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)