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()
