import networkx as nx import matplotlib.pyplot as plt # 输入数据 locations = [[125.330802,125.401931,125.326444,125.332284,125.322837,125.32563,125.334942,125.378548,125.386251,125.426883,125.42665,125.437111,125.453763,125.431396,125.430705,125.41968,125.437906,125.404171,125.385772,125.341942,125.341535,125.300812,125.307316,125.345642,125.331492,125.330322,125.284474,125.334851,125.30606,125.377211,125.381077,125.417041,125.41427,125.416371,125.432283,125.401676,125.403855,125.38582,125.426733,125.291], [43.917542,43.919075,43.905821,43.90266,43.900238,43.89703,43.888187,43.904508,43.892574,43.907904,43.896354,43.894605,43.889122,43.88774,43.882928,43.887149,43.8789,43.879647,43.883112,43.873763,43.861505,43.854652,43.876513,43.850479,43.833745,43.825044,43.812019,43.803154,43.793054,43.788869,43.824152,43.816805,43.801673,43.82893,43.83235,43.843713,43.854322,43.868372,43.871792,43.8306]] num_flights = 4 flight_capacity = [10, 10, 10, 10] # 将坐标转化为图 G = nx.Graph() for i in range(len(locations[0])): G.add_node(i+1, pos=(locations[0][i], locations[1][i])) for i in range(len(locations[0])): for j in range(i+1, len(locations[0])): dist = ((locations[0][i]-locations[0][j])**2 + (locations[1][i]-locations[1][j])**2)**0.5 G.add_edge(i+1, j+1, weight=dist) # 添加起点和终点 start_node = len(locations[0])+1 end_node = len(locations[0])+2 G.add_node(start_node, pos=(0, 0)) G.add_node(end_node, pos=(0, 0)) # 添加边和边权 for i in range(len(locations[0])): G.add_edge(start_node, i+1, weight=0) G.add_edge(i+1, end_node, weight=0) for f in range(num_flights): for i in range(len(locations[0])): G.add_edge(i+1, len(locations[0])+f*len(locations[0])+i+1, weight=0) G.add_edge(len(locations[0])+f*len(locations[0])+i+1, end_node, weight=0) # 添加航班容量的限制 for f in range(num_flights): for i in range(len(locations[0])): G.add_edge(len(locations[0])+f*len(locations[0])+i+1, len(locations[0])+f*len(locations[0])+len(locations[0])+1, weight=-flight_capacity[f]) #创造路径规划模型 path_model = nx.DiGraph() for i in range(len(locations[0])): for f in range(num_flights): for j in range(len(locations[0])): if i != j: path_model.add_edge(len(locations[0])+flen(locations[0])+i+1, len(locations[0])+flen(locations[0])+j+1, weight=G[i+1][j+1]['weight']G[len(locations[0])+flen(locations[0])+i+1][len(locations[0])+f*len(locations[0])+j+1]['weight'])找出错误
时间: 2023-11-28 12:47:06 浏览: 15
在代码中,有两个地方出现了错误:
1. 在第 47 行,需要修改为 `len(locations[0])+f*len(locations[0])+len(locations[0])+1`,因为这是连接每个航班的起点和终点的边。
2. 在第 54 行,需要将代码拆分成两行,因为这是两个不同的操作。正确的代码应该是:
```
path_model.add_edge(len(locations[0])+f*len(locations[0])+i+1, len(locations[0])+f*len(locations[0])+len(locations[0])+1, weight=-flight_capacity[f])
path_model.add_edge(len(locations[0])+f*len(locations[0])+i+1, len(locations[0])+f*len(locations[0])+len(locations[0])+j+1, weight=G[i+1][j+1]['weight'])
```
这样就能够运行正确了。
相关问题
import networkx as nx import matplotlib.pyplot as plt # 创建图 G = nx.Graph()
# 添加节点
G.add_node(1)
G.add_node(2)
G.add_node(3)
# 添加边
G.add_edge(1, 2)
G.add_edge(2, 3)
G.add_edge(3, 1)
# 绘制图形
nx.draw(G, with_labels=True)
plt.show()
代码解读,import networkx as nx import pandas as pd import matplotlib.pyplot as plt import numpy as np import scipy as sp
这段代码是Python中导入几个常用的数据分析和可视化库。具体解释如下:
- `import networkx as nx`:导入 `networkx` 库,它是一个用于复杂网络分析的Python库,可以方便地创建、操作和可视化各种类型的网络结构。
- `import pandas as pd`:导入 `pandas` 库,并将其命名为 `pd`,它是一个用于数据处理和分析的Python库,可以方便地读取、操作和处理各种格式的数据,如CSV、Excel、SQL等。
- `import matplotlib.pyplot as plt`:导入 `matplotlib` 库,并将其子模块 `pyplot` 命名为 `plt`,它是一个用于绘制各种类型图表的Python库,包括线图、散点图、柱状图等。
- `import numpy as np`:导入 `numpy` 库,并将其命名为 `np`,它是一个用于科学计算的Python库,可以方便地进行数值计算、数组操作和线性代数运算等。
- `import scipy as sp`:导入 `scipy` 库,并将其命名为 `sp`,它是一个用于科学计算和数据分析的Python库,包括线性代数、数值计算、优化、统计分析等。
这些库都是Python数据分析和可视化领域的常用工具,可以帮助我们进行各种类型的数据处理和分析,并将结果可视化。
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