mobicen / util / MyUtil.py @ 0a4aa24d
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from prettytable import PrettyTable 

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import numpy as np 
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import networkx as nx 
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import random as rnd 
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import matplotlib.pyplot as plt 
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plt.ion() 
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import matplotlib.colors as mcolors 
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import sys 
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import code # code.interact(local=dict(globals(), **locals())) 
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def draw(G, pos, xmax, ymax, measures=None): 
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lay = {k:pos[k] for k in range(0,len(pos))} 
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if not measures: 
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measures = nx.betweenness_centrality(G, normalized = False, weight = 'weight', endpoints=True) 
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measure_name = "Betweenness"

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nodes = nx.draw_networkx_nodes(G, lay, node_size=25, cmap=plt.cm.gnuplot2,

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node_color=measures.values(), 
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nodelist=measures.keys()) 
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nodes.set_norm(mcolors.SymLogNorm(linthresh=0.01, linscale=1)) 
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edges = nx.draw_networkx_edges(G, lay) 
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plt.title(measure_name) 
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plt.colorbar(nodes) 
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plt.xlim(0, xmax)

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plt.ylim(0, ymax)

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plt.draw() 
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plt.pause(0.01)

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plt.clf() 
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#plt.show()

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def dictAlikes(d1, d2, perc): 
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if (sorted(d1.keys()) != sorted(d2.keys())): 
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return False 
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for k in d1: 
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a = float(d1[k])

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b = float(d2[k])

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if (not b  (b*perc) <= a <= b + (b*perc)): 
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return False 
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return True 
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def summary(v): 
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vmin=min(v)

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vmax=max(v)

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tot=sum(v)

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avg=np.mean(v) 
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std=np.std(v) 
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t = PrettyTable(['min','max', 'mean','std','tot']) 
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s = "%.4f %.4f %.4f %.4f %.4f" % (vmin,vmax,avg,std,tot)

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t.add_row(s.split()) 
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print t

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return avg, std, vmax, vmin, tot
