def main(args):
e = edge_interpreter()
#edge = 29
t= travel()
t.set_up_for_test()
#t.load_path_times()
#print t.euler_tour(0,[],[])
'''for i in [0,3,len(t.path_times_list)-5]:
print i
t.graph_edge(i)
e = edge_interpreter()
time = e.interpret(t.path_times_list[i],3)
t.graph_range(time[0],time[1],'timing estimation - clustering - edge {0}'.format(i))
time = e.interpret(t.path_times_list[i],4)
t.graph_range(time[0],time[1],'timing estimation - exponential distribution - edge {0}'.format(i))'''
#t.graph_path_dates(False)
#for pt in t.path_times_list:
#print pt
#
#t.graph_edge(26,True)
#for j in [(0,28),(3,40),(0,40),(10,0),(10,43)]:
# print 'between', j
# for i in [600,610,620,630,640,650,660,670,680,690,700,710,720]:
# print 'time is', i
# print 'clust'
# route = t.a_star_route_advanced(i,3,j[0],j[1])
#
#temp =[]
#for n in route:
# temp.append(n.node_num)
#print temp
#print 'expo'
#route = t.a_star_route_advanced(i,4,j[0],j[1])
#temp =[]
#for n in route:
# temp.append(n.node_num)
#print temp
#t.graph_edge(i)
#times = e.interpret(t.path_times_list[i],3)
#t.graph_range(times[0],times[2][0:143],'entropy of edge {0}'.format(i))
#times = e.interpret(t.path_times_list[i],4)
#t.graph_range(times[0],times[2][0:143],'confidence intervals over edge {0}'.format(i))
# t.graph_range(times[0],times[1],'edge traversal estimations - exponential method on edge {0}'.format(i))
#t.graph_path_dates(True)
#for i in [61,68,71]:
# t.graph_each_edge_confidence_at_time(i,False)
# t.graph_each_edge_entropy_at_time(i,False)
# for i in range(len(t.path_times_list)):
#times = e.interpret(t.path_times_list[i],3)
# t.graph_edge(i,False)
# print i
#matplotlib.pyplot.show()
#matplotlib.pyplot.show()
#t.graph_path_times(True)
#times = e.interpret(t.path_times_list[edge],4)
#print times[2]
#times = e.interpret(t.path_times_list[45],4)
#print times[2]
#t.graph_edge(45,False)
#times = e.interpret(t.path_times_list[47],4)
#print times[2]
#t.graph_edge(47,True)
#print 'times', times[2]
#for i in range(len( times[2])):
# if times[2][i] < sys.maxint:
# print i ,times[2][i], times[1][i]
#print times[55],times[56],times[57], times[58], times[59], times[60],times[85],times[86],times[87],times[88],times[89]
#current_time = 10*int((datetime.today().hour*60+datetime.today().minute)/10)
#print current_time
# t=travel()
#t.set_up_for_test()
#t.load_path_times()
#t.graph_path_times(False)
#t.graph_path_dates(True)
# for i in range(len(t.path_times_list)):
# print i, t.path_times_list[i].edge.A, t.path_times_list[i].edge.B
#edge=26
e = edge_interpreter()
#times = e.interpret_clustering_ten_minutes(t.path_times_list[5].recordings,True,2,10,True)
#t.graph_range(times[0],times[2][0:(144*7)-1], 'entropies',True)
#print times
#t.temp_remove_records_after(30,7,2013)
# print t.path_times_list[26]
#print len(t.path_times_list[0].recordings)
# print(t.system_entropy_between_times(55,56))
#t.graph_each_edge_entropy_at_time(55,False)
#t.graph_each_edge_confidence_at_time(55,True)
# t.graph_edge_entropy(edge,True)
#t.load_path_times()
#t.temp_remove_records_after(1,8,2013)
# print t.path_times_list[26]
# print len(t.path_times_list[0].recordings)
# print(t.system_entropy_between_times(55,56))
#t.graph_each_edge_entropy_at_time(55,False)
#t.graph_each_edge_confidence_at_time(55,True)
# t.graph_edge_entropy(edge,True)
#t.load_path_times()
#t.temp_remove_records_after(2,8,2013)
# print t.path_times_list[26]
# print(t.system_entropy_between_times(55,56))
#t.graph_each_edge_entropy_at_time(55,False)
#t.graph_each_edge_confidence_at_time(55,True)
# t.graph_edge_entropy(edge,True)
#t.load_path_times()
#t.temp_remove_records_after(3,8,2013)
# print len(t.path_times_list[0].recordings)
# print(t.system_entropy_between_times(55,56))
#t.graph_each_edge_entropy_at_time(55,False)
#t.graph_each_edge_confidence_at_time(55,True)
# t.graph_edge_entropy(edge,True)
# t.load_path_times()
#t.temp_remove_records_after(5,8,2013)
# print t.path_times_list[26]
# for i in range(len(t.path_times_list)):
# print '{0} : {1}'.format(i, t.path_times_list[i])
# print len(t.path_times_list[0].recordings)
# print(t.system_entropy_between_times(55,56))
# t.graph_each_edge_entropy_at_time(55,False)
# t.graph_each_edge_confidence_at_time(55,True)
# t.graph_edge_entropy(edge,True)'''
'''print 'len',len(t.path_times_list[0].recordings)
print t.path_times_list[0]
t.temp_remove_records_after(4,8,2013)
print('removed')
print 'len',len(t.path_times_list[0].recordings)
print t.path_times_list[0]'''
'''time=580
edge=16
t= travel()
t.set_up_for_test()
t.load_path_times()
t.current=0
list1 =t.a_star_route_setup(time,4)
list2 =t.a_star_route_setup(time,3)
print('expected value: exponential--------clustering')
print(len(list1))
for i in range(len(list1)):
print( '{0} -- {1} -- diff:{2}'.format(list1[i], list2[i],(list1[i]-list2[i])))
start=15
end =6
route1= t.a_star_route_advanced(time,3,start,end,[])
route2 = t.a_star_route_advanced(time,4,start,end,[])
print('route: clustering - exponential')
if (len(route1)>len(route2)):
for i in range(len(route1)):
if len(route2)<i:
print '{0} , -'.format(route1[i].node_num)
else:
print '{0} , {1}'.format(route1[i].node_num,route2[i].node_num)
else:
for i in range(len(route2)):
if len(route2)<i:
print '- , {0}'.format(route2[i].node_num)
else:
print '{0} , {1}'.format(route1[i].node_num,route2[i].node_num)
'''
#route = t.generate_route_entropy_clusters([],570)
#print route
#print len(route)
#for i in range(len(t.path_times_list)):
# print i
# print t.path_times_list[i].edge.A.node_num, t.path_times_list[i].edge.B.node_num
#print t.path_times_list
#t.graph_path_times(True)end
#ent = t.generate_entropies()
#for i in range(len(ent)):
# print ent[i][55]
# print ent[55]
#t.current=0
#t.graph_path_times(False)
# t.graph_path_dates(True)
# for i in [1,7,8]:
# t.graph_edge(i,False)
# times = e.interpret(t.path_times_list[i],3)
# t.graph_range(times[0][60:80],times[1][60:79],'edge traversal estimations - clustering method on edge {0}'.format(i))
# times = e.interpret(t.path_times_list[i],4)
# t.graph_range(times[0][60:80],times[1][60:79],'edge traversal estimations - exponential method on edge {0}'.format(i))
matplotlib.pyplot.show()
def interpret(self, edge, time):
e = edge_interpreter()
print e.interpret(self.t.path_times_list[edge], 3)
