CF84 - 1 year ago 127

Python Question

I am working with an

`NxN`

This is how I generate the network:

`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)

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()

This is what I obtain: there is something wrong as the dict

`lengths`

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.

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Answer Source

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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