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network.tcp.sendbuffer.ipynb
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"language": "python",
"name": "python2"
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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",
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"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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" <th>std</th>\n",
" <th>min</th>\n",
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" <td>422.222222</td>\n",
" <td>433.333333</td>\n",
" <td>488.888889</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
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"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"default 10.0 431.111111 33.044012 400.0 405.555556 422.222222 \n",
"\n",
" 75% max \n",
"default 433.333333 488.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_select_search_suggestion'"
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" count mean std min 25% 50% 75% \\\n",
"default 10.0 7.222222 5.270463 5.555556 5.555556 5.555556 5.555556 \n",
"\n",
" max \n",
"default 22.222222 "
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{
"output_type": "display_data",
"text": [
"u'test_firefox_amazon_ail_type_in_search_field'"
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" </tr>\n",
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"</table>\n",
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"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
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"\n",
" max \n",
"default 22.222222 "
]
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{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_close_chat_tab'"
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},
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" <td>16.396995</td>\n",
" <td>44.444444</td>\n",
" <td>50.0</td>\n",
" <td>66.666667</td>\n",
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" <td>88.888889</td>\n",
" </tr>\n",
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"</table>\n",
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"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"default 10.0 64.444444 16.396995 44.444444 50.0 66.666667 66.666667 \n",
"\n",
" max \n",
"default 88.888889 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab'"
]
},
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" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>177.777778</td>\n",
" <td>59.027324</td>\n",
" <td>111.111111</td>\n",
" <td>133.333333</td>\n",
" <td>144.444444</td>\n",
" <td>233.333333</td>\n",
" <td>266.666667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"default 10.0 177.777778 59.027324 111.111111 133.333333 144.444444 \n",
"\n",
" 75% max \n",
"default 233.333333 266.666667 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_open_chat_tab_emoji'"
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" <td>12.883353</td>\n",
" <td>66.666667</td>\n",
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" <td>83.333333</td>\n",
" <td>88.888889</td>\n",
" <td>111.111111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"default 10.0 85.555556 12.883353 66.666667 77.777778 83.333333 \n",
"\n",
" 75% max \n",
"default 88.888889 111.111111 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_facebook_ail_click_photo_viewer_right_arrow'"
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},
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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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"</table>\n",
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" count mean std min 25% 50% 75% \\\n",
"default 10.0 50.0 7.856742 33.333333 44.444444 55.555556 55.555556 \n",
"\n",
" max \n",
"default 55.555556 "
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"output_type": "display_data",
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"u'test_firefox_facebook_ail_type_comment_1_txt'"
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" <td>33.333333</td>\n",
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" <td>55.555556</td>\n",
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"</table>\n",
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],
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"text": [
" count mean std min 25% 50% \\\n",
"default 10.0 32.222222 11.04921 11.111111 33.333333 33.333333 \n",
"\n",
" 75% max \n",
"default 33.333333 55.555556 "
]
},
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"output_type": "display_data",
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" <td>397.222222</td>\n",
" <td>422.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"default 10.0 375.555556 26.604867 344.444444 355.555556 372.222222 \n",
"\n",
" 75% max \n",
"default 397.222222 422.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_compose_new_mail'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <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>default</th>\n",
" <td>10.0</td>\n",
" <td>290.0</td>\n",
" <td>15.225781</td>\n",
" <td>266.666667</td>\n",
" <td>280.555556</td>\n",
" <td>288.888889</td>\n",
" <td>297.222222</td>\n",
" <td>322.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"default 10.0 290.0 15.225781 266.666667 280.555556 288.888889 \n",
"\n",
" 75% max \n",
"default 297.222222 322.222222 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_ymail_ail_type_in_reply_field'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>37.222222</td>\n",
" <td>62.638051</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>22.222222</td>\n",
" <td>200.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"default 10.0 37.222222 62.638051 5.555556 5.555556 8.333333 22.222222 \n",
"\n",
" max \n",
"default 200.0 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_select_search_suggestion'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>13.888889</td>\n",
" <td>11.490439</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>19.444444</td>\n",
" <td>33.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"default 10.0 13.888889 11.490439 5.555556 5.555556 8.333333 19.444444 \n",
"\n",
" max \n",
"default 33.333333 "
]
},
{
"output_type": "display_data",
"text": [
"u'test_firefox_youtube_ail_type_in_search_field'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>default</th>\n",
" <td>10.0</td>\n",
" <td>10.555556</td>\n",
" <td>6.651217</td>\n",
" <td>5.555556</td>\n",
" <td>5.555556</td>\n",
" <td>8.333333</td>\n",
" <td>11.111111</td>\n",
" <td>22.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% 75% \\\n",
"default 10.0 10.555556 6.651217 5.555556 5.555556 8.333333 11.111111 \n",
"\n",
" max \n",
"default 22.222222 "
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# show plot\n",
"plt.show()"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
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S1IAhS5IkqQFDliRJUgOGLEmSpAYMWZIkSQ38f9h0TSKbZWWpAAAAAElFTkSu\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x104ef6cd0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x104f71110>"
]
},
{
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"png": 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"text": [
"<matplotlib.figure.Figure at 0x105126b10>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x1051f58d0>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x105221410>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1052cbb50>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1052eead0>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x105370210>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x105447fd0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1054bf810>"
]
},
{
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"png": 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"text": [
"<matplotlib.figure.Figure at 0x105507090>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x105574510>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1055a0910>"
]
},
{
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pNuoSspLMA94PHALsBxyZZL8e65IkSZqNep3JOgBYVVXXVNX/AmcDh3ValyRJ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\nSZIkdWDIkiRJ6uD/A3Pv7sTBBLWHAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1056088d0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x105681d50>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10571a4d0>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x1057e4fd0>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x104f71090>"
]
},
{
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"png": 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"text": [
"<matplotlib.figure.Figure at 0x1058c6cd0>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1058ce050>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x10592add0>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x1059a80d0>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x105a9ae10>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x105aa3290>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x105af8c50>"
]
},
{
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"text": [
"<matplotlib.figure.Figure at 0x105b66950>"
]
},
{
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x105c4efd0>"
]
}
],
"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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