In [11]:

    

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


    

In [2]:

    

file = "/Users/mark/seriationct/seriations/seriationct-1/results/23d2out/23d2f6f2-ce8e-11e4-93cb-b8f6b1154c9b-0-minmax-by-weight.png.gml"


    

In [3]:

    

g = nx.read_gml(file)


    

In [12]:

    

%matplotlib inline


    

In [14]:

    

mean_degree = np.mean(g.degree().values())
stddev_degree = np.std(g.degree().values())
print "mean degree: %s stddev: %s" % (mean_degree, stddev_degree)


    

    




mean degree: 2.0 stddev: 0.632455532034

In [17]:

    

nx.diameter(g)


    

    
Out[17]:





34

In [21]:

    

nx.degree_histogram(g)


    

    
Out[21]:





[0, 10, 36, 8, 1]

In [22]:

    

dist_file = "/Users/mark/seriationct/seriations/seriationct-1/results/c896638e-cc06-11e4-8260-b8f6b1154c9b-3/c896638e-cc06-11e4-8260-b8f6b1154c9b-3-minmax-by-weight.png.gml"
gd = nx.read_gml(dist_file)


    

In [23]:

    

mean_degree = np.mean(gd.degree().values())
stddev_degree = np.std(gd.degree().values())
print "mean degree: %s stddev: %s" % (mean_degree, stddev_degree)


    

    




mean degree: 1.90909090909 stddev: 0.792527080644

In [24]:

    

nx.degree_histogram(gd)


    

    
Out[24]:





[0, 7, 11, 3, 1]

In [25]:

    

pfg_file = "/Users/mark/src/lipomadsen2015-idss-seriation-paper/seriation-analysis/output/pfg-cpl-minmax-by-weight.png.gml"
gp = nx.read_gml(pfg_file)


    

In [26]:

    

mean_degree = np.mean(gp.degree().values())
stddev_degree = np.std(gp.degree().values())
print "mean degree: %s stddev: %s" % (mean_degree, stddev_degree)


    

    




mean degree: 1.9 stddev: 0.888819441732

In [27]:

    

nx.degree_histogram(gp)


    

    
Out[27]:





[0, 7, 10, 1, 2]

In [33]:

    

hier_3_level_file = "/Users/mark/seriationct/seriations/seriationct-1/results/359c92a6-d0b1-11e4-b6ce-b8f6b1154c9b-0.txt/hier-2-359c92a6-d0b1-11e4-b6ce-b8f6b1154c9b-0-minmax-by-weight.png.gml"
ghier3 = nx.read_gml(hier_3_level_file)


    

In [34]:

    

mean_degree = np.mean(ghier3.degree().values())
stddev_degree = np.std(ghier3.degree().values())
print "mean degree: %s stddev: %s" % (mean_degree, stddev_degree)


    

    




mean degree: 1.94871794872 stddev: 0.932290203215

In [36]:

    

nx.degree_histogram(ghier3)


    

    
Out[36]:





[0, 14, 16, 7, 1, 1]

In [37]:

    




    

    




{'xCoordinate': 17.0, 'yCoordinate': 20.0, 'name': u'assemblage-20-17', 'size': 353.879641323, 'id': 0, 'label': u'assemblage-20-17'}

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