def test_metrics_update(self):
self._create_data()
with FakeClock(TIME):
PublicMetricsExport.export('123')
aggs1 = [a.asdict() for a in AggregateMetricsDao().get_all()]
with FakeClock(TIME2):
PublicMetricsExport.export('123')
aggs2 = [a.asdict() for a in AggregateMetricsDao().get_all()]
self.assertEquals(TIME2, MetricSetDao().get('123').lastModified)
self.assertEquals(aggs1, aggs2)
def test_metrics_redaction(self):
self._create_data()
with FakeClock(TIME):
PublicMetricsExport.export('123')
# Withdraw particpant.
pdao = ParticipantDao()
p1 = pdao.get(1)
p1.withdrawalStatus = WithdrawalStatus.NO_USE
pdao.update(p1)
PublicMetricsExport.export('123')
self.assert_total_count_per_key(2) # now, 2 qualified participants
def recalculate_public_metrics():
logging.info('generating public metrics')
aggs = PublicMetricsExport.export(LIVE_METRIC_SET_ID)
client_aggs = AggregateMetricsDao.to_client_json(aggs)
# summing all counts for one metric yields a total qualified participant count
participant_count = 0
if len(client_aggs) > 0:
participant_count = sum([a['count'] for a in client_aggs[0]['values']])
logging.info('persisted public metrics: {} aggregations over '
'{} participants'.format(len(client_aggs), participant_count))
# Same format returned by the metric sets API.
return json.dumps({'metrics': client_aggs})
def test_metrics_export(self):
self._create_data()
with FakeClock(TIME):
PublicMetricsExport.export('123')
self.assert_total_count_per_key(3) # 3 qualified participants