dbrgn/gist:9981928

## gistfile1.txt
{
 "metadata": {
  "name": "aes"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from collections import Counter, OrderedDict\nfrom base64 import b64decode\nimport matplotlib.pyplot as plt",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Decode bytes\naes_bytes = b64decode('''sRoK5vaaqJeIzKRMffh/GmkLbAY6CuydKyIglSRl+WtCwhg9OXtHVk8N\nURXhbhVI1DF6lMxqg2Y0lAHJSkVwoDjclOADJSw/WcyqOICiJt4AgmEmy8t1AKHGE3YNCcGSChHUiwDED\nLgmxHYqT5dHfyTkTLYYqJpdv4T5TSm+JBZD+fT6DPMkXRNbaMjJASxJJopgeOn5KDklAlMhR3AlsndOvN\n35eFmn1B9HqBg54dOsgQBtaH1r213GYByrjqrjEHVGbFZWAST30DL3H/s5Nw7AQ9rGHZSDZza8h6trDog\nJMzC7DCRTssNJ/Ydt/pPE3CJ4LJSjZ85y8/oKmzHoF7JgY8ht6uFve8bXo/JpWm58PerQisYQQhncLlGP\nMW2w8vtr8eyI6myE8ZPWh9chMslw/YHQY+axEf8PRxh4pDE='''.replace('\\n', ''))",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Count bytes\nbyte_counter = Counter(aes_bytes)\nbyte_counter",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": "Counter({36: 6, 71: 5, 148: 5, 198: 5, 249: 5, 0: 4, 10: 4, 24: 4, 38: 4, 49: 4, 57: 4, 107: 4, 109: 4, 120: 4, 1: 3, 12: 3, 37: 3, 44: 3, 86: 3, 93: 3, 96: 3, 108: 3, 112: 3, 135: 3, 136: 3, 168: 3, 178: 3, 196: 3, 201: 3, 204: 3, 208: 3, 212: 3, 220: 3, 225: 3, 234: 3, 9: 2, 13: 2, 14: 2, 16: 2, 17: 2, 19: 2, 21: 2, 26: 2, 31: 2, 33: 2, 34: 2, 50: 2, 56: 2, 61: 2, 66: 2, 67: 2, 73: 2, 76: 2, 79: 2, 81: 2, 83: 2, 89: 2, 99: 2, 103: 2, 104: 2, 105: 2, 110: 2, 117: 2, 118: 2, 123: 2, 125: 2, 127: 2, 129: 2, 131: 2, 132: 2, 138: 2, 147: 2, 151: 2, 154: 2, 163: 2, 164: 2, 170: 2, 171: 2, 177: 2, 188: 2, 200: 2, 203: 2, 215: 2, 230: 2, 236: 2, 241: 2, 242: 2, 243: 2, 247: 2, 250: 2, 251: 2, 253: 2, 2: 1, 3: 1, 6: 1, 11: 1, 15: 1, 22: 1, 23: 1, 25: 1, 28: 1, 29: 1, 32: 1, 40: 1, 41: 1, 42: 1, 43: 1, 46: 1, 48: 1, 51: 1, 52: 1, 54: 1, 55: 1, 58: 1, 63: 1, 69: 1, 70: 1, 72: 1, 74: 1, 77: 1, 78: 1, 90: 1, 91: 1, 97: 1, 101: 1, 102: 1, 106: 1, 111: 1, 114: 1, 119: 1, 122: 1, 124: 1, 128: 1, 130: 1, 139: 1, 142: 1, 143: 1, 146: 1, 149: 1, 155: 1, 157: 1, 160: 1, 161: 1, 162: 1, 167: 1, 172: 1, 176: 1, 182: 1, 184: 1, 187: 1, 190: 1, 191: 1, 192: 1, 193: 1, 194: 1, 195: 1, 206: 1, 211: 1, 214: 1, 218: 1, 219: 1, 221: 1, 222: 1, 224: 1, 227: 1, 228: 1, 232: 1, 233: 1, 244: 1, 246: 1, 248: 1, 254: 1, 255: 1})"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Sort dictionary\ndata = OrderedDict(sorted(byte_counter.items()))\n\n# Prepare figure & axes\nfig = plt.figure(figsize=(15,4), dpi=120)\nax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\nax.set_xlabel('Byte')\nax.set_ylabel('Count')\nplt.xticks(range(len(data)), list(data.keys()))\n\n# Only show every 4th tick label\nfor label in ax.xaxis.get_ticklabels()[1::4]:\n    label.set_visible(False)\nfor label in ax.xaxis.get_ticklabels()[2::4]:\n    label.set_visible(False)\nfor label in ax.xaxis.get_ticklabels()[3::4]:\n    label.set_visible(False)\n    \n# Plot bars\nplt.bar(range(len(data)), data.values(), align='center')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "<Container object of 173 artists>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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du7dSU1Mdx5s7d64WLVp0yY4bTH7+4j3++OO67777dNVVV+n+++/XggULHMfz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\n",
       "text": "<matplotlib.figure.Figure at 0x7f9dc194a048>"
      }
     ],
     "prompt_number": 4
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "aes"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "from collections import Counter, OrderedDict\nfrom base64 import b64decode\nimport matplotlib.pyplot as plt",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 1
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "# Decode bytes\naes_bytes = b64decode('''sRoK5vaaqJeIzKRMffh/GmkLbAY6CuydKyIglSRl+WtCwhg9OXtHVk8N\nURXhbhVI1DF6lMxqg2Y0lAHJSkVwoDjclOADJSw/WcyqOICiJt4AgmEmy8t1AKHGE3YNCcGSChHUiwDED\nLgmxHYqT5dHfyTkTLYYqJpdv4T5TSm+JBZD+fT6DPMkXRNbaMjJASxJJopgeOn5KDklAlMhR3AlsndOvN\n35eFmn1B9HqBg54dOsgQBtaH1r213GYByrjqrjEHVGbFZWAST30DL3H/s5Nw7AQ9rGHZSDZza8h6trDog\nJMzC7DCRTssNJ/Ydt/pPE3CJ4LJSjZ85y8/oKmzHoF7JgY8ht6uFve8bXo/JpWm58PerQisYQQhncLlGP\nMW2w8vtr8eyI6myE8ZPWh9chMslw/YHQY+axEf8PRxh4pDE='''.replace('\\n', ''))",
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "# Count bytes\nbyte_counter = Counter(aes_bytes)\nbyte_counter",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 76,
	"text": "Counter({36: 6, 71: 5, 148: 5, 198: 5, 249: 5, 0: 4, 10: 4, 24: 4, 38: 4, 49: 4, 57: 4, 107: 4, 109: 4, 120: 4, 1: 3, 12: 3, 37: 3, 44: 3, 86: 3, 93: 3, 96: 3, 108: 3, 112: 3, 135: 3, 136: 3, 168: 3, 178: 3, 196: 3, 201: 3, 204: 3, 208: 3, 212: 3, 220: 3, 225: 3, 234: 3, 9: 2, 13: 2, 14: 2, 16: 2, 17: 2, 19: 2, 21: 2, 26: 2, 31: 2, 33: 2, 34: 2, 50: 2, 56: 2, 61: 2, 66: 2, 67: 2, 73: 2, 76: 2, 79: 2, 81: 2, 83: 2, 89: 2, 99: 2, 103: 2, 104: 2, 105: 2, 110: 2, 117: 2, 118: 2, 123: 2, 125: 2, 127: 2, 129: 2, 131: 2, 132: 2, 138: 2, 147: 2, 151: 2, 154: 2, 163: 2, 164: 2, 170: 2, 171: 2, 177: 2, 188: 2, 200: 2, 203: 2, 215: 2, 230: 2, 236: 2, 241: 2, 242: 2, 243: 2, 247: 2, 250: 2, 251: 2, 253: 2, 2: 1, 3: 1, 6: 1, 11: 1, 15: 1, 22: 1, 23: 1, 25: 1, 28: 1, 29: 1, 32: 1, 40: 1, 41: 1, 42: 1, 43: 1, 46: 1, 48: 1, 51: 1, 52: 1, 54: 1, 55: 1, 58: 1, 63: 1, 69: 1, 70: 1, 72: 1, 74: 1, 77: 1, 78: 1, 90: 1, 91: 1, 97: 1, 101: 1, 102: 1, 106: 1, 111: 1, 114: 1, 119: 1, 122: 1, 124: 1, 128: 1, 130: 1, 139: 1, 142: 1, 143: 1, 146: 1, 149: 1, 155: 1, 157: 1, 160: 1, 161: 1, 162: 1, 167: 1, 172: 1, 176: 1, 182: 1, 184: 1, 187: 1, 190: 1, 191: 1, 192: 1, 193: 1, 194: 1, 195: 1, 206: 1, 211: 1, 214: 1, 218: 1, 219: 1, 221: 1, 222: 1, 224: 1, 227: 1, 228: 1, 232: 1, 233: 1, 244: 1, 246: 1, 248: 1, 254: 1, 255: 1})"
	}
	],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": "# Sort dictionary\ndata = OrderedDict(sorted(byte_counter.items()))\n\n# Prepare figure & axes\nfig = plt.figure(figsize=(15,4), dpi=120)\nax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\nax.set_xlabel('Byte')\nax.set_ylabel('Count')\nplt.xticks(range(len(data)), list(data.keys()))\n\n# Only show every 4th tick label\nfor label in ax.xaxis.get_ticklabels()[1::4]:\n label.set_visible(False)\nfor label in ax.xaxis.get_ticklabels()[2::4]:\n label.set_visible(False)\nfor label in ax.xaxis.get_ticklabels()[3::4]:\n label.set_visible(False)\n \n# Plot bars\nplt.bar(range(len(data)), data.values(), align='center')",
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 4,
	"text": "<Container object of 173 artists>"
	},
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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	"text": "<matplotlib.figure.Figure at 0x7f9dc194a048>"
	}
	],
	"prompt_number": 4
	}
	],
	"metadata": {}
	}
	]
	}