#導入模塊
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import scipy.sparse as sp

# 鄰接矩陣
Matrix = np.array(
    [
        [0, 1, 1, 1, 1, 1, 0, 0],  # a
        [0, 0, 1, 0, 1, 0, 0, 0],  # b
        [0, 0, 0, 1, 0, 0, 0, 0],  # c
        [0, 0, 0, 0, 1, 0, 0, 0],  # d
        [0, 0, 0, 0, 0, 1, 0, 0],  # e
        [0, 0, 1, 0, 0, 0, 1, 1],  # f
        [0, 0, 0, 0, 0, 1, 0, 1],  # g
        [0, 0, 0, 0, 0, 1, 1, 0]  # h
    ]
)

def get_matrix_triad(coo_matrix , data=False):
	'''
		獲取矩陣的元組表示  (row,col)
		data 為 True 時 (row,col,data)
	:dependent  scipy
	:param coo_matrix: 三元組表示的稀疏矩陣  類型可以為 numpy.ndarray
	:param data: 是否需要 data值
	:return:
		list
	'''
	# 檢查類型
	if not sp.isspmatrix_coo(coo_matrix):
		# 轉化為三元組表示的稀疏矩陣
		coo_matrix = sp.coo_matrix(coo_matrix)
	# nx3的矩陣  列分別為 矩陣行，矩陣列及對應的矩陣值
	temp = np.vstack((coo_matrix.row , coo_matrix.col , coo_matrix.data)).transpose()
	return temp.tolist()

edags = get_matrix_triad(Matrix)
-->
[[0.0, 0.0, 1.0],
 [0.0, 1.0, 1.0],
 [0.0, 2.0, 1.0],
 [0.0, 3.0, 1.0],
 [0.0, 4.0, 1.0],
 [0.0, 5.0, 1.0],
 [1.0, 1.0, 1.0],
 [1.0, 2.0, 1.0],
 [1.0, 4.0, 1.0],
 [2.0, 2.0, 1.0],
 [2.0, 3.0, 1.0],
 [3.0, 3.0, 1.0],
 [3.0, 4.0, 1.0],
 [4.0, 4.0, 1.0],
 [4.0, 5.0, 1.0],
 [5.0, 2.0, 1.0],
 [5.0, 5.0, 1.0],
 [5.0, 6.0, 1.0],
 [5.0, 7.0, 1.0],
 [6.0, 5.0, 1.0],
 [6.0, 6.0, 1.0],
 [6.0, 7.0, 1.0],
 [7.0, 5.0, 1.0],
 [7.0, 6.0, 1.0],
 [7.0, 7.0, 1.0]]

# 創建一個沒有邊，沒有節點的空圖Graph
G = nx.Graph()

H = nx.path_graph(Matrix.shape[0]) 
G.add_nodes_from(H)

G.add_edges_from(edags) #添加邊
# 若數據含有權重，及 get_matrix_triad() 中 data = True ,則使用
G.add_weighted_edges_from(edags)

colors = np.arange(Matrix.shape[0])
nx.draw(G,pos=nx.spring_layout(G),node_color=colors)
plt.show()

draw_networkx()