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query_file.py
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query_file.py
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"""
Runs the query algorithm
"""
from Algorithms.CSA.std_csa import std_csa
from Algorithms.RAPTOR.hypraptor import hypraptor
# from RAPTOR.one_to_many_rraptor import onetomany_rraptor
from Algorithms.RAPTOR.rraptor import rraptor
from Algorithms.RAPTOR.std_raptor import raptor
from Algorithms.TBTR.hyptbtr import hyptbtr
from Algorithms.TBTR.one_many_tbtr import onetomany_rtbtr
from Algorithms.TBTR.rtbtr import rtbtr
from Algorithms.TBTR.tbtr import tbtr
from Algorithms.TIME_EXPANDED_DIJKSTRA.TE_DIJ import custom_dij
from Algorithms.TRANSFER_PATTERNS.transferpattens import std_tp
from miscellaneous_func import *
print_logo()
def take_inputs() -> tuple:
"""
defines the use input
Returns:
algorithm (int): algorithm type. 0 for RAPTOR, 1 for TBTR, 2 for Transfer Patterns
variant (int): variant of the algorithm. 0 for normal version,
1 for range version,
2 for One-To-Many version,
3 for Hyper version
3 for Nested Hyper version
Examples:
>>> algorithm, variant = take_inputs()
"""
algorithm = int(input(
"Press 0 to enter RAPTOR environment \nPress 1 to enter TBTR environment\nPress 2 to enter Transfer Patterns environment\nPress 3 to enter CSA environment"
"\nPress 4 to enter Time Expanded Dijkstra\n: "))
variant = 0
print("***************")
if algorithm == 0:
variant = int(input(
"Press 0 for RAPTOR \nPress 1 for rRAPTOR \nPress 2 for One-To-Many rRAPTOR \nPress 3 for HypRAPTOR\nPress 4 for Multilevel-HypRAPTOR\n: "))
elif algorithm == 1:
variant = int(input("Press 0: TBTR \nPress 1: rTBTR \nPress 2: One-To-Many rTBTR \nPress 3: HypTBTR \nPress 4 for Multilevel-HypTBTR \n: "))
print("***************")
return algorithm, variant
def main() -> None:
"""
Runs the test case depending upon the values of algorithm, variant
Examples:
>>> main()
"""
algorithm, variant = take_inputs()
print_query_parameters(NETWORK_NAME, SOURCE, DESTINATION, D_TIME, MAX_TRANSFER, WALKING_FROM_SOURCE, variant, no_of_partitions=4,
weighting_scheme="S2", partitioning_algorithm="KaHyPar")
if algorithm == 0:
if variant == 0:
output = raptor(SOURCE, DESTINATION, D_TIME, MAX_TRANSFER, WALKING_FROM_SOURCE, CHANGE_TIME_SEC, PRINT_ITINERARY,
routes_by_stop_dict, stops_dict, stoptimes_dict, footpath_dict, idx_by_route_stop_dict)
print(f"Optimal arrival time are: {output}")
elif variant == 1:
output = rraptor(SOURCE, DESTINATION, d_time_groups, MAX_TRANSFER, WALKING_FROM_SOURCE, CHANGE_TIME_SEC, PRINT_ITINERARY,
OPTIMIZED, routes_by_stop_dict, stops_dict, stoptimes_dict, footpath_dict, idx_by_route_stop_dict)
if OPTIMIZED == 1:
print(f"Trips required to cover optimal journeys are {output}")
else:
print(f"Routes required to cover optimal journeys are {output}")
elif variant == 2:
pass
# output = onetomany_rraptor(SOURCE, DESTINATION_LIST, d_time_groups, MAX_TRANSFER, WALKING_FROM_SOURCE, CHANGE_TIME_SEC, PRINT_ITINERARY, OPTIMIZED, routes_by_stop_dict, stops_dict, stoptimes_dict, footpath_dict, idx_by_route_stop_dict)
# if OPTIMIZED == 1:
# print(f"Trips required to cover optimal journeys are {output}")
# else:
# print(f"Routes required to cover optimal journeys are {output}")
elif variant == 3:
output = hypraptor(SOURCE, DESTINATION, D_TIME, MAX_TRANSFER, WALKING_FROM_SOURCE, CHANGE_TIME_SEC, PRINT_ITINERARY,
stop_out, route_groups, routes_by_stop_dict, stops_dict, stoptimes_dict, footpath_dict, idx_by_route_stop_dict)
print(f"Optimal arrival time are: {output}")
elif variant == 4:
output = hypraptor(SOURCE, DESTINATION, D_TIME, MAX_TRANSFER, WALKING_FROM_SOURCE, CHANGE_TIME_SEC, PRINT_ITINERARY,
nested_stop_out, nested_route_groups, routes_by_stop_dict, stops_dict, stoptimes_dict, footpath_dict, idx_by_route_stop_dict)
print(f"Optimal arrival time are: {output}")
if algorithm == 1:
if variant == 0:
output = tbtr(SOURCE, DESTINATION, D_TIME, MAX_TRANSFER, WALKING_FROM_SOURCE, PRINT_ITINERARY, routes_by_stop_dict, stops_dict, stoptimes_dict,
footpath_dict, idx_by_route_stop_dict, trip_transfer_dict, trip_set)
print(f"Optimal arrival times are: {output[0]}")
elif variant == 1:
output = rtbtr(SOURCE, DESTINATION, d_time_groups, MAX_TRANSFER, WALKING_FROM_SOURCE, PRINT_ITINERARY, OPTIMIZED,
routes_by_stop_dict, stops_dict, stoptimes_dict, footpath_dict, idx_by_route_stop_dict, trip_transfer_dict, trip_set)
if OPTIMIZED == 1:
print(f"Trips required to cover optimal journeys are {output}")
else:
print(f"Routes required to cover optimal journeys are {output}")
elif variant == 2:
output = onetomany_rtbtr(SOURCE, DESTINATION_LIST, d_time_groups, MAX_TRANSFER, WALKING_FROM_SOURCE, PRINT_ITINERARY,
OPTIMIZED, routes_by_stop_dict, stops_dict, stoptimes_dict, footpath_dict, idx_by_route_stop_dict, trip_transfer_dict,
trip_set)
if OPTIMIZED == 1:
print(f"Trips required to cover optimal journeys are {output}")
else:
print(f"Routes required to cover optimal journeys are {output}")
elif variant == 3:
output = hyptbtr(SOURCE, DESTINATION, D_TIME, MAX_TRANSFER, WALKING_FROM_SOURCE, PRINT_ITINERARY, stop_out, trip_groups,
routes_by_stop_dict, stops_dict, stoptimes_dict, footpath_dict, idx_by_route_stop_dict, trip_transfer_dict, trip_set)
print(f"Optimal arrival times are: {output[0]}")
elif variant == 4:
output = hyptbtr(SOURCE, DESTINATION, D_TIME, MAX_TRANSFER, WALKING_FROM_SOURCE, PRINT_ITINERARY, nested_stop_out, nested_trip_groups,
routes_by_stop_dict, stops_dict, stoptimes_dict, footpath_dict, idx_by_route_stop_dict, trip_transfer_dict, trip_set)
print(f"Optimal arrival times are: {output[0]}")
if algorithm == 2:
if variant == 0:
output = std_tp(SOURCE, DESTINATION, D_TIME, footpath_dict, NETWORK_NAME, routesindx_by_stop_dict, stoptimes_dict, hub_count, hubstops)
print(f"Optimal arrival times are: {output}")
if algorithm == 3:
if variant == 0:
output = std_csa(SOURCE, DESTINATION, D_TIME, connections_list, WALKING_FROM_SOURCE, footpath_dict, PRINT_ITINERARY)
print(f"Optimal arrival times is: {output}")
if algorithm == 4:
if variant == 0:
output = custom_dij(SOURCE, D_TIME, DESTINATION, G, stops_group, stopevent_mapping, stop_times_file)
print(f"Optimal arrival times is: {output}")
return None
if __name__ == "__main__":
# Read network
USE_TESTCASE = int(input("Press 1 to use test case (anaheim), 0 to enter values manually. Example: 1\n: " ))
if USE_TESTCASE == 1:
NETWORK_NAME = './anaheim'
stops_file, trips_file, stop_times_file, transfers_file, stops_dict, stoptimes_dict, footpath_dict, routes_by_stop_dict, idx_by_route_stop_dict, routesindx_by_stop_dict = read_testcase(
NETWORK_NAME)
trip_transfer_dict, trip_set = load_TBTR(NETWORK_NAME)
connections_list = load_CSA(NETWORK_NAME)
G, stops_group, stopevent_mapping = load_TE_graph(NETWORK_NAME, stop_times_file)
print_network_details(transfers_file, trips_file, stops_file)
# Query parameters
SOURCE, DESTINATION, DESTINATION_LIST = 36, 52, [52, 43]
D_TIME = stop_times_file.arrival_time.sort_values().iloc[0]
MAX_TRANSFER, WALKING_FROM_SOURCE, CHANGE_TIME_SEC = 4, 1, 0
hub_count, hubstops = 0, set()
PRINT_ITINERARY, OPTIMIZED = 1, 0
# TODO add partition testcases
# stop_out, route_groups, _, trip_groups = read_partitions(stop_times_file, NETWORK_NAME, no_of_partitions=4, weighting_scheme="S2", partitioning_algorithm="kahypar")
# nested_stop_out, nested_route_groups, _, nested_trip_groups = read_nested_partitions(stop_times_file, NETWORK_NAME, no_of_partitions=4, weighting_scheme="S2")
else:
NETWORK_NAME = input("Enter Network name in small case. Example: anaheim\n: ")
stops_file, trips_file, stop_times_file, transfers_file, stops_dict, stoptimes_dict, footpath_dict, routes_by_stop_dict, idx_by_route_stop_dict, routesindx_by_stop_dict = read_testcase(
NETWORK_NAME)
trip_transfer_dict, trip_set = load_TBTR(NETWORK_NAME)
connections_list = load_CSA(NETWORK_NAME)
G, stops_group, stopevent_mapping = load_TE_graph(NETWORK_NAME, stop_times_file)
print_network_details(transfers_file, trips_file, stops_file)
SOURCE = int(input("Enter source stop id\n: "))
DESTINATION = int(input("Enter destination stop id\n: "))
D_TIME = pd.to_datetime(input("Enter departure time. Format: YYYY-MM-DD HH:MM:SS (24 hour format)\n: "))
MAX_TRANSFER = int(input("Maximum transfer limit\n: "))
WALKING_FROM_SOURCE = int(input("Press 1 to allow walking from source, else 0\n: "))
CHANGE_TIME_SEC = int(input("Enter change time (in seconds) \n: "))
PRINT_ITINERARY, OPTIMIZED = 1, 0
PARTITIONING_PARAMETER = int(input("Press 1 to enter partitioning related parameters. Else press 0\n: "))
if PARTITIONING_PARAMETER == 1:
NO_OF_PARTITION = int(input("Enter number of partitions\n: "))
WEIGHING_SCHEME = str(input("Enter weighing scheme [S1, S2, S3 S4 S5 S6]\n: "))
stop_out, route_groups, _, trip_groups = read_partitions(stop_times_file, NETWORK_NAME, no_of_partitions=NO_OF_PARTITION,
weighting_scheme=WEIGHING_SCHEME, partitioning_algorithm="kahypar")
nested_stop_out, nested_route_groups, _, nested_trip_groups = read_nested_partitions(stop_times_file, NETWORK_NAME,
no_of_partitions=NO_OF_PARTITION,
weighting_scheme=WEIGHING_SCHEME)
# main function
d_time_groups = stop_times_file.groupby("stop_id")
main()