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

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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
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']<min_flow:
min_flow = G[C[i]][C[i+1]]['capacity']
return min_flow
def getKPath (C,p):
d = 0
for i in range(len(C)-1):
d += distance.euclidean(p[C[i]],p[C[i+1]])
return d/distance.euclidean(p[C[0]],p[C[-1]])
def new_paths(G,edge):
for u,v,d in G.edges(data=True):
if d['capacity']<=0:
G.remove_edge(G[u][v])
def simplexNewPaths (G, old_paths,edge,points,capacity,V):
G.remove_edge (edge[0],edge[1])
new_paths = []
k = []
for p in old_paths:
new_paths.append (nx.shortest_path(G,p[0],p[-1]))
k.append(getKPath(p,points))
print ("Change paths",new_paths)
for p in new_paths:
k.append(getKPath(p,points))
size = len(old_paths)
A_e = []
A_u = [[]]
b_u = [capacity]
b_e = []
for i in range(size):
A_e.append([])
b_e.append(V[old_paths[i][0]][old_paths[i][-1]])
for j in range(size*2):
if j==2*i or j==2*i+1:
A_e[i].append(1)
else:
A_e[i].append(0)
for j in range(size*2):
if j%2==0:
A_u[0].append(1)
else:
A_u[0].append(0)
res = linprog(k, A_ub=A_u, b_ub=b_u, A_eq = A_e, b_eq=b_e,bounds=(0, None))
print('Optimal value:', res.fun, '\nX:', res.x)
#Создание и отрисовка случайного графа при помощи триангуляции Делоне
N = 6
p = shed.builtPoints(N,10,10)
tri = Delaunay(p)
e,f = shed.getEdgesDelaunay(tri)
G = nx.DiGraph()
for edge in e:
G.add_edge(edge[0],edge[1],weight = round(distance.euclidean(p[edge[0]],p[edge[1]]),3), capacity = np.random.randint(50)+1,flow=[],problem = True)
positions_vertexes = [(p[i][0], p[i][1]) for i in range(N)]
edge_labels=dict([((u,v,),(d['capacity'])) for u,v,d in G.edges(data=True)])
nx.draw_networkx_edge_labels(G,positions_vertexes,edge_labels=edge_labels,label_pos=0.75)
nx.draw_networkx(G, positions_vertexes, with_labels=True, arrows=True, node_color='Red')
plt.show()
#Создание случайной матрциы перевозок
V = builtRandomTransit(N)
shed.pprint (V)
#Поиск всех кратчайших путей
path = nx.all_pairs_dijkstra_path(G)
print (path)
#for i, u in path.items():
# for j, v in u.items():
# path[i][j]=(path[i][j], getMinFlow(path[i][j]),getKPath(path[i][j],p))
for i in range(N):
for j in range(N):
for k in range(len(path[i][j])-1):
# G[path[i][j][k]][path[i][j][k+1]]['capacity'] -= V[i][j]
G[path[i][j][k]][path[i][j][k+1]]['flow'].append(path[i][j])
edge_labels=dict([((u,v,),(d['capacity'])) for u,v,d in G.edges(data=True)])
nx.draw_networkx_edge_labels(G,positions_vertexes,edge_labels=edge_labels,label_pos=0.75)
nx.draw_networkx(G, positions_vertexes, with_labels=True, arrows=True, node_color='Red')
plt.show()
#
#for i, u in path.items():
# for j, v in u.items():
# print (path[i][j], getMinFlow(path[i][j]), V[i][j])
for u,v,d in G.edges(data=True):
sum_flow = sum([V[paths_edge[0]][paths_edge[-1]] for paths_edge in d['flow'] ])
if sum_flow>d['capacity']:
print (u,v,d['capacity'],d['flow'],sum_flow)
simplexNewPaths(G,d['flow'],(u,v),p, d['capacity'],V)