Python IonObject.configuration方法代码示例

# 需要导入模块: from pyon.public import IonObject [as 别名]
# 或者: from pyon.public.IonObject import configuration [as 别名]
    def test_tuple_in_dict(self):
        # create a resource with a tuple saved in a dict
        transform_obj = IonObject(RT.Transform)
        transform_obj.configuration = {}
        transform_obj.configuration["tuple"] = ('STRING',)
        transform_id, _ = self.resource_registry_service.create(transform_obj)

        # read the resource back
        returned_transform_obj = self.resource_registry_service.read(transform_id)

        self.assertEqual(transform_obj.configuration["tuple"], returned_transform_obj.configuration["tuple"])

# 需要导入模块: from pyon.public import IonObject [as 别名]
# 或者: from pyon.public.IonObject import configuration [as 别名]
    def test_multiple_visualization_queue(self):

        # set up a workflow with the salinity transform and the doubler. We will direct the original stream and the doubled stream to queues
        # and test to make sure the subscription to the queues is working correctly
        assertions = self.assertTrue

        # Build the workflow definition
        workflow_def_obj = IonObject(RT.WorkflowDefinition, name='Viz_Test_Workflow',description='A workflow to test collection of multiple data products in queues')

        workflow_data_product_name = 'TEST-Workflow_Output_Product' #Set a specific output product name
        #-------------------------------------------------------------------------------------------------------------------------
        #Add 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
        configuration = {'stream_name' : 'salinity'}
        workflow_step_obj.configuration = configuration
        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)

        aids = self.rrclient.find_associations(workflow_def_id, PRED.hasDataProcessDefinition)
        assertions(len(aids) == 1 )

        #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=30)

        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])

        # Now for each of the data_product_stream_ids create a queue and pipe their data to the queue


        user_queue_name1 = USER_VISUALIZATION_QUEUE + '1'
        user_queue_name2 = USER_VISUALIZATION_QUEUE + '2'

        # use idempotency to create queues
        xq1 = self.container.ex_manager.create_xn_queue(user_queue_name1)
        self.addCleanup(xq1.delete)
        xq2 = self.container.ex_manager.create_xn_queue(user_queue_name2)
        self.addCleanup(xq2.delete)
        xq1.purge()
        xq2.purge()

        # the create_subscription call takes a list of stream_ids so create temp ones

        dp_stream_id1 = list()
        dp_stream_id1.append(data_product_stream_ids[0])
        dp_stream_id2 = list()
        dp_stream_id2.append(data_product_stream_ids[1])

        salinity_subscription_id1 = self.pubsubclient.create_subscription( stream_ids=dp_stream_id1,
            exchange_name = user_queue_name1, name = "user visualization queue1")

        salinity_subscription_id2 = self.pubsubclient.create_subscription( stream_ids=dp_stream_id2,
            exchange_name = user_queue_name2, name = "user visualization queue2")

        # Create subscribers for the output of the queue
        subscriber1 = Subscriber(from_name=xq1)
        subscriber1.initialize()
        subscriber2 = Subscriber(from_name=xq2)
        subscriber2.initialize()

        # after the queue has been created it is safe to activate the subscription
        self.pubsubclient.activate_subscription(subscription_id=salinity_subscription_id1)
        self.pubsubclient.activate_subscription(subscription_id=salinity_subscription_id2)

        # Start input stream and wait for some time
        ctd_sim_pid = self.start_simple_input_stream_process(ctd_stream_id)
        gevent.sleep(5.0)  # Send some messages - don't care how many

        msg_count,_ = xq1.get_stats()
        log.info('Messages in user queue 1: %s ' % msg_count)
        msg_count,_ = xq2.get_stats()
        log.info('Messages in user queue 2: %s ' % msg_count)

        msgs1 = subscriber1.get_all_msgs(timeout=2)
        msgs2 = subscriber2.get_all_msgs(timeout=2)

        for x in range(min(len(msgs1), len(msgs2))):
            msgs1[x].ack()
            msgs2[x].ack()
#.........这里部分代码省略.........