def storeResultEntries(fname):
df = pandas.read_csv(fname)
for c in range(len(df)):
try:
user = df['user'][c]
stat = df['stat'][c]
ts = int(df['ts'][c])
reading = float(df['reading'][c])
storeResultEntry(user, stat, ts, reading)
except Exception as e:
print(e)
def storeResultEntries(fname): df = pandas.read_csv(fname) for c in range(len(df)): try: user = df['user'][c] stat = df['stat'][c] ts = int(df['ts'][c]) reading = float(df['reading'][c]) storeResultEntry(user, stat, ts, reading) except Exception as e: print(e)
def getScore(user_uuid, start, end):
components = getScoreComponents(user_uuid, start, end)
[pctClassified, mineMinusOptimal, allDriveMinusMine, sb375DailyGoal] = components
stats.storeResultEntry(user_uuid, stats.STAT_PCT_CLASSIFIED, time.time(), pctClassified)
stats.storeResultEntry(user_uuid, stats.STAT_MINE_MINUS_OPTIMAL, time.time(), mineMinusOptimal)
stats.storeResultEntry(user_uuid, stats.STAT_ALL_DRIVE_MINUS_MINE, time.time(), allDriveMinusMine)
stats.storeResultEntry(user_uuid, stats.STAT_SB375_DAILY_GOAL, time.time(), sb375DailyGoal)
return calcScore(components)
def getScore(user_uuid, start, end):
components = getScoreComponents(user_uuid, start, end)
[pctClassified, mineMinusOptimal, allDriveMinusMine, sb375DailyGoal] = components
stats.storeResultEntry(user_uuid, stats.STAT_PCT_CLASSIFIED, time.time(), pctClassified)
stats.storeResultEntry(user_uuid, stats.STAT_MINE_MINUS_OPTIMAL, time.time(), mineMinusOptimal)
stats.storeResultEntry(user_uuid, stats.STAT_ALL_DRIVE_MINUS_MINE, time.time(), allDriveMinusMine)
stats.storeResultEntry(user_uuid, stats.STAT_SB375_DAILY_GOAL, time.time(), sb375DailyGoal)
return calcScore(components)
def updateScoreForDay(user_uuid, today):
yesterday = today - timedelta(days = 1)
dayBeforeYesterday = today - timedelta(days = 2)
dayBeforeYesterdayStart = datetime.combine(dayBeforeYesterday, dttime.min)
yesterdayStart = datetime.combine(yesterday, dttime.min)
todayStart = datetime.combine(today, dttime.min)
user = User.fromUUID(user_uuid)
(discardedScore, prevScore) = getStoredScore(user)
# Using score from dayBeforeYesterday instead of yesterday because there is
# currently a significant lag in the time for e-mission to prompt for
# entries, so people might not confirm yesterday's trips until sometime
# today, which means that it won't be counted in their score
newScore = prevScore + getScore(user_uuid, dayBeforeYesterdayStart, yesterdayStart)
if newScore < 0:
newScore = 0
stats.storeResultEntry(user_uuid, stats.STAT_GAME_SCORE, time.time(), newScore)
setScores(user, prevScore, newScore)
def updateScoreForDay(user_uuid, today):
yesterday = today - timedelta(days = 1)
dayBeforeYesterday = today - timedelta(days = 2)
dayBeforeYesterdayStart = datetime.combine(dayBeforeYesterday, dttime.min)
yesterdayStart = datetime.combine(yesterday, dttime.min)
todayStart = datetime.combine(today, dttime.min)
user = User.fromUUID(user_uuid)
(discardedScore, prevScore) = getStoredScore(user)
# Using score from dayBeforeYesterday instead of yesterday because there is
# currently a significant lag in the time for e-mission to prompt for
# entries, so people might not confirm yesterday's trips until sometime
# today, which means that it won't be counted in their score
newScore = prevScore + getScore(user_uuid, dayBeforeYesterdayStart, yesterdayStart)
if newScore < 0:
newScore = 0
stats.storeResultEntry(user_uuid, stats.STAT_GAME_SCORE, time.time(), newScore)
setScores(user, prevScore, newScore)
def setCurrView(uuid, newView):
user = User.fromUUID(uuid)
user.setClientSpecificProfileFields({'curr_view': newView})
stats.storeResultEntry(uuid, stats.STAT_VIEW_CHOICE, time.time(), newView)
def runBackgroundTasksForDay(user_uuid, today):
today_dt = datetime.combine(today, time.max)
user = User.fromUUID(user_uuid)
# carbon compare results is a tuple. Tuples are converted to arrays
# by mongodb
# In [44]: testUser.setScores(('a','b', 'c', 'd'), ('s', 't', 'u', 'v'))
# In [45]: testUser.getScore()
# Out[45]: ([u'a', u'b', u'c', u'd'], [u's', u't', u'u', u'v'])
weekago = today_dt - timedelta(days=7)
carbonCompareResults = carbon.getFootprintCompareForRange(
user_uuid, weekago, today_dt)
setCarbonFootprint(user, carbonCompareResults)
(
myModeShareCount,
avgModeShareCount,
myModeShareDistance,
avgModeShareDistance,
myModeCarbonFootprint,
avgModeCarbonFootprint,
myModeCarbonFootprintNoLongMotorized,
avgModeCarbonFootprintNoLongMotorized, # ignored
myOptimalCarbonFootprint,
avgOptimalCarbonFootprint,
myOptimalCarbonFootprintNoLongMotorized,
avgOptimalCarbonFootprintNoLongMotorized) = carbonCompareResults
# We only compute server stats in the background, because including them in
# the set call means that they may be invoked when the user makes a call and
# the cached value is None, which would potentially slow down user response time
msNow = systime.time()
stats.storeResultEntry(user_uuid, stats.STAT_MY_CARBON_FOOTPRINT, msNow,
getCategorySum(myModeCarbonFootprint))
stats.storeResultEntry(
user_uuid, stats.STAT_MY_CARBON_FOOTPRINT_NO_AIR, msNow,
getCategorySum(myModeCarbonFootprintNoLongMotorized))
stats.storeResultEntry(user_uuid, stats.STAT_MY_OPTIMAL_FOOTPRINT, msNow,
getCategorySum(myOptimalCarbonFootprint))
stats.storeResultEntry(
user_uuid, stats.STAT_MY_OPTIMAL_FOOTPRINT_NO_AIR, msNow,
getCategorySum(myOptimalCarbonFootprintNoLongMotorized))
stats.storeResultEntry(
user_uuid, stats.STAT_MY_ALLDRIVE_FOOTPRINT, msNow,
getCategorySum(myModeShareDistance) * (278.0 / (1609 * 1000)))
stats.storeResultEntry(user_uuid, stats.STAT_MEAN_FOOTPRINT, msNow,
getCategorySum(avgModeCarbonFootprint))
stats.storeResultEntry(
user_uuid, stats.STAT_MEAN_FOOTPRINT_NO_AIR, msNow,
getCategorySum(avgModeCarbonFootprintNoLongMotorized))
def setCurrView(uuid, newView):
user = User.fromUUID(uuid)
user.setClientSpecificProfileFields({'curr_view': newView})
stats.storeResultEntry(uuid, stats.STAT_VIEW_CHOICE, time.time(), newView)
def runBackgroundTasksForDay(user_uuid, today):
today_dt = datetime.combine(today, time.max)
user = User.fromUUID(user_uuid)
# carbon compare results is a tuple. Tuples are converted to arrays
# by mongodb
# In [44]: testUser.setScores(('a','b', 'c', 'd'), ('s', 't', 'u', 'v'))
# In [45]: testUser.getScore()
# Out[45]: ([u'a', u'b', u'c', u'd'], [u's', u't', u'u', u'v'])
weekago = today_dt - timedelta(days=7)
carbonCompareResults = carbon.getFootprintCompareForRange(user_uuid, weekago, today_dt)
setCarbonFootprint(user, carbonCompareResults)
(myModeShareCount, avgModeShareCount,
myModeShareDistance, avgModeShareDistance,
myModeCarbonFootprint, avgModeCarbonFootprint,
myModeCarbonFootprintNoLongMotorized, avgModeCarbonFootprintNoLongMotorized, # ignored
myOptimalCarbonFootprint, avgOptimalCarbonFootprint,
myOptimalCarbonFootprintNoLongMotorized, avgOptimalCarbonFootprintNoLongMotorized) = carbonCompareResults
# We only compute server stats in the background, because including them in
# the set call means that they may be invoked when the user makes a call and
# the cached value is None, which would potentially slow down user response time
msNow = systime.time()
stats.storeResultEntry(user_uuid, stats.STAT_MY_CARBON_FOOTPRINT, msNow, getCategorySum(myModeCarbonFootprint))
stats.storeResultEntry(user_uuid, stats.STAT_MY_CARBON_FOOTPRINT_NO_AIR, msNow, getCategorySum(myModeCarbonFootprintNoLongMotorized))
stats.storeResultEntry(user_uuid, stats.STAT_MY_OPTIMAL_FOOTPRINT, msNow, getCategorySum(myOptimalCarbonFootprint))
stats.storeResultEntry(user_uuid, stats.STAT_MY_OPTIMAL_FOOTPRINT_NO_AIR, msNow, getCategorySum(myOptimalCarbonFootprintNoLongMotorized))
stats.storeResultEntry(user_uuid, stats.STAT_MY_ALLDRIVE_FOOTPRINT, msNow, getCategorySum(myModeShareDistance) * (278.0/(1609 * 1000)))
stats.storeResultEntry(user_uuid, stats.STAT_MEAN_FOOTPRINT, msNow, getCategorySum(avgModeCarbonFootprint))
stats.storeResultEntry(user_uuid, stats.STAT_MEAN_FOOTPRINT_NO_AIR, msNow, getCategorySum(avgModeCarbonFootprintNoLongMotorized))