exploring_digital_biomarkers_an_hour.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring dataset (an hour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking various types of data for an hour from 18:00 (2020-02-13), the food(Berry Smoothie) intake time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Food Log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "fl001_df = pd.read_csv('./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/001/Food_Log_001.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>time_begin</th>\n",
       "      <th>time_end</th>\n",
       "      <th>logged_food</th>\n",
       "      <th>amount</th>\n",
       "      <th>unit</th>\n",
       "      <th>searched_food</th>\n",
       "      <th>calorie</th>\n",
       "      <th>total_carb</th>\n",
       "      <th>dietary_fiber</th>\n",
       "      <th>sugar</th>\n",
       "      <th>protein</th>\n",
       "      <th>total_fat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>2020-02-13 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Berry Smoothie</td>\n",
       "      <td>20.00</td>\n",
       "      <td>fluid ounce</td>\n",
       "      <td>Strawberry Smoothie</td>\n",
       "      <td>456.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>83.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>20:30:00</td>\n",
       "      <td>2020-02-13 20:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chicken Leg</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chicken leg</td>\n",
       "      <td>475.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>20:30:00</td>\n",
       "      <td>2020-02-13 20:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asparagus</td>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asparagus</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>07:10:00</td>\n",
       "      <td>2020-02-14 07:10:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natrel Lactose Free 2 Percent</td>\n",
       "      <td>8.00</td>\n",
       "      <td>fluid ounce</td>\n",
       "      <td>(Natrel) Lactose Free 2% Partly Skimmed Milk</td>\n",
       "      <td>120.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>07:10:00</td>\n",
       "      <td>2020-02-14 07:10:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard Breakfast</td>\n",
       "      <td>0.75</td>\n",
       "      <td>cup</td>\n",
       "      <td>(Kellogg's) Frosted Flakes, Cereal</td>\n",
       "      <td>110.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>09:38:00</td>\n",
       "      <td>2020-02-14 09:38:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Breakfast Trail Mix</td>\n",
       "      <td>0.50</td>\n",
       "      <td>cup</td>\n",
       "      <td>(Giant) Breakfast Blend, Trail Mix</td>\n",
       "      <td>280.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>12:38:00</td>\n",
       "      <td>2020-02-14 12:38:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spinach Salad w/ strawberries and cheese</td>\n",
       "      <td>200.00</td>\n",
       "      <td>grams</td>\n",
       "      <td>Spinach And Strawberry Salad</td>\n",
       "      <td>286.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>12:38:00</td>\n",
       "      <td>2020-02-14 12:38:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Egg</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Large Egg</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>19:30:00</td>\n",
       "      <td>2020-02-14 19:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Acai Smoothie</td>\n",
       "      <td>20.00</td>\n",
       "      <td>fluid ounce</td>\n",
       "      <td>(Smoothie King) Acai Adventure Smoothie</td>\n",
       "      <td>440.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>2020-02-14 20:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(Trader Joe's) Mac and Cheese</td>\n",
       "      <td>0.50</td>\n",
       "      <td>cup</td>\n",
       "      <td>(Trader Joe's) Macaroni and Cheese</td>\n",
       "      <td>135.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         date      time           time_begin  time_end  \\\n",
       "0  2020-02-13  18:00:00  2020-02-13 18:00:00       NaN   \n",
       "1  2020-02-13  20:30:00  2020-02-13 20:30:00       NaN   \n",
       "2  2020-02-13  20:30:00  2020-02-13 20:30:00       NaN   \n",
       "3  2020-02-14  07:10:00  2020-02-14 07:10:00       NaN   \n",
       "4  2020-02-14  07:10:00  2020-02-14 07:10:00       NaN   \n",
       "5  2020-02-14  09:38:00  2020-02-14 09:38:00       NaN   \n",
       "6  2020-02-14  12:38:00  2020-02-14 12:38:00       NaN   \n",
       "7  2020-02-14  12:38:00  2020-02-14 12:38:00       NaN   \n",
       "8  2020-02-14  19:30:00  2020-02-14 19:30:00       NaN   \n",
       "9  2020-02-14  20:00:00  2020-02-14 20:00:00       NaN   \n",
       "\n",
       "                                logged_food  amount         unit  \\\n",
       "0                            Berry Smoothie   20.00  fluid ounce   \n",
       "1                               Chicken Leg    1.00          NaN   \n",
       "2                                 Asparagus    4.00          NaN   \n",
       "3             Natrel Lactose Free 2 Percent    8.00  fluid ounce   \n",
       "4                        Standard Breakfast    0.75          cup   \n",
       "5                       Breakfast Trail Mix    0.50          cup   \n",
       "6  Spinach Salad w/ strawberries and cheese  200.00        grams   \n",
       "7                                       Egg    1.00          NaN   \n",
       "8                             Acai Smoothie   20.00  fluid ounce   \n",
       "9             (Trader Joe's) Mac and Cheese    0.50          cup   \n",
       "\n",
       "                                  searched_food  calorie  total_carb  \\\n",
       "0                           Strawberry Smoothie    456.0        85.0   \n",
       "1                                   chicken leg    475.0         0.0   \n",
       "2                                     Asparagus     13.0         2.5   \n",
       "3  (Natrel) Lactose Free 2% Partly Skimmed Milk    120.0         9.0   \n",
       "4            (Kellogg's) Frosted Flakes, Cereal    110.0        26.0   \n",
       "5            (Giant) Breakfast Blend, Trail Mix    280.0        30.0   \n",
       "6                  Spinach And Strawberry Salad    286.0        14.0   \n",
       "7                                     Large Egg     72.0         0.4   \n",
       "8       (Smoothie King) Acai Adventure Smoothie    440.0        92.0   \n",
       "9            (Trader Joe's) Macaroni and Cheese    135.0        23.5   \n",
       "\n",
       "   dietary_fiber  sugar  protein  total_fat  \n",
       "0            1.7   83.0     16.0        3.3  \n",
       "1            0.0    0.0     62.0       23.0  \n",
       "2            1.2    0.8      1.4        0.1  \n",
       "3            NaN    8.0     12.0        NaN  \n",
       "4            NaN   10.0      1.0        NaN  \n",
       "5            NaN   22.0      4.0        NaN  \n",
       "6            NaN    8.5      7.6        NaN  \n",
       "7            NaN    0.2      6.3        NaN  \n",
       "8            NaN   75.0      5.0        NaN  \n",
       "9            NaN    1.5      5.0        NaN  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl001_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dexcom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "dexcom001_df = pd.read_csv('./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/001/Dexcom_001.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Timestamp (YYYY-MM-DDThh:mm:ss)</th>\n",
       "      <th>Event Type</th>\n",
       "      <th>Event Subtype</th>\n",
       "      <th>Patient Info</th>\n",
       "      <th>Device Info</th>\n",
       "      <th>Source Device ID</th>\n",
       "      <th>Glucose Value (mg/dL)</th>\n",
       "      <th>Insulin Value (u)</th>\n",
       "      <th>Carb Value (grams)</th>\n",
       "      <th>Duration (hh:mm:ss)</th>\n",
       "      <th>Glucose Rate of Change (mg/dL/min)</th>\n",
       "      <th>Transmitter Time (Long Integer)</th>\n",
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       "      <th>2</th>\n",
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       "      <td>PatientIdentifier</td>\n",
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       "      <td>2019-001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DateOfBirth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Device</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dexcom G6 Mobile App</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alert</td>\n",
       "      <td>Fall</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
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       "      <td>Alert</td>\n",
       "      <td>High</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alert</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alert</td>\n",
       "      <td>Signal Loss</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alert</td>\n",
       "      <td>Rise</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alert</td>\n",
       "      <td>Urgent Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>55.0</td>\n",
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       "      <td>iPhone G6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2020-02-13 17:23:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2020-02-13 17:28:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>2020-02-13 17:33:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2020-02-13 17:38:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>2020-02-13 17:43:31</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>2020-02-13 17:48:31</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2020-02-13 17:53:31</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12901.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>2020-02-13 17:58:31</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2020-02-13 18:03:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13501.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>2020-02-13 18:08:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13801.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>2020-02-13 18:13:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>2020-02-13 18:18:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>2020-02-13 18:23:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>2020-02-13 18:28:31</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>118.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>2020-02-13 18:33:31</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>2020-02-13 18:38:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>133.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>2020-02-13 18:43:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>139.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15901.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>2020-02-13 18:48:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16201.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Index Timestamp (YYYY-MM-DDThh:mm:ss)         Event Type    Event Subtype  \\\n",
       "0       1                             NaN          FirstName              NaN   \n",
       "1       2                             NaN           LastName              NaN   \n",
       "2       3                             NaN  PatientIdentifier              NaN   \n",
       "3       4                             NaN        DateOfBirth              NaN   \n",
       "4       5                             NaN             Device              NaN   \n",
       "5       6                             NaN              Alert             Fall   \n",
       "6       7                             NaN              Alert             High   \n",
       "7       8                             NaN              Alert              Low   \n",
       "8       9                             NaN              Alert      Signal Loss   \n",
       "9      10                             NaN              Alert             Rise   \n",
       "10     11                             NaN              Alert       Urgent Low   \n",
       "11     12                             NaN              Alert  Urgent Low Soon   \n",
       "12     13             2020-02-13 17:23:32                EGV              NaN   \n",
       "13     14             2020-02-13 17:28:32                EGV              NaN   \n",
       "14     15             2020-02-13 17:33:32                EGV              NaN   \n",
       "15     16             2020-02-13 17:38:32                EGV              NaN   \n",
       "16     17             2020-02-13 17:43:31                EGV              NaN   \n",
       "17     18             2020-02-13 17:48:31                EGV              NaN   \n",
       "18     19             2020-02-13 17:53:31                EGV              NaN   \n",
       "19     20             2020-02-13 17:58:31                EGV              NaN   \n",
       "20     21             2020-02-13 18:03:32                EGV              NaN   \n",
       "21     22             2020-02-13 18:08:32                EGV              NaN   \n",
       "22     23             2020-02-13 18:13:32                EGV              NaN   \n",
       "23     24             2020-02-13 18:18:32                EGV              NaN   \n",
       "24     25             2020-02-13 18:23:32                EGV              NaN   \n",
       "25     26             2020-02-13 18:28:31                EGV              NaN   \n",
       "26     27             2020-02-13 18:33:31                EGV              NaN   \n",
       "27     28             2020-02-13 18:38:32                EGV              NaN   \n",
       "28     29             2020-02-13 18:43:32                EGV              NaN   \n",
       "29     30             2020-02-13 18:48:32                EGV              NaN   \n",
       "\n",
       "   Patient Info           Device Info Source Device ID  Glucose Value (mg/dL)  \\\n",
       "0          2019                   NaN              NaN                    NaN   \n",
       "1           001                   NaN              NaN                    NaN   \n",
       "2      2019-001                   NaN              NaN                    NaN   \n",
       "3    1990-01-01                   NaN              NaN                    NaN   \n",
       "4           NaN  Dexcom G6 Mobile App        iPhone G6                    NaN   \n",
       "5           NaN                   NaN        iPhone G6                    NaN   \n",
       "6           NaN                   NaN        iPhone G6                  200.0   \n",
       "7           NaN                   NaN        iPhone G6                   80.0   \n",
       "8           NaN                   NaN        iPhone G6                    NaN   \n",
       "9           NaN                   NaN        iPhone G6                    NaN   \n",
       "10          NaN                   NaN        iPhone G6                   55.0   \n",
       "11          NaN                   NaN        iPhone G6                   55.0   \n",
       "12          NaN                   NaN        iPhone G6                   61.0   \n",
       "13          NaN                   NaN        iPhone G6                   59.0   \n",
       "14          NaN                   NaN        iPhone G6                   58.0   \n",
       "15          NaN                   NaN        iPhone G6                   59.0   \n",
       "16          NaN                   NaN        iPhone G6                   63.0   \n",
       "17          NaN                   NaN        iPhone G6                   67.0   \n",
       "18          NaN                   NaN        iPhone G6                   68.0   \n",
       "19          NaN                   NaN        iPhone G6                   63.0   \n",
       "20          NaN                   NaN        iPhone G6                   59.0   \n",
       "21          NaN                   NaN        iPhone G6                   60.0   \n",
       "22          NaN                   NaN        iPhone G6                   70.0   \n",
       "23          NaN                   NaN        iPhone G6                   86.0   \n",
       "24          NaN                   NaN        iPhone G6                  105.0   \n",
       "25          NaN                   NaN        iPhone G6                  118.0   \n",
       "26          NaN                   NaN        iPhone G6                  127.0   \n",
       "27          NaN                   NaN        iPhone G6                  133.0   \n",
       "28          NaN                   NaN        iPhone G6                  139.0   \n",
       "29          NaN                   NaN        iPhone G6                  143.0   \n",
       "\n",
       "    Insulin Value (u)  Carb Value (grams) Duration (hh:mm:ss)  \\\n",
       "0                 NaN                 NaN                 NaN   \n",
       "1                 NaN                 NaN                 NaN   \n",
       "2                 NaN                 NaN                 NaN   \n",
       "3                 NaN                 NaN                 NaN   \n",
       "4                 NaN                 NaN                 NaN   \n",
       "5                 NaN                 NaN                 NaN   \n",
       "6                 NaN                 NaN                 NaN   \n",
       "7                 NaN                 NaN                 NaN   \n",
       "8                 NaN                 NaN            00:20:00   \n",
       "9                 NaN                 NaN                 NaN   \n",
       "10                NaN                 NaN                 NaN   \n",
       "11                NaN                 NaN                 NaN   \n",
       "12                NaN                 NaN                 NaN   \n",
       "13                NaN                 NaN                 NaN   \n",
       "14                NaN                 NaN                 NaN   \n",
       "15                NaN                 NaN                 NaN   \n",
       "16                NaN                 NaN                 NaN   \n",
       "17                NaN                 NaN                 NaN   \n",
       "18                NaN                 NaN                 NaN   \n",
       "19                NaN                 NaN                 NaN   \n",
       "20                NaN                 NaN                 NaN   \n",
       "21                NaN                 NaN                 NaN   \n",
       "22                NaN                 NaN                 NaN   \n",
       "23                NaN                 NaN                 NaN   \n",
       "24                NaN                 NaN                 NaN   \n",
       "25                NaN                 NaN                 NaN   \n",
       "26                NaN                 NaN                 NaN   \n",
       "27                NaN                 NaN                 NaN   \n",
       "28                NaN                 NaN                 NaN   \n",
       "29                NaN                 NaN                 NaN   \n",
       "\n",
       "    Glucose Rate of Change (mg/dL/min)  Transmitter Time (Long Integer)  \n",
       "0                                  NaN                              NaN  \n",
       "1                                  NaN                              NaN  \n",
       "2                                  NaN                              NaN  \n",
       "3                                  NaN                              NaN  \n",
       "4                                  NaN                              NaN  \n",
       "5                                  3.0                              NaN  \n",
       "6                                  NaN                              NaN  \n",
       "7                                  NaN                              NaN  \n",
       "8                                  NaN                              NaN  \n",
       "9                                  3.0                              NaN  \n",
       "10                                 NaN                              NaN  \n",
       "11                                 NaN                              NaN  \n",
       "12                                 NaN                          11101.0  \n",
       "13                                 NaN                          11401.0  \n",
       "14                                 NaN                          11701.0  \n",
       "15                                 NaN                          12001.0  \n",
       "16                                 NaN                          12301.0  \n",
       "17                                 NaN                          12601.0  \n",
       "18                                 NaN                          12901.0  \n",
       "19                                 NaN                          13201.0  \n",
       "20                                 NaN                          13501.0  \n",
       "21                                 NaN                          13801.0  \n",
       "22                                 NaN                          14101.0  \n",
       "23                                 NaN                          14401.0  \n",
       "24                                 NaN                          14701.0  \n",
       "25                                 NaN                          15001.0  \n",
       "26                                 NaN                          15301.0  \n",
       "27                                 NaN                          15601.0  \n",
       "28                                 NaN                          15901.0  \n",
       "29                                 NaN                          16201.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dexcom001_df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dexcom001_df['Timestamp (YYYY-MM-DDThh:mm:ss)']=pd.to_datetime(dexcom001_df['Timestamp (YYYY-MM-DDThh:mm:ss)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dexcom001_df[(dexcom001_df['Timestamp (YYYY-MM-DDThh:mm:ss)'] >= '2020-02-13 18:00:00') & (dexcom001_df['Timestamp (YYYY-MM-DDThh:mm:ss)'] <= '2020-02-13 19:00:00')].plot(y='Glucose Value (mg/dL)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "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>Index</th>\n",
       "      <th>Timestamp (YYYY-MM-DDThh:mm:ss)</th>\n",
       "      <th>Event Type</th>\n",
       "      <th>Event Subtype</th>\n",
       "      <th>Patient Info</th>\n",
       "      <th>Device Info</th>\n",
       "      <th>Source Device ID</th>\n",
       "      <th>Glucose Value (mg/dL)</th>\n",
       "      <th>Insulin Value (u)</th>\n",
       "      <th>Carb Value (grams)</th>\n",
       "      <th>Duration (hh:mm:ss)</th>\n",
       "      <th>Glucose Rate of Change (mg/dL/min)</th>\n",
       "      <th>Transmitter Time (Long Integer)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>2020-02-14 00:03:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>111.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>94</td>\n",
       "      <td>2020-02-14 00:08:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>95</td>\n",
       "      <td>2020-02-14 00:13:31</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>2020-02-14 00:18:31</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>2020-02-14 00:23:32</td>\n",
       "      <td>EGV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iPhone G6</td>\n",
       "      <td>111.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Index Timestamp (YYYY-MM-DDThh:mm:ss) Event Type Event Subtype  \\\n",
       "92     93             2020-02-14 00:03:32        EGV           NaN   \n",
       "93     94             2020-02-14 00:08:32        EGV           NaN   \n",
       "94     95             2020-02-14 00:13:31        EGV           NaN   \n",
       "95     96             2020-02-14 00:18:31        EGV           NaN   \n",
       "96     97             2020-02-14 00:23:32        EGV           NaN   \n",
       "\n",
       "   Patient Info Device Info Source Device ID  Glucose Value (mg/dL)  \\\n",
       "92          NaN         NaN        iPhone G6                  111.0   \n",
       "93          NaN         NaN        iPhone G6                  110.0   \n",
       "94          NaN         NaN        iPhone G6                  110.0   \n",
       "95          NaN         NaN        iPhone G6                  110.0   \n",
       "96          NaN         NaN        iPhone G6                  111.0   \n",
       "\n",
       "    Insulin Value (u)  Carb Value (grams) Duration (hh:mm:ss)  \\\n",
       "92                NaN                 NaN                 NaN   \n",
       "93                NaN                 NaN                 NaN   \n",
       "94                NaN                 NaN                 NaN   \n",
       "95                NaN                 NaN                 NaN   \n",
       "96                NaN                 NaN                 NaN   \n",
       "\n",
       "    Glucose Rate of Change (mg/dL/min)  Transmitter Time (Long Integer)  \n",
       "92                                 NaN                          35100.0  \n",
       "93                                 NaN                          35400.0  \n",
       "94                                 NaN                          35700.0  \n",
       "95                                 NaN                          36000.0  \n",
       "96                                 NaN                          36300.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dexcom001_df[(dexcom001_df['Timestamp (YYYY-MM-DDThh:mm:ss)'] >= '2020-02-14 00:00:00') & (dexcom001_df['Timestamp (YYYY-MM-DDThh:mm:ss)'] <= '2020-02-14 23:59:59')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HP/4xTp48iZUrV6K6uhobNmzAG2+84fN7HD58GFVVVeLHd999J173xRdf4Iorruh3No0Q0j8sgzKxNBO5acIiggtdvjPDSY2PQzqdjgfA63S6Xtf19PTwR48e5Xt6eqJwZP3X0NDA33ffffywYcN4pVLJp6en89OmTeN///vf8waDQbwdAP69994TP//yyy/5CRMm8BqNhr/iiiv4t99+mwfA19TUiLfZsWMHf9lll/FqtZrPysri58yZw3d0dPA8Lzxu999/P5+Xl8er1Wr+8ssv57/99lvxa5988km+vLycT0lJ4XNycvgFCxbwZ86cEa9vbGzk77zzTvHrhw8fzi9btszn78jb7NmzeQAe34/neX7Dhg380KFDebVazVdWVvKbN2/mAfAHDx7keZ7nP//8cx6A+DPwPM+vW7eOLysr49PS0vg777yTf/rpp/khQ4Z43O/69ev5iy++mFepVHx2djZ/5ZVX8hs3bgx4jNOmTeNfeeUV8XNf3/v111/nMzMzPb7u17/+NT9p0iTxc71ez99xxx18amoqX1RUxD/33HP8tGnT+EceeYTneZ5/9NFH+cWLF/f6/h988AE/cuRIXq1W81dccQX/17/+1eP7s+Px/pDL5eJ9jBkzhv/nP//p92eM978dQuKB1WbnR/+/j/ghD2/hz7R283Oe38kPeXgLv6O6JdqHNiACnb+9cTwfxqCOGKHX65GZmQmdTgetVutxnclkQk1NDYYNG0ZbxhPJfPjhh1i5ciW+//57SXf9NRgMGDRoEP7whz9g6dKlmDhxIh599FHceuutkn0PAPj444/xi1/8AocOHYJC4bsfi/52CIm8Uy1dmPXcLqSq5Pj+8TlY8vq3+OLkBTx7yyQsmlIa7cOLuEDnb2+x3TlKSIy44YYbcPLkSdTX16OsrKzP93Pw4EEcP34c06ZNg06nwxNPPAEAWLBgASwWCxYuXCgu9ZaSwWDA66+/7jc4IYQMjLoOYX7U4JxUyGQc8jPUAIDWLupB8UavVoSEyHvaa189++yzqK6uhkqlwpQpU/DFF18gL09YYvjrX/9aku/hjTUfE0KiiwUiBVohS0kBin8UoBAygCoqKrB///5oHwYhJEpau4VAJD9d7fEvu5y40CoeQgghSe/xzUdw6yu7YTDbIvp9WKaEZU5cGRRTRL9vPErYACUOe38JiSr6myHJyu7gsX7POXx7th1bDjVE9Htd6BaWE/cOUCiD4i3hAhQ2yZPGeRMSHvY34z0Nl5BE19JlgtUuBOjv7A88u6m/WKaEBSYFFKD4lXA9KHK5HFlZWeLU0dTU1H7tAkxIouN5HkajES0tLcjKygo49p+QRMRW1gDA3rMdOHvBgKF5vneV7y+xxCP2oAjNsnqTDSarHZp+7t+WSBIuQAGAoqIiAAg6Gp0Q4pKVlSX+7RCSTOo6PDPuGw/UYcXsMX5u3T+uHhRhgqw2RQGVXAaL3YEL3WaUZqdG5PvGo4QMUDiOQ3FxMQoKCnptmkcI6U2pVFLmhCStunYhg5KqksNosWNPTXtEvo/JaofeJDThsswJxwmzUOo7e9DaRQGKu4QMUBi5XE4vuoQQQgJiJZ6LSjLx7dl2dBgjsy/OBedSYpVcBm2K6/Sb5xagEJeEa5IlhBBCwlHXKZR4Lhok7L7eYYxM5t19ibF7b2R+ulDuoVkonhI6g0IIIYQEwzIoE0qFvWE6jRbwPB/SAovv63V472A9eB6YPb4Qlw7P9XtbFqDkOVfuMLTU2DcKUAghhCQtu4NHQ6czQBmUBQCw2nkYLHakq4OfIn/13mEcqtMBADYerMOeX82EWuG7tcB7iiyT5/y8rTsypaV4FXaJZ9euXZg/fz5KSkrAcRw2bdrk97Y//vGPwXEcXnjhBY/L29vbsXjxYmi1WmRlZWHp0qXo7u4O91AIIYSQfmEzUBQyDkNzU6FWCKfFDkPwYMFic+BYox4AkK5WoNNoxfZj/lePek+RZVJVQiBktNj79DMkqrADFIPBgEmTJmHt2rUBb/fee+/hm2++QUlJSa/rFi9ejCNHjmDbtm3YsmULdu3aheXLl4d7KIQQQki/sPJOcZYGCrkM2alCP0gojbKnWrphtfPI0Cjwfy4dAgB494D/QW/+AxQh49JjjeyY/XgTdoAyb948PPXUU7jpppv83qa+vh73338/1q9f32sq5bFjx7B161a8+uqrmD59OmbMmIGXXnoJ//rXv9DQENkRw4QQQog7NgOlNEtY3puVKpyzQmmUZdmTccVaLJpSCgD4vLoVb+09j5PNXb1u7y9ASXEGKJRB8ST5Kh6Hw4Ef/ehHWLlyJcaPH9/r+t27dyMrKwtTp04VL5s1axZkMhn27Nnj8z7NZjP0er3HByGEENJfDZ3C6PlB2SkAIGZQOkPIoBx1BijlxVqMLEjHxWVZsDt4PPzuYdz0p6+hN3kGOc1655h7rx6UFOf02B4KUDxIHqD87ne/g0KhwAMPPODz+qamJhQUFHhcplAokJOTg6amJp9fs2bNGmRmZoofZWVlUh82IYSQJMSCiKwUIXOSnebMoITQg3K0wZlBKRFW/zz2g3GYVV6AnDQVus02fHSoUbyt3cHjZIvQazmywHOMvqvEQwGKO0kDlP379+OPf/wj3njjDUn3v1m1ahV0Op34UVtbK9l9E0IISV5GsxAUpDpX7GSJPSiBSzw8z4sZlHHFQoAyZUg2Xl1yCZZfORyA58aD59oMMFrsUCtkGJrrGaBQicc3SQOUL774Ai0tLRg8eDAUCgUUCgXOnTuHX/ziFxg6dCgAYZ8c7z1ybDYb2tvb/e4DolarodVqPT4IIYSQ/jJYhMbUNGeQkO3sQQlW4mnQmaDrsUIh4zCqMN3jupsqBkHGAfvOdaDmggEAcKxR6EkZW5QBhdzz1EslHt8kDVB+9KMf4dChQ6iqqhI/SkpKsHLlSnzyyScAgMrKSnR2dmL//v3i123fvh0OhwPTp0+X8nAIIYSQgLwzKNkhZlCOOcs7IwvSe809KdRqcOXofADCxoMAcLRRmJXCykHu2DJjKvF4CntQW3d3N06dOiV+XlNTg6qqKuTk5GDw4MHIzfWcoqdUKlFUVIQxY4SdIcvLyzF37lwsW7YMr7zyCqxWK+677z7cdtttPpckE0IIIZHSO4MS2jLjBp2wPNm7XMMsmlKKHdWteHd/HX4+a7SrX6W4d4BCGRTfws6g7Nu3DxUVFaioqAAArFixAhUVFVi9enXI97F+/XqMHTsWM2fOxPXXX48ZM2bgL3/5S7iHQgghhPQL6/tgWQyxSTZIgMKWDBdo1T6vn1VeCK1GgQadCbvPtLn6VXxkUFLcmmQdDr4PP0ViCjuDcvXVV4PnQ38Az5492+uynJwcbNiwIdxvTQghhEjKYHZmUNRCkCA2yRoCl3jEmSbpvgMUjVKO+ZNKsH7Pefx51xk0683gOGBMka8Sj6tEZLLZxWAp2dFuxoQQQpJWrwxKiHNQ/A1dc8eGt+060QpAKAf52t9Ho3QFKFTmcaEAhRBCSNIyWjwzKGwVj8Fih8Xm8Pt14sZ/AQKUi8uyMHlwlvj5rPICn7eTyzhxDyBaauxCeSRCCCFJy+BcxZPmzKBoNUrIOMDBC1mUAq3G59ddcGZQ8vyUeACA4zi8/ePL0KjrgUImQ6GffhVAKPOYbQ5ayeOGMiiEEEKSkt3BiwEB6wORybigw9p4ng8pgwII2ZHS7FQUZWoCDjCllTy9UYBCCCEkKblnK9LcekPYhoHtfsbd63qssNqFxSK56SpJjoWmyfZGAQohhJCkZHSu4JFxEHtAANfKnEbnrBNvrEE2K1XZa0hbX7mGtdkkub9EQAEKIYSQpGSwuPpP3Msv5c5hasecs0u8BVti3BeuEo//xtxkQwEKIYSQpMRmoKSqPbMgbNrrUX8BSoj9J+FwlXgog8JQgEIIISQpGS2eK3gYNu31aIPe52DSUGaghItlUEy0ikdEAQohhJCkxPbh8c6gjCxIh1zGocNoRZPe1OvrIlHiSaUm2V4oQCGEEJKUxJ2MvTIoGqUcI/PTAUDc5M9dRDIoFKD0QgEKIYSQpGT02snYnXuZx1tEelCoxNMLBSiEEEKSkrgPj4/9cQI1ykYig0Ilnt4oQCGEEJKUDCFkUHwtNW4JYcx9uFKcZSYKUFwoQCGEEJKU/PWgAK5ZKGfbjOg2u5b+9ljs4oTZkqwUyY4lRSmcjqnE40IBCiGEkKRk8NrJ2F1OmgrFmcJGgcfdsij1nUYAQIZGgcwUpWTHkipmUGgOCkMBCiGEkKQUKIMCuLIo7n0otR3C+PvS7FRJj4Wt4qHdjF0oQCGEEJKUAvWgAG6Nsm4reerEAEW68g5Auxn7QgEKIYSQpBRoFQ/gttS40T1AEUo8UgcotIqnNwpQCCGEJCW2F4/3qHuGZVCON3XBZhc28aujEs+AoQCFEEJIUnJlUHyXeAbnpCJNJYfF5kDNBQOACJZ4VFTi8UYBCiGEkKTk6kHxnUGRybhejbL1kSrxKGkOijcKUAghhCQl1yoe3xkUwHPkfY/FjgvdwgwUqUs8GpVwOu6x2n3uoJyMKEAhhBCSlFxzUHxnUADPpcaRmoECeC51Nlkdkt53vKIAhRBCSNLheV4sp/hbZgx4LjWubY9MgyzgWmYMUKMsQwEKIYSQpGO2OWB3CKUUf8uMAWBMUQZkHNBmsODA+Q4A0vefAIBcxkGlEE7JNE1WQAEKIYSQpOPejOqevfCmUcoxIj8dAPDW3loAwPC8tIgcUxrNQvFAAQohhJCkw8ooaoUMchkX8LasUZbtYvyDiSUROSbWC+O+OWEyowCFEEJI0ulxllFSAvSfMKwPBQDGFGbgokHaALfuu3QWoJgoQAEoQCGEEJKExCFtAco7DMugAMCiKaXguMAZl77K0AgBioEyKAAA/51BhBBCSIJiE1tDyaCML8mEUi4EJQsqIlPeAVwlni4KUAD0IYOya9cuzJ8/HyUlJeA4Dps2bfK4/vHHH8fYsWORlpaG7OxszJo1C3v27PG4TXt7OxYvXgytVousrCwsXboU3d3d/fpBCCGEkFAZraEHKDlpKry25BK8ec80FGRoInZMVOLxFHaAYjAYMGnSJKxdu9bn9aNHj8bLL7+Mw4cP48svv8TQoUMxe/ZstLa2irdZvHgxjhw5gm3btmHLli3YtWsXli9f3vefghBCCAlDj1jiCa2QcOXofFw2Ii+Sh0QlHi9hl3jmzZuHefPm+b3+jjvu8Pj8ueeew2uvvYZDhw5h5syZOHbsGLZu3Yq9e/di6tSpAICXXnoJ119/PZ599lmUlEQufUYIIYQArgBFE0IGZaCwPYFoFY8gok2yFosFf/nLX5CZmYlJkyYBAHbv3o2srCwxOAGAWbNmQSaT9SoFMWazGXq93uODEEII6StW4gmlSXagpGuoB8VdRAKULVu2ID09HRqNBs8//zy2bduGvDwhNdbU1ISCggKP2ysUCuTk5KCpqcnn/a1ZswaZmZniR1lZWSQOmxBCSJIwWYJvFDjQqAfFU0QClGuuuQZVVVX4+uuvMXfuXNx6661oaWnp8/2tWrUKOp1O/KitrZXwaAkhhCQbYwyWeFiAQj0ogogEKGlpaRg5ciQuvfRSvPbaa1AoFHjttdcAAEVFRb2CFZvNhvb2dhQVFfm8P7VaDa1W6/FBCCGE9JXRKgQBVOKJXQMyqM3hcMBsFkYEV1ZWorOzE/v37xev3759OxwOB6ZPnz4Qh0MIISTJUYkn9oW9iqe7uxunTp0SP6+pqUFVVRVycnKQm5uLp59+Gj/84Q9RXFyMCxcuYO3ataivr8ctt9wCACgvL8fcuXOxbNkyvPLKK7Barbjvvvtw22230QoeQgghAyIWSzziMmPazRhAHwKUffv24ZprrhE/X7FiBQBgyZIleOWVV3D8+HG8+eabuHDhAnJzc3HJJZfgiy++wPjx48WvWb9+Pe677z7MnDkTMpkMCxcuxIsvvijBj0MIIYQEF4ureNIog+Ih7ADl6quvBs/zfq/fuHFj0PvIycnBhg0bwv3WhBBCiCRcJZ7Y2fElnUbde6DNAgkhhCSdmCzxqJUAAIvNAYvNEeWjiT4KUAghhCSd2CzxuI6FlhpTgEIIISQJmcLYzXigKOQyaJTCaZnG3VOAQgghJAmxOSixFKAAQLqzzEMBCgUohBBCklCPRejxiKU5KACQ7izzUIBCAQohhJAk1OOcNZISQz0ogGuaLC01pgCFEEJIkuF5XmySjb0SjzNAoQwKBSiEEEKSi9nmABvnFUtzUAAKUNxRgEIIISSp9DhX8AAxWOKhabIiClAIIYQkFVbeUSlkkMu4KB+NJ7EHhTIoFKAQQghJLiyDEmvZE8BtPx4KUChAIYQQklx6xH14Yi9AyaASj4gCFEIIIUnFaInNIW0ANcm6owCFEEJIUumxUoknHlCAQgghJKnEdImHmmRFFKAQQghJKmIGJcZmoABue/FQDwoFKISQ5GG02GCy2oPfkCQ0o7iKJ/ZOgWm0F48o9n47hBASAXYHjzkv7MKs53bCandE+3BIFLlKPLGXQaESjwsFKISQpNBusKC2vQd1HT043dod7cMhUdQTo/vwAG4lHrMNPJvHn6QoQCGEJIVOo0X8/9EGfRSPhESbMYYHtbFJsnYHD7MtuTN9FKAQQpJCu4ECFCLocc5BicVVPKluQVNXkjfKUoBCCEkKHUar+P+jjRSgJDNW4tHEYAZFJuNoWJsTBSiEkKTgUeJp1Cd9fT+ZscwEa0iNNSxAMVCAQgghic89g9JptKJRZ4ri0ZBo6nAGq1mpqigfiW9sqTGVeAghJAm4Z1AA6kNJZh0GIVjNTlVG+Uh8S9e4VvIks9jMbxGSBP5zpAmvflkDh4PH3IuK8N9XDI/2ISU09yZZQCjzzBpXGKWjIdHEMijZMZpByaASDwAKUAiJCoeDx+Obj6DBWWY4WNuJBRcPQn6GOspHlrhYiacsJwW17T0422aI8hGRaOB5Hp3O50J2WmwGKGKJJ8kDFCrxEBIFu8+0oUFnglajQHmxFnYHj/er6qN9WAmNlXiG5qYBAPQ9yf3in6yMFjsszknCMVviof14AFCAQkhUvLO/DgDww4tLsHj6YADA2/vqaGVJBHV4Bygma6CbkwTFngcqhSwmB7UBrtVFyV7ioQCFkAHWZbLi4+8bAQALJ5di/sQSqBQyVDd34Qg1bkYMS+sPyU0FAOh7KEBJRmJ5J1UJjuOifDS+0YaBAgpQCBlg35xph8nqwLC8NFxcloXMVCWuKxeaNT850hTlo0tMDgePTmdAMjhHCFCSfQlnsmLN0rHaIAu4SjzJ/hwNO0DZtWsX5s+fj5KSEnAch02bNonXWa1WPPzww5gwYQLS0tJQUlKCO++8Ew0NDR730d7ejsWLF0Or1SIrKwtLly5Fdzdt3kWSQ7NeaIwdWZAuvoObNiwHAC19jZQukw12h1A+GyL2oFAGJRnF+goewLUfT7c5uZ+jYQcoBoMBkyZNwtq1a3tdZzQaceDAATz22GM4cOAANm7ciOrqavzwhz/0uN3ixYtx5MgRbNu2DVu2bMGuXbuwfPnyvv8UhMSR1i4zAHis2BlXogVAI9gjhZ2UUlVy5KULJ6YusytoIcnDtYInNhtkASDdWeIxmO1RPpLoCnuZ8bx58zBv3jyf12VmZmLbtm0el7388suYNm0azp8/j8GDB+PYsWPYunUr9u7di6lTpwIAXnrpJVx//fV49tlnUVJS0ocfg5D40drtDFDSXQHK2KIMAECjzoR2gwU5Mbr8MV65v2vO0LhOTN0mGzJjdCUHiYxYnyILuJV4qAclsnQ6HTiOQ1ZWFgBg9+7dyMrKEoMTAJg1axZkMhn27Nnj8z7MZjP0er3HByHxylcGJUOjFJs3j1EWRXLsXXNWqtJj9Qat5Ek+7k2ysUrcLDDJn58RDVBMJhMefvhh3H777dBqhRR2U1MTCgoKPG6nUCiQk5ODpibfDYJr1qxBZmam+FFWVhbJwyYkonwFKAAwrthZ5qE+FMl59x2wZZw66kNJOnHRgyJOkk3uEk/EAhSr1Ypbb70VPM9j3bp1/bqvVatWQafTiR+1tbUSHSUhAy9ogEIZFMl1eE0O1aYI754pg5J8OsRsWgwHKGKTbHKXeCIy6p4FJ+fOncP27dvF7AkAFBUVoaWlxeP2NpsN7e3tKCoq8nl/arUaajWNACfxj+d5nz0ogFujLGVQJNchLi0VAhOt8wRA02STD3su5MR0k6wrQHE4eMhksTmvJdIkz6Cw4OTkyZP49NNPkZub63F9ZWUlOjs7sX//fvGy7du3w+FwYPr06VIfDiExRW+ywWITxmz3yqA4A5RTrd0w25I7tSs178ZIyqAkr/hoknXlDozW5H0tCDuD0t3djVOnTomf19TUoKqqCjk5OSguLsaiRYtw4MABbNmyBXa7XewrycnJgUqlQnl5OebOnYtly5bhlVdegdVqxX333YfbbruNVvCQhMfKOxkaBTReY7aLtBoo5Rysdh7tBguKM1OicYgJyWgRXuTZ8k2tcyUPzUJJPq4m2dgNUDRKGeQyDnYHj26TzSNgSSZhZ1D27duHiooKVFRUAABWrFiBiooKrF69GvX19di8eTPq6upw8cUXo7i4WPz4+uuvxftYv349xo4di5kzZ+L666/HjBkz8Je//EW6n4qQGOWv/wQAOI5DZorwotlhoBOnlFhGigWF2hThBT/ZJ3UmG4vNIfZ1xPIqHo7j3Mo8yftaEHZYdvXVVwfc0CyUzc5ycnKwYcOGcL81IXHPX/8Jk5OmxIVus5iGJtIwWYWymlohvCcTMyhU4kkqnT3C35WMcz0HYlW6WgFdjxXdSbySh/biIWQABcqgAK66OAUo0jJZvTMorMRDGZRkwso7mSnKmG88ZRmUriQOoilAIWQABQtQWNqZLYUk0mABilrh1YOSxC/+yeiCM4MZD5Oa2Sj+tu7kfbNCAQohAyh4gCK8cHYakvdFKRLMzpVTGqWzxJPClhlTgJJMgv39xZLSbGGydF2HMcpHEj0UoBAygIL1oLhKPHTilJL/DAqVeJKJK0DRRPlIgivNFlbx1XX0RPlIoocCFEIk5nDw+J+PjuFf357vdV3oJR7KoEiJNcm6Mii0zDgZXXCWS/y9QYglrgxK8gYoybm4mpAIOljbib/sOgOVQoZFU0qhkLveBzTrTQCAAj/v4LKpSTYiXCUelkFxlnioByWpxFeJh2VQqMRDCJEI20vHYnPgzAWDeLnRYkO7s7dkULbvIWxZ1CQbEWaxxOOZQWGjxElyEEuscRSg1Hf2JO1zlAIUQiTmvpeO+//rnalarUaBzBTfMxjY6oJOyqBIyuQ1qI3tZszzQFeSb8iWTOIpg1Kk1UAuEyZLtziPO9lQgEKIxNx3I3b/P6slD3LWln0Rm2RpFY9k7A4eVrvwDpQFKGqFXOxHoT6U5CEGKHHQg6KQy1CcKZSC6zuTs8xDAQohErLZHTje6DuDwmrJpX7KO4CrSVZvssFmd0ToKJOL+8aLLCgBXCt5dBSgJAW7g0e7IX4yKACt5KEAhRAJnW0ziA2ZgJBBYds/sBeZQAGKe+mnk06ckmAreADXMmPA1e/TSf0+SaHNYIaDF8bcx8OgNoBW8lCAQoiEjjgzJuXFWshlHNoNFjTrhXdtrgDFf4lHIZeJK0yoD0UabAaKUs5B7jbenLYVSC6svJObrvZ4HsSyZF/JQwEKIRJiPSeTB2dhRH4aAOCY87JQSjwAkJ1Gw9qkJC4xdsueAEBOKjUkJ5N46j9hKINCCJHMwfOdAIBxJVqMK9YCcAUtoZR4AGqUlZo4RVbp+XLH9jqhQDA5xNMKHoa9VpxrowwKIaQf6jqM+LamHQBw1eh8jCtxBigNehgtNrQ5A45AJR7A1ShLvRHS8B5zz7BAsJ0CwaQQTzNQmJEF6QCA2g4jDEm4HJ4CFEIksvFAPQDgshG5KM1ORblbBoXNQMkIMAOFoWmy0vLeKJBxBYL0OCcDlkHJi6MST166GoVaNXgeON7UFe3DGXAUoBAiAZ7n8e6BOgDAoimlACAGKGfbDKhuFl5cBmUFLu8ArgClnU6ckgiWQaEST3KIxxIPgF6lYl9sdge6EnDbBgpQCJFAVW0nzrUZkaaSY+5FRQA83/1s/b4JQPDyDuD2zt6QeC840eC9USBDTbLJxZVBiY8lxoyYiW3wH6D84u3vMPWpTwPeJh5RgEKIBM60CnvuTB6SjVSVaw9O9u7no8ONAICKwVlB74uNYe9OwppzJJi9xtwz1CSbXFivUTyVeAC4etn8ZFCadCZ88F0DzDYH/rHn3EAeWsRRgEKIBPw14LEXFwcPcBxw8+RBQe8rVS0EKAYLBShSMFs9dzJmaA5KcmGBKBvQFy/Ym5zjjXqf06U3HqwD20vwg+8axJJmIqAAhRAJ+KtvjyvOFP8/Y2QeijOD96CkOTMwRnPivNBEE9sokO1kzLBeny6TDVbaViCh8TwvlvLiZYosMyQ3DakqOcw2B862GTyu43ke7+wXet84Tngu/+doczQOMyIoQCFEAv6GQLEMCuBqng0mVS2806cMijTYO0rvDEpmihKcc6AoLelObN1mG2zONAMLTOOFXMZhbFEGANekagD4+VtVGPvYVpxpNSBFKcfdlw0DADFgSQQUoBAiAX8ZlCE5qRhXrMXw/DTMHlcU0n2JGRQLZVCkYPbTJCuXceKGgdQom9hYAKpRynoFqvFgypBsAMB/jgjZkRPNXXjvYL24hP6/LinDksuGAAC+PNmKJp0pOgcqMQpQCJGA2IPilUGRyTh8+MAMfPLglUhRhfbCmOq8XTIOZooEV4mn9+OfQ9sKJAXWZxRv2RPmpgoh+7rtaDM6jRa868ySXDMmH9+smolfzx+HIblpmDY0Bw4eeO9gfTQPVzIUoBAigUAzFjiOg1Ie+p9amrNJtocyKJJgy4y9R90DroZJapRNbGwFT1acBijjSrQoL9bCYnfgvYP1YgDyX5cMRlGmBpyzVrlwitCE/87+WnEX9XhGAQoh/WS22aHrEd6BSzEEKk3l6kFJhBeZaBOXGfvIoGTTLJSkwEo82XG2gscd62H7/SfVaOkyIztViWvHFnjc5voJxdAoZTjdasDB2s4oHKW0KEAhpJ/auoWTm1LOBR1jHwq2zNjBu8a0k74z+VlmDLhnUKjEk8jivcQDAAsuLkGKUi72pt0ytQwqr5VpGRolZpUXAgB2nWgd8GOUmiL4TQghgbiv4GGp1v5IcTuRGsy2uGzqiyWuUfe9349l087RSYEFoGw4XzzKS1djywMzcLK5C2qFHJeNzPV5u4rB2dhyqBHHAozGjxcUoBDST1Lv8SGXcUhRytFjtcNoscP3yxAJlWuzQF8lHupBSQadCZBBAYAR+ekYkZ8e8DblxcKS5EB798QLKvG4OddmwJqPj+HVL870uu7z6hZ8eKgxCkdFYl0ktnFPo1koknHNQfGRQaFVPEnBNUU2vgOUULDJs7XtPWJvXLyiAMXNoTod/rzzDP76ZQ0cDldzYmuXGcve3Id7NxxAdRJueU0Ci8QuqWw/HwNNk+03NgfF5zJjKvEkBfb7jecm2VBlparEXdOPx3kWJewAZdeuXZg/fz5KSkrAcRw2bdrkcf3GjRsxe/Zs5ObmguM4VFVV9boPk8mEe++9F7m5uUhPT8fChQvR3Bz98bzXjStEhkaBBp0Ju8+0iZe/X1UvTiF890DiTOkj0vA3RbY/2CwUI2VQ+s21WWDvl7s8Z1B5wZkFI4kpEZpkwyHugJxsAYrBYMCkSZOwdu1av9fPmDEDv/vd7/zex89//nN88MEHePvtt7Fz5040NDTg5ptvDvdQJKdRyvHDSSUAXOOC3fc6AICNB+p9bthEkpe4jbukJR7KoEgl0CoeFlSy3yFJTJ1xulFgX4k7IDfEd4ASdpPsvHnzMG/ePL/X/+hHPwIAnD171uf1Op0Or732GjZs2IBrr70WAPD666+jvLwc33zzDS699NJwD0lSi6aUYv2e8/j4+0ZcPjIPnUYLjjd1QaWQIU0lx4VuM3adbMW1YwujepwkdvibItsfsZRB4XkeX59uQ7PehPwMNWaMzJNktdJA8bdZIOAqyxksdhjMNjEwJIkl2TIo4xIkgzLgf4379++H1WrFrFmzxMvGjh2LwYMHY/fu3T4DFLPZDLPZ9Q5Hr4/cg35xWRaG56fhTKsBv3z7O/Hy68YVojBDg79+VYN39tdRgEJEEcmgsB6UGJgmu+9cBxa/ukf8fMN/T8dlI/OieETh8bdZICBkqlJVwmyJC91mClASkNlmF2eHZMfZTsZ9Nd6ZQTnZ3I0ukxUZmvjMHA14k2xTUxNUKhWysrI8Li8sLERTU5PPr1mzZg0yMzPFj7KysogdH8dxeGrBRbh2bAGuHJ2PK0fnY874Qqy4brQ4ye/Toy3UVEdErFNeygY8tqOxMQb246lp9dzivcZry/dY51pm7PvlLo/KPAmNlXeEzSGTIwAtzU7B8Pw0WOwOfHzY93k1HsTFb2vVqlVYsWKF+Ller49okHLZyDy/7xDHFWtxtFGPDw414M7KoRE7BhIfHA4eXSbhBVAr4buUWMqgeM8I6YyzJbmuQW2+B97lZ6hxvt1IAUqCEvfhSVHGVWmyPziOw6IppXhmazXe2V+HWy+J3PkykgY8g1JUVASLxYLOzk6Py5ubm1FU5Hs7erVaDa1W6/ERLSyL4t44S5KXwWIDW5GulWDMPRNLGRTvGSHtcZQ95Hk+4GaBgFujLK3kSUgswE6WBlnm5opSyDjg27PtOBdnWU9mwAOUKVOmQKlU4rPPPhMvq66uxvnz51FZWTnQhxO2BReXQCHjcKhOh9Ot3dE+HBJlepMQQKjkMp9NmH0VUxkUZ0DCNjGMp6mrFrcVd/62DGCNspRBSUyujQKTo/+EKcrUYMaofADApoMNUT6avgm7xNPd3Y1Tp06Jn9fU1KCqqgo5OTkYPHgw2tvbcf78eTQ0CA9IdXU1ACFzUlRUhMzMTCxduhQrVqxATk4OtFot7r//flRWVkZ9BU8octPVmDwkG9/WtKPqfGfQscMksemd/SfaFIWk6eNYWsXDApLh+ek4XK+LqxIPy54AvnczBihASXSuDEpyBSiAsLhj14lWHKztiPah9EnYb/n27duHiooKVFRUAABWrFiBiooKrF69GgCwefNmVFRU4IYbbgAA3HbbbaioqMArr7wi3sfzzz+PH/zgB1i4cCGuvPJKFBUVYePGjVL8PAOCdUjH+xIu0n9igCJxl3wsTZJlAcnw/DQA8ZVBMTv7TzhO2G3aFwpQEpsrg5JcJR7A7VwVp/NQws6gXH311eB53u/1d911F+66666A96HRaLB27Vq/w95inbjGPE5/6UQ6rMSTIWH/CeDaiyeWMijD8oQAJZ4yKOIKHoXcb4aL9aDQNNnExEqUOUmyxNjd2KIMcBzQ0mVGa5dZ0u04BgLtxdMH49wyKIGCNZL4XBkUaRfEpcZSD4ozIGEBSjxlUAJtFMhQBiWxtSdxiSdVpcCwXOHv9lgcZvwpQOmDkQXpUMg46HqsaNCZon04JIr0bImx1BkUVWys4uF5Xtyqfnie0G+l67HC7oiPwLwnwJA2RgxQus30hiMBJXOJBwDK47glgQKUPlAr5BhZILxYU5knuel7hABC8h4U50RTY5QzKN1mm7hR5tC8VAAAzyNutnHvcT5+KSr/AUpuuvDO2mrn4+bnIqFL5iZZIL5bEihA6aNE2YyJ9I8rgyJtiYdlUAxR7kHpMAg/n0YpQ4ZGiQxnKSteyjxGZwYlNUCAolbIxRkZVOZJPMmeQRlHGZTkw6LSIw26KB8JiaaIreJhGZQor+Lx3mSN/dsZJwGKmEEJUOIBaFfjRCY+h5OwSRYAxjvPVWdau8WerHhBAUofTSzNAgB8c6Yt7n7pRDqR7kGx2B2w2BxBbh053ulx9i6UZVZinavEEzjD5d6HQhKH3eEq2yXbJFkmP0ONvHQVHDxQ3dQV7cMJCwUofTRlSDaKMzXQm2z47FhLtA+HRImrByUyq3gA10k2GrzT4yxQaY+TDAor8aQEWMUD0IaBiUrXYwXre062SbIMx3EoL47PMg8FKH0kl3G4efIgAMA7+2ujfDQkWrrMkcmgqBQycbCY0Rq9PpTeJR7h54yXEo/JwnpQQsygUICSUNjzN0OtgFKevKe7eO2ZTN7fmAQWThY2Dtx5ohWPbz6Cz6spk5JsIrWKB4iNabJsBkqWVwbFewPBWGUMYRUPQAFKomKBdFZacpZ3mHGUQUk+w/PTMXVINhw88MbXZ/Hjv++nZYpJhvWgZEq8igdwW8kTxVko7AWeTeFk/8ZLBoVln0JukqUelITCeqWStbzDsADlWKMejjiZYQRQgNJvz94yCQ/MHIXS7BSYbQ58eKgx2odEBgjP8xFbxQMA6RqWQYlmiYdlUOKzSdZV4qEMSjJK9hkozLC8NKgVMhgtdpxrN0b7cEJGAUo/Dc1Lw4rrRmNJ5VAA1I+STAwWO9ibEal7UAAgzbnUuCuaAYqB9aDEaZMslXiSWrLPQGEUchnGFmUAiK8+FApQJLKgogRyGYcD5ztxurU72odDBgDLnijlHNQK6f+U0p0BSrcplppkY6/Ec7K5CxsP1OHDQ429sk2uVTyhBSjtRgus9ugt6ybSavd6/iYz18C2+JndRQGKRAoyNLh6dD4AYMt3VOZJBuIMFI3S7065/cGmtkZzmmxnryZZZ4knRppkLTYHFq77Giv+/R3u3XAAL3520uP6UEs82akqyGUceB5oN8RO8EX6p5MCFJGrDyV+ZqFQgCKhGaPyAACH6+MnQiV9J67giUB5BwDSnKt4uqKYQWFZokznz8j+1cdIM3hLlwl6t8fnVItn9pKVeAJtFggIYwNynQ3AVOZJHGKTbJKv4gHic6kxBSgScu+UJonP1SAr/QoewNUk2x2lHhS7gxf7X1gQxv412xwxMUH5QrfF63PP4MK1F0/w3xH1oSQeapJ1GVMknJ+a9Ca0xclqNQpQJMS2ta7v7ImpGj2JjEiNuWcy1NFdxePe+8LKTRlqBVg1K5qZHYYFE+yYvIOLUEs8AI27T0TUJOuSrlZgaK6wI3m8lHkoQJGQVqNEWU4KgPgbiEPCF8klxoBrFU+0mmRZAKZRyqBWCCd4mYwTm3fZ9dHEAhKWvWztNoPnXXMe2ByUYCUegDYMTETeTd7JLt4aZSlAkZg4sS+O6nykb1jvgzYCQ9oAV4knWsuMdX4CMPZ5LPShsGBirDN9bbXzHsMSeyzCipywMigUoCQEh4NHm7PhOTedAhQg/s5PFKBIbFxxJgDKoCSDSGdQor3M2F8Ji32uj4UST7cJADAoO0VcYeQeYPQ4V0CFEqDk0TTZhNLZY4XdOagoN00d5aOJDa4MSnycnyhAkRh7AsRLjY/0XaR7UFiAEq1lxv52amafd8VQiSc/Q92rRMPzfMhzUNh9uH89iW/s95idqoQqAnOK4hF7A3261RATTe7B0G9NYixAOdncJT4BmvUmzH1hF57fdiKah0Yk5u8ELpWYz6D0xEAGhQUo6epeTa5mmwOsHSXYJFmAApRE4x68EkGhVo2cNBXsDh7VTbH/JpoCFImVZGpQpNXA5uCxw7m78fpvzuF4UxfW7TwNXYwMuCL9F/EMSpR7UNgqHb89KLGQQel2y6B4BRg9Ftc7xFAyKKXZQoN7XYcRFhtNk413rPxHAYoLx3GYPDgLAPCfo03RPZgQUIAiMY7jcGPFIADAO/vr4HDwePdAPQBh6uUHhxqieXhEQu6TZCMhPcrLjMUeG68mYPZ5tJtkeZ73zKB4lXhYeUcll0EhD/5SNygrBRkaBax2vtfANxJ/3J8bxOWmilIAwMYD9WKPTqyiACUCFk0RApTPq1vxwaEG1Hf2iNe9s78uWodFJOaaJBvZEo/RYo/KC4m/ACxWMijdZhtMViHTkZeh8ptBCaW8AwhvLsRVDnHSREj8oxKPbzPLC5CZokSjzoTdp9uifTgBUYASASMLMjCpLAt2B49VGw8DAGaPK4RcxqGqtpPenSWIiGdQ3HpbojFN1t8o/1jpQWEnoHS1AqkqRa8eFDFACaG8w7ia3ClAiXcUoPimUcoxf1IxAOCd/bVRPprAKECJkDumlQEQ3v1yHHDftSPFzQTfPUBZlHjH87xbCSQyAYpaIYdSLoxIjUaZx38GJTYGtXmfgHplUKyhT5Fl4m1OBPHPvT+JeFo0RTg/bT3SFPW/40AoQImQW6eWYf1/T8cr/2cy3r/3ckwszcKiKaz2VxfztT8SmMFiB/sVRiqDArit5IlKBsVfD0psDGoTT0DpvgMUo3N5dqglHsBzToT7RFoSf1w9KJooH0nsmVSaiZEF6TBZHfjoUGO0D8cvClAihOM4XD4yD3MvKsbE0iwAwLXlBchKVaJZb8aXpy5E9wBJv7CTs1LOQaOM3J+RuJInCkuN9UFX8cRGiYcFJmzQWrvRAqvd0acSz6iCDCjlHHQ9VjToTBIfMRlIVOLxj+M48Q1zLGf0KUAZQGqFHAsmlQCgZtl4517+4NhOdRGQrhaCgaiUePyUsGJlFY/3CSg7VQW5jAPPA+0Gi1jiCSeDolLIMCI/HQCVeeKZxeZAh3OkAwUovt1UMQgyDth7tgNnLxiifTg+UYAywG6aLESt2481Uwo5jvlrIJVaulo4uUalxCMGYd6TZGNjFc8FZ4knz7nPilzGITdN+H9rlxnGMHYydieWeShAiVttBuG5oZBxyIrw32i8KtRqcMWo2O6LDDtA2bVrF+bPn4+SkhJwHIdNmzZ5XM/zPFavXo3i4mKkpKRg1qxZOHnypMdt2tvbsXjxYmi1WmRlZWHp0qXo7k6OlS3lxRkAhB6GDhraFrdc+/BEZokxE61psg4HLwZF/lbxmKwOmG3RG5fdbhB+B9lpro3g3PtQ+lLiAdwaZeNkx1fSG8uu5aWrIZNFLsMZ71x9kfVwxGBfZNgBisFgwKRJk7B27Vqf1z/zzDN48cUX8corr2DPnj1IS0vDnDlzYDK56rmLFy/GkSNHsG3bNmzZsgW7du3C8uXL+/5TxBG1Qo5CrfAiWtdhjPLRkL6K9BRZJt2ZrRjoDEqX2SaOic/wCsIy1AqwqlY0emOYTqOwU212qo8ApdvsVuIJL4iMtw3VSG/UfxKa68YVIkOjQH1nD745E3szUcIOUObNm4ennnoKN910U6/reJ7HCy+8gEcffRQLFizAxIkT8be//Q0NDQ1ipuXYsWPYunUrXn31VUyfPh0zZszASy+9hH/9619oaEiOKaul2akAgLqOniC3JLEq0jsZM5Eu8eh6rNh7tr3X5eznUytkUCs8MxAyGSdmdqLZh9LhDFDYLsYAPKbJ9rnE48yg1Lb3QBflPhvSNxSghEajlOOHMdwXKWkPSk1NDZqamjBr1izxsszMTEyfPh27d+8GAOzevRtZWVmYOnWqeJtZs2ZBJpNhz549Pu/XbDZDr9d7fMQz9z0/SHxiK1i8swtSi/Qy4/v/eRC3vLIbX570XFUWLEMUCyt5WInUZwaly4wetsw4zBJPVqoKg7KEv9HjlEWJS0165z48NOY+qIXOMs9H3zfGxA7l7iQNUJqahM2HCgsLPS4vLCwUr2tqakJBQYHH9QqFAjk5OeJtvK1ZswaZmZniR1lZmZSHPeBcAQplUOJVpIe0MWwVTyQClNp2I3adaAUA7KnxTO8G26mZBWbRyqA4HLxY4snx1YPiUeIJL0ABgHIaeR/XTjQLO/WOKEiL8pHEvoqyLAzPT4PJ6sDHh2NrA8G4WMWzatUq6HQ68aO2NrbH8wZDJZ7452+Fi9TSWIknApkK98597xUrQTMoKdFdydNlsomD8jxKPBm9SzzhZlAAYJyzmZ1W8sQn9ntjgSbxz30myjsxtppH0gClqKgIANDc3OxxeXNzs3hdUVERWlpaPK632Wxob28Xb+NNrVZDq9V6fMQzlj6mEk/8GqhlxixTIXUGRdhl2y1A8coUBOuxEUs8UdqPh/WfpKrkHj0yLKV/ocsMUx9G3TOsUfZQnQ4nm7twsrmL+lEiyGyzi6uu+qvbbMO5duG1lQKU0NxcUQoZB3xb045zbbEzE0XSAGXYsGEoKirCZ599Jl6m1+uxZ88eVFZWAgAqKyvR2dmJ/fv3i7fZvn07HA4Hpk+fLuXhxCz3Eg/NQolPkd4okIlUiedgbQdq23uQ5jx5N+pMaDdYxOuD9diIw9qilEHp8LGCB/DMoBjMfS/xjCvOBABUN3fhuud34brnd+Hy3273eIyINHiex+L/3YPLfvsZOiR4fKub9OB5oFCrFqcLk8CKMjW4fGQeAODdA/VRPhqXsAOU7u5uVFVVoaqqCoDQGFtVVYXz58+D4zg8+OCDeOqpp7B582YcPnwYd955J0pKSnDjjTcCAMrLyzF37lwsW7YM3377Lb766ivcd999uO2221BSUiLlzxazSpwZFCPNQolbbHmt9z41UotUied0q/AuacrQHAzJFUqO7jv4tjsHXeWmqXp/MdwzKNF5/nY6/27cyzuAK0DpMttQ7exDYH9v4SjLScENE4qRk6ZCTpowobbbbKOSTwQcON+Jfec60GG04sD5jn7fH/sdjaPsSVjE0ff762JmJkrYr6779u3DNddcI36+YsUKAMCSJUvwxhtv4KGHHoLBYMDy5cvR2dmJGTNmYOvWrdBoXBs2rV+/Hvfddx9mzpwJmUyGhQsX4sUXX5Tgx4kPGqUcBRlqtHSZUddh9GjyI/FhoDIokSrxsGWYBRlqpKnkONdmxNEGvfguKtgyzWj3oPjLoKSrFdAoZTBZHWK2oy9pfo7jsHbxZPHzu1//Fp9Xt1JZNgLcl7cebdBjZnlhgFsHx8qVrExHQjNnfBEy1MJMlD017agckRvtQwo/QLn66qsDliU4jsMTTzyBJ554wu9tcnJysGHDhnC/dUIpzU5xBig94maCJH7E+yoe9wBkSE4qPv6+yaMPJWiAEsVNDAGIwYd3BoXjOORnqFHbLjSgD81NFZdq9wc1tkeGyWrHlu9c86+kWDXlyqBk9vu+kolGKccPJpXgn9+exzv762IiQImLVTyJiL3g1bbTO7JgzDY7Fqz9Co+8eyjahwJAqJn72+lXamkRGtTW2s22olf73HtGvD5YBiXKJR5f2Uf32RdSvYtOltlFL312EuNWb8WYRz/GmEc/xpQnt2H/ud6D/KSy7Wgzusw2yJ3j6PsToLyzvw4Tfv0JvqsTtiigDEr4WJnn4+8bo7JBqTcKUKJkqLPuf7o1OfYg6o9TLd34rrYTb+2rhdES/T+abrMNdmeNNjPSq3icGRSLTdp9by6wvUoy1BhdKCyprWkziLVnMYOSrvH59dEe1OaaIusjQHELqsqLpApQEj+DYjDbsG7naRgtdphtDphtDrQZLPjzzjMR+55s/s6NFw8CAJxrM/ZpWBjP83jxs5Pocp5URxWkY0hOqnQHmiQmD87CsLw0GC12fHS4MdqHQwFKtNB+H6HrcG4Kx/NAdVNXlI/G9e5drZD1aYVIOFgGBYC4KkUK7hmU4kwN5DIOFpsDF7rNcDh4XOgWAgD/GZToDmrrFKfI9g4Q3Y9Z+gxK4gYoH3/fBKPFjmF5afjy4Wvwr+WXAgC2H29Bm/P5IjWWtbtydB6KM4Vg+Hgf/sb3nu3A+XYj0tUKfP7Lq/HRz66gTQL7wH0mSizscEwBSpSw+uiJpm5Y7Y4oH01sY++WgdgI6Fj/g3eDZiQo5DJolMKfqZQpV/ceE4VchiKtcHKo7ehBh9EiZohy04Os4omxJlnAM+sjdYDS3GWK6g7OkfTOfmEA5sLJg1CanYpLh+diYmkmbA4e71dJv0+a3cGLwcj4Eq1rF+k+rJRix379hCIMy0uDUk6ntr66qWIQOA745kx71FsQ6LcYJaXZKchQK2CxO6jME0Sne4AS4WWedR1G3PPGXixa9zV+8e/vfAaPvjapiyTWKCtVQ6rJahfvi2Ub3HssWHYlJ03l94U+M6Vvg9rOtRmw5K/fYtG6r/HQO9/B1sfgvMPPMmPA9TNlpyrFwKu/ctJUSFHKwfNAY6cp+BfEmdp2I7450w6OA26aXCpeHsl30+faDDBa7NAoZRiWl+6zFyoURosNHx4SyhGLpsT3NiixoCQrBTPEmSjRzaJQgBIlMhnn2u+DZisE5D4r5liEMyj/3leH7cdbsO9cB949UIfdp3tvQR6oQTMSpF5qzLInKoVMXI0zyK2E4eo/8T/kimVQeqx2WGyhBxkvbz+FnSdase9cB/69rw7f+thJORSdATIoowvTAQDThuWA46RJ83Mc5/EYJZrdZ4Tn+dQh2eKkawCYP7EEKrkMRxr0kr9OsWzomCIt5DLOlUEJ82/8wLlOGCx2lGRqcMnQbEmPMVktdAap71c1RHWYKAUoUdTXdwzJxr3Ec7ypSyw/RIL378JXQBSovBAJrA9FqhKPe/8JO4G7N4GGslV9utuE2VCbGo0Wm9h4V+LsNzjW2LeeIlZm8xUkThmSjffvvRy/v2VSn+7bn0ReycOCrpEFGR6XZ6epMGucsLmr1O+mvQeqsdfD6uausDJrRxuFVTsVg7MlC0iT3ZzxRfh/15fjreWXRvUxpQAlitgf5rEmClACcR9/bbTYI7pXBAtIrhydD8D3u7lA5YVIYHM8uiTOoLgHIB4lnhACFLmMQ4aajbsP7bi2ft8Eg8WOIbmpuPUSIRXfl+C8x7nKBPD9O+A4DpPKsiRfAp7IjbIs6GI/oztW5tl0sF7SfjnvgWpl2cLMGovNgTMXQv8bd20MmBHkliRUKSo5ll05HAUSlUj7KrJzuklA7iUenucp+vfDezuAo416DM9Pl/z7dBotqO8UTj4LJw/CrhOtPk+ggcoLkSAOa5OoByVQgFIfYgYFEGahdJltIa/kYRNDF00uxdg+pvMBVwZLIeMkGcIWKleWKTYzKB8easQ+r5klco7DzZNLgzYL1zuDLl8BypWj8pGXrsaFbjN2Vrdi1rj+TXo932bE+j3ncOCcMNaevVETyt4Z2Hu2A0cb9OLy92BocmziogAlikYVpkMp59BhtOJEczfGFNE7AF9YQJCfoUZrlxknmrqAidJ/H/ZCV5aTgkuHC1MUT7d2w2S1Q6N0Lfcd6AwK60GRrMTjIwApc5586zt7PEpAoRxXKCt5dD1Wsc/hpsmDxGD8VEsXLDYHVIrQk7ktXa4m3oEM6tlcjUP1uph7Q9FhsOCBfx30Wf784uQFbH3wioDHWxcgQFHIZbipogT/+0UN3tlf1+8A5debv8fn1a0AAJVchrFur3vjirVCgNKox40Vg4Lel8lqF/eVosmxiYdKPFGkUcpx9RihvrsxBtacxyoWELB3Wq0RnskwrliLggw1ctNUcPiYvdIxgMuMAVcPimQlHh8BSFGmBjIOMNscYpkrlAwKENpKnmONwg6zg7JSUJqdipJMDTJTlLDaeZxsCa8P5bjz+EJ9hy2Vy0flQa2Q4UyrQZxWGiuONuphd/DIS1fh3mtG4N5rRuCnV4+AWiFDdXMXvq/3n6my2R1o0gsrk1iWyNtCZ5nns+PN/drRuUVvws4TQnCy7Iph+MudU5DmlgULd+HAiWahJy0nTYVCLe1cnGgoQIkyVt/deLC+z0suEx1L6bPVGSwDIDUxVVycCY7j/A7TY8czUKt4BqLEo5TLUJwpvHs+0dzd63pfwpmFIgZ/zseU4zixZyDcPpRopfS1GiXmXlQEQNjxNZawx/CSoTlYOWcsVs4Zi4fmjsXs8cLxsjkhvjTqTLA7eKjkMr9Zs7FFWkwYlAmrncfmqvo+H+d7B+vh4IXVQv/vhnHiGzTG/W8ulNUj7m8qYimjRaRBAUqUXTOmADlpKrR2mfHFyQvRPpyYY7U7xJkdo5wrDCIWoHidRNm7Oe+VPJ0JWOIBXEuNmeAZlNCnyR5r9FyxIfxfSMmH24fivfpjILE3FJu/a4DJGpmBbd/X69DQGV4jrq/HF3Ad7/vfNfgdMMfKO4OyUwJOX2X3tX7PeWw8UCd+vHewLqTj5Xne1Yc0pdTnbUYXZkAu49BusKBZH/zvnPpPEhsFKFGmUsiw4OISAMA7VObphQUDHAeMKIhcBkVvsuJUi5A5YO/s/U22HPBlxippNwz0F6CUeaX3C6TMoPg4kbD/hzPbxuHgXSfjKJyULhshjGTX9Vjx1Snp31AcbdDjhy9/idv/95uwltP7O1HPGJmHQq0anUYrvj7Ve6YPEHgFj7sfTiqBUs7hZEs3Vvz7O/Hj5299h6Vv7gt6jCeau3GypRtqhQzXTyz2eRuNUo4R+WkAQnteHDzfCYBW8CQqClBiAHs3se1IM3TG6IwOj1WsQVarUaLIOTujtdss+fCgDw81wubgMaogXRxU5X4CZZvomW12GC3CO9EBW8XjDASk6EHhed5vE+zSGcMwe1whrhydj5VzxvjciM9dqD0oFpsDJ51lI/d3+MPyhBNRbXvo2YLz7UYYLHaoFDIMd379QJLLOFw2Qpiyebhe+j6Uf++rhYMXNs37+nRoAZDJaheDa+8ARS7jcHmQ4w3UIOsuO02FJxZchCtH54sfl40QmslPhDC75FBdJwCgYnDgJeDi8yLIaqlTLV04XK+DXMZhxsj8gLcl8YlW8cSA8SWZKC/W4lijHpsPNeBHlw6J9iHFjA63TeHynPvCWO08dD3WoCfQcLinnlkte3heGlQKGQwWO863GzE0L03M6Mg4V+kl0thSWilKPHqTTZz86p1BGVeixV/unBryfWlDXMVzurUbFrsDGWqFx0mQ/b9Jb4LN7oAihP1TxOmjhRkh3T4SxpVo8e4B6Qcsmm12bHLr73hnfx2uGBX8xHuqpRs2B+93tP+4Ei02Hqz3e7yuACX47r+3TxuM26cNFj+3O3iMfexjWO08mvSmgPfh3uMVyKCs0HaOfme/8FhdMyY/aDmSxCfKoMQIlkV5J8aa76LNte+NCmqFXNwDpr9lHoeDx+nWbpxs7sJXpy5g/7kOyDhhoyxG4bYEkr24upd3Bmq3VBagSNEke8GZPcnQKDyWTveFWOIJ0oMiDtIq8WxkzE9XQyWXwe7g0agLvr/NhW6zuPVANPpPmL6OZA/m8+Mt6DRakeL8vWz9vimsBuRyP42iwY431BKPL3IZh5Ks0AbYefd4+RPKxF67g8d7B4XXyoWTffezkPhHAUqMWHBxCRQyDt/VduIMbR4ocg1FE06G7J1SfwOUX713GDP/sBPXPb8Li1/dA0CYHus9OdG7D4UtsRyoBlnANVZeih6UUPbZCZXYJBskcDrqp4FTJgt9f5ujDXpMe/pT/P2bc8J9RbEpkv0cdR090IU4pC4U7M3JnZcNwaiCdJhtDnETvEAO1Xd6HJc31ux9vt3oM+AJtcTjj/uQP394nvf7PPB3f4GeE3vOtKFZb0ZWqhLXlhf4vR2JbxSgxIi8dDUmDxY2uqqq7YzuwcQQV4lHKOewE2t/ZqHojFZsPFjvvF8lctJUGJSVgp9ePbLXbb2XGnd6Hc9AEEfdh7jnTSAsQMmTICUebgbFV1AR6v42X5xshYMXmspHF6Zj9vj+DQvrj8xUpdinJNXmla1dZnF42aLJpeLckWAZVavdga3fNwGAOFzQW3aaStz76LjX3kehzEAJpjSEkkxdRw+6TDYo5RxGFgSeAu2+L5Q/x52ziSqH50Kt6F8mkMQuClBiCG0e2Js4FM05c0SKDMoHhxpgsTkwtigDBx67Dgceuw5fPXItpg3L6XVb7wyKe8lpoIg9KBZ7v5uDQx1jHwrWJNsVIIMS7J2z+O47yDJVdh8/mzkK//n5VeLMlmiR+m/1/ap62B08JpVlYVRhBm6qGAQZB+w/14GaAPvS7KxuxYVuC/LSVbhqjP9+FX8rppr0wWegBBNKkMm+78iCjKBTg1lWrd1ggdHi+7nFgpeynL4FVSQ+UIASQ/wNBktmHREo8fhqiPWH7RnTpDeh3WBxy6AMfInH7uBhsvZvmF+oY+xDEcoy40adCboeKxQyDqMKe79zDuXdMhDd2Se+SN2H4tqnSOiBKtRqxA0rAw2FY19348WDoAzQNOxvyXyoM1ACKc0JXpIJtbwDAJkpSrEB21/ZqD99MyR+0CqeGOL+ohdre31IobXLjP/6y24snFyKe6/pXU7xxbXvjTQZlNOt3aiq7YRcxmHBxcH3+khXKzA0NxVn24w40qATe1AGaoosAKQq5eA4gOeBLrMVKaq+p7SlzaAILx9Gix1Wu8PnCZKdEEcWpPtMxYfy7lvYb8X3MtpoYcfx7oE6fPBdQ6/rh+Wl4d8/rgxpR+WjDXocb+qCSi7D/Ekl4uWLppRiR3Ur3j1Qh59fNxpyrwCiw2DBZ8ebAbhG0Qc7Xu+Aqr/9J8LXOoPMTv+/w1AbZN3v82ijHnUdPRjlY0sDKY6bxD7KoMSQUYXpUMg4dBqtIa1qiDffnGnDmVZDWCuVWN8FKyf0twdlzxlht9fpw3JCPkmz3qD/HGkWh3MNG8AZHDIZhzQVW2rcv+mlkgYoGiU0SuElpLbd98kp2DvnUBoiq5u64OCB3DRV0OFxA+WSoTnISlWC54X9i7w/jjd14f2q3oGLL3tqhJVJl4/M9SgdziovhFajQKPOJK5ecrf5uwZY7TzGl2jFRlh/2PXVzV2wus0rkSITwb62sdPkdxbKeefzgw1hC/U+/QWuruOmEk8iowAlhqgVcrGBLBH7UNjJsb6jRxx8Fgwbipbu3DCvvxmUo43CsKoJpaHvfHqTM+3+1r5a4Z2uQoZ5F/mehBkpUi01ljJAkck4jCkKXOoI9s6ZnWAadf5Pbu5TUmMlq5iTpsLuR2biy4ev6fXx81mjAYQ+MoA9RhNKszwu1yjlYkblXR9TpoONjXdXlp2KdLUCFpsDZ1pdPS3hzEDxpyBDA6Wcg83Bo9nP3yVb3l6Q0XtOiy+BSn+6Hqu4cow1K5PERAFKjInUjIVYwLIeFrsj5AwIG06W6swgsBPrhT5mUPrSy3DZiDwUaTXigLPrxhUicwB7UABXH0qXuX8reaTsQQH89zYwwTIo7rNQ2GqSXvcRY/0nTIpKjtLs1F4fiy8dLI4MOBXCTs2BHiMWfHz8faPHKq7jTXocrtdBKQ+tVCmTuW3O2OiaKMsyEf050XvMQvGRSbPZHWhzlkZDDYwDZdZYX0pOmspjJ2SSeChAiTHsneaRhtjazl0K7lmPYMtKGZZBSfMKUNoMlrB3f3Y4eHF54vgwehnkMg43T3adBEJ5xyq1NHX/Szx2B4828Z2sRAFKgMbuLpNVTO37K0GEMgsl3jaEy0tXi7v0smmn/ljtrm0AfD0nLy7Lwoj8NJisDixatxuL1n2NReu+xvK/7QcAXDu2IOR+KF/BpFS9HIFWY7UbLOB5YfpyqMfK7m/XiVbc88Zej9cLapBNHhSgxJgJg4TSwzdn2v3uPhqvPAOU0PZfETMozhJPdqoKchkHnncNTQvVuXYjjBY7NEoZhuUFnsXg7ZapZVApZBick4orRuaF9bVSyHL24LQb+r56qd1ggYMXNl6UqsmXnfR8zQPZdULo1ynLSRGXifvCloq6lx4Yh4PH8UbXpNR48UPnBqC7g+yn428bAIbjOHG0fHVzF/ad68C+cx1i4Oc+dj4Y72DSZneIvW797eUocS779rWrcYvz7z43Xd2r0def8mItZJyw/9T24y14a2+teB01yCYPyo/FmKlDc1Ck1aBJb8Jnx1pw/YSB7XWIpHADFJ7ne2VQ5DIOuWkqtHSZ0dJl7jX5NRD2znFMkTbkF0pmWF4aPv7ZFchQK6KyB8ygEKZ1BsMe/9w0lWQ/w9iiDHAc0Kw340K3GXlupaN39gsnlfkTS/x9OQCgvCgDu060+gxyor05YF+xZtBQl097bwPg7u7Lh2FMUUavvZjy0tWYOrT37B5/2B44xxq7wPO8OANFKef6nVEr0PrvDetLWbEsJxVb7r8CG749h398c97juSFF3wyJD5RBiTHu5YRE25fHve8klADFYnfA5mymZRkUAOJJMNyVPKz2Pq6PW7OPyE8PKyCSEusRqAsy0CwQ9njlSdR/Agilp2G5wsnY/STSojdh10khe9DXJbDul40tit7mgH3BTp5tAYaNAaH118hlHK4YlY+5FxV7fIQTnADCKkG5jEO7wYJmvVkMdgdl9X0GCsOCjwvdvbOafW3MHleiFftrPMtSVOJJFvHzF59E2Av6zhOteHzzEXxe3RLlI/Jt79l2PPHBUTy55SgO1wXumXHvfwBC60ExuvVbpLptbNfXlTyx2mwZilCW4wYj5Qoed+U+pqpuck5GnTw4CyPyA5fT2O/jeKO+1+queP2dZaYoxd2ufZU9mIHsr9Eo5Rjp/F0cbdRJmonId67O8ZlB6cfzjm3W2aAzoeaCAb//5DgOOrcCoQAl8VGAEoNG5KdjypBs2B083vj6LH789/0h7Wo60H7+VhX++lUNXvuyBo+9/33A27L+ByaUUoXB+c5TrZB5vHvuc4ASZ82W7thJpD8lnkbniTLUpZ6hYs2d35wRZnXwPC9m/4JlTwChfKZWyGCw2MXeCiYRfme1fn5nPRY7vnOebAcqAHP9rtpxuF54UyHFuHjxb9JHVpOtuOtLgJKhUWKw8/h+8o/9WPv5afHvfniYfWQk/kQkQOnq6sKDDz6IIUOGICUlBZdddhn27t0rXs/zPFavXo3i4mKkpKRg1qxZOHnyZCQOJW49e8skPDBzFEqzU0Le1XQgtRssHu/mz7b53y8EcL1IsTJ7XWfwWSis/yTVa3JqXwKUC91mNOvN4DiIszviSRkbhqXr8Ri0FQ62gmm0j5Hz/TFnfBEAYNfJC2jpMuH7ej1ONHdDpZDhB0H6TwBAIZdhTBFbAutZ5onXDAoQPOv1yZEmGCx2DM5JHbCfb7bzd/XewXpsdk7AnT2u/xsvBvqb7O8O2mKGzfn8/a+pZfjjbRdjaBz1JJG+iUiA8t///d/Ytm0b/v73v+Pw4cOYPXs2Zs2ahfp6YcndM888gxdffBGvvPIK9uzZg7S0NMyZMwcmU+JNT+2rYXlpWHHdaNxZOQRA7PWjsH6DvHRhdUan0Rpwt13Xu540yDjAYnMEnWXiPQOF6cs0WXa8Q3PTxKFn8SQvXQ2VQgYHDzT1ccpwpLIRI/LTMXlwFuwOHu8fbBCbY+eML0JmSmjzYnwtgW03WMTZKGPjOkDxXc4Us0yTS/vdAxKqa8cWIDtVidYuM9oNFuRnqHHFqP6vSmMBSrfZ1qvnpr+lRffna6FWjf+5eUJIs19I/JM8QOnp6cG7776LZ555BldeeSVGjhyJxx9/HCNHjsS6devA8zxeeOEFPProo1iwYAEmTpyIv/3tb2hoaMCmTZukPpy4d+PFrl1N/7b7LL6vj435KOyEP3VIjrhxXqAdadmLVElWirgTrb/UNyOu4FH3P4NyLIzNymKRTMahNIs9bqHNkPm+XieeHLvNNjHLFYnluoumlAEA/vntebzvfGcezrwYX42yrqAyNS6DykDTUOs7e/CVcwmy+4ydSFMpZB4n95srBknSfJymkiPF2Sd2ocuzUba1HyUewPNv9qaK0rBX4JH4JXmAYrPZYLfbodF41rlTUlLw5ZdfoqamBk1NTZg1a5Z4XWZmJqZPn47du3f7vE+z2Qy9Xu/xkSwKtBpc5dzVdPX7R3Dzuq/R7Gfi5kByH2EuvhC3BwhQ3JYaDgow1Mmd3wwKmyYbRoAS7mZlsSjYQDN3J5u7cOPar/Bff/4GNrsD1U168LzwDlTKVTzMDROLoVLIcOaCAZ1GKwq1aswIY14MOwl9V9spDuCL999ZoBLPx4cbwfPApcNzJOkBCYd74BhKj1AoOI5z60PxfH2SMoOyaAplTpKJ5AFKRkYGKisr8eSTT6KhoQF2ux3/+Mc/sHv3bjQ2NqKpqQkAUFjoWfcsLCwUr/O2Zs0aZGZmih9lZWVSH3ZMe2juWFw3rhCFWjUsNgfeOxh4OuVAcB/PHcqOtO4vUoXOpbotQQItKTMo4Wz3HqsCvSP39u99tbA5eNR39mDXydaI93Jkpijx9I0X4arR+bhmTD6evnFCWO90J5ZmITtViTaDBV86N2Rkv7PyOOwZAtymq/r4uzjkXPV2xaj8AT0mQGiUfWTeWPy/68sx2sdOwX3l6+/SZLWjy7lvTl8DlJKsFDx6QzlW/2AcRhZId7wk9kWkB+Xvf/87eJ7HoEGDoFar8eKLL+L222+HTNa3b7dq1SrodDrxo7a2NvgXJZDyYi3+986pWHGdaxMyng9ts71IMFntONUijOcWMijB39m7Byih9pCwVTz+MihdZht6LMGn7Zqsdpx2TimN13fjQPCeBsZqd+C9g66ddN/ZXzcgq2FumVqGN++ZhtfvnoZZYTZeupceWG9G3GdQsoSA8kK3pdfzNJqrkziOw4+vGoFlVw6X9H5ZP5p7gML+r1bIkNGPMt1/XzEc98wY1r8DJHEnIgHKiBEjsHPnTnR3d6O2thbffvstrFYrhg8fjqIioYu8ubnZ42uam5vF67yp1WpotVqPj2R0/YRiaJQynGrpxndB5o5E0qmWbtgcPLJSlSjO1IT0zt4jQAkxA8LmoKR5reLJUCugVghP3VA2DTzR3AW7g0dOmkqyPWiiIdRZKLtOtOJCt1nsCfj0aAv2nGkH4JomGotY6eE/R5vR0mXCqVZXEByPtCkK8aRc3+kKKnssdpxx/mzj4zij583X37V7/0ms7ERN4kdE56CkpaWhuLgYHR0d+OSTT7BgwQIMGzYMRUVF+Oyzz8Tb6fV67NmzB5WVlZE8nLiXoVFi3kXC6Hu2UiIa3MsFHMe5TTkNUOJxm2IaaoAiZlC83nm517tbQijzeB9vvAp1FsqmKiF7csf0wSgv1sJid+DMhdjPII0v0WJsUQYsNgee+88J2B08slOVKIrS9N7+4jjXRoi1bv1Z1c1dcPBCxkHqoXnRlJ/uHNbW3TuDkkg/Jxk4EQlQPvnkE2zduhU1NTXYtm0brrnmGowdOxZ33303OI7Dgw8+iKeeegqbN2/G4cOHceedd6KkpAQ33nhjJA4noSycLLzL/OC7Rpis0dlM0LufozQn+Dt71vCaoVGEnkGx+M6gAG6NsiFkUOJ52Jc7NgulSW8KuJPzwfMdAIBZ5YV4cNYoFGdqkJOmwpzxhRgywA2Z4eA4Tsyi/HufEICPC7BHTTwYUSDMnDnR3CVeJu6/E+cBszefGZR+zkAhyS0ia/d0Oh1WrVqFuro65OTkYOHChXj66aehVArLUR966CEYDAYsX74cnZ2dmDFjBrZu3dpr5Q/prXJELkoyNWjQCZsJ3jBx4DcT9O4NYBkUNgslQ9N79oXFJpxQ1Qo58tNDK8/4W8UDuM1CCTODEs/YLBSLTdiF1tfqD12PVQwUx5VokZmiFAepxYMFFw/Cmo+Pw+4c4hfvv7NxxVp8eKjR5/LpeA+YvQUKUPIog0L6ICIZlFtvvRWnT5+G2WxGY2MjXn75ZWRmumrfHMfhiSeeQFNTE0wmEz799FOMHj06EoeScITNBIV3mdEo8/A83+sFNkOjRFaQWShmZ4CiUsjEF7I2gyVgJsDfKh4g9JU8DgcvTqCM9xOC+ywUf9kq9rspzU4JeUhaLMnPUOOaMa6VLfH+O/M1gC4RVpT5ErAHhTIopA9oL5445L6ZYLClulKr6+hBl9kGlVzmsQlcqY9auzuLW4CSk6aCjAN4XpgW6k/ADEqAvT/c1XYY0W22QaWQYXgCjMYeFGQlTyJki1gZE4jtpt5QsADrdGs3TFY7HA5XgD8+zoMvb+5/k2yVIfWgkP6gACUODctLw5Qh2XDwwq6xA+mI8wQ4qjAdSvcN/JzvkDp8BBw8z8NiZyUeGeQyDrnpwZtcpcigsBP22KIMSSZmRluwFVOJ0G9zbXkBRhemY1RBOkbkx3dQWZChRm6aCg4eqG7qwrl2I4wWO9QKGYbmxvfP5o0tM7baeeh6hG0vKEAh/RH/r9hJijUTDvRMFH/paZbl8N6HA3CVdwAhgwKEtp+O0c8cFI+vDxagJFg6PdhSY/cGzHilVsjx4QNX4JMHr4z7oJLjOI8x/okWMLtTK+RiWZH9XVKAQvojsf5CksgNE4uhVshworlb3DZ9IPgbnpXiXGnTY+3dU2Jx6zNROV+UQ8mAuFbx9A5Q8sLMoMRzRsFdoGFtFpsDJ1uc/TZxHKAAgFIuG7AN9CLNvQ/laKPwt5ooz0dv7n/XPM9TDwrpFwpQ4pRWo8Tci4TVGQO507G/TfdSWYDiI4NiccugsAFroQQorjkoPko86b3r3b6I49Lj/ITNBMqgnGrphtXOI0OjEG9Hoo8FI0cadAnRIxSI+99ll9km/u1TBoX0BQUocYw1E27+rgFmW+RnonQaLeIqnXLvDIpzaqnRx+h5cQWPXCbOfQgpg2L2n0FhX2+xOaA39Q6KAKEBt1EnNBGPLUqMPTxYD4qvWSju5axEmq8R7yYMEhp9D9frcLC2E0ByZFDY33aGRgGNsvebDEKCoQAljl0+Mg9FWg06jVZsP9YS8e/HToBlOSnQes06cZV4egcorhkovZtqA/WguPbi6f3iplHKkaERAhd/QQ7L9gzJTfU5myUe5aeroZLLYHfwYvDFJFo5K1EMz0/H+BItrHYenUYrOA4YE6cbIAbjvpKH+k9If1GAEseEmSieG6xFUqD0NMug+Nq8z32JMROsh8Tu4GFy9rOk+dlkLFgWxl85Kp7JZJzbUmPPMo/Y35BAP2+iYE3tADA0Nw3p/dg4L5b5yqBQ/wnpKwpQ4hybibLjRCse33wEu060Brz92QsGvPrFGVgDDEjz51gja8DsPZuCZTl8l3iEy1Q+MigX/AQX7quBfGVQ3O/DXxYmUev9rL/EfSieMEAvMQbSJaIFFw+CUi6U3RLt+ejOfXUdZVBIf1GAEudG5KdjypBs2B083vj6LO7/58GATaMP/OsgnvrwGD463Bj29wo0YyPF2ScSaomnQCu8aDXrTT6PlwU6chnn8XXugmVQEmEmiC+sD+WscwNAAGjQmaDrsUIp5zCqIDH6bRJJTpoKM8cWAgAmlcX38LlAPDIo3RSgkP6hACUBPHvLJDwwcxQAYS+WTqPV5+2qm7pwqE4oA5xuNfi8jT8WmwOnnEtYy4t7nwADlXjMPko8bP8eg8UuDnVy55oiK/fb8BkoQDFZ7TjVImxpn2gBCmv4Pd7kNj7dmS0aWZDh8TiT2PE/N0/AEwvG487KodE+lIjJY5lR6kEhEqBXsgQwLC8NK64bjQLnC4G/IV7vHnD1qfgble7PyZYuWO08tBqFGFy4Sw2hSdb9xKlRysXJk76ON9AMFCZQgHKqpRs2B4/sVCWKtIm1CaU4+Kuhd4CSyOWDeJeTpsKdlUMTekWL+z5bTc4mbupBIX1FAUoCCTTEy2Z3YOMB11h8f0GMP+4rRHxlNFLEHhT/k2TVCs8X5kHi2PbexytmUHzMQGEC9aAk6pb2gCuD0qAziVsLsAZZX9ktQgaK+z5bbJNOyqCQvqIAJYEE2qdl79kOXOg2g52r68MMUNiLjb+BZ4FLPM4mWa/R3oGGjgVaYsywFz5fjbaJNuLeXYZGiSG5wu+arVRK1H4bEl/c99m6QD0opJ8oQEkggXa6rXE2VF5UIjToNep6wlrJc75duM/hbjsYuwulxKNWhh6gdDmHr3nPW3EXaEfjRJ8JUl7k2t9F12MVd5FOxICMxBf3kk5umgqjCymrR/qGApQEEuiEz4KWi8uyoFLI4OAh1ohDwe6z1Ef/CQCxru5rmTHbi6d3BsV/xkcfRoDS1m2G3eFaCSQsuU3sAMW9D+W482cdlJWCrFRVNA+LEI+MyY0Vgzx2PSckHPTMSSCBTvjssrKcFDHIqA2jUZYFOP72eGEZFLPN4REsAIDZ2rtJ1v2+fGV89M6VPdoU/02yuWlqyDjAwQtj7V3H2oMusw0quQwj/GR84p24AV2jPuH2GyLxzT0gcR9QR0i4KEBJIO4nfO/ZIq4AI9XvJFJ/dD1WseQyyG+A4gokTF5lHpZB8W6SLWMDxzp6eh2v3uQMUAJkUOQyDjlpwru1li5XNuiIs7wzuig9Yd+9sQzKyZZu7DvX4XEZIdHkcPtbpqCZ9EdivnonKffZIt6zUNjU0dLslIClIF9YcJObpvIIRNy5D1PzLvP4WmYsHK+Q8eky26Dv8Vz9wz7XpgTeQ6c4U1hC3NjpClASuUGWKc7UYEhuKuwOHh87h+4l8s9L4scvZ4/BuGIt3rxnWrQPhcQ5ClASiEYpF+u/7mPQzTY7mvVCI2lpdqpYCgp1JQ+7nb/yDiDsEcNW8nhnUNgqHu+JsCkq1ywU73KTK4MSeM8SFpS5l4mSYSYIx3HibtasojaeMigkBowr0eKjn12Bq0bnR/tQSJyjACXB+OrraHBmF1JVcmSnKgP2fvgiNsg6Axt//O3H42vUPcNmobgHVIB7D0rgDIqvbJCrQTZxR4oDEDeKBIAMtSJgAEkIIfGGApQEw4IItuwU8Gxw5TiuDyWe4BkUwH0lj2e5xl+Jx/0+a9u9MyjBV/G4fz07xk6jRQx2xib40LLS7FRUDs8FAJT7GaBHCCHxigKUBDPUOcDrdGu3eJl3BmRIbhoAoEHXI05sDSTYCh7G3ywUcS8eHw2rruP13BuoK+QMimcGhu3oW5aTEjS4SQTLrxoOGQfMu6go2odCCCGSClzgJ3HHffkp4x1g5KWrUZChRkuXGcebujBlSHbA+wy1xMPG3XtPk/U3qE043sxexwu49aAEWGYMAKU5nuWqZGiQdXfNmAIcf3IebRBICEk49KqWYNhS0+NNXbA5l/f6KtGIg768AgNfQs2giOPuw8igiMfbqBePl+d51yqeIFkQ1iTbYbSi22xza5BN7P4TdxScEEISEb2yJZiy7FSkqxWw2Bw44xxv7ysDImZaGgIHKLoeq9gP4m8GCuOvSVbcLNDHLq5DclKRqpLDbHPgbJtBvD2bnRKsxJOhUSIrVbhNfUcP7UlDCCEJggKUBCOTceKOtkcb9OB5XtyHp8w9QAkxg3LW+bV56f5noDB+Szx+Rt2z42W787IBa2wFj4wD0gJsFsiwzE7NhW6cahF6UChAIYSQ+EYBSgJy70Np1pvRbrBALuMwqtA19p1NeHQvrfgSzhj1FKUQwPQq8Tg/91eK8A6WXP0nypBWprAyz+fHW2G188hMUaLEOcCNEEJIfKIAJQG5byR3tFEHABiRnyYuAwaAoblpSFF6llZ8CWfomd85KHb/c1CE+870+F66EPtPGFa6+s/RJvFYacktIYTENwpQElC5WwblSL3vAEMu48Q5IUcC9KGE09PhKvGEPgfF/b7ZEuFQV/AwrMTT4RzvT+UdQgiJfxSgJKDRhRlQyDi0GyzYcsi5T4uPkzYLWlhg4M3h4F1TWUMq8QRZxeMnQBlTmAGOAy50m9HWbXZNkQ0xg+K9Y/HE0uRZwUMIIYlK8gDFbrfjsccew7Bhw5CSkoIRI0bgySef9Nitlud5rF69GsXFxUhJScGsWbNw8uRJqQ8laWmUclw9pgAAUN3sbBr1sew2WKPsuXYjjBY71AoZhuWlBf2+KUFH3ftueE1RyVGYIfSM1HX0iDsnhxqgzBiZhycXjMe914zAYz8Yh+snFIf0dYQQQmKX5IPafve732HdunV48803MX78eOzbtw933303MjMz8cADDwAAnnnmGbz44ot48803MWzYMDz22GOYM2cOjh49Co2GmhulsGhKKT491ix+Xu5j7Huwpcbs8rFFGVD4WIHjjfWgeG8WGGgvHqY0OwVNehPqOnrCLvHIZBx+VDk0pNsSQgiJD5JnUL7++mssWLAAN9xwA4YOHYpFixZh9uzZ+PbbbwEI2ZMXXngBjz76KBYsWICJEyfib3/7GxoaGrBp0yapDydpXTu2ANnO+SBFWg1y09W9bjO2SAuZs7TS0mXqdf2xMGeKpCj9zUEJvIoH8NzkkA1py0iCUfWEEEJ8kzxAueyyy/DZZ5/hxIkTAIDvvvsOX375JebNmwcAqKmpQVNTE2bNmiV+TWZmJqZPn47du3f7vE+z2Qy9Xu/xQQJTKWRYcLGw262/ACNFJRdLN76yKAfOdwAIbYkxuz8gvN2MGbYSxyODQgEKIYQkLclLPI888gj0ej3Gjh0LuVwOu92Op59+GosXLwYANDUJS0ELCws9vq6wsFC8ztuaNWvwm9/8RupDTXj3XzsSZpsDd0wb7Pc25cVanG414GijXuxbAYBGXQ92n2kDAFw1Oj+k7+evxBOsSRbwzKCkqYWnZaglHkIIIYlH8gzKv//9b6xfvx4bNmzAgQMH8Oabb+LZZ5/Fm2++2ef7XLVqFXQ6nfhRW1sr4REnrtx0NdbcPAETAqxqcZ+Z4m7jgXrwPDBtWI64+3EwbFCbewbF4eBhcwgN0v6aZAHXGH0hgxJekywhhJDEI/lb1JUrV+KRRx7BbbfdBgCYMGECzp07hzVr1mDJkiUoKhK2hW9ubkZxsWu1RXNzMy6++GKf96lWq6FW9+6hIP3HGmW/r9eh5oIBQ3OFUsu7B+oACM22ofI16t7iNqU2cAbFVeJJFTMoFKAQQkiykjyDYjQaIZN53q1cLofDIZyohg0bhqKiInz22Wfi9Xq9Hnv27EFlZaXUh0OCYBmUs21GXPPsDvxuazUO1nbiTKsBKUp5WEt2XZNkXYPazFa3ACXASqCSLGH1Vo/VLu7/o9VQiYcQQpKV5GeA+fPn4+mnn8bgwYMxfvx4HDx4EM899xzuueceAADHcXjwwQfx1FNPYdSoUeIy45KSEtx4441SHw4JoiBDg5srBmF7dQs6jVb889vzuNBtBgDMu6gI6erQnyIaBetBcQUlZruQTeE4QCn3P35erZCjUKtGs94MXY8VSjmH0YW9l0YTQghJDpIHKC+99BIee+wx/PSnP0VLSwtKSkrwf//v/8Xq1avF2zz00EMwGAxYvnw5Ojs7MWPGDGzdupVmoETJc/91MewOHjN+tx2NOhPe2R9+eQcANEohQ2K22cHzPDiOc425l8uC7o9Tmp2KZr0QHF07tgDZaapwfxRCCCEJQvIST0ZGBl544QWcO3cOPT09OH36NJ566imoVK6TDcdxeOKJJ9DU1ASTyYRPP/0Uo0ePlvpQSBjkMg43Tx4kfj4oKwWXDs8N6z7UzjkoDh6w2oXG2FBW8DBsJQ8ALJpSFtb3JoQQklhoLx4iunlyqdv/B0EmC29HYPc5JybncLZgY+7dsQAlN02Fq8eEtrSZEEJIYqIAhYhG5Kdj9rhCZKYo8V+XhJ/BUCtkYFUc1hwbypA25qrRBeA4YPmVw6EMYbQ+IYSQxEXLJIiHdf9nCuwOPqSSjDeO46BWyGCyOsRhbeYwApRpw3JQ/eS8gM20hBBCkgO9TSUe5DKuT8EJo3H2oZi9Sjyh3qdKEbyZlhBCSOKjAIVIimVK2FJjiz34RoGEEEKINzprEEl5Z1BYL0ooJR5CCCGEobMGkZT3sDY26p4yKIQQQsJBZw0iKbWSlXg8MyiBxtwTQggh3uisQSTFMihs9Y7ZHvocFEIIIYShAIVIqncGhZpkCSGEhI/OGkRSrEmW9aCwTEqKkjIohBBCQkcBCpGUa5mx3eNftpEgIYQQEgo6axBJuZYZC5mTHoszQFFRBoUQQkjoKEAhktJ49aD0sAwKNckSQggJAwUoRFLiHBQbK/E4e1Aog0IIISQMFKAQSbFVPGz+CcukUJMsIYSQcFCAQiTlmoNCTbKEEEL6js4aRFLey4zFHhTKoBBCCAkDBShEUt6D2kwUoBBCCOkDClCIpLxH3fdYaVAbIYSQ8FGAQiRFGRRCCCFSoACFSMrVg+IZoFAGhRBCSDgoQCGSYqPuXSUeZ4CioqcaIYSQ0NFZg0jKXwZFTZNkCSGEhIECFCIp92XGPM/TJFlCCCF9QgEKkZSrxGMXyzwANckSQggJDwUoRFLibsZWh7iTMQBoFPRUI4QQEjo6axBJibsZ2+xig6xSzkEhp6caIYSQ0NFZg0iKDWqz2nkYLTbhMirvEEIICRMFKERSardNATuNVgA0A4UQQkj4KEAhktK4LSdmAQplUAghhIRL8gBl6NCh4Diu18e9994LADCZTLj33nuRm5uL9PR0LFy4EM3NzVIfBokSmYyDytlv0tlDGRRCCCF9I3mAsnfvXjQ2Noof27ZtAwDccsstAICf//zn+OCDD/D2229j586daGhowM033yz1YZAoYmWeTqMFgKtxlhBCCAmVQuo7zM/P9/j8t7/9LUaMGIGrrroKOp0Or732GjZs2IBrr70WAPD666+jvLwc33zzDS699FKpD4dEgVohRxdsVOIhhBDSZxF9a2uxWPCPf/wD99xzDziOw/79+2G1WjFr1izxNmPHjsXgwYOxe/duv/djNpuh1+s9PkjsYhmTzh6WQaEAhRBCSHgiGqBs2rQJnZ2duOuuuwAATU1NUKlUyMrK8rhdYWEhmpqa/N7PmjVrkJmZKX6UlZVF8KhJf7GApINW8RBCCOmjiAYor732GubNm4eSkpJ+3c+qVaug0+nEj9raWomOkEQCG3evYwEK7cNDCCEkTJL3oDDnzp3Dp59+io0bN4qXFRUVwWKxoLOz0yOL0tzcjKKiIr/3pVaroVarI3WoRGIsg+Iq8VCTLCGEkPBE7Mzx+uuvo6CgADfccIN42ZQpU6BUKvHZZ5+Jl1VXV+P8+fOorKyM1KGQASb2oFCTLCGEkD6KSAbF4XDg9ddfx5IlS6BQuL5FZmYmli5dihUrViAnJwdarRb3338/KisraQVPAmHD2ihAIYQQ0lcRCVA+/fRTnD9/Hvfcc0+v655//nnIZDIsXLgQZrMZc+bMwZ/+9KdIHAaJEjYHpdss7MVDTbKEEELCFZEAZfbs2eB53ud1Go0Ga9euxdq1ayPxrUkMcB93D1APCiGEkPDRmYNITu2VMaEMCiGEkHBRgEIkl5um8vicelAIIYSEiwIUIrnyYq3H5xSgEEIICRcFKERy40o8AxQq8RBCCAkXBShEckNyUpHqNj2WMiiEEELCRQEKkZxMxnmUeVJU9DQjhBASHjpzkIgY5xagqBWUQSGEEBIeClBIRLj3odBmgYQQQsJFAQqJCPcMCvWgEEIICRcFKCQixhRliP9PV0ds02xCCCEJis4cJCI0Sjn+sXQ6jBYbMlOU0T4cQgghcYYCFBIxM0blRfsQCCGExCkq8RBCCCEk5lCAQgghhJCYQwEKIYQQQmIOBSiEEEIIiTkUoBBCCCEk5lCAQgghhJCYQwEKIYQQQmIOBSiEEEIIiTkUoBBCCCEk5lCAQgghhJCYQwEKIYQQQmIOBSiEEEIIiTkUoBBCCCEk5sTlbsY8zwMA9Hp9lI+EEEIIIaFi5212Hg8kLgOUrq4uAEBZWVmUj4QQQggh4erq6kJmZmbA23B8KGFMjHE4HGhoaEBGRgY4jovqsej1epSVlaG2thZarTaqxxJv6LHrG3rc+o4eu76jx65v6HHzxPM8urq6UFJSApkscJdJXGZQZDIZSktLo30YHrRaLT35+ogeu76hx63v6LHrO3rs+oYeN5dgmROGmmQJIYQQEnMoQCGEEEJIzKEApZ/UajV+/etfQ61WR/tQ4g49dn1Dj1vf0WPXd/TY9Q09bn0Xl02yhBBCCElslEEhhBBCSMyhAIUQQgghMYcCFEIIIYTEHApQCCGEEBJzKEAhhBBCSMyhAMWPXbt2Yf78+SgpKQHHcdi0aZPH9TzPY/Xq1SguLkZKSgpmzZqFkydPetymvb0dixcvhlarRVZWFpYuXYru7u4B/CkGXrDH7a677gLHcR4fc+fO9bhNMj5ua9aswSWXXIKMjAwUFBTgxhtvRHV1tcdtTCYT7r33XuTm5iI9PR0LFy5Ec3Ozx23Onz+PG264AampqSgoKMDKlSths9kG8kcZcKE8dldffXWv592Pf/xjj9sk22O3bt06TJw4UZxwWllZiY8//li8np5v/gV77Oj5Jg0KUPwwGAyYNGkS1q5d6/P6Z555Bi+++CJeeeUV7NmzB2lpaZgzZw5MJpN4m8WLF+PIkSPYtm0btmzZgl27dmH58uUD9SNERbDHDQDmzp2LxsZG8eOf//ynx/XJ+Ljt3LkT9957L7755hts27YNVqsVs2fPhsFgEG/z85//HB988AHefvtt7Ny5Ew0NDbj55pvF6+12O2644QZYLBZ8/fXXePPNN/HGG29g9erV0fiRBkwojx0ALFu2zON598wzz4jXJeNjV1pait/+9rfYv38/9u3bh2uvvRYLFizAkSNHANDzLZBgjx1AzzdJ8CQoAPx7770nfu5wOPiioiL+97//vXhZZ2cnr1ar+X/+8588z/P80aNHeQD83r17xdt8/PHHPMdxfH19/YAdezR5P248z/NLlizhFyxY4Pdr6HETtLS08AD4nTt38jwvPL+USiX/9ttvi7c5duwYD4DfvXs3z/M8/9FHH/EymYxvamoSb7Nu3Tpeq9XyZrN5YH+AKPJ+7Hie56+66ir+Zz/7md+vocdOkJ2dzb/66qv0fOsD9tjxPD3fpEIZlD6oqalBU1MTZs2aJV6WmZmJ6dOnY/fu3QCA3bt3IysrC1OnThVvM2vWLMhkMuzZs2fAjzmW7NixAwUFBRgzZgx+8pOfoK2tTbyOHjeBTqcDAOTk5AAA9u/fD6vV6vGcGzt2LAYPHuzxnJswYQIKCwvF28yZMwd6vd7jnV2i837smPXr1yMvLw8XXXQRVq1aBaPRKF6X7I+d3W7Hv/71LxgMBlRWVtLzLQzejx1Dz7f+i8vdjKOtqakJADyeXOxzdl1TUxMKCgo8rlcoFMjJyRFvk4zmzp2Lm2++GcOGDcPp06fxq1/9CvPmzcPu3bshl8vpcQPgcDjw4IMP4vLLL8dFF10EQHg+qVQqZGVledzW+znn6znJrksGvh47ALjjjjswZMgQlJSU4NChQ3j44YdRXV2NjRs3Akjex+7w4cOorKyEyWRCeno63nvvPYwbNw5VVVX0fAvC32MH0PNNKhSgkAF12223if+fMGECJk6ciBEjRmDHjh2YOXNmFI8sdtx77734/vvv8eWXX0b7UOKOv8fOvYdpwoQJKC4uxsyZM3H69GmMGDFioA8zZowZMwZVVVXQ6XR45513sGTJEuzcuTPahxUX/D1248aNo+ebRKjE0wdFRUUA0Kujvbm5WbyuqKgILS0tHtfbbDa0t7eLtyHA8OHDkZeXh1OnTgGgx+2+++7Dli1b8Pnnn6O0tFS8vKioCBaLBZ2dnR63937O+XpOsusSnb/Hzpfp06cDgMfzLhkfO5VKhZEjR2LKlClYs2YNJk2ahD/+8Y/0fAuBv8fOF3q+9Q0FKH0wbNgwFBUV4bPPPhMv0+v12LNnj1iDrKysRGdnJ/bv3y/eZvv27XA4HOKTlQB1dXVoa2tDcXExgOR93Hiex3333Yf33nsP27dvx7BhwzyunzJlCpRKpcdzrrq6GufPn/d4zh0+fNgjwNu2bRu0Wq2Yek5EwR47X6qqqgDA43mXjI+dN4fDAbPZTM+3PmCPnS/0fOujaHfpxqquri7+4MGD/MGDB3kA/HPPPccfPHiQP3fuHM/zPP/b3/6Wz8rK4t9//33+0KFD/IIFC/hhw4bxPT094n3MnTuXr6io4Pfs2cN/+eWX/KhRo/jbb789Wj/SgAj0uHV1dfG//OUv+d27d/M1NTX8p59+yk+ePJkfNWoUbzKZxPtIxsftJz/5CZ+Zmcnv2LGDb2xsFD+MRqN4mx//+Mf84MGD+e3bt/P79u3jKysr+crKSvF6m83GX3TRRfzs2bP5qqoqfuvWrXx+fj6/atWqaPxIAybYY3fq1Cn+iSee4Pft28fX1NTw77//Pj98+HD+yiuvFO8jGR+7Rx55hN+5cydfU1PDHzp0iH/kkUd4juP4//znPzzP0/MtkECPHT3fpEMBih+ff/45D6DXx5IlS3ieF5YaP/bYY3xhYSGvVqv5mTNn8tXV1R730dbWxt9+++18eno6r9Vq+bvvvpvv6uqKwk8zcAI9bkajkZ89ezafn5/PK5VKfsiQIfyyZcs8ltrxfHI+br4eMwD866+/Lt6mp6eH/+lPf8pnZ2fzqamp/E033cQ3NjZ63M/Zs2f5efPm8SkpKXxeXh7/i1/8grdarQP80wysYI/d+fPn+SuvvJLPycnh1Wo1P3LkSH7lypW8TqfzuJ9ke+zuuecefsiQIbxKpeLz8/P5mTNnisEJz9PzLZBAjx0936TD8TzPD1y+hhBCCCEkOOpBIYQQQkjMoQCFEEIIITGHAhRCCCGExBwKUAghhBAScyhAIYQQQkjMoQCFEEIIITGHAhRCCCGExBwKUAghhBAScyhAIYQQQkjMoQCFEEIIITGHAhRCCCGExJz/D7F+l4Sf970aAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dexcom001_df[(dexcom001_df['Timestamp (YYYY-MM-DDThh:mm:ss)'] >= '2020-02-14 00:00:00') & (dexcom001_df['Timestamp (YYYY-MM-DDThh:mm:ss)'] <= '2020-02-14 23:59:59')].plot(y='Glucose Value (mg/dL)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc001_df = pd.read_csv('./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/001/ACC_001.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "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>datetime</th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-13 15:28:50.000000</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-13 15:28:50.031250</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-13 15:28:50.062500</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-13 15:28:50.093750</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-13 15:28:50.125000</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     datetime   acc_x   acc_y   acc_z\n",
       "0  2020-02-13 15:28:50.000000   -34.0    17.0    55.0\n",
       "1  2020-02-13 15:28:50.031250   -34.0    17.0    52.0\n",
       "2  2020-02-13 15:28:50.062500   -34.0    19.0    51.0\n",
       "3  2020-02-13 15:28:50.093750   -36.0    18.0    51.0\n",
       "4  2020-02-13 15:28:50.125000   -35.0    17.0    52.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc001_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc001_df['datetime']=pd.to_datetime(acc001_df['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc001_df['datetime'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc001_df[(acc001_df['datetime'] >= '2020-02-13 18:00:00') & (acc001_df['datetime'] <= '2020-02-13 19:00:00')].plot(y=[' acc_x', ' acc_y', ' acc_z'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "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>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:00:00</th>\n",
       "      <td>62.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:05:00</th>\n",
       "      <td>-4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:10:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:15:00</th>\n",
       "      <td>9.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:20:00</th>\n",
       "      <td>45.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:25:00</th>\n",
       "      <td>-16.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:30:00</th>\n",
       "      <td>-23.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:35:00</th>\n",
       "      <td>2.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:40:00</th>\n",
       "      <td>2.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:45:00</th>\n",
       "      <td>18.0</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:50:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>-59.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 18:55:00</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-62.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-13 19:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      acc_x   acc_y   acc_z\n",
       "datetime                                   \n",
       "2020-02-13 18:00:00    62.0    10.0     1.0\n",
       "2020-02-13 18:05:00    -4.0    21.0    71.0\n",
       "2020-02-13 18:10:00     7.0    -3.0    68.0\n",
       "2020-02-13 18:15:00     9.0    47.0    41.0\n",
       "2020-02-13 18:20:00    45.0    -3.0    45.0\n",
       "2020-02-13 18:25:00   -16.0    56.0    27.0\n",
       "2020-02-13 18:30:00   -23.0    31.0    36.0\n",
       "2020-02-13 18:35:00     2.0    58.0    26.0\n",
       "2020-02-13 18:40:00     2.0    62.0    13.0\n",
       "2020-02-13 18:45:00    18.0   -34.0    38.0\n",
       "2020-02-13 18:50:00    13.0   -59.0     8.0\n",
       "2020-02-13 18:55:00     5.0   -62.0    16.0\n",
       "2020-02-13 19:00:00    10.0    54.0    33.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc001_df[(acc001_df['datetime'] >= '2020-02-13 18:00:00') & (acc001_df['datetime'] <= '2020-02-13 19:00:00')].set_index('datetime').resample('5Min').first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "eda001_df = pd.read_csv('./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/001/EDA_001.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "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>datetime</th>\n",
       "      <th>eda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-13 15:28:50.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-13 15:28:50.250</td>\n",
       "      <td>0.001281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-13 15:28:50.500</td>\n",
       "      <td>0.026905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-13 15:28:50.750</td>\n",
       "      <td>0.028186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-13 15:28:51.000</td>\n",
       "      <td>0.029467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime       eda\n",
       "0  2020-02-13 15:28:50.000  0.000000\n",
       "1  2020-02-13 15:28:50.250  0.001281\n",
       "2  2020-02-13 15:28:50.500  0.026905\n",
       "3  2020-02-13 15:28:50.750  0.028186\n",
       "4  2020-02-13 15:28:51.000  0.029467"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eda001_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "eda001_df['datetime']=pd.to_datetime(eda001_df['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda001_df[(eda001_df['datetime'] >= '2020-02-13 18:00:00') & (eda001_df['datetime'] <= '2020-02-13 19:00:00')].plot(y=' eda')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BVP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "bvp001_df = pd.read_csv('./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/001/BVP_001.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "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>datetime</th>\n",
       "      <th>bvp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-13 15:28:50.000000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-13 15:28:50.015625</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-13 15:28:50.031250</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-13 15:28:50.046875</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-13 15:28:50.062500</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     datetime   bvp\n",
       "0  2020-02-13 15:28:50.000000  -0.0\n",
       "1  2020-02-13 15:28:50.015625  -0.0\n",
       "2  2020-02-13 15:28:50.031250  -0.0\n",
       "3  2020-02-13 15:28:50.046875  -0.0\n",
       "4  2020-02-13 15:28:50.062500  -0.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bvp001_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "bvp001_df['datetime']=pd.to_datetime(bvp001_df['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yuEaPkjFQJFcwmFEAW+NNqhMj+VjlXb/cGWcLr5XiihvdBqQeWmtp0trrUQPOiiRbJA1mNmzYgFtvvRVxcXHQ6XRYunSp2fOCIGDatGmIjY1FUFAQUlNTcfCg+Wyc8+fPY9iwYQgNDUV4eDhGjhyJy5cvS1lsjzt3xfGgwnMDgMW7PbK2qwHv/onW01fxIqVRPK3qxvNHaiJpMHPlyhW0a9cOc+fOtfj87Nmz8f7772PevHnYsmULatWqhbS0NFy7ds24zbBhw7B3716sXr0ay5Ytw4YNGzB69Ggpiy0ZMeIDJV1fVu7JxZYj567/5vqL40WT3CHH54ddIdLh9YBcIekA4IEDB2LgwIEWnxMEAe+++y5eeOEF3H777QCAL7/8EtHR0Vi6dCmGDBmC/fv3Y+XKlfj777/RuXNnAMAHH3yA//znP5gzZw7i4uKkLL7X+XTDEYuPl5YbsPnIOXRMqINa+oqPzPHzRRizMBMAcPT1QR4rIxGRKxh/aptsY2ZycnKQm5uL1NRU42NhYWHo1q0bMjIyAAAZGRkIDw83BjIAkJqaCh8fH2zZssXqvouLi1FYWGj2oxVSdsnMXLHf+H/TO8/30w9i+P+2GnN9AEBu4TW4wtIFhXe52sQbbHIEl7ogMcgWzOTmVmTzjI6ONns8Ojra+Fxubi6ioqLMnvfz80NERIRxG0tmzZqFsLAw4098fLzIpfcMS4GLpyoI0wDjm7+PAwC25pz30NFJa8T63DoSy79mEpSTepWVG+xvRHSdJmczTZ06FQUFBcaf48ePy10k0YjZMOPOvpz52+Pni5B/yXpLDpNhaZNYn1Vn79znW+kurZR12v2p2fzISm9J5gm5i0AqIlswExMTAwDIyzNftycvL8/4XExMDPLz882eLysrw/nz543bWKLX6xEaGmr2Q865fK1qyrS7ldKiLcfQdWa6myXSPgZ1nlFmYAeYGhw9x2VQyHGyBTOJiYmIiYlBenpVJVdYWIgtW7YgJSUFAJCSkoKLFy8iMzPTuM2aNWtgMBjQrVs3j5fZFf/bmCN3EWpwJFfNBSdTvrMepur4mRDf5iPnsDY73/6GItDa+WMIq22Szma6fPkyDh06ZPw9JycHO3bsQEREBBISEjBx4kS8+uqraNasGRITE/Hiiy8iLi4OgwcPBgC0bNkSAwYMwKhRozBv3jyUlpZi/PjxGDJkiGpmMr2ybJ/x/85eGyzdqYuRNM8d10rLoffzYW4Yssv0I6IDcPTsFcSFByHAz/I9lMEgwMdHYzWoyIbM3wwA2PZCKiJr62UujTisXtN4iSEnSNoys23bNnTo0AEdOnQAAEyaNAkdOnTAtGnTAABPP/00Hn/8cYwePRpdunTB5cuXsXLlSgQGBhr3sWjRIiQlJaFfv374z3/+g549e2L+/PlSFlvZZPmCVx006cWVmLxkpxyFMPog/SCGzt+M4rJyWcshJa3dFa/LPoPec9bh3vkZFp//8+AZtJvxO5bvOu3hkrlG7hk4//x7Qdbjy6GkjAOCyTpJW2Z69+5t8w5ep9NhxowZmDFjhtVtIiIisHjxYimKJ7tyg4ByJ/vvZ6/Klqg0jvvxn5O4t7N8M8TeWn0AALB0+0nc2yVBtnKQbaZ33N/8fQwAsP3YRYvbDv/fVgDAuMX/YFBb87xFcrdGKoXptXT2qmyktozWREuWo4HhFxlH0b+19bGS9o9DWqbJ2Uxq0f+d9ej06mqn/uZ7GUb4W4pHlVC9FPNOzet4qsXq+PkiDHh3A5ZsU85MSNPv4aH8y/htj/X0FFpQ/Rpz8sJVWcpB6sBgRkaHz1zBpWueX2jR2t0xycu0ntb6kKRSF3OIOPu+5BZcw87jF50+zsu/7kVW7iVM+X6XxeevlMi/QOqpi6zciSoxmFEwqQbZHj9fVLF/N9pXqhfN0RtmjdfRXiWv8BoeW5SJzcb1ucyZfkZOF1TlGZq1Yj+aPf8b9p92LDO3O+NTus9Kx+1zNyEr17ks4EUltsdjiZGrxln87ohLEARcKZY/KCVxMJjxQpYuiku3n3Rq+4P5nr+Yk7I89+NurNida5xh46hPrie1e+v3irFP7gbtjgTlWmiNlGsGoSAIOHr2isePL/Xxnv1hN1pPX4Xtx7xvMLUWMZjxICUnRZv47Q6ntp/2816XjqPcd8B9x84Voc+cdVi05V+Htl9/4Ax+3mE9iFS6EyKNYdh9skCU/Whd9ardU5eT99MPofecdXh9ZZZnDmiF2NfPb6+Ph5q79pDZ44XXStF3zjq8IfPrJecwmPGgyzKMj1EjteawefnXvcg5ewXP/7THoe0f/GwrJnyzQ9pCSchei8gXfx11aD/OTrktuFqKg3ne1zJokOl78c4fFS1on6y3vUyEVizc/C+OnL2Cj9cdlrso5AQGMx50//+sr/StZJ4MLr7bdhwdX1mNHQ4M2lRazOPu7CoFN9y5ZJsbuVA2HzlndUxNu5d/x83vbMDeU9ZbdJ74ejum/Ww9qCxycwCvHOdKaZ93qcn1eg1c7kKVGMx4IwV/V5/+fhcuFJVi3KJ/5C6Kx3lbZWXNiQtFGDJ/Mwa+92eN50zHam08eNbqPn7ZeQpfZljv7pu90na+JnvngudKPJ4KDK13U2nsLsJLMZjxQs5ehx3ZXo7xQEpryVBaeaQmVYV+7PpsO0uWuZghuPqp+euw9UCIlMnLvl7kJAYzWmfhCsCLAimBWMGfHEsLsJtJO7ztJkSrGMzIZOUe+degcfTiaO+7vmBTjtPLMtij1kHA3sTdM+TIKb5aUo5y0w1F+li4GwDJUf9VH3AtdWuoXF/Bpddn+FU//JGzV0S/zgC8udMKBjMyGbPQ/pgQJVTnjgQVL/26T/S076dMkqx5owtFJXIXQRFaTluJAe9ukLsYiqDV+L7667I1G/DLjKOiH58tM9rAYEbB5F4ldsm24+gyMx0XikrtbrvPwWyuWubunbLpn6/am4eMw5Yz63obe/lsHEnrr4U4QK6p2XK4VlqOTYdqjmtavS9PhtKQGjCYUbC3r68OLbrrlaa9unfK97tw9nKxNGUgu+Zv8M48F/tOORcYn7vifCuWvc8+V+r2nOKymktHJL24Elm5lnMJGQyCqN3Qcoy5Upv8wmu45YM/8fXWY3IXxSoGMwqWc/aKJPu9dK0MuSJ34/jI0FbrRTeqqpG+Pw/P/7TbYgXlqFeX73doOwEypvhXwDF1AFbtzcUDn21F/iV1dstm5Rbi5ncc70YsKTOgz1vr8PCCv0UrA7uZ7HtjZTb2nCzE1B93y10Uq/zkLgBJzMpV9/Xf9qNRZC3PlkXjvOGaeOJCEWrr/RAeHGD2uCAI+G1PLh67nh8o0YHPlhiVyGMO5iO66EBXqdJZitse/SoTAPDqsv14f2gHD5fIfR+kH7K/kYnKRIz/nrM+fd8aq1lmvOGL66arpcrPXs9gxktdK5VnPI6YFw5ehDwr/9I19HxjLQDg6OuDzJ77dddpPPH1duPvYrf8WfPbnlyHtvtgzUFRj6u0j55aB4yzO08dVux27HsmJwYzXupycRm7aUQmdnAl98KkgiCYlWGvjbEsf+ecd2H/LhXLJUUlrnd7WSLLuan2frlShOrn1JtY+7hVjpkRBAGvrdiPDQeYUFGNOGbGS220MFPAExhAWaekKibn7BV0mZluPghZQ+fO3Qrd2lid+RsO46klOyUZy2NrNpMjr2fV3lx0fvUPi7OE5KKk68GarHx8+mcOsr1wEVMtYDBDqlFaLn3XmDuVkK3qpKTMgIN5lzwyYLW4rNztxfJeWbYPZy8X47UVWQ7lEKpel0p18++pus/V0/Taiix8n3lCkmn11YvkbBkf/SoT566UYNj/KWfBW0UEM9c/q+cuq7OrztMO5Ssz2GMwIwOPrspqo1L555jrqxrLoecbayQPBtL357v8t7ZKNuLzrbj5nQ34ecepim0leh2F10rRetoq3PNJhlv7Mc20OuX7Xfh4nfjTxOXs7bB06F0nLmLw3E3Y6kKXWXWV3VqHz1zG3LWHcKXY/QGUtj4zSmrVc4YSxswclWjWqFZVXsOUhsGMDH7fp4zBVI4kG3OUJyqmvMJijPpym/F3KeIBqZJy/XX9Tn3h5oqVnKUKJNdnn0GZQTDO+nBV9bf2jZVZNiseWytUi8m0Qhf7/A+dvxk7jl90KBA07dYpvGZ9plS/t9bjzVXZeP23LLfLV/3lHr9QNaNHiu+ftwytsTUWjNSDwYwMXEnypXSOXvjc7Y/+w42WE6k5c+2/XCzugFSlUUT3gQ2WPq9XXBwk/O5q+zOlMt0MLi35fNNR4//diTtW7D6NUV9uQ8FV86Ds0jXpp+Mq/XNC6sFghjwqT+FrLknd7F1ZiVrqMqg+iNPdG+Oftp+w+fzpgqtWuy4slk+1nRk1idnqkFfomc+0rYrfnQHNjy36B6v35eHdP8wzjl8WoWvMHk/GMtr59JIlDGZkoKVKwVliXrykaAZ3507RmQrFE/3OT3670+pzP20/gZRZa/D097skObbSuyj2nHSva6FuraqkgZINdhaEat1q0o6ZOS9Di7FSWmbUNn5QCoIgSLIquacwmJGBEga9AcDhM8oY+Cb3gpqmpP4yVwayUk2PdbRifed618iSTNutN6aqf27FGsTsTDDgqZQC9l5ZUmyo8f9S5G0pNwgY9P5GjPzCZIyYje2rF2H2yiz0nbOuRteR0pQZlPHdv2dehtc33QyZvxk3vbkWJWUGFBSVYu+pAovbKfVtYjAjA1tL3KvV8fOODSa29EVQSnAHAFuPuj6TxZkvuSOvOD0rH0u3n3S1OC6Zu/YQ3v4926N3zM4c66wCp89KcXHfd6oQ+04XYk1W1RgxZ96nj9YdxpGzV4wDzqtTSkvEuuwzku7f0ZmjZQYBJ847v0SClmzJOY8TF65i98mLuOGNNRj0/kZsc+N66GkMZsijvsg4KncRbJK8mdU4ZsaxzSd+u8Oh7SpbSS640VVwrbQcb67KxvtrDql24UJPKSqpGk/iqS41W0G/tdYha5/n0SazAiuVlhuwLtv2APu3fs+2+bzSbD9+0eFt31/j3DpRWlY5Xmqtnc+DkjCYkdABZpKsYfORmpG+VsYQyTVOZObyfej6WjrOXi7Giz/vdehvLJXVNMOskrr+lGb+hsOY8M0O4+9K+PQ6W4ay60GO6Z33it25GPG57dWoP1BZhV/mgUSbpAxcm0lCV0VeD4bMSdEVInX3iq1Kx9Vg6NM/cwAA/3f9XzFYeht2Hrfch26JJ9b/eWOl+7lbXPHaCvPj+oj4WlfuyUWAnw71agfWeE6Kz+a+0+7nWMkvvIZ6IXpFrvmknA5s9TiQd1nuIriELTMScuWLVFzmfQGQAq+Bslib5V6TrjPvo8WxS3Y+sO+lV+VTeWxRps0B5J5YtkExRPr8XiwqwZiFmXh4wTaUlNe8DjgzANj4NxKfhl92nkLX19Lx3E+7pT2QCGytbUVVVu1VRlJXZzGYkZArF/RTF8Udq6CGOCFXQbln3Lvg2X+3qyqdmsdZK/FgSGfYextW7FbnBc9hTnwMxKojTZPUlZXX3KmtwayXi8skmVptr5umcgzN11vtr99Vnae7Mn/dedrpv1HzVOXqTMd5OUpNQwAYzKjcF38dtfqcIChpnpB1WbmujS2SokUn/1Kx6Ps8lF/VbFtZ8Ulxk2jp7cj81/HZCKbvpyc/OWpvmftJpBln7nwmNh06h46vrHZ6DSh7b729utydMn/65xHX/9hBpuXLc2FQ+/eZzgdpSrR0+0m0mrYKn20UrytaaRjMKIyzrTnTf7E+4PPwGXX0fWqlS8JapZz69nrj/7dcX8TQU6/4ro8dW3By1d5ctxendJUiT7/MAZbp2lqVrQOOvE8nLpinSLD2mSy/3vJjb5fHJJyuLMXK4mLbfdLxcWJKVjkrcsayfRafN221UeT30QEMZjRMLS2kKimm4jnSwnEg7xIe+Gwrjp4zr6Qe/SrTLCuuWi9oUpi1Yj9W7Haui8KV1ibT1rA3V1VNgb7r47+c2o9p14i183jJwRac9P3SLLwKeKb1z91jqKmbxVXLd51Gq2mrLD5XLgj4IL3a2mMKbUrlbCYJWfoaaaUVQkze+JY48zkQBAHPL92DuLBAjO/bzK3jPvC/rcittpbQtVLxB50rcWaLqz7ZUNEdcvT1QQ7/jZif6R3Xc6U4WjHb6np21iwLq33nnL2CxMhaTpUJAE5dvIqNB8/i9g5x0Pv5wtPJfwtdyIasoY+xVU8tMV/2xPQ1//jPCeQVit/1LgW2zGjYtqMXkKOQJQuk4C1B0N5ThVi85Rjm/H7A/sZ2VA9kAKDzq3/UeMzd91aKVaK1ztZ7bjAIDp0TnQ5Y7kQrkit19Q9OLIFhKu3dDXj6h12Yez1XzcmLjmUNt8Ze6ouCq6WYZpJ36c+Dzi+FIea0e084evaK3YHgztxIWQpkXAkKPYEtMxLyVGVbXFYOvZ9vjcdnr8rCxSJlfvDMyROV/HPsAlbukWdWzgUnzosULSemLK2O7G4LoiPBjKKrCSdf/oOfbUWjusEuHeq7bcex52QBRvRoZHWbxs+tQFxYzdwzcvhw7SHkFV7Dm3e3c+rvKmdrrT94FpEherfH4xw5exmt48KsPj9rxX6zwfeusBTLHDtXhAZ1guDjo6xP8OmCq+g9Zx0A262IXWam45PhHdGpYYTF5/eesp17qLjMgKsl5fj8rxzc3DIazaJDXC6zmNgy42H26ogTF646nbWyxQsrLU5vVkvLhVxje+786C/M3yDejApPXdq+/fsYVu+rGMtwKL9qJpg39O8r1foDZ/BFhuV1kOx5+vtd+DLjX7N1mCw55UAKAx0805VduUCpI2uyXbpWipdMJiroALPfXTXo/Y2Ys8r68gpiTICo/p36Zusx9HpzbY2umfUHzuDEBXnXdtp9wrHBymcvF+PhBaYLmJp/Xs7YmdG5bOcpvLEyC7NXZuPmdzY4X1CJMJhRmAc+24qHFthOKW7Jd9tqTiFUy/icxxb9I3cRVCPn7BU888NujLq+ts62o+rvzlFZS75kxGhFtfReFpeVY5KFNb6kSPW/YFMOXv51r9m1Z/bKbCwwGcej04nXFvvhWmmXV6j+flYmjvzRZDr+xoNn8eBnW9HzjbWSlsWeL50Ipt3Jn3OpuMzsfCoFgxkFcqVv1xJ1hDKuU1oleKFImhWdTc/juctVd02F10rNnhPz/fBEa5mSP597T6lzSu74xdvxz7GLxt//PX8FX2X8a1b5Vvp9n/gzlV76dR8+33TUbIHHg/nmeaQqWo9EP3QNYrRUfvu3+U2ipXJvVcjK0hsPiVNvqBWDGUnV/OR79AKu5NqiGrW0Itnyt4OtJDudWMm3OtN36a1V2arpSrRFia/higzrqokxVbl6Asof/zmJV5fvt7it2GvHZeVWjbXYfuwiDAYBu08U1Fhc1jTTsac5m1Sw+ngydaQhtc/0equV7mkGMyK7XFxm/KC4lR1ThLEcxSpaMXaXg/29popUupDn7XM3ibKfnHNFmrm4qoGt5QTUSOy1iga8+6fx/68s24e7P8nArR9urLHdQTcH5brj/fSDKLxW6lSCyMvFZci2laVcidF4NbZabbVyDWEwI6J9pwrRZvoqY7ZFSxxtgZi5wvLdlDM8vfaJO26fuwm9ZjvX5/zhGmn7y8UgRouTrfsmqa6jnrjAqe1+sMjJWWXf/a3sVPhS18FKnJ6fc/YKZq3IwtYcx7uG+sxZh7R3N2DLEeVnLLam+rkWbDynVgxmRFS51sjPO07JXBJ1cnaqpqUpxUpSbhBwywc170ydZe3Ck3W60HzMjNtHqqKWRFme5Oz7+/QPuyQph1jcbZnJt5CzSFGsnDBnZzlVzu5Zvvu05e+F0gbvOcC0VbtYRTe9tjCYcdParHwcUckaSHKT8+JXVm5Aq2krMfGb7SgtN1ic/SW2PScL7OZscEf+pWIUm7QWWEqIpwZqqQs0cgNr5G6v2W0fitNd6q6SMgMe/GwrPl532KHtbXYZ2WB1tpCDQaHBIKC4zPNd4/amWmsFk+a5YdvR88Zp1EdfH1TjRsDicgYSlmf5LueXuPekrq+lS34Mg0GwmMxqzMJMFJWUY+mOU/jr8DlJVseWw/7TVRfm77a5lpmVlMHTzf0GQXArklRK8PzLzlNYf+AM1h84g7G9mxgft/TKSsoNKJAwg+2la6UICfS3+Fzj51YAABaP6oYeTSIlK0N1s0QYsqAGbJlxw87qg1ZNvj3p+/NEny1gz7jF3p2vZc/JAnR4ZbXFtWn+2F+VkMwTgYzpFGoxVR+DU+7pBW5EpLWWDlv2nS7ELzuV1f1sEARNDJi46sRYpssSz6Q6etZ+V/l9n24x/r/gaimW7TolaZbvc3aWN9AKBjMiMp3iNvKLbTWyRErp/eorm3qhKd/vQsHVUkyvll3U2VkoR8+6v57VlO93iTZryZYjIpRVLpX1qFrqU3cHcz/x9Xbb+3dr786b9vNe5DhQ+SqdM21L2yQelOxsQ9eNb6zB+MXb8ehXmWaPv7kqC2MXZko6g04QBElbqTyNwYyELLUASHXhLtPYtFFHrcvOx+p9eRAEATlnLY9d2u5kXpevNruWlt6UvdT0zjDNSzPk081mzzk6pX3Bphw0ena5aGUSwx/7xU/aJqV56x0bk6Emn23KkbsIkvHEWKyColJccmEiwjdbj6G03IDCyvWqDpwxe37u2sP4bU+upAn5er6xFu1e/l2y/Xsax8y4ofqdmloGMmrJiM8rxiyNuakJrpVa7nJxNnV39YCzrNwAP1/54n7TpGeuBMMZh8/hpV/3iVgi7zR37WFMSUtyaFtX7njlWvRUS7YdPY/OjSwvoCi2a6XlaDfDPBj44q+jDi3A+eyPux1abFaM7idr9ZK7q5YrDVtmRMRYRj6W7poLikqx5cg5i2NobPnrcFVa8P2nC9H8hd/w1u9VC9pN/XEXBr73J4rLynHpWin+OnzWrbVOpDa0WmsOVZjrwro+pxysACY72MVsmgsqR8VdhnIyrazXZufj33NXcO8nGTUyD4vNUjBQuQAnALszl95clWX3GGrpglUCtsyIyJGWGa1kW1S67NxLGDI/w6G7n+pMU8K/tmI/DALwwZpDmNy/BQDg660V07rX7M/H+2sOYf/pQiTFhIhTcC+zck8uxizMtL+hBN5clY0xNzXBucvF8LUwA86SwW6Og7pwpQR1agXgWmk5Plp3mGPdRPD6iqqgYO7aw5i71jPdgbY+MZ9vysHLv+5DQkQwNjzdx+I2lu5/qs/GzL90DUfOXEbft9ZjSloLjOvT1PK+rNxM7T1VgHXZZyw+pzUMZtxQvSlZzjVHyFzau+IsTV9Wbj34LBcE7D9dkUem+po45Bi5AplKTa5Pl3WUuzPhOryy2q2/p5pcGbMiBp2Vu1dBEPDy9W7dykSgK3bbT5vx0i978fOOk1j1ZC/jY8/8sNv4/8rg2yAIOHLmChIjayHAzwfbj12o0TJ9rbQc2bmXcLcTyzaoHYMZN3xgkk7/jo82YbvJarWkDabdR+UGwWzKtYJ7lohIYh+vs9xN+coy87wuD32+FWsdaB1ZcL07/PNNR61uYxp892waiYWPdMMdH/1VY7t7Pslwab07V8xZlY2n0lp45Fi2cMyMSBwNZNgHqg7nLhejtNyAUpM8Lrd9uNFsyYUvnRyLQ0TaYS1JZfUZYo4EMqYcHXu58dBZq895KpABgA/XHhJlDTp3sWXGw5Q8UJSqdHr1jxqP7T1VaNb/LHXOCiLyPs7UEJ7MZWZL4tQVSIoJwcqJvexvLBHVtMzMnTsXjRo1QmBgILp164atW7fKXSSXtJ6+Su4ikBs+dGEGDBGRoxxdYwoAvs9UzhImWbmXJM1kbI8qgplvv/0WkyZNwvTp0/HPP/+gXbt2SEtLQ36+eInJiIiIyHWr9sqXK0kVwczbb7+NUaNG4aGHHkKrVq0wb948BAcH47PPPpO7aERERARIntvHFsUHMyUlJcjMzERqaqrxMR8fH6SmpiIjw/K0s+LiYhQWFpr9EBERkXRkTJSu/GDm7NmzKC8vR3R0tNnj0dHRyM213KQ1a9YshIWFGX/i4+M9UVQiIiKv5SPjmj6KD2ZcMXXqVBQUFBh/jh8/LneRiIiINE3OJX0UPzU7MjISvr6+yMszX2E3Ly8PMTExFv9Gr9dDr9d7onhEREQEICw4QLZjK75lJiAgAJ06dUJ6errxMYPBgPT0dKSkpMhYMiIiIqpUJ9hftmMrPpgBgEmTJuHTTz/FF198gf3792Ps2LG4cuUKHnroIbmL5rQlYxiAqdmt7eLkLgIRaVhqyyi5i+CyG5vVk+3Yqghm7r33XsyZMwfTpk1D+/btsWPHDqxcubLGoGA1qFeb3V9qkBQTgilpLRAWVHWn8Z/kGLx1dzsZS0VEahMd6tw1v1PDCIe3Pfr6IGeLI4n64UF45952aBpVW7YyqCKYAYDx48fj33//RXFxMbZs2YJu3brJXSSX+PrIOUSKHBEVosfKib0wrk9T/Cc51vj4R8M6wc/K+WtQJ8hTxSMiBVtVLaX/5qn9kBhZq8Z2jevVfAywHfxsfKYPWsWGmj1WP1zea89bd7fDpmf74o4ODWQth2qCGSVyJTCRceYaOWjMTU2M/6++gJq187fxmb5SFomIrLilbaz9jTyoXoh5MKLT6fDto91rbJdcP8zi3zesaznIAYAGdYLRslowI7c6teQbJ2OKwYwbZg5uY/Z7r+b2+wv9fPiWq0n1hUF1JtHM1IFJiA0LxGcjOnu6WER0XfPoENmOXVtfc0KwwcEVpC39LWB+A/XjYz2QEBFs/rwDS1G2jqsIeJpF1ZZ8nJ8CFswGwGDGLdUjcEfUrS3f1DUtuq1dHPR+NT/GqS1dH09VZjAY/1/ZzRQXFlhju86NIpAxtR/6Jqlv7BaRViTFyBfMzH+gE2bc3trssbq1al7jdSYZWF4Y1BKdG9bBpJubGx8z7aY2bf2tV1uPianNAAB3dXS8G+d/D3bB432b4suRXfHk9b+XikEhwYzi88xojb+c+Z416P2hHQAAY77KxEqTRc5m3tEGf+zPs/ZnNvmatJ71blEPv47viUaRwTW2c/QOjMjU7e3j0KhuLbyXflDuomjCza2qbibeubcdnvx2p0eOm9Y6Gt0T6+JQ/mXjY//3QGez1ttKkbUDMKB1DHx9dHjkxsZ45MbGZs/rdMCg5FjkFV5D67iq7qcyg4A7OzZAt8Z1ERtacUOlq5aazlLXd0xYICb3bwEAyDl7xeXX6Ag5g0lTDGZIEzo1rGMWzNSy0oTriKFdq5a/0Ol0SG5guW+7ehcUOadBnSB0TKiDX3aekq0Me15OgyAICA7wQ5PnVnjkmO8N6QBBENAuPgybj5zH/A1HPHJcrTINHjx5f/HJ8M41jmnt8DqdDvOGd6rxeGxYIE4XXEO/pGi8dFtFC09ZeVXLcPn1VmLTQb6T+jfH+gNnMLx7Q4fKaW1op6+PTpRrWHxEzRs9ObCZQCSLR3WrMVjU00b0aCTr8eWU1roqG/QPY1PM+qOdfV+CAxwLhAweCmZ+GX+DR47jaTodMO3WVrIdPyTQD7X1fggJ9Hd4MP/el9PcOubvT1bMdNHpdOibFI3n/tPSrf2RuYZ1a+G3CTfiq5Fd5S6KQ34edwPeuCsZzw5MMj5m+lkss3CNqR8ehL+f74cJ17uP3rq7nc3Pb/WWnErWZmaqFYMZN5jGLj2aRMpXkOsiLPTVKsnScdJVygl1g5H5QioOzhxozNPwWO8mSIyshSdN+qYrffqA+4N2gwJ83d6HI0ICrc8WmHaLfMGAGCJr6zG0a4Isx979kvXAJNC/5qUx0N/H4Ra/Lo3qWHxcy1P4fxjbQ7Zj/zL+Brw3pD06NayDlrGhsiVvczY8iAoNxL1dEhDoX3UtMW1psjZI2HSbbo3rIvuVAdbLpK2YxSoGMxqi9M+stXwID9+QiIZ13W+qrFtbbzYm6ekBSVj7VG+zxHeVTPvZnTXj9tYY2TMR7ePDXd6HM2y1+Kn55qryjrGxhRwcSuRMK0qDOspoepdSaKB5RWt6p98iOgSbnvVcuoK2DcJxe/v6HjuePbEWJgw4Y+59HTHzjjYOf478TK57PZrUNXvOWjATJ3N+GrExmNGA8X2aonFkLTyQ0kjuorjk/u4JmHVHstN/52oCwj+f7uPS31V6IKURXryllcWBflJwtzNr90v9RSlH98aOZyZ1hlruHO9zogWpemIzLXqin/ksGR+TEzl3WAfUDw/C8id6erpYRtaS0onN0s3GH5NuwiuD22DndNe+e4PaxmJYN8fGxFRXfZKJtevUpw90xo3N5O9REAuDGTcoZfjnU2ktsOap3giTcZEvR9iqtFx5L5s5kTrb37fq4EoZsCYGR943W91UzmhcT9xU5WoJYir5OTET0VI31Y3NIh0ej6UGDeoEIcSkdcY0hVbo9c9c67gwNBKh1VUs090co2Ut0V11tfR+GN69ocVWYU+z9jVrGlUbX41UZyZ9SxjMKJBYd9JUpUujilaFSI2tjeVoPLD7pf7o2kialhU1cHb6qBRZVu1VHF0T1XN+Gterhf6tYsy6jkNNguao0KpuFrlu+ix9Nx66IdHl/VlakgAwX0tJiQG61gb6WsNgRmEyX0gV7U6aqrw3pAPG9WmCH+0MUlTixchVXRtF4H8PVgx0Dgn0x3durtgu1WQ9T3XXOaOhDK13cnRNuTpTbs3k3vCpVknGRwRjQr9meGGQsmdobXsh1eFtk2JC8NmIzmgdF4qP7+9oMfuutdQNSmEaWFqilRs8BjNukGIqtjv5UaqbbGEWj7eqF6LHlLQkJCigydtTF4/vxqSgnxuZkD0lQiFru8jN0zHdpJubo1mUewnPpt9akRvl8b5NAQBP3ty8RkI4uVjrTo6srcf2F292aB9prWPQNykay5+4EUkx2hwH1aeFPDO/xMZgRmEqL2gdE8Ld3lcjhc0SUd79d02eSBXkbKVlWibTRTDVrvJt4Hpl8ig3CG4HUClN6iLrlQHGbLNK8sZdbRHkbzl9Qh0H01joq4190mLSbzlzPYmJVxE3SPm5VkLeGk8RoI5Axxnijrmo+qRVTwDoqVw3UlBi95JoVPDaxMpgHWglYJBbdGggPryvg1v7qD5DVA3BjLMfvVoaGZTOYEahxvVp6tB2T6Za70pSwfVUs74a2dWtXDamTC+g1c9pRK2qLiuxjmejJGa/hQRq4yJY6ZvR3VEn2B8f3tfBpRuVANPZTiqo9coFwaViPj1Aea0wUqmetK6JnRmUSrjm2jqnIYF+ePSmxm4HeWN7K6+FmMGMQjl6xz3Bxoqo1tJYK406SumcyNp6p/KS2GJ6bTJ9r2JCA81+DxFxvJUj3G19qiy7JyoARyrt7o3r4p8Xb8YtbeOc3v/3Y1IsDg71NJ2uYnxYpQOvDrS6baCfL/x8nXvzByXHYqyTXZ0qiOsc9vJtrTG0azx+fEy+bMfuCA30x9SBLc0+47ZOT8O6wfhPcozZYxG1AvB436ZIbRmFN//bVqKSOo/BjBuk+JKaBiBNPJT0yVNsdSto6Hpn15T+LdDbiUF34ab5g0zewkB/H6vvW/ULkDh0Nn5znScquw4OjkFzpeurXXw4OtuZ9u5sK9a8+zvh1nbmQZUjn5nPR3RBW5NcKAF+1i/xQ7rG10iwZs9b97TTdvegHRG1AjDrzrbomGB5uQolEPP0/HuuCB8NM18gs3l0bQQH+OH/HuyCuzvHW/lLz2Mwo2ChIiRc6pcUJUJJSEz3dIk3y5ZqT1RIRd//5yO6oK5Jt9L86utLSVzHPNjDPCOp2xdND9aJYq5bVn1ZDotpPHQ6LHqkKq/MeAe7jSt1SAjHB0M7YO59HY2PveZClmxbou1M2bVEaeNj3O0u8TZ1LMwc1EoaGgYzGqbTiRMQadXrdyajU0MJ77BEuEjc2bFivZlb2sahT1IUfH10OPDqQBycORDNo6tNq5W4hSMpJhSpLauCY2cCMi1pWy2vSKBfRQVfvYXphqZVg/jbNgi3u1/TVtnK/yXFVp1jR06vGlpNImoFYPZ/24qSJNCVLkFvNiUtqcZjOp0Ou5xI1Kq0gLYSgxm3WL+8dE2MwPtD3btrEKP5XfmXtoovkxzlHNI1QdaVfh3x9j3tazwW4Odjt3vAnZwYi0d1s9o98fSAqouhCupNl9n67lV/zuL7cH2jZY/3xLv3tkdKtcX/LHF2/Iqj5B7XUH3AsI8OuKdzPBLrKqcbPVWkfExKHac48442GNkzEb2srMUUaiVR623tagaLL96izKncDGYk8t2jKejmwp2HmBWEoLA5zwoqikdUnwkBVE2tliLhIlCR0XVCv2YY3cvxxGWPVZuZ0KNJJO7q2MDituFs6avB1ne2Tf0wDO5gezXnCf2aISkmBMO7215Y0JHvT3RozYSM7oxriKztfvfcLW3j8PfzNbPuKmHAdKX3h7aXuwiSGtatoUuL41paY6x6N6tSMJhxQ/X6yN7vTu/fvT/XPKXPkujcsA7u724+o8lexeautg3C8eTNzZ1qCjZtbanU38o076jQQLw/tAP+74HObt+FqjW4rT6Y2OL74ESl8eTNzbFyYi/z4NfCn9v7uM++q63VFjlX1+fZ+pzjqf9tMZ1hVfniXMk+LFVFqqUFQN39XjmzgK+SMJghkohOp8Org5Mxrk8TNI2qjeVP9ET7+HC5i+WQ3i3qWe2Cu61dHFIlz2mjXA/dkIgEk1T5lXGLu7G1pfgn2CRFg63WvPiIINzTxXoLjKstvtXXXxLTg9USQDrC2WTRpuX3l6gbT2nEvMdTareZJQxm3HBj83qorfdD98YSrXar9KYHkbj6dVHLmI0paUn4Y9JNaB0n/oJ0UnVX6XQ6aQdHQx2DVS0J8PPBAym2u4RcYelUxoYFYUpaC0y/tRV8tTLt5Dpb08atMa1cx/Wxn+/GtEUqPNhyl1lKY/vjmbxB5cD2O610MSsdgxk31Nb74Z8Xb8bXo7pbfN7dPmExso2rJbL2jrCtSvU06WJw5z1cMiYFLWND8b0TK2vLPeYhY2pfyfbtzCurDMrE/KaZV9pN8dANiSLuXRvSWouTS0nUmFoBl1tXi7DokW5YOLKbU+PtlITBjJsC/HyMFzMx0rubfhDdrSyUduOrtPLIKcrCQM3qbmjquTvGLo0i8NuEG+0mfzNVWubm59Otv65otVCCytdh+m64MvjfvTJUvZuViz6qtVKyxfSa6MjNnqcbt/0VsGiqtdXC7QkJ9EfPZpFmLYBqumZrZ9STAky7tRXyCq+51BdsiRhfRDWsn6PTKeKGRnE+rZ4Uzw5Pv4dbj5738BFd5+xXyd57adpFZumCXyMHkAPEqndbxYXiwKsDzbpxtNhj7UgXq+mSG9VP0xP9mmHu2kN4flBLt8vyaK/GyM675NAUfKksHtUNP28/hafSvGftLFPKr+lUJDYsCD8+doPxd3cvIGJExYmRysnlQM5xdoaF1uorH504Xa1SU+KgbmfGo3z3aAru+SRDwtJIw5GPhvksKnOTbm6OJ/o2hZ+TSzpYMvU/7gdE7urRJBI9mljOI+OslMZ1kXHkHIZYWF9OqYExgxkFM10R2RU6qKuZ0JuoZSyTnL4e1R33zt8sdzHskmIFYUvf2zpWBrAC7nVJi5GJV63ECGS0aOEj3XChqASRtd2rgzyJZ1JhTJuvX729jZv7Ulb3DSvwKnIPnhWDuzk/7AXa7lQ00zyYpVRvXM7AvXNq7++VmkbeWWLdYCm1hUALfH10qgpkAAYzipZQ17WBXGrDIMd1rU1WSO4o8VTq6mLCnF+o0JSU5/3hnu7N/lFbPamm75A7QYiu2hQJokoMZohUrH54EOYP74QpaS1wn4X+bbVpaBbAu1dZ2RovYS2dgqOkDh3UE5rIR+yWmaFdXV/2geTHMTMSUsR9AwfNaF7/1jHo39rzx72vawIy/73g8t9L/dG0lWNOilknivi+exGnZ6jZ+bx56yrwzlJqFzlbZjRMEGzf4Q1qG+uxsgCwWhheQypYW9xRqe7sKO06U97WPlHLZG0mLa0VJCbTijQm1L1uzup4HVI3fmMUxpPfpy4N62D5rtMePKK4tDYAUIwVij1JiuUIxNyj2j4fgf6++OmxivWwggK0MdjXEnc/NkvGpOD8lRKXk8ORNjGYkZBU6+Y4SqcD+iZFyVoGtVHq8vZiWf1kL3yfeQKfbDgid1EUTa7vbocE1wZxq6lVwd23tosTWarJe7CbSePiwoPwz4s3Y3yfpja3szVYUutMF6y7vX2cjCWRXrPoEEUk+LKmfh1pgklPxCZqawkic2qaEeZp/VRwU8xgxgtE1ArAGDuJvf58uo/k5RD77lGs/ZlexDx1h8uKz7I3/9sOaa2jHZ5tdFs76YPPurUC0FXlrQFq+7g1rmc5c7k7AQeDFdc9Z7Lkg1LfR3YzScjX1nQKK6SqTGvrbZ9qrSTkcheDDM+pHHNjWnHFhQfhk+EVa1I5MlMq0F/6+7Etz/Vz6Lus06nn83NT83pmv7epH4o9Jws9cmzTa1zruFDsPVXzuMESjBlSU1ec0gSYJLDkbCYvJPZoe+ep6NurkKK68zX1RMWqRa3jwjDv/k5Y9nhPm9tVr4DtEeuS6+fr49BgZ7UEMgDw3pD2Zr8nyDSY1puXUiBx8eorISlme2iRTgeXah6xKg/TOw139vmiB1Poa4Hpt2NAmxi0MclmbMkXD3f1WOWnorgEgPP3AmFB/pKUQyxyBIZKbXEgxzCY0TC9EyvnkvvCgxyfWs041zXVWxAGJlvPlWT6FvP9ts0T4yBuvT6+6TE3FuaUMuBQ6lgQcgzHzGhYcgPbd7qeZu1SoaTmed6deY4YAUZvB7uelPQZUwI5Wo3fuacdnujbFE2janv82ID91isXhjiSgvDWXWHEvMjwu+lZ3RtXdIHEurkAoxZMTG0m6v6sjT9jV656+Pn6oFl0iGzjj+wdl/GuujGY8WJiVQT3d09Ay9hQN8ohSjHE4cYVrW5tPXZO74/1U1yc5q6k98FNUSH2AzpnZvt9PbpiqnZqy2gAQHiwssd8qI6FU3GrB6a9W2Pta8iuIPkptZWTwQy57dXByXjS3TtxjVyjwoL8EeDqWCWFXiTE9urgNoisrcfs/7Z1+G8SIyumb6e1jsZ3j6Zg7eTeEpXuOpWdCylaqOTOYO5pGrkEeS2OmfFinr5YiX3BlWJtlod7Joq+T0vUVk9Mvrm5U9vbOtX3d2+IYd0SXPo86HQ6uzOafptwo9P79SaWFghVWkUuRyClsq8kVcOWGZKdq0HO0K7xIpcEiJY9N5Bn3dvZsfewUaTljKyuknKsS2WXp2nlxIHdVd6+p71T2/v7Ki3UcU2XRq6te0XqwGCGbPpjUi9jE7+kXKhrXMmwTOaUvTozAxAy505Q+h8b0/gB5bVOkXMYzGiYGHe/TaNCHPqSc1YJVSf3Hf0jEnYZdlbZXb5Sx79YK5Ycs5lI3RjMkF1iXVd4KdGel29rDaDmGJkgf198NqKLm3t37xMTGy7NCtwA0LZBOH4Ym4LNU/sZH+vZNBIA0LCu55cGcPe7xYqebDH9eCgzLGYwQwrg6mVUqV8qlym4PrHWpfdgj0bIemUAbmlrPo13UNtYxIZJF0xYU33NISl1ahiBGJOcQu8P7YCn+jfHN6MdW/FbLZS0tIB7q2bbeZ4BnapxNhPZ5ZEmal5HFBmdPXRDI2w/dtGY38WS6iuuvzq4Dfx9fWTJH3R7+6qZOp7uWomoFYDxfcVNFugplk6VJyp3a4dQQq9YjyZ15S4COYHBjIa5eymaktbC6nPdG0dg85Hzbh7BOQG+PigpNxh/V3P8o5abwOm3tnb6b8R6baKmvVdA5UiOk/t07Z8xAIH+7LhQEwYzZNW4Pk0BVORzOXquyOy5nk0jzYIZxwYJu1ee7dNuxuXiMnR7Ld29HRF0uoq738pxHlJw9+46LMgffz+fCr2LlUq9EL17BfAitr6bagm87XHm46jsWX5kiWSh58yZM9GjRw8EBwcjPDzc4jbHjh3DoEGDEBwcjKioKEyZMgVlZWVm26xbtw4dO3aEXq9H06ZNsWDBAqmK7FVucnCBPgB487/tcEvbWETWrqocQoPESyfv6MWylt7PLA+M3HdvarZlaj8sHNkN/VpGyV0Um+qF6BEa6NpnLaWxF3UTqDTgsD6bid9uco5kwUxJSQnuvvtujB071uLz5eXlGDRoEEpKSvDXX3/hiy++wIIFCzBt2jTjNjk5ORg0aBD69OmDHTt2YOLEiXjkkUewatUqqYrtFWLDAvH+kA4Obx8TFogP7+tolnRK9MGdJteuxY90Q/rkm8TdP5mJCg1Ez2aREqXBF32XLnHntakuyZ6bxVVaLKSyd1/zAnyrQgWlpveSrJvp5ZdfBgCrLSm///479u3bhz/++APR0dFo3749XnnlFTzzzDN46aWXEBAQgHnz5iExMRFvvfUWAKBly5bYuHEj3nnnHaSlpUlVdM27v3tDhLm5UJ+Ud049JOz6UDSFXiSIyLtFhQbioRsaIcDPB8EByhydItsIp4yMDCQnJyM6umqWRFpaGgoLC7F3717jNqmpqWZ/l5aWhoyMDJv7Li4uRmFhodkP1eRsOGIrfnHkJtjatEoddF5XkWt59V8ljrHgnb5tSpuWHCZiN7ajImsHePyYajL91taYOrCl3MWwSrZgJjc31yyQAWD8PTc31+Y2hYWFuHr1qtV9z5o1C2FhYcaf+Hjx1/Ah+eldXZ1aBZwZ06QGD6Y0lLsIZEOwjQGvnu42vKNDfbSJC/PsQQE0qSfi7DnyOKdqg2effRY6nc7mT1ZWllRlddjUqVNRUFBg/Dl+/LjcRZKFwm62RDP55ua4tV0cuieKM8BTjlYSi+dGc80HVS9Iyjt/rQV+LrHz9tr7aD15c3O0iw/HrDuTRSuSIyx9LCb0kydXj1avl97Cqc6vyZMnY8SIETa3ady4sUP7iomJwdatW80ey8vLMz5X+W/lY6bbhIaGIijI+gBUvV4PvZ7TMsWmlC/74zJd7Mh7KGUQs6dE1tbj53E3AACm/rhb5tJIMwBbIZcvkohTwUy9evVQr544d0EpKSmYOXMm8vPzERVVMT109erVCA0NRatWrYzbrFixwuzvVq9ejZSUFFHKQK6rfqlxJ9BRSpDkSVp+zUofD9S9cYTcRRDFuD5NMHftYcn274l1Qr0taCTpSDbo4NixY9ixYweOHTuG8vJy7NixAzt27MDly5cBAP3790erVq0wfPhw7Ny5E6tWrcILL7yAcePGGVtVxowZgyNHjuDpp59GVlYWPvroI3z33Xd48sknpSo2SUjLFTiph6iZhTXouf8koUGdIEwZkCR3UWqopZdyJg0vUGom2Sdj2rRp+OKLL4y/d+hQkddk7dq16N27N3x9fbFs2TKMHTsWKSkpqFWrFh588EHMmDHD+DeJiYlYvnw5nnzySbz33nto0KAB/u///o/Tsh2k9DtkU8n1Kwb81a3lHTMK4utYWFlZPadLURy5uddiEjbT77eYH53RvZpgdK8mIu5RHEkxIXjfgwuJkrpIFswsWLDAbrbehg0b1uhGqq53797Yvn27iCVTruhQZY/zkao+0AEICfTH7pf6Q+/nHWnE7d1haqMVSxMvQlEGtY3F8l2nPXpMpXwWV07sJXcRSMG0O7dVIf7vgc4Ob7vxmb4SlkT5QgL9EaDh6dbeQimVnzWOtFgqtSFn7n0d5S4CkSKx5pBYaqto+xtd5+8r7ulwdYE+R1S/2KupS0uxFFqBuk5zL8gryRHYyXFMpQfhZBuDGY0KDfRDoL93dNkQkTpxZXMSC4MZhRiUHCvq/m5qYX01ZFcHQ0q2+J4Dd0QjejSS5tjkFZTabSQWpS1H4KiHb0jE7e3jzB6T66Wo8x2kSgxmFKJlbIio+/P0FzM6NFDS/Q/pqr0lKZ7q3xx3dKhf9YBGrqYaeRnkAUEBvnhvSAePHMtekKTWgJAqMJjRKE/fiLaKC8XMO9rY3IbXCnPj+zbDO/e2t/ic1lsSxCTFx0qyVkiZ8PNEWsdghlwSYSEfzLBu0i0myAHGRN4rLkzall9SPynTKZJCudqcanp317lhHTzRtymaMJsqWaGk1gDTjzxbCJWvdbVVs+/q1ECmkpBasGVGIRy98H87uru0BXGQTgdM6t8Ct7evb39jIglJETOpoSXQLECTrxiSuLdLvFlrjCden9beQ2/DYEZFpt/aCt0a15W7GC5TQwUhKwW1ZLhDiS0fSmolEosWX1MlXx8dHjSZwSjGS9Xy+0UMZlTjsd5N8NANiXIXQxIMcmpSYkDgDrW9Hq0NAHbH8JSKsXD9kqyneyCSG8fMkMN4eZeYyip8Uo/UltH4Y38eWkQ7nwJi6sCW6JsUhc4NIyQomXW83pAzGMxolBZXCSZ1aO5ChUnSeuvudvjhnxO4pZ3zyTkD/HxwY7N6EpRKWdTWekjm2M1EHrVm8k01HnPkIsILjXp0SKgjdxFs0uJHyd73IyzYHw/3TERUiPdOcbafNM8z5SBpMJghhznS2BN7fQZCQkRwjed0OqBxPeVN5eb4COnJPS6KZ5hI2xjMeDEpeqK+HtUdQ7sm4KuRXcXfORF5DU/3lMsdcJN7OGZGIbRy59goshZm3ZksdzGcopSLWMOIWnIXQTJqa/3ikDMidWHLDDksPNhfkv0qI5SQz9ejuuOJvk1xT2dmOXWFs4PdtRKncIwHURW2zGiUFBfsurWr1mNyZUkEXnstS2lSFylN1JsMkeSnlNZFqbCljOxhy4wX8tS07e8eTcFdHdnaQCQ1tXXjKZK240HNYzBDkumaGIFxfZrUeHzz1H6Yc3c7p/bVOFK740m0SknVq2n8zjpLHfoy4zA5gcEMScpShRYTFojeLaqScDnSZeXny4+qmqmtG0RJgZi3ahHD5IvkONYQ5Dg3r/CujLPxVnynyFtJFfjaTZonyVHJUxjMkCw4oI9IPGpr+VIi3mypG4MZD5iS1sLuNv1a2u4f1nLdz0tITVo73xygSkRSYjDjAc2ibKfwf/m21mgdFybKsfR+Faf0pubiLwznSnXEFhgiIpIa88woQELdmusYuWrD032w60QB+ilwJgBbYLyXlN0gyfXD8OfBsza3MW0ZcqQ7QQ1BuDd9nzzRsudN76cWMZjRmOjQQNzcyrGVceuF6CUujXXsdvAuUg5HeLxvMwT6+yK1ZbR0ByFZqSG4JHkxmPFig5JjseP4RXRMqCNrOTjujtwRFOCLJ/o1k7sYsuJ3iLwdgxkv5uOjw4u3tHJ4+5axruR94C0VERFJi8GMAiTXtz/4Vwk3Xre3q4/L18rQsaFrLTlmd4+McYjIgwJ8fW0+z9YtdWMwI6OJqc1wX9cERNaWb+yKM3x8dBie0kj0/TJHBknN2TEXUq0QT/aJHVQ80bcpsnIvoYedxVx5HVI3BjMyqq33Q1SoY4N1idRMbQM4nx2YhJMXrmJotwS5i0JumtTffp4vUj8GM0QKpIV7RLUFMKYia+vx9ejucheDPIjdTOrGpHkkqQZ1KnLo+PqYXynCTJrxg/W2+7JJ/VhREJGU2DLjASGByup/9+Qdc6C/L/a8nAY/H51ZsjK9ny/+erYvdDrAnytiq16QP8+hx3lRhKjiRj7yEAYzHtC9cQQeSGmIpnaWNdCq2nrLH7O48CAPl4Sk8lRaC+w9VYihXTnGhIg8j8GMB+h0Osy4vY3cxXBKHc7mICdEhQRi+RM3yl2MGuqF6HHmUjHSWsdgwV9H5S4OKZj3tHNpE4MZlbDUzPryba1d2pcjrdNfjezm0r6JlGTN5Jtw4sJVtIwNlbsoknJkvSkiLWNHt4wqV7h2VRsHku0pcd9Kw3pAu0IC/Y2BzF0dGwAAHrqhkYwlIiIpsGVGBk/1b471B87g7s7xcheFyCMiagXIXQS8dU87vHZnG+j9OHuOamoXHw4AaFg3WN6CkEsYzMhgfN9mGN/XuxfGI+/y8A2J2H2iAP1by7uyNQMZsqaW3g9ZrwyAnw+batWIwYyKsXuE1CIowBfzhneSuxhENgX6M9hVK46ZIVKQeiEV63T1bx0jc0lITXhfQ96OwYxKWEp0p+Z08WTZqom98MXDXXEvx1ORFbe0jQUADO3qPZ8RXuvIHnYzkZHezwfFZQa5i+HVImoF4Kbm9eQuhigE5m2VxAdDO+Cte9px/A+RCbbMkJG7U8WJSHo6nY6BDFE1rL2IiFSqcWQtAMCg611PRN6K3UxERCr1w9ge2PbvBfRpoY2uSSJXMZghIlKpOrUCcHMreXP3iI0pJ8gV7GZSCU8MpgwPlj9LKxERkbMYzJDR/Ac6Ibl+GBY81EXuohARETmM3Uwq5mpzrLWcDUkxofj18Z6uF4jIRFRIoNxFII3gNH+yh8EMEUkiolYAvh+TwhTxRCQ5BjPk9ZhdVDqdG0XIXQQi8gIcM6MWrHCJiIgsYjBDREREqiZZMHP06FGMHDkSiYmJCAoKQpMmTTB9+nSUlJSYbbdr1y7ceOONCAwMRHx8PGbPnl1jX0uWLEFSUhICAwORnJyMFStWSFVsIiKSEcdYkSskC2aysrJgMBjwySefYO/evXjnnXcwb948PPfcc8ZtCgsL0b9/fzRs2BCZmZl488038dJLL2H+/PnGbf766y8MHToUI0eOxPbt2zF48GAMHjwYe/bskaroREQkk8jaermLQCok2QDgAQMGYMCAAcbfGzdujOzsbHz88ceYM2cOAGDRokUoKSnBZ599hoCAALRu3Ro7duzA22+/jdGjRwMA3nvvPQwYMABTpkwBALzyyitYvXo1PvzwQ8ybN0+q4hMRkYcl1w+TuwikUh4dM1NQUICIiKrZDRkZGejVqxcCAqoyz6alpSE7OxsXLlwwbpOammq2n7S0NGRkZFg9TnFxMQoLC81+iIiISJs8FswcOnQIH3zwAR599FHjY7m5uYiONl9XpPL33Nxcm9tUPm/JrFmzEBYWZvyJj48X62XIhpOZpMO1YIgUjhdAssPpYObZZ5+FTqez+ZOVlWX2NydPnsSAAQNw9913Y9SoUaIV3pqpU6eioKDA+HP8+HHJjykH1sFEREQujJmZPHkyRowYYXObxo0bG/9/6tQp9OnTBz169DAb2AsAMTExyMvLM3us8veYmBib21Q+b4ler4dez0FkRERE3sDpYKZevXqoV6+eQ9uePHkSffr0QadOnfD555/Dx8e8ISglJQXPP/88SktL4e/vDwBYvXo1WrRogTp16hi3SU9Px8SJE41/t3r1aqSkpDhbdCIiItIgycbMnDx5Er1790ZCQgLmzJmDM2fOIDc312ysy3333YeAgACMHDkSe/fuxbfffov33nsPkyZNMm4zYcIErFy5Em+99RaysrLw0ksvYdu2bRg/frxURSciIiIVkWxq9urVq3Ho0CEcOnQIDRo0MHtOuL4YTlhYGH7//XeMGzcOnTp1QmRkJKZNm2aclg0APXr0wOLFi/HCCy/gueeeQ7NmzbB06VK0adNGqqIrksAFhIiIiCySLJgZMWKE3bE1ANC2bVv8+eefNre5++67cffdd4tUMiIiItISrs3khdjGQ0REWsJghoiIiFSNwYyK6VzM9sb8NESkJmxNJnskGzND0rmjQ32cvHgVbbmOCRF5AU6AIHsYzKiE6Xf5nXvby1YOIiIipWE3ExEREakagxkiIlIEgaNjyEUMZoiIiEjVGMwQERGRqjGYISIiIlVjMKNwkbX1AICbW0XLXBIiIiJl4tRshVs3pTdOXChCUkyo3EUhIpIF08yQPWyZUbjaej8GMkRERDYwmCEiIiJVYzBDREREqsZghhTLxXU0iYjIyzCY8UIcS2eOMRMRkboxmCEiIiJVYzBDREREqsZghhSrtp5pkIiIXeNkH4MZUqyvRnZDs6ja+PyhLnIXhYiIFIy3vqRY7ePDsXrSTXIXg4iIFI4tM0RERKRqDGaIiIhI1RjMEBERkaoxmCEiIkVIiAiWuwikUgxmyOtx2ieRvL57NAV3dKiPGbe3kbsopFKczURERLLqmhiBrokRcheDVIwtM0REpGgCm0/JDgYzREREpGoMZoiIiEjVGMwQERGRqjGY8UIhgRz3TURE2sFazYvMujMZa7LyMbRrgtxFURSd3AUgIiK3MJjxIkO7JjCQISIizWE3ExEREakagxkiIiJSNQYzRESkaAIXHSE7GMwQERGRqjGYISIiIlVjMENERESqxmCGiIiIVI3BDBEREakagxkiIiJSNQYzREREpGoMZoiISNHCgvzlLgIpHIMZIiJSpLfvaYfUltEYdWNjuYtCCseFJomISJHu7NgAd3ZsIHcxSAXYMkNERESqxmCGiIiIVI3BDJFOJ3cJiIjIDQxmiIiISNUYzBAREZGqMZghIiIiVWMwQ0RERKrGYIaIiIhUTdJg5rbbbkNCQgICAwMRGxuL4cOH49SpU2bb7Nq1CzfeeCMCAwMRHx+P2bNn19jPkiVLkJSUhMDAQCQnJ2PFihVSFpuIiIhURNJgpk+fPvjuu++QnZ2NH374AYcPH8Z///tf4/OFhYXo378/GjZsiMzMTLz55pt46aWXMH/+fOM2f/31F4YOHYqRI0di+/btGDx4MAYPHow9e/ZIWXQiIiJSCZ0gCIKnDvbLL79g8ODBKC4uhr+/Pz7++GM8//zzyM3NRUBAAADg2WefxdKlS5GVlQUAuPfee3HlyhUsW7bMuJ/u3bujffv2mDdvnkPHLSwsRFhYGAoKChAaGir+CyNVe3v1AbyffhAAcPT1QTKXhoiIKjlaf3tszMz58+exaNEi9OjRA/7+FSugZmRkoFevXsZABgDS0tKQnZ2NCxcuGLdJTU0121daWhoyMjKsHqu4uBiFhYVmP0RERKRNkgczzzzzDGrVqoW6devi2LFj+Pnnn43P5ebmIjo62mz7yt9zc3NtblP5vCWzZs1CWFiY8Sc+Pl6sl0NEREQK43Qw8+yzz0Kn09n8qewiAoApU6Zg+/bt+P333+Hr64sHHngAUvdsTZ06FQUFBcaf48ePS3o8IiIiko+fs38wefJkjBgxwuY2jRs3Nv4/MjISkZGRaN68OVq2bIn4+Hhs3rwZKSkpiImJQV5entnfVv4eExNj/NfSNpXPW6LX66HX6515WURERKRSTgcz9erVQ7169Vw6mMFgAFAxpgUAUlJS8Pzzz6O0tNQ4jmb16tVo0aIF6tSpY9wmPT0dEydONO5n9erVSElJcakMREREpC2SjZnZsmULPvzwQ+zYsQP//vsv1qxZg6FDh6JJkybGQOS+++5DQEAARo4cib179+Lbb7/Fe++9h0mTJhn3M2HCBKxcuRJvvfUWsrKy8NJLL2Hbtm0YP368VEUnIiIiFZEsmAkODsaPP/6Ifv36oUWLFhg5ciTatm2L9evXG7uAwsLC8PvvvyMnJwedOnXC5MmTMW3aNIwePdq4nx49emDx4sWYP38+2rVrh++//x5Lly5FmzZtpCo6ERERqYhH88zIhXlmyJaVe3IxZmEmAOaZISJSEkfrb6fHzBBpTVrraMz+b1u0iQuTuyhEROQCBjPk9XQ6He7pzFxERERqxVWziYiISNUYzBAREZGqMZghIiIiVWMwQ0RERKrGYIaIiIhUjcEMERERqRqDGSIiIlI1BjNERESkagxmiIiISNUYzBAREZGqMZghIiIiVWMwQ0RERKrGYIaIiIhUzStWzRYEAQBQWFgoc0mIiIjIUZX1dmU9bo1XBDOXLl0CAMTHx8tcEiIiInLWpUuXEBYWZvV5nWAv3NEAg8GAU6dOISQkBDqdTu7iSK6wsBDx8fE4fvw4QkND5S6O1+J5UAaeB2XgeVAGtZ0HQRBw6dIlxMXFwcfH+sgYr2iZ8fHxQYMGDeQuhseFhoaq4sOqdTwPysDzoAw8D8qgpvNgq0WmEgcAExERkaoxmCEiIiJVYzCjQXq9HtOnT4der5e7KF6N50EZeB6UgedBGbR6HrxiADARERFpF1tmiIiISNUYzBAREZGqMZghIiIiVWMwQ0RERKrGYEYmJ0+exP3334+6desiKCgIycnJ2LZtm/F5QRAwbdo0xMbGIigoCKmpqTh48KDZPs6fP49hw4YhNDQU4eHhGDlyJC5fvmy2za5du3DjjTciMDAQ8fHxmD17do2yLFmyBElJSQgMDERycjJWrFhh9rwjZVEre+dhxIgR0Ol0Zj8DBgww2wfPg3saNWpU4z3W6XQYN24cAODatWsYN24c6tati9q1a+Ouu+5CXl6e2T6OHTuGQYMGITg4GFFRUZgyZQrKysrMtlm3bh06duwIvV6Ppk2bYsGCBTXKMnfuXDRq1AiBgYHo1q0btm7dava8I2VRK3vnoXfv3jWeGzNmjNk+eB7cV15ejhdffBGJiYkICgpCkyZN8Morr5itTcT6wQKBPO78+fNCw4YNhREjRghbtmwRjhw5IqxatUo4dOiQcZvXX39dCAsLE5YuXSrs3LlTuO2224TExETh6tWrxm0GDBggtGvXTti8ebPw559/Ck2bNhWGDh1qfL6goECIjo4Whg0bJuzZs0f4+uuvhaCgIOGTTz4xbrNp0ybB19dXmD17trBv3z7hhRdeEPz9/YXdu3c7VRY1cuQ8PPjgg8KAAQOE06dPG3/Onz9vth+eB/fk5+ebvb+rV68WAAhr164VBEEQxowZI8THxwvp6enCtm3bhO7duws9evQw/n1ZWZnQpk0bITU1Vdi+fbuwYsUKITIyUpg6dapxmyNHjgjBwcHCpEmThH379gkffPCB4OvrK6xcudK4zTfffCMEBAQIn332mbB3715h1KhRQnh4uJCXl2fcxl5Z1MzeebjpppuEUaNGmW1TUFBg/HueB3HMnDlTqFu3rrBs2TIhJydHWLJkiVC7dm3hvffeM27D+qEmBjMyeOaZZ4SePXtafd5gMAgxMTHCm2++aXzs4sWLgl6vF77++mtBEARh3759AgDh77//Nm7z22+/CTqdTjh58qQgCILw0UcfCXXq1BGKi4vNjt2iRQvj7/fcc48waNAgs+N369ZNePTRRx0ui1rZOw+CUBHM3H777Vaf53kQ34QJE4QmTZoIBoNBuHjxouDv7y8sWbLE+Pz+/fsFAEJGRoYgCIKwYsUKwcfHR8jNzTVu8/HHHwuhoaHG9/zpp58WWrdubXace++9V0hLSzP+3rVrV2HcuHHG38vLy4W4uDhh1qxZgiAIDpVFS0zPgyBUBDMTJkywuj3PgzgGDRokPPzww2aP3XnnncKwYcMEQWD9YA27mWTwyy+/oHPnzrj77rsRFRWFDh064NNPPzU+n5OTg9zcXKSmphofCwsLQ7du3ZCRkQEAyMjIQHh4ODp37mzcJjU1FT4+PtiyZYtxm169eiEgIMC4TVpaGrKzs3HhwgXjNqbHqdym8jiOlEWt7J2HSuvWrUNUVBRatGiBsWPH4ty5c8bneB7EVVJSgoULF+Lhhx+GTqdDZmYmSktLzV53UlISEhISzL4LycnJiI6ONm6TlpaGwsJC7N2717iNrfe3pKQEmZmZZtv4+PggNTXVuI0jZdGK6ueh0qJFixAZGYk2bdpg6tSpKCoqMj7H8yCOHj16ID09HQcOHAAA7Ny5Exs3bsTAgQMBsH6whsGMDI4cOYKPP/4YzZo1w6pVqzB27Fg88cQT+OKLLwAAubm5AGB2Uaj8vfK53NxcREVFmT3v5+eHiIgIs20s7cP0GNa2MX3eXlnUyt55AIABAwbgyy+/RHp6Ot544w2sX78eAwcORHl5OQCeB7EtXboUFy9exIgRIwBUvO6AgACEh4ebbVf9vXH1/S0sLMTVq1dx9uxZlJeX2z0H9sqiFdXPAwDcd999WLhwIdauXYupU6fiq6++wv333298nudBHM8++yyGDBmCpKQk+Pv7o0OHDpg4cSKGDRsGgPWDNV6xarbSGAwGdO7cGa+99hoAoEOHDtizZw/mzZuHBx98UObSeQ9HzsOQIUOM2ycnJ6Nt27Zo0qQJ1q1bh379+slSbi373//+h4EDByIuLk7uong1S+dh9OjRxv8nJycjNjYW/fr1w+HDh9GkSRM5iqlJ3333HRYtWoTFixejdevW2LFjByZOnIi4uDjWDzawZUYGsbGxaNWqldljLVu2xLFjxwAAMTExAFBjdH5eXp7xuZiYGOTn55s9X1ZWhvPnz5ttY2kfpsewto3p8/bKolb2zoMljRs3RmRkJA4dOgSA50FM//77L/744w888sgjxsdiYmJQUlKCixcvmm1b/b1x9f0NDQ1FUFAQIiMj4evra/cc2CuLFlg6D5Z069YNAMy+CzwP7psyZYqxdSY5ORnDhw/Hk08+iVmzZgFg/WANgxkZ3HDDDcjOzjZ77MCBA2jYsCEAIDExETExMUhPTzc+X1hYiC1btiAlJQUAkJKSgosXLyIzM9O4zZo1a2AwGIwXmZSUFGzYsAGlpaXGbVavXo0WLVqgTp06xm1Mj1O5TeVxHCmLWtk7D5acOHEC586dQ2xsLACeBzF9/vnniIqKwqBBg4yPderUCf7+/mavOzs7G8eOHTP7Luzevdvs4r169WqEhoYag1V7729AQAA6depkto3BYEB6erpxG0fKogWWzoMlO3bsAACz7wLPg/uKiorg42NeNfv6+sJgMABg/WCVR4cbkyAIgrB161bBz89PmDlzpnDw4EFh0aJFQnBwsLBw4ULjNq+//roQHh4u/Pzzz8KuXbuE22+/3eLUuw4dOghbtmwRNm7cKDRr1sxs6t3FixeF6OhoYfjw4cKePXuEb775RggODq4x9c7Pz0+YM2eOsH//fmH69OkWp97ZK4sa2TsPly5dEp566ikhIyNDyMnJEf744w+hY8eOQrNmzYRr164Z98Pz4L7y8nIhISFBeOaZZ2o8N2bMGCEhIUFYs2aNsG3bNiElJUVISUkxPl85Jbh///7Cjh07hJUrVwr16tWzOCV4ypQpwv79+4W5c+danBKs1+uFBQsWCPv27RNGjx4thIeHm83OsVcWtbN2Hg4dOiTMmDFD2LZtm5CTkyP8/PPPQuPGjYVevXoZt+F5EMeDDz4o1K9f3zg1+8cffxQiIyOFp59+2rgN64eaGMzI5NdffxXatGkj6PV6ISkpSZg/f77Z8waDQXjxxReF6OhoQa/XC/369ROys7PNtjl37pwwdOhQoXbt2kJoaKjw0EMPCZcuXTLbZufOnULPnj0FvV4v1K9fX3j99ddrlOW7774TmjdvLgQEBAitW7cWli9f7nRZ1MrWeSgqKhL69+8v1KtXT/D39xcaNmwojBo1yuyiKgg8D2JYtWqVAMDi67l69arw2GOPCXXq1BGCg4OFO+64Qzh9+rTZNkePHhUGDhwoBAUFCZGRkcLkyZOF0tJSs23Wrl0rtG/fXggICBAaN24sfP755zWO9cEHHwgJCQlCQECA0LVrV2Hz5s1Ol0XNrJ2HY8eOCb169RIiIiIEvV4vNG3aVJgyZYpZnhlB4HkQQ2FhoTBhwgQhISFBCAwMFBo3biw8//zzZlOoWT/UpBMEk7SCRERERCrDMTNERESkagxmiIiISNUYzBAREZGqMZghIiIiVWMwQ0RERKrGYIaIiIhUjcEMERERqRqDGSIiIlI1BjNERESkagxmiIiISNUYzBAREZGqMZghIiIiVft/mF0s7osJU/8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bvp001_df[(bvp001_df['datetime'] >= '2020-02-13 18:00:00') & (bvp001_df['datetime'] <= '2020-02-13 19:00:00')].plot(y=' bvp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "hr001_df = pd.read_csv('./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/001/HR_001.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "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>datetime</th>\n",
       "      <th>hr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2/13/20 15:29</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/13/20 15:29</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/13/20 15:29</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/13/20 15:29</td>\n",
       "      <td>82.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2/13/20 15:29</td>\n",
       "      <td>82.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        datetime    hr\n",
       "0  2/13/20 15:29  94.0\n",
       "1  2/13/20 15:29  99.0\n",
       "2  2/13/20 15:29  94.0\n",
       "3  2/13/20 15:29  82.5\n",
       "4  2/13/20 15:29  82.6"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr001_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "hr001_df['datetime']=pd.to_datetime(hr001_df['datetime'], format='%m/%d/%y %H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OBXq5yfLnc/M3SZLQ7JKLe3mqiihOSKAQeeHaP23Cibc/g129o4XeFIIgEjDmC2JkUnZmbc1RBAVQBUXWAsUbmeIB1DQP2d5PxRcMwRfMvr07H5BAIXJOIBTGE+/0IMyAF3b0F3pzCIJIwBHFJt7psKDGYc3Z6+jVycM9ULQChU9gfmX3QFbrLjVGvQGcddfzuOBn/ykKkUIChcg52lHokwHj/ygIopzh6Z22HKZ3AE2KJ0svlNEYEZRT59QDAHb3jWW17lLjuff60OvxYW//ODYddBd6c5JCAoXIOd6AWkk/qhnqRRCE8egfldtzp9XkxqSNo1eKZzyqBgUAGqrlbR+iurcItBGlXUUg3kigEDlHG0EZpAMGQRia/jFFoFTnVqDwrhu9imRrHKpAqau0ASCBEs2e/nFxe3cR1AOSQCFyjjbXOThGBwyCMDIDSgSlMccRlGqbPjUo0V08gNxqDAAebxCBLI3gSokDg+p8ot39FEEhiIgUz+A4uTsShJEZUCIojdW2nL6OmuLJri4tVhePq8IKSbZZgXuC0sqAHMnmny0A7OolgUIQERGU4XE6WBCEkekXAiVfKZ7sjgmxunjMJgm1FXIH0vAERW2BqS3XfaM+uA3+3pBAIXKONoJCOWGCMDb8IqKuKrcRlGrF7n48BxEUQE3zUFpZ5tCwnN5Z0FKDmfWVAIB3Do8UcpOSQgKFyDnRbcaTWc7eIAgid3CTNh6ByBU8xTOabZtxjBoUQBUodFEkwyMoM+oqcMwM2Sfm7UMkUIgyRxtBAYAhg4cVCaKc4QLFlWOBoptRW4wuHgBwVcgChe9PuXPYzQVKpTCyM7qzNwkUIud4o8zZaCYPQRiTYCgsumLyJVByYXUPqNtPAkVGG0GZ1SCneNZs7jb0vCISKETO8UZZKlPIlSCMiUeTbsm1QOE2+tmYN4bDDONKyjg6xcO33z1JxxsAGBpXDfiOa68V9z/xzpECbVFySKAQOccXleKhqnqCMCa8q6PaboHFnNvTQ20lFxCZCxTewQPESvHI6/dQBAVAZKSprVaNongms4tg5RISKETOiY6gUFU9QRiTfNWfAGoRrmcygHCYZbQO3gFkNkmwWyJPZ1wAUYpHJtrQ7rxFzQAiRZ7RIIFC5JzoIlmKoBCEMcmnQHEqrxFmaidOunAPlWq7BRJ3ZlOgGpRIuJjjtTp6zULKJSRQiJzjC1ANCkEUA/kUKA6rGQ6rfArKNA0zFnXS1UICJRIuRPh7pdc06VxCAoXIObyLx6nkiCmCQhDGRHigVOZeoACaQtYM7ejjdfAAaoSGBAovJo5M8ejV5p1LSKAQOccXlFM8bbUVACiCQhBGZWQifxEUAKjN0qtEraswT3ksW/FTSkwEQmBKmQ8vJuajBjJNr+UDEihEzuERlFaXAwAJFIIwKryjxpXvCEqGrcCxJhlHr3vUG0QowyLcUoFHSbTFxFUUQSEItUi2VURQ6IqGIIxIPmtQAFUIZRpB4R4qzhjbq92HbLxWSgE+TqDKZhbFxDVUJEsQaptxq1OOoAxP+MFYeV/REIQRcec5xZNtGoZ7eDgdU7fXZjGh0ianfsq9DkUdB6C+TxRBIQhoUjxKBCUUZhGOlQRBGANPniMotVmaqXlEBGVqigegTh5OrFqdap2GNeYSEihEzuEpHqfDIn4UNI+HIIzHoGKHzicB55rsIyiKQIkRQdGunwTK1G4nftsXDCMYCsd8XqEhgULkHN7F47CaUVclHzAGSaAQhOEYVoRCQ5U9L6+XrdurJ0ENivb+cu/k4e3Y2mJi7W2j1qGQQCFyDjdqc1jNqK+Ur8wogkIQxiIUZsKjiF9I5Bpnll08ag0KpXgSwT1QtPOKbBYTrGa5YHbCH4r5vEJDAoXIOV4hUEyoU0LHQ2TWRhCGYmQyILwy6irzk+KpreQ+KJldwSeLoNSSQAGgqUGxRQq5SuVvEihE2eJVUjx2i1nktimCQhDGYkipP3E6LLDmeJIxR0Q4MrxgEQIlTg0KHW9kYqV4ALntGAAmDDowkAQKkXO0ERSe4iGzNoIwFtyfqKE6P/UngBrhcGdag6JEXlxxungoYiujthlHvk8VikDhgwSNBgkUIudEFsnKBwwqkiUIY8EjKHV5cpEF1AjKhD8EfzC9TpJwmCU0agPUfSn3CMpoHMdd/vdkgCIoRBkSCIWFzbTDYtbYT5d3TpggjAaPoNTnqYMHiBQW6daJjPuD4A728VI8vMYl0whNqTAeo80YgDCyowgKUZbw9A4A2K2qs6NRi7IIolwZEh4o+YugmE2S6MBJV6Bws0ebxQSHdeqwQEBTJFvubcZxBQovkqUIClGGcJM2ALBrrKcnSaAQhKHgXiH56uDhqPN40kvDJDNpAyiCwhlTIiTxIihGvWAkgULkFB5BsVtMkCRJKPZxg/4gCKJcyfckY05tBW81Tk9E8OXj2dwDag2Ke8KP9XsG8afXD2a4lcXNmJJSn9rFY+w24/ifLEHogC+omrQB0ERQjBlSJIhyhUdQuGDIF5na3acSQeE1LmEGXPbr1wAAx82sxYIWZyabWrSMx4ug2HkNijGPxzmJoIyOjuK6667DrFmzUFFRgdNOOw1vvPGGeJwxhltuuQWtra2oqKjA8uXLsWvXrlxsClFgeIrHYZW/ahRBIQhjwk/4tXmOoLgytLvnQ+6iW2e1OKxmVETVpxh5OF6uEDUoDkrx4Atf+AKeeeYZPPTQQ3jnnXdw3nnnYfny5Th8+DAA4K677sI999yD+++/Hxs2bEBVVRVWrFgBr9ebi80hCkj8CIoxfxAEUa5wu/naPE0y5mQaQZlU0sf8mBKP6LbpgEEH4+UKxpiwutdOMwbKsEh2cnISf/vb33DXXXfhrLPOwrx583Dbbbdh3rx5uO+++8AYw913342bbroJK1euxDHHHIMHH3wQ3d3dWLNmjd6bQxQYHkGxW3gERXUuZNxXmyCIgsMFQjxPkVyRqR09r2+LjpBE0+xyxHxeuTDhD4kRBjX2yM+WO8kaNaKtu0AJBoMIhUJwOCK/FBUVFXj55Zexb98+9PT0YPny5eIxl8uFZcuWYf369THX6fP54PF4Iv4RxYE3EBVBUXKgYaYauBEEUVgYY6JINu8pngwFCo/Cxmsx5rS5KqKeV17HHZ7eMUlqqp3DIyhGjWjrLlBqamrQ2dmJ733ve+ju7kYoFMIf/vAHrF+/HkeOHEFPTw8AoLm5OeJ5zc3N4rFo7rjjDrhcLvGvvb1d780mcoSoQbHIBxHt1Y5R854EUW54A2Hh5Fqb5zbj2gxrULzB1AQKn8fDmSyzCIrWA0WSpIjHyrJI9qGHHgJjDNOnT4fdbsc999yDyy67DCZTZi+3atUqjIyMiH9dXV06bzGRK3gNil1R7maTJFS8UfOeBFFu8PoTi0kSYf98odagpOeDwiMhFUm2t9kZ6YxbdgLFG9ukDTB+m3FOBMrcuXPx4osvYmxsDF1dXXj99dcRCAQwZ84ctLS0AAB6e3sjntPb2ysei8Zut8PpdEb8I4oDtYtHPYgYfcQ3QZQbosW40jrlKjvXuDL0QRERFEtigXLl6R1YvlCN2HvL7LgzHmcOD6CKO6NeLObUqK2qqgqtra0YHh7GU089hZUrV6KjowMtLS1Yt26dWM7j8WDDhg3o7OzM5eYQBSC6BgVQ0zwkUAjCGHCB4spzgSygpniG0+zi4UKjwpb4NFZtt+A3V5yEy06ZCaD8IiijcVqMAeNHUHJi1PbUU0+BMYajjz4au3fvxg033IAFCxbgs5/9LCRJwnXXXYfbb78d8+fPR0dHB26++Wa0tbXhoosuysXmEAWEX+XwLh5AbXWbMGjekyDKDW4zn+/6EwCYViOnYIbG/QiEwrCaU7tunoxx8ZMIfmFUbgIl3qBAwPg1KDkRKCMjI1i1ahUOHTqE+vp6XHzxxfj+978Pq1VWyjfeeCPGx8dx1VVXwe1244wzzsDatWundP4QxU+0URsAVBhctRNEuaG6yOY/glJfaYPFJCEYZhgY86E1qusmHrGis4ngkRajdqzkiniDAgE1gmJU0ZYTgXLJJZfgkksuifu4JElYvXo1Vq9enYuXJwyEL4ZXAS/CmzDoj4Igyg0xh6cAAsVkktBUY0f3iBe9ntQFymSKPigcvly5+aCMpVCDEggx+INh2CzGGs9nrK0hSo5YBxFh1mbQsCJBlBvD43KKp64q/ykeAJjmlKPnvZ7U3cQnYxTgJ8JRpimeRF08WhdeIxbKkkAhcoowU9L8ECjFQxDGYkgRKNGeIfmiWalD6UtDoMSKziaiokzHbCSqQbGaTSJqYsTjMQkUIqd4g5FGbYCa4im3KxmCMCrDEwUWKCKC4kv5OWqRbGqnsXItkh1TJhnHSvEAGrt7A0a0SaAUmHC4tOfRTIpWQG0Exbg/CIIoB4bG/RgcU8XAIE/xFKCLB1DN1PpGU4+gpF0kW7Y1KHJ9UbU99vvkzHDUQD4ggVJAfrj2PRzz3afxj82HC70pOSPWQC+jj/gmiFLmjf1DOOF7z6DzjucwoIgUXoPSUF0YgdKUSQQlxsVPIhxlGrkdVWpQahyxC6BrM5wmnQ9IoBSQB1/djzFfEKsf31boTckZscKwRh/xTRClzE+f2QkA8IfCeOKdIwCMEEFJv0g2lkt1IkSKp8wujFSBEjvFw71vhtMcNZAPSKAUCG8gJEZc+0t4qm+sMCxFUAiiMPR6vHh1z6D4+19bjiAQCouTWKFqUFoUgXLYPQnGkqe9Q2EGf0iZxZN2iqd0j7exGPXKkZG4EZQMhzXmAxIoBWL/4Li4XcpVKLHajKsMPuKbIEqVPX1jAAA+bmdzl1ukeUxSYXxQAGBWQyVMkny13z+aPM2jrSNJu4unbFM8sSModRRBIaLZ168KlDFfUEz9LTW8iYpkKcVDEHmla3gCAHDGvEbYLSb4Q2G8fWgEgBzqN5vyOyiQ47CaMauhCgCwSxFRidCKDHuK5mKU4oktULgoTXcWUj4ggVIgDrsnI/4eHjfel0MPRJtxjBRPuR0oCKLQdA3Jx52Z9ZXoaJQFwau7BwAA06rtBdsuAJg7rRoAsDsFgcIjKHaLCaYURZXWqC2VNFIp4AuGRCosXoqHF0b3jqRe/5MvSKAUiP6xyDDm4Hjq1evFhKi0jxAospIfJ4FCEHmFR1Da6ysxt0kWBC/tkgXKjLrULOZzxfxmeXt29Y0mXTbdFmMgMorrK+G6Py08egLENmoDgMVtLgDA24dH8rJN6ZCTWTxEcgZGI/N9pRhBYYzFnDhKERSCKAxdQ7JAmVlfKYrU9w3I6eZCC5R5aUVQ0iuQBQCHJhU04Q+lJW6KlVGNzX289B1/3/tHffAGjPW+UASlQAyUQQRFe5WibTOusvMuHqpBIYh8clBJ8bTXVWLutKqIx6YbJIKSikARxfcpeqAAgMVsgs1sinh+qaN28MSPRTgrLCK6El16UGhIoBQILkh4NIHPwigltJX2WlVeQSkegsg7k/6QuDBqr6/A/KaaiMc7GqsLsVkCXoMyMOYXxnHx4NHXVAtkOfxCqVyit8kKZAFAkiQ0KnUog2PGOg+RQCkQPMUzv1k+SCT7QRYj/CrFYpJgNWuM2hSx4g+GEQyVRy6YIArNgSE5lVPjsMBVYcXC1hq87+hpAOQW45Nn1xVy81Blt2B6rRzF2d2fOIrizSCCol2+XOzuk3mgcLj/zeCYsSL5VINSABhjIoJydHM1tnS5hZNjKTEeZ0iV9qAyGQihxkw6mSByDU+dzGuqhqQYofzus6dg3BeELxgWjqKFZG5TNQ67J3H7v7bhH189I+5yorbNkqZAKbOBgR5v/EnGWhqUDi6jnYfozFAARiYDCITkNrejlAhKKaZ4+DDAqqirHLvFBF6vVS6hVoIoNEKgTItM5VTZLQVzkI2muUY+UQ4kSTX4eJFsmhEUR5l5oaSS4gGABuXzN9p5iARKAeB54BqHRcygMNoXQw+4EVt0BEWSJM08nvI4UBBEodmliaAYla8tnw8A6B6ZTJj+jeVQnQrl5iabaoqHR8+MNjCQBEoB6FfqT6bV2MWVS0kKFCXFUxkjvFhB83gIIq9wm3veLWNEWl0VsJolMAb81/3rEQ7HNlTjAsNuTe8Ups7jKY/jzpgSQXEmiaA4K+THPV4SKGUPj6A0VtsNPQchW3gbcXSKB9DmgqnVmCByTSjMsFfxO5k3rSbJ0oXDbJJw9lFy4e7mLje2HHLHXM6baQTFWl4XRqmmeJxKhGWUBArBBcq0artGuZbeiXrMFzvFA2jN2qiLhyByTbd7Ev5gGDaLqeB+J8n49gcXitvxPFEyTfHwE7XRTsS5YtSXWoqHvy+eSWOdh0igFAA1gmKDUxnU5A+GSy7sOMG7eGJFUGxk1kYQ+aLHI89ZaXE6CjYQMFXmTKvGZafMBAAcGo5tHMaHkKbrelorItZlIlBSjaAo5yFK8RDCA6Wx2o5qm0WMPjfalyNbRibjq/fKMitWI4hC0jOiCpRigHeVuOOkvr0ZdvHUJ1lvqeERAiVxBIWneIx2DiKBUgD6RuWDRZPTDpNJQo2dhx1LK5ogUlk1U6ekllsumCAKSa9HPeYUA7WV8glzKE6kYzKQmZNsHV9vCTYlxCIVq3sAcFUY8xxEAqUAHFGuZniLsQivTRpLvWZL/6haDByNo8yq6QmikHBfkVgXC0ZE7W6M7WyaqZMspXhiwyMsnskAGIvdOVUISKAUAJ4PbnXJxWpqeM1Y6jVbeGdSfdXU8GK5OToSRCHhozQaDGLIlgxVoCSOoKRbJNukCLQ+5Rhc6vAIijPFFE+YGWtGGgmUPLN+z6Aww2lx8QgKr6AuLVXPa1B4hEiLmIlhoB8DQZQqQ8rFQl2RCJSGKllIJIugpFsk26bM+uke8SIcZvjf53bh0l+tF9HeUsIbCIlanWRW9w6rCVazXAxppPMQCZQ8893H3wUgD9BzKSduoxYoZcuI0rLmiiVQKIJCEHmD11zUG2DeTirUV6sGlrFSDqJINk2B0uJyQJLkrsmBcR9+/PROvLZ3CDf8dUv2G20wfvvyPnE71jFYiyRJGi8U40TySaDkGa78P3v6bHGfWoNinC+GHnDBFevHYRc1KOSDQhC5hqd4jDJzJxk8FRUIsZip70ydZK1mE6YpNXH7BybE/e92ezLdVMPy1oFhAMA5C5pgSqG1XHihGOhCmQRKnuFXMp84uV3cV4oRlEAoDH9QFh81doqgEEQhGZooLoHisJqFFUGsjpsJX2pTemPBU+ubDg6L+4bH/QgkmP1TjHAPmU93zkppeSM2a5BAySP+YFhcDfAcK1CaNSh8kjEAVNpjWd3LXz0SKASRW4KhsKgHK5YaFCBxJ89oFgKlqUYWKG9pBEowzHBwaCLeU4oOxhgOu2WBMqOuMqXnUIqnzOFXAmZN/QlQml083ObeZjHBap76NaMiWYLIDyOTAfAyjtoktQhGgqd5BsciIyiMMXF8qU7SPhuLZsUL5q2D7oj798Sx1S9GRiYD4j2akeJoAyMODCSBkkdUXxBbRE5QnYNgnC9GtvBJxvGucIQPSpAECkHkEuHobLfAEuNiwag0VPNOnkiBMuEPCcEVK32cDO4/Fd25s6d/PIOtNCY8vdNYbUu504m/l0Y6DxXPt7UE6B+Te++jzZJ47q+UBliN80nGMdI7gCpQJimCQhA5JVWzLqPBUzyDUQKFRwbMJgmONItkATWCwuH1cAeHSkeg9Csu3jydlQpGHFxLAiWPcFUbPQ+jFFM8vAalyhb7oKgWyZZWYRpBGI1s0iGFpKFKbTXWwgVXtd0CSUp/8GFT1PH3uPZaAFNTScXMmDf9z1ytQTHOhTIJlDzy7mG5lW1hqzPi/lIuko2X4hE1KFQkSxA5RZ3HUjz1J4Ba0DscJ4KSSYEsADRHRRWOaXcBmBqpKWb48bcmjffIiHYXJFDyyNbuEQDA4jZXxP2l2GY8ptSgVMarQbGQQCGIfOAp0hQPL+h1R124jWW5P9EpnmNn1AIABsdKx02Wi7iqNAQK+aCUMb5gCDt7RwEAS6ZHR1DkH6I3EIavRIpG1QhK7BqUChu1GRNEPlBP6MUVQVEH+0VHUOQTaKYRlLooN92jmmsAlFaKhzcppCNQnA4qki1bdveNIRBicFVYMb02su2rxm4BT6UaqQc9G0SRbJwaFCqSJYj8oK3ZKCbqKpUIStTk4dEM6iu0RLuq8qaFUV8Qdz75Hib8xX8M5sffeBeIsVCbNYyz/yRQ8kSfRw4fttdXTCnsMpkkcfDQQ72O+4L4wu/fQOcd6/BeT2EsnMeThBh5kawvGEY4bJzx3gRRaqgTbYtMoFTFjqDoLbicDosYlHf/i3vwm//sS/IM40MpHiIt3JPyj6y2IraTo56dPL94YTee3d6HIyNefPOvb2e9vkxI5oPCi2QBWaQQBJEbirXNuFaJoIxMBhDSXMTwk282+3PxCTMAAJ84qR2SJKFSE+ndengk4/UahYkkXZSx0BbJxhrQWAiK6xtbxAyPy6qU/+iicVZYcdg9qUsE5YUd/eL2RIFSKPwgEsvmHlCLZAG5DkUrWAiC0I9su14KBb+YY0yOLPOIih77860LFmBha40QKlpCJRDR5Rd96fjE8AibPxSGLxhO2eAtl1AEJU+4lTBldIEWh3859Mj/dSszGAD1x5xvkrUZm0wSbBYqlCWIXCPm1hRZkazNYkKVcuHS4/GK+9UUT+b7M63Gji+cOUeIHq0oOaw5fhYrXKDYLamLjCqbBbw8xyhpHhIoeWJYKfSqSxBBAbL/YgRCYfFagPzD9hcghTKWQojRrthuF2L7CKJc4POuKoswSskLYS/42X/EfbkwnvNrJhkfGp40TIojU3g3qD2NCEpkLaQxCmVJoOQJXuhVGzeCok+LlzYCYzVLYAzoGfEmeEZu4LU0rgTDyaxKBKXUxpyXC4fdk3hpZ3/RH8xLHR6hrDBAyD5dTp3TIG5zn5IxrzpbSC+0F0ljvqCYX1Ss+AI8gpLeKV6vC2W9IIGSJ3irXLwaFL0qqLnAqbZb0K6M2T7kzv8Ycb4dzgQCxUYRlKLm+kc34zP/9zp++dLeQm8KkQAuUIxQU5Au3/ngQnH73W65I1GPItlk8LEkxUomKR7AeF4oJFDyxHCyGhSdbIa58nc6LJjVIAuU3QUYI86/4IkjKHLC008RlKJkw74hAMA963YVeEuIRHCvoWIsRG9yOvDBpS0AgB09stFltj4osbjlQ4si/j40nP+LOj0RKZ60Iyj61ULqAQmUPMGFgyteDYpeERSvGrlYqlg4b+nKb9scY0wVShXxDyIUQSletGmdYIhSPEbGW8QpHgA4ull23n5PESi56Er67Omz8dIN78eHjmkFAHQNlUgEJc1pzzUGG7tSXH1nRcxIkogCj6Bkm/vkERinw4pjZ8gzf94+5M5qnekyGQghqFTFJ4qg2JTwI9WgFBePbDiIoXF1bkkwHAZjLKPJskTuUVM8xXk92l4vO2/3jcq1dLlI8UiShJkNlWivV9LixR5BCWSb4jFGBIUESh4Ih5n4UTnjtPrx1E+0rXO6qBEUC45RIii7+8dwaHgCM5SalFzDRZbFJCW8arMp7o0UQTE+4TDD1x7djPpKK36//kDkYwwIhplw4ySMA2Os6CMo9Uor8NC4H4wxMVsomzbjeLS55EnHRwrQWKAn2ad4jBFBKU5JXWSM+oLgEfF4KY+GavlHODie3URNUZzqsGJajR1zGqvAGHDGD5/HH147kOTZ+sDVt6vCmvCqmvugkEAxPu/1jOLxLd1TxAmHomDGxB8Kg1t8OIqwBgVQBcrwuB++YFhEZ/WsQeFwX5ToCcrFRtZFsiRQygcuGhxWU9wvTIPyw8h2oqa2BgUAzpjfKB67+9mdWa07VUZS6OABACuvQaGTm+GxJImOkMg0Jl6/+rkUawSFR5cHx/3i+CZJQGUO9oe7145kGckuNJnXoJAPStkxoolqxKOhWp6oOeEPZTVNU9vFAwAfP7FdPBYMs7x4VqTSYgxQBKWYSFYISyLTmPD6E4tJEhcExQaPoPiCYfSPyhHmaptlylRiPeA2EHx2WjESDIWFMy75oBBJiY5qxKLKZhZfpmyiKGJInyJQls5w4b3vnQ+zSYJ7IoC+0exSSMPjfnzh92/gVy/tibtMtEiKBz9gBqgLxPAEw6oAMZskRGfuSGQak2I2aeNU2sziYqZrSC5ezUV6B1CL+ocnAkVrQKgdvpppiofajMsITwonbEmS0KhEUQbHMxcoPPqinc7psJqFJ8qrewYyXjcgjyN/dnsffvDEe/jbxkMxl+GCLFEHD6CNoNAsHqOjFZFrvnI6Hr2qEz+79Djh5kkCxZhwD5RirT8B5GMjT4Ef5AIlR4MPeQ2KPxiGN1Cc32mtQLFlWCRLRm1lhLZoNBG8UPaie1/BcIYihU8vroqaIsxD9F9/dEtG65XXEca/3j4i/v75c7ENulKtQbFRBKVoCCopnDmNVVg6w4VTOuqx8rjp4gBIn6ExKfYWYw6vQzkwmNsISpXNDIuSOirWNA/v4LGaJZjTTINRkWwZkkqKB4gUMP/3yr6MXotPEa6MGtIXa6x4uuzsHYuY9Ll/cEK8nhatF0sibFQkWzTwzonoYlmqIzI2vhJI8QBqHUquIyiSJOnmSVUoMvVAAYzng0ICJQ+kUiQLAGFNzvOlnf0ZvZaIoEQJlC+c2SFuuycyuzLgVxTzmqrFGPRYNS3JTOk4wuqeTm6Gh7cRW0yRhwzqxDI2pVCDAqipFy5QcjmHh0efeT1fsaG2GKd/eucpnslAyBDWASRQ8oAnBdt3ADj7qGniNv8hpss4r0GJSvFU2S3ChCjT2TyqQZIFzU55Xb2eqYZGWrO4RNjM8jbSyc348BRhtBkbRVCMTTEPCtRSr3TX5DqCAqgXd7Giw8VApiZtQOT7aoRCWd0FSigUws0334yOjg5UVFRg7ty5+N73vhdREc0Ywy233ILW1lZUVFRg+fLl2LWrdAeOpRpRuPK0Dvz3B+YBkKvIuQNkOkz4YkdQAGBuUzUAYE9/ZgKFf2FrHBZMq5ELemNFUFIZFAioEZQAndwMD+/isZgpglJMFPOgQC08gsJPI7lwkeXwk3TxChTugZL+Z24xm0R03AiFsroLlB/+8Ie477778L//+7/Yvn07fvjDH+Kuu+7Cz3/+c7HMXXfdhXvuuQf3338/NmzYgKqqKqxYsQJeb3HbC8cjVYFis5hw/blHiavSPk/6LcEighLjgDRPESi7ejMVKGqqikdQ+mJEUPjk5mT7a6eTW9HAi2AtptgRFBKZxqTYbe45vIuHk6siWQCo5ALFX6QpnkDmKR7AWF4ouguUV199FStXrsSFF16I2bNn47/+679w3nnn4fXXXwcgR0/uvvtu3HTTTVi5ciWOOeYYPPjgg+ju7saaNWv03hxDkKpAAeQirRbl5N8T4+SfiFCYida4qhgh0AUtNQCAtw9nNt1YO0W0SYmgRKd4AqEw9g2MAwBmN1QlXJ/qg0InN6PDIyjRZl8kMo1NqdWgcGpymOKpFjUoxRpByTzFAxjLC0V3gXLaaadh3bp12LlTtlXfsmULXn75ZVxwwQUAgH379qGnpwfLly8Xz3G5XFi2bBnWr18fc50+nw8ejyfiXzGRatstp8WVmUDROtDGiqB0zpFt79/YPyQcGdNBm+IREZSo9fSMeBEIMdgsJsyoq0i4Pn717aOrb8MjIihRNSgiTUcCxZBM+jMP9xuJ+soogZLLIlklPT5WtAIl8y4eQGt3X/gIiu6f8re+9S14PB4sWLAAZrMZoVAI3//+93H55ZcDAHp6egAAzc3NEc9rbm4Wj0Vzxx134Lvf/a7em5o3RlL0QeHwCEpvmhM1eQeP2STFVM8zGyrR0ViFfQPj2NEzKupIUsUjBIoVTc7YEZRupQ25zeVIOCgQICfZYiIoUjyR3yveKk4i05h4g6UZQclliqeq6GtQlAhKht43JZ3i+fOf/4yHH34YjzzyCN566y38/ve/x49//GP8/ve/z3idq1atwsjIiPjX1dWl4xbnnlSdVTmZRlBUDxRzXHHA61B2942mtW5Ak+JxWNBUo44l//ObXcIfpXtE/r/VlTh6ApCTbDGhpnioi6eYUItki7thsz5KoMRKYesFbzOeKNcaFEX8lWSK54YbbsC3vvUtXHrppVi6dCk+/elP4+tf/zruuOMOAEBLSwsAoLe3N+J5vb294rFo7HY7nE5nxL9iwRsIiYN3qime5gxrUOJ5oGgRAiWDTh5eJCuneOQIyoHBCdz417fxjT/LDrXdbnmbW2sdSddHLqTFg5riid3FQykeY1IqRbJ1USme6KJZPeHip1xTPCKCYoAUj+4CZWJiAqaoMLDZbEZYuQLr6OhAS0sL1q1bJx73eDzYsGEDOjs79d6cgqMdD16dQDho4Sf//jS7eEQExR7/izlfRFDSFyjcB6XGbkGTM1KArN87iGAojCMjPMWTQgTFTFffxQK3uqcISnFRKj4oNospou4k3fR0OhR7m7E/C6M2QFODYoAIiu5xsg9/+MP4/ve/j5kzZ2Lx4sXYtGkTfvKTn+Bzn/scALlL5brrrsPtt9+O+fPno6OjAzfffDPa2tpw0UUX6b05BUeEWK3mlMeDT1OGBvaPpSdQUomgzJ3GvVDG01o3oC2StaLabkGVzRzRinfRL15Bs5L6aatNJ8VDJzejwyMk1jg1KPQZGpNS8UEBgOULm/H3TYdhM5vQUJU7gcLHhBRtm3G2NSgO40RQdBcoP//5z3HzzTfjK1/5Cvr6+tDW1oarr74at9xyi1jmxhtvxPj4OK666iq43W6cccYZWLt2LRyO5GmBYoOLhlhdNfHgVwfpdtok8kDh8Nbf/lEfJvzBKTN7EqFN8QByKmrvgCp0th72YKxB3oa2FFI8ZPJVPMTr4lHTdPQZGpFSaTMGgJsuXIhKmxnHtdemPaU3HYq/zVinFE8pRlBqampw99134+677467jCRJWL16NVavXq33yxuOiQyuYLhAGfMF0xIR2ghHPFyVVtRVWjE8EcCBwQksbE29nsejaTMGYtfU7FemjR6teK4kgiIoxUM8HxTRxUMCxZDwgsliT/EAQEO1Hd//6NKcv07xd/FkWyRbwl08RCQ8xFppTV0LVtstYjz6wGjqg/1SnfkzS4mi7EqjDsUbCImisQYlBcVrZQCgsVq97aqwilbpRPB6hlxdfWvHKxDZEYzjJGsVTrL0XhuRUoqg5As1xVOkAiWQnVGbkXxQSKDkGHGASCOCIkmSmuYZS72Th0dQkk1NPmlWHQDg2W29CZfjhMIMv3t1PwBZVPA2tNPmNopl6qvU12xxJvdAAdQfUC5SPE+/24NjbnsaT70b21uHSI94XTyiBiVUnPn6UqdUimTziVoka5zvdNfQhPCYSkY2s3gA9WIz1iDYfEMCJcdwd9d0r2BEoWwadShiinASE6NzF8kmea/uGUwpyvDg+v2488n3xHZx8fHJZTNx5Wmzcf+nTkStpg0w2rMgHqJFNQcpnpv/sRWjviCufmgjRiYCCIXpCj8byAelOCmlItl8wX1QjNJmPOkP4fy7X8Jpdz6HofHkEfVsUzyzGioByANr3ROpR/BzAQmUHDOZQZEsoNah9Hp8eGlnf0r5UBFBSeK3cmx7LWxmEwbGfDig1Iwk4m9vHVK3S5O6sZpNuO0ji3H+khbUVaqv2VCdmkCx5TCCoi0QO/H2Z3DF/72u+2uUE4E4TrJcsATJy8aQlIoPSj7hXZD+YNgQxd+9Hq/oKHpj/1DS5bOdxVNlV32u9g2k3+2pJyRQckwmRbKAKlB+tm4XPvN/r+Nrf9qU9Dl85k+yORUOqxnHzHABAF7bO5h0vdqTz7HK86LRRk1SNVGy5rBFlbvxAkAwzPDy7gFDFH0VK9wHJbqLx6wIliBFqAwJ1aCkj9aldsIAaZ4RTS1I11DyC0rVSTbzz7yjUa5TJIFS4vADRNoRlGr5BMtDes9u7xPRmHgMKL4pqXgEdM5tAAC8eWA47jKMMXz6txvwXo9si7/yuDZ89QPzYi6rdXpsqE7No8CWwzbjWLNhMjGnI2S4AIlO8fC/KYVmPBhjag1KkVvd5xObxSSOTWMGKJR1awTKoeHkdShqDUrmn3lHo+yXtTcDvyw9oW9tjpkQ3iTpdXTHckrcP5j4y8LrVZqcyQUCby/e1Rt/Js9re4fwn10DAOTCqbs/cZyYwRNNRAQlzRRPLqzuR2NUoA+OFTafWszwUHd0isdsomnGRsUfCoOXmFGRbHpwN+5JAwiUkQiBkkIEJcsUDwDMnWaMCEruJi4RANRx55mmeLR8++/v4K9fOk2cFLSEw0xEUFKxgT6qWfYp2dU3hnCYxXS57RtVq7hv+8iihJ05izR+KrUVKQoU5SolFGYIhVnM/cqUkRgCZbjABV/FDE/zTYmgmNTPkDAWXr8qGinFkx4VVjPcCIjjdyEZ0Ry3uobSiKBkkeJZedx0vO/oJsysr8x4HXpAEZQck2kOOJbI2HTQjX9sPhxzefdkQEQiUknxzG6ohM1swoQ/JCYRR8OLbs9b1IwPHdOWcH2nzmnAgpYaVNnMOGFWbdLXB1QPDUDfK3DGmKg3uf7co3DmfLkdemSCalAyhXfxRLcZc1FJNSjGgx97LCZpisEekRh+vObvYSGJqEEZnkjaeZntNGNAPv/Ma6rOqWNvKtC3Nsf4hA9Bem/17IbYyvWfW7pj3s/TO3WV1pS+VBazCTOV14jXycNbzGork09hNpkk/PlLnXjuG+9DawqDAgE1ggLErhnJFG8gLMTa58/oEPOHKIKSOYE4Rm28aJYLGMI4UIFs5jgMJFDcmgurCX8Iw0kutLKdxWMkin8PDE6mRkm1lTasWCz7ldz64UX47RUnAZC7bmKF03k6Jl6NSCymKwP9DrvjCRT5hxA96jweTocVzSk4yHK06QI9Iyj8isNsklBpMwuB5TaAM2KxEs/qntekUJux8eBF9Q7yQEkbnpJP1piQD6IdbZN18uiR4jEKVIOSY7gPgSODL8tPLjkObx0cxhnzGhFm8gndGwijx+MV4oLDIyjpjCGfXqcIlDiV4Vypu1KIoGSCJEmwmU3wh8K6thprDeskSUKt4gtDKZ7MiTcskFI8xoUiKJnD3zMejSgk0Y62XcMTOLa9Nu7y2Rq1GYni3wOD4w1k3vJVZbfgzPnTIEkSzCYJM+p4SmZqZXVGAkVEUGJbGo9MyimRVCMomZCLabh8hoRLESZ1SodRKi6MRGy4D8rUCAoJFKNCJm2ZI1I8BoigTERtw3tH4ndeAtpZPMX/uZNAyTHeoH4HCW5BfDBGzUgfbzHOSKDEDhkOixRPbiIogJrmyUkERREo3IafalAyJ54PikX4oFANitHwZlj/RmhSPAaoQeFWFccpURPuSxUPHkEpdIGrHhT/Hhgcr47jzmcpLV/7YwiUTCIo3G21zxN73g8vknWl2DacCfxHpGeRLK9B4UMT6xWB4qYUT8bEs7qnGhTjQoMCM6dCEXXGECjyNiyZLls5JPJCCYbC4mKiFIRp8e+BwfHqeJCY2SCb5xwcmpri4SZu0bUpieCpm3jFo6JItiqXEZRcpHjkKw6XiKDI/w9N+FMajkhMJRDH6l7t4qH31WjQoMDM4cdrrwFSPPxz5O6ufQkGyHo1F3qlIExJoMTh7md34qY17wiBkSl6hll5BCW6LTgcZtjVK9u4H9VSk/L6RHfLhB/hqBMMY0wIl1SN1zIhF9NweQ2Ks0KuAecut/5g2BBXRMVI0hoUcpI1HFSDkjlG8kHhXTzcNG14wh/396Y9X5VCkSx18cTgh2vfw30v7AEgfymuOmtuxuvSM4ISrwald9SLyUAIFpOUlvMfjzCEmTxzgqdEAGDUFxTtzKn4oGSKTURQ9LsCH4pKTVXazLBZTPAHwxga96c9doCI74NCXTzGhbp4MsdIPig8gjK9tgImST5eD4370RTD0oEva7eYEjp/FwvFL7F0ZtIfwq9f2iv+fuKdnuzWp+NBgrcFj/qCGNVM5uXzEtrrK9NyjHRYzWK7oltw3eMBZRlTTkOFIoIS0u9A0OuRu5JalJlEkiSJQl+qQ8mMeD4oVs24AsJYcJt28kFJH9UHpfCRQR5BqXFYUK+4hMdL8/C26FJI7wAkUKbQN+qNuBpMNqAvEaEwE0Wy2hHemVJps8DpkNfTM6K2BnOBEs99NhE8OhLd4eLOQ4sxoJ7g/EH9TnBHlPeGFwED6n5Qq3FmBJP4oORi4CORHRRByRz+nmWb4s+WsOYcUmkzo16pB/TEqRtUmzJK49ReGnuhI3zgXmO12vkRa/BcKkxoHAArdbqK4TbyPR5VoOxXBAovokoHnuaJjiwIk7aK3KV3ADXF49exhqFXEShaV1teh0KtxpkRb5oxT/lQm7HxoDbjzDFKDYr29eULVEWgeOMJFIqglDT9o/IJrL2+UrTsxvIdSQXeHmY2SboVLDUrUYEjEREUefs6GjOPoER38vAW45xHULhRm05FsuEwE+FP7Uwgvh/DFEHJiPg+KKaIxwnjQEWymePIgdX9izv70z6X8PSOJMlCk3s78U7FaEQEpQRM2gASKFNQIyh2je9IZmkeLlAqbWbdCpZalahAb4RAkTt4ZjdWpb0+0WocneJRIii5LJAF9I+gbNg3hGCYwSSpUTBAbZUeohqUjFBTPPG6eEigGA3yQckcvSMoL+3sxxX/9zo++otX0noeF0iVVvkcwlP8ySMopXFqL4290BFtimeW8B3JLIIy7pNVbpWOXSMigqKkeEJhhq4heZZORwYCpTZO8eigEmngNvG5wmbhNQzZC5RJfwiX/fo1AHKlu/ZkWh9HiBGpEeBFsnGmGVORrPEgH5TM0bsG5a8bDwFQj6upwufwVCjnEDWCEkegiEnGpfGZk0CJgqcHplXbRVsvr/FIF20ERS9aXZERlMExH/yhMExSZEojVXgEhQszziFFlKVj/JYJIoKiQ4rn9f1DcR/jdvcPrj8QUWBMJCcUZuD+dtERFFEkSzUohoOKZDOnwqavk6z2mJOOmJ8MKBe5dvkzVGtQkqR4SuQzJ4ESRZ8SmWh2OYRAOZBpBEXJH1ba9fuytDgja1D6FWFRX2UXJ4t0mK1Eifb2R4qwLsVOOR1flUywppHiCYWZ+Hxisa9/TNw+paM+4jHtCICvPLwx3c0sa7TRrSlOskrRLGOYYvZHFBaqQckcvYcFDmkit+kU6osIirI93HwyfhePkuIpAZM2gATKFA4Ny+mSNleFmuLJtEjWxyMo+qV4eOss7+LhM3i09RbpMK9Z7vzZ3TcWcT9Pa+VaoKTqJBsIhXHZr17DKT9Yhz+9fjDmMnxG0eyGStx3+QkRj500u07cfuugG2O+2FcgxFS0BbDW6C4ejWChQlljUWpX0/lEzxqUcJihS3ORm06hfnQUnkdQ4nWWClFaImk9EigafMGQOFEf1VIjimR7PN4puUjGGO59fjee3dYbd308glKl45eFR1CGxv3wBkJCUGWaipnXJAuUHo9XFF55AyH0KgME2/MUQUlWg/LCjn6RwvnTG10xlzmgFDNfddZcNFRHDk1sdVXg2g/ME39TN0/qaG21p3TxmLQChdI8RmKyxE5W+YS/Z3rUoHSPTEYMQ03Hi0lN8cgXudVKkWy8Cyz+OtTFU4JsPexBMMxQW2lFm8uB2kqrqJqOnn+zfs8gfvTUDnzhwTfjrm/Cx1M8+kVQaiutomW5z+NTIx0ZmLQBsiJvUtIfexRxxkVPtd0iHFhzhT3FCMqazYfF7Xe7R4RjopbDbnm7Z9TFFmvXn3e0SPWMxsnhElPRmrBFpxG1vigUQTEWokiWIihpw9+zQIhlXcAfnT6PN5w1FtEpHi5Uxv3xalCoi6dkeWX3AACgc04DJEmCJEmYXqdGUbTwkyEQv+hpXDlA6BlBkSQpIs3Dowazsoh0zFfSPDt7RwFAhCPb6ytzPs+Bp3gSOZGOegMRkapAiGH/wNS02xG3/Bm11U6dUcGptie+AiGmotrcS1O+D9oISohajQ0FRVAyR5sWyzaKsi+qySJ6rEgiuMgUERQuUHyxt4mM2kqYTy6bibs/cRw+3TlL3MethYfGfXGVdLzWVe4kq/dwOrVQdlJEdni9TCYc3ewEAOxUJiLzqEx7nEiEnvAUjy9BBOXpd3vhC4Yxp7EKx85wAQD29EfWzIx6AxhVREeibiZVoJAfSqoIDxTT1MOFySSBaxbq5DEWFEHJHHnYnnyb1/Jkyt6oYxUfI5IKPFLCRSa3rIgXAebbSm3GJUhjtR0XHT8dp81tFPfxNtyvP7oF5/7kRSE6tEVK8Xrbucqt0rGLB9AUyo54s07xAEDHNFnc8GhMV54KZIHkNSiMMfz6P/LwxouOn465TWq0Z0QZQxAKM9HV5HRYEs494gKFUjypI2zuzbGjabxwlrxQjANjjIzaskCSJN28UHYokWleLpDOwFKtURugjaBQioeAOsMFkLtEXtrZDyCy0CnaQ4STswiKIlD+sOEAJvwhSFL8uotU6FCiL7wL5oAmxZNreNFlPIHy6BtdeK9nFCYJ+Ojx00VR793P7sJpd67Dsd99Gkfd9CT+ubkbQHKhlqzIjJiKanMf+3BhJjdZw6GNSFKKJzP06OSZ9Ifw9qERAMDp8+QL37RqUPyRdYz8YncyEIp5QSBEKRXJlge1UbNoNnW5AUQKlMGxOBGUHNSgAMCSNjnNwR1k21wVsGfxheR+L7v7xhAMhfFejwcAML8p/eGD6aLWoMQWKE9u7QEg/7jb6ytx4dJWUffA399QmOF/n98NADhhZl3M9XBqeIqHIigpow4KjB1BEXb3FEExDFr/jlLxxMg3enih/GVjFyb8IbS5HMKbKR03a36cqhECRb3YjVUoW2qt5fTNTUJ9VBfL9iNyuG4wQqDEiaDkoIsHAFYsbonopsg2FdOmaVE++0cvCOGzqM2Z1XpTQRi1BWOf3LjPy+fO6AAg19o89PllcU3pTp5dH/N+DkVQ0ocXMMeLoKh291SDYhT4lbTNbJri/kukBo88ZRNBeevAMADgEyfPFNH4dFI8Y77IIlm7xSSizrHSPLy7kVI8ZUJjTaSfxntH5OjCcESKJ1kERV+BYrOY8NR1Z4m/sxUoZpOEBuXHo+1Oio4e5YJkNSjcdbFesy2dcxvw1HVn4t/XnoFtq1dELH/OwqaEr0c1KOkTTFKDYjYl78Qi8stkidUiFAI9Ujzvdsvni6UznOJ4mp5AkZflF1aSJKmtxjEECnXxlBnHtddG/N036sPgmC+i7mRwPEkNis5FsoCalgGAWY3Z14r89cunRfx91Vlzsl5nKiSqQWGMiVRafdTQwnlNNVjc5kKlzYIrT5sNAPhM56yk9T78hx6vyIyYSkB08cQpkqWBgYaDBgVmjyiSzTDFM+EPim7DJW0u1FYkdoGNBW+0qNacQ/gF71iMVmM1xVMap/bS2IscMqOuEvd/6kQ8+LlTMFsRBe/1jEZETbS3d/SM4jt/fwd7+seE1b3eERRAjjxcenI7jm6uweWnzEr+hCR0NFbh0pPbxd8nzkpcy6EXtgQRlAl/SBT7RQsULd+6YAH+8Pll+PYHFyZ9vRryQUkb1QclSZEsCRTDQHN4sseRZYpn+5FRhJk8B6zJ6dBMjk+jBkU5TlXb1VKD6gR1dN4SK5LV/8xZgpy/pAUAsKDFif2DE3jrwHDECU5bg3L3szvx5NYe7OgZVSuwc3QVc+fFx+i6vk+dOkvYyB+j+I3kGnVY4NSTG4+e2CymhO+hw2rGGfMb4z6uhWpQ0kf4oMRJ8Ygi2SwdNwn9oBbj7KmwZjfReOthuXtnsVLLV1shX2SN+0PwB8OiQSAR/Diltargt2Mdw7xKDUqp+KCQQEmDha1OrH23B//ZNRBxv7ajh3edvHlgWCjdRL4cRmLJdBf+fHUnAqFwQrMzPbHyLp4YRm28ELmhyqaboy2/EqEalNRRu3jiFcnK91MExThQiid7KrLs4nl9nzw77ESls7DGYYEkyZO/RyYDERPW48FT0TWaCEriGpTSSvEUx5nTICxsrQEAMbSOo+3oMZskkYvnCpcb9BQDvBUuX/D6hViD5g4rM4FaXfGt69Ml0dUHERvVByVZBIUEilGYpBRP1ogungwFypsH5PMEP6aaTBJcFVa4JwJwT/iTCpRQmIlpxtoISnWCeTxUJFvGLGyNbLudqziwjnqDCITCYIzFbH+tceR24F4xo9agTD25dQ3rbxinlztkOZEsgqLWoFCKxyhQDUr28OP2aAYXMz0jXvR6fDBJwFJNupwXyqZi1qYVINWai9yqBHV0PvJBKV9m1FUI9QoAx7XXgeuR4XE/JpTcoha7xZRSrrFcUX1Qpp7cxNDCOh0FSpZXReUIj4xY43yPeYqHuniMA6V4soePORmKM8okEVsOuQEARzXXRHQWptNqzItgrWYpwogznt29PxiGX7mYqM5BY0YhoDNnGkiShAUtNeLvuU1V4ks8OO6P6ShL0ZPEJPJBOaSkeNrr9auHqbTKP9xsvA3KDdHFk8RJlnxQjMMERVCyhg+KHc5AoPD24kVRUfd0OnnGRQdPpNiI18UzoYm46D3/rVCQQEmTU+c0iNtzGqtE++vQuD+mH4qzojSUbK6wWeL7oPAUzwwdIygOm1qZzxidUFMhkGIXD0VQjIOXIihZIyIoabQFc3qV4aUtUfVz6XihqB08kecQNcUTirm83VI67sGlsRd55OITZ4jbJ8+uFwKFIiiZYY1TgxIOMzWComeKR7miZCxyoBoRH9VJNrHVPdWgGAcqks0e7cVnuvR44giUFFM8jDGs3zsIIFYERf5Mo1M8qqlb6VwUl86e5ImOxir8+jMnocpmRkO1Xf0Sj/ngjZH3q6skgZII1Qcl8uTWP+aDPxiGSQJaa/Xr4tEesCf9oZIpJssloosnToqHW91TF49xIB+U7OFiIh3nV06PR46mNzsjj10uUSSbWPQ8+kYX7lq7A8BUH62qOF088SIuxUzp7EkeOXdRs7gtBMpEQOR950yrwt7+cQBqmJCIjbYGhTEm/E54gWyrqyKug2kmWMzysK1AiAlTIyIxaoon9udgpRSP4Zj0y4KfUjyZ49KkY8JhBlMcgR6LnhE5+tvijI6gKHUtCSIojKnT2QHgrYPuiMfjdfF4FCFVSmUFlOLJkgYRBvRhSEnxaAtpaymCkhDeZsxY5AmOp3dm1OlvGMdtoLmpUT5Ys+kwblrzTlo210aBp3ji+aDwNuMApXgMg2p5Tof4TOEChbH0jB37R33o9fggScDsxqqIx/j5YCSBQBkY84vjHwCcEjWhPV4XD4/08O0uBUpHahUIbZ6St4Id3ezEE+/IjrJ6Xv2XIlaLetILhBh4N51oMdbRA4Vjt5ox6gvmzQvlzf1DuO7RzQCAphoHrj1nfl5eVy8CYT4sME4EhdqMDYcYs1FC4f58Y7OYUGUzY9wfgnvSD1eKF5tvHRwGABzVVDNFLHC7+0QpHt4cUF9lw/XnHoXlC5sjHledZCOPXx6vEkEpobpHOntmSX217AY4OOYXE47bNDUTsbpTCBWtgNPWoQiTNh0LZDmOLGdspAsfuQ4Ae5X2w2JCLZJNHEGhGhTjkMtBpeVEOr4lHD6D59j2qfPM1Dbj+OvjF2fzplXjU6fOmlJoyyMoo97IdfCoTClFUEigZEmDJoKyo2cUQGRYj64qE2MxaSMoGoEypL8HCiffbrJ9o15xO5OCu0KjWt3H6eIhJ1nDoUZQqAYlG1xpOL9yuAfK0S3OKY+JwtsEAkWkt+Mc+1Sr+0irhFJM8ZBAyRKe4tnVN4a+UR/MJglL2lxYeVwbAOCK02YXcOuMjyRJGrv7qREUPT1QOLyzwZenGpT+UdUfp39sqleO0eEuv5Z4Rm2izZjEuFHgM1xKqeW0EKRjrMYZHpeFQmP11AYJ7oMy6gvGja4nc9DmJmyhMIuwSuDHFn5OKgVIoGRJQ9SXYX5TNSpsZvz0kuPw9m3nYe606gJtWfHAiy8DQfkEFwyFcUQxOspFBCXfKZ6hcfVqKZZXjtHhkZF4XTzUZmw8eAFldIsqkR6iqDWNCMqwImZidXA6K6zieNfr8U55HEg+g0ybttN28uwfzF3dXqEggZIltVFfwvnNcgePySSVVLFSLuEzXngNyp7+cYTCDFU2M5pr9PNA4TjynOIZ86kHt2I0hxOzeJJY3VMExTiIKbhUg5IVror0a1C4mInVwWk2SZjdIJcA7FGsKKIR6e04HYwmkySEJxeiw+N+bOlyAwBmkkAhODaLCTWaSZPzKGKSNtHzeN5WBm0tme5Ky3sgVVSBkh+xoK229xXhDKBkPig8xROiGhRDwBijGhSdSKWoNZpEERQAIqq+u29qwXwwFEa3WxYoMxviCw21UFb+nLWF+PObS+ccRAJFB7RpnnlNpfPlyBfRNSg8vTNnWlXc52RDviMoWr+CaMfcYkAMC0wyi4dSPMbAGwiD105SBCU7eM3IcIo1KN5ASFz4OOMUq3IBsbtvdMpjR0a8CIYZbGZTwuhxk1PuHu1RjpX9Y/L/p89riJh8XOyQQNGBak0EpZTUa76wKSkenv7gsy9y5cLLzavyVYOizRMHQgzhIkuFcOERv0hWqUEpsv0qVbQW6DSLJzvqlIvPVAWK9rdeE6dA+SilDIB3fWrh9SfT6yoSRo9bXXL654jiWMsL8acpthelAgkUHdh6WA2v8fwikTp2RTDwiMagIlByVY3O7b/zlW6JtqQutigKj2xZ47iS5nqacSjMcOE9/0HHqn9jV+/UgzoRibZANhcp0nKiXrlIGk5xYOCYknKptlvivvfcaXxn79iUieqpGlROr5UFymG3HDkZUIrvp9WQQEnI7NmzIUnSlH/XXHMNAMDr9eKaa65BQ0MDqqurcfHFF6O3t1fvzcgrWqc/G1lLpw0XDJNKYR8/GDTEaNPTA5HiyUPBajjMRMEip9gKZdVhgfG6eJQurBwJr2e39+Ldbg8YA+57cU9OXqOU4HUJpTQ0rlDUiVlr6UVQErV3z26sgtUsYcwXRPdIZCfPAd6Jk2TEB09//3XjIQBqBKWRIiiJeeONN3DkyBHx75lnngEAfPzjHwcAfP3rX8fjjz+Ov/zlL3jxxRfR3d2Nj33sY3pvRl759WdOxH2Xn4BXvvWBQm9KUcLD0JNTIii5+bE5oiI2uSR64iig+ooUC4EkTrK5trrfsHdI3C7GNu18w7tIaJJ69ggjzhS/d1wcatP+0VjNJnQoZp47oyKCO3vlwtn5SWoZF7XKJnADYz54A6GcR50Lhe4Se9q0aRF/33nnnZg7dy7OPvtsjIyM4Le//S0eeeQRfOAD8sn8gQcewMKFC/Haa6/h1FNP1Xtz8oIkSbhgaWuhN6NoiTZOGxpXDIdyVINit0ZGbHIJv6IymySYJQn+ULjoUjzBJF08agRFf4HCGMP/vbJP/P2OYiNOxIfXS/C5L0Tm8Dq4cX8I/mA4aYSc28/XJBAoADC/qQY7e8fw3pFR1FZYsWS6Cz9+egee3S5nE3idSjyOn1knbg+N+0VaL9nrFhs53Ru/348//OEPuP766yFJEjZu3IhAIIDly5eLZRYsWICZM2di/fr1RStQiOzQRlAYY8KJsT5HKZ6KPKZ4xjUh31CYyQKlyCIooosniQ9KLtqMufkUZ2jcD28gJEQtMRXeEkuT1LOnxmGBSQLCTHaTbXIm9mVKJcUDKM0U7wA/XPseAOCEmbV466BbPH5se23C55tNEhqrbRgY82NkMqA5zpTWZ57Tgok1a9bA7XbjyiuvBAD09PTAZrOhtrY2Yrnm5mb09PTEXY/P54PH44n4R5QOdo2z65gvKCIM0S69epHPNuMxn2o5zouBi02gJPVByaFRG4+mWc2SaHNOtaOiXOG27CRQssdkkkQUJZU6lLEUIxkLWyPn9GjFyS8/fWJK9UPcCNQzGdDUHZWWcM+pQPntb3+LCy64AG1tbVmt54477oDL5RL/2tvbddpCwghoh/fxFuNKmzlnV8nc6j4vNSg+9cChtlMXl1mbanUfZ5oxbzPOQYqHm9zNa6oRrs1DKXZUlCs8gpKrNv1ygxfr93mSz9Ea9aYWQVm+sBnnLGiacv+158zHisUtKW0XF6CD435R61Zqs5dyJlAOHDiAZ599Fl/4whfEfS0tLfD7/XC73RHL9vb2oqUl/oeyatUqjIyMiH9dXV252myiADg0KZ5ce6AA+Z1mrO2osBVpBEW1uo99uLDmMIIyoRx4q2xmTctn8U2Ezid88q6LIii60FYb6TmSiLEUUy1mk4TfXnky9t95IU6f1yDu75zTkOBZkXBL+/2D45oLIRIoKfHAAw+gqakJF154objvxBNPhNVqxbp168R9O3bswMGDB9HZ2Rl3XXa7HU6nM+IfUToIweBXBUquWoyB/Frda2tQuGNusQmUZF08ZiFQ9N8v3qJdabegrko+6Kfa8lmuuKlIVldmKUJg+5HkHjxjKXTxRLPqgoXi9omz6hIsGUlHo9zps6NnVKRhS02g5GRvwuEwHnjgAVxxxRWwWNSXcLlc+PznP4/rr78e9fX1cDqd+O///m90dnZSgWwZw39UY778tMvZ85ji0RbN8df1FVkXDz/4xbW6N+fOqO3NA8MA5AhKleKX4yaBkhA1xUMRFD1YNqcBv19/AC/s6AOwOOGyvIvHmYZAWTLdhf13Xph8wShmNXDhpNZkllqKJyd78+yzz+LgwYP43Oc+N+Wxn/70pzCZTLj44ovh8/mwYsUK/OIXv8jFZhBFgqtCHWl+SHFSbElSLZ8NjijflVyinWza61EESp6GFOoFj/jEm/FhMeWmBiUcZnhxRz8A4OiWGvQpZlRUg5IYXkRMKR59OGm2HNU4ODSRtIMs1S4ePZihmLlx75QKq1lEM0uFnKR4zjvvPDDGcNRRR015zOFw4N5778XQ0BDGx8fx2GOPJaw/IUofZ4X8Y/Z4A9jdL//Ycjl0sSKPKR5+NeuqsKk1KEUWQeHba09ida93imfjwWEcdk+ixmHBl86em7bteLmiGrVRikcPplXbUeOwIMzkeo9EpGLUphcz6iLt8EstvQPQLB7CAPAIytC4X4wgn5tDgaIaw+U+guKeVFs+bUoEothqUPj7FM+kypyjItldypXhSbPq4LCaRdfCsCL6oueYEPJ7Qj4o+iJJEuZMk49He/sTC5R8RlCaauwRadfqEmsxBkigEAbgaGV41u6+MRGunDctlwJFqUHJQ7svn5HRUGUr2iJZX5IUT66s7g8NRw5Oq9dMln3otQNYfOtT+Nfb3bq+ZrEz5gsKoUgRFP2Yq1jT71UivPFI1QdFD0wmSXQYAfmJ2uQbEihEwWmqcYgJn4A8pnx6beJhWdngUE60gRBDMMfpll6PPAysxeUQRbL+IvJBCYWZOOHFS/Hkyuq+a1hu62xXQtl1Gh+Um9dsxYQ/hB8/tUPX1yx2ePTEbjGR266O8Nk5B6KcjaNRpxnnJ3o1QzNUsMpGAoUgcsJHjlPN/JZMd+V0TDyfngwAEzlK8/BoQo8yrbTV5YDdzI3aiieCoo32xEvxqF08+u2XLxjCm/vlIYH8IMwny2pPEvsHJxDO0ZDCYoTSO7lhuvId7E7ihcJrUPI1E2dGrVqHUmpzeAASKIRB+MRJqjvwbOVqJVc4rGbxY07FHTJd1m7twdLbnsI3//o2PMoBq9npKEqjNu22xi+S1beLJxgK44M/+w+OKOJuyXQXAHV4JA+jc5IVLpYTvOaJ0jv6wlMph4fjCxRfMCQKyvOVbtFGUEqtxRgggUIYhIZqO655/1y0uhz40tlzcv56vI2ZRzj0Ihxm+M7f38GEP4RH35Qdj6tsZtQ4rEXZxcNt+U1S8mnGehXJrnuvD3s0xYj8IFxbFTsqQBOOVdSuMYqg6AmvgzrsnhTGhdHw9A6Qv3TLjHpVoNQ4Su8zJ4FCGIYbVizA+lXnYFZDbiMogFwTAgA9Hn0FSo/HK8zmOM3KaxVjkWyyAllANXDTq0j2bxsPidurLlgASZLXX2O3iJZmLZs0g9bKHW5iRxEUfWlzOVBttyAQYtg3MI4Newdx/t0v4ZENB8UyPLJXacufH8l0TYqHimQJokRQIyjJ52ukQyzzN/5awkm2GAWKNf6hQi2S1We/Drvlz+SBz56Mq8+eK+6XJEnUoQDA8TNrAQAv7erX5XVLAapByQ2SJGHuNPnCaVu3B5/41Wt4r2cU96zbJZbJd/0JEJniqSzBomgSKERZ0qpENY7onOKJZZ/PBYrNrPivFJVAUTxQ4qR3AP3bjD3CLnzqSbZeExm4cGkrANmbYjyqLqVcGRYChSIoesO9UG57/F1xX4/HKyKiqkDJnzhs1jhuF9NxJVVIoBBlCU+79Oqc4uHutFU2szipH68MACvmItlUIih61aB4JuUDvati6pWodojk7IYqKNkfMVSw3OFFslSDoj+81ZhHqTjf+fs7ANQ5PPmMoJhNEt539DRYTBLOXdSct9fNF6WXtCKIFMhVBIVHHNpqK/DtDy7Env4xfPKUmQBQpEWy8rYmiqAIq3sd9osxpjnQTz3JNlTbxe36ahsqrGZM+EN5GfxYDKjijgSK3syZFlkb1zmnAev3DuIvGw/hvz8wX7W5z3M3zS8uPwFD4/4p1velAEVQiLKEh0b1jqDwQYAOqxnvX9CEL5w5R0QY7JbiM2pLpUiWd/foEUEZ94fAVxMrxdOgqUGpr7SJuUoUQZEpxFV8udChsT+wmU347ZUnib9/+dIeUSQb63ubSyptlpIUJwAJFKJMaXXJxWUDY34R9dADfiUfyzOER1CKKVecSorHomOKh59grWZJjCTQor06ra+2CdO9fEymLgYKUahZLmgFyrQaOyptFnzxzA4AwNZuj/julqIfSaEggUKUJXWVqi+JnmZtXHzEshm3F2ENSipFsjxCFAqzrAf48RRFjcMq2ou1aLs3a+wWTQSFimQBYNQXPz1GZEelzSJGclywpAUAcOXpikA5PII+Ze4WiUP9oHeSKEskSUKL04GDQxM4MuIVRkypEAozBELhmCKER1BiXf0Xow+KGkFJ4INiUvc1FGbC+j4T1A6e2IcmrWiRJAmVSgSlVGtQvIFQWjN1eAQl3vtHZMeDnzsFW7tHcMa8aQCA6bUVaHM50D3ixcu7BgCUph9JoaAIClG2NFbz4XOpR1AYY1h578s496cvxmxtVVM8U08qpVoka9YIkmzTPIkKZAHgvMVypwIfJuko4RqUnzyzE0tufQrPbOtNaXm5wFgRKFQkmxOanA58YEFzxFyqE2fXAwD2Dsjux42aQm4iO0igEGUL73QYmQwkWVLFPRHA1sMedA1N4ultPVMe9yao2eCihRfSFgM+LrhSqEEBkgsU94Qfj2w4iEc2HMTWwyNY/fg2IUoANcXjjNFiDACL21x45utn4YmvnQlAHfw4WYIC5Z51uxAMM/z6pb0pLT/hDwkvGkoz5I8z5zVG/H3a3IYCbUnpQd9iomzhAoWfFFNB25b80s4BfPT4GRGPa7t4oinGCArf1niDAoFIgRJKMjDwqoc24vV9QxH3TfiDuPPiYwCoEZREnRDzm2vE7VJN8Wj3Z2AstQgfj56YTZKozSFyz4eObcX3/rUNo74gvvuRxcLQjcgeEihE2eLMIILSN6oKlPV7BsEYi6iL8CpFpY5EKZ4iqUEJhxl+/NROAIkFg3buSCAcf9+8gdAUcQIAf9l4CHd8bCkkSRLTn1ONAJRqikcrhIcm/AmWVNG2GMcqMCZyQ6XNgi23ngcG5G0GT7lAKR6ibMkkxTM4pp4sejxedA1FzvLxJkiJ8DqOYmkz/tV/9ooICu9eiIUkSRGdPPHgDpwmCWKuCX/Ow8rQNc9k8giKlsoSbTM+NDwhbrsnAhiZSP4dTVfcEfphMkkkTnIACRSibBEpHm8aAiWqoPbGv22J8FHhVveJIih6+q7kkn+93S1uf/jYtoTLWlIYGDisRALqq2z4zRUn44rOWWKo3bYjHgDqSTbVIk+eyii1GpTDw5HC98DQeNLniAiKnQpkidKABApRtmSS4hkYiwy3v7Z3CHet3SH+5kWlFbZYRbLFk+I5ODiBrYc9kCRg403LUZXEfMqSQgRleFx+72orbehorMJ3Vy7B15cfBQAiQuBJ0wm1wiYvV2oRlG53pEDZPzgRZ0kVMmkjSg0SKETZwtMI6QkUOYJy5ny1cn/NpsPiNj9RxipStGuKZLM1NMs1T70rdyh1zmmImH8Tj1QGBvJJu3WV6hU+d93kwkT18SjzCIo7cgTDtX/chINJREohpukSRC4hgUKULWoXT2KBcvVDb+Lcn7yICX9Q1KB8+Ng2/OCjSwEAg+N+DCrCRTVqi5/iYUy/yb+5ghdpLp3uSml5K5/Hk6CLh6d46irVeTrc1IrPMeGfRcoRFKXWZ6LEIihHRuQIygeXtoj7Hnptf8LnjCYxuSOIYoMEClG2qEWy8duM+0d9eOrdXuzqG8Pr+4ZEDcq0ajs+uWymMAzjIfjJFAQKYPw0D+9WmlaTmumUGkGJv1/uGAKlRomgjClX/8JJNsUalEolxeMtsQgKT/F8pnM2Vh4n1/8kS/Ok+94RhNEhgUKULa5KNYISL+Wy8YDaFrt/YBxbD8vFnA2KC22LK3Iq8qRSJBsrxaN1YzV6J0+/MlekSZn6nIxUalDGFRGhrWeJjqCkm+Jx2EqvzZgxJiJY02sr8BGlQLlnJPHk7aHxqQKQIIoZEihE2cJD4f5QOK5geG2vKlBue3ybuN1UI5+4W5QTOD958Ct57nCqxWI2iUiD0SMoQqCkGEGxKOIrkCDFM+mfWkDM6yVEBCXNFE9lCQ4LHBr3wxcMQ5KAZqcDzcp37EgSgcLTj/XVJFCI0oAEClG2VNstQjDEKpTtdk/id6/uj/lcHjnhJw81ghI/xQMUz8BAPpk11RRPKhEUb4wCYl4kO+oLYtPBYSEUU01TcCEzGmMuUrHSrRTITqu2w2YxoVX5rg2M+RJ+b3iNT0MVCRSiNCCBQpQtkiSJcDgXGFr+9/ndMZ/35ffNFbf5yYNf3Sbq4gG0dvfGTUlM+IMi5ZJqBCWVGpRY4k0bKfnoL14FAEiSWpuSDB6BSWdcgdHpVgpkW5X6pvoqmxC2sb6nnEFK8RAlBgkUoqyZ0yg7mu7tn2qEtemgGwBw3qJmrF/1AQByYe3ly2aKZZoVgdKjnDhElCBGigdQBYrXwAMD+zxy9KTCahYRjmRYUujimYyR/rJbTBGzfAC5/sSUoisnHyo4mobZntHZengEADC9Vv5uSZKEGXWyWNndPxb3ebwGpYFSPESJQAKFKGvmNsmDva57dDPueGK7uN8XDGF33ygA4OYPLUKrqwLvfncFXrrx/ZhRVymW4zUoXUMTYIxp2oxj/7TsRTAwkEeDWlyOlGe6pJLiiRVdkiRpSjon3iTjWPAIii8YLhqH3kT0erz4+XNy5G5xm9riffzMOgDAZx94Aw+8sm/K84KhsEhT1lOKhygRSKAQZc2iNqe4/cuX9mJXryxKdvSMIhBiqKu0iqvXKrtFtCZzjm6ugdkk4ciIF4eGJ0WRaNIUj4FrUPgcGL7fqWBOweo+Vg0KgCnvaZUtDYFit4BrKN4BVMz8+qW94vbHTpgubvNWYwD47uPbIoZWAoB7MgDeiFZLbcZEiUAChShr/uuEGTh3UbP4m8+E4e3ES6a7EkYRXJVWUafRNaT6VBRzkewhZQ6MNlKUDB5B8SbYL1GDEpX+ijYWG0uj4NVkklQ32jQcgY1I19AEHnrtAADgjo8tRatLFYhnHTUtIrX4/Ht9Ec8dEmMErCLdRhDFDn2TibKmwmbGrz9zEv7rxBkAIOzEt3bLdQDaMHs8uGcHr0ORJDWVE00xzONRBUrqERSLWRYo1/5xkzhZRiNqUKLEW7QA5B1EqcLff0+RR1Bu//c2+IJhOKwm8X2MePyiJfjESe0A1PooDn/PKb1DlBIkUAgCwKx6OVpwUImCvKsUKi6Z7oz7HA5PUfRqikvjRV14iuf5HX34zX/2ImjAWpRMUjxLNEJuyyF3zGW8cUzs9g1EFihfuLQ15dcFNK3GBiqUTXfWUjjM8OruQQDAH794qhgdoEWSJJw6tx4AsH8w8j0TAoU6eIgSggQKQQCY2SALlANDEwiFGd7rkWtRUoqgCIEiR1Di1Z8AgN0iP/bwhoO4/d/b8duXpxY8FpoDShSpvT71FM9XPzBP3OaW9tFMxulw0s6bufYD8/CNFUen/LqAJoKSw1ZjxhgODI4nLALm/OzZXTj1jnV4be9gyusfmvBj1BeEJCX+zs1qkLvO9g9E2t4PUgSFKEFIoBAE1HqLfQPjODw8CV8wDJvFJCIriXBFCZR49ScAcPq8xoi/H3+7O9NN1h1fMISrH3pTpKo6lJNhKtRW2nCRUsg5MJpeiufWDy/GlafNxhPXnonrzztazDdKFd7148lRBIUxhqsf2oizf/QCvvevbQmXdU/48dNnd6LX48Plv9mQ8pRl7kTcUGWPmNkUDf8+9ni8ougYAIZJoBAlCAkUgoDqDNs/6sNq5SQ0u6EyJT8OfoLsEQIl/s/qy++bizs/thTLOuRQ/a7esZSuyvPB0+/24ql3e8XfdWme7LjrbP/Y1BqScJjFddl1WM247SOLIzqq0oG3GucqxbN3YBxPb5Pfl4c3HJjSQcNZt70Xx61+RvwdCjN8/JevpvT5cnHb4kpsjFdfZUOVEoHitUIA1aAQpQkJFIIA0OZyiFTDs9vlk9HsFCMIPIIiDM7imLRxLj1lJh754qmwWUzwBcM4rDnRFBLeYg0AJ86qS/v5XNDEKpId18zKSXXOTqrwLqBcpXhe2T0gbgdCDH/beHjKMt5ACP/zly3i7zPny5GyrYc9eGP/0JTlo+H1Sy1JhjNKkiRSb13DapqHUjxEKUIChSAgH/h/dunxERbrC1pTu6LnAuWIYlGeqAaFYzZJwsV2TwJ30HyivSL/0X8dk/bzayvkk6N7YmokY9wnR0/MJiluh1Om8BqgXKV4/rNLFiizlDql3/xnb0R6BQBe3TMg9vsXl5+ABz93Cs5T2te5M2wiePQtlenRM7lA0bS1D43LAocEClFKkEAhCAWr2YQ509SoyRWds1J6Hp99wiP5iWpQtPC6l8NuY0RQ+BX5PZcdjznTqtN+fm2lLBRGJqdGULi3SZUtfodTpqhdPPpHUIKhMF7bIxe73nXxMWh22jE47sdf3uyKWI775lx8wgx8cGkrJEkSxa7bj4wiGb3cvTcFgcIjKLwlHoh0/yWIUoEECkFo+M6Fi9DqcuAXl5+AhurUBuW5KiOdO1OJoADqoEFeIFloeASlPY32Yi3cwTRWBIXXWNgsqb036aB28aQfQQmHGZ56twdbutwxH994YBijviBqK604aXY9vvI+uVvpV1Et4rzzqaNRLape2FoDQDX/SwSPoKQiUGZGtcQzxtCtiNx0C4wJwsiQQCEIDad01GP9qnPwwTS8OKKnxyarQeG0RA0a1Iu+US/ueHI7dvelnjryBUNiO9JxkNXiEhGUqUJh1WPvAAAGYhTQZgtP8bgzECh/33QYVz+0ESvvfSWiBofz3A7ZsfWkWfUwmyRcclI76qts6BqaxPV/3iLSSgeHZF+SmZq6JV70u7tvNKEx32t7B/Hizn4A6vDJRPBU017FP2Z4IiA8ZiiCQpQSJFAIIkuiZ584UowSNDv1j6AEQmGc8v11+OWLe/HTZ3am/Lwjbi8YkzuQGjOchlurCDV5Lkxk5wr3RlnQUpPRuhPBIwobDwyn1NYbCjPc98Ie/Oip93DHk+qAyL9vmlr8ul8RAafOkbuuKmxmfPnsuQCAf27pxvcelzu+eARF25Y+vbYCTocFgRCLKxb9wTC++shb4m9el5SIhUpt1N7+MUz6QyJ6Mq3GLnx2CKIUIIFCEFmSaQSlNQcRlDf3D4vbvBspFbqEe2xlxjUiXKj5g2FxRc/hXUGfO6Mjo3UnYn5ztejk0QqOePxzy2H8cO17uPf5PRgYU+tlXt0z1Vita0g++Wtrk75wZge+pIiUp7f1YsIfFPb8PLoByIXXXEy82x27UHZn76jYhlUXLEjJHK+pxo7GahvCDNjROyrEUTrOvwRRDJBAIYgsqXGoE3WB1ItkeQSlV8cIyqt71JbYYJhN6TaJR7b1JwBQaTPDqszkcUcVyvIiWW2XlF7YLWbc+uHFAIAH1x9I2jXz/Hv9EX8f1SwXBG89PIIJf2Sh7SGNcONIkoTrzz0KJklOZ3FR6HRYRBSJc/xMWZjxFE40u/rktNKyjnpcrYieZEQLH257n46xHkEUAyRQCCJLTCZJtBoDQLU9vQjKqC+Y1gTfRLx1UI2ghMLxUwvR8BPx9CwEiiRJcMVpNeYdNlU5ECgAcPGJM7B8odzWe/+Le+IuxxjD6/tkX5IHrjwZb992Hp667iy0uhwIhhk2a4bwjUwGxADC6OJTm8Ukoh3PK3Uqs2IIhAuWyN46z7/XF3PuEv985jal1zXFO4T+/EYXnlMmG89OIT1EEMUECRSC0AFtmqfVldpJvspuEa6g/WlO8I3H3n75arpSWe/t/96WkpNpt1uO4kyvzaxAlsNbjaMFCjdqq9bZpE3L506fDQD419tHsKMndmtv19AkejxeWM0STp3TAKfDCkmScPJsucbkZY0p22NvHQIgp05iCasORRA8rbjvdsQQCEunu1Bjt2DcH8LO3qlikc/USaX2RAsfYrnl0Ag2HpBFaefchrTWQRBGhwQKQejANE1LcjpRiEbFHl6P7pZAKCzaeflE4Nf2Dk3x7IgFL7Rsq82uC4TXoUR7oYx5c5fi4WhP0NHTfjmv7ZPrTI6dURtRK3SuYqr25zcPYcIfRLd7Ej9btwsARGtxNNxpmPvYHDNj6pA/k0nCse21AIBNXcNTHufiIpa4ScTyhc248rTZaFK+Py1OB06Ymb77L0EYGRIoBKEDK5RQvtkkRRRUJoMLGz0iKMMTfoQZIEnAV96vnlS1aZ94cKOvVKM/8YgXQRFGbTkUKJIkiTTP4NhUs7j9A+O4XZmztEzpyuGsWNyCGXUVGBjz4fZ/b8fHfvEq3BMBLG5z4uMnzYj5etqC2Gq7BR89fnrM5Y6fWQtAFovaNM9Pnt4hCqTTTc/w+UUbvn0Onrj2TDz3jbNhTmFuFEEUEyRQCEIHPn9GBx7/6hl4+AvL0FSTehRCDNjTQaCMKKLAVWFFR2MVfnH5CQAQN93BCYeZaHXONoIialA0niT+YBiBkJxmyqVAASBapAejIlK+YAjXPboZHm8Q1XYLPtM5O+Jxm8WE25RC20c2HESPxwunw4L7P3UirObYh0ntrKZbP7worrEfj2w8vqUbH/nfVzAyGcC67b2457ndynoqMy5wlSQJi9qcqLTl9n0liEJA32qC0ImlMUL8yWis1i/Fw0UBT7PwtMFBzcyWWAyM++APhSFJamdRpvBi4eEJNYIxrikArkqxBTtTGrhAiRpYeMcT72FzlxtOhwVrrjk95n4uX9SM5QubRXv2Dz62NGHb7ykd9Th+Zi3aXBX4rxNjR1kA4KTZdaivsmFo3I9tRzy48a9b8G637C57zoIm/OaKk3S3/yeIUoAECkEUED0jKDyt4lIKdtuUdM3wRACT/lBcf5YjSoFsU409brQgVXgEhhfrAmqBrN1igiXL9SejoWqq4Nt+xIOHNxwAAPzs0sRzhm77yCK8fciNWQ2V+OCSxG7CVXYL/v6V05NuU43Diie/dibWbe/Dzf/YiqfeVf1pfvzxY0mcEEQcKMVDEAWk2SmfUP/0RvJC1mRwt1YeQXFWWEQ3D5+0HAv+WLb1JwBwzIxaAMA7h1QvEj7JONfpHUATQdHUoPz0mZ0IhBjOXdSM9y9oSvj8GXWVeG3VOfjz1Z0w6VjT0ex04JPLZuJLZ88R9527qBl1NH2YIOJCAoUgCsi8JtX6nftpZAqfgcMLVSVJEl4rRxKYwaktxtkLlMVtTkiS7I7bNyqvl0dQKnOc3gHUlNnguBxB8XgDWKf4hNy44uiU1mEySTmLalx99lwRNbswjXlPBFGOkEAhiAJy7AyXmH78yIaDWa2Lp3i0s4GmKw6o3IgtFmoEJftBc1V2C+YpKRRuesZrUKrzGEHh9vFbutwIhRlm1ldifrP+c4DSxemw4olrz8SDnzsFHzm2rdCbQxCGhgQKQRQQi9mEf35VrmN4dntvUpv2RHB7eZfGNG620gq7byC+QOlWoit6TcI9dY7sR/KfXbLpGU/x5COC0qx0UA2N++ENhLBJEUnHKV4kRmBajR1nHTVN1xQSQZQiJFAIosDMb67B+YtbwBjw2FtTJ+qmSqwICm+F3TcQ3/K+VycPFM7p8xoBqCZkfL5NPmpQaiutYnDgvoFxYQN/guJFQhBE8UAChSAMwAVLZaO3VEzV4hFdgwIAR7fIaY3tR+J7oXCzsBZXbB+PdJndWBmxXp7iqcqDV4ckSaK9+gdPbMfmLjesZgnLFadYgiCKBxIoBGEAlk6XPVS2H/HEHCqXCrxzRTsXaHGbPLPl4NCEGJKnJRRm6PPIBaXZeqBwWp1yJIanWcb9SoonxSGK2cIFCk8x/fcH5kdMIyYIojjIiUA5fPgwPvWpT6GhoQEVFRVYunQp3nzzTfE4Ywy33HILWltbUVFRgeXLl2PXrl252BSCKApmN1Sh2m6BLxjG7v7UJhBrYYyhSzFkm6GZBVSrESuX/HL9lOftGxiHPxRGhdWsW4rHWWERhb89I968FskCmFIM+6FjqFuGIIoR3QXK8PAwTj/9dFitVjz55JPYtm0b/t//+3+oq1MHWd1111245557cP/992PDhg2oqqrCihUr4PXGb4UkiFLGZJItywFg62FP2s8fnghgVBEC0e6n154zX9yOtoB/+5AbgJwK0muWiyRJEY6uapFsfgTK0RqBMqOuIu1BfARBGAPdBcoPf/hDtLe344EHHsApp5yCjo4OnHfeeZg7dy4A+Urv7rvvxk033YSVK1fimGOOwYMPPoju7m6sWbNG780hiKJhSZuc5nm3O/1OngPK9N4WpwMOa2Qq5fpzjxIeJ3sHIqf8PvVuDwDgNM0kYD2oVwzI3BN+TQQlPymeZXPqRQTnvEUt5NRKEEWK7gLln//8J0466SR8/OMfR1NTE44//nj8+te/Fo/v27cPPT09WL58ubjP5XJh2bJlWL9+aggaAHw+HzweT8Q/gig1FrTKV/7JhvvFYlevnBaKFy3gE5b3atJHe/rH8PQ22Xb9I8fp68nBU0vDEwGNUVt+Iig1Div+fHUnblhxNP7nvKPy8poEQeiP7gJl7969uO+++zB//nw89dRT+PKXv4xrr70Wv//97wEAPT3yFVtzc2RVfXNzs3gsmjvuuAMul0v8a29v13uzCaLgLGjJXKA8tukQAGDJdGfMx+cowmXvwDj+sfkwLrr3FXz7sXfAGPCBBU1Y0BL7eZlSp3QSuSf8mPBzq/v8RFAAeXDjNe+fl5fWZoIgcoPuv95wOIyTTjoJP/jBDwAAxx9/PLZu3Yr7778fV1xxRUbrXLVqFa6//nrxt8fjIZFClBzzm2ogSXLdRv+oT1iiJ+PIyCRe2yt36Hz61Nkxl+GRlf0D4/jXliM47FZn8ySaxJspvJNoaNyPMV/+fFAIgigddI+gtLa2YtGiRRH3LVy4EAcPyjbeLS2y30Nvb2/EMr29veKxaOx2O5xOZ8Q/gig1KmxmYayWTh0Kd0ud31SNmQ2x22l5C3HPiDdCnNgtJnwgyQC9TOAC5Rcv7EGf4oeSDx8UgiBKB90Fyumnn44dO3ZE3Ldz507MmjULANDR0YGWlhasW7dOPO7xeLBhwwZ0dnbqvTkEUVScOEvuduMRkVToUZxgj0owa6ZRicZs0UwZtllMuOnChVOKavWgY5paC7N/UG5/rnaQQCEIInV0Fyhf//rX8dprr+EHP/gBdu/ejUceeQS/+tWvcM011wCQWxCvu+463H777fjnP/+Jd955B5/5zGfQ1taGiy66SO/NIYiignfTrN8zkPJzVCfY+EZrfMovZ0FLDXbefgE+3Tk7/Y1MgQ8uacH150YWqDalmLIiCIIAclCDcvLJJ+Pvf/87Vq1ahdWrV6OjowN33303Lr/8crHMjTfeiPHxcVx11VVwu90444wzsHbtWjgc+jhZEkSxwgftvXN4BN5AKKXoBo+gtCRwguW+JJxce4NYzCZce858nNJRj9+9sh+zGioxs57cXAmCSJ2cxFw/9KEP4UMf+lDcxyVJwurVq7F69epcvDxBFC2tLgecDgs83iD2DYxjYWvyeqtUIig1dgtsFhP8QdlGf3aezMtOndMgRBdBEEQ60CwegjAQkiRhXlM1AGB3X2qW9yKCkkCgSJKEaZo0D7mrEgRhdEigEITBmDtNFih7UpjJwxhTIyhJhv1pfUj4EEGCIAijQmX1BGEweATl7md3YU//OD56fBs+sKA55rLuiYBI2zQ5ExehaqccL0ohdUQQBFFISKAQhMHgAgUAHt/SjbcPueMKlCNKeqehyga7JXFB7ZffNxe7+sbw9XOPovk0BEEYHhIoBGEwjp9ZB4tJQjDMAAAHhybidvT0Kumd5iTpHQB439FNeOvmc/XdWIIgiBxBNSgEYTDqq2z42aXH4/pzj0KF1QzGgAOK2Vk0PILSmqBAliAIohghgUIQBuTCY1px7TnzcVSznO7ZG6dglhfINpNAIQiixCCBQhAGZo7S0bN3YDzm4z0j8lydZB08BEEQxQYJFIIwMHOVmTa7ekdjPr5/QE79zIozJJAgCKJYIYFCEAbm2PZaAMBLuwYQDIUjHguGwth+xAMgsvOHIAiiFCCBQhAGpnNOA2orrRga9+Otg+6Ix945PIJRXxBOhwULWsjXhCCI0oIECkEYGIvZhLOPmgYAeOytQxGPvbpnEADQObcBZhP5mhAEUVqQQCEIg3P+4hYAwJ/e6MKNf92CN/cPYcIfxNbDIwCAk2fXF3LzCIIgcgIZtRGEwVmxuAWXL5uJhzccxJ/fPIQ/v3kIS6Y7EQjKRm5zptHgP4IgSg+KoBCEwTGZJHz/o0vxs0uPE/dtPezBDqWzhw8XJAiCKCVIoBBEkbDyuOl48Yb3CfM2AKi0mdFeRy3GBEGUHiRQCKKImNVQhZXHTRd/H9VcAxMVyBIEUYKQQCGIImNRq9pSvKClpoBbQhAEkTtIoBBEkbFkukvcnl5bUcAtIQiCyB0kUAiiyJhWYxe3j59ZV8AtIQiCyB3UZkwQRcja687E7r4xnDG/sdCbQhAEkRNIoBBEEbKgxUn29gRBlDSU4iEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnCQQCEIgiAIwnAU5TRjxhgAwOPxFHhLCIIgCIJIFX7e5ufxRBSlQBkdHQUAtLe3F3hLCIIgCIJIl9HRUbhcroTLSCwVGWMwwuEwuru7UVNTA0mS0nqux+NBe3s7urq64HQ6c7SFxqMc97sc9xmg/S6n/S7HfQZov4t5vxljGB0dRVtbG0ymxFUmRRlBMZlMmDFjRlbrcDqdRfsBZ0M57nc57jNA+11OlOM+A7TfxUqyyAmHimQJgiAIgjAcJFAIgiAIgjAcZSdQ7HY7br31Vtjt9kJvSl4px/0ux30GaL/Lab/LcZ8B2u9y2e+iLJIlCIIgCKK0KbsICkEQBEEQxocECkEQBEEQhoMECkEQBEEQhoMECkEQBEEQhqNoBcro6Ciuu+46zJo1CxUVFTjttNPwxhtviMcZY7jlllvQ2tqKiooKLF++HLt27YpYx9DQEC6//HI4nU7U1tbi85//PMbGxiKWefvtt3HmmWfC4XCgvb0dd911V172LxbJ9vnKK6+EJEkR/84///yIdRh9n1966SV8+MMfRltbGyRJwpo1ayIez+fn+pe//AULFiyAw+HA0qVL8cQTT+i+vxw99nv27NlTPv8777wzYhkj7XeyfX7sscdw3nnnoaGhAZIkYfPmzVPW4fV6cc0116ChoQHV1dW4+OKL0dvbG7HMwYMHceGFF6KyshJNTU244YYbEAwGI5Z54YUXcMIJJ8But2PevHn43e9+p/Pequix3+973/umfNZf+tKXIpYppv0OBAL45je/iaVLl6KqqgptbW34zGc+g+7u7oh1FNtvW499Lrbfta6wIuWSSy5hixYtYi+++CLbtWsXu/XWW5nT6WSHDh1ijDF25513MpfLxdasWcO2bNnCPvKRj7COjg42OTkp1nH++eezY489lr322mvsP//5D5s3bx677LLLxOMjIyOsubmZXX755Wzr1q3sj3/8I6uoqGC//OUv876/jCXf5yuuuIKdf/757MiRI+Lf0NBQxDqMvs9PPPEE+853vsMee+wxBoD9/e9/j3g8X5/rK6+8wsxmM7vrrrvYtm3b2E033cSsVit75513DLvfs2bNYqtXr474/MfGxgy738n2+cEHH2Tf/e532a9//WsGgG3atGnKOr70pS+x9vZ2tm7dOvbmm2+yU089lZ122mni8WAwyJYsWcKWL1/ONm3axJ544gnW2NjIVq1aJZbZu3cvq6ysZNdffz3btm0b+/nPf87MZjNbu3at7vus136fffbZ7Itf/GLEZz0yMlK0++12u9ny5cvZo48+yt577z22fv16dsopp7ATTzwxYh3F9tvWY5+L7XetJ0UpUCYmJpjZbGb/+te/Iu4/4YQT2He+8x0WDodZS0sL+9GPfiQec7vdzG63sz/+8Y+MMca2bdvGALA33nhDLPPkk08ySZLY4cOHGWOM/eIXv2B1dXXM5/OJZb75zW+yo48+Ope7F5Nk+8yYLFBWrlwZdx3Fts/RP+h8fq6XXHIJu/DCCyO2Z9myZezqq6/WdR9jkcl+MyYfyH7605/GXa+R9zvWiZqzb9++mCdqt9vNrFYr+8tf/iLu2759OwPA1q9fzxiTTxAmk4n19PSIZe677z7mdDrF+3DjjTeyxYsXR6z7E5/4BFuxYoUOe5aYTPabMVmgfO1rX4u73mLeb87rr7/OALADBw4wxor/t53JPjNW3L/rbCnKFE8wGEQoFILD4Yi4v6KiAi+//DL27duHnp4eLF++XDzmcrmwbNkyrF+/HgCwfv161NbW4qSTThLLLF++HCaTCRs2bBDLnHXWWbDZbGKZFStWYMeOHRgeHs7lLk4h2T5zXnjhBTQ1NeHoo4/Gl7/8ZQwODorHim2fo8nn57p+/fqI1+HL8NfJJ6nsN+fOO+9EQ0MDjj/+ePzoRz+KCOkX234nY+PGjQgEAhHbu2DBAsycOTPi+7B06VI0NzeLZVasWAGPx4N3331XLFMs+6zl4YcfRmNjI5YsWYJVq1ZhYmJCPFYK+z0yMgJJklBbWwugNH/b0UTvM6ecftdainJYYE1NDTo7O/G9730PCxcuRHNzM/74xz9i/fr1mDdvHnp6egAg4sfJ/+aP9fT0oKmpKeJxi8WC+vr6iGU6OjqmrIM/VldXl5P9i0WyfQaA888/Hx/72MfQ0dGBPXv24Nvf/jYuuOACrF+/Hmazuej2OZp8fq49PT0JXyefpLLfAHDttdfihBNOQH19PV599VWsWrUKR44cwU9+8hOxnmLa72T09PTAZrNNOZhHfx9i7Q9/LNEyHo8Hk5OTqKioyNEeZM4nP/lJzJo1C21tbXj77bfxzW9+Ezt27MBjjz0GoPj32+v14pvf/CYuu+wyMRSvFH/bWmLtM1B+v2stRSlQAOChhx7C5z73OUyfPh1msxknnHACLrvsMmzcuLHQm5Yzku3zpZdeKpZdunQpjjnmGMydOxcvvPACzjnnnEJtNpEnrr/+enH7mGOOgc1mw9VXX4077rijbKyxy4WrrrpK3F66dClaW1txzjnnYM+ePZg7d24Btyx7AoEALrnkEjDGcN999xV6c/JCon0u5991UaZ4AGDu3Ll48cUXMTY2hq6uLrz++usIBAKYM2cOWlpaAGBKNX9vb694rKWlBX19fRGPB4NBDA0NRSwTax38sXyTaJ9jMWfOHDQ2NmL37t0AinOfteTzc423TCHeg1T2OxbLli1DMBjE/v37xXqKab+T0dLSAr/fD7fbHXF/9Pch0312Op2GjJ7EYtmyZQAQ8Vsvxv3mJ+oDBw7gmWeeiYgklOJvG0i8z7Eo9d+1lqIVKJyqqiq0trZieHgYTz31FFauXImOjg60tLRg3bp1YjmPx4MNGzags7MTANDZ2Qm32x0RcXnuuecQDofFj72zsxMvvfQSAoGAWOaZZ57B0UcfXdBUR6x9jsWhQ4cwODiI1tZWAMW9zwDy+rl2dnZGvA5fhr9OPkllv2OxefNmmEwmERYvtv1Oxoknngir1RqxvTt27MDBgwcjvg/vvPNOxImNnwQWLVoklimWfY4Hb0XW/taLbb/5iXrXrl149tln0dDQEPF4Kf62k+1zLEr9dx1Boat0M2Xt2rXsySefZHv37mVPP/00O/bYY9myZcuY3+9njMltmbW1tewf//gHe/vtt9nKlStjtqMef/zxbMOGDezll19m8+fPj2hZc7vdrLm5mX36059mW7duZX/6059YZWVlwdqME+3z6Ogo+8Y3vsHWr1/P9u3bx5599ll2wgknsPnz5zOv1yvWYfR9Hh0dZZs2bWKbNm1iANhPfvITtmnTJlHVnq/P9ZVXXmEWi4X9+Mc/Ztu3b2e33nprTtvyst3vV199lf30pz9lmzdvZnv27GF/+MMf2LRp09hnPvMZw+53sn0eHBxkmzZtYv/+978ZAPanP/2Jbdq0iR05ckSs40tf+hKbOXMme+6559ibb77JOjs7WWdnp3ict9ued955bPPmzWzt2rVs2rRpMdttb7jhBrZ9+3Z277335rTdNtv93r17N1u9ejV788032b59+9g//vEPNmfOHHbWWWcV7X77/X72kY98hM2YMYNt3rw5oqVW251SbL/tbPe5GH/XelK0AuXRRx9lc+bMYTabjbW0tLBrrrmGud1u8Xg4HGY333wza25uZna7nZ1zzjlsx44dEesYHBxkl112GauurmZOp5N99rOfZaOjoxHLbNmyhZ1xxhnMbrez6dOnszvvvDMv+xeLRPs8MTHBzjvvPDZt2jRmtVrZrFmz2Be/+MWINkPGjL/Pzz//PAMw5d8VV1zBGMvv5/rnP/+ZHXXUUcxms7HFixezf//734bd740bN7Jly5Yxl8vFHA4HW7hwIfvBD34QIU6Ntt/J9vmBBx6I+fitt94q1jE5Ocm+8pWvsLq6OlZZWck++tGPRggYxhjbv38/u+CCC1hFRQVrbGxk//M//8MCgcCUbTnuuOOYzWZjc+bMYQ888EBO9lmP/T548CA766yzWH19PbPb7WzevHnshhtuiPBBKbb95i3Vsf49//zzYh3F9tvOdp+L8XetJxJjjOkTiyEIgiAIgtCHoq9BIQiCIAii9CCBQhAEQRCE4SCBQhAEQRCE4SCBQhAEQRCE4SCBQhAEQRCE4SCBQhAEQRCE4SCBQhAEQRCE4SCBQhAEQRCE4SCBQhAEQRCE4SCBQhAEQRCE4SCBQhAEQRCE4SCBQhAEQRCE4fj/PeZZv7KaIG4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hr001_df[(hr001_df['datetime'] >= '2020-02-13 18:00:00') & (hr001_df['datetime'] <= '2020-02-13 19:00:00')].plot(y=' hr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IBI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "ibi001_df = pd.read_csv('./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/001/IBI_001.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "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>datetime</th>\n",
       "      <th>ibi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-13 15:33:22.059328</td>\n",
       "      <td>0.828163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-13 15:33:22.934368</td>\n",
       "      <td>0.875040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-13 15:34:21.593303</td>\n",
       "      <td>0.984420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-13 15:34:22.483969</td>\n",
       "      <td>0.890666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-13 15:34:23.421512</td>\n",
       "      <td>0.937543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     datetime       ibi\n",
       "0  2020-02-13 15:33:22.059328  0.828163\n",
       "1  2020-02-13 15:33:22.934368  0.875040\n",
       "2  2020-02-13 15:34:21.593303  0.984420\n",
       "3  2020-02-13 15:34:22.483969  0.890666\n",
       "4  2020-02-13 15:34:23.421512  0.937543"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibi001_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "ibi001_df['datetime']=pd.to_datetime(ibi001_df['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ibi001_df[(ibi001_df['datetime'] >= '2020-02-13 18:00:00') & (ibi001_df['datetime'] <= '2020-02-13 19:00:00')].plot(y=' ibi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TEMP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp001_df = pd.read_csv('./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/001/TEMP_001.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "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>datetime</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-13 15:28:50.000</td>\n",
       "      <td>30.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-13 15:28:50.250</td>\n",
       "      <td>30.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-13 15:28:50.500</td>\n",
       "      <td>30.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-13 15:28:50.750</td>\n",
       "      <td>30.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-13 15:28:51.000</td>\n",
       "      <td>30.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datetime   temp\n",
       "0  2020-02-13 15:28:50.000  30.21\n",
       "1  2020-02-13 15:28:50.250  30.21\n",
       "2  2020-02-13 15:28:50.500  30.21\n",
       "3  2020-02-13 15:28:50.750  30.21\n",
       "4  2020-02-13 15:28:51.000  30.21"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp001_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp001_df['datetime']=pd.to_datetime(temp001_df['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp001_df[(temp001_df['datetime'] >= '2020-02-13 18:00:00') & (temp001_df['datetime'] <= '2020-02-13 19:00:00')].plot(y=' temp')"
   ]
  }
 ],
 "metadata": {
  "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}