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network.http.max-persistent-connections-per-server.ipynb
{
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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"name": "ipython",
"version": 2
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"file_extension": ".py",
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"name": "python",
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"version": "2.7.10"
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"cells": [
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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",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\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>1</th>\n",
" <td>10.0</td>\n",
" <td>445.555556</td>\n",
" <td>45.496800</td>\n",
" <td>411.111111</td>\n",
" <td>425.000000</td>\n",
" <td>433.333333</td>\n",
" <td>441.666667</td>\n",
" <td>566.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>417.777778</td>\n",
" <td>13.042087</td>\n",
" <td>400.000000</td>\n",
" <td>405.555556</td>\n",
" <td>422.222222</td>\n",
" <td>422.222222</td>\n",
" <td>433.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>431.111111</td>\n",
" <td>33.044012</td>\n",
" <td>400.000000</td>\n",
" <td>405.555556</td>\n",
" <td>422.222222</td>\n",
" <td>433.333333</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",
"1 10.0 445.555556 45.496800 411.111111 425.000000 433.333333 \n",
"65536 10.0 417.777778 13.042087 400.000000 405.555556 422.222222 \n",
"default 10.0 431.111111 33.044012 400.000000 405.555556 422.222222 \n",
"\n",
" 75% max \n",
"1 441.666667 566.666667 \n",
"65536 422.222222 433.333333 \n",
"default 433.333333 488.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_select_search_suggestion'"
]
},
{
"html": [
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"\n",
" .dataframe thead th {\n",
" text-align: right;\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>1</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>65536</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>7.222222</td>\n",
" <td>5.270463</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",
"1 10.0 10.555556 8.050765 5.555556 5.555556 5.555556 18.055556 \n",
"65536 10.0 6.111111 1.756821 5.555556 5.555556 5.555556 5.555556 \n",
"default 10.0 7.222222 5.270463 5.555556 5.555556 5.555556 5.555556 \n",
"\n",
" max \n",
"1 22.222222 \n",
"65536 11.111111 \n",
"default 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_type_in_search_field'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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" }\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>1</th>\n",
" <td>10.0</td>\n",
" <td>12.777778</td>\n",
" <td>9.816562</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>19.444444</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>12.777778</td>\n",
" <td>6.953698</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</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",
"1 10.0 12.777778 9.816562 5.555556 5.555556 8.333333 \n",
"65536 10.0 13.888889 6.000686 5.555556 11.111111 11.111111 \n",
"default 10.0 12.777778 6.953698 5.555556 6.944444 11.111111 \n",
"\n",
" 75% max \n",
"1 19.444444 33.333333 \n",
"65536 19.444444 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",
" }\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>1</th>\n",
" <td>10.0</td>\n",
" <td>47.777778</td>\n",
" <td>14.861039</td>\n",
" <td>22.222222</td>\n",
" <td>36.111111</td>\n",
" <td>50.000000</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>58.888889</td>\n",
" <td>14.861039</td>\n",
" <td>22.222222</td>\n",
" <td>58.333333</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>64.444444</td>\n",
" <td>16.396995</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 47.777778 14.861039 22.222222 36.111111 50.000000 \n",
"65536 10.0 58.888889 14.861039 22.222222 58.333333 66.666667 \n",
"default 10.0 64.444444 16.396995 44.444444 50.000000 66.666667 \n",
"\n",
" 75% max \n",
"1 55.555556 66.666667 \n",
"65536 66.666667 66.666667 \n",
"default 66.666667 88.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab'"
]
},
{
"html": [
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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>1</th>\n",
" <td>10.0</td>\n",
" <td>152.222222</td>\n",
" <td>12.883353</td>\n",
" <td>133.333333</td>\n",
" <td>144.444444</td>\n",
" <td>155.555556</td>\n",
" <td>163.888889</td>\n",
" <td>166.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>164.444444</td>\n",
" <td>44.996571</td>\n",
" <td>133.333333</td>\n",
" <td>144.444444</td>\n",
" <td>150.000000</td>\n",
" <td>163.888889</td>\n",
" <td>288.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>177.777778</td>\n",
" <td>59.027324</td>\n",
" <td>111.111111</td>\n",
" <td>133.333333</td>\n",
" <td>144.444444</td>\n",
" <td>233.333333</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 152.222222 12.883353 133.333333 144.444444 155.555556 \n",
"65536 10.0 164.444444 44.996571 133.333333 144.444444 150.000000 \n",
"default 10.0 177.777778 59.027324 111.111111 133.333333 144.444444 \n",
"\n",
" 75% max \n",
"1 163.888889 166.666667 \n",
"65536 163.888889 288.888889 \n",
"default 233.333333 266.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab_emoji'"
]
},
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"html": [
"<div>\n",
"<style scoped>\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>1</th>\n",
" <td>10.0</td>\n",
" <td>74.444444</td>\n",
" <td>14.861039</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>72.222222</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>71.111111</td>\n",
" <td>18.294947</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>86.111111</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>85.555556</td>\n",
" <td>12.883353</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>83.333333</td>\n",
" <td>88.888889</td>\n",
" <td>111.111111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 74.444444 14.861039 44.444444 66.666667 72.222222 \n",
"65536 10.0 71.111111 18.294947 44.444444 66.666667 66.666667 \n",
"default 10.0 85.555556 12.883353 66.666667 77.777778 83.333333 \n",
"\n",
" 75% max \n",
"1 88.888889 88.888889 \n",
"65536 86.111111 100.000000 \n",
"default 88.888889 111.111111 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_photo_viewer_right_arrow'"
]
},
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"html": [
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" <td>94.444444</td>\n",
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" count mean std min 25% 50% \\\n",
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" count mean std min 25% 50% \\\n",
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"65536 10.0 28.888889 9.369712 11.111111 22.222222 33.333333 \n",
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"1 10.0 37.777778 14.054567 11.111111 33.333333 38.888889 \n",
"65536 10.0 50.000000 14.103284 22.222222 44.444444 55.555556 \n",
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"\n",
" 75% max \n",
"1 44.444444 55.555556 \n",
"65536 55.555556 66.666667 \n",
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" <td>18.518519</td>\n",
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" count mean std min 25% 50% \\\n",
"1 10.0 19.444444 10.877167 5.555556 11.111111 16.666667 \n",
"65536 10.0 16.111111 8.050765 5.555556 6.944444 22.222222 \n",
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"\n",
" 75% max \n",
"1 30.555556 33.333333 \n",
"65536 22.222222 22.222222 \n",
"default 22.222222 33.333333 "
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"65536 10.0 36.666667 10.540926 22.222222 33.333333 33.333333 \n",
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"\n",
" 75% max \n",
"1 44.444444 55.555556 \n",
"65536 33.333333 55.555556 \n",
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"65536 10.0 67.777778 11.049210 44.444444 66.666667 66.666667 \n",
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" 75% max \n",
"1 83.333333 88.888889 \n",
"65536 66.666667 88.888889 \n",
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" 75% max \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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>208.888889</td>\n",
" <td>67.647111</td>\n",
" <td>177.777778</td>\n",
" <td>180.555556</td>\n",
" <td>188.888889</td>\n",
" <td>197.222222</td>\n",
" <td>400.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 206.666667 15.887119 177.777778 200.000000 200.000000 \n",
"default 10.0 208.888889 67.647111 177.777778 180.555556 188.888889 \n",
"\n",
" 75% max \n",
"1 219.444444 233.333333 \n",
"default 197.222222 400.000000 "
]
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" <td>250.000000</td>\n",
" <td>13.094570</td>\n",
" <td>233.333333</td>\n",
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" <td>255.555556</td>\n",
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" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>230.000000</td>\n",
" <td>11.770555</td>\n",
" <td>211.111111</td>\n",
" <td>222.222222</td>\n",
" <td>233.333333</td>\n",
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" <td>255.555556</td>\n",
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" <td>10.0</td>\n",
" <td>237.777778</td>\n",
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" <td>233.333333</td>\n",
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" <td>266.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 250.000000 13.094570 233.333333 236.111111 255.555556 \n",
"65536 10.0 230.000000 11.770555 211.111111 222.222222 233.333333 \n",
"default 10.0 237.777778 22.951012 200.000000 225.000000 233.333333 \n",
"\n",
" 75% max \n",
"1 255.555556 266.666667 \n",
"65536 233.333333 255.555556 \n",
"default 255.555556 266.666667 "
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" <td>14.444444</td>\n",
" <td>7.027284</td>\n",
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" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
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" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 13.888889 6.000686 5.555556 11.111111 11.111111 \n",
"65536 10.0 20.000000 7.027284 11.111111 13.888889 22.222222 \n",
"default 10.0 14.444444 7.027284 5.555556 11.111111 11.111111 \n",
"\n",
" 75% max \n",
"1 19.444444 22.222222 \n",
"65536 22.222222 33.333333 \n",
"default 22.222222 22.222222 "
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" <td>104.444444</td>\n",
" <td>72.992098</td>\n",
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" <td>83.333333</td>\n",
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" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>96.666667</td>\n",
" <td>26.215316</td>\n",
" <td>77.777778</td>\n",
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" <td>88.888889</td>\n",
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" <td>8.198498</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",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 104.444444 72.992098 66.666667 77.777778 83.333333 \n",
"65536 10.0 96.666667 26.215316 77.777778 80.555556 88.888889 \n",
"default 10.0 65.555556 8.198498 44.444444 66.666667 66.666667 \n",
"\n",
" 75% max \n",
"1 88.888889 311.111111 \n",
"65536 100.000000 166.666667 \n",
"default 66.666667 77.777778 "
]
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" <td>9.816562</td>\n",
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" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>18.333333</td>\n",
" <td>8.302412</td>\n",
" <td>5.555556</td>\n",
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" <td>33.333333</td>\n",
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" <tr>\n",
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" <td>22.222222</td>\n",
" <td>7.407407</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</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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" count mean std min 25% 50% \\\n",
"1 10.0 23.888889 9.816562 5.555556 22.222222 22.222222 \n",
"65536 10.0 18.333333 8.302412 5.555556 11.111111 22.222222 \n",
"default 10.0 22.222222 7.407407 11.111111 22.222222 22.222222 \n",
"\n",
" 75% max \n",
"1 33.333333 33.333333 \n",
"65536 22.222222 33.333333 \n",
"default 22.222222 33.333333 "
]
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{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_type_searchbox'"
]
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" <td>5.399030</td>\n",
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" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>7.222222</td>\n",
" <td>5.270463</td>\n",
" <td>5.555556</td>\n",
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" <td>10.0</td>\n",
" <td>8.333333</td>\n",
" <td>5.399030</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>"
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" count mean std min 25% 50% 75% \\\n",
"1 10.0 8.333333 5.399030 5.555556 5.555556 5.555556 9.722222 \n",
"65536 10.0 7.222222 5.270463 5.555556 5.555556 5.555556 5.555556 \n",
"default 10.0 8.333333 5.399030 5.555556 5.555556 5.555556 9.722222 \n",
"\n",
" max \n",
"1 22.222222 \n",
"65536 22.222222 \n",
"default 22.222222 "
]
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]
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" <tr>\n",
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" <td>10.0</td>\n",
" <td>564.444444</td>\n",
" <td>313.911558</td>\n",
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" <td>886.111111</td>\n",
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" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>817.777778</td>\n",
" <td>323.144699</td>\n",
" <td>266.666667</td>\n",
" <td>677.777778</td>\n",
" <td>977.777778</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>908.888889</td>\n",
" <td>380.592275</td>\n",
" <td>222.222222</td>\n",
" <td>977.777778</td>\n",
" <td>994.444444</td>\n",
" <td>1033.333333</td>\n",
" <td>1477.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 564.444444 313.911558 233.333333 263.888889 505.555556 \n",
"65536 10.0 817.777778 323.144699 266.666667 677.777778 977.777778 \n",
"default 10.0 908.888889 380.592275 222.222222 977.777778 994.444444 \n",
"\n",
" 75% max \n",
"1 886.111111 955.555556 \n",
"65536 1033.333333 1144.444444 \n",
"default 1033.333333 1477.777778 "
]
},
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" <td>48.888889</td>\n",
" <td>23.541109</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>52.777778</td>\n",
" <td>111.111111</td>\n",
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" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>43.333333</td>\n",
" <td>14.296488</td>\n",
" <td>33.333333</td>\n",
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" <td>33.333333</td>\n",
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" <td>66.666667</td>\n",
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" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>16.563466</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>52.777778</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 48.888889 23.541109 33.333333 33.333333 44.444444 \n",
"65536 10.0 43.333333 14.296488 33.333333 33.333333 33.333333 \n",
"default 10.0 44.444444 16.563466 33.333333 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"1 52.777778 111.111111 \n",
"65536 52.777778 66.666667 \n",
"default 52.777778 77.777778 "
]
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" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>395.555556</td>\n",
" <td>18.294947</td>\n",
" <td>366.666667</td>\n",
" <td>380.555556</td>\n",
" <td>400.000000</td>\n",
" <td>400.000000</td>\n",
" <td>422.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>375.555556</td>\n",
" <td>26.604867</td>\n",
" <td>344.444444</td>\n",
" <td>355.555556</td>\n",
" <td>372.222222</td>\n",
" <td>397.222222</td>\n",
" <td>422.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 391.111111 23.306863 366.666667 369.444444 388.888889 \n",
"65536 10.0 395.555556 18.294947 366.666667 380.555556 400.000000 \n",
"default 10.0 375.555556 26.604867 344.444444 355.555556 372.222222 \n",
"\n",
" 75% max \n",
"1 400.000000 433.333333 \n",
"65536 400.000000 422.222222 \n",
"default 397.222222 422.222222 "
]
},
{
"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",
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" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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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>1</th>\n",
" <td>10.0</td>\n",
" <td>12.777778</td>\n",
" <td>19.074973</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>15.555556</td>\n",
" <td>10.075163</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</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% 75% \\\n",
"1 10.0 12.777778 19.074973 5.555556 5.555556 5.555556 9.722222 \n",
"65536 10.0 15.555556 10.075163 5.555556 5.555556 16.666667 22.222222 \n",
"\n",
" max \n",
"1 66.666667 \n",
"65536 33.333333 "
]
},
{
"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",
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" .dataframe tbody tr th {\n",
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"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
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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>1</th>\n",
" <td>10.0</td>\n",
" <td>313.333333</td>\n",
" <td>29.536892</td>\n",
" <td>266.666667</td>\n",
" <td>311.111111</td>\n",
" <td>316.666667</td>\n",
" <td>322.222222</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>293.333333</td>\n",
" <td>33.620165</td>\n",
" <td>233.333333</td>\n",
" <td>277.777778</td>\n",
" <td>283.333333</td>\n",
" <td>316.666667</td>\n",
" <td>355.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>290.000000</td>\n",
" <td>15.225781</td>\n",
" <td>266.666667</td>\n",
" <td>280.555556</td>\n",
" <td>288.888889</td>\n",
" <td>297.222222</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 313.333333 29.536892 266.666667 311.111111 316.666667 \n",
"65536 10.0 293.333333 33.620165 233.333333 277.777778 283.333333 \n",
"default 10.0 290.000000 15.225781 266.666667 280.555556 288.888889 \n",
"\n",
" 75% max \n",
"1 322.222222 366.666667 \n",
"65536 316.666667 355.555556 \n",
"default 297.222222 322.222222 "
]
},
{
"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>1</th>\n",
" <td>10.0</td>\n",
" <td>17.222222</td>\n",
" <td>12.129276</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>11.111111</td>\n",
" <td>30.555556</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>37.222222</td>\n",
" <td>62.638051</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>22.222222</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 17.222222 12.129276 5.555556 6.944444 11.111111 \n",
"65536 10.0 16.111111 12.129276 5.555556 6.944444 11.111111 \n",
"default 10.0 37.222222 62.638051 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"1 30.555556 33.333333 \n",
"65536 27.777778 33.333333 \n",
"default 22.222222 200.000000 "
]
},
{
"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>1</th>\n",
" <td>10.0</td>\n",
" <td>23.333333</td>\n",
" <td>11.049210</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>30.000000</td>\n",
" <td>9.147473</td>\n",
" <td>11.111111</td>\n",
" <td>25.000000</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>11.490439</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>19.444444</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",
"1 10.0 23.333333 11.049210 11.111111 11.111111 27.777778 \n",
"65536 10.0 30.000000 9.147473 11.111111 25.000000 33.333333 \n",
"default 10.0 13.888889 11.490439 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"1 33.333333 33.333333 \n",
"65536 33.333333 44.444444 \n",
"default 19.444444 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>1</th>\n",
" <td>10.0</td>\n",
" <td>17.777778</td>\n",
" <td>5.737753</td>\n",
" <td>11.111111</td>\n",
" <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>65536</th>\n",
" <td>10.0</td>\n",
" <td>24.444444</td>\n",
" <td>20.819955</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>30.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>10.555556</td>\n",
" <td>6.651217</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>11.111111</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",
"1 10.0 17.777778 5.737753 11.111111 11.111111 22.222222 \n",
"65536 10.0 24.444444 20.819955 11.111111 11.111111 11.111111 \n",
"default 10.0 10.555556 6.651217 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"1 22.222222 22.222222 \n",
"65536 30.555556 66.666667 \n",
"default 11.111111 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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GLEmSpAYYsiRJkhpgyJIkSWqAIUuSJKkBhixJkqQGGLIkSZIaYMiSJElqgCFL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\npAYYsiRJkhpgyJIkSWqAIUuSJKkB/x8wUmicjWe5OwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10e5ae6d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x112270110>"
]
},
{
"output_type": "display_data",
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eemsaqF3y5JPrr++LX1y/5vYAAIAqEKbQeZqb080jBw9O9ciRqVvtyitTfcop\n6XL/KVNSffvt6eq3Upg66qh0ZqrkttvKY5Yk6Ze/XH99hx1Wm+0AAGzVCFOonZdfTlezfeYz6SzP\niBEpMD36aKovvTR1rS1fnupp09L9lkrOPFOaX3Gh6I03prFMJeedt/76SlfCAQDQgbLvM4WtzJo1\n6RYBUrotwBVXpCvfJOmmm9K/i4uvabzzznTJf+nGk1Iaa1S6UeVZZ6WzSaWzS5ddVr6VgCQdf3x5\ngLckbbMNXW8AgLpDmELZmjXSrFnlu2xPn57upD1zZqrvuSedGSrdafvZZ6VvfjOdgZKk3XZL/5bu\nxfT5z6cvxC1d0XbmmdLvfpdCkSQdcEAKTG/jMAQAdF18im1NVq1K90V67bVUz5yZuuBKA7GfeSZ1\nlZW62hYuTN1ws2aletAg6bvfTYO8JekTn0hnofbfP9Uf+Uj6d9dd07/9+qWr5brTmwwA2HIRprZk\nixal+x6Vut+am6VDD5VGjUp1jx7pvkulbrn99pOuuUZ63/tSfcghaTxTKSTts490ySXpHkuStN12\nKVjR9QYA2IpxyqCD9d5vqN4/cmjHrfCrktYMk0YW90gasb+kn0sjf57qi7pJC4ZKpTb1kPTYz9q8\nut77SdLxbW8vAABdDGGqgy2fOkwzh225YWPg0NGd3QQAADoU3XwAAAAZCFMAAAAZCFMAAAAZCFMA\nAAAZCFMAAAAZCFMAAAAZCFMAAAAZCFMAAAAZuGknAABttCXfqHjHbXt0dhO6DMIUAABt0NHfZjFw\n6Ogt+hs0ujK6+QAAADIQpgAAADIQpgAAADIQpgAAADIQpgAAADIQpgAAADIQpgAAADIQpgAAADIQ\npgAAADJk3QHd9kxJyyWtlbQmIhrbo1FburZ8/cCsH32iBi3ZtD0uGrXZr+HrB5DDdttf+6O2vS4i\n2rxOoC04zrc87fF1Mh+NiAXtsJytQpu/CmAYvwjY8vEfPrYGHOdbHrr5AAAAMuSGqZD0sO1xts9p\njwYBAAB0JbndfP8UEXNs7yppjO1pETG2coYiZJ0jSbvvvnvm6gAAAOpL1pmpiJhT/Dtf0l2SDtrI\nPMMjojEiGhsaGnJWBwAAUHfaHKZsb2+7d+mxpGMkTWqvhgEAAHQFOd18fSXdVVzi2V3SzRHxYLu0\nCgAAoItoc5iKiJckHdCObQEAAOhyuDUCAABABsIUAABABsIUAABABsIUAABABsIUAABABsIUAABA\nBsIUAABABsIUAABABsIUAABABsIUAABABsIUAABABsIUAABABsIUAABABsIUAABABsIUAABABsIU\nAABABsIUAABABsIUAABABsJxL40bAAAFIElEQVQUAABABsIUAABABsIUAABABsIUAABABsIUAABA\nBsIUAABABsIUAABABsIUAABABsIUAABABsIUAABAhqwwZfvjtp+3/aLtoe3VKAAAgK6izWHKdjdJ\nv5B0rKRBkk61Pai9GgYAANAV5JyZOkjSixHxUkS8KelWSSe0T7MAAAC6hpww1V/SqxX17GIaAADA\nVqN7rVdg+xxJ5xTlCtvP13qdWM8ukhZ0diOAGuM4x9aA47zj7VHNTDlhao6kARX1bsW09UTEcEnD\nM9aDDLabIqKxs9sB1BLHObYGHOf1K6eb78+S9rG9p+2ekk6RdG/7NAsAAKBraPOZqYhYY/trkh6S\n1E3SdRExud1aBgAA0AVkjZmKiPsl3d9ObUFt0MWKrQHHObYGHOd1yhHR2W0AAADosvg6GQAAgAyE\nqS2U7etsz7c9qbPbAlTLdh/bd9ieZnuq7X+0fbHtObYnFD/HFfMOtL2yYvqvKpbzoO1nbU+2/avi\nGxtKz51XLH+y7cs7YzuB4rj+5iaeb7D9tO3xtg9tw/LPtH1V8fhEvqGktmp+nyl0mhGSrpJ0Qye3\nA9gcP5P0YER8trhKeDtJH5N0ZUT8ZCPzz4iIwRuZflJELLNtSXdI+pykW21/VOmbGg6IiNW2d63R\ndgC5jpT0XER8qR2WdaKkUZKmtMOysBGcmdpCRcRYSYs6ux1AtWzvKOkwSddKUkS8GRFL2rKsiFhW\nPOwuqaek0uDQf5E0LCJWF/PNz2o0sBlsf8f2dNtPSHpPMW3v4kzqONt/sP1e24MlXS7phOKs67a2\nr7bdVJxRvaRimTNt71I8brT9+Abr/JCkT0r6cbGsvTtqe7cmhCkA9WJPSc2Sri+6Nv7X9vbFc1+z\nPbHovt6p8jXFvL/fsCvE9kOS5ktarnR2SpL2lXRo0X3ye9v/UONtAiRJtg9Uuh/jYEnHSSode8Ml\nnRcRB0r6pqRfRsQESd+TdFtEDI6IlZK+U9yw8wOSPmL7A9WsNyKeVLoH5IXFsma064ZBEmEKQP3o\nLumDkq6OiCGS/ippqKSrJe2t9CE0V9IVxfxzJe1ezPtvkm62/fbSwiLiY5L6Seol6YiKdews6RBJ\nF0q6vegKBGrtUEl3RcQbxZnTeyVtI+lDkn5je4Kk/1E6ZjfmJNt/kTRe0vskMQaqjhCmANSL2ZJm\nR8TTRX2HpA9GxLyIWBsR6yRdI+kgSYqI1RGxsHg8TtIMpTNPfxMRqyTdozROqrSOOyN5RtI6pe87\nAzrD2yQtKc4YlX7223Am23sqnbU6MiI+IGm0UhCTpDUqf5Zvs+Fr0TEIUwDqQkS8LulV2+8pJh0p\naYrtyr/UPyVpkvS3q526FY/3krSPpJds71B6je3uko6XNK14/d2SPlo8t6/SeCq+OBYdYaykE4vx\nT70l/bOkNyS9bPtzkuTkgI289u1KZ2qX2u4r6diK52ZKOrB4/JkW1r1cUu/8TUBLCFNbKNu3SPqT\npPfYnm377M5uE1CF8yTdZHuiUrfef0q63PZzxbSPSvpGMe9hkiYW3SN3SDo3IhZJ2l7SvcX8E5TG\nTZVum3CdpL2KW4bcKumM4M7F6AAR8RdJt0l6VtIDSt9vK0mnSTrb9rOSJqt8FrXytc8qde9Nk3Sz\npD9WPH2JpJ/ZbpK0toXV3yrpwmJ8IQPQa4A7oAMAAGTgzBQAAEAGwhQAAEAGwhQAAEAGwhQAAEAG\nwhQAAEAGwhQAAEAGwhQAAEAGwhQAAECG/w9aEGkwmSvNvAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1125622d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1126a1390>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x11277afd0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x11280d890>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x112902f50>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x112996ed0>"
]
},
{
"output_type": "display_data",
"png": 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lSZJUwDAlSZJUwDAlSZJUwDAlSZJUwDAlSZJUwDAlSZJUwDAlSZJUwDAlSZJU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\n",
"text": [
"<matplotlib.figure.Figure at 0x112a39b90>"
]
},
{
"output_type": "display_data",
"png": 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oqUVtqZl6B/DBiHgS+CX59N4PgP4RUbsacHvg2ZYenFK6MKU0OqU0etCgQe0Q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\nikWN4SlW9QQe5+oJPM47qUg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"text": [
"<matplotlib.figure.Figure at 0x112a7e250>"
]
},
{
"output_type": "display_data",
"png": 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LiMkRMbmtra2Lh5MkSeqfuhSmMvOpzFyUmYuBHwM7L2fdCZk5NjPHjhgxoqvt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\nGKYkSZJqMExJkiTV8P8Bewt6pJ00lmsAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x112270250>"
]
},
{
"output_type": "display_data",
"png": 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XwF57waxZzX1ff33zLLuJE2G77Rz3pAFt6tSpjB49mohg9OjRTJ06tdVVUieG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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x112bfb450>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x112c06d50>"
]
},
{
"output_type": "display_data",
"png": 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pNuoSspLMA94PHALsBxyZZL8e65IkSZqNep3JOgBYVVXXVNX/AmcDh3ValyRJ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\nSZIkdWDIkiRJ6uD/A3Pv7sTBBLWHAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x112b96f50>"
]
},
{
"output_type": "display_data",
"png": 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JkiroMmRFxMURsSgiZrW07RwRN0bE/eXrTqU9IuKCiJgbEXdHxGtrTl6SJKm/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\n5tfCQZJ2kgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x112d33410>"
]
},
{
"output_type": "display_data",
"png": 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SJEmqwJAlSZJUgSFLkiSpAkOWJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIk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\nJEkVGLIkSZIq+P97oq760sxrgAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x112db6090>"
]
},
{
"output_type": "display_data",
"png": 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tb8OHP1yWl166zKbccVJASUPf975XuvCXXba099sPfvCD5takYak3V/Pt18Xq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\nSarBMCVJklSDYUqSJKmG/w+7lMKKF3jsBQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x112e89fd0>"
]
},
{
"output_type": "display_data",
"png": 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tG8/O/NJ6/+EjqR+vfW15NbzrXeXVcNZZcMYZzfLHP17u3mzYeOMSohq++tVy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\nSUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x112f17610>"
]
},
{
"output_type": "display_data",
"png": 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SJGl8KAlTuwMrGtr31OMkSZK2GBNHegMRMQOYUTcfi4g7R3qbeoZdgAfGughp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\nFA1xc3VSX5oAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x112fc3c90>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1130afcd0>"
]
},
{
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S1CeYgEk9afZs2HTT8moggE9/uiRfo0Y1Ni5JUp/iEKTUHZkwYwa88EJZp+uN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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x11314ad10>"
]
},
{
"output_type": "display_data",
"png": 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SpAoMWZIkSRUYsiRJkiowZEmSJFVgyJIkSarAkCVJklSBIUuSJKkCQ5YkSVIF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\n",
"text": [
"<matplotlib.figure.Figure at 0x113163850>"
]
},
{
"output_type": "display_data",
"png": 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qwJAlSZLUgCFLkiSpAUOWJElSA4YsSZKkBgxZkiRJDRiyJEmSGjBkSZIkNWDI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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1131e9b10>"
]
},
{
"output_type": "display_data",
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WiBjT6e4DgV2r2xOB3wDH9mJdkgaqxlIBUK6WmzULvvCF0m5M8L7tttK+777S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\nWIMAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x113282690>"
]
},
{
"output_type": "display_data",
"png": 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bhg0btjSHSpIk9XkdClkRMZgSsC7MzN8AZOazmbkwM98EzqTRJfgUsE7T4WtX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\nqQaGLEmSpBr8f4MoUqmH71ENAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11336eb10>"
]
},
{
"output_type": "display_data",
"png": 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8k+oPPkj1okWpLgWlhQtTvWJF43rlylS//XaqV61K9YIFjYPC/PmN67fealzP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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x113409e50>"
]
},
{
"output_type": "display_data",
"png": 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SUtoKmFK0JUndwQc/CI8/DkcemduPPQZLl1Zbk1SRRsNUSunFlNJ9xfYCYAaw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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x113460110>"
]
},
{
"output_type": "display_data",
"png": 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hSlKrkSPLo9UZZ5Qrs0eU9vnnw5w5jTD1zW+WXq4JE3q9VEnqTwxTktq24oqN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\navj/sp9+cT1TLXUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x11342acd0>"
]
}
],
"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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