stream_def_id) # create the DataProduct that is the input to the data processes input_dp_obj = IonObject(RT.DataProduct, name='input_data_product', description='input test stream') self.input_dp_id = self.dataproductclient.create_data_product( data_product=input_dp_obj, stream_definition_id=self.stream_def_id) # retrieve the Stream for this data product stream_ids, assoc_ids = self.rrclient.find_objects( self.input_dp_id, PRED.hasStream, RT.Stream, True) self.stream_id = stream_ids[0] self.start_event_listener() # create the DPD, DataProcess and output DataProduct dataprocessdef_id, dataprocess_id, dataproduct_id = self.create_data_process( ) self.dp_list.append(dataprocess_id) # validate the repository for data product algorithms persists the new resources NEW SA-1 # create_data_process call created one of each dpd_ids, _ = self.rrclient.find_resources( restype=OT.DataProcessDefinition, id_only=False) # there will be more than one becuase of the DPDs that reperesent the PFs in the data product above self.assertTrue(dpd_ids is not None) dp_ids, _ = self.rrclient.find_resources(restype=OT.DataProcess, id_only=False) # only one DP becuase the PFs that are in the code dataproduct above are not activated yet. self.assertEquals(len(dp_ids), 1) # validate the name and version label NEW SA - 2 dataprocessdef_obj = self.dataprocessclient.read_data_process_definition( dataprocessdef_id) self.assertEqual(dataprocessdef_obj.version_label, '1.0a') self.assertEqual(dataprocessdef_obj.name, 'add_arrays') # validate that the DPD has an attachment NEW SA - 21 attachment_ids, assoc_ids = self.rrclient.find_objects( dataprocessdef_id, PRED.hasAttachment, RT.Attachment, True) self.assertEqual(len(attachment_ids), 1) attachment_obj = self.rrclient.read_attachment(attachment_ids[0]) log.debug('attachment: %s', attachment_obj) # validate that the data process resource has input and output data products associated # L4-CI-SA-RQ-364 and NEW SA-3 outproduct_ids, assoc_ids = self.rrclient.find_objects( dataprocess_id, PRED.hasOutputProduct, RT.DataProduct, True) self.assertEqual(len(outproduct_ids), 1) inproduct_ids, assoc_ids = self.rrclient.find_objects( dataprocess_id, PRED.hasInputProduct, RT.DataProduct, True) self.assertEqual(len(inproduct_ids), 1) # Test for provenance. Get Data product produced by the data processes output_data_product_id, _ = self.rrclient.find_objects( subject=dataprocess_id, object_type=RT.DataProduct, predicate=PRED.hasOutputProduct, id_only=True) output_data_product_provenance = self.dataproductclient.get_data_product_provenance( output_data_product_id[0]) # Do a basic check to see if there were 3 entries in the provenance graph. Parent and Child and the # DataProcessDefinition creating the child from the parent. self.assertTrue(len(output_data_product_provenance) == 2) self.assertTrue(self.input_dp_id in output_data_product_provenance[ output_data_product_id[0]]['parents']) self.assertTrue(output_data_product_provenance[ output_data_product_id[0]]['parents'][self.input_dp_id] ['data_process_definition_id'] == dataprocessdef_id) # NEW SA - 4 | Data processing shall include the appropriate data product algorithm name and version number in # the metadata of each output data product created by the data product algorithm. output_data_product_obj, _ = self.rrclient.find_objects( subject=dataprocess_id, object_type=RT.DataProduct, predicate=PRED.hasOutputProduct, id_only=False) self.assertTrue(output_data_product_obj[0].name != None) self.assertTrue(output_data_product_obj[0]._rev != None) # retrieve subscription from data process subscription_objs, _ = self.rrclient.find_objects( subject=dataprocess_id, predicate=PRED.hasSubscription, object_type=RT.Subscription, id_only=False) log.debug('test_transform_worker subscription_obj: %s', subscription_objs[0]) # create a queue to catch the published granules self.subscription_id = self.pubsub_client.create_subscription( name='parsed_subscription', stream_ids=[self.stream_id], exchange_name=subscription_objs[0].exchange_name) self.addCleanup(self.pubsub_client.delete_subscription, self.subscription_id) self.pubsub_client.activate_subscription(self.subscription_id) self.addCleanup(self.pubsub_client.deactivate_subscription, self.subscription_id) stream_route = self.pubsub_client.read_stream_route(self.stream_id) self.publisher = StandaloneStreamPublisher(stream_id=self.stream_id, stream_route=stream_route) for n in range(1, 101): rdt = RecordDictionaryTool(stream_definition_id=self.stream_def_id) rdt['time'] = [0] # time should always come first rdt['conductivity'] = [1] rdt['pressure'] = [2] rdt['salinity'] = [8] self.publisher.publish(rdt.to_granule()) # validate that the output granule is received and the updated value is correct self.assertTrue(self.granule_verified.wait(self.wait_time)) # validate that the data process loaded into worker event is received (L4-CI-SA-RQ-182) self.assertTrue( self.worker_assigned_event_verified.wait(self.wait_time)) # validate that the data process create (with data product ids) event is received (NEW SA -42) self.assertTrue(self.dp_created_event_verified.wait(self.wait_time)) # validate that the data process heartbeat event is received (for every hundred granules processed) (L4-CI-SA-RQ-182) #this takes a while so set wait limit to large value self.assertTrue(self.heartbeat_event_verified.wait(200)) # validate that the code from the transform function can be retrieve via inspect_data_process_definition src = self.dataprocessclient.inspect_data_process_definition( dataprocessdef_id) self.assertIn('def add_arrays(a, b)', src) # now delete the DPD and DP then verify that the resources are retired so that information required for provenance are still available self.dataprocessclient.delete_data_process(dataprocess_id) self.dataprocessclient.delete_data_process_definition( dataprocessdef_id) in_dp_objs, _ = self.rrclient.find_objects( subject=dataprocess_id, predicate=PRED.hasInputProduct, object_type=RT.DataProduct, id_only=True) self.assertTrue(in_dp_objs is not None) dpd_objs, _ = self.rrclient.find_subjects( subject_type=RT.DataProcessDefinition, predicate=PRED.hasDataProcess, object=dataprocess_id, id_only=True) self.assertTrue(dpd_objs is not None) @attr('LOCOINT') @unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode') def test_transform_worker_with_instrumentdevice(self): # test that a data process (type: data-product-in / data-product-out) can be defined and launched. # verify that the output granule fields are correctly populated # test that the input and output data products are linked to facilitate provenance self.data_process_objs = [] self._output_stream_ids = [] self.event_verified = Event() # Create CTD Parsed as the initial data product # create a stream definition for the data from the ctd simulator self.parameter_dict_id = self.dataset_management_client.read_parameter_dictionary_by_name( 'ctd_parsed_param_dict', id_only=True) self.stream_def_id = self.pubsub_client.create_stream_definition( name='stream_def', parameter_dictionary_id=self.parameter_dict_id) # create the DataProduct that is the input to the data processes input_dp_obj = IonObject(RT.DataProduct, name='input_data_product', description='input test stream') self.input_dp_id = self.dataproductclient.create_data_product( data_product=input_dp_obj, stream_definition_id=self.stream_def_id) # retrieve the Stream for this data product stream_ids, assoc_ids = self.rrclient.find_objects( self.input_dp_id, PRED.hasStream, RT.Stream, True) self.stream_id = stream_ids[0] log.debug('new ctd_parsed_data_product_id = %s' % self.input_dp_id) # only ever need one device for testing purposes. instDevice_obj, _ = self.rrclient.find_resources( restype=RT.InstrumentDevice, name='test_ctd_device') if instDevice_obj: instDevice_id = instDevice_obj[0]._id else: instDevice_obj = IonObject(RT.InstrumentDevice, name='test_ctd_device', description="test_ctd_device", serial_number="12345") instDevice_id = self.imsclient.create_instrument_device( instrument_device=instDevice_obj) self.damsclient.assign_data_product(input_resource_id=instDevice_id, data_product_id=self.input_dp_id) # create the DPD, DataProcess and output DataProduct dataprocessdef_id, dataprocess_id, dataproduct_id = self.create_data_process( ) self.addCleanup(self.dataprocessclient.delete_data_process, dataprocess_id) self.addCleanup(self.dataprocessclient.delete_data_process_definition, dataprocessdef_id) # Test for provenance. Get Data product produced by the data processes output_data_product_id, _ = self.rrclient.find_objects( subject=dataprocess_id, object_type=RT.DataProduct, predicate=PRED.hasOutputProduct, id_only=True) output_data_product_provenance = self.dataproductclient.get_data_product_provenance( output_data_product_id[0]) # Do a basic check to see if there were 3 entries in the provenance graph. Parent and Child and the # DataProcessDefinition creating the child from the parent. self.assertTrue(len(output_data_product_provenance) == 3) self.assertTrue(self.input_dp_id in output_data_product_provenance[ output_data_product_id[0]]['parents']) self.assertTrue(instDevice_id in output_data_product_provenance[ self.input_dp_id]['parents']) self.assertTrue(output_data_product_provenance[instDevice_id]['type'] == 'InstrumentDevice') @attr('LOCOINT') @unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode') def test_transform_worker_with_platformdevice(self): # test that a data process (type: data-product-in / data-product-out) can be defined and launched. # verify that the output granule fields are correctly populated # test that the input and output data products are linked to facilitate provenance self.data_process_objs = [] self._output_stream_ids = [] self.event_verified = Event() # Create CTD Parsed as the initial data product # create a stream definition for the data from the ctd simulator self.parameter_dict_id = self.dataset_management_client.read_parameter_dictionary_by_name( 'ctd_parsed_param_dict', id_only=True) self.stream_def_id = self.pubsub_client.create_stream_definition( name='stream_def', parameter_dictionary_id=self.parameter_dict_id) # create the DataProduct that is the input to the data processes input_dp_obj = IonObject(RT.DataProduct, name='input_data_product', description='input test stream') self.input_dp_id = self.dataproductclient.create_data_product( data_product=input_dp_obj, stream_definition_id=self.stream_def_id) # retrieve the Stream for this data product stream_ids, assoc_ids = self.rrclient.find_objects( self.input_dp_id, PRED.hasStream, RT.Stream, True) self.stream_id = stream_ids[0] log.debug('new ctd_parsed_data_product_id = %s' % self.input_dp_id) # only ever need one device for testing purposes. platform_device_obj, _ = self.rrclient.find_resources( restype=RT.PlatformDevice, name='TestPlatform') if platform_device_obj: platform_device_id = platform_device_obj[0]._id else: platform_device_obj = IonObject(RT.PlatformDevice, name='TestPlatform', description="TestPlatform", serial_number="12345") platform_device_id = self.imsclient.create_platform_device( platform_device=platform_device_obj) self.damsclient.assign_data_product( input_resource_id=platform_device_id, data_product_id=self.input_dp_id) # create the DPD, DataProcess and output DataProduct dataprocessdef_id, dataprocess_id, dataproduct_id = self.create_data_process( ) self.addCleanup(self.dataprocessclient.delete_data_process, dataprocess_id) self.addCleanup(self.dataprocessclient.delete_data_process_definition, dataprocessdef_id) # Test for provenance. Get Data product produced by the data processes output_data_product_id, _ = self.rrclient.find_objects( subject=dataprocess_id, object_type=RT.DataProduct, predicate=PRED.hasOutputProduct, id_only=True) output_data_product_provenance = self.dataproductclient.get_data_product_provenance( output_data_product_id[0]) # Do a basic check to see if there were 3 entries in the provenance graph. Parent and Child and the # DataProcessDefinition creating the child from the parent. self.assertTrue(len(output_data_product_provenance) == 3) self.assertTrue(self.input_dp_id in output_data_product_provenance[ output_data_product_id[0]]['parents']) self.assertTrue(platform_device_id in output_data_product_provenance[ self.input_dp_id]['parents']) self.assertTrue(output_data_product_provenance[platform_device_id] ['type'] == 'PlatformDevice') @attr('LOCOINT') @unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode') def test_event_transform_worker(self): self.data_process_objs = [] self._output_stream_ids = [] self.event_verified = Event() # test that a data process (type: data-product-in / event-out) can be defined and launched. # verify that event fields are correctly populated self.parameter_dict_id = self.dataset_management_client.read_parameter_dictionary_by_name( name='ctd_parsed_param_dict', id_only=True) # create the StreamDefinition self.stream_def_id = self.pubsub_client.create_stream_definition( name='stream_def', parameter_dictionary_id=self.parameter_dict_id) self.addCleanup(self.pubsub_client.delete_stream_definition, self.stream_def_id) # create the DataProduct input_dp_obj = IonObject(RT.DataProduct, name='input_data_product', description='input test stream') self.input_dp_id = self.dataproductclient.create_data_product( data_product=input_dp_obj, stream_definition_id=self.stream_def_id) # retrieve the Stream for this data product stream_ids, assoc_ids = self.rrclient.find_objects( self.input_dp_id, PRED.hasStream, RT.Stream, True) self.stream_id = stream_ids[0] # create the DPD and two DPs self.event_data_process_id = self.create_event_data_processes() # retrieve subscription from data process subscription_objs, _ = self.rrclient.find_objects( subject=self.event_data_process_id, predicate=PRED.hasSubscription, object_type=RT.Subscription, id_only=False) log.debug('test_event_transform_worker subscription_obj: %s', subscription_objs[0]) # create a queue to catch the published granules self.subscription_id = self.pubsub_client.create_subscription( name='parsed_subscription', stream_ids=[self.stream_id], exchange_name=subscription_objs[0].exchange_name) self.addCleanup(self.pubsub_client.delete_subscription, self.subscription_id) self.pubsub_client.activate_subscription(self.subscription_id) self.addCleanup(self.pubsub_client.deactivate_subscription, self.subscription_id) stream_route = self.pubsub_client.read_stream_route(self.stream_id) self.publisher = StandaloneStreamPublisher(stream_id=self.stream_id, stream_route=stream_route) self.start_event_transform_listener() self.data_modified = Event() rdt = RecordDictionaryTool(stream_definition_id=self.stream_def_id) rdt['time'] = [0] # time should always come first rdt['conductivity'] = [1] rdt['pressure'] = [2] rdt['salinity'] = [8] self.publisher.publish(rdt.to_granule()) self.assertTrue(self.event_verified.wait(self.wait_time)) @attr('LOCOINT') @unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Skip test while in CEI LAUNCH mode') def test_bad_argument_map(self): self._output_stream_ids = [] # test that a data process (type: data-product-in / data-product-out) parameter mapping it validated during # data process creation and that the correct exception is raised for both input and output. self.parameter_dict_id = self.dataset_management_client.read_parameter_dictionary_by_name( name='ctd_parsed_param_dict', id_only=True) # create the StreamDefinition self.stream_def_id = self.pubsub_client.create_stream_definition( name='stream_def', parameter_dictionary_id=self.parameter_dict_id) self.addCleanup(self.pubsub_client.delete_stream_definition, self.stream_def_id) # create the DataProduct that is the input to the data processes input_dp_obj = IonObject(RT.DataProduct, name='input_data_product', description='input test stream') self.input_dp_id = self.dataproductclient.create_data_product( data_product=input_dp_obj, stream_definition_id=self.stream_def_id) # two data processes using one transform and one DPD dp1_func_output_dp_id = self.create_output_data_product() # Set up DPD and DP #2 - array add function tf_obj = IonObject( RT.TransformFunction, name='add_array_func', description='adds values in an array', function='add_arrays', module="ion_example.add_arrays", arguments=['arr1', 'arr2'], function_type=TransformFunctionType.TRANSFORM, uri= 'http://sddevrepo.oceanobservatories.org/releases/ion_example-0.1-py2.7.egg' ) add_array_func_id, rev = self.rrclient.create(tf_obj) dpd_obj = IonObject( RT.DataProcessDefinition, name='add_arrays', description='adds the values of two arrays', data_process_type=DataProcessTypeEnum.TRANSFORM_PROCESS) add_array_dpd_id = self.dataprocessclient.create_data_process_definition( data_process_definition=dpd_obj, function_id=add_array_func_id) self.dataprocessclient.assign_stream_definition_to_data_process_definition( self.stream_def_id, add_array_dpd_id, binding='add_array_func') # create the data process with invalid argument map argument_map = {"arr1": "foo", "arr2": "bar"} output_param = "salinity" with self.assertRaises(BadRequest) as cm: dp1_data_process_id = self.dataprocessclient.create_data_process( data_process_definition_id=add_array_dpd_id, inputs=[self.input_dp_id], outputs=[dp1_func_output_dp_id], argument_map=argument_map, out_param_name=output_param) ex = cm.exception log.debug(' exception raised: %s', cm) self.assertEqual( ex.message, "Input data product does not contain the parameters defined in argument map" ) # create the data process with invalid output parameter name argument_map = {"arr1": "conductivity", "arr2": "pressure"} output_param = "foo" with self.assertRaises(BadRequest) as cm: dp1_data_process_id = self.dataprocessclient.create_data_process( data_process_definition_id=add_array_dpd_id, inputs=[self.input_dp_id], outputs=[dp1_func_output_dp_id], argument_map=argument_map, out_param_name=output_param) ex = cm.exception log.debug(' exception raised: %s', cm) self.assertEqual( ex.message, "Output data product does not contain the output parameter name provided" ) def create_event_data_processes(self): # two data processes using one transform and one DPD argument_map = {"a": "salinity"} # set up DPD and DP #2 - array add function tf_obj = IonObject( RT.TransformFunction, name='validate_salinity_array', description='validate_salinity_array', function='validate_salinity_array', module="ion.processes.data.transforms.test.test_transform_worker", arguments=['a'], function_type=TransformFunctionType.TRANSFORM) add_array_func_id, rev = self.rrclient.create(tf_obj) dpd_obj = IonObject( RT.DataProcessDefinition, name='validate_salinity_array', description='validate_salinity_array', data_process_type=DataProcessTypeEnum.TRANSFORM_PROCESS, ) add_array_dpd_id = self.dataprocessclient.create_data_process_definition( data_process_definition=dpd_obj, function_id=add_array_func_id) self.dataprocessclient.assign_stream_definition_to_data_process_definition( self.stream_def_id, add_array_dpd_id, binding='validate_salinity_array') # create the data process dp1_data_process_id = self.dataprocessclient.create_data_process( data_process_definition_id=add_array_dpd_id, inputs=[self.input_dp_id], outputs=None, argument_map=argument_map) self.damsclient.register_process(dp1_data_process_id) self.addCleanup(self.dataprocessclient.delete_data_process, dp1_data_process_id) return dp1_data_process_id def create_data_process(self): # two data processes using one transform and one DPD dp1_func_output_dp_id = self.create_output_data_product() argument_map = {"arr1": "conductivity", "arr2": "pressure"} output_param = "salinity" # set up DPD and DP #2 - array add function tf_obj = IonObject( RT.TransformFunction, name='add_array_func', description='adds values in an array', function='add_arrays', module="ion_example.add_arrays", arguments=['arr1', 'arr2'], function_type=TransformFunctionType.TRANSFORM, uri= 'http://sddevrepo.oceanobservatories.org/releases/ion_example-0.1-py2.7.egg' ) add_array_func_id, rev = self.rrclient.create(tf_obj) dpd_obj = IonObject( RT.DataProcessDefinition, name='add_arrays', description='adds the values of two arrays', data_process_type=DataProcessTypeEnum.TRANSFORM_PROCESS, version_label='1.0a') add_array_dpd_id = self.dataprocessclient.create_data_process_definition( data_process_definition=dpd_obj, function_id=add_array_func_id) self.dataprocessclient.assign_stream_definition_to_data_process_definition( self.stream_def_id, add_array_dpd_id, binding='add_array_func') # create the data process dp1_data_process_id = self.dataprocessclient.create_data_process( data_process_definition_id=add_array_dpd_id, inputs=[self.input_dp_id], outputs=[dp1_func_output_dp_id], argument_map=argument_map, out_param_name=output_param) self.damsclient.register_process(dp1_data_process_id) #self.addCleanup(self.dataprocessclient.delete_data_process, dp1_data_process_id) # add an attachment object to this DPD to test new SA-21 import msgpack attachment_content = 'foo bar' attachment_obj = IonObject(RT.Attachment, name='test_attachment', attachment_type=AttachmentType.ASCII, content_type='text/plain', content=msgpack.packb(attachment_content)) att_id = self.rrclient.create_attachment(add_array_dpd_id, attachment_obj) self.addCleanup(self.rrclient.delete_attachment, att_id) return add_array_dpd_id, dp1_data_process_id, dp1_func_output_dp_id def create_output_data_product(self): dp1_outgoing_stream_id = self.pubsub_client.create_stream_definition( name='dp1_stream', parameter_dictionary_id=self.parameter_dict_id) dp1_output_dp_obj = IonObject(RT.DataProduct, name='data_process1_data_product', description='output of add array func') dp1_func_output_dp_id = self.dataproductclient.create_data_product( dp1_output_dp_obj, dp1_outgoing_stream_id) self.addCleanup(self.dataproductclient.delete_data_product, dp1_func_output_dp_id) # retrieve the id of the OUTPUT stream from the out Data Product and add to granule logger stream_ids, _ = self.rrclient.find_objects(dp1_func_output_dp_id, PRED.hasStream, None, True) self._output_stream_ids.append(stream_ids[0]) subscription_id = self.pubsub_client.create_subscription( 'validator', data_product_ids=[dp1_func_output_dp_id]) self.addCleanup(self.pubsub_client.delete_subscription, subscription_id) def on_granule(msg, route, stream_id): log.debug('recv_packet stream_id: %s route: %s msg: %s', stream_id, route, msg) self.validate_output_granule(msg, route, stream_id) self.granule_verified.set() validator = StandaloneStreamSubscriber('validator', callback=on_granule) validator.start() self.addCleanup(validator.stop) self.pubsub_client.activate_subscription(subscription_id) self.addCleanup(self.pubsub_client.deactivate_subscription, subscription_id) return dp1_func_output_dp_id def validate_event(self, *args, **kwargs): """ This method is a callback function for receiving DataProcessStatusEvent. """ data_process_event = args[0] log.debug("DataProcessStatusEvent: %s", str(data_process_event.__dict__)) # if data process already created, check origin if self.dp_list: self.assertIn(data_process_event.origin, self.dp_list) # if this is a heartbeat event then 100 granules have been processed if 'data process status update.' in data_process_event.description: self.heartbeat_event_verified.set() else: # else check that this is the assign event if 'Data process assigned to transform worker' in data_process_event.description: self.worker_assigned_event_verified.set() elif 'Data process created for data product' in data_process_event.description: self.dp_created_event_verified.set() def validate_output_granule(self, msg, route, stream_id): self.assertIn(stream_id, self._output_stream_ids) rdt = RecordDictionaryTool.load_from_granule(msg) log.debug('validate_output_granule rdt: %s', rdt) sal_val = rdt['salinity'] np.testing.assert_array_equal(sal_val, np.array([3])) def start_event_listener(self): es = EventSubscriber(event_type=OT.DataProcessStatusEvent, callback=self.validate_event) es.start() self.addCleanup(es.stop) def validate_transform_event(self, *args, **kwargs): """ This method is a callback function for receiving DataProcessStatusEvent. """ status_alert_event = args[0] np.testing.assert_array_equal(status_alert_event.origin, self.stream_id) np.testing.assert_array_equal(status_alert_event.values, np.array([self.event_data_process_id])) log.debug("DeviceStatusAlertEvent: %s", str(status_alert_event.__dict__)) self.event_verified.set() def start_event_transform_listener(self): es = EventSubscriber(event_type=OT.DeviceStatusAlertEvent, callback=self.validate_transform_event) es.start() self.addCleanup(es.stop) def test_download(self): egg_url = 'http://sddevrepo.oceanobservatories.org/releases/ion_example-0.1-py2.7.egg' egg_path = TransformWorker.download_egg(egg_url) import pkg_resources pkg_resources.working_set.add_entry(egg_path) from ion_example.add_arrays import add_arrays a = add_arrays(1, 2) self.assertEquals(a, 3)