def get_ontime_percentage_report_for_given_minutes(minutes, session, exclude_stops=None):
stop_names = manage.get_stop_names()
stop_passenger_ratio = get_stop_passenger_ratio()
stops = stop_names.keys()
data, trainstops = calc_ontime_data(stops, session, minutes, stop_names)
all_vals = [data[x]['all'] for x in stops]
rush = [data[x]['rush'] for x in stops]
non_rush = [data[x]['non_rush'] for x in stops]
stop_names_list = [stop_names[x] for x in stops]
exclude_inds = [stops.index(x) for x in exclude_stops]
order = np.argsort(all_vals)[::-1]
order = [i for i in order if i not in exclude_inds]
all_vals_ordered = [all_vals[i] for i in order]
rush_ordered = [rush[i] for i in order]
stop_names_list_ordered = [stop_names_list[i] for i in order]
title = 'Percentage of on-time trains per station. On time is when delay < {} minutes'.format(minutes)
vals_dict = {"all":all_vals_ordered, "rush":rush_ordered}
display_station_delay_bar_graph(vals_dict, stop_names_list_ordered, title, (22.0, 16.0))
print title
print "Name\tAll\tRush-hour"
for val in reversed(zip(stop_names_list_ordered, all_vals_ordered, rush_ordered)):
print val[0] + '\t' + str(val[1]) + '\t' + str(val[2])
print ""
passenger_weighted_all, passenger_weighted_rush = calc_passenger_weighted_ontime_percent(stops, stop_passenger_ratio, data)
print "Passenger ontime={}%, rush={}% (up to {} minutes delay)".format(passenger_weighted_all, passenger_weighted_rush, minutes)
def analyze_average_commute(date_val):
# Display a contiguous commute time from a source station to a target station
# Get DB data
session = manage.get_session()
stop_names = manage.get_stop_names()
station_id_dict = station_id2name('../data/stops_ids_and_names.txt')
# Create a graph representation of the train stops
G = create_train_graph(session, stop_names, station_id_dict, date_val)
target = 4600 # station_id for Tel-Aviv Hashalom
time_len = 60*24
hour_vec = np.array(range(24*60))/60
minute_vec = np.array(range(24*60)) % 60
time_indx_real = map(lambda x: '%04d'%x, hour_vec*100 + minute_vec)
# Calculation of the DOT graph algorithms
dist_vec, path_vec = dynamic_all_to_one(G, target, time_len)
display_graph(G, draw_edge_label=False)
station_id = 5410
xindx = range(len(time_indx_real))
plt.xticks(xindx[240::240], time_indx_real[240::240]), plt.plot(xindx[240:], dist_vec[station_id][240:], label=station_id_dict[station_id], linewidth=2.5), plt.grid(), plt.ylim(0, 150)
plt.xlabel('time of day'), plt.ylabel('time of commute in minutes'), plt.title(('Commute Time to %s') % (station_id_dict[4600]))
station_id = 3500
plt.plot(xindx[240:], dist_vec[station_id][240:], label=station_id_dict[station_id], linewidth=2.5)
station_id=8700
plt.plot(xindx[240:], dist_vec[station_id][240:], label=station_id_dict[station_id], linewidth=2.5)
plt.legend()
plt.show()
def analyze_average_commute(date_val):
# Display a contiguous commute time from a source station to a target station
# Get DB data
session = manage.get_session()
stop_names = manage.get_stop_names()
station_id_dict = station_id2name('../data/stops_ids_and_names.txt')
# Create a graph representation of the train stops
G = create_train_graph(session, stop_names, station_id_dict, date_val)
target = 4600 # station_id for Tel-Aviv Hashalom
time_len = 60 * 24
hour_vec = np.array(range(24 * 60)) / 60
minute_vec = np.array(range(24 * 60)) % 60
time_indx_real = map(lambda x: '%04d' % x, hour_vec * 100 + minute_vec)
# Calculation of the DOT graph algorithms
dist_vec, path_vec = dynamic_all_to_one(G, target, time_len)
display_graph(G, draw_edge_label=False)
station_id = 5410
xindx = range(len(time_indx_real))
plt.xticks(xindx[240::240], time_indx_real[240::240]), plt.plot(
xindx[240:],
dist_vec[station_id][240:],
label=station_id_dict[station_id],
linewidth=2.5), plt.grid(), plt.ylim(0, 150)
plt.xlabel('time of day'), plt.ylabel(
'time of commute in minutes'), plt.title(
('Commute Time to %s') % (station_id_dict[4600]))
station_id = 3500
plt.plot(xindx[240:],
dist_vec[station_id][240:],
label=station_id_dict[station_id],
linewidth=2.5)
station_id = 8700
plt.plot(xindx[240:],
dist_vec[station_id][240:],
label=station_id_dict[station_id],
linewidth=2.5)
plt.legend()
plt.show()
def get_ontime_percentage_report_for_given_minutes(minutes,
session,
exclude_stops=None):
stop_names = manage.get_stop_names()
stop_passenger_ratio = get_stop_passenger_ratio()
stops = stop_names.keys()
data, trainstops = calc_ontime_data(stops, session, minutes, stop_names)
all_vals = [data[x]['all'] for x in stops]
rush = [data[x]['rush'] for x in stops]
non_rush = [data[x]['non_rush'] for x in stops]
stop_names_list = [stop_names[x] for x in stops]
exclude_inds = [stops.index(x) for x in exclude_stops]
order = np.argsort(all_vals)[::-1]
order = [i for i in order if i not in exclude_inds]
all_vals_ordered = [all_vals[i] for i in order]
rush_ordered = [rush[i] for i in order]
stop_names_list_ordered = [stop_names_list[i] for i in order]
title = 'Percentage of on-time trains per station. On time is when delay < {} minutes'.format(
minutes)
vals_dict = {"all": all_vals_ordered, "rush": rush_ordered}
display_station_delay_bar_graph(vals_dict, stop_names_list_ordered, title,
(22.0, 16.0))
print title
print "Name\tAll\tRush-hour"
for val in reversed(
zip(stop_names_list_ordered, all_vals_ordered, rush_ordered)):
print val[0] + '\t' + str(val[1]) + '\t' + str(val[2])
print ""
passenger_weighted_all, passenger_weighted_rush = calc_passenger_weighted_ontime_percent(
stops, stop_passenger_ratio, data)
print "Passenger ontime={}%, rush={}% (up to {} minutes delay)".format(
passenger_weighted_all, passenger_weighted_rush, minutes)
path_vec[i][t] = [i] + path_vec[j][time_next]
return dist_vec, path_vec
### -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if __name__ == '__main__':
# Create graph
# time_len = 2
# G = create_toy_graph(10, 25, time_len=time_len)
# G = create_predefind_graph()
import manage
session = manage.get_session()
stop_names = manage.get_stop_names()
station_id_dict = station_id2name('../data/stops_ids_and_names.txt')
date_val = datetime.date(2014, 1, 1)
G = create_train_graph(session, stop_names, station_id_dict, date_val)
target = 4600
time_len = 60*24
hour_vec = np.array(range(24*60))/60
minuete_vec = np.array(range(24*60)) % 60
time_indx_real = map(lambda x: '%04d'%x, hour_vec*100 + minuete_vec)
dist_vec, path_vec = dynamic_all_to_one(G, target, time_len)
display_graph(G, draw_edge_label=False)