Data_mining_with_Anaconda_proof_of_concept.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proof of concept. Data mining of imported logfile content with Anaconda analytics.\n",
    "\n",
    "- Environment: Anaconda 3 x86_64  for windows 10 x86_64 (Python 3.5)\n",
    "- Python packages added:\n",
    "    - conda/pip install pandas_highcharts\n",
    "    - conda install seaborn\n",
    "- Installed Windows programs required for exporting this Jupyter notebook to PDF: \n",
    "    - Pandoc\n",
    "    - TeX Live 2015 (LaTeX)\n",
    "- Motivation:\n",
    "    - This proof of concept was developed by the author during a recruitment process in November 2015: a Python programming test to be completed in 3 hours. Only a first design with a basic prototype was delivered, but a couple of updates were handled over in the following days with the final outcome.\n",
    "    - The programming test was about importing a custom large log file where specific entries needed to be filtered out and imported for data mining purposes. \n",
    "    - The author of this notebook decided to deliver added value and develop a proof of concept based on Anaconda analytics instead of a traditional solution based on Python + vim + awk + sed.\n",
    "    - The 3 pages long detailed requirements are not included in order to preserve anonymity of the Company and privacy of their recruitment test. \n",
    "- Ideas for improvement:\n",
    "    - mmap module as the most efficient way to read large log files \n",
    "    - syslog module to deal with Syslog file rotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n",
      "WARNING: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pylab import has clobbered these variables: ['linalg', 'power', 'fft', 'draw_if_interactive', 'random', 'info']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "# Standard\n",
    "import tempfile\n",
    "import os\n",
    "import sys\n",
    "import pyparsing\n",
    "from pyparsing import Word, alphas, Suppress, Combine, nums, string, Optional, Regex, ParseException\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.random import randn\n",
    "# Stats\n",
    "from scipy import stats\n",
    "from pylab import *\n",
    "# Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "\n",
    "%pylab inline\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "logfilecontent = '''\n",
    "Feb 25 12:40:12 victim /opt/zl/bin/zdaemon[29412]: [zl::esink::Internals::timeout][/m0/zdaemon3]: Interval(5000): \\\n",
    "numCycles(173) numTimeouts(89) numMessages(241) numTMessages(2) numDescriptors(48) numPipes(0) task-events(0) num-timers(439) \n",
    "Feb 25 12:40:17 victim /opt/zl/bin/zdaemon[29412]: [zl::esink::Internals::timeout][/m0/zdaemon3]: Interval(5000): \\\n",
    "numCycles(17) numTimeouts(82) numMessages(247) numTMessages(3) numDescriptors(54) numPipes(0) task-events(0) num-timers(454)\n",
    "other logs 1\n",
    "Feb 25 12:40:22 victim /opt/zl/bin/zdaemon[29412]: [zl::esink::Internals::timeout][/m0/zdaemon3]: Interval(5000): \\\n",
    "numCycles(12) numTimeouts(2) numMessages(242) numTMessages(5) numDescriptors(67) numPipes(0) task-events(0) num-timers(499)\n",
    "Feb 25 12:40:27 victim /opt/zl/bin/zdaemon[29412]: [zl::esink::Internals::timeout][/m0/zdaemon3]: Interval(5000): \\\n",
    "numCycles(7) numTimeouts(32) numMessages(189) numTMessages(8) numDescriptors(60) numPipes(0) task-events(0) num-timers(413)\n",
    "Feb 25 12:40:32 victim /opt/zl/bin/zdaemon[29412]: [zl::esink::Internals::timeout][/m0/zdaemon3]: Interval(5000): \\\n",
    "numCycles(18) numTimeouts(21) numMessages(310) numTMessages(3) numDescriptors(78) numPipes(0) task-events(0) num-timers(505)\n",
    "other logs 2\n",
    "Feb 25 12:40:37 victim /opt/zl/bin/zdaemon[29412]: [zl::esink::Internals::timeout][/m0/zdaemon3]: Interval(5000): \\\n",
    "numCycles(22) numTimeouts(17) numMessages(233) numTMessages(6) numDescriptors(65) numPipes(0) task-events(0) num-timers(569)\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "month = Word(string.ascii_letters, string.ascii_letters, exact=3)\n",
    "integer = Word(nums)\n",
    "serverDateTime = Combine(month + \" \" + integer + \" \" + integer + \":\" + integer + \":\" + integer)\n",
    "hostname = Word(alphas + nums + \"_\" + \"-\")\n",
    "daemon = Word(alphas + \"/\" + \"-\" + \"_\" ) + Optional(Suppress(\"[\") + integer + Suppress(\"]\")) + Suppress(\":\")\n",
    "zl = Suppress(\"[\") + Word( alphas + \"::\" + alphas + \"::\" + alphas + \"::\" + alphas ) + Suppress(\"]\")\n",
    "m0 = Suppress(\"[\") + Word( alphas + nums + \"_\" + \"-\" + \"/\" ) + Suppress(\"]\") + Suppress(\":\")\n",
    "interval = Suppress(\"Interval\") + Suppress(\"(\") + Word(nums) + Suppress(\"):\") \n",
    "numCycles = Suppress(\"numCycles\") + Suppress(\"(\") + Word(nums) + Suppress(\")\") \n",
    "numTimeouts = Suppress(\"numTimeouts\") + Suppress(\"(\") + Word(nums) + Suppress(\")\")  \n",
    "numMessages = Suppress(\"numMessages\") + Suppress(\"(\") + Word(nums) + Suppress(\")\")  \n",
    "numTMessages = Suppress(\"numTMessages\") + Suppress(\"(\") + Word(nums) + Suppress(\")\") \n",
    "numDescriptors = Suppress(\"numDescriptors\") + Suppress(\"(\") + Word(nums) + Suppress(\")\") \n",
    "numPipes = Suppress(\"numPipes\") + Suppress(\"(\") + Word(nums) + Suppress(\")\") \n",
    "taskevents = Suppress(\"task-events\") + Suppress(\"(\") + Word(nums) + Suppress(\")\") \n",
    "numtimers = Suppress(\"num-timers\") + Suppress(\"(\") + Word(nums) + Suppress(\")\") \n",
    "bnf = (serverDateTime + hostname + daemon + zl + m0 + interval + numCycles + numTimeouts + \n",
    "       numMessages + numTMessages + numDescriptors + numPipes + taskevents + numtimers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(['Feb 25 12:40:12', 'victim', '/opt/zl/bin/zdaemon', '29412', 'zl::esink::Internals::timeout', '/m0/zdaemon3', '5000', '173', '89', '241', '2', '48', '0', '0', '439'], {}), (['Feb 25 12:40:17', 'victim', '/opt/zl/bin/zdaemon', '29412', 'zl::esink::Internals::timeout', '/m0/zdaemon3', '5000', '17', '82', '247', '3', '54', '0', '0', '454'], {}), (['Feb 25 12:40:22', 'victim', '/opt/zl/bin/zdaemon', '29412', 'zl::esink::Internals::timeout', '/m0/zdaemon3', '5000', '12', '2', '242', '5', '67', '0', '0', '499'], {}), (['Feb 25 12:40:27', 'victim', '/opt/zl/bin/zdaemon', '29412', 'zl::esink::Internals::timeout', '/m0/zdaemon3', '5000', '7', '32', '189', '8', '60', '0', '0', '413'], {}), (['Feb 25 12:40:32', 'victim', '/opt/zl/bin/zdaemon', '29412', 'zl::esink::Internals::timeout', '/m0/zdaemon3', '5000', '18', '21', '310', '3', '78', '0', '0', '505'], {}), (['Feb 25 12:40:37', 'victim', '/opt/zl/bin/zdaemon', '29412', 'zl::esink::Internals::timeout', '/m0/zdaemon3', '5000', '22', '17', '233', '6', '65', '0', '0', '569'], {})]\n"
     ]
    }
   ],
   "source": [
    "def make_tempfile():\n",
    "    fd, temp_file_name = tempfile.mkstemp()\n",
    "    os.close(fd)\n",
    "    f = open(temp_file_name,'wt')\n",
    "    try:\n",
    "        f.write(logfilecontent)\n",
    "    finally:\n",
    "        f.close()\n",
    "    return temp_file_name\n",
    "\n",
    "def cleanup(filename):\n",
    "    os.unlink(filename)\n",
    "        \n",
    "filename = make_tempfile()\n",
    "\n",
    "L=list()\n",
    "with open(filename) as infile:\n",
    "   for line in infile:\n",
    "        try:\n",
    "           results = bnf.parseString(line)\n",
    "        except ParseException as pe:\n",
    "            continue\n",
    "        else:\n",
    "           L.append(results)\n",
    "           \n",
    "print(L)\n",
    "cleanup(filename)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Columns naming \n",
    "dframe = pd.DataFrame(L,columns=['serverDateTime','hostname','daemon','daemon-p','zl','m0','interval','numcycles',\n",
    "                                 'numtimeouts','nummessages','numtmessages','numdescriptors','numpipes','task-events',\n",
    "                                 'num-timers'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dtype conversion to integer\n",
    "dframe[['interval','numcycles','numtimeouts','nummessages','numtmessages','numdescriptors','numpipes','task-events',\n",
    "        'num-timers']] = dframe[['interval','numcycles','numtimeouts','nummessages','numtmessages','numdescriptors',\n",
    "                                 'numpipes','task-events','num-timers']].astype(int)\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serverDateTime</th>\n",
       "      <th>hostname</th>\n",
       "      <th>daemon</th>\n",
       "      <th>daemon-p</th>\n",
       "      <th>zl</th>\n",
       "      <th>m0</th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>numpipes</th>\n",
       "      <th>task-events</th>\n",
       "      <th>num-timers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Feb 25 12:40:12</td>\n",
       "      <td>victim</td>\n",
       "      <td>/opt/zl/bin/zdaemon</td>\n",
       "      <td>29412</td>\n",
       "      <td>zl::esink::Internals::timeout</td>\n",
       "      <td>/m0/zdaemon3</td>\n",
       "      <td>5000</td>\n",
       "      <td>173</td>\n",
       "      <td>89</td>\n",
       "      <td>241</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb 25 12:40:17</td>\n",
       "      <td>victim</td>\n",
       "      <td>/opt/zl/bin/zdaemon</td>\n",
       "      <td>29412</td>\n",
       "      <td>zl::esink::Internals::timeout</td>\n",
       "      <td>/m0/zdaemon3</td>\n",
       "      <td>5000</td>\n",
       "      <td>17</td>\n",
       "      <td>82</td>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Feb 25 12:40:22</td>\n",
       "      <td>victim</td>\n",
       "      <td>/opt/zl/bin/zdaemon</td>\n",
       "      <td>29412</td>\n",
       "      <td>zl::esink::Internals::timeout</td>\n",
       "      <td>/m0/zdaemon3</td>\n",
       "      <td>5000</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feb 25 12:40:27</td>\n",
       "      <td>victim</td>\n",
       "      <td>/opt/zl/bin/zdaemon</td>\n",
       "      <td>29412</td>\n",
       "      <td>zl::esink::Internals::timeout</td>\n",
       "      <td>/m0/zdaemon3</td>\n",
       "      <td>5000</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>189</td>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Feb 25 12:40:32</td>\n",
       "      <td>victim</td>\n",
       "      <td>/opt/zl/bin/zdaemon</td>\n",
       "      <td>29412</td>\n",
       "      <td>zl::esink::Internals::timeout</td>\n",
       "      <td>/m0/zdaemon3</td>\n",
       "      <td>5000</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Feb 25 12:40:37</td>\n",
       "      <td>victim</td>\n",
       "      <td>/opt/zl/bin/zdaemon</td>\n",
       "      <td>29412</td>\n",
       "      <td>zl::esink::Internals::timeout</td>\n",
       "      <td>/m0/zdaemon3</td>\n",
       "      <td>5000</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>233</td>\n",
       "      <td>6</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    serverDateTime hostname               daemon daemon-p  \\\n",
       "0  Feb 25 12:40:12   victim  /opt/zl/bin/zdaemon    29412   \n",
       "1  Feb 25 12:40:17   victim  /opt/zl/bin/zdaemon    29412   \n",
       "2  Feb 25 12:40:22   victim  /opt/zl/bin/zdaemon    29412   \n",
       "3  Feb 25 12:40:27   victim  /opt/zl/bin/zdaemon    29412   \n",
       "4  Feb 25 12:40:32   victim  /opt/zl/bin/zdaemon    29412   \n",
       "5  Feb 25 12:40:37   victim  /opt/zl/bin/zdaemon    29412   \n",
       "\n",
       "                              zl            m0  interval  numcycles  \\\n",
       "0  zl::esink::Internals::timeout  /m0/zdaemon3      5000        173   \n",
       "1  zl::esink::Internals::timeout  /m0/zdaemon3      5000         17   \n",
       "2  zl::esink::Internals::timeout  /m0/zdaemon3      5000         12   \n",
       "3  zl::esink::Internals::timeout  /m0/zdaemon3      5000          7   \n",
       "4  zl::esink::Internals::timeout  /m0/zdaemon3      5000         18   \n",
       "5  zl::esink::Internals::timeout  /m0/zdaemon3      5000         22   \n",
       "\n",
       "   numtimeouts  nummessages  numtmessages  numdescriptors  numpipes  \\\n",
       "0           89          241             2              48         0   \n",
       "1           82          247             3              54         0   \n",
       "2            2          242             5              67         0   \n",
       "3           32          189             8              60         0   \n",
       "4           21          310             3              78         0   \n",
       "5           17          233             6              65         0   \n",
       "\n",
       "   task-events  num-timers  \n",
       "0            0         439  \n",
       "1            0         454  \n",
       "2            0         499  \n",
       "3            0         413  \n",
       "4            0         505  \n",
       "5            0         569  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['serverDateTime', 'hostname', 'daemon', 'daemon-p', 'zl', 'm0',\n",
       "       'interval', 'numcycles', 'numtimeouts', 'nummessages', 'numtmessages',\n",
       "       'numdescriptors', 'numpipes', 'task-events', 'num-timers'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3, 4, 5], dtype='int64')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['serverDateTime', 'hostname', 'daemon', 'daemon-p', 'zl', 'm0',\n",
       "       'interval', 'numcycles', 'numtimeouts', 'nummessages', 'numtmessages',\n",
       "       'numdescriptors', 'numpipes', 'task-events', 'num-timers'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    241\n",
       "1    247\n",
       "2    242\n",
       "3    189\n",
       "4    310\n",
       "5    233\n",
       "Name: nummessages, dtype: int32"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe['nummessages']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sample_string2 = dframe['num-timers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    439\n",
       "1    454\n",
       "2    499\n",
       "3    413\n",
       "4    505\n",
       "5    569\n",
       "Name: num-timers, dtype: int32"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_string2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del dframe['hostname']\n",
    "del dframe['daemon']\n",
    "del dframe['daemon-p']\n",
    "del dframe['zl']\n",
    "del dframe['m0']\n",
    "del dframe['numpipes']\n",
    "del dframe['task-events']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serverDateTime</th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Feb 25 12:40:12</td>\n",
       "      <td>5000</td>\n",
       "      <td>173</td>\n",
       "      <td>89</td>\n",
       "      <td>241</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb 25 12:40:17</td>\n",
       "      <td>5000</td>\n",
       "      <td>17</td>\n",
       "      <td>82</td>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Feb 25 12:40:22</td>\n",
       "      <td>5000</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>67</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feb 25 12:40:27</td>\n",
       "      <td>5000</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>189</td>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Feb 25 12:40:32</td>\n",
       "      <td>5000</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Feb 25 12:40:37</td>\n",
       "      <td>5000</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>233</td>\n",
       "      <td>6</td>\n",
       "      <td>65</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    serverDateTime  interval  numcycles  numtimeouts  nummessages  \\\n",
       "0  Feb 25 12:40:12      5000        173           89          241   \n",
       "1  Feb 25 12:40:17      5000         17           82          247   \n",
       "2  Feb 25 12:40:22      5000         12            2          242   \n",
       "3  Feb 25 12:40:27      5000          7           32          189   \n",
       "4  Feb 25 12:40:32      5000         18           21          310   \n",
       "5  Feb 25 12:40:37      5000         22           17          233   \n",
       "\n",
       "   numtmessages  numdescriptors  num-timers  \n",
       "0             2              48         439  \n",
       "1             3              54         454  \n",
       "2             5              67         499  \n",
       "3             8              60         413  \n",
       "4             3              78         505  \n",
       "5             6              65         569  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>233</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nummessages  numtmessages\n",
       "0          241             2\n",
       "1          247             3\n",
       "2          242             5\n",
       "3          189             8\n",
       "4          310             3\n",
       "5          233             6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe[['nummessages','numtmessages']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dframe2 = pd.DataFrame(dframe,columns=['numcycles','numtimeouts','nummessages','numtmessages'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>173</td>\n",
       "      <td>89</td>\n",
       "      <td>241</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>82</td>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>189</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>233</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   numcycles  numtimeouts  nummessages  numtmessages\n",
       "0        173           89          241             2\n",
       "1         17           82          247             3\n",
       "2         12            2          242             5\n",
       "3          7           32          189             8\n",
       "4         18           21          310             3\n",
       "5         22           17          233             6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.500000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>243.666667</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>64.630488</td>\n",
       "      <td>36.225681</td>\n",
       "      <td>38.790033</td>\n",
       "      <td>2.258318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.250000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>241.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>245.750000</td>\n",
       "      <td>5.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>173.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        numcycles  numtimeouts  nummessages  numtmessages\n",
       "count    6.000000     6.000000     6.000000      6.000000\n",
       "mean    41.500000    40.500000   243.666667      4.500000\n",
       "std     64.630488    36.225681    38.790033      2.258318\n",
       "min      7.000000     2.000000   189.000000      2.000000\n",
       "25%     13.250000    18.000000   235.000000      3.000000\n",
       "50%     17.500000    26.500000   241.500000      4.000000\n",
       "75%     21.000000    69.500000   245.750000      5.750000\n",
       "max    173.000000    89.000000   310.000000      8.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3, 4, 5], dtype='int64')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting copied dataframe with specific columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xdfeaddbc50>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v3vTp04cXXniBcDhMMBikpqaGoqKiNhVQW1t7+u9CjsmMWIQjFpEwRMIW4bBF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfeaddbf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot')\n",
    "rcParams['figure.figsize'] = 10, 5 # width, height in inches\n",
    "dframe2.plot()\n",
    "legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serverDateTime</th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Feb 25 12:40:12</td>\n",
       "      <td>5000</td>\n",
       "      <td>173</td>\n",
       "      <td>89</td>\n",
       "      <td>241</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb 25 12:40:17</td>\n",
       "      <td>5000</td>\n",
       "      <td>17</td>\n",
       "      <td>82</td>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Feb 25 12:40:22</td>\n",
       "      <td>5000</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>67</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feb 25 12:40:27</td>\n",
       "      <td>5000</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>189</td>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Feb 25 12:40:32</td>\n",
       "      <td>5000</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>310</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Feb 25 12:40:37</td>\n",
       "      <td>5000</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>233</td>\n",
       "      <td>6</td>\n",
       "      <td>65</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    serverDateTime  interval  numcycles  numtimeouts  nummessages  \\\n",
       "0  Feb 25 12:40:12      5000        173           89          241   \n",
       "1  Feb 25 12:40:17      5000         17           82          247   \n",
       "2  Feb 25 12:40:22      5000         12            2          242   \n",
       "3  Feb 25 12:40:27      5000          7           32          189   \n",
       "4  Feb 25 12:40:32      5000         18           21          310   \n",
       "5  Feb 25 12:40:37      5000         22           17          233   \n",
       "\n",
       "   numtmessages  numdescriptors  num-timers  \n",
       "0             2              48         439  \n",
       "1             3              54         454  \n",
       "2             5              67         499  \n",
       "3             8              60         413  \n",
       "4             3              78         505  \n",
       "5             6              65         569  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3, 4, 5], dtype='int64')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem: Monitor should obtain the following information\n",
    " - The number of events per second for numCycles, numTimeouts, numMessages and numTMessages\n",
    " - The current value of numDescriptors and num-timers \n",
    " - Solution: DataFrame manipulation \n",
    " - Issue: serverDateTime is not in datetime format, pd.to_datetime will solve it although year is not specified in origin and Python's default year is 1900."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   1900-02-25 12:40:12\n",
       "1   1900-02-25 12:40:17\n",
       "2   1900-02-25 12:40:22\n",
       "3   1900-02-25 12:40:27\n",
       "4   1900-02-25 12:40:32\n",
       "5   1900-02-25 12:40:37\n",
       "Name: serverDateTime, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe['interval'] = dframe['interval']/1000\n",
    "dframe['numcycles']=dframe['numcycles']/5\n",
    "dframe['numtimeouts']=dframe['numtimeouts']/5\n",
    "dframe['nummessages']=dframe['nummessages']/5\n",
    "dframe['numtmessages']=dframe['numtmessages']/5\n",
    "dframe['serverDateTime'] = pd.to_datetime(dframe['serverDateTime'],format='%b %d %H:%M:%S')\n",
    "dframe['serverDateTime'].dt.year\n",
    "dframe['serverDateTime']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Python's default year is updated \n",
    "- Year is not specified in logs' timestamp and default year in Python is 1900.\n",
    "- Adding years to our current datetime is not valid as each year has a different amount of days. \n",
    "    - daysToAdd = 365 * 115\n",
    "    - dframe['serverDateTime'] = dframe['serverDateTime'] + np.timedelta64(115, 'Y')\n",
    "    - dframe['serverDateTime'] = dframe['serverDateTime'] + np.timedelta64(daysToAdd, 'D')\n",
    "- Solution: Timestamps have a replace method (just like datetimes):     \n",
    "    - df.index.map(lambda t: t.replace(year=2015, month=2, day=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-02-25 12:40:12\n",
       "1   2015-02-25 12:40:17\n",
       "2   2015-02-25 12:40:22\n",
       "3   2015-02-25 12:40:27\n",
       "4   2015-02-25 12:40:32\n",
       "5   2015-02-25 12:40:37\n",
       "Name: serverDateTime, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe['serverDateTime'] = dframe['serverDateTime'].map(lambda t: t.replace(year=2015))\n",
    "dframe['serverDateTime'].dt.year\n",
    "dframe['serverDateTime']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem: Every 10 seconds, the program should emit all the counters to a file or a round-robin database for further processing and logging. \n",
    "- DatetimeIndex required for generating meaninful graphs and by dframe.resample\n",
    "- We setup serverDateTime column as index in order to use dframe.resample below (DatetimeIndex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serverDateTime</th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serverDateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:12</th>\n",
       "      <td>2015-02-25 12:40:12</td>\n",
       "      <td>5</td>\n",
       "      <td>34.6</td>\n",
       "      <td>17.8</td>\n",
       "      <td>48.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:17</th>\n",
       "      <td>2015-02-25 12:40:17</td>\n",
       "      <td>5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>54</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:22</th>\n",
       "      <td>2015-02-25 12:40:22</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:27</th>\n",
       "      <td>2015-02-25 12:40:27</td>\n",
       "      <td>5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>37.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>60</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:32</th>\n",
       "      <td>2015-02-25 12:40:32</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>78</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:37</th>\n",
       "      <td>2015-02-25 12:40:37</td>\n",
       "      <td>5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>46.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>65</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         serverDateTime  interval  numcycles  numtimeouts  \\\n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:12 2015-02-25 12:40:12         5       34.6         17.8   \n",
       "2015-02-25 12:40:17 2015-02-25 12:40:17         5        3.4         16.4   \n",
       "2015-02-25 12:40:22 2015-02-25 12:40:22         5        2.4          0.4   \n",
       "2015-02-25 12:40:27 2015-02-25 12:40:27         5        1.4          6.4   \n",
       "2015-02-25 12:40:32 2015-02-25 12:40:32         5        3.6          4.2   \n",
       "2015-02-25 12:40:37 2015-02-25 12:40:37         5        4.4          3.4   \n",
       "\n",
       "                     nummessages  numtmessages  numdescriptors  num-timers  \n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:12         48.2           0.4              48         439  \n",
       "2015-02-25 12:40:17         49.4           0.6              54         454  \n",
       "2015-02-25 12:40:22         48.4           1.0              67         499  \n",
       "2015-02-25 12:40:27         37.8           1.6              60         413  \n",
       "2015-02-25 12:40:32         62.0           0.6              78         505  \n",
       "2015-02-25 12:40:37         46.6           1.2              65         569  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dframe.set_index('serverDateTime', drop=False, verify_integrity=True)\n",
    "dframe.set_index('serverDateTime', drop=False, inplace=True)\n",
    "dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-02-25 12:40:12', '2015-02-25 12:40:17',\n",
       "               '2015-02-25 12:40:22', '2015-02-25 12:40:27',\n",
       "               '2015-02-25 12:40:32', '2015-02-25 12:40:37'],\n",
       "              dtype='datetime64[ns]', name='serverDateTime', freq=None)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. See the Time Series section. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serverDateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:10</th>\n",
       "      <td>5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>51.0</td>\n",
       "      <td>446.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:20</th>\n",
       "      <td>5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>63.5</td>\n",
       "      <td>456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:30</th>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>71.5</td>\n",
       "      <td>537.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     interval  numcycles  numtimeouts  nummessages  \\\n",
       "serverDateTime                                                       \n",
       "2015-02-25 12:40:10         5       19.0         17.1         48.8   \n",
       "2015-02-25 12:40:20         5        1.9          3.4         43.1   \n",
       "2015-02-25 12:40:30         5        4.0          3.8         54.3   \n",
       "\n",
       "                     numtmessages  numdescriptors  num-timers  \n",
       "serverDateTime                                                 \n",
       "2015-02-25 12:40:10           0.5            51.0       446.5  \n",
       "2015-02-25 12:40:20           1.3            63.5       456.0  \n",
       "2015-02-25 12:40:30           0.9            71.5       537.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame.resample will show NaN rows. We are copying dframe to a new one in order to avoid issues afterwards \n",
    "# (pd.notnull not working properly)\n",
    "\n",
    "dframe10s = dframe.copy(deep=True)\n",
    "#dframe.resample('10min', how='sum')\n",
    "#dframe10s.resample('10S', how='sum')   # 10S = 10 seconds\n",
    "dframe10s.resample('10S', how='mean')  # 10S = 10 seconds, mean = average\n",
    "#dframe10s[pd.notnull(dframe10s.resample('10S', how='mean'))]\n",
    "#dframe10s[np.isfinite(dframe10s.resample('10S', how='mean'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serverDateTime</th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serverDateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:12</th>\n",
       "      <td>2015-02-25 12:40:12</td>\n",
       "      <td>5</td>\n",
       "      <td>34.6</td>\n",
       "      <td>17.8</td>\n",
       "      <td>48.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:17</th>\n",
       "      <td>2015-02-25 12:40:17</td>\n",
       "      <td>5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>54</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:22</th>\n",
       "      <td>2015-02-25 12:40:22</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:27</th>\n",
       "      <td>2015-02-25 12:40:27</td>\n",
       "      <td>5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>37.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>60</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:32</th>\n",
       "      <td>2015-02-25 12:40:32</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>78</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:37</th>\n",
       "      <td>2015-02-25 12:40:37</td>\n",
       "      <td>5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>46.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>65</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         serverDateTime  interval  numcycles  numtimeouts  \\\n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:12 2015-02-25 12:40:12         5       34.6         17.8   \n",
       "2015-02-25 12:40:17 2015-02-25 12:40:17         5        3.4         16.4   \n",
       "2015-02-25 12:40:22 2015-02-25 12:40:22         5        2.4          0.4   \n",
       "2015-02-25 12:40:27 2015-02-25 12:40:27         5        1.4          6.4   \n",
       "2015-02-25 12:40:32 2015-02-25 12:40:32         5        3.6          4.2   \n",
       "2015-02-25 12:40:37 2015-02-25 12:40:37         5        4.4          3.4   \n",
       "\n",
       "                     nummessages  numtmessages  numdescriptors  num-timers  \n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:12         48.2           0.4              48         439  \n",
       "2015-02-25 12:40:17         49.4           0.6              54         454  \n",
       "2015-02-25 12:40:22         48.4           1.0              67         499  \n",
       "2015-02-25 12:40:27         37.8           1.6              60         413  \n",
       "2015-02-25 12:40:32         62.0           0.6              78         505  \n",
       "2015-02-25 12:40:37         46.6           1.2              65         569  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-02-25 12:40:12', '2015-02-25 12:40:17',\n",
       "               '2015-02-25 12:40:22', '2015-02-25 12:40:27',\n",
       "               '2015-02-25 12:40:32', '2015-02-25 12:40:37'],\n",
       "              dtype='datetime64[ns]', name='serverDateTime', freq=None)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Groupby per hour:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>serverDateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-02-25 - 2015-02-25</td>\n",
       "      <td>49.8</td>\n",
       "      <td>292.4</td>\n",
       "      <td>48.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2015-02-25 12:40:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Period  numcycles  nummessages  numtimeouts  \\\n",
       "12  2015-02-25 - 2015-02-25       49.8        292.4         48.6   \n",
       "\n",
       "    numtmessages      serverDateTime  \n",
       "12           5.4 2015-02-25 12:40:17  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def func(group):\n",
    "    return pd.Series({'numcycles': group.numcycles.sum(), 'numtimeouts': group.numtimeouts.sum(),\n",
    "        'nummessages': group.nummessages.sum(), 'numtmessages': group.numtmessages.sum(), 'serverDateTime': group.index[1], \n",
    "                      'Period': '{0} - {1}'.format(group.index[0].date(), group.index[-1].date())})\n",
    "\n",
    "dframe.groupby(lambda idx: idx.hour).apply(func)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serverDateTime</th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serverDateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:12</th>\n",
       "      <td>2015-02-25 12:40:12</td>\n",
       "      <td>5</td>\n",
       "      <td>34.6</td>\n",
       "      <td>17.8</td>\n",
       "      <td>48.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:17</th>\n",
       "      <td>2015-02-25 12:40:17</td>\n",
       "      <td>5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>54</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:22</th>\n",
       "      <td>2015-02-25 12:40:22</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:27</th>\n",
       "      <td>2015-02-25 12:40:27</td>\n",
       "      <td>5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>37.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>60</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:32</th>\n",
       "      <td>2015-02-25 12:40:32</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>78</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:37</th>\n",
       "      <td>2015-02-25 12:40:37</td>\n",
       "      <td>5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>46.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>65</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         serverDateTime  interval  numcycles  numtimeouts  \\\n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:12 2015-02-25 12:40:12         5       34.6         17.8   \n",
       "2015-02-25 12:40:17 2015-02-25 12:40:17         5        3.4         16.4   \n",
       "2015-02-25 12:40:22 2015-02-25 12:40:22         5        2.4          0.4   \n",
       "2015-02-25 12:40:27 2015-02-25 12:40:27         5        1.4          6.4   \n",
       "2015-02-25 12:40:32 2015-02-25 12:40:32         5        3.6          4.2   \n",
       "2015-02-25 12:40:37 2015-02-25 12:40:37         5        4.4          3.4   \n",
       "\n",
       "                     nummessages  numtmessages  numdescriptors  num-timers  \n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:12         48.2           0.4              48         439  \n",
       "2015-02-25 12:40:17         49.4           0.6              54         454  \n",
       "2015-02-25 12:40:22         48.4           1.0              67         499  \n",
       "2015-02-25 12:40:27         37.8           1.6              60         413  \n",
       "2015-02-25 12:40:32         62.0           0.6              78         505  \n",
       "2015-02-25 12:40:37         46.6           1.2              65         569  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### resample 10 min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serverDateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:00</th>\n",
       "      <td>30</td>\n",
       "      <td>49.8</td>\n",
       "      <td>48.6</td>\n",
       "      <td>292.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>372</td>\n",
       "      <td>2879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     interval  numcycles  numtimeouts  nummessages  \\\n",
       "serverDateTime                                                       \n",
       "2015-02-25 12:40:00        30       49.8         48.6        292.4   \n",
       "\n",
       "                     numtmessages  numdescriptors  num-timers  \n",
       "serverDateTime                                                 \n",
       "2015-02-25 12:40:00           5.4             372        2879  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe10min = dframe.copy(deep=True)\n",
    "dframe10min.resample('10min', how='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serverDateTime</th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
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       "    <tr>\n",
       "      <th>serverDateTime</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:12</th>\n",
       "      <td>2015-02-25 12:40:12</td>\n",
       "      <td>5</td>\n",
       "      <td>34.6</td>\n",
       "      <td>17.8</td>\n",
       "      <td>48.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:17</th>\n",
       "      <td>2015-02-25 12:40:17</td>\n",
       "      <td>5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>54</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:22</th>\n",
       "      <td>2015-02-25 12:40:22</td>\n",
       "      <td>5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:27</th>\n",
       "      <td>2015-02-25 12:40:27</td>\n",
       "      <td>5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>37.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>60</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:32</th>\n",
       "      <td>2015-02-25 12:40:32</td>\n",
       "      <td>5</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>78</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:37</th>\n",
       "      <td>2015-02-25 12:40:37</td>\n",
       "      <td>5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>46.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>65</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         serverDateTime  interval  numcycles  numtimeouts  \\\n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:12 2015-02-25 12:40:12         5       34.6         17.8   \n",
       "2015-02-25 12:40:17 2015-02-25 12:40:17         5        3.4         16.4   \n",
       "2015-02-25 12:40:22 2015-02-25 12:40:22         5        2.4          0.4   \n",
       "2015-02-25 12:40:27 2015-02-25 12:40:27         5        1.4          6.4   \n",
       "2015-02-25 12:40:32 2015-02-25 12:40:32         5        3.6          4.2   \n",
       "2015-02-25 12:40:37 2015-02-25 12:40:37         5        4.4          3.4   \n",
       "\n",
       "                     nummessages  numtmessages  numdescriptors  num-timers  \n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:12         48.2           0.4              48         439  \n",
       "2015-02-25 12:40:17         49.4           0.6              54         454  \n",
       "2015-02-25 12:40:22         48.4           1.0              67         499  \n",
       "2015-02-25 12:40:27         37.8           1.6              60         413  \n",
       "2015-02-25 12:40:32         62.0           0.6              78         505  \n",
       "2015-02-25 12:40:37         46.6           1.2              65         569  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-02-25 12:40:12', '2015-02-25 12:40:17',\n",
       "               '2015-02-25 12:40:22', '2015-02-25 12:40:27',\n",
       "               '2015-02-25 12:40:32', '2015-02-25 12:40:37'],\n",
       "              dtype='datetime64[ns]', name='serverDateTime', freq=None)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.4     1\n",
       "3.6     1\n",
       "2.4     1\n",
       "3.4     1\n",
       "34.6    1\n",
       "1.4     1\n",
       "Name: numcycles, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe['numcycles'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtain some values related with the machine load.\n",
    "- There's no load average for the last 1, 5 and 15 minutes in my windows environment. In any case this is trivial task.\n",
    "    - import psutil\n",
    "    - import os\n",
    "    - os.getloadavg()\n",
    "    - psutil.cpu_percent(percpu=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configurable threshold on the aggregated counters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serverDateTime</th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
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       "    <tr>\n",
       "      <th>serverDateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:17</th>\n",
       "      <td>2015-02-25 12:40:17</td>\n",
       "      <td>5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>54</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:27</th>\n",
       "      <td>2015-02-25 12:40:27</td>\n",
       "      <td>5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>37.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>60</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         serverDateTime  interval  numcycles  numtimeouts  \\\n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:17 2015-02-25 12:40:17         5        3.4         16.4   \n",
       "2015-02-25 12:40:27 2015-02-25 12:40:27         5        1.4          6.4   \n",
       "\n",
       "                     nummessages  numtmessages  numdescriptors  num-timers  \n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:17         49.4           0.6              54         454  \n",
       "2015-02-25 12:40:27         37.8           1.6              60         413  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe[(dframe.numtimeouts < 17) & (dframe.numtimeouts > 5)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numtimeouts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serverDateTime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:17</th>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:27</th>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     numtimeouts\n",
       "serverDateTime                  \n",
       "2015-02-25 12:40:17         16.4\n",
       "2015-02-25 12:40:27          6.4"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe[(dframe.numtimeouts < 17) & (dframe.numtimeouts > 5)] [['numtimeouts']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th>interval</th>\n",
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       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serverDateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:12</th>\n",
       "      <td>2015-02-25 12:40:12</td>\n",
       "      <td>5</td>\n",
       "      <td>34.6</td>\n",
       "      <td>17.8</td>\n",
       "      <td>48.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>48</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:17</th>\n",
       "      <td>2015-02-25 12:40:17</td>\n",
       "      <td>5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>54</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:27</th>\n",
       "      <td>2015-02-25 12:40:27</td>\n",
       "      <td>5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>37.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>60</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         serverDateTime  interval  numcycles  numtimeouts  \\\n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:12 2015-02-25 12:40:12         5       34.6         17.8   \n",
       "2015-02-25 12:40:17 2015-02-25 12:40:17         5        3.4         16.4   \n",
       "2015-02-25 12:40:27 2015-02-25 12:40:27         5        1.4          6.4   \n",
       "\n",
       "                     nummessages  numtmessages  numdescriptors  num-timers  \n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:12         48.2           0.4              48         439  \n",
       "2015-02-25 12:40:17         49.4           0.6              54         454  \n",
       "2015-02-25 12:40:27         37.8           1.6              60         413  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe[dframe['numtimeouts']>6] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Threshold checking via a conditional statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains rows with numtimeouts bigger than threshold!\n"
     ]
    }
   ],
   "source": [
    "if ~dframe[dframe['numtimeouts']>6].empty:\n",
    "    print('DataFrame contains rows with numtimeouts bigger than threshold!')\n",
    "    \n",
    "#dframe3 = dframe[dframe['numtimeouts']>6]\n",
    "#dframe3 is not None and isinstance(dframe3, pd.DataFrame) and not dframe3.empty  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display an index/row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serverDateTime</th>\n",
       "      <th>interval</th>\n",
       "      <th>numcycles</th>\n",
       "      <th>numtimeouts</th>\n",
       "      <th>nummessages</th>\n",
       "      <th>numtmessages</th>\n",
       "      <th>numdescriptors</th>\n",
       "      <th>num-timers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serverDateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-25 12:40:17</th>\n",
       "      <td>2015-02-25 12:40:17</td>\n",
       "      <td>5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>16.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>54</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         serverDateTime  interval  numcycles  numtimeouts  \\\n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:17 2015-02-25 12:40:17         5        3.4         16.4   \n",
       "\n",
       "                     nummessages  numtmessages  numdescriptors  num-timers  \n",
       "serverDateTime                                                              \n",
       "2015-02-25 12:40:17         49.4           0.6              54         454  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.ix['2015-02-25 12:40:17']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphs \n",
    "### Plotting with matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rcParams['figure.figsize'] = 10, 5 # width, height in inches\n",
    "\n",
    "#df_s = dframe['numcycles'].resample('1S', how='sum')\n",
    "#df_s.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xdfefc59dd8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sa9V3v/tdqqurs812g9FcfiInmONg2DbOCbikWeSU4zjkdXTib2nF296BQaorL1JYQGha\nkGhBQONBTWE5zeXn8Xi47LLLOP/88/nrX//Kt7/97T7NeV6vl3A4TCQSyXYLZp4PhUI5nZyI5CiR\ngI7O1K2zI33fBbYNpdNg1kyYMR0m0V/JIlNSVzf8Tz3UN0A01RpFQQBOOw2jYiY+j4cTN4iBTIRh\nv0VnzZrFjBkzgFRRWkFBAYcOHcquzwQpn8/XJ0D1D1iDUQuVyBhwHMx4Anc4jDscyd67+hWZOoZB\n3OsFAzxHm+FoM/Zb/x+RokLCJcVECwv017PICBnJJN62DvwtLeR1p37/2aZJuDRIKBgk7vOm/j0d\nPTrBZypjJacWqhdffJHDhw+zevVqWlpaCIfDLF26lH379rF48WL27NnDkiVLmD9/Pk888QSJRIJY\nLEZ9fX22ol9ExpDj4IpEe4WnCK5wGKtXoSv0XHId9/mI+7zEfT4S3rxsYLKiUXytbfhb2/C3teNv\naydpWURKigiVlBD3a8Z5kWM4Du5QCH9zusA8fbVZNJBPaFqQcFEhaKLhU9KwNVSJRIKtW7dy9OhR\nDMPgqquuoqCggPvuu49kMklFRQVf+MIXMAyDF198kZ07dwJw+eWXs3z58mFPQC1UIoMzksl0YOpp\ndXJHItnLrDMSHk82NGXubbdrZIEofRm3r6UNX1s7ViKR3We4pJhQSTFJb954vD2RKcOMx/G3tOFr\nacWd7tJLuN2EgyWEgiUk8zwTfIZyIgzVQjVsoBpvClQijLrLLhWavCTSAWrMCswdh7zOLnytbXjb\n2zHt1NdDzO8jXFJMuLg4FdRETgWOg7ejE39zC3kdndkC83BRIaFpQWKBfLXinmIUqEQmk1F02fVv\nderdZTfejGQSb3sHvtY28jq7Ur9MgGhBgHCwhEhhIY6lrg05+bgikVSXXmtbtsU25vMSCgYJlxTj\nuHSF7KkqpxoqETl+o+myC/erdxpxl904cSyLcLCEcLAEMx7H19aearnq7MLb2YVtmj3F7LoUXKY4\nI5nE19aOv7kFTygMpP6o6SqdRihYQsKva/RkaGqhEhkLk6XL7gSwIlH8rW34WltxxVID/yZdLsLF\nRYSDxSdkugyRMeE4eLpD+Jtbsl3cmVbY0LQgkcKCE1Jg/pvf/IYDBw6M6T4XLlx4wqdeORWohUpk\nLI3RVXZTVdKbR+fM6XTOKE9d7dTahre1ncDRZgJHm4nn5aXqrUqKVagrk5IZi+NvbcXf3Jr9oyfh\n8dAVLCEULMb2nBo/t3V1dbz22mtEo1EaGhqoqalhx44drFu3jsrKSp555hlaW1u5+OKL2bhxI+Xl\n5TQ1NbFixQoOHTrEgQMHOO+881i1ahU33XQT8+fP59ChQ/h8PpYuXcru3bvp6upi06ZN+Hw+tmzZ\nQn19PY7jcN111/HBD36QBx98kDfeeAPbtvnoRz9KTU0NTz/9NL/85S8xTZNFixaxdu1aDh06xNat\nW3Ech/b2dm666SYWL17Mc889x3/9139RWFiIy+VixYoVXHDBBSM+1lhSoBIZwlTusht3hkE8P5/2\n/HzaZ80kr7MrFa7aOyhsbKKwsYlYvp9QSTHh4iIcDR4qE8m2ewrM0zWBtmEQKikmFCyZ0ALzv//7\nv5+w1qRQKERtbS319fWsX7+eadOmDbhdY2MjmzdvJhwOc+WVV/Lkk0/i8XhYuXIlq1atAmDx4sWs\nXbuWr371q3i9XjZt2kRtbS179+6lubmZ4uJivvKVr9DR0cGNN97If/7nf/Liiy9y9913EwwG2bFj\nBwA7duzgy1/+Mh/4wAd45plnsG2bd999lxtuuIG5c+eya9cufvGLX1BRUcFPf/pTHnzwQVwuF+vW\nrQPgueeeG/GxxpK+4UTglOqyGxemSbSokGhRYc9gh61teLq6KO4OUfQ/DUQLCwiVFBPROD1yArnC\nEfwtLfha2rKtyDG/L11gXnTK/9tdsGABAGVlZcT6fd/1NnPmTHw+H5ZlEQwGCQQCQN95/TL7CgQC\nzJkzJ7sci8U4ePAgb731Fn/84x9xHAfbtuno6GD9+vU88MADtLa2ZodauvXWW/nf//t/09jYyOLF\ni3Ech9LSUh5++GG8Xi/d3d3k5+dTX1/P6aefjifdovg3f/M3AKM61lhSoJJTzyneZTfeHMsiPK2E\n8LQSzFgcX1tq8FBvRyfejs5UMXtxEaGSYl12LuPCSCRTP3fNrXjCqQLzpMuiq6w0VWDu807wGU5e\nHo+H5uZmKisr2b9/P2VlZcdsM1jp9VCTIs+ZM4fy8nKuvPJKYrEYjz76KD6fj5dffpk77rgDgGuu\nuYbzzz+f5557jnXr1uF2u7n11lt5++23uffee7n99tuZPXs2P/nJT2hqaqKiooLDhw8Ti8VwuVz8\n8Y9/ZPbs2aM6Vnl5+Rh8aikKVHJSU5fdxLI9brrLy+guL8MVjuBrbUuNzt7Sir+llaTbRbg4NXho\nIjNNh8jxcBw8Xd2p1qi2DgwnVWAeKSwgFCw5YQXmU5lhGFRXV3PPPfcwffp0SktL+6wbaHmo53r7\n5Cc/yaZNm/jyl79MKBTiU5/6FG63m4KCAm644Qby8vL40Ic+RHl5OXPnzuVLX/oSPp+PsrIyFi9e\nzIUXXsjXv/51CgoKKCsro729naKiImpqarjxxhspKCjIBqvhjuX1erPHGku6yk9ODuqymzrSV1b5\nWtvwtbVhJlNTd8S9vYrZT5GiYMmdFYvhSwf0zFWniTwPofQI5rbbPcFnKOMlmUzy+OOPc9VVVwFw\n4403snr1as4888xxO6au8pOTi7rspjbDIBbIJxbIp71iJt6OztT4Vh2dFP61icK/NhHNzyccLCZc\nVKRBFOVYto23vQN/S2tPgblpZkNULN+vf+enAMuyiEQifP7zn8fj8VBVVTWuYWo4aqGSSe2EzGUn\nk4KRSOJrb8fX0kZedzeQalGMFBYQLilWl43gDoVTXXqtPS2b0Xx/qkuvWAXmMv409YxMfuqyk16s\nWAxfazu+1lbckdREtLZlpgYPLSkmlq9i9lOFkUjgT9fducMRIDWQbKY1ShN3y4mkQCWTyxSZy04m\nAcdJzauWLma34ql51RJud7beSldsnYTSk3T7W1rxtvcqMC8qJBQsIVpYoO8BmRAKVDJh1GUnYyZz\nFVdrG962dkw7U8zuJRQsJlxcjO1RAfJUZkVj+Fta8bW04oqnCszjeXmEppUQLilWgblMOAUqGX/q\nspMTKT3qta+lFW9HJwbgALFAfmrwUNXTTBmGbeNta08VmHelauds0yRcUkQoGCTu19yQMnkoUMnY\ncBwM28ZIJjETSdyRiLrsZMIZiQS+tnZ8rW3kdYeAdDF7USGhkmKiBQEVs082jpMuMG9NFZjbmQLz\nfELTSogUFeFY+n82lRw6dIjOzk6WLl060acyrjRsgqT0DkRJGzOZTC8ney3bAz6XWR4sCvUMjNkT\noGy3W+FJxp3jchEqnUaodBpWNJYdPNTX1o6vrR3bsggXFxEKFhP363L6iWQmEtkxozIXGyTdLjrL\npqUKzPNOzQLz/KPP4+16a0z3GQmcSXfpJ8Z0n0N55ZVXKCkpOekD1VAUqKaScQxEg7FNE8eySLpd\n2N48HMvCTt+SeZ5seFL3ikwGyTwPXTPK6Zpehjscwdfaiq+1nfzmFvKbW0h4ehWze1XMfkI4Dnkd\nnT0F5qRaEFMhtyTVgqiQOyHq6up47bXXiEajNDQ0UFNTw44dO1i3bh2VlZU888wztLa2cvHFF7Nx\n40bKy8tpampixYoVHDp0iAMHDnDeeefxqU99irq6OtxuN2eccQabNm1i6dKlHDx4kMrKSoLBIHv3\n7sXj8XDnnXcSiUTYtGkTHR0dAHzxi19k7ty51NbW0tDQQCwWo7q6mgsvvJAHH3yQN954A9u2+ehH\nP0pNTQ179+7loYceAiAcDnP77bdTUVHBww8/zG9+8xuKi4uJRqNcd911LFiwYMTHypUC1Yk0yQKR\nY5np+97PWdi9ntcXnUxJhkHc7yPu99ExayZ5nV2pwUPbOyhoOkJB0xFiPl86XBWp2HkcWJFodooh\nK5G6OjPu9fYUmLv06yeju/QTJ7Q1qbdQKERtbS319fWsX7+eadOmDbhdY2MjmzdvJhwOc+WVV/Lk\nk0/i8Xioqalh1apVXHLJJQSDQRYtWkQoFOLCCy9k8eLFfPazn2XNmjVcd9113HTTTbz77rvs2rWL\nZcuWcdlll1FfX09tbS21tbW89dZb3HvvvQC8/vrrALz44ovcfffdBINBduzYAcC7777L7bffTjAY\nZPv27bz00kuce+657N69mwceeIBYLMaqVasA2L59+4iPlSv9RI+GApHI1GMYRAsLiBYWYCRtvB0d\nqXqrjk484TCFDX8lWhBIDR5aVKjW1hwYySTetg78LS3ZejbbNOkuDaYKzDVf46SzYMECAMrKyoj1\nu4iot5kzZ+Lz+bAsi2AwSCAQAMAcpD4xs99AIMCcOXMAsvPtHTx4kD179vDSSy/hOA6dnZ34fD7W\nrFnD5s2bCYVCXHDBBQCsX7+eBx54gNbWVpYvXw5AaWkp3//+9/H5fBw9epQlS5bw3nvvsWjRIiA1\nwfMZZ5wBMKpj5erUClQKRCKnNMcys11+ZjyBry1Vb+Xt7MLb2YVtporZwyXqihoxx8EdCuFvbsXX\naziLaCCQao0qKtRFAVOIx+OhubmZyspK9u/fT1lZ2THb9L6WLbNsGEaf5weaLDmzfs6cOXzgAx/g\n/PPPp62tjeeff56Wlhb279/Pxo0bicVi1NTUcMEFF/Dyyy9zxx13AHDNNdewYsUKNm/ezPbt2/H5\nfNx5550AzJ07l6effhqAWCzGgQMHRnysK664gosuumjQcDhSUytQKRCJyBix3S66y0rpLivFikbx\nt6TClb+1HX9rO0mXRbg4Fb506f6xzHg89Zm1tOKOpgrME2433WWl6QJzTXA91RiGQXV1Nffccw/T\np0+ntLS0z7qhls844wzuv/9+Zs+ePey2n/nMZ9i0aRPPPvssoVCIa665hmAwSEtLC2vXrsWyLK64\n4gpcLhcFBQXccMMN5OXl8aEPfYjp06dz4YUX8qUvfQmfz0dJSQlHjx5l7ty5LF++nBtuuIGioiLc\nbjcul2tEx6qpqck5TMEkGDah+Y9/OiGByB4w/CgQiUgv6cv5M1cKZoYCSeR5CKVbtk7VK9EAcBy8\nHZ34m1vIy4z/ZRiEiwoJTQsSC2hKIJkYbW1tvPzyy3zqU58iHo9z3XXXsWXLlgFb2HIxqYdNmHbo\nvSHXq4VIRE4YwyCe7yee76ejYiZ5HZ3ZYvbCxvcpbHyfmN9HqKSESEnRKVNY7YpEUl16rW3ZAvOY\nz0toWpBwcTGOS3VnMrGKior405/+RF1dHYZhcOmll455mBrOhLdQtb+xd8iQpEAkIhPNSCbxtqeL\n2Tu7siOzRwsLUoOHFhXinGR1QkYyia+tHX9zC55QGEgN1hsqKSYULCHh903wGYqceBopXURkjJjx\nOL7WdnytrXjCESDVkh4pKiQcLCYamMLF7I6DpzuEv7kFb3s7pp2alDhaECA0LUiksEAF5nJKU6AS\nERkHrkgkW2/liqUm8026XIRLigiXlEyZYQLMWBx/ayv+5tbs/JsJj4dQsIRQsBjbowJzEVCgEhEZ\nX+mWncyUN2a6mD2el0c4WEy4uHjyXfWWnmDa39yS7ca0DYNIegRzFZiLHEuBSkTkRLFtvL1GZjfS\nX7HRfH9qDKziIpwJLGZ3hSP4W1rwtfRcxRjz+wgFg4RLijSwqcgQFKhERCZAauTwdvytbXi6urPD\nDEQKCggHi09YTZKRSOJra8Pf3IonnCowT7oswiUlqQJzn+Y1lLGTmQPws5/97Jjts6WlhUceeYQb\nb7xxwPWxWIydO3dy6aWXjtkxBzKph00QETlZOZZFeFqQ8LQgZiyeCjUtbfg6OvB1dGCbJuHiIsIl\nxWPfxeY4eLq6U61RbamWMgeIFBYQCpaowHwSeaPxcf6743djus/KwuX87YyVY7rPiRQMBgcNU5AK\nXM8///y4B6qhKFCJiJwAtsdNd3kZ3eVluMKR9KjsbeS3tJLf0krS7SZUkgpXCd/xD0lgxWL40pMS\nZwrlE3l35G7sAAAgAElEQVQeQsFgqsBcE0FLWl1dHa+99hrRaJSGhgZqamrYsWMH69ato7KyMtvS\ndPHFF7Nx40bKy8tpampixYoVHDp0iAMHDnDuueeyevVq3nrrLX74wx9SWFiIaZosXrwYgJ/97Gfs\n2rULwzA4//zzufzyy3nllVd44okncLvdTJs2jQ0bNtDe3s53vvMdurq6APja177GCy+8wNtvv00k\nEuErX/kKtbW13HvvvVxzzTUsXbqUd999l8LCQm6//XYeffRR3nvvPR555BEuv/xyvv3tb9Pd3Y1t\n26xatYq//du/5brrruO0007D7XZz+eWX86Mf/QiXy4XX6+XrX/86vhz+3YEClYjICZfween0zaBz\n5nQ8Xd3ZYvaC949S8P5R4l4v4ZJiQiVFI7vCzrbxtnfgb2ntKTA3TbqDJYSDJcTy/Sown8T+dsbK\nCWtNCoVC1NbWUl9fz/r165k2bdqA2zU2NrJ582bC4TBXXnklTz75JB6Ph5UrV7J69WruueceNm7c\nSEVFBXfffTcA7733Hr/61a/4wQ9+gOM43HLLLZxzzjn86le/oqamho9+9KPs3LmTrq4uHnnkET7y\nkY/wyU9+kn379vGnP/0JgNNPP501a9bQ2NiYnbomGo1y4YUXcuaZZ3L//ffz85//nKuuuop3332X\nq6++mvvuu49zzjmH6upqjh49ype+9CUee+wxwuEwn/3sZ5k/fz733XcfK1as4F/+5V/47W9/S1dX\nlwKViMiUZRjECgLECgK0nzYLb2Zk9o5OCv/aSMFfG4kF8lPF7EVFx4xI7g6FU116rW2YyfSkxPn+\nVJdesQrMZXgLFiwAoKysjFh6yIyBzJw5E5/Ph2VZBINBAoEA0DM/X2trKxUVFQAsWbKEhoYGDh06\nRFNTEzfffDOO49DV1UV9fT033HAD27dv52c/+xmzZ8/mIx/5CP/93//NJz7xCQAWL17M4sWLeeih\nh6isrDzmXFwuF2eeeSYAf/M3f8Pu3bv7rH/vvfe44IILACgtLSU/P5/W1laA7P6uuuoqHn30UW6+\n+WZKS0uzLWq5UKASEZkMTJNIcRGR4iKMRAJfWwe+1lbyurrJ6+qm6H8aiBQWEC4pxorH8be04k4P\nLJp0uegsn5aalNh7Cs81KDnzeDw0NzdTWVnJ/v37B5y+ZaBr2UpLSzl8+DCzZ8/mz3/+MwUFBVRW\nVjJ37lzuvPNOAJ588knmz5/Ps88+y7XXXktRURFbtmzhN7/5DXPmzOGPf/wj8+bNY+/evbz22mvk\n5eX1mVw5I5FIcPDgQebNm8fbb7/N6aefjmmaJNNXrc6ZM4c333yTBQsWcOTIETo7OyksLAR6AuDO\nnTu55JJLuP7663nsscf4+c9/zr/+67/m9NkpUImITDKOy0WoNEioNIgVjeFra8PX0oavvQNfe0dq\nG0hNShwsIVpYoC49yZlhGFRXV3PPPfcwffp0SktL+6wbaDlj3bp1fOc73yE/Px+/309BQQHz58/n\nrLPO4otf/CLxeJyqqipKS0upqqritttuw+/34/f7Oe+88/jwhz/MXXfdxQsvvIBhGNx6663s2LFj\n0HN9/PHHaWpqYvr06axevRrbtkkmk2zbto2rrrqK2tpaXn75ZWKxGLfccguWZfU570WLFrFp0ya8\nXi+maXLzzTfn/vlp2AQRkSnAcVLF7O3t2JZFuEQF5nJqWrlyJY888giuCRjPTcMmiIhMdYZBwu+j\nU5MSyynOMIwBux0n2ohaqNrb27ntttu44447ME2TrVu3YhgGlZWVrF69GoAXXniBXbt2YVkW1dXV\nLFu2bEQnoBYqERERmQpyaqHK9Enm5aUKHR9++GFWrlxJVVUV27ZtY/fu3SxcuJC6ujpqa2uJRqNs\n2LCBpUuXTkhznIiIiMiJNuwwuY888ggXXXQRJSUlABw6dIiqqioAzjrrLN58803eeecdFi1ahGVZ\n+P1+ZsyYweHDh8f3zEVEREQmiSED1UsvvURhYSFLly7NPmfbdnbZ6/USDoeJRCL4/f4+z4dCoXE4\nXREREZHJZ8g+uV/96leYpslbb73Fu+++yw9/+EM6Ojqy6zNByufz9QlQ/QPWUIbqjxQRERGZCoYM\nVN/4xjf6LH/uc5/j0UcfZd++fSxevJg9e/awZMkS5s+fzxNPPEEikSAWi1FfX8/s2bNHdAIqShcR\nETl5xGIxdu7cyaWXXkpdXR1FRUWcd955E31aY2JMh024+uqruf/++0kmk1RUVHDuuediGAYf//jH\nueOOO4DUGBEqSBcRERleYf1f8ba1j+k+I8VFdFTMHNN9jlRLSwvPP/88l156KZdccsmEnMNE0MCe\nIiIiE2iiAlVdXR2vvfYa0WiUhoYGampq2LFjB+vWraOyspJnnnmG1tZWLr74YjZu3Eh5eTlNTU2s\nWLGCQ4cO8c477/DhD384O3xSxne/+11eeuklrrjiCmzbpqSkhNmzZ/PYY4/hdrs5cuQIn/zkJ9mz\nZw8HDx6kurqayy67jDfeeIMf//jHWJbFrFmzWLduHTt37uQXv/gFANdccw2//OUvaWhoIBaLUV1d\nzYUXXjimn9twNLCniIjIJNVRMXPCWpNCoRC1tbXU19ezfv16pk2bNuB2jY2NbN68mXA4zJVXXsmT\nTz6Jx+OhpqbmmEB11VVX8e6773L11Vfz0EMPZad8OXr0KA8++CB/+tOf2LhxI9u3b+fIkSNs2LCB\nyy67jC1btvCDH/yAoqIifvzjH1NXV4dlWRQWFvLNb36TcDjM5s2buffeewF4/fXXx/fDGSUFKhER\nkVPUggULACgrKyMWiw263cyZM/H5fFiWRTAYJBAIAGCaJpFIhK997WsYhsHZZ5/Nxz72sQH3MXfu\nXEzTJBAIMGvWLCzLIhAIEIvFaGtro7m5mW984xs4jkMsFuPss89m1qxZVFZWAuDz+VizZg2bN28m\nFApxwQUXjPGnkRsFKhEREQHA4/HQ3NxMZWUl+/fvp6ys7JhtelcKOY6D1+vl7rvvzj73/vvv9xli\naSD9q42Ki4spKyvjW9/6Fn6/n1dffRW/309jY2O2haulpYX9+/ezceNGYrEYV1xxBRdddBGmOeyQ\nmieEApWIiIhgGAbV1dXcc889TJ8+ndLS0j7rhlvOKC4uJpFIsG3bNjwez6DH6m/t2rXcdttt2LZN\nIBDga1/7Go2Njdn1wWCQlpYW1q5di2VZ1NTUTJowBSpKFxERERmRoYrSJ0+0ExEREZmiFKhERERE\ncqRAJSIiIpIjBSoRERGRHClQiYiIiORIgUpEREQkRwpUIiIiIjlSoBIRERHJkQKViIiISI4UqERE\nRERypEAlIiIikiMFKhEREZEcKVCJiIiI5EiBSkRERCRHClQiIiIiOVKgEhEREcmRApWIiIhIjhSo\nRERERHKkQCUiIiKSIwUqERERkRwpUImIiIjkSIFKREREJEcKVCIiIiI5UqASERERyZEClYiIiEiO\nFKhEREREcqRAJSIiIpIjBSoRERGRHClQiYiIiORIgUpEREQkRwpUIiIiIjlSoBIRERHJkQKViIiI\nSI4UqERERERy5BpuA9u2uf/++2loaMA0TT73uc/hcrnYunUrhmFQWVnJ6tWrAXjhhRfYtWsXlmVR\nXV3NsmXLxv0NiIiIiEy0YQPV66+/jmEYfPOb32Tfvn089thjAKxcuZKqqiq2bdvG7t27WbhwIXV1\nddTW1hKNRtmwYQNLly7F5Rr2ECIiIiJT2rBp50Mf+hBnn302AEeOHCEQCPDWW29RVVUFwFlnncXe\nvXsxDINFixZhWRZ+v58ZM2Zw+PBh5s2bN77vQERERGSCjaj5yDRN7r33Xnbv3s26det48803s+u8\nXi/hcJhIJILf7+/zfCgUGnbfs2bNOo7TFhEREZk8Rtwft2bNGtrb2/na175GLBbLPp8JUj6fr0+A\n6h+wBtPQ0DDKUxYRERE58YZqBBr2Kr9XXnmFp59+GgC3241pmsyfP599+/YBsGfPHqqqqpg/fz5/\n/vOfSSQShEIh6uvrmT179hi9BREREZHJy3Acxxlqg2g0ytatW2lra8O2bf75n/+ZiooK7rvvPpLJ\nJBUVFXzhC1/AMAxefPFFdu7cCcDll1/O8uXLhz0BtVCJiIjIVDBUC9WwgWq8KVCJiIjIVJBTl5+I\niIiIDE2BSkRERCRHClQiIiIiOVKgEhEREcmRApWIiIhIjhSoRERERHKkQCUiIiKSIwUqERERkRwp\nUImIiIjkSIFKREREJEcKVCIiIiI5UqASERERyZEClYiIiEiOFKhEREREcqRAJSIiIpIjBSoRERGR\nHClQiYiIiORIgUpEREQkRwpUIiIiIjlSoBIRERHJkQKViIiISI4UqERERERypEAlIiIikiMFKhER\nEZEcKVCJiIiI5EiBSkRERCRHClQiIiIiOVKgEhEREcmRApWIiIhIjhSoRERERHKkQCUiIiKSIwUq\nERERkRwpUImIiIjkSIFKREREJEcKVCIiIiI5UqASERERyZEClYiIiEiOFKhEREREcuQaamUymeRH\nP/oRR44cIZFIcPnll3PaaaexdetWDMOgsrKS1atXA/DCCy+wa9cuLMuiurqaZcuWnZA3ICIiIjLR\nhgxUv/71rykoKGDt2rV0d3fzla98hdNPP52VK1dSVVXFtm3b2L17NwsXLqSuro7a2lqi0SgbNmxg\n6dKluFxD7l5ERETkpDBk4jnvvPM499xzAbBtG8uyOHToEFVVVQCcddZZ7N27F8MwWLRoEZZl4ff7\nmTFjBocPH2bevHnj/w5EREREJtiQNVR5eXl4vV7C4TBbtmyhpqYGx3Gy6zPrIpEIfr+/z/OhUGj8\nzlpERERkEhm2T+7o0aNs3ryZiy++mI985CM8+uij2XWZIOXz+foEqP4BayizZs06jtMWERERmTyG\nDFRtbW38x3/8B6tWrWLJkiUAzJ07l3379rF48WL27NnDkiVLmD9/Pk888QSJRIJYLEZ9fT2zZ88e\n0Qk0NDTk/i5ERERExtlQjUBDBqqnn36aUCjEU089xVNPPQXAtddey49//GOSySQVFRWce+65GIbB\nxz/+ce644w4AVq5cqYJ0EREROWUYTu+iqAmgFioRERGZCoZqodLAniIiIiI5UqASERERyZEClYiI\niEiOFKhEREREcqRAJSIiIpIjBSoRERGRHClQiYiIiORIgUpEREQkRwpUIiIiIjlSoBIRERHJkQKV\niIiISI4UqERERERypEAlIiIikiMFKhEREZEcKVCJiIiI5EiBSkRERCRHClQiIiIiOVKgEhEREcmR\nApWIiIhIjhSoRERERHKkQCUiIiKSIwUqERERkRwpUImIiIjkSIFKREREJEcKVCIiIiI5UqASERER\nyZEClYiIiEiOFKhEREREcqRAJSIiIpIjBSoRERGRHClQiYiIiORIgUpEREQkRwpUIiIiIjlSoBIR\nERHJkQKViIiISI4UqERERERypEAlIiIikiMFKhEREZEcKVCJiIiI5Mg1ko0OHDjAY489xr//+7/T\n2NjI1q1bMQyDyspKVq9eDcALL7zArl27sCyL6upqli1bNq4nLiIiIjJZDBuonnnmGV555RW8Xi8A\nDz/8MCtXrqSqqopt27axe/duFi5cSF1dHbW1tUSjUTZs2MDSpUtxuUaU10RERESmtGG7/GbMmMEt\nt9ySfXzw4EGqqqoAOOuss3jzzTd55513WLRoEZZl4ff7mTFjBocPHx6/sxYRERGZRIZtQlq+fDlH\njhzJPnYcJ7vs9XoJh8NEIhH8fn+f50Oh0IhOYNasWaM5XxEREZFJZ9R9cqbZ06iVCVI+n69PgOof\nsIbS0NAw2lMQEREROeGGagQa9VV+c+fOZd++fQDs2bOHqqoq5s+fz5///GcSiQShUIj6+npmz559\n/GcsIiIiMoWMuoXq6quv5v777yeZTFJRUcG5556LYRh8/OMf54477gBg5cqVKkgXERGRU4bh9C6K\nmgDq8hMREZGpYEy7/ERERESkLwUqERERkRwpUImIiIjkSIFKREREJEe6FE9ERGQcOI5Dwo4QTXYS\nS3YRTXYRT4ZxmXm4TR8uy4vb9KWWTR+WqV/JU5n+74mIiAzDdmziyW6iya5UQEqkAlIsE5Z6PU7d\np262kxzxMUzDjdv04rZSActt+tLLPcEru87y9Qpj3uxjl+nDZeZhGMY4fhoyEAUqERE5pSTtWDb0\nRJNdfcJR38dd2dalWDIEjGyUIY+Vj8cK4HeXkmcVkGcF8LgC5FkB3KafhBMlkQwTtyPE7XB6OX1L\nRkjYYSKJDhJ25Ljen4GRDV29w1jvFrGe0HZsgMsEM7flxTQUE0ZKn5SIiExJA3WpRROd2dahnnDU\nt9UoYUdHtH8DizxXAK+rmKK80/D0C0f9H+dZBbitfExjbMqTHccmYUfTQSsVuBJ2Oogl08u9wlgi\nu00k+5pIop3OZCMOI28p680y3Me2iFnHtoz1PO/ts407Hcws4+RvNVOgEhGRCTfeXWouMw+PFSDg\nmZEOQwHyXAXZZU86EOW5epZdpndCQ4BhmKnAYvnAffz7cRwH24n3hK0+LWKZYBZJh7W+LWW9w1wk\n0T4mrWYDdVOOLJilXmca1vF/GONIgUrkFJC04z1/3SbDJJ04puHCMtyYhhvLdPd5bBrWSf/XpIyf\nE9ullgo/Pa1G6fteYSnPCmCZnvF905OYYRhYhif9GRTltC/bsUnYkQFaxyLHtJT1BLZIn/AWTrQR\nT4ZzaDXz9Oum9PYLZt5juzCzwcyb3c4yPGP6PadAJTJJ9W7u7/vldWxzf/8vrP5dBKMpjE0xsAxX\nr7Dl7vd48DBmmb22NdyYAz7u9Zr048GOp2A3cSZbl1qqJSl/0rZQnApMw8Rj+fFY/pxbzZJOPNs6\nduz3XHiIOrOers9IonXEP2/9GZhDXgDQu6UsE8xmzbp80P0pUImMsaSdGDDUDFf3EO/3V9/xNq2D\nkW1S97qKcJszjilINU03tpPAtuMknTi2k0jd93uctOPYTpykkyBuh0naHdl1I21NyJVpuAYJcJ7U\nfTaweVL32cDm7vd4gAA35LY9+0+12k3tYfvGu0vNMjzkuQr6dqmlu9V6d6n1rjea6C41mTiGYeAy\nPLhMD15Xrq1myT5dmcd0Yfb+jh3gOzluhwknWuiIhnGwhzzWh6sUqESG1Lc1qP8/zOGatSN9/mKy\nnfhxnUPvS6bzXIU9zdPH1BAM0axt+nCZHoxx/uXvOA4OyXTgSoexdABLOglsJ5a6HyqwZcNavN+2\n/R/3278dI+Z0Zx8P9wU4VgwsLLNXS5vh7vM4FdZ6r3f1am0bLMClQ2AmwJm9gmO//WeOZ2BhO3F1\nqU1CyWSSUChEd3f3oLdwOIzH4yEvL2/UN9Oc2qF+vJiGlf6Zzc9pP6lWs9gxdWbZ7/pkeMjXG47j\nnJg/MwfR0NAwkYeXKc52EgMGnkG7yPqFot5B6PhaXIx0sBni0uPe6/oUXvYe1M+LZebQfn4Ks51k\nOlwlsgHt2McjCXCpEJh5zrZ7Bblh9x8/jm7V42Ng4IzwZzXTpZYNQepSOy62bQ8ZlLq6ugiFQoRC\noSH343a78fl8xONxotEotj26Pwbcbjder5e8vDw8Hk92eSQ3t1vd52Nh1qxZg65TC5WccKm6jGhP\n3c8IihiP6T5LN+cmj7s1yMJt+nGZPvI95X0KFQdqGerfl+7KBqG8cW8NkqGZhoVpWLgm+H+D49jp\n1rmRBbikHRuw5a5vS1+81/qex5bhHqSlSF1qo2HbNuFweMiQ1N3dTSgUYqi2B5fLRSAQoLi4mEAg\nQH5+/oA3j6enFc9xHBKJBNFodNBbJBIhFotllzPPd3R0EIvFRvVeTdPMtox5vd5RBzLLUsgejlqo\nZFCOY6e/8BPpL/qeW6qYMNqnKbR/y1DvUNS7Ky1hh0f8F3Z/mYLBzKBz/cdFGbSLLL19ZlmtQSIn\nL8dxBg1K/QPTUL8CLcsiPz9/yJCUCUonOrjatp0NWyO59Q5k0WiUZHJ0LaoulysbrkYbyCbi8xkv\naqGapIYLLLaTxLYzf7Ume/7yHWr79F/Cg23Xs33vxz2vTWaPlxiz2hQDK9vyk+8p7RmDZMAxRoYa\nuVetQSKnMsdxiEQiQ4akTIvSUN1ppmkSCASYMWPGoCEpEAhM6iBgmiZerxev13tcrx+udax/K1km\nkIVCIVpbW4cMov0ZhjFo3dhIQpnLNTWiytQ4y+PU0wTf/5ZpPu8dWAYKG72CxiQOLMej57L0npvb\n9JNnZQpkXRi912P12dY0LCyj1yi5fcKQP3vvsXypK6RUTCkig3Ach2g0OmRIytyGC0r5+fmUl5cP\nGpLy8/PJyzv5R+0ejsvlwuVykZ8/+kJux3FG3DrWv9uyra2NeHx0pRqWZQ0YxHq3kg3WYubxeE7Y\n758JD1S/fP1+HJI4hp26JxU0UsupG0ayz3MYvdYbNvR6fWqdDUYSjAnszbRNcDI3AxwLx07d47hT\nz9kmTmYb20gt2wY4ZnrbzPPpbXutSy0bOHa/x8nUfhw7dQ62beAk6Xld0kj/ZWFk/8Lofz+4ZPqW\nGfOj47g+mswXWe8vtP7PDXc/knXH85r+rz3eczhR5328rzUMA7fbjdvtxuVy4fF4cLlc2ef63zLr\nTvVfQjI6mV+8Q3W5ZQLTUF1QhmGQn59PWVnZkEHJ61XN2IlgGEY2sByPZDI5qq7KTCALh8O0t7eP\nuph/oNaxTCAbrgvT5XKN+GdqwgNVq/c3x/W6VDDoCRqZMOLYFjguHPvYMNI3mPSEkczySLYnHV56\njndsyDEwgWN/uQ30i3qoX5Qj+SVtAOZA25oGmCPf3/GcFxwbxnqHst7PDfb8UK8Zyf1Itsn84xvN\nOYzmOIM9dzLqH7qGC2EjDWqWpZHZp5qBglL/kNTd3U0ikRh0H4Zh4Pf7mTZt2qAhKT8/H5/Pp5+P\nk4hlWfj9fvx+/6hf6zhO9irJ0d7a29tH3TpmmmafgHXTTTcNuu2EB6oP+K5MD5rnSl+t48LE1eux\nO3ufGoPFlZ0WY7Qh5HhDw2iDj8hYBMHjCXUjfU3mS6n3LZFIHPNc73WxWKzPNl1dXcTj8VH/tTiQ\n3i1muQS1gdapu3l04vH4oFe79Q5Kw/1i8vv9lJSUDBqSMkFJ/39kNDL1WB6Ph4KCglG/3rbtbMDq\nXRs2kltHx9C9MrrKT0Rykkwmhw1jowlqvdeNxdeTZVkjDmqjCXGj6QqYDBKJxLAhqbu7e9jL8X0+\n34Ahye/391lWUJKTjeM4VFRUDLp+wluoRGRqsywrWzQ6lhzHIZlMjiqoDRXQ4vE44XCYzs7OIbuh\nRmO4IDbY+uG6Q0cz5k8ikThm0Mn+Iam7u5todOj5zrxeLwUFBUMOE+D3+zUekZyyhvsDSoFKRCYl\nwzCyVyL5fL4x3bdt28eErlxa0jJFs2PVBWqa5qBhy+Vy9emWi0SGnvMxLy8ve+XbUEFpqlyaLjJZ\nTfi/oKTtYJlTp9lcRKa+zKjRvUeuHiv9W9Vy7Q7NFH/3r1nyeDxDXvmWuSkoiZwYE/4v7f/56Z8p\n9buZHnAzI+ChPOBmer6bGQWp+4I8Xf0jIlNHpgv0eAdcHExmqpJ4PJ5trRKRyWPCA9X8oJemrjh7\nG0Ps5diJJb0uMx223Nmw1Tt8eSd6Ai8RkROg95WQIjL5TJqr/CIJm/e74jR1xWnqjqXue93CiYHr\nEoq8FjMCbqbnp1u30uFresBNqd+t7kQREREZE0PN5TdpAtVQHMehM5qkqTveL2jFaOqO835XnOQA\n78I0oCw/1arVE7Y8TE+3dBV51Z0oIiIiIzPlA9VwkrZDSziRDVmNXamQ1dQdp7ErTmt44Euk8ywj\nFa4CbsoDnnRLV+axG79blweLiIhIykkfqIYTTdgcSbduNXbFeb+7b/Dqjg/cnViYZ6XCVX6mG9GT\nDWClfjduS61bIiIip4pTPlANpyvdndjYlardej8dvJrS4SthH/sRmQZM87nSAasnaE3PdzO9wEOJ\nuhNFREROKpM6UO3+f98FAwxI/weM7L3R7/HA96QnCe69n/73PdsbfV8/5GsMHBzaIwmOhBIcCcU5\n2p3g/e4YR7oTvB9KdSdm2rec7L2D2zIo8/fUbmVbugpS4Svfo+5EERGRqWRSB6r77943kYefELbj\nZEMepIKdQWai5VTrF8bwgbIn/B0bKAd/XU+gHHSbAfbbfxvDAJfLwO1J39y9lj0GbreJ22Pg8RhY\nLk0cLSIiU99QgWrCx6E68+zUlBKZWNdzn1lIt/wMcN97u2Nf3+s++5qht+17DGfIY/ffbrD9Jm2H\nWNIhlrBT90mHeNImnnSIZy5NdCAdXUjnGVyGgcsycBsGlmngMg1cJliGgWkYfd+/42AP9f6HeU8D\nbj+GDINjQpfHozAmIiInjwkPVKcvGNsJVacSx3FojSRTwz/0Hg6iO05TZ4zmcIIByrdwmQbl+e5+\ndVupsbimB9wEPGbOIWQkQTURd4jH0re4QzxmE4v1f67v43DIZjRTnQ0UxvoEMoUxERGZBCa8y28y\nFKVPVvGkw9HQseNuZR53RJMDvi7fbfbUbuX3LZovz3eTN4GjyzuOQzLJMUFrJGEsHnMUxkREZMJM\n6hoqBarjF4one40u3y94dcWJDjTaKVDic2XH2+pzy/cwze+atKPLT4Yw5nYbePIUxuTEcxwHxyH1\nc+yQrrPsqcHsXXcpIuPjhAUqx3F48MEHee+993C73Vx//fVMnz59yNf8979+YqwOL704QLvlp8lT\nxPueomPuj7gLsY1jW6pcdpLSeAflsXamx9uZHmtPLadvBckwU/Er2wFsw03c5SNu+VM3l4+4lbn1\nfuxP3acfJyw/tjny3nHDSeJOhHElw7iTYdzJUOo+0Xs5fZ9Zn0gtW3Z0Sn6+J4NUuaWJbbqwDQvH\ncK9w/wIAAA8GSURBVGEbLmzTwjas1LJh9Vufft7MLKceO2av7fu8zoWT2c7sWe9k95PZPnMMq8++\nGeDfbN83YWPgYKSLRw3HwcAGx0k/n0pjfZaP2Sb9Wuxe+0ntF6ff8322cdLb2KPaT5/XZNcNcX4D\nvR/HGcH5DfYejt1Pat1g52eP7DVDHDN1XPoUrRq9rhPXd0BKv08r/ZyBk70Kf7B1Pc+nPtX0toZx\nzDonfRXWgPsCMPoe5+z/9cSg5zumNVS7d+8mHo/zrW99iwMHDvDQQw9x6623juUhZIQMoDgZojgc\n4gPhvx6zPonBUXch73sKe4Wt4tSyu4g3C04fcL/eZIzpsXbK4u3k2Yn0lwykvjx6vhRSxfWpynez\n149j5jmj14+u0evLJbtN5vle22afd3ods8/z/V57zDlljkO/8w5jJEIY8b77NNLHcgDDMDFMD5ge\nDCN1j5W+Nz04Zl52GdND3Mwj7sr//9u719goqr+B498zbWmFUto/C16AEgsoRaAoBQXNY9SAUUiE\nFwQxgBfuFwmKRSj6QFNLRKQSpUUCFf5gkNgAXuILDWopEQNySQoUgVhLY7AFoe2T0m5hZ+Z5sbvT\nnb2Uwm4v4O+TbHcu58yZM2fPzG9nOzPUx/4H1E3cIsPUUcY1NL0RZTSi6U40oxFNb0QznO533UmU\n4XTP0xuJMhqJ0hvQjGto/tsl2LY2A7edZtq3UbPb2sSe1zpg3Hhbm2igNEwVjak08AQKZsArGkPT\nMHGnM7Roa167BiytRJk6mqGjTBea6UIzdaLM62h6A5qpo5kudxrThWboeE9RmUrzHBA0z8HE0wpK\nYaI1jXvS+s5zHyi8y4h2T1cKPPlMzykw23J8yhNtw/p/1qYpQYa8w/bzI83NN4OkcZcH3s7re7ol\nWPpgw74luHu+57MaZA3dAYvfhVJgBUVB1y9Y2WZz2yTYcPDt1HxZ7r/DCS2iAdXvv//OsGHDABgw\nYABlZWU3zBP3v8t8xgJPlqkg0wInteQkW0tPxN3ism5pnULUryXlBSS5+fp1Be4PsezregP/53RR\n26hT2+ii1qm7x506tY0613UFuJ96b91OAe976Ha03bIhyDop/3cVOM/bHbxd1fQs19ttA8sOXK+m\ndb659E1pdKABaPBZ1+DL1HB3tGgUUSj3FZwoonFftdk0z311Z5R3HgrbYUt5g5VOQCcgwWeme5sY\nmOiYuDCtYe/L8EzXPWk1T101QFPurWebhum+lYd3GOV5d1+VqinvsGdZqmmZvi9vHbzzCdiuTUE4\nAfODf4OH6yh3BEywzw+YPm1mYt+FeoaV6TvWtNv3WSfrs6awL0P5rov/NJ86qeBpVEB6z/byme/7\n5YQgny0TZXV7w3NU8n51MXHvErxpTE+a0NM8L9Od1zD9x1VTGp9ybeG26fcN33TXwvDWwjZf+eyy\nfMJw7xHWKse+R7CduzFV0PKaxjVb3sA03iO5bUtbW9+7vsoq15vensY/n7cO3iu3Mf2W6zOmBfl8\nu3Pa95cKE5S9JO90/31lwGfH+x6kr1n5VeD+zn8ZvutoXxf/8zpBluc/33+aCpxvG/btN/jW03dh\ngfsR//y2OvjVx54/2L7fOy10SBXRgKq+vp7OnTtb41FRURiGgaaF/jaTdKEgkqsgIsjhHdCAzp6X\nECIkwy/QcA9jBQb2MMwzzSfQ8J/vc/cYezBB04HM/+CnfAJdhWmNe/N00H+RFOK2F9GAqnPnzjid\nTmv8RsEUQPT//DeSqyCEEEII0eYi+kP4gw8+yLFjxwA4e/YsycnJkVy8EEIIIUSH1CpX+VVUVAAw\nb968Zi8xFEIIIYS4E7T7faiEEEIIIW53cu2rEEIIIUSY2v1ZfneCc+fOsXPnTlauXEl5eTlbt25F\n0zRiYmJYuHAhCQkJAXlqa2tZtmwZ7777Lvfddx+VlZXk5+ejlKJPnz7MnDnzhmUBlJeX8/7773Pv\nvfcCMHbsWEaNGtV6lb3DtEXb6brOxo0buXTpEi6Xi4kTJ5Kent7iNhehtWf7Sd8LT1u0nWEYbNq0\niQsXLqBpGrNmzaJ3797S9yKgPduvo/Y9CajC9M0331BcXExcXBwA27ZtY8aMGSQnJ7Nv3z6++uor\npk+fbsuj6zqbN28mNrbpwdDbt29nypQppKamsnnzZn777TdGjBjRbFkAZWVljB8/nvHjx7diLe9M\nbdV2Bw4coGvXrixcuJC6ujqWLl1Kenp6i9pchNbe7Sd979a1VdsdPXoUpRTZ2dmUlpbyxRdfkJGR\nIX0vTO3Vfjt37mTp0qUdtu/JT35huueee3jrrbes8cWLF1tXN+q6TqdOnQDYsGEDly9fBmDHjh2M\nHTuWpKQkK19ZWRmpqakAPPzww5w4cSIgn39Z3nzHjx9n5cqVfPrpp7bbVojmtVXbjRo1ismTJwPu\nCzeioqKazSdapiO0n/S9W9NWbTdixAhmz54NwMWLF+nSpUuz+UTLtFf7xcfHW/k6Yt+TgCpMI0eO\ntHawAImJiQCcOXOG77//nnHjxgGwcOFCunfvTlFREQkJCQwdOjTkMuPi4qivr7flC1YWQP/+/Zk6\ndSpZWVn07NmTwsLCiNbvTtZWbRcbG0tcXBwNDQ3k5uYyZcoUwP64BN98omXau/2k7926ttxvappG\nXl4e27Zt44knngCk74Wrvduvo/Y9+cmvFRw8eJC9e/eyfPlyunbtapv3888/o2kaJ06coLy8nLy8\nPDIyMmxPiXc6nbY7zjdn5MiRVtqRI0eydevWyFXkX6i12u6ff/5h3bp1PPvss4wePRrAdtPbm2lz\nEVpbtp/0vchqzf3mggULqK2tJTMzk9zcXOl7raCt2u+jjz7qsH1PAqoIKy4u5scff2TVqlXW6WVf\nWVlZtuHZs2eTmJjI/fffT2lpKYMGDeL48eMMHjw4ZBm+365ycnJ47bXX6NevHydPniQlJSWyFfoX\naa22q6mpIScnhxkzZtjm3Uybixtr6/aTvhc5rdV2xcXFXLlyhQkTJhATE4OmaWiaJn0vwtqy/ZRS\nHbbvSUAVQYZhsG3bNnr06MGHH34IwKBBg5g0aRIbNmxgypQp1mlML29wNG3aNDZt2oSu6/Tq1YvH\nHnsMIGg+36h+1qxZFBQUEB0dTWJiInPmzGntat6RWqPt8vLyePHFF/n222+pr69n9+7d7N69G4DM\nzMyQ+cTNa4/2k74XGa2533z00UfJz89n5cqVGIbBK6+8QkxMjPS9CGqP9uuofU9u7CmEEEIIESb5\np3QhhBBCiDBJQCWEEEIIESYJqIQQQgghwiQBlRBCCCFEmCSgEkIIIYQIkwRUQgghhBBhkvtQCSFu\nK/n5+ZSUlNCtWzdM00TXdfr27cv06dOtR2CEcvHiRXbs2MGSJUuaTZebm0tVVRWmaXL+/HmSk5NR\nShEfH8/rr79Obm4u2dnZkayWEOI2J/ehEkLcVvLz80lOTrY9aX7v3r38+uuvrFmzxnbjW3+nTp1i\n69at1g0IW2Ly5MkUFBRYD2YVQohg5AyVEKLFnE4n+fn5VFVVoZQiJSWF2bNnc+TIEfbu3YvL5SI2\nNpZp06YxYMAACgsLOXv2LDU1NfTp04fTp0+TkZFhPSpi/fr1PPTQQ4wZM4Y9e/Zw+PBhTNOkR48e\nzJw5k8TERLKysujSpQt///03Y8aMCbpeEydOpKioiJKSEtLS0tizZw9Hjhzh+vXrNDY2Mm3aNIYP\nH86mTZuorq5m9erVZGZmcubMGXbu3EljYyNKKSZNmsQjjzzS7Da4dOkSS5YsYfv27RQWFlJVVUVV\nVRXV1dX079+foUOHsn//fi5dusTUqVOtZ/+Fqp8Q4s4gAZUQosUOHz5MY2Mja9aswTAMtmzZQmVl\nJbt27WLVqlXEx8fz119/kZ2dzSeffAK4Hyycm5uLUorCwkKKiopISUmhrq6OEydOMGfOHIqLi6mo\nqGD16tVomsa+ffvYuHEjy5cvByA+Pp5169YB7jNUwfTt25eKigp69erFqVOnyMrKIiYmhoMHD/Ll\nl18yYsQI5s6dy2effUZmZiZXr15l48aNvPPOOzgcDqqrq8nMzOS9994LeFSGP9+zYGfOnGHt2rVo\nmsbcuXPp3r07WVlZHDlyhB07djB69Gj279/fbP2EELc/CaiEEC02cOBAdu3aRVZWFkOGDOH555+n\npKSEmpoasrOzrWd0aZpGZWUlAAMGDLACkKeeeorly5fz8ssv88svvzB8+HDuuusujh49yh9//MGy\nZcsA97O+rl27ZpWbmpp6w3VTShEbG4vD4WD+/PkcOHCAyspKzp07h9PpDEh/9uxZqqurWbt2rW29\nz58/f8OAyteQIUOIi4sDICkpiWHDhgFw9913c/XqVQCOHTvWbP2EELc/CaiEEC3Ws2dPPv74Y0pL\nSzl58iTZ2dmMHTuWwYMHs3jxYivd5cuXSUpK4tChQ1awAeBwOEhJSeHo0aMUFRXx6quvAu4HrL7w\nwgvWT3oul8sKRgDbMkIpKyvjueee488//+SDDz5g/PjxpKWlMWjQILZs2RKQ3jAMevfuTU5OjjWt\nurqabt263dQ2iY6270ajoqKCluVfv7q6upsqRwjRscltE4QQLfbDDz+Qn5/P0KFDeemll0hLS6Oi\nooKSkhIuXLgAuM/GZGRk4HK5gi7j6aef5uuvv+batWs88MADAKSlpfHTTz/R0NAAwK5du9iwYUOL\n1skwDAoLC0lISGDgwIGcPn2afv36MW7cOFJTUzl8+DCGYQDuM1C6rgPuM2eVlZWcPn0agPLychYt\nWsSVK1dufQOFEKx+eXl5ES9HCNF+5AyVEKLFnnzySUpLS3njjTeIi4vD4XAwb948SkpKWL9+PeA+\nQ/P222/TqVOnoMtIT0+noKCACRMmWNOeeeYZqqurWbFiBUopHA4HCxYsCLke3333HQcOHADcAVW/\nfv2s/0d6/PHHOXToEG+++SYxMTEMHjyYuro6nE4nffr0QSnFihUryMnJYcmSJXz++efWz2+LFi3C\n4XDc8vYJdYVhsPrNnz//lssRQnQ8ctsEIYQQQogwyU9+QgghhBBhkoBKCCGEECJMElAJIYQQQoRJ\nAiohhBBCiDBJQCWEEEIIESYJqIQQQgghwiQBlRBCCCFEmP4fx7PFxn9F+FAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdff045a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dframe.plot()\n",
    "legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xdfefd70550>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6DgAACBLhWZxatpIZNFw6dEDatNbpOAAAIEiEZXGSJHP1OElVC2L66fp4AAAQ5MK3OKWk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\nLmNizlDSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfefd70160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dframe.resample(\"1D\", how=\"sum\")\n",
    "dframe['numcycles'].plot()\n",
    "legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfefddd6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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s5K233iIWi9Hc3Ex9fT0vvvgia9eupaamhueee45QKMRll13G+vXrqaqq4siR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfefc59668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure();dframe.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdfefe14eb8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sFwDgDwQnBD2G9QIA/IXghKDGsF4AgD8RnBC0GNYLAPA3ghOCEsN6AQCBQHBC\n0GFYLwAgUAhOCCp29neyZj0j5R6V0fVOGT37M0IFAOA3BCcEjQuH9RrdehKaAAB+RXBCULD3filr\nziSp4EzxsN5fdQt0SQCAMERwguMxrBcA4BQEJzjaecN6Hx0roxXDegEAgUNwgmMxrBcA4DQEJzgO\nw3oBAE5FcIKjMKwXAOBkBCc4BsN6AQBOR3CCIzCsFwAQDAhOCDiG9QIAggXBCQHFsF4AQDAhOCFg\nGNYLAAg2BCcEBMN6AQDBiOAEv2NYLwAgWBGc4FcM6wUABDOCE/yGYb0AgGBHcIJfMKwXABAKCE7w\nOYb1AgBCBcEJPsOwXgBAqCE4wScY1gsACEUEJ3gdw3oBAKGK4ASvYlgvACCUeRScRo4cqZiYGElS\nfHy8unXrpqlTpyohIUGS1LVrV3Xu3Nl3VSIoXDysd7SM6JhAlwUAgNe4DU7nzp2TJI0bN67ka+vW\nrVP37t3VvXt331WGoMKwXgBAOHAbnDIzM1VYWKjJkyfLsiz17t1b+/bt0+HDh7VlyxYlJCSoX79+\nio6O9ke9cCCG9QIAwoVh27Zd3gYHDx7U119/rZtuukmHDx/WlClT1KNHDzVt2lRNmzbVm2++qdOn\nT6tv377lnigrK8urhcMZGNYLAAg1iYllfwrc7R2nxMRE1a9fX5KUkJCg2NhYtW3bVnXq1JEkdezY\nUYsXL65UEQhOZ7/5j44+P0Y6kada9z+q2F79CE0AgJDmNjitW7dOBw8eVFpamnJzc3XmzBnNmDFD\nAwYMUIsWLbRr1y41a9bM7Ym44xRaLhzWe+pnt+jU4cOBLgsAgEor72aP26W6oqIipaen69ixYzIM\nQ/fee6+qVKmiRYsWKTIyUrVr19agQYPcvseJ4BQ6zhvWO2AIw3oBACGlUsHJWwhOoeG8Yb0PjWZY\nLwAg5FTqPU7AjxjWCwAIdwQnuMWwXgAAihGcUC6G9QIA8D8EJ5TKtm3p692y/v43aedWhvUCACCC\nEy5gnzsre/NHstetkQ7uK/7iFSkyHxnDsF4AQNgjOEGSZOcek73x77I3vS+d+kEyTOma62Xe1F26\n8moaWwIAIIJTWPtxOc7+cI3sbZ9KliVVj5Vx6+9k/Oo2GXXrBbpEAAAcheAUhkpdjmvYRMZN3WVc\n90sZVaoGtkAAAByK4BRGWI4DAKByCE4hjuU4AAC8h+AUoliOAwDA+whOIYblOAAAfIfgFAJYjgMA\nwD8ITkEoJNQ5AAAN+ElEQVSM5TgAAPyL4BSEWI4DACAwCE5BguU4AAACj+DkcCzHAQDgHAQnh2I5\nDgAA5yE4OQjLcQAAOBvByQFYjgMAIDgQnAKI5TgAAIILwcnPWI4DACB4EZz8hOU4AACCH8HJx1iO\nAwAgdBCcfIDlOAAAQhPByYtYjgMAILQRnLyA5TgAAMIDwekSsRwHAED4IThVEMtxAACEL4KTh8pc\njuvSXbqC5TgAAMIBwakcLMcBAICfIjiVguU4AABQGo+C08iRIxUTEyNJio+PV48ePZSeni7DMJSU\nlKS0tDSfFukvLMcBAIDyuA1O586dkySNGzeu5GvTp09Xnz59lJycrIULF2rLli3q0KGD76r0IZbj\nAACAp9wGp8zMTBUWFmry5MmyLEu9e/fW/v37lZycLElq166dduzYEXTBieU4AABQUW6DU5UqVfSb\n3/xGN910kw4fPqwpU6YU36X5r+joaOXn5/u0SG9iOQ4AAFwqt8EpMTFR9evXlyQlJCQoNjZW+/fv\nL3m8oKCg5P1P7o4TKLZt62zGdp1cvUJnPlkvWS6ZsbVUvef9qnF7L0XG1w9YbQAAIHi4DU7r1q3T\nwYMHlZaWptzcXJ05c0atW7dWRkaGUlJStG3bNrVs2dLtibKysrxScEWUtxyn636p/CpVlV9kSQGo\nDQAAOFN5N3sM+6frbqUoKipSenq6jh07JsMwlJqaqtjYWM2fP18ul0sNGjTQoEGD3C5x+TM4lboc\n164Ty3EAAMCtSgUnb/F1cCrz03E/78qn4wAAgMfKC05B3wCTT8cBAAB/CdrgxKfjAACAvwVVcKJZ\nJQAACKSgCE4sxwEAACdwdHBiOQ4AADiJ44ITy3EAAMCpHBOcWI4DAABOF/DgxHIcAAAIFgEJTizH\nAQCAYOTX4MRyHAAACGZ+C07WqqUsxwEAgKDmt+Bkv7uS5TgAABDU/BacjPseYzkOAAAENb8FJ/Pn\nXf11KgAAAJ8wA10AAABAsCA4AQAAeIjgBAAA4CGCEwAAgIcITgAAAB4iOAEAAHiI4AQAAOAhghMA\nAICHCE4AAAAeIjgBAAB4iOAEAADgIYITAACAhwhOAAAAHiI4AQAAeIjgBAAA4CGCEwAAgIcITgAA\nAB4iOAEAAHgo0pONTpw4oVGjRukPf/iDzp49q6lTpyohIUGS1LVrV3Xu3NmnRQIAADiB2+Dkcrm0\ncOFCVa1aVZK0b98+de/eXd27d/d5cQAAAE7iNjgtXbpUXbt21apVqyQVB6fDhw9ry5YtSkhIUL9+\n/RQdHe3zQgEAAAKt3Pc4bdiwQTVr1lTr1q1LvtaiRQulpqZqwoQJio+P18qVK31eJAAAgBOUe8dp\n/fr1Mk1TO3fu1IEDBzR37lyNGDFCtWrVkiR17NhRixcv9uhEiYmJla8WAAAggAzbtm1PNpwwYYIG\nDhyouXPnqn///mrRooXee+89HT9+XPfee6+v6wQAAAg4jz5V91MDBw7UokWLFBkZqdq1a2vQoEG+\nqAsAAMBxPL7jBAAAEO5ogAkAAOChCi/VhbOvvvpKy5Yt07hx43TgwAEtXrxYpmkqKipKjz32mGrW\nrHnRPj9tHpqYmKjs7Gylp6fLMAwlJSUpLS3N7bkk6cCBAzQerSR/XD+Xy6V58+bp6NGjKioqUo8e\nPdS+fXuPrztKF8hrx3Ov8vxx/SzL0oIFC5SVlSXTNDVw4EA1bNiQ514lBfLaOfW5R3Dy0OrVq7Vp\n06aSnlVLlizRgAED1KhRI33wwQd66623dN999523z4XNQyXp1VdfVZ8+fZScnKyFCxdqy5Yt6tCh\nQ7nnkmg8Wln+un4fffSRYmNj9dhjj+nUqVMaMWKE2rdv79F1R+kCfe147lWOv67f559/LsMwNGnS\nJGVkZGj58uUaPnw4z71KCNS1W7ZsmUaMGOHY5x5LdR6qX7++hg0bVvL3wYMHq1GjRpKKf1GqVKki\nSZozZ46OHz8u6X/NQy+77LKS/fbt26fk5GRJUrt27bRz586L9rvwXD/ut23bNo0bN07z589XQUGB\nj77T0OSv69e5c2fdfffdkiTbthUREVHufnDPCdeO596l89f169Chgx588EFJ0pEjR1S9evVy94N7\ngbp2NWrUKNnPic89gpOHOnbsWPJCKkm1a9eWJO3Zs0fvv/++br/9dknSY489prp165baPPRC0dHR\nys/PP2+/0s4l0Xi0svx1/apWraro6GidOXNGM2fOVJ8+fSQV/0Nc2n5wL9DXjude5fjztdM0Tc2d\nO1dLlizRz372M0k89yoj0NfOqc89luoq4ZNPPtGqVas0evRoxcbGnvdYac1Dhw8fLsMwSrYpKChQ\nTEyMR+fq2LFjybYVaTyKsvnq+h07dkwvvPCCbrnlFl1//fWSil8U3O0Hz/nz2vHc8z5fvnY++uij\nOnHihMaMGaOZM2fy3PMyf127WbNmOfa5R3C6RJs2bdKHH36o8ePHl9wS/qkJEyac9+cHH3xQtWvX\nVtOmTZWRkaGUlBRt27ZNLVu2LPMcP/2f0uTJk/XAAw+oefPm2rVrl5o1a+bdbyjM+Or6ff/995o8\nebIGDBhw3mMVue4on7+vHc897/LV9du0aZNyc3N15513KioqSqZpyjRNnnte5M9rZxiGY597BKdL\nYFmWlixZonr16un555+XJKWkpKhXr16aM2eO+vTpU3L78Uc/hqC+fftqwYIFcrlcatCggTp16iRJ\npe7305RO41Hv8cX1mzt3rnr37q01a9YoPz9fb7zxht544w1J0pgxY8rcDxUTiGvHc897fPnaed11\n1yk9PV3jxo2TZVnq16+foqKieO55SSCunVOfezTABAAA8BBvDgcAAPAQwQkAAMBDBCcAAAAPEZwA\nAAA8RHACAADwEMEJAADAQ/RxAuBI6enp2rFjh2rVqiXbtuVyudS4cWPdd999JaMfynLkyBEtXbpU\nQ4cOLXe7mTNnKicnR7ZtKzMzU40aNZJhGKpRo4Yef/xxzZw5U5MmTfLmtwUgyNHHCYAjpaenq1Gj\nRudNRl+1apU+/fRTTZs27bwGsRf68ssvtXjx4pJGfZ64++67tWjRopIBowBQGu44AbhIQUGB0tPT\nlZOTI8Mw1KxZMz344IPaunWrVq1apaKiIlWtWlV9+/bVFVdcoZUrV2rv3r36/vvvlZSUpN27d2v4\n8OElIxJmz56tq6++WjfffLPefPNNbd68WbZtq169ekpLS1Pt2rU1YcIEVa9eXYcPH9bNN99cal09\nevTQhg0btGPHDrVp00Zvvvmmtm7dqnPnzqmwsFB9+/bVtddeqwULFigvL09TpkzRmDFjtGfPHi1b\ntkyFhYUyDEO9evXSNddcU+7P4OjRoxo6dKheffVVrVy5Ujk5OcrJyVFeXp5atGih1q1ba+PGjTp6\n9KhSU1NLZtuV9f0BCA0EJwAX2bx5swoLCzVt2jRZlqVXXnlF2dnZWrFihcaPH68aNWro0KFDmjRp\nkl566SVJxQNyZ86cKcMwtHLlSm3YsEHNmjXTqVOntHPnTg0aNEibNm3SwYMHNWXKFJmmqQ8++EDz\n5s3T6NGjJUk1atTQCy+8IKn4jlNpGjdurIMHD6pBgwb68ssvNWHCBEVFRemTTz7R66+/rg4dOuih\nhx7Sn/70J40ZM0anT5/WvHnzNHbsWMXFxSkvL09jxozRs88+e9GIiAv99K7Wnj17NGPGDJmmqYce\nekh169bVhAkTtHXrVi1dulTXX3+9Nm7cWO73ByD4EZwAXOSqq67SihUrNGHCBLVq1Uq33XabduzY\noe+//16TJk0qmUFlmqays7MlSVdccUVJ0Ljxxhs1evRo3X///fr444917bXXqlq1avr888/1zTff\naNSoUZKKZ1mdPXu25LzJycluazMMQ1WrVlVcXJweeeQRffTRR8rOztZXX32lgoKCi7bfu3ev8vLy\nNGPGjPPqzszMdBucfqpVq1aKjo6WJF122WVq27atJOnyyy/X6dOnJUlffPFFud8fgOBHcAJwkfj4\neP3xj39URkaGdu3apUmTJqlr165q2bKlBg8eXLLd8ePHddlll+mzzz4rCRWSFBcXp2bNmunzzz/X\nhg0b1L9/f0nFg0J/+9vflizFFRUVlYQOSecdoyz79u1Tt27dtH//fk2fPl3du3dXmzZtlJKSolde\neeWi7S3LUsOGDTV58uSSr+Xl5alWrVoV+plERp7/chkREVHquS78/k6dOlWh8wBwNtoRALjIP/7x\nD6Wnp6t169a655571KZNGx08eFA7duxQVlaWpOK7K8OHD1dRUVGpx7jpppv09ttv6+zZs7ryyisl\nSW3atNG6det05swZSdKKFSs0Z84cj2qyLEsrV65UzZo1ddVVV2n37t1q3ry5br/9diUnJ2vz5s2y\nLEtS8R0ll8slqfhOWHZ2tnbv3i1JOnDggJ544gnl5uZe+g+oDKV9f3PnzvX6eQAEDnecAFzkl7/8\npTIyMvTUU08pOjpacXFxevjhh7Vjxw7Nnj1bUvEdl5EjR6pKlSqlHqN9+/ZatGiR7rzzzpKvdenS\nRXl5eXr66adlGIbi4uL06KOPllnHO++8o48++khScXBq3rx5yfuFbrjhBn322WcaMmSIoqKi1LJl\nS506dUoFBQVKSkqSYRh6+umnNXnyZA0dOlSvvfZaybLZE088obi4uEv++ZT1ib7Svr9HHnnkks8D\nwHloRwAAAOAhluoAAAA8RHACAADwEMEJAADAQwQnAAAADxGcAAAAPERwAgAA8BDBCQAAwEP/H8al\n28pewOz9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdff05ef0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dframe.plot(x='serverDateTime',y='numdescriptors')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting with Seaborn\n",
    "### Scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xdff08dff28>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xdff08dfcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('numtimeouts','nummessages', data=dframe, fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Density Plot. Kernel density estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdff0a8eac8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hc/qc9sILL+DxeNi4cSPbt2/noYceYvny5YwdO/a09zuxl0wcapeTqU0G5la7BNuP0tiy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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdff0a96be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(dframe.nummessages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdff09785c0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MmMD4pOH+9g+PbCK/4RCToofx46zAcqdH8bC8dD2f1+wi1mDjjpEXc1lqDtGG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6Uh02joayO9MV+2PUBvxlerp7RVuTHXe6jc0aOgrQfQ3dODj4vm/pFIvSbu9Jw8eLiYtLeLWV13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\nQuiQJGNCCCGEEDr0/wGX4KUiZMG0QAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdff09c7630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(dframe.nummessages, dframe.numtimeouts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdff09d0550>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R0bWfphfKzs6O6HmxRw7hvPAk9OtP3dV/e/pjH6zCOVTd9j6G5UHJBhrLSqhe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdff0be1a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dframe.nummessages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GeoxhaZh6Rpr+eU05la4tY4U9L6uvdnjI68YHr+ejwQf/DPK+IeOqpaUlBw8eHLz9wbBK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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdff0c06f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dframe.nummessages, alpha = .3)\n",
    "sns.rugplot(dframe.nummessages);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Violin Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdff0b1a160>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pGADQLihMaE+UJvyLwNgJKjztXNMxAKBNir73fQXGHW46BrIIpQl7lHfk8cpj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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdff0b81978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot([dframe.numtimeouts,dframe.nummessages])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdff0baea90>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LxYzVasVqsxGSIwE2m4O5kGLFarMSYrFkt01Jcbe1hJhztDVzISV/E7CLzp6/QPy+JMYM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\nCYaIiIh43P8BxDbl5rlngaEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdff0bba860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap([dframe.numtimeouts,dframe.nummessages], annot=True, fmt=\"f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting with Pandas Highcharts \n",
    "- conda/pip install pandas_highcharts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script src=\"//code.highcharts.com/stock/highstock.js\"></script>\n",
       "<script src=\"//code.highcharts.com/highcharts-more.js\"></script>\n",
       "<script src=\"//code.highcharts.com/modules/exporting.js\"></script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.compat import StringIO\n",
    "from pandas.io.common import urlopen\n",
    "from IPython.display import display, display_pretty, Javascript, HTML\n",
    "from pandas_highcharts.core import serialize\n",
    "from pandas_highcharts.display import display_charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"chart_idtUOxGCjp\"</div>\n",
       "    <script type=\"text/javascript\">new Highcharts.Chart({\"chart\":{\"renderTo\":\"chart_idtUOxGCjp\"},\"series\":[{\"data\":[[1424868012000,0.4],[1424868017000,0.6],[1424868022000,1.0],[1424868027000,1.6],[1424868032000,0.6],[1424868037000,1.2]],\"yAxis\":0,\"name\":\"numtmessages\"},{\"data\":[[1424868012000,48],[1424868017000,54],[1424868022000,67],[1424868027000,60],[1424868032000,78],[1424868037000,65]],\"yAxis\":0,\"name\":\"numdescriptors\"},{\"data\":[[1424868012000,17.8],[1424868017000,16.4],[1424868022000,0.4],[1424868027000,6.4],[1424868032000,4.2],[1424868037000,3.4]],\"yAxis\":0,\"name\":\"numtimeouts\"},{\"data\":[[1424868012000,5.0],[1424868017000,5.0],[1424868022000,5.0],[1424868027000,5.0],[1424868032000,5.0],[1424868037000,5.0]],\"yAxis\":0,\"name\":\"interval\"},{\"data\":[[1424868012000,439],[1424868017000,454],[1424868022000,499],[1424868027000,413],[1424868032000,505],[1424868037000,569]],\"yAxis\":0,\"name\":\"num-timers\"},{\"data\":[[1424868012000,48.2],[1424868017000,49.4],[1424868022000,48.4],[1424868027000,37.8],[1424868032000,62.0],[1424868037000,46.6]],\"yAxis\":0,\"name\":\"nummessages\"},{\"data\":[[1424868012000,34.6],[1424868017000,3.4],[1424868022000,2.4],[1424868027000,1.4],[1424868032000,3.6],[1424868037000,4.4]],\"yAxis\":0,\"name\":\"numcycles\"}],\"title\":{\"text\":\"logs\"},\"xAxis\":{\"type\":\"datetime\",\"title\":{\"text\":\"serverDateTime\"}},\"legend\":{\"enabled\":true},\"yAxis\":[{}]});</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_charts(dframe, title=\"logs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"chart_idFnoXvRoI\"</div>\n",
       "    <script type=\"text/javascript\">new Highcharts.Chart({\"chart\":{\"type\":\"column\",\"renderTo\":\"chart_idFnoXvRoI\"},\"series\":[{\"data\":[[1424868012000,0.4],[1424868017000,0.6],[1424868022000,1.0],[1424868027000,1.6],[1424868032000,0.6],[1424868037000,1.2]],\"yAxis\":0,\"name\":\"numtmessages\"},{\"data\":[[1424868012000,48],[1424868017000,54],[1424868022000,67],[1424868027000,60],[1424868032000,78],[1424868037000,65]],\"yAxis\":0,\"name\":\"numdescriptors\"},{\"data\":[[1424868012000,17.8],[1424868017000,16.4],[1424868022000,0.4],[1424868027000,6.4],[1424868032000,4.2],[1424868037000,3.4]],\"yAxis\":0,\"name\":\"numtimeouts\"},{\"data\":[[1424868012000,5.0],[1424868017000,5.0],[1424868022000,5.0],[1424868027000,5.0],[1424868032000,5.0],[1424868037000,5.0]],\"yAxis\":0,\"name\":\"interval\"},{\"data\":[[1424868012000,439],[1424868017000,454],[1424868022000,499],[1424868027000,413],[1424868032000,505],[1424868037000,569]],\"yAxis\":0,\"name\":\"num-timers\"},{\"data\":[[1424868012000,48.2],[1424868017000,49.4],[1424868022000,48.4],[1424868027000,37.8],[1424868032000,62.0],[1424868037000,46.6]],\"yAxis\":0,\"name\":\"nummessages\"},{\"data\":[[1424868012000,34.6],[1424868017000,3.4],[1424868022000,2.4],[1424868027000,1.4],[1424868032000,3.6],[1424868037000,4.4]],\"yAxis\":0,\"name\":\"numcycles\"}],\"title\":{\"text\":\"rate\"},\"xAxis\":{\"type\":\"datetime\",\"title\":{\"text\":\"serverDateTime\"}},\"legend\":{\"enabled\":true},\"yAxis\":[{}]});</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_charts(dframe, kind=\"bar\", title=\"rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"chart_idwKFPPDBq\"</div>\n",
       "    <script type=\"text/javascript\">new Highcharts.Chart({\"chart\":{\"type\":\"bar\",\"renderTo\":\"chart_idwKFPPDBq\"},\"series\":[{\"data\":[[1424868012000,0.4],[1424868017000,0.6],[1424868022000,1.0],[1424868027000,1.6],[1424868032000,0.6],[1424868037000,1.2]],\"yAxis\":0,\"name\":\"numtmessages\"},{\"data\":[[1424868012000,48],[1424868017000,54],[1424868022000,67],[1424868027000,60],[1424868032000,78],[1424868037000,65]],\"yAxis\":0,\"name\":\"numdescriptors\"},{\"data\":[[1424868012000,17.8],[1424868017000,16.4],[1424868022000,0.4],[1424868027000,6.4],[1424868032000,4.2],[1424868037000,3.4]],\"yAxis\":0,\"name\":\"numtimeouts\"},{\"data\":[[1424868012000,5.0],[1424868017000,5.0],[1424868022000,5.0],[1424868027000,5.0],[1424868032000,5.0],[1424868037000,5.0]],\"yAxis\":0,\"name\":\"interval\"},{\"data\":[[1424868012000,439],[1424868017000,454],[1424868022000,499],[1424868027000,413],[1424868032000,505],[1424868037000,569]],\"yAxis\":0,\"name\":\"num-timers\"},{\"data\":[[1424868012000,48.2],[1424868017000,49.4],[1424868022000,48.4],[1424868027000,37.8],[1424868032000,62.0],[1424868037000,46.6]],\"yAxis\":0,\"name\":\"nummessages\"},{\"data\":[[1424868012000,34.6],[1424868017000,3.4],[1424868022000,2.4],[1424868027000,1.4],[1424868032000,3.6],[1424868037000,4.4]],\"yAxis\":0,\"name\":\"numcycles\"}],\"title\":{\"text\":\"rate\"},\"xAxis\":{\"type\":\"datetime\",\"title\":{\"text\":\"serverDateTime\"}},\"legend\":{\"enabled\":true},\"yAxis\":[{}]});</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_charts(dframe, kind=\"barh\", title=\"rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"chart_idScUBPSDP\"</div>\n",
       "    <script type=\"text/javascript\">new Highcharts.Chart({\"chart\":{\"height\":700,\"type\":\"column\",\"renderTo\":\"chart_idScUBPSDP\",\"width\":1000},\"series\":[{\"data\":[[1424868012000,0.4],[1424868017000,0.6],[1424868022000,1.0],[1424868027000,1.6],[1424868032000,0.6],[1424868037000,1.2]],\"yAxis\":0,\"name\":\"numtmessages\"},{\"data\":[[1424868012000,48],[1424868017000,54],[1424868022000,67],[1424868027000,60],[1424868032000,78],[1424868037000,65]],\"yAxis\":0,\"name\":\"numdescriptors\"},{\"data\":[[1424868012000,17.8],[1424868017000,16.4],[1424868022000,0.4],[1424868027000,6.4],[1424868032000,4.2],[1424868037000,3.4]],\"yAxis\":0,\"name\":\"numtimeouts\"},{\"data\":[[1424868012000,5.0],[1424868017000,5.0],[1424868022000,5.0],[1424868027000,5.0],[1424868032000,5.0],[1424868037000,5.0]],\"yAxis\":0,\"name\":\"interval\"},{\"data\":[[1424868012000,439],[1424868017000,454],[1424868022000,499],[1424868027000,413],[1424868032000,505],[1424868037000,569]],\"yAxis\":0,\"name\":\"num-timers\"},{\"data\":[[1424868012000,48.2],[1424868017000,49.4],[1424868022000,48.4],[1424868027000,37.8],[1424868032000,62.0],[1424868037000,46.6]],\"yAxis\":0,\"name\":\"nummessages\"},{\"data\":[[1424868012000,34.6],[1424868017000,3.4],[1424868022000,2.4],[1424868027000,1.4],[1424868032000,3.6],[1424868037000,4.4]],\"yAxis\":0,\"name\":\"numcycles\"}],\"title\":{\"text\":\"rate\"},\"xAxis\":{\"type\":\"datetime\",\"title\":{\"text\":\"serverDateTime\"}},\"legend\":{\"enabled\":null},\"yAxis\":[{}]});</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_charts(dframe, title=\"rate\", legend=None, kind=\"bar\", figsize = (1000, 700))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"chart5\"</div>\n",
       "    <script type=\"text/javascript\">new Highcharts.Chart({\"chart\":{\"type\":\"column\",\"renderTo\":\"chart5\",\"zoomType\":\"xy\"},\"series\":[{\"data\":[[1424868012000,0.4],[1424868017000,0.6],[1424868022000,1.0],[1424868027000,1.6],[1424868032000,0.6],[1424868037000,1.2]],\"yAxis\":0,\"name\":\"numtmessages\"},{\"data\":[[1424868012000,48],[1424868017000,54],[1424868022000,67],[1424868027000,60],[1424868032000,78],[1424868037000,65]],\"yAxis\":0,\"name\":\"numdescriptors\"},{\"data\":[[1424868012000,17.8],[1424868017000,16.4],[1424868022000,0.4],[1424868027000,6.4],[1424868032000,4.2],[1424868037000,3.4]],\"yAxis\":0,\"name\":\"numtimeouts\"},{\"data\":[[1424868012000,5.0],[1424868017000,5.0],[1424868022000,5.0],[1424868027000,5.0],[1424868032000,5.0],[1424868037000,5.0]],\"yAxis\":0,\"name\":\"interval\"},{\"data\":[[1424868012000,439],[1424868017000,454],[1424868022000,499],[1424868027000,413],[1424868032000,505],[1424868037000,569]],\"yAxis\":0,\"name\":\"num-timers\"},{\"data\":[[1424868012000,48.2],[1424868017000,49.4],[1424868022000,48.4],[1424868027000,37.8],[1424868032000,62.0],[1424868037000,46.6]],\"yAxis\":0,\"name\":\"nummessages\"},{\"data\":[[1424868012000,34.6],[1424868017000,3.4],[1424868022000,2.4],[1424868027000,1.4],[1424868032000,3.6],[1424868037000,4.4]],\"yAxis\":0,\"name\":\"numcycles\"}],\"title\":{\"text\":\"rate\"},\"xAxis\":{\"type\":\"datetime\",\"title\":{\"text\":\"serverDateTime\"}},\"legend\":{\"enabled\":true},\"yAxis\":[{}]});</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_charts(dframe, title=\"rate\", kind=\"bar\", render_to=\"chart5\", zoom=\"xy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n"
   ]
  }
 ],
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