def clean_aggregate_schedules(args):
data = args[0]
seed = args[1]
activityAttribs = args[2]
dailyStatusAttribs = args[3]
dependencyAttribs = args[4]
t = time.time()
data["actobjects"] = data.apply(lambda x: return_act(x, activityAttribs), axis=1)
pschedulesGrouped = data.groupby(level=[0,1], sort=False)
print "Size is %d and time taken to run the df apply - %.4f" %(data.shape[0], time.time()-t)
t = time.time()
actList = []
for (hid,pid), pidSchedules in pschedulesGrouped:
#print workStatus_df[(hid, pid)]
personObject = Person(hid, pid)
activityList = list(pidSchedules["actobjects"].values)
personObject.add_episodes(activityList)
reconciledSchedules = personObject.clean_schedules_for_in_home_episodes()
actList += reconciledSchedules
print "Time taken to loop through all schedules - %.4f" %(time.time()-t)
return actList
def clean_aggregate_schedules(args):
data = args[0]
seed = args[1]
activityAttribs = args[2]
dailyStatusAttribs = args[3]
dependencyAttribs = args[4]
t = time.time()
data["actobjects"] = data.apply(lambda x: return_act(x, activityAttribs),
axis=1)
pschedulesGrouped = data.groupby(level=[0, 1], sort=False)
print "Size is %d and time taken to run the df apply - %.4f" % (
data.shape[0], time.time() - t)
t = time.time()
actList = []
for (hid, pid), pidSchedules in pschedulesGrouped:
#print workStatus_df[(hid, pid)]
personObject = Person(hid, pid)
activityList = list(pidSchedules["actobjects"].values)
personObject.add_episodes(activityList)
reconciledSchedules = personObject.clean_schedules_for_in_home_episodes(
)
actList += reconciledSchedules
print "Time taken to loop through all schedules - %.4f" % (time.time() - t)
return actList
def reconcile_schedules(args):
data = args[0]
seed = args[1]
activityAttribs = args[2]
t = time.time()
data["actobjects"] = data.apply(lambda x: return_act(x, activityAttribs), axis=1)
pschedulesGrouped = data.groupby(level=[0,1], sort=False)
print "Size is %d and time taken to run the df apply - %.4f" %(data.shape[0], time.time()-t)
t = time.time()
actList = []
for (hid,pid), pidSchedules in pschedulesGrouped:
personObject = Person(hid, pid)
activityList = list(pidSchedules["actobjects"].values)
personObject.add_episodes(activityList)
reconciledSchedules = personObject.reconcile_activity_schedules(seed)
if not personObject._check_for_conflicts():
raise Exception, "THE SCHEDULES ARE STILL MESSED UP"
actList += reconciledSchedules
print "Time taken to loop through all schedules - %.4f" %(time.time()-t)
return actList
def process_dependency(args):
data = args[0]
seed = args[1]
activityAttribs = args[2]
dailyStatusAttribs = args[3]
dependencyAttribs = args[4]
specification = args[5]
t = time.time()
data["actobjects"] = data.apply(lambda x: return_act(x, activityAttribs),
axis=1)
hschedulesGrouped = data.groupby(level=0, sort=False)
pschedulesGrouped = data.groupby(level=[0, 1], sort=False)
workStatus_df = pschedulesGrouped[dailyStatusAttribs.workStatusName].min()
schoolStatus_df = pschedulesGrouped[
dailyStatusAttribs.schoolStatusName].min()
childDependency_df = pschedulesGrouped[
dependencyAttribs.childDependencyName].min()
#print workStatus_df.shape
#print workStatus_df
print "Size is %d and time taken to run the df apply - %.4f" % (
data.shape[0], time.time() - t)
t = time.time()
actList = []
for hid, hidSchedules in hschedulesGrouped:
householdObject = Household(hid)
for pid, pidSchedules in hidSchedules.groupby(level=1, axis=0):
personObject = Person(hid, pid)
activityList = list(pidSchedules["actobjects"].values)
personObject.add_episodes(activityList)
workStatus = workStatus_df[(hid, pid)]
schoolStatus = schoolStatus_df[(hid, pid)]
childDependency = childDependency_df[(hid, pid)]
personObject.add_status_dependency(workStatus, schoolStatus,
childDependency)
householdObject.add_person(personObject)
if specification.terminalEpisodesAllocation:
householdObject.allocate_terminal_dependent_activities(seed)
elif specification.childDepProcessingType == 'Allocation':
householdObject.allocate_dependent_activities(seed)
elif specification.childDepProcessingType == 'Resolution':
householdObject.lineup_activities(seed)
elif specification.childDepProcessingType == 'Fix Trip Purpose':
householdObject.fix_trippurpose(seed)
elif specification.childDepProcessingType == 'Extract Tour Attributes':
householdObject.extract_tripattributes(seed)
reconciledSchedules = householdObject._collate_results()
actList += reconciledSchedules
print "Time taken to loop through all schedules - %.4f" % (time.time() - t)
return actList
def process_dependency(args):
data = args[0]
seed = args[1]
activityAttribs = args[2]
dailyStatusAttribs = args[3]
dependencyAttribs = args[4]
specification = args[5]
t = time.time()
data["actobjects"] = data.apply(lambda x: return_act(x, activityAttribs), axis=1)
hschedulesGrouped = data.groupby(level=0, sort=False)
pschedulesGrouped = data.groupby(level=[0,1], sort=False)
workStatus_df = pschedulesGrouped[dailyStatusAttribs.workStatusName].min()
schoolStatus_df = pschedulesGrouped[dailyStatusAttribs.schoolStatusName].min()
childDependency_df = pschedulesGrouped[dependencyAttribs.childDependencyName].min()
#print workStatus_df.shape
#print workStatus_df
print "Size is %d and time taken to run the df apply - %.4f" %(data.shape[0], time.time()-t)
t = time.time()
actList = []
for hid, hidSchedules in hschedulesGrouped:
householdObject = Household(hid)
for pid, pidSchedules in hidSchedules.groupby(level=1, axis=0):
personObject = Person(hid, pid)
activityList = list(pidSchedules["actobjects"].values)
personObject.add_episodes(activityList)
workStatus = workStatus_df[(hid, pid)]
schoolStatus = schoolStatus_df[(hid, pid)]
childDependency = childDependency_df[(hid, pid)]
personObject.add_status_dependency(workStatus, schoolStatus,
childDependency)
householdObject.add_person(personObject)
if specification.terminalEpisodesAllocation:
householdObject.allocate_terminal_dependent_activities(seed)
elif specification.childDepProcessingType == 'Allocation':
householdObject.allocate_dependent_activities(seed)
elif specification.childDepProcessingType == 'Resolution':
householdObject.lineup_activities(seed)
elif specification.childDepProcessingType == 'Fix Trip Purpose':
householdObject.fix_trippurpose(seed)
elif specification.childDepProcessingType == 'Extract Tour Attributes':
householdObject.extract_tripattributes(seed)
reconciledSchedules = householdObject._collate_results()
actList += reconciledSchedules
print "Time taken to loop through all schedules - %.4f" %(time.time()-t)
return actList
def clean_schedules(args):
data = args[0]
seed = args[1]
activityAttribs = args[2]
dailyStatusAttribs = args[3]
dependencyAttribs = args[4]
t = time.time()
data["actobjects"] = data.apply(lambda x: return_act(x, activityAttribs),
axis=1)
pschedulesGrouped = data.groupby(level=[0, 1], sort=False)
workStatus_df = pschedulesGrouped[dailyStatusAttribs.workStatusName].min()
schoolStatus_df = pschedulesGrouped[
dailyStatusAttribs.schoolStatusName].min()
childDependency_df = pschedulesGrouped[
dependencyAttribs.childDependencyName].min()
#print workStatus_df.shape
#print workStatus_df
print "Size is %d and time taken to run the df apply - %.4f" % (
data.shape[0], time.time() - t)
t = time.time()
actList = []
for (hid, pid), pidSchedules in pschedulesGrouped:
#print workStatus_df[(hid, pid)]
personObject = Person(hid, pid)
activityList = list(pidSchedules["actobjects"].values)
personObject.add_episodes(activityList)
workStatus = workStatus_df[(hid, pid)]
schoolStatus = schoolStatus_df[(hid, pid)]
childDependency = childDependency_df[(hid, pid)]
personObject.add_status_dependency(workStatus, schoolStatus,
childDependency)
reconciledSchedules = personObject.clean_schedules()
actList += reconciledSchedules
print "Time taken to loop through all schedules - %.4f" % (time.time() - t)
return actList
def clean_schedules(args):
data = args[0]
seed = args[1]
activityAttribs = args[2]
dailyStatusAttribs = args[3]
dependencyAttribs = args[4]
t = time.time()
data["actobjects"] = data.apply(lambda x: return_act(x, activityAttribs), axis=1)
pschedulesGrouped = data.groupby(level=[0, 1], sort=False)
workStatus_df = pschedulesGrouped[dailyStatusAttribs.workStatusName].min()
schoolStatus_df = pschedulesGrouped[dailyStatusAttribs.schoolStatusName].min()
childDependency_df = pschedulesGrouped[dependencyAttribs.childDependencyName].min()
# print workStatus_df.shape
# print workStatus_df
print "Size is %d and time taken to run the df apply - %.4f" % (data.shape[0], time.time() - t)
t = time.time()
actList = []
for (hid, pid), pidSchedules in pschedulesGrouped:
# print workStatus_df[(hid, pid)]
personObject = Person(hid, pid)
activityList = list(pidSchedules["actobjects"].values)
personObject.add_episodes(activityList)
workStatus = workStatus_df[(hid, pid)]
schoolStatus = schoolStatus_df[(hid, pid)]
childDependency = childDependency_df[(hid, pid)]
personObject.add_status_dependency(workStatus, schoolStatus, childDependency)
reconciledSchedules = personObject.clean_schedules()
actList += reconciledSchedules
print "Time taken to loop through all schedules - %.4f" % (time.time() - t)
return actList
