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network.buffer.cache.size
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network.buffer.cache.size.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",
"mimetype": "text/x-python",
"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",
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" .dataframe tbody tr th:only-of-type {\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>458.888889</td>\n",
" <td>20.319803</td>\n",
" <td>433.333333</td>\n",
" <td>444.444444</td>\n",
" <td>461.111111</td>\n",
" <td>466.666667</td>\n",
" <td>488.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>428.888889</td>\n",
" <td>29.256915</td>\n",
" <td>400.000000</td>\n",
" <td>402.777778</td>\n",
" <td>422.222222</td>\n",
" <td>450.000000</td>\n",
" <td>477.777778</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 10.0 458.888889 20.319803 433.333333 444.444444 461.111111 \n",
"65536 10.0 428.888889 29.256915 400.000000 402.777778 422.222222 \n",
"default 10.0 431.111111 33.044012 400.000000 405.555556 422.222222 \n",
"\n",
" 75% max \n",
"1 466.666667 488.888889 \n",
"65536 450.000000 477.777778 \n",
"default 433.333333 488.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_select_search_suggestion'"
]
},
{
"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>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>65536</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>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 9.444444 6.953698 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 7.222222 5.270463 5.555556 5.555556 5.555556 5.555556 \n",
"\n",
" max \n",
"1 22.222222 \n",
"65536 22.222222 \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",
" .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>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>65536</th>\n",
" <td>10.0</td>\n",
" <td>16.666667</td>\n",
" <td>5.856070</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>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 7.777778 5.367177 5.555556 5.555556 5.555556 \n",
"65536 10.0 16.666667 5.856070 11.111111 11.111111 16.666667 \n",
"default 10.0 12.777778 6.953698 5.555556 6.944444 11.111111 \n",
"\n",
" 75% max \n",
"1 5.555556 22.222222 \n",
"65536 22.222222 22.222222 \n",
"default 19.444444 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_close_chat_tab'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\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>60.000000</td>\n",
" <td>15.887119</td>\n",
" <td>33.333333</td>\n",
" <td>47.222222</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>46.666667</td>\n",
" <td>13.658584</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>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 60.000000 15.887119 33.333333 47.222222 66.666667 \n",
"65536 10.0 46.666667 13.658584 33.333333 33.333333 44.444444 \n",
"default 10.0 64.444444 16.396995 44.444444 50.000000 66.666667 \n",
"\n",
" 75% max \n",
"1 66.666667 88.888889 \n",
"65536 55.555556 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>177.777778</td>\n",
" <td>61.974817</td>\n",
" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>144.444444</td>\n",
" <td>238.888889</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>220.000000</td>\n",
" <td>73.292170</td>\n",
" <td>133.333333</td>\n",
" <td>155.555556</td>\n",
" <td>216.666667</td>\n",
" <td>286.111111</td>\n",
" <td>300.000000</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 10.0 177.777778 61.974817 133.333333 133.333333 144.444444 \n",
"65536 10.0 220.000000 73.292170 133.333333 155.555556 216.666667 \n",
"default 10.0 177.777778 59.027324 111.111111 133.333333 144.444444 \n",
"\n",
" 75% max \n",
"1 238.888889 266.666667 \n",
"65536 286.111111 300.000000 \n",
"default 233.333333 266.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab_emoji'"
]
},
{
"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>73.333333</td>\n",
" <td>13.042087</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>68.888889</td>\n",
" <td>8.764563</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>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 73.333333 13.042087 55.555556 66.666667 66.666667 \n",
"65536 10.0 68.888889 8.764563 55.555556 66.666667 66.666667 \n",
"default 10.0 85.555556 12.883353 66.666667 77.777778 83.333333 \n",
"\n",
" 75% max \n",
"1 77.777778 100.000000 \n",
"65536 66.666667 88.888889 \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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" <td>91.111111</td>\n",
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" <td>77.777778</td>\n",
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" <td>80.555556</td>\n",
" <td>88.888889</td>\n",
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" count mean std min 25% 50% \\\n",
"1 10.0 84.444444 10.734353 66.666667 77.777778 83.333333 \n",
"65536 10.0 91.111111 14.628458 66.666667 77.777778 100.000000 \n",
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"\n",
" 75% max \n",
"1 88.888889 100.000000 \n",
"65536 100.000000 111.111111 \n",
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" count mean std min 25% 50% \\\n",
"1 10.0 44.444444 10.475656 33.333333 33.333333 44.444444 \n",
"65536 10.0 48.888889 9.369712 33.333333 44.444444 55.555556 \n",
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"\n",
" 75% max \n",
"1 55.555556 55.555556 \n",
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" count mean std min 25% 50% \\\n",
"1 10.0 40.000000 11.944086 33.333333 33.333333 33.333333 \n",
"65536 10.0 34.444444 9.728834 11.111111 33.333333 33.333333 \n",
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"\n",
" 75% max \n",
"1 41.666667 66.666667 \n",
"65536 41.666667 44.444444 \n",
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" <th>65536</th>\n",
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" <td>27.777778</td>\n",
" <td>7.856742</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
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" count mean std min 25% 50% \\\n",
"1 10.0 46.666667 10.210406 33.333333 36.111111 50.000000 \n",
"65536 10.0 40.000000 14.998857 22.222222 25.000000 44.444444 \n",
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"\n",
" 75% max \n",
"1 55.555556 55.555556 \n",
"65536 44.444444 66.666667 \n",
"default 33.333333 44.444444 "
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" <th>65536</th>\n",
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" <td>28.888889</td>\n",
" <td>18.848905</td>\n",
" <td>5.555556</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>9.0</td>\n",
" <td>18.518519</td>\n",
" <td>10.758287</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
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" count mean std min 25% 50% \\\n",
"1 10.0 32.222222 13.302433 11.111111 33.333333 33.333333 \n",
"65536 10.0 28.888889 18.848905 5.555556 13.888889 33.333333 \n",
"default 9.0 18.518519 10.758287 5.555556 11.111111 22.222222 \n",
"\n",
" 75% max \n",
"1 33.333333 55.555556 \n",
"65536 33.333333 66.666667 \n",
"default 22.222222 33.333333 "
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" <td>44.444444</td>\n",
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"1 2.0 33.333333 31.426968 11.111111 22.222222 33.333333 \n",
"65536 10.0 40.000000 9.369712 33.333333 33.333333 33.333333 \n",
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"\n",
" 75% max \n",
"1 44.444444 55.555556 \n",
"65536 44.444444 55.555556 \n",
"default 44.444444 55.555556 "
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"65536 10.0 65.555556 8.198498 44.444444 66.666667 66.666667 \n",
"default 10.0 60.000000 9.369712 44.444444 55.555556 66.666667 \n",
"\n",
" 75% max \n",
"65536 66.666667 77.777778 \n",
"default 66.666667 66.666667 "
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"1 10.0 180.000000 26.604867 133.333333 166.666667 177.777778 \n",
"65536 10.0 206.666667 9.369712 200.000000 200.000000 200.000000 \n",
"default 10.0 212.222222 58.666012 166.666667 177.777778 200.000000 \n",
"\n",
" 75% max \n",
"1 200.000000 222.222222 \n",
"65536 211.111111 222.222222 \n",
"default 222.222222 366.666667 "
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" <td>366.666667</td>\n",
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"65536 10.0 228.888889 39.958827 200.000000 202.777778 216.666667 \n",
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"\n",
" 75% max \n",
"1 213.888889 366.666667 \n",
"65536 233.333333 333.333333 \n",
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"\n",
" 75% max \n",
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" 75% max \n",
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"default 10.0 65.555556 8.198498 44.444444 66.666667 66.666667 \n",
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" 75% max \n",
"65536 100.000000 166.666667 \n",
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" count mean std min 25% 50% \\\n",
"65536 10.0 20.000000 11.172669 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",
"65536 30.555556 33.333333 \n",
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"1 10.0 5.555556 0.000000 5.555556 5.555556 5.555556 5.555556 \n",
"65536 10.0 8.888889 7.027284 5.555556 5.555556 5.555556 5.555556 \n",
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"\n",
" max \n",
"1 5.555556 \n",
"65536 22.222222 \n",
"default 22.222222 "
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"default 10.0 908.888889 380.592275 222.222222 977.777778 994.444444 \n",
"\n",
" 75% max \n",
"65536 975.000000 1055.555556 \n",
"default 1033.333333 1477.777778 "
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"65536 10.0 46.666667 13.658584 33.333333 33.333333 44.444444 \n",
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" <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 10.0 416.666667 56.716270 366.666667 377.777778 400.000000 \n",
"65536 10.0 364.444444 14.628458 344.444444 355.555556 366.666667 \n",
"default 10.0 375.555556 26.604867 344.444444 355.555556 372.222222 \n",
"\n",
" 75% max \n",
"1 430.555556 544.444444 \n",
"65536 375.000000 388.888889 \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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" .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>30.000000</td>\n",
" <td>12.883353</td>\n",
" <td>11.111111</td>\n",
" <td>25.000000</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>16.666667</td>\n",
" <td>10.475656</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 30.000000 12.883353 11.111111 25.000000 33.333333 \n",
"65536 10.0 16.666667 10.475656 5.555556 11.111111 11.111111 \n",
"\n",
" 75% max \n",
"1 33.333333 55.555556 \n",
"65536 22.222222 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",
" }\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>564.444444</td>\n",
" <td>466.425570</td>\n",
" <td>233.333333</td>\n",
" <td>291.666667</td>\n",
" <td>327.777778</td>\n",
" <td>650.000000</td>\n",
" <td>1700.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>286.666667</td>\n",
" <td>29.068766</td>\n",
" <td>244.444444</td>\n",
" <td>266.666667</td>\n",
" <td>277.777778</td>\n",
" <td>308.333333</td>\n",
" <td>333.333333</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 10.0 564.444444 466.425570 233.333333 291.666667 327.777778 \n",
"65536 10.0 286.666667 29.068766 244.444444 266.666667 277.777778 \n",
"default 10.0 290.000000 15.225781 266.666667 280.555556 288.888889 \n",
"\n",
" 75% max \n",
"1 650.000000 1700.000000 \n",
"65536 308.333333 333.333333 \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>34.444444</td>\n",
" <td>19.910637</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>41.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>15.000000</td>\n",
" <td>13.870358</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>19.444444</td>\n",
" <td>44.444444</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 34.444444 19.910637 11.111111 22.222222 33.333333 \n",
"65536 10.0 15.000000 13.870358 5.555556 5.555556 8.333333 \n",
"default 10.0 37.222222 62.638051 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"1 41.666667 66.666667 \n",
"65536 19.444444 44.444444 \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>65536</th>\n",
" <td>10.0</td>\n",
" <td>13.333333</td>\n",
" <td>6.521043</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>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",
"65536 10.0 13.333333 6.521043 5.555556 11.111111 11.111111 \n",
"default 10.0 13.888889 11.490439 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"65536 19.444444 22.222222 \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>12.222222</td>\n",
" <td>8.606630</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</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>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",
" <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% 75% \\\n",
"1 10.0 12.222222 8.606630 5.555556 5.555556 5.555556 22.222222 \n",
"65536 10.0 10.555556 6.651217 5.555556 5.555556 8.333333 11.111111 \n",
"default 10.0 10.555556 6.651217 5.555556 5.555556 8.333333 11.111111 \n",
"\n",
" max \n",
"1 22.222222 \n",
"65536 22.222222 \n",
"default 22.222222 "
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# show plot\n",
"plt.show()"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
"png": 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k8B8RsTNwFvCdlNJWwBzg8DL/4cCcMv47ZT5JkqQ+pd2QlbIXS3H18kjAe4Gr\nyvgLgX3K8N6lTJm+W0REp9VYkiSpF+hQn6yI6BcRE4GZwK3Ao8DclNLiMss0YGgZHgpMBSjT5wEb\ndmalJUmSeroOhayU0isppZHAMOAtwOtXdsURMToixkfE+FmzZq3s4iRJknqU5bq6MKU0F/gd8DZg\nUET0L5OGAdPL8HRgOECZvh4wu41lnZdSGpVSGjV48OAVrL4kSVLP1JGrCwdHxKAyvCawO/AAOWzt\nW2Y7BLiuDF9fypTpv00ppc6stCRJUk/Xv/1Z2AS4MCL6kUPZlSmlGyLifuDyiDgN+BtwQZn/AuDi\niJgMPAccWKHekiRJPVq7ISuldC+wYxvjHyP3z2o9fiGwX6fUTpIkqZfyju+SJEkVGLIkSZIqMGRJ\nkiRVYMiSJEmqwJAlSZJUgSFLkiSpAkOWJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIkVWDIkiRJ\nqsCQJUmSVIEhS5IkqQJDliRJUgWGLEmSpAoMWZIkSRUYsiRJkiowZEmSJFVgyJIkSarAkCVJklSB\nIUuSJKkCQ5YkSVIFhixJkqQKDFmSJEkVGLIkSZIqMGRJkiRVYMiSJEmqwJAlSZJUgSFLkiSpAkOW\nJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIkVWDIkiRJqsCQJUmSVIEhS5IkqQJDliRJUgWGLEmS\npAoMWZIkSRUYsiRJkipoN2RFxPCI+F1E3B8RkyLic2X8BhFxa0Q8Uv6uX8ZHRHw/IiZHxL0RsVPt\njZAkSeppOnImazHwxZTS9sDOwNERsT1wAnBbSmlr4LZSBtgD2Lo8RgPndHqtJUmSerh2Q1ZK6emU\n0l/L8AvAA8BQYG/gwjLbhcA+ZXhv4KKU3QkMiohNOr3mkiRJPdhy9cmKiBHAjsBdwJCU0tNl0gxg\nSBkeCkxtetq0Mk6SJKnP6HDIioh1gF8An08pPd88LaWUgLQ8K46I0RExPiLGz5o1a3meKkmS1ON1\nKGRFxOrkgHVpSunqMvqZRjNg+TuzjJ8ODG96+rAybgkppfNSSqNSSqMGDx68ovWXJEnqkTpydWEA\nFwAPpJS+3TTpeuCQMnwIcF3T+E+Wqwx3BuY1NStKkiT1Cf07MM87gIOB+yJiYhl3InAmcGVEHA48\nAexfpv0K2BOYDMwHDuvUGkuSJPUC7YaslNIfgFjK5N3amD8BR69kvSRJkno17/guSZJUgSFLkiSp\nAkOWJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIkVWDIkiRJqsCQJUmSVIEhS5IkqQJDliRJUgWG\nLEmSpAoMWZIkSRUYsiRJkir1OaJ2AAAHuUlEQVQwZEmSJFVgyJIkSarAkCVJklSBIUuSJKkCQ5Yk\nSVIFhixJkqQKDFmSJEkVGLIkSZIqMGRJkiRVYMiSJEmqwJAlSZJUgSFLkiSpAkOWJElSBYYsSZKk\nCgxZkiRJFRiyJEmSKjBkSZIkVWDIkiRJqsCQJUmSVIEhS5IkqQJDliRJUgWGLEmSpAoMWZIkSRUY\nsiRJkiowZEmSJFVgyJIkSarAkCVJklRBuyErIn4SETMj4u9N4zaIiFsj4pHyd/0yPiLi+xExOSLu\njYidalZekiSpp+rImayfAf/RatwJwG0ppa2B20oZYA9g6/IYDZzTOdWUJEnqXdoNWSmlO4DnWo3e\nG7iwDF8I7NM0/qKU3QkMiohNOquykiRJvcWK9skaklJ6ugzPAIaU4aHA1Kb5ppVxkiRJfcpKd3xP\nKSUgLe/zImJ0RIyPiPGzZs1a2WpIkiT1KCsasp5pNAOWvzPL+OnA8Kb5hpVx/yKldF5KaVRKadTg\nwYNXsBqSJEk904qGrOuBQ8rwIcB1TeM/Wa4y3BmY19SsKEmS1Gf0b2+GiBgHvBvYKCKmAV8DzgSu\njIjDgSeA/cvsvwL2BCYD84HDKtRZkiSpx2s3ZKWUPraUSbu1MW8Cjl7ZSkmSJPV23vFdkiSpAkOW\nJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBkSZIkVWDIkiRJqsCQJUmSVIEhS5IkqQJDliRJUgWGLEmS\npAoMWZIkSRUYsiRJkiowZEmSJFVgyJIkSarAkCVJklSBIUuSJKkCQ5YkSVIFhixJkqQKDFmSJEkV\nGLIkSZIqMGRJkiRVYMiSJEmqwJAlSZJUgSFLkiSpAkOWJElSBYYsSZKkCgxZkiRJFRiyJEmSKjBk\nSZIkVWDIkiRJqsCQJUmSVIEhS5IkqQJDliRJUgWGLEmSpAoMWZIkSRUYsiRJkiowZEmSJFVgyJIk\nSarAkCVJklSBIUuSJKkCQ5YkSVIFVUJWRPxHRDwUEZMj4oQa65AkSerJOj1kRUQ/4IfAHsD2wMci\nYvvOXo8kSVJPVuNM1luAySmlx1JK/wAuB/ausB5JkqQeq0bIGgpMbSpPK+MkSZL6jP7dteKIGA2M\nLsUXI+Kh7qpLH7UR8Gx3V0KqzONcfYHHedfbvCMz1QhZ04HhTeVhZdwSUkrnAedVWL86ICLGp5RG\ndXc9pJo8ztUXeJz3XDWaC/8CbB0RW0TEGsCBwPUV1iNJktRjdfqZrJTS4oj4DHAz0A/4SUppUmev\nR5IkqSer0icrpfQr4Fc1lq1OY1Ot+gKPc/UFHuc9VKSUursOkiRJqxx/VkeSJKkCQ1YfExE/iYiZ\nEfH37q6L1FERMSgiroqIByPigYh4W0ScEhHTI2JieexZ5h0REQuaxo9tWs6vI+KeiJgUEWPLL1Q0\nph1Tlj8pIr7RHdspleP6S8uYPjgi7oqIv0XEriuw/EMj4gdleB9/kaWubrtPlrrNz4AfABd1cz2k\n5fE94NcppX3LVctrAR8AvpNS+mYb8z+aUhrZxvj9U0rPR0QAVwH7AZdHxHvIv0yxQ0ppUURsXGk7\npJW1G3BfSumITljWPsANwP2dsCy1wTNZfUxK6Q7gue6uh9RREbEe8E7gAoCU0j9SSnNXZFkppefL\nYH9gDaDRKfXTwJkppUVlvpkrVWlpOUTEf0fEwxHxB2DbMm7LcuZ1QkT8PiJeHxEjgW8Ae5eztGtG\nxDkRMb6cgT21aZlTImKjMjwqIm5vtc63Ax8G/l9Z1pZdtb19iSFLUk+3BTAL+GlpIvlxRKxdpn0m\nIu4tzeDrNz+nzPt/rZtUIuJmYCbwAvlsFsA2wK6lGeb/IuLNlbdJAiAi3kS+n+RIYE+gceydBxyT\nUnoT8CXgRymlicDJwBUppZEppQXAf5cbkf478K6I+PeOrDel9CfyPSyPK8t6tFM3TIAhS1LP1x/Y\nCTgnpbQj8BJwAnAOsCX5y+lp4Ftl/qeBzcq8xwKXRcRrGgtLKX0A2AQYALy3aR0bADsDxwFXliZF\nqbZdgWtSSvPLmdbrgYHA24GfR8RE4FzyMduW/SPir8DfgDcA9rHqQQxZknq6acC0lNJdpXwVsFNK\n6ZmU0isppVeB84G3AKSUFqWUZpfhCcCj5DNV/5RSWghcR+6H1VjH1Sm7G3iV/HtwUndYDZhbzjA1\nHtu1nikitiCf5dotpfTvwI3kgAawmJbv+IGtn6uuYciS1KOllGYAUyNi2zJqN+D+iGj+z/4jwN/h\nn1df9SvDrwO2Bh6LiHUaz4mI/sBewIPl+dcC7ynTtiH31/IHd9UV7gD2Kf2r1gU+BMwHHo+I/QAi\n26GN576GfGZ3XkQMAfZomjYFeFMZ/s+lrPsFYN2V3wQtjSGrj4mIccCfgW0jYlpEHN7ddZI64Bjg\n0oi4l9w8eAbwjYi4r4x7D/CFMu87gXtLM8tVwJiU0nPA2sD1Zf6J5H5Zjds7/AR4Xbm1yeXAIck7\nNasLpJT+ClwB3APcRP79X4BPAIdHxD3AJFrOujY/9x5yM+GDwGXAH5smnwp8LyLGA68sZfWXA8eV\n/ot2fK/AO75LkiRV4JksSZKkCgxZkiRJFRiyJEmSKjBkSZIkVWDIkiRJqsCQJUmSVIEhS5IkqQJD\nliRJUgX/H8CHlLBJLvMXAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10d3d06d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111092110>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x111453710>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111532c90>"
]
},
{
"output_type": "display_data",
"png": 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2UqomTNttl6vt770XeveGe+5Z9MbLAwfWJ0ZJXdtyy8E221TLp5wC+++/6GfX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\nB/PVBCDRo/0TAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11158c250>"
]
},
{
"output_type": "display_data",
"png": 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33TXH1lBJGo6UUtVf++23X5rsdvzClaN7wH33Tenoo3vjurqU3vve3njXXVNq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\nsx8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x11155fdd0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x11166a790>"
]
},
{
"output_type": "display_data",
"png": 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MU5IkSTUYpiRJkmowTEmSJNVgmJIkSarBMCVJklSDYUqSJKkGw5QkSVINhilJ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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x111743bd0>"
]
},
{
"output_type": "display_data",
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"text": [
"<matplotlib.figure.Figure at 0x1117e87d0>"
]
},
{
"output_type": "display_data",
"png": 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stVYe790b3vUuGD06l2fMgJkzGxeb1EnZZkqS1LwxY+Caa6rlb3wDHnwQ/vWv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\n3gkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x11183fa50>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1118d5e50>"
]
},
{
"output_type": "display_data",
"png": 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JNRimJEmSajBMSZIk1dBlmIqI4RHxt4i4LSKmR8Q3qtc3jogbI+KBiDgnIlbr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\nI+2h0O2XAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x111a0b3d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111a02fd0>"
]
},
{
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1XWYZZqDV/LZkCfz3f5fEB0r/oz33LCN7Q7m7DjoeVLtoUXkw6lVXlXiVVcpD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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x111b31d90>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111b3a550>"
]
},
{
"output_type": "display_data",
"png": 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4E3BeRBwOPAwc0PdmSpIkDS69DlmZ+SCw2UKm/wXYpS+NkiRJGuy847skSVIF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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x111c1b250>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111c68790>"
]
},
{
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MGlUmU23Yait47LHeqxhfegn237+ELYAbb4QDDihXSb75zfCHP5TpKNrbnfdL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\nJElSTf4/6otblmUrYoIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x111da1f90>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111d99810>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111e3da10>"
]
},
{
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YRGwN7A/MAsjMJzPzQeAQYHbVbDZwaPX8EOBHWVwObBMRzx9wzyVJkkaowYyA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\njyop8rSC5QAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x111cfacd0>"
]
},
{
"output_type": "display_data",
"png": 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SJNXQa5iKiJERcXNE3BERd0XEMdX69SLipoi4PyLOi4il219dSZKkztJKy9Sr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\n",
"text": [
"<matplotlib.figure.Figure at 0x111fc9ad0>"
]
},
{
"output_type": "display_data",
"png": 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AQ5YkSVIDDFmSJEkN6DRkRcTwiLgqIv4WEXdExGF1/VoRMTki7qk/16zrIyJO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\n1ID/D+UzRtjCpKBdAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x112077dd0>"
]
},
{
"output_type": "display_data",
"png": 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U28aNg512KtMvvgiHHw5velNpz5oFxxwDDz/cuvok1WKYkqS1aYst4NxzG+Hq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\nJKmG/w98p3Kdk7a1KAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x11207f410>"
]
},
{
"output_type": "display_data",
"png": 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fTpZbroT/++4r4X/ZZeHBB+Huu8svGv37w733wh13wCc+UdozeXL5heKgg0p7\nb7qp1DWmS1x3Xdn+yCNL+aqryvEan/1LLy3zUI89tpQvuKCcc8yYUv7lL8vns/Hv96c/LW0+6aRS\nPv308svJt75VyieeWP7tnnqqPb16gwGsD6i7V8Crw/qIZZeF4cPbyqutBjvu2FYeMgS+8IW28kYb\nzTv/bcst4W9/ayvvskv5EmnYZ58yp2+llUp5t93ghhtK2ALYZpvyRbT66qW82WblfI3tV18d1l23\nfIECPPlk+dLrX/23c+215YvqxBPbznnIIW0B7LTTysUVTz9dykcfDePHl8AHZWj2llvg+utL+eST\n4aGHSvCDcvuUZ55pG6a+9NISGj75yVK+5prSa9eY03jFFeXLe4cdSvnii0ug/mj1b+FXvyqv8a67\nth1/zTVhjz1K+XvfK6H34x8v5W9+s8xRbJzvK18pvZeNEH7EEWWoep99SvmAA8ocw0b5Yx8r++67\nL7z+enl/DjiglF96qWx7+OFl+8aVw1/8Ytvw9Xbbld7MvfYqoXibbeDUU0v7HnoIPvCBEpr33LME\nmK22Ku/nbruV8PL+95fA/NGPlvd9yy1Lj+gOO5R5jttsU4b0t922vJY77FDC9pZblgttdt21vD8t\nLWW/T3wCbr+9BOorrijzOe+7r/yCcNllJSw99FD5zIwfX8LS9OllSsCll8JRR5XnNXBg6cU95hh4\n7rkyxP6HP8DXvlaCX79+pXziiXDggSW0X3pp+aw1Atj48aW3txHALrsMfvObtgB25ZWlrhHArrmm\nhLZGALvxxhLoGgHs9ttLQGt44IHyeW+YMaOEUGDu8C+xMkumMqP1zh5uRd/hbSj0bwxgqs3cuaWn\nkKXgdivfeq2EtcZFKiNGlDD1jW+U8siRJTAcc0wpb7JJCQyf/WwJYFttVcoHHVR6U3beufRA7r13\nuW/fXnuV8u67lws9Dj64bL/DDmX+4Oc/D4ceWnqRnniihIlDDinD0TNmlN6agw8uw+DTp5fh7gMO\nKD2tjz4KP/tZGY5ef/1yNfT555cQNWxY6Q265JISGAcPLuU//amtx3Xq1BK4d9kF1lij7H/TTaVt\nq6xSjj95cgnDK65Yesfuv7+EwOWXL1c+P/xw6REbMKC0f/r08hr161ee38yZJcwts0zpfZs1q7Qt\norw+L71UAjOU12/27HJuKK/v3Lnzzu9cTJbU0QywB6yho7eh6HQAi4h1gPOANYEEzs7MH0bECcCn\ngZnVpl/LzCuqfb4KjAbmAJ/LzD+3dx4DWP0MYOoJi/1z98ILpQdsjTVKeerU8nPYsPLzscfKl3ej\nh++pp8qXe6OH77nnSu/dW948cB4mAAAHGklEQVRSyq+9Vrbv3/5Agv+m1FP87NWvjvuAvQ58KTNv\njYiVgUkRMaFa9/3MPH2+Bm0E7ANsDKwN/CUiNsjMOV1og6QlSM/0Dty12M+wRM11VI/oyn3A4jvt\nb7MgvX2ErK/rdADLzBnAjGr5+Yi4Fxi8iF12By7IzFeBhyNiCrA5cENn2yBpyeFcR2nhDENLnm65\nEWtEDAfeCzTusHlkRNwREWMjouq/ZzDwaNNu01hIYIuIQyOiNSJaZ86cuaBNJEmS+qwuB7CIWAm4\nGPhCZj4HnAmsB4yi9JCd8WaPmZlnZ2ZLZrYMGjSoq02UtATr7B/UnvqdXfxj3JJ6TJduQxERAyjh\n6/zM/D1AZj7RtP5nwB+r4nRgnabdh1R1ktRpDs1I6os6HcCi/Br4C+DezPxeU/1a1fwwgD1pm+E6\nHvhNRHyPMgl/feDmzp5f7XPSpiRJvVNXesDeD3wKuDMiGrfr/hqwb0SMotya4hHgMwCZeXdEXAjc\nQ7mC8givgFy8DEOSJPVOXbkKciKwoC6WKxaxzynAKQtbL0mStDTolqsgJUmS1HEGMEmSpJoZwCRJ\nkmpmAJMkSaqZAUySJKlmBjBJkqSaGcAkSZJqZgCTJEmqmQFMkiSpZgYwSZKkmhnAJEmSamYAkyRJ\nqpkBTJIkqWYGMEmSpJoZwCRJkmpmAJMkSaqZAUySJKlmBjBJkqSaGcAkSZJqZgCTJEmqmQFMkiSp\nZgYwSZKkmhnAJEmSamYAkyRJqpkBTJIkqWYGMEmSpJoZwCRJkmpWewCLiB0j4v6ImBIRx9V9fkmS\npJ5WawCLiH7AT4CdgI2AfSNiozrbIEmS1NPq7gHbHJiSmQ9l5mvABcDuNbdBkiSpR9UdwAYDjzaV\np1V1kiRJS43+Pd2ABYmIQ4FDq+ILEXF/T7ZnKTQQeKqnGyEtZn7OtTTwc16/YR3ZqO4ANh1Yp6k8\npKqbR2aeDZxdV6M0r4hozcyWnm6HtDj5OdfSwM9571X3EOQtwPoRsW5ELAvsA4yvuQ2SJEk9qtYe\nsMx8PSKOBP4M9APGZubddbZBkiSpp9U+BywzrwCuqPu8elMc/tXSwM+5lgZ+znupyMyeboMkSdJS\nxT9FJEmSVDMDmN4QEWMj4smIuKun2yJ1RESsFhG/i4j7IuLeiPjPiDghIqZHxOTqsXO17fCIeLmp\n/qym4/wpIm6PiLsj4qzqr3Y01h1VHf/uiPhuTzxPqfpcH7OI9YMi4qaIuC0itu7E8Q+MiP+plvfw\nr9Qsfr3yPmDqMecA/wOc18PtkDrqh8CfMvMT1ZXVbwF2AL6fmacvYPsHM3PUAur3ysznIiKA3wGf\nBC6IiG0pf61jk8x8NSLetpieh9RV2wF3ZuYh3XCsPYA/Avd0w7G0EPaA6Q2ZeR3wTE+3Q+qIiFgV\n+ADwC4DMfC0zn+3MsTLzuWqxP7As0Jgcezhwama+Wm33ZJcaLb0JETEmIv4ZEROBDau69aoe20kR\n8feIeFdEjAK+C+xe9e6uEBFnRkRr1XN7YtMxH4mIgdVyS0T8bb5zbgnsBpxWHWu9up7v0sYAJqmv\nWheYCfyyGnb5eUSsWK07MiLuqIbVV2/ep9r22vmHaSLiz8CTwPOUXjCADYCtq6GdayPifYv5OUkA\nRMRmlHtljgJ2BhqfvbOBozJzM+AY4KeZORn4BvDbzByVmS8DY6obsL4H+GBEvKcj583M6yn35/xy\ndawHu/WJ6Q0GMEl9VX9gU+DMzHwv8CJwHHAmsB7li2sGcEa1/QxgaLXtF4HfRMQqjYNl5g7AWsBy\nwIeazrEGsAXwZeDCaphSWty2Bi7JzJeqHtrxwPLAlsBFETEZ+F/KZ3ZB9oqIW4HbgI0B53T1MgYw\nSX3VNGBaZt5UlX8HbJqZT2TmnMycC/wM2BwgM1/NzKer5UnAg5Qerjdk5ivApZR5X41z/D6Lm4G5\nlL+tJ/WEZYBnq56pxmPE/BtFxLqU3rHtMvM9wOWU8AbwOm3f/cvPv6/qYwCT1Cdl5uPAoxGxYVW1\nHXBPRDT3COwJ3AVvXCXWr1p+B7A+8FBErNTYJyL6Ax8F7qv2/wOwbbVuA8r8MP+wsepwHbBHNZ9r\nZWBX4CXg4Yj4JEAUmyxg31UoPcKzImJNYKemdY8Am1XLH1/IuZ8HVu76U9CiGMD0hogYB9wAbBgR\n0yJidE+3SWrHUcD5EXEHZcjxW8B3I+LOqm5b4Ohq2w8Ad1RDN78DDsvMZ4AVgfHV9pMp88Aat6gY\nC7yjujXLBcAB6d2rVYPMvBX4LXA7cCXlbykD7AeMjojbgbtp661t3vd2ytDjfcBvgH80rT4R+GFE\ntAJzFnL6C4AvV/MlnYS/mHgnfEmSpJrZAyZJklQzA5gkSVLNDGCSJEk1M4BJkiTVzAAmSZJUMwOY\nJElSzQxgkiRJNTOASZIk1ez/AwplyhrXf+6jAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x112128810>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x112173b50>"
]
},
{
"output_type": "display_data",
"png": 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S+Kl8affw4Sn8VCZsHTUqfUl3Zf24cakVqxKmDjssBabK1Ynrr99yWosHHqh+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\n6niO3yN+eDIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1122022d0>"
]
},
{
"output_type": "display_data",
"png": 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OkiRpyOl1mIqIDSLiWa3HwJ7ATX3VMEmSpOGgpptvM+AnUe7TNhL4QWb+ok9a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\nkiRJFQxTkiRJFf4/gFkD4a2Zlb8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1122b2b90>"
]
}
],
"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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