# -----------------------------------------------------------
# SideLine version 1.0.0 - AES_SCA.py
#
# Simple python script for AES traces visualization 
# and CPA computation.
#
# (C) 2020 https://github.com/Remote-HWA/SideLine/
# -----------------------------------------------------------

from scipy import signal
import numpy as np
import os
import matplotlib.pyplot as plt
import time

class AES_SCA(object):

    def __init__(self):

        ##### User main parameters #####
        self.inPath = "E:\\These\\SideLine_git\\aes_sideline" # name of the file which contains the DLL traces
        self.nSample = 200 # number of sample per DLL trace
        self.nTrace = 10000000 # number of DLL traces

        ##### Advanced parameters #####
        self.outFolder = os.path.join(os.path.dirname(self.inPath),"DLL_AES_results")
        self.startTime = time.time() # For progression information
        self.moduloval = 10000 # Refresh Rate (processed traces)
        self.lineCounter = 0
        self.keyArray = np.zeros(16,np.uint8)
        self.ptArray = np.zeros(16,np.uint8)
        self.dataArray = np.zeros(self.nSample,dtype=np.double)

        ##### Butterworth Filter #####
        fs = 16000 # DLL Sampling frequency (Khz)
        fc = 2000 # Filter CutOff frequency (KHz)
        order = 4 # Filter order
        w = fc / (fs / 2) # Normalize the frequency
        self.b,self.a = signal.butter(order, w , btype='highpass',output='ba') # HP butterworth filter

        ##### CPA parameters #####
        self.nByte = 16 # number of key bytes considered for the CPA
        self.nClass = 256 # number of classes (8bit = 256 classes)
        self.nHyp = 256 # number of key hypotheses (8bit = 256 hyp)

        ##### CPA accumulators #####
        self.sumAvg      = np.zeros(self.nSample,dtype=np.double)
        self.sumPopClass = np.zeros((self.nByte,self.nClass),dtype=np.uint32)
        self.sumVar      = np.zeros(self.nSample,dtype=np.double)
        self.sumAvgClass = np.zeros((self.nByte,self.nClass,self.nSample),dtype=np.double)
        self.sumVarClass = np.zeros((self.nByte,self.nClass,self.nSample),dtype=np.double)

        ##### Create a folder to save CPA results #####
        counterlog = 0
        try:
            os.mkdir(self.outFolder)
        except OSError:  
            while(1):
                if(os.path.isdir(self.outFolder+"_%02d"%(counterlog))):
                    counterlog = counterlog + 1
                else:
                    os.mkdir(self.outFolder+"_%02d"%(counterlog))
                    self.outFolder = self.outFolder+"_%02d"%(counterlog)
                    break  

    def Check_Data_Integrity(self,dataFile): # check if the uart log file data is corrupted 

        valid = 1
        state = -1

        #Check plaintext 
        while state == -1:
            
            linePlain = dataFile.readline().decode("utf-8")
            state = linePlain.find("plaintext : ")
            self.lineCounter += 1

            if not linePlain:
                print("no more line")
                return 2

            if linePlain == "":
                print("empty line")
                return 2

        try: 
            ptString =  linePlain[12:45]
            self.ptArray = [int(ptString[i*2]+ptString[i*2+1],16) for i in range(0,16)]
        except:
            print("plain line error: %d: line %d"%(len(linePlain),self.lineCounter))
            valid = 0         
        
        #Check samples
        lineSample = dataFile.readline() 
        self.lineCounter += 1

        try:
            self.dataArray = np.frombuffer(lineSample,dtype=np.uint8)[0:self.nSample]
        except:
            print("sample line error: %d > %d: line %d"%(self.nSample,len(lineSample),self.lineCounter))
            valid = 0

        #Check ciphertext
        #self.lineCipher = dataFile.readline() 
        #self.lineCounter += 1

        #if len(self.lineCipher) != 46:
        #    print("cipher line error: %d: line %d"%(len(self.lineCipher),self.lineCounter))
        #    valid = 0  

        return valid


    def Data_Acquisition(self):

        iTrace = 0
        counterOK = 0
        counterERR = 0
        keyFound = -1
        globalDataArray = np.zeros(self.nSample)
        globalFilteredDataArray = np.zeros(self.nSample)
        dataFile = open(self.inPath,'rb') # file that contains the DLL samples 

        # Parse the key and put it in a byte array
        while keyFound == -1:
            lineKey = dataFile.readline() 
            keyFound = lineKey.decode("utf-8").find("key : ")

        keyString = lineKey[6:39].decode("utf-8")
        self.keyArray = [int(keyString[i*2]+keyString[i*2+1],16) for i in range(0,16)]

        while iTrace < self.nTrace :

            #print treatment progression
            if (iTrace%self.moduloval)==0 and iTrace != 0:
                print("Processed traces: %d - Time: %.2f/%.2fmin - Progression: %.2f%%"%(iTrace,(time.time() - self.startTime)/60,((time.time() - self.startTime)/(iTrace/self.nTrace))/60,(iTrace/self.nTrace)*100),end="\r")

            if self.Check_Data_Integrity(dataFile) == 1:

                globalDataArray += self.dataArray

                self.dataArray = signal.filtfilt(self.b,self.a, self.dataArray)

                for iByte in range(0,self.nByte):
                    self.sumPopClass[iByte][self.ptArray[iByte]] += 1
                    self.sumAvgClass[iByte][self.ptArray[iByte]] += self.dataArray
                    self.sumVarClass[iByte][self.ptArray[iByte]] += np.square(self.dataArray)
                self.sumVar += np.square(self.dataArray)
                self.sumAvg += self.dataArray
                counterOK += 1
                iTrace += 1

            elif self.Check_Data_Integrity(dataFile) == 2:

                print("No more line in file: exiting with %d traces"%iTrace)
                break

            else:
                counterERR += 1

        #Close data file
        print("\n\n%d values removed"%(counterERR))
        print("%d values validated"%(counterOK))
        self.nTrace = counterOK
        dataFile.close()

        #Compute average & variance 
        self.sumAvg =  np.divide(self.sumAvg,self.nTrace) 
        self.sumVar =  np.divide(self.sumVar,self.nTrace) - np.square(self.sumAvg)

        ####### SAVE AVERAGE VALUE png AND BIN #######

        globalDataArray = np.divide(globalDataArray,self.nTrace)

        fig = plt.figure(1)
        ax = fig.add_subplot(111)
        plt.rc('font', size=7)
        plt.title("Averaged value for %d traces"%(self.nTrace))
        plt.text(1.02,0.7,"Nb Traces: %d\nNb Samples: %d\n"%(self.nTrace, self.nSample),horizontalalignment='left',verticalalignment='center', transform = ax.transAxes)   
        plt.xlabel("Time (samples)")
        plt.ylabel("Quantification")
        plt.plot(globalDataArray,linewidth=0.7, linestyle="-")
        plt.savefig(self.outFolder+"\\unfiltered_avg.png",dpi=600,transparent=True,bbox_inches='tight')
        plt.close('all')

        fig = plt.figure(1)
        ax = fig.add_subplot(111)
        plt.rc('font', size=7)
        plt.title("Filtered averaged value for %d traces"%(self.nTrace))
        plt.text(1.02,0.7,"Nb Traces: %d\nNb Samples: %d\n"%(self.nTrace, self.nSample),horizontalalignment='left',verticalalignment='center', transform = ax.transAxes)   
        plt.xlabel("Time (samples)")
        plt.ylabel("Quantification")
        plt.plot(self.sumAvg,linewidth=0.7, linestyle="-")
        plt.savefig(self.outFolder+"\\filtered_avg.png",dpi=600,transparent=True,bbox_inches='tight')
        plt.close('all')

        print("\nEnd Acquisition\n")



    def Compute_Correlation(self,iByte):

        #Turn Global variance into standard deviation
        Correlation = np.zeros((self.nClass,self.nSample),dtype=np.double)
        hammingDistance = np.zeros(self.nClass,dtype=np.double)

        #Compute Class Variance & Average and save it in binary files
        for iClass in range(0, self.nClass):
            if(self.sumPopClass[iByte][iClass]>0):
                self.sumAvgClass[iByte][iClass] = np.divide(self.sumAvgClass[iByte][iClass],self.sumPopClass[iByte][iClass])
                self.sumVarClass[iByte][iClass] = np.divide(self.sumVarClass[iByte][iClass],self.sumPopClass[iByte][iClass]) - np.square(self.sumAvgClass[iByte][iClass])

        #Turn Global variance into standard deviation
        stdDev = np.sqrt(self.sumVar)

        print("Compute Byte %d..."%iByte)

        for iHyp in range(0, self.nHyp):

            DY = 0;
            YVAR = 0;

            for iClass in range(0,self.nClass): 

                vClass = iClass ^ iHyp

                if iByte == 0  or iByte == 5 or iByte == 10 or iByte == 15:
                    vClass = self.Te0[vClass]
                elif iByte == 4 or iByte == 9 or iByte == 14 or iByte == 3:
                    vClass = self.Te1[vClass]
                elif iByte == 8 or iByte == 13 or iByte == 2 or iByte == 7:
                    vClass = self.Te2[vClass]
                else:
                    vClass = self.Te3[vClass]

                hammingDistance[iClass] = bin(vClass).count("1")

                DY                  += np.multiply(self.sumPopClass[iByte][iClass],hammingDistance[iClass])
                YVAR                += np.multiply(self.sumPopClass[iByte][iClass], np.square(hammingDistance[iClass]))

            DY = np.divide(DY,self.nTrace)          
            YVAR = np.divide(YVAR,self.nTrace) - np.square(DY) 
            YSTDDEV = np.sqrt(YVAR)

            DX = np.zeros(self.nSample)
            XVAR = np.zeros(self.nSample)
            DXY = np.zeros(self.nSample)

            #Classification stats
            for iClass in range(0,self.nClass):
                #Acumulator for sum of products
                DXY     += np.multiply(self.sumPopClass[iByte][iClass],np.multiply(self.sumAvgClass[iByte][iClass],hammingDistance[iClass]))

            #Covariance : average of product minus product of averages
            Correlation[iHyp] = np.divide(DXY,self.nTrace) - np.multiply(self.sumAvg,DY)

            #normalize covariance if correlation requested
            X = np.divide(Correlation[iHyp], YSTDDEV)
            Correlation[iHyp] = np.divide(X,stdDev) 

        return Correlation


    def Save_Correlation_Results(self,Correlation,iByte):

        currentMax = 0.0
        currentArgMax = 0
        hyprank = np.zeros(256)
        minArray = np.full(len(Correlation[0]),1000000.0)
        maxArray = np.full(len(Correlation[0]),-1000000.0)

        for iClass in range(0,self.nClass):
            if np.amax(abs(Correlation[iClass])) > currentMax:
                currentArgMax = iClass
                currentMax = np.amax(abs(Correlation[iClass]))

        for iClass in range(0,self.nClass):
            for iSample in range(0, self.nSample):

                if (Correlation[iClass][iSample] > maxArray[iSample]) and iClass != currentArgMax:
                    maxArray[iSample] = Correlation[iClass][iSample]

                if (Correlation[iClass][iSample] < minArray[iSample]) and iClass != currentArgMax:
                    minArray[iSample] = Correlation[iClass][iSample]

        fig = plt.figure(0)
        ax = fig.add_subplot(111)
        plt.plot(maxArray,linewidth=0.7, linestyle="-",color='#C0C0C0')
        plt.plot(minArray,linewidth=0.7, linestyle="-",color='#C0C0C0')

        if(self.keyArray[iByte] == currentArgMax):
            plt.plot(Correlation[currentArgMax],linewidth=0.7, linestyle="-",color='r')
        else:
            plt.plot(Correlation[currentArgMax],linewidth=0.5, linestyle='dotted',color='r')
            plt.plot(Correlation[self.keyArray[iByte]],linewidth=0.7, linestyle="-",color='g')

        plt.fill_between(range(0,len(Correlation[0])),minArray, maxArray,color='#C0C0C0')

        plt.title("Temporal Correlation")
        plt.xlabel("Time (samples)")
        plt.ylabel("Correlation Rate")
        plt.text(0.02,0.98,"Best Correlation Rate : %.3f%%\n hyp : %x"%(currentMax*100,currentArgMax),horizontalalignment='left',
        verticalalignment='top', transform = ax.transAxes)
        plt.savefig(os.path.join(self.outFolder,"corr_%02d.png"%(iByte)),dpi=600,transparent=True,bbox_inches='tight') 
        plt.close('all')

    #AES 32-BIT TTABLE 0
    Te0 = (
        0xc66363a5, 0xf87c7c84, 0xee777799, 0xf67b7b8d,
        0xfff2f20d, 0xd66b6bbd, 0xde6f6fb1, 0x91c5c554,
        0x60303050, 0x02010103, 0xce6767a9, 0x562b2b7d,
        0xe7fefe19, 0xb5d7d762, 0x4dababe6, 0xec76769a,
        0x8fcaca45, 0x1f82829d, 0x89c9c940, 0xfa7d7d87,
        0xeffafa15, 0xb25959eb, 0x8e4747c9, 0xfbf0f00b,
        0x41adadec, 0xb3d4d467, 0x5fa2a2fd, 0x45afafea,
        0x239c9cbf, 0x53a4a4f7, 0xe4727296, 0x9bc0c05b,
        0x75b7b7c2, 0xe1fdfd1c, 0x3d9393ae, 0x4c26266a,
        0x6c36365a, 0x7e3f3f41, 0xf5f7f702, 0x83cccc4f,
        0x6834345c, 0x51a5a5f4, 0xd1e5e534, 0xf9f1f108,
        0xe2717193, 0xabd8d873, 0x62313153, 0x2a15153f,
        0x0804040c, 0x95c7c752, 0x46232365, 0x9dc3c35e,
        0x30181828, 0x379696a1, 0x0a05050f, 0x2f9a9ab5,
        0x0e070709, 0x24121236, 0x1b80809b, 0xdfe2e23d,
        0xcdebeb26, 0x4e272769, 0x7fb2b2cd, 0xea75759f,
        0x1209091b, 0x1d83839e, 0x582c2c74, 0x341a1a2e,
        0x361b1b2d, 0xdc6e6eb2, 0xb45a5aee, 0x5ba0a0fb,
        0xa45252f6, 0x763b3b4d, 0xb7d6d661, 0x7db3b3ce,
        0x5229297b, 0xdde3e33e, 0x5e2f2f71, 0x13848497,
        0xa65353f5, 0xb9d1d168, 0x00000000, 0xc1eded2c,
        0x40202060, 0xe3fcfc1f, 0x79b1b1c8, 0xb65b5bed,
        0xd46a6abe, 0x8dcbcb46, 0x67bebed9, 0x7239394b,
        0x944a4ade, 0x984c4cd4, 0xb05858e8, 0x85cfcf4a,
        0xbbd0d06b, 0xc5efef2a, 0x4faaaae5, 0xedfbfb16,
        0x864343c5, 0x9a4d4dd7, 0x66333355, 0x11858594,
        0x8a4545cf, 0xe9f9f910, 0x04020206, 0xfe7f7f81,
        0xa05050f0, 0x783c3c44, 0x259f9fba, 0x4ba8a8e3,
        0xa25151f3, 0x5da3a3fe, 0x804040c0, 0x058f8f8a,
        0x3f9292ad, 0x219d9dbc, 0x70383848, 0xf1f5f504,
        0x63bcbcdf, 0x77b6b6c1, 0xafdada75, 0x42212163,
        0x20101030, 0xe5ffff1a, 0xfdf3f30e, 0xbfd2d26d,
        0x81cdcd4c, 0x180c0c14, 0x26131335, 0xc3ecec2f,
        0xbe5f5fe1, 0x359797a2, 0x884444cc, 0x2e171739,
        0x93c4c457, 0x55a7a7f2, 0xfc7e7e82, 0x7a3d3d47,
        0xc86464ac, 0xba5d5de7, 0x3219192b, 0xe6737395,
        0xc06060a0, 0x19818198, 0x9e4f4fd1, 0xa3dcdc7f,
        0x44222266, 0x542a2a7e, 0x3b9090ab, 0x0b888883,
        0x8c4646ca, 0xc7eeee29, 0x6bb8b8d3, 0x2814143c,
        0xa7dede79, 0xbc5e5ee2, 0x160b0b1d, 0xaddbdb76,
        0xdbe0e03b, 0x64323256, 0x743a3a4e, 0x140a0a1e,
        0x924949db, 0x0c06060a, 0x4824246c, 0xb85c5ce4,
        0x9fc2c25d, 0xbdd3d36e, 0x43acacef, 0xc46262a6,
        0x399191a8, 0x319595a4, 0xd3e4e437, 0xf279798b,
        0xd5e7e732, 0x8bc8c843, 0x6e373759, 0xda6d6db7,
        0x018d8d8c, 0xb1d5d564, 0x9c4e4ed2, 0x49a9a9e0,
        0xd86c6cb4, 0xac5656fa, 0xf3f4f407, 0xcfeaea25,
        0xca6565af, 0xf47a7a8e, 0x47aeaee9, 0x10080818,
        0x6fbabad5, 0xf0787888, 0x4a25256f, 0x5c2e2e72,
        0x381c1c24, 0x57a6a6f1, 0x73b4b4c7, 0x97c6c651,
        0xcbe8e823, 0xa1dddd7c, 0xe874749c, 0x3e1f1f21,
        0x964b4bdd, 0x61bdbddc, 0x0d8b8b86, 0x0f8a8a85,
        0xe0707090, 0x7c3e3e42, 0x71b5b5c4, 0xcc6666aa,
        0x904848d8, 0x06030305, 0xf7f6f601, 0x1c0e0e12,
        0xc26161a3, 0x6a35355f, 0xae5757f9, 0x69b9b9d0,
        0x17868691, 0x99c1c158, 0x3a1d1d27, 0x279e9eb9,
        0xd9e1e138, 0xebf8f813, 0x2b9898b3, 0x22111133,
        0xd26969bb, 0xa9d9d970, 0x078e8e89, 0x339494a7,
        0x2d9b9bb6, 0x3c1e1e22, 0x15878792, 0xc9e9e920,
        0x87cece49, 0xaa5555ff, 0x50282878, 0xa5dfdf7a,
        0x038c8c8f, 0x59a1a1f8, 0x09898980, 0x1a0d0d17,
        0x65bfbfda, 0xd7e6e631, 0x844242c6, 0xd06868b8,
        0x824141c3, 0x299999b0, 0x5a2d2d77, 0x1e0f0f11,
        0x7bb0b0cb, 0xa85454fc, 0x6dbbbbd6, 0x2c16163a,
    )

    #AES 32-BIT TTABLE 1
    Te1 = (
        0xa5c66363, 0x84f87c7c, 0x99ee7777, 0x8df67b7b,
        0x0dfff2f2, 0xbdd66b6b, 0xb1de6f6f, 0x5491c5c5,
        0x50603030, 0x03020101, 0xa9ce6767, 0x7d562b2b,
        0x19e7fefe, 0x62b5d7d7, 0xe64dabab, 0x9aec7676,
        0x458fcaca, 0x9d1f8282, 0x4089c9c9, 0x87fa7d7d,
        0x15effafa, 0xebb25959, 0xc98e4747, 0x0bfbf0f0,
        0xec41adad, 0x67b3d4d4, 0xfd5fa2a2, 0xea45afaf,
        0xbf239c9c, 0xf753a4a4, 0x96e47272, 0x5b9bc0c0,
        0xc275b7b7, 0x1ce1fdfd, 0xae3d9393, 0x6a4c2626,
        0x5a6c3636, 0x417e3f3f, 0x02f5f7f7, 0x4f83cccc,
        0x5c683434, 0xf451a5a5, 0x34d1e5e5, 0x08f9f1f1,
        0x93e27171, 0x73abd8d8, 0x53623131, 0x3f2a1515,
        0x0c080404, 0x5295c7c7, 0x65462323, 0x5e9dc3c3,
        0x28301818, 0xa1379696, 0x0f0a0505, 0xb52f9a9a,
        0x090e0707, 0x36241212, 0x9b1b8080, 0x3ddfe2e2,
        0x26cdebeb, 0x694e2727, 0xcd7fb2b2, 0x9fea7575,
        0x1b120909, 0x9e1d8383, 0x74582c2c, 0x2e341a1a,
        0x2d361b1b, 0xb2dc6e6e, 0xeeb45a5a, 0xfb5ba0a0,
        0xf6a45252, 0x4d763b3b, 0x61b7d6d6, 0xce7db3b3,
        0x7b522929, 0x3edde3e3, 0x715e2f2f, 0x97138484,
        0xf5a65353, 0x68b9d1d1, 0x00000000, 0x2cc1eded,
        0x60402020, 0x1fe3fcfc, 0xc879b1b1, 0xedb65b5b,
        0xbed46a6a, 0x468dcbcb, 0xd967bebe, 0x4b723939,
        0xde944a4a, 0xd4984c4c, 0xe8b05858, 0x4a85cfcf,
        0x6bbbd0d0, 0x2ac5efef, 0xe54faaaa, 0x16edfbfb,
        0xc5864343, 0xd79a4d4d, 0x55663333, 0x94118585,
        0xcf8a4545, 0x10e9f9f9, 0x06040202, 0x81fe7f7f,
        0xf0a05050, 0x44783c3c, 0xba259f9f, 0xe34ba8a8,
        0xf3a25151, 0xfe5da3a3, 0xc0804040, 0x8a058f8f,
        0xad3f9292, 0xbc219d9d, 0x48703838, 0x04f1f5f5,
        0xdf63bcbc, 0xc177b6b6, 0x75afdada, 0x63422121,
        0x30201010, 0x1ae5ffff, 0x0efdf3f3, 0x6dbfd2d2,
        0x4c81cdcd, 0x14180c0c, 0x35261313, 0x2fc3ecec,
        0xe1be5f5f, 0xa2359797, 0xcc884444, 0x392e1717,
        0x5793c4c4, 0xf255a7a7, 0x82fc7e7e, 0x477a3d3d,
        0xacc86464, 0xe7ba5d5d, 0x2b321919, 0x95e67373,
        0xa0c06060, 0x98198181, 0xd19e4f4f, 0x7fa3dcdc,
        0x66442222, 0x7e542a2a, 0xab3b9090, 0x830b8888,
        0xca8c4646, 0x29c7eeee, 0xd36bb8b8, 0x3c281414,
        0x79a7dede, 0xe2bc5e5e, 0x1d160b0b, 0x76addbdb,
        0x3bdbe0e0, 0x56643232, 0x4e743a3a, 0x1e140a0a,
        0xdb924949, 0x0a0c0606, 0x6c482424, 0xe4b85c5c,
        0x5d9fc2c2, 0x6ebdd3d3, 0xef43acac, 0xa6c46262,
        0xa8399191, 0xa4319595, 0x37d3e4e4, 0x8bf27979,
        0x32d5e7e7, 0x438bc8c8, 0x596e3737, 0xb7da6d6d,
        0x8c018d8d, 0x64b1d5d5, 0xd29c4e4e, 0xe049a9a9,
        0xb4d86c6c, 0xfaac5656, 0x07f3f4f4, 0x25cfeaea,
        0xafca6565, 0x8ef47a7a, 0xe947aeae, 0x18100808,
        0xd56fbaba, 0x88f07878, 0x6f4a2525, 0x725c2e2e,
        0x24381c1c, 0xf157a6a6, 0xc773b4b4, 0x5197c6c6,
        0x23cbe8e8, 0x7ca1dddd, 0x9ce87474, 0x213e1f1f,
        0xdd964b4b, 0xdc61bdbd, 0x860d8b8b, 0x850f8a8a,
        0x90e07070, 0x427c3e3e, 0xc471b5b5, 0xaacc6666,
        0xd8904848, 0x05060303, 0x01f7f6f6, 0x121c0e0e,
        0xa3c26161, 0x5f6a3535, 0xf9ae5757, 0xd069b9b9,
        0x91178686, 0x5899c1c1, 0x273a1d1d, 0xb9279e9e,
        0x38d9e1e1, 0x13ebf8f8, 0xb32b9898, 0x33221111,
        0xbbd26969, 0x70a9d9d9, 0x89078e8e, 0xa7339494,
        0xb62d9b9b, 0x223c1e1e, 0x92158787, 0x20c9e9e9,
        0x4987cece, 0xffaa5555, 0x78502828, 0x7aa5dfdf,
        0x8f038c8c, 0xf859a1a1, 0x80098989, 0x171a0d0d,
        0xda65bfbf, 0x31d7e6e6, 0xc6844242, 0xb8d06868,
        0xc3824141, 0xb0299999, 0x775a2d2d, 0x111e0f0f,
        0xcb7bb0b0, 0xfca85454, 0xd66dbbbb, 0x3a2c1616,
    )

    #AES 32-BIT TTABLE 2
    Te2 = (
        0x63a5c663, 0x7c84f87c, 0x7799ee77, 0x7b8df67b,
        0xf20dfff2, 0x6bbdd66b, 0x6fb1de6f, 0xc55491c5,
        0x30506030, 0x01030201, 0x67a9ce67, 0x2b7d562b,
        0xfe19e7fe, 0xd762b5d7, 0xabe64dab, 0x769aec76,
        0xca458fca, 0x829d1f82, 0xc94089c9, 0x7d87fa7d,
        0xfa15effa, 0x59ebb259, 0x47c98e47, 0xf00bfbf0,
        0xadec41ad, 0xd467b3d4, 0xa2fd5fa2, 0xafea45af,
        0x9cbf239c, 0xa4f753a4, 0x7296e472, 0xc05b9bc0,
        0xb7c275b7, 0xfd1ce1fd, 0x93ae3d93, 0x266a4c26,
        0x365a6c36, 0x3f417e3f, 0xf702f5f7, 0xcc4f83cc,
        0x345c6834, 0xa5f451a5, 0xe534d1e5, 0xf108f9f1,
        0x7193e271, 0xd873abd8, 0x31536231, 0x153f2a15,
        0x040c0804, 0xc75295c7, 0x23654623, 0xc35e9dc3,
        0x18283018, 0x96a13796, 0x050f0a05, 0x9ab52f9a,
        0x07090e07, 0x12362412, 0x809b1b80, 0xe23ddfe2,
        0xeb26cdeb, 0x27694e27, 0xb2cd7fb2, 0x759fea75,
        0x091b1209, 0x839e1d83, 0x2c74582c, 0x1a2e341a,
        0x1b2d361b, 0x6eb2dc6e, 0x5aeeb45a, 0xa0fb5ba0,
        0x52f6a452, 0x3b4d763b, 0xd661b7d6, 0xb3ce7db3,
        0x297b5229, 0xe33edde3, 0x2f715e2f, 0x84971384,
        0x53f5a653, 0xd168b9d1, 0x00000000, 0xed2cc1ed,
        0x20604020, 0xfc1fe3fc, 0xb1c879b1, 0x5bedb65b,
        0x6abed46a, 0xcb468dcb, 0xbed967be, 0x394b7239,
        0x4ade944a, 0x4cd4984c, 0x58e8b058, 0xcf4a85cf,
        0xd06bbbd0, 0xef2ac5ef, 0xaae54faa, 0xfb16edfb,
        0x43c58643, 0x4dd79a4d, 0x33556633, 0x85941185,
        0x45cf8a45, 0xf910e9f9, 0x02060402, 0x7f81fe7f,
        0x50f0a050, 0x3c44783c, 0x9fba259f, 0xa8e34ba8,
        0x51f3a251, 0xa3fe5da3, 0x40c08040, 0x8f8a058f,
        0x92ad3f92, 0x9dbc219d, 0x38487038, 0xf504f1f5,
        0xbcdf63bc, 0xb6c177b6, 0xda75afda, 0x21634221,
        0x10302010, 0xff1ae5ff, 0xf30efdf3, 0xd26dbfd2,
        0xcd4c81cd, 0x0c14180c, 0x13352613, 0xec2fc3ec,
        0x5fe1be5f, 0x97a23597, 0x44cc8844, 0x17392e17,
        0xc45793c4, 0xa7f255a7, 0x7e82fc7e, 0x3d477a3d,
        0x64acc864, 0x5de7ba5d, 0x192b3219, 0x7395e673,
        0x60a0c060, 0x81981981, 0x4fd19e4f, 0xdc7fa3dc,
        0x22664422, 0x2a7e542a, 0x90ab3b90, 0x88830b88,
        0x46ca8c46, 0xee29c7ee, 0xb8d36bb8, 0x143c2814,
        0xde79a7de, 0x5ee2bc5e, 0x0b1d160b, 0xdb76addb,
        0xe03bdbe0, 0x32566432, 0x3a4e743a, 0x0a1e140a,
        0x49db9249, 0x060a0c06, 0x246c4824, 0x5ce4b85c,
        0xc25d9fc2, 0xd36ebdd3, 0xacef43ac, 0x62a6c462,
        0x91a83991, 0x95a43195, 0xe437d3e4, 0x798bf279,
        0xe732d5e7, 0xc8438bc8, 0x37596e37, 0x6db7da6d,
        0x8d8c018d, 0xd564b1d5, 0x4ed29c4e, 0xa9e049a9,
        0x6cb4d86c, 0x56faac56, 0xf407f3f4, 0xea25cfea,
        0x65afca65, 0x7a8ef47a, 0xaee947ae, 0x08181008,
        0xbad56fba, 0x7888f078, 0x256f4a25, 0x2e725c2e,
        0x1c24381c, 0xa6f157a6, 0xb4c773b4, 0xc65197c6,
        0xe823cbe8, 0xdd7ca1dd, 0x749ce874, 0x1f213e1f,
        0x4bdd964b, 0xbddc61bd, 0x8b860d8b, 0x8a850f8a,
        0x7090e070, 0x3e427c3e, 0xb5c471b5, 0x66aacc66,
        0x48d89048, 0x03050603, 0xf601f7f6, 0x0e121c0e,
        0x61a3c261, 0x355f6a35, 0x57f9ae57, 0xb9d069b9,
        0x86911786, 0xc15899c1, 0x1d273a1d, 0x9eb9279e,
        0xe138d9e1, 0xf813ebf8, 0x98b32b98, 0x11332211,
        0x69bbd269, 0xd970a9d9, 0x8e89078e, 0x94a73394,
        0x9bb62d9b, 0x1e223c1e, 0x87921587, 0xe920c9e9,
        0xce4987ce, 0x55ffaa55, 0x28785028, 0xdf7aa5df,
        0x8c8f038c, 0xa1f859a1, 0x89800989, 0x0d171a0d,
        0xbfda65bf, 0xe631d7e6, 0x42c68442, 0x68b8d068,
        0x41c38241, 0x99b02999, 0x2d775a2d, 0x0f111e0f,
        0xb0cb7bb0, 0x54fca854, 0xbbd66dbb, 0x163a2c16,
    )

    #AES 32-BIT TTABLE 3
    Te3 = (
        0x6363a5c6, 0x7c7c84f8, 0x777799ee, 0x7b7b8df6,
        0xf2f20dff, 0x6b6bbdd6, 0x6f6fb1de, 0xc5c55491,
        0x30305060, 0x01010302, 0x6767a9ce, 0x2b2b7d56,
        0xfefe19e7, 0xd7d762b5, 0xababe64d, 0x76769aec,
        0xcaca458f, 0x82829d1f, 0xc9c94089, 0x7d7d87fa,
        0xfafa15ef, 0x5959ebb2, 0x4747c98e, 0xf0f00bfb,
        0xadadec41, 0xd4d467b3, 0xa2a2fd5f, 0xafafea45,
        0x9c9cbf23, 0xa4a4f753, 0x727296e4, 0xc0c05b9b,
        0xb7b7c275, 0xfdfd1ce1, 0x9393ae3d, 0x26266a4c,
        0x36365a6c, 0x3f3f417e, 0xf7f702f5, 0xcccc4f83,
        0x34345c68, 0xa5a5f451, 0xe5e534d1, 0xf1f108f9,
        0x717193e2, 0xd8d873ab, 0x31315362, 0x15153f2a,
        0x04040c08, 0xc7c75295, 0x23236546, 0xc3c35e9d,
        0x18182830, 0x9696a137, 0x05050f0a, 0x9a9ab52f,
        0x0707090e, 0x12123624, 0x80809b1b, 0xe2e23ddf,
        0xebeb26cd, 0x2727694e, 0xb2b2cd7f, 0x75759fea,
        0x09091b12, 0x83839e1d, 0x2c2c7458, 0x1a1a2e34,
        0x1b1b2d36, 0x6e6eb2dc, 0x5a5aeeb4, 0xa0a0fb5b,
        0x5252f6a4, 0x3b3b4d76, 0xd6d661b7, 0xb3b3ce7d,
        0x29297b52, 0xe3e33edd, 0x2f2f715e, 0x84849713,
        0x5353f5a6, 0xd1d168b9, 0x00000000, 0xeded2cc1,
        0x20206040, 0xfcfc1fe3, 0xb1b1c879, 0x5b5bedb6,
        0x6a6abed4, 0xcbcb468d, 0xbebed967, 0x39394b72,
        0x4a4ade94, 0x4c4cd498, 0x5858e8b0, 0xcfcf4a85,
        0xd0d06bbb, 0xefef2ac5, 0xaaaae54f, 0xfbfb16ed,
        0x4343c586, 0x4d4dd79a, 0x33335566, 0x85859411,
        0x4545cf8a, 0xf9f910e9, 0x02020604, 0x7f7f81fe,
        0x5050f0a0, 0x3c3c4478, 0x9f9fba25, 0xa8a8e34b,
        0x5151f3a2, 0xa3a3fe5d, 0x4040c080, 0x8f8f8a05,
        0x9292ad3f, 0x9d9dbc21, 0x38384870, 0xf5f504f1,
        0xbcbcdf63, 0xb6b6c177, 0xdada75af, 0x21216342,
        0x10103020, 0xffff1ae5, 0xf3f30efd, 0xd2d26dbf,
        0xcdcd4c81, 0x0c0c1418, 0x13133526, 0xecec2fc3,
        0x5f5fe1be, 0x9797a235, 0x4444cc88, 0x1717392e,
        0xc4c45793, 0xa7a7f255, 0x7e7e82fc, 0x3d3d477a,
        0x6464acc8, 0x5d5de7ba, 0x19192b32, 0x737395e6,
        0x6060a0c0, 0x81819819, 0x4f4fd19e, 0xdcdc7fa3,
        0x22226644, 0x2a2a7e54, 0x9090ab3b, 0x8888830b,
        0x4646ca8c, 0xeeee29c7, 0xb8b8d36b, 0x14143c28,
        0xdede79a7, 0x5e5ee2bc, 0x0b0b1d16, 0xdbdb76ad,
        0xe0e03bdb, 0x32325664, 0x3a3a4e74, 0x0a0a1e14,
        0x4949db92, 0x06060a0c, 0x24246c48, 0x5c5ce4b8,
        0xc2c25d9f, 0xd3d36ebd, 0xacacef43, 0x6262a6c4,
        0x9191a839, 0x9595a431, 0xe4e437d3, 0x79798bf2,
        0xe7e732d5, 0xc8c8438b, 0x3737596e, 0x6d6db7da,
        0x8d8d8c01, 0xd5d564b1, 0x4e4ed29c, 0xa9a9e049,
        0x6c6cb4d8, 0x5656faac, 0xf4f407f3, 0xeaea25cf,
        0x6565afca, 0x7a7a8ef4, 0xaeaee947, 0x08081810,
        0xbabad56f, 0x787888f0, 0x25256f4a, 0x2e2e725c,
        0x1c1c2438, 0xa6a6f157, 0xb4b4c773, 0xc6c65197,
        0xe8e823cb, 0xdddd7ca1, 0x74749ce8, 0x1f1f213e,
        0x4b4bdd96, 0xbdbddc61, 0x8b8b860d, 0x8a8a850f,
        0x707090e0, 0x3e3e427c, 0xb5b5c471, 0x6666aacc,
        0x4848d890, 0x03030506, 0xf6f601f7, 0x0e0e121c,
        0x6161a3c2, 0x35355f6a, 0x5757f9ae, 0xb9b9d069,
        0x86869117, 0xc1c15899, 0x1d1d273a, 0x9e9eb927,
        0xe1e138d9, 0xf8f813eb, 0x9898b32b, 0x11113322,
        0x6969bbd2, 0xd9d970a9, 0x8e8e8907, 0x9494a733,
        0x9b9bb62d, 0x1e1e223c, 0x87879215, 0xe9e920c9,
        0xcece4987, 0x5555ffaa, 0x28287850, 0xdfdf7aa5,
        0x8c8c8f03, 0xa1a1f859, 0x89898009, 0x0d0d171a,
        0xbfbfda65, 0xe6e631d7, 0x4242c684, 0x6868b8d0,
        0x4141c382, 0x9999b029, 0x2d2d775a, 0x0f0f111e,
        0xb0b0cb7b, 0x5454fca8, 0xbbbbd66d, 0x16163a2c,
    )


#---------------- MAIN ---------------#

cd = AES_SCA()

##### Acquire Data and compute CPA accumulators #####
cd.Data_Acquisition()

##### Compute CPA #####
for iByte in range(0,cd.nByte):
    Correlation = cd.Compute_Correlation(iByte)
    cd.Save_Correlation_Results(Correlation,iByte)