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askeing/report-input-latency-obs-win10-win7-fps-90-60.ipynb

Created April 19, 2017 04:04
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2017-04-18 Win10 OBS / Win7 FFmpeg with 60 /90 FPS
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report-input-latency-obs-win10-win7-fps-90-60.ipynb
{
"metadata": {
"celltoolbar": "Raw Cell Format",
"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": "2.7.12"
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"nbformat": 3,
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import glob\n",
"import json \n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from os import environ\n",
"from IPython.display import display, HTML\n",
"\n",
"key_index = ['avg_time',\n",
" 'max_time', \n",
" 'med_time',\n",
" 'min_time',\n",
" 'std_dev']\n",
"\n",
"#\n",
"# Read report filename from environment varilable 'file' \n",
"#\n",
"filename = environ['file']\n",
"with open(filename) as data_file:\n",
" data = json.load(data_file)\n",
" d = pd.DataFrame(data) \n",
"\n",
"# drop empty 'time_list'\n",
"for c in d:\n",
" if (d[c]['time_list'] == []) :\n",
" d.drop(c, axis=1, inplace=True)\n",
" \n",
"# Select columns and rows\n",
"d.loc[key_index, :]"
],
"language": "python",
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [
{
"html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win10_obs_60fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win10_obs_90fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_60fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_90fps</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>avg_time</th>\n",
" <td>23.8889</td>\n",
" <td>23.6715</td>\n",
" <td>48.3804</td>\n",
" <td>57.4566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max_time</th>\n",
" <td>33.3333</td>\n",
" <td>44.4444</td>\n",
" <td>66.6667</td>\n",
" <td>68.1818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>med_time</th>\n",
" <td>16.6667</td>\n",
" <td>22.2222</td>\n",
" <td>50</td>\n",
" <td>56.1798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min_time</th>\n",
" <td>16.6667</td>\n",
" <td>11.1111</td>\n",
" <td>33.3333</td>\n",
" <td>44.9438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std_dev</th>\n",
" <td>8.25893</td>\n",
" <td>9.05424</td>\n",
" <td>10.8238</td>\n",
" <td>7.34465</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 1,
"text": [
" test_chrome_gdoc_ail_type_10p_txt_win10_obs_60fps \\\n",
"avg_time 23.8889 \n",
"max_time 33.3333 \n",
"med_time 16.6667 \n",
"min_time 16.6667 \n",
"std_dev 8.25893 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win10_obs_90fps \\\n",
"avg_time 23.6715 \n",
"max_time 44.4444 \n",
"med_time 22.2222 \n",
"min_time 11.1111 \n",
"std_dev 9.05424 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_60fps \\\n",
"avg_time 48.3804 \n",
"max_time 66.6667 \n",
"med_time 50 \n",
"min_time 33.3333 \n",
"std_dev 10.8238 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_90fps \n",
"avg_time 57.4566 \n",
"max_time 68.1818 \n",
"med_time 56.1798 \n",
"min_time 44.9438 \n",
"std_dev 7.34465 "
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Retrive 'run_time' from each run\n",
"runtime = pd.DataFrame([pd.DataFrame(d[c]['time_list'])['run_time'] for c in d]).T\n",
"runtime.columns = d.columns\n",
"runtime"
],
"language": "python",
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [
{
"html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win10_obs_60fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win10_obs_90fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_60fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_90fps</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>16.666667</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.943820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>16.666667</td>\n",
" <td>44.444444</td>\n",
" <td>33.333333</td>\n",
" <td>44.943820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>44.943820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>44.943820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>45.454545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>16.666667</td>\n",
" <td>11.111111</td>\n",
" <td>33.898305</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>16.666667</td>\n",
" <td>33.333333</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>16.666667</td>\n",
" <td>33.333333</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>16.666667</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>16.666667</td>\n",
" <td>11.111111</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>33.333333</td>\n",
" <td>11.111111</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>33.333333</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>56.818182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>33.333333</td>\n",
" <td>11.111111</td>\n",
" <td>50.000000</td>\n",
" <td>56.818182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>33.333333</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>56.818182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>33.333333</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>56.818182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>33.333333</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>67.415730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>33.333333</td>\n",
" <td>NaN</td>\n",
" <td>50.000000</td>\n",
" <td>67.415730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>33.333333</td>\n",
" <td>NaN</td>\n",
" <td>50.847458</td>\n",
" <td>67.415730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>33.333333</td>\n",
" <td>NaN</td>\n",
" <td>66.666667</td>\n",
" <td>67.415730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>33.333333</td>\n",
" <td>NaN</td>\n",
" <td>66.666667</td>\n",
" <td>67.415730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>33.333333</td>\n",
" <td>NaN</td>\n",
" <td>66.666667</td>\n",
" <td>67.415730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>33.333333</td>\n",
" <td>NaN</td>\n",
" <td>66.666667</td>\n",
" <td>68.181818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>33.333333</td>\n",
" <td>NaN</td>\n",
" <td>66.666667</td>\n",
" <td>68.181818</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 2,
"text": [
" test_chrome_gdoc_ail_type_10p_txt_win10_obs_60fps \\\n",
"0 16.666667 \n",
"1 16.666667 \n",
"2 16.666667 \n",
"3 16.666667 \n",
"4 16.666667 \n",
"5 16.666667 \n",
"6 16.666667 \n",
"7 16.666667 \n",
"8 16.666667 \n",
"9 16.666667 \n",
"10 16.666667 \n",
"11 16.666667 \n",
"12 16.666667 \n",
"13 16.666667 \n",
"14 16.666667 \n",
"15 16.666667 \n",
"16 16.666667 \n",
"17 33.333333 \n",
"18 33.333333 \n",
"19 33.333333 \n",
"20 33.333333 \n",
"21 33.333333 \n",
"22 33.333333 \n",
"23 33.333333 \n",
"24 33.333333 \n",
"25 33.333333 \n",
"26 33.333333 \n",
"27 33.333333 \n",
"28 33.333333 \n",
"29 33.333333 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win10_obs_90fps \\\n",
"0 33.333333 \n",
"1 44.444444 \n",
"2 22.222222 \n",
"3 22.222222 \n",
"4 22.222222 \n",
"5 22.222222 \n",
"6 22.222222 \n",
"7 11.111111 \n",
"8 22.222222 \n",
"9 22.222222 \n",
"10 22.222222 \n",
"11 33.333333 \n",
"12 22.222222 \n",
"13 33.333333 \n",
"14 44.444444 \n",
"15 22.222222 \n",
"16 11.111111 \n",
"17 11.111111 \n",
"18 22.222222 \n",
"19 11.111111 \n",
"20 22.222222 \n",
"21 22.222222 \n",
"22 22.222222 \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_60fps \\\n",
"0 33.333333 \n",
"1 33.333333 \n",
"2 33.333333 \n",
"3 33.333333 \n",
"4 33.333333 \n",
"5 33.333333 \n",
"6 33.333333 \n",
"7 33.898305 \n",
"8 50.000000 \n",
"9 50.000000 \n",
"10 50.000000 \n",
"11 50.000000 \n",
"12 50.000000 \n",
"13 50.000000 \n",
"14 50.000000 \n",
"15 50.000000 \n",
"16 50.000000 \n",
"17 50.000000 \n",
"18 50.000000 \n",
"19 50.000000 \n",
"20 50.000000 \n",
"21 50.000000 \n",
"22 50.000000 \n",
"23 50.000000 \n",
"24 50.847458 \n",
"25 66.666667 \n",
"26 66.666667 \n",
"27 66.666667 \n",
"28 66.666667 \n",
"29 66.666667 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_90fps \n",
"0 44.943820 \n",
"1 44.943820 \n",
"2 44.943820 \n",
"3 44.943820 \n",
"4 45.454545 \n",
"5 56.179775 \n",
"6 56.179775 \n",
"7 56.179775 \n",
"8 56.179775 \n",
"9 56.179775 \n",
"10 56.179775 \n",
"11 56.179775 \n",
"12 56.179775 \n",
"13 56.179775 \n",
"14 56.179775 \n",
"15 56.179775 \n",
"16 56.179775 \n",
"17 56.179775 \n",
"18 56.818182 \n",
"19 56.818182 \n",
"20 56.818182 \n",
"21 56.818182 \n",
"22 67.415730 \n",
"23 67.415730 \n",
"24 67.415730 \n",
"25 67.415730 \n",
"26 67.415730 \n",
"27 67.415730 \n",
"28 68.181818 \n",
"29 68.181818 "
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Report percentile\n",
"runtime.quantile([0.01, 0.95, 0.99], interpolation='nearest')"
],
"language": "python",
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [
{
"html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win10_obs_60fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win10_obs_90fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_60fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_90fps</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.01</th>\n",
" <td>16.666667</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>44.943820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>68.181818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.99</th>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>68.181818</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 3,
"text": [
" test_chrome_gdoc_ail_type_10p_txt_win10_obs_60fps \\\n",
"0.01 16.666667 \n",
"0.95 33.333333 \n",
"0.99 33.333333 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win10_obs_90fps \\\n",
"0.01 11.111111 \n",
"0.95 44.444444 \n",
"0.99 44.444444 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_60fps \\\n",
"0.01 33.333333 \n",
"0.95 66.666667 \n",
"0.99 66.666667 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_90fps \n",
"0.01 44.943820 \n",
"0.95 68.181818 \n",
"0.99 68.181818 "
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Report summary\n",
"runtime.describe()"
],
"language": "python",
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [
{
"html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win10_obs_60fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win10_obs_90fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_60fps</th>\n",
" <th>test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_90fps</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>30.000000</td>\n",
" <td>23.000000</td>\n",
" <td>30.000000</td>\n",
" <td>30.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>23.888889</td>\n",
" <td>23.671498</td>\n",
" <td>48.380414</td>\n",
" <td>57.456588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8.400116</td>\n",
" <td>9.054237</td>\n",
" <td>11.008812</td>\n",
" <td>7.470213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>16.666667</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>44.943820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>37.923729</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>56.179775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>33.333333</td>\n",
" <td>22.222222</td>\n",
" <td>50.000000</td>\n",
" <td>64.766343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>68.181818</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 4,
"text": [
" test_chrome_gdoc_ail_type_10p_txt_win10_obs_60fps \\\n",
"count 30.000000 \n",
"mean 23.888889 \n",
"std 8.400116 \n",
"min 16.666667 \n",
"25% 16.666667 \n",
"50% 16.666667 \n",
"75% 33.333333 \n",
"max 33.333333 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win10_obs_90fps \\\n",
"count 23.000000 \n",
"mean 23.671498 \n",
"std 9.054237 \n",
"min 11.111111 \n",
"25% 22.222222 \n",
"50% 22.222222 \n",
"75% 22.222222 \n",
"max 44.444444 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_60fps \\\n",
"count 30.000000 \n",
"mean 48.380414 \n",
"std 11.008812 \n",
"min 33.333333 \n",
"25% 37.923729 \n",
"50% 50.000000 \n",
"75% 50.000000 \n",
"max 66.666667 \n",
"\n",
" test_chrome_gdoc_ail_type_10p_txt_win7_ffmpeg_90fps \n",
"count 30.000000 \n",
"mean 57.456588 \n",
"std 7.470213 \n",
"min 44.943820 \n",
"25% 56.179775 \n",
"50% 56.179775 \n",
"75% 64.766343 \n",
"max 68.181818 "
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot input latency histogram\n",
"runtime.plot.hist(bins=20, \n",
" histtype='bar', \n",
" color=['green','orange', 'red', 'blue'], \n",
" subplots=True, layout=(len(runtime.columns)/2,2), \n",
" figsize=(20,10))\n",
"runtime.plot.box(subplots=True,\n",
" layout=(len(runtime.columns)/2,2),\n",
" sharey=True, sharex=True, figsize=(20, 10))\n",
"plt.show()\n",
"plt.show()"
],
"language": "python",
"metadata": {
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
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"text": [
"<matplotlib.figure.Figure at 0x10bb9ef90>"
]
},
{
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Jmt9S8a1JHt3dyw4PXbmquk+Sd2V+Ib6PTeuuTPLl3f3Me7iWlRyXxVTV9Um+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\nJEmSWuT/AdyNhZJMVQevAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10e8f55d0>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": true,
"input": [],
"language": "python",
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [],
"prompt_number": 6
}
],
"metadata": {}
}
]
}
Raw
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