Edited encryption2.py to test lesmis novel, failed

Edited graphs.py to add ranked comparison test
Edited keytestsutie.py to change autocorr kmax parameter to 100
Edited TRNG2.py to create 5 separate TRNG outputs

Author: AAK05

TRNG0.json

@@ -85,7 +85,17 @@ def gaussian(self, N, offset=0):
 
 if __name__ == "__main__":
     provide = Provide("Projects\\EE\\2022-08-all.bin")
-    integers = provide.integer(1000000, 26)
+    integers = provide.integer(5000000, 26)
     integers = integers + 1
-    with open("Projects\\EE\\TRNG2.json","w") as f:
-        json.dump(integers.tolist(),f)
+    lst = integers.tolist()
+    lst0 = lst[0:1000000]
+    lst1 = lst[1000000:2000000]
+    lst2 = lst[2000000:3000000]
+    lst3 = lst[3000000:4000000]
+    lst4 = lst[4000000:]
+    lsts = (lst0,lst1,lst2,lst3,lst4)
+    count = 0
+    for i in lsts:
+        with open("Projects\\EE\\TRNG{}.json".format(str(count)),"w") as f:
+            json.dump(i,f)
+        count += 1

TRNG1.json

@@ -40,7 +40,7 @@ def loadkey(filename):
 
 def loadtxt(fname):
     out = ""
-    with open(fname,"r") as f:
+    with open(fname,"r",encoding="UTF-8") as f:
         for line in f:
             n = line.replace(" ","")
             n = n.translate(str.maketrans("","",string.punctuation))
@@ -101,9 +101,12 @@ def decrypt(ciphertext,keyfile):
 y = decrypt(x,"pi-with-coprime2023-b26.json")
 print(y)
 
-writetxt(encrypt(loadtxt("Amber-Spyglass-Clean.txt"),"testkeyfinal.json"),"Amber-Spyglass-Encrypted.txt")
-writejson(FormatTextToNum(loadtxt("Amber-Spyglass-Encrypted.txt")),"Amber-Spyglass_Encrypted-Enumerated.json")
+#writetxt(encrypt(loadtxt("Amber-Spyglass-Clean.txt"),"testkeyfinal.json"),"Amber-Spyglass-Encrypted.txt")
+#writejson(FormatTextToNum(loadtxt("Amber-Spyglass-Encrypted.txt")),"Amber-Spyglass_Encrypted-Enumerated.json")
 #b26 = convertbase26(loadkeysbase10("coprime-digits-mod10-2023-1000k.json","pi-digits.json",length=1000000))
 #b26 = convertbase26(loadkeysbase10("pi-digits.json",length=1000000))
 #writejson(b26,"test26v2.json")
-#print(convertbase26([3,1,4,1,5,9,2,6,5,3]))
+#print(convertbase26([3,1,4,1,5,9,2,6,5,3]))
+writetxt(loadtxt("lesmis.txt"),"lesmis-clean.txt")
+writetxt(encrypt(loadtxt("lesmis-Clean.txt"),"testkeyfinal.json"),"lesmis-Encrypted.txt")
+writejson(FormatTextToNum(loadtxt("lesmis-Encrypted.txt"))[0:700000],"lesmis-Encrypted-Enumerated-cut.json")

TRNG2.json

@@ -16,7 +16,16 @@
 trng = trng[0:len(cipher)]
 
 def plotcorr(data,maxk=100):
-    plot_correlogram(data,maxk=maxk)
+    graph = correlogram(data,maxk)
+    x = np.linspace(1,maxk,maxk)
+    y = np.array([graph[i] for i in range(1,maxk+1)])
+    plt.grid()
+    plt.plot(x,y,marker=".",color="b",label="Ciphertext Correlogram")
+    plt.xlabel("Lag (k)")
+    plt.ylabel("Autocorrelation")
+    plt.legend()
+    plt.show()
+    return x,y
 
 def plotcorrdouble(data1,data2,maxk=100):
     #import numpy as np
@@ -43,6 +52,34 @@ def plotfreq(data):
     plt.xlabel("Character")
     plt.show()
 
+def plotfreqerr(data):
+    expected = len(data)/26
+    dist = freq_analysis(data)
+    x = np.linspace(1,26,26)
+    y = np.array([(100*(dist[i]-expected)/expected) for i in range(1,27)])
+    plt.grid()
+    plt.plot(x,y,color="r",marker=".")
+    plt.xlabel("Character")
+    plt.ylabel("% Error")
+    plt.show()
+    return x,y
+
+def plotfreqerrdouble(data1,data2):
+    expected = len(data1)/26
+    dist1 = freq_analysis(data1)
+    x1 = np.linspace(1,26,26)
+    y1 = np.array([(100*(dist1[i]-expected)/expected) for i in range(1,27)])
+    dist2 = freq_analysis(data2)
+    x2 = np.linspace(1,26,26)
+    y2 = np.array([(100*(dist2[i]-expected)/expected) for i in range(1,27)])
+    plt.grid()
+    plt.plot(x1,y1,color="b",marker=".",label="Frequency Error Ciphertext")
+    plt.plot(x2,y2,marker=".",color="r",label="Frequency Error TRNG")
+    plt.xlabel("Character")
+    plt.ylabel("% Error")
+    plt.legend()
+    plt.show()
+
 def plotcarlo(data):
     data1 = data
     data2 = data1[::-1]
@@ -76,10 +113,13 @@ def plotnextdig(data):
     plt.xlabel("Difference")
     plt.ylabel("% Error")
     plt.show()
+    return x,y
 
 def plotnextdighist(data):
     dist = difftest(data)
     plt.bar(dist.keys(), dist.values(), 1, color='r',edgecolor="black",linewidth=1.2)
+    plt.xlabel("Difference")
+    plt.ylabel("Frequency")
     plt.show()
 
 def plotnextdigdouble(data1,data2):
@@ -108,6 +148,7 @@ def plotslope(data):
     plt.xlabel("Difference")
     plt.ylabel("% Error")
     plt.show()
+    return x,y
 
 def plotslopedouble(data1,data2):
     expected = len(data1)/26
@@ -128,7 +169,28 @@ def plotslopedouble(data1,data2):
 def plotslopehist(data):
     dist = slopetest(data)
     plt.bar(dist.keys(), dist.values(), 1, color='r',edgecolor="black",linewidth=1.2)
+    plt.xlabel("Difference")
+    plt.ylabel("Frequency")
+    plt.show()
+
+def plotcomparison(data1,data2,test):
+    x1,lst1 = test(data1)
+    x2,lst2 = test(data2)
+    lst1.sort()
+    lst2.sort()
+    y1 = np.array(lst1)
+    y2 = np.array(lst2)
+    #diffs = [abs(lst2[i]-lst1[i]) for i in range(len(lst1))]
+    #print("Max diff: " + str(max(diffs)))
+    print("Correlation Coeff: " + str(np.corrcoef(y1,y2)[1,0]))
+    plt.grid()
+    plt.plot(x1,y1,color="b",marker=".",label="Cipher Ranked Autocorrelation")
+    plt.plot(x2,y2,color="r",marker=".",label="TRNG Ranked Autocorrelation")
+    plt.legend()
+    plt.xlabel("Rank")
+    plt.ylabel("Autocorrelation")
     plt.show()
+    
 
 if __name__ == "__main__":
     #plotcorr(cipher)
@@ -139,4 +201,6 @@ def plotslopehist(data):
     #plotnextdighist(trng)
     #plotslope(trng)
     #plotslopedouble(cipher,trng)
-    plotslopehist(trng)
+    plotslopehist(trng)
+    #plotfreqerrdouble(cipher,trng)
+    #plotcomparison(cipher,trng,plotfreqerr)

TRNG2.py

@@ -15,7 +15,7 @@
         data = [x for x in str(f.readline())]
     print(len(data))
 
-data1 = data[0:664044]
+data1 = data[0:664044]#[0:700000]
 data2 = data1[::-1]
 
 #datax and datax2 adds together 1st and 2nd, 3rd and 4th, etc
@@ -69,8 +69,8 @@
         n = (data2[i]) + (data2[i+1]) + (data2[i+2]) #+ (data2[i+3]) + (data2[i+4])
         datax2.append(int(n))"""
 
-print("Maximum autocorrelation (k,autocorrelation): " + str(max_autocorr(data1,20)))
-print("Minimum autocorrelation (k,autocorrelation): " + str(min_autocorr(data1,20)))
+print("Maximum autocorrelation (k,autocorrelation): " + str(max_autocorr(data1,100)))
+print("Minimum autocorrelation (k,autocorrelation): " + str(min_autocorr(data1,100)))
 #plot_correlogram(data1,100)
 #print("Maximum pattern error from expected (pattern,%): " + str(max_pattern(data1)))
 #print("Minimum pattern error from expected (pattern,%): " + str(min_pattern(data1)))