time_start = time.time() time_compute = time.time()

`time.time()` 是 Python 中 `time` 模块提供的一个函数，它返回当前时间的时间戳（timestamp），时间戳是从1970年1月1日（UTC/GMT的午夜）开始所经过的秒数，不包括闰秒。这个时间戳通常是一个浮点数，可以用来计算时间差或者作为时间标签。

在你提供的代码中，`time_start = time.time()` 这行代码记录了脚本执行开始的时间点，而 `time_compute = time.time()` 这行代码记录了某个计算或者操作完成的时间点。通过这两个时间点，我们可以计算出脚本或代码片段执行所需的时间，即计算时间。具体的计算方法是用 `time_compute` 减去 `time_start`。

例如：

import time

time_start = time.time()  # 记录开始时间

# 这里执行你的代码，比如某个复杂的计算或者操作

time_compute = time.time()  # 记录操作完成时间
time_taken = time_compute - time_start  # 计算操作耗时

print("操作耗时：{:.4f}秒".format(time_taken))

这段代码可以帮助你了解某段代码执行所需的时间，对于性能调优和时间敏感的编程场景很有帮助。

相关问题

优化代码“def calTravelCost(route_list, model): timetable_list = [] distance_of_routes = 0 time_of_routes = 0 obj = 0 for route in route_list: timetable = [] vehicle = model.vehicle_dict[route[0]] v_type = route[0] free_speed = vehicle.free_speed fixed_cost = vehicle.fixed_cost variable_cost = vehicle.variable_cost for i, node_id in enumerate(route): if i == 0: next_node_id = route[i + 1] travel_distance, travel_time, departure = _compute_departure_time(model, v_type, next_node_id, free_speed, 0) elif i < len(route) - 1: last_node_id = route[i - 1] current_node = model.demand_dict[node_id] travel_distance, travel_time, arrival, departure = _compute_arrival_and_departure_time(model, last_node_id, current_node, free_speed, timetable[-1][1]) timetable.append((int(arrival), int(departure))) else: last_node_id = route[i - 1] travel_distance, travel_time, departure = _compute_departure_time(model, last_node_id, v_type, free_speed, timetable[-1][1]) timetable.append((int(departure), int(departure))) distance_of_routes += travel_distance time_of_routes += travel_time if model.opt_type == 0: obj += fixed_cost + distance_of_routes * variable_cost else: obj += fixed_cost + time_of_routes * variable_cost timetable_list.append(timetable) return timetable_list, time_of_routes, distance_of_routes, obj def _compute_departure_time(model, from_node_id, to_node_id, free_speed, arrival_time): travel_distance = model.distance_matrix[from_node_id, to_node_id] travel_time = travel_distance / free_speed departure_time = max(arrival_time, model.demand_dict[to_node_id].start_time - travel_time) return travel_distance, travel_time, departure_time def _compute_arrival_and_departure_time(model, from_node_id, to_node, free_speed, arrival_time): travel_distance = model.distance_matrix[from_node_id, to.id] travel_time = travel_distance / free_speed arrival_time = max(arrival_time + travel_time, to.start_time) departure_time = arrival_time + to.service_time return travel_distance, travel_time, arrival_time, departure_time”

这段代码主要是计算一组路线的时间和距离成本，并返回每个节点的出发和到达时间。其中，_compute_departure_time()函数计算从一个节点到下一个节点的出发时间，_compute_arrival_and_departure_time()函数计算到达一个节点的时间和离开时间，calTravelCost()函数是对这两个函数的封装，用于计算整个路线的时间和距离成本，并返回时间表、时间成本、距离成本和总成本。如果需要优化这段代码，可以考虑使用并行计算来提高计算效率，或者使用更高效的算法来计算时间和距离成本。此外，还可以考虑优化代码结构和变量命名，以提高代码的可读性和可维护性。

降低这段代码的重复率：def calTravelCost(route_list,model): timetable_list=[] distance_of_routes=0 time_of_routes=0 obj=0 for route in route_list: timetable=[] vehicle=model.vehicle_dict[route[0]] travel_distance=0 travel_time=0 v_type = route[0] free_speed=vehicle.free_speed fixed_cost=vehicle.fixed_cost variable_cost=vehicle.variable_cost for i in range(len(route)): if i == 0: next_node_id=route[i+1] travel_time_between_nodes=model.distance_matrix[v_type,next_node_id]/free_speed departure=max(0,model.demand_dict[next_node_id].start_time-travel_time_between_nodes) timetable.append((int(departure),int(departure))) elif 1<= i <= len(route)-2: last_node_id=route[i-1] current_node_id=route[i] current_node = model.demand_dict[current_node_id] travel_time_between_nodes=model.distance_matrix[last_node_id,current_node_id]/free_speed arrival=max(timetable[-1][1]+travel_time_between_nodes,current_node.start_time) departure=arrival+current_node.service_time timetable.append((int(arrival),int(departure))) travel_distance += model.distance_matrix[last_node_id, current_node_id] travel_time += model.distance_matrix[last_node_id, current_node_id]/free_speed+\ + max(current_node.start_time - arrival, 0) else: last_node_id = route[i - 1] travel_time_between_nodes = model.distance_matrix[last_node_id,v_type]/free_speed departure = timetable[-1][1]+travel_time_between_nodes timetable.append((int(departure),int(departure))) travel_distance += model.distance_matrix[last_node_id,v_type] travel_time += model.distance_matrix[last_node_id,v_type]/free_speed distance_of_routes+=travel_distance time_of_routes+=travel_time if model.opt_type==0: obj+=fixed_cost+travel_distance*variable_cost else: obj += fixed_cost + travel_time *variable_cost timetable_list.append(timetable) return timetable_list,time_of_routes,distance_of_routes,obj

Here is a possible way to reduce the repetition in the code:

def calTravelCost(route_list, model):
    timetable_list = []
    distance_of_routes = 0
    time_of_routes = 0
    obj = 0

    for route in route_list:
        timetable = []
        vehicle = model.vehicle_dict[route[0]]
        v_type = route[0]
        free_speed = vehicle.free_speed
        fixed_cost = vehicle.fixed_cost
        variable_cost = vehicle.variable_cost

        for i, node_id in enumerate(route):
            if i == 0:
                next_node_id = route[i + 1]
                travel_distance, travel_time, departure = _compute_departure_time(model, v_type, next_node_id, free_speed, 0)
            elif i < len(route) - 1:
                last_node_id = route[i - 1]
                current_node = model.demand_dict[node_id]
                travel_distance, travel_time, arrival, departure = _compute_arrival_and_departure_time(model, last_node_id, current_node, free_speed, timetable[-1][1])
                timetable.append((int(arrival), int(departure)))
            else:
                last_node_id = route[i - 1]
                travel_distance, travel_time, departure = _compute_departure_time(model, last_node_id, v_type, free_speed, timetable[-1][1])
                timetable.append((int(departure), int(departure)))

            distance_of_routes += travel_distance
            time_of_routes += travel_time

        if model.opt_type == 0:
            obj += fixed_cost + distance_of_routes * variable_cost
        else:
            obj += fixed_cost + time_of_routes * variable_cost

        timetable_list.append(timetable)

    return timetable_list, time_of_routes, distance_of_routes, obj

def _compute_departure_time(model, from_node_id, to_node_id, free_speed, arrival_time):
    travel_distance = model.distance_matrix[from_node_id, to_node_id]
    travel_time = travel_distance / free_speed
    departure_time = max(arrival_time, model.demand_dict[to_node_id].start_time - travel_time)
    return travel_distance, travel_time, departure_time

def _compute_arrival_and_departure_time(model, from_node_id, to_node, free_speed, arrival_time):
    travel_distance = model.distance_matrix[from_node_id, to.id]
    travel_time = travel_distance / free_speed
    arrival_time = max(arrival_time + travel_time, to.start_time)
    departure_time = arrival_time + to.service_time
    return travel_distance, travel_time, arrival_time, departure_time

In this refactored code, I extracted two helper functions `_compute_departure_time` and `_compute_arrival_and_departure_time` to avoid duplication of code. I also simplified the loop that iterates over the nodes in each route by using the `enumerate` function to get both the index and the value of each node. Finally, I moved the computation of `distance_of_routes`, `time_of_routes`, and `obj` inside the loop to avoid redundancy.