Temperature Analysis using Lagrange's Interpolation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "83a5a62e",
   "metadata": {},
   "source": [
    "# Temperature Estimation using Lagrange's Interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93334c2b",
   "metadata": {},
   "source": [
    "The data used for analysis is retrieved from Kaggle."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54f4464",
   "metadata": {},
   "source": [
    "### *Shireen Jain*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3bb0d2",
   "metadata": {},
   "source": [
    "It is important to be able to obtain relevant information from available weather data due to the impact of weather on military activities, such as traffic, health, usage and functionality of electronic equipment etc. Interpolation can be used to estimate values based on existing data. Using Lagrange's Interpolation, we can estimate the temperature recordings or every month in 2015 by interpolating the temperatures from 2011 to 2016. A comparison can be drawn between the estimated and actual values to note the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c5faf5fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing relevant Python libraries for analysing and plotting the data\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbd91fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implementing Lagrange's Polynomial Interpolation using Scipy\n",
    "\n",
    "import scipy\n",
    "from numpy.polynomial.polynomial import Polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9dfde16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Given dataset contains the temperatures in Celsius for each month during the years 2011 to 2016\n",
    "\n",
    "sample_data = pd.read_csv(\"https://raw.githubusercontent.com/shireenjain/temp-estimation/main/sample-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ac45c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>18.32</td>\n",
       "      <td>20.79</td>\n",
       "      <td>24.11</td>\n",
       "      <td>26.10</td>\n",
       "      <td>28.92</td>\n",
       "      <td>28.60</td>\n",
       "      <td>27.60</td>\n",
       "      <td>27.20</td>\n",
       "      <td>26.70</td>\n",
       "      <td>25.51</td>\n",
       "      <td>22.84</td>\n",
       "      <td>19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>18.25</td>\n",
       "      <td>20.43</td>\n",
       "      <td>23.98</td>\n",
       "      <td>26.89</td>\n",
       "      <td>28.72</td>\n",
       "      <td>28.91</td>\n",
       "      <td>27.98</td>\n",
       "      <td>27.31</td>\n",
       "      <td>26.65</td>\n",
       "      <td>24.85</td>\n",
       "      <td>22.26</td>\n",
       "      <td>19.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>18.88</td>\n",
       "      <td>21.07</td>\n",
       "      <td>24.53</td>\n",
       "      <td>26.97</td>\n",
       "      <td>29.06</td>\n",
       "      <td>28.24</td>\n",
       "      <td>27.50</td>\n",
       "      <td>27.22</td>\n",
       "      <td>26.87</td>\n",
       "      <td>25.63</td>\n",
       "      <td>22.18</td>\n",
       "      <td>19.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>18.81</td>\n",
       "      <td>20.35</td>\n",
       "      <td>23.34</td>\n",
       "      <td>26.91</td>\n",
       "      <td>28.45</td>\n",
       "      <td>29.42</td>\n",
       "      <td>28.07</td>\n",
       "      <td>27.42</td>\n",
       "      <td>26.61</td>\n",
       "      <td>25.38</td>\n",
       "      <td>22.53</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>19.02</td>\n",
       "      <td>21.23</td>\n",
       "      <td>23.52</td>\n",
       "      <td>26.52</td>\n",
       "      <td>28.82</td>\n",
       "      <td>28.15</td>\n",
       "      <td>28.03</td>\n",
       "      <td>27.64</td>\n",
       "      <td>27.04</td>\n",
       "      <td>25.82</td>\n",
       "      <td>22.95</td>\n",
       "      <td>20.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>20.92</td>\n",
       "      <td>23.58</td>\n",
       "      <td>26.61</td>\n",
       "      <td>29.56</td>\n",
       "      <td>30.41</td>\n",
       "      <td>29.70</td>\n",
       "      <td>28.18</td>\n",
       "      <td>28.17</td>\n",
       "      <td>27.72</td>\n",
       "      <td>26.81</td>\n",
       "      <td>23.90</td>\n",
       "      <td>21.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR    JAN    FEB    MAR    APR    MAY    JUN    JUL    AUG    SEP    OCT  \\\n",
       "0  2011  18.32  20.79  24.11  26.10  28.92  28.60  27.60  27.20  26.70  25.51   \n",
       "1  2012  18.25  20.43  23.98  26.89  28.72  28.91  27.98  27.31  26.65  24.85   \n",
       "2  2013  18.88  21.07  24.53  26.97  29.06  28.24  27.50  27.22  26.87  25.63   \n",
       "3  2014  18.81  20.35  23.34  26.91  28.45  29.42  28.07  27.42  26.61  25.38   \n",
       "4  2015  19.02  21.23  23.52  26.52  28.82  28.15  28.03  27.64  27.04  25.82   \n",
       "5  2016  20.92  23.58  26.61  29.56  30.41  29.70  28.18  28.17  27.72  26.81   \n",
       "\n",
       "     NOV    DEC  \n",
       "0  22.84  19.84  \n",
       "1  22.26  19.91  \n",
       "2  22.18  19.69  \n",
       "3  22.53  19.50  \n",
       "4  22.95  20.21  \n",
       "5  23.90  21.89  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the data\n",
    "\n",
    "sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2febb8ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The temperatures of every month in 2015 are to be interpolated \n",
    "\n",
    "years = [2011,2012,2013,2014,2015,2016]\n",
    "years.remove(2015)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "855c2b13",
   "metadata": {},
   "source": [
    "## Estimating the temperature of January 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd2c714c",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_january = [18.32,18.25,18.88,18.81,19.02,20.92]\n",
    "temp_january.remove(19.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c710d56c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_jan = pd.DataFrame({\"years\": years, \"temp_january\": temp_january})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "816e9e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>years</th>\n",
       "      <th>temp_january</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>18.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>18.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>18.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>18.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>20.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   years  temp_january\n",
       "0   2011         18.32\n",
       "1   2012         18.25\n",
       "2   2013         18.88\n",
       "3   2014         18.81\n",
       "4   2016         20.92"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "50f4f787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for January 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_january)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(18,21)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of January 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c1d19ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.865000000000002"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for January 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_january)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7deb3e0",
   "metadata": {},
   "source": [
    "## Estimating the temperature of February 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d6286eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_february = [20.79,20.43,21.07,20.35,21.23,23.58]\n",
    "temp_february.remove(21.23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d93996f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_feb = pd.DataFrame({\"years\": years, \"temp_february\": temp_february})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fcc8dde6",
   "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>years</th>\n",
       "      <th>temp_february</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>20.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>20.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>20.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>23.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_february\n",
       "0   2011          20.79\n",
       "1   2012          20.43\n",
       "2   2013          21.07\n",
       "3   2014          20.35\n",
       "4   2016          23.58"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_feb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a285b31f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for February 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_february)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(20,24)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of February 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b6032b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.965"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for February 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_february)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5918edf1",
   "metadata": {},
   "source": [
    "## Estimating the temperature of March 2015\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba85391c",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_march = [24.11,23.98,24.53,23.34,23.52,26.61]\n",
    "temp_march.remove(23.52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "36544d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mar = pd.DataFrame({\"years\": years, \"temp_march\": temp_march})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e32c4cee",
   "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>years</th>\n",
       "      <th>temp_march</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>24.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>23.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>24.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>23.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>26.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_march\n",
       "0   2011       24.11\n",
       "1   2012       23.98\n",
       "2   2013       24.53\n",
       "3   2014       23.34\n",
       "4   2016       26.61"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d2228cf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for March 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_march)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(23,27)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of March 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "49fa90b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.975"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for March 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_march)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf4652cc",
   "metadata": {},
   "source": [
    "## Estimating the temperature of April 2015\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c4685f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_april = [26.1,26.89,26.97,26.91,26.52,29.56]\n",
    "temp_april.remove(26.52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6aa83a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_apr = pd.DataFrame({\"years\": years, \"temp_april\": temp_april})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "717e68bb",
   "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>years</th>\n",
       "      <th>temp_april</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>26.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>26.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>26.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>26.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>29.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_april\n",
       "0   2011       26.10\n",
       "1   2012       26.89\n",
       "2   2013       26.97\n",
       "3   2014       26.91\n",
       "4   2016       29.56"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_apr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a0537b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for April 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_april)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(26,30)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of April 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f7cc25ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.235"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for April 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_april)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c40361",
   "metadata": {},
   "source": [
    "## Estimating the temperature of May 2015\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0492217e",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_may = [28.92,28.72,29.06,28.45,28.82,30.41]\n",
    "temp_may.remove(28.82)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "48f56383",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_may = pd.DataFrame({\"years\": years, \"temp_may\": temp_may})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4a910da4",
   "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>years</th>\n",
       "      <th>temp_may</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>28.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>28.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>29.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>28.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>30.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_may\n",
       "0   2011     28.92\n",
       "1   2012     28.72\n",
       "2   2013     29.06\n",
       "3   2014     28.45\n",
       "4   2016     30.41"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_may"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "34900e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for May 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_may)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(28,31)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of May 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3dacf794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.43"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for May 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_may)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "611e1b97",
   "metadata": {},
   "source": [
    "## Estimating the temperature of June 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d571edd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_june = [28.6,28.91,28.24,29.42,28.15,29.7]\n",
    "temp_june.remove(28.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e0c36364",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_jun = pd.DataFrame({\"years\": years, \"temp_june\": temp_june})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34cb6c2f",
   "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>years</th>\n",
       "      <th>temp_june</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>28.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>28.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>28.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>29.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>29.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_june\n",
       "0   2011      28.60\n",
       "1   2012      28.91\n",
       "2   2013      28.24\n",
       "3   2014      29.42\n",
       "4   2016      29.70"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d00a3ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for June 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_june)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(28,30)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of June 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e72e7d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.560000000000002"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for June 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_june)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49a0a02",
   "metadata": {},
   "source": [
    "## Estimating the temperature of July 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9a46f30a",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_july = [27.6,27.98,27.5,28.07,28.03,28.18]\n",
    "temp_july.remove(28.03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3e5c9b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_jul = pd.DataFrame({\"years\": years, \"temp_july\": temp_july})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "113f40f3",
   "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>years</th>\n",
       "      <th>temp_july</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>27.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>27.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>28.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>28.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_july\n",
       "0   2011      27.60\n",
       "1   2012      27.98\n",
       "2   2013      27.50\n",
       "3   2014      28.07\n",
       "4   2016      28.18"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "704bdac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for July 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_july)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(27,29)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of July 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "731fd1dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.125"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for July 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_july)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3166891b",
   "metadata": {},
   "source": [
    "## Estimating the temperature of August 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "421a68ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_august = [27.2,27.31,27.22,27.42,27.64,28.17]\n",
    "temp_august.remove(27.64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4ea53bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_aug = pd.DataFrame({\"years\": years, \"temp_august\": temp_august})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1dd11b31",
   "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>years</th>\n",
       "      <th>temp_august</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>27.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>27.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>27.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>27.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>28.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_august\n",
       "0   2011        27.20\n",
       "1   2012        27.31\n",
       "2   2013        27.22\n",
       "3   2014        27.42\n",
       "4   2016        28.17"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_aug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "21927796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for August 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_august)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(27,28.5)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of August 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "219cb3f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.795"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for August 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_august)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc506e2a",
   "metadata": {},
   "source": [
    "## Estimating the temperature of September 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2ba9676e",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_september = [26.7,26.65,26.87,26.61,27.04,27.72]\n",
    "temp_september.remove(27.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dc2bbc04",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sep = pd.DataFrame({\"years\": years, \"temp_september\": temp_september})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "589265f8",
   "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>years</th>\n",
       "      <th>temp_september</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>26.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>26.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>26.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>27.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_september\n",
       "0   2011           26.70\n",
       "1   2012           26.65\n",
       "2   2013           26.87\n",
       "3   2014           26.61\n",
       "4   2016           27.72"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "85bc1699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ek15RycrcJnd/V8E1IpMUfKrXTdKlxT22GJnhfmeY2d7wOAsUvP+U9T3rVQVjx+cq6KwYGy6P1/tNSV5VMExwh4Jx4xe4e+53OL+L80cFn0zMsq/nOv9XzPpLFcyas0PSPZIucvctMesLZ9qRgk9F98esu1nB+bNTwUw5o9z9g3K0DVWEBUNogarDzF5QcPHnb0t98MEfq5qCMZNXuPv7iT5eRQh7l58Ox7MCkWdBsZkMSTW/4ycuOAALirT1cPflyW4LUBnRo45Kz8wGhkMRqoUfOZ+r0nuzDuZ4Z5hZk/Aj6F8oGHdY5l55AAAAKYFB3cw6WFBad5EFMx3cHC7va2afmtlcM0s3s0EH2P5MM1tiZsvN7K6Y5c3M7G0zWxZ+b5qo54BK4xAF0/rtUVCw4gZ3/yyBxztGQZGRrQo+ej3P3feXvAkAAMA3JWzoi5m1kdTG3eeEF//MVlBY4CFJf3X3/5nZ2ZLudPcTi2xbXcG41dMUDBuYJekyd//CzO5VMN3ePWGAb+ruP0vIkwAAAACSJGE96u6+0d3nhLczFcwB207BjAeFMzs01tfTOMUaJGm5u68ML+p6XsFwBYXfJ4a3JyoI/wAAAEClUqMiDhJeyNNPwZRnt0iaYmb3K/hH4dhiNmmnb87+sE5fX+3eOpwOTe6+sXCKp2KOOVrSaEmqX7/+gEMPLU+tGAAAAKD8Zs+evdXdW5b+yNIlPKhbML/xJEm3uPtuM/ujpJ+6+6RwOrKxCopGfGOzYnZVrjE67v64grLdSktL8/T09PI3HgAAACgHM1td+qPKJqGzvphZTQUh/Rl3nxwuHiap8PZLCoa5FLVO36x0VlhpTJI2hePfC8fBb453uwEAAIBkS+SsL6agt3yRuz8Ys2qDvq7IdrKkZcVsPktSDwsqDtZSUFSgsGLXawrCvsLvr8a77QAAAECyJXLoy3EKqoTNN7O54bJfKKg097ew3G+WwnHkZtZW0hh3P9vd88zsJgWVC6tLGufuC8N93CPpRTO7RtIaBVUgAQAAgEqlSlQmZYw6AAAAKoKZzXb3tHjsi8qkAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEZSwoG5mHczsfTNbZGYLzezmcPkLZjY3/FplZnOL2bZXzGPmmtluM7slXHe3ma2PWXd2op4DAAAAkCw1ErjvPEm3ufscM2soabaZve3uPyx8gJk9IGlX0Q3dfYmkvuFjqktaL+nfMQ/5q7vfn8C2AwAAAEmVsKDu7hslbQxvZ5rZIkntJH0hSWZmki6RdHIpuzpF0gp3X52otgIAAABRUyFj1M2ss6R+kmbELB4qaZO7Lytl80slPVdk2U1mNs/MxplZ0wMcc7SZpZtZ+pYtW75r0wEAAICkSHhQN7MGkiZJusXdd8esukzfDuBFt60l6RxJL8UsflRSNwVDYzZKeqC4bd39cXdPc/e0li1bfvcnAAAAACRBIseoy8xqKgjpz7j75JjlNSRdIGlAKbs4S9Icd99UuCD2tpk9Ien1uDYaAAAAiIBEzvpiksZKWuTuDxZZfaqkxe6+rpTdfKvX3czaxNw9X9KCg20rAAAAEDWJHPpynKSrJJ1czFSK3xp3bmZtzeyNmPv1JJ0mabK+6V4zm29m8ySdJOmnCXsGAAAAQJIkctaXaZLsAOuGF7Nsg6SzY+7vk9S8mMddFb9WAgAAANFEZVIAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiAN3j+v+COoAAADAQdq4a7/Oe+TjuO4zYQWPAAAAgKpg1/5cDR83S+t37o/rfulRBwAAAL6j7Lx8XfdUulZu3aPHrhoQ133Tow4AAAB8BwUFrtte/Fyfrtyuv13aV8d1bxHX/dOjDgAAAHwH//e/RXp93kbdddahOrdvu7jvn6AOAAAAlNPYaRl6YmqGhh/bWdcd3zUhxyCoAwAAAOXw33kb9cf/fqEzDz9Ev/5+b5lZQo5DUAcAAADK6NOV2/TTF+YqrVNTPXRpX1WvlpiQLhHUAQAAgDJZuilTo59MV8fm9fTE1WmqU7N6Qo9HUAcAAABK8eWuLA0bN1N1albXhBED1aRerYQfk+kZAQAAgBLszsrV8PEzlZmVpxeuG6z2TetVyHHpUQcAAAAOIDsvX9c9OVvLN+/Rv64coMPbNq6wY9OjDgAAABSjoMB1x0vz9MnKbfrrD4/SkB7xLWhUGnrUAQAAgGLc8+Zivfb5Bv3szEN1fr/2FX58gjoAAABQxPiPM/T4Ryt19TGddP0JiSloVBqCOgAAABDjjfkb9fvXv9AZh7fWb39weMIKGpWGoA4AAACEZmZs1y0vzNWAjk31t0v7JbSgUWkI6gAAAICkZZsyde3EWerQtG6FFDQqDUEdAAAAVV5hQaPaNatrwohBalo/8QWNSkNQBwAAQJVWWNBo1/5cTRgxUB2aVUxBo9IwjzoAAACqrJy8Al3/VFDQaPyIgRVa0Kg0BHUAAABUSQUFrjte/lzTV2zTg5ccpaE9Wia7Sd/A0BcAAABUSX+Zslivzt2gO8/spQv6V3xBo9IQ1AEAAFDlTPg4Q499uFJXDe6kG07oluzmFIugDgAAgCrlf/M36nevf6HTe7fW3eckr6BRaQjqAAAAqDJmrdqum1+Yq/4dm+rhy5Jb0Kg0BHUAAABUCcs3Z+raielq37SuxkSgoFFpCOoAAACo9DbtztKwcbNUq0Y1TYxIQaPSENQBAABQqWVm5WrYuJnauS9H44dHp6BRaZhHHQAAAJVWTl6Brn86KGg0bvhA9WkXnYJGpSGoAwAAoFIqKHDd+fLn+nj5Nj1w8VE6vme0ChqVhqEvAAAAqJTunbJEr8zdoDvO6KULB0SvoFFpCOoAAACodCZOX6V/fbhCVw7uqB+dGM2CRqUhqAMAAKBSeXPBRt39n4U6rXdr/e6cPpEtaFQagjoAAAAqjfRV23Xz83PVt0MTPXxptAsalYagDgAAgEph+eZMXTMxXe2a1NXYYQNVt1a0CxqVhqAOAACAlLc5LGhUs3o1TRw5SM1SoKBRaQjqAAAASGmZWbkaPn6Wdu7L0YQRqVPQqDTMow4AAICUlZNXoBuenqOlmzI1NsUKGpWGoA4AAICU5O66a9I8TVu+VfdddKROSLGCRqVh6AsAAABS0n1TlmjyZ+t1++k9dXFah2Q3J+4I6gAAAEg5T32ySv/8YIUuP7qjbjype7KbkxAEdQAAAKSUNxd8qd+8tlCnHtZavz/n8JQtaFQagjoAAABSxuzV23Xz85+pb4cm+vtl/VSjeuWNs5X3mQEAAKBSWbFlj66ZmK62laSgUWkI6gAAAIi8oKDRTNWoZpo4onIUNCoN0zMCAAAg0vZk52nEhFnavjdHL4w+Rh2bV46CRqUhqAMAACCygoJGs7X4y0yNHZamI9pXnoJGpWHoCwAAACLJ3XXX5Hmaumyr7rngCJ3Yq1Wym1ShCOoAAACIpPvfWqLJc9br1tMqZ0Gj0hDUAQAAEDlPfbpaj7y/QpcN6qgfn1w5CxqVhqAOAACASHlr4Zf67asLdOphrfSHcytvQaPSJOxiUjPrIOlJSYdIKpD0uLv/zcxekNQrfFgTSTvdvW8x26+SlCkpX1Keu6eFy5tJekFSZ0mrJF3i7jsS9TwAAABQcWav3qEfP/eZjmzfRA9X8oJGpUnkrC95km5z9zlm1lDSbDN7291/WPgAM3tA0q4S9nGSu28tsuwuSe+6+z1mdld4/2fxbjwAAAAqVlDQaJbaNK6jscPSVK9W1Z6gMGH/orj7RnefE97OlLRIUrvC9RZ8hnGJpOfKuetzJU0Mb0+UdN5BNxYAAABJtTkzpqDRyEFq3qB2spuUdBXyWYKZdZbUT9KMmMVDJW1y92UH2MwlvWVms81sdMzy1u6+UQr+GZBU7Dw9ZjbazNLNLH3Lli0H/RwAAACQGHuy8zQyLGg0bvhAdWpeP9lNioSEB3UzayBpkqRb3H13zKrLVHJv+nHu3l/SWZJuNLPjy3Ncd3/c3dPcPa1ly5blbjcAAAASLze/QD96Zo4WbczUI1f015HtmyS7SZGR0KBuZjUVhPRn3H1yzPIaki5QcFFosdx9Q/h9s6R/SxoUrtpkZm3C/bSRtDkxrQcAAEAiubvumjRfHy3dov87/widVMUKGpUmYUE9HIM+VtIid3+wyOpTJS1293UH2LZ+eAGqzKy+pNMlLQhXvyZpWHh7mKRX4912AAAAJN4Dby3VpDnr9NNTe+qSgVWvoFFpEtmjfpykqySdbGZzw6+zw3WXqsiwFzNra2ZvhHdbS5pmZp9Lminpv+7+ZrjuHkmnmdkySaeF9wEAAJBCnpmxWv94f7kuG9RBPzmlahY0Ko25e7LbkHBpaWmenp6e7GYAAABA0ttfbNJ1T6XrxF6t9PhVAyrVXOlmNruw/s/BqjyvCgAAACJvzpod+vFzc3RE+yb6x+VVu6BRaXhlAAAAUCFWbtmjaybM0iGNKGhUFgR1AAAAJNzmzCwNGz9T1SwoaNSCgkal4t8YAAAAJNTe7DxdMyFdWzNz9PzowRQ0KiOCOgAAABKmsKDRFxt364mrB+ioDk2S3aSUwdAXAAAAJIS76+eT5+vDpVv05/P76ORDWye7SSmFoA4AAICE+OvbS/Xy7HW65dQe+uHAjsluTsohqAMAACDunpmxWg+/t1yXDuygm0/pkezmpCSCOgAAAOLqnS826devLNBJvVrqj+f1kZklu0kpiaAOAACAuPlszQ7d9NwcHdGusR65oj8FjQ4CrxwAAADiImPrXl0zMV2tG9XR2OEDKWh0kAjqAAAAOGhbMrM1bNxMmaSJIyhoFA/8mwMAAICDsjc7TyMnzNKWzGw9N3qwOregoFE80KMOAACA7yw3v0A3PjtHCzfs0j8u76e+FDSKG3rUAQAA8J24u3757/n6YMkW/d8FR+iUwyhoFE/0qAMAAOA7+es7y/Ri+jr95JQeumwQBY3ijaAOAACAcntu5ho9/O4yXZLWXj89lYJGiUBQBwAAQLm8u2iTfvnv+TqxV0v96fwjKGiUIAR1AAAAlNnctTt107Of6fC2jfXI5f1Vk4JGCcMrCwAAgDJZtXWvRk6YpZYNa2vc8IGqX5t5SRKJoA4AAIBSbd2TrWHjZ0qSJo4cpJYNKWiUaAR1AAAAlKiwoNGm3VkaOyxNXShoVCEI6gAAADigvPwC3fTsHC1Yv0uPXN5f/To2TXaTqgwGFgEAAKBYQUGjBXp/yRb9+XwKGlU0etQBAABQrL+9u0wvpK/VT07ursuPpqBRRSOoAwAA4Fuen7lGD72zTBcPaK+fntYz2c2pkgjqAAAA+Ib3Fm/SL19ZoBN6ttSfL6CgUbIQ1AEAAPCVz9fu1I3PfKbebRrpn1dQ0CiZeOUBAAAg6euCRi0a1qKgUQQQ1AEAAPBVQaMCd00cQUGjKODfJAAAgCpuX06ergkLGj07arC6tmyQ7CZB9KgDAABUaUFBo880f/0u/f2y/upPQaPIoEcdAACginJ3/eqVBXpv8Wb96fw+Oq03BY2ihB51AACAKurhd5fr+VlrddNJ3XXF0Z2S3RwUQVAHAACogl6ctVZ/fWepLuzfXredTkGjKCKoAwAAVDHvL96sn/97vo7v2VL3XEhBo6giqAMAAFQhn6/dqR89M0eHtWlIQaOI4ycDAABQRazeFhQ0at4gKGjUgIJGkUZQBwAAqAK27cnWsHFhQaORg9SqYZ1kNwml4N8oAACASm5fTp5GTkzXxl1BQaNuFDRKCfSoAwAAVGJ5+QX68bOfaf66nfr7Zf00oBMFjVIFPeoAAACVlLvr168u0LuLN+uP5/XR6YcfkuwmoRzoUQcAAKik/v7ecj03c61uPKmbrhxMQaNUQ1AHAACohF5MX6sH316qC/q30+2n90p2c/AdENQBAAAqmfeXbNbPJ8/X0B4t9JcLj6SgUYoiqAMAAFQi89bt1I3PzNGhhzTUo1cOoKBRCuMnBwAAUEms2bZPIyfMUrP6tTR+BAWNUh1BHQAAoBLYtidbw8bPVF4BBY0qC4I6AABAitufk69rJqZrw879GjssjYJGlQRBHQAAIIXl5Rfox899pnnrdurhy/ppQKdmyW4S4oSBSwAAACnK3fWb1xbqnUWb9IdzD9cZFDSqVOhRBwAASFGPvL9cz85Yox+d2E1XHdM52c1BnBHUAQAAUtBL6Wt1/1tLdUG/drrjDAoaVUYEdQAAgBTz4dIt+vnk+RrSvYXuoaBRpUVQBwAASCHz1+3SDU/PVs/WDfXolf1VqwZxrrLiJwsAAJAi1m7fpxETZqlpvVqaMGKgGtapmewmIYGY9QUAACAFbN+bo2HjZiqvoEDPjxysVo0oaFTZ0aMOAAAQcUFBo1lav3O/xlydpu6tKGhUFdCjDgAAEGH5Ba6fPP+Z5q7dqUevGKC0zhQ0qioS1qNuZh3M7H0zW2RmC83s5nD5C2Y2N/xaZWZzy7ptuO5uM1sfs4+zE/UcAAAAksnd9dvXFujtLzbp7h8crjP7UNCoKklkj3qepNvcfY6ZNZQ028zedvcfFj7AzB6QtKsc234Rrv+ru9+fwLYDAAAk3T8/WKGnP12j60/opmHHdk52c1DBEtaj7u4b3X1OeDtT0iJJ7QrXWzDh5yWSnivvtgAAAJXdy7PX6b4pS3R+v3a6k4JGVVKFXExqZp0l9ZM0I2bxUEmb3H3Zd9j2JjObZ2bjzKzpAbYbbWbpZpa+ZcuWg2o/AABARfpo6RbdNWmehnRvob9ceKSqVaOgUVWU8KBuZg0kTZJ0i7vvjll1mYrpTS/Dto9K6iapr6SNkh4oblt3f9zd09w9rWXLlgf3JAAAACrIgvVBQaMeFDSq8hI664uZ1VQQtJ9x98kxy2tIukDSgPJu6+6bYh7zhKTXE9B0AACACldY0KgJBY2gxM76YpLGSlrk7g8WWX2qpMXuvq6825pZm5i750taEL9WAwAAJMeOvTkaNn6mcvIKNHHkQLWmoFGVl8jPUo6TdJWkk4uZSvFSFRn2YmZtzeyNMmx7r5nNN7N5kk6S9NMEPgcAAICEy8oNChqt27FfY4alqXurhsluEiIgYUNf3H2apGKvfHD34cUs2yDp7DJse1X8WgkAAJBc+QWunzz3mT5bu1OPXtFfAylohBBXJwAAACSJu+vu1xbqrS826bff760z+7QpfSNUGQR1AACAJHn0wxV66tPVuu6Erhp+XJdkNwcRQ1AHAABIgslz1uneN5fovL5t9bMzDk12cxBBBHUAAIAKNnXZFt358jwd17257r3oKAoaoVgEdQAAgAq0YP0uXf/UbHVv1UCPXjmAgkY4IM4MAACAClJY0Khx3ZqaOHKQGlHQCCUgqAMAAFSAwoJG2bn5mjhyEAWNUKpSg7qZdTOz2uHtE83sJ2bWJOEtAwAAqCSycvN17ZPpYUGjgerRmoJGKF1ZetQnSco3s+6SxkrqIunZhLYKAACgksgvcN38/Geas2aHHvphXw3qQkEjlE1ZgnqBu+dJOl/SQ+7+U0nMxg8AAFAKd9fv/rNQUxZu0m++31tnH0GEQtmVJajnmtllkoZJej1cxpUPAAAApfjXhyv15CerNfr4rhpBQSOUU1mC+ghJx0j6k7tnmFkXSU8ntlkAAACp7d+frdNf3lysc45qq7vOpKARyq9GaQ9w9y8k/STmfoakexLZKAAAgFQ2bdlW3fHSPB3Ttbnuu/hIChrhOyk1qJtZhiQvutzduyakRQAAACls4YZduv7poKDRY1cPUO0a1ZPdJKSoUoO6pLSY23UkXSyJy5UBAACKWLdjn4aPn6VGdWpowggKGuHglDpG3d23xXytd/eHJJ2c+KYBAACkjp37cjRsXFDQaMLIQTqkMQWNcHDKMvSlf8zdagp62JmlHwAAIJSVm69rJ6Zr7fb9euqaQepJQSPEQVmGvjwQcztP0ipJlySkNQAAACkmv8B1y/NzNXvNDv3jsv46umvzZDcJlURZZn05qSIaAgAAkGrcXb//z0K9ufBL/eb7vfW9IylohPg5YFA3syvd/Wkzu7W49e7+YOKaBQAAEH2PfbRSEz9ZrVFDu2jkEAoaIb5K6lGvH35nkBUAAEARr85dr3v+t1g/OKqtfn7WYcluDiqhAwZ1d38s/P67imsOAABA9H28fKtuf+lzDe7aTPdT0AgJUur0jGZ2r5k1MrOaZvaumW01sysronEAAABR88WG3bruqdnq2qKBHrsqjYJGSJhSg7qk0919t6TvS1onqaekOxLaKgAAgAgKChrNVMM6NTRh5EA1rktBIyROWYJ64Rl4tqTn3H17AtsDAAAQSTv35Wj4+Fnan5uviSMHqU3jusluEiq5ssyj/h8zWyxpv6QfmVlLSVmJbRYAAEB0ZOXma/STs7Vm2z49SUEjVJBSe9Td/S5Jx0hKc/dcSXslnZvohgEAAERBfoHr1hfnauaq7Xrwh0dpMAWNUEFKmkf9gmKWxd6dnIgGAQAARIW76w+vf6E35n+pX33vMH3/yLbJbhKqkJKGvvyghHUugjoAAKjknpi6UhOmr9K1Q7ro2qFdk90cVDElzaM+oiIbAgAAECWvzl2vP7+xWN8/so1+cTYFjVDxyjKPemszG2tm/wvv9zazaxLfNAAAgOSYHhY0OrpLMz1wyVEUNEJSlGV6xgmSpkgqHJS1VNItCWoPAABAUi3aGBQ06tKivh6/moJGSJ6yBPUW7v6ipAJJcvc8SfkJbRUAAEASrN+5X8PHz1T92jU0YcQgChohqcoyj/peM2uu4AJSmdlgSbsS2ioAAIAKtmtfroaPm6l9Ofl66fpj1LYJBY2QXGUJ6rdKek1SNzP7WFJLSRcltFUAAAAVKCs3X6OeTNfqbfs0ceQgHXpIo2Q3CSg9qLv7HDM7QVIvSSZpSVj4CAAAIOUVFLhue/FzzVy1XQ9f1k/HdKOgEaKhpIJHV0oyd38qHJe+MFw+ysz2uvuzFdVIAACARHB3/eG/X+i/8zfqV987TOccRUEjREdJF5PeJumVYpa/EK4DAABIaWOmZmj8x6s08jgKGiF6Sgrq1d09s+hCd98tiUugAQBASnvt8w360xuL9L0j2+hX36OgEaKnpKBe08zqF11oZg0l1UpckwAAABJr+oqtuu3FuRrUpZkeuJiCRoimkoL6WEkvm1nnwgXh7efDdQAAACln8Ze7dd2TQUGjJ65KU52aFDRCNB3wYlJ3v9/M9kj60MwaKJhHfa+ke9z90YpqIAAAQLxs2Llfw8fN+rqgUT1G8yK6Spye0d3/JelfYVC34sasAwAApIJd+3M1fPxM7c3O00s3UNAI0VeWgkdy9z2JbggAAECiZOXma/ST6crYupeCRkgZZQrqAAAAqaqgwHXbS59rRkZQ0OjYbi2S3SSgTEq6mBQAACDl/emNRfrvvI36xdmHUtAIKaVMPepmdqykzrGPd/cnE9QmAACAuBgzdaXGTsvQiOM6axQFjZBiSg3qZvaUpG6S5krKDxe7JII6AACIrP98vkF//O8inX3EIfr193rLjLnSkVrK0qOeJqm3u3uiGwMAABAPn6zYptte/FyDOjfTg5f0paARUlJZxqgvkHRIohsCAAAQD4u/3K3RT6WrU/N6euJqChohdZWlR72FpC/MbKak7MKF7n5OwloFAADwHWzcFRQ0qleruiaMpKARUltZgvrdiW4EAADAwdq1P1fDx83S3uw8vXj9MWpHQSOkuFKDurt/WBENAQAA+K6y84KCRiu37tHEEYN0WBsKGiH1HTCom9k0dx9iZpkKZnn5apUkd3d+AwAAQNIVFLhuezEoaPS3S/vq2O4UNELlcMCg7u5Dwu8NK645AAAA5fPnNxbp9Xkb9fOzDtW5fdsluzlA3FCZFAAApKyx0zI0ZlqGhh/bWaOPp6ARKheCOgAASEmvz9ugP/73C53V5xD9+vsUNELlQ1AHAAAp59OV23TrC58rrVNT/fWHfVWdgkaohMoU1M2sk5mdGt6ua2aMWwcAAEmx5MtMjXoyXR0paIRKrtSgbmajJL0s6bFwUXtJr5Rhuw5m9r6ZLTKzhWZ2c7j8BTObG36tMrO5B9j+TDNbYmbLzeyumOXNzOxtM1sWfm9a+tMEAACVwcZd+zV8/EzVrVldE0cOUpN6tZLdJCBhytKjfqOk4yTtliR3XyapVRm2y5N0m7sfJmmwpBvNrLe7/9Dd+7p7X0mTJE0uuqGZVZf0iKSzJPWWdJmZ9Q5X3yXpXXfvIend8D4AAKjkdmflasT4WcrMytOEEYMoaIRKryxBPdvdcwrvmFkNfXNe9WK5+0Z3nxPezpS0SNJXcyZZcMXHJZKeK2bzQZKWu/vK8NjPSzo3XHeupInh7YmSzivDcwAAACksOy9f1z05Wyu27NFjVw1Q77aUc0HlV5ag/qGZ/UJSXTM7TdJLkv5TnoOYWWdJ/STNiFk8VNKmsIe+qHaS1sbcX6evQ35rd98oBf8M6AC9+2Y22szSzSx9y5Yt5WkuAACIkIIC1+0vzdMnK7fpvouO0nEUNEIVUZag/jNJWyTNl3SdpDck/aqsBzCzBgqGuNzi7rtjVl2m4nvTpaD6aVGl9uJ/48Huj7t7mruntWzZsjybAgCACLnnzcX6z+cbdNdZh+q8fhQ0QtVxwMqkkmRm1STNc/c+kp4o787NrKaCkP6Mu0+OWV5D0gWSBhxg03WSOsTcby9pQ3h7k5m1cfeNZtZG0ubytgsAAKSGcdMy9PhHKzXsmE66joJGqGJK7FF39wJJn5tZx/LuOByDPlbSInd/sMjqUyUtdvd1B9h8lqQeZtbFzGpJulTSa+G61yQNC28Pk/RqedsGAACi7435G/WH/36hMw5vrd/84HAKGqHKKbFHPdRG0kIzmylpb+FCdz+nlO2Ok3SVpPkxUzD+wt3fUBC8vzHsxczaShrj7me7e56Z3SRpiqTqksa5+8LwofdIetHMrpG0RtLFZXgOAAAghcxYuU23vDBXAzo21d8u7UdBI1RJ5l7y0G8zO6G45e7+YUJalABpaWmenp6e7GYAAIAyWLopUxc9Ol0tG9bWpBuOZa50pBQzm+3uafHYV6k96qkUyAEAQGr7cleWho+bqToUNAJKD+pmlqmvZ1ypJammpL3uzgSmAAAgbnZn5Wr4+JnanZWnF64brPZN6yW7SUBSlaVHvWHsfTM7T0FBIgAAgLjIySvQ9U/N1vLNezR+xEAd3rZxspsEJF1Z5lH/Bnd/RdLJ8W8KAACoigoKXHe8/Lmmr9imey86UkN7UP8EkMo29OWCmLvVJKWpnMWHAAAADuQvby7Wq3M36M4ze+mC/u2T3RwgMsoyPeMPYm7nSVol6dyEtAYAAFQp4z/O0GMfrdTVx3TSDSd0S3ZzgEgpS1Af4+4fxy4ws+NERVAAAHAQ/jd/o37/elDQ6LcUNAK+pSxj1P9exmUAAABlMjNju25+Ya76U9AIOKAD9qib2TGSjpXU0sxujVnVSEG1UAAAgHJbtilTo55MV/umdTXm6jTVqUmsAIpT0tCXWpIahI+JnaJxt6SLEtkoAABQOW3anaXh42epVo1qmjhikJrWp6ARcCAHDOphRdIPzWyCu6+uwDYBAIBKaHdWroaNm6md+3L0wnXHqEMzChoBJSnLxaT7zOw+SYdLqlO40N2ZSx0AAJRJTl6Bbnj664JGfdpR0AgoTVkuJn1G0mJJXST9TsH0jLMS2CYAAFCJFBS47nz5c328fJv+ciEFjYCyKktQb+7uYyXluvuH7j5S0uAEtwsAAFQS905ZolfmbtAdZ/TShQMoaASUVVmGvuSG3zea2fckbZDEbxkAACjVxOmr9K8PV+jKwR31oxMpaASUR1mC+h/NrLGk2xTMn95I0k8T2ioAAJDy3lywUXf/Z6FO791avzunDwWNgHIqMaibWXVJPdz9dUm7JJ1UIa0CAAApbdaq7frJ83PVr0MTPXwZBY2A76LEMeruni/pnApqCwAAqASWb87UtRPT1b5JXY0dNpCCRsB3VJahL9PN7B+SXpC0t3Chu89JWKsAAEBK2rQ7S8PGzVLN6tU0cSQFjYCDUZagfmz4/fcxy1wS86gDAICvZGblavj4WRQ0AuKk1KDu7oxLBwAAJQoKGs3Rsk2ZGjecgkZAPJQ6j7qZtTazsWb2v/B+bzO7JvFNAwAAqcDd9bNJ8zRt+Vbdc+GROr4nBY2AeChLwaMJkqZIahveXyrplgS1BwAApJh7pyzRvz9br9tP76mLKGgExE1ZgnoLd39RUoEkuXuepPyEtgoAAKSEJz9ZpUc/WKErju6oG0/qnuzmAJVKWYL6XjNrruACUpnZYAVzqgMAgCrszQVf6revLdSph7XW78+loBEQb2WZ9eVWSa9J6mZmH0tqKemihLYKAABEWvqq7br5+c/Ut0MT/Z2CRkBClGXWlzlmdoKkXpJM0hJ3z014ywAAQCQt37xH10xMV9uwoFHdWhQ0AhKh1KBuZnUk/UjSEAXDX6aa2b/cPSvRjQMAANGyeXeWho2bGRQ0GjFIzShoBCRMWYa+PCkpU9Lfw/uXSXpK0sWJahQAAIiewoJGO/bl6IXRx6hjcwoaAYlUlqDey92Pirn/vpl9nqgGAQCA6MnJK9CPnpmjJZsyNXZYmo5oT0EjINHKMuvLZ+FML5IkMzta0seJaxIAAIgSd9ddk+Zp6rKtuueCI3Rir1bJbhJQJZSlR/1oSVeb2ZrwfkdJi8xsviR39yMT1joAAJB0901Zosmfrddtp/XUxWkdkt0coMooS1A/M+GtAAAAkfTUp6v1zw9W6PKjO+qmkyloBFSkskzPuNrMmkrqEPt4d5+TyIYBAIDkmrLwS/321QU69bBW+v05h1PQCKhgZZme8Q+ShktaobA6afj95MQ1CwAqTkGB693Fm/Vi+lp1a9lA15/QVU3qMeUcqrbZq7frJ899piPbN9HfL+uvGtXLclkbgHgqy9CXSyR1c/ecRDcGACrS/px8vTxnncZNy1DG1r1q0aC23lm0Sc/MWK3rju+qEcd1Uf3aZXmbBCqXFVtiCxqlUdAISJKy/AVaIKmJpM2JbQoAVIzNu7P05Cer9fSM1dq5L1dHtW+sv1/WT2f1OUTLt+zR/VOW6P63lmrC9FW66aTuuuzojqpdg6CCqmFzZlDQqEY108QRg9S8Qe1kNwmosszdS36AWZqkVxUE9uzC5e5+TmKbFj9paWmenp6e7GYASLJFG3dr7LQMvTZ3g3ILCnR679a6dmhXpXVq+q2xt7NXb9e9by7RjIztatekrn56Wk+d36+dqldjjC4qrz3ZefrhY58oY+tePT96sI5s3yTZTQJSjpnNdve0uOyrDEF9oaTHJM2XVFC43N0/jEcDKgJBHai63F0fLt2iMVMzNG35VtWtWV2XpLXXiOO6qHOL+qVu+9GyrbpvymItWL9b3Vs10O2n99QZhx/CRXWodHLzCzRywixNX7FNY4al6STmSge+k3gG9bIMfdnq7g/H42AAUFGycvP16tz1GjM1Q8s271HrRrV155m9dPmgjmW+UNTMdELPlhravYX+t+BLPfD2El3/9Bwd1b6x7jzzUB3XvUWCnwVQMdxdPwsLGt170ZGEdCAiytKj/qCCIS+v6ZtDX1JmekZ61IGqY9uebD396Ro99ekqbd2To8PaNNKooV30/SPbqlaNg5u1Ii+/QJPnrNdD7yzVhl1ZOq57c91xxqHq26FJfBoPJMn9U5boH+8v162n9dRPTumR7OYAKa2ih768X8xid/eUmZ6RoA5Ufss379HYaRmaPGedsvMKdFKvlho1tKuO6dY87sNUsnLz9cyMNXrk/eXavjdHp/durdvP6KWerRvG9ThARXj609X61SsLdNmgDvrz+UcwrAs4SBUa1CsDgjpQObm7PlmxTWOmZei9xZtVq0Y1Xdi/na4Z0kXdWyU+NO/JztPYqRl6YupK7cvJ03n92umnp/ZUh2b1En5sIB7eWvilrn96tk7q1UqPXTWAudKBOKjoHvXWkv4sqa27n2VmvSUd4+5j49GAikBQByqXnLwCvT5vg8ZMzdAXG3eref1auuqYTrpycCe1SMJUctv35ujRD5Zr4ier5e66fFBH3XRyD7VsyLR2iK7Zq3foijGfqtchjfTcqKNVrxY1A4B4qOig/j9J4yX90t2PMrMakj5z9yPi0YCKQFAHKodd+3L1zMzVmjh9lTbtzlb3Vg107ZAuOq9fO9Wpmfx5zjfu2q+H312mF9PXqVb1aho5pLNGH99NjevWTHbTgG9YuWWPLnx0uhrXralJNxzLXOlAHFVIUDezGu6eZ2az3H2gmX3m7v3CdXPdvW88GlARCOpAalu9ba/GTcvQi+nrtD83X0O6t9A1Q7vohB4tVS2C85qv3LJHD769VK/P26jGdWvq+hO6afixnanuiEjYnJmlCx+drn3Z+Zr8o2PVqXnJ05QCKJ+Kmp5xpqT+kvaaWXNJHh58sKRd8Tg4AByIu2v26h16YupKvfXFJtWoZjrnqHa6dmgXHdamUbKbV6KuLRvoH5f31/Un7NL9by3RX95crPEfZ+jHp/TQpQM7qCbjgJEke7LzNHLCLG3NzNHzowcT0oGIK6lH/TN372dm/SX9XVIfBdVJW0q6yN3nVVwzDw496kDqyMsv0JsLv9QTUzP0+dqdaly3pq4c3FHDjumsVo3qJLt538nMjO26983FSl+9Qx2b1dOtp/XUOUe1jeSnAai8cvMLdM3EdH28fKvGXJ2mkw5lrnQgESpq6Ms6SQ+Gd6tJqi3JFMylnu/uDxa7YQQR1IHoy8zK1Quz1mr8x6u0fud+dW5eT9cM6aILB7SvFBe5ubveX7JZ9765RIu/zNShhzTU7af30imHtWI6PCScu+v2l+Zp0px1uvfCI3XJwA7JbhJQaVXU0JfqkhooCOexmHcMQNys37lf46dl6PlZa7UnO0+DOjfTb3/QW6cc1lrVK1GPs5np5ENb68SerfSfeRv04NtLde2T6RrQqanuOKOXBndtnuwmohJ78O2lmjRnnW45tQchHUghJfWoz3H3/hXcnoSgRx2Ins/X7tQTU1fqfwu+lCR974g2umZIFx1VRap85uYX6MX0tXr43WXatDtbx/dsqTvP6KU+7Ronu2moZJ6ZsVq//PcCXTqwg/7vAgoaAYlWUUNfvprlJdUR1IFoyC9wvbNok8ZMXalZq3aoYe0auuzojhp2bGe1a1I32c1LiqzcfE2cvkqPfrhCO/fl6ntHtNGtp/dUt5YNkt00VAJvf7FJ1z2VrhN7tdLjFDQCKkRFBfVm7r49HgdJNoI6kFz7cvL08ux1GjctQ6u27VO7JnU1ckgXXZLWXg3rMMe4JO3OytUTH63U2GkZys4r0EX92+vmU3uobRX9BwYHb86aHbr8iU/Vq3VDPTd6cKW41gNIBRVa8KgyIKgDybFpd5YmTl+lZ2as0a79uerboYlGDe2qMw5vTc/eAWzJzNYj7y/XszPWSCZdNbiTfnRiNwrSoFwKCxo1CgsaJaNiL1BVEdTLiaAOVKwvNuzWmGkr9Z/PNyivwHVG70M06vgu6t+xKeNjy2jdjn362zvLNGnOOtWtWV3XDu2qa4d24RMIlGpLZrYuePRj7cvO16QbjlXnFsyVDlQkgno5EdSBxCsocH24dIvGTFupj5dvU71a1XVJWgeNOK4zRVUOwvLNmbp/ylK9ufBLNa1XUzee1F1XDu6kOjWpcopv25udp0sf/1TLN+/Rc6MHq28VuTgbiBKCejkR1IHEycrN178/W6+x0zK0fPMetW5UW8OP7aLLB3VU43r0/sbL52t36v63lmjqsq1q07iObj6lhy4a0J4hRPhKbn6Brp2YrmnLt+qJqwfo5ENbJ7tJQJVEUC8ngjoQf1v3ZOupT1br6U9Xa9veHPVu00ijju+i7x3RVrVqEB4TZfryrfrLlCX6fO1OdWlRX7ed3lNn92lDldMqzt1158vz9NLsdbrngiN06aCOyW4SUGVVVMEjAPiWZZsyNXZahiZ/tl45eQU65dBWunZoVw3u2ozx5xXg2O4t9Eq35nr7i026/60luunZz3R42xW6/YxeOrFnS34GVdRf316ql2av082n9CCkA5VIwoK6mXWQ9KSkQyQVSHrc3f8WrvuxpJsk5Un6r7vfWWTbXpJeiFnUVdJv3P0hM7tb0ihJW8J1v3D3NxL1PAAEvXXTV2zTE1NX6oMlW1S7RjVdNKC9rhnShfm+k8DMdPrhh+iUw1rr1bnr9eDbSzVi/CwN6txMd57ZS2mdmyW7iahAz85Yo4ffW64fpnXQLaf2SHZzAMRRwoa+mFkbSW3cfY6ZNZQ0W9J5klpL+qWk77l7tpm1cvfNJeynuqT1ko5299VhUN/j7veXtS0MfQG+m5y8Ar32+QaNmbpSi7/MVIsGtXT1MZ115eBOala/VrKbh1BOXoGen7VGD7+7XFv3ZOuUQ1vpttN7qXfbRsluGhLs3UWbNOrJdJ3Qs6UevzpNNblmAUi6lBj64u4bJW0Mb2ea2SJJ7RT0ht/j7tnhugOG9NApkla4++pEtRXAN+3cl6NnZqzRxOmrtDkzWz1bN9C9Fx6pc/q2ZbaRCKpVo5quPqazLhrQXuM/XqXHPlyh7/19qn5wZFvdelpPpuerpD5bs0M3PjtHfdo11j8u709IByqhCrmY1Mw6S/pIUp/w+6uSzpSUJel2d59VwrbjJM1x93+E9++WNFzSbknpkm5z9x3FbDda0mhJ6tix44DVq8n5QGkytu7VuGkZenn2Ou3PzdfQHi107dCuOr5HC8Y+p5Bd+3L1r49WaPzHGcrLd10ysINuPqWHWjeqk+ymIU4ytu7VhY9OV8M6NShoBERMSs36YmYNJH0o6U/uPtnMFkh6T9LNkgYqGIve1YtpiJnVkrRB0uHuvilc1lrSVkku6Q8KhteMLKkNDH0BDszdNWvVDj0xdaXeWbRJNatV07l92+qaoV106CEMnUhlm3dn6e/vLddzM9eoejXT8GM764YTu6lJPYYtpbItmdm68NHp2pOdp8kUNAIiJyWGvkiSmdWUNEnSM+4+OVy8TtLkMJjPNLMCSS309cWhsc5S0Ju+qXBB7G0ze0LS64lqP1CZ5eYX6H8LvtSYqSs1b90uNalXUzed1F1XHdNJrRrS81oZtGpUR384r49GDe2qv76zVI9PXalnZ6zR6OO7auSQLqpfm4m/Us3e7DxdM3GWNmdm6blRgwnpQCWXyFlfTNJYSYvc/cGYVa9IOlnSB2bWU1ItBT3kxblM0nNF9tsmHP8uSedLWhDPdgOV3e6sXL0wc63Gf5yhDbuy1LVFff3xvD66sH971a3F+PPKqGPzevrrD/vquhO66v4pS/XA20s18ZNVuvGk7rr86I6qXYOfeyrIzS/Qjc/O0YL1u/TE1Wnq17FpspsEIMESOevLEElTJc1XMD2jJP1C0juSxknqKylHwRj198ysraQx7n52uH09SWsVDIvZFbPfp8JtXdIqSdfFBPdiMfQFkNZu36cJ01fphVlrtSc7T0d3aaZRQ7vq5ENbUSynipm9eofum7JYn67crnZN6uqWU3vogv7tVZ3zILLcXT+bNE8vpq/T/11whC5jrnQgslJqjHoUENRRlX22ZofGTM3Q/xZsVDUzfe/INrp2SFcd0b5xspuGJHJ3TV22VfdNWaL563epe6sGuv30njrj8EO4cDiC/vr2Uv3t3WX6ySk9dOtpPZPdHAAlSJkx6gCSI7/A9fYXX2rM1Aylr96hhnVqaNTQrhp2bGe1bVI32c1DBJiZju/ZUkN7tND/Fnyp+99aouufnqOj2jfWHWccqiE9WiS7iQg9N3ON/vbuMl2S1l4/paARUKXQow5UInuz8/RS+lqN+3iV1mzfpw7N6mrkcV10cVoHNeDCQZQgL79Ak+es10PvLNWGXVk6tltz3XFGL8ZBJ9l7izdp1JOzNaR7C40ZRkEjIBUw9KWcCOqo7Dbu2q+J01fr2RmrtTsrT/07NtGooV11+uGHMO4Y5ZKVm69nZqzRI+8v1/a9OTq9d2vdfkYv9WzdMNlNq3Lmrt2pyx7/VN1bNdDzowczSw+QIgjq5URQR2W1YP0ujZ2Wof98vkEF7jqzzyG6ZkhXDehELygOzp7sPI2dmqEnpq7U3pw8nd+vnX56ak91aFYv2U2rElZt3asLHp2uBrWDgkYtG1LQCEgVBPVyIqijMikocL2/ZLPGTM3QJyu3qX6t6rpkYAeNPK4LIQpxt31vjh79YLkmfrJa7q7LB3XUTSf3IDgm0NY9QUGjzKw8TbrhWHVhrnQgpRDUy4mgjsogKzdfk+as09hpGVq5Za/aNK6j4cd21qWDOqpx3ZrJbh4quY279uvhd5frxfS1qlW9mkYO6azRx3fj3IuzfTl5uuzxT7VkU6aeGzWYawSAFERQLyeCOlLZlsxsPfXJKj09Y422781Rn3aNNGpoV519RBsuLEOFy9i6Vw++vVT/+XyDGtetqetP6Kbhx3amWFYc5OUXaNST6fpw6RY9flWaTu3dOtlNAvAdENTLiaCOVLR0U6bGTF2pVz7boNyCAp1yaGtdO7SLju7SjHmukXQLN+zS/VOW6P0lW9SyYW395JQe+mFaB9WqwT+P34W76+eT5+v5WWv1p/P76IqjOyW7SQC+I4J6ORHUkSrcXdOWb9UTUzP00dItqlOzmi4a0F4jj+uiri0bJLt5wLfMzNiu+6Ys1qxVO9SxWT399LQeOueodsw2VE4PvbNUD72zTD8+ubtuO71XspsD4CAQ1MuJoI6oy87L16tzN2js1Awt2ZSpFg1qa/ixnXT50Z3UrH6tZDcPKJG764MlW3TvlCVatHG3erVuqDvO6KVTDmvFpz9l8MKsNfrZpPm6aEB73XfRkbxmQIojqJcTQR1RtWNvjp7+dLUmfrJaW/dk69BDGuqaIV10Tt+2ql2DMb9ILQUFrtfnb9SDby3Rqm371L9jE91xxqE6plvzZDctst5fvFnXPplOQSOgEiGolxNBHVGzcssejZ2WoUlz1ikrt0DH92ypUUO7aEj3FvSmIeXl5hfopfR1+tu7S7Vpd7aG9mihO884VEe0b5zspkXK52t36tLHP1W3VvX1wuhjKGgEVBIE9XLqfvhR/sx/3lPrRnXUqlFt1avFmyEqnrtrRsZ2jZm6Uu8u3qya1arpvH5tdc2Qrup1CFUfUflk5ebryU9W6Z8frNDOfbk6+4hDdOtpvdS9FddbrN62Vxf8c7rq1a6uSTccq1YN6yS7SQDihKBeTrXb9PA2wx766n7D2jXUqlFttW5UJwjvDWurVaM6ah0ua9Wwtlo1rMN0Y4iL3PwCvTF/o8ZMzdD89bvUtF5NXTW4k646pjNFY1Al7M7K1ZiPVmrMtAxl5ebrogHtdfOpPdWuSd1kNy0ptoUFjXbtz9WkG47lQnGgkiGol1Ofo/r7v15+S5t2Z2lTZpY2787W5swsbdqdrU27g/s5+QXf2q5RnRpf9cK3bljnqzDfquHXob5lw9qqU5NAj2/btT9Xz89cownTV2njrix1bVlf1w7pqgv6t+OcQZW0dU+2Hnl/uZ75dI0k6crBnXTjSd3UvEHV+Yd1X06eLntihpZ8uVvPjhqs/hQ0Aiodgno5lTZG3d21a3/uV8F90+4sbc7M1ubdYZiPCfe5+d9+vRrXrRnTG18Y7MP7X/XY1+biwCpi7fZ9Gvdxhl6ctVZ7c/J1TNfmGnV8F53Ys5WqMWUdoHU79ulv7yzTpDnrVLdmdV0ztKtGDe2ihnUqd5XTvPwCjX5qtj5YslmPXZWm0yhoBFRKBPVyitfFpAUFrp37c78K8kFvfBDmC3voN4fr8gq+/bo2rVfzq1741kWH2oTDcFo2qE3BkBQ1e/UOjZ22Um8u+FLVzPSDo9rqmiFd1KcdF9ABxVm+OVMPvLVU/1vwpZrWq6kfndhdVx3TqVJ+4uTu+sW/5+u5mWv1x/P66MrBFDQCKiuCejlV9KwvBQWuHftyYnrjgx75TUXC/ObMbOUXE+ib1a+lVjFhvnCoTauYMfUtG9ZmGq8IyC9wTVn4pcZMXak5a3aqUZ0auvzoThp+bGcd0piLw4CymLdup+6bskRTl23VIY3q6OZTe+jiAe1VoxK9xz387jI9+PZS3XRSd91+BgWNgMqMoF5OUZ2eMb/AtX1vjjbtztKWzMJhNzHhPly2JTNbRfO8mdS8fq2YoTZfh/mvQ34dtWhQq1L9sYuKPdl5enHWWo2fnqG12/erY7N6GnlcZ12c1oEp1oDv6JMV23TvlMX6bM1OdWlRX7ee1lPfO6JNyg8Ze3HWWt05aZ4u7N9e919MQSOgsiOol1NUg3pZ5Re4tu3J/iq4f3URbOE4+rCnftueAwX62l8Ns2ndqLZaFl4M27DOVxfLNq9PoC+Ljbv2a8LHq/TszDXKzMpTWqemunZoF53W+xBKpgNx4O56Z9Fm3T9liZZsytThbRvp9jN66cSeLVMy4L6/ZLOunZiu47q30FgKGgFVAkG9nFI9qJdVXn6BtoU99EXHzX89rj5b2/Zmq+iPvZpJLRoUHTP/9f2vA33tKhlIF6zfpSemrtR/521UgbvOOqKNrh3SRf2YsQFIiPwC12ufr9eDby/V2u37NahzM915Zi+ldW6W7KaV2bx1O/XDx4KCRs+PPkYN+LQNqBII6uVUVYJ6WeXmF2jrnuxg3PzuLG0Ke+aLjqPftjfnW9tWr2Zq0aDWVzPcFJ2uslV4v3n9Win/cXVBgevdxZs1ZupKzcjYrga1a+iHAzto+LGd1aFZvWQ3D6gScvIK9MKsNXr4veXakpmtkw9tpdtP76XebRslu2klWrNtny549GPVqVldk39EQSOgKiGolxNB/bvJyQsCfWEP/ZaYuee/CveZ2dpeTKCvUc3UsmHsmPmvh9q0jBlT37Re9AL9/px8vTxnncZNy1DG1r1q27iORhzXRT8c1EGNKvn0cUBU7cvJ04Tpq/SvD1Zod1aezjmqrW49rac6t6if7KZ9y7Y92broX59ox74cTbrhWHWjoBFQpRDUy4mgnljZefnhxbBFwnw4/Kawp37nvtxvbVuzuqllg2Iqw4YXwxb22DetVzPh41M3Z2bpyemr9fSM1dq5L1dHtm+sa4d21Vl9DmFcKRARu/bl6rGPVmj8x6uUm1+gSwZ20E9O7hGZWZb25+Trsic+1aKNQUGjAZ0YHgdUNQT1ciKoR0NWbhDov1EVNvPr6rCF93ft/3agr1W9Wjj/fPHTVRaG+sZ1yx/oF3+5W2OmZui1uRuUW1CgUw9rrVFDu2pg56YpefEaUBVszszSP95brudmrlE1Mw0/trOuP6GbmtavlbQ25eUX6PqnZ+u9xZv16JUDdMbhhyStLQCSh6BeTgT11JKVm/9VL/zX4+i/rg5bGPIzs/K+tW2tGtW+NQd9q5hhN4XLGtapoanLt2rM1JWaumyr6tasrovT2mvEcV3UJYIfpQMo3trt+/TXt5fq33PXq0GtGhp9fFeNHNKlwqdJDQoaLdBzM9foD+f10VUUNAKqLIJ6ORHUK6f9OfnfCO7Fz0efrT3Z3w70NaubcvNdLRvW1vBjO+uKozuqSb3k9cQBODhLvszU/W8t0dtfbFKLBrV040nddfnRHVW7RsVUOf37u8v0wNtL9aMTu+nOMw+tkGMCiCaCejkR1Ku2vdl5MXPQZ4XDb7LVq3VDff+oNhX2hxxA4s1Zs0P3vblEn6zcpnZN6uqWU3vogv7tEzqt7Evpa3XHy/N0Qf92euDioxgyB1RxBPVyIqgDQNXh7pq2fKvum7JE89btUvdWDXTbaT11Zp9D4h6iP1iyWddMTNex3Zpr7LCBqlWDC8+Bqi6eQZ13FABApWJmGtqjpV698Tg9ekV/ubtueGaOzn3kY01btjVux5m/bpd+9MwcHXpIQz165QBCOoC4410FAFApmZnOOqKNptxyvO696Eht25OjK8fO0OVPfKrP1uw4qH2v2bZPIybMVNN6tTR++ECqjgJICII6AKBSq1G9mi5J66D3bj9Bv/l+by35MlPn/3O6Rj2ZrqWbMsu9v+17czRs/EzlFbgmjhykVo2iMYc7gMqHoA4AqBJq16iukUO66MM7T9Ktp/XUpyu26YyHPtKtL8zV2u37yrSP/Tn5umbiLG3YuV9jrk5T91ZUHQWQOFxMCgCoknbszdG/PlyhCdNXqcBdlw3qqJtO7q5WDYvvIQ8KGs3Ru4s36dErBujMPhQ0AvBtXEwKAMBBalq/ln5+9mH68I6TdHFaBz0zY41OuPcD3fvm4m9VSHZ3/ea1hXpn0Sb97pzDCekAKgRBHQBQpR3SuI7+fP4ReufWE3Ra79b65wcrNPQv7+mfHyzX/px8SdIj7y/XszPW6IYTu+nqYzont8EAqgyGvgAAEOOLDbt1/1tL9N7izWrZsLZO791az8xYo/P7tdODl1DQCEDJGPoCAECC9G7bSOOGD9RL1x+jLs3r65kZazSkewv95cIjCekAKhQTvwIAUIyBnZvphesGa8H63erRugEFjQBUOII6AAAHYGY6on3jZDcDQBVF9wAAAAAQQQR1AAAAIIII6gAAAEAEEdQBAACACCKoAwAAABFEUAcAAAAiiKAOAAAARBBBHQAAAIgggjoAAAAQQQR1AAAAIIII6gAAAEAEEdQBAACACCKoAwAAABFEUAcAAAAiiKAOAAAARBBBHQAAAIgggjoAAAAQQQR1AAAAIIII6gAAAEAEJSyom1kHM3vfzBaZ2UIzuzlm3Y/NbEm4/N4DbL/KzOab2VwzS49Z3szM3jazZeH3pol6DgAAAECy1EjgvvMk3ebuc8ysoaTZZva2pNaSzpV0pLtnm1mrEvZxkrtvLbLsLknvuvs9ZnZXeP9niXgCAAAAQLIkrEfd3Te6+5zwdqakRZLaSbpB0j3unh2u21zOXZ8raWJ4e6Kk8+LSYAAAACBCKmSMupl1ltRP0gxJPSUNNbMZZvahmQ08wGYu6S0zm21mo2OWt3b3jVLwz4CkYnvkzWy0maWbWfqWLVvi9lwAAACAipDIoS+SJDNrIGmSpFvcfbeZ1ZDUVNJgSQMlvWhmXd3di2x6nLtvCIfGvG1mi939o7Ie190fl/S4JKWlpRXdNwAAABBpCe1RN7OaCkL6M+4+OVy8TtJkD8yUVCCpRdFt3X1D+H2zpH9LGhSu2mRmbcL9t5FU3qEzAAAAQOQlctYXkzRW0iJ3fzBm1SuSTg4f01NSLUlbi2xbP7wAVWZWX9LpkhaEq1+TNCy8PUzSqwl6CgAAAEDSJHLoy3GSrpI038zmhst+IWmcpHFmtkBSjqRh7u5m1lbSGHc/W8HMMP8Osr5qSHrW3d8M93GPguEy10haI+niBD4HAAAAICns20PDK5+0tDRPT08v/YEAAADAQTCz2e6eFo99UZkUAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgghIW1M2sg5m9b2aLzGyhmd0cs+7HZrYkXH5vObe928zWm9nc8OvsRD0HAAAAIFlqJHDfeZJuc/c5ZtZQ0mwze1tSa0nnSjrS3bPNrFVZt3X3L8L1f3X3+xPYdgAAACCpEhbU3X2jpI3h7UwzWySpnaRRku5x9+xw3eZybPtF0ccCAAAAlVGFjFE3s86S+kmaIamnpKFmNsPMPjSzgeXYttBNZjbPzMaZWdMDbDfazNLNLH3Lli1xeR4AAABARUl4UDezBpImSbrF3Xcr6MVvKmmwpDskvWhmVsZtJelRSd0k9VXQ6/5Acdu6++PunubuaS1btozjMwIAAAASL6FB3cxqKgjaz7j75HDxOkmTPTBTUoGkFmXcVu6+yd3z3b1A0hOSBiXyOQAAAADJkMhZX0zSWEmL3P3BmFWvSDo5fExPSbUkbS3jtjKzNjF3z5e0IO6NBwAAAJIskT3qx0m6StLJRaZSHCepq5ktkPS8pGHu7mbW1szeKGVbSbrXzOab2TxJJ0n6aQKfAwAAAJAUiZz1ZZqkYseeS7qymMdvkHR2adu6+1XxaiMAAAAQVVQmBQAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBBBHUAAAAgggjqAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiKCEBXUz62Bm75vZIjNbaGY3x6z7sZktCZffe4Dtzwwfs9zM7opZ3szM3jazZeH3pol6DgAAAECyJLJHPU/Sbe5+mKTBkm40s95mdpKkcyUd6e6HS7q/6IZmVl3SI5LOktRb0mVm1jtcfZekd929h6R3w/sAAABApZKwoO7uG919Tng7U9IiSe0k3SDpHnfPDtdtLmbzQZKWu/tKd8+R9LyCcK/w+8Tw9kRJ5yXqOQAAAADJUqMiDmJmnSX1kzRD0n2ShprZnyRlSbrd3WcV2aSdpLUx99dJOjq83drdN0rBPwNm1uoAxxwtaXR4N9vMFsTjuaBSaSFpa7IbgcjhvEBxOC9QHM4LFKdXvHaU8KBuZg0kTZJ0i7vvNrMakpoqGA4zUNKLZtbV3T12s2J25cUsOyB3f1zS42Eb0t097Ts9AVRanBcoDucFisN5geJwXqA4ZpYer30ldNYXM6upIKQ/4+6Tw8XrJE32wExJBQr+I421TlKHmPvtJW0Ib28yszbh/ttIKm7oDAAAAJDSEjnri0kaK2mRuz8Ys+oVSSeHj+kpqZa+/bHRLEk9zKyLmdWSdKmk18J1r0kaFt4eJunVhDwBAAAAIIkS2aN+nKSrJJ1sZnPDr7MljZPUNRwz/rykYe7uZtbWzN6QJHfPk3STpCkKLkJ90d0Xhvu9R9JpZrZM0mnh/dI8HtdnhsqC8wLF4bxAcTgvUBzOCxQnbueFfXNoOAAAAIAooDIpAAAAEEEEdQAAACCCUjKom1kHM3vfzBaZ2UIzuzlc3szM3jazZeH3puHy5uHj95jZP4rs609mttbM9iTjuSB+4nVemFk9M/uvmS0O91OW6yAQUXF+v3jTzD4P9/OvsIoyUlA8z4uYfb5GzY7UFuf3iw/MbEnMdXrF1n1B9MX5vKhlZo+b2dIwZ1xY0rFTMqhLypN0m7sfpmA+9hvNrLekuyS96+49JL0b3peCwkq/lnR7Mfv6j4JKqEh98Twv7nf3QxUU6jrOzM5KeOuRKPE8Ly5x96Mk9ZHUUtLFiW48Eiae54XM7AJJdPikvrieF5KucPe+4RfTSaeueJ4Xv5S02d17Suot6cOSDpySQd3dN7r7nPB2poKZYdpJOlfSxPBhEyWdFz5mr7tPU/DCFd3Xp4WVTpHa4nVeuPs+d38/vJ0jaY6CufyRguL8frE7vFlDwdSyXI2fouJ5XlhQ2O9WSX9MfMuRSPE8L1B5xPm8GCnp/8LHFbh7iZVtUzKoxzKzzgp6PWdIal0YusPvfMxURcXrvDCzJpJ+oOA/ZaS4eJwXZjZFQaG1TEkvJ6alqEhxOC/+IOkBSfsS1UZUvDj9HRkfDnv5tZkVV3UdKeZgzoswU0jSH8xsjpm9ZGatS9ompYN62IsxSdItMT1dqOLidV6YWQ1Jz0l62N1Xxqt9SI54nRfufoakNpJqKyzehtR1sOeFmfWV1N3d/x3vtiF54vR+cYW7HyFpaPh1Vbzah+SIw3lRQ8En9B+7e39Jn0i6v6QNUjaom1lNBS/WM+4+OVy8yczahOvbKOj1QhUS5/PicUnL3P2huDcUFSre7xfunqWgSvK58W4rKk6czotjJA0ws1WSpknqaWYfJKbFqAjxer9w9/Xh90xJz4rr4VJanM6LbQo+eSv8x/4lSf1L2iAlg3r48dFYSYvc/cGYVa9JGhbeHibp1YpuG5InnueFmf1RUmNJt8S5mahg8TovzKxBzBtyDUlnS1oc/xajIsTrvHD3R929rbt3ljRE0lJ3PzH+LUZFiOP7RQ0zaxHerinp+5KYEShFxfH9whVMYnJiuOgUSV+UeOxUrExqZkMkTZU0X1JBuPgXCsYLvSipo6Q1ki529+3hNqskNVJwAdhOSae7+xdmdq+kyyW1lbRB0hh3v7uingviJ17nhaTdktYqCGHZ4X7+4e5jKuJ5IL7ieF5sk/S6giEv1SW9J+mn7p5XQU8FcRTPvyMx++ws6XV371MhTwJxF8f3i9WSPpJUU8H7xTuSbnX3/Ap6KoijOOfOTpKektRE0hZJI9x9zQGPnYpBHQAAAKjsUnLoCwAAAFDZEdQBAACACCKoAwAAABFEUAcAAAAiiKAOAAAARBBBHQCqAAtMM7OzYpZdYmZvJrNdAIADY3pGAKgizKyPgkp4/RTM7TxX0pnuvuI77Ks6c0IDQGIR1AGgCgmLvO2VVD/83knSEZJqSLrb3V8NC/c8FT5Gkm5y9+lmdqKk30raKKmvpIEKin20VxD8/+DuL1TUcwGAyo6gDgBViJnVlzRHUo6CSqsL3f1pM2siaaaC3naXVODuWWbWQ9Jz7p4WBvX/Surj7hlmdqGCHvlR4b4bu/uuCn9SAFBJEdQBoIoxs99L2iPpEkl1JOWFq5pJOkPSBkn/UNBrni+pp7vXK+xRd/eTwv30lDRFQa/66+4+teKeBQBUfjWS3QAAQIUrCL9M0oXuviR2pZndLWmTpKMUTDqQFbN6b+ENd19qZgMknS3p/8zsLXf/fYLbDgBVBrO+AEDVNUXSj83MJMnM+oXLG0va6O4Fkq5SMP78W8ysraR97v60pPsl9U98kwGg6qBHHQCqrj9IekjSvDCsr5L0fUn/lDTJzC6W9L5ietGLOELSfWZWIClX0g2JbjAAVCWMUQcAAAAiiKEvAAAAQAQR1AEAAIAIIqgDAAAAEURQBwAAACKIoA4AAABEEEEdAAAAiCCCOgAAABBB/w/t+vuLgg37YQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for September 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_september)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(26,28)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of September 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f11968b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.165"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for September 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_september)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db543266",
   "metadata": {},
   "source": [
    "## Estimating the temperature of October 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "de5d5623",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_october = [25.51,24.85,25.63,25.38,25.82,26.81]\n",
    "temp_october.remove(25.82)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8b5b913f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_oct = pd.DataFrame({\"years\": years, \"temp_october\": temp_october})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b055a174",
   "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>years</th>\n",
       "      <th>temp_october</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>25.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>24.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>25.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>25.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>26.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_october\n",
       "0   2011         25.51\n",
       "1   2012         24.85\n",
       "2   2013         25.63\n",
       "3   2014         25.38\n",
       "4   2016         26.81"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_oct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "42d12cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oa+Xj8v2z/bn/PhPke92Vfvm7CyTP+/IdVLxQvg0ZE/xZAn2NLMml8v3NjbKfjxee4J//Z/K9Pn8t3+9wowptxJGvoB+Rb4Sb4f6f/+i/ra187y+Z/lzZ8h0vAhwX8+3WClRPZvaGfAdh/qnMO5/4ssLk28o0zDn3daiXVx78W4unOOdalHFXoELwb/FdLynyOD9BAYCgYws5qhUzS/LvQhDm/6j4YpW9depElvcbM6vj/6j29/LtXxjwVnYAAFD1hayQm1lLM/vafKMmLDOz2/3Xv2FmC/1fG8xsYQnTn2tmP5nZGjMrbt8v4Hg0kW+4vIPy7fN3o3NuQQiXN0C+0Qh2y/cR9SXOuSMhXB4AAKhkQrbLipk1ldTUOTfff+DNPPnKyPJC9/mXpAPOuYeLTBsu376kZ8v3Ef9c+cYOXS4AAACgCgnZFnLn3Dbn3Hz/z5nyjZv637FDzczkG+KsuGGr+kla45xb5z+g6nX5di0AAAAAqpTiBv8POv9BNL3lO1r7qFPlG+9zdTGTNNfPR1hIVwlHlJvZWEljJSk2NrZvp07Hcr4UAAAA4NjMmzdvt3OuYdn3DEzIC7n5xgR+R9IdzrmMQjcNUfFbx6XiB9Yvdt8a59xL8p2KWomJiS4tLe0E0gIAAAClM7ONZd8rcCEt5GYWKV8Zn+qcm1Ho+gj5zgTWt4RJ0/XzM3e10P/O3AUAAABUGaEcZcXkG9x/hXPu8SI3nyXf2M/pJUw+V1IH8511L0q+kwh8EKqsAAAAgFdCOQ75QEkjJJ1RaJjD8/23Xa0iu6uYWTMz+0SS/CdruEW+s/etkPSmc25ZCLMCAAAAngjZLivOue9V/L7gcs6NLOa6rZLOL3T5E/lOjQ4AAABUWZypEwAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAAADwEIUcAAAACFBBgQv6PCnkAAAAQIAe/Wxl0OdJIQcAAAACMDV1o8Z/uy7o86WQAwAAAGX4ZtUuPfD+Mv26Y8Ogz5tCDgAAAJRixbYM3Tx1vk5qXFvPDu0T9PlTyAEAAIAS7MjI0uiUuYqtEa6JIxNVq0ZE0JcR/DkCAAAAVcCh7DyNeXWuDhzJ1ZvjBqhpfExIlsMWcgAAAKCI/AKn219foOVbM/Ts0N7q1jw+ZMtiCzkAAABQxCMfLdeXK3bq4Yu76oxOjUO6LLaQAwAAAIVMmrVeKbM3aPTANrpmQOuQL49CDgAAAPh9uXyHHvlouc7u0lh/uKBzuSyTQg4AAABIWrrlgG6dvkDdmsfrqat7KTzMymW5FHIAAABUe1v3H9HolLmqFxulV65NVM2o8jvUkkIOAACAai0zK1ejU+bqSE6+Jo5MUqPa0eW6fEZZAQAAQLWVl1+gm6ct0OqdBzVpZJI6Nqld7hnYQg4AAIBqyTmnBz5Ypm9X7dKfL+mmX53U0JMcFHIAAABUSy9/t07TUjfphtPaaUi/BM9yUMgBAABQ7Xy6ZJv++slKXdC9qe75TUdPs1DIAQAAUK0s2LRPd7yxUH0S6uhfV/ZUWDkNb1gSCjkAAACqjc17D+v619LUOC5aL1+TqOjIcK8jMcoKAAAAqocDR3I1KmWucvIK9PrYJNWvVcPrSJIo5AAAAKgGcvIKdOOUedq455BeG52s9o1qeR3pvyjkAAAAqNKcc/rDu0s0e+0e/euKnhrQrr7XkX6GfcgBAABQpT339Rq9NS9dt53ZQYP7tvA6zi9QyAEAAFBlvb9wi/75+Spd2ru5fntWB6/jFItCDgAAgCpp7oa9uvutxerXup7+Pri7zLwd3rAkFHIAAABUOet3H9LY19LUvG6Mxo/oqxoR3g9vWBIKOQAAAKqUfYdyNDplriRp0sgk1Y2N8jhR6RhlBQAAAFVGdl6+xk2epy37j2jadclq3SDW60hlYgs5AAAAqgTnnO55e7HmbNirf17RU4mt63kdKSAUcgAAAFQJT3yxSu8v3Kq7f9NRF/Vs5nWcgFHIAQAAUOm9PS9dT/97ja5MbKGbft3O6zjHhEIOAACASm322t26b8ZiDWxfX3+5tOIOb1gSCjkAAAAqrTU7M3XD5HlqXT9Wzw/rq8jwyldvK19iAAAAQNLug9kalTJXURFhmjgySfExkV5HOi4MewgAAIBKJys3X9e/lqZdmdl6fewAtaxX0+tIx41CDgAAgEqloMDpzjcXauHm/XphWF/1alnH60gnhF1WAAAAUKk8OnOlPlmyXb8/r7PO7dbE6zgnjEIOAACASmNa6iaN/2adhiUn6LpT23gdJygo5AAAAKgUvl21S/e/v1SnndRQD13UtdINb1gSCjkAAAAqvJXbM3TT1Pnq0KiWnh3aWxGVcHjDklSdRwIAAIAqaWdGlkZPmqvYGuGaNCpJtaMr5/CGJWGUFQAAAFRYh3PyNObVNO0/kqs3xw1Q0/gYryMFHVvIAQAAUCHlFzjdNn2hlm09oGeG9Fa35vFeRwoJCjkAAAAqpL98vEJfrtihP13YVWd2bux1nJChkAMAAKDCeXX2Bk2ctV6jBrbWtSe39jpOSFHIAQAAUKF8tWKHHvpwmc7q3Fh/vKCL13FCjkIOAACACmPplgO6dfoCdW0Wr6eH9FJ4WNUYa7w0FHIAAABUCFv3H9HolLmqExOpCdcmqmZU9RgQsHo8SgAAAFRoB7PzNDplrg7n5OvtGweoUVy015HKDYUcAAAAnsrLL9DNU+dr9c6DmjQySZ2axHkdqVyxywoAAAA845zTnz5Ypm9W7dKfL+mmX53U0OtI5Y5CDgAAAM+88t16TU3dpHGntdWQfglex/FEyAq5mbU0s6/NbIWZLTOz2wvddquZ/eS//rESpt9gZkvMbKGZpYUqJwAAALzx2dJt+uunK3R+9yb63W86eR3HM6HchzxP0l3OuflmVlvSPDP7QlJjSRdL6uGcyzazRqXM43Tn3O4QZgQAAIAHFm7erzveWKieLero8St7KawaDG9YkpAVcufcNknb/D9nmtkKSc0lXS/p7865bP9tO0OVAQAAABXP5r2Hdd2rc9Wwdg29cm2ioiPDvY7kqXLZh9zMWkvqLSlV0kmSTjWzVDP7xsySSpjMSfrczOaZ2dhS5j3WzNLMLG3Xrl1Bzw4AAIDgOXAkV6NT5ionr0CTRiapQa0aXkfyXMiHPTSzWpLekXSHcy7DzCIk1ZXUX1KSpDfNrK1zzhWZdKBzbqt/l5YvzGylc+7bovN3zr0k6SVJSkxMLDoPAAAAVBA5eQW6aeo8bdhzSK+O7qf2jWp7HalCCOkWcjOLlK+MT3XOzfBfnS5phvOZI6lAUoOi0zrntvq/75T0rqR+ocwKAACA0HHO6Y/vLdGsNXv0t8t66OR2v6h/1VYoR1kxSRMkrXDOPV7opvckneG/z0mSoiTtLjJtrP9AUJlZrKRzJC0NVVYAAACE1vP/Was309J12xntdXnfFl7HqVBCucvKQEkjJC0xs4X+634vaaKkiWa2VFKOpGudc87Mmkl6xTl3vnwjsbzr6/SKkDTNOfdZCLMCAAAgRD5YtFX/mPmTLu7VTL89+ySv41Q4oRxl5XtJJY1fM7yY+2+VdL7/53WSeoYqGwAAAMpH2oa9+r+3FimpdV09dnkP+Te4ohDO1AkAAICQ2LD7kK5/LU3N68TopRGJqhFRvYc3LAmFHAAAAEG371CORqXMlSRNGpmkurFRHiequEI+7CEAAACql+y8fI2bPE9b9h3R1OuT1bpBrNeRKjQKOQAAAILGOaffvb1Yczbs1VNX91JS63peR6rw2GUFAAAAQfPEl6v13sKt+r9zTtLFvZp7HadSoJADAAAgKN6Zl66nv1qty/u20M2nt/c6TqVBIQcAAMAJ+2HtHt07Y7FObldff720O8MbHgMKOQAAAE7Imp0HNW5ymlrVj9ULw/sqKoKKeSx4tgAAAHDc9hzM1qiUOYqKCNOkkUmKj4n0OlKlwygrAAAAOC5Zufm67rU07czI1utj+6tlvZpeR6qUKOQAAAA4ZgUFTne9uUgLN+/X80P7qHdCXa8jVVrssgIAAIBj9o/Pf9LHS7bpvvM66bzuTb2OU6lRyAEAAHBMps/ZpBf+s1ZDkxN0/altvY5T6VHIAQAAELDvVu/SH99bqtNOaqiHL+rK8IZBQCEHAABAQH7anqmbpsxXh0a19OzQ3ooIp0oGA88iAAAAyrQzI0ujU+YqJipcE0cmqXY0wxsGC6OsAAAAoFSHc/J03Wtp2nsoR2/dMEDN6sR4HalKYQs5AAAASpRf4HT76wu1dMsBPTOkt7o1j/c6UpVDIQcAAECJ/vrJCn2xfIceGNRFZ3Vp7HWcKolCDgAAgGK99sMGTfh+vUae3FojB7bxOk6VRSEHAADAL/x75Q49+MEyndW5ke4f1MXrOFUahRwAAAA/s3TLAd0ybYG6NIvTU1f3VngYY42HEoUcAAAA/7XtwBGNeXWu6sREasK1SYqtwaB8ocYzDAAAAEnSwew8jU5J06HsfL11wwA1jov2OlK1QCEHAACA8vILdMu0+Vq1I1MTRyapc9M4ryNVG+yyAgAAUM055/Tgh8v0n5926ZGLu+m0kxp6HalaoZADAABUcxO+X68pP27SuF+11dDkBK/jVDsUcgAAgGrss6Xb9ZdPVui8bk30u3M7eR2nWqKQAwAAVFOLNu/XHW8sUM8WdfTEVb0UxvCGnqCQAwAAVEOb9x7WmFfT1KBWDb18TaKiI8O9jlRtMcoKAABANXPgSK5Gp8xVdl6+Xh+brIa1a3gdqVpjCzkAAEA1kptfoJunztf63Yc0fnhftW9U2+tI1R5byAEAAKoJ55z++O5Sfb9mt/5xeQ+d3L6B15EgtpADAABUG8//Z63eSNusW89orysSW3odB34UcgAAgGrgw0Vb9Y+ZP+niXs1059kneR0HhVDIAQAAqrh5G/fqrrcWKal1XT06uIfMGN6wIqGQAwAAVGEb9xzS9a/NU7P4aI0fwfCGFRGFHAAAoIrafzhHoybNVYFzmjSqn+rFRnkdCcWgkAMAAFRB2Xn5Gjt5ntL3HdHL1ySqTYNYryOhBAx7CAAAUMU453TvO0s0Z/1ePXV1LyW1rud1JJSCLeQAAABVzFNfrda7C7borrNP0sW9mnsdB2WgkAMAAFQhM+an68kvV+vyvi10yxntvY6DAFDIAQAAqogf1+3R795ZrAFt6+uvl3ZneMNKgkIOAABQBazddVDjJs9TQr2aenF4X0VFUPMqC35TAAAAldyeg9kaNWmuIsJMKaP6Kb5mpNeRcAwYZQUAAKASy8rN1/WvpWlHRpZeH9tfLevV9DoSjhGFHAAAoJIqKHC6661Fmr9pv14Y1ke9E+p6HQnHgV1WAAAAKql/fv6TPl68Tfed10nndW/qdRwcJwo5AABAJfT6nE16/j9rNaRfgsb+qq3XcXACKOQAAACVzHerd+kP7y3Vr05qqEcu7srwhpUchRwAAKAS+Wl7pm6aMl8dGtXSc0N7KyKcOlfZ8RsEAACoJHZmZml0ylzFRIVr4sgk1Y5meMOqgFFWAAAAKoEjOfm67tU07T2UozfHDVCzOjFeR0KQUMgBAAAquPwCp9tfX6AlWw7opRGJ6t4i3utICCJ2WQEAAKjg/vbJCn2+fIceGNRFZ3dp7HUcBBmFHAAAoAKb/MMGvfL9eo08ubVGDWzjdRyEAIUcAACggvp65U796YNlOqtzI90/qIvXcRAiFHIAAIAKaNnWA7pl2nx1bhqnp67urfAwxhqvqijkAAAAFcy2A0c0JiVNcTGRmjgySbE1GIejKqOQAwAAVCAHs/M0JiVNmVm5mjgySY3jor2OhBDj3y0AAIAKIi+/QLdOm6+fdmRqwrWJ6tw0zutIKAdsIQcAAKgAnHN66MPl+vqnXXr44q76dcdGXkdCOaGQAwAAVAATvl+vyT9u1NhftdWw5FZex0E5opADAAB4bOay7frLJyt0XrcmuvfcTl7HQTmjkAMAAHhocfp+3f76AvVoUUePX9lLYQxvWO1QyAEAADySvu+wRqekqUGtGnrlmkTFRIV7HQkeYJQVAAAAD2Rk5Wp0ylxl5+Vr+vXJali7hteR4BG2kAMAAJSz3PwC3TRlvtbtOqQXh/dVh8a1vY4ED7GFHAAAoBw55/THd5fq+zW79djlPTSwfQOvI8FjbCEHAAAoRy9+s05vpG3WLae315WJLb2OgwqAQg4AAFBOPlq8VY9+tlIX9mymO88+yes4qCAo5AAAAOVg3sZ9uvPNRUpsVVf/uLwHwxvivyjkAAAAIbZxzyFd/1qamsVH66VrEhUdyfCG+B8KOQAAQAjtP5yjUSlzVeCcJo3qp3qxUV5HQgVDIQcAAAiRnLwCjZs8T+l7j+ilEYlq0yDW60iogBj2EAAAIAScc7r3ncVKXb9XT13dS/3a1PM6EiootpADAACEwNNfrdGMBVt059kn6eJezb2OgwqMQg4AABBk7y5I1xNfrtLgPi106xntvY6DCo5CDgAAEESp6/bonrcXq3/bevrbZd1lxvCGKB2FHAAAIEjW7jqosZPnKaFeTY0fnqioCKoWysZaAgAAEAR7DmZr1KS5iggzTRrZT/E1I72OhEqCUVYAAABOUFZuvsZOnqcdGVmaPra/EurX9DoSKhEKOQAAwAkoKHD6v7cWad7GfXpuaB/1SajrdSRUMuyyAgAAcAL+9cVP+mjxNt17Xidd0KOp13FQCVHIAQAAjtObczfrua/Xaki/lhr3q7Zex0ElRSEHAAA4Dt+v3q3fv7tEp3ZooIcv7sbwhjhuFHIAAIBjtGpHpm6cMk/tGtbSc8P6KDKcSoXjx9oDAABwDHZmZmnUpLmKjgrXxFFJiotmeEOcGAo5AABAgI7k5Ov6V9O091COJl6bpOZ1YryOhCqAYQ8BAAACkF/gdMcbC7R4ywGNH95X3VvEex0JVQRbyAEAAALw909XaOayHbr/gi46p2sTr+OgCqGQAwAAlGHyjxv18nfrde2AVho1sLXXcVDFUMgBAABK8fXKnfrT+0t1ZqdGeuDCrgxviKCjkAMAAJRg+dYM3TJtvjo3jdPTQ3orPIwyjuCjkAMAABRj+4EsjU6Zq9rRkZpwbZJiazAWBkKDQg4AAFDEoew8jU6Zq8ysXE0cmaQm8dFeR0IVxr96AAAAheTlF+jW6Qv0045MvXJtoro0i/M6Eqo4tpADAAD4Oef08EfL9e+VO/XgRV11esdGXkdCNUAhBwAA8Js0a4Ne+2Gjrj+1jUb0b+V1HFQTFHIAAABJny/brkc+Xq5zuzbRfed19joOqpGQFXIza2lmX5vZCjNbZma3F7rtVjP7yX/9YyVMf67/PmvM7N5Q5QQAAFicvl+3v75QPZrH64mreimM4Q1RjkJ5UGeepLucc/PNrLakeWb2haTGki6W1MM5l21mv9g5y8zCJT0n6WxJ6ZLmmtkHzrnlIcwLAACqofR9hzXm1TTVi43SK9cmKSYq3OtIqGbK3EJuZu3MrIb/51+b2W1mVqes6Zxz25xz8/0/Z0paIam5pBsl/d05l+2/bWcxk/eTtMY5t845lyPpdflKPAAAQNBkZOVqTEqasnLzlTIqSQ1r1/A6EqqhQHZZeUdSvpm1lzRBUhtJ045lIWbWWlJvSamSTpJ0qpmlmtk3ZpZUzCTNJW0udDndf11x8x5rZmlmlrZr165jiQUAAKqx3PwC3Tx1vtbuOqgXh/dVh8a1vY6EaiqQQl7gnMuTdKmkJ51zv5XUNNAFmFkt+Ur9Hc65DPl2k6krqb+kuyW9aWZFd9QqbsctV9z8nXMvOecSnXOJDRs2DDQWAACoxpxzeuD9pfpu9W799dLuGti+gdeRUI0FUshzzWyIpGslfeS/LjKQmZtZpHxlfKpzbob/6nRJM5zPHEkFkor+FaRLalnocgtJWwNZJgAAQFnGf7tO0+ds1s2nt9OVSS3LngAIoUAK+ShJAyT9xTm33szaSJpS1kT+rd4TJK1wzj1e6Kb3JJ3hv89JkqIk7S4y+VxJHcysjZlFSbpa0gcBZAUAACjVx4u36e+frtSgHk1119kdvY4DlD3Kin9kk9sKXV4v6e8BzHugpBGSlpjZQv91v5c0UdJEM1sqKUfStc45Z2bNJL3inDvfOZdnZrdImikpXNJE59yyY3hcAAAAvzBv4z799s2F6tuqrv55RU+GN0SFUGYhN7P1Kmb/bedc29Kmc859r+L3BZek4cXcf6uk8wtd/kTSJ2XlAwAACMSmPYc19rU0NY2P1svXJCo6kuENUTEEMg55YqGfoyVdIaleaOIAAAAE34HDuRqZMkd5BU6TRiapXmyU15GA/ypzH3Ln3J5CX1ucc0/Kvw84AABARZeTV6BxU9KUvveIXhrRV20b1vI6EvAzgeyy0qfQxTD5tpgzUCcAAKjwnHO6d8Zi/bhur568qpeS29b3OhLwC4HssvKvQj/nSdog6cqQpAEAAAiiZ/69RjPmb9FvzzpJl/Qu9hyDgOcCGWXl9PIIAgAAEEzvLdiix79Ypcv6NNdtZ7b3Og5QohILuZkNd85NMbM7i7u9yNjiAAAAFUbquj265+3F6t+2nv5+WQ/98qTgQMVR2hbyWP939hcHAACVxrpdBzVuyjy1qBej8cMTFRURyHkQAe+UWMidc+P93x8qvzgAAADHb++hHI1OmatwM6WM7Kf4mpFeRwLKVOa/jGb2mJnFmVmkmX1lZrvN7Bcn9gEAAPBSVm6+xr6Wpq0HsvTSNYlKqF/T60hAQAL5DOcc51yGpEGS0iWdJOnukKYCAAA4BgUFTne/vVhpG/fpiSt7qW+rul5HAgIWSCE/+lnP+ZKmO+f2hjAPAADAMXv8i1X6cNFW/e7cTrqgR1Ov4wDHJJBxyD80s5WSjki6ycwaSsoKbSwAAIDAvDl3s579eo2uTmqpG05r63Uc4JiVuYXcOXevpAGSEp1zuZIOSbo41MEAAADKMmvNbv3+3SU6tUMDPXJJN4Y3RKVU2jjklxVzXeGLM0IRCAAAIBCrd2Tqhinz1K5hLT03rI8iwxneEJVTabusXFjKbU4UcgAA4JFdmdkalTJX0ZHhmjgqSXHRDG+Iyqu0cchHlWcQAACAQBzJydd1r6Vp98FsvTlugJrXifE6EnBCAhmHvLGZTTCzT/2Xu5jZmNBHAwAA+LmCAqffvrFQi9P36+mre6tHizpeRwJOWCA7W6VImimpmf/yKkl3hCgPAABAif7+2Up9tmy7/nhBF53TtYnXcYCgCKSQN3DOvSmpQJKcc3mS8kOaCgAAoIgpP27US9+u0zUDWmn0wNZexwGCJpBCfsjM6st3IKfMrL+kAyFNBQAAUMh/ftqpP32wTGd0aqQHBnVheENUKYGcGOhOSR9IamdmsyQ1lHR5SFMBAAD4Ld+aoZunzlfHxrX1zJDeimB4Q1QxZRZy59x8MztNUkdJJukn/wmCAAAAQmpHRpbGvDpXtaMjNXFkkmJrBLItEahcSjsx0HBJ5pyb7N9vfJn/+uvN7JBzblp5hQQAANXPoew8jU6Zq4wjuXrrhpPVJD7a60hASJT2mc9dkt4r5vo3/LcBAACERH6B023TF2jFtgw9O7SPujSL8zoSEDKlFfJw51xm0SudcxmSOB0WAAAImUc+Wq6vVu7UQxd30+mdGnkdBwip0gp5pJnFFr3SzGpLigpdJAAAUJ1NmrVeKbM36LpT2mhE/1ZexwFCrrRCPkHS22bW+ugV/p9f998GAAAQVF8s36GHP1qu33RtrPvO7+x1HKBclHhQp3Pun2Z2UNI3ZlZLvnHID0n6u3PuhfIKCAAAqocl6Qd02/QF6tE8Xk9e1VvhYYw1juqh1LGDnHMvSnrRX8ituH3KAQAATtSW/Uc0+tW5qhcbpZevTVRMVLjXkYByE9Bgns65g6EOAgAAqqfMrFyNnjRXWTn5mnpTshrVZnhDVC+Mrg8AADyTm1+gm6bO19pdB5Uyqp9Oalzb60hAuaOQAwAATzjn9MD7y/Td6t16dHB3ndKhgdeRAE8EVMjN7GRJrQvf3zn3WogyAQCAauClb9dp+pxNuunX7XRVUoLXcQDPlFnIzWyypHaSFkrK91/tJFHIAQDAcflkyTb97dOVGtSjqf7vnI5exwE8FcgW8kRJXZxzLtRhAABA1Td/0z799o2F6tuqrv55RU+FMbwhqrnSTgx01FJJTUIdBAAAVH2b9x7W9a+mqXFctF4a0VfRkQxvCASyhbyBpOVmNkdS9tErnXMXhSwVAACocg4cztXISXOUV+A0aVSS6teq4XUkoEIIpJA/GOoQAACgasvJK9ANU+Zp097DmjwmWe0a1vI6ElBhlFnInXPflEcQAABQNTnndN+MJfph3R49cVVP9W9b3+tIQIVSYiE3s++dc6eYWaZ8o6r89yZJzjkXF/J0AACg0nv232v0zvx03XFWB13au4XXcYAKp8RC7pw7xf+dU2YBAIDj8v7CLfrXF6t0We/muv3MDl7HASqkQEZZAQAAOGZz1u/V3W8tVnKbevrb4O4yY3hDoDgUcgAAEHTrdx/S2MlpalEvRuNH9FWNCIY3BEpCIQcAAEG191CORk2aozAzTRqZpDo1o7yOBFRoARVyM2tlZmf5f44xM/YrBwAAv5CVm6+xr6Vp64EsvXxNX7WqH+t1JKDCK7OQm9n1kt6WNN5/VQtJ74UwEwAAqIQKCpzueXux0jbu0+NX9lTfVvW8jgRUCoFsIb9Z0kBJGZLknFstqVEoQwEAgMrniS9X6YNFW3XPuR01qEczr+MAlUYghTzbOZdz9IKZRejn45IDAIBq7q20zXrm32t0VWJL3XhaO6/jAJVKIIX8GzP7vaQYMztb0luSPgxtLAAAUFnMXrNb981YolPaN9CfL+3G8IbAMQqkkP9O0i5JSySNk/SJpD+GMhQAAKgc1uzM1Lgp89SmQayeH95HkeEM4AYcqxLP1ClJZhYmabFzrpukl8snEgAAqAx2ZWZr5KS5qhERrkmjkhQXHel1JKBSKvXfWOdcgaRFZpZQTnkAAEAlcCQnX9e9lqbdB7M14dpEtahb0+tIQKVV6hZyv6aSlpnZHEmHjl7pnLsoZKkAAECFVVDgdOebC7U4fb9eHN5XPVvW8ToSUKkFUsgfCnkKAABQaTz62Up9unS7/nhBZ/2maxOv4wCVXpmF3Dn3TXkEAQAAFd/U1I0a/+06jejfSmNOaeN1HKBKKLOQm1mm/jfueJSkSEmHnHNxoQwGAAAqlv/8tFMPvL9Mp3dsqD9d2IXhDYEgCWQLee3Cl83sEkn9QhUIAABUPCu2ZeiWaQt0UuPaemZoH0UwvCEQNMf81+Sce0/SGcGPAgAAKqIdGVkanTJXsTXCNXFkomrVCOQQNACBCmSXlcsKXQyTlKj/7cICAACqsEPZeRqdMlcHjuTqrRsGqGl8jNeRgConkH9xLyz0c56kDZIuDkkaAABQYeQXON3++gKt2JahCdcmqWuzeK8jAVVSIIX8FefcrMJXmNlASTtDEwkAAFQEj3y0XF+u2KlHLu6q0zs18joOUGUFsg/5MwFeBwAAqohJs9YrZfYGjTmljUYMaO11HKBKK3ELuZkNkHSypIZmdmehm+IkhYc6GAAA8MaXy3fokY+W6+wujfX78zt7HQeo8krbZSVKUi3/fQoPfZgh6fJQhgIAAN5Ykn5At05foG7N4/XU1b0UHsZY40ColVjI/Wfo/MbMUpxzG8sxEwAA8MDW/Uc05tW5qhcbpVeuTVTNKIY3BMpDIH9ph83sH5K6Soo+eqVzjrHIAQCoIjKzcjU6Za6O5ORr8o3JalQ7uuyJAARFIAd1TpW0UlIbSQ/JN+zh3BBmAgAA5Sgvv0A3T1ugNTsP6vnhfdSxSe2yJwIQNIEU8vrOuQmScp1z3zjnRkvqH+JcAACgHDjn9MAHy/Ttql368yXddGqHhl5HAqqdQHZZyfV/32ZmF0jaKqlF6CIBAIDy8vJ36zQtdZNu/HU7Xd0vwes4QLUUSCH/s5nFS7pLvvHH4yT9NqSpAABAyH26ZJv++slKXdCjqe4+p6PXcYBqq9RCbmbhkjo45z6SdEDS6eWSCgAAhNSCTft0xxsL1Sehjv51RU+FMbwh4JlS9yF3zuVLuqicsgAAgHKwee9hXf9amhrHRevlaxIVHcn5/gAvBbLLymwze1bSG5IOHb3SOTc/ZKkAAEBIHDicq1Epc5Wb7zRpVJLq16rhdSSg2gukkJ/s//5woeucJMYhBwCgEsnJK9CNU+dp455Dem10sto1rOV1JAAKoJA759hvHACASs45p9+/u0Sz1+7Rv67oqQHt6nsdCYBfmeOQm1ljM5tgZp/6L3cxszGhjwYAAILlua/X6O156br9zA4a3JfRi4GKJJATA6VImimpmf/yKkl3hCgPAAAIsvcXbtE/P1+lS3s31x1ndfA6DoAiAinkDZxzb0oqkCTnXJ6k/JCmAgAAQTF3w17d/dZi9WtTT38f3F1mDG8IVDSBFPJDZlZfvgM5ZWb95RuTHAAAVGDrdx/S2NfS1KJujF4a0Vc1IhjeEKiIAhll5U5JH0hqZ2azJDWUdHlIUwEAgBOy71CORqfMlZlp0qgk1akZ5XUkACUIZJSV+WZ2mqSOkkzST8653JAnAwAAxyU7L19jJ6dpy/4jmnZdslrVj/U6EoBSlFnIzSxa0k2STpFvt5XvzOxF51xWqMMBAIBj45zTPW8v1twN+/TMkN5KbF3P60gAyhDILiuvScqU9Iz/8hBJkyVdEapQAADg+DzxxSq9v3Cr7v5NR13Ys1nZEwDwXCCFvKNzrmehy1+b2aJQBQIAAMfn7Xnpevrfa3RVYkvd9Ot2XscBEKBARllZ4B9ZRZJkZsmSZoUuEgAAOFaz1+7WfTMWa2D7+vrzpd0Y3hCoRALZQp4s6Roz2+S/nCBphZktkeSccz1Clg4AAJRpzc5M3TB5nlrXj9Xzw/oqMjyQ7W0AKopACvm5IU8BAOUkMytXHy/epl93bKQm8dFexwFO2O6D2RqVMldREeGaODJJ8TGRXkcCcIwCGfZwo5nVldSy8P2dc/NDGQwAgq2gwOm26Qv09U+7FB5mOqtzIw3v30oD2zVQWBgf76PyycrN13WvpmlXZrZeHztALevV9DoSgOMQyLCHj0gaKWmt/Gfr9H8/I3SxACD4nvpqtb7+aZfuOvskHczJ01tp6Zq5bIda16+pockJurxvS9WL5eQpqBwKCpzufHOhFqXv1wvD+qpXyzpeRwJwnALZZeVKSe2cczmhDgMAofLVih166qvVuqJvC91yRnuZme48+yR9tnS7pvy4UX/9ZKX++fkqXdC9qYYlJ6hvq7ocFIcK7dGZK/XJku364wWddW63Jl7HAXACAinkSyXVkbQztFEAIDTW7z6kO95YqO7N4/XIJf8bfaJGRLgu7tVcF/dqrp+2Z2pq6kbNmL9F7y7Yok5NamtYcoIu6d1ctaPZJxcVy7TUTRr/zToN75+gMae08ToOgBNkzrnS72CWKOl9+Yp59tHrnXMXhTbasUtMTHRpaWlexwBQgRzKztNlz8/WzswsfXjrKWpRt/R9bA9l5+nDRVs1JXWjlm7JUM0oX2kflpygbs3jyyk1ULJvVu3S6JS5OrVDA71yTaIiGFEFKHdmNs85lxis+QWyhfxVSY9KWiKpIFgLBoBQc87pnncWa/XOTL02OrnMMi5JsTUidHW/BF2V1FKL0w9oaupGvbsgXdPnbFKvlnU0LDlBF/ZspujI8HJ4BMDPrdyeoZunzleHRrX07NA+lHGgighkC/k3zrnTyinPCWELOYDCXv52nf7yyQrde14n3XDa8Z+18MDhXM1YkK4pP27U2l2HFBcdocv7ttTQ5AS1b1QriImBku3MyNIlz81SvnN67+aBahof43UkoNoK9hbyQAr54/LtqvKBfr7LSoUb9pBCDuCo2Wt2a/iEVJ3brYmeG9onKAdoOueUun6vpvy4UTOXbVduvtOAtvU1rH+CzunSRFERbK1EaBzOydNV43/U2l0H9ea4Aew+BXjMi11Wevu/9y90HcMeAqiwtu4/olumL1C7hrX02OU9gzZaipmpf9v66t+2vnZlZuvNtM2alrpJt0xboAa1auiqpBa6OimBsaARVPkFTrdNX6hlWw/olWsTKeNAFVTmFvLKhC3kALJy83XV+B+0btchvXfLQLVrGNpdSvILnL5dvUtTf9yof6/cKSfp9I6NNCw5Qb/u2EjhnHAIJ+ihD5dp0qwNevjirrpmQGuv4wCQB1vIzayxpL9KauacO8/Mukga4JybEKwQABAsD36wTIvSD+ilEX1DXsYlKTzMdHrHRjq9YyNt2X9Er8/ZpNfnbtaYV9PUvE6MhvRrqSuTWqpR7eiQZ0HVkzJrvSbN2qDRA9tQxoEqLJB9yD+VNEnSH5xzPc0sQtIC51z38gh4LNhCDlRv0+ds0n0zlujWM9rrrnM6epYjN79AXy7foSmpGzVrzR5FhJl+07WJhvVP0IC29TnhEALy1Yoduv61NJ3ZubFeHN6XT1uACqTctpCbWYRzLk9SA+fcm2Z2nyQ55/LMLD+AoC0lvSapiXzDJb7knHvKzB6UdL2kXf67/t4590kx02+QlCkpX1JeMB80gKpnwaZ9+tP7y3TaSQ11x1kneZolMjxM53VvqvO6N9W6XQc1LXWT3pqXro+XbFPbBrEampygy/u2UJ2aUZ7mRMW1dMsB3TJtgbo2i9dTV/eijANVXIlbyM1svnOuj5n9R9JgSV/4L/eX9GhZQyGaWVNJTZ1z882stqR5ki6RdKWkg865f5Yx/QZJic653YE+GLaQA9XTrsxsXfjM94qKCNMHtwyskEU3KzdfnyzZpik/btT8TftVIyJMg3o00/D+CerVsg5bzfFfW/cf0SXPzVJkeJjevelkNYpjdyegoinPfciPvjvcKd+Qh+3MbJakhpIuL2vGzrltkrb5f840sxWSmp9YXAD4udz8At0ybb72H8nRjBsrZhmXpOjIcF3Wp4Uu69NCy7dmaGrqRr23YIvemZ+uLk3jNKx/gi7p1VyxNQIZ/ApVVWZWrkanzNWRnHxNvjGZMg5UE6VtIU+X9Lj/YpikGvKV9GxJ+c65x4udsPh5tZb0raRu8hX8kZIyJKVJuss5t6+YadZL2iffEIvjnXMvlTDvsZLGSlJCQkLfjRs3BhoLQBXwyEfLNeH79Xryql66pHfl+p//YHae3luwRVN+3KiV2zNVq0aELundTMP7t1KnJnFex0M5y8sv0JhX0/T9mt2aNDJJvzqpodeRAJSg3E4MZGbbJL2g/20p/xnn3EMBLcCslqRvJP3FOTfDP2rLbvmK9iPy7dYyupjpmjnntppZI0lfSLrVOfdtactilxWgenl/4Rbd/vpCjRrYWn+6sKvXcY6bc04LNu/XlB836qPF25STV6C+repqeP8EndetqaIjw72OiBBzzumP7y3V1NRN+ttl3TWkX4LXkQCUojwL+XznXJ8TmrlZpKSPJM0sbou6f8v5R865bmXM50EFsN85hRyoPlZsy9Clz89Sj+Z1NPX6ZEWGV42zZO4/nKO356Vrauomrd99SHVqRuqKvi00NLmV2jSI9ToeQuTlb9fpL5+s0A2ntdO953XyOg6AMnixD/lxMd8RShMkrShcxs2sqX//ckm6VNLSYqaNlRTm3/c8VtI5kh4+kTwAqo4Dh3N1w5R5io+J1LPDeleZMi5JdWpG6bpT22rMKW00e+0eTU3dqEmzNujl79brlPYNNLx/gs7s3LhKPebq7rOl2/TXT1fogu5Ndc9vvBuuE4B3SivkZ57gvAdKGiFpiZkt9F/3e0lDzKyXfLusbJA0TvLtoiLpFefc+ZIaS3rXP+pAhKRpzrnPTjAPgCqgoMDpjjcWaOv+I3p97IAqe8IdM9PA9g00sH0D7czI0htzN2v6nE26Ycp8NapdQ1f3S9DVSS3VrE6M11FxAhZu3q873lioXi3r6F9X9lQYwxsC1VKZJwaqTNhlBaj6Hv9ilZ7+arX+fEk3De/fyus45Sq/wOnrlTs1NXWj/rNql0zSGZ0aa3j/BP2qQ0PKXCWzee9hXfr8LMVEhevdmwaqQa0aXkcCEKDy3GUFACqUL5fv0NNfrdYVfVtoWHL1O+gtPMx0VpfGOqtLY23ee1jT52zSm2mb9eWKHWpZL0ZD+7XSFYktKHaVwIEjuRqVMlc5eQV6fewAfmdANccWcgCVwvrdh3TRM9+rTcNYvTluACOP+OXkFWjmsu2amrpRP67bq8hw07ndmmp4coL6tanHCYcqoJy8Ao2cNEdzN+zVa6OTNaBdfa8jAThGbCEHUO0cys7TuMlpiowI0wvD+1LGC4mKCNOFPZvpwp7NtGZnpqambtI789L14aKt6tColoYlJ+jSPi0UHxPpdVTo6PCGSzR77R7984qelHEAkthCDqCCc87plukL9OmSbZo8JlkD2zfwOlKFdyQnXx8u3qqpqZu0aPN+xUSG66KezTSsf4J6tKjjdbxq7bmv1+gfM3/SbWd20J1nn+R1HADHiS3kpahC/1sA8Hvlu/X6ePE23XdeJ8p4gGKiwnVlYktdmdhSS7cc0NTUjXpvwVa9kbZZ3ZvHa3j/BF3Ys5lqRlWpt4AK74NFW/WPmT/pkl7N9NuzOngdB0AFUqW2kDds08VtW7NUEYzPC1QJs9fs1vAJqTq3WxM9N7QP+0OfgIysXL23YIum/LhRq3YcVO3oCA3u00JDkxN0UuPaXser8tI27NXQV1LVq0UdTb6un2pEsNsVUJmV25k6K6MaTTu4W59+W48N7sHwX0Alt2X/EV34zPeqHxuld28eqFo12JobDM45pW3cp6k/btQnS7YrJ79A/drU07DkBJ3brQlFMQQ27D6kS5+fpTo1ozTjxpNVNzbK60gAThCFvBQtT+rmwi97VKMHttH9gzqzNQ2opLJy83Xl+B+0ftchvXfLQLVrWMvrSFXS3kM5eitts6bN2aSNew6rfmyUrkhsqaH9EpRQv6bX8aqEfYdydNkLs7X/cI7evWmgWjeI9ToSgCBgH/JSNI6L1oUDW2virPWKj4nU7eyjB1Q6zjk98P5SLU4/oJevSaSMh1C92CiNO62drj+1rb5fs1tTUzfq5e/Wafy3a/WrDg01LDlBZ3RqxG6Axyk7L1/jJs/Tln1HNO36ZMo4gBJVqUIuSfdf0EWZWXl64stViouJ0KiBbbyOBOAYTJ+zWW+mpeu2M9rr7C6NvY5TLYSFmX51UkP96qSG2n4gS6/P3aTX52zW2Mnz1DQ+WlcnJejqfi3VOC7a66iVhnNOv3t7seZs2Kunh/RWYut6XkcCUIFVqV1Wjg57mJdfoJumztfny3foX1f01OC+LbyOBiAA8zft01Xjf9DA9g004dokhXMsiGfy8gv01cqdmpq6Sd+u2qXwMNPZnRtrWP8EDWzXgON0yvD4F6v09FerdfdvOurm09t7HQdAkLEPeSkKj0OelZuv0Slzlbp+r14Y1kfndG3icToApdmVma0Ln/leURFh+uCWgapTkwPfKoqNew5p2pxNeistXXsP5ah1/Zoampygy/u2VD0OUPyFd+al6663FunKxBZ6dHAPjmcCqiAKeSmKnhjoYHaehr2SqhVbM5QyKkknM4YxUCHl5hdo+CupWpS+XzNuHKguzeK8joRiZOfl67Ol2zXlx42au2GfoiLCdEH3phqWnKC+repSPCX9sHaPrpmYqn5t6illVD9Fsv89UCVRyEtR3Jk69x/O0ZXjf1D6viOadn1/9WpZx5twAEr08IfLNXHWej15VS9d0ru513EQgJ+2Z2pa6kbNmL9Fmdl56tSktoYlJ+iS3s1VOzrS63ieWLPzoC57fpYaxUXrnRtPVnxM9XwegOqAQl6K4gq5JO3IyNIVL/6gjKxcvTF2gDo24SQYQEXx/sItuv31hRo1sLX+dGFXr+PgGB3KztOHi7ZqSupGLd2SoZpR4bq4V3MNS05Qt+bxXscrN3sOZuuS52fpSE6+3r1poFrWY9hIoCqjkJeipEIuSZv2HNblL86WJL19w8mMsQtUACu2ZejS52epR4s6mnpdMh/vV3KLNu/X1NSN+mDRVmXlFqhXyzoalpygC3s2U3Rk1T3hUFZuvoa8/KOWb83QG+MG8EksUA1QyEtRWiGXfB+xXjn+B8XHROrtGwaoEUN4AZ45cDhXFz77vbLz8vXRraeqYe0aXkdCkBw4nKsZC9I1NXWT1uw8qLjoCF3et6WGJieofaOqNa58QYHTrdMX6JOl2/TCsD46t1tTryMBKAcU8lKUVcglacGmfRr2Sqpa1q2pN8b1ZyQHwAMFBU6jX52rWWt2641xA9Qnoa7XkRACzjmlrt+rqamb9NnSbcrNdxrQtr6G9U/QOV2aKCqi8n8i8vdPV+rFb9bqD+d31vW/aut1HADlhEJeikAKuSTNWrNboybNVZdmcZp6XbJia1S58yMBFdrRMZr/cmk3DUtu5XUclINdmdl6a95mTUvdpPR9R9SgVg1dldRCQ/olqEXdyrkL4fQ5m3TfjCUalpygP1/SjVFmgGqEQl6KQAu5JM1ctl03TZ2v/m3raeLIJNWIqLr7NwIVyZfLd+i619IYo7mayi9w+nb1Lk39cZP+vXKHnKTTOzbS8P4JOu2kRpXmZFDfrtqlUSlzdUr7BppwbaIiOP4BqFYo5KU4lkIu/e/kDb/p2ljPDe3DCyoQYut3H9JFz3yvNg1j9ea4AVX6QD+Ubcv+I3pjzia9PnezdmZmq3mdGA3p11JXJrVUo9oV9xifn7ZnavALs9WibozeumFAtR3mEajOKOSlONZCLkmTZq3XQx8u1+V9W+ixwT04HTQQIoey83TJc7O051COPrz1FDWvE+N1JFQQufkF+nL5Dk1J3ahZa/YoIsz0m65NNKx/gga0rV+hPkXZmZGlS5+frbyCAr1700A1Yz0GqqVgF/Jqv/P0qIFtdOBIrp78crXioiN1/6DOFerFH6gKnHO65+3FWrvroCaPSaaM42ciw8N0XvemOq97U63bdVDT52zSW/PS9fGSbWrbIFZDkxN0ed8Wnh+EfzgnT2NeTdO+wzl6c9wAyjiAoKn2hVySbj+zg/YfztXEWesVHxOp28/q4HUkoEp5+bt1+njJNt13XicNbN/A6ziowNo2rKU/XNBFd53TUZ8s2aapqZv0549X6B8zf9KgHs00vH+CerWsU+4bTvILnG5/faGWbT2gl69JrFYnPQIQehRySWamBwZ1UWZWnp74cpXiYiI0amAbr2MBVcLsNbv1909X6oLuTTWWYeEQoOjIcF3Wp4Uu69NCy7dmaNqcjXp3/ha9Mz9dXZrGaXj/Vrq4V7NyGyXrLx+v0BfLd+ihi7rqzM6Ny2WZAKqPar8PeWF5+QW6aep8fb58h/51RU8N7tsiiOmA6mfL/iO68JnvVT82Su/dPJAhRnFCDmbn6f2FWzTlx01asS1DtWpE6NLezTWsf4I6NYkL2XJfnb1Bf/pgmUYNbK0/Xdg1ZMsBUHlwUGcpTrSQS75TII9OmavU9Xv1wrA+OqdrkyClA6qXrNx8XTn+B63fdUjv3zJQbRtWrTM0wjvOOS3YvF9TftyojxZvU05egfq2qqvh/RN0XremQR29598rd+i6V9N0RqfGGj+ib6UZlhFAaFHISxGMQi75tsIMeyVVK7ZmKGVUkk5mn1fgmDjn9Lt3FuvNtHS9fE2izu7CR/wIjf2Hc/T2vHRNS92kdbsPqU7NSF3Rt4WGJrdSmwaxJzTvpVsO6MrxP6itf5jOmlF8wgPAh0JeimAVcsn3In/l+B+Uvu+Ipl3fX71a1gnKfIHqYGrqRv3h3aW67Yz2uvOcjl7HQTXgnNMPa/doauomzVy2XXkFTqe0b6Dh/RN0ZufGijzG80xsO3BElzw3S+Fmeu/mgWoUV3HHRQdQ/ijkpQhmIZekHRlZuuLFH5SRlas3xg5Qxya1gzZvoKqav2mfrhr/gwa2b6AJ1ybxET/K3c6MLL2ZtlnT52zWlv1H1Kh2DV3dL0FXJ7UMaKjCg9l5uuLFH7R572G9feOAkO6fDqByopCXItiFXJI27Tmsy1+cLUl658aT1bJezaDOH6hKdmVma9Az36lGRLg+uGWg5+NGo3rLL3D6z087NeXHjfrPql0ySWd2bqxhyQn6VYeGxZ4ILi+/QNe9lqbvVu/WxJFJOu2khuUfHECFRyEvRSgKueQ7TfKV439QfEyk3r5hAB9dAsXIzS/QsFdStTh9v2bcOFBdmrFVERXH5r2H9frcTXpj7mbtPpijlvViNLRfK12R2EINatWQ5Nvt5f73l2rKj5v010u7a2hygsepAVRUFPJShKqQS9KCTfs07JVUtaxbU2+M68+WP6CIhz9cromz1uupq3vp4l7NvY4DFCsnr0CfL9+uKT9u1I/r9ioy3HRut6YanpygxekH9JdPVmjcaW1133mdvY4KoAKjkJcilIVckmat2a1Rk+aqS7M4Tb0umTGVAb/3F27R7a8v1OiBbfTAhV28jgMEZM3Og5qaulHvzEtXRlaeJOn87k307JA+xe7OAgBHUchLEepCLkkzl23XTVPnq3/bepo4Mkk1IoI33i1QGa3YlqFLn5+lHi3qaOp1ycc8mgXgtSM5+fpo8Vb9tD1T//ebjkEdxxxA1RTsQs475zH6TdcmenRwD81as0e3TV+gvPwCryMBntl/OEfjJs9TfEyknhvahzKOSikmKlxXJLbUHwd1oYwD8ATvnsfh8r4t9MCgLpq5bIfunbFEBQVV51MGIFD5BU63v75Q2w4c0QvD+6ph7RpeRwIAoFJiJ+jjNPqUNjpwJFdPfbVacdGRun9QZ5mxzyGqj6e+XKVvVu3SXy7tpj4Jdb2OAwBApUUhPwF3nNVBB47kauKs9YqPidTtZ3XwOhJQLr5YvkNP/3uNrkxsoaH9GBoOAIATQSE/AWamBwZ1UWZWnp74cpXiYiI0amAbr2MBIbVu10Hd+cZC9WgRr4cv7sYnQwAAnCAK+QkKCzM9Ori7MrNy9dCHyxUXHanBfVt4HQsIiUPZeRo3eZ4iI8L0wvC+HAAHAEAQcFBnEESEh+npIb11crv6uuedxfp82XavIwFB55zTPW8v1tpdB/XskN5qXifG60gAAFQJFPIgiY4M10vXJKpb83jdMm2BZq/Z7XUkIKhe/m6dPl6yTfee10knt2/gdRwAAKoMCnkQ1aoRoVdHJal1g5q67rU0Ldy83+tIQFDMWrNbf/90pS7o3lTXn9rW6zgAAFQpFPIgq1MzSpPHJKtBrRoaOWmOVu3I9DoScELS9x3WLdPmq13DWnrs8h4cxAkAQJBRyEOgcVy0poxJVlR4mEZMSNXmvYe9jgQcl6zcfN04Zb7y8p3Gj+ir2BocBw4AQLBRyEMkoX5NTR6TrKzcAg17JVU7M7K8jgQcE+ec7n9vqZZsOaDHr+qltg1reR0JAIAqiUIeQh2b1FbKqCTtPpitERPmaP/hHK8jAQGbNmeT3pqXrtvO7KCzuzT2Og4AAFUWhTzEeifU1cvXJGr97kMaOWmuDmXneR0JKNP8Tfv04AfLdHrHhrrjTM5ACwBAKFHIy8HA9g309JDeWpy+X2Mnpyk7L9/rSECJdmVm68Yp89Q0PkZPXtVbYWEcxAkAQChRyMvJud2a6LHLe2rWmj26bfoC5eUXeB0J+IXc/ALdPHW+DhzJ1fgRfRVfM9LrSAAAVHkU8nJ0ed8WemBQF81ctkP3zliiggLndSTgZ/76yQrN2bBXjw7uoc5N47yOAwBAtcAYZuVs9CltdOBIrp76arXioiN1/6DOjOuMCuG9BVs0adYGjR7YRhf3au51HAAAqg0KuQfuOKuDDhzJ1cRZ6xUfE6nbz+KgOXhr+dYM3Ttjsfq1qaf7zu/kdRwAAKoVCrkHzEwPDOqizKw8PfHlKsXFRGjUwDZex0I1tf9wjsZNSVOdmCg9N7SPIsPZkw0AgPJEIfdIWJjp0cHdlZmVq4c+XK646EgN7tvC61ioZvILnG5/faF2HMjWG+P6q2HtGl5HAgCg2mFTmIciwsP09JDeOrldfd3zzmJ9vmy715FQzTz15Sp9s2qXHryoq3on1PU6DgAA1RKF3GPRkeF66ZpEdWser1umLdDsNbu9joRq4ovlO/T0v9foqsSWGtKvpddxAACotijkFUCtGhF6dVSSWjeoqeteS9PCzfu9joQqbu2ug7rzjYXq0SJeD13clZF+AADwEIW8gqhTM0qTxySrQa0aGjlpjlbtyPQ6Eqqog9l5umHyPEVGhOmF4X0VHRnudSQAAKo1CnkF0jguWlPGJCsqPEwjJqRq897DXkdCFeOc0z1vL9LaXQf17JDeal4nxutIAABUexTyCiahfk1NHpOsrNwCDXslVTszsryOhCrkpW/X6ZMl23XveZ10cvsGXscBAACikFdIHZvUVsqoJO0+mK0RE+Zo/+EcryOhCpi1Zrce/WylLujRVNef2tbrOAAAwI9CXkH1Tqirl69J1PrdhzRy0lwdys7zOhIqsfR9h3XLtPlq36iWHhvcg4M4AQCoQCjkFdjA9g309JDeWpy+X2Mnpyk7L9/rSKiEsnLzdeOU+crLdxo/IlGxNTgfGAAAFQmFvII7t1sTPXZ5T81as0e3TV+gvPwCryOhEnHO6f73lmrJlgN64qpeatMg1utIAACgCAp5JXB53xZ6YFAXzVy2Q/fOWKKCAud1JFQSU1M36a156brtzA46q0tjr+MAAIBi8Nl1JTH6lDY6cCRXT321WnHRkbp/UGf2A0ap5m3cp4c+XKbTOzbUHWd28DoOAAAoAYW8ErnjrA46cCRXE2etV3xMpG4/i5KF4u3MzNJNU+epaXyMnryqt8LC+OcNAICKikJeiZiZHhjURZlZeXriy1WKi4nQqIFtvI6FCiY3v0C3TF2gjCN5mnFTP8XXjPQ6EgAAKAWFvJIJCzM9Ori7MrNy9dCHyxUXHanBfVt4HQsVyF8/WaE5G/bqqat7qXPTOK/jAACAMnBQZyUUER6mp4f01snt6uuedxbr82XbvY6ECuK9BVs0adYGjTmljS7u1dzrOAAAIAAU8koqOjJcL12TqG7N43XLtAWavWa315HgseVbM3TvjMVKblNP957Xyes4AAAgQBTySqxWjQiljExS6wY1df1raVq4eb/XkeCR/YdzNG5KmurEROnZoX0UGc6fNgAAlQXv2pVc3dgoTR6TrHq1ojRy0hyt2pHpdSSUs/wCp9teX6gdB7L1wvA+ali7hteRAADAMaCQVwGN46I1ZUyyIsPDNGJCqjbvPex1JJSjJ79cpW9X7dKDF3VV74S6XscBAADHiEJeRbSqH6spY5KVlVugYa+kamdGlteRUA4+X7Zdz/x7ja5KbKkh/Vp6HQcAABwHCnkV0rFJbaWMStLug9kaMWGO9h/O8ToSQmjtroO6881F6tkiXg9d3JUztwIAUElRyKuY3gl19fI1iVq/+5BGTpqrQ9l5XkdCCBzMztMNk+epRkSYXhjeV9GR4V5HAgAAx4lCXgUNbN9ATw/prcXp+zV2cpqy8/K9joQgcs7pnrcXae2ug3pmaG81qxPjdSQAAHACKORV1Lndmuixy3tq1po9um36AuXlF3gdCUEy/tt1+mTJdt13Xmed3K6B13EAAMAJopBXYZf3baEHBnXRzGU7dO+MJSoocF5Hwgn6fvVuPfbZSl3Qo6muO7WN13EAAEAQRHgdAKE1+pQ2OnAkV099tVpx0ZG6f1BnDv6rpNL3Hdat0+erfaNaemxwD36PAABUERTyauCOszrowJFcTZy1XvExkbr9rA5eR8IxysrN1w1T5ikv32n8iETF1uBPFwCAqoJ39WrAzPTAoC7KzMrTE1+uUlxMhEYNZHeHysI5pz++t1RLt2RowrWJatMg1utIAAAgiCjk1URYmOnRwd2VkZWrhz5crrjoSA3u28LrWAjA1NRNenteum4/s4PO7NzY6zgAACDIOKizGokID9MzQ3rr5Hb1dc87i/X5su1eR0IZ5m3cp4c+XKbTOzbU7WeyqxEAAFURhbyaiY4M10vXJKpb83jdMn2BZq/d7XUklGBnZpZumjpPzerE6MmreissjIM4AQCoiijk1VCtGhFKGZmk1vVr6vpX07Rw836vI6GI3PwC3Tx1vjKO5OnF4X0VXzPS60gAACBEKOTVVN3YKE0ek6x6taI0ctIcrdqR6XUkFPKXj1do7oZ9+vvg7urcNM7rOAAAIIQo5NVY47hoTRmTrMjwMI2YkKrNew97HQmS3l2QrpTZGzTmlDa6uFdzr+MAAIAQo5BXc63qx2rKmGRl5RZo2Cup2pmR5XWkam3Z1gO6b8YSJbepp3vP6+R1HAAAUA4o5FDHJrWVMipJuw9ma8SEOdp/OMfrSNXS/sM5umHKPNWJidKzQ/soMpw/TwAAqgPe8SFJ6p1QVy9fk6j1uw9p5KS5OpSd53WkaiW/wOm21xdqx4FsvTC8jxrWruF1JAAAUE4o5Pivge0b6OkhvbU4fb/GTk5Tdl6+15GqjSe/XKVvV+3SQxd3Ve+Eul7HAQAA5YhCjp85t1sTPXZ5T81as0e3TV+gvPwCryNVeTOXbdcz/16jq5Naaki/BK/jAACAckYhxy9c3reFHhjURTOX7dC9M5aooMB5HanKWrvroO56c5F6tojXgxd19ToOAADwQITXAVAxjT6ljQ4cydVTX61WXHSk7h/UWWacKTKYDmbnadzkeaoREaYXhvdVdGS415EAAIAHKOQo0R1nddCBI7maOGu94mMidftZHbyOVGU453T3W4u0btdBTbkuWc3qxHgdCQAAeIRCjhKZmR4Y1EUZWbl64stViouJ0KiBbbyOVSWM/3adPl26XX84v7NObtfA6zgAAMBDIduH3MxamtnXZrbCzJaZ2e3+6x80sy1mttD/dX4J059rZj+Z2RozuzdUOVG6sDDTY4N76OwujfXQh8s1Y36615Eqve9X79Zjn63UoB5Ndd2p/IMDAEB1F8qDOvMk3eWc6yypv6SbzayL/7YnnHO9/F+fFJ3QzMIlPSfpPEldJA0pNC3KWUR4mJ4Z0lsnt6uvu99erM+Xbfc6UqWVvu+wbp0+Xx0a1dZjl/dgv3wAABC6Qu6c2+acm+//OVPSCknNA5y8n6Q1zrl1zrkcSa9Lujg0SRGI6MhwvXRNoro1j9ct0xdo9trdXkeqdLJy83XDlHnKK3B6cURf1YxijzEAAFBOwx6aWWtJvSWl+q+6xcwWm9lEMyvuLCjNJW0udDldJZR5MxtrZmlmlrZr165gxkYRtWpEKGVkklrXr6nrX03Tws37vY5UaTjn9Id3l2rplgw9eVUvtWkQ63UkAABQQYS8kJtZLUnvSLrDOZch6QVJ7ST1krRN0r+Km6yY64odDNs595JzLtE5l9iwYcPghEaJ6sZGafKYZNWrFaWRk+Zo1Y5MryNVClNSN+md+em6/cwOOrNzY6/jAACACiSkhdzMIuUr41OdczMkyTm3wzmX75wrkPSyfLunFJUuqWWhyy0kbQ1lVgSucVy0poxJVmR4mEZMSNXmvYe9jlShzdu4Vw9/uEynd2yo289k6EgAAPBzoRxlxSRNkLTCOfd4oeubFrrbpZKWFjP5XEkdzKyNmUVJulrSB6HKimPXqn6spoxJVlZugYa9kqqdGVleR6qQdmZm6cYp89WsToyevKq3wsI4iBMAAPxcKLeQD5Q0QtIZRYY4fMzMlpjZYkmnS/qtJJlZMzP7RJKcc3mSbpE0U76DQd90zi0LYVYch45NaitlVJJ2H8zWiAlztP9wjteRKpTc/ALdPHW+MrPyNH5EX8XXjPQ6EgAAqIDMuWJ3za6UEhMTXVpamtcxqp1Za3Zr1KS56tIsTlOvS1ZsDUYPkaQHP1imlNkb9PSQ3rqoZzOv4wAAgCAxs3nOucRgza9cRllB1TawfQM9PaS3Fqfv19jJacrOy/c6kufeXZCulNkbdN0pbSjjAACgVBRyBMW53Zro0cE9NGvNHt02fYHy8gu8juSZZVsP6L4ZS9S/bT3de14nr+MAAIAKjkKOoLkisaXuH9RFM5ft0L0zlqigoOrsDhWo/YdzNG7yPNWJidKzQ/soIpw/MQAAUDp29kVQjTmljQ4cydXTX61WXHSk7h/UudqcHj6/wOm21xdqZ0a23hjXXw1q1fA6EgAAqAQo5Ai6357VQRlHcjVx1nrFx0Tq9rOqx9jbT3yxSt+u2qW/XdZdvROKOwEtAADAL1HIEXRmpgcGdVFGVq6e+HKV4mMiNHJgG69jhdTMZdv17NdrdHVSSw3pl+B1HAAAUIlQyBESYWGmxwb3UGZWnh78cLniYiJ1WZ8WXscKibW7DuquNxepZ4t4PXhRV6/jAACASoYjzhAyEeFhemZIb53crr7ufnuxPl+23etIQXcwO0/jJs9TjYgwvTC8r6Ijw72OBAAAKhkKOUIqOjJcL12TqG7N43XL9AWavXa315GCxjmnu99apPW7D+mZob3VrE6M15EAAEAlRCFHyNWqEaGUkUlqXb+mrn81TQs37/c6UlC8+M06fbp0u+47r5NObtfA6zgAAKCSopCjXNSNjdLkMcmqVytKIyfN0aodmV5HOiHfrd6lf8xcqUE9mmrMKVX7gFUAABBaFHKUm8Zx0ZoyJlmR4WEaMSFVm/ce9jrScdm897Bum75AHRrV1mOX96g246wDAIDQoJCjXLWqH6spY5KVlVugYa+kamdGlteRjklWbr5unDpPeQVOL47oq5pRDFQEAABODIUc5a5jk9pKGZWk3QezNWLCHO0/nON1pIA45/SHd5dq6ZYMPXlVL7VpEOt1JAAAUAVQyOGJ3gl19dKIRK3ffUgjJ83Voew8ryOVaUrqJr0zP123n9lBZ3Zu7HUcAABQRVDI4ZlTOjTQ00N6aXH6fo2dnKbsvHyvI5Vo3sa9evjDZTqzUyPdfmYHr+MAAIAqhEIOT53brakeHdxDs9bs0W3TFygvv8DrSL+wMyNLN06Zr2Z1YvT4Vb0UFsZBnAAAIHgo5PDcFYktdf+gLpq5bIfunbFEBQXO60j/lZNXoJumzldmVp7Gj+ir+JhIryMBAIAqhiEiUCGMOaWNDhzJ1dNfrVZcdKTuH9S5Qgwn+NdPViht4z49PaS3OjWJ8zoOAACogijkqDB+e1YHZRzJ1cRZ61WnZqRu83hf7Rnz05Uye4OuO6WNLurZzNMsAACg6qKQo8IwMz0wqIsysnL1+BerFBcdoZEDvTkL5tItB3TfjCXq37ae7j2vkycZAABA9UAhR4USFmZ6bHAPZWbl6cEPlysuJlKX9WlRrhn2H87RDVPmqV5slJ4d2kcR4RxqAQAAQoemgQonIjxMzwzprZPb1dfdby/W58u2l9uy8wucbnt9oXZmZOuF4X3VoFaNcls2AAConijkqJCiI8P10jWJ6tY8XrdMX6DZa3eXy3If/+Infbtqlx6+uKt6taxTLssEAADVG4UcFVatGhFKGZmk1vVr6vpX07Rw8/6QLu+zpdv13NdrNaRfS13dLyGkywIAADiKQo4KrW5slCaPSVa9WlEaOWmOVu3IDMly1uw8qP97a5F6tqyjBy/qGpJlAAAAFIdCjgqvcVy0poxJVmR4mEZMSNXmvYeDOv+D2XkaNzlNNSLC9MKwPqoRER7U+QMAAJSGQo5KoVX9WE0e009ZuQUa9kqqdmZkBWW+zjn935uLtGHPYT0ztLea1YkJynwBAAACRSFHpdGpSZwmjUrS7oPZGjFhjvYfzjnheb74zTp9tmy77juvk05u1yAIKQEAAI4NhRyVSp+EunppRKLW7z6kkZPm6lB23nHP67vVu/SPmSt1Yc9mGnOKNycgAgAAoJCj0jmlQwM9PaSXFqfv19jJacrOyz/meWzee1i3Tl+gDo1q69HB3WVmIUgKAABQNgo5KqVzuzXVo4N7aNaaPbpt+gLl5RcEPG1Wbr5umDJP+QVO40f0Vc0oTlgLAAC8QyFHpXVFYkvdP6iLZi7boXtnLFFBgStzGuecfv/uEi3bmqGnru6l1g1iyyEpAABAydg0iEptzCltdOBIrp7+arXioiN1/6DOpe5+MuXHjZoxf4vuOKuDzujUuByTAgAAFI9Cjkrvt2d1UMaRXE2ctV51akbqtjM7FHu/eRv36qEPl+vMTo102xnF3wcAAKC8UchR6ZmZHhjURRlZuXr8i1WKi47QyIE/HzVlZ0aWbpwyX83rxujxq3opLIyDOAEAQMVAIUeVEBZmemxwD2Vm5enBD5crLiZSl/VpIUnKySvQTVPnKzMrT6+N6af4mEiP0wIAAPwPB3WiyogID9MzQ3rr5Hb1dffbi/X5su2SpL9+skJpG/fp0ct7qFOTOI9TAgAA/ByFHFVKdGS4XromUd2ax+uW6Qv08IfLlTJ7g64/tY0u6tnM63gAAAC/QCFHlVOrRoRSRiapVb2amjhrvfq3raffndvJ61gAAADFYh9yVEl1Y6M05bpkpczeoOtOaaOIcP73BAAAFROFHFVW47hotowDAIAKj82GAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIco5AAAAICHKOQAAACAhyjkAAAAgIdCVsjNrKWZfW1mK8xsmZndXuT2/zMzZ2YNSph+g5ktMbOFZpYWqpwAAACAlyJCOO88SXc55+abWW1J88zsC+fccjNrKelsSZvKmMfpzrndIcwIAAAAeCpkW8idc9ucc/P9P2dKWiGpuf/mJyTdI8mFavkAAABAZRDKLeT/ZWatJfWWlGpmF0na4pxbZGalTeYkfW5mTtJ459xLJcx7rKSx/ovZZrY0eMlRBTSQxKcsKIr1AsVhvUBxWC9QnI7BnFnIC7mZ1ZL0jqQ75NuN5Q+Szglg0oHOua1m1kjSF2a20jn3bdE7+Yv6S/5lpTnnEoMWHpUe6wSKw3qB4rBeoDisFyhOsI9vDOkoK2YWKV8Zn+qcmyGpnaQ2khaZ2QZJLSTNN7MmRad1zm31f98p6V1J/UKZFQAAAPBCKEdZMUkTJK1wzj0uSc65Jc65Rs651s651pLSJfVxzm0vMm2s/0BQmVmsfFvU2RUFAAAAVU4ot5APlDRC0hn+oQsXmtn5Jd3ZzJqZ2Sf+i40lfW9miyTNkfSxc+6zAJZZ7H7mqNZYJ1Ac1gsUh/UCxWG9QHGCul6Ycwx0AgAAAHiFM3UCAAAAHqKQAwAAAB6q0IXczFqa2ddmtsLMlpnZ7f7r65nZF2a22v+9rv/6+v77HzSzZ4vM6y9mttnMDnrxWBAcwVonzKymmX1sZiv98/m7V48JJy7IrxWfmdki/3xeNLNwLx4TTlww14tC8/yA811UbkF+vfiPmf1U6Fi5Rl48Jpy4IK8XUWb2kpmt8veMwWUtv0IXcvnGLb/LOddZUn9JN5tZF0n3SvrKOddB0lf+y5KUJel+Sf9XzLw+FEMnVgXBXCf+6ZzrJN9Jqwaa2XkhT49QCeZ6caVzrqekbpIaSroi1OERMsFcL2Rml0lio07lF9T1QtIw51wv/9fOEGdH6ARzvfiDpJ3OuZMkdZH0TVkLr9CF3Dm3zTk33/9zpqQVkppLuljSq/67vSrpEv99DjnnvpfvSSo6rx+dc9vKIzdCJ1jrhHPusHPua//POZLmyzcuPiqhIL9WZPh/jJAUJd9Zg1EJBXO9MN9J7u6U9OfQJ0coBXO9QNUR5PVitKS/+e9X4Jwr80yvFbqQF2ZmreXbkpkqqfHRcu3/zkdE1VCw1gkzqyPpQvn+80UlF4z1wsxmStopKVPS26FJivIUhPXiEUn/knQ4VBlR/oL0PjLJv7vK/WZmoUmK8nQi64W/U0jSI2Y238zeMrPGZS2zUhRy/5aJdyTdUWjrFaqxYK0TZhYhabqkp51z64KVD94I1nrhnPuNpKaSakg6I0jx4JETXS/MrJek9s65d4OdDd4J0uvFMOdcd0mn+r9GBCsfvBGE9SJCvk/cZznn+kj6QdI/y5qowhdyM4uU74mZ6pyb4b96h5k19d/eVL4tWagmgrxOvCRptXPuyaAHRbkK9muFcy5L0gfyfVyJSipI68UASX3NbIOk7yWdZGb/CU1ilIdgvV4457b4v2dKmiaOVavUgrRe7JHvk7Sj/8C/JalPWcuu0IXc/9HPBEkrnHOPF7rpA0nX+n++VtL75Z0N3gjmOmFmf5YUL+mOIMdEOQvWemFmtQq98EZIOl/SyuAnRnkI1nrhnHvBOdfMOdda0imSVjnnfh38xCgPQXy9iDCzBv6fIyUNksQIPJVUEF8vnHwDifzaf9WZkpaXufyKfKZOMztF0neSlkgq8F/9e/n26XlTUoKkTZKucM7t9U+zQVKcfAdj7Zd0jnNuuZk9JmmopGaStkp6xTn3YHk9FgRHsNYJSRmSNstXtrL983nWOfdKeTwOBFcQ14s9kj6Sb1eVcEn/lvRb51xeOT0UBFEw30MKzbO1pI+cc93K5UEg6IL4erFR0reSIuV7vfhS0p3OufxyeigIoiB3zlaSJkuqI2mXpFHOuU2lLr8iF3IAAACgqqvQu6wAAAAAVR2FHAAAAPAQhRwAAADwEIUcAAAA8BCFHAAAAPAQhRwAqgjz+d7Mzit03ZVm9pmXuQAApWPYQwCoQsysm3xnhust39jICyWd65xbexzzCmdMZQAIPQo5AFQx/hOhHZIU6//eSlJ3SRGSHnTOve8/wc1k/30k6Rbn3Gwz+7WkP0naJqmXpCT5TorRQr6C/4hz7o3yeiwAUB1QyAGgijGzWEnzJeXId+bRZc65KWZWR9Ic+baeO0kFzrksM+sgabpzLtFfyD+W1M05t97MBsu3hf16/7zjnXMHyv1BAUAVRiEHgCrIzB6WdFDSlZKiJeX5b6on6TeStkp6Vr6t4PmSTnLO1Ty6hdw5d7p/PidJminfVvKPnHPfld+jAIDqIcLrAACAkCjwf5mkwc65nwrfaGYPStohqad8B/hnFbr50NEfnHOrzKyvpPMl/c3MPnfOPRzi7ABQrTDKCgBUbTMl3WpmJklm1tt/fbykbc65Akkj5Ns//BfMrJmkw865KZL+KalP6CMDQPXCFnIAqNoekfSkpMX+Ur5B0iBJz0t6x8yukPS1Cm0VL6K7pH+YWYGkXEk3hjowAFQ37EMOAAAAeIhdVgAAAAAPUcgBAAAAD1HIAQAAAA9RyAEAAAAPUcgBAAAAD1HIAQAAAA9RyAEAAAAP/T9hypyBmdmgTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for October 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_october)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(24.5,27)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of October 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4fe05ff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.095"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for October 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_october)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e72a8d",
   "metadata": {},
   "source": [
    "## Estimating the temperature of November 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "406569ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_november = [22.84,22.26,22.18,22.53,22.95,23.9]\n",
    "temp_november.remove(22.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6f2c36ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_nov = pd.DataFrame({\"years\": years, \"temp_november\": temp_november})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "23c18696",
   "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>years</th>\n",
       "      <th>temp_november</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>22.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>22.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>22.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>22.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>23.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_november\n",
       "0   2011          22.84\n",
       "1   2012          22.26\n",
       "2   2013          22.18\n",
       "3   2014          22.53\n",
       "4   2016          23.90"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d0e6bbc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for November 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_november)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(22,24)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of November 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c058c7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.215"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for November 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_november)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71f0cd0b",
   "metadata": {},
   "source": [
    "## Estimating the temperature of December 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "715a03f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_december = [19.84,19.91,19.69,19.5,20.21,21.89]\n",
    "temp_december.remove(20.21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "05675aea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dec = pd.DataFrame({\"years\": years, \"temp_december\": temp_december})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "be4a52b9",
   "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>years</th>\n",
       "      <th>temp_december</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>19.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>19.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>21.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  temp_december\n",
       "0   2011          19.84\n",
       "1   2012          19.91\n",
       "2   2013          19.69\n",
       "3   2014          19.50\n",
       "4   2016          21.89"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5f9e9c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the temperature for December 2015 using Lagrange's Interpolation with 5 values\n",
    "lagrange_interpolation = scipy.interpolate.lagrange(years, temp_december)\n",
    "\n",
    "# Based on the graph the value can be estimated \n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(years, lagrange_interpolation(years))\n",
    "plt.xlim(2011,2016)\n",
    "plt.ylim(19,22)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Temperature in Celsius\")\n",
    "plt.title(\"Lagrange's Interpolation to estimate the temperature of December 2015\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2c8804e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.695"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of temperature for December 2015 based on the graph\n",
    "\n",
    "np.interp(2015, years, temp_december)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e039cb2e",
   "metadata": {},
   "source": [
    "## Comparing the estimated values with the actual values of temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "697cfb00",
   "metadata": {},
   "outputs": [],
   "source": [
    "estimated_values = [np.interp(2015, years, temp_january),\n",
    "                    np.interp(2015, years, temp_february),\n",
    "                    np.interp(2015, years, temp_march),\n",
    "                    np.interp(2015, years, temp_april),\n",
    "                    np.interp(2015, years, temp_may),\n",
    "                    np.interp(2015, years, temp_june),\n",
    "                    np.interp(2015, years, temp_july),\n",
    "                    np.interp(2015, years, temp_august),\n",
    "                    np.interp(2015, years, temp_september),\n",
    "                    np.interp(2015, years, temp_october),\n",
    "                    np.interp(2015, years, temp_november),\n",
    "                    np.interp(2015, years, temp_december)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "41eea981",
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_values = [19.02,21.23,23.52,26.52,28.82,28.15,28.03,27.64,27.04,25.82,22.95,20.21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "639ebaa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9f153beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the average temperature of 2015 (based on both estimated and actual values)\n",
    "\n",
    "estimated_vals_avg = statistics.mean(estimated_values)\n",
    "actual_vals_avg = statistics.mean(actual_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138d4e72",
   "metadata": {},
   "source": [
    "## Computing the Mean Absolute Percentage Error (MAPE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "009cfd80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining a function to calculate Mean Absolute Percentage Error\n",
    "\n",
    "def mape(actual, pred): \n",
    "    actual, pred = np.array(actual), np.array(pred)\n",
    "    return np.mean(np.abs((actual - pred) / actual)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "cec48aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.732898478006351"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of average temperature of the year 2015\n",
    "\n",
    "mape(actual_vals_avg, estimated_vals_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "aede843c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.442691903259739"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in January 2015\n",
    "\n",
    "mape(actual_values[0], estimated_values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "62fe5177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4620819594912833"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in February 2015\n",
    "\n",
    "mape(actual_values[1], estimated_values[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "10b15651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.186224489795927"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in March 2015\n",
    "\n",
    "mape(actual_values[2], estimated_values[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ffbe900a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.46681749622926"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in April 2015\n",
    "\n",
    "mape(actual_values[3], estimated_values[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "63dee61b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.116585704371962"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in May 2015\n",
    "\n",
    "mape(actual_values[4], estimated_values[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9abdc4d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.008880994671417"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in June 2015\n",
    "\n",
    "mape(actual_values[5], estimated_values[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "61c130ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3389225829468386"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in July 2015\n",
    "\n",
    "mape(actual_values[6], estimated_values[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6cdae913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.560781476121567"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in August 2015\n",
    "\n",
    "mape(actual_values[7], estimated_values[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "92d5c5f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46227810650887574"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in September 2015\n",
    "\n",
    "mape(actual_values[8], estimated_values[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7e4c8dc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0650658404337667"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in October 2015\n",
    "\n",
    "mape(actual_values[9], estimated_values[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8a02d9a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1546840958605689"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in November 2015\n",
    "\n",
    "mape(actual_values[10], estimated_values[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "74774c19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3998020781791163"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error Calculation for estimated and actual values of temperature in December 2015\n",
    "\n",
    "mape(actual_values[11], estimated_values[11])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d0c258",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab62b105",
   "metadata": {},
   "source": [
    "Based on given analysis, Lagrange's Interpolation has been verified with real-life data. The values estimated based on it have been almost accurate with only 0.5% to 6.4% error occuring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "960f5a97",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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