{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concentration de CO2 dans l'atmosphère depuis 1958"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hideCode": true
   },
   "source": [
    "Ce notebook montre la concetration de CO2 dans l'atmosphère depuis 1958 et fait une extrapolation sur les années à venir.\n",
    "\n",
    "Ce notebook a été exécuté pour la dernière fois le :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-09-22 12:23:52.398459\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "print(datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importation des modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importation des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Yr</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Date</th>\n",
       "      <th>Date</th>\n",
       "      <th>CO2</th>\n",
       "      <th>seasonally</th>\n",
       "      <th>fit</th>\n",
       "      <th>seasonally</th>\n",
       "      <th>CO2</th>\n",
       "      <th>seasonally</th>\n",
       "      <th>Sta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>adjusted</td>\n",
       "      <td></td>\n",
       "      <td>adjusted fit</td>\n",
       "      <td>filled</td>\n",
       "      <td>adjusted filled</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Excel</td>\n",
       "      <td></td>\n",
       "      <td>[ppm]</td>\n",
       "      <td>[ppm]</td>\n",
       "      <td>[ppm]</td>\n",
       "      <td>[ppm]</td>\n",
       "      <td>[ppm]</td>\n",
       "      <td>[ppm]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>01</td>\n",
       "      <td>21200</td>\n",
       "      <td>1958.0411</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>02</td>\n",
       "      <td>21231</td>\n",
       "      <td>1958.1260</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>03</td>\n",
       "      <td>21259</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.43</td>\n",
       "      <td>316.20</td>\n",
       "      <td>314.91</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.43</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yr   Mn      Date         Date       CO2  seasonally          fit  \\\n",
       "0                                                adjusted                \n",
       "1                Excel                  [ppm]      [ppm]         [ppm]   \n",
       "2  1958   01     21200    1958.0411    -99.99      -99.99       -99.99   \n",
       "3  1958   02     21231    1958.1260    -99.99      -99.99       -99.99   \n",
       "4  1958   03     21259    1958.2027    315.71      314.43       316.20   \n",
       "\n",
       "     seasonally           CO2       seasonally   Sta  \n",
       "0  adjusted fit        filled  adjusted filled   NaN  \n",
       "1         [ppm]         [ppm]            [ppm]   NaN  \n",
       "2        -99.99        -99.99           -99.99   MLO  \n",
       "3        -99.99        -99.99           -99.99   MLO  \n",
       "4        314.91        315.71           314.43   MLO  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"monthly_in_situ_co2_mlo.csv\"\n",
    "\n",
    "if filename not in os.listdir():\n",
    "    response = requests.get(data_url)\n",
    "    \n",
    "    with open(filename, \"wb\") as f:\n",
    "        f.write(response.content)\n",
    "    \n",
    "raw_data = pd.read_csv(filename, skiprows=61, sep=\",\", na_values=-99.99)\n",
    "raw_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nettoyage des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data.columns = [\"Yr\",\"Mn\",\"Date_excel\",\"Date\",\"CO2\",\"seasonally_adjusted\",\"fit\",\"seasonally_adjusted_fit\",\"CO2_filled\",\"seasonally_adjusted_filled\",\"Sta\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Date_excel</th>\n",
       "      <th>Date</th>\n",
       "      <th>CO2</th>\n",
       "      <th>seasonally_adjusted</th>\n",
       "      <th>fit</th>\n",
       "      <th>seasonally_adjusted_fit</th>\n",
       "      <th>CO2_filled</th>\n",
       "      <th>seasonally_adjusted_filled</th>\n",
       "      <th>Sta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>01</td>\n",
       "      <td>21200</td>\n",
       "      <td>1958.0411</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>02</td>\n",
       "      <td>21231</td>\n",
       "      <td>1958.1260</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>03</td>\n",
       "      <td>21259</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.43</td>\n",
       "      <td>316.20</td>\n",
       "      <td>314.91</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.43</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1958</td>\n",
       "      <td>04</td>\n",
       "      <td>21290</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.15</td>\n",
       "      <td>317.31</td>\n",
       "      <td>314.99</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.15</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1958</td>\n",
       "      <td>05</td>\n",
       "      <td>21320</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.68</td>\n",
       "      <td>317.89</td>\n",
       "      <td>315.07</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.68</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yr   Mn Date_excel         Date       CO2 seasonally_adjusted  \\\n",
       "2  1958   01      21200    1958.0411    -99.99              -99.99   \n",
       "3  1958   02      21231    1958.1260    -99.99              -99.99   \n",
       "4  1958   03      21259    1958.2027    315.71              314.43   \n",
       "5  1958   04      21290    1958.2877    317.45              315.15   \n",
       "6  1958   05      21320    1958.3699    317.51              314.68   \n",
       "\n",
       "           fit seasonally_adjusted_fit   CO2_filled  \\\n",
       "2       -99.99                  -99.99       -99.99   \n",
       "3       -99.99                  -99.99       -99.99   \n",
       "4       316.20                  314.91       315.71   \n",
       "5       317.31                  314.99       317.45   \n",
       "6       317.89                  315.07       317.51   \n",
       "\n",
       "  seasonally_adjusted_filled   Sta  \n",
       "2                     -99.99   MLO  \n",
       "3                     -99.99   MLO  \n",
       "4                     314.43   MLO  \n",
       "5                     315.15   MLO  \n",
       "6                     314.68   MLO  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = raw_data.drop([0,1])\n",
    "raw_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Date_excel</th>\n",
       "      <th>Date</th>\n",
       "      <th>CO2</th>\n",
       "      <th>seasonally_adjusted</th>\n",
       "      <th>fit</th>\n",
       "      <th>seasonally_adjusted_fit</th>\n",
       "      <th>CO2_filled</th>\n",
       "      <th>seasonally_adjusted_filled</th>\n",
       "      <th>Sta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>03</td>\n",
       "      <td>21259</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.43</td>\n",
       "      <td>316.20</td>\n",
       "      <td>314.91</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.43</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1958</td>\n",
       "      <td>04</td>\n",
       "      <td>21290</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.15</td>\n",
       "      <td>317.31</td>\n",
       "      <td>314.99</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.15</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1958</td>\n",
       "      <td>05</td>\n",
       "      <td>21320</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.68</td>\n",
       "      <td>317.89</td>\n",
       "      <td>315.07</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.68</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1958</td>\n",
       "      <td>07</td>\n",
       "      <td>21381</td>\n",
       "      <td>1958.5370</td>\n",
       "      <td>315.87</td>\n",
       "      <td>315.20</td>\n",
       "      <td>315.85</td>\n",
       "      <td>315.22</td>\n",
       "      <td>315.87</td>\n",
       "      <td>315.20</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1958</td>\n",
       "      <td>08</td>\n",
       "      <td>21412</td>\n",
       "      <td>1958.6219</td>\n",
       "      <td>314.93</td>\n",
       "      <td>316.23</td>\n",
       "      <td>313.95</td>\n",
       "      <td>315.29</td>\n",
       "      <td>314.93</td>\n",
       "      <td>316.23</td>\n",
       "      <td>MLO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yr   Mn Date_excel         Date     CO2  seasonally_adjusted     fit  \\\n",
       "4  1958   03      21259    1958.2027  315.71               314.43  316.20   \n",
       "5  1958   04      21290    1958.2877  317.45               315.15  317.31   \n",
       "6  1958   05      21320    1958.3699  317.51               314.68  317.89   \n",
       "8  1958   07      21381    1958.5370  315.87               315.20  315.85   \n",
       "9  1958   08      21412    1958.6219  314.93               316.23  313.95   \n",
       "\n",
       "   seasonally_adjusted_fit  CO2_filled  seasonally_adjusted_filled   Sta  \n",
       "4                   314.91      315.71                      314.43   MLO  \n",
       "5                   314.99      317.45                      315.15   MLO  \n",
       "6                   315.07      317.51                      314.68   MLO  \n",
       "8                   315.22      315.87                      315.20   MLO  \n",
       "9                   315.29      314.93                      316.23   MLO  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in [\"CO2\",\"seasonally_adjusted\",\"fit\",\"seasonally_adjusted_fit\",\"CO2_filled\",\"seasonally_adjusted_filled\"]:\n",
    "    raw_data[col] = raw_data[col].astype(float)\n",
    "    \n",
    "clean_data = raw_data[~raw_data.eq(-99.99).any(axis=1)]\n",
    "clean_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Premier graphique de la concentration de CO2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vAvDLX/6SAwcOsHHjRkJDQ2lra+PVV18lLCyMjRs38pWvfIUnn3ySb3zjG2zdupX7778fgF//+tfeZ/zMZz7DTTfdxM0338xDDz3E7bffzp/+9CcAqquref3119m/fz9XXXUV1113nd/49PX1cejQIXJycrzHNm/ezObNm1myZAlXXHEFf/zjH7nuuuvo7OzkrLPO4gc/+AHf+MY3+PrXv+7tT0REBK+++ir33XcfV199Ndu2bSM5OZn8/HzuuOMOUlJSWLp0KVu2bBn270pERETOXJq5HkfW2mEXvRlj+M53vsOXvvQlbr311oD2jRs3cttttxEW5nweSk5OpqSkhNzcXObPnw/AzTffzKuvvuq95tprrwVg9erVlJeXe+/z6U9/2nvOjBkz2LRpE8XFxZx//vmsXLmShx9+mCNHjpzwPsFcf/313pSM1tZWrr/+epYuXcodd9zB3r17TzY8vPXWW3z4wx8G4GMf+xivv/66t+2aa64hJCSExYsXU1tbG3BtQ0MDSUlJfsfWrl1Lbm4uoaGh3Hjjjd77hYSE8I//+I8AfPSjH/V7n6uuugqAZcuWsWTJEmbNmkVkZCR5eXkcO3YMgNDQUCIiImhvbz/pM4mIiMi7V9fWQ0NH70R3Y0Sm9cz1iWaYx8qSJUuGTVtYsmSJX/ALcOjQIeLi4oiPj+euu+6is7PTm9rgK1hgfrKFkJGRkYATDA7mQQ93n0svvZTHHntsxPcJJjY21vv6P//zP1m/fj1PPfUU5eXlFBYWnrCvwfj2c7APg/09XnR0dEAZvOOf80QfbI5/n5CQEL/3DAkJ8Xv23t5eoqKiRvIYIiIi8i5d8N8v0zfg5vB3rpz01bk0cz3KLrnkEnp7e/n5z3/uPbZlyxZeeeUVPvKRj/D666+zceNGwKlucfvtt3PnnXcCTj70bbfd5s2n9nXZZZfxs5/9zBvgNTU1sXDhQsrLyzl48CAAjzzyCBdffPEJ+3fZZZd5UyDASQs599xzeeONN7z36erq8lY3GU58fPwJZ25bW1vJysoC/FM/TnTdunXrePzxxwEnf/qCCy44YR98zZgxA5fL5Rdgb968mfLyctxuN0888YT3fm632/sB6NFHHz2l9wFobGwkLS1NOzGKiIiMg4rmLvoGnHVTbx1qnODenJyC61FmjOGpp57ihRdeID8/nyVLlvC1r32NzMxMoqOj+fOf/8w999zjXSB49tlnexf0XXHFFbjdbh5++OGAGeJPfOITzJkzh+XLl7NixQoeffRRoqKi+NWvfsX111/PsmXLCAkJ8VtIGczdd99Nc3MzS5cuZcWKFbz88sukpaXx61//mhtvvJHly5dz7rnnehdADucDH/gATz31lHdB4/HuvPNO7rrrLs4//3y/0njr16+nuLjYu6DR149//GN+9atfsXz5ch555BHuu+++E/bheJdddplfisd5553H1772NZYuXUpubi4f/OAHAWeGfTD//aWXXvLmlo/Uyy+/zJVXXnlK14iIiMjpeWl/nff1yz6vJyszFWssD1qzZo3dunWr37F9+/axaNGiCerRyGn789G3fft27r33Xh555BGKior4/ve/z2OPPRYwznFxcd5qKKfj2muv5Tvf+Q4LFiwIaJsqP39joaio6LTSf+TUaJzHj8Z6fGicx8dUHufbH9vOlvImEqPDyZ4RzS9uPntC+mGM2WatXXOy86Z1zrWcWVatWsX69evHfBOZa665JmhgLSIiIqNvT2Ury7ISCQ0xlNRO/mICSguRaeXWW28lNDSUwsJCnnnmmaDnvJtZ64iICG666abTvl5ERERGrr2nn0MNnSzLSiQ3NZajjV0MuNwT3a0TUnAtIiIiIpPSzmOtACzNdoLrAbelorl7gnt1YtMyuJ7KeeQydennTkREZHQ9vbOSuMgwzslNJi8tjvjIMOoneb3raZdzHRUVRWNjIykpKZO+DqJMH9ZaGhsbVftaRETkFO2taqWlq591+f6xW1NnH8/squb9y2cRExHGWXOS2PW1yyZ9fDftguvs7GwqKiqor6+f6K6cUE9PjwKxcTCe4xwVFUV2dva4vJeIiMh0UN7QyYd+9hadfS6+efUSPnZejrft/71aRne/i3++MA8YfjO4yWbaBdfh4eHk5uZOdDdOqqioiFWrVk10N6Y9jbOIiMjk9b8vH2TA7aRVvrS/zi+4fn5vLZcsSKcgY2qVLp52wbWIiIiITH5ut+Wl/XVcvmQmsZFhPLOzCpfbEhpiaOnq43BDJ9evmXq/EZ6WCxpFREREZHLbUdFCY2cfGxalszZ3Bu29A5TWOXWsdxxrAWDl7KSJ7OJpUXAtIiIiIuPupX11hIYYCuenk5caB8CxJqfM3o5jLRgDy7OnXnCttBARERERGXcb99WyZu4MEmPCmTXgFB+oaXWC690VreSnxREXOfVCVc1ci4iIiMi4aursY39NOxcvSAMgNS6S8FBDVWsPALsrW1melTiRXTxtCq5FREREZEzc9NBm7vrj7oCN1vbXtAGwNNMJoENCDBkJUVS3dFPb1kNdey9Lp2hwPfXm2kVERERk0qtp7eHVA86+I+sXpHHZkpnetpIaZ+HiwplDZfZmJUZR1drDrgpny/Pl2VMzuNbMtYiIiIiMuldLhzb023qk2a+tpKadGTHhpMVHeo/NSoymprWH3ZWthBhYnJkwbn0dTQquRURERGTUbT7cRGpcBAtnxnOgtt2vbW9VGwtmxvvtupg9I5rq1m62HG5iXnocMRFTM8FCwbWIiIiIjLryhk7y0uKYnxFPaW2H93hdmzM7fX5+qt/5a3OT6XdZ3jrUOGXzrUHBtYiIiIiMgSNNXcxNjmHBzHgqW7rp6B0A4IV9tQB+OdgA5+SmeF9fPD9t/Do6yhRci4iIiMio6uoboL69l7kpMRSkOxvElHpSQzYfbmJWYhTzM+L8romOCOUDKzK5sCCVq1ZkjnufR8uYB9fGmFBjzHZjzDOe7//HGLPfGLPLGPOUMSbJ59y7jDEHjTElxpjLx7pvIiIiInJ6dle0stOzTfnxBndanJMSy/wMpyLIYGrIvuo2lmQm+OVbD/rJjav4za1rg7ZNFeMxc/1ZYJ/P9y8AS621y4EDwF0AxpjFwA3AEuAK4AFjTOg49E9ERERETkFH7wDXPPAGV//vG/x9b01A+5byJgDmJscwOzmGyLAQDtS209Pvoqy+k0Wzhq8EMpUDaxjj4NoYkw28D/jF4DFr7fPW2gHPt5uAbM/rq4HHrbW91trDwEFg7Vj2T0RERERO3V93VeFyOxvD7Kls9Wura+/h63/Zy8KZ8SyYGU9oiGFeehwH6jo4WNeBy21PGFxPdWNd4+RHwJ1A/DDttwJPeF5n4QTbgyo8x/wYYz4JfBIgIyODoqKi0erruOro6JiyfZ9KNM7jR2M9PjTO40djPT40zuNjtMf5sW09pMc4M8xvFx+mKKLa27apeoB+l+Ufc/vZ9MZrACTQw56j7fzxZScQbzu6j6LGklHrz2QyZsG1Meb9QJ21dpsxpjBI+38AA8DvBg8FuY0NOGDtg8CDAGvWrLGFhQG3nhKKioqYqn2fSjTO40djPT40zuNHYz0+NM7jY7TH+ctvvsi6+cm0dvdT09ZLYeGF3ra//3E38ZFVfOz96wkLdZIk9lHGW8/tpzUinZiIaj703vWEhEzt9I/hjGVayPnAVcaYcuBx4BJjzG8BjDE3A+8HPmKHNpuvAGb7XJ8NVI1h/0REREQkiO89t58nthwNON7VN8CV971GTVsPS7MSyUuL43BDB25PisiAy01RSR1rc5O9gTXgrQzyl51VLJgZP20DaxjD4Npae5e1Nttam4OzUPEla+1HjTFXAP8OXGWt7fK55GngBmNMpDEmFygANo9V/0REREQk0Pajzfy0qIx/f3I3r5c2+LW9UlJPcXUb4GxPnpcWS0+/m+q2HgCe3VNDdWsPN6yd43fdYMWQPpd7Wudbw8TUub4fJwf7BWPMDmPMzwCstXuB3wPFwHPAp621rgnon4iIiMgZ69G3h2as91T5L1Z8odjZACY5NoJlWYnkpToz0ofqnTJ7z+2pJjMxig0L0/2uy0qK9r4+a86MMen3ZDEum7Zba4uAIs/reSc471vAt8ajTyIiIiLiz1rLKwfq+cCKTN442MCRxi6/9tcONnDNykx+dMMqAPLTYgE4VN/JhQVpFFe1sWJ2UkDaR0iI4b1LZ9LvsnxwVUC9imllXIJrEREREZkc6tt7SYwOJyIsMIGhrL6DuvZezs9P4VhTF0caO71tde091Lf3sjzbu/8fafGRxEaEcqi+g/aefsobu7hudXbAfQF++tHVo/8wk5C2PxcRERE5Q/T0uzj7Wxv5/O93BG0vKqkHYF1+KjkpMX4z1/uqne3LF2cO5UwbY8hLi+NQQyf7awLbz0QKrkVERETOEFvLmwF4Zle1dxOYQW635bebjnDWnCTmpMQwJyWW6tZuegecJXDFVc5CxkUz/YPn/LRYSms7vO2LZyWO9WNMagquRURERM4Qr5bWe19v9WxRPmh3ZSvljV189Ny5gLN1udtCRXM3AMXVbWQlRZMYE+533fLsJGraeti4r5aU2AgyEiLH+CkmNwXXIiIiImeILeVN5KU6ixAHS+r5tgGcPy8VgJzUGACOelJD9lW3BS2jt3quU/3jtdIGFmcmYMz0rWE9EgquRURERM4AbrelpKadi+ankRAVRpmnfN6greXNzEmOISMhCoA5yU4QfqSxk+4+F4fqO4LmUy/OTCA81AmoF0/zGtYjoeBaREREZJroG3AztPm1v6NNXXT1uVg0K5789DgO1g0F19Zath5pYs3coRrUqXERxESEUt7YRUltO24bPHgODw3hzssXMjMhisIF6QHtZxoF1yIiIiLTQE+/i/l3/40fbiwN2r6/xrMgcVYC89LiKKsfKrNX3thFQ0cfa3KSvceMMcxJjuFoUxd7Kp3NZJYMUwnkny/KY9NXNnBefspoPc6UpeBaREREZBrY66nW8eMXA4Nray1/2FpBZFgIBenOzHV9ey+t3f3A0OLGs3P8d0/MSYnlUH0H2440kxYfSfaM6IB7iz8F1yIiIiLTwK6KFu/rg3Xtfm27G1y8uL+OL12+gOiIUOalOduWD+ZdbylvIikmnHzP8UFLMhMob+zixX21nJ0z44xfrDgSCq5FREREpoGdx4aC68ENXwZtPDpAalwkN52XA0B+uie4ruvAWktRST3n5aUEbFu+YrazG2NbzwCr5yYjJ6fgWkRERGQa2FXRyoUFThk9323Lu/tc7Glw8Q+rs7xbns+eEU1EaAgH6zvYW9VGXXsvGxZlBNxzefbQhjBXLps5xk8wPYRNdAdERERE5N1p7ernUEMn/7A6mwO17ZT7bFteXN2K28Ian5nnsNAQclJjKKvr5K2yRgAump8acN+kmAjW5aewcGYCsxKVbz0SCq5FREREprhdlU5KyIrsJOYmx/rNXO+ucCp9LMvy35Z8fkY824+2kBQTTlp8JOnxUUHv/eg/nztGvZ6elBYiIiIiMsW9UuJsa74sO5G5KTF+M9e7K9tIiDAB25KvnJ1EZUs3r5c2sCAjflz7O50puBYRERGZAurbe4NuELPzWAu/eP0w16zMJDE6nLw0T5m9LqfMXnF1G3MTQgIqfQxuW17T1sN8BdejRsG1iIiIyCTX0NHL2d/ayBf+sDOg7eE3y4mLDOOeDy4DYOEsJ1DeV9NGv8tNWV0Hs+MDQ74lmUNpIvMz4gLa5fQouBYRERGZ5AbTPv74TiWltUNl9noHXDyzu5prVmUSF+kspRvconxfdRvlDZ30udxkxQXWp44IC+HnN63htovzuWKpKoGMFi1oFBEREZnkXiqp874urm6jwJPGUVbXSd+Am3Nyh7YdT4+PJDk2gn3VbaTFO3nW2UFmrgEuXZzBpYsDS/DJ6dPMtYiIiMgEGnC5+e7f9vtV+PDVN+Dm1ZJ6Prgqi9AQw8G6Dm/b/hpny/NFs4Zypo0xLJoVz/6adkpq2gkNMWTGKeQbLxppERERkQn09uEmfvZKGZ95dPsw7Y209w5w5bJZzEmO8W5ZDrC/pp2IsBByUmL9rlk4M4EfqOWbAAAgAElEQVSSmnaKq9rITY0lPETblo8XBdciIiIiE2jjvloA9lS10tU3ENheXEtUeAgXzEslPy2WsrqhGe591W0UpMcRFuof0i2alUDvgJsX99epzN44U3AtIiIiMoFeK20AwNqhDV8GWWt5obiWC+alER0RSn56HIcbOnG5LX0DbrYdafaW1PPlmyayYKaC6/Gk4FpERERkgvQNuDnc0MkHV2UBcKjBP+9606Emqlp7uMyz6DA/LY4+l5tjTV3sONZCV5+LdfmB25bPSx8qrbdQwfW4UnAtIiIiMkHKG51Z6AsLUokMC+GQTz51v8vNHU/sYE5yDFcsc0rl5ac5QXNZfQdvH2rEGDgvLyXgvpFhoTzzbxfwzWuWsn5h+vg8jAAKrkVERETGzC9fP8yaezZS5FNKz9dg5Y/5GfHkpsZSVj80c11S005NWw9fuGw+CVHhAOSnOQsXy+o7KKvvIDMxmsSY8KD3XpqVyMfOnUt4qMK98aTRFhERERkj9208QENHLz/cWBp06/LB4DovLZb8tDi/mevdlU7+9crZSd5jSTERpMZFUFbXSXljFzmpMWP8BHKqFFyLiIiIjIGuvgHaegZIi49k57EWjjR2+bX39Lv40/ZK5mfEERMRRn5aLMeau+kdcAGwq6KVhKgw5iT7B9AF6fHsr2mjvLEzoASfTDwF1yIiIiJjoLq1B8C7A+LRJv/g+ldvlHOooZO737cYgLy0OFxuy1FPEL67soXl2UkY41+jell2IjsrWmnp6ic3VcH1ZKPgWkRERGQMVLc4wfXZOU6pvIrmbm9bT7+LnxYd5JKF6Vw0Pw3wXazYSU+/i5KadpZlJwbcd1nW0LG5mrmedMImugMiIiIi01FVqxNMr5w9g/BQQ0Xz0Mx1cXUbbT0DfGjNbO+xXJ/FirMSo+h3WZZnBQbXy30C7rPmJAW0y8RScC0iIiIyBmo8aSGZSVFkJkX7zVwPbhbjGyjHRYaRkRDJofpOEqKdCiDBZq7nJMdw49rZvG9ZJilxkWP5CHIaFFyLiIiIjIGK5i5SYiOIDAslKynab+Z6d2UrqXERzEqM8rsmPy2OQw0dhIZAcmwEWUnRAfc1xvCda5ePef/l9CjnWkRERGSU9fS7eKG4lnPykgHInuE/c73jWAtLsxIDFivmpcVSVtfBropWlgVpl8lPwbWIiIjIaTjc0Mml975CcVVbQNvL++to7urnw2vnApA9I4a69l56+l0cbujkYF0HFxWkBVyXlxpHW88A+2va/VJGZOpQcC0iIiJyGr785C5K6zr4047KgLbtx1qICAvxm7kGqGrp5vm9NQBcvnRmwHX56XHe18uCLGaUyU/BtYiIiMgp6htws/VIMwB7PDsp+tpd0cqimfHercezZzgbwVQ0d7O7spU5yTFB86nzfOpWn5ObMhZdlzGmBY0iIiIip+hIYycutyUpJpztR1vod7m9gbS1lj1VrXxgRab3/MGZ64rmbo40dpEzzOYv2TOi+VRhPlcum0ViTPjYP4iMOs1ci4iIiJyisvoOAG44ew7d/S6/2evi6jbaewZY4ZMznZEQRViIU+u6vKGT3JSYgHuCUwnkzisWslQpIVOWgmsRERGR4zR09PKVp3ZT3dodtL2svhOAG852NoHZUt7kbXt88zEiwkK4fMlQTnVoiCFrRjTbjjTT3jugnRWnMQXXIiIiIsf5wfMlPPr2Ub7xl+Kg7QfrOshMjCInNZbc1Fg2Hx4Krv+2p5rLl8wkKSbC75pVs5N423NeTmrwmWuZ+hRci4iIiPgYcLl58h2nAshze2to7+kPOGdXRQsLZyUAzi6L+2vaAWjs6KWho4+VswO3JT8nb2iBYkF6/Fh0XSaBMQ+ujTGhxpjtxphnPN8nG2NeMMaUev6c4XPuXcaYg8aYEmPM5WPdNxEREZHjHWnqom/AzXsWZWAtHG3q8mtv7uyjrL6T1XOdECYzKZrath5cbktpnZOLXeBTUm/QeZ7gevXcGcxO1sz1dDUeM9efBfb5fP9l4EVrbQHwoud7jDGLgRuAJcAVwAPGmNBx6J+IiIiIV2mtEyBfsjAdgGPHBdebPfnVa3yC636XpaGjl9JaZwa7ICMwuM5JjeW3/3QOv7l17Zj1XSbemAbXxphs4H3AL3wOXw087Hn9MHCNz/HHrbW91trDwEFAP30iIiIyqqpaurnup29S1TLcYkUnuC5c4OygeKxp6Ly+ATf/8/cSZiVGscKT+pGVFAVAZUs3pXUdxEeGMTMhKui9LyhIJTZSlZCns7H+2/0RcCfgm1iUYa2tBrDWVhtj0j3Hs4BNPudVeI75McZ8EvgkQEZGBkVFRWPQ7bHX0dExZfs+lWicx4/GenxonMePxnp8TMQ4P13Wx9Yj/dz9u1e5aUlkQPvru3pIjjIc2PE2MWHw1p5SCtxHAdhVP8DBul4+vTKSTW+8BkBluxuAjW9uY/PRftKj4JVXXhm/BxoB/TyPnzELro0x7wfqrLXbjDGFI7kkyDEbcMDaB4EHAdasWWMLC0dy68mnqKiIqdr3qUTjPH401uND4zx+NNbjYyLG+ZX2vVBaTl9kEoWF5wS0f3fHqyybE0Vh4Vrydr+GOzqSwkLnl+lFT+8lKvwo//YP64kKd7JXW7v7+c83nmdGZh4NB8u4ZGE6hYUrxvWZTkY/z+NnLNNCzgeuMsaUA48DlxhjfgvUGmNmAXj+rPOcXwHM9rk+G6gaw/6JiIjIGWhfdRvg1KbuG3D7tfX0uyit62CZZxOXuSmxHPLUtAYoKqnj/PxUb2ANkBAVRlxkGLsqW2no6GN+hiqBnMnGLLi21t5lrc221ubgLFR8yVr7UeBp4GbPaTcDf/a8fhq4wRgTaYzJBQqAzWPVPxERETnzWGvZX9NOfGQYvQNuSuva/dqLq9twua13h8SC9DiONXfR0++itauf8sYu1uQk+11jjGF+Rhx/2enMCc4LUilEzhwTUef6u8ClxphS4FLP91hr9wK/B4qB54BPW2tdE9A/ERERmaZKattp6ernI+fOBWBvVZu3rbvPxbf/6hQ4W549GFzHY62zyHFvtbPF+ZLMhID7LvPZrnzBTM1cn8nGJbi21hZZa9/ved1ord1grS3w/Nnkc963rLX51toF1tq/jUffRERE5MzxSkk9ADedN5fYiFD2VrZ629442MDWI8189f2LyUyKBoZK6h2s66DYE4gvDhZcZzuVQ2IiQpmVGD2mzyCTm2rBiIiIyBnB7bY8s6uaBRnxZCZFszgzgeLqoZnrHcdaCA0x3Lh2jvdYTkosoSGG0toOqlq6yUiIJDUusMLICs9M9y3rcsb8OWRy0/bnIiIiMm28XFLHqm88z+GGzoC2P+2oZHdlK5+8KA+A/LQ4v8WKOytaWJART3TE0GLFiLAQclJiKK1rp7i6jcWzAmetAQoy4nnucxfyxcsWjPITyVSj4FpERESmBZfb8s8Pb6W5q9+7uNDXS/vrmJkQxbVnOdto5KXF0tjZR2tXP263ZeexFu/GML4K0uPZU9lGaV1H0JSQQQtnJhASEqyysJxJFFyLiIjItFDZ3M2A29ki48X9dX5t1lo2H27inLxkjHEC4LxUJ5+6rKGD8sZO2noGWDk7keMVZMRR2dKNy21ZkhnYLuJLOdciIiIyLRxqcLYtv2BeKm+UNdDW009CVDgARxq7qGvvZW3uUBm9vLRY57r6TkI9040rZ88IuK9vab3h0kJEBmnmWkRERKaFck+e9bVnZWEt7K4YqgSy9UgzAGf71KienRxDWIihrL6DHUdbiIkIDVqjermnEsj6BWnMTYkZy0eQaUAz1yIiIjItlDd2ERcZxoaFGYBT/eP8eakAbDvSTHxUGPPShoLn8NAQ5qTEcKi+g5q2XpZlJRIaJGc6NzWWd/7zUmbEhHtTSkSGo5lrERERmRL6XW42HWrEWhu0/VBDJzmpMSTGhJOXFsuOYy3etu1Hm1k5OylgwWFeahwlNe3sq2pjZZDFjIOSYyMUWMuIKLgWERGRKeHBVw9xw4ObeG5PTUCby23ZVdHCggwnJ3rhzHjK6pwc7Paefkpq21k9NzCfOj8tlvLGLvpc7hMG1yIjpeBaREREpoSXPRVA7n/5YEDb7spWWrr6uWi+kwaSmxrL0aYu+l1udh5rxVo4a05gcD24qBFguYJrGQUKrkVERGTC1bX38EDRQVzu4CkfDR29vHO02dmyvKqNqpZub5vLbXnw1TKMgQsL0gAn3WPAbTna1MW2I80YAyvnBAbP8zPiAViXn0JWkrYtl3dPwbWIiIhMuLue3M1/P1fCW2WNQdtfKK7FbeGeDy4F4I2DDd62jftqeXZ3DZ9/z3ySYyMA/zJ7O441U5Ae5y3L52vl7CSe/Nfz+M2ta0f7keQMpeBaREREJpTbbdl21CmVt3FfbdBznt1dzdyUGK5ekUVqXASbDjV52zYdaiQqPIR/uTjfeyzPUxXkYF0HpXUdLJwZvD61MYbVc5MJC1VIJKNDP0kiIiIyofbVtNHS1Q9AUUldQPv+mjZeK23gmpVZhIQYCtLjKW/s9LZvKW9i5ewkIsKGwprE6HCyZ0SzpbyJypZu8tMC61eLjAUF1yIiIjKhth91SuZdtzqbI01ddPe5/Np/8uJB4iLD+Pj5OQDMTo7mWFMXAJ29AxRXtbHWZ3OYQStnJ/HS/jqshfz02IB2kbGg4FpEREQm1DtHm0mNi2D9gnSshbL6Dm/bwbp2nt1TzS3rckiKcfKps2fEUNfeS0+/i/01bbjt0C6KvnxL6wXbeVFkLCi4FhERkQn1zpFmVs6e4Q2AfYPrVw40YC189Ny53mPZM5yqHpUt3RRXtQGwODMwp/qi+Wne1zkpmrmW8aHtz0VERGTC1HW5KW/s5uZ1OeSkxhBi8G7+ArC7ooWZCVHMTIzyHsueEQNARXM3xdVtJMWEM8unfdD8jHhe/dJ6Wrv7iQoPHfuHEUEz1yIiIjLGdle08rnHtwfkUgPsqneOrV+QTmRYKHNTYjnoM3O9u7KVpVmJftfMTnZmro80drKnso3FsxKG3Zp8TkoMy7ITg7aJjAUF1yIiIjKmPvv4dv60o4qH3jgc0La/yUX2jGhyUp20jfy0OA76bFt+qKGTZccF1zMTokiLj+S10gaKq9tYFWRzGJGJouBaRERExsz+mjYONThl83636UhA+5E2Nyuy/RceHm7oZMDlZkt5E9bC2bn+25YbYzg3L4UXimtxuS2r5wZuay4yURRci4iIyJh5cZ9Tt/oTF+RS1dpDU2eft621q5/6bsuSrKHFiPPS4+h3OduWv1XWSERYCGfNCQyez8tL8b4O1i4yURRci4iIyGmrae3h8c1HsdYGbS8qqWNpVgKFC9IB2Ffd5m3bU9UK4Jf2MVgx5GBdB28dauSsOUlBFyO+f8UsPnFBLp97T4G3RJ/IZKDgWkRERE7bfS+W8uU/7ub3W48FtPX0u9h+tIUL5qV5S+Xt9QTU4GxbbvCvUT0YXO+pbGV/dfuws9IJUeHc/f7FfO4980fxaUTePQXXIiIictoaOnoBePDVQwFtB2rbGXBblmcnkhwbwcyEKPbXtHvbi0rqmZcUQmJ0uPdYXGQYc5Jj+NOOKgbcNmAxo8hkp+BaRERETttgTeryxi56B/xL7e2pdFJAlmY6AXJeWiyH6p3FjU2dfeyubGVZWmDKx8KZ8Rz1bG9+fBk+kclOwbWIiIiclp5+F+WNncxNicHltpQ3dPm1v36wnoSoMG9daie47sBay55KJz2kICkwuC7IcFJD4qPCvLsxikwVCq5FREQkqN4BF/XtvcO2l9S047Zw5bJZAN761ABbypt4dncNHztvrneDl7zUONp6Bmjs7GOvZ9vyOQmBoch5eakA/Pc/LB92cxiRyUrBtYiIiARo6uzj0ntf5eL/eZnKlu6g57xQXEuIgY+eOxdjoLRuKJ/6lZJ6wkIMn1lf4D2Wl+ZsFHOovpPi6jaykqKJDQ8Mni8oSKXknit4rydoF5lKFFyLiIhIgNcPNnC0qYuuPhf3Pn8goP2JLUe5/+WDnJObQlZSNJmJ0ZR7NosBZ9vygox4oiOG0j4GK4GU1LZTXNXqrSASTGRYYLqIyFSg4FpEREQCHKxtJ8TAlctm8lppvV9b74CL7z1XAsBthfkA5KTGUN7o5FwP5lQvPS54zkqKJjk2gk2HGjnU0MmSEwTXIlOVgmsREZEzUE1rD28ebBi2/WB9B3OSYzg7J5m69l5q23q8bS/tq6Ops49ff/xsLp6fBsCc5FiONDoz1zVtPTR29gUEz8YYVs5O4q+7qrEWFs9ScC3Tj4JrERGRM9CVP36ND//ibRo7gi9YPFjXwbz0OJZnO6Xwdh5r8ba9UdZAfGQYFxakeY/lpMTQ3NVPa1c/mw83AbB6bnLAfVfOHtow5kRpISJTlYJrERGRM8z2o800dfYB8NzemoD27j4X5Q1d5KfFsXhWIsbgre4BsONYC8tnJxIaMrQYcW6Ks1jxSFMnb5U1khAVFjR4ft/yoUWKWUkqsyfTj4JrERGRM8wOzyx0XGQYLxTXBrQ/tb2SPpebDYsyiI4IJT0+0lsxpKffxf7qdlb4bFkOkO+pBLKvuo03yxo5Jy/FL/geOi+Ov3zmAn558xqV2ZNpKWyiOyAiIiLj60BtB4nR4WxYmM4bZYF510++U8HCmfGcnTMDgMykaKpbneB6b1UrA27rl94BTiWQmQlR/HbTUY42dXHLupxh339ZdiKgnRdletLMtYiIyBmmtLad+RlxLJgZT21bLy1dfd62tp5+dhxr4dLFGd6Z5cykaKpanAWN2486s97HB9fGGDYsSme3Z+fFdfNSxuNRRCYdBdciIiJnEGstpXUdFGTEM39mPODMZA/aVNaIy205f16q91hWUjRVLd1Ya9lZ0UpmYhTpCVEB9/7wOXO8r+enx4/hU4hMXkoLERERmWae3V3N3qpWPnFBHjNiI/zath5pprW7nxXZiSzIcALgktp21uY6lT1e2l9HbEQoZ82Z4b1mVmIUvQNumjr72HGsmRXHzVoPWpKZyLO3X0h3/wAhQfKtRc4ECq5FRESmkTfLGvjU794B4HBDJw98ZLVf+/0vHSQlNoKrVmQRFR5CfGQYB2qcbcsHXG7+vreG9yzOICJs6JfbmZ6qHrsrWznW1M1Hz5k77PurvJ6c6ZQWIiIiMo0MVgK5fEkGmw83Ya31tu2uaOWVA/X804W5REeEYoxh/sx4DtQ6wfU7R1to7urniiUz/e453zPD/es3y4HAfGsRGTJmwbUxJsoYs9kYs9MYs9cY83XP8ZXGmE3GmB3GmK3GmLU+19xljDlojCkxxlw+Vn0TERGZrsrqOslIiOT8eak0dPRR1Tq0s+JT2yuJCg/ho+cOzTzPz3CCa2stuyqcwHxNjv/mL7mpseSlxVJUUk+IgaVZqvQhMpwTBtfGmGxjzBeNMX82xmwxxrxqjHnAGPM+Y8zJAvNe4BJr7QpgJXCFMeZc4L+Br1trVwJf9XyPMWYxcAOwBLgCeMAYE/ruHk9ERGR6eauskU2HGodtL6vvID8tjmWeAHiXz86KuytbWJKZSEJUuPfYgow4mrv6qe/opbiqjfT4SNLiIwPue7lnNnvhzARiI5VVKjKcYQNkY8yvgIeAPuB7wI3Ap4CNOMHv68aYi4a73joGlx+He76s52swISsRqPK8vhp43Frba609DBwE1iIiIiIAvHKgnht/vokbHtxE74AroN1a6w2uF81KwBhnsSKA223ZW9XG0uNyor0VQ2o62FPVOuys9KcK87nvhpX85MOrRvmpRKaXE330/IG1dk+Q43uAPxpjIoA5Qdq9PDPP24B5wP9aa982xnwO+Lsx5vs4wf06z+lZwCafyys8x0RERARn2/JB28qbWedTLg+cLcrbewaYnxFHVHgosxKiONLYBcDhxk66+lwBwfNgxZCtR5ooq+8MyLceFB8VztUr9b9lkZMZNrgeJrD2be/DmV0+0TkuYKUxJgl4yhizFPgkcIe19kljzIeAXwLvAYLV7LHHHzDGfNJzDzIyMigqKjpRFyatjo6OKdv3qUTjPH401uND4zx+JuNYv13cQ2w49AzAbza+Q1+Ff5m9n2x32pM7DlNUVE5iaB87D1VTVNTCW1UDAPRUl1JUVOZ3XUIE/PyVUlxuiO2ooKioetyeaTKO83SkcR4/J02aMsa8H/gmMNdzvsHJ+hhxrR1rbYsxpggnneRm4LOepj8Av/C8rgBm+1yWzVDKiO+9HgQeBFizZo0tLCwcaTcmlaKiIqZq36cSjfP40ViPD43z+JmMY/2jvW+wcm4oXX0uWkNCKSw819s24HLzqZee5x9Wz+V9ly4F4O9Nu3luTzWFhYW88ddiIsKOcMOVhYSH+meFLi3dxJtljcRHhnHr1esD2sfSZBzn6UjjPH5G8q/nRzgBcYq1NsFaGz+SwNoYk+aZscYYE40zO70fJ2C+2HPaJUCp5/XTwA3GmEhjTC5QAGw+pacRERGZxsobO5mbEktBehyldR1+bXur2ujqc3k3gwHITY2huauf1q5+dle2smhWQtDAeU5yDAAfWJk5roG1yHQ0kuW+x4A91rdQ5sjMAh725F2HAL+31j5jjGkB7jPGhAE9eFI8rLV7jTG/B4qBAeDTnrQSERGRM8ILxbXsr27jny/KIyrcv2BWS1cfLV395KbEAvD7rRU0d/Z5d2DcUt4EwNk+ZfTmes491NDB3so2rlqZGfR9P3PJPBZnJvDhtSdcSiUiIzCS4PpO4FljzCs45fUAsNbee6KLrLW7gIAlxdba14HVgVeAtfZbwLdG0CcREZFppd/l5t8ee4eefjftvQN85cpFfu1byp3FjEuyEugdcANwsL6Ds2OdYHrjvlry02KZmRjlvSY31QmuXyttoL13wFue73jZM2K46byc0X4kkTPSSH738y2gC4gC4n2+REREZJS8uK+Wnn4naN58uCmg/ZUDdcREhLJmbjIF6XEAlNY6qSF17T28fbiJ9y33n5mekxyDMfDMLmcJkzZ/ERl7I5m5TrbWXjbmPRERETmDbT7cTFR4CDecPYfHtxzF5baEhjiFtHr6XTy/t5Z1+alEhIWQmRhNdHgopXVODes3DjZgrbPlua/BcnwHajsIDzXebcxFZOyMZOZ6ozFGwbWIiMgY2lPZyuJZCSzLSqSn382h+qEFi4++fZS69l4+cWEuACEhhnnpcRz0LGosrmojIizEW7Pa12De9YKZ8USEabGiyFgbyb+yTwPPGWN6jDHtnq+2se6YiIjImcLZPdHZHXGxZwfF4uqh/9U+s6uK5dmJnJuX4j1W4BNc76tuZ0FGPGFBKn1EhTvHrl89O6BNREbfSYNrT+m9EGttlOf1iErxiYiIyMjsrWqj07N74uAixPIGZ2fF1q5+dhxroXBBut818zLiqG7tob2nn+LqNhbPCv6/5juvWMjd71vETefNHduHEBFgZDnXGGOuBS7A2THxNWvtn8a0VyIiItPMGwcbKK1t54a1cwLK7P1o4wESosK4dFEGUeGhZCZGcaSxE4C3DjXgtnBhgf9W5wXp8Z77NtLU2ceiWcHzqRfNSmDRMIG3iIy+kezQ+AAwD3jMc+g2Y8yl1tpPj2nPREREpol+l5tPP/oOLV39VLf1cNd7h8rs1bX38FJJHf+2fp63ZvXclFgOe4LrLeXNRIaFsCI7ye+e8zwVQ/6y06kEogBaZHIYycz1xcDSwU1kjDEPA7vHtFciIiLTyEv762jp6gdg0yH/Mnt/31ODtfD+FUNl9HJSY3luTzUA2482sywrMWAx4uwZ0USEhfDX3c55izIVXItMBiNZ0FgC+G7ZNBvYNTbdERERmX5ePVBPfGQY/3RBLvuq2ugdGNqA+NndNcxLj/Mrkze4bXldew97Kts4a+6MgHuGhYaQ58nPzp4RTUJU+Ng/iIic1EiC6xRgnzGmyBhThLM9eZox5mljzNNj2jsREZEp4OmdVTy5rWLY9m1Hmlk1dwar586gz+Vmf7VTn7qho5e3Dzdy5dKZfucPBtr/t62CPpebs+YkBdwTYG5KDACr5gQG3yIyMUaSFvLVMe+FiIjIFHb7Y9sBuHRJRsAMcltPPyW17bx36SwWzHSC5rL6DlbMTuKl/XW4LVyxdJbfNYPblD/8ZjkwfPD8zxfmkZMay79clD+ajyMi78JJg2tr7SvGmJnAWpxqIVustTVj3jMREZEp4FhTl/f1U+9UcvO6HL/2xzcfxVo4Lz+FrKRoACqbuwFn85eYiFAWzvSv9JESF0lWUjSVLd1kJUWTkRAV9L3X5CSzJid5FJ9GRN6tk6aFGGM+AWwGrgWuAzYZY24d646JiIhMBW+WNXhfbzvS7NfW2tXPfRtL2bAwnbNzZhAVHkpqXARVrU5wXVrXTkF6HCGebc59rZjtzF6vDpJvLSKT10jSQr4ErLLWNgIYY1KAN4GHxrJjIiIiU8GT2yqZnRzNvLQ4DtS2+7U9sqmczj4XX7x8AcY4AXRmUjQVnpnrA7UdXDw/Leh9v3LlItYvSB+2XUQmp5EsaKwAfP9r0Q4cG5vuiIiITC5ut8XltkHbiqva2FzexMfOncvCWQmU1XfQ73J725/dXcPa3GS/GtRZSdFUtXTT0tVHfXsvCzKCb/6SPSOG69fMJn2YlBARmZxGElxXAm8bY75mjPkvYBNw0BjzeWPM58e2eyIiIhNnx7EWzv3Oi5zz7Y28c7Q5oP03b5UTFR7Ch9bMZkFGPP0uy+EGZ/OXlq4+9tW0ccE8/50VM5OiqWrpoaTGmbcqyIgb8+cQkfEzkuC6DPgTzmJGgD8D1UC850tERGTKae3qZ3dFK5490oL6+WuHGHBbevvd/GGrf6m9vgE3T++s4qoVmSTFRHiD5NLaDgA2H27CWjg3L8XvurkpMaU2JecAACAASURBVHT3u3i+uBbAr761iEx9I6kW8vXx6IiIiPx/9u47vq37PPT/5wuAEwD33lsStactWbIl2bGd4Tpxlm+bNKvNTZt1m9ub3DS9rdMkt72dv660STOaNE1cZzjxjic1rEENihoUKe69ABIkAE4A398fBwAJAlRsR6Q1nvfrpVeo8z04POdYL+rJo+f7PGI1/dXzzfzgRA8fvaOcP3mgNmo9oDXH2hwcWJvDoGuGpsHJiPUL/S6m5vwcXJsDQFmmMdClKzi2vKHXRZxZhTcmhtxbm8cjT1zi20c7sSdYyE+Vsg8hbibLZq6VUt9USm1cZs2qlPqoUuq3Vu7WhBBCiJVzptsFGJsOB4PdOxbrdQcYn5pnb1UW6/JTaBmajKi9Do0x31VuZKatCRZy7AnhspBLA5NU59hJsJgjrpuXmhgOyNOt8eGNjkKIm8PVykK+DvwfpdRlpdSPlVJfV0p9Ryl1BKNbiB34yarcpRBCCHENzfkCtI24ecemfOb9mp+cjp6ueMlpjCg3gms7M/OBcOAMcKhllDW5djKs8eFj5VlWuhxetNY0DUxQW5ASdV2A//3WtQDhoTJCiJvHsmUhWutzwPuUUjZgB5APTAOXtdYtq3R/QgghxDV3ZdjNvF9z/4Y8upxejrQ5+PTd1RHnNDkC1OTayElJZG2eESS3jbipyrHRPuqhvmuMz9+/JuIz5VlWXmgaZtQ9i8Mzx/plguuqHDvP/Y995NilJESIm81rqbn2AHUrfytCCCHEtfPY6V6K0pLYs6RbB8CRVmPwy+aiNPZWZfOtIx14Zn3YEoy/Fi/0TdA87ueDu43Plmcb9dTto0bm+olzA5gUvGd7UcR1K7KtOL1zvBocLLO+ILLeerFQwC6EuLm8lm4hQgghxA2l3zXN539ynt/81kl6nFMRa4GA5tFTPewqz6A4I5ld5en4AprmRRsW/+jxC6TGKz5xVyUAtgQLuSkJdASD6/rOMWoLUqIyz6F+1qHOIuvypexDiFuNBNdCCCFuOo/W94S//unZyHrqY+1Oup1T/OauEgBKMoysdO+4EYQ7PbNc6J/grmILuYsGuFRk2ehweJjzBWjoHWdHaUbU9w1lqo+1OynNTMaeGHdtH0wIcd17Q8G1UkqCciGEENetI60OdpalU5ufwunusYi1H53qIS05jvs35AFQlJ4EQI/T6BjyarsTgA2ZkV0+KrKtdIx6aRqcZGY+wM6y6OA6wxpPerIRUC9Xby2EuLm95iBZKfVtpdTzSqk1wFdX8J6EEEKIN2x6zs/F/gl2lmWwqzyDhh5XeCS5zx/gUMsob92QR2KcETwnxpnJTUkIZ65PdjixJ1goS438K7Im187E9DwvBoe/LO1fHXJXTTYAD20tirkuhLi5vZ4MtBN4J/AloHhlbkcIIYT49ZzrdeELaHaWZbC1JI2pOX94auKF/gk8sz7uWLLJsSQjmZ4xI7g+3zfBxqJUTEv6T28sMoLpH9X3kJoUR2FaUszv/xfv3sTFL9/HPbW51/rRhBA3gNcTXI9praeA3wfKVuZ2hBBCiF+td2yKyZn5mGunu8ZQCraVpIf7SLeNGsH1sWDJx9KR5MXpyfSNTTHr89M8NMmmorSo69bmp2A2KZzeOTYUpiw7/CUxzhzuOiKEuPW8puA62Ov6H8Fozae13reidyWEEEIso298in1/+Qqf+6/GmOunusdZk2snNTmO8iwrJgVtw24AGntdVGRZybIlRHymOCOZwckZzvdNMO/XbC6KLvlIjDNTmpkMwNbi9Gv8VEKIm8VVg2ul1O8rpXqAbqBHKdWtlPr91bk1IYQQIto/vdwGwIuXh9FaR6z5A5qz3ePsKDOC3wSLmdJMazhzfWlgkvWF0YFzcUYyWsOzF4YA2FQcnbkGeOSB9fzR29byyQNV1+x5hBA3l2WDa6XUHwPvAPZrrTO11pnAAeCtwTUhhBBi1Z3vmwh/3bWkh/WrbQ48sz72Lqqprsy20TrsYdw7R79rmg0xungUBzuGPHV+gCxbPAWpsScn3lmTzcfvrCQp3hxzXQghrpa5/iDwkNa6I3Qg+PX7gN9e6RsTQghx69Fa8xfPNvPS5eGY6/6Apm3Uwz3rjM2Cx4KTEEN+eraP1KQ4DqzNCR+rzrXR5fRyrs8FxJ6aWBIs9xhxz7KpKG3ZemohhPhVrloWorWeiXFsGgis2B0JIYS4ZTX0uvjXQ+187HunGZyYjlrvdnqZ8wW4d30ueSmJ4Q2KAHO+AC9dHuFtG/NIsCxklquybcz7NT8504dJxW6hl7to0uKmGPXWQgjxWl0tuO5TSt299KBS6iAwuHK3JIQQ4lb1vWNd4a9D9c+LXQm21FuTa2dPZSYn2p3huuvT3WN4Zn0cWJMT8ZnqXBsAT58fZH1BasypiSbTQqb6/Tul26wQ4o27Wq+gzwC/UEodBc4AGtgJ3AE8uAr3JoQQ4hYy4p7hmQuDfOSOMn55cYizPeN8lPKIc55sHMCeYKEm186u8gx+1tBPt3OKsiwrh66MEmdWUT2sK7Nt4a9vr4ieqhjyT7+5FYvJRH5q7P7VQgjxWiybudZaXwI2AIcx+lpXBL/eEFwTQgghXrNAQPMfx7voCHbuWOrHp/uY92t+e3cZW0vSaehxRaz3jk3xzMVBPri7lKR4czgj3R68XtPAJGvzUrAu6TG9+Pfv3r781MR3bCoIj0QXQog3atnMtVKqCsjVWn9nyfF9SqkBrXX7it+dEEKIm8a/HGrnr37ZQnFGEof+8EBEKYbWmp+e7WNXeQblWVa2lqTx9IVBnJ5ZMoM9qR9v6Edr+M3bSgCoyDKC645RL3evM/53Z1ns/tPf/+gurAkW1uZFdwoRQohr6Wo11/8f4I5xfDq4JoQQQrxmvzjXD0Dv2DRH2iK7fDQPuekY9fKurYXAQilHp8MLGMH34w393FaeQVG60dkj3RpPhjWeDocH76yPftc0VTk2YrmzJpvtpTL4RQix8q4WXJdprc8vPai1Po2MPxdCCPE6zPsDdDq8fGh3KSYFZ7vHI9ZDXT/uqskGoCzLCiwE1w29LjodXt69LbKsoyLLSvuIl45R47zF9dVCCPFmuFpwHbuDvkF2ewghhHjNup1eY6x4cRo1uXbO9UbWU5/ocFKamUxBmvHXS1F6EmaTostpBM1PnBsgwWLi/o2RNdHlWVY6HF7aRo1/aK1cJnMthBCr5WrB9Sml1O8uPaiU+hhG9xAhhBAizD0zHzWOPCTUQq86x86W4jTO9brC5wYCmvrOMW4vzwyfH2c2UZyeRJfDmMDY0OtiW0k6KUva6JVmJuPwzHK220W82UR5MOMthBBvlqsF1/8D+IhSqk4p9TfBX4eA3wE+uzq3J4QQ4kbQPDTJxkee5+t1sfe6XxqYQCmozLGyNs/OxPQ8Tu8cAC3Dbiam59lVHtkmryzLSpfTi88foHlwktpYY8szjPrrF5qGqc61EWe+6mw0IYRYcVdrxTestd4DfBnoCv76stZ6t9Y6urP/EkqpRKVUvVKqUSl1SSn15UVrn1ZKtQSP/+Wi419USrUF1+77dR5MCCHE6vmzJ5sA+LsXrjDvjxziq7Xml5eG2VWWQXK8JbwhsW/cmMB4qmsMIDq4zrTS5fDS5fQy6wtQm798cD00OcO6GOtCCLHarjZEBgCt9SvAK2/g2rPAQa21RykVBxxVSj2LUa/9ILBJaz2rlMoBUErVAg8D64EC4EWlVI3W2v8GvrcQQohVMjPv53TXOBXZVjpGvZzocLKvOju8fmXYQ9uIhw89uB6AwnSjrrpvfIotxWmc7BwjPzWRovTI7Txlmcl45/zUtYwCxAyeS4LBNRAz+BZCiNW2Yv9+pg2hSQFxwV8a+D3gL7TWs8HzRoLnPAg8qrWe1Vp3Am3ArpW6PyGEEK+N1prvHetiwDUdc72hx8WcP8BnDlYD0Lhks+LT5wcwKbh/Qz6wEFz3j0+jteZU5xg7yzJQSkV8LtQx5NFTvcGpjNGbFTOt8eGv37Yx/w0+oRBCXDu/MnP961BKmTE2P1YB/6y1PqmUqgH2KaW+BswAf6i1PgUUAicWfbwveGzpNT8OfBwgNzeXurq6lXyEFePxeG7Ye7+RyHtePfKuV8eb8Z7bxv189eQM337lMn92R3SzqJ+3zaGAOMcV8qyKl861s8Fk9LTWWvPYyWnWpJu4dOZ4+DPWODh5qY10bzcj7lnS5h1RzzUyZZSXtI142JJt5uiRwzHvb3eBmcxEE80NJ2i+No8MyJ/p1SLveXXIe149KxpcB0s6tiil0oDHlVIbgt8zHbgd2Ak8ppSqAFSsS8S45jeBbwLs2LFD79+/f4XufmXV1dVxo977jUTe8+qRd706Xu97HvPOkZ4cF5UVDhmamOED3z7JJ+6q5D3LjAZ/+RcXgW563AFqt91OTkpkp9Zvtp5gXf48b3/LPl4cO8fRNkf4HpsGJhn65RE+fe969t9WGv5M2fkj6KQEVE4+cJ4P3Hc7Nbn2iOv6/AE+f/hZAH7jtjXs31se8/5W6o+d/JleHfKeV4e859WzKtuqtdYuoA64HyMj/bNg2Ug9EACygseLF32sCBhYjfsTQoib0cX+CbZ95QV+HpyMGMtf/bKFthEPf/jjRkbcM1HrWmueuzhEfqoRUDf2TUSsz/kCnO0Z57YKYzPiunw7o+5ZXFNGJ5CnLwRLQtZH96e+MuyhaXCSpDgzVTGGv1jMJj5wewnv21HEw7uKo9aFEOJ6tGLBtVIqO5ixRimVBNwDNAM/Bw4Gj9cA8YADeAJ4WCmVoJQqB6qB+pW6PyGEuNn9qL4HgKfPD8Zc9/kDPHdxMDwy/PKgO+qc5iE3I+5ZPhbMGneMeiLWj7SOMjMf4LZgp4/yrMix5UdaHewoyyDTlhDxuS3FafS7pjnW5qQqx4bJFDuz/tV3buQv37OZ5PgV/YdWIYS4ZlYyc50PvKKUOg+cAl7QWj8FfAeoUEpdBB4FPhTMYl8CHgOagOeAT0qnECGEeGO01jx70eia2tg3EXO4S8uwG++cn/fvMLLC3cFpiIsdumJ06nhgcwGZ1vjwmPHQ9/jL51oozUzmwNocgPAQly6nl3l/gOZBN1uL06KuuyV4rGXYTXWMjYpCCHGjWrFUgNb6PLA1xvE54APLfOZrwNdW6p6EEOJW0Tc+zZh3jo2FqVzon6DbORXuvhFytnscgPvW5/G3L1wJT0NcrL5zjMpsK7kpiUarPcdC5npwYoaWYTePPFBLgsUMGK3xTAo6R720DnuY8wdYX5gadd0Ni45V59ij1oUQ4kYlo6yEEOImdKHfqI1+aJvRdClUprHYqa5xsu0JFGckUZqZTM9Y5Dlaa872jLOj1Cj5qMiyRWSuLwa/x8aihcx0vMVEUXoync6p8PqGGJMVE+PMfO4tNcBCFlsIIW4GElwLIcQNaGbez58/cxmnZzbm+vm+CeLMivs3GBsJlwbXWmuOtTvZU5mJUorSzGS6nJGZ67YRD66pebaXpgNQkW3F6Z1jYmoegEsDk5iUsYlxscpsK63Dbi4OTGBLsFCWGZkxD/nM3dWc+eN7uL0iI+a6EELciCS4FkKIG9AvzvXzjcMd/PXzV2KuX+h3sTYvhbyURFISLVHB9ZVhDw7PLHdUZgHGqPEe5xT+wEJt9l8/30KCxcTeauOcimBHj/ZgacilgUkqsm1Rmw3X5afQPuqhocdFbUHKspsVATJtCcu2CRRCiBuRBNdCCHEDOtrmBKChZzxqTWvNhb4JNhSmopSiPNsWFVw/dX4ApWBfjRE4l2ZamfMHGJo02vENTkzzy0vDfOKuSgrSjMExFdlGBrpz1IvWmsY+Fxtj1FOvy09h3q+50D/BhoLodSGEuJlJcC2EEDeY6Tk/dc0jgNEqb2gisj91z9gUkzM+NhUZgW15ZnJEcD3vD/DoqV4OrskhP9UInMsykwHoDp53qssI2t9Smxv+XElGMhaTosPhYWhyhlH3LJuLooPn2kU11hsKo+uthRDiZibBtRBCXGdOdDh5778e48pwdN9pgOcuDeKe9fH5+9cA0Njnilg/12v8PpRVLs+y0e+aZmbe6G56qnOMUfcs792xMJGxJBRcjxl116e7xrDGm1mbt1BPHWc2UZKRTMeol8bg99gcYzNiWaaVgtREKrOtHAy26BNCiFuFBNdCCHEd8Qc0v/O905zqGucrTzXFPOexU32UZibzkT3lWEyK80uC67qWUdKT41iXb2SNy4PlHN3BDYsvXB4m3mLizprs8GfyU5OIN5voci5krreVpmMxR/41UZFtpWPUy7leY8Nk6HssZjYpjn7hIC9+7i7SkuPf4JsQQogbkwTXQghxHel2evHM+qjItnKk1UHfeGQHj5GpAMc7nLx3exFJ8WZqcu2cXzSS3B/Q1LWMcGBNDubgRsKKYH/rzuBGxLqWUe6ozIzYiGg2KYozkuhyeJmcmad5aDLcgm+ximwbnU4vZ3vGWZefQmKcOeZzmExKNioKIW5JElwLIcQqW9yRY6mWIaMU5BN3VgJwqmssYr1+yAfAQ9uMko71BSk0Dy2Uj/z4dC/jU/PhFnxAeHhMh8PLiHuGToeXPcEuIYuVZxkbH892j6M17CxLjzqnIsvKnC9AfecYm4ukP7UQQiwlwbUQQqyiM91jbP/qC7wS3JC4VPOQG6Xg7ZvySUm0UN8ZGVxfdPhZl58S7uBRmWNj1D3LxLTRe/ofX25jR2l6xEZEW4KFbHsCnaNeTgc3Ku6IEThXZlvpckxxrN2J2aTYUhIdPIfa8UHsemshhLjVSXAthBCr6O9fasM1Nc9nHm3A5w9ErTcNTlKWacWaYGFrSTrnehdKPryzPlrHA9xZs5B1rgz1nh71MDI5Q79rmrduzI8qySjPstLl9HKqa4zEOFPE+PGQimyjHd83D3dwR1VWVP/q0Dkhe6uis99CCHGrk+BaCCFWiWtqjsNXRinNTMY94+PKsCdifcQ9w6GWUfYFh7aUZ1npcRo9pQGOtzvxa7iremEjYlVOMLge8XBxIDiOPFbgnGWl0+E1+l8XpBJnjv7xvzgr/c4tBTGfIcuWwBfuX8uTn9pLXmri63l8IYS4JUhwLYQQq6Qh2L7uE3cZ9dSLW+j5/AE+/5Pz+AIBPrynDIDSzGS8c36c3jkADl0ZJd4M2xeVdBSnJxFnVrSPernYPwlE9pkOKc+y4vDMcbp7PGbWGhay4PFmE2/dkL/sc/ze/ko2xuhvLYQQQoJrIYRYNQ09LkwKHthcQEqiJaKFXn3nGHUto3zp7bXhDHJJhtF7uifYe/pI6yjrMswkWBY6dFjMJsoyrbSNeLjYP0FFlhVbQnQ5R2hTI8TObANkWON57L/v5vT/uYek+NhdQIQQQlydBNdCCHGNjEzO8MgTl/DM+qLWtNa82uZgTV4KtgQLG4tSw5lmgHPBQPvd2wrDx0qDg116nFMMT87Q5ZxiXUZ00FuZbaNj1Aiul8tKVywKrjcXL5913lWeQUpi3K94UiGEEMuJTm8IIYR4Q/7lUDv/fqyL5Hgzn79/bcTaKy0jnOke55EHagGoybXzaH0vgYDGZFKc63FRnmWNGLpSlB6cmuicwmI2NijWZETnRKpybDx3aQhYftx4VY6NP32gltyURKpy7DHPEUII8euTzLUQQlwDWmsOXxkF4If1PeFNiCHPXRwiLTmO37q9FIDqHDvT8376XdNorWnsc7FlSWu7xDgzhWlJdDg8nO4aJynOTIk9+sd2Zc5CVnpDQeystFKKj9xRzts2Ll9LLYQQ4tcnmWshhLgGBidmaB/1Up1jo3XEw8DEDIXBXtQA53pdbC1OC3fpqMk16qpbR4wBMMOTs2yN0Ve6KsdG24jRVWRbaRoW00z0OdkLmWjZaCiEEG8uyVwLIcRr4J6Z579O9Sw7XfFiv9EG7707jMmJV4YXpiZOzszTOuJha8lCl4/qYGnGlWEPJ4ODYm4rz4y6blWOjUsDk1waiD2OHIxSkL94aCNHv3AAu9RLCyHEm0qCayGEeA3++ZV2vvDTCzxzYTDm+sWByXAnEIC2RT2sz/dOoDURZR+pyXHk2BNoHfZQ3+kkLTmO6hxb1HUXH9tZFju4Vkrx8K6ScI22EEKIN48E10II8SvMzPv5UX0PAP92pCPmOZf6J6jKsZGfmkSWLSEic93QY4wcXzouvCbXTuuIm/rOMXaWZWAyRU5VBKjOXSj52FYq48aFEOJ6J8G1EOKW5w/oiGB4qeYhNxPT89Tmp3ChfwLvklZ7/oDmTM84m4qM4Le2IIWLA4va7PW6qMqxkZoUWbJRlWPjfN8EXc4pbiuPnZXeVpLG3z+8hfo/ujvmOHIhhBDXFwmuhRC3vP/3XDP3/t1hLvRNxFxvGTIC5Ye2FaI1UYH4ud5xXFPz7F9jjCXfUpRKy9AkU3M+tNY0BDczLlWzKCu9a5ngWinFg1sKyUmRUeNCCHEjkOBaCHFL88z6wqUe3zoau+Tj8qCb5Hgzb6nNDf9+sR+f7sNsUuyrCgbXJWkENFzom6B91MOYd47tpelR1w31pM5PTaQ2P3Z/aiGEEDcW+TdGIcQt7cWmYbQ2SjReujyC1hqlImufW4bc1OTaKU5PxpZg4fLgQsnHxf4JHj3Vy8f2lpOabJR9bA6WhzT2uUiKMyYq7q6M7gSyqSiNZz+7j6ocGxaz5DqEEOJmID/NhRA3tcZeF488cYlZnz/m+tMXBslLSeRDe8rwzPoYmIjsIz3r83O+z8WGwhRMJkVVjo320YVOIM83DWNS8OmDVeFjmbYE8lMTuTzo5niHk4LUREoyYnfyWJefEu59LYQQ4sYnmWshxE3LPTPPg//8KgDr8u28f2dJxHogoDnW5uBd2wpZl2fUP7cMTUYMfznRMYZ3zs/da42SkIosK8c7nOH1o62jbCpKixhbbny/FJoGJpmYnue2ioyobLgQQoibk6RLhBA3rcbehQ2K/36sO2q9y+nFO+dnU1FauOVdy5An4pynGgdIjjeHyzrKs6wMTswwNefDM+ujsW+CvVVZUddel2+nZdjN0ORMuExECCHEzU+CayHETasl2NXjt3eX0jw0iWdJC71Qu7wNBamkJsVRkJoY7gwC4PTM8ovGAd61tZDEYO10ebYVgC7HFOd7XfgDmp0xOn3U5i+MId9cLCPJhRDiViHBtRDihjUz76f1Kv2prwy5ybTGc2BtDjrYvWOxi/0TxJtNVOcaUxBr8uy0LJqs+KP6HuZ8AT5yR1n4WEWWcW6nw8vZ4HCYLTHa7B1Ymx3+enGgLYQQ4uYmwbUQ4ob1589c5i1/d5jTXWMx11uG3VTn2sJlGef7XBHrR1odbClJC28oXJNrp33Ew7w/gM8f4D9OdLOvOouqnIV+1BXZVuLNJhp6xmnoiT0cBiA53sKpL93Df338dpLizdfqkYUQQlznJLgWQtyQ/AHNT8/2A/DVpy9HrXtnfTQPTbIuP4UMazwFqYkRLfQGXNNcHpzknnU54WNr8uzM+QN0O71cHnQzPDnLe7YXRVw3Mc7MbRUZvNIysuxwmJBsewK3VUS34BNCCHHzkuBaCHFDOtfrwjProyQjmYv9E0zPRbbae+bCIDPzAd6xKR+Asiwr3WNTEesAd6/LDR+rWbSpsaHXKPmINfxl/5oc2ke9jHnn2FoSvS6EEOLWJcG1EOK6NO8PMDI5s+x6aAT5R+4owxfQXByIrKf+6dk+yrOsbAsGv6WZyXQ7jeA6END858ketpemU5ltC3+mKseGSRnt+M71uMi2J0S05Qt5+8b88NdbS6QTiBBCiAUSXAshrkufe6yRXf/3JSam52Outw57SIoz8/ZgZvps93h4rXdsihMdYzy0tTDcX7o008qYd47JmXmaBifpdHh5/87iiGsmxpkpy7LSMuymodfFluK0mP2p81ITWRvsix3KdgshhBAgwbUQ4jrU75rmycYBAJ4I/u9SrSNuqnJs5NgTKUxLCrfVA3i8wajFfte2wvCxskxjQmKPc4pTwQ2Qd8ToT702z87JzjE6Hd6YXUBCfvJ7ezj0v/ZjNslwGCGEEAskuBZCvCl8/sCya4+f7QPAlmDhyXPLBNfDHqpzjJIOYxqiURaiteZnZ/vYXZFJUfrCyPGSjGB/aqeXU11jFKYlxSz5qMm145oysuVX26xoS7BQmmm92iMKIYS4BUlwLYRYdX/yi4sc/JtDUZsQQ55oHGBnWToP7yzmXJ+LOV9kIH6me4yhyZlwvfP6ghQ6HF6m5nxcHnTT5ZyKyFqDUXMN0O2c4my3ix1lsTci7ihdGAizsUj6UwshhHh9JLgWQqyqU11jfP94Nz1jU/zkTG/Ueu/YFFeGPbx1Qz7bS9OZ8wW4tGSz4ndf7SItOY6Hthlt8moLUtAaLg+6aR0xNjouLemwJljItifQ0DPO0OQMGwpiB857q7P48m+s57/fVYE9Mbp/tRBCCHE1ljf7BoQQt5YXm4aJMyuK0pN58vwgH9xdFrF+osMJGPXQ6clGcHumezzc8s4f0BxpdXBvbS7WBONHWKg8pMvhpcvpxWxSlMUo2SjLTObFyyOAEZAv50N7ypZdE0IIIa5GMtdCiGtqxD1DfWfsiYkAh1sd7CjN4LbyDNpGPFHrxzucZFjjqc6xkZOSSIY1ng6HN7zeNDDJxPQ8e6sXNiMWBGun+13TtI14KM1IJt4S/eMtVHcNRp22EEIIca1JcC2EuKZ+7wdned83jvPY6eiSjxH3DJcHJ9lXk0VVjo0x7xxOz2x4fdbn58WmYe6qycYU7MJRnJFMj3Nh+Etdi5F53l25MPkwMc5Mli2B/nEjuK7MWehdvVhxhhGEZ9kSyLDG//oPK4QQQiyxYsG1UipRKVWvlGpUSl1SSn15yfofKqW0Uipr0bEvKqXalFItSqn7VurehBAro23Ew5lgv+kf1fdErb/a5gDgp3vhVAAAIABJREFUzupsqoIB8OLs9aGWUSZnfDy4pSB8rDQjmZ5FkxWfvjDIjtJ0cuyJEdcuTE+iedhN+6hn2ay00UEkiX/8b1vf4BMKIYQQV7eSmetZ4KDWejOwBbhfKXU7gFKqGHgLEP7bVylVCzwMrAfuB76ulDKv4P0JIa6x7x/vIt5s4oHNBbQMuQkEdMT6kSsOMqzx1OanUB0cvtI2uhBcv3R5hJRES0T/6ZKMZPpd08z7A7SPemgecodHmi9WlJZEY6+LgIY7FmW1F7utIpOjXzgYkfUWQgghrqUVC661IfS3ZlzwV+hv2r8DPr/o9wAPAo9qrWe11p1AG7Brpe5PCHFtzfo1Pz3Txzs257O3KpOpOT+94wsZZ601h1sd7K3KwmRSFKQmkhxvpnXYE14/2uZgd2UmceaFH00lGcn4A5oB13S4lnv/mpyo71+YbpR8JMaZwpsfhRBCiNW2ot1CgpnnM0AV8M9a65NKqd8A+rXWjUvGChcCJxb9vi94bOk1Pw58HCA3N5e6uroVuvuV5fF4bth7v5HIe762XLMBJmY1pSnR/6jUNuLFO6co1A68A0bHjx+/cJwdecaPmV53AIdnlpyAI/zfJCdRc6qlh7q6UUamAvS7pjlY4I/4bzY2ZvTCfuLl45wc8mOPh84L9XQtGUtumvABcE+xmWNHD1/rR79uyJ/p1SPvenXIe14d8p5Xz4oG11prP7BFKZUGPK6U2gR8Cbg3xumxZgjrqANafxP4JsCOHTv0/v37r90Nr6K6ujpu1Hu/kch7vna01jz0L8do6HHxyAO1fPiO8oj1o//5AjDHuw7eTn5qEl858RyWrBL2768B4BuH2oFmfveBfeSmGPXS24bPcbzDyf79+/nJmT6gkQ/edzs1wZIRgA2eWf6i/kWS8ysZ6O7m9qpMDhzYEXV/d2nNH7w3QILl5q4mkz/Tq0fe9eqQ97w65D2vnlXpFqK1dgF1GKUf5UCjUqoLKALOKqXyMDLVxYs+VgTEnnsshFh1x9qdNPS4AGNT4VL9Hk282URJRjJJ8WaK05MjNiseaXWwJtceDqwBKnNsDE7M4J6Zp6FnHHuCharsyE4foc4eJzucdDi8bC+NXfKhlLrpA2shhBDXv5XsFpIdzFijlEoC7gEatNY5WusyrXUZRkC9TWs9BDwBPKyUSlBKlQPVQP1K3Z8Q4vV5vKEfe6KF9+8o5vJg9GbFfneAimwrlmC9dHWOLRxcT8/5qe8aY9+i3tShcwDaR72c63WxuTgt3IJvsaocG883DQMsG1wLIYQQ14OVzFznA68opc4Dp4AXtNZPLXey1voS8BjQBDwHfDJYViKEWAVPnx/kr3/ZgmNR3+mQeX+AF5qGeUttLltK0vDM+ugbnw6vv9w8zAWHn9srFrpwVOfa6Rj14vMHqO8aY84XYF9NdsR1Q+34LvS5aB5yR40sD6nJXchmbyyMPbZcCCGEuB6sWM211vo8cNVmssHs9eLffw342krdkxAitucvDfHJH54FYHBihr953+aI9cZeFxPT89xbm0d+qlHW0TQ4QUlmMgA/qu8lI1Hxv9+6NvyZ6hwbc/4A3WNTHLkySrzFxK6yjIjrlmQkE2828XhDP/6AZmtJ7OD6rRvy+cGJHm6vyCAxTko/hBBCXL9WdEOjEOLG8PSFQbJsCWwsTOFMd/To8tPBwTA7y9KxJlgwKWgadHP/hny01jT0uFiTYY4IfKuD2ebWYQ9HWh3sKssgKT4yMLaYTZRnWTkbrOVeLnN9R1UWjX9yLyaZKSuEEOI6J39VCXELaBqYjBk0AwQCmqOtDvZVZ7GzPIMu5xTj3rmIc850j1OeZSXTlkBinJmKbBtNA5MA9LumcXhmqUiN/HESKvl4tc1By7A7qt46pCLbChijyTNtCcs+Q2pyHPbEuNf2wEIIIcSbRIJrIW5ygYDmbf9whHf/y3EGXNNR681DbpzeOfZWZYUzx419rvB679gUh66MsmfRVMPa/BQuDxrB9ble49zKtMgfJ8nxForSk/iPE90A7KuOrLcO2REsFfndfRVv9BGFEEKI64YE10Lc5A5dGQ1//W9HOqLWz/YYJR+7yjNYn29sFrwy7A6vf+NwOyYFnzpYFT62Lj+Fftc0E1PznOtxEW8xUWyP/nFSGWyrl2VLYF2+PWod4MN7yrjwyL389u6y1/9wQgghxHVGgmshbnKvtIyQHG9mR2k6Z4K104s19LjIssVTlJ5EanIcWbaEiP7Ux9qd7KnMIj81KXws1L2j3eGhodfFxsJULDFa6IU2KH7w9lKWTGQNM5uUlHsIIYS4aUhwLcRN7lyvi01Fqewsz6BpYJKZ+YUOl1NzPo61O9hSnB4OfqtyrOHg2umZpWPUy84lXT5KM4066bYRDxf7J5bdiPh7+ys58cW7+ew91SvxaEIIIcR1R4JrIW5gWmu++2onPzzZE3N9Zt5P08AkW0vS2VKchi+guTQwEV7/+5daGZqc4aN3lIWPVQWHv2itOdW10CVkseKMJJSCX14cYtYXWDa4TrCYyUtNjLkmhBBC3IwkuBbiBvazs/18+ckm/ujxC3SMeqLWLw1M4AtothanUZufAhit8UKevTDEgTU57Kla6ORRlW1jcsbHqGeWw62jWOPNbCqKDJ4TLGYKUpN4qXkEWL6FnhBCCHGrkeBaiOvYlWE3XQ7vsus/P2eMJI83m/jBiejsdUOof3RJGvmpicSZFd1jUwD0OKfoGZviziUt8qpyjI2HbSMeDrWMsqcqi3hL9I+K0uAAmVC9thBCCCEkuBbiujUz7+fevzvM/r+uo8c5FbXunfVxsmOM9+8oZmtJGqdj9LFu6HVRmJZEjj0Ri9lEcXoy3U4jWD/W7gBgb3XskeQvNo3Q75rmrprYLfRqco0gfHtp+rKbFYUQQohbjQTXQrxJJmfmIzYXLvXEuYHw14+eis5KH2t3MucPcHBtDjvK0rk0MMnUnC+8Pj3n52THGFsWjRQvzUymy2EE6o19LlKT4qgMDnEJyU1JwJZg4cenewGiNjOG/O+3ruW7H97Jl39jw2t4WiGEEOLWIMG1EG8C98w8O77yIp/64dllz3nx8jDFGUmsybXTPOSOWn+5eQRbgoUdZRlsL03HH9Bc6FvYrPivh9pxeGb5wG2l4WOlmVa6nV601lzon2BTUWpU1lkpRWWODfesD3uChepgJnupxDgzB9bmyIZFIYQQYhEJroVYAT8+3curbY5l179xqIM5f4AXL49EjRoHowvI6e5xdpVlsjbfTsuS4DoQ0NS1jLA3WA8dKtHoWFSf/ctLQ+ypzGT3osmKFdlWvHN+Oh1eWobcbCxMjXl/W4qM45U5Nkwx+lcLIYQQIjYJroW4xqbmfPyvn5znt751MlzfvNRLzSOkJRuDU569OBS13j7qZcw7x86ydNbk2el3TTM5Mx9er+8aY3Bihvs35AGQn5pEvNkU3vzonpmnZdjNrvLIko5Qice3jnYy79dsKoodXH/hrWv5zN3VfP6+Na/z6YUQQohbmwTXQlxjR1sXMtY/Pdsfte70zHJ5cJLf2VtOYVoShxeNJw955sIgAHsqs1iXZ7TQW5y9/tnZPqzxZu5bbwTXZpOiJDOZzmBwfa7XhdawrSSyP/WaXDuZ1vhwX+zl6qmT4y187i01ES36hBBCCPGrSXAtxDX2SssI9gQLa/Ps1Hc6o9aPBstF9lRlsbcqi1fbHfj8gfC6P6D5wYlu7qzJpiQzmTV5RslHqO5aa80rLaMcXJdLUrw5/LmyTCtdwUz52W4XShGxmRHAZFLh7h/xFhOZtoRr+ORCCCGEkOBaiGvseLuT2yoy2VOZRUOPi1lfZEeQJxsHyU1JYHNRGnuqMnHP+CI2LJ7tGWfEPcv7dhQBkJ+aiD3RQvPgJACtIx5G3bPsW5JVLs9Kpts5RSCgOdMzzppcOymJcVH394fBUo/3bC+6ps8thBBCCAmuhbimBiem6XJOsbsykx1l6cz6AjQPLgTO3lkfh66M8I5NBZhNamFq4sjCOS9eHibOvJBhVkqxLi8lXBYSKjvZU7WwURGgMtvGrC9A99gUDT3jbF1SEhJSkJbEmT++hz95R+21e3AhhBBCABJcC/G6vdI8Qu9Y9FAXgCPBwHd3RSbr8qNrpRv7XMz7NXuDUxFLM62YTYq2EWMkudaa5y4OcXtFJvZFWec1eUbHEK01x9odlGUmU5SeHPG9Q9/vycYB3DM+tpfGDq4BMm0JJMaZl10XQgghxBsjwbUQr8OzFwb5yL+f4hM/OBNz/fGz/ZRlJrMu305pRjJJcWaaguUcAGe7xwHYVmwEvvEWE2WZybQOG8H12Z5xup1TPLilMOK6a/PtuGd99IxNcaJjLOZGwzV5dkwKfnCiG+CqwbUQQgghVoYE10K8Do+eMqYWNg+5o6YrDk/OcLzDyUPbilBKYTIp1uTZaR5aCK7PdI9TmW0lNXkhK12VY6Nt1Aiun2wcJMFiCrfYC1kb3NT4X6d68cz62BsjuE6MM1ORbWPEPUuGNZ6yzOSoc4QQQgixsiS4FmKR7xztpKFnfNn1DoeHtOQ4/AHN8fbITiAvN48AhNvjAazLN6Yraq0Zcc9wtM3BncFa6pCqHBvdzinmfAEOt45ye0UmtgRLxDmhITHfebUTpYyyk1j2BctNCtISoyYvCiGEEGLlSXAtRJDDM8ufPdXEu75+jAHXdNT6zLyfvvFp/tuuEpLjzbzUPByx/nLzCIVpSdTkLowLX5efgmtqnqHJGb5/rJt5v+aDt5dGfK46x44/YNRSd4x6wwHyYvbEOIrSk5iZD7Am1066NT7mM3z+vrW8bWMenzpQ/UZegRBCCCF+TRJci1tCXcsIf/t8C0MTM8uec6JjIRP9XIypiV1OL1obJRp3VGXxSvMoWmsAZn1+Xm1zcGBtdkTGeG1wAMyLTcN880gH79iUT0W2LeK6VTnG7793rAuAfdWRme2Qd2016rD3r8lZ9hmS4s18/be2R5WVCCGEEGJ1SHAtbgmPPHGJf3i5jQ9/tz6qVjrkWLsTW4KFgtRE6jvHotZ/3jAAGC3v9q/Jpt81TbfT6BpysmOMqTk/B9dGBr6hATD/95lm5v0Bvvi2dVHXrcy2oRS80jJKjj0hIvO92P+8dw0/+/09fObuqtf+4EIIIYRYVRJcixteIKD582cuc7F/Iub68OQMXc4pdpSm0zzk5pVgbfRiY945ftHQz8G1OeyuzKK+ayyclQY43+fiXw+1U51jozrXxqZCY/JhqBPIy80jJFhM7K6ILOlITYqjMC2J6Xk/GwpSKUxLivreSfHm8PG9VVlXrZXeVpJOcrxl2XUhhBBCvLkkuBY3vGPtTr5xuIMPfPtkzPVQuccfvX0d8RYTZ2NsWPz6K21Mzfv51MEqtpakMeadY3BRCcmPT/eRYDHxk0/sIcFipjrXhsWkuDQwERxHPsKeysyIceQhf/CWGgDetjF/2Wf49MEqbivP4OFdJa/r2YUQQghxfZEUmLjh/bDe6Ovsmprn8uBkeJhKyKGWUdKS49hclMbGwlQaelwR650OL9873sV7txdRk2vH4ZkFoGPUS0FaEjPzfp5oHOC+9XnhFnqJcWaqcmw0DUzS4fDS7ZziY3vLY97fe7YXsbUkjfJM67LP8P6dJbx/pwTWQgghxI1OMtfihjYz7+eV5lEe2FxAnFnxeEN/xLovoHmpeYS71+ZiNim2laRxvn+CeX8gfM6fP3OZeLOJP7xvDWDUQIPRdg/gpcsjTEzP857tRRHXri1I4dLAJK+2GVMZ99csv9GwMtuGySSt8YQQQoibnQTX4ro35wssu3aiw8n0vJ+HthVyV002TzYORNRKXxkPMDE9z73rcwHYUJjKnC9Ae3Boy7h3jhcuD/OhPWXk2BMByLEnYI030zHqBYxx4rkpCdyxZHBLbX4KI+5Znrs4RF5KIsUZ0fXUQgghhLi1SHAtrmv9rmm2f/UFfud7p2MG2YevOIIbCTO5vSKTwYkZXFPz4fWzwz4SLKZw7+jaYMlI04CxEfFomwOt4Z7a3PBnlFJUZNvocHgJBDQnO53sq87GvCTzvL4gFTBqvreXpcvQFiGEEEJIcC3ePLM+P3/+zGX6xqeWPecfXmzFPePjxcvDHLoyGrV+pmeczcVpJMaZKckwxn33jBnX01pzdsTPvurscIeN8iwrCRZTOLg+fGWU1CSjHnux8iwrHaMe2kY9jE/Ns6s8I+p71y6q7d5VFr0uhBBCiFuPBNfiTfOjkz1843AHf/XLlpjrPn+Apy8M8s4tBcSbTZzqiuw9PTPvp2lggm0l6QCUBjcMdgeD60sDk4zN6HBJCIDFbGJtnp3LQ5NorTnS6mBvVVZUVroi20q/a5rDwYD+thjBdWpyHOnJcVhMivfuKIpaF0IIIcStR7qFiBWhteZC/wTr8lOIM8f+/3DfP250+TjVOUYgoKM2/DX2ufDM+rh3fR79rumowS5PnR9k3q/ZUWoE1+HMtdOolX7+0hAKuHvJYJfaghSeuzhE64iHocmZmOPGK7JtaA2Pne4lNyUhfO2lnv3sndgSLdJ7WgghhBCAZK7FCvnByR5+459e5VM/PBuxwTBk1D1Lh8NLdY6NgYkZWkc8UeccvuJAKdhdkcm20nQuDSx0+ZiZ9/PlJy+xozSd/WuMceFJ8WZy7AnhqYmHroxSlWYi05YQcd3a/BTGp+Z5tL4XgH010ePGK7KMLPiVYQ+3lWcuW0+dl5qILUECayGEEEIYJLgW15w/oPmr55oB+OWlYVqG3VHnnOs1ek1/+I4yAJoGI6craq35xbl+dpZlkG6NpzrHzrxfh+upT3eN457x8ckDVVgWZcbLsqy0jXqYmvNxcWCSNRnRQ11CfbC/82ontfkpMacmlmct9KSOVW8thBBCCBGLBNfimrsy7GZyxsenD1YB0NjrijrnbM84FpPiwS2FxC/aYBhyvMNJl3OK9+0oBqAqx+g93R7McB9pHSXOrLitIjLw3VqSxsX+CU52jOEPaGrSo/+Ir120EfG+9Xkxn8GaYGFfdRYH1mTzrq2Fr/XRhRBCCHGLk+BaXHOnu43x4u/ZXkRKoiWcpQ7xzvr48ek+bqvIwJZgYU2uncuDkdntf3iplWx7Am8PjgyvzDYyyW3B/tTHO5xsLUmPqnXeWZrBvF/zb0c6UAoq06Iz17YEC597Sw0H1mRfdSPif3zsNr77kV1YpexDCCGEEK+RBNe3kHHvHF95qgnPrG/Zcyam5/nqU01XbY9X3znG9493MTE9v+x6tt3YBLi5OI1zvQslH9Nzfh74x6M4vbP8wT01gFED3TQ4Ga7NPtbu4ETHGJ/cX0lSvBEc2xPjyE1JoG3Eg88foHnIzeai1KjvvaMsPXgNJ5uL0rDGxa6V/szd1Xz3I7soiFESIoQQQgjxRklwfQv518PtfPtoJ9892rnsOd871sW3jnby8DdP4A9Eb0Sc9fn50Hfq+ZNfXOK3vnWCWZ8/ar2ueYT9NdkopajNT6F91AiIAc50j9Ph8PKX797EjmBv6HX5dsa8cwxPzgLwz6+0kZ+ayMO7SiKuXZVjo33US4fDy5wvQG1BCkulJcezrcToWR2rfZ4QQgghxEqS4PoWcuSKA4Cfn+uPuT7r84fb4/WNT4dHhC92vm+C6Xk/99bmcrF/kqfPD0asv9rmwD3r423Bco6qHBtzvgC949MAnOoaw6Tg/g0Ltc61wUmHlwcnmZn3U985xoNbCkmMiyzpqMq20T7iCddn1+ZHZ64B/vSB9WTZ4nlom/SeFkIIIcTqWrHgWimVqJSqV0o1KqUuKaW+HDz+V0qpZqXUeaXU40qptEWf+aJSqk0p1aKUum+l7u1mNO6d49tHO5me88dcH3XP0jQ4SbY9gfZRL07PbNQ5TzYO4vDM8ifvqAXgQt9E1DknO5wA/MW7N1GVY+NH9T0R689eGMKeYGFPVSawsBGxLbgR8Uz3OGvzUrAnxoU/szbfDkDT4CSNvS7m/ZqdwfKOxSpzbHhmfTzfNESCxURFtjXqHIDNxWmc/uO3sCbPHnNdCCGEEGKlrGTmehY4qLXeDGwB7ldK3Q68AGzQWm8CrgBfBFBK1QIPA+uB+4GvK6Wid6Pdgqbn/Dx1fiBmv+iQz//0PF95qolHnrgUc/1ivxEoP7zT6L5xZTgyK6215ttHO6nJtfHbu0tJjjdzoT8yuPb5Azx1fpC1eXYyrPHcXpERcZ15f4AXLg9zT20uCRbjP10ouG4dcePzBzjbMx6uiw5JSYyjOCOJpsHJ8GbI7aXRwXVVtnGtZy4Msbsyc9nhNEIIIYQQb5YVi060IRR5xQV/aa3181rr0I66E0Do3+4fBB7VWs9qrTuBNmDXSt3f9SQQo7Z5sb9/qZVP/bCBFy+PxFxvHXbzQtMwAI+d6Y2Zlb7QP4FS8M5gW7krS3pPH+9wcnlwko/eUY7FbKI2PyUckIc80ThA85Cbz9xdDUBxejIT0/NMzhgbGxt7Xbim5rm3dmHcuD0xjryURNqGPVwedDM15w/XWi+2Li+FywOTnO4aoyrHRlpyfNQ5oUAd4K4Yg1+EEEIIId5sK5r6U0qZlVLngBHgBa31ySWnfBR4Nvh1IdC7aK0veOym9vylIWr/9DmahyaXPefSgBHkPtk4EHP9ycYBTAq+8+EdaG1MJlzqQv8E5VlWKrKspCRaooLr7xztJMMaHw6+1+TZaR3xRGTLn7s4REFqIm8N1ksXB0eC9wYHu5zocKIU3F6RGXHt6lwbbaMeTnUZ48tjlXzUFqTQ4fBytM0Rcx0g257Ah3aXsqEwhbduyI95jhBCCCHEm2lFG/hqrf3AlmBd9eNKqQ1a64sASqkvAT7gP4Onx+qZFpXSVUp9HPg4QG5uLnV1dStx6yvO4/FQV1fHP52eYWY+wKf//VW+dHt0WzhfQHOqwwhen7s4wIsvu7CYIl/VT+unjGEpg02kxCt+fOQiGZNt4fU5v+bolSl25lk4dOgQeUkBTl3po67OqJ8enQrw0uVpHqiM48SrR4wPTcwzMT3PL55/hbQEE3N+TV3LFHsLjWsAjEwY9d3PHTnFaK6FZ85MU2Qz0XjqWMT9Jc7NUj/o45c+DxmJipaGk7QseU6/0/jHjHm/xjo9vOx/1wOpcGAjNDecoPlXv+bwexYrT9716pD3vHrkXa8Oec+rQ97z6lmV6Rhaa5dSqg6jlvqiUupDwDuAu/VCarQPKF70sSIgKlWrtf4m8E2AHTt26P3796/gna+curo69u67k8/UvQBA20SAHbv3YlsysORI6ygz/nretbWQxxv6yazawtaShcyu0zNL/3Mv8vn7azi4v4qtnfU4vbPs378vfM6TjQNM+xr43fu2sa86mxfGL/DU+UHuuusulFL88GQPmgt89p17qAzWNVtaHfxn80lyqjaxpzKLnzf0M+c/x8fu3c4dVVkAuKbmeOT4C6QWVFC8NoeW5w/zu/sq2L9/bcQz9Cd180L3RS6Owe7KbPbv3xn1PqrGp/jHhlcA+Ng79pKfem36T9fV1XGj/hm50ci7Xh3ynlePvOvVIe95dch7Xj0r2S0kO9QJRCmVBNwDNCul7ge+APyG1nrxpJIngIeVUglKqXKgGqhfqfu7HjQNTjI54+OhbYVoTVSNM8CzF4dIjjfzP+81Bq7Ud45FrJ/qMjYAhno6l2Ym0+2cCpdzDE5M86XHL1CZbWV3sFyjJtfOxPQ8I+7Z4DXGyLLFU5G10H1jaZePH5zopjxr4RoAqUlxpCRaaB/18K0jHSRYTPzOvvKoZ6jOMbp2eOf8rM2L7k0NUJSezPc/uoujXzhwzQJrIYQQQojVtpI11/nAK0qp88ApjJrrp4B/AuzAC0qpc0qpfwXQWl8CHgOagOeATwbLSm5YrcNu/uzJJjod3pjrjcGx4B/eUxbx+xB/QPP8pSEOrMmhKD2ZssxkzgS7aYQ8e3GQ5HgzGwuNjoYlGcm4Z3y4poxNhk+fH2Ryxsc3PrgDS7C7Rk2uEeyG6q7rO8fYWZaBUgvlJrkpCdgTLLQOexiZnOF09zjv2V6EaVFJilKKXeUZHGl10NDj4rbyDLJsCVHPGWq1t/Trpe6syaYoPXnZdSGEEEKI692KlYVorc8DW2Mcr7rKZ74GfG2l7mk1aa35459f5GTnGMc7nDz72X1R5zT2TZBpjWdjYSoFqYk0DUZuajzdNYbDMxceuLIuP4XmoYWNiF0OL082DvA7+yqItxiBc2mmkX3uHpsi3RrPKy0j1OTaIjpt1OQaX7cMuanMttHvmuZjeyMzzkopqnJttI14qAtukDywJifqGfavyQl3MblnXW7UOhit9h7YXMCTjQOsL4g9+EUIIYQQ4mYgjYLfgFmfn799voX/91zzsr2nL/ZPcrJzDGu8mZYhY/LgYlprznaPs7k4DaUUZVlWesamIs556vwg8RYTB9YaQW11rp0upzd8rV+cG0BDRGBcmmlkfrudXjyzPuo7x6KC4kxbAlm2eFqHFzp47IoxKrwq20briIeXL4+QY09gXYys88G1C9deH2Mcecjfvm8zP/v9PZRnxR78IoQQQghxM5Dg+g043u7kH15u41/q2mmMMcUQ4OXmEZSCL75tHQENrUuGtjSO+ulweMM9oUsyksMt7QCm5nz8vKGft23IC29yXJNrR+uFOuhnLw6yozSd3JTE8OdKMpKxmBQtQ26OtjqY9+twcL5YTa6dlmE39Z1j2BIsrMuPDoyrc204PLM8d2mId2wqiCgbCSlIS2JftbHBcWPR8lnpOLOJbSWxW+wJIYQQQtwsJLh+A1oWlWb84lx/1Pq8P8DTFwbYVJjK7kpjA+DiPtZaax5vm6ckI5l3bzdm6BRnJOPwzOGdNVrSPXV+EPesj9+8rTT8uVA5x5VhN10OL81Dbu5f0u85Mc7M5uI0jrU7qWvAAQU/AAAL5ElEQVQZwZ5oiTntsCbXTuuwm2PtTnaUpWM2RQfOi0tJ3r19+Zbj//bbO/jxJ3ZLvbQQQgghbnkSXL8BLcNuclMSuLc2l/+/vTuP8aO87zj+/u7ha+21gxffNsbBR2zwhXGMIUAgXI2KjVACIS1ESaVGAoX0SAVqq4rwT4OqKqqI1EYqES1NQpOmKqQcii2agys1YHzgK3aCbby2sY3j9VLW19M/Znb92/Vev/Xsb+P1+yWNdvaZQ/P7sKy/O/PM8/x4XSMnO8yw+DdPb2TrvqOsXDSZ6WPrGFpT1a4gX71pP+8cOcVXbpjZNoX3tNYJWd7P7l5/97WdXDJuZLsJVaY31FFbHWzdd5TnNuwFaOuPXWr5R8eybvdhntuwl+VdTBM+a/womo+d5NcHmrlmZuezHV4+7QKuvqSBr6+Y121f6WG11VzRyayLkiRJ5xuL6z7YsreJ2RPquW3hJN5rauG1HQfbtn14/CTPrN3D7Ysm84Xl06muCmaNH9XuRcR/X7OL0UODlQsntbW1Ftc7D37AzoMfsHbXYe5cMrVdV4za6ipmNIxk274mnl3fyIIpo5k85sxh6z4x80JOJfjt/x1n6cVjz9gOsPiiMW3r187uvLgePaKWJ//o49xz5fTeBSNJknSes7gu08lTiW37jzJ7/EhumDOeuiHVPF0yLfnPtx2gqeUEKxae7qM8e8Lp4vpQ8zFe3LKfKydWtw2NBzA9H+Vj+3vNvLLjAACfnHNm0Ttz/EhWb97P+nd/2zZVeUdLSrqBLO3ijvKcCfU8/oUl/OmNs9qNby1JkqS+s7gu08lTiUfvmM9tCyYzfEg1N82bwLPrG2k5kY3g8ez6RkYPr22bxRBgzoRRHDjawoGjLfx43R6On0xcNbm23XlHj6hlQv0wtu5r4pXtB2kYObRttsRSpf2gVy7svLiuqgr+cFnWV7uzET5aXT9nPF+5YWanLypKkiSpfBbXZRpSU8XKRZPbRsb4/QUTOfLhCV7bcYiWEydZ9fY+bpo7vl0/59ZZCTc3NvGjN97lYxPrmTrqzOhb73C/uuMQy2Zc0GnR2zr1+dduns1H6oZ0eZ0P3zaPzY/c0u7uuCRJkvpXv00ic764fFrW7WLjniPUVlfR1HKCm+e1f8nwsimjqakKXti4l3W7D3P/Jy8BGs8415wJo/hpPmHLshmd95W+ZmYDLz94PZM66WtdqqoqGFZV3YdPJEmSpL6yuD5Lo0fUMmn0MLbsPUIiGzWk49B3o4fXsvySBv711XcAmD9lDOw/s7ieWzIJS+sQfh1FRI+FtSRJkgaGfQYK0Nqd482dh5nRUNdpd41PX3b6bvb8LiZbufXSiXxsYj2Txwz3JUNJkqRzkMV1AWZPqGfz3iZWbdrHwmljOt3n9kVT2tbHlcyoWGpITRXP3H8Vz3/1E75kKEmSdA6yW0gBVi6axK73P+CjF47kziumdrrPkJoq/ufPr+PIh8e7PVdNdRWjfAlRkiTpnGRxXYA5E+r51t2Le9xvul09JEmSBjVvkUqSJEkFsbiWJEmSCmJxLUmSJBXE4lqSJEkqiMW1JEmSVBCLa0mSJKkgFteSJElSQSyuJUmSpIJYXEuSJEkFsbiWJEmSCmJxLUmSJBXE4lqSJEkqiMW1JEmSVJBIKQ30NfRZRLwHvDPQ19FHDcCBgb6I84A5V45ZV4Y5V45ZV4Y5V4Y5n72LUkoX9rTTOV1cn8siYk1KaclAX8dgZ86VY9aVYc6VY9aVYc6VYc6VY7cQSZIkqSAW15IkSVJBLK4HzrcH+gLOE+ZcOWZdGeZcOWZdGeZcGeZcIfa5liRJkgrinWtJkiSpIBbXBYmIxyNif0RsKGlbEBGvRMT6iHgmIupLts3Pt23Mtw/L2y/Pv/9VRPxDRMRAfJ7fZeVkHRGfj4i1JcupiFiYbzPrbpSZc21EPJG3b4qIh0qOMecelJn1kIj4Tt7+VkRcV3KMWXcjIqZGxIv5z+jGiHggb78gIn4SEdvyrx8pOeahPM8tEXFzSbtZd6HcnCNibL7/0Yh4rMO5zLkLfcj5xoh4Pc/z9Yi4vuRc5lyklJJLAQtwDbAY2FDS9r/Atfn6F4FH8vUaYB2wIP9+LFCdr/8SuBII4Dng1oH+bL9rSzlZdzjuMmBHyfdmXVDOwN3A9/P1EcBvgOnm3C9Z3wd8J18fB7wOVJl1r3KeCCzO10cBW4G5wKPAg3n7g8A38vW5wFvAUOBiYLu/q/sl5zrgauDLwGMdzmXOxeW8CJiUr18KvGvO/bN457ogKaWfAYc6NM8Gfpav/wS4I1+/CViXUnorP/ZgSulkREwE6lNKr6Tsp/1fgJX9f/XnljKzLvU54HsAZt2zMnNOQF1E1ADDgWPAEXPunTKznguszo/bDxwGlph1z1JKjSmlN/L1JmATMBlYATyR7/YEp3NbQfZHY0tK6dfAr4ClZt29cnNOKTWnlH4BfFh6HnPuXh9yfjOltCdv3wgMi4ih5lw8i+v+tQG4LV//DDA1X58FpIh4ISLeiIi/yNsnA7tLjt+dt6lnXWVd6k7y4hqz7quucv4h0Aw0AjuBv0spHcKcz0ZXWb8FrIiImoi4GLg832bWZYiI6WR38l4DxqeUGiErWMieCECW366Sw1ozNete6mXOXTHnXupDzncAb6aUWjDnwllc968vAvdFxOtkj2yO5e01ZI/APp9/vT0ibiB7HNORw7n0TldZAxARHwc+SCm19mk1677pKuelwElgEtnj8z+LiBmY89noKuvHyf7xWwN8E3gZOIFZ91pEjAT+A/hqSulId7t20pa6aVeJMnLu8hSdtJlzB+XmHBHzgG8Af9za1Mlu5nwWagb6AgazlNJmsi4gRMQs4NP5pt3AT1NKB/Jtz5L1t3wSmFJyiinAHtSjbrJudRen71pD9t/ArMvUTc53A8+nlI4D+yPiJWAJ8HPMuU+6yjqldAL4k9b9IuJlYBvwPmbdo4ioJStE/i2l9KO8eV9ETEwpNeaPyPfn7btp/xSsNVN/f/SgzJy7Ys49KDfniJgC/CdwT0ppe95szgXzznU/iohx+dcq4K+Af8w3vQDMj4gReR/Va4G388c3TRGxLH9T9x7gvwbg0s853WTd2vYZ4PutbWbdN93kvBO4PjJ1wDJgszn3XVdZ57836vL1G4ETKSV/f/RCnss/A5tSSn9fsulp4N58/V5O5/Y0cFfeL/ViYCbwS7PuXh9y7pQ5d6/cnCNiDPDfwEMppZdadzbnfjDQb1QOloXsrmgjcJzsr8AvAQ+Qvb27Ffhb8kl78v3/gOyFgg3AoyXtS/K27cBjpce49Dnr64BXOzmPWReUMzAS+EH+M/028DVz7respwNbyF5eWgVcZNa9zvlqssfd64C1+fJ7ZCM2rSZ7ArAauKDkmL/M89xCyQgKZl14zr8he6n3aP7/wFxzLjZnsj/Sm0v2XQuMM+fiF2dolCRJkgpitxBJkiSpIBbXkiRJUkEsriVJkqSCWFxLkiRJBbG4liRJkgpicS1Jg0A+xvgvIuLWkrbPRsTzA3ldknS+cSg+SRokIuJSsvHGFwHVZOPY3pJOz8TWl3PWpGxWSElSL1hcS9IgEhGPkk0UUQc0pZQeiYh7gfuAIcDLwP0ppVMR8W1gMTAceCql9PX8HLuBfwJuAb6ZUvrBAHwUSTon1Qz0BUiSCvUw8AZwDFiS382+HVieUjqRF9R3Ad8FHkwpHYqIGuDFiPhhSunt/DzNKaWrBuIDSNK5zOJakgaRlFJzRDwFHE0ptUTEp4ArgDURAdld6l357p+LiC+R/VswCZhLNn09wFOVvXJJGhwsriVp8DmVLwABPJ5S+uvSHSJiJvAAsDSldDgingSGlezSXJErlaRBxtFCJGlwWwV8NiIaACJibERMA+qBJuBIREwEbh7Aa5SkQcM715I0iKWU1kfEw8CqiKgCjgNfBtaQdQHZAOwAXhq4q5SkwcPRQiRJkqSC2C1EkiRJKojFtSRJklQQi2tJkiSpIBbXkiRJUkEsriVJkqSCWFxLkiRJBbG4liRJkgpicS1JkiQV5P8BSHQH60tinJAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "co2_data = clean_data.dropna(subset=[\"CO2\"]).copy()\n",
    "co2_data[\"date\"] = pd.to_datetime(dict(year=clean_data[\"Yr\"], month=clean_data[\"Mn\"], day=15))\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(co2_data[\"date\"], co2_data[\"CO2_filled\"], label=\"CO₂ concentration (ppm)\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CO₂ (ppm)\")\n",
    "plt.title(\"Monthly CO₂ concentration at Mauna Loa\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modèle de prediction pour extrapoler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Préparation des variables\n",
    "X = co2_data[\"date\"].map(pd.Timestamp.toordinal).values.reshape(-1, 1)  # dates en nombres\n",
    "y = co2_data[\"CO2\"].values\n",
    "\n",
    "# Modèle polynomial (ordre 3 par exemple)\n",
    "degree = 3\n",
    "model = make_pipeline(PolynomialFeatures(degree), LinearRegression())\n",
    "model.fit(X, y)\n",
    "\n",
    "# Prédictions sur données existantes\n",
    "y_pred = model.predict(X)\n",
    "\n",
    "# Prédictions futures (20 ans)\n",
    "last_date = co2_data[\"date\"].max()\n",
    "future_dates = pd.date_range(start=last_date, periods=12*20, freq=\"M\")\n",
    "X_future = future_dates.map(pd.Timestamp.toordinal).values.reshape(-1, 1)\n",
    "y_future = model.predict(X_future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(co2_data[\"date\"], y, label=\"Observed CO₂\")\n",
    "plt.plot(co2_data[\"date\"], y_pred, label=f\"Polynomial trend (deg={degree})\", color=\"orange\")\n",
    "plt.plot(future_dates, y_future, label=\"Future prediction\", color=\"red\", linestyle=\"--\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CO₂ (ppm)\")\n",
    "plt.title(\"Polynomial regression model of CO₂ at Mauna Loa\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coefficients de la regression :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [ 0.00000000e+00 -1.32833320e-13 -9.66139988e-08  9.14194538e-14]\n",
      "Intercept: 16290.44586886284\n"
     ]
    }
   ],
   "source": [
    "linreg = model.named_steps[\"linearregression\"]\n",
    "\n",
    "# Coefficients\n",
    "print(\"Coefficients:\", linreg.coef_)\n",
    "print(\"Intercept:\", linreg.intercept_)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}