{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,inspect\n",
    "currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n",
    "parentdir = os.path.dirname(currentdir)\n",
    "sys.path.insert(0,parentdir) \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import collections\n",
    "import string\n",
    "%matplotlib inline\n",
    "\n",
    "from cipher.railfence import *\n",
    "from cipher.hill import *\n",
    "from support.utilities import *\n",
    "from support.text_prettify import *\n",
    "from support.language_models import *\n",
    "from support.plot_frequency_histogram import *\n",
    "\n",
    "\n",
    "c6a = open('6a.ciphertext').read()\n",
    "c6b = open('6b.ciphertext').read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c5027da58>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c5027d160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freqs = pd.Series(english_counts)\n",
    "freqs.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c502dacc0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c0ed65ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freqs_6a = pd.Series(collections.Counter([l.lower() for l in c6a if l in string.ascii_letters]))\n",
    "freqs_6a.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c502da780>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c0fd5a208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freqs_6b = pd.Series(collections.Counter([l.lower() for l in c6b if l in string.ascii_letters]))\n",
    "freqs_6b.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mtaeglatcleptenopeautelebiiootatwnantateituiiagaeostgvetabdresiacqobwavgrhrsihssaekajbwwttdrsmeetnyafsegilegtkrreocuantteomsgstnsiaeluutrbaiaeteeserhxgtooarrbhpcklialhnaesvearhbepiydcesewtaxuyaerywoeinhteegeisieireaassrbitnhtuorooleewsttereoahyakhlsmsaeodslthsutigqimnidsgetpmwtrnnotfhvselkaumrndvcnrluceryhyeetlnigouncnanrhpnosbhshpslreclvrinfoehniaeennhcrbenrgunruesmlrehiutgteordroeaeoisoeusiknteeslohthdcrmisuteoteaeoshfaiaesemritrseisaigwyrmhrbtetncoenuhorcadeodlcrncomnctosihudtcinagesntisutigytmshthyalatlsnhilguimtlbfldyhrfrnetsaosteetaefhlgokhretcakuteihrlrtlsetshlcpeadhthyutaeennhryraeennihrnbhnsnehyutsdtoywmtiatalwhvbepetlxihuscrtadtikhnxmsaesnwluevgnrcpegvnhteruigeuealsdntikeaeomctwrybusiilephkyodhrsyhecaatrmrltrarretstuoetnuesiduaidoesisaeetbllerpntroisiatsiasesomihsieiaunsaitneelacrfnrnngvetteenslhvpepteonedtnaooutgsotancetimiiwoetiuihclsewtcniieotslfbeecohenpoelsdoctceeemiiirttmhbiuovecegaitjuaborcleentatruyinetsidlaeehitwencceohwvohoatwkteroarhcseer'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c6as = sanitise(c6a)\n",
    "c6as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hwssswxfewhhrfewpdrvttdhxbccleayphalnadhiehaoudrotwnrrvysabjlttbaytmelrkaidopthatlelrtwaamaneksvvzrvllatkcrjquicizgtoqcpnrrkttowandqehtqrvtbaydqealannohulanuzlwextlvjrvivhnohdqmgykaclmswrupdetfioftfelhzpxhaswftwprrsweiseohefpdrvttnvagdvswgoerbetnharvaeevtlltbmgaiatgelinmdawevhatterdhrznbnvoutnefoteveaehlaymhacglzeptvvdimworfisgtuzlwibeqohubtghamqornjnnrumqvjtxeltfovgawdaeevllgrtxibgtibevmpsaateoasevaeyqohameonncfuidoefafattemuimnflznbekofobrliaehhauihnnnwzaeevtlltpaalnanvtzlzuucptaelinanpaahewfthaosetaribnbnvhaevdhyytlmuxb'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c6bs = sanitise(c6b)\n",
    "c6bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, -2314.997881051078)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_a, score = railfence_break(c6as)\n",
    "key_a, score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mark the last message told usa lot the scuttling equipment is designed to pump water in and out of\n",
      "the vessel like a submarine dive control but clearly they werent planning to turn a container ship\n",
      "into a sub this ship is a largescale version of something i have seen in the caribbean drug runners\n",
      "use a similar technique to get below radar coverage for inshore runs sinking the vessel so that the\n",
      "deck remains just below the wave tops the fda pirates seem more interested in staying away from\n",
      "shore but getting close enough to track and record electronic communications without detection i am\n",
      "guessing this scuttling system is what they call nautilus in their log but i am still baffled by the\n",
      "references to seahorse the next page of the log looks harder to crack but the cipher clerk tells me\n",
      "it is a hill cipher and that they must have been in a hurry or have been enciphering by hand since\n",
      "they just used a two by two matrix actually we have been pretty lax with our security and i think\n",
      "the next message is end will use avi genere cipher given that we are using secure cables i dont\n",
      "think we have too much to worry about so i will keep the keyword short say three characters more\n",
      "later harry\n"
     ]
    }
   ],
   "source": [
    "print(prettify(railfence_decipher(c6as, key_a)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(matrix([[0, 1],\n",
       "         [1, 1]]), -666.1299098341699)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_b, score = hill_break(c6bs)\n",
    "key_b, score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "phase six seahorse operated exactly as planned with good forward visibility at the trial depths the\n",
      "crew managed several tasks requiring concentration and dexterity and we plan to run a full test\n",
      "overnight on dummy cables dropped from the ship the software seems to be operating as designed but\n",
      "there are still bugs in the firmware that need ironing out before we deploy the collective is\n",
      "working full time to hunt them down and remove them though we are all getting tired mistakes are\n",
      "easy to make and could be fatal time is no longer on our side though and we are still planning to\n",
      "launch the final phase of the operation in three days timex\n"
     ]
    }
   ],
   "source": [
    "print(prettify(hill_decipher(key_b, c6bs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}