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```
#### Expect
```
-size exec file contents
- ./
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-5566017 count_2w.txt 781c0596c3eea532d30bef9f3dba1d5137d652f00376260822c761a7584dfb8c
-220441 count_3l.txt 8702c95530c7d0d182ab94dc03ed7681fcf969819f6db011a58de31411dc6365
-320508 count_big.txt 3ba257fba1934bd138413d8274e79b56c5992431a27692fd562929aa43ec01a3
-3355 find_best_affine_break_parameters.py 6b11004bb93ac26ec7d42d33504e758edbaf9d55365ae2e4ca2fca7589263f25
-3027 find_best_caesar_break_parameters.py 0347d80309179d937a88fd1c8684490a513ccd086366c5a0dd55b8a2fe5c565f
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-5645 language_models.py bfd5b60cdef8af20cdb061b24a1691f569984be3be333782c3d76e3370e16d14
-368 lettercount.py ed36497d62cf75b91994055e4a18848b2fabe5ce793cd76a77fabfc94d81d4f3
-592 make-cracking-dictionary.py 71791e64e4853cd9ca292cb436bbe8c72dd60f509811174df93ed2067683d5c1
-7077 norms.py a657a36c1741e6f3a513386b318fcc99e6b11f98ec64a48284b47462ff2acf30
-8411 norms.pyc ac7a18765c7bcc27e406d8f38d943408097b3384a271502185d53482e6ec0da7|002b186e716cec64869a00bd2d72e16614931e696daa0cf3529d634a0f270e42
-112847 plot-caesar-parameters.ipynb 639459b4b2e434f9f0852c012ed9a8a8d87bd1cb6c2d65ca5abfdb0e42c3dea6
-881 segment.py 94d257cc6151861ef3d3033c4d2d03d8c121b0a982344abf400f65fd507fed28
-4538523 shakespeare.txt 6f9c770efced5c3d87efa6197cd3091b982341372e36c6357f865df91ddecde6
-592309 sherlock-holmes.txt 0027de6f4110440ea51d67a2f3af3484898c630808f13b1d4db108e6283e67a3|2034ee1ebdec47e839607124d22b674d4614e1cc6209d758f7b6e99e69ae8e08
- slides/
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-3320 aims.html a45e8f4b0cc2987656a1b008351e089dcdc2a717aa1b6aa540bf4e8a4435fe17
-6835 alternative-plaintext-scoring.html c313d095c04faa6dae6c57fc20dd5b94f08746c4e8352c73178f335328a8a02d
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-3793 caesar-encipher.html 66d4b2d96571a9d7edb10f79d176327efbc3923af2e19c7f61800eea376716e3
-7860 caesarwheel1.gif fd5a9d2d8ef261b0b7ec4536fc56a27ebec94ac91ff2ca136b74d5c066f3ecce|4c536ee4df63c73c2e5e213ca1f96c2fea0bc058efe991f78fd304ff215e6fcd
-8200 english_frequency_histogram.png c4a1afec62d2fcda15824f71df3c997088c4a952ee5fe0bc6fa753ede08b2098|6eabd4ebdc616722c9fe3a419e8ca5fc309452120e5023b2e5cb783b9d6d2df8
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-2714 further-work.html 66339213b16c1a98e4afc5c1ec4878bb4839c43ae963e56dd4be624fc60b8895
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-2870 index.html f6c2cbaaaeb43db84e9e1bbd2654c31103e22ae3bdf683bfe6d6719a074c9024
-5312 keyword-break.html 3217d573ee8ce0ce73e57d23122dec2de39088d006eb0af16d14405361e99d6c
-5456 keyword-encipher.html d8d54fc7ebc88b52bade6c3da2ba81bceb110beeb1a82ff5ffd3fa2593ecdab8
-2989 pocket-enigma-break.html aff9bd947680764ebc7fbf7e62cee948f290b1b81f4c2c2f819734ed24c34da1
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-409764 pocket-enigma.jpg aeb79ca953b7fac863340d71f675a50029687aa97bae8af4068927414aeec96c|0fd2d5b1683451ecee1a6501c4ccfa311293e15bcdb063314776493e5746a04b
-3460 transposition-break.html 5d3df37e584ac2b0cf2a6b49cc9276bf0964cf4290bda35e808467c12224b2ea
-5758 transposition-encipher.html 2c1c3e64dc875de00a481eefe782fd1a812a9035a10a27e993ccf9414e087762
-52398 typingmonkeylarge.jpg 04eba431d38b1d9b5a89065c6a6571e28cddb7052ade379b532d4475ab99cf4a|1357b8bbc859ae4487dac3e431463a2df0ac46851baf24e2811a70bc3b11100c
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-451530 spell-errors.txt a4abe6ce6c24280f9a8d0485cbf78ddd2e58279ca01293692630a08ba4b13407
-45378 unknown-word-probability-investigation.ipynb a866c807557980091a284bec04ad49a22c3ff14554ea6dca3edd71157318d52a
-3291641 war-and-peace.txt 3ed0f41cfdf660846878943bad5b9d575bcae1e4a92ee9a7f43d3c9dba2af344|6799e48d3fd0a6f4c40b9951ec86de6da81f0b9cd36e413490ac511542ca54d3
-868202 words.txt aa77abbcba3c6dee1306d93adcedc2b2ccb8a4e0344a39d0676732ff58ebd5e5
-868384 words_2013.txt 57faa4841fe28dd82a5da4488b6381c194df6e1ecc04e61fb9f60e842bbca18c
+size exec file contents
+ ./
+384 .gitignore a93de2ae5c2a47a38599751d1f914566569dfa09dd1778e207117db6c71421dd
+ 2012/
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+3323 1b.plaintext a7fde879259e64fccca52618a9c008f2111e485ed1c6bfad863c90d8e6ceacae
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+ 2013/
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+1495 1b.plaintext 717b1f8955515a5d5d2f049f0ffe137820d71f8b2a1689e1a29ec30ed7c9d473
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+1565 7b.ciphertext adeef9d9e985c534c40acee5c6734d04195895a32efd9452a89cafef1fdc91b8
+182 mona-lisa-words.txt d7d05c9c86f6282fa66df5f4ca795c89f01cef88a9ce8c921ce4484b3d6078a7
+1190 solutions.txt bd14d8cb1b6694ee4b07d7f38f4df7931e89c10247f101878b6056647bf67cee
+ 2014/
+ 2015/
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+2195 sol.py.zip 4c22e57eaacd8a91ef9b0c338429e400210a03dec9fbacec4a45fe1faed50720|1fd0a62af53125eacfa11016088451e2c58269d98aaef74c320730ebc7601b66
+18025 LICENSE a01259a1b522cf0de95824f9860613b453153eebac468e96196d5d7dba84786c
+429647 Programming+for+A+level+Computing+through+automated+cipher+breaking.pdf 2f4008ad7217d7902c99bbfc7394c9b9128a3073f4c5d096ef4653d753358b9d|6c1eb93f74c8e3fd5a180e80ce7b315cb642a9f5f66805a4398f6b793dd9b852
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+6357 course-blurb.docx b363ea532c9a12f47e7282ddfcb2a92d72ab6d62d669ea2e5fe31d864486f2f8|dfadc44f8ebac5145acd01156029539bc1e2b697d461ddd9e042f1ce9cc74bbd
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+4538523 shakespeare.txt 6f9c770efced5c3d87efa6197cd3091b982341372e36c6357f865df91ddecde6
+592309 sherlock-holmes.txt 0027de6f4110440ea51d67a2f3af3484898c630808f13b1d4db108e6283e67a3|2034ee1ebdec47e839607124d22b674d4614e1cc6209d758f7b6e99e69ae8e08
+ slides/
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+2989 pocket-enigma-break.html aff9bd947680764ebc7fbf7e62cee948f290b1b81f4c2c2f819734ed24c34da1
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+3460 transposition-break.html 5d3df37e584ac2b0cf2a6b49cc9276bf0964cf4290bda35e808467c12224b2ea
+5758 transposition-encipher.html 2c1c3e64dc875de00a481eefe782fd1a812a9035a10a27e993ccf9414e087762
+52398 typingmonkeylarge.jpg 04eba431d38b1d9b5a89065c6a6571e28cddb7052ade379b532d4475ab99cf4a|1357b8bbc859ae4487dac3e431463a2df0ac46851baf24e2811a70bc3b11100c
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+8918 word-segmentation.html f39cb54c31412c00c5bdeadcc235f261663596293ecb639921d6dc79ac1fafa8
+451530 spell-errors.txt a4abe6ce6c24280f9a8d0485cbf78ddd2e58279ca01293692630a08ba4b13407
+3291641 war-and-peace.txt 3ed0f41cfdf660846878943bad5b9d575bcae1e4a92ee9a7f43d3c9dba2af344|6799e48d3fd0a6f4c40b9951ec86de6da81f0b9cd36e413490ac511542ca54d3
+868202 words.txt aa77abbcba3c6dee1306d93adcedc2b2ccb8a4e0344a39d0676732ff58ebd5e5
+868384 words_2013.txt 57faa4841fe28dd82a5da4488b6381c194df6e1ecc04e61fb9f60e842bbca18c
```
#### Ignore
+++ /dev/null
-"name",500,250,100,50,20\r
-"Pletters",2442,2425,2550,2461,2067\r
-"cosine_similarity + euclidean_scaled",2487,2492,2513,2455,1731\r
-"cosine_similarity + normalised",2503,2573,2454,2435,1680\r
-"l1 + euclidean_scaled",2495,2477,2506,2419,1812\r
-"l1 + normalised",2488,2521,2465,2466,1895\r
-"l2 + euclidean_scaled",2524,2467,2529,2470,1763\r
-"l2 + normalised",2566,2545,2481,2433,1756\r
-"l3 + euclidean_scaled",2444,2458,2560,2426,1582\r
-"l3 + normalised",2476,2524,2463,2354,1433\r
+++ /dev/null
-"name",100,50,30,20,10,5\r
-"Pletters",4995,4997,4971,4893,4092,2401\r
-"cosine_similarity + euclidean_scaled",4997,4980,4915,4671,3590,2174\r
-"cosine_similarity + normalised",4999,4993,4931,4660,3559,2207\r
-"l1 + euclidean_scaled",4997,4995,4950,4761,3751,2175\r
-"l1 + normalised",4999,4993,4950,4770,3662,2188\r
-"l2 + euclidean_scaled",4997,4991,4914,4664,3579,2209\r
-"l2 + normalised",4998,4988,4915,4637,3536,2195\r
-"l3 + euclidean_scaled",4997,4979,4822,4477,3243,1978\r
-"l3 + normalised",4999,4959,4774,4351,2966,1949\r
+++ /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": 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- "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": 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- "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": 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- "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": 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- "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": 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- "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": 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- "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']"
- ]
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- "prompt_number": 22
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "c6bs"
- ],
- "language": "python",
- "metadata": {},
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- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 23,
- "text": [
- "'hithhnfrferteaofftnasltyorrreqthimserulrhfsetaeeiuiwsihbnrrtsioiienirhmrpytdtoeircitalnbrpnhtohorliewtshtesposotoynuwhredhhcpicmnootahashtijeanetofrneusragmxurttowleatbrtepptraaesenethhrisnrststrnfldoghaumgseseakuntiahyttnyuntnnvhigwlewfiapasrelfaiebapcmcplahethtolhwsuladseuveaeotaeutateefehlbhtrshgatliaceehtrnhgaasietufnnpurehrelittmudyinnuegicouendeeiahsuelhtnoahtieainyfdniauoenatiyefohedaluaroeryhuraeraterahiaongtwiswsaitbultaomxletoreototonaoortgbcvaipnleewfyrtaeoaduwwetrrtnlrpfpaiellrimntrowieqrecuarndotafnroteletpenrpmshnddetdewhefoypedennsneegtateeanesertgdlueosrburowhdgbrpahenyttekrertrrhnfnedoltswsianiypothmouoctyeieeponhkrnntairlteeiuremnitaphoeaohhnouumtkvsenedeeoidpwkiegtiiiosenhuufanbeuithraerhieehuayasrusiwgadsisitrlgfphiltwiisihesurmromaceisoilvdeoelusairideotnfiesobntsapofgsodrtfirpltanetrlyiosotyoessfntpheaigeaoraieitdmtaaegrhyasrrcfelrtleeanehvehivmhnassieroixlmpsjttesofbmrhndiochecnbttncompusesoehtenetfienootnwooteeriltoigdosmehoifmlroeffdtuesoflerwiiyqdbzottnrfantdigontalaepdsheamnthlpsnbtuoahaalplemnpdrshvnaiaelnhcaliskacdoaedtjghoieadgwayndetenhegeeueoeednoryyrfctemttwntgdetasuoootfwymihrgrhoesinrraofretfenpoyaissesstnhhayeulhiwpcteatuapeanlueelwahaoeluresnbiieasaeteagcdcsorepsroenselvosedudsitlteittfseoesnlnginoernteieiwwlwhteirsiilhtdsrndesrfhrntlgrsuasedadoytasadutenwitcnametertpoesytotocnyutsogcmudlpadleadotefdsbfeleahaxrsonidtieiprashicdcshipbielslnalhorfrentatoraiemklagretmrdcwsnaynwsvnuldorekutnnoutiiunnhvnieilohltefnaeatuifttacetlnnrbhbeglosnaonmifkoaronsnvnfretrcfthdetpswsucpercwtpoawfhdayisooagoaelndfigngrtebltllgfo'"
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- "prompt_number": 23
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- {
- "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"
- ]
- }
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- "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)]"
- ]
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- "cell_type": "code",
- "collapsed": false,
- "input": [
- "[c for c in chunks(c6bs, 522)]"
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- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 29,
- "text": [
- "['hithhnfrferteaofftnasltyorrreqthimserulrhfsetaeeiuiwsihbnrrtsioiienirhmrpytdtoeircitalnbrpnhtohorliewtshtesposotoynuwhredhhcpicmnootahashtijeanetofrneusragmxurttowleatbrtepptraaesenethhrisnrststrnfldoghaumgseseakuntiahyttnyuntnnvhigwlewfiapasrelfaiebapcmcplahethtolhwsuladseuveaeotaeutateefehlbhtrshgatliaceehtrnhgaasietufnnpurehrelittmudyinnuegicouendeeiahsuelhtnoahtieainyfdniauoenatiyefohedaluaroeryhuraeraterahiaongtwiswsaitbultaomxletoreototonaoortgbcvaipnleewfyrtaeoaduwwetrrtnlrpfpaiellrimntrowieqrecuarndotafnrotel',\n",
- " 'etpenrpmshnddetdewhefoypedennsneegtateeanesertgdlueosrburowhdgbrpahenyttekrertrrhnfnedoltswsianiypothmouoctyeieeponhkrnntairlteeiuremnitaphoeaohhnouumtkvsenedeeoidpwkiegtiiiosenhuufanbeuithraerhieehuayasrusiwgadsisitrlgfphiltwiisihesurmromaceisoilvdeoelusairideotnfiesobntsapofgsodrtfirpltanetrlyiosotyoessfntpheaigeaoraieitdmtaaegrhyasrrcfelrtleeanehvehivmhnassieroixlmpsjttesofbmrhndiochecnbttncompusesoehtenetfienootnwooteeriltoigdosmehoifmlroeffdtuesoflerwiiyqdbzottnrfantdigontalaepdsheamnthlpsnbtuoahaalplemnpdrshvna',\n",
- " 'iaelnhcaliskacdoaedtjghoieadgwayndetenhegeeueoeednoryyrfctemttwntgdetasuoootfwymihrgrhoesinrraofretfenpoyaissesstnhhayeulhiwpcteatuapeanlueelwahaoeluresnbiieasaeteagcdcsorepsroenselvosedudsitlteittfseoesnlnginoernteieiwwlwhteirsiilhtdsrndesrfhrntlgrsuasedadoytasadutenwitcnametertpoesytotocnyutsogcmudlpadleadotefdsbfeleahaxrsonidtieiprashicdcshipbielslnalhorfrentatoraiemklagretmrdcwsnaynwsvnuldorekutnnoutiiunnhvnieilohltefnaeatuifttacetlnnrbhbeglosnaonmifkoaronsnvnfretrcfthdetpswsucpercwtpoawfhdayisooagoaelndfigngrteb',\n",
- " 'ltllgfo']"
- ]
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- "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]"
- ]
- }
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- "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'"
- ]
- }
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- "prompt_number": 34
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- "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']"
- ]
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- "prompt_number": 38
- },
- {
- "cell_type": "code",
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- "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",
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- " 'siprahicdcshp',\n",
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- " 'nnkutnoutiiun',\n",
- " 'iehvneilohltf',\n",
- " 'acnaetuifttae',\n",
- " 'notlnrbhbegls',\n",
- " 'nonaomifkoarn',\n",
- " 'ntsnvfretrcfh',\n",
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- " '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": [
- "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": 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w+O3J4XDoyJEjSktLk91u91lXWFioxYsXa+LEiTrnnHN0zjnn+Az2FStW6Oyz\nz9a1116rzs5OTZo0SR6PR06n02fPl1xyiRITE7t9/h5//HEdPHhQF198sSTpwgsv1FNPPeV3mr6E\nD+oi7I4ePaqBAweqra1NU6ZM0dNPP628vLxe6eXzzz/v1WNbva22tlbTp0/Xrl27gp520aJFGjRo\nkO64444IdBa7Ojs7NW7cOK1evVrnn39+b7djNHbxIexuueUW5efna9y4cbr66qt7LZwOHDigSZMm\n6a677uqV5ZuiJ7vpomH3bDSpqalRRkaGpk6dSjgFgBEUAMBIjKAAAEYioAAARiKgAABGIqAAAEYi\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": 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- "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": 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- "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": 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- "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": 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- "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": 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- "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
+++ /dev/null
-import string
-import collections
-import math
-from enum import Enum
-from itertools import zip_longest, cycle, chain, count
-import numpy as np
-from numpy import matrix
-from numpy import linalg
-from language_models import *
-
-
-modular_division_table = [[0]*26 for _ in range(26)]
-for a in range(26):
- for b in range(26):
- c = (a * b) % 26
- modular_division_table[b][c] = a
-
-
-def every_nth(text, n, fillvalue=''):
- """Returns n strings, each of which consists of every nth character,
- starting with the 0th, 1st, 2nd, ... (n-1)th character
-
- >>> every_nth(string.ascii_lowercase, 5)
- ['afkpuz', 'bglqv', 'chmrw', 'dinsx', 'ejoty']
- >>> every_nth(string.ascii_lowercase, 1)
- ['abcdefghijklmnopqrstuvwxyz']
- >>> every_nth(string.ascii_lowercase, 26) # doctest: +NORMALIZE_WHITESPACE
- ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
- 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
- >>> every_nth(string.ascii_lowercase, 5, fillvalue='!')
- ['afkpuz', 'bglqv!', 'chmrw!', 'dinsx!', 'ejoty!']
- """
- split_text = chunks(text, n, fillvalue)
- return [''.join(l) for l in zip_longest(*split_text, fillvalue=fillvalue)]
-
-def combine_every_nth(split_text):
- """Reforms a text split into every_nth strings
-
- >>> combine_every_nth(every_nth(string.ascii_lowercase, 5))
- 'abcdefghijklmnopqrstuvwxyz'
- >>> combine_every_nth(every_nth(string.ascii_lowercase, 1))
- 'abcdefghijklmnopqrstuvwxyz'
- >>> combine_every_nth(every_nth(string.ascii_lowercase, 26))
- 'abcdefghijklmnopqrstuvwxyz'
- """
- 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
-
- >>> transpose(['a', 'b', 'c', 'd'], (0,1,2,3))
- ['a', 'b', 'c', 'd']
- >>> transpose(['a', 'b', 'c', 'd'], (3,1,2,0))
- ['d', 'b', 'c', 'a']
- >>> transpose([10,11,12,13,14,15], (3,2,4,1,5,0))
- [13, 12, 14, 11, 15, 10]
- """
- transposed = [''] * len(transposition)
- for p, t in enumerate(transposition):
- transposed[p] = items[t]
- return transposed
-
-def untranspose(items, transposition):
- """Undoes a transpose
-
- >>> untranspose(['a', 'b', 'c', 'd'], [0,1,2,3])
- ['a', 'b', 'c', 'd']
- >>> untranspose(['d', 'b', 'c', 'a'], [3,1,2,0])
- ['a', 'b', 'c', 'd']
- >>> untranspose([13, 12, 14, 11, 15, 10], [3,2,4,1,5,0])
- [10, 11, 12, 13, 14, 15]
- """
- transposed = [''] * len(transposition)
- for p, t in enumerate(transposition):
- transposed[t] = items[p]
- return transposed
-
-def deduplicate(text):
- return list(collections.OrderedDict.fromkeys(text))
-
-
-def caesar_encipher_letter(accented_letter, shift):
- """Encipher a letter, given a shift amount
-
- >>> caesar_encipher_letter('a', 1)
- 'b'
- >>> caesar_encipher_letter('a', 2)
- 'c'
- >>> caesar_encipher_letter('b', 2)
- 'd'
- >>> caesar_encipher_letter('x', 2)
- 'z'
- >>> caesar_encipher_letter('y', 2)
- 'a'
- >>> caesar_encipher_letter('z', 2)
- 'b'
- >>> caesar_encipher_letter('z', -1)
- 'y'
- >>> caesar_encipher_letter('a', -1)
- 'z'
- >>> caesar_encipher_letter('A', 1)
- 'B'
- >>> caesar_encipher_letter('é', 1)
- 'f'
- """
- letter = unaccent(accented_letter)
- if letter in string.ascii_letters:
- if letter in string.ascii_uppercase:
- alphabet_start = ord('A')
- else:
- alphabet_start = ord('a')
- return chr(((ord(letter) - alphabet_start + shift) % 26) +
- alphabet_start)
- else:
- return letter
-
-def caesar_decipher_letter(letter, shift):
- """Decipher a letter, given a shift amount
-
- >>> caesar_decipher_letter('b', 1)
- 'a'
- >>> caesar_decipher_letter('b', 2)
- 'z'
- """
- return caesar_encipher_letter(letter, -shift)
-
-def caesar_encipher(message, shift):
- """Encipher a message with the Caesar cipher of given shift
-
- >>> caesar_encipher('abc', 1)
- 'bcd'
- >>> caesar_encipher('abc', 2)
- 'cde'
- >>> caesar_encipher('abcxyz', 2)
- 'cdezab'
- >>> caesar_encipher('ab cx yz', 2)
- 'cd ez ab'
- >>> caesar_encipher('Héllo World!', 2)
- 'Jgnnq Yqtnf!'
- """
- enciphered = [caesar_encipher_letter(l, shift) for l in message]
- return ''.join(enciphered)
-
-def caesar_decipher(message, shift):
- """Decipher a message with the Caesar cipher of given shift
-
- >>> caesar_decipher('bcd', 1)
- 'abc'
- >>> caesar_decipher('cde', 2)
- 'abc'
- >>> caesar_decipher('cd ez ab', 2)
- 'ab cx yz'
- >>> caesar_decipher('Jgnnq Yqtnf!', 2)
- 'Hello World!'
- """
- return caesar_encipher(message, -shift)
-
-def affine_encipher_letter(accented_letter, multiplier=1, adder=0, one_based=True):
- """Encipher a letter, given a multiplier and adder
-
- >>> ''.join([affine_encipher_letter(l, 3, 5, True) \
- for l in string.ascii_uppercase])
- 'HKNQTWZCFILORUXADGJMPSVYBE'
- >>> ''.join([affine_encipher_letter(l, 3, 5, False) \
- for l in string.ascii_uppercase])
- 'FILORUXADGJMPSVYBEHKNQTWZC'
- """
- letter = unaccent(accented_letter)
- if letter in string.ascii_letters:
- if letter in string.ascii_uppercase:
- alphabet_start = ord('A')
- else:
- alphabet_start = ord('a')
- letter_number = ord(letter) - alphabet_start
- if one_based: letter_number += 1
- cipher_number = (letter_number * multiplier + adder) % 26
- if one_based: cipher_number -= 1
- return chr(cipher_number % 26 + alphabet_start)
- else:
- return letter
-
-def affine_decipher_letter(letter, multiplier=1, adder=0, one_based=True):
- """Encipher a letter, given a multiplier and adder
-
- >>> ''.join([affine_decipher_letter(l, 3, 5, True) \
- for l in 'HKNQTWZCFILORUXADGJMPSVYBE'])
- 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
- >>> ''.join([affine_decipher_letter(l, 3, 5, False) \
- for l in 'FILORUXADGJMPSVYBEHKNQTWZC'])
- 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
- """
- if letter in string.ascii_letters:
- if letter in string.ascii_uppercase:
- alphabet_start = ord('A')
- else:
- alphabet_start = ord('a')
- cipher_number = ord(letter) - alphabet_start
- if one_based: cipher_number += 1
- plaintext_number = (
- modular_division_table[multiplier]
- [(cipher_number - adder) % 26])
- if one_based: plaintext_number -= 1
- return chr(plaintext_number % 26 + alphabet_start)
- else:
- return letter
-
-def affine_encipher(message, multiplier=1, adder=0, one_based=True):
- """Encipher a message
-
- >>> affine_encipher('hours passed during which jerico tried every ' \
- 'trick he could think of', 15, 22, True)
- 'lmyfu bkuusd dyfaxw claol psfaom jfasd snsfg jfaoe ls omytd jlaxe mh'
- """
- enciphered = [affine_encipher_letter(l, multiplier, adder, one_based)
- for l in message]
- return ''.join(enciphered)
-
-def affine_decipher(message, multiplier=1, adder=0, one_based=True):
- """Decipher a message
-
- >>> affine_decipher('lmyfu bkuusd dyfaxw claol psfaom jfasd snsfg ' \
- 'jfaoe ls omytd jlaxe mh', 15, 22, True)
- 'hours passed during which jerico tried every trick he could think of'
- """
- enciphered = [affine_decipher_letter(l, multiplier, adder, one_based)
- for l in message]
- return ''.join(enciphered)
-
-
-class KeywordWrapAlphabet(Enum):
- from_a = 1
- from_last = 2
- from_largest = 3
-
-
-def keyword_cipher_alphabet_of(keyword, wrap_alphabet=KeywordWrapAlphabet.from_a):
- """Find the cipher alphabet given a keyword.
- wrap_alphabet controls how the rest of the alphabet is added
- after the keyword.
-
- >>> keyword_cipher_alphabet_of('bayes')
- 'bayescdfghijklmnopqrtuvwxz'
- >>> keyword_cipher_alphabet_of('bayes', KeywordWrapAlphabet.from_a)
- 'bayescdfghijklmnopqrtuvwxz'
- >>> keyword_cipher_alphabet_of('bayes', KeywordWrapAlphabet.from_last)
- 'bayestuvwxzcdfghijklmnopqr'
- >>> keyword_cipher_alphabet_of('bayes', KeywordWrapAlphabet.from_largest)
- 'bayeszcdfghijklmnopqrtuvwx'
- """
- if wrap_alphabet == KeywordWrapAlphabet.from_a:
- cipher_alphabet = ''.join(deduplicate(sanitise(keyword) +
- string.ascii_lowercase))
- else:
- if wrap_alphabet == KeywordWrapAlphabet.from_last:
- last_keyword_letter = deduplicate(sanitise(keyword))[-1]
- else:
- last_keyword_letter = sorted(sanitise(keyword))[-1]
- last_keyword_position = string.ascii_lowercase.find(
- last_keyword_letter) + 1
- cipher_alphabet = ''.join(
- deduplicate(sanitise(keyword) +
- string.ascii_lowercase[last_keyword_position:] +
- string.ascii_lowercase))
- return cipher_alphabet
-
-
-def keyword_encipher(message, keyword, wrap_alphabet=KeywordWrapAlphabet.from_a):
- """Enciphers a message with a keyword substitution cipher.
- wrap_alphabet controls how the rest of the alphabet is added
- after the keyword.
- 0 : from 'a'
- 1 : from the last letter in the sanitised keyword
- 2 : from the largest letter in the sanitised keyword
-
- >>> keyword_encipher('test message', 'bayes')
- 'rsqr ksqqbds'
- >>> keyword_encipher('test message', 'bayes', KeywordWrapAlphabet.from_a)
- 'rsqr ksqqbds'
- >>> keyword_encipher('test message', 'bayes', KeywordWrapAlphabet.from_last)
- 'lskl dskkbus'
- >>> keyword_encipher('test message', 'bayes', KeywordWrapAlphabet.from_largest)
- 'qspq jsppbcs'
- """
- cipher_alphabet = keyword_cipher_alphabet_of(keyword, wrap_alphabet)
- cipher_translation = ''.maketrans(string.ascii_lowercase, cipher_alphabet)
- return unaccent(message).lower().translate(cipher_translation)
-
-def keyword_decipher(message, keyword, wrap_alphabet=KeywordWrapAlphabet.from_a):
- """Deciphers a message with a keyword substitution cipher.
- wrap_alphabet controls how the rest of the alphabet is added
- after the keyword.
- 0 : from 'a'
- 1 : from the last letter in the sanitised keyword
- 2 : from the largest letter in the sanitised keyword
-
- >>> keyword_decipher('rsqr ksqqbds', 'bayes')
- 'test message'
- >>> keyword_decipher('rsqr ksqqbds', 'bayes', KeywordWrapAlphabet.from_a)
- 'test message'
- >>> keyword_decipher('lskl dskkbus', 'bayes', KeywordWrapAlphabet.from_last)
- 'test message'
- >>> keyword_decipher('qspq jsppbcs', 'bayes', KeywordWrapAlphabet.from_largest)
- 'test message'
- """
- cipher_alphabet = keyword_cipher_alphabet_of(keyword, wrap_alphabet)
- cipher_translation = ''.maketrans(cipher_alphabet, string.ascii_lowercase)
- return message.lower().translate(cipher_translation)
-
-
-def vigenere_encipher(message, keyword):
- """Vigenere encipher
-
- >>> vigenere_encipher('hello', 'abc')
- 'hfnlp'
- """
- shifts = [ord(l) - ord('a') for l in sanitise(keyword)]
- pairs = zip(message, cycle(shifts))
- return ''.join([caesar_encipher_letter(l, k) for l, k in pairs])
-
-def vigenere_decipher(message, keyword):
- """Vigenere decipher
-
- >>> vigenere_decipher('hfnlp', 'abc')
- 'hello'
- """
- shifts = [ord(l) - ord('a') for l in sanitise(keyword)]
- 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
-
-
-def transpositions_of(keyword):
- """Finds the transpostions given by a keyword. For instance, the keyword
- 'clever' rearranges to 'celrv', so the first column (0) stays first, the
- second column (1) moves to third, the third column (2) moves to second,
- and so on.
-
- If passed a tuple, assume it's already a transposition and just return it.
-
- >>> transpositions_of('clever')
- (0, 2, 1, 4, 3)
- >>> transpositions_of('fred')
- (3, 2, 0, 1)
- >>> transpositions_of((3, 2, 0, 1))
- (3, 2, 0, 1)
- """
- if isinstance(keyword, tuple):
- return keyword
- else:
- key = deduplicate(keyword)
- transpositions = tuple(key.index(l) for l in sorted(key))
- return transpositions
-
-def pad(message_len, group_len, fillvalue):
- padding_length = group_len - message_len % group_len
- if padding_length == group_len: padding_length = 0
- padding = ''
- for i in range(padding_length):
- if callable(fillvalue):
- padding += fillvalue()
- else:
- padding += fillvalue
- return padding
-
-def column_transposition_encipher(message, keyword, fillvalue=' ',
- fillcolumnwise=False,
- emptycolumnwise=False):
- """Enciphers using the column transposition cipher.
- Message is padded to allow all rows to be the same length.
-
- >>> column_transposition_encipher('hellothere', 'abcdef', fillcolumnwise=True)
- 'hlohr eltee '
- >>> column_transposition_encipher('hellothere', 'abcdef', fillcolumnwise=True, emptycolumnwise=True)
- 'hellothere '
- >>> column_transposition_encipher('hellothere', 'abcdef')
- 'hellothere '
- >>> column_transposition_encipher('hellothere', 'abcde')
- 'hellothere'
- >>> column_transposition_encipher('hellothere', 'abcde', fillcolumnwise=True, emptycolumnwise=True)
- 'hellothere'
- >>> column_transposition_encipher('hellothere', 'abcde', fillcolumnwise=True, emptycolumnwise=False)
- 'hlohreltee'
- >>> column_transposition_encipher('hellothere', 'abcde', fillcolumnwise=False, emptycolumnwise=True)
- 'htehlelroe'
- >>> column_transposition_encipher('hellothere', 'abcde', fillcolumnwise=False, emptycolumnwise=False)
- 'hellothere'
- >>> column_transposition_encipher('hellothere', 'clever', fillcolumnwise=True, emptycolumnwise=True)
- 'heotllrehe'
- >>> column_transposition_encipher('hellothere', 'clever', fillcolumnwise=True, emptycolumnwise=False)
- 'holrhetlee'
- >>> column_transposition_encipher('hellothere', 'clever', fillcolumnwise=False, emptycolumnwise=True)
- 'htleehoelr'
- >>> column_transposition_encipher('hellothere', 'clever', fillcolumnwise=False, emptycolumnwise=False)
- 'hleolteher'
- >>> column_transposition_encipher('hellothere', 'cleverly')
- 'hleolthre e '
- >>> column_transposition_encipher('hellothere', 'cleverly', fillvalue='!')
- 'hleolthre!e!'
- >>> column_transposition_encipher('hellothere', 'cleverly', fillvalue=lambda: '*')
- 'hleolthre*e*'
- """
- transpositions = transpositions_of(keyword)
- message += pad(len(message), len(transpositions), fillvalue)
- if fillcolumnwise:
- rows = every_nth(message, len(message) // len(transpositions))
- else:
- rows = chunks(message, len(transpositions))
- transposed = [transpose(r, transpositions) for r in rows]
- if emptycolumnwise:
- return combine_every_nth(transposed)
- else:
- return ''.join(chain(*transposed))
-
-def column_transposition_decipher(message, keyword, fillvalue=' ',
- fillcolumnwise=False,
- emptycolumnwise=False):
- """Deciphers using the column transposition cipher.
- Message is padded to allow all rows to be the same length.
-
- >>> column_transposition_decipher('hellothere', 'abcde', fillcolumnwise=True, emptycolumnwise=True)
- 'hellothere'
- >>> column_transposition_decipher('hlohreltee', 'abcde', fillcolumnwise=True, emptycolumnwise=False)
- 'hellothere'
- >>> column_transposition_decipher('htehlelroe', 'abcde', fillcolumnwise=False, emptycolumnwise=True)
- 'hellothere'
- >>> column_transposition_decipher('hellothere', 'abcde', fillcolumnwise=False, emptycolumnwise=False)
- 'hellothere'
- >>> column_transposition_decipher('heotllrehe', 'clever', fillcolumnwise=True, emptycolumnwise=True)
- 'hellothere'
- >>> column_transposition_decipher('holrhetlee', 'clever', fillcolumnwise=True, emptycolumnwise=False)
- 'hellothere'
- >>> column_transposition_decipher('htleehoelr', 'clever', fillcolumnwise=False, emptycolumnwise=True)
- 'hellothere'
- >>> column_transposition_decipher('hleolteher', 'clever', fillcolumnwise=False, emptycolumnwise=False)
- 'hellothere'
- """
- transpositions = transpositions_of(keyword)
- message += pad(len(message), len(transpositions), fillvalue)
- if emptycolumnwise:
- rows = every_nth(message, len(message) // len(transpositions))
- else:
- rows = chunks(message, len(transpositions))
- untransposed = [untranspose(r, transpositions) for r in rows]
- if fillcolumnwise:
- return combine_every_nth(untransposed)
- else:
- return ''.join(chain(*untransposed))
-
-def scytale_encipher(message, rows, fillvalue=' '):
- """Enciphers using the scytale transposition cipher.
- Message is padded with spaces to allow all rows to be the same length.
-
- >>> scytale_encipher('thequickbrownfox', 3)
- 'tcnhkfeboqrxuo iw '
- >>> scytale_encipher('thequickbrownfox', 4)
- 'tubnhirfecooqkwx'
- >>> scytale_encipher('thequickbrownfox', 5)
- 'tubn hirf ecoo qkwx '
- >>> scytale_encipher('thequickbrownfox', 6)
- 'tqcrnxhukof eibwo '
- >>> scytale_encipher('thequickbrownfox', 7)
- 'tqcrnx hukof eibwo '
- """
- # transpositions = [i for i in range(math.ceil(len(message) / rows))]
- # return column_transposition_encipher(message, transpositions,
- # fillvalue=fillvalue, fillcolumnwise=False, emptycolumnwise=True)
- transpositions = [i for i in range(rows)]
- return column_transposition_encipher(message, transpositions,
- fillvalue=fillvalue, fillcolumnwise=True, emptycolumnwise=False)
-
-def scytale_decipher(message, rows):
- """Deciphers using the scytale transposition cipher.
- Assumes the message is padded so that all rows are the same length.
-
- >>> scytale_decipher('tcnhkfeboqrxuo iw ', 3)
- 'thequickbrownfox '
- >>> scytale_decipher('tubnhirfecooqkwx', 4)
- 'thequickbrownfox'
- >>> scytale_decipher('tubn hirf ecoo qkwx ', 5)
- 'thequickbrownfox '
- >>> scytale_decipher('tqcrnxhukof eibwo ', 6)
- 'thequickbrownfox '
- >>> scytale_decipher('tqcrnx hukof eibwo ', 7)
- 'thequickbrownfox '
- """
- # transpositions = [i for i in range(math.ceil(len(message) / rows))]
- # return column_transposition_decipher(message, transpositions,
- # fillcolumnwise=False, emptycolumnwise=True)
- transpositions = [i for i in range(rows)]
- return column_transposition_decipher(message, transpositions,
- fillcolumnwise=True, emptycolumnwise=False)
-
-
-def railfence_encipher(message, height, fillvalue=''):
- """Railfence cipher.
- Works by splitting the text into sections, then reading across them to
- generate the rows in the cipher. The rows are then combined to form the
- ciphertext.
-
- Example: the plaintext "hellotherefriends", with a height of four, written
- out in the railfence as
- h h i
- etere*
- lorfns
- l e d
- (with the * showing the one character to finish the last section).
- Each 'section' is two columns, but unfolded. In the example, the first
- section is 'hellot'.
-
- >>> railfence_encipher('hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers', 2, fillvalue='!')
- 'hlohraateerishsslnpeefetotsigaleccpeselteevsmhatetiiaogicotxfretnrifneihr!'
- >>> railfence_encipher('hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers', 3, fillvalue='!')
- 'horaersslpeeosglcpselteevsmhatetiiaogicotxfretnrifneihr!!lhateihsnefttiaece!'
- >>> railfence_encipher('hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers', 5, fillvalue='!')
- 'hresleogcseeemhetaocofrnrner!!lhateihsnefttiaece!!ltvsatiigitxetifih!!oarspeslp!'
- >>> railfence_encipher('hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers', 10, fillvalue='!')
- 'hepisehagitnr!!lernesge!!lmtocerh!!otiletap!!tseaorii!!hassfolc!!evtitffe!!rahsetec!!eixn!'
- >>> railfence_encipher('hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers', 3)
- 'horaersslpeeosglcpselteevsmhatetiiaogicotxfretnrifneihrlhateihsnefttiaece'
- >>> railfence_encipher('hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers', 5)
- 'hresleogcseeemhetaocofrnrnerlhateihsnefttiaeceltvsatiigitxetifihoarspeslp'
- >>> railfence_encipher('hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers', 7)
- 'haspolsevsetgifrifrlatihnettaeelemtiocxernhorersleesgcptehaiaottneihesfic'
- """
- sections = chunks(message, (height - 1) * 2, fillvalue=fillvalue)
- n_sections = len(sections)
- # Add the top row
- rows = [''.join([s[0] for s in sections])]
- # process the middle rows of the grid
- for r in range(1, height-1):
- rows += [''.join([s[r:r+1] + s[height*2-r-2:height*2-r-1] for s in sections])]
- # process the bottom row
- rows += [''.join([s[height - 1:height] for s in sections])]
- # rows += [' '.join([s[height - 1] for s in sections])]
- return ''.join(rows)
-
-def railfence_decipher(message, height, fillvalue=''):
- """Railfence decipher.
- Works by reconstructing the grid used to generate the ciphertext, then
- unfolding the sections so the text can be concatenated together.
-
- Example: given the ciphertext 'hhieterelorfnsled' and a height of 4, first
- work out that the second row has a character missing, find the rows of the
- grid, then split the section into its two columns.
-
- 'hhieterelorfnsled' is split into
- h h i
- etere
- lorfns
- l e d
- (spaces added for clarity), which is stored in 'rows'. This is then split
- into 'down_rows' and 'up_rows':
-
- down_rows:
- hhi
- eee
- lrn
- led
-
- up_rows:
- tr
- ofs
-
- These are then zipped together (after the up_rows are reversed) to recover
- the plaintext.
-
- Most of the procedure is about finding the correct lengths for each row then
- splitting the ciphertext into those rows.
-
- >>> railfence_decipher('hlohraateerishsslnpeefetotsigaleccpeselteevsmhatetiiaogicotxfretnrifneihr!', 2).strip('!')
- 'hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers'
- >>> railfence_decipher('horaersslpeeosglcpselteevsmhatetiiaogicotxfretnrifneihr!!lhateihsnefttiaece!', 3).strip('!')
- 'hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers'
- >>> railfence_decipher('hresleogcseeemhetaocofrnrner!!lhateihsnefttiaece!!ltvsatiigitxetifih!!oarspeslp!', 5).strip('!')
- 'hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers'
- >>> railfence_decipher('hepisehagitnr!!lernesge!!lmtocerh!!otiletap!!tseaorii!!hassfolc!!evtitffe!!rahsetec!!eixn!', 10).strip('!')
- 'hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers'
- >>> railfence_decipher('horaersslpeeosglcpselteevsmhatetiiaogicotxfretnrifneihrlhateihsnefttiaece', 3)
- 'hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers'
- >>> railfence_decipher('hresleogcseeemhetaocofrnrnerlhateihsnefttiaeceltvsatiigitxetifihoarspeslp', 5)
- 'hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers'
- >>> railfence_decipher('haspolsevsetgifrifrlatihnettaeelemtiocxernhorersleesgcptehaiaottneihesfic', 7)
- 'hellothereavastmeheartiesthisisalongpieceoftextfortestingrailfenceciphers'
- """
- # find the number and size of the sections, including how many characters
- # are missing for a full grid
- n_sections = math.ceil(len(message) / ((height - 1) * 2))
- padding_to_add = n_sections * (height - 1) * 2 - len(message)
- # row_lengths are for the both up rows and down rows
- row_lengths = [n_sections] * (height - 1) * 2
- for i in range((height - 1) * 2 - 1, (height - 1) * 2 - (padding_to_add + 1), -1):
- row_lengths[i] -= 1
- # folded_rows are the combined row lengths in the middle of the railfence
- folded_row_lengths = [row_lengths[0]]
- for i in range(1, height-1):
- folded_row_lengths += [row_lengths[i] + row_lengths[-i]]
- folded_row_lengths += [row_lengths[height - 1]]
- # find the rows that form the railfence grid
- rows = []
- row_start = 0
- for i in folded_row_lengths:
- rows += [message[row_start:row_start + i]]
- row_start += i
- # split the rows into the 'down_rows' (those that form the first column of
- # a section) and the 'up_rows' (those that ofrm the second column of a
- # section).
- down_rows = [rows[0]]
- up_rows = []
- for i in range(1, height-1):
- down_rows += [''.join([c for n, c in enumerate(rows[i]) if n % 2 == 0])]
- up_rows += [''.join([c for n, c in enumerate(rows[i]) if n % 2 == 1])]
- down_rows += [rows[-1]]
- up_rows.reverse()
- return ''.join(c for r in zip_longest(*(down_rows + up_rows), fillvalue='') for c in r)
-
-def make_cadenus_keycolumn(doubled_letters = 'vw', start='a', reverse=False):
- """Makes the key column for a Cadenus cipher (the column down between the
- rows of letters)
-
- >>> make_cadenus_keycolumn()['a']
- 0
- >>> make_cadenus_keycolumn()['b']
- 1
- >>> make_cadenus_keycolumn()['c']
- 2
- >>> make_cadenus_keycolumn()['v']
- 21
- >>> make_cadenus_keycolumn()['w']
- 21
- >>> make_cadenus_keycolumn()['z']
- 24
- >>> make_cadenus_keycolumn(doubled_letters='ij', start='b', reverse=True)['a']
- 1
- >>> make_cadenus_keycolumn(doubled_letters='ij', start='b', reverse=True)['b']
- 0
- >>> make_cadenus_keycolumn(doubled_letters='ij', start='b', reverse=True)['c']
- 24
- >>> make_cadenus_keycolumn(doubled_letters='ij', start='b', reverse=True)['i']
- 18
- >>> make_cadenus_keycolumn(doubled_letters='ij', start='b', reverse=True)['j']
- 18
- >>> make_cadenus_keycolumn(doubled_letters='ij', start='b', reverse=True)['v']
- 6
- >>> make_cadenus_keycolumn(doubled_letters='ij', start='b', reverse=True)['z']
- 2
- """
- index_to_remove = string.ascii_lowercase.find(doubled_letters[0])
- short_alphabet = string.ascii_lowercase[:index_to_remove] + string.ascii_lowercase[index_to_remove+1:]
- if reverse:
- short_alphabet = ''.join(reversed(short_alphabet))
- start_pos = short_alphabet.find(start)
- rotated_alphabet = short_alphabet[start_pos:] + short_alphabet[:start_pos]
- keycolumn = {l: i for i, l in enumerate(rotated_alphabet)}
- keycolumn[doubled_letters[0]] = keycolumn[doubled_letters[1]]
- return keycolumn
-
-def cadenus_encipher(message, keyword, keycolumn, fillvalue='a'):
- """Encipher with the Cadenus cipher
-
- >>> cadenus_encipher(sanitise('Whoever has made a voyage up the Hudson ' \
- 'must remember the Kaatskill mountains. ' \
- 'They are a dismembered branch of the great'), \
- 'wink', \
- make_cadenus_keycolumn(doubled_letters='vw', start='a', reverse=True))
- 'antodeleeeuhrsidrbhmhdrrhnimefmthgeaetakseomehetyaasuvoyegrastmmuuaeenabbtpchehtarorikswosmvaleatned'
- >>> cadenus_encipher(sanitise('a severe limitation on the usefulness of ' \
- 'the cadenus is that every message must be ' \
- 'a multiple of twenty-five letters long'), \
- 'easy', \
- make_cadenus_keycolumn(doubled_letters='vw', start='a', reverse=True))
- 'systretomtattlusoatleeesfiyheasdfnmschbhneuvsnpmtofarenuseieeieltarlmentieetogevesitfaisltngeeuvowul'
- """
- rows = chunks(message, len(message) // 25, fillvalue=fillvalue)
- columns = zip(*rows)
- rotated_columns = [col[start:] + col[:start] for start, col in zip([keycolumn[l] for l in keyword], columns)]
- rotated_rows = zip(*rotated_columns)
- transpositions = transpositions_of(keyword)
- transposed = [transpose(r, transpositions) for r in rotated_rows]
- return ''.join(chain(*transposed))
-
-def cadenus_decipher(message, keyword, keycolumn, fillvalue='a'):
- """
- >>> cadenus_decipher('antodeleeeuhrsidrbhmhdrrhnimefmthgeaetakseomehetyaa' \
- 'suvoyegrastmmuuaeenabbtpchehtarorikswosmvaleatned', \
- 'wink', \
- make_cadenus_keycolumn(reverse=True))
- 'whoeverhasmadeavoyageupthehudsonmustrememberthekaatskillmountainstheyareadismemberedbranchofthegreat'
- >>> cadenus_decipher('systretomtattlusoatleeesfiyheasdfnmschbhneuvsnpmtof' \
- 'arenuseieeieltarlmentieetogevesitfaisltngeeuvowul', \
- 'easy', \
- make_cadenus_keycolumn(reverse=True))
- 'aseverelimitationontheusefulnessofthecadenusisthateverymessagemustbeamultipleoftwentyfiveletterslong'
- """
- rows = chunks(message, len(message) // 25, fillvalue=fillvalue)
- transpositions = transpositions_of(keyword)
- untransposed_rows = [untranspose(r, transpositions) for r in rows]
- columns = zip(*untransposed_rows)
- rotated_columns = [col[-start:] + col[:-start] for start, col in zip([keycolumn[l] for l in keyword], columns)]
- rotated_rows = zip(*rotated_columns)
- # return rotated_columns
- return ''.join(chain(*rotated_rows))
-
-
-def hill_encipher(matrix, message_letters, fillvalue='a'):
- """Hill cipher
-
- >>> hill_encipher(np.matrix([[7,8], [11,11]]), 'hellothere')
- 'drjiqzdrvx'
- >>> hill_encipher(np.matrix([[6, 24, 1], [13, 16, 10], [20, 17, 15]]), \
- 'hello there')
- 'tfjflpznvyac'
- """
- n = len(matrix)
- sanitised_message = sanitise(message_letters)
- if len(sanitised_message) % n != 0:
- padding = fillvalue[0] * (n - len(sanitised_message) % n)
- else:
- padding = ''
- message = [ord(c) - ord('a') for c in sanitised_message + padding]
- message_chunks = [message[i:i+n] for i in range(0, len(message), n)]
- # message_chunks = chunks(message, len(matrix), fillvalue=None)
- enciphered_chunks = [((matrix * np.matrix(c).T).T).tolist()[0]
- for c in message_chunks]
- return ''.join([chr(int(round(l)) % 26 + ord('a'))
- for l in sum(enciphered_chunks, [])])
-
-def hill_decipher(matrix, message, fillvalue='a'):
- """Hill cipher
-
- >>> hill_decipher(np.matrix([[7,8], [11,11]]), 'drjiqzdrvx')
- 'hellothere'
- >>> hill_decipher(np.matrix([[6, 24, 1], [13, 16, 10], [20, 17, 15]]), \
- 'tfjflpznvyac')
- 'hellothereaa'
- """
- adjoint = linalg.det(matrix)*linalg.inv(matrix)
- inverse_determinant = modular_division_table[int(round(linalg.det(matrix))) % 26][1]
- inverse_matrix = (inverse_determinant * adjoint) % 26
- return hill_encipher(inverse_matrix, message, fillvalue)
-
-
-# Where each piece of text ends up in the AMSCO transpositon cipher.
-# 'index' shows where the slice appears in the plaintext, with the slice
-# from 'start' to 'end'
-AmscoSlice = collections.namedtuple('AmscoSlice', ['index', 'start', 'end'])
-
-class AmscoFillStyle(Enum):
- continuous = 1
- same_each_row = 2
- reverse_each_row = 3
-
-def amsco_transposition_positions(message, keyword,
- fillpattern=(1, 2),
- fillstyle=AmscoFillStyle.continuous,
- fillcolumnwise=False,
- emptycolumnwise=True):
- """Creates the grid for the AMSCO transposition cipher. Each element in the
- grid shows the index of that slice and the start and end positions of the
- plaintext that go to make it up.
-
- >>> amsco_transposition_positions(string.ascii_lowercase, 'freddy', \
- fillpattern=(1, 2)) # doctest: +NORMALIZE_WHITESPACE
- [[AmscoSlice(index=3, start=4, end=6),
- AmscoSlice(index=2, start=3, end=4),
- AmscoSlice(index=0, start=0, end=1),
- AmscoSlice(index=1, start=1, end=3),
- AmscoSlice(index=4, start=6, end=7)],
- [AmscoSlice(index=8, start=12, end=13),
- AmscoSlice(index=7, start=10, end=12),
- AmscoSlice(index=5, start=7, end=9),
- AmscoSlice(index=6, start=9, end=10),
- AmscoSlice(index=9, start=13, end=15)],
- [AmscoSlice(index=13, start=19, end=21),
- AmscoSlice(index=12, start=18, end=19),
- AmscoSlice(index=10, start=15, end=16),
- AmscoSlice(index=11, start=16, end=18),
- AmscoSlice(index=14, start=21, end=22)],
- [AmscoSlice(index=18, start=27, end=28),
- AmscoSlice(index=17, start=25, end=27),
- AmscoSlice(index=15, start=22, end=24),
- AmscoSlice(index=16, start=24, end=25),
- AmscoSlice(index=19, start=28, end=30)]]
- """
- transpositions = transpositions_of(keyword)
- fill_iterator = cycle(fillpattern)
- indices = count()
- message_length = len(message)
-
- current_position = 0
- grid = []
- current_fillpattern = fillpattern
- while current_position < message_length:
- row = []
- if fillstyle == AmscoFillStyle.same_each_row:
- fill_iterator = cycle(fillpattern)
- if fillstyle == AmscoFillStyle.reverse_each_row:
- fill_iterator = cycle(current_fillpattern)
- for _ in range(len(transpositions)):
- index = next(indices)
- gap = next(fill_iterator)
- row += [AmscoSlice(index, current_position, current_position + gap)]
- current_position += gap
- grid += [row]
- if fillstyle == AmscoFillStyle.reverse_each_row:
- current_fillpattern = list(reversed(current_fillpattern))
- return [transpose(r, transpositions) for r in grid]
-
-def amsco_transposition_encipher(message, keyword,
- fillpattern=(1,2), fillstyle=AmscoFillStyle.reverse_each_row):
- """AMSCO transposition encipher.
-
- >>> amsco_transposition_encipher('hellothere', 'abc', fillpattern=(1, 2))
- 'hoteelhler'
- >>> amsco_transposition_encipher('hellothere', 'abc', fillpattern=(2, 1))
- 'hetelhelor'
- >>> amsco_transposition_encipher('hellothere', 'acb', fillpattern=(1, 2))
- 'hotelerelh'
- >>> amsco_transposition_encipher('hellothere', 'acb', fillpattern=(2, 1))
- 'hetelorlhe'
- >>> amsco_transposition_encipher('hereissometexttoencipher', 'encode')
- 'etecstthhomoerereenisxip'
- >>> amsco_transposition_encipher('hereissometexttoencipher', 'cipher', fillpattern=(1, 2))
- 'hetcsoeisterereipexthomn'
- >>> amsco_transposition_encipher('hereissometexttoencipher', 'cipher', fillpattern=(1, 2), fillstyle=AmscoFillStyle.continuous)
- 'hecsoisttererteipexhomen'
- >>> amsco_transposition_encipher('hereissometexttoencipher', 'cipher', fillpattern=(2, 1))
- 'heecisoosttrrtepeixhemen'
- >>> amsco_transposition_encipher('hereissometexttoencipher', 'cipher', fillpattern=(1, 3, 2))
- 'hxtomephescieretoeisnter'
- >>> amsco_transposition_encipher('hereissometexttoencipher', 'cipher', fillpattern=(1, 3, 2), fillstyle=AmscoFillStyle.continuous)
- 'hxomeiphscerettoisenteer'
- """
- grid = amsco_transposition_positions(message, keyword,
- fillpattern=fillpattern, fillstyle=fillstyle)
- ct_as_grid = [[message[s.start:s.end] for s in r] for r in grid]
- return combine_every_nth(ct_as_grid)
-
-
-def amsco_transposition_decipher(message, keyword,
- fillpattern=(1,2), fillstyle=AmscoFillStyle.reverse_each_row):
- """AMSCO transposition decipher
-
- >>> amsco_transposition_decipher('hoteelhler', 'abc', fillpattern=(1, 2))
- 'hellothere'
- >>> amsco_transposition_decipher('hetelhelor', 'abc', fillpattern=(2, 1))
- 'hellothere'
- >>> amsco_transposition_decipher('hotelerelh', 'acb', fillpattern=(1, 2))
- 'hellothere'
- >>> amsco_transposition_decipher('hetelorlhe', 'acb', fillpattern=(2, 1))
- 'hellothere'
- >>> amsco_transposition_decipher('etecstthhomoerereenisxip', 'encode')
- 'hereissometexttoencipher'
- >>> amsco_transposition_decipher('hetcsoeisterereipexthomn', 'cipher', fillpattern=(1, 2))
- 'hereissometexttoencipher'
- >>> amsco_transposition_decipher('hecsoisttererteipexhomen', 'cipher', fillpattern=(1, 2), fillstyle=AmscoFillStyle.continuous)
- 'hereissometexttoencipher'
- >>> amsco_transposition_decipher('heecisoosttrrtepeixhemen', 'cipher', fillpattern=(2, 1))
- 'hereissometexttoencipher'
- >>> amsco_transposition_decipher('hxtomephescieretoeisnter', 'cipher', fillpattern=(1, 3, 2))
- 'hereissometexttoencipher'
- >>> amsco_transposition_decipher('hxomeiphscerettoisenteer', 'cipher', fillpattern=(1, 3, 2), fillstyle=AmscoFillStyle.continuous)
- 'hereissometexttoencipher'
- """
-
- grid = amsco_transposition_positions(message, keyword,
- fillpattern=fillpattern, fillstyle=fillstyle)
- transposed_sections = [s for c in [l for l in zip(*grid)] for s in c]
- plaintext_list = [''] * len(transposed_sections)
- current_pos = 0
- for slice in transposed_sections:
- plaintext_list[slice.index] = message[current_pos:current_pos-slice.start+slice.end][:len(message[slice.start:slice.end])]
- current_pos += len(message[slice.start:slice.end])
- return ''.join(plaintext_list)
-
-
-class PocketEnigma(object):
- """A pocket enigma machine
- The wheel is internally represented as a 26-element list self.wheel_map,
- where wheel_map[i] == j shows that the position i places on from the arrow
- maps to the position j places on.
- """
- def __init__(self, wheel=1, position='a'):
- """initialise the pocket enigma, including which wheel to use and the
- starting position of the wheel.
-
- The wheel is either 1 or 2 (the predefined wheels) or a list of letter
- pairs.
-
- The position is the letter pointed to by the arrow on the wheel.
-
- >>> pe.wheel_map
- [25, 4, 23, 10, 1, 7, 9, 5, 12, 6, 3, 17, 8, 14, 13, 21, 19, 11, 20, 16, 18, 15, 24, 2, 22, 0]
- >>> pe.position
- 0
- """
- self.wheel1 = [('a', 'z'), ('b', 'e'), ('c', 'x'), ('d', 'k'),
- ('f', 'h'), ('g', 'j'), ('i', 'm'), ('l', 'r'), ('n', 'o'),
- ('p', 'v'), ('q', 't'), ('s', 'u'), ('w', 'y')]
- self.wheel2 = [('a', 'c'), ('b', 'd'), ('e', 'w'), ('f', 'i'),
- ('g', 'p'), ('h', 'm'), ('j', 'k'), ('l', 'n'), ('o', 'q'),
- ('r', 'z'), ('s', 'u'), ('t', 'v'), ('x', 'y')]
- if wheel == 1:
- self.make_wheel_map(self.wheel1)
- elif wheel == 2:
- self.make_wheel_map(self.wheel2)
- else:
- self.validate_wheel_spec(wheel)
- self.make_wheel_map(wheel)
- if position in string.ascii_lowercase:
- self.position = ord(position) - ord('a')
- else:
- self.position = position
-
- def make_wheel_map(self, wheel_spec):
- """Expands a wheel specification from a list of letter-letter pairs
- into a full wheel_map.
-
- >>> pe.make_wheel_map(pe.wheel2)
- [2, 3, 0, 1, 22, 8, 15, 12, 5, 10, 9, 13, 7, 11, 16, 6, 14, 25, 20, 21, 18, 19, 4, 24, 23, 17]
- """
- self.validate_wheel_spec(wheel_spec)
- self.wheel_map = [0] * 26
- for p in wheel_spec:
- self.wheel_map[ord(p[0]) - ord('a')] = ord(p[1]) - ord('a')
- self.wheel_map[ord(p[1]) - ord('a')] = ord(p[0]) - ord('a')
- return self.wheel_map
-
- def validate_wheel_spec(self, wheel_spec):
- """Validates that a wheel specificaiton will turn into a valid wheel
- map.
-
- >>> pe.validate_wheel_spec([])
- Traceback (most recent call last):
- ...
- ValueError: Wheel specification has 0 pairs, requires 13
- >>> pe.validate_wheel_spec([('a', 'b', 'c')]*13)
- Traceback (most recent call last):
- ...
- ValueError: Not all mappings in wheel specificationhave two elements
- >>> pe.validate_wheel_spec([('a', 'b')]*13)
- Traceback (most recent call last):
- ...
- ValueError: Wheel specification does not contain 26 letters
- """
- if len(wheel_spec) != 13:
- raise ValueError("Wheel specification has {} pairs, requires 13".
- format(len(wheel_spec)))
- for p in wheel_spec:
- if len(p) != 2:
- raise ValueError("Not all mappings in wheel specification"
- "have two elements")
- if len(set([p[0] for p in wheel_spec] +
- [p[1] for p in wheel_spec])) != 26:
- raise ValueError("Wheel specification does not contain 26 letters")
-
- def encipher_letter(self, letter):
- """Enciphers a single letter, by advancing the wheel before looking up
- the letter on the wheel.
-
- >>> pe.set_position('f')
- 5
- >>> pe.encipher_letter('k')
- 'h'
- """
- self.advance()
- return self.lookup(letter)
- decipher_letter = encipher_letter
-
- def lookup(self, letter):
- """Look up what a letter enciphers to, without turning the wheel.
-
- >>> pe.set_position('f')
- 5
- >>> ''.join([pe.lookup(l) for l in string.ascii_lowercase])
- 'udhbfejcpgmokrliwntsayqzvx'
- >>> pe.lookup('A')
- ''
- """
- if letter in string.ascii_lowercase:
- return chr(
- (self.wheel_map[(ord(letter) - ord('a') - self.position) % 26] +
- self.position) % 26 +
- ord('a'))
- else:
- return ''
-
- def advance(self):
- """Advances the wheel one position.
-
- >>> pe.set_position('f')
- 5
- >>> pe.advance()
- 6
- """
- self.position = (self.position + 1) % 26
- return self.position
-
- def encipher(self, message, starting_position=None):
- """Enciphers a whole message.
-
- >>> pe.set_position('f')
- 5
- >>> pe.encipher('helloworld')
- 'kjsglcjoqc'
- >>> pe.set_position('f')
- 5
- >>> pe.encipher('kjsglcjoqc')
- 'helloworld'
- >>> pe.encipher('helloworld', starting_position = 'x')
- 'egrekthnnf'
- """
- if starting_position:
- self.set_position(starting_position)
- transformed = ''
- for l in message:
- transformed += self.encipher_letter(l)
- return transformed
- decipher = encipher
-
- def set_position(self, position):
- """Sets the position of the wheel, by specifying the letter the arrow
- points to.
-
- >>> pe.set_position('a')
- 0
- >>> pe.set_position('m')
- 12
- >>> pe.set_position('z')
- 25
- """
- self.position = ord(position) - ord('a')
- return self.position
-
-
-if __name__ == "__main__":
- import doctest
- doctest.testmod(extraglobs={'pe': PocketEnigma(1, 'a')})
+++ /dev/null
-"""A set of functions to break the ciphers give in ciphers.py.
-"""
-
-import string
-import collections
-import norms
-import logging
-import random
-import math
-from itertools import starmap
-from segment import segment
-from multiprocessing import Pool
-
-import matplotlib.pyplot as plt
-
-logger = logging.getLogger(__name__)
-logger.addHandler(logging.FileHandler('cipher.log'))
-logger.setLevel(logging.WARNING)
-#logger.setLevel(logging.INFO)
-#logger.setLevel(logging.DEBUG)
-
-from cipher import *
-from language_models import *
-
-# To time a run:
-#
-# import timeit
-# c5a = open('2012/5a.ciphertext', 'r').read()
-# timeit.timeit('keyword_break(c5a)', setup='gc.enable() ; from __main__ import c5a ; from cipher import keyword_break', number=1)
-# timeit.repeat('keyword_break_mp(c5a, chunksize=500)', setup='gc.enable() ; from __main__ import c5a ; from cipher import keyword_break_mp', repeat=5, number=1)
-
-transpositions = collections.defaultdict(list)
-for word in keywords:
- transpositions[transpositions_of(word)] += [word]
-
-def frequencies(text):
- """Count the number of occurrences of each character in text
-
- >>> sorted(frequencies('abcdefabc').items())
- [('a', 2), ('b', 2), ('c', 2), ('d', 1), ('e', 1), ('f', 1)]
- >>> sorted(frequencies('the quick brown fox jumped over the lazy ' \
- 'dog').items()) # doctest: +NORMALIZE_WHITESPACE
- [(' ', 8), ('a', 1), ('b', 1), ('c', 1), ('d', 2), ('e', 4), ('f', 1),
- ('g', 1), ('h', 2), ('i', 1), ('j', 1), ('k', 1), ('l', 1), ('m', 1),
- ('n', 1), ('o', 4), ('p', 1), ('q', 1), ('r', 2), ('t', 2), ('u', 2),
- ('v', 1), ('w', 1), ('x', 1), ('y', 1), ('z', 1)]
- >>> sorted(frequencies('The Quick BROWN fox jumped! over... the ' \
- '(9lazy) DOG').items()) # doctest: +NORMALIZE_WHITESPACE
- [(' ', 8), ('!', 1), ('(', 1), (')', 1), ('.', 3), ('9', 1), ('B', 1),
- ('D', 1), ('G', 1), ('N', 1), ('O', 2), ('Q', 1), ('R', 1), ('T', 1),
- ('W', 1), ('a', 1), ('c', 1), ('d', 1), ('e', 4), ('f', 1), ('h', 2),
- ('i', 1), ('j', 1), ('k', 1), ('l', 1), ('m', 1), ('o', 2), ('p', 1),
- ('r', 1), ('t', 1), ('u', 2), ('v', 1), ('x', 1), ('y', 1), ('z', 1)]
- >>> sorted(frequencies(sanitise('The Quick BROWN fox jumped! over... '\
- 'the (9lazy) DOG')).items()) # doctest: +NORMALIZE_WHITESPACE
- [('a', 1), ('b', 1), ('c', 1), ('d', 2), ('e', 4), ('f', 1), ('g', 1),
- ('h', 2), ('i', 1), ('j', 1), ('k', 1), ('l', 1), ('m', 1), ('n', 1),
- ('o', 4), ('p', 1), ('q', 1), ('r', 2), ('t', 2), ('u', 2), ('v', 1),
- ('w', 1), ('x', 1), ('y', 1), ('z', 1)]
- >>> frequencies('abcdefabcdef')['x']
- 0
- """
- return collections.Counter(c for c in text)
-
-
-def caesar_break(message, fitness=Pletters):
- """Breaks a Caesar cipher using frequency analysis
-
- >>> caesar_break('ibxcsyorsaqcheyklxivoexlevmrimwxsfiqevvmihrsasrxliwyrh' \
- 'ecjsppsamrkwleppfmergefifvmhixscsymjcsyqeoixlm') # doctest: +ELLIPSIS
- (4, -130.849989015...)
- >>> caesar_break('wxwmaxdgheetgwuxztgptedbgznitgwwhpguxyhkxbmhvvtlbhgtee' \
- 'raxlmhiixweblmxgxwmhmaxybkbgztgwztsxwbgmxgmert') # doctest: +ELLIPSIS
- (19, -128.82410410...)
- >>> caesar_break('yltbbqnqnzvguvaxurorgenafsbezqvagbnornfgsbevpnaabjurer' \
- 'svaquvzyvxrnznazlybequrvfohgriraabjtbaruraprur') # doctest: +ELLIPSIS
- (13, -126.25403935...)
- """
- sanitised_message = sanitise(message)
- best_shift = 0
- best_fit = float('-inf')
- for shift in range(26):
- plaintext = caesar_decipher(sanitised_message, shift)
- fit = fitness(plaintext)
- logger.debug('Caesar break attempt using key {0} gives fit of {1} '
- 'and decrypt starting: {2}'.format(shift, fit,
- plaintext[:50]))
- if fit > best_fit:
- best_fit = fit
- best_shift = shift
- logger.info('Caesar break best fit: key {0} gives fit of {1} and '
- 'decrypt starting: {2}'.format(best_shift, best_fit,
- caesar_decipher(sanitised_message, best_shift)[:50]))
- return best_shift, best_fit
-
-def affine_break(message, fitness=Pletters):
- """Breaks an affine cipher using frequency analysis
-
- >>> affine_break('lmyfu bkuusd dyfaxw claol psfaom jfasd snsfg jfaoe ls ' \
- 'omytd jlaxe mh jm bfmibj umis hfsul axubafkjamx. ls kffkxwsd jls ' \
- 'ofgbjmwfkiu olfmxmtmwaokttg jlsx ls kffkxwsd jlsi zg tsxwjl. jlsx ' \
- 'ls umfjsd jlsi zg hfsqysxog. ls dmmdtsd mx jls bats mh bkbsf. ls ' \
- 'bfmctsd kfmyxd jls lyj, mztanamyu xmc jm clm cku tmmeaxw kj lai ' \
- 'kxd clm ckuxj.') # doctest: +ELLIPSIS
- ((15, 22, True), -340.601181913...)
- """
- sanitised_message = sanitise(message)
- best_multiplier = 0
- best_adder = 0
- best_one_based = True
- best_fit = float("-inf")
- for one_based in [True, False]:
- for multiplier in [x for x in range(1, 26, 2) if x != 13]:
- for adder in range(26):
- plaintext = affine_decipher(sanitised_message,
- multiplier, adder, one_based)
- fit = fitness(plaintext)
- logger.debug('Affine break attempt using key {0}x+{1} ({2}) '
- 'gives fit of {3} and decrypt starting: {4}'.
- format(multiplier, adder, one_based, fit,
- plaintext[:50]))
- if fit > best_fit:
- best_fit = fit
- best_multiplier = multiplier
- best_adder = adder
- best_one_based = one_based
- logger.info('Affine break best fit with key {0}x+{1} ({2}) gives fit of '
- '{3} and decrypt starting: {4}'.format(
- best_multiplier, best_adder, best_one_based, best_fit,
- affine_decipher(sanitised_message, best_multiplier,
- best_adder, best_one_based)[:50]))
- return (best_multiplier, best_adder, best_one_based), best_fit
-
-def keyword_break(message, wordlist=keywords, fitness=Pletters):
- """Breaks a keyword substitution cipher using a dictionary and
- frequency analysis.
-
- >>> keyword_break(keyword_encipher('this is a test message for the ' \
- 'keyword decipherment', 'elephant', KeywordWrapAlphabet.from_last), \
- wordlist=['cat', 'elephant', 'kangaroo']) # doctest: +ELLIPSIS
- (('elephant', <KeywordWrapAlphabet.from_last: 2>), -52.834575011...)
- """
- best_keyword = ''
- best_wrap_alphabet = True
- best_fit = float("-inf")
- for wrap_alphabet in KeywordWrapAlphabet:
- for keyword in wordlist:
- plaintext = keyword_decipher(message, keyword, wrap_alphabet)
- fit = fitness(plaintext)
- logger.debug('Keyword break attempt using key {0} (wrap={1}) '
- 'gives fit of {2} and decrypt starting: {3}'.format(
- keyword, wrap_alphabet, fit,
- sanitise(plaintext)[:50]))
- if fit > best_fit:
- best_fit = fit
- best_keyword = keyword
- best_wrap_alphabet = wrap_alphabet
- logger.info('Keyword break best fit with key {0} (wrap={1}) gives fit of '
- '{2} and decrypt starting: {3}'.format(best_keyword,
- best_wrap_alphabet, best_fit, sanitise(
- keyword_decipher(message, best_keyword,
- best_wrap_alphabet))[:50]))
- return (best_keyword, best_wrap_alphabet), best_fit
-
-def keyword_break_mp(message, wordlist=keywords, fitness=Pletters,
- number_of_solutions=1, chunksize=500):
- """Breaks a keyword substitution cipher using a dictionary and
- frequency analysis
-
- >>> keyword_break_mp(keyword_encipher('this is a test message for the ' \
- 'keyword decipherment', 'elephant', KeywordWrapAlphabet.from_last), \
- wordlist=['cat', 'elephant', 'kangaroo']) # doctest: +ELLIPSIS
- (('elephant', <KeywordWrapAlphabet.from_last: 2>), -52.834575011...)
- >>> keyword_break_mp(keyword_encipher('this is a test message for the ' \
- 'keyword decipherment', 'elephant', KeywordWrapAlphabet.from_last), \
- wordlist=['cat', 'elephant', 'kangaroo'], \
- number_of_solutions=2) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
- [(('elephant', <KeywordWrapAlphabet.from_last: 2>), -52.834575011...),
- (('elephant', <KeywordWrapAlphabet.from_largest: 3>), -52.834575011...)]
- """
- with Pool() as pool:
- helper_args = [(message, word, wrap, fitness)
- for word in wordlist
- for wrap in KeywordWrapAlphabet]
- # Gotcha: the helper function here needs to be defined at the top level
- # (limitation of Pool.starmap)
- breaks = pool.starmap(keyword_break_worker, helper_args, chunksize)
- if number_of_solutions == 1:
- return max(breaks, key=lambda k: k[1])
- else:
- return sorted(breaks, key=lambda k: k[1], reverse=True)[:number_of_solutions]
-
-def keyword_break_worker(message, keyword, wrap_alphabet, fitness):
- plaintext = keyword_decipher(message, keyword, wrap_alphabet)
- fit = fitness(plaintext)
- logger.debug('Keyword break attempt using key {0} (wrap={1}) gives fit of '
- '{2} and decrypt starting: {3}'.format(keyword,
- wrap_alphabet, fit, sanitise(plaintext)[:50]))
- return (keyword, wrap_alphabet), fit
-
-def monoalphabetic_break_hillclimbing(message, max_iterations=10000000,
- alphabet=None, fitness=Pletters):
- ciphertext = unaccent(message).lower()
- if not alphabet:
- alphabet = list(string.ascii_lowercase)
- random.shuffle(alphabet)
- alphabet = ''.join(alphabet)
- return monoalphabetic_break_hillclimbing_worker(ciphertext, alphabet,
- max_iterations, fitness)
-
-def monoalphabetic_break_hillclimbing_mp(message, workers=10,
- max_iterations = 10000000, alphabet=None, fitness=Pletters, chunksize=1):
- worker_args = []
- ciphertext = unaccent(message).lower()
- for i in range(workers):
- if alphabet:
- this_alphabet = alphabet
- else:
- this_alphabet = list(string.ascii_lowercase)
- random.shuffle(this_alphabet)
- this_alphabet = ''.join(this_alphabet)
- worker_args.append((ciphertext, this_alphabet, max_iterations, fitness))
- with Pool() as pool:
- breaks = pool.starmap(monoalphabetic_break_hillclimbing_worker,
- worker_args, chunksize)
- return max(breaks, key=lambda k: k[1])
-
-def monoalphabetic_break_hillclimbing_worker(message, alphabet,
- max_iterations, fitness):
- def swap(letters, i, j):
- if i > j:
- i, j = j, i
- if i == j:
- return letters
- else:
- return (letters[:i] + letters[j] + letters[i+1:j] + letters[i] +
- letters[j+1:])
- best_alphabet = alphabet
- best_fitness = float('-inf')
- for i in range(max_iterations):
- alphabet = swap(alphabet, random.randrange(26), random.randrange(26))
- cipher_translation = ''.maketrans(string.ascii_lowercase, alphabet)
- plaintext = message.translate(cipher_translation)
- if fitness(plaintext) > best_fitness:
- best_fitness = fitness(plaintext)
- best_alphabet = alphabet
- print(i, best_alphabet, best_fitness, plaintext)
- return best_alphabet, best_fitness
-
-
-def vigenere_keyword_break_mp(message, wordlist=keywords, fitness=Pletters,
- chunksize=500):
- """Breaks a vigenere cipher using a dictionary and frequency analysis.
-
- >>> vigenere_keyword_break_mp(vigenere_encipher(sanitise('this is a test ' \
- 'message for the vigenere decipherment'), 'cat'), \
- wordlist=['cat', 'elephant', 'kangaroo']) # doctest: +ELLIPSIS
- ('cat', -52.947271216...)
- """
- with Pool() as pool:
- helper_args = [(message, word, fitness)
- for word in wordlist]
- # Gotcha: the helper function here needs to be defined at the top level
- # (limitation of Pool.starmap)
- breaks = pool.starmap(vigenere_keyword_break_worker, helper_args,
- chunksize)
- return max(breaks, key=lambda k: k[1])
-vigenere_keyword_break = vigenere_keyword_break_mp
-
-def vigenere_keyword_break_worker(message, keyword, fitness):
- plaintext = vigenere_decipher(message, keyword)
- fit = fitness(plaintext)
- logger.debug('Vigenere keyword break attempt using key {0} gives fit of '
- '{1} and decrypt starting: {2}'.format(keyword,
- fit, sanitise(plaintext)[:50]))
- return keyword, fit
-
-
-def vigenere_frequency_break(message, max_key_length=20, fitness=Pletters):
- """Breaks a Vigenere 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. I jump every time I hear a footstep on the stairs, " \
- "certain that the theft has been discovered and that I will " \
- "be caught. The SS officer visits less often now that he is " \
- "sure"), 'florence')) # doctest: +ELLIPSIS
- ('florence', -307.5473096791...)
- """
- def worker(message, key_length, fitness):
- splits = every_nth(sanitised_message, key_length)
- key = ''.join([chr(caesar_break(s)[0] + ord('a')) for s in splits])
- plaintext = vigenere_decipher(message, key)
- fit = fitness(plaintext)
- return key, fit
- sanitised_message = sanitise(message)
- results = starmap(worker, [(sanitised_message, i, fitness)
- for i in range(1, max_key_length+1)])
- return max(results, key=lambda k: k[1])
-
-
-def beaufort_frequency_break(message, max_key_length=20, fitness=Pletters):
- """Breaks a Beaufort cipher with frequency analysis
-
- >>> beaufort_frequency_break(beaufort_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. I jump every time I hear a footstep on the stairs, " \
- "certain that the theft has been discovered and that I will " \
- "be caught. The SS officer visits less often now " \
- "that he is sure"), 'florence')) # doctest: +ELLIPSIS
- ('florence', -307.5473096791...)
- """
- def worker(message, key_length, fitness):
- splits = every_nth(sanitised_message, key_length)
- key = ''.join([chr(-caesar_break(s)[0] % 26 + ord('a'))
- for s in splits])
- plaintext = beaufort_decipher(message, key)
- fit = fitness(plaintext)
- return key, fit
- sanitised_message = sanitise(message)
- results = starmap(worker, [(sanitised_message, i, fitness)
- for i in range(1, max_key_length+1)])
- return max(results, key=lambda k: k[1])
-
-
-def column_transposition_break_mp(message, translist=transpositions,
- fitness=Pbigrams, chunksize=500):
- """Breaks a column transposition cipher using a dictionary and
- n-gram frequency analysis
-
- >>> 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 \
- 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), False, False), -709.4646722...)
- >>> 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 \
- 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']}, \
- fitness=Ptrigrams) # doctest: +ELLIPSIS
- (((2, 0, 5, 3, 1, 4, 6), False, False), -997.0129085...)
- """
- with Pool() as pool:
- helper_args = [(message, trans, fillcolumnwise, emptycolumnwise,
- fitness)
- for trans in translist.keys()
- for fillcolumnwise in [True, False]
- for emptycolumnwise 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 max(breaks, key=lambda k: k[1])
-column_transposition_break = column_transposition_break_mp
-
-def column_transposition_break_worker(message, transposition,
- fillcolumnwise, emptycolumnwise, fitness):
- plaintext = column_transposition_decipher(message, transposition,
- fillcolumnwise=fillcolumnwise, emptycolumnwise=emptycolumnwise)
- fit = fitness(sanitise(plaintext))
- logger.debug('Column transposition break attempt using key {0} '
- 'gives fit of {1} and decrypt starting: {2}'.format(
- transposition, fit,
- sanitise(plaintext)[:50]))
- return (transposition, fillcolumnwise, emptycolumnwise), fit
-
-
-def scytale_break_mp(message, max_key_length=20,
- fitness=Pbigrams, chunksize=500):
- """Breaks a scytale cipher using a range of lengths and
- n-gram frequency analysis
-
- >>> scytale_break_mp(scytale_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."), \
- 5)) # doctest: +ELLIPSIS
- (5, -709.4646722...)
- >>> scytale_break_mp(scytale_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."), \
- 5), \
- fitness=Ptrigrams) # doctest: +ELLIPSIS
- (5, -997.0129085...)
- """
- with Pool() as pool:
- helper_args = [(message, trans, False, True, fitness)
- for trans in
- [[col for col in range(math.ceil(len(message)/rows))]
- for rows in range(1,max_key_length+1)]]
- # 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)
- best = max(breaks, key=lambda k: k[1])
- return math.trunc(len(message) / len(best[0][0])), best[1]
-scytale_break = scytale_break_mp
-
-
-def railfence_break(message, max_key_length=20,
- fitness=Pletters, chunksize=500):
- """Breaks a hill cipher using a matrix of given rank and letter frequencies
-
-
- """
-
- sanitised_message = sanitise(message)
- results = starmap(worker, [(sanitised_message, i, fitness)
- for i in range(2, max_key_length+1)])
- return max(results, key=lambda k: k[1])
-
-
-def railfence_break(message, max_key_length=20,
- fitness=Pbigrams, chunksize=500):
- """Breaks a railfence cipher using a range of lengths and
- n-gram frequency analysis
-
- >>> railfence_break(railfence_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."), \
- 7)) # doctest: +ELLIPSIS
- (7, -709.46467226...)
- >>> railfence_break(railfence_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."), \
- 7), \
- fitness=Ptrigrams) # doctest: +ELLIPSIS
- (7, -997.0129085...)
- """
- def worker(message, height, fitness):
- plaintext = railfence_decipher(message, height)
- fit = fitness(plaintext)
- return height, fit
-
- sanitised_message = sanitise(message)
- results = starmap(worker, [(sanitised_message, i, fitness)
- for i in range(2, max_key_length+1)])
- return max(results, key=lambda k: k[1])
-
-def amsco_break(message, translist=transpositions, patterns = [(1, 2), (2, 1)],
- fillstyles = [AmscoFillStyle.continuous,
- AmscoFillStyle.same_each_row,
- AmscoFillStyle.reverse_each_row],
- fitness=Pbigrams,
- chunksize=500):
- """Breaks an AMSCO transposition cipher using a dictionary and
- n-gram frequency analysis
-
- >>> amsco_break(amsco_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']}, \
- patterns=[(1, 2)]) # doctest: +ELLIPSIS
- (((2, 0, 5, 3, 1, 4, 6), (1, 2)), -709.4646722...)
- >>> amsco_break(amsco_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', fillpattern=(2, 1)), \
- 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']}, \
- patterns=[(1, 2), (2, 1)], fitness=Ptrigrams) # doctest: +ELLIPSIS
- (((2, 0, 5, 3, 1, 4, 6), (2, 1)), -997.0129085...)
- """
- with Pool() as pool:
- helper_args = [(message, trans, pattern, fillstyle, fitness)
- for trans in translist.keys()
- for pattern in patterns
- for fillstyle in fillstyles]
- # Gotcha: the helper function here needs to be defined at the top level
- # (limitation of Pool.starmap)
- breaks = pool.starmap(amsco_break_worker, helper_args, chunksize)
- return max(breaks, key=lambda k: k[1])
-
-def amsco_break_worker(message, transposition,
- pattern, fillstyle, fitness):
- plaintext = amsco_transposition_decipher(message, transposition,
- fillpattern=pattern, fillstyle=fillstyle)
- fit = fitness(sanitise(plaintext))
- logger.debug('AMSCO transposition break attempt using key {0} and pattern'
- '{1} ({2}) gives fit of {3} and decrypt starting: '
- '{4}'.format(
- transposition, pattern, fillstyle, fit,
- sanitise(plaintext)[:50]))
- return (transposition, pattern, fillstyle), fit
-
-
-def hill_break(message, matrix_size=2, fitness=Pletters,
- number_of_solutions=1, chunksize=500):
-
- all_matrices = [np.matrix(list(m))
- for m in itertools.product([list(r)
- for r in itertools.product(range(26), repeat=matrix_size)],
- repeat=matrix_size)]
- valid_matrices = [m for m, d in
- zip(all_matrices, (int(round(linalg.det(m))) for m in all_matrices))
- if d != 0
- if d % 2 != 0
- if d % 13 != 0 ]
- with Pool() as pool:
- helper_args = [(message, matrix, fitness)
- for matrix in valid_matrices]
- # Gotcha: the helper function here needs to be defined at the top level
- # (limitation of Pool.starmap)
- breaks = pool.starmap(hill_break_worker, helper_args, chunksize)
- if number_of_solutions == 1:
- return max(breaks, key=lambda k: k[1])
- else:
- return sorted(breaks, key=lambda k: k[1], reverse=True)[:number_of_solutions]
-
-def hill_break_worker(message, matrix, fitness):
- plaintext = hill_decipher(matrix, message)
- fit = fitness(plaintext)
- logger.debug('Hill cipher break attempt using key {0} gives fit of '
- '{1} and decrypt starting: {2}'.format(matrix,
- fit, sanitise(plaintext)[:50]))
- return matrix, fit
-
-
-def pocket_enigma_break_by_crib(message, wheel_spec, crib, crib_position):
- """Break a pocket enigma using a crib (some plaintext that's expected to
- be in a certain position). Returns a list of possible starting wheel
- positions that could produce the crib.
-
- >>> pocket_enigma_break_by_crib('kzpjlzmoga', 1, 'h', 0)
- ['a', 'f', 'q']
- >>> pocket_enigma_break_by_crib('kzpjlzmoga', 1, 'he', 0)
- ['a']
- >>> pocket_enigma_break_by_crib('kzpjlzmoga', 1, 'll', 2)
- ['a']
- >>> pocket_enigma_break_by_crib('kzpjlzmoga', 1, 'l', 2)
- ['a']
- >>> pocket_enigma_break_by_crib('kzpjlzmoga', 1, 'l', 3)
- ['a', 'j', 'n']
- >>> pocket_enigma_break_by_crib('aaaaa', 1, 'l', 3)
- []
- """
- pe = PocketEnigma(wheel=wheel_spec)
- possible_positions = []
- for p in string.ascii_lowercase:
- pe.set_position(p)
- plaintext = pe.decipher(message)
- if plaintext[crib_position:crib_position+len(crib)] == crib:
- possible_positions += [p]
- return possible_positions
-
-
-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__":
- import doctest
- doctest.testmod()
+++ /dev/null
-import random
-import collections
-from cipher import *
-from cipherbreak import *
-import itertools
-import csv
-
-corpus = sanitise(''.join([open('shakespeare.txt', 'r').read(),
- open('sherlock-holmes.txt', 'r').read(),
- open('war-and-peace.txt', 'r').read()]))
-corpus_length = len(corpus)
-
-euclidean_scaled_english_counts = norms.euclidean_scale(english_counts)
-
-metrics = [{'func': norms.l1, 'invert': True, 'name': 'l1'},
- {'func': norms.l2, 'invert': True, 'name': 'l2'},
- {'func': norms.l3, 'invert': True, 'name': 'l3'},
- {'func': norms.cosine_similarity, 'invert': False, 'name': 'cosine_similarity'}]
- # {'func': norms.harmonic_mean, 'invert': True, 'name': 'harmonic_mean'},
- # {'func': norms.geometric_mean, 'invert': True, 'name': 'geometric_mean'}]
-scalings = [{'corpus_frequency': normalised_english_counts,
- 'scaling': norms.normalise,
- 'name': 'normalised'},
- {'corpus_frequency': euclidean_scaled_english_counts,
- 'scaling': norms.euclidean_scale,
- 'name': 'euclidean_scaled'}]
-message_lengths = [2000, 1000, 500, 250, 100, 50, 20]
-
-trials = 5000
-
-scores = {}
-
-
-def make_frequency_compare_function(target_frequency, frequency_scaling, metric, invert):
- def frequency_compare(text):
- counts = frequency_scaling(frequencies(text))
- if invert:
- score = -1 * metric(target_frequency, counts)
- else:
- score = metric(target_frequency, counts)
- return score
- return frequency_compare
-
-def scoring_functions():
- return [{'func': make_frequency_compare_function(s['corpus_frequency'],
- s['scaling'], m['func'], m['invert']),
- 'name': '{} + {}'.format(m['name'], s['name'])}
- for m in metrics
- for s in scalings] + [{'func': Pletters, 'name': 'Pletters'}]
-
-def eval_scores():
- [eval_one_score(f, l)
- for f in scoring_functions()
- for l in message_lengths]
-
-def eval_one_score(scoring_function, message_length):
- print(scoring_function['name'], message_length, ': ', end='', flush=True)
- if scoring_function['name'] not in scores:
- scores[scoring_function['name']] = collections.defaultdict(int)
- for _ in range(trials):
- sample_start = random.randint(0, corpus_length - message_length)
- sample = corpus[sample_start:(sample_start + message_length)]
- multiplier = random.choice([x for x in range(1, 26, 2) if x != 13])
- adder = random.randint(0, 25)
- one_based = random.choice([True, False])
- key = (multiplier, adder, one_based)
- ciphertext = affine_encipher(sample, multiplier, adder, one_based)
- found_key, _ = affine_break(ciphertext, scoring_function['func'])
- if found_key == key:
- scores[scoring_function['name']][message_length] += 1
- print(scores[scoring_function['name']][message_length], '/', trials)
- return scores[scoring_function['name']][message_length]
-
-def show_results():
- with open('affine_break_parameter_trials.csv', 'w') as f:
- writer = csv.DictWriter(f, ['name'] + message_lengths,
- quoting=csv.QUOTE_NONNUMERIC)
- writer.writeheader()
- for scoring in sorted(scores.keys()):
- scores[scoring]['name'] = scoring
- writer.writerow(scores[scoring])
-
-print('Starting...')
-eval_scores()
-show_results()
+++ /dev/null
-import random
-import collections
-from cipher import *
-from cipherbreak import *
-import itertools
-import csv
-
-corpus = sanitise(''.join([open('shakespeare.txt', 'r').read(),
- open('sherlock-holmes.txt', 'r').read(),
- open('war-and-peace.txt', 'r').read()]))
-corpus_length = len(corpus)
-
-euclidean_scaled_english_counts = norms.euclidean_scale(english_counts)
-
-metrics = [{'func': norms.l1, 'invert': True, 'name': 'l1'},
- {'func': norms.l2, 'invert': True, 'name': 'l2'},
- {'func': norms.l3, 'invert': True, 'name': 'l3'},
- {'func': norms.cosine_similarity, 'invert': False, 'name': 'cosine_similarity'}]
- # {'func': norms.harmonic_mean, 'invert': True, 'name': 'harmonic_mean'},
- # {'func': norms.geometric_mean, 'invert': True, 'name': 'geometric_mean'}]
-scalings = [{'corpus_frequency': normalised_english_counts,
- 'scaling': norms.normalise,
- 'name': 'normalised'},
- {'corpus_frequency': euclidean_scaled_english_counts,
- 'scaling': norms.euclidean_scale,
- 'name': 'euclidean_scaled'}]
-message_lengths = [100, 50, 30, 20, 10, 5]
-
-trials = 5000
-
-scores = {}
-
-
-def make_frequency_compare_function(target_frequency, frequency_scaling, metric, invert):
- def frequency_compare(text):
- counts = frequency_scaling(frequencies(text))
- if invert:
- score = -1 * metric(target_frequency, counts)
- else:
- score = metric(target_frequency, counts)
- return score
- return frequency_compare
-
-def scoring_functions():
- return [{'func': make_frequency_compare_function(s['corpus_frequency'],
- s['scaling'], m['func'], m['invert']),
- 'name': '{} + {}'.format(m['name'], s['name'])}
- for m in metrics
- for s in scalings] + [{'func': Pletters, 'name': 'Pletters'}]
-
-def eval_scores():
- [eval_one_score(f, l)
- for f in scoring_functions()
- for l in message_lengths]
-
-def eval_one_score(scoring_function, message_length):
- print(scoring_function['name'], message_length)
- if scoring_function['name'] not in scores:
- scores[scoring_function['name']] = collections.defaultdict(int)
- for _ in range(trials):
- sample_start = random.randint(0, corpus_length - message_length)
- sample = corpus[sample_start:(sample_start + message_length)]
- key = random.randint(1, 25)
- ciphertext = caesar_encipher(sample, key)
- found_key, _ = caesar_break(ciphertext, scoring_function['func'])
- if found_key == key:
- scores[scoring_function['name']][message_length] += 1
- return scores[scoring_function['name']][message_length]
-
-def show_results():
- with open('caesar_break_parameter_trials.csv', 'w') as f:
- writer = csv.DictWriter(f, ['name'] + message_lengths,
- quoting=csv.QUOTE_NONNUMERIC)
- writer.writeheader()
- for scoring in sorted(scores.keys()):
- scores[scoring]['name'] = scoring
- writer.writerow(scores[scoring])
-
-eval_scores()
-show_results()
+++ /dev/null
-import urllib.request
-import urllib.parse
-import json
-import time
-
-initial_request_url = "http://en.wikipedia.org/w/api.php?action=query&list=allpages&format=json&aplimit=10&apminsize=5000"
-request_url = "http://en.wikipedia.org/w/api.php?action=query&list=allpages&format=json&aplimit=10&apminsize=5000&apcontinue={}"
-titles_file = '/opt/sources/wp-titles.txt'
-
-def titles_of(result):
- return [p['title'] for p in result['query']['allpages'] ]
-
-def next_title(result):
- return result['query-continue']['allpages']['apcontinue']
-
-def write_titles(titles):
- with open(titles_file, 'a') as f:
- print('\n'.join(titles), file=f)
-
-def request_again(start_title):
- request = urllib.request.Request(request_url.format(urllib.parse.quote(start_title)))
- request.add_header('User-Agent','neil.wpspider@njae.me.uk')
- result = json.loads(urllib.request.urlopen(request).read().decode())
- return titles_of(result), next_title(result)
-
-f = open(titles_file, 'w')
-f.close()
-
-result = json.loads(urllib.request.urlopen(initial_request_url).read().decode())
-n_title = next_title(result)
-titles = titles_of(result)
-while titles != []:
- write_titles(titles)
- time.sleep(0.5)
- titles, n_title = request_again(n_title)
+++ /dev/null
-"""Language-specific functions, including models of languages based on data of
-its use.
-"""
-
-import string
-import random
-import norms
-import collections
-import unicodedata
-import itertools
-from math import log10
-import os
-
-unaccent_specials = ''.maketrans({"’": "'"})
-
-def letters(text):
- """Remove all non-alphabetic characters from a text
- >>> letters('The Quick')
- 'TheQuick'
- >>> letters('The Quick BROWN fox jumped! over... the (9lazy) DOG')
- 'TheQuickBROWNfoxjumpedoverthelazyDOG'
- """
- return ''.join([c for c in text if c in string.ascii_letters])
-
-def unaccent(text):
- """Remove all accents from letters.
- It does this by converting the unicode string to decomposed compatability
- form, dropping all the combining accents, then re-encoding the bytes.
-
- >>> unaccent('hello')
- 'hello'
- >>> unaccent('HELLO')
- 'HELLO'
- >>> unaccent('héllo')
- 'hello'
- >>> unaccent('héllö')
- 'hello'
- >>> unaccent('HÉLLÖ')
- 'HELLO'
- """
- translated_text = text.translate(unaccent_specials)
- return unicodedata.normalize('NFKD', translated_text).\
- encode('ascii', 'ignore').\
- decode('utf-8')
-
-def sanitise(text):
- """Remove all non-alphabetic characters and convert the text to lowercase
-
- >>> sanitise('The Quick')
- 'thequick'
- >>> sanitise('The Quick BROWN fox jumped! over... the (9lazy) DOG')
- 'thequickbrownfoxjumpedoverthelazydog'
- >>> sanitise('HÉLLÖ')
- 'hello'
- """
- # sanitised = [c.lower() for c in text if c in string.ascii_letters]
- # return ''.join(sanitised)
- return letters(unaccent(text)).lower()
-
-
-def datafile(name, sep='\t'):
- """Read key,value pairs from file.
- """
- with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), name), 'r') as f:
- for line in f:
- splits = line.split(sep)
- yield [splits[0], int(splits[1])]
-
-english_counts = collections.Counter(dict(datafile('count_1l.txt')))
-normalised_english_counts = norms.normalise(english_counts)
-
-english_bigram_counts = collections.Counter(dict(datafile('count_2l.txt')))
-normalised_english_bigram_counts = norms.normalise(english_bigram_counts)
-
-english_trigram_counts = collections.Counter(dict(datafile('count_3l.txt')))
-normalised_english_trigram_counts = norms.normalise(english_trigram_counts)
-
-with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'words.txt'), 'r') as f:
- keywords = [line.rstrip() for line in f]
-
-
-def weighted_choice(d):
- """Generate random item from a dictionary of item counts
- """
- target = random.uniform(0, sum(d.values()))
- cuml = 0.0
- for (l, p) in d.items():
- cuml += p
- if cuml > target:
- return l
- return None
-
-def random_english_letter():
- """Generate a random letter based on English letter counts
- """
- return weighted_choice(normalised_english_counts)
-
-
-def ngrams(text, n):
- """Returns all n-grams of a text
-
- >>> ngrams(sanitise('the quick brown fox'), 2) # doctest: +NORMALIZE_WHITESPACE
- ['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn',
- 'nf', 'fo', 'ox']
- >>> ngrams(sanitise('the quick brown fox'), 4) # doctest: +NORMALIZE_WHITESPACE
- ['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow',
- 'rown', 'ownf', 'wnfo', 'nfox']
- """
- return [text[i:i+n] for i in range(len(text)-n+1)]
-
-
-class Pdist(dict):
- """A probability distribution estimated from counts in datafile.
- Values are stored and returned as log probabilities.
- """
- def __init__(self, data=[], estimate_of_missing=None):
- data1, data2 = itertools.tee(data)
- self.total = sum([d[1] for d in data1])
- for key, count in data2:
- self[key] = log10(count / self.total)
- self.estimate_of_missing = estimate_of_missing or (lambda k, N: 1./N)
- def __missing__(self, key):
- return self.estimate_of_missing(key, self.total)
-
-def log_probability_of_unknown_word(key, N):
- """Estimate the probability of an unknown word.
- """
- return -log10(N * 10**((len(key) - 2) * 1.4))
-
-Pw = Pdist(datafile('count_1w.txt'), log_probability_of_unknown_word)
-Pw_wrong = Pdist(datafile('count_1w.txt'), lambda _k, N: log10(1/N))
-Pl = Pdist(datafile('count_1l.txt'), lambda _k, _N: 0)
-P2l = Pdist(datafile('count_2l.txt'), lambda _k, _N: 0)
-P3l = Pdist(datafile('count_3l.txt'), lambda _k, _N: 0)
-
-def Pwords(words):
- """The Naive Bayes log probability of a sequence of words.
- """
- return sum(Pw[w.lower()] for w in words)
-
-def Pwords_wrong(words):
- """The Naive Bayes log probability of a sequence of words.
- """
- return sum(Pw_wrong[w.lower()] for w in words)
-
-def Pletters(letters):
- """The Naive Bayes log probability of a sequence of letters.
- """
- return sum(Pl[l.lower()] for l in letters)
-
-def Pbigrams(letters):
- """The Naive Bayes log probability of the bigrams formed from a sequence
- of letters.
- """
- return sum(P2l[p] for p in ngrams(letters, 2))
-
-def Ptrigrams(letters):
- """The Naive Bayes log probability of the trigrams formed from a sequence
- of letters.
- """
- return sum(P3l[p] for p in ngrams(letters, 3))
-
-
-def cosine_similarity_score(text):
- """Finds the dissimilarity of a text to English, using the cosine distance
- of the frequency distribution.
-
- >>> cosine_similarity_score('abcabc') # doctest: +ELLIPSIS
- 0.26228882...
- """
- return norms.cosine_similarity(english_counts,
- collections.Counter(sanitise(text)))
-
-
-if __name__ == "__main__":
- import doctest
- doctest.testmod()
+++ /dev/null
-from language_models import sanitise
-import collections
-
-corpora = ['shakespeare.txt', 'sherlock-holmes.txt', 'war-and-peace.txt']
-counts = collections.Counter()
-
-for corpus in corpora:
- text = sanitise(open(corpus).read())
- counts.update(text)
-
-with open('count_1l.txt', 'w') as f:
- for l, c in counts.most_common():
- f.write("{}\t{}\n".format(l, c))
+++ /dev/null
-import language_models
-
-american = set(open('/usr/share/dict/american-english', 'r').readlines())
-british = set(open('/usr/share/dict/british-english', 'r').readlines())
-cracklib = set(open('/usr/share/dict/cracklib-small', 'r').readlines())
-
-words = american | british | cracklib
-
-# sanitised_words = set()
-
-# for w in words:
- # sanitised_words.add(language_models.sanitise(w))
-
-sanitised_words = set(language_models.sanitise(w) for w in words)
-
-sanitised_words.discard('')
-
-with open('words.txt', 'w') as f:
- f.write('\n'.join(sorted(sanitised_words, key=lambda w: (len(w), w))))
+++ /dev/null
-import collections
-from math import log10
-
-def normalise(frequencies):
- """Scale a set of frequencies so they sum to one
-
- >>> sorted(normalise({1: 1, 2: 0}).items())
- [(1, 1.0), (2, 0.0)]
- >>> sorted(normalise({1: 1, 2: 1}).items())
- [(1, 0.5), (2, 0.5)]
- >>> sorted(normalise({1: 1, 2: 1, 3: 1}).items()) # doctest: +ELLIPSIS
- [(1, 0.333...), (2, 0.333...), (3, 0.333...)]
- >>> sorted(normalise({1: 1, 2: 2, 3: 1}).items())
- [(1, 0.25), (2, 0.5), (3, 0.25)]
- """
- length = sum(f for f in frequencies.values())
- return collections.defaultdict(int, ((k, v / length)
- for (k, v) in frequencies.items()))
-
-def euclidean_scale(frequencies):
- """Scale a set of frequencies so they have a unit euclidean length
-
- >>> sorted(euclidean_scale({1: 1, 2: 0}).items())
- [(1, 1.0), (2, 0.0)]
- >>> sorted(euclidean_scale({1: 1, 2: 1}).items()) # doctest: +ELLIPSIS
- [(1, 0.7071067...), (2, 0.7071067...)]
- >>> sorted(euclidean_scale({1: 1, 2: 1, 3: 1}).items()) # doctest: +ELLIPSIS
- [(1, 0.577350...), (2, 0.577350...), (3, 0.577350...)]
- >>> sorted(euclidean_scale({1: 1, 2: 2, 3: 1}).items()) # doctest: +ELLIPSIS
- [(1, 0.408248...), (2, 0.81649658...), (3, 0.408248...)]
- """
- length = sum([f ** 2 for f in frequencies.values()]) ** 0.5
- return collections.defaultdict(int, ((k, v / length)
- for (k, v) in frequencies.items()))
-
-def identity_scale(frequencies):
- return frequencies
-
-
-def l2(frequencies1, frequencies2):
- """Finds the distances between two frequency profiles, expressed as dictionaries.
- Assumes every key in frequencies1 is also in frequencies2
-
- >>> l2({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1})
- 0.0
- >>> l2({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
- 1.73205080...
- >>> l2(normalise({'a':2, 'b':2, 'c':2}), normalise({'a':1, 'b':1, 'c':1}))
- 0.0
- >>> l2({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
- 1.732050807...
- >>> l2(normalise({'a':0, 'b':2, 'c':0}), \
- normalise({'a':1, 'b':1, 'c':1})) # doctest: +ELLIPSIS
- 0.81649658...
- >>> l2({'a':0, 'b':1}, {'a':1, 'b':1})
- 1.0
- """
- total = 0
- for k in frequencies1:
- total += (frequencies1[k] - frequencies2[k]) ** 2
- return total ** 0.5
-euclidean_distance = l2
-
-def l1(frequencies1, frequencies2):
- """Finds the distances between two frequency profiles, expressed as
- dictionaries. Assumes every key in frequencies1 is also in frequencies2
-
- >>> l1({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1})
- 0
- >>> l1({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1})
- 3
- >>> l1(normalise({'a':2, 'b':2, 'c':2}), normalise({'a':1, 'b':1, 'c':1}))
- 0.0
- >>> l1({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1})
- 3
- >>> l1({'a':0, 'b':1}, {'a':1, 'b':1})
- 1
- """
- total = 0
- for k in frequencies1:
- total += abs(frequencies1[k] - frequencies2[k])
- return total
-
-def l3(frequencies1, frequencies2):
- """Finds the distances between two frequency profiles, expressed as
- dictionaries. Assumes every key in frequencies1 is also in frequencies2
-
- >>> l3({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1})
- 0.0
- >>> l3({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
- 1.44224957...
- >>> l3({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
- 1.4422495703...
- >>> l3(normalise({'a':0, 'b':2, 'c':0}), \
- normalise({'a':1, 'b':1, 'c':1})) # doctest: +ELLIPSIS
- 0.718144896...
- >>> l3({'a':0, 'b':1}, {'a':1, 'b':1})
- 1.0
- >>> l3(normalise({'a':0, 'b':1}), normalise({'a':1, 'b':1})) # doctest: +ELLIPSIS
- 0.6299605249...
- """
- total = 0
- for k in frequencies1:
- total += abs(frequencies1[k] - frequencies2[k]) ** 3
- return total ** (1/3)
-
-def geometric_mean(frequencies1, frequencies2):
- """Finds the geometric mean of the absolute differences between two frequency profiles,
- expressed as dictionaries.
- Assumes every key in frequencies1 is also in frequencies2
-
- >>> geometric_mean({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1})
- 1
- >>> geometric_mean({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1})
- 1
- >>> geometric_mean({'a':2, 'b':2, 'c':2}, {'a':1, 'b':5, 'c':1})
- 3
- >>> geometric_mean(normalise({'a':2, 'b':2, 'c':2}), \
- normalise({'a':1, 'b':5, 'c':1})) # doctest: +ELLIPSIS
- 0.01382140...
- >>> geometric_mean(normalise({'a':2, 'b':2, 'c':2}), \
- normalise({'a':1, 'b':1, 'c':1})) # doctest: +ELLIPSIS
- 0.0
- >>> geometric_mean(normalise({'a':2, 'b':2, 'c':2}), \
- normalise({'a':1, 'b':1, 'c':0})) # doctest: +ELLIPSIS
- 0.009259259...
- """
- total = 1
- for k in frequencies1:
- total *= abs(frequencies1[k] - frequencies2[k])
- return total
-
-def harmonic_mean(frequencies1, frequencies2):
- """Finds the harmonic mean of the absolute differences between two frequency profiles,
- expressed as dictionaries.
- Assumes every key in frequencies1 is also in frequencies2
-
- >>> harmonic_mean({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1})
- 1.0
- >>> harmonic_mean({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1})
- 1.0
- >>> harmonic_mean({'a':2, 'b':2, 'c':2}, {'a':1, 'b':5, 'c':1}) # doctest: +ELLIPSIS
- 1.285714285...
- >>> harmonic_mean(normalise({'a':2, 'b':2, 'c':2}), \
- normalise({'a':1, 'b':5, 'c':1})) # doctest: +ELLIPSIS
- 0.228571428571...
- >>> harmonic_mean(normalise({'a':2, 'b':2, 'c':2}), \
- normalise({'a':1, 'b':1, 'c':1})) # doctest: +ELLIPSIS
- 0
- >>> harmonic_mean(normalise({'a':2, 'b':2, 'c':2}), \
- normalise({'a':1, 'b':1, 'c':0})) # doctest: +ELLIPSIS
- 0.2
- """
- total = 0
- for k in frequencies1:
- if abs(frequencies1[k] - frequencies2[k]) == 0:
- return 0
- total += 1 / abs(frequencies1[k] - frequencies2[k])
- return len(frequencies1) / total
-
-
-def cosine_similarity(frequencies1, frequencies2):
- """Finds the distances between two frequency profiles, expressed as dictionaries.
- Assumes every key in frequencies1 is also in frequencies2
-
- >>> cosine_similarity({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
- 1.0000000000...
- >>> cosine_similarity({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
- 1.0000000000...
- >>> cosine_similarity({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
- 0.5773502691...
- >>> cosine_similarity({'a':0, 'b':1}, {'a':1, 'b':1}) # doctest: +ELLIPSIS
- 0.7071067811...
- """
- numerator = 0
- length1 = 0
- length2 = 0
- for k in frequencies1:
- numerator += frequencies1[k] * frequencies2[k]
- length1 += frequencies1[k]**2
- for k in frequencies2.keys():
- length2 += frequencies2[k]**2
- return numerator / (length1 ** 0.5 * length2 ** 0.5)
-
-
-
-if __name__ == "__main__":
- import doctest
- doctest.testmod()
+++ /dev/null
-{
- "metadata": {
- "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import csv\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "with open('caesar_break_parameter_trials.csv') as c:\n",
- " results = [l for l in csv.DictReader(c, quoting=csv.QUOTE_NONNUMERIC)]\n",
- "results"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 6,
- "text": [
- "[{5.0: 2401.0,\n",
- " 10.0: 4092.0,\n",
- " 20.0: 4893.0,\n",
- " 50.0: 4997.0,\n",
- " 100.0: 4995.0,\n",
- " 'name': 'Pletters',\n",
- " 30.0: 4971.0},\n",
- " {5.0: 2174.0,\n",
- " 10.0: 3590.0,\n",
- " 20.0: 4671.0,\n",
- " 50.0: 4980.0,\n",
- " 100.0: 4997.0,\n",
- " 'name': 'cosine_similarity + euclidean_scaled',\n",
- " 30.0: 4915.0},\n",
- " {5.0: 2207.0,\n",
- " 10.0: 3559.0,\n",
- " 20.0: 4660.0,\n",
- " 50.0: 4993.0,\n",
- " 100.0: 4999.0,\n",
- " 'name': 'cosine_similarity + normalised',\n",
- " 30.0: 4931.0},\n",
- " {5.0: 2175.0,\n",
- " 10.0: 3751.0,\n",
- " 20.0: 4761.0,\n",
- " 50.0: 4995.0,\n",
- " 100.0: 4997.0,\n",
- " 'name': 'l1 + euclidean_scaled',\n",
- " 30.0: 4950.0},\n",
- " {5.0: 2188.0,\n",
- " 10.0: 3662.0,\n",
- " 20.0: 4770.0,\n",
- " 50.0: 4993.0,\n",
- " 100.0: 4999.0,\n",
- " 'name': 'l1 + normalised',\n",
- " 30.0: 4950.0},\n",
- " {5.0: 2209.0,\n",
- " 10.0: 3579.0,\n",
- " 20.0: 4664.0,\n",
- " 50.0: 4991.0,\n",
- " 100.0: 4997.0,\n",
- " 'name': 'l2 + euclidean_scaled',\n",
- " 30.0: 4914.0},\n",
- " {5.0: 2195.0,\n",
- " 10.0: 3536.0,\n",
- " 20.0: 4637.0,\n",
- " 50.0: 4988.0,\n",
- " 100.0: 4998.0,\n",
- " 'name': 'l2 + normalised',\n",
- " 30.0: 4915.0},\n",
- " {5.0: 1978.0,\n",
- " 10.0: 3243.0,\n",
- " 20.0: 4477.0,\n",
- " 50.0: 4979.0,\n",
- " 100.0: 4997.0,\n",
- " 'name': 'l3 + euclidean_scaled',\n",
- " 30.0: 4822.0},\n",
- " {5.0: 1949.0,\n",
- " 10.0: 2966.0,\n",
- " 20.0: 4351.0,\n",
- " 50.0: 4959.0,\n",
- " 100.0: 4999.0,\n",
- " 'name': 'l3 + normalised',\n",
- " 30.0: 4774.0}]"
- ]
- }
- ],
- "prompt_number": 6
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "for r in results:\n",
- " plt.plot(sorted([k for k in r.keys() if k != 'name']), sorted([r[k] for k in r.keys() if k != 'name']))\n",
- "plt.legend([r['name'] for r in results], loc='center', bbox_to_anchor=(2, 0.5))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 7,
- "text": [
- "<matplotlib.legend.Legend at 0x7eff2f9fd250>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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b4djOFJuWNhxc8zRxG9X06juAxo0bG9Ll58O6dXD0qH5505lNPBLwCM5WztUc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- "text": [
- "<matplotlib.figure.Figure at 0x7eff2f9f82d0>"
- ]
- }
- ],
- "prompt_number": 7
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "for r in results:\n",
- " plt.plot(sorted([k for k in [5.0, 10.0, 20.0, 30.0]]), sorted([r[k] for k in [5.0, 10.0, 20.0, 30.0]]))\n",
- "plt.legend([r['name'] for r in results], loc='center', bbox_to_anchor=(2, 0.5))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 8,
- "text": [
- "<matplotlib.legend.Legend at 0x7eff2e0aba90>"
- ]
- },
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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OhoEvNqe/p2uDBwmtXktaYRqphamkFSRToDqOIv0M1oUp2FXmYW9WhkMzsHQCo6uEpADz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\nAAAAAElFTkSuQmCC\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7eff2e0abd50>"
- ]
- }
- ],
- "prompt_number": 8
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "bests = {}\n",
- "for l in [k for k in results[0].keys() if k != 'name']:\n",
- " bests[l] = max(results, key=lambda r: r[l])['name']\n",
- "bests"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 9,
- "text": [
- "{5.0: 'Pletters',\n",
- " 10.0: 'Pletters',\n",
- " 20.0: 'Pletters',\n",
- " 30.0: 'Pletters',\n",
- " 50.0: 'Pletters',\n",
- " 100.0: 'cosine_similarity + normalised'}"
- ]
- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
+++ /dev/null
-"""Segment a collection of letters into words"""
-
-import language_models
-import sys
-from functools import lru_cache
-sys.setrecursionlimit(1000000)
-
-@lru_cache()
-def segment(text):
- """Return a list of words that is the best segmentation of text.
- """
- if not text: return []
- candidates = ([first]+segment(rest) for first, rest in splits(text))
- return max(candidates, key=language_models.Pwords)
-
-@lru_cache()
-def segment_wrong(text):
- """Return a list of words that is the best segmentation of text.
- """
- if not text: return []
- candidates = ([first]+segment(rest) for first, rest in splits(text))
- return max(candidates, key=language_models.Pwords_wrong)
-
-
-def splits(text, L=20):
- """Return a list of all possible (first, rest) pairs, len(first)<=L.
- """
- return [(text[:i+1], text[i+1:])
- for i in range(min(len(text), L))]
-
+++ /dev/null
-{
- "metadata": {
- "name": "",
- "signature": "sha256:6a52b786de0fbcf68d2da20d0ad155b76414b0e5add50e4d11d9f765911eb884"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%matplotlib inline\n",
- "import matplotlib.pyplot as plt\n",
- "from math import log10\n",
- "from cipherbreak import *\n",
- "from language_models import *"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 1
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# What fraction of possible words are present in the dictionary?"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "fractions = [1.0]\n",
- "for wordlen in range(1, 20):\n",
- " known_words = len([w for w in keywords if len(w) == wordlen]) # Words in the dictionary\n",
- " possible_words = 26 ** wordlen\n",
- " fractions += [known_words / possible_words]\n",
- "fractions"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 2,
- "text": [
- "[1.0,\n",
- " 1.0,\n",
- " 0.34615384615384615,\n",
- " 0.05974055530268548,\n",
- " 0.007654668954168271,\n",
- " 0.0005913456488541394,\n",
- " 3.6129588927177356e-05,\n",
- " 1.8010883826943671e-06,\n",
- " 7.107795165409739e-08,\n",
- " 2.4355817367258945e-09,\n",
- " 7.469162662303339e-11,\n",
- " 1.979378237044537e-12,\n",
- " 4.875878520286003e-14,\n",
- " 1.1095648437193016e-15,\n",
- " 2.199659830168053e-17,\n",
- " 4.185399254019551e-19,\n",
- " 6.83349209784038e-21,\n",
- " 1.199477188056649e-22,\n",
- " 2.0013901045062867e-24,\n",
- " 1.5656245928341226e-26]"
- ]
- }
- ],
- "prompt_number": 2
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Plot the fractions"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "plt.plot(fractions)\n",
- "plt.ylabel(\"Probability of word\")\n",
- "plt.xlabel(\"Word length\")\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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whuh4KWHyWGZMmgR/yMSC35KyLE6ncg64GXg2uj4J+BhwX0Ix1RxLRpKyIM741IcJM4yX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\nkiRJqlX/D1Q+4n26HL3CAAAAAElFTkSuQmCC\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7f9d3321cfd0>"
- ]
- }
- ],
- "prompt_number": 3
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Log plot"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "plt.plot([log10(f) for f in fractions])\n",
- "plt.plot([log10(f) for f in fractions], 'bo')\n",
- "plt.ylabel(\"Log probability of word\")\n",
- "plt.xlabel(\"Word length\")\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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MGROePSk0u0lEJMItXZpNRkbWiT0pxoxJCdvsJiUJEZE4EU2L6UREJAooSYiI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\nSUVORK5CYII=\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7f9d31cd14a8>"
- ]
- }
- ],
- "prompt_number": 4
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Guess some lines of best fit"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "plt.plot([log10(f) for f in fractions], 'bo')\n",
- "plt.plot([i for i in range(2,20)], [-(i-2) for i in range(2,20)], 'g-')\n",
- "plt.plot([i for i in range(2,20)], [-(i-3)*1.5 for i in range(2,20)], 'r-')\n",
- "plt.plot([i for i in range(2,20)], [-(i-2)*1.4 for i in range(2,20)], 'r^-')\n",
- "plt.ylabel(\"Log probability of word\")\n",
- "plt.xlabel(\"Word length\")\n",
- "plt.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "display_data",
- "png": 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F616UH/4OEvF4bmbKCRKv+nIhP1OQkMpj1SoYMgT++18YPZrkM2dI6dvXJuRd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\nRK5CYII=\n",
- "text": [
- "<matplotlib.figure.Figure at 0x7f9d31d314a8>"
- ]
- }
- ],
- "prompt_number": 6
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "' '.join(segment(sanitise('HELMU TSCOU SINSA REISU PPOSE KINDI NTHEI ROWNW AYBUT THERE ISLIT TLEWA RMTHI NTHEK INDNE SSIRE CEIVE ')))\n"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "metadata": {},
- "output_type": "pyout",
- "prompt_number": 9,
- "text": [
- "'helmut s cousins are i suppose kind in their own way but there is little warmth in the kindness i receive'"
- ]
- }
- ],
- "prompt_number": 9
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
projects / cipher-training.git / commitdiff