samples = [x for x in xrange((numBits/parallelism)*numTraces)] deltaSamples = [x for x in xrange(((numBits-1)/parallelism)*numTraces)] #elif algorithm == 'sbox1': # pp = [x for x in peaks.values()] # samples = [x for x in xrange(numTraces)] # deltaSamples = [x for x in xrange(numTraces-1)] plt.plot(samples, pp, 'g') plt.savefig(folder+'/currentPlot.png') plt.clf() plt.hist(pp, bins=100) plt.savefig(folder+'/currentDistribution.png') plt.clf() correctKeyHammings = dpa.getHammings(keys) plt.plot(samples, correctKeyHammings, color = 'r') plt.savefig(folder+'/correctKeyHammingsPlot.png') plt.clf() plt.hist(correctKeyHammings, (max(correctKeyHammings)-min(correctKeyHammings))+1) plt.savefig(folder+'/correctKeyHammingsDistribution.png') plt.clf() correctKeyDeltas = dpa.getDeltas(keys) plt.plot(deltaSamples, correctKeyDeltas, color = 'r') plt.savefig(folder+'/correctKeyDeltasPlot.png') plt.clf() plt.hist(correctKeyDeltas, (max(correctKeyDeltas)-min(correctKeyDeltas))+1) plt.savefig(folder+'/correctKeyDeltasDistribution.png') plt.clf()