--- /dev/null
+{
+ "metadata": {
+ "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from cipherbreak import *\n",
+ "with open('2013/mona-lisa-words.txt') as f:\n",
+ " mlwords = [line.rstrip() for line in f]\n",
+ "mltrans = collections.defaultdict(list)\n",
+ "for word in mlwords:\n",
+ " mltrans[transpositions_of(word)] += [word]\n",
+ "c6a = open('2013/6a.ciphertext').read()\n",
+ "c6b = open('2013/6b.ciphertext').read()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c1a = open('2013/1a.ciphertext').read()\n",
+ "c1b = open('2013/1b.ciphertext').read()\n",
+ "c2a = open('2013/2a.ciphertext').read()\n",
+ "c2b = open('2013/2b.ciphertext').read()\n",
+ "c3a = open('2013/3a.ciphertext').read()\n",
+ "c3b = open('2013/3b.ciphertext').read()\n",
+ "c4a = open('2013/4a.ciphertext').read()\n",
+ "c4b = open('2013/4b.ciphertext').read()\n",
+ "c5a = open('2013/5a.ciphertext').read()\n",
+ "c5b = open('2013/5b.ciphertext').read()\n",
+ "\n",
+ "p1a = caesar_decipher(c1a, 8)\n",
+ "p1b = caesar_decipher(c1b, 14)\n",
+ "p2a = affine_decipher(c2a, 3, 3, True)\n",
+ "p2b = caesar_decipher(c2b, 6)\n",
+ "p3a = affine_decipher(c3a, 7, 8, True)\n",
+ "p3b = keyword_decipher(c3b, 'louvigny', 2)\n",
+ "p4a = keyword_decipher(c4a, 'montal', 2)\n",
+ "p4b = keyword_decipher(c4b, 'salvation', 2)\n",
+ "p5a = keyword_decipher(c5a, 'alfredo', 2)\n",
+ "p5b = vigenere_decipher(sanitise(c5b), 'florence')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(frequencies(sanitise(c6a)))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "/home/neil/.virtualenvs/p3basic/lib/python3.3/site-packages/matplotlib/figure.py:372: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
+ " \"matplotlib is currently using a non-GUI backend, \"\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAEkCAYAAABt4jWqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaebe642c>"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6af = frequencies(sanitise(c6a))\n",
+ "plot_frequency_histogram(c6af, sort_key=lambda l: c6af[l])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAEkCAYAAABt4jWqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRhFEAACjCCIAgFEEEQDAKIIIAGDU/wAb7nFhOTPwsAAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xae9d774c>"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(normalised_english_counts, sort_key=lambda l: normalised_english_counts[l])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEkCAYAAABzKwUZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwv8DbMnA/BWkp54AAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaea2d2cc>"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6bf = frequencies(sanitise(c6b))\n",
+ "plot_frequency_histogram(c6bf, sort_key=lambda l: c6bf[l])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEkCAYAAAB6wKVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaea3550c>"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(c6bf)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEkCAYAAAB6wKVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaea9556c>"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(normalised_english_counts)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEkCAYAAABzKwUZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEFgAACsQWAAAKxBYAAArEFgAACv8P5ZWvQDqsWNNAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xae9bd72c>"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 9,
+ "text": [
+ "'CPYYL GVVIR PDDVU BCSUP QOWPW SYBYP ODBCS PBBPR CSIOZ PTSTV HYVTW PYOZC OGCRV TTPUI BVGVS YOUGZ ZSRYS BPYLY SHSYY OUGBV BCSWP OUBOU GPUIR DSPYD LTSUB OVUOU GZPYP ZSSTZ DONSB CSLNU SJPAV EBBCS ZRPTV EYHYO SUIDL ZZVHH ORSYJ PZWDP UUOUG BVWED DBCSL WORNS IEWCS YBYPO DBCSU OGCBP HBSYZ CSJSU BTOZZ OUGAE BLVER PUYSP IPAVE BBCPB OUBCS OYYSW VYBZB ODDUV MVLVU GSBBO UGPRR SZZBV BCSVY OGOUP DTOZZ TVUPO UBCSG PDDSY LPUIO PTASG OUUOU GBVBC OUNJS TOGCB USSIB VYVDD VEBPA DPRNA PGMVA LVEJP UBBVI VOBVY ZCPDD OALBC SJPLO JPZZE YWYOZ SIALB COZYS WVYBB CSVBC SYWPW SYZJS CPFSH YVTBC SUPQO ZPBBC OZDSF SDPYS SURYL WBSIE ZOUGA OHOIV YWDPL HPOYZ BLDSR OWCSY ZBCOZ VUSOZ UBTPL ASBCS ROWCS YRDSY NJPZV HHIEB LBCPB IPLJS GVBDE RNL\\n'"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "len(sanitise(c6b))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 18,
+ "text": [
+ "1573"
+ ]
+ }
+ ],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6as = sanitise(c6a)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 35
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "frequencies(ngrams(c6as, 2))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 48,
+ "text": [
+ "Counter({'bc': 21, 'cs': 20, 'ou': 15, 'sy': 12, 'oz': 10, 'ug': 10, 'bv': 8, 'ub': 8, 'bb': 7, 'su': 7, 'py': 6, 'pu': 6, 'vy': 6, 'jp': 6, 'dd': 6, 'zz': 6, 'ys': 6, 'yo': 6, 've': 6, 'ds': 5, 'eb': 5, 'po': 5, 'bo': 5, 'si': 5, 'yz': 5, 'vu': 5, 'vb': 5, 'co': 5, 'cp': 5, 'wp': 4, 'dp': 4, 'pz': 4, 'pl': 4, 'pd': 4, 'pb': 4, 'js': 4, 'og': 4, 'bp': 4, 'zb': 4, 'sb': 4, 'yl': 4, 'yb': 4, 'uo': 4, 'ui': 4, 'up': 4, 'hy': 3, 'wc': 3, 'pt': 3, 'pr': 3, 'sw': 3, 'bl': 3, 'zc': 3, 'rp': 3, 'zp': 3, 'zs': 3, 'zv': 3, 'od': 3, 'ow': 3, 'gb': 3, 'gc': 3, 'gv': 3, 'gp': 3, 'lb': 3, 'sr': 3, 'ss': 3, 'sp': 3, 'rn': 3, 'st': 3, 'sj': 3, 'lv': 3, 'db': 3, 'sg': 3, 'to': 3, 'ie': 3, 'tv': 3, 'yy': 3, 'yp': 3, 'al': 3, 'yv': 3, 'yw': 3, 'us': 3, 'vt': 3, 'vh': 3, 'pa': 3, 'wv': 2, 'ws': 2, 'ip': 2, 'wd': 2, 'ho': 2, 'hh': 2, 'pw': 2, 'er': 2, 'mv': 2, 'oy': 2, 'ry': 2, 'lw': 2, 'so': 2, 'qo': 2, 'by': 2, 'bs': 2, 'bt': 2, 'zd': 2, 'rs': 2, 'zo': 2, 'or': 2, 'rd': 2, 'ga': 2, 'go': 2, 'ey': 2, 'gz': 2, 'sz': 2, 'ro': 2, 'sv': 2, 'dv': 2, 'sh': 2, 'do': 2, 'sl': 2, 'dl': 2, 'io': 2, 'ts': 2, 'ir': 2, 'tp': 2, 'iv': 2, 'as': 2, 'av': 2, 'nj': 2, 'uu': 2, 'ns': 2, 'fs': 2, 'ib': 2, 'pq': 2, 'vi': 2, 'cb': 2, 'hs': 1, 'hp': 1, 'wy': 1, 'we': 1, 'wb': 1, 'wo': 1, 'hi': 1, 'lj': 1, 'lo': 1, 'du': 1, 'pi': 1, 'ph': 1, 'ed': 1, 'pf': 1, 'zj': 1, 'pg': 1, 'lp': 1, 'oa': 1, 'lt': 1, 'yn': 1, 'nl': 1, 'vv': 1, 'lz': 1, 'ze': 1, 'ao': 1, 'bi': 1, 'ap': 1, 'bd': 1, 'bz': 1, 'de': 1, 'bu': 1, 'on': 1, 'oj': 1, 'oh': 1, 'oi': 1, 'rr': 1, 'ob': 1, 'rv': 1, 'zr': 1, 'zt': 1, 'zw': 1, 'rc': 1, 'op': 1, 'dt': 1, 'gm': 1, 'ej': 1, 'gs': 1, 'zu': 1, 'la': 1, 'lg': 1, 'ld': 1, 'ov': 1, 'ly': 1, 'sf': 1, 'sd': 1, 'lh': 1, 'os': 1, 'ia': 1, 'ta': 1, 'id': 1, 'tz': 1, 'tw': 1, 'tt': 1, 'ad': 1, 'ae': 1, 'yr': 1, 'yh': 1, 'yj': 1, 'yd': 1, 'ln': 1, 'un': 1, 'na': 1, 'uy': 1, 'nu': 1, 'uv': 1, 'ez': 1, 'ur': 1, 'tb': 1, 'vw': 1, 'ew': 1, 'vs': 1, 'sc': 1, 'hb': 1, 'vd': 1, 'vg': 1, 'va': 1, 'vm': 1, 'vl': 1, 'vo': 1, 'zy': 1, 'cr': 1})"
+ ]
+ }
+ ],
+ "prompt_number": 48
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "' '.join(segment(letters(c6a).translate(''.maketrans({'B':'t', 'C':'h', 'S':'e', 'O':'i', 'U':'n', 'G':'g', 'A':'b', 'V':'o', 'N':'k', 'J':'w', 'T':'m', 'I':'d', 'P':'a', 'W':'p', 'R':'c', 'Y':'r', 'L':'y', 'D':'l', 'H':'f', 'Z':'s', 'Q':'z', 'E':'u', 'F':'v', 'M':'j'}))))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 65,
+ "text": [
+ "'harry good call on the nazi paper trail the attached is a memo from paris high command to goering s secretary referring to the painting and clearly mentioning sara seems like they knew about the scam our friendly ss officer was planning to pull they picked up her trail the night after she went missing but you can read about that in their report still no joy on getting access to the original miss mona in the gallery and i am beginning to think we might need to rollout a black bag job you want to do it or shall i by the way i was surprised by this report the other papers we have from the nazis at this level are encrypted using bifid or playfair style ciphers this one isnt maybe the cipher clerk was off duty that day we got lucky'"
+ ]
+ }
+ ],
+ "prompt_number": 65
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "trans={'B':'t', 'C':'h', 'S':'e', 'O':'i', 'U':'n', 'G':'g', 'A':'b', 'V':'o', 'N':'k', 'J':'w', 'T':'m', 'I':'d', 'P':'a', 'W':'p', 'R':'c', 'Y':'r', 'L':'y', 'D':'l', 'H':'f', 'Z':'s', 'Q':'z', 'E':'u', 'F':'v', 'M':'j'}"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 66
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "sorted(trans.keys(), key=lambda k: trans[k])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 84,
+ "text": [
+ "['P',\n",
+ " 'A',\n",
+ " 'R',\n",
+ " 'I',\n",
+ " 'S',\n",
+ " 'H',\n",
+ " 'G',\n",
+ " 'C',\n",
+ " 'O',\n",
+ " 'M',\n",
+ " 'N',\n",
+ " 'D',\n",
+ " 'T',\n",
+ " 'U',\n",
+ " 'V',\n",
+ " 'W',\n",
+ " 'Y',\n",
+ " 'Z',\n",
+ " 'B',\n",
+ " 'E',\n",
+ " 'F',\n",
+ " 'J',\n",
+ " 'L',\n",
+ " 'Q']"
+ ]
+ }
+ ],
+ "prompt_number": 84
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "keyword_decipher(c6as, 'parishighcommand', 2)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 86,
+ "text": [
+ "'harrygoodcallonthenazipapertrailtheattachedisamemofromparishighcommandtogoeringssecretaryreferringtothepaintingandclearlymentioningsaraseemsliketheyknewaboutthescamourfriendlyssofficerwasplanningtopulltheypickeduphertrailthenightaftershewentmissingbutyoucanreadaboutthatintheirreportstillnojoyongettingaccesstotheoriginalmissmonainthegalleryandiambeginningtothinkwemightneedtorolloutablackbagjobyouwanttodoitorshallibythewayiwassurprisedbythisreporttheotherpaperswehavefromthenazisatthislevelareencryptedusingbifidorplayfairstyleciphersthisoneisntmaybethecipherclerkwasoffdutythatdaywegotlucky'"
+ ]
+ }
+ ],
+ "prompt_number": 86
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
--- /dev/null
+CPYYL GVVIR PDDVU BCSUP QOWPW SYBYP ODBCS PBBPR CSIOZ PTSTV HYVTW PYOZC OGCRV TTPUI BVGVS YOUGZ ZSRYS BPYLY SHSYY OUGBV BCSWP OUBOU GPUIR DSPYD LTSUB OVUOU GZPYP ZSSTZ DONSB CSLNU SJPAV EBBCS ZRPTV EYHYO SUIDL ZZVHH ORSYJ PZWDP UUOUG BVWED DBCSL WORNS IEWCS YBYPO DBCSU OGCBP HBSYZ CSJSU BTOZZ OUGAE BLVER PUYSP IPAVE BBCPB OUBCS OYYSW VYBZB ODDUV MVLVU GSBBO UGPRR SZZBV BCSVY OGOUP DTOZZ TVUPO UBCSG PDDSY LPUIO PTASG OUUOU GBVBC OUNJS TOGCB USSIB VYVDD VEBPA DPRNA PGMVA LVEJP UBBVI VOBVY ZCPDD OALBC SJPLO JPZZE YWYOZ SIALB COZYS WVYBB CSVBC SYWPW SYZJS CPFSH YVTBC SUPQO ZPBBC OZDSF SDPYS SURYL WBSIE ZOUGA OHOIV YWDPL HPOYZ BLDSR OWCSY ZBCOZ VUSOZ UBTPL ASBCS ROWCS YRDSY NJPZV HHIEB LBCPB IPLJS GVBDE RNL
--- /dev/null
+HITHH NFRFE RTEAO FFTNA SLTYO RRREQ THIMS ERULR HFSET AEEIU IWSIH BNRRT SIOII ENIRH MRPYT DTOEI RCITA LNBRP NHTOH ORLIE WTSHT ESPOS OTOYN UWHRE DHHCP ICMNO OTAHA SHTIJ EANET OFRNE USRAG MXURT TOWLE ATBRT EPPTR AAESE NETHH RISNR STSTR NFLDO GHAUM GSESE AKUNT IAHYT TNYUN TNNVH IGWLE WFIAP ASREL FAIEB APCMC PLAHE THTOL HWSUL ADSEU VEAEO TAEUT ATEEF EHLBH TRSHG ATLIA CEEHT RNHGA ASIET UFNNP UREHR ELITT MUDYI NNUEG ICOUE NDEEI AHSUE LHTNO AHTIE AINYF DNIAU OENAT IYEFO HEDAL UAROE RYHUR AERAT ERAHI AONGT WISWS AITBU LTAOM XLETO REOTO TONAO ORTGB CVAIP NLEEW FYRTA EOADU WWETR RTNLR PFPAI ELLRI MNTRO WIEQR ECUAR NDOTA FNROT ELETP ENRPM SHNDD ETDEW HEFOY PEDEN NSNEE GTATE EANES ERTGD LUEOS RBURO WHDGB RPAHE NYTTE KRERT RRHNF NEDOL TSWSI ANIYP OTHMO UOCTY EIEEP ONHKR NNTAI RLTEE IUREM NITAP HOEAO HHNOU UMTKV SENED EEOID PWKIE GTIII OSENH UUFAN BEUIT HRAER HIEEH UAYAS RUSIW GADSI SITRL GFPHI LTWII SIHES URMRO MACEI SOILV DEOEL USAIR IDEOT NFIES OBNTS APOFG SODRT FIRPL TANET RLYIO SOTYO ESSFN TPHEA IGEAO RAIEI TDMTA AEGRH YASRR CFELR TLEEA NEHVE HIVMH NASSI EROIX LMPSJ TTESO FBMRH NDIOC HECNB TTNCO MPUSE SOEHT ENETF IENOO TNWOO TEERI LTOIG DOSME HOIFM LROEF FDTUE SOFLE RWIIY QDBZO TTNRF ANTDI GONTA LAEPD SHEAM NTHLP SNBTU OAHAA LPLEM NPDRS HVNAI AELNH CALIS KACDO AEDTJ GHOIE ADGWA YNDET ENHEG EEUEO EEDNO RYYRF CTEMT TWNTG DETAS UOOOT FWYMI HRGRH OESIN RRAOF RETFE NPOYA ISSES STNHH AYEUL HIWPC TEATU APEAN LUEEL WAHAO ELURE SNBII EASAE TEAGC DCSOR EPSRO ENSEL VOSED UDSIT LTEIT TFSEO ESNLN GINOE RNTEI EIWWL WHTEI RSIIL HTDSR NDESR FHRNT LGRSU ASEDA DOYTA SADUT ENWIT CNAME TERTP OESYT OTOCN YUTSO GCMUD LPADL EADOT EFDSB FELEA HAXRS ONIDT IEIPR ASHIC DCSHI PBIEL SLNAL HORFR ENTAT ORAIE MKLAG RETMR DCWSN AYNWS VNULD OREKU TNNOU TIIUN NHVNI EILOH LTEFN AEATU IFTTA CETLN NRBHB EGLOS NAONM IFKOA RONSN VNFRE TRCFT HDETP SWSUC PERCW TPOAW FHDAY ISOOA GOAEL NDFIG NGRTE BLTLL GFO
--- /dev/null
+WWPA, AWHCRH MDY IOT NJHGK HJWLSBALH HI AWL BBHLJT, X DTUI PC VC MGPSHN HCK AHXK AVL BCAXS PMILG JAVHPCN IPBL. PZ ESPUCLS AWL RYPAT DPZ SLAPKLGLS AD AWLXY NHGK DU UYXKPF PMILGUDVC, ADV AHIL UVG HCFDUT AD WGVRLHZ XA, PUS AWL QVPYS DPZ THHF IV SLIHRO UYDT IOT IPZT. LMJTSALCA XKTH IV BHZL IOT WPPCAXUV SDVZ SXRT WPYI VU AWL QVM IN AWL LHN - UD-VCL LHH NDPCN IV IBGU XA DCTY IV AVDR XM IOTF SPSU’I RCVL PI DPZ IOTYT, HCK IOTF LLGL EYTAIF JUAPZLAF IV QL AVDRXUV MDY HVBLDUT AD ZBBVNAL P WPPCAXUV PC HCFLHN.
+HRJTZH AD AWL VHASTYN DPZ HAGHXNWAUVGDPYS HCK XA IVDR PYDBCK IDTUIF BPCBILH AD ZLPIJW AWL RVEF LPIO IOT VGPVPCHA. P WPS AWL EHXUIPCN PTDUV H QBCJW VU YTWGVSBRAXVCZ XU IOT TJZTBB ZWVE HCK RHBWTK DBI MDY IOT UXNWA XU IOT NJHGKH’ IPAWYDVB. AJYCZ DBI AWLN WGLULG AD BHL IOT KXYTJIVGZ’ UHRPAPIPTZ JW DU IOT ADW USDVG. DXAW AWL CLL LMOXIXAXVC VELCPCN DU HHIBGKPF IOT WAHRL LHH IJZN LCVJNW AD ZAPE VJA UPGZI AWPCN PUS, HH HGYPUVLS, P BHSL HBGL X DPZ UPGZI PCAD AWL HODW. X UDD WHKL IOT ODUDBG VU OPCXUV IDBVOI AWL ROTHELHA TCTY LVGR QF SH KPCJX. VG UDA.
+IOT E-GHN YTZJSIZ RHBL QHRR IOXZ BVGUXUV HCK, PZ NVJ ZJZELRATK, IOXZ XZ DUT VU ZPYP’Z UHZLH. P PT IVAK XA XZ RSDZT AD WTYULRA, QBI, OXKSLC BCKTY IOT SPFTYH VU WPPCA, HOT ZRYXIQSTK P WXJIBGL DM IOT UPGX LPNAL TTQSTT XU ALPK. HOT ZXNCLS PI Z IVD. AWL ILRO VBNZ IOXUZ ZWL BHN OPCT BHLS H QPI VU VAK EPEL APZL P JGHNVC AD KTMPJT AWL QVPYS ITMDYT ZWL HAPYILS DDYZ VC PI. HCFLHN, AWHI STHKLH AWL FBTZIPDU DM LOTYT AWL WLAS IOT YTHA WPPCAXUV TXNWA QL. XA’H OPYS AD ITSXLKL IOPA HOT STMI PI DXAW AWL HZ PUS P RHC’A HLT OTY VVXUV VC AWL GBC DXAW PI ZIBRR JUSLG OTY RVPA. XA’H UDA APZL HOT JDBAK GVAS XA JW.
+X DDYZLS AWYDBVO HVBL BVGL DM IOT UPGX WPWTYH HCK UVJUS AWPH UDAT. HI STHHA XA ILASH BH DWLGL HOT DTUI. SDVZZ APZL IOT JXWWLG JALGR LHH IPJZ IN AWL LHN. P WHKL BVKLS VC AD CTUXJT AD AGF IV UPCK PUN AGHRL DM HHGH IOTYT, IJA X OPCT H ULTSXUV AWL HVABIPDU IV IOXZ BFHATYN PH IPJZ PC WPYXZ LOTYT PI HAS QLVHC. P ALUA IOT WPPCAXUV HI AWL EHGPH VUMXJT. TPFQL NVJ JDBAK PYGHCNT AD YTAJYC PI? PI ZWVJSS IT LPZXLG AD NTA XA XU IOPU XA LHH AD LMAGHRA XA.
+PSA AWL QLHA,
+WHGYN
--- /dev/null
+TTFTK IESFO EYTYL ETTKF OYUHT TIENH HNTPR NMEPL KLFHF NEFEN NNTMM OAIOH KTITM URPSO EGEOF CMYMH TEPOY ITKCO YTFTG PGTYM RHTEP CRNYD EFUOT CTKYR CCTIM ANRMQ LHMKT TTCTS THRYC YUHVG MTHYD EFYHE FYFNO PMHOE TGEET TTXKR QCTOF YNYTF HTMYE MNNFV NEHHT YTRFR TUOHT MOHRG RTXYO HNTUH RGHUB FHHTH OTGWG KITTY QHEOW TNHCY TGPGY WCWFM AIFTM RPCYF MTOET YIHEO NYDHT PTFAE TRHKY UTYTT NEAFH FMFUW CTHUY OMQGF TYIRT HHNOO TMNRG NMTTO HFMKO CYFTF HHTKO CAYQP HAECX YETFO TMHVO VHYIH EOYWC RTHUY OFMCH TETMF PUYMH TKRUG OOYTF TTNEA FFTTG PGKTQ OPBHN NYPTT MTTKY XLOCK KYHMA CCTYE KYUGM EVHMT XTUMT MTMMT HMKTS CFPOR HOTHT OTBRP IOTUB OOYRY YKFTY YNYEW GTTYH KYURT EMMOA YUEKL FRHYO TCMHY TECIY FTFNE FENCO TRQBB NUOYU HUAYQ TMMOO TATNY UFWIY HNTXT TONTY TURND CHFLI IPTNR TOOOR TDTTI EXYHO TETIE ETBZC LGBEW FBWWH YIWEO GOUOT ATBBZ VVAWI YMDVI ZGGBF ACDSI WAAGU EYNNZ LHALF XORPB ALEAE NFBID UATBV NXWLL YFIXT BHEUH YWVLH IYYMX DALXG SFDEG DEDDW EBVDK IDGGV LMBUD EWLKU WAWMV AAYHK DALXD OSWMK CNWUL GEWUM BHLOH NPYDE WIPWT BDGEU DEUVD FGXLI VEUDM AMERP FFCNK MZGLH WZHDC GKMZA BYOZL HKSOE EZGBT BQAKE NIHVV AVLHE UQRZH BUDFE WYKLA YKYBG EWQZK ENHHA IIYSB WFSIL LQBWV LWHXB WHWYF KRVMA BIGKZ IWOIW GFAHY NILXC MZHUL HLTWX RMAOT BDGHU LRAVQ DIVMM ATIUS BZOLS OLOAQ ZKENQ RVGLA BIGPF DKWTS XXAIX CTFNP GKYFI XTBWH XBWAF OHEUL WWNWL GDTWW WOEFH LOHBM ALGSG WWTHI UMGXT GRLOH YORKR OWKEY EHSXE LUDKD LELLR MBWWP MVFRQ RAGRD IYNGY BKYXD FRUOR TSLQB WCLWV FYIKX GBFBL DGMBL NNHFY LOTBC DSIWC DLBUD ABBTN PIABC PZOXE NLBNW VKTFI ZIGUO RGLFG HTQRP AVAHB ILWYS TMHFM GEGAF BBZHW XNRSB LBRL
-1a: caesar_decipher(c1a, 8)
-1b: caesar_decipher(c1b, 14)
-2a: affine_decipher(c2a, 3, 3, True)
-2b: caesar_decipher(c2b, 6)
-3a: affine_decipher(c3a, 7, 8, True)
# with open('2013/mona-lisa-words.txt') as f: mona_lisa_words = [line.rstrip() for line in f]
# keyword_break(c4a, wordlist=mona_lisa_words)
-3b: keyword_decipher(c3b, 'louvigny', 2)
-4a: keyword_decipher(c4a, 'montal', 2)
-4b: keyword_decipher(c4b, 'salvation', 2)
-5a: keyword_decipher(c5a, 'alfredo', 2)
-5b: vigenere_decipher(c5bs, 'florence')
+
+c1a = open('2013/1a.ciphertext').read()
+c1b = open('2013/1b.ciphertext').read()
+c2a = open('2013/2a.ciphertext').read()
+c2b = open('2013/2b.ciphertext').read()
+c3a = open('2013/3a.ciphertext').read()
+c3b = open('2013/3b.ciphertext').read()
+c4a = open('2013/4a.ciphertext').read()
+c4b = open('2013/4b.ciphertext').read()
+c5a = open('2013/5a.ciphertext').read()
+c5b = open('2013/5b.ciphertext').read()
+c6a = open('2013/6a.ciphertext').read()
+c6b = open('2013/6b.ciphertext').read()
+c7a = open('2013/6a.ciphertext').read()
+c7b = open('2013/6b.ciphertext').read()
+
+p1a = caesar_decipher(c1a, 8)
+p1b = caesar_decipher(c1b, 14)
+p2a = affine_decipher(c2a, 3, 3, True)
+p2b = caesar_decipher(c2b, 6)
+p3a = affine_decipher(c3a, 7, 8, True)
+p3b = keyword_decipher(c3b, 'louvigny', 2)
+p4a = keyword_decipher(c4a, 'montal', 2)
+p4b = keyword_decipher(c4b, 'salvation', 2)
+p5a = keyword_decipher(c5a, 'alfredo', 2)
+p5b = vigenere_decipher(sanitise(c5b), 'florence')
+p6a = keyword_decipher(c6a, 'parishighcommand', 2)
+p7a = vigenere_decipher(sanitise(c7a), 'hp')
+++ /dev/null
-{
- "metadata": {
- "name": "Untitled0"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": []
-}
\ No newline at end of file
--- /dev/null
+{
+ "metadata": {
+ "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from cipherbreak import *\n",
+ "with open('2013/mona-lisa-words.txt') as f:\n",
+ " mlwords = [line.rstrip() for line in f]\n",
+ "mltrans = collections.defaultdict(list)\n",
+ "for word in mlwords:\n",
+ " mltrans[transpositions_of(word)] += [word]\n",
+ "c6a = open('2013/6a.ciphertext').read()\n",
+ "c6b = open('2013/6b.ciphertext').read()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c1a = open('2013/1a.ciphertext').read()\n",
+ "c1b = open('2013/1b.ciphertext').read()\n",
+ "c2a = open('2013/2a.ciphertext').read()\n",
+ "c2b = open('2013/2b.ciphertext').read()\n",
+ "c3a = open('2013/3a.ciphertext').read()\n",
+ "c3b = open('2013/3b.ciphertext').read()\n",
+ "c4a = open('2013/4a.ciphertext').read()\n",
+ "c4b = open('2013/4b.ciphertext').read()\n",
+ "c5a = open('2013/5a.ciphertext').read()\n",
+ "c5b = open('2013/5b.ciphertext').read()\n",
+ "\n",
+ "p1a = caesar_decipher(c1a, 8)\n",
+ "p1b = caesar_decipher(c1b, 14)\n",
+ "p2a = affine_decipher(c2a, 3, 3, True)\n",
+ "p2b = caesar_decipher(c2b, 6)\n",
+ "p3a = affine_decipher(c3a, 7, 8, True)\n",
+ "p3b = keyword_decipher(c3b, 'louvigny', 2)\n",
+ "p4a = keyword_decipher(c4a, 'montal', 2)\n",
+ "p4b = keyword_decipher(c4b, 'salvation', 2)\n",
+ "p5a = keyword_decipher(c5a, 'alfredo', 2)\n",
+ "p5b = vigenere_decipher(sanitise(c5b), 'florence')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(frequencies(sanitise(c6a)))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "/usr/local/lib/python3.3/dist-packages/matplotlib/figure.py:372: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
+ " \"matplotlib is currently using a non-GUI backend, \"\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAEkCAYAAABt4jWqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaeb9980c>"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6af = frequencies(sanitise(c6a))\n",
+ "plot_frequency_histogram(c6af, sort_key=lambda l: c6af[l])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAEkCAYAAABt4jWqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGg9JREFUeJzt3X1wFPUdx/HP8mBByEESycaCNYwQQiCQ43EQI4chwZGB\nAkLQIr1KxdFOpx2nlcDYltgHPcbYFqwPlbaodUqH8keMaKkCPUa0SEGkOIi0lRShySleDsKzSbZ/\nUKIoyV4ud/kll/dr5sYk/L77+yZe7pPd/e2e5TiOIwAADOlmugEAQNdGEAEAjCKIAABGEUQAAKMI\nIgCAUQQRAMAo1yB677335PV6mx79+vXT6tWrFQ6HVVRUpOzsbBUXFysSibRHvwCAJGO15jqixsZG\nDRw4UDt37tRjjz2mq666SkuXLtXKlStVW1urQCCQyF4BAEmoVYfmNm/erCFDhuiaa65RZWWl/H6/\nJMnv96uioiIhDQIAklurguiPf/yjbr/9dklSKBSSbduSJNu2FQqF4t8dACDpRX1o7vz58xo4cKD2\n79+vAQMGKDU1VbW1tU3/npaWpnA4nLBGAQDJqUe0A//85z9r7NixGjBggKQLe0E1NTXKzMxUdXW1\nMjIyvlCTn5+vvXv3xq9bAECnNXr0aL399ttf+HrUh+bWrVvXdFhOkmbNmqVnn31WkvTss89q9uzZ\nX6jZu3evHMfp0o8VK1YkvKY95qCGGmqoaeujuR2TqILo1KlT2rx5s+bOndv0tWXLlunVV19Vdna2\ntm7dqmXLlkWbaQAANInq0FyfPn107NixS76WlpamzZs3J6QpAEDX0b2srKwsURt/8MEHlcDNdxpZ\nWVkJr2mPOaihhhpq2qK5TGjVBa2tZVmWErh5AEAn0lwmcK85AIBRBBEAwCiCCABgFEEEADCKIAIA\nGEUQAQCMIogAAEYRRAAAowgiAIBRBBEAwCiCCABgFEEEADCKIAIAGEUQAQCMIogAAEYRRAAAowgi\nAIBRBBEAwCiCCABgFEEEADCKIAIAGEUQAQCMIogAAEYRRADQRXg8abIsK6qHx5PWbn1ZjuM4Cdu4\nZSmBmwcAtIJlWZKifU2O/+t3c5nAHhEAwKiogigSiWjevHkaPny4cnNz9eabbyocDquoqEjZ2dkq\nLi5WJBJJdK8AgCQUVRB997vf1S233KJ3331X//jHP5STk6NAIKCioiIdPHhQhYWFCgQCie4VAJCE\nXM8RHT9+XF6vV++///4lX8/JydG2bdtk27Zqamrk8/l04MCBSzfOOSIA6DA67TmiQ4cOacCAAbrz\nzjs1ZswYLVmyRKdOnVIoFJJt25Ik27YVCoXi2jAAoGtwDaL6+nq99dZb+ta3vqW33npLffr0+cJh\nuIvL/QAAaK0ebgMGDRqkQYMGafz48ZKkefPm6eGHH1ZmZqZqamqUmZmp6upqZWRkXLa+rKys6WOf\nzyefzxeXxgEAHVswGFQwGHQdF9V1RDfeeKN+85vfKDs7W2VlZTp9+rQkKT09XaWlpQoEAopEIpfd\nU+IcEQB0DB31HFFUQbR3717dddddOn/+vK677jqtXbtWDQ0NKikp0eHDh5WVlaX169erf//+UU0K\nAGh/nTqI4j0pAKD9ddQg4s4KAACjCCIAgFEEEQDAKIIIADqhjvqWDrFgsQIAdEKxLDxgsQIAAJdB\nEAEAjCKIAABGEUQAAKMIIgCAUQQRAMAogggAYBRBBAAwiiACABhFEAEAjCKIAABGEUQAAKMIIgCA\nUQQRAMAogggAYBRBBAAwiiACABhFEAEAjCKIAABGEUQAAKMIIgCAUQQRAMAogggAYFSPaAZlZWXJ\n4/Goe/fu6tmzp3bu3KlwOKwFCxboP//5j7KysrR+/Xr1798/0f0CAJJMVHtElmUpGAxqz5492rlz\npyQpEAioqKhIBw8eVGFhoQKBQEIbBQAkp6gPzTmOc8nnlZWV8vv9kiS/36+Kior4dgYA6BKi3iOa\nNm2axo0bpzVr1kiSQqGQbNuWJNm2rVAolLguAQBJK6pzRK+//rquvvpqffTRRyoqKlJOTs4l/25Z\nlizLSkiDAIDkFlUQXX311ZKkAQMGaM6cOdq5c6ds21ZNTY0yMzNVXV2tjIyMy9aWlZU1fezz+eTz\n+drcNAAkG48nTXV1ta7jUlJSdeJEuB06artgMKhgMOg6znI+f/Lnc06fPq2GhgalpKTo1KlTKi4u\n1ooVK7R582alp6ertLRUgUBAkUjkCwsWLMv6wrklAMAXXTiqFM3r5YXX1ejHt60mnprLBNcgOnTo\nkObMmSNJqq+v18KFC7V8+XKFw2GVlJTo8OHDzS7fJogAIDoEUYIQRAAQna4cRNxZAQBgFEEEADCK\nIAIAGEUQAUCceTxpTddXtvTweNJMt9ohsFgBAOKstQsPYqlhsQIAAHFCEAEAjCKIAABGEUQAAKMI\nIgCAUQQRAMAogggAYBRBBAAwiiACABhFEAEAjCKIAABGEUQAAKMIIgCAUQQRAMAogggAYBRBBAAw\niiACABhFEAEAjCKIAABGEUQAAKMIIgBogceTJsuyXB8eT5rpVjsty3EcJ2EbtywlcPMAkHCWZUmK\n5nXs09e79qiJfnzbauKpuUxgjwgAYFRUQdTQ0CCv16uZM2dKksLhsIqKipSdna3i4mJFIpGENgkA\nSF5RBdGqVauUm5v7/906KRAIqKioSAcPHlRhYaECgUBCmwQAJC/XIDpy5Ihefvll3XXXXU3H9ior\nK+X3+yVJfr9fFRUVie0SAJC0XIPovvvu0yOPPKJu3T4dGgqFZNu2JMm2bYVCocR1CABIai0G0caN\nG5WRkSGv19vs6omLSxcBAIhFj5b+8Y033lBlZaVefvllnT17VidOnNCiRYtk27ZqamqUmZmp6upq\nZWRkNLuNsrKypo99Pp98Pl+8egcAdGDBYFDBYNB1XNTXEW3btk3l5eV68cUXtXTpUqWnp6u0tFSB\nQECRSOSyCxa4jghAZ8d1RPETl+uILh6CW7ZsmV599VVlZ2dr69atWrZsWXy6BAB0OdxZAUCn5PGk\nqa6u1nVcSkqqTpwIx1zDHlH8NJcJBBGATqmjBkR71SRTEHGLHwCAUQQRAMAogggAYBRBBAAwiiAC\nABhFEAEAjCKIAABGEUQAAKMIIgCAUQQRAMAogggAYBRBBAAwiiACABhFEAEAjCKIAABGEUQAAKMI\nIgCAUQQRAMAogggAYBRBBAAwiiACABhFEAGIK48nTZZluT48nrQ21SB5WI7jOAnbuGUpgZsH0AFZ\nliUpmt/7T18fqGl9TfTj21YTT81lAntEAACjCCIAgFEEEQDAKIIIAGBUi0F09uxZTZw4Ufn5+crN\nzdXy5cslSeFwWEVFRcrOzlZxcbEikUi7NAsASD6uq+ZOnz6tK6+8UvX19brhhhtUXl6uyspKXXXV\nVVq6dKlWrlyp2tpaBQKBL26cVXNAl9NRV5klW02XWjV35ZVXSpLOnz+vhoYGpaamqrKyUn6/X5Lk\n9/tVUVER12YBAF2HaxA1NjYqPz9ftm1r6tSpGjFihEKhkGzbliTZtq1QKJTwRgEAyamH24Bu3brp\n7bff1vHjxzV9+nT99a9/veTfL17xDABALFyD6KJ+/fppxowZ2r17t2zbVk1NjTIzM1VdXa2MjIxm\n68rKypo+9vl88vl8bekXANBJBINBBYNB13EtLlY4duyYevToof79++vMmTOaPn26VqxYob/85S9K\nT09XaWmpAoGAIpEIixUASOq4J/eTrSaZFiu0uEdUXV0tv9+vxsZGNTY2atGiRSosLJTX61VJSYl+\n+9vfKisrS+vXr49rswCAroObngKIq466B5FsNcm0R8SdFQAARhFEAACjCCIAgFEEEQDAKIIIAGAU\nQQQAMIogAgAYRRABaJbHk9Z0P8mWHh5PmulW0YlFfa85AF1PXV2torkAsq6OGx8jduwRAQCMIogA\nAEYRRAAAowgiAIBRBBEAwCiCCABgFEEEADCKIAIAGEUQAQCMIogAAEYRRAAAowgiAIBRBBEAwCiC\nCABgFEEEADCKIAIAGEUQAQCMIogAAEYRRAAAo1yD6IMPPtDUqVM1YsQIjRw5UqtXr5YkhcNhFRUV\nKTs7W8XFxYpEIglvFgCQfCzHcZyWBtTU1Kimpkb5+fk6efKkxo4dq4qKCq1du1ZXXXWVli5dqpUr\nV6q2tlaBQODSjVuWXDYPoAOzLEtSNL/Dn/6uU9M+NdGPb1tNPDWXCa57RJmZmcrPz5ck9e3bV8OH\nD9fRo0dVWVkpv98vSfL7/aqoqIhrwwCArqFV54iqqqq0Z88eTZw4UaFQSLZtS5Js21YoFEpIgwCA\n5BZ1EJ08eVK33nqrVq1apZSUlEv+zbKs/+/yAQDQOj2iGfTJJ5/o1ltv1aJFizR79mxJF/aCampq\nlJmZqerqamVkZFy2tqysrOljn88nn8/X5qaBrs7jSVNdXa3ruJSUVJ04EY65BmiLYDCoYDDoOs51\nsYLjOPL7/UpPT9cvfvGLpq8vXbpU6enpKi0tVSAQUCQSYbEC0E466gl0alis0OIWm8kE1yDavn27\nbrzxRo0aNarp8NvDDz+sCRMmqKSkRIcPH1ZWVpbWr1+v/v37RzUpgLbpqC+O1BBELW4x1iBKxKQA\n2qajvjhSQxC1uMVYl28DAJBIBBEAwCiCCABgFEEEADCKIAIAGEUQAQCMIogAAEYRREAceTxpTfde\nbOnh8aS1qQZIJlzQCsRRR734kZrkq+GCVgAA4oQgAgAYRRABAIwiiAAARhFEAACjCCJ0CSyrBjqu\nqN4qHOjsLrxFtvtS1Lo6q001AFqPPSIAgFEEEQDAKIIIAGAUQQQAMIogAgAYRRABAIwiiNDpcH0P\nkFy4jgidDtf3AMmFPSIAgFEEEQDAKIIIAGAUQQQAMMo1iBYvXizbtpWXl9f0tXA4rKKiImVnZ6u4\nuFiRSCShTQIAkpdrEN15553atGnTJV8LBAIqKirSwYMHVVhYqEAgkLAG0XlEu6z6s0urY6kBkFws\nx3Fc18FWVVVp5syZ2rdvnyQpJydH27Ztk23bqqmpkc/n04EDB764cctSFJtHkrAsS9Esq/7/aDmO\nk+CaT59/1FCTbDXt9fsWT81lQkzniEKhkGzbliTZtq1QKNS27gAAXVabFytcPGwCAEAsYrqzwsVD\ncpmZmaqurlZGRkazY8vKypo+9vl88vl8sUyJdubxpP3/DgbuUlJSdeJEOMEdAehsgsGggsGg67iY\nzhEtXbpU6enpKi0tVSAQUCQSueyCBc4RdV4d73xPLDUd/zg/NdRwjiiKILr99tu1bds2HTt2TLZt\n68c//rG++tWvqqSkRIcPH1ZWVpbWr1+v/v37Rz0pOr6OFyqx1HT8FxNqqCGIotwjivek6Pg6XqjE\nUtPxX0yooYYg4s4KAADDCCIAgFEEEQDAKIIIAGAUQQQAMIogAgAYRRABAIwiiLoA3moBQEcW073m\n0LlcuGdcdBem1dVxA1sA7Ys9IgCAUQQRAMAogggAYBRBBAAwiiACABhFEAEAjCKIAABGEUQAAKMI\nIgCAUQQRAMAogggAYBRBBAAwiiACABhFEAEAjCKIAABGEUQAAKMIojiJ5V1Q26sGADoyy3Gc6N66\nM5aNW5YSuPkOxbIsRfsuqNKFnws1ia759PlHDTXJVtNev2/x1FwmsEcEADCqTUG0adMm5eTkaOjQ\noVq5cmW8egIAdCExB1FDQ4O+/e1va9OmTdq/f7/WrVund999N569JYlgO9S0xxzUUEMNNYkRcxDt\n3LlTQ4YMUVZWlnr27KnbbrtNL7zwQjx7SxLBdqhpjzmooYYaahIj5iA6evSorrnmmqbPBw0apKNH\nj8alKQBA1xFzEF1YfQEAQBs5Mfrb3/7mTJ8+venzhx56yAkEApeMGT16tKMLawV58ODBg0cXf4we\nPfqyeRLzdUT19fUaNmyYtmzZoi9/+cuaMGGC1q1bp+HDh8eyOQBAF9Uj5sIePfSrX/1K06dPV0ND\ng775zW8SQgCAVkvonRUAAHDDnRWSwOrVq5Wbm6tFixYlfK7JkydHNa6qqkp5eXkJ7iZ2x48f15NP\nPtnqumi/fxP69u3brvOVlZXp0Ucfbdc5cXkd/ffNDUGUBJ588klt3rxZv//97xM+1+uvv57wOdpD\nbW2tnnjiiVbXteX7dxwnofdebO+VrKycRbwQRAnys5/9TMOGDVNBQYG+9rWvtfiXY1VVlXJycnTn\nnXdq2LBhWrhwoV555RVNnjxZ2dnZ+vvf/95s7T333KP3339fN998s375y1+69lVVVaXhw4fr7rvv\n1siRIzV9+nSdPXs26u+rNX91NzQ0tHqeOXPmaNy4cRo5cqTWrFkT1TzPP/+8Jk6cKK/Xq3vuuUeN\njY2uNcuWLdO///1veb1elZaWRjWP1Pq9jqqqKg0bNkx+v195eXk6cuTIZcc98sgjeuyxxyRJ9913\nnwoLCyVJW7du1R133NGqOaP1k5/8RDk5OVE9Ry/67PP6vffecx3/61//Wl6vV16vV4MHD9ZNN90U\nVW/PPfecRo8erfz8fH39619vcezy5csv+aMi2j21z+9FlJeX68EHH2x2/IoVK7Rq1aqmzx944AGt\nXr3adZ6f//znysvLU15e3iX1LfWVk5OjO+64Q7m5uZo/f77OnDnjWldfX9/qmlOnTmnGjBnKz89X\nXl6e1q9f71qTELEu30bzdu3a5eTl5TlnzpxxTpw44QwZMsR59NFHmx1/6NAhp0ePHs4777zjNDY2\nOmPHjnUWL17sOI7jvPDCC87s2bNbnC8rK8v5+OOPo+rt4lx79+51HMdxSkpKnOeffz7K78xx+vbt\nm9B5wuGw4ziOc/r0aWfkyJGu39f+/fudmTNnOvX19Y7jOM69997rPPfcc67zVFVVOSNHjnQd93nR\nfv8XHTp0yOnWrZvz5ptvtjhux44dzvz58x3HcZwbbrjBmThxovPJJ584ZWVlztNPPx333nbu3Onk\n5+c7586dc+rq6pyhQ4e2+Bx1nNY/rz/rk08+cQoKCpyNGze6jn3nnXec7Ozspv/3F58TzdmzZ48z\nZcqUps9zc3OdI0eOuM5z6NChS54D5eXlTllZWbPjq6qqnDFjxjiO4zgNDQ3Odddd59rbxZ/Z6dOn\nnZMnTzojRoxw9uzZ49qXZVnOG2+84TiO4yxevNgpLy+Pe43jOM6GDRucJUuWNH1+/Phx15pEYI8o\nAV577TXNnTtXvXr1UkpKimbNmuV6SGbw4MEaMWKELMvSiBEjNG3aNEnSyJEjVVVVFdf+Bg8erFGj\nRkmSxo4dG/ftt2WeVatWKT8/X5MmTdKRI0f0z3/+s8XxW7Zs0e7duzVu3Dh5vV5t3bpVhw4dcp3H\n7f9HPF177bWaMGFCi2PGjBmj3bt3q66uTr169dKkSZO0a9cubd++XQUFBXHv6fXXX9fs2bN1xRVX\nqG/fvpo5c6brzySW5/VF3/nOd1RYWKgZM2a4jt26datKSkqUlnbh/bRSU1NbHJ+fn68PP/xQ1dXV\n2rt3r1JTUzVw4MCo+mqNa6+9Vunp6Xr77bf1yiuvaMyYMa69bd++XXPnzlXv3r3Vp08fzZ07V6+9\n9prrXNdcc40mTZokSbrjjju0ffv2hNSMGjVKr776qpYtW6bt27fL4/G41iRCzMu30bzPv+dGNL+s\nX/rSl5o+7tatm6644oqmj+vr6+Pa32fn6t69e1S78O0xTzAY1JYtW7Rjxw716tVLU6dO1blz51zn\n8fv9euihh9rcb6L06dPHdUzPnj01ePBgPfPMM7r++us1atQobd26Vf/617+Uk5MT955ieY7GUiNJ\nzzzzjD744IOoz8nF8j5m8+fP14YNG1RTU6PbbrstqpoePXpcchg3mt+Du+66S2vXrlUoFNLixYtd\nx1/uZxbNubXPjklkzdChQ7Vnzx699NJL+sEPfqDCwkL98Ic/dK2LN/aIEuDGG29URUWFzp49q7q6\nOm3cuJETu1E4ceKEUlNT1atXLx04cEA7duxwrSksLNSGDRv00UcfSZLC4bAOHz7sWpeSkqK6uro2\n9xxPBQUFKi8v15QpU1RQUKCnnnpKY8aMSchckydP1osvvqhz587p5MmTeumll1yfo7E8r3fv3q1H\nH320VQtpbrrpJv3pT39SOByWpKb/tmTBggVat26dNmzYoPnz50c1j23b+vDDDxUOh3Xu3Dlt3LjR\ntWbOnDnatGmTdu3apenTp7uOLygoUEVFhc6cOaNTp06poqIiqj3cw4cPNz3///CHPySsprq6Wr16\n9dLChQv1/e9/X2+99ZZrTSKwR5QAXq9XCxYs0OjRo5WRkaHx48e7/oX3+V/oz37u9sve2pBraa7W\n1sZznptvvllPPfWUcnNzNWzYsKbDDC0ZPny4fvrTn6q4uFiNjY3q2bOnnnjiCX3lK19psS49PV2T\nJ09WXl6ebrnllqjfTyuWPyiirSkoKNBDDz2kSZMmqXfv3urdu3erDsu1prdx48Zp1qxZGjVqlGzb\nVl5envr169dizeef126HGyXp8ccfV21traZOnSpJGj9+vJ5++ukWa3Jzc/XAAw9oypQp6t69u8aM\nGaPf/e53rjUnT57UoEGDZNu2a1/Shb3QH/3oR5owYYIGDhyo3Nxc159hz549ddNNNyk1NTWqn7fX\n69U3vvGNpp/VkiVLNHr0aNe6YcOG6fHHH9fixYs1YsQI3XvvvS2Otyyr1TWStG/fPt1///1NR2Fi\nuaQhHrigtR08+OCD6tu3r773ve+ZbqVNPv7444SeU0L7OnXqlPr06aPTp09rypQpWrNmjfLz8023\n1aE1NjZq7Nix2rBhg6677rqEzFFVVaWZM2dq3759Cdl+R8ShuXbS2Q/N/fe//9X111+v+++/33Qr\niJO7775bXq9XY8eO1bx58wghF/v379fQoUM1bdq0hIXQRZ399aK12CMCABjFHhEAwCiCCABgFEEE\nADCKIAIAGEUQAQCMIogAAEb9D13scWHcdPNhAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xae9bdf4c>"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(normalised_english_counts, sort_key=lambda l: normalised_english_counts[l])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEkCAYAAABzKwUZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwv8DbMnA/BWkp54AAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaea606ec>"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6bf = frequencies(sanitise(c6b))\n",
+ "plot_frequency_histogram(c6bf, sort_key=lambda l: c6bf[l])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEkCAYAAAB6wKVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xae9b112c>"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(c6bf)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEkCAYAAAB6wKVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaea4c52c>"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(normalised_english_counts)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEkCAYAAABzKwUZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEFgAACsQWAAAKxBYAAArEFgAACv8P5ZWvQDqsWNNAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xae9fc90c>"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 10,
+ "text": [
+ "'CPYYL GVVIR PDDVU BCSUP QOWPW SYBYP ODBCS PBBPR CSIOZ PTSTV HYVTW PYOZC OGCRV TTPUI BVGVS YOUGZ ZSRYS BPYLY SHSYY OUGBV BCSWP OUBOU GPUIR DSPYD LTSUB OVUOU GZPYP ZSSTZ DONSB CSLNU SJPAV EBBCS ZRPTV EYHYO SUIDL ZZVHH ORSYJ PZWDP UUOUG BVWED DBCSL WORNS IEWCS YBYPO DBCSU OGCBP HBSYZ CSJSU BTOZZ OUGAE BLVER PUYSP IPAVE BBCPB OUBCS OYYSW VYBZB ODDUV MVLVU GSBBO UGPRR SZZBV BCSVY OGOUP DTOZZ TVUPO UBCSG PDDSY LPUIO PTASG OUUOU GBVBC OUNJS TOGCB USSIB VYVDD VEBPA DPRNA PGMVA LVEJP UBBVI VOBVY ZCPDD OALBC SJPLO JPZZE YWYOZ SIALB COZYS WVYBB CSVBC SYWPW SYZJS CPFSH YVTBC SUPQO ZPBBC OZDSF SDPYS SURYL WBSIE ZOUGA OHOIV YWDPL HPOYZ BLDSR OWCSY ZBCOZ VUSOZ UBTPL ASBCS ROWCS YRDSY NJPZV HHIEB LBCPB IPLJS GVBDE RNL\\n'"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "len(sanitise(c6b))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 11,
+ "text": [
+ "1573"
+ ]
+ }
+ ],
+ "prompt_number": 11
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6as = sanitise(c6a)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "frequencies(ngrams(c6as, 2))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 13,
+ "text": [
+ "Counter({'bc': 21, 'cs': 20, 'ou': 15, 'sy': 12, 'oz': 10, 'ug': 10, 'ub': 8, 'bv': 8, 'su': 7, 'bb': 7, 'zz': 6, 'yo': 6, 'dd': 6, 'ys': 6, 'py': 6, 'pu': 6, 'jp': 6, 've': 6, 'vy': 6, 'cp': 5, 'co': 5, 'si': 5, 'yz': 5, 'ds': 5, 'po': 5, 'bo': 5, 'eb': 5, 'vb': 5, 'vu': 5, 'sb': 4, 'zb': 4, 'yb': 4, 'dp': 4, 'pl': 4, 'pd': 4, 'pb': 4, 'pz': 4, 'bp': 4, 'js': 4, 'wp': 4, 'og': 4, 'up': 4, 'uo': 4, 'ui': 4, 'yl': 4, 'tv': 3, 'to': 3, 'lv': 3, 'lb': 3, 'yv': 3, 'sj': 3, 'sg': 3, 'sp': 3, 'sr': 3, 'ss': 3, 'sw': 3, 'zs': 3, 'zv': 3, 'zc': 3, 'al': 3, 'yw': 3, 'rn': 3, 'rp': 3, 'db': 3, 'us': 3, 'yy': 3, 'yp': 3, 'st': 3, 'ie': 3, 'gv': 3, 'gp': 3, 'zp': 3, 'gb': 3, 'gc': 3, 'pa': 3, 'pt': 3, 'pr': 3, 'bl': 3, 'wc': 3, 'ow': 3, 'od': 3, 'vh': 3, 'vt': 3, 'hy': 3, 'tp': 2, 'ts': 2, 'cb': 2, 'lw': 2, 'fs': 2, 'sh': 2, 'sl': 2, 'so': 2, 'sz': 2, 'sv': 2, 'zo': 2, 'ro': 2, 'wv': 2, 'rd': 2, 'ry': 2, 'rs': 2, 'dl': 2, 'do': 2, 'dv': 2, 'ir': 2, 'ip': 2, 'qo': 2, 'io': 2, 'ib': 2, 'gz': 2, 'ga': 2, 'av': 2, 'go': 2, 'pw': 2, 'pq': 2, 'by': 2, 'bs': 2, 'bt': 2, 'wd': 2, 'oy': 2, 'or': 2, 'ws': 2, 'iv': 2, 'zd': 2, 'ey': 2, 'er': 2, 'ns': 2, 'vi': 2, 'nj': 2, 'ho': 2, 'uu': 2, 'hh': 2, 'mv': 2, 'as': 2, 'tz': 1, 'tt': 1, 'tw': 1, 'ta': 1, 'tb': 1, 'lp': 1, 'lt': 1, 'ly': 1, 'lz': 1, 'la': 1, 'ld': 1, 'lg': 1, 'lh': 1, 'lj': 1, 'cr': 1, 'lo': 1, 'ln': 1, 'wb': 1, 'sc': 1, 'sd': 1, 'sf': 1, 'na': 1, 'zr': 1, 'ap': 1, 'wo': 1, 'zw': 1, 'zu': 1, 'zt': 1, 'zy': 1, 'ad': 1, 'ae': 1, 'zj': 1, 'ao': 1, 'rc': 1, 'hb': 1, 'rr': 1, 'rv': 1, 'yj': 1, 'yh': 1, 'yn': 1, 'hi': 1, 'de': 1, 'yd': 1, 'yr': 1, 'du': 1, 'dt': 1, 'nl': 1, 'gm': 1, 'wy': 1, 'ia': 1, 'id': 1, 'gs': 1, 'pi': 1, 'ph': 1, 'pg': 1, 'pf': 1, 'bz': 1, 'bu': 1, 'bi': 1, 'bd': 1, 'we': 1, 'ov': 1, 'op': 1, 'os': 1, 'on': 1, 'oi': 1, 'oj': 1, 'oa': 1, 'ob': 1, 'ej': 1, 'ze': 1, 'ed': 1, 'ez': 1, 'ew': 1, 'vg': 1, 'vd': 1, 'va': 1, 'vo': 1, 'oh': 1, 'nu': 1, 'vl': 1, 'vw': 1, 'vv': 1, 'vs': 1, 'uy': 1, 'uv': 1, 'ur': 1, 'un': 1, 'hp': 1, 'hs': 1, 'vm': 1})"
+ ]
+ }
+ ],
+ "prompt_number": 13
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "' '.join(segment(letters(c6a).translate(''.maketrans({'B':'t', 'C':'h', 'S':'e', 'O':'i', 'U':'n', 'G':'g', 'A':'b', 'V':'o', 'N':'k', 'J':'w', 'T':'m', 'I':'d', 'P':'a', 'W':'p', 'R':'c', 'Y':'r', 'L':'y', 'D':'l', 'H':'f', 'Z':'s', 'Q':'z', 'E':'u', 'F':'v', 'M':'j'}))))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 14,
+ "text": [
+ "'harry good call on the nazi paper trail the attached is a memo from paris high command to goering s secretary referring to the painting and clearly mentioning sara seems like they knew about the scam our friendly ss officer was planning to pull they picked up her trail the night after she went missing but you can read about that in their report still no joy on getting access to the original miss mona in the gallery and i am beginning to think we might need to rollout a black bag job you want to do it or shall i by the way i was surprised by this report the other papers we have from the nazis at this level are encrypted using bifid or playfair style ciphers this one isnt maybe the cipher clerk was off duty that day we got lucky'"
+ ]
+ }
+ ],
+ "prompt_number": 14
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "trans={'B':'t', 'C':'h', 'S':'e', 'O':'i', 'U':'n', 'G':'g', 'A':'b', 'V':'o', 'N':'k', 'J':'w', 'T':'m', 'I':'d', 'P':'a', 'W':'p', 'R':'c', 'Y':'r', 'L':'y', 'D':'l', 'H':'f', 'Z':'s', 'Q':'z', 'E':'u', 'F':'v', 'M':'j'}"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 15
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "''.join(sorted(trans.keys(), key=lambda k: trans[k]))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 16,
+ "text": [
+ "'PARISHGCOMNDTUVWYZBEFJLQ'"
+ ]
+ }
+ ],
+ "prompt_number": 16
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "' '.join(segment(keyword_decipher(c6as, 'parishighcommand', 2)))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 17,
+ "text": [
+ "'harry good call on the nazi paper trail the attached is a memo from paris high command to goering s secretary referring to the painting and clearly mentioning sara seems like they knew about the scam our friendly ss officer was planning to pull they picked up her trail the night after she went missing but you can read about that in their report still no joy on getting access to the original miss mona in the gallery and i am beginning to think we might need to rollout a black bag job you want to do it or shall i by the way i was surprised by this report the other papers we have from the nazis at this level are encrypted using bifid or playfair style ciphers this one isnt maybe the cipher clerk was off duty that day we got lucky'"
+ ]
+ }
+ ],
+ "prompt_number": 17
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6bs = sanitise(c6b)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "len(c6bs)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 19,
+ "text": [
+ "1573"
+ ]
+ }
+ ],
+ "prompt_number": 19
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from itertools import permutations"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 20
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "list(permutations(range(4)))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 21,
+ "text": [
+ "[(0, 1, 2, 3),\n",
+ " (0, 1, 3, 2),\n",
+ " (0, 2, 1, 3),\n",
+ " (0, 2, 3, 1),\n",
+ " (0, 3, 1, 2),\n",
+ " (0, 3, 2, 1),\n",
+ " (1, 0, 2, 3),\n",
+ " (1, 0, 3, 2),\n",
+ " (1, 2, 0, 3),\n",
+ " (1, 2, 3, 0),\n",
+ " (1, 3, 0, 2),\n",
+ " (1, 3, 2, 0),\n",
+ " (2, 0, 1, 3),\n",
+ " (2, 0, 3, 1),\n",
+ " (2, 1, 0, 3),\n",
+ " (2, 1, 3, 0),\n",
+ " (2, 3, 0, 1),\n",
+ " (2, 3, 1, 0),\n",
+ " (3, 0, 1, 2),\n",
+ " (3, 0, 2, 1),\n",
+ " (3, 1, 0, 2),\n",
+ " (3, 1, 2, 0),\n",
+ " (3, 2, 0, 1),\n",
+ " (3, 2, 1, 0)]"
+ ]
+ }
+ ],
+ "prompt_number": 21
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "[column_transposition_decipher(c6bs, p, fillvalue=' ') for p in permutations(range(4))]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 22,
+ "text": [
+ "['hithhnfrferteaofftnasltyorrreqthimserulrhfsetaeeiuiwsihbnrrtsioiienirhmrpytdtoeircitalnbrpnhtohorliewtshtesposotoynuwhredhhcpicmnootahashtijeanetofrneusragmxurttowleatbrtepptraaesenethhrisnrststrnfldoghaumgseseakuntiahyttnyuntnnvhigwlewfiapasrelfaiebapcmcplahethtolhwsuladseuveaeotaeutateefehlbhtrshgatliaceehtrnhgaasietufnnpurehrelittmudyinnuegicouendeeiahsuelhtnoahtieainyfdniauoenatiyefohedaluaroeryhuraeraterahiaongtwiswsaitbultaomxletoreototonaoortgbcvaipnleewfyrtaeoaduwwetrrtnlrpfpaiellrimntrowieqrecuarndotafnroteletpenrpmshnddetdewhefoypedennsneegtateeanesertgdlueosrburowhdgbrpahenyttekrertrrhnfnedoltswsianiypothmouoctyeieeponhkrnntairlteeiuremnitaphoeaohhnouumtkvsenedeeoidpwkiegtiiiosenhuufanbeuithraerhieehuayasrusiwgadsisitrlgfphiltwiisihesurmromaceisoilvdeoelusairideotnfiesobntsapofgsodrtfirpltanetrlyiosotyoessfntpheaigeaoraieitdmtaaegrhyasrrcfelrtleeanehvehivmhnassieroixlmpsjttesofbmrhndiochecnbttncompusesoehtenetfienootnwooteeriltoigdosmehoifmlroeffdtuesoflerwiiyqdbzottnrfantdigontalaepdsheamnthlpsnbtuoahaalplemnpdrshvnaiaelnhcaliskacdoaedtjghoieadgwayndetenhegeeueoeednoryyrfctemttwntgdetasuoootfwymihrgrhoesinrraofretfenpoyaissesstnhhayeulhiwpcteatuapeanlueelwahaoeluresnbiieasaeteagcdcsorepsroenselvosedudsitlteittfseoesnlnginoernteieiwwlwhteirsiilhtdsrndesrfhrntlgrsuasedadoytasadutenwitcnametertpoesytotocnyutsogcmudlpadleadotefdsbfeleahaxrsonidtieiprashicdcshipbielslnalhorfrentatoraiemklagretmrdcwsnaynwsvnuldorekutnnoutiiunnhvnieilohltefnaeatuifttacetlnnrbhbeglosnaonmifkoaronsnvnfretrcfthdetpswsucpercwtpoawfhdayisooagoaelndfigngrtebltllgfo ',\n",
+ " 'hihthnrffetreafoftanslytorrreqhtimesrurlhfestaeeiuwisibhnrtrsiioieinrhrmpydttoierctialbnrphntoohrleiwthstepsostooyunwherdhchpimcnotoahsahtjieaentorfnesuramgxutrtolweabtrtpeptaraeesnehthrsinrtsstnrflodghuamgessekaunitahtytnuyntnnvhgiwlwefipaaserlfiaebpacmpclaehthotlhswuldasevueaoetauetaetefhelbthrsghatilaceehtnrhgaasiteufnnpuerhrleitmtudiynneugiocuedneeaihseulhntoathieianydfniuaoeantieyfoehdaulareoryuhrareatreahaiontgwiwssatibutlaoxmleotretootnoaorotgcbvapinleewfrytaoeadwuwertrtlnrppfailelrmintorwiqereucardnotfanrtoeltepernpmhsndedtdweheofypdeensnnegetaeteaensetrgduleorsbuorwhgdbrapheynttkeretrrrnhfndeolstwsainipyotmhoucotyieeeopnhrknnatirtleeuirenmitpahoaeohnhoumutksvendeeeiodpkwietgiioisehnuuafnbueitrhaehrieheuaaysrsuiwagdssiitlrgfhpilwtiiisheusrmormaecisiolvedoeulsariidoetnifesbontaspogfsordtfriplatnertlyoisoytoessfnptheiageoaraeiitmdtaeagryhasrrcflerteleaenhvheivhmnassieorixmlpstjteosfbrmhnidocehcntbtnocmpsueseohtneetifenootnowoteeritloidgosemhofimlorefdftuseofelrwiiyqbdzottnrafntidgotnaleapdhseanmthplsntbuohaaapllenmpdsrhvanialenhacliksacodaetdjgohiedagwyandteenehgeueeoeednroyyfrctmettnwtgedtausootofwmyihgrrheosirnraforeftenopyasisesstnhhayuelhwipcetataupenalueelwhaaoleursenbiieaasetaegccdsoerpsoreneslvsoeddusilttetitfesoenslnignorentieeiwwlwtheisriihltdrsndserfrhntglrsauseaddotyasdautnewictnaemtetrposeyttoocynutosgcumdlapdlaedoetfdbsfeelahxarsnoiditeirpasihcdschibpiesllnlahofrretnatroaimeklgaremtrdwcsnyanwvsnudlorkeutnnouitiunnhvineiolhletfneaatiuftatceltnnbrhbgelonsaomnifokarnosnnvfrterctfhdtepsswuceprctwpowafhadyiosoaogaenldfgingtrebtlllfgo ',\n",
+ " 'htihhfnrfreteoaffntastlyorrretqhismerlurhsfeteaeiiuwshibnrrtsoiiineirmhrptydteoirictanlbrnphthoorilewsthtsepoostonyuwrhedhhcpcimnootaahshitjenaetfornuesrgamxruttwoletabretpprtaaseentehhirsnsrtsrtnfdlogahumsgesaekutniayhttynunntnvihgwelwfaiparselafieabpccmplhaettholwhsualdsueveeaoteauttaeeefhlhbtrhsgaltiaecehrtnhagaseitunfnprueherlittmuydinunegciounedeieahuselthnohatiaeinfydnaiuoneatyiefhoedlauaorerhyurearaetraihaogntwsiwsiatblutamoxlteoroetootnaoortbgcviapnelewyfrteaoaudwwterrntlrfppaeillirmnrtoweiqrceuanrdoatfnorteeltpnerpsmhnddetedwhfeoyepdennsneegttaeenaesretglduesorbruowdhgbprahneytetkrretrhrnfendotlswisanyipohtmooucteyiepeonkhrntnailrteieurmeniatpheoaohhnouumtvkseendeoeidwpkigetiiiosnehufuanebuihtrarehieehuyaasursigwadissirtlgpfhitlwisiihseurrmomcaeiosildveoleusiariedotfnieosbnstapfogsdortifrptlanterliyostoyosesftnphaeigaeoriaeidtmtaaeghryarsrceflrlteenaehevhimvhnsasireoilxmpjsttseofmbrhdniohcecbnttcnomupseosehetneftieonotwnooeterlitogidomsehiofmrloeffdteusolferiwiydqbztotnfrandtignotaalepsdhemantlhpsbntuaohalaplmenprdshnvaiealnchalsikadcoadetjhgoiaedgawynedtehnegeeueeoedonryryfcetmtwtntdgetsauoootfywmirhgrohesnirroafrtefepnoyiasssesthnhaeyulihwptceautapaenleuelawhaeoluersnibiesaaeetagdccsroeprsoesnelovseuddstiltiettsfeosenlgnineornetiewiwlhwterisilihtsdrnedsrhfrnltgrusasdeadyotaasduetnwticnmaetretpeosyottoncyustogmcudpladeladtoefsdbfleeaahxrosnitdiepirahsiccdshpibileslanlhrofrnetaotraeimkalgrtemrcdwsanynswvnludoerkuntnotuiinunhnvieliohtlefaneautifttactelnrnbhebglsonanomikfoaornsvnnfertrfcthedtpwssupcerwctpaowfdhaysioogaoalendifgnrgtelbtlglfo ',\n",
+ " 'hhithrnffterefaofatnsyltorrrehqtiemsrrulhefsteaeiwuisbihntrrsiioiienrrhmpdyttioertciablnrhpntoohreliwhtstpesotsoouynwehrdchhpmicntooashahjtieeantrofnseurmagxturtlowebatrptepatraeesnhethsrintrssntrfoldguhamegsskeauintathytunynntnvghiwwlefpiaaesrlifaepbacpmcleahtohtlshwudlasveueoaetuaeteatehfeltbhrgshaitlaecehntrhagastieunfnpeurhlreimttuidynenugoicudeneaeihesulnhtotahiieandyfnuiaoaenteiyfeohdualaeroruyhrraearteaahiotngwwisstaibtulaxomloetrteoontoarootcgbvpainelewrfytoaeawduwretrltnrppfalielmrinotrwqieruecadrnoftantroetleprenphmsneddtwdehoefydpeesnnngeeteateeanstergudlerosbourwghdbarphyentkterterrnrhfdneosltwasinpiyomthocuotiyeeoepnrhknantitrleueirnemiptahaoeonhhomuutskvedneeieodkpwitegioiishenuaufnubeirthaheriheeuaayssruiawgdssiiltrghfpiwltiiishuesromrmeaciisolevdouelsraiiodetinfebsonatspgofsrodtrfipaltnretloyisyotosesfpnthieagoeareaiimtdteaagyrharsrclferetleeanhhveihvmnsasioerimxlptsjtoesfrbmhindoechctnbtoncmspueesohnteeitfeonotonwoetertilodigoesmhfoimolredfftsueoeflriwiybqdztotnarfnitdgtonaelaphdsenamtphlstnbuhoaapallnempsdrhavnilaenahclkisaocdatedjoghideagywantdeeenhgueeeeoedrnoyfyrcmtetntwtegdtuasotoofmwyighrrehosrinrfaorfeteonpysaissesthnhauyelwhipectaatupnealeuelhwaaloeusrenibieaaseategccdseorposreenslsvoedduslittteitefsoneslingnroeniteewiwltwhesirihiltrdsnsderrfhngtlrasusaeddtoyadsauntewcitneamtterpsoeyttooycnuotsgucmdalpdaledeotfbdsfeelaxharnsoiidteripaishcsdchbipiselllnahforrtenartoamiekglarmetrwdcsynanvwsndulokreuntnoiutinunhivneoilheltfenaaitufattcletnbnrhgbelnosamoniofkanrosnnvftrertcfhtdepsswuecprtcwpwoafahdyoisooaganeldgfintgretbllflgo ',\n",
+ " 'hthihfrnfrteeofafnatstylorrrethqisemrlruhsefteeaiiwushbinrtrsoiiiniermrhptdyteioritcanblrnhpthoorielwshttspeootsonuywrehdhchpcminotoaashhijteneatfronusergmaxrtutwloetbareptprataseenthehisrnstrsrntfdolgauhmsegsakeutinaythtyunnnntvighwewlfapiareslaifeapbccpmlheattohlwshuadlsuveeeoateuatteaeehflhtbrhgsalitaeechrnthaagsetiunnfpreuhelritmtuyidnuengcoiundeeiaehuesltnhohtaiaienfdynauionaetyeifheodluaaoerrhuyreraaertaiahogtnwswisitabltuamxoltoeroteoontaorotbcgvipaneelwyrfteoaauwdwtrernltrfppaelilimrnrotweqircueandroaftnotreetlpnrepshmndedtewdhfoeyedpensnnegetteaeneasrtegludesrobrouwdghbparhnyetektrrterhnrfednotslwiasnypiohmtoocuteiyepoenkrhntaniltreiuermneiaptheaoohnhoumutvskeedneoiedwkpigteiioisnheufauneubihrtarheieheuyaasusrigawdissirltgphfitwlisiihsuerrommceaioisldevoluesiraieodtfineobsnsatpfgosdrotirfptalntrelioystyoosseftpnhaiegaoerieaidmttaeaghyrarrscelfrleteneahehvimhvnssairoeilmxpjtstsoefmrbhdinoheccbtntconmuspeoeshentefiteoontwonoeetrltiogdiomeshifomrolefdftesuolefriiwydbqzttonfarnditgntoaaelpshdemnatlphsbtnuahoalpalmneprsdhnavielancahlskiadocadtejhogiadegaywnetdehengeueeeeodornyrfycemttwnttdegtsuaootofymwirghroehsnrirofartfeeponyisasssethhnaeuyliwhptecauatpaneleeulahwaelouesrniibesaaeeatgdccsreoprosesenlosveuddstlititetsefosnelginneroneitewwilhtwersiilhitsrdnesdrhrfnlgtruassdaedytoaadsuentwtcinmeatrtepesoyottonycusotgmucdpaldealdteofsbdfleeaaxhronsitidepriahisccsdhpbiilselalnhrfornteaortaemikaglrtmercwdsaynnsvwnlduoekrunntotiuinnuhniveloihtelfaenauitftatctlenrbnhegblsnoanmoikofaonrsvnnfetrrftchetdpwssupecrwtcpawofdahysoiogoaalnedigfnrtgeltblgflo ',\n",
+ " 'hhtihrfnftreefoafantsytlorrrehtqiesmrrluhesfteeaiwiusbhintrrsioiiinerrmhpdtytieorticabnlrhnptohoreilwhsttpseotosounywerhdchhpmcintooasahhjiteenatrfonsuermgaxtrutlwoebtarpetpartaesenhtehsirntsrsnrtfodlguahmesgskaeuitnatyhtuynnnntvgihwwelfpaiaersliafepabcpcmlehatothlswhudalsvueeoeatueatetaeheflthbrghsailtaeechnrthaagsteiunnfperuhlerimttuiydneungociudneeaieheuslnthothaiiaendfynuaioaneteyifehodulaaeorruhyrreaaretaaihotgnwwsistiabtluaxmolotertoeonotarootcbgvpianeelwryftoeaawudwrterlntrpfpaleilmirnortwqeiruceadnrofatntoretelprnephsmneddtwedhofeydepesnnngeetetaeenastreguldersoboruwgdhbaprhynetketrtrernhrfdenostlwaisnpyiomhtocoutieyeopenrkhnatnitlreuiernmeipathaeoonhhomuutsvkedeneioedkwpitgeioiishneuafunuebirhtahreiheeuayassuriagwdsisilrtghpfiwtliisihuserormmecaiiosledvoulesriaioedtifnebosnastpgfosrdotrifpatlnrteloiysytoossefptnhiaegoaereiaimdtteaagyhrarrsclefrelteenahhevihmvnssaioreimlxptjstosefrmbhidnoehcctbntocnmsupeeoshneteifteoontownoeetrtliodgioemshfiomorledfftseuoelfriiwybdqzttonafrnidtgtnoaealphsdenmatplhstbnuhaoaplalnmepsrdhanvileanachlksiaodcatdejohgidaegyawntedeehngueeeeeodronyfrycmettnwttedgtusaotoofmywigrhreohsrnirfoarfteeopnysiasssethhnaueylwihpetcaautpnaeleeulhawaleouserniibeasaeaetgcdcseroporseesnlsovedudsltittietesfonselignnreonietewwilthwesriihlitrsdnsedrrhfngltraussadedtyoadasunetwctinemattrepseoytotoyncuostgumcdapldaeldetofbsdfeleaxahrnosiitderpiaihscscdhbpiislellanhfrortnearotameikgalrmterwcdsyannvswndluokerunntoituinnuhinveolihetlfeanaiutfattcltenbrnhgeblnsoamnoiokfanorsnvnfterrtfchtedpswsuepcrtwcpwaofadhyosioogaanledgifntrgetlblfglo ',\n",
+ " 'ihthnhfrefrtaeoftfnalstyrorrqethmiseurlrfhseateeuiiwishbrnrtisoieinihrmryptdoteicritlanbprnhotholrietwshetspsootyonuhwrehdhcipcmonothaasthijaeneotfrenusargmuxrtotwlaetbtreptpraeaseenthrhisrnsttsrnlfdohgaugmseesaknutihaytntyutnnnhviglwewifapsareflaibeapmccpalhehttohlwsluadesuvaeeoateuattefeehblhtsrhgtalicaeethrnghaaisetfunnuprerheltitmduyinnueigcoeundeeiashuehltnaohteiaiynfdinaueonaityeofheadluraoeyrhuarertaerhaianogtiwswasitubltoamxeltoerottoonoaorgtbcaviplneefwyrateodauwewtrtrnlprfpiaelrlimtnroiweqercurandtoafrnotleetepnrmpshdndedtewehfopyednensenegatteaeneesrtdgluoesrubrohwdgrbpaehnyttekerrtrrhnnfedlotsswiainyptohmuoocyteieepohnkrnntarilteeiuermntiapoheahohnuoumktvsneedeeoipdwkeigtiiioesnhuufabneutihrearheiehauyarsuswigasdistirlfgphlitwiisiehsumrroamcesioivldeeoluasirdieontfiseobtnsaopfgosdrftirlptaentrylioostyeossnftpehaiegaoarietidmataerghysarrfceltrleaenevhehvimhansseiroxilmspjtetsobfmrnhdicohencbtntcopmusseoethentefineoontwotoeeirltiogdsomeohiflmrofefdutesfolewriiqydbozttrnfatndiogntlaaedpshaemnhtlpnsbtouahaalpelmndprsvhnaaielhncailskcadoeadtgjhoeiadwgaydnetneheegeuoeeendoryyrftcemttwngtdeatsuoootwfymhirghroeisnrarofertfnepoayisesssnthhyaeuhliwcptetauaepanuleewlahoaelruesbniiaesateeacgdcosrespronesevlosdeudistletitftseeosnnlgionertneiiewwwlhtiersiilhdtsrdnesfrhrtnlgsruaesdaodytsaadtueniwtcanmeetrtopestyotconytusocgmuldpaldeaodtedfsbeflehaaxsronditiieprsahidccsihpbeilsnlalohrfernttaoriaemlkagertmdrcwnsaywnsvunldroektunnuotiuinnvhniielolhtenfaetauitftaectlnnrbbhegolsnoanmfikoraonnsvnrfetcrftdhetspwscupecrwtopawhfdaiysoaogoealnfdiggnrtbeltllgf o ',\n",
+ " 'ihhtnhrfeftraefotfanlsytrorrqehtmiesurrlfhesateeuiwiisbhrntrisioeiinhrrmypdtotiecrtilabnprhnotohlreitwhsetpssotoyounhwerhdchipmcontohasathjiaeenotrfensuarmguxtrotlwaebttrpetpareaesenhtrhsirntstsnrlfodhguagmeseskanuithatyntuytnnnhvgilwweifpasaerfliabepamcpcalehhtothlswludaesvuaeoeatueatetfeheblthsrghtailcaeethnrghaaistefunnuperrhletimtduiynneuigoceudneeaisheuhlntaotheiiayndfinuaeoaniteyofehadulraeoyruharretarehaainotgiwwsastiubtloaxmelotertotonooarogtcbavpilneefwryatoedawuewrttrlnprpfialerlmitnoriwqeerucradntofarntoleteeprnmphsdneddtweehofpydenesnengeatetaeenestrdguloersuborhwgdrbapehynttkeertrrrnhnfdelostswaiinpytomhuocoytieeeophnrknnatritleeuiernmtipaohaehonhuomuktsvnedeeeiopdkweitgiioieshnuuafbnuetirheahreiheauayrssuwiagsdsitilrfghpliwtiiisehusmroramecsiiovledeoulasridioentifsebotnasopgfosrdftrilpatenrtyloiosyteossnfptehiaegoaareitimdateargyhsarrfcletrelaeenvhhevihmansseiorximlsptjetosbfrmnhidcoehnctbntocpmsuseeothneteifneoontowtoeeirtliodgsoemohfilmorfedfutsefoelwriiqybdozttrnaftnidogtnlaeadphsaenmhtplnstbouhaaaplelnmdpsrvhanailehnacilkscaodeatdgjoheidawgyadnteneehegueoeeendroyyfrtcmettnwgtedatusootowfmyhigrhreoisrnarfoerftneopaysiesssnthhyauehlwicpettaauepnauleewlhaoalerusebniiaeasteaecgcdosersporneesvlsodeduisltettifteseonsnligonretnieiewwwlthiesriihldtrsdnsefrrhtnglsrauesadodtysadatuneiwctanemettropsetytocoyntuoscgumldapldaeodetdfbsefelhaxasrnodiitierpsaihdcscihbpeislnllaohfrertntaroiamelkgaermtdrwcnsyawnvsundlroketunnuoituinnvhinieollhetnfeataiutfatecltnnbrbhgeolnsoamnfiokranonsnvrftecrtfdhtespswcuepcrtwopwahfadiyosaoogeanlfdgigntrbetlllfg o ',\n",
+ " 'thihfhnrrfetoeafnftatslyrorrteqhsimelrurshfeetaeiiuwhsibrnrtosiinieimrhrtpydetoiirctnalbnrphhtooirleswthstepoostnoyurwhehdhccpimonotaahsihtjneaeftorunesgramrxutwtolteabertprptasaeetnehihrssnrtrstndfloaghusmgeasektuniyahtytnunntnivhgewlwafiprasealfiaebpccmphlaetthowlhsaulduseveeaoetauttaeeefhhlbthrsglatieacerhtnahgaesitnufnrpueehrltitmyudiunnecgionuedieeauhsetlhnhoataieifnydaniunoeaytiehfoeldauoarehryuerareatriahagontswiwisatlbutmaoxtleooretootnoaorbtgcivapenleywfretaouadwtwernrtlfrppeaililrmrntoewiqcreunardaotfonrteeltnperspmhdndeetdwfheoeypdnensenegttaeneaersetlgduseorrbuodwhgpbranheyettkrrethrrnefndtolsiwsayniphotmooucetyipeeoknhrtnnalirtieeumrenaitpehoahohnuoumvtkseendoeeiwdpkgietiiionsehfuuaenbuhitrraeheiehyuaausrsgiwaidssritlpgfhtilwsiiisheurrmocmaeoisidlveloeuisareidoftnioesbsntafpogdsoritfrtplatnerilyotsoysoestfnpaheiageoiraeditmataehgryrasrecfllrteneaeehvhmivhsnasrieolixmjpststeomfbrdhnihocebcntctnoumpsoeseehtnfetioenowtnoeotelritgoidmoseihofrmlofefdetuslofeirwidyqbtzotfnradntingotaalespdhmeanlthpbsntauohlaapmlenrpdsnhvaeialcnhaslikdacodaethjgoaiedagwyendtheneegeueeoeodnrryyfectmwttndtgestauoootyfwmrihgorhensiroraftrefpenoiyassseshtnheayuilhwtpceuataapeneluealwheaoleursinbiseaaeetadgccrsoerpsoseneolvsueddtsilitetstfesoenglnienorentiweiwhlwtreisliihstdrendshrfrlntgursadseaydotaasdeutntwicmnaerteteposoyttnocysutomgcupdlaedlatdoesfdblfeeaahxorsntidipeirhasiccdsphibliesalnlrhofnretoatreaimaklgtremcrdwasnysnwvlnudeorknutntouiniunnhvileiothleafneuatitftatcelrnnbehbgslonnaomkifooarnvsnnefrtfrctehdtwpsspucewrctapowdfhasyiogoaolaenidfgrngtlebtgllf o ',\n",
+ " 'hhitrhnftferfeaoaftnysltrorrheqteimsrrulehfsetaewiuibsihtnrrisioiienrrhmdpytitoetrcibalnhrpnotoherlihwtsptestosouoynewhrcdhhmpictnoosahajhtieeanrtofsneumragtxurltowbeatprteaptreaeshnetshritnrsnstrofldughaemgskseaiunttahyutnynntngvhiwwlepfiaeasrilfapebapcmcelahothtslhwdulavseuoeaeutaeetathefetlbhgrshiatleacenhtrahgatsienufnepurlhremittiudyennuogicduenaeeiehsunlhttoahiieadnyfuniaaoenetiyefohudalearouryhrraerateaahitongwwistsaitbulxaomolettreonotoraooctgbpvaienlerwfyotaewadurwetlrtnprpflaiemlriontrqwieurecdarnfotatnrotelerpenhpmsenddwtdeohefdypesenngneeetateeantserugdlreosoburgwhdabrpyhenkttetrernrrhdfnesoltawsipniymothcouoityeoeeprnhkannttirlueeinrempitaahoenohhmouustkvdeneieeokdpwtiegoiiihsenauufunberithhaerhieeauayssruaiwgsdsilitrhgfpwiltiiisuhesormremaciisoelvduoelrsaioideitnfbesoantsgpofrsodrtfiapltrnetolyiysotsoespfntiheaogeaeraimitdetaaygrhrasrlcfeertleeanhhvehivmsnasoiermixltpsjotesrfbmihndeochtcnbotncsmpueesonhteietfoenootnweotetrildoigeosmfhoiomlrdeffstueeoflirwibyqdtzotanrfintdtgonealahpdsneampthltsnbhuoapaalnlemspdrahvnliaeanhcklisoacdtaedojghdieaygwatndeeenhugeeeeoerdnofyyrmctenttwetgdutastooomfwygihrerhorsinfraofretoenpsyaisseshtnhuayewlhiepctaatunpeaeluehlwalaoesureinbiaeasaetecgcdesoropsreensslvodedulsittteietfsnoesilngrnoeinteweiwtlwhseirhiilrtdssnderrfhgntlarsuasedtdoydasanutecwitenamtterspoetytoyocnoutsugcmadlpadleedotbfdsefelxahanrsoiidtreipiashscdcbhipsielllnafhortrenratomaiegklamretwrdcysnavnwsdnulkorenutnioutniunihvnoeilehltefnaiatuafttlcetbnnrghbenlosmaonoifknaronsnvtfretrcfthdespsweucptrcwwpoaafhdoyisooagnaelgdfitngrteblfllg o ',\n",
+ " 'thhifhrnrfteoefanfattsylrorrtehqsiemlrrushefeteaiiwuhsbirntrosiiniiemrrhtpdyetioirtcnablnrhphtooirelswhtstpeootsnouyrwehhdchcpmiontoaashihjtneeaftrounsegrmarxtuwtlotebaerptrpatsaeetnheihsrsntrrsntdfolaguhsmegasketuinyathytunnnntivghewwlafpiraesalifaepbccpmhleattohwlshaudlusveeeoaetuatteaeehfhltbhrgslaiteaecrhntahagestinunfrpeuehlrtimtyuidunencgoinudeieaeuhestlnhhotaaiiefndyanuinoaeyteihfeolduaoaerhruyerraeartiaahgotnswwiistalbtumaxotloeorteoontoarobtcgivpaenelywrfetoauawdtwrenrltfrppealiilmrrnotewqicruenadraoftontreetlnpresphmdnedetwdfhoeeydpnesnengetteaneearstelgudserorboudwghpbarnhyeetktrrtehrnrefdntosliwasynpihomtoocuetiypeoeknrhtnanlitrieuemrneaiptehaohonhuomuvtskeednoeiewdkpgiteiioinshefuauenubhirtraheeiheyuaaussrgiawidssriltpghftiwlsiiishuerromcmeaoiisdlevloueisraeiodftinoebssnatfpgodsroitrftpaltnreiloytsyososetfpnahieagoeireadimtateahgyrrarseclflretneeaehhvmihvsnsarioelimxjptsstoemfrbdhinhoecbctnctonumspoeesehntfeitoeonwtoneoetlrtigodimoesihformolfedfetsuloefiriwdybqtztofnardnitngtoaaelsphdmenaltphbstnauholapamlnerpsdnhaveilacnahslkidaocdatehjogaideagywentdheenegueeeeoodrnryfyecmtwtntdtegstuaootoyfmwrighorehnsriorfatrfepeoniysasssehthneauyilwhtpecuaatapneeleualhwealoeusrinibseaaeeatdgccrseorposseenolsvueddtsliittestefsoneglinenroenitwewihltwresilihistrdensdhrrflngturasdsaeydtoaadseunttwcimneartteepsooyttnoycsuotmgucpdaledaltdeosfbdlfeeaaxhornstiidperihaisccsdphbiliseallnrhfonrteoarteamiakgltrmecrwdasynsnvwlndueokrnunttoiuninunhivleoithelafenuaittfattclernbnehgbslnonamokiofoanrvsnneftrfrtcehtdwpsspuecwrtcapwodfahsyoigooalaneidgfrntgletbglfl o ',\n",
+ " 'hhtirhfntfrefeoaafntystlrorrhetqeismrrluehsfeteawiiubshitnrrisoiiinerrmhdptyiteotricbanlhrnpothoerilhwstptsetoosuonyewrhcdhhmpcitnoosaahjhiteenartfosnuemrgatxrultwobetapretaprteasehnteshirtnsrnsrtofdlugahemsgksaeiutntayhutynnnntgvihwwelpfaiearsilafpeabpccmelhaotthslwhdualvsueoeeauteaettaheeftlhbgrhsialteaecnhrtahagtseinunfeprulhermittiuydenunogciduneaeieehusnlthtohaiiaednfyunaiaoneetyiefhoudlaeaorurhyrrearaetaaihtognwwsitsiatbluxamooltetroenootraooctbgpviaenelrwyfoteawaudrwtelrntprfplaeimlironrtqweiurcedanrfoattnorteelrpnehpsmenddwtedohfedyepsenngneeettaeenatsreugldresoobrugwdhabpryhnektettrrenrhrdfensotlawispnyimohtcoouiteyoepernkhantntilrueienrmepiataheonohhmouustvkdeenieoekdwptigeoiiihsneaufuunebrihtharehieeauyassuraigwsdislirthgpfwitliisiuhseorrmemcaiioseldvuolersiaoieditfnbeosanstgpforsdortifaptlrnteoliyystososepftnihaeogaeeriamidtetaayghrrarslceferlteenahhevhimvsnsaoiremilxtpjsotserfmbihdneohctcbnotcnsmupeeosnhetieftoeonotwneoettrlidogieomsfhioomrldeffsteueolfiriwbydqtztoanfrindttgnoeaalhpsdnemaptlhtsbnhuaopalanlmesprdahnvlieaanchklsioadctadeojhgdiaeygawtnedeehnugeeeeeordonfyrymcetntwtetdgutsatooomfywgirherohrsnifroafrteoepnsyiasssehthnuaeywliheptcaautnpaeeleuhlawlaeosuerinibaesaaeetcgdcesrooprseesnslovdeudlstittieetsfnoseilgnrneoinetwewitlhwserihilirtsdsnedrrhfgnltarusasdetdyodaasnuetcwtienmattrespeotyotyoncoustugmcadpladeledtobfsdeflexaahnrosiitdrepiiahssccdbhpisilellanfhrotrneraotmaeigkalmrtewrcdysanvnswdnlukoernuntiotuninuihnvoeliehtlefaniautafttlctebnrnghebnlsomanooikfnaornsvntfertrfcthedspwseupctrwcwpaoafdhoysiooganalegdiftnrgtelbflgl o ',\n",
+ " 'ithhnfhrerftaoeftnfaltsyrrorqtehmsieulrrfsheaeteuiiwihsbrrntiosieniihmrrytpdoeticirtlnabpnrhohtoliretswhestpsootynouhrwehhdcicpmoonthaastihjaneeoftreunsagrmurxtowtlatebterptrpaesaeetnhrihsrsnttrsnldfohagugsmeeaskntuihyatnytutnnnhivglewwiafpsraefalibaepmccpahlehttohwlslaudeusvaeeoaetuattefeehbhltshrgtlaiceaetrhngahaiestfnunurperehlttimdyuinuneicgoenudeieasuhehtlnahoteaiiyfndianuenoaiyteohfealduroaeyhruaerrtearhiaangotiswwaistulbtomaxetloeorttoonooargbtcaivplenefywraetoduawetwrtnrlpfrpiealrilmtrnoiewqecrurnadtaofrontleetenprmsphddnedetwefhopeydnneseengatteaneeerstdlguoserurbohdwgrpbaenhytetkerrtrhrnnefdltossiwaiynpthomuoocyetiepeohknrntnarliteieuemrntaipoehahhonuuomkvtsneedeoeipwdkegitiiioenshufuabenuthireraheeihayuarusswgiasidstrilfpghltiwisiieshumrroacmesoiivdleelouaisrdeionftisoebtsnaofpgodsrfitrltpaetnryilootsyesosntfpeahieagoairetdimaaterhgysrarfecltlreaneevehhvmihasnserioxlimsjptestobmfrndhichoenbctnctopumssoeetehntfeinoeonwtoteoeilrtigodsmoeoihflrmoffeduetsfloewiriqdybotztrfnatdniongtlaaedsphamenhltpnbstoauhalapemlndrpsvnhaaeilhcnaislkcdaoedatghjoeaidwagydentnheeeeguoeeenodryryftecmtwtngdteastuoootwyfmhrighoreinsraorfetrfnpeoaiysesssnhthyeauhilwctpetuaaeapnuelewalhoealreusbiniaseateeacdgcorsesrponseevolsdueditsleittfsteesonnglioenrteniiwewwhltiresilihdstrdensfhrrtlngsuraedsaoydtsaadteunitwcamneerttoepstoytcnoytsuocmgulpdaledaotdedsfbelfehaaxsorndtiiipershaidccsiphbelisnallorhfenrttoarieamlakgetrmdcrwnasywsnvulndreoktnunutoiuninvnhiileolthenafetuaittfaetclnrnbbehgoslnonamfkioroannvsnreftcfrtdehtswpscpuecwrtoapwhdfaisyoagooelanfidggrntbletlglf o ',\n",
+ " 'ihhtnrhfetfrafeotafnlystrrorqhetmeisurrlfehsaeteuwiiibshrtnriisoeiinhrrmydptoitectrilbanphrnoothlerithwseptsstooyuonhewrhcdhimpcotnohsaatjhiaeenortfesnuamrgutxroltwabettpretapreeasehntrshirtnstnsrlofdhugagemseksaniuthtaynutytnnnhgvilwweipfasearfilabpeampccaelhhotthslwlduaevsuaoeeauteaettfheebtlhsgrhtialceaetnhrgahaitsefnunueprrlhetmitdiuynenuiogceduneaeisehuhnltatoheiiaydnfiunaeaonietyoefhaudlreaoyurharretraehaaintogiwwsatsiutbloxameoltetrotnoooraogctbapvilenefrwyaotedwauerwttlrnpprfilaermlitonriqweeurcrdantfoartnolteeerpnmhpsdenddwteeohfpdyensenegneaettaeenetsrdugloresuobrhgwdrabpeyhntkteetrrrnrhndfelsotsawiipnytmohucooyiteeoephrnknantrtileueienrmtpiaoahehnohumoukstvndeeeieopkdwetigioiiehsnuaufbunetriheharehieaauyrssuwaigssditlirfhgplwitiiiseuhsmorraemcsiioveldeuolarsidoienitfsbeotansogpforsdfrtilapterntyolioystesosnpfteihaeogaaeritmidaetaryghsrarflceterlaeenvhhevhimasnseoirxmilstpjeotsbrfmnihdceohntcbnotcpsmuseeotnhetiefnoeonotwteoeitrlidogseomofhilomrfdefustefeolwiriqbydotztranftindotgnleaadhpsanemhptlntsbohuaapalenlmdsprvahnaliehanciklscoadetadgojhediawygadtneneeheugeoeeenrdoyfyrtmcetntwgetdautsotoowmfyhgirheroirsnafroefrtnoepasyiesssnhthyuaehwlicepttaauenpauelewhlaolaersuebiniaaestaeeccgdoesrsoprneesvsloddeuilstettifetsenosnilgornetineiwewwtlhiserihildrtsdsnefrrhtgnlsarueasdotdysdaatnueicwtaenmettrospettyocyontouscugmladpladeoedtdbfseeflhxaasnrodiitirepsiahdsccibhpesilnllaofhretrntraoimaelgkaemrtdwrcnysawvnsudnlrkoetnunuiotuninvihnioellehtnefatiautaftelctnbnrbgheonlsomanfoikrnaonnsvrtfectrfdthesspwceupctrwowpahafdioysaoogenalfgdigtnrbtellflg o ',\n",
+ " 'tihhfnhrreftoaefntfatlsyrrortqehsmielurrsfheeateiuiwhisbrrntoisineiimhrrtypdeotiicrtnlabnprhhotoilrestwhsetposotnyourhwehhdccipmoontahasithjnaeefotruensgarmruxtwotltaebetrprtpaseaetenhirhssrntrtsndlfoahgusgmeaesktnuiyhatyntuntnnihvgelwwaifprsaeafliabepcmcphalethtowhlsaluduesveaeoeatutateefehhblthsrgltaiecaerthnaghaeistnfunrupeerhlttimyduiunnecigoneudieeaushethlnhaotaeiifyndainuneoayitehofeladuoraehyruearretarihaagnotsiwwiastlubtmoaxtelooertotonooarbgtciavpelneyfwreatoudawtewrntrlfprpeialirlmrtnoeiwqcerunradatofornteletneprsmphddneedtwfehoepydnneseengtatenaeerestldgusoerrubodhwgprbanehyettkrerthrrnenfdtlosiswayinphtomouoceytipeeokhnrtnnalritieeumernatipeohahhonuuomvktsenedoeeiwpdkgeitiiioneshfuuaebnuhtirreaheeihyauaurssgwiaisdsrtilpfghtliwsiiisehurmrocameosiidvleleouiasrediofntiosebstnafopgdosriftrtlpatenriylotosyseostnfpaehiaegoiaredtimaatehrgyrsarefclltrenaeeevhhmvihsansreiolximjsptsetombfrdnhihcoebnctcntoupmsoseeethnfteioneowntoetoelirtgiodmsoeiohfrlmoffedeutslfoeiwridqybtoztfrnadtninogtalaesdphmaenlhtpbnstaouhlaapmelnrdpsnvhaeailchnasilkdcaodeathgjoaeidawgyednthneeeegueoeeondrryyfetcmwttndgtesatuoootywfmrhigohrenisroarfterfpneoiayssesshntheyauihlwtcpeutaaaepneuleawlheoalerusibnisaeaeteadcgcrosersposneeovlsudedtisliettsfteseongnlieonretniwiewhwltriesliihsdtrednshfrrltngusradesayodtasadetuntiwcmaneretteopsotytncoystuomcgupldaeldatodesdfblefeahaxosrntdiipierhsaicdcspihbleisanllrohfnertotareiamalkgtermcdrwansyswnvlunderokntuntuoinuinnvhilieotlheanfeutaittfateclrnnbebhgsolnnoamkfiooranvnsnerftfcrtedhtwspspcuewcrtaopwdhfasiyogaooleanifdgrgntlbetgllf o ',\n",
+ " 'hihtrnhftefrfaeoatfnylstrrorhqetemisrurlefhseatewuiibishtrnriisoieinrhrmdyptiotetcriblanhprnoothelrihtwspetstsoouyonehwrchdhmipctonoshaajthieaenrotfsenumargtuxrlotwbaetptreatpreeashentsrhitrnsntsrolfduhgaegmskesainutthayuntyntnnghviwlwepifaesariflapbeapmccealhohttshlwdluavesuoaeeuateeatthfeetblhgsrhitalecaenthraghatisenfuneuprlrhemtitiduyennuoigcdeunaeeieshunhlttaohieiadynfuinaaeoneityeofhuadleraouyrhrarertaeahaitnogwiwstasitublxoamoeltterontooroaocgtbpavielnerfwyoatewdaurewtltrnpprfliaemrliotnrqiweuercdranftoatrnotleerepnhmpsedndwdteoehfdpyesnengeneeatteaentesrudglroesoubrghwdarbpyehnktteterrnrrhdnfeslotaswipinymtohcuooiyteoeeprhnkannttrilueeinermptiaaohenhohmuousktvdneeieeokpdwteigoiiihesnauufubnertihhearheieaauysrsuawigssdiltirhfgpwlitiiisuehsomrreamcisioevldueolrasiodieintfbseoatnsgopfrosdrftialptrentoyliyostseospnftiehaoegaearimtideatayrghrsarlfceetrleaenhvhehvimsansoeirmxiltspjoetsrbfminhdecohtncbontcspmueseontheitefoneoontwetoetirldiogesomfohiolmrdfefsuteefoliwribqydtoztarnfitndtognelaahdpsnaemphtltnsbhouapaalnelmsdpravhnlaieahnckilsocadteadogjhdeiaywgatdneenehuegeeoeerndofyyrmtcenttwegtduatstooomwfyghirehrorisnfarofertonepsayisesshnthuyaewhliecptataunepaeulehwlaloaesrueibniaaesateeccgdeosrosprenessvloddeulisttetieftsneosinlgroneitnewiewtwlhsierhiilrdtssdnerfrhgtnlasruaesdtodydsaantueciwteanmtetrsopettyoyconotusucgmaldpaldeeodtbdfseeflxhaansroiditriepisahsdccbihpseillnlafohrternrtaomiaeglkamertwdrcynsavwnsdunlkroentuniuotnuinivhnoielelhtenfaitauatftlectbnnrgbhenolsmoanofiknraonnsvtrfetcrftdhesspwecuptcrwwopaahfdoiysoaognealgfditgnrtbelfllg o ',\n",
+ " 'thhifrhnrtfeofeanafttyslrrortheqseimlrrusehfeetaiwiuhbsirtnroisiniiemrrhtdpyeitoitrcnbalnhrphotoierlshwtspteotosnuoyrewhhcdhcmpiotnoasahijhtneeafrtousnegmrartxuwltotbeaeprtraptseaethneishrstnrrnstdoflaughsemgaksetiunytahyutnnnntigvhewwlapfireasailfapebcpcmhelatothwslhaduluvseeoeaeutatetaehefhtlbhgrsliateeacrnhtaahgetsinnufrepuelhrtmityiuduenncogindueiaeeuehstnlhhtoaaiiefdnyauninaoeyetihefoludaoearhuryerraeratiaahgtonswwiitsaltbumxaotoleotreonotoraobctgipvaeenlyrwfeotauwadtrwenlrtfprpelaiimlrronteqwicurendarafototnretelnrpeshpmdendewtdfoheedypnsenegnetetaneeartselugdsreorobudgwhpabrnyheekttrtrehnrredfntsoliawsypnihmotocoueitypoeekrnhtannltiriueemnreapiteahohnohumouvstkedenoieewkdpgtieioiinhsefauueunbhritrhaeehieyauaussrgaiwisdsrlitphgftwilsiiisuherormcemaoiisdelvluoeirsaeoidfitnobessantfgpodrsoirtftapltrneiolytysossoetpfnaiheaogeieradmitaetahygrrraselcflertneeaehhvmhivssnaroielmixjtpssotemrfbdihnheocbtcncotnusmpoeesenhtfietooenwotneeotltrigdoimeosifhoromlfdefestuleofiirwdbyqttzofanrdintntgoaealshpdmnealpthbtsnahuolpaamnlerspdnahveliacanhsklidoacdtaehojgadieaygwetndheeneugeeeeoordnrfyyemctwnttdetgsutaotooymfwrgihoerhnrsiofratfrepoenisyasssehhtneuayiwlhtepcuaatanpeeeluahlwelaoesuriinbsaeaeaetdcgcresoropsseenoslvudedtlsiittesetfsnoegilnernoeintwweihtlwrseilhiisrtdesndhrrflgntuarsdaseytdoadasenuttcwimenartteespootytnyocsoutmugcpadleadltedosbfdlefeaxahonrstiidpreihiascscdpbhilsieallnrfhontreoratemaiagkltmrecwrdaysnsvnwldnuekornnuttiounniunihvloeitehlaefnuiattafttlcerbnneghbsnlonmaokoifonarvnsnetfrftrcethdwspspeucwtrcawpodafhsoyigooalnaeigdfrtngltebgfll o ',\n",
+ " 'hthirfhntrfefoeaanftytslrrorhteqesimrlrueshfeetawiiubhsitrnriosiiniermrhdtpyietotircbnalhnrpohtoeirlhswtpstetoosunoyerwhchdhmcpitonosaahjihtenearftosunemgratrxulwtobteapertarptesaehtnesihrtsnrnrstodfluaghesmgkaseituntyahuytnnnntgivhwewlpafierasialfpaebpccmehlaotthswlhdaulvuseoeeauetaettaheefthlbghrsilateeacnrhtaahgtesinnuferpulehrmtitiyudeunnocgidnueaieeeuhsntlhthoaiaiedfnyuanianoeeytiehfouldaeoaruhryrerareataiahtgonwswitisatlbuxmaootletorenootroaocbtgpivaeenlrywfoetawuadrtwelnrtpfrpleaimilrorntqewiucrednarfaottonrteelrnpehspmedndwetdofhedeypsnengeneettaeneatrseulgdrseoorbugdwhapbrynheketttrrenhrrdefnstolaiwspynimhotcoouietyopeerknhatnntliruieenmrepaitaehonhohmuousvtkdeenioeekwdptgieoiiihnseafuuuenbrhithraeheieayuasusragiwsidslrithpgfwtilisiiusheorrmecmaioisedlvuloerisaoeidiftnboesasntgfpordsoritfatplrtneoilyytsossoeptfniaheoageeiramditeatayhgrrraslecfelrteneahehvhmivssnaoriemlixtjpsostermfbidhnehoctbcnoctnsumpeoesnehtifetooenowtneeottlridgoiemosfihoormldfefsetuelofiirwbdyqttzoafnridnttngoeaalhspdnmeaplthtbsnhauoplaanmlesrpdanhvleiaacnhksliodactdaeohjgdaieyagwtendehenuegeeeeorodnfryymectnwttedtgustatooomyfwgriheorhrnsiforaftreopensiyasssehhtnueaywilhetpcauatnapeeeluhalwleaoseuriinbaseaaeetcdgcersoorpsesensolvduedltsititeestfnsoeiglnrenoientwweithlwsreihliirstdsendrhrfglntaursadsetydodaasneutctwiemnatrtesepotoytynocosutumgcapdlaedletdobsfdelfexaahnorsitidrpeiihassccdbphislielalnfrhotnreroatmeaigaklmtrewcrdyasnvsnwdlnukeornnutitounniuinhvoleiethleafniuatatftltcebrnngehbnslomnaookifnoarnvsntefrtfrctehdswpsepuctwrcwapoadfhosyiogoanlaegidftrngtlebfgll o ',\n",
+ " 'ithhnfrhertfaofetnafltysrrroqthemseiulrrfsehaeetuiwiihbsrrtnioiseniihmrrytdpoeitcitrlnbapnhrohotliertshwesptsotoynuohrewhhcdicmpootnhasatijhaneeofrteusnagmrurtxowltatbeteprtrapeseaethnrishrstntrnsldofhauggsemeaksntiuhytanyuttnnnhigvlewwiapfsreafailbapemcpcahelhtothwslladueuvsaeoeaeutatetfehebhtlshgrtliaceeatrnhgaahietsfnnuureprelhttmidyiunuenicogendueiaesuehhtnlahtoeaiiyfdniaunenaoiyetohefaludroeayhuraerrterahiaangtoiswwaitsultbomxaetoleotrtonoooragbctaipvleenfyrwaeotduwaetrwtnlrpfprielarimltronieqwecurrndataforotnleteenrpmshpddendewtefohpedynnseeegnatetaneeertsdlugosreurobhdgwrpabenyhtektertrrhnrnedfltsosiawiypnthmouocoyeitepoehkrnntanrltieiueemnrtapioeahhhnouumokvstnedeeoiepwkdegtiiioienhsufaubeunthrierhaeehiayaurusswgaisisdtrlifphgltwiisiiesuhmroracemsoiivdeleluoairsdeoinfitsobetsanofgpodrsfirtltapetrnyiolotysessontpfeaiheaogaiertdmiaaetrhygsrrafelctleraneevehhvmhiassneroixlmisjtpesotbmrfndihcheonbtcncotpusmsoeetenhtfienooenwotteeoiltrigdosmeooifhlromffdeuestfleowiirqdbyottzrfantdinontglaeadshpamnehlptnbtsoahualpaemnldrspvnahaelihcanisklcdoaedtaghojeadiwaygdetnnheeeeugoeeenordyrfytemctwntgdetasutootowymfhrgihoerinrsaofretfrnpoeaisyesssnhhtyeuahiwlcteptuaaeanpueelwahloelaresubiinasaeteaecdcgoressropnseevosldudeitlseittfsetesnongiloernteiniwwewhtlirseilhidsrtdesnfhrrtlgnsuaredasoytdsadatenuitcwamenerttoesptotycnyotsoucmuglpadleadoteddsbfelefhaxasonrdtiiipreshiadcscipbhelsinallorfhentrtoraiemalagketmrdcwrnayswsvnuldnrekotnnuutiounnivnihiloeltehnaeftuiattafetlcnrbnbeghosnlonmafkoironanvnsretfcftrdethswspcpeucwtroawphdafisoyagooelnafigdgrtnbltelgfl o',\n",
+ " 'ihthnrfhetrfafoetanflytsrrroqhtemesiurlrfeshaeetuwiiibhsrtrniioseinihrmrydtpoietctirlbnaphnroohtleirthswepststooyunoherwhchdimcpotonhsaatjihaeneorftesunamgrutrxolwtabtetpertarpeesaehtnrsihrtsntnrslodfhuaggesmekasnituhtyanuyttnnnhgivlwewipafserafialbpaempccaehlhotthswlldauevusaoeeauetaettfheebthlsghrtilaceeatnrhgaahitesfnnuuerprlehtmtidiyuneuniocgednueaieseuhhntlathoeiaiydfniuaneanoieytoehfauldreoayuhrarertreahaiantgoiwswatisutlboxmaeotletortnoooroagcbtapivleenfrywaoetdwuaertwtlnrppfrilearmiltorniqeweucrrdnatfaortonlteeernpmhspdedndweteofhpdeynsneegenaettaeneetrsdulgorseuorbhgdwrapbeynhtketetrrrnhrndeflstosaiwipyntmhoucooyieteopehrknnatnrtlieuieenmrtpaioaehhnhoumuoksvtndeeeioepkwdetgiioiiehnsuafubuentrhiehraeheiaayursuswagissidtlrifhpglwtiiisieushmorraecmsioivedleuloarisdoeiniftsboetasnogfpordsfritlatpertnyoiloytsessonptfeiaheoagaeirtmdiaeatryhgsrraflectelraenevhehvhmiassneorixmlistjpeostbrmfnidhcehontbcnoctpsumseoetnehtifenooenowtteeoitlridgosemoofihlormfdfeusetfelowiirqbdyottzrafntidnotngleaadhspanmehpltntbsohauaplaenmldsrpvanhaleihacnikslcodaetdagohjedaiwyagdtenneheeuegoeeenrodyfrytmectnwtgedtaustotoowmyfhgriheorirnsaforeftrnopeasiyesssnhhtyueahwilcetptauaenapueelwhalolearseubiinaasetaeeccdgoerssorpnesevsolddueiltsetitfestensoniglorentieniwwewthlisreihlidrstdsenfrhrtglnsaureadsotydsdaatneuictwaemnetrtosepttoycynotosucumglapdlaedoetddbsfeelfhxaasnorditiirpesihadsccibpheslinlalofrhetnrtroaimealgakemtrdwcrnyaswvsnudlnrkeotnnuuitounnivinhiolelethneaftiuatatfeltcnbrnbgehonslomnafokirnoannvsrtefctfrdtehsswpcepuctwrowaphadfiosyaogoenlafgidgtrnbtlelfgl o',\n",
+ " 'tihhfnrhretfoafentaftlysrrrotqhesmeilurrsfeheaetiuwihibsrrtnoiisneiimhrrtydpeoitictrnlbanphrhootilersthwseptostonyuorhewhhcdcimpootnahsaitjhnaeefortuesngamrrutxwolttabeetprrtapseeatehnirshsrtnrtnsdlofahugsgemaekstniuyhtaynutntnnihgvelwwaipfrseaafilabpecmpchaelthotwhslalduuevseaoeeauttaetefhehbtlhsgrltiaeceartnhagaheitsnfnurueperlhttmiydiuunenciogneduieaeusehthnlhatoaeiifydnaiunneaoyiethoeflaudoreahyurearretraihaagntosiwwiatslutbmoxateoloetrotnooorabgctiapvelenyfrweaotudwaterwntlrfppreilairmlrtoneiqwceurnrdaatfoortneltenerpsmhpddenedwtfeohepdynnseeegntaetnaeeretsldugsoreruobdhgwprabneyhetktretrhrnrendftlsoisawyipnhtmooucoeyitpeoekhrntnanlrtiieuemenratpieoahhhnouumovkstendeoeiewpkdgetiiioinehsfuauebunhtrirehaeehiyaauurssgwaiissdrtlipfhgtlwisiiiseuhrmorcaemosiidvelleuoiarsedoifnitosbestanfogpdorsifrttlapterniyoltoyssesotnpfaeihaeogiaerdtmiaaethrygrsraeflclternaeeevhhmvhisasnreoilxmijstpseotmbrfdnihhceobntccnotupsmoseeetnhftieonoewnoteteolitrgidomseoiofhrlomffdeeustlfeoiwirdqbytotzfrandtinnotgaleasdhpmanelhptbntsaohulapamenlrdspnvahealichansikldcoadetahgojaediawygedtnhneeeeugeoeeonrdryfyetmcwtntdgetsautootoywmfrhgiohernirsoafrtefrpnoeiasysesshnhteyuaihwltceputaaaenpeuelawhleolaersuibinsaaeetaedccgroesrsopsneeovsluddetilsiettsfetsenognileornetinwiwehwtlriselihisdrtedsnhfrrltgnusardeasyotdasdaetnuticwmaenretteospottyncyostoumcugpladeladtoedsdbfleefahxaosnrtdiipirehsiacdscpibhlesianllrofhnetrotraeimaalgktemrcdwranysswvnludnerkontnutuionuninvihlioetlehanefutiattaftelcrnbnebghsonlnomakfoiornavnnsertffctredthwssppceuwctraowpdhafsioygaoolenaifgdrgtnlbteglfl o',\n",
+ " 'hithrnfhterffaoeatnfyltsrrrohqteemsirulrefsheaetwuiibihstrrniiosienirhmrdytpioettcirblnahpnroohtelirhtswpesttsoouynoehrwchhdmicptoonshaajtiheaneroftseunmagrturxlowtbatepteratrpeesahetnsrihtrsnntrsoldfuhagegsmkeasintuthyaunytntnnghivwlewpiafesraifalpbaepmcceahlohttshwldlauveusoaeeuaeteatthfeetbhlgshritlaeceantrhagahtiesnfnueurplrehmttiidyuenunoicgdenuaeieesuhnhtltahoieaidyfnuianaenoeiyteohfualderoauyhrraerrteaahiatngowiswtaistulbxomaoetlteorntoorooacgbtpaivelenrfywoaetwduaretwltnrppfrlieamrilotrnqiewuecrdrnaftaotrontleerenphmspeddnwdetoefhdpeysnnegeeneatteanetersudlgroseourbghdwarpbyenhktetterrnrhrdnefsltoasiwpiynmthocuooiyetoeperhknantntrliueienemrptaiaoehnhhomuuoskvtdneeieoekpwdtegioiiihensaufuubenrthiheraheeiaayusrusawgissidltrihfpgwltiiisiueshomrreacmisoievdlueloraisodeiinftbsoeatsngofprodsrfitaltpretnoyilyotssesopntfieahoeageairmtdieaatyrhgrsralfecetlreanehvehhvmisasnoerimxlitsjpoestrbmfindhechotnbconctspumesoentehitfeonoeonwteteotilrdigoesmofoiholrmdffesuetefloiwirbqdytotzarfnitdntongelaahdspnamephlttnbshoaupalanemlsdrpavnhlaeiahcnkislocdatedaoghjdeaiywagtdenenheueegeoeernodfyrymtecntwtegdtuasttooomwyfghriehorrinsfaorfetronpesaiysesshnhtuyeawhilectpatuaneapeuelhwalloeasreuibinaaseateeccdgeorsosrpensesvoldduelitsteitefstnesoinglroenitenwiwetwhlsirehilirdstsdenrfhrgtlnasuraedstoyddsaanteucitweamntertsoepttoyycnootsuucmgalpdaledeotdbdsfeelfxhaansoridtiripeishasdccbiphselilnalforhtenrrtoamieaglakmetrwdcrynasvwsndulnkreontnuiutonuniivnhoileelthenafituaattfletcbnrngbehnoslmonaofkinroannvstreftcfrtdehsswpecputcwrwoapahdfoisyoagonelagfidtgrntbleflgl o',\n",
+ " 'thihfrnhrtefofaenatftylsrrrothqesemilrursefheeatiwuihbisrtrnoiisnieimrhrtdypeiotitcrnblanhprhootielrshtwspetotsonuyorehwhchdcmipotonashaijthneaefrotusengmarrtuxwlottbaeeptrratpseeathenisrhstrnrntsdolfauhgsegmakestinuythayuntnntnighvewlwapifresaaiflapbecpmchealtohtwshladluuveseoaeeuatteatehfehtblhgsrlitaeecarnthaaghetisnnfureupelrhtmtiyiduuenncoigndeuiaeeueshtnhlhtaoaieifdynauinnaeoyeitheofluadoerahuyrerarertaiahagtnoswiwitasltubmxoatoeloterontooroabcgtipaveelnyrfweoatuwdatrewnltrfppreliaimrlrotneqiwcuerndraaftootrnetlenrepshmpdednewdtfoehedpynsneegenteatneaertesludgsroeroubdghwparbnyehekttrterhnrrednftsloiaswypinhmtoocuoeiytpoeekrhntannltriiueemneraptieaohhnhoumuovsktedneoieewkpdgteiioiinhesfauueubnhrtirheaeheiyaauusrsgawiissdrltiphfgtwlisiiisuehromrceamoisidevllueoiraseodifintobsesatnfgopdrosirfttalptrenioyltyossseotpnfaiehaoegieardmtiaeathyrgrrsaelfcletrneaeehvhmhvissanroeilmxijtspsoetmrbfdinhhecobtnccontuspmoeseenthfiteoonewonteetoltirgdiomesoifohrolmfdfeesutlefoiiwrdbqyttozfarnditnntogaelashdpmnaelphtbtnsahoulpaamnelrsdpnavhelaicahnskildocadteahogjadeiaywgetdnheneeuegeeoeorndrfyyemtcwnttdegtsuatotooymwfrghioehrnrisofartferponeisaysseshhnteuyaiwhltecpuataanepeeulahwleloaesruiibnsaaeeatedccgreosrospseneosvluddetlisitetseftsneoginleroneitnwwiehtwlrsielhiisrdtesdnhrfrlgtnuasrdaesytodadsaentutciwmeanrtetesopottynycosotumucgpaldealdteodsbdfleefaxhaonsrtidipriehisacsdcpbihlseialnlrfohnterortaemiaaglktmercwdraynssvwnldunekronntutiuonnuinivhloietelhaenfuitatatftlecrbnnegbhsnolnmoakofionravnnsetrfftcretdhwssppecuwtcrawopdahfsoiygoaolneaigfdrtgnltbegfll o',\n",
+ " 'htihrfnhtreffoaeantfytlsrrrohtqeesmirluresfheeatwiuibhistrrnioisineirmhrdtypieotticrbnlahnprohoteilrhstwpsettosounyoerhwchhdmciptoonsahajithenaerfotsuenmgartruxlwotbtaepetrartpeseahtensirhtsrnnrtsodlfuahgesgmkaesitnutyhauyntnntngihvwelwpaifersaiaflpabepcmcehalothtswhldaluvuesoeaeueatetathefethblghsriltaeecanrthaaghteisnnfueruplerhmttiiydueunnocigdneuaieeeushnthlthaoiaeidfynuainaneoeyitehofuladeorauhyrrearretaaihatgnowsiwtiastlubxmoaoteltoernotorooacbgtpiaveelnryfwoeatwudartewlntrpfprleiamirlortnqeiwucerdnrafatotorntelernephsmpeddnwedtofehdepysnnegeenetatenaetresuldgrsoeorubgdhwaprbynehketttrernhrrdenfstloaiswpyinmhtocouoieytopeerkhnatnntlriuieenmerpatiaeohnhhomuuosvktdeneioeekwpdtgeioiiihnesafuuuebnrhtihreaheeiayausursagwisisdlrtihpfgwtliisiiusehormrecamiosiedvluleoriasoediifntboseastngfoprdosriftatlprtenoiylytossseoptnfiaehoaegeiarmdtieaatyhrgrrsalefceltrenaehevhhmvissanoreimlxitjsposetrmbfidnhehcotbncocntsupmeosenethifteooneownteetotlirdgioemsofiohorlmdffeseutelfoiiwrbdqyttozafrnidtntnogealahsdpnmaeplhttbnshaouplaanmelsrdpanvhleaiachnksilodcatdeaohgjdaeiyawgtednehneueegeeoerondfryymetcnwttedgtusattooomywfgrhieohrrnisfoarfteropnesiaysseshhntueyawihletcpautanaepeeulhawlleoaseruiibnasaeaetecdcgerosorspesnesovldudeltistietesftnseoignlreonietnwwiethwlsriehliirsdtsednrhfrgltnausradestyoddasanetuctiwemantretseoptotyyncoostuumcgapldaeldetodbsdfelefxahanosritdirpieihsascdcbpihsleilanlfrohtnerrotameiagalkmterwcdryansvswndlunkeronntuituonnuiinvholieetlheanfiutaattfltecbrnngebhnsolmnoaokfinoranvnsterftfcrtedhswspepcutwcrwaopadhfosiyogaonleagifdtrgntlbefgll o']"
+ ]
+ }
+ ],
+ "prompt_number": 22
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6bs"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 23,
+ "text": [
+ "'hithhnfrferteaofftnasltyorrreqthimserulrhfsetaeeiuiwsihbnrrtsioiienirhmrpytdtoeircitalnbrpnhtohorliewtshtesposotoynuwhredhhcpicmnootahashtijeanetofrneusragmxurttowleatbrtepptraaesenethhrisnrststrnfldoghaumgseseakuntiahyttnyuntnnvhigwlewfiapasrelfaiebapcmcplahethtolhwsuladseuveaeotaeutateefehlbhtrshgatliaceehtrnhgaasietufnnpurehrelittmudyinnuegicouendeeiahsuelhtnoahtieainyfdniauoenatiyefohedaluaroeryhuraeraterahiaongtwiswsaitbultaomxletoreototonaoortgbcvaipnleewfyrtaeoaduwwetrrtnlrpfpaiellrimntrowieqrecuarndotafnroteletpenrpmshnddetdewhefoypedennsneegtateeanesertgdlueosrburowhdgbrpahenyttekrertrrhnfnedoltswsianiypothmouoctyeieeponhkrnntairlteeiuremnitaphoeaohhnouumtkvsenedeeoidpwkiegtiiiosenhuufanbeuithraerhieehuayasrusiwgadsisitrlgfphiltwiisihesurmromaceisoilvdeoelusairideotnfiesobntsapofgsodrtfirpltanetrlyiosotyoessfntpheaigeaoraieitdmtaaegrhyasrrcfelrtleeanehvehivmhnassieroixlmpsjttesofbmrhndiochecnbttncompusesoehtenetfienootnwooteeriltoigdosmehoifmlroeffdtuesoflerwiiyqdbzottnrfantdigontalaepdsheamnthlpsnbtuoahaalplemnpdrshvnaiaelnhcaliskacdoaedtjghoieadgwayndetenhegeeueoeednoryyrfctemttwntgdetasuoootfwymihrgrhoesinrraofretfenpoyaissesstnhhayeulhiwpcteatuapeanlueelwahaoeluresnbiieasaeteagcdcsorepsroenselvosedudsitlteittfseoesnlnginoernteieiwwlwhteirsiilhtdsrndesrfhrntlgrsuasedadoytasadutenwitcnametertpoesytotocnyutsogcmudlpadleadotefdsbfeleahaxrsonidtieiprashicdcshipbielslnalhorfrentatoraiemklagretmrdcwsnaynwsvnuldorekutnnoutiiunnhvnieilohltefnaeatuifttacetlnnrbhbeglosnaonmifkoaronsnvnfretrcfthdetpswsucpercwtpoawfhdayisooagoaelndfigngrtebltllgfo'"
+ ]
+ }
+ ],
+ "prompt_number": 23
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "''.join([c[0] for c in every_nth(c6bs, 3)])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 24,
+ "text": [
+ "'hit'"
+ ]
+ }
+ ],
+ "prompt_number": 24
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6bs.find('e', 13)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 25,
+ "text": [
+ "28"
+ ]
+ }
+ ],
+ "prompt_number": 25
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "len(c6bs) / 978"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 26,
+ "text": [
+ "1.6083844580777096"
+ ]
+ }
+ ],
+ "prompt_number": 26
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6bs[55:60]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 27,
+ "text": [
+ "'bnrrt'"
+ ]
+ }
+ ],
+ "prompt_number": 27
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "[(c6bs[0] + c6bs[i] + c6bs[2*i] + c6bs[3*i], i) for i in range(int(len(c6bs) / 3)) if c6bs[i] == 'e' and c6bs[2*i] == 'i' ]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 28,
+ "text": [
+ "[('heih', 177), ('heit', 207), ('heip', 307), ('heil', 522)]"
+ ]
+ }
+ ],
+ "prompt_number": 28
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "[c for c in chunks(c6bs, 522)]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 29,
+ "text": [
+ "['hithhnfrferteaofftnasltyorrreqthimserulrhfsetaeeiuiwsihbnrrtsioiienirhmrpytdtoeircitalnbrpnhtohorliewtshtesposotoynuwhredhhcpicmnootahashtijeanetofrneusragmxurttowleatbrtepptraaesenethhrisnrststrnfldoghaumgseseakuntiahyttnyuntnnvhigwlewfiapasrelfaiebapcmcplahethtolhwsuladseuveaeotaeutateefehlbhtrshgatliaceehtrnhgaasietufnnpurehrelittmudyinnuegicouendeeiahsuelhtnoahtieainyfdniauoenatiyefohedaluaroeryhuraeraterahiaongtwiswsaitbultaomxletoreototonaoortgbcvaipnleewfyrtaeoaduwwetrrtnlrpfpaiellrimntrowieqrecuarndotafnrotel',\n",
+ " 'etpenrpmshnddetdewhefoypedennsneegtateeanesertgdlueosrburowhdgbrpahenyttekrertrrhnfnedoltswsianiypothmouoctyeieeponhkrnntairlteeiuremnitaphoeaohhnouumtkvsenedeeoidpwkiegtiiiosenhuufanbeuithraerhieehuayasrusiwgadsisitrlgfphiltwiisihesurmromaceisoilvdeoelusairideotnfiesobntsapofgsodrtfirpltanetrlyiosotyoessfntpheaigeaoraieitdmtaaegrhyasrrcfelrtleeanehvehivmhnassieroixlmpsjttesofbmrhndiochecnbttncompusesoehtenetfienootnwooteeriltoigdosmehoifmlroeffdtuesoflerwiiyqdbzottnrfantdigontalaepdsheamnthlpsnbtuoahaalplemnpdrshvna',\n",
+ " 'iaelnhcaliskacdoaedtjghoieadgwayndetenhegeeueoeednoryyrfctemttwntgdetasuoootfwymihrgrhoesinrraofretfenpoyaissesstnhhayeulhiwpcteatuapeanlueelwahaoeluresnbiieasaeteagcdcsorepsroenselvosedudsitlteittfseoesnlnginoernteieiwwlwhteirsiilhtdsrndesrfhrntlgrsuasedadoytasadutenwitcnametertpoesytotocnyutsogcmudlpadleadotefdsbfeleahaxrsonidtieiprashicdcshipbielslnalhorfrentatoraiemklagretmrdcwsnaynwsvnuldorekutnnoutiiunnhvnieilohltefnaeatuifttacetlnnrbhbeglosnaonmifkoaronsnvnfretrcfthdetpswsucpercwtpoawfhdayisooagoaelndfigngrteb',\n",
+ " 'ltllgfo']"
+ ]
+ }
+ ],
+ "prompt_number": 29
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c6b"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 30,
+ "text": [
+ "'HITHH NFRFE RTEAO FFTNA SLTYO RRREQ THIMS ERULR HFSET AEEIU IWSIH BNRRT SIOII ENIRH MRPYT DTOEI RCITA LNBRP NHTOH ORLIE WTSHT ESPOS OTOYN UWHRE DHHCP ICMNO OTAHA SHTIJ EANET OFRNE USRAG MXURT TOWLE ATBRT EPPTR AAESE NETHH RISNR STSTR NFLDO GHAUM GSESE AKUNT IAHYT TNYUN TNNVH IGWLE WFIAP ASREL FAIEB APCMC PLAHE THTOL HWSUL ADSEU VEAEO TAEUT ATEEF EHLBH TRSHG ATLIA CEEHT RNHGA ASIET UFNNP UREHR ELITT MUDYI NNUEG ICOUE NDEEI AHSUE LHTNO AHTIE AINYF DNIAU OENAT IYEFO HEDAL UAROE RYHUR AERAT ERAHI AONGT WISWS AITBU LTAOM XLETO REOTO TONAO ORTGB CVAIP NLEEW FYRTA EOADU WWETR RTNLR PFPAI ELLRI MNTRO WIEQR ECUAR NDOTA FNROT ELETP ENRPM SHNDD ETDEW HEFOY PEDEN NSNEE GTATE EANES ERTGD LUEOS RBURO WHDGB RPAHE NYTTE KRERT RRHNF NEDOL TSWSI ANIYP OTHMO UOCTY EIEEP ONHKR NNTAI RLTEE IUREM NITAP HOEAO HHNOU UMTKV SENED EEOID PWKIE GTIII OSENH UUFAN BEUIT HRAER HIEEH UAYAS RUSIW GADSI SITRL GFPHI LTWII SIHES URMRO MACEI SOILV DEOEL USAIR IDEOT NFIES OBNTS APOFG SODRT FIRPL TANET RLYIO SOTYO ESSFN TPHEA IGEAO RAIEI TDMTA AEGRH YASRR CFELR TLEEA NEHVE HIVMH NASSI EROIX LMPSJ TTESO FBMRH NDIOC HECNB TTNCO MPUSE SOEHT ENETF IENOO TNWOO TEERI LTOIG DOSME HOIFM LROEF FDTUE SOFLE RWIIY QDBZO TTNRF ANTDI GONTA LAEPD SHEAM NTHLP SNBTU OAHAA LPLEM NPDRS HVNAI AELNH CALIS KACDO AEDTJ GHOIE ADGWA YNDET ENHEG EEUEO EEDNO RYYRF CTEMT TWNTG DETAS UOOOT FWYMI HRGRH OESIN RRAOF RETFE NPOYA ISSES STNHH AYEUL HIWPC TEATU APEAN LUEEL WAHAO ELURE SNBII EASAE TEAGC DCSOR EPSRO ENSEL VOSED UDSIT LTEIT TFSEO ESNLN GINOE RNTEI EIWWL WHTEI RSIIL HTDSR NDESR FHRNT LGRSU ASEDA DOYTA SADUT ENWIT CNAME TERTP OESYT OTOCN YUTSO GCMUD LPADL EADOT EFDSB FELEA HAXRS ONIDT IEIPR ASHIC DCSHI PBIEL SLNAL HORFR ENTAT ORAIE MKLAG RETMR DCWSN AYNWS VNULD OREKU TNNOU TIIUN NHVNI EILOH LTEFN AEATU IFTTA CETLN NRBHB EGLOS NAONM IFKOA RONSN VNFRE TRCFT HDETP SWSUC PERCW TPOAW FHDAY ISOOA GOAEL NDFIG NGRTE BLTLL GFO\\n'"
+ ]
+ }
+ ],
+ "prompt_number": 30
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "[i for i in range(len(c6bs)) if c6bs[i] == 'q']"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 42,
+ "text": [
+ "[29, 503, 985]"
+ ]
+ }
+ ],
+ "prompt_number": 42
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "''.join([c[0] for c in every_nth(c6bs, 11)])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 32,
+ "text": [
+ "'hithhnfrfer'"
+ ]
+ }
+ ],
+ "prompt_number": 32
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "''.join([c[0] for c in every_nth(c6bs, 13)])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 33,
+ "text": [
+ "'hithhnfrferte'"
+ ]
+ }
+ ],
+ "prompt_number": 33
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "''.join([c[0] for c in chunks(c6bs, 121)])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 34,
+ "text": [
+ "'hhrnrnumeodti'"
+ ]
+ }
+ ],
+ "prompt_number": 34
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "''.join([c[0] for c in chunks(c6bs, 13)])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 35,
+ "text": [
+ "'harrseehthoexttnsneitetghuydoihuouocttiuedeeerrsohiaeefhifsiitiohtreiehseiiszomhhihtdtoeentencsllilrdieodhpbrlakhntnsdwon'"
+ ]
+ }
+ ],
+ "prompt_number": 35
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "'l' in ''.join([c[0] for c in chunks(c6bs, 13)])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 36,
+ "text": [
+ "True"
+ ]
+ }
+ ],
+ "prompt_number": 36
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "every_nth(c6bs, 11)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 38,
+ "text": [
+ "['httmtbnoreohaegaasdetgrmlvtshtenenfiargurredrmcrpwnaegennckehmoinhutioddnrtegmranmmneieseetoetmaaoeoceyiesieaidnseewtrdtttmoaicnagariounanfcfor',\n",
+ " 'ieysaniepwthhtmtenoanwecheehtulueodyratleteupnuomhsnobkeitriotiibisrimeettrsetrnaprbseemfrtnaunecitettmnntwaoecsiorhdnaeeouthesatryeuhirostphat',\n",
+ " 'taoeerrintocaoxbsrgkyllpwaegrfieianeotwtogwwftatsenesrrdyynuekdoeeilsaoosflsaacesshtonrefwntmopldeeeeairpnpneasetentstdnrcdeaihloenknlfbnnhedee',\n",
+ " 'horrerhrhsypsfureshuueflsefanntgahifeeiatbfwprrehfesrpeopenravpsuewgicetaiyfoafhsjnteoihdiranadnoandmshrohcllsollsterlowtnlfxpihrtwuntthmvdralb',\n",
+ " 'hfruitmcthnihrrtntannwaauoethntihtaorrsoocyeaonlnoeebarloiteosweihgfhelnprinreevitdnholotiflthrhadhnturayhtuuarvtneingyipypdrrpoamsthetbinecynl',\n",
+ " 'nfrlusriotuctnteesuttfihlthlgpmcsiuhyawmtvrtiwdedygruhttteamhekntuapeiufopotagleetictttiuyaahascegeotogoaaeereeoelirdrttouassabrirvnvfaefftwidt',\n",
+ " 'fteriipthewmietpttminieeaaliauuoueoehhsxoatreiotdpttrersheinhnihhadhsssiflspirrhreooenofeqnelahadwgrworfiyaeetpsinesesacetdbosifednnnncgkrptsfl',\n",
+ " 'rnqhwoyaoshnjuophrganabtdebaardueaeduialniarletpeeagonrwmprineeurysiuoaegtohehtioscmnwimsdtpplvltaeynohrsetlsesetgiisusnsslfnhermcuoiaeloespoil',\n",
+ " 'fatfsitlrproeswthnshvpahsuhcseyelinaraieapetlqaetdtdwyhsooltodguaailriissateiylviohpeoglobddspnijyeyttoesuuwnardtiwiraaayoeeiilekwlueetoatwoogg',\n",
+ " 'eshsiidnloeoarlrrfeyhaptetteihinhnalaottononrrfndeelhtniuntauetfesstmlroonyataemxfeutodrfzisnlasgnurgfetelaabgoufnwlfsdmtgaldcsnlsdtialsrrsaanf',\n",
+ " 'rliehetbisdtnaeailstiscouareerndtytuenboolalienreneudtfaohepueiarriwrvibdeoidsehlbcsftoologhbeikhdefdwsfshphicedsolhheueocdetdltanoiltnnocuwggo']"
+ ]
+ }
+ ],
+ "prompt_number": 38
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "every_nth(c6bs, 143)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 64,
+ "text": [
+ "['hetuehroera',\n",
+ " 'iteloirnnny',\n",
+ " 'toetsectptn',\n",
+ " 'hffarefaolw',\n",
+ " 'hreobhelygs',\n",
+ " 'nnhmuulaarv',\n",
+ " 'felxrareisn',\n",
+ " 'rubloytpsuu',\n",
+ " 'fshewaldsal',\n",
+ " 'ertthsesesd',\n",
+ " 'rarodrehseo',\n",
+ " 'tgsrguaesdr',\n",
+ " 'emhebsnatae',\n",
+ " 'axgoriemndk',\n",
+ " 'ouatpwhnhou',\n",
+ " 'frtoagvthyt',\n",
+ " 'ftlthaehatn',\n",
+ " 'ttioedhlyan',\n",
+ " 'noannsipeso',\n",
+ " 'awcayivsuau',\n",
+ " 'sleotsmnldt',\n",
+ " 'leeotihbhui',\n",
+ " 'tahretntiti',\n",
+ " 'ytttkrauweu',\n",
+ " 'obrgrlsopnn',\n",
+ " 'rrnbegsacwn',\n",
+ " 'rthcrfihtih',\n",
+ " 'regvtpeaetv',\n",
+ " 'epaarhraacn',\n",
+ " 'qpairioltni',\n",
+ " 'ttsphlipuae',\n",
+ " 'hrinntxlami',\n",
+ " 'iaelfwlepel',\n",
+ " 'matenimmeto',\n",
+ " 'seueeipnaeh',\n",
+ " 'esfwdsspnrl',\n",
+ " 'renfoijdltt',\n",
+ " 'unnylhtrupe',\n",
+ " 'leprtetseof',\n",
+ " 'rtutsseheen',\n",
+ " 'hhrawusvlsa',\n",
+ " 'fheesronwye',\n",
+ " 'srhoimfaata',\n",
+ " 'eiraarbihot',\n",
+ " 'tsednomaatu',\n",
+ " 'anluimreooi',\n",
+ " 'eriwyahlecf',\n",
+ " 'estwpcnnlnt',\n",
+ " 'itteoedhuyt',\n",
+ " 'usmttiicrua',\n",
+ " 'iturhsoaetc',\n",
+ " 'wrdrmoclsse',\n",
+ " 'snytoihinot',\n",
+ " 'ifinulesbgl',\n",
+ " 'hlnlovckicn',\n",
+ " 'bdnrcdnaimn',\n",
+ " 'nouptebceur',\n",
+ " 'rgefyotdadb',\n",
+ " 'rhgpeetoslh',\n",
+ " 'taiailnaapb',\n",
+ " 'sucieuceeae',\n",
+ " 'imoeesodtdg',\n",
+ " 'ogulpamtell',\n",
+ " 'iseloipjaeo',\n",
+ " 'ienrnruggas',\n",
+ " 'esdihishcdn',\n",
+ " 'neemkdeodoa',\n",
+ " 'iaenresicto',\n",
+ " 'rkitnooesen',\n",
+ " 'huarnteaofm',\n",
+ " 'mnhotnhdrdi',\n",
+ " 'rtswaftgesf',\n",
+ " 'piuiiiewpbk',\n",
+ " 'yaeerenasfo',\n",
+ " 'thlqlseyrea',\n",
+ " 'dyhrtotnolr',\n",
+ " 'ttteebfdeeo',\n",
+ " 'otncenienan',\n",
+ " 'enouitetshs',\n",
+ " 'iyaausneean',\n",
+ " 'ruhrraonlxv',\n",
+ " 'cntnepohvrn',\n",
+ " 'itidmoteosf',\n",
+ " 'tneonfngsor',\n",
+ " 'anatigweene',\n",
+ " 'lviatsoedit',\n",
+ " 'nhnfaoouudr',\n",
+ " 'biynpdtedtc',\n",
+ " 'rgfrhreosif',\n",
+ " 'pwdooteeiet',\n",
+ " 'nlntefretih',\n",
+ " 'heieaiidlpd',\n",
+ " 'twalorlntre',\n",
+ " 'ofuehptoeat',\n",
+ " 'hiothlorisp',\n",
+ " 'oaepntiyths',\n",
+ " 'rpneoagytiw',\n",
+ " 'laanundrfcs',\n",
+ " 'istrueofsdu',\n",
+ " 'eripmtscecc',\n",
+ " 'weymtrmtosp',\n",
+ " 'tleskleeehe',\n",
+ " 'sffhvyhmsir',\n",
+ " 'haonsiotnpc',\n",
+ " 'tihdeoitlbw',\n",
+ " 'eeednsfwnit',\n",
+ " 'sbdeeomngep',\n",
+ " 'paatdtltilo',\n",
+ " 'opldeyrgnsa',\n",
+ " 'scueeoodolw',\n",
+ " 'omawoeeeenf',\n",
+ " 'tcrhisftrah',\n",
+ " 'opoedsfanld',\n",
+ " 'ylefpfdstha',\n",
+ " 'narowntueoy',\n",
+ " 'uhyyktuoiri',\n",
+ " 'wehpipeoefs',\n",
+ " 'htueehsoiro',\n",
+ " 'rhrdgeotweo',\n",
+ " 'etaetaffwna',\n",
+ " 'doeniilwltg',\n",
+ " 'hlrnigeywao',\n",
+ " 'hhasiermhta',\n",
+ " 'cwtnoawitoe',\n",
+ " 'pseesoiherl',\n",
+ " 'iureeririan',\n",
+ " 'clagnaygrid',\n",
+ " 'mahthiqrsef',\n",
+ " 'ndiauedhimi',\n",
+ " 'osatuiboikg',\n",
+ " 'oeoeftzelln',\n",
+ " 'tuneadoshag',\n",
+ " 'avganmtitgr',\n",
+ " 'hetnbttndrt',\n",
+ " 'aaweeanrsee',\n",
+ " 'seisuarrrtb',\n",
+ " 'hoseiefanml',\n",
+ " 'ttwrtgaodrt',\n",
+ " 'iasthrnfedl',\n",
+ " 'jeagrhtrscl',\n",
+ " 'euidayderwg',\n",
+ " 'attleaitfsf',\n",
+ " 'nabursgfhno']"
+ ]
+ }
+ ],
+ "prompt_number": 64
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "[(q, u) for q in [i for i in range(len(c6bs)) if c6bs[i] == 'q'] for u in [i for i in range(len(c6bs)) if c6bs[i] == 'u'] if abs(q-u) < 13]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 44,
+ "text": [
+ "[(29, 37), (503, 507), (985, 973)]"
+ ]
+ }
+ ],
+ "prompt_number": 44
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "every_nth(c6bs, 13)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 54,
+ "text": [
+ "['harrseehthoexttnsneitetghuydoihuouocttiuedeeerrsohiaeefhifsiitiohtreiehseiiszomhhihtdtoeentencsllilrdieodhpbrlakhntnsdwon',\n",
+ " 'iorhiniterttuehfeywehueagrieaaernltvanmalddeoptwukuongaiwpuldsrsedrheseenlfoonnavsoenwtsnhelbdetnwhnotsgoarieayuvalanetog',\n",
+ " 'tfefhiroseaorphlaufbtvetaenehudagtoaelnreeeasarsorrhetneghrveapoamcvrocsotmftttankinonfiphawiclegwttycyctxaengntnenovtpar',\n",
+ " 'hfqsbrchpdhftprdkniaoeflahnitoaetatiortnttnnrhricnehdibeaimdopltitfeofnooolltahlaaehrtwnoataisviildltntmersltrwniannnpogt',\n",
+ " 'httenhiooharttioutaplaeisruaielrwoopaprdpdnebehatnmneiehdlretotygaehibbetirenllpicaeygyryyuheootnwsgaaoufshsaesnetrmfsaoe',\n",
+ " 'nnhtrmtrshsnorsgnnpchehaieehenuaimnndfooeessunnnytnoeiuustoonfaoealixmthngorrapladdgydmraeaaarstohrrsmtddoilttvoiubirwwab',\n",
+ " 'faiarralochewanhtnamwolcelgsaaatsxalupwtnwneryfieaiuooiaiwmefgneaervlrttwdewfeseeogereiaiuposeefetnsaeolsncnomnulihfesfel',\n",
+ " 'rsmetplitptularaivscstbetiiuitrewloewaiarherotnyiituistysialisesogtmmhneoofiapnmlawefthosleeapdsredudtcpbidarrutofbktuhlt',\n",
+ " 'flsesyneoiiseesuahrpuaheutceniorseoewiefpeetwteperamdehaiicueotsrrlhpncnosfindbnneaucarfshalesuenieauenafdcladlihteorcdnl',\n",
+ " 'eteiitbwycjrastmhiellethftolyyeaatrweeqnmfgghedoelptpnrstsessdrfahensdoetmdytstphdyetsgreinutrdotrsstrydetshicdiltgacpadl',\n",
+ " 'ryruodrtnmeatesgyglaaurtnmuhferhiotftlrrsotddkotpthkwharriiaorlniyeajimteetqdhudctnoeureswlreoseesreetullihoewoutalrfeyfg',\n",
+ " 'touiitpsunagbntstwfhdtsrnuetdfyitrgyrleohyalgrlhoeovkueulhsibtyteaastopfehudieorajdemohtspueaeisiifdnpteeeirmsrnecootriif',\n",
+ " 'erlwionhwonmreretlaesahnpdnnnohabebrrrctnptubetmneesiursgeornfipisnstcuiroebgaaslgeetooftcesgntneihawosaaipfknenfesnhcsgo']"
+ ]
+ }
+ ],
+ "prompt_number": 54
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "[''.join(transpose(l, (3, 11, 0, 1, 2, 4, 5, 6,7, 8, 9, 10, 12))) for l in chunks(c6bs, 13)]"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 61,
+ "text": [
+ "['hthithnfrfere',\n",
+ " 'foaoftnasltyr',\n",
+ " 'qurrethimserl',\n",
+ " 'sirhfetaeeiuw',\n",
+ " 'bisihnrrtsioi',\n",
+ " 'rtenihmrpytdo',\n",
+ " 'cpeiritalnbrn',\n",
+ " 'hshtoorliewth',\n",
+ " 'putesosotoynw',\n",
+ " 'dnhrehhcpicmo',\n",
+ " 'haotaashtijen',\n",
+ " 'fgetorneusram',\n",
+ " 'tbxurtowleatr',\n",
+ " 'pnteptraaesee',\n",
+ " 'rtthhisnrstsr',\n",
+ " 'dsnfloghaumge',\n",
+ " 'ktseauntiahyt',\n",
+ " 'nwnyutnnvhigl',\n",
+ " 'ifewfapasrela',\n",
+ " 'ahiebpcmcplae',\n",
+ " 'odthtlhwsulas',\n",
+ " 'eteuvaeotaeua',\n",
+ " 'fsteeehlbhtrh',\n",
+ " 'lrgatiaceehtn',\n",
+ " 'anhgasietufnp',\n",
+ " 'huurerelittmd',\n",
+ " 'neyinuegicoun',\n",
+ " 'itdeeahsuelhn',\n",
+ " 'tdoahieainyfn',\n",
+ " 'ofiauenatiyeo',\n",
+ " 'ayhedluaroerh',\n",
+ " 'eiurarateraha',\n",
+ " 'ttongwiswsaib',\n",
+ " 'arultomxletoe',\n",
+ " 'tgotoonaoortb',\n",
+ " 'iycvapnleewfr',\n",
+ " 'ortaeaduwwetr',\n",
+ " 'rltnlpfpaielr',\n",
+ " 'teimnrowieqrc',\n",
+ " 'nouardotafnrt',\n",
+ " 'thelepenrpmsn',\n",
+ " 'tyddedewhefop',\n",
+ " 'naedensneegtt',\n",
+ " 'nleeaesertgdu',\n",
+ " 'rgeosburowhdb',\n",
+ " 'hrrpaenytteke',\n",
+ " 'rlrtrhnfnedot',\n",
+ " 'ihswsaniypotm',\n",
+ " 'coouotyeieepn',\n",
+ " 'nehkrntairlte',\n",
+ " 'eoiurmnitaphe',\n",
+ " 'hvaohnouumtks',\n",
+ " 'dkeneeeoidpwi',\n",
+ " 'iuegtiiosenhu',\n",
+ " 'befaneuithrar',\n",
+ " 'euhiehuayasrs',\n",
+ " 'aliwgdsisitrg',\n",
+ " 'ihfphltwiisie',\n",
+ " 'mssurromaceio',\n",
+ " 'diilveoelusar',\n",
+ " 'obidetnfieson',\n",
+ " 'pttsaofgsodrf',\n",
+ " 'lyirptanetrli',\n",
+ " 'ttosoyoessfnp',\n",
+ " 'ieheageaoraii',\n",
+ " 'tatdmaaegrhys',\n",
+ " 'farrcelrtleen',\n",
+ " 'esehvhivmhnas',\n",
+ " 'otierixlmpsjt',\n",
+ " 'foesobmrhndic',\n",
+ " 'nphecbttncomu',\n",
+ " 'ofsesehteneti',\n",
+ " 'oeenotnwooter',\n",
+ " 'ohiltigdosmeo',\n",
+ " 'luifmroeffdte',\n",
+ " 'ldsoferwiiyqb',\n",
+ " 'tizotnrfantdg',\n",
+ " 'aeontlaepdsha',\n",
+ " 'homntlpsnbtua',\n",
+ " 'lrhaaplemnpds',\n",
+ " 'aahvniaelnhcl',\n",
+ " 'ajiskcdoaedtg',\n",
+ " 'edhoiadgwayne',\n",
+ " 'hetenegeeueoe',\n",
+ " 'rmdnoyyrfctet',\n",
+ " 'totwngdetasuo',\n",
+ " 'whotfymihrgro',\n",
+ " 'ntesirraofref',\n",
+ " 'osenpyaissest',\n",
+ " 'apnhhyeulhiwc',\n",
+ " 'tuteauapeanle',\n",
+ " 'aeelwhaoelurs',\n",
+ " 'ianbieasaeteg',\n",
+ " 'secdcorepsron',\n",
+ " 'viselosedudst',\n",
+ " 'isltettfseoen',\n",
+ " 'iilngnoerntee',\n",
+ " 'liiwwwhteirsi',\n",
+ " 'dflhtsrndesrh',\n",
+ " 'ldrntgrsuasea',\n",
+ " 'tndoyasadutew',\n",
+ " 'npitcameterto',\n",
+ " 'ttesyotocnyus',\n",
+ " 'meogcudlpadla',\n",
+ " 'eedotfdsbfela',\n",
+ " 'rehaxsonidtii',\n",
+ " 'siprahicdcshp',\n",
+ " 'lrbieslnalhof',\n",
+ " 'tmrenatoraiek',\n",
+ " 'rslagetmrdcwn',\n",
+ " 'wraynsvnuldoe',\n",
+ " 'nnkutnoutiiun',\n",
+ " 'iehvneilohltf',\n",
+ " 'acnaetuifttae',\n",
+ " 'notlnrbhbegls',\n",
+ " 'nonaomifkoarn',\n",
+ " 'ntsnvfretrcfh',\n",
+ " 'prdetswsucpec',\n",
+ " 'oiwtpawfhdays',\n",
+ " 'giooaoaelndfg',\n",
+ " 'tfngrebltllgo']"
+ ]
+ }
+ ],
+ "prompt_number": 61
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
--- /dev/null
+{
+ "metadata": {
+ "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from cipherbreak import *\n",
+ "with open('2013/mona-lisa-words.txt') as f:\n",
+ " mlwords = [line.rstrip() for line in f]\n",
+ "mltrans = collections.defaultdict(list)\n",
+ "for word in mlwords:\n",
+ " mltrans[transpositions_of(word)] += [word]\n",
+ "c7a = open('2013/7a.ciphertext').read()\n",
+ "c7b = open('2013/7b.ciphertext').read()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c1a = open('2013/1a.ciphertext').read()\n",
+ "c1b = open('2013/1b.ciphertext').read()\n",
+ "c2a = open('2013/2a.ciphertext').read()\n",
+ "c2b = open('2013/2b.ciphertext').read()\n",
+ "c3a = open('2013/3a.ciphertext').read()\n",
+ "c3b = open('2013/3b.ciphertext').read()\n",
+ "c4a = open('2013/4a.ciphertext').read()\n",
+ "c4b = open('2013/4b.ciphertext').read()\n",
+ "c5a = open('2013/5a.ciphertext').read()\n",
+ "c5b = open('2013/5b.ciphertext').read()\n",
+ "\n",
+ "p1a = caesar_decipher(c1a, 8)\n",
+ "p1b = caesar_decipher(c1b, 14)\n",
+ "p2a = affine_decipher(c2a, 3, 3, True)\n",
+ "p2b = caesar_decipher(c2b, 6)\n",
+ "p3a = affine_decipher(c3a, 7, 8, True)\n",
+ "p3b = keyword_decipher(c3b, 'louvigny', 2)\n",
+ "p4a = keyword_decipher(c4a, 'montal', 2)\n",
+ "p4b = keyword_decipher(c4b, 'salvation', 2)\n",
+ "p5a = keyword_decipher(c5a, 'alfredo', 2)\n",
+ "p5b = vigenere_decipher(sanitise(c5b), 'florence')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(frequencies(sanitise(c7a)))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "/usr/local/lib/python3.3/dist-packages/matplotlib/figure.py:372: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
+ " \"matplotlib is currently using a non-GUI backend, \"\n"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEkCAYAAAB6wKVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noAAARiKgAABG+j/og6x5jaDpPwAAAABJRU5ErkJggg==\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xb55607cc>"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c7af = frequencies(sanitise(c7a))\n",
+ "plot_frequency_histogram(c7af, sort_key=lambda l: c7af[l])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEkCAYAAAB6wKVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHKlJREFUeJzt3X90U/X9x/FXsEUU2tF2a3pskTJpKaGlP1A4wCrRkuJ0\neBClCuoqnTplO9txKjB1WvZVmilMcROdOnXMI5NxdgqipwcGJxxRsSKIaHVMoYOWtk5LoVCs0t7v\nH4xipUnaNKGfJs/HOTk0yTuf+w65zaufe29ubJZlWQIAwDAD+roBAAC6QkABAIxEQAEAjERAAQCM\nREABAIxEQAEAjOQzoEpKSmS325WVlXXafUuXLtWAAQPU2NjYcVtZWZnS0tKUkZGh9evXB79bAEDE\n8BlQc+fOVUVFxWm379+/Xxs2bNDw4cM7bquqqtLLL7+sqqoqVVRUaN68eWpvbw9+xwCAiOAzoPLz\n8xUXF3fa7b/61a/08MMPd7ptzZo1mj17tqKjo5WamqqRI0eqsrIyuN0CACJGj/dBrVmzRikpKRo7\ndmyn2w8cOKCUlJSO6ykpKaqtre19hwCAiBTVk+KWlhYtXrxYGzZs6LjN15mSbDZb4J0BACJajwLq\n008/VXV1tbKzsyVJNTU1GjdunN5++20lJydr//79HbU1NTVKTk4+bYycnBzt3Lmzl20DAMJBdna2\n3nvvva7vtPzYu3evlZmZ2eV9qamp1hdffGFZlmV9+OGHVnZ2ttXa2mrt2bPH+v73v2+1t7ef9phu\nLLJfe+CBB8K21pQ+qDWrD2p7XhvqsfsTX5ngcx/U7NmzNWnSJO3evVvDhg3T888/3+n+b27Cczgc\nKioqksPh0A9/+EMtX76cTXwAgID53MS3cuVKnw/es2dPp+v33HOP7rnnnt53BQCIeGeVlpaWnskF\nLlq0SGd4kWdcampq2Naa0ge1ZvVBbc9rQz12f+ErE2z/2wZ4xthsNp9H/gEAIoevTOBcfAAAIxFQ\nAAAjEVAAACMRUABgkNjYeNlsNp+X2Nj4vm7zjOAgCQAwyInPj/p7jwyf91EOkgAA9DsEFADASAQU\nAMBIBBQAwEgEFADASAQUAMBIBBQAwEgEFADASAQUAMBIBBQAwEgEFADASAQUAMBIBBQAwEgEFADA\nSAQUAMBIBBQAwEgEFADASAQUAMBIBBQAwEgEFADASAQUAMBIPgOqpKREdrtdWVlZHbfdfffdGj16\ntLKzszVz5kwdOnSo476ysjKlpaUpIyND69evD13XAICw5zOg5s6dq4qKik63FRYW6sMPP9TOnTuV\nnp6usrIySVJVVZVefvllVVVVqaKiQvPmzVN7e3voOgeAfiI2Nl42m83nJTY2vq/bNI7PgMrPz1dc\nXFyn21wulwYMOPGwCRMmqKamRpK0Zs0azZ49W9HR0UpNTdXIkSNVWVkZorYBoP9obj4oyfJ5OVGD\nb+rVPqjnnntOl19+uSTpwIEDSklJ6bgvJSVFtbW1vesOABCxAg6ohx56SAMHDtScOXO81thstkCH\nBwBEuKhAHvTCCy/otdde08aNGztuS05O1v79+zuu19TUKDk5ucvHl5aWdvzsdDrldDoDaQMA0M94\nPB55PJ5u1dosy7J8FVRXV2v69OnatWuXJKmiokJ33nmnNm/erO9+97sddVVVVZozZ44qKytVW1ur\nqVOn6pNPPjltFmWz2eRnkQAQVk68D/p73zvx3tiT2nDgKxN8zqBmz56tzZs36/PPP9ewYcO0aNEi\nlZWV6auvvpLL5ZIkTZw4UcuXL5fD4VBRUZEcDoeioqK0fPlyNvEBAALmdwYV9AUygwIQBmJj4/0e\neRcTE6fDhxuZQfngKxMIKAAIQKhCh4A6hVMdAcD/8IFaszCDAoD/MWFWxAzqFGZQAAAjEVAAACMR\nUAAAIxFQAAAjEVAAACMRUAAAIxFQAAAjEVAAACMRUAAAIxFQAAAjEVAAACMRUAAAIxFQAAAjEVAA\nACMRUAAAIxFQAAAjEVAAACMRUAAAIxFQAAAjEVAAACMRUAAAIxFQAAAjEVAAACMRUAAAIxFQAAAj\n+QyokpIS2e12ZWVlddzW2Ngol8ul9PR0FRYWqqmpqeO+srIypaWlKSMjQ+vXrw9d1wCAsOczoObO\nnauKiopOt7ndbrlcLu3evVsFBQVyu92SpKqqKr388suqqqpSRUWF5s2bp/b29tB1DgAIaz4DKj8/\nX3FxcZ1uW7t2rYqLiyVJxcXFKi8vlyStWbNGs2fPVnR0tFJTUzVy5EhVVlaGqG0AQLjr8T6ohoYG\n2e12SZLdbldDQ4Mk6cCBA0pJSemoS0lJUW1tbZDaBIDAxMbGy2az+bzExsb3dZvoQlRvHnzyxfV1\nPwD0pebmg5IsPzW8V5moxwFlt9tVX1+vpKQk1dXVKTExUZKUnJys/fv3d9TV1NQoOTm5yzFKS0s7\nfnY6nXI6nT1tA0AEi42N/1/weBcTE6fDhxvPUEfoLo/HI4/H061am2VZPv+0qK6u1vTp07Vr1y5J\n0vz585WQkKAFCxbI7XarqalJbrdbVVVVmjNnjiorK1VbW6upU6fqk08+OW0WZbPZ5GeRAODTifcV\nf+8jJ95rwrk2HPjKBJ8zqNmzZ2vz5s36/PPPNWzYMP32t7/VwoULVVRUpD//+c9KTU3VqlWrJEkO\nh0NFRUVyOByKiorS8uXL2cQHAAiY3xlU0BfIDApAL5kwezGhNhz4ygTOJAEAMBIBBQAwEgEFADAS\nAQUAMBIBBQAwEgEFADASAQUAMBIBBQAwEgEFADASAQUAMBIBBQAwEgEFADASAQUAMBIBBQAwEgEF\nADASAQUAMBIBBQAwEgEFADASAQUAMBIBBQAwEgEFADASAQUAMBIBBQAwEgEFwAixsfGy2WxeL7Gx\n8X3dIs6wqL5uAAAkqbn5oCTLx/22M9cMjMAMCgBgJAIKAGAkAgoAYKSAA6qsrExjxoxRVlaW5syZ\no9bWVjU2Nsrlcik9PV2FhYVqamoKZq8AgAgSUEBVV1frmWee0fbt27Vr1y61tbXpb3/7m9xut1wu\nl3bv3q2CggK53e5g9wsAiBABBVRsbKyio6PV0tKi48ePq6WlReedd57Wrl2r4uJiSVJxcbHKy8uD\n2iwAIHIEFFDx8fG68847df755+u8887T0KFD5XK51NDQILvdLkmy2+1qaGgIarMA+hc+24TeCCig\nPv30Uz322GOqrq7WgQMHdOTIEb344oudak6ugAAi16nPNnV9OXE/0LWAPqi7bds2TZo0SQkJCZKk\nmTNn6q233lJSUpLq6+uVlJSkuro6JSYmdvn40tLSjp+dTqecTmcgbQAA+hmPxyOPx9OtWptlWd4/\nuu3Fzp07df311+udd97RoEGDdNNNN2n8+PH6z3/+o4SEBC1YsEBut1tNTU2nHShhs9kUwCIB9EMn\ntqL4+n0/9X4Q3NpT9eFcGw58ZUJAASVJDz/8sP7yl79owIABysvL07PPPqvm5mYVFRVp3759Sk1N\n1apVqzR06NBuNwMgvBBQBJQ/IQmoUDQDILwQUASUP74ygTNJAACMREABAIxEQAEAjERAAQCMREAB\nAIxEQAEAjERAAQCMREABAIxEQAEAjERAARHO31difPNrMXpSC/QWpzoCIlwoT8PDqY441ZE/nOoI\nANDvEFAAACMRUAAAIxFQAAAjEVAAACMRUAAAIxFQAAAjEVAAACMRUAAAIxFQQBjilEQIB1F93QCA\n4GtuPih/p8tpbradmWaAADGDAgAYiYACABiJgAIAGImAAgAYiYACABiJgAIAGCnggGpqatI111yj\n0aNHy+Fw6O2331ZjY6NcLpfS09NVWFiopqamYPYKAIggAQfUL3/5S11++eX66KOP9P777ysjI0Nu\nt1sul0u7d+9WQUGB3G53MHsFAEQQmxXAF9sfOnRIubm52rNnT6fbMzIytHnzZtntdtXX18vpdOrj\njz/uvEAf3z8PIDhsNpv8fVBXOvG7GKra7vURqtrAeu5vteHAVyYENIPau3evvve972nu3LnKy8vT\nLbfcoqNHj6qhoUF2u12SZLfb1dDQEHjXADrh9EWINAEF1PHjx7V9+3bNmzdP27dv1+DBg0/bnHfy\nFwZAcJw6fZH3y4kaIDwEdC6+lJQUpaSk6KKLLpIkXXPNNSorK1NSUpLq6+uVlJSkuro6JSYmdvn4\n0tLSjp+dTqecTmcgbQD9XmxsvN9QiYmJ0+HDjWeoIyC0PB6PPB5Pt2oD2gclSRdffLGeffZZpaen\nq7S0VC0tLZKkhIQELViwQG63W01NTV3OrMJl2ynQWybsx2AfVP+tDQe+MiHggNq5c6duvvlmffXV\nV7rgggv0/PPPq62tTUVFRdq3b59SU1O1atUqDR06tNvNAJHGhDc5Aqr/1oaDkARUKJoBIo0Jb3IE\nVP+tDQdBP4oPAIBQI6AAAEYioAAARiKgAABGIqAAAEYioAAARiKgAABGIqAAAEYioAAARiKggCDj\nazGA4AjobOYAvDv1tRi+avgqGsAfZlAAACMRUEA3sNkOOPPYxAd0A5vtgDOPGRQAwEgEFADASAQU\nAMBIBBQAwEgEFCIWR+YBZuMoPkQsjswDzMYMCkbr6SzHXz0zIqD/YAYFo/V0luOvnhkR0H8wgwIA\nGImAAgAYiYACABiJgAIAGImAAgAYiYACABipVwHV1tam3NxcTZ8+XZLU2Ngol8ul9PR0FRYWqqmp\nKShNAgAiT68CatmyZXI4HLLZTny2xO12y+Vyaffu3SooKJDb7Q5KkwCAyBNwQNXU1Oi1117TzTff\nLMs68cHItWvXqri4WJJUXFys8vLy4HQJAIg4AQfUHXfcoUceeUQDBpwaoqGhQXa7XZJkt9vV0NDQ\n+w4BABEpoIBat26dEhMTlZub2zF7+raT5z4DACAQAZ2L780339TatWv12muv6csvv9Thw4d14403\nym63q76+XklJSaqrq1NiYmKXjy8tLe342el0yul0BtIGAKCf8Xg88ng83aq1Wd6mQN20efNmLVmy\nRK+88ormz5+vhIQELViwQG63W01NTacdKGGz2bzOuoBvOzEL97e+nFqn/Nf3pPZUPbU9r5WC/Xrw\n2n27Nhz4yoSgfA7q5Ka8hQsXasOGDUpPT9emTZu0cOHCYAyPMMMXBQLojl7PoHq8QGZQEa///MUe\nWB/UMoM6U7XhIOQzKAAAgo2AQlCw2Q5AsPGNugiKnn7zLQD4wwwKAGAkAgoAYCQCCgBgJAIKAGAk\nAgpecWQegL7EUXzwiiPzAPQlZlAAACMRUAAAIxFQEYb9SgD6C/ZBRRj2KwHoL5hBAQCMREABAIxE\nQAEAjERAAQCMREABAIxEQAEAjERAAQCMREABAIxEQAEAjERAAQCMREABAIxEQAEAjERAAQCMREAB\nAIxEQAEAjBRQQO3fv1+XXHKJxowZo8zMTD3++OOSpMbGRrlcLqWnp6uwsFBNTU1BbRYAEDkCCqjo\n6Gg9+uij+vDDD7V161Y98cQT+uijj+R2u+VyubR7924VFBTI7XYHu18AQIQIKKCSkpKUk5MjSRoy\nZIhGjx6t2tparV27VsXFxZKk4uJilZeXB69TAEBE6fU+qOrqau3YsUMTJkxQQ0OD7Ha7JMlut6uh\noaHXDQIAIlOvAurIkSO6+uqrtWzZMsXExHS6z2azyWaz9ao5AEDkigr0gV9//bWuvvpq3XjjjZox\nY4akE7Om+vp6JSUlqa6uTomJiV0+trS0tONnp9Mpp9MZaBsAgH7E4/HI4/F0q9ZmWZbV0wVYlqXi\n4mIlJCTo0Ucf7bh9/vz5SkhI0IIFC+R2u9XU1HTagRI2m00BLBJBcmJW6+///8Rr1N9qpe48v57U\nhr7ncK6Vgv168Np9uzYc+MqEgAJqy5YtuvjiizV27NiOzXhlZWUaP368ioqKtG/fPqWmpmrVqlUa\nOnRot5tBYGJj49XcfNBnTUxMnA4fbjTil6r/vCGGvudwrpUIKALKv6AHVKiawSmEjglviKHvOZxr\nJQKKgPLPVyYEvA8KoXUinHyvgM3NHIQCIHxxqiMAgJEIKACAkQgoAICRCCgAgJEIqDMoNja+4wwb\n3i6xsfF93SYAGIGj+M4gjswDgO5jBgUAMBIBBQAwEgHVS/72K7FPCQACwz6oXvK3X4l9SgAQGGZQ\nAAAjEVAAACMRUAAAIxFQAAAjEVAAACMRUAAAIxFQAAAjEVAAACMRUAAAIxFQAAAjEVAAACMRUAAA\nIxFQAAAjEVAAACMRUAAAIxFQAAAjEVAAACMFPaAqKiqUkZGhtLQ0/e53vwv28ACACBHUgGpra9PP\nf/5zVVRUqKqqSitXrtRHH30UzEX0A54wrg3l2NT2vDaUY1Mb2tpQjx0eghpQlZWVGjlypFJTUxUd\nHa3rrrtOa9asCeYi+gFPGNeGcmxqe14byrGpDW1tqMcOD0ENqNraWg0bNqzjekpKimpra4O5CABA\nhAhqQNlstmAOBwCIZFYQvfXWW9a0adM6ri9evNhyu92darKzsy1JXLhw4cKFi5Wdne01U2yWZVkK\nkuPHj2vUqFHauHGjzjvvPI0fP14rV67U6NGjg7UIAECEiArqYFFR+uMf/6hp06apra1NP/nJTwgn\nAEBAgjqDAgAgWDiTRD/w+OOPy+Fw6MYbb+yT5VdXVysrKyvky5k8eXKf94DQKy0t1dKlS4M6pq91\n55sOHTqkJ598MqjLDmTdHDJkSFB7CFcEVD/w5JNP6p///Kf++te/hmwZlmWpryfTb7zxRp8uH2dG\nKI727e66c/DgQS1fvjzoy+8pjnjuHgIqSF588UVNmDBBubm5uu2229Te3u6z/qGHHtKoUaOUn5+v\nOXPmeP2L8rbbbtOePXt02WWX6bHHHuuyprq6WhkZGZo7d65GjRql66+/XuvXr9fkyZOVnp6ud955\nx+vjRo0apeLiYmVlZammpsbv89yzZ4/y8vL07rvveh1z9OjRuvXWW5WZmalp06bpyy+/9Duu5P+v\nyuPHj+uGG26Qw+HQrFmzdOzYsS7rHnjgAS1btqzj+r333qvHH3/c59j/93//p4yMDL+vhyT9/ve/\nV1ZWlrKysjot59uOHj2qK664Qjk5OcrKytKqVau81v7pT39Sbm6ucnNzNWLECF166aU++12xYoWy\ns7OVk5OjH//4x17r3nnnHWVnZ6u1tVVHjx5VZmamqqqquqz99kxgyZIlWrRo0Wl1jzzyiP7whz9I\nku644w4VFBRIkjZt2qQbbrihy7G/ub7/61//8trvr3/9604B0t3ZVndnJAsXLtSnn36q3NxcLViw\nwGvdyd+p7qxv0omz6ASyzvtz1VVX6cILL1RmZqaeeeaZoIzZrwTzMPNIVVVVZU2fPt06fvy4ZVmW\ndfvtt1srVqzwWr9t2zYrKyvLOnbsmHX48GFr5MiR1tKlS73Wp6amWl988YXX+/fu3WtFRUVZH3zw\ngdXe3m6NGzfOKikpsSzLstasWWPNmDHD6+MGDBhgvf322z6f3969e63MzEzr448/tnJzc63333/f\nby87d+60LMuyioqKrBdffNHn+CcNGTLE57g2m8168803LcuyrJKSEmvJkiVd1lZXV1t5eXmWZVlW\nW1ubdcEFF1iNjY1ex66srLRycnKs1tZWq7m52UpLS/P6epx87VpaWqwjR45YY8aMsXbs2NFl7erV\nq61bbrml4/qhQ4e89nDS119/beXn51vr1q3zWvPBBx9Y6enpHeuEr+dmWZZ13333WXfddZf1s5/9\n7LSPfXzTydf5pCVLllilpaWn1W3dutWaNWuWZVmW9YMf/MCaMGGC9fXXX1ulpaXW008/fVp9T9b3\nHTt2WFOmTOm47nA4rJqaGp/Pz7J8rzvfVF1d3ek5etOT9S2Qdb67/Z58bVtaWqzMzEyf7wPhiBlU\nEGzcuFHvvvuuLrzwQuXm5mrTpk3au3ev1/rXX39dM2fO1KBBgxQTE6Mrr7yy15vXRowYoTFjxshm\ns2nMmDGaOnWqJCkzM1PV1dVeHzd8+HCNHz/e7/ifffaZZsyYoZdeesnv9vYRI0Zo7NixkqRx48b5\nXH5PDBs2TBMnTpQk3XDDDdqyZUuXdcOHD1dCQoLee+89rV+/Xnl5eYqLi/M67htvvKEZM2Zo4MCB\nGjJkiKZPn+719diyZYtmzpypc845R4MHD9bMmTP1+uuvd1k7duxYbdiwQQsXLtSWLVsUGxvr9zn+\n4he/UEFBga644gqvNZs2bVJRUZHi4+Mlyedzk6T7779f69ev17Zt2zR//ny/Pfhzcgbd3NysQYMG\naeLEidq2bZu2bNmi/Pz80+p7sr7n5OTos88+U11dnXbu3Km4uDglJyf3uueTevJ71t31TQrdOr9s\n2TLl5ORo4sSJqqmp0b///e+gjNtfBPUw80hWXFysxYsXd6vWZrN1+kXpbThJ0tlnn93x84ABAzRw\n4MCOn48fP+71cYMHD+7W+EOHDtXw4cP1+uuvKyMjo9u9nHXWWT43jfTEN7fbW5blczv+zTffrOef\nf14NDQ0qKSnxO253X4+uar31kZaWph07dujVV1/Vfffdp4KCAv3mN7/xOvYLL7yg/fv3+91H8u0e\n/Pn888919OhRtbW16dixYzr33HO7rIuKiuq0adrb6xYdHa0RI0bohRde0KRJkzR27Fht2rRJn3zy\nSZfrRk/X91mzZmn16tWqr6/Xdddd152nGBI9Wd9Csc57PB5t3LhRW7du1aBBg3TJJZeotbW11+P2\nJ8yggqCgoECrV6/Wf//7X0lSY2Oj9u3b57X+4osvVnl5ub788ks1Nzdr3bp1xu80HThwoP7xj39o\nxYoVWrlyZZ/0sG/fPm3dulWS9NJLL3X51/pJV111lSoqKrRt2zZNmzbN57iTJ0/WK6+8otbWVh05\nckSvvvqq19cjPz9f5eXlOnbsmI4ePary8nKvfdTV1WnQoEG6/vrrddddd2n79u1ee3j33Xe1dOnS\nbh0Ic+mll+rvf/+7GhsbJanjX29++tOf6sEHH9ScOXN87nex2+367LPP1NjYqNbWVq1bt85rbX5+\nvpYsWaIpU6YoPz9fTz31lPLy8rqs7en6fu2112rlypVavXq1Zs2a5fO59VRMTIyam5u7VduT9S0U\nDh8+rLi4OA0aNEgff/xxRy+RhBlUEIwePVoPPvigCgsL1d7erujoaC1fvlznn39+l/W5ubm69tpr\nlZ2drcTERF100UV+/2r359s137zu6/HdDUabzaZzzz1X69atk8vlUkxMjH70ox/1uBd/y/B136hR\no/TEE0+opKREY8aM0e233+61Pjo6Wpdeeqni4uL8Lv/CCy/UlVdeqbFjx8putysrK0vf+c53uqzN\nzc3VTTfd1LFZ9JZbblF2dnaXtbt27dLdd9/dMaP1dXjzE088oYMHD+qSSy6RJF100UV6+umnu6x1\nOBy69957NWXKFJ111lnKy8vTc88912XtihUrdPbZZ+u6665Te3u7Jk2aJI/HI6fTeVptdHS07r//\nfo0fP17JyclyOBw+g3rx4sWaOHGizjnnHJ1zzjle38C/vb7726TscDh05MgRpaSkyG63+6w9qbvr\nWEJCgiZPnqysrCxdfvnlPr+zrifrW0/X+e70e9lll+mpp56Sw+HQqFGjOjY3RhI+qGuARYsWaciQ\nIbrzzjv7upU+88UXXwR12317e7vGjRun1atX64ILLvBbf/ToUQ0ePFgtLS2aMmWKnnnmGeXk5ASl\nF/Q/1dXVmj59unbt2tXXrUQ0NvEZwvRNfKF04MABTZo0SXfffXdQxquqqlJaWpqmTp3arXCSpFtv\nvVW5ubkaN26crrnmGsIJEf07aQpmUAAAIzGDAgAYiYACABiJgAIAGImAAgAYiYACABiJgAIAGOn/\nARTQq3riH9t+AAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaeb102cc>"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(normalised_english_counts, sort_key=lambda l: normalised_english_counts[l])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEkCAYAAABzKwUZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwv8DbMnA/BWkp54AAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaeae5cec>"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c7bf = frequencies(sanitise(c7b))\n",
+ "plot_frequency_histogram(c7bf, sort_key=lambda l: c7bf[l])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEkCAYAAAB6wKVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\neawAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaeb95f8c>"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(c7bf)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEkCAYAAAB6wKVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noAAARiKgAABGIqAAAEYioAAARvp/fPP9PvjmFnQAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaeaa372c>"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "plot_frequency_histogram(normalised_english_counts)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEkCAYAAABzKwUZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEFgAACsQWAAAKxBYAAArEFgAACv8P5ZWvQDqsWNNAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0xaeb47b6c>"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "c7a"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 10,
+ "text": [
+ "'WWPA, AWHCRH MDY IOT NJHGK HJWLSBALH HI AWL BBHLJT, X DTUI PC VC MGPSHN HCK AHXK AVL BCAXS PMILG JAVHPCN IPBL. PZ ESPUCLS AWL RYPAT DPZ SLAPKLGLS AD AWLXY NHGK DU UYXKPF PMILGUDVC, ADV AHIL UVG HCFDUT AD WGVRLHZ XA, PUS AWL QVPYS DPZ THHF IV SLIHRO UYDT IOT IPZT. LMJTSALCA XKTH IV BHZL IOT WPPCAXUV SDVZ SXRT WPYI VU AWL QVM IN AWL LHN - UD-VCL LHH NDPCN IV IBGU XA DCTY IV AVDR XM IOTF SPSU\u2019I RCVL PI DPZ IOTYT, HCK IOTF LLGL EYTAIF JUAPZLAF IV QL AVDRXUV MDY HVBLDUT AD ZBBVNAL P WPPCAXUV PC HCFLHN. \\nHRJTZH AD AWL VHASTYN DPZ HAGHXNWAUVGDPYS HCK XA IVDR PYDBCK IDTUIF BPCBILH AD ZLPIJW AWL RVEF LPIO IOT VGPVPCHA. P WPS AWL EHXUIPCN PTDUV H QBCJW VU YTWGVSBRAXVCZ XU IOT TJZTBB ZWVE HCK RHBWTK DBI MDY IOT UXNWA XU IOT NJHGKH\u2019 IPAWYDVB. AJYCZ DBI AWLN WGLULG AD BHL IOT KXYTJIVGZ\u2019 UHRPAPIPTZ JW DU IOT ADW USDVG. DXAW AWL CLL LMOXIXAXVC VELCPCN DU HHIBGKPF IOT WAHRL LHH IJZN LCVJNW AD ZAPE VJA UPGZI AWPCN PUS, HH HGYPUVLS, P BHSL HBGL X DPZ UPGZI PCAD AWL HODW. X UDD WHKL IOT ODUDBG VU OPCXUV IDBVOI AWL ROTHELHA TCTY LVGR QF SH KPCJX. VG UDA. \\nIOT E-GHN YTZJSIZ RHBL QHRR IOXZ BVGUXUV HCK, PZ NVJ ZJZELRATK, IOXZ XZ DUT VU ZPYP\u2019Z UHZLH. P PT IVAK XA XZ RSDZT AD WTYULRA, QBI, OXKSLC BCKTY IOT SPFTYH VU WPPCA, HOT ZRYXIQSTK P WXJIBGL DM IOT UPGX LPNAL TTQSTT XU ALPK. HOT ZXNCLS PI Z IVD. AWL ILRO VBNZ IOXUZ ZWL BHN OPCT BHLS H QPI VU VAK EPEL APZL P JGHNVC AD KTMPJT AWL QVPYS ITMDYT ZWL HAPYILS DDYZ VC PI. HCFLHN, AWHI STHKLH AWL FBTZIPDU DM LOTYT AWL WLAS IOT YTHA WPPCAXUV TXNWA QL. XA\u2019H OPYS AD ITSXLKL IOPA HOT STMI PI DXAW AWL HZ PUS P RHC\u2019A HLT OTY VVXUV VC AWL GBC DXAW PI ZIBRR JUSLG OTY RVPA. XA\u2019H UDA APZL HOT JDBAK GVAS XA JW. \\nX DDYZLS AWYDBVO HVBL BVGL DM IOT UPGX WPWTYH HCK UVJUS AWPH UDAT. HI STHHA XA ILASH BH DWLGL HOT DTUI. SDVZZ APZL IOT JXWWLG JALGR LHH IPJZ IN AWL LHN. P WHKL BVKLS VC AD CTUXJT AD AGF IV UPCK PUN AGHRL DM HHGH IOTYT, IJA X OPCT H ULTSXUV AWL HVABIPDU IV IOXZ BFHATYN PH IPJZ PC WPYXZ LOTYT PI HAS QLVHC. P ALUA IOT WPPCAXUV HI AWL EHGPH VUMXJT. TPFQL NVJ JDBAK PYGHCNT AD YTAJYC PI? PI ZWVJSS IT LPZXLG AD NTA XA XU IOPU XA LHH AD LMAGHRA XA. \\nPSA AWL QLHA, \\nWHGYN\\n'"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "vigenere_frequency_break(sanitise(c7a))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 12,
+ "text": [
+ "('hp', 0.03214089578198264)"
+ ]
+ }
+ ],
+ "prompt_number": 12
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "' '.join(segment(vigenere_decipher(sanitise(c7a), 'hp')))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 15,
+ "text": [
+ "'phil thanks for the guard schedules at the museum i went in on friday and laid low until after closing time as planned the crate was delivered to their yard on friday afternoon too late for anyone to process it and the board was easy to detach from the base excellent idea to make the painting look like part of the box by the way no one was going to turn it over to look if they didnt know it was there and they were pretty unlikely to be looking for someone to smuggle a painting in anyway access to the gallery was straightforward and it took around twenty minutes to switch the copy with the original i hid the painting among a bunch of reproductions in the museum shop and camped out for the night in the guards bathroom turns out they prefer to use the directors facilities upon the top floor with the new exhibition opening on saturday the place was busy enough to slip out first thing and as arranged i made sure i was first into the shop i now have the honour of having bought the cheapest ever work by davinci or not the xray results came back this morning and as you suspected this is one of saras fake siam told it is close to perfect but hidden under the layers of paint she scribbled a picture of the nazi eagle emblem in leads he signed its too the tech guys think she may have used abit of old pipe like a crayon to deface the board before she started work on it anyway that leaves the question of where the hell the real painting might be its hard to believe that she left it with the ss and icant see her going on the run with it stuck under her coat its not like she could roll it up i worked through some more of the nazi papers and found this note atleast it tells us where she went looks like the cipher clerk was back by the way i have moved on to venice to try to find any trace of sara there but i have a feeling the solution to this mystery is back in paris where it all began i left the painting at the paris office maybe you could arrange to return it it should be easier to get it in than it was to extract it all the best harry'"
+ ]
+ }
+ ],
+ "prompt_number": 15
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "vigenere_frequency_break(sanitise(c7b))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 16,
+ "text": [
+ "('aattuualptaaauaaaa', 0.10312795085805967)"
+ ]
+ }
+ ],
+ "prompt_number": 16
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "' '.join(segment(vigenere_decipher(sanitise(c7b),'aattuualptaaauaaaa' )))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 17,
+ "text": [
+ "'ttmaqoehqveytelettkf vfantitlnhhttprnmews qrfwquefetnntmmohpun kit a murvsoegeomjsemwelpo yotkcoytmamvgijtrhtk pcrnydlmautreryrciti man rtxrnmzeatctythrycyb obmmisfdefehefyfnvws note neetztxkrqcavlennemh tmeemnnfvulnntneyfrt a ohtmohynxzxnzontunrg hubfooznoirdgkiztyqh eodatncnenpgyccwfmai masxprjmmtoktyiheouf jnteemaetxhkyutyaatk ausmmfuccthuyotxmltn tythhtootmnrnusztdsm mkoiyftfhharuianbwha eixyetfoatnboksfiheu ywcrthbfulmrsaetmlpu ymhtryamodjafttteaff ttnwmqtfzwbhntypttmt are dldnrkyhsacctyerfamm tgomtxzumtmtmtanskid jfpoxhothtoaixvidebb ooeryykftffteelratyh qyurtemtvgeutvsfrheo tcmhyalioyuemnefknco trqiitaonfouaywtmmoo that eu uhpyhnzxttontfaaxnsn of lioptnrtovvxzdiepexy not etielahfcariewfhwwh yiwlvmuudehtbbfvvawi ytkbozvrifacjsiwaagb let now oalfdorpballhktfqtku at hvnxwllfmodtqsluhycv lhiyytejglmrzfdemded dweicjqisrnvlmhudewl kb dgcmklhyhkjalxdosdtq in lfs gewambhlohuwejeltww tbjgeudeucklmxatceud same rpfmjtqmorshwzndcgkm zhieuzasrsoekzgbtbqh rktiwgcavlneuqrzhibj leljrlayqybgewqgrkth wlpiyshwfsillxicblls ebwhcyfkrvmhiomkotdo iwmfahyniseiszwfshlt cxrmaotikmnuachvqdov mmatibzhfoadvloawzke nqrcnrgbxrwfdkctsxxa iejzlnerryfidtbwhxbd hluhtfswwnclgdtwwdvk lhazobmargsgwwtopasg men rl on york rodr keewdeelujkdlellythc wexcfrqxagrdiyunehkn ikfruurtslqbdjrcvujp kxghfbldgmistthujsot bids iwcdsiajaqmanpigbcp zoxlurhnlgrtfifiguor gsm mntfcwavanbilwysatnl mvpnafbhzhwxnrzirhra'"
+ ]
+ }
+ ],
+ "prompt_number": 17
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "len(sanitise(c7b))"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 18,
+ "text": [
+ "1304"
+ ]
+ }
+ ],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
return ''.join([''.join(l)
for l in zip_longest(*split_text, fillvalue='')])
+def chunks(text, n, fillvalue=None):
+ """Split a text into chunks of n characters
+
+ >>> chunks('abcdefghi', 3)
+ ['abc', 'def', 'ghi']
+ >>> chunks('abcdefghi', 4)
+ ['abcd', 'efgh', 'i']
+ >>> chunks('abcdefghi', 4, fillvalue='!')
+ ['abcd', 'efgh', 'i!!!']
+ """
+ if fillvalue:
+ padding = fillvalue[0] * (n - len(text) % n)
+ else:
+ padding = ''
+ return [(text+padding)[i:i+n] for i in range(0, len(text), n)]
+
def transpose(items, transposition):
"""Moves items around according to the given transposition
transposed[t] = items[p]
return transposed
-
-
def deduplicate(text):
return list(collections.OrderedDict.fromkeys(text))
-
def caesar_encipher_letter(letter, shift):
"""Encipher a letter, given a shift amount
transpositions = tuple(key.index(l) for l in sorted(key))
return transpositions
-def column_transposition_encipher(message, keyword, fillvalue=' '):
+def column_transposition_encipher(message, keyword, fillvalue=' ',
+ columnwise=False):
"""Enciphers using the column transposition cipher.
Message is padded to allow all rows to be the same length.
'hleolteher'
>>> column_transposition_encipher('hellothere', 'cleverly', fillvalue='!')
'hleolthre!e!'
+ >>> column_transposition_encipher('hellothere', 'clever', columnwise=True)
+ 'htleehoelr'
"""
- return column_transposition_worker(message, keyword, encipher=True,
- fillvalue=fillvalue)
+ transpositions = transpositions_of(keyword)
+ columns = every_nth(message, len(transpositions), fillvalue=fillvalue)
+ transposed_columns = transpose(columns, transpositions)
+ if columnwise:
+ return ''.join(transposed_columns)
+ else:
+ return combine_every_nth(transposed_columns)
-def column_transposition_decipher(message, keyword, fillvalue=' '):
+def column_transposition_decipher(message, keyword, fillvalue=' ',
+ columnwise=False):
"""Deciphers using the column transposition cipher.
Message is padded to allow all rows to be the same length.
'hellothere'
>>> column_transposition_decipher('hleolthre!e!', 'cleverly', fillvalue='?')
'hellothere!!'
- """
- return column_transposition_worker(message, keyword, encipher=False,
- fillvalue=fillvalue)
-
-def column_transposition_worker(message, keyword,
- encipher=True, fillvalue=' '):
- """Does the actual work of the column transposition cipher.
- Message is padded with spaces to allow all rows to be the same length.
-
- >>> column_transposition_worker('hellothere', 'clever')
- 'hleolteher'
- >>> column_transposition_worker('hellothere', 'clever', encipher=True)
- 'hleolteher'
- >>> column_transposition_worker('hleolteher', 'clever', encipher=False)
+ >>> column_transposition_decipher('htleehoelr', 'clever', columnwise=True)
'hellothere'
"""
transpositions = transpositions_of(keyword)
- columns = every_nth(message, len(transpositions), fillvalue=fillvalue)
- if encipher:
- transposed_columns = transpose(columns, transpositions)
+ if columnwise:
+ columns = chunks(message, int(len(message) / len(transpositions)))
else:
- transposed_columns = untranspose(columns, transpositions)
- return combine_every_nth(transposed_columns)
+ columns = every_nth(message, len(transpositions), fillvalue=fillvalue)
+ untransposed_columns = untranspose(columns, transpositions)
+ return combine_every_nth(untransposed_columns)
+
def vigenere_encipher(message, keyword):
"""Vigenere encipher
pairs = zip(message, cycle(shifts))
return ''.join([caesar_decipher_letter(l, k) for l, k in pairs])
+beaufort_encipher=vigenere_decipher
+beaufort_decipher=vigenere_encipher
if __name__ == "__main__":
import collections
import norms
import logging
-from itertools import zip_longest, cycle
+from itertools import zip_longest, cycle, permutations
from segment import segment, Pwords
from multiprocessing import Pool
+from math import log10
+
+import matplotlib.pyplot as plt
from cipher import *
letter_frequencies = frequencies
+def bigram_likelihood(bigram, bf, lf):
+ return bf[bigram] / (lf[bigram[0]] * lf[bigram[1]])
+
def caesar_break(message,
metric=norms.euclidean_distance,
sanitise(scytale_decipher(message, best_key))[:50]))
return best_key, best_fit
-def column_transposition_break(message,
- translist=transpositions,
- metric=norms.euclidean_distance,
- target_counts=normalised_english_bigram_counts,
- message_frequency_scaling=norms.normalise):
- """Breaks a column transposition cipher using a dictionary and
- n-gram frequency analysis
-
- >>> column_transposition_break(column_transposition_encipher(sanitise( \
- "It is a truth universally acknowledged, that a single man in \
- possession of a good fortune, must be in want of a wife. However \
- little known the feelings or views of such a man may be on his \
- first entering a neighbourhood, this truth is so well fixed in the \
- minds of the surrounding families, that he is considered the \
- rightful property of some one or other of their daughters."), \
- 'encipher'), \
- translist={(2, 0, 5, 3, 1, 4, 6): ['encipher'], \
- (5, 0, 6, 1, 3, 4, 2): ['fourteen'], \
- (6, 1, 0, 4, 5, 3, 2): ['keyword']}) # doctest: +ELLIPSIS
- ((2, 0, 5, 3, 1, 4, 6), 0.0628106372...)
- >>> column_transposition_break(column_transposition_encipher(sanitise( \
- "It is a truth universally acknowledged, that a single man in \
- possession of a good fortune, must be in want of a wife. However \
- little known the feelings or views of such a man may be on his \
- first entering a neighbourhood, this truth is so well fixed in the \
- minds of the surrounding families, that he is considered the \
- rightful property of some one or other of their daughters."), \
- 'encipher'), \
- translist={(2, 0, 5, 3, 1, 4, 6): ['encipher'], \
- (5, 0, 6, 1, 3, 4, 2): ['fourteen'], \
- (6, 1, 0, 4, 5, 3, 2): ['keyword']}, \
- target_counts=normalised_english_trigram_counts) # doctest: +ELLIPSIS
- ((2, 0, 5, 3, 1, 4, 6), 0.0592259560...)
- """
- best_transposition = ''
- best_fit = float("inf")
- ngram_length = len(next(iter(target_counts.keys())))
- for transposition in translist.keys():
- if len(message) % len(transposition) == 0:
- plaintext = column_transposition_decipher(message, transposition)
- counts = message_frequency_scaling(frequencies(
- ngrams(sanitise(plaintext), ngram_length)))
- fit = metric(target_counts, counts)
- logger.debug('Column transposition break attempt using key {0} '
- 'gives fit of {1} and decrypt starting: {2}'.format(
- translist[transposition][0], fit,
- sanitise(plaintext)[:50]))
- if fit < best_fit:
- best_fit = fit
- best_transposition = transposition
- logger.info('Column transposition break best fit with key {0} gives fit '
- 'of {1} and decrypt starting: {2}'.format(
- translist[best_transposition][0],
- best_fit, sanitise(
- column_transposition_decipher(message,
- best_transposition))[:50]))
- return best_transposition, best_fit
-
def column_transposition_break_mp(message,
translist=transpositions,
translist={(2, 0, 5, 3, 1, 4, 6): ['encipher'], \
(5, 0, 6, 1, 3, 4, 2): ['fourteen'], \
(6, 1, 0, 4, 5, 3, 2): ['keyword']}) # doctest: +ELLIPSIS
- ((2, 0, 5, 3, 1, 4, 6), 0.0628106372...)
+ (((2, 0, 5, 3, 1, 4, 6), False), 0.0628106372...)
>>> column_transposition_break_mp(column_transposition_encipher(sanitise( \
"It is a truth universally acknowledged, that a single man in \
possession of a good fortune, must be in want of a wife. However \
(5, 0, 6, 1, 3, 4, 2): ['fourteen'], \
(6, 1, 0, 4, 5, 3, 2): ['keyword']}, \
target_counts=normalised_english_trigram_counts) # doctest: +ELLIPSIS
- ((2, 0, 5, 3, 1, 4, 6), 0.0592259560...)
+ (((2, 0, 5, 3, 1, 4, 6), False), 0.0592259560...)
"""
ngram_length = len(next(iter(target_counts.keys())))
with Pool() as pool:
- helper_args = [(message, trans, metric, target_counts, ngram_length,
+ helper_args = [(message, trans, columnwise, metric, target_counts, ngram_length,
message_frequency_scaling)
- for trans in translist.keys()]
+ for trans in translist.keys() for columnwise in [True, False]]
# Gotcha: the helper function here needs to be defined at the top level
# (limitation of Pool.starmap)
breaks = pool.starmap(column_transposition_break_worker, helper_args, chunksize)
return min(breaks, key=lambda k: k[1])
+column_transposition_break = column_transposition_break_mp
-def column_transposition_break_worker(message, transposition, metric, target_counts,
+def column_transposition_break_worker(message, transposition, columnwise, metric, target_counts,
ngram_length, message_frequency_scaling):
- plaintext = column_transposition_decipher(message, transposition)
+ plaintext = column_transposition_decipher(message, transposition, columnwise=columnwise)
counts = message_frequency_scaling(frequencies(
ngrams(sanitise(plaintext), ngram_length)))
fit = metric(target_counts, counts)
'gives fit of {1} and decrypt starting: {2}'.format(
transposition, fit,
sanitise(plaintext)[:50]))
- return transposition, fit
+ return (transposition, columnwise), fit
+
+
+def transposition_break_exhaustive(message):
+ best_transposition = ''
+ best_pw = -float('inf')
+ for keylength in range(1, 21):
+ if len(message) % keylength == 0:
+ for transposition in permutations(range(keylength)):
+ for columnwise in [True, False]:
+ plaintext = column_transposition_decipher(message,
+ transposition, columnwise=columnwise)
+ # pw = Pwords(segment(plaintext))
+ pw = sum([log10(bigram_likelihood(b,
+ normalised_english_bigram_counts,
+ normalised_english_counts))
+ for b in ngrams(plaintext, 2)])
+ logger.debug('Column transposition break attempt using key {0} {1} '
+ 'gives fit of {2} and decrypt starting: {3}'.format(
+ transposition, columnwise, pw,
+ sanitise(plaintext)[:50]))
+ if pw > best_pw:
+ best_transposition = transposition
+ best_columnwise = columnwise
+ best_pw = pw
+ return (best_transposition, best_columnwise), best_pw
+
def vigenere_keyword_break(message,
wordlist=keywords,
vigenere_decipher(message, best_key))[:50]))
return best_key, best_fit
+def beaufort_frequency_break(message,
+ metric=norms.euclidean_distance,
+ target_counts=normalised_english_counts,
+ message_frequency_scaling=norms.normalise):
+ """Breaks a Beaufort cipher with frequency analysis
+
+ >>> vigenere_frequency_break(vigenere_encipher(sanitise("It is time to " \
+ "run. She is ready and so am I. I stole Daniel's pocketbook this " \
+ "afternoon when he left his jacket hanging on the easel in the " \
+ "attic."), 'florence')) # doctest: +ELLIPSIS
+ ('florence', 0.077657073...)
+ """
+ best_fit = float("inf")
+ best_key = ''
+ sanitised_message = sanitise(message)
+ for trial_length in range(1, 20):
+ splits = every_nth(sanitised_message, trial_length)
+ key = ''.join([chr(caesar_break(s, target_counts=target_counts)[0] + ord('a')) for s in splits])
+ plaintext = beaufort_decipher(sanitised_message, key)
+ counts = message_frequency_scaling(frequencies(plaintext))
+ fit = metric(target_counts, counts)
+ logger.debug('Beaufort key length of {0} ({1}) gives fit of {2}'.
+ format(trial_length, key, fit))
+ if fit < best_fit:
+ best_fit = fit
+ best_key = key
+ logger.info('Beaufort break best fit with key {0} gives fit '
+ 'of {1} and decrypt starting: {2}'.format(best_key,
+ best_fit, sanitise(
+ beaufort_decipher(message, best_key))[:50]))
+ return best_key, best_fit
+
+
+
+def plot_frequency_histogram(freqs, sort_key=None):
+ x = range(len(freqs.keys()))
+ y = [freqs[l] for l in sorted(freqs.keys(), key=sort_key)]
+ f = plt.figure()
+ ax = f.add_axes([0.1, 0.1, 0.9, 0.9])
+ ax.bar(x, y, align='center')
+ ax.set_xticks(x)
+ ax.set_xticklabels(sorted(freqs.keys(), key=sort_key))
+ f.show()
if __name__ == "__main__":
projects / cipher-tools.git / commitdiff