Funnel Performance Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Funnel Performance Analysis\n",
    "\n",
    "cohort by first contect date and closed_month\n",
    "\n",
    "#### Analysis of Conversion Rate\n",
    "\n",
    "1. Distribution and evolution of conversion rate, conversions volume and conversions rate\n",
    "2. Evolution of leads generated across channels MoM\n",
    "3. Evolution of conversion rate across channels MoM\n",
    "4. Which channel is the most effective in generating leads and converting users?\n",
    "\n",
    "#### Analysis of Deal Velocity\n",
    "\n",
    "5. Distribution of deal velocity across cohorts\n",
    "6. Evolution of deal velocity across cohorts \n",
    "7. Distribution of deal velocity across channels\n",
    "8. Distribution of deal velocity across SRs\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load funnel datasets\n",
    "mql=pd.read_csv('olist_marketing_qualified_leads_dataset.csv', parse_dates=True)\n",
    "closed_deals=pd.read_csv('olist_closed_deals_dataset.csv', parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join funnel datasets\n",
    "\n",
    "The two dataframes that are going to be joint are 'mql' containing data regarding marketing qulified leads that reached out to Olist sales reps and 'closed_deals' containg data about the marketing qualified leads that converted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mql_id</th>\n",
       "      <th>first_contact_date</th>\n",
       "      <th>landing_page_id</th>\n",
       "      <th>origin</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>sdr_id</th>\n",
       "      <th>sr_id</th>\n",
       "      <th>won_date</th>\n",
       "      <th>business_segment</th>\n",
       "      <th>lead_type</th>\n",
       "      <th>lead_behaviour_profile</th>\n",
       "      <th>has_company</th>\n",
       "      <th>has_gtin</th>\n",
       "      <th>average_stock</th>\n",
       "      <th>business_type</th>\n",
       "      <th>declared_product_catalog_size</th>\n",
       "      <th>declared_monthly_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dac32acd4db4c29c230538b72f8dd87d</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>88740e65d5d6b056e0cda098e1ea6313</td>\n",
       "      <td>social</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",
       "      <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>1</th>\n",
       "      <td>8c18d1de7f67e60dbd64e3c07d7e9d5d</td>\n",
       "      <td>2017-10-20</td>\n",
       "      <td>007f9098284a86ee80ddeb25d53e0af8</td>\n",
       "      <td>paid_search</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",
       "      <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>2</th>\n",
       "      <td>b4bc852d233dfefc5131f593b538befa</td>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>a7982125ff7aa3b2054c6e44f9d28522</td>\n",
       "      <td>organic_search</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",
       "      <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>3</th>\n",
       "      <td>6be030b81c75970747525b843c1ef4f8</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>d45d558f0daeecf3cccdffe3c59684aa</td>\n",
       "      <td>email</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",
       "      <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>4</th>\n",
       "      <td>5420aad7fec3549a85876ba1c529bd84</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>b48ec5f3b04e9068441002a19df93c6c</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>2c43fb513632d29b3b58df74816f1b06</td>\n",
       "      <td>a8387c01a09e99ce014107505b92388c</td>\n",
       "      <td>4ef15afb4b2723d8f3d81e51ec7afefe</td>\n",
       "      <td>2018-02-26 19:58:54</td>\n",
       "      <td>pet</td>\n",
       "      <td>online_medium</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                             mql_id first_contact_date  \\\n",
       "0  dac32acd4db4c29c230538b72f8dd87d         2018-02-01   \n",
       "1  8c18d1de7f67e60dbd64e3c07d7e9d5d         2017-10-20   \n",
       "2  b4bc852d233dfefc5131f593b538befa         2018-03-22   \n",
       "3  6be030b81c75970747525b843c1ef4f8         2018-01-22   \n",
       "4  5420aad7fec3549a85876ba1c529bd84         2018-02-21   \n",
       "\n",
       "                    landing_page_id          origin  \\\n",
       "0  88740e65d5d6b056e0cda098e1ea6313          social   \n",
       "1  007f9098284a86ee80ddeb25d53e0af8     paid_search   \n",
       "2  a7982125ff7aa3b2054c6e44f9d28522  organic_search   \n",
       "3  d45d558f0daeecf3cccdffe3c59684aa           email   \n",
       "4  b48ec5f3b04e9068441002a19df93c6c  organic_search   \n",
       "\n",
       "                          seller_id                            sdr_id  \\\n",
       "0                               NaN                               NaN   \n",
       "1                               NaN                               NaN   \n",
       "2                               NaN                               NaN   \n",
       "3                               NaN                               NaN   \n",
       "4  2c43fb513632d29b3b58df74816f1b06  a8387c01a09e99ce014107505b92388c   \n",
       "\n",
       "                              sr_id             won_date business_segment  \\\n",
       "0                               NaN                  NaN              NaN   \n",
       "1                               NaN                  NaN              NaN   \n",
       "2                               NaN                  NaN              NaN   \n",
       "3                               NaN                  NaN              NaN   \n",
       "4  4ef15afb4b2723d8f3d81e51ec7afefe  2018-02-26 19:58:54              pet   \n",
       "\n",
       "       lead_type lead_behaviour_profile has_company has_gtin average_stock  \\\n",
       "0            NaN                    NaN         NaN      NaN           NaN   \n",
       "1            NaN                    NaN         NaN      NaN           NaN   \n",
       "2            NaN                    NaN         NaN      NaN           NaN   \n",
       "3            NaN                    NaN         NaN      NaN           NaN   \n",
       "4  online_medium                    cat         NaN      NaN           NaN   \n",
       "\n",
       "  business_type  declared_product_catalog_size  declared_monthly_revenue  \n",
       "0           NaN                            NaN                       NaN  \n",
       "1           NaN                            NaN                       NaN  \n",
       "2           NaN                            NaN                       NaN  \n",
       "3           NaN                            NaN                       NaN  \n",
       "4      reseller                            NaN                       0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#joining funnel datasets\n",
    "funnel = mql.merge(closed_deals, on='mql_id', how='outer')\n",
    "#check results\n",
    "funnel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join closed_deal dataset\n",
    "first contenct date needed to compute deal velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>has_company</th>\n",
       "      <th>has_gtin</th>\n",
       "      <th>average_stock</th>\n",
       "      <th>business_type</th>\n",
       "      <th>declared_product_catalog_size</th>\n",
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       "      <th>landing_page_id</th>\n",
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       "  </thead>\n",
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       "      <td>4ef15afb4b2723d8f3d81e51ec7afefe</td>\n",
       "      <td>2018-02-26 19:58:54</td>\n",
       "      <td>pet</td>\n",
       "      <td>online_medium</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>b48ec5f3b04e9068441002a19df93c6c</td>\n",
       "      <td>organic_search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a555fb36b9368110ede0f043dfc3b9a0</td>\n",
       "      <td>bbb7d7893a450660432ea6652310ebb7</td>\n",
       "      <td>09285259593c61296eef10c734121d5b</td>\n",
       "      <td>d3d1e91a157ea7f90548eef82f1955e3</td>\n",
       "      <td>2018-05-08 20:17:59</td>\n",
       "      <td>car_accessories</td>\n",
       "      <td>industry</td>\n",
       "      <td>eagle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-04-04</td>\n",
       "      <td>2f08471dfb83baef522f952438a36f6e</td>\n",
       "      <td>referral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>327174d3648a2d047e8940d7d15204ca</td>\n",
       "      <td>612170e34b97004b3ba37eae81836b4c</td>\n",
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       "      <td>6565aa9ce3178a5caf6171827af3a9ba</td>\n",
       "      <td>2018-06-05 17:27:23</td>\n",
       "      <td>home_appliances</td>\n",
       "      <td>online_big</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>1ceb590cd1e00c7ee95220971f82693d</td>\n",
       "      <td>organic_search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f5fee8f7da74f4887f5bcae2bafb6dd6</td>\n",
       "      <td>21e1781e36faf92725dde4730a88ca0f</td>\n",
       "      <td>56bf83c4bb35763a51c2baab501b4c67</td>\n",
       "      <td>d3d1e91a157ea7f90548eef82f1955e3</td>\n",
       "      <td>2018-01-17 13:51:03</td>\n",
       "      <td>food_drink</td>\n",
       "      <td>online_small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-01-14</td>\n",
       "      <td>22c29808c4f815213303f8933030604c</td>\n",
       "      <td>paid_search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ffe640179b554e295c167a2f6be528e0</td>\n",
       "      <td>ed8cb7b190ceb6067227478e48cf8dde</td>\n",
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       "      <td>d3d1e91a157ea7f90548eef82f1955e3</td>\n",
       "      <td>2018-07-03 20:17:45</td>\n",
       "      <td>home_appliances</td>\n",
       "      <td>industry</td>\n",
       "      <td>wolf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>manufacturer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10-09</td>\n",
       "      <td>a0fbc0263cb98bdb0d72b9f9f283a172</td>\n",
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      ],
      "text/plain": [
       "                             mql_id                         seller_id  \\\n",
       "0  5420aad7fec3549a85876ba1c529bd84  2c43fb513632d29b3b58df74816f1b06   \n",
       "1  a555fb36b9368110ede0f043dfc3b9a0  bbb7d7893a450660432ea6652310ebb7   \n",
       "2  327174d3648a2d047e8940d7d15204ca  612170e34b97004b3ba37eae81836b4c   \n",
       "3  f5fee8f7da74f4887f5bcae2bafb6dd6  21e1781e36faf92725dde4730a88ca0f   \n",
       "4  ffe640179b554e295c167a2f6be528e0  ed8cb7b190ceb6067227478e48cf8dde   \n",
       "\n",
       "                             sdr_id                             sr_id  \\\n",
       "0  a8387c01a09e99ce014107505b92388c  4ef15afb4b2723d8f3d81e51ec7afefe   \n",
       "1  09285259593c61296eef10c734121d5b  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "2  b90f87164b5f8c2cfa5c8572834dbe3f  6565aa9ce3178a5caf6171827af3a9ba   \n",
       "3  56bf83c4bb35763a51c2baab501b4c67  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "4  4b339f9567d060bcea4f5136b9f5949e  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "\n",
       "              won_date business_segment      lead_type lead_behaviour_profile  \\\n",
       "0  2018-02-26 19:58:54              pet  online_medium                    cat   \n",
       "1  2018-05-08 20:17:59  car_accessories       industry                  eagle   \n",
       "2  2018-06-05 17:27:23  home_appliances     online_big                    cat   \n",
       "3  2018-01-17 13:51:03       food_drink   online_small                    NaN   \n",
       "4  2018-07-03 20:17:45  home_appliances       industry                   wolf   \n",
       "\n",
       "  has_company has_gtin average_stock business_type  \\\n",
       "0         NaN      NaN           NaN      reseller   \n",
       "1         NaN      NaN           NaN      reseller   \n",
       "2         NaN      NaN           NaN      reseller   \n",
       "3         NaN      NaN           NaN      reseller   \n",
       "4         NaN      NaN           NaN  manufacturer   \n",
       "\n",
       "   declared_product_catalog_size  declared_monthly_revenue first_contact_date  \\\n",
       "0                            NaN                       0.0         2018-02-21   \n",
       "1                            NaN                       0.0         2018-04-04   \n",
       "2                            NaN                       0.0         2018-04-03   \n",
       "3                            NaN                       0.0         2018-01-14   \n",
       "4                            NaN                       0.0         2017-10-09   \n",
       "\n",
       "                    landing_page_id          origin  \n",
       "0  b48ec5f3b04e9068441002a19df93c6c  organic_search  \n",
       "1  2f08471dfb83baef522f952438a36f6e        referral  \n",
       "2  1ceb590cd1e00c7ee95220971f82693d  organic_search  \n",
       "3  22c29808c4f815213303f8933030604c     paid_search  \n",
       "4  a0fbc0263cb98bdb0d72b9f9f283a172         unknown  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_deals=closed_deals.merge(mql, on='mql_id')\n",
    "closed_deals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating plotting function for variable distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define plot_distribution_subplot\n",
    "def plot_distribution_subplot(data, title, axlabel, ax):\n",
    "    plt.figure(figsize=(12,6))\n",
    "    sns.distplot(data, axlabel=axlabel,ax=ax)\n",
    "    ax.axvline(data.mean(), c='green', ymax=0.95)\n",
    "    ax.axvline(data.median(), c='orange', ymax=0.95)\n",
    "    ax.axvline(data.describe()[4], c='yellow', ymax=0.95)\n",
    "    ax.axvline(data.describe()[6], c='red', ymax=0.95)\n",
    "    ax.set_title(title)\n",
    "    ax.legend(labels=['mean', 'median', '1st quartile', '3rd quartile'])\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create variable 'converted'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>mql_id</th>\n",
       "      <th>first_contact_date</th>\n",
       "      <th>landing_page_id</th>\n",
       "      <th>origin</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>sdr_id</th>\n",
       "      <th>sr_id</th>\n",
       "      <th>won_date</th>\n",
       "      <th>business_segment</th>\n",
       "      <th>lead_type</th>\n",
       "      <th>lead_behaviour_profile</th>\n",
       "      <th>has_company</th>\n",
       "      <th>has_gtin</th>\n",
       "      <th>average_stock</th>\n",
       "      <th>business_type</th>\n",
       "      <th>declared_product_catalog_size</th>\n",
       "      <th>declared_monthly_revenue</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dac32acd4db4c29c230538b72f8dd87d</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>88740e65d5d6b056e0cda098e1ea6313</td>\n",
       "      <td>social</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8c18d1de7f67e60dbd64e3c07d7e9d5d</td>\n",
       "      <td>2017-10-20</td>\n",
       "      <td>007f9098284a86ee80ddeb25d53e0af8</td>\n",
       "      <td>paid_search</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b4bc852d233dfefc5131f593b538befa</td>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>a7982125ff7aa3b2054c6e44f9d28522</td>\n",
       "      <td>organic_search</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6be030b81c75970747525b843c1ef4f8</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>d45d558f0daeecf3cccdffe3c59684aa</td>\n",
       "      <td>email</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5420aad7fec3549a85876ba1c529bd84</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>b48ec5f3b04e9068441002a19df93c6c</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>2c43fb513632d29b3b58df74816f1b06</td>\n",
       "      <td>a8387c01a09e99ce014107505b92388c</td>\n",
       "      <td>4ef15afb4b2723d8f3d81e51ec7afefe</td>\n",
       "      <td>2018-02-26 19:58:54</td>\n",
       "      <td>pet</td>\n",
       "      <td>online_medium</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mql_id first_contact_date  \\\n",
       "0  dac32acd4db4c29c230538b72f8dd87d         2018-02-01   \n",
       "1  8c18d1de7f67e60dbd64e3c07d7e9d5d         2017-10-20   \n",
       "2  b4bc852d233dfefc5131f593b538befa         2018-03-22   \n",
       "3  6be030b81c75970747525b843c1ef4f8         2018-01-22   \n",
       "4  5420aad7fec3549a85876ba1c529bd84         2018-02-21   \n",
       "\n",
       "                    landing_page_id          origin  \\\n",
       "0  88740e65d5d6b056e0cda098e1ea6313          social   \n",
       "1  007f9098284a86ee80ddeb25d53e0af8     paid_search   \n",
       "2  a7982125ff7aa3b2054c6e44f9d28522  organic_search   \n",
       "3  d45d558f0daeecf3cccdffe3c59684aa           email   \n",
       "4  b48ec5f3b04e9068441002a19df93c6c  organic_search   \n",
       "\n",
       "                          seller_id                            sdr_id  \\\n",
       "0                               NaN                               NaN   \n",
       "1                               NaN                               NaN   \n",
       "2                               NaN                               NaN   \n",
       "3                               NaN                               NaN   \n",
       "4  2c43fb513632d29b3b58df74816f1b06  a8387c01a09e99ce014107505b92388c   \n",
       "\n",
       "                              sr_id             won_date business_segment  \\\n",
       "0                               NaN                  NaN              NaN   \n",
       "1                               NaN                  NaN              NaN   \n",
       "2                               NaN                  NaN              NaN   \n",
       "3                               NaN                  NaN              NaN   \n",
       "4  4ef15afb4b2723d8f3d81e51ec7afefe  2018-02-26 19:58:54              pet   \n",
       "\n",
       "       lead_type lead_behaviour_profile has_company has_gtin average_stock  \\\n",
       "0            NaN                    NaN         NaN      NaN           NaN   \n",
       "1            NaN                    NaN         NaN      NaN           NaN   \n",
       "2            NaN                    NaN         NaN      NaN           NaN   \n",
       "3            NaN                    NaN         NaN      NaN           NaN   \n",
       "4  online_medium                    cat         NaN      NaN           NaN   \n",
       "\n",
       "  business_type  declared_product_catalog_size  declared_monthly_revenue  \\\n",
       "0           NaN                            NaN                       NaN   \n",
       "1           NaN                            NaN                       NaN   \n",
       "2           NaN                            NaN                       NaN   \n",
       "3           NaN                            NaN                       NaN   \n",
       "4      reseller                            NaN                       0.0   \n",
       "\n",
       "  converted  \n",
       "0        No  \n",
       "1        No  \n",
       "2        No  \n",
       "3        No  \n",
       "4       Yes  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating variable 'converted' to establish which leads converted\n",
    "funnel['converted']=funnel['seller_id'].apply(lambda x: 'No' if pd.isnull(x) else 'Yes')\n",
    "#check results\n",
    "funnel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>mql_id</th>\n",
       "      <th>first_contact_date</th>\n",
       "      <th>origin</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dac32acd4db4c29c230538b72f8dd87d</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>social</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8c18d1de7f67e60dbd64e3c07d7e9d5d</td>\n",
       "      <td>2017-10-20</td>\n",
       "      <td>paid_search</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b4bc852d233dfefc5131f593b538befa</td>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6be030b81c75970747525b843c1ef4f8</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>email</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5420aad7fec3549a85876ba1c529bd84</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mql_id first_contact_date          origin  \\\n",
       "0  dac32acd4db4c29c230538b72f8dd87d         2018-02-01          social   \n",
       "1  8c18d1de7f67e60dbd64e3c07d7e9d5d         2017-10-20     paid_search   \n",
       "2  b4bc852d233dfefc5131f593b538befa         2018-03-22  organic_search   \n",
       "3  6be030b81c75970747525b843c1ef4f8         2018-01-22           email   \n",
       "4  5420aad7fec3549a85876ba1c529bd84         2018-02-21  organic_search   \n",
       "\n",
       "  converted  \n",
       "0        No  \n",
       "1        No  \n",
       "2        No  \n",
       "3        No  \n",
       "4       Yes  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting columns\n",
    "funnel=funnel[['mql_id','first_contact_date','origin','converted']]\n",
    "#check results\n",
    "funnel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Assessing & Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8000 entries, 0 to 7999\n",
      "Data columns (total 4 columns):\n",
      "mql_id                8000 non-null object\n",
      "first_contact_date    8000 non-null object\n",
      "origin                7940 non-null object\n",
      "converted             8000 non-null object\n",
      "dtypes: object(4)\n",
      "memory usage: 312.5+ KB\n"
     ]
    }
   ],
   "source": [
    "funnel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check duplicates\n",
    "funnel.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning Issues:\n",
    "\n",
    " 1. **first_contact_date** is an object and not datetime: convert 'first_contact_date' to datetime.\n",
    " \n",
    " 2. **origin** has 60 missing values: change NaN to 'unknown'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8000 entries, 0 to 7999\n",
      "Data columns (total 4 columns):\n",
      "mql_id                8000 non-null object\n",
      "first_contact_date    8000 non-null datetime64[ns]\n",
      "origin                7940 non-null object\n",
      "converted             8000 non-null object\n",
      "dtypes: datetime64[ns](1), object(3)\n",
      "memory usage: 312.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# 1. convert 'first_contact_date' to datetime\n",
    "funnel['first_contact_date']=pd.to_datetime(funnel['first_contact_date'])\n",
    "#check results\n",
    "funnel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8000 entries, 0 to 7999\n",
      "Data columns (total 4 columns):\n",
      "mql_id                8000 non-null object\n",
      "first_contact_date    8000 non-null datetime64[ns]\n",
      "origin                8000 non-null object\n",
      "converted             8000 non-null object\n",
      "dtypes: datetime64[ns](1), object(3)\n",
      "memory usage: 312.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# 2. change NaN to 'unknown'\n",
    "funnel['origin'].fillna('unknown', inplace=True)\n",
    "#check results\n",
    "funnel.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Distribution and evolution of conversion rate, conversions volume and conversions rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define funnel_ts in order to resample data (datetime index is needed)\n",
    "funnel_ts=funnel.set_index('first_contact_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get montlhy conversion rate\n",
    "monthly_conv_rate=(funnel_ts.resample('M')['converted'].value_counts(normalize=True).unstack().fillna(0))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get montlhy conversion volume\n",
    "monthly_conv_vol=funnel_ts.resample('M')['converted'].value_counts().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get monthly leads volume\n",
    "monthly_leads_vol=funnel_ts.resample('M')['mql_id'].count().rename('leads_vol').to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Leads Volume')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xbNky7rzzTgDS09NJT09v9f4PPfQQr732GgD5+fls3bqVmJgYAgICmD59OgCjR4/m/fff9/aPKiIi0im8tW4PtQ0eHrg0jeFap0mAaUN7cM95A3hwyZcMSYjge2f1cTqST1OxS0REpJMLDPx60VqXy9Xy3OVy0dDQgNvtJjMzk4EDBx517Yl2dFq6dClLlixh+fLlhISEMHnyZGpqagDw9/dvud7tdtPQ0NBWP5KIiEintjC7gP7dw0hPinQ6iviQO87txxd7y/jd4s30jw9v2c1RjqZpjCIiIl3cBRdcwMMPP9wyJH7NmjUATJo0iRdffBGADRs2sG7duqOuLS0tJSoqipCQEL744gs+//zz9gsuIiLSCe08UEn2rkNcOTrphB86Sdfichn+cvVwBvaI4PYFq9lRVOF0JJ+lYpeIiEgXd99991FfX096ejpDhw7lvvvuA+AHP/gBFRUVpKen86c//YkxY8Ycde20adNoaGggPT2d++67j3HjxrV3fBERkU7lX6sLcBm4fGSi01HEB4UE+PHUjaPxd7v43nNZlNXUOx3JJ2kao4iISCeWkpLChg0bWp7Pnz+/1XNPPPHEUdcGBwfz8ssvt3rf3Nzclsdvv/12q20qKr7+tHHmzJnMnDnz20QXERHpcjwey79WFzKxfxzxWoxejiEpKoTHrhvFdX9fwZ0vreHpm87A7dIowMNpZJeIiIiIiIiID/h8x0EKS6qZOTrJ6Sji48b2ieGBGWks3VLEn979wuk4Pkcju0RERERERER8wMLVBYQH+XH+kHino0gHcN3Y3mzeU8YTH+9gcI8ILtPU1xYa2SUiIiIiIiLisMraBt7ZsJfp6QkE+budjiMdxJxL0hibGs3PMtexNr/E6Tg+wyvFLmPMNGPMFmPMNmPMz1s5H2iMeaX5/ApjTErz8euMMTmHfXmMMSO8kVFERERERETEV7y9YS9VdY1cOUpTGOXk+btd/O26UcSGBXLr81nsL6txOpJPaPNilzHGDTwKXAgMAWYZY4Yc0ey7wCFrbT/gQeCPANbaF621I6y1I4AbgFxrbU5bZxQRERERERHxJZnZBaTEhDC6d5TTUaSDiQkL5O83ZVBe08B/vZBNTX2j05Ec542RXWOAbdbaHdbaOuBlYMYRbWYA/2h+vBCYYow5cuuAWcBLXsgnIiIiIiIi4jPyi6tYvuMgV45K4ug/jUVObHBCBP939XDW5JXwq39vwFrrdCRHeaPYlQjkH/a8oPlYq22stQ1AKRBzRJvvoGKXiIjIabnlllvo3r07Q4cOPWHbpUuX8p///KcdUh2/38cff5znnnsOgNmzZ7Nw4cJ2zyQiItKeXltTCMDlo7TAuJy6aUMTuGtKfxZmF/D0pzudjuMobxS7WitDH1lSPG4bY8xYoMpau+GYnRhzqzEmyxiTVVRUdGpJRUREOrnZs2fzzjvvnFRbJ4pdDQ0NR/V72223ceONN7ZrDhEREadYa/nX6gLG94khKSrE6TjSwd01pT/T0nrwu8WbWfZl162V+HnhngVAr8OeJwG7j9GmwBjjB0QCxYedv4YTjOqy1j4JPAmQkZHRtcfniYhIB3A30NbLUI4A/nrcFpMmTSI3N/eo4w899BCPP/44fn5+DBkyhD/84Q88/vjjuN1uXnjhBR5++GHOOuuslvYHDx5k1qxZFBUVMWbMGN555x2ys7OpqKhg+vTpbNjQ9PnUvHnzqKioYO7cuTz11FM8+eST1NXV0a9fP55//nlCQkKYPXs20dHRrFmzhujoaD777LNv9PvBBx8QFhbGvffe+43M2dnZ/PjHP6aiooLY2Fjmz59PQkLCab+KIiIiTsredYjcg1Xcfm5/p6NIJ+ByGf5y9XCufKyS2xes5vXbJ5IaG+p0rHbnjZFdq4D+xphUY0wATYWrRUe0WQTc1Px4JvChbZ5QaoxxAVfRtNaXiIiIeMEf/vAH1qxZw7p163j88cdJSUnhtttu45577iEnJ+cbhS6ABx54gIkTJ7JmzRouvfRS8vLyTtjHFVdcwapVq1i7di2DBw/m6aefbjn35ZdfsmTJEjIzM4/b71fq6+u54447WLhwIdnZ2dxyyy388pe/PL0XQURExAcszC4gJMDNhUN7OB1FOonQQD+eujEDt8vw/eeyKKupdzpSu2vzkV3W2gZjzO3Au4AbeMZau9EY82sgy1q7CHgaeN4Ys42mEV3XHHaLSUCBtXZHW2cTERFxzvFHYLW39PR0rrvuOi677DIuu+yyE7ZftmwZ//rXvwC4+OKLiYo68U5RGzZs4Fe/+hUlJSVUVFRwwQUXtJy76qqrcLvdJ513y5YtbNiwgalTpwLQ2NioUV0iItLh1dQ38ta6PVw4NIHQQG9MvJKuqld0CH+7bjQ3PL2Cu1/OaSl+dRVe+W2y1i4GFh9x7P7DHtfQNHqrtWuXAuO8kUtERESavPXWWyxbtoxFixbxm9/8ho0bN57wmtZ2h/Lz88Pj8bQ8r6mpaXk8e/Zs/v3vfzN8+HDmz5/P0qVLW86Fhn674fTWWtLS0li+fPm3uk68zxjzDDAd2G+tHdp87M/AJUAdsB242Vpb4lxKERHf9O7GvZTXNnDlaC1ML21vfN8Y5lyaxn3/3sC897bws2mDnI7UbrwxjVFERER8mMfjIT8/n3POOYc//elPLSOvwsPDKS8vb/WaSZMm8eKLLwLw9ttvc+jQIQDi4+PZv38/Bw8epLa2ljfffLPlmvLychISEqivr2+5tjXH6/crAwcOpKioqKXYVV9ff1IFOmkX84FpRxx7HxhqrU0HvgR+0d6hREQ6gszVhSR2C2ZcaozTUaSTumFcb64dm8xjS7fzek6h03HajYpdIiIindisWbMYP348W7ZsISkpiaeffprGxkauv/56hg0bxsiRI7nnnnvo1q0bl1xyCa+99hojRozgk08++cZ95syZw7Jlyxg1ahTvvfceycnJAPj7+3P//fczduxYpk+fzqBBX39i+Jvf/IaxY8cyderUbxw/0vH6/UpAQAALFy7kZz/7GcOHD2fEiBHtvnOktM5au4xvbjSEtfY9a21D89PPadqwSEREDrO3tIZPtxZxxahEXF1oepm0v7mXpDEmJZqfLlzHuoKuMdDaNK8L36FlZGTYrKwsp2OIHGXBihMv4NzWpgz+DgAfbH6l3fsGmFLY3H9i+/X/vyua+vzV2K/7vHZscrv13+lMntz0/bApZ3JqNm/ezODBg52O4RUpKSlkZWURGxvrdJSjtPa6G2OyrbUZDkXq9IwxKcCbX01jPOLcG8Ar1toXWjl3K3ArQHJy8uhdu3Z5OamIiO94bOl2/vjOF3x07+QuuVuetK+DFbVc+shnNHosi+6YQPfwIKcjnZKTfU+nkV0iIiIi4hXGmF8CDUCr81ittU9aazOstRlxcXHtG05ExEHWWjJXF5DRO0qFLmkXMWGBPHVjBqXV9dz2fDa1DY1OR/IqFbtERETkW8vNzfXJUV3iO4wxN9G0cP11tjNMJRARaUPrCkrZtr+CK0drlre0nyE9I/jL1cNZnVfCr17bQGf+v2cVu0RERESkTRljpgE/Ay611lY5nUdExNdkri4g0M/FxekJTkeRLuaiYQnceW4/Xs0u4NnPcp2O4zUqdomIiIjIKTPGvAQsBwYaYwqMMd8FHgHCgfeNMTnGmMcdDSki4kNqGxp5PWc356f1ICLI3+k40gXdfd4Azh8Sz28Xb+bTrQecjuMVKnaJiIiIyCmz1s6y1iZYa/2ttUnW2qettf2stb2stSOav25zOqeIiK/4cPN+SqvrmakpjOIQl8vw4HdG0C8ujB8tWE3ugUqnI7U5FbtERERERERE2knm6gLiIwKZ2E9rX4pzQgP9eOrGDIyB7z2XRXlNvdOR2pSKXSIiIp1UTU0NY8aMYfjw4aSlpTFnzpyTui43N5ehQ4d6Od3RfS5YsKDleVZWFnfeeScA8+fP5/bbb2/XPCIiIt5woKKWpVuKuGxkIm6XcTqOdHHJMSH87bpR7DxQyT2v5ODxdJ4F61XsEhER6aQCAwP58MMPWbt2LTk5Obzzzjt8/vnnR7VrbHR26+mGhoajil0ZGRk89NBDDqYSERFpe6/n7KbBY5k5SlMYxTec2TeWOZcMYcnm/fzl/S1Ox2kzfk4HEBER6RLuvhtyctr2niNGwF//eszTxhjCwsIAqK+vp76+HmOaPkVOSUnhlltu4b333uP222+nf//+3HLLLYSEhDBx4sRW72et5Y477uDDDz8kNTUVay233HILM2fOJCUlhaysLGJjY8nKyuLee+9l6dKlrFy5krvvvpvq6mqCg4N59tlnGThwIPPnz+ett96ipqaGyspKqqqq2Lx5MyNGjOCmm25i5MiRzJs3jzfffPMbGYqKirjtttvIy8sD4K9//SsTJkxoi1dTRETE6xZmF5CeFEn/+HCno4i0uGFcbzbvKePRj7YzqEcElwzv6XSk06Zil4iISCfW2NjI6NGj2bZtGz/60Y8YO3Zsy7mgoCA+/fRTANLT03n44Yc5++yz+clPftLqvV577TW2bNnC+vXr2bdvH0OGDOGWW245bv+DBg1i2bJl+Pn5sWTJEv7nf/6HzMxMAJYvX866deuIjo5m6dKl3yhuLV26tNX73XXXXdxzzz1MnDiRvLw8LrjgAjZv3vxtXxYREZF2t2l3GZv3lPHrGWlORxH5BmMMD1w6lG37K/jJwrWkxoYyNDHS6VinRcUuERGR9nCcEVje5Ha7ycnJoaSkhMsvv5wNGza0rMf1ne98B4DS0lJKSko4++yzAbjhhht4++23j7rXsmXLmDVrFm63m549e3LuueeesP/S0lJuuukmtm7dijGG+vqvFz+dOnUq0dHR3+rnWbJkCZs2bWp5XlZWRnl5OeHh+oRcRER8W+bqAvzdhkvSO/6oGel8AvxcPHb9aC59+FO+/1wWi26fSFx4oNOxTpnW7BIREekCunXrxuTJk3nnnXdajoWGhgJN0xO/mt54Isdq5+fnh8fjAZoWxv/KfffdxznnnMOGDRt44403vnHuq/6/DY/Hw/Lly8nJySEnJ4fCwkIVukRExOfVN3p4PaeQKYPiiQoNcDqOSKtiwwJ56qYMDlXVcdsL2dQ2OLuu6+lQsUtERKSTKioqoqSkBIDq6mqWLFnCoEGDjmrXrVs3IiMjW6Y0vvjii63eb9KkSbz88ss0NjayZ88ePvroo5ZzKSkpZGdnA7RMU4SmkV2JiYlA066KxxIeHk55efkJf6bzzz+fRx55pOV5TluvgyYiIuIFH28p4kBFHVeO1sL04tvSekYy76rhZO86xP3/3oi1HXOHRhW7REREOqk9e/ZwzjnnkJ6ezhlnnMHUqVOZPn16q22fffZZfvSjHzF+/HiCg4NbbXP55ZfTv39/hg0bxg9+8IOWaY8Ac+bM4a677uKss87C7Xa3HP/pT3/KL37xCyZMmHDcXR/T09Px8/Nj+PDhPPjgg8ds99BDD5GVlUV6ejpDhgzh8ccfP9HLICIi4rjM1QXEhAYweWCc01FETmh6ek/uOLcfr2Tl84//5Dod55SYjlqlO1xGRobNyspyOobIURasyGv3PqcMblqD54PNr7R73wBTCpv7T2y//v93RVOfvxr7dZ/Xjk1ut/47ncmTm74fY4FwOXmbN29m8ODBTsfwmtmzZzN9+nRmzpzpdJRvaO11N8ZkW2szHIokJ0Hv50SksyqpqmPMbz/g+nG9uf+SIU7HETkpHo/lv17I5sMv9vPcLWOY0C/W6UjAyb+n08guERERERERES95Y+1u6ho9XDk60ekoIifN5TI8+J0R9I0L5UcLVrPrYKXTkb4VFbtERETklMyfP9/nRnWJiIj4moWrCxnUI5y0npFORxH5VsIC/XjqxqZBVN9/LouK2gaHE508rxS7jDHTjDFbjDHbjDE/b+V8oDHmlebzK4wxKYedSzfGLDfGbDTGrDfGBHkjo4iISHvoDMsFdCR6vUVExJds21/O2vwSZmpheumgeseE8ui1o9heVMk9r+Tg8XSM91ptXuwyxriBR4ELgSHALGPMkROTvwscstb2Ax4E/th8rR/wAnCbtTYNmAzUt3VGERH2QFrVAAAgAElEQVSR9hAUFMTBgwdVgGkn1loOHjxIUJA+JxMREd+wMLsQt8swY4SmMErHNaFfLPddPJj3N+3jwSVfOh3npPh54Z5jgG3W2h0AxpiXgRnApsPazADmNj9eCDxijDHA+cA6a+1aAGvtQS/kExERaRdJSUkUFBRQVFTkdJQuIygoiKQkfXouIiLOa/RYXltTwOQBccSFBzodR+S03HRmCpv3lPPwh9sY2COc6ek9nY50XN4odiUC+Yc9LwDGHquNtbbBGFMKxAADAGuMeReIA1621v6ptU6MMbcCtwIkJ2vXNRER8T3+/v6kpqY6HUNEREQc8Nm2A+wrq2XOJfoQRjo+Ywy/viyNbUUV3PvqWlJiQhma6Lvr0HljzS7TyrEj528cq40fMBG4rvn75caYKa11Yq190lqbYa3NiIuLO528IiIiIiIiIm1qYXYBkcH+TBnc3ekoIm0i0M/N49ePJiokgFufy+JARa3TkY7JG8WuAqDXYc+TgN3HatO8TlckUNx8/GNr7QFrbRWwGBjlhYwiIiIiIiIiXlFWU8+7G/dyyfAEAv3cTscRaTNx4YE8dWMGxVV1/OCFbOoaPE5HapU3pjGuAvobY1KBQuAa4Noj2iwCbgKWAzOBD621X01f/KkxJgSoA86maQF7ETkFJVV1FByqZl95DfvKajlYUUvjYbtnBPi5iAsLpHtEEPHhgSRGBRMe5O9gYhERERGRjm/xuj3UNniYObrXiRuLdDBDEyP588zh3PHSGuYs2sDvLh9G0zLsvqPNi13Na3DdDrwLuIFnrLUbjTG/BrKstYuAp4HnjTHbaBrRdU3ztYeMMf9HU8HMAouttW+1dUaRzqzR46GitpHHlm4j/1A10DRvOCo0gO7hgfi5TMu84toGD9uLKliTX9LSLjkmhKE9I0nrGUG3kABHfgYRERERkY4sc3UBfeNCGZ7ku2saiZyOS4b35Iu9ZTz60XYGJ0Rw4/gUpyN9gzdGdmGtXUzTFMTDj91/2OMa4KpjXPsC8II3col0ZnvLaliyaR994qoAaPBYpqX1oE9cKN3DgwjwO/as5eq6RvaX17BtfwUbd5fx1vo9vLV+D/27h3H2gDhSY0N9rlIvIiK+wRjzDDAd2G+tHdp8LBp4BUgBcoGrrbWHnMooItKecg9Usir3ED+dNlDvoaVT+++pA9myt5wH3thEv7gwzuwX63SkFt5Ys0tE2lFxZR2vZuXz8Adb2V5UQbfgAJK6hXDHuf2ZNCCOpKiQ4xa6AIID3PSOCWXK4HjunNKfH583gCmDu7O7tIa/f7qTxz/ezqbdZVh75F4TIiIizAemHXHs58AH1tr+wAfNz0VEuoR/rS7AGLh8ZKLTUUS8yuUyPPidEfSJDeWHC1aTd7DK6UgtvDKyS0S8r9Fj+fCL/Sz7sghj4Kz+sUwaEEdU6OlPPYwND2TKoHgm9Y8je9chPtlaxAsrdpESE8Ilw3uSEBncBj+Bdy1Yked0hA5rSlnTriofnMJreO3Y5LaOIyI+zlq7zBiTcsThGcDk5sf/AJYCP2u3UCIiDvF4LJmrC5nYL7ZDvGcWOV3hQf48dWMGMx79jO8/l0XmD88kLND5UpNGdol0QAcqanli2XY+2rKfYUmR/Pf5A5k2NIGQgLb9j4q/28W4PjH8eOpALh+RyP7yWh79aBtvrttNTX1jm/YlIiKdSry1dg9A8/fuDucREWkXK3YWU1hSzczRSU5HEWk3KbGhPHrtKLYVVfDjV3LweJyfEeR8uU1ETpq1lqzcQ7y5fjd+LhezxiQzLNH7i166XYYzUqNJS4zg/U37WL79IOsLSrlydBID4sO93r+IiHROxphbgVsBkpM1MlREOr7M1QWEBfpx/pAeTkcRaVcT+8dy38WDafBYfGGpOhW7RDqIRo/ljXW7WbmzmH5xYVw5OonIYP92zRAS4MeMEYlk9I5m4ep85v8nl/F9Y5iW1gN/twaKioh0ZMaYiUB/a+2zxpg4IMxau/MUb7fPGJNgrd1jjEkA9rfWyFr7JPAkQEZGhvMfA4uInIbK2gYWr9/DJek9CQ5wOx1HpN3NnpDqdIQW+utUpAOobWjkhc93sXJnMWcPiGP2hJR2L3QdLjEqmB9O7seZfWNYvv0gf1u6jb2lNY7lERGR02OMmUPTmlq/aD7kz+ntjr0IuKn58U3A66dxLxGRDuGdDXupqmvkSk1hFHGcil0iPq68pp6nPtnBl/vKmTGiJxek9cDlA+NC/d0upqf3ZPaZKVTWNvLYx9tYW1DidCwRETk1lwOXApUA1trdwEnNUzfGvAQsBwYaYwqMMd8F/gBMNcZsBaY2PxcR6dQyVxeQHB3CGSlRTkcR6fI0jVHEh5VU1fHUJzuoqG3ghvG9GdQjwulIRxkQH84d5/Zjwco8XlmVz+y0WqLbYEdIERFpV3XWWmuMsQDGmNCTvdBaO+sYp6a0STIRkQ6gsKSa5TsOcveUARgf+GBapKvTyC4RH1VWU8/Tn+6kqq6R703s45OFrq+EB/nz3YmpjE2NprS6nr1lNVTVNTgdS0RETt4/jTFPAN2MMd8HlgB/dziTiEiH8drqAqyFK0YlOh1FRFCxS8QnVdQ28MynOymvaWD2mSn0ig5xOtIJ+blczBiRSGxYINX1Hh7/eAfFlXVOxxIRkZNgrZ0HLAQygYHA/dbah5xNJSLSMVhryVxdyNjU6A7xvl2kK1CxS8THVNU18OxnOymurOPG8b3pHXPSM0l8QniQPwkRQVTWNvDYx9spOFTldCQRETkBY8wfrbXvW2t/Yq2911r7vjHmj07nEhHpCFbnHWLngUotTC/iQ1TsEvEh9Y0enlu+i/3ltdwwrjd94sKcjnRKgvzd/NfZfQhwG576ZAdf7ClzOpKIiBzf1FaOXdjuKUREOqCF2YUE+7u5aFiC01FEpJmKXSI+wlrLa2sKySuu4uqMXvSPP6lNsHxW9/Agbju7L93Dg3j+811k7yp2OpKIiBzBGPMDY8x6mnZSXHfY105gndP5RER8XU19I2+u282FQ3sQFqj930R8hX4bRXzE0i+LyMkv4bzB8QxLjHQ6TpsID/Ln+2f14cUVu8hcXUhNvYcJ/WKdjiUiIl9bALwN/B74+WHHy621+pRCROQE3t+0j/KaBk1hFPExGtkl4gPWF5by/qZ9jOjVjXMGxjkdp00F+Lm4YVxv0npG8Nb6PXz4xX6stU7HEhERwFpbaq3NtdbOstbuAqoBC4QZY5Idjici4vMWZhfQMzKI8X1inI4iIodRsUvEYYUl1SzMzic5OoTLRyZijHE6Upvzc7u45oxkRiV3Y8nmfbyzYa8KXiIiPsQYc4kxZiuwE/gYyKVpxJeIiBzDvrIaPtlaxOWjEnG5Ot97eJGOTNMYRRxUXdfIghW7CAnw47qxyfi7O2/92e0yXDEqiQA/N59sO0BNg4cZI3ri6oTFPRGRDuh/gXHAEmvtSGPMOcAshzOJiPi0f68pxGPhylGawijiazrvX9YiPs5aS+bqAkqr65l1Ri/Cg/ydjuR1LmO4JD2ByQPiWJVbzD+z8mn0aISXiIgPqLfWHgRcxhiXtfYjYITToUREfNVX7+VHJXfrsDuoi3RmGtkl4pDlOw6yaU8ZFw7tQXJMqNNx2o0xhvPTehDo7+bdjXupa/Awa0znHtUmItIBlBhjwoBlwIvGmP1Ag8OZRER81vrCUr7cV8FvLx/qdBQRaYVX/ro0xkwzxmwxxmwzxvy8lfOBxphXms+vMMakNB9PMcZUG2Nymr8e90Y+EaflF1fx9vq9DOoRzsQuujvh2QPiuHR4T7bsLecfy3Opa/A4HUlEpCubAVQB9wDvANuBSxxNJCLiwzKzCwjwczE9vafTUUSkFW1e7DLGuIFHgQuBIcAsY8yQI5p9Fzhkre0HPAj88bBz2621I5q/bmvrfCJOq65r5OVVeYQH+TFzdFKnXJD+ZI3rE8PM0UnsLKrk2c92UlPf6HQkEZEuyVpbaa31WGsbrLX/oOm93DSnc4mI+KK6Bg+L1u5m6pB4IoM7/1IkIh2RN0Z2jQG2WWt3WGvrgJdp+rTwcDOAfzQ/XghMMV35L37pUt5ct5vS6nquGZNMSIBmEo9MjuKaMcnkH6rimc92UlWnWTMiIu3FGBNhjPmFMeYRY8z5psntwA7gaqfziYj4og+/2M+hqnpmjtbC9CK+yhvFrkQg/7DnBc3HWm1jrW0ASoGY5nOpxpg1xpiPjTFnHasTY8ytxpgsY0xWUVFR26UX8aINhaWsyS9h8sDuJEeHOB3HZwxLjOT6sb3ZU1rD3z/ZSUWtCl4iIu3keWAgsB74HvAecBUww1p75IeVIiICZK4uIC48kLO66HIkIh2BN4pdrY3QOnK7tWO12QMkW2tHAj8GFhhjIlrrxFr7pLU2w1qbERcXd1qBRdpDeU09/84pJLFbMOcM7O50HJ8zKCGCG8f35mBlLU8t20FZdb3TkUREuoI+1trZ1tongFlABjDdWpvjcC4REZ90sKKWj77Yz+UjE/HTBksiPssbv50FQK/DnicBu4/VxhjjB0QCxdba2uZtr7HWZtO0OOoAL2QUaVfWWl5bU0hdg4erRifhdmnWbmv6dw9n9pmplNbU8+QnOzhUVed0JBGRzq7lkwVrbSOw01pb7mAeERGf9nrObho8litHaQqjiC/zRrFrFdDfGJNqjAkArgEWHdFmEXBT8+OZwIfWWmuMiWte4B5jTB+gP01rRoh0aNm7DvHF3nIuSOtB94ggp+P4tNTYUL47IZWqugaeXLaDgxW1TkcSEenMhhtjypq/yoH0rx4bY8qcDici4msyVxcwNDGCgT3CnY4iIsfR5sWu5jW4bgfeBTYD/7TWbjTG/NoYc2lzs6eBGGPMNpqmK/68+fgkYJ0xZi1NC9ffZq0tbuuMIu2ppKqOt9bvoU9sKOP7xpz4AqFXdAjfm9iH+kYPT36yg31lNU5HEhHplKy1bmttRPNXuLXW77DHrS4lISLSVW3eU8bG3WXM1KguEZ/nla3grLWLgcVHHLv/sMc1NC1+euR1mUCmNzKJOMFay6K1u/FYyxWjknBp09GT1rNbMN8/qw/PfLqTJ5ftYPaZKfTSov4iIiIi4pDM7AL83YZLRxy5/5qI+BqtqCfiResLS/libzlTB8cTHRrgdJwOJz4iiP86uy/BAW6e/nQnW/drGRkRERERaX8NjR7+nbObcwZ21/t6kQ5AxS4RLympquONdXtI7BbM+L7alvhURYcGcOukPkSHBvDcf3axvrDU6UgiInKSjDH3GGM2GmM2GGNeMsZo4UoR6ZCWbS3iQEUtV47WFEaRjkDFLhEv+e1bm6mua+CKUYnaffE0RQT58/2z+pAUFczLK/P4z/YDTkcSEZETMMYkAncCGdbaoYCbpo2LREQ6nMzsQqJDAzhnYHeno4jISVCxS8QLPtt2gFezCzirfxwJkcFOx+kUggPc3DwhlcEJEby5bg+L1+/BY63TsUREOgVjzBXGmK3GmNI23o3RDwg2xvgBIcDuNriniEi7Kq2q5/1N+7h0eE8C/PQntEhHoN9UkTZWU9/IL19bT0pMCOcO0ic/bSnAz8W1Y5MZ3zeGT7cd4KWVedQ3epyOJSLSGfwJuNRaG9lWuzFaawuBeUAesAcotda+d3gbY8ytxpgsY0xWUVHR6XQnIuI1i9btpq7Rw0xNYRTpMFTsEmljTy7bQe7BKn5z2VD83foVa2suY7gkvScXD0tg0+4y/v7JDho8KniJiJymfdbazW15Q2NMFDADSAV6AqHGmOsPb2OtfdJam2GtzYiLi2vL7kVE2kxmdgED48NJ63lanwGISDvSX+IibSi/uIpHP9rGxcMSOKu/3rR704R+sVw7Npl9ZbXsLqmmpqHR6UgiIh1ZljHmFWPMrOYpjVcYY644zXueB+y01hZZa+uBfwFnnn5UEZH2s72ogpz8Eq4cnYgxWodXpKNQsUukDc1dtBG3y/Cr6YOdjtIlpPWM5Laz+4Ix7CmtYfWuQ05HEhHpqCKAKuB84JLmr+mnec88YJwxJsQ0/YU4BWjT0WMiIt6WmV2A22W4bESi01FE5FvwczqASGfx/qZ9fPDFfv7nokFalL4d9YgMIrFbMPvLali4uoD8Q1VcNCxBU0hFRL4Fa+3NXrjnCmPMQmA10ACsAZ5s635ERLyl0WP51+pCJvWPpXtEkNNxRORb0F+DIm2guq6RuYs2MiA+jJsnpDodp8txG0OPyCDO6h/Lip3FPLZ0O/vKapyOJSLSYRhjkowxrxlj9htj9hljMo0xp70Ss7V2jrV2kLV2qLX2BmttbVvkFRFpD//ZfoC9ZTVcqYXpRTocFbtE2sBjS7dRWFLNr2doUXqnGAwXDk1g9pkplNfU87el21i1sxhrrdPRREQ6gmeBRTQtJJ8IvNF8TESky8rMLiAiyI/zBsc7HUVEviVNYxQ5TfnFVTyxbAeXDu/JuD4xTsfp8gbEh3PnlP68mlXAazmFbNxTyowRiUSFBDgdzeustVTWNXKwopYDFXWUVNdRXdfY9FXfSKPH4ucyuF0GP7eLsEA/okIDiArxJzo0gNiwQFxaeFWkq4qz1h5e3JpvjLnbsTQiIg4rr6nnnY17uWJUEkH+bqfjiMi3pGKXyGn6/dubcRnDLy4a5HQUaRYe5M/sCSl8vuMg723cx/9bspWpQ+IZ3zemUxVzSqvryS+uouBQNQUlVU27UtZ7vtEm0M9FsL+b4AA3bpeh0WNp9FgaPJay6noaPF+PfAv2d9M7JoSM6jqC/N1Ya7XrkEjXccAYcz3wUvPzWcBBB/OIiDjq7fV7qan3MFNTGEU6JBW7RE7D8u0HWbx+Lz+eOkCL0vsYlzGc2TeWwQkRvJ5TyFvr97C2oIQLhyaQGhvqdLxTUlZTz46iCnYUVbLjQCXFlXXA12uWpSd2Iy48kNiwplFa3UICcLuOXazyWEtlbQOHKusoqqhj18FKcg9+fd8Hl3zJiF7dGNEriujQzj8yTqSLuwV4BHgQsMB/mo+JiHRJC7ML6BMbyshe3ZyOIiKnQMUukVPU0OjhgTc2ktgtmFsn9XE6jhxDVEgAN41PYV1BKW9v2MNTn+xgUI9wzk/rQQ8f31Wn0WPJK67iy33lfLmvnD2lTYvuB/m7SI0NY3yfGJKjQ0iIDMLvFNaKcxlDeJA/4UH+JMeEMrp3FADJC0OoqmskPMifJZv3s2TzfvrEhXLOwO70iQ3VaC+RTshamwdc6nQOERFfkHewipW5xfzkgoF63yPSQanYJXKKXl6Vzxd7y/nbdaM0j9/HGWMY3qsbgxMiWL79AB9vLeLhD7aSnhTJhH6xJEWFOB2xRUlVHVv3VfDl/nK27a+gtsGDy0DvmFAuGBJPv/hwEiKDvDod0+1yER7k4vtn9aGkqo6c/BKW7zjI05/upFdUMOcM6s7A+HC9+RPpBIwxP7XW/skY8zBNI7q+wVp7pwOxREQclbm6AGPg8pGJTkcRkVOkYpfIKSitqucv721hXJ9oLhzaw+k4cpIC/FycPbA7Z6REs/TLIlbmFrO2oJTe0SGc2S+WIQkRx5325w3VdY3sOFDBtv0VbC+q5EBFLQCRwf6kJ3VjQHwYfePCHCuodgsJYPLA7kzoF0v2rkMs21rEc8t3kRITyqUjevr86DgROaHNzd+zHE0hIuIjPB7Lv9YUMKFvLD27aZkSkY5KxS6RU/Do0m2UVNdz//Q0jW7pgEIC/bhoWALnDupO1q5DLN9+gJdW5hHs72ZIQgRDEyPo2z0MP9e3nxp4InUNHvKKq9he1FTg2l1SjQUC3C5SY0MZkxJF//hwuocH+tS/LX+3i3F9YjgjJZqsXcW8t3Efj3y4lQl9Yzl3cHcC/TS6UaQjsta+0fz9H18dM8a4gDBrbZljwUREHLIyt5j84mp+PHWA01FE5DSo2CXyLeUXVzH/s1xmjkpiSM8Ip+PIaQjydzOxXyxn9o3hy73lrC8sZcPuUrLzDhHo5yI5OoTk6BB6RYeQ1C2Y4AD3SRegPNZSXtPAwYpa9pfXUniomsKSavaX1+Cx4DLQKzqEcwd1p29cGEnRwV4prrU1t8swNjWGoT0jeXfjXj7ZdoC1BSXMHN2Lft3DnI4nIqfIGLMAuA1oBLKBSGPM/1lr/+xsMhGR9pWZXUBogJsL0jR7Q6Qj80qxyxgzDfh/gBv4u7X2D0ecDwSeA0bTtK31d6y1uYedTwY2AXOttfO8kVHkVP353S24XPDf5w90Ooq0EZcxDEqIYFBCBA2NHrYXVbB5Tzl5xVV8+MX+lkVsAvxcdAv2p1uIPyEBfriNweUyuF1Q12Cprm+kuq6RqroGDlXVUd/49fI3IQFukqKCGZzQneToYFJiQzv0aKjQQD+uGJVERu8oMlcX8uxnO5k0II6rMpLwP4XF8kXEcUOstWXGmOuAxcDPaCp6qdglIl1GVV0Di9fv4aJhCYQEaFyISEfW5r/Bxhg38CgwFSgAVhljFllrNx3W7LvAIWttP2PMNcAfge8cdv5B4O22ziZyutbml7Bo7W7uOLcfPSK1VlFn5Od2MbBHBAN7NI3aq6lvpOBQNXtLqymprqekqp6S6jqKymvx2KYdExs9lgA/F8H+boL83cSGBTIgPpzo0ABiwgKICwskMtjfp6YltpXkmFB+dE4/3ly3m4+/LOLqJ5bz0DUj6RXtO4v+i8hJ8TfG+AOXAY9Ya+uNMUctWC8i0pm9u3EvlXWNXDk6yekoInKavFGuHgNss9buADDGvAzMoGmk1ldmAHObHy8EHjHGGGutNcZcBuwAKr2QTeSUWWv57eLNxIYF8F9n93U6jrSTIH83/bqHaYrecQT4ubhiVBL9uofx1ro9XPzQJzx+/WjO7BfrdDQROXmPA7nAWmCZMaY3oDW7RKRLycwupFd0MGNSop2OIiKnyRtzTRKB/MOeFzQfa7WNtbYBKAVijDGhNA2bf+BEnRhjbjXGZBljsoqKitokuMjxvL9pHyt3FnP3eQMIC9SwZpEjpSd14607zyI+Iogbn1nJK6vynI4kIieheUH6fdbaRGvtRdZaC+QB5zgcTUSk3ewuqeaz7Qe4YmQSrnbenVtE2p43il2t/ZfhyGHwx2rzAPCgtbbiRJ1Ya5+01mZYazPi4uJOIabIyatv9PCHt7+gb1wo15zRy+k4Ij4rOSaEzB+eyfi+Mfwscz2/X7wZj0czoUR8mbXWA9x+xDHb/IGkiEiX8NqaQqyFK0dpCqNIZ+CNYlcBcHg1IAnYfaw2xhg/IBIoBsYCfzLG5AJ3A/9jjLkdEYe9vDKPHQcq+cWFg/HT4tsixxUR5M+zs8/ghnG9eWLZDm5/aTV1DR6nY4nI8b1vjLnXGNPLGBP91ZfToURE2oO1lszsAsakRJMco3VHRToDb8zFWgX0N8akAoXANcC1R7RZBNwELAdmAh82D5k/66sGxpi5QIW19hEvZBQ5aeU19fx1yVbG9YlmyuDuTscR6RD83C5+PSON3jEh/O9bm6muy+Kx60cT5N9xd6AU6eRuaf7+o8OOWaCPA1lERNrVmvz/z959x1dV338cf32ySQIh7BEgQBgCIiMMhSq4qraKC3HW1aJ1a9XaZa3tr9UOV7VVHHXUiopVseIWVBxIQED2RsLeO/vz++MeakwBGbn3JLnv5+ORR+4959xz3/nm5uTez/l+v2czi9fv4IpjdMgTqSuqvdjl7mVBb6y3gETgCXefZWZ3AgXuPhZ4HHjGzBYS6dF1bnXnEKkuD3+wiA07SnjylG518mp6ItFiZvzwOx1IT0niF698yeVPTebRH+TrUt4iNZC7tw87g4hIWMZMKSQtOYFTDm8ZdhQRqSZR+cTh7uOAcVWW3V7pdhEw/Fv2cUc0sokciJWbd/HYR0s4vVcrDs/JCjuOSK10/oC2pCUncPOL0/nB45/zxKX9aJCWHHYsEanEzNKBm4C27j7SzDoBXdz9PyFHExGJqqLScv4zfSUndW9Bfb0/EakzNPmQyD785e35OHDzd7uEHUWkVjuzTw5/Pa8P05Zv5vInJ7OrpDzsSCLyTf8ASoCjgvuFwO/CiyMiEhvvzlnD1qIyzuqrielF6hIVu0T2YtbKLfz7i0IuHZRLTrYmqhQ5VN/r2ZL7z+3NlGWbuOKfUyguU8FLpAbp6O5/BEoB3H0Xe756tohInfLSlEJaNEjjqI5Nwo4iItVIxS6RPXB3fj9uDg3rJXPVkLyw44jUGd/r2ZK7zuzJh/PXccPoaZSV6yqNIjVEiZnVIzIpPWbWESgON5KISHSt3VrEhwvWc0af1iQmqL4vUpeo2CWyBxPmr+PjhRu47rhOZNXT2H2R6nROvzb86vvdeGPmam7795dUVHjYkUQE7gDeBNqY2bPAe8Cth7pTM2toZmPMbK6ZzTGzIw91nyIi1eWVaSsor3DO6qMhjCJ1jS6JJVJFWXkFfxg3h9zG6VwwoF3YcUTqpMsHt2dbUSn3vbuAZvVTufWkrmFHEolr7v62mU0BBhIZvni9u6+vhl3fD7zp7mebWQqgeQFEpEZwd16asoJebRqS1ywz7DgiUs1U7BKpYsyUQuav2c7fL+hDSpI6P4pEy/XHdWLttmL+NmERbRqlc17/tmFHEolbZjYWeA4Y6+47qmmfDYCjgUsA3L2EyCT4IiKhm7VyK/PWbOO3p/cIO4qIRIE+yYtUsqO4jHvemU/fdtmc1KNF2HFE6jQz487TunNM56b88pWZfDB/XdiRROLZX4DvALPN7EUzO9vM0g5xnx2AdcA/zOwLM3vMzDIqb2BmI82swMwK1q3TMUBEYmfMlEJSEhM4tWfLsKOISBSo2CVSyaMfLWbttmJ+fsphmGmSSpFoS0pM4KEL+tC5eX2ufnYqs1duDTuSSFxy9w/c/SoiBapRwDnA2kPcbRLQB/i7u78e5h0AACAASURBVPcGdgC3VXneUe6e7+75TZs2PcSnExHZPyVlFbw6bQUndGtOw/SUsOOISBSo2CUSWLu1iFEfLuZ7h7ekb7vssOOIxI3M1CT+cUk/MlOTuPypyazdVhR2JJG4FFyN8SzgSqAf8NQh7rIQKHT3ScH9MUSKXyIioRo/by2bdpZyVt/WYUcRkShRsUskcO+78yktr+DWk7qEHUUk7rTISuPxS/LZvLOUH/9zKsVl5WFHEokrZvY8MAc4FngI6Oju1x7KPt19NbDczHb/Yz0OmH1IQUVEqsFLUwppkpnK0Z3Uo1SkrlKxSwSYv2Ybz09ezkUDc2nXOOPbHyAi1a57qyz+PPwIpizbxK9fnYW7hx1JJJ78g0iB60p3f9/dK6ppv9cCz5rZDKAX8Ptq2q+IyEHZuKOE8fPWcnqvViQl6uOwSF2lqzGKAH8YN4eM1CSuPTYv7Cgice17PVsyZ1UeD45fSPdWDbjoyNywI4nEBXd/08yOMrNcKr0/dPenD3G/04D8Q0snIlJ9xk5bQWm5c1bfnLCjiEgUqdglce/jhesZP28dPz+lK9kZmqBSJGw3ndCZOau28pvXZpPXrD5HdmwcdiSROs/MngE6AtOA3eOIHTikYpeISE3i7oyZWkj3Vg04rGWDsOOISBSp36bEtYoK5/fj5tC6YT1+oB4kIjVCQoJx77m9aNc4nWv+NZXVWzRhvUgM5AOD3P0qd782+Lou7FAiItXB3XlvzhpOe/BjZq7Yyoh+bcKOJCJRpmKXxLVXpq1g1sqt3HpSF9KSE8OOIyKBBmnJPHJRX3aVlnP1v6ZSWl5d0weJyF7MBFqEHUJEpDpVLnJd/lQBW3aV8sezenLhgHZhRxORKNMwRolbRaXl/OmtefTMyeLUnq3CjiMiVeQ1q8/dZ/Xk2ue+4A/j5nL7qd3CjiRSlzUBZpvZ50Dx7oXuflp4kUREDo678/7ctdz37gK+XLGFto3S+ePZPTmjd2uSNSm9SFxQsUvi1uMTl7BqSxH3juhFQoKFHUdE9uDUI1oxZdkmnvh4CX3bZfO9ni3DjiRSV90RdgARkUOlIpeI7KZil8SlDduL+fuERZzQrTkDO2jya5Ga7OenHMaMws3cOmY6XVrUJ69ZZtiRROocd//AzJoD/YJFn7v72jAziYjsLxW5RKQq/eVLXLr/vQXsKi3ntpO7hh1FRL5FSlICD13Qh9TkRK7511SKSsu//UEickDM7Bzgc2A4cA4wyczODjeViMi+VZ2Ta/OuEv54dk/e+8kxnJPfRoUukTgWlb9+MzvJzOaZ2UIzu20P61PN7Plg/SQzyw2W9zezacHXdDM7Ixr5JL4tWredf036ivP7t6VjU/UQEakNWmbV48/DezJ39TZ+P25O2HFE6qJfAP3c/WJ3/wHQH/hVyJlERPYo0pNrDcMeqlTkOqsn7/9kiIpcIgJEYRijmSUCDwEnAIXAZDMb6+6zK212ObDJ3fPM7FzgbmAEkSsB5bt7mZm1BKab2WvuXlbdOSV+3f3GXNKSE7n++E5hRxGRA3Bs1+b8cHB7Hpu4hKM6NuGkHrpwnEg1SqgybHEDGgEgIjWMuzN+XmS44ozCLbRpVI8/ntWTM/pouKKIfFM05uzqDyx098UAZjYaGAZULnYN4+uJUMcAD5qZufvOStukAR6FfBLHPl+ykbdnr+GW73ahSWZq2HFE5ADdelJXJi3ZyK1jptOjdQNystPDjiRSV7xpZm8BzwX3RwBvhJhHROS/VOQSkQMVjSNDa2B5pfuFwbI9bhP02toCNAYwswFmNgv4Erhyb726zGykmRWYWcG6deuq+UeQuqiiwvm/cXNo0SCNywa1DzuOiByElKQE/npebyocrh89jbLyirAjidQJ7n4L8AjQEzgCGOXut4abSkTiXeXhipc9WcCmnZWGK/bTcEUR2btoHB1sD8uq9tDa6zbuPsnduxO5GtDPzCxtT0/i7qPcPd/d85s2bXpIgSU+vDp9BdOXb+bm73ahXkpi2HFE5CDlNsng/87owZRlm7jv3QVhxxGp1cwsz8wGAbj7v939Jne/EdhgZh1DjicicUpFLhE5VNEYxlgItKl0PwdYuZdtCs0sCcgCNlbewN3nmNkOoAdQEIWcEkd2lpRx9xvzOCInizN7V+1oKCK1zbBerfl44XoemrCQozo25qi8JmFHEqmt7gN+voflO4N1p8Y2jojEMw1XFJHqEo1i12Sgk5m1B1YA5wLnV9lmLHAx8ClwNvC+u3vwmOXBBPXtgC7A0ihklDjz8AeLWb21iIcu6E1Cwp46FopIbXPHad2ZsmwT1z8/jTeu/47m4RM5OLnuPqPqQncv2H21bBGRaFORS0SqW7UXu4JC1TXAW0Ai8IS7zzKzO4ECdx8LPA48Y2YLifToOjd4+GDgNjMrBSqAq9x9fXVnlPiyYvMuHvlgEace0Yq+7RqFHUdEqkl6ShIPnt+HYQ99zM0vTueJi/upmC1y4PY4XUSgXsxSiEhcUpFLRKIlGj27cPdxwLgqy26vdLsIGL6Hxz0DPBONTBK/7n5jLgC3ndw15CQiUt0Oa9mAX32/G796ZSaPTVzMyKM1xZDIAZpsZj9y90crLzSzy4EpIWUSkTpORS4RibaoFLtEaoopyzYydvpKrjs2j9YNdYJapC66cEBbPl6wnj++OY+BHRrTM6dh2JFEapMbgJfN7AK+Lm7lAynAGaGlEpE6qWqRKye7HnefdThn9slRkUtEqpWKXVJnVVQ4d742m+YNUrniGPX2EKmrzIy7z+rJyfd/yPWjp/GfaweTkap/byL7w93XAEeZ2VAiFwUCeN3d3w8xlojUMSpyiUis6dOA1FmvTFvB9MIt3HPOEfrgK1LHZaUnc++IXpz76GfcMXYWfxp+RNiRRGoVdx8PjA87h4jULe7OhHnruO/d+UxXkUtEYkgVAKmTdhSXcfebczkiJ4vTe7UOO46IxMCADo25Zmgef31/IUd3bsqpR7QKO5KIiEhcUpFLRMKmYpfUSY98sIg1W4v52wV9dHU2kThy3XGdmLhwPT9/+Ut6t21ITnZ62JFERETihopcIlJT6IgjdU7hpp088uFiTjuiFX3bNQo7jojEUHJiAveP6I073DB6GmXlFWFHEolrZpZoZl+Y2X/CziIi0ePujJ+7ltMf+phLn5zMhh0l3H3W4Yy/eQgj+rVVoUtEYk49u6TOufvNeZjBT0/uGnYUEQlB28bp/O70Htzw/DQeGr+I64/vFHYkkXh2PTAHaBB2EBGpXiVlFcxcuYWCpRt5fcYq9eQSkRpFxS6pUwqWbuS16Su57rhOtG5YL+w4IhKS03u35oP567j/vfkMymtMfq56eYrEmpnlAN8D/g+4KeQ4InKItuwqZepXmyhYupHJSzcxfflmissiPag7Ns1QkUtEahQVu6TOKK9wbn91Fi2z0rjymA5hxxGRkN05rDtTlm3i+tHTGHf9d8iqlxx2JJF4cx9wK1B/bxuY2UhgJEDbtm1jFEtE9seKzbuCwtZGCpZuYt6abbhDYoLRo1UDLhzYjvx22fTNzaZZ/bSw44qIfIOKXVJnPDtpGbNXbeWh8/uQnqKXtki8q5+WzP3n9uLshz/lFy9/yV/P642ZLlghEgtm9n1grbtPMbMhe9vO3UcBowDy8/M9RvFEpIryCmfe6m0ULIsUtgqWbmTlliIAMlIS6dMum5N7tKRfbja92jbUe20RqfF0lJI6Yf32Yv701jwG5zXhlMNbhB1HRGqI3m2zuemEzvzprXkM6dKMs/vmhB1JJF4MAk4zs1OANKCBmf3T3S8MOZeIALtKypm2fDNTlkWGJE5dtoltxWUANG+QSr/cRoxsl01+biO6tqhPkoYmikgto2KX1Al3vTGXotJy7jitu3puiMg3XHlMRz6cv47bX51JfrtscptkhB1JpM5z958BPwMIenbdrEKXSHg2bC+mYNnX823NXLGFsopIZ8ouzetzaq9W9MvNJr9dI3Ky6+n9tIjUeip2Sa03ZdlGxkwp5MpjOpLXLDPsOCJSwyQmGPeO6MXJ93/EdaO/YMyVR5GSpDPUIiJSN7k7SzfsDObaigxLXLx+BwApiQkc0SaLHx3dgX652fRpm03D9JSQE4uIVD8Vu6RWKyuv4FevRCalv/bYvLDjiEgN1aphPe4683B+/OxU7nlnPred3DXsSCJxw90nABNCjiFSZ5WWVzBr5db/TiY/Zdkm1m8vAaBhejL57bI5p18b+uVm06N1FqlJiSEnFhGJPhW7pFZ76tOvJ6XPSNXLWUT27uTDW3Je/zY88uEivtOpCYPymoQdSURE5IBtKypl6leb/9tr64vlmygqrQCgbaN0ju7clH65jchvl03HppkkJGhIoojEH1UHpNZauXkXf3l7HkO7NNWk9CKyX371/W5MWrKRm16YxhvXH02jDA3dEBGRmm3Vll1MDq6QWLB0E3NXb6XCIcGge6sszuvflvx2jcjPzaZ5g7Sw44qI1Agqdkmt5O7c/uos3OHOYT00iaaI7Jf0lCQeOLc3Z/7tE3760gxGXdRXxw8REalRikrLeWvWasbPXcvkpZtYsXkXAOkpifRpm821x3aiX24jerVtSKZGNoiI7JGOjlIrvTVrNe/OWcPPT+lKm0bpYccRkVqkR+ssbj2pC797fQ7PTvqKCwe2CzuSiIgIM1ds4fnJy3l12gq2FpXRJDOV/u2zuXxwe/rlNuKwlvVJStQFVkRE9oeKXVLrbCsq5ddjZ3FYywZcOqh92HFEpBa6bFB7Plywnt/+ZzYD2jeiU/P6YUcSEZE4tHlnCa9OW8nzk5cze9VWUpISOLlHC0bkt2Fgh8aab0tE5CBFpdhlZicB9wOJwGPufleV9anA00BfYAMwwt2XmtkJwF1AClAC3OLu70cjo9Ref35rHmu3FfPIRfkk6+yWiByEhATjz8N7cvJ9H3Htc1/wytWDSEvW1alERCT6KiqcTxZt4PmC5bw1azUlZRX0aN2A3w7rzmlHtCYrPTnsiCIitV61F7vMLBF4CDgBKAQmm9lYd59dabPLgU3unmdm5wJ3AyOA9cCp7r7SzHoAbwGtqzuj1F4FSzfy9GfL+MHAdvRq0zDsOCJSizWrn8afhx/BpU9O5q435nLHad3DjiQiInXYis27eLFgOS8WFLJi8y6y6iVzfv+2DM/PoXurrLDjiYjUKdHo2dUfWOjuiwHMbDQwDKhc7BoG3BHcHgM8aGbm7l9U2mYWkGZmqe5eHIWcUssUlZZz65gZtMqqx60ndQ07jojUAUO7NuOSo3J58pOlDOzQiJN6tAw7koiI1CHFZeW8PWsNLxQsZ+LC9QAMzmvCT0/uyondmqtXsYhIlESj2NUaWF7pfiEwYG/buHuZmW0BGhPp2bXbWcAXeyt0mdlIYCRA27Ztqye51Gj3vjOfxet38M/LB5ChK8+ISDX52Sld+eKrTdzy4gy6tmhAbpOMsCOJiEgtN3vlVl4oWM4r01aweWcprRvW47pjO3F23xxdXElEJAaiUTHY0yyKfiDbmFl3IkMbT9zbk7j7KGAUQH5+ftX9Sx3zxVebePSjxZzXvw2DOzUJO46I1CGpSYk8dEEfvvfARH787FRevuoonWkXEZEDtmVXKWOnr+SFycv5csUWUhITOLF7c0b0a8NRHZuQqMnmRURiJhrFrkKgTaX7OcDKvWxTaGZJQBawEcDMcoCXgR+4+6Io5JNaprgsMnyxeYM0fnbKYWHHEZE6KCc7nXtHHMFlTxZwx9hZ3HVWz7AjiYhILVBR4Xy2ZAMvTF7OGzNXU1xWwWEtG3DHqd0Y1qs12RkpYUcUEYlL0Sh2TQY6mVl7YAVwLnB+lW3GAhcDnwJnA++7u5tZQ+B14Gfu/nEUskktdP+7C1iwdjtPXtqPBmm6Oo2IRMexXZtz9dCOPDR+Efm5jTi7b07YkUREpIZatWUXYwoKeXFKIV9t3En9tCTOyW/DOflt6NG6AWbqxSUiEqZqL3YFc3BdQ+RKionAE+4+y8zuBArcfSzwOPCMmS0k0qPr3ODh1wB5wK/M7FfBshPdfW1155TaoWDpRh7+YBHD++YwpEuzsOOISB134/GdmbJsE7985Ut6tG5A1xYNwo4kIiI1RElZBe/OiUw2/+H8dVQ4HNmhMTed0JmTerTQEHgRkRokKrN8u/s4YFyVZbdXul0EDN/D434H/C4amaT22V5cxk0vTKd1dj1+fVr3sOOISBxISkzggfN6870HJnLVP6cy9trBZOqCGCIicW3e6m28ULCcl79YwcYdJbRokMbVQ/MY3rcNbRtrsnkRkZpI7+Clxvrta7Mp3LSTF644Uh82RSRmmtVP46/n9eb8Rz/jpy/N4MHzems4iohInNlWVMpr01fxfMFypi/fTHKicUK35gzPb8PRnZpqsnkRkRpOFQSpkd6atZrnC5Zz1ZCO5Oc2CjuOiMSZgR0ac8t3u3L3m3Ppn9uIi4/KDTuSiIhEmbvz+ZKNvFBQyLgvV7GrtJzOzTP55fcO44zerWmcmRp2RBER2U8qdkmNs25bMT/7d2S+nBuO7xx2HBGJU1cc3YGCpRv53euzOTwniz5ts8OOJCIiUbBmaxEvTS3kxYJClqzfQWZqEqf3bs2Ifm04IidLvXtFRGohFbukRqmocG56YRo7isu4b0QvUpISwo4kInEqIcG455xefP/Bj7jymSm8du1gmjdICzuWiEiNsnpLEeXupCQmkJqcQGpSAimJCTW+QFRaXsH7c9fywuTlTJi/jvIKp3/7RlwzNI+TD29Beoo+JomI1GY6ikuN8rcJC/lowXruOvNw8prVDzuOiMS5rPRkHv1BPmf+7ROueGYKo0cO1NW2RESALwu3cM878xg/b90e16ckRQpfqUmJwfeEyLLkr+9XXv/f7YP1XxfPKq8PHpu8e32VxyYlfr0uac8Ft4Vrt/NCwXL+PbWQ9dtLaFY/lSuO7sDw/Da0b5IR7WYTEZEYUbFLaoxJizdwzzvzGdarFSP6tQk7jogIAF1bNOCec47gyn9O5ZevzORPZ/es8T0WRESiZe7qrdz7znzemrWGhunJ3Hh8Z1pmpVFcXkFxaTnFZRXBVzklu2+XVrlfVs724jI27vj6fnFpBSXlX29b4YeeNaVKUS0hAZZv3EVSgnFs12aM6NeGYzo3JSlRIwlEROoaFbukRtiwvZjrRn9Bu8YZ/N8Zh+uDpIjUKCf1aMn1x3Xi/vcW0L1VAy4d1D7sSCI1npm1AZ4GWgAVwCh3vz/cVHKwFq3bzn3vLuA/M1aSmZLEjcd35rLBudRPS67253J3yir8G8WxqsWw4rKKYH351wW1KgW3qutLyiu4aGA7zuidQ9P6mmxeRKQuU7FLQldR4fzkxels2lnKE5f0IzNVL0sRqXmuP64Tc1Zt5Xevz6Fj00yO7tw07EgiNV0Z8BN3n2pm9YEpZvaOu88OO5jsv6827OT+9xbw8heFpCUnctWQjvzoOx1omJ4Stec0M5ITjeTEBDJUkxIRkYOgqoKE7oH3FzBh3jp+e3oPurfKCjuOiMgeJSQY94zoxfCHP+XqZ6cy5sdH0aWF5hYU2Rt3XwWsCm5vM7M5QGtAxa5aYOXmXfz1/YW8WLCcxATjskHtuXJIR5pkqvokIiI1n4pdEqp3Zq/hvncXcGaf1lw4oG3YcURE9ikzNYknLsln2IMfc9mTk3n56qNoVl9XaBT5NmaWC/QGJoWbRL7N2q1F/G3CIv416Ssc5/wBbbl6aJ6uRisiIrWKil0SmoVrt3Pj89M4vHUWv9c8XSJSS7TMqscTl/Rj+MOf8qOnChg98kjqpegKjSJ7Y2aZwEvADe6+tcq6kcBIgLZtddIrTBt3lPDIB4t46tOllJY7w/vmcM2xeeRkp4cdTURE5ICp2CWh2FpUyshnCkhNSuCRi/qSlqwPiiJSe/RoncUD5/Vm5DMF3Pj8NB66oA+JCSrYi1RlZslECl3Puvu/q65391HAKID8/PxquP6eHKgtu0p57KPFPDFxCbtKyzm9V2uuO64TuU0ywo4mIiJy0FTskpgrr3BuHD2Nrzbs5NkfDqBVw3phRxIROWAndGvO7d/vxm9em82vx87kt8N6qIeqSCUW+YN4HJjj7veEnUe+aXtxGf+YuIRRHy1mW1EZ3+vZkhuP70ReM81FKCIitZ+KXRJzv3t9Nu/NXctvh3VnQIfGYccRETlolw5qz5qtxTz8wSKaZKZyw/Gdw44kUpMMAi4CvjSzacGyn7v7uBAzxb1dJeU8/elSHv5gEZt2lnJCt+bceHxnurVqEHY0ERGRaqNil8TUExOX8I+Pl3L54PZcdGRu2HFERA7ZT0/qwobtxdz37gIaZ6Zy0cB2YUcSqRHcfSKg7o41RFFpOc99/hUPjV/E+u3FHNO5KTed0Jkj2jQMO5qIiEi1U7FLYubtWav57euz+W735vz8lMPCjiMiUi3MjD+ceTgbd5Rw+6szyU5P5vs9W4UdS0QEgJKyCl6cspwH31/Iqi1FDOzQiIcv7EN+bqOwo4mIiESNil0SE9OWb+a60V/QM6ch943orYmcRaROSUpM4MHz+3DR45O4YfQ0UpMSOaFb87BjiUgcKyuv4OUvVvDA+wtYvnEXfdtl85fhR3BUXpOwo4mIiERdQtgBpO5bsGYbl/7jc5rWT+WxH+RTL0VXXhSRuqdeSiJPXNqP7q2zuOrZKYyftzbsSCIShyoqnFenreDEez/kljEzaFgvhX9c2o8xVx6pQpeIiMQNFbskqr7asJMLHptEUmICz1w2gKb1U8OOJCISNQ3Sknn60v50bl6fK56ZwsQF68OOJCJxwt15c+YqTrr/Q64fPY2UpAQeuagvY68ZxNAuzXS1WBERiStRKXaZ2UlmNs/MFprZbXtYn2pmzwfrJ5lZbrC8sZmNN7PtZvZgNLJJ7KzeUsQFj39GSXkF/7x8ALlNMsKOJCISdVnpyfzz8gF0aJLBD5+ezCcLVfASkehxd96fu4ZTH5zIlf+cSlmF89fzejPuuu/w3e4tVOQSEZG4VO3FLjNLBB4CTga6AeeZWbcqm10ObHL3POBe4O5geRHwK+Dm6s4lsbVhezEXPj6JTTtKeerS/nRpUT/sSCIiMZOdkcI/fziAdo0yuOTJybw/d03YkUSkjnF3Ji5Yz5l//4TLnixgy65S/jL8CN6+4WhOPaIVCZofVURE4lg0enb1Bxa6+2J3LwFGA8OqbDMMeCq4PQY4zszM3XcEl6kuikIuiZG124o4d9RnLN+4k8cvztclrUUkLjXJTGX0yIF0bVGfkU9P4fUZq8KOJCJ1xOdLNnLuqM+48PFJrNlSxB/OPJz3fzKEs/rmkJSoWUpERESicTXG1sDySvcLgQF728bdy8xsC9AY2O+xHmY2EhgJ0LZt20PJK9Vo1ZZdXPDoJFZvLeLJS/szoEPjsCOJiIQmOyOFZ384gMuenMy1z01lZ0lPhue3CTuWiNRS05Zv5i9vz+OjBetpWj+V35zWnXP7tyE1SRf/ERERqSwaxa499Zn2g9hmn9x9FDAKID8//4AeK9GxfONOzn/sMzbvKOWZy/vTt12jsCOJiISufloyT13WnyuemcItY2awfnsJVx7TQfPoiMh+m7VyC/e+M59356ylUUYKvzjlMC4c2E5XuBYREdmLaBS7CoHKp61zgJV72abQzJKALGBjFLJIjCxYs42Ln/icHSXlPPujAfTM0dBFEZHd0lOSeOzifG5+cQZ3vzmXwk07+c1p3TXcSET2acGabdz77nzGfbmaBmlJ3HxiZy4Z1J7M1Gi8hRcREak7ovGfcjLQyczaAyuAc4Hzq2wzFrgY+BQ4G3jf3dU7q5b6dNEGRj5TQFpyIs/9aCDdWjUIO5KISI2TmpTI/SN6kZNdj79PWMTKzbt48Pw+ZOhDq4hUsWT9Du5/dz6vTl9JenIi1x2bx+Xf6UBWveSwo4mIiNQK1f4OO5iD6xrgLSAReMLdZ5nZnUCBu48FHgeeMbOFRHp0nbv78Wa2FGgApJjZ6cCJ7j67unNK9XjlixXcMmY6uY0z+Mel/cjJTg87kohIjZWQYPz0pK7kZNfjV6/M5JxHPuWRi/rq2CkiLFm/g/Fz1zJ+3lo+WbSB5ERj5NEduOLojjTKSAk7noiISK0SldPJ7j4OGFdl2e2VbhcBw/fy2NxoZJLq5e48NH4hf357PgM7NOKRC/PJStfZRhGR/XHBgHa0yqrHdc99wWkPfsyD5/XmqLwmYccSkRgqKi3n08Ub+GDeOsbPW8uyDTsB6Ng0gx99pwOXDc6lWf20kFOKiIjUTho7IQdse3EZt7w4nTdmrub0Xq24++yeugqQiMgBGtq1Ga9eM4iRz0zhwscn8fNTDuPywe01cb1IHfbVhp2Mn7eWCUHvreKyCtKSEziqYxN+OLg9Q7o0o00j9fQUERE5VCp2yQFZuHY7VzxTwNINO/nFKYfxw+/og5mIyMHq0DSTV64exM0vTOd3r8/hi6828/szD9e8PCJ1RHFZOZ8v2cj4ueuYMH8ti9ftACC3cTrn9W/L0K7NGNC+EWnJOmkoIiJSnVTskv027stV3DpmBqlJCTxzeX+O6qghNyIihyozNYm/X9iHhz9YzJ/fnse05Zu5/9xe5Oc2CjuaiByEwk07mTBvHRPmreXjhRvYVVpOSlICR3ZozEUD2zGkSzPaN8kIO6aIiEidpmKXfKttRaX85rXZjJlSSK82Dfn7hX1omVUv7FgiInWGmfHjIR0Z2KER14+exjmPfMp1x3XimqF5JCUmhB1PRPahpKyCgqUbmTB/HePnrmXB2u0A5GTXY3h+DkO6NOXIDk2ol6LeWyIiIrGiYpfsU8HSjdz4wjRWbNrFdcfmce1xnUjWBy8Rkajo3Tab168bzK9fncV97y7g/blrufusnhzWhhxqXQAAIABJREFUskHY0USkklVbdv2399bEBevZUVJOcqIxoH1jRvRrw5AuzejYNENTPYiIiIRExS7Zox3FZdz37nwen7iE1tn1ePHKI+nbTkNqRESirX5aMveM6MWxhzXj16/O4tS/TuSqIR25+tg8XQxEJCSl5RVMXbaJ8UGBa+7qbQC0ykpjWO/WDO3SjKM6NiYjVW+tRUREagL9R5b/8d6cNdz+6ixWbN7Fef3b8vNTulI/TZMli4jE0vd7tmJQxyb89j+zeeD9hYybuZrfnNadQXmaL1EkFtZuLWLC/Ehx66MF69lWVEZSgpGfm83PTu7K0K7N6NQsU723REREaiAVu+S/lm/cye/HzeGNmavp1CyTMVceqQmSRURClJ2Rwj0jenFar1b88pWZXPDYJE7s1pxffO8w2jXWBNci1am8wpm2fBPj565j/Ly1zFq5FYDmDVI5pUdLhnZtyqC8JjoBKCIiUguo2CVs2VnKg+MX8NQny0hIgFu+24UffacDKUmam0tEpCYY0qUZ7950DI9PXMJD4xdywj0fcungXH58TEcapqeEHU9kr8zsJOB+IBF4zN3vCjnSN6zfXswH89YxYf46Ppy/ji27SklMMPq2zebWk7owpHMzDmtZX723REREahkVu+LYzpIy/vnZMh4av4itRaUM75vDTSd0oUVWWtjRRESkirTkRK4emsfZfXO4+825jPpwMc9+9hWXDW7P5YPbk1VPvU2kZjGzROAh4ASgEJhsZmPdfXZYmcornBmFmxk/bx0fzFvLjBVbcIcmmamc0K05Q7s0Y3CnJvp7EhERqeVU7IpDW4tKeebTZTw+cQkbd5RwdOem/Ozkrrral4hILdC8QRr3nNOLK47uyL3vzOeB9xbw5MdLuGxwey4a2I7GmalhRxTZrT+w0N0XA5jZaGAYEPNi1+szVvHO7NV8uGA9G3eUkGDQq01Dbjq+M0O7NqNbywYkJKj3loiISF2hYlccKdy0k2cnfcU/P1vGtqIyhnZpyjXH5ukqiyIitVCXFvV5+KK+zFyxhfveXcB97y7g7xMWcWafHC4f3J68ZplhRxRpDSyvdL8QGFB5AzMbCYwEaNu2bdSC/OvzZcxZtY0hnZtyTJemHN2pKdkZGgIsIiJSV6nYVcdVVDifLNrAU58u5b05awD4bvcWXD00jx6ts8INJyIih6xH6yweuzifhWu38fjEJbw0tZDnPv+KwXlNOLd/G07o1pzUpMSwY0p82lNXKf/GHfdRwCiA/Px838P21eKBc3uTnZ6i3lsiIiJxQsWuOmrRuu28PHUFL3+xghWbd9EoI4Urj+nIBQPb0bphvbDjiYhINctrVp8/nNmTn5zYhWc/+4oXCpZzzb++oFFGCmf0bs2wXq04vHWWJtqWWCoE2lS6nwOsDCOIhveKiIjEFxW76pBF67bz9qw1vDlzFdMLt5BgMCivCbd8twsn9WhBWrLO7IuI1HVNMlO5/vhOXHNsHhMXruf5yV/x9KdLeXziEto2Suf7PVtyyuEt6d6qgQpfEm2TgU5m1h5YAZwLnB9uJBEREYkHKnbVYkWl5RQs3cRHC9fx7uw1LFq3A4DDW2fxi1MO47RerWjeQFdWFBGJR4kJxjGdm3JM56Zs3lnCW7NW858Zq3jkw8X8bcIimjdIZWiXZgzt2oyjOjamfpquPifVy93LzOwa4C0gEXjC3WeFHEtERETigIpdtciO4jKmL9/MlGWb+GzJBiYv3URJWQXJiUa/3Eb84MhcTujWnFYapigiIpU0TE9hRL+2jOjXlg3bi3lv7lrGz13Lf2asYvTk5SRY5ETJgA6NGdC+EX3aZmvybqkW7j4OGBd2DhEREYkvKnbVUNuLy5i7aiuzV21l9sqtzCjcwtzVW6kIpm7t2qI+Fw1sx+C8JvRv34iMVP0qRUTk2zXOTOWc/Dack9+GkrIKCpZt5NNFG5i0eCNPfryUUR8uBqBd43R65jTkiJwsurZoQJcW9WmSmaKhjyIiIiJS46lCEqLtxWUsXb+DZRt2snTDjv/eXrJhB+u2Ff93u+z0ZLq3yuKaoXn0aZdN7zbZZKVruImIiByalKQEjurYhKM6NgEiw+OnfrWJ6cu3MKNwM1OWbuS16V/PJ94oI4W8Zpm0bZROm+x02jauR5vsdNo0SqdpZqqudCciIiIiNUJUil1mdhJwP5H5GR5z97uqrE8Fngb6AhuAEe6+NFj3M+ByoBy4zt3fikbG6lRWXsGOknJ2FJexs6SMHcWR29uLy9iwo4T124pZv72Y9dtLWLc9cnvdtmK2FZV9Yz/N6qeS2ziDIZ2bktskg64t6tO9VRbNG6TqTLqIiERdWnLiN4pfAOu3FzN/9Tbmrt7GvNXbWLRuOx8tWMearcXfeGxqUgI52fVoWj+VRhkpwVcqjdKTaZSZSuOMFLLqJVMvJZF6ycFXSiKpSQn6HyciIiIi1arai11mlgg8BJxA5JLTk81srLvPrrTZ5cAmd88zs3OBu4ERZtaNyJV6ugOtgHfNrLO7l1d3zv2xassuzn90EuUV/vWXOxUVTllF5HtxeQUlZRXfuq/6aUk0rZ9Kk8xUDmvRgO/kpdA8K432jTNo1ziDdo3TNRRRRERqnCaZqTTJS+WovCbfWF5UWk7hpl0s37STwo07+WrjTgo37WL99mLmrd7Gxh0lbN5Vivu+928G9ZITSU9J5LaTD+PsvjlR/GlEREREJB5Eo7rSH1jo7osBzGw0MAyoXOwaBtwR3B4DPGiR07rDgNHuXgwsMbOFwf4+jULOb5WSmECP1lkkGiQmJJCYELm6VYIZSQlGQoKRkphARmoS6SmJZKYmkZ6aREZKIhmpSWSkJNEoM4XGGSmkJSeG8SOIiIhERVpyInnNMslrlrnXbcrKK9i8q5RNO0rYsKOELbtKKSotZ1dJObtKy9lZUv7f+ztLy8nJ1gVWREREROTQRaPY1RpYXul+ITBgb9sEl6XeAjQOln9W5bGt9/QkZjYSGBnc3W5m8w49erVqAqwPO0TI1AahtkG7cJ72f58/Zm1wQeg/8z7Vvr+HgQfenhfse3Xta4PqpzbYRxv8IbrPW6MPEAJTpkxZb2bLovgUWcCWKO6/OtWmrFC78iprdNSmrFC78rYFvgo7xH6qTe1am7JC7cob7az79Z4uGsWuPU28UXUQw9622Z/HRha6jwJGHVi02DGzAnfPDztHmNQGagNQG+ymdlAbgNoA1Aayd+7eNJr7N7NR7j7y27cMX23KCrUrr7JGR23KCrUrr5mtqy3/N2tZu9aarFC78taUrAlR2Gch0KbS/Rxg5d62MbMkIpW/jfv5WBERERGRA/Va2AEOQG3KCrUrr7JGR23KCrUr7+awAxyA2tSutSkr1K68NSJrNIpdk4FOZtbezFKITDg/tso2Y4GLg9tnA++7uwfLzzWzVDNrD3QCPo9CRhERERGJI+5eI95874/alBVqV15ljY7alBVqXd7aMnStVrVrbcoKtStvTcla7cMYgzm4rgHeAhKBJ9x9lpndCRS4+1jgceCZYAL6jUQKYgTbvUBkMvsy4OqwrsRYDWrsEMsYUhuoDUBtsJvaQW0AagNQG4iIiBwI/d8UOQjm33ZNcBERERERERERkVoiGsMYRURERERCYWbbw85woL4ts5lNMLPQJqg2szPMzM2sa1gZ9peZ/cLMZpnZDDObZmZVrwpfo5hZjpm9amYLzGyRmd0fTAWzt+1vMLP0WGYMntfN7C+V7t9sZnfEOsf+MLPy4Hc/y8ymm9lNZqbPvdWgUtvu/srdx7ZDzOw/sUv3P8/vZvZMpftJZrYuzEzfpjYca2tTu+qPXkRERERE9uU8YCLB1CM1lZkdCXwf6OPuPYHjgeXhpto7MzPg38Ar7t4J6AxkAv+3j4fdAMS82AUUA2eaWZMQnvtA7XL3Xu7eHTgBOAX4dciZ6ordbbv7a2nYgfZhB9DDzOoF908AVhzIDoKL6cXSQR1rzSwxOnH26JDbNVZU7KpGZjY8OINQUfXsm5n9zMwWmtk8M/tuWBljzczuMLMVlar/p4SdKVbM7KTg973QzG4LO08YzGypmX0Z/O4Lws4TC2b2hJmtNbOZlZY1MrN3grO275hZdpgZY2Ev7RBXxwMza2Nm481sTvC/4fpgedy8HvbRBnH1WpDYq9qjwMweNLNLgttLzew3ZjY1+B9VI86g7ytzmMwsExgEXE7wAexb2vcUM5trZhPN7IEYn+1vCax392IAd1/v7ivNrK+ZfWBmU8zsLTNrGWSdYGb3mdknZjbTzPrHMCvAsUCRu/8jyFsO3AhcZmYZZvbn4DU6w8yuNbPrgFbAeDMbH+OsZUTmjrqx6goza2dm7wU53zOztmaWFfytJQTbpJvZcjNLjmVod18LjASusYhEM/uTmU0O8l5R6ee4NWjv6WZ2VyxzBs9f63qlQqTQsrc2BRqY2ctmNtvMHrbY97B7A/hecPs84LndK8ysf/C3/0XwvUuw/BIze9HMXgPejlXQfRxrP9xTG5rZdjO708wmAUfGKmfgYNr1IzPrVWm7j82sZzRDqthVvWYCZwIfVl5oZt2IvGC7AycBf7PYVl/Ddm+l6v+4sMPEQvD7fQg4GegGnBe8DuLR0OB3H9rwixh7ksjfeWW3Ae8FZ23fC+7XdU/yv+0A8XU8KAN+4u6HAQOBq4PjQDy9HvbWBhBfrwWpeda7ex/g78DNYYep4U4H3nT3+cBGM+uztw3NLA14BDjZ3QcDTWOUcbe3gTZmNt/M/mZmxwTFlb8CZ7t7X+AJvtlzKsPdjwKuCtbFUndgSuUF7r4V+Ar4IdAe6B30UnvW3R8AVhJ5bzU0xlkh8t72AjPLqrL8QeDp3TmBB9x9CzAdOCbY5lTgLXcvjVnagLsvJvK5txmRQsIWd+8H9AN+ZGbtzexkIq/1Ae5+BPDHWOesJepVOlH1crBsj20arOsP/AQ4HOhI5LNyLI0Gzg2OTT2BSZXWzQWOdvfewO3A7yutOxK42N2PjVnSvR9r99aGGcBMdx/g7hNjmBMOrl0fAy4BMLPOQKq7z4hmSBW7qpG7z3H3eXtYNQwY7e7F7r4EWEjkRSt1V39gobsvdvcSIgeEYSFnkhhw9w+JXGW2smHAU8Htp4j8M6vT9tIOccXdV7n71OD2NmAO0Jo4ej3sow1Ewvbv4PsUIDfEHLXBeUTexxB8P28f23YFFgfvd6HS2f5YcPftQF8iPXnWAc8DVwA9gHfMbBrwSyCn0sOeCx77IZFeKA1jGNmAPV0tzICjgYfdvSzIF/r/1KAQ9zRwXZVVRwL/Cm4/AwwObj8PjAhunxvcD4sF308EfhC8FiYBjYFORIa8/sPdd0J47W1mmUHvuN09T4cFy3Mt0kv6UYv0lH7bvh5GFkuVhzGeESzbW5sCfB58Hion8rc2+H93GT1BMSWXyHGr6om1LOBFi4xCuJdI8Xm3d0J4DeztWLu3NiwHXoptxIiDbNcXge8HJyAuI3JiPKpU7IqN1nxzvoBC4uvN/jVBl9YnrA4P16ki3n/nuznwdjBsYGTYYULU3N1XQeTDP5Ezi/EqHo8HWGQC195E3gTG5euhShtAnL4WJGbK+Ob73LQq64uD7+VArOdk2ZtvyxxzZtaYyFC7x8xsKXALkeJFOXvOaoTM3cvdfYK7/xq4BjgLmFXpA/rh7n5i5YdU3UXMwsIsoOrUJw2ANuy9EBa2+4j05MnYxza7c48FTjazRkSKkO9HOdsemVkHIq/ZtUTa9dpKr4f27v42Nae9i4Azgp6nQ4G/mNnuv6tOwEPBXGSbiby2a4K9tSmE+/e121jgz/xv8f23wHh370Gk52HlY+6OGGUD9nms3dPrcvf9oqAAFpYDategkPwOkZO+5/B1gTxqVOw6QGb2rkXG9Ff92levnT39468JB9Nq8S1t8nci3S17AauAv+xzZ3VHnf6dH4BBwT/rk4kMXzo67EASqrg8HgRzMLwE3BCcFY87e2iDuHwtSEwtA7qZWWow5Oq4sAPth5qY+Wwiw9PauXuuu7cBdvfa2lPWuUAH+/oKbSOIITPrYmadKi3qRaRHaVOLTF6PmSWbWeUeHCOC5YOJDMXaErPAkaHs6Wb2gyBDIpHj4ZNEhmReacEE2UHBCGAbUD+GGb8h6O3yApGC126f8PWE2hcQmWB7d0+7z4H7gf+E8cHczJoCDwMPursDbwE/DnqXYGadzSyDSHtfZsGVLiu1d8wjA783sxnAu0ROljcP1i1x92nB7ZrUK3VvbQrQPxgmmkDkby3Ww+0gMjz5Tnf/ssryLL6eWP2SmCb6X3s71g6mZrThnhxMuz4GPABMjkXPuZpyJqvWcPfjD+JhhUTO0OyWQ2S8fZ2wv21iZo8CNe6SpFFSp3/n+8vdVwbf1wbj+vtTZU67OLHGzFq6+yqLTIq7NuxAYXD3Nbtvx8vxIHjj9xKRuVZ2D5uKq9fDntogHl8LEhtBYaDY3Zeb2QvADGAB8EW4yfauhmc+D6g6UfdLwPlECh7fyOruu8zsKuBNM1tPpNARS5nAX4OhiGVEpg4ZSWRi9QeCwlwSkd5Js4LHbDKzT4AGRIbWxIy7u5mdQWQ+318R6YgwDvg5kZ5InYEZZlYKPEpkbqxRwBtmtiqkebsgUpC7ptL964AnzOwWIsNHL6207nkiw5eGxCxdMK8UkEzkdfAMcE+w7jEiRaKpQY+pdcDp7v6mRSbPLjCzEr7+PcTaBUTmuuvr7qVBL5/dPY6KK21XDoQxjHFP9timwbpPiRxDDifyGeDlPe0gmty9kEjBtao/Ak+Z2U2E1Ouwkr0da39MDWjDPTmYdnX3KWa2FfhHDCJikQK3VCczmwDc7O4Fwf3uRLrp9SdyBZX3gE4hdzuMid0f6ILbNxKZ9LFGX7a6OgRvXOcTOdO5ApgMnO/us/b5wDokOKOT4O7bgtvvEKn+vxlytKgLzmj/J+i+i5n9Cdjg7ndZ5Mqcjdz91hAjxsQe2iGujgfBG76ngI3ufkOl5XHzethHG8TVa0Fix8yOAB5191ozN2ptzLwvZpbp7tuDv/+HgAXufm/Yufak6nt2kTBZ5GqMvwDy3P1aMxtKpFiwe7L3yu+pbgYy3f2OUMJKTJjZECLHqO+HnaU6mFkrYALQ1d0rov186tlVjYIzM38lUo1/3cymuft33X1WcKZuNpGzC1fHQ6Er8MfgLIkDS4lMElrnuXuZmV1DpFtvIvBEPBW6As2Bl4NpBpKAf8VJoes5Imcvm5hZIfBrImdjXjCzy4lcYWl4eAljYy/tMCTOjgeDgIuAL4MzzBA5SxxPr4e9tcF5cfZakBgwsyuJ9DC54du2rSlqY+b98CMzuxhIIdLj65GQ84jUeLt7eBK5muVrZlYATCMyNFik1guGa/8fcFMsCl2gnl0iIiIiIiIioalrPTxFagJNUC8iIiIiIiISgqCH53PAL8POIlKXqGeXiIiIiIiIiIjUGerZJSIiIiIiIhIDZtbGzMab2Rwzm2Vm1wfLG5nZO2a2IPieHSzvamafmllxMDF95X3dGOxjppk9Z2Zpe3pOkXikYpeIiIiIiIhIbJQBP3H3w4CBwNVm1g24DXjP3TsB7wX3ATYSuZDFnyvvxMxaB8vzg6s0JgK6srFIQMUuERERERERkRhw91XuPjW4vQ2YA7QGhgFPBZs9BZwebLPW3ScDpXvYXRJQL7iaYzqwMsrxRWoNFbtE5KCYWQszG21mi8xstpmNM7POYefazcxOM7Pbvn3L/dpXuZlNC7qIv2ZmDb9l+4ZmdlV1PLeIiIiI1E1mlgv0BiYBzd19FUQKYkCzfT3W3VcQ6e31FbAK2OLub0czr0htomKXiBwwMzPgZWCCu3d0927Az4HmIWRJ3NNydx/r7ndV09PscvdeQRfxjcDV37J9Q0DFLhERERHZIzPLBF4CbnD3rQfx+GwivcHaA62ADDO7sHpTitReKnaJyMEYCpS6+8O7F7j7NHf/yCL+FPSC+tLMRgCY2RAzm2BmY8xsrpk9G2x7spm9sHs/wXavBbdPDCbknGpmLwZvCjCzpWZ2u5lNBIab2XVB77IZZjY62OYSM3swuN3OzN4L1r9nZm2D5U+a2QNm9omZLTazs/fjZ/+USFdzzCwz2N/U4GcdFmxzF9Ax6A32p2DbW8xscpDhN4fS+CIiIiJSe5lZMpFC17Pu/u9g8Rozaxmsbwms/ZbdHA8scfd17l4K/Bs4KlqZRWqbpLADiEit1AOYspd1ZwK9gCOAJsBkM/swWNcb6E5kPoGPgUHAO8AjZpbh7juAEcDzZtYE+CVwvLvvMLOfAjcBdwb7KnL3wQBmthJo7+7Fexli+CDwtLs/ZWaXAQ8QzIMAtAQGA12BscCYvf3QQS+y44DHd2cAznD3rUHez8xsLJEJRXu4e6/gcScCnYD+gAFjzexod//wf55EREREROqsYITE48Acd7+n0qqxwMVETppeDLz6Lbv6ChhoZunALiLvUQuqP7FI7aSeXSJS3QYDz7l7+f+zd9/xVdX3H8dfn2zCJgyBgGHvALIEBBkiVnEPxNG6q3UUbbVaF7W11dZWK/ZXxYqodeOiDqygFFlikC0CASIEEEgg7OzP749caAoBAhkn4/18PO4j557z/Z7zvheRm8/9fr/H3bcA/wH6hI7Nd/dUd88HFgEJ7p4LTAXODS2ueQ4F/7ifCnQGZpvZIgr+0T+50HXeLLS9BHg1NHQ7t4hM/YHXQtuvhDIe8L6757v7txx5GmaNUIZ0oAEFBTooKFz93syWANMoGPFV1DnODD0WAt9QUFhrd4RriYiIiEjVNRC4GhgWmgWwyMzOpqDINcLMVgMjQs8PrJObSsGXvg+YWaqZ1XH3ryj4kvYbYCkFv9tPCOD1iFRIGtklIidiOXCkKX92lH5Zhbbz+O//g96kYB2s7cDX7r479K3XZ+4+5gjn2lto+xxgMHAe8KCZdTlGfj9CpiNl3+/uPcysLvBhKOvTwJVAI6CXu+eYWQoQU0R/A/7g7s8dI5eIiIiIVGHuPosjf+YcXkT7H4D4I5zrYeDh0ksnUnVoZJeInIjPgWgzu/HADjPrY2anAzOB0WYWbmaNKChCzT/G+WYApwA38t8RW/OAgWbWNnT+2KLu9mhmYUALd/8CuIeCxeFrHdJsDnB5aPtKYFZxX2hh7r4TuAP4ZWithbrA1lChayj/HXm2G6hdqOunwHWF1hxrbmZHvcOOiIiIiIiInBiN7BKR4+bubmYXAk+Z2b0UrF2VAoyloNjVH1hMwQiqe9z9BzPreJTz5ZnZh8A1FExXxN23mdk1wOtmFh1q+gCw6pDu4cA/Q6OuDHjS3TMKBoYddAcw0czuBrYB15bgtS80s8UUFM9eBf5lZkkUTMv8LtQm3cxmm9ky4BN3v9vMOgFzQ7n2AFdx7IVHRURERERE5DiZux+7lYiIiIiIiIiISCWgaYwiIiIiIiIiIlJlqNglIiIiIiIiIiJVhopdIiIiIiIiIiJSZajYJSIiIiIiUk2Y2R1mtsLMdoRuNFTcfglmdkVZZit0rSFmNqAE/X99nO3Hmdkvj9HmAjPrfKKZRKR8qdglIiIiIiJSffwMONvd67v7Y4ceNLOII/RLAMql2AUMAU642AUcV7GrmC4AVOwSqSRU7BIREREREakGzOxZoDUwxczuNLNnQvsnmdlfzOwL4HEzO93MFoUeC82sNvAYMCi0784jnD/czJ4ws6VmtsTMbg/tHx46z1Izm2hm0aH9KWb2GzP7JnSso5klADcDd4auNcjMzjWzr0LnmGZmTUL9a5nZi4Wud7GZPQbUCPV99Sjvxf1mttLMpgEdCu2/0cy+NrPFZvaOmcWGRpmdB/wpdN42ocdUM1tgZl+aWccS/vGISCkydw86g4iIiIiIiJQDM0sBegOjgN7ufpuZTQIaAue7e56Z/Qt4zN1nm1ktIBM4Dfilu486yrlvAc4ARrt7rpk1APYBq4Hh7r7KzF4GvnH3p0JZ/uzu483sZ8Ap7n6DmY0D9rj7E6Hz1gcy3N3N7Aagk7v/wsweB6LdfeyBdu6+w8z2uHuto+TsBUwC+gERwDfAs+7+hJnFuXt6qN3vgC2hfJOAD919cujYdOBmd19tZv2AP7j7sOL/SYhIWTrSEFURERERERGpPt5297zQ9mzgL6GRUe+6e6qZFeccZ1BQNMoFcPftZtYdWOfuq0JtXgJuBZ4KPX839HMBcNERzhsPvGlmTYEoYF2h611+oJG77yhOSGAQ8J677wMwsymFjnUNFbnqAbWATw/tHCoADgDeLvS+RBfz2iJSDjSNUURERERERPYe2Ait5XUDUAOYdxxT9Aw4dOrQsapkWaGfeRx5MMZ44Bl37wb8FIg5yvWK60j9JgG3ha71m0LXKiyMgpFmPQo9Op1gDhEpAyp2iYiIiIiIyEFm1sbdl7r740AS0BHYDdQ+Rtd/AzcfWOQ+NI3xOyDBzNqG2lwN/OcY5zn0WnWBjaHtnxxyvdsK5a4f2swxs8ijnH8mcKGZ1QitR3ZuoWO1gc2h/lcWlcnddwHrzOzS0HUtNIJNRCoIFbtERERERESksLFmtszMFgP7gU+AJUBuaOH2IheoB/4BrAeWhPpe4e6ZwLUUTPlbCuQDzx7j+v+ioBi1yMwGAeNC/b8E0gq1+x1Qv1DWoaH9E0IZilyg3t2/Ad4EFgHvAF8WOvwg8BXwGQWFugPeAO4OLZLfhoJC2PWh6y4Hzj/GaxKRcqQF6kVEREREREREpMrQyC4REREREREREakydDdGERERERERKTYzGwk8fsjude5+YRB5jsTM4oDpRRwa7u7p5Z1HRMqPpjGKiIiIiIiIiEiVoWmMIiIiIiIiIiJSZajYJSIiIiIiIiIiVUaVWLOrYcOGnpCQEHQMERERqaAWLFiQ5u6Ngs4hR6bPcyLKb33LAAAgAElEQVQiInIsxf1MVyWKXQkJCSQlJQUdQ0RERCooM/s+6AxydPo8JyIiIsdS3M90msYoIiIiIiIiIiJVhopdIiIiIiIiIiJSZajYJSIiIiIiIiIiVUaVWLNLRESkIsrJySE1NZXMzMygo1QbMTExxMfHExkZGXQUEREREQmIil0iIiJlJDU1ldq1a5OQkICZBR2nynN30tPTSU1NpVWrVkHHEREREZGAaBqjiIhIGcnMzCQuLk6FrnJiZsTFxWkknYiIiEg1p2KXiIhIGVKhq3zp/RYRERERFbtERERERERERCqYPVm5TF6Qyg87NWr9eGnNLpEKY2zo51NFH14QOt7rCMcrqbFTC17XU2dVrddV5saG/nt4Su+biIiIiEhVsj59H5PmpPB20gZ2Z+XSPb4uk28ZQGS4xisVl4pdIuXota/WH/HY8E5fATB9RdFthm8MHc898jkqo3+vKnhdr9Uv3dd1Rb+WpXq+CmfRoqATSCWRkpLCWWedxWmnnca8efPo3r071157LQ8//DBbt27l1VdfpUuXLtx+++0sXbqU3Nxcxo0bx/nnn09KSgpXX301e/fuBeCZZ55hwIABzJgxg3HjxtGwYUOWLVtGr169+Oc//6kphCIiIiInyN2Zt3Y7E2evY9qKLYSbcU5iUzqeVIfHp37HM58nc+eI9kHHrDRU7BIRESkHY6eOZdEPpVuk7HFSj2KNikxOTubtt99mwoQJ9OnTh9dee41Zs2YxZcoUfv/739O5c2eGDRvGxIkTycjIoG/fvpxxxhk0btyYzz77jJiYGFavXs2YMWNISkoCYOHChSxfvpxmzZoxcOBAZs+ezWmnnVaqr09ERESkqsvMyWPK4k28ODuFFZt30aBmFLcOacvV/U+mSZ0YAFZv2c0zXyQztGNjerSoF3DiykHFLhERkSquVatWdOvWDYAuXbowfPhwzIxu3bqRkpJCamoqU6ZM4YknngAK7iK5fv16mjVrxm233caiRYsIDw9n1apVB8/Zt29f4uPjAejRowcpKSkqdomIiIgU09Zdmfxz3ve8+tV60vdm0/Gk2jx+cTfO79GcmMjw/2k77vwuzFubzl1vLuKjOwZRIyr8CGeVA1TsEhERKQdBrksXHR19cDssLOzg87CwMHJzcwkPD+edd96hQ4cO/9Nv3LhxNGnShMWLF5Ofn09MTEyR5wwPDyc3N7eMX4WIiIhI5bc0dScTZ6/jwyWbyM13hndszHUDW9G/TdwRl4SoExPJE5d254p/fMUfPlnBI+d3LefUlY+KXSIiItXcyJEjGT9+POPHj8fMWLhwIT179mTnzp3Ex8cTFhbGSy+9RF5eXtBRRURERCqd3Lx8/v3tFibOWkfS9zuoGRXOlf1O5poBCSQ0rFmscwxo25DrT2vFC7PWMbxTE05v36iMU1duKnaJiIhUcw8++CBjx44lMTERdychIYEPP/yQn/3sZ1x88cW8/fbbDB06lJo1i/dhTERERERg574c3vh6PS/P/Z6NGftp2SCWB0d15tLe8dSJiTzu8909sgMzV23j7rcX8+87B1MvNqoMUlcNKnaJiIhUYQkJCSxbtuzg80mTJhV57Lnnnjusb7t27ViyZMnB53/4wx8AGDJkCEOGDDm4/5lnninl1FLRmNlEYBSw1d27hva9CRyY+1oPyHD3HmaWAKwAVoaOzXP3m8s3sYiISHCSt+5h0px1vLNgI/tz8ujfOo6Hz+3M8E5NCA878btXx0SG8+ToHlz4f7O5//1lPDOmp+6GfQQqdomIiIjIsUwCngFePrDD3Ucf2DazPwM7C7Vf4+49yi2diIhIwPLznZmrt/Hi7BT+s2obURFhXNCjGdcMaEXnZnVK7Tpdm9dl7Bnt+dOnKzmzcxPO79G81M5dlajYJSIiIiJH5e4zQyO2DmMFXylfBgwrz0wiIiIVwb7sXN75ZiOTZq9jzba9NKodzS9GtOeKfi2JqxV97BOcgJ8Obs30FVt48P1l9EloQLN6NcrkOpWZil0iIiIiUhKDgC3uvrrQvlZmthDYBTzg7l8W1dHMbgJuAmjZsmWZBxURESktGzP28/KcFF6fv55dmbkkxtflydHdOadbM6Iiwsr02hHhYfzlsh6c/fSX3D15Ma9c14+wEkyPrIpK9CdgZmeZ2UozSzaze4s4Hm1mb4aOf1X4G0Ezuy+0f6WZjSy0v56ZTTaz78xshZn1L0lGERERESlTY4DXCz3fDLR0957AXcBrZlbk/A13n+Duvd29d6NGuquUiIhUbO5OUsp2fvbqAgb/8Que/3Itg9o1YvLN/fng1oFc2DO+zAtdByQ0rMkD53RmdnI6L81NKZdrViYnPLLLzMKBvwEjgFTgazOb4u7fFmp2PbDD3dua2eXA48BoM+sMXA50AZoB08ysvbvnAX8Fprr7JWYWBcSeaEYRERERKTtmFgFcBPQ6sM/ds4Cs0PYCM1sDtAeSAgkpIiJSQtm5+Xy0dBMTZ6WwdONO6sREcMOgVvy4fwLNA5xCOKZvC6at2MJjn3zHoHYNadu4dmBZKpqSTGPsCyS7+1oAM3sDOB8oXOw6HxgX2p4MPBNa1+F84I3Qh6F1ZpYM9DWz5cBg4BoAd88GskuQUURERETKzhnAd+6eemCHmTUCtrt7npm1BtoBa4MKKCIicqLS9mTx2lfreWXe92zbnUWbRjX53QVdueiU5sRGBb8qlJnx2MXdGPnkTO58czHv/mwAkeHlM7KsoivJu9Ac2FDoeWpoX5Ft3D2Xgrv0xB2lb2tgG/CimS00s3+YWc2iLm5mN5lZkpklbdu2rQQvQ0RERIpjyJAhJCUVDM45++yzycjICDiRlBczex2YC3Qws1Qzuz506HL+dwojFHxxucTMFlPwZefN7r69/NKKiIiUzLebdnH324sZ8Njn/OWzVXRuWoeXruvLZ3eezlWnnlwhCl0HNK4dwx8u6sbSjTsZP331sTtUEyX5Eypq9TMvZpsj7Y8ATgFud/evzOyvwL3Ag4c1dp8ATADo3bv3odcVERGRMvTxxx8HHUHKkbuPOcL+a4rY9w7wTllnEhERKU15+c60FVt4cfY65q3dTo3IcC7rHc81A1rRtnGtoOMd1Vldm3LxKfE880UyQzo25pSW9YOOFLiSjOxKBVoUeh4PbDpSm9CaDnWB7UfpmwqkuvtXof2TKSh+iYiIyAlISUmhY8eO3HDDDXTt2pUrr7ySadOmMXDgQNq1a8f8+fPZu3cv1113HX369KFnz5588MEHAOzfv5/LL7+cxMRERo8ezf79+w+eNyEhgbS0NAAuuOACevXqRZcuXZgwYcLBNrVq1eL++++ne/funHrqqWzZsqV8X7yIiIjIMezOzOGFWesY8sQX/PSVBaxP38d9P+rIvPuG87sLulX4QtcBD5/XmaZ1a3DXm4vYl50bdJzAlWRk19dAOzNrBWykYBj7FYe0mQL8hIJh75cAn7u7m9kUCu7M8xcKFqhvB8wPre2wwcw6uPtKYDj/uwaYiIhI5bRgLOxYVLrnrN8Dej11zGbJycm8/fbbTJgwgT59+vDaa68xa9YspkyZwu9//3s6d+7MsGHDmDhxIhkZGfTt25czzjiD5557jtjYWJYsWcKSJUs45ZSiv3+aOHEiDRo0YP/+/fTp04eLL76YuLg49u7dy6mnnsqjjz7KPffcw/PPP88DDzxQuu+BiIiIyAlISdvLpDkpvJ20gb3ZefQ+uT73/agTZ3ZuQkQlXPeqTkwkf76sO2Oen8fvP17B7y7oFnSkQJ1wscvdc83sNuBTIByY6O7LzewRIMndpwAvAK+EFqDfTkFBjFC7tygoZOUCt4buxAhwO/Bq6E6Ma4FrTzSjiIiIQKtWrejWreADT5cuXRg+fDhmRrdu3UhJSSE1NZUpU6bwxBNPAJCZmcn69euZOXMmd9xxBwCJiYkkJiYWef6nn36a9957D4ANGzawevVq4uLiiIqKYtSoUQD06tWLzz77rKxfqoiIiMhRLfh+O3+fsYbp320lIswYldiMawcmkBhfL+hoJXZq6zhuOK0Vz3+5juGdmjC0Q+OgIwWmRKuqufvHwMeH7Huo0HYmcOkR+j4KPFrE/kVA75LkEhERqXCKMQKrrERHRx/cDgsLO/g8LCyM3NxcwsPDeeedd+jQocNhfQtuonxkM2bMYNq0acydO5fY2FiGDBlCZmYmAJGRkQf7h4eHk5urIfUiIiISjHlr0xn/+WpmJ6dTPzaS24e25apTT6ZxnZigo5WqX5zZgZmr0rhn8hL+PXYw9WtGBR0pEJVvbJ6IiIiUqpEjRzJ+/HjcC+73snDhQgAGDx7Mq6++CsCyZctYsmTJYX137txJ/fr1iY2N5bvvvmPevHnlF1xERETkKNydWavTuOy5uVw+YR4rf9jD/Wd3Yva9w7jrzA5VrtAFEBMZzpOje5CxL5v731968PNddVNx7pcpIiIigXjwwQcZO3YsiYmJuDsJCQl8+OGH3HLLLVx77bUkJibSo0cP+vbte1jfs846i2effZbExEQ6dOjAqaeeGsArEBEREfkvd2fGqm2Mn76ab9Zn0KRONA+f25kxfVsSExkedLwy17lZHe4c0Z4/Tl3J+4s2cmHP+KAjlTsVu0RERKqwhIQEli1bdvD5pEmTijz23HPPHda3Ro0avPHGG0WeNyUl5eD2J598UmSbPXv2HNy+5JJLuOSSS44nuoiIiMhxcXemrdjK+M9XsyR1J83r1eC3F3Tl0l7x1aLIVdhPB7fh8xVbeeiD5fRtFUfzejWCjlSuNI1RRERERERERCqt/Hznk6WbOfvpWdz4chIZ+3J47KJufPHLIVx96snVrtAFEB5m/OWyHuTnO798azH5+dVrOqNGdomIiIiIiIhIpZOX73y0dDPPfL6aVVv20KphTf58aXfO79GMiHCN7WkZF8tD53bmV+8s5cU5KVx/WqugI5UbFbtEREREREREpNLIzctnyuJNPPNFMmu37aVd41r89fIejEpsRnjY0e8kXd1c1rsFn327hcenfsegdg1p36R20JHKhUqdIiIiIiIiIlLh5eTl89bXGxj+l/9w11uLiQoP4/+uPIVPxw7m/B7NVegqgpnxh4sSqR0dwZ1vLiI7Nz/oSOVCI7tEREREREREpMLKys1j8oJU/u+LNWzM2E/X5nWYcHUvzujUhDAVuI6pUe1o/nBRN256ZQF/nb6Ku0d2DDpSmVOxS0REREREREQqnMycPN6Yv57nZq5l885MerSox+8u6MqQDo0wU5HreJzZ5SQu7RXP32esYVjHxvQ6uUHQkcqUpjGKiIhUYddddx2NGzema9eux2w7Y8YM5syZUw6pjn7dZ599lpdffhmAa665hsmTJ5d7JhEREQnOvuxc/vHlWgb98QvG/etb4uvX4JXr+/LezwYwtGNjFbpO0EPndqZZvRrc9dZi9mblBh2nTKnYJSIiUoVdc801TJ06tVhtgyh25ebmHnbdm2++mR//+MflmkNERESCtycrl7/PWMOgx7/gdx+toF3jWrx+46m89dP+DGqn0VwlVTsmkr9c1oP12/fxu49WBB2nTGkao4iISBU2ePBgUlJSDtv/9NNP8+yzzxIREUHnzp157LHHePbZZwkPD+ef//wn48ePZ9CgQQfbp6enM2bMGLZt20bfvn2ZOnUqCxYsYM+ePYwaNYply5YB8MQTT7Bnzx7GjRvH888/z4QJE8jOzqZt27a88sorxMbGcs0119CgQQMWLlxIgwYNmD179v9cd/r06dSqVYtf/vKX/5N5wYIF3HXXXezZs4eGDRsyadIkmjZtWqbvn4iIiJS9XZk5vDQ7hRdmryNjXw6D2zfijmFt6Z1QtafaBaFvqwbcNLg1z/1nLSM6N2ZYxyZBRyoTKnaJiIiUi7HAolI+Zw/gqRPq+dhjj7Fu3Tqio6PJyMigXr163HzzzUUWmQB+85vfcNppp/HQQw/x0UcfMWHChGNe46KLLuLGG28E4IEHHuCFF17g9ttvB2DVqlVMmzaN8PBwxo0b9z/XnT59+mHnysnJ4fbbb+eDDz6gUaNGvPnmm9x///1MnDjxhF6/iIiIBC9jXzYTZ6fw4ux17M7MZXjHxtw+vB09WtQLOlqVdteI9vxn5TbumbyUT8fWI65WdNCRSp2KXSIiItVQYmIiV155JRdccAEXXHDBMdvPnDmTd999F4BzzjmH+vXrH7PPsmXLeOCBB8jIyGDPnj2MHDny4LFLL72U8PDwYudduXIly5YtY8SIEQDk5eVpVJeIiEgltX1vNv/4ci0vz/2ePVm5jOzShNuHtaNr87pBR6sWoiPCeXJ0D85/Zjb3v7eMv191SpWbIqpil4iISLk4sRFYZeWjjz5i5syZTJkyhd/+9rcsX778mH2K+hAUERFBfn7+weeZmZkHt6+55href/99unfvzqRJk5gxY8bBYzVr1jyuvO5Oly5dmDt37nH1ExERkYpj2+4snv9yLa/M/Z7M3DzO7taU24e1peNJdYKOVu10alqHX5zZnj988h3vfrORi3vFBx2pVGmBehERkWomPz+fDRs2MHToUP74xz8eHHlVu3Ztdu/eXWSfwYMH8+qrrwLwySefsGPHDgCaNGnC1q1bSU9PJysriw8//PBgn927d9O0aVNycnIO9i3K0a57QIcOHdi2bdvBYldOTk6xCnQiIiISvC27MvnNv5Zz2uOf848v1zKySxM+u3Mwf7viFBW6AnTDoNb0TWjAw1OWk7pjX9BxSpVGdomIiFRhY8aMYcaMGaSlpREfH89vfvMbfvzjH3PVVVexc+dO3J0777yTevXqce6553LJJZfwwQcfHLZA/cMPP8yYMWM45ZRTOP3002nZsiUAkZGRPPTQQ/Tr149WrVrRsWPHg31++9vf0q9fP04++WS6det2xILWodctSlRUFJMnT+aOO+5g586d5ObmMnbsWLp06VKK75YciZlNBEYBW929a2jfOOBGYFuo2a/d/ePQsfuA64E84A53/7TcQ4uIVHJj31jInDXpNKgZRVytKBrUjCauZhQNakZRv2bUwe0DP+vFRhEeVrGmom3M2M+zM9bwZtIG8vKdC3s259ahbWnV8PhGeEvZCA8z/nxZd3701y/5xVuLef3GUwmrYP8NnShz96AzlFjv3r09KSkp6Bgix/TaV+uPeGx4p9EATF/xZtHHN4aONy/6eGX1u68KXtcD/Ur3dV3Rr2Wpnq/CGTKk4GehaWFS8axYsYJOnToFHaNMJCQkkJSURMOGDYOOcpii3nczW+DuvQOKVOmZ2WBgD/DyIcWuPe7+xCFtOwOvA32BZsA0oL275x3tGvo8JyLyX7szc+jxyGd0aVaHxrVj2L43i+17s0nfm83uzNwi+4QZ1IstKHw1OKQYVv/gdvTB4ln92CiiIspmsteG7fv4vxnJTF6QCsAlveK55fS2tIyLLZPrScm8lbSBeyYv4f6zO3Hj4NZBxzmq4n6m08guERERETkqd59pZgnFbH4+8Ia7ZwHrzCyZgsKXFlwTESmm+eu2k5fv3Pujjgxo879fLGXn5rNjXzbpe7JDBbAsduzNPlgMO/Bz9dY9bN+bzY592RxpjEvtmIiDRbGDI8dqhQpksf/dPlAoqxF19JvLrEvby9++SOa9hRsJN+PyPi25eUgbmterUVpvjZSBS3vFM+3bLfzp05UMbt+IDifVDjpSianYJSIiIsctJSUl6AhSMdxmZj8GkoBfuPsOoDkwr1Cb1NC+w5jZTcBNwMGpsSIiArOT04mOCOOUloff/TgqIowmdWJoUiemWOfKy3cy9v23CLajUFHsvwWyLFJ37GNJagbb92aTm190daxGZHihaZWFR5BF890Pu/jX4k1Ehofxk/4J/PT01sXOKMEyM/5wUTdGPjWTsW8u4v1bBxAdUfy7ZldEKnaJiIiIyIn4O/BbwEM//wxcBxS12EeRvzW5+wRgAhRMYyybmCIilc+cNWn0TqhPTGTJCw7hYUZcrWjiakXTrhjt3Z1dmbmhYlgW2/fmsH1vVkFRbM9/C2Tpe7JZvWUP6XuzyMzJp0ZkODcMas0Ng1rRuLaKXJVNXK1oHrsokRteTuKpaav51Vkdj92pAlOxS0RERESOm7tvObBtZs8DB27FmQq0KNQ0HthUjtFERCq1tD1ZfPfDbu4e2SGQ65sZdWtEUrdGZLEXkt+XnUuYWakU5yQ4Z3RuwuV9WvDcf9YwrGNj+iQ0CDrSCSub1ehEREREpEozs6aFnl4ILAttTwEuN7NoM2sFtAPml3c+EZHKau6adAAGtIkLOEnxxUZFqNBVRTwwqjPN69fgrrcWsSer6JshVAYqdomIiIjIUZnZ6xQsMN/BzFLN7Hrgj2a21MyWAEOBOwHcfTnwFvAtMBW49Vh3YhQRkf+asyaN2tERdGteN+goUg3Vio7gyct6sHHHfn734bdBxzlhKnaJiIhUUZmZmfTt25fu3bvTpUsXHn744WL1S0lJoWvXrmWc7vBrvvbaawefJyUlcccddwAwadIkbrvttnLNI//L3ce4e1N3j3T3eHd/wd2vdvdu7p7o7ue5++ZC7R919zbu3sHdPwkyu4hIZTNnTTr9WscREa5f1yUYvRMa8NPT2/DG1xv47Nstx+5QAelvj4iISBUVHR3N559/zuLFi1m0aBFTp05l3rx5h7XLywt20E1ubu5hxa7evXvz9NNPB5hKRESk/KXu2Mf36fsq1RRGqZruPKM9nZrW4b53l5C+JyvoOMetRMUuMzvLzFaaWbKZ3VvE8WgzezN0/CszSyh07L7Q/pVmNrLQ/pTQkPhFZpZUknwiIiLVmZlRq1YtAHJycsjJycGs4EZ5CQkJPPLII5x22mm8/fbbLFiwgO7du9O/f3/+9re/FXk+d+e2226jc+fOnHPOOZx99tlMnjz54PnS0tKAglFZQ4YMAWD+/PkMGDCAnj17MmDAAFauXAkUjNa69NJLOffccznzzDO59957+fLLL+nRowdPPvkkM2bMYNSoUYdl2LZtGxdffDF9+vShT58+zJ49u1TfMxERkSDNSS5Yr2tg24YBJ5HqLioijKdG92DX/lzue3cp7pXrpsknfDdGMwsH/gaMoOCuO1+b2RR3Lzyp83pgh7u3NbPLgceB0WbWGbgc6AI0A6aZWftC6zkMdfe0E80mIiJS4YwdC4sWle45e/SAp546apO8vDx69epFcnIyt956K/369Tt4LCYmhlmzZgGQmJjI+PHjOf3007n77ruLPNd7773HypUrWbp0KVu2bKFz585cd911R71+x44dmTlzJhEREUybNo1f//rXvPPOOwDMnTuXJUuW0KBBA2bMmMETTzzBhx8W3NBvxowZRZ7v5z//OXfeeSennXYa69evZ+TIkaxYseKoGURERCqLOWvSaFgrivZNagUdRYQOJ9Xm7pEdePTjFby9IJXLerc4dqcK4oSLXUBfINnd1wKY2RvA+RQsRnrA+cC40PZk4Bkr+Er5fOANd88C1plZcuh8c0uQR0RERA4RHh7OokWLyMjI4MILL2TZsmUH1+MaPXo0ADt37iQjI4PTTz8dgKuvvppPPjl8maWZM2cyZswYwsPDadasGcOGDTvm9Xfu3MlPfvITVq9ejZmRk5Nz8NiIESNo0OD4bmk9bdo0vv32vx81du3axe7du6ldu/ZxnUdERKSicXdmr0mnf5uGB0diiwTt+tNaMf27LTzyr2/p3zqOFg1ig45ULCUpdjUHNhR6ngr0O1Ibd881s51AXGj/vEP6Ng9tO/BvM3PgOXefUIKMIiIiFcMxRmCVtXr16jFkyBCmTp16sNhVs2ZNoODDdXE/VB+pXUREBPn5+UDBwvgHPPjggwwdOpT33nuPlJSUg9MbC1//eOTn5zN37lxq1Khx3H1FREQqsuSte9i2O4uBWq9LKpCwMOOJS7vzo6e+5BdvLeb1m04lPKziF2NLsmZXUa/u0EmcR2pztL4D3f0U4EfArWY2uMiLm91kZklmlrRt27biZhYREak2tm3bRkZGBgD79+9n2rRpdOzY8bB29erVo27dugenNL766qtFnm/w4MG88cYb5OXlsXnzZr744ouDxxISEliwYAHAwWmKUDCyq3nzgu+zJk2adMSstWvXZvfu3cd8TWeeeSbPPPPMweeLSntqqIiISEDmrNF6XVIxxdePZdx5XZifsp1/fLk26DjFUpJiVypQeMJmPLDpSG3MLAKoC2w/Wl93P/BzK/AeBdMbD+PuE9y9t7v3btSoUQlehoiISNW0efNmhg4dSmJiIn369GHEiBFFLvoO8OKLL3LrrbfSv3//I46auvDCC2nXrh3dunXjlltuOTjtEeDhhx/m5z//OYMGDSI8PPzg/nvuuYf77ruPgQMHHvWuj4mJiURERNC9e3eefPLJI7Z7+umnSUpKIjExkc6dO/Pss88e620QERGpFGYnpxFfv0almSYm1ctFpzTnrC4n8ed/r2LF5l1BxzkmO9EV9UPFq1XAcGAj8DVwhbsvL9TmVqCbu98cWqD+Ine/zMy6AK9RUMhqBkwH2gExQJi77zazmsBnwCPuPvVoWXr37u1JSbpxo1R8r321/ojHhncqWDtn+oo3iz6+MXS8edHHK6vffVXwuh7oV7qv64p+LUv1fBXOgalgR1jEWyqGFStW0KlTp6BjlJlrrrmGUaNGcckllwQd5X8U9b6b2QJ37x1QJCkGfZ4TkeosL9/p+ci/+VHXpjx+SWLQcUSKtH1vNmc+OZOGtaL44LaBREeEH7tTKSvuZ7oTHtnl7rnAbcCnwArgLXdfbmaPmNl5oWYvAHGhBejvAu4N9V0OvEXBYvZTgVtDd2JsAswys8XAfOCjYxW6RERERERERCqz5Zt2siszlwFttV6XVFwNakbxx0u68d0Pu/nLZ6uCjmyUZwoAACAASURBVHNUJVmgHnf/GPj4kH0PFdrOBC49Qt9HgUcP2bcW6F6STCIiIlI+jrYGl4iIiBTf7OSC9boGtNF6XVKxDevYhDF9WzJh5lqGdWhMv9YVs0BbkjW7RERERERERKSE5qxJo32TWjSqHR10FJFjeuCcTrRsEMsv3l7M7sycoOMUScUuERGRMnSia2PKidH7LSIilU1Wbh5fp2zXqC6pNGpGR/CXy3qwKWM/v/3w26DjFEnFLhERkTISExNDenq6CjDlxN1JT08nJiYm6CgiIiLFtnB9Bpk5+Qxsq2KXVB69Tq7PLUPa8FZSKv9e/kPQcQ5TojW7RERE5Mji4+NJTU1l27ZtQUepNmJiYoiPjw86hoiISLHNSU4jzKBvqwZBRxE5Lj8f3p4ZK7dx37tL6dmyfoWahqtil0iF4mzfm01Wbh6xURHUiAwnMtwws6CDicgJiIyMpFWrVkHHEBERkQpszpp0usXXo26NyKCjiByXqIgwnhrdg3PGz+K+d5fw/I97V5jfXVXsEglQVk4eSzfuZNWW3XQ4aR+5ec4T/175P20iwoymdWM4pXk2sVHh5LsTVkH+ByIiIiIiIidub1YuizZkcNPg1kFHETkh7ZrU5ldndeS3H37LW0kbGN2nZdCRABW7RAKRkraXpO93sGzjTrLz8qkXG0lkeBixUcZFPZsTHRlOZnYe+3Ly2JuVS0r6XjL2ZZOxDx5b/h39WjdgQOuG1IgKD/qliIhIJWNmJwPt3H2amdUAItx9d9C5RESqo/nrtpOb71qcXiq1awckMH3FFh7517f0b92QlnGxQUdSsUukPG3fm82HSzbx3Q+7iY4IIzG+Lr1Ork/LBrE0qVOwoHLvhKLn6rfcUJP9ObnE16/B9BVbmbU6jQFtGjKwbRyxUfqrLCIix2ZmNwI3AQ2ANkA88CwwPMhcIiLV1ezkNKIiwuidUD/oKCInLCzM+NOl3TnrqZnc9dYi3vppf8LCgp2NpN+QRcpBZk4ez/1nLeM/X01YmPGjrifRr1UcURHFvyFqeJhRKzqSH/dPYFPGfr5YuZUvVm5l7to0RiU2o2eLehVmfrSIiFRYtwJ9ga8A3H21mTUONpKISPU1Z006vVrWJyZSMzakcmterwaPX5xImFnghS5QsUukzCVv3cNNrySxdtteujWvy9ndmpZ48clm9WpwZb+T+WFnJh8s3sjkBal8u2kXF/RsTq1o/bUWEZEjynL37ANfjphZBODBRhIRqZ62783m2827+MWI9kFHESkVZ3drGnSEg/RbsUgZ+mLlVu54bSHRkWG8cn1fNmzfX6rnP6luDDcOas3s5DQ++3YLT01bxcWnxNOpaZ1SvU5l9NpX64OOUKaG78oCYHrAr/OKfhVjAUoRKbb/mNmvgRpmNgL4GfCvY3Uys4nAKGCru3cN7fsTcC6QDawBrnX3DDNLAFYAB+64Ms/dby7tFyIiUtnNXZMOwIC2Wq9LpLQVfw6ViBSbuzNh5hqum/Q1LRrE8sFtpzGoXaMyuVaYGYPaNeLWoW2pFxvJK/O+5z+rtuGuL+pFROQw9wLbgKXAT4GPgQeK0W8ScNYh+z4Durp7IrAKuK/QsTXu3iP0UKFLRKQIc9akUSs6gu7xdYOOIlLlaGSXSCnLz3fuf38Zr89fzzndmvKnSxPLZQH5JnVi+OngNrzzTSqfLv+B9D1ZnNejGRFhqmmLiEgBd88Hng89jqffzNCIrcL7/l3o6TzgkpLmExGpTuasSadvqwZEhOvzukhp098qkVLk7jw0paDQdcuQNjxzRc9yvVNiZHgYo3u3YGiHxiR9v4NJc1LYn51XbtcXEZGKzcxGmdlCM9tuZrvMbLeZ7SqFU18HfFLoeavQdf5jZoNK4fwiIlXKpoz9rEvby4A2cUFHEamSVOwSKSXuziMffss/563np6e35p6RHQK5O6KZMaJzEy7pFc/3aft4YdZa9mXnlnsOERGpkJ4CfgLEuXsdd6/t7iVa6NHM7gdygVdDuzYDLd29J3AX8JqZFXkNM7vJzJLMLGnbtm0liSEiUqnMCa3XNVDrdYmUCRW7REqBu/PY1O94cXYK1w5M4N6zOgZS6CrslJb1ubr/yWzZncXEWetU8BIREYANwDIvpYUdzewnFCxcf+WBc7p7lrunh7YXULB4fZG3GnP3Ce7e2917N2pUNmtbiohURHOS04irGUWHJrWDjiJSJanYJVIKnpu5luf+s5arTm3JQ6M6B17oOqB9k9pc1S9U8JqtgpeIiHAP8LGZ3Wdmdx14nMiJzOws4FfAee6+r9D+RmYWHtpuDbQD1pZCdhGRKsHdmb0mjVPbxBEWVjF+bxCpalTsEimh6Su28PjU7xiV2JRHzutaYQpdB3Q4qTZX9WvJll0FBS+t4SUiUq09CuwDYoDahR5HZWavA3OBDmaWambXA8+E+n5mZovM7NlQ88HAEjNbDEwGbnb37aX/UkREKqe1aXvZsiuLgW00hVGkrOhujCIlkLx1Nz9/YxFdmtXhT5d0r7DfzHQ4qQ5X9mvJq/PW88+vvufaAQm664uISPXUwN3PPN5O7j6miN0vHKHtO8A7x3sNEZHqYk5yGgAD22pxepGyot92RU7Qzn053PBSEjGR4Uy4ujc1osKDjnRUHU+qw8W94lmXtpe3F6SSXzrLtYiISOUyzcyOu9glIiKlZ3ZyOs3r1aBlg9igo4hUWSp2iZyA3Lx8bnv9GzZm7Oe5q0+hWb0aQUcqlh4t6nFWl5NYunEnU5f9EHQcEREpf7cCU81sv5ntMrPdZrYr6FAiItVFfr4zd206A9rEVbjlT0SqEk1jFDkBf/tiDV+uTuOPFyfS6+QGQcc5LoPaNSRjfw6zktOoWyNStzsWEalG3F23/RIRCdC3m3exc3+OPoOLlDEVu0SO04Lvt/P056u5sGdzLuvTIug4x83MGJXYlN2ZOXy8dDNxNaPo2LRO0LFERKQcmNngova7+8zyziIiUh3NDq3X1b+N1usSKUsqdokch12ZOfz8jUU0qxfDI+d3CTrOCQsz49JeLZiwbw1vJm3gltPb0LhOTNCxRESk7N1daDsG6AssAIYFE0dEpHqZsyadto1r0USfvUXKlNbsEjkOD72/jM07M3lqdE9qx0QGHadEoiLCuKrfyUSEh/HKvO/Zn50XdCQRESlj7n5uoccIoCuwJehcIiLVQXZuPvPXbWegRnWJlDkVu0SK6f2FG3l/0SbuGNaOXifXDzpOqagXG8VV/VqSsS+HN75eT16+7tAoIlLNpFJQ8BIRkTK2aEMG+3Py6N9G63WJlDVNYxQphh92ZvLg+8vofXJ9bh3aJug4perkuJqc16MZ7y3cyKfLf+Dsbk2DjiQiImXEzMYDB77ZCAN6AIuDSyQiUn3MTk4jzKB/a43sEilrJRrZZWZnmdlKM0s2s3uLOB5tZm+Gjn9lZgmFjt0X2r/SzEYe0i/czBaa2YclySdSGtydBz9YRnZePk9c2p2I8Ko3ILJPQgNObd2AWclpLN+0M+g4IiJSdpIoWKNrATAX+JW7XxVsJBGR6mHumnS6Nq9L3djKvRyKSGVwwiO7zCwc+BswgoIh8F+b2RR3/7ZQs+uBHe7e1swuBx4HRptZZ+ByoAvQDJhmZu3d/cCiQT8HVgC6RZwE7uOlP/DZt1u470cdSWhYM+g4Zebsrk1J3bGfyQtSOalODHG1ooOOJCIipczdXwo6g4hIdbQvO5eFG3Zw3Wmtgo4iUi2UZIhKXyDZ3de6ezbwBnD+IW3OBw58qJoMDDczC+1/w92z3H0dkBw6H2YWD5wD/KME2URKRca+bB6esoxuzetyfRX/hykiPIwxfVsSZsbr89eTk5cfdCQRESklZrbUzJYU8VhqZkuCziciUtXNX7ednDxnoNbrEikXJVmzqzmwodDzVKDfkdq4e66Z7QTiQvvnHdK3eWj7KeAeoPbRLm5mNwE3AbRs2fLEXoHIMTz60Qp27Mvhpev6Vsnpi4eqHxvFpb3ieXne93y0ZDMX9Gx+7E4iIlIZjAo6gIhIdTZ3TTpR4WH0SWgQdBSRaqEkv71bEfsOvZXbkdoUud/MRgFb3X3BsS7u7hPcvbe7927UqNGx04ocp1mr03h7QSo3DW5Nl2Z1g45Tbjo2rcPgdo2Yn7KdRRt2BB1HRERKgbt/f+ABZALdQo/9oX0iIlKGZq9Jo2fLetSICg86iki1UJJiVyrQotDzeGDTkdqYWQRQF9h+lL4DgfPMLIWCaZHDzOyfJcgockKycvN44P2ltGpYk58Pbxd0nHI3onMTEuJieW/hRrbsygw6joiIlBIzuwyYD1wKXAZ8ZWaXBJtKRKRqy9iXzfJNuxigKYwi5aYkxa6vgXZm1srMoihYcH7KIW2mAD8JbV8CfO7uHtp/eehuja2AdsB8d7/P3ePdPSF0vs91hyAJwguz1pGSvo9x53UhJrL6ffsSHmZc3qclURHhvDZ/Pdm5Wr9LRKSKuB/o4+4/cfcfU7Bm6oMBZxIRqdLmrU3HHQa2jQs6iki1ccLFLnfPBW4DPqXgzolvuftyM3vEzM4LNXsBiDOzZOAu4N5Q3+XAW8C3wFTg1kJ3YhQJ1Oad+xk/PZkzOzfh9PbVd4psnRqRjO7dgrTdWby/aCMFdWoREankwtx9a6Hn6ZTsy08RETmG2cnpxEaF071FvaCjiFQbJVmgHnf/GPj4kH0PFdrOpGCYfFF9HwUePcq5ZwAzSpJP5EQ8+tEK8t15cFTnoKMErm3jWgzr1JjpK7aSEFeTvq20oKaISCU31cw+BV4PPR/NIZ/lRESkdM1ek0bfVg2IrAY3vBKpKPS3TaSQuWvS+XDJZm4+vQ0tGsQGHadCGNqhMe0a1+LDJZvYlLE/6DgiIlIC7n438ByQCHQHJrj7r4JNJSJSdf2wM5O12/YyUOt1iZQrFbtEQnLy8hk3ZTnx9Wtwy5A2QcepMMLMuLR3C2Kjwnl9/noyczTjWESksjGzZ8xsAIC7v+vud7n7ne7+XtDZRESqsjlr0gAYoPW6RMqVil0iIa/O+56VW3bzwDmdq+Wi9EdTKzqC0X1asn1vttbvEhGpnFYDfzazFDN73Mx6BB1IRKQ6mJ2cTv3YSDqdVCfoKCLViopdIsDO/Tn8dfpq+reOY2SXJkHHqZBaNazJGZ2bsCR1J1+n7Ag6joiIHAd3/6u79wdOB7YDL5rZCjN7yMzaBxxPRKRKcnfmrkmjf5s4wsIs6Dgi1YqKXSLA/81IJmN/Dvef0wkz/UN0JKe3b0Tb0Ppdm3dq/S4RkcrG3b9398fdvSdwBXAhBXfVPiozm2hmW81sWaF9DczsMzNbHfpZP7TfzOxpM0s2syVmdkqZvSARkQosJX0fm3ZmMkDrdYmUOxW7pNrbsH0fL85K4aKe8XRtXjfoOBVamBmX9W5BjahwXp+/gaxcrd8lIlKZmFmkmZ1rZq8CnwCrgIuL0XUScNYh++4Fprt7O2B66DnAj4B2ocdNwN9LIbqISKUzOzm0XlcbrdclUt5U7JJq70+frsQMfjlSsziKo1Z0BKN7tyB9TxYfLNqk9btERCoBMxthZhOBVAoKUB8Dbdx9tLu/f6z+7j6TgumPhZ0PvBTafgm4oND+l73APKCemTUtjdchIlKZzF2TTtO6MbRqWDPoKCLVjopdUq0t2pDBlMWbuHFQa5rWrRF0nEqjdaNaDOvUmEUbMljwvdbvEhGpBH4NzAU6ufu57v6qu+8t4TmbuPtmgNDPxqH9zYENhdqlhvYdxsxuMrMkM0vatm1bCeOIiFQc+fnOnDVpDGjTUMukiARAxS6pttyd33+0goa1orh5SJug41Q6Qzs0pnWjmvxrySZ+2JUZdBwRETkKdx/q7s+7+6Gjs8pCUb/VFTkM2N0nuHtvd+/dqFGjMo4lIlJ+Vvywix37cjSFUSQgKnZJtTVtxVbmp2xn7BntqRUdEXScSifMjNG9WxAdEc7r89eTnZsfdCQRESlfWw5MTwz93Branwq0KNQuHthUztlERAI1JzkdgIFttTi9SBBU7JJqKS/f+dOn39G6YU0u79Pi2B2kSLVjIrmsdwvSdmfxwaKNWr9LRKR6mQL8JLT9E+CDQvt/HLor46nAzgPTHUVEqos5a9Jo3agmJ9WNCTqKSLWkYpdUS+8v3MiqLXv4xZkdiAjXX4OSaNu4FkM7Nmbhhgzmp5TH7BgRESlvZvY6BWt+dTCzVDO7HngMGGFmq4ERoedQsPj9WiAZeB74WQCRRUQCk5OXz/x12xnYRqO6RIKiuVtS7WTl5vHktFV0a16XH3U9Keg4VcKwjo3ZsH0fHy7ZTPN6NYivHxt0JBERKYKZXQQ8TsFi8hZ6uLvXOVo/dx9zhEPDi2jrwK0ljCoiUmkt3pDB3uw8rdclEiANaZFq5/Wv1pO6Yz/3nNWBsDDdGaU0HFi/q3ZMBK99tZ69WblBRxIRkaL9ETjP3eu6ex13r32sQpeIiByfOWvSMYP+KnaJBEbFLqlW9mTlMv7zZPq3juM0LRZZqmKjI7iib0t2Z+XyVtIG8rV+l4hIRbTF3VcEHUJEpCqbnZxGl2Z1qBcbFXQUkWpLxS6pVibOWkf63mzuOasDZhrVVdri68dybmIzVm/dw7QVW4KOIyIih0syszfNbIyZXXTgEXQoEZGqYn92HgvXZzBA63WJBEprdkm1kbEvmwkz13Jm5yb0bFk/6DhVVp+E+qTu2MeMlds4qU4MifH1go4kIiL/VQfYB5xZaJ8D7wYTR0Skakn6fjvZeflar0skYCp2SbXx/Jdr2ZOVy11ntg86SpVmZpzXvRnbdmfxzjepNKwVTbN6NYKOJSIigLtfG3QGEZGqbHZyOhFhRt9WDYKOIlKtaRqjVAvb92YzaXYK5yQ2peNJWoe3rEWEh3FFv5bERkXwyrzv2Z2ZE3QkEREBzCzezN4zs61mtsXM3jGz+KBziYhUFXPWpNGzZT1iozSuRCRIKnZJtfD8l2vZl5PH2OHtgo5SbdSOieTqU09mX3Yur361nty8/KAjiYgIvAhMAZoBzYF/hfaJiEgJ7dyXw7KNO7Vel0gFoGKXVHnpe7J4aU4KoxKb0a5J7aDjVCvN6tXgkl4tWL99H5O/SdUdGkVEgtfI3V9099zQYxLQKOhQIiJVwbx16eT/P3t3Hh9XWfZ//HPNZN+bNGmTNN33ltKNrsi+FARBZCkCgqIggoj78vxc8JHnUR9REVFkU0DZBS2CArJKC91oKXRP0i1t2mZpszV77t8fc9KmJV3T5MxMvu/Xa16ZOefM5JszyWTmOvd9HQezddZ3Ed9pbKVEvfveKqahuZWvaFSXL07IT6dybD9eWrWDjMRY5ozP9TtSxGttc7S0tdHa5thQXgeAAcGAkZYYS2pCDAGdbVREOlduZlcDj3u3rwQqfMwjIhI1FhSWkxgbZGKBTtAk4jcVuySqldc28sg7m/jEiXkMz0nxO06vdcrIbHbXN/PW+nLSE2OZqaHdR8Q5R1V9M1t21VOyaw8lu+opr22ktqGFEZV7gNAU3QMFzUhPiiU7JZ7BWUkMyU4hPyORYEAFMBHhc8BvgV8ROgvjAm+ZiIh00YKiCk4akklcjCZQifhNxS6Jan94s4jGllZu1aguX5kZF56YR3VDC/9YUUpaYizj8tL9jhWWWtscmyrrWFNaw+rSairqmoDQqK3c9ARG5qSSnhRL35R4ggHjc7OHAOBwtLaFimO79zSza08T26saWLujBthBXDDA8JwUJg/sw6j+qSp8ifRSzrnNwCf8ziEiEm12Vjewfmctl07ROT9EwoGKXRK1dtY08Oi7m7h4Uj5DszWqy28BM66YWsCDbxfz5OItfGZmUKPtPM45tu6uZ+mmXawoqaK+uZVgwBjaN5kZQ7MYmJlEbnoCMcF9RwlTE2IBDrsPaxtb2FBeR3FZLSu3VbOqtJrkuNDw+hlDs8hKie/Wn01EwoOZfcs593Mzu5vQiK79OOdu9SGWiEjUWFAUmhGu5vQi4UHFLola975RTHOr49YzNKorXMTFBLh25mAeeHsDj767kWtnDfY7kq8amltZumkXSzZVsqO6kZiAMTYvjfF56YzISSE+Ntjl75ESH8MJ+emckJ/OBRPyWL+zhvc27eLdDZW8U1zBxIIMTh+Vo6KXSPRb7X1d4msKEZEotaAo1K5jbF6a31FEhC4Wu8xsDnAXEAQecM799ID18cAjwBRCzU+vcM5t9NZ9F7geaAVudc69ZGYJwFtAvJftGefcD7uSUXqnHdUN/GXhJj45KZ/BfZP9jiMdJMXH8LmTh/DAf4p5ZMEmWjJbSTgORZ1IUlnXxIKicpZs2kVTSxsD+iRy0cQ8JuRnkBjXffsiGDBG909jdP80ahqa+c/6ct4trmD5lt1MLMjgnLH9SUuM7bbvLyL+cc497319uH2ZmQWAFOdctW/BRESigHOO+YUVzByapVYRImHimItdZhYE7gHOBkqAxWY2zzm3qsNm1wO7nHPDzWwu8DPgCjMbC8wFxgF5wL/NbCTQCJzhnKs1s1jgbTP7p3Pu3WPNKb3T798ooqVNo7rCVUp8DNefPIT7/1PMquoG+qcn+B2pR5RW1fP6mp2s3FaNGUwYkMHsYX3J75PY41lSE2I5/4RcPjaiL2+tK2PhhkpWbqvmnLH9mD40S2dzFIlSZvYY8EVCBxuXAulm9kvn3P/5m0xEJHJtrtzD1t31fPHUoX5HERFPV04TMQ0odM4VO+eagCeAiw7Y5iKg/QjiM8CZZmbe8iecc43OuQ1AITDNhdR628d6l4/0lRA5lO1VDTy2aDOXTh7AwKwkv+PIQaQmxHL9yUMJBoztVQ2s21Hjd6Rus3V3PX9+dxN3v1bI+p21nDIym2+dO5rLpxb4UujqKDUhlo9PyOMrZ45gYGYSz68o5fdvFLFtd72vuUSk24z1RnJdDLwIDASu8TeSiEhkm18Y6telM46LhI+uFLvygS0dbpd4yzrdxjnXAlQBWYe6r5kFzWw5sBN4xTm3sLNvbmY3mNkSM1tSVlbWhR9Dos3v3iikrc1xyxnD/Y4ih5GeGOs1XjceeWcjyzbv8jvScbWlcg+PvLORe14vpLi8ljNH5/Ctc0dz7rjwmy6YlRLPdbMGc8XUAqrqm/ndG4W8ta6MNqfjDSJRJtYbPX8x8HfnXDM6sCgi0iULisrplxbPsGy1TxEJF13p2dXZHJcD3ywdbJuD3tc51wpMNLMM4DkzG++c+/AjGzt3H3AfwNSpU/UmTQDYtrueJxZt4bKpBRRkalRXJIgJBMhLTyQvKZmnl5ZQ09DCx0b0xSJ4Gt3myj28tmYH63bUkhgb5Oyx/Zg5NCvse5OZGScWZDCyXyrPLivhXyu3U1xey6VTCkiJ1/lMRKLEvcBG4H3gLTMbBKhnl4jIMWprc7xTVMGpI7Mj+v2rSLTpyqeXEqCgw+0BwLaDbFNiZjFAOlB5JPd1zu02szeAOcBHil0inbnn9UIcGtUVaQJmXDdrME8vDRVYKuoauWBCHrHBrgw+7XmbKup4dc1OCnfWkhQX5Nxx/ZkxJPO4nFWxJyXGBfn0tIEs2ljJCytKufu19VwxtYCh2Sl+RxORLvAa0u9wzuV3WLYZON2/VCIikW3tjhoq6pqYOSzL7ygi0kFXPkkuBkaY2RAziyPUcH7eAdvMA671rl8KvOacc97yuWYWb2ZDgBHAIjPL9kZ0YWaJwFnAmi5klF6kZNcenlqyJdQHKcPfPkhy9GKCAa44qYBTRmSzeOMu/vBmERW1jX7HOiIbyut48O1i/vBWMaW765kzrj/fPHcUp47MjrhCVzszY/qQLG46bRgJMUEemr+BxRsq/Y4lIl3gnGsDbjlgmfNaTYiIyDFYUBTq1zV7uPp1iYSTYx7Z5ZxrMbNbgJeAIPCQc26lmf0YWOKcmwc8CDxqZoWERnTN9e670syeAlYBLcDNzrlWM8sFHvbO9BgAnnLO/aMrP6D0Hve8Xohh3Hy6RnVFqoAZc8b3Z1BWEk8v3cI9bxRy6eQBjM1L9ztap4rLa3lt9U6Ky+tIjo/hvPH9mT4ki7iYyBqRdii56YncdNownli8meeWb6WstpE54/vrbI0ikesVM/sG8CRQ177QOXdM1WwzG+U9VruhwA+ADOALQHtj1e855148psQiImFsQWE5Q/omk6eD7SJhpUtNWLw3LS8esOwHHa43AJcd5L53AHccsGwFMKkrmaR32lK5h6eXlPDp6QP1jyYKjMlN45bTR/D4os38eeFmJg/MYM743LDoG+Wco6isjtfX7mRDeR2p8TGcf0Iu0wZnRlWRq6OE2CDXzBjMix+U8nZhOeW1jVxxUgHxMZE5ak2kl/uc9/XmDsscoSLVUXPOrQUmQugkQ8BW4Dngs8CvnHO/OPaoIiLhraW1jYUbKvnExDy/o4jIAfz/5ChyHPz2tUICAeNLp2lUV7TITI7jxlOG8tqanfxnfTmrS2uYM74/Uwb18WVUUUtbGytKqphfWE5pVQOpCTF8/IRcpg3JjLjeYsciGDAuPDGPvqnx/OP9bTz49gaumzXY71gicpScc0O68eHPBIqcc5vUpFlEeoMVW6uobWxh9jBNYRQJNyp2ScTbVFHHM++VcM2MQfRPT/A7jhxHMcEA54zrz8SCDP7+/jaeW7aVJRsrOWN0DiP7pfbIGW8q65p4b/MuFm+spKahhZzUeC6ZlM+JBRm9osh1oJlDs0hPiOXxxZt54D8b+MTEPHJS9XcnEinMLAn4GjDQOXeDmY0ARh2nthFzgcc73L7FzD4DLAG+7pzbdRy+h4hI2FhQWA6g5vQiYaj3fVKTqHP3a4XEBIybThvmdxTpJjlpCXz+5CFcOmUA1Q0tPPzOJu5+rZDlW3bR2uaO+/draG5l+ZZdPPh2Mb94eS2vr9lJbnoC180azFfOCD1ISgAAIABJREFUHMHUwb1jNNfBjM1L49qZg6moa+Tye9+hZNcevyOJyJH7I9AEzPJulwA/6eqDeicr+gTwtLfo98AwQlMcS4E7D3K/G8xsiZktKSsr62wTEZGwNb+wgjG5aWQmx/kdRUQOoJFdEtE2ltfx3LKtXDtzMP3SNLokmpkZkwf24cQBGbxfspu31pXx1JISXvhgO2Nz0xiXl8bQ7GRiAkdfhHLOsWtPM2t31LCmtJrisjpanaNPUixnjclh8sA+ZCTpTUxHw3NS+NzsITy2aDOX3/sOj98wg0FZyX7HEpHDG+acu8LMrgRwztXb8Rkmex7wnnNuh/e4O9pXmNn9QKcjx5xz9wH3AUydOvX4H70QEekmDc2tLN28i8/MGOR3FBHphIpdEtF+89p6YoPGF087pr66EoGCgVDRa2JBBmu317B8y27e37KbxRsrSYgNUNAniX5pCfRPSyAnLZ6EmCAxQds7Equ2sSV0aWihrLaRkl17KNlVz56mVgD6psQza3gWY/qnMTArSWcdPIRBWck8ccMMrn5gIZ++fyFP3jiDAX2S/I4lIofWZGaJhJrSY2bDgMbj8LhX0mEKo5nlOudKvZufBD48Dt9DRCRsLN20i6aWNmYPV78ukXCkYpdErKKyWv62bCvXnzxEPYN6oYAZY3LTGJObRnNrG4U7a1ldWs223fW8W1xByxFMbzSgX1oCY/qnkd8nkWHZKWSnxnd/+CgyLi+dR6+fzqfvf3dvwSs3XWdEFQljPwL+BRSY2V+A2cB1XXlArw/Y2cCNHRb/3MwmEiqqbTxgnYhIxJtfWE5MwDhpSKbfUUSkEyp2ScS6+9X1xMcEufFU9erq7WKDgb2FL4DWNkdlXRNlNY00tbbS3OJobmvDOUhJiCElPobU+BgykuKIi+m9vbeOl/H56Txy/XSufmAhV92/kCdunKECtEiYcs69bGZLgRmEav5fcc6Vd/Ex9wBZByy7piuPKSIS7hYUVXBiQQYp8fpILRKO9JcpEalwZw3z3t/GFz42lL4pGokj+wsGjOzUeI3S6kETCzL402dP4jMPLeLqBxby1I0z1edMJAyZ2TxC0w3nOefq/M4jIhKJqhuaWVGym1tOH+53FBE5CA1pkIh016uFJMQGueEU9eoSCRdTB2fywLVT2Vi+h+sfXkK91wdNRMLKncDHgFVm9rSZXWpmGoopInIUFhZX0uZglvp1iYQtFbsk4qzbUcM/Vmzj2lmDydKoLpGwMmtYX349dyLvbd7Flx9/j5bWNr8jiUgHzrk3nXNfAoYSOgvi5cBOf1OJiESWBUXlJMQGmDQww+8oInIQKnZJxLnr1fUkxQa54WMa1SUSjs4/IZcfXzSef6/eyfee+wDnDn+yABHpOd7ZGD8FfBE4CXjY30QiIpFlQWEFJw3OJD4m6HcUETkI9eySiLK6tJoXVpRy8+nD6JOsfkAi4eqaGYMoq2nkN6+up19aAl8/Z5TfkUQEMLMngemEzsh4D/CGc05DMEVEjlBZTSNrd9Rw8aR8v6OIyCGo2CUR5c6X15GaEMMNH9MZGEXC3VfPGsHO6gbufq2QgswkLp9a4HckEYE/Ap92zqmpnojIMXinuAKAWcOyDrOliPhJxS6JGMu37Obfq3fw9bNHkp4U63ccETkMM+O/Lx7P1t31fO/ZDxiQkahGriI+c879y8xmmdlgOrwPdM494lsoEZEIsqCwnLSEGMbnp/sdRUQOQT27JGLc+fJaMpPj+OzJQ/yOIiJHKDYY4J6rJjM0O5kv/nkphTtr/I4k0quZ2aPAL4CTCfXrOgmY6msoEZEIMr+onBlDswgGzO8oInIIKnZJRFhYXMF/1pdz06nDSInXgESRSJKWEMuD155EXEyAz/5pMeW1jX5HEunNpgKznXNfcs592bvc6ncoEZFIsKVyD1sq6zWFUSQCqNglYc85x50vryMnNZ5rZg7yO46IHIOCzCQeuPYkdlY38qU/v0dTi/phi/jkQ6C/3yFERCLRgqJyAGarLYNI2FOxS8Lef9aXs2hjJV8+YzgJsTq9r0ikmliQwc8vncCijZXc/vxKv+OI9FZ9gVVm9pKZzWu/+B1KRCQSzC+sIDs1nuE5KX5HEZHD0HwwCWuhUV1ryc9I5PKTdCY3kUh30cR8VpfWcO+bRYzNS+Oq6RqtKdLDfuR3ABGRSOScY0FRBbOHZ2Gmfl0i4U4juySsvbJqB++XVPGVM0cQH6NRXSLR4JvnjuK0Udn88O8rWbSh0u84Ir2Kc+5NYA2Q6l1We8tEROQQ1u+spby2kdnDNIVRJBKo2CVhq63N8ctX1jGkbzKXTM73O46IHCfBgHHX3EkUZCZx05+XsnV3vd+RRHoNM7scWARcBlwOLDSzS/1NJSIS/uYXhvp1zRqu5vQikUDFLglbL3xQyprtNdx21ghigvpVFYkm6Ymx3P+ZqTS1tHHjo0uob2r1O5JIb/FfwEnOuWudc58BpgHf9zmTiEjYm19YwcDMJAb0SfI7iogcAVUQJCy1tLbxq3+vY1S/VC6ckOd3HBHpBsNzUvj13Ims3FbNt/+6Auec35FEeoOAc25nh9sV6P2giMghtbS2sbA41K9LRCKD3txIWHpu2VaKy+r42jkjCQTUAFIkWp05ph/fOGcU897fxn1vFfsdR6Q3+Jd3JsbrzOw64AXgnz5nEhEJax9uq6amsYVZ6tclEjF0NkYJO00tbdz16npOyE/nnLH9/I4jIt3sS6cNY9W2an76rzWM6p/KaaNy/I4kErWcc980s0uAkwED7nPOPedzLBGRsNber2vmMI3sEokUGtklYefJxZsp2VXP188ZqdP6ivQCZsb/XTaB0f3T+PLjy9hYXud3JJGoY2bDzWw2gHPuWefc15xzXwUqzGyYz/FERMLaO0UVjO6fSt+UeL+jiMgRUrFLwkpdYwt3vVrItCGZnDoy2+84ItJDkuJiuO+aKQQDxo2PLmVPU4vfkUSiza+Bmk6W7/HWiYhIJxqaW1m8sVJTGEUiTJeKXWY2x8zWmlmhmX2nk/XxZvakt36hmQ3usO673vK1Znaut6zAzF43s9VmttLMvtKVfBJ57v9PMeW1jXz3vNEa1SXSyxRkJvGbuZNYt7OGb//1AzWsFzm+BjvnVhy40Dm3BBjc83FERCLDe5t30djSpub0IhHmmItdZhYE7gHOA8YCV5rZ2AM2ux7Y5ZwbDvwK+Jl337HAXGAcMAf4nfd4LcDXnXNjgBnAzZ08pkSpsppG7n+rmPNP6M+kgX38jiMiPjhlZDbfOGcUz7+/jYfmb/Q7jkg0STjEusSuPLCZbTSzD8xsuZkt8ZZlmtkrZrbe+6p/7CISkd4pqiAYMKYNyfQ7iogcha6M7JoGFDrnip1zTcATwEUHbHMR8LB3/RngTAsN17kIeMI51+ic2wAUAtOcc6XOufcAnHM1wGogvwsZJYLc/dp6Glra+MY5o/yOIiI++tJpwzh3XD/+58XVvFtc4XcckWix2My+cOBCM7seWHocHv9059xE59xU7/Z3gFedcyOAV73bIiIRZ35hORMGpJOaEOt3FBE5Cl0pduUDWzrcLuGjham92zjnWoAqIOtI7utNeZwELOzsm5vZDWa2xMyWlJWVHfMPIeFhQ3kdjy3czJXTChianeJ3HBHxkZnxi8tOZFBWErc89h6lVfV+RxKJBrcBnzWzN8zsTu/yJvB5oDvaRnQ84PkwcHE3fA8RkW5V09DM+yVVzFa/LpGI05ViV2cNlQ5ssHKwbQ55XzNLAf4K3Oacq+7smzvn7nPOTXXOTc3OViPzSPeLl9cSFxPg1jNH+B1FRMJAakIs910zhfqmVm7683s0trT6HUkkojnndjjnZgG3Axu9y+3OuZnOue1dfXjgZTNbamY3eMv6OedKve9dCuR0dkcdvBSRcLZ4YyWtbY5Zw9SvSyTSdKXYVQIUdLg9ANh2sG3MLAZIByoPdV8ziyVU6PqLc+7ZLuSTCLF8y25eWFHK508eQk7qoVqKiEhvMjwnlTsvP5HlW3Zz+/Or/I4jEhWcc6875+72Lq8dp4ed7ZybTKiP681mdspR5NHBSxEJW/MLK4iPCTB5kNoOikSarhS7FgMjzGyImcURajg/74Bt5gHXetcvBV5zodNrzQPmemdrHAKMABZ5/bweBFY7537ZhWwSIZxz/Pc/VpGdGs8Npw7zO46IhJk543O56bRhPLZwM08t3nL4O4hIj3PObfO+7gSeI9TXdYeZ5QJ4X3f6l1BE5NjMLyxn6uA+JMQG/Y4iIkfpmItdXg+uW4CXCDWSf8o5t9LMfmxmn/A2exDIMrNC4Gt4zUmdcyuBp4BVwL+Am51zrcBs4BrgDO+MPsvN7PxjzSjh7x8rSlm6aRffOGckKfExfscRkTD0jXNGcfLwvvy/v3/IipLdfscRkQ7MLNnMUtuvA+cAH7L/Ac9rgb/7k1BE5NiU1zayZnsNs9SvSyQidam64Jx7EXjxgGU/6HC9AbjsIPe9A7jjgGVv03k/L4lCDc2t/PSfaxibm8alUwoOfwcR6ZWCAeM3V07iwrvf5ouPLuX5L59MVkq837FEJKQf8FxocD4xwGPOuX+Z2WLgKe9sj5s5yPtBEZFw1X5GaPXrEolMXZnGKNIlD83fwNbd9fy/j48hGFCNU0QOLjM5jnuvnkJ5XRO3PrGMltY2vyOJCOCcK3bOnehdxnkHM3HOVTjnznTOjfC+VvqdVUTkaMwvrCA1PoYT8tP9jiIix0DFLvFFWU0jv3u9iLPG9GPWcA0NFpHDO2FAOndcPJ75hRX838tr/Y4jIiIiUWxBUTnTh2YSE9RHZpFIpL9c8cUvX1lLQ3Mr3zt/tN9RRCSCXDa1gKtnDOQPbxbz4gelfscRERGRKFSyaw+bKvaoX5dIBFOxS3rch1ureGLxFj4zczBDs1P8jiMiEeYHF4xj0sAMvvn0+6zfUeN3HBEREYkyC4pC/bpmawaKSMRSsUt6VFub4/t//5Cs5Di+ctYIv+OISASKiwnw+6umkBgXw42PLqW6odnvSCIiIhJFFhSW0zcljpH9dGBeJFKp2CU96q/vlbBs826+c94Y0hNj/Y4jIhGqf3oCv7tqMpsr9/D1p96nrc35HUlERESigHOOBUUVzBzWF+9MsyISgVTskh5TVd/Mz/61hskDM7hkUr7fcUQkwk0bksl/fXwMr6zawe/fLPI7joiIiESBorJadtY0MntYlt9RRKQLVOySHvOrV9ZRUdfEjy8aTyCgoyQi0nXXzRrMxRPz+MXLa3lzXZnfcURERCTCzS9Uvy6RaKBil/SI1aXVPPLORq6aPpDx+el+xxGRKGFm/O8lExjVL5VbH1/GxvI6vyOJiIhIBFtQVM6APokUZCb5HUVEukDFLul2bW2O7//tQ9ITY/nGOaP8jiMiUSYxLsj9n5lKwOD6hxerYb2IiIgck9Y2xztFFcweplFdIpFOxS7pdk8s3sKSTbv43vljyEiK8zuOiEShgswkfn/1FDZV7OHLjy2jVQ3rRURE5Cit3FZFdUMLs4arX5dIpFOxS7rVzuoG/vefq5k5NItLpwzwO46IRLEZQ7P474vH8+a6Mv73xdV+xxEREZEIs6Ao1K9rpprTi0S8GL8DSHS7/flVNLa08T+XnKBT94pIt7ty2kDWbq/hgbc3MLJ/KpdPLfA7koiIiESI+YXljOyXQk5qgt9RRKSLNLJLus2rq3fwwgel3HrGcIb0TfY7joj0Ev/v42P42Ii+/NdzH7BkY6XfcURERCQCNLa0snhjJbPUr0skKqjYJd2irrGF7//tQ0b2S+GGU4b5HUdEepGYYIDfXjmZAX2SuPHRpZTs2uN3JBEREQlzyzfvpqG5jVmawigSFVTskm7xs3+tobS6gf+95ATiYvRrJiI9Kz0plgeunUpTaxuff3gJdY0tfkcSERGRMDa/qIKAwfShKnaJRANVIeS4m19YziPvbOKzs4YwZVCm33FEpJcalp3CPZ+ezLodNXz1yeW06QyNIiIichALCss5YUAG6YmxfkcRkeNAxS45rmoamvnWMysY2jeZb80Z5XccEenlThmZzfcvGMvLq3bw85fW+h1HREREwlBdYwvLt+zWFEaRKKKzMcpx9ZN/rKa0qp5nbppFQmzQ7zgiIlw3azCFO2u5980iBvRJ5OoZg/yOJCIiImFk0cZKWtocs9WcXiRqqNglx83ra3by5JIt3HTaMCYP7ON3HBERAMyM2z8xjtKqBn7w9w/JTU/gzDH9/I4lIiIiYWJBYTlxwQBTB+szjEi00DRGOS5272niO8+uYFS/VG47a4TfcURE9hMTDHD3lZMYl5fOLY8t44OSKr8jiYiISJiYX1jB5EEZmpkiEkVU7JIuc87xzWdWUFnXxJ2Xn0h8jP5JiEj4SY6P4cHrppKZHMdn/7SYLZV7/I4kEvHMrMDMXjez1Wa20sy+4i3/kZltNbPl3uV8v7OKiHRmV10Tq0qrNYVRJMqo2CVd9ui7m3hl1Q6+PWc04/PT/Y4jInJQOakJPPy5k2hubeOaBxdSVtPodySRSNcCfN05NwaYAdxsZmO9db9yzk30Li/6F1FE5ODeKa4AYNZwFbtEoomKXdIlq7ZV85MXVnP6qGyuP3mI33FERA5reE4qD113EturG7juj4uoaWj2O5JIxHLOlTrn3vOu1wCrgXx/U4mIHLn5heWkxMdw4gAdtBeJJip2yTHb09TCLY+/R0ZiLL+47ETMzO9IIiJHZMqgPvz+6ims3V7DDY8spaG51e9IIhHPzAYDk4CF3qJbzGyFmT1kZp12fTazG8xsiZktKSsr66GkIiL7vFNUwbQhmcQE9dFYJJroL1qO2Y/mrWRDeR2/vmIiWSnxfscRETkqp4/K4ReXncg7xRV85YlltLS2+R1JJGKZWQrwV+A251w18HtgGDARKAXu7Ox+zrn7nHNTnXNTs7OzeyyviAhAaVU9xeV1zBqW5XcUETnOulTsMrM5ZrbWzArN7DudrI83sye99Qu9I37t677rLV9rZud2WP6Qme00sw+7kk261+OLNvPUkhJuPm245reLSMS6eFI+P7hgLC+t3ME3nn6f1jbndySRiGNmsYQKXX9xzj0L4Jzb4Zxrdc61AfcD0/zMKCLSmfmFoX5ds/V5RiTqHHOxy8yCwD3AecBY4MoODUnbXQ/scs4NB34F/My771hgLjAOmAP8zns8gD95yyRMLdu8ix/+fSUfG9GXr5490u84IiJd8rmTh/DNc0fxt+Xb+N6zH9CmgpfIEbNQD4MHgdXOuV92WJ7bYbNPAjqIKSJhZ0FROZnJcYzql+p3FBE5zmK6cN9pQKFzrhjAzJ4ALgJWddjmIuBH3vVngN96b4ouAp5wzjUCG8ys0Hu8d5xzb3UcASbhpaymkZv+/B790uO5+8pJBAPq0yUike/m04fT2NzKb14rJC4mwI8vGqc+hCJHZjZwDfCBmS33ln2P0EHQiYADNgI3+hNPRKRzzjkWFFYwc1gWAX2mEYk6XSl25QNbOtwuAaYfbBvnXIuZVQFZ3vJ3D7jvUZ25x8xuAG4AGDhw4FEFl2PT3NrGzX95j931TTx702wykuL8jiQictx89eyRNLa08Ye3iokNBvj+BWNU8BI5DOfc20Bnfygv9nQWEZGjUVxex/bqBmYP0xRGkWjUlWJXZ29sDpz7cbBtjuS+h+Scuw+4D2Dq1Kmac9LNnHP8+PlVLNpYyV1zJzI2L83vSCIix5WZ8Z3zRtPU2sZD8zfQ2tbGDy8cp6O9IiIiUWhBUahfl5rTi0SnrhS7SoCCDrcHANsOsk2JmcUA6UDlEd5XwsiDb2/g0Xc3ceMpQ7lo4lENwhMRiRhmxg8uGEtsMMB9bxXT1NrGHRefoIKXiIjIQTjnaGptIz4mePiNw8iCwnLyMxIZlJXkdxQR6QZdKXYtBkaY2RBgK6GG858+YJt5wLXAO8ClwGvOOWdm84DHzOyXQB4wAljUhSzSjf75QSl3vLia80/oz7fnjPY7johItzIzvnveaOKCAX77eiFNLY6fXzpBPQpFRKTXaWtzlNc2UlrVwPbqBrZXNYSuV9Xvt6yxpY3BWUmMzUtjXF669zWNnNQEv3+ETrW1Od4pruDsMf3UskAkSh1zscvrwXUL8BIQBB5yzq00sx8DS5xz8widnedRrwF9JaGCGN52TxFqZt8C3OycawUws8eB04C+ZlYC/NA59+Ax/4TSJUs37eK2J5czqSCDX14+UaMbRKRXMDO+ce4o4mIC/PKVddQ3t/CrKyZG3FFrERGRg2lubWNnTeO+wpV3Ka3ed31HdQMtB5ylOC4YoF96PLlpiZw4IIM54xJIiA2ybkcNK7dV8+IH2/dum50az9jcUOFrXF464/LSGJiZ5PtnilWl1eze08ys4ZrCKBKtujKyC+fcixzQgNQ594MO1xuAyw5y3zuAOzpZfmVXMsnxs6G8ji88soTc9AQeuPYkEmL1IU9EepdbzxxBUlyQn7ywmt17FvOHa6aQmhDrdywREZFDamhuZUd1w94iVqlXuCqtqt97u6y2EXdA5+PE2CC56Qn0T09g+tBM73oiuWmhZbnpCWQmxx1yNFR1QzOrt1Wz0rusKq1m/lvFe4tmKfExjMlN3W8E2IicVOJiAt25S/azoKgcgFlqTi8StbpU7JLoVbJrD1fdHzph5h8/O43MZJ15UUR6p89/bChZKXF88+kVzL3vXf702Wlkp8b7HUtERHqpusaWDkWs+o+Mxtpe3UBlXdNH7peWEEN/r3g1un/a3uJV6Gsi/dMTSEuI6fK0vrSEWKYPzWL60H2jphpbWlm/o5aV26pY5RXBnl6yhbqmVgBig8aInFRvBFga4/LTGZObRkp893xcnV9YwfCcFPqlhec0SxHpOhW75CO2VzXw6fsXUtvYwmNfmMGQvsl+RxIR8dUnJw0gIymOL/35PS69dwF/vO4khman+B1LRESiUFV9M6tLq9lQXrd/fyyvkFXT0PKR+2Qlx9EvLVS8mjQwY9+ILK+Y1T8tgeRuKhwdifiYIOPz0xmfn753WVubY2NF3d7RXyu3VfP62p08vbRk7zaDs5L2GwE29jj0AWtqaWPRhkoumzqgS48jIuFNxS7ZT3ltI1c98C4VtY38+fPT9/uHJCLSm50+KofHvjCd6x9ewid/t4DfXz1Z0x9EROSYOefYUd2432inlaVVbKms37uNGeSkxtM/LYGh2cnMHt5334istNCIrJy0+IhsNxIIGEOzUxiancKFJ+YBoX2ys2b/ffLB1ipe+KB07/2yU+P3jQDLS2ds7tH1AXu/ZDf1za36Hy4S5VTskr0qahu5+oGFbN1dz8OfncakgX38jiQiElYmDezD3740m+sfXsxnHlzETy4ez9xpA/2OJSIiYa6tzbGhfRTTtuq9xZyKDtMNB2clMSE/g7knDQz1seqXSk5qPLHBnutl5Tczo19aAv3SEjhjdL+9y6sbmlm1d9+F9t/b68v36wM2NjdtvxFgB+sDNr+wnIDBzKFqTi8SzVTsEgBKq+q5+oGFlOyq54Frp+43x15ERPYZmJXEX780i1seW8Z3nv2A9Ttr+e55o4npRR9GRETk4BpbWlm3PdSfqn2K3urSavZ06E81sl8qZ4zO6ZH+VNEgLSGWGUOzmNHhM0pDc6gP2KrSqr3N8J9asmXvfo4LBhjRLyVU/Mrdt58XFFYwPj+d9CSdcEYkmukVVdhYXsdVDyykqr6ZRz43TYUuEZHDSEuI5aFrp/KTF1bz4Nsb+HBrFXd/elKX+4iIiEhkaR9xtLLDaK3CnbUfGXF0+dQC3848GK0SYoOcMCCdEwbsa7vS2ubY5I2ga39OXl29k6eWhPqAmYFzcOOpQ/2KLSI9RMWuXm7N9mqufmARrW1tPP6FGfv9sxARkYOLCQb40SfGMWFAOt977gMu+M3b/O6qyUwdnOl3NBEROc469pJaubXz/lrtvaRCI7bSGZd3dL2kpOuCh+kDtnJrNRsq6ph7kloQiEQ7Fbt6sf+sL+NLf3mP5LgYnrhhJsNzUv2OJCIScS6ZPIAxuWnc9OelzL3vXb49ZzTXnzxEH25EJKpV1Daycls1RWW1xAYDpCbEkBLvXRJiSI2PJcVbFmmjmNr7ax04Yutg/bXaR2xpdG94OlgfMBGJbip29VKPvLOR259fxYicFB64dioD+iT5HUlEJGKNyU1j3pdP5ptPv88dL67mrfVl/OKyE+mXpg8+IhLZnHOU7Krf23+qvcH69uqGI36MuJgAqV4RrL0glpoQQ/J+xbH267F71++3LiGGxNggZsf3QEJ7f62OfZ8O7K81IifUX2usd/a/MbmppCao35OISDhTsauXaWlt4/bnV/Hou5s4c3QOd105Sc0wRUSOg7SEWO69egqPL9rCf/9jFXN+/RY//dQEzh3X3+9oIiJHpLm1jaKy2n3T9LZVsaq0mpqGFgACBsNzUpgxNHPvNL0R/VJxzlHT2EJtQwu1jS3UeF9rG5pDtzusa/9aWtWw93ZNYwtNLW2HzRcwvEJYqCCWHB8kJSG2Q6Es5iOFsn23Y0mKC7J1d/1+I7Y69tdKjgsyNm9ff62xuWmM7Kf+WiIikUhVjl5kR3UDtz6+jIUbKrnxlKF8a85ogppmIyJy3JgZn54+kOlDM7ntieXc+OhSLpmcz/c/PpY+yXF+xxMR2WtPUwurS2tY1WHE1todNXuLTgmxAUb3T+PCE/NCZwzMS2d0/1QSYoOdPl5OF/M0trRS19jqFb+a9xXH9iue7btd562rqm9m6649e9fXeSOyDqdvivpriYhEMxW7eok31u7ka0+9T31TK3dediKfmjLA70giIlFrWHYKf71pFne/tp7fv1HEW+vK+OGF47hgQu5xn4IjInI4FbWNrCqt3u8MdRvK63ChAU1kJMXtlBKOAAAgAElEQVQyLi+Na2cO2lv4GdI3mZhgz41oio8JEh8TJLOLBwZa2xx1TZ2NMmuhtrGZnLQExuWmkaNp5iIiUU3FrijX3NrGnS+v4943ixjVL5V7rpqkRvQiIj0gLibA188Zxfkn5PLtv67gy48v4+/Lt/LDC8dRkKk+iSJy/O3rr1W934itjv218jMSGZuXxoUTvBFb+enkpSdETSE+GDDSEmJJU08tEZFeTcWuKPbh1iq+9cwKVpVWc+W0gfzwwrEHHXouIiLdY0xuGs/eNIs/zt/IL19Zx5m/fJMbTxnKTacNIylO/4ZF5Ni0tLZR6PXXCo3aCp0xsLpDf61h2aH+Wu2N1cfmpmlKtYiI9Ap6lx2FGppbufu19dz7ZjGZyXHce/UU5oxXg2QREb/EBAN84ZShfHxCLj/95xrufq2QZ5aW8J3zRnPhhDz1iBGRQ+rYX6t9OuKa7fv6a8XHBBidm8YFXn+tsblpjO6fRmKcDnKKiEjvpGJXlHlrXRm3P7+SorI6Lp0ygO9/fCzpSRrGLSISDvIyEvnNlZO4ZuYgfjRvJV95Yjm/f6OIb547ijNG50TNNCIROToH9pnaUd3gTUXc11/LO2Eg6Yn799cam5fG0B7uryUiIhLuVOyKEkVltfzPC6t5dc1OBmYm8fDnpnHqyGy/Y4mISCdOGpzJvFtO5h8rtvHLV9Zx/cNLmDwwg6+ePZKTh/dV0UuihpnNAe4CgsADzrmf+hzpuGpqadvb/PxIziBY29jcYXvvjIKHOINgXnoCY/PSucDrrzU2L438jES9RoiIiByGil0Rbmd1A797o4g/v7uJhNgg3z1vNNfNHkx8jIati4iEs2DAuGhiPuefkMvTS0r4zavruebBRYzPT+OLpw7jvPG5BDW9USKYmQWBe4CzgRJgsZnNc86t8jOXc449Ta3UNYYKTh89a1+oINVxXXtxqmMxq7axZe80wkMJGKTEx5CaEEtKfAwpCTFkJMUxIDOJlLjQ7dD6mL3rM5PjGNNf/bVERESOlYpdEWp7VQP3vlnEY4s209rmuHxqAV87eyTZqfF+RxMRkaMQGwzw6ekDuWRyPs8t28p9bxVzy2PLGJS1lmtnDuZTUwaQnqjp6BKRpgGFzrliADN7ArgI6PFi15cfX8bSjZV7R1O1Twk8lLiYAKnx+4pRKfEx5GUkkBIfQ7K3PDW+vUAV+5GCVft9E2ODGoklIiLSw1TsijBrt9fw8DsbeWZpCW1tjksm53Pz6cMZlJXsdzQREemChNggV04byOVTC3h55Xbu+08xP/7HKn7+0houOjGfq2cMYnx+mj40SyTJB7Z0uF0CTO+4gZndANwAMHDgwG4LMiw7mbhgYL9i1H7Fqb0FqlhSEmJIjg9qlLyIiEgEU7ErArS0tvHqmp38af5G3imuID4mwKcm5/Ol04ZTkJnkdzwRETmOggHjvBNyOe+EXD7cWsWf393E35dv48klWxjVL5WLJ+Vz8aQ8ctMT/Y4qcjidVWb3G1PlnLsPuA9g6tSpRzDe6tjcdtbI7npoERERCUMqdoUp5xwrt1Xz7Htbmff+NsprG8nPSOTbc0Yz96QC9XAQEekFxuen89NPTeC7549h3vvbeO69En72rzX8/KU1TBucyZzx/TlnXH/yM1T4krBUAhR0uD0A2OZTFhEREelFVOwKI845PtxazSurtvOvldtZt6OWuGCAM0bncMnkfM4YnaPTSouI9ELpibFcM2MQ18wYxKaKOp5btpUXVpRy+/OruP35VZyQn86ZY3I4ZWQ2E/LT9b9CwsViYISZDQG2AnOBT/sbSURERHoDFbt8tquuiXeKK5hfWM5ra3ZSWtVAwGDq4Ex+cvF4LpiQS0aSRnGJiEjIoKxkbjtrJLedNZLislpeWrmDl1Zu565X1/Prf68nLSGG2cP7MmNoFlMH92F0/zSd1VF84ZxrMbNbgJeAIPCQc26lz7FERESkF1Cxqwc55yjZVc+yLbtZtnkXizZUsqq0GucgOS7I7OF9+fo5ozhjdA6ZmqYoIiKHMTQ7hZtOS+Gm04axq66J+UXl/GddOW8XlvPPD7cDkBofw6RBfThxQDrj89OZMCCd/mkJanQvPcI59yLwot85REREpHdRsaub1DW2sKG8jnU7alizvYbVpdWsLq2mvLYJgITYACcOyOCrZ41k9vAsJgzIIFbTTkRE5Bj1SY7jggl5XDAhD4Ctu+tZvKGSxRsrWbppF797o5zWtlD/7z5JsQzPSWFYdgpDs5MZlh26PqBPoqZAioiIiEjE61Kxy8zmAHcRGpr+gHPupwesjwceAaYAFcAVzrmN3rrvAtcDrcCtzrmXjuQxw0FjSyu79zRTWddEWU0jpVX1bN3dQOnuejZV7mFjeR07axr3bh8XE2BkvxROG5XDxIIMJhZkMKp/qopbIiLSbfIzEsmflM/Fk/IBqG9qZfX2aj4oqWLN9mqKyur49+odlC9u2nufuGCAQVlJ5PdJpF9qAv3SE+iXFk//tAT6eZes5DgCmhYpIiIiImHsmItdZhYE7gHOJnS2ncVmNs85t6rDZtcDu5xzw81sLvAz4AozG0uoSek4IA/4t5m1nxP6cI/ZY3ZWN3DHi6uprGvaW9zavaeJuqbWj2xrBjmp8QzMTOLUkdkM7pvM4KxkRvVPYXBWso6Ui4iIrxLjgkwe2IfJA/vst3z3niaKyuooLqvd+3V7dQOrtlVTVtuIc/s/jhmkxMeQlhBLakIMaYmxpCXsux0fGyQ2aMQGA8QGA8QFA6HbMQECZrS2uf0uLW2ONudoaXWcPjqbCQMyenCviIiIiEg06srIrmlAoXOuGMDMngAuAjoWpi4CfuRdfwb4rYWahFwEPOGcawQ2mFmh93gcwWP2HINlm3fTJzmOvilxjMhJoU9yHH2SYumTHEdmUhxZKfHkZYSOdmukloiIRJqMpDimDIpjyqA+H1nX0tpGWW0jO6ob2V7VwI7qBipqG6luaKG6oZmahhaq65vZuruBNQ01VNc309TaRktrqIh1tDKTY1XsEhEREZEu60qxKx/Y0uF2CTD9YNt4Z+SpArK85e8ecN987/rhHrPH5KQm8Na3Tvfr24uIiPgqJhggNz2R3PREKDi6+7a1OZrb2mhudTS3tNHc2karcwQDRtCMmECAQABiAoHQsoCh2ZEiIiIicjx0pdjV2VvSAw/jHmybgy3vbGhUp4eGzewG4AbvZq2ZrT1IToG+QLnfIQQ4oudi0GEe4nDrI9NVPf9zRcffxQx/fx+uOj4PEx3PRXSI5uciOl88o8jSpUvLzWxTN36LdKCqGx//eIqkrBBZeZW1e0RSVoisvAOBzX6HOEKRtF8jKStEVt7uznpE7+m6UuwqYf/jvAOAbQfZpsTMYgj90JWHue/hHhMA59x9wH3HGr43MbMlzrmpfucQPRfhRM9F+NBzET70XIifnHPZ3fn4Znafc+6Gw2/pv0jKCpGVV1m7RyRlhcjKa2ZlkfK/OcL2a8RkhcjKGy5Zu9JkajEwwsyGmFkcoYbz8w7YZh5wrXf9UuA155zzls81s3gzGwKMABYd4WOKiIiIiByt5/0OcBQiKStEVl5l7R6RlBUiK+9uvwMchUjar5GUFSIrb1hkPeaRXV4PrluAl4Ag8JBzbqWZ/RhY4pybBzwIPOo1oK8kVLzC2+4pQo3nW4CbnXOtAJ095rH/eCIiIiIi4JwLizffRyKSskJk5VXW7hFJWSHi8kbK1LWI2q+RlBUiK2+4ZDV34DnFJeqY2Q3etE/xmZ6L8KHnInzouQgfei5ERETCi/43ixwbFbtERERERERERCRqdKVnl4iIiIhIWDGzWr8zHK3DZTazN8zMtwbVZvZJM3NmNtqvDEfKzP7LzFaa2QozW25m0/3OdChmNsDM/m5m682syMzu8noXH2z728wsqSczet/XmdmdHW5/w8x+1NM5joSZtXrP/Uoze9/MvmZm+tx7HHTYt+2XwYfY9jQz+0fPpfvI93dm9miH2zFmVuZnpsOJhNfaSNqv+qOPMmZ2mffC3nbgmyIz+66ZFZrZWjM7t8PyOd6yQjP7Ts+n7h20n3uWmT1kZjvN7MMOyzLN7BXvDeUrZtbHW25m9hvvuVlhZpP9Sx5dzKzAzF43s9Xea9NXvOV6LnqYmSWY2SLvjf9KM7vdWz7EzBZ6z8WT7R+yvJPIPOk9FwsP9YZWRKLelcDbeP13w5WZzQQuACY75yYAZwFb/E11cGZmwLPA35xzI4CRQApwxyHudhvQ48UuoBG4xMz6+vC9j1a9c26ic24ccDZwPvBDnzMdVoQU6tv3bftlo9+BDqEOGG9mid7ts4GtR/MAZnbMPc6P0TG91ppZsHvidKrL+7WnqNgVfT4ELgHe6rjQzMYS+qMZB8wBfmdmQe8P4x7gPGAscKW3rRxH2s+++BOh3/WOvgO86r2hfNW7DaHnZYR3uQH4fQ9l7A1agK8758YAM4Cbvd99PRc9rxE4wzl3IjARmGNmM4CfAb/ynotdwPXe9tcDu5xzw4FfeduJRIQDRxSY2W/N7Drv+kYzu93M3jOzD8LlCPqhMvvJzFKA2YReE+Z6yw61f883szVm9rZ38KInj/bnAuXOuUYA51y5c26bmU0xszfNbKmZvWRmuV7WN8zs12a2wMw+NLNpPZgV4AygwTn3Ry9vK/BV4HNmlmxmv/B+R1eY2ZfN7FYgD3jdzF7v4awtwH1evv2Y2SAze9XL+aqZDTSzdO9vLeBtk2RmW8wstidDO+d2Eno/cYt3QC1oZv9nZou9vDd2+Dm+5e3v983spz2ZM5Idap8CaWb2nJmtMrN7redH2P0T+Lh3/Urg8fYVZjbN+9tf5n0d5S2/zsyeNrPngZd7KughXmvf6mwfmlmtmf3YzBYCM3sqp+dY9ut/zGxih+3mm9mE7gypYleUcc6tds6t7WTVRcATzrlG59wGoBCY5l0KnXPFzrkm4AlvWzm+tJ97mHPuLUJnge3oIuBh7/rDwMUdlj/iQt4FMtrfCEvXOOdKnXPveddrgNVAPnouepy3T9uPGsd6F0fow9Yz3vIDn4v25+gZ4ExvFIJINCh3zk0mVFD/ht9hwtzFwL+cc+uASjvEiFszSwD+AJznnDsZyO6hjO1eBgrMbJ2Z/c7MTvWKK3cDlzrnpgAPsf/IqWTn3CzgS966njQOWNpxgXOuGtgMfB4YAkzyRqn9xTn3G2AbcLpz7vQezgqhA7dXmVn6Act/S+h/9wTgL8BvnHNVwPvAqd42FwIvOeeaeyytxzlXTOhzbw6hQkKVc+4k4CTgCxYa4Xweod/16d5BoZ/3dE4IFTy8gmF7Mf4ib/lgC42Sv99Co7Nftn0ja3pSou2bwvict6zTfeqtmwZ8HTgBGEZoUEZPegKY6702TQAWdli3BjjFOTcJ+AHwPx3WzQSudc6d0WNJD/5ae7B9mAx86Jyb7px7uwdzwrHt1weA6wDMbCQQ75xb0Z0hVezqPfLZfxh3ibfsYMvl+NJ+Dg/9nHOlECrCEHrTA3p+eoSFpsFNIvQPUc+FD7yjr8uBncArQBGw2znX4m3ScX/vfS689VVAVs8mFuk2z3pflwKDfcwRCa4k9MEG7+uVh9h2NFDsHViFDkf7e4JX0J9CaCRPGfAkcCMwHnjFe/37f8CADnd73LvvW4RGoWT0YGQjdNChs+WnAPe2vz475w48gNfjvELcI8CtB6yaCTzmXX8UONm7/iRwhXd9rnfbL+0Ha84BPuP9Liwk9H9tBKEpr390zu0BX/d3A/BJrxh/OnBnhwNNI4B7vOmZu4FP+ZCv4zTGT3rLDrZPARZ5B/tbCf2tnfzRh+w+XjFlMKHXrRcPWJ0OPG2hlie/IlR8bveKD78DB3utPdg+bAX+2rMRQ45xvz4NXOAdgPgcoVk43aqn56DKcWBm/wb6d7Lqv5xzfz/Y3TpZ5ui84KlTdB5/B9v/Eh70/HQzb2j2X4HbnHPVhxggpOeiG3lvlCZ6H+aeA8Z0tpn3Vc+FRLIW9n+Pk3DA+kbvayvh8374cJl7nJllERr9Od7MHBAk9Dowj86z+j7603udewN4w8w+AG4GVjrnDjbN58DXtZ58nVvJAQULM0sDCoDiHs5ypH4NvAf88RDbtOeeB/yvmWUSKkK+1s3ZOmVmQwn9re8k9Dv6ZefcSwdsM4fw2N8G/I+ZnQK0ETrw1M9bt8E5t9y7Hk6F+oPt09Pw9++r3TzgF8Bp7H/Q7r+B151zn/QOyL7RYV1dD2UDDvla+yIH34cN3uudX45qvzrn9pjZK4RmDlwOdPtJVzSyKwI5585yzo3v5HKwQheEjtYXdLg9gNAw6IMtl+NL+zk87LB9fTpyCb3pAT0/3co7gvNXQlMw2kdT6LnwkXNuN6E3HzMITRVt/7DfcX/vfS689el8dGqwSLjaBIy10IkW0oEz/Q50BMIx86WEpqcNcs4Nds4VAO2jtjrLugYYavtOaHEFPcjMRpnZiA6LJhKaPp9toeb1mFmsmXUcwXGFt/xkQlOxqnoscKhnZZKZfcbLEATuJDTi4WXgi+2vz17BCKAGSO3BjPvxRrs8xb7+jgAL2NdQ+ypCDbbbR9otAu4C/uHHB3MzywbuBX7rnHPAS8BN3nsTzGykmSUT2t+fM+9Mlx32d0+7itD03ynOuf/P3n2HV1WlfR//3ikkhNASWiBAgtIhdAKCGETAgmNDERRFZsZxxoLOax1lQGfGyoyOOs+D+IhYQBERxY4gGURpAZFiaEqACJIQegmkrPePs4kBEhIhyUn5fa4rV/ZZe+217r1Dwj73WWvtzsBOfkkmH81Xrzwl6gu7pgA9vWmiAfh+18p6uh34pic/5pxbfVJ5bX5ZWH1UmUZ0qsL+1valfFzDgpzJdf0/4HlgWVmMnFOyq+qYjW9ebYg3h7olvv98lgEtvV+gavj+o5rtxzgrK13n8mE2cLO3fTPwQb7ym8ynF76b3R3+CLCy8YbevwIkO+f+lW+XfhZlzMzqH5+e463zcRG+N4Hz8d1kwak/i+M/o6HAl94bBZFyy0sMHHXObcP3hnwVvjWEvvVrYKdRzmMejm8UaH4zgREUEKtz7gi+ta8+M7OF+N6ol2XyKBx4zXwLOa/C91Cgv+L7G/aUmX0HrATOy3fMHjP7Bl9C5LcnN1iavL+pVwHXmtlGYAO+aWx/wfemcCuwyot7hHfYJOBTK/sF6vP7J5D/qYx3Abd413wkMCbfvunAjZTtFMbj60qtBebiS2Q96u37P+B7YIU3zeolIMg59xm+//eSvOl4/lrLrzaQ5pzLMrP+QHM/xfFrFHhNvX2LgCfxPURtM6f+PSl1zrlU59y/C9j1NL6Rh1/jG0nlT6f7W+v3a1iQM7muzrnlwH5OPzK0xJjuWysXM7sK3yKc9fHN5V7pnBvs7XsY3/zYbHxTiT71yi/FNyQ5EJjsnDvd447lDOk6ly0zewvfsNp6+G62xwHv47s5b4bvBvJa59xuLyHzIr6nNx4GbnHOJfkj7srG+6T8K2A1vuH44LuJX4J+FmXKfE+8eQ3f36AA4B3n3GPe9I63gQh8b1hvdM4d9RYdfQPfOmu7geu9RX5Fyi0z6wS87Jwr66fqnbGKGPPpmFm4c+6g9/f8P8BG59yz/o6rIGaWCNyr/2fE37yk906gNfAhvofIrMT3dL5LvGofOec6ePXvBcKdc+PLPlopK95U0Hudc0P8HUtJMLPG+GYWtHHO5RZR/ez7U7JLRERERCo6M7sN3wiTu51zZfa4+LNREWMuipndg29UaDV8CfTfH1/0u7xRskvKi8qW9JaSUZmSXd507X8Af3bOzSiTPpXsEhERERERESl7lTHpLVIeKNklIiIiIiIiIiKVhhaoFxERERERERGRSkPJLhEREREREZEyYGZNzWy+mSWb2VozG+OVR5jZF2a20fte1ytvY2aLzOyotzB9/rbu8dpYY2ZveQ+XERGU7BIREREREREpK9nA/3POtQV6AbebWTvgQWCec64lMM97Db4nIt8FTMjfiJk18cq7e09pDASuL5tTECn/lOwSkROYWSMze9vMfjCz783sEzNr5e+4jjOz35jZg0XXPKO2x5vZEyeVdTaz5CKOSzGzeqURk4iIiIhUHs65Hc65Fd72ASAZaAJcAbzmVXsNuNKrk+acWwZkFdBcEFDdzIKAMGB7KYcvUmEo2SUieczMgFlAonPuHOdcO+AvQEM/xBJYULlzbrZz7slS6vYtYNhJZdcD00qpPxERERGposwsBugCLAEaOud2gC8hBjQ43bHOuZ/wjfbaCuwA9ulpjiK/ULJLRPLrD2Q55yYeL3DOrXTOfWU+z3hrAqw2s2EAZpZgZolm9q6ZrTOzqV7dS8zsnePtePU+9LYHeWsPrDCzGWYW7pWnmNlfzWwhcK2Z3eWNLltlZm97dUaZ2YvednMzm+ftn2dmzbzyKWb2vJl9Y2Y/mtlQrzzKzBaY2UrvPM7Pf/LOufXAXjOLz1d8HXC87+Heua8xs6dOvnhmFmNma/K9vtfMxnvbiWb2rNd/spn1MLP3vHUZ/p7vmBvNbKkX40uFJf1EREREpOLy7n9nAnc75/afwfF18Y0GiwUaAzXM7MaSjVKk4lKyS0Ty6wAsL2Tf1UBnoBNwEfCMmUV5+7oAdwPtgBZAH+ALoJeZ1fDqDAOme9P9HgEucs51BZKAP+frJ9M519c59za+tQq6OOfigNsKiOlF4HVv/1Tg+Xz7ooC+wBDg+EiwEcDnzrnj57GygDbfwlvvwMx6ARnOuY1m1hh4CrjQuw49zOzKQq5VYY455/oBE4EPgNvxXfNRZhZpZm3xXac+Xow5wA2/sg8RERERKcfMLBhfomuqc+49r3jn8Xtr73taEc1cBGx2zqU757KA94DzSitmkYpGyS4RKa6+wFvOuRzn3E7gv0APb99S51yqcy4XXwIpxjmXDXwGXO6tI3AZvgRPL3xJsa/NbCVwM9A8Xz/T822vAqZ6n1JlFxBTb36ZYviGF+Nx7zvncp1z3/PLNMxlwC3eaKuO3joJJ3sbGGpmAfiSXm955T3wTe9M985tKtCvwCtVuNne99XAWm/NhqPAj0BTYADQDVjmXZsB+JKHIiIiIlIJeMuGvAIkO+f+lW/XbHz3xXjfPyiiqa34PlgO89ocgG/9LxHBt6CdiMhxa4Ghheyz0xx3NN92Dr/8bZmOb/TSbmCZc+6A95/xF8654YW0dSjf9mX4Ekq/AcaaWfsi4neFxGQAzrkFZtbPa/cNM3vGOff6CQ04t83MUoALgGvwJdTy2ihCNid+iHDy45+Px5R7Uny5+K6ZAa855x4qRl8iIiIiUvH0AUYCq70PN8G3Ru6TwDtm9lt8iaxrwffwKHwzIWoBuWZ2N9DOObfEzN4FVuC7B/0WmFSmZyJSjmlkl4jk9yUQYma/P17grS11AbAAGGZmgWZWH18SamkR7SUCXYHf88uIrcVAHzM712s/zAp42qM3sqqpc24+cD9QBwg/qdo3/PKI5RuAhacLxsyaA2nOuZfxfaLWtZCqbwHPAj8451K9siXABWZWz1tHazi+0W357QQaeFMSQ/BNofw15uEbVdbAizfCi1lEREREKgHn3ELnnDnn4pxznb2vT5xzGc65Ac65lt733V79n51z0c65Ws65Ot72fm/fOOdcG+dcB+fcSG/GgIigZJeI5OOcc8BVwEAz+8HM1gLj8T3GeBa+aYXf4UuK3e+c+7mI9nKAj4BLvO8459KBUcBbZrYKX/KrTQGHBwJvmtlqfJ9UPeuc23tSnbvwTUtche8TsjFFnGICsNLMvsU3auvfhdSbAbTHW5jei3sH8BAwH981WOGcO2F4ubdewmP4EmMfAeuKiOcE3pTLR4A53jl9gW/tMRERERERESkm8723FRERERERERERqfg0sktERERERERERCoNJbtERERERERERKTSULJLREREREREREQqDSW7RERERERERESk0lCyS0REREREpIows7vMLNnM9pjZg7/iuBgzG1GaseXrK8HMzjuL4//yK+uPN7N7i6hzpZm1O9OYRKRsKdklIiIiIiJSdfwJuNQ5V9c59+TJO80sqJDjYoAySXYBCcAZJ7uAX5XsKqYrASW7RCoIJbtERERERESqADObCLQAZpvZPWb2olc+xcz+ZWbzgafM7AIzW+l9fWtmNYEngfO9snsKaT/QzCaY2WozW2Vmd3rlA7x2VpvZZDML8cpTzOxRM1vh7WtjZjHAbcA9Xl/nm9nlZrbEa2OumTX0jg83s1fz9XeNmT0JVPeOnXqaa/Gwma03s7lA63zlvzezZWb2nZnNNLMwb5TZb4BnvHbP8b4+M7PlZvaVmbU5yx+PiJQgc875OwYREREREREpA2aWAnQHhgDdnXN3mNkUoB5whXMux8w+BJ50zn1tZuFAJtAXuNc5N+Q0bf8RuAgY5pzLNrMI4DCwERjgnNtgZq8DK5xzz3mx/NM594KZ/Qno6pz7nZmNBw465yZ47dYF9jrnnJn9DmjrnPt/ZvYUEOKcu/t4PefcHjM76JwLP02c3YApQDwQBKwAJjrnJphZpHMuw6v3d2CnF98U4CPn3LvevnnAbc65jWYWDzzhnLuw+D8JESlNhQ1RFRERERERkapjhnMux9v+GviXNzLqPedcqpkVp42L8CWNsgGcc7vNrBOw2Tm3wavzGnA78Jz3+j3v+3Lg6kLajQamm1kUUA3YnK+/649Xcs7tKU6QwPnALOfcYQAzm51vXwcvyVUHCAc+P/lgLwF4HjAj33UJKWbfIlIGNI1RREREREREDh3f8Nby+h1QHVj8K6boGXDy1KGismRHve85FD4Y4wXgRedcR+APQOhp+iuuwo6bAtzh9fVovr7yC8A30qxzvq+2ZxiHiJQCJbtEREREREQkj5md46VYFlMAACAASURBVJxb7Zx7CkgC2gAHgJpFHDoHuO34IvfeNMZ1QIyZnevVGQn8t4h2Tu6rNvCTt33zSf3dkS/uut5mlpkFn6b9BcBVZlbdW4/s8nz7agI7vONvKCgm59x+YLOZXev1a94INhEpJ5TsEhERERERkfzuNrM1ZvYdcAT4FFgFZHsLtxe4QD3wf8BWYJV37AjnXCZwC74pf6uBXGBiEf1/iC8ZtdLMzgfGe8d/BezKV+/vQN18sfb3yid5MRS4QL1zbgUwHVgJzAS+yrd7LLAE+AJfou64t4H7vEXyz8GXCPut1+9a4IoizklEypAWqBcRERERERERkUpDI7tERERERERERKTS0NMYRUREREREpNjMbDDw1EnFm51zV/kjnsKYWSQwr4BdA5xzGWUdj4iUHU1jFBERERERERGRSkPTGEVEREREREREpNKoFNMY69Wr52JiYvwdhoiIiJRTy5cv3+Wcq+/vOMo7M5sMDAHSnHMdTtp3L/AMUN85t8vMDPg3cClwGBjlPeEMM7sZeMQ79O/OudeK6lv3cyIiIlKU4t7TVYpkV0xMDElJSf4OQ0RERMopM9vi7xgqiCnAi8Dr+QvNrCkwENiar/gSoKX3FQ/8LxBvZhHAOKA74IDlZjbbObfndB3rfk5ERESKUtx7Ok1jFBEREREAnHMLgN0F7HoWuB9f8uq4K4DXnc9ioI6ZRQGDgS+cc7u9BNcXwMWlHLqIiIhIHiW7RERERKRQZvYb4Cfn3Hcn7WoCbMv3OtUrK6y8oLZvNbMkM0tKT08vwahFRESkKlOyS0REREQKZGZhwMPAXwvaXUCZO035qYXOTXLOdXfOda9fX0uqiYiISMmoFGt2iYiIlEdZWVmkpqaSmZnp71CqjNDQUKKjowkODvZ3KJXFOUAs8J1vPXqigRVm1hPfiK2m+epGA9u98oSTyhPPpHP9DpUM/V6IiEhVo2SXiIhIKUlNTaVmzZrExMTgJQqkFDnnyMjIIDU1ldjYWH+HUyk451YDDY6/NrMUoLv3NMbZwB1m9ja+Ber3Oed2mNnnwONmVtc7bBDw0Jn0r9+hs6ffCxERqYo0jVFERKSUZGZmEhkZqTfpZcTMiIyM1Cigs2BmbwGLgNZmlmpmvz1N9U+AH4FNwMvAnwCcc7uBvwHLvK/HvLJfTb9DZ0+/FyIiUhVpZJeIiEgp0pv0sqXrfXacc8OL2B+Tb9sBtxdSbzIwuSRi0s/07OkaiohIVaORXSIiIiIiIiIiUmloZJeIiIiIiIiISDmzOnUfT3yaTIAZbRrVpG1ULdpE1eTcBuGEBAX6O7xyTckukRI0bclWf4dQpK7NHgVgxdZxJdPeLq+9eiXT3tl4I9kXy8i24xgR38zP0ZQzd9/t+/7cc/6NQ0SkggkPD+fgwYNn3c6UKVNISkrixRdfLIGo/NeHiIiUvsysHP49byOTFvxIRI1qNKoVyuuLt3AsOxeAoADjnPrhtInyEmBeIqxBzRBNXfco2SVSxdSt8X3Jtne0ZNs7G1v2l59Yyp2VK/0dgfhJSkoKF198MX379mXx4sV06tSJW265hXHjxpGWlsbUqVNp3749d955J6tXryY7O5vx48dzxRVXkJKSwsiRIzl06BAAL774Iueddx6JiYmMHz+eevXqsWbNGrp168abb76pmysRERGRs7R8yx7uf/c7fkg/xLXdonlkSDtqVw8mOyeXlIxDJO84wLqf95O84wDLNu/mg5Xb846NqFGNNo1q0qZRLdp6ibBzG4QTGlz1RoEp2SUiIlIG7v7sblb+XLJJx86NOvPcxUWP1tu0aRMzZsxg0qRJ9OjRg2nTprFw4UJmz57N448/Trt27bjwwguZPHkye/fupWfPnlx00UU0aNCAL774gtDQUDZu3Mjw4cNJSkoC4Ntvv2Xt2rU0btyYPn368PXXX9O3b98SPT+R/B79cC3fb99fom22a1yLcZe3L3b9Z555hnfeeYejR49y1VVX8eijvhHFV155Jdu2bSMzM5MxY8Zw6623AvDqq6/yxBNPEBUVRatWrQgJCQFgxowZPProowQGBlK7dm0WLFhQYH/x8fFMnjyZ9u19MSYkJPDPf/6T2NhYRo8ezY8//khYWBiTJk0iLi7ubC6FiIj42ZFjOUyYs57JX28mqlYor43uyQWt6uftDwoM4NwGNTm3QU0u79Q4r3zv4WOs+/kA63bsz0uETVu6hcws3yiwwACjRb0atInyEmCNfFMhG9UKrdQfVCrZJSIiUsnFxsbSsWNHANq3b8+AAQMwMzp27EhKSgqpqanMnj2bCRMmAJCZmcnWrVtp3Lgxd9xxBytXriQwMJANGzbktdmzZ0+io6MB6Ny5MykpKUp2SaU2Z84cNm7cyNKlS3HO8Zvf/IYFCxbQr18/Jk+eTEREBEeOHKFHjx5cc801HDt2jHHjxrF8+XJq165N//796dKlCwCPPfYYn3/+OU2aNGHv3r2F9nn99dfzzjvv8Oijj7Jjxw62b99Ot27duPPOO+nSpQvvv/8+X375JTfddBMrNYJXRKTCWvxjBg/MXMWWjMPcEN+MBy9pQ83Q4GIdWyesGr1aRNKrRWReWU6uIyXjEOvyRoHtZ8WWPXz43fZ8xwXnTX88ngBr1bBmpRkFpmSXiIhIGSjOCKzScnw0CUBAQEDe64CAALKzswkMDGTmzJm0bt36hOPGjx9Pw4YN+e6778jNzSU0NLTANgMDA8nOzi7ls5Cq7teMwCoNc+bMYc6cOXkJq4MHD7Jx40b69evH888/z6xZswDYtm0bGzdu5OeffyYhIYH69X2fyg8bNiwvYdynTx9GjRrFddddx9VXX11on9dddx0DBw7k0Ucf5Z133uHaa68FYOHChcycOROACy+8kIyMDPbt21dq5y4iIqXj0NFsnvpsHa8v2kLTiOpM+108551b76zbDfTW9DqnfjiXxUXlle87ksX6n3+ZBpm8Yz9vL93GkawcAAIMYuvV8CXA8q0FFlW74o0CU7JLRESkihs8eDAvvPACL7zwAmbGt99+S5cuXdi3bx/R0dEEBATw2muvkZOT4+9QRfzGOcdDDz3EH/7whxPKExMTmTt3LosWLSIsLIyEhAQyMzMBCn1jMHHiRJYsWcLHH39M586dWblyJZGRkafUa9KkCZGRkaxatYrp06fz0ksv5cVysor2JkREpKpbuHEXD8xcxfZ9Rxh1Xgz3X9yasGqlm6KpXT2YnrER9IyNyCvLzXVs3X2Y5B37SfamQ36XupePVu3Iq1MrNIg2UbVo5yXA2kTVonXDmlSvVn5HgSnZJSIiUsWNHTuWu+++m7i4OJxzxMTE8NFHH/GnP/2Ja665hhkzZtC/f39q1Kjh71BF/Gbw4MGMHTuWG264gfDwcH766SeCg4PZt28fdevWJSwsjHXr1rF48WLAt97WmDFjyMjIoFatWsyYMYNOnToB8MMPPxAfH098fDwffvgh27ZtKzDZBb6pjE8//TT79u3Lm47cr18/pk6dytixY0lMTKRevXrUqlWrbC6EiIiclf2ZWTz+cTJvL9tGi3o1eOcPvekRE1H0gaUkIMCIqVeDmHo1uKTjL6PADmRmsWHnAb7f4UuArfv5ADOStnHomO/DTzOIjayRNwLs+JpgTepULxcfwCjZJSIiUonFxMSwZs2avNdTpkwpcN/xESP5tWzZklWrVuW9fuKJJwDfItkJCQl55S+++GIJRy1S/gwaNIjk5GR69+4NQHh4OG+++SYXX3wxEydOJC4ujtatW9OrVy8AoqKiGD9+PL179yYqKoquXbvmjY6877772LhxI845BgwYkJcEK8jQoUMZM2YMY8eOzSsbP348t9xyC3FxcYSFhfHaa6+V4pmLiEhJmb8ujYfeW03agUz+cEEL7rmoVbldI6tmaDDdmkfQrfmJo8BS9xwh2VsHbN2OA6zdvo+PV/8yCqxLszrM+lMff4R8AiW7REREREQKcfDgwbztMWPGMGbMmFPqfPrppwUee8stt3DLLbecUv7ee+8Vu/+GDRuesiZeREQEH3zwwSl1R40axahRo4rdtoiIlI29h4/x2Eff896Kn2jVMJyJI/vQuWkdf4f1qwUEGM0iw2gWGcbg9o3yyg8dzWb9zgOs23GAoED/j+oCJbtERERERERERErF52t/5pH317D70DHuvPBc7rjwXEKCyudorjNVIySIrs3q0rVZXX+HkkfJLhERERERP/r888954IEHTiiLjY3Ne8KjiIhUPBkHjzJu9lo+WrWDdlG1eHVUDzo0qe3vsKoMJbtEREREpNxyzpWLhW5L0+DBgxk8eHCptV/Q0xtFRKR0OOf4cNUOxs9ey4HMLP7fwFbclnAOwYEB/g6tSlGyS0RERETKpdDQUDIyMoiMjKz0Ca/S4pwjIyOD0NBQf4ciIlLppe3P5JH31zDn+510iq7N00N70bpRTX+HVSUp2SUiIiIi5VJ0dDSpqamkp6f7O5QKLTQ0lOjoaH+HISJSaTnneG/FTzz20fccycrhwUva8Lu+sQRpNJffFCvZZWYXA/8GAoH/c849edL+EOB1oBuQAQxzzqV4+x4CfgvkAHc55z4/XZtmNgB4BggADgKjnHObzu40RURE5GwlJCQwYcIEunfvzqWXXsq0adOoU6fiPUlIKo7g4GBiY2P9HYaIiEihduw7wl/eW8389el0a16Xp4fGcU79cH+HVeUVmewys0DgP8BAIBVYZmaznXPf56v2W2CPc+5cM7seeAoYZmbtgOuB9kBjYK6ZtfKOKazN/wWucM4lm9mfgEeAUSVwriIiIlJCPvnkE3+HICIiIuI3zjneXraNxz9OJjvXMe7ydtzUO4bAAE27Lw+KM6auJ7DJOfejc+4Y8DZwxUl1rgBe87bfBQaYb2GFK4C3nXNHnXObgU1ee6dr0wG1vO3awPYzOzURERFJSUmhTZs2/O53v6NDhw7ccMMNzJ07lz59+tCyZUuWLl3KoUOHGD16ND169KBLly588MEHABw5coTrr7+euLg4hg0bxpEjR/LajYmJYdeuXQBceeWVdOvWjfbt2zNp0qS8OuHh4Tz88MN06tSJXr16sXPnzrI9eREREZFSsG33YW58ZQkPvbea9k1q8dnd53NLn1glusqR4kxjbAJsy/c6FYgvrI5zLtvM9gGRXvnik45t4m0X1ubvgE/M7AiwH+hVjBhFRETKt+V3w56VJdtm3c7Q7bkiq23atIkZM2YwadIkevTowbRp01i4cCGzZ8/m8ccfp127dlx44YVMnjyZvXv30rNnTy666CJeeuklwsLCWLVqFatWraJr164Ftj958mQiIiI4cuQIPXr04JprriEyMpJDhw7Rq1cv/vGPf3D//ffz8ssv88gjj5TsNRAREREpI7m5jjeXbOHJT9dhwN+v7MCIns0IUJKr3ClOsqugn9rJzy8urE5h5QWNKDve5j3Apc65JWZ2H/AvfAmwEzs0uxW4FaBZs2YFRy4iIiLExsbSsWNHANq3b8+AAQMwMzp27EhKSgqpqanMnj2bCRMmAJCZmcnWrVtZsGABd911FwBxcXHExcUV2P7zzz/PrFmzANi2bRsbN24kMjKSatWqMWTIEAC6devGF198UdqnKiIiIlIqUnYd4v6Zq1i6eTfnt6zHk9fE0aROdX+HJYUoTrIrFWia73U0p04tPF4n1cyC8E0/3F3EsaeUm1l9oJNzbolXPh34rKCgnHOTgEkA3bt3Pzn5JiIiUr4UYwRWaQkJCcnbDggIyHsdEBBAdnY2gYGBzJw5k9atW59yrG9VgsIlJiYyd+5cFi1aRFhYGAkJCWRmZgK+xcWPHx8YGEh2dnZJnZKIiIhImcjJdbz69WYmzFlPcGAATw+N49pu0UXeI4l/FWfNrmVASzOLNbNq+Bacn31SndnAzd72UOBL55zzyq83sxAziwVaAktP0+YeoHa+RewHAslnfnoiIiJSlMGDB/PCCy/g+68bvv32WwD69evH1KlTAVizZg2rVq065dh9+/ZRt25dwsLCWLduHYsXLz6ljoiIiEhFtCntAEMnfsPfP06mzzn1+OKeC7iue1MluiqAIpNdzrls4A7gc3yJp3ecc2vN7DEz+41X7RUg0sw2AX8GHvSOXQu8A3yPb4TW7c65nMLa9Mp/D8w0s++AkcB9JXe6IiIicrKxY8eSlZVFXFwcHTp0YOzYsQD88Y9/5ODBg8TFxfH000/Ts2fPU469+OKLyc7OJi4ujrFjx9Krl5barMjMbLKZpZnZmnxlz5jZOjNbZWazzKxOvn0PmdkmM1tvZoPzlV/slW0yswfL+jxERETORnZOLv+Zv4lL/72QzbsO8dywzvzfzd1pVDvU36FJMdnxT3Ersu7du7ukpCR/hyHCtCVb/R1CkQa0HQbAvOTpJdPeT157TUqmvbPx9yW+WB6Jn86IeK3ld4KEBN/3xER/RlHlJCcn07ZtW3+HUeUUdN3NbLlzrrufQqowzKwfcBB43TnXwSsbhG/UfraZPQXgnHvAzNoBb+F7ynZjYC5wfHT+Bnwj9FPxjegf7pz7/nR9635ORETKg+Qd+7n/3VWs/mkfl3ZsxKO/6UD9miFFHyhlorj3dMVZs0tEREREqgDn3AIzizmpbE6+l4vxLVkBcAXwtnPuKLDZG+F/fPjfJufcjwBm9rZX97TJLhEREX86lp3L/yRu4j/zN1G7ejD/c0NXLu0Y5e+w5Awp2SUiIiIixTUa3wOEAJrgS34dl+qVAWw7qTy+oMb0dG0RESkPVqfu4753v2Pdzwe4snNj/np5eyJqVPN3WHIWlOwSERERkSKZ2cNANjD1eFEB1RwFrwlb4LoZerq2iIj4U2ZWDs/P28hLC34kskY1Xr6pOwPbNfR3WFIClOwSERERkdMys5uBIcAA98uCr6lA03zVooHt3nZh5SIiIuXCiq17uP/dVWxKO8i13aJ5ZEg7alcP9ndYUkKU7BIRERGRQpnZxcADwAXOucP5ds0GppnZv/AtUN8SWIpvxFdLM4sFfgKuB0aUbdQiIiIFO3g0m+e+2MArX28mqlYor43uyQWt6vs7LClhSnaJiIiICABm9haQANQzs1RgHPAQEAJ8YWYAi51ztznn1prZO/gWns8GbnfO5Xjt3AF8DgQCk51za8v8ZERERPI5lp3LW0u38vy8jWQcOsYN8c148JI21AzVaK7KSMkuERGRSmz06NF89NFHNGjQgDVr1py2bmJiItWqVeO8884ro+gK7nfixImEhYVx0003MWrUKIYMGcLQoUOLaEVKgnNueAHFr5ym/j+AfxRQ/gnwSQmGJiIickZycx0frd7BhM/Xs3X3YXq1iOCVS9rSuWkdf4cmpUjJLhERkUps1KhR3HHHHdx0001F1k1MTCQ8PLxMk13Z2dmn9HvbbbeVWf8iIiJSeX29aRdPfrqO1T/to02jmrx6Sw8SWtXHG6kslZiSXSIiIpVYv379SElJOaX8+eefZ+LEiQQFBdGuXTuefPJJJk6cSGBgIG+++SYvvPAC559/fl79jIwMhg8fTnp6Oj179uSzzz5j+fLlHDx4kCFDhuSNGpswYQIHDx5k/PjxvPzyy0yaNIljx45x7rnn8sYbbxAWFsaoUaOIiIjg22+/JSIigq+//vqEfufNm0d4eDj33nvvCTEvX76cP//5zxw8eJB69eoxZcoUoqKiSvX6iYiISMWz5qd9PPXZOr7auIsmdarzr+s6cUXnJgQGKMlVVSjZJSIiUibuBlaWcJudgefO6Mgnn3ySzZs3ExISwt69e6lTpw633XZbgUkmgEcffZS+ffvy17/+lY8//phJkyYV2cfVV1/N73//ewAeeeQRXnnlFe68804ANmzYwNy5cwkMDGT8+PEn9Dtv3rxT2srKyuLOO+/kgw8+oH79+kyfPp2HH36YyZMnn9H5i4iIlHfZObmYmRI0v8K23YeZMGc9H6zcTp2wYB65rC039mpOaHCgv0OTMqZkl4iISBUUFxfHDTfcwJVXXsmVV15ZZP0FCxbw3nvvAXDZZZdRt27dIo9Zs2YNjzzyCHv37uXgwYMMHjw4b9+1115LYGDxbzzXr1/PmjVrGDhwIAA5OTka1SUiIpXa9ZMWs2X3YYb3aMqI+OY0qh3q75DKrYyDR3lx/ibeXLyFwADjTwnncFvCOdTS4vNVlpJdIiIiZeLMRmCVlo8//pgFCxYwe/Zs/va3v7F2bdEPyytofYugoCByc3PzXmdmZuZtjxo1ivfff59OnToxZcoUEhMT8/bVqFHjV8XrnKN9+/YsWrToVx0nIiJSEW3fe4SkLXtoHhnGC/M38Z/EHxjUriEjezend4tIrTnlOXwsm1e+2sxLC37k8LFshvVoypgBrZQYFAL8HYCIiIiUrdzcXLZt20b//v15+umn80Ze1axZkwMHDhR4TL9+/Zg6dSoAn376KXv27AGgYcOGpKWlkZGRwdGjR/noo4/yjjlw4ABRUVFkZWXlHVuQ0/V7XOvWrUlPT89LdmVlZRUrQSciIlIR/XdDOgAv39Sd/97bn9/1jWXRjxmMeHkJg55dwOuLUjiQmeXfIP0oKyeXNxdv4YJnEvnnFxvoc24kc+65gCeujlOiSwAlu0RERCq14cOH07t3b9avX090dDSvvPIKOTk53HjjjXTs2JEuXbpwzz33UKdOHS6//HJmzZpF586d+eqrr05oZ9y4cSxYsICuXbsyZ84cmjVrBkBwcDB//etfiY+PZ8iQIbRp0ybvmL/97W/Ex8czcODAE8pPdrp+j6tWrRrvvvsuDzzwAJ06daJz58588803JXCFREREyp/569JoXDuUlg3CaRYZxkOXtmXxQwN4Zmgc1asF8tcP1tLr8Xk88v5qNuw8/QdGlYlzjk9W72DQswt45P01xESGMfOP5/HSyO6c2yDc3+FJOWLOOX/HcNa6d+/ukpKS/B2GCNOWbPV3CEUa0HYYAPOSp5dMez957TUpmfbOxt+X+GJ5JH46I+Kb+TmaciYhwfc93zQyKX3Jycm0bdvW32GUipiYGJKSkqhXr56/QzlFQdfdzJY757r7KSQpBt3PiYj4HMvOpctjc7iiSxMev6pjgXW+27aX1xdt4cNV2zmWnUt8bAQ39Y5hUPuGBAdWzjEti37I4MnP1vHdtr20bBDOAxe3YUDbBprSWcUU955Oa3aJiIiIiIiIlBNJKbs5dCyH/q0bFFqnU9M6/LNpHR6+rC3vJG3jzcVbuH3aChrWCmF4z2aM6NmMBrUqx3S+5B37eeqzdSSuTyeqdihPD43jmq7RekqlnJaSXSIiIvKrpaSk+DsEERGRSmn++jSqBQZw3jmRRdaNqFGN2y44h9+f34LE9Wm8vmgLz83dyItfbmJwh0bc1Ks5PWMjKuTop9Q9h/nXFxuY9e1P1AwJ4qFL2nDzeTGEBhf/ac5SdSnZJSIiIiIiIlJOJK5Pp2dsBDVCiv92PTDAGNC2IQPaNiRl1yHeXLyFd5K28fGqHbRuWJMbezfn6i5NflWb/rLn0DH+M38Try/aAga39mvBny44l9phwf4OTSqQ8v8vXURERERERKQKSN1zmI1pBxnWo+kZtxFTrwaPDGnH/xvUmtnf/cTri7Yw9v01PPXpOq7p2oSRvZtzboOaJRh1yThyLIfJX29mYuIPHDqWzdBu0dx9USsa16nu79CkAlKyS0RERERERKQcSFyfDkDCadbrKq7q1QIZ1qMZ13Vvyrfb9vLGoi28tXQbry3awnnnRHJT7+Zc1LYhQX5e0D47J5d3l6fy7NwN7Nx/lIvaNuC+wW1o3aj8JeSk4lCyS0RERERERKQcSFyfRtOI6pxTv0aJtWlmdG1Wl67N6vLwZW2Zvmwb05Zs5bY3VxBVO5QRPZtxfc9m1K8ZUmJ9Fodzjjnf7+Tpz9bxQ/ohujarwwvDu9IzNqJM45DKqXI+k1RERETIzMykZ8+edOrUifbt2zNu3LhiHZeSkkKHDh1KObpT+5w2bVre66SkJO666y4ApkyZwh133FGm8YiIiJS1zKwcvt6UQUKrBqW2oHy98BBu738uC+7vz6SR3Ti3QTj//GID5z05j7ve+paklN0450ql7/yWpezmmv/9hj+8sRyAl0Z2Y+Yfz1OiS0qMRnaJiIhUUiEhIXz55ZeEh4eTlZVF3759ueSSS+jVq9cJ9XJycggM9N+TjbKzs/OSXSNGjACge/fudO/e3W8xiYiIlLVlKbs5kpVD/zb1S72vwABjUPtGDGrfiB/SD/Lm4i28uzyV2d9tp21ULUb2as6VXRoTVq1kUwYbdh7g6c/WMTc5jQY1Q3ji6o5c2y3a71MppfJRsktERKSSMjPCw8MByMrKIisrK++T4piYGEaPHs2cOXO44447aNmyJaNHjyYsLIy+ffsW2J5zjjvvvJMvv/yS2NhYnHOMHj2aoUOHEhMTQ1JSEvXq1SMpKYl7772XxMREli5dyt13382RI0eoXr06r776Kq1bt2bKlCl8/PHHZGZmcujQIQ4fPkxycjKdO3fm5ptvpkuXLkyYMIGPPvrohBjS09O57bbb2Lp1KwDPPfccffr0KcWrKCIiUjbmr0unWlAAvVvUK9N+z6kfzrjL23Pf4Na8/+12Xl+Uwl9mreaJT5MZ2i2akb2a06J++Fn1sX3vEZ6bu4F3l6dSo1oQ9w1uzeg+sVSv5r8P26RyU7JLpApyzrFt92HSDxwl1zmcg1wcYdWCaFw7lIga1Upt6LRIlXX33bByZcm22bkzPPfcaavk5OTQrVs3Nm3axO233058fHzevtDQUBYuXAhAXFwcL7zwAhdccAH33XdfgW3NmjWL9evXs3r1anbu3Em7du0YPXr0aftv06YNCxYsICgoiLlz5/KXv/yFmTNnArBo0SJWrVpFREQEiYmJJyS3EhMTC2xvzJgxL9HEWAAAIABJREFU3HPPPfTt25etW7cyePBgkpOTTxuDiIhIRZC4IY1eLSL9lgAKqxbEiPhmDO/ZlKQte3h90RbeXLyFV79O4fyW9RjZqzkD2jYkMKD47xP2Hc7if/67iSlfp+AcjO4Ty+39z6VujWqleCYiSnaJVBk792eyYsse2kYd4Vh2Lv/73x8KrRsaHEDj2tVp3agmXZrVJTxEfypEKqrAwEBWrlzJ3r17ueqqq1izZk3eelzDhg0DYN++fezdu5cLLrgAgJEjR/Lpp5+e0taCBQsYPnw4gYGBNG7cmAsvvLDI/vft28fNN9/Mxo0bMTOysrLy9g0cOJCIiF+3NsfcuXP5/vvv817v37+fAwcOULOmntgkIiIV15aMQ/yYfoiRvZr7OxTMjB4xEfSIiSDtQFumL93G1CVbufWN5TSpU50R8c24vkdTIsMLX9A+MyuH175J4T/zN3HgaDZXdWnCnwe2IrpuWBmeiVRlegcrUonlOsfGnQf45ocMNqYdJDDA+DNQq3oQI3o2o1HtUIICDDPDgAOZ2Wzfe4Sf9h0hdfdhPl3zM5+v/Zm2UbXoERNBywbhGvElcqaKGIFV2urUqUNCQgKfffZZXrKrRg3fk56cc8X+3S6sXlBQELm5uYBvYfzjxo4dS//+/Zk1axYpKSkkJCTk7Tve/6+Rm5vLokWLqF69+q8+VkREpLxKXJ8OQELrBn6O5EQNaoZy54CW/DHhHOYm7+T1RVt45vP1/HvuRi6Li+LGXs3p2qxO3v1BTq5j5opUnv1iAzv2ZZLQuj4PXNyGtlG1/HwmUtUo2SVSSW3JOMT7K39i5/6j1AwNYmC7hvSIiaBxHd8bxA5Nap9yTK3qwTSpW50e3uud+zNZvmUPK7buYe32/TStW53L4hrTLEKfyIhUBOnp6QQHB1OnTh2OHDnC3LlzeeCBB06pV6dOHWrXrs3ChQvp27cvU6dOLbC9fv368dJLL3HTTTeRlpbG/Pnz8xaUj4mJYfny5VxyySV50xTBN7KrSZMmgO+pioWpWbMmBw4cKPKcBg0axIsvvpg31XLlypV07ty5yONERETKs8T1acREhhFb79d/EFQWggIDuLhDFBd3iGJT2gHeWLSFmSt+Yta3P9G+cS1u6t2cumHVmDBnPRt2HqRTdG3+eV0nzjunbNcfEzlOjzwQqWSOZufw4XfbmbTgR45m5XJd92juG9ya/q0b/OrpiA1rhXJpxygevKQNV3dpwt7DWUz87w9MX7aVvYePldIZiEhJ2bFjB/379ycuLo4ePXowcOBAhgwZUmDdV199ldtvv53evXsXOmrqqquuomXLlnTs2JE//vGPedMeAcaNG8eYMWM4//zzT3iy4/33389DDz1Enz59yMnJKTTWuLg4goKC6NSpE88++2yh9Z5//nmSkpKIi4ujXbt2TJw4sajLICIiUq5lZuXwzQ8Z5W5UV2HObVCTR6/owOK/DOBvV3YgKyeXB2au5tY3lpOV4/ifG7ry/u19lOgSvzLnnL9jOGvdu3d3SUlJ/g5DhGlLtvq1/x/SDzJzRSr7DmfRq0Ukg9o1JCT4xAUuB7T1rdEzL3n6r27/aFYO/92YzsKNuzCDy+Ma80DwbYAxr8mvb6+k/X2J79weiZ/OiPhmfo6mnDk+dayQRb+ldCQnJ9O2bVt/h1FqRo0axZAhQxg6dKi/QzlBQdfdzJY757r7KSQpBt3PiUhVlbg+jVGvLmPKLT0qTMIrP+ccSzfvJv3gUQa3b0RwoMbUSOkp7j2d/hWKVBJLN+/m1a83ExQQwK39WnB5p8anJLrOVkhwIIPaNeKega1oWjeM9779iTTviY4iIlLxmdlkM0szszX5yiLM7Asz2+h9r+uVm5k9b2abzGyVmXXNd8zNXv2NZnazP85FRKSiSFyfTmhwAL1aRPo7lDNiZsS3iGRIXGMluqTc0JpdIhVcrnN8vuZnvtq0i1YNwxneo1mJJ7lOVjesGqP7xrJgQzqHMrM5mp3L1rDDWstLpIo53RpcUmFNAV4EXs9X9iAwzzn3pJk96L1+ALgEaOl9xQP/C8SbWQQwDugOOGC5mc12zu0ps7MQEalA5q9Po3eLSEJL+R5epCpR2lWkAjuWnctbS7fy1aZd9GoRwcheMaWe6DouwIyE1g1oXNu3ts/LX/3Id9v2lknfIhVJZVguoCLR9T47zrkFwO6Tiq8AXvO2XwOuzFf+uvNZDNQxsyhgMPCFc263l+D6Ari49KMXEal4Nu86xJaMw/RvU/GmL4qUZ0p2iVRQWTm5vLl4C99v389lHaO4PK4xgQFW5nGEBAfSpHZ1mtatzvSkbSSuT9ObTRFPaGgoGRkZ+p0oI845MjIyCA0N9XcolU1D59wOAO/78XdkTYBt+eqlemWFlZ/CzG41syQzS0pPTy/xwEVEyrv569IASGilZJdISdI0RpEKKCfX8faybWxKP8jQrtF0bV7Xr/EEBBij+8Qyc0Uqc77fye5Dx7iicxO/JN9EypPo6GhSU1PRm/iyExoaSnR0tL/DqCoK+iPvTlN+aqFzk4BJ4FugvuRCExGpGBI3pNOifg2aRWo5EJGSpGSXSAWT6xzvLt9G8o79XN6psd8TXccFBQZwXfemRNQIYf76NA5kZjMivpkWqZQqLTg4mNjYWH+HIXK2dppZlHNuhzdNMc0rTwWa5qsXDWz3yhNOKk8sgzhFRCqUw8eyWfxjBiN7Nfd3KCKVjt6FilQgzjk+WLmd71L3MbhdQ3qXsye2mBkD2zXkis6NWb/zAG8u3sKx7Fx/hyUiImdnNnD8iYo3Ax/kK7/JeypjL2CfN83xc2CQmdX1ntw4yCsTEZF8Fv2QwbHsXBJa1/d3KCKVjpJdIhXIgg3pLEvZzQWt6nNB6/I7rz8+NpJrujZhU9pBXl+UooSXiEgFYWZvAYuA1maWama/BZ4EBprZRmCg9xrgE+BHYBPwMvAnAOfcbuBvwDLv6zGvTERE8klcn0714EB6xkb4OxSRSkfTGEUqiPU/72fO9zuJi67NoHYN/R1Okbo1jyAwwJiRlMqr32xmVO+ye1KkiIicGefc8EJ2DSigrgNuL6SdycDkEgxNRKRScc4xf30afc6tR0iQ7pFFSppGdolUALsOHGV60jYa1Q7l6i7RmFWMhd87N63LsB5N2bb7MG8s3kJWjkZ4iYiIiIj8kH6I1D1HNIVRpJQo2SVSzmVm5fDG4i0EmHFjr+ZUC6pYv7Zx0XUY2i2azbsO8dbSreTk6mFbIiIiIlK1Ja73PetDyS6R0lGx3jWLVDHOOWYsTyXj0FFG9GxG3bBq/g7pjHRuWpffdG7Mup8PMGP5NnKdEl4iIiIiUnXNX59GywbhRNcN83coIpWS1uwSKccW/5hB8o79XNYxihb1w/0dzlmJj43kaFYun639mZCgAK7s3KRUp2NOW7K11NquiAbsPwrAPD9clxHxzcq8TxEREZHy6tDRbJZu3s0tfWL9HYpIpaVkl0g5tXN/Jp+u+ZnWDWty3jmR/g6nRPRrVZ/MrBwSN6RTMzSYi9qW/4X2RURERERK0tebdpGV4zSFUaQUFWsao5ldbGbrzWyTmT1YwP4QM5vu7V9iZjH59j3kla83s8FFtWk+/zCzDWaWbGZ3nd0pilQ82Tm5TF+2jZCgAK7uWrojoMrawHYN6da8Ll+uS2NZip5ELyIiIiJVS+KGdGpUC6R78wh/hyJSaRU5ssvMAoH/AAOBVGCZmc12zn2fr9pvgT3OuXPN7HrgKWCYmbUDrgfaA42BuWbWyjumsDZHAU2BNs65XDNrUBInKlKRzPl+Jz/vz+SmXs2pGRrs73BKlJlxZecmHMjM4oOVP1ErNIjWjWr5OywRERERkVLnnCNxXRp9W9arcA+eEqlIivPb1RPY5Jz70Tl3DHgbuOKkOlcAr3nb7wIDzDcU5QrgbefcUefcZmCT197p2vwj8JhzLhfAOZd25qcnUvFsSjvIwk27iI+NoE1U5UwCBQYYw3s2o1HtUKYt3UrqnsP+DklEREREpNRt2HmQ7fsySWitMR0ipak4ya4mwLZ8r1O9sgLrOOeygX1A5GmOPV2b5+AbFZZkZp+aWcuCgjKzW706Senp6cU4DZHy72hWDjNXpFI/PIRLOkT5O5xSFRIUyM29YwgPCeK1b1LIOHjU3yGJiIiIiJSqxPW+sRxar0ukdBUn2VXQYkGumHV+bTlACJDpnOsOvAxMLigo59wk51x351z3+vX1h0IqhznJO9l/JItrujapEsOaa4YGM+q8WBww5ZsUDh7N9ndIIiIiIiKlZv76NNo0qklU7er+DkWkUivOu+lUfGtoHRcNbC+sjpkFAbWB3ac59nRtpgIzve1ZQFwxYhSp8LbuPsziHzLo1SKSZpE1/B1OmalfM4SRvZqz70gWbyxK4Vh2rr9DEhEREREpcQcys0hK2aMpjCJloDjJrmVASzOLNbNq+Bacn31SndnAzd72UOBL55zzyq/3ntYYC7QElhbR5vvAhd72BcCGMzs1kYojOzeX91akUqt6MIPaNfR3OGWueWQNhvVoSuqeI0xftpWc3JMHj4qIiIiIVGxfb9pFdq6jv6YwipS6IpNd3hpcdwCfA8nAO865tWb2mJn9xqv2ChBpZpuAPwMPeseuBd4Bvgc+A253zuUU1qbX1pPANWa2GngC+F3JnKpI+fXfDemkHTjKFZ0bExIc6O9w/KJ949oM6dSY5J8P8OGq7fjy5SIiIiIilcP8denUDAmia/O6/g5FpNILKk4l59wnwCcnlf0133YmcG0hx/4D+Edx2vTK9wKXFScukcogbX8mievTiYuuTZtGlfPpi8XVu0Uk+w5nsWBjOnWqB2uIt4jIGTKzhsDjQGPn3CVm1g7o7Zx7xc+hiYhUSc45EjekcX6regQHVv61eUX8Tb9lIn7knGP2qu1UCwxgSFxjf4dTLgxq35DOTesw5/udrNi6x9/hiIhUVFPwjaA//p/LBuBuv0UjIlLFJe84wM79R/VhrkgZUbJLxI/WbN/Pj+mHGNiuIeEhxRpoWekFmHF11yacU78G761IZePOA/4OSUSkIqrnnHsHyIW8ZSly/BuSiEjVlbghDYCEVlqvS6QsKNkl4ifHsnP5ZPUOomqH0jM2wt/hlCtBAQHcEN+chrVCmbp0K9v3HvF3SCIiFc0hM4sEHICZ9QL2+TckEZGqK3FdOu0b16JBrVB/hyJSJSjZJeIniRvS2Hcki8vjGhNg5u9wyp3Q4EBu7h1DWHAgr32Twp5Dx/wdkohIRfJnfE+6PsfMvgZeB+70b0giIlXTviNZLN+6hwQ9hVGkzCjZJeIHGQeP8tXGXXRuWoeYejX8HU65Vat6MDefF0NWbi6vfpPC4aPZ/g5JRKRCcM6tAC4AzgP+ALR3zq3yb1QiIlXTwo27yMl19Nd6XSJlRskuET/4ePUOAgOMi9s38nco5V7DWqGM7BXD3sPHeH3xFrJycv0dkohIuWdmgcClwABgEHCnmf3Zv1GJiFRN89enUbt6MJ2b1vn/7N13fFRV/v/x15n03kMICRAgCYQOoRdB7GVREcWKfW1rX3V/rl9313VXV1fXsrYVFXvBAnZdpQgC0jshoSahJIE00pM5vz8yuiyLSklyM8n7+XjMY2bu3LnzHktm5nPP+Ryno4i0Gyp2ibSwTXvK2bi7nOPT4wkP8nM6jldIiQ1hcmYyufsqeXtJLg1u63QkEZHW7iPgMiAGCDvgIiIiLcjttszdVMiY1Fh8ffTzW6SlaPk3kRbktpbP1u4iOsSfkd1jnI7jVfp2iqC8X0c+Xr2LD1bkc86gTup1JiLy05Kstf2cDiEi0t6t31VGYXmNpjCKtDCVlkVa0PLtxewpq+Hk3gk6s3MURnaPZUKveJbvKOaT1buwViO8RER+wmfGmJOcDiEi0t7N3lgAwNg0NacXaUn6tS3SQmrqG/hqwx6So4LokxjudByvdXx6PKN7xLJwy16+Wr/H6TgiIq3VIuADY0yVMabMGFNujCk7lgMaY241xqwzxqw1xrxpjAk0xqQYYxYbY7KNMW8bY/w9+wZ47ud4Hu/aBO9JRMTrzNlUSL+kCOLCApyOItKuqNgl0kLmZxdRXl3PaX07YjT97qgZYzi1TwJDukYxZ1Mhc7IKnI4kItIa/R0YAQRba8OttWHW2qM+02KM6QTcBGRaa/sAPsAU4CHgMWttKlAMXOl5ypVAsbW2B/CYZz8RkXalpLKWFTuKGacpjCItTsUukRZQVl3HvOxC+nSKoEtMiNNxvJ4xhokDOjEgOZIv1+/hm40qeImIHCQbWGubdr63LxBkjPEFgoFdwPHADM/j04GzPLcneu7jeXyC0ZkeEWln5mUX4bYwLl1TGEVamhrUi7SAf6/fg9sNJ2d0cDpKm+EyhnMHJ2GAf2/Yg9taLBaDfkuJiNBYiJpjjPkMqPlho7X20aM5mLU23xjzCLADqAK+BJYBJdbaes9ueUAnz+1OQK7nufXGmFIaV4YsOvC4xphrgGsAOnfufDTRRERarTkbC4gK9qN/UqTTUUTaHY3sEmlmBWXVLNtezPBu0cSEaq5+U3IZw6TBSWR2ieKbjQUUV9ZhUdN6ERFgK/A14A+EHXA5KsaYKBpHa6UAiUAIcOohdv3hj/Chzjz8zx9oa+3z1tpMa21mXJxGPohI2+F2W+ZuKmRsWhw+Lp2MFWlpGtkl0sy+2rAHf1+X5uo3E5cxnDWwEy6XYVZ+LW5rcVuLS7NlRKQds9b+sYkPeQKw1VpbCGCMeR8YCUQaY3w9o7uSgJ2e/fOAZCDPM+0xAtjXxJlERFqtNfml7K2oZbx+A4g4QiO7RJpR7r5K1u0sY3RqLCEBqi03F5cxTOyfSESQH2VVdbyxeAe19W6nYznKbS37a+rZV1HL7tJqauobqKlvYF9FLdV1DTRtGx8RaW2MMbONMd8cfDmGQ+4Ahhtjgj29tyYA64HZwLmefaYCMz23Z3nu43n8mybuHyYi0qrNzirAGBibplGrIk7Qr2+RZvTl+t2E+Pswunus01HaPGMMMSEB+Pq42LCrjGnzt3DJiK6EtoMiY229m+37Ktixt5KC8hqK9jde6hr+87tyZEkVAI98mQWAj8sQHxZAUlQwyVFBJEcHEx8WoJVCRdqOOw64HQhMAup/Yt9fZK1dbIyZASz3HGcF8DzwCfCWMebPnm3TPE+ZBrxqjMmhcUTXlKN9bRERbzQnq5D+SZFEh/g7HUWkXWr7vwJFHJJTsJ/NhRWc3rcjAX4+TsdpNyIC/Th7WGfeXpLLs3M3c8nwLnQID3Q6VpNyW0t+cRXrd5WxpXA/+SVVuG1jg5yoEH9iQ/3pFhtCVIg/gb4++Pu6SPgkEGth0qAkKmvr2V9Tz+7Satbkl7BkW+PMothQf/onRzIgKVL95US8nLV22UGbFhhj5h7jMe8D7jto8xZg6CH2rQYmH8vriYh4q737a1iVV8ItE9KcjiLSbqnYJdIMrLV8sW43kUF+DEuJdjpOu9M7MYKrxvjx6qLtPD0nh7MHdmJAcpTTsY6J21q2FlWwNr+UDbvKKKuux2UgKSqYMalxpMSG0CU6+CcLq0F+jX/uB3f5738O1lr2VtSypbCCVXklfLOhgK83FNAlOphx6fGkdQjVaC8RL2SMOfDDxwUMBhIciiMi0q7Myy7EWhiXrimMIk5RsUukGazbWUZ+SRXnDkrC10et8ZzQOTqY34zvwVtLdvDO0jy2FVVyer+O+HnZv4+9+2tYvqOYFTtKKKmqw8/HkNYhjIyO4fRMCCfI/9hGDRpjiA0NIDY0gKEp0ZRW1bEqt4RFW/cyfeE2kqOCmNCrA6nxKnqJeJllNK5+aGicdrgVuNLRRCIi7cScrEJiQvzp2ynC6Sgi7ZaKXSJNzG0t/96wh7iwAAZ0jnQ6TrsWHuTHlaO78dX6PczLLiS3uJJJg5JIjAxyOtrPqq5rYE1+Kct3FLN9byUG6BEfysl9EsjoGN6sBbuIID/GpsUxskcMy7eXMCergJe/20ZKbAjnDOyk6Y0iXsJam+J0BhGR9qjBbZm7qZDje8bjculEoYhTVOwSaWJr8kspKK9hypBkXBoJ4zgfl+GUPgl0iQnmgxX5PD0nh9E9Yjm+Zwf8fVvPKC+3tWwu3M+KHSWs21lKXYMlLjSAk3snMCA5koggvxbN4+tyMTQlmkFdIlmyrZiv1u/m8a+zOTGjA6N6xOq/bZFWyhhzzs89bq19v6WyiIi0R6vySiiprGNcerzTUUTaNRW7RJqQ21q+2VhAfFgAfTRsuVXp1TGcrjEhfLZ2F/Oyi1iTX8qZ/RNJ7xDm6PS8gvJqVuwoYcWOYsqq6wn0czGocxSDOkeRFBXk+NRBX5eLEd1iyOgYzqyV+Xy2djdr8kuZPDiZuDCN8hJphc78mccsoGKXiEgzmrOxAJeBsalajV3ESSp2iTShNfmlFJbXcMHQzhr50goF+ftwzqAkBiRH8uHKfF5ZuJ2kqCAm9OzQoo3YK2vrWZ3XOE0xr7gKl4HU+DBO7xdFz4SwVtlXLCLIj4uHd2FNfimzVu3k6Tk5TB6cTEZiuNPRROQA1trLnc4gItKezc4qZGDnKCKD/Z2OItKuqdgl0kQa3JZvNjSO6uqtAkCr1i0ulJsmpLJiRwmzswqYvnAbSVFBjOweS0bH8GaZ3lhRU8/G3eWs31XGpj3lNLgtCeGBnNqncZpiWGDLTlM8GsYY+iVF0jk6mNcX7+C1xds5vmd8Y08KFXdFWhVjTARwHzDWs2ku8CdrbalzqURE2rbC8hrW5Jdyx0lpTkcRafdU7BJpIh+v3knhfo3q8ha+LhdDukYzsHMkK3Y0NmJ/Z2ku/j4ueieG0z85kq4xIUdd+Kqtd5NbXMn2vRXkFOxn+95KLI0jpIalRDOocxQdIwIdn6Z4NCKD/blmbDdmrtzJNxsL2FlSxfmZyQT4HdvKkCLSpF4E1gLnee5fArwE/GxPLxEROXpzNxUCqF+XSCugYpdIE2hwW574OpsO4RrV5W1+KHoN7hLF9r2VrMwtZk1+KStyS3AZ6BAeSOfoYBIjgggL9CUkwJfQAF98fQx1DZa6Bjd1DW6KK+vYu7+Gov21FJRXs6ukmgZrMUBCRCDj0uPJ6BhOYqR3FrgO5ufjYtKgTnSKDOSTNbuYtmArl43sSrC/PlZEWonu1tpJB9z/ozFmpWNpRETagTlZBcRplodIq6BfJSJN4OPVO9lcWKFRXV7MZQwpsSGkxIZwZr9ENhdWsGNfBbn7qliZW8LirfsO6zhhgb7EhgYwqkcsXWOD6RIdQpB/2xzxZIxhRPdYIoL8eXPJDv717RYuH5VCuBdMyRRpB6qMMaOttfMBjDGjgCqHM4mItFn1DW7mbSrk5N4JbeLEpoi3U7FL5Bg1uC2Pf51Nz4QwncVpI3x9XKQnhJGeEAY0rrJZWlVHRU09+6vr2V9TT73b4u/jwtfH4O/jIjzIj5hQfwJ822Zh6+dkJIYzdURXXlu0nefnbeHKUSlEhagpq4jDrgOme3p3ARQDUx3MIyLSpq3ILaGsul5TGEVaCRW7RI7Rx6t3sqWwgmcuGkRxZZ3TcaQZuIwhKtifKK2q85N6xIdy5egUXv5uG89/u4VrxnRTwUvEAcaYDtbaPdbalUB/Y0w4gLW2zOFoIiJt2pysAnxchtGpsU5HERGg9a1vL+JFDhzVdXLvBKfjiDgqOTqYq8akUFvvZtqCrZRXq/gr4oBVxpivjDFXGGPCrbVlKnSJiDS/2RsLGdwlioggtXMQaQ1U7BI5Bh+tahzVdfOEVFwuzc0X6RgRxNSRXdlfXc9LC7ZRVdvgdCSR9qYT8AgwBsg2xnxojDnfGBPkcC4RkTZrT1k163eVMS49zukoIuKhYpfIUfphBUaN6hL5b52jg7l4eBcK99fw8ndbqaipdzqSSLthrW2w1n5hrb0cSAZeAs4CthpjXnc2nYhI2zQ3qxCA8erXJdJqqNglcpRmrcpnS1EFt5ygUV0iB+sRH8qUIcnkFVdx3evLqW9wOx1JpN2x1tYC64ENQBmQ4WwiEZG2aXZWAQnhgfT0LG4kIs5TsUvkKNQ3uHny6xx6JoRxUoZGdYkcSu/ECM4a0Il5mwr508frnY4j0m4YYzobY35rjFkOfAz4ABOttQMdjiYi0ubUNbiZn13EuPQ4jNEJcJHWQqsxihyFj1bvZEtRBc9ePFijukR+xpCUaGLD/PnXt1vpER/KpSO6Oh1JpE0zxnxHY9+ud4FrrLVLHY4kItKmLdteTHlNPeM0hVGkVVGxS+QI1Te4eeLrHHp1DOekjA5OxxFp9e4+tRdbiyr440fr6RITwnFpat4q0ox+B8yz1lqng4iItAdzsgrxdRlG9YhxOoqIHEDTGEWO0KxVO9lapBUYRQ6Xj8vw+JSBpMaHcuPry8kpKHc6kkibZa2dq0KXiEjLmZNVwJCu0YQF+jkdRUQOoGKXyBGob3Dz5Dc5ZHQM5+TeGtUlcrhCAnyZdtkQAvxc/PrVZezXCo0iIiLi5XaWVLFxdznje2rUukhro2KXyBGYudIzquuEVDWgFDlCnSKDePKCQWwtquCu91ajwSciIiLizeZuKgRQvy6RVkjFLpHD1DiqK5sM9eoSOWojusfw25N78snqXby0YJvTcUTaLGPMzcaYcNNomjFmuTHmJKdziYi0JbM3FtApMojU+FCno4jIQVTsEjlMM1fuZNveSm7RqC6RY3Ltcd04MaMDf/l0A0u37XM6jkhbdYW1tgw4CYgDLgcePJYDGmMijTEzjDEbjTEbjDEjjDHRxpivjDHZnusoz77GGPOEMSbHGLPaGDPo2N+SiEjrUVvvZkFOEcelx+m3gUgrpGKXyGE4cFTXiRrVJXJMjDE8Mrk/naKCuOGN5RTtr3E6kkhb9MMvr9OAl6y1qw7Lh5wjAAAgAElEQVTYdrQeBz631vYE+gMbgLuBr621qcDXnvsApwKpnss1wDPH+NoiIq3K0m37qKhtYLymMIq0SodV7DLGnGKMyfKcnbv7EI8HGGPe9jy+2BjT9YDHfufZnmWMOfkIjvmkMWb/0b0tkab1oUZ1iTSpiCA/nrloMMWVddw5Q/27RJrBMmPMlzQWu74wxoQB7qM9mDEmHBgLTAOw1tZaa0uAicB0z27TgbM8tycCr9hGi4BIY0zHo319EZHWZnZWAf4+LkZ2j3E6iogcwi8Wu4wxPsA/aTxDlwFcYIzJOGi3K4Fia20P4DHgIc9zM4ApQG/gFOBpY4zPLx3TGJMJRB7jexNpEj+M6uqdqFFdIk0pIzGc/3dqT77ZWMDL321zOo5IW3MljaOshlhrKwF/GqcyHq1uQCHwkjFmhTHmBWNMCNDBWrsLwHP9wxCHTkDuAc/P82z7L8aYa4wxS40xSwsLC48hnohIy5qTVcjQlGhCAnydjiIih3A4I7uGAjnW2i3W2lrgLRrP1h3owLN6M4AJpnH4y0TgLWttjbV2K5DjOd5PHtNTCHsYuPPY3ppI0/hgRT7b91ZyywlpGtUl0sSmjuzKhJ7x/PXTjazbWep0HBGvZ4wZ5OmPNcCzqZvnfhfgWH6R+QKDgGestQOBCv4zZfGQUQ6x7X+GcFprn7fWZlprM+Pi4o4hnohIy8ndV0l2wX7GpevvlkhrdTjFrsM5M/fjPtbaeqAUiPmZ5/7cMW8EZv1wlvCn6EygtIT6BjdPzc6hT6dwTuil+fgiTc0Yw8OT+xMZ7MdNb66gsrbe6Ugi3u7vnss/gUXA88C/gMXAE8dw3Dwgz1q72HN/Bo3Frz0/TE/0XBccsH/yAc9PAnYew+uLiLQaczY1/v4cp35dIq3W4RS7DufM3E/tc0TbjTGJwGTgyV8KpTOB0hJ+HNU1QaO6RJpLdIg/j50/gC1FFdz/8Xqn44h4NWvteGvteGA7MNjzXWkwMJDGEfZHe9zdQK4xJt2zaQKwHpgFTPVsmwrM9NyeBVzqWZVxOFD6SycyRUS8xdysApKjg+geF+J0FBH5CYcznP1wzsz9sE+eMcYXiAD2/cJzD7V9INADyPEUFoKNMTmeXmAiLaquwc2T3zSO6pqgUV0izWpUj1h+PbY7z87dzISeHThB/fFEjlVPa+2aH+5Ya9caYwb83BMOw2+A140x/sAWGnuAuYB3jDFXAjtoPGkJ8CmNzfFzgEqOrV+YiEirUV3XwIKcvUzOTNLJcJFW7HCKXUuAVGNMCpBPY8P5Cw/a54ezeguBc4FvrLXWGDMLeMMY8yiQSOPy09/TOLLrf45prV0HJPxwUGPMfhW6xCkfrMhnx75KXrg0Ux9kIi3gthPTmLupkLvfX80XnccSExrgdCQRb7bBGPMC8BqNo+ovBjYcywGttSuBzEM8NOEQ+1rghmN5PRGR1mjJtn1U1TWoX5dIK/eL0xg9PbhuBL6g8UvSO9badcaYPxljfuXZbRoQY4zJAW7D07DUU7x6h8Zh7p8DN1hrG37qmE371kSOXl2Dm6e+yaFvpwiN6hJpIf6+Lh49rz9lVfX8/sO1NP5WFpGjdDmwDrgZuIXG72IaXSUicoxmbyzE39fFiG6xTkcRkZ9xWKvyWGs/pXE4+oHb/u+A29X8Z9j6wc99AHjgcI55iH1CDyefSFP7YHnjqK5pUzWqS6Ql9eoYzq0npvHQ5xuZuXInZw08eD0UETkcnu9mj3kuIiLSROZkFTC8WwxB/j5ORxGRn3E4DepF2pW6BjdPzs6mX1IEx/fUqC6RlnbN2G4M7hLFvTPXsqu0yuk4Il7JGJNqjJlhjFlvjNnyw8XpXCIi3mz73gq2FFUwXlMYRVo9FbtEDvLB8nxy91VxywmpGtUl4gAfl+HR8/rT4LbcOWO1pjOKHJ2XgGeAemA88ArwqqOJRES83JysQgDGp+uEuEhrp2KXyAEOHNWlDzER53SJCeHuU3vybXYR7y7LczqOiDcKstZ+DRhr7XZr7R+A4x3OJCLi1eZkFdA1JpiusSFORxGRX6Bil8gB3l+ep1FdIq3ExcO6MLRrNH/+eD0FZdVOxxHxNtXGGBeQbYy50RhzNqCzOCIiR6m6roHvNu9lnE6Ii3gFFbtEPOoa3Dz5TQ79NapLpFVwuQwPTupLTb2be7Q6o8iRugUIBm4CBgMXA1MdTSQi4sUWbtlLTb2bcerXJeIVVOwS8XhvWR55xVXcckKaRnWJtBLd4kK57cQ0vlq/h49X73I6jojXsNYusdbuB4qttZdbaydZaxc5nUtExFvNzSok0M/F8G4xTkcRkcOgYpcIUFPfwBNfZzMgOVJna0RamStHp9AvKYI/zFrHvopap+OIeAVjzAhjzHpgg+d+f2PM0w7HEhHxWrOzChjZPZZAPx+no4jIYVCxSwR4c/EOdpZW89uT0zWqS6SV8fVx8bdz+1FWXccfP1rndBwRb/EP4GRgL4C1dhUw1tFEIiJeamtRBdv3VuqkuIgXUbFL2r3K2nqemr2Z4d2iGdldw5JFWqOeCeHcML4HM1fu5N/r9zgdR8QrWGtzD9rU4EgQEREvN3tjAQDj0tTXV8RbqNgl7d4rC7dTtL+GO07SqC6R1uz6cT3omRDGPR+uobSqzuk4Iq1drjFmJGCNMf7GmDvwTGkUEZEjMzurgG5xIXSOCXY6iogcJhW7pF0rq67j2bmbGZceR2bXaKfjiMjP8Pd18dCkfhSW1/DXT/WbXeQXXAvcAHQC8oABwPWOJhIR8UKVtfUs3rpPq7WLeBkVu6Rde3H+Vkoq67j9xHSno4jIYeifHMnVY7vx1pJc5mcXOR1HpNWy1hZZay+y1naw1sZbay8GLnU6l4iIt1m4eS+19W4Vu0S8jIpd0m4VV9TywrdbOaV3An2TIpyOIyKH6dYT0kiJDeHu91dTUVPvdBwRb3Kb0wFERLzNnKxCgv19GJIS5XQUETkCKnZJu/XcvC1U1NZz20lpTkcRkSMQ6OfDQ5P6kVdcxSNfZjkdR8SbqDGliMgRsNYyO6uAkd1jCfD1cTqOiBwBFbukXSoor+bl77YysX8iaR3CnI4jIkdoaEo0Fw/vzMvfbWPFjmKn44h4C+t0ABERb7K5cD95xVWMS49zOoqIHCEVu6Rdenr2ZuoaLLecoFFdIt7qrlN6khAeyN3vraG23u10HJFWwRhTbowpO8SlHEh0Op+IiDeZk1UIoGKXiBfydTqASEvLL6nijcU7mDw4ia6xIU7HEZGjFBbox5/P6sOV05fyzJzN3HxCqtORRBxnrdVwZRFpFhU19ewuq2ZPaTW7SqvZXVbNbs+1y8C9Z2SQFBXsdMwmNTurgLQOoW3ufYm0Byp2Sbvz1DfZAPxmgn4Yi3i7Cb06cEa/jjw1O5vT+iaQqmnJIiIiR8Ray76K2v8qXu0uPeh2WTXl1f+7KExksB8J4YHkF1cx6ZnvmH7FUHomhDvwLppeRU0932/dx+WjUpyOIiJHQcUuaVe2FVXwztI8LhnehU6RQU7HEZEm8Idf9WZ+ThF3vbeaGdeOxOVSD24RERGAugY3BeU1BxWvqthdVuO5rmZPWc3/tANwGYgPC6RDRCDd40IZ1SOWhIhAEsID/+s60K+xaXvW7nIufXExk59dyLSpQxiaEu3E221SC3KKqGuwmsIo4qVU7JJ25R//3oSfj+H68d2djiIiTSQ2NIB7T8/g9ndX8eqi7Uwd2dXpSCIiIs3u56YV/nBdtL8Ge9DSFAG+LjpGBNIhPJDBnaPoEBFIxx+KWBFBJIQHEhvqj6/P4bd3Tk8I473rRnLpi99zybTFPHnBQE7qndDE77hlzc4qJMTfh8wu3l+4E2mPVOySdmP9zjJmrtrJNWO7ER8W6HQcEWlC5wzqxIcr8/nb5xs5IaODRm6KiIhXq65rYHPh/h+LVocqaB1qWmFEkN+PhazeieF0CA9svB/ReJ0QHkhEkB/GNP0o6KSoYGZcO5IrXl7Cta8t44Gz+3LB0M5N/jotwVrL3KwCRqfG4u+rNd1EvJGKXdJuPPT5RsID/bj+uB5ORxGRJmaM4S9n9+Wkx+ZxzwdreOmyIc3yRV6kvTPG+ABLgXxr7RnGmBTgLSAaWA5cYq2tNcYEAK8Ag4G9wPnW2m0OxRbxCtZa1u0s4+0lucxcmU/ZAcUsl4G4sAASIoLoFhfCqB6x/ylkHXAd5O/j4DuA6BB/3rh6GNe/vpzfvb+GwvIafnN8D6/7TN60Zz87S6u5ST1+RbyWil3SLizIKWLupkLuOa0XEcF+TscRkWaQHB3MHSenc//H65m1aicTB3RyOpJIW3QzsAH4oQP1Q8Bj1tq3jDHPAlcCz3iui621PYwxUzz7ne9EYJHWrriilpkr83l7aR4bdpUR4Ovi1D4JnJiRQGJk4/TCuNCAI5pW6KRgf1/+dWkmd723mke/2kTR/hruO7M3Pl7UU3NOVgEAx6lfl4jXUrFL2jy32/LXzzbQKTKIS0Z0cTqOiDSjy0Z25aNVO/njR+sZkxpHdIi/05FE2gxjTBJwOvAAcJtpHKpxPHChZ5fpwB9oLHZN9NwGmAE8ZYwx1h7cPUikfWpwWxbkFPH20ly+WreH2gY3/ZIiuP+sPvyqfyIRQd59ctbPx8XfJ/cnLjSA5+ZtoWh/DY+dP4AAX2dHnh2u2VkF9EwIo2OE2iKIeCsVu6TN+2j1Ttbml/Hoef1/XDFGRNomH5fhoUn9OOPJb/nTR+v4x5SBTkcSaUv+AdwJhHnuxwAl1tof5lrlAT8MqewE5AJYa+uNMaWe/YsOPKAx5hrgGoDOnb2zt4/IkcjdV8m7y/KYsTSXnaXVRAb7cdHwzkwenExGYvgvH8CLGGP43Wm9iAsL4M+fbKC4YgnPXTqY8MDWXcgrr65j6bZirhrTzekoInIMVOySNq2mvoFHvsyiV8dwztKUJpF2IT0hjOvG9eCJr7OZOLAT49PjnY4k4vWMMWcABdbaZcaYcT9sPsSu9jAe+88Ga58HngfIzMzUqC8vU1vv5vut++geH6IRMD+juq6BL9bt5u0luXy3eS/GwNjUOO45PYMTMuK9ZrTT0bpqTDdiQwO4491VTHluES9fMaRVLxa1IKeIerdlvKYwing1FbukTXt90Q5y91Ux/Yq+uLyoT4CIHJsbxnfn0zW7uOf9NXx523GEBujjTuQYjQJ+ZYw5DQiksWfXP4BIY4yvZ3RXErDTs38ekAzkGWN8gQhgX8vHluZgreWTNbt4+Isstu+tBCA1PpQxqXGMSYtlWEo0wf7t+++utZa1+WW8s/Q/zeaTo4O4/cQ0Jg1OIrGdrRp81sBORIX4c91ry5j0zHe8esUwusaGOB3rkGZvLCQs0JdBXaKcjiIix6B9fwpJm1ZSWcvjX2czukcsY1NjnY4jIi0owNeHhyb15dxnF/LIF1n84Ve9nY4k4tWstb8DfgfgGdl1h7X2ImPMu8C5NK7IOBWY6XnKLM/9hZ7Hv1G/rrZh4ea9PPjZBlbllZLeIYwnLhjIntJq5mUX8vri7by4YCv+Pi4yu0Y1Fr9SY8noGN5uTjoWV9Ty4cp83l6Sy8bd5T82mz9vSDLDU2LazT+HQzkuLY43rh7OFS8vYdIz3/Hy5UPpmxThdKz/Yq1lzqYCxqTG4uclCwKIyKGp2CVt1uNfZ1NeXcc9p/fyuuWOReTYDe4SzaXDuzB94TbO7J/IYJ2hFWkOdwFvGWP+DKwApnm2TwNeNcbk0Diia4pD+aSJbNxdxkOfbWR2ViEdIwJ5+Nx+nDMo6ccV9q4e243qugaWbNvHt9lFzNtUyEOfb+ShzyEmxJ/RqbE/Fr86hLfeKWxHo8FtmZ9TxDtttNl8UxqQHMmMa0dwybTvmfL8Qp67JJPRreik9IZd5ewpq2GcWiCIeD0Vu6RN2ly4n1cXbuf8Icn06ti2mn2KyOH77Sk9+Wr9Hu56bzWf3DS6zfdFEWkJ1to5wBzP7S3A0EPsUw1MbtFg0ix2llTx6FebeG95HqEBvtx9ak8uG9n1kIv+BPr5eApacfy/03pRUFbN/Jwivs0u4tvsQmaubJzlmt4hjDGpsYxJi2NYSrTXLiC0Y28lM5blMmNZHjtLq4nyNJs/L1PfP39Ot7hQ3r9+JFNf/J7LX/6ev583gF/1T3Q6FtC4CiPAuDT16xLxdip2SZv0l082EOjnw20npjsdRUQcFBrgywPn9OXyl5bw9OzN3HpimtORRES8QmlVHU/PyeHlBduwFq4ancIN43sQGex/2MeIDw/knEFJnDMoCbfbsnF3Od9mF/JtdhGvLNrOC/O34u/rYlhKdGPxKzWOnglhrXpEfnVdA5+v3c07S9tns/mm0iE8kLd/PYKrX1nKTW+uYO/+Gi4fleJ0LOZmFdI7MZz4Njb6UKQ9UrFL2pz52UV8vbGAu07pSVxYgNNxRMRh49PjmTggkafn5HB6v46kdQhzOpKISKtVXdfAqwu389TsHMqq6zh7QCduOymNpKjgYzquy2XISAwnIzGcXx/XnaraBr7fto9vNzUWv/7y6UZgI7GhAYxNjWVMWiyjesS2ilX7rLWsyS/1NJvfSXk7bzbfVCKC/HjliqHc/NYK/vjRegrKa7jz5HTHip2lVXUs21HMdcd1d+T1RaRpqdglbUqD2/LnT9aTFBXE5aO6Oh1HRFqJ/zsjg3mbCrlzxmreu27kjz1mRESkkdttmbkqn0e+2ER+SRVj0+K465R0eic2TwPxIH8fjkuL4zjPdLHdpdU/jvqas6mQ91fkA9CrY3hj8Ss1jsyuUS065XFfRS0frsjnnaX/aTZ/Wt+OTM5MavfN5ptKoJ8PT180mN9/uJZn5mymqLyGv57TF18HmsPPzy6iwW0Zl64pjCJtgYpd0qa8tWQHG3eX888LB3lt/wcRaXoxoQHcd2Zvbnl7JdO/28YVo52fKiEi0lrM21TIg59tZP2uMvp0Cudv5/ZjVI+WbRqeEBHI5MxkJmcm43Zb1u8qY152Id9uKuLFBVt5bt4WAnxdDOsW82PxK61DaJOPAmpwW77NLuTdpXl8tb6x2Xz/pAj+fFYfzlSz+Wbh4zL85ew+xIcF8PjX2eyrqOWpCwcR5N+y3+VnZxUQEeTHgOTIFn1dEWkeKnZJm1FcUcvDX2QxLCWa0/omOB1HRFqZiQMS+XBlPo98mcWJGR1Ijj62KTkiIt5ubX4pD362kfk5RSRFBfH4lAGc2S/R8RFLLpehT6cI+nSK4PpxPaisrWfxln2Nxa/sIv78yQZgA/FhAYxJjWOsZ8pjbOjRt6/YsbeSdz3N5nd5ms1fPLwL5w1JomeCms03N2MMt56YRlxYAPfOXMtFLyzixcuGHFGPuGPhdlvmZBUyNi3OkVFlItL0VOySNuNvX2RRXl3Pnyb2adWNTUXEGcYYHji7Lyc+Opd7PlzL9MuH6G+FiLRLufsqeeTLLGau3ElUsB/3npHBxcM7t9rm6sH+vozvGc/4nvFA4wqR87OLmJddyNcb9/De8jwAeieGNxa/UmMZ3DXqF99PdV0Dn63dxTtL8li4ZS8uA2PT4rj3jAwm9FKzeSdcPLwLMSH+3PzWSs59diGvXDG0RXqird9VRtH+Gq3CKNKGqNglbcLqvBLeWrKDy0emkJ6g5tMicmidIoO48+R0/vDRet5dlsd5mclORxIRaTHFFbU8+U0Ory3ajssF14/rzrXjuhMe6F1T8xIjgzhvSDLnDUmmwW1Zt7OUb7OLmLepkBe+3cKzczcT5OfDsG7RPxa/esQ3Tnm01rI6r7HZ/KxVjc3mO0cHc8dJjc3mO0ao2bzTTu3bkchgf655ZSmTnvmOV64YSmozLy4ze2MBAMepX5dIm6Fil3g9t9vyfzPXERMSwC0npjodR0RauUtHdOXTtbu5/6P1jO4Rq1W0RKTNq6pt4MUFW3l2zmYqauuZPDiZW09MIyHC+ZUOj5WPy9AvKZJ+SZHcML4H+2vqWbxlb2PxK7uQ+z9eD0BCeCAju8ewflfZfzWbPy8zmWEp0Y5P3ZT/NqJ7DG//egRTX/qec59dyIuXZTK4S3Szvd6cTYX0S4o4pqmwItK6qNglXu/dZbmszC3h0fP6e92ZSRFpeS6X4ZFz+3PK4/O4673VvHLFUE1nFJE2qcFteW9ZHo9+tYndZdWc0CueO0/pSVozj5JxUmiALxN6dWBCrw4A5BVXMj+76MdVHpOjgvjzWX341YBEfW9s5TISw3n/upFc+uL3XPTCYp66YBAnZHRo8tcprqhlxY5ibjxeJ81F2hIVu8SrlVTW8tDnWQzpGsXZAzs5HUdEvETnmGB+d1ov7v1wLW98v4OLhnVxOpKISJOx1vLNxgIe+nwjm/bsZ0ByJI9PGcCwbjFOR2txSVHBTBnamSlDOzsdRY5CcnQwM64dweUvL+HXry3jr+f0bfIWBPOyC3FbGK8pjCJtipaaEK/21083UlpVxx9/pab0InJkLh7WmdE9Ynngkw3k7qt0Oo6ISJNYsaOY859fxJXTl1LXYHnmokF8cP3IdlnokrYhJjSAN64ezsjuMdw5YzVPz8nBWttkx5+bVUhUsB/9kiKb7Jgi4jwVu8RrLdy8l7eX5nL1mG5kJGpJaBE5MsYYHjq3Hy5juOPdVbjdTffFWUSkpW0tquD615dx9tPfsaVwP/ef1Ycvbx3LqX076oSgeL3QAF+mTR3CxAGJ/O3zLP740fom+dx2uy1zNxVyXFocPurbJtKmaBqjeKXqugbu+WANnaODuXmC5teLyNHpFBnE/52RwZ3vrWba/K1cPbab05FERI5IYXkNT3ydzZvf78Df18XNE1K5emw3QgP0NV/aFn9fF4+dN4CYkABeXLCVvRW1/H1yf/x9j378xpr8UvZW1DIuPb4Jk4pIa3BYfxmMMacYY7KMMTnGmLsP8XiAMeZtz+OLjTFdD3jsd57tWcaYk3/pmMaY1z3b1xpjXjTGqHOk/I+nZ+ewpaiCB87uQ5C/j9NxRMSLTc5M4qSMDjz8RRYbdpU5HUdE5LBU1NTz+L+zGffwbN74fgdThiYz57fjuPXENBW6pM1yuQz3ntGLu0/tyUerdnLFy0vYX1N/1MebnVWAMTA2Tf26RNqaXyx2GWN8gH8CpwIZwAXGmIyDdrsSKLbW9gAeAx7yPDcDmAL0Bk4BnjbG+PzCMV8HegJ9gSDgqmN6h9LmbNpTzjNzN3POwE6MSdUHk4gcG2MMD07qR0SwH7e8tZLquganI4mI/KS6BjevLdrOcQ/P4bF/b2JsWhxf3TqWP5/Vl/iwQKfjiTQ7YwzXHtedRyb3Z+GWvVzw/CKK9tcc1bFmZxUyIDmS6BD/Jk4pIk47nJFdQ4Eca+0Wa20t8BYw8aB9JgLTPbdnABNMY3OAicBb1toaa+1WIMdzvJ88prX2U+sBfA8kHdtblLakwW353ftrCA3w5Z7TezkdR0TaiOgQfx4+tx9Ze8p5+Issp+OIiPwPay2fr93FyY/N4/cfrqVbbAjvXz+SZy4eTLe4UKfjibS4cwcn8a9LB5NdUM65z3zHjr1HttjM3v01rM4rYVyapjCKtEWHU+zqBOQecD/Ps+2Q+1hr64FSIOZnnvuLx/RMX7wE+PxQoYwx1xhjlhpjlhYWFh7G25C24KUFW1m2vZh7z8ggJjTA6Tgi0oaMS4/n0hFdmDZ/K/Ozi5yOIyLyoyXb9jHpme+49rXl+LgML1yaydu/Hs6gzlFORxNx1PE9O/D6VcMpqarjnGe+Y93O0sN+7rzsQqyF8T01U0SkLTqcYtehlqU4eOmLn9rnSLcf6GlgnrX220OFstY+b63NtNZmxsXpD1R7kFOwn799kcUJvTpw9sCD660iIsfud6f2ontcCLe9s5K9RzklQkSkqeQUlHPV9KVMfnYh+SVVPDSpL5/dPIYTMjpohUURj8Fdophx7Qj8fQznP7eI7zYf3gmrOVmFxIb60ycxopkTiogTDqfYlQckH3A/Cdj5U/sYY3yBCGDfzzz3Z49pjLkPiANuO5w3IW1ffYOb299dRbC/D385p4++4IlIswjy9+HJCwZRUlXH7e+uapJlzUVEjtSesmrufm81Jz02j8Vb9vLbk9OZc8d4zh/SGV+fo195TqSt6hEfxnvXj6RjRCCXvbiET1bv+tn9G9yWuZsKGZsWh8ul3xUibdHhfFouAVKNMSnGGH8aG87POmifWcBUz+1zgW88PbdmAVM8qzWmAKk09uH6yWMaY64CTgYusNa6j+3tSVvx/LdbWJVbwv0T+6j5qog0q4zEcO49vRdzsgqZNn+r03FEpJ15ffF2jnt4Nu8tz2PqyK7MvXM8N4zvodWnRX5Bx4gg3r12BP2SIrjxzeW8unDbT+67MreEkso6xqerX5dIW/WL6xJba+uNMTcCXwA+wIvW2nXGmD8BS621s4BpwKvGmBwaR3RN8Tx3nTHmHWA9UA/cYK1tADjUMT0v+SywHVjoGb3zvrX2T032jsXrZO0u5x9fZXN6346c2T/R6Tgi0g5cPLwL83OKeOjzjQxNiaZ/cqTTkUSkjXO7LQ99vpHn5m3huLQ47p/Yh84xwU7HEvEqkcH+vHrlMH7z5nLunbmOwvIabj0x7X9mhczNKsBlYExqrENJRaS5/WKxCxpXSAQ+PWjb/x1wuxqY/BPPfQB44HCO6dl+WJmkfaiua+CWt1cSHuTL/Wf1cTqOiLQTxhj+Nqk/pz3xLTe+uZxPbhpDeKCf07FEpI2qrmvg9ndW8cmaXVwyvAv3nZmh6YoiRynI34dnLx7MPR+s5YlvcijcX8P9E/v81/9Ts7MKGdQ5ishgfweTikhz0qeotGoPfraRDbvK+Nu5/YgO0YeRiLSciGA/nrhgADtLqrnz3dU0zs4XEWla+ypqueiFxXy6dhe/P70Xf5rYW4UukWPk6+PiwRG1KhMAACAASURBVEl9uXF8D978PpfrX19OdV0DAIXlNazJL2VcuhY5E2nL9EkqrdbXG/bw8nfbuHxUV47v2cHpOCLSDg3uEs1dp6Tz+brd/OvbLU7HEXGMMSbZGDPbGLPBGLPOGHOzZ3u0MeYrY0y25zrKs90YY54wxuQYY1YbYwY5+w5ap61FFZzz9ALW5pfy9IWDuGpMNy3CI9JEjDHccXI6fzgzg6827OHSad9TWlXH3E2FAIxTvy6RNk1TBqVVKiir5rczVtOrYzh3n9rT6Tgi0o5dPaYbK3aU8NDnWfRLimR4txinI4k4oR643Vq73BgTBiwzxnwFXAZ8ba190BhzN3A3cBdwKo0LE6UCw4BnPNfisXTbPq5+ZSnGGN64ejiDu0Q5HUmkTbpsVAqxYQHc+vZKznt2IbFh/sSHBdA7MdzpaCLSjDSyS1odt9ty6zsrqapt4MkLBhLgq9WHRMQ5xhj+dm4/usQEc+MbK9hTVu10JJEWZ63dZa1d7rldDmwAOgETgeme3aYDZ3luTwResY0WAZHGmI4tHLvV+nj1Ti58YTGRwf58cP1IFbpEmtkZ/RJ5+fKh5BVXsiBnL8elxWkUpUgbp2KXtDpPz8lhQc5e7jszgx7xoU7HEREhLNCPZy8eTEVNPTe8vpzaerfTkUQcY4zpCgwEFgMdrLW7oLEgBvwwL6gTkHvA0/I82w4+1jXGmKXGmKWFhYXNGbtVsNby7NzN3PjGCvonRfD+dSPpEhPidCyRdmFUj1je/vUI+idHMmVoZ6fjiEgzU7FLWpW5mwr5+1ebmDggkfOHJDsdR0TkR2kdwnjo3H4s3V7MfbPWqWG9tEvGmFDgPeAWa23Zz+16iG3/8z+NtfZ5a22mtTYzLq5tN4uub3Dz+w/X8uBnGzmjX0devXIYUVp8R6RF9ekUwcwbRmk0pUg7oJ5d0mrk7qvk5rdWkN4hjL+e01dDi0Wk1flV/0Q27CrjmTmb6ZkQxtSRXZ2OJNJijDF+NBa6XrfWvu/ZvMcY09Fau8szTbHAsz0POPCsVRKws+XSti4VNfXc+MZyZmcVct247vz2pHRcLn3PERERaS4a2SWtQnVdA9e9vowGt+XZiwcT7K86rIi0Tr89KZ0TesXzp4/XMz+7yOk4Ii3CNJ6BmgZssNY+esBDs4CpnttTgZkHbL/UsyrjcKD0h+mO7c2esmrOe24h87KL+MvZfbnrlJ4qdImIiDQzFbvEcdZa7v1wLWvzy3jsvAF0jVXvChFpvVwuwz+mDKRHXCjXv76MrUUVTkcSaQmjgEuA440xKz2X04AHgRONMdnAiZ77AJ8CW4Ac4F/A9Q5kdtzG3WWc9c8FbCuqYNrUTC4cpj5BIiIiLUHDZ8RxLy3YxrvL8vjN8T04IaOD03FERH5RaIAvL0zN5FdPzefKl5fw3nUj1XtH2jRr7XwO3YcLYMIh9rfADc0aqpWbn13Eda8tIzjAh3euHUHvxAinI4mIiLQbGtkljvr3+j3c/8l6TsrowK0npDkdR0TksCVHB/PcJZnklVRx5fQlVNc1OB1JRFqJd5bkctlL39MpKogPbxilQpeIiEgLU7FLHLM2v5Sb3lpBn8QI/jFlgPpXiIjXGZoSzT/OH8CK3BJuenMFDW6t0CjSnllr+fuXWdz53mpGdI/h3WtH0DEiyOlYIiIi7Y6KXeKI3aXVXDV9KRFBfkybmqmG9CLitU7r25H/OyODL9fv4Q+z1tE4e0tE2pua+gZufXslT36Tw/mZybx42RDCAv2cjiUiItIuqcIgLa6suo4rXl5CeXUd7147kvjwQKcjiYgck8tHpbC7tJrn5m2hQ3gANx6f6nQkEWlBpZV1/Pq1pSzaso/fnpzO9eO607iApYiIiDhBxS5pUVW1DVz18lI27SnnX1MzyUgMdzqSiEiTuOuUnhSU1/DIl5sI9vflitEpTkcSkRaQu6+Sy176ntx9VTw+ZQATB3RyOpKIiEi7p2KXtJi6BjfXv76MJdv38cSUgYxPj3c6kohIk3G5DA+f24+q2gb+9PF6Av18uHBYZ6djiUgzWplbwlXTl1DXYHn1yqEM6xbjdCQRERFBPbukhTS4Lbe/s4rZWYU8cFZfzuyf6HQkEZEm5+vj4okLBjI+PY57PlzDe8vynI4kIs3ky3W7mfL8QoL8fXjvupEqdImIiLQiKnZJs3O7Lfd8sIZZq3Zy1yk9NdJBRNo0f18Xz1w8mJHdY/jtjFXMXJnvdCQRaWIvLdjKr19bRnpCOB9cP4oe8aFORxIREZEDqNglzarBbfntjNW8tSSXG8f34Lpx3Z2OJCLS7AL9fPjXpZkMTYnmlrdX8tb3O5yOJCJNoMFt+eNH6/jjR+s5KaMDb109nNjQAKdjiYiIyEFU7JJmU9fg5pa3V/Le8jxuOzGNO05OdzqSiEiLCfb35aXLhjI2NY6731/DtPlbnY4kIsegqraB615bxksLtnHl6BSevmgwQf4+TscSERGRQ1CDemkWtfVubnpzBZ+v283dp/bk2uM0oktE2p8gfx+ev3QwN7+5kvs/Xk9lTT03Ht8DY4zT0UTkCBSW13DV9CWszi/lD2dmcNkorbYqIiLSmmlklzS5suo6rnh5CZ+v2819Z2ao0CUi7VqArw9PXTiQcwZ24u9fbeL/Zq6jvsHtdCwROUw5Bfs555kFZO0p57mLB6vQJSIi4gU0skua1K7SKi5/aQk5Bft5ZHJ/zh2c5HQkERHH+fq4eGRyf+LCAnhu3hbyS6p48oKBhAToY1ikNVu0ZS/XvLIUf18f3r5mBP2TI52OJCIiIodBI7ukyazfWcbZ//yO/OIqpl8xVIUuEZEDuFyG353Wi/vP6sOcrALOe24he8qqnY4lIj/hwxX5XDJtMfHhgXxw/UgVukRERLyIil3SJL5ct5vznlsIwLvXjWBUj1iHE4mItE6XDO/CtKlD2FpUwcSnFrBiR7HTkUTkANZanvomm1veXsngLlG8d+1IkqODnY4lIiIiR0DFLjkmDW7Lw19s5JpXl9EtLoQPbhhJz4Rwp2OJiLRq43vG8+61I/D1MZz/3CJeW7Qda63TsUTavboGN3e9t5pHvtzE2QM7Mf2KoUQE+zkdS0RERI6Qil1y1PZV1HLZS9/zz9mbuWBoMu/8egQdI4KcjiUi4hV6J0bw8W9GM6J7DL//cC13vLua6roGp2OJtFs/LLDzztI8bpqQyqPn9SfA18fpWCIiInIU1BlXjsrCzXu5/Z2VFFXU8tCkvpw/pLPTkUREvE5ksD8vXjaEJ77O5vGvs1mbX8o/pgygV0eNkBVpSTtLqrji5cYFdh4+tx+TM5OdjiQiIiLHQCO75IjU1Dfwl083cOELiwjw82HGtSNU6BIROQY+LsOtJ6bx8uVD2FtRy8SnFvD8vM243ZrWKNIS1u0s5eynF5BfXMXLlw9VoUtERKQNULFLDtuGXWWeH2FbuHBoZz65aTT9krQykYhIUxiXHs8Xt4xhXHocf/l0Ixe+sIjcfZVOxxJp02ZnFXDeswvxMYYZ141kdKoW2BEREWkLVOySX1RZW89fPt3AGU/Op2h/DS9elskDZ/cl2F+zYEVEmlJMaADPXTKYhyb1ZU1eKSc9No9n526mrsHtdDSRNuf1xdu5avpSUuJC+OCGUaQnhDkdSURERJqIqhXys75av4c/zFpHfkkVU4Ykc9cpPYkK8Xc6lohIm2WM4fwhnRmdGsd9M9fx4Gcb+XBFPg+c3ZfBXaKcjifi9dxuy0NfbOS5uVsYnx7HUxcOIiRAX4lFRETaEn2yyyGt21nKg59t5NvsItI7hDHj2hFkdo12OpaISLvRKTKIF6Zm8sW63dw3cx2TnvmOswYkcvtJ6SRHBzsdT8QrVdc1cPu7q/hk9S4uGtaZP/6qN74+muggIiLS1qjYJf8lr7iSR7/cxAcr8wkP9OP3p/di6siu+OmLoIiII07uncCoHrH8c3YOL87fyqdrdnPZqK7cMK4HEcF+TscT+VnGmFOAxwEf4AVr7YNOZdlXUcs1ryxl6fZi/t9pPbl6TDeMMU7FERERkWakYpcAsGNvJc/O28yMpXlg4Jqx3bj+OP2QEhFpDUIDfLnrlJ5cMrwLj3yZxb++3cJb3+/gslEpXD6yq6aXS6tkjPEB/gmcCOQBS4wxs6y161s6y7aiCi5/eQn5JVX888JBnN6vY0tHEBERkRakYlc7t3F3Gc/O2cysVTvxdbmYNDiJG4/vQafIIKejiYjIQRIjg3j0vAFcOTqFJ77O5omvs3nh2y1cMrwLV45OIT480OmIIgcaCuRYa7cAGGPeAiYCLVrscrst1762jJLKWt68ehiDu6gtg4iISFunYlc7VFvv5ot1u3l10Xa+37qPYH8frhrTjStHp9BBP5RERFq93okRPHdJJlm7y3l6Tg7/+nYLLy7Yyil9OnLpiC5kdonS9CxpDToBuQfczwOGHbiDMeYa4BqAzp07N0sIl8vw9/P6E+LvS9fYkGZ5DREREWldVOxqR7J2l/PBinxmLMujaH8NydFB/O7UnpyXmawpMCIiXig9IYzHpwzkthPTeGXhdt5ZmstHq3bSq2M452cmcWb/RGJCA5yOKe3XoSqu9r/uWPs88DxAZmamPcT+TaJ3YkRzHVpERERaIRW72rjcfZV8umYXH6zIZ+PucnxchnFpcVw8ogvHpcbhcunMv4iIt+sSE8K9Z2Rw+0lpzFz5/9u79zirynqP458vw53hDiqChAoKSt7Da2Vpx9QKLTtqHo+dLLM00/KUdepkHSs1u6mnPGZe6pSYoScsU9EgzSuKCIKaJmQIIshNbgPD/M4fzzOyGffckNl79sz3/Xrt1177Wc9a67d/s/bae571rGct4pcP/52L75jHJX94hnfvMZQT9h/Oe8fuQJ8e/tq3kloI7FLwegSwqEyxmJmZWSfiX70dzOa6YM7Lq7h33hKmzlvCc0teB2D/kQP45of25vh9hjHEZ/nNzDqk3t27cuqEkZw6YSTPvrKa2598md89uYj7nn2V7l27cPjug/mnvXfiqLE7eHwvK4UZwBhJuwIvA6cAHytvSGZmZtYZuLGrwtXVBS8sXcPDf3uNB19YxiMvvsbqDbVUdRHvGDWQr39gL943bkdGDu5d7lDNzKyExu7Uj68c248vHTOWGQuWM3XeEu6Z9wrTbpsDwJ479uWw0YM5YvQQ3rHrIPr19N13bfuKiFpJ5wJ3A1XA9RExt8xhmZmZWSfgxq4KEhG8snoD8xatZvbCVcx8aQWz/rGS1zfUAjBiYC+OHT+Mw0YP5t17DGVAb4/DZWbW2VV1EYfsNphDdhvM144fx7OvvM7055by0N+W8etHX+KGBxcgwZgdqjlg5EAOGDmQt4/oz+gdqulW1aXc4VuFi4g7gTvLHYeZmZl1Lm7saodqN9fxyuoNzF+2lheXruVvS9fw/JI1PPPKalau2wRAF8GeO/Xjg/vuzAEjBzJh1CD33jIzsyZJYtywfowb1o/PHLk7GzZtZubfVzBjwQpmvrSCO+csZtKMdPO87lVdGLNjNWN36sduQ/vwtsG9GTW4DyMH93YvMDMzMzNr11rU2CXp/cCPSV3Qr4uISxvM7wH8AjgQeA04OSIW5HlfAc4ENgPnRcTdTa0zj+swCRgEzAROj4iNb+1ttg91dcHqDZtYuW4TK9ZtZNmajSxauZ5Fq9azaOUGFq1cz+KV63ll9QbqCu5HVN2jK7sP7cOx43di3LB+7DWsH2OH9aPaAw2bmdlb0LNbFYeNHsJho4cA6XvqxWVrmbd4NfMWrWbe4tU88PxSJs9cuNVyg/t0Z2Ru/BrWvydD+/ZgSHV6DO3bnSHVPejfqxuSb4JiZmZmZqXXbGuJpCrgv4H3ke6qM0PSlIiYV1DtTGBFRIyWdApwGXCypL1Ig5HuDewM3Ctpj7xMY+u8DPhhREySdE1e90+3x5ttrbU1tUydt4S6CDbXBRGk6QjqIl1WuLG2jpraOtZv3Mz6TemxIU+v27iZVes3sWp9atxatX4TUeSm2t2rujBsQE+G9e/JIbsPZuf+vdh5QC9GDenN6KHVDO3bw/8wmJlZm+vSRYzeoZrRO1TzoX13fqN83cZaXlq+jgXL1vH319ay4LX0/Nj85SxZvYHaujd/uXWrEoP6dKe6R1eqe3SlT36un+7Toyu9ulXRvWuXNx4H7zqIPXbsW8q3bGZmZmYdUEu6Bk0AXoiIFwEkTQImAoWNXROBi/P0b4GrlVpnJgKTIqIGmC/phbw+iq1T0jPAe9lyp56b8nrL0ti1fO1Gzr9lVovqdlG6C1bPbl3o2a2KXt2q6NW9iv69ujFiYC8G9u7OwN7d6J+fB/TuxuA+Pdh5QC8G9+lOly5uzDIzs/apd/eujN2pH2N36vemeXV1wcr1m1i2poZlr9ewdE0NS1+vYdmajSxfW8OamlrW1GxmbU0ty9eu4/UNtazdWMvamlo2bd66keySE8a7scvMzMzM3rKWNHYNB/5R8HohcHBjdfKdd1YBg3P5Iw2WHZ6ni61zMLAyImqL1N+KpLOAs/LLNZKea8F7KWYIsGwbl+1MnKfmVViO3lau9bV5nk7b7u+t5NouR4eUPjentc1qK+zzVjYVlafTL4PT2271FX9g6OieeOKJZZL+3oab6A+sasP1b0+VFCtUVryOtW1UUqxQWfGOBF4qdxAtVEl5raRYobLibetYW/SbriWNXcW6HDW8XqGxOo2VF7u9U1P131wYcS1wbbF5rSHp8Yg46K2up6NznprnHLWM89Q856h5zlHLOE9WSSJiaFuuX9K1EXFW8zXLr5JihcqK17G2jUqKFSorXklLK+W7vMLyWjGxQmXF215ibck9xRcCuxS8HgEsaqyOpK6klrzlTSzbWPkyYEBeR2PbMjMzMzNrrTvKHUArVFKsUFnxOta2UUmxQmXFu7LcAbRCJeW1kmKFyoq3XcTaksauGcAYSbtK6k4acH5KgzpTgDPy9EnAnyIicvkpknrkuyyOAR5rbJ15mWl5HeR1/m7b356ZmZmZGUREu/jx3RKVFCtUVryOtW1UUqxQcfFWyqVrFZXXSooVKive9hJrs5cx5jG4zgXuBqqA6yNirqRvAY9HxBTg58Av8wD0y0mNV+R6vyENZl8LnBMRmwGKrTNv8svAJEmXAE/mdbelt3wpZCfhPDXPOWoZ56l5zlHznKOWcZ7MzMwqm7/LzbaBUmcqMzMzMzMzMzOzyteSyxjNzMzMzCqCpDXljqG1motZ0nRJZRugWtKJkkLS2HLF0FKS/kPSXEmzJc2S1PAu8u2KpBGSfifpeUl/k/TjPMxLY/XPl9S7lDHm7Yak7xe8vlDSxaWOoyUkbc5/+7mSnpL0BUn+v3c7KMht/WNUE3WPlPT70kX3pu2HpF8WvO4qaWk5Y2pOJRxrKymv/tCbmZmZmVlTTgX+Qh6qpL2SdCjwAeCAiNgHOBr4R3mjapwkAbcB/xcRY4A9gGrg200sdj5Q8sYuoAb4sKQhZdh2a62PiP0iYm/gfcBxwDfKHFNHUZ/b+seCcgfUhLXAeEm98uv3AS+3ZgUFN84rlW061kqqaptwinrLeS2VDt/YJemjuVW/ruEZMUlfkfSCpOckHVNQ/v5c9oKkiwrKd5X0aD7zcktTZ10qlaSLJb1c0Fp/XMG8VuWrM3EOtpC0QNKcvP88nssGSZqaPztTJQ3M5ZJ0Zc7bbEkHlDf6tiHpekmvSnq6oKzVOZF0Rq7/vKQzim2rkjWSJx+TCkjaRdI0Sc/k77bP53LvT2YF1KBHgaSrJX08Ty+Q9E1JM/P3Vbs4g95UzOUkqRo4HDiT/A9YM/k9TtKzkv6Sjz+lPNs/DFgWETUAEbEsIhZJOlDSnyU9IeluScNyrNMl/UjSQ5KeljShhLECvBfYEBE35Hg3AxcAn5DUR9IVeR+dLelzks4DdgamSZpW4lhrSWNHXdBwhqS3Sbovx3mfpJGS+ufPWpdcp7ekf0jqVsqgI+JV4Czg3PydWCXpe5Jm5Hg/XfA+vpTz/ZSkS0sZZ95+xfVKhdTQ0lhOgX6Sbpc0T9I1Kn0Puz8Cx+fpU4Gb62dImpA/+0/m5z1z+ccl3SrpDuCeUgXaxLH2/mI5lLRG0rckPQocWqo4s23J6wOS9iuo96CkfdoyyA7f2AU8DXwYuL+wUNJepJ1ob+D9wE/yB7UK+G/gWGAv4NRcF+Ay4If5zMsK0o7YEf2woLX+TtjmfHUKzkFR78n7T30D80XAffmzc19+DSlnY/LjLOCnJY+0NG4kfW4KtSonkgaRzkoeDEwAvlHfoNGB3Mib8wQ+JhWqBb4YEeOAQ4Bz8vvz/mTWOssi4gDSZ+LCcgfTzp0A3BURfwWWq4kTU5J6Av8DHBsRRwBDSxRjvXuAXST9VdJPJL07N65cBZwUEQcC17N1z6k+EXEY8Nk8r5T2Bp4oLIiI1cBLwCeBXYH9cy+1X0XElcAi0u+s95Q4Vkjfr6dJ6t+g/GrgF/VxAldGxCrgKeDduc4HgbsjYlPJos0i4kXS/707kP5/WxUR7wDeAXxKqUPDsaR9/eCI2Be4vNRxVohe2nIC8vZcVjSned4E4IvA24HdSf+Xl9Ik4JR8bNoHeLRg3rPAuyJif+A/ge8UzDsUOCMi3luySBs/1jaWwz7A0xFxcET8pYRxwrbl9Trg4wCS9gB6RMTstgyywzd2RcQzEfFckVkTgUkRURMR84EXSDvSBOCFiHgxIjaS/pATJYl09uW3efmbSDtkZ9GqfJUxznJwDpo3kfSZga0/OxNJP44iIh4BBtSfbe1IIuJ+0p1qC7U2J8cAUyNieUSsAKZSvGGoYjWSp8Z0ymNSRCyOiJl5+nXgGWA43p/MWuu2/PwEMKqMcVSCU0nHUvLzqU3UHQu8mI/LUHC2vxQiYg1wIKlxfylwC/BpYDwwVdIs4GvAiILFbs7L3k/qhTKghCELKHa3MAHvAq6JiNocX0u/H9tMboj7BXBeg1mHAr/O078EjsjTtwAn5+lT8utyUX7+J+Bf877wKDCYdELoaOCGiFgH5cu3pOrcO66+5+nEXD5KqVf3z5R6dt+jLZeRlVLhZYwn5rLGcgrwWP5Ntpn0WTvizatsO7kxZRTpuHVng9n9gVuVrij4Ianxud7UMuwDjR1rG8vhZmByaUNMtjGvtwIfyCcgPkE6yd2mSn0NansyHHik4PXCXAZbX9u/kHTmezCwsv4Lp0H9juZcSf8KPE7qQbCC1uerMxmOc1AogHskBfA/EXEtsGNELIb0z7qkHXLdYrkbDiwuZcBl0tqcNFbeGfiYVITSoLD7k35Yen8y21otW5/U7dlgfk1+3kz7+T3cXMwlJ2kw6WTv+Py9XkX6np9C8VhFmeV/CKcD0yXNAc4B5kZEY5f5NGxsKuWt6ucCHykskNQP2AV4scSxtNSPgJnADU3UqY97CvDd3Jv4QOBPbRxbUZJ2I33WXyXto5+LiLsb1Hk/7SPfG4ATI2K10vhoj0iakueNAU6NiE9J+g1p3/nfcgVaoLGcHkl5P1/1pgBXAEeS/qev91/AtIg4Mf+mml4wb22JYgOaPNbeSeM53JCPd+XSqrxGxDpJU0knYv8ZaPObrnSInl2S7lW6zr7ho6mz+cW+jGMbyitOM/n6Kal75H6kBof6u650+Ly8Bc7B1g7Pl4YcS7rE6l1N1HXu3syfta35mFREHtdhMnB+PtPeaNUiZZ0mT9ap/R3YS1KPfMnVUeUOqAXaY8wnkXqHvi0iRkXELkB9r61isT4L7KYtd2g7mRKStKekMQVF+5F6wA5VGrweSd0kFfbgODmXH0G6FGtVyQJOl533zid06ofG+D6px8M9wNnKA2TnBiOA14G+JYxxK7m3y2/YejiXh9gyoPZppAG263vaPQb8GPh9Of4xlzQUuAa4OiICuBv4TO5dgqQ9JPUh5fsTyne6LMh3yUMGviNpNnAv6UTUjnne/IiYlafbU6/UxnIKMCFfJtqF9Fkr9eV2kC5P/lZEzGlQ3p8tA6t/vKQRvVljx9ojaB85LGZb8nodcCUwoxQ959rLmay3JCKO3obFFpLOmtQbQboGnkbKl5Eu/+iae3cV1q8oLc2XpJ8B9YOKtjZfnUlTuel0ImJRfn41X8s/AVgiaVjucTKMdGYNOnfuWpuThaQzJ4Xl00sQZ1lFxJL6aR+TkvxjcjJp/Jb6S7G8P5nxxp2zaiLiH7nnw2zgeeDJ8kbWuHYe86lAw4G6JwMfIzV4bBVrRKyX9FngLknLSA0dpVQNXJUvRawlXeJ+Fmlg9Stzw1xXUu+kuXmZFZIeAvqRLq0pmYgISSeSxp38Oqkjwp3AV0k9kfYAZkvaBPyMNDbWtcAfJS2O8ozbBalB7tyC1+cB10v6d9Llo/9WMO8W0uVLR5YsujyuFNCNtB/8EvhBnncdqZFoZh6mZilwQkTcpTR49uOSNrLl71Bqp5HGujswIjZJWsCWnpM1BfU2A+W4jLGYojnN8x4mHUPeThpD+/ZiK2hLEbGQ1ODa0OXATZK+QJl6HRZo7Fj7GdpBDovZlrxGxBOSVtN0z9DtJyI6xYP0I/6ggtd7kwZN7EEa/PFFUnfBrnl6V6B7rrN3XuZW4JQ8fQ3w2XK/rzbI07CC6QtIY+JsU746y8M52CoXfYC+BdMPkcYB+h5wUS6/CLg8Tx9PupuHSINtP1bu99CGuRlFGkSy/nWrcgIMIp3hGZgf84FB5X5fJciTj0lb50ek8VJ+1KDc+5MffkQA7Ftp3yWVGHMz76c6Pwv4CXBBuWNqItbpFPx/4Icf5XwAa4DPA1fl1+8h9boeVeT30YXAxeWO2Y823yeOJPWKLHss2+n97Az8FehSiu11iJ5dTclnS64itZD/QdKsiDgmIubms2fzSC3+50TuWivpXFJ3zCrg+oioP/PzZWCSn2QZ7AAACa5JREFUpEtIZ7B+XuK3UwqX57MaASwgDerJNuarU4iI2s6egwI7Arenkzp0BX4d6UzZDOA3ks4k3WHoo7n+ncBxpDOv69j6TGCHIelm0pfVEEkLSXfBu5RW5CQilkv6L2BGrvetaAeD1W5PjeTpSB+TtnI4cDowJ5+1hnTm2fuTdXqSzib1MDm/3LG0VCXG3AKfknQG6YTDk6S7M5pZE+p7eJLuZnmHpMeBWaRLg80qXr5c+9vAFyKiriTbzC1sZmZmZmZmZlZikvYFfhYRE8odi1lH0SEGqDczMzMzMzOrNLmH583A18odi1lH4p5dZmZmZmZmZmbWYbhnl5mZmZmZmZmZdRhu7DIzMzMzMzMrAUm7SJom6RlJcyV9PpcPkjRV0vP5eWAuHyvpYUk1ki5ssK4L8jqelnSzpJ7leE9m7ZEbu8zMzMzMzMxKoxb4YkSMAw4BzpG0F3ARcF9EjAHuy68BlpPu2npF4UokDc/lB0XEeNJdqE8pzVswa//c2GVm242kNW2wzumSDmpBvSMlPdygrKukJZKGNbHcjZJO2h6xmpmZmZk1JSIWR8TMPP068AwwHJgI3JSr3QSckOu8GhEzgE1FVtcV6CWpK9AbWNTG4ZtVDDd2mVlHcT8wQtKogrKjgacjYnFZIjIzMzMza0T+3bo/8CiwY/1v1vy8Q1PLRsTLpN5eLwGLgVURcU9bxmtWSdzYZWZtStJQSZMlzciPw3P5BEkPSXoyP++Zy3tJmiRptqRbgF65vCr3wnpa0hxJFxRuJyLqgFuBkwuKTyHdyhlJ+0l6JK/39vpxEBrEukDSkDx9kKTpefpiSTdJuifX+bCky3Mcd0nqlusdKOnPkp6QdHdTPcrMzMzMrPOSVA1MBs6PiNXbsPxAUm+wXYGdgT6S/mX7RmlWudzYZWZt7cfADyPiHcBHgOty+bPAuyJif+A/ge/k8s8A6yJiH+DbwIG5fD9geESMj4i3AzcU2dbN5LEKJPUAjiP9iAD4BfDlvN45wDda+T52B44n/aj4X2BajmM9cHxu8LoKOCkiDgSuz/GbmZmZmb0h/26cDPwqIm7LxW8MvZGfX21mNUcD8yNiaURsAm4DDmurmM0qTddyB2BmHd7RwF6S6l/3k9QX6A/cJGkMEEC3PP9dwJUAETFb0uxc/iKwm6SrgD8Ab+qmHREzJFXnXmLjgEciYoWk/sCAiPhzrnoTqRdYa/wxIjZJmkMaAPSuXD4HGAXsCYwHpub3WkXqUm5mZmZmBoDSD8WfA89ExA8KZk0BzgAuzc+/a2ZVLwGHSOpNOvl6FPD49o/YrDK5scvM2loX4NCIWF9YmButpkXEiXm8gukFs6PhSnKj1b7AMcA5wD8DnyiyvUmk3l3jyJcwtkItW3q8Nrx1c02Oo07Spoioj7GOdCwVMDciDm3lNs3MzMys8zgcOB2YI2lWLvsqqZHrN5LOJDVkfRRA0k6kRqx+QJ2k84G9IuJRSb8FZpJ+wz4JXFvSd2LWjrmxy8za2j3AucD3II2dFRGzSD27Xs51Pl5Q/37gNGCapPHAPnm5IcDGiJgs6W/AjY1s72bSmbD+wJkAEbFK0gpJ74yIB0g/MP5cZNkFpMsm/0i65LI1ngOGSjo0Ih7O3dP3iIi5rVyPmZmZmXVQEfEX0knSYo4qUv8VYEQj6/oGrR+aw6xT8JhdZrY99Za0sODxBeA84KA8MPw84Oxc93Lgu5IeJF3yV++nQHW+fPFLwGO5fDgwPZ8BuxH4SrEAImIesA74U0SsLZh1BvC9vN79gG8VWfybwI8lPQBsbs0bj4iNwEnAZZKeAmbhcRPMzMzMzMxKTluuxDEzMzMzMzMzM6ts7tllZmZmZmZmZmYdhhu7zMzMzMzMzMysw3Bjl5mZmZmZmZmZdRhu7DIzMzMzMzMzsw7DjV1mZmZmZmadhKTzJD0jaYWki1qx3ChJH2vL2Aq2daSkbb6rtaSvtrL+xZIubKbOCZL22taYzKy03NhlZmZmZmbWeXwWOC4iBkbEpQ1nSurayHKjgJI0dgFHAtvc2AW0qrGrhU4A3NhlViHc2GVmZmZmZtYJSLoG2A2YIukCSVfn8hsl/UDSNOAySe+WNCs/npTUF7gUeGcuu6CR9VdJukLSHEmzJX0ulx+V1zNH0vWSeuTyBZK+KWlmnjdW0ijgbOCCvK13SvqgpEfzOu6VtGNevlrSDQXb+4ikS4FeedlfNZGL/5D0nKR7gT0Lyj8laYakpyRNltQ79zL7EPC9vN7d8+MuSU9IekDS2Lf45zGz7UgRUe4YzMzMzMzMrAQkLQAOAj4AHBQR50q6ERgCTIyIzZLuAC6NiAclVQMbgCOACyPiA02s+zPA0cDJEVEraRCwDngeOCoi/irpF8DMiPhRjuX7EXGVpM8CB0TEJyVdDKyJiCvyegcCKyMiJH0SGBcRX5R0GdAjIs6vrxcRKyStiYjqJuI8ELgROBjoCswEromIKyQNjojXcr1LgCU5vhuB30fEb/O8+4CzI+J5SQcD342I97b8L2FmbamxLqpmZmZmZmbWedwaEZvz9IPAD3LPqNsiYqGklqzjaFKjUS1ARCyXtC8wPyL+muvcBJwD/Ci/vi0/PwF8uJH1jgBukTQM6A7ML9jeKfWVImJFS4IE3gncHhHrACRNKZg3PjdyDQCqgbsbLpwbAA8Dbi3IS48WbtvMSsCXMZqZmZmZmdna+ok8ltcngV7AI624RE9Aw0uHmmslq8nPm2m8M8ZVwNUR8Xbg00DPJrbXUo0tdyNwbt7WNwu2VagLqafZfgWPcdsYh5m1ATd2mZmZmZmZ2Rsk7R4RcyLiMuBxYCzwOtC3mUXvAc6uH+Q+X8b4LDBK0uhc53Tgz82sp+G2+gMv5+kzGmzv3IK4B+bJTZK6NbH++4ETJfXK45F9sGBeX2BxXv60YjFFxGpgvqSP5u0q92Azs3bCjV1mZmZmZmZW6HxJT0t6ClgP/BGYDdTmgduLDlAPXAe8BMzOy34sIjYA/0a65G8OUAdc08z27yA1Rs2S9E7g4rz8A8CygnqXAAMLYn1PLr82x1B0gPqImAncAswCJgMPFMz+OvAoMJXUUFdvEvDveZD83UkNYWfm7c4FJjbznsyshDxAvZmZmZmZmZmZdRju2WVmZmZmZmZmZh2G78ZoZmZmZmZmLSbpGOCyBsXzI+LEcsTTGEmDgfuKzDoqIl4rdTxmVjq+jNHMzMzMzMzMzDoMX8ZoZmZmZmZmZmYdhhu7zMzMzMzMzMysw3Bjl5mZmZmZmZmZdRhu7DIzMzMzMzMzsw7j/wF8h7kbFVjlaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "fig=plt.figure(figsize=(20,8))\n",
    "\n",
    "plt.subplots_adjust(left=0.1, right=0.9, bottom=-0.5, top=0.9)\n",
    "\n",
    "ax1=fig.add_subplot(3,2,1)\n",
    "ax2=fig.add_subplot(3,2,2)\n",
    "ax3=fig.add_subplot(3,2,3)\n",
    "ax4=fig.add_subplot(3,2,4)\n",
    "ax5=fig.add_subplot(3,2,5)\n",
    "ax6=fig.add_subplot(3,2,6)\n",
    "\n",
    "plot_distribution_subplot(monthly_conv_rate.Yes, None, 'Conversion Rate',ax1)\n",
    "monthly_conv_rate.Yes.plot(ax=ax2).set_ylabel('Conversion Rate')\n",
    "plot_distribution_subplot(monthly_conv_vol.Yes, None, 'Conversions Volume',ax3)\n",
    "monthly_conv_vol.Yes.plot(ax=ax4).set_ylabel('Conversion Volume')\n",
    "plot_distribution_subplot(monthly_leads_vol.leads_vol, None, 'Leads Volume',ax5)\n",
    "monthly_leads_vol.plot(ax=ax6).set_ylabel('Leads Volume')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite a drop in leads volume and conversions volume happened in December 2017, the conversion rate grew steadily. Although less leads reached out to the company, the company incresed its effevtivness in converting leads.\n",
    "\n",
    "The distributions of the three variables are very similar, rightly skewed and two pikes, this suggests that they might be correlated.\n",
    "\n",
    "The most interesting relation to look into is between conversion rate and leads volume. The relation between leads volume and conversions volume, to parity of conversion rate, is quite obvious. \n",
    "\n",
    "This does not mean that the volume of leads directly affects the conversion rate. \n",
    "\n",
    "The factors that influnce the conversion are the willingness of the lead to become customer and the performance of the SDR/SR. Having a large pool of leads certaninly helps in increasing conversions but the conversion rate is mostly influenced by the performance of the sales team and the quality of the product.\n",
    "\n",
    "The overall increase of the conversion rate could have been driven by:\n",
    "\n",
    "- Improved quality of product. As the number of customers increased the company could have collected more feedback and translated it into a better product.\n",
    "- Increased pool of leads led the sales team to have more insights on leads behaviour, translating them into more effective negotiations.\n",
    "- Sales team became more structured."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Evolution of leads generated across channels MoM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>mql_id</th>\n",
       "      <th>first_contact_date</th>\n",
       "      <th>origin</th>\n",
       "      <th>converted</th>\n",
       "      <th>cohort_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dac32acd4db4c29c230538b72f8dd87d</td>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>social</td>\n",
       "      <td>No</td>\n",
       "      <td>2018-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8c18d1de7f67e60dbd64e3c07d7e9d5d</td>\n",
       "      <td>2017-10-20</td>\n",
       "      <td>paid_search</td>\n",
       "      <td>No</td>\n",
       "      <td>2017-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b4bc852d233dfefc5131f593b538befa</td>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>No</td>\n",
       "      <td>2018-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6be030b81c75970747525b843c1ef4f8</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>email</td>\n",
       "      <td>No</td>\n",
       "      <td>2018-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5420aad7fec3549a85876ba1c529bd84</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2018-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mql_id first_contact_date          origin  \\\n",
       "0  dac32acd4db4c29c230538b72f8dd87d         2018-02-01          social   \n",
       "1  8c18d1de7f67e60dbd64e3c07d7e9d5d         2017-10-20     paid_search   \n",
       "2  b4bc852d233dfefc5131f593b538befa         2018-03-22  organic_search   \n",
       "3  6be030b81c75970747525b843c1ef4f8         2018-01-22           email   \n",
       "4  5420aad7fec3549a85876ba1c529bd84         2018-02-21  organic_search   \n",
       "\n",
       "  converted cohort_group  \n",
       "0        No      2018-02  \n",
       "1        No      2017-10  \n",
       "2        No      2018-03  \n",
       "3        No      2018-01  \n",
       "4       Yes      2018-02  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Create cohorts based on first contact date to then aggregate data\n",
    "#set index mql_id to \n",
    "funnel.set_index('mql_id', inplace=True)\n",
    "#create cohort label based in first contact date\n",
    "funnel['cohort_group'] = funnel.groupby(level=0)['first_contact_date'].min().apply(lambda x: x.strftime('%Y-%m'))\n",
    "funnel.reset_index(inplace=True)\n",
    "funnel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d0ec7bf6d8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,6))\n",
    "sns.heatmap(pd.crosstab(funnel.origin, funnel.cohort_group), cmap='Greens', annot=True, fmt='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Evolution of conversion rate across channel MoM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_convrate_ev=funnel.groupby(['cohort_group','origin'])['converted'].value_counts(normalize=True).unstack().Yes.unstack().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d0ebedb8d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,6))\n",
    "sns.heatmap(channel_convrate_ev*100, cmap='Greens', annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Which channel is the most effective in generating leads and converting users?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get conversions volume by channel\n",
    "conv_vol_channel=pd.crosstab(funnel.origin, funnel.converted).Yes.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get conversion rate by channel\n",
    "conv_rate_channel=(pd.crosstab(funnel.origin, funnel.converted, normalize='index').Yes.sort_values(ascending=False))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get leads volume by channel\n",
    "leads_vol_channel=funnel['origin'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>leads_vol</th>\n",
       "      <th>conversion_rate</th>\n",
       "      <th>conv_vol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>organic_search</th>\n",
       "      <td>2296</td>\n",
       "      <td>11.803136</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paid_search</th>\n",
       "      <td>1586</td>\n",
       "      <td>12.295082</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>social</th>\n",
       "      <td>1350</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>1159</td>\n",
       "      <td>16.652286</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direct_traffic</th>\n",
       "      <td>499</td>\n",
       "      <td>11.222445</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email</th>\n",
       "      <td>493</td>\n",
       "      <td>3.042596</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>referral</th>\n",
       "      <td>284</td>\n",
       "      <td>8.450704</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>150</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>display</th>\n",
       "      <td>118</td>\n",
       "      <td>5.084746</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other_publicities</th>\n",
       "      <td>65</td>\n",
       "      <td>4.615385</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   leads_vol  conversion_rate  conv_vol\n",
       "organic_search          2296        11.803136       271\n",
       "paid_search             1586        12.295082       195\n",
       "social                  1350         5.555556        75\n",
       "unknown                 1159        16.652286       193\n",
       "direct_traffic           499        11.222445        56\n",
       "email                    493         3.042596        15\n",
       "referral                 284         8.450704        24\n",
       "other                    150         2.666667         4\n",
       "display                  118         5.084746         6\n",
       "other_publicities         65         4.615385         3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_df=leads_vol_channel.rename('leads_vol').to_frame().merge(conv_rate_channel.rename('conversion_rate').to_frame(),right_index=True, left_index=True).merge(conv_vol_channel.rename('conv_vol').to_frame(),right_index=True, left_index=True)\n",
    "channel_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>leads_vol</th>\n",
       "      <th>conversion_rate</th>\n",
       "      <th>conv_vol</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>organic_search</th>\n",
       "      <td>2296</td>\n",
       "      <td>11.803136</td>\n",
       "      <td>271</td>\n",
       "      <td>organic_search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paid_search</th>\n",
       "      <td>1586</td>\n",
       "      <td>12.295082</td>\n",
       "      <td>195</td>\n",
       "      <td>paid_search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>social</th>\n",
       "      <td>1350</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>75</td>\n",
       "      <td>social</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>1159</td>\n",
       "      <td>16.652286</td>\n",
       "      <td>193</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direct_traffic</th>\n",
       "      <td>499</td>\n",
       "      <td>11.222445</td>\n",
       "      <td>56</td>\n",
       "      <td>direct_traffic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email</th>\n",
       "      <td>493</td>\n",
       "      <td>3.042596</td>\n",
       "      <td>15</td>\n",
       "      <td>email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>referral</th>\n",
       "      <td>284</td>\n",
       "      <td>8.450704</td>\n",
       "      <td>24</td>\n",
       "      <td>referral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>150</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>4</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>display</th>\n",
       "      <td>118</td>\n",
       "      <td>5.084746</td>\n",
       "      <td>6</td>\n",
       "      <td>display</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other_publicities</th>\n",
       "      <td>65</td>\n",
       "      <td>4.615385</td>\n",
       "      <td>3</td>\n",
       "      <td>other_publicities</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   leads_vol  conversion_rate  conv_vol            channel\n",
       "organic_search          2296        11.803136       271     organic_search\n",
       "paid_search             1586        12.295082       195        paid_search\n",
       "social                  1350         5.555556        75             social\n",
       "unknown                 1159        16.652286       193            unknown\n",
       "direct_traffic           499        11.222445        56     direct_traffic\n",
       "email                    493         3.042596        15              email\n",
       "referral                 284         8.450704        24           referral\n",
       "other                    150         2.666667         4              other\n",
       "display                  118         5.084746         6            display\n",
       "other_publicities         65         4.615385         3  other_publicities"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create col 'channel' for legend\n",
    "channel_df['channel']=channel_df.index\n",
    "channel_df.sort_values(by='conv_vol', ascending=False)\n",
    "channel_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1d0ec1d7c18>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.figure(figsize=(16,6))\n",
    "plot=sns.scatterplot(x=channel_df.leads_vol, y=channel_df.conversion_rate, size=channel_df.conv_vol/channel_df.conv_vol.sum(), hue=channel_df.channel, legend=False)\n",
    "\n",
    "plt.title('Which channel is the most effective in generating leads and converting users?')\n",
    "plt.ylabel('Conversion Rate')\n",
    "plt.xlabel('Leads Volume')\n",
    "\n",
    "for line in range(0,channel_df.shape[0]):\n",
    "     plot.text(channel_df.leads_vol[line]+0.2, channel_df.conversion_rate[line]-0.2, \n",
    "               channel_df.channel[line], \n",
    "               horizontalalignment='center', verticalalignment='top', color='black')\n",
    "        \n",
    "plt.axhline(channel_df.conversion_rate.median(), color='orange', alpha=0.5)\n",
    "plt.axvline(channel_df.leads_vol.median(), color='orange', alpha=0.5)\n",
    "\n",
    "plt.axhline(channel_df.conversion_rate.quantile(0.9), color='green', alpha=0.5)\n",
    "plt.axvline(channel_df.leads_vol.quantile(0.9), color='green', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The 10.5% of the users converted over the period under examination. The monthly average conversion rate is 6.98%, the median is 4.77%. The conversion rate consistently grew until February 2018, the overall percent change of the conversion rate is 1092.24% during the period under observation.\n",
    "- Despite a drop in leads volume and conversions volume happened in December 2017, the conversion rate grew steadily. Although less leads reached out to the company, the company incresed its effevtivness in converting leads.\n",
    "- The most effective channel in converting users (ignoring unkown) is paid search (12.3% conversion rate), the second one is organic search (11.8%) and the third is direct traffic (11.22%).\n",
    "- Despite being slightly less effective than paid search, organic search converted more users, 271 versus 195, and attracted more leads, 2296 versus 1586.\n",
    "- Social positions as third most effective channel to attract leads but presents quite a low conversion rate (5.55%). \n",
    "- Direct traffic is quite effective in converting users, but the ability to attract leads and the volume of conversions is not as attractive as paid and organic search.\n",
    "- The monthly conversion rate is strongly correlated with the monthly leads volume. The correlation between conversion rate and conversions volume weakens if the analysis is performed across channels instead of comparing monthly metrics. In general conversion rate could depend on the volume of leads that reached out to the company, but when the data is grouped by channel, the idiosyncracities of each channel take over influencing the relation between the conversion rate and the leads volume.\n",
    "- The factors that influnce the conversion are the willingness of the lead to become customer and the performance of the SDR/SR. Having a large pool of leads certaninly helps in increasing conversions but the conversion rate is mostly influenced by the performance of the sales team and the quality of the product. The overall increase of the conversion rate could have been driven by:\n",
    "     - Improved quality of product. As the number of customers increased the company could have collected more feedback and translated it into a better product.\n",
    "     - Increased pool of leads led the sales team to have more insights on leads behaviour, translating them into more effective negotiations.\n",
    "     - Sales team became more structured.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Limitations\n",
    "\n",
    "- There is no complete view in the overall marketing funnel, for instance additional data that could have produce a more extensive analyisis would be:\n",
    "    - Amount of impressions for paid search, social media and ads displayed on websites.\n",
    "    - Amount of emails sent to potential leads.\n",
    "- Important economic data are not included such as:\n",
    "    - Customer acquisition cost across channels.\n",
    "    - Budget for SEO."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Closed Deals Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to datetime\n",
    "closed_deals['won_date']=pd.to_datetime(closed_deals['won_date'])\n",
    "closed_deals['first_contact_date']=pd.to_datetime(closed_deals['first_contact_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create variable deal_lenght\n",
    "closed_deals['deal_lenght']=(closed_deals.won_date-closed_deals.first_contact_date)/np.timedelta64(1,'D')\n",
    "closed_deals['deal_lenght']=closed_deals['deal_lenght'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>mql_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>sdr_id</th>\n",
       "      <th>sr_id</th>\n",
       "      <th>won_date</th>\n",
       "      <th>business_segment</th>\n",
       "      <th>lead_type</th>\n",
       "      <th>lead_behaviour_profile</th>\n",
       "      <th>has_company</th>\n",
       "      <th>has_gtin</th>\n",
       "      <th>average_stock</th>\n",
       "      <th>business_type</th>\n",
       "      <th>declared_product_catalog_size</th>\n",
       "      <th>declared_monthly_revenue</th>\n",
       "      <th>first_contact_date</th>\n",
       "      <th>landing_page_id</th>\n",
       "      <th>origin</th>\n",
       "      <th>deal_lenght</th>\n",
       "      <th>cohort_group_first_contact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5420aad7fec3549a85876ba1c529bd84</td>\n",
       "      <td>2c43fb513632d29b3b58df74816f1b06</td>\n",
       "      <td>a8387c01a09e99ce014107505b92388c</td>\n",
       "      <td>4ef15afb4b2723d8f3d81e51ec7afefe</td>\n",
       "      <td>2018-02-26 19:58:54</td>\n",
       "      <td>pet</td>\n",
       "      <td>online_medium</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>b48ec5f3b04e9068441002a19df93c6c</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a555fb36b9368110ede0f043dfc3b9a0</td>\n",
       "      <td>bbb7d7893a450660432ea6652310ebb7</td>\n",
       "      <td>09285259593c61296eef10c734121d5b</td>\n",
       "      <td>d3d1e91a157ea7f90548eef82f1955e3</td>\n",
       "      <td>2018-05-08 20:17:59</td>\n",
       "      <td>car_accessories</td>\n",
       "      <td>industry</td>\n",
       "      <td>eagle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-04-04</td>\n",
       "      <td>2f08471dfb83baef522f952438a36f6e</td>\n",
       "      <td>referral</td>\n",
       "      <td>34</td>\n",
       "      <td>2018-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>327174d3648a2d047e8940d7d15204ca</td>\n",
       "      <td>612170e34b97004b3ba37eae81836b4c</td>\n",
       "      <td>b90f87164b5f8c2cfa5c8572834dbe3f</td>\n",
       "      <td>6565aa9ce3178a5caf6171827af3a9ba</td>\n",
       "      <td>2018-06-05 17:27:23</td>\n",
       "      <td>home_appliances</td>\n",
       "      <td>online_big</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>1ceb590cd1e00c7ee95220971f82693d</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>63</td>\n",
       "      <td>2018-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f5fee8f7da74f4887f5bcae2bafb6dd6</td>\n",
       "      <td>21e1781e36faf92725dde4730a88ca0f</td>\n",
       "      <td>56bf83c4bb35763a51c2baab501b4c67</td>\n",
       "      <td>d3d1e91a157ea7f90548eef82f1955e3</td>\n",
       "      <td>2018-01-17 13:51:03</td>\n",
       "      <td>food_drink</td>\n",
       "      <td>online_small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-01-14</td>\n",
       "      <td>22c29808c4f815213303f8933030604c</td>\n",
       "      <td>paid_search</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ffe640179b554e295c167a2f6be528e0</td>\n",
       "      <td>ed8cb7b190ceb6067227478e48cf8dde</td>\n",
       "      <td>4b339f9567d060bcea4f5136b9f5949e</td>\n",
       "      <td>d3d1e91a157ea7f90548eef82f1955e3</td>\n",
       "      <td>2018-07-03 20:17:45</td>\n",
       "      <td>home_appliances</td>\n",
       "      <td>industry</td>\n",
       "      <td>wolf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>manufacturer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10-09</td>\n",
       "      <td>a0fbc0263cb98bdb0d72b9f9f283a172</td>\n",
       "      <td>unknown</td>\n",
       "      <td>267</td>\n",
       "      <td>2017-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mql_id                         seller_id  \\\n",
       "0  5420aad7fec3549a85876ba1c529bd84  2c43fb513632d29b3b58df74816f1b06   \n",
       "1  a555fb36b9368110ede0f043dfc3b9a0  bbb7d7893a450660432ea6652310ebb7   \n",
       "2  327174d3648a2d047e8940d7d15204ca  612170e34b97004b3ba37eae81836b4c   \n",
       "3  f5fee8f7da74f4887f5bcae2bafb6dd6  21e1781e36faf92725dde4730a88ca0f   \n",
       "4  ffe640179b554e295c167a2f6be528e0  ed8cb7b190ceb6067227478e48cf8dde   \n",
       "\n",
       "                             sdr_id                             sr_id  \\\n",
       "0  a8387c01a09e99ce014107505b92388c  4ef15afb4b2723d8f3d81e51ec7afefe   \n",
       "1  09285259593c61296eef10c734121d5b  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "2  b90f87164b5f8c2cfa5c8572834dbe3f  6565aa9ce3178a5caf6171827af3a9ba   \n",
       "3  56bf83c4bb35763a51c2baab501b4c67  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "4  4b339f9567d060bcea4f5136b9f5949e  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "\n",
       "             won_date business_segment      lead_type lead_behaviour_profile  \\\n",
       "0 2018-02-26 19:58:54              pet  online_medium                    cat   \n",
       "1 2018-05-08 20:17:59  car_accessories       industry                  eagle   \n",
       "2 2018-06-05 17:27:23  home_appliances     online_big                    cat   \n",
       "3 2018-01-17 13:51:03       food_drink   online_small                    NaN   \n",
       "4 2018-07-03 20:17:45  home_appliances       industry                   wolf   \n",
       "\n",
       "  has_company has_gtin average_stock business_type  \\\n",
       "0         NaN      NaN           NaN      reseller   \n",
       "1         NaN      NaN           NaN      reseller   \n",
       "2         NaN      NaN           NaN      reseller   \n",
       "3         NaN      NaN           NaN      reseller   \n",
       "4         NaN      NaN           NaN  manufacturer   \n",
       "\n",
       "   declared_product_catalog_size  declared_monthly_revenue first_contact_date  \\\n",
       "0                            NaN                       0.0         2018-02-21   \n",
       "1                            NaN                       0.0         2018-04-04   \n",
       "2                            NaN                       0.0         2018-04-03   \n",
       "3                            NaN                       0.0         2018-01-14   \n",
       "4                            NaN                       0.0         2017-10-09   \n",
       "\n",
       "                    landing_page_id          origin  deal_lenght  \\\n",
       "0  b48ec5f3b04e9068441002a19df93c6c  organic_search            5   \n",
       "1  2f08471dfb83baef522f952438a36f6e        referral           34   \n",
       "2  1ceb590cd1e00c7ee95220971f82693d  organic_search           63   \n",
       "3  22c29808c4f815213303f8933030604c     paid_search            3   \n",
       "4  a0fbc0263cb98bdb0d72b9f9f283a172         unknown          267   \n",
       "\n",
       "  cohort_group_first_contact  \n",
       "0                    2018-02  \n",
       "1                    2018-04  \n",
       "2                    2018-04  \n",
       "3                    2018-01  \n",
       "4                    2017-10  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# COHORTS ON first_contact_data\n",
    "#set index mql_id to \n",
    "closed_deals.set_index('mql_id', inplace=True)\n",
    "#create cohort label based in first contact date\n",
    "closed_deals['cohort_group_first_contact'] = closed_deals.groupby(level=0)['first_contact_date'].min().apply(lambda x: x.strftime('%Y-%m'))\n",
    "closed_deals.reset_index(inplace=True)\n",
    "closed_deals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>mql_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>sdr_id</th>\n",
       "      <th>sr_id</th>\n",
       "      <th>won_date</th>\n",
       "      <th>business_segment</th>\n",
       "      <th>lead_type</th>\n",
       "      <th>lead_behaviour_profile</th>\n",
       "      <th>has_company</th>\n",
       "      <th>has_gtin</th>\n",
       "      <th>average_stock</th>\n",
       "      <th>business_type</th>\n",
       "      <th>declared_product_catalog_size</th>\n",
       "      <th>declared_monthly_revenue</th>\n",
       "      <th>first_contact_date</th>\n",
       "      <th>landing_page_id</th>\n",
       "      <th>origin</th>\n",
       "      <th>deal_lenght</th>\n",
       "      <th>cohort_group_first_contact</th>\n",
       "      <th>cohort_group_won_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5420aad7fec3549a85876ba1c529bd84</td>\n",
       "      <td>2c43fb513632d29b3b58df74816f1b06</td>\n",
       "      <td>a8387c01a09e99ce014107505b92388c</td>\n",
       "      <td>4ef15afb4b2723d8f3d81e51ec7afefe</td>\n",
       "      <td>2018-02-26 19:58:54</td>\n",
       "      <td>pet</td>\n",
       "      <td>online_medium</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>b48ec5f3b04e9068441002a19df93c6c</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-02</td>\n",
       "      <td>2018-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a555fb36b9368110ede0f043dfc3b9a0</td>\n",
       "      <td>bbb7d7893a450660432ea6652310ebb7</td>\n",
       "      <td>09285259593c61296eef10c734121d5b</td>\n",
       "      <td>d3d1e91a157ea7f90548eef82f1955e3</td>\n",
       "      <td>2018-05-08 20:17:59</td>\n",
       "      <td>car_accessories</td>\n",
       "      <td>industry</td>\n",
       "      <td>eagle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-04-04</td>\n",
       "      <td>2f08471dfb83baef522f952438a36f6e</td>\n",
       "      <td>referral</td>\n",
       "      <td>34</td>\n",
       "      <td>2018-04</td>\n",
       "      <td>2018-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>327174d3648a2d047e8940d7d15204ca</td>\n",
       "      <td>612170e34b97004b3ba37eae81836b4c</td>\n",
       "      <td>b90f87164b5f8c2cfa5c8572834dbe3f</td>\n",
       "      <td>6565aa9ce3178a5caf6171827af3a9ba</td>\n",
       "      <td>2018-06-05 17:27:23</td>\n",
       "      <td>home_appliances</td>\n",
       "      <td>online_big</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>1ceb590cd1e00c7ee95220971f82693d</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>63</td>\n",
       "      <td>2018-04</td>\n",
       "      <td>2018-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f5fee8f7da74f4887f5bcae2bafb6dd6</td>\n",
       "      <td>21e1781e36faf92725dde4730a88ca0f</td>\n",
       "      <td>56bf83c4bb35763a51c2baab501b4c67</td>\n",
       "      <td>d3d1e91a157ea7f90548eef82f1955e3</td>\n",
       "      <td>2018-01-17 13:51:03</td>\n",
       "      <td>food_drink</td>\n",
       "      <td>online_small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-01-14</td>\n",
       "      <td>22c29808c4f815213303f8933030604c</td>\n",
       "      <td>paid_search</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-01</td>\n",
       "      <td>2018-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ffe640179b554e295c167a2f6be528e0</td>\n",
       "      <td>ed8cb7b190ceb6067227478e48cf8dde</td>\n",
       "      <td>4b339f9567d060bcea4f5136b9f5949e</td>\n",
       "      <td>d3d1e91a157ea7f90548eef82f1955e3</td>\n",
       "      <td>2018-07-03 20:17:45</td>\n",
       "      <td>home_appliances</td>\n",
       "      <td>industry</td>\n",
       "      <td>wolf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>manufacturer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10-09</td>\n",
       "      <td>a0fbc0263cb98bdb0d72b9f9f283a172</td>\n",
       "      <td>unknown</td>\n",
       "      <td>267</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>2018-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mql_id                         seller_id  \\\n",
       "0  5420aad7fec3549a85876ba1c529bd84  2c43fb513632d29b3b58df74816f1b06   \n",
       "1  a555fb36b9368110ede0f043dfc3b9a0  bbb7d7893a450660432ea6652310ebb7   \n",
       "2  327174d3648a2d047e8940d7d15204ca  612170e34b97004b3ba37eae81836b4c   \n",
       "3  f5fee8f7da74f4887f5bcae2bafb6dd6  21e1781e36faf92725dde4730a88ca0f   \n",
       "4  ffe640179b554e295c167a2f6be528e0  ed8cb7b190ceb6067227478e48cf8dde   \n",
       "\n",
       "                             sdr_id                             sr_id  \\\n",
       "0  a8387c01a09e99ce014107505b92388c  4ef15afb4b2723d8f3d81e51ec7afefe   \n",
       "1  09285259593c61296eef10c734121d5b  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "2  b90f87164b5f8c2cfa5c8572834dbe3f  6565aa9ce3178a5caf6171827af3a9ba   \n",
       "3  56bf83c4bb35763a51c2baab501b4c67  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "4  4b339f9567d060bcea4f5136b9f5949e  d3d1e91a157ea7f90548eef82f1955e3   \n",
       "\n",
       "             won_date business_segment      lead_type lead_behaviour_profile  \\\n",
       "0 2018-02-26 19:58:54              pet  online_medium                    cat   \n",
       "1 2018-05-08 20:17:59  car_accessories       industry                  eagle   \n",
       "2 2018-06-05 17:27:23  home_appliances     online_big                    cat   \n",
       "3 2018-01-17 13:51:03       food_drink   online_small                    NaN   \n",
       "4 2018-07-03 20:17:45  home_appliances       industry                   wolf   \n",
       "\n",
       "  has_company has_gtin average_stock business_type  \\\n",
       "0         NaN      NaN           NaN      reseller   \n",
       "1         NaN      NaN           NaN      reseller   \n",
       "2         NaN      NaN           NaN      reseller   \n",
       "3         NaN      NaN           NaN      reseller   \n",
       "4         NaN      NaN           NaN  manufacturer   \n",
       "\n",
       "   declared_product_catalog_size  declared_monthly_revenue first_contact_date  \\\n",
       "0                            NaN                       0.0         2018-02-21   \n",
       "1                            NaN                       0.0         2018-04-04   \n",
       "2                            NaN                       0.0         2018-04-03   \n",
       "3                            NaN                       0.0         2018-01-14   \n",
       "4                            NaN                       0.0         2017-10-09   \n",
       "\n",
       "                    landing_page_id          origin  deal_lenght  \\\n",
       "0  b48ec5f3b04e9068441002a19df93c6c  organic_search            5   \n",
       "1  2f08471dfb83baef522f952438a36f6e        referral           34   \n",
       "2  1ceb590cd1e00c7ee95220971f82693d  organic_search           63   \n",
       "3  22c29808c4f815213303f8933030604c     paid_search            3   \n",
       "4  a0fbc0263cb98bdb0d72b9f9f283a172         unknown          267   \n",
       "\n",
       "  cohort_group_first_contact cohort_group_won_date  \n",
       "0                    2018-02               2018-02  \n",
       "1                    2018-04               2018-05  \n",
       "2                    2018-04               2018-06  \n",
       "3                    2018-01               2018-01  \n",
       "4                    2017-10               2018-07  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# COHORTS ON won_date\n",
    "#set index mql_id to \n",
    "closed_deals.set_index('mql_id', inplace=True)\n",
    "#create cohort label based in first contact date\n",
    "closed_deals['cohort_group_won_date'] = closed_deals.groupby(level=0)['won_date'].min().apply(lambda x: x.strftime('%Y-%m'))\n",
    "closed_deals.reset_index(inplace=True)\n",
    "closed_deals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of deal velocity across cohorts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cohort_group_first_contact</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07</th>\n",
       "      <td>2.0</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>41.012193</td>\n",
       "      <td>369.0</td>\n",
       "      <td>383.5</td>\n",
       "      <td>398.0</td>\n",
       "      <td>412.50</td>\n",
       "      <td>427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08</th>\n",
       "      <td>9.0</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>70.927780</td>\n",
       "      <td>141.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>335.00</td>\n",
       "      <td>371.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09</th>\n",
       "      <td>7.0</td>\n",
       "      <td>283.714286</td>\n",
       "      <td>91.107472</td>\n",
       "      <td>130.0</td>\n",
       "      <td>226.5</td>\n",
       "      <td>336.0</td>\n",
       "      <td>347.00</td>\n",
       "      <td>373.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10</th>\n",
       "      <td>14.0</td>\n",
       "      <td>240.214286</td>\n",
       "      <td>72.897376</td>\n",
       "      <td>107.0</td>\n",
       "      <td>188.5</td>\n",
       "      <td>246.5</td>\n",
       "      <td>306.00</td>\n",
       "      <td>341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11</th>\n",
       "      <td>18.0</td>\n",
       "      <td>155.111111</td>\n",
       "      <td>106.727740</td>\n",
       "      <td>14.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>222.50</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12</th>\n",
       "      <td>11.0</td>\n",
       "      <td>122.363636</td>\n",
       "      <td>114.057242</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>54.0</td>\n",
       "      <td>189.00</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01</th>\n",
       "      <td>152.0</td>\n",
       "      <td>43.651316</td>\n",
       "      <td>59.239357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>50.75</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02</th>\n",
       "      <td>149.0</td>\n",
       "      <td>42.281879</td>\n",
       "      <td>62.316550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03</th>\n",
       "      <td>167.0</td>\n",
       "      <td>37.616766</td>\n",
       "      <td>53.702072</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04</th>\n",
       "      <td>183.0</td>\n",
       "      <td>23.819672</td>\n",
       "      <td>40.496442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.50</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05</th>\n",
       "      <td>130.0</td>\n",
       "      <td>32.807692</td>\n",
       "      <td>45.832032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.25</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count        mean         std    min    25%  \\\n",
       "cohort_group_first_contact                                                \n",
       "2017-07                       2.0  398.000000   41.012193  369.0  383.5   \n",
       "2017-08                       9.0  296.000000   70.927780  141.0  296.0   \n",
       "2017-09                       7.0  283.714286   91.107472  130.0  226.5   \n",
       "2017-10                      14.0  240.214286   72.897376  107.0  188.5   \n",
       "2017-11                      18.0  155.111111  106.727740   14.0   71.0   \n",
       "2017-12                      11.0  122.363636  114.057242    4.0   40.5   \n",
       "2018-01                     152.0   43.651316   59.239357    0.0    8.0   \n",
       "2018-02                     149.0   42.281879   62.316550    0.0    7.0   \n",
       "2018-03                     167.0   37.616766   53.702072   -1.0    6.0   \n",
       "2018-04                     183.0   23.819672   40.496442    0.0    3.0   \n",
       "2018-05                     130.0   32.807692   45.832032    0.0    3.0   \n",
       "\n",
       "                              50%     75%    max  \n",
       "cohort_group_first_contact                        \n",
       "2017-07                     398.0  412.50  427.0  \n",
       "2017-08                     317.0  335.00  371.0  \n",
       "2017-09                     336.0  347.00  373.0  \n",
       "2017-10                     246.5  306.00  341.0  \n",
       "2017-11                     163.0  222.50  340.0  \n",
       "2017-12                      54.0  189.00  321.0  \n",
       "2018-01                      17.5   50.75  306.0  \n",
       "2018-02                      16.0   41.00  260.0  \n",
       "2018-03                      12.0   41.00  210.0  \n",
       "2018-04                       9.0   21.50  207.0  \n",
       "2018-05                      10.0   40.25  178.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_deals.groupby('cohort_group_first_contact')['deal_lenght'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d0ecc0ef28>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#order cohorts\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.boxenplot(y=closed_deals.cohort_group_first_contact, x=closed_deals.deal_lenght)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deal lenght by cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d0ecddec50>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "closed_deals.groupby('cohort_group_first_contact')['deal_lenght'].median().plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>cohort_group_won_date</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "      <th>2018-08</th>\n",
       "      <th>2018-09</th>\n",
       "      <th>2018-10</th>\n",
       "      <th>2018-11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cohort_group_first_contact</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07</th>\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>369.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>427.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>141.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>223.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>314.5</td>\n",
       "      <td>335.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>130.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>341.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>373.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>267.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11</th>\n",
       "      <td>17.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>306.0</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>207.5</td>\n",
       "      <td>246.5</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>116.5</td>\n",
       "      <td>159.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>206.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>120.0</td>\n",
       "      <td>156.5</td>\n",
       "      <td>191.5</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cohort_group_won_date       2017-12  2018-01  2018-02  2018-03  2018-04  \\\n",
       "cohort_group_first_contact                                                \n",
       "2017-07                         NaN      NaN      NaN      NaN      NaN   \n",
       "2017-08                         NaN    141.0      NaN      NaN    223.0   \n",
       "2017-09                         NaN    130.0      NaN      NaN    206.0   \n",
       "2017-10                         NaN      NaN    107.0    151.0    189.0   \n",
       "2017-11                        17.0     64.0     87.0      NaN    168.0   \n",
       "2017-12                         4.0     40.0     48.5      NaN      NaN   \n",
       "2018-01                         NaN      7.0     20.0     54.0     85.0   \n",
       "2018-02                         NaN      NaN      6.0     19.0     55.0   \n",
       "2018-03                         NaN      NaN      NaN      6.0     21.0   \n",
       "2018-04                         NaN      NaN      NaN      NaN      5.0   \n",
       "2018-05                         NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "cohort_group_won_date       2018-05  2018-06  2018-07  2018-08  2018-09  \\\n",
       "cohort_group_first_contact                                                \n",
       "2017-07                         NaN      NaN    369.0      NaN    427.0   \n",
       "2017-08                         NaN    314.5    335.0    356.0      NaN   \n",
       "2017-09                       247.0      NaN      NaN    341.0      NaN   \n",
       "2017-10                       226.0      NaN    267.0    306.0    321.0   \n",
       "2017-11                       179.0      NaN    237.0    254.0      NaN   \n",
       "2017-12                       144.5      NaN    216.0      NaN      NaN   \n",
       "2018-01                       116.0    148.0    191.0    210.0    228.0   \n",
       "2018-02                        88.0    130.0    153.0    199.0    207.5   \n",
       "2018-03                        57.0     97.0    116.5    159.0    183.0   \n",
       "2018-04                        17.0     58.0     82.5    120.0    156.5   \n",
       "2018-05                         3.0     19.0     65.0     93.0    122.0   \n",
       "\n",
       "cohort_group_won_date       2018-10  2018-11  \n",
       "cohort_group_first_contact                    \n",
       "2017-07                         NaN      NaN  \n",
       "2017-08                         NaN      NaN  \n",
       "2017-09                       373.0      NaN  \n",
       "2017-10                       341.0      NaN  \n",
       "2017-11                       329.0      NaN  \n",
       "2017-12                       306.0    321.0  \n",
       "2018-01                       292.0    306.0  \n",
       "2018-02                       246.5    260.0  \n",
       "2018-03                       206.5      NaN  \n",
       "2018-04                       191.5    206.0  \n",
       "2018-05                       159.5      NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(closed_deals, index='cohort_group_first_contact', columns='cohort_group_won_date', values='deal_lenght', aggfunc=np.median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d0ec303278>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(pd.pivot_table(closed_deals, index='cohort_group_first_contact', columns='cohort_group_won_date', values='deal_lenght', aggfunc=np.median))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deal velocity across channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d0eeb97d68>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "closed_deals.groupby('origin')['deal_lenght'].median().sort_values(ascending=True).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}