nMAP tests

nMAP_tests

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import os\n",
      "import subprocess as sp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Collect data for bitrate statistics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      ">>> import os\n",
      ">>> import subprocess as sp\n",
      ">>> for dirname, subdirs, files in os.walk('.'):\n",
      "...     with open('python_test_results', 'a+') as f:\n",
      "...             if 'test.log' in files:\n",
      "...                     f.write(' '.join([dirname, \n",
      "                                    sp.check_output([\"grep Mbps \" + dirname + \"/test.log \" + \"| grep -v average | cut -d' ' -f4\"], \n",
      "                                                    shell=True).strip().replace('\\n', ' ')]) + '\\n')\n",
      "'''\n",
      "\n",
      "df = pd.read_csv('python_test_results', sep=' ', \n",
      "                 header=None, names=['path', 'stream1', 'stream2'])\n",
      "df.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>path</th>\n",
        "      <th>stream1</th>\n",
        "      <th>stream2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> ./03-12_21-00_DualWAP/192.168.1.10</td>\n",
        "      <td> 1.659238</td>\n",
        "      <td> 1.642646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  ./03-12_21-00_DualWAP/192.168.1.3</td>\n",
        "      <td> 1.377036</td>\n",
        "      <td> 1.443399</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> ./03-12_21-00_DualWAP/192.168.1.15</td>\n",
        "      <td> 0.514400</td>\n",
        "      <td> 0.481213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  ./03-12_21-00_DualWAP/192.168.1.7</td>\n",
        "      <td> 1.476616</td>\n",
        "      <td> 1.360478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> ./03-12_21-00_DualWAP/192.168.1.11</td>\n",
        "      <td> 1.443402</td>\n",
        "      <td> 1.343857</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "                                 path   stream1   stream2\n",
        "0  ./03-12_21-00_DualWAP/192.168.1.10  1.659238  1.642646\n",
        "1   ./03-12_21-00_DualWAP/192.168.1.3  1.377036  1.443399\n",
        "2  ./03-12_21-00_DualWAP/192.168.1.15  0.514400  0.481213\n",
        "3   ./03-12_21-00_DualWAP/192.168.1.7  1.476616  1.360478\n",
        "4  ./03-12_21-00_DualWAP/192.168.1.11  1.443402  1.343857\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Statistics for dual nMAP tests; average stream (application) bitrate per stream, averaged over all RPis "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.path.apply(lambda x: 'Dual' in x)].mean(axis=1).describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "count    120.000000\n",
        "mean       1.123260\n",
        "std        0.388230\n",
        "min        0.447986\n",
        "25%        0.638797\n",
        "50%        1.306548\n",
        "75%        1.410224\n",
        "max        1.650981\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Statistics for single nMAP tests; average stream (application) bitrate per stream, averaged over all RPis"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.path.apply(lambda x: 'Singe' in x)].mean(axis=1).describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "count    60.000000\n",
        "mean      1.659164\n",
        "std       0.000387\n",
        "min       1.659057\n",
        "25%       1.659097\n",
        "50%       1.659121\n",
        "75%       1.659133\n",
        "max       1.662104\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Collect data for queue sizes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "res = []\n",
      "\n",
      "for dirname, subdirs, files in os.walk('./test_logs/'):\n",
      "    if 'test.log' in files:\n",
      "        df2 = pd.read_csv(dirname + '/test.log', sep=' ', \n",
      "                          header=None, names=['date', 'time', 'sid', 'qsize'], \n",
      "                          error_bad_lines=False).dropna()\n",
      "        df2 = df2[df2.sid.apply(lambda x: ':' not in x)]\n",
      "        df2.sid = df2.sid.apply(lambda x: x.strip(',')).astype(int)\n",
      "        df2.qsize = df2.qsize.astype(int)\n",
      "        res.append( [dirname, df2.groupby('sid')['qsize'].max().max()] )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df3 = pd.DataFrame(res, columns=['path', 'max_qsize'])\n",
      "df3.tail()"
     ],
     "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>path</th>\n",
        "      <th>max_qsize</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>175</th>\n",
        "      <td> ./test_logs/03-13_06-00_SingeWAP\\192.168.1.11</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>176</th>\n",
        "      <td> ./test_logs/03-13_06-00_SingeWAP\\192.168.1.12</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>177</th>\n",
        "      <td>  ./test_logs/03-13_06-00_SingeWAP\\192.168.1.3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>178</th>\n",
        "      <td>  ./test_logs/03-13_06-00_SingeWAP\\192.168.1.7</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>179</th>\n",
        "      <td>  ./test_logs/03-13_06-00_SingeWAP\\192.168.1.9</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "                                              path  max_qsize\n",
        "175  ./test_logs/03-13_06-00_SingeWAP\\192.168.1.11          1\n",
        "176  ./test_logs/03-13_06-00_SingeWAP\\192.168.1.12          1\n",
        "177   ./test_logs/03-13_06-00_SingeWAP\\192.168.1.3          1\n",
        "178   ./test_logs/03-13_06-00_SingeWAP\\192.168.1.7          1\n",
        "179   ./test_logs/03-13_06-00_SingeWAP\\192.168.1.9          1\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Max queue sizes per stream for dual vs single nMAP, maximum over all RPis"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df3.groupby(df3.path.map(lambda x: 'Dual' in x))['max_qsize'].max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "path\n",
        "False     1\n",
        "True     70\n",
        "Name: max_qsize, dtype: int64"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "up to 70 requests are queued for dual nMAP tests."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}

Do you have any update distro nmap have no update distro yet