def check_tempsf_instrument_data_product(self, reference_designator):
passing = True
info_list = []
passing &= self.check_data_product_reference(reference_designator, info_list)
if not passing: return passing
data_product_id, stream_def_id, dataset_id = info_list.pop()
now = time.time()
ntp_now = now + 2208988800
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [ntp_now]
rdt['temperature'] = [[ 25.3884, 26.9384, 24.3394, 23.3401, 22.9832,
29.4434, 26.9873, 15.2883, 16.3374, 14.5883, 15.7253, 18.4383,
15.3488, 17.2993, 10.2111, 11.5993, 10.9345, 9.4444, 9.9876,
10.9834, 11.0098, 5.3456, 4.2994, 4.3009]]
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ParameterHelper.publish_rdt_to_data_product(data_product_id, rdt)
passing &= self.assertTrue(dataset_monitor.wait())
if not passing: return passing
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
passing &= self.assert_array_almost_equal(rdt['time'], [ntp_now])
passing &= self.assert_array_almost_equal(rdt['temperature'], [[
25.3884, 26.9384, 24.3394, 23.3401, 22.9832, 29.4434, 26.9873,
15.2883, 16.3374, 14.5883, 15.7253, 18.4383, 15.3488, 17.2993,
10.2111, 11.5993, 10.9345, 9.4444, 9.9876, 10.9834, 11.0098,
5.3456, 4.2994, 4.3009]])
return passing
def test_instrument_simple(self):
instrument_model_id = self.create_instrument_model()
instrument_agent_id = self.create_instrument_agent(instrument_model_id)
instrument_device_id = self.create_instrument_device(instrument_model_id)
instrument_agent_instance_id = self.create_instrument_agent_instance(instrument_agent_id, instrument_device_id)
raw_dp_id, parsed_dp_id = self.create_instrument_data_products(instrument_device_id)
self.start_instrument_agent_instance(instrument_agent_instance_id)
agent_process_id = self.poll_instrument_agent_instance(instrument_agent_instance_id, instrument_device_id)
agent_client = ResourceAgentClient(instrument_device_id,
to_name=agent_process_id,
process=FakeProcess())
self.agent_state_transition(agent_client, ResourceAgentEvent.INITIALIZE, ResourceAgentState.INACTIVE)
self.agent_state_transition(agent_client, ResourceAgentEvent.GO_ACTIVE, ResourceAgentState.IDLE)
self.agent_state_transition(agent_client, ResourceAgentEvent.RUN, ResourceAgentState.COMMAND)
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(parsed_dp_id)
for i in xrange(10):
monitor = DatasetMonitor(dataset_id=dataset_id)
agent_client.execute_resource(AgentCommand(command=SBE37ProtocolEvent.ACQUIRE_SAMPLE))
if not monitor.event.wait(30):
raise AssertionError('Failed on the %ith granule' % i)
monitor.stop()
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(dataset_id))
self.assertEquals(len(rdt), 10)
def init_check(self):
self.dp_id = self.create_data_product(name=uuid4().hex, stream_def_id=self.stream_def_id)
self.data_product_management.activate_data_product_persistence(self.dp_id)
self.addCleanup(self.data_product_management.suspend_data_product_persistence, self.dp_id)
self.dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(self.dp_id)
self.dataset_monitor = DatasetMonitor(self.dataset_id)
self.addCleanup(self.dataset_monitor.stop)
def test_logger(self):
data_product_id = self.make_ctd_data_product()
# Clone the data product so we have an output
clone_id = self.clone_data_product(data_product_id)
data_process_id = self.create_data_process_logger(data_product_id, clone_id, {'x':'temp'})
dataset_monitor = DatasetMonitor(data_product_id=data_product_id)
self.addCleanup(dataset_monitor.stop)
# Put some data into the the data product
rdt = self.ph.rdt_for_data_product(data_product_id)
rdt['time'] = np.arange(40)
rdt['temp'] = np.arange(40)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
# Watch the output
dataset_monitor = DatasetMonitor(data_product_id=clone_id)
self.addCleanup(dataset_monitor.stop)
# Run the replay
self.data_process_management.activate_data_process(data_process_id)
# Make sure data came out
self.assertTrue(dataset_monitor.wait())
def test_coefficient_compatibility(self):
data_product_id = self.create_data_product(
name='Calibration Coefficient Test Data product',
stream_def_id=self.stream_def_id)
self.data_product_management.activate_data_product_persistence(
data_product_id)
self.addCleanup(
self.data_product_management.suspend_data_product_persistence,
data_product_id)
rdt = RecordDictionaryTool(stream_definition_id=self.stream_def_id)
rdt['time'] = np.arange(10)
rdt['temp'] = [10] * 10
rdt['cc_coefficient'] = [2] * 10
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(
data_product_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
rdt2 = RecordDictionaryTool.load_from_granule(
self.data_retriever.retrieve(dataset_id))
np.testing.assert_array_equal(rdt2['offset'], [12] * 10)
def test_append_parameter(self):
# Make a CTDBP Data Product
data_product_id = self.make_ctd_data_product()
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(
data_product_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
# Throw some data in it
rdt = self.ph.rdt_for_data_product(data_product_id)
rdt['time'] = np.arange(30)
rdt['temp'] = np.arange(30)
rdt['pressure'] = np.arange(30)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
dataset_monitor.event.clear()
# Grab the egg
egg_url = self.egg_url
egg_path = TransformWorker.download_egg(egg_url)
import pkg_resources
pkg_resources.working_set.add_entry(egg_path)
self.addCleanup(os.remove, egg_path)
# Make a parameter function
owner = 'ion_example.add_arrays'
func = 'add_arrays'
arglist = ['a', 'b']
pf = ParameterFunction(name='add_arrays',
function_type=PFT.PYTHON,
owner=owner,
function=func,
args=arglist)
pfunc_id = self.dataset_management.create_parameter_function(pf)
self.addCleanup(self.dataset_management.delete_parameter_function,
pfunc_id)
# Make a context (instance of the function)
context = ParameterContext(name='array_sum',
units="1",
fill_value="-9999",
parameter_function_id=pfunc_id,
parameter_type="function",
value_encoding="float32",
display_name="Array Summation",
parameter_function_map={
'a': 'temp',
'b': 'pressure'
})
#pfunc = DatasetManagementService.get_coverage_function(pf)
#pfunc.param_map = {'a':'temp', 'b':'pressure'}
#ctxt = ParameterContext('array_sum', param_type=ParameterFunctionType(pfunc))
#ctxt_dump = ctxt.dump()
#ctxt_id = self.dataset_management.create_parameter_context('array_sum', ctxt_dump)
ctxt_id = self.dataset_management.create_parameter(context)
self.dataset_management.add_parameter_to_dataset(ctxt_id, dataset_id)
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['array_sum'], np.arange(0, 60, 2))
def test_instrument_simple(self):
instrument_model_id = self.create_instrument_model()
instrument_agent_id = self.create_instrument_agent(instrument_model_id)
instrument_device_id = self.create_instrument_device(instrument_model_id)
instrument_agent_instance_id = self.create_instrument_agent_instance(instrument_agent_id, instrument_device_id)
raw_dp_id, parsed_dp_id = self.create_instrument_data_products(instrument_device_id)
self.start_instrument_agent_instance(instrument_agent_instance_id)
agent_process_id = self.poll_instrument_agent_instance(instrument_agent_instance_id, instrument_device_id)
agent_client = ResourceAgentClient(instrument_device_id,
to_name=agent_process_id,
process=FakeProcess())
self.agent_state_transition(agent_client, ResourceAgentEvent.INITIALIZE, ResourceAgentState.INACTIVE)
self.agent_state_transition(agent_client, ResourceAgentEvent.GO_ACTIVE, ResourceAgentState.IDLE)
self.agent_state_transition(agent_client, ResourceAgentEvent.RUN, ResourceAgentState.COMMAND)
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(parsed_dp_id)
for i in xrange(10):
monitor = DatasetMonitor(dataset_id=dataset_id)
agent_client.execute_resource(AgentCommand(command=SBE37ProtocolEvent.ACQUIRE_SAMPLE))
if not monitor.wait():
raise AssertionError('Failed on the %ith granule' % i)
monitor.stop()
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(dataset_id))
self.assertEquals(len(rdt), 10)
def test_add_parameter_function(self):
# req-tag: NEW SA - 31
# Make a CTDBP Data Product
data_product_id = self.make_ctd_data_product()
self.data_product_id = data_product_id
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(data_product_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
# Throw some data in it
rdt = self.ph.rdt_for_data_product(data_product_id)
rdt['time'] = np.arange(30)
rdt['temp'] = np.arange(30)
rdt['pressure'] = np.arange(30)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
dataset_monitor.event.clear()
#--------------------------------------------------------------------------------
# This is what the user defines either via preload or through the UI
#--------------------------------------------------------------------------------
# Where the egg is
egg_url = self.egg_url
# Make a parameter function
owner = 'ion_example.add_arrays'
func = 'add_arrays'
arglist = ['a', 'b']
pf = ParameterFunction(name='add_arrays', function_type=PFT.PYTHON, owner=owner, function=func, args=arglist, egg_uri=egg_url)
pfunc_id = self.dataset_management.create_parameter_function(pf)
#--------------------------------------------------------------------------------
self.addCleanup(self.dataset_management.delete_parameter_function, pfunc_id)
# Make a data process definition
dpd = DataProcessDefinition(name='add_arrays', description='Sums two arrays')
dpd_id = self.data_process_management.create_data_process_definition(dpd, pfunc_id)
# TODO: assert assoc exists
argmap = {'a':'temp', 'b':'pressure'}
dp_id = self.data_process_management.create_data_process(dpd_id, [data_product_id], argument_map=argmap, out_param_name='array_sum')
# Verify that the function worked!
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['array_sum'], np.arange(0,60,2))
# Verify that we can inspect it as well
source_code = self.data_process_management.inspect_data_process_definition(dpd_id)
self.assertEquals(source_code, 'def add_arrays(a, b):\n return a+b\n')
url = self.data_process_management.get_data_process_definition_url(dpd_id)
self.assertEquals(url, 'http://sddevrepo.oceanobservatories.org/releases/ion_example-0.1-py2.7.egg')
dpd_ids, _ = self.resource_registry.find_resources(name='dataqc_spiketest', restype=RT.DataProcessDefinition, id_only=True)
dpd_id = dpd_ids[0]
url = self.data_process_management.get_data_process_definition_url(dpd_id)
self.assertEquals(url, 'https://github.com/ooici/ion-functions/blob/master/ion_functions/qc/qc_functions.py')
def create_device_site_deployment(self, dep_name="Deployment", starting=''):
from interface.objects import StreamConfiguration, StreamConfigurationType, InstrumentDevice
from interface.objects import InstrumentModel, PlatformAgent, InstrumentSite, TemporalBounds, Deployment
from interface.objects import RemotePlatformDeploymentContext
stream_conf = StreamConfiguration(stream_name="CTD 1 Parsed Stream", parameter_dictionary_name='ctd_parsed_param_dict', stream_type=StreamConfigurationType.PARSED)
pdict_id = self.dataset_management.read_parameter_dictionary_by_name(name='ctd_parsed_param_dict')
stream_def_id = self.create_stream_definition(name='CTD 1', parameter_dictionary_id=pdict_id)
data_product_id = self.create_data_product(name="DDP_1", stream_def_id=stream_def_id, stream_configuration=stream_conf)
self.activate_data_product(data_product_id)
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(data_product_id)
stream_def = self.resource_registry.find_objects(data_product_id, PRED.hasStreamDefinition)[0][0]
param_dict = self.resource_registry.find_objects(stream_def._id, PRED.hasParameterDictionary)[0][0]
# Add data to the DataProduct
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
rdt = self.ph.get_rdt(stream_def._id)
rdt_ = self.ph.rdt_for_data_product(data_product_id)
self.assertEquals(rdt.fields, rdt_.fields)
rdt['time'] = [0, 1, 2, 3]
rdt['temp'] = [10, 11, 12, 13]
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
# Create Device
device = InstrumentDevice(name='Device 1')
device_id = self.instrument_management.create_instrument_device(device)
self.data_acquisition_management.register_instrument(device_id)
self.data_acquisition_management.assign_data_product(device_id, data_product_id)
# Create Model
model = InstrumentModel(name='Model 1')
model_id = self.instrument_management.create_instrument_model(model)
self.instrument_management.assign_instrument_model_to_instrument_device(model_id, device_id)
# Create AgentDefinition
ad = PlatformAgent(stream_configurations=[stream_conf])
ad_id, _ = self.resource_registry.create(ad)
# Create Site
site = InstrumentSite(name='Site 1', stream_configurations=[stream_conf])
site_id, _ = self.resource_registry.create(site)
self.resource_registry.create_association(site_id, PRED.hasModel, model_id)
self.resource_registry.create_association(site_id, PRED.hasAgentDefinition, ad_id)
# TemporalBounds of the Deployment
temp_bounds = TemporalBounds(start_datetime=starting, end_datetime='')
# Create Deployment
deployment = Deployment(name=dep_name, type="RemotePlatform", context=RemotePlatformDeploymentContext(),
constraint_list=[temp_bounds])
deployment_id = self.observatory_management.create_deployment(deployment=deployment, site_id=site_id, device_id=device_id)
return site_id, device_id, dataset_id, deployment_id, param_dict, data_product_id
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()
def test_append_parameter(self):
# Make a CTDBP Data Product
data_product_id = self.make_ctd_data_product()
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(data_product_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
# Throw some data in it
rdt = self.ph.rdt_for_data_product(data_product_id)
rdt['time'] = np.arange(30)
rdt['temp'] = np.arange(30)
rdt['pressure'] = np.arange(30)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
dataset_monitor.event.clear()
# Grab the egg
egg_url = self.egg_url
egg_path = TransformWorker.download_egg(egg_url)
import pkg_resources
pkg_resources.working_set.add_entry(egg_path)
self.addCleanup(os.remove, egg_path)
# Make a parameter function
owner = 'ion_example.add_arrays'
func = 'add_arrays'
arglist = ['a', 'b']
pf = ParameterFunction(name='add_arrays', function_type=PFT.PYTHON, owner=owner, function=func, args=arglist)
pfunc_id = self.dataset_management.create_parameter_function(pf)
self.addCleanup(self.dataset_management.delete_parameter_function, pfunc_id)
# Make a context (instance of the function)
context = ParameterContext(name='array_sum',
units="1",
fill_value="-9999",
parameter_function_id=pfunc_id,
parameter_type="function",
value_encoding="float32",
display_name="Array Summation",
parameter_function_map={'a':'temp','b':'pressure'})
#pfunc = DatasetManagementService.get_coverage_function(pf)
#pfunc.param_map = {'a':'temp', 'b':'pressure'}
#ctxt = ParameterContext('array_sum', param_type=ParameterFunctionType(pfunc))
#ctxt_dump = ctxt.dump()
#ctxt_id = self.dataset_management.create_parameter_context('array_sum', ctxt_dump)
ctxt_id = self.dataset_management.create_parameter(context)
self.dataset_management.add_parameter_to_dataset(ctxt_id, dataset_id)
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['array_sum'], np.arange(0,60,2))
def test_dca_ingestion_pause_resume(self):
data_product_id, dataset_id = self.make_ctd_data_product()
streamer = Streamer(data_product_id, interval=1)
self.addCleanup(streamer.stop)
# Let a couple samples accumulate
self.use_monitor(dataset_id, samples=2)
# Go into DCA and get an editable handle to the coverage
with DirectCoverageAccess() as dca:
with dca.get_editable_coverage(dataset_id) as cov: # <-- This pauses ingestion
monitor = DatasetMonitor(dataset_id)
monitor.event.wait(7) # <-- ~7 Samples should accumulate on the ingestion queue
self.assertFalse(monitor.event.is_set()) # Verifies that nothing was processed (i.e. ingestion is actually paused)
monitor.stop()
# Stop the streamer
streamer.stop()
cont = True
while cont:
monitor = DatasetMonitor(dataset_id)
if not monitor.event.wait(10):
cont = False
monitor.stop()
with DirectCoverageAccess() as dca:
with dca.get_read_only_coverage(dataset_id) as cov:
self.assertGreaterEqual(cov.num_timesteps, 8)
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()
def init_check(self):
self.dp_id = self.create_data_product(name=uuid4().hex, stream_def_id=self.stream_def_id)
self.data_product_management.activate_data_product_persistence(self.dp_id)
self.addCleanup(self.data_product_management.suspend_data_product_persistence, self.dp_id)
self.dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(self.dp_id)
self.dataset_monitor = DatasetMonitor(self.dataset_id)
self.addCleanup(self.dataset_monitor.stop)
def test_retrieve_process(self):
data_product_id = self.make_ctd_data_product()
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(data_product_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
rdt = self.ph.rdt_for_data_product(data_product_id)
date0 = datetime(2014, 1, 1, 0, 0) # 2014-01-01T00:00Z
time0 = calendar.timegm(date0.timetuple()) + 2208988800 # NTP
rdt['time'] = np.arange(time0, time0+30)
rdt['temp'] = np.arange(30)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
dataset_monitor.event.clear()
retrieve_process = RetrieveProcess(dataset_id)
rdt = retrieve_process.retrieve(date0, date0 + timedelta(hours=1))
np.testing.assert_array_equal(rdt['temp'], np.arange(30))
def test_retrieve_process(self):
data_product_id = self.make_ctd_data_product()
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(
data_product_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
rdt = self.ph.rdt_for_data_product(data_product_id)
date0 = datetime(2014, 1, 1, 0, 0) # 2014-01-01T00:00Z
time0 = calendar.timegm(date0.timetuple()) + 2208988800 # NTP
rdt['time'] = np.arange(time0, time0 + 30)
rdt['temp'] = np.arange(30)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
dataset_monitor.event.clear()
retrieve_process = RetrieveProcess(dataset_id)
rdt = retrieve_process.retrieve(date0, date0 + timedelta(hours=1))
np.testing.assert_array_equal(rdt['temp'], np.arange(30))
def test_coefficient_compatibility(self):
data_product_id = self.create_data_product(name='Calibration Coefficient Test Data product', stream_def_id=self.stream_def_id)
self.data_product_management.activate_data_product_persistence(data_product_id)
self.addCleanup(self.data_product_management.suspend_data_product_persistence, data_product_id)
rdt = RecordDictionaryTool(stream_definition_id=self.stream_def_id)
rdt['time'] = np.arange(10)
rdt['temp'] = [10] * 10
rdt['cc_coefficient'] = [2] * 10
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(data_product_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
rdt2 = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(dataset_id))
np.testing.assert_array_equal(rdt2['offset'],[12]*10)
def check_presta_instrument_data_products(self, reference_designator):
# Check the parsed data product make sure it's got everything it needs and can be published persisted etc.
# Absolute Pressure (SFLPRES_L0) is what comes off the instrumnet, SFLPRES_L1 is a pfunc
# Let's go ahead and publish some fake data!!!
# According to https://alfresco.oceanobservatories.org/alfresco/d/d/workspace/SpacesStore/63e16865-9d9e-4b11-b0b3-d5658faa5080/1341-00230_Data_Product_Spec_SFLPRES_OOI.pdf
# Appendix A. Example 1.
# p_psia_tide = 14.8670
# the tide should be 10.2504
passing = True
info_list = []
passing &= self.check_data_product_reference(reference_designator,
info_list)
if not passing:
return passing
data_product_id, stream_def_id, dataset_id = info_list.pop()
now = time.time()
ntp_now = now + 2208988800.
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [ntp_now]
rdt['absolute_pressure'] = [14.8670]
passing &= self.assert_array_almost_equal(rdt['seafloor_pressure'],
[10.2504], 4)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ParameterHelper.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(
dataset_monitor.wait()) # Bumped to 20 to keep buildbot happy
if not passing: return passing
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
passing &= self.assert_array_almost_equal(rdt['time'], [ntp_now])
passing &= self.assert_array_almost_equal(rdt['seafloor_pressure'],
[10.2504], 4)
passing &= self.assert_array_almost_equal(rdt['absolute_pressure'],
[14.8670], 4)
return passing
def test_array_flow_paths(self):
data_product_id, stream_def_id = self.make_array_data_product()
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(data_product_id)
dm = DatasetMonitor(dataset_id)
self.addCleanup(dm.stop)
# I need to make sure that we can fill the RDT with its values
# Test for one timestep
# Test for multiple timesteps
# Publishes
# Ingests correctly
# Retrieves correctly
#--------------------------------------------------------------------------------
# Ensure that the RDT can be filled with ArrayType values
#--------------------------------------------------------------------------------
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [0]
rdt['temp_sample'] = [[0,1,2,3,4]]
np.testing.assert_array_equal(rdt['temp_sample'], np.array([[0,1,2,3,4]]))
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dm.event.wait(10))
dm.event.clear()
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['temp_sample'], np.array([[0,1,2,3,4]]))
#--------------------------------------------------------------------------------
# Ensure that it deals with multiple values
#--------------------------------------------------------------------------------
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [1,2,3]
rdt['temp_sample'] = [[0,1,2,3,4],[1],[5,5,5,5,5]]
m = rdt.fill_value('temp_sample') or np.finfo(np.float32).max
np.testing.assert_equal(m,np.finfo(np.float32).max)
np.testing.assert_array_equal(rdt['temp_sample'], [[0,1,2,3,4],[1,m,m,m,m],[5,5,5,5,5]])
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dm.event.wait(10))
dm.event.clear()
#--------------------------------------------------------------------------------
# Retrieve and Verify
#--------------------------------------------------------------------------------
retrieved_granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(retrieved_granule)
np.testing.assert_array_equal(rdt['time'], np.array([0,1,2,3]))
np.testing.assert_array_equal(rdt['temp_sample'], np.array([[0,1,2,3,4],[0,1,2,3,4],[1,m,m,m,m],[5,5,5,5,5]]))
def test_get_data_from_FDW(self):
# generate a data product and check that the FDW can get data
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_extended_parsed()
stream_def_id = self.pubsub_management.create_stream_definition(
'example', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition,
stream_def_id)
dp = DataProduct(name='example')
data_product_id = self.data_product_management.create_data_product(
dp, stream_def_id)
self.addCleanup(self.data_product_management.delete_data_product,
data_product_id)
self.data_product_management.activate_data_product_persistence(
data_product_id)
self.addCleanup(
self.data_product_management.suspend_data_product_persistence,
data_product_id)
dataset_id = self.resource_registry.find_objects(data_product_id,
PRED.hasDataset,
id_only=True)[0][0]
monitor = DatasetMonitor(dataset_id)
self.addCleanup(monitor.stop)
rdt = ph.get_rdt(stream_def_id)
ph.fill_rdt(rdt, 100)
ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(monitor.event.wait(10))
gevent.sleep(
1) # Yield to other greenlets, had an issue with connectivity
print "--------------------------------"
print dataset_id
coverage_path = DatasetManagementService()._get_coverage_path(
dataset_id)
print coverage_path
print "--------------------------------"
#verify table exists in the DB (similar to above)
# ....code...
# check that the geoserver layer exists as above
# ... code ....
# make a WMS/WFS request...somet like this (or both)
url = self.gs_host + '/geoserver/geonode/ows?service=WFS&version=1.0.0&request=GetFeature&typeName=geonode:ooi_' + dataset_id + '_ooi&maxFeatures=1&outputFormat=csv'
r = requests.get(url)
assertTrue(r.status_code == 200)
def test_create_dataset_verify_geoserver_layer(self):
#generate layer and check that the service created it in geoserver
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_extended_parsed()
stream_def_id = self.pubsub_management.create_stream_definition(
'example', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition,
stream_def_id)
dp = DataProduct(name='example')
data_product_id = self.data_product_management.create_data_product(
dp, stream_def_id)
self.addCleanup(self.data_product_management.delete_data_product,
data_product_id)
self.data_product_management.activate_data_product_persistence(
data_product_id)
self.addCleanup(
self.data_product_management.suspend_data_product_persistence,
data_product_id)
dataset_id = self.resource_registry.find_objects(data_product_id,
PRED.hasDataset,
id_only=True)[0][0]
monitor = DatasetMonitor(dataset_id)
self.addCleanup(monitor.stop)
rdt = ph.get_rdt(stream_def_id)
ph.fill_rdt(rdt, 100)
ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(monitor.event.wait(10))
gevent.sleep(
1) # Yield to other greenlets, had an issue with connectivity
log.debug("--------------------------------")
log.debug(dataset_id)
coverage_path = DatasetManagementService()._get_coverage_path(
dataset_id)
log.debug(coverage_path)
log.debug("--------------------------------")
# verify that the layer exists in geoserver
try:
r = requests.get(self.gs_rest_url + '/layers/ooi_' + dataset_id +
'_ooi.xml',
auth=(self.username, self.PASSWORD))
self.assertTrue(r.status_code == 200)
except Exception as e:
log.error("check service and layer exist...%s", e)
self.assertTrue(False)
def test_logger(self):
data_product_id = self.make_ctd_data_product()
# Clone the data product so we have an output
clone_id = self.clone_data_product(data_product_id)
data_process_id = self.create_data_process_logger(
data_product_id, clone_id, {'x': 'temp'})
dataset_monitor = DatasetMonitor(data_product_id=data_product_id)
self.addCleanup(dataset_monitor.stop)
# Put some data into the the data product
rdt = self.ph.rdt_for_data_product(data_product_id)
rdt['time'] = np.arange(40)
rdt['temp'] = np.arange(40)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
# Watch the output
dataset_monitor = DatasetMonitor(data_product_id=clone_id)
self.addCleanup(dataset_monitor.stop)
# Run the replay
self.data_process_management.activate_data_process(data_process_id)
# Make sure data came out
self.assertTrue(dataset_monitor.wait())
def check_presta_instrument_data_products(self, reference_designator):
# Check the parsed data product make sure it's got everything it needs and can be published persisted etc.
# Absolute Pressure (SFLPRES_L0) is what comes off the instrumnet, SFLPRES_L1 is a pfunc
# Let's go ahead and publish some fake data!!!
# According to https://alfresco.oceanobservatories.org/alfresco/d/d/workspace/SpacesStore/63e16865-9d9e-4b11-b0b3-d5658faa5080/1341-00230_Data_Product_Spec_SFLPRES_OOI.pdf
# Appendix A. Example 1.
# p_psia_tide = 14.8670
# the tide should be 10.2504
passing = True
info_list = []
passing &= self.check_data_product_reference(reference_designator, info_list)
if not passing:
return passing
data_product_id, stream_def_id, dataset_id = info_list.pop()
now = time.time()
ntp_now = now + 2208988800.
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [ntp_now]
rdt['absolute_pressure'] = [14.8670]
passing &= self.assert_array_almost_equal(rdt['seafloor_pressure'], [10.2504], 4)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ParameterHelper.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait()) # Bumped to 20 to keep buildbot happy
if not passing: return passing
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
passing &= self.assert_array_almost_equal(rdt['time'], [ntp_now])
passing &= self.assert_array_almost_equal(rdt['seafloor_pressure'], [10.2504], 4)
passing &= self.assert_array_almost_equal(rdt['absolute_pressure'], [14.8670], 4)
return passing
def test_dca_ingestion_pause_resume(self):
data_product_id, dataset_id = self.make_ctd_data_product()
streamer = Streamer(data_product_id, interval=1)
self.addCleanup(streamer.stop)
# Let a couple samples accumulate
self.use_monitor(dataset_id, samples=2)
# Go into DCA and get an editable handle to the coverage
with DirectCoverageAccess() as dca:
with dca.get_editable_coverage(dataset_id) as cov: # <-- This pauses ingestion
monitor = DatasetMonitor(dataset_id)
monitor.event.wait(7) # <-- ~7 Samples should accumulate on the ingestion queue
self.assertFalse(monitor.event.is_set()) # Verifies that nothing was processed (i.e. ingestion is actually paused)
monitor.stop()
# Stop the streamer
streamer.stop()
cont = True
while cont:
monitor = DatasetMonitor(dataset_id)
if not monitor.event.wait(10):
cont = False
monitor.stop()
with DirectCoverageAccess() as dca:
with dca.get_read_only_coverage(dataset_id) as cov:
self.assertGreaterEqual(cov.num_timesteps, 8)
def test_array_visualization(self):
data_product_id, stream_def_id = self.make_array_data_product()
# Make a granule with an array type, give it a few values
# Send it to google_dt transform, verify output
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(2208988800, 2208988810)
rdt['temp_sample'] = np.arange(10*4).reshape(10,4)
rdt['cond_sample'] = np.arange(10*4).reshape(10,4)
granule = rdt.to_granule()
dataset_monitor = DatasetMonitor(self.RR2.find_dataset_id_of_data_product_using_has_dataset(data_product_id))
self.addCleanup(dataset_monitor.stop)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
dataset_monitor.event.wait(10)
gdt_pdict_id = self.dataset_management.read_parameter_dictionary_by_name('google_dt',id_only=True)
gdt_stream_def = self.create_stream_definition('gdt', parameter_dictionary_id=gdt_pdict_id)
gdt_data_granule = VizTransformGoogleDTAlgorithm.execute(granule, params=gdt_stream_def)
rdt = RecordDictionaryTool.load_from_granule(gdt_data_granule)
testval = {'data_content': [
[0.0 , 0.0 , 1.0 , 2.0 , 3.0 , 0.0 , 2.0 , 4.0 , 6.0 , 0.0 , 1.0 , 2.0 , 3.0] ,
[1.0 , 4.0 , 5.0 , 6.0 , 7.0 , 8.0 , 10.0 , 12.0 , 14.0 , 4.0 , 5.0 , 6.0 , 7.0] ,
[2.0 , 8.0 , 9.0 , 10.0 , 11.0 , 16.0 , 18.0 , 20.0 , 22.0 , 8.0 , 9.0 , 10.0 , 11.0] ,
[3.0 , 12.0 , 13.0 , 14.0 , 15.0 , 24.0 , 26.0 , 28.0 , 30.0 , 12.0 , 13.0 , 14.0 , 15.0] ,
[4.0 , 16.0 , 17.0 , 18.0 , 19.0 , 32.0 , 34.0 , 36.0 , 38.0 , 16.0 , 17.0 , 18.0 , 19.0] ,
[5.0 , 20.0 , 21.0 , 22.0 , 23.0 , 40.0 , 42.0 , 44.0 , 46.0 , 20.0 , 21.0 , 22.0 , 23.0] ,
[6.0 , 24.0 , 25.0 , 26.0 , 27.0 , 48.0 , 50.0 , 52.0 , 54.0 , 24.0 , 25.0 , 26.0 , 27.0] ,
[7.0 , 28.0 , 29.0 , 30.0 , 31.0 , 56.0 , 58.0 , 60.0 , 62.0 , 28.0 , 29.0 , 30.0 , 31.0] ,
[8.0 , 32.0 , 33.0 , 34.0 , 35.0 , 64.0 , 66.0 , 68.0 , 70.0 , 32.0 , 33.0 , 34.0 , 35.0] ,
[9.0 , 36.0 , 37.0 , 38.0 , 39.0 , 72.0 , 74.0 , 76.0 , 78.0 , 36.0 , 37.0 , 38.0 , 39.0]] ,
'data_description': [('time', 'number', 'time'),
('temp_sample[0]', 'number', 'temp_sample[0]', {'precision': '5'}),
('temp_sample[1]', 'number', 'temp_sample[1]', {'precision': '5'}),
('temp_sample[2]', 'number', 'temp_sample[2]', {'precision': '5'}),
('temp_sample[3]', 'number', 'temp_sample[3]', {'precision': '5'}),
('temp_offset[0]', 'number', 'temp_offset[0]', {'precision': '5'}),
('temp_offset[1]', 'number', 'temp_offset[1]', {'precision': '5'}),
('temp_offset[2]', 'number', 'temp_offset[2]', {'precision': '5'}),
('temp_offset[3]', 'number', 'temp_offset[3]', {'precision': '5'}),
('cond_sample[0]', 'number', 'cond_sample[0]', {'precision': '5'}),
('cond_sample[1]', 'number', 'cond_sample[1]', {'precision': '5'}),
('cond_sample[2]', 'number', 'cond_sample[2]', {'precision': '5'}),
('cond_sample[3]', 'number', 'cond_sample[3]', {'precision': '5'})],
'viz_product_type': 'google_dt'}
self.assertEquals(rdt['google_dt_components'][0], testval)
def check_tempsf_instrument_data_product(self, reference_designator):
passing = True
info_list = []
passing &= self.check_data_product_reference(reference_designator,
info_list)
if not passing: return passing
data_product_id, stream_def_id, dataset_id = info_list.pop()
now = time.time()
ntp_now = now + 2208988800
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [ntp_now]
rdt['temperature'] = [[
25.3884, 26.9384, 24.3394, 23.3401, 22.9832, 29.4434, 26.9873,
15.2883, 16.3374, 14.5883, 15.7253, 18.4383, 15.3488, 17.2993,
10.2111, 11.5993, 10.9345, 9.4444, 9.9876, 10.9834, 11.0098,
5.3456, 4.2994, 4.3009
]]
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ParameterHelper.publish_rdt_to_data_product(data_product_id, rdt)
passing &= self.assertTrue(dataset_monitor.wait())
if not passing: return passing
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
passing &= self.assert_array_almost_equal(rdt['time'], [ntp_now])
passing &= self.assert_array_almost_equal(rdt['temperature'], [[
25.3884, 26.9384, 24.3394, 23.3401, 22.9832, 29.4434, 26.9873,
15.2883, 16.3374, 14.5883, 15.7253, 18.4383, 15.3488, 17.2993,
10.2111, 11.5993, 10.9345, 9.4444, 9.9876, 10.9834, 11.0098,
5.3456, 4.2994, 4.3009
]])
return passing
def test_create_dataset(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_extended_parsed()
stream_def_id = self.pubsub_management.create_stream_definition(
'example', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition,
stream_def_id)
dp = DataProduct(name='example')
data_product_id = self.data_product_management.create_data_product(
dp, stream_def_id)
self.addCleanup(self.data_product_management.delete_data_product,
data_product_id)
self.data_product_management.activate_data_product_persistence(
data_product_id)
self.addCleanup(
self.data_product_management.suspend_data_product_persistence,
data_product_id)
dataset_id = self.resource_registry.find_objects(data_product_id,
PRED.hasDataset,
id_only=True)[0][0]
monitor = DatasetMonitor(dataset_id)
self.addCleanup(monitor.stop)
rdt = ph.get_rdt(stream_def_id)
ph.fill_rdt(rdt, 100)
ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(monitor.event.wait(10))
# Yield to other greenlets, had an issue with connectivity
gevent.sleep(1)
log.debug("--------------------------------")
log.debug(dataset_id)
coverage_path = DatasetManagementService()._get_coverage_path(
dataset_id)
log.debug(coverage_path)
log.debug("--------------------------------")
breakpoint(locals(), globals())
def test_fdt_created_during(self):
# generate a data product and check that the FDT exists
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_extended_parsed()
stream_def_id = self.pubsub_management.create_stream_definition(
'example', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition,
stream_def_id)
dp = DataProduct(name='example')
data_product_id = self.data_product_management.create_data_product(
dp, stream_def_id)
self.addCleanup(self.data_product_management.delete_data_product,
data_product_id)
self.data_product_management.activate_data_product_persistence(
data_product_id)
self.addCleanup(
self.data_product_management.suspend_data_product_persistence,
data_product_id)
dataset_id = self.resource_registry.find_objects(data_product_id,
PRED.hasDataset,
id_only=True)[0][0]
monitor = DatasetMonitor(dataset_id)
self.addCleanup(monitor.stop)
rdt = ph.get_rdt(stream_def_id)
ph.fill_rdt(rdt, 100)
ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(monitor.event.wait(10))
gevent.sleep(
1) # Yield to other greenlets, had an issue with connectivity
print "--------------------------------"
print dataset_id
coverage_path = DatasetManagementService()._get_coverage_path(
dataset_id)
print coverage_path
print "--------------------------------"
def test_creation_args(self):
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict')
stream_def_id = self.create_stream_definition('ctd', parameter_dictionary_id=pdict_id)
data_product_id = self.create_data_product('ctd', stream_def_id=stream_def_id)
self.activate_data_product(data_product_id)
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(data_product_id)
breakpoint(locals())
rdt = self.ph.get_rdt(stream_def_id)
rdt['time'] = np.arange(20)
rdt['temp'] = np.arange(20)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
self.ph.publish_rdt_to_data_product(data_product_id,rdt)
dataset_monitor.event.wait(10)
breakpoint(locals())
granule = self.data_retriever.retrieve(dataset_id)
breakpoint(locals())
def test_pydap_handlers(self):
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict')
stream_def_id = self.create_stream_definition('ctd', parameter_dictionary_id=pdict_id)
data_product_id = self.create_data_product('ctd', stream_def_id=stream_def_id)
self.activate_data_product(data_product_id)
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(data_product_id)
rdt = self.ph.get_rdt(stream_def_id)
rdt['time'] = np.arange(20)
rdt['temp'] = np.arange(20)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
self.ph.publish_rdt_to_data_product(data_product_id,rdt)
dataset_monitor.event.wait(10)
from pydap.client import open_url
pydap_host = CFG.get_safe('server.pydap.host','localhost')
pydap_port = CFG.get_safe('server.pydap.port',8001)
url = 'http://%s:%s/%s' %(pydap_host, pydap_port, dataset_id)
ds = open_url(url)
ds['temp']['temp'][:]
def setup_resource(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_extended_parsed()
stream_def_id = self.pubsub_management.create_stream_definition(
'example', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition,
stream_def_id)
dp = DataProduct(name='example')
data_product_id = self.data_product_management.create_data_product(
dp, stream_def_id)
self.addCleanup(self.data_product_management.delete_data_product,
data_product_id)
self.data_product_management.activate_data_product_persistence(
data_product_id)
self.addCleanup(
self.data_product_management.suspend_data_product_persistence,
data_product_id)
dataset_id = self.resource_registry.find_objects(data_product_id,
PRED.hasDataset,
id_only=True)[0][0]
monitor = DatasetMonitor(dataset_id)
self.addCleanup(monitor.stop)
rdt = ph.get_rdt(stream_def_id)
ph.fill_rdt(rdt, 100)
ph.publish_rdt_to_data_product(data_product_id, rdt)
# Yield to other greenlets, had an issue with connectivity
gevent.sleep(1)
self.offering_id = dataset_id
class TestCoverageQC(TestQCFunctions):
def init_check(self):
self.dp_id = self.create_data_product(name=uuid4().hex, stream_def_id=self.stream_def_id)
self.data_product_management.activate_data_product_persistence(self.dp_id)
self.addCleanup(self.data_product_management.suspend_data_product_persistence, self.dp_id)
self.dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(self.dp_id)
self.dataset_monitor = DatasetMonitor(self.dataset_id)
self.addCleanup(self.dataset_monitor.stop)
def test_qc_functions(self):
self.check_fill_values()
self.check_global_range()
self.check_spike()
self.check_stuck_value()
self.check_gradient()
self.check_trend()
self.check_localrange()
def check_global_range(self):
TestQCFunctions.check_global_range(self)
self.init_check()
flagged = Event()
def cb(event, *args, **kwargs):
times = event.temporal_values
self.assertEquals(times,[0.0, 7.0])
flagged.set()
event_subscriber = EventSubscriber(event_type=OT.ParameterQCEvent,origin=self.dp_id, callback=cb, auto_delete=True)
event_subscriber.start()
self.addCleanup(event_subscriber.stop)
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_almost_equal(rdt['tempwat_glblrng_qc'], [0, 1, 1, 1, 1, 1, 1, 0])
self.assertTrue(flagged.wait(10))
def check_fill_values(self):
log.info('check_fill_values')
self.new_rdt()
self.init_check()
self.rdt['time'] = np.arange(5)
self.rdt['temp'] = [12] * 5
self.rdt.fetch_lookup_values()
np.testing.assert_array_equal(self.rdt['tempwat_glblrng_qc'], [-99] * 5)
np.testing.assert_array_equal(self.rdt['tempwat_spketst_qc'], [-99] * 5)
np.testing.assert_array_equal(self.rdt['tempwat_stuckvl_qc'], [-99] * 5)
np.testing.assert_array_equal(self.rdt['tempwat_trndtst_qc'], [-99] * 5)
np.testing.assert_array_equal(self.rdt['tempwat_gradtst_qc'], [-99] * 5)
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_equal(rdt['tempwat_glblrng_qc'], [-99] * 5)
np.testing.assert_array_equal(rdt['tempwat_spketst_qc'], [-99] * 5)
np.testing.assert_array_equal(rdt['tempwat_stuckvl_qc'], [-99] * 5)
np.testing.assert_array_equal(rdt['tempwat_trndtst_qc'], [-99] * 5)
np.testing.assert_array_equal(rdt['tempwat_gradtst_qc'], [-99] * 5)
def check_spike(self):
log.info('check_spike')
TestQCFunctions.check_spike(self)
self.init_check()
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_almost_equal(rdt['tempwat_spketst_qc'], [1, 1, 0, 1, 1, 1, 1, 1])
def check_stuck_value(self):
log.info('check_stuck_value')
TestQCFunctions.check_stuck_value(self)
self.init_check()
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_almost_equal(rdt['tempwat_stuckvl_qc'], [1, 1, 0, 0, 0, 0, 1, 1, 1, 1])
def check_gradient(self):
log.info('check_gradient')
TestQCFunctions.check_gradient(self)
self.init_check()
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_equal(rdt['tempwat_gradtst_qc'], [1, 1, 0, 0, 1])
def check_trend(self):
log.info('check_trend')
TestQCFunctions.check_trend(self)
self.init_check()
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_almost_equal(rdt['tempwat_trndtst_qc'], [1] * 10)
def check_localrange(self):
log.info('check_localrange')
TestQCFunctions.check_localrange(self)
self.init_check()
flagged = Event()
def cb(event, *args, **kwargs):
times = event.temporal_values
if not event.qc_parameter == 'tempwat_loclrng_qc':
return
np.testing.assert_array_equal( times, np.array([ 3580144708.7555027, 3580144709.7555027, 3580144710.7555027, 3580144711.7555027, 3580144712.7555027]))
flagged.set()
event_subscriber = EventSubscriber(event_type = OT.ParameterQCEvent, origin=self.dp_id, callback=cb, auto_delete=True)
event_subscriber.start()
self.addCleanup(event_subscriber.stop)
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_almost_equal(rdt['tempwat_loclrng_qc'], [1 ,1 ,1 ,1 ,1 ,0 ,0 ,0 ,0 ,0])
self.assertTrue(flagged.wait(10))
def use_monitor(self, dataset_id, samples=10, wait=10):
for x in xrange(samples):
monitor = DatasetMonitor(dataset_id)
monitor.event.wait(wait)
monitor.stop()
def test_add_parameter_function(self):
# req-tag: NEW SA - 31
# Make a CTDBP Data Product
data_product_id = self.make_ctd_data_product()
self.data_product_id = data_product_id
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(
data_product_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
# Throw some data in it
rdt = self.ph.rdt_for_data_product(data_product_id)
rdt['time'] = np.arange(30)
rdt['temp'] = np.arange(30)
rdt['pressure'] = np.arange(30)
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
dataset_monitor.event.clear()
#--------------------------------------------------------------------------------
# This is what the user defines either via preload or through the UI
#--------------------------------------------------------------------------------
# Where the egg is
egg_url = self.egg_url
# Make a parameter function
owner = 'ion_example.add_arrays'
func = 'add_arrays'
arglist = ['a', 'b']
pf = ParameterFunction(name='add_arrays',
function_type=PFT.PYTHON,
owner=owner,
function=func,
args=arglist,
egg_uri=egg_url)
pfunc_id = self.dataset_management.create_parameter_function(pf)
#--------------------------------------------------------------------------------
self.addCleanup(self.dataset_management.delete_parameter_function,
pfunc_id)
# Make a data process definition
dpd = DataProcessDefinition(name='add_arrays',
description='Sums two arrays')
dpd_id = self.data_process_management.create_data_process_definition(
dpd, pfunc_id)
# TODO: assert assoc exists
argmap = {'a': 'temp', 'b': 'pressure'}
dp_id = self.data_process_management.create_data_process(
dpd_id, [data_product_id],
argument_map=argmap,
out_param_name='array_sum')
# Verify that the function worked!
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['array_sum'], np.arange(0, 60, 2))
# Verify that we can inspect it as well
source_code = self.data_process_management.inspect_data_process_definition(
dpd_id)
self.assertEquals(source_code,
'def add_arrays(a, b):\n return a+b\n')
url = self.data_process_management.get_data_process_definition_url(
dpd_id)
self.assertEquals(
url,
'http://sddevrepo.oceanobservatories.org/releases/ion_example-0.1-py2.7.egg'
)
dpd_ids, _ = self.resource_registry.find_resources(
name='dataqc_spiketest',
restype=RT.DataProcessDefinition,
id_only=True)
dpd_id = dpd_ids[0]
url = self.data_process_management.get_data_process_definition_url(
dpd_id)
self.assertEquals(
url,
'https://github.com/ooici/ion-functions/blob/master/ion_functions/qc/qc_functions.py'
)
def test_activateInstrumentSample(self):
self.loggerpids = []
# Create InstrumentModel
instModel_obj = IonObject(RT.InstrumentModel,
name='SBE37IMModel',
description="SBE37IMModel")
instModel_id = self.imsclient.create_instrument_model(instModel_obj)
log.debug( 'new InstrumentModel id = %s ', instModel_id)
raw_config = StreamConfiguration(stream_name='raw', parameter_dictionary_name='raw')
parsed_config = StreamConfiguration(stream_name='parsed', parameter_dictionary_name='ctd_parsed_param_dict')
# Create InstrumentAgent
instAgent_obj = IonObject(RT.InstrumentAgent,
name='agent007',
description="SBE37IMAgent",
driver_uri=DRV_URI_GOOD,
stream_configurations = [raw_config, parsed_config])
instAgent_id = self.imsclient.create_instrument_agent(instAgent_obj)
log.debug('new InstrumentAgent id = %s', instAgent_id)
self.imsclient.assign_instrument_model_to_instrument_agent(instModel_id, instAgent_id)
# Create InstrumentDevice
log.debug('test_activateInstrumentSample: Create instrument resource to represent the SBE37 (SA Req: L4-CI-SA-RQ-241) ')
instDevice_obj = IonObject(RT.InstrumentDevice,
name='SBE37IMDevice',
description="SBE37IMDevice",
serial_number="12345" )
instDevice_id = self.imsclient.create_instrument_device(instrument_device=instDevice_obj)
self.imsclient.assign_instrument_model_to_instrument_device(instModel_id, instDevice_id)
log.debug("test_activateInstrumentSample: new InstrumentDevice id = %s (SA Req: L4-CI-SA-RQ-241) " , instDevice_id)
port_agent_config = {
'device_addr': CFG.device.sbe37.host,
'device_port': CFG.device.sbe37.port,
'process_type': PortAgentProcessType.UNIX,
'binary_path': "port_agent",
'port_agent_addr': 'localhost',
'command_port': CFG.device.sbe37.port_agent_cmd_port,
'data_port': CFG.device.sbe37.port_agent_data_port,
'log_level': 5,
'type': PortAgentType.ETHERNET
}
instAgentInstance_obj = IonObject(RT.InstrumentAgentInstance, name='SBE37IMAgentInstance',
description="SBE37IMAgentInstance",
port_agent_config = port_agent_config,
alerts= [])
instAgentInstance_id = self.imsclient.create_instrument_agent_instance(instAgentInstance_obj,
instAgent_id,
instDevice_id)
tdom, sdom = time_series_domain()
sdom = sdom.dump()
tdom = tdom.dump()
parsed_pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
parsed_stream_def_id = self.pubsubcli.create_stream_definition(name='parsed', parameter_dictionary_id=parsed_pdict_id)
raw_pdict_id = self.dataset_management.read_parameter_dictionary_by_name('raw', id_only=True)
raw_stream_def_id = self.pubsubcli.create_stream_definition(name='raw', parameter_dictionary_id=raw_pdict_id)
#-------------------------------
# Create Raw and Parsed Data Products for the device
#-------------------------------
dp_obj = IonObject(RT.DataProduct,
name='the parsed data',
description='ctd stream test',
temporal_domain = tdom,
spatial_domain = sdom)
data_product_id1 = self.dpclient.create_data_product(data_product=dp_obj, stream_definition_id=parsed_stream_def_id)
log.debug( 'new dp_id = %s' , data_product_id1)
self.dpclient.activate_data_product_persistence(data_product_id=data_product_id1)
self.damsclient.assign_data_product(input_resource_id=instDevice_id, data_product_id=data_product_id1)
# Retrieve the id of the OUTPUT stream from the out Data Product
stream_ids, _ = self.rrclient.find_objects(data_product_id1, PRED.hasStream, None, True)
log.debug('Data product streams1 = %s', stream_ids)
# Retrieve the id of the OUTPUT stream from the out Data Product
dataset_ids, _ = self.rrclient.find_objects(data_product_id1, PRED.hasDataset, RT.Dataset, True)
log.debug('Data set for data_product_id1 = %s' , dataset_ids[0])
self.parsed_dataset = dataset_ids[0]
pid = self.create_logger('ctd_parsed', stream_ids[0] )
self.loggerpids.append(pid)
dp_obj = IonObject(RT.DataProduct,
name='the raw data',
description='raw stream test',
temporal_domain = tdom,
spatial_domain = sdom)
data_product_id2 = self.dpclient.create_data_product(data_product=dp_obj, stream_definition_id=raw_stream_def_id)
log.debug('new dp_id = %s', data_product_id2)
self.damsclient.assign_data_product(input_resource_id=instDevice_id, data_product_id=data_product_id2)
self.dpclient.activate_data_product_persistence(data_product_id=data_product_id2)
# setup notifications for the device and parsed data product
user_id_1 = self._create_notification( user_name='user_1', instrument_id=instDevice_id, product_id=data_product_id1)
#---------- Create notifications for another user and verify that we see different computed subscriptions for the two users ---------
user_id_2 = self._create_notification( user_name='user_2', instrument_id=instDevice_id, product_id=data_product_id2)
# Retrieve the id of the OUTPUT stream from the out Data Product
stream_ids, _ = self.rrclient.find_objects(data_product_id2, PRED.hasStream, None, True)
log.debug('Data product streams2 = %s' , str(stream_ids))
# Retrieve the id of the OUTPUT stream from the out Data Product
dataset_ids, _ = self.rrclient.find_objects(data_product_id2, PRED.hasDataset, RT.Dataset, True)
log.debug('Data set for data_product_id2 = %s' , dataset_ids[0])
self.raw_dataset = dataset_ids[0]
def start_instrument_agent():
self.imsclient.start_instrument_agent_instance(instrument_agent_instance_id=instAgentInstance_id)
gevent.joinall([gevent.spawn(start_instrument_agent)])
#cleanup
self.addCleanup(self.imsclient.stop_instrument_agent_instance,
instrument_agent_instance_id=instAgentInstance_id)
#wait for start
inst_agent_instance_obj = self.imsclient.read_instrument_agent_instance(instAgentInstance_id)
gate = AgentProcessStateGate(self.processdispatchclient.read_process,
instDevice_id,
ProcessStateEnum.RUNNING)
self.assertTrue(gate.await(30), "The instrument agent instance (%s) did not spawn in 30 seconds" %
gate.process_id)
#log.trace('Instrument agent instance obj: = %s' , str(inst_agent_instance_obj))
# Start a resource agent client to talk with the instrument agent.
self._ia_client = ResourceAgentClient(instDevice_id,
to_name=gate.process_id,
process=FakeProcess())
log.debug("test_activateInstrumentSample: got ia client %s" , str(self._ia_client))
cmd = AgentCommand(command=ResourceAgentEvent.INITIALIZE)
retval = self._ia_client.execute_agent(cmd)
log.debug("test_activateInstrumentSample: initialize %s" , str(retval))
state = self._ia_client.get_agent_state()
self.assertEqual(ResourceAgentState.INACTIVE, state)
log.debug("(L4-CI-SA-RQ-334): Sending go_active command ")
cmd = AgentCommand(command=ResourceAgentEvent.GO_ACTIVE)
reply = self._ia_client.execute_agent(cmd)
log.debug("test_activateInstrument: return value from go_active %s" , str(reply))
state = self._ia_client.get_agent_state()
self.assertEqual(ResourceAgentState.IDLE, state)
cmd = AgentCommand(command=ResourceAgentEvent.GET_RESOURCE_STATE)
retval = self._ia_client.execute_agent(cmd)
state = retval.result
log.debug("(L4-CI-SA-RQ-334): current state after sending go_active command %s" , str(state))
cmd = AgentCommand(command=ResourceAgentEvent.RUN)
reply = self._ia_client.execute_agent(cmd)
log.debug("test_activateInstrumentSample: run %s" , str(reply))
state = self._ia_client.get_agent_state()
self.assertEqual(ResourceAgentState.COMMAND, state)
cmd = AgentCommand(command=ResourceAgentEvent.PAUSE)
retval = self._ia_client.execute_agent(cmd)
state = self._ia_client.get_agent_state()
self.assertEqual(ResourceAgentState.STOPPED, state)
cmd = AgentCommand(command=ResourceAgentEvent.RESUME)
retval = self._ia_client.execute_agent(cmd)
state = self._ia_client.get_agent_state()
self.assertEqual(ResourceAgentState.COMMAND, state)
cmd = AgentCommand(command=ResourceAgentEvent.CLEAR)
retval = self._ia_client.execute_agent(cmd)
state = self._ia_client.get_agent_state()
self.assertEqual(ResourceAgentState.IDLE, state)
cmd = AgentCommand(command=ResourceAgentEvent.RUN)
retval = self._ia_client.execute_agent(cmd)
state = self._ia_client.get_agent_state()
self.assertEqual(ResourceAgentState.COMMAND, state)
for i in xrange(10):
monitor = DatasetMonitor(dataset_id=self.parsed_dataset)
self._ia_client.execute_resource(AgentCommand(command=SBE37ProtocolEvent.ACQUIRE_SAMPLE))
if not monitor.wait():
raise AssertionError('Failed on the %ith granule' % i)
monitor.stop()
# cmd = AgentCommand(command=SBE37ProtocolEvent.ACQUIRE_SAMPLE)
# for i in xrange(10):
# retval = self._ia_client.execute_resource(cmd)
# log.debug("test_activateInstrumentSample: return from sample %s" , str(retval))
log.debug( "test_activateInstrumentSample: calling reset ")
cmd = AgentCommand(command=ResourceAgentEvent.RESET)
reply = self._ia_client.execute_agent(cmd)
log.debug("test_activateInstrumentSample: return from reset %s" , str(reply))
#--------------------------------------------------------------------------------
# Now get the data in one chunk using an RPC Call to start_retreive
#--------------------------------------------------------------------------------
replay_data_raw = self.dataretrieverclient.retrieve(self.raw_dataset)
self.assertIsInstance(replay_data_raw, Granule)
rdt_raw = RecordDictionaryTool.load_from_granule(replay_data_raw)
log.debug("RDT raw: %s", str(rdt_raw.pretty_print()) )
self.assertIn('raw', rdt_raw)
raw_vals = rdt_raw['raw']
all_raw = "".join(raw_vals)
# look for 't' entered after a prompt -- ">t"
t_commands = all_raw.count(">t")
if 10 != t_commands:
log.error("%s raw_vals: ", len(raw_vals))
for i, r in enumerate(raw_vals): log.error("raw val %s: %s", i, [r])
self.fail("Expected 10 't' strings in raw_vals, got %s" % t_commands)
else:
log.debug("%s raw_vals: ", len(raw_vals))
for i, r in enumerate(raw_vals): log.debug("raw val %s: %s", i, [r])
replay_data_parsed = self.dataretrieverclient.retrieve(self.parsed_dataset)
self.assertIsInstance(replay_data_parsed, Granule)
rdt_parsed = RecordDictionaryTool.load_from_granule(replay_data_parsed)
log.debug("test_activateInstrumentSample: RDT parsed: %s", str(rdt_parsed.pretty_print()) )
self.assertIn('temp', rdt_parsed)
temp_vals = rdt_parsed['temp']
pressure_vals = rdt_parsed['pressure']
if 10 != len(temp_vals):
log.error("%s temp_vals: %s", len(temp_vals), temp_vals)
self.fail("Expected 10 temp_vals, got %s" % len(temp_vals))
log.debug("l4-ci-sa-rq-138")
"""
Physical resource control shall be subject to policy
Instrument management control capabilities shall be subject to policy
The actor accessing the control capabilities must be authorized to send commands.
note from maurice 2012-05-18: Talk to tim M to verify that this is policy. If it is then talk with Stephen to
get an example of a policy test and use that to create a test stub that will be
completed when we have instrument policies.
Tim M: The "actor", aka observatory operator, will access the instrument through ION.
"""
#--------------------------------------------------------------------------------
# Get the extended data product to see if it contains the granules
#--------------------------------------------------------------------------------
extended_product = self.dpclient.get_data_product_extension(data_product_id=data_product_id1, user_id=user_id_1)
def poller(extended_product):
return len(extended_product.computed.user_notification_requests.value) == 1
poll(poller, extended_product, timeout=30)
self._check_computed_attributes_of_extended_product( expected_data_product_id = data_product_id1, extended_data_product = extended_product)
#--------------------------------------------------------------------------------
# Get the extended instrument
#--------------------------------------------------------------------------------
extended_instrument = self.imsclient.get_instrument_device_extension(instrument_device_id=instDevice_id, user_id=user_id_1)
#--------------------------------------------------------------------------------
# For the second user, check the extended data product and the extended intrument
#--------------------------------------------------------------------------------
extended_product = self.dpclient.get_data_product_extension(data_product_id=data_product_id2, user_id=user_id_2)
self._check_computed_attributes_of_extended_product(expected_data_product_id = data_product_id2, extended_data_product = extended_product)
#--------------------------------------------------------------------------------
# Get the extended instrument
#--------------------------------------------------------------------------------
extended_instrument = self.imsclient.get_instrument_device_extension(instrument_device_id=instDevice_id, user_id=user_id_2)
self._check_computed_attributes_of_extended_instrument(expected_instrument_device_id = instDevice_id, extended_instrument = extended_instrument)
#--------------------------------------------------------------------------------
# Deactivate loggers
#--------------------------------------------------------------------------------
for pid in self.loggerpids:
self.processdispatchclient.cancel_process(pid)
self.dpclient.delete_data_product(data_product_id1)
self.dpclient.delete_data_product(data_product_id2)
def test_lookup_values(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_lookups()
stream_def_id = self.pubsubcli.create_stream_definition(
'lookup', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsubcli.delete_stream_definition, stream_def_id)
data_product = DataProduct(name='lookup data product')
tdom, sdom = time_series_domain()
data_product.temporal_domain = tdom.dump()
data_product.spatial_domain = sdom.dump()
data_product_id = self.dpsc_cli.create_data_product(
data_product, stream_definition_id=stream_def_id)
self.addCleanup(self.dpsc_cli.delete_data_product, data_product_id)
data_producer = DataProducer(name='producer')
data_producer.producer_context = DataProcessProducerContext()
data_producer.producer_context.configuration['qc_keys'] = [
'offset_document'
]
data_producer_id, _ = self.rrclient.create(data_producer)
self.addCleanup(self.rrclient.delete, data_producer_id)
assoc, _ = self.rrclient.create_association(
subject=data_product_id,
object=data_producer_id,
predicate=PRED.hasDataProducer)
self.addCleanup(self.rrclient.delete_association, assoc)
document_keys = self.damsclient.list_qc_references(data_product_id)
self.assertEquals(document_keys, ['offset_document'])
svm = StoredValueManager(self.container)
svm.stored_value_cas('offset_document', {'offset_a': 2.0})
self.dpsc_cli.activate_data_product_persistence(data_product_id)
dataset_ids, _ = self.rrclient.find_objects(subject=data_product_id,
predicate=PRED.hasDataset,
id_only=True)
dataset_id = dataset_ids[0]
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [0]
rdt['temp'] = [20.]
granule = rdt.to_granule()
stream_ids, _ = self.rrclient.find_objects(subject=data_product_id,
predicate=PRED.hasStream,
id_only=True)
stream_id = stream_ids[0]
route = self.pubsubcli.read_stream_route(stream_id=stream_id)
publisher = StandaloneStreamPublisher(stream_id, route)
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(10))
granule = self.data_retriever.retrieve(dataset_id)
rdt2 = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['temp'], rdt2['temp'])
np.testing.assert_array_almost_equal(rdt2['calibrated'],
np.array([22.0]))
svm.stored_value_cas('updated_document', {'offset_a': 3.0})
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ep = EventPublisher(event_type=OT.ExternalReferencesUpdatedEvent)
ep.publish_event(origin=data_product_id,
reference_keys=['updated_document'])
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [1]
rdt['temp'] = [20.]
granule = rdt.to_granule()
gevent.sleep(2) # Yield so that the event goes through
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(10))
granule = self.data_retriever.retrieve(dataset_id)
rdt2 = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt2['temp'], np.array([20., 20.]))
np.testing.assert_array_almost_equal(rdt2['calibrated'],
np.array([22.0, 23.0]))
def test_qc_interval_integration(self):
# 1 need to make a dataset that only has one discrete qc violation
# 2 Launch the process
# 3 Setup the scheduler to run it say three times
# 4 Get the Events and verify the data
#--------------------------------------------------------------------------------
# Make a dataset that has only one discrete qc violation
#--------------------------------------------------------------------------------
dp_id, dataset_id, stream_def_id = self.make_data_product()
ph = ParameterHelper(self.dataset_management, self.addCleanup)
monitor = DatasetMonitor(dataset_id)
self.addCleanup(monitor.stop)
for rdt in self.populate_vectors(stream_def_id, 1,
lambda x: [41] + [39] * (x - 1)):
ph.publish_rdt_to_data_product(dp_id, rdt)
self.assertTrue(
monitor.event.wait(10)) # Give it 10 seconds to populate
#--------------------------------------------------------------------------------
# Launch the process
#--------------------------------------------------------------------------------
interval_key = uuid4().hex
config = DotDict()
config.process.interval_key = interval_key
config.process.qc_params = [
'glblrng_qc'
] # The others are tested in other tests for completeness
self.sync_launch(config)
async_queue = Queue()
def callback(event, *args, **kwargs):
times = event.temporal_values
self.assertEquals(len(times), 1)
async_queue.put(1)
es = EventSubscriber(event_type=OT.ParameterQCEvent,
origin=dp_id,
callback=callback,
auto_delete=True)
es.start()
self.addCleanup(es.stop)
#--------------------------------------------------------------------------------
# Setup the scheduler
#--------------------------------------------------------------------------------
timer_id = self.scheduler_service.create_interval_timer(
start_time=time.time(),
end_time=time.time() + 13,
interval=5,
event_origin=interval_key)
#--------------------------------------------------------------------------------
# Get the events and verify them
#--------------------------------------------------------------------------------
try:
for i in xrange(2):
async_queue.get(timeout=10)
except Empty:
raise AssertionError('QC Events not raised')
def use_monitor(self, dataset_id, samples=10, wait=10):
for x in xrange(samples):
monitor = DatasetMonitor(dataset_id)
monitor.event.wait(wait)
monitor.stop()
def check_vel3d_instrument_data_products(self, reference_designator):
passing = True
info_list = []
passing &= self.check_data_product_reference(reference_designator, info_list)
if not passing:
return passing
data_product_id, stream_def_id, dataset_id = info_list.pop()
pdict = self.RR2.find_parameter_dictionary_of_stream_definition_using_has_parameter_dictionary(stream_def_id)
self.assertEquals(pdict.name, 'vel3d_b_sample')
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
lat = 14.6846
lon = -51.044
ts = np.array([3319563600, 3319567200, 3319570800, 3319574400,
3319578000, 3319581600, 3319585200, 3319588800, 3319592400,
3319596000], dtype=np.float)
ve = np.array([ -3.2, 0.1, 0. , 2.3, -0.1, 5.6, 5.1, 5.8,
8.8, 10.3])
vn = np.array([ 18.2, 9.9, 12. , 6.6, 7.4, 3.4, -2.6, 0.2,
-1.5, 4.1])
vu = np.array([-1.1, -0.6, -1.4, -2, -1.7, -2, 1.3, -1.6, -1.1, -4.5])
ve_expected = np.array([-0.085136, -0.028752, -0.036007, 0.002136,
-0.023158, 0.043218, 0.056451, 0.054727, 0.088446, 0.085952])
vn_expected = np.array([ 0.164012, 0.094738, 0.114471, 0.06986, 0.07029,
0.049237, -0.009499, 0.019311, 0.012096, 0.070017])
vu_expected = np.array([-0.011, -0.006, -0.014, -0.02, -0.017, -0.02,
0.013, -0.016, -0.011, -0.045])
rdt['time'] = ts
rdt['lat'] = [lat] * 10
rdt['lon'] = [lon] * 10
rdt['turbulent_velocity_east'] = ve
rdt['turbulent_velocity_north'] = vn
rdt['turbulent_velocity_up'] = vu
passing &= self.assert_array_almost_equal(rdt['eastward_turbulent_velocity'],
ve_expected)
passing &= self.assert_array_almost_equal(rdt['northward_turbulent_velocity'],
vn_expected)
passing &= self.assert_array_almost_equal(rdt['upward_turbulent_velocity'],
vu_expected)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ParameterHelper.publish_rdt_to_data_product(data_product_id, rdt)
passing &= self.assertTrue(dataset_monitor.wait())
if not passing: return passing
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
passing &= self.assert_array_almost_equal(rdt['eastward_turbulent_velocity'],
ve_expected)
passing &= self.assert_array_almost_equal(rdt['northward_turbulent_velocity'],
vn_expected)
passing &= self.assert_array_almost_equal(rdt['upward_turbulent_velocity'],
vu_expected)
return passing
def test_pydap(self):
if not CFG.get_safe('bootstrap.use_pydap', False):
raise unittest.SkipTest('PyDAP is off (bootstrap.use_pydap)')
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_extended_parsed()
stream_def_id = self.pubsub_management.create_stream_definition(
'example', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition,
stream_def_id)
tdom, sdom = time_series_domain()
dp = DataProduct(name='example')
dp.spatial_domain = sdom.dump()
dp.temporal_domain = tdom.dump()
data_product_id = self.data_product_management.create_data_product(
dp, stream_def_id)
self.addCleanup(self.data_product_management.delete_data_product,
data_product_id)
self.data_product_management.activate_data_product_persistence(
data_product_id)
self.addCleanup(
self.data_product_management.suspend_data_product_persistence,
data_product_id)
dataset_id = self.resource_registry.find_objects(data_product_id,
PRED.hasDataset,
id_only=True)[0][0]
monitor = DatasetMonitor(dataset_id)
self.addCleanup(monitor.stop)
rdt = ph.get_rdt(stream_def_id)
ph.fill_rdt(rdt, 10)
ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(monitor.event.wait(10))
gevent.sleep(
1) # Yield to other greenlets, had an issue with connectivity
pydap_host = CFG.get_safe('server.pydap.host', 'localhost')
pydap_port = CFG.get_safe('server.pydap.port', 8001)
url = 'http://%s:%s/%s' % (pydap_host, pydap_port, dataset_id)
for i in xrange(
3
): # Do it three times to test that the cache doesn't corrupt the requests/responses
ds = open_url(url)
np.testing.assert_array_equal(ds['time'][:], np.arange(10))
untested = []
for k, v in rdt.iteritems():
if k == rdt.temporal_parameter:
continue
context = rdt.context(k)
if isinstance(context.param_type, QuantityType):
np.testing.assert_array_equal(ds[k][k][:][0], rdt[k])
elif isinstance(context.param_type, ArrayType):
if context.param_type.inner_encoding is None:
values = np.empty(rdt[k].shape, dtype='O')
for i, obj in enumerate(rdt[k]):
values[i] = str(obj)
np.testing.assert_array_equal(ds[k][k][:][0], values)
elif len(rdt[k].shape) > 1:
values = np.empty(rdt[k].shape[0], dtype='O')
for i in xrange(rdt[k].shape[0]):
values[i] = ','.join(
map(lambda x: str(x), rdt[k][i].tolist()))
elif isinstance(context.param_type, ConstantType):
np.testing.assert_array_equal(ds[k][k][:][0], rdt[k])
elif isinstance(context.param_type, CategoryType):
np.testing.assert_array_equal(ds[k][k][:][0], rdt[k])
else:
untested.append('%s (%s)' % (k, context.param_type))
if untested:
raise AssertionError('Untested parameters: %s' % untested)
def test_lookup_values(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_lookups()
stream_def_id = self.pubsubcli.create_stream_definition('lookup', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsubcli.delete_stream_definition, stream_def_id)
data_product = DataProduct(name='lookup data product')
data_product_id = self.dpsc_cli.create_data_product(data_product, stream_definition_id=stream_def_id)
self.addCleanup(self.dpsc_cli.delete_data_product, data_product_id)
data_producer = DataProducer(name='producer')
data_producer.producer_context = DataProcessProducerContext()
data_producer.producer_context.configuration['qc_keys'] = ['offset_document']
data_producer_id, _ = self.rrclient.create(data_producer)
self.addCleanup(self.rrclient.delete, data_producer_id)
assoc,_ = self.rrclient.create_association(subject=data_product_id, object=data_producer_id, predicate=PRED.hasDataProducer)
self.addCleanup(self.rrclient.delete_association, assoc)
document_keys = self.damsclient.list_qc_references(data_product_id)
self.assertEquals(document_keys, ['offset_document'])
svm = StoredValueManager(self.container)
svm.stored_value_cas('offset_document', {'offset_a':2.0})
self.dpsc_cli.activate_data_product_persistence(data_product_id)
dataset_ids, _ = self.rrclient.find_objects(subject=data_product_id, predicate=PRED.hasDataset, id_only=True)
dataset_id = dataset_ids[0]
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [0]
rdt['temp'] = [20.]
granule = rdt.to_granule()
stream_ids, _ = self.rrclient.find_objects(subject=data_product_id, predicate=PRED.hasStream, id_only=True)
stream_id = stream_ids[0]
route = self.pubsubcli.read_stream_route(stream_id=stream_id)
publisher = StandaloneStreamPublisher(stream_id, route)
publisher.publish(granule)
self.assertTrue(dataset_monitor.wait())
granule = self.data_retriever.retrieve(dataset_id)
rdt2 = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['temp'], rdt2['temp'])
np.testing.assert_array_almost_equal(rdt2['calibrated'], np.array([22.0]))
svm.stored_value_cas('updated_document', {'offset_a':3.0})
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ep = EventPublisher(event_type=OT.ExternalReferencesUpdatedEvent)
ep.publish_event(origin=data_product_id, reference_keys=['updated_document'])
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [1]
rdt['temp'] = [20.]
granule = rdt.to_granule()
gevent.sleep(2) # Yield so that the event goes through
publisher.publish(granule)
self.assertTrue(dataset_monitor.wait())
granule = self.data_retriever.retrieve(dataset_id)
rdt2 = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt2['temp'],np.array([20.,20.]))
np.testing.assert_array_almost_equal(rdt2['calibrated'], np.array([22.0,23.0]))
def check_vel3d_instrument_data_products(self, reference_designator):
passing = True
info_list = []
passing &= self.check_data_product_reference(reference_designator,
info_list)
if not passing:
return passing
data_product_id, stream_def_id, dataset_id = info_list.pop()
pdict = self.RR2.find_parameter_dictionary_of_stream_definition_using_has_parameter_dictionary(
stream_def_id)
self.assertEquals(pdict.name, 'vel3d_b_sample')
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
lat = 14.6846
lon = -51.044
ts = np.array([
3319563600, 3319567200, 3319570800, 3319574400, 3319578000,
3319581600, 3319585200, 3319588800, 3319592400, 3319596000
],
dtype=np.float)
ve = np.array([-3.2, 0.1, 0., 2.3, -0.1, 5.6, 5.1, 5.8, 8.8, 10.3])
vn = np.array([18.2, 9.9, 12., 6.6, 7.4, 3.4, -2.6, 0.2, -1.5, 4.1])
vu = np.array([-1.1, -0.6, -1.4, -2, -1.7, -2, 1.3, -1.6, -1.1, -4.5])
ve_expected = np.array([
-0.085136, -0.028752, -0.036007, 0.002136, -0.023158, 0.043218,
0.056451, 0.054727, 0.088446, 0.085952
])
vn_expected = np.array([
0.164012, 0.094738, 0.114471, 0.06986, 0.07029, 0.049237,
-0.009499, 0.019311, 0.012096, 0.070017
])
vu_expected = np.array([
-0.011, -0.006, -0.014, -0.02, -0.017, -0.02, 0.013, -0.016,
-0.011, -0.045
])
rdt['time'] = ts
rdt['lat'] = [lat] * 10
rdt['lon'] = [lon] * 10
rdt['turbulent_velocity_east'] = ve
rdt['turbulent_velocity_north'] = vn
rdt['turbulent_velocity_up'] = vu
passing &= self.assert_array_almost_equal(
rdt['eastward_turbulent_velocity'], ve_expected)
passing &= self.assert_array_almost_equal(
rdt['northward_turbulent_velocity'], vn_expected)
passing &= self.assert_array_almost_equal(
rdt['upward_turbulent_velocity'], vu_expected)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ParameterHelper.publish_rdt_to_data_product(data_product_id, rdt)
passing &= self.assertTrue(dataset_monitor.wait())
if not passing: return passing
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
passing &= self.assert_array_almost_equal(
rdt['eastward_turbulent_velocity'], ve_expected)
passing &= self.assert_array_almost_equal(
rdt['northward_turbulent_velocity'], vn_expected)
passing &= self.assert_array_almost_equal(
rdt['upward_turbulent_velocity'], vu_expected)
return passing
def check_trhph_instrument_data_products(self, reference_designator):
passing = True
info_list = []
passing &= self.check_data_product_reference(reference_designator,
info_list)
if not passing:
return passing
data_product_id, stream_def_id, dataset_id = info_list.pop()
pdict = self.RR2.find_parameter_dictionary_of_stream_definition_using_has_parameter_dictionary(
stream_def_id)
passing &= self.assertEquals(pdict.name, 'trhph_sample')
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
# calibration constants
a = 1.98e-9
b = -2.45e-6
c = 9.28e-4
d = -0.0888
e = 0.731
V_s = 1.506
V_c = 0.
T = 11.8
r1 = 0.906
r2 = 4.095
r3 = 4.095
ORP_V = 1.806
Cl = np.nan
offset = 2008
gain = 4.0
# Normally this would be 50 per the DPS but the precision is %4.0f which truncates the values to the nearest 1...
ORP = ((ORP_V * 1000.) - offset) / gain
ntp_now = time.time() + 2208988800
rdt['cc_a'] = [a]
rdt['cc_b'] = [b]
rdt['cc_c'] = [c]
rdt['cc_d'] = [d]
rdt['cc_e'] = [e]
rdt['ref_temp_volts'] = [V_s]
rdt['resistivity_temp_volts'] = [V_c]
rdt['eh_sensor'] = [ORP_V]
rdt['resistivity_5'] = [r1]
rdt['resistivity_x1'] = [r2]
rdt['resistivity_x5'] = [r3]
rdt['cc_offset'] = [offset]
rdt['cc_gain'] = [gain]
rdt['time'] = [ntp_now]
passing &= self.assert_array_almost_equal(rdt['vent_fluid_temperaure'],
[T], 2)
passing &= self.assert_array_almost_equal(
rdt['vent_fluid_chloride_conc'], [Cl], 4)
passing &= self.assert_array_almost_equal(rdt['vent_fluid_orp'], [ORP],
4)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ParameterHelper.publish_rdt_to_data_product(data_product_id, rdt)
passing &= self.assertTrue(dataset_monitor.wait())
if not passing: return passing
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
passing &= self.assert_array_almost_equal(rdt['vent_fluid_temperaure'],
[T], 2)
passing &= self.assert_array_almost_equal(
rdt['vent_fluid_chloride_conc'], [Cl], 4)
passing &= self.assert_array_almost_equal(rdt['vent_fluid_orp'], [ORP],
4)
return passing
def check_trhph_instrument_data_products(self, reference_designator):
passing = True
info_list = []
passing &= self.check_data_product_reference(reference_designator, info_list)
if not passing:
return passing
data_product_id, stream_def_id, dataset_id = info_list.pop()
pdict = self.RR2.find_parameter_dictionary_of_stream_definition_using_has_parameter_dictionary(stream_def_id)
passing &= self.assertEquals(pdict.name, 'trhph_sample')
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
# calibration constants
a = 1.98e-9
b = -2.45e-6
c = 9.28e-4
d = -0.0888
e = 0.731
V_s = 1.506
V_c = 0.
T = 11.8
r1 = 0.906
r2 = 4.095
r3 = 4.095
ORP_V = 1.806
Cl = np.nan
offset = 2008
gain = 4.0
# Normally this would be 50 per the DPS but the precision is %4.0f which truncates the values to the nearest 1...
ORP = ((ORP_V * 1000.) - offset) / gain
ntp_now = time.time() + 2208988800
rdt['cc_a'] = [a]
rdt['cc_b'] = [b]
rdt['cc_c'] = [c]
rdt['cc_d'] = [d]
rdt['cc_e'] = [e]
rdt['ref_temp_volts'] = [V_s]
rdt['resistivity_temp_volts'] = [V_c]
rdt['eh_sensor'] = [ORP_V]
rdt['resistivity_5'] = [r1]
rdt['resistivity_x1'] = [r2]
rdt['resistivity_x5'] = [r3]
rdt['cc_offset'] = [offset]
rdt['cc_gain'] = [gain]
rdt['time'] = [ntp_now]
passing &= self.assert_array_almost_equal(rdt['vent_fluid_temperaure'], [T], 2)
passing &= self.assert_array_almost_equal(rdt['vent_fluid_chloride_conc'], [Cl], 4)
passing &= self.assert_array_almost_equal(rdt['vent_fluid_orp'], [ORP], 4)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ParameterHelper.publish_rdt_to_data_product(data_product_id, rdt)
passing &= self.assertTrue(dataset_monitor.wait())
if not passing: return passing
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
passing &= self.assert_array_almost_equal(rdt['vent_fluid_temperaure'], [T], 2)
passing &= self.assert_array_almost_equal(rdt['vent_fluid_chloride_conc'], [Cl], 4)
passing &= self.assert_array_almost_equal(rdt['vent_fluid_orp'], [ORP], 4)
return passing
def test_pydap(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_extended_parsed()
stream_def_id = self.pubsub_management.create_stream_definition('example', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id)
dp = DataProduct(name='example')
data_product_id = self.data_product_management.create_data_product(dp, stream_def_id)
self.addCleanup(self.data_product_management.delete_data_product, data_product_id)
self.data_product_management.activate_data_product_persistence(data_product_id)
self.addCleanup(self.data_product_management.suspend_data_product_persistence, data_product_id)
dataset_id = self.resource_registry.find_objects(data_product_id, PRED.hasDataset, id_only=True)[0][0]
monitor = DatasetMonitor(dataset_id)
self.addCleanup(monitor.stop)
rdt = ph.get_rdt(stream_def_id)
ph.fill_rdt(rdt,10)
ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(monitor.wait())
gevent.sleep(1) # Yield to other greenlets, had an issue with connectivity
pydap_host = CFG.get_safe('server.pydap.host','localhost')
pydap_port = CFG.get_safe('server.pydap.port',8001)
url = 'http://%s:%s/%s' %(pydap_host, pydap_port, data_product_id)
for i in xrange(3): # Do it three times to test that the cache doesn't corrupt the requests/responses
ds = open_url(url)
np.testing.assert_array_equal(list(ds['data']['time']), np.arange(10))
untested = []
for k,v in rdt.iteritems():
if k==rdt.temporal_parameter:
continue
context = rdt.context(k)
if isinstance(context.param_type, QuantityType):
np.testing.assert_array_equal(list(ds['data'][k]), rdt[k])
elif isinstance(context.param_type, ArrayType):
if context.param_type.inner_encoding is None:
values = np.empty(rdt[k].shape, dtype='O')
for i,obj in enumerate(rdt[k]):
values[i] = str(obj)
np.testing.assert_array_equal(list(ds['data'][k]), values)
elif len(rdt[k].shape)>1:
values = np.empty(rdt[k].shape[0], dtype='O')
for i in xrange(rdt[k].shape[0]):
values[i] = ','.join(map(lambda x : str(x), rdt[k][i].tolist()))
elif isinstance(context.param_type, ConstantType):
np.testing.assert_array_equal(list(ds['data'][k]), rdt[k])
elif isinstance(context.param_type, CategoryType):
np.testing.assert_array_equal(list(ds['data'][k]), rdt[k].astype('|S'))
else:
untested.append('%s (%s)' % (k,context.param_type))
if untested:
raise AssertionError('Untested parameters: %s' % untested)
def create_device_site_deployment(self,
dep_name="Deployment",
starting=''):
from interface.objects import StreamConfiguration, StreamConfigurationType, InstrumentDevice
from interface.objects import InstrumentModel, PlatformAgent, InstrumentSite, TemporalBounds, Deployment
from interface.objects import RemotePlatformDeploymentContext
stream_conf = StreamConfiguration(
stream_name="CTD 1 Parsed Stream",
parameter_dictionary_name='ctd_parsed_param_dict',
stream_type=StreamConfigurationType.PARSED)
pdict_id = self.dataset_management.read_parameter_dictionary_by_name(
name='ctd_parsed_param_dict')
stream_def_id = self.create_stream_definition(
name='CTD 1', parameter_dictionary_id=pdict_id)
data_product_id = self.create_data_product(
name="DDP_1",
stream_def_id=stream_def_id,
stream_configuration=stream_conf)
self.activate_data_product(data_product_id)
dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(
data_product_id)
stream_def = self.resource_registry.find_objects(
data_product_id, PRED.hasStreamDefinition)[0][0]
param_dict = self.resource_registry.find_objects(
stream_def._id, PRED.hasParameterDictionary)[0][0]
# Add data to the DataProduct
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
rdt = self.ph.get_rdt(stream_def._id)
rdt_ = self.ph.rdt_for_data_product(data_product_id)
self.assertEquals(rdt.fields, rdt_.fields)
rdt['time'] = [0, 1, 2, 3]
rdt['temp'] = [10, 11, 12, 13]
self.ph.publish_rdt_to_data_product(data_product_id, rdt)
self.assertTrue(dataset_monitor.wait())
# Create Device
device = InstrumentDevice(name='Device 1')
device_id = self.instrument_management.create_instrument_device(device)
self.data_acquisition_management.register_instrument(device_id)
self.data_acquisition_management.assign_data_product(
device_id, data_product_id)
# Create Model
model = InstrumentModel(name='Model 1')
model_id = self.instrument_management.create_instrument_model(model)
self.instrument_management.assign_instrument_model_to_instrument_device(
model_id, device_id)
# Create AgentDefinition
ad = PlatformAgent(stream_configurations=[stream_conf])
ad_id, _ = self.resource_registry.create(ad)
# Create Site
site = InstrumentSite(name='Site 1',
stream_configurations=[stream_conf])
site_id, _ = self.resource_registry.create(site)
self.resource_registry.create_association(site_id, PRED.hasModel,
model_id)
self.resource_registry.create_association(site_id,
PRED.hasAgentDefinition,
ad_id)
# TemporalBounds of the Deployment
temp_bounds = TemporalBounds(start_datetime=starting, end_datetime='')
# Create Deployment
deployment = Deployment(name=dep_name,
type="RemotePlatform",
context=RemotePlatformDeploymentContext(),
constraint_list=[temp_bounds])
deployment_id = self.observatory_management.create_deployment(
deployment=deployment, site_id=site_id, device_id=device_id)
return site_id, device_id, dataset_id, deployment_id, param_dict, data_product_id
class TestCoverageQC(TestQCFunctions):
def init_check(self):
self.dp_id = self.create_data_product(name=uuid4().hex, stream_def_id=self.stream_def_id)
self.data_product_management.activate_data_product_persistence(self.dp_id)
self.addCleanup(self.data_product_management.suspend_data_product_persistence, self.dp_id)
self.dataset_id = self.RR2.find_dataset_id_of_data_product_using_has_dataset(self.dp_id)
self.dataset_monitor = DatasetMonitor(self.dataset_id)
self.addCleanup(self.dataset_monitor.stop)
def test_qc_functions(self):
self.check_fill_values()
self.check_global_range()
self.check_spike()
self.check_stuck_value()
self.check_gradient()
self.check_trend()
self.check_localrange()
def check_global_range(self):
TestQCFunctions.check_global_range(self)
self.init_check()
flagged = Event()
def cb(event, *args, **kwargs):
times = event.temporal_values
self.assertEquals(times,[0.0, 7.0])
flagged.set()
event_subscriber = EventSubscriber(event_type=OT.ParameterQCEvent,origin=self.dp_id, callback=cb, auto_delete=True)
event_subscriber.start()
self.addCleanup(event_subscriber.stop)
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_almost_equal(rdt['tempwat_glblrng_qc'], [0, 1, 1, 1, 1, 1, 1, 0])
self.assertTrue(flagged.wait(10))
def check_fill_values(self):
log.info('check_fill_values')
self.new_rdt()
self.init_check()
self.rdt['time'] = np.arange(5)
self.rdt['temp'] = [12] * 5
self.rdt.fetch_lookup_values()
np.testing.assert_array_equal(self.rdt['tempwat_glblrng_qc'], [-99] * 5)
np.testing.assert_array_equal(self.rdt['tempwat_spketst_qc'], [-99] * 5)
np.testing.assert_array_equal(self.rdt['tempwat_stuckvl_qc'], [-99] * 5)
np.testing.assert_array_equal(self.rdt['tempwat_trndtst_qc'], [-99] * 5)
np.testing.assert_array_equal(self.rdt['tempwat_gradtst_qc'], [-99] * 5)
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_equal(rdt['tempwat_glblrng_qc'], [-99] * 5)
np.testing.assert_array_equal(rdt['tempwat_spketst_qc'], [-99] * 5)
np.testing.assert_array_equal(rdt['tempwat_stuckvl_qc'], [-99] * 5)
np.testing.assert_array_equal(rdt['tempwat_trndtst_qc'], [-99] * 5)
np.testing.assert_array_equal(rdt['tempwat_gradtst_qc'], [-99] * 5)
def check_spike(self):
log.info('check_spike')
TestQCFunctions.check_spike(self)
self.init_check()
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_almost_equal(rdt['tempwat_spketst_qc'], [1, 1, 0, 1, 1, 1, 1, 1])
def check_stuck_value(self):
log.info('check_stuck_value')
TestQCFunctions.check_stuck_value(self)
self.init_check()
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_almost_equal(rdt['tempwat_stuckvl_qc'], [1, 1, 0, 0, 0, 0, 1, 1, 1, 1])
def check_gradient(self):
log.info('check_gradient')
TestQCFunctions.check_gradient(self)
self.init_check()
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_equal(rdt['tempwat_gradtst_qc'], [1, 1, 0, 0, 1])
def check_trend(self):
log.info('check_trend')
TestQCFunctions.check_trend(self)
self.init_check()
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_almost_equal(rdt['tempwat_trndtst_qc'], [1] * 10)
def check_localrange(self):
log.info('check_localrange')
TestQCFunctions.check_localrange(self)
self.init_check()
flagged = Event()
def cb(event, *args, **kwargs):
times = event.temporal_values
if not event.qc_parameter == 'tempwat_loclrng_qc':
return
np.testing.assert_array_equal( times, np.array([ 3580144708.7555027, 3580144709.7555027, 3580144710.7555027, 3580144711.7555027, 3580144712.7555027]))
flagged.set()
event_subscriber = EventSubscriber(event_type = OT.ParameterQCEvent, origin=self.dp_id, callback=cb, auto_delete=True)
event_subscriber.start()
self.addCleanup(event_subscriber.stop)
self.ph.publish_rdt_to_data_product(self.dp_id, self.rdt)
self.assertTrue(self.dataset_monitor.wait())
rdt = RecordDictionaryTool.load_from_granule(self.data_retriever.retrieve(self.dataset_id))
np.testing.assert_array_almost_equal(rdt['tempwat_loclrng_qc'], [1 ,1 ,1 ,1 ,1 ,0 ,0 ,0 ,0 ,0])
self.assertTrue(flagged.wait(10))
