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network.buffer.cache.count
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network.buffer.cache.count.ipynb
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"metadata": {
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"name": "ipython",
"version": 2
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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
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"outputs": [
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_hover_related_product_thumbnail'"
]
},
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"<div>\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>485.555556</td>\n",
" <td>12.883353</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>1000</th>\n",
" <td>10.0</td>\n",
" <td>440.000000</td>\n",
" <td>30.180072</td>\n",
" <td>400.000000</td>\n",
" <td>422.222222</td>\n",
" <td>433.333333</td>\n",
" <td>452.777778</td>\n",
" <td>511.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>442.222222</td>\n",
" <td>35.447366</td>\n",
" <td>388.888889</td>\n",
" <td>425.000000</td>\n",
" <td>438.888889</td>\n",
" <td>461.111111</td>\n",
" <td>500.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 485.555556 12.883353 466.666667 477.777778 488.888889 \n",
"1000 10.0 440.000000 30.180072 400.000000 422.222222 433.333333 \n",
"default 10.0 442.222222 35.447366 388.888889 425.000000 438.888889 \n",
"\n",
" 75% max \n",
"1 497.222222 500.000000 \n",
"1000 452.777778 511.111111 \n",
"default 461.111111 500.000000 "
]
},
{
"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>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>1000</th>\n",
" <td>10.0</td>\n",
" <td>9.444444</td>\n",
" <td>6.953698</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"1 10.0 9.444444 6.953698 5.555556 5.555556 5.555556 9.722222 \n",
"1000 10.0 9.444444 6.953698 5.555556 5.555556 5.555556 9.722222 \n",
"default 10.0 7.777778 5.367177 5.555556 5.555556 5.555556 5.555556 \n",
"\n",
" max \n",
"1 22.222222 \n",
"1000 22.222222 \n",
"default 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_type_in_search_field'"
]
},
{
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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>17.222222</td>\n",
" <td>6.651217</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>16.666667</td>\n",
" <td>7.856742</td>\n",
" <td>11.111111</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>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% \\\n",
"1 10.0 17.222222 6.651217 5.555556 11.111111 22.222222 \n",
"1000 10.0 16.666667 7.856742 11.111111 11.111111 11.111111 \n",
"default 10.0 7.222222 5.270463 5.555556 5.555556 5.555556 \n",
"\n",
" 75% max \n",
"1 22.222222 22.222222 \n",
"1000 22.222222 33.333333 \n",
"default 5.555556 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_close_chat_tab'"
]
},
{
"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",
" <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>52.222222</td>\n",
" <td>20.319803</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>50.000000</td>\n",
" <td>63.888889</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>47.777778</td>\n",
" <td>13.907395</td>\n",
" <td>33.333333</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>45.555556</td>\n",
" <td>12.227833</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 52.222222 20.319803 33.333333 33.333333 50.000000 \n",
"1000 10.0 47.777778 13.907395 33.333333 33.333333 50.000000 \n",
"default 10.0 45.555556 12.227833 33.333333 33.333333 44.444444 \n",
"\n",
" 75% max \n",
"1 63.888889 88.888889 \n",
"1000 55.555556 66.666667 \n",
"default 55.555556 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>1</th>\n",
" <td>10.0</td>\n",
" <td>173.333333</td>\n",
" <td>61.797492</td>\n",
" <td>111.111111</td>\n",
" <td>133.333333</td>\n",
" <td>144.444444</td>\n",
" <td>222.222222</td>\n",
" <td>288.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>176.666667</td>\n",
" <td>36.080234</td>\n",
" <td>144.444444</td>\n",
" <td>158.333333</td>\n",
" <td>166.666667</td>\n",
" <td>183.333333</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>220.000000</td>\n",
" <td>77.654223</td>\n",
" <td>111.111111</td>\n",
" <td>158.333333</td>\n",
" <td>227.777778</td>\n",
" <td>286.111111</td>\n",
" <td>311.111111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 173.333333 61.797492 111.111111 133.333333 144.444444 \n",
"1000 10.0 176.666667 36.080234 144.444444 158.333333 166.666667 \n",
"default 10.0 220.000000 77.654223 111.111111 158.333333 227.777778 \n",
"\n",
" 75% max \n",
"1 222.222222 288.888889 \n",
"1000 183.333333 266.666667 \n",
"default 286.111111 311.111111 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab_emoji'"
]
},
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"<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>70.000000</td>\n",
" <td>13.907395</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>65.555556</td>\n",
" <td>15.225781</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>64.444444</td>\n",
" <td>10.210406</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</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 70.000000 13.907395 44.444444 66.666667 66.666667 \n",
"1000 10.0 65.555556 15.225781 44.444444 55.555556 66.666667 \n",
"default 10.0 64.444444 10.210406 55.555556 55.555556 66.666667 \n",
"\n",
" 75% max \n",
"1 77.777778 88.888889 \n",
"1000 66.666667 100.000000 \n",
"default 66.666667 88.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_photo_viewer_right_arrow'"
]
},
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" <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>80.000000</td>\n",
" <td>7.027284</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>86.111111</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>91.111111</td>\n",
" <td>10.210406</td>\n",
" <td>77.777778</td>\n",
" <td>80.555556</td>\n",
" <td>94.444444</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>95.555556</td>\n",
" <td>5.737753</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 80.000000 7.027284 66.666667 77.777778 77.777778 \n",
"1000 10.0 91.111111 10.210406 77.777778 80.555556 94.444444 \n",
"default 10.0 95.555556 5.737753 88.888889 88.888889 100.000000 \n",
"\n",
" 75% max \n",
"1 86.111111 88.888889 \n",
"1000 100.000000 100.000000 \n",
"default 100.000000 100.000000 "
]
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" <td>10.734353</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>52.777778</td>\n",
" <td>66.666667</td>\n",
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" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>10.475656</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>43.333333</td>\n",
" <td>11.049210</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>38.888889</td>\n",
" <td>55.555556</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 48.888889 10.734353 33.333333 44.444444 44.444444 \n",
"1000 10.0 44.444444 10.475656 33.333333 33.333333 44.444444 \n",
"default 10.0 43.333333 11.049210 33.333333 33.333333 38.888889 \n",
"\n",
" 75% max \n",
"1 52.777778 66.666667 \n",
"1000 55.555556 55.555556 \n",
"default 55.555556 55.555556 "
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" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>66.666667</td>\n",
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" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>36.666667</td>\n",
" <td>7.499428</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",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>28.888889</td>\n",
" <td>9.369712</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</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",
"1 10.0 32.222222 14.296488 11.111111 25.000000 33.333333 \n",
"1000 10.0 36.666667 7.499428 33.333333 33.333333 33.333333 \n",
"default 10.0 28.888889 9.369712 11.111111 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"1 33.333333 66.666667 \n",
"1000 33.333333 55.555556 \n",
"default 33.333333 33.333333 "
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" <td>10.0</td>\n",
" <td>36.666667</td>\n",
" <td>10.540926</td>\n",
" <td>22.222222</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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" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>45.555556</td>\n",
" <td>8.198498</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>52.777778</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>23.333333</td>\n",
" <td>11.049210</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 36.666667 10.540926 22.222222 33.333333 33.333333 \n",
"1000 10.0 45.555556 8.198498 33.333333 44.444444 44.444444 \n",
"default 10.0 23.333333 11.049210 11.111111 11.111111 27.777778 \n",
"\n",
" 75% max \n",
"1 33.333333 55.555556 \n",
"1000 52.777778 55.555556 \n",
"default 33.333333 33.333333 "
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>10.555556</td>\n",
" <td>6.651217</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>23.888889</td>\n",
" <td>22.383673</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>16.666667</td>\n",
" <td>33.333333</td>\n",
" <td>77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>11.490439</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>19.444444</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 10.555556 6.651217 5.555556 5.555556 8.333333 \n",
"1000 10.0 23.888889 22.383673 5.555556 6.944444 16.666667 \n",
"default 10.0 13.888889 11.490439 5.555556 5.555556 8.333333 \n",
"\n",
" 75% max \n",
"1 11.111111 22.222222 \n",
"1000 33.333333 77.777778 \n",
"default 19.444444 33.333333 "
]
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" <td>42.222222</td>\n",
" <td>13.658584</td>\n",
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" <td>36.111111</td>\n",
" <td>44.444444</td>\n",
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" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>35.555556</td>\n",
" <td>7.027284</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>41.666667</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>48.888889</td>\n",
" <td>9.369712</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 42.222222 13.658584 11.111111 36.111111 44.444444 \n",
"1000 10.0 35.555556 7.027284 22.222222 33.333333 33.333333 \n",
"default 10.0 48.888889 9.369712 33.333333 44.444444 44.444444 \n",
"\n",
" 75% max \n",
"1 52.777778 55.555556 \n",
"1000 41.666667 44.444444 \n",
"default 55.555556 66.666667 "
]
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" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>67.777778</td>\n",
" <td>11.049210</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
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" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>60.000000</td>\n",
" <td>14.054567</td>\n",
" <td>44.444444</td>\n",
" <td>47.222222</td>\n",
" <td>61.111111</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 66.666667 12.830006 44.444444 58.333333 66.666667 \n",
"1000 10.0 67.777778 11.049210 44.444444 66.666667 66.666667 \n",
"default 10.0 60.000000 14.054567 44.444444 47.222222 61.111111 \n",
"\n",
" 75% max \n",
"1 75.000000 88.888889 \n",
"1000 66.666667 88.888889 \n",
"default 66.666667 88.888889 "
]
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" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>200.000000</td>\n",
" <td>18.144368</td>\n",
" <td>166.666667</td>\n",
" <td>191.666667</td>\n",
" <td>200.0</td>\n",
" <td>211.111111</td>\n",
" <td>222.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>200.000000</td>\n",
" <td>5.237828</td>\n",
" <td>188.888889</td>\n",
" <td>200.000000</td>\n",
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" <td>211.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>202.222222</td>\n",
" <td>13.658584</td>\n",
" <td>177.777778</td>\n",
" <td>200.000000</td>\n",
" <td>200.0</td>\n",
" <td>208.333333</td>\n",
" <td>222.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 200.000000 18.144368 166.666667 191.666667 200.0 \n",
"1000 10.0 200.000000 5.237828 188.888889 200.000000 200.0 \n",
"default 10.0 202.222222 13.658584 177.777778 200.000000 200.0 \n",
"\n",
" 75% max \n",
"1 211.111111 222.222222 \n",
"1000 200.000000 211.111111 \n",
"default 208.333333 222.222222 "
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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>211.111111</td>\n",
" <td>67.076927</td>\n",
" <td>177.777778</td>\n",
" <td>180.555556</td>\n",
" <td>194.444444</td>\n",
" <td>200.000000</td>\n",
" <td>400.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>225.555556</td>\n",
" <td>43.524428</td>\n",
" <td>177.777778</td>\n",
" <td>200.000000</td>\n",
" <td>222.222222</td>\n",
" <td>233.333333</td>\n",
" <td>333.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>217.777778</td>\n",
" <td>54.734676</td>\n",
" <td>177.777778</td>\n",
" <td>191.666667</td>\n",
" <td>200.000000</td>\n",
" <td>216.666667</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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" count mean std min 25% 50% \\\n",
"1 10.0 211.111111 67.076927 177.777778 180.555556 194.444444 \n",
"1000 10.0 225.555556 43.524428 177.777778 200.000000 222.222222 \n",
"default 10.0 217.777778 54.734676 177.777778 191.666667 200.000000 \n",
"\n",
" 75% max \n",
"1 200.000000 400.000000 \n",
"1000 233.333333 333.333333 \n",
"default 216.666667 366.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_reply_mail'"
]
},
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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>228.888889</td>\n",
" <td>14.054567</td>\n",
" <td>200.000000</td>\n",
" <td>222.222222</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>227.777778</td>\n",
" <td>9.442629</td>\n",
" <td>211.111111</td>\n",
" <td>225.000000</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>247.777778</td>\n",
" <td>11.770555</td>\n",
" <td>233.333333</td>\n",
" <td>236.111111</td>\n",
" <td>250.000000</td>\n",
" <td>255.555556</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 10.0 228.888889 14.054567 200.000000 222.222222 233.333333 \n",
"1000 10.0 227.777778 9.442629 211.111111 225.000000 233.333333 \n",
"default 10.0 247.777778 11.770555 233.333333 236.111111 250.000000 \n",
"\n",
" 75% max \n",
"1 233.333333 255.555556 \n",
"1000 233.333333 233.333333 \n",
"default 255.555556 266.666667 "
]
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"output_type": "display_data",
"text": [
"u'test_firefox_gmail_ail_type_in_reply_field'"
]
},
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
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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>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>1000</th>\n",
" <td>10.0</td>\n",
" <td>17.222222</td>\n",
" <td>6.651217</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>18.888889</td>\n",
" <td>7.499428</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <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 8.466022 5.555556 11.111111 16.666667 \n",
"1000 10.0 17.222222 6.651217 5.555556 11.111111 22.222222 \n",
"default 10.0 18.888889 7.499428 11.111111 11.111111 22.222222 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"1000 22.222222 22.222222 \n",
"default 22.222222 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_image_cat'"
]
},
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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",
" <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>105.555556</td>\n",
" <td>99.965701</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>72.222222</td>\n",
" <td>86.111111</td>\n",
" <td>388.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>97.777778</td>\n",
" <td>36.962889</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" <td>97.222222</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>116.666667</td>\n",
" <td>111.511624</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>83.333333</td>\n",
" <td>88.888889</td>\n",
" <td>433.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 105.555556 99.965701 66.666667 66.666667 72.222222 \n",
"1000 10.0 97.777778 36.962889 77.777778 77.777778 88.888889 \n",
"default 10.0 116.666667 111.511624 66.666667 77.777778 83.333333 \n",
"\n",
" 75% max \n",
"1 86.111111 388.888889 \n",
"1000 97.222222 200.000000 \n",
"default 88.888889 433.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_select_search_suggestion'"
]
},
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>15.555556</td>\n",
" <td>8.996265</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>16.666667</td>\n",
" <td>10.475656</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>25.555556</td>\n",
" <td>9.147473</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 15.555556 8.996265 5.555556 11.111111 11.111111 \n",
"1000 10.0 16.666667 10.475656 5.555556 11.111111 11.111111 \n",
"default 10.0 25.555556 9.147473 11.111111 22.222222 27.777778 \n",
"\n",
" 75% max \n",
"1 22.222222 33.333333 \n",
"1000 22.222222 33.333333 \n",
"default 33.333333 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_type_searchbox'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
" .dataframe tbody tr th {\n",
" 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",
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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>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>1000</th>\n",
" <td>10.0</td>\n",
" <td>10.555556</td>\n",
" <td>9.604669</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>9.444444</td>\n",
" <td>5.270463</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 6.111111 1.756821 5.555556 5.555556 5.555556 5.555556 \n",
"1000 10.0 10.555556 9.604669 5.555556 5.555556 5.555556 9.722222 \n",
"default 10.0 9.444444 5.270463 5.555556 5.555556 8.333333 11.111111 \n",
"\n",
" max \n",
"1 11.111111 \n",
"1000 33.333333 \n",
"default 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_clicktab_0'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
" .dataframe tbody tr th {\n",
" 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>1</th>\n",
" <td>10.0</td>\n",
" <td>990.000000</td>\n",
" <td>76.524291</td>\n",
" <td>833.333333</td>\n",
" <td>977.777778</td>\n",
" <td>1000.000000</td>\n",
" <td>1019.444444</td>\n",
" <td>1133.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>907.777778</td>\n",
" <td>199.178698</td>\n",
" <td>455.555556</td>\n",
" <td>916.666667</td>\n",
" <td>988.888889</td>\n",
" <td>1008.333333</td>\n",
" <td>1066.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>892.222222</td>\n",
" <td>246.848448</td>\n",
" <td>266.666667</td>\n",
" <td>880.555556</td>\n",
" <td>961.111111</td>\n",
" <td>977.777778</td>\n",
" <td>1200.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 990.000000 76.524291 833.333333 977.777778 1000.000000 \n",
"1000 10.0 907.777778 199.178698 455.555556 916.666667 988.888889 \n",
"default 10.0 892.222222 246.848448 266.666667 880.555556 961.111111 \n",
"\n",
" 75% max \n",
"1 1019.444444 1133.333333 \n",
"1000 1008.333333 1066.666667 \n",
"default 977.777778 1200.000000 "
]
},
{
"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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" .dataframe tbody tr th {\n",
" 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>1</th>\n",
" <td>10.0</td>\n",
" <td>65.555556</td>\n",
" <td>71.520811</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</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",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>55.555556</td>\n",
" <td>12.830006</td>\n",
" <td>33.333333</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</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",
"1 10.0 65.555556 71.520811 33.333333 33.333333 44.444444 \n",
"1000 10.0 41.111111 13.907395 33.333333 33.333333 33.333333 \n",
"default 10.0 55.555556 12.830006 33.333333 55.555556 55.555556 \n",
"\n",
" 75% max \n",
"1 55.555556 266.666667 \n",
"1000 41.666667 66.666667 \n",
"default 66.666667 66.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gslide_ail_pagedown_0'"
]
},
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"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\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>316.666667</td>\n",
" <td>15.930232</td>\n",
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" <td>344.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% 75% \\\n",
"1 10.0 316.666667 15.930232 300.0 302.777778 311.111111 330.555556 \n",
"\n",
" max \n",
"1 344.444444 "
]
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{
"output_type": "display_data",
"text": [
"u'test_firefox_gslide_ail_type_0'"
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" count mean std min 25% 50% 75% max\n",
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]
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"output_type": "display_data",
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" <td>342.222222</td>\n",
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" <td>66.666667</td>\n",
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" <td>422.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"1 10.0 388.888889 20.951312 366.666667 377.777778 383.333333 \n",
"1000 10.0 342.222222 97.260134 66.666667 366.666667 366.666667 \n",
"default 10.0 393.333333 22.345338 355.555556 380.555556 394.444444 \n",
"\n",
" 75% max \n",
"1 397.222222 433.333333 \n",
"1000 375.000000 388.888889 \n",
"default 408.333333 422.222222 "
]
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"u'test_firefox_outlook_ail_type_composemail_0'"
]
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" <td>17.777778</td>\n",
" <td>11.653432</td>\n",
" <td>5.555556</td>\n",
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" <td>33.333333</td>\n",
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" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
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" <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",
"1 10.0 20.555556 24.989710 5.555556 6.944444 11.111111 \n",
"1000 10.0 17.777778 11.653432 5.555556 11.111111 11.111111 \n",
"default 10.0 26.666667 10.734353 11.111111 22.222222 27.777778 \n",
"\n",
" 75% max \n",
"1 22.222222 88.888889 \n",
"1000 30.555556 33.333333 \n",
"default 33.333333 44.444444 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_compose_new_mail'"
]
},
{
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <th>1000</th>\n",
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" <td>323.333333</td>\n",
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" <td>266.666667</td>\n",
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" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>343.333333</td>\n",
" <td>184.699363</td>\n",
" <td>266.666667</td>\n",
" <td>277.777778</td>\n",
" <td>277.777778</td>\n",
" <td>300.000000</td>\n",
" <td>866.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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" count mean std min 25% 50% \\\n",
"1 10.0 263.333333 17.411347 233.333333 258.333333 266.666667 \n",
"1000 10.0 323.333333 58.666012 266.666667 277.777778 311.111111 \n",
"default 10.0 343.333333 184.699363 266.666667 277.777778 277.777778 \n",
"\n",
" 75% max \n",
"1 266.666667 300.000000 \n",
"1000 355.555556 433.333333 \n",
"default 300.000000 866.666667 "
]
},
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"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_type_in_reply_field'"
]
},
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" .dataframe tbody tr th:only-of-type {\n",
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" <th>min</th>\n",
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" <th>75%</th>\n",
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" <td>10.0</td>\n",
" <td>12.777778</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>23.333333</td>\n",
" <td>16.932044</td>\n",
" <td>11.111111</td>\n",
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" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
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" <td>37.497142</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>122.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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" count mean std min 25% 50% \\\n",
"1 10.0 12.777778 8.302412 5.555556 5.555556 8.333333 \n",
"1000 10.0 23.333333 16.932044 11.111111 11.111111 22.222222 \n",
"default 10.0 27.777778 37.497142 11.111111 11.111111 11.111111 \n",
"\n",
" 75% max \n",
"1 22.222222 22.222222 \n",
"1000 22.222222 66.666667 \n",
"default 11.111111 122.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_select_search_suggestion'"
]
},
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"<style scoped>\n",
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" <th>75%</th>\n",
" <th>max</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>17.222222</td>\n",
" <td>12.129276</td>\n",
" <td>5.555556</td>\n",
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" <td>22.222222</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>11.111111</td>\n",
" <td>9.442629</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>11.111111</td>\n",
" <td>6.415003</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"1 10.0 17.222222 12.129276 5.555556 6.944444 16.666667 \n",
"1000 10.0 11.111111 9.442629 5.555556 5.555556 5.555556 \n",
"default 10.0 11.111111 6.415003 5.555556 5.555556 11.111111 \n",
"\n",
" 75% max \n",
"1 22.222222 44.444444 \n",
"1000 11.111111 33.333333 \n",
"default 11.111111 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_type_in_search_field'"
]
},
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"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",
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" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>10.0</td>\n",
" <td>10.555556</td>\n",
" <td>9.604669</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>10.0</td>\n",
" <td>7.222222</td>\n",
" <td>5.270463</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>10.555556</td>\n",
" <td>9.604669</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"1 10.0 10.555556 9.604669 5.555556 5.555556 5.555556 9.722222 \n",
"1000 10.0 7.222222 5.270463 5.555556 5.555556 5.555556 5.555556 \n",
"default 10.0 10.555556 9.604669 5.555556 5.555556 5.555556 9.722222 \n",
"\n",
" max \n",
"1 33.333333 \n",
"1000 22.222222 \n",
"default 33.333333 "
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# show plot\n",
"plt.show()"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
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JklQBQ5YkSVIFDFmSJEkVMGRJkiRVwJAlSZJUAUOWJElSBQxZkiRJFTBkSZIk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\nqQL/H85QSnW2xseBAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1019ee690>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1056b20d0>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x105b06b50>"
]
},
{
"output_type": "display_data",
"png": 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EysD/A/YGtgI+EBFb9VXDJEmShoKSK1NvB+allB5OKb0KXAYc0DfNkiRJGhpK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\nrFa96j5hYwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x105b54150>"
]
},
{
"output_type": "display_data",
"png": 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SSreWUG+p5axcmbsm2HLLXL7llty/U6Wvpu99D2bOzP09Qb5U99hj1QcLz54N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\n7JZmmMcrujEAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x105c3b1d0>"
]
},
{
"output_type": "display_data",
"png": 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mlHIGcEaF7WsKkoyUUvp7XYdUk/1ci4H9fO6qcbnwO8AhSQ5O8gjgWOC8CtuR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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x105c81b50>"
]
},
{
"output_type": "display_data",
"png": 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SpH7ISxAD1bvfDWeeWV6vuy789KdlGsAqq8C3vw2bbtp79ZMkqZ+LbHRm7kVj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\nGhiyJEmSamDIkiRJqsH/B9Nm9MQGFGP3AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x105cc6710>"
]
},
{
"output_type": "display_data",
"png": 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kSdKQ1G2Yysw/Ac/tZPwCSv8pSZKkjZZPQJckSarBMCVJklSDYUqSJKkGw5Qk\nSVINhilJkqQaDFOSJEk1GKYkSZJqMExJkiTVYJiSJEmqwTAlSZJUg2FKkiSpBsOUJElSDYYpSZKk\nGgxTkiRJNRimJEmSajBMSZIk1WCYkiRJqsEwJUmSVINhSpIkqQbDlCRJUg2GKUmSpBoMU5IkSTUY\npiRJkmowTEmSJNVgmJIkSarBMCVJklSDYUqSJKkGw5QkSVIN3YapiNg5In4RETdFxI0R8Z5q/NiI\nmB0R86vf2/Z/cyVJkoaWnlyZWgN8IDMnAi8E3hURE4FpwJzM3B2YU9WSJEkblW7DVGYuzsw/VMMP\nAjcDOwJHArOq2WYBk/qrkZIkSUPVevWZiogJwHOB64Dxmbm4mrQEGN+nLZMkSRoGehymImJL4AfA\nezPzgdZpmZlAdrHcCRHRHhHty5cvr9VYSZKkoaZHYSoiRlGC1Lcz84fV6KURsUM1fQdgWWfLZubM\nzGzLzLZx48b1RZslSZKGjJ58mi+A84CbM/OslkmXAVOq4SnApX3fPEmSpKFtZA/meTHwFuDPETGv\nGjcdOAO4KCKOAxYCr++fJkqSJA1d3YapzLwGiC4mH9S3zZEkSRpefAK6JElSDYYpSZKkGgxTkiRJ\nNRimJEmSajBMSZIk1WCYkiRJqsEwJUmSVINhSpIkqQbDlCRJUg2GKUmSpBoMU5IkSTUYpiRJkmow\nTEmSJNVgmJIkSarBMCVJklSDYUqSJKkGw5QkSVINhilJkqQaDFOSJEk1GKYkSZJqMExJkiTVYJiS\nJEmqwTAlSZJUg2FKkiSpBsOUJElSDYYpSZKkGgxTkiRJNRimJEmSaug2TEXE+RGxLCL+0jJubETM\njoj51e9t+7eZkiRJQ1NPrkxdALxyrXHTgDmZuTswp6olSZI2Ot2Gqcz8FbByrdFHArOq4VnApD5u\nlyRJ0rDQ2z5T4zNzcTW8BBjfR+2RJEkaVmp3QM/MBLKr6RFxQkS0R0T78uXL625OkiRpSOltmFoa\nETsAVL+XdTVjZs7MzLbMbBs3blwvNydJkjQ09TZMXQZMqYanAJf2TXMkSZKGl548GuFC4Fpgj4i4\nJyKOA84ADomI+cDBVS1JkrTRGdndDJl5VBeTDurjtkiSJA07PgFdkiSpBsOUJElSDYYpSZKkGgxT\nkiRJNRimJEmSajBMSZIk1WCYkiRJqsEwJUmSVINhSpIkqQbDlCRJUg2GKUmSpBoMU5IkSTUYpiRJ\nkmowTEmSJNVgmJIkSarBMCVJklSDYUqSJKkGw5QkSVINhilJkqQaDFOSJEk1GKYkSZJqMExJkiTV\nYJiSJEmqwTAlSZJUg2FKkiTscRtsAAAEHklEQVSpBsOUJElSDYYpSZKkGgxTkiRJNdQKUxHxyoi4\nNSJuj4hpfdUoSZKk4aLXYSoiNgG+ChwGTASOioiJfdUwSZKk4aDOlakXALdn5oLMfBz4LnBk3zRL\nkiRpeKgTpnYE7m6p76nGSZIkbTRG9vcGIuIE4ISqfCgibu3vbaqD7YH7BrsRUj/zPNfGwPN84O3S\nk5nqhKlFwM4t9U7VuA4ycyYws8Z2VENEtGdm22C3Q+pPnufaGHieD111bvNdD+weEbtGxKbAG4HL\n+qZZkiRJw0Ovr0xl5pqIOBH4GbAJcH5m3thnLZMkSRoGavWZyswrgSv7qC3qH95i1cbA81wbA8/z\nISoyc7DbIEmSNGz5dTKSJEk1GKY2UBFxfkQsi4i/DHZbpPXV2fkbEWMjYnZEzK9+b1uNj4j4UvW1\nVn+KiOe1LDOlmn9+REwZjH2ROhMRn4qID65j+riIuC4i/hgRL+nF+o+NiK9Uw5P8hpL+ZZjacF0A\nvHKwGyH10gX84/k7DZiTmbsDc6oaylda7V79nAB8HUr4Aj4J7E/5xoZPNgKYNAwcBPw5M5+bmb+u\nua5JlK99Uz8xTG2gMvNXwMrBbofUG12cv0cCs6rhWZQ3iMb4b2bxO2CbiNgBeAUwOzNXZuYqYDb+\nB0ODKCI+GhG3RcQ1wB7VuN0i4qcRMTcifh0Re0bEvsB/A0dGxLyI2Cwivh4R7RFxY0Sc0rLOuyJi\n+2q4LSKuXmubBwCvAT5XrWu3gdrfjUm/PwFdkvrI+MxcXA0vAcZXw119tZVfeaUhIyKeT3ke476U\n994/AHMpn9CbmpnzI2J/4GuZeWBEfAJoy8wTq+U/mpkrI2ITYE5E/Gtm/qm77WbmbyPiMuDyzLy4\nn3Zvo2eYkjTsZGZGhB9F1nDyEuCSzHwEoAo4Y4ADgO9HRGO+0V0s//rq69lGAjtQbtt1G6Y0MAxT\nkoaLpRGxQ2Yurm7jLavGd/XVVouAl601/uoBaKfUUyOA1Zm577pmiohdgQ8C+2Xmqoi4gBLEANbQ\n7LIzppPFNQDsMyVpuLgMaHwibwpwacv4Y6pP9b0QuL+6Hfgz4NCI2LbqeH5oNU4aDL8CJlX9n7YC\njgAeAe6MiNfB3z+Zuk8ny24NPAzcHxHjKR+6aLgLeH41PLmLbT8IbFV/F9QVw9QGKiIuBK4F9oiI\neyLiuMFuk9RTXZy/ZwCHRMR84OCqhvItDAuA24FzgXcCZOZK4DOU7xG9Hvh0NU4acJn5B+B7wA3A\nTyjnJMDRwHERcQNwI+UDFWsvewPwR+AW4DvAb1omnwJ8MSLagSe72Px3gQ9Vj1mwA3o/8AnokiRJ\nNXhlSpIkqQbDlCRJUg2GKUmSpBoMU5IkSTUYpiRJkmowTEmSJNVgmJIkSarBMCVJklTD/wdsO4QO\nms8yvAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x105e09f50>"
]
},
{
"output_type": "display_data",
"png": 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T9dzmWwG8bsi4s4FVmXkYsKpsq0m0tbXR19e3w7i+vj7a2tomqSJJ0ljMmjWL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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x105e09f10>"
]
},
{
"output_type": "display_data",
"png": 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hhfmpvIp11rHek6SuFwE77FD9vtl0U3jrW3P1AoBp0+Af/6hffFIbTKYa2aJF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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x105dffc10>"
]
},
{
"output_type": "display_data",
"png": 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J/ZZhSlrVzJ4NkyaVM9OgDP5eZx24++7m+l/8orn+wAPL2J81qo+DCRNK4GoM\n4t5xx9Laspyz29SFLbeErbcu9yNKK1ajmzMCfvMbeN/7SnnhwjIYfsGCUn7gAdhmGzjjjFKeP78E\n2VtvLeUXXjBoSf2EYUoaaJ58Es47r7RaAPz5z2Wczm9+U8pz5pRpBW65pZT32AP+8z+bZ8Adfnhp\neXrJS0p5l13KsoHeHTfQDB5cWqZ22KGUR4yAm2+Go44q5fXWK0Fq771LefbsMpi/MWHpH/5QWrlm\nzizl++8vXa5OWCr1OsOU1N+88EIJSLNnl/Kjj5a5lhrTCyxYUAZCN7reNtsM9t232dX2ylfCY4+V\nZVBmCD/ppDIQGmxhGig22qicbbj99qU8Zkzpgm2Eq003heOOa66/5hr46EfL+wVKK9gOOzTfR/fe\nC5dc0uy+ldRjDFNSX3jyyWYLQmb50pxWTeH9wgvw8pfD1KmlvMEGpQuu0Q235ZZwww3lNH4ok0+e\neWbZB2DNNcs+WvVENKdt2H57OOUU2GKLUj7iiNJq1QhXw4fD7ruXFi8orZlvfzv84x+lfOaZ8I53\nNMPV7NllmorWsxcltcWpEaROWbSonDYPZZDxuuvC0UeX8k47lZajM88sX5A33wyjR5d1a65Z5m3a\ndddSHjy4nDXWsMYasOeevfUsNFBElFbKhre8pdwajjkG3vCG0n0I5f35zDPNsXFf+xr8+Mdl7BaU\nLsW//rXMRg+lxWv99ZvvaUn/ZJiSesJll5UBwv/6r6W8337l7KzLqovInXNOaUFqhKlJk5otClAm\nh2x1wAGdr7NWL8OHw2tf2yyPH19uDRMmlIDf6Aa+9dbS3dxw9NGl9eqmm0p56lQYNqzZQvrii83W\nU2k1Y5iS2nH33fCXv8D+1fXmTjyxzK101VWlPHVquexJI0y95z1LXnZk5swl/6N/73t7p95Su176\n0nJr+M53llz/wQ+W7umGqVNh442bYerVry6tqWeeWco/+Um5NM+rXtXZekv9gGFKgvIlcdddZdxR\nROnu+O//ht/+tpSnTClddc88U/77Hj26DPJuOO205vXdoHzxtLJrRAPdO9+5ZPnqq5tzlUGZ2LVx\nkgOUwfCHH94MU694RQlejUldL7mkXJpn1KjO1lvqBbbJavWxcGEZJwJlMsWjjmoGojPPLB/28+eX\ncmYJUY2JLD/ykdLy1HDMMeU09IYRI5xaQKuXiCXf85/7HIwd2yzfdRd8/vPl/qJFZWD8JpuU8lNP\nlcHwP/xhKT/7bOlivPjiUl68GO64wzMPNWAYprRqaZyJNHt2mXm6MRfTjBllzEhj7qVHH4Urr4SH\nq4vI7b8/nH9+s3Xpve8trVIYxS38AAAKQUlEQVSNwbqjR5f/oh0TIrVnxIjmgPghQ0q3X6N7e+jQ\n8g9No1t8wYLyj00jPN17b5n/rHGB7AcegH/7N7jttlJetAiee673novUjW6/GSLiBxExLyJubVk2\nPCIui4i7q5+r6bUk1GeeeqpcQ+6uu0r59ttLWJoxo5QffbSEqcaH7557lmvMjRxZygccUALXTjuV\n8rbblkt5tHbVSeqMIUPKGKvG7PCjRpWTMBrXhNx00/L3/aY3lfLf/lZO4micaXjFFaVV7OqrS/nO\nO8vfd6NlWepl7YyZOgv4LvDDlmUTgcsz85SImFiVj+/56q16dv/ipTz+7KIV3m/2pN4/u2vr4y9c\n4X02WGsIN528b/2Dv/hi+XAdPrxMPPjEE/DWt5Yzjj7wgdItcNRR5aKxO+5YPowPO6w5/mL33cv4\npmHDSnnLLeFTn6pfL0mdt9FGzYHtUM5CXLCg2fK89dZw8snN2eOvvRY+85kymS3AWWeV9ddeW8Zx\n3XZbOYnkHe8oU49IPazbMJWZV0bE6KUWHwi8ubo/DbgCw1RbHn92Efefsv+K73jKwJhIb/TEi9rf\neP780lS/5Zal/P73l3FLjQvFvuUt8OEPwze+UbrbRo5sdrttskkZU7HNNqW8wQZLXnx30KDm5IaS\nVg2NaRt22KGEpYajjy6tWo3JarfaqrRqbbppKZ9zDnz5y80B86edVq5Pedllpev+L38pk5nutlvv\nPRetUlb2bL6RmVldJZWHgZE9VB+tSjLh6afLZJUAp55aJqD86EdL+bWvhZe9rHzQQbnwbuMir2us\nAb/6Vel+g/Ih+stfNh87otlFJ0kbtYw22Wuvcmv49KfLdCWNVqk11yz/mDXGQJ5yCvz6183rHn7j\nG/DII2UiUyifTRts4Ekm6lLtqREyMyOiy2aTiBgPjAfYaqut6h5O/dmvf71k+YADyhiHa64p5Usu\nKd1ujTD1ta81L3XRWN+qdfZmSVpZ669fuv4bPvShcmv47Gebg+GhnLjSuKYhwPveB48/3pxc99RT\nyxAENi7lf/zD7sPV3MqGqUciYrPMnBsRmwFdXqY8M6cAUwDGjBkzMPqqtGx33llmPz7ssFL+/Ofh\n5z9vDvI+91zY9D3N7Y86qoxbavjVr5a8yO673tX5OktSd3baacmW7tNOW3L9Jz7RnFYF4Oyzyxm+\no48q5d13Ly3tjetpnnFGmcC0dcZ5rdJW9jzvGUBjQpGxwAU9Ux31qYUL4fe/LxfaBZg+vVypvvEh\n8pOflP/eGqcv77IL7LNPc1DoN7+55OMdfngZLN7QGqQkaaDYf//mmYYAf/hDOduw4QMfKPNmQTl5\n5mMfK/9oQvl83G675ozymfCzn5UzFLXKaGdqhOnAtcCOEfFARIwDTgH2iYi7gbdWZfV3maXvvxGG\nrrsODj64LINyVfk3vhHmzCnlYcPKAM7GJSQmTChTETRm8z7iiNLc3QhJG27Ye89FkvrS4JaOndYz\nCddYA+bNgxNOKOXnniufq40Tbf7+9/K5e/75pbxgQbkAdWOYw3PPwQ03LNmqr36v2zCVmUdk5maZ\nOSQzt8jMqZn5aGbunZnbZ+ZbM3NBb1RWK2jOHJg4sTkX06WXltOEG/3+zzxTJrFszM2y335l3FNj\nHNO73lW65oYPL+XNNy//YTlxpSR1bb31mp+ba60FP/hBc1jDhhvCjTfCoYeW8hNPlM/Uxufq7beX\ny1o1xqDec085W/GOO3r3OWiFRGbvDWMaM2ZMzmq9JMdq6KXTXtr9RgPcLWNv6esqqI+NnnjRyk0B\nMkCs6s9P7fHzfNUXEddn5pjutvNCx73syTtO6b0P4RdfLLfBvfdrXqF5piRpAOvVz/M+4Od5+wxT\nq7LWpmNJktQRftNKkiTVYJiSJEmqwW4+SR2xKo+32GCtIX1dBUn9iGFKUo/r7UG5nl0nqS/ZzSdJ\nklSDYUqSJKkGu/n6wMqMJZk96YAO1GT5tj7+whXex7EkqiNqXL8xJq3cfr05cbFWPX6eC5wBXZIk\naZnanQHdbj5JkqQaDFOSJEk1GKYkSZJqMExJkiTVYJiSJEmqwTAlSZJUg2FKkiSpBsOUJElSDYYp\nSZKkGgxTkiRJNRimJEmSajBMSZIk1VArTEXE2yPiroi4JyIm9lSlJEmSBoqVDlMRMQj4HrAfsAtw\nRETs0lMVkyRJGgjqtEy9ErgnM+/LzH8AZwMH9ky1JEmSBoY6YWoUMKel/EC1TJIkabUxuNMHiIjx\nwPiq+FRE3NXpY2oJmwB/7+tKSB3m+1yrA9/nvW/rdjaqE6YeBLZsKW9RLVtCZk4BptQ4jmqIiFmZ\nOaav6yF1ku9zrQ58n/dfdbr5/gRsHxHbRMSawOHAjJ6pliRJ0sCw0i1Tmbk4Ij4KXAIMAn6Qmbf1\nWM0kSZIGgFpjpjLzV8Cveqgu6gy7WLU68H2u1YHv834qMrOv6yBJkjRgeTkZSZKkGgxTq6iI+EFE\nzIuIW/u6LtKKWtb7NyKGR8RlEXF39XOjanlExHeqy1rdHBEva9lnbLX93RExti+ei7QsEfGFiPj0\nctaPiIjrIuLGiHjDSjz++yPiu9X9g7xCSWcZplZdZwFv7+tKSCvpLP73+3cicHlmbg9cXpWhXNJq\n++o2HjgdSvgCTgZeRbliw8mNACYNAHsDt2Tmnpn5+5qPdRDlsm/qEMPUKiozrwQW9HU9pJXRxfv3\nQGBadX8a5QuisfyHWfwB2DAiNgPeBlyWmQsycyFwGf6DoT4UESdGxF8i4ipgx2rZthHx64i4PiJ+\nHxE7RcQewNeAAyPizxGxVkScHhGzIuK2iPhiy2PeHxGbVPfHRMQVSx3ztcA7gf+qHmvb3nq+q5OO\nz4AuST1kZGbOre4/DIys7nd1aSsveaV+IyJeTpmPcQ/Kd+8NwPWUM/QmZObdEfEq4P9l5l4R8e/A\nmMz8aLX/iZm5ICIGAZdHxP/NzJu7O25mXhMRM4ALM/O8Dj291Z5hStKAk5kZEZ6KrIHkDcD5mfkM\nQBVwhgGvBX4aEY3thnax/6HV5dkGA5tRuu26DVPqHYYpSQPFIxGxWWbOrbrx5lXLu7q01YPAm5da\nfkUv1FNq1xrAY5m5x/I2iohtgE8Dr8jMhRFxFiWIASymOWRn2DJ2Vy9wzJSkgWIG0DgjbyxwQcvy\n91Vn9b0aeLzqDrwE2DciNqoGnu9bLZP6wpXAQdX4p/WAfwGeAf4aEYfAP89M3X0Z+64PPA08HhEj\nKSddNNwPvLy6/54ujv0ksF79p6CuGKZWURExHbgW2DEiHoiIcX1dJ6ldXbx/TwH2iYi7gbdWZShX\nYbgPuAc4A/gwQGYuAP6Tch3RPwH/US2Tel1m3gCcA9wEXEx5TwIcCYyLiJuA2ygnVCy9703AjcCd\nwE+Aq1tWfxH4dkTMAl7o4vBnA5+ppllwAHoHOAO6JElSDbZMSZIk1WCYkiRJqsEwJUmSVINhSpIk\nqQbDlCRJUg2GKUmSpBoMU5IkSTUYpiRJkmr4/86oFGDT77eqAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x105ebfb90>"
]
},
{
"output_type": "display_data",
"png": 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Nntxcv88+cPbZ5flzz5WxWPfd1/v17IrWk58eccTigeiVVxbf9pprFp//a8QI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\n",
"text": [
"<matplotlib.figure.Figure at 0x105fe3750>"
]
},
{
"output_type": "display_data",
"png": 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/NekBYPKAtkySJGkE6HeYioitgQuBf8zMx3pPy8wEcgPvOzYieiKiZ+nSpbUa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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x106082e10>"
]
},
{
"output_type": "display_data",
"png": 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FXtTtTkhjsJafPYZXApeVUg4HLmvL0Nza6/D2sRw4D5pQBpwJPIfmDhVnDgQz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\nwJAlSZJUwf8H8vBYUiq0628AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x105f73910>"
]
},
{
"output_type": "display_data",
"png": 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6aAlyUMLcFVc0b1S77bZlYn/jd9K8eWV9o6fr+uvL77EFC0q5q6tcNNAYrrz6\n6vJJBI1PWtCg1esPiI6IbYCfA1Mz8wcRsTQzt29Z/0hm7hARVwHnZObMqv5a4COZ2b3O8aZQhhPZ\nY489Dryv0QsxQm3qHYLvO/e1NbTmqe35kas2ep/t1qzitqO3KVfWzZ5d7kjd1QUnnACzZpVu+ssu\nKx/Rcs898L73lcvFX/Si5vDfUUeV4YE1a8p/pYaiIWdz3pV6IHgnbMEI+SD0k+8Y6CYMqH79gOiI\nGA18H7g0M39QVS+KiF0zc2E1HLi4ql8A7N6y+25V3Voy8yLgIoD29vYRP3tw2WOrN+1T1M/ZTC9d\nd3eZGLr33qXb+gMfKP+JHXdc6ZHaZZdyX5wzzii9VM94Rvlw3Q9/GJYtY+JnZpaPCYFyv55zzy3d\n51D+Q3zggWbP06RJ8NOfNs89fnyZb9HQ6LHSkLNJ7/E+mHjmjzf7OaXls88Z1u+74f7PUn/qzdWF\nAUwDZmfmf7SsuhKoZg1yMvCjlvq3VVcZHgIsaxlW1GCxaFGZvNnw5S83P5gWyiTOM89slo86qnll\nzxZblDtDz5pVylttVW5P0LgCb5ttSih717tKebvtytc3vKG5/owzSm8WlG7w8eMNT5KkYaU3PVkv\nBd4K3BERt1Z1ZwHnAFdERAdwH9C45v1q4NXAHOBR4B392mL1j6OPLjcH/K//KuULLyxDdo2Jne3t\npdeq4fvfLxM1G/70p7WP97nPNZcjysezSJI0gvUYsqq5VRv6nI0j17N9AqeuZ1s9hW3bzuT5l5zZ\n84b95XSAP0Bj7sCHA5jVLL8Q4Dq45EvNfeYDv9y0023bBjB8u88lSVqXd3wfJBzDl6ThYzj/zttu\nq9E9byTAkCVJUr/yAg81eA28JElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg28unAQ8ZJfSZKG\nD0PWIOElv5IkDS+GrCGufLTkJu577qbtV27qL20+vs81Evg+H34MWUOcPyAaCXyfayTwfT78OPFd\nkiSpBoYsSZKkGhiyJEmSamDIkiRJqoEhS5IkqQaGLEmSpBoYsiRJkmpgyJIkSaqBIUuSJKkGhixJ\nkqQaGLIkSZJq0GPIioivR8TiiLizpe4TEbEgIm6tHq9uWffRiJgTEXdHxCvqargkSdJg1puerIuB\nV66n/guZuX/1uBogIvYBTgD2rfb5SkSM6q/GSpIkDRU9hqzM/AXwcC+PdyxwWWauysw/AnOAg/rQ\nPkmSpCGpL3Oy3hcRt1fDiTtUdROAeS3bzK/qJEmSRpRNDVkXAHsB+wMLgc9v7AEiYkpEdEdE95Il\nSzaxGZIkSYPTJoWszFyUmWvixVyNAAAIgklEQVQy80ngazSHBBcAu7dsultVt75jXJSZ7ZnZPm7c\nuE1phiRJ0qC1SSErInZtKf4d0Ljy8ErghIgYExHPAiYBv+5bEyVJkoaeLXvaICK6gMOAnSNiPvBx\n4LCI2B9IYC7wboDMnBURVwB3AU8Ap2bmmnqaLkmSNHhFZg50G2hvb8/u7u6BboYkSVKPIuLmzGzv\naTvv+C5JklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmS\nVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElS\nDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg16DFkR8fWIWBwRd7bU7RgR0yPinurrDlV9RMR5ETEn\nIm6PiBfW2XhJkqTBqjc9WRcDr1yn7kzg2sycBFxblQFeBUyqHlOAC/qnmZIkSUNLjyErM38BPLxO\n9bHAJdXyJcBxLfXfzOJXwPYRsWt/NVaSJGmo2NQ5WeMzc2G1/AAwvlqeAMxr2W5+VSdJkjSi9Hni\ne2YmkBu7X0RMiYjuiOhesmRJX5shSZI0qGxqyFrUGAasvi6u6hcAu7dst1tV939k5kWZ2Z6Z7ePG\njdvEZkiSJA1OmxqyrgROrpZPBn7UUv+26irDQ4BlLcOKkiRJI8aWPW0QEV3AYcDOETEf+DhwDnBF\nRHQA9wFvrDa/Gng1MAd4FHhHDW2WJEka9HoMWZl54gZWHbmebRM4ta+NkiRJGuq847skSVINDFmS\nJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmS\nJNXAkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmS\nVANDliRJUg0MWZIkSTXYsi87R8RcYDmwBngiM9sjYkfgcmAiMBd4Y2Y+0rdmSpIkDS390ZN1eGbu\nn5ntVflM4NrMnARcW5UlSZJGlDqGC48FLqmWLwGOq+EckiRJg1pfQ1YC10TEzRExpaobn5kLq+UH\ngPF9PIckSdKQ06c5WcChmbkgIv4GmB4Rv2tdmZkZEbm+HatQNgVgjz326GMzJEmSBpc+9WRl5oLq\n62Lgh8BBwKKI2BWg+rp4A/telJntmdk+bty4vjRDkiRp0NnkkBURT4+IbRvLwDHAncCVwMnVZicD\nP+prIyVJkoaavgwXjgd+GBGN43wnM/8nIn4DXBERHcB9wBv73kxJkqShZZNDVmbeC+y3nvqHgCP7\n0ihJkqShzju+S5Ik1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXAkCVJklQDQ5YkSVINDFmSJEk1\nMGRJkiTVwJAlSZJUA0OWJElSDQxZkiRJNTBkSZIk1cCQJUmSVANDliRJUg0MWZIkSTUwZEmSJNXA\nkCVJklQDQ5YkSVINDFmSJEk1MGRJkiTVwJAlSZJUg9pCVkS8MiLujog5EXFmXeeRJEkajGoJWREx\nCvhP4FXAPsCJEbFPHeeSJEkajOrqyToImJOZ92bm48BlwLE1nUuSJGnQqStkTQDmtZTnV3WSJEkj\nwpYDdeKImAJMqYorIuLugWrLCLUz8OBAN0Kqme9zjQS+zze/PXuzUV0hawGwe0t5t6rurzLzIuCi\nms6vHkREd2a2D3Q7pDr5PtdI4Pt88KpruPA3wKSIeFZEPA04AbiypnNJkiQNOrX0ZGXmExHxPuCn\nwCjg65k5q45zSZIkDUa1zcnKzKuBq+s6vvrMoVqNBL7PNRL4Ph+kIjMHug2SJEnDjh+rI0mSVAND\n1ggTEV+PiMURcedAt0XaFOt7D0fEjhExPSLuqb7uUNVHRJxXfbzX7RHxwpZ9Tq62vyciTh6I5yKt\nKyI+EREfeor14yLipoi4JSJetgnHf3tEfLlaPs5PY6mXIWvkuRh45UA3QuqDi/m/7+EzgWszcxJw\nbVWG8tFek6rHFOACKKEM+DhwMOUTKj7eCGbSIHckcEdmHpCZ/9vHYx1H+eg71cSQNcJk5i+Ahwe6\nHdKm2sB7+Fjgkmr5Esofj0b9N7P4FbB9ROwKvAKYnpkPZ+YjwHT850MDJCI6I+L3ETETeG5Vt1dE\n/E9E3BwR/xsRz4uI/YHPAsdGxK0RsVVEXBAR3RExKyLObjnm3IjYuVpuj4jr1znnS4DXA/9eHWuv\nzfV8R5IBu+O7JPWj8Zm5sFp+ABhfLW/oI7786C8NChFxIOVekvtT/ib/FriZcsXgKZl5T0QcDHwl\nM4+IiI8B7Zn5vmr/zsx8OCJGAddGxAsy8/aezpuZv4yIK4GrMvN7NT29Ec+QJWlYycyMCC+b1lDx\nMuCHmfkoQBV8xgIvAb4bEY3txmxg/zdWH1O3JbArZfivx5ClzcOQJWk4WBQRu2bmwmo4cHFVv6GP\n+FoAHLZO/fWboZ1Sb2wBLM3M/Z9qo4h4FvAh4EWZ+UhEXEwJaABP0JwSNHY9u2szcE6WpOHgSqBx\nheDJwI9a6t9WXWV4CLCsGlb8KXBMROxQTXg/pqqTNrdfAMdV86u2BV4HPAr8MSLeAH+9Sna/9ez7\nDOAvwLKIGE+50KNhLnBgtfwPGzj3cmDbvj8FbYgha4SJiC7gRuC5ETE/IjoGuk3SxtjAe/gc4OiI\nuAc4qipD+dSJe4E5wNeA9wJk5sPApyifs/ob4JNVnbRZZeZvgcuB24CfUN6PACcBHRFxGzCLchHH\nuvveBtwC/A74DnBDy+qzgS9FRDewZgOnvwz4cHU7CCe+18A7vkuSJNXAnixJkqQaGLIkSZJqYMiS\nJEmqgSFLkiSpBoYsSZKkGhiyJEmSamDIkiRJqoEhS5IkqQb/H3ARFSdnVO2QAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1062078d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10629d0d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1061dc910>"
]
},
{
"output_type": "display_data",
"png": 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U3m671L1z+eWpPHJk2vbvfpfKe+yR3jR++MNUPuooadddpcMOS+X//d8U0Hbf\nPZXb2tKb0vD8FXgLFqT9r0JX9bp+Hazrx7dWWbIkBbFBg1JIefXVFLwqQe7ll6UnnpDe8Y7UBTp3\nbgpMO+yQgvVzz6Vr/1//NT3nmWdSd+oBB6Sg+fjj6W/u8MOlt7wlfTi49FLpW9/S9jd8pN5HX9xD\nhz1U7yrUVXe7C2nJAroSIf3zn9VP2M88I02dmj49S+mT7sMPS1/7Wir/5jfS3/4mnXVWKjc3p0+/\nt9ySyp/7XBrb8sgjqXzCCWmw9MSJqTx+fBrcXQlZjz5aHWgtpU/ltWNejjmm2oolSRdckN40Ku6/\nv/P6t9/e+fiWbg35+lI3CNe2xEjV8wD0Zv36df67WG+9zq1uG2yQWqAqNt5YGjOmWh46VPr0p6vl\nt72tc0vjO9+ZWuoqdtghPSS91HHqmgnbS5asfOttD+D74LqPkIV106JFUv/+6T+g559Pn1h32in9\nRztpUmqyP+KIFD5uvFG65JLUrTZoUGqe/9nP0p1m/funFqQf/Si1QNmppefMM1NrlJQ+zZ53XjVk\nPflkClUVw4ZVu8ek1KUwc2a1fNJJ1bvXpNTiVNuFddFFnY+t0q1X8YUvdC7Xds9I1RYzAOuWOgQs\nrBxCFnqHNPw5/acxf35qvRk5Mo25mTYtjVs44IA0lmHixDTO53vfS91Yt9wiffvbaYzDNtukO6i+\n8IUUrLbdNt3i39QkPf10Wr+tLd3l9elPp/Eg06alu7kWLEgha7PNUnfbq6+mbrG9905BZfFiacCA\ntK199kn1tVPoOfHE6rGcckp6VBx1VOdj/dSnOpfzp9/X1bZCrYNW5VPw2nTXFSCt2609XOcroTuj\n40s/uLtwLb1bo3JH1quvRjz4YMRzz6Xyiy+mu3g6OlJ5xox0fPfck8qPPx7R2Bhxyy2pfO+9EXbE\n9den8p13psh1002pfNttqdzaWi1vuWXEhAmpfPfdEfvvH/Hkk6k8aVLED38YMXNmKj/zTMSf/pTu\nVopIdwTNmrVyd6EBQC+1Vr5/rOXUzbsLaWvsqx57LH2RYsUll6S70qTUz3/00an1SErdYh/6UHWg\n9dy5qTvrjDNSec6c1BpTGWj90kuptecvf0nlSlfYlCnp38GD08DRyl1Mw4dL3/1uGoAqpVakq6+u\njpf4wAfSl0lWxkCNGZPq/v73p/Kuu6Yuu9rnf/e71bughg+X9t23OpZogw3SQFaa2gEABfEu01ft\ntlv1CwyldDt/5fbvfv3SdGUg9qBBKaAMGpTKG26Yuuca840Vm2ySbqWu3Jo+dGjqmquMFaoMNj3w\nwPTv8OHptukPfziV3/pW6Qd5EimwAAAFRElEQVQ/kN797ur2PvWpakgaPDh9BcFAmqgBAGsPvsKh\nl+gT34Tdx2/5BYAS+OqQNY+vcFjLrLFbfutkXR4ECgDAstBdCAAAUAAtWb0It7YDQN/lVfgVhdef\ne1rX6yxLbxgytC4jZPUSq9xVeCp/IACwLiDwrHvoLgQAACiAkAUAAFAAIQsAAKAAQhYAAEABhCwA\nAIACCFkAAAAFELIAAAAKIGQBAAAUQMgCAAAogJAFAABQACELAACgAEIWAABAAYQsAACAAghZAAAA\nBRCyAAAACiBkAQAAFEDIAgAAKICQBQAAUAAhCwAAoABCFgAAQAGELAAAgAIIWQAAAAUQsgAAAAog\nZAEAABRAyAIAACiAkAUAAFAAIQsAAKAAQhYAAEABhCwAAIACCFkAAAAFELIAAAAKIGQBAAAUUCRk\n2d7X9mO2J9s+vsQ+AAAAerMeD1m2+0v6X0n7SdpO0iG2t+vp/QAAAPRmJVqydpE0OSKeiohXJV0q\naf8C+wEAAOi1SoSsYZKm1pSn5XkAAAB9xoB67dj2kZKOzMX5th+rV136qM0lPV/vSgCFcZ2jL+A6\nX/O27s5KJULWdEnDa8pb5XmdRMS5ks4tsH90g+32iGisdz2AkrjO0RdwnfdeJboL75M00vbbba8n\n6WBJ1xTYDwAAQK/V4y1ZEfGa7W9IulFSf0m/joiHe3o/AAAAvVmRMVkRcYOkG0psGz2Grlr0BVzn\n6Au4znspR0S96wAAALDO4Wd1AAAACiBk9TG2f217pu1J9a4LsCqWdQ3b3tT2zbafyP9ukufb9s/z\nT3xNtP3+muccltd/wvZh9TgWYGm2T7J9zAqWD7F9j+37be++Cts/3PYv8vQB/CJLWYSsvucCSfvW\nuxLAarhAb7yGj5d0a0SMlHRrLkvp571G5seRks6WUiiTdKKkXZV+peLESjADerk9JT0UEe+LiDtX\nc1sHKP38HQohZPUxEXGHpNn1rgewqpZzDe8v6cI8faHSm0dl/m8juVvSxra3kLSPpJsjYnZEzJF0\ns/jwgTqx3Wz7cdttkt6V521j+8+2J9i+0/a7be8o6SeS9rf9gO31bZ9tu932w7ZPrtnm07Y3z9ON\ntm9fap8flPQpSf+Tt7XNmjrevqRu3/gOAD1oaETMyNPPShqap5f3M1/8/Bd6Bds7KX2f5I5K78l/\nkzRB6Y7Br0bEE7Z3lfTLiPiI7e9LaoyIb+TnN0fEbNv9Jd1q+70RMbGr/UbEX21fI+m6iPhDocPr\n8whZANYpERG2uW0aa4vdJV0ZEQskKQefwZI+KOly25X1Bi3n+Qfln6kbIGkLpe6/LkMW1gxCFoB1\nwXO2t4iIGbk7cGaev7yf+ZouaY+l5t++BuoJdEc/SXMjYscVrWT77ZKOkbRzRMyxfYFSQJOk11Qd\nEjR4GU/HGsCYLADrgmskVe4QPEzS1TXzv5jvMtxN0rzcrXijpL1tb5IHvO+d5wFr2h2SDsjjq94k\n6ZOSFkj6u+0Dpdfvkt1hGc99s6SXJc2zPVTpRo+KpyXtlKc/vZx9vyTpTat/CFgeQlYfY7tF0v9J\nepftabab6l0nYGUs5xo+VdJetp+Q9NFcltIvTzwlabKk8yQdJUkRMVvSD5V+a/U+ST/I84A1KiL+\nJukySQ9K+pPS9ShJh0pqsv2gpIeVbuJY+rkPSrpf0qOSfifprprFJ0s603a7pMXL2f2lko7NXwfB\nwPcC+MZ3AACAAmjJAgAAKICQBQAAUAAhCwAAoABCFgAAQAGELAAAgAIIWQAAAAUQsgAAAAogZAEA\nABTw/wFHmN2Fxbrf8gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10624c6d0>"
]
},
{
"output_type": "display_data",
"png": 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S1H/89Kew//6tNyb+2c9gww3rXZWkBmeYktR/rLdeDk+LF9e7Ekl9iGFKUt+1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\n",
"text": [
"<matplotlib.figure.Figure at 0x10641bed0>"
]
},
{
"output_type": "display_data",
"png": 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2371nPgeAshCmAKAcEateedbUJI0dm4ZPPz116J40KdWnnJI6cE+fnupXXy1e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\n",
"text": [
"<matplotlib.figure.Figure at 0x1064b75d0>"
]
},
{
"output_type": "display_data",
"png": 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Xf25RrRsRcWZEzI+I2yNin5EcW5IkaX010jlgZwD/lpmvAV4H9AAnA9dk5u7A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\n+/8BeUZ0cdkVY2cAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10655bed0>"
]
},
{
"output_type": "display_data",
"png": 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99allrtQ1M3q09OijqctUSq1sM2emFjRJ+t73pLffTt1AkjRuXLrbszQp42OP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\nRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10669cf50>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1066e8b90>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10611ddd0>"
]
},
{
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W8yRJkvqMLoesiFgTuBT4Smb+vf2yzEwgl2XHEXFQREyLiGlz5sxZlqdKkiT1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\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1067c0950>"
]
},
{
"output_type": "display_data",
"png": 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nziz13XeX+oorSn3ffaW+7LLy79y55d9LLimPN8am/PjHpW5cd+z880v93HOl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\n",
"text": [
"<matplotlib.figure.Figure at 0x1067702d0>"
]
},
{
"output_type": "display_data",
"png": 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YNmRIuIrokEPC1Tu77hqugDv55NBi1KVL+BZVWRkC18YbZ++tNHWqtNdeoXXq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\nAAAgAUIWAABAAoQsAACABAhZAAAACfx/fURPQNfUZj8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x106880b90>"
]
},
{
"output_type": "display_data",
"png": 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WcNJJ8KIXlfKee5Ygv/POpTxuXGkBbQS3OXPgC18oi7tC6cocNaq0gAH89rdw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\noUBrAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x106998e50>"
]
},
{
"output_type": "display_data",
"png": 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Oj4ip5DlU45unJEmSpPajKcN870sp3QXcVdx/Dtip+UuSJElqPzwDuiRJUgmGKUmSpBIM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\ncC5/ADUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1069ef790>"
]
},
{
"output_type": "display_data",
"png": 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jhKkJKiLulLRxvOsAKjVCHz5OUme636nij8TA9G9G4R5JL7I9U9KbJC2OiI0R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\nCgAAIMP/B1HQbP61KjIpAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1069cc050>"
]
}
],
"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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