def getSingleDC(row):
output = [] # [tokeep t/f, breakdown T/F, deadhead or date, cost or run]
busRun = schedule[(schedule["ServiceDate"] == row[1].ServiceDate) & (schedule["Run"] == row[1].Run)]
totalPass = sum(busRun.NumOn)
if 8 in busRun.Activity.unique():
output = [False, True, row[1].Run, row[1].ServiceDate]
elif totalPass <= 0:
output = [False, False, None, None]
else:
time = float(runTime(busRun["ETA"]))
if time < 14400: # 4 hours
time = 14400.0
cost = time / totalPass
deadhead = deadheadPct(busRun[["ETA", "TotalPass"]])
output = [True, False, deadhead, cost]
def getSingleDC(row):
output = [] # [tokeep t/f, breakdown T/F, deadhead or date, cost or run]
busRun = schedule[(schedule['ServiceDate'] == row[1].ServiceDate)
& (schedule['Run'] == row[1].Run)]
totalPass = sum(busRun.NumOn)
if 8 in busRun.Activity.unique():
output = [False, True, row[1].Run, row[1].ServiceDate]
elif totalPass <= 0:
output = [False, False, None, None]
else:
time = float(runTime(busRun['ETA']))
if time < 14400: # 4 hours
time = 14400.0
cost = time / totalPass
deadhead = deadheadPct(busRun[['ETA', 'TotalPass']])
output = [True, False, deadhead, cost]
def getSingleDC(row, schedule,results,pnum,total):
output = []
busRun = schedule[(schedule['ServiceDate'] == row[1].ServiceDate) &
(schedule['Run'] == row[1].Run)]
totalPass = sum(busRun.NumOn)
if 8 in busRun.Activity.unique():
output = [row[1].ServiceDate, row[1].Run, False, True, None, None]
elif totalPass <= 0:
output = [row[1].ServiceDate, row[1].Run, False, False, None, None]
else:
time = float(runTime(busRun['ETA']))
if time < 14400: # 4 hours
time = 14400.0
cost = time / totalPass
deadhead = deadheadPct(busRun[['ETA', 'TotalPass']])
output = [row[1].ServiceDate, row[1].Run, True, False, deadhead, cost]
results[pnum] = output
print(str(len(results)/float(total)) + '%' + ' done.')
def deadheadVsCost(schedule):
"""
schedule: a full schedule
with columns [ServiceDate, Run, Activity, ETA, NumOn, TotalPass,]
"""
uniqueDR = schedule[["ServiceDate", "Run"]].drop_duplicates()
toKeep = []
print (uniqueDR)
numOfRuns = len(uniqueDR)
deadhead = []
cost = []
breakdownDate = []
breakdownRun = []
loopcount = 0
numDropped = 0
# get deadhead pct for each Date/Run pair
for row in uniqueDR.iterrows():
busRun = schedule[(schedule["ServiceDate"] == row[1].ServiceDate) & (schedule["Run"] == row[1].Run)]
totalPass = sum(busRun.NumOn)
if 8 in busRun.Activity.unique():
print "warning! run " + str(row[1].Run) + " on " + str(row[1].ServiceDate) + " breaks down."
toKeep.append(False)
breakdownRun.append(row[1].Run)
breakdownDate.append(row[1].ServiceDate)
elif totalPass > 0:
# approximates average cost per boarding
time = float(runTime(busRun["ETA"]))
if time < 14400: # 4 hours
time = 14400.0
icost = time / totalPass
cost.append(icost)
deadhead.append(deadheadPct(busRun[["ETA", "TotalPass"]]))
toKeep.append(True)
else:
print "warning! run " + str(row[1].Run) + " on " + str(row[1].ServiceDate) + " has no passengers."
toKeep.append(False)
numDropped = numDropped + 1
loopcount = loopcount + 1
os.system(["clear", "cls"][os.name == "nt"])
print row[1].Run
print row[1].ServiceDate
print str(float(loopcount) / numOfRuns * 100) + "%" + " done\n"
print cost
print deadhead
print ("")
print ('Number "dropped": ' + str(numDropped))
uniqueDRLen = len(uniqueDR)
print ("Number needed: " + str(uniqueDRLen - len(cost)))
if False in toKeep:
print ("Drop in toKeep found.")
# remove rows with bad data
uniqueDR = uniqueDR[toKeep]
print ("Number dropped: " + str(uniqueDRLen - len(uniqueDR)))
# write breakdowns to file
# pd.DataFrame({'ServiceDate' : breakdownDate,
#'Run' : breakdownRun}).to_csv('../../data/4mo_broken_buses.csv')
# write results to file
cost = np.asarray(cost)
deadhead = np.asarray(deadhead)
uniqueDR["CostProxy"] = cost
uniqueDR["PctDeadhead"] = deadhead
# IMPORTANT: change file name if not 4mo
uniqueDR.to_csv("../../data/4mo_deadhead_cost_nptest.csv", index=False)
# regressions
results = smf.ols("cost ~ deadhead", data=uniqueDR).fit()
print results.summary()
results = smf.ols("np.log(cost) ~ deadhead", data=uniqueDR).fit()
print results.summary()
def deadheadVsCost(schedule):
'''
schedule: a full schedule
with columns [ServiceDate, Run, Activity, ETA, NumOn, TotalPass,]
'''
uniqueDR = schedule[['ServiceDate', 'Run']].drop_duplicates()
toKeep = []
print(uniqueDR)
numOfRuns = len(uniqueDR)
deadhead = []
cost = []
breakdownDate = []
breakdownRun = []
loopcount = 0
numDropped = 0
# get deadhead pct for each Date/Run pair
for row in uniqueDR.iterrows():
busRun = schedule[(schedule['ServiceDate'] == row[1].ServiceDate)
& (schedule['Run'] == row[1].Run)]
totalPass = sum(busRun.NumOn)
if 8 in busRun.Activity.unique():
print 'warning! run ' + str(row[1].Run) + ' on ' + str(
row[1].ServiceDate) + ' breaks down.'
toKeep.append(False)
breakdownRun.append(row[1].Run)
breakdownDate.append(row[1].ServiceDate)
elif totalPass > 0:
# approximates average cost per boarding
time = float(runTime(busRun['ETA']))
if time < 14400: # 4 hours
time = 14400.0
icost = time / totalPass
cost.append(icost)
deadhead.append(deadheadPct(busRun[['ETA', 'TotalPass']]))
toKeep.append(True)
else:
print 'warning! run ' + str(row[1].Run) + ' on ' + str(
row[1].ServiceDate) + ' has no passengers.'
toKeep.append(False)
numDropped = numDropped + 1
loopcount = loopcount + 1
os.system(['clear', 'cls'][os.name == 'nt'])
print row[1].Run
print row[1].ServiceDate
print str(float(loopcount) / numOfRuns * 100) + '%' + ' done\n'
print cost
print deadhead
print('')
print('Number "dropped": ' + str(numDropped))
uniqueDRLen = len(uniqueDR)
print('Number needed: ' + str(uniqueDRLen - len(cost)))
if False in toKeep:
print('Drop in toKeep found.')
# remove rows with bad data
uniqueDR = uniqueDR[toKeep]
print('Number dropped: ' + str(uniqueDRLen - len(uniqueDR)))
# write breakdowns to file
#pd.DataFrame({'ServiceDate' : breakdownDate,
#'Run' : breakdownRun}).to_csv('../../data/4mo_broken_buses.csv')
# write results to file
cost = np.asarray(cost)
deadhead = np.asarray(deadhead)
uniqueDR['CostProxy'] = cost
uniqueDR['PctDeadhead'] = deadhead
# IMPORTANT: change file name if not 4mo
uniqueDR.to_csv('../../data/4mo_deadhead_cost_nptest.csv', index=False)
# regressions
results = smf.ols('cost ~ deadhead', data=uniqueDR).fit()
print results.summary()
results = smf.ols('np.log(cost) ~ deadhead', data=uniqueDR).fit()
print results.summary()
