def testResetActiveTransportation(self):
self.testAutomaticRewardActiveTransportation()
#Get user data before resetting
user_before = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertEqual(int(user_before['bike_count']), 1500)
habits_before = proxy.habiticaProxy(self.testUUID, 'GET', "/api/v3/tasks/user?type=habits", None).json()
bike_pts_before = [habit['history'] for habit in habits_before['data'] if habit['text'] == self.new_task_text]
#Reset
reset_ts = arrow.Arrow(2016,5,3,9).timestamp
autocheck.reset_all_tasks_to_ts(self.testUUID, reset_ts, is_dry_run=False)
# Check timestamp
user_after = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertEqual(int(user_after['last_timestamp']), reset_ts)
# Re-score points
# This should give points for the second and third sections
# So I expect to see an additional distance of 2.5 + 3.5 km = 6km
autocheck.give_points_for_all_tasks(self.testUUID)
#Get user data after scoring and check results
# We already had bike_count = 1500, and this is a round number, so it
# should continue to be 1500
user_after = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertEqual(int(user_after['bike_count']), 0)
# and we should have 6 points more?
habits_after = proxy.habiticaProxy(self.testUUID, 'GET', "/api/v3/tasks/user?type=habits", None).json()
bike_pts_after = [habit['history'] for habit in habits_after['data'] if habit['text'] == self.new_task_text]
logging.debug("bike_pts_after = %s" % (len(bike_pts_after[0]) - len(bike_pts_before[0])))
self.assertTrue(len(bike_pts_after[0]) - len(bike_pts_before[0]) == 3)
def testResetActiveTransportation(self):
self.testAutomaticRewardActiveTransportation()
#Get user data before resetting
user_before = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertEqual(int(user_before['bike_count']), 1500)
habits_before = proxy.habiticaProxy(self.testUUID, 'GET',
"/api/v3/tasks/user?type=habits",
None).json()
bike_pts_before = [
habit['history'] for habit in habits_before['data']
if habit['text'] == self.new_task_text
]
#Reset
reset_ts = arrow.Arrow(2016, 5, 3, 9).timestamp
autocheck.reset_all_tasks_to_ts(self.testUUID,
reset_ts,
is_dry_run=False)
# Check timestamp
user_after = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertEqual(int(user_after['last_timestamp']), reset_ts)
# Re-score points
# This should give points for the second and third sections
# So I expect to see an additional distance of 2.5 + 3.5 km = 6km
autocheck.give_points_for_all_tasks(self.testUUID)
#Get user data after scoring and check results
# We already had bike_count = 1500, and this is a round number, so it
# should continue to be 1500
user_after = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertEqual(int(user_after['bike_count']), 0)
# and we should have 6 points more?
habits_after = proxy.habiticaProxy(self.testUUID, 'GET',
"/api/v3/tasks/user?type=habits",
None).json()
bike_pts_after = [
habit['history'] for habit in habits_after['data']
if habit['text'] == self.new_task_text
]
logging.debug("bike_pts_after = %s" %
(len(bike_pts_after[0]) - len(bike_pts_before[0])))
self.assertTrue(len(bike_pts_after[0]) - len(bike_pts_before[0]) == 3)
def testAutomaticRewardActiveTransportation(self):
# Create a task that we can retrieve later
self.new_task_text = randomGen()
new_habit = {
'type':
"habit",
'text':
self.new_task_text,
'notes':
'AUTOCHECK: {"mapper": "active_distance",'
'"args": {"walk_scale": 1000, "bike_scale": 3000}}'
}
habit_id = proxy.create_habit(self.testUUID, new_habit)
self.dummy_task = enehat.Task()
self.dummy_task.task_id = habit_id
logging.debug("in testAutomaticRewardActiveTransportation,"
"the new habit id is = %s and task is %s" %
(habit_id, self.dummy_task))
#Create test data -- code copied from TestTimeGrouping
key = (2016, 5, 3)
test_section_list = []
#
# Since PST is UTC-7, all of these will be in the same UTC day
# 13:00, 17:00, 21:00
# so we expect the local date and UTC bins to be the same
test_section_list.append(
self._createTestSection(
arrow.Arrow(2016, 5, 3, 6, tzinfo=tz.gettz(PST)), PST))
test_section_list.append(
self._createTestSection(
arrow.Arrow(2016, 5, 3, 10, tzinfo=tz.gettz(PST)), PST))
test_section_list.append(
self._createTestSection(
arrow.Arrow(2016, 5, 3, 14, tzinfo=tz.gettz(PST)), PST))
self._fillModeDistanceDuration(test_section_list)
#logging.debug("durations = %s" % [s.data.duration for s in test_section_list])
summary_ts = earmt.group_by_timestamp(
self.testUUID,
arrow.Arrow(2016, 5, 1).timestamp,
arrow.Arrow(2016, 6, 1).timestamp, None, [earmts.get_distance])
logging.debug(
"in testAutomaticRewardActiveTransportation, result = %s" %
summary_ts)
#Get user data before scoring
user_before = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertIsNone(user_before)
# Needed to work, otherwise sections from may won't show up in the query!
modification = {
"last_timestamp": arrow.Arrow(2016, 5, 1).timestamp,
"bike_count": 0,
"walk_count": 0
}
autocheck.save_task_state(self.testUUID, self.dummy_task, modification)
user_before = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertEqual(int(user_before['bike_count']), 0)
habits_before = proxy.habiticaProxy(self.testUUID, 'GET',
"/api/v3/tasks/user?type=habits",
None).json()
bike_pts_before = [
habit['history'] for habit in habits_before['data']
if habit['text'] == self.new_task_text
]
#Score points
autocheck.give_points_for_all_tasks(self.testUUID)
#Get user data after scoring and check results
user_after = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertEqual(int(user_after['bike_count']), 1500)
habits_after = proxy.habiticaProxy(self.testUUID, 'GET',
"/api/v3/tasks/user?type=habits",
None).json()
bike_pts_after = [
habit['history'] for habit in habits_after['data']
if habit['text'] == self.new_task_text
]
self.assertTrue(len(bike_pts_after[0]) - len(bike_pts_before[0]) == 2)
def testAutomaticRewardActiveTransportation(self):
# Create a task that we can retrieve later
self.new_task_text = randomGen()
new_habit = {'type': "habit", 'text': self.new_task_text,
'notes': 'AUTOCHECK: {"mapper": "active_distance",'
'"args": {"walk_scale": 1000, "bike_scale": 3000}}'}
habit_id = proxy.create_habit(self.testUUID, new_habit)
self.dummy_task = enehat.Task()
self.dummy_task.task_id = habit_id
logging.debug("in testAutomaticRewardActiveTransportation,"
"the new habit id is = %s and task is %s" % (habit_id, self.dummy_task))
#Create test data -- code copied from TestTimeGrouping
key = (2016, 5, 3)
test_section_list = []
#
# Since PST is UTC-7, all of these will be in the same UTC day
# 13:00, 17:00, 21:00
# so we expect the local date and UTC bins to be the same
test_section_list.append(
self._createTestSection(arrow.Arrow(2016,5,3,6, tzinfo=tz.gettz(PST)),
PST))
test_section_list.append(
self._createTestSection(arrow.Arrow(2016,5,3,10, tzinfo=tz.gettz(PST)),
PST))
test_section_list.append(
self._createTestSection(arrow.Arrow(2016,5,3,14, tzinfo=tz.gettz(PST)),
PST))
self._fillModeDistanceDuration(test_section_list)
#logging.debug("durations = %s" % [s.data.duration for s in test_section_list])
summary_ts = earmt.group_by_timestamp(self.testUUID,
arrow.Arrow(2016,5,1).timestamp,
arrow.Arrow(2016,6,1).timestamp,
None, [earmts.get_distance])
logging.debug("in testAutomaticRewardActiveTransportation, result = %s" % summary_ts)
#Get user data before scoring
user_before = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertIsNone(user_before)
# Needed to work, otherwise sections from may won't show up in the query!
modification = {"last_timestamp": arrow.Arrow(2016,5,1).timestamp, "bike_count": 0, "walk_count":0}
autocheck.save_task_state(self.testUUID, self.dummy_task, modification)
user_before = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertEqual(int(user_before['bike_count']), 0)
habits_before = proxy.habiticaProxy(self.testUUID, 'GET', "/api/v3/tasks/user?type=habits", None).json()
bike_pts_before = [habit['history'] for habit in habits_before['data'] if habit['text'] == self.new_task_text]
#Score points
autocheck.give_points_for_all_tasks(self.testUUID)
#Get user data after scoring and check results
user_after = autocheck.get_task_state(self.testUUID, self.dummy_task)
self.assertEqual(int(user_after['bike_count']),1500)
habits_after = proxy.habiticaProxy(self.testUUID, 'GET', "/api/v3/tasks/user?type=habits", None).json()
bike_pts_after = [habit['history'] for habit in habits_after['data'] if habit['text'] == self.new_task_text]
self.assertTrue(len(bike_pts_after[0]) - len(bike_pts_before[0]) == 2)
def run_intake_pipeline_for_user(uuid):
uh = euah.UserCacheHandler.getUserCacheHandler(uuid)
with ect.Timer() as uct:
logging.info("*" * 10 + "UUID %s: moving to long term" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: moving to long term" % uuid + "*" * 10)
uh.moveToLongTerm()
esds.store_pipeline_time(uuid, ecwp.PipelineStages.USERCACHE.name,
time.time(), uct.elapsed)
# Hack until we delete these spurious entries
# https://github.com/e-mission/e-mission-server/issues/407#issuecomment-2484868
if edb.get_timeseries_db().find({"user_id": uuid}).count() == 0:
logging.debug("Found no entries for %s, skipping" % uuid)
return
with ect.Timer() as aft:
logging.info("*" * 10 + "UUID %s: filter accuracy if needed" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: filter accuracy if needed" % uuid + "*" * 10)
eaicf.filter_accuracy(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.ACCURACY_FILTERING.name,
time.time(), aft.elapsed)
with ect.Timer() as tst:
logging.info("*" * 10 + "UUID %s: segmenting into trips" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: segmenting into trips" % uuid + "*" * 10)
eaist.segment_current_trips(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.TRIP_SEGMENTATION.name,
time.time(), tst.elapsed)
with ect.Timer() as sst:
logging.info("*" * 10 + "UUID %s: segmenting into sections" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: segmenting into sections" % uuid + "*" * 10)
eaiss.segment_current_sections(uuid)
esds.store_pipeline_time(uuid,
ecwp.PipelineStages.SECTION_SEGMENTATION.name,
time.time(), sst.elapsed)
with ect.Timer() as jst:
logging.info("*" * 10 + "UUID %s: smoothing sections" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: smoothing sections" % uuid + "*" * 10)
eaicl.filter_current_sections(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.JUMP_SMOOTHING.name,
time.time(), jst.elapsed)
with ect.Timer() as crt:
logging.info("*" * 10 +
"UUID %s: cleaning and resampling timeline" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: cleaning and resampling timeline" % uuid + "*" * 10)
eaicr.clean_and_resample(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.CLEAN_RESAMPLING.name,
time.time(), crt.elapsed)
with ect.Timer() as act:
logging.info(
"*" * 10 +
"UUID %s: checking active mode trips to autocheck habits" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: checking active mode trips to autocheck habits" % uuid +
"*" * 10)
autocheck.give_points_for_all_tasks(uuid)
esds.store_pipeline_time(uuid, "AUTOCHECK_POINTS", time.time(),
act.elapsed)
with ect.Timer() as ogt:
logging.info("*" * 10 + "UUID %s: storing views to cache" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: storing views to cache" % uuid + "*" * 10)
uh.storeViewsToCache()
esds.store_pipeline_time(uuid, ecwp.PipelineStages.OUTPUT_GEN.name,
time.time(), ogt.elapsed)
def run_intake_pipeline_for_user(uuid):
uh = euah.UserCacheHandler.getUserCacheHandler(uuid)
with ect.Timer() as uct:
logging.info("*" * 10 + "UUID %s: moving to long term" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: moving to long term" % uuid + "*" * 10)
uh.moveToLongTerm()
esds.store_pipeline_time(uuid, ecwp.PipelineStages.USERCACHE.name,
time.time(), uct.elapsed)
# Hack until we delete these spurious entries
# https://github.com/e-mission/e-mission-server/issues/407#issuecomment-2484868
# Hack no longer works after the stats are in the timeseries because
# every user, even really old ones, have the pipeline run for them,
# which inserts pipeline_time stats.
# Let's strip out users who only have pipeline_time entries in the timeseries
# I wonder if this (distinct versus count) is the reason that the pipeline has
# become so much slower recently. Let's try to actually delete the
# spurious entries or at least mark them as obsolete and see if that helps.
if edb.get_timeseries_db().find({
"user_id": uuid
}).distinct("metadata.key") == ["stats/pipeline_time"]:
logging.debug("Found no entries for %s, skipping" % uuid)
return
with ect.Timer() as aft:
logging.info("*" * 10 + "UUID %s: filter accuracy if needed" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: filter accuracy if needed" % uuid + "*" * 10)
eaicf.filter_accuracy(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.ACCURACY_FILTERING.name,
time.time(), aft.elapsed)
with ect.Timer() as tst:
logging.info("*" * 10 + "UUID %s: segmenting into trips" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: segmenting into trips" % uuid + "*" * 10)
eaist.segment_current_trips(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.TRIP_SEGMENTATION.name,
time.time(), tst.elapsed)
with ect.Timer() as sst:
logging.info("*" * 10 + "UUID %s: segmenting into sections" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: segmenting into sections" % uuid + "*" * 10)
eaiss.segment_current_sections(uuid)
esds.store_pipeline_time(uuid,
ecwp.PipelineStages.SECTION_SEGMENTATION.name,
time.time(), sst.elapsed)
with ect.Timer() as jst:
logging.info("*" * 10 + "UUID %s: smoothing sections" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: smoothing sections" % uuid + "*" * 10)
eaicl.filter_current_sections(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.JUMP_SMOOTHING.name,
time.time(), jst.elapsed)
with ect.Timer() as crt:
logging.info("*" * 10 +
"UUID %s: cleaning and resampling timeline" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: cleaning and resampling timeline" % uuid + "*" * 10)
eaicr.clean_and_resample(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.CLEAN_RESAMPLING.name,
time.time(), crt.elapsed)
with ect.Timer() as crt:
logging.info("*" * 10 +
"UUID %s: inferring transportation mode" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: inferring transportation mode" % uuid + "*" * 10)
eacimp.predict_mode(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.MODE_INFERENCE.name,
time.time(), crt.elapsed)
with ect.Timer() as act:
logging.info(
"*" * 10 +
"UUID %s: checking active mode trips to autocheck habits" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: checking active mode trips to autocheck habits" % uuid +
"*" * 10)
autocheck.give_points_for_all_tasks(uuid)
esds.store_pipeline_time(uuid, "AUTOCHECK_POINTS", time.time(),
act.elapsed)
with ect.Timer() as ogt:
logging.info("*" * 10 + "UUID %s: storing views to cache" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: storing views to cache" % uuid + "*" * 10)
uh.storeViewsToCache()
esds.store_pipeline_time(uuid, ecwp.PipelineStages.OUTPUT_GEN.name,
time.time(), ogt.elapsed)
def run_intake_pipeline_for_user(uuid):
uh = euah.UserCacheHandler.getUserCacheHandler(uuid)
with ect.Timer() as uct:
logging.info("*" * 10 + "UUID %s: moving to long term" % uuid + "*" * 10)
print(str(arrow.now()) + "*" * 10 + "UUID %s: moving to long term" % uuid + "*" * 10)
uh.moveToLongTerm()
esds.store_pipeline_time(uuid, ecwp.PipelineStages.USERCACHE.name,
time.time(), uct.elapsed)
# Hack until we delete these spurious entries
# https://github.com/e-mission/e-mission-server/issues/407#issuecomment-2484868
# Hack no longer works after the stats are in the timeseries because
# every user, even really old ones, have the pipeline run for them,
# which inserts pipeline_time stats.
# Let's strip out users who only have pipeline_time entries in the timeseries
# I wonder if this (distinct versus count) is the reason that the pipeline has
# become so much slower recently. Let's try to actually delete the
# spurious entries or at least mark them as obsolete and see if that helps.
if edb.get_timeseries_db().find({"user_id": uuid}).distinct("metadata.key") == ["stats/pipeline_time"]:
logging.debug("Found no entries for %s, skipping" % uuid)
return
with ect.Timer() as aft:
logging.info("*" * 10 + "UUID %s: filter accuracy if needed" % uuid + "*" * 10)
print(str(arrow.now()) + "*" * 10 + "UUID %s: filter accuracy if needed" % uuid + "*" * 10)
eaicf.filter_accuracy(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.ACCURACY_FILTERING.name,
time.time(), aft.elapsed)
with ect.Timer() as tst:
logging.info("*" * 10 + "UUID %s: segmenting into trips" % uuid + "*" * 10)
print(str(arrow.now()) + "*" * 10 + "UUID %s: segmenting into trips" % uuid + "*" * 10)
eaist.segment_current_trips(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.TRIP_SEGMENTATION.name,
time.time(), tst.elapsed)
with ect.Timer() as sst:
logging.info("*" * 10 + "UUID %s: segmenting into sections" % uuid + "*" * 10)
print(str(arrow.now()) + "*" * 10 + "UUID %s: segmenting into sections" % uuid + "*" * 10)
eaiss.segment_current_sections(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.SECTION_SEGMENTATION.name,
time.time(), sst.elapsed)
with ect.Timer() as jst:
logging.info("*" * 10 + "UUID %s: smoothing sections" % uuid + "*" * 10)
print(str(arrow.now()) + "*" * 10 + "UUID %s: smoothing sections" % uuid + "*" * 10)
eaicl.filter_current_sections(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.JUMP_SMOOTHING.name,
time.time(), jst.elapsed)
with ect.Timer() as crt:
logging.info("*" * 10 + "UUID %s: cleaning and resampling timeline" % uuid + "*" * 10)
print(str(arrow.now()) + "*" * 10 + "UUID %s: cleaning and resampling timeline" % uuid + "*" * 10)
eaicr.clean_and_resample(uuid)
esds.store_pipeline_time(uuid, ecwp.PipelineStages.CLEAN_RESAMPLING.name,
time.time(), crt.elapsed)
with ect.Timer() as act:
logging.info("*" * 10 + "UUID %s: checking active mode trips to autocheck habits" % uuid + "*" * 10)
print(str(arrow.now()) + "*" * 10 + "UUID %s: checking active mode trips to autocheck habits" % uuid + "*" * 10)
autocheck.give_points_for_all_tasks(uuid)
esds.store_pipeline_time(uuid, "AUTOCHECK_POINTS",
time.time(), act.elapsed)
with ect.Timer() as ogt:
logging.info("*" * 10 + "UUID %s: storing views to cache" % uuid + "*" * 10)
print(str(arrow.now()) + "*" * 10 + "UUID %s: storing views to cache" % uuid + "*" * 10)
uh.storeViewsToCache()
esds.store_pipeline_time(uuid, ecwp.PipelineStages.OUTPUT_GEN.name,
time.time(), ogt.elapsed)
def run_intake_pipeline_for_user(uuid):
uh = euah.UserCacheHandler.getUserCacheHandler(uuid)
logging.info("*" * 10 + "UUID %s: moving to long term" % uuid + "*" * 10)
print(
str(arrow.now()) + "*" * 10 + "UUID %s: moving to long term" % uuid +
"*" * 10)
uh.moveToLongTerm()
# Hack until we delete these spurious entries
# https://github.com/e-mission/e-mission-server/issues/407#issuecomment-2484868
if edb.get_timeseries_db().find({"user_id": uuid}).count() == 0:
logging.debug("Found no entries for %s, skipping" % uuid)
return
logging.info("*" * 10 + "UUID %s: filter accuracy if needed" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: filter accuracy if needed" % uuid + "*" * 10)
eaicf.filter_accuracy(uuid)
logging.info("*" * 10 + "UUID %s: segmenting into trips" % uuid + "*" * 10)
print(
str(arrow.now()) + "*" * 10 + "UUID %s: segmenting into trips" % uuid +
"*" * 10)
eaist.segment_current_trips(uuid)
logging.info("*" * 10 + "UUID %s: segmenting into sections" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: segmenting into sections" % uuid + "*" * 10)
eaiss.segment_current_sections(uuid)
logging.info("*" * 10 + "UUID %s: smoothing sections" % uuid + "*" * 10)
print(
str(arrow.now()) + "*" * 10 + "UUID %s: smoothing sections" % uuid +
"*" * 10)
eaicl.filter_current_sections(uuid)
logging.info("*" * 10 +
"UUID %s: cleaning and resampling timeline" % uuid + "*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: cleaning and resampling timeline" % uuid + "*" * 10)
eaicr.clean_and_resample(uuid)
logging.info("*" * 10 +
"UUID %s: checking active mode trips to autocheck habits" %
uuid + "*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: checking active mode trips to autocheck habits" % uuid +
"*" * 10)
autocheck.give_points_for_all_tasks(uuid)
logging.info("*" * 10 + "UUID %s: storing views to cache" % uuid +
"*" * 10)
print(
str(arrow.now()) + "*" * 10 +
"UUID %s: storing views to cache" % uuid + "*" * 10)
uh.storeViewsToCache()