import matplotlib.pyplot as plt
+from counts import *
from cipher import *
# To time a run:
# 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)
-
-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)
-normalised_english_counts = norms.normalise(english_counts)
-
-english_bigram_counts = collections.defaultdict(int)
-with open('count_2l.txt', 'r') as f:
- for line in f:
- (bigram, count) = line.split("\t")
- english_bigram_counts[bigram] = int(count)
-normalised_english_bigram_counts = norms.normalise(english_bigram_counts)
-
-english_trigram_counts = collections.defaultdict(int)
-with open('count_3l.txt', 'r') as f:
- for line in f:
- (trigram, count) = line.split("\t")
- english_trigram_counts[trigram] = int(count)
-normalised_english_trigram_counts = norms.normalise(english_trigram_counts)
-
-
with open('words.txt', 'r') as f:
keywords = [line.rstrip() for line in f]
'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
+ 'bfmctsd kfmyxd jls lyj, mztanamyu xmc jm clm cku tmmeaxw kj lai ' \
+ 'kxd clm ckuxj.') # doctest: +ELLIPSIS
((15, 22, True), 0.0598745365924...)
"""
sanitised_message = sanitise(message)
vigenere_decipher(message, best_keyword))[:50]))
return best_keyword, best_fit
-def vigenere_keyword_break_mp(message,
- wordlist=keywords,
- metric=norms.euclidean_distance,
- target_counts=normalised_english_counts,
- message_frequency_scaling=norms.normalise,
+def vigenere_keyword_break_mp(message,
+ wordlist=keywords,
+ metric=norms.euclidean_distance,
+ target_counts=normalised_english_counts,
+ message_frequency_scaling=norms.normalise,
chunksize=500):
"""Breaks a vigenere cipher using a dictionary and
frequency analysis