13_timeseries2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Moving Window Functions\n",
    "* sometimes we want to do manipulation on a chunk of a dataframe by time period, e.g. 5 mins, instead of using groups\n",
    "* we can do this by just indexing into the dataframe with a date range comparison\n",
    "* let's look at examples on the stock market with amazon\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997-05-15</td>\n",
       "      <td>2.437500</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.927083</td>\n",
       "      <td>1.958333</td>\n",
       "      <td>1.958333</td>\n",
       "      <td>72156000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997-05-16</td>\n",
       "      <td>1.968750</td>\n",
       "      <td>1.979167</td>\n",
       "      <td>1.708333</td>\n",
       "      <td>1.729167</td>\n",
       "      <td>1.729167</td>\n",
       "      <td>14700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1997-05-19</td>\n",
       "      <td>1.760417</td>\n",
       "      <td>1.770833</td>\n",
       "      <td>1.625000</td>\n",
       "      <td>1.708333</td>\n",
       "      <td>1.708333</td>\n",
       "      <td>6106800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1997-05-20</td>\n",
       "      <td>1.729167</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.635417</td>\n",
       "      <td>1.635417</td>\n",
       "      <td>1.635417</td>\n",
       "      <td>5467200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1997-05-21</td>\n",
       "      <td>1.635417</td>\n",
       "      <td>1.645833</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>1.427083</td>\n",
       "      <td>1.427083</td>\n",
       "      <td>18853200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close  Adj Close    Volume\n",
       "0  1997-05-15  2.437500  2.500000  1.927083  1.958333   1.958333  72156000\n",
       "1  1997-05-16  1.968750  1.979167  1.708333  1.729167   1.729167  14700000\n",
       "2  1997-05-19  1.760417  1.770833  1.625000  1.708333   1.708333   6106800\n",
       "3  1997-05-20  1.729167  1.750000  1.635417  1.635417   1.635417   5467200\n",
       "4  1997-05-21  1.635417  1.645833  1.375000  1.427083   1.427083  18853200"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"datasets/AMZN.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8e599eb70>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Let's look at the highs and lows\n",
    "amzn=df[[\"High\",\"Low\"]].set_index(pd.to_datetime(df[\"Date\"]))\n",
    "# just by this year\n",
    "amzn=amzn[amzn.index> '2020-01-01']\n",
    "import matplotlib.pyplot as plt\n",
    "amzn.plot()\n",
    "# notice we have two lines!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* The `rolling()` operator lets us specify a window size and apply a function that works on that much data to every point in our time series\n",
    "* Close your eyes and imagine now one of those stock prices over time. How would we calculate the 7 day moving average?\n",
    "* First, what does the 7 day moving average even mean?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Now, what's the algorithm you would use to calculate it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "3108.1614467142854\n",
      "3144.00143\n",
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      "3185.7214355714286\n",
      "3197.2157157142856\n",
      "3200.7814244285714\n",
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      "3218.0871581428573\n",
      "3256.9785852857144\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "for i in range(7,len(amzn)):\n",
    "    print(np.mean(amzn.iloc[i-7:i][\"High\"]))\n",
    "\n",
    "#a loop. ickers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* In pandas, the `rolling` function allows us to do this with arbitrary window sizes and a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: Currently, 'apply' passes the values as ndarrays to the applied function. In the future, this will change to passing it as Series objects. You need to specify 'raw=True' to keep the current behaviour, and you can pass 'raw=False' to silence this warning\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2020-01-06</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-10</th>\n",
       "      <td>1905.364258</td>\n",
       "      <td>1877.561436</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-13</th>\n",
       "      <td>1905.362828</td>\n",
       "      <td>1879.940011</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-14</th>\n",
       "      <td>1905.492833</td>\n",
       "      <td>1879.090018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-15</th>\n",
       "      <td>1901.945696</td>\n",
       "      <td>1878.388585</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-16</th>\n",
       "      <td>1897.902832</td>\n",
       "      <td>1874.671439</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-17</th>\n",
       "      <td>1894.422834</td>\n",
       "      <td>1870.501448</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-21</th>\n",
       "      <td>1891.058559</td>\n",
       "      <td>1865.387155</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>1890.424282</td>\n",
       "      <td>1865.864293</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>1889.278564</td>\n",
       "      <td>1864.715716</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>1890.404279</td>\n",
       "      <td>1863.128558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>1884.995710</td>\n",
       "      <td>1857.449986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>1881.069999</td>\n",
       "      <td>1852.307129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>1879.371425</td>\n",
       "      <td>1851.988560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>1876.314279</td>\n",
       "      <td>1850.647130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>1898.202846</td>\n",
       "      <td>1867.637137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>1920.848563</td>\n",
       "      <td>1885.849993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-04</th>\n",
       "      <td>1944.392857</td>\n",
       "      <td>1909.840001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <td>1977.252860</td>\n",
       "      <td>1940.791434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>2005.565726</td>\n",
       "      <td>1968.617153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>2037.534302</td>\n",
       "      <td>1994.771432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>2075.067174</td>\n",
       "      <td>2028.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>2093.671457</td>\n",
       "      <td>2047.354283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>2112.492885</td>\n",
       "      <td>2069.502860</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-13</th>\n",
       "      <td>2128.275739</td>\n",
       "      <td>2087.592861</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-01</th>\n",
       "      <td>3440.671456</td>\n",
       "      <td>3357.947161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-02</th>\n",
       "      <td>3465.232875</td>\n",
       "      <td>3390.680001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-03</th>\n",
       "      <td>3483.948591</td>\n",
       "      <td>3395.822858</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-04</th>\n",
       "      <td>3473.914307</td>\n",
       "      <td>3362.474261</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-08</th>\n",
       "      <td>3445.035749</td>\n",
       "      <td>3327.045689</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-09</th>\n",
       "      <td>3426.437151</td>\n",
       "      <td>3298.259975</td>\n",
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       "    <tr>\n",
       "      <th>2020-09-10</th>\n",
       "      <td>3405.707136</td>\n",
       "      <td>3264.767125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-11</th>\n",
       "      <td>3363.345703</td>\n",
       "      <td>3210.049979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-14</th>\n",
       "      <td>3311.222831</td>\n",
       "      <td>3154.237130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-15</th>\n",
       "      <td>3266.452846</td>\n",
       "      <td>3126.511405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-16</th>\n",
       "      <td>3238.701416</td>\n",
       "      <td>3121.228550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-17</th>\n",
       "      <td>3207.069964</td>\n",
       "      <td>3098.735700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-18</th>\n",
       "      <td>3169.158552</td>\n",
       "      <td>3058.812849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-21</th>\n",
       "      <td>3113.745710</td>\n",
       "      <td>3016.019985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-22</th>\n",
       "      <td>3101.838553</td>\n",
       "      <td>3004.051409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-23</th>\n",
       "      <td>3093.211426</td>\n",
       "      <td>2989.248535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-24</th>\n",
       "      <td>3078.108573</td>\n",
       "      <td>2968.688546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-25</th>\n",
       "      <td>3065.865723</td>\n",
       "      <td>2957.952846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>3086.667167</td>\n",
       "      <td>2978.612828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-29</th>\n",
       "      <td>3108.161447</td>\n",
       "      <td>3011.041399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>3144.001430</td>\n",
       "      <td>3048.611398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>3156.860003</td>\n",
       "      <td>3073.154262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-02</th>\n",
       "      <td>3166.688581</td>\n",
       "      <td>3091.814279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05</th>\n",
       "      <td>3185.721436</td>\n",
       "      <td>3116.935721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-06</th>\n",
       "      <td>3197.215716</td>\n",
       "      <td>3129.935721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-07</th>\n",
       "      <td>3200.781424</td>\n",
       "      <td>3132.110003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-08</th>\n",
       "      <td>3207.214286</td>\n",
       "      <td>3138.174282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>3218.087158</td>\n",
       "      <td>3147.294294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>3256.978585</td>\n",
       "      <td>3171.230015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-13</th>\n",
       "      <td>3299.347133</td>\n",
       "      <td>3214.261440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low\n",
       "Date                                \n",
       "2020-01-02          NaN          NaN\n",
       "2020-01-03          NaN          NaN\n",
       "2020-01-06          NaN          NaN\n",
       "2020-01-07          NaN          NaN\n",
       "2020-01-08          NaN          NaN\n",
       "...                 ...          ...\n",
       "2020-10-07  3200.781424  3132.110003\n",
       "2020-10-08  3207.214286  3138.174282\n",
       "2020-10-09  3218.087158  3147.294294\n",
       "2020-10-12  3256.978585  3171.230015\n",
       "2020-10-13  3299.347133  3214.261440\n",
       "\n",
       "[198 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here, let's do a 7 day rolling on the dataframe\n",
    "# Note the output we get back\n",
    "amzn.rolling(7).apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: Currently, 'apply' passes the values as ndarrays to the applied function. In the future, this will change to passing it as Series objects. You need to specify 'raw=True' to keep the current behaviour, and you can pass 'raw=False' to silence this warning\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8e58a7358>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# there are the usual benefits (faster, less error prone, not icky) versus iteration\n",
    "# we can also control those nan's by specifying how much data we want to be present\n",
    "# before applying the function\n",
    "amzn.rolling(7,min_periods=1).apply(np.mean).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: Currently, 'apply' passes the values as ndarrays to the applied function. In the future, this will change to passing it as Series objects. You need to specify 'raw=True' to keep the current behaviour, and you can pass 'raw=False' to silence this warning\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8e58b9390>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# here's a 30 day moving average\n",
    "amzn.rolling(30,min_periods=1).apply(np.mean).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Time Series and Gaps\n",
    "What do we do when we have missing data in a time series?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DD</th>\n",
       "      <th>MM</th>\n",
       "      <th>YY</th>\n",
       "      <th>London</th>\n",
       "      <th>Bristol</th>\n",
       "      <th>Liverpool</th>\n",
       "      <th>Manchester</th>\n",
       "      <th>Newcastle</th>\n",
       "      <th>Birmingham</th>\n",
       "      <th>Sheffield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>511.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DD   MM    YY  London  Bristol  Liverpool  Manchester  Newcastle  \\\n",
       "0  10.0  1.0  48.0     NaN      3.0       40.0        22.0       58.0   \n",
       "1  17.0  1.0  48.0   240.0      4.0       51.0        19.0       52.0   \n",
       "2  24.0  1.0  48.0   284.0      3.0       54.0        23.0       34.0   \n",
       "3  31.0  1.0  48.0   340.0      5.0       54.0        31.0       25.0   \n",
       "4   7.0  2.0  48.0   511.0      1.0       89.0        66.0       27.0   \n",
       "\n",
       "   Birmingham  Sheffield  \n",
       "0        78.0        9.0  \n",
       "1        84.0       11.0  \n",
       "2        65.0       11.0  \n",
       "3       106.0        4.0  \n",
       "4       142.0        7.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# let's look at some data reported cases of measles in England and Wales\n",
    "df=pd.read_csv(\"datasets/ewcitmeas.txt\", delim_whitespace=True, dtype=float, na_values=\"*\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>London</th>\n",
       "      <th>Bristol</th>\n",
       "      <th>Liverpool</th>\n",
       "      <th>Manchester</th>\n",
       "      <th>Newcastle</th>\n",
       "      <th>Birmingham</th>\n",
       "      <th>Sheffield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1948-01-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-01-17</th>\n",
       "      <td>240.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-01-24</th>\n",
       "      <td>284.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-01-31</th>\n",
       "      <td>340.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-02-07</th>\n",
       "      <td>511.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            London  Bristol  Liverpool  Manchester  Newcastle  Birmingham  \\\n",
       "1948-01-10     NaN      3.0       40.0        22.0       58.0        78.0   \n",
       "1948-01-17   240.0      4.0       51.0        19.0       52.0        84.0   \n",
       "1948-01-24   284.0      3.0       54.0        23.0       34.0        65.0   \n",
       "1948-01-31   340.0      5.0       54.0        31.0       25.0       106.0   \n",
       "1948-02-07   511.0      1.0       89.0        66.0       27.0       142.0   \n",
       "\n",
       "            Sheffield  \n",
       "1948-01-10        9.0  \n",
       "1948-01-17       11.0  \n",
       "1948-01-24       11.0  \n",
       "1948-01-31        4.0  \n",
       "1948-02-07        7.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what a painful date time format! Welcome to my world!\n",
    "df.rename(columns={'DD': 'day', 'MM': 'month', 'YY': 'year'}, inplace=True)\n",
    "df['year'] = df['year'] + 1900\n",
    "df=df.set_index(pd.to_datetime(df[['year', 'month', 'day']])).drop([\"day\",\"month\",\"year\"], axis='columns')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# we can setup some features for matplotlib here\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.figsize'] = 12, 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8e5717e48>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# instead of the line plots we have been using, let's look at a scatter plot\n",
    "df[\"London\"].plot(style=\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* We notice several things here, first, the seasonality of the measels is shown. Second that the resultion of weekly makes sense sometimes, but at other times it seems like we could be helped with more frequent data collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1967-10-13      72.0\n",
       "1987-12-11      95.0\n",
       "1948-12-18     180.0\n",
       "1971-09-10      92.0\n",
       "1949-03-19    1231.0\n",
       "Name: London, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I want to show you how to deal with holes in your data\n",
    "# I'm just going to pull out 500 obsertvations as an example into a new dataframe\n",
    "df2=df[\"London\"].sample(500)\n",
    "# Now, look at the randomness of dates/values in the dataframe itself\n",
    "display(df2.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1948-01-31    340.0\n",
       "1948-02-01    340.0\n",
       "1948-02-02    340.0\n",
       "1948-02-03    340.0\n",
       "1948-02-04    340.0\n",
       "Freq: D, Name: London, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One way of filling holes is to resample and forward fill values\n",
    "df_ffill=df2.resample(\"D\").fillna(method=\"ffill\")\n",
    "df_ffill.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8dd6a4550>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's compare these two\n",
    "df2.plot(style=\".\")\n",
    "plt.figure()\n",
    "df_ffill.plot(style=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: Currently, 'apply' passes the values as ndarrays to the applied function. In the future, this will change to passing it as Series objects. You need to specify 'raw=True' to keep the current behaviour, and you can pass 'raw=False' to silence this warning\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8dd4528d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What if we applied a rolling window?\n",
    "import numpy as np\n",
    "df2.resample(\"D\").asfreq().rolling(10,min_periods=1).apply(np.nanmean).dropna().plot(style=\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* We have another great option in pandas, which is the `interpolate()` function.\n",
    "* interpolate fills na values in different ways depending upon the parameters you pass\n",
    "* it actually has some specific time series benefits but only for higher frequency data (day or better)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8dd36e320>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.resample(\"D\").interpolate(method=\"linear\").plot(style=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8b3c90908>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7GYoZf0lGMsEFTWSXvq4U1q3tQe+SOfj0Cl60ExHF3YcZjb0BwINCiK8DGAJwLQBIKXcLIR4E8DqAAoBbpJTe0u9vAvgBgJkAnir/RzEzMDgM11eG8ZkL5nNBE1klncnhO4/vQr4oAZzEc28cxeZvRL/wrkNpX6duExGRGZMKmKWUzwF4rvz1MIDP1fm+2wHcHrB/O4DeyZ4knV28kgy3PLzkP944inQmx6CZrKEO1/H6IEf9HO1V2tfNbknirmf3o7+7na8PIiKDPkyGmeiMSV+KuVDUE4wQNaq/ux1NCVHOMOvrg7z74Mmq7f/1wpuQUqI56bC1HBGRQQyYSTu1JMMRbCtHdunrSmHzzZ/Clh1ZCABXr+rUEqyqDWOK5ReKrgw3EREFY8BM2vV3t6Mp6SBfcCEEcNOl5zMQIOv0daW0Py/VkoymhIDrSk76IyIyjAEzGeG6pUbMUgLf/+UBXNGzkEEzWSWdyWnPMOdG8hAYb1x/7eqlWDJ3JmuYiYgMY8BM2qmDS3i7mWyTzuTw1Xu2VmqYH0pntXTJSLU2V8oyJEoZ5+vXLIv0mERENLFGBpcQTSlvQZWHt5vJNvW6ZETNyzADpQyzrgmDREQUjgEzadfXlcJ3fr8Xy+fPwvJzz8F3fq+H2WWyiqmLOjXDnOKkPyIiK7Akg7SrHgoBfOdfd2PlwjYGzWQNU10y1BpmZpiJiOzAgJm0MzUUgmgyTHTJUDPMp06PcXAJEZEFGDCTdqaGQhBNVjqTw8DgsLaA1Z9hBoCNL7wJl4NLiIiMY8BM2nk1zA+8PIQFs2fgTz7zEQYCZJ10JocbNg4gX3C1Baz+DDNQGlwiwbswRESmcdEfaZfO5LD+id14NXsSP//1Eex955TpUyKqMTA4jHzBhSvNdMkAgIQjkBC8C0NEZBozzKTdwOAwRsdcSAAFV2LdY7u46I+sk2pthiNKBRImumQApSmYbTObWMNMRGQYM8ykXX93OxLOeB7NlVJL9o6oUd5dkKIr4QiBdWv1tD70Z5gdAG0zm3DLZcsZLBMRGcaAmbTr60rhpkvPhyNKrbOaebuZLOOVY0gAUkpt7d36u9vRlHQgACT5uiAisgYDZtIuncnhB1sPQMpSjaau7B1Ro/q729GcdMzUD0tZ/ScRERnHGmbSzp+9K7oSuw6eNH1KRFX6ulK476b+yuASXR7ZkcVYUVZeG+yMQURkB2aYSbv+7nYkyzXMEsDD6SzSmZzZkyIK8MiOLDa/NIQbNg5E/hxNZ3J4aPtblUV/iYSDVGsz7np2P18fRESGMWAm7fq6UvjsynMr28WinpZdRJOhu63cwOAwCm4pXBYAPnPBfKx/Yjfu+OleLQE7ERHVx4CZtEtncnhu75HKdiLBxU1kH911zP3d7UgmSgv+mpIOzm1r0d4HmoiIgrGGmbRTM2l/0NfJOk2yjlfHrHM0tn/BX8/iOWhOOhgruBxcQkRkGANm0s7L3I0VXCQcAYFS1plBM9kknclpDZa9C0lvwV9uJK8/YCciokAMmEm7vq4U1q3twQMvD+H1Q+9i80tD2LIji/tu6mdQQFZIZ3K4YeMA8gUXzUlHy3PTK8kYK7iVMqW+rhRfE0REFmANM2nnTVF7NXsSY0XJGk2yju4FfxXswUxEZCUGzKSdvw8zgMoiJ9Zoki28siEHgBACqdbmyI+plmTwApKIyB4MmEk7f/eB5oTA9WuWsRyDrOKVDTmOgCsl1j+xO/K2bv4uGewcQ0RkFwbMpJ0XjFy8vAN/fMn5WDx3pulTIqqRG8nDlZpLhpSSjHQmx8ElREQW4KI/0s6rYR4dc/GLfcfgCGhbWEXUKH83Fx0lQ/6SjIIrsWVHFo/syGpdeEhERMEYMJN2ag2zP4PHgIBsobsPc6q1GeX25HAlcOzUaM3CQ74+iIjMYMBM2nmZu/yYCxeAo2mSGtFk6WzrlhvJQwCQKNXKdbS1cHAJEZElGDCTdv7MXaq1GbmRPAczUOz1d7ejqRwgJ5MOrlnViWtWdXJwCRGRBRgwk1ErF7YxECDyKIv+OLiEiMgODJhJO/8UtaQjcO3qpbh6VScDA7JKOpPDlh1ZCEDL8zOoDzNfE0REdmDATNr5p6jlixKbtnE0Ntklncnhq/dsRb5YyvQ+lM5i8zeifX7q7spBRESNYx9m0s4LDER5W4KjsckuA4PDGCuOj6fW8fz09ydft7aHF49ERBZhhpm08xb9bdmRxcPpLIpFZtTILv3d7WhKiEqGWcfz0+tPni+4ePnAcaxc2AYAXPRHRGQBBsxkzJK5M/Gd3+thlwyyTl9XCptv/pT2GmZ/32UOLiEisgcDZtLOv+iPgQDZSneHiv7udiQTpRrmRKJUssTBJUREdmANM2lXtehvzMWdP3sD6UzO9GkRmedrK9ezeA6akw4SHOxDRGQcM8yknTrp78X9x/DygePMNFOsqW3lciN5raO5iYioPmaYSTtv0d8lKzrgCFTdciaKK68kQwBIJJxKkHzLZcsZLBMRGcaAmYxZOq8VyQRvOZO90pkc7np2v76SIWXSHxER2YElGaSdOunvuouWcdIfWUf34lRO+iMishczzKSdf9Ff0ZVYPHcmAwOyjtrmLeqSoaCSDO0ZbiIiCsQMM2nnHwGccAQOnjiNdCbHoJmskmpthiMEAKmvZMhXkrH3nVOVQSZsv0hEZBYzzKSdNwK4d8kcSACbXxrCDRsHmEUja3hT94quhCOEllHVaknGU7sOac1wExFRfQyYSTsvGHk1exJjRcmAgKzjlWNIAFKWWrxFzbvz4i2CvbJ3EfswExFZgiUZpJ0/GAEAAQYEZBd/2ZCu56Z35+WpXYdwZe8iXL9mGVYubGMfZiIiCzBgJu3UGuZrVy9llwyyitcrXGew6t15yRdcvHzgOFYubNM+npuIiIIxYCbtTAQjRJOlO1gN6srB1wYRkR1Yw0xG7X3nFNtmkbV0tnULaitHRER2YIaZtPMGQoyOleqYHQG2zSLr6B5cAqBm0l86k+OdGCIiCzBg1ogffiXqoj/egiYb6S6RUNvKbdmRxSM7suzDTERkAQbMmqQzOVx79y/hylJG9aH/fHFsP/y8RX/5MRcuSr8Pdskg2+julKEeTwCsaSYisoSQ3i1AS61evVpu377d9Gl8aFfe+Tz2vHOqsv2xhW146tZPGzwjs7xse6q1GbmRfOyz7mQn3XeFNm0bqrSVW7mwDTdsHKgE0MwwExFFSwiRllKuDnqMGWZNfnP0vdDtuGG7LJoOdD5P1bZy993Uz24yRESWYMCsSWtzEvnTY1XbccYMM1G1oJrpWy5bztcFEZEF4h21abRozgyc8AXMi+bMMHg2ZrFLBk0HussxvLZyYwWXbeWIiCzDgFmTd0cLodtxwi4ZZDsjLeUAtpUjIrIUA2ZNZjYlQrfjhF0yyHb+8ojRMRdbdmQjD1jZVo6IyF4MmDXpXTwb+4+8V7UdV/7R2KxhJhv1d7cj6Qjki6UA9uF0Ftes6oz0Ocq2ckRE9uJobE2G38+HbsfVyoVtXNhE1unrSuHa1UshytvFYilgjfqY69b24OLlHVi3tgdXr+pEc9KBA0AIgVRrc6THJyKi+iYMmIUQM4QQLwkhfiWE2C2E+Ovy/nlCiKeFEPvKf6Z8P/NtIcR+IcReIcQXfPv7hBCvlR/7ByGECDrm2ahn0ezQ7Tjx6kPv+OlefPWerfjLH7+GdCZn+rSIqly9qhMtTQ4SmkqGvLZyL+4/hvVP7AYArFvbAwig4Eqse3wXXydERIY0kmEeBfC7UsrfAfBxAF8UQvQDuA3AM1LKFQCeKW9DCHEhgOsA9AD4IoDvCiG8gt3vAbgZwIryf1+cwn+L1bjob5y/PjRflNi0bQg3bBxgMEBW8UqHvvX5lVrqh4Payj239wjc8jrAQlHin/7jN5GeAxERBZswYJYlXvFtU/k/CeAqAD8s7/8hgC+Xv74KwP1SylEp5ZsA9gO4SAixCMBsKeVWWRov+CPfz5z1jp0aDd2OE69W07u9IDEeIBDZpK8rpa1kyHtd+DPauw+9W/U9gzEfeEREZEpDNcxCiIQQYieAIwCellJuA7BASnkIAMp/nlv+9iUA3vL9eLa8b0n5a3V/LHS0tYRux4mXufvqmmU1AQJRXKk1zABwMHe66nvmzWIdMxGRCQ11yZBSFgF8XAgxF8CPhRC9Id8eVJcsQ/bX/gVC3IxS6QaWLVvWyClar3fxnNDtOFoydya+83s97JJBhNrR2Nes6qx5g5zDhX9EREZMqq2clPKEEOI5lGqPDwshFkkpD5XLLY6Uvy0LYKnvxzoBHCzv7wzYH3ScewDcAwCrV68ODKqni03bhvDUrkMYHStW7d998KShMzLP2FAIIoupNczeFEzX9w4Ym1XSRESWaaRLxvxyZhlCiJkALgfwawCPA/ha+du+BuCx8tePA7hOCNEihDgfpcV9L5XLNk4JIfrL3TFu9P3MWWnTtiH8xY9fwy/2HcNLB6oXtE3rq4APKWhxE5GN0pkc7np2v5YFqd5obAEgkXBwzapOXP6xBVXfE+dSLiIikxrJMC8C8MNypwsHwINSyieEEFsBPCiE+DqAIQDXAoCUcrcQ4kEArwMoALilXNIBAN8E8AMAMwE8Vf7vrPX9FwbrPja7Jb4zY/wDGhKOwMETp5HO5JhlJqsYuROijMbu7phV9XCc3zeIiEya8N1XSvkqgE8E7B8G8Lk6P3M7gNsD9m8HEFb/fHYJaTOtrn6PE2/R35YdWTyczmLzS0PYsiPL0gyyStCdkCifn+po7IHBYWxV7r7E+X2DiMgkTvqLUNj46yt7F2k8Ezu9dXwEhSJLM8hOqdZmOELA0dTFRW0rl2ptrlnrwPcNIiIzeH8vQvXGXyccgZUL2zSfjT28W92jY+MLm9hWjmzidawouhIJR2Dd2p7I7354beWe2nUIV/YuQm4kj6I7/rgAYv2+QURkEjPMEao3/tot326NK+9Wt0TpCXjJ8g6WY5BV/M9RKSVyI8EXv1NJHY1dynCPPy4BbNmRrfvzREQUHQbMEao3/tpxBFIx7qfqv/Xc3OTg1ssvYLBMVgmauhc1tWY6N5LH55QuGWwrR0RkBksyIlTvw811JdY/sRsrF7bFMlD0Fv0NDA4j1dpcybbH8XdBdvI/R3UN1fF3j/GC9FOnx6q+p4cDj4iIjGDAHKGrV3Xi/peHquoQgdKtVR2r7m3m/bs5wIRs1deV0vp8VGuYAWDjC29WfY+O0hAiIqrFgDlCe985VRMsA1zkls7kMDA4jIMnTmtt20Vks6DR2AW3esRRnEu5iIhMYsAcoad2HarZlxBA75I5+Monl8UyOPQPg0g6AsmEg2LRjfUFBNnLu7jTUZYROBobgP+ae5fSZo6IiPRgwByhK3sX4Rf7jlXtK0rg1exJ7D0czxpmf1BQdCW+ctFSLJk7U1udKFGjdE/6U2uYexfPKS2E8CWZueiPiMgMBswRWrmwDU0JgbFi9W3VONcwq0HBNas6Y/c7oOlB96S/oD7MSkUGF/0RERnCgDlCA4PDKKqfeChlieJagmCi+wDRmQjqWhEltYZ53doeOAKVoFmAi/6IiExhwByh/u52JBMO8oVSFaIjSv/1LI5vDbNKZ40o0WTovrhTM9rP7j1SlWF2HMTyIpuIyAYMmKMmS594AsCiuTNx5N0P8Nrb8a1hVhf9QQgUimwrR3bS2VpOzWgfefeDqscDblYREZEmnPQXoYHB4Ur9sgTwdu40xoqyqiYybqqyaEWJMaVGlCiuvBrmi5d3YN3aHpzfMavqcSnB1wgRkSHMMEeov7sdCUfU9FKNcw2zP4uWKGeY2VaObKS7XEitYe6YVd1zWYAlGUREpjBgjlBfVwrrr+rFXz36GrxGGY4AfivGfZjVulAArGEm6+huKQfU1jCP5ItVjy+eO4OvESIiQ1iSEbHr1yzDN/637kr/VFcCr719Euuf2I10Jmf03GzQ15XCLZctZyBAVvEHr6NjLrbsyEZ+TO/uS6I8CfQPVy+tevyWy1ZEfg5ERBSMGeaIbdo2hHt+MeifPRDrUdAmMndEk9Xf3Y6kI5AvSkgAD6ezWnqGX72qE6L8Z19XCsvaZ+GBl2DUnRcAACAASURBVIewYPYMrFzYFumxiYioPmaYI5TO5LDusV01q9sdEd8aZvW285YdWdz17H5m28kqfV0pXLt6aeXOULEY7aJU70Ly/peGqrLZKxe2Ye/hU/jZnsO4YeMAXydERIYwYI5Q0OCShAN89aJlsc2s+m87JxyBh9NZ3PHTvQwGyDpXr+pES9N4iUSUF7hBUwXD9hMRkV4syYhQf3c7WpocfDDmVvZJFxg6PmLwrMzyL/o7eOI0Nr80FOsSFbKXzsEl9aYK6p42SEREwYSUdnfDX716tdy+fbvp0zhjm7YNVXXJAEolGazfHb8N7QUDcf99ULxt2jaEp3YdwpW9i3D9mmWV/ZyGSUSkhxAiLaVcHfQYM8wRy43ka2qY455R9QcAOkcPE9lK7cHsnwKqc9ogEREFY8AcsaDhJXEeXBLUJeOWy5abPi0io4JqlRkkExHZg4v+IuYNL0k4AgKlRX+/3TkH69b2xPIDkV0yiGqpPZj9F9PpTI6vESIiw5hhjlg6k8Oze49AylI/16JbGlyy9/DuqtuucaGOxn44nUWhyJ7MZCdd9cN9XSmsW9tTqWH2jsW+5UREdmDAHKF0Joev3rMV+WJ1EXOcb7uySwZNFzqD1Xo1zCzVICKyA0syIjQwOIyxYm0XkjgPLvHrWTyn7m1oItN09kCud6ywUg0iItKHGeYI9Xe3oykhqjLMCQe47pPLKqNv40bN2q1b24PcSJ5dMsg6qdZmOEIAkJEHq/3d7UgmyqVKifFj6ewFTURE9TFgjlBfVwqbb/4U/nzLq9h/5D0AHFyiZtJyI3l2ySDreCUSRVci4Qg9i3S9nvhKb3y2lSMiMo8lGRoMDb9f+doF8OL+Y7EdBc1bzDQdeBd2EoCUErmRfOTHK7jewmBZVf7BLhlEROYxwxyxoDrmOC/gUW8xA8Bdz+7n7Wayiu6R1PWOxy4ZRER2YMAcMQ4uqW/vO6cqnQEYDJBNdNcO12srxy4ZRER2YMCswWUfPRfP7DkMKYFEQuArq5dy0V/BhSMEXCkZDJCVdNYO12srpzvTTUREwRgwR8gLDj8Yc8d3yto2c3Hiz5hBSjiOgNDQhYDoTOgaXFIvk8wuGUREdmDAHKGBwWGM+oNlAAUX2LRtCFt2ZGNZgqBmzNhWjmyls344LJPMLhlEROYxYI5QUP0yAEjEtwSBGTOaLnTWD9erYSYiIjswYI5QX1cK66/qxV89+hr8jTLiPumPGTOaDnTWD9erYSYiIjswYI7Y9WuW4bm9R/DT1w8DKHXIWDavFTd/+iOx/UD014UCYLaZrKTzbkhYNltXHTUREdXHgDli6UwOz+09UtmWKE36W//E7lhmkfx1oUlHAEKgUGRbObKTrrsh7MNMRGQ3TvqL2ESDS+KmKpNWlBhTsmpEceTVMF+8vKNqDHdQ5pmIiPRjhjliHFxSzZ9JS5QzzMUie8xSvLEPMxGR3Rgwa/CJZXOx/UAOEkAy5oNLgkZjsz6TbJTO5LBlRxYCiPz1yj7MRER2Y8AcoXQmh6/esxV5X0mGjPngEhU7ZpCN1NfuQ+ksNn+DfZiJiOKKAXOEguqXizEfXKIuYuLgErKR+tplH2YionhjwByh/u52NCVEVYYZiPfgEv+t5/yYi3WP7YIrJTsAkFXU167JPsxsK0dEZB4D5gj1daWw+eZPYcNTe/DygVxlf5wHl/hvPQsh4EqpZZIa0WR4r13TNcxsK0dEZAcGzBq8MnSiavuS5R249fILYvnB51/ElGptxvondrMDAFlJZ+1wvRpmneO5iYioPgbMERsYHK5qKQcAM5sShs7GLisXtrEDABHq1zCzrRwRkR0YMEesv7sdCQH4y5iffv0wnt93NJa3V4NuMd9y2XLTp0VkVL0aZraVIyKyAyf9aSAcUbXtX/QXN5xcRtNJOpPDXc/uRzqTm/ibP4Sw10VfVwq3XLacwTIRkUHMMEfskR1ZFJQuGVz0N36LOdXajLue3c/sGVlH54I7ll4QEdmNAXOE0pkcHtr+Vs1+LvqrXvTHDgBkI50L7tiHmYjIbizJiFDQ4BKAi/68W8y5kTzLM8haXtY3oeGOkFfD/OL+Y1j/xO6qEhBdZSFERFQfM8wR6u9uR8IRNV0y4rzoDxgfxJBqbeZtaLKWzgV37MNMRGQ3BswR6utKYf1VvfjLH78Gf8gc50l/HI1N04muXszsw0xEZDcGzBFbubANjiNQ9GWZBfQu+rv1/lfw3BtH8dkL5uPO6z6h5Zj1qAFAbiTPtnIUe+zDTERkNwbMERsYHK4KlgHgigsX4E8+8xEtmaJb738Fj+48CAB4dOdBHDj2Ph79vy6N/Lj1sEsGTRfpTE7baGz2YSYishsD5oilWptr9h1+9wNtx//33YertndmT2LTtiFcv2aZtnPwY5cMmg7SmRy+es9W5MuLdh9KZ7H5G9E9P8NKL3SO6CYiomDskhGx3Ei+Zt+vsidxw8YBLavem5OiZt8DLw9FftxG7D54kl0yyEpqh5uon586O3IQEdHkMcMcsVOnxwL361rAs3JBG146UB2YtyTNXSf5F/0lHYFkwkGxyPpMskt/dzuaEqKSYY76+ck+zEREdmPAHLEnXj1YtS2gd9Lf3ICSkBUL2iI/bj3+W89FV+IrFy3FkrkzWZ9JVunrSmHzzZ8yXsPsf5x1zERE5jBgjlA6k0P2RHW9clNC4E8vv8DoB1/P4jlGjgvULvq7JuJAhOhM6awdDqthZi9mIiLzJrw3L4RYKoR4VgixRwixWwjxp+X984QQTwsh9pX/TPl+5ttCiP1CiL1CiC/49vcJIV4rP/YPQojaAtuzSFDNo9oxw4RdB08aO7a36O9bn1+J+27qBwBOMaPYC6thDgqmiYhIr0aKWQsA/kxK+TEA/QBuEUJcCOA2AM9IKVcAeKa8jfJj1wHoAfBFAN8VQnizoL8H4GYAK8r/fXEK/y3W6e9uh3pFIAHc8dO92hb9dbS11Oyz5Spl7zuncMPGAa2/DyIbeTXMFy/vwLq1PVUZZC4IJCIyb8KSDCnlIQCHyl+fEkLsAbAEwFUAPlv+th8CeA7An5f33y+lHAXwphBiP4CLhBAHAMyWUm4FACHEjwB8GcBTU/jvsUpfVwoPf/NifOuBnTh08jQWz52JoeMjWqd29QaUX5gsyfDfXnaEgCslp5iRtXTVDofVMLMXMxGReZOqYRZCnAfgEwC2AVhQDqYhpTwkhDi3/G1LAAz4fixb3jdW/lrdH3Scm1HKRGPZMjP9gqdKX1cK//HfLgMwHizqnNq1O6D8IqjVnS7+28uQEo4jICCZOSPr6KwdnmgENnsxExGZ1XDALIQ4B8AWALdKKd8NKT8OekCG7K/dKeU9AO4BgNWrV5sv+p0iJjJFQb+8oGEquqiL/tat7UFuJM/MGVlnoiB2KnEENhGR3RoKmIUQTSgFy/dJKR8p7z4shFhUzi4vAnCkvD8LYKnvxzsBHCzv7wzYTxEKKskwmWHm7WWaLnQGsezDTERktwkD5nIni3sB7JFS/r3voccBfA3AhvKfj/n2bxJC/D2AxSgt7ntJSlkUQpwSQvSjVNJxI4D/OWX/kmnARHuooJIMkxlmFfvLkq10XtxN1IeZiIjMaiTDfAmAPwLwmhBiZ3nfX6AUKD8ohPg6gCEA1wKAlHK3EOJBAK+j1GHjFillsfxz3wTwAwAzUVrsd9Yu+PP4A0Kdt3g9QSUZJjPM6qQ/CIFCkf1lyU66aocnem/ghSURkVmNdMl4AfU7kX2uzs/cDuD2gP3bAfRO5gSnMzWjvG5tj/Y6RbUkI+HAaH1kVWBQlAAkJNglg+yjM0jt725HMlF6b0gkqt8bOLiEiMg8TvqLkJo1yo3ktdfv1maTzXZh9teFJsoZ5mKRC53ILl6QOjpWep6uv6oX16+JuGOPlNV/lpm4M0VERNUYMEfIhpXvar2y60qjH7jq4qaVC9t4q5msMzA4jNExFxJAwZVY99iuSOuKBwaHUXBLd1uKymvUhvcRIqK4Y8AcIXXREADtt1bVDLMjzJZkqIub7rupH7dcttzY+RAF6e9uR8IRKJRH2bsy2gvNsKCYnWWIiMxjwBwx/6Khu57dr/3Wak1HjPr9s7VQby9v2ZFlIEDW6etKYf1VvVj32C64UqLZcFs5Di4hIjIrVgHzjfduw0sHjuOi8+bhR19fo/34Jm6tqhnmoivxlX/aios/0m78d5BwBB5OZ412ybjgL59EvijRnBB44/YvaT022e36Ncu0lQyxrRwRkd0c0yegy433bsPz+47hgzEXz+87hhvv3ab9HLxbq9/6/EptwWFQz+WCK634HVy7eikKxeqMu05esAwA+aLE+bf9ROvxyX59XSncctnyyF+rQQv7iIjIHrEJmF/cfyx0+2wV1nPZ9O+gZ/EcNCcdJASMLGbygmWPBHDr/a9oPQeyWzqTw13P7kc6k4v0ON6dl3qvBV3nQUREwWJTkiEEqqZ46Crl9fdyBfQv+gub6pdw9NczB/Wmzo3kralhfvK1Q7jzuk+YPg2ygM7+x2E1zOzDTERkXmwC5lRrM46+l6/ajpr6QXfNqk7ti/7CMswfmX9OpMcOEtSb2lSXjI62Zhw7Vf37KRSDZiNSHOnsfxxWw8w+zERE5sWmJKPjnJbQ7SioH3QS0F6CEHZh0DZD//WSeus51dps7FZzS6L26e8C2LRtSPu5kH0mKpOYSmE1zDrPg4iIgsUmw/zuaCF0OwpqV4xrVnXimlWdhif9jXv75AeRH1/l7ymbam2uZNVM3GpOOsHXi995fFf0U93Iejr7H7MPMxGR3WITMAtl3Ky6HYWgDzrdmdT+7nYkEyKw1GBmU0Lruah2Hzxp7FZzOpND5vhI4GPqYkCKL139j23tw5zO5PCtB3bi0MnT6O8204qSiMgG0yJgTmdy2PDUHrx1fARf/vgS3Palj03671g0dyayJz6o2tbB/0FnavFOvbqbyz96buTHVvl/B0lHIJlwUCzqH/n7yI5s3cdam81eSJAd0pkctuzIQgC4elVn7PowpzM5XPO9X1a2vVaUDJqJKI6sD5hH8kVce/cvUZ5Qi7ufHwSASQfNai2vjkV/KhOLdx7Zka2bMW2b2RTpsYP4fwdFV+IrFy3Fkrkztd9qPnpqtO5jly7v0HYeZKd0Joev3rO18tp5KJ3F5m9Ed4Fr48K+oIvKX/4mHu04iYhU1i/6y43kK8Gy59Gdb0/671FDRhM33XUv3klncnho+1t1Hzdx0aD+Dq5Z1allMMRkzG+LfkEo2W1gcBhjvgvNqIeJlEqnHAgAiYQdfZiDLipnGC7jIiIyxfoMc6EooYYvZxLoqR2HdXUg9vdhnqhOcaoNDA6joF5t+IQtCIxK0O9A/R2Z1rN4julTIMP6u9vRlBCVDLOWkiFvXYWyvsKmPswXLppt5LhERKZZHzAHWTqv1fQpNCRoSIfOOsVUa3NNdl59XDe1VhOA0U4ZKgEzFxJkl76uFDbf/CltNczexa1EqVTJX5JhqlyjI+BOy1wD7xlERDaYlgHzmdwyP6EEQTpKMtQPuqd2HdL6wZcbyUMZcFjzuG6mfycTkTBzIUH20dmZIqytXNhjUeoNuNPCciUiiivrA+ZkorZ4YrK3zNOZXE3tn46SDPWD7sreRXj5wHFtH3yp1ubQCwOTNcymficTYYaZ/HSVC4WVa5nqwxz0OmC5EhHFlfUB88ymBFxl366DJyf1dwwMDteUJjz3xlGkM7nIPwTVD7qVC9u0ffDZmGFWA4Pr1yzT+juZCDPM5NFZOzxRWzkTfZiDXgeTfe8lIjpbWB8wnx4r1iz6m2x2OCjTWizquf1vauAAUF64VM7mCoGaiwYbaphXLmzTfg5hHMEMM5XorB22sa1c0OtA12JpIiLbWB8wB5nsbUE10yqgadW7wshqd1laSKQONjRVeqAGBlt2ZEu9og0v+vMCgaQjjJeFkB101g57beXGCm5gWzkTgi6oWZJBRHFlfcAcVJIx2UBPzTB/5Nxz8MeXnK89MNOdRQprK+cIGPlQVoMQAViRWROifFEhmEOjEu21w3XaygH6aqn9gt5nWZJBRHFlfcBccGXNdJXJlhKoGebfHHkP65/YHXlbN/VDTvdq99C2coYCQzUIAYAtO7LaF/2pLbO835OuUh2aHvyt3fzbUy2srZypPsxB77O8nCSiuLI+YE46YsozzBLRZzPrfcjpHFwStuhP/VA2xVQHALVlVsIBXBdIsCSDfHQFq2EX06bqm9klg4honPUBcxQZZiD6wCjoQw6A9sEltrWVqxeA6A7cdyu3lit3wVmSQWXpTA53/uwNLcFq2MW0qT7M/d3tSDgCRd9tKi6IJaK4sj5gVjPMZ7JYLTBwjDgwCvqQ050pCssw27Loz7uQ0J1hPnpqtGqbJRnk513YjY65kCjV/EcZrIa1lTN1FwZATT01Wy4SUVxZHzCrGeYzWawWFDhGHRjV+5DTmSny2srlC2pRC+A4diz6S7U2G6nPVDnCXPcUso93YScBOAAuWd6BWy+/wFhbORN3YYL61zPDTERxZX3AXFPDfAaZYTXDrCswUj/kvCB6y46svsUzASvuS8wt+vPfes6N5K3okrFyQRs62lq01JaT/fwXdglHYNm81kiPl2pthiNKl/W2XLQF3ZljhpmI4kotD7aO2hbNW6w2GV6G2dPV3op1a3uMBUaP7Mhi80tDuGHjQM3I7qkU1lbOdSW27MhGdux6vFvPL+4/hvVP7EaqtRnNSQeJiG95q9QuGW8cea9yTlH+P6Hpwbu4/cpFywAhIn29eq+JoivhCGH0vckvKJvMDDMRxZX1AXPSqc2ETjbLoWZKMsMjWgKjdCaHu57dX3WcejW8UQhrKycBPJzOag8O1X9/biSP+27qx7c+v1JrOYbaJcN1pZb/J0GuuOM5dH/7J7jijue0HpfC9XWlsGTuTBSK0b5e/eUfUsrAoDTovSRq/d3tSCqfEMwwE1FcWR8wB2VIJ5vlqOmIgOgDI2/R0B0/3VuVmfJu9erIqKqZdZVXx62TN9FMAEYnmqnPCVGuYdbdVu6KO57DvqPvw5XAvqPv4+N//e/ajm2zG+/dho/+1VO48d5tRs9Dx+t1otdEvfeSqPV1pfC7H11Q2Ta1UJiIyAbWB8zntCSR+JBZDjXk1lHDXC+T7NXwXry8I/JbrxO1lTPWc9g30WzvO6eMBANql4wKzW3l9h19v2r7xOkCNjy5R+s52ObGe7fh+X3H8MGYi+f3HTMaNHulGZHfAQmZ8qfzrpRfOpPDz399ePwUwQwzEcWX9QEzgKqI90yyHOrt94+ce07kwWq9zJRawxtlgDhRhtlEz2F1otlTuw4ZCQZUriw9zUxk3VUPbn/L6PFNe3H/sdDts03QlD8/nXel1PMqKg12mGEmoriyvkvGe6MFJHwB81S0ldMxGrteWzmdvZgnyjCb6DmstpW7sncRXj5w3Pho7IQjAKm/Q8GslgTeHy1W7XPrdjaJB6H0gDQ5S0bHpL+JBpOY6sPc390Ox0FV0MwMMxHFlfUBcxRt5XSMxgaCe6fqnNoVNrgEMFOSobaVu37NMgDQNi7cM7ul+qn/e7+9CMPv57W3lZvZXBswz5rRpO34Nkq1NuPoe/mqbVN0XOCGTfnzf4+Rzhnsw0xEBGAaBMzq4BLvluVkPjzUwNHkgIpGPhynStjgEgBGUnfqRDNA77hw7xz+1wtvVu174tVDcKXUdg6eFrVAH7XBfNx0nNNSFTC3Npv7fei4wA2b8mfSwOAwikrAzAwzEcWV9TXMUbSV01HDXI/OGmYAIYNLgIKBmmE1Y2eihvmRHVkUfd1XhCiVQZioo14yd2bNvjG1cDRmxpTOOEPHR7S3P/TauAGIfNFfI4v6zLWVq37/ZYaZiOLK+oBZbSt3Jov+1MVvXg3z2d6HOWxwCQC40J8x8iaaOeUFTFf2LtK+oEntkPHRBW1GFlXVM29WvLN481qrS1IkoPUiRm3jtvedU5Eez+a2cmt/e1HVPmaYiSiurL/3e05LEvmEQKF8b1AC+N5z+7Hv8Cnced0nGvo7TNQw11sspLOGOWxwCVBaQKkzY+SfaJZwShPNrl+zDCsXtmld0KQu+PtEVwp/9KnztNdRA8DbJz+o2Tc3xkFJOpPDaEAJkc5AzX9Rmx9zse6xXXCljGzRH4BJt5XT8RxNZ3J44tVDVft2Kf3LiYjiwvqAGUDNh8h7o0U8uvMgADQUNJuoYa73IaezhnmiRX9JzYv+GplopoPaZnB2S9JIDWk6k8PB3OnIjzNd3Hr/K5XXtUrHc2XDk3vwzy++WSkJSQhACFFTrjPVz42gtnL+Y+i8yFbPq6hccT+czuKaVZ1W1FgTEelkfcD83mgBiTolnf/66sGGAuagGuY/vuR8LX2Y1Q85nQt8Jmorp3vRX9DvREfbLpU65W+roQzeIzuy4f9/YmTDk3vqBstA9BnmDU/uwd3PD1btO6+9FTd/+iNY/8TuSINVryRjrOAGlmSYbCvX0uRgdMytPE8LGl8fREQ2sT5grmkr59Po2qi49mGeKMOs+8MvKLt+17P7tQerag1zS9IxksGrO20whh7d+Xbo41FnmB9MZ2v2HTxxWl/bw5CSDMBMWznv9fr9FwaxvzyR0sS6ByIiG1gfMKtt5fxmJhtbs2hbH+akIzBWlJH3QZ4ow6z7wy8ou27qdrPf3NZmIxm8euYrNdZxkGptxjvv1r+AiPp5GtCMB/NmNWu5IzRRSYYp3r99dGw8M6F73QMRkS2s75IR1FbOkwh5zE+9Ba+rD3PdVlBeKUTEJRETjcbW/eFXL7u+bm0PLl7eYazVH4DKgsyBwWHtLcxUPUqNdRx8YoL/71E/T7s7ZtXs++3OuVq62kzUJQMw01bOv+bAo3vdAxGRLaZ1hvm9fBHpTG7CIEvNsi6Y3YL/8rkLIg3O6tXmDgwOo1AsfQhFPZpazTB/ekUH3jh8CkdOjUJK/R9+QbWatgxtMFFLXU8cOxGoCzFVUWeY63Um0XYHJKQkw9Rz0/u358fc8bI4k3PKiYgMmtYZZqCx/qzqh/Hhd0cj78NcLzPllWQIRD+a2p9hFgDWdLfjrhv6kPSmy5n48FMCA519qT1qW7mOthYj51HPsRjWNk+UQTZVBqDjDkhQSYb6uInnprcO45IVHZX3ERPDjoiIbGB9wBw2eANoLPOklmT4a5ij4mVnAodhaCrJ8GeYZXl7YHAYY+XbrLo//PyBQaEcGIT+niKiXkD1Lp5j5Dxo3ESvY1MLzXRM5pyoJMP0c3NGU6LyPsJFf0QUV9aXZJzTksSoI2r6gXoayTwFdSOI+oMnrEuGrpIM9UJh98GT6Fk8x9iHn3+QiitL2zr7UnuCfi/Xr1mm/TzqieOiPxszzPPr3HnQPbjEVFs5rxTEv+jvTCatEhGdDawPmAHUbbUEnFnAN29WM/7r51dG/sFjukuG+luTqG41p/vDz39sp7xtooZZvYA6emrUmlpqIJ6L/mzMMB89NYqexXMir2FupEuGibZyQYv+vDtVRERxY31JxnujhdDxzmcS8B1/Px95DXMoTSUZQaUHQWUauvR3t6MpWbr1nCwHH7bUDttyHkA8F/3ZmGF++vXDWP/Ebvyfnzov0hrmRrpkmOCVgvjfpZhhJqK4sj5gTjoitJfwmQZ8OoKioFZQOmuI1UV/uZF84D6tlFvPOhdBhjFdJ+oXx0V/pjPM6kJQoHRBmR9zsfGFNyOtYS4dLHxwiYm2cl4pyOUXLqjsY4aZiOLK+pKM02NFhFV0NhLwvXV8pGZf1MFZvVZQ/gxv1DXE9bLJpjLM9Rb9lTLt0mjLKlN1okF+vvdIQ+0SzxY33rsNL+w/VvdxHRd2QW3tBADHEXCljLSGOeh14T+G6ZaHH4wVK18zw0xEcWV9hjn3fvib80QB36ZtQ9jzzqnaByIOzurd4teZ4Q1a3GYywxy06C9oEWRcBGU1AaBQlNiyo3ZU89noxnu34fl9x0LLrnRc2KmvFQC44sIFWH9Vb+R3QIJeF36myoW8QP0X+8YvZnRfZN96/yv4+Pqf4tb7X9F2TCKiINZnmMPKMRqZVPfAy0OB+wtRrnhH/YEHOmuIgxa3+btk6P7wC1pwqHMRZBgTWbywYR1xKctoJPjTcWEX1Ennd5bOxcqFbZHfAQlaDOtnany8F6j76bzIvvX+V/DozoMAUPnzzus+oeXYREQq6zPMYRqZVKe+4XuiLofwbvF/6/Mrq4IvnRneoAEduZE8vFkwukdj171Y0LQIMoyJLF5QVtMTl9Zy57RMfM2u48IuKNuv6w5I0GJYv3rvJVELWvSn8yL7ydcOhW4TEek0rQPmRgKssWJwwAwA/7L1wNSdS4OiyjB/+R9fwPK/eBJf/scXKvtGRgtV3+MN6PBuMesejR10sWBLSYaJRX9BWU1PXFrLzZs18fPfVA2z/w5I5ItSJ1j019eVwi2XLddau+wF6peu6KjsYw0zEcXVtA6YGwmwmhL1/4l73jmFDU/umerTAjB+i/+On+7FDRsHKqvbo8gwf/kfX8DO7EkUXImd2ZP48j++gE3bhiq3MT2VYxnK6AZdLNjSJUPHCOTJiEtruUYCZlM1zLrugAQt+lOZ6JLhmdGUqHytM8PclKx+757ZnKjznURE0bO+hjlMIwFWWIYZAB7d+TZu+9LHpvK0AATf4le7ZEzVh8/O7Mma7YMn91btEwKVvsdqWztdwWHQIkQdNaKNsGlwCQCY+03oNbeB57+OrGZQXrfeHZCpfl5MtOjPVJcMk5P+0pkc3h8tVu07Z0ZT5MclIqpnWmeYGwmwJspgRZUtqXeLX80cfthMYr2M05FT1R9q5zQntLe1UwUtQtTZl9pzIuAD30QNc70uGUB8SjIaoSOrqZZkOAI4eOI0Uq3Nkd8B8d91Clr0IKL/fgAAIABJREFUZ6pLhslJf3/24M6afT2LZuOKO55D97d/givueC7ycyAi8pvWGeZGMj4TZbBWRZSpqdfXV+1+8GG7ITzSYPuxU+VszVQH7JPx/BtHa/bpDuDTmRy2KxcZHW0tRjoRhHXJiEtJRiN0ZDXVux+uBDa/NFQ9OCmiOyDeor+xghu46M9UlwzvuKNj40GzrgxzZri2d/4rQzkcfa907H1H38cVdzyHp//ss5GfCxERMM0D5qnI+ESZyevrSkV+63Tf4YAe0yHUj3xdt/4v3fAMPlA6lrx1fCSw1VyUHtmRrer56wjgmlWdlRrmp3YdwpW9i7Tc8g7rkhGXtnJhWXaPjqxmUEmGK4Gxoqw8HlVJRukA9Rf9mRqq4x337v/4DZ5+/XDp9GBu0p8XLHv2HX3fyHkQUTxNWJIhhPi+EOKIEGKXb988IcTTQoh95T9Tvse+LYTYL4TYK4T4gm9/nxDitfJj/yDE5NM1889phgAq/51Jxqdz7ozxfxuiDdB0LNQ5HjDYJRnwf7U5UfpdqRcIum79Z098ULNv7zuntPalBmrLQlaXL2q8GubIRyD7hF3sxKWtXFiW3WNy0l9TQiCRiLYko5FFfya6ZHjH/fjSuVqHHaUzudD++0REJjRSw/wDAF9U9t0G4Bkp5QoAz5S3IYS4EMB1AHrKP/NdIYS3tPl7AG4GsKL8n/p3TujzPQvxX7+wEkLgjNuQtfkWjkQZoNXrkjHVgrqAJJzafW/c/iUAevtA+wUF8a7B8/F4JTu660SDSkP84lLDHJZl9+i4kAp63l26ogPf+f1eOBF3yZho0R9grktGOpPDzrdOaL2orVdmpr6HeEkAIiIdJgyYpZTPAziu7L4KwA/LX/8QwJd9+++XUo5KKd8EsB/ARUKIRQBmSym3SiklgB/5fqZhEph0GzL1lq/aNSOqWlFdAVhQF5DZM6orbT5/4YLK17ozup5Vy2ozY51zZxg7H5XuPsxqaYgqLjXMYb2oPToupE6dHqvaTjjArZdfgNxIPpJFqRue3IPP/t2z2PDkngkvGnVdfKu843rlGJ6/eeL1yNpxAvWnu6qJgFa2mSMijc60S8YCKeUhACj/eW55/xIAb/m+L1vet6T8tbp/Uiq3TSeR8VFvtapdM6LKUdQLwNQODUEdGyYjqAvIrJDpaaYyussXtFVttzY5eOG2zxldhOinuw+zGhR0nFP9/5E1zOOivpBKZ3LY+MKbyt7SqySKRakbntyDu58fxIHhEdz9/CC2DQ6HXjSa7pKhOj1WxN3PD0YWNF+zqjNwv6O8WYf12I/SJ//2aZx320/wyb992sjxiciMqX7HCYo/Zcj+4L9EiJuFENuFENv9f/GZTIab6JZvVLe+642zVWuOg2qQJyOoC8iBgBXmHlMZXfXC5b+v7QEA7FfqeNVtXXTXMKu/j+6OWVXbrGEeF/WF3cDgMIpKul/KUi1xFKPk//mXB6q2X/zNcGhbORNTKP3HrZdU+NHWA5EdO+iYp8eqg/fmJv0Z5k/+7dOVxYdH38vjY//9Ke3nQERmnGnAfLhcZoHyn0fK+7MAlvq+rxPAwfL+zoD9gaSU90gpV0spV1f2YXwy3GQ+PNSofLTgGq2ZVbMi3ip8XUxlmIOGlgDA2ydOV+1Xt3XRncUzdSFnGxtqmP0XkR5vbHwUo+TVrG3RlWgqB6ZBbeXqXXxHzTvub3cGPxdHJxgKdaYGBodrbh6q2WUAmB1yJy0qaqeO0wU30vIUIrLHmb7jPA7gawA2lP98zLd/kxDi7wEsRmlx30tSyqIQ4pQQoh/ANgA3Avifkz3o7oMncf2aZZNq/6VmsM7vmIVflSfj6Vj0p07nalZWrmSOjyCdyUX6Iei/7W0qw6wGJN62+qF7Qqkl1UV3r1u1dvftk9VdREyUpvSu+ze8ly/1604I4Df/93+K/JiNXC5GfWHnb204flChfD11kyibEgJ534Vyc0KEtpUD9LSorOf90ULg/taIMrz93e1IJpyqC4ugX8sbR96L/L2zEQ9ufyuSabFEZJdG2sptBrAVwEohRFYI8XWUAuUrhBD7AFxR3oaUcjeABwG8DuDfANwipfTmm34TwEaUFgL+BsCk72VJTP7WuZrBevPY+1oyrPUyll/55LKa793S4PCRM+W/aDCVYVYvXLztlmT1h+77o0Vs2jak5Zz8dNcwq/Jj1WOAddcw+4NlAChKoPu2n0R/3DqZ9KQvpWgiw1wouLjzZ2/gkR3ZKV30l87kqoJlAJjRlJiwrZyJLhneRf/+Ov2OT4+50Z2PEiEH9smu87uK0syAdj9unYscIjq7TJhhllJ+tc5Dn6vz/bcDuD1g/3YAvZM6O0Xv4jmBgWhYcKO+lTUnHS0Z1noZy+vXLMO/bD2APe+M1+pGGRypQbGpDHO9ASU9i2bj7Vx1Gcb3XxjE9WtqLyyi5F2I5QsuXj5wHCsXthnPXOnkD5Y9LhB5Bq/eBVvP4tmVO0FAtBn3oLIQF8CL+49hq8CULvoLapmWam3Gux+UsrhBbeXq3a2KWr1Ff56CK/EH3/slHv7mxVN6Pl5faj8Hpd+/n4muOiKgNiQZ1DOTiM460+qVvvvgyUm3lVMzWKnWZi0Z1rC6w6XzWiM5ZhD1Q8VUV4p6gfqffOYjNd974gP9ZRmmOhHY4MZ7t9V97FsP7Iz02EEBjyOABbNnVO2LsuOuelF9TksCAqXgVY0XP+zrJaiNXpNvYV3Qe5Kp56Z30R9G4sPdIdu0bQiXbHgGq/7m6UotsFeS4SfqnIbu0qWg01DvkhHR2WlajcaufNhMoqZQzR6dGMlb0fdXJ//vwNRo7HoZ5r6uFOaf01y1mMbEB5DuGma1nZqJFf+eF/Yfq/tY5nj9jitTod6iP7VrSJSLINWL6vdGS9l2R5Seq/4KiiheL/Nam7C//HXQe5Lu5+aN927DSweO46Lz5uG+m/rxXzbvwNsBkzo9Z3qHbNO2IfzFj1+rbN/9/CAA4IqehTUlGW6dRLfO0SWbtg0F3olZMmdGwHcT0dlmWgXMHW0tgW3lJlOSobZxiypDYeo2ahD/78DUaOywUpDzO2ZVBcxRfgDV64Pt1TA3upj0w1KDtNktSbwd6RHrCxugErWgQ0sJbFWyqFFmEoMW/TkALlnegZ5FsyuBHBDN62Vu+a6XejHp0fncvPHebXh+X+kC6vl9x3DoxGm8G9FC3O8+u69m34Pb30LbzKaqkgyB0sVLUEMhnd1kvv+i2qu7JKi1JxGdfaZVwNy7eA5WLmybVLYlcHCJbxFLVBmKsFprtcdu1D13/b8DUyUZ9TLMutXrg627hlnNrL5bpxOBaVFn8IIW/UkALUopQJTnEdhWLiFw6+UXlFqcIdrnrQRC73rpfG6+qNxt2Fdnwd9UOD5SG4i7UlZKMsYKLhyn9Bw5v2MWHt1Z3YlU9/vI6bydr1Ei0mPa1TBPtpuBPzBxAMxRPoyiylCE9YueyizvRJPS1A8VNfDQ1Y0hLMM81cNcwqiTEb1t3XWiai2r2iVDl4m6HEQdMDfShxmINpMYeA6idtJfVCVcArCmhlmcQeu8M73gb0qGLKCTpa4hRRd47e2T+Ndf1bbt111SN3tGk7ZjEZF9plXAPNm2culMDg9sH5/UnQyYWhVVhjVs0d9UtnabaFKa+qFy9apOJBPjv4Wf7z2ipVWV/9+sTjOrF8RGQb196m1PdjFp1D7syPRG/T9PhQ9dcIHI2vylMzk85Ht9+ql9qZ/deyTw+6ZCUFmIV+6l445MboJ1FTon/c2aEVxLn3Tqf1ic6cVM0GjrlmSipkuGK4PLMQC9i/7GIhrUQkTTw7QKmOu1lavnkR1ZFHzvtJ+9YH7N9+judwtMbdZqogydGpz2daXwuyvPrWwXijLyPtBA6UO/3jSzekGsdl52bYoGVHwYLx/IabmQeTU7ccBxV0Ct6VQIah/mef+D6tvfP/91dBd26kWnwHhgqr4/RPF+kVNKE9TXtM5Jf0FB7PJzz8EDf3IxLr9wQeDPTGXQumTOjJouGY4oBexBdL5So7yQJyL7TauAebJt5dSP4o62Fm31w96ivzt+uhc3bByo+rCfyqxVUJsqoPS7qTdqV/03a/vQmWCamQ5vKV0fvExu0GJSndSa3Q/brqtR+QayZmpAN1VSrc11Fxw6ovQc9kQ5pEINUD8yfxY+vWI+HtmR1ZLpn9dafavf5BiMloCAeSJn+v4RdKzKxbKUpfd4AfzWkjlYtSz4IiEuI+SJyLxptehvsm3l6k2X84vqDTds0d9Utnar/4FePzg10SnDyyb6p5np7hqSzuSqBsYAwGi50a7u1l2qxXNnIqu07tp/+FSd75463kvJk3BKgczI2HggHdVVdeBI6rI/XL0UwHirsSjrVdXjDx57vzLdTn1tqhdcU0FdV6G+T+nsuDN7RhPehvI8PPIebtg4gE+vqL1DB5z5+0fQsYDq94qiLN0FCZgXon3RX5RrK4jIftMqw1yvrVw9QbXCurpE6Fj0l87Uv21fdFF31K6JThn+bKI6zUxX1j9oypo3qtz0aOy5rc1YkppZtU/HB7SjXHQ6QtRciEZVual2p+ia14qFs1vwnz/djdu+9LGaziFRPU/VANWf9VaD6T3vnMKt979yxsdSL3AFgHPbWqxZ9FevTnes4NZdYHym/1/qHUstyZAIbn2oe9EfSzKI4m1aZZi9tnJJR2CsKCcsyQiqFf7VWyeqvieqGuaw3qlT1WJtYHB4wh66QaN2TQwv8f+b1bpqXRlvtXzlYwvbKiO4bRiNrfZiDqonnWoy4A5Ec0JgRNmOgloKcemKDtz+v/9WZVtH/XDQefiz3smEqFoHAQCP7jyIi85vP6Px7W8eq27TJgG8P1poaNGfjrsfattNYLym+5pVnXho+1sYU34fZ/rsqPf8/pt/3V01krteH2aT7SmJKH6mVYa58sHW4OIs9YOw0RZWUyGsm8dULfoL6h+rCvpQMVGSEbboT1fGW83utc0Yv14cGBzG6Fgpi5cfMzMa++h7o6HbUy2dydWMfnaEGG/tVaZuTxX1uWuqdlc9rve2knAE1v9+b+DPnOlCyKC7Bo/7WqaFDS4xdffjigsXVMpA3AZKvBoV1Ht8+4Hj2KksRJVAYFSuO8Nc745P1H30p5ugcedEZ4NplWE+emp0UpP+TH4gh9UwT1WGuZELgKAPFVPDS+ot+tOVSQzr9+y/+HBhZmT6iDJ2V92eakEt5TrOacaopvZZjawxMHEelaenlHVfm2eyEHLDk3sC26P57xIF3TXTefdDzYADwOF3x+uME8JBUSnSOdP3r3cDfq7e7zVoNLYj9GaYg7LvABce+tUbd37blz5m6pSIpsy0yjADk+uX28gH8lQs4rl0wzM4/7af4NINz1SdZ70a5qnKMNfrkOGnlj8A+gJUv6BFf7qF9XvOjeQrC4t0fxB7pFJfMxrxMBN1ASQA3HLZikiP6TeV/cg/DPXC03FE1et2xfxZNT9zJm+c/zKQmfB7brr0/JpgWFcN86ZtQ1Uj6j2/yp6sdPr50m8trHn8TN6/Njy5B6dGa5/f9ZIaQdUbSc390uu1u4yyR3g9H//rf8d5t/0E3d/+iVVZ3P/3339ds++f64wUJ5puplXAXFl00mBJRtAHsnr7bM87pz7UYIZLNzyD7IkPIAFkT3yA3nX/BiD8NqrOQCGorZwJYYv+bOC/ENPxQRzU3u4DpT6iKBHph6EaoDcnRGBd7umIMt06pug1Qg3SVi2bW/W6ffrPPlvzM2eSgz89wQWQANA2s3aanK7BJQ+8XP990AvU77zuEzivvbXqsTO5Q+VlHhsxI+ngdz+6oLYqw4J+6QDw9OuH0f8/fqYtcP34X/87TpwulbO4svS7tCVoPhlwh2C03tQZomlm2gTMyYTANas6MTA4jLFCqSSjMEG2JegD+epVnTXfF/ZBMRG1Fdh7+SI2PLlHSw1zI2Ox/6CvsyZjpasrhV/YpD9d5zPhCG5Ng0vqtbcLOuy9LzQeWEzWmBIwe2OR586sfj6+P1qMZNqfLRlm9c7TzrdO4IV9x7DusV2Vf/fcmdXVa2eyEDIR1BvNp957ga4aZrUXuMc/yAUALlneUfP4ZHhJhUZ9UHDx818frrmwmej9f6qF9eR+591RbYGrFyz7/WjrgciP24j/v73zj47rqu79945Gsiz/kmw5JI6RZCdOcH6Ag5LINMb8CCbYpq2BklLx6veaUBqadC3WY70iEmNICEHvrWcW662kpBQDL6+49AckaZGNm2ZhXBciEgVDHJzEiWM5cuLGPyT/kCXrx9z3x8yV7+y7z7nn3Ln3zoy9P2tlxTMaje6cOfecffb+7r0zbCQg/esQhCSomqn8/isvQntrk5XWlNuQ21ubsPTiWUWvU20UYagWx3945jVtGDUuQyFM8+lltlPKkfTX1FCHmoyDjAPU1erL7M2aloy0XifJsDmIlYqqvN0KYogAwHgZuvHedtOiwHOlHCpVVIqHmUoyxifPSYc2Pr4Hff2DsSRCNkzj207rrgXQJxCnwQ1tTUW1n0tdP05HiFhwsvq0cw1ou3aOx3YfCn1NEtDoVLmYWR9cu2cmtJ4LQtpUjcHsYWNsqjbkWcxNbUv31r3KsGLOdadqiToAamqS0TCHJv0pOuqlnfTnbfiTORcZx9FKVADgb/59f+pGQVxJf1t6D6KtqwdtXT247As97Gv2kYYkrXMb0NnRgkdu7wi8NqGKbgCAWuLx9B53drTg4tnFXv6xBDbkclax8UNzAfyjknPzevtp2WJjlz42waRMIJeX4D98j47n8MlvP1VSLWgVqioQzx4sLsVZjsgAN3RpRiX6+gfx+uBI6OvSMOC5JSEkeJEa3ByfUR+UGVUq6x7chcvv3op1D+4q96UIFUjVGMye/MDG2FRtyIeGihc++tiELb9Ue9ymFghFVYi4NpywpD9Vcl3aSX/ehp9vQBCsPLB88byiBX/STaYttE6SEUfSH80Qn3TBGs3UUzXhmx809B/H4U4FNYH9j6l84ORo/O2xw6rY0BB4Gm2qAaBlbgMyTv7erCtIEWYTLxl9HBeczMrT13uMjufw2O7XYzeaVY05Jsk6UuqB/9LGeqvXXzSrjm2NnWZU4kfPDhhVWXpnwiX/tvQeZK+jhtNCVAhJ3Stxs+7BXdg9cAITORe7B06I0SwEqNy7jODJD2yMTdWGTMtmRSmjpUvgmT0tW9zeNeYNxyNMw1wpyXVhCX/trU0BXWYSRrxOkhFH0t83/u3FwHNcvssh4qk6O3FuLlFvTKLeGXKQq836jGQqqE5A1x1WxSZUcx4T9D4aGBqB6+YPDV40hB4YotTInmbgYVbKrJjx3/rcG9bXoENVBSJD7odSI1SXNk4Pf5GPo6fH2IhTmtVsqHOCHmw9kpa3fUdRcWJ6beVu5aqOjpXGb0j9790DJ9DW1YNl924v0xUJlUbl3mUEzztsY2wmWeeVthT2Mz6Z02a2xyWJCPs8ldIJS5fw56HarNMkh0Ib3oi/f3yY98L6N3su4emsT9OZlidzS+9BnCEC6Zl15/5WGtcRJsmg4d2kOh8GWmMXDrquPxpC7neVEacja+AF5GQpXu15im4NioLKg9/e0lh0oE27U2jO5Vtjp1lWjo6Nai4mvd4OjfDvX8myhzQ6lsaB6nYaGpkQo1kAUEUGs3fCp3rXB3p+q9y4VBsyPfFGOQHrQuVzZ9RpM9vj2nDCNJ/lTKTyo+vy55FGpQydx/KHzw5MtUCemHTxX77da53xTjXBHg//7JWpf3MJT/56tHHMTRM4T9VVPsMxjesIk2SkNRaB1tgFOYa/zvvVl8wO/J7/ew2jr38Q/QY13zmZFZUseSxqDtaHLgWVB/9Xrw0VrbGlJv3ZRApqMg4yUBgzKZaVo9eskmeUa72tBNlDX/8gjp4KfrdJyLnipq9/kI0GenCVSYQLj6oxmDkNM5A3QD72zZ+zRrNqQx6fKP4JfWzC/Jlqg66xoU6b2R5XlYowDXOleJgBKPXcHmlU7tB5LOnWOzI+aV0mKqvI0PvVQXNPJL3G8YRqmHKeqjvec5nyOpLwEoVFgNLyMNMkzKkp6jPI/sw3Nh6vHjlt/Df+2sK45uA8rG+d2xB8sgRUGuaJyWJJWakRMvo9Xqw5HN902TxkMg77+dMsK0fHRmWql2u9rQTZA1f9BwAODY2mnsRtSzkaaQnVR9UYzN5mqlqcu5k2v6oNmX5o2sDBhLAFSldWLq0qFZXiYTbp8pfGmJw8W+wl8H+HKgP9+73h3dk85s7gN35/i2uuhKFfD1lHft5//Ewim83EBJVj1BRFQdLw7oZJMqhn6vUT9sm5JtB20G7hv/GJ3FTyaXtrE1qJgWpjwO8nxnVDXQ1rdHF5CU/tP8YajHFHYVSyqGxNsfShlKThvv5BvEgOKFwpMo89r59ETnHITrOsnKlkrFzrbVIHaxvowdOPTTSmHJh8b0nUoheqi6oxmL3NVLU4v/DGSeXv0Md06x+ZyFkbJbrNcujMmLasXFxVKsKS/lR64bQx6fKXdOWOvv7BQLKd32ukMtBt9qEJxcbub3JRXxecN7u/dMvUv//whmCnvSQqhoyRD0bvCV2CZFzQCEkgYkJC7idGJmLftLh20N5fdQH8U9/A1NowkSseJZtQM10vZk/LsmF9Li9BtZnHHYVRaZjfW6iBHwc/enagyPjPOPq5Nb02g7qCnItSURG0AuW6nv7jZ8pu0OmkNv5ozKpNO7D4Cz1YtWlHCldlhklJy68/EUzqFi4sqsZg9tZYlVeF66KlkmTMbQgmSNiegHUet6mFQyFDiEuvG5b0VyltsSuhoxsXLrz8Leca2KhCrNSrqEVhMPubXDjkJbTEVmdHS+Bvvqzx3ESlqCIG8zgN6IGPPuZ0w9+JufMhp+W+oe2ccTg56YsOlVA5hK4XY7kcO+e4jVt1v8QdhVEZPDtefDO2KAc9FF3f2oQlb5mleDVw2fyZ2Pjhq9E6L3gfphlBo4cJ1fpfzojeQz/dhz/e3Fs2w1l38PF+tmrTDuw7MoycC+w7MlwxyXQmfpHBhKr0CNVD1RjMnnHItbYG+CxhlSTjz9+3JPDa31puPmGLg66sXFx63aiNS9LGpLJJ0kl/NFzoAEVdEFXfgY1OdHZIpvqW3oOB5JGrmL9bS7TQSZRTo4Y7fZxGSbcwDTOnG37jZHi3NRsODRYn4mUz+dbYHv7oUCmVQ6iHuS6TYTdpLi9BZYTFfcQx1TBT4/E3rw3RXzGmsaFOu/699J+ncN+Pn8eBY8GEyTTLytH5r1LxldPjfWhoFP++7yjufvS5snubKZ6kZd+RYvnT0MhEKu3EwzCpoJVijqlQoVSNwewZh6rQILd5qSQZnR0tmEXa1Eapxazi1OgE/vX5w3Ach5VkxKXXjdq4JG1MPMxJJ/3RZiHzZtUVzaU4vHVhOl/Om3kHYxSmIYegkgz6OI1rCNMwt7c2BZMxz9q3VdYxSkrrTeTy9w2Qn6t/0L5wap6Uouum+vkz45PGBq/KCIv7HjGtw3yW6N8PnzpbktGju/da5jYou0ymWVaOzn/VISZpD7NJLW8ARQ2U0kJ3qPYOWdycL1c7cT8mkoxKbg4jpEPVzAD/dr5sYXCj4DYvnUZyWp19a1s/1ADzs/fwKfx64AQmCx5m6uml+twkQu6AWi9MPUSvGZS7KgUTD3PiSX/kO6AbTxzfQVgS2MhYsdHUPLOOPQBSwyWJGtX0SsMkGadG4y+rFKphRjBUGnfqIc3BrHEwVT99Wm2mKApRUtUOMv8cxywMDKiNsLS8mS65dk5n//BOM6kMJ8NR5Svc0NaEz69eWtTl0M9YofxjEm3CKfQeVOUrJP2dmNTy9li/uTfBKwmiux88Y3p2Q9CxFaXNfNyY3IuqsqGCOdXeerxqDGZ/yOSxu1aggRi8nAdMp5GkN6nNTdvXP4jXB80z9qmnl8oNnukfTKQSgsqbSz1Eew+fSrTsj4mHOemkPyqXoI9LlRz09Q/ihcN6o5t2OFsccx1dU/r6BwP1oP1NS4D054gNcV7HgsZiyc3CpgZl/XSa5GeT9Efnm83WqzLC4vZmqg7OrltcdquzI2gwm8LJcFTyq+m1hTVZEwsfGZ9MpE04hToZhhXffZIeZtNa3h479x1N7Fo4dBEXb3/mjOraGgfdW/eirasHbV09WLphW2LXqMJEkjEeoZqWcI7zofV41RjMNGSy4vLmosecF06nkSxFj/ijZweMvUNA0NP70XcuLGpEkHOjVUIIq5Kh8uamVYnBI65W4KVAQ+L08eL5M0t6/7/+2SvKOTHCNCvRQTdnVfWCqHA1gamWOu05ooJz6myIMdxM9eITuRzu+/Hz2LXvKDY+vqdYC1pC0h+db1QCo0N1v8QZhenrH8RexYGPSjJ07xEGJ8NRSUt+sf+YssshJe424RR6oFZ9f0l6mFV1jisFnWzL2585ScnLR4aLIhQjE7nUjeZN//pC6GtolZwkWPfgrqmDww33P5H430uT3Uzr8WqjagzmKGc7nUayFD0iDR3Pn6k3AKlXtb21CdeTUHwUjyo9EHDGBbeAd3a0YOnFxZnpcXt06TWUu0pGIAGSPOYSzADz5ENaZ9fP8NlJbOk9aJxIl3TCHddIhWqp06rWEQY9GAP5RLC44BpSnB3PTdUM3/j4nilDMOohe+mGbYGShso2vMy9obpfSr1n12/uxdu+uA3rN/fqjTFGfnDVJcHKFiaVhjgZjsrwz7kuli+epywrlyZ0ntQpmhQl6QzQ1TmuFsISoz1GFLr1JFj34C4cGw6PFk3mkq3F7HlgPY6cHjvvjOZqp2oMZpOQCUWnkYwzqWlR8wzWWPUw8apG8SLSA8ElJOQPzd+lrb3j9mLSayi3hzlMktHe2oQ7Vi4O/J5pYlXY/Pn7pw8aa2CTTrgbJh4iWqq3AAAgAElEQVTvadkMq6VOo1pHGI/c3hF4LsnI6Kz6WhL9OSeninLIXrphG7v5Tyg8lNwYq7y7pVSSWb+5Fzv3HcXoeA479x3FPz7zmvK1OTfYCe0r664NvM620pCHyvBf2NiA9tYm/Ld3tYW+B5XoxQ2NYKru3SSdATRvxkRCn6aMyiTpz8YxlValDxtP50M/3ZfYdfyauQ5aI75aqbSqLVGpCoOZ80omHbbWwf0t6jH2w11/HF5EeiBwGE+QKvs3jbJhqmswyUiOmzBJBgB0rVmKG9uKv0fTsDfdUKkHamwiZ2xwJZ30RxP8uGYqQPKGO9XMqu7hQGJejKsWnfcnR8fh+CzmrK/CDTWSThskQqo8ZaruddwYt7c2BcYAKK1KBtW36iQi3CG3vbWpqEMlEL3SkMrwv2lJM/r6B/HtXa+GRhi5sqJxQuemSmaVqDMgQpnQNDvsmST92STKJmmc+lH5upYtnBNYxwfPmOct2HI+K6T/SvFdVkpejClVYTBzC7aJwafbkEsxGLnf1Rk13PWXlHGvYBazaahuwiT+vgpVA5lUCZFkeNAmClFDwQ6JuZ8cHU+lVJsJaX73KjjNLE009KBjSR+XAv3sDoCcoqwcbVt++NTZyJ4T1Zir1hHOq55E+3gVnOeUGqlRqx2oDP9rFsyZqmcfhk0OShRo+3TV4eDuR59DW1cyXexoVKzOoGJGVK9/FMKS/kwSo/0cTcm72jCNn7dccQGVFCcpqsJAM+C44qBR6S3TKVXzfVCvZJjxEbYhl2K8RPlduuGUoqH2oF4PzmuqkrLE8fdNCWtQkQZhkgyPqPWg6XcxSRJEbLxv5Yye+EkyCsFpZrlEQwDIEANZ5Z21pa9/EC8SXejSBXOUZeW46/v7p/UGs2p7vWZBsIuhjvra4KaeZN4BhfOclpI47Udl+D//+gksXzxPK3fzKHX9WtH9JBZ19WBF95OBn3Ht08PqIe87Mhy70UzXd6NPnGK3jfGQRFbbZPk0kuwAYLpGzkMPhUlGMrgZlUP1eWE5VGVL0zzQxUHVGMz0RgsLW3OVAFQbsi3c3w4zauL2MPf1D+IZgxtJpalL09uZpiRDtfEdPV1sXHCHCyB6PWhqTNL5Oi1bUzFJf6YkOUdoAlPr3AZlubLZRG8fV/LNj54dKPLcZpx88uPGD1+Nay6dg5VL5he9vrOjBRfPLpYPqJpqAED31r1KA2HddXzHUlXlm/XLWwPPRa2fHqXJCHfPRimzZ1OH2ca4KmVuruh+EgNDo3ABDAyNBtYO7lA0x+Dv0a52JRPhoJi0591j1aYd2pJ3x4fHQhttUcrZqHZhYz0A4NI59UXP08dxsaX3oPIAVG1eWA6lfVNl7ROrxmAO80pSg5VWLWieWVe0IcdtlIT9Pt1wjhADjj4Og9vsr74k6LUqR4IdxaRBRRzQjc/LMO7rHwwmTyhW46j1oANZ9CRcOnta1tgArRTpRpJaaprApGoEAQDXtQTzA76zy6xRhg46D708hC//8x78euAE/vW3/4k/+punijw8M4kBovNsbvklb9Q3Ts/ipy++GXietmv307VmKS6aVTz+UWtj/7+n+q1/h61MYllmb92DuwIHHd267kkyki5/OzA0qn1MD0WtcxvYtZYS9+ZKo2Im729TKzwq6x7cFXo4CPM+s6RkMB89Fdy7d3XdnM4fL6DS+ALAE7/9TzbyoaNaGoSkdaCLi6owmLmkubDmH/QGnUEaM1AjpFQdZ5hRQ+992jnNtpMat9m/98qLAq9TeXMPDY1oH1cjdKM7cnoMff2DRnWHPei8Mq1EQI1JakadHB03NkCTTvo7SxKWJhRe0iSlIWcnJrWP/XAl/4YSMAQaG+rw1P5jRWvH+ESuqEKETWRoZDz4mRqnZ7H7S7fgVaYM4YLGerZaicdnP3Bl4LnubfbeYu66KLS73hvM+mAjyaAlszx00SZPkmEiG00yCkMPRbU1jrIMpZ/Z0+M1BgK1vE0kCyl48EyqTPQfP2MdEUlDjrDs3u2B5+Y2nDuYpBXtU2l8PbjIh4pqahCSpBQ0CarCYOaS5sKaf4R5cGly1wsldjILM2qoJ4XqMEudOI0Ndax8QHlIL6EJQ6XCaR0f/tkreP6Nk4Hnad1hj6gaZkrgUhzH2ABN0lDt6x/EqbPFBtOkwoVXKdIQriJDUixfPK+oCkdtNlNU1s1GhkBbSmczwO4v3QKAP2CH1ajlJCtPH7Bfs0xudZesHC7zSzZ5EFzJLCB/8FcdSqeuwEjDHM0dqZL2+J/nDkntrU1Yt2yB9r2zXGmTUoigUUhKQhBGLfPRw7oUcgejO/+2L6Yr4hkaCTqqbr3+rVP/Tivap9L4+hkYGsU1G38S+rpqahBSruhpVKrCYOY8zGHNP8aIx4o+/ug7Fxatwy5K0wqFGTX0+ukinHNL12Vy8gFVyDOuhJ2kiGIkXvmWYDOFV4+cDmw0zTPrlJ68qBpmer10+Zs9LRuQIZhqmPcfjU8LyXnb36YIL1eKNARIwPjQkCnIaWoyDr78u8XtsW0OmtRxH3YmVunqw7BtC02TKDlqyGuyzO/YeNt1ph53KM3WOPjYOxfiqf3HQscNyBtjURweqqRNT/Kz7N7tgeRx73v6xieuQ9u8hsDvJgU9UNHvqFxwVzGeQ+CQG+YU+t13BA8gh1NMbPXoWrN06t9JR/s8HMOz0OmxSa3RXKkJgqooZrVRFQazqtkF9RL7PRX0+6GP21ubsKCpuNEHFyY1Jcz7RpsPcFnyX3/ixch/X4Uq5BklYSdJqJfp6QOD1jf/mydHA8/V1mQCG838mWqZBV38TbekQOtcEi49cvpsoNvbIkU7btqm++jpsdgKv3Pe9q7VS5lXJgtdQCtlQS1qxey6gYOu6UGTM2L9eyJ3CBqOeA/++DevR/o9HdRj+8aJoCTDxsOsU7xxh9L3X3kR2lubrHIwojg8VEmbQ6PjWNzVw3og/QfwK5hDukfUMnsqDp8k3SINyoekERVSlWWjFSV0B7U5DVl84xPXsT9r6+qJfnEh0G6m9HFa6OqgU04raoADwP9kJFopV8IL0Nc/yN9HKF/UMipVYTBnwFd7UIXPTbPA6WZnqmPmQua6381mEPBofp4xUoYsiqKbhu1VCXa0zNmhodHEtE4m11qqx3/95l4cY8ZvfDJn1LTEg86pWYaed67Fsh/uu1XJQjh9ZFj5MlOoVnhOQ1bpbU9SkkE3CJsNI0m8VswZ5Os9U4PN5KC5pfcgHtsdNGKn+XYuTgWj2lT8LFsY9MbaJsVNGow1fQUn26Frnk4WoduzdS3XuXV/uiLaEKVElcrIHzw9pqxaYNreWRe1W3bvdrR19aBNUcqOsqX3IAbPFM+PZoOITxpRIe7bmJ7NBD6/bnf15pJqniS1N9FupvRxWphIMkyg0RAAWNhUnkOAB2fEe5SjB0ApVMXV1tVm2PawqvA5l50+g6m1GLUWMWdI6Dy0nBOjvbUpUATdpih6qcZM4/TgQppUggAt+k+lCUDpHv9dpHOZR21NxirBbPDMWNGi/e1dr0YKc1FvCm2+cKkmwau9tSng6dCVLysF3YKVaIMTIpOplIVoqhWzkzcS7/vx80XfPz1o0sdbeg/i7kefY9/7T25aNPXvW9v5ahhhPHbXioBRUW8hV+neuteofi99z5subw68hq6XKlnE0g3b2DXQQ7d2LV88LzA3VA1uonQaVBmUujOFv0SlLilYVflo2b3biw5HJgld3IH5zvctCewZaZt7W3oPBnIiAGDv/asDn1/3/XgRpj9buZj9eVI63DDZWdQkcFts1tb5M9WHIK6XTTkOAdds/AnaunqwqKsHzx5U75+S9Bczb5ldj+9/ajlrXKhKgHEL6j1rrwo8F9Ug4G6y02Nq75Bqumbp37PQpJ0mXlJbT8Jtvs3bT9wLk01JN5qgYvOZVLed7Q2ZT/o69z1M5tyiKgkq6KYf+G4JYV4qWrS/HJIZOnZxXUNf/yDOjBe/d9LLpreAt3X1YOmGbdpr+/auV5Fz817WMVIlg4bZ6eN7FMYyUKyN7FqzNOAp1W2EfgKNFizWDVWpO8rbF87ByiXNqK/NYOWSZjxye0fgNVQ6BBQnXgP5cVe1Bwfy0aaw+9yhy6Ti1lK1q46bYd/f+aiiDCCgllJxkQRa4YdCD8wXz5qGzo4WvPTVNVPzaHo2g8vmzyh6XdIh73v/5fnAcw985FoAwBnyfegiId6YdK1ZiiXkMyRJmEY5riTwMGwkac0aSeHMumBUI2pCbFSu2fiTKdmIC95h6CFJfzFz0axpSk+cKtRfX1f8seprM2yGeVQPM3eTjU+oJ+W7lwS9M0Bw8E2/jO6te3H4ZNB7YZMop2oSETdcOEZV0i0JbL2i7a1N+NSKc4cJlX6eQhclnU46/3r9XAvzZKYBXcxeHxqNJamEmxNNDcl10PIv4AAwMpHD0g3b2PUjn2R27rvMOE5RdIuGmWkynGoV4IyAvfevnjKS58+sw9MbVhl9nlIiUyYl5Tweub0DL3xlNWssA7x0iMordHpLIG/Q0VwU4FxTFi7pb2EjH2IeYTydYfRpvF9KfAd+XRlAID4pFZWR1fgOW3vvX40D3Wux9/7VqSbqrtq0I+Cc8gx5ILpx8cTn3lvahVkQJheMmgRuA6fx1d3Sur2Dc8T0Hz8z5Sxo6+qZ6k+QFGH3fDVT8QazDpUsgRpJM+t5LVmcIee5ig1f5Z0Bgl41E5NoRfeTeHhnsGnDoROjbC1l22oTcU+IPa+bl3RLQjNr2xAGCG5OYYtkX/9goGRSY0MtmmepN6uwuRbmyYyKTbIdNWRcBD2IUeB0dne+b4nVe9h4E7kFfGQix8635YvnTYUwaxzgvt+/psgo0skQVHKmJfNnKI2ApzeswoHutcbGMhCsGJJEBRGTe6+9tSkwx23ruS+aP5P10npJkdxh9aYlzWxJN9sj5YruJ40qcFBs/HXUM6zLr9HJ4WxkZX5s6/vbwDUrmfQdJmwO+TS6wuUzxpX47CeschGNYuv09lHhKhctap6BA91rcaB7LZZeXLwO6zzGtKMtx5HTY4kZzes391q9XpL+UqTU03TUShG0APvQmTH8ObPh64xlwN5T5HWy4xgeHWcT/Gwn5FtjzhKm5fxqHLVXJuoBRreQHqVyEANsu/1FacMe5mFOquyfTbIdZ8jEsWG4JDZbV+OERjyo5n747GTJGyi9N6bWD89rzEgdOBmCl5zK1RrWGctRKeUwRWtDq1BVcAmDGklhtvwd77mMXQ88zyWX9HfNgjnKigpX3LPV8ErDZRAqbrn6YuPX0vtcJ4mJQw5HPb5Ru0GGoXpP//dFew2o4KIrzYw86SFNNzwVN9z/hNKr2tc/GFq5KKxBWhxwlYtuW3FOy11HbiJdCcVhQ0dCQCYZE7te5nOJVKQtFymVqjaYVfqjacTQoo+niNC8o69/MOAhOzuRQ2dHCx74yLWYMz2LbMYJNZaBoGdoPCTdXbfAnxmbDCSWAfqNj9NMxp0gQO1C3UeMKpEJC3seZ1qfxgld8OY0ZEMNwLDDQKmt05V/l2Rj67Kz21ubcClJxIzDI0C/1TC9N8Br7k3aY+s8elx3raf2H8N4weiYzLnY+Pieos2JkyF41Rm4qZ1EeJkeniYt9I+mTj9VFMiGvv5BrX5x5ZLmUEkD52H2SmVyMpc0qq1QY13XwITe5zaSGD+mkSHuoB5HVIiiqnzgv6x5IQ6s5ll1yugK19XS1vlxw/1PTBmGR06PBeoXc44OOu/DGqTFAY0WzJhWU7R/cN+psoJUhOY2JqzofrJI1qHCtmJP1Prp5aKqDWYVVMejSrCK4sXTeRM7O1rw6y/dgpcfWBNqLAPxe83efmlQG6zb+J7esCrgAYq7zAu9f3T3U1QPc1gFCfrTRsOyUKbQBc+7bp3BH9akgibM0MdRyRGDImyMo5Ze1GFjtHt0drSggbQPOzIcfogwTXID8uHZ5YvnkQ2yOOmT6zqYtr6cepwOnzprtG709Q8ayQla5zaEGrImcGtlfTaDxoZarFu2oGiNpCF47zEnh/IiaarDiGmln2mWUpYaBzjQvTbw/Dc+cR3WLVuAxoZazCQ1ieOqAmAaGersaAlU2DGNCvkNorBmOJzUjsIZvX5063BnR0tAx2vqsb7inq1o6+oJeFFPjxXvr/tJFSauclFYg7QkoEm9nR0tAecWV0GKS6ZWYZP3wEW2VUazQWnwALrysese3IXL795aMe29z0uD2dRTGUWSQW+y5pl1kRPobL1murm45tpL8NhdK7Bs4RzUOMAls6fhh5/5ndCNj55eS20R7ofbxHWfIapExkS35ccf7koSXSJomAaxlMQuFVwJqLDDQxINbiLnDpAIkG58PWw8et534m8Ika0JlrTkNMM2UoBS4TxOJt72z/3DbqP3n4jJS/UrJqFu4+9ejd0bPxjw0v4e6fLmPeZmfbMvTM5FyUykDW1dPcrydJRsJm8ov/K1oLHs8Y1PXIfdGz+It5Kat7YHTFVExCZBnEYJTaJC1AB6bPfrWqOZq8sNFH8fYfti2DpMI08mHSrbunq0UQZ/NPI1Isc4NcKvbUl197OBVsag82rVph342Dd/bvx+Nl0iVZFtTq9cE2IxH+hea2T8A3ljeffACUzkXOweOGFUrzxpzkuD2XRDpp6hIcUN48e2GoKOzo4WzCIeiSGNQcLVkgbyIUFvA3rsrhV45Wtr8Yu7P2DkJYq7Rbif/7X9hcBz02vVUy5qZQhT3RYQDHdxmDaFCaMUI9clvxqHh/mvGA1g2KaVRLWOqF3+kjhEUJ7afww5nzHwnivmh95HR0+NsZs012QkDmzXDY/+Y2cCz108m1m/YjKYT5KEs5qM2ojye2n96xlXxusa33M2yZIetp3j6iw04lFlZR7f/Y9X2efpwU8n37PN7VElaj3ONN/xqGUMI06LrHPih8rWyN/g/qYfk+/Viyps6T0YODCdjFBlJQ5M1kPdvFq1aQebgKljZCJXsmNsJ9P7IJejEUxnKnnRi86EGf8e9OA7MDSaSOKnDVVtMKsMG9pCVBX+tpVEcNUQwkLrYWTIpq9aYLf0HlSWa1ElwJgQd4twP1x3u/Xvaovlvf3YTOKwRRcIZvo/HxJ+VC141Oi1oYYUmx2bdI07WKqgmt1ag2S7JBghYUNT3WnS1SEa62sDkoyfvXQksLGYGkGP3bUizssrYpri8KyDG+UWJsk3y3U/UKDbyCfoOIV8zZ6X1r+ecZKM50so7XX53fZtlq3WlxBnzUTIXD/L/Lx7695AI5U4D4v/rmj6pLtSegc01NWwh5eXH1gbMJoz4KUtFHoo0B0STEP2nrf4a1t/a/T6NDCR29B55G8GZmsse5g4xmwM1C29BwPzlGuqVErUMkriZ5zEk3pfJriyUFwLUVUr1dtuWhToyvXQT/cpjQgu5GFa3kcFDSurwsyqxDZdwokpl86pL8oWjlq70691UhWf9zdvCMO0dFhYsqQfE+OMRhqGxybRvXUve+2LunoCG4vnDc6V4Iy9tX1hoHzg93v7rcaPUpt1AN8t0zAtnlJ1NvT1DwY8Ow2Gxl8U751pVQjgnLfdhQPPXJiYzDcuKSotZyAFSaDSW0ksUnjfuDJwxy0iKnQszvi8dPRWi5KPx5mFtBpQjRN8b+5+7d66V5uEqMJmfaFzcu/hU9Yebcr3fnEg8NwnO1qVr6ffaVipvyjxBBuJyMsPhBvHJug0zLYVRrjuhCo7IWlMxpIalZNufr/lqvZ4TM9m8Ld/ulwp1TBpJc9FJf1s6T2IL//zHuW+ys5TRbEFL0nTAfD7CrtmkHHCpUmFLet2cKd5m7BzZ0dL4KSu+kJU3j2bDj0cpmFmmtiWcYqlGKUQ1u3IBJoYEOXUS8tjmSZB0oVUJxs0STAbZhbTbzG1r9sYYxk4J9uh9TP9hGmHu9YsDdycoxHn2oruJ7GoqwcnyUHSKUNFHy4R7LoWswQz08OlR1iVBj81Tn49oJIM2rgEMFs04zIS4uCKe7YqjSJOYmPhYA7IxMYnXWzpPYhl9263uEI1Jp3VrmTus+/39gee4+rXm2CabAbEkxRL17xRJpFLe3AmBsmhodGpZD6qtQ+LWql+Tg8RSaS9zib9E8Yn3aJoj797ZxzsvX81+3xcEj0VY+Qe5MaS6yQ8MDSqjA5Mz2aw9/7VaG9twg8/8zu4mGnpbSKvCys/d/ejzymN5ZoMP09ps6es4xRVNHGR19BznBmb1HZq5Vi/uRdv++I26xrRHFVtMHOte+kXrNPNAUAdTSxQvE6Vbc+1ObWBhu1VYXya2DZvRl0sxjIQz4JgUtNUI18GAFx9yezAc1FCMNlMRplcGNVIpEuLTv/lTanPr1ZvaiaJh7QFsM3G3dc/iKu+uA1tXT0YGBqFi6AnycTbXqomk8LVHDUtYUanTyDkT/gDiyQYb//3Ny5xAHxqxaKAhjnM42gSbk4T1fe8ckkz+33SSio6uMPOQz/dx66LCxvrjd/X46cvvhl4jq5PX1l3beA1oxO5oqoPuqRMB8AHr3qL9bVx2CbFZhmJ2H2+dtNRjEFdtaexSReLfe+5eRevmfZ45BcHAs9xUSITR4Qt3NzqLpSzo907OQ50r8WNbcXvMXSGb1euo9SGWsvu3Y62rh72ELml92Dg/uQibifO8HYGd6c+8JFri4z/9tYmPHXPB7SNtFTY7DmU6Qrt/3FS3ej48FmrutBep1YT1m/uxc59RzE6nsPOfUdLNpqr2mCmp/lDQ6OBGznsNjbVSUWtnxmGqdeMFvDnCvpHJYkOexy3hxiJXI3bsNqbff2DgUTMWfVZpZeZ6mc5VHISP6o6pADw4bfnw0m6ZDET7TCVdJjaqn39g/jYN38eWmLI5Oan8/F0xM5hiwuGC20UMLOuxriEmUOMi7GCN5ND5f1XSdj9r835nvvOzw8EDke6cmQPfCRovJUTnQfxkds72I34jMVax92zhxSH511dNxu/rweXT0G7s7W3NgUkMHRNUB0aDnSvxavda9nP4cHpMFXYJsV++O2XBJ7zIkmqqgBhVxNmtOdwbl6EHf649fKLjz0XeG7+jOjJ7yq47+SFwoE7zFj27kNOnsLpl6dpNOGldAS+ZuNPpg6PQyMTAaOZi4ibRtxUEpJS8lLWPbir6KBpUplEBfWce1AbnJPHhDFiGDqkiYk79x0tKReoqg1mWo+UI2xc6QnK9kTFlTWywdRrRj9HiUqQIkptEW6acBGmv21vbQrU3pwIEQKrQvy2BdT9cPVd6ZBw7Z094vL8cx/BJLP51ofNPKsm2kza8n0yFx7GpbR19ShDtjb6UE7iwkUgdG1fP/1u/aHtqf3HihKzxidyRXWYAaDzRvWGlFYSJfUMq/T+nP4V0HvBlY2eGEwPOzqDRAd7eGfWaCfCxu4/GOs+h04vTKGJ5GGo1orP/uBXyqjdP37md7TvaWK0P7xzf2itZYBfg/a9GTzEJFGqs721KVANpibjhHoXp2czU/chlTKcHptgDbQ/YUq8erx+otjoPng8WG2Go69/MGDY08jLm0xNZy7ixkVnTI1GU5bduz2gBTctvcihOqTGVbc+rL23aq98eOd+5R7W1z+ImpnzlK08q9pgDms/bAI1kG1aNXJldGzhvGarNu2ITXNjQqm1dk0SLkz3S2o/hd1bqhC/6tzDtVzloF7EyVzxDUjbO3vEGY7nui7e+bd92t/p6x+MlFylgmv5zulDVYQdpmwOqJzEhdtwdOE91aHN25BoZ7lMJqhhLiXxMi5Gx4pvjOGzk2zIl9O/+r3gtAkLkEwlmy2fflek3zvNVCG6itE1R/GEmXZhtPm+udr6KnTOFpWG846Vi0MPKcZVXDRl41RcfncPu0cmdVCkVaSGRiZCDUW/HIHmbaikDbrv+AQxcofHJou8sG1dPUXr3NINeSmcKtnOHzngxpL7fnd13WxUGWXlkmblz3TyuraunpLlpWmjW+cv+4J6/IG80ex9d973ccU9W/Gxb/4cNTObLlX9XlUbzCY3aRQPsMnJu7bGKdlYBniv2b4jw1Oam6UbtrFh56geG45Sau2aehv/NMSz52HqVfUSPlQh/jkNfFLdnYwByMHNm26fDIMrq8QZy5xnYKZhVYi11wbDtYcVXaYuv7tHu0hzmBweOjtaAouEKmTvddjy9KLrN/eGHqZsqlhwGwkt06XTfHobDv2eFjbWT8kFAlInxfVxd19SdZc5OFnI0MhE0edXrWP+dXP3l26ZMpozTt4gS+JAELVzID2sALwHzqRcpB9OdtWoWDNs6OxoMaqQ4ne26Iwcisl3Y1LFxQbPcXP53T2xRjZNsP0s9N6OYyRMZtbugRNTBliYQe+vJ2xzffOZxD2KrsOwSv5po5O3uctUfSOWXKSu7MGh2y85h0xbV4+V02igkBRrktNT1WXlwjDxAM+bUYc3TxVvko/tfj0QKpuM023n4/Orl2qNnJGJHDY+vifwvC6ElCammeemm3DGCXqZP/7Nn2O/byHUJXx4If6/vOVtgZKBgLkn5M/ftyTw+y/4vNmmZZV2dd1cVEFkZl0N9tz3IaNr+MYnrjPyAkXNEjc9PNCZz6lk/Jvp2KRrfE2lRuf816brBFVX4+Clr66ZeqyKBFAPc85FoKwcALzavbaopOCyhXMSrbtM6byxRXnvtXX1sKXWVOz+0i0lXcv8mXVab4+NQUh55PYOrN/ci6f2H8OCxunYdOsy1vi2nUacd5lbM6KUGlPZS6o598jtHUb3i2kJ0bkNtZEbHbXNa8AB0uBm576jsVWisGXMsGyramznzajFsWF9xDQsZ+UdC+dYl60L4+5Hn2P3Jx3T6/TmWpgHms7ks+OT1h1KZzdklV56yj1rr2Kf/8q6a60cO95+yZVw3T1woqjCRtJUvcFcV+OwJwPT0PhnP3AlO3HDFghbj4YKEw1L/PEAAA1xSURBVM/LBBP+j9MLRPWPx06ZTT7TRdTGGb7i8uaAUD9X+FvZTL5cly7hY7JgzXmGsf+7tZFLdHa0YOPjzxVtfn5vJt2QVAkOQLRkJx2rNu2Y2vBV9XVNMD080NnnIrqRTomhClfotdh879TDzEkyPF4tYzWMrjVLtYdVlbFsktBqy9MbVmm/A53XywST37cxElVj4N0P9zz6HFycK80VB2HRh5VLgusexTQ3gjvsm7CwsR6bbl1mZcwACOiM48TEo6071Hxr/Q2hnydMmvPYXStSOzCovLIA3zfCj98hwFFXWwOMFddJt3UEzp8xLdRgzjjA/euuVe4vXqk7k3nmX7t/f9kC1oGUlrEMlEGS4TjOhxzHedFxnJcdx+kq9f1e+uqawMnK1jCKwtWXphd+TRrqMfQMIu/0uWrTDiz+Qg9Wbdox9dhmAbG5JXWb40Qu3DjyL7CdHS2Btpw20HEZKZSqWrVpR8CjZaN9t6GBqcW378jwVPgv7K/G0dgmSbyKIqZwZbh02JYyox7meIK6yRDF+2mq27WFu79MO7rFAae19/DPmCXzZ2jHoLOjBa8W1ouoxjL3tYRFHx65vUMberapvqLa08KkYLu6bo4knfnebaUdiHSY6HZ131N7a5P2PjGVx3Fa/yRQeWWB/PfKHfZmKuSAlFvbF5Z0bUB4cueB7rXY/7W1oXZVe2sTDnSvDUgfncJ7cHt2XMn0pZCqwew4Tg2AhwCsBnAVgD9yHEc9Qwx56atrSjKMouicuzQ1dm3RnSrT4Jar+RqkXmh935Fh5NxzhpptUxKbskxA+TouUVTODe7zx+Ep5djw4asj/67X2IYzum2Iy4O0btmCKQPWidh4hyvDpcPWu089zLkcAlUyKoW996+2uleSLnnnX4MPdK8tklEljWqDvmPl4ikD+ED32sQODH787aCzGfNDw577PsR6oh/4iNpbp+KHpJLGHSsXa6Vg/mvUlU30U+Pk/05UfboJuiTKZQvnGI2tzqA2lcf5tf5+4lz2HYQ78J743Huxckkz6mszWLmkGQe61xp/BpOodH02E5g7fjo7WmLN1Xh6wyosbKyHg7xzIyxqV4q8Kw4cm6Sbkv+Y47wLwJdd172l8PgLAOC67tdUv3P99de7zzzzTOLXZhtyidNz4tXNNaVxerZk3SElrpDTkvkzAgZllESiqNcTp5bU5hri6rpY6nUAwbm5pfdgIJRnc73c79sS58ZqOh5R7lHus0YxWNImbEyohvt8xT8OSSUwVgt9/YN4av8xLF88r+jeW7phW1Fimsl6QfHkcWnQvXUvfvL8YXzo6otL+j4Xk/KWcUc/vNyauhoHf/fpd+G/bu7F6bFJOMg7xXRSwhoHeOVryY9n2DrhrXXc6/x2B/fzsOhNXJRiq/i/c9X7vPG9z+Ls4X1saCNtDfOlAF7zPR4AkFw8x4KwxBU/JmEiG2wNibiNZSB/Ui41AdqbjKs27cC+I8OozTi4fcWiSIvcge611jfG9Gwm1sQrzvhXkWS46I6Vi42TK7lNwDP2vv7Eixg+O4lbrn6L1fV2drRgw6PPRZ4fNs1JTFjYWB/aWTLqZsg1BIqzSVBSHOheGzCEPDII1zeeL1Ral8Vy0t7axN53YVKTzo4WrcFsk7gcB11rlsZy8Ek64kHHhD7m9rOkPfSUsH3V2ysOkKRm6qRrnJ4tKkPnIDm5F0X3GbycNs6eoZ5x1b7qTpxVbvppe5g/DuAW13U/VXj8xwBudF33L8jrPg3g0wDQ0tLS3t9vXve1FLhsS7oRJeWp4TJAKRkke9NHPbklvYDqsmBrnHzTgmsWzE6kSoFn/OuIox53GN1b94YazUkbC7SsFP17/mogSV+X/2/FeU9Wq4eZsn5zL3554DhubJtbctKdcOHx2R/8ik2wkgNJaSy7dzuGRiZQn83g+3+6PFVj2Q/dT6LYFt5nSSLibYK/WhZng3Rv3YvNu/ZjwgXecSkfefaiF8NnJzB4ZhzXLJiNx//i3X2u617P/U2RZFQIXs9zStqbteo6ONIKIwG8IZOGoepHdaBIcxNRHawqZSOjRnOlXJcpdz/6XKDu+Sc7WvDVCmt5LQhJQ43maruXBSEKjuNUjMGcBfASgJsBHALwNIBO13WfV/3OhWIwA7xBJotUZbHuwV1TNTnTNtiF5Lnn0efwfWIwt81rwI7/8b4yXZEgCIKQFjqDOVUNs+u6E47j3AVgO4AaAN/RGcsXGlSbI8Zy5ZFmcwohfT76zoUBg/noab67oiAIgnDhkHrjEtd1twKway9zASFGsiCUj/bWpkATiQ8s5csuCoIgCBcOlVHwVhAEoUJ45PYOrFu2AI0NtYmWCxQEQRCqh6pvjS0IghA3YiQLgiAIfsTDLAiCIAiCIAgaxGAWBEEQBEEQBA1iMAuCIAiCIAiCBjGYBUEQBEEQBEGDGMyCIAiCIAiCoEEMZkEQBEEQBEHQIAazIAiCIAiCIGgQg1kQBEEQBEEQNIjBLAiCIAiCIAgaxGAWBEEQBEEQBA1iMAuCIAiCIAiCBjGYBUEQBEEQBEGDGMyCIAiCIAiCoEEMZkEQBEEQBEHQIAazIAiCIAiCIGgQg1kQBEEQBEEQNDiu65b7GrQ4jnMKwIsRf30OgBMlXkIzgKMlvkcc11Hqe1TCNQAynnG/h4xnvO8h4xnve1TCeFbKWJwv4xnHe1TCNQCVMZ6VMhYynnmudF13FvsT13Ur+j8Az5Twu98q59+P+TpKeo9KuAYZTxnPSr4GGc/zczwraCzOi/E8z76Tso9nBY2FjGfIZzjfJRn/Uu4LKBDHdZT6HpVwDXFRCZ+lEq4hLirhs1TCNcRFJXyWSriGuDhfxuJ8Gc843qMSriEuzpexkPEMoRokGc+4rnv9hfr3zzdkPONFxjNeZDzjRcYzXmQ840XGM17Oh/HUfYZq8DB/6wL/++cbMp7xIuMZLzKe8SLjGS8ynvEi4xkv58N4Kj9DxXuYBUEQBEEQBKGcVIOHWRAEQRAEQRDKxgVnMDuO8x3Hcd50HGeP77l3OI7zC8dxnnMc518cx5ldeL7NcZwRx3F2F/572Pc77YXXv+w4zv9xHMcpx+cpNzGO5w7HcV70/eyicnyecmMznoWfvb3ws+cLP68vPC/zE7GOp8xPWN/vn/SN127HcXKO4ywr/EzmJ2IdT5mfsB7PWsdx/m/h+b2O43zB9zsyPxHreJ4f87PUEiDV9h+AlQDeCWCP77mnAbyn8O/bAHyl8O82/+vI+/wSwLsAOAC2AVhd7s9W5eO5A8D15f485f7PcjyzAH4D4B2Fx/MA1BT+LfMz3vGU+Wk5nuT3rgWw3/dY5me84ynz03I8AXQC+EHh3w0ADgBoKzyW+RnveJ4X8/OC8zC7rrsTwHHy9JUAdhb+/QSAj+new3GcSwDMdl33F25+NjwCYF3c11oNxDGewjksx/ODAH7juu6vC797zHXdSZmf54hjPFO50CqhhPv9jwD8HSDrp584xlM4h+V4ugBmOI6TBTAdwBiAkzI/zxHHeKZxnWlxwRnMCvYA+L3Cvz8O4K2+ny1yHOdXjuP8zHGcdxeeuxTAgO81A4XnhDy24+nx3UK45osXaghMgWo8rwDgOo6z3XGcZx3H+cvC8zI/9diOp4fMTx7d/e7xhzhn4Mn81GM7nh4yP3lU4/lPAIYBvAHgIID/7bruccj8DMN2PD2qfn6KwZznNgB3Oo7TB2AW8icjIP/Ft7iuex2A/w5gS0Gvw33ZUm7kHLbjCQCfdF33WgDvLvz3xylfcyWjGs8sgBUAPln4/0ccx7kZMj/DsB1PQOanDtV4AgAcx+kAcMZ1XU8HKfNTj+14AjI/dajG80YAkwAWAFgE4HOO4yyGzM8wbMcTOE/mpxjMAFzXfcF13Q+6rtuO/Kn9lcLzZ13XPVb4d1/h+SuQP3Eu9L3FQgCvp3vVlUuE8YTruocK/z8FYAvyN58A9XgiPw9/5rruUdd1zwDYirzeTOanhgjjKfNTg2Y8PT6BYm+ozE8NEcZT5qcGzXh2AviJ67rjruu+CeA/AFwPmZ9aIozneTM/xWAG4GVsOo6TAbABwMOFx/Mdx6kp/HsxgCXIJ1q8AeCU4zjLC6GF9QAeL8vFVyC24+k4TtZxnObC87UAPox82EeAejwBbAfwdsdxGgq6sfcA+K3MTz224ynzU49mPL3nPg7gB95zMj/12I6nzE89mvE8COD9Tp4ZAJYDeEHmpx7b8Tyf5me23BeQNo7j/B2A9wJodhxnAMCXAMx0HOfOwkt+BOC7hX+vBHCf4zgTyIca7vBpcj4D4HvIi9u3Ff674IhjPAs31/bCzVQD4N8A/E2KH6NisBlP13UHHcf5OvJZyy6Ara7r9hReJ/MT8YynzM9zWN7vQP6eH3Bddz95K5mfiG08p0HmJwDr8Xyo8O89yMswvuu67m8KP5P5iXjG83xaP6XTnyAIgiAIgiBoEEmGIAiCIAiCIGgQg1kQBEEQBEEQNIjBLAiCIAiCIAgaxGAWBEEQBEEQBA1iMAuCIAiCIAiCBjGYBUEQBEEQBEGDGMyCIAiCIAiCoEEMZkEQBEEQBEHQ8P8BrhfaRiRO18MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.resample(\"D\").interpolate(method=\"polynomial\", order=3).plot(style=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position_lat</th>\n",
       "      <th>position_long</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>Power</th>\n",
       "      <th>heart_rate</th>\n",
       "      <th>enhanced_altitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-08-21 03:24:16</td>\n",
       "      <td>201.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>504612085.0</td>\n",
       "      <td>-999397826.0</td>\n",
       "      <td>2019-08-21 03:24:17</td>\n",
       "      <td>210.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>504614191.0</td>\n",
       "      <td>-999397963.0</td>\n",
       "      <td>2019-08-21 03:24:18</td>\n",
       "      <td>216.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>314.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>504614830.0</td>\n",
       "      <td>-999398356.0</td>\n",
       "      <td>2019-08-21 03:24:19</td>\n",
       "      <td>231.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>319.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>504614846.0</td>\n",
       "      <td>-999398479.0</td>\n",
       "      <td>2019-08-21 03:24:20</td>\n",
       "      <td>231.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>312.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   position_lat  position_long            timestamp  Power  heart_rate  \\\n",
       "0           NaN            NaN  2019-08-21 03:24:16  201.0       119.0   \n",
       "1   504612085.0   -999397826.0  2019-08-21 03:24:17  210.0       120.0   \n",
       "2   504614191.0   -999397963.0  2019-08-21 03:24:18  216.0       122.0   \n",
       "3   504614830.0   -999398356.0  2019-08-21 03:24:19  231.0       122.0   \n",
       "4   504614846.0   -999398479.0  2019-08-21 03:24:20  231.0       121.0   \n",
       "\n",
       "   enhanced_altitude  \n",
       "0              294.0  \n",
       "1              308.0  \n",
       "2              314.4  \n",
       "3              319.2  \n",
       "4              312.2  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what does interpolate work on?\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df=pd.read_csv(\"datasets/run.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>position_lat</th>\n",
       "      <th>position_long</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>Power</th>\n",
       "      <th>heart_rate</th>\n",
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       "      <th>2019-08-21 03:24:16</th>\n",
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       "      <td>2019-08-21 03:24:18</td>\n",
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       "      <th>2019-08-21 03:24:45</th>\n",
       "      <td>504616122.0</td>\n",
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       "      <td>2019-08-21 03:24:45</td>\n",
       "      <td>296.0</td>\n",
       "      <td>127.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     position_lat  position_long            timestamp  Power  \\\n",
       "timestamp                                                                      \n",
       "2019-08-21 03:24:16           NaN            NaN  2019-08-21 03:24:16  201.0   \n",
       "2019-08-21 03:24:17   504612085.0   -999397826.0  2019-08-21 03:24:17  210.0   \n",
       "2019-08-21 03:24:18   504614191.0   -999397963.0  2019-08-21 03:24:18  216.0   \n",
       "2019-08-21 03:24:19   504614830.0   -999398356.0  2019-08-21 03:24:19  231.0   \n",
       "2019-08-21 03:24:20   504614846.0   -999398479.0  2019-08-21 03:24:20  231.0   \n",
       "2019-08-21 03:24:21   504615114.0   -999398697.0  2019-08-21 03:24:21  234.0   \n",
       "2019-08-21 03:24:22   504615254.0   -999398935.0  2019-08-21 03:24:22  261.0   \n",
       "2019-08-21 03:24:23   504615283.0   -999399273.0  2019-08-21 03:24:23  283.0   \n",
       "2019-08-21 03:24:24   504615385.0   -999399615.0  2019-08-21 03:24:24  287.0   \n",
       "2019-08-21 03:24:25   504615427.0   -999399902.0  2019-08-21 03:24:25  290.0   \n",
       "2019-08-21 03:24:36   504615768.0   -999403755.0  2019-08-21 03:24:36  295.0   \n",
       "2019-08-21 03:24:37   504615752.0   -999404141.0  2019-08-21 03:24:37  296.0   \n",
       "2019-08-21 03:24:38   504615753.0   -999404467.0  2019-08-21 03:24:38  298.0   \n",
       "2019-08-21 03:24:39   504615783.0   -999404812.0  2019-08-21 03:24:39  299.0   \n",
       "2019-08-21 03:24:40   504615783.0   -999405171.0  2019-08-21 03:24:40  299.0   \n",
       "2019-08-21 03:24:41   504615772.0   -999405519.0  2019-08-21 03:24:41  301.0   \n",
       "2019-08-21 03:24:42   504615800.0   -999405791.0  2019-08-21 03:24:42  301.0   \n",
       "2019-08-21 03:24:43   504615852.0   -999406103.0  2019-08-21 03:24:43  300.0   \n",
       "2019-08-21 03:24:44   504615976.0   -999406398.0  2019-08-21 03:24:44  298.0   \n",
       "2019-08-21 03:24:45   504616122.0   -999406646.0  2019-08-21 03:24:45  296.0   \n",
       "\n",
       "                     heart_rate  enhanced_altitude  \n",
       "timestamp                                           \n",
       "2019-08-21 03:24:16       119.0              294.0  \n",
       "2019-08-21 03:24:17       120.0              308.0  \n",
       "2019-08-21 03:24:18       122.0              314.4  \n",
       "2019-08-21 03:24:19       122.0              319.2  \n",
       "2019-08-21 03:24:20       121.0              312.2  \n",
       "2019-08-21 03:24:21       120.0              309.6  \n",
       "2019-08-21 03:24:22       119.0              306.6  \n",
       "2019-08-21 03:24:23       119.0              304.0  \n",
       "2019-08-21 03:24:24       119.0              302.4  \n",
       "2019-08-21 03:24:25       120.0              303.8  \n",
       "2019-08-21 03:24:36       125.0              298.0  \n",
       "2019-08-21 03:24:37       125.0              297.2  \n",
       "2019-08-21 03:24:38       125.0              296.6  \n",
       "2019-08-21 03:24:39       126.0              296.2  \n",
       "2019-08-21 03:24:40       126.0              296.0  \n",
       "2019-08-21 03:24:41       126.0              295.6  \n",
       "2019-08-21 03:24:42       127.0              295.4  \n",
       "2019-08-21 03:24:43       127.0              295.4  \n",
       "2019-08-21 03:24:44       127.0              295.2  \n",
       "2019-08-21 03:24:45       127.0              295.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[0:10].append(df[20:100])\n",
    "df=df.set_index(pd.to_datetime(df[\"timestamp\"]))\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8b3ab42e8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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P1FlcajIxPsbRQ9Mc2Ldn3b5hGUc/xyhJ20HPLxjMzGsz80nAUeDqqjlWW7TXfQBExJURMRMRMydOnNjIpiSp7+qz8ywuNWkmnFxqUp+dL+oblnH0c4yStB1sxt02PgC8uPr6buBJbX3nA99aZZ37IuJcgOrz/d02npk3ZGYtM2t79+7dhOFKUv9MT00yMT7GroDd42NMT00W9Q3LOPo5RknaDiJz/QvDEXEB8LHMvLh6fGFmfqX6+rXAszPzJRHxVFph+lLgPFovBrwwMx9esb3rgPnMfHtEXAOcnZmvW28ctVotZ2ZmzuT5SdLANeYWqM/OMz01edqUh7X6hmUc/RyjJA2LiGhkZu209vXCc0TcDFwGnAPcB7wZuAK4CGgCc8BVmXlPtfy1wKuBJeC3M/OTVfsR4PrMnImISeDDwJOBbwAvzcyOFyWuxvAsSZKkfug5PA8Tw7MkSZL6oVt49h0GJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0k6A425BQ4fO05jbuGM+vq1r80egySp0/igByBJ20VjboGDR+osLjWZGB/j6KFpDuzbs25fv/a12WOQJJ3OK8+SVKg+O8/iUpNmwsmlJvXZ+aK+fu1rs8cgSTqd4VmSCk1PTTIxPsaugN3jY0xPTRb19Wtfmz0GSdLpIjMHPYZitVotZ2ZmBj0MSTtYY26B+uw801OTp02JWKuvX/va7DFI0k4VEY3MrJ3WbniWJEmSOnULz07bkCRJkgoZniVJkqRChmdJkiSpkOFZkiRJKmR4liRJkgoZniVJkqRChmdJkiSpkOFZkiRJKmR4liRJkgoZniVJkqRChmdJkiSpkOFZkiRJKmR4liRJkgoZniVJkqRChmdJW6Ixt8DhY8dpzC0UtW9F31bsS5K0s40PegCSRk9jboGDR+osLjWZGB/j6KFpDuzb07V9rXV67duKfUmS5JVnSZuuPjvP4lKTZsLJpSb12fk127eibyv2JUmS4VnSppuemmRifIxdAbvHx5iemlyzfSv6tmJfkiRFZg56DMVqtVrOzMwMehiSCjTmFqjPzjM9Ndkx7aFb+1b0bcW+JEk7Q0Q0MrN2WrvhWZIkSerULTw7bUOSJEkqZHiWJEmSCq0bniPixoi4PyLuamu7LiK+GBF3RsRHIuKsqv1gRNzR9tGMiP2rbPMtEXFP23JXbO7TkiRJkjZfyZXnm4DLV7TdClycmZcAXwbeAJCZRzNzf2buB34d+Hpm3tFlu+8+tWxmfqK34UuSJEn9s254zszbgQdWtN2SmUvVwzpw/iqr/gpw84ZHKEmSJA2JzZjz/Grgk6u0v5y1w/PV1bSPGyPCe0FJkiRp6G0oPEfEtcAScHRF+zOA72fmXauuCO8FngLsB+4F3rXGPq6MiJmImDlx4sRGhitJkiRtSM/hOSJeCTwXOJin3yz6l1njqnNm3peZD2dmE3gfcOkay96QmbXMrO3du7fX4UqSJEkbNt7LShFxOfB64NmZ+f0VfWPAS4FnrbH+uZl5b/XwRUC3K9SSJEnS0Ci5Vd3NwF8BF0XE3RHxGuA9wI8Ct1a3mru+bZVnAXdn5uyK7RyJiFPv0vLOiPh8RNwJ/DzwzzfjyUiSJElbybfnliRJklbw7bklSZKkDTI8S5IkSYUMz5IkSVIhw7PUB425BQ4fO05jbqG4r5d1hmlfkiSNop5uVSepXGNugYNH6iwuNZkYH+PooWkO7NuzZl8v6wzTviRJGlVeeZa2WH12nsWlJs2Ek0tN6rPz6/b1ss4w7UuSpFFleJa22PTUJBPjY+wK2D0+xvTU5Lp9vawzTPuSJGlUeZ9nqQ8acwvUZ+eZnpo8bWpDt75e1hmmfUmStJ11u8+z4VmSJElawTdJkSRJkjbI8CxJkiQVMjxLkiRJhQzPkiRJUiHDsyRJklTI8CxJkiQVMjxLkiRJhQzPkiRJUiHDsyRJklTI8CxJkiQVMjxLkiRJhQzPkiRJUiHDsyRJklTI8CytojG3wOFjx2nMLRS1r9cnSZJGw/igByANm8bcAgeP1FlcajIxPsbRQ9Mc2Lena/ta60iSpNHilWdphfrsPItLTZoJJ5ea1Gfn12xfr0+SJI0Ow7O0wvTUJBPjY+wK2D0+xvTU5Jrt6/VJkqTREZk56DEUq9VqOTMzM+hhaAdozC1Qn51nemqyY/pFt/b1+iRJ0vYSEY3MrJ3WbniWJEmSOnULz07bkCRJkgoZniVJkqRChmdJkiSpkOFZkiRJKmR4liRJkgoZniVJkqRChmdJkiSpkOFZkiRJKmR4liRJkgoZniVJkqRChmdJkiSpkOFZkiRJKrRueI6IGyPi/oi4q63tuoj4YkTcGREfiYizqvYLIuIHEXFH9XF9l22eHRG3RsRXqs97Nu8pSZIkSVuj5MrzTcDlK9puBS7OzEuALwNvaOv7amburz6u6rLNa4DbMvNC4LbqsSRJkjTU1g3PmXk78MCKtlsyc6l6WAfOP8P9vgB4f/X1+4EXnuH60g815hY4fOw4jbmFTeuTJElazfgmbOPVwIfaHv9YRHwOeBD4vcz89CrrPD4z7wXIzHsj4nGbMA7tQI25BQ4eqbO41GRifIyjh6Y5sG/PhvokSZK62dALBiPiWmAJOFo13Qs8OTOfDvwO8IGIeMwG93FlRMxExMyJEyc2simNoPrsPItLTZoJJ5ea1GfnN9wnSZLUTc/hOSJeCTwXOJiZCZCZD2XmfPV1A/gq8BOrrH5fRJxbbedc4P5u+8nMGzKzlpm1vXv39jpcjajpqUkmxsfYFbB7fIzpqckN90mSJHUTVe5de6GIC4CPZebF1ePLgT8Cnp2ZJ9qW2ws8kJkPR8QU8GngaZn5wIrtXQfMZ+bbI+Ia4OzMfN1646jVajkzM1P85LQzNOYWqM/OMz01edrUi177JEnSzhYRjcysnda+XniOiJuBy4BzgPuAN9O6u8YjgFP/665n5lUR8WLg92lN5XgYeHNm/nm1nSPA9Zk5ExGTwIeBJwPfAF66MmCvxvAsSZKkfug5PA8Tw7MkSZL6oVt49h0GJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpELrhueIuDEi7o+Iu9rarouIL0bEnRHxkYg4q2r/xYhoRMTnq8+/0GWbb4mIeyLijurjis17SpIkSdLWKLnyfBNw+Yq2W4GLM/MS4MvAG6r2bwPPy8ynAa8E/myN7b47M/dXH584s2FLkiRJ/bdueM7M24EHVrTdkplL1cM6cH7V/rnM/FbV/gXgkRHxiE0cryRJkjQwmzHn+dXAJ1dpfzHwucx8qMt6V1fTPm6MiD2bMA5JkiRpS20oPEfEtcAScHRF+1OBdwC/1WXV9wJPAfYD9wLvWmMfV0bETETMnDhxYiPDlSRJkjak5/AcEa8EngsczMxsaz8f+Ajwisz86mrrZuZ9mflwZjaB9wGXdttPZt6QmbXMrO3du7fX4UqSJEkb1lN4jojLgdcDz8/M77e1nwV8HHhDZn5mjfXPbXv4IuCubstKkiRJw6LkVnU3A38FXBQRd0fEa4D3AD8K3Frdau76avGrgR8H3th2G7rHVds5EhG1arl3VrezuxP4eeCfb/LzkiRJkjZdtM24GHq1Wi1nZmYGPQxJkiSNuIhoZGZtZbvvMChJkiQVMjxLkiRJhQzPkiRJUqGRDs+NuQUOHztOY27hjPokSZKk1YwPegBbpTG3wMEjdRaXmkyMj3H00DQH9u1Zt0+SJEnqZmSvPNdn51lcatJMOLnUpD47X9QnSZIkdTOy4Xl6apKJ8TF2BeweH2N6arKoT5IkSepmpO/z3JhboD47z/TU5GnTMtbqkyRJ0s7W7T7PIzvnGeDAvj1dg/FafZIkSdJqRnbahiRJkrTZDM+SJElSIcOzJEmSVMjwLEmSJBUyPEuSJEmFDM+SJElSIcOzJEmSVMjwLEmSJBUyPEuSJEmFDM+SJElSIcOzJEmSVMjwLEmSJBUyPEuSJEmFDM+raMwtcPjYcRpzC4MeiiRJkobI+KAHMGwacwscPFJncanJxPgYRw9Nc2DfnkEPS5IkSUPAK88r1GfnWVxq0kw4udSkPjs/6CFJkiRpSBieV5iemmRifIxdAbvHx5iemhz0kCRJkjQknLaxwoF9ezh6aJr67DzTU5NO2ZAkSdIPGZ5XcWDfHkOzJEmSTuO0DUmSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpEKGZ0mSJKmQ4VmSJEkqZHiWJEmSChmeJUmSpELrhueIuDEi7o+Iu9rarouIL0bEnRHxkYg4q63vDRFxPCK+FBHP6bLNsyPi1oj4SvV5z+Y8HUmSJGnrlFx5vgm4fEXbrcDFmXkJ8GXgDQAR8VPALwNPrdb504jYtco2rwFuy8wLgduqx5IkSdJQWzc8Z+btwAMr2m7JzKXqYR04v/r6BcAHM/OhzPwacBy4dJXNvgB4f/X1+4EX9jD2H2rMLXD42HEacwsb2cyO3pckSZLWN74J23g18KHq6yfSCtOn3F21rfT4zLwXIDPvjYjH9brzxtwCB4/UWVxqMjE+xtFD0xzYtzWzQEZ1X5IkSSqzoRcMRsS1wBJw9FTTKovlBvdxZUTMRMTMiRMnTuuvz86zuNSkmXByqUl9dn4ju1vTqO5LkiRJZXoOzxHxSuC5wMHMPBWQ7wae1LbY+cC3Vln9vog4t9rOucD93faTmTdkZi0za3v37j2tf3pqkonxMXYF7B4fY3pqssdntL5R3ZckSZLKxHLuXWOhiAuAj2XmxdXjy4E/Ap6dmSfalnsq8AFa85zPo/ViwAsz8+EV27sOmM/Mt0fENcDZmfm69cZRq9VyZmbmtPbG3AL12Xmmpya3fGrDqO5LkiRJyyKikZm109rXC88RcTNwGXAOcB/wZlp313gEcGouQT0zr6qWv5bWPOgl4Lcz85NV+xHg+syciYhJ4MPAk4FvAC/NzI4XJa6mW3iWJEmSNlPP4XmYGJ4lSZLUD93Cs+8wKEmSJBUyPEuSJEmFDM+SJElSIcOzJEmSVMjwLEmSJBUyPEuSJEmFDM+SJElSIcOzJEmSVMjwLEmSJBUyPEuSJEmFDM+SJElSIcOzJEmSVMjwLEmSJBXaVuH5/u89RGNuYdDD6Koxt8DhY8dPG2O39vX6JEmSNFzGBz2AM3Hfg3/HwSN1jh6a5sC+PYMeTofG3AIHj9RZXGoyMT72wzF2a19rHUmSJA2nbXXlGeDkUpP67Pygh3Ga+uw8i0tNmtk5xm7t6/VJkiRp+Gy78Lx7fIzpqclBD+M001OTTIyPsSs6x9itfb0+SZIkDZ/IzEGPodiTL3pafuSW24d2akNjboH67DzTU5MdY+zWvl6fJEmSBiMiGplZO619O4XnWq2WMzMzgx6GJEmSRly38Lztpm1IkiRJg2J4liRJkgoZniVJkqRChmdJkiSpkOFZkiRJKmR4liRJkgoZniVJkqRChmdJkiSpkOFZkiRJKmR4liRJkgoZniVJkqRChmdJkiSpkOFZkiRJKmR4liRJkgpFZg56DMUi4gQw16fdnQN8u0/72g6sxzJr0cl6dLIey6xFJ+uxzFp0sh6dhqUe+zJz78rGbRWe+ykiZjKzNuhxDAvrscxadLIenazHMmvRyXossxadrEenYa+H0zYkSZKkQoZnSUZS4A8AAA3gSURBVJIkqZDhubsbBj2AIWM9llmLTtajk/VYZi06WY9l1qKT9eg01PVwzrMkSZJUyCvPkiRJUqEdH54jIgY9hmFiPZZZi07WY5m16GQ9OlmPZdaik/XotF3rsSPDc0Q8OiJeGxFPAR5ZtW3Lb+BmsB7LrEUn67HMWnSyHp2sxzJr0cl6dBqFeuy4Oc8R8fPAe4G7gHngocz8XwY7qsGxHsusRSfrscxadLIenazHMmvRyXp0GpV67MQrz08EPpiZLwHeCPxsRLwGICJ2DXRkg2E9llmLTtZjmbWg4+qQ9ehkPZZZi07Wo9NI1GPkw3NEnB8RT21r+kngfwBk5v3A64C3Vo8f7v8I+ysinhwRP9vWdBE7tB4RccGpH9rKTwF/CzuvFgARcWFE/GFb00+yQ+sREU+KiL/f1rTTf2/sAcjlf1X+PXbosQGeV9p5TunkeaXTqJ5XRjY8R8R4RLwDuA34o4h4V9X1n4HfOrVcZt4GfDYifq9ab1vNuykVEbsj4m3A/w28LiL+ddV1OzuwHpX3Ar8fEc+rHn8GuOpU506pRfWz8jbgKPB3bV2fZmfW418Bfwm8PSLeGRFnAZ8A/umpZXZQLXZFxLuBL0fEs9u6bmdnHhueVyqeU7ryvMLon1dGNjwDrwcuoPVXzquAn4iIZ2Xmfwa+FRFvbVv2RuAJEbG77crKqDlVj58GXgL8w4j4icy8FbinCgynjHQ92v41NAO8D/iNiHh0Zn4cuHcHHhu/B7wGeFZm/stTjZn5/wB377Bj4xzgQuApwMuAh4E3Z+ZfAv81Iv6gbfGRrkXlRcB5wJ/QOk6AjmNjp/2seF5Z5jmljeeV04z0eWWUw/MHgf85W+4F7gV+qer7TeDXI+KZ1eOLgHsy8+QAxrml2v6Ke2dmHqye47OA+2j9Owng1bTq8azq8cjWAzr+NfQ04LPAt4DfqNpeDbxiJ9Si7dh4D/AdYE9E/IOIeFNEvKzq21HHBrAI/APgcZn5HeBDQEbEr9G6mnZwJ9Si7dj4T8A1wB9UzVe2LXYlO+T3aJsdf17xnLI6zystO+W8MnLhue2vv29m5rfbHj+S1r9iycwv0ToZ/GpE3A78Gq2DfRSdOpBPAkTE04F/C9wK/GZEXAt8k9bE/ZGuR0SMVZ8nImIcmAU+BfwZ8MKIOALcD/xL4FdGuRaViIjIzG/TqsGXgXcAC8BvV1dKRvbYqI6BU1+PAWTmg7QC869WXZ8H/opWoL4PeAsjWAvorAfV743MXMzMr2XmEvAu4J9GxKOqvi8Df8gOqMdOP6+sdmywg88pq/3u2MnnlZXHx444r2Tmtv4Afh/4perrWKV/ovr8ceB/WtH3COCfDPo59LMeK5atAf8B2F89fiRwxaCfQx+PjZto/Yv+bcCDwF+cWnbUatGtHu11Af5X4Efajo0PAReP6LHxJlpzmB+xSt/zaYWBp7XV4qPAY0exFuvVY8Xx8n/QuuLY3jcxgr9H16vHjjmvrFeLFcuO9Dml8NjYaeeV0+qxE84r2/rKc0RMAb8N/E5EjGVmVpPUb4yIn4PWlZOIeDzwcGZ+NiJeHhGvi4g9mflQtuYjjYSSerTLzBngMVRXEDLz7zLzE/0d9dZYpxbPjIhTz/sztH7R/SrwYEQ8M1tGphbQvR7Av43qhWCZ+ceZ+YPq6xngbKBZPR6JekTEcyPib4Ap4KOZ+VDVPh4RN0VEDWgAX6X1S7+9Fk+oHo9ELWDderT/Hj01D/H1tOa2viUirouI87J1dXokfo+eQT1G/rxSWot2I35OWa8eO+q8slY92AnnlUGn914+gLHq86OA64HPAVe39U+uWP45wB20rhJ8Grh00M9hUPVoW/aZwJ8DNwNnDfo5DKgWB4Fa2+NfAy4c9HMYVD3a2tqPjccO+jlsYi0eB/x74N+0tf1Ie3/b14+nddeAG4C/pnW1ZGRqUViPvaus83Ra/369A3jmoJ/DIOsxyueVM6nFqJ9TCuvR/rtjJ5xXevndMVLnlW3zDoMR8Vhar+z9r1lNKo+IXwCeAdwCfCAzL+qy7sto/Rv2UGZ+qD8j3lq91qOaq3cZcB3wJ5n57/o15q1yprWo5mPlim2MZWazf6PeOhv8WXkG8G+APx6xY+O/Zetq4ctovajp87TmMT+S1qvjP5KZ320/NiJiL7AfeEJm/tlAnsAmO9N6rFh3ktYfYLdl5vV9HfgW2WA9Ruq80mstRvGcAj397tiVK+5TPKLnlV5+VkbqvALb5AWDEfEvaH1T/hVwY0Q8ser6KvBjmdkA7oiIz0XEe9vWe01EnAv8X7T+Et72v+BgY/Wg9RfybbTm6W37g7iXWmRmVsfGeae2M0K/4Dbys3JeZv41cGAEj41/F603+vgkrX8rvxP4AfAXwC/SOvG3HxvnZ+aJzLx1hILzGdejWu81EfGkzJwHXjZCwXkj9Rip88pGasGInVOg598dD++Q88qZ/qyM1HnllKEPzxHxSuAf0voX4fNo/fv51K2ALgS+ExEHgEto3Zv1k22rn6Q1J+2hlX8RblebUY/q6tq2r8cGa7EILEVsjxuyl9iEY2NphI+NHwH+cWZ+j9aL3p6fmW/MzA/SeiHlU6o5rNA6NhZH/NjoqR4r/2OzXW2wHiN1XtmMWozK7w3YlJ+VUT+vnOnxMTLnlXZDP20jIs6nNc5vVo//N1rziX6l+uv/OPA14BBwOfBzmfmPBjbgLWY9llmLTtZj2Vq1WGXZl9OqxWv7PMy+sR6drMcya9HJenSyHqsbX3+RwYiI8WzdW/T+zFxs6zqb1oszAOaByzLzs9U6f83y/VlHivVYZi06WY9lhbU4tewFwNW0rqq8vV9j7Cfr0cl6LLMWnaxHJ+uxtqEJzxHxCuC/A1/IzHuqbxqnvmkR8Yhs3QrlfFq3kjrV99kV/UcHMf7NZj2WWYtO1mNZL7Vo82vV52dm5t/2a8xbyXp0sh7LrEUn69HJepyZgU/biIifpfWXyt8CXwCeTOvVyw9GxNto3THgaNvyn6B1K5iLaU1O/9PM/O/9H/nWsB7LrEUn67FsA7V4Gq1bjL0D+EFuo7eDXYv16GQ9llmLTtajk/XoUQ7uPoG7gN20Xr354qrtQlr3VT2rerxnxToX0/qL5z8B/wV43qDGbz2shfWwFoP+sB7Ww1pYD+sxgPoN4Bs2DvzvwJ8AP0fnWzoeAf6G1l8zT6zaxtr6a8D/AF476MJZD2thPayF9RiOD+thLayH9ejnR1+nbVS3bzlM696AnwReBXyU1l86L6X1Jg6fAv4+8NOZ+fxqvRcB38jMRkQ8OkdkTo31WGYtOlmPZdaik/XoZD2WWYtO1qOT9dhEff6L5zHAXwI/Wj1+Dq2/fl68Yrk9tN4a95Lq8QuBnxz0XxrWw1pYD2sx6A/rYT2shfWwHoP96OubpGTmg8DXaf21A/AZWvNnfiEintC26EXAHPDfqvU+mplf7N9I+8N6LLMWnazHMmvRyXp0sh7LrEUn69HJemyeQbzD4EeA/RFxbrYu/d8JPAQ8MSJ+LCKupfUe6DO5M169aT2WWYtO1mOZtehkPTpZj2XWopP16GQ9NsEgwvNf0HrDhlcBZOb/B1wKNGndTuvHgedm5h8PYGyDYD2WWYtO1mOZtehkPTpZj2XWopP16GQ9NkHf3yQlM++NiI8Cb4+I47TeuOEk8DDwvsy8od9jGiTrscxadLIey6xFJ+vRyXossxadrEcn67E5BvYmKRHxj2m9uvNngPdk5nsGMpAhYT2WWYtO1mOZtehkPTpZj2XWopP16GQ9Nmag7zAYEbuBzOptIHc667HMWnSyHsusRSfr0cl6LLMWnaxHJ+vRu4G/PbckSZK0XQziBYOSJEnStmR4liRJkgoZniVJkqRChmdJkiSpkOFZkiRJKmR4lqQBiIizIuKfVV+fFxH/cQv3tT8irtiq7UvSTmJ4lqTBOAv4ZwCZ+a3MfMkW7ms/YHiWpE3gfZ4laQAi4oPAC4AvAV8B/l5mXhwRrwJeCOwCLgbeBUwAvw48BFyRmQ9ExFOAw8Be4PvAb2bmFyPipcCbab3d7neBfwQcB34EuAf4Q+BrwL+u2n4A/EZmfukM9v0p4A7gUuAxwKsz879sTaUkabh45VmSBuMa4KuZuR/4Fyv6LgZ+lVY4fRvw/cx8OvBXwCuqZW4AXpuZB4DfBf60an8T8JzM/Gng+Zm5WLV9KDP3Z+aHgC8Cz6q2+SbgD85w3wCPysyfoXX1/MaNlUKSto/xQQ9AknSaY5n5PeB7EfFd4M+r9s8Dl0TEo4GfAf5DRJxa5xHV588AN0XEh4H/s8v2Hwu8PyIuBBLYXbrvtuVuBsjM2yPiMRFxVmZ+p8fnK0nbhuFZkobPQ21fN9seN2n93h4DvlNdte6QmVdFxDOAfwLcERGnLQO8lVZIflFEXAB86gz2/cNdrdz1Gs9HkkaG0zYkaTC+B/xoLytm5oPA16r5zUTLT1dfPyUz/zoz3wR8G3jSKvt6LK35zwCv6m34vLza388B383M7/a4HUnaVgzPkjQAmTkPfCYi7gKu62ETB4HXRMTfAF+g9eJDgOsi4vPVdm8H/gY4BvxURNwRES8H3gn8YUR8htaLA3uxEBF/CVwPvKbHbUjStuPdNiRJZ6S628bvZubMoMciSf3mlWdJkiSpkFeeJUmSpEJeeZYkSZIKGZ4lSZKkQoZnSZIkqZDhWZIkSSpkeJYkSZIKGZ4lSZKkQv8/84WHo7k6LQwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"heart_rate\"].plot(style=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
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      ],
      "text/plain": [
       "                     position_lat  position_long            timestamp  \\\n",
       "timestamp                                                               \n",
       "2019-08-21 03:24:16           NaN            NaN  2019-08-21 03:24:16   \n",
       "2019-08-21 03:24:17   504612085.0  -9.993978e+08  2019-08-21 03:24:17   \n",
       "2019-08-21 03:24:18   504614191.0  -9.993980e+08  2019-08-21 03:24:18   \n",
       "2019-08-21 03:24:19   504614830.0  -9.993984e+08  2019-08-21 03:24:19   \n",
       "2019-08-21 03:24:20   504614846.0  -9.993985e+08  2019-08-21 03:24:20   \n",
       "2019-08-21 03:24:21   504615114.0  -9.993987e+08  2019-08-21 03:24:21   \n",
       "2019-08-21 03:24:22   504615254.0  -9.993989e+08  2019-08-21 03:24:22   \n",
       "2019-08-21 03:24:23   504615283.0  -9.993993e+08  2019-08-21 03:24:23   \n",
       "2019-08-21 03:24:24   504615385.0  -9.993996e+08  2019-08-21 03:24:24   \n",
       "2019-08-21 03:24:25   504615427.0  -9.993999e+08  2019-08-21 03:24:25   \n",
       "2019-08-21 03:24:26   504615458.0  -9.994003e+08                  NaN   \n",
       "2019-08-21 03:24:27   504615489.0  -9.994006e+08                  NaN   \n",
       "2019-08-21 03:24:28   504615520.0  -9.994010e+08                  NaN   \n",
       "2019-08-21 03:24:29   504615551.0  -9.994013e+08                  NaN   \n",
       "2019-08-21 03:24:30   504615582.0  -9.994017e+08                  NaN   \n",
       "2019-08-21 03:24:31   504615613.0  -9.994020e+08                  NaN   \n",
       "2019-08-21 03:24:32   504615644.0  -9.994024e+08                  NaN   \n",
       "2019-08-21 03:24:33   504615675.0  -9.994027e+08                  NaN   \n",
       "2019-08-21 03:24:34   504615706.0  -9.994031e+08                  NaN   \n",
       "2019-08-21 03:24:35   504615737.0  -9.994034e+08                  NaN   \n",
       "\n",
       "                          Power  heart_rate  enhanced_altitude  \n",
       "timestamp                                                       \n",
       "2019-08-21 03:24:16  201.000000  119.000000         294.000000  \n",
       "2019-08-21 03:24:17  210.000000  120.000000         308.000000  \n",
       "2019-08-21 03:24:18  216.000000  122.000000         314.400000  \n",
       "2019-08-21 03:24:19  231.000000  122.000000         319.200000  \n",
       "2019-08-21 03:24:20  231.000000  121.000000         312.200000  \n",
       "2019-08-21 03:24:21  234.000000  120.000000         309.600000  \n",
       "2019-08-21 03:24:22  261.000000  119.000000         306.600000  \n",
       "2019-08-21 03:24:23  283.000000  119.000000         304.000000  \n",
       "2019-08-21 03:24:24  287.000000  119.000000         302.400000  \n",
       "2019-08-21 03:24:25  290.000000  120.000000         303.800000  \n",
       "2019-08-21 03:24:26  290.454545  120.454545         303.272727  \n",
       "2019-08-21 03:24:27  290.909091  120.909091         302.745455  \n",
       "2019-08-21 03:24:28  291.363636  121.363636         302.218182  \n",
       "2019-08-21 03:24:29  291.818182  121.818182         301.690909  \n",
       "2019-08-21 03:24:30  292.272727  122.272727         301.163636  \n",
       "2019-08-21 03:24:31  292.727273  122.727273         300.636364  \n",
       "2019-08-21 03:24:32  293.181818  123.181818         300.109091  \n",
       "2019-08-21 03:24:33  293.636364  123.636364         299.581818  \n",
       "2019-08-21 03:24:34  294.090909  124.090909         299.054545  \n",
       "2019-08-21 03:24:35  294.545455  124.545455         298.527273  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample(\"1s\").interpolate(method=\"time\").head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8b3a3b278>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.resample(\"1s\").interpolate(method=\"time\")[\"heart_rate\"].plot(style=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa8b3928e80>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df.resample(\"1s\").interpolate(method=\"time\")[\"heart_rate\"].plot(style=\"b.\")\n",
    "plt.figure()\n",
    "df.resample(\"1s\").fillna(method=\"ffill\")[\"heart_rate\"].plot(style=\"r.\")\n",
    "plt.figure()\n",
    "df.resample(\"1s\").fillna(method=\"nearest\")[\"heart_rate\"].plot(style=\"g.\")"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}