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juliantaylor/casa-logs.ipynb

Last active August 29, 2015 14:17
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casa log parse
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casa-logs.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:a62328819ee51c59b87bcb21de0ca30a948cfdd8f409f00c2c79d57b8914c9bd"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib nbagg\n",
"import pandas\n",
"import datetime\n",
"from dateutil.parser import parse\n",
"import re\n",
"from collections import OrderedDict, defaultdict\n",
"import matplotlib.pyplot as plt\n",
"#plt.style.use('ggplot')\n",
"matplotlib.rcParams['figure.figsize'] = (16,8)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"inpfile = '/tmp/X164_sequential_casapy-20150313-142649.log'\n",
"inpfile = '/tmp/X164_1thread_parallel_casapy-20150318-083910.log'\n",
"inpfile = '/tmp/X291_sequential_casapy-20150323-142036.log'\n",
"inpfile = '/tmp/X291_16subms_casapy-20150325-115256.log'\n",
"ls = open(inpfile).readlines()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!head $inpfile"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2015-03-25 11:53:04\tWARN\tpipeline.infrastructure.renderer.logger::pipeline.infrastructure.renderer.logger::casa\tImageMagick is not installed. Thumbnails will not be generated, leading to slower web logs.\r\n",
"2015-03-25 11:53:06\tINFO\tcasa::::@almahpc03:MPIClient\t---\r\n",
"2015-03-25 11:53:06\tINFO\tcasa::::@almahpc03:MPIClient\tCASA Version 4.3.0 (DEV r32769)\r\n",
"2015-03-25 11:53:06\tINFO\tcasa::::@almahpc03:MPIClient\t Tagged on: 2015-03-19 19:40:15 +0000 (Thu, 19 Mar 2015)\r\n",
"2015-03-25 11:53:06\tINFO\tcasa::casapy::@almahpc03:MPIClient\tMPI Enabled at host almahpc03 with rank 0 as MPIClient using MPI version 2.1 from Open MPI v1.6.5 implementation \r\n",
"2015-03-25 11:53:06\tINFO\tcasa::::@almahpc03:MPIClient\tManaging Sakura lifecycle\r\n",
"2015-03-25 11:53:06\tINFO\tsakura::initialize_sakura \tinitialize_sakura is called. sakura was initialized successfully. \r\n",
"2015-03-25 11:53:13\tINFO\th_init::::@almahpc03:MPIClient\t\r\n",
"2015-03-25 11:53:13\tINFO\th_init::::@almahpc03:MPIClient+\t##########################################\r\n",
"2015-03-25 11:53:13\tINFO\th_init::::@almahpc03:MPIClient+\t##### Begin Task: h_init #####\r\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls[0].split('\\t')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"['2015-03-25 11:53:04',\n",
" 'WARN',\n",
" 'pipeline.infrastructure.renderer.logger::pipeline.infrastructure.renderer.logger::casa',\n",
" 'ImageMagick is not installed. Thumbnails will not be generated, leading to slower web logs.\\n']"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tasks = defaultdict(list)\n",
"\n",
"log = []\n",
"for i, l in enumerate(ls[:]):\n",
" tup = l.strip().split('\\t')\n",
" if not tup:\n",
" continue\n",
" if len(tup) < 4:\n",
" continue\n",
" ts, level, origin = tup[:3]\n",
" message = '\\t'.join(tup[3:])\n",
" ts = parse(ts)\n",
" begin = re.search('##### Begin Task:(.*)#####', message)\n",
" end = re.search('##### End Task:(.*)#####', message)\n",
" if begin and 'MPIServer-' not in origin:\n",
" tasks[begin.groups()[0].strip()].append(ts)\n",
" if end and 'MPIServer-' not in origin:\n",
" tasks[end.groups()[0].strip()].append(ts)\n",
" log.append((ts, level, origin, message))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ntasks = dict()\n",
"taskcount = defaultdict(int)\n",
"for k, v in tasks.items():\n",
" for i in range(0, len(v), 2):\n",
" taskcount[k] += 1\n",
" ntasks['%s_%d' % (k, taskcount[k])] = v[i:i+2]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pandas.DataFrame.from_dict(ntasks, orient='index')\n",
"df.columns = ['start', 'end']\n",
"df['duration'] = (df['end'] - df['start']) / np.timedelta64(1, 's')\n",
"df['startts'] = (df['start'].astype(int) - df['start'].astype(int).min()) * 1e-9\n",
"df['task'] = df.index\n",
"df['task'] = df['task'].str.replace(r'(\\w)[0-9]+', '')\n",
"del df['end']\n",
"df = df.set_index('start')\n",
"df = df.sort_index()\n",
"#df['duration'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['task'][0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"'h_init'"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#pandas.set_option(\"display.max_rows\", None)\n",
"#pandas.set_option(\"display.float_format\", lambda x: \"%.5g\" % x)\n",
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>duration</th>\n",
" <th>startts</th>\n",
" <th>task</th>\n",
" </tr>\n",
" <tr>\n",
" <th>start</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-03-25 11:53:13</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> h_init</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 11:53:13</th>\n",
" <td> 3955</td>\n",
" <td> 0</td>\n",
" <td> hifa_importdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 11:53:13</th>\n",
" <td> 819</td>\n",
" <td> 0</td>\n",
" <td> importasdm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 12:10:05</th>\n",
" <td> 871</td>\n",
" <td> 1012</td>\n",
" <td> importasdm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 12:27:54</th>\n",
" <td> 980</td>\n",
" <td> 2081</td>\n",
" <td> importasdm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 12:47:44</th>\n",
" <td> 170</td>\n",
" <td> 3271</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 12:50:34</th>\n",
" <td> 176</td>\n",
" <td> 3441</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 12:53:30</th>\n",
" <td> 187</td>\n",
" <td> 3617</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 12:56:44</th>\n",
" <td> 10</td>\n",
" <td> 3811</td>\n",
" <td> listobs</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 12:57:25</th>\n",
" <td> 11</td>\n",
" <td> 3852</td>\n",
" <td> listobs</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 12:58:03</th>\n",
" <td> 12</td>\n",
" <td> 3890</td>\n",
" <td> listobs</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 12:59:09</th>\n",
" <td> 1003</td>\n",
" <td> 3956</td>\n",
" <td> partition</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:15:53</th>\n",
" <td> 748</td>\n",
" <td> 4960</td>\n",
" <td> partition</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:28:22</th>\n",
" <td> 802</td>\n",
" <td> 5709</td>\n",
" <td> partition</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:42:02</th>\n",
" <td> 581</td>\n",
" <td> 6529</td>\n",
" <td> hifa_flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:42:22</th>\n",
" <td> 135</td>\n",
" <td> 6549</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:45:01</th>\n",
" <td> 142</td>\n",
" <td> 6708</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:47:47</th>\n",
" <td> 117</td>\n",
" <td> 6874</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:50:43</th>\n",
" <td> 17</td>\n",
" <td> 7050</td>\n",
" <td> flagcmd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:51:00</th>\n",
" <td> 18</td>\n",
" <td> 7067</td>\n",
" <td> flagcmd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:51:19</th>\n",
" <td> 18</td>\n",
" <td> 7086</td>\n",
" <td> flagcmd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:51:45</th>\n",
" <td> 482</td>\n",
" <td> 7112</td>\n",
" <td> hifa_fluxcalflag</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:53:20</th>\n",
" <td> 24</td>\n",
" <td> 7207</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:53:47</th>\n",
" <td> 4</td>\n",
" <td> 7234</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:53:53</th>\n",
" <td> 25</td>\n",
" <td> 7240</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:55:57</th>\n",
" <td> 27</td>\n",
" <td> 7364</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:56:26</th>\n",
" <td> 4</td>\n",
" <td> 7393</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:56:32</th>\n",
" <td> 27</td>\n",
" <td> 7399</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:58:35</th>\n",
" <td> 23</td>\n",
" <td> 7522</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:59:00</th>\n",
" <td> 4</td>\n",
" <td> 7547</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:59:06</th>\n",
" <td> 25</td>\n",
" <td> 7553</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 13:59:49</th>\n",
" <td> 550</td>\n",
" <td> 7596</td>\n",
" <td> hif_refant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:09:00</th>\n",
" <td> 798</td>\n",
" <td> 8147</td>\n",
" <td> hifa_tsyscal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:09:09</th>\n",
" <td> 14</td>\n",
" <td> 8156</td>\n",
" <td> gencal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:09:34</th>\n",
" <td> 17</td>\n",
" <td> 8181</td>\n",
" <td> gencal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:10:01</th>\n",
" <td> 14</td>\n",
" <td> 8208</td>\n",
" <td> gencal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:10:29</th>\n",
" <td> 35</td>\n",
" <td> 8236</td>\n",
" <td> plotbandpass</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:11:06</th>\n",
" <td> 170</td>\n",
" <td> 8273</td>\n",
" <td> plotbandpass</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:14:18</th>\n",
" <td> 29</td>\n",
" <td> 8465</td>\n",
" <td> plotbandpass</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:14:49</th>\n",
" <td> 172</td>\n",
" <td> 8496</td>\n",
" <td> plotbandpass</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:18:03</th>\n",
" <td> 45</td>\n",
" <td> 8690</td>\n",
" <td> plotbandpass</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:18:50</th>\n",
" <td> 181</td>\n",
" <td> 8737</td>\n",
" <td> plotbandpass</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:22:20</th>\n",
" <td> 1678</td>\n",
" <td> 8947</td>\n",
" <td> hifa_tsysflag</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:22:30</th>\n",
" <td> 0</td>\n",
" <td> 8957</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:22:37</th>\n",
" <td> 0</td>\n",
" <td> 8964</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:22:58</th>\n",
" <td> 0</td>\n",
" <td> 8985</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:23:04</th>\n",
" <td> 0</td>\n",
" <td> 8991</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:23:25</th>\n",
" <td> 0</td>\n",
" <td> 9012</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:23:31</th>\n",
" <td> 0</td>\n",
" <td> 9018</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:23:52</th>\n",
" <td> 0</td>\n",
" <td> 9039</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:24:01</th>\n",
" <td> 1</td>\n",
" <td> 9048</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:24:07</th>\n",
" <td> 1</td>\n",
" <td> 9054</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:24:54</th>\n",
" <td> 0</td>\n",
" <td> 9101</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:25:00</th>\n",
" <td> 0</td>\n",
" <td> 9107</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:25:19</th>\n",
" <td> 0</td>\n",
" <td> 9126</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:25:27</th>\n",
" <td> 0</td>\n",
" <td> 9134</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:25:29</th>\n",
" <td> 0</td>\n",
" <td> 9136</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:25:41</th>\n",
" <td> 0</td>\n",
" <td> 9148</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:25:47</th>\n",
" <td> 0</td>\n",
" <td> 9154</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-03-25 14:26:05</th>\n",
" <td> 0</td>\n",
" <td> 9172</td>\n",
" <td> flagdata</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1805 rows \u00d7 3 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
" duration startts task\n",
"start \n",
"2015-03-25 11:53:13 0 0 h_init\n",
"2015-03-25 11:53:13 3955 0 hifa_importdata\n",
"2015-03-25 11:53:13 819 0 importasdm\n",
"2015-03-25 12:10:05 871 1012 importasdm\n",
"2015-03-25 12:27:54 980 2081 importasdm\n",
"2015-03-25 12:47:44 170 3271 flagdata\n",
"2015-03-25 12:50:34 176 3441 flagdata\n",
"2015-03-25 12:53:30 187 3617 flagdata\n",
"2015-03-25 12:56:44 10 3811 listobs\n",
"2015-03-25 12:57:25 11 3852 listobs\n",
"2015-03-25 12:58:03 12 3890 listobs\n",
"2015-03-25 12:59:09 1003 3956 partition\n",
"2015-03-25 13:15:53 748 4960 partition\n",
"2015-03-25 13:28:22 802 5709 partition\n",
"2015-03-25 13:42:02 581 6529 hifa_flagdata\n",
"2015-03-25 13:42:22 135 6549 flagdata\n",
"2015-03-25 13:45:01 142 6708 flagdata\n",
"2015-03-25 13:47:47 117 6874 flagdata\n",
"2015-03-25 13:50:43 17 7050 flagcmd\n",
"2015-03-25 13:51:00 18 7067 flagcmd\n",
"2015-03-25 13:51:19 18 7086 flagcmd\n",
"2015-03-25 13:51:45 482 7112 hifa_fluxcalflag\n",
"2015-03-25 13:53:20 24 7207 flagdata\n",
"2015-03-25 13:53:47 4 7234 flagdata\n",
"2015-03-25 13:53:53 25 7240 flagdata\n",
"2015-03-25 13:55:57 27 7364 flagdata\n",
"2015-03-25 13:56:26 4 7393 flagdata\n",
"2015-03-25 13:56:32 27 7399 flagdata\n",
"2015-03-25 13:58:35 23 7522 flagdata\n",
"2015-03-25 13:59:00 4 7547 flagdata\n",
"2015-03-25 13:59:06 25 7553 flagdata\n",
"2015-03-25 13:59:49 550 7596 hif_refant\n",
"2015-03-25 14:09:00 798 8147 hifa_tsyscal\n",
"2015-03-25 14:09:09 14 8156 gencal\n",
"2015-03-25 14:09:34 17 8181 gencal\n",
"2015-03-25 14:10:01 14 8208 gencal\n",
"2015-03-25 14:10:29 35 8236 plotbandpass\n",
"2015-03-25 14:11:06 170 8273 plotbandpass\n",
"2015-03-25 14:14:18 29 8465 plotbandpass\n",
"2015-03-25 14:14:49 172 8496 plotbandpass\n",
"2015-03-25 14:18:03 45 8690 plotbandpass\n",
"2015-03-25 14:18:50 181 8737 plotbandpass\n",
"2015-03-25 14:22:20 1678 8947 hifa_tsysflag\n",
"2015-03-25 14:22:30 0 8957 flagdata\n",
"2015-03-25 14:22:37 0 8964 flagdata\n",
"2015-03-25 14:22:58 0 8985 flagdata\n",
"2015-03-25 14:23:04 0 8991 flagdata\n",
"2015-03-25 14:23:25 0 9012 flagdata\n",
"2015-03-25 14:23:31 0 9018 flagdata\n",
"2015-03-25 14:23:52 0 9039 flagdata\n",
"2015-03-25 14:24:01 1 9048 flagdata\n",
"2015-03-25 14:24:07 1 9054 flagdata\n",
"2015-03-25 14:24:54 0 9101 flagdata\n",
"2015-03-25 14:25:00 0 9107 flagdata\n",
"2015-03-25 14:25:19 0 9126 flagdata\n",
"2015-03-25 14:25:27 0 9134 flagdata\n",
"2015-03-25 14:25:29 0 9136 flagdata\n",
"2015-03-25 14:25:41 0 9148 flagdata\n",
"2015-03-25 14:25:47 0 9154 flagdata\n",
"2015-03-25 14:26:05 0 9172 flagdata\n",
" ... ... ...\n",
"\n",
"[1805 rows x 3 columns]"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"grp = df.groupby('task').sum().sort('duration', ascending=False)\n",
"grp['count'] = df.groupby('task').size()\n",
"#grp = grp[grp['duration'] > 100]\n",
"tasknames = list(grp.index)\n",
"grp"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>duration</th>\n",
" <th>startts</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>task</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>hif_applycal</th>\n",
" <td> 13984</td>\n",
" <td> 34866</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>plotms</th>\n",
" <td> 11059</td>\n",
" <td> 2755954</td>\n",
" <td> 66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gaincal</th>\n",
" <td> 9960</td>\n",
" <td> 1051040</td>\n",
" <td> 49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hifa_timegaincal</th>\n",
" <td> 6676</td>\n",
" <td> 28190</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hifa_gfluxscale</th>\n",
" <td> 4667</td>\n",
" <td> 23522</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>applycal</th>\n",
" <td> 4269</td>\n",
" <td> 220827</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hifa_importdata</th>\n",
" <td> 3955</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hif_bandpass</th>\n",
" <td> 3631</td>\n",
" <td> 18596</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>plotbandpass</th>\n",
" <td> 3522</td>\n",
" <td> 363524</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>setjy</th>\n",
" <td> 3094</td>\n",
" <td> 357683</td>\n",
" <td> 15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hif_setjy</th>\n",
" <td> 2778</td>\n",
" <td> 15817</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hifa_wvrgcalflag</th>\n",
" <td> 2720</td>\n",
" <td> 10626</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>importasdm</th>\n",
" <td> 2670</td>\n",
" <td> 3093</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>partition</th>\n",
" <td> 2553</td>\n",
" <td> 14625</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hif_lowgainflag</th>\n",
" <td> 2469</td>\n",
" <td> 13347</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>flagdata</th>\n",
" <td> 1929</td>\n",
" <td> 1112612</td>\n",
" <td> 81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hifa_tsysflag</th>\n",
" <td> 1678</td>\n",
" <td> 8947</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bandpass</th>\n",
" <td> 1618</td>\n",
" <td> 204466</td>\n",
" <td> 12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hif_bpflagchans</th>\n",
" <td> 1293</td>\n",
" <td> 22228</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hifa_tsyscal</th>\n",
" <td> 798</td>\n",
" <td> 8147</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>wvrgcal</th>\n",
" <td> 645</td>\n",
" <td> 45603</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hifa_flagdata</th>\n",
" <td> 581</td>\n",
" <td> 6529</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hif_refant</th>\n",
" <td> 550</td>\n",
" <td> 7596</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hifa_fluxcalflag</th>\n",
" <td> 482</td>\n",
" <td> 7112</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>plotcal</th>\n",
" <td> 477</td>\n",
" <td> 50237344</td>\n",
" <td> 1488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hif_makeimlist</th>\n",
" <td> 292</td>\n",
" <td> 48851</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hif_makeimages</th>\n",
" <td> 86</td>\n",
" <td> 49144</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>flagcmd</th>\n",
" <td> 53</td>\n",
" <td> 21203</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gencal</th>\n",
" <td> 45</td>\n",
" <td> 24545</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fluxscale</th>\n",
" <td> 33</td>\n",
" <td> 76466</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>listobs</th>\n",
" <td> 33</td>\n",
" <td> 11553</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h_save</th>\n",
" <td> 1</td>\n",
" <td> 49231</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>h_init</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>calstat</th>\n",
" <td> 0</td>\n",
" <td> 952250</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>34 rows \u00d7 3 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
" duration startts count\n",
"task \n",
"hif_applycal 13984 34866 1\n",
"plotms 11059 2755954 66\n",
"gaincal 9960 1051040 49\n",
"hifa_timegaincal 6676 28190 1\n",
"hifa_gfluxscale 4667 23522 1\n",
"applycal 4269 220827 6\n",
"hifa_importdata 3955 0 1\n",
"hif_bandpass 3631 18596 1\n",
"plotbandpass 3522 363524 24\n",
"setjy 3094 357683 15\n",
"hif_setjy 2778 15817 1\n",
"hifa_wvrgcalflag 2720 10626 1\n",
"importasdm 2670 3093 3\n",
"partition 2553 14625 3\n",
"hif_lowgainflag 2469 13347 1\n",
"flagdata 1929 1112612 81\n",
"hifa_tsysflag 1678 8947 1\n",
"bandpass 1618 204466 12\n",
"hif_bpflagchans 1293 22228 1\n",
"hifa_tsyscal 798 8147 1\n",
"wvrgcal 645 45603 4\n",
"hifa_flagdata 581 6529 1\n",
"hif_refant 550 7596 1\n",
"hifa_fluxcalflag 482 7112 1\n",
"plotcal 477 50237344 1488\n",
"hif_makeimlist 292 48851 1\n",
"hif_makeimages 86 49144 1\n",
"flagcmd 53 21203 3\n",
"gencal 45 24545 3\n",
"fluxscale 33 76466 3\n",
"listobs 33 11553 3\n",
"h_save 1 49231 1\n",
"h_init 0 0 1\n",
"calstat 0 952250 24\n",
"\n",
"[34 rows x 3 columns]"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['r'] = 0.\n",
"df['g'] = 0.\n",
"df['b'] = 0.\n",
"df['a'] = 0.\n",
"#longtasks = list(grp[grp['duration'] < 100].index)\n",
"longtasks = list(grp.index)\n",
"longtasks = [x for x in longtasks if not x.startswith('hif')]\n",
"cm = plt.cm.Paired_r\n",
"lin = np.linspace(0, 1., len(longtasks))\n",
"for i, tn in zip(lin, longtasks):\n",
" df['r'][df['task'] == tn] = cm(i)[0]\n",
" df['g'][df['task'] == tn] = cm(i)[1]\n",
" df['b'][df['task'] == tn] = cm(i)[2]\n",
" df['a'][df['task'] == tn] = 1.0#cm(i)[3]\n",
" \n",
"for tn in set(longtasks).symmetric_difference(set(tasknames)):\n",
" df['r'][df['task'] == tn] = 0.\n",
" df['g'][df['task'] == tn] = 0.\n",
" df['b'][df['task'] == tn] = 0.5\n",
" df['a'][df['task'] == tn] = 0.4#cm(i)[3]\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_data(sfile, flags='-u', interval=[]):\n",
" import subprocess as sp\n",
" from datetime import datetime\n",
" if not isinstance(flags, list):\n",
" flags = [flags]\n",
" cmd = ['sadf', '-p', '-U'] + [sfile] + map(str, interval) + ['--'] + flags\n",
" env = os.environ.copy()\n",
" env['LC_NUMERIC'] = 'en_US.UTF-8'\n",
" p = sp.Popen(cmd, stdout=sp.PIPE, stderr=sp.PIPE, env=env)\n",
" res = p.communicate()\n",
" if p.returncode != 0:\n",
" print \" \".join(cmd)\n",
" print res[1]\n",
" return None\n",
"\n",
" out = [x.split() for x in res[0].splitlines()]\n",
" data = defaultdict(list)\n",
" times = defaultdict(list)\n",
" for x in out:\n",
" data[x[-2]].append(float(x[-1]))\n",
" times[x[-2]].append(datetime.fromtimestamp(float(x[2])))\n",
" data = dict([(k.replace('%%', '%'), np.array(v)) for k, v in data.items()] + [('t', np.array(times.values()[0]))])\n",
" return data\n",
"\n",
"def get_sar(file, flags='-u'):\n",
" d = get_data(sfile=file, flags=flags)\n",
" #t = d.pop('t')\n",
" d = pandas.DataFrame.from_dict(d)\n",
"\n",
" d = d.set_index('t')\n",
" d = d.sort_index()\n",
" d = d[(d.index > df.index.min()) & (d.index <= df.index.max())]\n",
" d['startts'] = (d.index.astype(int) - df.index.astype(int).min()) * 1e-9\n",
" return d\n",
"\n",
"sarfile = '/tmp/sa25'\n",
"dcpu = get_sar(sarfile)\n",
"ddisk = get_sar(sarfile, flags='-b')\n",
"ddisk['read'] = ddisk['bread/s'] * 512 / 1024.**2\n",
"ddisk['write'] = ddisk['bwrtn/s'] * 512 / 1024.**2\n",
"dsar = pandas.merge(dcpu,ddisk)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib nbagg\n",
"df['normdur'] = df['duration'] / df['duration'].sum()\n",
"fig = plt.figure(figsize=(14,24))\n",
"ax = fig.add_subplot(311)\n",
"handles = dict()\n",
"for i, row in enumerate(df.sort('duration', ascending=False).iterrows()):\n",
" index, sdf = row\n",
" t = sdf['task']\n",
" if sdf['duration'] != 0:\n",
" bar = ax.bar(left=sdf['startts'], height=sdf['duration'], width=sdf['duration'],\n",
" color=np.array((sdf['r'], sdf['g'], sdf['b'], sdf['a'])).T, label=t)\n",
" handles[t] = bar\n",
"for i, t in enumerate(tasknames):\n",
" sdf = df[df['task'] == t]\n",
" if sdf['normdur'][0] > 0.001:\n",
" ax.annotate(sdf['task'][0], xy=(sdf['startts'][0], sdf['duration'][0]),\n",
" xytext=(sdf['startts'][0], sdf['duration'][0] + 500),\n",
" arrowprops=dict(facecolor='black', width=0.002, headwidth=0.002))\n",
" \n",
"lgd = ax.legend(handles=[handles[x] for x in tasknames if not x.startswith('hif') and x in handles],\n",
" bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.);\n",
"ax.set_xlabel('time [s]')\n",
"ax.set_ylabel('time in task')\n",
"ax.set_title(inpfile);\n",
"ax = fig.add_subplot(312)\n",
"ax.plot(dsar['startts'], dsar['%user'], label='user')\n",
"ax.plot(dsar['startts'], dsar['%system'], label='system')\n",
"ax.plot(dsar['startts'], dsar['%iowait'], '--', label='iowait')\n",
"ax.plot(dsar['startts'], dsar['%user'] + dsar['%system'], '--', label='user + system')\n",
"ax.set_ylabel('%cpu')\n",
"ax.legend()\n",
"ax = fig.add_subplot(313)\n",
"ax.plot(dsar['startts'], dsar['read'], label='read')\n",
"ax.plot(dsar['startts'], dsar['write'], label='write')\n",
"ax.plot(dsar['startts'], dsar['tps'] * (dsar['read'].max() / dsar['tps'].max()), label='norm. tps')\n",
"#ax.plot(dsar['startts'], dsar['wtps'] * (dsar['read'].max() / dsar['tps'].max()), label='rtps', '--')\n",
"#ax.plot(dsar['startts'], dsar['rtps'] * (dsar['read'].max() / dsar['tps'].max()), label='wtps', '--')\n",
"ax.set_ylabel('MB/s')\n",
"ax.legend()\n",
"\n",
"fig.savefig('/tmp/%s.pdf' % os.path.basename(inpfile), bbox_extra_artists=(lgd,), bbox_inches='tight')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox \u2265 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.focus_on_mousover = false;\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this.root.attr('style', 'display: inline-block');\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
" canvas_div.resizable({ resize: mpl.debounce_resize(\n",
" function(event, ui) { fig.request_resize(ui.size.width, ui.size.height); }\n",
" , 50)});\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both;');\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0;\")\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
" function mouse_event_fn(event) {\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keydown('key_release', canvas_keyboard_event);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width);\n",
" canvas.attr('height', height);\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'];\n",
" var y0 = fig.canvas.height - msg['y0'];\n",
" var x1 = msg['x1'];\n",
" var y1 = fig.canvas.height - msg['y1'];\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (this.focus_on_mouseover && name === 'motion_notify')\n",
" {\n",
" this.canvas.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x;\n",
" var y = canvas_pos.y;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
" /* Don't fire events just when a modifier is changed. Modifiers are\n",
" sent along with other keys. */\n",
" if (event.keyCode >= 16 && event.keyCode <= 20) {\n",
" return;\n",
" }\n",
"\n",
" value = '';\n",
" if (event.ctrlKey) {\n",
" value += \"ctrl+\";\n",
" }\n",
" if (event.altKey) {\n",
" value += \"alt+\";\n",
" }\n",
" value += String.fromCharCode(event.keyCode).toLowerCase();\n",
"\n",
" this.send_message(name, {key: value});\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" var format_dropdown = this.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" this.ondownload(this, format);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"\n",
"mpl.debounce_event = function(func, time){\n",
" var timer;\n",
" return function(event){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event); }, time);\n",
" };\n",
"}\n",
"\n",
"mpl.debounce_resize = function(func, time){\n",
" var timer;\n",
" return function(event, ui){\n",
" clearTimeout(timer);\n",
" timer = setTimeout(function(){ func(event, ui); }, time);\n",
" };\n",
"}\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" function() { },\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
" // Disable right mouse context menu.\n",
" $(fig.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
" fig.send_message('closing', {});\n",
" fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Close figure', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type == 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (cell.output_area.outputs[j]['text/html'] == html_output) {\n",
" var output = cell.output_area.outputs[j];\n",
" return [cell, output, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x7fc4dbe5e810>"
]
},
{
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\">"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7fc4dbe5e8d0>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dsar"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>%idle</th>\n",
" <th>%iowait</th>\n",
" <th>%nice</th>\n",
" <th>%steal</th>\n",
" <th>%system</th>\n",
" <th>%user</th>\n",
" <th>startts</th>\n",
" <th>bread/s</th>\n",
" <th>bwrtn/s</th>\n",
" <th>rtps</th>\n",
" <th>tps</th>\n",
" <th>wtps</th>\n",
" <th>read</th>\n",
" <th>write</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 97.61</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.93</td>\n",
" <td> 1.45</td>\n",
" <td> 108</td>\n",
" <td> 0.45</td>\n",
" <td> 151.96</td>\n",
" <td> 0.04</td>\n",
" <td> 2.76</td>\n",
" <td> 2.72</td>\n",
" <td> 0.000220</td>\n",
" <td> 0.074199</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 97.61</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.93</td>\n",
" <td> 1.46</td>\n",
" <td> 708</td>\n",
" <td> 0.28</td>\n",
" <td> 164.33</td>\n",
" <td> 0.03</td>\n",
" <td> 1.94</td>\n",
" <td> 1.91</td>\n",
" <td> 0.000137</td>\n",
" <td> 0.080239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 97.61</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.94</td>\n",
" <td> 1.45</td>\n",
" <td> 1008</td>\n",
" <td> 0.32</td>\n",
" <td> 163.86</td>\n",
" <td> 0.04</td>\n",
" <td> 1.76</td>\n",
" <td> 1.72</td>\n",
" <td> 0.000156</td>\n",
" <td> 0.080010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 97.63</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.92</td>\n",
" <td> 1.45</td>\n",
" <td> 1309</td>\n",
" <td> 0.27</td>\n",
" <td> 164.00</td>\n",
" <td> 0.03</td>\n",
" <td> 1.97</td>\n",
" <td> 1.94</td>\n",
" <td> 0.000132</td>\n",
" <td> 0.080078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 97.62</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.93</td>\n",
" <td> 1.45</td>\n",
" <td> 1608</td>\n",
" <td> 0.85</td>\n",
" <td> 171.84</td>\n",
" <td> 0.05</td>\n",
" <td> 2.55</td>\n",
" <td> 2.50</td>\n",
" <td> 0.000415</td>\n",
" <td> 0.083906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 97.62</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.93</td>\n",
" <td> 1.44</td>\n",
" <td> 1908</td>\n",
" <td> 1.25</td>\n",
" <td> 173.63</td>\n",
" <td> 0.06</td>\n",
" <td> 2.27</td>\n",
" <td> 2.21</td>\n",
" <td> 0.000610</td>\n",
" <td> 0.084780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 97.63</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.93</td>\n",
" <td> 1.44</td>\n",
" <td> 2208</td>\n",
" <td> 0.43</td>\n",
" <td> 171.80</td>\n",
" <td> 0.03</td>\n",
" <td> 2.22</td>\n",
" <td> 2.19</td>\n",
" <td> 0.000210</td>\n",
" <td> 0.083887</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 97.63</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.93</td>\n",
" <td> 1.44</td>\n",
" <td> 2508</td>\n",
" <td> 0.59</td>\n",
" <td> 171.98</td>\n",
" <td> 0.03</td>\n",
" <td> 2.52</td>\n",
" <td> 2.48</td>\n",
" <td> 0.000288</td>\n",
" <td> 0.083975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 97.63</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.94</td>\n",
" <td> 1.43</td>\n",
" <td> 2808</td>\n",
" <td> 0.88</td>\n",
" <td> 162.47</td>\n",
" <td> 0.05</td>\n",
" <td> 2.03</td>\n",
" <td> 1.98</td>\n",
" <td> 0.000430</td>\n",
" <td> 0.079331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 97.60</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.95</td>\n",
" <td> 1.45</td>\n",
" <td> 3108</td>\n",
" <td> 0.32</td>\n",
" <td> 138.08</td>\n",
" <td> 0.03</td>\n",
" <td> 1.59</td>\n",
" <td> 1.56</td>\n",
" <td> 0.000156</td>\n",
" <td> 0.067422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 97.60</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.94</td>\n",
" <td> 1.46</td>\n",
" <td> 3408</td>\n",
" <td> 0.40</td>\n",
" <td> 153.08</td>\n",
" <td> 0.04</td>\n",
" <td> 1.71</td>\n",
" <td> 1.67</td>\n",
" <td> 0.000195</td>\n",
" <td> 0.074746</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 92.53</td>\n",
" <td> 0.76</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 3.83</td>\n",
" <td> 2.88</td>\n",
" <td> 3708</td>\n",
" <td> 11738.08</td>\n",
" <td> 26481.00</td>\n",
" <td> 73.02</td>\n",
" <td> 186.49</td>\n",
" <td> 113.47</td>\n",
" <td> 5.731484</td>\n",
" <td> 12.930176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 89.99</td>\n",
" <td> 2.94</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 3.97</td>\n",
" <td> 3.10</td>\n",
" <td> 4309</td>\n",
" <td> 46276.16</td>\n",
" <td> 110146.77</td>\n",
" <td> 122.85</td>\n",
" <td> 440.29</td>\n",
" <td> 317.44</td>\n",
" <td> 22.595781</td>\n",
" <td> 53.782603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 95.71</td>\n",
" <td> 0.07</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.32</td>\n",
" <td> 2.89</td>\n",
" <td> 4608</td>\n",
" <td> 6007.62</td>\n",
" <td> 718.51</td>\n",
" <td> 18.84</td>\n",
" <td> 41.52</td>\n",
" <td> 22.68</td>\n",
" <td> 2.933408</td>\n",
" <td> 0.350835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 91.40</td>\n",
" <td> 2.02</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 3.52</td>\n",
" <td> 3.07</td>\n",
" <td> 4908</td>\n",
" <td> 36597.26</td>\n",
" <td> 110394.31</td>\n",
" <td> 94.71</td>\n",
" <td> 455.83</td>\n",
" <td> 361.12</td>\n",
" <td> 17.869756</td>\n",
" <td> 53.903472</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 90.16</td>\n",
" <td> 3.40</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 3.59</td>\n",
" <td> 2.85</td>\n",
" <td> 5208</td>\n",
" <td> 37475.28</td>\n",
" <td> 147772.19</td>\n",
" <td> 98.22</td>\n",
" <td> 542.32</td>\n",
" <td> 444.10</td>\n",
" <td> 18.298477</td>\n",
" <td> 72.154390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 91.35</td>\n",
" <td> 0.59</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 4.27</td>\n",
" <td> 3.80</td>\n",
" <td> 5508</td>\n",
" <td> 65388.68</td>\n",
" <td> 8911.84</td>\n",
" <td> 177.82</td>\n",
" <td> 234.00</td>\n",
" <td> 56.17</td>\n",
" <td> 31.928066</td>\n",
" <td> 4.351484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 94.71</td>\n",
" <td> 0.65</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 2.24</td>\n",
" <td> 2.41</td>\n",
" <td> 5808</td>\n",
" <td> 13294.22</td>\n",
" <td> 32336.87</td>\n",
" <td> 36.47</td>\n",
" <td> 171.67</td>\n",
" <td> 135.20</td>\n",
" <td> 6.491318</td>\n",
" <td> 15.789487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 90.16</td>\n",
" <td> 2.79</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 4.12</td>\n",
" <td> 2.93</td>\n",
" <td> 6108</td>\n",
" <td> 40995.49</td>\n",
" <td> 131682.44</td>\n",
" <td> 102.74</td>\n",
" <td> 486.50</td>\n",
" <td> 383.77</td>\n",
" <td> 20.017329</td>\n",
" <td> 64.298066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 90.38</td>\n",
" <td> 2.30</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 4.32</td>\n",
" <td> 2.99</td>\n",
" <td> 6408</td>\n",
" <td> 48864.95</td>\n",
" <td> 115628.90</td>\n",
" <td> 127.57</td>\n",
" <td> 476.67</td>\n",
" <td> 349.10</td>\n",
" <td> 23.859839</td>\n",
" <td> 56.459424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 91.22</td>\n",
" <td> 0.29</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 4.61</td>\n",
" <td> 3.88</td>\n",
" <td> 6708</td>\n",
" <td> 56123.35</td>\n",
" <td> 988.48</td>\n",
" <td> 153.05</td>\n",
" <td> 184.57</td>\n",
" <td> 31.52</td>\n",
" <td> 27.403979</td>\n",
" <td> 0.482656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 96.07</td>\n",
" <td> 0.02</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.91</td>\n",
" <td> 3.00</td>\n",
" <td> 7008</td>\n",
" <td> 492.81</td>\n",
" <td> 279.31</td>\n",
" <td> 9.63</td>\n",
" <td> 13.31</td>\n",
" <td> 3.68</td>\n",
" <td> 0.240630</td>\n",
" <td> 0.136382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 93.85</td>\n",
" <td> 0.04</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.83</td>\n",
" <td> 5.27</td>\n",
" <td> 7308</td>\n",
" <td> 504.82</td>\n",
" <td> 362.28</td>\n",
" <td> 10.69</td>\n",
" <td> 15.96</td>\n",
" <td> 5.27</td>\n",
" <td> 0.246494</td>\n",
" <td> 0.176895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> 68.66</td>\n",
" <td> 21.14</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 5.17</td>\n",
" <td> 5.03</td>\n",
" <td> 7908</td>\n",
" <td> 92332.35</td>\n",
" <td> 68371.08</td>\n",
" <td> 232.90</td>\n",
" <td> 617.53</td>\n",
" <td> 384.63</td>\n",
" <td> 45.084155</td>\n",
" <td> 33.384316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 66.74</td>\n",
" <td> 24.93</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 4.56</td>\n",
" <td> 3.77</td>\n",
" <td> 8208</td>\n",
" <td> 147597.26</td>\n",
" <td> 145404.20</td>\n",
" <td> 331.61</td>\n",
" <td> 881.90</td>\n",
" <td> 550.30</td>\n",
" <td> 72.068975</td>\n",
" <td> 70.998145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> 88.70</td>\n",
" <td> 6.34</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 3.16</td>\n",
" <td> 1.79</td>\n",
" <td> 8508</td>\n",
" <td> 130107.96</td>\n",
" <td> 142334.71</td>\n",
" <td> 332.75</td>\n",
" <td> 673.91</td>\n",
" <td> 341.16</td>\n",
" <td> 63.529277</td>\n",
" <td> 69.499370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> 64.20</td>\n",
" <td> 23.86</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 6.38</td>\n",
" <td> 5.56</td>\n",
" <td> 8809</td>\n",
" <td> 137830.15</td>\n",
" <td> 127559.41</td>\n",
" <td> 368.16</td>\n",
" <td> 949.76</td>\n",
" <td> 581.60</td>\n",
" <td> 67.299878</td>\n",
" <td> 62.284868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> 57.18</td>\n",
" <td> 35.03</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 4.16</td>\n",
" <td> 3.64</td>\n",
" <td> 9109</td>\n",
" <td> 149699.52</td>\n",
" <td> 146952.43</td>\n",
" <td> 332.41</td>\n",
" <td> 903.64</td>\n",
" <td> 571.22</td>\n",
" <td> 73.095469</td>\n",
" <td> 71.754116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> 77.46</td>\n",
" <td> 8.01</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 8.78</td>\n",
" <td> 5.75</td>\n",
" <td> 9408</td>\n",
" <td> 96351.58</td>\n",
" <td> 81561.66</td>\n",
" <td> 269.42</td>\n",
" <td> 1433.92</td>\n",
" <td> 1164.51</td>\n",
" <td> 47.046670</td>\n",
" <td> 39.825029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> 52.02</td>\n",
" <td> 35.51</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 9.21</td>\n",
" <td> 3.26</td>\n",
" <td> 9712</td>\n",
" <td> 156103.85</td>\n",
" <td> 131278.46</td>\n",
" <td> 348.25</td>\n",
" <td> 1108.06</td>\n",
" <td> 759.81</td>\n",
" <td> 76.222583</td>\n",
" <td> 64.100811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> 63.29</td>\n",
" <td> 24.03</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 8.63</td>\n",
" <td> 4.05</td>\n",
" <td> 10008</td>\n",
" <td> 127101.64</td>\n",
" <td> 135936.61</td>\n",
" <td> 296.78</td>\n",
" <td> 2373.04</td>\n",
" <td> 2076.25</td>\n",
" <td> 62.061348</td>\n",
" <td> 66.375298</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> 83.49</td>\n",
" <td> 1.33</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 2.10</td>\n",
" <td> 13.07</td>\n",
" <td> 10308</td>\n",
" <td> 20019.63</td>\n",
" <td> 29175.39</td>\n",
" <td> 129.03</td>\n",
" <td> 423.77</td>\n",
" <td> 294.74</td>\n",
" <td> 9.775210</td>\n",
" <td> 14.245796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> 73.73</td>\n",
" <td> 0.59</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.11</td>\n",
" <td> 24.57</td>\n",
" <td> 10608</td>\n",
" <td> 16625.15</td>\n",
" <td> 54250.68</td>\n",
" <td> 53.43</td>\n",
" <td> 623.05</td>\n",
" <td> 569.63</td>\n",
" <td> 8.117749</td>\n",
" <td> 26.489590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> 91.18</td>\n",
" <td> 0.05</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.95</td>\n",
" <td> 7.83</td>\n",
" <td> 10908</td>\n",
" <td> 222.46</td>\n",
" <td> 5702.34</td>\n",
" <td> 2.69</td>\n",
" <td> 62.23</td>\n",
" <td> 59.54</td>\n",
" <td> 0.108623</td>\n",
" <td> 2.784346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> 90.82</td>\n",
" <td> 0.04</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.92</td>\n",
" <td> 8.22</td>\n",
" <td> 11508</td>\n",
" <td> 233.13</td>\n",
" <td> 3128.16</td>\n",
" <td> 7.16</td>\n",
" <td> 45.62</td>\n",
" <td> 38.45</td>\n",
" <td> 0.113833</td>\n",
" <td> 1.527422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> 94.67</td>\n",
" <td> 0.01</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.92</td>\n",
" <td> 4.40</td>\n",
" <td> 11808</td>\n",
" <td> 76.15</td>\n",
" <td> 1076.28</td>\n",
" <td> 0.50</td>\n",
" <td> 15.78</td>\n",
" <td> 15.28</td>\n",
" <td> 0.037183</td>\n",
" <td> 0.525527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> 92.12</td>\n",
" <td> 0.91</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.26</td>\n",
" <td> 5.71</td>\n",
" <td> 12108</td>\n",
" <td> 246597.13</td>\n",
" <td> 756.70</td>\n",
" <td> 626.95</td>\n",
" <td> 636.56</td>\n",
" <td> 9.61</td>\n",
" <td> 120.408755</td>\n",
" <td> 0.369482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> 91.93</td>\n",
" <td> 1.17</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.34</td>\n",
" <td> 5.57</td>\n",
" <td> 12408</td>\n",
" <td> 279104.11</td>\n",
" <td> 423.80</td>\n",
" <td> 696.47</td>\n",
" <td> 704.48</td>\n",
" <td> 8.01</td>\n",
" <td> 136.281304</td>\n",
" <td> 0.206934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> 91.53</td>\n",
" <td> 1.46</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.61</td>\n",
" <td> 5.39</td>\n",
" <td> 12709</td>\n",
" <td> 273649.34</td>\n",
" <td> 585.47</td>\n",
" <td> 679.69</td>\n",
" <td> 694.79</td>\n",
" <td> 15.10</td>\n",
" <td> 133.617842</td>\n",
" <td> 0.285874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> 91.63</td>\n",
" <td> 1.54</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.45</td>\n",
" <td> 5.38</td>\n",
" <td> 13008</td>\n",
" <td> 262699.21</td>\n",
" <td> 310.16</td>\n",
" <td> 658.65</td>\n",
" <td> 663.17</td>\n",
" <td> 4.53</td>\n",
" <td> 128.271099</td>\n",
" <td> 0.151445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> 91.69</td>\n",
" <td> 1.75</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.35</td>\n",
" <td> 5.21</td>\n",
" <td> 13308</td>\n",
" <td> 258480.25</td>\n",
" <td> 1198.59</td>\n",
" <td> 635.53</td>\n",
" <td> 655.31</td>\n",
" <td> 19.78</td>\n",
" <td> 126.211060</td>\n",
" <td> 0.585249</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> 91.67</td>\n",
" <td> 2.03</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.37</td>\n",
" <td> 4.93</td>\n",
" <td> 13609</td>\n",
" <td> 259427.48</td>\n",
" <td> 511.82</td>\n",
" <td> 642.83</td>\n",
" <td> 655.16</td>\n",
" <td> 12.32</td>\n",
" <td> 126.673574</td>\n",
" <td> 0.249912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td> 91.63</td>\n",
" <td> 2.03</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.59</td>\n",
" <td> 4.75</td>\n",
" <td> 13908</td>\n",
" <td> 259713.76</td>\n",
" <td> 1271.45</td>\n",
" <td> 639.72</td>\n",
" <td> 661.43</td>\n",
" <td> 21.71</td>\n",
" <td> 126.813359</td>\n",
" <td> 0.620825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> 93.93</td>\n",
" <td> 0.61</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.36</td>\n",
" <td> 4.10</td>\n",
" <td> 14208</td>\n",
" <td> 48764.62</td>\n",
" <td> 589.02</td>\n",
" <td> 137.50</td>\n",
" <td> 146.76</td>\n",
" <td> 9.26</td>\n",
" <td> 23.810850</td>\n",
" <td> 0.287607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td> 94.63</td>\n",
" <td> 0.19</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.04</td>\n",
" <td> 4.14</td>\n",
" <td> 14508</td>\n",
" <td> 13878.14</td>\n",
" <td> 1181.64</td>\n",
" <td> 39.71</td>\n",
" <td> 55.44</td>\n",
" <td> 15.73</td>\n",
" <td> 6.776436</td>\n",
" <td> 0.576973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td> 94.66</td>\n",
" <td> 0.06</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.95</td>\n",
" <td> 4.32</td>\n",
" <td> 15108</td>\n",
" <td> 6531.28</td>\n",
" <td> 1525.06</td>\n",
" <td> 16.23</td>\n",
" <td> 36.67</td>\n",
" <td> 20.44</td>\n",
" <td> 3.189102</td>\n",
" <td> 0.744658</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td> 94.52</td>\n",
" <td> 0.19</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.09</td>\n",
" <td> 4.20</td>\n",
" <td> 15408</td>\n",
" <td> 19409.83</td>\n",
" <td> 1287.18</td>\n",
" <td> 45.08</td>\n",
" <td> 62.57</td>\n",
" <td> 17.49</td>\n",
" <td> 9.477456</td>\n",
" <td> 0.628506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td> 94.68</td>\n",
" <td> 0.07</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.96</td>\n",
" <td> 4.29</td>\n",
" <td> 15708</td>\n",
" <td> 7488.10</td>\n",
" <td> 2104.38</td>\n",
" <td> 15.90</td>\n",
" <td> 38.67</td>\n",
" <td> 22.77</td>\n",
" <td> 3.656299</td>\n",
" <td> 1.027529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td> 94.61</td>\n",
" <td> 0.16</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.03</td>\n",
" <td> 4.20</td>\n",
" <td> 16008</td>\n",
" <td> 11680.98</td>\n",
" <td> 1187.50</td>\n",
" <td> 33.07</td>\n",
" <td> 49.88</td>\n",
" <td> 16.81</td>\n",
" <td> 5.703604</td>\n",
" <td> 0.579834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> 94.62</td>\n",
" <td> 0.14</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.03</td>\n",
" <td> 4.21</td>\n",
" <td> 16308</td>\n",
" <td> 18109.12</td>\n",
" <td> 1717.74</td>\n",
" <td> 37.84</td>\n",
" <td> 58.31</td>\n",
" <td> 20.46</td>\n",
" <td> 8.842344</td>\n",
" <td> 0.838740</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td> 94.68</td>\n",
" <td> 0.02</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.86</td>\n",
" <td> 4.43</td>\n",
" <td> 16608</td>\n",
" <td> 871.41</td>\n",
" <td> 651.24</td>\n",
" <td> 3.88</td>\n",
" <td> 12.34</td>\n",
" <td> 8.46</td>\n",
" <td> 0.425493</td>\n",
" <td> 0.317988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td> 94.76</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.81</td>\n",
" <td> 4.43</td>\n",
" <td> 16908</td>\n",
" <td> 0.48</td>\n",
" <td> 222.77</td>\n",
" <td> 0.05</td>\n",
" <td> 3.67</td>\n",
" <td> 3.62</td>\n",
" <td> 0.000234</td>\n",
" <td> 0.108774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td> 92.44</td>\n",
" <td> 0.57</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.28</td>\n",
" <td> 5.71</td>\n",
" <td> 17208</td>\n",
" <td> 37566.09</td>\n",
" <td> 795.06</td>\n",
" <td> 140.09</td>\n",
" <td> 152.71</td>\n",
" <td> 12.63</td>\n",
" <td> 18.342817</td>\n",
" <td> 0.388213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td> 94.65</td>\n",
" <td> 0.02</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.91</td>\n",
" <td> 4.42</td>\n",
" <td> 17508</td>\n",
" <td> 761.49</td>\n",
" <td> 720.77</td>\n",
" <td> 8.81</td>\n",
" <td> 18.20</td>\n",
" <td> 9.40</td>\n",
" <td> 0.371821</td>\n",
" <td> 0.351938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td> 91.12</td>\n",
" <td> 0.14</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.93</td>\n",
" <td> 7.81</td>\n",
" <td> 17808</td>\n",
" <td> 1669.84</td>\n",
" <td> 1061.17</td>\n",
" <td> 11.93</td>\n",
" <td> 24.83</td>\n",
" <td> 12.89</td>\n",
" <td> 0.815352</td>\n",
" <td> 0.518149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td> 94.47</td>\n",
" <td> 0.26</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 1.18</td>\n",
" <td> 4.10</td>\n",
" <td> 18108</td>\n",
" <td> 35267.46</td>\n",
" <td> 700.49</td>\n",
" <td> 89.09</td>\n",
" <td> 100.76</td>\n",
" <td> 11.67</td>\n",
" <td> 17.220439</td>\n",
" <td> 0.342036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td> 92.72</td>\n",
" <td> 0.18</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.96</td>\n",
" <td> 6.14</td>\n",
" <td> 18708</td>\n",
" <td> 10930.26</td>\n",
" <td> 850.76</td>\n",
" <td> 27.21</td>\n",
" <td> 38.95</td>\n",
" <td> 11.74</td>\n",
" <td> 5.337041</td>\n",
" <td> 0.415410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td> 94.63</td>\n",
" <td> 0.13</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 0.97</td>\n",
" <td> 4.26</td>\n",
" <td> 19008</td>\n",
" <td> 16508.65</td>\n",
" <td> 1017.81</td>\n",
" <td> 47.57</td>\n",
" <td> 60.74</td>\n",
" <td> 13.17</td>\n",
" <td> 8.060864</td>\n",
" <td> 0.496978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td> 94.68</td>\n",
" <td> 0.02</td>\n",
" <td> 0.04</td>\n",
" <td> 0</td>\n",
" <td> 0.88</td>\n",
" <td> 4.39</td>\n",
" <td> 19308</td>\n",
" <td> 220.33</td>\n",
" <td> 734.83</td>\n",
" <td> 2.44</td>\n",
" <td> 11.69</td>\n",
" <td> 9.25</td>\n",
" <td> 0.107583</td>\n",
" <td> 0.358804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td> 90.85</td>\n",
" <td> 0.45</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 3.52</td>\n",
" <td> 5.17</td>\n",
" <td> 19608</td>\n",
" <td> 56365.64</td>\n",
" <td> 3041.25</td>\n",
" <td> 133.80</td>\n",
" <td> 369.09</td>\n",
" <td> 235.28</td>\n",
" <td> 27.522285</td>\n",
" <td> 1.484985</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>135 rows \u00d7 14 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
" %idle %iowait %nice %steal %system %user startts bread/s \\\n",
"0 97.61 0.00 0.00 0 0.93 1.45 108 0.45 \n",
"1 97.61 0.00 0.00 0 0.93 1.46 708 0.28 \n",
"2 97.61 0.00 0.00 0 0.94 1.45 1008 0.32 \n",
"3 97.63 0.00 0.00 0 0.92 1.45 1309 0.27 \n",
"4 97.62 0.00 0.00 0 0.93 1.45 1608 0.85 \n",
"5 97.62 0.00 0.00 0 0.93 1.44 1908 1.25 \n",
"6 97.63 0.00 0.00 0 0.93 1.44 2208 0.43 \n",
"7 97.63 0.00 0.00 0 0.93 1.44 2508 0.59 \n",
"8 97.63 0.00 0.00 0 0.94 1.43 2808 0.88 \n",
"9 97.60 0.00 0.00 0 0.95 1.45 3108 0.32 \n",
"10 97.60 0.00 0.00 0 0.94 1.46 3408 0.40 \n",
"11 92.53 0.76 0.00 0 3.83 2.88 3708 11738.08 \n",
"12 89.99 2.94 0.00 0 3.97 3.10 4309 46276.16 \n",
"13 95.71 0.07 0.00 0 1.32 2.89 4608 6007.62 \n",
"14 91.40 2.02 0.00 0 3.52 3.07 4908 36597.26 \n",
"15 90.16 3.40 0.00 0 3.59 2.85 5208 37475.28 \n",
"16 91.35 0.59 0.00 0 4.27 3.80 5508 65388.68 \n",
"17 94.71 0.65 0.00 0 2.24 2.41 5808 13294.22 \n",
"18 90.16 2.79 0.00 0 4.12 2.93 6108 40995.49 \n",
"19 90.38 2.30 0.00 0 4.32 2.99 6408 48864.95 \n",
"20 91.22 0.29 0.00 0 4.61 3.88 6708 56123.35 \n",
"21 96.07 0.02 0.00 0 0.91 3.00 7008 492.81 \n",
"22 93.85 0.04 0.00 0 0.83 5.27 7308 504.82 \n",
"23 68.66 21.14 0.00 0 5.17 5.03 7908 92332.35 \n",
"24 66.74 24.93 0.00 0 4.56 3.77 8208 147597.26 \n",
"25 88.70 6.34 0.00 0 3.16 1.79 8508 130107.96 \n",
"26 64.20 23.86 0.00 0 6.38 5.56 8809 137830.15 \n",
"27 57.18 35.03 0.00 0 4.16 3.64 9109 149699.52 \n",
"28 77.46 8.01 0.00 0 8.78 5.75 9408 96351.58 \n",
"29 52.02 35.51 0.00 0 9.21 3.26 9712 156103.85 \n",
"30 63.29 24.03 0.00 0 8.63 4.05 10008 127101.64 \n",
"31 83.49 1.33 0.00 0 2.10 13.07 10308 20019.63 \n",
"32 73.73 0.59 0.00 0 1.11 24.57 10608 16625.15 \n",
"33 91.18 0.05 0.00 0 0.95 7.83 10908 222.46 \n",
"34 90.82 0.04 0.00 0 0.92 8.22 11508 233.13 \n",
"35 94.67 0.01 0.00 0 0.92 4.40 11808 76.15 \n",
"36 92.12 0.91 0.00 0 1.26 5.71 12108 246597.13 \n",
"37 91.93 1.17 0.00 0 1.34 5.57 12408 279104.11 \n",
"38 91.53 1.46 0.00 0 1.61 5.39 12709 273649.34 \n",
"39 91.63 1.54 0.00 0 1.45 5.38 13008 262699.21 \n",
"40 91.69 1.75 0.00 0 1.35 5.21 13308 258480.25 \n",
"41 91.67 2.03 0.00 0 1.37 4.93 13609 259427.48 \n",
"42 91.63 2.03 0.00 0 1.59 4.75 13908 259713.76 \n",
"43 93.93 0.61 0.00 0 1.36 4.10 14208 48764.62 \n",
"44 94.63 0.19 0.00 0 1.04 4.14 14508 13878.14 \n",
"45 94.66 0.06 0.00 0 0.95 4.32 15108 6531.28 \n",
"46 94.52 0.19 0.00 0 1.09 4.20 15408 19409.83 \n",
"47 94.68 0.07 0.00 0 0.96 4.29 15708 7488.10 \n",
"48 94.61 0.16 0.00 0 1.03 4.20 16008 11680.98 \n",
"49 94.62 0.14 0.00 0 1.03 4.21 16308 18109.12 \n",
"50 94.68 0.02 0.00 0 0.86 4.43 16608 871.41 \n",
"51 94.76 0.00 0.00 0 0.81 4.43 16908 0.48 \n",
"52 92.44 0.57 0.00 0 1.28 5.71 17208 37566.09 \n",
"53 94.65 0.02 0.00 0 0.91 4.42 17508 761.49 \n",
"54 91.12 0.14 0.00 0 0.93 7.81 17808 1669.84 \n",
"55 94.47 0.26 0.00 0 1.18 4.10 18108 35267.46 \n",
"56 92.72 0.18 0.00 0 0.96 6.14 18708 10930.26 \n",
"57 94.63 0.13 0.00 0 0.97 4.26 19008 16508.65 \n",
"58 94.68 0.02 0.04 0 0.88 4.39 19308 220.33 \n",
"59 90.85 0.45 0.00 0 3.52 5.17 19608 56365.64 \n",
" ... ... ... ... ... ... ... ... \n",
"\n",
" bwrtn/s rtps tps wtps read write \n",
"0 151.96 0.04 2.76 2.72 0.000220 0.074199 \n",
"1 164.33 0.03 1.94 1.91 0.000137 0.080239 \n",
"2 163.86 0.04 1.76 1.72 0.000156 0.080010 \n",
"3 164.00 0.03 1.97 1.94 0.000132 0.080078 \n",
"4 171.84 0.05 2.55 2.50 0.000415 0.083906 \n",
"5 173.63 0.06 2.27 2.21 0.000610 0.084780 \n",
"6 171.80 0.03 2.22 2.19 0.000210 0.083887 \n",
"7 171.98 0.03 2.52 2.48 0.000288 0.083975 \n",
"8 162.47 0.05 2.03 1.98 0.000430 0.079331 \n",
"9 138.08 0.03 1.59 1.56 0.000156 0.067422 \n",
"10 153.08 0.04 1.71 1.67 0.000195 0.074746 \n",
"11 26481.00 73.02 186.49 113.47 5.731484 12.930176 \n",
"12 110146.77 122.85 440.29 317.44 22.595781 53.782603 \n",
"13 718.51 18.84 41.52 22.68 2.933408 0.350835 \n",
"14 110394.31 94.71 455.83 361.12 17.869756 53.903472 \n",
"15 147772.19 98.22 542.32 444.10 18.298477 72.154390 \n",
"16 8911.84 177.82 234.00 56.17 31.928066 4.351484 \n",
"17 32336.87 36.47 171.67 135.20 6.491318 15.789487 \n",
"18 131682.44 102.74 486.50 383.77 20.017329 64.298066 \n",
"19 115628.90 127.57 476.67 349.10 23.859839 56.459424 \n",
"20 988.48 153.05 184.57 31.52 27.403979 0.482656 \n",
"21 279.31 9.63 13.31 3.68 0.240630 0.136382 \n",
"22 362.28 10.69 15.96 5.27 0.246494 0.176895 \n",
"23 68371.08 232.90 617.53 384.63 45.084155 33.384316 \n",
"24 145404.20 331.61 881.90 550.30 72.068975 70.998145 \n",
"25 142334.71 332.75 673.91 341.16 63.529277 69.499370 \n",
"26 127559.41 368.16 949.76 581.60 67.299878 62.284868 \n",
"27 146952.43 332.41 903.64 571.22 73.095469 71.754116 \n",
"28 81561.66 269.42 1433.92 1164.51 47.046670 39.825029 \n",
"29 131278.46 348.25 1108.06 759.81 76.222583 64.100811 \n",
"30 135936.61 296.78 2373.04 2076.25 62.061348 66.375298 \n",
"31 29175.39 129.03 423.77 294.74 9.775210 14.245796 \n",
"32 54250.68 53.43 623.05 569.63 8.117749 26.489590 \n",
"33 5702.34 2.69 62.23 59.54 0.108623 2.784346 \n",
"34 3128.16 7.16 45.62 38.45 0.113833 1.527422 \n",
"35 1076.28 0.50 15.78 15.28 0.037183 0.525527 \n",
"36 756.70 626.95 636.56 9.61 120.408755 0.369482 \n",
"37 423.80 696.47 704.48 8.01 136.281304 0.206934 \n",
"38 585.47 679.69 694.79 15.10 133.617842 0.285874 \n",
"39 310.16 658.65 663.17 4.53 128.271099 0.151445 \n",
"40 1198.59 635.53 655.31 19.78 126.211060 0.585249 \n",
"41 511.82 642.83 655.16 12.32 126.673574 0.249912 \n",
"42 1271.45 639.72 661.43 21.71 126.813359 0.620825 \n",
"43 589.02 137.50 146.76 9.26 23.810850 0.287607 \n",
"44 1181.64 39.71 55.44 15.73 6.776436 0.576973 \n",
"45 1525.06 16.23 36.67 20.44 3.189102 0.744658 \n",
"46 1287.18 45.08 62.57 17.49 9.477456 0.628506 \n",
"47 2104.38 15.90 38.67 22.77 3.656299 1.027529 \n",
"48 1187.50 33.07 49.88 16.81 5.703604 0.579834 \n",
"49 1717.74 37.84 58.31 20.46 8.842344 0.838740 \n",
"50 651.24 3.88 12.34 8.46 0.425493 0.317988 \n",
"51 222.77 0.05 3.67 3.62 0.000234 0.108774 \n",
"52 795.06 140.09 152.71 12.63 18.342817 0.388213 \n",
"53 720.77 8.81 18.20 9.40 0.371821 0.351938 \n",
"54 1061.17 11.93 24.83 12.89 0.815352 0.518149 \n",
"55 700.49 89.09 100.76 11.67 17.220439 0.342036 \n",
"56 850.76 27.21 38.95 11.74 5.337041 0.415410 \n",
"57 1017.81 47.57 60.74 13.17 8.060864 0.496978 \n",
"58 734.83 2.44 11.69 9.25 0.107583 0.358804 \n",
"59 3041.25 133.80 369.09 235.28 27.522285 1.484985 \n",
" ... ... ... ... ... ... \n",
"\n",
"[135 rows x 14 columns]"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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