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ShakoHo/layers.tile-height.ipynb

Created December 7, 2017 02:40
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layers.tile-height.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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"nbformat": 3,
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
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"input": [
"import os \n",
"import json\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"from IPython.display import display, display_markdown\n",
"\n",
"#\n",
"# Hide the plotbox if case miss any one data source\n",
"#\n",
"HIDE_MISSING_CASE = False\n",
"\n",
"DATA_LIST = 'time_list'\n",
"DATA_NAME = 'run_time'\n",
"DELETE_KEY = \"video-recording-fps\"\n",
"\n",
"#\n",
"# Input data source\n",
"#\n",
"input_json_fp = os.getenv(\"input_json_fp\")\n",
"#input_json_fp = \"output/javascript.options.baselinejit.threshold.json\"\n",
"with open(input_json_fp) as read_fh:\n",
" data_dict = json.load(read_fh)\n",
" for item in data_dict:\n",
" if DELETE_KEY in data_dict[item]:\n",
" data_dict[item].pop(DELETE_KEY)\n",
"\n",
"\n",
"#\n",
"# Definde layout and figure size\n",
"#\n",
"layout_row = 1\n",
"layout_column = 1\n",
"fig_size_w = 10\n",
"fig_size_h = 5\n",
"matplotlib.rcParams.update({'figure.max_open_warning': 0})\n",
"\n",
"#\n",
"# generate merged data\n",
"#\n",
"casename_list = list(set([casename for lable, data in data_dict.items() for casename in data.keys()]))\n",
"\n",
"case_result = []\n",
"for casename in casename_list:\n",
" result = {\n",
" \"casename\": casename,\n",
" \"result\": {}\n",
" }\n",
" for lable, data in data_dict.items():\n",
" result[\"result\"][lable] = {}\n",
" \n",
" for lable, data in data_dict.items():\n",
" if data.get(casename):\n",
" result[\"result\"][lable][DATA_LIST] = data.get(casename).get(DATA_LIST)\n",
" else:\n",
" result[\"result\"][lable][DATA_LIST] = []\n",
" case_result.append(result)\n",
"\n",
"nightly_better_list = []\n",
"for case in sorted(case_result):\n",
" casename = case.get('casename')\n",
" result = case.get('result')\n",
" d = pd.DataFrame(result)\n",
" \n",
" # drop empty 'DATA_LIST'\n",
" for c in d:\n",
" if (d[c][DATA_LIST] == []) :\n",
" d.drop(c, axis=1, inplace=True)\n",
"\n",
" # Retrive the value of 'DATA_NAME' from each run\n",
" value = pd.DataFrame([pd.DataFrame(d[c][DATA_LIST])[DATA_NAME] for c in d]).T\n",
" value.columns = d.columns\n",
" \n",
" # Plot input latency boxplot\n",
" value_min = min(value.min())\n",
" value_max = max(value.max())\n",
" value_median = pd.DataFrame([pd.DataFrame(d[c][DATA_LIST])[DATA_NAME].median() for c in d]).T\n",
" value_median.columns = d.columns\n",
" \n",
" # When HIDE_MISSING_CASE is enabled, skip the case if the case data less then input data amount.\n",
" if HIDE_MISSING_CASE and len(INPUT_SOURCE) > len(value.T):\n",
" # display summary\n",
" display_markdown('### {}\\nskip draw plot box'.format(casename), raw=True)\n",
" display(value.describe())\n",
" else:\n",
" \n",
" fig, ax = plt.subplots()\n",
" ax.plot(list(range(1,len(list(value_median.median()))+1)), list(value_median.median()), \"r:\")\n",
" \n",
" value.plot.box(layout=(layout_row, layout_column),\n",
" sharey=True, sharex=True, figsize=(fig_size_w, fig_size_h),\n",
" ylim=(0, value_max*1.1), ax=ax)\n",
" plt.title(casename)\n",
" if \"Nightly\" in value_median and \"ESR52\" in value_median:\n",
" if value_median['Nightly'][0] < value_median['ESR52'][0]:\n",
" nightly_better_list.append(casename)\n",
" # display summary\n",
" display(casename)\n",
" display(value.describe().T)"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_hover_related_product_thumbnail'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\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>128</th>\n",
" <td>10.0</td>\n",
" <td>483.333333</td>\n",
" <td>9.442629</td>\n",
" <td>466.666667</td>\n",
" <td>480.555556</td>\n",
" <td>488.888889</td>\n",
" <td>488.888889</td>\n",
" <td>488.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>498.888889</td>\n",
" <td>43.963447</td>\n",
" <td>477.777778</td>\n",
" <td>477.777778</td>\n",
" <td>488.888889</td>\n",
" <td>488.888889</td>\n",
" <td>622.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>476.666667</td>\n",
" <td>9.728834</td>\n",
" <td>466.666667</td>\n",
" <td>466.666667</td>\n",
" <td>477.777778</td>\n",
" <td>486.111111</td>\n",
" <td>488.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>482.222222</td>\n",
" <td>10.734353</td>\n",
" <td>466.666667</td>\n",
" <td>477.777778</td>\n",
" <td>483.333333</td>\n",
" <td>488.888889</td>\n",
" <td>500.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>493.333333</td>\n",
" <td>38.203843</td>\n",
" <td>466.666667</td>\n",
" <td>477.777778</td>\n",
" <td>483.333333</td>\n",
" <td>488.888889</td>\n",
" <td>600.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"128 10.0 483.333333 9.442629 466.666667 480.555556 488.888889 \n",
"256 10.0 498.888889 43.963447 477.777778 477.777778 488.888889 \n",
"512 10.0 476.666667 9.728834 466.666667 466.666667 477.777778 \n",
"64 10.0 482.222222 10.734353 466.666667 477.777778 483.333333 \n",
"768 10.0 493.333333 38.203843 466.666667 477.777778 483.333333 \n",
"\n",
" 75% max \n",
"128 488.888889 488.888889 \n",
"256 488.888889 622.222222 \n",
"512 486.111111 488.888889 \n",
"64 488.888889 500.000000 \n",
"768 488.888889 600.000000 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_select_search_suggestion'"
]
},
{
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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>128</th>\n",
" <td>10.0</td>\n",
" <td>11.666667</td>\n",
" <td>7.612891</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>19.444444</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>7.522258</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>15.000000</td>\n",
" <td>8.302412</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",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>16.111111</td>\n",
" <td>8.050765</td>\n",
" <td>5.555556</td>\n",
" <td>6.944444</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>11.111111</td>\n",
" <td>7.856742</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>19.444444</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 11.666667 7.612891 5.555556 5.555556 8.333333 19.444444 \n",
"256 10.0 13.888889 7.522258 5.555556 6.944444 11.111111 22.222222 \n",
"512 10.0 15.000000 8.302412 5.555556 11.111111 11.111111 19.444444 \n",
"64 10.0 16.111111 8.050765 5.555556 6.944444 22.222222 22.222222 \n",
"768 10.0 11.111111 7.856742 5.555556 5.555556 5.555556 19.444444 \n",
"\n",
" max \n",
"128 22.222222 \n",
"256 22.222222 \n",
"512 33.333333 \n",
"64 22.222222 \n",
"768 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_type_in_search_field'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>128</th>\n",
" <td>10.0</td>\n",
" <td>8.333333</td>\n",
" <td>5.399030</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>8.333333</td>\n",
" <td>5.399030</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>9.722222</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>7.777778</td>\n",
" <td>2.868877</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>11.666667</td>\n",
" <td>9.240722</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>13.333333</td>\n",
" <td>7.943559</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</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% 75% \\\n",
"128 10.0 8.333333 5.399030 5.555556 5.555556 5.555556 9.722222 \n",
"256 10.0 8.333333 5.399030 5.555556 5.555556 5.555556 9.722222 \n",
"512 10.0 7.777778 2.868877 5.555556 5.555556 5.555556 11.111111 \n",
"64 10.0 11.666667 9.240722 5.555556 5.555556 8.333333 11.111111 \n",
"768 10.0 13.333333 7.943559 5.555556 5.555556 11.111111 22.222222 \n",
"\n",
" max \n",
"128 22.222222 \n",
"256 22.222222 \n",
"512 11.111111 \n",
"64 33.333333 \n",
"768 22.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_close_chat_tab'"
]
},
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"html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>128</th>\n",
" <td>10.0</td>\n",
" <td>146.666667</td>\n",
" <td>16.396995</td>\n",
" <td>111.111111</td>\n",
" <td>136.111111</td>\n",
" <td>155.555556</td>\n",
" <td>155.555556</td>\n",
" <td>166.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>151.111111</td>\n",
" <td>19.737648</td>\n",
" <td>133.333333</td>\n",
" <td>144.444444</td>\n",
" <td>144.444444</td>\n",
" <td>152.777778</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>151.111111</td>\n",
" <td>14.054567</td>\n",
" <td>133.333333</td>\n",
" <td>136.111111</td>\n",
" <td>155.555556</td>\n",
" <td>163.888889</td>\n",
" <td>166.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>160.000000</td>\n",
" <td>19.029974</td>\n",
" <td>133.333333</td>\n",
" <td>147.222222</td>\n",
" <td>161.111111</td>\n",
" <td>175.000000</td>\n",
" <td>188.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>143.333333</td>\n",
" <td>28.903130</td>\n",
" <td>100.000000</td>\n",
" <td>116.666667</td>\n",
" <td>144.444444</td>\n",
" <td>166.666667</td>\n",
" <td>177.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"128 10.0 146.666667 16.396995 111.111111 136.111111 155.555556 \n",
"256 10.0 151.111111 19.737648 133.333333 144.444444 144.444444 \n",
"512 10.0 151.111111 14.054567 133.333333 136.111111 155.555556 \n",
"64 10.0 160.000000 19.029974 133.333333 147.222222 161.111111 \n",
"768 10.0 143.333333 28.903130 100.000000 116.666667 144.444444 \n",
"\n",
" 75% max \n",
"128 155.555556 166.666667 \n",
"256 152.777778 200.000000 \n",
"512 163.888889 166.666667 \n",
"64 175.000000 188.888889 \n",
"768 166.666667 177.777778 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab'"
]
},
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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>128</th>\n",
" <td>10.0</td>\n",
" <td>238.888889</td>\n",
" <td>12.001372</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>256.666667</td>\n",
" <td>52.495999</td>\n",
" <td>211.111111</td>\n",
" <td>233.333333</td>\n",
" <td>244.444444</td>\n",
" <td>255.555556</td>\n",
" <td>400.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>263.333333</td>\n",
" <td>58.102129</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>238.888889</td>\n",
" <td>263.888889</td>\n",
" <td>422.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>368.888889</td>\n",
" <td>97.962788</td>\n",
" <td>244.444444</td>\n",
" <td>258.333333</td>\n",
" <td>433.333333</td>\n",
" <td>444.444444</td>\n",
" <td>455.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>373.333333</td>\n",
" <td>154.954748</td>\n",
" <td>233.333333</td>\n",
" <td>266.666667</td>\n",
" <td>277.777778</td>\n",
" <td>483.333333</td>\n",
" <td>633.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"128 10.0 238.888889 12.001372 233.333333 233.333333 233.333333 \n",
"256 10.0 256.666667 52.495999 211.111111 233.333333 244.444444 \n",
"512 10.0 263.333333 58.102129 233.333333 233.333333 238.888889 \n",
"64 10.0 368.888889 97.962788 244.444444 258.333333 433.333333 \n",
"768 10.0 373.333333 154.954748 233.333333 266.666667 277.777778 \n",
"\n",
" 75% max \n",
"128 233.333333 266.666667 \n",
"256 255.555556 400.000000 \n",
"512 263.888889 422.222222 \n",
"64 444.444444 455.555556 \n",
"768 483.333333 633.333333 "
]
},
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"output_type": "display_data",
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" <td>144.444444</td>\n",
" <td>20.286021</td>\n",
" <td>111.111111</td>\n",
" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>166.666667</td>\n",
" <td>166.666667</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>152.222222</td>\n",
" <td>22.253065</td>\n",
" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>150.000000</td>\n",
" <td>155.555556</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>145.555556</td>\n",
" <td>14.296488</td>\n",
" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>138.888889</td>\n",
" <td>155.555556</td>\n",
" <td>166.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>165.555556</td>\n",
" <td>19.910637</td>\n",
" <td>133.333333</td>\n",
" <td>155.555556</td>\n",
" <td>166.666667</td>\n",
" <td>175.000000</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>167.777778</td>\n",
" <td>47.271216</td>\n",
" <td>133.333333</td>\n",
" <td>136.111111</td>\n",
" <td>155.555556</td>\n",
" <td>166.666667</td>\n",
" <td>288.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text": [
" count mean std min 25% 50% \\\n",
"128 10.0 144.444444 20.286021 111.111111 133.333333 133.333333 \n",
"256 10.0 152.222222 22.253065 133.333333 133.333333 150.000000 \n",
"512 10.0 145.555556 14.296488 133.333333 133.333333 138.888889 \n",
"64 10.0 165.555556 19.910637 133.333333 155.555556 166.666667 \n",
"768 10.0 167.777778 47.271216 133.333333 136.111111 155.555556 \n",
"\n",
" 75% max \n",
"128 166.666667 166.666667 \n",
"256 155.555556 200.000000 \n",
"512 155.555556 166.666667 \n",
"64 175.000000 200.000000 \n",
"768 166.666667 288.888889 "
]
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" <td>10.0</td>\n",
" <td>236.666667</td>\n",
" <td>27.241742</td>\n",
" <td>211.111111</td>\n",
" <td>222.222222</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>275.555556</td>\n",
" <td>89.319942</td>\n",
" <td>200.000000</td>\n",
" <td>213.888889</td>\n",
" <td>233.333333</td>\n",
" <td>327.777778</td>\n",
" <td>444.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>228.888889</td>\n",
" <td>41.968046</td>\n",
" <td>200.000000</td>\n",
" <td>211.111111</td>\n",
" <td>222.222222</td>\n",
" <td>222.222222</td>\n",
" <td>344.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>210.000000</td>\n",
" <td>26.423791</td>\n",
" <td>166.666667</td>\n",
" <td>200.000000</td>\n",
" <td>205.555556</td>\n",
" <td>222.222222</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>223.333333</td>\n",
" <td>14.296488</td>\n",
" <td>200.000000</td>\n",
" <td>213.888889</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" <td>233.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"128 10.0 236.666667 27.241742 211.111111 222.222222 233.333333 \n",
"256 10.0 275.555556 89.319942 200.000000 213.888889 233.333333 \n",
"512 10.0 228.888889 41.968046 200.000000 211.111111 222.222222 \n",
"64 10.0 210.000000 26.423791 166.666667 200.000000 205.555556 \n",
"768 10.0 223.333333 14.296488 200.000000 213.888889 233.333333 \n",
"\n",
" 75% max \n",
"128 233.333333 300.000000 \n",
"256 327.777778 444.444444 \n",
"512 222.222222 344.444444 \n",
"64 222.222222 266.666667 \n",
"768 233.333333 233.333333 "
]
},
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" <td>10.734353</td>\n",
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" <td>10.0</td>\n",
" <td>52.222222</td>\n",
" <td>5.367177</td>\n",
" <td>44.444444</td>\n",
" <td>47.222222</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>7.407407</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>41.111111</td>\n",
" <td>7.499428</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>47.777778</td>\n",
" <td>7.499428</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 48.888889 10.734353 33.333333 44.444444 50.000000 55.555556 \n",
"256 10.0 52.222222 5.367177 44.444444 47.222222 55.555556 55.555556 \n",
"512 10.0 44.444444 7.407407 33.333333 44.444444 44.444444 44.444444 \n",
"64 10.0 41.111111 7.499428 33.333333 33.333333 44.444444 44.444444 \n",
"768 10.0 47.777778 7.499428 33.333333 44.444444 44.444444 55.555556 \n",
"\n",
" max \n",
"128 66.666667 \n",
"256 55.555556 \n",
"512 55.555556 \n",
"64 55.555556 \n",
"768 55.555556 "
]
},
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" <td>11.049210</td>\n",
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" <td>33.333333</td>\n",
" <td>38.888889</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>42.222222</td>\n",
" <td>8.764563</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>43.333333</td>\n",
" <td>9.728834</td>\n",
" <td>33.333333</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>64</th>\n",
" <td>10.0</td>\n",
" <td>40.000000</td>\n",
" <td>9.369712</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>43.333333</td>\n",
" <td>14.296488</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>38.888889</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 43.333333 11.049210 33.333333 33.333333 38.888889 55.555556 \n",
"256 10.0 42.222222 8.764563 33.333333 33.333333 44.444444 44.444444 \n",
"512 10.0 43.333333 9.728834 33.333333 33.333333 44.444444 52.777778 \n",
"64 10.0 40.000000 9.369712 33.333333 33.333333 33.333333 44.444444 \n",
"768 10.0 43.333333 14.296488 22.222222 33.333333 38.888889 55.555556 \n",
"\n",
" max \n",
"128 55.555556 \n",
"256 55.555556 \n",
"512 55.555556 \n",
"64 55.555556 \n",
"768 66.666667 "
]
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" <td>64.444444</td>\n",
" <td>17.213259</td>\n",
" <td>33.333333</td>\n",
" <td>58.333333</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>51.111111</td>\n",
" <td>19.737648</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>58.888889</td>\n",
" <td>13.907395</td>\n",
" <td>44.444444</td>\n",
" <td>47.222222</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>71.111111</td>\n",
" <td>14.054567</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>"
],
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"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 50.000000 16.769232 22.222222 36.111111 50.000000 66.666667 \n",
"256 10.0 64.444444 17.213259 33.333333 58.333333 66.666667 66.666667 \n",
"512 10.0 51.111111 19.737648 33.333333 33.333333 44.444444 66.666667 \n",
"64 10.0 58.888889 13.907395 44.444444 47.222222 55.555556 66.666667 \n",
"768 10.0 71.111111 14.054567 55.555556 66.666667 66.666667 75.000000 \n",
"\n",
" max \n",
"128 66.666667 \n",
"256 88.888889 \n",
"512 88.888889 \n",
"64 88.888889 \n",
"768 100.000000 "
]
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" <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>128</th>\n",
" <td>10.0</td>\n",
" <td>48.888889</td>\n",
" <td>5.737753</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>46.666667</td>\n",
" <td>8.764563</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>7.0</td>\n",
" <td>50.793651</td>\n",
" <td>8.742175</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>44.444444</td>\n",
" <td>7.407407</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>52.222222</td>\n",
" <td>7.499428</td>\n",
" <td>44.444444</td>\n",
" <td>44.444444</td>\n",
" <td>55.555556</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 48.888889 5.737753 44.444444 44.444444 44.444444 55.555556 \n",
"256 10.0 46.666667 8.764563 33.333333 44.444444 44.444444 55.555556 \n",
"512 7.0 50.793651 8.742175 44.444444 44.444444 44.444444 55.555556 \n",
"64 10.0 44.444444 7.407407 33.333333 44.444444 44.444444 44.444444 \n",
"768 10.0 52.222222 7.499428 44.444444 44.444444 55.555556 55.555556 \n",
"\n",
" max \n",
"128 55.555556 \n",
"256 55.555556 \n",
"512 66.666667 \n",
"64 55.555556 \n",
"768 66.666667 "
]
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" <td>57.777778</td>\n",
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" <td>61.111111</td>\n",
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" <td>60.000000</td>\n",
" <td>10.734353</td>\n",
" <td>44.444444</td>\n",
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" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
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" <th>512</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",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>64.444444</td>\n",
" <td>4.684856</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>68.888889</td>\n",
" <td>11.475506</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>75.000000</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% 75% \\\n",
"128 10.0 57.777778 11.475506 33.333333 55.555556 61.111111 66.666667 \n",
"256 10.0 60.000000 10.734353 44.444444 50.000000 66.666667 66.666667 \n",
"512 10.0 63.333333 5.367177 55.555556 58.333333 66.666667 66.666667 \n",
"64 10.0 64.444444 4.684856 55.555556 66.666667 66.666667 66.666667 \n",
"768 10.0 68.888889 11.475506 44.444444 66.666667 66.666667 75.000000 \n",
"\n",
" max \n",
"128 66.666667 \n",
"256 66.666667 \n",
"512 66.666667 \n",
"64 66.666667 \n",
"768 88.888889 "
]
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" <th>128</th>\n",
" <td>10.0</td>\n",
" <td>80.000000</td>\n",
" <td>10.210406</td>\n",
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" <td>69.444444</td>\n",
" <td>83.333333</td>\n",
" <td>88.888889</td>\n",
" <td>88.888889</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>76.666667</td>\n",
" <td>8.198498</td>\n",
" <td>66.666667</td>\n",
" <td>69.444444</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>83.333333</td>\n",
" <td>9.442629</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>83.333333</td>\n",
" <td>88.888889</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>77.777778</td>\n",
" <td>9.072184</td>\n",
" <td>66.666667</td>\n",
" <td>69.444444</td>\n",
" <td>77.777778</td>\n",
" <td>86.111111</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>75.555556</td>\n",
" <td>7.027284</td>\n",
" <td>66.666667</td>\n",
" <td>69.444444</td>\n",
" <td>77.777778</td>\n",
" <td>77.777778</td>\n",
" <td>88.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 80.000000 10.210406 66.666667 69.444444 83.333333 88.888889 \n",
"256 10.0 76.666667 8.198498 66.666667 69.444444 77.777778 77.777778 \n",
"512 10.0 83.333333 9.442629 66.666667 77.777778 83.333333 88.888889 \n",
"64 10.0 77.777778 9.072184 66.666667 69.444444 77.777778 86.111111 \n",
"768 10.0 75.555556 7.027284 66.666667 69.444444 77.777778 77.777778 \n",
"\n",
" max \n",
"128 88.888889 \n",
"256 88.888889 \n",
"512 100.000000 \n",
"64 88.888889 \n",
"768 88.888889 "
]
},
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" <th>128</th>\n",
" <td>10.0</td>\n",
" <td>267.777778</td>\n",
" <td>16.932044</td>\n",
" <td>244.444444</td>\n",
" <td>258.333333</td>\n",
" <td>266.666667</td>\n",
" <td>283.333333</td>\n",
" <td>288.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>270.000000</td>\n",
" <td>23.453541</td>\n",
" <td>233.333333</td>\n",
" <td>255.555556</td>\n",
" <td>266.666667</td>\n",
" <td>288.888889</td>\n",
" <td>300.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>252.222222</td>\n",
" <td>18.182130</td>\n",
" <td>222.222222</td>\n",
" <td>236.111111</td>\n",
" <td>255.555556</td>\n",
" <td>266.666667</td>\n",
" <td>277.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>266.666667</td>\n",
" <td>25.660012</td>\n",
" <td>222.222222</td>\n",
" <td>255.555556</td>\n",
" <td>266.666667</td>\n",
" <td>288.888889</td>\n",
" <td>300.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>295.555556</td>\n",
" <td>31.514144</td>\n",
" <td>266.666667</td>\n",
" <td>269.444444</td>\n",
" <td>283.333333</td>\n",
" <td>316.666667</td>\n",
" <td>355.555556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"128 10.0 267.777778 16.932044 244.444444 258.333333 266.666667 \n",
"256 10.0 270.000000 23.453541 233.333333 255.555556 266.666667 \n",
"512 10.0 252.222222 18.182130 222.222222 236.111111 255.555556 \n",
"64 10.0 266.666667 25.660012 222.222222 255.555556 266.666667 \n",
"768 10.0 295.555556 31.514144 266.666667 269.444444 283.333333 \n",
"\n",
" 75% max \n",
"128 283.333333 288.888889 \n",
"256 288.888889 300.000000 \n",
"512 266.666667 277.777778 \n",
"64 288.888889 300.000000 \n",
"768 316.666667 355.555556 "
]
},
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" <td>466.666667</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>320.000000</td>\n",
" <td>39.475297</td>\n",
" <td>277.777778</td>\n",
" <td>300.000000</td>\n",
" <td>311.111111</td>\n",
" <td>322.222222</td>\n",
" <td>422.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>320.000000</td>\n",
" <td>37.697809</td>\n",
" <td>266.666667</td>\n",
" <td>300.000000</td>\n",
" <td>316.666667</td>\n",
" <td>330.555556</td>\n",
" <td>411.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>323.333333</td>\n",
" <td>72.473447</td>\n",
" <td>266.666667</td>\n",
" <td>291.666667</td>\n",
" <td>305.555556</td>\n",
" <td>322.222222</td>\n",
" <td>522.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>347.777778</td>\n",
" <td>88.432470</td>\n",
" <td>277.777778</td>\n",
" <td>313.888889</td>\n",
" <td>322.222222</td>\n",
" <td>341.666667</td>\n",
" <td>588.888889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"128 10.0 321.111111 55.543209 266.666667 291.666667 305.555556 \n",
"256 10.0 320.000000 39.475297 277.777778 300.000000 311.111111 \n",
"512 10.0 320.000000 37.697809 266.666667 300.000000 316.666667 \n",
"64 10.0 323.333333 72.473447 266.666667 291.666667 305.555556 \n",
"768 10.0 347.777778 88.432470 277.777778 313.888889 322.222222 \n",
"\n",
" 75% max \n",
"128 322.222222 466.666667 \n",
"256 322.222222 422.222222 \n",
"512 330.555556 411.111111 \n",
"64 322.222222 522.222222 \n",
"768 341.666667 588.888889 "
]
},
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" <td>8.764563</td>\n",
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" <td>333.333333</td>\n",
" <td>333.333333</td>\n",
" <td>344.444444</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>314.444444</td>\n",
" <td>24.595493</td>\n",
" <td>266.666667</td>\n",
" <td>311.111111</td>\n",
" <td>311.111111</td>\n",
" <td>330.555556</td>\n",
" <td>355.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>338.888889</td>\n",
" <td>20.454372</td>\n",
" <td>300.000000</td>\n",
" <td>333.333333</td>\n",
" <td>333.333333</td>\n",
" <td>352.777778</td>\n",
" <td>366.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>341.111111</td>\n",
" <td>28.712663</td>\n",
" <td>311.111111</td>\n",
" <td>325.000000</td>\n",
" <td>333.333333</td>\n",
" <td>350.000000</td>\n",
" <td>400.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>344.444444</td>\n",
" <td>22.831163</td>\n",
" <td>322.222222</td>\n",
" <td>333.333333</td>\n",
" <td>333.333333</td>\n",
" <td>344.444444</td>\n",
" <td>400.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"128 10.0 331.111111 8.764563 311.111111 333.333333 333.333333 \n",
"256 10.0 314.444444 24.595493 266.666667 311.111111 311.111111 \n",
"512 10.0 338.888889 20.454372 300.000000 333.333333 333.333333 \n",
"64 10.0 341.111111 28.712663 311.111111 325.000000 333.333333 \n",
"768 10.0 344.444444 22.831163 322.222222 333.333333 333.333333 \n",
"\n",
" 75% max \n",
"128 333.333333 344.444444 \n",
"256 330.555556 355.555556 \n",
"512 352.777778 366.666667 \n",
"64 350.000000 400.000000 \n",
"768 344.444444 400.000000 "
]
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>31.111111</td>\n",
" <td>12.614360</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>55.555556</td>\n",
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" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>31.111111</td>\n",
" <td>8.764563</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>25.555556</td>\n",
" <td>7.499428</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>26.666667</td>\n",
" <td>10.734353</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 28.888889 10.734353 11.111111 25.000000 33.333333 33.333333 \n",
"256 10.0 31.111111 12.614360 11.111111 33.333333 33.333333 33.333333 \n",
"512 10.0 31.111111 8.764563 11.111111 33.333333 33.333333 33.333333 \n",
"64 10.0 25.555556 7.499428 11.111111 22.222222 22.222222 33.333333 \n",
"768 10.0 26.666667 10.734353 11.111111 22.222222 27.777778 33.333333 \n",
"\n",
" max \n",
"128 44.444444 \n",
"256 55.555556 \n",
"512 44.444444 \n",
"64 33.333333 \n",
"768 44.444444 "
]
},
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"u'test_firefox_gsearch_ail_select_image_cat'"
]
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" <td>175.353846</td>\n",
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" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>150.000000</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>190.000000</td>\n",
" <td>179.425237</td>\n",
" <td>111.111111</td>\n",
" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>141.666667</td>\n",
" <td>700.000000</td>\n",
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" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>163.333333</td>\n",
" <td>103.046862</td>\n",
" <td>111.111111</td>\n",
" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>455.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>186.666667</td>\n",
" <td>168.898635</td>\n",
" <td>111.111111</td>\n",
" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>141.666667</td>\n",
" <td>666.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>202.222222</td>\n",
" <td>136.666165</td>\n",
" <td>133.333333</td>\n",
" <td>133.333333</td>\n",
" <td>138.888889</td>\n",
" <td>155.555556</td>\n",
" <td>533.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"128 10.0 191.111111 175.353846 111.111111 133.333333 133.333333 \n",
"256 10.0 190.000000 179.425237 111.111111 133.333333 133.333333 \n",
"512 10.0 163.333333 103.046862 111.111111 133.333333 133.333333 \n",
"64 10.0 186.666667 168.898635 111.111111 133.333333 133.333333 \n",
"768 10.0 202.222222 136.666165 133.333333 133.333333 138.888889 \n",
"\n",
" 75% max \n",
"128 150.000000 688.888889 \n",
"256 141.666667 700.000000 \n",
"512 133.333333 455.555556 \n",
"64 141.666667 666.666667 \n",
"768 155.555556 533.333333 "
]
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"u'test_firefox_gsearch_ail_select_search_suggestion'"
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" <td>14.392118</td>\n",
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" <td>30.555556</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>24.444444</td>\n",
" <td>10.210406</td>\n",
" <td>11.111111</td>\n",
" <td>13.888889</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
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" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>27.777778</td>\n",
" <td>7.856742</td>\n",
" <td>11.111111</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>64</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>768</th>\n",
" <td>10.0</td>\n",
" <td>22.777778</td>\n",
" <td>10.621948</td>\n",
" <td>5.555556</td>\n",
" <td>13.888889</td>\n",
" <td>22.222222</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% 75% \\\n",
"128 10.0 18.888889 14.392118 5.555556 5.555556 16.666667 30.555556 \n",
"256 10.0 24.444444 10.210406 11.111111 13.888889 27.777778 33.333333 \n",
"512 10.0 27.777778 7.856742 11.111111 22.222222 33.333333 33.333333 \n",
"64 10.0 15.555556 8.996265 5.555556 11.111111 11.111111 22.222222 \n",
"768 10.0 22.777778 10.621948 5.555556 13.888889 22.222222 33.333333 \n",
"\n",
" max \n",
"128 44.444444 \n",
"256 33.333333 \n",
"512 33.333333 \n",
"64 33.333333 \n",
"768 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsearch_ail_type_searchbox'"
]
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" <th>256</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>512</th>\n",
" <td>10.0</td>\n",
" <td>67.777778</td>\n",
" <td>51.970763</td>\n",
" <td>11.111111</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>91.666667</td>\n",
" <td>155.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>46.111111</td>\n",
" <td>38.583457</td>\n",
" <td>5.555556</td>\n",
" <td>12.500000</td>\n",
" <td>38.888889</td>\n",
" <td>63.888889</td>\n",
" <td>122.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>58.888889</td>\n",
" <td>32.309376</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" <td>88.888889</td>\n",
" <td>111.111111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 10.555556 8.050765 5.555556 5.555556 5.555556 18.055556 \n",
"256 10.0 16.111111 6.651217 5.555556 11.111111 16.666667 22.222222 \n",
"512 10.0 67.777778 51.970763 11.111111 33.333333 44.444444 91.666667 \n",
"64 10.0 46.111111 38.583457 5.555556 12.500000 38.888889 63.888889 \n",
"768 10.0 58.888889 32.309376 22.222222 33.333333 44.444444 88.888889 \n",
"\n",
" max \n",
"128 22.222222 \n",
"256 22.222222 \n",
"512 155.555556 \n",
"64 122.222222 \n",
"768 111.111111 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_clicktab_0'"
]
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" <th>128</th>\n",
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" <td>126.111111</td>\n",
" <td>90.648101</td>\n",
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" <td>75.000000</td>\n",
" <td>133.333333</td>\n",
" <td>141.666667</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>130.555556</td>\n",
" <td>68.755845</td>\n",
" <td>5.555556</td>\n",
" <td>116.666667</td>\n",
" <td>166.666667</td>\n",
" <td>166.666667</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>150.000000</td>\n",
" <td>84.983659</td>\n",
" <td>44.444444</td>\n",
" <td>69.444444</td>\n",
" <td>161.111111</td>\n",
" <td>188.888889</td>\n",
" <td>300.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>127.777778</td>\n",
" <td>73.935055</td>\n",
" <td>11.111111</td>\n",
" <td>91.666667</td>\n",
" <td>138.888889</td>\n",
" <td>169.444444</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>171.666667</td>\n",
" <td>101.647944</td>\n",
" <td>5.555556</td>\n",
" <td>116.666667</td>\n",
" <td>150.000000</td>\n",
" <td>200.000000</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",
"128 10.0 126.111111 90.648101 5.555556 75.000000 133.333333 \n",
"256 10.0 130.555556 68.755845 5.555556 116.666667 166.666667 \n",
"512 10.0 150.000000 84.983659 44.444444 69.444444 161.111111 \n",
"64 10.0 127.777778 73.935055 11.111111 91.666667 138.888889 \n",
"768 10.0 171.666667 101.647944 5.555556 116.666667 150.000000 \n",
"\n",
" 75% max \n",
"128 141.666667 322.222222 \n",
"256 166.666667 200.000000 \n",
"512 188.888889 300.000000 \n",
"64 169.444444 255.555556 \n",
"768 200.000000 344.444444 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_type_0'"
]
},
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" <th>128</th>\n",
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" <td>17.411347</td>\n",
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" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>86.111111</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>74.444444</td>\n",
" <td>17.411347</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>86.111111</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>73.333333</td>\n",
" <td>22.951012</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>75.000000</td>\n",
" <td>133.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>80.000000</td>\n",
" <td>26.084173</td>\n",
" <td>55.555556</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>86.111111</td>\n",
" <td>144.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>81.111111</td>\n",
" <td>26.215316</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>77.777778</td>\n",
" <td>97.222222</td>\n",
" <td>133.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 74.444444 17.411347 44.444444 66.666667 66.666667 86.111111 \n",
"256 10.0 74.444444 17.411347 44.444444 66.666667 66.666667 86.111111 \n",
"512 10.0 73.333333 22.951012 44.444444 66.666667 66.666667 75.000000 \n",
"64 10.0 80.000000 26.084173 55.555556 66.666667 66.666667 86.111111 \n",
"768 10.0 81.111111 26.215316 44.444444 66.666667 77.777778 97.222222 \n",
"\n",
" max \n",
"128 100.000000 \n",
"256 100.000000 \n",
"512 133.333333 \n",
"64 144.444444 \n",
"768 133.333333 "
]
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" <td>594.444444</td>\n",
" <td>50.307695</td>\n",
" <td>500.000000</td>\n",
" <td>569.444444</td>\n",
" <td>594.444444</td>\n",
" <td>616.666667</td>\n",
" <td>666.666667</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>611.111111</td>\n",
" <td>28.688766</td>\n",
" <td>566.666667</td>\n",
" <td>591.666667</td>\n",
" <td>622.222222</td>\n",
" <td>622.222222</td>\n",
" <td>666.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>586.666667</td>\n",
" <td>26.084173</td>\n",
" <td>533.333333</td>\n",
" <td>577.777778</td>\n",
" <td>588.888889</td>\n",
" <td>600.000000</td>\n",
" <td>633.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>630.000000</td>\n",
" <td>25.147030</td>\n",
" <td>588.888889</td>\n",
" <td>613.888889</td>\n",
" <td>633.333333</td>\n",
" <td>650.000000</td>\n",
" <td>666.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>590.000000</td>\n",
" <td>25.364287</td>\n",
" <td>555.555556</td>\n",
" <td>569.444444</td>\n",
" <td>600.000000</td>\n",
" <td>600.000000</td>\n",
" <td>633.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" count mean std min 25% 50% \\\n",
"128 10.0 594.444444 50.307695 500.000000 569.444444 594.444444 \n",
"256 10.0 611.111111 28.688766 566.666667 591.666667 622.222222 \n",
"512 10.0 586.666667 26.084173 533.333333 577.777778 588.888889 \n",
"64 10.0 630.000000 25.147030 588.888889 613.888889 633.333333 \n",
"768 10.0 590.000000 25.364287 555.555556 569.444444 600.000000 \n",
"\n",
" 75% max \n",
"128 616.666667 666.666667 \n",
"256 622.222222 666.666667 \n",
"512 600.000000 633.333333 \n",
"64 650.000000 666.666667 \n",
"768 600.000000 633.333333 "
]
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" <td>27.777778</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>33.333333</td>\n",
" <td>13.857990</td>\n",
" <td>11.111111</td>\n",
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" <td>33.333333</td>\n",
" <td>33.333333</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
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" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>28.333333</td>\n",
" <td>22.291558</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>66.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>24.444444</td>\n",
" <td>12.614360</td>\n",
" <td>11.111111</td>\n",
" <td>11.111111</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>23.888889</td>\n",
" <td>12.297746</td>\n",
" <td>5.555556</td>\n",
" <td>13.888889</td>\n",
" <td>22.222222</td>\n",
" <td>33.333333</td>\n",
" <td>44.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 27.777778 16.769232 11.111111 13.888889 27.777778 33.333333 \n",
"256 10.0 33.333333 13.857990 11.111111 33.333333 33.333333 33.333333 \n",
"512 10.0 28.333333 22.291558 5.555556 11.111111 22.222222 33.333333 \n",
"64 10.0 24.444444 12.614360 11.111111 11.111111 27.777778 33.333333 \n",
"768 10.0 23.888889 12.297746 5.555556 13.888889 22.222222 33.333333 \n",
"\n",
" max \n",
"128 66.666667 \n",
"256 66.666667 \n",
"512 66.666667 \n",
"64 44.444444 \n",
"768 44.444444 "
]
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" <td>66.346970</td>\n",
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" <td>583.333333</td>\n",
" <td>744.444444</td>\n",
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>530.000000</td>\n",
" <td>36.307632</td>\n",
" <td>488.888889</td>\n",
" <td>511.111111</td>\n",
" <td>516.666667</td>\n",
" <td>541.666667</td>\n",
" <td>611.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>584.444444</td>\n",
" <td>89.227749</td>\n",
" <td>411.111111</td>\n",
" <td>555.555556</td>\n",
" <td>572.222222</td>\n",
" <td>655.555556</td>\n",
" <td>700.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>594.444444</td>\n",
" <td>101.328622</td>\n",
" <td>477.777778</td>\n",
" <td>522.222222</td>\n",
" <td>561.111111</td>\n",
" <td>677.777778</td>\n",
" <td>755.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>546.666667</td>\n",
" <td>34.664767</td>\n",
" <td>511.111111</td>\n",
" <td>525.000000</td>\n",
" <td>533.333333</td>\n",
" <td>563.888889</td>\n",
" <td>622.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% \\\n",
"128 10.0 576.666667 66.346970 511.111111 544.444444 555.555556 \n",
"256 10.0 530.000000 36.307632 488.888889 511.111111 516.666667 \n",
"512 10.0 584.444444 89.227749 411.111111 555.555556 572.222222 \n",
"64 10.0 594.444444 101.328622 477.777778 522.222222 561.111111 \n",
"768 10.0 546.666667 34.664767 511.111111 525.000000 533.333333 \n",
"\n",
" 75% max \n",
"128 583.333333 744.444444 \n",
"256 541.666667 611.111111 \n",
"512 655.555556 700.000000 \n",
"64 677.777778 755.555556 \n",
"768 563.888889 622.222222 "
]
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>83.333333</td>\n",
" <td>92.407222</td>\n",
" <td>11.111111</td>\n",
" <td>25.000000</td>\n",
" <td>33.333333</td>\n",
" <td>100.000000</td>\n",
" <td>255.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>63.333333</td>\n",
" <td>63.731691</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>44.444444</td>\n",
" <td>66.666667</td>\n",
" <td>211.111111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>58.888889</td>\n",
" <td>80.131921</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>27.777778</td>\n",
" <td>61.111111</td>\n",
" <td>277.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>62.222222</td>\n",
" <td>82.850453</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>27.777778</td>\n",
" <td>33.333333</td>\n",
" <td>244.444444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"text": [
" count mean std min 25% 50% 75% \\\n",
"128 10.0 17.777778 11.653432 5.555556 11.111111 11.111111 30.555556 \n",
"256 10.0 83.333333 92.407222 11.111111 25.000000 33.333333 100.000000 \n",
"512 10.0 63.333333 63.731691 11.111111 22.222222 44.444444 66.666667 \n",
"64 10.0 58.888889 80.131921 11.111111 22.222222 27.777778 61.111111 \n",
"768 10.0 62.222222 82.850453 11.111111 22.222222 27.777778 33.333333 \n",
"\n",
" max \n",
"128 33.333333 \n",
"256 255.555556 \n",
"512 211.111111 \n",
"64 277.777778 \n",
"768 244.444444 "
]
},
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"text": [
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" <td>9.816562</td>\n",
" <td>5.555556</td>\n",
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" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>21.666667</td>\n",
" <td>9.955319</td>\n",
" <td>5.555556</td>\n",
" <td>13.888889</td>\n",
" <td>22.222222</td>\n",
" <td>30.555556</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>18.888889</td>\n",
" <td>12.339505</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>22.222222</td>\n",
" <td>30.555556</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>11.666667</td>\n",
" <td>9.240722</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</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",
"128 10.0 27.777778 7.856742 11.111111 22.222222 33.333333 33.333333 \n",
"256 10.0 18.333333 9.816562 5.555556 11.111111 16.666667 22.222222 \n",
"512 10.0 21.666667 9.955319 5.555556 13.888889 22.222222 30.555556 \n",
"64 10.0 18.888889 12.339505 5.555556 5.555556 22.222222 30.555556 \n",
"768 10.0 11.666667 9.240722 5.555556 5.555556 8.333333 11.111111 \n",
"\n",
" max \n",
"128 33.333333 \n",
"256 33.333333 \n",
"512 33.333333 \n",
"64 33.333333 \n",
"768 33.333333 "
]
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" <th>256</th>\n",
" <td>10.0</td>\n",
" <td>12.777778</td>\n",
" <td>8.302412</td>\n",
" <td>5.555556</td>\n",
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" <td>22.222222</td>\n",
" <td>22.222222</td>\n",
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" <th>512</th>\n",
" <td>10.0</td>\n",
" <td>11.111111</td>\n",
" <td>7.856742</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>19.444444</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>10.0</td>\n",
" <td>20.555556</td>\n",
" <td>10.492011</td>\n",
" <td>5.555556</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" <td>30.555556</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>768</th>\n",
" <td>10.0</td>\n",
" <td>9.444444</td>\n",
" <td>8.705674</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",
"128 10.0 26.666667 19.737648 11.111111 13.888889 22.222222 30.555556 \n",
"256 10.0 12.777778 8.302412 5.555556 5.555556 8.333333 22.222222 \n",
"512 10.0 11.111111 7.856742 5.555556 5.555556 5.555556 19.444444 \n",
"64 10.0 20.555556 10.492011 5.555556 11.111111 22.222222 30.555556 \n",
"768 10.0 9.444444 8.705674 5.555556 5.555556 5.555556 9.722222 \n",
"\n",
" max \n",
"128 77.777778 \n",
"256 22.222222 \n",
"512 22.222222 \n",
"64 33.333333 \n",
"768 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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WJElSBVocsiKiS0T8LSJuKeUNIuL+iJgcEddGxIplfPdSnlym96+m6ZIkSR1X\na3qyTgYm1ZUvAH6UUvow8DIwtIwfCrxcxv+o1JMkSVqutChkRUQ/YB/gp6UcwK7A9aXKGOCzZXi/\nUqZM363UlyRJWm60tCfrf4GvA/NLeQ3glZTSe6U8DehbhvsCUwHK9FdLfUmSpOVGsyErIj4NzEwp\nPdiWC46I4yJiYkRMnDVrVlvOWpIkqd21pCfr48BnImIKcA35MuFFQK+I6Frq9AOml+HpwLoAZfpq\nwOzGM00pXZZSGpRSGtS7d+8lWglJkqSOptmQlVI6M6XUL6XUHzgY+FNK6VBgHLB/qXYkcFMZvrmU\nKdP/lFJKbdpqSZKkDm5JfifrdGB4REwm33M1uowfDaxRxg8HzliyJkqSJHU+XZuv0iCldBdwVxl+\nFtiuiTpvAwe0QdskSZI6LX/xXZIkqQKGLEmSpAoYsiRJkipgyJIkSaqAIUuSJKkChixJkqQKGLIk\nSZIqYMiSJEmqgCFLkiSpAoYsSZKkChiyJEmSKmDIkiRJqoAhS5IkqQKGLEmSpAoYsiRJkipgyJIk\nSaqAIUuSJKkChixJkqQKGLIkSZIqYMiSJEmqgCFLkiSpAoYsSZKkChiyJEmSKmDIkiRJqoAhS5Ik\nqQKGLEmSpAoYsiRJkipgyJIkSaqAIUuSJKkChixJkqQKGLIkSZIq0GzIioiVIuKBiHgkIp6IiHPK\n+A0i4v6ImBwR10bEimV891KeXKb3r3YVJEmSOp6W9GS9A+yaUtoC2BLYKyJ2AC4AfpRS+jDwMjC0\n1B8KvFzG/6jUkyRJWq40G7JS9kYpdiuPBOwKXF/GjwE+W4b3K2XK9N0iItqsxZIkSZ1Ai+7Jiogu\nEfEwMBO4A/gH8EpK6b1SZRrQtwz3BaYClOmvAmu0ZaMlSZI6uhaFrJTSvJTSlkA/YDtgkyVdcEQc\nFxETI2LirFmzlnR2kiRJHUqrvl2YUnoFGAfsCPSKiK5lUj9gehmeDqwLUKavBsxuYl6XpZQGpZQG\n9e7dezGbL0mS1DG15NuFvSOiVxleGfgkMIkctvYv1Y4EbirDN5cyZfqfUkqpLRstSZLU0XVtvgpr\nA2Miogs5lF2XUrolIp4EromIc4G/AaNL/dHAVRExGXgJOLiCdkuSJHVozYaslNKjwFZNjH+WfH9W\n4/FvAwe0SeskSZI6KX/xXZIkqQKGLEmSpAoYsiRJkipgyJIkSaqAIUuSJKkChixJkqQKGLIkSZIq\nYMiSJEmqgCFLkiSpAoYsSZKkChiyJEmSKmDIkiRJqoAhS5IkqQKGLEmSpAoYsiRJkipgyJIkSaqA\nIUuSJKkChixJkqQKGLIkSZK5wOHLAAAKgklEQVQqYMiSJEmqgCFLkiSpAoYsSZKkChiyJEmSKmDI\nkiRJqoAhS5IkqQKGLEmSpAoYsiRJkipgyJIkSaqAIUuSJKkChixJkqQKGLIkSZIq0GzIioh1I2Jc\nRDwZEU9ExMll/OoRcUdEPFP+fqCMj4i4OCImR8SjEbF11SshSZLU0bSkJ+s94NSU0mbADsCJEbEZ\ncAZwZ0ppAHBnKQPsDQwoj+OAS9u81ZIkSR1csyErpTQjpfRQGX4dmAT0BfYDxpRqY4DPluH9gCtT\nNgHoFRFrt3nLJUmSOrBW3ZMVEf2BrYD7gT4ppRll0vNAnzLcF5ha97RpZVzjeR0XERMjYuKsWbNa\n2WxJkqSOrcUhKyJWAX4NnJJSeq1+WkopAak1C04pXZZSGpRSGtS7d+/WPFWSJKnDa1HIiohu5ID1\ni5TSDWX0C7XLgOXvzDJ+OrBu3dP7lXGSJEnLjZZ8uzCA0cCklNIP6ybdDBxZho8Ebqobf0T5luEO\nwKt1lxUlSZKWC11bUOfjwOHAYxHxcBl3FnA+cF1EDAWeAw4s024FPgVMBt4Ejm7TFkuSJHUCzYas\nlNJ4IBYyebcm6ifgxCVslyRJUqfmL75LkiRVwJAlSZJUAUOWJElSBQxZkiRJFTBkSZIkVcCQJUmS\nVAFDliRJUgUMWZIkSRUwZEmSJFXAkCVJklQBQ5YkSVIFDFmSJEkVMGRJkiRVwJAlSZJUAUOWJElS\nBQxZkiRJFTBkSZIkVcCQJUmSVAFDliRJUgUMWZIkSRUwZEmSJFXAkCVJklQBQ5YkSVIFDFmSJEkV\nMGRJkiRVwJAlSZJUAUOWJElSBQxZkiRJFTBkSZIkVcCQJUmSVIFmQ1ZE/CwiZkbE43XjVo+IOyLi\nmfL3A2V8RMTFETE5Ih6NiK2rbLwkSVJH1ZKerCuAvRqNOwO4M6U0ALizlAH2BgaUx3HApW3TTEmS\npM6l2ZCVUroHeKnR6P2AMWV4DPDZuvFXpmwC0Csi1m6rxkqSJHUWi3tPVp+U0owy/DzQpwz3BabW\n1ZtWxkmSJC1XlvjG95RSAlJrnxcRx0XExIiYOGvWrCVthiRJUoeyuCHrhdplwPJ3Zhk/HVi3rl6/\nMu59UkqXpZQGpZQG9e7dezGbIUmS1DEtbsi6GTiyDB8J3FQ3/ojyLcMdgFfrLitKkiQtN7o2VyEi\nxgK7AGtGxDTgW8D5wHURMRR4DjiwVL8V+BQwGXgTOLqCNkuSJHV4zYaslNIhC5m0WxN1E3DikjZK\nkiSps/MX3yVJkipgyJIkSaqAIUuSJKkChixJkqQKGLIkSZIqYMiSJEmqgCFLkiSpAoYsSZKkChiy\nJEmSKmDIkiRJqoAhS5IkqQKGLEmSpAoYsiRJkipgyJIkSaqAIUuSJKkChixJkqQKGLIkSZIqYMiS\nJEmqgCFLkiSpAoYsSZKkChiyJEmSKmDIkiRJqoAhS5IkqQKGLEmSpAoYsiRJkipgyJIkSaqAIUuS\nJKkChixJkqQKGLIkSZIqYMiSJEmqgCFLkiSpApWErIjYKyKejojJEXFGFcuQJEnqyNo8ZEVEF+D/\ngL2BzYBDImKztl6OJElSR1ZFT9Z2wOSU0rMppXeBa4D9KliOJElSh1VFyOoLTK0rTyvjJEmSlhtd\n22vBEXEccFwpvhERT7dXW5aCNYEX27sRWizuu87N/de5uf86r2V9363fkkpVhKzpwLp15X5l3AJS\nSpcBl1Ww/A4nIiamlAa1dzvUeu67zs3917m5/zov911WxeXCvwIDImKDiFgROBi4uYLlSJIkdVht\n3pOVUnovIr4C3AZ0AX6WUnqirZcjSZLUkVVyT1ZK6Vbg1irm3UktF5dFl1Huu87N/de5uf86L/cd\nECml9m6DJEnSMsd/qyNJklQBQ9YSioifRcTMiHi8btz3I+KpiHg0Im6MiF5lfLeIGBMRj0XEpIg4\ns/1aLoCIWDcixkXEkxHxREScXMafHRHTI+Lh8vhU3XM+GhF/KfUfi4iV2m8Nlm8RMaXsg4cjYmIZ\nd0DZN/MjYlBd3U9GxIOl/oMRsWv7tVyNRUSviLi+vHdOiogd66adGhEpItZszzaqQURsXPf++HBE\nvBYRp5Rpw8p+fCIivlfGLZfnv3b7naxlyBXAj4Er68bdAZxZvgRwAXAmcDpwANA9pfSRiOgBPBkR\nY1NKU5Zym9XgPeDUlNJDEbEq8GBE3FGm/SildGF95YjoClwNHJ5SeiQi1gDmLt0mq5EhKaX63+N5\nHPg88JNG9V4E9k0p/TsiBpK/nOMPJXccFwF/SCntX76Z3gPyByFgD+Bf7dk4LSil9DSwJfzn3+lN\nB26MiCHk//KyRUrpnYhYqzxluTz/2ZO1hFJK9wAvNRp3e0rpvVKcQP6tMIAE9Cwn6pWBd4HXllZb\n9X4ppRkppYfK8OvAJBZ94t0DeDSl9Eh5zuyU0rzqW6qWSilNKieAxuP/llL6dyk+AawcEd2XbuvU\nlIhYDdgZGA2QUno3pfRKmfwj4Ovk9091TLsB/0gpPQd8GTg/pfQOQEppZqmzXJ7/DFnV+xLw+zJ8\nPTAHmEH+VHZhSumlhT1RS1dE9Ae2Au4vo75SLvn+LCI+UMZtBKSIuC0iHoqIr7dDU9UgAbeXy3/H\nNVu7wReAh2onArW7DYBZwM8j4m8R8dOI6BkR+wHTax9q1GEdDIwtwxsBO0XE/RFxd0RsW8Yvl+c/\nQ1aFImIE+XLUL8qo7YB5wDrkN5VTI2LDdmqe6kTEKsCvgVNSSq8BlwIfIneHzwB+UKp2BQYDh5a/\nn4uI3ZZ+i1UMTiltDewNnBgROzf3hIjYHLgA+O+qG6cW6wpsDVyaUtqKfDI+GzgL+GY7tkvNKJd2\nPwP8qozqCqwO7AB8DbguIoLl9PxnyKpIRBwFfBo4NDX8TsYXyfcczC1dqPcCy/2/HWhvEdGNHLB+\nkVK6ASCl9EJKaV5KaT5wOfkNAvI/PL8npfRiSulN8u/Bbd0e7RaklKaXvzOBG2nYT02KiH6l3hEp\npX9U30K10DRgWkqp1ot8Pfl1tQHwSERMId928VBEfLB9mqiF2JvcK/xCKU8DbkjZA8B88v8xXC7P\nf4asCkTEXuR7CD5TTsQ1/wJ2LXV6kpP+U0u/haopn7BGA5NSSj+sG792XbXPkW+mhnyz9Ecioke5\nt+ATwJNLq71qUC4nrVobJt8v9/gi6vcCfgeckVK6d+m0Ui2RUnoemBoRG5dRu5FP3GullPqnlPqT\nT95bl7rqOA6h4VIhwG+AIQARsRGwIvlLJ8vl+c8fI11CETEW2IWc1F8AvkX+NmF3YHapNiGldHy5\nJPVzYDMggJ+nlL6/1But/4iIwcCfgcfIn7ggX6I4hHypMAFTgP9OKc0ozzmMvI8TcGtKyfuy2kG5\n1HBjKXYFfplSGhkRnwMuAXoDrwAPp5T2jIhvkPfbM3Wz2aPuxly1o4jYEvgp+aT8LHB0SunluulT\ngEGNvkmqdlTC0r+ADVNKr5ZxKwI/I79/vgucllL60/J6/jNkSZIkVcDLhZIkSRUwZEmSJFXAkCVJ\nklQBQ5YkSVIFDFmSJEkVMGRJkiRVwJAlSZJUAUOWJElSBf4/r7JjCqJ30fUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10ff75b90>"
]
},
{
"output_type": "display_data",
"png": 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1MJmSJEmqgcmUJElSDUymJEmSamAyJUmSVAOTKUmSpBqYTEmSJNXA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\nSUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10c4fca90>"
]
},
{
"output_type": "display_data",
"png": 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1dUv9kGFKklYHTzxRxjE99lhpX3NNOfHl3dW5BrbZplyb7m9/K+0TT4SZM9v3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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x111070d10>"
]
},
{
"output_type": "display_data",
"png": 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Zq1l+BjBjVfOlThs2bOUarFGjSiDW+KLfYosyCGDjjsrMEnw0OnDPn1/uhvvp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\nSaqJgZYkSVJNDLQkSZJqYqAlSZJUk/8PphamFeOGD7YAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x11112e850>"
]
},
{
"output_type": "display_data",
"png": 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Ms4Gza9h/jxMR0zJzVLPjUOd57no3z1/v5vnrvTx3RR3NhX8BNomI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\n",
"text": [
"<matplotlib.figure.Figure at 0x1112004d0>"
]
},
{
"output_type": "display_data",
"png": 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eaDPtisxcUhX/TDlXGEACa1df1msBLwJP91RdtazMnJ+ZN1X3nwHm\nsPIv3r2BWzLzb9U6j2fm0vprqo7KzDnVF0Db6X/NzIer4m3AWhGxRs/WTssTEesBuwPTADLzxcx8\nspp9MvB5ymen+qY9gXsy837g/wAnZeYLAJm5oFpmUH73GbJ6xhHAZdX984F/APMpv8y+lZlPrGhF\n9ZyIGAXsDFxfTfpE1d17ekSsX017NZARcXlE3BQRn++FqqopgSuq7r8j21266f3ATY0vAvW6VwIL\ngTMi4q8R8bOIWDsiJgDzGj9q1Gd9EJhe3X818NaIuD4iromIN1TTB+V3nyGrZhExhdIldW41aVdg\nKbAZ5YPl2IjYupeqp0pErANcAHw6M58GTgFeRWkOnw98u1p0KDAOOLj6+76I2LPna6zKuMzcBXgX\ncHRE7N7eChHxGuDrwFF1V04dNhTYBTglM3emfBmfAHwR+HIv1kvtqLp23wP8upo0FNgAeBNwHHBe\nRASD9LvPkFWjiDgM2A84OJvnyjiIMu5gcdWM+j/AoL/0QG+KiGGUgHVuZl4IkJmPZubSzHwJOI3y\nAQHlYufXZuZjmflPyrngdumNegsyc171dwFwEc39tFwRsXm13Icz8576a6gOegh4KDMbrcjnU/6v\nXgn8LSLmUoZc3BQRr+idKmoF3kVpFX60Kj8EXJjFDcBLlOsYDsrvPkNWTSJiH8o4gvdUX8YNDwBv\nr5ZZm5L27+j5Ggqg+oU1DZiTmd9pmb5py2LvowymhjJYeseIeFk1tuBtwO09VV81Vd1J6zbuU8bL\n3bqS5YcDvwcmZ+b/9Ewt1RGZ+QjwYERsV03ak/LFvXFmjsrMUZQv712qZdV3TKTZVQjwG2A8QES8\nGlidctDJoPzu82Sk3SAipgN7UNL6o8BXKEcTrgE8Xi3258z8WNUtdQawAxDAGZn5zR6vtACIiHHA\nfwOzKb+4oHRRTKR0FSYwFzgqM+dX63yIsn8TuDQzHZfVC6quhouq4lDgF5k5NSLeB/wAGAE8Cdyc\nme+MiP+g7Le7Wjazd8vAXPWiiNgJ+BnlS/le4PDMXNQyfy4wts2RpOpFVVh6ANg6M5+qpq0OnE75\n/HwR+FxmXj1Yv/sMWZIkSTWwu1CSJKkGhixJkqQaGLIkSZJqYMiSJEmqgSFLkiSpBoYsSZKkGhiy\nJEmSamDIkiRJqsH/B8xueIssn7DkAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1112c8210>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1112bfe10>"
]
},
{
"output_type": "display_data",
"png": 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jgZIkSX1ZR49MfRf4MrC0ql8JzMvMxVX9GLBZF7dNkiSpz2s3TEXEB4DZmXnb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\n",
"text": [
"<matplotlib.figure.Figure at 0x11139a510>"
]
},
{
"output_type": "display_data",
"png": 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lfV566Waz3dZbl9rBpZYq8bhx89/kXgOCNVOSNJS9//2lw3rDHXfMv/zKK0vN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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11151a210>"
]
},
{
"output_type": "display_data",
"png": 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LUjcxyVK+EfPmm1fvn3XEEfmu75Uuyf3l9jZ9zYYbwnrr5ekHH8xj9wwdmssL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\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x11154d990>"
]
},
{
"output_type": "display_data",
"png": 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ccMIJHHvssQwbNowJEyY8P14abDxmSpIkaQU8ZkqSJGkAGKYkSZJqMExJkiTV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\ng2FKkiSpBsOUJElSDYYpSZKkGv4/0r8t86PTa6cAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1115f58d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111771050>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10ffdce10>"
]
},
{
"output_type": "display_data",
"png": 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WfM7SwOb/nlR09jlZnbmStRj4ambeFxHDgHsjovpRJ87PzO8st+LtgSOBHYA3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\nvJruk5n5eP01VCdNB6ZnZuM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"text": [
"<matplotlib.figure.Figure at 0x111912e50>"
]
},
{
"output_type": "display_data",
"png": 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pKf/0p6V81VWl3N2d+eIXl/rMzJ/8JHOffcqg4ar9bide1ncw+9nPMt/xjr4g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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1119dafd0>"
]
},
{
"output_type": "display_data",
"png": 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k0WgoA99XAidn5vURsTVwXURcVS07PTP/tXXliNgTOBrYC3gBcHVEvDgznVxI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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1119f91d0>"
]
},
{
"output_type": "display_data",
"png": 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lwlSAT32qDO5fsaIUqv/6r6Xlr8uHP9yQNDX4RnUxVftijxtp2onnD/k+JWnA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\n",
"text": [
"<matplotlib.figure.Figure at 0x111a97bd0>"
]
},
{
"output_type": "display_data",
"png": 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Yr3JLS3mBPvVUeTSU53zguyw9/fD1zp+Q8owqf1+1qsQ8juU5Xd8bOX6bsj4o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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x111b83350>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111c54f90>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111d91490>"
]
},
{
"output_type": "display_data",
"png": 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897ynlJcsKS3NG21UyrffXsZM3nBDKd91V5lv7q67SvnZZ0uIU+975pkSlBqt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\n",
"text": [
"<matplotlib.figure.Figure at 0x111dab050>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x111e88990>"
]
},
{
"output_type": "display_data",
"png": 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1fkTESRExJSJujohtm3wBkiRJA9GStGTtmJnbZObYWj4SuDwzNwMur2WA3YDN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\nhhyahXeLTLgAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x111f88050>"
]
},
{
"output_type": "display_data",
"png": 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OtazPnvs6fZ6p5cgZwE+An7csuxw4suqMfzxwJHAE8DFgpczcKiJW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\nUg0mU5IkSTWYTEmSJNVgMiVJklTD/wfSixqDYaDyvQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1120bf090>"
]
},
{
"output_type": "display_data",
"png": 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3L2MwWjbe2Kv5JPXYgExd1I8G6zhrry5UcyJK337rSsg11ihfPttq0m6FjUMO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\n",
"text": [
"<matplotlib.figure.Figure at 0x1121991d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x112112ed0>"
]
},
{
"output_type": "display_data",
"png": 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zpj/2uqY9zt2bvE/ZmM5f/ug8ZEyjH/PZdcdlIZLN2+XyZxq1fbtW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\nKfTDAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1122bd250>"
]
},
{
"output_type": "display_data",
"png": 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HPyr/Fy1a/X2kbjCZGugi2q9ltc02Zc6qxq+wyy+HD3wAHnmkdfFJ6r8atdjD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\n",
"text": [
"<matplotlib.figure.Figure at 0x11233db50>"
]
}
],
"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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