def clearRelatedDb(self):
edb.get_timeseries_db().remove({"user_id": self.androidUUID})
edb.get_analysis_timeseries_db().remove({"user_id": self.androidUUID})
edb.get_pipeline_state_db().remove({"user_id": self.androidUUID})
edb.get_timeseries_db().remove({"user_id": self.iosUUID})
edb.get_analysis_timeseries_db().remove({"user_id": self.iosUUID})
edb.get_pipeline_state_db().remove({"user_id": self.iosUUID})
def testEmptyCall(self):
# Check call to the entire filter accuracy with a zero length timeseries
import emission.core.get_database as edb
edb.get_timeseries_db().remove({"user_id": self.testUUID})
# We expect that this should not throw
eaicf.filter_accuracy(self.testUUID)
self.assertEqual(len(self.ts.get_data_df("background/location")), 0)
def move_all_filters_to_data():
for entry in edb.get_timeseries_db().find():
if "filter" in entry["metadata"]:
curr_filter = entry["metadata"]["filter"]
if is_location_entry(entry):
entry["data"]["filter"] = curr_filter
logging.debug("for entry %s, found key %s, moved filter %s into data" %
(entry["_id"], get_curr_key(entry), curr_filter))
# For all cases, including the location one, we want to delete the filter from metadata
del entry["metadata"]["filter"]
edb.get_timeseries_db().save(entry)
logging.debug("for entry %s, for key %s, deleted filter %s from metadata" %
(entry["_id"], get_curr_key(entry), curr_filter))
else:
pass
# logging.warning("No filter found for entry %s, skipping" % entry)
if "filter" not in entry["data"] and is_location_entry(entry):
# This must be an entry from before the time that we started sending
# entries to the server. At that time, we only sent time entries,
# so set it to time in this case
entry["data"]["filter"] = "time"
logging.debug("No entry found in either data or metadata, for key %s setting to 'time'" % entry["metadata"]["key"])
edb.get_timeseries_db().save(entry)
def tearDown(self):
edb.get_timeseries_db().remove({"user_id": self.testUUID})
edb.get_analysis_timeseries_db().remove({"user_id": self.testUUID})
edb.get_timeseries_db().remove({"user_id": "new_fake"})
edb.get_analysis_timeseries_db().remove({"user_id": "new_fake"})
edb.get_common_trip_db().drop()
edb.get_common_place_db().drop()
def tearDown(self):
edb.get_timeseries_db().remove({"user_id": self.androidUUID})
edb.get_timeseries_db().remove({"user_id": self.iosUUID})
edb.get_usercache_db().remove({"user_id": self.androidUUID})
edb.get_usercache_db().remove({"user_id": self.iosUUID})
edb.get_place_db().remove()
edb.get_trip_new_db().remove()
def clearRelatedDb(self):
edb.get_timeseries_db().remove()
edb.get_place_db().remove()
edb.get_stop_db().remove()
edb.get_trip_new_db().remove()
edb.get_section_new_db().remove()
def clearRelatedDb(self):
edb.get_timeseries_db().delete_many({"user_id": self.testUUID})
edb.get_analysis_timeseries_db().delete_many({"user_id": self.testUUID})
edb.get_pipeline_state_db().delete_many({"user_id": self.testUUID})
edb.get_timeseries_db().delete_many({"user_id": self.testUUID1})
edb.get_analysis_timeseries_db().delete_many({"user_id": self.testUUID1})
edb.get_pipeline_state_db().delete_many({"user_id": self.testUUID1})
def clearRelatedDb(self):
edb.get_timeseries_db().remove({"user_id": self.androidUUID})
edb.get_analysis_timeseries_db().remove({"user_id": self.androidUUID})
edb.get_pipeline_state_db().remove({"user_id": self.androidUUID})
edb.get_timeseries_db().remove({"user_id": self.iosUUID})
edb.get_analysis_timeseries_db().remove({"user_id": self.iosUUID})
edb.get_pipeline_state_db().remove({"user_id": self.iosUUID})
def tearDown(self):
edb.get_timeseries_db().remove({"user_id": self.androidUUID})
edb.get_timeseries_db().remove({"user_id": self.iosUUID})
edb.get_usercache_db().remove({"user_id": self.androidUUID})
edb.get_usercache_db().remove({"user_id": self.iosUUID})
edb.get_analysis_timeseries_db().remove({"user_id": self.androidUUID})
edb.get_analysis_timeseries_db().remove({"user_id": self.iosUUID})
def getPublicData():
ids = request.json['phone_ids']
all_uuids = map(lambda id: UUID(id), ids)
uuids = [uuid for uuid in all_uuids if uuid in estag.TEST_PHONE_IDS]
from_ts = request.query.from_ts
to_ts = request.query.to_ts
time_range = estt.TimeQuery("metadata.write_ts", float(from_ts),
float(to_ts))
time_query = time_range.get_query()
user_queries = map(lambda id: {'user_id': id}, uuids)
for q in user_queries:
q.update(time_query)
num_entries = map(lambda q: edb.get_timeseries_db().find(q).count(),
user_queries)
total_entries = sum(num_entries)
logging.debug("Total entries requested: %d" % total_entries)
threshold = 200000
if total_entries > threshold:
data_list = None
else:
data_list = map(
lambda q: list(edb.get_timeseries_db().find(q).sort(
"metadata.write_ts")), user_queries)
return {'phone_data': data_list}
def testEmptyCall(self):
# Check call to the entire filter accuracy with a zero length timeseries
import emission.core.get_database as edb
edb.get_timeseries_db().remove({"user_id": self.testUUID})
# We expect that this should not throw
eaicf.filter_accuracy(self.testUUID)
self.assertEqual(len(self.ts.get_data_df("background/location")), 0)
def clearRelatedDb(self):
edb.get_timeseries_db().remove({'user_id': self.testUUID})
edb.get_place_db().remove({'user_id': self.testUUID})
edb.get_stop_db().remove({'user_id': self.testUUID})
edb.get_trip_new_db().remove({'user_id': self.testUUID})
edb.get_section_new_db().remove({'user_id': self.testUUID})
def clearRelatedDb(self):
edb.get_timeseries_db().remove({"user_id": {"$in": self.testUUIDList}})
edb.get_analysis_timeseries_db().remove(
{"user_id": {
"$in": self.testUUIDList
}})
edb.get_usercache_db().remove({"user_id": {"$in": self.testUUIDList}})
def testLocalRangeRolloverQuery(self):
"""
Search for all entries between 8:18 and 8:20 local time, both inclusive
"""
start_local_dt = ecwl.LocalDate({
'year': 2015,
'month': 8,
'hour': 8,
'minute': 18
})
end_local_dt = ecwl.LocalDate({
'year': 2015,
'month': 8,
'hour': 9,
'minute': 8
})
final_query = {"user_id": self.testUUID}
final_query.update(
esdl.get_range_query("data.local_dt", start_local_dt,
end_local_dt))
entries = edb.get_timeseries_db().find(final_query).sort(
'data.ts', pymongo.ASCENDING)
self.assertEqual(448,
edb.get_timeseries_db().count_documents(final_query))
entries_list = list(entries)
# Note that since this is a set of filters, as opposed to a range, this
# returns all entries between 18 and 8 in both hours.
# so 8:18 is valid, but so is 9:57
self.assertEqual(ecwe.Entry(entries_list[0]).data.local_dt.hour, 8)
self.assertEqual(ecwe.Entry(entries_list[0]).data.local_dt.minute, 18)
self.assertEqual(ecwe.Entry(entries_list[-1]).data.local_dt.hour, 9)
self.assertEqual(ecwe.Entry(entries_list[-1]).data.local_dt.minute, 57)
def tearDown(self):
edb.get_timeseries_db().remove({"user_id": self.androidUUID})
edb.get_timeseries_db().remove({"user_id": self.iosUUID})
edb.get_usercache_db().remove({"user_id": self.androidUUID})
edb.get_usercache_db().remove({"user_id": self.iosUUID})
edb.get_place_db().remove()
edb.get_trip_new_db().remove()
def clearRelatedDb(self):
edb.get_timeseries_db().remove()
edb.get_place_db().remove()
edb.get_stop_db().remove()
edb.get_trip_new_db().remove()
edb.get_section_new_db().remove()
def tearDown(self):
os.remove(self.analysis_conf_path)
edb.get_timeseries_db().remove({"user_id": self.androidUUID})
edb.get_timeseries_db().remove({"user_id": self.iosUUID})
edb.get_pipeline_state_db().remove({"user_id": self.androidUUID})
edb.get_pipeline_state_db().remove({"user_id": self.iosUUID})
edb.get_analysis_timeseries_db().remove({"user_id": self.androidUUID})
edb.get_analysis_timeseries_db().remove({"user_id": self.iosUUID})
def loadPointsForTrip(self, trip_id):
import emission.core.get_database as edb
entries = json.load(open("emission/tests/data/smoothing_data/%s" % trip_id),
object_hook=bju.object_hook)
for entry in entries:
entry["user_id"] = self.testUUID
edb.get_timeseries_db().save(entry)
def loadPointsForTrip(self, trip_id):
import emission.core.get_database as edb
entries = json.load(open("emission/tests/data/smoothing_data/%s" %
trip_id),
object_hook=bju.object_hook)
for entry in entries:
entry["user_id"] = self.testUUID
edb.get_timeseries_db().save(entry)
def clearRelatedDb(self):
edb.get_timeseries_db().delete_many({"user_id": self.testUUID})
edb.get_analysis_timeseries_db().delete_many(
{"user_id": self.testUUID})
edb.get_pipeline_state_db().delete_many({"user_id": self.testUUID})
edb.get_timeseries_db().delete_many({"user_id": self.testUUID1})
edb.get_analysis_timeseries_db().delete_many(
{"user_id": self.testUUID1})
edb.get_pipeline_state_db().delete_many({"user_id": self.testUUID1})
def convert_collection(collection, key):
result_cursor = collection.find()
logging.info("About to convert %s entries" % result_cursor.count())
for i, wrapper in enumerate(result_cursor):
entry = convert_wrapper_to_entry(key, wrapper)
if entry.get_id() != wrapper["_id"]:
logging.warn("entry.id = %s, wrapper.id = %s" % (entry.get_id(), wrapper["_id"]))
if i % 10000 == 0:
print("converted %s -> %s" % (wrapper, entry))
edb.get_timeseries_db().insert(entry)
def testInsertFilters(self):
edb.get_timeseries_db().remove({"user_id": self.testUUID,
"metadata.key": "background/filtered_location"})
for entry in edb.get_timeseries_db().find({'user_id': self.testUUID,
'metadata.filter': 'time',
"metadata.key": "background/location"}):
del entry["_id"]
del entry["metadata"]["filter"]
entry["metadata"]["key"] = "background/filtered_location"
edb.get_timeseries_db().insert(entry)
# At this point, all the filtered_location entries will not have any filters
self.assertEquals(edb.get_timeseries_db().find({'user_id': self.testUUID,
'metadata.filter': 'time',
"metadata.key": "background/filtered_location"}).count(), 0)
self.assertEquals(edb.get_timeseries_db().find({'user_id': self.testUUID,
'metadata.filter': 'time',
"metadata.key": "background/filtered_location"}).count(), 0)
# Now, move all filters
estfm.move_all_filters_to_data()
# The entries should now be set to "time"
# At this point, all the filtered_location entries will not have any filters
self.assertEquals(edb.get_timeseries_db().find({'user_id': self.testUUID,
'data.filter': 'distance',
"metadata.key": "background/filtered_location"}).count(), 0)
self.assertEquals(edb.get_timeseries_db().find({'user_id': self.testUUID,
'data.filter': 'time',
"metadata.key": "background/filtered_location"}).count(), 738)
def testOneOverride(self):
cfg_1 = copy.copy(self.dummy_config)
cfg_1['metadata']['write_ts'] = 1440700000
edb.get_timeseries_db().insert(cfg_1)
tq = estt.TimeQuery("metadata.write_ts", 1440658800, 1440745200)
eacc.save_all_configs(self.androidUUID, tq)
saved_entries = list(edb.get_usercache_db().find({'user_id': self.androidUUID, 'metadata.key': 'config/sensor_config'}))
self.assertEqual(len(saved_entries), 1)
logging.debug(saved_entries[0])
self.assertEqual(saved_entries[0]['data']['is_duty_cycling'], cfg_1['data']['is_duty_cycling'])
def insert(self, entry):
"""
"""
logging.debug("insert called")
if "user_id" not in entry:
entry["user_id"] = self.user_id
elif entry["user_id"] != self.user_id:
raise AttributeError("Saving entry for %s in timeseries for %s" % (entry["user_id"], self.user_id))
else:
logging.debug("entry was fine, no need to fix it")
logging.debug("Inserting entry %s into timeseries" % entry)
edb.get_timeseries_db().insert(entry)
def setupRealExampleWithEntries(testObj):
tsdb = edb.get_timeseries_db()
for entry in testObj.entries:
entry["user_id"] = testObj.testUUID
# print "Saving entry with write_ts = %s and ts = %s" % (entry["metadata"]["write_fmt_time"],
# entry["data"]["fmt_time"])
edb.save(tsdb, entry)
logging.info("After loading, timeseries db size = %s" % edb.get_timeseries_db().count())
logging.debug("First few entries = %s" %
[e["data"]["fmt_time"] if "fmt_time" in e["data"] else e["metadata"]["write_fmt_time"] for e in
list(edb.get_timeseries_db().find({"user_id": testObj.testUUID}).sort("data.write_ts",
pymongo.ASCENDING).limit(10))])
def testOldOverride(self):
cfg_1 = copy.copy(self.dummy_config)
cfg_1['metadata']['write_ts'] = 1440500000
edb.get_timeseries_db().insert(cfg_1)
cfg_2 = copy.copy(self.dummy_config)
cfg_2['metadata']['write_ts'] = 1440610000
edb.get_timeseries_db().insert(cfg_2)
tq = estt.TimeQuery("metadata.write_ts", 1440658800, 1440745200)
eacc.save_all_configs(self.androidUUID, tq)
saved_entries = list(edb.get_usercache_db().find({'user_id': self.androidUUID, 'metadata.key': 'config/sensor_config'}))
self.assertEqual(len(saved_entries), 0)
def setupRealExampleWithEntries(testObj):
tsdb = edb.get_timeseries_db()
for entry in testObj.entries:
entry["user_id"] = testObj.testUUID
# print "Saving entry with write_ts = %s and ts = %s" % (entry["metadata"]["write_fmt_time"],
# entry["data"]["fmt_time"])
tsdb.save(entry)
logging.info("After loading, timeseries db size = %s" % edb.get_timeseries_db().count())
logging.debug("First few entries = %s" %
[e["data"]["fmt_time"] if "fmt_time" in e["data"] else e["metadata"]["write_fmt_time"] for e in
list(edb.get_timeseries_db().find({"user_id": testObj.testUUID}).sort("data.write_ts",
pymongo.ASCENDING).limit(10))])
def setUp(self):
# We need to access the database directly sometimes in order to
# forcibly insert entries for the tests to pass. But we put the import
# in here to reduce the temptation to use the database directly elsewhere.
import emission.core.get_database as edb
import uuid
self.testUUID = uuid.uuid4()
self.entries = json.load(open("emission/tests/data/smoothing_data/tablet_2015-11-03"),
object_hook=bju.object_hook)
for entry in self.entries:
entry["user_id"] = self.testUUID
edb.get_timeseries_db().save(entry)
self.ts = esta.TimeSeries.get_time_series(self.testUUID)
def clearRelatedDb(self):
edb.get_timeseries_db().delete_many(
{"user_id": {
"$in": self.testUUIDList
}})
edb.get_analysis_timeseries_db().delete_many(
{"user_id": {
"$in": self.testUUIDList
}})
edb.get_usercache_db().delete_many(
{"user_id": {
"$in": self.testUUIDList
}})
edb.get_uuid_db().delete_many({"user_id": {"$in": self.testUUIDList}})
def setUp(self):
# We need to access the database directly sometimes in order to
# forcibly insert entries for the tests to pass. But we put the import
# in here to reduce the temptation to use the database directly elsewhere.
import emission.core.get_database as edb
import uuid
self.testUUID = uuid.uuid4()
self.entries = json.load(
open("emission/tests/data/smoothing_data/tablet_2015-11-03"),
object_hook=bju.object_hook)
for entry in self.entries:
entry["user_id"] = self.testUUID
edb.get_timeseries_db().save(entry)
self.ts = esta.TimeSeries.get_time_series(self.testUUID)
def setupRealExample(testObj, dump_file):
logging.info("Before loading, timeseries db size = %s" % edb.get_timeseries_db().count())
testObj.entries = json.load(open(dump_file), object_hook = bju.object_hook)
testObj.testUUID = uuid.uuid4()
for entry in testObj.entries:
entry["user_id"] = testObj.testUUID
# print "Saving entry with write_ts = %s and ts = %s" % (entry["metadata"]["write_fmt_time"],
# entry["data"]["fmt_time"])
edb.get_timeseries_db().save(entry)
logging.info("After loading, timeseries db size = %s" % edb.get_timeseries_db().count())
logging.debug("First few entries = %s" %
[e["data"]["fmt_time"] for e in
list(edb.get_timeseries_db().find({"user_id": testObj.testUUID}).sort("data.write_ts",
pymongo.ASCENDING).limit(10))])
def post_check(unique_user_list, all_rerun_list):
import emission.core.get_database as edb
import numpy as np
logging.info(
"For %s users, loaded %s raw entries, %s processed entries and %s pipeline states"
% (len(unique_user_list), edb.get_timeseries_db().count_documents(
{"user_id": {
"$in": list(unique_user_list)
}}), edb.get_analysis_timeseries_db().count_documents(
{"user_id": {
"$in": list(unique_user_list)
}}), edb.get_pipeline_state_db().count_documents({
"user_id": {
"$in": list(unique_user_list)
}
})))
all_rerun_arr = np.array(all_rerun_list)
# want to check if no entry needs a rerun? In this case we are done
# no entry needs a rerun = all entries are false, not(all entries) are true
if np.all(np.logical_not(all_rerun_list)):
logging.info(
"all entries in the timeline contain analysis results, no need to run the intake pipeline"
)
# if all entries need to be re-run, we must have had raw data throughout
elif np.all(all_rerun_list):
logging.info(
"all entries in the timeline contain only raw data, need to run the intake pipeline"
)
else:
logging.info(
"timeline contains a mixture of analysis results and raw data - complain to shankari!"
)
def fix_trips_or_sections(collection):
tsdb = edb.get_timeseries_db()
for entry in collection.find():
start_loc_entry = tsdb.find_one({'user_id': entry['user_id'],
'metadata.key': 'background/location', 'data.ts': entry['start_ts']})
end_loc_entry = tsdb.find_one({'user_id': entry['user_id'],
'metadata.key': 'background/location', 'data.ts': entry['end_ts']})
if start_loc_entry is not None:
start_tz = start_loc_entry['metadata']['time_zone']
else:
logging.warn("No start_loc_entry found for trip %s, returning default" % entry)
start_tz = "America/Los_Angeles"
if end_loc_entry is not None:
end_tz = end_loc_entry['metadata']['time_zone']
else:
logging.warn("No end_loc_entry found for trip %s, returning default" % entry)
end_tz = "America/Los_Angeles"
logging.debug("Found entries with metadata = %s, %s" % (start_tz, end_tz))
entry['start_local_dt'] = get_local_date(entry['start_fmt_time'], start_tz)
entry['end_local_dt'] = get_local_date(entry['end_fmt_time'], end_tz)
collection.save(entry)
def fix_stops_or_places(collection):
tsdb = edb.get_timeseries_db()
for entry in collection.find():
if 'enter_ts' in entry:
enter_loc_entry = tsdb.find_one({'user_id': entry['user_id'],
'metadata.key': 'background/location', 'data.ts': entry['enter_ts']})
if enter_loc_entry is not None:
enter_tz = enter_loc_entry['metadata']['time_zone']
else:
enter_tz = "America/Los_Angeles"
logging.debug("entry metadata timezone = %s" % enter_tz)
entry['enter_local_dt'] = get_local_date(entry['enter_fmt_time'],
enter_tz)
else:
logging.warning("No entry timestamp found, skipping")
if 'exit_ts' in entry:
exit_loc_entry = tsdb.find_one({'user_id': entry['user_id'],
'metadata.key': 'background/location', 'data.ts': entry['exit_ts']})
if exit_loc_entry is not None:
exit_tz = exit_loc_entry['metadata']['time_zone']
else:
exit_tz = "America/Los_Angeles"
logging.debug("exit metadata timezone = %s" % exit_tz)
entry['exit_local_dt'] = get_local_date(entry['exit_fmt_time'], exit_tz)
else:
logging.warning("No exit timestamp found, skipping")
collection.save(entry)
def setUp(self):
etc.setupRealExample(
self, "emission/tests/data/real_examples/shankari_2015-aug-27")
# eaicf.filter_accuracy(self.testUUID)
etc.runIntakePipeline(self.testUUID)
# estfm.move_all_filters_to_data()
logging.info("After loading, timeseries db size = %s" %
edb.get_timeseries_db().count())
self.day_start_ts = 1440658800
self.day_end_ts = 1440745200
self.day_start_dt = esdldq.get_local_date(self.day_start_ts,
"America/Los_Angeles")
self.day_end_dt = esdldq.get_local_date(self.day_end_ts,
"America/Los_Angeles")
# If we don't delete the time components, we end up with the upper and
# lower bounds = 0, which basically matches nothing.
del self.day_start_dt['hour']
del self.day_end_dt['hour']
del self.day_start_dt['minute']
del self.day_end_dt['minute']
del self.day_start_dt['second']
del self.day_end_dt['second']
def getPublicData():
ids = request.json['phone_ids']
all_uuids = map(lambda id: UUID(id), ids)
uuids = [uuid for uuid in all_uuids if uuid in estag.TEST_PHONE_IDS]
from_ts = request.query.from_ts
to_ts = request.query.to_ts
time_range = estt.TimeQuery("metadata.write_ts", float(from_ts), float(to_ts))
time_query = time_range.get_query()
user_queries = map(lambda id: {'user_id': id}, uuids)
for q in user_queries:
q.update(time_query)
num_entries_ts = map(lambda q: edb.get_timeseries_db().find(q).count(), user_queries)
num_entries_uc = map(lambda q: edb.get_usercache_db().find(q).count(), user_queries)
total_entries = sum(num_entries_ts + num_entries_uc)
logging.debug("Total entries requested: %d" % total_entries)
threshold = 200000
if total_entries > threshold:
data_list = None
else:
data_list = map(lambda u: esdc.find_entries(u, None, time_range), all_uuids)
return {'phone_data': data_list}
def fix_stops_or_places(collection):
tsdb = edb.get_timeseries_db()
for entry in collection.find():
if 'enter_ts' in entry:
enter_loc_entry = tsdb.find_one({
'user_id': entry['user_id'],
'metadata.key': 'background/location',
'data.ts': entry['enter_ts']
})
else:
logging.info("No entry timestamp found, skipping")
if 'exit_ts' in entry:
exit_loc_entry = tsdb.find_one({
'user_id': entry['user_id'],
'metadata.key': 'background/location',
'data.ts': entry['exit_ts']
})
else:
logging.info("No exit timestamp found, skipping")
logging.debug("Found entries with metadata = %s, %s" %
(enter_loc_entry['metadata']['time_zone'],
exit_loc_entry['metadata']['time_zone']))
if 'enter_local_dt' in entry:
entry['enter_local_dt'] = get_local_date(
entry['enter_fmt_time'],
enter_loc_entry['metadata']['time_zone'])
if 'exit_local_dt' in entry:
entry['exit_local_dt'] = get_local_date(
entry['exit_fmt_time'],
exit_loc_entry['metadata']['time_zone'])
collection.save(entry)
def export_timeline(user_id_str, day_str, file_name):
logging.info(
"Extracting timeline for user %s day %s and saving to file %s" %
(user_id_str, day_str, file))
# day_dt = pydt.datetime.strptime(day_str, "%Y-%m-%d").date()
day_dt = pydt.datetime.strptime(day_str, "%Y-%m-%d")
logging.debug("day_dt is %s" % day_dt)
day_end_dt = day_dt + pydt.timedelta(days=1)
# TODO: Convert to call to get_timeseries once we get that working
# Or should we even do that?
entry_list = list(edb.get_timeseries_db().find({
'user_id':
uuid.UUID(user_id_str),
'metadata.write_local_dt': {
'$gt': day_dt,
"$lt": day_end_dt
}
}))
logging.info("Found %d entries" % len(entry_list))
json.dump(entry_list,
open(file_name, "w"),
default=bju.default,
allow_nan=False,
indent=4)
def setupRealExample(testObj, dump_file):
logging.info("Before loading, timeseries db size = %s" % edb.get_timeseries_db().count())
with open(dump_file) as dfp:
testObj.entries = json.load(dfp, object_hook = bju.object_hook)
testObj.testUUID = uuid.uuid4()
print("Setting up real example for %s" % testObj.testUUID)
setupRealExampleWithEntries(testObj)
def fix_stops_or_places(collection):
tsdb = edb.get_timeseries_db()
for entry in collection.find():
if 'enter_ts' in entry:
enter_loc_entry = tsdb.find_one({'user_id': entry['user_id'],
'metadata.key': 'background/location', 'data.ts': entry['enter_ts']})
else:
logging.info("No entry timestamp found, skipping")
if 'exit_ts' in entry:
exit_loc_entry = tsdb.find_one({'user_id': entry['user_id'],
'metadata.key': 'background/location', 'data.ts': entry['exit_ts']})
else:
logging.info("No exit timestamp found, skipping")
logging.debug("Found entries with metadata = %s, %s" %
(enter_loc_entry['metadata']['time_zone'],
exit_loc_entry['metadata']['time_zone']))
if 'enter_local_dt' in entry:
entry['enter_local_dt'] = get_local_date(entry['enter_fmt_time'],
enter_loc_entry['metadata']['time_zone'])
if 'exit_local_dt' in entry:
entry['exit_local_dt'] = get_local_date(entry['exit_fmt_time'],
exit_loc_entry['metadata']['time_zone'])
collection.save(entry)
def export_timeline(user_id_str, day_str, file_name):
logging.info(
"Extracting timeline for user %s day %s and saving to file %s" %
(user_id_str, day_str, file_name))
# day_dt = pydt.datetime.strptime(day_str, "%Y-%m-%d").date()
day_dt = pydt.datetime.strptime(day_str, "%Y-%m-%d")
logging.debug("day_dt is %s" % day_dt)
# TODO: Convert to call to get_timeseries once we get that working
# Or should we even do that?
user_query = {'user_id': uuid.UUID(user_id_str)}
date_query = {
'metadata.write_local_dt.year': day_dt.year,
'metadata.write_local_dt.month': day_dt.month,
'metadata.write_local_dt.day': day_dt.day
}
final_query = user_query
final_query.update(date_query)
entry_list = list(edb.get_timeseries_db().find(final_query))
logging.info("Found %d entries" % len(entry_list))
json.dump(entry_list,
open(file_name, "w"),
default=bju.default,
allow_nan=False,
indent=4)
def purge_entries_for_user(curr_uuid, is_purge_state, db_array=None):
logging.info("For uuid = %s, deleting entries from the timeseries" % curr_uuid)
if db_array is not None:
[ts_db, ats_db, udb, psdb] = db_array
logging.debug("db_array passed in with databases %s" % db_array)
else:
import emission.core.get_database as edb
ts_db = edb.get_timeseries_db()
ats_db = edb.get_analysis_timeseries_db()
udb = edb.get_uuid_db()
psdb = edb.get_pipeline_state_db()
logging.debug("db_array not passed in, looking up databases")
timeseries_del_result = ts_db.remove({"user_id": curr_uuid})
logging.info("result = %s" % timeseries_del_result)
logging.info("For uuid = %s, deleting entries from the analysis_timeseries" % curr_uuid)
analysis_timeseries_del_result = ats_db.remove({"user_id": curr_uuid})
logging.info("result = %s" % analysis_timeseries_del_result)
logging.info("For uuid %s, deleting entries from the user_db" % curr_uuid)
user_db_del_result = udb.remove({"uuid": curr_uuid})
logging.info("result = %s" % user_db_del_result)
if is_purge_state:
logging.info("For uuid %s, deleting entries from the pipeline_state_db" % curr_uuid)
psdb_del_result = psdb.remove({"user_id": curr_uuid})
logging.info("result = %s" % psdb_del_result)
def __init__(self, user_id):
super(BuiltinTimeSeries, self).__init__(user_id)
self.key_query = lambda(key): {"metadata.key": key}
self.type_query = lambda(entry_type): {"metadata.type": entry_type}
self.user_query = {"user_id": self.user_id} # UUID is mandatory for this version
self.timeseries_db = edb.get_timeseries_db()
self.analysis_timeseries_db = edb.get_analysis_timeseries_db()
self.ts_map = {
"background/location": self.timeseries_db,
"background/filtered_location": self.timeseries_db,
"background/motion_activity": self.timeseries_db,
"background/battery": self.timeseries_db,
"statemachine/transition": self.timeseries_db,
"config/sensor_config": self.timeseries_db,
"segmentation/raw_trip": self.analysis_timeseries_db,
"segmentation/raw_place": self.analysis_timeseries_db,
"segmentation/raw_section": self.analysis_timeseries_db,
"segmentation/raw_stop": self.analysis_timeseries_db,
"analysis/smoothing": self.analysis_timeseries_db,
"analysis/cleaned_trip": self.analysis_timeseries_db,
"analysis/cleaned_place": self.analysis_timeseries_db,
"analysis/cleaned_section": self.analysis_timeseries_db,
"analysis/cleaned_stop": self.analysis_timeseries_db,
"analysis/recreated_location": self.analysis_timeseries_db,
}
def setupRealExample(testObj, dump_file):
logging.info("Before loading, timeseries db size = %s" %
edb.get_timeseries_db().count())
with open(dump_file) as dfp:
testObj.entries = json.load(dfp, object_hook=bju.object_hook)
testObj.testUUID = uuid.uuid4()
setupRealExampleWithEntries(testObj)
def testMoveToLongTerm(self):
# 5 mins of data, every 30 secs = 10 entries per entry type. There are
# 3 entry types, so 30 entries
# First all the entries are in the usercache
self.assertEqual(len(self.uc1.getMessage()), 30)
self.assertEqual(len(list(self.ts1.find_entries())), 0)
self.assertEqual(len(self.uc2.getMessage()), 30)
self.assertEqual(len(list(self.ts2.find_entries())), 0)
self.assertEqual(len(self.ucios.getMessage()), 30)
self.assertEqual(len(list(self.tsios.find_entries())), 0)
# Then we move entries for user1 into longterm
enuah.UserCacheHandler.getUserCacheHandler(self.testUserUUID1).moveToLongTerm()
# So we end up with all user1 entries in longterm
self.assertEqual(len(self.uc1.getMessage()), 0)
self.assertEqual(len(list(self.ts1.find_entries())), 30)
# Then, we move entries for the ios user into longterm
enuah.UserCacheHandler.getUserCacheHandler(self.testUserUUIDios).moveToLongTerm()
self.assertEqual(len(self.ucios.getMessage()), 0)
self.assertEqual(len(list(self.tsios.find_entries())), 30)
# 30 entries from android + 30 entries from ios = 60
self.assertEqual(edb.get_timeseries_db().find().count(), 60)
self.assertEqual(edb.get_timeseries_error_db().find().count(), 0)
# But all existing entries still in usercache for the second user
self.assertEqual(len(self.uc2.getMessage()), 30)
self.assertEqual(len(list(self.ts2.find_entries())), 0)
def __init__(self, user_id):
super(BuiltinTimeSeries, self).__init__(user_id)
self.key_query = lambda (key): {"metadata.key": key}
self.type_query = lambda (entry_type): {"metadata.type": entry_type}
self.user_query = {
"user_id": self.user_id
} # UUID is mandatory for this version
self.timeseries_db = edb.get_timeseries_db()
self.analysis_timeseries_db = edb.get_analysis_timeseries_db()
self.ts_map = {
"background/location": self.timeseries_db,
"background/filtered_location": self.timeseries_db,
"background/motion_activity": self.timeseries_db,
"background/battery": self.timeseries_db,
"statemachine/transition": self.timeseries_db,
"config/sensor_config": self.timeseries_db,
"segmentation/raw_trip": self.analysis_timeseries_db,
"segmentation/raw_place": self.analysis_timeseries_db,
"segmentation/raw_section": self.analysis_timeseries_db,
"segmentation/raw_stop": self.analysis_timeseries_db,
"analysis/smoothing": self.analysis_timeseries_db,
"analysis/cleaned_trip": self.analysis_timeseries_db,
"analysis/cleaned_place": self.analysis_timeseries_db,
"analysis/cleaned_section": self.analysis_timeseries_db,
"analysis/cleaned_stop": self.analysis_timeseries_db,
"analysis/recreated_location": self.analysis_timeseries_db,
}
def request_data(server_url, from_ts, to_ts, phone_ids, debug):
url = server_url + "/eval/publicData/timeseries?from_ts=" + str(from_ts) + "&to_ts=" + str(to_ts)
ids = {'phone_ids': phone_ids}
headers = {'Content-Type': 'application/json'}
r = requests.get(url, data=json.dumps(ids), headers = headers)
dic = json.loads(r.text, object_hook = bju.object_hook)
phone_list = dic['phone_data']
if phone_list == None:
print "Requested amount of data exceeds the threshold value."
else:
# Load data to the local server
tsdb = edb.get_timeseries_db()
for index, entry_list in enumerate(phone_list):
if debug:
logging.debug("phone" + str(index+1) + " first entry (in Pacific Time):")
if len(entry_list) == 0:
logging.debug("...has no data...")
else:
logging.debug(str(entry_list[0].get('metadata').get('write_fmt_time')))
for entry in entry_list:
tsdb.save(entry)
def setUp(self):
self.clearRelatedDb()
etc.setupRealExample(self, "emission/tests/data/real_examples/shankari_2015-aug-27")
eaicf.filter_accuracy(self.testUUID)
estfm.move_all_filters_to_data()
logging.info("After loading, timeseries db size = %s" % edb.get_timeseries_db().count())
self.day_start_ts = 1440658800
self.day_end_ts = 1440745200
def fix_key(check_field, new_key):
print("First entry for "+new_key+" is %s" % list(edb.get_timeseries_db().find(
{"metadata.key": "config/sensor_config",
check_field: {"$exists": True}}).sort(
"metadata/write_ts").limit(1)))
udb = edb.get_usercache_db()
tdb = edb.get_timeseries_db()
for i, entry in enumerate(edb.get_timeseries_db().find(
{"metadata.key": "config/sensor_config",
check_field: {"$exists": True}})):
entry["metadata"]["key"] = new_key
if i % 10000 == 0:
print(udb.insert(entry))
print(tdb.remove(entry["_id"]))
else:
udb.insert(entry)
tdb.remove(entry["_id"])
def get_data_df(self, key, time_query = None):
sort_key = self._get_sort_key(time_query)
logging.debug("curr_query = %s, sort_key = %s" % (self._get_query([key], time_query), sort_key))
result_it = edb.get_timeseries_db().find(self._get_query([key], time_query), {"data": True,
"metadata.write_ts": True}).sort(sort_key, pymongo.ASCENDING)
logging.debug("Found %s results" % result_it.count())
# Dataframe doesn't like to work off an iterator - it wants everything in memory
return pd.DataFrame([BuiltinTimeSeries._to_df_entry(e) for e in list(result_it)])
