mobicen / timeAnalysis.py @ c16015e2
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import code # code.interact(local=dict(globals(), **locals())) 

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from collections import deque 
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from scipy import stats 
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import matplotlib.pyplot as plt 
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from collections import defaultdict 
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import os 
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import sys 
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from statsmodels.graphics.tsaplots import plot_acf, acf 
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import operator 
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from mpl_toolkits import mplot3d 
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import pandas as pd 
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from pprint import pprint 
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import numpy as np 
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import glob 
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import matplotlib 
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import seaborn as sns 
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sns.set() 
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folder = sys.argv[1]

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lags = int(sys.argv[2]) 
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nick = folder.split('/')[2].split('_')[0]+"_" 
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os.chdir(folder) 
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dfn = pd.DataFrame() # rows=nodes columns=BC at columnindex timeinstant

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print "Loading data from", folder, "..." 
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for snap in sorted(glob.glob('./stats*')): 
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# print snap

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node_id = int(snap.strip('.csv').strip('./stats')) 
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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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nodes = dfn.columns.tolist() 
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initialCentrality = {} 
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for n in nodes: 
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initialCentrality[int(n)] = dfn.iloc[0][n] 
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sorted_x = sorted(initialCentrality.items(),

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key=operator.itemgetter(1), reverse=True) 
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srtNodes = [e[0] for e in sorted_x] 
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dfACF = pd.DataFrame() # rows=TimeLags, columns = nodes

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print "Processing data..." 
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for node in nodes: 
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print "Autocorr of node", node 
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nodeACF = pd.DataFrame([dfn[node].autocorr(lag) for lag in range(lags)]) 
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dfACF = pd.concat([dfACF, nodeACF], axis=1)

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'''

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X ==> timelag

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Y ==> i nodi in ordine di centralita a tempo_0

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Z ==> l'acf del nodo y al timelag x

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'''

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if not os.path.exists("plots"+nick): 
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os.makedirs("plots"+nick)

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os.chdir("plots"+nick)

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# Plotting

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# Mean AutoCorrelation and RankCorrelation

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# lags=20

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firstRank = dfn.iloc[0,:]

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x = range(0, lags) 
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meanACF = [] 
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rankCorr = [] 
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weightedRankCorr = [] 
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for i in x: 
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#code.interact(local=dict(globals(), **locals()))

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meanACF.append(np.mean(dfACF.iloc[i])) 
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rankCorr.append(stats.spearmanr(firstRank, dfn.iloc[i,:])[0])

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#weightedRankCorr.append(stats.weightedtau(firstRank, dfn.iloc[i,:])[0])

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plt.plot(x, meanACF, lw="1.5", label='Mean Autocorrelation') 
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plt.plot(x, rankCorr, lw="1.5", label='RankCorrelation (with rank at t_0)') 
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#plt.plot(x, weightedRankCorr, lw="1.5",

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# label='WeightedRankCorrelation (with rank at t_0)')

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plt.ylabel('Corr coeff: [ACF, Spearman rho]')

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plt.xlabel('Timelags / Time')

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plt.grid() 
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plt.legend() 
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# plt.ylim(1.0,1.0)

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plt.xlim(0, lags)

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plt.savefig(nick+"autoCorrMeanRankSpearman.pdf", format='pdf') 
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plt.clf() 
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'''

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nodes2coreInst = defaultdict(list)

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#nodes2rankInst = defaultdict(list)

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for t in range(len(dfn.iloc[0])):

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coreT, coreRankT = coreNodesAtTime(dfn, t, 5)

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for n in coreT:

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nodes2coreInst[n].append((t,coreT[n],coreRankT[n]))

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for n in [5,38,59,92]:

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points = nodes2coreInst[n]

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x = [p[0] for p in points]

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y = [len(nodes2coreInst)p[2] for p in points]

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color = n / float(len(nodes2coreInst.keys()))

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#rgba = cmap(color)

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#plt.scatter(x,y, rgba)

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plt.plot(x,y, 'o')

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plt.ylim(0, len(nodes2coreInst))

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plt.show()

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

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plt.show()

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exit()

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# Core Persitence

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plags=100

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x = range(0, plags)

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y = []

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for i in x:

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print "cacca"

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plt.clf()

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'''

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X, Y, Z = [], [], [] 
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for node in srtNodes: 
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print "n:", node 
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for lag in range(lags): 
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print "\tn:%d lag:%d" % (node,lag) 
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#code.interact(local=dict(globals(), **locals()))

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X.append(lag) 
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Y.append(node) 
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Z.append(list(dfACF.iloc[lag])[node])

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fig = plt.figure() 
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ax = plt.axes(projection='3d')

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ax.set_xlabel('TimeLag')

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ax.set_ylabel('Nodes sorted by BC at t_0')

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ax.set_zlabel('ACF at timelag x of node y')

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ax.plot_trisurf(X, Y, Z, linewidth=0.2, antialiased=True) 
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ax.set_xlim(0, lags)

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ax.set_ylim(0, len(srtNodes)) 
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#ax.set_zlim(1.0, 1.0)

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plt.savefig(nick+"autoBC3d.pdf", format="pdf") 
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print "THE END" 