Networkx和Plotly边缘不匹配

Question

我正在尝试使用Networkx 2.1和Plotly 2.7.0生成一个使用距离矩阵作为输入的最小生成树图。我正在使用Python 2.7。

问题是Plotly图中显示的节点的边缘与Networkx最小生成树的边缘列表不匹配。

距离矩阵（csv文件）具有以下格式：

,, , ,

这是Python脚本：

ST0_1,0,0,1109,1109,1091,1091,1125,1126,1126,1125,1127,1126,1127,1127,1125,1125,1125,1126,1126,1127,1127,1127,1127,1122,1130,1126,1127
ST0_2,0,0,1109,1109,1091,1091,1125,1126,1126,1125,1127,1126,1127,1127,1125,1125,1125,1126,1126,1127,1127,1127,1127,1122,1130,1126,1127
ST1_3,1109,1109,0,0,1107,1107,1047,1048,1048,1047,1049,1048,1049,1049,1049,1049,1049,1047,1049,1049,1047,1047,1047,1050,1050,1049,1049
ST1_4,1109,1109,0,0,1107,1107,1047,1048,1048,1047,1049,1048,1049,1049,1049,1049,1049,1047,1049,1049,1047,1047,1047,1050,1050,1049,1049
ST22_5,1091,1091,1107,1107,0,0,1100,1101,1101,1100,1102,1101,1102,1102,1101,1101,1101,1101,1100,1102,1101,1101,1102,1099,1103,1102,1102
ST22_6,1091,1091,1107,1107,0,0,1100,1101,1101,1100,1102,1101,1102,1102,1101,1101,1101,1101,1100,1102,1101,1101,1102,1099,1103,1102,1102
ST2,1125,1125,1047,1047,1100,1100,0,5,6,8,8,9,8,10,7,7,8,11,16,12,8,7,11,72,43,11,17
ST7,1126,1126,1048,1048,1101,1101,5,0,1,7,3,4,3,5,6,6,7,10,15,13,7,6,10,72,43,6,16
ST9,1126,1126,1048,1048,1101,1101,6,1,0,8,2,3,2,4,7,7,8,11,16,14,8,7,11,73,44,5,17
ST5,1125,1125,1047,1047,1100,1100,8,7,8,0,10,11,10,12,7,7,8,7,16,16,10,9,13,73,44,13,17
ST13,1127,1127,1049,1049,1102,1102,8,3,2,10,0,3,2,4,9,9,10,13,18,16,10,9,13,75,46,7,19
ST11,1126,1126,1048,1048,1101,1101,9,4,3,11,3,0,3,1,10,10,11,14,19,17,11,10,14,76,47,8,20
ST10,1127,1127,1049,1049,1102,1102,8,3,2,10,2,3,0,4,9,9,10,13,18,16,10,9,13,75,46,7,19
ST12,1127,1127,1049,1049,1102,1102,10,5,4,12,4,1,4,0,11,11,12,15,20,18,12,11,15,77,48,9,21
ST16_7,1125,1125,1049,1049,1101,1101,7,6,7,7,9,10,9,11,0,0,3,10,15,15,9,8,12,72,43,12,16
ST16_8,1125,1125,1049,1049,1101,1101,7,6,7,7,9,10,9,11,0,0,3,10,15,15,9,8,12,72,43,12,16
ST15,1125,1125,1049,1049,1101,1101,8,7,8,8,10,11,10,12,3,3,0,11,16,16,10,9,13,73,44,13,17
ST6,1126,1126,1047,1047,1101,1101,11,10,11,7,13,14,13,15,10,10,11,0,17,15,11,12,12,70,43,14,18
ST18,1126,1126,1049,1049,1100,1100,16,15,16,16,18,19,18,20,15,15,16,17,0,22,16,17,18,77,49,21,7
ST17,1127,1127,1049,1049,1102,1102,12,13,14,16,16,17,16,18,15,15,16,15,22,0,14,15,15,75,50,15,23
ST4,1127,1127,1047,1047,1101,1101,8,7,8,10,10,11,10,12,9,9,10,11,16,14,0,1,3,73,44,13,17
ST3,1127,1127,1047,1047,1101,1101,7,6,7,9,9,10,9,11,8,8,9,12,17,15,1,0,4,74,45,12,18
ST8,1127,1127,1047,1047,1102,1102,11,10,11,13,13,14,13,15,12,12,13,12,18,15,3,4,0,72,47,14,20
ST21,1122,1122,1050,1050,1099,1099,72,72,73,73,75,76,75,77,72,72,73,70,77,75,73,74,72,0,83,74,79
ST20,1130,1130,1050,1050,1103,1103,43,43,44,44,46,47,46,48,43,43,44,43,49,50,44,45,47,83,0,49,50
ST14,1126,1126,1049,1049,1102,1102,11,6,5,13,7,8,7,9,12,12,13,14,21,15,13,12,14,74,49,0,22
ST19,1127,1127,1049,1049,1102,1102,17,16,17,17,19,20,19,21,16,16,17,18,7,23,17,18,20,79,50,22,0

这是输出HTML的屏幕截图：

如果运行命令import csv import numpy as np import networkx as nx from plotly.offline import plot import plotly.graph_objs as go from networkx.drawing.nx_agraph import graphviz_layout def read_dist_matrix(file_name): """ Reads a distance matrix in PHYLIP format. Requires: file_name is the name of a file that has the distance matrix in PHYLIP format. Ensures: distance matrix as numpy array and a list with the names/ids of each sequence. """ with open(file_name) as f: #first_line = f.readline().strip() reader = csv.reader(f, delimiter=',') names = list(zip(*reader))[0] cols = tuple(list(range(1,len(names)+1))) dist_matrix = np.loadtxt(file_name, delimiter=',', usecols=cols) return [dist_matrix, names] def dist_to_graph(matrix, names): """ Converts numpy array representing distance matrix to graph. Requires: matrix as a numpy array. names as a list with the names of each row/column. Ensures: networkx graph labeled with the right names. """ G = nx.from_numpy_matrix(matrix) nodes = list(G.nodes()) names_dict = {} for n in range(len(nodes)): names_dict[nodes[n]] = names[n] G = nx.relabel_nodes(G, names_dict) return G ola = read_dist_matrix("out.csv") ola_g = dist_to_graph(ola[0], ola[1]) mst = nx.minimum_spanning_tree(ola_g, algorithm='prim') pos = graphviz_layout(mst, prog='neato') labels = list(mst.nodes.keys()) X = [] Y = [] for k in pos: X.append(pos[k][0]) Y.append(pos[k][1]) tracer = go.Scatter(x=X, y=Y, mode='lines', line=go.Line(color='#888', width=2), hoverinfo = 'none', showlegend=False) tracer_marker = go.Scatter(x=X, y=Y, mode='markers+text', text=labels, textposition='top', marker = go.Marker(size= 15, line = dict(width = 2)), hoverinfo = 'none', showlegend=False) layout = dict(title='Test', showlegend=False, xaxis=dict(title='', titlefont=dict(size=20), showgrid=False, zeroline=False, showline=False, ticks='', showticklabels=False, showspikes=False), yaxis=dict(title='', titlefont=dict(size=20), showgrid=False, zeroline=False, showline=False, ticks='', showticklabels=False, showspikes=False) ) fig = dict(data=[tracer, tracer_marker], layout=layout) plot(fig, filename='./test_plotly.html', auto_open=True, show_link=False) ，则会看到节点ST7和ST5已连接，但在Plotly图中它们未连接。

有人问过类似的问题here，但它在R上：

有人可以帮我吗？

Answer 1

您有多个正在使用的字典。字典中键返回的顺序无法准确预测。

我认为您无法保证 .global _start .equ TAM, 100 .set end, 0 _start: mov R0, #0 Loop: cmp R5, #3 beq done ldr R3, =i lsl R4, R5, #2 add R4, R4, R3 mov r0, #1 @ Comando de saida ldr r1, =msg_prod_nome @ Endereco da mensagem ldr r2, =len_prod_nome @ Tamanho mensagem a ser escrita mov r7, #4 @ Sinaliza a saída no console svc 0x055 mov r0, #0 @ Comando de entrada ldr r1, =produto_nome @ Endereco da mensagem ldr r2, =TAM @ Tamanho maximo a ser lido mov r7, #3 @ Sinaliza a entrada no console svc #0x55 str r1, [R4] add R5, R5, #1 b Loop done: mov R5, #0 readloop: cmp R5, #3 beq readdone ldr R3, =i lsl R4, R5, #2 add R4, R4, R3 mov r0, #1 @ Comando de saida @ Endereco da mensagem ldr r1, [R4] ldr r2, =TAM mov r7, #4 @ Sinaliza a saída no console svc 0x055 mov r0, #1 @ Comando de saida ldr r1, =quebra_linha @ Endereco da mensagem ldr r2, =len @ Tamanho mensagem a ser escrita mov r7, #4 @ Sinaliza a saída no console svc 0x055 add R5, R5, #1 b readloop @ ------------------------------ readdone: mov r0, #0 mov r7, #1 svc #0x55 i: .skip 100 produto_nome: .skip TAM quebra_linha: .ascii "\r\n\r" len = . - quebra_linha 与list(mst.nodes.keys())和X的顺序相同。因此，我怀疑标签Y是否已应用于其他节点。