Tidied up keyword ciphers and writeups
[cipher-training.git] / slides / keyword-break.html
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4 <title>Breaking keyword ciphers</title>
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47
48 # Breaking keyword ciphers
49
50 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
51 --|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|--
52 k | e | y | w | o | r | d | a | b | c | f | g | h | i | j | l | m | n | p | q | s | t | u | v | x | z
53
54 ---
55
56 # Duplicate and extend your `affine_break()` function
57
58 * How to cycle through all the keys? What _are_ all the keys?
59
60 * Look at `words.txt`
61
62 ---
63
64 # Test it.
65
66 * `2013/4a.ciphertext`
67 * `2013/4b.ciphertext`
68
69 This will take a while. Fire up a system monitor. What's wrong?
70
71 ---
72
73 # Python, threads, and the GIL
74
75 Thread-safe shared-memory code is hard.
76
77 Python's Global Interpreter Lock prevents shooting yourself in the foot.
78
79 Where you want true parallelism, need different threads (Python processes).
80
81 * Thread-safe shared-memory code is hard.
82
83 The `multiprocessing` library makes this easier.
84
85 But before we get there, a couple of diversions...
86
87 ---
88
89 # map()
90
91 A common task is to apply a function to each item in a sequence, returning a sequence of the results.
92
93 ```python
94 def double(x):
95 return x * 2
96
97 >>> map(double, [1,2,3])
98 [2,4,6]
99 ```
100
101 * `map()` is a higher-order function: its first argument is the function that's applied.
102
103 How can we use this for keyword cipher breaking?
104
105 ---
106
107 # Mapping keyword decipherings
108
109 Define a function that takes a possible key (keyword and cipher type) and returns the key and its fitness.
110
111 * (Also pass in the message and the fitness function)
112
113 Use `map()` and `max()` to find the best key
114
115 ---
116
117 # Arity of print()
118
119 How many arguments does this take?
120
121 How do you write a function that takes this many arguments?
122
123 ---
124
125 # Function arguments
126
127 ## Positional, keyword
128
129 * Common or garden parameters, as you're used to.
130 * `def keyword_encipher(message, keyword, Keyword_wrap_alphabet.from_a):`
131
132 ## Excess positional
133 * `def mean(x, *xs):`
134
135 First number goes in `x`, remaining go in the tuple `xs`
136
137 ## Excess keyword
138
139 * `def myfunc(arg1=0, **kwargs):`
140
141 `kwargs` will be a Dict of the remaining keywords (not `arg1`)
142
143 ---
144
145 # Back to `multiprocessing`
146
147 What does `Pool.starmap()` do?
148
149 ---
150
151 ```python
152 from multiprocessing import Pool
153
154 def keyword_break_mp(message, wordlist=keywords, fitness=Pletters, chunksize=500):
155 helper_args = [??? for word in wordlist] # One tuple for each possible key
156 with Pool() as pool:
157 breaks = pool.starmap(keyword_break_worker, helper_args, chunksize)
158 return max(breaks, key=lambda k: k[1])
159
160 def keyword_break_worker(???):
161 ???
162 return (key, fitness)
163 ```
164
165 * Gotcha: the function in `Pool.starmap()` must be defined at the top level
166 * This is definitely a "feature"
167
168 ---
169
170 # Performance and chunksize
171
172 Try the multiprocessing keyword break. Is it using all the resources?
173
174 Setting `chunksize` is an art.
175
176 ## Map-reduce as a general pattern for multiprocessing
177
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