def test_end_to_end(self):
dao = BiobankStoredSampleDao()
self.assertEquals(dao.count(), 0)
# Create 3 participants and pass their (random) IDs into sample rows.
summary_dao = ParticipantSummaryDao()
biobank_ids = []
participant_ids = []
for _ in xrange(3):
participant = self.participant_dao.insert(Participant())
summary_dao.insert(self.participant_summary(participant))
participant_ids.append(participant.participantId)
biobank_ids.append(participant.biobankId)
self.assertEquals(
summary_dao.get(
participant.participantId).numBaselineSamplesArrived, 0)
test1, test2, test3 = random.sample(_BASELINE_TESTS, 3)
samples_file = test_data.open_biobank_samples(*biobank_ids,
test1=test1,
test2=test2,
test3=test3)
input_filename = 'cloud%s.csv' % self._naive_utc_to_naive_central(
clock.CLOCK.now()).strftime(
biobank_samples_pipeline.INPUT_CSV_TIME_FORMAT)
self._write_cloud_csv(input_filename, samples_file.read())
biobank_samples_pipeline.upsert_from_latest_csv()
self.assertEquals(dao.count(), 3)
self._check_summary(participant_ids[0], test1, '2016-11-29T12:19:32')
self._check_summary(participant_ids[1], test2, '2016-11-29T12:38:58')
self._check_summary(participant_ids[2], test3, '2016-11-29T12:41:26')
def test_end_to_end(self):
dao = BiobankStoredSampleDao()
self.assertEquals(dao.count(), 0)
# Create 3 participants and pass their (random) IDs into sample rows.
summary_dao = ParticipantSummaryDao()
biobank_ids = []
participant_ids = []
nids = 16 # equal to the number of parent rows in 'biobank_samples_1.csv'
cids = 1 # equal to the number of child rows in 'biobank_samples_1.csv'
for _ in xrange(nids):
participant = self.participant_dao.insert(Participant())
summary_dao.insert(self.participant_summary(participant))
participant_ids.append(participant.participantId)
biobank_ids.append(participant.biobankId)
self.assertEquals(
summary_dao.get(
participant.participantId).numBaselineSamplesArrived, 0)
test_codes = random.sample(_BASELINE_TESTS, nids)
samples_file = test_data.open_biobank_samples(biobank_ids=biobank_ids,
tests=test_codes)
lines = samples_file.split('\n')[1:] # remove field name line
input_filename = 'cloud%s.csv' % self._naive_utc_to_naive_central(
clock.CLOCK.now()).strftime(
biobank_samples_pipeline.INPUT_CSV_TIME_FORMAT)
self._write_cloud_csv(input_filename, samples_file)
biobank_samples_pipeline.upsert_from_latest_csv()
self.assertEquals(dao.count(), nids - cids)
for x in range(0, nids):
cols = lines[x].split('\t')
if cols[10].strip(): # skip child sample
continue
# If status is 'In Prep', then sample confirmed timestamp should be empty
if cols[2] == 'In Prep':
self.assertEquals(len(cols[11]), 0)
else:
status = SampleStatus.RECEIVED
ts_str = cols[11]
# DA-814 - Participant Summary test status should be: Unset, Received or Disposed only.
# If sample is disposed, then check disposed timestamp, otherwise check confirmed timestamp.
# DA-871 - Only check status is disposed when reason code is a bad disposal.
if cols[2] == 'Disposed' and get_sample_status_enum_value(
cols[8]) > SampleStatus.UNKNOWN:
status = SampleStatus.DISPOSED
ts_str = cols[9]
ts = datetime.datetime.strptime(ts_str, '%Y/%m/%d %H:%M:%S')
self._check_summary(participant_ids[x], test_codes[x], ts,
status)