"cells": [
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 6,
+ "metadata": {},
"outputs": [],
"source": [
"import os,sys,inspect\n",
"import string\n",
"%matplotlib inline\n",
"\n",
- "from cipherbreak import *\n",
+ "from cipher.affine import *\n",
+ "from cipher.keyword_cipher 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",
"c2a = open('2a.ciphertext').read()\n",
"c2b = open('2b.ciphertext').read()"
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 3,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x7f8012c71eb8>"
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd7aa938668>"
]
},
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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yN7hfkfRR4Crgv1QqGBEfBz4u6c8i4j112i33GeBhSfenvr0T+KykU8jORJ6X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\nzAocGMzMrOD/A5ZV4vqjDJn1AAAAAElFTkSuQmCC\n",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAD7CAYAAACWq8i5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAGXNJREFUeJzt3X+0XWV95/H3p1B+TAsSNKYsgsZOM1rK+ANSSFdtR6WFgJ2GTpXBtpJxKJkW7KLj9Ec67ZQR6xTbaW0Z23RYkhrUqaLVIa1gmhW11iqYIBQEyuIWZUgWQiT8cLTV4nznj/OkHq7nnPvckHAuyfu11lln7+9+nv3sc+6593P2j3NuqgpJknp8y7Q3QJL09GFoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqdui0N2Bfe9aznlXLli2b9mZI0tPKTTfd9MWqWjxXuwMuNJYtW8b27dunvRmS9LSS5N6edh6ekiR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LU7YD7cN+BbNm6D42sf/7yVz7FWyLpYOWehiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG5zhkaS5ye5Zej2WJKfT3Jski1J7m73i1r7JLkiyUySW5OcPLSuNa393UnWDNVPSXJb63NFkrT6yDEkSdMxZ2hU1V1V9eKqejFwCvAV4IPAOmBrVS0HtrZ5gLOA5e22FlgPgwAALgVOA04FLh0KgfXAhUP9VrX6uDEkSVMw38NTpwN/V1X3AquBja2+ETinTa8Grq6BG4BjkhwHnAlsqardVfUwsAVY1ZYdXVU3VFUBV89a16gxJElTMN/QOA/4kza9pKrub9NfAJa06eOB+4b67Gi1SfUdI+qTxniCJGuTbE+yfdeuXfN8SJKkXt2hkeQw4EeB981e1vYQah9u1zeZNEZVXVlVK6pqxeLFi/fnZkjSQW0+expnAZ+pqgfa/APt0BLt/sFW3wmcMNRvaatNqi8dUZ80hiRpCuYTGq/hG4emADYBe66AWgNcO1Q/v11FtRJ4tB1i2gyckWRROwF+BrC5LXssycp21dT5s9Y1agxJ0hR0/Y/wJN8G/DDwH4bKlwPXJLkAuBc4t9WvA84GZhhcafU6gKraneRNwLbW7rKq2t2mLwLeARwJXN9uk8aQJE1BV2hU1ZeBZ86qPcTgaqrZbQu4eMx6NgAbRtS3AyeNqI8cQ5I0HX4iXJLUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR16wqNJMckeX+Sv01yZ5LvS3Jski1J7m73i1rbJLkiyUySW5OcPLSeNa393UnWDNVPSXJb63NFkrT6yDEkSdPRu6fx+8CHq+oFwIuAO4F1wNaqWg5sbfMAZwHL220tsB4GAQBcCpwGnApcOhQC64ELh/qtavVxY0iSpmDO0EjyDOAHgasAquprVfUIsBrY2JptBM5p06uBq2vgBuCYJMcBZwJbqmp3VT0MbAFWtWVHV9UNVVXA1bPWNWoMSdIU9OxpPA/YBfxxkpuTvD3JtwFLqur+1uYLwJI2fTxw31D/Ha02qb5jRJ0JYzxBkrVJtifZvmvXro6HJEnaGz2hcShwMrC+ql4CfJlZh4naHkLt+83rG6OqrqyqFVW1YvHixftzMyTpoNYTGjuAHVV1Y5t/P4MQeaAdWqLdP9iW7wROGOq/tNUm1ZeOqDNhDEnSFMwZGlX1BeC+JM9vpdOBO4BNwJ4roNYA17bpTcD57SqqlcCj7RDTZuCMJIvaCfAzgM1t2WNJVrarps6fta5RY0iSpuDQznY/B7w7yWHAPcDrGATONUkuAO4Fzm1trwPOBmaAr7S2VNXuJG8CtrV2l1XV7jZ9EfAO4Ejg+nYDuHzMGJKkKegKjaq6BVgxYtHpI9oWcPGY9WwANoyobwdOGlF/aNQYkqTp8BPhkqRuhoYkqZuhIUnqZmhIkroZGpKkbr2X3ErS1C1b96GR9c9f/sqneEsOXu5pSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6dYVGks8nuS3JLUm2t9qxSbYkubvdL2r1JLkiyUySW5OcPLSeNa393UnWDNVPaeufaX0zaQxJ0nTM56vRX15VXxyaXwdsrarLk6xr878MnAUsb7fTgPXAaUmOBS4FVgAF3JRkU1U93NpcCNwIXAesAq6fMIZ00Bn3teDgV4PrqfNkDk+tBja26Y3AOUP1q2vgBuCYJMcBZwJbqmp3C4otwKq27OiquqGqCrh61rpGjSFJmoLe0CjgL5LclGRtqy2pqvvb9BeAJW36eOC+ob47Wm1SfceI+qQxniDJ2iTbk2zftWtX50OSJM1X7+Gpl1bVziTPBrYk+dvhhVVVSWrfb17fGFV1JXAlwIoVK/brdkjSwawrNKpqZ7t/MMkHgVOBB5IcV1X3t0NMD7bmO4EThrovbbWdwMtm1T/W6ktHtGfCGF3815CStG/NeXgqybclOWrPNHAG8FlgE7DnCqg1wLVtehNwfruKaiXwaDvEtBk4I8midhXUGcDmtuyxJCvbVVPnz1rXqDEkSVPQs6exBPhguwr2UOB/VdWHk2wDrklyAXAvcG5rfx1wNjADfAV4HUBV7U7yJmBba3dZVe1u0xcB7wCOZHDV1PWtfvmYMSRJUzBnaFTVPcCLRtQfAk4fUS/g4jHr2gBsGFHfDpzUO4YkaTr8RLgkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkrr1/o9waZ/yX/FKT0+Ghp60cQEAhsA4Pmd6uvLwlCSpW3doJDkkyc1J/rzNPy/JjUlmkrw3yWGtfnibn2nLlw2t41da/a4kZw7VV7XaTJJ1Q/WRY0iSpmM+exqXAHcOzb8FeGtVfRfwMHBBq18APNzqb23tSHIicB7wPcAq4A9bEB0C/AFwFnAi8JrWdtIYkqQp6DqnkWQp8ErgzcAbkgR4BfATrclG4L8C64HVbRrg/cDbWvvVwHuq6qvA55LMAKe2djNVdU8b6z3A6iR3ThhD0tOcF0M8PfXuafwe8EvA/2vzzwQeqarH2/wO4Pg2fTxwH0Bb/mhr/0/1WX3G1SeN8QRJ1ibZnmT7rl27Oh+SJGm+5gyNJD8CPFhVNz0F27NXqurKqlpRVSsWL1487c2RpANWz+Gp7wd+NMnZwBHA0cDvA8ckObTtCSwFdrb2O4ETgB1JDgWeATw0VN9juM+o+kMTxpAkTcGcexpV9StVtbSqljE4kf2RqvpJ4KPAq1qzNcC1bXpTm6ct/0hVVauf166ueh6wHPg0sA1Y3q6UOqyNsan1GTeGJGkKnsznNH6ZwUnxGQbnH65q9auAZ7b6G4B1AFV1O3ANcAfwYeDiqvp624t4PbCZwdVZ17S2k8aQJE3BvD4RXlUfAz7Wpu/hG1c/Dbf5B+DVY/q/mcEVWLPr1wHXjaiPHEOSNB1+IlyS1M3vnpL0pPmZi4OHexqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSus0ZGkmOSPLpJH+T5PYkb2z15yW5MclMkvcmOazVD2/zM235sqF1/Uqr35XkzKH6qlabSbJuqD5yDEnSdPTsaXwVeEVVvQh4MbAqyUrgLcBbq+q7gIeBC1r7C4CHW/2trR1JTgTOA74HWAX8YZJDkhwC/AFwFnAi8JrWlgljSJKmYM7QqIH/22a/td0KeAXw/lbfCJzTple3edry05Ok1d9TVV+tqs8BM8Cp7TZTVfdU1deA9wCrW59xY0iSpqDrnEbbI7gFeBDYAvwd8EhVPd6a7ACOb9PHA/cBtOWPAs8crs/qM67+zAljzN6+tUm2J9m+a9eunockSdoLXaFRVV+vqhcDSxnsGbxgv27VPFXVlVW1oqpWLF68eNqbI0kHrHldPVVVjwAfBb4POCbJoW3RUmBnm94JnADQlj8DeGi4PqvPuPpDE8aQJE1Bz9VTi5Mc06aPBH4YuJNBeLyqNVsDXNumN7V52vKPVFW1+nnt6qrnAcuBTwPbgOXtSqnDGJws39T6jBtDkjQFh87dhOOAje0qp28BrqmqP09yB/CeJL8B3Axc1dpfBbwzyQywm0EIUFW3J7kGuAN4HLi4qr4OkOT1wGbgEGBDVd3e1vXLY8aQJE3BnKFRVbcCLxlRv4fB+Y3Z9X8AXj1mXW8G3jyifh1wXe8YkqTp8BPhkqRuhoYkqZuhIUnq1nMiXPvBsnUfGln//OWvfIq3RJL6uachSermnoY0i3uB0njuaUiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSus0ZGklOSPLRJHckuT3JJa1+bJItSe5u94taPUmuSDKT5NYkJw+ta01rf3eSNUP1U5Lc1vpckSSTxpAkTUfPnsbjwH+qqhOBlcDFSU4E1gFbq2o5sLXNA5wFLG+3tcB6GAQAcClwGnAqcOlQCKwHLhzqt6rVx40hSZqCOUOjqu6vqs+06S8BdwLHA6uBja3ZRuCcNr0auLoGbgCOSXIccCawpap2V9XDwBZgVVt2dFXdUFUFXD1rXaPGkCRNwbzOaSRZBrwEuBFYUlX3t0VfAJa06eOB+4a67Wi1SfUdI+pMGGP2dq1Nsj3J9l27ds3nIUmS5qE7NJJ8O/CnwM9X1WPDy9oeQu3jbXuCSWNU1ZVVtaKqVixevHh/boYkHdS6/t1rkm9lEBjvrqoPtPIDSY6rqvvbIaYHW30ncMJQ96WtthN42az6x1p96Yj2k8aQpC7++959q+fqqQBXAXdW1e8OLdoE7LkCag1w7VD9/HYV1Urg0XaIaTNwRpJF7QT4GcDmtuyxJCvbWOfPWteoMSRJU9Czp/H9wGuB25Lc0mr/GbgcuCbJBcC9wLlt2XXA2cAM8BXgdQBVtTvJm4Btrd1lVbW7TV8EvAM4Eri+3ZgwhiRpCuYMjar6BJAxi08f0b6Ai8esawOwYUR9O3DSiPpDo8aQJE2HnwiXJHXrOhEuaTJPtupg4Z6GJKmboSFJ6mZoSJK6GRqSpG6eCJekJ2nchRBw4F0M4Z6GJKmboSFJ6ubhKUl6mlgIh8EMjX3AD3ZJOlgYGgewhfCuRNKBxXMakqRuhoYkqZuhIUnqZmhIkrp5IlxP4MlzSZO4pyFJ6jZnaCTZkOTBJJ8dqh2bZEuSu9v9olZPkiuSzCS5NcnJQ33WtPZ3J1kzVD8lyW2tzxVJMmkMSdL09OxpvANYNau2DthaVcuBrW0e4CxgebutBdbDIACAS4HTgFOBS4dCYD1w4VC/VXOMIUmakjlDo6o+DuyeVV4NbGzTG4FzhupX18ANwDFJjgPOBLZU1e6qehjYAqxqy46uqhuqqoCrZ61r1BiSpCnZ2xPhS6rq/jb9BWBJmz4euG+o3Y5Wm1TfMaI+aYxvkmQtgz0bnvOc58z3sUgHLC9s0L72pE+Etz2E2gfbstdjVNWVVbWiqlYsXrx4f26KJB3U9nZP44Ekx1XV/e0Q04OtvhM4Yajd0lbbCbxsVv1jrb50RPtJY+xXfvmgJI23t3sam4A9V0CtAa4dqp/frqJaCTzaDjFtBs5IsqidAD8D2NyWPZZkZbtq6vxZ6xo1hiRpSubc00jyJwz2Ep6VZAeDq6AuB65JcgFwL3Bua34dcDYwA3wFeB1AVe1O8iZgW2t3WVXtObl+EYMrtI4Erm83JowhSZqSOUOjql4zZtHpI9oWcPGY9WwANoyobwdOGlF/aNQYkqTp8RPhkqRuhoYkqZuhIUnq5rfcStIQPxA5mXsakqRuhoYkqZuHp3RA8xP+0r7lnoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSui340EiyKsldSWaSrJv29kjSwWxBf2FhkkOAPwB+GNgBbEuyqarumO6WaRr88kEdSJ6u/7djQYcGcCowU1X3ACR5D7AaMDSk/eTp+sdMo+3rN1upqiezPftVklcBq6rqp9v8a4HTqur1s9qtBda22ecDd41Y3bOAL85zE+wz/z4Ldbvss3C3yz4LY7ueW1WL51xDVS3YG/Aq4O1D868F3raX69pun/3fZ6Ful30W7nbZZ+Fu16jbQj8RvhM4YWh+aatJkqZgoYfGNmB5kuclOQw4D9g05W2SpIPWgj4RXlWPJ3k9sBk4BNhQVbfv5equtM9T0mehbpd9Fu522Wfhbtc3WdAnwiVJC8tCPzwlSVpADA1JUjdDQ9KTkoET5m6pA8EBHxpJFiU5NckP7rnN0f6IJG9I8oEkf5rkPyY54qna3gnblSQ/leTX2/xzkpw6pu072/0lT+U27g9JPtHuv5TksVm3R5N8LslFE/qfMqL2I/tzm/enJC9K8vp2e1FH+/3+eq7BidHr5tsvyauTHNWmf61t48kT2r+lp/Zktefr+Hn2eVeSC5O8YB59ThxRe9kcfX4uyaJ5btvWJGfPqu31CfED+kR4kp8GLmHw+Y5bgJXAp6rqFRP6XAN8CXhXK/0EcExVvXpCn43AJVX1SJtfBPxOVf37We3eMGl7q+p3J4yxHvh/wCuq6rvbGH9RVd87ou0dwA8B1wMvAzJrnN0Txhm1jY8CN1XVLWP6HA78OLCMoSvyquqycePsK0meCXyyqp4/ZvlngPOr6rNt/jXAz1fVaft4O1YAvwo8l8FzEAZ/T184oc+8nrf2JuBC4AOt9GPAlVX1PyaMsTev53m/TtvvwNuqatukvrP63FpVL0zyUuA3gN8Gfn3czybJZ6rq5FHrmGOcXx/zOMY9z5cC5wK7gfcC76uqB+YY4+XAD7TbPwduBj5eVb8/oc9ngXcCvwUc0e5XVNX3TejzGww+evAZYAOwueb4I57kHuA+4CNV9cZW+6bnsteCvuR2H7gE+F7ghqp6eXsX8N/m6HNSVQ2/A/ho+yM8yQv3BAZAVT2c5CUj2h3V7p/ftmvPZ07+NfDpOcY4rapOTnLz0BiHjWn7R8BW4DuBm3hiaFSrj7Oi3f6szf8IcCvwM0neV1W/NaLPtbRgAb466UEk+URVvTTJl9q2/NOiwcOqoyf1n62qHprj3dmrgPcn+QkGv9DnA2eM2bbZ2zSfbXs38IvAbQzCvUf389ZcwOB18OW2vW8BPgWMDQ327vW8gtGvz7sn9DkN+Mkk9wJfpiM0ga+3+1cyCL8PtT+KT5DkZ4GLgO9McuvQoqOAv57jsdC2Z48jGLym7xzXuP1hfWOSFwL/FvjLJDuq6ocm9Ploko8zeN5eDvwM8D3A2NBg8Jy9BfhkeyzvBr5/0gOpql9L8l8YvIZfB7ytvTG4qqr+bky3R4DTgSuS/BnwU5PGmNOT/Uj5Qr4B29r9LcDhbfr2Ofq8C1g5NH8acPUcff4GWDQ0fyxw24T2HweOGpo/isG7kklj3MjgsyqfafOLgZvn6LN+L56zjwPfPjT/7cBfAkcCd4zp89lp/6zneEz/gsGXXH4YOHI/jfGJvegzr+eNQSAdMTR/xKTXWWuzN6/nvXl9PnfUbY4+fw78T+Ae4BjgcOBvRrR7BoO9sT+Ztf5j9/JndTjwsY523wH8HINgunWOtluBG4C3Av8GeHbH+g9jsHd1CzADnDePx/Ai4PeAvwXWM9iz+a0xbW8emv537XW0Y2+eu6o64Pc0diQ5BvjfwJYkDwP3jmqY5DYG7zK/Ffhkkv/T5p/L4Aczye8An0ryvjb/auDNE9ovAb42NP+1VpvkCuCDwLOTvJnBO+hfm9Shqn52jnWO8mye+K73H4ElVfX3Sca9G/5kkn9ZVbftxXj7xdDPc49jGYTujUmoOQ5p7IVLk7ydwR+Pf3qequoD47vM+3n7Ywbb/8E2fw5w1Rx9TuEbr2eA5wB37Xl+xjwP8359VtXI36s5nAusAv57VT2S5DgGe2uz1/0ogz2y1+zFGKP8MwaHrEdq58jOZfDG7H3AhTX3v2O4lcFzfRKDbX0kyaeq6u8n9NnGYG/zexl8keAfJfnxmnzo8BIGe8tfBN4O/GJV/WOSb2GwJ/hLI7r90Z6JqnpH+9lfPMfjGeuAPqcxLMm/YvCO5cNV9bURy587qf9cvxTtpNaecyUfmfQiS/KrDF6Uw7/8762q35xjjBcw2M0MsLWqxu5i76226/tjDF7MMDg0sYlBMF5ZVT851HbPH+ZDgeUM3jF+lb5DE/vVk/157sV47wJeANzONw5PVc06rzWrzx3AdwGfo/N5ayeKX9pm/6qqbp5ju+b9POzt63OhmvUG4hAGYXBZVb1tTPvfZPB4R57Dm2Osoxi8m/8F4Duq6vAJbVdU1fZZtddW1Tsn9Hkjg2/GGPVz++798Tfhm8Y5WEJjoWm//D/QZj8+1y//U6md1N1zbPWvZ7+wh9o9pX+YF7Ikd9WYk/ET+ox8/hbC87aQX5/zNet5fhx4oKoe38djvJ7B83UK8HngrxiE+kf25TgLgaEh7QNJ/hj47Y7DGDoAJfkFBkFx074OpIXG0JD2gSR3MrjUsvtQk/R0ZGhI+8BCPtQk7UuGhiSp2wH/NSKSpH3H0JAkdTM0JEndDA1JUrf/D01s6fDIlGlbAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f8012c71898>"
+ "<matplotlib.figure.Figure at 0x7fd7abfb7860>"
]
},
"metadata": {},
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 4,
+ "metadata": {},
"outputs": [
{
"data": {
"((5, 25, True), -761.8388033231918)"
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 5,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "calm weather allowed us to complete the hull survey and establish its integrity no major remedial works were required and the pumps and extra bulkheads were installed out in deep waters over the next five days we are now testing the system for reliability and safety before moving on to phase three of the operation operation trojan remains on target\n"
+ "calm weather allowed us to complete the hull survey and establish its integrity no major remedial\n",
+ "works were required and the pumps and extra bulkheads were installed out in deep waters over the\n",
+ "next five days we are now testing the system for reliability and safety before moving on to phase\n",
+ "three of the operation operation trojan remains on target\n"
]
}
],
"source": [
- "print(' '.join(segment(sanitise(keyword_decipher(c2b, key_b[0], key_b[1])))))"
+ "print(lcat(tpack(segment(sanitise(keyword_decipher(c2b, key_b[0], key_b[1]))))))"
]
},
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x7f800850a358>"
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd7a6ada710>"
]
},
"execution_count": 9,
},
{
"data": {
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+ "image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f800b778a58>"
+ "<matplotlib.figure.Figure at 0x7fd7a6adad68>"
]
},
"metadata": {},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": []
}
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.4.3"
+ "version": "3.6.3"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
projects / cipher-tools.git / blobdiff