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javascript.options.baselinejit.threshold
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javascript.options.baselinejit.threshold.ipynb
{
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
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"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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"nbformat": 3,
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"worksheets": [
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"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'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe tbody tr th {\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>1</th>\n",
" <td>10.0</td>\n",
" <td>422.222222</td>\n",
" <td>16.563466</td>\n",
" <td>400.000000</td>\n",
" <td>411.111111</td>\n",
" <td>422.222222</td>\n",
" <td>433.333333</td>\n",
" <td>444.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>415.555556</td>\n",
" <td>17.529125</td>\n",
" <td>388.888889</td>\n",
" <td>411.111111</td>\n",
" <td>411.111111</td>\n",
" <td>422.222222</td>\n",
" <td>455.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>433.333333</td>\n",
" <td>26.707787</td>\n",
" <td>400.000000</td>\n",
" <td>416.666667</td>\n",
" <td>433.333333</td>\n",
" <td>441.666667</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 10.0 422.222222 16.563466 400.000000 411.111111 422.222222 \n",
"50 10.0 415.555556 17.529125 388.888889 411.111111 411.111111 \n",
"default 10.0 433.333333 26.707787 400.000000 416.666667 433.333333 \n",
"\n",
" 75% max \n",
"1 433.333333 444.444444 \n",
"50 422.222222 455.555556 \n",
"default 441.666667 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",
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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>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",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>5.555556</td>\n",
" <td>0.000000</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</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 7.222222 5.270463 5.555556 5.555556 5.555556 5.555556 \n",
"50 10.0 5.555556 0.000000 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",
"50 5.555556 \n",
"default 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_type_in_search_field'"
]
},
{
"html": [
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\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>11.666667</td>\n",
" <td>7.612891</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>19.444444</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</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>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",
"1 10.0 11.666667 7.612891 5.555556 5.555556 8.333333 \n",
"50 10.0 17.777778 5.737753 11.111111 11.111111 22.222222 \n",
"default 10.0 16.111111 6.651217 5.555556 11.111111 16.666667 \n",
"\n",
" 75% max \n",
"1 19.444444 22.222222 \n",
"50 22.222222 22.222222 \n",
"default 22.222222 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",
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" }\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>38.888889</td>\n",
" <td>18.332398</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>56.666667</td>\n",
" <td>14.296488</td>\n",
" <td>22.222222</td>\n",
" <td>55.555556</td>\n",
" <td>61.111111</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>56.666667</td>\n",
" <td>29.373896</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>133.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 38.888889 18.332398 11.111111 22.222222 44.444444 \n",
"50 10.0 56.666667 14.296488 22.222222 55.555556 61.111111 \n",
"default 10.0 56.666667 29.373896 33.333333 36.111111 55.555556 \n",
"\n",
" 75% max \n",
"1 55.555556 55.555556 \n",
"50 66.666667 66.666667 \n",
"default 55.555556 133.333333 "
]
},
{
"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",
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" <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>9.147473</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>72.222222</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>80.000000</td>\n",
" <td>17.213259</td>\n",
" <td>44.444444</td>\n",
" <td>69.444444</td>\n",
" <td>83.333333</td>\n",
" <td>88.888889</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>73.333333</td>\n",
" <td>9.369712</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</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 74.444444 9.147473 66.666667 66.666667 72.222222 \n",
"50 10.0 80.000000 17.213259 44.444444 69.444444 83.333333 \n",
"default 10.0 73.333333 9.369712 66.666667 66.666667 66.666667 \n",
"\n",
" 75% max \n",
"1 77.777778 88.888889 \n",
"50 88.888889 100.000000 \n",
"default 77.777778 88.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab_emoji'"
]
},
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"html": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <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>58.888889</td>\n",
" <td>10.540926</td>\n",
" <td>44.444444</td>\n",
" <td>47.222222</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>57.777778</td>\n",
" <td>12.614360</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>61.111111</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>61.111111</td>\n",
" <td>15.044516</td>\n",
" <td>44.444444</td>\n",
" <td>47.222222</td>\n",
" <td>61.111111</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 58.888889 10.540926 44.444444 47.222222 66.666667 \n",
"50 10.0 57.777778 12.614360 44.444444 44.444444 61.111111 \n",
"default 10.0 61.111111 15.044516 44.444444 47.222222 61.111111 \n",
"\n",
" 75% max \n",
"1 66.666667 66.666667 \n",
"50 66.666667 77.777778 \n",
"default 66.666667 88.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_photo_viewer_right_arrow'"
]
},
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"html": [
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" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
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" <tbody>\n",
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" <td>10.210406</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>72.222222</td>\n",
" <td>86.111111</td>\n",
" <td>88.888889</td>\n",
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" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>77.777778</td>\n",
" <td>12.830006</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>72.222222</td>\n",
" <td>88.888889</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>75.555556</td>\n",
" <td>8.764563</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</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",
"1 10.0 75.555556 10.210406 66.666667 66.666667 72.222222 \n",
"50 10.0 77.777778 12.830006 66.666667 66.666667 72.222222 \n",
"default 10.0 75.555556 8.764563 66.666667 66.666667 77.777778 \n",
"\n",
" 75% max \n",
"1 86.111111 88.888889 \n",
"50 88.888889 100.000000 \n",
"default 77.777778 88.888889 "
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" <td>43.333333</td>\n",
" <td>6.307180</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
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" <th>50</th>\n",
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" <td>45.555556</td>\n",
" <td>9.728834</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</td>\n",
" <td>44.444444</td>\n",
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" <td>55.555556</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>48.888889</td>\n",
" <td>5.737753</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
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" count mean std min 25% 50% \\\n",
"1 10.0 43.333333 6.307180 33.333333 44.444444 44.444444 \n",
"50 10.0 45.555556 9.728834 33.333333 36.111111 44.444444 \n",
"default 10.0 48.888889 5.737753 44.444444 44.444444 44.444444 \n",
"\n",
" 75% max \n",
"1 44.444444 55.555556 \n",
"50 55.555556 55.555556 \n",
"default 55.555556 55.555556 "
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" <td>7.768954</td>\n",
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" <td>33.333333</td>\n",
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" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>40.000000</td>\n",
" <td>11.944086</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
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" <td>66.666667</td>\n",
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" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>41.111111</td>\n",
" <td>10.540926</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>52.777778</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
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" count mean std min 25% 50% \\\n",
"1 10.0 37.777778 7.768954 33.333333 33.333333 33.333333 \n",
"50 10.0 40.000000 11.944086 33.333333 33.333333 33.333333 \n",
"default 10.0 41.111111 10.540926 33.333333 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"1 41.666667 55.555556 \n",
"50 41.666667 66.666667 \n",
"default 52.777778 55.555556 "
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" <td>10.0</td>\n",
" <td>51.111111</td>\n",
" <td>19.737648</td>\n",
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" <td>32.222222</td>\n",
" <td>12.227833</td>\n",
" <td>11.111111</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>23.888889</td>\n",
" <td>19.253917</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>16.666667</td>\n",
" <td>30.555556</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",
"1 10.0 51.111111 19.737648 22.222222 36.111111 50.000000 \n",
"50 10.0 32.222222 12.227833 11.111111 25.000000 33.333333 \n",
"default 10.0 23.888889 19.253917 5.555556 11.111111 16.666667 \n",
"\n",
" 75% max \n",
"1 63.888889 88.888889 \n",
"50 33.333333 55.555556 \n",
"default 30.555556 66.666667 "
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>31.111111</td>\n",
" <td>18.555519</td>\n",
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" <td>33.333333</td>\n",
" <td>41.666667</td>\n",
" <td>55.555556</td>\n",
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" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>20.555556</td>\n",
" <td>9.091065</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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>34.444444</td>\n",
" <td>21.880082</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>38.888889</td>\n",
" <td>52.777778</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",
"1 10.0 31.111111 18.555519 5.555556 16.666667 33.333333 \n",
"50 10.0 20.555556 9.091065 5.555556 13.888889 22.222222 \n",
"default 10.0 34.444444 21.880082 11.111111 11.111111 38.888889 \n",
"\n",
" 75% max \n",
"1 41.666667 55.555556 \n",
"50 22.222222 33.333333 \n",
"default 52.777778 66.666667 "
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" <td>10.0</td>\n",
" <td>61.111111</td>\n",
" <td>9.442629</td>\n",
" <td>44.444444</td>\n",
" <td>58.333333</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
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" <td>10.0</td>\n",
" <td>34.444444</td>\n",
" <td>3.513642</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>57.777778</td>\n",
" <td>17.992530</td>\n",
" <td>11.111111</td>\n",
" <td>58.333333</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</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",
"1 10.0 61.111111 9.442629 44.444444 58.333333 66.666667 \n",
"50 10.0 34.444444 3.513642 33.333333 33.333333 33.333333 \n",
"default 10.0 57.777778 17.992530 11.111111 58.333333 66.666667 \n",
"\n",
" 75% max \n",
"1 66.666667 66.666667 \n",
"50 33.333333 44.444444 \n",
"default 66.666667 66.666667 "
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" <td>64.444444</td>\n",
" <td>8.764563</td>\n",
" <td>44.444444</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>88.888889</td>\n",
" <td>18.885257</td>\n",
" <td>66.666667</td>\n",
" <td>69.444444</td>\n",
" <td>94.444444</td>\n",
" <td>100.000000</td>\n",
" <td>122.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 83.333333 18.332398 66.666667 66.666667 77.777778 \n",
"50 10.0 64.444444 8.764563 44.444444 66.666667 66.666667 \n",
"default 10.0 88.888889 18.885257 66.666667 69.444444 94.444444 \n",
"\n",
" 75% max \n",
"1 100.000000 111.111111 \n",
"50 66.666667 77.777778 \n",
"default 100.000000 122.222222 "
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" <td>233.333333</td>\n",
" <td>22.831163</td>\n",
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" <td>241.666667</td>\n",
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" <td>10.0</td>\n",
" <td>207.777778</td>\n",
" <td>13.907395</td>\n",
" <td>188.888889</td>\n",
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" <td>233.333333</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>251.111111</td>\n",
" <td>66.088024</td>\n",
" <td>211.111111</td>\n",
" <td>222.222222</td>\n",
" <td>227.777778</td>\n",
" <td>252.777778</td>\n",
" <td>433.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 233.333333 22.831163 200.000000 216.666667 233.333333 \n",
"50 10.0 207.777778 13.907395 188.888889 200.000000 200.000000 \n",
"default 10.0 251.111111 66.088024 211.111111 222.222222 227.777778 \n",
"\n",
" 75% max \n",
"1 241.666667 277.777778 \n",
"50 219.444444 233.333333 \n",
"default 252.777778 433.333333 "
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"</style>\n",
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" <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>246.666667</td>\n",
" <td>17.992530</td>\n",
" <td>211.111111</td>\n",
" <td>236.111111</td>\n",
" <td>244.444444</td>\n",
" <td>263.888889</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>206.666667</td>\n",
" <td>21.722783</td>\n",
" <td>177.777778</td>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>216.666667</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>243.333333</td>\n",
" <td>16.101530</td>\n",
" <td>222.222222</td>\n",
" <td>233.333333</td>\n",
" <td>238.888889</td>\n",
" <td>252.777778</td>\n",
" <td>277.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 246.666667 17.992530 211.111111 236.111111 244.444444 \n",
"50 10.0 206.666667 21.722783 177.777778 200.000000 200.000000 \n",
"default 10.0 243.333333 16.101530 222.222222 233.333333 238.888889 \n",
"\n",
" 75% max \n",
"1 263.888889 266.666667 \n",
"50 216.666667 255.555556 \n",
"default 252.777778 277.777778 "
]
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"output_type": "display_data",
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]
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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>265.555556</td>\n",
" <td>37.203330</td>\n",
" <td>200.000000</td>\n",
" <td>241.666667</td>\n",
" <td>283.333333</td>\n",
" <td>288.888889</td>\n",
" <td>300.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>224.444444</td>\n",
" <td>11.475506</td>\n",
" <td>200.000000</td>\n",
" <td>222.222222</td>\n",
" <td>227.777778</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>261.111111</td>\n",
" <td>12.001372</td>\n",
" <td>244.444444</td>\n",
" <td>250.000000</td>\n",
" <td>266.666667</td>\n",
" <td>266.666667</td>\n",
" <td>277.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 265.555556 37.203330 200.000000 241.666667 283.333333 \n",
"50 10.0 224.444444 11.475506 200.000000 222.222222 227.777778 \n",
"default 10.0 261.111111 12.001372 244.444444 250.000000 266.666667 \n",
"\n",
" 75% max \n",
"1 288.888889 300.000000 \n",
"50 233.333333 233.333333 \n",
"default 266.666667 277.777778 "
]
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"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_type_in_reply_field'"
]
},
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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>1</th>\n",
" <td>10.0</td>\n",
" <td>18.888889</td>\n",
" <td>7.499428</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
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" <tr>\n",
" <th>50</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",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>20.000000</td>\n",
" <td>7.027284</td>\n",
" <td>11.111111</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",
"1 10.0 18.888889 7.499428 11.111111 11.111111 22.222222 \n",
"50 10.0 16.111111 6.651217 5.555556 11.111111 16.666667 \n",
"default 10.0 20.000000 7.027284 11.111111 13.888889 22.222222 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"50 22.222222 22.222222 \n",
"default 22.222222 33.333333 "
]
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"text": [
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]
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" <th>min</th>\n",
" <th>25%</th>\n",
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" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>117.777778</td>\n",
" <td>88.300522</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>94.444444</td>\n",
" <td>100.000000</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>124.444444</td>\n",
" <td>97.118993</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
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" <td>400.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>133.333333</td>\n",
" <td>140.740741</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>97.222222</td>\n",
" <td>533.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 117.777778 88.300522 77.777778 77.777778 94.444444 \n",
"50 10.0 124.444444 97.118993 77.777778 88.888889 100.000000 \n",
"default 10.0 133.333333 140.740741 77.777778 88.888889 88.888889 \n",
"\n",
" 75% max \n",
"1 100.000000 366.666667 \n",
"50 100.000000 400.000000 \n",
"default 97.222222 533.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_search_suggestion'"
]
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" <th>min</th>\n",
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" <th>75%</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>10.0</td>\n",
" <td>22.222222</td>\n",
" <td>10.475656</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>25.555556</td>\n",
" <td>9.147473</td>\n",
" <td>11.111111</td>\n",
" <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>default</th>\n",
" <td>10.0</td>\n",
" <td>15.555556</td>\n",
" <td>7.314229</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",
"1 10.0 22.222222 10.475656 11.111111 11.111111 22.222222 \n",
"50 10.0 25.555556 9.147473 11.111111 22.222222 27.777778 \n",
"default 10.0 15.555556 7.314229 5.555556 11.111111 16.666667 \n",
"\n",
" 75% max \n",
"1 33.333333 33.333333 \n",
"50 33.333333 33.333333 \n",
"default 22.222222 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_type_searchbox'"
]
},
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"html": [
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"<style scoped>\n",
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" <th>min</th>\n",
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" <th>75%</th>\n",
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" <td>5.555556</td>\n",
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" <th>50</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>"
],
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"text": [
" count mean std min 25% 50% 75% \\\n",
"1 10.0 5.555556 0.000000 5.555556 5.555556 5.555556 5.555556 \n",
"50 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 5.555556 \n",
"50 11.111111 \n",
"default 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_type_0'"
]
},
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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>47.777778</td>\n",
" <td>12.883353</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>53.333333</td>\n",
" <td>13.658584</td>\n",
" <td>33.333333</td>\n",
" <td>47.222222</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>58.888889</td>\n",
" <td>16.604824</td>\n",
" <td>44.444444</td>\n",
" <td>47.222222</td>\n",
" <td>55.555556</td>\n",
" <td>63.888889</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",
"1 10.0 47.777778 12.883353 33.333333 36.111111 44.444444 \n",
"50 10.0 53.333333 13.658584 33.333333 47.222222 55.555556 \n",
"default 10.0 58.888889 16.604824 44.444444 47.222222 55.555556 \n",
"\n",
" 75% max \n",
"1 55.555556 66.666667 \n",
"50 55.555556 77.777778 \n",
"default 63.888889 100.000000 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gslide_ail_pagedown_0'"
]
},
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>337.777778</td>\n",
" <td>13.042087</td>\n",
" <td>322.222222</td>\n",
" <td>333.333333</td>\n",
" <td>333.333333</td>\n",
" <td>333.333333</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>351.111111</td>\n",
" <td>10.734353</td>\n",
" <td>333.333333</td>\n",
" <td>344.444444</td>\n",
" <td>350.000000</td>\n",
" <td>355.555556</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>332.222222</td>\n",
" <td>8.198498</td>\n",
" <td>322.222222</td>\n",
" <td>325.000000</td>\n",
" <td>333.333333</td>\n",
" <td>333.333333</td>\n",
" <td>344.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 337.777778 13.042087 322.222222 333.333333 333.333333 \n",
"50 10.0 351.111111 10.734353 333.333333 344.444444 350.000000 \n",
"default 10.0 332.222222 8.198498 322.222222 325.000000 333.333333 \n",
"\n",
" 75% max \n",
"1 333.333333 366.666667 \n",
"50 355.555556 366.666667 \n",
"default 333.333333 344.444444 "
]
},
{
"output_type": "display_data",
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]
},
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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>50</th>\n",
" <td>10.0</td>\n",
" <td>106.666667</td>\n",
" <td>5.737753</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>111.111111</td>\n",
" <td>111.111111</td>\n",
" <td>111.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>100.000000</td>\n",
" <td>13.857990</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>133.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"50 10.0 106.666667 5.737753 100.000000 100.000000 111.111111 \n",
"default 10.0 100.000000 13.857990 88.888889 88.888889 100.000000 \n",
"\n",
" 75% max \n",
"50 111.111111 111.111111 \n",
"default 100.000000 133.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_outlook_ail_composemail_0'"
]
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" .dataframe tbody tr th:only-of-type {\n",
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" <td>413.333333</td>\n",
" <td>27.616818</td>\n",
" <td>377.777778</td>\n",
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" <td>411.111111</td>\n",
" <td>419.444444</td>\n",
" <td>477.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>396.666667</td>\n",
" <td>24.031301</td>\n",
" <td>355.555556</td>\n",
" <td>383.333333</td>\n",
" <td>400.000000</td>\n",
" <td>408.333333</td>\n",
" <td>433.333333</td>\n",
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" <tr>\n",
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" <td>411.111111</td>\n",
" <td>422.222222</td>\n",
" <td>433.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 413.333333 27.616818 377.777778 400.000000 411.111111 \n",
"50 10.0 396.666667 24.031301 355.555556 383.333333 400.000000 \n",
"default 10.0 411.111111 17.371910 377.777778 400.000000 411.111111 \n",
"\n",
" 75% max \n",
"1 419.444444 477.777778 \n",
"50 408.333333 433.333333 \n",
"default 422.222222 433.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_outlook_ail_type_composemail_0'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe tbody tr th {\n",
" 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",
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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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" <td>18.333333</td>\n",
" <td>18.527775</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</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>241.111111</td>\n",
" <td>342.829748</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>550.000000</td>\n",
" <td>766.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 18.333333 18.527775 5.555556 11.111111 11.111111 \n",
"50 10.0 9.444444 6.953698 5.555556 5.555556 5.555556 \n",
"default 10.0 241.111111 342.829748 11.111111 22.222222 33.333333 \n",
"\n",
" 75% max \n",
"1 11.111111 66.666667 \n",
"50 9.722222 22.222222 \n",
"default 550.000000 766.666667 "
]
},
{
"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",
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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></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>1</th>\n",
" <td>10.0</td>\n",
" <td>303.333333</td>\n",
" <td>60.869331</td>\n",
" <td>266.666667</td>\n",
" <td>266.666667</td>\n",
" <td>283.333333</td>\n",
" <td>308.333333</td>\n",
" <td>466.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>282.222222</td>\n",
" <td>37.843081</td>\n",
" <td>244.444444</td>\n",
" <td>255.555556</td>\n",
" <td>277.777778</td>\n",
" <td>297.222222</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>332.222222</td>\n",
" <td>108.922897</td>\n",
" <td>255.555556</td>\n",
" <td>269.444444</td>\n",
" <td>288.888889</td>\n",
" <td>341.666667</td>\n",
" <td>611.111111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 303.333333 60.869331 266.666667 266.666667 283.333333 \n",
"50 10.0 282.222222 37.843081 244.444444 255.555556 277.777778 \n",
"default 10.0 332.222222 108.922897 255.555556 269.444444 288.888889 \n",
"\n",
" 75% max \n",
"1 308.333333 466.666667 \n",
"50 297.222222 366.666667 \n",
"default 341.666667 611.111111 "
]
},
{
"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",
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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>14.444444</td>\n",
" <td>9.868824</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>25.000000</td>\n",
" <td>30.344388</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>23.888889</td>\n",
" <td>20.629663</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 14.444444 9.868824 5.555556 5.555556 11.111111 \n",
"50 10.0 25.000000 30.344388 5.555556 6.944444 11.111111 \n",
"default 10.0 23.888889 20.629663 5.555556 11.111111 22.222222 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"50 22.222222 100.000000 \n",
"default 22.222222 77.777778 "
]
},
{
"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",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <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>15.555556</td>\n",
" <td>8.996265</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>50</th>\n",
" <td>10.0</td>\n",
" <td>15.555556</td>\n",
" <td>11.355341</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</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>22.222222</td>\n",
" <td>5.237828</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>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 15.555556 8.996265 5.555556 11.111111 11.111111 \n",
"50 10.0 15.555556 11.355341 5.555556 5.555556 11.111111 \n",
"default 10.0 22.222222 5.237828 11.111111 22.222222 22.222222 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"50 22.222222 33.333333 \n",
"default 22.222222 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>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",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>11.111111</td>\n",
" <td>9.442629</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>20.000000</td>\n",
" <td>29.652769</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>19.444444</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% 75% \\\n",
"1 10.0 7.777778 5.367177 5.555556 5.555556 5.555556 5.555556 \n",
"50 10.0 11.111111 9.442629 5.555556 5.555556 5.555556 11.111111 \n",
"default 10.0 20.000000 29.652769 5.555556 5.555556 5.555556 19.444444 \n",
"\n",
" max \n",
"1 22.222222 \n",
"50 33.333333 \n",
"default 100.000000 "
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# show plot\n",
"plt.show()"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
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GLEmSpBoYsiRJkmpgyJIkSaqBIUuSJKkGhixJkqQaGLIkSZJqYMiSJEmqgSFL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\nT/+PYiZ7AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10ac096d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10e957490>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10ec704d0>"
]
},
{
"output_type": "display_data",
"png": 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9tpRHjixX7q1cWcqnngq/+11z+0MPhXe9q3/rLEkaEIYsqdWKFWV25sZ90a66\nqsx0fs89pXzTTSVk3XtvKb/jHWU26oYZM8rg9cYtP8aOLVcGSpKGHUOWhrelS+Hkk5szPv/85zBp\nUrnVBpRJA3fdtdnFd9hhZSqF3XYr5T33hPe+d8DmnZIkDV6GLK3bnn++zHx+//2lvHQpTJzYvG3F\nc8/BZz9bJvIEeOUry73HXlZNJviKV8AllzRvZrvRRuVqQEmSumHI0rqhceeCzNIy9f3vl/LKleXm\ns1//eimPGVNuFzNmTCnvuGNpmTryyFIePbrczuJFL+rX6kuS1j2GLA09t99e7mzf8LrXlbFQUOaO\nOv/8cv8wgA02gIsugqOPLuWRI0sAO+ig5vp29UmSauAUDhqcVq5sDh4/88zSzXfSSaX8/vfDiBFw\nzTWlfMABsN12zW3vvLMsb5g6tX/qLElSC1uyNPB++9tyRV7D8ceXLr2G668vk3o2fPGLJXg1nHxy\nsyULVg1YkiQNEFuy1H8yS/fcFVfAOefABReUQHT22fD5z5dpE9ZfH/bbr9yzr7H+2WeXnw2vfvXA\nnYMkST1kS5b63tKl5Qq95ctL+ZvfLD8feqj8XLas3IPv978v5WOOKfNPNboHDz+8dA02glVrwJIk\naYgwZKl3nnmmOcv57bfD294GN99cyjfdVOaOuummUn7Vq8rPRoiaNq1M+Nm4SfJOO5UJP9fz6yhJ\nWnf4V03de+YZOO+8cksZgDvugE03Lffog3IF3z33wGOPlfLkySV47bNPKb/0peVn642PJUlaxxmy\nVDz9dOnGA/jTn+CQQ5o3L37hBTjqKLj44lLeeedya5k99yzlXXYpE35OmVLKm25aJvwcNapfT0GS\npMHEge/D1bx5ZebyxvQGEyaUW8Z87Wtl8Pkf/9i8CfKmm8Jdd5V1oLRc/dM/DUStJUkaMiIbM2UP\noI6OjlzYOrnkMPSy81420FWo3S3TbhnoKmiIigG4+GEw/NuooWnCzMu5d84hA12N2qzr59cTEXFD\nZnZ0t54tWYPEU4vm9O+X9pFHys2PR/bPV2DCzMv75ThaNxl4JA1FhqzhqnFlnyRJqoUD3yVJkmpg\nyJIkSapB2yErIkZExK8i4rKqvHNEXBcRd0fEBRGxfvvVlCRJGlr6oiXrI8CilvJpwBcyc1fgcWB6\nHxxDkiRpSGkrZEXEeOAQ4OyqHMABwIXVKucBh7ZzDEmSpKGo3ZasLwKfBF6oylsDT2Tmyqr8ILB9\nm8eQJEkacnodsiLircAjmXlDL7efERELI2LhssbtXCRJktYR7bRk7Qu8PSLuBb5D6Sb8EjA6Ihrz\nb40HFq9u48w8KzM7MrNj7NhNDT7sAAAHvUlEQVSxbVRDkiRp8Ol1yMrMT2fm+MycALwb+J/MPBKY\nDxxWrTYNuKTtWkqSJA0xdcyT9SngYxFxN2WM1twajiFJkjSo9cltdTLzauDq6vnvgL37Yr+SJElD\nlTO+S5Ik1cCQJUmSVIM+6S6UJElNE2ZePtBVqM0WG40a6CoMGYYsSZL60L1zDunX402YeXm/H1M9\nY3ehJElSDQxZkiRJNTBkSZIk1cAxWZIkDQIR0fttT+vddpnZ62Oqe4YsSZIGAQPPusfuQkmSpBoY\nsiRJkmpgyJIkSaqBIUuSJKkGhixJkqQaGLIkSZJqYMiSJEmqgSFLkiSpBoYsSZKkGhiyJEmSamDI\nkiRJqoEhS5IkqQaGLEmSpBoYsiRJkmpgyJIkSaqBIUuSJKkGhixJ65x58+YxadIkRowYwaRJk5g3\nb95AV0nSMDRyoCsgSX1p3rx5zJo1i7lz5zJ58mQWLFjA9OnTATjiiCMGuHaShhNbsiStU2bPns3c\nuXOZMmUKo0aNYsqUKcydO5fZs2cPdNUkDTO9bsmKiB2AbwLjgATOyswvRcRWwAXABOBe4J2Z+Xj7\nVV33TZh5+UBXoTZbbDRqoKugYWLRokVMnjx5ldcmT57MokWLBqhGkoardroLVwIfz8wbI2Iz4IaI\nuBI4CrgqM+dExExgJvCp9qu6brt3ziH9erwJMy/v92NK/WHixIksWLCAKVOm/Pm1BQsWMHHixAGs\nlaThqNfdhZm5JDNvrJ4/BSwCtgemAudVq50HHNpuJSWpp2bNmsX06dOZP38+K1asYP78+UyfPp1Z\ns2YNdNUkDTN9MvA9IiYArwCuA8Zl5pJq0VJKd6Ik9YvG4PbjjjuORYsWMXHiRGbPnu2gd0n9ru2Q\nFRGbAt8HPpqZT0bEn5dlZkZEdrHdDGAGwI477thuNSTpz4444ghDlaQB19bVhRExihKwvp2ZF1Uv\nPxwR21bLtwUeWd22mXlWZnZkZsfYsWPbqYYkSdKg0+uQFaXJai6wKDP/o2XRpcC06vk04JLeV0+S\nJGloaqe7cF/g74FbIuKm6rUTgTnAdyNiOnAf8M72qihJkjT09DpkZeYCILpYfGBv9ytJkrQucMZ3\nSZKkGhiyJEmSamDIkiRJqoEhS5IkqQaGLEmSpBoYsiRJkmpgyJIkSaqBIUuSJKkGhixJkqQaGLIk\nSZJqYMiSJEmqQTs3iNYgENHV7SN7sO1pvdsuM3t9TEmShgtD1hBn4JEkaXCyu1CSJKkGhixJkqQa\nGLIkSZJqYMiSJEmqgSFLkiSpBoYsSZKkGhiyJEmSamDIkiRJqoEhS5IkqQaGLEmSpBoYsiRJkmpg\nyJIkSaqBIUuSJKkGhixJkqQaGLIkSZJqUFvIioiDIuLOiLg7ImbWdRxJkqTBqJaQFREjgC8DBwN7\nAEdExB51HEuSJGkwqqsla2/g7sz8XWb+CfgOMLWmY0mSJA06dYWs7YEHWsoPVq9JkiQNCyMH6sAR\nMQOYURWfjog7B6ouw9QY4NGBroRUM7/nGg78nve/nXqyUl0hazGwQ0t5fPXan2XmWcBZNR1f3YiI\nhZnZMdD1kOrk91zDgd/zwauu7sL/A3aLiJ0jYn3g3cClNR1LkiRp0KmlJSszV0bEh4GfACOAczLz\ntjqOJUmSNBjVNiYrM68Arqhr/2qbXbUaDvyeazjwez5IRWYOdB0kSZLWOd5WR5IkqQaGrGEmIs6J\niEci4taBrovU1yLi3oi4JSJuioiF1WtbRcSVEXFX9XPLga6n1JWIODkiPrGG5WMj4rqI+FVE7NeL\n/R8VEWdUzw/1biz1MmQNP+cCBw10JaQaTcnMvVouaZ8JXJWZuwFXVWVpqDoQuCUzX5GZP29zX4dS\nbn2nmhiyhpnMvAZ4bKDrIfWjqcB51fPzKH9YpEEjImZFxG8iYgHwkuq1XSLixxFxQ0T8PCJ2j4i9\ngH8FplattRtFxFcjYmFE3BYRp7Ts896IGFM974iIqzsdcx/g7cC/Vfvapb/OdzgZsBnfJakGCfw0\nIhI4s5r0eFxmLqmWLwXGDVjtpE4i4lWUuST3ovxNvhG4gXLF4DGZeVdEvAb4SmYeEBGfAToy88PV\n9rMy87GIGAFcFRF7ZuavuztuZv4yIi4FLsvMC2s6vWHPkCVpXTI5MxdHxDbAlRFxR+vCzMwqgEmD\nxX7ADzLzGYAq+GwI7AN8LyIa623QxfbvrG5TNxLYltL9123IUv8wZElaZ2Tm4urnIxHxA2Bv4OGI\n2DYzl0TEtsAjA1pJqXvrAU9k5l5rWikidgY+Abw6Mx+PiHMpAQ1gJc0hQRuuZnP1A8dkSVonRMQm\nEbFZ4znwJuBWyi29plWrTQMuGZgaSqt1DXBoNb5qM+BtwDPAPRFxOEAUL1/NtpsDfwCWR8Q44OCW\nZfcCr6qe/20Xx34K2Kz9U1BXDFnDTETMA/4XeElEPBgR0we6TlIfGQcsiIibgeuByzPzx8Ac4I0R\ncRfwhqosDQqZeSNwAXAz8CPKvX8BjgSmV9/n2ygXcHTe9mbgV8AdwH8Bv2hZfArwpWoqk+e7OPx3\ngH+opoNw4HsNnPFdkiSpBrZkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5Yk\nSVINDFmSJEk1+P/oamwhZVnLQQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10ee30410>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10ee30350>"
]
},
{
"output_type": "display_data",
"png": 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u0IiYHhHT586dW9RYSZKkvqapMBURy5CD1MUppSurl1+MiFHV8FHAS21Nm1I6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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10ef24310>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10f00bb10>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10f095bd0>"
]
},
{
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1k4OABye6CKnD3M81Fbifj79D2+lUE6buBZ7X0j6kjNtJZq4AVlSsRxUioi8z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\nkiRJFf4/M9L8uF9QNpEAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10f0d3610>"
]
},
{
"output_type": "display_data",
"png": 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Aj3+8Eq5uuQWOPDIPx7DbbvDCCzBqVGWAUQ05bz37Vp57ZWVNt1nLYTk2Hz2K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\nSZKkEgxTkiRJJRimJEmSSvj/6lh7TP/ZZZMAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10f1890d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10f1c3ed0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10f15cd90>"
]
},
{
"output_type": "display_data",
"png": 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AKcB1mbkfcF3VliRJGlS6HbIi4oXA0cB0gMx8LjOXAhOAmdVhM4GTe1qkJElS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\nGe0u51MAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10f2faf90>"
]
},
{
"output_type": "display_data",
"png": 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m5wN3AaOAA4BJZbVJQHNFxwOAC1J2E7BGRAy+qy5Kg1XrOJ3XvrZ9lxS0v87Y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\nFRiyJEmSKjBkSZIkVWDIkiRJquD/A5612WTNIDCUAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10f3b7f90>"
]
},
{
"output_type": "display_data",
"png": 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JUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJU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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10f44f650>"
]
},
{
"output_type": "display_data",
"png": 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JqoEhS5IkqQaGLEmSpBoYsiRJkmpgyJIkSaqBIUuSJKkG7YasiDg3IuZHxF0t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\nYMiSJEmqgSFLkiSpBv8fcCsf5NPHt/QAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10f526a90>"
]
},
{
"output_type": "display_data",
"png": 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q77prugZiafzYgQdKv/ylNHBg9WsD0CURpgB0jMGD0wSUkvTxj0tvvintuWdq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\nWFNqw63NV/8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10f4d5b90>"
]
},
{
"output_type": "display_data",
"png": 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xpW5oaP3a//736Zpa3H9/6+133dW6Hju2df3b37aur7uudX355a3rX/2qdf3T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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10f4f6bd0>"
]
},
{
"output_type": "display_data",
"png": 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XAgfXK0hJkoD8IOVHH80JFsBJJ8E228DSpY2NS33aSnVAj4jRwE7APcCIlNLc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\n/hgAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10f5d7290>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10f753090>"
]
},
{
"output_type": "display_data",
"png": 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aazVu3DiCFoAWR8gC0K7ccsstGjt2rEaNGqU99thDo0aN0tixY3XLLbfUumkA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\neEsMECgAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10f775650>"
]
},
{
"output_type": "display_data",
"png": 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gAfCNF3LgzDw3M9sys23IkCEvZFNJkqRer1MhKyIGUgLWhZn5M4DMfDgzl2fm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\nDQxZkiRJNfj/4Bw0+1ffMXgAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10f883310>"
]
},
{
"output_type": "display_data",
"png": 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VYMiSJEmqwJAlSZJUgSFLkiSpAkOWJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBk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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10f91ee90>"
]
},
{
"output_type": "display_data",
"png": 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wLHB4j7ZYkiSpH+gyZGXmJCCWMnnPJcyfwJEr2S5JkqR+zW98lyRJqsCQJUmS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\nCgxZkiRJFRiyJEmSKjBkSZIkVfD/AXFUfiabgOVMAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10f9c5150>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10fa0b250>"
]
},
{
"output_type": "display_data",
"png": 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qq64qPSbtv0W3tJRbE01NpT733NK79eUvl/r000svy1e/WuqTTio9dF//eqm/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\nSZJq8P8BIrW/Rfz3i9AAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10fb36550>"
]
},
{
"output_type": "display_data",
"png": 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g7JcYS6OWIUvS6PH003Djjd0h6YYb4IADcniCfGrvxBPhj3/M9X77wUc/2v0J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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10fb82c50>"
]
},
{
"output_type": "display_data",
"png": 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D7LZbOSIlSd1kmJK0fps5E97yFvjhD0v7fe+D666Dv/qr5tYlab1hmJK0/pkw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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10fc588d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10fcba8d0>"
]
}
],
"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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