def _write_move(self, all_buses, prefix):
all_movements = self._assign.compute_all_movements(
self._assign.get_buses())
if len(all_movements) > 0:
specific_dir = self._output_directory + prefix
create_dir(specific_dir)
result_move = open(specific_dir + "/results_move.csv", "w+")
move_heading = trip_heading_prefix
for _bus in all_buses:
move_heading += _bus.__str__() + ", "
move_heading += "\n"
result_move.write(move_heading)
for movement_dict in all_movements:
for movement in movement_dict.keys():
move_content = movement.__content__()
for _bus in all_buses:
if _bus.__key__() == movement_dict[movement].__key__():
move_content += "1,"
else:
move_content += "0,"
move_content += "\n"
result_move.write(move_content)
result_move.flush()
os.fsync(result_move.fileno())
result_move.close()
def split_files(file_name, lines_limit=500000):
"""
Args:
file_name: name of the file, probably the file_name for energy estimates
lines_limit: number of lines for each smaller files generated from the large file
"""
import pandas as pd
dir_name = file_name.replace(".csv", "")
df = pd.read_csv(file_name,
usecols=[
"Start_Lat", "Start_Lon", "End_Lat", "End_Lon",
"Path_Timestamp", "Predicted_Values"
],
index_col=False)
df.to_csv(file_name,
columns=[
"Start_Lat", "Start_Lon", "End_Lat", "End_Lon",
"Path_Timestamp", "Predicted_Values"
],
index=False)
input_file = open(file_name, "r+")
contents = input_file.readlines()
number_of_sub_portion = int(len(contents) / lines_limit)
create_dir(dir_name)
for i in range(number_of_sub_portion + 2):
out_file = open(dir_name + "/{0}.csv".format(str(i)), "w+")
out_file.writelines(contents[i * lines_limit:(i + 1) * lines_limit])
out_file.close()
input_file.close()
def write(self, prefix):
create_dir(self._output_directory)
all_buses = self._assign.get_buses()
filtered_trips = []
for trip in self._assign.get_trips():
if isinstance(trip, OperatingTrip):
filtered_trips.append(trip)
self._write_assign(filtered_trips, all_buses, prefix)
self._write_move(all_buses, prefix)
def generate_random_dump_data_internal(info_enabled=True, dump_config=None, operating_trips=None, suffix=""):
"""
Args:
info_enabled: this is a boolean value to determine updating info
dump_config: dump configuration
operating_trips: available operating trips
suffix: additional suffix to contain multiple dump_structure with same dump_config
Returns:
selected operating trips
Note : this function also update the dump.info file
"""
_selected_trips, _route_values = extract_specific_data_random(dump_config, operating_trips)
_filtered_trips = []
if info_enabled:
dump_info_file_name = dump_info_directory + dump_config.__key__() + suffix + "_dump.info"
create_dir(dump_info_directory)
route_details = FileWriter(dump_info_file_name)
route_details.write("Total number of bus-lines : " + str(dump_config.route_limit))
route_details.write("Maximum number of trips per bus-line : " + str(dump_config.trip_limit))
route_details.write("\n[BUS LINE NUMBERS]\n" + "\n".join([str(route_no) for route_no in _route_values]))
for route_no in _route_values:
random.seed(random_seed)
sample_trips = random.sample(_selected_trips[route_no],
min(len(_selected_trips[route_no]), dump_config.trip_limit))
route_details.write("\n[TRIPS FOR BUS LINE NUMBER : " + str(route_no) + "]")
route_details.write("\nNo, Trip ID, Start Time, End Time, Duration")
route_details_dict = {}
for trip in sample_trips:
_filtered_trips.append(trip)
start_time_in_sec = trip.start_s()
if start_time_in_sec in route_details_dict:
start_time_in_sec += random.randint(1, 100)
route_details_dict[start_time_in_sec] = trip
counter = 1
for time_in_sec in sorted(route_details_dict.keys()):
trip = route_details_dict[time_in_sec]
route_details.write(", ".join([str(counter), str(trip.trip_id), str(trip.start_time.time),
str(trip.end_time.time), str(trip.duration.time)]))
counter += 1
route_details.close()
else:
for route_no in _route_values:
random.seed(random_seed)
sample_trips = random.sample(_selected_trips[route_no],
min(len(_selected_trips[route_no]), dump_config.trip_limit))
for trip in sample_trips:
_filtered_trips.append(trip)
cleaned_trips = []
for _trip in _filtered_trips:
_duration = diff(_trip.end_time, _trip.start_time)
_trip.end_time = diff(_trip.end_time, time(_duration.time_in_seconds * default_time_tol))
_trip.duration = diff(_trip.end_time, _trip.start_time)
cleaned_trips.append(_trip)
return cleaned_trips
def plot(obj):
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv(obj.summary_file_name)
plt.figure()
df.plot(x="iteration", y="energy_cost")
plt.xlabel("Iterations")
plt.ylabel("Energy Cost")
image_file_name = obj.summary_file_name.replace(".csv", ".png")
create_dir(image_directory)
image_file_name = image_file_name.replace(summary_directory, image_directory)
plt.savefig(image_file_name)
def update_dump_log(self, dump_config, start, end):
if self.log_enabled:
time_taken = end - start
create_dir(self.log_directory)
dump_log_file_name = self.log_directory + dump_config.__key__() + \
self.get_suffix() + self.dump_prefix + ".log"
file_writer = FileAppender(dump_log_file_name)
file_writer.append("Dump generation started at " + str(start))
file_writer.append("Dump generation finished at " + str(end))
file_writer.append("Time taken for dump generation is " + str(time_taken))
file_writer.close()
def run(self, arg):
start_time = datetime.now()
self._init_population(arg)
convergence_limit = 0
minimum_cost = math.inf
new_population = self.sys_population
generation = 0
self.summary_file_name = summary_directory + "genetic_algorithm.csv"
create_dir(summary_directory)
summary_file = FileWriter(self.summary_file_name)
summary_file.write("iteration,energy_cost")
while convergence_limit < gen_alg_convergence_limit and generation < self.generation_limit:
s_print("Current Generation {}".format(str(generation)))
scores = score_population(new_population)
best = new_population[scores.index(min(scores))]
fitness_cost = fitness(best)
self.best_costs.append(fitness_cost)
if fitness_cost < minimum_cost:
convergence_limit = 0
minimum_cost = fitness_cost
self.over_all_best = best
else:
convergence_limit += 1
summary_file.write([generation, minimum_cost])
new_population = select(
new_population,
max(int(len(new_population) * selection_ratio), min_pop))
s_print("Creating next generation ")
new_population.extend(self._create_crossover(new_population))
new_population.extend(self._create_mutation(new_population))
self.population_limit = len(new_population)
self.generation_count = generation
generation += 1
summary_file.close()
end_time = datetime.now()
self.time_consumed = (end_time - start_time).total_seconds()
s_print("Total time taken is " + time(int(self.time_consumed)).time)
if self.over_all_best is not None:
if isinstance(self.over_all_best, Assignment):
self.over_all_best.write(self.dump_structure.__key__())
self.over_all_best.write_bus_stat(
self.dump_structure.__key__(), do_print=True)
def write_bus_stat(prefix,
output_dir=output_directory,
additional_prefix="",
bus_stats=None):
specific_dir = output_dir + prefix
if additional_prefix != "":
specific_dir = specific_dir + "_" + additional_prefix
create_dir(specific_dir)
result_bus_stat = open(specific_dir + "/bus_stats.csv", "w+")
result_bus_stat.write(bus_stat_heading + "\n")
for _bus in bus_stats:
if _bus is not None:
if not is_dummy(_bus):
bus_stat = bus_stats[_bus]
bus_stat_content = _bus.__str__(
) + "," + bus_stat.get_min_csv_line()
bus_stat_content += "\n"
result_bus_stat.write(bus_stat_content)
result_bus_stat.flush()
os.fsync(result_bus_stat.fileno())
result_bus_stat.close()
def _write_assign(self, filtered_trips, all_buses, prefix):
if len(filtered_trips) > 0:
specific_dir = self._output_directory + prefix
create_dir(specific_dir)
result_assign = open(specific_dir + "/results_assign.csv", "w+")
assign_heading = trip_heading_prefix
for _bus in all_buses:
assign_heading += _bus.__str__() + ", "
assign_heading += "\n"
result_assign.write(assign_heading)
for trip in filtered_trips:
assign_content = trip.__content__()
for _bus in all_buses:
if _bus.__key__() == self._assign.get(trip).__key__():
assign_content += "1,"
else:
assign_content += "0,"
assign_content += "\n"
result_assign.write(assign_content)
result_assign.flush()
os.fsync(result_assign.fileno())
result_assign.close()
def _assist_inner(self, args=None):
dump_config = DumpConfigWTC(3, 50, 17, 230, 1)
day = self._selected_date.day
month = self._selected_date.month
month_val, day_val = convert_month_day_num_to_str(
str(month) + "/" + str(day))
file_name = data_main_directory + "trips/" + month_val + "/" + day_val + ".csv"
date_str = self._selected_date.strftime("%Y/%m/%d")
print(
"Assigning trips to buses as of data for date {}".format(date_str))
dump_structure = self._dump_util.load_filtered_data(dump_config)
df = pd.read_csv(file_name)
buses_dicts = {}
for x in range(101, 152):
buses_dicts[x] = bus(str(x), gas_bus_type)
for x in range(501, 508):
buses_dicts[x] = bus(str(x), gas_bus_type)
for x in range(751, 755):
buses_dicts[x] = bus(str(x), electric_bus_type)
trips_dicts = {}
for trip in dump_structure.filtered_trips:
trips_dicts[trip.get_trip_id()] = trip
trip_vehicle_pairs = []
real_times = {}
real_times_stamps = {}
trips = []
for i in range(len(df)):
entry = df.iloc[i]
try:
trip_id = str(int(entry["tatripid"]))
except ValueError:
trip_id = "null"
vehicle_id = str(entry["vid"])
start_time_stamp = entry["start_timestamp"]
end_time_stamp = entry["end_timestamp"]
start_time = entry["start_tmstmp"].split(" ")[1] + ":00"
end_time = entry["end_tmstmp"].split(" ")[1] + ":00"
route = str(entry["route_id"])
current_trip = None
current_vehicle = None
start_time_stamp = int(float(start_time_stamp))
end_time_stamp = int(float(end_time_stamp))
if route not in ["33", "34", "U", "PI", "PO"]:
if trip_id in trips_dicts.keys():
current_trip = trips_dicts[trip_id]
if int(vehicle_id) in buses_dicts.keys():
current_vehicle = buses_dicts[int(vehicle_id)]
if start_time_stamp != end_time_stamp:
if current_trip in trips:
if current_trip is not None and current_vehicle is not None:
(p_start_time, p_end_time
) = real_times[current_trip.get_trip_id()]
(p_start_time_stamp, p_end_time_stamp, p_bus_name
) = real_times_stamps[current_trip.get_trip_id()]
if p_bus_name == current_vehicle.bus_name:
if p_start_time_stamp < start_time_stamp:
start_time_stamp = p_start_time_stamp
start_time = p_start_time
if p_end_time_stamp > end_time_stamp:
end_time_stamp = p_end_time_stamp
end_time = p_end_time
real_times[current_trip.get_trip_id()] = (
start_time, end_time)
real_times_stamps[current_trip.get_trip_id(
)] = (start_time_stamp, end_time_stamp,
current_vehicle.bus_name)
else:
if int(float(end_time_stamp)) - int(float(start_time_stamp)) >= 300 >= \
int(float(p_end_time_stamp)) - int(float(p_start_time_stamp)):
previous_vehicle = buses_dicts[int(
p_bus_name)]
trip_vehicle_pairs.remove(
[current_trip, previous_vehicle])
trip_vehicle_pairs.append(
[current_trip, current_vehicle])
real_times[current_trip.get_trip_id()] = (
start_time, end_time)
real_times_stamps[current_trip.get_trip_id(
)] = (start_time_stamp, end_time_stamp,
current_vehicle.bus_name)
else:
trips.append(current_trip)
if current_trip is not None and current_vehicle is not None:
trip_vehicle_pairs.append(
[current_trip, current_vehicle])
real_times[current_trip.get_trip_id()] = (
start_time, end_time)
real_times_stamps[current_trip.get_trip_id()] = (
start_time_stamp, end_time_stamp,
current_vehicle.bus_name)
print("Number of filtered trips for forced assign {}".format(
str(len(trip_vehicle_pairs))))
trip_count = 0
assign_pairs = {}
for [selected_trip, selected_bus] in trip_vehicle_pairs:
if selected_trip in trips_dicts.values(
) and selected_bus in buses_dicts.values():
trip_count += 1
time_in_sec = selected_trip.start_s()
assigns = []
if time_in_sec in assign_pairs.keys():
assigns = assign_pairs[time_in_sec]
assigns.append((selected_trip, selected_bus))
assign_pairs[time_in_sec] = assigns.copy()
specific_trip_dir = trips_directory + month_val + "/" + day_val + "/"
create_dir(specific_trip_dir)
fu_assign = ForcedUpdate(dump_structure=dump_structure,
result_dir=specific_trip_dir,
date=date_str)
_ = fu_assign.force_update(assign_pairs, real_times=real_times)
self._assignment = fu_assign.assignment
def _open_writer(self):
create_dir(self._output_dir)
self.__summary = FileWriter(self._path)
def _save_model(self):
create_dir(self._ip_file_name)
self.write(self._ip_file_name)
def dump_obj(obj, file_name):
create_dir(file_name)
dump_file = open(file_name, "wb")
pickle.dump(obj, dump_file)
dump_file.close()
def _make_dump_directory(self):
create_dir(self.dump_directory)
import os
import sys
sys.path.append(os.getcwd())
from base.dump.with_charge.DumpConfig import DumpConfigWTC
from base.dump.with_charge.DumpUtil import DumpUtilIPWTC, DumpUtilWTC
from common.configs.global_constants import run_mode, max_ev_count, max_gv_count, dump_directory
from common.mode.RunMode import get_r_t_h_values, RunMode
from common.util.common_util import create_dir
if run_mode == RunMode.FULL:
dump_util = DumpUtilWTC()
else:
dump_util = DumpUtilIPWTC()
create_dir(dump_directory)
for (r, t, h) in get_r_t_h_values(run_mode):
e = max_ev_count
g = min(max_gv_count, 5 * r - e)
dump_config = DumpConfigWTC(e, g, r, t, h)
dump_util.load_filtered_data(dump_config)
def run(self, dump_structure, args):
cycle_count = int(args.cycle_count)
start_prob = float(args.start_prob)
end_prob = float(args.end_prob)
swap_prob = float(args.swap_prob)
swap_condition = 0 < swap_prob < 1
start_end_condition = 0 < end_prob < start_prob < 1
s_print("Simulated annealing configs: \ncycle Count: {}\nstart prob: {}\nend prob: {}\nswap prob: {}".
format(args.cycle_count, args.start_prob, args.end_prob, args.swap_prob))
if not swap_condition or not start_end_condition:
raise ValueError("inconsistent parameters")
assignment = greedy_assign(dump_structure, AssignUtilConfig(do_print=True), args=args)
energy_cost = assignment.total_energy_cost()
self.min_assign = assignment
self.min_cost = energy_cost
temp_start = -1.0 / math.log(start_prob)
temp_end = -1.0 / math.log(end_prob)
rate_of_temp = (temp_end / temp_start) ** (1.0 / (cycle_count - 1.0))
selected_temp = temp_start
delta_e_avg = 0.0
number_of_accepted = 1
prefix = "{}_{}_{}_".format(args.start_prob, args.end_prob, args.swap_prob)
prefix = prefix.replace(".", "_")
self.summary_file_name = summary_directory + prefix + "simulated_annealing.csv"
create_dir(summary_directory)
summary_file = FileWriter(self.summary_file_name)
summary_file.write("iteration,energy_cost")
summary_file.write([0, energy_cost])
for i in range(cycle_count):
s_print('Cycle: {} with Temperature: {}'.format(str(i), str(selected_temp)))
nn_assignment = nearest_neighbour(assignment, swap_prob)
nn_energy_cost = nn_assignment.total_energy_cost()
delta_e = abs(nn_energy_cost - energy_cost)
if nn_energy_cost > energy_cost:
if i == 0:
delta_e_avg = delta_e
denominator = (delta_e_avg * selected_temp)
p = math.exp(-1 * math.inf) if denominator == 0 else math.exp(-delta_e / denominator)
accept = True if random.random() < p else False
else:
accept = True
# save current minimum to avoid losing details due to crash
if self.min_cost > nn_energy_cost:
self.min_assign = nn_assignment.copy()
self.min_cost = nn_energy_cost
nn_assignment.write("current_min")
nn_assignment.write_bus_stat("current_min")
if accept:
assignment = nn_assignment
energy_cost = nn_energy_cost
summary_file.write([i, energy_cost])
delta_e_avg = delta_e_avg + (delta_e - delta_e_avg) / number_of_accepted
number_of_accepted += 1
selected_temp = rate_of_temp * selected_temp
summary_file.close()
improve_perc = round(100.0 * (energy_cost - self.min_cost) / energy_cost, 3)
s_print("Improvement in energy cost {}%".format(str(improve_perc)))
