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layers.tile-height.ipynb
{
"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": "2.7.10"
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"name": ""
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
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"input": [
"import os \n",
"import json\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"from IPython.display import display, display_markdown\n",
"\n",
"#\n",
"# Hide the plotbox if case miss any one data source\n",
"#\n",
"HIDE_MISSING_CASE = False\n",
"\n",
"DATA_LIST = 'time_list'\n",
"DATA_NAME = 'run_time'\n",
"DELETE_KEY = \"video-recording-fps\"\n",
"\n",
"#\n",
"# Input data source\n",
"#\n",
"input_json_fp = os.getenv(\"input_json_fp\")\n",
"#input_json_fp = \"output/javascript.options.baselinejit.threshold.json\"\n",
"with open(input_json_fp) as read_fh:\n",
" data_dict = json.load(read_fh)\n",
" for item in data_dict:\n",
" if DELETE_KEY in data_dict[item]:\n",
" data_dict[item].pop(DELETE_KEY)\n",
"\n",
"\n",
"#\n",
"# Definde layout and figure size\n",
"#\n",
"layout_row = 1\n",
"layout_column = 1\n",
"fig_size_w = 10\n",
"fig_size_h = 5\n",
"matplotlib.rcParams.update({'figure.max_open_warning': 0})\n",
"\n",
"#\n",
"# generate merged data\n",
"#\n",
"casename_list = list(set([casename for lable, data in data_dict.items() for casename in data.keys()]))\n",
"\n",
"case_result = []\n",
"for casename in casename_list:\n",
" result = {\n",
" \"casename\": casename,\n",
" \"result\": {}\n",
" }\n",
" for lable, data in data_dict.items():\n",
" result[\"result\"][lable] = {}\n",
" \n",
" for lable, data in data_dict.items():\n",
" if data.get(casename):\n",
" result[\"result\"][lable][DATA_LIST] = data.get(casename).get(DATA_LIST)\n",
" else:\n",
" result[\"result\"][lable][DATA_LIST] = []\n",
" case_result.append(result)\n",
"\n",
"nightly_better_list = []\n",
"for case in sorted(case_result):\n",
" casename = case.get('casename')\n",
" result = case.get('result')\n",
" d = pd.DataFrame(result)\n",
" \n",
" # drop empty 'DATA_LIST'\n",
" for c in d:\n",
" if (d[c][DATA_LIST] == []) :\n",
" d.drop(c, axis=1, inplace=True)\n",
"\n",
" # Retrive the value of 'DATA_NAME' from each run\n",
" value = pd.DataFrame([pd.DataFrame(d[c][DATA_LIST])[DATA_NAME] for c in d]).T\n",
" value.columns = d.columns\n",
" \n",
" # Plot input latency boxplot\n",
" value_min = min(value.min())\n",
" value_max = max(value.max())\n",
" value_median = pd.DataFrame([pd.DataFrame(d[c][DATA_LIST])[DATA_NAME].median() for c in d]).T\n",
" value_median.columns = d.columns\n",
" \n",
" # When HIDE_MISSING_CASE is enabled, skip the case if the case data less then input data amount.\n",
" if HIDE_MISSING_CASE and len(INPUT_SOURCE) > len(value.T):\n",
" # display summary\n",
" display_markdown('### {}\\nskip draw plot box'.format(casename), raw=True)\n",
" display(value.describe())\n",
" else:\n",
" \n",
" fig, ax = plt.subplots()\n",
" ax.plot(list(range(1,len(list(value_median.median()))+1)), list(value_median.median()), \"r:\")\n",
" \n",
" value.plot.box(layout=(layout_row, layout_column),\n",
" sharey=True, sharex=True, figsize=(fig_size_w, fig_size_h),\n",
" ylim=(0, value_max*1.1), ax=ax)\n",
" plt.title(casename)\n",
" if \"Nightly\" in value_median and \"ESR52\" in value_median:\n",
" if value_median['Nightly'][0] < value_median['ESR52'][0]:\n",
" nightly_better_list.append(casename)\n",
" # display summary\n",
" display(casename)\n",
" display(value.describe().T)"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_hover_related_product_thumbnail'"
]
},
{
"html": [
"<div>\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>432.222222</td>\n",
" <td>21.880082</td>\n",
" <td>400.000000</td>\n",
" <td>422.222222</td>\n",
" <td>433.333333</td>\n",
" <td>444.444444</td>\n",
" <td>466.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>430.000000</td>\n",
" <td>36.307632</td>\n",
" <td>388.888889</td>\n",
" <td>400.000000</td>\n",
" <td>422.222222</td>\n",
" <td>452.777778</td>\n",
" <td>500.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>434.444444</td>\n",
" <td>28.424569</td>\n",
" <td>400.000000</td>\n",
" <td>413.888889</td>\n",
" <td>422.222222</td>\n",
" <td>452.777778</td>\n",
" <td>488.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 432.222222 21.880082 400.000000 422.222222 433.333333 \n",
"2 10.0 430.000000 36.307632 388.888889 400.000000 422.222222 \n",
"default 10.0 434.444444 28.424569 400.000000 413.888889 422.222222 \n",
"\n",
" 75% max \n",
"16384 444.444444 466.666667 \n",
"2 452.777778 500.000000 \n",
"default 452.777778 488.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_select_search_suggestion'"
]
},
{
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>6.666667</td>\n",
" <td>2.342428</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>6.111111</td>\n",
" <td>1.756821</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>7.777778</td>\n",
" <td>5.367177</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"16384 10.0 6.666667 2.342428 5.555556 5.555556 5.555556 5.555556 \n",
"2 10.0 6.111111 1.756821 5.555556 5.555556 5.555556 5.555556 \n",
"default 10.0 7.777778 5.367177 5.555556 5.555556 5.555556 5.555556 \n",
"\n",
" max \n",
"16384 11.111111 \n",
"2 11.111111 \n",
"default 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_type_in_search_field'"
]
},
{
"html": [
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"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>15.000000</td>\n",
" <td>6.441677</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>15.000000</td>\n",
" <td>7.878536</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>6.000686</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>19.444444</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 15.000000 6.441677 5.555556 11.111111 11.111111 \n",
"2 10.0 15.000000 7.878536 5.555556 6.944444 16.666667 \n",
"default 10.0 13.888889 6.000686 5.555556 11.111111 11.111111 \n",
"\n",
" 75% max \n",
"16384 22.222222 22.222222 \n",
"2 22.222222 22.222222 \n",
"default 19.444444 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_close_chat_tab'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>22.222222</td>\n",
" <td>11.111111</td>\n",
" <td>25.000000</td>\n",
" <td>44.444444</td>\n",
" <td>63.888889</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>50.000000</td>\n",
" <td>12.001372</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>50.000000</td>\n",
" <td>12.001372</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 44.444444 22.222222 11.111111 25.000000 44.444444 \n",
"2 10.0 50.000000 12.001372 33.333333 44.444444 50.000000 \n",
"default 10.0 50.000000 12.001372 33.333333 44.444444 50.000000 \n",
"\n",
" 75% max \n",
"16384 63.888889 77.777778 \n",
"2 55.555556 66.666667 \n",
"default 55.555556 66.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab'"
]
},
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" <th></th>\n",
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" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>218.888889</td>\n",
" <td>69.101623</td>\n",
" <td>144.444444</td>\n",
" <td>161.111111</td>\n",
" <td>194.444444</td>\n",
" <td>283.333333</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>205.555556</td>\n",
" <td>81.186334</td>\n",
" <td>122.222222</td>\n",
" <td>133.333333</td>\n",
" <td>200.000000</td>\n",
" <td>286.111111</td>\n",
" <td>288.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>235.555556</td>\n",
" <td>67.036014</td>\n",
" <td>133.333333</td>\n",
" <td>172.222222</td>\n",
" <td>266.666667</td>\n",
" <td>275.000000</td>\n",
" <td>300.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 218.888889 69.101623 144.444444 161.111111 194.444444 \n",
"2 10.0 205.555556 81.186334 122.222222 133.333333 200.000000 \n",
"default 10.0 235.555556 67.036014 133.333333 172.222222 266.666667 \n",
"\n",
" 75% max \n",
"16384 283.333333 322.222222 \n",
"2 286.111111 288.888889 \n",
"default 275.000000 300.000000 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab_emoji'"
]
},
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" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>64.444444</td>\n",
" <td>8.764563</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>65.555556</td>\n",
" <td>12.227833</td>\n",
" <td>44.444444</td>\n",
" <td>58.333333</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>81.111111</td>\n",
" <td>18.921540</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>72.222222</td>\n",
" <td>88.888889</td>\n",
" <td>122.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 64.444444 8.764563 44.444444 66.666667 66.666667 \n",
"2 10.0 65.555556 12.227833 44.444444 58.333333 66.666667 \n",
"default 10.0 81.111111 18.921540 66.666667 66.666667 72.222222 \n",
"\n",
" 75% max \n",
"16384 66.666667 77.777778 \n",
"2 66.666667 88.888889 \n",
"default 88.888889 122.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_photo_viewer_right_arrow'"
]
},
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
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" <td>97.777778</td>\n",
" <td>11.475506</td>\n",
" <td>77.777778</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>111.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>95.555556</td>\n",
" <td>7.768954</td>\n",
" <td>77.777778</td>\n",
" <td>91.666667</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>10.0</td>\n",
" <td>111.111111</td>\n",
" <td>11.712139</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>111.111111</td>\n",
" <td>122.222222</td>\n",
" <td>122.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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" count mean std min 25% 50% \\\n",
"16384 10.0 97.777778 11.475506 77.777778 100.000000 100.000000 \n",
"2 10.0 95.555556 7.768954 77.777778 91.666667 100.000000 \n",
"default 10.0 111.111111 11.712139 100.000000 100.000000 111.111111 \n",
"\n",
" 75% max \n",
"16384 100.000000 111.111111 \n",
"2 100.000000 100.000000 \n",
"default 122.222222 122.222222 "
]
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" <td>51.111111</td>\n",
" <td>7.768954</td>\n",
" <td>33.333333</td>\n",
" <td>47.222222</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>42.222222</td>\n",
" <td>10.210406</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>38.888889</td>\n",
" <td>52.777778</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>46.666667</td>\n",
" <td>8.764563</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"16384 10.0 51.111111 7.768954 33.333333 47.222222 55.555556 \n",
"2 10.0 42.222222 10.210406 33.333333 33.333333 38.888889 \n",
"default 10.0 46.666667 8.764563 33.333333 44.444444 44.444444 \n",
"\n",
" 75% max \n",
"16384 55.555556 55.555556 \n",
"2 52.777778 55.555556 \n",
"default 55.555556 55.555556 "
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" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>24.444444</td>\n",
" <td>8.764563</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
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" <td>33.333333</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>36.666667</td>\n",
" <td>7.499428</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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" count mean std min 25% 50% \\\n",
"16384 10.0 34.444444 16.101530 11.111111 25.000000 33.333333 \n",
"2 10.0 24.444444 8.764563 11.111111 22.222222 22.222222 \n",
"default 10.0 36.666667 7.499428 33.333333 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"16384 44.444444 55.555556 \n",
"2 33.333333 33.333333 \n",
"default 33.333333 55.555556 "
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" <td>27.777778</td>\n",
" <td>15.930232</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>66.666667</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>29.444444</td>\n",
" <td>9.091065</td>\n",
" <td>5.555556</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
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" <tr>\n",
" <th>default</th>\n",
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" <td>36.666667</td>\n",
" <td>15.757072</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>41.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"16384 10.0 27.777778 15.930232 11.111111 22.222222 22.222222 \n",
"2 10.0 29.444444 9.091065 5.555556 33.333333 33.333333 \n",
"default 10.0 36.666667 15.757072 11.111111 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"16384 33.333333 66.666667 \n",
"2 33.333333 33.333333 \n",
"default 41.666667 66.666667 "
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" </tr>\n",
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" <td>10.0</td>\n",
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" <td>10.227186</td>\n",
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" <td>22.222222</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>18.333333</td>\n",
" <td>12.015651</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>16.666667</td>\n",
" <td>30.555556</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>26.111111</td>\n",
" <td>15.724392</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 25.000000 10.227186 5.555556 22.222222 27.777778 \n",
"2 10.0 18.333333 12.015651 5.555556 6.944444 16.666667 \n",
"default 10.0 26.111111 15.724392 5.555556 11.111111 33.333333 \n",
"\n",
" 75% max \n",
"16384 33.333333 33.333333 \n",
"2 30.555556 33.333333 \n",
"default 33.333333 55.555556 "
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" <td>41.111111</td>\n",
" <td>12.883353</td>\n",
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" <td>38.888889</td>\n",
" <td>44.444444</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>46.666667</td>\n",
" <td>11.475506</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</td>\n",
" <td>44.444444</td>\n",
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" <td>66.666667</td>\n",
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" <tr>\n",
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" <td>37.777778</td>\n",
" <td>7.768954</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>41.666667</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"16384 10.0 41.111111 12.883353 22.222222 33.333333 38.888889 \n",
"2 10.0 46.666667 11.475506 33.333333 36.111111 44.444444 \n",
"default 10.0 37.777778 7.768954 33.333333 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"16384 44.444444 66.666667 \n",
"2 55.555556 66.666667 \n",
"default 41.666667 55.555556 "
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" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>57.777778</td>\n",
" <td>10.210406</td>\n",
" <td>44.444444</td>\n",
" <td>47.222222</td>\n",
" <td>61.111111</td>\n",
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" <td>66.666667</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>68.888889</td>\n",
" <td>12.614360</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"16384 10.0 70.000000 9.147473 55.555556 66.666667 66.666667 \n",
"2 10.0 57.777778 10.210406 44.444444 47.222222 61.111111 \n",
"default 10.0 68.888889 12.614360 44.444444 66.666667 66.666667 \n",
"\n",
" 75% max \n",
"16384 75.000000 88.888889 \n",
"2 66.666667 66.666667 \n",
"default 66.666667 88.888889 "
]
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" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>191.111111</td>\n",
" <td>12.614360</td>\n",
" <td>177.777778</td>\n",
" <td>177.777778</td>\n",
" <td>194.444444</td>\n",
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" <td>211.111111</td>\n",
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" <tr>\n",
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" <td>10.0</td>\n",
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" <td>15.887119</td>\n",
" <td>177.777778</td>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>219.444444</td>\n",
" <td>233.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"16384 10.0 211.111111 32.288140 188.888889 200.000000 200.000000 \n",
"2 10.0 191.111111 12.614360 177.777778 177.777778 194.444444 \n",
"default 10.0 206.666667 15.887119 177.777778 200.000000 200.000000 \n",
"\n",
" 75% max \n",
"16384 200.000000 300.000000 \n",
"2 200.000000 211.111111 \n",
"default 219.444444 233.333333 "
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>210.000000</td>\n",
" <td>21.880082</td>\n",
" <td>177.777778</td>\n",
" <td>200.0</td>\n",
" <td>200.000000</td>\n",
" <td>219.444444</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>217.777778</td>\n",
" <td>30.180072</td>\n",
" <td>200.000000</td>\n",
" <td>200.0</td>\n",
" <td>211.111111</td>\n",
" <td>219.444444</td>\n",
" <td>300.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>214.444444</td>\n",
" <td>27.241742</td>\n",
" <td>177.777778</td>\n",
" <td>200.0</td>\n",
" <td>216.666667</td>\n",
" <td>230.555556</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 210.000000 21.880082 177.777778 200.0 200.000000 \n",
"2 10.0 217.777778 30.180072 200.000000 200.0 211.111111 \n",
"default 10.0 214.444444 27.241742 177.777778 200.0 216.666667 \n",
"\n",
" 75% max \n",
"16384 219.444444 255.555556 \n",
"2 219.444444 300.000000 \n",
"default 230.555556 266.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_reply_mail'"
]
},
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" <th></th>\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>235.555556</td>\n",
" <td>4.684856</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>244.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>246.666667</td>\n",
" <td>12.614360</td>\n",
" <td>233.333333</td>\n",
" <td>236.111111</td>\n",
" <td>244.444444</td>\n",
" <td>252.777778</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>230.000000</td>\n",
" <td>11.770555</td>\n",
" <td>200.000000</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>244.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 235.555556 4.684856 233.333333 233.333333 233.333333 \n",
"2 10.0 246.666667 12.614360 233.333333 236.111111 244.444444 \n",
"default 10.0 230.000000 11.770555 200.000000 233.333333 233.333333 \n",
"\n",
" 75% max \n",
"16384 233.333333 244.444444 \n",
"2 252.777778 266.666667 \n",
"default 233.333333 244.444444 "
]
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"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_type_in_reply_field'"
]
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
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" <td>10.0</td>\n",
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" <td>6.651217</td>\n",
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" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>18.333333</td>\n",
" <td>6.441677</td>\n",
" <td>5.555556</td>\n",
" <td>13.888889</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>16.111111</td>\n",
" <td>6.651217</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 17.222222 6.651217 5.555556 11.111111 22.222222 \n",
"2 10.0 18.333333 6.441677 5.555556 13.888889 22.222222 \n",
"default 10.0 16.111111 6.651217 5.555556 11.111111 16.666667 \n",
"\n",
" 75% max \n",
"16384 22.222222 22.222222 \n",
"2 22.222222 22.222222 \n",
"default 22.222222 22.222222 "
]
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{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_image_cat'"
]
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" <th></th>\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>100.000000</td>\n",
" <td>47.430550</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>233.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>113.333333</td>\n",
" <td>124.259121</td>\n",
" <td>66.666667</td>\n",
" <td>69.444444</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>466.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>96.666667</td>\n",
" <td>40.588943</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>211.111111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 100.000000 47.430550 77.777778 77.777778 88.888889 \n",
"2 10.0 113.333333 124.259121 66.666667 69.444444 77.777778 \n",
"default 10.0 96.666667 40.588943 77.777778 77.777778 88.888889 \n",
"\n",
" 75% max \n",
"16384 88.888889 233.333333 \n",
"2 77.777778 466.666667 \n",
"default 88.888889 211.111111 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_search_suggestion'"
]
},
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" <th>min</th>\n",
" <th>25%</th>\n",
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" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>10.000000</td>\n",
" <td>5.105203</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>24.444444</td>\n",
" <td>10.210406</td>\n",
" <td>11.111111</td>\n",
" <td>13.888889</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>19.444444</td>\n",
" <td>7.965116</td>\n",
" <td>5.555556</td>\n",
" <td>13.888889</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 10.000000 5.105203 5.555556 5.555556 11.111111 \n",
"2 10.0 24.444444 10.210406 11.111111 13.888889 27.777778 \n",
"default 10.0 19.444444 7.965116 5.555556 13.888889 22.222222 \n",
"\n",
" 75% max \n",
"16384 11.111111 22.222222 \n",
"2 33.333333 33.333333 \n",
"default 22.222222 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_type_searchbox'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
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" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <th>16384</th>\n",
" <td>10.0</td>\n",
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" <td>0.000000</td>\n",
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" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>9.444444</td>\n",
" <td>6.953698</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>9.444444</td>\n",
" <td>6.953698</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"16384 10.0 5.555556 0.000000 5.555556 5.555556 5.555556 5.555556 \n",
"2 10.0 9.444444 6.953698 5.555556 5.555556 5.555556 9.722222 \n",
"default 10.0 9.444444 6.953698 5.555556 5.555556 5.555556 9.722222 \n",
"\n",
" max \n",
"16384 5.555556 \n",
"2 22.222222 \n",
"default 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_clicktab_0'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>count</th>\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>1015.555556</td>\n",
" <td>81.683252</td>\n",
" <td>944.444444</td>\n",
" <td>977.777778</td>\n",
" <td>988.888889</td>\n",
" <td>1022.222222</td>\n",
" <td>1233.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8.0</td>\n",
" <td>888.888889</td>\n",
" <td>253.511494</td>\n",
" <td>388.888889</td>\n",
" <td>858.333333</td>\n",
" <td>955.555556</td>\n",
" <td>966.666667</td>\n",
" <td>1266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>965.555556</td>\n",
" <td>147.819079</td>\n",
" <td>566.666667</td>\n",
" <td>980.555556</td>\n",
" <td>994.444444</td>\n",
" <td>1008.333333</td>\n",
" <td>1111.111111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 1015.555556 81.683252 944.444444 977.777778 988.888889 \n",
"2 8.0 888.888889 253.511494 388.888889 858.333333 955.555556 \n",
"default 10.0 965.555556 147.819079 566.666667 980.555556 994.444444 \n",
"\n",
" 75% max \n",
"16384 1022.222222 1233.333333 \n",
"2 966.666667 1266.666667 \n",
"default 1008.333333 1111.111111 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_type_0'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>7.407407</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>62.222222</td>\n",
" <td>46.021406</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>188.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>11.712139</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>52.777778</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 44.444444 7.407407 33.333333 44.444444 44.444444 \n",
"2 10.0 62.222222 46.021406 33.333333 36.111111 55.555556 \n",
"default 10.0 44.444444 11.712139 33.333333 33.333333 44.444444 \n",
"\n",
" 75% max \n",
"16384 44.444444 55.555556 \n",
"2 55.555556 188.888889 \n",
"default 52.777778 66.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gslide_ail_pagedown_0'"
]
},
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>310.000000</td>\n",
" <td>48.981394</td>\n",
" <td>244.444444</td>\n",
" <td>275.000000</td>\n",
" <td>311.111111</td>\n",
" <td>341.666667</td>\n",
" <td>400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>275.555556</td>\n",
" <td>16.396995</td>\n",
" <td>244.444444</td>\n",
" <td>266.666667</td>\n",
" <td>272.222222</td>\n",
" <td>288.888889</td>\n",
" <td>300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>290.000000</td>\n",
" <td>82.560200</td>\n",
" <td>122.222222</td>\n",
" <td>255.555556</td>\n",
" <td>266.666667</td>\n",
" <td>363.888889</td>\n",
" <td>400.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 310.000000 48.981394 244.444444 275.000000 311.111111 \n",
"2 10.0 275.555556 16.396995 244.444444 266.666667 272.222222 \n",
"default 10.0 290.000000 82.560200 122.222222 255.555556 266.666667 \n",
"\n",
" 75% max \n",
"16384 341.666667 400.0 \n",
"2 288.888889 300.0 \n",
"default 363.888889 400.0 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gslide_ail_type_0'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>103.333333</td>\n",
" <td>11.770555</td>\n",
" <td>88.888889</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>108.333333</td>\n",
" <td>122.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>100.000000</td>\n",
" <td>15.713484</td>\n",
" <td>77.777778</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>100.000000</td>\n",
" <td>133.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>104.444444</td>\n",
" <td>9.369712</td>\n",
" <td>100.000000</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>100.000000</td>\n",
" <td>122.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"16384 10.0 103.333333 11.770555 88.888889 100.0 100.0 108.333333 \n",
"2 10.0 100.000000 15.713484 77.777778 100.0 100.0 100.000000 \n",
"default 10.0 104.444444 9.369712 100.000000 100.0 100.0 100.000000 \n",
"\n",
" max \n",
"16384 122.222222 \n",
"2 133.333333 \n",
"default 122.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_outlook_ail_composemail_0'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>402.222222</td>\n",
" <td>33.864087</td>\n",
" <td>344.444444</td>\n",
" <td>372.222222</td>\n",
" <td>411.111111</td>\n",
" <td>433.333333</td>\n",
" <td>433.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>381.111111</td>\n",
" <td>18.182130</td>\n",
" <td>355.555556</td>\n",
" <td>366.666667</td>\n",
" <td>377.777778</td>\n",
" <td>397.222222</td>\n",
" <td>411.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>380.000000</td>\n",
" <td>17.992530</td>\n",
" <td>344.444444</td>\n",
" <td>369.444444</td>\n",
" <td>377.777778</td>\n",
" <td>397.222222</td>\n",
" <td>400.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 402.222222 33.864087 344.444444 372.222222 411.111111 \n",
"2 10.0 381.111111 18.182130 355.555556 366.666667 377.777778 \n",
"default 10.0 380.000000 17.992530 344.444444 369.444444 377.777778 \n",
"\n",
" 75% max \n",
"16384 433.333333 433.333333 \n",
"2 397.222222 411.111111 \n",
"default 397.222222 400.000000 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_outlook_ail_type_composemail_0'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>18.888889</td>\n",
" <td>18.555519</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>19.444444</td>\n",
" <td>11.785113</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>22.222222</td>\n",
" <td>30.555556</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>15.000000</td>\n",
" <td>15.055909</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 18.888889 18.555519 5.555556 5.555556 16.666667 \n",
"2 10.0 19.444444 11.785113 5.555556 6.944444 22.222222 \n",
"default 10.0 15.000000 15.055909 5.555556 6.944444 11.111111 \n",
"\n",
" 75% max \n",
"16384 22.222222 66.666667 \n",
"2 30.555556 33.333333 \n",
"default 11.111111 55.555556 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_compose_new_mail'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>307.777778</td>\n",
" <td>19.633123</td>\n",
" <td>277.777778</td>\n",
" <td>291.666667</td>\n",
" <td>305.555556</td>\n",
" <td>322.222222</td>\n",
" <td>333.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>295.555556</td>\n",
" <td>9.369712</td>\n",
" <td>277.777778</td>\n",
" <td>288.888889</td>\n",
" <td>300.000000</td>\n",
" <td>300.000000</td>\n",
" <td>311.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>302.222222</td>\n",
" <td>33.864087</td>\n",
" <td>266.666667</td>\n",
" <td>269.444444</td>\n",
" <td>300.000000</td>\n",
" <td>322.222222</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 307.777778 19.633123 277.777778 291.666667 305.555556 \n",
"2 10.0 295.555556 9.369712 277.777778 288.888889 300.000000 \n",
"default 10.0 302.222222 33.864087 266.666667 269.444444 300.000000 \n",
"\n",
" 75% max \n",
"16384 322.222222 333.333333 \n",
"2 300.000000 311.111111 \n",
"default 322.222222 366.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_type_in_reply_field'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>16.666667</td>\n",
" <td>10.475656</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>20.000000</td>\n",
" <td>8.764563</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>31.111111</td>\n",
" <td>29.068766</td>\n",
" <td>11.111111</td>\n",
" <td>13.888889</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 16.666667 10.475656 5.555556 11.111111 11.111111 \n",
"2 10.0 20.000000 8.764563 11.111111 11.111111 22.222222 \n",
"default 10.0 31.111111 29.068766 11.111111 13.888889 22.222222 \n",
"\n",
" 75% max \n",
"16384 22.222222 33.333333 \n",
"2 22.222222 33.333333 \n",
"default 22.222222 100.000000 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_select_search_suggestion'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>15.000000</td>\n",
" <td>7.878536</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>12.222222</td>\n",
" <td>7.314229</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>19.444444</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>16.111111</td>\n",
" <td>12.129276</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>11.111111</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 15.000000 7.878536 5.555556 6.944444 16.666667 \n",
"2 10.0 12.222222 7.314229 5.555556 5.555556 11.111111 \n",
"default 10.0 16.111111 12.129276 5.555556 6.944444 11.111111 \n",
"\n",
" 75% max \n",
"16384 22.222222 22.222222 \n",
"2 19.444444 22.222222 \n",
"default 27.777778 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_type_in_search_field'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>9.444444</td>\n",
" <td>6.953698</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>10.555556</td>\n",
" <td>8.050765</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>18.055556</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>7.777778</td>\n",
" <td>5.367177</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"16384 10.0 9.444444 6.953698 5.555556 5.555556 5.555556 9.722222 \n",
"2 10.0 10.555556 8.050765 5.555556 5.555556 5.555556 18.055556 \n",
"default 10.0 7.777778 5.367177 5.555556 5.555556 5.555556 5.555556 \n",
"\n",
" max \n",
"16384 22.222222 \n",
"2 22.222222 \n",
"default 22.222222 "
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# show plot\n",
"plt.show()"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
"png": 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PUkqXlsmPt4YBy98nyvQZwMaNm48s0xaSUjo3pTQ6pTR62LBhS9t+SZKkPqmd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\nklSBIUuSJKkCQ5YkSVIFhixJkqQK/j+2kLp+JdDDOgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1042ae690>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x107f730d0>"
]
},
{
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Lb78Nv/89LFiQw1RKeSb23XfPoUtNwQEvPclll+XDzE89ldtHH52PPvXtm9sD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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108391c10>"
]
},
{
"output_type": "display_data",
"png": 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wJUmSVINhSpIkqYZWvptPA2zSjAuGuglts8nYdYa6CZIkDSrD1CC7Z+ZBg7q9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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108469c90>"
]
},
{
"output_type": "display_data",
"png": 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upXWncXPKPfcsrU9rrlnK//RP8Ic/wJveVMpHHFEC32qrlfLxx5c6NQZff+1r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\n",
"text": [
"<matplotlib.figure.Figure at 0x108475c50>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1085bec50>"
]
},
{
"output_type": "display_data",
"png": 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UzT8T2KeH25AkSRpwuh2yMnMacCLwBCVcvQDcDszOzIXVYk8B6/W0kZIkSQNN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\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x108650050>"
]
},
{
"output_type": "display_data",
"png": 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73lNufdLwwAMwdGizfM45i9Zn9dU779wk9RnLFaYy81bg1mr5EWCnzq+SJC3F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\n",
"text": [
"<matplotlib.figure.Figure at 0x10865b3d0>"
]
},
{
"output_type": "display_data",
"png": 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JzWLAANh222q5rS3fpwxyy8KJJ8JJJ+UwlRJ8//v5Zs2jRzekumoiK1dWx0eb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\n",
"text": [
"<matplotlib.figure.Figure at 0x1086f7d90>"
]
},
{
"output_type": "display_data",
"png": 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jO+ywejElSRpzLKakkfboo/03vrz5ZthqK7j66hy/8IW5mOq7fcE//APcdBPs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\nWZ4iAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1087d8810>"
]
},
{
"output_type": "display_data",
"png": 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rr841VvPmNSY+dW7XX5/vNn3jjVw+7zy45x6b8KROyGRK6ij77AP33ZcHCYV8\np9XChbnJBnJz4Gc/6/ALPdW77+aO5JXBNTfeOA/LUUm2t9kmT+gtqdMxmZIa5bjjYOrUPEo7wKJF\nufaqUvNw8slwxhkNC08dpDKdUVMTfOpT1U7lu+wCN95YHfNMUqdlnympszjttOXLr72Wp7ep+PSn\n4cAD89yD6h6OPDKPaXbFFfmu0AceqE51JKnLaLVmKiI2jog7I+LJiHgiIr5arF8vIqZExFPFz3Xr\nH67Ug/z619W7BRcvzlOAzJ+fy+++m8e4shNy1zJzZnWSbchDaeywQ7X8oQ85OrnUBbWlmW8p8I2U\n0tbAh4ETI2JrYDxwR0ppC+COoiypHvr3hz/+MTf9AcyenW+Tr3Rqf/bZPMfgE080LES1YPHi6hAG\nN90EX/96/v1BnoT7m99sXGyS2kWryVRKaU5K6c/F8kJgOjAMGANMLHabCBxRryAlrWCTTeDxx6tj\nCz33HNx7b7VW4557YNw4mDu3cTEK/vKX3Gl8ypRc/sIXcg1jZaBXSd3CKnVAj4iRwE7A/cDQlNKc\nYtPLwNB2jUxS6yqd1ffZJ39Jb7llLs+cCTffXJ365qqr4JRTqp2dVR/LlsGVV8LvfpfLW22VE973\nvS+X11mnOjSGpG6jzclURKwFXAd8LaX0Ru22lFICmr2fOyLGRcS0iJjW1NRUKlhJKxFRTa5OOAFe\neimPjA3w6KN5zKs+xT0nF164/OjtKqfSly0Czj0XLrssl/v1y8s779y42CTVXZuSqYjoS06krkwp\nXV+sfiUiNiy2bwg0256QUrokpTQ6pTR6yJAh7RGzpLboVfPxPvtseOSRavnOO+GOO6rl00+Ha6/t\nuNi6k299C7beOk/vEpGT1uuua3RUkjpQW+7mC2ACMD2l9JOaTTcDxxfLxwM3tX94ktpN7V1iN9xQ\n/cJ/773cDHj//bmcEnz1q3kMLP1fzz+ff772Wv550EG5U/mSJbm80UbLJ7KSur22fOL3AI4D9ouI\nR4rHocA5wEcj4inggKIsqavoW0xK3bs3zJgBP/hBLs+ZkweOnDEjlxcsyMnCX//amDg7g0WLqiOR\nL1iQf/7v/+af++yT78gbOLAhoUlqvLbczTc1pRQppe1TSjsWj1tTSq+llPZPKW2RUjogpeQEY1JX\nFQFrrJGXN9ooj8Z93HG5/Nhj8POfV+8MnD4dvve9nnOn4OLF+e7J738/l7ffPv88zAnEJWXWRUv6\nv3r3riZXe+2VO1jvvnsu33cfnHlmdQ7Bu+/OHdoXL25MrPXw29/Cd7+bl/v3h+98J49WLknNMJmS\n1LoBA6p3Ap5wQu4vNLQYDeX4Rv3EAAAKq0lEQVSWW3JyVWk2vOGGxsRYVqVZE+DBB/O8eO++m8sn\nnQR77NGYuCR1eiZTklbd4MHV5R/9KPenqnRwv/TS5ff9z/9c/s7BzmjSJBg1Ch56KJfPPDMPuNmv\nX2PjktQlmExJKm+DDarLt9xSXU4JzjgjzzNY8dOf5nGvGun11+HLX4bJk3P5kEPgvPNg001zecCA\n6phdktQKkylJ7at2WIAI+Nvfcu0V5DvhTj65msQsWQLnn5+nw6m3N9+szl245pp5ipfKHYqDB8PX\nvgbrOl+7pFXXp9EBSOrm1lijOp3K4MHw6qt52hXIA4mefDJsvDGMGJGHZfj97+GII9o/sRkzBl5+\nOc9p2KdPviuxj38CJZVnzZSkjjV4MKy3Xl7eZZdcK3Xwwbk8ZUru4D57di4/+SRcfz28886qn+eO\nO+CAA6p3GZ5+OvzqV9XtJlKS2onJlKTG+sAHcrMb5LGtHnsMttkml3/zG/jUp6oTNN93X54KJzUz\nFWhKMG1aHiOrYu7cPAE0wN575+Ed7AslqZ2ZTEnqPCJg222rCc+ZZ+ZhCirJ1rnnwhe+UN1+++35\nrjuAZ5/NNV0TJuTyfvvlju5bbNGx1yCpx7GeW1Ln1a8f7LBDtTxx4vKd1U86KY9Ofuut+U68a6/N\nTXtgDZSkDmMyJandDRo1nu0mjq/fCf5c/BzfB3gBJm5X3XbjGfU7b2HQKACnk5GUmUxJancLp5/D\nrHO6b7IxcvzkRocgqROxz5QkSVIJJlOSJEklmExJkiSVYDIlSZJUgsmUJElSCSZTkiRJJZhMSZIk\nlWAyJUmSVILJlCRJUgkmU5IkSSWYTEmSJJVgMiVJklSCyZQkSVIJJlOSJEklmExJkiSVYDIlSZJU\ngsmUJElSCSZTkiRJJZhMSZIklWAyJUmSVILJlCRJUgkmU5IkSSX0aXQAapuIWP1jf7h6x6WUVvuc\nktQTjBw/udEh1M06A/o2OoQuw2SqizCxkaTOZdY5h3Xo+UaOn9zh51TbtNrMFxGXRcTciHi8Zt16\nETElIp4qfq5b3zAlSZI6p7b0mbocOHiFdeOBO1JKWwB3FGVJkqQep9VkKqV0DzBvhdVjgInF8kTg\niHaOS5IkqUtY3bv5hqaU5hTLLwNDW9oxIsZFxLSImNbU1LSap5MkSeqcSg+NkHLP6BZ7R6eULkkp\njU4pjR4yZEjZ00mSJHUqq5tMvRIRGwIUP+e2X0iSJEldx+omUzcDxxfLxwM3tU84kiRJXUtbhkaY\nBNwHbBkRL0bEWOAc4KMR8RRwQFGWJEnqcVodtDOldEwLm/Zv51gkSZK6HOfmkyRJKsFkSpIkqQST\nKUmSpBJMpiRJkkowmZIkSSrBZEqSJKkEkylJkqQSTKYkSZJKaHXQTklaHSPHT250CHWzzoC+jQ5B\nUidiMiWp3c0657AOPd/I8ZM7/JySVGEznyRJUgkmU93UpEmT2HbbbenduzfbbrstkyZNanRIkiR1\nSzbzdUOTJk3i1FNPZcKECey5555MnTqVsWPHAnDMMS3NWy1JklaHNVPd0FlnncWECRPYd9996du3\nL/vuuy8TJkzgrLPOanRokiR1O5FS6rCTjR49Ok2bNq3DztdT9e7dm8WLF9O3b/WOoyVLltC/f3/e\ne++9BkYmrVxEdPg5O/JvoAS+z7uSiHgopTS6tf2smeqGRo0axdSpU5dbN3XqVEaNGtWgiKS2SSl1\n+EPqaL7Pux+TqW7o1FNPZezYsdx5550sWbKEO++8k7Fjx3Lqqac2OjRJkrodO6B3Q5VO5ieddBLT\np09n1KhRnHXWWXY+lySpDuwzJUmS1Az7TEmSJHUAkylJkqQSTKYkSZJKMJmSJEkqwWRKkiSpBJMp\nSZKkEkymJEmSSjCZkiRJKsFkSpIkqQSTKUmSpBJMpiRJkkowmZIkSSrBZEqSJKkEkylJkqQSTKYk\nSZJKMJmSJEkqoVQyFREHR8RfI2JmRIxvr6AkSZK6itVOpiKiN/Az4BBga+CYiNi6vQKTJEnqCsrU\nTO0KzEwpPZNSehe4ChjTPmFJkiR1DWWSqWHACzXlF4t1kiRJPUafep8gIsYB44rimxHx13qfU8vZ\nAHi10UFIdeb7XD2B7/OON6ItO5VJpmYDG9eUhxfrlpNSugS4pMR5VEJETEspjW50HFI9+T5XT+D7\nvPMq08z3ILBFRGwSEf2ATwE3t09YkiRJXcNq10yllJZGxL8C/w30Bi5LKT3RbpFJkiR1AaX6TKWU\nbgVubadYVB82saon8H2unsD3eScVKaVGxyBJktRlOZ2MJElSCSZTXUBEXBYRcyPi8RXWnxQRMyLi\niYg4t1i3a0Q8UjwejYh/rNn/5GLfxyNiUkT0X+H5LoyINzvmqqRyImLjiLgzIp4s3tdfbXRMUltF\nxBkR8W8r2T4kIu6PiIcjYq/VeP7PRcRFxfIRzlBSXyZTXcPlwMG1KyJiX/KI8zuklLYBflxsehwY\nnVLasTjmlxHRJyKGAV8ptm1LvmngUzXPNxpYt94XIrWjpcA3UkpbAx8GTvQLQ93I/sBjKaWdUkp/\nLPlcR5CnfVOdmEx1ASmle4B5K6z+F+CclNI7xT5zi5+LUkpLi336A7Wd4voAAyKiDzAQeAn+Ps/i\nj4BT6nYRUjtLKc1JKf25WF4ITMdZGNSJRcSpEfG3iJgKbFms2ywifh8RD0XEHyNiq4jYETgXGFO0\nMgyIiF9ExLSiFvbMmuecFREbFMujI+KuFc75D8DHgR8Vz7VZR11vT2Iy1XV9ENirqAa+OyJ2qWyI\niN0i4gngMeBLKaWlKaXZ5Nqr54E5wOsppT8Uh/wrcHNKaU4HX4PULiJiJLATcH9jI5GaFxEfIrcG\n7AgcClT+Zl8CnJRS+hDwb8DPU0qPAKcBV6eUdkwpvQ2cWgzYuT3wkYjYvi3nTSndSx4D8pvFcz3d\nrhcmoAOmk1Hd9AHWIzdv7AJcExGbpux+YJuIGAVMjIjbgAHkZsFNgAXAbyPiM8D/AJ8E9mnANUil\nRcRawHXA11JKbzQ6HqkFewE3pJQWAUTEzeTWg38g/z2u7LdGC8cfVUzP1gfYkNxs95e6Rqw2M5nq\nul4Erk95bIsHImIZed6mpsoOKaXpRYfybclJ1LMppSaAiLie/CGeD2wOzCw+zAMjYmZKafMOvRpp\nNUREX3IidWVK6fpGxyOtol7AgqKPa4siYhNyrdUuKaX5EXE5ORGD3Hew0srUv5nD1QFs5uu6bgT2\nBYiIDwL9gFeL6X36FOtHAFsBs8jNex+OiIGRs6b9gekppckppfenlEamlEYCi0yk1BUU7+MJ5Pfx\nTxodj9SKe4Ajiv5Pg4CPAYuAZyPik5Df0xGxQzPHrg28BbweEUOBQ2q2zQI+VCz/UwvnXggMKn8J\naonJVBcQEZOA+4AtI+LFiBgLXAZsWgyXcBVwfFFLtSfwaEQ8AtwAfDml9GrR9Hct8GdyX6peOJqu\nurY9gOOA/WqGAzm00UFJzSlulrgaeBS4jTy/LcCxwNiIeBR4gtwdY8VjHwUeBmYA/wX8qWbzmcAF\nETENeK+F018FfLMYZsEO6HXgCOiSJEklWDMlSZJUgsmUJElSCSZTkiRJJZhMSZIklWAyJUmSVILJ\nlCRJUgkmU5IkSSWYTEmSJJXw/wEKUNJmR34hUAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108830a90>"
]
},
{
"output_type": "display_data",
"png": 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ZKjVzZmmpaoSpb3yjdBm2vxG0NEB5OZUkqXvtsku5UXjDZZfBnDnN8jnnwC9+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\n",
"text": [
"<matplotlib.figure.Figure at 0x108910ad0>"
]
},
{
"output_type": "display_data",
"png": 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1YJiSJElqwDAlSZLUgGFKkiSpAcOUJElSA4YpSZKkBgxTkiRJDRimJEmSGjBM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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1089f8110>"
]
},
{
"output_type": "display_data",
"png": 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AAFAAIQsAAKAAQhYAAEABhCwAAIACCFkAAAAFELIAAAAK6DJk2T7L9pO276rp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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x108a04650>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x108a8cb10>"
]
},
{
"output_type": "display_data",
"png": 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u2z+3PdX207Z3z9l4AACAzqotPVnjJB2y0rKzJT0QETtIeqCoJelQSTsUt5Mk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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x108b81750>"
]
},
{
"output_type": "display_data",
"png": 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kVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBM\nSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIk\nVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBM\nSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIk\nVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVagKUxGxZ0TcFhF3RMSxfdWUJEnS\nUNHrMBURqwHfBd4KbAkcGBFb9lVjkiRJQ0HNnqntgDsy887MfBr4KbBv37QlSZI0NNSEqQ2BezvV\n9zX3SZIkDRsj+3sFEXEocGhTLoyI2/p7nXqO9YH57W5C6mdu5xoO3M4H3sY9makmTN0PTOxUv7i5\n7zky84fADyvWowoRMSszJ7e7D6k/uZ1rOHA7H7xqhvn+F3hJRGwSEasD7wF+2TdtSZIkDQ293jOV\nmUsi4uPAb4HVgGmZObvPOpMkSRoCqo6ZysyLgIv6qBf1D4dYNRy4nWs4cDsfpCIz292DJEnSkOXl\nZCRJkioYpoaAiJgWEQ9HxE1d7j88Im6NiNkR8ZXmvu0i4obmdmNE/FOn+T/ZzHtTRJwdEWt2Wd4p\nEbFwYF6VVC8iJkbE5RFxc7NtH9nunqSeiIjjIuJTy3l8XETMjIjrI2LnXiz/kIj4TjO9n1co6V+G\nqaHhdGDPzndExJsoZ5zfJjNfAXyteegmYHJmbts85wcRMTIiNgSOaB7bivKlgfd0Wt5kYL3+fiFS\nH1sCHJ2ZWwLbA1P9o6FVxG7AXzLzVZl5VeWy9qNc9k39xDA1BGTmlcCjXe7+KHBiZj7VzPNw83NR\nZi5p5lkT6HxQ3EhgdESMBNYCHoD/f53FrwKf7rcXIfWDzHwwM69rpp8AbsErMWiQioh/jYi/RsQf\ngJc1920WEb+JiGsj4qqIeHlEbAt8Bdi3GWUYHRHfj4hZzR7Y4zstc05ErN9MT46IK7qs8/XA24Gv\nNsvabKBe73BimBq6Xgrs3OwG/n1EvLb1QES8LiJmA38BDsvMJZl5P2Xv1T3Ag8BjmXlx85SPA7/M\nzAcH+DVIfSYiJgGvAma2txPp/4qI11BGA7YF9gJa/2b/EDg8M18DfAr4XmbeAPw78LPM3DYzFwP/\n2pyw85XAGyPilT1Zb2b+kXIOyGOaZf2tT1+YgAG4nIz6zUjg+ZShjdcC50TEplnMBF4REVsA0yPi\n18BoyrDgJsAC4NyIOAj4HfDtUHvNAAAB6ElEQVQuYJc2vAapT0TE2sB5wCcy8/F29yMtw87AzzNz\nEUBE/JIyevB6yr/HrfnW6Ob5BzSXZxsJTKAM2/25XztWjxmmhq77gPOznNviTxGxlHLdpnmtGTLz\nluaA8q0oIequzJwHEBHnU36J/w5sDtzR/DKvFRF3ZObmA/pqpF6KiFGUIHVmZp7f7n6klTACWNAc\n49qtiNiEstfqtZn594g4nRLEoBw32BplWnMZT9cAcJhv6LoAeBNARLwUWB2Y31zeZ2Rz/8bAy4E5\nlOG97SNirSipaTfglsyckZkbZOakzJwELDJIaahotuX/omzL32h3P9JyXAns1xz/tA6wD7AIuCsi\n3gVle46IbZbx3OcB/wAei4jxwFs7PTYHeE0z/c5u1v0EsE79S1B3DFNDQEScDVwDvCwi7ouIDwLT\ngE2b0yX8FDi42Uu1E3BjRNwA/Bz4WGbOb4b+/hu4jnIs1Qg8m66Gvh2B9wG7djolyF7tbkrqqvmi\nxM+AG4FfU65vCzAF+GBE3AjMphyO0fW5NwLXA7cCZwFXd3r4eOBbETELeLab1f8UOKY5zYIHoPcD\nz4AuSZJUwT1TkiRJFQxTkiRJFQxTkiRJFQxTkiRJFQxTkiRJFQxTkiRJFQxTkiRJFQxTkiRJFf4f\nZvAWdqIkhQcAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x108a8cbd0>"
]
},
{
"output_type": "display_data",
"png": 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Vccst1fr666tXDSPSf/rTplXr556LeO216BHeeiuFnErgnjMn4m9/q76QT5mS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\nyAIAACiAkAUAAFDA/we/mPgEKiXK1AAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108c5ed90>"
]
},
{
"output_type": "display_data",
"png": 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kbqWqZCoi+pITqetTSrcUs2dGxJBi+RBg1sq2TSldmVIalVIaNWjQoI6IWWoM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\n1E4R0ZecSF2fUrql3vFIq6A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"text": [
"<matplotlib.figure.Figure at 0x108bc5f50>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x108de60d0>"
]
},
{
"output_type": "display_data",
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RgMxMINdkoxFxRETMiIgZ8+bN60PzJEmSBqa+BLCHgYcz8+aq/gklkD3WeWqx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\ncbQAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x108e408d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x108f12f10>"
]
},
{
"output_type": "display_data",
"png": 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yHwINB37PJQ1GzvguSZLUAEOWJElSAwxZkiRJDTBkSZIkNcCQJUmS1ABDliRJ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\nkhpgyJIkSWrA/wctRXbW6P0qoAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108ffdc10>"
]
},
{
"output_type": "display_data",
"png": 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jfSDJ3dvmqqSxSbJvkqVJftA+r1/f75qkrZHkHUnevIXtM5NcnuR7SZ4/iuO/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\nv8wAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x109017050>"
]
},
{
"output_type": "display_data",
"png": 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ivhEREyLiDxHxmpqNlyRJGqh605P1fWC/bsNOBH6ZmdsCv2xqgP2BbZvHMcB3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\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x109065d90>"
]
},
{
"output_type": "display_data",
"png": 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CVsYwNQxERDvwW2CHiHgwIiYD3wVeU02X8BPg2KqVal/g1oi4BbgE\n+FhmPl51/V0M3EQZS7UOzqar4e3NwDHA/i3TgbxzsCsldac6WeIC4Fbgl5Tr2wK8D5gcEbcCd1CG\nY3R97q3AzcBdwPnAr1sePh34WkR0AstWsvufAP9STbPgAPR+4AzokiRJNdgyJUmSVINhSpIkqQbD\nlCRJUg2GKUmSpBoMU5IkSTUYpiRJkmowTEmSJNVgmJIkSarh/wFYiMUr4nQJMQAAAABJRU5ErkJg\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x109199e50>"
]
},
{
"output_type": "display_data",
"png": 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GrMwcBzzdzfXtCVyUmS9m5sPAZGC7XrRPkiRpQOrNPVmfjYh7anfimnXc+sBj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\n/H8B90kna3A2MwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x108ffdb90>"
]
},
{
"output_type": "display_data",
"png": 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XeqWV2vOnJElaDl7CQSPPWWeVuVCHHlrqHXaACRPat4u59tr2vKeVViqT0V/+\n8lJHlE/4ddpyy6FptyRphWLI0vDzm9+UK53vu2+pDz+8DPfNmFHqM8+Ep55qh6xPfKI90Rzgl79c\neH9veEP9NktSh3HHXLjslUaotVZ1zml/GbI09B5/vNysePvtS/21r5VP7bV6mE45BX70I7jvvlL3\n9bUnmkO5afEaa7TrQw4ZmnZLUj/cfsJbhvR44465cMiPqf5xsonqmD+/fcHNyy8v359+unw/6aRy\nf73580udWa4/1bpswuTJcMUV7X29//3t28xA+fRfLOm2mZIkDQ+GLHXvzjtL79ODD5Z6+vQysfyO\nO0p9++3le6tn6oAD4PvfbwelI48sVzkfNarUm23WnkMlSdIIZcjSsj31FPziF+2QdN115Z55rd6m\nP/6xfKLvhhtKveOOcNxx7cnnreG8jTcu37fZBvbfv9zsWJKk5ylDloqHHmpfBuFPf4KDDoKf/KTU\nc+eWe+i17qk3dmwJWa0Lbr761XD//bDXXqUeP76ErPXXL7VDe5KkFZAha0X07LMwZUp7ovlTT5Xr\nRX3lK6VeY43SWzVvXqk32wwuvrj9ab+NNoJzz4Vddin1KquUi3MapiRJek5kZq/bQF9fX86aNavX\nzeip7aZt1+smVHfjYTf2ugnqsYF+rP2OE/cd5JYs26ZH/2i5t1lr1THccJyXDNHARA/+UB0OGWAk\niohrMrNvmesNhzfYkDX0/MivJEkD09+Q5XChJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIkVWDI\nkiRJqsCQJUmSVIEhS5IkqQJDliRJUgWGLEmSpAoMWZIkSRUYsiRJkioY3esGqDvd3LU9ThzYdsPh\npuKSJA13hqwRzsAjSdLw5HChJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIkVWDIkiRJqsCQJUmS\nVIEhS5IkqYIqISsi9omI30fELRFxTI1jSJIkDWeDHrIiYhTwZeBNwDbAwRGxzWAfR5IkaTir0ZO1\nC3BLZt6amX8FvgPsV+E4kiRJw1aNkLUhcFdHPbdZJkmStMLo2Q2iI+II4IimfCIift+rtqyg1gUe\n7HUjpMo8z7Ui8Dwfepv2Z6UaIetuYOOOeqNm2UIy8zTgtArHVz9ExKzM7Ot1O6SaPM+1IvA8H75q\nDBdeDWwREZtFxMrAu4DzKxxHkiRp2Br0nqzMXBAR/wL8FBgFnJ6Zswf7OJIkScNZlTlZmXkRcFGN\nfWvQOFSrFYHnuVYEnufDVGRmr9sgSZL0vONtdSRJkiowZI1gEXF6RDwQETctsnxSRPwuImZHxOea\nZbtExPXN1w0RsX/H+kc1694TaTjkAAADy0lEQVQUEdMjYpVF9ndKRDwxNK9K6l5EbBwRMyLit825\n/cFet0nqj4j4ZER8ZCnPj42IKyPiuojYfQD7f29EfKl5/HbvyFKXIWtkOwPYp3NBROxJucL+Dpm5\nLfBfzVM3AX2ZuWOzzdcjYnREbAh8oHluAuXDCu/q2F8f8OLaL0QaZAuAD2fmNsCuwD/7y0TPE3sD\nN2bmTpn5yy739XbK7e9UiSFrBMvMy4GHFll8JHBCZv6lWeeB5vtTmbmgWWcVoHMy3mhg1YgYDawG\n3APP3YfyP4GPVXsRUgWZeW9mXts8fhyYg3ee0DAVEZMj4g8RMRPYqln28oj4SURcExG/jIitI2JH\n4HPAfs2oxKoR8dWImNX02H6qY5+3R8S6zeO+iLhskWO+Bngb8J/Nvl4+VK93RWLIev7ZEti96U7+\nRUS8svVERLwqImYDNwLvz8wFmXk3pbfrTuBe4NHMvLjZ5F+A8zPz3iF+DdKgiYhxwE7Alb1tifR/\nRcQrKKMHOwJvBlr/Z58GTMrMVwAfAb6SmdcD/w6cnZk7ZuafgcnNhUi3B/42Irbvz3Ez838p17D8\naLOvPw7qCxPQw9vqqJrRwNqUIZJXAt+NiJdlcSWwbUSMB6ZFxI+BVSnDi5sBjwDfi4j3AJcCBwB7\n9OA1SIMiItYAvg98KDMf63V7pMXYHfhBZj4FEBHnU0YbXkP5/7i13guWsP2BzW3qRgPrU4b/flO1\nxeo3Q9bzz1zg3CzX5rgqIp6l3NdqXmuFzJzTTGSfQAlXt2XmPICIOJfyj/thYHPgluYf+WoRcUtm\nbj6kr0YaoIgYQwlYZ2Xmub1uj7QcVgIeaebQLlFEbEbp5XplZj4cEWdQAhqUeYmt0apVFrO5hoDD\nhc8/PwT2BIiILYGVgQeb2xyNbpZvCmwN3E4ZJtw1IlaLkqb2BuZk5oWZ+dLMHJeZ44CnDFgaKZpz\neSrlXD6p1+2RluJy4O3N/Ko1gbcCTwG3RcQBUM7niNhhMdu+EHgSeDQi1gPe1PHc7cArmsd/t4Rj\nPw6s2f1L0JIYskawiJgO/ArYKiLmRsRE4HTgZc1lHb4DHNb0au0G3BAR1wM/AP4pMx9shhDPAa6l\nzNVaCa8erJHvtcAhwF4dly55c68bJS2q+YDG2cANwI8p9/8FeDcwMSJuAGZTpnUsuu0NwHXA74D/\nAa7oePpTwBcjYhbwzBIO/x3go83lIJz4XoFXfJckSarAnixJkqQKDFmSJEkVGLIkSZIqMGRJkiRV\nYMiSJEmqwJAlSZJUgSFLkiSpAkOWJElSBf8fftejKMO3xY0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10923e710>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1092574d0>"
]
},
{
"output_type": "display_data",
"png": 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nfYbk6aMl7SNpudLw3sG2t3dKTYdKWhYRN0fEX0XEmIgYI2k9QQqNIh/Lc5WO\n5Vn1rgfYjLskHZuvf9pJ0rslrZf0iO33Sel4tn1AF4/9C0nPS1pre1dJR1TMWy7prfn+8d30vU7S\nTuWbgO4QphqA7VZJP5P0JtuP2W6WdJmk1+evS/i2pFPyWaoJkpbYXizpOkkfiYg1eejvu5J+qXQt\n1SDxbbpofH8r6WRJf1/xlSBH1rsooLP8QYmrJC2R9AOl37eVpPdLara9RNL9SpdjdH7sEkm/kvRb\nSVdK+mnF7AskXWJ7oaQXu+n+25LOzl+zwAXo/YBvQAcAACjAmSkAAIAChCkAAIAChCkAAIAChCkA\nAIAChCkAAIAChCkAAIAChCkAAIAChCkAAIAC/wfXpyvCz4FOsQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1093c5790>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Case list which Nightly peform better than ESR52"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nightly_better_list"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 3,
"text": [
"[]"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
}
],
"metadata": {}
}
]
}
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