def on_start(self):
#print ">>>>>>>>>>>>>>>>>>>>>> MPL CFG = ", self.CFG
self.pubsub_management = PubsubManagementServiceProcessClient(process=self)
self.ssclient = SchedulerServiceProcessClient(process=self)
self.rrclient = ResourceRegistryServiceProcessClient(process=self)
self.data_retriever_client = DataRetrieverServiceProcessClient(process=self)
self.dsm_client = DatasetManagementServiceProcessClient(process=self)
self.pubsub_client = PubsubManagementServiceProcessClient(process = self)
self.stream_info = self.CFG.get_safe('process.publish_streams',{})
self.stream_names = self.stream_info.keys()
self.stream_ids = self.stream_info.values()
if not self.stream_names:
raise BadRequest('MPL Transform has no output streams.')
graph_time_periods= self.CFG.get_safe('graph_time_periods')
# If this is meant to be an event driven process, schedule an event to be generated every few minutes/hours
self.event_timer_interval = self.CFG.get_safe('graph_gen_interval')
if self.event_timer_interval:
event_origin = "Interval_Timer_Matplotlib"
sub = EventSubscriber(event_type="ResourceEvent", callback=self.interval_timer_callback, origin=event_origin)
sub.start()
self.interval_timer_id = self.ssclient.create_interval_timer(start_time="now" , interval=self._str_to_secs(self.event_timer_interval),
event_origin=event_origin, event_subtype="")
super(VizTransformMatplotlibGraphs,self).on_start()
def setUp(self):
# Start container
logging.disable(logging.ERROR)
self._start_container()
self.container.start_rel_from_url('res/deploy/r2deploy.yml')
# simulate preloading
preload_ion_params(self.container)
logging.disable(logging.NOTSET)
#Instantiate a process to represent the test
process=VisualizationServiceTestProcess()
# Now create client to DataProductManagementService
self.rrclient = ResourceRegistryServiceProcessClient(node=self.container.node, process=process)
self.damsclient = DataAcquisitionManagementServiceProcessClient(node=self.container.node, process=process)
self.pubsubclient = PubsubManagementServiceProcessClient(node=self.container.node, process=process)
self.ingestclient = IngestionManagementServiceProcessClient(node=self.container.node, process=process)
self.imsclient = InstrumentManagementServiceProcessClient(node=self.container.node, process=process)
self.dataproductclient = DataProductManagementServiceProcessClient(node=self.container.node, process=process)
self.dataprocessclient = DataProcessManagementServiceProcessClient(node=self.container.node, process=process)
self.datasetclient = DatasetManagementServiceProcessClient(node=self.container.node, process=process)
self.workflowclient = WorkflowManagementServiceProcessClient(node=self.container.node, process=process)
self.process_dispatcher = ProcessDispatcherServiceProcessClient(node=self.container.node, process=process)
self.data_retriever = DataRetrieverServiceProcessClient(node=self.container.node, process=process)
self.vis_client = VisualizationServiceProcessClient(node=self.container.node, process=process)
self.ctd_stream_def = SBE37_CDM_stream_definition()
def setUp(self):
# Start container
self._start_container()
self.container.start_rel_from_url('res/deploy/r2deploy.yml')
# simulate preloading
preload_ion_params(self.container)
#Instantiate a process to represent the test
process=WorkflowServiceTestProcess()
# Now create client to DataProductManagementService
self.rrclient = ResourceRegistryServiceProcessClient(node=self.container.node, process=process)
self.damsclient = DataAcquisitionManagementServiceProcessClient(node=self.container.node, process=process)
self.pubsubclient = PubsubManagementServiceProcessClient(node=self.container.node, process=process)
self.ingestclient = IngestionManagementServiceProcessClient(node=self.container.node, process=process)
self.imsclient = InstrumentManagementServiceProcessClient(node=self.container.node, process=process)
self.dataproductclient = DataProductManagementServiceProcessClient(node=self.container.node, process=process)
self.dataprocessclient = DataProcessManagementServiceProcessClient(node=self.container.node, process=process)
self.datasetclient = DatasetManagementServiceProcessClient(node=self.container.node, process=process)
self.workflowclient = WorkflowManagementServiceProcessClient(node=self.container.node, process=process)
self.process_dispatcher = ProcessDispatcherServiceProcessClient(node=self.container.node, process=process)
self.data_retriever = DataRetrieverServiceProcessClient(node=self.container.node, process=process)
self.ctd_stream_def = SBE37_CDM_stream_definition()
def on_start(self):
SimpleProcess.on_start(self)
self.data_retriever = DataRetrieverServiceProcessClient(process=self)
self.interval_key = self.CFG.get_safe('process.interval_key', None)
self.qc_params = self.CFG.get_safe('process.qc_params', [])
validate_is_not_none(
self.interval_key,
'An interval key is necessary to paunch this process')
self.event_subscriber = EventSubscriber(event_type=OT.TimerEvent,
origin=self.interval_key,
callback=self._event_callback,
auto_delete=True)
self.add_endpoint(self.event_subscriber)
self.resource_registry = self.container.resource_registry
self.run_interval = self.CFG.get_safe(
'service.qc_processing.run_interval', 24)
def on_start(self):
SimpleProcess.on_start(self)
self.data_retriever = DataRetrieverServiceProcessClient(process=self)
self.interval_key = self.CFG.get_safe('process.interval_key',None)
self.qc_params = self.CFG.get_safe('process.qc_params',[])
validate_is_not_none(self.interval_key, 'An interval key is necessary to paunch this process')
self.event_subscriber = EventSubscriber(event_type=OT.TimerEvent, origin=self.interval_key, callback=self._event_callback, auto_delete=True)
self.add_endpoint(self.event_subscriber)
self.resource_registry = self.container.resource_registry
def on_start(self):
#print ">>>>>>>>>>>>>>>>>>>>>> MPL CFG = ", self.CFG
self.pubsub_management = PubsubManagementServiceProcessClient(
process=self)
self.ssclient = SchedulerServiceProcessClient(process=self)
self.rrclient = ResourceRegistryServiceProcessClient(process=self)
self.data_retriever_client = DataRetrieverServiceProcessClient(
process=self)
self.dsm_client = DatasetManagementServiceProcessClient(process=self)
self.pubsub_client = PubsubManagementServiceProcessClient(process=self)
self.stream_info = self.CFG.get_safe('process.publish_streams', {})
self.stream_names = self.stream_info.keys()
self.stream_ids = self.stream_info.values()
if not self.stream_names:
raise BadRequest('MPL Transform has no output streams.')
graph_time_periods = self.CFG.get_safe('graph_time_periods')
# If this is meant to be an event driven process, schedule an event to be generated every few minutes/hours
self.event_timer_interval = self.CFG.get_safe('graph_gen_interval')
if self.event_timer_interval:
event_origin = "Interval_Timer_Matplotlib"
sub = EventSubscriber(event_type="ResourceEvent",
callback=self.interval_timer_callback,
origin=event_origin)
sub.start()
self.interval_timer_id = self.ssclient.create_interval_timer(
start_time="now",
interval=self._str_to_secs(self.event_timer_interval),
event_origin=event_origin,
event_subtype="")
super(VizTransformMatplotlibGraphs, self).on_start()
class TestWorkflowManagementIntegration(VisualizationIntegrationTestHelper):
def setUp(self):
# Start container
self._start_container()
self.container.start_rel_from_url('res/deploy/r2deploy.yml')
#Instantiate a process to represent the test
process=WorkflowServiceTestProcess()
# Now create client to DataProductManagementService
self.rrclient = ResourceRegistryServiceProcessClient(node=self.container.node, process=process)
self.damsclient = DataAcquisitionManagementServiceProcessClient(node=self.container.node, process=process)
self.pubsubclient = PubsubManagementServiceProcessClient(node=self.container.node, process=process)
self.ingestclient = IngestionManagementServiceProcessClient(node=self.container.node, process=process)
self.imsclient = InstrumentManagementServiceProcessClient(node=self.container.node, process=process)
self.dataproductclient = DataProductManagementServiceProcessClient(node=self.container.node, process=process)
self.dataprocessclient = DataProcessManagementServiceProcessClient(node=self.container.node, process=process)
self.datasetclient = DatasetManagementServiceProcessClient(node=self.container.node, process=process)
self.workflowclient = WorkflowManagementServiceProcessClient(node=self.container.node, process=process)
self.process_dispatcher = ProcessDispatcherServiceProcessClient(node=self.container.node, process=process)
self.data_retriever = DataRetrieverServiceProcessClient(node=self.container.node, process=process)
self.ctd_stream_def = SBE37_CDM_stream_definition()
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),'Not integrated for CEI')
def test_SA_transform_components(self):
assertions = self.assertTrue
#The list of data product streams to monitor
data_product_stream_ids = list()
#Create the input data product
ctd_stream_id, ctd_parsed_data_product_id = self.create_ctd_input_stream_and_data_product()
data_product_stream_ids.append(ctd_stream_id)
###
### Setup the first transformation
###
# Salinity: Data Process Definition
ctd_L2_salinity_dprocdef_id = self.create_salinity_data_process_definition()
l2_salinity_all_data_process_id, ctd_l2_salinity_output_dp_id = self.create_transform_process(ctd_L2_salinity_dprocdef_id,ctd_parsed_data_product_id, 'salinity' )
## get the stream id for the transform outputs
stream_ids, _ = self.rrclient.find_objects(ctd_l2_salinity_output_dp_id, PRED.hasStream, None, True)
assertions(len(stream_ids) > 0 )
sal_stream_id = stream_ids[0]
data_product_stream_ids.append(sal_stream_id)
###
### Setup the second transformation
###
# Salinity Doubler: Data Process Definition
salinity_doubler_dprocdef_id = self.create_salinity_doubler_data_process_definition()
salinity_double_data_process_id, salinity_doubler_output_dp_id = self.create_transform_process(salinity_doubler_dprocdef_id, ctd_l2_salinity_output_dp_id, 'salinity' )
stream_ids, _ = self.rrclient.find_objects(salinity_doubler_output_dp_id, PRED.hasStream, None, True)
assertions(len(stream_ids) > 0 )
sal_dbl_stream_id = stream_ids[0]
data_product_stream_ids.append(sal_dbl_stream_id)
#Start the output stream listener to monitor and collect messages
results = self.start_output_stream_and_listen(ctd_stream_id, data_product_stream_ids)
#Stop the transform processes
self.dataprocessclient.deactivate_data_process(salinity_double_data_process_id)
self.dataprocessclient.deactivate_data_process(l2_salinity_all_data_process_id)
#Validate the data from each of the messages along the way
self.validate_messages(results)
@attr('LOCOINT')
@attr('SMOKE')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),'Not integrated for CEI')
def test_transform_workflow(self):
assertions = self.assertTrue
log.debug("Building the workflow definition")
workflow_def_obj = IonObject(RT.WorkflowDefinition,
name='Salinity_Test_Workflow',
description='tests a workflow of multiple transform data processes')
workflow_data_product_name = 'TEST-Workflow_Output_Product' #Set a specific output product name
#-------------------------------------------------------------------------------------------------------------------------
log.debug( "Adding a transformation process definition for salinity")
#-------------------------------------------------------------------------------------------------------------------------
ctd_L2_salinity_dprocdef_id = self.create_salinity_data_process_definition()
workflow_step_obj = IonObject('DataProcessWorkflowStep',
data_process_definition_id=ctd_L2_salinity_dprocdef_id,
persist_process_output_data=False) #Don't persist the intermediate data product
workflow_def_obj.workflow_steps.append(workflow_step_obj)
#-------------------------------------------------------------------------------------------------------------------------
log.debug( "Adding a transformation process definition for salinity doubler")
#-------------------------------------------------------------------------------------------------------------------------
salinity_doubler_dprocdef_id = self.create_salinity_doubler_data_process_definition()
workflow_step_obj = IonObject('DataProcessWorkflowStep',
data_process_definition_id=salinity_doubler_dprocdef_id, )
workflow_def_obj.workflow_steps.append(workflow_step_obj)
log.debug( "Creating workflow def in the resource registry")
workflow_def_id = self.workflowclient.create_workflow_definition(workflow_def_obj)
aids = self.rrclient.find_associations(workflow_def_id, PRED.hasDataProcessDefinition)
assertions(len(aids) == 2 )
#The list of data product streams to monitor
data_product_stream_ids = list()
log.debug( "Creating the input data product")
ctd_stream_id, ctd_parsed_data_product_id = self.create_ctd_input_stream_and_data_product()
data_product_stream_ids.append(ctd_stream_id)
log.debug( "Creating and starting the workflow")
workflow_id, workflow_product_id = self.workflowclient.create_data_process_workflow(workflow_def_id,
ctd_parsed_data_product_id,
persist_workflow_data_product=True, output_data_product_name=workflow_data_product_name, timeout=300)
workflow_output_ids,_ = self.rrclient.find_subjects(RT.Workflow, PRED.hasOutputProduct, workflow_product_id, True)
assertions(len(workflow_output_ids) == 1 )
log.debug( "persisting the output product")
#self.dataproductclient.activate_data_product_persistence(workflow_product_id)
dataset_ids,_ = self.rrclient.find_objects(workflow_product_id, PRED.hasDataset, RT.Dataset, True)
assertions(len(dataset_ids) == 1 )
dataset_id = dataset_ids[0]
log.debug( "Verifying the output data product name matches what was specified in the workflow definition")
workflow_product = self.rrclient.read(workflow_product_id)
assertions(workflow_product.name.startswith(workflow_data_product_name), 'Nope: %s != %s' % (workflow_product.name, workflow_data_product_name))
log.debug( "Walking the associations to find the appropriate output data streams to validate the messages")
workflow_dp_ids,_ = self.rrclient.find_objects(workflow_id, PRED.hasDataProduct, RT.DataProduct, True)
assertions(len(workflow_dp_ids) == 2 )
for dp_id in workflow_dp_ids:
stream_ids, _ = self.rrclient.find_objects(dp_id, PRED.hasStream, None, True)
assertions(len(stream_ids) == 1 )
data_product_stream_ids.append(stream_ids[0])
log.debug( "data_product_stream_ids: %s" % data_product_stream_ids)
log.debug( "Starting the output stream listener to monitor to collect messages")
results = self.start_output_stream_and_listen(ctd_stream_id, data_product_stream_ids)
log.debug( "results::: %s" % results)
log.debug( "Stopping the workflow processes")
self.workflowclient.terminate_data_process_workflow(workflow_id, False, timeout=250) # Should test true at some point
log.debug( "Making sure the Workflow object was removed")
objs, _ = self.rrclient.find_resources(restype=RT.Workflow)
assertions(len(objs) == 0)
log.debug( "Validating the data from each of the messages along the way")
self.validate_messages(results)
log.debug( "Checking to see if dataset id = %s, was persisted, and that it can be retrieved...." % dataset_id)
self.validate_data_ingest_retrieve(dataset_id)
log.debug( "Cleaning up to make sure delete is correct.")
self.workflowclient.delete_workflow_definition(workflow_def_id)
workflow_def_ids,_ = self.rrclient.find_resources(restype=RT.WorkflowDefinition)
assertions(len(workflow_def_ids) == 0 )
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),'Not integrated for CEI')
def test_google_dt_transform_workflow(self):
assertions = self.assertTrue
# Build the workflow definition
workflow_def_obj = IonObject(RT.WorkflowDefinition, name='GoogleDT_Test_Workflow',description='Tests the workflow of converting stream data to Google DT')
#Add a transformation process definition
google_dt_procdef_id = self.create_google_dt_data_process_definition()
workflow_step_obj = IonObject('DataProcessWorkflowStep', data_process_definition_id=google_dt_procdef_id, persist_process_output_data=False)
workflow_def_obj.workflow_steps.append(workflow_step_obj)
#Create it in the resource registry
workflow_def_id = self.workflowclient.create_workflow_definition(workflow_def_obj)
#The list of data product streams to monitor
data_product_stream_ids = list()
#Create the input data product
ctd_stream_id, ctd_parsed_data_product_id = self.create_ctd_input_stream_and_data_product()
data_product_stream_ids.append(ctd_stream_id)
#Create and start the workflow
workflow_id, workflow_product_id = self.workflowclient.create_data_process_workflow(workflow_def_id, ctd_parsed_data_product_id, timeout=60)
workflow_output_ids,_ = self.rrclient.find_subjects(RT.Workflow, PRED.hasOutputProduct, workflow_product_id, True)
assertions(len(workflow_output_ids) == 1 )
#Walk the associations to find the appropriate output data streams to validate the messages
workflow_dp_ids,_ = self.rrclient.find_objects(workflow_id, PRED.hasDataProduct, RT.DataProduct, True)
assertions(len(workflow_dp_ids) == 1 )
for dp_id in workflow_dp_ids:
stream_ids, _ = self.rrclient.find_objects(dp_id, PRED.hasStream, None, True)
assertions(len(stream_ids) == 1 )
data_product_stream_ids.append(stream_ids[0])
#Start the output stream listener to monitor and collect messages
results = self.start_output_stream_and_listen(ctd_stream_id, data_product_stream_ids)
#Stop the workflow processes
self.workflowclient.terminate_data_process_workflow(workflow_id=workflow_id,delete_data_products=False, timeout=60) # Should test true at some point
#Validate the data from each of the messages along the way
self.validate_google_dt_transform_results(results)
"""
# Check to see if ingestion worked. Extract the granules from data_retrieval.
# First find the dataset associated with the output dp product
ds_ids,_ = self.rrclient.find_objects(workflow_dp_ids[len(workflow_dp_ids) - 1], PRED.hasDataset, RT.Dataset, True)
retrieved_granule = self.data_retriever.retrieve(ds_ids[0])
#Validate the data from each of the messages along the way
self.validate_google_dt_transform_results(retrieved_granule)
"""
#Cleanup to make sure delete is correct.
self.workflowclient.delete_workflow_definition(workflow_def_id)
workflow_def_ids,_ = self.rrclient.find_resources(restype=RT.WorkflowDefinition)
assertions(len(workflow_def_ids) == 0 )
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),'Not integrated for CEI')
def test_mpl_graphs_transform_workflow(self):
assertions = self.assertTrue
# Build the workflow definition
workflow_def_obj = IonObject(RT.WorkflowDefinition, name='Mpl_Graphs_Test_Workflow',description='Tests the workflow of converting stream data to Matplotlib graphs')
#Add a transformation process definition
mpl_graphs_procdef_id = self.create_mpl_graphs_data_process_definition()
workflow_step_obj = IonObject('DataProcessWorkflowStep', data_process_definition_id=mpl_graphs_procdef_id, persist_process_output_data=False)
workflow_def_obj.workflow_steps.append(workflow_step_obj)
#Create it in the resource registry
workflow_def_id = self.workflowclient.create_workflow_definition(workflow_def_obj)
#The list of data product streams to monitor
data_product_stream_ids = list()
#Create the input data product
ctd_stream_id, ctd_parsed_data_product_id = self.create_ctd_input_stream_and_data_product()
data_product_stream_ids.append(ctd_stream_id)
#Create and start the workflow
workflow_id, workflow_product_id = self.workflowclient.create_data_process_workflow(workflow_def_id, ctd_parsed_data_product_id,
persist_workflow_data_product=True, timeout=60)
workflow_output_ids,_ = self.rrclient.find_subjects(RT.Workflow, PRED.hasOutputProduct, workflow_product_id, True)
assertions(len(workflow_output_ids) == 1 )
#Walk the associations to find the appropriate output data streams to validate the messages
workflow_dp_ids,_ = self.rrclient.find_objects(workflow_id, PRED.hasDataProduct, RT.DataProduct, True)
assertions(len(workflow_dp_ids) == 1 )
for dp_id in workflow_dp_ids:
stream_ids, _ = self.rrclient.find_objects(dp_id, PRED.hasStream, None, True)
assertions(len(stream_ids) == 1 )
data_product_stream_ids.append(stream_ids[0])
#Start the output stream listener to monitor and collect messages
results = self.start_output_stream_and_listen(ctd_stream_id, data_product_stream_ids)
#Stop the workflow processes
self.workflowclient.terminate_data_process_workflow(workflow_id=workflow_id,delete_data_products=False, timeout=60) # Should test true at some point
#Validate the data from each of the messages along the way
self.validate_mpl_graphs_transform_results(results)
# Check to see if ingestion worked. Extract the granules from data_retrieval.
# First find the dataset associated with the output dp product
ds_ids,_ = self.rrclient.find_objects(workflow_dp_ids[len(workflow_dp_ids) - 1], PRED.hasDataset, RT.Dataset, True)
retrieved_granule = self.data_retriever.retrieve_last_data_points(ds_ids[0], 10)
#Validate the data from each of the messages along the way
self.validate_mpl_graphs_transform_results(retrieved_granule)
#Cleanup to make sure delete is correct.
self.workflowclient.delete_workflow_definition(workflow_def_id)
workflow_def_ids,_ = self.rrclient.find_resources(restype=RT.WorkflowDefinition)
assertions(len(workflow_def_ids) == 0 )
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),'Not integrated for CEI')
def test_multiple_workflow_instances(self):
assertions = self.assertTrue
# Build the workflow definition
workflow_def_obj = IonObject(RT.WorkflowDefinition, name='Multiple_Test_Workflow',description='Tests the workflow of converting stream data')
#Add a transformation process definition
google_dt_procdef_id = self.create_google_dt_data_process_definition()
workflow_step_obj = IonObject('DataProcessWorkflowStep', data_process_definition_id=google_dt_procdef_id, persist_process_output_data=False)
workflow_def_obj.workflow_steps.append(workflow_step_obj)
#Create it in the resource registry
workflow_def_id = self.workflowclient.create_workflow_definition(workflow_def_obj)
#The list of data product streams to monitor
data_product_stream_ids = list()
#Create the first input data product
ctd_stream_id1, ctd_parsed_data_product_id1 = self.create_ctd_input_stream_and_data_product('ctd_parsed1')
data_product_stream_ids.append(ctd_stream_id1)
#Create and start the first workflow
workflow_id1, workflow_product_id1 = self.workflowclient.create_data_process_workflow(workflow_def_id, ctd_parsed_data_product_id1, timeout=60)
#Create the second input data product
ctd_stream_id2, ctd_parsed_data_product_id2 = self.create_ctd_input_stream_and_data_product('ctd_parsed2')
data_product_stream_ids.append(ctd_stream_id2)
#Create and start the second workflow
workflow_id2, workflow_product_id2 = self.workflowclient.create_data_process_workflow(workflow_def_id, ctd_parsed_data_product_id2, timeout=60)
#Walk the associations to find the appropriate output data streams to validate the messages
workflow_ids,_ = self.rrclient.find_resources(restype=RT.Workflow)
assertions(len(workflow_ids) == 2 )
#Start the first input stream process
ctd_sim_pid1 = self.start_sinusoidal_input_stream_process(ctd_stream_id1)
#Start the second input stream process
ctd_sim_pid2 = self.start_simple_input_stream_process(ctd_stream_id2)
#Start the output stream listener to monitor a set number of messages being sent through the workflows
results = self.start_output_stream_and_listen(None, data_product_stream_ids, message_count_per_stream=5)
# stop the flow of messages...
self.process_dispatcher.cancel_process(ctd_sim_pid1) # kill the ctd simulator process - that is enough data
self.process_dispatcher.cancel_process(ctd_sim_pid2)
#Stop the first workflow processes
self.workflowclient.terminate_data_process_workflow(workflow_id=workflow_id1,delete_data_products=False, timeout=60) # Should test true at some point
#Stop the second workflow processes
self.workflowclient.terminate_data_process_workflow(workflow_id=workflow_id2,delete_data_products=False, timeout=60) # Should test true at some point
workflow_ids,_ = self.rrclient.find_resources(restype=RT.Workflow)
assertions(len(workflow_ids) == 0 )
#Cleanup to make sure delete is correct.
self.workflowclient.delete_workflow_definition(workflow_def_id)
workflow_def_ids,_ = self.rrclient.find_resources(restype=RT.WorkflowDefinition)
assertions(len(workflow_def_ids) == 0 )
aid_list = self.rrclient.find_associations(workflow_def_id, PRED.hasDataProcessDefinition)
assertions(len(aid_list) == 0 )
class QCPostProcessing(SimpleProcess):
'''
QC Post Processing Process
This process provides the capability to ION clients and operators to evaluate the automated quality control flags on
various data products. This process should be run periodically with overlapping spans of data to ensure complete
dataset QC verification.
This parameters that this process accepts as configurations are:
- dataset_id: The dataset identifier, required.
- start_time: Unix timestamp, defaults to 24 hours in the past
- end_time: Unix timestamp, defaults to current time
- qc_params: a list of qc functions to evaluate, currently supported functions are: ['glblrng_qc',
'spketst_qc', 'stuckvl_qc'], defaults to all
'''
qc_suffixes = ['glblrng_qc', 'spketst_qc', 'stuckvl_qc']
def on_start(self):
SimpleProcess.on_start(self)
self.data_retriever = DataRetrieverServiceProcessClient(process=self)
self.interval_key = self.CFG.get_safe('process.interval_key',None)
self.qc_params = self.CFG.get_safe('process.qc_params',[])
validate_is_not_none(self.interval_key, 'An interval key is necessary to paunch this process')
self.event_subscriber = EventSubscriber(event_type=OT.TimerEvent, origin=self.interval_key, callback=self._event_callback, auto_delete=True)
self.add_endpoint(self.event_subscriber)
self.resource_registry = self.container.resource_registry
self.run_interval = self.CFG.get_safe('service.qc_processing.run_interval', 24)
def _event_callback(self, *args, **kwargs):
log.info('QC Post Processing Triggered')
dataset_ids, _ = self.resource_registry.find_resources(restype=RT.Dataset, id_only=True)
for dataset_id in dataset_ids:
log.info('QC Post Processing for dataset %s', dataset_id)
try:
self.process(dataset_id)
except BadRequest as e:
if 'Problems reading from the coverage' in e.message:
log.error('Failed to read from dataset %s', dataset_id, exc_info=True)
def process(self, dataset_id, start_time=0, end_time=0):
if not dataset_id:
raise BadRequest('No dataset id specified.')
now = time.time()
start_time = start_time or (now - (3600*(self.run_interval+1))) # Every N hours with 1 of overlap
end_time = end_time or now
qc_params = [i for i in self.qc_params if i in self.qc_suffixes] or self.qc_suffixes
self.qc_publisher = EventPublisher(event_type=OT.ParameterQCEvent)
log.debug('Iterating over the data blocks')
for st,et in self.chop(int(start_time),int(end_time)):
log.debug('Chopping %s:%s', st, et)
log.debug("Retrieving data: data_retriever.retrieve('%s', query={'start_time':%s, 'end_time':%s')", dataset_id, st, et)
try:
granule = self.data_retriever.retrieve(dataset_id, query={'start_time':st, 'end_time':et})
except BadRequest:
data_products, _ = self.container.resource_registry.find_subjects(object=dataset_id, predicate=PRED.hasDataset, subject_type=RT.DataProduct)
for data_product in data_products:
log.exception('Failed to perform QC Post Processing on %s', data_product.name)
log.error('Calculated Start Time: %s', st)
log.error('Calculated End Time: %s', et)
raise
log.debug('Retrieved Data')
rdt = RecordDictionaryTool.load_from_granule(granule)
qc_fields = [i for i in rdt.fields if any([i.endswith(j) for j in qc_params])]
log.debug('QC Fields: %s', qc_fields)
for field in qc_fields:
val = rdt[field]
if val is None:
continue
if not np.all(val):
log.debug('Found QC Alerts')
indexes = np.where(val==0)
timestamps = rdt[rdt.temporal_parameter][indexes[0]]
self.flag_qc_parameter(dataset_id, field, timestamps.tolist(),{})
def flag_qc_parameter(self, dataset_id, parameter, temporal_values, configuration):
log.info('Flagging QC for %s', parameter)
data_product_ids, _ = self.resource_registry.find_subjects(object=dataset_id, subject_type=RT.DataProduct, predicate=PRED.hasDataset, id_only=True)
for data_product_id in data_product_ids:
self.qc_publisher.publish_event(origin=data_product_id, qc_parameter=parameter, temporal_values=temporal_values, configuration=configuration)
@classmethod
def chop(cls, start_time, end_time):
while start_time < end_time:
yield (start_time, min(start_time+3600, end_time))
start_time = min(start_time+3600, end_time)
return
class VizTransformMatplotlibGraphs(TransformStreamPublisher, TransformEventListener, TransformStreamListener):
"""
This class is used for instantiating worker processes that have subscriptions to data streams and convert
incoming data from CDM format to Matplotlib graphs
"""
output_bindings = ['graph_image_param_dict']
event_timer_interval = None
def on_start(self):
#print ">>>>>>>>>>>>>>>>>>>>>> MPL CFG = ", self.CFG
self.pubsub_management = PubsubManagementServiceProcessClient(process=self)
self.ssclient = SchedulerServiceProcessClient(process=self)
self.rrclient = ResourceRegistryServiceProcessClient(process=self)
self.data_retriever_client = DataRetrieverServiceProcessClient(process=self)
self.dsm_client = DatasetManagementServiceProcessClient(process=self)
self.pubsub_client = PubsubManagementServiceProcessClient(process = self)
self.stream_info = self.CFG.get_safe('process.publish_streams',{})
self.stream_names = self.stream_info.keys()
self.stream_ids = self.stream_info.values()
if not self.stream_names:
raise BadRequest('MPL Transform has no output streams.')
graph_time_periods= self.CFG.get_safe('graph_time_periods')
# If this is meant to be an event driven process, schedule an event to be generated every few minutes/hours
self.event_timer_interval = self.CFG.get_safe('graph_gen_interval')
if self.event_timer_interval:
event_origin = "Interval_Timer_Matplotlib"
sub = EventSubscriber(event_type="ResourceEvent", callback=self.interval_timer_callback, origin=event_origin)
sub.start()
self.interval_timer_id = self.ssclient.create_interval_timer(start_time="now" , interval=self._str_to_secs(self.event_timer_interval),
event_origin=event_origin, event_subtype="")
super(VizTransformMatplotlibGraphs,self).on_start()
# when tranform is used as a data process
def recv_packet(self, packet, in_stream_route, in_stream_id):
#Check to see if the class instance was set up as a event triggered transform. If yes, skip the packet
if self.event_timer_interval:
return
log.info('Received packet')
mpl_data_granule = VizTransformMatplotlibGraphsAlgorithm.execute(packet, params=self.get_stream_definition())
for stream_name in self.stream_names:
publisher = getattr(self, stream_name)
publisher.publish(mpl_data_granule)
def get_stream_definition(self):
stream_id = self.stream_ids[0]
stream_def = self.pubsub_management.read_stream_definition(stream_id=stream_id)
return stream_def._id
def process_event(self, msg, headers):
return
def interval_timer_callback(self, *args, **kwargs):
#Find out the input data product to this process
in_dp_id = self.CFG.get_safe('in_dp_id')
print " >>>>>>>>>>>>>> IN DP ID from cfg : ", in_dp_id
# get the dataset_id associated with the data_product. Need it to do the data retrieval
ds_ids,_ = self.rrclient.find_objects(in_dp_id, PRED.hasDataset, RT.Dataset, True)
if ds_ids is None or not ds_ids:
return None
# retrieve data for the specified time interval. Setup up query from pass config first
query = {}
param_list_str = self.CFG.get_safe('parameters')
if param_list_str:
query['parameters'] = param_list_str.split(', ')
# append time if not present in list of parameters
if not 'time' in query['parameters']:
query['parameters'].append('time')
query['start_time'] = query['end_time'] = ntplib.system_to_ntp_time(time.time()) # Now
query['stride_time'] = 1
if self.CFG.get_safe('graph_time_period'):
query['start_time'] = query['end_time'] - self._str_to_secs(self.CFG.get_safe('graph_time_period'))
#print " >>>>>>>>>>>>>> QUERY = ", query
#retrieved_granule = self.data_retriever_client.retrieve(ds_ids[0],{'start_time':start_time,'end_time':end_time})
retrieved_granule = self.data_retriever_client.retrieve(ds_ids[0], query=query)
# add extra parameters to query passed in config that are not needed by data retrieval
if self.CFG.get_safe('resolution'):
query['resolution'] = self.CFG.get_safe('resolution')
# send the granule through the Algorithm code to get the matplotlib graphs
mpl_pdict_id = self.dsm_client.read_parameter_dictionary_by_name('graph_image_param_dict',id_only=True)
mpl_stream_def = self.pubsub_client.create_stream_definition('mpl', parameter_dictionary_id=mpl_pdict_id)
fileName = self.CFG.get_safe('graph_time_period')
mpl_data_granule = VizTransformMatplotlibGraphsAlgorithm.execute(retrieved_granule, config=query, params=mpl_stream_def, fileName=fileName)
if mpl_data_granule == None:
return None
# publish on all specified output streams
for stream_name in self.stream_names:
publisher = getattr(self, stream_name)
publisher.publish(mpl_data_granule)
return
def _str_to_secs(self, time_period):
# this method converts commonly used time periods to its actual seconds counterpart
#separate alpha and numeric parts of the time period
time_n = time_period.lower().rstrip('abcdefghijklmnopqrstuvwxyz ')
time_a = time_period.lower().lstrip('0123456789. ')
# determine if user specified, secs, mins, hours, days, weeks, months, years
factor = None
if time_a == 'sec' or time_a == "secs" or time_a == 'second' or time_a == "seconds":
factor = 1
if time_a == "min" or time_a == "mins" or time_a == "minute" or time_a == "minutes":
factor = 60
if time_a == "hr" or time_a == "hrs" or time_a == "hour" or time_a == "hours":
factor = 60 * 60
if time_a == "day" or time_a == "days":
factor = 60 * 60 * 24
if time_a == "wk" or time_a == "wks" or time_a == "week" or time_a == "weeks":
factor = 60 * 60 * 24 * 7
if time_a == "mon" or time_a == "mons" or time_a == "month" or time_a == "months":
factor = 60 * 60 * 24 * 30
if time_a == "yr" or time_a == "yrs" or time_a == "year" or time_a == "years":
factor = 60 * 60 * 24 * 365
time_period_secs = float(time_n) * factor
return time_period_secs
def on_quit(self):
#Cancel the timer
if hasattr(self, 'interval_timer_id'):
self.ssclient.cancel_timer(self.interval_timer_id)
super(VizTransformMatplotlibGraphs,self).on_quit()
class QCPostProcessing(SimpleProcess):
'''
QC Post Processing Process
This process provides the capability to ION clients and operators to evaluate the automated quality control flags on
various data products. This process should be run periodically with overlapping spans of data to ensure complete
dataset QC verification.
This parameters that this process accepts as configurations are:
- dataset_id: The dataset identifier, required.
- start_time: Unix timestamp, defaults to 24 hours in the past
- end_time: Unix timestamp, defaults to current time
- qc_params: a list of qc functions to evaluate, currently supported functions are: ['glblrng_qc',
'spketst_qc', 'stuckvl_qc'], defaults to all
'''
qc_suffixes = ['glblrng_qc', 'spketst_qc', 'stuckvl_qc']
def on_start(self):
SimpleProcess.on_start(self)
self.data_retriever = DataRetrieverServiceProcessClient(process=self)
self.interval_key = self.CFG.get_safe('process.interval_key', None)
self.qc_params = self.CFG.get_safe('process.qc_params', [])
validate_is_not_none(
self.interval_key,
'An interval key is necessary to paunch this process')
self.event_subscriber = EventSubscriber(event_type=OT.TimerEvent,
origin=self.interval_key,
callback=self._event_callback,
auto_delete=True)
self.add_endpoint(self.event_subscriber)
self.resource_registry = self.container.resource_registry
self.run_interval = self.CFG.get_safe(
'service.qc_processing.run_interval', 24)
def _event_callback(self, *args, **kwargs):
log.info('QC Post Processing Triggered')
dataset_ids, _ = self.resource_registry.find_resources(
restype=RT.Dataset, id_only=True)
for dataset_id in dataset_ids:
log.info('QC Post Processing for dataset %s', dataset_id)
try:
self.process(dataset_id)
except BadRequest as e:
if 'Problems reading from the coverage' in e.message:
log.error('Failed to read from dataset')
def process(self, dataset_id, start_time=0, end_time=0):
if not dataset_id:
raise BadRequest('No dataset id specified.')
now = time.time()
start_time = start_time or (now - (3600 * (self.run_interval + 1))
) # Every N hours with 1 of overlap
end_time = end_time or now
qc_params = [i for i in self.qc_params if i in self.qc_suffixes
] or self.qc_suffixes
self.qc_publisher = EventPublisher(event_type=OT.ParameterQCEvent)
log.debug('Iterating over the data blocks')
for st, et in self.chop(int(start_time), int(end_time)):
log.debug('Chopping %s:%s', st, et)
log.debug(
"Retrieving data: data_retriever.retrieve('%s', query={'start_time':%s, 'end_time':%s')",
dataset_id, st, et)
granule = self.data_retriever.retrieve(dataset_id,
query={
'start_time': st,
'end_time': et
})
log.debug('Retrieved Data')
rdt = RecordDictionaryTool.load_from_granule(granule)
qc_fields = [
i for i in rdt.fields
if any([i.endswith(j) for j in qc_params])
]
log.debug('QC Fields: %s', qc_fields)
for field in qc_fields:
val = rdt[field]
if val is None:
continue
if not np.all(val):
log.debug('Found QC Alerts')
indexes = np.where(val == 0)
timestamps = rdt[rdt.temporal_parameter][indexes[0]]
self.flag_qc_parameter(dataset_id, field,
timestamps.tolist(), {})
def flag_qc_parameter(self, dataset_id, parameter, temporal_values,
configuration):
log.info('Flagging QC for %s', parameter)
data_product_ids, _ = self.resource_registry.find_subjects(
object=dataset_id,
subject_type=RT.DataProduct,
predicate=PRED.hasDataset,
id_only=True)
for data_product_id in data_product_ids:
self.qc_publisher.publish_event(origin=data_product_id,
qc_parameter=parameter,
temporal_values=temporal_values,
configuration=configuration)
@classmethod
def chop(cls, start_time, end_time):
while start_time < end_time:
yield (start_time, min(start_time + 3600, end_time))
start_time = min(start_time + 3600, end_time)
return
class VizTransformMatplotlibGraphs(TransformStreamPublisher,
TransformEventListener,
TransformStreamListener):
"""
This class is used for instantiating worker processes that have subscriptions to data streams and convert
incoming data from CDM format to Matplotlib graphs
"""
output_bindings = ['graph_image_param_dict']
event_timer_interval = None
def on_start(self):
#print ">>>>>>>>>>>>>>>>>>>>>> MPL CFG = ", self.CFG
self.pubsub_management = PubsubManagementServiceProcessClient(
process=self)
self.ssclient = SchedulerServiceProcessClient(process=self)
self.rrclient = ResourceRegistryServiceProcessClient(process=self)
self.data_retriever_client = DataRetrieverServiceProcessClient(
process=self)
self.dsm_client = DatasetManagementServiceProcessClient(process=self)
self.pubsub_client = PubsubManagementServiceProcessClient(process=self)
self.stream_info = self.CFG.get_safe('process.publish_streams', {})
self.stream_names = self.stream_info.keys()
self.stream_ids = self.stream_info.values()
if not self.stream_names:
raise BadRequest('MPL Transform has no output streams.')
graph_time_periods = self.CFG.get_safe('graph_time_periods')
# If this is meant to be an event driven process, schedule an event to be generated every few minutes/hours
self.event_timer_interval = self.CFG.get_safe('graph_gen_interval')
if self.event_timer_interval:
event_origin = "Interval_Timer_Matplotlib"
sub = EventSubscriber(event_type="ResourceEvent",
callback=self.interval_timer_callback,
origin=event_origin)
sub.start()
self.interval_timer_id = self.ssclient.create_interval_timer(
start_time="now",
interval=self._str_to_secs(self.event_timer_interval),
event_origin=event_origin,
event_subtype="")
super(VizTransformMatplotlibGraphs, self).on_start()
# when tranform is used as a data process
def recv_packet(self, packet, in_stream_route, in_stream_id):
#Check to see if the class instance was set up as a event triggered transform. If yes, skip the packet
if self.event_timer_interval:
return
log.info('Received packet')
mpl_data_granule = VizTransformMatplotlibGraphsAlgorithm.execute(
packet, params=self.get_stream_definition())
for stream_name in self.stream_names:
publisher = getattr(self, stream_name)
publisher.publish(mpl_data_granule)
def get_stream_definition(self):
stream_id = self.stream_ids[0]
stream_def = self.pubsub_management.read_stream_definition(
stream_id=stream_id)
return stream_def._id
def process_event(self, msg, headers):
return
def interval_timer_callback(self, *args, **kwargs):
#Find out the input data product to this process
in_dp_id = self.CFG.get_safe('in_dp_id')
print " >>>>>>>>>>>>>> IN DP ID from cfg : ", in_dp_id
# get the dataset_id associated with the data_product. Need it to do the data retrieval
ds_ids, _ = self.rrclient.find_objects(in_dp_id, PRED.hasDataset,
RT.Dataset, True)
if ds_ids is None or not ds_ids:
return None
# retrieve data for the specified time interval. Setup up query from pass config first
query = {}
param_list_str = self.CFG.get_safe('parameters')
if param_list_str:
query['parameters'] = param_list_str.split(', ')
# append time if not present in list of parameters
if not 'time' in query['parameters']:
query['parameters'].append('time')
query['start_time'] = query['end_time'] = ntplib.system_to_ntp_time(
time.time()) # Now
query['stride_time'] = 1
if self.CFG.get_safe('graph_time_period'):
query['start_time'] = query['end_time'] - self._str_to_secs(
self.CFG.get_safe('graph_time_period'))
#print " >>>>>>>>>>>>>> QUERY = ", query
#retrieved_granule = self.data_retriever_client.retrieve(ds_ids[0],{'start_time':start_time,'end_time':end_time})
retrieved_granule = self.data_retriever_client.retrieve(ds_ids[0],
query=query)
# add extra parameters to query passed in config that are not needed by data retrieval
if self.CFG.get_safe('resolution'):
query['resolution'] = self.CFG.get_safe('resolution')
# send the granule through the Algorithm code to get the matplotlib graphs
mpl_pdict_id = self.dsm_client.read_parameter_dictionary_by_name(
'graph_image_param_dict', id_only=True)
mpl_stream_def = self.pubsub_client.create_stream_definition(
'mpl', parameter_dictionary_id=mpl_pdict_id)
fileName = self.CFG.get_safe('graph_time_period')
mpl_data_granule = VizTransformMatplotlibGraphsAlgorithm.execute(
retrieved_granule,
config=query,
params=mpl_stream_def,
fileName=fileName)
if mpl_data_granule == None:
return None
# publish on all specified output streams
for stream_name in self.stream_names:
publisher = getattr(self, stream_name)
publisher.publish(mpl_data_granule)
return
def _str_to_secs(self, time_period):
# this method converts commonly used time periods to its actual seconds counterpart
#separate alpha and numeric parts of the time period
time_n = time_period.lower().rstrip('abcdefghijklmnopqrstuvwxyz ')
time_a = time_period.lower().lstrip('0123456789. ')
# determine if user specified, secs, mins, hours, days, weeks, months, years
factor = None
if time_a == 'sec' or time_a == "secs" or time_a == 'second' or time_a == "seconds":
factor = 1
if time_a == "min" or time_a == "mins" or time_a == "minute" or time_a == "minutes":
factor = 60
if time_a == "hr" or time_a == "hrs" or time_a == "hour" or time_a == "hours":
factor = 60 * 60
if time_a == "day" or time_a == "days":
factor = 60 * 60 * 24
if time_a == "wk" or time_a == "wks" or time_a == "week" or time_a == "weeks":
factor = 60 * 60 * 24 * 7
if time_a == "mon" or time_a == "mons" or time_a == "month" or time_a == "months":
factor = 60 * 60 * 24 * 30
if time_a == "yr" or time_a == "yrs" or time_a == "year" or time_a == "years":
factor = 60 * 60 * 24 * 365
time_period_secs = float(time_n) * factor
return time_period_secs
def on_quit(self):
#Cancel the timer
if hasattr(self, 'interval_timer_id'):
self.ssclient.cancel_timer(self.interval_timer_id)
super(VizTransformMatplotlibGraphs, self).on_quit()
class TestWorkflowManagementIntegration(VisualizationIntegrationTestHelper):
def setUp(self):
# Start container
self._start_container()
self.container.start_rel_from_url('res/deploy/r2deploy.yml')
# simulate preloading
preload_ion_params(self.container)
#Instantiate a process to represent the test
process = WorkflowServiceTestProcess()
# Now create client to DataProductManagementService
self.rrclient = ResourceRegistryServiceProcessClient(
node=self.container.node, process=process)
self.damsclient = DataAcquisitionManagementServiceProcessClient(
node=self.container.node, process=process)
self.pubsubclient = PubsubManagementServiceProcessClient(
node=self.container.node, process=process)
self.ingestclient = IngestionManagementServiceProcessClient(
node=self.container.node, process=process)
self.imsclient = InstrumentManagementServiceProcessClient(
node=self.container.node, process=process)
self.dataproductclient = DataProductManagementServiceProcessClient(
node=self.container.node, process=process)
self.dataprocessclient = DataProcessManagementServiceProcessClient(
node=self.container.node, process=process)
self.datasetclient = DatasetManagementServiceProcessClient(
node=self.container.node, process=process)
self.workflowclient = WorkflowManagementServiceProcessClient(
node=self.container.node, process=process)
self.process_dispatcher = ProcessDispatcherServiceProcessClient(
node=self.container.node, process=process)
self.data_retriever = DataRetrieverServiceProcessClient(
node=self.container.node, process=process)
self.ctd_stream_def = SBE37_CDM_stream_definition()
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),
'Not integrated for CEI')
def test_SA_transform_components(self):
assertions = self.assertTrue
#The list of data product streams to monitor
data_product_stream_ids = list()
#Create the input data product
ctd_stream_id, ctd_parsed_data_product_id = self.create_ctd_input_stream_and_data_product(
)
data_product_stream_ids.append(ctd_stream_id)
###
### Setup the first transformation
###
# Salinity: Data Process Definition
ctd_L2_salinity_dprocdef_id = self.create_salinity_data_process_definition(
)
l2_salinity_all_data_process_id, ctd_l2_salinity_output_dp_id = self.create_transform_process(
ctd_L2_salinity_dprocdef_id, ctd_parsed_data_product_id,
'salinity')
## get the stream id for the transform outputs
stream_ids, _ = self.rrclient.find_objects(
ctd_l2_salinity_output_dp_id, PRED.hasStream, None, True)
assertions(len(stream_ids) > 0)
sal_stream_id = stream_ids[0]
data_product_stream_ids.append(sal_stream_id)
###
### Setup the second transformation
###
# Salinity Doubler: Data Process Definition
salinity_doubler_dprocdef_id = self.create_salinity_doubler_data_process_definition(
)
salinity_double_data_process_id, salinity_doubler_output_dp_id = self.create_transform_process(
salinity_doubler_dprocdef_id, ctd_l2_salinity_output_dp_id,
'salinity')
stream_ids, _ = self.rrclient.find_objects(
salinity_doubler_output_dp_id, PRED.hasStream, None, True)
assertions(len(stream_ids) > 0)
sal_dbl_stream_id = stream_ids[0]
data_product_stream_ids.append(sal_dbl_stream_id)
#Start the output stream listener to monitor and collect messages
results = self.start_output_stream_and_listen(ctd_stream_id,
data_product_stream_ids)
#Stop the transform processes
self.dataprocessclient.deactivate_data_process(
salinity_double_data_process_id)
self.dataprocessclient.deactivate_data_process(
l2_salinity_all_data_process_id)
#Validate the data from each of the messages along the way
self.validate_messages(results)
@attr('LOCOINT')
@attr('SMOKE')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),
'Not integrated for CEI')
def test_transform_workflow(self):
assertions = self.assertTrue
log.debug("Building the workflow definition")
workflow_def_obj = IonObject(
RT.WorkflowDefinition,
name='Salinity_Test_Workflow',
description='tests a workflow of multiple transform data processes'
)
workflow_data_product_name = 'TEST-Workflow_Output_Product' #Set a specific output product name
#-------------------------------------------------------------------------------------------------------------------------
log.debug("Adding a transformation process definition for salinity")
#-------------------------------------------------------------------------------------------------------------------------
ctd_L2_salinity_dprocdef_id = self.create_salinity_data_process_definition(
)
workflow_step_obj = IonObject(
'DataProcessWorkflowStep',
data_process_definition_id=ctd_L2_salinity_dprocdef_id,
persist_process_output_data=False
) #Don't persist the intermediate data product
workflow_def_obj.workflow_steps.append(workflow_step_obj)
#-------------------------------------------------------------------------------------------------------------------------
log.debug(
"Adding a transformation process definition for salinity doubler")
#-------------------------------------------------------------------------------------------------------------------------
salinity_doubler_dprocdef_id = self.create_salinity_doubler_data_process_definition(
)
workflow_step_obj = IonObject(
'DataProcessWorkflowStep',
data_process_definition_id=salinity_doubler_dprocdef_id,
)
workflow_def_obj.workflow_steps.append(workflow_step_obj)
log.debug("Creating workflow def in the resource registry")
workflow_def_id = self.workflowclient.create_workflow_definition(
workflow_def_obj)
aids = self.rrclient.find_associations(workflow_def_id,
PRED.hasDataProcessDefinition)
assertions(len(aids) == 2)
#The list of data product streams to monitor
data_product_stream_ids = list()
log.debug("Creating the input data product")
ctd_stream_id, ctd_parsed_data_product_id = self.create_ctd_input_stream_and_data_product(
)
data_product_stream_ids.append(ctd_stream_id)
log.debug("Creating and starting the workflow")
workflow_id, workflow_product_id = self.workflowclient.create_data_process_workflow(
workflow_def_id,
ctd_parsed_data_product_id,
persist_workflow_data_product=True,
output_data_product_name=workflow_data_product_name,
timeout=300)
workflow_output_ids, _ = self.rrclient.find_subjects(
RT.Workflow, PRED.hasOutputProduct, workflow_product_id, True)
assertions(len(workflow_output_ids) == 1)
log.debug("persisting the output product")
#self.dataproductclient.activate_data_product_persistence(workflow_product_id)
dataset_ids, _ = self.rrclient.find_objects(workflow_product_id,
PRED.hasDataset,
RT.Dataset, True)
assertions(len(dataset_ids) == 1)
dataset_id = dataset_ids[0]
log.debug(
"Verifying the output data product name matches what was specified in the workflow definition"
)
workflow_product = self.rrclient.read(workflow_product_id)
assertions(
workflow_product.name.startswith(workflow_data_product_name),
'Nope: %s != %s' %
(workflow_product.name, workflow_data_product_name))
log.debug(
"Walking the associations to find the appropriate output data streams to validate the messages"
)
workflow_dp_ids, _ = self.rrclient.find_objects(
workflow_id, PRED.hasDataProduct, RT.DataProduct, True)
assertions(len(workflow_dp_ids) == 2)
for dp_id in workflow_dp_ids:
stream_ids, _ = self.rrclient.find_objects(dp_id, PRED.hasStream,
None, True)
assertions(len(stream_ids) == 1)
data_product_stream_ids.append(stream_ids[0])
log.debug("data_product_stream_ids: %s" % data_product_stream_ids)
log.debug(
"Starting the output stream listener to monitor to collect messages"
)
results = self.start_output_stream_and_listen(ctd_stream_id,
data_product_stream_ids)
log.debug("results::: %s" % results)
log.debug("Stopping the workflow processes")
self.workflowclient.terminate_data_process_workflow(
workflow_id, False, timeout=250) # Should test true at some point
log.debug("Making sure the Workflow object was removed")
objs, _ = self.rrclient.find_resources(restype=RT.Workflow)
assertions(len(objs) == 0)
log.debug(
"Validating the data from each of the messages along the way")
self.validate_messages(results)
log.debug(
"Checking to see if dataset id = %s, was persisted, and that it can be retrieved...."
% dataset_id)
self.validate_data_ingest_retrieve(dataset_id)
log.debug("Cleaning up to make sure delete is correct.")
self.workflowclient.delete_workflow_definition(workflow_def_id)
"""
workflow_def_ids,_ = self.rrclient.find_resources(restype=RT.WorkflowDefinition)
assertions(len(workflow_def_ids) == 0 )
"""
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),
'Not integrated for CEI')
def test_highcharts_transform_workflow(self):
assertions = self.assertTrue
# Build the workflow definition
workflow_def_obj = IonObject(
RT.WorkflowDefinition,
name='HighCharts_Test_Workflow',
description=
'Tests the workflow of converting stream data to HighCharts')
#Add a transformation process definition
highcharts_procdef_id = self.create_highcharts_data_process_definition(
)
workflow_step_obj = IonObject(
'DataProcessWorkflowStep',
data_process_definition_id=highcharts_procdef_id,
persist_process_output_data=False)
workflow_def_obj.workflow_steps.append(workflow_step_obj)
#Create it in the resource registry
workflow_def_id = self.workflowclient.create_workflow_definition(
workflow_def_obj)
#The list of data product streams to monitor
data_product_stream_ids = list()
#Create the input data product
ctd_stream_id, ctd_parsed_data_product_id = self.create_ctd_input_stream_and_data_product(
)
data_product_stream_ids.append(ctd_stream_id)
#Create and start the workflow
workflow_id, workflow_product_id = self.workflowclient.create_data_process_workflow(
workflow_def_id, ctd_parsed_data_product_id, timeout=60)
workflow_output_ids, _ = self.rrclient.find_subjects(
RT.Workflow, PRED.hasOutputProduct, workflow_product_id, True)
assertions(len(workflow_output_ids) == 1)
#Walk the associations to find the appropriate output data streams to validate the messages
workflow_dp_ids, _ = self.rrclient.find_objects(
workflow_id, PRED.hasDataProduct, RT.DataProduct, True)
assertions(len(workflow_dp_ids) == 1)
for dp_id in workflow_dp_ids:
stream_ids, _ = self.rrclient.find_objects(dp_id, PRED.hasStream,
None, True)
assertions(len(stream_ids) == 1)
data_product_stream_ids.append(stream_ids[0])
#Start the output stream listener to monitor and collect messages
results = self.start_output_stream_and_listen(ctd_stream_id,
data_product_stream_ids)
#Stop the workflow processes
self.workflowclient.terminate_data_process_workflow(
workflow_id=workflow_id, delete_data_products=False,
timeout=60) # Should test true at some point
#Validate the data from each of the messages along the way
self.validate_highcharts_transform_results(results)
#Cleanup to make sure delete is correct.
self.workflowclient.delete_workflow_definition(workflow_def_id)
"""
workflow_def_ids,_ = self.rrclient.find_resources(restype=RT.WorkflowDefinition)
assertions(len(workflow_def_ids) == 0 )
"""
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),
'Not integrated for CEI')
def test_mpl_graphs_transform_workflow(self):
assertions = self.assertTrue
# Build the workflow definition
workflow_def_obj = IonObject(
RT.WorkflowDefinition,
name='Mpl_Graphs_Test_Workflow',
description=
'Tests the workflow of converting stream data to Matplotlib graphs'
)
#Add a transformation process definition
mpl_graphs_procdef_id = self.create_mpl_graphs_data_process_definition(
)
workflow_step_obj = IonObject(
'DataProcessWorkflowStep',
data_process_definition_id=mpl_graphs_procdef_id,
persist_process_output_data=False)
workflow_def_obj.workflow_steps.append(workflow_step_obj)
#Create it in the resource registry
workflow_def_id = self.workflowclient.create_workflow_definition(
workflow_def_obj)
#The list of data product streams to monitor
data_product_stream_ids = list()
#Create the input data product
ctd_stream_id, ctd_parsed_data_product_id = self.create_ctd_input_stream_and_data_product(
)
data_product_stream_ids.append(ctd_stream_id)
#Create and start the workflow
workflow_id, workflow_product_id = self.workflowclient.create_data_process_workflow(
workflow_def_id,
ctd_parsed_data_product_id,
persist_workflow_data_product=True,
timeout=60)
workflow_output_ids, _ = self.rrclient.find_subjects(
RT.Workflow, PRED.hasOutputProduct, workflow_product_id, True)
assertions(len(workflow_output_ids) == 1)
#Walk the associations to find the appropriate output data streams to validate the messages
workflow_dp_ids, _ = self.rrclient.find_objects(
workflow_id, PRED.hasDataProduct, RT.DataProduct, True)
assertions(len(workflow_dp_ids) == 1)
for dp_id in workflow_dp_ids:
stream_ids, _ = self.rrclient.find_objects(dp_id, PRED.hasStream,
None, True)
assertions(len(stream_ids) == 1)
data_product_stream_ids.append(stream_ids[0])
#Start the output stream listener to monitor and collect messages
results = self.start_output_stream_and_listen(ctd_stream_id,
data_product_stream_ids)
#Stop the workflow processes
self.workflowclient.terminate_data_process_workflow(
workflow_id=workflow_id, delete_data_products=False,
timeout=60) # Should test true at some point
#Validate the data from each of the messages along the way
self.validate_mpl_graphs_transform_results(results)
# Check to see if ingestion worked. Extract the granules from data_retrieval.
# First find the dataset associated with the output dp product
ds_ids, _ = self.rrclient.find_objects(
workflow_dp_ids[len(workflow_dp_ids) - 1], PRED.hasDataset,
RT.Dataset, True)
retrieved_granule = self.data_retriever.retrieve_last_data_points(
ds_ids[0], 10)
#Validate the data from each of the messages along the way
self.validate_mpl_graphs_transform_results(retrieved_granule)
#Cleanup to make sure delete is correct.
self.workflowclient.delete_workflow_definition(workflow_def_id)
"""
workflow_def_ids,_ = self.rrclient.find_resources(restype=RT.WorkflowDefinition)
assertions(len(workflow_def_ids) == 0 )
"""
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False),
'Not integrated for CEI')
def test_multiple_workflow_instances(self):
assertions = self.assertTrue
# Build the workflow definition
workflow_def_obj = IonObject(
RT.WorkflowDefinition,
name='Multiple_Test_Workflow',
description='Tests the workflow of converting stream data')
#Add a transformation process definition
highcharts_procdef_id = self.create_highcharts_data_process_definition(
)
workflow_step_obj = IonObject(
'DataProcessWorkflowStep',
data_process_definition_id=highcharts_procdef_id,
persist_process_output_data=False)
workflow_def_obj.workflow_steps.append(workflow_step_obj)
#Create it in the resource registry
workflow_def_id = self.workflowclient.create_workflow_definition(
workflow_def_obj)
#The list of data product streams to monitor
data_product_stream_ids = list()
#Create the first input data product
ctd_stream_id1, ctd_parsed_data_product_id1 = self.create_ctd_input_stream_and_data_product(
'ctd_parsed1')
data_product_stream_ids.append(ctd_stream_id1)
#Create and start the first workflow
workflow_id1, workflow_product_id1 = self.workflowclient.create_data_process_workflow(
workflow_def_id, ctd_parsed_data_product_id1, timeout=60)
#Create the second input data product
ctd_stream_id2, ctd_parsed_data_product_id2 = self.create_ctd_input_stream_and_data_product(
'ctd_parsed2')
data_product_stream_ids.append(ctd_stream_id2)
#Create and start the second workflow
workflow_id2, workflow_product_id2 = self.workflowclient.create_data_process_workflow(
workflow_def_id, ctd_parsed_data_product_id2, timeout=60)
#Walk the associations to find the appropriate output data streams to validate the messages
workflow_ids, _ = self.rrclient.find_resources(restype=RT.Workflow)
assertions(len(workflow_ids) == 2)
#Start the first input stream process
ctd_sim_pid1 = self.start_sinusoidal_input_stream_process(
ctd_stream_id1)
#Start the second input stream process
ctd_sim_pid2 = self.start_simple_input_stream_process(ctd_stream_id2)
#Start the output stream listener to monitor a set number of messages being sent through the workflows
results = self.start_output_stream_and_listen(
None, data_product_stream_ids, message_count_per_stream=5)
# stop the flow of messages...
self.process_dispatcher.cancel_process(
ctd_sim_pid1
) # kill the ctd simulator process - that is enough data
self.process_dispatcher.cancel_process(ctd_sim_pid2)
#Stop the first workflow processes
self.workflowclient.terminate_data_process_workflow(
workflow_id=workflow_id1, delete_data_products=False,
timeout=60) # Should test true at some point
#Stop the second workflow processes
self.workflowclient.terminate_data_process_workflow(
workflow_id=workflow_id2, delete_data_products=False,
timeout=60) # Should test true at some point
workflow_ids, _ = self.rrclient.find_resources(restype=RT.Workflow)
assertions(len(workflow_ids) == 0)
#Cleanup to make sure delete is correct.
self.workflowclient.delete_workflow_definition(workflow_def_id)
"""
