Edge color based on weights in Networkx-CodePudding

I am generating a graph with nodes and edges. I want the array Edges to take values in the array Weights such that Edges[0]=Weights[0],Edges[1]=Weights[1], Edges[2]=Weights[2] and display different colors as shown in the expected output.

import networkx as nx
import numpy as np

N = 1

def pos():
    x, y = 1, N   3 - 1
    for _ in range(2 * N * (N   1)):
        yield (x, y)
        y -= (x   2) // (N   3)
        x = (x   2) % (N   3)

G = nx.Graph()
it_pos = pos()
for u in range(2 * N * (N   1)):
    G.add_node(u   1, pos=next(it_pos))
    if u % (2 * N   1) < N:
        for v in (u - 2 * N - 1, u - N - 1, u - N):
            if G.has_node(v   1):
                G.add_edge(u   1, v   1)
    elif u % (2 * N   1) == N:
        G.add_edge(u   1, u - N   1)
    elif u % (2 * N   1) < 2 * N:
        for v in (u - 1, u - N - 1, u - N):
            G.add_edge(u   1, v   1)
    else:
        for v in (u - 1, u - N - 1):
            G.add_edge(u   1, v   1)

nx.draw(G, nx.get_node_attributes(G, 'pos'), with_labels=True, font_weight='bold')
A=nx.adjacency_matrix(G).todense()
A = np.squeeze(np.asarray(A))
print([A])

##################################################
Edges=np.array([[1,2],[1,3],[1,4]])
Weights=np.array([[1.7],[2.9],[8.6]])
##################################################

The current output is

The expected output is

CodePudding user response：

Try this:

import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable

N = 1

def pos():
    x, y = 1, N   3 - 1
    for _ in range(2 * N * (N   1)):
        yield (x, y)
        y -= (x   2) // (N   3)
        x = (x   2) % (N   3)

G = nx.Graph()
it_pos = pos()
for u in range(2 * N * (N   1)):
    G.add_node(u   1, pos=next(it_pos))
    if u % (2 * N   1) < N:
        for v in (u - 2 * N - 1, u - N - 1, u - N):
            if G.has_node(v   1):
                G.add_edge(u   1, v   1)
    elif u % (2 * N   1) == N:
        G.add_edge(u   1, u - N   1)
    elif u % (2 * N   1) < 2 * N:
        for v in (u - 1, u - N - 1, u - N):
            G.add_edge(u   1, v   1)
    else:
        for v in (u - 1, u - N - 1):
            G.add_edge(u   1, v   1)


nx.draw(G, nx.get_node_attributes(G, 'pos'), with_labels=True, font_weight='bold')
Edges=np.array([[1,2],[1,3],[1,4]])
Weights=np.array([[1.7],[2.9],[8.6]])
flat_weights = Weights.flatten()
weights_normalized = [x / max(flat_weights) for x in flat_weights]
edge_weight_map = dict(zip([tuple(e) for e in Edges.tolist()],weights_normalized))
my_cmap = plt.cm.get_cmap('Oranges')
colors = my_cmap([edge_weight_map.get(tuple(e), 0) for e in Edges.tolist()])
pos = nx.get_node_attributes(G, 'pos')
sm = ScalarMappable(cmap=my_cmap, norm=plt.Normalize(0,max(flat_weights)))
nx.draw_networkx_edges(G, pos, edge_color=colors, 
                       edgelist=[tuple(e) for e in Edges.tolist()],
                       width=5);
plt.colorbar(sm)

Output: