analysis/bytes_included_in_crc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import dateutil\n",
    "import analysis\n",
    "\n",
    "packets = analysis.parse_packet_file('data/all.txt')\n",
    "packets += analysis.parse_packet_file('data/temp_basals.txt')\n",
    "\n",
    "# Select valid packets\n",
    "packets = filter(lambda x: x.is_valid() and x.body_len == 3, packets)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-06-26T20:33:28.412197 ID1:1f01482a PTYPE:PDM SEQ:13 ID2:1f01482a B9:10 BLEN:3 MTYPE:0e01 BODY:00802c CRC:88\n"
     ]
    }
   ],
   "source": [
    "print packets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1f01482aad1f01482a10030e0100802c88\n"
     ]
    }
   ],
   "source": [
    "print packets[0].tx_data().encode('hex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def split_parts(data, idx):\n",
    "    return (ord(data[idx]), (data[4:idx] + data[idx+1:-3]).encode('hex'), data[-3:-1].encode('hex'))\n",
    "\n",
    "def get_parts_df(pos, packets):\n",
    "    parts = [split_parts(p.tx_data(), pos) for p in packets] \n",
    "    parts_df = pd.DataFrame(parts, columns=[\"byte\", \"other\", \"hash\"])\n",
    "    parts_df.drop_duplicates(inplace=True)\n",
    "    return parts_df\n",
    "\n",
    "def analyze_parts(parts_df):\n",
    "    # Number of times byte changes independently of 'other'\n",
    "    independent_change_count = parts_df.groupby(['other']).count()['byte'].max() - 1\n",
    "    # Max number of times byte changed independently of hash\n",
    "    # Higher number here means not likely to be included in hash\n",
    "    hash_independent_change_count = parts_df.groupby(['hash', 'other']).count()['byte'].max() - 1\n",
    "    # Number of times hash changed dependent on byte\n",
    "    dependent_change_count = parts_df.groupby(['other']).count()['hash'].max() - 1\n",
    "    return (independent_change_count, hash_independent_change_count, dependent_change_count)\n",
    "\n",
    "plen = len(packets[0].tx_data())\n",
    "# For all bytes except the last three (2-byte hash and crc8)\n",
    "stats = [analyze_parts(get_parts_df(i, packets)) for i in range(4, plen-3)]\n",
    "stats = pd.DataFrame(stats, columns=['ichange', 'hichange', 'dchange'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112895450>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD8CAYAAAC8TPVwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhhjaktSQ/wev7UM0eyyqNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1125da7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats['ichange'].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112c4ef90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD8CAYAAAC8TPVwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzi/4WBx6wkYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112c57b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats['hichange'].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112e04290>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAD8CAYAAAC8TPVwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhhjaktSQ/wev7UM0eyyqNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e040d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats['dchange'].plot.bar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}