Python/인접 행렬 시각화

import matplotlib.pyplot as plt
import networkx as nx
import numpy as np

adj = [
    [0, 1, 1, 0],
    [1, 0, 0, 1],
    [1, 0, 0, 1],
    [0, 1, 1, 0]]

print (adj)

G = nx.from_numpy_matrix(np.array(adj))
nx.draw(G, with_labels=True)

plt.axis('equal')
plt.show()

[[0, 1, 1, 0], [1, 0, 0, 1], [1, 0, 0, 1], [0, 1, 1, 0]]

[PNG image (24.8 KB)]

# 5 X 5
adj = np.zeros((25,25))

for i in range(0, 25):
    u = i - 5
    d = i + 5
    l = i - 1
    r = i + 1

    if 0 < u:
        adj[i][u] = 1
    if d < 25:
        adj[i][d] = 1
    if 0 <= l and (l % 5) != 4:
        adj[i][l] = 1
    if r < 25 and (r % 5) != 0:
        adj[i][r] = 1
print (adj)

G = nx.from_numpy_matrix(np.array(adj))
nx.draw(G, with_labels=True)

plt.axis('equal')
plt.show()

[[0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0.]]

[PNG image (51.66 KB)]

nx.draw_random(G, with_labels=True)

[PNG image (89.05 KB)]

# DFS visualization
adj = np.zeros((25,25))
adjTrail = np.zeros((25,25))
visited = np.zeros(25)

def dfs(v):
    global adjTrail
    global visited

    visited[v] = 1

    for i in range(0, 25):
        if adj[v][i] and not visited[i]:
            adjTrail[v][i] = 1
            adjTrail[i][v] = 1
            dfs(i)

for i in range(0, 25):
    u = i - 5
    d = i + 5
    l = i - 1
    r = i + 1

    if 0 < u:
        adj[i][u] = 1
    if d < 25:
        adj[i][d] = 1
    if 0 <= l and (l % 5) != 4:
        adj[i][l] = 1
    if r < 25 and (r % 5) != 0:
        adj[i][l] = 1

dfs(0)

G = nx.from_numpy_matrix(np.array(adj))

pos = nx.spring_layout(G)

trail_edges = [(u, v) for (u, v, d) in G.edges(data=True) if adjTrail[u][v] == 1]
null_edges = [(u, v) for (u, v, d) in G.edges(data=True) if adjTrail[u][v] == 0]

# nodes
nx.draw_networkx_nodes(G, pos)

# edges
nx.draw_networkx_edges(G, pos, edgelist=trail_edges)
nx.draw_networkx_edges(G, pos, edgelist=null_edges, edge_color="lightgrey", style="dashed")

# labels
nx.draw_networkx_labels(G, pos, font_family="sans-serif")

plt.axis('equal')
plt.show()

0->1->2->3->4->9->8->7->6->5->10->11->12->13->14->19->18->17->16->15->20->21->22->23->24

[PNG image (40.1 KB)]