{
 "metadata": {
  "name": "bluetooth_database_analytics"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas\n",
      "import json\n",
      "import os\n",
      "import datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#the following is the path to your client json files \n",
      "data_path=\"../../data/json_archive\"\n",
      "has_json_ext = lambda x: True if os.path.splitext(x)[-1] == \".json\" else False\n",
      "data_list=[]\n",
      "for path, deeper_dirs, filenames in os.walk(data_path):\n",
      "    for filename in filter(has_json_ext, filenames):\n",
      "        f = open(os.path.join(path, filename), 'r')\n",
      "        json_data = json.load(f)\n",
      "        f.close()\n",
      "        data_list.append(json_data)\n",
      "df = pandas.DataFrame(data_list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### High level stats"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uniq_devs = df[\"bluetoothAddress\"].nunique()\n",
      "total_entries = len(df)\n",
      "print \"%-25s %-25s\" % (\"Uniq Devs:\", uniq_devs)\n",
      "print \"%-25s %-25s\" % (\"Total Sightings:\", total_entries)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Uniq Devs:                2489                     \n",
        "Total Sightings:          9362                     \n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Epoc to datetime"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "epoc_to_datetime = lambda x:datetime.datetime.fromtimestamp(float(x))\n",
      "df[\"timestamp\"] = df.timestamp.apply(epoc_to_datetime)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 5 entries, 0 to 4\n",
        "Data columns (total 10 columns):\n",
        "bluetoothAddress    5  non-null values\n",
        "bluetoothName       5  non-null values\n",
        "clientType          0  non-null values\n",
        "clientVersion       0  non-null values\n",
        "deviceMajor         0  non-null values\n",
        "deviceMinor         0  non-null values\n",
        "latatude            5  non-null values\n",
        "longitude           5  non-null values\n",
        "timestamp           5  non-null values\n",
        "type                0  non-null values\n",
        "dtypes: datetime64[ns](1), float64(3), object(6)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "    bluetoothAddress        bluetoothName clientType clientVersion  \\\n",
        "0  14:10:9F:E5:00:22                 eth1        NaN           NaN   \n",
        "1  04:0C:CE:E0:55:55  Katie\u2019s MacBook Air        NaN           NaN   \n",
        "2  04:0C:CE:E0:55:55  Katie\u2019s MacBook Air        NaN           NaN   \n",
        "3  04:0C:CE:E0:55:55  Katie\u2019s MacBook Air        NaN           NaN   \n",
        "4  14:10:9F:E5:00:22                 eth1        NaN           NaN   \n",
        "\n",
        "   deviceMajor  deviceMinor   latatude   longitude                  timestamp  \\\n",
        "0          NaN          NaN  30.257667  -97.794550 2013-05-11 13:44:02.069268   \n",
        "1          NaN          NaN  30.257667  -97.794550 2013-05-11 13:44:05.004672   \n",
        "2          NaN          NaN  30.257590  -97.794682 2013-05-11 13:46:13.040933   \n",
        "3          NaN          NaN  30.257656  -97.794611 2013-05-11 13:47:54.032942   \n",
        "4          NaN          NaN  30.257656  -97.794611 2013-05-11 13:47:56.232570   \n",
        "\n",
        "   type  \n",
        "0   NaN  \n",
        "1   NaN  \n",
        "2   NaN  \n",
        "3   NaN  \n",
        "4   NaN  "
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Device discovery over time"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_devs = df[[\"timestamp\"]]\n",
      "all_devs.sort(\"timestamp\")\n",
      "all_devs = all_devs.set_index(\"timestamp\")\n",
      "all_devs[\"total\"] = 1\n",
      "all_devs[\"total\"] = all_devs.total.cumsum()\n",
      "fig = all_devs.plot(figsize=(15,5))\n",
      "fig.set_title(\"Cumulitive sightings over time\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "<matplotlib.text.Text at 0x1087b4a50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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goCAtJSIiIiIiUk/YPnGNSY4cOWL7LEMmbiYVzg6Hw+4mSDWUj9mUj9mUj/mUkdmUj9mU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\n3gAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_devs = df[[\"timestamp\"]]\n",
      "all_devs[\"timestamp\"] = df.timestamp.apply(lambda x: x.date())\n",
      "grouped = all_devs.groupby(\"timestamp\")\n",
      "all_by_day = grouped.size()\n",
      "fig = all_by_day.plot(figsize=(15,5))\n",
      "fig.set_title(\"Sightings by day\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "<matplotlib.text.Text at 0x1097705d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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sqnlzFta8ERERERGRNVavBj75BPjf/2y7rrAQ6NQJ+PNP5/TLUarXvBERERER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\ngiDYAaK8CYIgCIIgCIIg2AH/D0RhnXFi3F3HAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_devs = df[\"timestamp\"]\n",
      "ts = pandas.Series(0, all_devs)\n",
      "all_by_day += ts.resample('D')\n",
      "fig = all_by_day.plot(kind=\"bar\", figsize=(15,5))\n",
      "fig.set_title(\"Sightings by day (with NA)\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "<matplotlib.text.Text at 0x1097441d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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wYYJ27dqlcePGadmyZZKk0tJSFRUVqbS0VMXFxZo/f76OHDkSUWHXzsN1KdNk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\nAAAAEcCbNwAAAAAAgAj4G3HzQbh+9zAjAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Unique discovery over time"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uniq_devs = df.groupby(\"bluetoothAddress\")\n",
      "uniq_devs = uniq_devs.timestamp.min()\n",
      "uniq_devs = uniq_devs.reset_index()\n",
      "uniq_devs = uniq_devs[[\"timestamp\"]]\n",
      "uniq_devs.sort(\"timestamp\")\n",
      "uniq_devs = uniq_devs.set_index(\"timestamp\")\n",
      "uniq_devs[\"total\"] = 1\n",
      "uniq_devs[\"total\"] = uniq_devs.total.cumsum()\n",
      "fig = uniq_devs.plot(figsize=(15,5))\n",
      "fig.set_title(\"Cumulitive unique over time\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "<matplotlib.text.Text at 0x1098a7dd0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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HacOGC2sQz4Ym8TRHjhxhhLEUJl0yxO12KzY21ukycAbkYzbyMR8ZmY18zEY+ZiOfsn3y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\nRCGEEEIIIYQQmv8HwwyUFlARf5QAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uniq_devs = df.groupby(\"bluetoothAddress\")\n",
      "uniq_devs = uniq_devs.timestamp.min()\n",
      "uniq_devs = uniq_devs.reset_index()\n",
      "uniq_devs = uniq_devs[[\"timestamp\"]]\n",
      "uniq_devs[\"timestamp\"] = uniq_devs.timestamp.apply(lambda x: x.date())\n",
      "grouped = uniq_devs.groupby(\"timestamp\")\n",
      "uniq_by_day = grouped.size()\n",
      "fig = uniq_by_day.plot(figsize=(15,5))\n",
      "fig.set_title(\"Unique sightings by day\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "<matplotlib.text.Text at 0x109a486d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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cZO3atUhISAAAJCQkYPXq1QCANWvWID4+Hl5eXggICEBgYCB27dql1vMgIiIiIiIq49Ah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\nSUVORK5CYII=\n"
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uniq_devs = df.groupby(\"bluetoothAddress\")\n",
      "uniq_devs = uniq_devs.timestamp.min()\n",
      "uniq_devs = uniq_devs.reset_index()\n",
      "uniq_devs = uniq_devs[[\"timestamp\"]]\n",
      "ts = pandas.Series(0, uniq_devs[\"timestamp\"])\n",
      "ts = ts.resample('D')\n",
      "uniq_devs[\"timestamp\"] = uniq_devs.timestamp.apply(lambda x: x.date())\n",
      "grouped = uniq_devs.groupby(\"timestamp\")\n",
      "uniq_by_day = grouped.size()\n",
      "uniq_by_day += ts \n",
      "fig = uniq_by_day.plot(figsize=(15,5), kind='bar')\n",
      "fig.set_title(\"Unique sightings by day (with NA)\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "<matplotlib.text.Text at 0x109d23f10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Z+vDDDzVv3jzFxcXp1ltvlSSNGTNGc+bM0dChQxuGsiYNAAAAQBNYkya1b2m4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\n"
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Device sighting prev"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prev = df.groupby(\"bluetoothAddress\")\n",
      "prev = prev.size()\n",
      "prev = prev.reset_index()\n",
      "prev.columns = [\"bluetoothAddress\", \"prev\"]\n",
      "df_w_prev = pandas.merge(df, prev, on=\"bluetoothAddress\")\n",
      "df_w_prev.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 5 entries, 0 to 4\n",
        "Data columns (total 11 columns):\n",
        "bluetoothAddress    5  non-null values\n",
        "bluetoothName       5  non-null values\n",
        "clientType          0  non-null values\n",
        "clientVersion       0  non-null values\n",
        "deviceMajor         0  non-null values\n",
        "deviceMinor         0  non-null values\n",
        "latatude            5  non-null values\n",
        "longitude           5  non-null values\n",
        "timestamp           5  non-null values\n",
        "type                0  non-null values\n",
        "prev                5  non-null values\n",
        "dtypes: datetime64[ns](1), float64(3), int64(1), object(6)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "    bluetoothAddress bluetoothName clientType clientVersion  deviceMajor  \\\n",
        "0  14:10:9F:E5:00:22          eth1        NaN           NaN          NaN   \n",
        "1  14:10:9F:E5:00:22          eth1        NaN           NaN          NaN   \n",
        "2  14:10:9F:E5:00:22          eth1        NaN           NaN          NaN   \n",
        "3  14:10:9F:E5:00:22          eth1        NaN           NaN          NaN   \n",
        "4  14:10:9F:E5:00:22          eth1        NaN           NaN          NaN   \n",
        "\n",
        "   deviceMinor   latatude   longitude                  timestamp  type  prev  \n",
        "0          NaN  30.257667  -97.794550 2013-05-11 13:44:02.069268   NaN    67  \n",
        "1          NaN  30.257656  -97.794611 2013-05-11 13:47:56.232570   NaN    67  \n",
        "2          NaN  30.257601  -97.794624 2013-05-11 16:34:56.083478   NaN    67  \n",
        "3          NaN  30.257601  -97.794624 2013-05-11 16:35:52.096570   NaN    67  \n",
        "4          NaN  30.257601  -97.794624 2013-05-11 16:37:48.111033   NaN    67  "
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Device movement"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "def distance(origin, destination):\n",
      "    lat1, lon1 = origin\n",
      "    lat2, lon2 = destination\n",
      "    radius = 6371 # km\n",
      "    dlat = math.radians(lat2-lat1)\n",
      "    dlon = math.radians(lon2-lon1)\n",
      "    a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) \\\n",
      "        * math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)\n",
      "    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\n",
      "    d = radius * c\n",
      "    return d"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#TOP MOVERS...\n",
      "multi_sightings = df_w_prev[df_w_prev[\"prev\"] > 1]\n",
      "multi_sightings[\"longitude\"] = multi_sightings.longitude.apply(lambda x: abs(float(x)))\n",
      "multi_sightings[\"latatude\"] = multi_sightings.latatude.apply(lambda x: abs(float(x)))\n",
      "ms_grouped = multi_sightings.groupby(\"bluetoothAddress\")\n",
      "movement = ms_grouped.agg({\"latatude\":lambda x: x.max() - x.min(),\n",
      "    \"longitude\":lambda x: x.max() - x.min(),\n",
      "    \"bluetoothName\":lambda x: set(i for i in x),\n",
      "    \"prev\":lambda x: x.max()})\n",
      "movement[\"moved\"] = movement[\"latatude\"] + movement[\"longitude\"]\n",
      "movement.sort(\"moved\", ascending=False).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Index: 5 entries, 00:05:4F:7A:9B:59 to 40:5F:BE:B2:40:F6\n",
        "Data columns (total 5 columns):\n",
        "latatude         5  non-null values\n",
        "prev             5  non-null values\n",
        "bluetoothName    5  non-null values\n",
        "longitude        5  non-null values\n",
        "moved            5  non-null values\n",
        "dtypes: float64(3), int64(1), object(1)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "                   latatude  prev                           bluetoothName  \\\n",
        "bluetoothAddress                                                            \n",
        "00:05:4F:7A:9B:59  0.482951     3  set([zumo 660 #3660760110, Handsfree])   \n",
        "00:23:3D:04:4C:E3  0.351182   136                set([VW UHV, Handsfree])   \n",
        "98:B8:E3:69:EE:46  0.207068     6     set([Tommy\u2019s iPhone, Mobile Phone])   \n",
        "2C:44:01:60:6F:14  0.206404     2           set([SGH-A847, Mobile Phone])   \n",
        "40:5F:BE:B2:40:F6  0.130841     8    set([BlackBerry 9800, Mobile Phone])   \n",
        "\n",
        "                   longitude     moved  \n",
        "bluetoothAddress                        \n",
        "00:05:4F:7A:9B:59   0.277812  0.760763  \n",
        "00:23:3D:04:4C:E3   0.213312  0.564494  \n",
        "98:B8:E3:69:EE:46   0.089510  0.296578  \n",
        "2C:44:01:60:6F:14   0.080094  0.286498  \n",
        "40:5F:BE:B2:40:F6   0.131978  0.262819  "
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Max Distance Traveled (also add in origin, dest lat/long in result)\n",
      "multi_sightings = df_w_prev[df_w_prev[\"prev\"] > 1]\n",
      "multi_sightings[\"longitude\"] = multi_sightings.longitude.apply(lambda x: float(x))\n",
      "multi_sightings[\"latatude\"] = multi_sightings.latatude.apply(lambda x: float(x))\n",
      "ms_grouped = multi_sightings.groupby(\"bluetoothAddress\")\n",
      "traveled_data = []\n",
      "for device, group in ms_grouped:\n",
      "    max_dist = 0\n",
      "    for i in range(len(group)):\n",
      "        origin = (group.irow(i)[\"latatude\"], group.irow(i)[\"longitude\"])\n",
      "        for j in range(len(group)):\n",
      "            destination = (group.irow(j)[\"latatude\"], group.irow(j)[\"longitude\"])\n",
      "            tmp = distance(origin, destination)\n",
      "            if tmp > max_dist:\n",
      "                max_dist = tmp\n",
      "    traveled_data.append({\"bluetoothAddress\":device, \"km_traveled\":max_dist})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = pandas.DataFrame(traveled_data)\n",
      "df2.sort(\"km_traveled\", ascending=False).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### UAP stats"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### most common uap"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uap_df = pandas.DataFrame(df[\"bluetoothAddress\"].drop_duplicates())\n",
      "get_uap = lambda x: x.split(':')[2]\n",
      "uap_df[\"UAP\"] = uap_df[\"bluetoothAddress\"].apply(get_uap)\n",
      "uap_stats = uap_df.groupby(\"UAP\").size()\n",
      "uap_stats = uap_stats.reset_index()\n",
      "uap_stats.columns = [\"UAP\",\"count\"]\n",
      "uap_stats.sort(\"count\", ascending=False).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>UAP</th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>73 </th>\n",
        "      <td> 4F</td>\n",
        "      <td> 192</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>226</th>\n",
        "      <td> FC</td>\n",
        "      <td>  85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>162</th>\n",
        "      <td> B2</td>\n",
        "      <td>  72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>117</th>\n",
        "      <td> 7E</td>\n",
        "      <td>  62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>145</th>\n",
        "      <td> A0</td>\n",
        "      <td>  61</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "    UAP  count\n",
        "73   4F    192\n",
        "226  FC     85\n",
        "162  B2     72\n",
        "117  7E     62\n",
        "145  A0     61"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### NAP stats"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### most common nap"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nap_df = pandas.DataFrame(df[\"bluetoothAddress\"].drop_duplicates())\n",
      "get_uap = lambda x: x.split(':')[2]\n",
      "get_nap = lambda x: ':'.join(x.split(':')[0:2])\n",
      "nap_df[\"NAP\"] = uap_df[\"bluetoothAddress\"].apply(get_nap)\n",
      "nap_df[\"UAP\"] = uap_df[\"bluetoothAddress\"].apply(get_uap)\n",
      "\n",
      "nap_stats = nap_df.groupby(\"NAP\").size()\n",
      "nap_stats = nap_stats.reset_index()\n",
      "nap_stats.columns = [\"NAP\",\"count\"]\n",
      "nap_stats.sort(\"count\", ascending=False).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>NAP</th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 00:05</td>\n",
        "      <td> 182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td> 00:22</td>\n",
        "      <td>  97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>71</th>\n",
        "      <td> 10:C6</td>\n",
        "      <td>  85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td> 00:26</td>\n",
        "      <td>  80</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td> 00:1E</td>\n",
        "      <td>  75</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "      NAP  count\n",
        "5   00:05    182\n",
        "32  00:22     97\n",
        "71  10:C6     85\n",
        "36  00:26     80\n",
        "29  00:1E     75"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### most common nap by uap"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nap_df = pandas.DataFrame(df[\"bluetoothAddress\"].drop_duplicates())\n",
      "get_uap = lambda x: x.split(':')[2]\n",
      "get_nap = lambda x: ':'.join(x.split(':')[0:2])\n",
      "nap_df[\"NAP\"] = uap_df[\"bluetoothAddress\"].apply(get_nap)\n",
      "nap_df[\"UAP\"] = uap_df[\"bluetoothAddress\"].apply(get_uap)\n",
      "nap_df.groupby([\"UAP\",\"NAP\"]).size()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "UAP  NAP  \n",
        "00   00:17    1\n",
        "     00:25    8\n",
        "     A8:06    2\n",
        "01   00:24    2\n",
        "     2C:44    4\n",
        "02   6C:9B    2\n",
        "03   94:51    1\n",
        "     9C:DF    2\n",
        "04   00:07    2\n",
        "     00:13    1\n",
        "     70:1A    3\n",
        "     78:CA    1\n",
        "     A0:4E    2\n",
        "05   70:81    4\n",
        "06   00:17    1\n",
        "...\n",
        "F8   8C:71     2\n",
        "F9   38:59     1\n",
        "     C0:38     5\n",
        "     E4:7C     2\n",
        "FA   88:9F     2\n",
        "FB   00:21     2\n",
        "     00:25     1\n",
        "FC   10:C6    83\n",
        "     18:9E     2\n",
        "FD   00:1D     1\n",
        "     14:89     2\n",
        "FE   00:16     1\n",
        "     00:21     3\n",
        "     3C:8B     6\n",
        "FF   00:26     1\n",
        "Length: 590, dtype: int64"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Vendor stats"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Anomalies"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### same address diff names"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#do all bluetooth APIs return generic names on bad reception?\n",
      "name_anom = df.groupby(\"bluetoothAddress\")\n",
      "exclude = [\"Handsfree\", \"Misc\", \"Computer\", \"Mobile Phone\", \"Laptop\", \"Headset\", \"PDA\", \"Peripheral\", \"Mouse\", \"Keyboard\"]\n",
      "set_agg = lambda x: set(i for i in x if i not in exclude)\n",
      "name_anom = name_anom.bluetoothName.apply(set_agg)\n",
      "name_anom = name_anom.reset_index()\n",
      "name_anom.columns = [\"bluetoothAddress\", \"names\"]\n",
      "name_anom[\"name_count\"] = name_anom.names.apply(len)\n",
      "name_anom = name_anom[name_anom[\"name_count\"] > 1]\n",
      "name_anom.sort(\"name_count\", ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 16 entries, 2407 to 16\n",
        "Data columns (total 3 columns):\n",
        "bluetoothAddress    16  non-null values\n",
        "names               16  non-null values\n",
        "name_count          16  non-null values\n",
        "dtypes: int64(1), object(2)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "       bluetoothAddress                                              names  \\\n",
        "2407  F0:1C:13:A8:3C:4A                    set([LG GT365, Cordless Phone])   \n",
        "2349  E4:7C:F9:2A:A9:A9                      set([Cordless Phone, 1000PS])   \n",
        "1898  90:27:E4:F2:64:2D         set([NetBoot001, Kathleen Nash\u2019s MacBook])   \n",
        "1869  90:03:B7:30:C4:4A  set([Parrot MINIKIT Neo v, Parrot MINIKIT Neo ...   \n",
        "1715  70:F1:A1:E6:21:A0                    set([Jabra CLI, Jabra CLIPPER])   \n",
        "1566  5C:59:48:40:F9:C3                       set([iPhone, ryan's iPhone])   \n",
        "1299  2C:30:68:43:0F:EF                                set([P9050, Xhela])   \n",
        "1184  14:10:9F:DF:DC:34                set([Jordan\u2019s MacBook Pro, jordan])   \n",
        "1179  10:F9:6F:EA:33:C4               set([LG Rumor Reflex, LG Rumor Ref])   \n",
        "1003  00:AA:70:37:44:81               set([LG Rumor Reflex, LG Rumor Ref])   \n",
        "569   00:1D:DF:74:9A:F9                          set([Audio, OnBeat aWak])   \n",
        "567   00:1D:DF:5D:A8:C7                   set([OnBeat aWake, OnBeat aWak])   \n",
        "566   00:1D:DF:5D:A8:B1                   set([OnBeat aWake, OnBeat aWak])   \n",
        "565   00:1D:DF:5D:45:55                   set([OnBeat aWake, OnBeat aWak])   \n",
        "512   00:18:E4:26:28:B1                     set([BluNote+Chat, BluNote+C])   \n",
        "16    00:02:72:EF:9D:6F                       set([Belkin H8, Belkin H87])   \n",
        "\n",
        "      name_count  \n",
        "2407           2  \n",
        "2349           2  \n",
        "1898           2  \n",
        "1869           2  \n",
        "1715           2  \n",
        "1566           2  \n",
        "1299           2  \n",
        "1184           2  \n",
        "1179           2  \n",
        "1003           2  \n",
        "569            2  \n",
        "567            2  \n",
        "566            2  \n",
        "565            2  \n",
        "512            2  \n",
        "16             2  "
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Generic stats"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### most common names"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "name_anom = df.groupby(\"bluetoothAddress\")\n",
      "set_agg = lambda x: set(i for i in x)\n",
      "name_anom = name_anom.bluetoothName.apply(set_agg)\n",
      "name_anom = name_anom.reset_index()\n",
      "name_anom.columns = [\"bluetoothAddress\", \"names\"]\n",
      "name_dict = {}\n",
      "for i in name_anom[\"names\"]:\n",
      "    for j in i:\n",
      "        if j in name_dict:\n",
      "            name_dict[j] += 1\n",
      "        else:\n",
      "            name_dict[j] = 1\n",
      "formated_dict = {\"name\":[], \"count\":[]}\n",
      "excude = [\"Handsfree\", \"Misc\", \"Computer\", \"Mobile Phone\", \"Laptop\", \"Headset\", \"PDA\", \"Peripheral\"]\n",
      "for k,v in name_dict.iteritems():\n",
      "    if k in excude:\n",
      "        continue\n",
      "    formated_dict[\"name\"].append(k)\n",
      "    formated_dict[\"count\"].append(v)\n",
      "name_df = pandas.DataFrame(formated_dict)\n",
      "name_df = name_df.set_index(\"name\")\n",
      "name_df.sort(\"count\", ascending=False)[:20].plot(kind='barh', figsize=(15,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109faf9d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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khBBCCCGEKEzFuoO9fft21q5dS0xMDPv27WPTpk3UrVu3qJuVbwaDAW9vbyIi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\n"
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Map example"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get latest sighting for each device\n",
      "grouped = df.groupby(\"bluetoothAddress\")\n",
      "latest_times = grouped.timestamp.max()\n",
      "latest_times = latest_times.reset_index()\n",
      "latest_times.columns = [\"bluetoothAddress\",\"timestamp\"]\n",
      "latest_times[\"latest\"]= True\n",
      "latest_times = latest_times.set_index([\"bluetoothAddress\", \"timestamp\"])\n",
      "\n",
      "df[\"timestamp\"] = df.timestamp.apply(pandas.tslib.Timestamp)\n",
      "df2 = df.set_index([\"bluetoothAddress\", \"timestamp\"])\n",
      "latest_times = pandas.merge(latest_times, df2, left_index=True, right_index=True)\n",
      "latest_times[:20]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}