def fill_temporal_gap(self, dataset_id, gap_coverage_path=None, gap_coverage_id=None):
if gap_coverage_path is None and gap_coverage_id is None:
raise ValueError('Must specify either \'gap_coverage_path\' or \'gap_coverage_id\'')
if gap_coverage_path is None:
gap_coverage_path = self.get_coverage_path(gap_coverage_id)
from coverage_model import AbstractCoverage
gap_cov = AbstractCoverage.load(gap_coverage_path)
self.pause_ingestion(self.get_stream_id(dataset_id))
DatasetManagementService._splice_coverage(dataset_id, gap_cov)
def splice_coverage(self, dataset_id, coverage):
log.info('Splicing new coverage')
DatasetManagementService._splice_coverage(dataset_id, coverage)
def splice_coverage(self, dataset_id, coverage):
log.info('Splicing new coverage')
DatasetManagementService._splice_coverage(dataset_id, coverage)
def test_fill_temporal_gap(self):
from ion.services.dm.inventory.dataset_management_service import DatasetManagementService
data_product_id, dataset_id = self.make_ctd_data_product()
pdict = DatasetManagementService.get_parameter_dictionary_by_name('ctd_parsed_param_dict')
streamer = Streamer(data_product_id, interval=0.5)
self.addCleanup(streamer.stop)
self.use_monitor(dataset_id, samples=10)
streamer.stop()
gap_times = []
waiter = Event()
while not waiter.wait(1):
gap_times.append(time.time() + 2208988800)
if len(gap_times) == 10:
waiter.set()
# Simulate a gap by appending a new SimplexCoverage with times after the above gap
with DirectCoverageAccess() as dca:
dca.pause_ingestion(dataset_id)
with dca.get_read_only_coverage(dataset_id) as cov:
beforecovtimes = cov.get_time_values()
with DatasetManagementService._create_simplex_coverage(dataset_id, pdict, None, None) as scov:
scov.insert_timesteps(3)
now = time.time() + 2208988800
ts = [now, now + 1, now + 2]
scov.set_time_values(ts)
aftercovtimes = scov.get_time_values()
DatasetManagementService._splice_coverage(dataset_id, scov)
# Start streaming data again
streamer.start()
# Create the gap-fill coverage
with DatasetManagementService._create_simplex_coverage(dataset_id, pdict, None, None) as scov:
scov.insert_timesteps(len(gap_times))
scov.set_time_values(gap_times)
gap_cov_path = scov.persistence_dir
gapcovtimes = scov.get_time_values()
# Fill the gap and capture times to do some assertions
with DirectCoverageAccess() as dca:
with dca.get_read_only_coverage(dataset_id) as cov:
otimes = cov.get_time_values()
dca.fill_temporal_gap(dataset_id, gap_coverage_path=gap_cov_path)
with dca.get_read_only_coverage(dataset_id) as cov:
agtimes = cov.get_time_values()
self.use_monitor(dataset_id, samples=5)
with DirectCoverageAccess() as dca:
with dca.get_read_only_coverage(dataset_id) as cov:
ntimes = cov.get_time_values()
self.assertLess(len(otimes), len(agtimes))
self.assertLess(len(agtimes), len(ntimes))
bctl = len(beforecovtimes)
gctl = len(gapcovtimes)
actl = len(aftercovtimes)
np.testing.assert_array_equal(beforecovtimes, ntimes[:bctl])
np.testing.assert_array_equal(gapcovtimes, ntimes[bctl+1:bctl+gctl+1])
np.testing.assert_array_equal(aftercovtimes, ntimes[bctl+gctl+1:bctl+gctl+actl+1])
np.testing.assert_array_equal(agtimes, ntimes[:len(agtimes)])
def test_fill_temporal_gap(self):
from ion.services.dm.inventory.dataset_management_service import DatasetManagementService
data_product_id, dataset_id = self.make_ctd_data_product()
pdict = DatasetManagementService.get_parameter_dictionary_by_name(
'ctd_parsed_param_dict')
streamer = Streamer(data_product_id, interval=0.5)
self.addCleanup(streamer.stop)
self.use_monitor(dataset_id, samples=10)
streamer.stop()
gap_times = []
waiter = Event()
while not waiter.wait(1):
gap_times.append(time.time() + 2208988800)
if len(gap_times) == 10:
waiter.set()
# Simulate a gap by appending a new SimplexCoverage with times after the above gap
with DirectCoverageAccess() as dca:
dca.pause_ingestion(dataset_id)
with dca.get_read_only_coverage(dataset_id) as cov:
beforecovtimes = cov.get_time_values()
with DatasetManagementService._create_simplex_coverage(
dataset_id, pdict, None, None) as scov:
scov.insert_timesteps(3)
now = time.time() + 2208988800
ts = [now, now + 1, now + 2]
scov.set_time_values(ts)
aftercovtimes = scov.get_time_values()
DatasetManagementService._splice_coverage(dataset_id, scov)
# Start streaming data again
streamer.start()
# Create the gap-fill coverage
with DatasetManagementService._create_simplex_coverage(
dataset_id, pdict, None, None) as scov:
scov.insert_timesteps(len(gap_times))
scov.set_time_values(gap_times)
gap_cov_path = scov.persistence_dir
gapcovtimes = scov.get_time_values()
# Fill the gap and capture times to do some assertions
with DirectCoverageAccess() as dca:
with dca.get_read_only_coverage(dataset_id) as cov:
otimes = cov.get_time_values()
dca.fill_temporal_gap(dataset_id, gap_coverage_path=gap_cov_path)
with dca.get_read_only_coverage(dataset_id) as cov:
agtimes = cov.get_time_values()
self.use_monitor(dataset_id, samples=5)
with DirectCoverageAccess() as dca:
with dca.get_read_only_coverage(dataset_id) as cov:
ntimes = cov.get_time_values()
self.assertLess(len(otimes), len(agtimes))
self.assertLess(len(agtimes), len(ntimes))
bctl = len(beforecovtimes)
gctl = len(gapcovtimes)
actl = len(aftercovtimes)
np.testing.assert_array_equal(beforecovtimes, ntimes[:bctl])
np.testing.assert_array_equal(gapcovtimes,
ntimes[bctl + 1:bctl + gctl + 1])
np.testing.assert_array_equal(
aftercovtimes, ntimes[bctl + gctl + 1:bctl + gctl + actl + 1])
np.testing.assert_array_equal(agtimes, ntimes[:len(agtimes)])
