Python Course Lecture 8b Time Data.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Introduction to Scientific Programming in Python\n",
    "## Lecture 8b: Time Data and Time Series Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Datetimes: Core Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The Datetime Package has a Datetime Type\n",
    "```python\n",
    "datetime(year, month, day[, hour[, minute[, second[, microsecond[,tzinfo]]]]])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "datetime(year=2016, month=6, day=6, \n",
    "         hour=12, minute=3, second=5, microsecond=1)\n",
    "# now = datetime.now()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Datetime Properties and Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 2016)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now = datetime.now()\n",
    "now.hour, now.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, '2016-06-07T13:23:00.791760')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now.weekday(), now.isoformat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Datetimes can be printed and read nicely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-06-07 13:23:00.821273'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'It is 13:23, on Jun 07, 2016.'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.now().strftime('It is %H:%M, on %h %d, %Y.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1986, 1, 14, 0, 0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.strptime('1.14.86', '%m.%d.%y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iFrame src=\"https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior\" width=900 height400></iFrame>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<iFrame src=\"https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior\" width=900 height400></iFrame>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Datetime Math produces TimeDeltas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(200, 38219, 125625)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now = datetime.now()\n",
    "christmas = datetime(2016, 12, 25, 0, 0, 0)\n",
    "christmas - now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'200 days, 10:36:59.125625'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(christmas - now)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## TimeDeltas Can Also Be Created Directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2016, 6, 8, 13, 23, 0, 874375)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "a_day = timedelta(days=1)\n",
    "now + a_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, datetime.timedelta(0, 0, 1))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_day.days, a_day.resolution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Dates and Times can also be Created Seperately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.date(2015, 4, 5), datetime.time(4, 2, 1))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import date, time\n",
    "date(2015, 4, 5), time(4, 2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-04-05 and 04:02:01\n"
     ]
    }
   ],
   "source": [
    "print(date(2015, 4, 5), 'and', time(4, 2, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Caution: There is also a different Time package, which is for measuring time passing on the computer, not representing data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.15702"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "time.clock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "waiting...done!"
     ]
    }
   ],
   "source": [
    "print('waiting...', end='')\n",
    "time.sleep(2)\n",
    "print('done!', end='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Datetime in NumPy, and Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## NumPy has a Datetime64 dtype, converted from strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2015-04-21')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2015-04-09') + 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2016-06-07T13:23:02.960833+0200',\n",
       "       '2016-06-07T13:23:03.960867+0200', '2016-06-07T13:23:04.960878+0200'], dtype='datetime64[us]')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mins = np.array([str(datetime.now() + timedelta(seconds=el)) for el in range(100)],\n",
    "               dtype=np.datetime64)\n",
    "mins[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Pandas Uses NumPy's Datetime dtype\n",
    "However, hdf5 files don't understand datetimes, so we need to convert after reading."
   ]
  },
  {
   "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>Time</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>Session</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136.783342</td>\n",
       "      <td>-0.168612</td>\n",
       "      <td>0.497566</td>\n",
       "      <td>-0.384482</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>137.616675</td>\n",
       "      <td>-0.146270</td>\n",
       "      <td>0.289156</td>\n",
       "      <td>-0.308755</td>\n",
       "      <td>1448890319922000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>138.450009</td>\n",
       "      <td>-0.043644</td>\n",
       "      <td>0.256933</td>\n",
       "      <td>-0.290230</td>\n",
       "      <td>1448890319922000000</td>\n",
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       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>140.116676</td>\n",
       "      <td>-0.051600</td>\n",
       "      <td>0.284099</td>\n",
       "      <td>-0.572804</td>\n",
       "      <td>1448890319922000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time         X         Y         Z              Session\n",
       "0    136.783342 -0.168612  0.497566 -0.384482  1448890319922000000\n",
       "100  137.616675 -0.146270  0.289156 -0.308755  1448890319922000000\n",
       "200  138.450009 -0.043644  0.256933 -0.290230  1448890319922000000\n",
       "300  139.283342  0.060798  0.259256 -0.454783  1448890319922000000\n",
       "400  140.116676 -0.051600  0.284099 -0.572804  1448890319922000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_hdf('rel_data.h5', 'behav')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### After Conversion..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>Session</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136.783342</td>\n",
       "      <td>-0.168612</td>\n",
       "      <td>0.497566</td>\n",
       "      <td>-0.384482</td>\n",
       "      <td>2015-11-30 13:31:59.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>137.616675</td>\n",
       "      <td>-0.146270</td>\n",
       "      <td>0.289156</td>\n",
       "      <td>-0.308755</td>\n",
       "      <td>2015-11-30 13:31:59.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>138.450009</td>\n",
       "      <td>-0.043644</td>\n",
       "      <td>0.256933</td>\n",
       "      <td>-0.290230</td>\n",
       "      <td>2015-11-30 13:31:59.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>139.283342</td>\n",
       "      <td>0.060798</td>\n",
       "      <td>0.259256</td>\n",
       "      <td>-0.454783</td>\n",
       "      <td>2015-11-30 13:31:59.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>140.116676</td>\n",
       "      <td>-0.051600</td>\n",
       "      <td>0.284099</td>\n",
       "      <td>-0.572804</td>\n",
       "      <td>2015-11-30 13:31:59.922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time         X         Y         Z                 Session\n",
       "0    136.783342 -0.168612  0.497566 -0.384482 2015-11-30 13:31:59.922\n",
       "100  137.616675 -0.146270  0.289156 -0.308755 2015-11-30 13:31:59.922\n",
       "200  138.450009 -0.043644  0.256933 -0.290230 2015-11-30 13:31:59.922\n",
       "300  139.283342  0.060798  0.259256 -0.454783 2015-11-30 13:31:59.922\n",
       "400  140.116676 -0.051600  0.284099 -0.572804 2015-11-30 13:31:59.922"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Session'] = pd.DatetimeIndex(data['Session'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Experiment Time is More Accurately Described As a TimeDelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>Session</th>\n",
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       "    <tr>\n",
       "      <th>Time</th>\n",
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       "      <th></th>\n",
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       "      <th>1970-01-01 00:02:16.783342</th>\n",
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       "      <td>2015-11-30 13:31:59.922</td>\n",
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       "    <tr>\n",
       "      <th>1970-01-01 00:02:17.616675</th>\n",
       "      <td>-0.146270</td>\n",
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       "      <td>-0.308755</td>\n",
       "      <td>2015-11-30 13:31:59.922</td>\n",
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       "      <th>1970-01-01 00:02:18.450009</th>\n",
       "      <td>-0.043644</td>\n",
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       "      <td>-0.290230</td>\n",
       "      <td>2015-11-30 13:31:59.922</td>\n",
       "    </tr>\n",
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       "      <th>1970-01-01 00:02:19.283342</th>\n",
       "      <td>0.060798</td>\n",
       "      <td>0.259256</td>\n",
       "      <td>-0.454783</td>\n",
       "      <td>2015-11-30 13:31:59.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:02:20.116676</th>\n",
       "      <td>-0.051600</td>\n",
       "      <td>0.284099</td>\n",
       "      <td>-0.572804</td>\n",
       "      <td>2015-11-30 13:31:59.922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   X         Y         Z  \\\n",
       "Time                                                       \n",
       "1970-01-01 00:02:16.783342 -0.168612  0.497566 -0.384482   \n",
       "1970-01-01 00:02:17.616675 -0.146270  0.289156 -0.308755   \n",
       "1970-01-01 00:02:18.450009 -0.043644  0.256933 -0.290230   \n",
       "1970-01-01 00:02:19.283342  0.060798  0.259256 -0.454783   \n",
       "1970-01-01 00:02:20.116676 -0.051600  0.284099 -0.572804   \n",
       "\n",
       "                                           Session  \n",
       "Time                                                \n",
       "1970-01-01 00:02:16.783342 2015-11-30 13:31:59.922  \n",
       "1970-01-01 00:02:17.616675 2015-11-30 13:31:59.922  \n",
       "1970-01-01 00:02:18.450009 2015-11-30 13:31:59.922  \n",
       "1970-01-01 00:02:19.283342 2015-11-30 13:31:59.922  \n",
       "1970-01-01 00:02:20.116676 2015-11-30 13:31:59.922  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_t = data.copy()\n",
    "data_t['Time'] = pd.to_datetime(data['Time'], unit='s')\n",
    "data_t = data_t.set_index('Time')\n",
    "data_t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Plotting Data with Time Axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0dd0829b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JOg/FLYYSVaFs3cpxA7nQmi40NXUtADhsGGd9Odnhh9deS95NI1V5bS0nE/zP//A5Ovdc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\ntU/5IgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0dc02e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sess = data_t[data_t['Session'] == data_t.ix[0, 'Session']]\n",
    "fg, ax = plt.subplots()\n",
    "sess.plot(y='X', ax=ax)\n",
    "# tick_labels = ax.xaxis.major.formatter.seq\n",
    "# ax.xaxis.major.formatter.seq = [label.split(':')[-1] if label else \"\" for label in tick_labels]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Resampling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.tseries.resample.DatetimeIndexResampler'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0d9c2ff28>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XvX9/fcE07eELOvp5+umhsU+c9WVd528i/t4PnzfvcVGcf9bif+CA/ty7dw8Wi6QNvp2d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\n7MKzxahRwUM624p92tp0z8Xa6Kcs4p9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PAgMiMsXurlsnrWNxqVLqNgCl1K3oGKHomByL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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0d9772dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resampler = sess.resample('5s')\n",
    "print(type(resampler))\n",
    "sess_r = resampler.bfill()\n",
    "sess_r.head()\n",
    "sess_r.plot(y='X')  # Timedeltas seem to still have some bugs regarding plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "del sess['Session']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Rolling Mean: rolling() works like agg() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0d9677f98>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7A716AS+9pPy5dTqgeHFOYPD3V/78riAhgWM/SUkuUfwXLwKNGvFbUqiQ7ev3\n7+ccB5eyciWbRevXO3wKczPlNeXyIqJMAG8B2AggBsAiIjolhHhdCDEsa9n/AJQCMF0IES2EcG1p\nqMQ2dDrgm29YeRgzbBgwf76bbH47OHOGfc72ZAEJwZ1d1RaYd+Vu3MuLXUcubjyoKCdPsmXlIivy\n11+BgQNtUyYAEBQEfPgh8Pbbbijp0bu8XHAhTSkUACCi9URUjYjCiWhi1mOziGh21u//IaIAImpI\nRA2IyMnB2RJFmDKFd/udO+d9LiSEA99q64Wlv+nYS+PGwKFDysvjKI8e8RZYH7dyBW5oPKgop08D\n1au75NSZmRwWfO01+4577z0uz1qwwCVi5VC+PA9Xc8H4Zs0pFIkGefgQGD+et23mdoTDhwMzZ7pX\nLmvExHDA2V4aNVKXhXLyJKczu7I2xg2NBxXlzBmXKdjNm9nraW+HG19f9gqPGuWGel8XbQCkQpG4\nnnXr+IYTFmZ+TffuvGNSU7bQyZOOKRS9y0st8bnjx13fAFFrCsWFFsq8ecDQoY4dW78+u73efVdZ\nmfKgD8wrjFQoaubiRR4vq3VWreKAsCUKFAB693YqUKg4jiqUChXYEtNX2Xsad/SrCg8H7twBbt50\n7XWUwkUK5eZNrtcdONDxc3z4IXtMXVrO1KwZZwAojFQoaiUykv33Vauqv9+VJXQ6VhK29Jdw09xr\nm0hL42LA8HD7j1VbYN4dFoqXl/pcfebQF/ZVqaL4qRcs4DRgZ5Ld/Py4Q86oUfz1cQmNGwNHj3Kb\nIAWRCkUNHDjA/7mGzJgBTJrEbdP//NMzcinBwYPsUA4Ksr62cWOuTFcDsbF8wylY0LHj1RSYd4GF\ncusWZ1PnQitur9hYzs31UbYMj8g5d5chAwaw0f7jj86fyyTFinGn6GPHFD2tVCieZs8ebuzTvn1O\ncVhSEve4evFF/nROn+7SGQYuZetW05ldpggJ4TuVGl7riROOubv0qGW3npDABaUK1sRkZvJ/aUgI\nMG2aQahIKwrFRQH5/fvZsFWiobMQHKCfOpXdZ7/8wk6Lq3nKuJ2geXPF3V5SoXiaTz8FfvqJazRe\neIFjJgsWcL9rf3/e6T73HNCxI99stcaxY3lnsJvDy4uLzdQQmD92zLlBVGoJzOvbiyhYbzFnDm9w\nd+3i3xctynrChfUNiuKi+IneOlHqrQ4LY4O9RQtWJv/7H3+VQkK4yD0lxckLNGsG7LNY+20/RPTE\n/vDL9yCQizFYAAAgAElEQVR37xIVLUqUkkKk0xGNHEkUGkoUEEB06FDOOp2O6O23ifr185ysjlK7\ndu7XYo0BA4j++MN18tjKs88SLVvm3DnKlye6cEEZeRzl+++J3nzT6dPodPx2nDpFVKYM0ZEj/Pja\ntUR16/LzpNMRVa7Mi9TMiy8S/faboqd88IDI35/o2jVFT5uHzEx+e/v0IfrySydPdvw4UXi4Q4dm\n3Tvz3FOlheJJ9u3jnayfH29rJk9mH8LJk7k73ArBUbo9ezgFd8IE3nVu2eI52W3h8WOek2GP6yg0\n1CUFV3bjrIUCqMPtdeAAWw5OMnUqMHgwG5CDBuW8NV278r/r14M/p08/zWlOasYFFsrff3NOSfny\nip42D15eLPr48cAPPzg5+aFGDU5OSEpSTj7FziQBwJmiNWsCtWtz5WtmpoXFxu4gLy92dZUpk3dt\n4cIcqO/bl4/r14/niaqZM2c4sG1r/wmA7fm4OJeJZIpp0zhj+bJ+MMLt25wC62wWkBoyvfbtY5+J\nE8TF8Q1s3z6uUf3++5znhAA++IBvbgDUr1CIXBJDmTfP/sp4Z6henadSz5jhxEm8vfkzqmA9ilQo\nCvPNN7xr++MP4PBhHv1hFnvbZ/fowW00Fi5kbXXokPr6XxniSHZRSIhbLZS1a9kwDA8HunXLylzS\n/794Ofn18HSm1/XrvIV1JPXZgIkTgTff5JtYkSJ5n+/dm8fZ3rkDjvXt2MHRaTVy9SpQtKiiTSzj\n49kQd+vkRfCclcmTecSQwygcmJcKRUGI2PR96y2gQQNO+ZswgXd1Jjl3zvGW7cWK8S5LLWm2pnCk\n/sGNLq/TpzmIOn8+Z2i3acN5EZnRCri7gByXl6eC1Pv2ceDViSjx9evcYu2tt8yvKVaMd8urVoEn\nSdWowe5ZNeICd9f69byJVDgL2Sq1a/Nn1uKm1RoKB+alQlGQU6d48xMSwn/XrQu0asXWikkuXQIq\nVzbzpA00bKh+hWKvhfLUU+xyevTINTJlcekSZ2tPnMjj7YXgDUBGBhCz8KgyhYDlyrGrMj7e+XM5\nwt69Tru7fviBO94HBlpe17s3sHp11h+dOqk3vucCd9f69fxZ8gRffMFTHxzOtG/WjF1eClVQSoWi\nIJGRQLt2uR975RWDtEpDHj8GEhP5BmoFnQ6YNYvz2xcuNHiiQQN1tww/csT+nb63NxdBuvAmvHUr\nj7QfNQr4v//LedzHh4cc6aKPQVdbocpyT7q99u1jl4aD3L/PacGjRllf26EDf/51OnBN1bZtDl/X\npShsoTx+zK/b1lIrpalRg9vgOTxKqFw5oEQJHnCnAFKhKEhkJO92DenShTfqeQqSrl7llBAb7OSh\nQ7mw6aWXgI8/NvCgVK3KVb9q5OpV/rbZUiFvjAsD848esZKfPZsHRRpTv04mqmbEICpVocpyT2V6\npadzEK+p49Mb5s1jYyM42PraypXZ9RUTA84qO3LESkaKh1DYQtm7l7+GpUsrdkq7GTuWa58dHhPf\nogW/EAWQCkUhdDrelLVvn/vxggXZHWA49RYAN360cLO9c4fvx1u2cIxz2zbg9dc5YSrbyxUeznEY\nNRIVxTcWR/z3CgbmJ01iq37sWA5efvMNGw1mA6ixsUjzL4uFa4orcn2PZXodO8ZZaiVKOHyKv/7i\n2We2km2YlCjBmYpuztazCYUtFE+6u/QEBfGm89NPHTyBVCjq4+hRICAAqFQp73PPPw8sX270oJn4\nSVISjwapWJF/Bg7kwH7hwvx8jx4GvuqKFbl63umSWRegVyiOoFBgftIk4LffeEjkuXP8xVuwwEp/\npB07INq0xtKljrmVZ882ikfr506kp9t/MmdwMn5y5Qr/F7RpY/sxHToYeLrq1lW8T5TTJCdzO2BH\nrGYzbNjgeYUCsDJZs4aNUruRCkV9bNrE7gFTtGrFm9SMDIMHTVgoRNy+Kz2dv8x79vCwnv79c9b0\n6JGVTQNwvCE4WB2FgMY4o1AUcHn98QfP69q0CejTh/trbtzI8UdTSj+byEj494pAyZL2f8euXGEr\nsls3gy45AQFsKbg7juJk/GTVKrbi7Jl+3L49sH17liKuV099CuXsWe5n4u2tyOnu3GGPsxNvs2KU\nKMEB+vfecyCpsF49JwtacpAKRSFWr+aaLlOUKMG6I9f3y4SFMmkS9/KbORMoW5Y/+8bJRq1a8b32\n2rWsB8LCVBFH2bmTkwa2bgV/og8edE6hOKkkv/uOB0Qa9kRs2JCzWs1ClB0I69+fU8DtYdYsTq/t\n3Zsto2w8EaR2UqGsXcs1tvZQvjx7uo4ehTotFIXdXXv2sAFqj9J1JUOHcivAZcvsPLBAAaB1a0Vk\nkApFAY4d4/tft27m17RoYZTubWSh7NnDFdtr1lj+gBYowLVjmzdnPRAW5vE4yp07PD+rdWvg5ZeB\njDNxnD9dtqxjJwwJ4SwvB1MZz59nxWz3dyQ2lpMkgoPRrx/HvWwVIS2NM6LefJOtlNmzDXaK7dtn\naVo3kZTEGYQO3jzT0jhu50jmUrbuVKNCUTggv3u3YvdhRfD25hY5H37ouXpnqVAUYNo0YMQIy4qg\neXMjF4qRhTJpEjBunG1dxtu2NZhDFR7ucQtlwQIu7Bo/nuU/Nd/J/lFFirBZd/26Q4f/+y8PObLb\ns6FP0xMC1atz5s7u3bYdum0b6/bq1XnzULCggVHSoQNXI+cZIOIioqI4u8xB187u3ZyOGhBg/7HZ\nCiU0lLW6u16zLShsoezapS6FAvD7X7s2W8ueQCoUJ0lKAu4u3oBRJy038slloRDlUigPH3I2l61j\nQ3MNNjRjoRw+7J5u90S8G9dnA/XqBdzeEOVUuioApwLz//7LcRO7Mcr7tsfttW1bzo5eCO7rlO32\nKl6c7zzr1lk9z/nzXJnuFAcOOPX+O5O5FBHBn80M8uaJo8ePOyyH4ihooaSl8XdMDfETYz79lAtS\nPdGgQSoUJ1m3RocZutdR6M95Fl1PNWpwgsnNm+A7vZ8fu4XAgczGjTmP3xbq1OGc88REmIyh3LzJ\nG9SmTV2fAKYfKqQv6OzcGSh2xomAvB4HA/MJCVwL0aGDnQcaxE/09OvHN3dbyim2bs2dMv788xxX\ny56w2qcPazorDB/Ox544YZf0uXFSoaxbZ9l9a4kyZTj5MDoavFXWD43zNDodB+UVUiiHD3P9ia3f\nWXfStCng66toz0ebkQrFSc7N3ATvwJKcnrVrl9l1Xl78H71/P/LETyxliJnC25vXr1wJtnLu3s3q\nzMf89htXgNeoYX9g2V6WL+dxpfpyk0b1MlA15QiulW/k3IkdDMyvWMHuN7sn9xrET/RUrco3SGtu\nr3v32JvSrFnOYxUq8Puf3YGkRw/e+ltomnj2LAe0P/yQ4zEOQcQuLwcVyuXL7Gl0Zj+Q7faqVUs9\nCuXKFW4IWVyZ+qIdO9Tn7tIjBCdUbNjg/mtLheIEZ88CLQ7+hCIfv8VmgxXzvnnzLLeXUfxk0yb7\nA6CvvcZtQu4ne/OJd+7ElSscyxk/nrONXn/d9b5UfWM8Pd5nTuJ+sYpYusnxgjoADru8nHJ3tW+f\npxDTFrfXjh2sTIyV2PPPA0uXZv1RrhzfYC1ke/38M2fqjBjBcSmH2pldusS7l4oVHTg4x93lTGZt\ndj1KzZrqUSinTysakN++PW+bJTXRubNB4o4b0ZxCEUJ0FUKcFkKcFUJ8bGbND0KIWCHEESGEjfNn\nbYMImDKFXQLv9TqPNr77UGjwQM5qsaJQsuuHLl7MVihXr/KOsJGdG/quXfmnQwcguUkEYuduR8OG\nvAE7cSKnGjw2ljdnruDaNb5/5doMR0XBp2UT/PCDk503HHB5paSw/94hd42pvjlgt9fSpZZfi7G7\nS89zz7HFlH1s794mKlyZBw+4duaNN7hspWlTE90VbEHv7nKww/DatY67u/S0b8+f89vlVWShnD3r\neGdvIzIy2GpVYna8q2jTht2OTg3gcgBNKRQhhBeAnwB0AVALwEAhRHWjNd0AhBJROIDXAcx09rq3\nbwNvv80erYEDuUiuY0dgXJFvUWj4YC5jr1PHappks2bsjciMz3F5LVrEX2BHdoRTp/JusvfUCKRu\niMTq1dw9V58p5uPDCsdVO5WNG/l9yNWO7MABlO7WBAEBBgWYjuCAhbJnD9do2e3XNhE/0RMezv07\ns5MgTBy6apXpmo3KlfnYbF92796sYUzkIs+fz++lvujy9de5HslunIifPH7MloWzld/+/tyw8Pdt\nlbg6/fZt506oBHFx/JlSgKNH2QD0ZP8uaxQuzG7L7dvde11NKRQATQHEEtFFIkoHsAhAL6M1vQD8\nDgBEtB9ACSGEgwURjJ8fpwSPGsU36507gQ8G3UCTuEXw+vQTXlSxIid/37xp9jylSvGAxoRdsUBY\nGNLSeEDOBx84JpcQwNdfAxM2N0Ft37NoWvVunjWdO7NLzRWYbDsRFQXRtAlGjWJLzmHKlePtldlh\nMnkx1UvNJmJj+T/YzIRGfU2KKbZv582AuabKzzzDu34ArJ0CAvIMNCLi5n6GM0e6d+fEC7s7Yjih\nUHbvVq7R4X/+A8ydJ0A1a/JIa0+joEJRu7tLjyu/++bQmkKpAOCywd9Xsh6ztOaqiTV24efHN/4D\nB3j8qZ8fOCL+zDNAyZK8SAjOarGSnvPf/wKpx2Ox8GA4XnmF3WANGjgjHdC4pS9Es2Ymt9GdOrGF\nonQKIRHHDnJlU6Wmsq+6fn089xx79hy2joSwu62MwwrFoP7EFJayvebM4a7F5jxMuRQKYNLtdfgw\nx+oNXSg+PryBGT3ajlhKRgafrHFjGw/IjTPZXca0bcsWz83SKnF7nT//RCoUd8dRtKZQFGfcuHHZ\nP5GRkbYfePhw3uZ71atzrrsFunbKQJC4hM3nQ/DoEbelV4SICJP2bXAwu4CULge4dIlvsLlamx84\nwIFnPz/4+HB9yuDBXKvjEHa4vR48YI9jy5YOXMeMu0tPWBhbpjt25H48LY1vwr17mz91ixb8ErJb\ni+vThw00/B9/AIMG5VVKw4ZxTOzrr218HWfOsGWn3+TYiZIKRV+Lsy1RBQqFiP8T9JPvnECny2kz\npHYaNODPXZ7RGQ4QGRmZ615pFiLSzA+A5gDWG/w9GsDHRmtmAnjB4O/TAMqaOR85TNu2RJs3537s\n22+JRo60fFxsLFFQkOPXNcfOnUSNGpl8avhwou++U/ZyCxcS9elj9OCXXxK9/36uh957j9fpdA5c\nZORImwVfu5YoIsKBaxARVahAdO6cxSUTJ/L7aMiffxJ16mT99C++SPTVV1l/6HREFSsSxcQQEdHR\no0QBAUTx8aaPvXCBqFQpoocPrV+HFiwg6t/fhoV5uXSJKDCQKCPDocNNcuMGUd8i6ym9bQflTuoI\nV68SlSmjyKmOHiUKD1fkVG7h+eeJfvtN+fNm3Tvz3FO1ZqFEAQgTQgQJIXwBDACw0mjNSgCvAIAQ\nojmAu0SUoLgkp05xoYEhNlgoiI1lX7rSNGnC176bN47SqZPyvtQ9e0xYAzt25Nm6TZjA7muH5l7b\nUYtiLtPKKgkJnB5mZffavz+7vQzLSGbN4uC5Nb78kmMkP/0E3rpnub10Oj7v5MlmwzcICuJ6h1yT\nOs1x5AgH6Rxg3TpubqpQI14A3MqtePOaSD/qYQslLk4R6wRQX/8ua3Tr5mRyjJ1oSqEQUSaAtwBs\nBBADYBERnRJCvC6EGJa1Zi2AeCHEOQCzAIxQXJBbt/jOUr587serVeMYgiVcpVAKFuQ0MhPFlRER\nrABytc93ku3bjb5YGRlcZGP0bStYkG/EU6ey66hHD4v1n7mxw+W1aZMD1fFAzk3YSpptcDAv+/NP\n/vvUKc5E7WWcEmKCkBB+/8eOzXo5ffoAy5djwwbOxnn5ZcvHjxjBCtlqHMxJhaKUu8uQdi9VhC75\nkRN+TwVQMCC/Zw93/NYKffrwd8NdLdU0pVAAgIjWE1E1IgonoolZj80iotkGa94iojAiqkdEjoyc\nsYzeOjG+CQUHc1GJpX4nJ05wnMEVtG5tNN2J0Q/+Umr8fFIScOGCUe1MdDRvs030hw8LY+NpyRK+\n6ffrZ+MH3MZalJgY1vEOzZOy4yb8+eesFFJScuJDtrYuDwriIPvw4cD1sDZAXBwWTLxiMaCvp3Nn\nfn0W9ypE/FrMpZtZQKl0YVP06CkQo6uJ1EMetFIUVCi7dzsYp/MQJUvyhtKGrj+KoDmFogrOnDHd\ntdTHhz+4lrr/Hj5sfxWjrbRoYVKhAHyzWGnsHHSQ7dt5l5brZhoZaXG8n7c3Bwnfe49dU3Pn2nCh\n4OCc6L8F9EFth9w10dE2p9m1aMFNCb7/ni2VoUPtu9RHH7HOrVmvAFbTswg+tgIDBlg/zsuLFbHF\nmoKEBFYqxlazDSiZLmxMQACQWLoW4lZ5MHVYIYVy/Tp7lBUsuHcLL75oo8tUAaRCcYQLF4zSmwyw\nFEd5/Jhz8o2nZilFgwZcdWXCN/LSS3wTVCJ9eONGE/GKDRvMTxgz4t13OZ5gtZK+UCEgMNBiqb9O\nx7PPX3rJpkvnxU430YQJwJgxLJa996gCBdiySUwEqozqg8/qLLe555iZJL4c9I0PHaiQX73aNe4u\nPb4NauHOLuUslDt3OC5gbbKyvoP3td3nkVnFeYWin6rspbG7Zo8e7I1OUD6SnAcf60skebh4kcua\nTWEpjnLyJCsi/YB4pSlThoMWV6/m6eXUsCHf0Pbvd67ldnIyu65yuc8ePuQT22hXN23Koq5Zw3NL\nLKIPzJuZA75vH6dF16ljm/y5ePiQLSA7ZmSEhbHudHR2GMD/D7Xf7QxMGMRFJn5+Vo9p145rmIjM\n6AwHY3NxcVylHxVl96E2U7lbLdz/rzKRYSJWfgULsmI5f54N/mrV+OWnpvJjZ87w12D0aMBvaRym\nbwjF206m+ppMRNEAhQtzoeySJbmLZ12BxnStSjAxDz6b6tXNK5TDh/nO7krMNKkUgnfxf/zh3On/\n/pu/VLnmsm/ZwlrCjp4nb75pY+NKK4H5xYuBF16w+bK5OX6cGxjaOcP16acdClXkpmhRLoS18U4e\nHMximvWmOtCrKj2d40D//a95g1sJwnvXQpWUGEXqIQ4f5mYUkZFcd3T1Ks//qFuXOyU/esSfz/Hj\nef/2xkv3UaJACj6fWdbpwPTu3doKyBvy4os5CSWuRCoUR1C7QjHTU2zIEO4ddvSo46dfscJoEBgR\nj5t89VW7ztO7N2d7WW3zZCEwn5nJuy6HFYod8ROX0Lq1+SZhRgjBVorZ2ls7LZTUVG5eWbIkV/q7\nEu9KT6GITxq2LHZ+4tvKldzFWW+llSjBCv6tt3io1Pjx3PYlIoIHfyIuDl6hIejUWWD2bEtntkxq\nKn+tnB3z4yk6d2aF68x33xakQrGXjAyOzplrD16tGu8WTQ0j96CFAnC198yZ/OGyKShuxOPH7MfP\nlZ67YwdvGV980a5zFSvG9THLlllZaMFC2bOHYxkOB0mPHPGsQmnTxo4caitxlNhYuyyUTz/lWMCS\nJcrWnphECKRUqYnT/zgfRzHZP84SWQH5jz/m3nIWxtFY5NAhTuwsUsSx4z1NgQKcYfjTT669jlQo\n9nL1KgcAfH1NP1+iBN8tje37zEze4jhYJ2AzVtro9+vHQfWvvjIY/mQj//7L4pcrZ/DgN9/wRCgH\n7krDh3OQ22KvKgvFjStXWm57YpXoaNf/f1iiVSuO9NrY519voeRJrNDp+MYZFmbTeZKTgXnzgBkz\nzH+MlaZY81pIPRTj2IyXLG7fZjeWXW6nLIXSoAF7NxcvduzaWo2fGDJsGG/gLl503TWkQrEXS+4u\nPabcXmfPckpnCScHT1mjZk3erVpIf6lfn9vcjx5t+8wSItYd775r8GB0NO/yrVXmmaFzZzbYvvvO\nwiIzLi8idr/ZUlhokowM12bc2ULp0vyZsLHRWmgot4bPkxl+5Qr7rmzcPq9axdlKDmQYO0yhhrXQ\nutRJ8y47G9iyhY06u6ZxGjSFfO89tlIcyXTUcvxET5kyPG9nzJjcjxOx80QJpEKxF4PhWGYxpVAO\nH3aPe8XPjyPmVlrA9O/PceEJE2w77dq1rHyys7KIgHfe4Uq/QoUcFnfCBGDatFwTjHNTujT72oxa\nypw+zZaNwx7E06fZbVm0qIMnUAg74yivv87WZS7sDMgvWuRE3MlRatVCY78YrF7t+CnsyEzPwaAG\npWtXjoXYq9SI8oeFAnAt1ObNucci/P47Wy9KlBRIhWIvtlgoNWrknQHhRFsMu7FhHLGXF2d8ffut\nhZt5FkTAF18An31mkLKqn1H72mtOiRoWxm6ryZPNLBDCpNtrxQpWbg4OJvR8QF5PmzY2KxSA3YTx\n8UYt8e0IyN+5w1XxTrkKHaFWLTx1NwarVjl24yJiV63d1fwGCsXLi2+cf/1l/ykKFjTKbNQoxYvz\nd37ECDbSExNZycyZ48R3yQCpUOzl0iXrCqV27bwtu48eVSDX1EZsGEcM8Aa9a1frVbQLF7J18txz\nWQ+kpnKu6Y8/KhLR/ewzbp54y1wSkInAvFPuLsC9Ct4SjRvb1RPH15eV73vvGQSYz52zOX6ydCnv\n8v39HZDVGcqXhzcyUZZuWBsZZJLTp/mGZ1cCxuPHPKfa4PvapYv984Hyi3WiZ+BA9pDOmMEu7P/7\nP+X2VlKh2IstFkqtWtyzy/BT606F0qCBwdxZywweDPz6q/nnk5M51jJ1qkGF8MyZXE3mUPOsvFSp\nwskCZmMpRhbKjRt8g7EwwsQ6arFQwsJ4k2JH+tEzz7CHK3sipo2tRYh4JzpokIOyOoMQEPXr49UG\nRx1ye+ndXXbtoi9e5NRGgzqjGjX4rbZnurTW+ndZQwhuNvrOO5xkaGm8ib1IhWIvtiiUMmV4566f\nqnTjBtuX+mHvrqZtW65ctyGlpnNnzoI2t2v8/nv+MuVqIvzbb9zpUEE+/TSnLUkejALzq1bxTtPh\nDCV9I0U1WCi+vvx5OnfOrsOmTWMFfPky8igUnY5rS2rU4I/iwoXsJ69fny/Xo4fCr8FW6tVD59JH\nHFIoa9c66O4yalsvhP3jHLTWYdgWatTgW8S6dco27pAKxR6IbHN5GY8D1lsnSjgpbaFECdYCS5da\nXertzQHaf/7J+1xGBruiPv/c4MG4ONZACn/DKlXi9hB//23iSSOXl9PurkuXOJHAmf4pSlKjhvWx\nB0aEhHAx32tDCWQ03nbuXC7AX7KEU6tHjWJl/e23HJD2WC+qevUQ+vAoTpyw4N40wZ073GKna1c7\nr2dm7G/HjranzN+9y6373OVccCdNmyrf+FwqFHu4eZOzqGzJDDJUKA62FXeK0aPZpg0I4JjK+++b\ndav07Gm6E/Hy5eyOyuW3XraM7+YuqIbr3p13THkwsFCSk7mW0qlmhnv38uwYtVC9Oo9EsJNPPwXK\n0g3czywCXRFue/PoESdQTJvGH8HmzVn/79rFLiMfT3bvq18f3ieOokMHYP162w9bs4aLae1OyDPj\nCuzYkRMTTNUeG7N3L4e57OzO88QiFYo92OLu0mNsobjbvdK+PWcP7dvHjvPoaA6im6BVK96FGTb1\nTU/nYHku/yoRB1wcrDuxRufOLHIeT11QEBeKpqdj40bWBU4FldVWVGCpXY8FChQA5oyOwxXfUHz4\nIf/3TJ/OO09VtgipVg2Ii0OPbhl2ub2WLQP69nXgemYUSsWK3GHBllwItX1U1I5UKPZgz1wFw0wv\ndwbkjWUID+c78Ndfm+234uPDgV7DUaFjxvBLzeW31ld1u2gGasmSbEzlaS/i68tVeJcvO+/uAtSX\ntlOjhkMWCgAUvHwOYV1CsWkTu4TGj+eRw6qkUCGgXDl0r3MRGzZYbj2v5949dk917+7A9Sx8Xzt1\nss3tpbWRv55GKhR7OHPG9gKyWrVYoaSksC+3Zk3XymaNZs3422mmXW3fvmzI7N/PN/R589gYyRX2\nmTOH605cGAt65hnzbq/003FYvdqGlveWePiQrQFXDTlzhGrV+LNliw/GmLg4FKwZiqgoNkq/+851\nA0EVITwcZe7FompVriuxxpQpPMbWxCBQyxCZjaEA7PayFphPT+dYlELJjE8EUqHYg36IkS34+3Nw\nfOVKPsZdTZPM4eUFPPssO6RN0Ls379refJP7PM6YwRlC2dy7x828/u//XCpmt25mFEpoKE6vPY9q\n1aw3KrDIgQPsfrSrf4eL8ffn/m8WBomZJWsXXrAgh80GD1ZePEUJDwdiY/Huu9zKxxIPHnB662ef\nOXCdGze4FY2ZkQodOrDBnZxs/hTR0Ry+c3W3pPyEVCj2YI+FArDL6dtv+U6tBp591szdmo2OSZOA\ngwc5XJFdxKhn0SJ+Hbm0jPLUr883kjxZtCEhiN9y3t6mxnlRa1FB1aqWR0ebQ8F56W4hS6H068c1\nh5aaBMyZw5aEjTWbuTGRMmxIiRIca9q82fwpZPzEfqRCsRUi+4cYDRjAPbxcFMS2mzZtOEifkWH/\nsXPm8KAJFyOEaSsl+l4IvC/G5Z7F4ghqLSoIC7O7FgUAu3Us3DhVR5ZC8fEBPviAC2ZNoS/CfPNN\nB69jwd2lp0eP3HFDY6RCsR+pUGwlIYHdJPY4c199lW1qtSSxBwZycaWN3W2ziY7mlGk3WVq9erFB\npG80kJgIfDI3FG0rnrffl25IRgYrVDU6xR1RKA8e8Ocr1zwBlZOlUACuf9q8mb2pxuzdyyElhwPi\nNlhuPXqwB9hU6IpIBuQdQSoUWzlxwrHAuqvmxzuKHd1ts5k7l8c9unwSE/PMM3yfrF+fG1i+9BLQ\n4qUQFEuIc64l6saNbGGqpaDRkLAws5MpzRIfz4VC7iqYVYLgYI4VpaejZElOsPj+e/5v/eEHzvnQ\nz2sZMsSJl2aDQgkN5f3h/v15n4uP57CjrVUCEkYqFFs5fFhdmUGO0qqViYEaFkhJYXNhyBDXyWRE\ngdGowk4AACAASURBVAL8dk+YwDebGjWAT78vxd9wqzODLfDLL8DQocoJqiSOWCjx8a4dBu8KfH3Z\nSo6PB8D/x/Pnc6LFggUcVxk4kJs8OJX/YWNsadAg7iRkjN7dpSVdrQakQrGVQ4fU0UzQWRo2ZBeW\nrSxdyinHbu7d7eXFlsqRI7xz9fGBxemNVrl5k/0rbh8EYiOhoXwTtMcCi4/XVvxEj4Hbq2JFzuLe\nto1v4kuWcP1wv35OevJsVCivvMLXNM722rVLxk8cQTMKRQhRUgixUQhxRgixQQiRJ5lPCFFRCLFV\nCBEjhDguhHhHMQHcMQ/eHVSvzh0FHz7M+9zixXmT83//3a3WiUXMTG+0iQUL2L+i1hzQ4sW5t8j1\n67Yfo0ULBWCZL1zI/tPPjw20AgU40/foUbM1uLZhR2ypQgUOqRn2siPir0H79k7I8ISiGYUCYDSA\nzURUDcBWAJ+YWJMBYBQR1QLQAsCbQojqTl/53j22xe0axqBSChTgWNCxY7kfP3WKs9H69eNsNoC/\nmPv3O9CVz0WYmItiE0Q5Tnk1Y6/bS6sKpUqVXApFcc6f5/fFRn/V0KH88dCj/2rUqeMC2fI5WlIo\nvQDMz/p9PoA8M+eI6AYRHcn6/SGAUwCc7xnv5cU7dY921lOQBg3yNjL6+WeujPv8cx48TcSxk/bt\nPT8mV4+jLq9Dh7hBWNu2ysukJPYqFP2NU2u4WqHYWZvTvTu73fTt1P79l6vzZfzEfrSkUMoQUQLA\nigOAxQo7IUQVAPUBmMjhsJNixUxU+mmYxo05fVZPSgoPzfjPf3g26LVr3Gr4++8Vn3viFI66vNat\n4zuEx/q224g9CoWIb8pSoeTFToXi6wu8/jq3u9Pp2Dvav7/rxMvPqGrLLYTYBMAwp1MAIACmmi+Y\njV4KIYoCWApgZJalYpZxBu10IyIiEBERgSpVquDixYt2SK5egoKCcMH4y/v009z9Uafjm+yCBRyB\n1AfeZ85ky6R7d6BdO7fLbBZHXV47dnArf7UTFsbbY1u4eZProtQaE7KEOxSKnf6qjz7icrH33+dM\n/6ZNXSSbRomMjERkZKT1hUSkiR+w+6ps1u/lAJwys84HwHqwMrF2TjKFuce1iNnXUq0a0cGDRJmZ\nRNWrE23blvv5lBQinc7l8tlFejqRry9Raqrtx+h0RKVKEV2/7jq5lOLAAaIGDWxbu28fUaNGrpXH\nVeh0RH5+RA8euOb8nToRrVtn92HbtxMBRL/84gKZ8hlZ95U891SV+wBysRLAq1m//x+AFWbW/QLg\nJBFNc4dQmuXZZ3nQxMyZvMs1tkT8/NTnRPbxYSvKHuvx+nUuyNRCNbm+uNGW1GGtxk8A/lwFBbnO\nSnGwv1nbtpz8qPoGmypGVS4vK3wD4G8hxBAAFwH0BwAhRHkAc4iouxCiFYCXABwXQkSD3WL/JSI7\n5sM9IQwfzlXjJUtyPEVtysMc+sC8rT3Vjh3TTrpOyZKs/G7dAkqXtrxWqxleevRur9q1lT1vejrH\nAB0scS9SRFlxnjQ0o1CI6DaAPM2kiOg6gO5Zv+8G4J7+IFonPJxjC4GB9jW89DT2BuYPHdJWhwN9\nYN4WhaKl12WMq+Io8fFcXOLpcRFPKFpyeUmUpk0b7muiJfQV5bZy8CBntWkFWzO9tOzyAvIUNypG\nbCxvliQeQSoUibYwaNthE/lVodg7SkFtuMpCOXfOwQEqEiWQCkVjJCcnIzg4GAsXLsx+7OHDhwgK\nCsKyZcs8KJmb0I/LtYWEBG7BoaWdvC0KJTmZ4yxOja70MK5SKNJC8ShSoWiMIkWKYNasWRg5ciSS\nkpIAAB9++CGaNm2Kvn37elg6NxAaCly6BDx+bH2tPn6ilYQDwDaFcu4cvw9uGifgEqRCyZdIhaJB\nnn76aXTv3h1vv/02tm/fjqVLl2L69OmeFss9+Ppy6rAtcRStubsAViixsZZTh7Xu7gI46SAlhfvF\nKUlsrHR5eRCpUDTK5MmTERkZieeffx7ff/89SlvLCspP2Or20qJC0f8/JiaaX5MfFIoQbKUo2ZEi\nORm4cUObLf3zCVKhOIAQyvw4g7+/P2rVqoVHjx6hT58+yrwwrWCPQtFaaq0QXJsRE2N+zenT+aPz\ntdJur5Mn+X3JL01cNYhUKA7ADRqc/3GGBQsW4OLFi+jUqRM++ugjZV6YVrBFoVy7xnEWLc5wrVWL\nR06bIzqaG09pHaUVyokTyhdKSuxCKhQNkpiYiFGjRmHu3LmYOXMmlixZgt27d3taLPdhi0I5dIjd\nXVoKyOuxZKEkJ3PxXn64cSqtUI4f105XhHyKVCga5K233kLfvn3Rtm1blCtXDt988w1ee+01pKen\ne1o092CLQtFi/ESPJQtlxw6eHJofKsGlhZLvkApFY6xYsQJ79uzBpEmTsh8bOnQoKlSogC+++MKD\nkrmRsmW5Z1NW2rRJtBg/0VOrFlsopvyiK1dyY8/8gFQo+Q5BzjrzNYwQgky9fiEE8sv7kp9eSy6a\nNQOmTAFatsz7HBF3Fz54MGfGi9YoW5bddhUr5jyWksKvJzpa20WNehITeRz1rVvOnyspibO77t7V\npptTY2TdV/K80dJCkWgTS26vq1f5X8ObsdaoXTuv22vJEqB58/yhTABla1FiYvg9k8rEo0iFItEm\nVatyPYYp9O4uLd9c6tUDjhzJ/dicOcCwYZ6RxxUoWYty/Lh0d6kAqVAk2sRSk0gtB+T1NG7Mr0NP\nTAx3GM4v8RM9SnUdPnFCZnipAKlQJNokvyuUJk2AqKicv+fOBYYMyX9Fe0oF5qWFogry2adT8sQQ\nHs5NEolyu7aItDdUyxShocC9exy4Ll4cWLAAOHDA01IpT5UqXFfjDJmZwNGjQP36iogkcRypUCTa\npFgx/rl2jSf06bl0iXfxTz3lOdmUwMuLrZS9ezlzqUEDbbXht5UqVXgEtTPExgJlygD+/oqIJHEc\nqVAk2iU8nAPzhgpF7+7SckBeT8+ebJkcPAj89punpXENSri8Dh/mYk+Jx5ExFIl2MRVHyQ/xEz0v\nvQQsXcrWSbt2npbGNUiFkq+QCkWiXUwplPwQP9FTqhRw5w4rlfxKYCCQmgrcv+/4OfbuZfegxONI\nhaIhXn75ZQwZMiTXY9u3b0dgYCASEhI8JJUHMVYoRNpuuWIKf3+Op+RXnK1FuXePA/KtWikqlsQx\n8vEnNf8xbdo0rF+/Hlu2bAEApKWlYdiwYZgyZQrKli3rYek8gHFxY3w8ULgwUL6852SS2I8zbq/t\n27l7gJ+fkhJJHEQqFA1RqlQp/PDDDxg2bBhSUlIwbtw4hIWF4eWXX/a0aJ6henXe2epbd+Q36+RJ\nwZnU4U2bgM6dFRVH4jiaUShCiJJCiI1CiDNCiA1CiBIW1noJIQ4LIVa6U0Z38Pzzz6Nhw4YYOHAg\n5s6dizlz5nhaJM/h68stSvQV5fv2cdNIibYICzNfpGqNzZuBTp2UlUfiMFpKGx4NYDMRTRJCfAzg\nk6zHTDESwEkAxV0hiPhcmZRUGutYF+Cff/4ZoaGhmDBhAp7Ser2FszRrBuzfD7RvD+zaBXz7racl\nkthLrVrA6tX2H3flCnDzJmfBSVSBlhRKLwD63Mn5ACJhQqEIISoCeAbA1wBGuUIQRxWBUpQpUwaB\ngYGoWbOmR+VQBc2bAwsXctfamBiZ7aNFatbkXlzGXQ+ssXkz0KFD/k5a0Bha+p8oQ0QJAEBENwCU\nMbNuCoAPAeTDISCSPLRqxZbJvn3cHLBwYU9LJLGXChVYkVy6ZN9xmzfL+InKUJWFIoTYBMAwXUmA\nFcNnJpbnURhCiGcBJBDRESFERNbxFhk3blz27xEREYiIiLBLZomHqViR52q8+SYwYICnpZE4ghC8\nMdi9GwgKsu0YIlYoX37pWtkkAIDIyEhERkZaXaeZiY1CiFMAIogoQQhRDsA2IqphtGY8gEEAMgD4\nASgGYBkRvWLmnJqd2BgSEoK5c+eiQ4cOFtdp4bU4zV9/AR99xLEUwzYsEu0weTI3+5w+3bb1x48D\nvXsDcXGulUtiEnMTG7WkUL4BcJuIvskKypckInNBeQgh2gF4n4h6WlijWYViK/nptUjyMQcOAP/5\nDxcp2sLkyVyDNHOma+WSmCQ/jAD+BkBnIcQZAB0BTAQAIUR5IYQDKSISiUQ1NGjAA8Tu3bNtvUwX\nViWasVBcgbRQJBIVEREBjB4NdO1qed3jx9wD7MIF7ncmcTv5wUKRSCT5mYgIYNs2088RAbdv8+97\n9wLVqkllokKkQpFIJOqgUydgzRpWHoY8fAj07w8EBABjxwKTJgF9+nhGRolFpEKRSCTqoGVL/nfT\nppzH4uM5pbhoUQ7CnzvH0xnfftszMkosImMoMoYikaiH338H5s8HtmzhjK+uXTmu8s47+WMKZz5B\n82nDrkAqFIlEZaSnc3ykTBmuMZk+HejXz9NSSYyQCsUEUqFIJCrk7l3u7VW6NCsXieqQCsUEUqFI\nJBKJ/ci04XzCX3/9hWLFiqF48eLZP8WKFYOXlxe++uorT4snkUieYKSFkg8slHnz5uF///sfoqOj\n84wC1tprkUgk6ke6vEyQHxRKdHQ02rZti7Vr16JNmzZ5ntfSa5FIJNpAurzyIffu3UO/fv0wduxY\nk8pEIpFI3Im0UByxUJTKh3fyve/Zsyd8fHywbNkys2ukhSKRSJTGnIWiqgFbmkEFN+iJEyfi1KlT\nOHTokKdFkUgkEgDSQtFkDCUyMhK9evXCzp07UbduXYtr1f5aJBKJ9pAxlHzC9evXMXDgQEydOtWq\nMpFIJBJ3IhWKxpg7dy4SExMxcuTIXHUoxYsXx4gRIzwtnkQieYKRLi8NurzsIT+9FolEog6ky0si\nkUgkLkUqFIlEIpEoglQoEolEIlEEqVAkEolEoghSoUgkEolEEaRCkUgkEokiyNYrJggKCoLIJ/Or\ng4KCPC2CRCJ5QpB1KE/w65dIJBJH0HwdihCipBBioxDijBBigxCihJl1JYQQS4QQp4QQMUKIZu6W\nVU9kZKSnLq0qGQB1yKEGGQAph9pkANQhhxpkAJyTQzMKBcBoAJuJqBqArQA+MbNuGoC1RFQDQD0A\np9wkXx7U8AFRgwyAOuRQgwyAlENtMgDqkEMNMgBPjkLpBWB+1u/zAfQ2XiCEKA6gDRH9CgBElEFE\n990nokQikTy5aEmhlCGiBAAgohsAyphYEwzglhDiVyHEYSHEbCGEn1ullEgkkicUVQXlhRCbAJQ1\nfAgAAfgMwG9EVMpgbRIRBRgd3wjAPgAtiOigEGIqgHtENNbM9dTz4iUSiURDqH5iIxF1NvecECJB\nCFGWiBKEEOUAJJpYdgXAZSI6mPX3UgAfW7he/sgNlkgkEhWgJZfXSgCvZv3+fwBWGC/IcoldFkJU\nzXqoI4CTbpFOIpFInnBU5fKyhBCiFIC/AVQCcBFAfyK6K4QoD2AOEXXPWlcPwFwABQCcBzCYiO55\nSGyJRCJ5YtCMQpFIJBKJutGSy0sikUgkKkYqFCcQQtQUQgRk/e6RAL8aZFCLHGqQQS1yqEEGtcih\nBhmeFDlUleWlFYQQweCkgJsACgshhgI4CyDjSZJBLXKoQQa1yKEGGdQihxpkeNLkkBaKY3QHsISI\nOoKzz4YDePoJlEEtcqhBBrXIoQYZ1CKHGmR4ouSQCsVGhBBFDP6sCKB81u9TwfUvLYUQLu0VrwYZ\n1CKHGmRQixxqkEEtcqhBhidZDqlQrCCE6COEOAngByHE6KyHtwNIE0IEE9EjANsAFAZQJ7/KoBY5\n1CCDWuRQgwxqkUMNMkg5pEKxSFZF/igAbwOYCKB/lt/xBAAdgA4AQERRAPwAVMk6TrFAlxpkUIsc\napBBLXKoQQa1yKEGGaQcjFQoRgghDBMVCoOLI48TUSyANwG8C+AxgGgA9YQQXbLWHgYQBADOTu1S\ngwxqkUMNMqhFDjXIoBY51CCDlMMERCR/sn7AM1aWARgGwB/AUwDWA6hisGY+gO+zfh8C4ByALwFc\nBdA1P8igFjnUIINa5FCDDGqRQw0ySDnMyKLUibT+A+A/YB9jBwC/A/gRQFEAUwBMNlhXFsAlAOWz\n/u4IYCSAOvlBBrXIoQYZ1CKHGmRQixxqkEHKYUEeJU+m1R9wm/zZAJ7J+jsUrL2/BFAcwFEArZHT\nqmYOgPD8JoNa5FCDDGqRQw0yqEUONcgg5bD880TGUIQQhYQQk4QQg4UQ1Ynf7atgUxBE/9/euUfb\nVVV3+PvlASEkEsgboSUBQmqAZMgjhMoboVSqtEhqQSgWAcMQaO1AwUoAsfKQAkogKvJsKIlVhCFi\nC6KIiqSgVolieZX6RFoFg4CAyewfc53cneu94T722Wfem/kbY4279uuc7869zl6vueeyx4HPAvPw\nhbwuwLuT75evsbIz8NRQZ4jCEYEhCkcEhigcERiSo3/a6CoUSVPw8cbJeOTimyXtAFwPbC5p33Lq\nj4FvAAeY2XLgEnx8ErxF8NxQZojCEYEhCkcEhigcERiSo//aGEOvbAlMNLM/BZA0Hl9f5V+BzwF/\nD9xrZr+UNAF4EcDMvifpfWa2dpgwROGIwBCFIwJDFI4IDMnRTw37HoqkbSWdKmm6pNHAz4BHJe1V\nTrkSmAbMxld43FTSBfK3R+fgrnYADPSmRGCIwhGBIQpHBIYoHBEYkmPwGtYViqS3A18GFuATVWeU\nLt+vgR0kjTGzJ/HJqwPN7Ff4y0BrgBW4H/fSoc4QhSMCQxSOCAxROCIwJEdNsjbO+Hc6AWcCf1Py\nO+Juc7sAfwYsARaUY+PKsa0r1242XBiicERgiMIRgSEKRwSG5KgnDas5FEk7AaPNbJWkEcAOwOMA\nZvaopEuAT5rZAkl7AO+Q9CwwErgfeL71WeaxboYkQxSOCAxROCIwROGIwJAcbVIna7Maa/TR+Es9\nDwP34nFsRgPHAfd3O/c7wOEl/7fAvwOP4mvPD2mGKBwRGKJwRGCIwhGBITnamzoOUMs/4TX6ipLf\nGbgYOL9srwKOrZx7GrC4sv0HwIjhwBCFIwJDFI4IDFE4IjAkR3vTcJmU3wzYTdIIM1uFLx4zWdL+\nwMn4iz07l3PXdScBzOxHVo8XRASGKBwRGKJwRGCIwhGBITnaqCFZoUhdYZZL/jE8tv/by+7/BB7E\nu4jfwF/+OVXSSmAP3DtiyDNE4YjAEIUjAkMUjggMydGwOt1F6kf3cBzuGjeT4skAjCx/xwDHAx8H\nppZ9fwIsL/lR+Nuihw11higcERiicERgiMIRgSE5OpdaQcNCS9IBwFLg+8D/AS+Z2WnlmMzMJM2m\nK6bNeyVtAtwF/IWZ/XI4METhiMAQhSMCQxSOCAzJ0WF1ukbrS8K7hOeW/BTgW8AJZXt05bwZwA+A\nS3HPicuATYcLQxSOCAxROCIwROGIwJAcnU0dB+jlRmwLvL6y/SH8bdHW9sHAz3q5dibwZuAtQ50h\nCkcEhigcERiicERgSI5YqeMAPRj2Q3jEzDtxN7oJwN7AE93Ouw34QMmPBP4OGDVcGKJwRGCIwhGB\nIQpHBIbkiJdCeXlJmoSHGtgeWIjHpjnHzO4DfiDpw5XTrwWmSRptZmuA3wCjq54UQ5UhCkcEhigc\nERiicERgSI6YClWh4BEyFwBTzOxZPNCZyYOlnQwco664/zsBPzWzVwDM7Goze9FK1T/EGaJwRGCI\nwhGBIQpHBIbkCKgQFYqkkQBmthq/GUeXQw8B38Rv1i+Ac4GjJd2LT3j9R40MIzrFIGlUJd8RDklj\nK/mO3Q/5Wg4d54jEEIUjAkPh6NhvtRtHCHuEUtNjbKUiXgTshdfo3Y+9GbgG2KVs7w7cCmxRtsdQ\n1lAeJMMHgUNKXp1gKJ+1uNjj97w6GrTFeXhMoW06xVA+60x8SdN9O2iL9wFHAbM6bIsT8XUtxnXQ\nFu8C5gLjO2yLGRs41iTHX+MhUl7TSY7IqdEeiqQjJD0IvBEfa7yo7Jek6yXtjr8p+jhwOoCZPQhs\nhS8mg5n91szuGCTHTDzA2nvkYQ9M0qiGGQ6X9F3cu+NWM3up7G+MQ9I4STcAs4DzzOwnTTOU71so\n6T5gPh4kb3SF47qGbDFf0reA1wO74g+HTpTNPUu5OBR4G3B55diNDdlijqTvAIfjjZ0rmmYo3/VG\nSc8D10iaXNnfWLko3/fn5Z4cCbwXn4BvHbuhKY4hoaZqLuC1wEeAfcr2fviPpfXW6OTKuVPxB8sn\ngZV4d3KLGhhGlL+bA5/AI3i+u3J8UrsZymdPAZYBn6js26x6vCGO6cBdle0J1e9tiGE2cDOwd9n+\nNHB6B+7JO4HTWt8JfJTyrkBT96N8/kmtMom/Kf1lvLIHmNaQLfYHlpb8OODzwMUNM2yO95BOwNdS\n/ysqwRAbLBfTgesovWZgN3xifbcmOYZKau+Hu+vcnpUfZjXkwB3Al0rhHVf2q3LtZLwnc2wNDHtQ\ncc0DDgTOKoXjvzZwbS0M3ThatliIDzUdiVe0S/AwDK0uctttgXulXIVHLr0WuB2PH9TT0FfdtlhX\nLrodOx64o933pIf7cQXwKXyo4hbg28C7Ww+Mbg+zdpaLfwYurBy/EniW8iBvU7kY2217EXB5ZXs7\n4Bngte1i6IWj9X0LS9nc0NBXOznmVfJTgLtbbO3kGIqpfR/sLa1fAF/Aa/jXlf0j8HHq88vDdAnr\nt9RP6OmBVgPDtcCcsn874OqSX4H3VJa2g6EHjhvwN2M3xd+MfaLY4m3ATfhCOu22xfX4UNsE4Ov4\nfNJiYCweKuKWhmxxXeWetBobh+KV3IRu17XLFjfirdBJ+DDo94sNdi5l5vaGbHEj3sKdAfwvcEz5\nnVyI9+CWtMkWZ5Xve0dl3w74GuZbVfZdBlzfRlusx0G30OzAvwBnAJtXj7ebo9sxAVvia5HMZP2K\ntVaOoZraMociaQzu4bCPmb0J+AlwkqS55iGXl5jZ2Wb2WbxlOFvStHL5y8DLNfjKd2f4aWGYDUwE\nVkvaDR8v3x74YuXyWhh64fgRvpDOdHzI74hii+X4g317SVPr5OiB4cd0LeZzMz7ZuNLMXjCzRcB8\nSXPK5a/UwdALR6tc7Gzukw/wc7xSac0ptb63Xbb4H+D9+LDW5XjP+RTzcOInArMkbV8nwwY4zgZW\n4y3ybfGyeRVwNfBMxRuwLlv8MT5vdQGwqDBhZo/hdqiuS34jMLLihVenLX6Pw8zWShrR8qTChyAP\nw3sI4ENi4O98tI2j7B8heewt3FFivJk9YWZW8Y78XV0cQ1rtqqmAH9I1X7Ij3gL+YA/nHUWl9dVm\nhnOAf8C7pavx1uheuFvflxq0xTmUGD/dzvtL4IqGGM7DH6TCW1zvBMbjw1+fBiZ2qlzgLfZjGi6b\n55Tth+maz1mA9xrbsk53Dxzntji6nXcm8J42Mcwqf5ex/lDbpnjv+ciyfVS7ymZvHHT1Qlp/PwAs\nB+7p6VnSbo6SPxrvyU4pLIvaZZOhmGrtoUga2fIRx4cL3gK+LjLue72lpHnl3DmSPoI/1L7WEMNK\nvGcwA3iDmc0xs/vxh+t1dTH0kWNixRbbydeNPhMfgmqC4Zt4K3hr/Ic6Ff8RfR6412qMdNrPcjEW\nHwYaWWdrrw8M0yRthbfKL5W0Ap+/uMdqXKf7VTjuByZVbLGNpI/jS8I+UCdDK29mj5TsRcCbJM0q\n+1/Cw4IcJOkufFh2ZV0MfeGw0ksBrBybCxwC3GZmixvmaPUOd8GfF18EvmZmS0mt06AqFEnHSdpP\n0hYAZrbGulYR+zowQdLBZfsR/GH+27J9HB7LZh8zW9EgwzRgtZl9r1y/ibluGijDADmqtjge/9E0\nbYspuEPEA2b2j3hXf4GZLRkowwA51tnCzF7AW37LrTQJG2KYik/Afwzvrd2Oe/ZcPVCGAXJUy8Ue\n+BDgfDMbcKOrJ4Zux2VmD+GNifNb+83sNjM7BfgnYHczWzZQhoFymNlaM7NSya4C/tDMLusAx+/K\n4Z3wxuc+ZnblYDiGpfrbpcGHSKbjK43djbvf3kSXN8zFwFuB1wCn4A+HllfRHcDBJT/g8MyDZPgC\ncEAd3bsabbFJBxmi2GLQHDUwHBTEFgeV/Mg2MlxEt2FFfJjrq8CHgY8BsxuwxatxXAHMDMBxFd4A\nG1NHGRmuqb83peXaOAtYVvIjy02/pWxPrJw/AffO+AzeRfwqvbjbDSWGKBwRGKJwRGCIwtFfhsp1\nW+PL0v43cFTTtuiFY+Fw4dgYUl9vyCjgErzVsi8eZuDayvERwFPAfq3zqzezXHPiIAtFxxmicERg\niMIRgSEKxwAYRnZjuAY4uwO2GNYcG1Pqy00R3t1bBhyLjyueATwN7Fo5713AVyrbRwB71gIZgCEK\nRwSGKBwRGKJwDJJhr5If8NBrcmQyM9ZFud2AxgPzgEPN7DlJz+ATU0/htf8hxRPjVuBASduZ2ZP4\nJPNzffj8vigCQxSOCAxROCIwROEYDMOvAczs5eSonWOj0qt6eZmHZn4S90QCj1XzNPBvwFxJJ5p7\nrWwDrCk3BXMPkYfrgIzAEIUjAkMUjggMUTgiMCRHqq9uw58D5kmaXm7UI8Cv8BfC5km6HZ9Y/Das\n93ZznYrAEIUjAkMUjggMUTgiMCTHxqy+jIvh7nYXAWdV9t1HV8TNA6jBQyY6QxSOCAxROCIwROGI\nwJAcG3fqyxwKZvZzSbcBF0p6DH9r90XKuhVm9pW+fM5gFIEhCkcEhigcERiicERgSI6NXP2s8Q/D\nw0X8kMo6Ik2mCAxROCIwROGIwBCFIwJDcmycScXgfZak0V4PrQtF0LgiMEThiMAQhSMCQxSOCAzJ\nsfGp3xVKKpVKpVI9qdE15VOpVCo1fJUVSiqVSqVqUVYoqVQqlapFWaGkUqlUqhZlhZJKpVKpWpQV\nSiqVSqVqUZ/elE+lUoOTfL36u/FottOBNXiwQgHPm9kbOoiXStWifA8llWpYkhYDvzGzSzvN+j5W\nEgAAARxJREFUkkrVqRzySqWa13pRbSU9V/7uJ+keSbdKekzSBZKOlrRS0nclzSjnTZL0mbJ/paS9\nO/FPpFLdlRVKKtV5VYcJdgVOAl6HrzS4o5nNx5ejPbWc81Hg0rL/rcCnGmRNpXpVzqGkUrH0gJk9\nDSDpceDOsv8hYP+SPxj4o8r6HeMkjTWzFxolTaW6KSuUVCqWXqrk11a219L1exUw38xeaRIslXo1\n5ZBXKtV59XelwDuB09ddLM2tFyeVGpiyQkmlOq/eXC172386sHuZqF8FnNwerFSqf0q34VQqlUrV\nouyhpFKpVKoWZYWSSqVSqVqUFUoqlUqlalFWKKlUKpWqRVmhpFKpVKoWZYWSSqVSqVqUFUoqlUql\natH/AyoWxDK0xbh4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb0d96f8780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sess_rm = sess.rolling(window=50, center=True).mean()\n",
    "sess_rm.head(10)\n",
    "sess_rm.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Conclusions\n",
    "  - Timeseries Analysis Works in Tabular Data\n",
    "  - Datetime and TimeDelta Representation has nice features, but still Needs to be fully implemented in Numpy and Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Discussion/Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}