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herrfz/nMAP_tests

Created March 20, 2014 14:07
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nMAP tests
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nMAP_tests
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"name": ""
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"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": {}
}
]
}
@Masud2017
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Do you have any update distro nmap have no update distro yet

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