{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import os,sys,inspect\n",
"import string\n",
"%matplotlib inline\n",
"\n",
- "from cipherbreak import *\n",
+ "from cipher.caesar import *\n",
+ "from cipher.affine import *\n",
+ "from cipher.column_transposition import *\n",
+ "from cipher.keyword_cipher import *\n",
+ "from cipher.vigenere import *\n",
+ "from support.utilities import *\n",
+ "from support.text_prettify import *\n",
+ "from support.language_models import *\n",
+ "from support.plot_frequency_histogram import *\n",
+ "\n",
"with open('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",
+ " \n",
"c6a = open('6a.ciphertext').read()\n",
"c6b = open('6b.ciphertext').read()"
]
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"c1a = open('1a.ciphertext').read()\n",
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 3,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x7f86015e3f28>"
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7f5d44974358>"
]
},
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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yN7hfkfRR4Crgv1QqGBEfBz4u6c8i4j112i33GeBhSfenvr0T+KykU8jORJ6X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\nzAocGMzMrOD/A5ZV4vqjDJn1AAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f8601638208>"
+ "<matplotlib.figure.Figure at 0x7f5d44979748>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 4,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x7f8601425f60>"
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7f5d05a43be0>"
]
},
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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yN7hfkfRR4Crgv1QqGBEfBz4u6c8i4j112i33GeBhSfenvr0T+KykU8jORJ6X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\nzAocGMzMrOD/A5ZV4vqjDJn1AAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f86014ba6d8>"
+ "<matplotlib.figure.Figure at 0x7f5d03f1d748>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 5,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x7f8601372080>"
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7f5d03e91128>"
]
},
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f860137cc88>"
+ "<matplotlib.figure.Figure at 0x7f5d03e1bfd0>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 6,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x7f86012ed668>"
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7f5d03dc10b8>"
]
},
- "execution_count": 8,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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yN7hfkfRR4Crgv1QqGBEfBz4u6c8i4j112i33GeBhSfenvr0T+KykU8jORJ6X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\nzAocGMzMrOD/A5ZV4vqjDJn1AAAAAElFTkSuQmCC\n",
+ "image/png": "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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f86013413c8>"
+ "<matplotlib.figure.Figure at 0x7f5d03dce5f8>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 7,
+ "metadata": {},
"outputs": [
{
"data": {
"'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'"
]
},
- "execution_count": 9,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 8,
+ "metadata": {},
"outputs": [
{
"data": {
"1573"
]
},
- "execution_count": 10,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 9,
+ "metadata": {},
"outputs": [],
"source": [
"c6as = sanitise(c6a)"
},
{
"cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 10,
+ "metadata": {},
"outputs": [
{
"data": {
" 'zz': 6})"
]
},
- "execution_count": 12,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 11,
+ "metadata": {},
"outputs": [
{
"data": {
"'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'"
]
},
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 12,
+ "metadata": {},
"outputs": [],
"source": [
"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'}"
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 39,
+ "metadata": {},
"outputs": [
{
"data": {
"'PARISHGCOMNDTUVWYZBEFJLQ'"
]
},
- "execution_count": 15,
+ "execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "''.join(sorted(trans.keys(), key=lambda k: trans[k]))"
+ "cat(sorted(trans.keys(), key=lambda k: trans[k]))"
]
},
{
"cell_type": "code",
"execution_count": 41,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "'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'"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "harry good call on the nazi paper trail the attached is a memo from paris high command to goering s\n",
+ "secretary referring to the painting and clearly mentioning sara seems like they knew about the scam\n",
+ "our friendly ss officer was planning to pull they picked up her trail the night after she went\n",
+ "missing but you can read about that in their report still no joy on getting access to the original\n",
+ "miss mona in the gallery and i am beginning to think we might need to rollout a black bag job you\n",
+ "want to do it or shall i by the way i was surprised by this report the other papers we have from the\n",
+ "nazis at this level are encrypted using bifid or playfair style ciphers this one isnt maybe the\n",
+ "cipher clerk was off duty that day we got lucky\n"
+ ]
}
],
"source": [
- "' '.join(segment(keyword_decipher(c6as, 'parishighcommand', KeywordWrapAlphabet.from_largest)))"
+ "print(prettify(keyword_decipher(c6as, 'parishighcommand', KeywordWrapAlphabet.from_largest)))"
]
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 15,
+ "metadata": {},
"outputs": [],
"source": [
"c6bs = sanitise(c6b)"
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 16,
+ "metadata": {},
"outputs": [
{
"data": {
"1573"
]
},
- "execution_count": 18,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 17,
+ "metadata": {},
"outputs": [],
"source": [
"from itertools import permutations"
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 18,
+ "metadata": {},
"outputs": [
{
"data": {
" (3, 2, 1, 0)]"
]
},
- "execution_count": 20,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 19,
+ "metadata": {},
"outputs": [
{
"data": {
" 'htihrfnhtreffoaeantfytlsrrrohtqeesmirluresfheeatwiuibhistrrnioisineirmhrdtypieotticrbnlahnprohoteilrhstwpsettosounyoerhwchhdmciptoonsahajithenaerfotsuenmgartruxlwotbtaepetrartpeseahtensirhtsrnnrtsodlfuahgesgmkaesitnutyhauyntnntngihvwelwpaifersaiaflpabepcmcehalothtswhldaluvuesoeaeueatetathefethblghsriltaeecanrthaaghteisnnfueruplerhmttiiydueunnocigdneuaieeeushnthlthaoiaeidfynuainaneoeyitehofuladeorauhyrrearretaaihatgnowsiwtiastlubxmoaoteltoernotorooacbgtpiaveelnryfwoeatwudartewlntrpfprleiamirlortnqeiwucerdnrafatotorntelernephsmpeddnwedtofehdepysnnegeenetatenaetresuldgrsoeorubgdhwaprbynehketttrernhrrdenfstloaiswpyinmhtocouoieytopeerkhnatnntlriuieenmerpatiaeohnhhomuuosvktdeneioeekwpdtgeioiiihnesafuuuebnrhtihreaheeiayausursagwisisdlrtihpfgwtliisiiusehormrecamiosiedvluleoriasoediifntboseastngfoprdosriftatlprtenoiylytossseoptnfiaehoaegeiarmdtieaatyhrgrrsalefceltrenaehevhhmvissanoreimlxitjsposetrmbfidnhehcotbncocntsupmeosenethifteooneownteetotlirdgioemsofiohorlmdffeseutelfoiiwrbdqyttozafrnidtntnogealahsdpnmaeplhttbnshaouplaanmelsrdpanvhleaiachnksilodcatdeaohgjdaeiyawgtednehneueegeeoerondfryymetcnwttedgtusattooomywfgrhieohrrnisfoarfteropnesiaysseshhntueyawihletcpautanaepeeulhawlleoaseruiibnasaeaetecdcgerosorspesnesovldudeltistietesftnseoignlreonietnwwiethwlsriehliirsdtsednrhfrgltnausradestyoddasanetuctiwemantretseoptotyyncoostuumcgapldaeldetodbsdfelefxahanosritdirpieihsascdcbpihsleilanlfrohtnerrotameiagalkmterwcdryansvswndlunkeronntuituonnuiinvholieetlheanfiutaattfltecbrnngebhnsolmnoaokfinoranvnsterftfcrtedhswspepcutwcrwaopadhfosiyogaonleagifdtrgntlbefgll o']"
]
},
- "execution_count": 21,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 20,
+ "metadata": {},
"outputs": [
{
"data": {
"'hithhnfrferteaofftnasltyorrreqthimserulrhfsetaeeiuiwsihbnrrtsioiienirhmrpytdtoeircitalnbrpnhtohorliewtshtesposotoynuwhredhhcpicmnootahashtijeanetofrneusragmxurttowleatbrtepptraaesenethhrisnrststrnfldoghaumgseseakuntiahyttnyuntnnvhigwlewfiapasrelfaiebapcmcplahethtolhwsuladseuveaeotaeutateefehlbhtrshgatliaceehtrnhgaasietufnnpurehrelittmudyinnuegicouendeeiahsuelhtnoahtieainyfdniauoenatiyefohedaluaroeryhuraeraterahiaongtwiswsaitbultaomxletoreototonaoortgbcvaipnleewfyrtaeoaduwwetrrtnlrpfpaiellrimntrowieqrecuarndotafnroteletpenrpmshnddetdewhefoypedennsneegtateeanesertgdlueosrburowhdgbrpahenyttekrertrrhnfnedoltswsianiypothmouoctyeieeponhkrnntairlteeiuremnitaphoeaohhnouumtkvsenedeeoidpwkiegtiiiosenhuufanbeuithraerhieehuayasrusiwgadsisitrlgfphiltwiisihesurmromaceisoilvdeoelusairideotnfiesobntsapofgsodrtfirpltanetrlyiosotyoessfntpheaigeaoraieitdmtaaegrhyasrrcfelrtleeanehvehivmhnassieroixlmpsjttesofbmrhndiochecnbttncompusesoehtenetfienootnwooteeriltoigdosmehoifmlroeffdtuesoflerwiiyqdbzottnrfantdigontalaepdsheamnthlpsnbtuoahaalplemnpdrshvnaiaelnhcaliskacdoaedtjghoieadgwayndetenhegeeueoeednoryyrfctemttwntgdetasuoootfwymihrgrhoesinrraofretfenpoyaissesstnhhayeulhiwpcteatuapeanlueelwahaoeluresnbiieasaeteagcdcsorepsroenselvosedudsitlteittfseoesnlnginoernteieiwwlwhteirsiilhtdsrndesrfhrntlgrsuasedadoytasadutenwitcnametertpoesytotocnyutsogcmudlpadleadotefdsbfeleahaxrsonidtieiprashicdcshipbielslnalhorfrentatoraiemklagretmrdcwsnaynwsvnuldorekutnnoutiiunnhvnieilohltefnaeatuifttacetlnnrbhbeglosnaonmifkoaronsnvnfretrcfthdetpswsucpercwtpoawfhdayisooagoaelndfigngrtebltllgfo'"
]
},
- "execution_count": 22,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 21,
+ "metadata": {},
"outputs": [
{
"data": {
"'hit'"
]
},
- "execution_count": 23,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 22,
+ "metadata": {},
"outputs": [
{
"data": {
"28"
]
},
- "execution_count": 24,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 23,
+ "metadata": {},
"outputs": [
{
"data": {
"1.6083844580777096"
]
},
- "execution_count": 25,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 24,
+ "metadata": {},
"outputs": [
{
"data": {
"'bnrrt'"
]
},
- "execution_count": 26,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 25,
+ "metadata": {},
"outputs": [
{
"data": {
"[('heih', 177), ('heit', 207), ('heip', 307), ('heil', 522)]"
]
},
- "execution_count": 27,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 26,
+ "metadata": {},
"outputs": [
{
"data": {
" 'ltllgfo']"
]
},
- "execution_count": 28,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 27,
+ "metadata": {},
"outputs": [
{
"data": {
"'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'"
]
},
- "execution_count": 29,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 28,
+ "metadata": {},
"outputs": [
{
"data": {
"[29, 503, 985]"
]
},
- "execution_count": 30,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 29,
+ "metadata": {},
"outputs": [
{
"data": {
"'hithhnfrfer'"
]
},
- "execution_count": 31,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 30,
+ "metadata": {},
"outputs": [
{
"data": {
"'hithhnfrferte'"
]
},
- "execution_count": 32,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 31,
+ "metadata": {},
"outputs": [
{
"data": {
"'hhrnrnumeodti'"
]
},
- "execution_count": 33,
+ "execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 32,
+ "metadata": {},
"outputs": [
{
"data": {
"'harrseehthoexttnsneitetghuydoihuouocttiuedeeerrsohiaeefhifsiitiohtreiehseiiszomhhihtdtoeentencsllilrdieodhpbrlakhntnsdwon'"
]
},
- "execution_count": 34,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 33,
+ "metadata": {},
"outputs": [
{
"data": {
"True"
]
},
- "execution_count": 35,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 34,
+ "metadata": {},
"outputs": [
{
"data": {
" 'rliehetbisdtnaeailstiscouareerndtytuenboolalienreneudtfaohepueiarriwrvibdeoidsehlbcsftoologhbeikhdefdwsfshphicedsolhheueocdetdltanoiltnnocuwggo']"
]
},
- "execution_count": 36,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 35,
+ "metadata": {},
"outputs": [
{
"data": {
" 'nabursgfhno']"
]
},
- "execution_count": 37,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 36,
+ "metadata": {},
"outputs": [
{
"data": {
"[(29, 37), (503, 507), (985, 973)]"
]
},
- "execution_count": 38,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 37,
+ "metadata": {},
"outputs": [
{
"data": {
" 'erlwionhwonmreretlaesahnpdnnnohabebrrrctnptubetmneesiursgeornfipisnstcuiroebgaaslgeetooftcesgntneihawosaaipfknenfesnhcsgo']"
]
},
- "execution_count": 39,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 38,
+ "metadata": {},
"outputs": [
{
"data": {
" 'tfngrebltllgo']"
]
},
- "execution_count": 40,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": []
}
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.4.3"
+ "version": "3.6.3"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
projects / cipher-tools.git / blobdiff