X-Git-Url: https://git.njae.me.uk/?a=blobdiff_plain;f=cipher.py;h=248155edb8a54956df51348a64fc26c9532db339;hb=3d8f7067b9c3a48ef140d7cff834d18ee91f58b3;hp=0fc9e8897f12b8920d07819f25c29b8e9efb59fb;hpb=8e6bdd888ded10a3f7abb660b241f168d617e58d;p=cipher-tools.git diff --git a/cipher.py b/cipher.py index 0fc9e88..248155e 100644 --- a/cipher.py +++ b/cipher.py @@ -1,228 +1,31 @@ -import string -import collections +# 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 * +# import pprint + + + +from utilities import * +from segment import * +from text_prettify import * +from plot_frequency_histogram import * + +from caesar import * +from affine import * +from keyword import * +from polybius import * +from column_transposition import * +from railfence import * +from cadenus import * +from hill import * +from amsco import * +from bifid import * +from autokey import * +from pocket_enigma import * - -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' - """ - sanitised = [c.lower() for c in text if c in string.ascii_letters] - return ''.join(sanitised) - -def letter_frequencies(text): - """Count the number of occurrences of each character in text - - >>> sorted(letter_frequencies('abcdefabc').items()) - [('a', 2), ('b', 2), ('c', 2), ('d', 1), ('e', 1), ('f', 1)] - >>> sorted(letter_frequencies('the quick brown fox jumped over the lazy dog').items()) - [(' ', 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(letter_frequencies('The Quick BROWN fox jumped! over... the (9lazy) DOG').items()) - [(' ', 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(letter_frequencies(sanitise('The Quick BROWN fox jumped! over... the (9lazy) DOG')).items()) - [('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)] - """ - counts = collections.defaultdict(int) - for c in text: - counts[c] += 1 - return counts - - -def normalise_frequencies(frequencies): - """Scale a set of letter frequenies so they add to 1 - - >>> sorted(normalise_frequencies(letter_frequencies('abcdefabc')).items()) - [('a', 0.2222222222222222), ('b', 0.2222222222222222), ('c', 0.2222222222222222), ('d', 0.1111111111111111), ('e', 0.1111111111111111), ('f', 0.1111111111111111)] - >>> sorted(normalise_frequencies(letter_frequencies('the quick brown fox jumped over the lazy dog')).items()) - [(' ', 0.18181818181818182), ('a', 0.022727272727272728), ('b', 0.022727272727272728), ('c', 0.022727272727272728), ('d', 0.045454545454545456), ('e', 0.09090909090909091), ('f', 0.022727272727272728), ('g', 0.022727272727272728), ('h', 0.045454545454545456), ('i', 0.022727272727272728), ('j', 0.022727272727272728), ('k', 0.022727272727272728), ('l', 0.022727272727272728), ('m', 0.022727272727272728), ('n', 0.022727272727272728), ('o', 0.09090909090909091), ('p', 0.022727272727272728), ('q', 0.022727272727272728), ('r', 0.045454545454545456), ('t', 0.045454545454545456), ('u', 0.045454545454545456), ('v', 0.022727272727272728), ('w', 0.022727272727272728), ('x', 0.022727272727272728), ('y', 0.022727272727272728), ('z', 0.022727272727272728)] - >>> sorted(normalise_frequencies(letter_frequencies('The Quick BROWN fox jumped! over... the (9lazy) DOG')).items()) - [(' ', 0.1568627450980392), ('!', 0.0196078431372549), ('(', 0.0196078431372549), (')', 0.0196078431372549), ('.', 0.058823529411764705), ('9', 0.0196078431372549), ('B', 0.0196078431372549), ('D', 0.0196078431372549), ('G', 0.0196078431372549), ('N', 0.0196078431372549), ('O', 0.0392156862745098), ('Q', 0.0196078431372549), ('R', 0.0196078431372549), ('T', 0.0196078431372549), ('W', 0.0196078431372549), ('a', 0.0196078431372549), ('c', 0.0196078431372549), ('d', 0.0196078431372549), ('e', 0.0784313725490196), ('f', 0.0196078431372549), ('h', 0.0392156862745098), ('i', 0.0196078431372549), ('j', 0.0196078431372549), ('k', 0.0196078431372549), ('l', 0.0196078431372549), ('m', 0.0196078431372549), ('o', 0.0392156862745098), ('p', 0.0196078431372549), ('r', 0.0196078431372549), ('t', 0.0196078431372549), ('u', 0.0392156862745098), ('v', 0.0196078431372549), ('x', 0.0196078431372549), ('y', 0.0196078431372549), ('z', 0.0196078431372549)] - >>> sorted(normalise_frequencies(letter_frequencies(sanitise('The Quick BROWN fox jumped! over... the (9lazy) DOG'))).items()) - [('a', 0.027777777777777776), ('b', 0.027777777777777776), ('c', 0.027777777777777776), ('d', 0.05555555555555555), ('e', 0.1111111111111111), ('f', 0.027777777777777776), ('g', 0.027777777777777776), ('h', 0.05555555555555555), ('i', 0.027777777777777776), ('j', 0.027777777777777776), ('k', 0.027777777777777776), ('l', 0.027777777777777776), ('m', 0.027777777777777776), ('n', 0.027777777777777776), ('o', 0.1111111111111111), ('p', 0.027777777777777776), ('q', 0.027777777777777776), ('r', 0.05555555555555555), ('t', 0.05555555555555555), ('u', 0.05555555555555555), ('v', 0.027777777777777776), ('w', 0.027777777777777776), ('x', 0.027777777777777776), ('y', 0.027777777777777776), ('z', 0.027777777777777776)] - """ - total = sum(frequencies.values()) - return dict((k, v / total) for (k, v) in frequencies.items()) - -def l2_norm(frequencies1, frequencies2): - """Finds the distances between two frequency profiles, expressed as dictionaries. - Assumes every key in frequencies1 is also in frequencies2 - - >>> l2_norm({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1}) - 0.0 - >>> l2_norm({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) - 0.0 - >>> l2_norm({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) - 0.816496580927726 - >>> l2_norm({'a':0, 'b':1}, {'a':1, 'b':1}) - 0.7071067811865476 - """ - f1n = normalise_frequencies(frequencies1) - f2n = normalise_frequencies(frequencies2) - total = 0 - for k in f1n.keys(): - total += (f1n[k] - f2n[k]) ** 2 - return total ** 0.5 -euclidean_distance = l2_norm - -def l1_norm(frequencies1, frequencies2): - """Finds the distances between two frequency profiles, expressed as dictionaries. - Assumes every key in frequencies1 is also in frequencies2 - - >>> l1_norm({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1}) - 0.0 - >>> l1_norm({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) - 0.0 - >>> l1_norm({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) - 1.3333333333333333 - >>> l1_norm({'a':0, 'b':1}, {'a':1, 'b':1}) - 1.0 - """ - f1n = normalise_frequencies(frequencies1) - f2n = normalise_frequencies(frequencies2) - total = 0 - for k in f1n.keys(): - total += abs(f1n[k] - f2n[k]) - return total - -def l3_norm(frequencies1, frequencies2): - """Finds the distances between two frequency profiles, expressed as dictionaries. - Assumes every key in frequencies1 is also in frequencies2 - - >>> l3_norm({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1}) - 0.0 - >>> l3_norm({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) - 0.0 - >>> l3_norm({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) - 0.7181448966772946 - >>> l3_norm({'a':0, 'b':1}, {'a':1, 'b':1}) - 0.6299605249474366 - """ - f1n = normalise_frequencies(frequencies1) - f2n = normalise_frequencies(frequencies2) - total = 0 - for k in f1n.keys(): - total += abs(f1n[k] - f2n[k]) ** 3 - return total ** (1/3) - -def cosine_distance(frequencies1, frequencies2): - """Finds the distances between two frequency profiles, expressed as dictionaries. - Assumes every key in frequencies1 is also in frequencies2 - - >>> cosine_distance({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1}) - -2.220446049250313e-16 - >>> cosine_distance({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) - -2.220446049250313e-16 - >>> cosine_distance({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) - 0.42264973081037416 - >>> cosine_distance({'a':0, 'b':1}, {'a':1, 'b':1}) - 0.29289321881345254 - """ - numerator = 0 - length1 = 0 - length2 = 0 - for k in frequencies1.keys(): - numerator += frequencies1[k] * frequencies2[k] - length1 += frequencies1[k]**2 - for k in frequencies2.keys(): - length2 += frequencies2[k] - return 1 - (numerator / (length1 ** 0.5 * length2 ** 0.5)) - - - - -def caesar_encipher_letter(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' - """ - 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' - """ - enciphered = [caesar_encipher_letter(l, shift) for l in message] - return ''.join(enciphered) - -def caesar_decipher(message, shift): - """Encipher 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' - """ - return caesar_encipher(message, -shift) - -def caesar_break(message, metric=euclidean_distance): - sanitised_message = sanitise(message) - best_shift = 0 - best_fit = float("inf") - for shift in range(1, 25): - plaintext = caesar_decipher(sanitised_message, shift) - frequencies = letter_frequencies(plaintext) - fit = metric(english_counts, frequencies) - if fit < best_fit: - best_fit = fit - best_shift = shift - return best_shift, best_fit - - - - - -english_counts = collections.defaultdict(int) -with open('count_1l.txt', 'r') as f: - for line in f: - (letter, count) = line.split("\t") - english_counts[letter] = int(count) - - -if __name__ == "__main__": - import doctest - doctest.testmod()