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Created November 17, 2017 09:42
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layers.tile-width
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layers.tile-width.ipynb
{
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
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"name": ""
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"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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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>486.666667</td>\n",
" <td>11.475506</td>\n",
" <td>466.666667</td>\n",
" <td>477.777778</td>\n",
" <td>488.888889</td>\n",
" <td>497.222222</td>\n",
" <td>500.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>475.555556</td>\n",
" <td>25.552872</td>\n",
" <td>411.111111</td>\n",
" <td>469.444444</td>\n",
" <td>477.777778</td>\n",
" <td>488.888889</td>\n",
" <td>500.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>445.555556</td>\n",
" <td>24.258552</td>\n",
" <td>411.111111</td>\n",
" <td>433.333333</td>\n",
" <td>444.444444</td>\n",
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" <td>477.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 486.666667 11.475506 466.666667 477.777778 488.888889 \n",
"2 10.0 475.555556 25.552872 411.111111 469.444444 477.777778 \n",
"default 10.0 445.555556 24.258552 411.111111 433.333333 444.444444 \n",
"\n",
" 75% max \n",
"16384 497.222222 500.000000 \n",
"2 488.888889 500.000000 \n",
"default 466.666667 477.777778 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_select_search_suggestion'"
]
},
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>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>2</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>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",
"16384 10.0 7.777778 5.367177 5.555556 5.555556 5.555556 5.555556 \n",
"2 10.0 8.888889 7.027284 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",
"16384 22.222222 \n",
"2 22.222222 \n",
"default 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_type_in_search_field'"
]
},
{
"html": [
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"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>18.333333</td>\n",
" <td>6.441677</td>\n",
" <td>5.555556</td>\n",
" <td>13.888889</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>16.111111</td>\n",
" <td>6.651217</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>17.777778</td>\n",
" <td>7.768954</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 18.333333 6.441677 5.555556 13.888889 22.222222 \n",
"2 10.0 16.111111 6.651217 5.555556 11.111111 16.666667 \n",
"default 10.0 17.777778 7.768954 11.111111 11.111111 16.666667 \n",
"\n",
" 75% max \n",
"16384 22.222222 22.222222 \n",
"2 22.222222 22.222222 \n",
"default 22.222222 33.333333 "
]
},
{
"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",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>40.000000</td>\n",
" <td>14.998857</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>52.777778</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>46.666667</td>\n",
" <td>15.537909</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>50.000000</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>63.333333</td>\n",
" <td>5.367177</td>\n",
" <td>55.555556</td>\n",
" <td>58.333333</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 40.000000 14.998857 11.111111 33.333333 44.444444 \n",
"2 10.0 46.666667 15.537909 22.222222 33.333333 50.000000 \n",
"default 10.0 63.333333 5.367177 55.555556 58.333333 66.666667 \n",
"\n",
" 75% max \n",
"16384 52.777778 55.555556 \n",
"2 55.555556 66.666667 \n",
"default 66.666667 66.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab'"
]
},
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>185.555556</td>\n",
" <td>65.010287</td>\n",
" <td>133.333333</td>\n",
" <td>147.222222</td>\n",
" <td>161.111111</td>\n",
" <td>175.000000</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>245.555556</td>\n",
" <td>80.371224</td>\n",
" <td>144.444444</td>\n",
" <td>155.555556</td>\n",
" <td>300.000000</td>\n",
" <td>300.000000</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>253.333333</td>\n",
" <td>13.658584</td>\n",
" <td>233.333333</td>\n",
" <td>244.444444</td>\n",
" <td>255.555556</td>\n",
" <td>266.666667</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",
"16384 10.0 185.555556 65.010287 133.333333 147.222222 161.111111 \n",
"2 10.0 245.555556 80.371224 144.444444 155.555556 300.000000 \n",
"default 10.0 253.333333 13.658584 233.333333 244.444444 255.555556 \n",
"\n",
" 75% max \n",
"16384 175.000000 322.222222 \n",
"2 300.000000 322.222222 \n",
"default 266.666667 266.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab_emoji'"
]
},
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"html": [
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" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>54.444444</td>\n",
" <td>12.227833</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>66.666667</td>\n",
" <td>11.712139</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>72.222222</td>\n",
" <td>13.094570</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>75.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 54.444444 12.227833 33.333333 44.444444 55.555556 \n",
"2 10.0 66.666667 11.712139 44.444444 66.666667 66.666667 \n",
"default 10.0 72.222222 13.094570 55.555556 66.666667 66.666667 \n",
"\n",
" 75% max \n",
"16384 66.666667 66.666667 \n",
"2 66.666667 88.888889 \n",
"default 75.000000 100.000000 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_photo_viewer_right_arrow'"
]
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" <th>75%</th>\n",
" <th>max</th>\n",
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" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>86.666667</td>\n",
" <td>12.61436</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>83.333333</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>91.111111</td>\n",
" <td>12.61436</td>\n",
" <td>66.666667</td>\n",
" <td>80.555556</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>83.333333</td>\n",
" <td>5.85607</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>83.333333</td>\n",
" <td>88.888889</td>\n",
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" </tr>\n",
" </tbody>\n",
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" count mean std min 25% 50% \\\n",
"16384 10.0 86.666667 12.61436 66.666667 77.777778 83.333333 \n",
"2 10.0 91.111111 12.61436 66.666667 80.555556 100.000000 \n",
"default 10.0 83.333333 5.85607 77.777778 77.777778 83.333333 \n",
"\n",
" 75% max \n",
"16384 100.000000 100.000000 \n",
"2 100.000000 100.000000 \n",
"default 88.888889 88.888889 "
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" <td>43.333333</td>\n",
" <td>8.198498</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>42.222222</td>\n",
" <td>11.475506</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
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" <td>55.555556</td>\n",
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" <td>10.0</td>\n",
" <td>50.000000</td>\n",
" <td>9.442629</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>50.000000</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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" count mean std min 25% 50% \\\n",
"16384 10.0 43.333333 8.198498 33.333333 36.111111 44.444444 \n",
"2 10.0 42.222222 11.475506 33.333333 33.333333 33.333333 \n",
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"\n",
" 75% max \n",
"16384 44.444444 55.555556 \n",
"2 55.555556 55.555556 \n",
"default 55.555556 66.666667 "
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" <td>33.333333</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>25.555556</td>\n",
" <td>10.540926</td>\n",
" <td>11.111111</td>\n",
" <td>13.888889</td>\n",
" <td>33.333333</td>\n",
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" <td>33.333333</td>\n",
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" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>37.777778</td>\n",
" <td>9.369712</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
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" count mean std min 25% 50% \\\n",
"16384 10.0 36.666667 7.499428 33.333333 33.333333 33.333333 \n",
"2 10.0 25.555556 10.540926 11.111111 13.888889 33.333333 \n",
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"\n",
" 75% max \n",
"16384 33.333333 55.555556 \n",
"2 33.333333 33.333333 \n",
"default 33.333333 55.555556 "
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" <td>32.222222</td>\n",
" <td>17.723683</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>41.666667</td>\n",
" <td>66.666667</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>37.777778</td>\n",
" <td>14.054567</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
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" <td>55.555556</td>\n",
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" <td>10.0</td>\n",
" <td>22.222222</td>\n",
" <td>12.559870</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</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",
"16384 10.0 32.222222 17.723683 11.111111 22.222222 22.222222 \n",
"2 10.0 37.777778 14.054567 11.111111 33.333333 33.333333 \n",
"default 10.0 22.222222 12.559870 5.555556 11.111111 27.777778 \n",
"\n",
" 75% max \n",
"16384 41.666667 66.666667 \n",
"2 50.000000 55.555556 \n",
"default 33.333333 33.333333 "
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" </tr>\n",
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" <td>10.0</td>\n",
" <td>22.777778</td>\n",
" <td>24.630326</td>\n",
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" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>16.666667</td>\n",
" <td>11.415581</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>16.666667</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>21.111111</td>\n",
" <td>9.369712</td>\n",
" <td>5.555556</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"16384 10.0 22.777778 24.630326 5.555556 11.111111 11.111111 \n",
"2 10.0 16.666667 11.415581 5.555556 5.555556 16.666667 \n",
"default 10.0 21.111111 9.369712 5.555556 22.222222 22.222222 \n",
"\n",
" 75% max \n",
"16384 22.222222 88.888889 \n",
"2 22.222222 33.333333 \n",
"default 22.222222 33.333333 "
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" <td>7.768954</td>\n",
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" <td>33.333333</td>\n",
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" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>40.000000</td>\n",
" <td>14.054567</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>38.888889</td>\n",
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" <td>55.555556</td>\n",
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" <td>42.222222</td>\n",
" <td>10.210406</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>38.888889</td>\n",
" <td>52.777778</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"16384 10.0 37.777778 7.768954 33.333333 33.333333 33.333333 \n",
"2 10.0 40.000000 14.054567 11.111111 33.333333 38.888889 \n",
"default 10.0 42.222222 10.210406 33.333333 33.333333 38.888889 \n",
"\n",
" 75% max \n",
"16384 41.666667 55.555556 \n",
"2 52.777778 55.555556 \n",
"default 52.777778 55.555556 "
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" <th>2</th>\n",
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" <td>63.333333</td>\n",
" <td>7.499428</td>\n",
" <td>44.444444</td>\n",
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" <td>66.666667</td>\n",
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" <tr>\n",
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" <td>63.333333</td>\n",
" <td>10.540926</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>61.111111</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"16384 10.0 68.888889 7.027284 66.666667 66.666667 66.666667 \n",
"2 10.0 63.333333 7.499428 44.444444 66.666667 66.666667 \n",
"default 10.0 63.333333 10.540926 55.555556 55.555556 61.111111 \n",
"\n",
" 75% max \n",
"16384 66.666667 88.888889 \n",
"2 66.666667 66.666667 \n",
"default 66.666667 88.888889 "
]
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" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>206.666667</td>\n",
" <td>18.294947</td>\n",
" <td>188.888889</td>\n",
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" <td>194.444444</td>\n",
" <td>12.001372</td>\n",
" <td>177.777778</td>\n",
" <td>183.333333</td>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>211.111111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"16384 10.0 212.222222 14.296488 200.000000 200.000000 205.555556 \n",
"2 10.0 206.666667 18.294947 188.888889 200.000000 200.000000 \n",
"default 10.0 194.444444 12.001372 177.777778 183.333333 200.000000 \n",
"\n",
" 75% max \n",
"16384 222.222222 233.333333 \n",
"2 208.333333 244.444444 \n",
"default 200.000000 211.111111 "
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>232.222222</td>\n",
" <td>39.003359</td>\n",
" <td>200.000000</td>\n",
" <td>211.111111</td>\n",
" <td>216.666667</td>\n",
" <td>233.333333</td>\n",
" <td>333.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>216.666667</td>\n",
" <td>49.204927</td>\n",
" <td>177.777778</td>\n",
" <td>191.666667</td>\n",
" <td>200.000000</td>\n",
" <td>219.444444</td>\n",
" <td>344.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>226.666667</td>\n",
" <td>108.512830</td>\n",
" <td>177.777778</td>\n",
" <td>180.555556</td>\n",
" <td>194.444444</td>\n",
" <td>208.333333</td>\n",
" <td>533.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 232.222222 39.003359 200.000000 211.111111 216.666667 \n",
"2 10.0 216.666667 49.204927 177.777778 191.666667 200.000000 \n",
"default 10.0 226.666667 108.512830 177.777778 180.555556 194.444444 \n",
"\n",
" 75% max \n",
"16384 233.333333 333.333333 \n",
"2 219.444444 344.444444 \n",
"default 208.333333 533.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_reply_mail'"
]
},
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>236.666667</td>\n",
" <td>11.770555</td>\n",
" <td>211.111111</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>244.444444</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>250.000000</td>\n",
" <td>15.044516</td>\n",
" <td>233.333333</td>\n",
" <td>236.111111</td>\n",
" <td>250.000000</td>\n",
" <td>255.555556</td>\n",
" <td>277.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>228.888889</td>\n",
" <td>14.998857</td>\n",
" <td>211.111111</td>\n",
" <td>213.888889</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 236.666667 11.770555 211.111111 233.333333 233.333333 \n",
"2 10.0 250.000000 15.044516 233.333333 236.111111 250.000000 \n",
"default 10.0 228.888889 14.998857 211.111111 213.888889 233.333333 \n",
"\n",
" 75% max \n",
"16384 244.444444 255.555556 \n",
"2 255.555556 277.777778 \n",
"default 233.333333 255.555556 "
]
},
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"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_type_in_reply_field'"
]
},
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>15.000000</td>\n",
" <td>6.441677</td>\n",
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" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>18.333333</td>\n",
" <td>12.015651</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>16.666667</td>\n",
" <td>30.555556</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>17.777778</td>\n",
" <td>5.737753</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 15.000000 6.441677 5.555556 11.111111 11.111111 \n",
"2 10.0 18.333333 12.015651 5.555556 6.944444 16.666667 \n",
"default 10.0 17.777778 5.737753 11.111111 11.111111 22.222222 \n",
"\n",
" 75% max \n",
"16384 22.222222 22.222222 \n",
"2 30.555556 33.333333 \n",
"default 22.222222 22.222222 "
]
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"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_image_cat'"
]
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>103.333333</td>\n",
" <td>61.764187</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>83.333333</td>\n",
" <td>88.888889</td>\n",
" <td>277.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>106.666667</td>\n",
" <td>76.119897</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>98.888889</td>\n",
" <td>86.772648</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>83.333333</td>\n",
" <td>344.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 103.333333 61.764187 77.777778 77.777778 83.333333 \n",
"2 10.0 106.666667 76.119897 66.666667 77.777778 88.888889 \n",
"default 10.0 98.888889 86.772648 66.666667 66.666667 66.666667 \n",
"\n",
" 75% max \n",
"16384 88.888889 277.777778 \n",
"2 88.888889 322.222222 \n",
"default 83.333333 344.444444 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_search_suggestion'"
]
},
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>19.444444</td>\n",
" <td>9.532991</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",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>26.111111</td>\n",
" <td>10.492011</td>\n",
" <td>5.555556</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</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>14.444444</td>\n",
" <td>8.764563</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</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",
"16384 10.0 19.444444 9.532991 5.555556 11.111111 22.222222 \n",
"2 10.0 26.111111 10.492011 5.555556 22.222222 33.333333 \n",
"default 10.0 14.444444 8.764563 5.555556 11.111111 11.111111 \n",
"\n",
" 75% max \n",
"16384 22.222222 33.333333 \n",
"2 33.333333 33.333333 \n",
"default 19.444444 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_type_searchbox'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
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" <th></th>\n",
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" <th>min</th>\n",
" <th>25%</th>\n",
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" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>6.666667</td>\n",
" <td>2.342428</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>6.111111</td>\n",
" <td>1.756821</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>5.555556</td>\n",
" <td>0.000000</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"16384 10.0 6.666667 2.342428 5.555556 5.555556 5.555556 5.555556 \n",
"2 10.0 6.111111 1.756821 5.555556 5.555556 5.555556 5.555556 \n",
"default 10.0 5.555556 0.000000 5.555556 5.555556 5.555556 5.555556 \n",
"\n",
" max \n",
"16384 11.111111 \n",
"2 11.111111 \n",
"default 5.555556 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_clicktab_0'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" 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>16384</th>\n",
" <td>10.0</td>\n",
" <td>1005.555556</td>\n",
" <td>46.921702</td>\n",
" <td>944.444444</td>\n",
" <td>969.444444</td>\n",
" <td>994.444444</td>\n",
" <td>1033.333333</td>\n",
" <td>1100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>1050.000000</td>\n",
" <td>341.273738</td>\n",
" <td>400.000000</td>\n",
" <td>941.666667</td>\n",
" <td>977.777778</td>\n",
" <td>1191.666667</td>\n",
" <td>1622.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>944.444444</td>\n",
" <td>382.038428</td>\n",
" <td>144.444444</td>\n",
" <td>1088.888889</td>\n",
" <td>1100.000000</td>\n",
" <td>1122.222222</td>\n",
" <td>1233.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 1005.555556 46.921702 944.444444 969.444444 994.444444 \n",
"2 10.0 1050.000000 341.273738 400.000000 941.666667 977.777778 \n",
"default 10.0 944.444444 382.038428 144.444444 1088.888889 1100.000000 \n",
"\n",
" 75% max \n",
"16384 1033.333333 1100.000000 \n",
"2 1191.666667 1622.222222 \n",
"default 1122.222222 1233.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_type_0'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
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" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>43.333333</td>\n",
" <td>15.225781</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",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>47.777778</td>\n",
" <td>14.861039</td>\n",
" <td>33.333333</td>\n",
" <td>36.111111</td>\n",
" <td>44.444444</td>\n",
" <td>52.777778</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>41.111111</td>\n",
" <td>13.907395</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>41.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 43.333333 15.225781 33.333333 33.333333 33.333333 \n",
"2 10.0 47.777778 14.861039 33.333333 36.111111 44.444444 \n",
"default 10.0 41.111111 13.907395 33.333333 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"16384 52.777778 77.777778 \n",
"2 52.777778 77.777778 \n",
"default 41.666667 66.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gslide_ail_pagedown_0'"
]
},
{
"html": [
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>334.444444</td>\n",
" <td>11.049210</td>\n",
" <td>311.111111</td>\n",
" <td>333.333333</td>\n",
" <td>333.333333</td>\n",
" <td>333.333333</td>\n",
" <td>355.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>278.888889</td>\n",
" <td>16.932044</td>\n",
" <td>266.666667</td>\n",
" <td>266.666667</td>\n",
" <td>277.777778</td>\n",
" <td>277.777778</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>322.222222</td>\n",
" <td>10.475656</td>\n",
" <td>300.000000</td>\n",
" <td>322.222222</td>\n",
" <td>322.222222</td>\n",
" <td>330.555556</td>\n",
" <td>333.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 334.444444 11.049210 311.111111 333.333333 333.333333 \n",
"2 10.0 278.888889 16.932044 266.666667 266.666667 277.777778 \n",
"default 10.0 322.222222 10.475656 300.000000 322.222222 322.222222 \n",
"\n",
" 75% max \n",
"16384 333.333333 355.555556 \n",
"2 277.777778 322.222222 \n",
"default 330.555556 333.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gslide_ail_type_0'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>101.111111</td>\n",
" <td>3.513642</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>111.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>100.000000</td>\n",
" <td>7.407407</td>\n",
" <td>88.888889</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>111.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>93.333333</td>\n",
" <td>11.944086</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>97.222222</td>\n",
" <td>122.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 101.111111 3.513642 100.000000 100.000000 100.000000 \n",
"2 10.0 100.000000 7.407407 88.888889 100.000000 100.000000 \n",
"default 10.0 93.333333 11.944086 77.777778 88.888889 88.888889 \n",
"\n",
" 75% max \n",
"16384 100.000000 111.111111 \n",
"2 100.000000 111.111111 \n",
"default 97.222222 122.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_outlook_ail_composemail_0'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>415.555556</td>\n",
" <td>87.989275</td>\n",
" <td>355.555556</td>\n",
" <td>366.666667</td>\n",
" <td>394.444444</td>\n",
" <td>411.111111</td>\n",
" <td>655.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>401.111111</td>\n",
" <td>40.723902</td>\n",
" <td>366.666667</td>\n",
" <td>380.555556</td>\n",
" <td>388.888889</td>\n",
" <td>400.000000</td>\n",
" <td>511.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>398.888889</td>\n",
" <td>17.723683</td>\n",
" <td>377.777778</td>\n",
" <td>388.888889</td>\n",
" <td>400.000000</td>\n",
" <td>400.000000</td>\n",
" <td>433.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 415.555556 87.989275 355.555556 366.666667 394.444444 \n",
"2 10.0 401.111111 40.723902 366.666667 380.555556 388.888889 \n",
"default 10.0 398.888889 17.723683 377.777778 388.888889 400.000000 \n",
"\n",
" 75% max \n",
"16384 411.111111 655.555556 \n",
"2 400.000000 511.111111 \n",
"default 400.000000 433.333333 "
]
},
{
"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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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>128.333333</td>\n",
" <td>228.288347</td>\n",
" <td>5.555556</td>\n",
" <td>13.888889</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>566.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>25.000000</td>\n",
" <td>11.490439</td>\n",
" <td>5.555556</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>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 128.333333 228.288347 5.555556 13.888889 22.222222 \n",
"2 10.0 25.000000 11.490439 5.555556 22.222222 22.222222 \n",
"\n",
" 75% max \n",
"16384 33.333333 566.666667 \n",
"2 33.333333 44.444444 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_compose_new_mail'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>295.555556</td>\n",
" <td>20.420813</td>\n",
" <td>277.777778</td>\n",
" <td>277.777778</td>\n",
" <td>288.888889</td>\n",
" <td>308.333333</td>\n",
" <td>333.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>367.777778</td>\n",
" <td>281.015696</td>\n",
" <td>266.666667</td>\n",
" <td>266.666667</td>\n",
" <td>277.777778</td>\n",
" <td>297.222222</td>\n",
" <td>1166.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>290.000000</td>\n",
" <td>21.243897</td>\n",
" <td>266.666667</td>\n",
" <td>269.444444</td>\n",
" <td>294.444444</td>\n",
" <td>300.000000</td>\n",
" <td>333.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 295.555556 20.420813 277.777778 277.777778 288.888889 \n",
"2 10.0 367.777778 281.015696 266.666667 266.666667 277.777778 \n",
"default 10.0 290.000000 21.243897 266.666667 269.444444 294.444444 \n",
"\n",
" 75% max \n",
"16384 308.333333 333.333333 \n",
"2 297.222222 1166.666667 \n",
"default 300.000000 333.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_type_in_reply_field'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>25.555556</td>\n",
" <td>29.068766</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>30.555556</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>18.333333</td>\n",
" <td>8.302412</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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>16.111111</td>\n",
" <td>22.748400</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>18.055556</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"16384 10.0 25.555556 29.068766 5.555556 11.111111 11.111111 \n",
"2 10.0 18.333333 8.302412 5.555556 11.111111 22.222222 \n",
"default 10.0 16.111111 22.748400 5.555556 5.555556 5.555556 \n",
"\n",
" 75% max \n",
"16384 30.555556 100.000000 \n",
"2 22.222222 33.333333 \n",
"default 18.055556 77.777778 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_select_search_suggestion'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>9.166199</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>11.111111</td>\n",
" <td>19.444444</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>12.222222</td>\n",
" <td>8.996265</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>11.111111</td>\n",
" <td>8.281733</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"16384 10.0 13.888889 9.166199 5.555556 6.944444 11.111111 19.444444 \n",
"2 10.0 12.222222 8.996265 5.555556 5.555556 11.111111 11.111111 \n",
"default 10.0 11.111111 8.281733 5.555556 5.555556 11.111111 11.111111 \n",
"\n",
" max \n",
"16384 33.333333 \n",
"2 33.333333 \n",
"default 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_type_in_search_field'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16384</th>\n",
" <td>10.0</td>\n",
" <td>9.444444</td>\n",
" <td>6.953698</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.0</td>\n",
" <td>17.777778</td>\n",
" <td>8.996265</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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>10.227186</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</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",
"16384 10.0 9.444444 6.953698 5.555556 5.555556 5.555556 \n",
"2 10.0 17.777778 8.996265 5.555556 11.111111 22.222222 \n",
"default 10.0 13.888889 10.227186 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"16384 9.722222 22.222222 \n",
"2 22.222222 33.333333 \n",
"default 22.222222 33.333333 "
]
}
],
"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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AkCVJklQBQ5YkSVIFDFmSJEkVMGRJkiRVwJAlSZJUAUOWJElSBQxZkiRJFTBk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\nSZIkVcCQJUmSVAFDliRJUgX+PxfLYs3eVqyZAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10922e690>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10cef10d0>"
]
},
{
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qocNjptT9ho26urdbaJmhgwf1dguSJPUow1QPmzr6Az26vGGjru7xZUqStDxx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\n",
"text": [
"<matplotlib.figure.Figure at 0x10d2a4050>"
]
},
{
"output_type": "display_data",
"png": 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i4tKIWK151ZQkSRqYenJm6pPAzIbyqcDpmbkNsBA4oTcrJkmSNBh0K0xFxKbA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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10d3a7650>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10d3ffe50>"
]
},
{
"output_type": "display_data",
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NwvOfn++M3nsvqS22gDe8oRKmpBpqb2+npaWFRYsWsXr1ahYtWkRLS4tBS9KY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\nJEmSSmDIkiRJKoEhS5IkqQT/H8uk56ohANf+AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10d4995d0>"
]
},
{
"output_type": "display_data",
"png": 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WAR5pb+PMPCszJ2bmxLFjx/aiGpIkSYNPb0LWQ8COETEmIgLYE7gb+D1wQLXO\nJOCK3lVRkiRp6OlxyMrMmygD3P8G3FHt6yzgWOC/ImIGsAZwTh/UU5IkaUjp1WV1MvME4IQ2d98H\nbN+b/UqSJA11zvguSZJUAy8QPYh4arskDQ89+X/+4Kn71VCTJVv/2Ku6vY3/z7vOkDVIeGq7JA0P\nPf7fOjn7tiIacHYXSpIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmS\nJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmS\nJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1WDUQFdA\nvRMRPd/21J5tl5k9fkypJ3ydSxqKDFlDnB8EGgl8nUsaiuwulCRJqoEhS5IkqQaGLEmSpBoYsiRJ\nkmpgyJIkSaqBIUuSJKkGhixJkqQaGLIkSZJqYMiSJEmqgSFLkiSpBoYsSZKkGvQqZEXEqhFxaUTc\nExHTI2KniFg9Iq6NiHur36v1VWUlSZKGit62ZH0H+FVmbgZsDUwHPgf8NjM3AX5blSVJkkaUHoes\niFgFeANwDkBm/isz5wPvAKZUq00B9u9tJSVJkoaa3rRkbQDMAc6LiFsj4uyIWAEYl5mzqnUeA8b1\ntpKSJElDTW9C1ijgtcAZmbkt8CxtugYzM4Fsb+OIOCIipkbE1Dlz5vSiGpIkSYNPb0LWTGBmZt5U\nlS+lhK7HI2I8QPV7dnsbZ+ZZmTkxMyeOHTu2F9WQJEkafHocsjLzMeDhiNi0umtP4G7gSmBSdd8k\n4Ipe1VCSJGkIGtXL7Y8ELoyIZYD7gA9RgttPIuJQ4EHgwF4+hiRJ0pDTq5CVmbcBE9tZtGdv9itJ\nkjTUOeO7JElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5Yk\nSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIk\nSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk\n1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJU\nA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVINeh6yIWDoibo2Iq6ryBhFxU0TMiIhLImKZ3ldTkiRp\naOmLlqxPAtNbyqcC38rMjYEG4tsqAAAI7UlEQVR5wKF98BiSJElDSq9CVkSsA+wLnF2VA9gDuLRa\nZQqwf28eQ5IkaSjqbUvWt4FjgJeq8hrA/MxcVJVnAmv38jEkSZKGnB6HrIjYD5idmbf0cPsjImJq\nREydM2dOT6shSZI0KPWmJWsX4O0R8QBwMaWb8DvAqhExqlpnHeCR9jbOzLMyc2JmThw7dmwvqiFJ\nkjT49DhkZebnM3OdzJwAHAz8LjPfB/weOKBabRJwRa9rKUmSNMTUMU/WscB/RcQMyhitc2p4DEmS\npEFtVOerdC4zrwOuq27fB2zfF/uVJEkaqpzxXZIkqQaGLEmSpBoYsiRJkmpgyJIkSaqBIUuSJKkG\nhixJkqQaGLIkSZJqYMiSJEmqgSFLkiSpBoYsSZKkGhiyJEmSamDIkiRJqoEhS5IkqQaGLEmSpBoY\nsiRJkmpgyJIkSaqBIUuSJKkGhixJkqQaGLIkSZJqYMiSJEmqgSFLkiSpBoYsSZKkGhiyJEmSamDI\nkiRJqoEhS5IkqQaGLEmSpBoYsiRJkmpgyJIkSaqBIUuSJKkGhixJkqQaGLIkSZJqYMiSJEmqgSFL\nkiSpBoYsSZKkGhiyJEmSamDIkiRJqoEhS5IkqQaGLEmSpBoYsiRJkmpgyJIkSaqBIUuSJKkGPQ5Z\nEbFuRPw+Iu6OiLsi4pPV/atHxLURcW/1e7W+q64kSdLQ0JuWrEXApzNzc2BH4OMRsTnwOeC3mbkJ\n8NuqLEmSNKL0OGRl5qzM/Ft1+2lgOrA28A5gSrXaFGD/3lZSkiRpqOmTMVkRMQHYFrgJGJeZs6pF\njwHjOtjmiIiYGhFT58yZ0xfVkCRJGjR6HbIiYkXgZ8CnMvOp1mWZmUC2t11mnpWZEzNz4tixY3tb\nDUmSpEGlVyErIkZTAtaFmXlZdffjETG+Wj4emN27KkqSJA09vTm7MIBzgOmZ+c2WRVcCk6rbk4Ar\nel49SZKkoWlUL7bdBfgAcEdE3FbddxwwGfhJRBwKPAgc2LsqSpIkDT09DlmZ+UcgOli8Z0/3K0mS\nNBw447skSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJ\nUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJ\nNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTV\nwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQD\nQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUg1pCVkS8JSL+HhEzIuJzdTyGJEnSYNbnISsilgb+F9gH\n2Bx4T0Rs3tePI0mSNJjV0ZK1PTAjM+/LzH8BFwPvqOFxJEmSBq06QtbawMMt5ZnVfZIkSSPGqIF6\n4Ig4AjiiKj4TEX8fqLqMUGsCTwx0JaSa+TrXSODrvP+t35WV6ghZjwDrtpTXqe57mcw8CzirhsdX\nF0TE1MycOND1kOrk61wjga/zwauO7sK/AptExAYRsQxwMHBlDY8jSZI0aPV5S1ZmLoqITwC/BpYG\nzs3Mu/r6cSRJkgazWsZkZeY1wDV17Ft9xq5ajQS+zjUS+DofpCIzB7oOkiRJw46X1ZEkSaqBIWsI\ni4hzI2J2RNzZ5v4jI+KeiLgrIr5a3bd9RNxW/UyLiHe2rH90te6dEXFRRCzXZn+nRcQz/XNUUu9F\nxLoR8fuIuLt6bX9yoOskdUVEfCkiPrOE5WMj4qaIuDUidu3B/g+JiNOr2/t7RZZ6GbKGtvOBt7Te\nERFvpMywv3VmbgF8vVp0JzAxM7eptjkzIkZFxNrAUdWyLSknKxzcsr+JwGp1H4jUxxYBn87MzYEd\ngY/7YaJhYk/gjszcNjNv6OW+9qdc/k41MWQNYZn5B2Bum7s/CkzOzBerdWZXv5/LzEXVOssBrYPx\nRgHLR8QoYAzwKPz/61B+DTimtoOQapCZszLzb9Xtp4HpeOUJDVIR8YWI+EdE/BHYtLpvo4j4VUTc\nEhE3RMRmEbEN8FXgHVWvxPIRcUZETK1abE9s2ecDEbFmdXtiRFzX5jF3Bt4OfK3a10b9dbwjiSFr\n+HkVsGvVnHx9RGzXWBARO0TEXcAdwEcyc1FmPkJp7XoImAUsyMzfVJt8ArgyM2f18zFIfSYiJgDb\nAjcNbE2kfxcRr6P0HmwDvBVo/M8+CzgyM18HfAb4XmbeBvw3cElmbpOZzwNfqCYi3QrYLSK26srj\nZuaNlDksP1vt6599emACBvCyOqrNKGB1ShfJdsBPImLDLG4CtoiIVwNTIuKXwPKU7sUNgPnATyPi\n/cDvgHcDuw/AMUh9IiJWBH4GfCoznxro+kjt2BW4PDOfA4iIKym9DTtT/h831lu2g+0PrC5TNwoY\nT+n+u73WGqvLDFnDz0zgsixzc9wcES9Rrms1p7FCZk6vBrJvSQlX92fmHICIuIzy5p4HbAzMqN7k\nYyJiRmZu3K9HI/VQRIymBKwLM/Oyga6P1A1LAfOrMbQdiogNKK1c22XmvIg4nxLQoIxLbPRWLdfO\n5uoHdhcOPz8H3ggQEa8ClgGeqC5zNKq6f31gM+ABSjfhjhExJkqa2hOYnplXZ+ZamTkhMycAzxmw\nNFRUr+VzKK/lbw50faQl+AOwfzW+aiXgbcBzwP0R8W4or+eI2LqdbVcGngUWRMQ4YJ+WZQ8Ar6tu\nv6uDx34aWKn3h6COGLKGsIi4CPgzsGlEzIyIQ4FzgQ2raR0uBiZVrVqvB6ZFxG3A5cDHMvOJqgvx\nUuBvlLFaS+HswRr6dgE+AOzRMnXJWwe6UlJb1QkalwDTgF9Srv8L8D7g0IiYBtxFGdbRdttpwK3A\nPcCPgT+1LD4R+E5ETAUWd/DwFwOfraaDcOB7DZzxXZIkqQa2ZEmSJNXAkCVJklQDQ5YkSVINDFmS\nJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNfh/YSRfETbqncAAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10d5c7910>"
]
},
{
"output_type": "display_data",
"png": 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NN5XZ2wcMKC0w/T0YXHklXHcdrFidhTdmzIJj2g45pGfqJalHGaa62fSTd+vW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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d65b5d0>"
]
},
{
"output_type": "display_data",
"png": 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46zruwIi4oT5ujIj/3TH/++u8yyJicURM6be+v42IxzbPXkltIuLZEbEkIm6p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\n",
"text": [
"<matplotlib.figure.Figure at 0x10d67cf10>"
]
},
{
"output_type": "display_data",
"png": 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qcePKRR+tWnucf/zx5in6zNJG6+CDS/mvfy1XI953XykvW1a6JWkc1XrmmdIx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\nmJIkSarBMCVJklSDYUqSJKmG/w8gM8Za0kogDQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d70c2d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10d7a8dd0>"
]
},
{
"output_type": "display_data",
"png": 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5RNxV/dyk75srSZI0uHSnZ+p54JOZOQnYEzg6IiYBM4ArMnMH4IqqliRJWqN0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\nDFOSJEk1/D/venGtVvBwUAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d8d4f10>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10d922c10>"
]
},
{
"output_type": "display_data",
"png": 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SJKkCQ5YkSVIFhixJkqQKDFmSJEkVGLIkSZIqMGRJkiRVYMiSJEmqwJAlSZJU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\n",
"text": [
"<matplotlib.figure.Figure at 0x10d970210>"
]
},
{
"output_type": "display_data",
"png": 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LddtHOGSZW7W8QfDdl9E+gc932ENJA1J/vv177dWHtLoL6knN88MGDy43ZdCC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\nXwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d97efd0>"
]
},
{
"output_type": "display_data",
"png": 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BIUuSJKkCQ5YkSVIFhixJkqQKDFmSJEkVGLIkSZIqMGRJkiRVYMiSJEmqwJAl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\nl5Y2AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10d900450>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10dac0fd0>"
]
},
{
"output_type": "display_data",
"png": 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33x+x5ZYRjzwSEfn1SRF//3uaP3ZsxB57lI+NlpaIs88uB/CXX06/qw76faB7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\n",
"text": [
"<matplotlib.figure.Figure at 0x10dc29590>"
]
},
{
"output_type": "display_data",
"png": 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E3BoRd0bEm7uw/SMj4n+K8UN8Qkl9mUw1hwuBAypnRMR+5B7nd00p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\n",
"text": [
"<matplotlib.figure.Figure at 0x10dc7ed90>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10dd5e9d0>"
]
},
{
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8Ged225VHr512Ko9e06aVR2V5z+ljNoyP1X+/kgbPO+GPAnX+UXJESpKkTc+b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\n",
"text": [
"<matplotlib.figure.Figure at 0x10ddb6950>"
]
},
{
"output_type": "display_data",
"png": 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zywXWt9yyT8pVz7JnanE3e3ZzqO6228o3cOOyK5MmlYUvH3mktD/5yXImy9JL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\nCQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10deefad0>"
]
},
{
"output_type": "display_data",
"png": 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U33Z3FST1II7JkiRJqoEhS5IkqQaGLEmSpBoYsiRJkmpgyJIkSaqBVxdK6lJr\ncwXeI2e8p4aarN6OX7hqjY/ZbENvoSOplSFLUpdZ6+kbTu/++fwkaU3ZXShJklQDQ5YkSVINDFmS\nJEk1MGRJkiTVwJAlSZJUA0OWJElSDdoMWRGxfURMiYj7I2JGRHyyWv+1iJgTEXdXj0ObjvliRMyK\niD9FxMF1vgBJkqSeqD3zZL0MnJKZd0bEIOCOiLi+2vadzDyreeeI2B04Fngd8Crghoh4TWYu7cyK\nS5Ik9WRttmRl5tzMvLNaXgjMBIas5pDDgYsz86XMfAiYBezTGZWVJEnqLdZoTFZEDAX2Am6rVn08\nIu6NiB9FxBbVuiHAY02HzWb1oUySJGmd0+6QFRGbAL8CPpWZzwHnAzsDI4C5wLfX5MQRMTYipkXE\ntHnz5q3JoZIkST1eu0JWRAygBKyfZeZlAJn5ZGYuzcxlwA9p7RKcA2zfdPh21brlZOYFmdmSmS2D\nBw/uyGuQJEnqcdpzdWEAk4CZmXl20/ptm3Z7HzC9Wr4SODYiNoiInYBdgds7r8qSJEk9X3uuLnwz\n8CHgvoi4u1r3JWB0RIwAEngY+FeAzJwREZcC91OuTDzZKwslSVJf02bIysypQKxk09WrOWYCMKED\n9ZIkSerVnPFdkiSpBoYsSZKkGhiyJEmSamDIkiRJqoEhS5IkqQaGLEmSpBoYsiRJkmpgyJIkSaqB\nIUuSJKkGhixJkqQaGLIkSZJqYMiSJEmqgSFLkiSpBoYsSZKkGhiyJEmSamDIkiRJqoEhS5IkqQaG\nLEmSpBoYsiRJkmpgyJIkSaqBIUuSJKkGhixJkqQaGLIkSZJqYMiSJEmqgSFLkiSpBoYsSZKkGrQZ\nsiJi+4iYEhH3R8SMiPhktX7LiLg+Iv5S/btFtT4i4tyImBUR90bEG+p+EZIkST1Ne1qyXgZOyczd\ngf2AkyNid+BU4MbM3BW4sSoDvAvYtXqMBc7v9FpLkiT1cG2GrMycm5l3VssLgZnAEOBw4KJqt4uA\nI6rlw4GfZHErsHlEbNvpNZckSerB1mhMVkQMBfYCbgO2ycy51aYngG2q5SHAY02Hza7WSZIk9Rnt\nDlkRsQnwK+BTmflc87bMTCDX5MQRMTYipkXEtHnz5q3JoZIkST1eu0JWRAygBKyfZeZl1eonG92A\n1b9PVevnANs3Hb5dtW45mXlBZrZkZsvgwYPXtv6SJEk9UnuuLgxgEjAzM89u2nQlcEK1fAJwRdP6\n46urDPcDFjR1K0qSJPUJ/duxz5uBDwH3RcTd1bovAacDl0bEGOAR4Ohq29XAocAs4EXgw51aY0mS\npF6gzZCVmVOBWMXmg1ayfwKvLQB8AAALCUlEQVQnd7BekiRJvZozvkuSJNXAkCVJklQDQ5YkSVIN\nDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUw\nZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQ\nJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklSDNkNWRPwoIp6KiOlN674WEXMi4u7qcWjTti9G\nxKyI+FNEHFxXxSVJknqy9rRk/Rg4ZCXrv5OZI6rH1QARsTtwLPC66pjzIqJfZ1VWkiSpt2gzZGXm\nLcDf2vl8hwMXZ+ZLmfkQMAvYpwP1kyRJ6pU6Mibr4xFxb9WduEW1bgjwWNM+s6t1kiRJfcrahqzz\ngZ2BEcBc4Ntr+gQRMTYipkXEtHnz5q1lNSRJknqmtQpZmflkZi7NzGXAD2ntEpwDbN+063bVupU9\nxwWZ2ZKZLYMHD16bakiSJPVYaxWyImLbpuL7gMaVh1cCx0bEBhGxE7ArcHvHqihJktT79G9rh4iY\nDLwN2DoiZgNfBd4WESOABB4G/hUgM2dExKXA/cDLwMmZubSeqkuSJPVckZndXQdaWlpy2rRp3V0N\nSZKkNkXEHZnZ0tZ+zvguSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUw\nZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQ\nJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OW\nJElSDdoMWRHxo4h4KiKmN63bMiKuj4i/VP9uUa2PiDg3ImZFxL0R8YY6Ky9JktRTtacl68fAISus\nOxW4MTN3BW6sygDvAnatHmOB8zunmpIkSb1LmyErM28B/rbC6sOBi6rli4Ajmtb/JItbgc0jYtvO\nqqwkSVJvsbZjsrbJzLnV8hPANtXyEOCxpv1mV+v+SUSMjYhpETFt3rx5a1kNSZKknqnDA98zM4Fc\ni+MuyMyWzGwZPHhwR6shSZLUo6xtyHqy0Q1Y/ftUtX4OsH3TfttV6yRJkvqUtQ1ZVwInVMsnAFc0\nrT++uspwP2BBU7eiJElSn9G/rR0iYjLwNmDriJgNfBU4Hbg0IsYAjwBHV7tfDRwKzAJeBD5cQ50l\nSZJ6vDZDVmaOXsWmg1aybwInd7RSkiRJvZ0zvkuSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTV\nwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQD\nQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0M\nWZIkSTUwZEmSJNWgf0cOjoiHgYXAUuDlzGyJiC2BS4ChwMPA0Zn5TMeqKUmS1Lt0RkvWqMwckZkt\nVflU4MbM3BW4sSpLkiT1KXV0Fx4OXFQtXwQcUcM5JEmSerSOhqwErouIOyJibLVum8ycWy0/AWzT\nwXNIkiT1Oh0akwWMzMw5EfEK4PqIeKB5Y2ZmROTKDqxC2ViAHXbYoYPVkCRJ6lk61JKVmXOqf58C\nLgf2AZ6MiG0Bqn+fWsWxF2RmS2a2DB48uCPVkCRJ6nHWOmRFxMYRMaixDLwTmA5cCZxQ7XYCcEVH\nKylJktTbdKS7cBvg8ohoPM/PM/PaiPgjcGlEjAEeAY7ueDUlSZJ6l7UOWZn5ILDnStY/DRzUkUpJ\nkiT1ds74LkmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAl\nSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5Yk\nSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg1qC1kR\ncUhE/CkiZkXEqXWdR5IkqSeqJWRFRD/ge8C7gN2B0RGxex3nkiRJ6onqasnaB5iVmQ9m5t+Bi4HD\nazqXJElSj1NXyBoCPNZUnl2tkyRJ6hP6d9eJI2IsMLYqPh8Rf+quuvRRWwPzu7sSUs38nKsv8HPe\n9XZsz051haw5wPZN5e2qdf+QmRcAF9R0frUhIqZlZkt310Oqk59z9QV+znuuuroL/wjsGhE7RcT6\nwLHAlTWdS5IkqceppSUrM1+OiI8DvwP6AT/KzBl1nEuSJKknqm1MVmZeDVxd1/Orw+yqVV/g51x9\ngZ/zHioys7vrIEmStM7xtjqSJEk1MGT1chHxo4h4KiKmr7B+XEQ8EBEzIuLMat0+EXF39bgnIt7X\ntP+nq32nR8TkiBi4wvOdGxHPd82rkjomIraPiCkRcX/1uf5kd9dJao+I+FpEfHY12wdHxG0RcVdE\nvGUtnv/EiPivavkI78ZSL0NW7/dj4JDmFRExijLD/p6Z+TrgrGrTdKAlM0dUx/wgIvpHxBDgE9W2\n4ZSLFY5ter4WYIu6X4jUiV4GTsnM3YH9gJP9Y6J1xEHAfZm5V2b+bwef6wjKre9UE0NWL5eZtwB/\nW2H1x4DTM/Olap+nqn9fzMyXq30GAs0D8voDG0ZEf2Aj4HH4x30ovwV8vrYXIXWyzJybmXdWywuB\nmXjXCfVQETE+Iv4cEVOB3ap1O0fEtRFxR0T8b0S8NiJGAGcCh1c9EhtGxPkRMa1qsT2t6Tkfjoit\nq+WWiPifFc75JuC9wLeq59q5q15vX2LIWje9BnhL1aR8c0Ts3dgQEftGxAzgPuCkzHw5M+dQWrse\nBeYCCzLzuuqQjwNXZubcLn4NUqeIiKHAXsBt3VsT6Z9FxBspPQcjgEOBxu/rC4BxmflG4LPAeZl5\nN/AV4JLMHJGZi4Dx1USkrwcOiIjXt+e8mfkHyvyVn6ue66+d+sIEdONtdVSr/sCWlG6SvYFLI+LV\nWdwGvC4ihgEXRcQ1wIaU7sWdgGeBX0TEB4GbgKOAt3XDa5A6LCI2AX4FfCozn+vu+kgr8Rbg8sx8\nESAirqT0NLyJ8ru4sd8Gqzj+6Oo2df2BbSndf/fWWmO1myFr3TQbuCzL/By3R8Qyyr2t5jV2yMyZ\n1UD24ZRw9VBmzgOIiMso/8GfAXYBZlX/0TeKiFmZuUuXvhppLUTEAErA+llmXtbd9ZHWwHrAs9X4\n2VWKiJ0orVx7Z+YzEfFjSkCDMi6x0Vs1cCWHqwvYXbhu+jUwCiAiXgOsD8yvbnPUv1q/I/Ba4GFK\nN+F+EbFRlDR1EDAzM3+bma/MzKGZORR40YCl3qD6HE+ifI7P7u76SKtxC3BENb5qEHAY8CLwUEQc\nBeXzHBF7ruTYTYEXgAURsQ3wrqZtDwNvrJY/sIpzLwQGdfwlaFUMWb1cREwG/g/YLSJmR8QY4EfA\nq6tpHS4GTqhatUYC90TE3cDlwL9l5vyqC/GXwJ2UsVrr4QzC6t3eDHwIOLBp2pJDu7tS0oqqCzQu\nAe4BrqHc+xfgOGBMRNwDzKAM6Vjx2HuAu4AHgJ8Dv2/afBrw3YiYBixdxekvBj5XTQfhwPcaOOO7\nJElSDWzJkiRJqoEhS5IkqQaGLEmSpBoYsiRJkmpgyJIkSaqBIUuSJKkGhixJkqQaGLIkSZJq8P8B\noKYp8/SjfFcAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10dedb2d0>"
]
},
{
"output_type": "display_data",
"png": 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KkiT1dq74LkmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmS\nJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmS\nJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmS\nVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk16HTIioh+EXF7RFxZ1ZtExC0R\n8UBEXBQRq3S+mZIkSb1LV/RkHQ1Ma1OfAvwgM98NPAeM6oJzSJIk9SqdClkRsSHwSeB/qzqA3YBL\nql0mAvt25hySJEm9UWd7sn4IfBNYWNVrA7Myc0FVPw5s0MlzSJIk9TodDlkRsTfwTGbe1sH3HxER\nLRHRMmPGjI42Q5IkqUfqTE/Wh4BPR8TDwC8pw4Q/AtaIiP7VPhsCTyzpzZl5VmYOz8zhgwcP7kQz\nJEmSep4Oh6zMPC4zN8zMIcDBwB8y8xBgMnBAtdtI4DedbqUkSVIvU8c6WWOBMRHxAGWO1jk1nEOS\nJKlH69/+Lu3LzBuAG6rnDwLv74rjSpIk9Vau+C5JklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJU\nA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVIN\nDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUw\nZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1aDD\nISsi3hkRkyPi3oi4JyKOrravFRHXRcT91Z9rdl1zJUmSeofO9GQtAI7JzC2BHYGvRMSWwLHA9Zm5\nGXB9VUuSJPUpHQ5ZmTk9M/9WPX8BmAZsAOwDTKx2mwjs29lGSpIk9TZdMicrIoYA2wG3AOtm5vTq\npaeAdZfyniMioiUiWmbMmNEVzZAkSeoxOh2yImJ14FLgq5n5fNvXMjOBXNL7MvOszByemcMHDx7c\n2WZIkiT1KJ0KWRGxMiVgXZiZl1Wbn46I9arX1wOe6VwTJUmSep/OfLswgHOAaZl5WpuXrgBGVs9H\nAr/pePMkSZJ6p/6deO+HgEOBuyPijmrb8cDJwMURMQp4BDioc02UJEnqfTocsjJzChBLeXn3jh5X\nkiRpReCK75IkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZ\nkiRJNTBkSZIk1cCQJUmSVAOzl8m3AAAFbUlEQVRDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5Yk\nSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIk\nSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUg9pCVkTsERH3RcQDEXFsXeeR\nJEnqiWoJWRHRDzgD2BPYEhgREVvWcS5JkqSeqK6erPcDD2Tmg5n5CvBLYJ+aziVJktTj1BWyNgAe\na1M/Xm2TJEnqE/p314kj4gjgiKp8MSLu66629FHrADO7uxFSzfycqy/wc778bbwsO9UVsp4A3tmm\n3rDa9prMPAs4q6bzqx0R0ZKZw7u7HVKd/JyrL/Bz3nPVNVz4V2CziNgkIlYBDgauqOlckiRJPU4t\nPVmZuSAijgKuBfoB52bmPXWcS5IkqSeqbU5WZl4NXF3X8dVpDtWqL/Bzrr7Az3kPFZnZ3W2QJEla\n4XhbHUmSpBoYsnq5iDg3Ip6JiKmLbW+KiL9HxD0R8b1q2/sj4o7qcWdEfKbN/l+r9p0aEZMiYuBi\nxzs9Il5cPlcldU5EvDMiJkfEvdXn+ujubpO0LCLi2xHx9Td4fXBE3BIRt0fEhztw/MMi4sfV8329\nG0u9DFm93/nAHm03RMSulBX235uZWwGnVi9NBYZn5rbVe86MiP4RsQHwn9VrwyhfVji4zfGGA2vW\nfSFSF1oAHJOZWwI7Al/xHxOtIHYH7s7M7TLzj5081r6UW9+pJoasXi4zbwKeXWzzkcDJmflytc8z\n1Z9zM3NBtc9AoO2EvP7AqhHRHxgEPAmv3Yfy+8A3a7sIqYtl5vTM/Fv1/AVgGt51Qj1URIyLiH9E\nxBRg82rbphHx24i4LSL+GBFbRMS2wPeAfaoRiVUj4qcR0VL12J7Y5pgPR8Q61fPhEXHDYuf8IPBp\n4PvVsTZdXtfblxiyVkzvAT5cdSnfGBE7NF6IiA9ExD3A3cDozFyQmU9QerseBaYDszPzd9VbjgKu\nyMzpy/kapC4REUOA7YBburcl0utFxPaUkYNtgb2Axt/XZwFNmbk98HXgJ5l5B/BfwEWZuW1mvgSM\nqxYi3Qb4SERssyznzcw/Udav/EZ1rH926YUJ6Mbb6qhW/YG1KMMkOwAXR8S7srgF2CoihgITI+Ia\nYFXK8OImwCzgVxHxOeAPwIHALt1wDVKnRcTqwKXAVzPz+e5uj7QEHwYuz8y5ABFxBWWk4YOUv4sb\n+w1YyvsPqm5T1x9YjzL8d1etLdYyM2StmB4HLsuyPsetEbGQcm+rGY0dMnNaNZF9GCVcPZSZMwAi\n4jLKf+DPAe8GHqj+Qx8UEQ9k5ruX69VIHRARK1MC1oWZeVl3t0d6E1YCZlXzZ5cqIjah9HLtkJnP\nRcT5lIAGZV5iY7Rq4BLeruXA4cIV06+BXQEi4j3AKsDM6jZH/avtGwNbAA9Thgl3jIhBUdLU7sC0\nzLwqM9+RmUMycwgw14Cl3qD6HJ9D+Ryf1t3tkd7ATcC+1fyqtwCfAuYCD0XEgVA+zxHx3iW8963A\nHGB2RKwL7NnmtYeB7avn+y/l3C8Ab+n8JWhpDFm9XERMAv4MbB4Rj0fEKOBc4F3Vsg6/BEZWvVo7\nA3dGxB3A5cCXM3NmNYR4CfA3ylytlXAFYfVuHwIOBXZrs2zJXt3dKGlx1Rc0LgLuBK6h3PsX4BBg\nVETcCdxDmdKx+HvvBG4H/g78Ari5zcsnAj+KiBbg1aWc/pfAN6rlIJz4XgNXfJckSaqBPVmSJEk1\nMGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg3+P0JyNEmoFiN5AAAA\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10dccd710>"
]
},
{
"output_type": "display_data",
"png": 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70UalJ+Wuu+ANbyg9UU89VXq+Ro8uPTqLFpVexpEjV6hnUAPT6v6VNY497H1P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\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10e088190>"
]
},
{
"output_type": "display_data",
"png": 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XtDpJkqTNxrRNfE+yCBj7joM7klw5XX3RrLMTcPN0d0LSJsf3Fk3WHpNp1CNk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\nZEmSJHVgyJIkSerg/wPE6neo8nZ5NQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10e0d7650>"
]
},
{
"output_type": "display_data",
"png": 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SPXduxNVXV38X5s1L66oEogUL0nFVCUSLFqXAUvldW7o0/T5UAhDewev52u29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\n",
"text": [
"<matplotlib.figure.Figure at 0x10df1a250>"
]
},
{
"output_type": "display_data",
"png": 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LFAp77FHKG24IH/kIvPSl/fs+pL6y9trNObn22w8eeKDZ8tqYaLZxt+tRR5Wf\ngcYYw3nzmssoSVohQ5YGv+eeW3K27E9+Ek47rVl+4xvL1AhQ/sCst16ZFgFg/fXLnX2Nu/1e/OKy\n8PGOO5byaqt5a7xWba0z+H/oQ2W6kMa2HXeEvfZqlv/5n5e8EeP660vwkrRUTuGgweess8pYqIMO\nKuXttoMJE5rLxVx3XXPc02qrlcHor3xlKUeUO/xaNcavSFrSP/3TkuVJk5Zc6PqAA2DrreH8avW0\ns88uywltvXX/1VEawAxZGnhuvLHcct5YYPeQQ0p334wZpfyjH5Vb1Rsh69hjm90dAL/97ZLHe7sz\nE0t9orEIdsPUqc3xh88+W1rCPvlJ+MpXShfjcceV1zRahqUhxpCl/vf442Wx4le/upRPPbXctddo\nYfrWt+DnPy9jRaAM0m0MNIeyvEjrDNkHHtg/9Za0pIkTm49Hjix3MjaW/LnnHjjppPKzu+OOpVvx\niCPK3bqGLg0RDjZRPZ55pnln0pVXls8Lq7W8Tj65rK/XWAA3swymbUybMHlymbW64WMfay4zA+Xu\nv1jWspmSOiKi3DAydmwpb7JJuUux0eV4111lvq5Gd+PVV8Ob3wy33FLKjcH10irEkKX23X13aX16\n+OFSnj69DCy/665SnjOnfG60TO23X1l3rRGUDjusTKDY+A94s82aY6gkDV5rrNG8yaSrq6yI0Gj9\neuqp8s9Vo6v/jDNg3LjmslLz55ebWqRBzJClFXvqKfjNb5oh6U9/KmvmNVqbbr+9jMO44YZS3n77\nMhajMfi80Z238cbl8zbbwHvfWxY7ljR0RDT/udp5Z/jDH8ri1lB+P7zlLTBmTCmfdBJssEGzBfzW\nW8ti2NIgYshS8cgjzWkQ/va3ctfQL35RynPnll+IjTX1Ro8uIasx4PUNbyjLdey6aymPH19C1oYb\nlrJde5JWZPfdy00tjSlT9tgDjj+++Xvm6KNLCGu47LKyuoI0gBmyhqLnn4cTT2wONH/qqTJf1He/\nW8prr11aqxrz32y2GVx6afNuv7Fj4bzz4HWvK+XVVy+TcxqmJPWVt74VjjyyWf7sZ+Hb326Wjzyy\nbGuYMqV5B7I0QEQOgMGGXV1d2d3d3elqdNS207btdBVqN+vgWZ2ugjps3DEXrXinQWzUGiO44Tin\nDOkX991Xlr3aeuty08zo0WX1hm98owyinzQJ9t+/uVpDPxp3zEXMOWmvfj9vf1nV39/KiIhrM7Nr\nRfs5hcMA0d8BxB8SdUJ/X3Ne56uwl7+8fEC5aeaBB0qrPJShDzNmwGtfW8qPPgp77glf+EJZb7TR\nuGDru2pmd6EkafAbObK5EPZUvfWlAAAHU0lEQVT665e5+P7lX0r54YfL2K7GzTbd3WXYw+9/X8pP\nP10+pD5myJIkrZoag+i32KLM19cYOD9yZLmZp7EQ/HnnwahRZTJVKF2Rc+Y4d5faZsiSJA0t221X\n1kDdaKNSnjABPv3p5vx8p55aHjcmTr322rJcl6FLL5BjsiRJQ9t225WPhg99qExF01i+66tfLV2L\nd99dyj/9KQwfDu97X//XVYOKIUuSpFZbblk+GqZMaQYsgFNOgbXWaoasz30ONt+8uWi9VLG7UJKk\n5Rk9Gl7zmmb5yitLdyOULsSLLy5rMbY644zmY7sZhyxDliRJL8Tw4WUCZijTQHR3wze/WcqNcVyL\nFpXPjz9epppohLLFi+GJJ/q3vuoYQ5YkSe1qLHC/1lrl8yGHlM9PPFEmRG3cyXjjjeVOxsaKGwsW\nlHUZbe1aJRmyJEmqy4Ybwg9/CBMnlvJ668Hkyc2B9pdcUsZ/XXddKd9+O1x+OTz3XGfqqz5lyJIk\nqb9summZeX7s2FJ+05vgtNPg1a8u5bPOKrPSNyZHveKKMr5r8eJO1FZtMmRJktQpY8fCRz9aZqQH\n+MQnSrAaNaqUzzwTPvOZ5sSq3/8+fP3rHamqXjhDliRJA8W66zZnpocSqq65prnO4owZzfFcAB//\n+JLTS2hAiRwAg+26urqyu7u709UYlKIDC5wOhGtGQ4vXuQaTbadt2+kq1G7WwbM6XYWOiohrM7Nr\nRfs5Gekg5x8CDQVe5xpMhnoAUZPdhZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTV\nwJAlSZJUA0OWJElSDWoJWRGxR0T8JSJui4hj6jiHJEnSQNbnISsihgHfAd4JbAN8ICK26evzSJIk\nDWR1tGS9DrgtM+/IzOeAHwN713AeSZKkAauOkLURcE9LeW61TZIkacjo2ALREXEocGhVfCIi/tKp\nugxRGwAPd7oSUs28zjUUeJ33v01XZqc6Qta9wMYt5bHVtiVk5mnAaTWcXyshIrozs6vT9ZDq5HWu\nocDrfOCqo7vwGmCLiNgsIkYC7wcuqOE8kiRJA1aft2Rl5qKI+Dfgl8Aw4PTMvLmvzyNJkjSQ1TIm\nKzMvBi6u49jqM3bVaijwOtdQ4HU+QEVmdroOkiRJqxyX1ZEkSaqBIWsQi4jTI+KhiLipx/bDI+LP\nEXFzRHyl2va6iLi++rghIt7bsv+R1b43RcT0iFi9x/G+FRFP9M+7ktoXERtHxIyIuKW6tj/Z6TpJ\nKyMiPh8R/7Gc50dHxFUR8aeIeHMvjv/hiPh29XgfV2SplyFrcDsD2KN1Q0TsQplhf7vMfBXwteqp\nm4CuzNy+es1/R8TwiNgI+ET13ATKzQrvbzleF/CSut+I1McWAZ/KzG2AnYCP+8dEq4jdgFmZuUNm\n/rbNY+1DWf5ONTFkDWKZeSXwSI/NhwEnZeaz1T4PVZ+fysxF1T6rA62D8YYDa0TEcGBN4D74+zqU\nXwWOqu1NSDXIzPsz87rq8ePAbFx5QgNUREyOiL9GxExgq2rbKyPiFxFxbUT8NiK2jojtga8Ae1e9\nEmtExPciortqsT2+5ZhzImKD6nFXRFzR45xvBN4DfLU61iv76/0OJYasVc+WwJur5uTfRMRrG09E\nxOsj4mZgFvCxzFyUmfdSWrvuBu4H5mfmpdVL/g24IDPv7+f3IPWZiBgH7ABc1dmaSP8oIl5D6T3Y\nHtgTaPzOPg04PDNfA/wH8N3MvB74HPCTzNw+M58GJlcTkb4aeGtEvHplzpuZv6fMYfnp6li39+kb\nE9DBZXVUm+HAepQuktcCP42IV2RxFfCqiBgPTIuIS4A1KN2LmwGPAWdHxIeAXwP7ATt34D1IfSIi\n1gbOBY7IzAWdro+0FG8GfpaZTwFExAWU3oY3Un4fN/Z70TJev3+1TN1wYENK99+NtdZYK82QteqZ\nC5yXZW6OqyPiecq6VvMaO2Tm7Gog+wRKuLozM+cBRMR5lB/uR4HNgduqH/I1I+K2zNy8X9+N1EsR\nMYISsM7KzPM6XR/pBVgNeKwaQ7tMEbEZpZXrtZn5aEScQQloUMYlNnqrVl/Ky9UP7C5c9fwfsAtA\nRGwJjAQerpY5Gl5t3xTYGphD6SbcKSLWjJKmdgNmZ+ZFmfmyzByXmeOApwxYGiyqa3kq5Vo+udP1\nkZbjSmCfanzVOsC7gaeAOyNiPyjXc0Rst5TXvhh4EpgfEWOAd7Y8Nwd4TfX4/y3j3I8D67T/FrQs\nhqxBLCKmA38AtoqIuRExCTgdeEU1rcOPgYOrVq2JwA0RcT3wM+BfM/PhqgvxHOA6ylit1XD2YA1+\nbwIOBHZtmbpkz05XSuqpukHjJ8ANwCWU9X8BPghMiogbgJspwzp6vvYG4E/An4H/BX7X8vTxwDcj\nohtYvIzT/xj4dDUdhAPfa+CM75IkSTWwJUuSJKkGhixJkqQaGLIkSZJqYMiSJEmqgSFLkiSpBoYs\nSZKkGhiyJEmSamDIkiRJqsH/Bw6Ep10vqGJYAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10e1acf90>"
]
},
{
"output_type": "display_data",
"png": 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XzsDjfOLt389EXcLUdcC+Pe192mH3U1WnAqd2WI86SDJYVQOTXYc0njzOtTPw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\nSZI6MExJkiR1YJiSJEnq4P8DU35fn+OIa8IAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10e247590>"
]
},
{
"output_type": "display_data",
"png": 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6KqlzDRsGn/xk08jqr77q90DdjiOgS7UyaRJ8/etw8cW5/MEPWhulnmerrfJ3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\nkqQS/j9dquQ+q9J4KgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10e3828d0>"
]
}
],
"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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