def compare():
G = create_graph()
# print(G.degree(1))
# path = nx.dijkstra_path(G, 1, 1200)
# print(path)
# nx.draw(G)
# plt.title("graph")
# plt.axis('on')
# plt.xticks([])
# plt.yticks([])
# plt.show()
dist1 = np.zeros(G.number_of_nodes() + 1)
for i in range(1, G.number_of_nodes() + 1):
dist1[i] = nx.dijkstra_path_length(G, 2, i)
print(np.max(dist1))
print(np.min(dist1))
reader = DataReader()
net_array = reader.data_reader()
net_distance = NetDistance(net_array)
node_dist1 = net_distance.node_distance(2)
node_dist1[np.where(node_dist1 == float("inf"))] = 0
print(np.max(node_dist1))
# print(np.where(node_dist == 7))
print(dist1.size, node_dist1.size)
print(np.where(dist1 == node_dist1))
def test_delete_node_distance():
reader = DataReader()
net_array = reader.data_reader()
x_list = [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
for i in x_list:
# 删除i比例的节点
nodes_deleted = delete_nodes(net_array, i)
net_distance = NetDistance(nodes_deleted)
all_dict = net_distance.all_pair_node_distance()
distri_array = net_distance.all_pair_lenth_distribution(all_dict)
net_distance.write_distance_distribution_to_file(distri_array, file_path="../data/distance/distance_distribution_test_"+str(i)+".json")
'''
:param clustering: 每个节点的聚类系数
:param rootPath: 保存文件的根目录
:return:
'''
# filename = input("输入保存的文件名:")
# file_path = os.path.join(rootPath, filename)
file_path = rootPath
with open(file_path, 'w') as fp:
json.dump(clustering, fp)
if __name__ == "__main__":
filename = "../data/clustering coefficient.json"
data = DataReader()
adj_matrix = data.data_reader()
# 计算每个节点的聚类系数
my_cal = cal_clustring(adj_matrix)
# 计算clustering的最大和最小值
# max_num, ave_num = clu_pro(filename)
# print("节点聚类系数最大值{}, 平均值{}".format(max_num, ave_num))
# 保存文件到data/cluster文件夹
# rootPath = '../data/cluster/'
rootPath = '../data/cluster/cluster.json'
write_cluster_tofile(my_cal, rootPath)
# 与nexworkx进行对比
if v not in seen:
seen.add(v)
for i in np.where(net_array[v] == 1)[0]:
if i not in next_level:
next_level.append(i)
return seen
def write_submap_to_file(net_array,
file_path="../data/submap/submap_counts.json"):
x_list = [0, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
y_dict = dict()
for t in range(10):
y_list = []
for i in x_list:
# 删除i比例的节点
nodes_deleted = delete_nodes(net_array, i)
y_list.append(calculate_unicom_submap(nodes_deleted))
y_dict.update({str(t): y_list})
submap_dict = {"x": x_list, "y": y_dict}
with open(file_path, 'w') as f:
f.write(json.dumps(submap_dict))
if __name__ == "__main__":
reader = DataReader()
net_array = reader.data_reader()
write_submap_to_file(net_array)