Revision 23c7ab1e plotterBCrealization.py
plotterBCrealization.py  

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import seaborn as sns 
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sns.set() 
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# https://en.wikipedia.org/wiki/Sample_entropy 

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def SampEn(U, m, r): 

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"""Compute Sample entropy""" 

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def _maxdist(x_i, x_j): 

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return max([abs(ua  va) for ua, va in zip(x_i, x_j)]) 

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def _phi(m): 

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x = [[U[j] for j in range(i, i + m  1 + 1)] for i in range(N  m + 1)] 

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C = [len([1 for j in range(len(x)) if i != j and _maxdist(x[i], x[j]) <= r]) for i in range(len(x))] 

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return sum(C) 

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N = len(U) 

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return np.log(_phi(m+1) / _phi(m)) 

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ss = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/sunspotarea.csv') 

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a10 = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv') 

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rand_small = np.random.randint(0, 100, size=36) 

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rand_big = np.random.randint(0, 100, size=136) 

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print(SampEn(ss.value, m=2, r=0.2*np.std(ss.value))) # 0.78 

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print(SampEn(a10.value, m=2, r=0.2*np.std(a10.value))) # 0.41 

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print(SampEn(rand_small, m=2, r=0.2*np.std(rand_small))) # 1.79 

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print(SampEn(rand_big, m=2, r=0.2*np.std(rand_big))) # 2.42 

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folder = sys.argv[1] 
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interval = 100 
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if len(sys.argv) > 2: 
...  ...  
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df = pd.read_csv(snap, names=['time', str(node_id)], skiprows=1) 
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dfn = pd.concat([dfn, df[str(node_id)]], axis=1) 
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code.interact(local=dict(globals(), **locals())) 

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print "Processing and plotting..." 
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if not os.path.exists("plots"+nick): 
...  ...  
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# 'label': '\"Core Or Not\" (Blue or White)'}) 
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small=pd.DataFrame(resDF.iloc[:,0:1000]) 
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sns.heatmap(small, cmap=cmap1, xticklabels=range(1000), yticklabels=range(len(small)), cbar_kws={ 

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#code.interact(local=dict(globals(), **locals())) 

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sns.heatmap(small.applymap(int), cmap=cmap1, xticklabels=range(1000), yticklabels=range(len(small)), cbar_kws={ 

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'label': '\"Core Or Not\" (Blue or White)'}) 
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plt.savefig(nick+"coreResistMapEntryTOP10LeavingTOP20.pdf", format='pdf') 
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f = open(nick + "stats.txt", 'w') 
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f.write(str(pd.DataFrame(allint).describe())) 
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f.close() 

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f.close() 
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