From 8e6bdd888ded10a3f7abb660b241f168d617e58d Mon Sep 17 00:00:00 2001
From: Neil Smith <neil.github@njae.me.uk>
Date: Thu, 3 Oct 2013 12:02:42 +0100
Subject: [PATCH] Created Neil's branch

---
 cipher.py      | 229 ++++++++++++++++++++++++++++++++++++++++++++++---
 lettercount.py |  21 +++++
 2 files changed, 238 insertions(+), 12 deletions(-)
 create mode 100644 lettercount.py

diff --git a/cipher.py b/cipher.py
index f938f36..0fc9e88 100644
--- a/cipher.py
+++ b/cipher.py
@@ -1,23 +1,228 @@
 import string
+import collections
 
 
-def caesar_cipher_letter(letter, shift):
+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_lowercase:
-            return chr((ord(letter) - ord('a') + shift) % 26 + ord('a'))
+        if letter in string.ascii_uppercase:
+            alphabet_start = ord('A')
         else:
-            new_letter = letter.lower()
-            yolo = chr((ord(new_letter) - ord('a') + shift) % 26 + ord('a'))
-            return yolo.upper()
+            alphabet_start = ord('a')
+        return chr(((ord(letter) - alphabet_start + shift) % 26) + alphabet_start)
     else:
         return letter
 
 def caesar_decipher_letter(letter, shift):
-    return caesar_cipher_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)
 
-def caesar_cipher_message(message, shift):
-    big_cipher = [caesar_cipher_letter(l, shift) for l in message]
-    return ''.join(big_cipher)
 
-def caesar_decipher_message(message, shift):
-    return caesar_cipher_message(message, -shift)
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
diff --git a/lettercount.py b/lettercount.py
new file mode 100644
index 0000000..4a7082d
--- /dev/null
+++ b/lettercount.py
@@ -0,0 +1,21 @@
+import collections
+import string
+
+def sanitise(text):
+    return [l.lower() for l in text if l in string.ascii_letters]
+
+corpora = ['shakespeare.txt', 'sherlock-holmes.txt', 'war-and-peace.txt']
+counts = collections.defaultdict(int)
+
+for corpus in corpora:
+    text = sanitise(open(corpus, 'r').read())
+    for letter in text:
+        counts[letter] += 1
+
+sorted_letters = sorted(counts, key=counts.get, reverse=True)
+
+with open('count_1l.txt', 'w') as f:
+    for l in sorted_letters:
+        f.write("{0}\t{1}\n".format(l, counts[l]))
+        
+    
\ No newline at end of file
-- 
2.34.1