def OptimalEnergy(intersections, routes, number, vehicle, console):
'''Get optimal route from pre-generated routes'''
t0 = time.time()
calcRoutes = []
for route, distance in routes:
energy = 0
t = 0 #can't use time, because of module time
for int1, int2, int3 in zip(route[:-2], route[1:-1], route[2:]):
energy += intersections[int1][int2][int3].energy
t += intersections[int1][int2][int3].time
energy += intersections[route[-2]][route[-1]].micropath.energy
t += intersections[route[-2]][route[-1]].micropath.time
calcRoutes.append((route, energy, t, distance))
calcRoutes.sort(key=lambda a: a[1]) #sort by energy usage
top = calcRoutes[:min(number*5, len(calcRoutes))] #get best routes, more than you need to filter near-duplicates
newTop = util.filterRoutes(top, f=3) #take out very similar routes of greater energy
newTop = newTop[:min(len(newTop), number)] #take only what you need
output = []
for route, energy, t, distance in newTop:
path = [intersections[route[0]][route[1]][route[2]][0]]
for int1, int2, int3 in zip(route[:-2], route[1:-1], route[2:]):
path.extend(intersections[int1][int2][int3][1:-1])
output.append(Longpath(path, vehicle, ('energy',energy), ('distance',distance), ('time',t)))
console.add('Optimal Energy', error=': '+str(round(time.time()-t0, 3)))
return output
def OptimalEnergy(intersections, routes, number, vehicle, console):
'''Get optimal route from pre-generated routes'''
t0 = time.time()
calcRoutes = []
for route, distance in routes:
energy = 0
t = 0 #can't use time, because of module time
for int1, int2, int3 in zip(route[:-2], route[1:-1], route[2:]):
energy += intersections[int1][int2][int3].energy
t += intersections[int1][int2][int3].time
energy += intersections[route[-2]][route[-1]].micropath.energy
t += intersections[route[-2]][route[-1]].micropath.time
calcRoutes.append((route, energy, t, distance))
calcRoutes.sort(key=lambda a: a[1]) #sort by energy usage
top = calcRoutes[:min(
number * 5, len(calcRoutes)
)] #get best routes, more than you need to filter near-duplicates
newTop = util.filterRoutes(
top, f=3) #take out very similar routes of greater energy
newTop = newTop[:min(len(newTop), number)] #take only what you need
output = []
for route, energy, t, distance in newTop:
path = [intersections[route[0]][route[1]][route[2]][0]]
for int1, int2, int3 in zip(route[:-2], route[1:-1], route[2:]):
path.extend(intersections[int1][int2][int3][1:-1])
output.append(
Longpath(path, vehicle, ('energy', energy), ('distance', distance),
('time', t)))
console.add('Optimal Energy', error=': ' + str(round(time.time() - t0, 3)))
return output
def OptimalEnergyInitial(intersections, routes, number, console):
'''Get optimal route using pre-generated routes.'''
t0 = time.time()
#get list of most promising routes
output = []
for route, distance in routes:
total = 0
for index, inter in enumerate(route[:-1]):
total += intersections[inter][route[index + 1]].energy
output.append((route, total))
considered = sorted(output, key=lambda a: a[1])
considered = considered[:min(len(output), 100)]
print len(considered)
output = util.filterRoutes(considered, f=2)
print len(output)
console.add('Initial Energy', error=': ' + str(time.time() - t0))
output = output[:min(len(output), max(number * 3, 15))]
print len(output)
return output
def OptimalEnergyInitial(intersections, routes, number, console):
'''Get optimal route using pre-generated routes.'''
t0 = time.time()
#get list of most promising routes
output = []
for route, distance in routes:
total = 0
for index, inter in enumerate(route[:-1]):
total += intersections[inter][route[index+1]].energy
output.append((route, total))
considered = sorted(output, key=lambda a: a[1])
considered = considered[:min(len(output), 100)]
print len(considered)
output = util.filterRoutes(considered, f=2)
print len(output)
console.add('Initial Energy', error=': '+str(time.time()-t0))
output = output[:min(len(output), max(number*3, 15))]
print len(output)
return output
