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dom.idle_period.throttled_length.ipynb
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"metadata": {
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"display_name": "Python 2",
"language": "python",
"name": "python2"
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"name": "ipython",
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"file_extension": ".py",
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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'"
]
},
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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>417.777778</td>\n",
" <td>15.887119</td>\n",
" <td>400.000000</td>\n",
" <td>402.777778</td>\n",
" <td>416.666667</td>\n",
" <td>430.555556</td>\n",
" <td>444.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>446.666667</td>\n",
" <td>25.552872</td>\n",
" <td>422.222222</td>\n",
" <td>433.333333</td>\n",
" <td>433.333333</td>\n",
" <td>458.333333</td>\n",
" <td>500.000000</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 10.0 417.777778 15.887119 400.000000 402.777778 416.666667 \n",
"65536 10.0 446.666667 25.552872 422.222222 433.333333 433.333333 \n",
"default 10.0 431.111111 33.044012 400.000000 405.555556 422.222222 \n",
"\n",
" 75% max \n",
"1 430.555556 444.444444 \n",
"65536 458.333333 500.000000 \n",
"default 433.333333 488.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_select_search_suggestion'"
]
},
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>7.222222</td>\n",
" <td>2.683588</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>10.000000</td>\n",
" <td>6.829292</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</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>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 2.683588 5.555556 5.555556 5.555556 9.722222 \n",
"65536 10.0 10.000000 6.829292 5.555556 5.555556 5.555556 11.111111 \n",
"default 10.0 7.222222 5.270463 5.555556 5.555556 5.555556 5.555556 \n",
"\n",
" max \n",
"1 11.111111 \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",
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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>16.111111</td>\n",
" <td>8.466022</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>65536</th>\n",
" <td>10.0</td>\n",
" <td>17.222222</td>\n",
" <td>8.466022</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>16.666667</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>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 16.111111 8.466022 5.555556 11.111111 11.111111 \n",
"65536 10.0 17.222222 8.466022 5.555556 11.111111 16.666667 \n",
"default 10.0 12.777778 6.953698 5.555556 6.944444 11.111111 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"65536 22.222222 33.333333 \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",
" .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>54.444444</td>\n",
" <td>17.723683</td>\n",
" <td>22.222222</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>65536</th>\n",
" <td>10.0</td>\n",
" <td>65.555556</td>\n",
" <td>3.513642</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>64.444444</td>\n",
" <td>16.396995</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 54.444444 17.723683 22.222222 44.444444 61.111111 \n",
"65536 10.0 65.555556 3.513642 55.555556 66.666667 66.666667 \n",
"default 10.0 64.444444 16.396995 44.444444 50.000000 66.666667 \n",
"\n",
" 75% max \n",
"1 66.666667 77.777778 \n",
"65536 66.666667 66.666667 \n",
"default 66.666667 88.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab'"
]
},
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"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>210.000000</td>\n",
" <td>84.205314</td>\n",
" <td>122.222222</td>\n",
" <td>133.333333</td>\n",
" <td>205.555556</td>\n",
" <td>286.111111</td>\n",
" <td>311.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>191.111111</td>\n",
" <td>61.485944</td>\n",
" <td>144.444444</td>\n",
" <td>155.555556</td>\n",
" <td>166.666667</td>\n",
" <td>183.333333</td>\n",
" <td>311.111111</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 10.0 210.000000 84.205314 122.222222 133.333333 205.555556 \n",
"65536 10.0 191.111111 61.485944 144.444444 155.555556 166.666667 \n",
"default 10.0 177.777778 59.027324 111.111111 133.333333 144.444444 \n",
"\n",
" 75% max \n",
"1 286.111111 311.111111 \n",
"65536 183.333333 311.111111 \n",
"default 233.333333 266.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab_emoji'"
]
},
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" <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>72.222222</td>\n",
" <td>14.103284</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>86.111111</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>71.111111</td>\n",
" <td>13.042087</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>83.333333</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 72.222222 14.103284 44.444444 66.666667 66.666667 \n",
"65536 10.0 71.111111 13.042087 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 86.111111 88.888889 \n",
"65536 83.333333 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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" <th>75%</th>\n",
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" <td>13.042087</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>100.0</td>\n",
" <td>122.222222</td>\n",
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" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>95.555556</td>\n",
" <td>7.768954</td>\n",
" <td>77.777778</td>\n",
" <td>91.666667</td>\n",
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" <td>100.0</td>\n",
" <td>100.000000</td>\n",
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" <td>10.0</td>\n",
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" <td>80.555556</td>\n",
" <td>88.888889</td>\n",
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" <td>100.000000</td>\n",
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" count mean std min 25% 50% 75% \\\n",
"1 10.0 93.333333 13.042087 77.777778 88.888889 88.888889 100.0 \n",
"65536 10.0 95.555556 7.768954 77.777778 91.666667 100.000000 100.0 \n",
"default 10.0 90.000000 9.728834 77.777778 80.555556 88.888889 100.0 \n",
"\n",
" max \n",
"1 122.222222 \n",
"65536 100.000000 \n",
"default 100.000000 "
]
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" <td>8.198498</td>\n",
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" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>52.777778</td>\n",
" <td>55.555556</td>\n",
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" <tr>\n",
" <th>65536</th>\n",
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" <td>47.777778</td>\n",
" <td>7.499428</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
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" <td>55.555556</td>\n",
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" <td>7.856742</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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" count mean std min 25% 50% \\\n",
"1 10.0 45.555556 8.198498 33.333333 44.444444 44.444444 \n",
"65536 10.0 47.777778 7.499428 33.333333 44.444444 44.444444 \n",
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"\n",
" 75% max \n",
"1 52.777778 55.555556 \n",
"65536 55.555556 55.555556 \n",
"default 55.555556 55.555556 "
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" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
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" <th>65536</th>\n",
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" <td>11.111111</td>\n",
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" <td>27.777778</td>\n",
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" <td>32.222222</td>\n",
" <td>11.049210</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>55.555556</td>\n",
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" count mean std min 25% 50% \\\n",
"1 10.0 20.000000 7.027284 11.111111 13.888889 22.222222 \n",
"65536 10.0 26.666667 7.768954 11.111111 22.222222 27.777778 \n",
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"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"65536 33.333333 33.333333 \n",
"default 33.333333 55.555556 "
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" <tr>\n",
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" <td>10.0</td>\n",
" <td>37.777778</td>\n",
" <td>9.369712</td>\n",
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" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
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" <th>65536</th>\n",
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" <td>42.222222</td>\n",
" <td>8.764563</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
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" <td>55.555556</td>\n",
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" <th>default</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",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 37.777778 9.369712 33.333333 33.333333 33.333333 \n",
"65536 10.0 42.222222 8.764563 33.333333 33.333333 44.444444 \n",
"default 10.0 27.777778 7.856742 22.222222 22.222222 22.222222 \n",
"\n",
" 75% max \n",
"1 33.333333 55.555556 \n",
"65536 44.444444 55.555556 \n",
"default 33.333333 44.444444 "
]
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" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>21.666667</td>\n",
" <td>15.146744</td>\n",
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" <td>6.944444</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
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" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>17.222222</td>\n",
" <td>11.249143</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 21.666667 15.146744 5.555556 6.944444 22.222222 \n",
"65536 10.0 17.222222 11.249143 5.555556 11.111111 11.111111 \n",
"default 9.0 18.518519 10.758287 5.555556 11.111111 22.222222 \n",
"\n",
" 75% max \n",
"1 33.333333 44.444444 \n",
"65536 27.777778 33.333333 \n",
"default 22.222222 33.333333 "
]
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" <td>41.111111</td>\n",
" <td>11.770555</td>\n",
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" <td>9.442629</td>\n",
" <td>33.333333</td>\n",
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" <td>44.444444</td>\n",
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" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 41.111111 11.770555 11.111111 44.444444 44.444444 \n",
"65536 10.0 38.888889 9.442629 33.333333 33.333333 33.333333 \n",
"default 10.0 44.444444 7.407407 33.333333 44.444444 44.444444 \n",
"\n",
" 75% max \n",
"1 44.444444 55.555556 \n",
"65536 41.666667 55.555556 \n",
"default 44.444444 55.555556 "
]
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" <th>65536</th>\n",
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" <td>11.049210</td>\n",
" <td>44.444444</td>\n",
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" <tr>\n",
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" <td>60.000000</td>\n",
" <td>9.369712</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</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 67.777778 16.101530 44.444444 55.555556 66.666667 \n",
"65536 10.0 65.555556 11.049210 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",
"1 83.333333 88.888889 \n",
"65536 66.666667 88.888889 \n",
"default 66.666667 66.666667 "
]
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" <td>200.0</td>\n",
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" <td>200.000000</td>\n",
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" <td>166.666667</td>\n",
" <td>177.777778</td>\n",
" <td>200.0</td>\n",
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" <td>366.666667</td>\n",
" </tr>\n",
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"</table>\n",
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" count mean std min 25% 50% \\\n",
"1 10.0 201.111111 12.227833 177.777778 200.000000 200.0 \n",
"65536 10.0 208.888889 12.614360 200.000000 200.000000 200.0 \n",
"default 10.0 212.222222 58.666012 166.666667 177.777778 200.0 \n",
"\n",
" 75% max \n",
"1 208.333333 222.222222 \n",
"65536 219.444444 233.333333 \n",
"default 222.222222 366.666667 "
]
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" <td>219.444444</td>\n",
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" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>220.000000</td>\n",
" <td>23.306863</td>\n",
" <td>177.777778</td>\n",
" <td>211.111111</td>\n",
" <td>222.222222</td>\n",
" <td>230.555556</td>\n",
" <td>266.666667</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>208.888889</td>\n",
" <td>67.647111</td>\n",
" <td>177.777778</td>\n",
" <td>180.555556</td>\n",
" <td>188.888889</td>\n",
" <td>197.222222</td>\n",
" <td>400.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 212.222222 23.099950 177.777778 200.000000 205.555556 \n",
"65536 10.0 220.000000 23.306863 177.777778 211.111111 222.222222 \n",
"default 10.0 208.888889 67.647111 177.777778 180.555556 188.888889 \n",
"\n",
" 75% max \n",
"1 219.444444 255.555556 \n",
"65536 230.555556 266.666667 \n",
"default 197.222222 400.000000 "
]
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"u'test_firefox_gmail_ail_reply_mail'"
]
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" <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>237.777778</td>\n",
" <td>15.887119</td>\n",
" <td>200.0</td>\n",
" <td>233.333333</td>\n",
" <td>238.888889</td>\n",
" <td>244.444444</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>230.000000</td>\n",
" <td>15.757072</td>\n",
" <td>200.0</td>\n",
" <td>225.000000</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>237.777778</td>\n",
" <td>22.951012</td>\n",
" <td>200.0</td>\n",
" <td>225.000000</td>\n",
" <td>233.333333</td>\n",
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" <td>266.666667</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 237.777778 15.887119 200.0 233.333333 238.888889 \n",
"65536 10.0 230.000000 15.757072 200.0 225.000000 233.333333 \n",
"default 10.0 237.777778 22.951012 200.0 225.000000 233.333333 \n",
"\n",
" 75% max \n",
"1 244.444444 255.555556 \n",
"65536 233.333333 255.555556 \n",
"default 255.555556 266.666667 "
]
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"text": [
"u'test_firefox_gmail_ail_type_in_reply_field'"
]
},
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" <th>min</th>\n",
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" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>14.444444</td>\n",
" <td>5.367177</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>19.444444</td>\n",
" <td>22.222222</td>\n",
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" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>14.444444</td>\n",
" <td>7.027284</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
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" <td>22.222222</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>14.444444</td>\n",
" <td>7.027284</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 14.444444 5.367177 11.111111 11.111111 11.111111 \n",
"65536 10.0 14.444444 7.027284 5.555556 11.111111 11.111111 \n",
"default 10.0 14.444444 7.027284 5.555556 11.111111 11.111111 \n",
"\n",
" 75% max \n",
"1 19.444444 22.222222 \n",
"65536 22.222222 22.222222 \n",
"default 22.222222 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_image_cat'"
]
},
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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>85.555556</td>\n",
" <td>37.055551</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>188.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>94.444444</td>\n",
" <td>27.342265</td>\n",
" <td>66.666667</td>\n",
" <td>80.555556</td>\n",
" <td>88.888889</td>\n",
" <td>97.222222</td>\n",
" <td>166.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>65.555556</td>\n",
" <td>8.198498</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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" count mean std min 25% 50% \\\n",
"1 10.0 85.555556 37.055551 66.666667 66.666667 77.777778 \n",
"65536 10.0 94.444444 27.342265 66.666667 80.555556 88.888889 \n",
"default 10.0 65.555556 8.198498 44.444444 66.666667 66.666667 \n",
"\n",
" 75% max \n",
"1 77.777778 188.888889 \n",
"65536 97.222222 166.666667 \n",
"default 66.666667 77.777778 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_search_suggestion'"
]
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" <td>10.0</td>\n",
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" <td>9.604669</td>\n",
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" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>21.111111</td>\n",
" <td>11.049210</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>16.666667</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>22.222222</td>\n",
" <td>7.407407</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 17.222222 9.604669 5.555556 6.944444 22.222222 \n",
"65536 10.0 21.111111 11.049210 11.111111 11.111111 16.666667 \n",
"default 10.0 22.222222 7.407407 11.111111 22.222222 22.222222 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"65536 33.333333 33.333333 \n",
"default 22.222222 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_type_searchbox'"
]
},
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"<div>\n",
"<style scoped>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>8.333333</td>\n",
" <td>2.928035</td>\n",
" <td>5.555556</td>\n",
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" <td>8.333333</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>8.888889</td>\n",
" <td>7.027284</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>8.333333</td>\n",
" <td>5.399030</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% 75% \\\n",
"1 10.0 8.333333 2.928035 5.555556 5.555556 8.333333 11.111111 \n",
"65536 10.0 8.888889 7.027284 5.555556 5.555556 5.555556 5.555556 \n",
"default 10.0 8.333333 5.399030 5.555556 5.555556 5.555556 9.722222 \n",
"\n",
" max \n",
"1 11.111111 \n",
"65536 22.222222 \n",
"default 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_clicktab_0'"
]
},
{
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
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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>895.555556</td>\n",
" <td>246.912346</td>\n",
" <td>211.111111</td>\n",
" <td>936.111111</td>\n",
" <td>955.555556</td>\n",
" <td>983.333333</td>\n",
" <td>1111.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>724.444444</td>\n",
" <td>312.466185</td>\n",
" <td>300.000000</td>\n",
" <td>400.000000</td>\n",
" <td>933.333333</td>\n",
" <td>969.444444</td>\n",
" <td>1011.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>908.888889</td>\n",
" <td>380.592275</td>\n",
" <td>222.222222</td>\n",
" <td>977.777778</td>\n",
" <td>994.444444</td>\n",
" <td>1033.333333</td>\n",
" <td>1477.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 895.555556 246.912346 211.111111 936.111111 955.555556 \n",
"65536 10.0 724.444444 312.466185 300.000000 400.000000 933.333333 \n",
"default 10.0 908.888889 380.592275 222.222222 977.777778 994.444444 \n",
"\n",
" 75% max \n",
"1 983.333333 1111.111111 \n",
"65536 969.444444 1011.111111 \n",
"default 1033.333333 1477.777778 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_type_0'"
]
},
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"html": [
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\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>52.222222</td>\n",
" <td>11.770555</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>63.888889</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>41.111111</td>\n",
" <td>14.861039</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>38.888889</td>\n",
" <td>44.444444</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>16.563466</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>52.777778</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 52.222222 11.770555 33.333333 44.444444 50.000000 \n",
"65536 10.0 41.111111 14.861039 22.222222 33.333333 38.888889 \n",
"default 10.0 44.444444 16.563466 33.333333 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"1 63.888889 66.666667 \n",
"65536 44.444444 77.777778 \n",
"default 52.777778 77.777778 "
]
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" <td>300.000000</td>\n",
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" <td>322.222222</td>\n",
" <td>333.333333</td>\n",
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" count mean std min 25% 50% \\\n",
"1 10.0 311.111111 15.713484 288.888889 300.000000 316.666667 \n",
"65536 10.0 275.555556 29.997714 244.444444 266.666667 266.666667 \n",
"\n",
" 75% max \n",
"1 322.222222 333.333333 \n",
"65536 266.666667 355.555556 "
]
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" <td>28.808054</td>\n",
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"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 116.666667 28.808054 77.777778 100.000000 116.666667 \n",
"65536 10.0 127.777778 19.065982 111.111111 113.888889 122.222222 \n",
"\n",
" 75% max \n",
"1 141.666667 155.555556 \n",
"65536 130.555556 166.666667 "
]
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"output_type": "display_data",
"text": [
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" <td>366.666667</td>\n",
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" <td>372.222222</td>\n",
" <td>397.222222</td>\n",
" <td>422.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 390.000000 19.910637 344.444444 380.555556 394.444444 \n",
"65536 10.0 407.777778 26.215316 366.666667 400.000000 400.000000 \n",
"default 10.0 375.555556 26.604867 344.444444 355.555556 372.222222 \n",
"\n",
" 75% max \n",
"1 400.000000 411.111111 \n",
"65536 419.444444 466.666667 \n",
"default 397.222222 422.222222 "
]
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"output_type": "display_data",
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"u'test_firefox_outlook_ail_type_composemail_0'"
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" <td>5.555556</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"1 10.0 18.888889 15.537909 5.555556 11.111111 11.111111 22.222222 \n",
"65536 10.0 21.111111 14.054567 5.555556 11.111111 22.222222 22.222222 \n",
"\n",
" max \n",
"1 55.555556 \n",
"65536 44.444444 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_compose_new_mail'"
]
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" <td>304.444444</td>\n",
" <td>19.029974</td>\n",
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" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>310.000000</td>\n",
" <td>108.164636</td>\n",
" <td>244.444444</td>\n",
" <td>250.000000</td>\n",
" <td>266.666667</td>\n",
" <td>313.888889</td>\n",
" <td>600.000000</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>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 304.444444 19.029974 277.777778 291.666667 305.555556 \n",
"65536 10.0 310.000000 108.164636 244.444444 250.000000 266.666667 \n",
"default 10.0 290.000000 15.225781 266.666667 280.555556 288.888889 \n",
"\n",
" 75% max \n",
"1 319.444444 333.333333 \n",
"65536 313.888889 600.000000 \n",
"default 297.222222 322.222222 "
]
},
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"output_type": "display_data",
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]
},
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" <tbody>\n",
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" <td>31.111111</td>\n",
" <td>26.084173</td>\n",
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" <td>13.888889</td>\n",
" <td>22.222222</td>\n",
" <td>30.555556</td>\n",
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" <tr>\n",
" <th>65536</th>\n",
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" <td>21.666667</td>\n",
" <td>18.416385</td>\n",
" <td>5.555556</td>\n",
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" <td>22.222222</td>\n",
" <td>66.666667</td>\n",
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" <tr>\n",
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" <td>37.222222</td>\n",
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" <td>5.555556</td>\n",
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" <td>200.000000</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 26.084173 11.111111 13.888889 22.222222 \n",
"65536 10.0 21.666667 18.416385 5.555556 6.944444 22.222222 \n",
"default 10.0 37.222222 62.638051 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"1 30.555556 88.888889 \n",
"65536 22.222222 66.666667 \n",
"default 22.222222 200.000000 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_select_search_suggestion'"
]
},
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" <td>13.333333</td>\n",
" <td>11.770555</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>19.444444</td>\n",
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" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>17.777778</td>\n",
" <td>10.409977</td>\n",
" <td>5.555556</td>\n",
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" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>11.490439</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>19.444444</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 13.333333 11.770555 5.555556 5.555556 5.555556 \n",
"65536 10.0 17.777778 10.409977 5.555556 11.111111 16.666667 \n",
"default 10.0 13.888889 11.490439 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"1 19.444444 33.333333 \n",
"65536 22.222222 33.333333 \n",
"default 19.444444 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_type_in_search_field'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <td>10.0</td>\n",
" <td>10.000000</td>\n",
" <td>6.829292</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
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" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65536</th>\n",
" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>8.784105</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>13.888889</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>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 10.000000 6.829292 5.555556 5.555556 5.555556 11.111111 \n",
"65536 10.0 13.888889 8.784105 5.555556 5.555556 13.888889 22.222222 \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": [
{
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i11osSZK0HOjRNVkRMSAi7gKeBK4D/gbMzsz5dZZHgWF1eBgwDaBOfxZYrzcb\nLUmS1N/1KGRl5quZuS0wHNgR2HJpVxwRR0bExIiYOHPmzKVdnCRJUr+yWN8uzMzZwI3Ae4AhETGw\nThoOTK/D04GNAer0dYCnu1jWOZk5KjNHDR06dAmbL0mS1D/15NuFQyNiSB1eHfggMJkStvavsx0K\nXF6Hr6g1dfoNmZm92WhJkqT+bmD3s7AhcH5EDKCEsksz88qI+AtwcUScCtwJjK/zjwcujIgpwDPA\nwQ20W5IkqV/rNmRl5j3A33Ux/iHK9Vmdx88DDuiV1kmSJC2n/MV3SZKkBhiyJEmSGmDIkiRJaoAh\nS5IkqQGGLEmSpAYYsiRJkhpgyJIkSWqAIUuSJKkBhixJkqQGGLIkSZIaYMiSJElqgCFLkiSpAYYs\nSZKkBhiyJEmSGmDIkiRJaoAhS5IkqQGGLEmSpAYYsiRJkhpgyJIkSWqAIUuSJKkBhixJkqQGGLIk\nSZIaYMiSJElqgCFLkiSpAYYsSZKkBhiyJEmSGmDIkiRJaoAhS5IkqQGGLEmSpAYYsiRJkhpgyJIk\nSWqAIUuSJKkBhixJkqQGGLIkSZIaYMiSJElqgCFLkiSpAYYsSZKkBhiyJEmSGmDIkiRJaoAhS5Ik\nqQHdhqyI2DgiboyIv0TE/RHxxTp+3Yi4LiL+Wv++sY6PiPhhREyJiHsiYrumN0KSJKm/6UlP1nzg\nuMzcCtgJODoitgJOAK7PzM2B62sNsBeweb0dCZzd662WJEnq57oNWZn5eGb+uQ4/B0wGhgH7AufX\n2c4H9qvD+wIXZHEbMCQiNuz1lkuSJPVji3VNVkSMAP4OuB3YIDMfr5NmABvU4WHAtLa7PVrHSZIk\nrTR6HLIi4g3Ar4AvZeac9mmZmUAuzooj4siImBgRE2fOnLk4d5UkSer3ehSyImJVSsD6RWb+uo5+\nouM0YP37ZB0/Hdi47e7D67gFZOY5mTkqM0cNHTp0SdsvSZLUL/Xk24UBjAcmZ+YZbZOuAA6tw4cC\nl7eN/3T9luFOwLNtpxUlSZJWCgN7MM//Aj4F3BsRd9VxJwKnAZdGxGjgEeDAOu0qYG9gCjAXOLxX\nWyxJkrQc6DZkZeYtQCxk8ge6mD+Bo5eyXZIkScs1f/FdkiSpAYYsSZKkBhiyJEmSGmDIkiRJaoAh\nS5IkqQGGLEmSpAYYsiRJkhpgyJIkSWqAIUuSJKkBhixJkqQGGLIkSZIaYMiSJElqgCFLkiSpAYYs\nSZKkBhiyJEmSGmDIkiRJaoAhS5IkqQGGLEmSpAYYsiRJkhpgyJIkSWqAIUuSJKkBhixJkqQGGLIk\nSZIaYMiSJElqgCFLkiSpAYbaFljHAAAHaElEQVQsSZKkBhiyJEmSGmDIkiRJaoAhS5IkqQGGLEmS\npAYYsiRJkhpgyJIkSWqAIUuSJKkBhixJkqQGGLIkSZIaYMiSJElqgCFLkiSpAYYsSZKkBhiyJEmS\nGmDIkiRJaoAhS5IkqQHdhqyIODcinoyI+9rGrRsR10XEX+vfN9bxERE/jIgpEXFPRGzXZOMlSZL6\nq570ZJ0HfKjTuBOA6zNzc+D6WgPsBWxeb0cCZ/dOMyVJkpYv3YaszLwZeKbT6H2B8+vw+cB+beMv\nyOI2YEhEbNhbjZUkSVpeLOk1WRtk5uN1eAawQR0eBkxrm+/ROu5/iIgjI2JiREycOXPmEjZDkiSp\nf1rqC98zM4Fcgvudk5mjMnPU0KFDl7YZkiRJ/cqShqwnOk4D1r9P1vHTgY3b5htex0mSJK1UljRk\nXQEcWocPBS5vG//p+i3DnYBn204rSpIkrTQGdjdDREwAdgXWj4hHgZOA04BLI2I08AhwYJ39KmBv\nYAowFzi8gTZLkiT1e92GrMw8ZCGTPtDFvAkcvbSNkiRJWt75i++SJEkNMGRJkiQ1wJAlSZLUAEOW\nJElSAwxZkiRJDTBkSZIkNcCQJUmS1ABDliRJUgMMWZIkSQ0wZEmSJDXAkCVJktQAQ5YkSVIDDFmS\nJEkNMGRJkiQ1wJAlSZLUAEOWJElSAwxZkiRJDTBkSZIkNcCQJUmS1ABDliRJUgMMWZIkSQ0wZEmS\nJDXAkCVJktQAQ5YkSVIDDFmSJEkNMGRJkiQ1wJAlSZLUAEOWJElSAwxZkiRJDTBkSZIkNcCQJUmS\n1ABDliRJUgMMWZIkSQ0wZEmSJDXAkCVJktQAQ5YkSVIDDFmSJEkNMGRJkiQ1wJAlSZLUAEOWJElS\nAxoJWRHxoYj4r4iYEhEnNLEOSZKk/qzXQ1ZEDAD+DdgL2Ao4JCK26u31SJIk9WdN9GTtCEzJzIcy\n82XgYmDfBtYjSZLUbzURsoYB09rqR+s4SZKklcbAvlpxRBwJHFnL5yPiv/qqLSup9YGn+roRUsPc\nz7UycD9f9jbtyUxNhKzpwMZt9fA6bgGZeQ5wTgPrVw9ExMTMHNXX7ZCa5H6ulYH7ef/VxOnCPwGb\nR8RbImI14GDgigbWI0mS1G/1ek9WZs6PiM8D1wADgHMz8/7eXo8kSVJ/1sg1WZl5FXBVE8tWr/FU\nrVYG7udaGbif91ORmX3dBkmSpBWO/1ZHkiSpAYaslUxEnBsRT0bEfX3dFqmnImJIRFwWEQ9ExOSI\neE9EnBwR0yPirnrbu847IiJebBs/rm05V0fE3RFxf0SMq/+homPaMXX590fEd/tiO6W6X39lEdOH\nRsTtEXFnROyyBMs/LCJ+VIf38z+yNKvPfidLfeY84EfABX3cDmlxnAlcnZn7128trwHsCfwgM7/f\nxfx/y8xtuxh/YGbOiYgALgMOAC6OiN0o/5niXZn5UkS8qaHtkJbWB4B7M/MzvbCs/YArgb/0wrLU\nBXuyVjKZeTPwTF+3Q+qpiFgHeB8wHiAzX87M2UuyrMycUwcHAqsBHRel/jNwWma+VOd7cqkaLS2G\niBgTEQ9GxC3A2+u4t9ae10kR8f8iYsuI2Bb4LrBv7aVdPSLOjoiJtQf2lLZlTo2I9evwqIi4qdM6\n3wt8FPheXdZbl9X2rkwMWZL6u7cAM4Gf1VMkP42INeu0z0fEPfU0+Bvb71Pn/UPnUyoRcQ3wJPAc\npTcLYAtgl3oa5g8RsUPD2yQBEBHbU35Pcltgb6Bj3zsHOCYztwe+Avw4M+8CvgVckpnbZuaLwJj6\nQ6TbAO+PiG16st7M/E/Kb1h+tS7rb726YQIMWZL6v4HAdsDZmfl3wAvACcDZwFspb06PA/9a538c\n2KTOeyxwUUSs3bGwzNwT2BAYBOzeto51gZ2ArwKX1lOKUtN2AX6TmXNrT+sVwGDgvcAvI+Iu4N8p\n+2xXDoyIPwN3Au8AvMaqHzFkServHgUezczba30ZsF1mPpGZr2bma8BPgB0BMvOlzHy6Dk8C/kbp\nqXpdZs4DLqdch9Wxjl9ncQfwGuX/wUl9YRVgdu1h6riN7DxTRLyF0sv1gczcBvgdJaABzKf1Hj+4\n8321bBiyJPVrmTkDmBYRb6+jPgD8JSLaP9n/A3AfvP7tqwF1eDNgc+ChiHhDx30iYiDwYeCBev/f\nArvVaVtQrtfyH+5qWbgZ2K9eX7UW8BFgLvBwRBwAEMW7urjv2pSe3WcjYgNgr7ZpU4Ht6/A/LmTd\nzwFrLf0maGEMWSuZiJgA/BF4e0Q8GhGj+7pNUg8cA/wiIu6hnB78DvDdiLi3jtsN+HKd933APfU0\ny2XAUZn5DLAmcEWd/y7KdVkdP+9wLrBZ/WmTi4FD019q1jKQmX8GLgHuBn5P+f+/AJ8ARkfE3cD9\ntHpd2+97N+U04QPARcCtbZNPAc6MiInAqwtZ/cXAV+v1i1743gB/8V2SJKkB9mRJkiQ1wJAlSZLU\nAEOWJElSAwxZkiRJDTBkSZIkNcCQJUmS1ABDliRJUgMMWZIkSQ34/yZ8dZCUMsbYAAAAAElFTkSu\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10180c690>"
]
},
{
"output_type": "display_data",
"png": 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B9wG3Nff/MbBns2xHyvVU/nCshsI1wL7N9U8bAx8AVgB/jogPA0SxWxf33YQy\nUrs8IrYEprYtuwt4Q3N7v27afhzYuH4T1B3D1BoqIuYDvwJ2ioh7ImLacPdJ6oPpwPcj4veU03qz\ngK9GxE3NvD2BzzXrvh34fXN65ALgiMx8GNgQuKhZfxHluqnW1yacCbyy+cqQ84CD028u1hDIzN8C\n5wM3Av+P8vu2AAcB0yLiRuBmOkZR2+97I+X03m3AucB/tS0+CTg9IhYAz3bT/HnAsc31hV6APgj8\nBnRJkqQKjkxJkiRVMExJkiRVMExJkiRVMExJkiRVMExJkiRVMExJkiRVMExJkiRVMExJkiRV+P/x\nlY09D6f9TwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1054cf450>"
]
},
{
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drYP8/eIX5XbTTeWf6EMPlX5QmaV89tnll2mDQxNIA9enP71g+ctfhuHDy/1X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\nhilJkqQa/j90xxX2kGuy0AAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1057fb310>"
]
},
{
"output_type": "display_data",
"png": 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2iYjRETExIiZExFer1zePiDsi4pnq52Yd31xJkqTupZk+U0uAb6aUdgU+AHwl\nInYFRgB3pZR2Au6qakmSpDVKm2EqpTQzpfRQ9fw1YBIwADgUuKIa7QrgsI5qpCRJUne1UlfzRcQg\n4L3AWKBfSmlmNWgW0K9dWyZJktQDNB2mImIj4HrgaymlBY3DUu4d3WIP6Yg4ISLGRcS4uXPnFjVW\nkiSpu2kqTEXE2uQgdVVK6Ybq5dkRsXU1fGtgTkvTppQuSykNSSkN6du3b3u0WZIkqdto5mq+AEYB\nk1JKP24YdDNwbPX8WOCm9m+eJElS99bMfaY+DHwOeDwiHqle+zYwEvhdRAwHngeO7JgmSpIkdV9t\nhqmU0higtdtvH9i+zZEkSepZ/G4+SZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYp\nSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKk\nAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYp\nSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKk\nAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYpSZKkAoYp\nSZKkAoYpSZKkAoYpSZKkAkVhKiI+ERFPRcTkiBjRXo2SJEnqKVY5TEVEL+BC4CBgV+DoiNi1vRom\nSZLUE5QcmRoKTE4pTUkpvQX8Fji0fZolSZLUM5SEqQHAtIZ6evWaJEnSGqN3Ry8gIk4ATqjKhRHx\nVEcvU8vYEnipqxshdTDf51oT+D7vfNs1M1JJmJoBbNNQD6xeW0ZK6TLgsoLlqEBEjEspDenqdkgd\nyfe51gS+z7uvktN8fwd2iojtI2Id4DPAze3TLEmSpJ5hlY9MpZSWRMT/BG4HegG/SClNaLeWSZIk\n9QBFfaZSSn8C/tRObVHH8BSr1gS+z7Um8H3eTUVKqavbIEmS1GP5dTKSJEkFDFOrqYj4RUTMiYgn\nurotUrMiYtOIuC4inoyISRHxwYj4fkTMiIhHqsfB1biDIuIfDa9f0jCf2yLi0YiYEBGXVN/YUBt2\nUjX/CRFxblesp1S9r09ZwfC+ETE2Ih6OiH1WYf7HRcQF1fPD/IaSjtXh95lSl/kVcAHw6y5uh7Qy\nfgrcllI6vLpKeAPg48D5KaX/amH8Z1NKe7bw+pEppQUREcB1wBHAbyNif/I3NbwnpfRmRGzVQesh\nlToQeDyldHw7zOsw4BZgYjvrgTq2AAADGElEQVTMSy3wyNRqKqV0L/BKV7dDalZEbALsC4wCSCm9\nlVKavyrzSiktqJ72BtYBap1DvwSMTCm9WY03p6jR0kqIiNMj4umIGAPsXL22Q3UkdXxE3BcRu0TE\nnsC5wKHVUdf1I+LiiBhXHVE9o2GeUyNiy+r5kIi4e7llfgj4V+D/VPPaobPWd01imJLUXWwPzAV+\nWZ3auDwiNqyG/c+IeKw6fb1Z4zTVuPcsfyokIm4H5gCvkY9OAbwL2Kc6fXJPRLyvg9dJAiAi9ibf\nj3FP4GCg9t67DDgppbQ3cApwUUrpEeC7wLUppT1TSv8ATq9u2LkH8JGI2KOZ5aaU7iffA/LUal7P\ntuuKCTBMSeo+egN7ARenlN4LvA6MAC4GdiB/CM0EzqvGnwlsW437DeDqiNi4NrOU0seBrYF1gQMa\nlrE58AHgVOB31alAqaPtA/whpbSoOnJ6M7Ae8CHg9xHxCHAp+T3bkiMj4iHgYeDdgH2guhHDlKTu\nYjowPaU0tqqvA/ZKKc1OKb2dUloK/BwYCpBSejOl9HL1fDzwLPnI0z+llN4AbiL3k6ot44aUPQgs\nJX/fmdQV1gLmV0eMao/By48UEduTj1odmFLaA7iVHMQAllD/LF9v+WnVOQxTkrqFlNIsYFpE7Fy9\ndCAwMSIa/1P/H8AT8M+rnXpVz98J7ARMiYiNatNERG/gEODJavobgf2rYe8i96fyi2PVGe4FDqv6\nP/UBPgUsAp6LiCMAIntPC9NuTD5S+2pE9AMOahg2Fdi7ev7pVpb9GtCnfBXUGsPUaioirgEeAHaO\niOkRMbyr2yQ14STgqoh4jHxa70fAuRHxePXa/sDXq3H3BR6rTo9cB5yYUnoF2BC4uRr/EXK/qdpt\nE34BvLO6ZchvgWOTdy5WJ0gpPQRcCzwK/Jn8/bYAxwDDI+JRYAL1o6iN0z5KPr33JHA18P8aBp8B\n/DQixgFvt7L43wKnVv0L7YDeAbwDuiRJUgGPTEmSJBUwTEmSJBUwTEmSJBUwTEmSJBUwTEmSJBUw\nTEmSJBUwTEmSJBUwTEmSJBX4//QdNsD7poyyAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10591ef90>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x105a5c190>"
]
},
{
"output_type": "display_data",
"png": 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HDz+cz/moo3L84IO59u6II3JcqbU77LAcT56ca+UOPTTHf/97rnU7+OAc339/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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x105af1250>"
]
},
{
"output_type": "display_data",
"png": 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llr1fb/VYZD84XXXMmDE5vTFuZxn1ljPf0tdVqN3tB97e11XQABV90PLZH94b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\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x105ae7110>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x105a805d0>"
]
},
{
"output_type": "display_data",
"png": 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eYw/44AfhjTdy+Vvfyo9MqfjIR/JglRXDh9u6JEmrCcOUBqfnnsstR5XhAW6+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\n",
"text": [
"<matplotlib.figure.Figure at 0x105cc3610>"
]
},
{
"output_type": "display_data",
"png": 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u4dU33+nUY7bnc9VeKyzVn0e//fFOO153ZjLVyUw01BtMOWOf9m14RurYQFQf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\nmEbHJLXCWOCyiHiMfFlvHHB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"text": [
"<matplotlib.figure.Figure at 0x105ba8c90>"
]
},
{
"output_type": "display_data",
"png": 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/HddeOwepzTbLoXnQoDyxpqSGMkxJ3clnPlNdXrgQpk2rzmi9ZAlccEG+1M3w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\nklSCYUqSJKkEw5QkSVIJ/x8NIwcJVCS15wAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x105dfc590>"
]
},
{
"output_type": "display_data",
"png": 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6dZUlS3LrE+RB+ttum2cXrzjnnOoA/Q02gOOOc7JMaYAyTEkqr3X31QUXVJ+n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\nKsEwJUmSVIJhSpIkqQTDlCRJUgn/D/JelVqZgPKKAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x105e97bd0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x105f2bb10>"
]
},
{
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WJElSDwxZkiRJPTBkSZIk9cCQJUmS1INRQ1aSM5LckuTKgbpHJ/lSkmvbv49q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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x105e07e10>"
]
},
{
"output_type": "display_data",
"png": 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bO4euL3yh82z899zTuXvzkEPguONKCxmUsWDHHttx3ksvLYP+d9+9d5/Hq1jX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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x105fea210>"
]
},
{
"output_type": "display_data",
"png": 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FhixJkqQKDFmSJEkVGLIkSZIqMGRJkiRVYMiSJEmqwJAlSZJUgSFLkiSpAkOW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\n",
"text": [
"<matplotlib.figure.Figure at 0x1060b4fd0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x106163390>"
]
},
{
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OFVeUsU6NsUQf+UiZIuD//t9S3nffcjVVY0bwd7wDXvSicvk6lPEjzV1pb3pT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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10619af10>"
]
},
{
"output_type": "display_data",
"png": 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L+cMfzpfLb7NNLj/8cO5rVWn1uOQS+MQnqmNeeeXgknvrrTyO03335fLjj8NG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\n",
"text": [
"<matplotlib.figure.Figure at 0x1062f9990>"
]
},
{
"output_type": "display_data",
"png": 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ttDlMRcQ6ETGw9hzYB3iovRomSZLUE5QM8w0Bfhn5Ng99gKtTSr9pl1ZJkiT1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\nVMAwJUmSVMAwJUmSVOD/A6Cm0piRB03LAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x106389b50>"
]
},
{
"output_type": "display_data",
"png": 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13agqz6u2j+2NC5AkSRpouh3AImI74F+B5sxsAoYBHwFOAk7JzJ2AJ4CWapcW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\nSZKkmv1/dvv/jOc80NoAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1064c2e50>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1064cb250>"
]
},
{
"output_type": "display_data",
"png": 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YUy2PBe5tOmxOtU6SJGm90eeQFRGbABcDH8zMx5q3ZWYCuSYXjogpEdEdEd0L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\nSUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10655e310>"
]
},
{
"output_type": "display_data",
"png": 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keEmSpEoMWpIkSZUYtCRJkioxaEmSJFVi0JIkSarEoCVJklSJQUuSJKkSg5Yk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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x106605250>"
]
},
{
"output_type": "display_data",
"png": 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WJElSAwxZkiRJDTBkSZIkNcCQJUmS1ABDliRJUgMMWZIkSQ0wZEmSJDXAkCVJ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\n",
"text": [
"<matplotlib.figure.Figure at 0x106663710>"
]
},
{
"output_type": "display_data",
"png": 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mrbdecq6mv/8d+vdvbjdmDJek1ZhhSlqVZZaJLYcMKe0f/agEpUZX3UUXlQv8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\nGKYkSZJqMExJkiTV8P8BOjuS6cSHGXgAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x106389510>"
]
},
{
"output_type": "display_data",
"png": 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dWq/FmmuWQNpqRRwypOz73e8u5Y03Lq1Ne+1VytttV57vnnuW8lZbldf+ne8s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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1067eea10>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1068820d0>"
]
},
{
"output_type": "display_data",
"png": 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TUk+YMQOuvbZMv+ENsPPO8OY3l/LQoXYql5qNH186qLebMgXuuadRPv10+Nvf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\nsqY9AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x106882550>"
]
},
{
"output_type": "display_data",
"png": 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b16V2wTAlQb7Uyy235OVu3fLA1BNOqG1NktqvoUMry3Pm5KsbvPxybi9cmK8f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\nbpOaAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1069cfd50>"
]
}
],
"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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