def constructDT(gpsPoints, testPhones):
reload(tripActivitySeparatorMongo)
reload(modeChainSeparatorMongo)
minDuration, maxRadius, minSamplingRate, gpsAccuracyThreshold = 360000, 50, 300000, 200
minSeparationDistance, minSeparationTime = 100, 360000
maxWalkSpeed, maxWalkAcceleration, minSegmentDuration, minSegmentLength = 5, 1620, 90000, 200
hcrThreshold, srThreshold, vcrTheshold = 19, 7.6, 0.26
modeData = []
for testPhone in testPhones:
print 'Processing data for ' + str(testPhone)
try:
query = {'phNum': testPhone}
projection = {'_id': 0, 'gpsReading': 1, 'epochTime': 1, 'groundTruth': 1, 'movesTime': 1}
gpsTraces = list(gpsPoints.find(query, projection).sort('epochTime'))
trips, activities, holes = tripActivitySeparatorMongo.inferTripActivity(gpsTraces, minDuration,
maxRadius, minSeparationDistance, minSeparationTime, minSamplingRate, gpsAccuracyThreshold)
for trip in trips:
modeChains = modeChainSeparatorMongo.inferModeChain(gpsTraces, trip, maxWalkSpeed, maxWalkAcceleration,
minSegmentDuration, minSegmentLength, gpsAccuracyThreshold)
determineModes(modeChains, modeData, gpsTraces, hcrThreshold, srThreshold, vcrTheshold, minSamplingRate)
except:
print "Unexpected error during inference:", sys.exc_info()[0]
pass
return modeData
def addActivities(gpsPoints, segments, testPhones):
reload(tripActivitySeparatorMongo)
minDuration, maxRadius, minSamplingRate, gpsAccuracyThreshold = 360000, 50, 300000, 200
minSeparationDistance, minSeparationTime = 100, 360000
for testPhone in testPhones:
print 'Processing data for ' + str(testPhone)
query = {'phNum': testPhone}
projection = {'_id': 1, 'gpsReading': 1, 'epochTime': 1, 'groundTruth': 1, 'movesTime': 1}
gpsTraces = list(gpsPoints.find(query, projection).sort('epochTime'))
purposes = list(gpsPoints.distinct('groundTruth.purpose'))
trips, activities, holes = tripActivitySeparatorMongo.inferTripActivity(gpsTraces, minDuration, maxRadius,
minSeparationDistance, minSeparationTime, minSamplingRate, gpsAccuracyThreshold)
for activity in activities:
trackPoints, wiFiNetwork, location = [], [], []
purposeCount = {}
for purpose in purposes:
purposeCount[purpose] = 0
for i in range(activity[0], activity[1]):
trackPoint = {'location': gpsTraces[i]['gpsReading']['location'],
'time': gpsTraces[i]['movesTime'],
'id': gpsTraces[i]['_id']}
trackPoints.append(trackPoint)
if 'wiFiNetwork' in gpsTraces[i]:
if gpsTraces[i]['wiFiNetwork'] not in wiFiNetwork:
wiFiNetwork.append(gpsTraces[i]['wiFiNetwork'])
if 'purpose' in gpsTraces[i]['groundTruth']:
for gtPurpose in gpsTraces[i]['groundTruth']['purpose']:
purposeCount[gtPurpose] += 1
if 'exactLocation' in gpsTraces[i]['groundTruth']:
if gpsTraces[i]['groundTruth']['exactLocation'] not in location:
location.append(gpsTraces[i]['groundTruth']['exactLocation'])
centroid = calCentroid(trackPoints)
centroid = {'coordinates': [centroid[0], centroid[1]],
'type': 'Point'}
maxCount, gtPurpose = purposeCount[max(purposeCount, key = purposeCount.get)], []
if maxCount > (0.5 * (activity[1] - activity[0])):
for purpose in purposes:
if purposeCount[purpose] == maxCount:
gtPurpose.append(purpose)
startTime = gpsTraces[activity[0]]['movesTime']
endTime = gpsTraces[activity[1]]['movesTime']
duration = (dateutil.parser.parse(endTime) - dateutil.parser.parse(startTime)).total_seconds()
record = {'type': 'Activity',
'phNum': testPhone,
'startTime': startTime,
'endTime': endTime,
'duration': duration,
'purpose': gtPurpose,
'location': location,
'wiFiNetwork': wiFiNetwork,
'centroid': centroid,
'trackPoints': trackPoints}
if 'Sleep' in gtPurpose:
record['mainPurpose'] = 'Sleep'
elif 'Waiting' in gtPurpose:
record['mainPurpose'] = 'Waiting'
elif ('Work' in gtPurpose) or ('School' in gtPurpose):
record['mainPurpose'] = 'Mandatory'
elif ('Household_chores' in gtPurpose) or ('Meals' in gtPurpose) or ('Medical' in
gtPurpose) or ('Personal_services' in gtPurpose) or ('Shopping' in gtPurpose):
record['mainPurpose'] = 'Maintenance'
elif ('Recreation' in gtPurpose) or ('Relaxing' in gtPurpose) or ('Social' in gtPurpose):
record['mainPurpose'] = 'Discretionary'
if segments.find({'startTime': startTime, 'endTime': endTime, 'phNum': testPhone}).count() == 0:
segments.insert(record)
print testPhone, startTime, endTime, maxCount, gtPurpose
def modeChainSeparator(gpsPoints, testPhones):
print
reload(tripActivitySeparatorMongo)
minDuration, maxRadius, minSamplingRate, gpsAccuracyThreshold = 360000, 50, 300000, 200
minSeparationDistance, minSeparationTime = 100, 360000
maxWalkSpeed, maxWalkAcceleration, minSegmentDuration, minSegmentLength = 5, 1620, 90000, 200
timeTotTrips, timeInfTrips, distTotTrips, distInfTrips = 0, 0, 0, 0
segTotTrips, segInfTrips, segWalkInfNonWalkTrips, segNonWalkInfWalkTrips = 0, 0, 0, 0
for testPhone in testPhones:
print "Processing data for " + str(testPhone)
try:
query = {"phNum": testPhone}
projection = {"_id": 0, "gpsReading": 1, "epochTime": 1, "groundTruth": 1, "movesTime": 1}
gpsTraces = list(gpsPoints.find(query, projection).sort("epochTime"))
trips, activities, holes = tripActivitySeparatorMongo.inferTripActivity(
gpsTraces,
minDuration,
maxRadius,
minSeparationDistance,
minSeparationTime,
minSamplingRate,
gpsAccuracyThreshold,
)
for trip in trips:
modeChains = inferModeChain(
gpsTraces,
trip,
maxWalkSpeed,
maxWalkAcceleration,
minSegmentDuration,
minSegmentLength,
gpsAccuracyThreshold,
)
(
timeTotal,
timeInferred,
distTotal,
distInferred,
segTotal,
segInferred,
segWalkInfNonWalk,
segNonWalkInfWalk,
) = calInfAccuray(modeChains, gpsTraces)
timeTotTrips += timeTotal
timeInfTrips += timeInferred
distTotTrips += distTotal
distInfTrips += distInferred
segTotTrips += segTotal
segInfTrips += segInferred
segWalkInfNonWalkTrips += segWalkInfNonWalk
segNonWalkInfWalkTrips += segNonWalkInfWalk
except:
print "Unexpected error during inference:", sys.exc_info()[0]
pass
print
print "Accuracy in terms of time: %.0f%% of %.0f hours" % ((timeInfTrips * 100.0) / timeTotTrips, timeTotTrips)
print "Accuracy in terms of distance: %.0f%% of %.0f miles" % ((distInfTrips * 100.0) / distTotTrips, distTotTrips)
print "Accuracy in terms of number of segments: %.0f%% of %.0f segments" % (
(segInfTrips * 100.0) / segTotTrips,
segTotTrips,
)
print (
"Percentage of total segments that are walk but inferred as non-walk: %.0f%% of %.0f segments"
% ((segWalkInfNonWalkTrips * 100.0) / segTotTrips, segTotTrips)
)
print (
"Percentage of total segments that are non-walk but inferred as walk: %.0f%% of %.0f segments"
% ((segNonWalkInfWalkTrips * 100.0) / segTotTrips, segTotTrips)
)
