# import numpy as np import shed import matplotlib pyplot as plt from sc

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155``` ```import numpy as np import shed import matplotlib.pyplot as plt from scipy.spatial import Delaunay import networkx as nx from scipy.spatial import distance from scipy.optimize import linprog class path: def __init__ (self,C,f): self.C = C self.flow = f def getK (self,p): d = 0 for i in range(len(self.C)-1): d += distance.euclidean(p[self.C[i]],p[self.C[i+1]]) return d/distance.euclidean(p[self.C[0]],p[self.C[-1]]) def builtRandomTransit (n): f = [] for i in range(n): f.append([0] * n) for i in range(n): for j in range(n): f[i][j]=np.random.randint(10)+1 if i==j: f[i][j]=0 return f def getMinFlow (C): if len(C)==1: return 0 min_flow = np.inf for i in range(len(C)-1): if G[C[i]][C[i+1]]['capacity']d['capacity']: print (u,v,d['capacity'],sum_flow) newPaths, flows = simplexNewPaths(G,d['flow'],(u,v),p, d['capacity'],V) oldPaths = [i for i in d['flow']] print (oldPaths, newPaths, flows) size = len (newPaths) for i in range(size): if flows[i*2]==0: for j in range(len(oldPaths[i])-1): G[oldPaths[i][j]][oldPaths[i][j+1]]['flow'].remove(oldPaths[i]) if flows[i*2+1]!=0: for j in range(len(newPaths[i])-1): G[newPaths[i][j]][newPaths[i][j+1]]['flow'].append(newPaths[i]) for u,v,d in G.edges(data=True): print (u,v,d['capacity'],d['flow']) ```