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convert_demand.py
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convert_demand.py
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from tables import openFile, IsDescription, Filters
from collections import defaultdict
import time, random, sys, os
import pandas as pd
import numpy as np
from tables import openFile
from config import *
from util_functions import *
from person import Person
random.seed("rbtz")
start = time.time()
hfilename = os.path.join(OUTPUT_DIR, FILE_OUT_HH)
pfilename = os.path.join(OUTPUT_DIR, FILE_OUT_PERSON)
outfilename = os.path.join(OUTPUT_DIR, FILE_OUT_TRIP)
if INPUT_TYPE=="CHAMP":
infile = openFile(FILE_INPUT_ABM_DEMAND, mode="r")
rownum = 1
outfile = open(outfilename, mode="w")
outfile.write("person_id,person_trip_id,person_tour_id,o_taz,d_taz,mode,purpose,departure_time,arrival_time,time_target,vot,PNR_ids\n")
tempfilename = os.path.join(OUTPUT_DIR, FILE_TEMP)
temp_file = open(tempfilename, mode="w")
temp_file.write("hh_id,hh_vehicles,hh_income,hh_size,hh_workers,hh_presch,hh_elders,")
temp_file.write("person_id,age,gender,worker_status,work_athome,multiple_jobs,transit_pass,disability\n")
# read the preferred departure & arrival time distributions
pref_dep_time_dist = readDistributionCDFs(FILE_DEFAULT_DEPTIME_CDF)
pref_arr_time_dist = readDistributionCDFs(FILE_DEFAULT_ARRTIME_CDF)
# keep track of the simulated version
sim_dep_time_dist = defaultdict(int)
sim_arr_time_dist = defaultdict(int)
person = None
for row in infile.root.records:
if rownum % 1000000 == 0: print "Read %10d rows" % rownum
rownum += 1
if person:
if person._persid != row['persid']:
# the previous person is done - write them out
person.sortTrips()
person.choosePreferredTimes(pref_dep_time_dist, pref_arr_time_dist)
person.write(outfile, sim_arr_time_dist, sim_dep_time_dist)
person.write_temp(temp_file)
# initialize the new person, add the trip
person = Person(row)
else:
# add the trip
person.addTrip(row)
else:
# initialize the new person, add the trip
person = Person(row)
infile.close()
print "Read %s in %5.2f mins" % (FILE_INPUT_ABM_DEMAND, (time.time() - start)/60.0)
temp_file.close()
print "Reading visitor demand"
# visitor demand
if VISITOR_DEMAND_FLAG:
visitor_trip_id = 0
for idx, tp in TIMEPERIODS_NUM_TO_STR.iteritems():
visit_file_name = os.path.join(VISITOR_DEMAND_DIR, tp.lower()+"VISIT.h5")
visit_file = openFile(visit_file_name, "r")
visitors = visit_file.get_node("/", "2") # this is the matrix with WLRT trips in it
org_zones = visitors.shape[0]
des_zones = visitors.shape[1]
# print sum(sum(visitors))
for i in range(org_zones):
for j in range(des_zones):
if visitors[i][j] > 0:
num_trips = getIntTrips(visitors[i][j])
for t in range(num_trips):
person_id = 0
visitor_trip_id += 1
otaz = i+1
dtaz = j+1
mode = 'walk-transit-walk'
purpose = 'visitor'
dep_time = chooseTimeFromDistribution(pref_dep_time_dist[tp])
arr_time = -1
time_target = 'departure'
# person_id,person_trip_id,person_tour_id,o_taz,d_taz,mode,purpose,departure_time,arrival_time,time_target,vot,PNR_ids
outfile.write("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%.2f,%s\n" %
(person_id, visitor_trip_id, '', otaz, dtaz, mode, purpose,
convertTripTime(dep_time), convertTripTime(arr_time),
time_target, -1,''))
visit_file.close()
outfile.close()
print "Creating household and person files"
# household and person files
hh_cols = ['hh_id', 'hh_vehicles', 'hh_income', 'hh_size', 'hh_workers', 'hh_presch', 'hh_elders']
per_cols = ['person_id', 'hh_id', 'age', 'gender', 'worker_status', 'work_athome', 'multiple_jobs', 'transit_pass', 'disability']
all_persons = pd.read_csv(tempfilename, usecols=per_cols)
all_hh = pd.read_csv(tempfilename, usecols=hh_cols)
all_hh = all_hh.drop_duplicates(subset='hh_id')
all_persons.to_csv(pfilename, index=False)
all_persons['hh_grdsch'] = 0
all_persons.loc[(all_persons.age >= 5) & (all_persons.age <= 15), 'hh_grdsch'] = 1
all_persons['hh_hghsch'] = 0
all_persons.loc[(all_persons.age >= 16) & (all_persons.age <= 17), 'hh_hghsch'] = 1
outhh_df = pd.DataFrame(all_persons.groupby(['hh_id'])[['hh_grdsch','hh_hghsch']].sum()).reset_index()
outhh_df = all_hh.merge(outhh_df, how='left', on='hh_id')
outhh_df.to_csv(hfilename, index=False)
os.remove(tempfilename)
elif INPUT_TYPE=="CHTS":
# read in survey files
hh_df = pd.read_csv(os.path.join(INPUT_DIR, INFILE_HH), sep=' ')
person_df = pd.read_csv(os.path.join(INPUT_DIR, INFILE_PERSON), sep=' ')
hh_df = hh_df.rename(columns={'hhno':'hh_id','hhvehs':'hh_vehicles','hhincome':'hh_income','hhsize':'hh_size','hhwkrs':'hh_workers',
'hhcu5':'hh_presch','hh515':'hh_grdsch','hhhsc':'hh_hghsch'})
person_df = person_df.rename(columns={'hhno':'hh_id','pno':'person_id','pagey':'age'})
### prepare and write out hh file
person_df['hh_elders'] = 0
person_df.loc[person_df['age'] >= 65, 'hh_elders'] = 1
agg_df = pd.DataFrame(person_df.groupby(['hh_id'])[['hh_elders']].sum()).reset_index()
hh_df = hh_df.merge(agg_df, how='left', on='hh_id')
# hh_df['hh_income'] = hh_df['hh_income'].astype(int)
# hh_df.loc[hh_df['hh_income']<0,'hh_income'] = np.nan
hh_cols = ['hh_id', 'hh_vehicles', 'hh_income', 'hh_size', 'hh_workers', 'hh_presch', 'hh_grdsch', 'hh_hghsch', 'hh_elders']
hh_df.to_csv(hfilename, columns=hh_cols, index=False)
### prepare and write out person file
person_df['gender'] = ''
person_df.loc[person_df['pgend']==1 ,'gender'] = 'male'
person_df.loc[person_df['pgend']==2 ,'gender'] = 'female'
person_df['worker_status'] = 'unemployed'
person_df.loc[person_df['pwtyp']==1 ,'worker_status'] = 'full-time'
person_df.loc[person_df['pwtyp']==2 ,'worker_status'] = 'part-time'
person_df['work_athome'] = 'False'
person_df['multiple_jobs'] = 'False'
person_df['transit_pass'] = 'False'
person_df['disability'] = 'none'
person_df['person_id'] = person_df['hh_id'].astype(str) + '_' + person_df['person_id'].astype(str)
per_cols = ['person_id', 'hh_id', 'age', 'gender', 'worker_status', 'work_athome', 'multiple_jobs', 'transit_pass', 'disability']
person_df.to_csv(pfilename, columns=per_cols, index=False)
if not GPS_TRIPS:
tour_df = pd.read_csv(os.path.join(INPUT_DIR, INFILE_TOUR), sep=' ')
trip_df = pd.read_csv(os.path.join(INPUT_DIR, INFILE_TRIP), sep=' ')
### prepare and write out trip file
trip_df = trip_df.loc[trip_df['mode'].isin(range(6,16)),] # keep only transit trips
trip_df = trip_df.loc[(trip_df['otaz']>0) & (trip_df['dtaz']>0),]
trip_df = trip_df.merge(tour_df[['hhno','pno','tour','parent']], how='left', on=['hhno','pno','tour'])
trip_df = trip_df.rename(columns={'hhno':'hh_id','pno':'person_id','otaz':'o_taz','dtaz':'d_taz','tour':'person_tour_id','mode':'survey_mode'})
trip_df['person_id'] = trip_df['hh_id'].astype(str) + '_' + trip_df['person_id'].astype(str)
# 6=walk local 7=walk lrt 8=walk prem 9=walk ferry 10=walk bart
# 11=drive local 12=drive lrt 13=drive prem 14=drive ferry 15=drive bart
trip_df['mode'] = 'walk-transit-walk'
trip_df.loc[(trip_df['survey_mode'].isin(range(11,16))) & (trip_df['half']==1), 'mode'] = 'PNR-transit-walk'
trip_df.loc[(trip_df['survey_mode'].isin(range(11,16))) & (trip_df['half']==2), 'mode'] = 'walk-transit-PNR'
trip_df = trip_df.merge(hh_df[['hh_id','hh_income','hh_workers']], how='left', on=['hh_id'])
trip_df = trip_df.merge(person_df[['person_id','pptyp','age','worker_status']], how='left', on=['person_id'])
trip_df['purpose'] = 'other'
trip_df.loc[trip_df['dpurp']==1, 'purpose'] = 'work'
trip_df.loc[(trip_df['dpurp']==2) & (trip_df['pptyp']==7), 'purpose'] = 'grade_school'
trip_df.loc[(trip_df['dpurp']==2) & (trip_df['pptyp']==6), 'purpose'] = 'high_school'
trip_df.loc[(trip_df['dpurp']==2) & (trip_df['pptyp']==5), 'purpose'] = 'college'
trip_df.loc[trip_df['parent']>0, 'purpose'] = 'work_based'
trip_df['vot'] = trip_df.apply(calculateVOT,axis=1)
trip_df['departure_time'] = trip_df['deptm'].apply(convertTripTime, args=(100,))
trip_df['arrival_time'] = trip_df['arrtm'].apply(convertTripTime, args=(100,))
trip_df['time_target'] = 'departure'
trip_df.loc[trip_df['half']==1, 'time_target'] = 'arrival'
trip_df['person_trip_id'] = trip_df['person_tour_id'].astype(str) + '_' + trip_df['half'].astype(str) + '_' + trip_df['tseg'].astype(str)
else:
transit_legs = pd.read_csv(GPS_TRIP_FILE)
transit_legs['A_id'] = transit_legs['A_stop_id']
transit_legs['B_id'] = transit_legs['B_stop_id']
transit_legs.loc[transit_legs['linkmode']=='access', 'A_id'] = transit_legs.loc[transit_legs['linkmode']=='access', 'A_TAZ']
transit_legs.loc[transit_legs['linkmode']=='egress', 'B_id'] = transit_legs.loc[transit_legs['linkmode']=='egress', 'B_TAZ']
trip_df = transit_legs.drop_duplicates(['person_id','trip_list_id_num'])[['person_id','trip_list_id_num','A_id','new_A_time','mode']]
# trip_count = trip_df.groupby('person_id').count().reset_index()
# trip_df['person_trip_id'] = [item for l in trip_count['trip_list_id_num'].apply(range).tolist() for item in l]
# trip_df['person_trip_id'] = trip_df['person_trip_id'] + 1
# trip_df['person_trip_id'] = trip_df['person_id'].astype(str) + '_' + trip_df['person_trip_id'].astype(str)
trip_df = trip_df.rename(columns={'mode':'mode1'})
temp_df = transit_legs.drop_duplicates(['person_id','trip_list_id_num'], keep='last')[['person_id','trip_list_id_num','B_id','new_B_time','mode']]
trip_df = trip_df.merge(temp_df, on=['person_id','trip_list_id_num'], how='left')
trip_df = trip_df.rename(columns={'A_id':'o_taz','B_id':'d_taz','new_A_time':'departure_time','new_B_time':'arrival_time','mode':'mode2',
'trip_list_id_num':'person_trip_id'})
trip_df['arr_hour'] = trip_df['arrival_time'].apply(lambda x: int(x.split(":")[0]))
trip_df['dep_hour'] = trip_df['departure_time'].apply(lambda x: int(x.split(":")[0]))
trip_df['time_target'] = 'departure'
trip_df.loc[trip_df['arr_hour']<=12, 'time_target'] = 'arrival'
trip_df['hh_id'] = trip_df['person_id'].apply(lambda x: int(x.split("_")[0]))
trip_df = trip_df.merge(hh_df[['hh_id','hh_income','hh_workers']], how='left', on=['hh_id'])
trip_df = trip_df.merge(person_df[['person_id','pptyp','age','worker_status']], how='left', on=['person_id'])
trip_df.loc[pd.isnull(trip_df['age']), 'age'] = -1
trip_df.loc[pd.isnull(trip_df['hh_workers']), 'hh_workers'] = -1
trip_df['tp'] = ''
trip_df.loc[(trip_df['dep_hour']>=6) & (trip_df['dep_hour']<=9), 'tp'] = 'AM'
trip_df['purpose'] = 'other' # default
trip_df.loc[(trip_df['tp']=='AM') & (((trip_df['pptyp']==1)) | (trip_df['pptyp']==2)), 'purpose'] = 'work'
trip_df.loc[(trip_df['tp']=='AM') & (trip_df['pptyp']==7), 'purpose'] = 'grade_school'
trip_df.loc[(trip_df['tp']=='AM') & (trip_df['pptyp']==6), 'purpose'] = 'high_school'
trip_df.loc[(trip_df['tp']=='AM') & (trip_df['pptyp']==5), 'purpose'] = 'college'
trip_df['vot'] = trip_df.apply(calculateVOT,axis=1)
trip_df['mode'] = 'walk-transit-walk'
trip_df.loc[trip_df['mode1'].isin(['PNR_access','KNR_access']), 'mode'] = 'PNR-transit-walk'
trip_df.loc[trip_df['mode2'].isin(['PNR_egress','KNR_egress']), 'mode'] = 'walk-transit-PNR'
trip_df = trip_df.loc[(pd.notnull(trip_df['o_taz'])) & (pd.notnull(trip_df['d_taz'])),]
trip_df[['o_taz','d_taz']] = trip_df[['o_taz','d_taz']].astype(int)
trip_df = trip_df.loc[trip_df['person_id'].isin(person_df['person_id']),]
trip_df = trip_df.sort_values(['person_id','person_trip_id'])
trip_cols = ['person_id', 'person_trip_id', 'person_tour_id', 'o_taz', 'd_taz', 'mode', 'purpose', 'departure_time', 'arrival_time', 'time_target', 'vot']
trip_df.to_csv(outfilename, columns=trip_cols, index=False)
print "Finished conversion in %5.2f mins" % ((time.time() - start)/60.0)