FC_Simple-Linear-Regression-Co2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Objectives\n",
    "\n",
    "* Use scikit-learn to implement simple Linear Regression\n",
    "* Create a model, train it, test it and use the model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Needed packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Understanding the Data\n",
    "\n",
    "### `FuelConsumption.csv`:\n",
    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
    "\n",
    "- **MODELYEAR** e.g. 2014\n",
    "- **MAKE** e.g. Acura\n",
    "- **MODEL** e.g. ILX\n",
    "- **VEHICLE CLASS** e.g. SUV\n",
    "- **ENGINE SIZE** e.g. 4.7\n",
    "- **CYLINDERS** e.g 6\n",
    "- **TRANSMISSION** e.g. A6\n",
    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the data in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=\"FuelConsumptionCo2.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MODELYEAR</th>\n",
       "      <th>MAKE</th>\n",
       "      <th>MODEL</th>\n",
       "      <th>VEHICLECLASS</th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>TRANSMISSION</th>\n",
       "      <th>FUELTYPE</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
       "\n",
       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
       "0          AS5        Z                   9.9                  6.7   \n",
       "1           M6        Z                  11.2                  7.7   \n",
       "2          AV7        Z                   6.0                  5.8   \n",
       "3          AS6        Z                  12.7                  9.1   \n",
       "4          AS6        Z                  12.1                  8.7   \n",
       "\n",
       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
       "0                   8.5                        33           196  \n",
       "1                   9.6                        29           221  \n",
       "2                   5.9                        48           136  \n",
       "3                  11.1                        25           255  \n",
       "4                  10.6                        27           244  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"FuelConsumptionCo2.csv\")\n",
    "\n",
    "# take a look at the dataset\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MODELYEAR</th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1067.0</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>1067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.346298</td>\n",
       "      <td>5.794752</td>\n",
       "      <td>13.296532</td>\n",
       "      <td>9.474602</td>\n",
       "      <td>11.580881</td>\n",
       "      <td>26.441425</td>\n",
       "      <td>256.228679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.415895</td>\n",
       "      <td>1.797447</td>\n",
       "      <td>4.101253</td>\n",
       "      <td>2.794510</td>\n",
       "      <td>3.485595</td>\n",
       "      <td>7.468702</td>\n",
       "      <td>63.372304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>10.850000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>294.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>488.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
       "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
       "mean      2014.0     3.346298     5.794752             13.296532   \n",
       "std          0.0     1.415895     1.797447              4.101253   \n",
       "min       2014.0     1.000000     3.000000              4.600000   \n",
       "25%       2014.0     2.000000     4.000000             10.250000   \n",
       "50%       2014.0     3.400000     6.000000             12.600000   \n",
       "75%       2014.0     4.300000     8.000000             15.550000   \n",
       "max       2014.0     8.400000    12.000000             30.200000   \n",
       "\n",
       "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
       "count          1067.000000           1067.000000               1067.000000   \n",
       "mean              9.474602             11.580881                 26.441425   \n",
       "std               2.794510              3.485595                  7.468702   \n",
       "min               4.900000              4.700000                 11.000000   \n",
       "25%               7.500000              9.000000                 21.000000   \n",
       "50%               8.800000             10.900000                 26.000000   \n",
       "75%              10.850000             13.350000                 31.000000   \n",
       "max              20.500000             25.800000                 60.000000   \n",
       "\n",
       "       CO2EMISSIONS  \n",
       "count   1067.000000  \n",
       "mean     256.228679  \n",
       "std       63.372304  \n",
       "min      108.000000  \n",
       "25%      207.000000  \n",
       "50%      251.000000  \n",
       "75%      294.000000  \n",
       "max      488.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
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       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>196</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.6</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.1</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
       "0         2.0          4                   8.5           196\n",
       "1         2.4          4                   9.6           221\n",
       "2         1.5          4                   5.9           136\n",
       "3         3.5          6                  11.1           255\n",
       "4         3.5          6                  10.6           244\n",
       "5         3.5          6                  10.0           230\n",
       "6         3.5          6                  10.1           232\n",
       "7         3.7          6                  11.1           255\n",
       "8         3.7          6                  11.6           267"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
    "viz.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's plot each of these features against the Emission, to see how linear their relationship is:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
    "plt.xlabel(\"Cylinders\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating train and test dataset\n",
    "Train/Test Split involves splitting the dataset into training and testing sets that are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
    "\n",
    "Let's split our dataset into train and test sets. 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using __np.random.rand()__ function: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Regression Model\n",
    "Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the actual value y in the dataset, and the predicted value yhat using linear approximation. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train data distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modeling\n",
    "Using sklearn package to model data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[38.60389544]]\n",
      "Intercept:  [126.86606305]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "regr = linear_model.LinearRegression()\n",
    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
    "regr.fit(train_x, train_y)\n",
    "# The coefficients\n",
    "print ('Coefficients: ', regr.coef_)\n",
    "print ('Intercept: ',regr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned before, __Coefficient__ and __Intercept__ in the simple linear regression, are the parameters of the fit line. \n",
    "Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data. \n",
    "Notice that all of the data must be available to traverse and calculate the parameters.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot outputs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the fit line over the data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Emission')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gw4YN6NChA1JSUuxt+vTpg5KSEuzatcvjNcvKylBSUqK4EVFgxMSIqYxZs8R9JE5dAf6NcnhaySQv5x42TNwvXmzOZoOehEIydUBt2CBGc956q+rY/feLX+aVV5rXL9LM1H123n//fWzduhWbNm1yeayoqAgxMTGo61Q3JCUlBUVFRfY2joGO/Lj8mCeTJ0/GpEmT/Ow9EVEVvUvGCwpE3otjAJOeLvJ2HBN7feXHWCwimMzKCl4+j1wby1cyddjVxjp/HmjZ0jWqPH4cuOgic/pEfjFtZOfQoUMYPXo03nvvPdSsWTOo154wYQKKi4vtt0OHDgX1+kQUefSMcmgpCxGK+THhlEyt2quvAnFxyjc7P1+8wQx0wpZpwc6WLVtw/PhxXHbZZahRowZq1KiBtWvX4l//+hdq1KiBlJQUlJeX49SpU4rnHTt2DKl/VaFLTU11WZ0l/5zqpaJdbGwsEhISFDciIn9orQCudSWT2ZsNehIKtbHOnRP7AfXpI+7PndNxkoMHxS/pgQeqjl15pVhmPmyYYX0lc5gW7PTs2RM7duzAtm3b7LcrrrgCw4cPt/93dHQ0Vq1aZX/Onj17cPDgQWRmZgIAMjMzsWPHDhw/ftzeZsWKFUhISEDbtm2D/pqIqPrSOsqhdaQmlPNjsrOBX34BCgvFIEhhodgHKBiBzqBBYk+n2bOB5cvFfXy8a3kqjyRJdLRxY+XxHTuAb78Ns2Ep8ihICdOqOK7GkiRJGjlypNSoUSNp9erV0ubNm6XMzEwpMzPT/nhFRYXUvn17qXfv3tK2bdukZcuWSRdddJE0YcIETdflaiwiMoq71VIZGa6rpbSuZJJXe1ksxq32CndZWd7fu6wsHydYvtz1SY8/HoSek1HUfn+HdCHQGTNmICoqCoMHD0ZZWRn69OmDOXPm2B+3Wq1YsmQJ7r//fmRmZqJWrVoYMWIEnnnmGRN7TUTVWXa2SBL2VQFc60iNPHI0ZIgYKXKc/grb/Bg/nDsnVqd5s3ixaBcX5/RAaSmQkiK28JZFRQEnTwKJiYb3lczHchFguQgiCj69ZSHcrd7KyBCBTsSVZfAiN1dMWfmSkyNyju2eew544glloyVLgAEDDO0fCeXlwJw5ouhv8+aiEK6R20+o/f4O6ZEdIqJIpXekRu3IUaTbu1djuz17gNatlQ/27y8CHU9Z5eSX8eOB6dOVO3w//LAoFRPsHdRN31SQiKi60ruSyXmzweoW6ABiGxw1LmlRCVx3nWugs38/8NlnDHQCZPx4YNo011ImNps4Pn58cPvDaSxwGoso3Nls4T3SEe79N8O5c2LVlTfZWIiFGKI8+NJLogQ9BUx5ufjdeKvZZrWKlCl/p7Q4jUVE1YLaHYhDmTxSQ+rFxYnpPHdJyvVwEieRpDx40UVifbyvCIn8NmeO7+K0NptoN2ZMULrEaSwiCl9adiCuLjzV2YpEixaJgMfRyxjnGuisWSNKPTDQCYr9+41tZwQGO0QUlrTuQFwdFBSIFV49egC33CLumzSJ7KBv0SIxHTL55u8gwYJxmFH14K23ApV/5exQ0DRvbmw7IzBnB8zZIQpHa9aIL3NfCgtDf4rIiJwdeZTL+V90Of820KUbzp0D8vLE6qeWLUUSqsv+NoFw4QLwt78Bu3crjx8+DKSlBaED5CwUc3Y4skNEYSlUa0VpZcRojNmjXH6XbNBr3jzxbekY6Lz1lnjRDHRMExMjlpd7M26csfvt+MIEZSIKS6FcK8qTkyfFjMqRI+K7+OGHgTvvdA1S5JwjT6MxziNBNpv6OltqR7nUbgY3aJDnnYwXLxaPL1qk7pqqHT3qGsy0bg18/31wv0HJI3kfHed9dqxWc/bZ4TQWOI1FFI707kBsltRU4Ngx9e217KBcv74IpHzJz1dXwNvdZnDuvqTULP8GxHSFYVNad9wBvP228tjmzcDllxt0ATJSqOygzGksIgpLWquMm0lroAO4Vj0HPK8+UxPoAOpGubRsBpeXp+66att5tW6d+MU6BjoPPijeKAY6ISsmRkyhzpol7s0aeGOwQ0RhS+8OxMF08qT2QMeRnHPkLS/HF4tF1M/q1s17u/JyMaLjzfTpoh2go2SDHufOiSjt2muVx//4oyraJfKBwQ4RhbXsbLFXXGGhmKYpLBRTP6EQ6AD+r3qWR2PWrfOel+OJllEuLZvBAepLNqht52LmTDFPVlRUdeyDD0TEl5Tk8WlEzpigTERhL5R3ID5yRN/z5JwdeTRG7aoy5/yd9HT1FdG1bgY3bZq6yuPTpqk7r92BA0CzZspjV18NrF0bGvOSFHYY7BARBVBamvqcGpm70Ri1q8oWLBArsfUkhGrdDC4mBqhZEzh/3nPbuDgNeRqSBAwcKCqRO9q9G2jTRuVJiFxxNRa4GouIAufkSe0zLhkZrqMxalaf1a8vggu9dcK0bgZn6MaOy5YB/fopjz39NPDUU74vQNUWC4ESEYWA+vWBlBTvScopKcD773vfQVlefTZkiAhsHAMe+ecTJ1zP7WvPHkfyZnDepp0cN4MzZGPHkhKgQQOxE7IsNhb4/XegTh11FyDygQnKREQBVlQkAhp3UlLE4927iz1wunf3nJbiafXZxRd7Hj3SuoPy1Kliqbi7YCsvT7nPjt8bOz71FJCYqAx0Pv9czIsx0CEDcRoLnMYiouBw3kF57Vox8qOVux2Ue/Xy/TwtdcLUbAane2PH3buBdu2UjQcOFFstO2+aROQFp7GIqNowopBmMNSvD+zY4Xpc6y6zzqvPFixQd30tdcLkzeC88TW1BjgtebfZxC9nwwbliQ4cEFGTF4HeiZciG6exiCisGVFI00zjx4uk4LFjgVdfFffx8cqdin0xs06Y6o0dP/gAqFFDGejMnCkiJB+BjhHvEVVvnMYCp7GIwpVcPsH5XzF5VCFUdlGWOY9OHDrkfcdi5xwZT0KhTpjHkZcTJ0QCsqO0NNGwZk2f55XLV3ii9j2iyKT2+5vBDhjsEIUj+Qve067CoVYI1F1xTV8cl3n7Igd+gPvppEAGfu6Kk6anA4UdH0SLpbOUjb/6SmwQqILWpfBU/bAQKBFFNF/lE9wV0jSLp+KavjiWZvDFrDph7oqTXo7NOPSbRRno3HGH+KWoDHQA7eUriDxhgjIRhSVD9ngJAjXFNb1Zu1YsT1eTeJ2dDWRlBS9Z27k4aTTKsQMd0Ao/Kdv9dhTWi1M1n19r+QoiTziyQ0RhycykXC3UjE54s2iRtsRreaWWrz17jOA4unY33kI5YhWBzh2YBwskrNurPdABtJevIPKEIztEFJa6dRNTNL6ScuVCmsHkuBR+7VrjzqtlN+RgOHoUSMNhHEa64vgOtMdl2IoKRAMAVq3SN9I0ahTw8MO+c3ZGjdL7Cqi64MgOEYUleY8XwHUfOrd7vASJ81L4RYuMO7fW3ZADSpJw/b+HuwQ6nbAVHbHDHugAwHPP6dsWQC5f4Y1j+QoiTxjsEFHY8pWUm5UlilUuWCDuAx0guEvWNZrRidfnzgG5uUCfPuL+3DkVT1q7FoiKQsqqfPuhl/AQLJCwDZ28PlUenVIb8GgpX0HkCZeeg0vPicKdux2UFy92vxxabQVwPX3wthTeaPn5Ii/HH4MGiffJWVaWhxGps2eBRo1cKo7Wxwn8CfV1L/RsC8AdlMkd7rOjAYMdoshixmaDa9aIaZpg0VLnyh1PgY7MJeB56SUxlOLg0zsXYuA8/W+kv6+BiPvsEFG15Lwc2lEgc16CtcTdYgEyMvxLvD53znugA4jHp04FJt2+X1zUMdDp3h2w2bC8ln8Ro9nbAlD1wdVYRBRRtGw2aOSoQrCWuEuS/4nXTgM0nq6Ejo/0Q198oTz8449Aq1YA/F/ybfa2AFR9cGSHiCJKIDYbLC8XAcYDD4j78nLXNvJSeOeVYcFms/lOyt671/s5BmAJJEQpAp3H8RwskDD+363sx0aN0hd0GTE6RaQFgx0iiihGbzaotuK2vBQ+0FmQFovnaTi1FeBbtnR/7gQUQ4IFS3Cj/dhp1EZtnMYLeByA2A1aDvbULA1313/AnG0BqPpisENEEaVbNyApyXubpCR1owqealrZbOK4c8DzzTfezzd6NJCTA/TuLe4XLBBf/lpGgzwtPfe07N3dUm93VcSfw+MoRl3FsRuwHAk4jTOobT+mtRZV7drKnwNdq4vIHa7GAldjEUUSm03UknJaHa2QlAQcO+Z9ZEFrxW29FbrdVQxXw3HpuZ4K8PJqrHbYiZ3ooGj/EQbjH/gQgPsoLDcXmDVL/WteulT8PgJdq4uqH67GIqJqad0674EOIB73tSmf1orbeit0Z2cDv/wilmHn5wMzZng/h8xxGk5PBfhFH1Xgp7qdXQKdRvgV/8BH8BToAFWJyWpf8+7dwanVReSJqcHO3Llz0bFjRyQkJCAhIQGZmZn4/PPP7Y93794dFotFcRs5cqTiHAcPHsSAAQMQHx+P5ORk5OXloaKiItgvhYhCxOHDxrTTWnHbnwrdjsU7H3jAe6Kzu+RezUnZ+flAdDRantpsf+zV1q9i5H0SDkc18noOx1pUvhKdZWrbEQWKqUvP09PTMWXKFLRs2RKSJOHtt99GVlYWvvvuO7Rr1w4AcM899+CZZ56xPyc+Pt7+3zabDQMGDEBqairWr1+Po0eP4vbbb0d0dDReeOGFoL8eIjLf778b005rxW2jKnTLic5DhojAxjHRwFNyr9pk60ZxvwOWZOXBxo2BPXuQGxsLAEhIcJ/TI3OsRaU218jsFWpEkEJMvXr1pLfeekuSJEm67rrrpNGjR3tsu3TpUikqKkoqKiqyH5s7d66UkJAglZWVqb5mcXGxBEAqLi7W3W8i0qasTJJmzJCk3Fxxr+F/Wa/eeUeSRIjg/fbOO777Z7V6P4fVWtVvre19WbhQktLTlc/PyBDHnVVUiLYWi/vrWiyS9E6tka4PbNjg9tp5ea6vxWoVxwPxXhPppfb7O2Rydmw2G95//32cOXMGmZmZ9uPvvfceGjRogPbt22PChAk4e/as/bENGzagQ4cOSElJsR/r06cPSkpKsGvXLo/XKisrQ0lJieJGRMGjdjm3Hr7yddS201px258K3e728XHO5SksFAnG7lYxeasA3wXfolKy4LYzr1UdvOceEYdcdZXbfk6dKhKpZ8wQycgzZoifnYtuZmR4f71a2xEFiuk7KO/YsQOZmZk4f/48ateujY8//hht27YFANxyyy1o3Lgx0tLSsH37djzyyCPYs2cPCv5aQ1lUVKQIdADYfy4qKvJ4zcmTJ2PSpEkBekVE5I28nNuZvJwb8K+S9UUXGdtOC7nf06crE3etVhHouHtd48e7tn/44ar27nZ5dlf4VK4AL6/sikEZfkAbNMMB5ZOPHQOSk11P6iQmRuzn4428zN/XyjduHkimC9JIk0dlZWXS3r17pc2bN0uPPvqo1KBBA2nXrl1u265atUoCIO3bt0+SJEm65557pN69eyvanDlzRgIgLV261OM1z58/LxUXF9tvhw4d4jQWURAYPdXjzsqV6qZWVq4MXF89TdGdOiVJV18tpqOuvlqSHnzQ+/mdp40kyf30Vnp61fRWRYUk7Rkzx/Vk776r/031oKJCkpKSvL+GpCTRjigQ1E5jmR7sOOvZs6d07733un2stLRUAiAtW7ZMkiRJeuKJJ6RLL71U0ebnn3+WAEhbt25VfU3m7BAFx4wZ6gKRGTP0X8OoYMfovjZvru583oKphQvd5+VYLOK29PWDrg9edpkkXbig9+30qrBQ3esoLAzI5YnCL2dHVllZibKyMrePbdu2DQDQ8K+lB5mZmdixYweOHz9ub7NixQokJCTYp8KIKHT4szxbLYd/DvxqZ+Sy6hYt9L0mx315vFdzl/C+dDP63ee0bPz774EtW4AagclYCEQdMqJAMDVnZ8KECejXrx8aNWqE06dPIz8/H2vWrMEXX3yB/fv3Iz8/H/3790dSUhK2b9+OsWPH4tprr0XHjh0BAL1790bbtm1x2223YerUqSgqKsLEiRORk5OD2L+WURJR6DBqebY3RtXGMmpZdXGxf8Gb/FxPGwdej1VYhV7Kg48+CkyerP+iKhldh4woUEwNdo4fP47bb78dR48eRWJiIjp27IgvvvgCN9xwAw4dOoSVK1di5syZOHPmDDIyMjB48GBMnDjR/nyr1YolS5bg/vvvR2ZmJmrVqoURI0Yo9uUhotAxapRIvvVVXkDetE4Pufr44cPuR0Hk0gm+kmY7d1Z3PV/tBgxQdx5P5MDPeXQkHmdwBGlIhHI16ftz/0TR+brY/4B47qhR7leAGcGo95oo4IIzqxbamLNDFDx5edqTcrWSc1uc81vkY+72qnE2daq6fJSpU72fJyNDe66Ou5wdx/yYRzDZpfGNWCwBkhQV5XoOI95TTxYu9P4a1LzXRHqFbc4OEUW2qVOBvDzXGklWqzj+3HOue85oJS/DTktTHr/4YvUVt7//Xt21fLVr5L36gleO+/J06wZck7IXEiyYggn2NstxA6Jgw6cYCACorFSew1OFdqO8845/jxMFA4MdIgo6T5vWAcZtNvjNN65TP0eOiONqlJYa0+6zz9Sdx5Ec+Nn35amshLV3T6w7domiXQvsRR8sh6Tin/Lp0/UFjt6cOycqp3uzeLFoR2Qm0zcVJKLI5m4DPKvVddM6Izcb9HSuykr15zIq+TYxUeTOeEtSbtZMjGTt3+8mz2bxYmDQIEX7FxKm4PGSRxTXKC723g95ZZevjQK1yMtT3+7VV427LpFWFklyl1ZWvZSUlCAxMRHFxcVISEgwuztEEaOgoGpHX1l6uiht4DiVVF4uRnB8JS6fPes72daoc737LnD77d6vBYhpmttu893O0/Lz5s2BffvcPOHPP4H69ZXH6tUDfvsNtth4RQD54YdVS9S9yc0FZs3y3U6tG24AVq703a5XL2DFCuOuSyRT+/3NaSwiP9hswJo1wIIF4t7bF2x1U1AgKnc7L5c+fFgc/6vqCwDxRe3rvXPcc8Ybo85ldN2nfftEWYX27UUM0769+NltoDN+vGugs3o1cPIkEB8Pq1WUkRg2TNy3bKmuD/4s6XdH7fQUp7HIbAx2iHQqKACaNAF69ABuuUXcN2mi/BKvrrxvgCfux4ypCkqM3GzQqHN16aLuPGrbFRQAl14K7NwpYpadO8XPis/L99+L9dqOc3BDh4r5tx49PJ571CjXhG9n/i7pd6dDB2PbEQUKgx0iHbSMWlRHnjbAk0kScOiQaAcYu9mgUed6/XV151HTztfn5eMPK4C//U3cHB06JIYNfexc6E/FdX+0amVsO6JAYbBDpJHWUYvqSGsZASNHJow6l1HlInx9Xm6T3sFNN0cr17C/9pp4MD1dXSfge0m/P5XkPTFrRIlIKwY7RBppHbWojrSuZDJyZMKocxlVLsLT5yUZxyDBgrcxoupgixZAWRlw333qLu7kqquAlBTlsZQUcTwQzBpRItKKwQ6RRix+6JtcRsBTIGCxiMRexzIC/oxMOCeKT57s/yiHUTk77j4Hb+L/cAypimNfPPOtGCbSGRnIU2VHjrheP5BTq2aMKBFpxX12iDSq7sUPPe2b48hqFcvLhwwRgY3jFI4cAM2c6fq8qVPFDspz5njYc8YNb8vbT58WX7h794oVS9OmAXFx6l6n8+7Lets5fg4ysR7rcbXi8dkYhVzMRqEf9aN8TZVZLGJqNSvL97ST4zl9/Z5len5vREEVlOIVIY61sUiLigpJSk93rbvkWH8pI0O0izQLF4rX7vh609M91z9y1z4jw7h6SXINLHe/A0CSkpLU99XZypXq6letXOn9PBUVktT84nPSQaS7PLkBjhvyeXGsm+XtVlio7nxaf89EZmFtLKIAkUctANdpGm+jFuFOzwq07Gzgl1+AwkIgP1/cHzigrjaVL2oSxU+cUN9XZ8ePq+uHr3bWua9i3+E4ZKDqjRuKBbBAwgnLRQD8/7wYObXKlYYUiRjsEOlgRKHJcOLPCjTnDfB8famr3ajRV6K4O1pWy/k9XfnrryL6feAB+6Hvoq+EFRX4H4YCENNtRnxejJpa9ef3XF7ufwFXokBhsEPkB7UrdsJdsFagadmoUW8CuNq+6kmytl/gpptExx3t2IGO577FqkKr4aNcuvvqRO/vefx44wq4EgUCgx0iHSJpqF/NSEowVqBpfU/9TQD31Vdd05XLlwNRUcCiRVXHJk4UUUL79ppHudQyampVz+9ZLrrq/LmRC7gy4KGQEKQcopDGBGXSQk5Q9pQEGk4JymoTUY1OgHWm5z31lSgeyGRdlyTr06clKS5O2SgqSpJOndL3hujkb0K41t9zWZkkWa3e21qtoh1RIDBBmShAImVTQS0jKd26AUlJ3s+XlOR7msQTPe+pt9EMbywWMeVjs6kr4OozyfrZZ4E6dZTVLpcsESdNTFTfMQP4mxCudTrMn6KrLKJLwcR9dog0ioRNBQOxL4s/9L6ncqK48z47SUliJZa7PX4kScQlvXpVHZf35fEUFMjTTwo//gi0aaM81r+/CHRMTOZy21cNz9WyP9KePerO69zO295IkZbcT6GBIztEGkXCpoJaR1LWrXNdxu3sxAn9o1n+vKfuRjOOHQMWLhSr4xzVr1/VV0e+cq0UoxCrKyF1u9Y10Nm/H/jss7DPWpcDSOf3zt3KsaIided0bBdJ+W4UPjiyQ6SRPNR/+LD7kRF5mkTvlI5aWna4daZ1JCXQo1ldu4q+e5vKsFpFO0+POY9mZGeLkSn5PUpOBu64w/3zvY1mOY5CZGMhFmKI8skvvQQ89JCPVyj8/jtw5ZXi/qKLgI0bxX2ocX7vPH2+tAapoTaiSNUHgx0ijfSWQjCSv9MAWr+kAj2atX69utyP9eu1TdE4BkFr1qgfzZKfI49C1JVOQoIyaekYkrEh/xcMGqau/kTdukBxcdXPZ86IACwxETh1SuULCiI102GXXKLuXHI7LSOKeqfiiNzhNBaRDlqG+o1mxDSA1kRUo/Zx8SQYeVBaryGPQrwkjcNJp0DnWqxFQ8sxPPhInKrEWudAx1FxsXg8HI0a5Tuot1pFOyAy8t0oPDHYIdIpkKUQPPFnh1tHWvdlkdu7u658bX9Gs4KRB6X1Gt/9eysO/WbBOMywP/YOboMFlViHa1Wvuvv9d8+Bjqy4WLQLNzExwLhx3tuMG1dVEFRtUBeuwR+FLgY7RH4I1CZxnqidBpg1y/eSXq2jU998471vvh73JtAjR/I1VC2fv+oC0K4drrjvcsVjaTiMEXgHgLKTvkYhrrxSXf/UtvMl2Eu6p04VleWdP/tWqzg+dWrVsU8+UXdOte2I1GKwQxRG1A7vjx3ru9wCoH50qrwcmD7d+zWnT9dfDylUiqsOOz8P1rgYYPdu+7G78RYskHAUaW6f42sUQu2IjREjO1rKbRhp6lTg7FlgxgwgN1fcnz2rDHQA4Oef1Z1PbTsitRjsEIURPdM4vnJ51IxO+bN5nFqBzoPytnw+FUchwYJZZ+6yHztarw2iUY7/4G6v5/U1CqF2tZW/q7LMXtIdEyOmUGfNEvfy1JWjli3VnUttOyK1GOwQhRFf0z3uaMnl8WT/fmPbeRLIPCj3o2IS5mOE66jN5s24o/NuVCDa53l9jUJs3Kiuf+7aqa0kblQuV6BNm2ZsOyK1GOwQhRG9JRL8LWHRvLmx7bwJVB6U86jYNVgHCVF/5eEIMzEaawol4PLLDRuFuOgi31UjEhNdR3a0VBIPlxImcXFiDx1vsrJEOyIjWSTJ0/qK6qOkpASJiYkoLi5GQkKC2d0h8qmgQPy1f+SItufddpv4Ym3eXCwHdjfV4E55ufgCqqz03CYqSpRhcD6nP5sfGslmE/krJ347h5/RFKk4pni8Af5AfEYSDhwQ/Tt3TgQXvpw9q+7L2dPyc3f77MiVxD1xTvxdsEDk6PiSny+CSLMNGgQsXux6PCtLWTCeyBe1398c2SEKQ998I0oiaPXuu75HCdyxWn1/8deq5RrEmJUw647VCnzWawbOIl4R6PwDHyDKIuGkJcllub3a86px6hRw/Lh4/bVqifvjx10DHT3J4OFWwmTRIhEk5uQAvXuL+7NnGehQAAWlBnuIU1sinigU5OVJkpiYMOaWl+f7moWF6s5VWFj1nIULJclicW1jsYjbwoWBeofc+Plnl46sw9VSFCokQJIyMlz7M2OGutc8Y4axXdVz3YoKSapd23v7OnVEO6JIovb7myM7RGFEzV/9WqlZMq5392FPCbOSFKSEWUkC/v53oFkzxWHbjt2oKPwK/823ekyCDlZStrO9e7W3s9nEyIg3Z86Yn6BMZBYGO0RhRM0ScK3ULBnXOk3iK2EWCELC7Oefi0Sizz6rOvb004Akwdq+jc8k6GAmZTtSm3ju2G7OHO/5VIB4fNy44G02SBRKGOwQhRGjRxHUnleuSu6NY1Xyw4fVXVdtO01KSoDoaKB//6pjsbHi+FNPqT7N8OHGtlOrSxft7dR+LmbPNj93isgMDHaIwojRowhqz6ulKjkQ3F2DFZ56SixvqqioOrZsGXD+PFCnjqZT3XSTse3UysjQ3k7P5yJYmw0ShYIaep946tQpbNy4EcePH0el0/jp7bff7nfHiMjVqFHAww8bOwXhWJXaE605O8HaNdhu926gXTvlsaws4OOPtW1I5ODXX41tp5a8caS3aUDnOmF6PheSJN6aMWPEW2XGdgBEwaIr2Pn0008xfPhwlJaWIiEhARaHf0wsFguDHaIAkatMG7nDrGNVak+05uw4l3zwRG07j2w24JprXKuQHjgg5mn8ULeu77wjuZ2R5I0jBw/23Ma5Tpjez4XjZoPdu+vpLVF40DWN9dBDD+Guu+5CaWkpTp06hT///NN+O3nypOrzzJ07Fx07dkRCQgISEhKQmZmJzz//3P74+fPnkZOTg6SkJNSuXRuDBw/GMafNRQ4ePIgBAwYgPj4eycnJyMvLQ4XjEDZRhPFUZTrKx//Nzo+7q0rtieqK4d2q2qene2/vbxVzfPABUKOGMtCZOVN8g/sIdNRUBh8zRl031LZTe11AX4V5T58LNdSO3BGFLT3r2uPj46X9+/frWhPv6JNPPpE+++wz6aeffpL27NkjPfbYY1J0dLS0c+dOSZIkaeTIkVJGRoa0atUqafPmzdJVV10lde3a1f78iooKqX379lKvXr2k7777Tlq6dKnUoEEDacKECZr6wX12KByVlYm9VnJzJenllyXp4os977FisUhSerpol5srnldWpv5aFRWSlJTkfR+XpCTlPi6e9tmR+6N7n53ff3c94cUXS9K5c6qevnCheC8cn56e7tqflSvV7XezcqW6bqu9blmZJFmt3q9ptXr+/Tl+LnJytO+PRBRO1H5/6wp2brrpJul///ufro75Uq9ePemtt96STp06JUVHR0sffvih/bEffvhBAiBt2LBBkiRJWrp0qRQVFSUVFRXZ28ydO1dKSEiQyjT8S85gh8Kdnk3/ZBUV4nh+vrh3t/Gc3vO7+4J3t4Gfarm5rhf96itVr0Huj9oAzMhgR8sGi0ZuZlhRId5/b685I4ObDVL4Uvv9rStnZ8CAAcjLy8Pu3bvRoUMHREcrKwMPHDhQ8zltNhs+/PBDnDlzBpmZmdiyZQsuXLiAXr162du0bt0ajRo1woYNG3DVVVdhw4YN6NChA1JSUuxt+vTpg/vvvx+7du1Cp06d3F6rrKwMZWVl9p9LSko095colGhNIJYVFIjN/xxzU9LTRc6I4yZ7es+fnS2SX/2ujbV5M9C5s/LYnXcC//mPeA1NfL8GbxsdAlUbHcrJusePq+uar3a+Nlh0ThI2cjNDOf9nyBBxHcc+yKmWzvk/RJFIV7Bzzz33AACeeeYZl8csFgtsGpYE7NixA5mZmTh//jxq166Njz/+GG3btsW2bdsQExODuk7ZfykpKSgqKgIAFBUVKQId+XH5MU8mT56MSZMmqe4jUajTUxupoEB8CTp/CctLkj/6qCpY8Kf2klzFXJfycqB9e9dthY8eBVJTNb0GLRsddu9uXL0pLRXJu3c3fjPD7GzxPrgLamfOdN05migS6UpQrqys9HjTEugAQKtWrbBt2zZ8++23uP/++zFixAjs3r1bT7dUmzBhAoqLi+23Q4cOBfR6FLnUJpzqbe/NuXNAbi7Qpw/w/vu+E5StVhE7LFgArFrlfbQBUJZz8DBI6kJtO1XefFNsBugY6MyfLzqYmupzxARQvgatGx3KSdaeVq5bLOqSrLWOit13n7r2rVq5/xyVl4sg5oEHxH15uQhofvkFKCwUlc89lcjwh5GfbSLDBWlaTbWePXtK9957r7Rq1SoJgPTnn38qHm/UqJE0ffp0SZIk6YknnpAuvfRSxeM///yzBEDaunWr6msyZ4f0UJtwqre9N1lZ6vI6/L3JOTiDBqlrP2iQ3nfTwW+/uZ64QwdJKi9XNNOaR6QnF2bhQu9t1fzutPZTbXt3n6O8PNfkZqtVXbFXfxj52SbSIuCFQNeuXYsbb7wRLVq0QIsWLTBw4ECsM6DQTWVlJcrKynD55ZcjOjoaq1atsj+2Z88eHDx4EJmZmQCAzMxM7NixA8cdJs1XrFiBhIQEtG3b1u++EHkiT584T0/89pv7XWk9tdezi+2gQcDixbq6rZk82hCUopiSJGoZOK1ZL17zHbB9uygB4aZvvpi20eFfunXzva+hxVI1QqRnGfjhw2JfnmnTXEdUbDZxfPx47edVQ+v/C0Sm0BNJvfvuu1KNGjWkm2++WXrllVekV155Rbr55pul6Oho6b333lN9nkcffVRau3atdODAAWn79u3So48+KlksFmn58uWSJIml540aNZJWr14tbd68WcrMzJQyMzPtz5eXnvfu3Vvatm2btGzZMumiiy7i0nMKKHmFi7e/tB1XuPhqr2VFzNmzwRnRCfrIjpvhjGl4yP5jSoqqp3h9DVpXV2n9PXty/Li66x4/ru11ab15W66ul1HvEZFeAV163rp1a/tUkqOXX35Zat26terz3HXXXVLjxo2lmJgY6aKLLpJ69uxpD3QkSZLOnTsnjRo1SqpXr54UHx8v3XTTTdLRo0cV5/jll1+kfv36SXFxcVKDBg2khx56SLpw4YKm18Ngh7QI1LSEmr1O1O6b4u/NOQA7fVrd806fVv8+lpVJ0qwXz0ilsfVdTlQXJ13O7RzwaF1WrTXYMer31qSJuvM0aVL1unztaaT3pma5uhZGfraJ9Ajo0vOff/4ZN954o8vxgQMH4rHHHlN9nn//+99eH69ZsyZmz56N2bNne2zTuHFjLF26VPU1ifylNdFV77Jtd5wXJQWCuyXJtWuLld+bNnl+XufOop0a48cDlpem4UVJObdyEwqwCO4rax47Bpw8CdSvL37WuqzaywJNBbmdUZXbTSuK6oZf04xumFrdnkgDXTk7GRkZilwa2cqVK5GhtmQvRRwzV2O4W4ESKFq/vIxawgwALVuqO5c/0tOVS7Zljz7q/Xm+HpdNuWc/pk6zKAKd1eiBKNg8Bjqy665T/iwvq3auseXuNWj9vRkVpKgNAOV269YBJ06oe45WeqqjexNKgRyRN7pGdh566CE8+OCD2LZtG7p27QoA+PrrrzF//ny88sorhnaQwoPazekCYfx4YPp0ZXD18MOiMKKauk9aaU10lZcwHz6sHH2QWSzicTV1oqZNA7wMdOoSFQUsWwb88YfnTf/kZd7e+KyeXVmJyj798OjK5YrDl2AP9uISVX09csT1mNqNC7X+3nzVApP5ajdpEjBypO/zyFt/BapOlZrq9lqZlfRNpJWuYOf+++9HamoqXn75ZXzwwQcAgDZt2uB///sfsrKyDO0ghT4tG7sZbfx495We5RUogPEBj9aK3kbuYhsX53s6SavKSrHQadgwz220bsjn4tNPgYEDFUPJj+F5TIb6aW8ASEtzf1zNxoVaf29qR1d8tWvVSt155HZqRwIdOX+u3FFT3V6roFW3J/JXkHKIQhoTlPUzcqWRVv4WTNRL7woUI+pEqbm2ntt//+v9uv/9r87znDrl0qgYdaRaOK2rnydOqH+v9Lx3jr833a/Zz+v6SryWP9fuPkfB3meHq7HIbAHfZ4cI0LYVvtHmzFG3Y/GcOcZe12r1PgoCAEOHuo7UGLGLrZoRFj185VToys147DHAqdzLwpErkIgSnIHKRBYHKSlVycl6yCNs3nZEdhxhM2rUQut15fbyY85tLRaxY7a7z9HUqcDZs8CMGWJ37RkzxM+BmM51fm2e+sraWxQKVAc79evXxx9//AEAqFevHurXr+/xRtWHkSuNtArKRndu2GzAyy97b/Pyy+4DMXm6Zdgwca/1SyBQ+Ry+cio05Wbs2CG+5SZPrnpgyBCgshI3vtLL5wZ77qSkqF9N5Y2c0Oy0byEyMlynW+VcK2/UlIvQel3H9p4Sr2/ykscdEyPyp2bNEvdGT10505IkTmQW1Tk7M2bMQJ06dez/bdHzLxZFHCNXGmlldMFEtT77TOS5eFNZKdoNHGjstZOTjT2fzNfohJpRDisqMPCZq4Cftigf+PVXoFEj1X2xWIA2bURwk5YGrF3r34iOM7UJzY65VoB/uVZaruur/eLFQJMm5iwG8MSw6vZEAWKRJF9pbZGvpKQEiYmJKC4uRkJCgtndCSs2m/ir21uSZlKS2CPF6H/4ysuB+HjvU1lWqxjGN/Kv24wMdVNJ6eliCs9Iq1YBvXoZe87oaFFU1Nvvx2Zz/YJ1NAz5yMdw5cHZs12W/8ycCYwd67tPOTnA1VeHxpemu5WGGRnmVAz3tBhADr44kkLVjdrvb105O1u3bsWOHTvsPy9evBiDBg3CY489hvJAbnBC5CAmRqww8SYQK1D+/NPYdlo4lIEzzIULItjxxjGPxNFFOA4JFmWg06QJcP6823XOaqcUZ88WZbJ69BCnM7O+Una26LdjHsy+fcEPKrRWeSeiKrqCnfvuuw8//fQTALGb8j//+U/Ex8fjww8/xPhAVZujkKRmA7QTJwKToAyIxMu8PPdTEHl5gUnMrFXL2HZaBGI6EABuu813m2++Uf48FyNxHCnKgxs2iGzZ2Fi359AzpainWKrMiI0uCwpEv8eOBV59Vdw3bx78AMzMxQBEYU/PUq+EhARp3759kiRJ0pQpU6TevXtLkiRJX331lZSenq7nlKbi0nP98vPVLc/Nzw9sP8rKRN2f3Fxxb/Ryc0d33KHuNd9xh/HXDtTS83btvF/XcZn/lfjG5QSv4V5Vy/zVbBfg7qZnCwN3S/3T07Ut9V+40P0ScItF3LScS5JE/wsLxf8PhYXaXk+o/L9GFEoCuvRckiRU/pWhuXLlSvTv3x+AKCMhr9ii6sHMBGVHwVyBcuqUse20sFqByy83/rxnznh/fM4cwGorw89oim9xleKxZBzDSLyuapm/mqlHd7SOWsi5Lc4jIb/9pn6UyOhpo4ICMSXXo4e+KbpQ+X/NEzPLxRD5oivYueKKK/Dcc8/h3Xffxdq1azFgwAAAwIEDB5CSkuLj2RRJ5OW53vYQUbs8N1w0aGBsOy3Ky4ElS4w/b7Nm3h9vuGguylATTfGL/dhw/BcWSPgdVUvE1OTkeJp6VEPN0ntvQQogjqsJUoycNvIUfGmZogvl/9f8DeSIAk1XsDNz5kxs3boVubm5ePzxx9GiRQsAwEcffWSvlUXVg68N0IDQ3FTMn79CN2wwtp0WajZS1OP4caBPH5GAq0hWPnQIsFjwz7VVycabcTlq4ILr6iuoz8lx3vwuJ0fd89SMWmgpbeGNUXtIGTVCFKr/rxkRyBEFnJFzZ+fOnZPKy8uNPGVQMGfHf8Hept4f/uZyNG6sLneicWPj+z5qlPH5Ou5uWQMrJWnIEJcHOuB7j8/xpzSHrxIJWnJ2jCrzUFio7jyFhcE5j8yIsiNGMbNcDJEkBThn59ChQ/jNIYzfuHEjxowZg3feeQfR0dEGhWEULgoKgJdecv3L1GYTx0PpLzsj/go1qhq2HsHYy/N6rMKiT6LEpi2yRx/F+DwJO9DR4/P8WeZv5KiFrtIWbhg1bWT0LuNGlB0xCleIUbjQFezccsstKCwsBAAUFRXhhhtuwMaNG/H444/jmWeeMbSDFNp85UcAobP3h1HTCfKOur6obSf3Tc20WufO6s+pVTzOoBgJWAWnXQv//BOYPNnwZf7l5SKAeeABcf/3vxtTdkBTaQsvjArAApFY7G/ZEaOYWS6GSAtdwc7OnTtx5ZVXAgA++OADtG/fHuvXr8d7772H+fPnG9k/CnHh9JedUX0tLVV3PbXttCR3BmKjQgB4FJNxBrWRgNP2YwOxGLk5kqKYp1GFJsePF7tfO+5dEx8v9vLxd9TCqAKegDF1n0I5sdhfob5CjEimujaWowsXLiD2r03DVq5ciYF/FQBq3bo1jjKEr1bC6S87o/oapfJPBDXtPG3/L0+rOX+hqh21UKslfsJPaKU4thw3oC+WQUIUeu91fY68zF+v8eOBadNcj9tsVcf92QxSDi68BbZaggt/6z7JI0SDB7t/XJJCM4lfDfm9PnzY/YipxSIeD8dAjiKLrpGddu3a4bXXXsO6deuwYsUK9O3bFwBw5MgRJAUiUYFCVjj9Zae2iKavdmoXHPpqp2dazahgx4JKrERPl0CnBfaiD5ZD+uufhpYtjbmerLwcmD7de5vp00U7vTyVtnCkNbjwd9rIefdprY+HqlBdIUbkTFew8+KLL+L1119H9+7dMWzYMFx66aUAgE8++cQ+vUXVQyQP0Xuye7f+do65ObNmaZ9WcyhJp1sWFqESVvTEavux8XgRFkjYjxaKtu5GYPyhZum8ms0Jw0kwAjwzGTHVRxRouqaxunfvjj/++AMlJSWoV6+e/fi9996L+Ph4wzpHoS+chujVFtH01e6XX9Sdx7mdu+rZajhOq/38s7bnOqqLP/En6iuOnUQ9pOM3nIPr/7dZWUBcnP7rubNnj7Ht3JFHzLwZM0a8vmB8LrUEeP5MD5rJ36k+okDTNbIDAFarVRHoAECTJk2QrHaugCJGuAzRGzXlpnbjPMd2npa8a+2P3qXnL2K8S6DTA6uRhJMeA51Fi/Rdy5uiImPbuWPUpoJGUVvpXW27UBUqK8SI3FE9snPZZZdh1apVqFevHjp16gSLl391t27dakjnKPSpHaJ/7rnA1qtSw6hkyvvuE6uHfLnvPnGvZnm+J1arMvdH69Lzjvge3+NvimP5GIbheA9A1f/DbdqI6caWLcXUldEjOrJg5HgdPmxsO0D8DvWOWugJjonIWKqDnaysLPsKrEGDBgWqPxRmwmmIXp5yGzJEBDaOwYeWZMpvv1V3vW+/FX/hqhlp8MRmA9avF+cB1C89t6ICW3A5LsV2xfF0HMJhpLu0r18f+OILfX3U4pJLjG3njlGbCsrcTT+mp4vPkpp8lFGjgIcf9v7/idUq2hFRYKgOdp566im3/03VW7gN0cvJlO6+vGbOVPflpXUJu7/L7h2fr2ax4214B+9ghOLYfXgNb+A+j8+pWVNv77QJxhe/UZsKAp63BpCrp6tJwJUrvXtL9vZn92ki8k1XgrKj0tJSVFZWKo4lJCT4e1oKE02aGNsuGPxNptRaLsLfNDbHKR1vydPJOIZjSFUc+wkt0R47cQHev0kDNW3lLBhf/Kmpvtuoaae2erqaRGd536Dp05WBntUqXq8/+woRkW+6gp0DBw4gNzcXa9aswfnz5+3HJUmCxWKBLRRqA1BQdOhgbDu9tOZUyMmUeqhd/r1jB9C7t75ryJKSlDlEJ0+6b/cm/g//h38rjnXGRmyGuiSf2rXV98mf/BUgfL74tSQ6q/ksTZ0qctfmzBEjnc2bixEsjugQBZ6uYOfWW2+FJEn4z3/+g5SUFK/JyhTZjM6P0MPfnAqttE7dqV3yrobzrsyZWI/1uFpxbDZGIRezNZ337Fl17Yx6rwP5xW/Uiq9AJDr7u/s0EemjK9j5/vvvsWXLFrRq1cp3Y4poZgc7RuRUaKU1Z8eflUUnTihHDrp3F0FCLM5jH1ogHcpv2gb4HSfQQPN1fv9dbHTobaRGa2kLXwL1xW/UZ9Lsz3a48XfEjyiQdO2z07lzZxw6dMjovlAYMjIZVCu1ORVGz6rWr++7jWO7rl39+0ffMbjq0gXIxSycR5wi0BmKBbBA0hXoAMCGDd6LkBpVMT4YjPpMmvnZDjdaitkSmUHXyM5bb72FkSNH4vDhw2jfvj2io6MVj3fs2NGQzlHoM7LCtFZG51SopXUX4PXr/QsC7CNDv/6K2k2aYJbDY9+gC67G16iEcX9Cuxup0VIx3sj3Wg+jPpNmfrbDidEjfkSBoCvY+f3337F//37ceeed9mMWi4UJytWQ0RWmfTl3DsjLA/buBZwWAXqkJadCDbV5JXI7f65vtQJdMyVg0E3A4sWKx9phJ3ajnf6TeyBJYt8hx5VG4VTd3qjPZLA/2+HI14if8+eIyCy6prHuuusudOrUCRs2bMDPP/+MAwcOKO6p+pA36rNY3Fc9tliMq401aBAQHw/Mng0sXw6sXKnueUbnVGjN5fDn+tfbliOmZpQi0HkWE2GBFJBAR+ZchDScqtsb9ZkM5mc7XGkZ8SMyk65g59dff8WLL76ILl26oEmTJmjcuLHiRtVLMKoeDxrkMrChmtE5FSkp2trpuX5tnMZZxGE5+tiPSVFWPHB7MZ7Es9pPqJM8UhNu1e2N+kyyord34TTiR9Wbrmms66+/Ht9//z1atGhhdH8oTAWy6vG5c/oDHcD4nIoWLYBVq9S103P9iXgWz+JJxbH++AyfV/ZHrYXazuUveUNEo0ptBJNRn0lW9PYsnEb8qHrTFezceOONGDt2LHbs2IEOHTq4JCgPHDjQkM5RePFnoz5v8vL0P9fTaIM/y2SbNdPWTk3uBwC0wo/4EW0Ux5ZgAG7Ep5CLdp45o+7agWBEqY1gM+ozGajPdrgzqrguUaDpCnZGjhwJAHjmmWdcHmOCMhntp5+0P8fbaIO/G+P98ou6PsjtHEdF3H4hoBJrcR264SvF8WbYjwNQGVkFyJEjyp85ykGOwnHEj6onXTk7lZWVHm9aAp3Jkyejc+fOqFOnDpKTkzFo0CDscVrX2717d1gsFsVNDrZkBw8exIABAxAfH4/k5GTk5eWhoqJCz0ujEFSrlvbneMqpkJfJOo+yyMtk1ewLoidPQR4VSXcqOD6i9kJUwqoIdMbhZVggmR7oAO4rvMujHMOGiXt+kVVvzGuicKAp2Onfvz+Ki4vtP0+ZMgWnTp2y/3zixAm0bdtW9fnWrl2LnJwcfPPNN1ixYgUuXLiA3r1744zTWP0999yDo0eP2m9THYrn2Gw2DBgwAOXl5Vi/fj3efvttzJ8/H08++aTz5SjAbDZgzRqxE++aNcZtMDdokLp2jzwC5OcDhYXAgQOu/8gatTGe3s3msrNFaYQZM4Dx/3cSEiyYXzrE/ngRUhCHs5iBceouEAQcpCU1srPFSGZhoff/B4lMI2kQFRUlHTt2zP5znTp1pP3799t/LioqkqKiorScUuH48eMSAGnt2rX2Y9ddd500evRoj89ZunSpFBUVJRUVFdmPzZ07V0pISJDKyspUXbe4uFgCIBUXF+vue3W3cKEkpadLkggbxC09XRz3V2Gh8ryeboWFwTnPoEHqzjNokPJ58ns0HWNcGnfDWlXnDPYtJ0f/742IKNDUfn9rGtmRnP4kdv7ZX/KoUX2n/fjfe+89NGjQAO3bt8eECRNw1qFq4YYNG9ChQwekOKwH7tOnD0pKSrBr1y631ykrK0NJSYniRvp5mhqS61P5u2W8nATpjZplz0Ytk9WzAqWgAMgdfBRzfrsRYzHTfvwd3AYLKrEO16o7aZB16WJ2D6q3QI2WElU3uhKUA6GyshJjxozB1Vdfjfbt29uP33LLLWjcuDHS0tKwfft2PPLII9izZw8K/voGLSoqUgQ6AOw/F3koazx58mRMmjQpQK+kelFbn8qfHVTlJMjBgz23UZMEadQy2UsuUXceuZ2t3Iatd7+GH/AYElEVWDfEERQhtNfkpqWZ3YPqy6gK80SkMWdHThB2PmaEnJwc7Ny5E++//77i+L333os+ffqgQ4cOGD58ON555x18/PHH2L9/v+5rTZgwAcXFxfYbi5rqp6U+lT+++ca/xwHjNsYbNcrzORzPNWoUgO++w5lLM/HcqVwkogQb0RmdsBUWSCEf6JB5jEikJ6IqmkZ2JEnCHXfcgdjYWADA+fPnMXLkSNT6a7lMWVmZrk7k5uZiyZIl+PLLL5HuY76iy1/j6vv27UPz5s2RmpqKjRs3KtocO3YMAJCamur2HLGxsfbXQP5RGyf6E0+WlwPTp3tvM3068Nxz3utWBXOZbG3pNKwPPwnM/hcSKitRjARMwGS8jvsMLdoZaMePm92D6of1poiMp2lkZ8SIEUhOTkZiYiISExNx6623Ii0tzf5zcnIybr/9dtXnkyQJubm5+Pjjj7F69Wo0bdrU53O2bdsGAGj411xDZmYmduzYgeMO/yqvWLECCQkJmlaGkT7ulib7086dOXN85yrYbKKdL0Ysk50zx/O0HSBhED7GLrSFddZMoLISx3v8E63xI+ZiVFgFOgB3vjUD600RGU/TyM68efMMvXhOTg7y8/OxePFi1KlTx55jk5iYiLi4OOzfvx/5+fno378/kpKSsH37dowdOxbXXnstOnbsCADo3bs32rZti9tuuw1Tp05FUVERJk6ciJycHI7eBIHaHHV/ctnVbiqotp2/G+N5mkFthF8xCw9gID4FAPye0AwX/W82km7oixpNAHj5AktPB+bPFyMpu3eLUSqzWa1A165m96L6Yb0pIuPp2lTQKHPnzkVxcTG6d++Ohg0b2m//+9//AAAxMTFYuXIlevfujdatW+Ohhx7C4MGD8emnn9rPYbVasWTJElitVmRmZuLWW2/F7bff7nZ3ZzJey5bGtnMnEP/4+7MxXvPmyp9r4AIexjTsRlsMxKcoRzSew+N4//GdQN++sFrFdbwZNgzo2bPqPhTYbMD69Wb3ovphvSki41kko9ePh6GSkhIkJiaiuLgYCQkJZncnrJSWAnXq+G53+jRQu7a+a9x/P/Daa77bde0KXHaZCEZGjfKev+OP8nIgPl4EA1dhA17DSFyK7QCAL9ENI/EafrK2xdmzog82G9CkifepiYwMsQmb1ao8v9ny830HamQs+fPiq96U/Hkhqs7Ufn+bOrJD4e+tt4xt506rVurarV8PvPoqMHasCBbGj9d/TW9iYoDHR/2JuRiJr3E1LsV2/IEk3In/4DqsxQ9oi3HjqoItrSvW1q8PjUAH4OiBGeREesB11R/rTRHpw2CH/KJ2BwA/dgrAqFHa/2G32YBp0zwHPLo3a5MkID8fk/7XGiPxOqIgYR7uQGv8iPm4E1FRFuTlAQ4VTTRPw4VCLobaZfgUGKw3RWQsBjvklyZNjG3nTkwMME5nuajp08W0kKOCAtGfHj2AW24R902aqNi7ZO9eoHdvYPhw4PhxHKvfGj0sa3AX5uEEGnh8mtYcjORkde0DhaMHoYH1poiMw2CH/NKhg7HtjOa8JF3XZm1lZcAzz4gXsXIlULMmll3zHBqd3IY10nWKppWVriNKRm1maLSoKLEqzXlrK44ehA5WmCcyBoMd8stf+zca1s4dNZsKeiNPoemqel5YCFx6KfDUUyLo6d0b5Vt34u8bHkc5PG9t4DiiJOdgeCup4TiKEsyN/C65hKMHRBT5GOyQX0JlU0Fv5KXimjZr+/13YMQI4PrrgT17gJQUkeCzbBnmfNHcsE0O3QlWUrA8CjVhAkcPiCiyMdipRgJRQfn8eWPbubNnj/7nWq1/1aiCusRfCyoRl/+WWAL2zjtVRa5+/BEYOhSwWLB3r7pry+3kESWP17QoR5R8TXsZzV1eExFRJGGwU03oTsr1Qe2IzapV+oOsHTs0d8vOcQm4rxGTttiFL3Eturx5D/Dnn8Df/gZs2ADMng3UrWtvpzYIkdtp3f7f17SX0fwZhSIiCgcMdqqBUKig/Msv+oOsuDj9173qqqr/9jRiEoezeAETsA1/wzX4GlKtWsDLLwObNgF/FZ515OaQW3I7PTtAq6nibiR/tgYgIgp1DHYinK6kXA30LCnXGmSp3VTQmfP0kLvN2vphKXahHSZgCqJRgSNXDoJl924xJFTDfem41FR115fbaV167m9Cth7OJTCIiCIJg50IF+gKyg08by/j9ZqA+iBr4kTt15Cv4/zasrOBhx8GLrYcwQf4B5ZiAJriFxxEBubftBhp334MNGrk9bzffafu+nI7rUvP/U3I1soxr4mIKBIx2Ilwga6gvHKlvudpCbKGDNF3DZnjayv40Ibz02ZhV2Vr/AMfoQJWvISH0Ba7cdeigapGmxzq0Kpqp3X7/2BPKTnmNRERRSIGOxEu0BWU/R2BUBNk/fqrf9eQR59sG7egxfAu+BceRAJO4xt0weXYgjy8hDMQVUrVjDapfc2O7bRs/x+sKSWrFS6lLYiIIhGDnQgnT6F448/uvdHR+p4nUxNkOSyE0mXPphJg9GhEZV6Jjhe24BQSMRJz0RXrsR2X2tupHW2qX1/ddZ3bqd3+X08tMLVatgRyc4EZM4CzZxnoEFH14D4DkyKG1So2i5s2zXOboUP1f7nWqaPveRaLCMLUBFljxgD/9396riIhGwW49YUHgTNHYAGQj2EYh+k4Bs9Zxr5Gm9S+V+7aydv/exMTA1x2mVgMZrRXXgH69TP+vEREoYwjOxHOZhP723jz/vv6p6N8jRq5o7XQZFqa9ms0xi9Ygr9jIYag7pkjQPPm+H7qFxiOfK+BDuB7tCkhQV0f1LZzVl4ObN2q77ne1Kwp6pgSEVU3DHYinK/VWIB/q7F++EH7c7QWmnz9dfXnroELGI8XsRttMQBLUY5ofHbZE8COHWg/rrchBTmHDlXXF7XtnAVqNdZ777EUBBFVTwx2IlygV2OdOKGuXWys/kKTP/+srl1XfI2tuAwv4lHE4xzW4Dp0xHbMqPcMEBeneVWUJ2rLV+gtc8EN/oiIjMVgJ8IFejVW7drq2tWtq7/QpK/VSfVwEm/gHnyNa9ABO/E7GmAE5qMHCrEHrRUV17WsivLkwAF1/VbbzlkgVmM5b7BIRFSdMNiJcFo3tNNqwgRj27nz7rueHpFwK97Fj2iNe/AWAOAt3I3W+BHvYAQA8aKTkpTPUrsqypPGjY1t5ywQq7H83TySiCicMdiJcL6KSkqS+kRhdz780Nh27tSuDXTurDx2CfZgJXrhXdyOZPyOXWiLbvgS9+AtnIQyuqmocD2nvCpK72hTIMXEiI3+AkHvdCURUThjsEMelZeLQOiBB8R9eblrm+JidedS284Tebl2LM7jaTyF7eiInliNc6iJCXgBnfAdvoL74an27f27tjO1mxz6sxni1Kliwz+jgzC905VEROGMwU6EkwuBeuMul2P8eCA+Hhg7Fnj1VXEfHy+OO2raVF0/1LZzRy6MeT1WYTs64ik8g1iUYyn6oR12YQom4AI81zswOgdG7fn8ve7UqWLjvxkzqjYCHDlS37n8na4kIgpnDHYinJ6l5+PHi00InQMgm00cdwx4unZV1w+17dyZ/+IxzLfdilXohUuwF0fQEP/ABxiAz3AAzfSfWCc1OTVGFdeMiRHB6KxZ4l5PNXSt+xoREUUaBjsR7vBhbe3kURRvpk+vmtIK6MqkykrgjTdw63OtcSveQyUsmIVctMEP+Aj/gJyA7IvRS7nV5NQEqrhmXByQleW9Tc2ayp+17mtERBRpGOxEuN9/19ZOzYZ2NptoBwC7dqk7v9p2djt2ANdcA9x3H+LLT2ELLsOV2IgHMQslSNR0qp07NV5bhauu8u9xfyxa5DngycoCSkv1rzQjIopErI0V4ZyXXftqp3YURG5XQ+UnSG07nDkDTJokho9sNqB2bcxJew4P/pQDm86Pq/NIh79sNuDee723ufdeEXgEatpo0SLg3DmRxLx3ryjwOW2aGPkBfNffIiKqThjsRDi1OxzL7bQm327frq69qnZLlohMXHkZU3Y28Mor2D0lHbaf1F3Hndat9T/XnTVrfL+vJ06Idj17GnttR3FxInmciIi84zRWhLvoIm3ttCbfqt2R12u7334Tgc2NN4pAp3Fj4NNPgYULgfR0TJmi7hqeeKv4rsfq1ca208tmEwHVggXinrsjExG5x2AnwjmXRfDVTmvybWWluvO7bVdRIZYItWkDfPyxiKLGjxcJPn//u73Z5s3qruFOVlbV1I5RDh40tp0eBQVAkyZAjx7ALbeI+yZNxHEiIlJisBPG1Pxl362b77ydpCTl/ivyhnZRTp+OqChxfOrUqmMZGer66tJu0ybgyivFBj6lpUBmJvDdd8CLLwK1aima6t31NytL5LYYrVEjY9tpVVAADBniuqXA4cPiOAMeIiIlBjthKtB/2V91letuuw0buq4y0jpyhOJikZfTpYsIburWBd54A/jqK6BDB7fPrVNHU9ftjhzR9zxfrr/e2HZayJtEuiv/IR9jwU8iIiUGO2FIy1/269apS6Z13FRQPr/zHj1Hjrief+1adX1eu0YCPvhATFnNni2+mW+9FdizB7jnHtdhJAdvvKHuGs7kwSOjde+ubrQsECuifG0SyYKfRESuGOyEGa1/2Wvd9M/X+SVJef4LF3yfuyl+xuKK/sA//ynmpFq2BFauFOXMk5N9Pv+XX9S9Bnc2bRKzZEayWn0HYG+8EZhl52qn9Fjwk4ioCoOdMKP1L/t//1vdeeV2WstLeBmQQTTKMQEvYBfaoR+Wiazmp54S69A1rMn2t8bUbbf593x3srPti8UU0tPF8UBt4qe2kCcLfhIRVeE+O2FG61/2WquSay0v0bWrSLdxdg3W4TWMRDvsBgBsSbwel2+cC1xyiboLOHj3Xf15O4DYdC8QsrNFEvS6deL9bthQJHoHsv5Ut24ioDp82P3om8UiHmfBTyKiKhzZCTNa/7Jv0UJde7md1vISl17qdB7sxR9Iwjpci3bYjeO4CLfiXcwbvlJXoAMAtWsDnTvreiqAqkKYgWC1itycYcPEfaALbVqtwCuvuA90AHGcBT+JiJQY7IQZ+S97T1/gFotY5i3/Zf/uu+rOK7fTugmhvGGfBZVYiZ7Yi0uQhJMAgDdwD1rjR7yHWzHtJf8iDn+Sff2puE5EROHP1GBn8uTJ6Ny5M+rUqYPk5GQMGjQIe/bsUbQ5f/48cnJykJSUhNq1a2Pw4ME4duyYos3BgwcxYMAAxMfHIzk5GXl5eaioqAjmSwkarX/Zx8UB0dHezxkdXbXxXmqqun7I7Ww2IAuLUAkreqJqy+BczMJ9eAN/or69nV5qKrF7o3NAKSTJCeSeWCxcek5E5MzUYGft2rXIycnBN998gxUrVuDChQvo3bs3zpw5Y28zduxYfPrpp/jwww+xdu1aHDlyBNkO2Z82mw0DBgxAeXk51q9fj7fffhvz58/Hk08+acZLCjnr1vleMXXhgs6lyqdOoXYdCxbhJvuhE6iPeJzBbOQqmvqTJKymErs3HrbvCUtcek5EpIMUQo4fPy4BkNauXStJkiSdOnVKio6Olj788EN7mx9++EECIG3YsEGSJElaunSpFBUVJRUVFdnbzJ07V0pISJDKyspUXbe4uFgCIBUXFxv4agKjokKS0tPlReCuN4tFkjIyRDtJkqT58z23dbzNny/av/OOuvY7Box3OXgdCj22b9NG/2u+9151ffJ0y8/3/30PFfn51e81ExF5ovb7O6Rydor/WhJUv76Y+tiyZQsuXLiAXr162du0bt0ajRo1woYNGwAAGzZsQIcOHZCSkmJv06dPH5SUlGDXrl1ur1NWVoaSkhLFLVyo/ct+1ixRRuLNN9WdVy6r8O233tt1xPeQYEH7z6pqRryHW2BBJdaiu8fnnTyprh/uePg1qhZJy7C59JyISLuQWXpeWVmJMWPG4Oqrr0b79u0BAEVFRYiJiUHdunUVbVNSUlBUVGRv4xjoyI/Lj7kzefJkTJo0yeBXEBxql56PHavtvPLMoafpIisqsAWX41JsVxy/rvlv+HK/75oR9epp64+j2Fj9zwWAK67w7/mhhEvPiYi0C5mRnZycHOzcuRPvv/9+wK81YcIEFBcX22+HDh0K+DWNEqi/2OUkXndLlm/DO6hAtCLQWdD9dUCS8ONpdcWx/BnZKSvT/1wAePRR/54fSuQEdcB1RZ78M5eeExEphUSwk5ubiyVLlqCwsBDpDlvSpqamory8HKdOnVK0P3bsGFL/Wg6UmprqsjpL/jnVw9Ki2NhYJCQkKG7homvXwHyRTZwo7h33s0nGMUiw4B2MsB/bg0sQgzKU33EvALEpshpq27nTrp3+5wLATz/59/xQk50NfPSRaxHW9HRxPFC7NxMRhStTgx1JkpCbm4uPP/4Yq1evRtOmTRWPX3755YiOjsaqVavsx/bs2YODBw8iMzMTAJCZmYkdO3bg+PHj9jYrVqxAQkIC2rZtG5wXEkTr1wdmWfGQIeL+zz/F/Zv4PxyDMli8ApvQGntwATH2dvK9L2rbueNr6bwvtWr59/xQlJ0taoYVFgL5+eL+wAEGOkRE7pias5OTk4P8/HwsXrwYderUsefYJCYmIi4uDomJibj77rsxbtw41K9fHwkJCXjggQeQmZmJq666CgDQu3dvtG3bFrfddhumTp2KoqIiTJw4ETk5OYj1N9kjBAWqwOPBg+K+zZ/rIeFqxWOzkIsHMUtxTN5U0FttLEdq27nTpYsolK7XoEH6n0tEROHP1GBn7ty5AIDuTtvjzps3D3fccQcAYMaMGYiKisLgwYNRVlaGPn36YM6cOfa2VqsVS5Yswf3334/MzEzUqlULI0aMwDPPPBOslxFUSUmBOW+L9PNAegv0cSqO1QC/4wQauLSXp1Di44HTp32fPz5ef98yMvQ/FwAaN/bv+aGooEBsLui4Mi89XeTzcHSHiEjJIkme9uKtPkpKSpCYmIji4uKQz995+WXg4YeNPWcuZmEWHlQcuxn/w4e42W37uDgR4FitQM2a6hKIY2OB8+f19c9mA5o08V2N3Z2kJODYschK2C0oENOOnlZjMW+HiKoLtd/fIZGgTOrt32/cuRrhV0iwKAIdW5dMWFHhMdABgHPnRAkHwPfuzDK17dxxXIFU3cnlIryVC2G5CCIiJQY7YcaYnB0JH2MQfkUT5eFduzD6ivWohO9hkLw8cV+zprorqm3nyTff6HveiRORVTrB16aSAMtFEBE5Y7ATZtQW6szMBHJzAaf9GHEDlkNCFAZhsf3YJDyJRhkS0LYtfvhB3fnldnffra692nbu+FsI1CkNKaz9+qux7YiIqoOQ2UGZ1GnVSl27DRvETVYLpTiOZMTjnP1YOaLRAH/gNBJwdSNx7Oef1Z1fbqe2uLw/Rej9LQT6++/6nxtq5LIeatqNGOGzGRFRtcCRnTAzapT2ZdwT8SxKUUcR6PTDUsSiHKchEro++0wcV7snjdzOeRdfT9S2c8ffPCV5mXwkkMt6GNWOiKg6YLATZqxW9cu4W+FHSLDgWTxpP/Yp/g4LKrEM/RTnrF1b/HfLlurOLbdr1Ehde7Xt3HHaa1IztVN/4UAu62FUOyKi6oDBTphZtw4oLfXexoJKfIlu+BFtFMeb4mcMxKcAlMMsNltVQuu776rrh9zuwAF17dW2c6dDB/3PjTTTphnbjoioOmCwE2Z81SzNxkJUwopu+Mp+bAxmwAIJv8DzEIk8VVS7NpCY6P0aiYlVI0H79qnptfp27vzxh/7nAsBfG3NHhLg4ICvLe5usLNGOiIgEBjthxtOS4no4CQkWLMQQ+7GjSEUczuIVjPF53pkzxf25c0Bxsfe2xcWiHSA27FNDbTt3kpP1PxeIrARlQCQfewp4srLUJzETEVUXDHbCjOMKK9l0jMVJKOtIdMOXSMNRnIe6P/HlwvLy/jm+yO3UJv+amSQcSQnKskWLgLNngZwcoHdvcX/2LAMdIiJ3uPQ8zDjuRNwJW7EVlysen48RuBPz4JyX44tcP2rvXnXt5Xa+prxkatu541DQXhe5jlekiYsDXn3V7F4QEYU+juyEmcxMoAYuYBfaugQ6DXEEd2I+tAY6APDvf4v75s3VtZfb9e2rrr3adu40bKj/uRkZQLdu+p9PREThj8FOmGnx5X9wATFoi6qtju/Ef2CBhCLojwquu07cDxyorr3cTuvqLT26dRMVvfXs1TN0aGQVAXVkswFr1gALFoh71sMiInKPwU64OHoUsFjw+M9VdRd2oh2iUY75uNPv0//5p7g/cUJde7mdnOvji9p27jgWAtUa8Lz/fmQGAQUFohJ8jx7ALbeI+yZNxHEiIlJisBPqJAm4/XYgLU1xuBO2ogN2ogLRhl0GUL9ySW4XjB2UASA7G/joI+35N5FYFLOgABgyxLUg6OHD4jgDHiIiJQY7oWzdOlEbwmEOqGzUWFggYRs6GXqpJk3EfVKS12Z2cruuXdW1V9vOm+xs4JdfgMJCID8fePRRdc/ztTdROLHZgNGjq4JTR/KxMWMiczSLiEgvBjuh6Nw5ICUFuPZa5fETJxA7ezpSUoy/ZEmJ/RKqyO3atPHeTqa2nS9WK9C9OzBsGHD6tLrnfPutMdcOBevWuY7oOJKkyBzNIiLyB4OdUDNjhih+5bje+qOPxLdY/fqw2fyrIO6JPFKjdd+cUaN8JwBbraKd0dyNbvjTLhwcPWpsOyKi6oDBTqg4cEAktowbV3Xs2mvFfMTgwfZDa9aoH33RQi4cqTYnRm4XE6Pssjvjxol2RtNatDQSqN1N2t9dp4mIIgmDHbNJEjBgANCsmfL4jz8Ca9eKnB0Ha9YEphtyWlC3br7zdpKSlHvXXHWV9/a+Htdr1CiXt8dFVFRgRpWIiCh8MNgx09Kl4tt46dKqY88+KwKgVq2C1o3OnasKe2olJ8x6YrEELmHWahUzft7Ex0fWPjtqd5P2d9dpIqJIwmDHDCUlQI0aYkRHFh8vMm4nTvT61O7dje+O46qmdet8T5OdOFGVAGtmwuy6dUBpqfc2paWRlayrdjdpf3adJiKKNAx2gu3JJ0WhKMehjuXLgTNnVA2vGLGE25HzyIvWBFgzE2YPHza2XTjwtZu0xcISGUREzhjsBMuuXeKb6Nlnq47ddBNQWQnccIPq07z+urHdch550TpyYOZIg9YNECOBt92k5Z9nzoysqTsiIn8x2Ak0m01U72zfXnn8l1/EVrcatxZWW5VcK3nkpWtXdUvJ5RGmrl19vwSLxfgRKUD7MvlI4Wk36fR0cTw725x+ERGFKgY7gbRmjcjN+eabqmOzZonhlMaNdZ3S37ILnsgjL+vX+04mttlEO0AsGPO1j40kiXZG07pMPpI47yZdWCh2L2CgQ0TkqobZHYhojsnGjRoBP/0ExMb6dcouXYDZs/3slwOLRYwIyDkeWnNwtFQ91zBbp4qcv+ItQTqS81fk3aSJiMg7juwEUl4ecM01wNdfA7/+6negA7jUA/WbJClzPLRuWieXmfBFbTst5PwVi8V9/orFwvwVIiJisBNYWVki81dFworNJma9FiwQ9+FSyFFt8GV0kCZj/goREfnCYCcEFBSIquM9egC33CLumzQRx50ZvVmc89LzI0fUPU9u17mzuvZq2+nB/BUiIvKGwY7JCgqAIUNc804OHxbHnQMeo5dwOy89V1shXG63ZYu69mrb6eVYDb17d05dERFRFQY7JpJLLbhbzSQfcy614GtTOb3khGO102dyO63tiYiIgo3Bjon0lFrwtqmcP+QRI7UjInI7re2JiIiCjcGOifSWWvCUlKuHc3mBLl3UPU9up7U9ERFRsDHYMZE/pRack3JnzNB+fXflBbSursrIUNdebTsiIiKjcVPBALHZxPTT0aMiWOnWzXUqR86/OXzYfd6O84Z/zhw3lbPZgJdf9nwuAIiKEqW4ZBdfLKbE/Fm1VN039iMiotDHkZ0AULuU3MiijmrOVa+e7/OoXdout3Pc2M8dbuxHRERmY7BjMK1LyY3cFM/TuerXF/cnTvjuk56pNfm66enKNhkZ3NiPiIjMZ2qw8+WXX+LGG29EWloaLBYLFi1apHj8jjvugMViUdz69u2raHPy5EkMHz4cCQkJqFu3Lu6++26UlpYG8VVU0bOUHDB2Uzznc61cCcTFuW/rrk++lrY7JzQH4jUQEREZydScnTNnzuDSSy/FXXfdhWwP34p9+/bFvHnz7D/HOtWXGj58OI4ePYoVK1bgwoULuPPOO3HvvfciPz8/oH13R8tScucCjkYWdXQ815o12vokT0sNGSICG8fAzdfUGgtTEhFRKDI12OnXrx/69evntU1sbCxSU1PdPvbDDz9g2bJl2LRpE6644goAwKxZs9C/f3+89NJLSAtUQSYP9C4lB9QlNAerT/K01OjRykApPV0EOhytISKicBLyOTtr1qxBcnIyWrVqhfvvvx8nHBJPNmzYgLp169oDHQDo1asXoqKi8K2XugdlZWUoKSlR3Iygdym5ltpYweoTp6WIiChShPTS8759+yI7OxtNmzbF/v378dhjj6Ffv37YsGEDrFYrioqKkJycrHhOjRo1UL9+fRQVFXk87+TJkzFp0iTD+9utG1C7NuAtZah2bWW+i5zQ7JznIycP60nwdRwlSk7Wv7xd67RUoEangn0NIiKKLCEd7AwdOtT+3x06dEDHjh3RvHlzrFmzBj179tR93gkTJmDcuHH2n0tKSpBhwK53Nhtw9qz3NmfPinZWq++EZrkieVaW+i/0ggLX6aekpKrzacnB0cLdddPT/d/HJ9jXICKiyBPy01iOmjVrhgYNGmDfvn0AgNTUVBx32himoqICJ0+e9JjnA4g8oISEBMXNCHPmKDftc6eyUrQD1Cc0z5oFLFggko29FdT0tOz95ElxLy9Bl+lZ3q7lup6W24fqNYiIKDKFVbDz22+/4cSJE2j4V4JJZmYmTp06hS1bttjbrF69GpWVlehiQjGm/fu1tVObPDx2rO9cHjWjRHFxYim6kTk4epfbh9o1iIgocpka7JSWlmLbtm3Ytm0bAODAgQPYtm0bDh48iNLSUuTl5eGbb77BL7/8glWrViErKwstWrRAnz59AABt2rRB3759cc8992Djxo34+uuvkZubi6FDhwZ9JRYANG+urZ3a5GFHnkYy1IwS/fabmK4aNqxqmbm/9FRuD8VrEBFR5DI12Nm8eTM6deqETp06AQDGjRuHTp064cknn4TVasX27dsxcOBAXHLJJbj77rtx+eWXY926dYq9dt577z20bt0aPXv2RP/+/XHNNdfgjTfeMOX1jBrlO4CwWkU7wPcGfu54GsnwZ9m7P4JxXbNeGxERRQZTE5S7d+8OyVPVSgBffPGFz3PUr1/flA0E3YmJAcaNA6ZN89xm3DjRDvC+gZ837jYn9KeCuj+CcV2zXhsREUWGsMrZCQdTpwJ5ea4jPFarOD51qvK4p3pWajiOZOgt8+CvYFxXzTUuvhjYsgV44AGxuqy8XP/1iIgosjDYCYCpU8US8xkzgNxccX/2rGug40jtqI4jx5EMIyuoayFf11P/Jcn/6/p6bZIkAr+HHwZefVUkdMfHA+PH678mERFFDgY7ARITI/JqZs0S9/LUlTN5SfXhw+rP7a0Yp7tRoosvDv/q455eW61a4t55yb/NJqYTGfAQERGDHRN5W1LtiZpRGufz6Rk1Ukt+DZ7IGyMasSzcuYTFF18A5855f8706ZzSIiKq7hjsmMjXkmp3vG0E6GmU6MiRwG28F+xl4XIJi2HDgN27fQdRNlvVJo5ERFQ9hXS5iEindqn0xIlA27bea0EFovSEGmYuC9e6iSMREVVPDHZMpHapdM+evgtyahlh0VLc0xczl4Vr3cSRiIiqJ05jmcjIZdtmjbCYteQd0L6JIxERVU8Mdkxk5HLxQIyw2Gyi+Ki3IqRmLXkHqjZx9MZxE0ciIqqeGOyYzNOSaq0VyY0eYSkoEEVHe/TwXYTUqNegh9ZNHImIqPqxSN7qNVQTJSUlSExMRHFxMRISEkzpg80m8mmOHvWeiOyNvBoLUCYqywGQ2sBDPo/zJ8PXeYx4DXqVl4tVV/v3ixydUaM4okNEFOnUfn8z2EFoBDtGKSgQq7Ick5UzMsRUkppAx2YTIziekp0tFjFic+CAayBjZrBDRETVj9rvb67GijDZ2WJ5ud6gQ++qLndBVnq6yOcJ552biYgo/DHYiUDyxnt66FnV5Wna6/BhcTzcS1UQEVF4Y4IyKSQna2vnazNDwLhyEURERHow2CG/BLtcBBERkVYMdkjh+HFt7cwsF0FERKQGgx1S0Lo5oZnlIoiIiNRgsEMKWjcnNLNcBBERkRoMdkhBa/kHM8tFEBERqcFgh1xoLf9gZrkIIiIiX7iDMiJrB2Ujad0RmTsoExFRMHEHZfKb1s0J/dnMkIiIKFA4jUVEREQRjcEOERERRTQGO0RERBTRGOwQERFRRGOCMhmGq7GIiCgUMdghQxQUiOrnjkVB09PFhoPcZ4eIiMzEaawIZLMBa9YACxaIe5stsNcrKACGDHGtfn74sDheUBDY6xMREXnDYCfCFBQATZoAPXoAt9wi7ps0CVzAYbOJER13W1PKx8aMCXzARURE5AmDnQhixgjLunWu13MkScChQ6IdERGRGRjsRAizRliOHjW2HRERkdEY7EQIs0ZYGjY0th0REZHRGOxECLNGWLp1E6uuLBb3j1ssQEaGaEdERGQGBjsRwqwRFqtVLC8HXAMe+eeZM7nfDhERmYfBToQwc4QlOxv46CPg4ouVx9PTxXHus0NERGbipoIRQh5hGTJEBDaOicrBGGHJzgaysriDMhERhR5TR3a+/PJL3HjjjUhLS4PFYsGiRYsUj0uShCeffBINGzZEXFwcevXqhb179yranDx5EsOHD0dCQgLq1q2Lu+++G6WlpUF8FaHD7BEWqxXo3h0YNkzcM9AhIqJQYGqwc+bMGVx66aWYPXu228enTp2Kf/3rX3jttdfw7bffolatWujTpw/Onz9vbzN8+HDs2rULK1aswJIlS/Dll1/i3nvvDdZLCDnZ2cAvvwCFhUB+vrg/cIBTSUREVH1ZJMndzizBZ7FY8PHHH2PQoEEAxKhOWloaHnroITz88MMAgOLiYqSkpGD+/PkYOnQofvjhB7Rt2xabNm3CFVdcAQBYtmwZ+vfvj99++w1paWmqrl1SUoLExEQUFxcjISEhIK+PiIiIjKX2+ztkE5QPHDiAoqIi9OrVy34sMTERXbp0wYYNGwAAGzZsQN26de2BDgD06tULUVFR+Pbbbz2eu6ysDCUlJYobERERRaaQDXaKiooAACkpKYrjKSkp9seKioqQnJyseLxGjRqoX7++vY07kydPRmJiov2WkZFhcO+JiIgoVIRssBNIEyZMQHFxsf126NAhs7tEREREARKywU5qaioA4NixY4rjx44dsz+WmpqK48ePKx6vqKjAyZMn7W3ciY2NRUJCguJGREREkSlkg52mTZsiNTUVq1atsh8rKSnBt99+i8zMTABAZmYmTp06hS1bttjbrF69GpWVlejSpUvQ+0xEREShx9RNBUtLS7Fv3z77zwcOHMC2bdtQv359NGrUCGPGjMFzzz2Hli1bomnTpnjiiSeQlpZmX7HVpk0b9O3bF/fccw9ee+01XLhwAbm5uRg6dKjqlVhEREQU2UwNdjZv3owePXrYfx43bhwAYMSIEZg/fz7Gjx+PM2fO4N5778WpU6dwzTXXYNmyZahZs6b9Oe+99x5yc3PRs2dPREVFYfDgwfjXv/4V9NdCREREoSlk9tkxE/fZISIiCj9hv88OERERkRFYCBRit2YA3FyQiIgojMjf274mqRjsADh9+jQAcHNBIiKiMHT69GkkJiZ6fJw5OwAqKytx5MgR1KlTBxaLxbDzlpSUICMjA4cOHao2uUB8zXzNkYqvma85UoXza5YkCadPn0ZaWhqiojxn5nBkB0BUVBTS09MDdv7quHEhX3P1wNdcPfA1Vw/h+pq9jejImKBMREREEY3BDhEREUU0BjsBFBsbi6eeegqxsbFmdyVo+JqrB77m6oGvuXqoDq+ZCcpEREQU0TiyQ0RERBGNwQ4RERFFNAY7REREFNEY7BAREVFEY7ATAF9++SVuvPFGpKWlwWKxYNGiRWZ3KeAmT56Mzp07o06dOkhOTsagQYOwZ88es7sVUHPnzkXHjh3tG3FlZmbi888/N7tbQTNlyhRYLBaMGTPG7K4E1NNPPw2LxaK4tW7d2uxuBdThw4dx6623IikpCXFxcejQoQM2b95sdrcCqkmTJi6/Z4vFgpycHLO7FhA2mw1PPPEEmjZtiri4ODRv3hzPPvuszxpT4Yo7KAfAmTNncOmll+Kuu+5Cdna22d0JirVr1yInJwedO3dGRUUFHnvsMfTu3Ru7d+9GrVq1zO5eQKSnp2PKlClo2bIlJEnC22+/jaysLHz33Xdo166d2d0LqE2bNuH1119Hx44dze5KULRr1w4rV660/1yjRuT+0/nnn3/i6quvRo8ePfD555/joosuwt69e1GvXj2zuxZQmzZtgs1ms/+8c+dO3HDDDfjHP/5hYq8C58UXX8TcuXPx9ttvo127dti8eTPuvPNOJCYm4sEHHzS7e4aL3P9jTdSvXz/069fP7G4E1bJlyxQ/z58/H8nJydiyZQuuvfZak3oVWDfeeKPi5+effx5z587FN998E9HBTmlpKYYPH44333wTzz33nNndCYoaNWogNTXV7G4ExYsvvoiMjAzMmzfPfqxp06Ym9ig4LrroIsXPU6ZMQfPmzXHdddeZ1KPAWr9+PbKysjBgwAAAYmRrwYIF2Lhxo8k9CwxOY1FAFBcXAwDq169vck+Cw2az4f3338eZM2eQmZlpdncCKicnBwMGDECvXr3M7krQ7N27F2lpaWjWrBmGDx+OgwcPmt2lgPnkk09wxRVX4B//+AeSk5PRqVMnvPnmm2Z3K6jKy8vx3//+F3fddZehxaFDSdeuXbFq1Sr89NNPAIDvv/8eX331VcT+oc6RHTJcZWUlxowZg6uvvhrt27c3uzsBtWPHDmRmZuL8+fOoXbs2Pv74Y7Rt29bsbgXM+++/j61bt2LTpk1mdyVounTpgvnz56NVq1Y4evQoJk2ahG7dumHnzp2oU6eO2d0z3M8//4y5c+di3LhxeOyxx7Bp0yY8+OCDiImJwYgRI8zuXlAsWrQIp06dwh133GF2VwLm0UcfRUlJCVq3bg2r1QqbzYbnn38ew4cPN7trAcFghwyXk5ODnTt34quvvjK7KwHXqlUrbNu2DcXFxfjoo48wYsQIrF27NiIDnkOHDmH06NFYsWIFatasaXZ3gsbxL92OHTuiS5cuaNy4MT744APcfffdJvYsMCorK3HFFVfghRdeAAB06tQJO3fuxGuvvVZtgp1///vf6NevH9LS0szuSsB88MEHeO+995Cfn4927dph27ZtGDNmDNLS0iLy98xghwyVm5uLJUuW4Msvv0R6errZ3Qm4mJgYtGjRAgBw+eWXY9OmTXjllVfw+uuvm9wz423ZsgXHjx/HZZddZj9ms9nw5Zdf4tVXX0VZWRmsVquJPQyOunXr4pJLLsG+ffvM7kpANGzY0CVYb9OmDRYuXGhSj4Lr119/xcqVK1FQUGB2VwIqLy8Pjz76KIYOHQoA6NChA3799VdMnjyZwQ6RJ5Ik4YEHHsDHH3+MNWvWVIuERncqKytRVlZmdjcComfPntixY4fi2J133onWrVvjkUceqRaBDiAStPfv34/bbrvN7K4ExNVXX+2ybcRPP/2Exo0bm9Sj4Jo3bx6Sk5PtibuR6uzZs4iKUqbtWq1WVFZWmtSjwGKwEwClpaWKv/oOHDiAbdu2oX79+mjUqJGJPQucnJwc5OfnY/HixahTpw6KiooAAImJiYiLizO5d4ExYcIE9OvXD40aNcLp06eRn5+PNWvW4IsvvjC7awFRp04dlxysWrVqISkpKaJzsx5++GHceOONaNy4MY4cOYKnnnoKVqsVw4YNM7trATF27Fh07doVL7zwAm6++WZs3LgRb7zxBt544w2zuxZwlZWVmDdvHkaMGBHR2wsAYjXp888/j0aNGqFdu3b47rvvMH36dNx1111mdy0wJDJcYWGhBMDlNmLECLO7FjDuXi8Aad68eWZ3LWDuuusuqXHjxlJMTIx00UUXST179pSWL19udreC6rrrrpNGjx5tdjcC6p///KfUsGFDKSYmRrr44oulf/7zn9K+ffvM7lZAffrpp1L79u2l2NhYqXXr1tIbb7xhdpeC4osvvpAASHv27DG7KwFXUlIijR49WmrUqJFUs2ZNqVmzZtLjjz8ulZWVmd21gLBIUoRul0hEREQE7rNDREREEY7BDhEREUU0BjtEREQU0RjsEBERUURjsENEREQRjcEOERERRTQGO0RERBTRGOwQERFRRGOwQ0RhZf78+ahbt66pfejevTvGjBljah+ISD3uoExEhrjjjjvw9ttvuxzv06cPli1bZth1zp07h9OnTyM5Odmwc2p18uRJREdHo06dOqb1gYjUi+xKZ0QUVH379sW8efMUx2JjYw29RlxcnOnFZevXr2/q9YlIG05jEZFhYmNjkZqaqrjVq1fP/rjFYsFbb72Fm266CfHx8WjZsiU++eQTxTk++eQTtGzZEjVr1kSPHj3w9ttvw2Kx4NSpUwBcp7Gefvpp/O1vf8O7776LJk2aIDExEUOHDsXp06ftbSorKzF58mQ0bdoUcXFxuPTSS/HRRx95fS1z5syx9yMlJQVDhgyxP+Y4jbVmzRpYLBaX2x133GFvv3jxYlx22WWoWbMmmjVrhkmTJqGiokLju0tEejHYIaKgmjRpEm6++WZs374d/fv3x/Dhw3Hy5EkAwIEDBzBkyBAMGjQI33//Pe677z48/vjjPs+5f/9+LFq0CEuWLMGSJUuwdu1aTJkyxf745MmT8c477+C1117Drl27MHbsWNx6661Yu3at2/Nt3rwZDz74IJ555hns2bMHy5Ytw7XXXuu2bdeuXXH06FH7bfXq1ahZs6a9/bp163D77bdj9OjR2L17N15//XXMnz8fzz//vNa3joj0MrfoOhFFihEjRkhWq1WqVauW4vb888/b2wCQJk6caP+5tLRUAiB9/vnnkiRJ0iOPPCK1b99ecd7HH39cAiD9+eefkiRJ0rx586TExET740899ZQUHx8vlZSU2I/l5eVJXbp0kSRJks6fPy/Fx8dL69evV5z37rvvloYNG+b2tSxcuFBKSEhQnNPRddddJ40ePdrl+B9//CE1a9ZMGjVqlP1Yz549pRdeeEHR7t1335UaNmzo9txEZDzm7BCRYXr06IG5c+cqjjnnt3Ts2NH+37Vq1UJCQgKOHz8OANizZw86d+6saH/llVf6vG6TJk0UycINGza0n3Pfvn04e/YsbrjhBsVzysvL0alTJ7fnu+GGG9C4cWM0a9YMffv2Rd++fe1Tb55cuHABgwcPRuPGjfHKK6/Yj3///ff4+uuvFSM5NpsN58+fx9mzZ72ek4iMwWCHiAxTq1YttGjRwmub6Ohoxc8WiwWVlZV+XdfbOUtLSwEAn332GS6++GJFO0/J03Xq1MHWrVuxZs0aLF++HE8++SSefvppbNq0yeOy9/vvvx+HDh3Cxo0bUaNG1T+tpaWlmDRpErKzs12eU7NmTdWvkYj0Y7BDRCGjVatWWLp0qeLYpk2b/Dpn27ZtERsbi4MHD+K6665T/bwaNWqgV69e6NWrF5566inUrVsXq1evdhu0TJ8+HR988AHWr1+PpKQkxWOXXXYZ9uzZ4zMIJKLAYbBDRIYpKytDUVGR4liNGjXQoEEDVc+/7777MH36dDzyyCO4++67sW3bNsyfPx+AGK3Ro06dOnj44YcxduxYVFZW4pprrkFxcTG+/vprJCQkYMSIES7PWbJkCX7++Wdce+21qFevHpYuXYrKykq0atXKpe3KlSsxfvx4zJ49Gw0aNLC//ri4OCQmJuLJJ5/E3//+dzRq1AhDhgxBVFQUvv/+e+zcuRPPPfecrtdERNpwNRYRGWbZsmVo2LCh4nbNNdeofn7Tpk3x0UcfoaCgAB07dsTcuXPtq7H82a/n2WefxRNPPIHJkyejTZs26Nu3Lz777DM0bdrUbfu6deuioKAA119/Pdq0aYPXXnsNCxYsQLt27VzafvXVV7DZbBg5cqTidY8ePRqA2FRxyZIlWL58OTp37oyrrroKM2bMQOPGjXW/HiLShjsoE1FIe/755/Haa6/h0KFDZneFiMIUp7GIKKTMmTMHnTt3RlJSEr7++mtMmzYNubm5ZneLiMIYgx0iCil79+7Fc889h5MnT6JRo0Z46KGHMGHCBLO7RURhjNNYREREFNGYoExEREQRjcEOERERRTQGO0RERBTRGOwQERFRRGOwQ0RERBGNwQ4RERFFNAY7REREFNEY7BAREVFE+3+amtxMZ81i0QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
    "plt.xlabel(\"Engine size\")\n",
    "plt.ylabel(\"Emission\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation\n",
    "We compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\n",
    "\n",
    "There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set: \n",
    "* Mean Absolute Error: It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.\n",
    "\n",
    "* Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It’s more popular than Mean Absolute Error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n",
    "\n",
    "* Root Mean Squared Error (RMSE). \n",
    "\n",
    "* R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 23.17\n",
      "Residual sum of squares (MSE): 951.80\n",
      "R2-score: 0.78\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
    "test_y_ = regr.predict(test_x)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## regression model using the `FUELCONSUMPTION_COMB` feature\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
    "\n",
    "test_x = test[[\"FUELCONSUMPTION_COMB\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now train a Linear Regression Model using the `train_x` you created and the `train_y` created previously\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the predictions using the model's `predict` function and the `test_x` data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "regr.fit(train_x, train_y)\n",
    "predictions = regr.predict(test_x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally use the `predictions` and the `test_y` data and find the Mean Absolute Error value using the `np.absolute` and `np.mean` function like done previously\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 19.83\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the MAE is much worse when we train using `ENGINESIZE` than `FUELCONSUMPTION_COMB`.\n"
   ]
  }
 ],
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