def test_extend_names(self):
tc = TaxyTool()
tc.add_taxonomy_set('1','a')
tc.add_taxonomy_set('2','a')
self.assertEquals(tc.get_handles('1'),{0,})
self.assertEquals(tc.get_handles('2'),{1,})
self.assertEquals(tc.get_names_by_handle(0),{'1', 'a',})
self.assertEquals(tc.get_names_by_handle(1),{'2', 'a',})
tc.extend_names_by_nick_name('1', 'c', 'e')
tc.extend_names_by_nick_name('2', 'z', 'x')
tc.extend_names_by_nick_name('1', 'd', 'f')
self.assertEquals(tc.get_handles('a'),{0,1})
self.assertEquals(tc.get_handles('z'),{1,})
self.assertEquals(tc.get_handles('c'),{0,})
#Test for a name that isn't in the taxonomy
self.assertEquals(tc.get_handles('b'),{-1,})
self.assertEquals(tc.get_names_by_handle(0),{'1', 'a', 'c', 'e', 'd', 'f',})
self.assertEquals(tc.get_names_by_handle(1),{'2', 'a', 'z', 'x',})
tc.extend_names_by_anyname('a', 'extend')
self.assertEquals(tc.get_handles('extend'),{0,1,})
def test_extend_names(self):
tc = TaxyTool()
tc.add_taxonomy_set('1', 'a')
tc.add_taxonomy_set('2', 'a')
self.assertEquals(tc.get_handles('1'), {
0,
})
self.assertEquals(tc.get_handles('2'), {
1,
})
self.assertEquals(tc.get_names_by_handle(0), {
'1',
'a',
})
self.assertEquals(tc.get_names_by_handle(1), {
'2',
'a',
})
tc.extend_names_by_nick_name('1', 'c', 'e')
tc.extend_names_by_nick_name('2', 'z', 'x')
tc.extend_names_by_nick_name('1', 'd', 'f')
self.assertEquals(tc.get_handles('a'), {0, 1})
self.assertEquals(tc.get_handles('z'), {
1,
})
self.assertEquals(tc.get_handles('c'), {
0,
})
#Test for a name that isn't in the taxonomy
self.assertEquals(tc.get_handles('b'), {
-1,
})
self.assertEquals(tc.get_names_by_handle(0), {
'1',
'a',
'c',
'e',
'd',
'f',
})
self.assertEquals(tc.get_names_by_handle(1), {
'2',
'a',
'z',
'x',
})
tc.extend_names_by_anyname('a', 'extend')
self.assertEquals(tc.get_handles('extend'), {
0,
1,
})
def test_get_nick_names(self):
tc = TaxyTool()
tc.add_taxonomy_set('1','a')
tc.add_taxonomy_set('2','a')
self.assertEquals(tc.get_nick_name(0),'1')
self.assertEquals(tc.get_nick_names('a'),['1', '2'])
self.assertEquals(tc.get_handle('1'),0)
self.assertEquals(tc.get_handles('a'),{0,1})
def test_get_nick_names(self):
tc = TaxyTool()
tc.add_taxonomy_set('1', 'a')
tc.add_taxonomy_set('2', 'a')
self.assertEquals(tc.get_nick_name(0), '1')
self.assertEquals(tc.get_nick_names('a'), ['1', '2'])
self.assertEquals(tc.get_handle('1'), 0)
self.assertEquals(tc.get_handles('a'), {0, 1})
def _setup_resources(self):
stream_id = self.create_stream_and_logger(name='fibonacci_stream')
tx = TaxyTool()
tx.add_taxonomy_set('data', 'external_data')
#TG: Build TaxonomyTool & add to dh_cfg.taxonomy
self.DVR_CONFIG['dh_cfg'] = {
'TESTING': True,
'stream_id': stream_id,
'data_producer_id': 'fibonacci_data_producer_id',
'taxonomy': tx.dump(),
'max_records': 4,
}
def _setup_resources(self):
stream_id = self.create_stream_and_logger(name='fibonacci_stream')
tx = TaxyTool()
tx.add_taxonomy_set('data', 'external_data')
#TG: Build TaxonomyTool & add to dh_cfg.taxonomy
self.DVR_CONFIG['dh_cfg'] = {
'TESTING':True,
'stream_id':stream_id,
'data_producer_id':'fibonacci_data_producer_id',
'taxonomy':tx.dump(),
'max_records':4,
}
def _setup_resources(self):
stream_id = self.create_stream_and_logger(name='dummydata_stream')
tx = TaxyTool()
tx.add_taxonomy_set('data', 'external_data')
self.DVR_CONFIG['dh_cfg'] = {
'TESTING':True,
'stream_id':stream_id,#TODO: This should probably be a 'stream_config' dict with stream_name:stream_id members
'data_producer_id':'dummy_data_producer_id',
'taxonomy':tx.dump(),
'max_records':4,
}
def _setup_resources(self):
stream_id = self.create_stream_and_logger(name='dummydata_stream')
tx = TaxyTool()
tx.add_taxonomy_set('data', 'external_data')
self.DVR_CONFIG['dh_cfg'] = {
'TESTING': True,
'stream_id':
stream_id, #TODO: This should probably be a 'stream_config' dict with stream_name:stream_id members
'data_producer_id': 'dummy_data_producer_id',
'taxonomy': tx.dump(),
'max_records': 4,
}
def launch_benchmark(transform_number=1, primer=1, message_length=4):
import gevent
from gevent.greenlet import Greenlet
from pyon.util.containers import DotDict
from pyon.net.transport import NameTrio
from pyon.net.endpoint import Publisher
import numpy
from pyon.ion.granule.record_dictionary import RecordDictionaryTool
from pyon.ion.granule.taxonomy import TaxyTool
from pyon.ion.granule.granule import build_granule
tt = TaxyTool()
tt.add_taxonomy_set('a')
import uuid
num = transform_number
msg_len = message_length
transforms = list()
pids = 1
TransformBenchTesting.message_length = message_length
cc = Container.instance
pub = Publisher(to_name=NameTrio(get_sys_name(),
str(uuid.uuid4())[0:6]))
for i in xrange(num):
tbt = cc.proc_manager._create_service_instance(
str(pids), 'tbt', 'prototype.transforms.linear',
'TransformInPlaceNewGranule',
DotDict({
'process': {
'name': 'tbt%d' % pids,
'transform_id': pids
}
}))
tbt.init()
tbt.start()
gevent.sleep(0.2)
for i in xrange(primer):
rd = RecordDictionaryTool(tt, message_length)
rd['a'] = numpy.arange(message_length)
gran = build_granule(data_producer_id='dp_id',
taxonomy=tt,
record_dictionary=rd)
pub.publish(gran)
g = Greenlet(tbt.perf)
g.start()
transforms.append(tbt)
pids += 1
def test_combine_granule(self):
tt = TaxyTool()
tt.add_taxonomy_set('a')
rdt = RecordDictionaryTool(tt)
rdt['a'] = numpy.array([1,2,3])
granule1 = build_granule('test',tt,rdt)
rdt = RecordDictionaryTool(tt)
rdt['a'] = numpy.array([4,5,6])
granule2 = build_granule('test',tt,rdt)
granule3 = combine_granules(granule1,granule2)
rdt = RecordDictionaryTool.load_from_granule(granule3)
self.assertTrue(numpy.allclose(rdt['a'],numpy.array([1,2,3,4,5,6])))
def test_combine_granule(self):
tt = TaxyTool()
tt.add_taxonomy_set('a')
rdt = RecordDictionaryTool(tt)
rdt['a'] = numpy.array([1, 2, 3])
granule1 = build_granule('test', tt, rdt)
rdt = RecordDictionaryTool(tt)
rdt['a'] = numpy.array([4, 5, 6])
granule2 = build_granule('test', tt, rdt)
granule3 = combine_granules(granule1, granule2)
rdt = RecordDictionaryTool.load_from_granule(granule3)
self.assertTrue(
numpy.allclose(rdt['a'], numpy.array([1, 2, 3, 4, 5, 6])))
def test_yamlize(self):
tc = TaxyTool()
tc.add_taxonomy_set('1', 'a')
tc.add_taxonomy_set('2', 'b')
tc.extend_names_by_nick_name('1', 'x')
tc.extend_names_by_anyname('a', 'z')
tc.extend_names_by_anyname('b', 'c')
s = tc.dump()
tc2 = TaxyTool.load(s)
#@todo - a list is not a set and the yaml dump/ion serialization can not handle sets...
self.assertEquals(tc2._cnt, 1)
self.assertEquals(tc2.get_names_by_handle(0), {
'1',
'x',
'a',
'z',
})
self.assertEquals(tc2.get_names_by_handle(1), {
'2',
'b',
'c',
})
self.assertEquals(tc._cnt, 1)
self.assertEquals(tc.get_names_by_handle(0), {
'1',
'x',
'a',
'z',
})
self.assertEquals(tc.get_names_by_handle(1), {
'2',
'b',
'c',
})
def test_yamlize(self):
tc = TaxyTool()
tc.add_taxonomy_set('1','a')
tc.add_taxonomy_set('2','b')
tc.extend_names_by_nick_name('1','x')
tc.extend_names_by_anyname('a','z')
tc.extend_names_by_anyname('b','c')
s = tc.dump()
tc2 = TaxyTool.load(s)
#@todo - a list is not a set and the yaml dump/ion serialization can not handle sets...
self.assertEquals(tc2._cnt,1)
self.assertEquals(tc2.get_names_by_handle(0),{'1','x','a','z',})
self.assertEquals(tc2.get_names_by_handle(1),{'2','b','c',})
self.assertEquals(tc._cnt,1)
self.assertEquals(tc.get_names_by_handle(0),{'1','x','a','z',})
self.assertEquals(tc.get_names_by_handle(1),{'2','b','c',})
def launch_benchmark(transform_number=1, primer=1,message_length=4):
import gevent
from gevent.greenlet import Greenlet
from pyon.util.containers import DotDict
from pyon.net.transport import NameTrio
from pyon.net.endpoint import Publisher
import numpy
from pyon.ion.granule.record_dictionary import RecordDictionaryTool
from pyon.ion.granule.taxonomy import TaxyTool
from pyon.ion.granule.granule import build_granule
tt = TaxyTool()
tt.add_taxonomy_set('a')
import uuid
num = transform_number
msg_len = message_length
transforms = list()
pids = 1
TransformBenchTesting.message_length = message_length
cc = Container.instance
pub = Publisher(to_name=NameTrio(get_sys_name(),str(uuid.uuid4())[0:6]))
for i in xrange(num):
tbt=cc.proc_manager._create_service_instance(str(pids), 'tbt', 'prototype.transforms.linear', 'TransformInPlaceNewGranule', DotDict({'process':{'name':'tbt%d' % pids, 'transform_id':pids}}))
tbt.init()
tbt.start()
gevent.sleep(0.2)
for i in xrange(primer):
rd = RecordDictionaryTool(tt, message_length)
rd['a'] = numpy.arange(message_length)
gran = build_granule(data_producer_id='dp_id',taxonomy=tt, record_dictionary=rd)
pub.publish(gran)
g = Greenlet(tbt.perf)
g.start()
transforms.append(tbt)
pids += 1
def test_build_granule_and_load_from_granule(self):
#Define a taxonomy and add sets. add_taxonomy_set takes one or more names and assigns them to one handle
tx = TaxyTool()
tx.add_taxonomy_set('temp', 'long_temp_name')
tx.add_taxonomy_set('cond', 'long_cond_name')
tx.add_taxonomy_set('pres', 'long_pres_name')
tx.add_taxonomy_set('rdt')
# map is {<local name>: <granule name or path>}
#Use RecordDictionaryTool to create a record dictionary. Send in the taxonomy so the Tool knows what to expect
rdt = RecordDictionaryTool(taxonomy=tx)
#Create some arrays and fill them with random values
temp_array = numpy.random.standard_normal(100)
cond_array = numpy.random.standard_normal(100)
pres_array = numpy.random.standard_normal(100)
#Use the RecordDictionaryTool to add the values. This also would work if you used long_temp_name, etc.
rdt['temp'] = temp_array
rdt['cond'] = cond_array
rdt['pres'] = pres_array
#You can also add in another RecordDictionaryTool, providing the taxonomies are the same.
rdt2 = RecordDictionaryTool(taxonomy=tx)
rdt2['temp'] = temp_array
rdt['rdt'] = rdt2
g = build_granule(data_producer_id='john', taxonomy=tx, record_dictionary=rdt)
l_tx = TaxyTool.load_from_granule(g)
l_rd = RecordDictionaryTool.load_from_granule(g)
# Make sure we got back the same Taxonomy Object
self.assertEquals(l_tx._t, tx._t)
self.assertEquals(l_tx.get_handles('temp'), tx.get_handles('temp'))
self.assertEquals(l_tx.get_handles('testing_2'), tx.get_handles('testing_2'))
# Now test the record dictionary object
self.assertEquals(l_rd._rd, rdt._rd)
self.assertEquals(l_rd._tx._t, rdt._tx._t)
for k, v in l_rd.iteritems():
self.assertIn(k, rdt)
if isinstance(v, numpy.ndarray):
self.assertTrue( (v == rdt[k]).all())
else:
self.assertEquals(v._rd, rdt[k]._rd)
def test_build_granule_and_load_from_granule(self):
#Define a taxonomy and add sets. add_taxonomy_set takes one or more names and assigns them to one handle
tx = TaxyTool()
tx.add_taxonomy_set('temp', 'long_temp_name')
tx.add_taxonomy_set('cond', 'long_cond_name')
tx.add_taxonomy_set('pres', 'long_pres_name')
tx.add_taxonomy_set('rdt')
# map is {<local name>: <granule name or path>}
#Use RecordDictionaryTool to create a record dictionary. Send in the taxonomy so the Tool knows what to expect
rdt = RecordDictionaryTool(taxonomy=tx)
#Create some arrays and fill them with random values
temp_array = numpy.random.standard_normal(100)
cond_array = numpy.random.standard_normal(100)
pres_array = numpy.random.standard_normal(100)
#Use the RecordDictionaryTool to add the values. This also would work if you used long_temp_name, etc.
rdt['temp'] = temp_array
rdt['cond'] = cond_array
rdt['pres'] = pres_array
#You can also add in another RecordDictionaryTool, providing the taxonomies are the same.
rdt2 = RecordDictionaryTool(taxonomy=tx)
rdt2['temp'] = temp_array
rdt['rdt'] = rdt2
g = build_granule(data_producer_id='john',
taxonomy=tx,
record_dictionary=rdt)
l_tx = TaxyTool.load_from_granule(g)
l_rd = RecordDictionaryTool.load_from_granule(g)
# Make sure we got back the same Taxonomy Object
self.assertEquals(l_tx._t, tx._t)
self.assertEquals(l_tx.get_handles('temp'), tx.get_handles('temp'))
self.assertEquals(l_tx.get_handles('testing_2'),
tx.get_handles('testing_2'))
# Now test the record dictionary object
self.assertEquals(l_rd._rd, rdt._rd)
self.assertEquals(l_rd._tx._t, rdt._tx._t)
for k, v in l_rd.iteritems():
self.assertIn(k, rdt)
if isinstance(v, numpy.ndarray):
self.assertTrue((v == rdt[k]).all())
else:
self.assertEquals(v._rd, rdt[k]._rd)
def granule_example(nsdict):
"""
Usage:
from examples.granule import granule_example
granule_example(locals())
tx, g, rdt, rdt2... etc, are now local variables in your shell!
"""
#Define a taxonomy and add sets. add_taxonomy_set takes one or more names and assigns them to one handle
tx = TaxyTool()
tx.add_taxonomy_set('temp', 'long_temp_name')
tx.add_taxonomy_set('cond', 'long_cond_name')
tx.add_taxonomy_set('pres', 'long_pres_name')
tx.add_taxonomy_set('rdt')
# map is {<local name>: <granule name or path>}
#Use RecordDictionaryTool to create a record dictionary. Send in the taxonomy so the Tool knows what to expect
rdt = RecordDictionaryTool(taxonomy=tx)
#Create some arrays and fill them with random values
temp_array = numpy.random.standard_normal(100)
cond_array = numpy.random.standard_normal(100)
pres_array = numpy.random.standard_normal(100)
#Use the RecordDictionaryTool to add the values. This also would work if you used long_temp_name, etc.
rdt['temp'] = temp_array
rdt['cond'] = cond_array
rdt['pres'] = pres_array
#You can also add in another RecordDictionaryTool, providing the taxonomies are the same.
rdt2 = RecordDictionaryTool(taxonomy=tx)
rdt2['temp'] = temp_array
rdt['rdt'] = rdt2
#you can get a string representation of the RecordDictionaryTool
print rdt
print rdt.pretty_print()
#Determine the length of the RecordDictionary using the len function
print len(rdt)
#Delete an item in the RecordDictionary
del rdt['temp']
g = build_granule(data_producer_id='john', taxonomy=tx, record_dictionary=rdt)
nsdict.update(locals())
def test_taxonomy_set(self):
nick_name = 'nick_name'
a = 'a'
b = 'b'
c = 'c'
tc = TaxyTool()
tc.add_taxonomy_set(nick_name=nick_name)
self.assertEquals(tc.get_handles('a name'), {-1,})
self.assertRaises(KeyError,tc.get_names_by_handle,5)
self.assertEquals(tc.get_names_by_handle(0), {nick_name,})
tc = TaxyTool()
tc.add_taxonomy_set(a)
self.assertEquals(tc.get_handle(a),0)
self.assertEquals(tc.get_names_by_handle(0),{a,})
self.assertEquals(tc.get_names_by_nick_name(a),{a,})
tc = TaxyTool()
tc.add_taxonomy_set(a)
tc.add_taxonomy_set(b)
self.assertEquals(tc.get_handle(a),0)
self.assertEquals(tc.get_names_by_handle(0),{a,})
self.assertEquals(tc.get_handle(b),1)
self.assertEquals(tc.get_names_by_handle(1),{b,})
tc = TaxyTool()
tc.add_taxonomy_set(nick_name, a, b, c)
self.assertEquals(tc.get_handle(nick_name),0)
self.assertEquals(tc.get_handles(a),{0,})
self.assertEquals(tc.get_handles(b),{0,})
self.assertEquals(tc.get_handles(c),{0,})
self.assertEquals(tc.get_names_by_handle(0),{nick_name,a,b,c,})
self.assertEquals(tc.get_names_by_nick_name(nick_name),{nick_name,a,b,c,})
class ExampleDataProducer(TransformStreamPublisher):
"""
This Example process inherits some business logic from the above example.
It is not infrastructure - it is a demonstration of the infrastructure applied to an example.
"""
def init(self):
### Taxonomies are defined before hand out of band... somehow.
self._tx = TaxyTool()
self._tx.add_taxonomy_set('temp','long name for temp')
self._tx.add_taxonomy_set('cond','long name for cond')
self._tx.add_taxonomy_set('lat','long name for latitude')
self._tx.add_taxonomy_set('lon','long name for longitude')
self._tx.add_taxonomy_set('pres','long name for pres')
self._tx.add_taxonomy_set('time','long name for time')
def on_start(self):
super(ExampleDataProducer, self).on_start()
stream_id = self.CFG.process.out_stream_id
g = Greenlet(self._trigger_func, stream_id)
log.debug('Starting publisher thread for simple ctd data.')
g.start()
log.warn('Publisher Greenlet started in "%s"' % self.__class__.__name__)
self.greenlet_queue = []
self.greenlet_queue.append(g)
def publish(self, msg, stream_id):
self.publisher.publish(msg=msg, stream_id=stream_id)
def on_quit(self):
for greenlet in self.greenlet_queue:
greenlet.kill()
super(ExampleDataProducer,self).on_quit()
def _trigger_func(self, stream_id):
self.last_time = 0
#@todo - add lots of comments in here
while True:
length = 10
#Explicitly make these numpy arrays...
c = numpy.array([random.uniform(0.0,75.0) for i in xrange(length)])
t = numpy.array([random.uniform(-1.7, 21.0) for i in xrange(length)])
p = numpy.array([random.lognormvariate(1,2) for i in xrange(length)])
lat = numpy.array([random.uniform(-90.0, 90.0) for i in xrange(length)])
lon = numpy.array([random.uniform(0.0, 360.0) for i in xrange(length)])
tvar = numpy.array([self.last_time + i for i in xrange(1,length+1)])
self.last_time = max(tvar)
rdt = RecordDictionaryTool(taxonomy=self._tx)
rdt['temp'] = ExampleDataProducer_algorithm.execute(t)
rdt['cond'] = ExampleDataProducer_algorithm.execute(c)
rdt['pres'] = ExampleDataProducer_algorithm.execute(p)
rdt['time'] = tvar
rdt['lat'] = lat
rdt['lon'] = lon
log.info("logging published Record Dictionary:\n %s", rdt.pretty_print())
g = build_granule(data_producer_id=stream_id, taxonomy=self._tx, record_dictionary=rdt)
log.info('Sending %d values!' % length)
if(isinstance(g, Granule)):
self.publish(g, stream_id)
time.sleep(2.0)
def test_activateDatasetAgent(self):
# Create ExternalDatasetModel
datsetModel_obj = IonObject(RT.ExternalDatasetModel,
name='ExampleDatasetModel',
description="ExampleDatasetModel",
datset_type="FibSeries")
try:
datasetModel_id = self.damsclient.create_external_dataset_model(
datsetModel_obj)
except BadRequest as ex:
self.fail("failed to create new ExternalDatasetModel: %s" % ex)
log.debug(
"TestExternalDatasetAgentMgmt: new ExternalDatasetModel id = %s",
str(datasetModel_id))
# Create ExternalDatasetAgent
datasetAgent_obj = IonObject(RT.ExternalDatasetAgent,
name='datasetagent007',
description="datasetagent007",
handler_module=EDA_MOD,
handler_class=EDA_CLS)
try:
datasetAgent_id = self.damsclient.create_external_dataset_agent(
datasetAgent_obj, datasetModel_id)
except BadRequest as ex:
self.fail("failed to create new ExternalDatasetAgent: %s" % ex)
log.debug(
"TestExternalDatasetAgentMgmt: new ExternalDatasetAgent id = %s",
str(datasetAgent_id))
# Create ExternalDataset
log.debug(
'TestExternalDatasetAgentMgmt: Create external dataset resource ')
extDataset_obj = IonObject(RT.ExternalDataset,
name='ExtDataset',
description="ExtDataset")
try:
extDataset_id = self.damsclient.create_external_dataset(
extDataset_obj, datasetModel_id)
except BadRequest as ex:
self.fail("failed to create new external dataset resource: %s" %
ex)
log.debug(
"TestExternalDatasetAgentMgmt: new ExternalDataset id = %s ",
str(extDataset_id))
#register the dataset as a data producer
dproducer_id = self.damsclient.register_external_data_set(
extDataset_id)
# create a stream definition for the data from the ctd simulator
ctd_stream_def = SBE37_CDM_stream_definition()
ctd_stream_def_id = self.pubsubcli.create_stream_definition(
container=ctd_stream_def)
log.debug(
"TestExternalDatasetAgentMgmt: new Stream Definition id = %s",
str(ctd_stream_def_id))
log.debug(
"TestExternalDatasetAgentMgmt: Creating new data product with a stream definition"
)
dp_obj = IonObject(RT.DataProduct,
name='eoi dataset data',
description=' stream test')
try:
data_product_id1 = self.dpclient.create_data_product(
dp_obj, ctd_stream_def_id)
except BadRequest as ex:
self.fail("failed to create new data product: %s" % ex)
log.debug("TestExternalDatasetAgentMgmt: new dp_id = %s",
str(data_product_id1))
self.damsclient.assign_data_product(input_resource_id=extDataset_id,
data_product_id=data_product_id1)
self.dpclient.activate_data_product_persistence(
data_product_id=data_product_id1,
persist_data=True,
persist_metadata=True)
# 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("TestExternalDatasetAgentMgmt: Data product streams1 = %s",
str(stream_ids))
stream_id = stream_ids[0]
# Build a taxonomy for the dataset
tx = TaxyTool()
tx.add_taxonomy_set('data', 'external_data')
# Augment the DVR_CONFIG with the necessary pieces
self.DVR_CONFIG['dh_cfg'] = {
'TESTING': True,
'stream_id':
stream_id, #TODO: This should probably be a 'stream_config' dict with stream_name:stream_id members
'data_producer_id': dproducer_id,
# 'external_dataset_res':extDataset_obj, # Not needed - retrieved by EDA based on resource_id
'taxonomy': tx.dump(), #TODO: Currently does not support sets
'max_records': 4,
}
# Create agent config.
self._stream_config = {}
agent_config = {
'driver_config': self.DVR_CONFIG,
'stream_config': self._stream_config,
'agent': {
'resource_id': EDA_RESOURCE_ID
},
'test_mode': True
}
extDatasetAgentInstance_obj = IonObject(
RT.ExternalDatasetAgentInstance,
name='DatasetAgentInstance',
description="DatasetAgentInstance",
dataset_driver_config=self.DVR_CONFIG,
dataset_agent_config=agent_config)
extDatasetAgentInstance_id = self.damsclient.create_external_dataset_agent_instance(
external_dataset_agent_instance=extDatasetAgentInstance_obj,
external_dataset_agent_id=datasetAgent_id,
external_dataset_id=extDataset_id)
log.debug(
"TestExternalDatasetAgentMgmt: Dataset agent instance obj: = %s",
str(extDatasetAgentInstance_obj))
log.debug(
"TestExternalDatasetAgentMgmt: Dataset agent instance id: = %s",
str(extDatasetAgentInstance_id))
#Check that the instance is currently not active
id, active = self.damsclient.retrieve_external_dataset_agent_instance(
extDataset_id)
log.debug(
"TestExternalDatasetAgentMgmt: Dataset agent instance id: = %s active 1 = %s ",
str(id), str(active))
self.damsclient.start_external_dataset_agent_instance(
extDatasetAgentInstance_id)
dataset_agent_instance_obj = self.damsclient.read_external_dataset_agent_instance(
extDatasetAgentInstance_id)
log.debug(
"TestExternalDatasetAgentMgmt: Dataset agent instance obj: = %s",
str(dataset_agent_instance_obj))
# now the instance process should be active
id, active = self.damsclient.retrieve_external_dataset_agent_instance(
extDataset_id)
log.debug(
"TestExternalDatasetAgentMgmt: Dataset agent instance id: = %s active 2 = %s ",
str(id), str(active))
# Start a resource agent client to talk with the instrument agent.
self._dsa_client = ResourceAgentClient(extDataset_id,
process=FakeProcess())
print 'TestExternalDatasetAgentMgmt: got ia client %s', self._dsa_client
log.debug("TestExternalDatasetAgentMgmt: got dataset client %s",
str(self._dsa_client))
# cmd=AgentCommand(command='initialize')
# _ = self._dsa_client.execute_agent(cmd)
#
# cmd = AgentCommand(command='go_active')
# _ = self._dsa_client.execute_agent(cmd)
#
# cmd = AgentCommand(command='run')
# _ = self._dsa_client.execute_agent(cmd)
#
# log.info('Send an unconstrained request for data (\'new data\')')
# cmd = AgentCommand(command='acquire_data')
# self._dsa_client.execute(cmd)
#
# log.info('Send a second unconstrained request for data (\'new data\'), should be rejected')
# cmd = AgentCommand(command='acquire_data')
# self._dsa_client.execute(cmd)
#
# cmd = AgentCommand(command='reset')
# _ = self._dsa_client.execute_agent(cmd)
# cmd = AgentCommand(command='get_current_state')
# retval = self._dsa_client.execute_agent(cmd)
# state = retval.result
# TODO: Think about what we really should be testing at this point
# The following is taken from ion.agents.data.test.test_external_dataset_agent.ExternalDatasetAgentTestBase.test_states()
# TODO: Do we also need to show data retrieval?
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.UNINITIALIZED)
cmd = AgentCommand(command='initialize')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.INACTIVE)
cmd = AgentCommand(command='go_active')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.IDLE)
cmd = AgentCommand(command='run')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.OBSERVATORY)
cmd = AgentCommand(command='pause')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.STOPPED)
cmd = AgentCommand(command='resume')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.OBSERVATORY)
cmd = AgentCommand(command='clear')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.IDLE)
cmd = AgentCommand(command='run')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.OBSERVATORY)
cmd = AgentCommand(command='pause')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.STOPPED)
cmd = AgentCommand(command='clear')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.IDLE)
cmd = AgentCommand(command='run')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.OBSERVATORY)
cmd = AgentCommand(command='reset')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.UNINITIALIZED)
#-------------------------------
# Deactivate InstrumentAgentInstance
#-------------------------------
self.damsclient.stop_external_dataset_agent_instance(
extDatasetAgentInstance_id)
from pyon.ion.granule.taxonomy import TaxyTool
from pyon.ion.granule.record_dictionary import RecordDictionaryTool
from prototype.sci_data.stream_defs import SBE37_CDM_stream_definition, SBE37_RAW_stream_definition
from prototype.sci_data.stream_parser import PointSupplementStreamParser
from prototype.sci_data.constructor_apis import PointSupplementConstructor, RawSupplementConstructor
import StringIO
from numpy import array, append
# Matplotlib related imports
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
tx = TaxyTool()
tx.add_taxonomy_set("matplotlib_graphs", "Matplotlib generated graphs for a particular data product")
class VizTransformMatplotlibGraphs(TransformFunction):
"""
This class is used for instantiating worker processes that have subscriptions to data streams and convert
incoming data from CDM format to Matplotlib graphs
"""
outgoing_stream_def = SBE37_RAW_stream_definition()
incoming_stream_def = SBE37_CDM_stream_definition()
def on_start(self):
super(VizTransformMatplotlibGraphs, self).on_start()
def _setup_resources(self):
# TODO: some or all of this (or some variation) should move to DAMS'
# Build the test resources for the dataset
dams_cli = DataAcquisitionManagementServiceClient()
dpms_cli = DataProductManagementServiceClient()
rr_cli = ResourceRegistryServiceClient()
eda = ExternalDatasetAgent()
eda_id = dams_cli.create_external_dataset_agent(eda)
eda_inst = ExternalDatasetAgentInstance()
eda_inst_id = dams_cli.create_external_dataset_agent_instance(
eda_inst, external_dataset_agent_id=eda_id)
# Create and register the necessary resources/objects
# Create DataProvider
dprov = ExternalDataProvider(institution=Institution(),
contact=ContactInformation())
dprov.contact.name = 'Christopher Mueller'
dprov.contact.email = '*****@*****.**'
# Create DataSource
dsrc = DataSource(protocol_type='DAP',
institution=Institution(),
contact=ContactInformation())
dsrc.connection_params['base_data_url'] = ''
dsrc.contact.name = 'Tim Giguere'
dsrc.contact.email = '*****@*****.**'
# Create ExternalDataset
ds_name = 'usgs_test_dataset'
dset = ExternalDataset(name=ds_name,
dataset_description=DatasetDescription(),
update_description=UpdateDescription(),
contact=ContactInformation())
# The usgs.nc test dataset is a download of the R1 dataset found here:
# http://thredds-test.oceanobservatories.org/thredds/dodsC/ooiciData/E66B1A74-A684-454A-9ADE-8388C2C634E5.ncml
dset.dataset_description.parameters[
'dataset_path'] = 'test_data/usgs.nc'
dset.dataset_description.parameters['temporal_dimension'] = 'time'
dset.dataset_description.parameters['zonal_dimension'] = 'lon'
dset.dataset_description.parameters['meridional_dimension'] = 'lat'
dset.dataset_description.parameters['vertical_dimension'] = 'z'
dset.dataset_description.parameters['variables'] = [
'water_temperature',
'streamflow',
'water_temperature_bottom',
'water_temperature_middle',
'specific_conductance',
'data_qualifier',
]
# Create DataSourceModel
dsrc_model = DataSourceModel(name='dap_model')
dsrc_model.model = 'DAP'
dsrc_model.data_handler_module = 'N/A'
dsrc_model.data_handler_class = 'N/A'
## Run everything through DAMS
ds_id = dams_cli.create_external_dataset(external_dataset=dset)
ext_dprov_id = dams_cli.create_external_data_provider(
external_data_provider=dprov)
ext_dsrc_id = dams_cli.create_data_source(data_source=dsrc)
ext_dsrc_model_id = dams_cli.create_data_source_model(dsrc_model)
# Register the ExternalDataset
dproducer_id = dams_cli.register_external_data_set(
external_dataset_id=ds_id)
# Or using each method
dams_cli.assign_data_source_to_external_data_provider(
data_source_id=ext_dsrc_id, external_data_provider_id=ext_dprov_id)
dams_cli.assign_data_source_to_data_model(
data_source_id=ext_dsrc_id, data_source_model_id=ext_dsrc_model_id)
dams_cli.assign_external_dataset_to_data_source(
external_dataset_id=ds_id, data_source_id=ext_dsrc_id)
dams_cli.assign_external_dataset_to_agent_instance(
external_dataset_id=ds_id, agent_instance_id=eda_inst_id)
# dams_cli.assign_external_data_agent_to_agent_instance(external_data_agent_id=self.eda_id, agent_instance_id=self.eda_inst_id)
# Generate the data product and associate it to the ExternalDataset
dprod = DataProduct(name='usgs_parsed_product',
description='parsed usgs product')
dproduct_id = dpms_cli.create_data_product(data_product=dprod)
dams_cli.assign_data_product(input_resource_id=ds_id,
data_product_id=dproduct_id,
create_stream=True)
stream_id, assn = rr_cli.find_objects(subject=dproduct_id,
predicate=PRED.hasStream,
object_type=RT.Stream,
id_only=True)
stream_id = stream_id[0]
log.info('Created resources: {0}'.format({
'ExternalDataset': ds_id,
'ExternalDataProvider': ext_dprov_id,
'DataSource': ext_dsrc_id,
'DataSourceModel': ext_dsrc_model_id,
'DataProducer': dproducer_id,
'DataProduct': dproduct_id,
'Stream': stream_id
}))
#CBM: Use CF standard_names
ttool = TaxyTool()
ttool.add_taxonomy_set('time', 'time')
ttool.add_taxonomy_set('lon', 'longitude')
ttool.add_taxonomy_set('lat', 'latitude')
ttool.add_taxonomy_set('z', 'water depth')
ttool.add_taxonomy_set('water_temperature',
'average water temperature')
ttool.add_taxonomy_set('water_temperature_bottom',
'water temperature at bottom of water column')
ttool.add_taxonomy_set('water_temperature_middle',
'water temperature at middle of water column')
ttool.add_taxonomy_set('streamflow', 'flow velocity of stream')
ttool.add_taxonomy_set('specific_conductance',
'specific conductance of water')
ttool.add_taxonomy_set('data_qualifier', 'data qualifier flag')
ttool.add_taxonomy_set('coords',
'This group contains coordinate parameters')
ttool.add_taxonomy_set('data', 'This group contains data parameters')
# Create the logger for receiving publications
self.create_stream_and_logger(name='usgs', stream_id=stream_id)
self.EDA_RESOURCE_ID = ds_id
self.EDA_NAME = ds_name
self.DVR_CONFIG['dh_cfg'] = {
'TESTING': True,
'stream_id': stream_id,
'taxonomy': ttool.dump(),
'data_producer_id':
dproducer_id, #CBM: Should this be put in the main body of the config - with mod & cls?
'max_records': 4,
}
from pyon.core.exception import BadRequest
from pyon.public import IonObject, RT, log
from datetime import datetime
import numpy
from pyon.ion.granule.granule import build_granule
from pyon.ion.granule.taxonomy import TaxyTool
from pyon.ion.granule.record_dictionary import RecordDictionaryTool
from prototype.sci_data.stream_defs import SBE37_CDM_stream_definition, SBE37_RAW_stream_definition
from prototype.sci_data.stream_parser import PointSupplementStreamParser
from prototype.sci_data.constructor_apis import PointSupplementConstructor, RawSupplementConstructor
tx = TaxyTool()
tx.add_taxonomy_set('google_dt_components','Google DT components for part or entire datatable')
class VizTransformGoogleDT(TransformFunction):
"""
This class is used for converting incoming data from CDM format to JSON style Google DataTables
Note: One behaviour that this class is expected to achieve specifically is to determine if its supposed
to work as a realtime transform (exists indefinitely and maintains a sliding window of data) or as
a replay transform (one-shot).
[2] This transform behaves as an instantaneous forwarder. There is no waiting for the entire stream
to create the complete datatable. As the granules come in, they are translated to the datatable
'components'. Components, because we are not creating the actual datatable in this code. That's the job
of the viz service to put all the components of a datatable together in JSON format before sending it
# Inherit some old machinery for this example
from ion.processes.data.ctd_stream_publisher import SimpleCtdPublisher
### For new granule and stream interface
from pyon.ion.granule.record_dictionary import RecordDictionaryTool
from pyon.ion.granule.taxonomy import TaxyTool
from pyon.ion.granule.granule import build_granule
from pyon.public import log
import numpy
import random
import time
### Taxonomies are defined before hand out of band... somehow.
tx = TaxyTool()
tx.add_taxonomy_set('temp','long name for temp')
tx.add_taxonomy_set('cond','long name for cond')
tx.add_taxonomy_set('lat','long name for latitude')
tx.add_taxonomy_set('lon','long name for longitude')
tx.add_taxonomy_set('pres','long name for pres')
tx.add_taxonomy_set('time','long name for time')
# This is an example of using groups it is not a normative statement about how to use groups
tx.add_taxonomy_set('group1','This group contains coordinates...')
tx.add_taxonomy_set('group0','This group contains data...')
tx.add_taxonomy_set('raw_fixed','Fixed length bytes in an array of records')
tx.add_taxonomy_set('raw_blob','Unlimited length bytes in an array')
class ExampleDataProducer(SimpleCtdPublisher):
"""
def test_eq(self):
tx1 = Taxonomy(map={1:('nick_name',{'nick_name','a'}),
2:('nick2',{'nick2','a','b','cc'})} )
# pass with tt1._t.map "is"
tt1 = TaxyTool(tx1)
tt2 = TaxyTool(tx1)
self.assertEquals(tt1, tt2)
# after changes - thy are still the same
tt2.add_taxonomy_set('new_name','p','q','r')
self.assertEquals(tt2,tt1)
# pass with 'is'
tt3 = tt1
self.assertEquals(tt3, tt1)
# after changes - thy are still the same
tt3.add_taxonomy_set('new_name','p','q','r')
self.assertEquals(tt1,tt3)
# pass with tt1._t.map '=='
tx1 = Taxonomy(map={1:('nick_name',{'nick_name','a'}),
2:('nick2',{'nick2','a','b','cc'})} )
tx2 = Taxonomy(map={1:('nick_name',{'nick_name','a'}),
2:('nick2',{'nick2','a','b','cc'})} )
self.assertNotEquals(tx1, tx2)
self.assertEquals(tx1.map, tx2.map)
tt1 = TaxyTool(tx1)
tt2 = TaxyTool(tx2)
self.assertEquals(tt1, tt2)
# fail with tt1._t.map '=='
tx2 = Taxonomy(map={1:('nick_name',{'nick_name','as'}),
2:('nick2',{'nick2','a','b','cc'})} )
tt1 = TaxyTool(tx1)
tt2 = TaxyTool(tx2)
self.assertNotEquals(tt1, tt2)
# Use the interface to build a complex one and test equality as they go in and out of sync...
tt1 = TaxyTool()
tt2 = TaxyTool()
tt1.add_taxonomy_set('a name','junk','1','2')
tt2.add_taxonomy_set('a name','junk','1','2')
self.assertEquals(tt1, tt2)
tt2.add_taxonomy_set('new_name','1')
self.assertNotEquals(tt1,tt2)
tt1.extend_names_by_nick_name('a name','3')
tt2.extend_names_by_nick_name('a name','3')
tt1.add_taxonomy_set('new_name','1')
self.assertEquals(tt1, tt2)
def test_eq(self):
tx1 = Taxonomy(
map={
1: ('nick_name', {'nick_name', 'a'}),
2: ('nick2', {'nick2', 'a', 'b', 'cc'})
})
# pass with tt1._t.map "is"
tt1 = TaxyTool(tx1)
tt2 = TaxyTool(tx1)
self.assertEquals(tt1, tt2)
# after changes - thy are still the same
tt2.add_taxonomy_set('new_name', 'p', 'q', 'r')
self.assertEquals(tt2, tt1)
# pass with 'is'
tt3 = tt1
self.assertEquals(tt3, tt1)
# after changes - thy are still the same
tt3.add_taxonomy_set('new_name', 'p', 'q', 'r')
self.assertEquals(tt1, tt3)
# pass with tt1._t.map '=='
tx1 = Taxonomy(
map={
1: ('nick_name', {'nick_name', 'a'}),
2: ('nick2', {'nick2', 'a', 'b', 'cc'})
})
tx2 = Taxonomy(
map={
1: ('nick_name', {'nick_name', 'a'}),
2: ('nick2', {'nick2', 'a', 'b', 'cc'})
})
self.assertNotEquals(tx1, tx2)
self.assertEquals(tx1.map, tx2.map)
tt1 = TaxyTool(tx1)
tt2 = TaxyTool(tx2)
self.assertEquals(tt1, tt2)
# fail with tt1._t.map '=='
tx2 = Taxonomy(
map={
1: ('nick_name', {'nick_name', 'as'}),
2: ('nick2', {'nick2', 'a', 'b', 'cc'})
})
tt1 = TaxyTool(tx1)
tt2 = TaxyTool(tx2)
self.assertNotEquals(tt1, tt2)
# Use the interface to build a complex one and test equality as they go in and out of sync...
tt1 = TaxyTool()
tt2 = TaxyTool()
tt1.add_taxonomy_set('a name', 'junk', '1', '2')
tt2.add_taxonomy_set('a name', 'junk', '1', '2')
self.assertEquals(tt1, tt2)
tt2.add_taxonomy_set('new_name', '1')
self.assertNotEquals(tt1, tt2)
tt1.extend_names_by_nick_name('a name', '3')
tt2.extend_names_by_nick_name('a name', '3')
tt1.add_taxonomy_set('new_name', '1')
self.assertEquals(tt1, tt2)
def test_taxonomy_set(self):
nick_name = 'nick_name'
a = 'a'
b = 'b'
c = 'c'
tc = TaxyTool()
tc.add_taxonomy_set(nick_name=nick_name)
self.assertEquals(tc.get_handles('a name'), {
-1,
})
self.assertRaises(KeyError, tc.get_names_by_handle, 5)
self.assertEquals(tc.get_names_by_handle(0), {
nick_name,
})
tc = TaxyTool()
tc.add_taxonomy_set(a)
self.assertEquals(tc.get_handle(a), 0)
self.assertEquals(tc.get_names_by_handle(0), {
a,
})
self.assertEquals(tc.get_names_by_nick_name(a), {
a,
})
tc = TaxyTool()
tc.add_taxonomy_set(a)
tc.add_taxonomy_set(b)
self.assertEquals(tc.get_handle(a), 0)
self.assertEquals(tc.get_names_by_handle(0), {
a,
})
self.assertEquals(tc.get_handle(b), 1)
self.assertEquals(tc.get_names_by_handle(1), {
b,
})
tc = TaxyTool()
tc.add_taxonomy_set(nick_name, a, b, c)
self.assertEquals(tc.get_handle(nick_name), 0)
self.assertEquals(tc.get_handles(a), {
0,
})
self.assertEquals(tc.get_handles(b), {
0,
})
self.assertEquals(tc.get_handles(c), {
0,
})
self.assertEquals(tc.get_names_by_handle(0), {
nick_name,
a,
b,
c,
})
self.assertEquals(tc.get_names_by_nick_name(nick_name), {
nick_name,
a,
b,
c,
})
def _setup_resources(self):
# TODO: some or all of this (or some variation) should move to DAMS'
# Build the test resources for the dataset
dams_cli = DataAcquisitionManagementServiceClient()
dpms_cli = DataProductManagementServiceClient()
rr_cli = ResourceRegistryServiceClient()
eda = ExternalDatasetAgent()
eda_id = dams_cli.create_external_dataset_agent(eda)
eda_inst = ExternalDatasetAgentInstance()
eda_inst_id = dams_cli.create_external_dataset_agent_instance(eda_inst, external_dataset_agent_id=eda_id)
# Create and register the necessary resources/objects
# Create DataProvider
dprov = ExternalDataProvider(institution=Institution(), contact=ContactInformation())
dprov.contact.name = 'Christopher Mueller'
dprov.contact.email = '*****@*****.**'
# Create DataSource
dsrc = DataSource(protocol_type='FILE', institution=Institution(), contact=ContactInformation())
dsrc.connection_params['base_data_url'] = ''
dsrc.contact.name='Tim Giguere'
dsrc.contact.email = '*****@*****.**'
# Create ExternalDataset
ds_name = 'ruv_test_dataset'
dset = ExternalDataset(name=ds_name, dataset_description=DatasetDescription(), update_description=UpdateDescription(), contact=ContactInformation())
dset.dataset_description.parameters['dataset_path'] = 'test_data/RDLi_SEAB_2011_08_24_1600.ruv'
dset.dataset_description.parameters['temporal_dimension'] = None
dset.dataset_description.parameters['zonal_dimension'] = None
dset.dataset_description.parameters['meridional_dimension'] = None
dset.dataset_description.parameters['vertical_dimension'] = None
dset.dataset_description.parameters['variables'] = [
]
# Create DataSourceModel
dsrc_model = DataSourceModel(name='ruv_model')
dsrc_model.model = 'RUV'
dsrc_model.data_handler_module = 'N/A'
dsrc_model.data_handler_class = 'N/A'
## Run everything through DAMS
ds_id = dams_cli.create_external_dataset(external_dataset=dset)
ext_dprov_id = dams_cli.create_external_data_provider(external_data_provider=dprov)
ext_dsrc_id = dams_cli.create_data_source(data_source=dsrc)
ext_dsrc_model_id = dams_cli.create_data_source_model(dsrc_model)
# Register the ExternalDataset
dproducer_id = dams_cli.register_external_data_set(external_dataset_id=ds_id)
# Or using each method
dams_cli.assign_data_source_to_external_data_provider(data_source_id=ext_dsrc_id, external_data_provider_id=ext_dprov_id)
dams_cli.assign_data_source_to_data_model(data_source_id=ext_dsrc_id, data_source_model_id=ext_dsrc_model_id)
dams_cli.assign_external_dataset_to_data_source(external_dataset_id=ds_id, data_source_id=ext_dsrc_id)
dams_cli.assign_external_dataset_to_agent_instance(external_dataset_id=ds_id, agent_instance_id=eda_inst_id)
# dams_cli.assign_external_data_agent_to_agent_instance(external_data_agent_id=self.eda_id, agent_instance_id=self.eda_inst_id)
# Generate the data product and associate it to the ExternalDataset
dprod = DataProduct(name='ruv_parsed_product', description='parsed ruv product')
dproduct_id = dpms_cli.create_data_product(data_product=dprod)
dams_cli.assign_data_product(input_resource_id=ds_id, data_product_id=dproduct_id, create_stream=True)
stream_id, assn = rr_cli.find_objects(subject=dproduct_id, predicate=PRED.hasStream, object_type=RT.Stream, id_only=True)
stream_id = stream_id[0]
log.info('Created resources: {0}'.format({'ExternalDataset':ds_id, 'ExternalDataProvider':ext_dprov_id, 'DataSource':ext_dsrc_id, 'DataSourceModel':ext_dsrc_model_id, 'DataProducer':dproducer_id, 'DataProduct':dproduct_id, 'Stream':stream_id}))
#CBM: Use CF standard_names
ttool = TaxyTool()
ttool.add_taxonomy_set('data','test data')
#CBM: Eventually, probably want to group this crap somehow - not sure how yet...
# Create the logger for receiving publications
self.create_stream_and_logger(name='ruv',stream_id=stream_id)
self.EDA_RESOURCE_ID = ds_id
self.EDA_NAME = ds_name
self.DVR_CONFIG['dh_cfg'] = {
'TESTING':True,
'stream_id':stream_id,
'external_dataset_res':dset,
'taxonomy':ttool.dump(),
'data_producer_id':dproducer_id,#CBM: Should this be put in the main body of the config - with mod & cls?
'max_records':20,
}
def _setup_resources(self):
# TODO: some or all of this (or some variation) should move to DAMS'
# Build the test resources for the dataset
dams_cli = DataAcquisitionManagementServiceClient()
dpms_cli = DataProductManagementServiceClient()
rr_cli = ResourceRegistryServiceClient()
eda = ExternalDatasetAgent()
eda_id = dams_cli.create_external_dataset_agent(eda)
eda_inst = ExternalDatasetAgentInstance()
eda_inst_id = dams_cli.create_external_dataset_agent_instance(eda_inst, external_dataset_agent_id=eda_id)
# Create and register the necessary resources/objects
# Create DataProvider
dprov = ExternalDataProvider(institution=Institution(), contact=ContactInformation())
dprov.contact.name = 'Christopher Mueller'
dprov.contact.email = '*****@*****.**'
# Create DataSource
dsrc = DataSource(protocol_type='FILE', institution=Institution(), contact=ContactInformation())
dsrc.connection_params['base_data_url'] = ''
dsrc.contact.name='Tim Giguere'
dsrc.contact.email = '*****@*****.**'
# Create ExternalDataset
ds_name = 'slocum_test_dataset'
dset = ExternalDataset(name=ds_name, dataset_description=DatasetDescription(), update_description=UpdateDescription(), contact=ContactInformation())
dset.dataset_description.parameters['dataset_path'] = 'test_data/ru05-2012-021-0-0-sbd.dat'
dset.dataset_description.parameters['temporal_dimension'] = None
dset.dataset_description.parameters['zonal_dimension'] = None
dset.dataset_description.parameters['meridional_dimension'] = None
dset.dataset_description.parameters['vertical_dimension'] = None
dset.dataset_description.parameters['variables'] = [
'c_wpt_y_lmc',
'sci_water_cond',
'm_y_lmc',
'u_hd_fin_ap_inflection_holdoff',
'sci_m_present_time',
'm_leakdetect_voltage_forward',
'sci_bb3slo_b660_scaled',
'c_science_send_all',
'm_gps_status',
'm_water_vx',
'm_water_vy',
'c_heading',
'sci_fl3slo_chlor_units',
'u_hd_fin_ap_gain',
'm_vacuum',
'u_min_water_depth',
'm_gps_lat',
'm_veh_temp',
'f_fin_offset',
'u_hd_fin_ap_hardover_holdoff',
'c_alt_time',
'm_present_time',
'm_heading',
'sci_bb3slo_b532_scaled',
'sci_fl3slo_cdom_units',
'm_fin',
'x_cycle_overrun_in_ms',
'sci_water_pressure',
'u_hd_fin_ap_igain',
'sci_fl3slo_phyco_units',
'm_battpos',
'sci_bb3slo_b470_scaled',
'm_lat',
'm_gps_lon',
'sci_ctd41cp_timestamp',
'm_pressure',
'c_wpt_x_lmc',
'c_ballast_pumped',
'x_lmc_xy_source',
'm_lon',
'm_avg_speed',
'sci_water_temp',
'u_pitch_ap_gain',
'm_roll',
'm_tot_num_inflections',
'm_x_lmc',
'u_pitch_ap_deadband',
'm_final_water_vy',
'm_final_water_vx',
'm_water_depth',
'm_leakdetect_voltage',
'u_pitch_max_delta_battpos',
'm_coulomb_amphr',
'm_pitch',
]
# Create DataSourceModel
dsrc_model = DataSourceModel(name='slocum_model')
dsrc_model.model = 'SLOCUM'
dsrc_model.data_handler_module = 'N/A'
dsrc_model.data_handler_class = 'N/A'
## Run everything through DAMS
ds_id = dams_cli.create_external_dataset(external_dataset=dset)
ext_dprov_id = dams_cli.create_external_data_provider(external_data_provider=dprov)
ext_dsrc_id = dams_cli.create_data_source(data_source=dsrc)
ext_dsrc_model_id = dams_cli.create_data_source_model(dsrc_model)
# Register the ExternalDataset
dproducer_id = dams_cli.register_external_data_set(external_dataset_id=ds_id)
# Or using each method
dams_cli.assign_data_source_to_external_data_provider(data_source_id=ext_dsrc_id, external_data_provider_id=ext_dprov_id)
dams_cli.assign_data_source_to_data_model(data_source_id=ext_dsrc_id, data_source_model_id=ext_dsrc_model_id)
dams_cli.assign_external_dataset_to_data_source(external_dataset_id=ds_id, data_source_id=ext_dsrc_id)
dams_cli.assign_external_dataset_to_agent_instance(external_dataset_id=ds_id, agent_instance_id=eda_inst_id)
# dams_cli.assign_external_data_agent_to_agent_instance(external_data_agent_id=self.eda_id, agent_instance_id=self.eda_inst_id)
# Generate the data product and associate it to the ExternalDataset
dprod = DataProduct(name='slocum_parsed_product', description='parsed slocum product')
dproduct_id = dpms_cli.create_data_product(data_product=dprod)
dams_cli.assign_data_product(input_resource_id=ds_id, data_product_id=dproduct_id, create_stream=True)
stream_id, assn = rr_cli.find_objects(subject=dproduct_id, predicate=PRED.hasStream, object_type=RT.Stream, id_only=True)
stream_id = stream_id[0]
log.info('Created resources: {0}'.format({'ExternalDataset':ds_id, 'ExternalDataProvider':ext_dprov_id, 'DataSource':ext_dsrc_id, 'DataSourceModel':ext_dsrc_model_id, 'DataProducer':dproducer_id, 'DataProduct':dproduct_id, 'Stream':stream_id}))
#CBM: Use CF standard_names
ttool = TaxyTool()
ttool.add_taxonomy_set('c_wpt_y_lmc'),
ttool.add_taxonomy_set('sci_water_cond'),
ttool.add_taxonomy_set('m_y_lmc'),
ttool.add_taxonomy_set('u_hd_fin_ap_inflection_holdoff'),
ttool.add_taxonomy_set('sci_m_present_time'),
ttool.add_taxonomy_set('m_leakdetect_voltage_forward'),
ttool.add_taxonomy_set('sci_bb3slo_b660_scaled'),
ttool.add_taxonomy_set('c_science_send_all'),
ttool.add_taxonomy_set('m_gps_status'),
ttool.add_taxonomy_set('m_water_vx'),
ttool.add_taxonomy_set('m_water_vy'),
ttool.add_taxonomy_set('c_heading'),
ttool.add_taxonomy_set('sci_fl3slo_chlor_units'),
ttool.add_taxonomy_set('u_hd_fin_ap_gain'),
ttool.add_taxonomy_set('m_vacuum'),
ttool.add_taxonomy_set('u_min_water_depth'),
ttool.add_taxonomy_set('m_gps_lat'),
ttool.add_taxonomy_set('m_veh_temp'),
ttool.add_taxonomy_set('f_fin_offset'),
ttool.add_taxonomy_set('u_hd_fin_ap_hardover_holdoff'),
ttool.add_taxonomy_set('c_alt_time'),
ttool.add_taxonomy_set('m_present_time'),
ttool.add_taxonomy_set('m_heading'),
ttool.add_taxonomy_set('sci_bb3slo_b532_scaled'),
ttool.add_taxonomy_set('sci_fl3slo_cdom_units'),
ttool.add_taxonomy_set('m_fin'),
ttool.add_taxonomy_set('x_cycle_overrun_in_ms'),
ttool.add_taxonomy_set('sci_water_pressure'),
ttool.add_taxonomy_set('u_hd_fin_ap_igain'),
ttool.add_taxonomy_set('sci_fl3slo_phyco_units'),
ttool.add_taxonomy_set('m_battpos'),
ttool.add_taxonomy_set('sci_bb3slo_b470_scaled'),
ttool.add_taxonomy_set('m_lat'),
ttool.add_taxonomy_set('m_gps_lon'),
ttool.add_taxonomy_set('sci_ctd41cp_timestamp'),
ttool.add_taxonomy_set('m_pressure'),
ttool.add_taxonomy_set('c_wpt_x_lmc'),
ttool.add_taxonomy_set('c_ballast_pumped'),
ttool.add_taxonomy_set('x_lmc_xy_source'),
ttool.add_taxonomy_set('m_lon'),
ttool.add_taxonomy_set('m_avg_speed'),
ttool.add_taxonomy_set('sci_water_temp'),
ttool.add_taxonomy_set('u_pitch_ap_gain'),
ttool.add_taxonomy_set('m_roll'),
ttool.add_taxonomy_set('m_tot_num_inflections'),
ttool.add_taxonomy_set('m_x_lmc'),
ttool.add_taxonomy_set('u_pitch_ap_deadband'),
ttool.add_taxonomy_set('m_final_water_vy'),
ttool.add_taxonomy_set('m_final_water_vx'),
ttool.add_taxonomy_set('m_water_depth'),
ttool.add_taxonomy_set('m_leakdetect_voltage'),
ttool.add_taxonomy_set('u_pitch_max_delta_battpos'),
ttool.add_taxonomy_set('m_coulomb_amphr'),
ttool.add_taxonomy_set('m_pitch'),
#CBM: Eventually, probably want to group this crap somehow - not sure how yet...
# Create the logger for receiving publications
self.create_stream_and_logger(name='slocum',stream_id=stream_id)
self.EDA_RESOURCE_ID = ds_id
self.EDA_NAME = ds_name
self.DVR_CONFIG['dh_cfg'] = {
'TESTING':True,
'stream_id':stream_id,
'external_dataset_res':dset,
'taxonomy':ttool.dump(),
'data_producer_id':dproducer_id,#CBM: Should this be put in the main body of the config - with mod & cls?
'max_records':20,
}
def test_activateDatasetAgent(self):
# Create ExternalDatasetModel
datsetModel_obj = IonObject(RT.ExternalDatasetModel, name='ExampleDatasetModel', description="ExampleDatasetModel", datset_type="FibSeries" )
try:
datasetModel_id = self.damsclient.create_external_dataset_model(datsetModel_obj)
except BadRequest as ex:
self.fail("failed to create new ExternalDatasetModel: %s" %ex)
log.debug("TestExternalDatasetAgentMgmt: new ExternalDatasetModel id = %s", str(datasetModel_id) )
# Create ExternalDatasetAgent
datasetAgent_obj = IonObject(RT.ExternalDatasetAgent, name='datasetagent007', description="datasetagent007", handler_module=EDA_MOD, handler_class=EDA_CLS )
try:
datasetAgent_id = self.damsclient.create_external_dataset_agent(datasetAgent_obj, datasetModel_id)
except BadRequest as ex:
self.fail("failed to create new ExternalDatasetAgent: %s" %ex)
log.debug("TestExternalDatasetAgentMgmt: new ExternalDatasetAgent id = %s", str(datasetAgent_id) )
# Create ExternalDataset
log.debug('TestExternalDatasetAgentMgmt: Create external dataset resource ')
extDataset_obj = IonObject(RT.ExternalDataset, name='ExtDataset', description="ExtDataset" )
try:
extDataset_id = self.damsclient.create_external_dataset(extDataset_obj, datasetModel_id)
except BadRequest as ex:
self.fail("failed to create new external dataset resource: %s" %ex)
log.debug("TestExternalDatasetAgentMgmt: new ExternalDataset id = %s ", str(extDataset_id))
#register the dataset as a data producer
dproducer_id = self.damsclient.register_external_data_set(extDataset_id)
# create a stream definition for the data from the ctd simulator
ctd_stream_def = SBE37_CDM_stream_definition()
ctd_stream_def_id = self.pubsubcli.create_stream_definition(container=ctd_stream_def)
log.debug("TestExternalDatasetAgentMgmt: new Stream Definition id = %s", str(ctd_stream_def_id))
log.debug("TestExternalDatasetAgentMgmt: Creating new data product with a stream definition")
dp_obj = IonObject(RT.DataProduct,name='eoi dataset data',description=' stream test')
try:
data_product_id1 = self.dpclient.create_data_product(dp_obj, ctd_stream_def_id)
except BadRequest as ex:
self.fail("failed to create new data product: %s" %ex)
log.debug("TestExternalDatasetAgentMgmt: new dp_id = %s", str(data_product_id1) )
self.damsclient.assign_data_product(input_resource_id=extDataset_id, data_product_id=data_product_id1)
self.dpclient.activate_data_product_persistence(data_product_id=data_product_id1, persist_data=True, persist_metadata=True)
# 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("TestExternalDatasetAgentMgmt: Data product streams1 = %s", str(stream_ids) )
stream_id = stream_ids[0]
# Build a taxonomy for the dataset
tx = TaxyTool()
tx.add_taxonomy_set('data', 'external_data')
# Augment the DVR_CONFIG with the necessary pieces
self.DVR_CONFIG['dh_cfg'] = {
'TESTING':True,
'stream_id':stream_id,#TODO: This should probably be a 'stream_config' dict with stream_name:stream_id members
'data_producer_id':dproducer_id,
# 'external_dataset_res':extDataset_obj, # Not needed - retrieved by EDA based on resource_id
'taxonomy':tx.dump(), #TODO: Currently does not support sets
'max_records':4,
}
# Create agent config.
self._stream_config = {}
agent_config = {
'driver_config' : self.DVR_CONFIG,
'stream_config' : self._stream_config,
'agent' : {'resource_id': EDA_RESOURCE_ID},
'test_mode' : True
}
extDatasetAgentInstance_obj = IonObject(RT.ExternalDatasetAgentInstance, name='DatasetAgentInstance', description="DatasetAgentInstance", dataset_driver_config = self.DVR_CONFIG, dataset_agent_config = agent_config)
extDatasetAgentInstance_id = self.damsclient.create_external_dataset_agent_instance(external_dataset_agent_instance=extDatasetAgentInstance_obj, external_dataset_agent_id=datasetAgent_id, external_dataset_id=extDataset_id)
log.debug("TestExternalDatasetAgentMgmt: Dataset agent instance obj: = %s", str(extDatasetAgentInstance_obj) )
log.debug("TestExternalDatasetAgentMgmt: Dataset agent instance id: = %s", str(extDatasetAgentInstance_id) )
#Check that the instance is currently not active
id, active = self.damsclient.retrieve_external_dataset_agent_instance(extDataset_id)
log.debug("TestExternalDatasetAgentMgmt: Dataset agent instance id: = %s active 1 = %s ", str(id), str(active) )
self.damsclient.start_external_dataset_agent_instance(extDatasetAgentInstance_id)
dataset_agent_instance_obj= self.damsclient.read_external_dataset_agent_instance(extDatasetAgentInstance_id)
log.debug("TestExternalDatasetAgentMgmt: Dataset agent instance obj: = %s", str(dataset_agent_instance_obj) )
# now the instance process should be active
id, active = self.damsclient.retrieve_external_dataset_agent_instance(extDataset_id)
log.debug("TestExternalDatasetAgentMgmt: Dataset agent instance id: = %s active 2 = %s ", str(id), str(active) )
# Start a resource agent client to talk with the instrument agent.
self._dsa_client = ResourceAgentClient(extDataset_id, process=FakeProcess())
print 'TestExternalDatasetAgentMgmt: got ia client %s', self._dsa_client
log.debug("TestExternalDatasetAgentMgmt: got dataset client %s", str(self._dsa_client))
# cmd=AgentCommand(command='initialize')
# _ = self._dsa_client.execute_agent(cmd)
#
# cmd = AgentCommand(command='go_active')
# _ = self._dsa_client.execute_agent(cmd)
#
# cmd = AgentCommand(command='run')
# _ = self._dsa_client.execute_agent(cmd)
#
# log.info('Send an unconstrained request for data (\'new data\')')
# cmd = AgentCommand(command='acquire_data')
# self._dsa_client.execute(cmd)
#
# log.info('Send a second unconstrained request for data (\'new data\'), should be rejected')
# cmd = AgentCommand(command='acquire_data')
# self._dsa_client.execute(cmd)
#
# cmd = AgentCommand(command='reset')
# _ = self._dsa_client.execute_agent(cmd)
# cmd = AgentCommand(command='get_current_state')
# retval = self._dsa_client.execute_agent(cmd)
# state = retval.result
# TODO: Think about what we really should be testing at this point
# The following is taken from ion.agents.data.test.test_external_dataset_agent.ExternalDatasetAgentTestBase.test_states()
# TODO: Do we also need to show data retrieval?
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.UNINITIALIZED)
cmd = AgentCommand(command='initialize')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.INACTIVE)
cmd = AgentCommand(command='go_active')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.IDLE)
cmd = AgentCommand(command='run')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.OBSERVATORY)
cmd = AgentCommand(command='pause')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.STOPPED)
cmd = AgentCommand(command='resume')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.OBSERVATORY)
cmd = AgentCommand(command='clear')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.IDLE)
cmd = AgentCommand(command='run')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.OBSERVATORY)
cmd = AgentCommand(command='pause')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.STOPPED)
cmd = AgentCommand(command='clear')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.IDLE)
cmd = AgentCommand(command='run')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.OBSERVATORY)
cmd = AgentCommand(command='reset')
retval = self._dsa_client.execute_agent(cmd)
cmd = AgentCommand(command='get_current_state')
retval = self._dsa_client.execute_agent(cmd)
state = retval.result
self.assertEqual(state, InstrumentAgentState.UNINITIALIZED)
#-------------------------------
# Deactivate InstrumentAgentInstance
#-------------------------------
self.damsclient.stop_external_dataset_agent_instance(extDatasetAgentInstance_id)
def _setup_resources(self):
# TODO: some or all of this (or some variation) should move to DAMS'
# Build the test resources for the dataset
dams_cli = DataAcquisitionManagementServiceClient()
dpms_cli = DataProductManagementServiceClient()
rr_cli = ResourceRegistryServiceClient()
eda = ExternalDatasetAgent()
eda_id = dams_cli.create_external_dataset_agent(eda)
eda_inst = ExternalDatasetAgentInstance()
eda_inst_id = dams_cli.create_external_dataset_agent_instance(eda_inst, external_dataset_agent_id=eda_id)
# Create and register the necessary resources/objects
# Create DataProvider
dprov = ExternalDataProvider(institution=Institution(), contact=ContactInformation())
dprov.contact.name = 'Christopher Mueller'
dprov.contact.email = '*****@*****.**'
# Create DataSource
dsrc = DataSource(protocol_type='DAP', institution=Institution(), contact=ContactInformation())
dsrc.connection_params['base_data_url'] = ''
dsrc.contact.name='Tim Giguere'
dsrc.contact.email = '*****@*****.**'
# Create ExternalDataset
ds_name = 'usgs_test_dataset'
dset = ExternalDataset(name=ds_name, dataset_description=DatasetDescription(), update_description=UpdateDescription(), contact=ContactInformation())
# The usgs.nc test dataset is a download of the R1 dataset found here:
# http://thredds-test.oceanobservatories.org/thredds/dodsC/ooiciData/E66B1A74-A684-454A-9ADE-8388C2C634E5.ncml
dset.dataset_description.parameters['dataset_path'] = 'test_data/usgs.nc'
dset.dataset_description.parameters['temporal_dimension'] = 'time'
dset.dataset_description.parameters['zonal_dimension'] = 'lon'
dset.dataset_description.parameters['meridional_dimension'] = 'lat'
dset.dataset_description.parameters['vertical_dimension'] = 'z'
dset.dataset_description.parameters['variables'] = [
'water_temperature',
'streamflow',
'water_temperature_bottom',
'water_temperature_middle',
'specific_conductance',
'data_qualifier',
]
# Create DataSourceModel
dsrc_model = DataSourceModel(name='dap_model')
dsrc_model.model = 'DAP'
dsrc_model.data_handler_module = 'N/A'
dsrc_model.data_handler_class = 'N/A'
## Run everything through DAMS
ds_id = dams_cli.create_external_dataset(external_dataset=dset)
ext_dprov_id = dams_cli.create_external_data_provider(external_data_provider=dprov)
ext_dsrc_id = dams_cli.create_data_source(data_source=dsrc)
ext_dsrc_model_id = dams_cli.create_data_source_model(dsrc_model)
# Register the ExternalDataset
dproducer_id = dams_cli.register_external_data_set(external_dataset_id=ds_id)
# Or using each method
dams_cli.assign_data_source_to_external_data_provider(data_source_id=ext_dsrc_id, external_data_provider_id=ext_dprov_id)
dams_cli.assign_data_source_to_data_model(data_source_id=ext_dsrc_id, data_source_model_id=ext_dsrc_model_id)
dams_cli.assign_external_dataset_to_data_source(external_dataset_id=ds_id, data_source_id=ext_dsrc_id)
dams_cli.assign_external_dataset_to_agent_instance(external_dataset_id=ds_id, agent_instance_id=eda_inst_id)
# dams_cli.assign_external_data_agent_to_agent_instance(external_data_agent_id=self.eda_id, agent_instance_id=self.eda_inst_id)
# Generate the data product and associate it to the ExternalDataset
dprod = DataProduct(name='usgs_parsed_product', description='parsed usgs product')
dproduct_id = dpms_cli.create_data_product(data_product=dprod)
dams_cli.assign_data_product(input_resource_id=ds_id, data_product_id=dproduct_id, create_stream=True)
stream_id, assn = rr_cli.find_objects(subject=dproduct_id, predicate=PRED.hasStream, object_type=RT.Stream, id_only=True)
stream_id = stream_id[0]
log.info('Created resources: {0}'.format({'ExternalDataset':ds_id, 'ExternalDataProvider':ext_dprov_id, 'DataSource':ext_dsrc_id, 'DataSourceModel':ext_dsrc_model_id, 'DataProducer':dproducer_id, 'DataProduct':dproduct_id, 'Stream':stream_id}))
#CBM: Use CF standard_names
ttool = TaxyTool()
ttool.add_taxonomy_set('time','time')
ttool.add_taxonomy_set('lon','longitude')
ttool.add_taxonomy_set('lat','latitude')
ttool.add_taxonomy_set('z','water depth')
ttool.add_taxonomy_set('water_temperature', 'average water temperature')
ttool.add_taxonomy_set('water_temperature_bottom','water temperature at bottom of water column')
ttool.add_taxonomy_set('water_temperature_middle', 'water temperature at middle of water column')
ttool.add_taxonomy_set('streamflow', 'flow velocity of stream')
ttool.add_taxonomy_set('specific_conductance', 'specific conductance of water')
ttool.add_taxonomy_set('data_qualifier','data qualifier flag')
ttool.add_taxonomy_set('coords','This group contains coordinate parameters')
ttool.add_taxonomy_set('data','This group contains data parameters')
# Create the logger for receiving publications
self.create_stream_and_logger(name='usgs',stream_id=stream_id)
self.EDA_RESOURCE_ID = ds_id
self.EDA_NAME = ds_name
self.DVR_CONFIG['dh_cfg'] = {
'TESTING':True,
'stream_id':stream_id,
'taxonomy':ttool.dump(),
'data_producer_id':dproducer_id,#CBM: Should this be put in the main body of the config - with mod & cls?
'max_records':4,
}
