class Path(object): def __init__(self,path,distancecost,timecost): self.__path = path self.__distancecost = distancecost self.__timecost = timecost #路径上最后一个节点 def getLastNode(self): return self.__path[-1] #获取路径路径 @property def path(self): return self.__path #判断node是否为路径上最后一个节点 def isLastNode(self, node): return node == self.getLastNode() #增加加点和成本产生一个新的path对象 def addNode(self, node, dprice, tprice): return Path(self.__path+[node],self.__distancecost + dprice,self.__timecost + tprice) #输出当前路径 def printPath(self): for n in self.__path: if self.isLastNode(node=n): print(n) else: print(n, end="->") print(f"最短路径距离(self.__distancecost:.0f)m") print(f"红绿路灯个数（self.__timecost:.0f）个") #获取路径总成本的只读属性 @property def dCost(self): return self.__distancecost @property def tCost(self): return self.__timecost class DirectedGraph(object): def __init__(self, d): if isinstance(d, dict): self.__graph = d else: self.__graph = dict() print('Sth error') #通过递归生成所有可能的路径 def __generatePath(self, graph, path, end, results, distancecostIndex, timecostIndex): current = path.getLastNode() if current == end: results.append(path) else: for n in graph[current]: if n not in path.path: self.__generatePath(graph, path.addNode(n,self.__graph[path.getLastNode()][n][distancecostIndex][timecostIndex]), end, results, distancecostIndex, timecostIndex) #搜索start到end之间时间或空间最短的路径，并输出 def __searchPath(self, start, end, distancecostIndex, timecostIndex): results = [] self.__generatePath(self.__graph, Path([start],0,0), end, results,distancecostIndex,timecostIndex) results.sort(key=lambda p: p.distanceCost) results.sort(key=lambda p: p.timeCost) print('The {} shortest path from '.format("spatially" if distancecostIndex==0 else "temporally"), start, ' to ', end, ' is:', end="") print('The {} shortest path from '.format("spatially" if timecostIndex==0 else "temporally"), start, ' to ', end, ' is:', end="") results[0].printPath() #调用__searchPath搜索start到end之间的空间最短的路径，并输出 def searchSpatialMinPath(self,start, end): self.__searchPath(start,end,0,0) #调用__searc 优化这个代码

这段代码中可以进行如下优化：

1. 将类属性的命名改为下划线开头的私有属性，避免外部直接修改属性值，可以使用@property装饰器来获取属性的值。

2. 可以将类的构造函数中的参数改为可选参数，避免在初始化时需要传入参数，可以在需要时再传入参数。

3. 在递归函数的参数中加入默认值，避免在调用递归函数时需要传入所有参数。

4. 可以使用f-string格式化输出语句，代码更加简洁易读。

5. 可以使用列表推导式对列表进行过滤。

6. 在搜索最短路径时可以使用heapq模块的堆排序来优化效率。

优化后的代码如下：

import heapq

class Path(object):
    def __init__(self, path = [], distance_cost = 0, time_cost = 0):
        self._path = path
        self._distance_cost = distance_cost
        self._time_cost = time_cost
        
    def get_last_node(self):
        return self._path[-1]
    
    def is_last_node(self, node):
        return node == self.get_last_node()
    
    def add_node(self, node, d_price, t_price):
        return Path(self._path + [node], self._distance_cost + d_price, self._time_cost + t_price)
    
    def print_path(self):
        for n in self._path:
            print(n, end="->" if not self.is_last_node(n) else "\n")
        print(f"最短路径距离({self._distance_cost:.0f}m)")
        print(f"红绿路灯个数({self._time_cost:.0f})个")
    
    @property
    def d_cost(self):
        return self._distance_cost
    
    @property
    def t_cost(self):
        return self._time_cost

class DirectedGraph(object):
    def __init__(self, d = {}):
        if isinstance(d, dict):
            self._graph = d
        else:
            self._graph = dict()
            print('Something went wrong!')
    
    def __generate_path(self, graph, path, end, results, distance_cost_index=0, time_cost_index=0):
        current = path.get_last_node()
        if current == end:
            results.append(path)
        else:
            for n in graph[current]:
                if n not in path._path:
                    self.__generate_path(graph, path.add_node(n, self._graph[path.get_last_node()][n][distance_cost_index][time_cost_index]), end, results, distance_cost_index, time_cost_index)
    
    def __search_path(self, start, end, distance_cost_index=0, time_cost_index=0):
        results = []
        self.__generate_path(self._graph, Path([start], 0, 0), end, results, distance_cost_index, time_cost_index)
        results = heapq.nsmallest(1, results, key=lambda p: (p.d_cost, p.t_cost))
        print(f"The {'spatially' if distance_cost_index==0 else 'temporally'} shortest path from {start} to {end} is:")
        results[0].print_path()
    
    def search_spatial_min_path(self, start, end):
        self.__search_path(start, end, 0, 0)
    
    def search_temporal_min_path(self, start, end):
        self.__search_path(start, end, 1, 1)