From: Neil Smith Date: Sun, 27 Mar 2016 12:39:39 +0000 (+0100) Subject: Deleted unnecessary files X-Git-Url: https://git.njae.me.uk/?a=commitdiff_plain;h=refs%2Fheads%2Fpresentation-slides;p=cipher-training.git Deleted unnecessary files --- diff --git a/SIGNED.md b/SIGNED.md deleted file mode 100644 index d07ec2a..0000000 --- a/SIGNED.md +++ /dev/null @@ -1,130 +0,0 @@ -##### Signed by https://keybase.io/neilnjae -``` ------BEGIN PGP SIGNATURE----- -Version: GnuPG v2 - -iQIcBAABCAAGBQJWjpkcAAoJEJPB2e07PgbqyX4QAIMe/BAgFiLDcxDXp6IUQvd8 -TSkZitbbe4WVSiGZ2rsS/LbuHeJr7WGBnUYACp1AeMeJqE62mPl0xbForExH7rRS -PWF9qw8H+Ie6Cu6KeF9hPbAje+24pntjUT/49slb3zNwcmPP3IytA20w50T6Arw7 -aDq7pZ8IUfWnxTR6ZeWLWDo1rRjNMumQ4CmjcP24HElEm84IJJE9sBpd1uqGDHzW -q12wajgwO8jANVdAAxXHye3QieEINzvkCNBShhIwE1/oxYs0Gl94UwSVtHuAFqxv -oul5THS9WFdDCafhgqQkjJC0iOgk9VY833TGIBYzHVA4ktPJmxVvjiDa2aG9vybZ -JJ5MUriSjnf/K65KJl/5mt9bZtskteS5xP0njzjv2579vD2qiqTsX7h8WgYmTjKe -ZxyICi/c5fg2188J0qyr0AUYX9blF3ixlGnovZqXbGa1hIjW2HgZUUrTrvkhyLw5 -RwH5B98LIsmVyI0ZNAQejkyHweUYscwyBS0SZDmu5r+23FJ4wlLNUHi2ThK0ep7e -2ix7QQ3qKsaISDcl6xxARSnBPqScJVHwb8cxI47KeBkVwE48zqaw6BfxjF7IskeN -ypU/7vjy5XjgwxnTanjYZr8LfnpO84I6qMibL1SRz6YgUkzml6Yp+Nq3viBuv1nh -6rhe8Q/XLCg5I3VvegZt -=Ry9+ ------END PGP SIGNATURE----- - -``` - - - -### Begin signed statement - -#### Expect - -``` -size exec file contents - ./ -384 .gitignore a93de2ae5c2a47a38599751d1f914566569dfa09dd1778e207117db6c71421dd -18025 LICENSE a01259a1b522cf0de95824f9860613b453153eebac468e96196d5d7dba84786c -61 README.md 277247b410300ee16477b12ca54ad878d81c8061f6134e2e1cadccaf299de3a3 -469 affine_break_parameter_trials.csv 1a9d635d0af2f41fc6f1e83ae87d6372034259321ba288a11fb024e98ed52f4f|dd9c840434de596a30c84e79de26a9824b36c217a84876c2aab0579b76999735 -6488666 big.txt fa066c7d40f0f201ac4144e652aa62430e58a6b3805ec70650f678da5804e87b -514 caesar_break_parameter_trials.csv 6586223bcc00e06e3ff79d107202d6c29ef962a6dd544add00610c5907407e85|1cb7cc77831ef3ef4f994a9ea77e82a841b38acdde45ede9cedbe7a54f1e8e46 -135303 challenge6.ipynb 5b37a8b10db4c8d9831827a2acdffdcdb65369557d15b3e08a900ee8e088da73 -75506 challenge7.ipynb ee9a99fa7a9845ad7db77199927fe8ad1d05a6b0272e50d8175e874e7a3e4cdf -318 cipher-training.sublime-project 58e5d5b4e54fb29abecaef2d41266e3355adccb8b6a70bd595e509bd07c16587 -42922 cipher.py 58637b8946b4fb973b19a374a2066a896d86c928dacaa1ccd2252e6f8bb6e810 -28908 cipherbreak.py 0fb22645ddce4e04c7e441a1f7bdc0e4a397a3c9b2cfb3098bcb213e79a361c9 -11564 count_1edit.txt 3bf563ef032ba151ec1a4b2d1f33f50c49f4a47e4dc5b8152394bc5b63f57655|b5fbacbebcc25f5011ce97bc9ac967a09c50eef28b4aa98379a6c426df6ac08b -223 count_1l.txt 335388d457db6ef1da05d8b55ab879e9be7d4e021085efc8d9dfeac0e4a79aa9 -4956241 count_1w.txt 51df159fd3de12b20e403c108f526e96dbd723d9cabdd5f17955cdc16059e690 -9270 count_2l.txt bc2895f800189070c193907cd8bca956ad65fed2e25c14300d4bb5b6a243ba99 -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 -1236 find_wikipedia_titles.py f040bf855dfec7fff9d8e5eba2fb509179bc53bc02a20b26b7fc61fef983aa45 -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 - 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-#### Ignore - -``` -/SIGNED.md -``` - -#### Presets - -``` -git # ignore .git and anything as described by .gitignore files -``` - - - -### End signed statement - -
- -#### Notes - -With keybase you can sign any directory's contents, whether it's a git repo, -source code distribution, or a personal documents folder. It aims to replace the drudgery of: - - 1. comparing a zipped file to a detached statement - 2. downloading a public key - 3. confirming it is in fact the author's by reviewing public statements they've made, using it - -All in one simple command: - -```bash -keybase dir verify -``` - -There are lots of options, including assertions for automating your checks. - -For more info, check out https://keybase.io/docs/command_line/code_signing \ No newline at end of file diff --git a/affine_break_parameter_trials.csv b/affine_break_parameter_trials.csv deleted file mode 100644 index 75f3f16..0000000 --- a/affine_break_parameter_trials.csv +++ /dev/null @@ -1,10 +0,0 @@ -"name",500,250,100,50,20 -"Pletters",2442,2425,2550,2461,2067 -"cosine_similarity + euclidean_scaled",2487,2492,2513,2455,1731 -"cosine_similarity + normalised",2503,2573,2454,2435,1680 -"l1 + euclidean_scaled",2495,2477,2506,2419,1812 -"l1 + normalised",2488,2521,2465,2466,1895 -"l2 + euclidean_scaled",2524,2467,2529,2470,1763 -"l2 + normalised",2566,2545,2481,2433,1756 -"l3 + euclidean_scaled",2444,2458,2560,2426,1582 -"l3 + normalised",2476,2524,2463,2354,1433 diff --git a/caesar_break_parameter_trials.csv b/caesar_break_parameter_trials.csv deleted file mode 100644 index a414d22..0000000 --- a/caesar_break_parameter_trials.csv +++ /dev/null @@ -1,10 +0,0 @@ -"name",100,50,30,20,10,5 -"Pletters",4995,4997,4971,4893,4092,2401 -"cosine_similarity + euclidean_scaled",4997,4980,4915,4671,3590,2174 -"cosine_similarity + normalised",4999,4993,4931,4660,3559,2207 -"l1 + euclidean_scaled",4997,4995,4950,4761,3751,2175 -"l1 + normalised",4999,4993,4950,4770,3662,2188 -"l2 + euclidean_scaled",4997,4991,4914,4664,3579,2209 -"l2 + normalised",4998,4988,4915,4637,3536,2195 -"l3 + euclidean_scaled",4997,4979,4822,4477,3243,1978 -"l3 + normalised",4999,4959,4774,4351,2966,1949 diff --git a/challenge6.ipynb b/challenge6.ipynb deleted file mode 100644 index ca4e493..0000000 --- a/challenge6.ipynb +++ /dev/null @@ -1,1141 +0,0 @@ -{ - "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": [ - "" - ] - } - ], - "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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CCwBgBQILAGAFAgsAYIWggVVaWqqkpCQlJCRo6dKlLdYsXLhQCQkJ8nq9Ki8vb3ZfIBBQ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\n4cOaO3eu4/hee+0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- "text": [ - "" - ] - } - ], - "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": [ - 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TOWLE ATBRT EPPTR AAESE NETHH RISNR STSTR NFLDO GHAUM GSESE AKUNT IAHYT TNYUN TNNVH IGWLE WFIAP ASREL FAIEB APCMC PLAHE THTOL HWSUL ADSEU VEAEO TAEUT ATEEF EHLBH TRSHG ATLIA CEEHT RNHGA ASIET UFNNP UREHR ELITT MUDYI NNUEG ICOUE NDEEI AHSUE LHTNO AHTIE AINYF DNIAU OENAT IYEFO HEDAL UAROE RYHUR AERAT ERAHI AONGT WISWS AITBU LTAOM XLETO REOTO TONAO ORTGB CVAIP NLEEW FYRTA EOADU WWETR RTNLR PFPAI ELLRI MNTRO WIEQR ECUAR NDOTA FNROT ELETP ENRPM SHNDD ETDEW HEFOY PEDEN NSNEE GTATE EANES ERTGD LUEOS RBURO WHDGB RPAHE NYTTE KRERT RRHNF NEDOL TSWSI ANIYP OTHMO UOCTY EIEEP ONHKR NNTAI RLTEE IUREM NITAP HOEAO HHNOU UMTKV SENED EEOID PWKIE GTIII OSENH UUFAN BEUIT HRAER HIEEH UAYAS RUSIW GADSI SITRL GFPHI LTWII SIHES URMRO MACEI SOILV DEOEL USAIR IDEOT NFIES OBNTS APOFG SODRT FIRPL TANET RLYIO SOTYO ESSFN TPHEA IGEAO RAIEI TDMTA AEGRH YASRR CFELR TLEEA NEHVE HIVMH NASSI EROIX LMPSJ TTESO FBMRH NDIOC HECNB TTNCO MPUSE SOEHT ENETF IENOO TNWOO TEERI LTOIG DOSME HOIFM LROEF FDTUE SOFLE RWIIY QDBZO TTNRF ANTDI GONTA LAEPD SHEAM NTHLP SNBTU OAHAA LPLEM NPDRS HVNAI AELNH CALIS KACDO AEDTJ GHOIE ADGWA YNDET ENHEG EEUEO EEDNO RYYRF CTEMT TWNTG DETAS UOOOT FWYMI HRGRH OESIN RRAOF RETFE NPOYA ISSES STNHH AYEUL HIWPC TEATU APEAN LUEEL WAHAO ELURE SNBII EASAE TEAGC DCSOR EPSRO ENSEL VOSED UDSIT LTEIT TFSEO ESNLN GINOE RNTEI EIWWL WHTEI RSIIL HTDSR NDESR FHRNT LGRSU ASEDA DOYTA SADUT ENWIT CNAME TERTP OESYT OTOCN YUTSO GCMUD LPADL EADOT EFDSB FELEA HAXRS ONIDT IEIPR ASHIC DCSHI PBIEL SLNAL HORFR ENTAT ORAIE MKLAG RETMR DCWSN AYNWS VNULD OREKU TNNOU TIIUN NHVNI EILOH LTEFN AEATU IFTTA CETLN NRBHB EGLOS NAONM IFKOA RONSN VNFRE TRCFT HDETP SWSUC PERCW TPOAW FHDAY ISOOA GOAEL NDFIG NGRTE BLTLL GFO\\n'" - ] - } - ], - "prompt_number": 30 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[i for i in range(len(c6bs)) if c6bs[i] == 'q']" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 42, - "text": [ - "[29, 503, 985]" - ] - } - ], - "prompt_number": 42 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "''.join([c[0] for c in every_nth(c6bs, 11)])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 32, - "text": [ - "'hithhnfrfer'" - ] - } - ], - "prompt_number": 32 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "''.join([c[0] for c in every_nth(c6bs, 13)])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 33, - "text": [ - "'hithhnfrferte'" - ] - } - ], - "prompt_number": 33 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "''.join([c[0] for c in chunks(c6bs, 121)])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 34, - "text": [ - "'hhrnrnumeodti'" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "''.join([c[0] for c in chunks(c6bs, 13)])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 35, - "text": [ - "'harrseehthoexttnsneitetghuydoihuouocttiuedeeerrsohiaeefhifsiitiohtreiehseiiszomhhihtdtoeentencsllilrdieodhpbrlakhntnsdwon'" - ] - } - ], - "prompt_number": 35 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "'l' in ''.join([c[0] for c in chunks(c6bs, 13)])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 36, - "text": [ - "True" - ] - } - ], - "prompt_number": 36 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "every_nth(c6bs, 11)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 38, - "text": [ - "['httmtbnoreohaegaasdetgrmlvtshtenenfiargurredrmcrpwnaegennckehmoinhutioddnrtegmranmmneieseetoetmaaoeoceyiesieaidnseewtrdtttmoaicnagariounanfcfor',\n", - " 'ieysaniepwthhtmtenoanwecheehtulueodyratleteupnuomhsnobkeitriotiibisrimeettrsetrnaprbseemfrtnaunecitettmnntwaoecsiorhdnaeeouthesatryeuhirostphat',\n", - " 'taoeerrintocaoxbsrgkyllpwaegrfieianeotwtogwwftatsenesrrdyynuekdoeeilsaoosflsaacesshtonrefwntmopldeeeeairpnpneasetentstdnrcdeaihloenknlfbnnhedee',\n", - " 'horrerhrhsypsfureshuueflsefanntgahifeeiatbfwprrehfesrpeopenravpsuewgicetaiyfoafhsjnteoihdiranadnoandmshrohcllsollsterlowtnlfxpihrtwuntthmvdralb',\n", - " 'hfruitmcthnihrrtntannwaauoethntihtaorrsoocyeaonlnoeebarloiteosweihgfhelnprinreevitdnholotiflthrhadhnturayhtuuarvtneingyipypdrrpoamsthetbinecynl',\n", - " 'nfrlusriotuctnteesuttfihlthlgpmcsiuhyawmtvrtiwdedygruhttteamhekntuapeiufopotagleetictttiuyaahascegeotogoaaeereeoelirdrttouassabrirvnvfaefftwidt',\n", - " 'fteriipthewmietpttminieeaaliauuoueoehhsxoatreiotdpttrersheinhnihhadhsssiflspirrhreooenofeqnelahadwgrworfiyaeetpsinesesacetdbosifednnnncgkrptsfl',\n", - " 'rnqhwoyaoshnjuophrganabtdebaardueaeduialniarletpeeagonrwmprineeurysiuoaegtohehtioscmnwimsdtpplvltaeynohrsetlsesetgiisusnsslfnhermcuoiaeloespoil',\n", - " 'fatfsitlrproeswthnshvpahsuhcseyelinaraieapetlqaetdtdwyhsooltodguaailriissateiylviohpeoglobddspnijyeyttoesuuwnardtiwiraaayoeeiilekwlueetoatwoogg',\n", - " 'eshsiidnloeoarlrrfeyhaptetteihinhnalaottononrrfndeelhtniuntauetfesstmlroonyataemxfeutodrfzisnlasgnurgfetelaabgoufnwlfsdmtgaldcsnlsdtialsrrsaanf',\n", - " 'rliehetbisdtnaeailstiscouareerndtytuenboolalienreneudtfaohepueiarriwrvibdeoidsehlbcsftoologhbeikhdefdwsfshphicedsolhheueocdetdltanoiltnnocuwggo']" - ] - } - ], - "prompt_number": 38 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "every_nth(c6bs, 143)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 64, - "text": [ - "['hetuehroera',\n", - " 'iteloirnnny',\n", - " 'toetsectptn',\n", - " 'hffarefaolw',\n", - " 'hreobhelygs',\n", - " 'nnhmuulaarv',\n", - " 'felxrareisn',\n", - " 'rubloytpsuu',\n", - " 'fshewaldsal',\n", - " 'ertthsesesd',\n", - " 'rarodrehseo',\n", - " 'tgsrguaesdr',\n", - " 'emhebsnatae',\n", - " 'axgoriemndk',\n", - " 'ouatpwhnhou',\n", - " 'frtoagvthyt',\n", - " 'ftlthaehatn',\n", - " 'ttioedhlyan',\n", - " 'noannsipeso',\n", - " 'awcayivsuau',\n", - " 'sleotsmnldt',\n", - " 'leeotihbhui',\n", - " 'tahretntiti',\n", - " 'ytttkrauweu',\n", - " 'obrgrlsopnn',\n", - " 'rrnbegsacwn',\n", - " 'rthcrfihtih',\n", - " 'regvtpeaetv',\n", - " 'epaarhraacn',\n", - " 'qpairioltni',\n", - " 'ttsphlipuae',\n", - " 'hrinntxlami',\n", - " 'iaelfwlepel',\n", - " 'matenimmeto',\n", - " 'seueeipnaeh',\n", - " 'esfwdsspnrl',\n", - " 'renfoijdltt',\n", - " 'unnylhtrupe',\n", - " 'leprtetseof',\n", - " 'rtutsseheen',\n", - " 'hhrawusvlsa',\n", - " 'fheesronwye',\n", - " 'srhoimfaata',\n", - " 'eiraarbihot',\n", - " 'tsednomaatu',\n", - " 'anluimreooi',\n", - " 'eriwyahlecf',\n", - " 'estwpcnnlnt',\n", - " 'itteoedhuyt',\n", - " 'usmttiicrua',\n", - " 'iturhsoaetc',\n", - " 'wrdrmoclsse',\n", - " 'snytoihinot',\n", - " 'ifinulesbgl',\n", - " 'hlnlovckicn',\n", - " 'bdnrcdnaimn',\n", - " 'nouptebceur',\n", - " 'rgefyotdadb',\n", - " 'rhgpeetoslh',\n", - " 'taiailnaapb',\n", - " 'sucieuceeae',\n", - " 'imoeesodtdg',\n", - " 'ogulpamtell',\n", - " 'iseloipjaeo',\n", - " 'ienrnruggas',\n", - " 'esdihishcdn',\n", - " 'neemkdeodoa',\n", - " 'iaenresicto',\n", - " 'rkitnooesen',\n", - " 'huarnteaofm',\n", - " 'mnhotnhdrdi',\n", - " 'rtswaftgesf',\n", - " 'piuiiiewpbk',\n", - " 'yaeerenasfo',\n", - " 'thlqlseyrea',\n", - " 'dyhrtotnolr',\n", - " 'ttteebfdeeo',\n", - " 'otncenienan',\n", - " 'enouitetshs',\n", - " 'iyaausneean',\n", - " 'ruhrraonlxv',\n", - " 'cntnepohvrn',\n", - " 'itidmoteosf',\n", - " 'tneonfngsor',\n", - " 'anatigweene',\n", - " 'lviatsoedit',\n", - " 'nhnfaoouudr',\n", - " 'biynpdtedtc',\n", - " 'rgfrhreosif',\n", - " 'pwdooteeiet',\n", - " 'nlntefretih',\n", - " 'heieaiidlpd',\n", - " 'twalorlntre',\n", - " 'ofuehptoeat',\n", - " 'hiothlorisp',\n", - " 'oaepntiyths',\n", - " 'rpneoagytiw',\n", - " 'laanundrfcs',\n", - " 'istrueofsdu',\n", - " 'eripmtscecc',\n", - " 'weymtrmtosp',\n", - " 'tleskleeehe',\n", - " 'sffhvyhmsir',\n", - " 'haonsiotnpc',\n", - " 'tihdeoitlbw',\n", - " 'eeednsfwnit',\n", - " 'sbdeeomngep',\n", - " 'paatdtltilo',\n", - " 'opldeyrgnsa',\n", - " 'scueeoodolw',\n", - " 'omawoeeeenf',\n", - " 'tcrhisftrah',\n", - " 'opoedsfanld',\n", - " 'ylefpfdstha',\n", - " 'narowntueoy',\n", - " 'uhyyktuoiri',\n", - " 'wehpipeoefs',\n", - " 'htueehsoiro',\n", - " 'rhrdgeotweo',\n", - " 'etaetaffwna',\n", - " 'doeniilwltg',\n", - " 'hlrnigeywao',\n", - " 'hhasiermhta',\n", - " 'cwtnoawitoe',\n", - " 'pseesoiherl',\n", - " 'iureeririan',\n", - " 'clagnaygrid',\n", - " 'mahthiqrsef',\n", - " 'ndiauedhimi',\n", - " 'osatuiboikg',\n", - " 'oeoeftzelln',\n", - " 'tuneadoshag',\n", - " 'avganmtitgr',\n", - " 'hetnbttndrt',\n", - " 'aaweeanrsee',\n", - " 'seisuarrrtb',\n", - " 'hoseiefanml',\n", - " 'ttwrtgaodrt',\n", - " 'iasthrnfedl',\n", - " 'jeagrhtrscl',\n", - " 'euidayderwg',\n", - " 'attleaitfsf',\n", - " 'nabursgfhno']" - ] - } - ], - "prompt_number": 64 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[(q, u) for q in [i for i in range(len(c6bs)) if c6bs[i] == 'q'] for u in [i for i in range(len(c6bs)) if c6bs[i] == 'u'] if abs(q-u) < 13]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 44, - "text": [ - "[(29, 37), (503, 507), (985, 973)]" - ] - } - ], - "prompt_number": 44 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "every_nth(c6bs, 13)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 54, - "text": [ - "['harrseehthoexttnsneitetghuydoihuouocttiuedeeerrsohiaeefhifsiitiohtreiehseiiszomhhihtdtoeentencsllilrdieodhpbrlakhntnsdwon',\n", - " 'iorhiniterttuehfeywehueagrieaaernltvanmalddeoptwukuongaiwpuldsrsedrheseenlfoonnavsoenwtsnhelbdetnwhnotsgoarieayuvalanetog',\n", - " 'tfefhiroseaorphlaufbtvetaenehudagtoaelnreeeasarsorrhetneghrveapoamcvrocsotmftttankinonfiphawiclegwttycyctxaengntnenovtpar',\n", - " 'hfqsbrchpdhftprdkniaoeflahnitoaetatiortnttnnrhricnehdibeaimdopltitfeofnooolltahlaaehrtwnoataisviildltntmersltrwniannnpogt',\n", - " 'httenhiooharttioutaplaeisruaielrwoopaprdpdnebehatnmneiehdlretotygaehibbetirenllpicaeygyryyuheootnwsgaaoufshsaesnetrmfsaoe',\n", - " 'nnhtrmtrshsnorsgnnpchehaieehenuaimnndfooeessunnnytnoeiuustoonfaoealixmthngorrapladdgydmraeaaarstohrrsmtddoilttvoiubirwwab',\n", - " 'faiarralochewanhtnamwolcelgsaaatsxalupwtnwneryfieaiuooiaiwmefgneaervlrttwdewfeseeogereiaiuposeefetnsaeolsncnomnulihfesfel',\n", - " 'rsmetplitptularaivscstbetiiuitrewloewaiarherotnyiituistysialisesogtmmhneoofiapnmlawefthosleeapdsredudtcpbidarrutofbktuhlt',\n", - " 'flsesyneoiiseesuahrpuaheutceniorseoewiefpeetwteperamdehaiicueotsrrlhpncnosfindbnneaucarfshalesuenieauenafdcladlihteorcdnl',\n", - " 'eteiitbwycjrastmhiellethftolyyeaatrweeqnmfgghedoelptpnrstsessdrfahensdoetmdytstphdyetsgreinutrdotrsstrydetshicdiltgacpadl',\n", - " 'ryruodrtnmeatesgyglaaurtnmuhferhiotftlrrsotddkotpthkwharriiaorlniyeajimteetqdhudctnoeureswlreoseesreetullihoewoutalrfeyfg',\n", - " 'touiitpsunagbntstwfhdtsrnuetdfyitrgyrleohyalgrlhoeovkueulhsibtyteaastopfehudieorajdemohtspueaeisiifdnpteeeirmsrnecootriif',\n", - " 'erlwionhwonmreretlaesahnpdnnnohabebrrrctnptubetmneesiursgeornfipisnstcuiroebgaaslgeetooftcesgntneihawosaaipfknenfesnhcsgo']" - ] - } - ], - "prompt_number": 54 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[''.join(transpose(l, (3, 11, 0, 1, 2, 4, 5, 6,7, 8, 9, 10, 12))) for l in chunks(c6bs, 13)]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 61, - "text": [ - "['hthithnfrfere',\n", - " 'foaoftnasltyr',\n", - " 'qurrethimserl',\n", - " 'sirhfetaeeiuw',\n", - " 'bisihnrrtsioi',\n", - " 'rtenihmrpytdo',\n", - " 'cpeiritalnbrn',\n", - " 'hshtoorliewth',\n", - " 'putesosotoynw',\n", - " 'dnhrehhcpicmo',\n", - " 'haotaashtijen',\n", - " 'fgetorneusram',\n", - " 'tbxurtowleatr',\n", - " 'pnteptraaesee',\n", - " 'rtthhisnrstsr',\n", - " 'dsnfloghaumge',\n", - " 'ktseauntiahyt',\n", - " 'nwnyutnnvhigl',\n", - " 'ifewfapasrela',\n", - " 'ahiebpcmcplae',\n", - " 'odthtlhwsulas',\n", - " 'eteuvaeotaeua',\n", - " 'fsteeehlbhtrh',\n", - " 'lrgatiaceehtn',\n", - " 'anhgasietufnp',\n", - " 'huurerelittmd',\n", - " 'neyinuegicoun',\n", - " 'itdeeahsuelhn',\n", - " 'tdoahieainyfn',\n", - " 'ofiauenatiyeo',\n", - " 'ayhedluaroerh',\n", - " 'eiurarateraha',\n", - " 'ttongwiswsaib',\n", - " 'arultomxletoe',\n", - " 'tgotoonaoortb',\n", - " 'iycvapnleewfr',\n", - " 'ortaeaduwwetr',\n", - " 'rltnlpfpaielr',\n", - " 'teimnrowieqrc',\n", - " 'nouardotafnrt',\n", - " 'thelepenrpmsn',\n", - " 'tyddedewhefop',\n", - " 'naedensneegtt',\n", - " 'nleeaesertgdu',\n", - " 'rgeosburowhdb',\n", - " 'hrrpaenytteke',\n", - " 'rlrtrhnfnedot',\n", - " 'ihswsaniypotm',\n", - " 'coouotyeieepn',\n", - " 'nehkrntairlte',\n", - " 'eoiurmnitaphe',\n", - " 'hvaohnouumtks',\n", - " 'dkeneeeoidpwi',\n", - " 'iuegtiiosenhu',\n", - " 'befaneuithrar',\n", - " 'euhiehuayasrs',\n", - " 'aliwgdsisitrg',\n", - " 'ihfphltwiisie',\n", - " 'mssurromaceio',\n", - " 'diilveoelusar',\n", - " 'obidetnfieson',\n", - " 'pttsaofgsodrf',\n", - " 'lyirptanetrli',\n", - " 'ttosoyoessfnp',\n", - " 'ieheageaoraii',\n", - " 'tatdmaaegrhys',\n", - " 'farrcelrtleen',\n", - " 'esehvhivmhnas',\n", - " 'otierixlmpsjt',\n", - " 'foesobmrhndic',\n", - " 'nphecbttncomu',\n", - " 'ofsesehteneti',\n", - " 'oeenotnwooter',\n", - " 'ohiltigdosmeo',\n", - " 'luifmroeffdte',\n", - " 'ldsoferwiiyqb',\n", - " 'tizotnrfantdg',\n", - " 'aeontlaepdsha',\n", - " 'homntlpsnbtua',\n", - " 'lrhaaplemnpds',\n", - " 'aahvniaelnhcl',\n", - " 'ajiskcdoaedtg',\n", - " 'edhoiadgwayne',\n", - " 'hetenegeeueoe',\n", - " 'rmdnoyyrfctet',\n", - " 'totwngdetasuo',\n", - " 'whotfymihrgro',\n", - " 'ntesirraofref',\n", - " 'osenpyaissest',\n", - " 'apnhhyeulhiwc',\n", - " 'tuteauapeanle',\n", - " 'aeelwhaoelurs',\n", - " 'ianbieasaeteg',\n", - " 'secdcorepsron',\n", - " 'viselosedudst',\n", - " 'isltettfseoen',\n", - " 'iilngnoerntee',\n", - " 'liiwwwhteirsi',\n", - " 'dflhtsrndesrh',\n", - " 'ldrntgrsuasea',\n", - " 'tndoyasadutew',\n", - " 'npitcameterto',\n", - " 'ttesyotocnyus',\n", - " 'meogcudlpadla',\n", - " 'eedotfdsbfela',\n", - " 'rehaxsonidtii',\n", - " 'siprahicdcshp',\n", - " 'lrbieslnalhof',\n", - " 'tmrenatoraiek',\n", - " 'rslagetmrdcwn',\n", - " 'wraynsvnuldoe',\n", - " 'nnkutnoutiiun',\n", - " 'iehvneilohltf',\n", - " 'acnaetuifttae',\n", - " 'notlnrbhbegls',\n", - " 'nonaomifkoarn',\n", - " 'ntsnvfretrcfh',\n", - " 'prdetswsucpec',\n", - " 'oiwtpawfhdays',\n", - " 'giooaoaelndfg',\n", - " 'tfngrebltllgo']" - ] - } - ], - "prompt_number": 61 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/challenge7.ipynb b/challenge7.ipynb deleted file mode 100644 index d7d18f4..0000000 --- a/challenge7.ipynb +++ /dev/null @@ -1,321 +0,0 @@ -{ - "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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- "text": [ - "" - ] - } - ], - "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": [ - "" - ] - } - ], - "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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] - } - ], - "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 diff --git a/cipher.py b/cipher.py deleted file mode 100644 index 266237a..0000000 --- a/cipher.py +++ /dev/null @@ -1,1056 +0,0 @@ -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')}) diff --git a/cipherbreak.py b/cipherbreak.py deleted file mode 100644 index 0ac8ae5..0000000 --- a/cipherbreak.py +++ /dev/null @@ -1,603 +0,0 @@ -"""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', ), -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', ), -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', ), -52.834575011...), - (('elephant', ), -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() diff --git a/find_best_affine_break_parameters.py b/find_best_affine_break_parameters.py deleted file mode 100644 index e3d6bad..0000000 --- a/find_best_affine_break_parameters.py +++ /dev/null @@ -1,85 +0,0 @@ -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() diff --git a/find_best_caesar_break_parameters.py b/find_best_caesar_break_parameters.py deleted file mode 100644 index 7a550aa..0000000 --- a/find_best_caesar_break_parameters.py +++ /dev/null @@ -1,80 +0,0 @@ -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() diff --git a/find_wikipedia_titles.py b/find_wikipedia_titles.py deleted file mode 100644 index 8d56124..0000000 --- a/find_wikipedia_titles.py +++ /dev/null @@ -1,35 +0,0 @@ -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) diff --git a/language_models.py b/language_models.py deleted file mode 100644 index bf00875..0000000 --- a/language_models.py +++ /dev/null @@ -1,177 +0,0 @@ -"""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() diff --git a/lettercount.py b/lettercount.py deleted file mode 100644 index 956eca1..0000000 --- a/lettercount.py +++ /dev/null @@ -1,13 +0,0 @@ -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)) diff --git a/make-cracking-dictionary.py b/make-cracking-dictionary.py deleted file mode 100644 index 37de917..0000000 --- a/make-cracking-dictionary.py +++ /dev/null @@ -1,19 +0,0 @@ -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)))) diff --git a/norms.py b/norms.py deleted file mode 100644 index 6645294..0000000 --- a/norms.py +++ /dev/null @@ -1,189 +0,0 @@ -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() diff --git a/plot-caesar-parameters.ipynb b/plot-caesar-parameters.ipynb deleted file mode 100644 index 91f09ee..0000000 --- a/plot-caesar-parameters.ipynb +++ /dev/null @@ -1,207 +0,0 @@ -{ - "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": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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l3xjx7Qhe6/waL7R/ocZmfEIIIYQQQtwrJGCqB4mJMHMm7NqlD1zKpV7Mxyo8nwERDxrW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- "text": [ - "" - ] - } - ], - "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": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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OhoEvNqe/p2uDBwmtXktaYRqphamkFSRToDqOIv0M1oUp2FXmYW9WhkMzsHQCo6uEpADz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\n0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- "text": [ - "" - ] - } - ], - "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 diff --git a/segment.py b/segment.py deleted file mode 100644 index a64ea5d..0000000 --- a/segment.py +++ /dev/null @@ -1,30 +0,0 @@ -"""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))] - diff --git a/unknown-word-probability-investigation.ipynb b/unknown-word-probability-investigation.ipynb deleted file mode 100644 index ff3767b..0000000 --- a/unknown-word-probability-investigation.ipynb +++ /dev/null @@ -1,203 +0,0 @@ -{ - "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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- "text": [ - "" - ] - } - ], - "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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- "text": [ - "" - ] - } - ], - "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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