Python：常规网络的边长分布 [英] Python: edge length distribution of a regular network

问题描述

我正在使用 NxN 常规网络，我想绘制其边缘长度分布。

I am working with an NxN regular network and I want to plot its edge length distribution.

这是我如何生成网络：

import networkx as nx
import matplotlib.pyplot as plt
N=30 #This can be changed
G=nx.grid_2d_graph(N,N)
pos = dict( (n, n) for n in G.nodes() )
labels = dict( ((i, j), i + (N-1-j) * N ) for i, j in G.nodes() )
nx.relabel_nodes(G,labels,False)
inds=labels.keys()
vals=labels.values()
inds.sort()
vals.sort()
pos2=dict(zip(vals,inds))
nx.draw_networkx(G, pos=pos2, with_labels=False, node_size = 15)

这是我如何计算边长分布：

This is how I compute the edge length distribution:

def plot_edge_length_distribution(): #Euclidean distances from all nodes
    lengths={}
    for node in G.nodes():
        neigh=nx.all_neighbors(G,node) #The connected neighbors of node n
        for n in neigh:
            lengths[node]=((pos2[n][1]-pos2[node][1])**2)+((pos2[n][0]-pos2[node][0])**2) #The square distance
    items=sorted(lengths.items())
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.plot([k for (k,v) in items],[v/(num_edges) for (k,v) in items],'ks-')
    ax.set_xscale("linear")
    ax.set_yscale("linear")
    plt.yticks(numpy.arange(0.94, 1.00, 0.02))
    title_string=('Edge Length Distribution')
    subtitle_string=('Lattice Network | '+str(N)+'x'+str(N)+' nodes') 
    plt.suptitle(title_string, y=0.99, fontsize=17)
    plt.title(subtitle_string, fontsize=9)
    plt.xlabel('Edge Length L')
    plt.ylabel('p(L)')
    ax.grid(True,which="both")
    plt.show()
plot_edge_length_distribution()

这是我获得的：有由于常规网格的性质，dict 长度应该只包含一个值作为错误。

This is what I obtain: there is something wrong as the dict lengths should contain only ones as values, due to the nature of the regular grid.

这是我想要的：一个情节告诉我，长度= 1有概率p（l）= 1，因为规则网格只有长度为1的边。我的代码有什么问题？ p>

This is what I want: a plot telling me that length=1 has a probability p(l)=1 because the regular grid only features edges of length 1. What is wrong in my code?

推荐答案

迭代边缘并计算每一个距离更容易和更快：

It's easier and faster to iterate over the edges and compute the distance on each one:

In [1]: import networkx as nx

In [2]: from math import sqrt

In [3]: from collections import Counter

In [4]: G = nx.grid_2d_graph(100,100)

In [5]: d = Counter(sqrt((x-a)**2 + (y-b)**2) for (x,y),(a,b) in G.edges())

In [6]: print(d)
Counter({1.0: 19800})

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