Notebook4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "609c29e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "328433dd",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>Value</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2026</td>\n",
       "      <td>April</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>405.67</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2035</td>\n",
       "      <td>July</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>423.20</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2044</td>\n",
       "      <td>December</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>445.10</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>January</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>396.04</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2027</td>\n",
       "      <td>July</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>406.57</td>\n",
       "      <td>PPM</td>\n",
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       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>2043</td>\n",
       "      <td>August</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>439.02</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>2035</td>\n",
       "      <td>August</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>420.46</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>2048</td>\n",
       "      <td>December</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>455.69</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>2004</td>\n",
       "      <td>October</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>361.59</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>2047</td>\n",
       "      <td>March</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>453.40</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year     Month   Indicator   Value Unit\n",
       "0    2026     April  CO2 levels  405.67  PPM\n",
       "1    2035      July  CO2 levels  423.20  PPM\n",
       "2    2044  December  CO2 levels  445.10  PPM\n",
       "3    2023   January  CO2 levels  396.04  PPM\n",
       "4    2027      July  CO2 levels  406.57  PPM\n",
       "..    ...       ...         ...     ...  ...\n",
       "749  2043    August  CO2 levels  439.02  PPM\n",
       "750  2035    August  CO2 levels  420.46  PPM\n",
       "751  2048  December  CO2 levels  455.69  PPM\n",
       "752  2004   October  CO2 levels  361.59  PPM\n",
       "753  2047     March  CO2 levels  453.40  PPM\n",
       "\n",
       "[754 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a4a816d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2026</td>\n",
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       "      <td>405.67</td>\n",
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       "      <th>1</th>\n",
       "      <td>2035</td>\n",
       "      <td>July</td>\n",
       "      <td>423.20</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2044</td>\n",
       "      <td>December</td>\n",
       "      <td>445.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>January</td>\n",
       "      <td>396.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2027</td>\n",
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       "      <td>August</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
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       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>2004</td>\n",
       "      <td>October</td>\n",
       "      <td>361.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>2047</td>\n",
       "      <td>March</td>\n",
       "      <td>453.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year     Month   Value\n",
       "0    2026     April  405.67\n",
       "1    2035      July  423.20\n",
       "2    2044  December  445.10\n",
       "3    2023   January  396.04\n",
       "4    2027      July  406.57\n",
       "..    ...       ...     ...\n",
       "749  2043    August  439.02\n",
       "750  2035    August  420.46\n",
       "751  2048  December  455.69\n",
       "752  2004   October  361.59\n",
       "753  2047     March  453.40\n",
       "\n",
       "[754 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.drop(['Indicator','Unit'],axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9d51ce2",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>713</th>\n",
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       "      <td>347.45</td>\n",
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       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>1986</td>\n",
       "      <td>August</td>\n",
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       "      <th>507</th>\n",
       "      <td>1986</td>\n",
       "      <td>April</td>\n",
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       "      <td>1986</td>\n",
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       "      <td>August</td>\n",
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       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>2048</td>\n",
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       "      <td>455.77</td>\n",
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       "      <td>November</td>\n",
       "      <td>454.43</td>\n",
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       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2048</td>\n",
       "      <td>April</td>\n",
       "      <td>458.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2048</td>\n",
       "      <td>May</td>\n",
       "      <td>459.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year     Month   Value\n",
       "200  1986   October  343.67\n",
       "713  1986      July  347.45\n",
       "169  1986    August  346.42\n",
       "507  1986     April  349.20\n",
       "71   1986  December  346.14\n",
       "..    ...       ...     ...\n",
       "361  2048    August  454.06\n",
       "693  2048  February  455.77\n",
       "349  2048  November  454.43\n",
       "256  2048     April  458.10\n",
       "181  2048       May  459.04\n",
       "\n",
       "[754 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by='Year')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3bf35bd1",
   "metadata": {},
   "outputs": [
    {
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       "      <td>455.77</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>2048</td>\n",
       "      <td>November</td>\n",
       "      <td>454.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2048</td>\n",
       "      <td>April</td>\n",
       "      <td>458.10</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2048</td>\n",
       "      <td>May</td>\n",
       "      <td>459.04</td>\n",
       "      <td>1</td>\n",
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       "<p>754 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year     Month   Value  Day\n",
       "200  1986   October  343.67    1\n",
       "713  1986      July  347.45    1\n",
       "169  1986    August  346.42    1\n",
       "507  1986     April  349.20    1\n",
       "71   1986  December  346.14    1\n",
       "..    ...       ...     ...  ...\n",
       "361  2048    August  454.06    1\n",
       "693  2048  February  455.77    1\n",
       "349  2048  November  454.43    1\n",
       "256  2048     April  458.10    1\n",
       "181  2048       May  459.04    1\n",
       "\n",
       "[754 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day'] = 1\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2222121f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year      0\n",
       "Month     0\n",
       "Value    18\n",
       "Day       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8696ddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'January': 1,\n",
       " 'February': 2,\n",
       " 'March': 3,\n",
       " 'April': 4,\n",
       " 'May': 5,\n",
       " 'June': 6,\n",
       " 'July': 7,\n",
       " 'August': 8,\n",
       " 'September': 9,\n",
       " 'October': 10,\n",
       " 'November': 11,\n",
       " 'December': 12}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {\n",
    "'January':1,\n",
    "'February':2,\n",
    "'March':3,\n",
    "'April':4,\n",
    "'May':5,\n",
    "'June':6,\n",
    "'July':7,\n",
    "'August':8,\n",
    "'September':9,\n",
    "'October':10,\n",
    "'November':11,\n",
    "'December':12\n",
    "}\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f046edb",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>1986</td>\n",
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       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>1986</td>\n",
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       "      <td>349.20</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1986</td>\n",
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       "      <td>454.06</td>\n",
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       "    <tr>\n",
       "      <th>693</th>\n",
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       "      <td>455.77</td>\n",
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       "    <tr>\n",
       "      <th>256</th>\n",
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       "      <td>458.10</td>\n",
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       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2048</td>\n",
       "      <td>5</td>\n",
       "      <td>459.04</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Month   Value  Day\n",
       "200  1986     10  343.67    1\n",
       "713  1986      7  347.45    1\n",
       "169  1986      8  346.42    1\n",
       "507  1986      4  349.20    1\n",
       "71   1986     12  346.14    1\n",
       "..    ...    ...     ...  ...\n",
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       "693  2048      2  455.77    1\n",
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       "256  2048      4  458.10    1\n",
       "181  2048      5  459.04    1\n",
       "\n",
       "[754 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Month'] = df['Month'].map(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0df77ea4",
   "metadata": {},
   "outputs": [
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       "     Year  Month   Value  Day\n",
       "107  1986      3  347.27    1\n",
       "507  1986      4  349.20    1\n",
       "727  1986      5  349.26    1\n",
       "253  1986      6  348.96    1\n",
       "713  1986      7  347.45    1\n",
       "..    ...    ...     ...  ...\n",
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       "431  2048      9  452.67    1\n",
       "565  2048     10  452.66    1\n",
       "349  2048     11  454.43    1\n",
       "751  2048     12  455.69    1\n",
       "\n",
       "[754 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.sort_values(by=['Year','Month'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dc4324ee",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     Year  Month   Value  Day       Date\n",
       "107  1986      3  347.27    1 1986-03-01\n",
       "507  1986      4  349.20    1 1986-04-01\n",
       "727  1986      5  349.26    1 1986-05-01\n",
       "253  1986      6  348.96    1 1986-06-01\n",
       "713  1986      7  347.45    1 1986-07-01\n",
       "..    ...    ...     ...  ...        ...\n",
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       "\n",
       "[754 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df[['Year','Month','Day']])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e71685e",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     Year  Month   Value  Day       Date\n",
       "0    1986      3  347.27    1 1986-03-01\n",
       "1    1986      4  349.20    1 1986-04-01\n",
       "2    1986      5  349.26    1 1986-05-01\n",
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       "4    1986      7  347.45    1 1986-07-01\n",
       "..    ...    ...     ...  ...        ...\n",
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       "751  2048     10  452.66    1 2048-10-01\n",
       "752  2048     11  454.43    1 2048-11-01\n",
       "753  2048     12  455.69    1 2048-12-01\n",
       "\n",
       "[754 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = range(len(df))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20b55f2e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>752</th>\n",
       "      <td>11</td>\n",
       "      <td>454.43</td>\n",
       "      <td>2048-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>12</td>\n",
       "      <td>455.69</td>\n",
       "      <td>2048-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month   Value       Date\n",
       "0        3  347.27 1986-03-01\n",
       "1        4  349.20 1986-04-01\n",
       "2        5  349.26 1986-05-01\n",
       "3        6  348.96 1986-06-01\n",
       "4        7  347.45 1986-07-01\n",
       "..     ...     ...        ...\n",
       "749      8  454.06 2048-08-01\n",
       "750      9  452.67 2048-09-01\n",
       "751     10  452.66 2048-10-01\n",
       "752     11  454.43 2048-11-01\n",
       "753     12  455.69 2048-12-01\n",
       "\n",
       "[754 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Year','Day'],axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8975bad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28d4d602e90>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.index,df.Value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2ec4cca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([33, 112, 469, 683, 734], dtype='int64')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = df[df.Value>600].index\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "288e58e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\idipa\\AppData\\Local\\Temp\\ipykernel_7104\\706382596.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Value'][i] = np.nan\n",
      "C:\\Users\\idipa\\AppData\\Local\\Temp\\ipykernel_7104\\706382596.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Value'][i] = np.nan\n",
      "C:\\Users\\idipa\\AppData\\Local\\Temp\\ipykernel_7104\\706382596.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Value'][i] = np.nan\n",
      "C:\\Users\\idipa\\AppData\\Local\\Temp\\ipykernel_7104\\706382596.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Value'][i] = np.nan\n",
      "C:\\Users\\idipa\\AppData\\Local\\Temp\\ipykernel_7104\\706382596.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Value'][i] = np.nan\n"
     ]
    },
    {
     "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>Month</th>\n",
       "      <th>Value</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>347.27</td>\n",
       "      <td>1986-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>349.20</td>\n",
       "      <td>1986-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>349.26</td>\n",
       "      <td>1986-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>348.96</td>\n",
       "      <td>1986-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>347.45</td>\n",
       "      <td>1986-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>8</td>\n",
       "      <td>454.06</td>\n",
       "      <td>2048-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>9</td>\n",
       "      <td>452.67</td>\n",
       "      <td>2048-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>10</td>\n",
       "      <td>452.66</td>\n",
       "      <td>2048-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>11</td>\n",
       "      <td>454.43</td>\n",
       "      <td>2048-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>12</td>\n",
       "      <td>455.69</td>\n",
       "      <td>2048-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month   Value       Date\n",
       "0        3  347.27 1986-03-01\n",
       "1        4  349.20 1986-04-01\n",
       "2        5  349.26 1986-05-01\n",
       "3        6  348.96 1986-06-01\n",
       "4        7  347.45 1986-07-01\n",
       "..     ...     ...        ...\n",
       "749      8  454.06 2048-08-01\n",
       "750      9  452.67 2048-09-01\n",
       "751     10  452.66 2048-10-01\n",
       "752     11  454.43 2048-11-01\n",
       "753     12  455.69 2048-12-01\n",
       "\n",
       "[754 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in index:\n",
    "    df['Value'][i] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9e22ede1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([ 17,  33,  92, 112, 150, 225, 282, 296, 323, 341, 370, 419, 452, 469,\n",
       "       487, 514, 560, 591, 638, 669, 683, 734, 742],\n",
       "      dtype='int64')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = df[df.Value.isnull()].index\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b1ab95a3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Value</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-03-01</th>\n",
       "      <td>347.27</td>\n",
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       "    <tr>\n",
       "      <th>1986-04-01</th>\n",
       "      <td>349.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-05-01</th>\n",
       "      <td>349.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-06-01</th>\n",
       "      <td>348.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-07-01</th>\n",
       "      <td>347.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2048-08-01</th>\n",
       "      <td>454.06</td>\n",
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       "      <th>2048-09-01</th>\n",
       "      <td>452.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048-10-01</th>\n",
       "      <td>452.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048-11-01</th>\n",
       "      <td>454.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048-12-01</th>\n",
       "      <td>455.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Value\n",
       "Date              \n",
       "1986-03-01  347.27\n",
       "1986-04-01  349.20\n",
       "1986-05-01  349.26\n",
       "1986-06-01  348.96\n",
       "1986-07-01  347.45\n",
       "...            ...\n",
       "2048-08-01  454.06\n",
       "2048-09-01  452.67\n",
       "2048-10-01  452.66\n",
       "2048-11-01  454.43\n",
       "2048-12-01  455.69\n",
       "\n",
       "[754 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = df.copy()\n",
    "d.index = d.Date\n",
    "d.drop(['Month','Date'],axis=1,inplace=True)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ffc4fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-03-01</th>\n",
       "      <td>347.270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-02</th>\n",
       "      <td>347.332258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-03</th>\n",
       "      <td>347.394516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-04</th>\n",
       "      <td>347.456774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-03-05</th>\n",
       "      <td>347.519032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048-11-27</th>\n",
       "      <td>455.522000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048-11-28</th>\n",
       "      <td>455.564000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048-11-29</th>\n",
       "      <td>455.606000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048-11-30</th>\n",
       "      <td>455.648000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048-12-01</th>\n",
       "      <td>455.690000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22922 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Value\n",
       "Date                  \n",
       "1986-03-01  347.270000\n",
       "1986-03-02  347.332258\n",
       "1986-03-03  347.394516\n",
       "1986-03-04  347.456774\n",
       "1986-03-05  347.519032\n",
       "...                ...\n",
       "2048-11-27  455.522000\n",
       "2048-11-28  455.564000\n",
       "2048-11-29  455.606000\n",
       "2048-11-30  455.648000\n",
       "2048-12-01  455.690000\n",
       "\n",
       "[22922 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.resample('1D').mean().interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9978de2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>349.20</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>349.26</td>\n",
       "      <td>1986-05-01</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>348.96</td>\n",
       "      <td>1986-06-01</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>347.45</td>\n",
       "      <td>1986-07-01</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>8</td>\n",
       "      <td>454.06</td>\n",
       "      <td>2048-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>9</td>\n",
       "      <td>452.67</td>\n",
       "      <td>2048-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>10</td>\n",
       "      <td>452.66</td>\n",
       "      <td>2048-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>11</td>\n",
       "      <td>454.43</td>\n",
       "      <td>2048-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>12</td>\n",
       "      <td>455.69</td>\n",
       "      <td>2048-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month   Value       Date\n",
       "0        3  347.27 1986-03-01\n",
       "1        4  349.20 1986-04-01\n",
       "2        5  349.26 1986-05-01\n",
       "3        6  348.96 1986-06-01\n",
       "4        7  347.45 1986-07-01\n",
       "..     ...     ...        ...\n",
       "749      8  454.06 2048-08-01\n",
       "750      9  452.67 2048-09-01\n",
       "751     10  452.66 2048-10-01\n",
       "752     11  454.43 2048-11-01\n",
       "753     12  455.69 2048-12-01\n",
       "\n",
       "[754 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Value=d.values\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1762ed23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>17</th>\n",
       "      <td>8</td>\n",
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       "      <td>1987-08-01</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>1988-12-01</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993-11-01</td>\n",
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       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1998-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2025-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2026-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2029-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2032-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2035-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2039-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2041-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2043-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2047-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2048-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Value       Date\n",
       "17       8    NaN 1987-08-01\n",
       "33      12    NaN 1988-12-01\n",
       "92      11    NaN 1993-11-01\n",
       "112      7    NaN 1995-07-01\n",
       "150      9    NaN 1998-09-01\n",
       "225     12    NaN 2004-12-01\n",
       "282      9    NaN 2009-09-01\n",
       "296     11    NaN 2010-11-01\n",
       "323      2    NaN 2013-02-01\n",
       "341      8    NaN 2014-08-01\n",
       "370      1    NaN 2017-01-01\n",
       "419      2    NaN 2021-02-01\n",
       "452     11    NaN 2023-11-01\n",
       "469      4    NaN 2025-04-01\n",
       "487     10    NaN 2026-10-01\n",
       "514      1    NaN 2029-01-01\n",
       "560     11    NaN 2032-11-01\n",
       "591      6    NaN 2035-06-01\n",
       "638      5    NaN 2039-05-01\n",
       "669     12    NaN 2041-12-01\n",
       "683      2    NaN 2043-02-01\n",
       "734      5    NaN 2047-05-01\n",
       "742      1    NaN 2048-01-01"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[index,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b0c24fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month    0\n",
       "Value    0\n",
       "Date     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in index:\n",
    "    df.iloc[i,1] = (df.iloc[i-1,1]+df.iloc[i+1,1])/2\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2af31ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Value.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "66887463",
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX = df.Value.max()\n",
    "MIN = df.Value.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "af5688bd",
   "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>Month</th>\n",
       "      <th>Value</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.031204</td>\n",
       "      <td>1986-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.047933</td>\n",
       "      <td>1986-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.048453</td>\n",
       "      <td>1986-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>0.045852</td>\n",
       "      <td>1986-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>0.032764</td>\n",
       "      <td>1986-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>8</td>\n",
       "      <td>0.956835</td>\n",
       "      <td>2048-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>9</td>\n",
       "      <td>0.944786</td>\n",
       "      <td>2048-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>10</td>\n",
       "      <td>0.944700</td>\n",
       "      <td>2048-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>11</td>\n",
       "      <td>0.960042</td>\n",
       "      <td>2048-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>12</td>\n",
       "      <td>0.970963</td>\n",
       "      <td>2048-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month     Value       Date\n",
       "0        3  0.031204 1986-03-01\n",
       "1        4  0.047933 1986-04-01\n",
       "2        5  0.048453 1986-05-01\n",
       "3        6  0.045852 1986-06-01\n",
       "4        7  0.032764 1986-07-01\n",
       "..     ...       ...        ...\n",
       "749      8  0.956835 2048-08-01\n",
       "750      9  0.944786 2048-09-01\n",
       "751     10  0.944700 2048-10-01\n",
       "752     11  0.960042 2048-11-01\n",
       "753     12  0.970963 2048-12-01\n",
       "\n",
       "[754 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Value = (df.Value.values - MIN)/(MAX-MIN)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8e34080b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28d508bd360>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Date'],df.Value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e2cc79",
   "metadata": {},
   "source": [
    "# Splitting :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "703ec411",
   "metadata": {},
   "outputs": [],
   "source": [
    "window = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "72468ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Create_data(series,lag):\n",
    "    start = 0\n",
    "    end = lag\n",
    "    X = []\n",
    "    Y = []\n",
    "    while(end<len(series)):\n",
    "        X.append(series[start:end])\n",
    "        Y.append(series[end])\n",
    "        start += 1\n",
    "        end +=1\n",
    "    X = np.array(X)\n",
    "    Y = np.array(Y)\n",
    "    X = X.reshape(len(X),lag,1)\n",
    "    return X,Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ae1b0edd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,y_train = Create_data(df['Value'].values[:600+window],window)\n",
    "x_test,y_test = Create_data(df['Value'].values[600:],window)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e3fa15f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "744"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)+len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8ca020ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Predict:\n",
    "    def __init__(self,window:int,prev_data:np.ndarray,model):\n",
    "        self.__prev_data = list(prev_data)\n",
    "        self.predictions = []\n",
    "        self.__model = model\n",
    "    def predict(self,num_prediction:int):\n",
    "        del self.predictions\n",
    "        self.predictions = []\n",
    "        for _ in range(num_prediction):\n",
    "            print(self.__prev_data)\n",
    "            pred = self.__model.predict(np.array(self.__prev_data).reshape(1,window,1)).reshape(-1,)\n",
    "            print(pred)\n",
    "            self.predictions.append(pred[0])\n",
    "            self.__prev_data.append(pred[0])\n",
    "            self.__prev_data.pop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ac867b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "309f0b4d",
   "metadata": {},
   "source": [
    "# Model :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2598c825",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM,Dense\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de92a02d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(128,input_shape=(window,1),activation='relu'))\n",
    "model.add(Dense(10,activation='relu'))\n",
    "model.add(Dense(10,activation='relu'))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer=Adam(),loss='mean_squared_error',metrics=['MeanSquaredError'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f3c8961",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f68ed2bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9d3f9bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test).reshape(-1,)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "214b635f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(range(len(y_test)),y_test,color='green',label='Original')\n",
    "plt.plot(range(len(y_pred)),y_pred,color='red',label='Predicted')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7ccb13c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(x_train).reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b556c6c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(range(len(y_train)),y_train,color='green',label='Original')\n",
    "plt.plot(range(len(pred)),pred,color='red',label='Predicted')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5b89d4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "prev_data = df.Value.values[-window:]\n",
    "prev_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7fcfddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "ob = Predict(window=window,prev_data=prev_data,model=model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6c333b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ob.predict(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a648b873",
   "metadata": {},
   "outputs": [],
   "source": [
    "ob.predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b3220a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(range(len(ob.predictions)),ob.predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac8aa35",
   "metadata": {},
   "source": [
    "# Arima :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "621764c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = df.Value[:600]\n",
    "x_test = df.Value[600:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ac565559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.031204\n",
       "1      0.047933\n",
       "2      0.048453\n",
       "3      0.045852\n",
       "4      0.032764\n",
       "         ...   \n",
       "595    0.657363\n",
       "596    0.670018\n",
       "597    0.684233\n",
       "598    0.699402\n",
       "599    0.702089\n",
       "Name: Value, Length: 600, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "04bcf258",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a67ddb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2      0.017249\n",
       "3     -0.002080\n",
       "4     -0.015689\n",
       "5     -0.022016\n",
       "6     -0.025397\n",
       "         ...   \n",
       "595   -0.008234\n",
       "596    0.014302\n",
       "597    0.026870\n",
       "598    0.029384\n",
       "599    0.017856\n",
       "Name: Value, Length: 598, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = (x_train-x_train.shift(2))\n",
    "L.dropna(inplace=True)\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1a6aafb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7vnaHydOjeo1AxMmYR2GtfxS/1//y0W/TGy65lf7xmrvh73rTveW+osz/D7T/Lf/xA7r6rgfpc9fl4IeI6MB9x+F1Tp1uj9bc/bBr5hO5iIznw5fCSAbi7YYmTAClQDmdbg+uw9t2VuWvOGAvWE6uwZlf4akBOA3NO40zgDOTEE6e9n2f8dEZtWl3JL5ordtUScPzrUaebVrHAA5fuL90a2Fe+M5P7Agf7jtige3pgInqlvseCbV1PmgL26h4FBUHAt6J0JsDZw3xfW3KdDJOYeL2c+Rjc70BXFMG9CvuwH46ponMGfcWWDiIaXCsjL//+K276Yx/uo5e8xnbT4cn34ucDxXRvJgh4Kx824wlv6NIs4hZrdHgNNSQosUs1JFLCYMSj5jZEzLSDkp8rfkl2yxmmpo9SjP164jSL5kpg/d53wDobQnnWH9j9DQ4y5nvz3znAinaUP2/ZWdlorNME166/Uh/uOSOTFTdbq9sk6fB4QB1/WY/7N96Hx1Di+Cdw2UFPizda6z83zpaJpm+PCAd0eAIvxijD/z5WPOLD2MLKGlggjQv4uiI6S697bIf0N9cdpu6rwE4DTUkiG8q1sK6c7pD3/nxQ+4m2RFq1t3Lr2Q2iC82HARYEvZMaf3mHXTel26nD1zxI3rbZT+gI//mcvrx+rk/boFvhHwz3m9xPcCRzrH+ZuBpGw/erzJlPDzH/mC9Xo/e89Uf0ueu/7nJw+fGViZE8HfPx8ejjpaG86FcQL1eTwBLtIHzDXuHYw7jdU3UHOlgm8Iwv247125YgQ/8OVrtiQRNRKKW+Nidmva1YEQOwOlyDU6Xvv+LjZk/UyRXEG/zw1sn6bPX3Uv/57qf00aWgLPJg9NQQ4pkeCqepG+/7Hb6o09cn0kMspzZSVa1uxKXwLZOYjPFbNJfXXorffrae+gjV/6YPtv3afiHq7GfhEeDHp/ANyo+DCIAh/vgmBqcgA/OSLtaWn+5EW+ED2/ZSc/9+6vofV+/q7ZdHt36i030T9/+Gb31P+y5wTdRDl46IooqpsERp1wj7Yx6bHWbpafB4XwP1AAc631YQFVrcPhzsXJzcRBlhXdHku/xZx0xUVngja95pg8Ou/WHv9xMf3DBd+m3//abkkc9C7Qm2PmMMEjWv80HNQCnoXknvvFaC+slNxZOw5eu/YVZTqdnT7SGYnkyZpMeAUcrLAos/jO9h28GXaHBGUPsgjiWsoBfJEycbzzc34nTF266n37+8Da6wEnXHyHeDnuT53OsMlFNiSiq6t4tjganW6M10IACPSfeHi+KqhvQmHj1EEkBipO2UG1m0XXWxsyfl/WsY9mFeRSVNS95vfXv1XqOvC4rw3RuovJBqeBl7W9MVA39ytBdE4/SnROb57yeaA6G2nLYrfMtOSxEiizas0loYxkK4AyY6Zr3p2uYqCw/kl4AJKfx6oWJi43ZcI7l/hyDJDHUxKMQrRB4Dta4EDGMDw4HNUj7ot87BDgdDpJiTsae4zPR4BocTVyDY61D/Lo1PmImKlam5RfDfV4CWp4dpg9S9b81PiIHknrh44l0zqH5jtBsAE5DkLbunKaTPvQtOvlD355R5s8IdR0V5yAU2Zz2ZLLOTkKainUbt884X4xe7Ihi0q2mRaOD5UnimwFvAtfgPBo4/8fyTUrFe/OCl7Nu03Z6/3/eRWt//ojgWcIAzi8eGS7xHJHso/XOOOgTPjg84aNhxtLEzZtIa6DNHTgiJ5ZUkI9ZL3SdKAYCPNq8o3p21ruNHHEyuAanHuDUObwTEW2fnKaPXvlj+q5ybo74I0aACZrPROrgXnXffK/DDcBZgNTr9ehfb7yPfrQLHDItuufhKk+KZdudLapzSIxSNN34bNHWndMLIkImSlbUiJbMJjbtoGf97Tfpee+72izr9nWb6N/W/sLtP5L4xgcEK8U91TIVed5cIuaL8ihz5vnOjx+il1z4XbpOZaQVWsCajWey0zXbw7t+8fX30ke/+RM69R+uheUQEd07g4SAHCxs2o4dmjmP9MHB2i5uxtK0neV3QdqXTkcDHN+fIxo4ULcO2VFUQYCzveqXCXCYs69VbmSMD+qDY41F/nz+fe399P4rfkSv+OfrTZ5Ie9D3og2WBgfPt6KcJg9OQ4q+dOs6+l//9n164Qe/NW9tWLdx14URd2ZJg7Mro6h+9tBW+o13fJ1WX3zznNbDqW5z7/Vwgq5EpgZHPatv/ehBIiLasHWS7tuwTWQ9TvTij3yH/urSW+nL37fPekLvchgTFQc4nlkokWVG4P//2eduolvv20gvv+g6cS8fN2vufhhmIk7l9Hr2eOXP2tKG8E3kh+uGNwXzcmwTFTb1WP4TnomKa3CQiSrX4Pgmqu1THfraD35Jb7jklgzs8KFZl2agLqFgHT3KNThGWZEjTsZZiHnE783MKRMw3XMweYfhThDJA5Q7B/th4pzE2WaND05DdXTjPY/UM80x8YV9rnw0EvGJMxPfGb6pzFXoc6KUQv6rPxjsMMdh6Z6HttIz3vMNMwqp1+vRK/75ejr9outMIGNqcNT7fYBljH3Oe6+il/3jGnMD/o+bbKdvtEgOA3B4OvpILh0+hniXI4st53nzF34AMxF3AtI3H9M8/w4HIBxM/f3X76KfPVRpTQch/v5sE1VV13bjNHFxVIOTj4qbpdD7yP05kIlK+uD82eduov+4+X5xNIMua6rT8x27jdcb0WAQKROV6dQb8MFhGhxL6xTJg8Onz2RNRB+R7WRsrQfCGV/x/HLTDtqg0htYazPXdmnBsgE4DWU01yYhTp1uj/7sc2vp3C98X1y/VwCcOfbB4QBnBmBqV0ZRDbNRz4TeetkP6KEtk+Vhd5q2TXbo2rsfpht+toHuewSbOyJhnkREDz6aJ1T7/i82wntvNQ7dI8pNFUTSTBSlNrvHmxtpg5o0VOaRMREZNrycLTum6TNr7qGfPyzBCS9nhLWfCw56rFvPeJD2bDQOpLQ2J3HYptKqWMTPskKp/yMOq3wj5HU9tEWOPQ1OhkkWGRWauKYtYqKyyh1hB2Bus3y9AgBH+IPN4AwpO7iAARP1nM/61Pfo+HdfIZ63NX+4k7HuSgNwGspoV2V8JSK6/qcP09d+MEH/94b7hHljVwIcOUlnKYpqjrVO0Y362p88NCsHLt7zkF8G38i5L4HgMR6tNkE+uCUHONYp21rKE+WCxc1Sc3vEx6UVcfPP3/4p/eZ5/0n/vlYeusibENnoIuOPbwaf+M7P6O1fvJ2e+/dXCx4uEfMNgM8rvfh74dIeSR+ceg2OuHeIwza31WlwIiYq413os8C0ZmEY4Q8BbUQhgNOxtRWJxJEXgQMwLe1MNwKCBtRKijbwaEM4V4kuv22Cfa/X4Oj5M9/RrA3AWYBUd2LrbNKNLLKDj18uxc/1IBWb0AyAiYzGws9w47ZJetdXfki3r7M1DxEaDYQu33zvI/Tf//n6WTlw0TpJORHvrhkKaixQ2pz3AEiJ31aAjmslTAdI5KhojKWHt+yk8750O901kTvWR9LRv/urxcGJb7z0VjNMPAKu0LDRiz//fnP/4EavzXwD+PnDTIOjyq2LErKIP+eID4643sWbkwck+CGSOExc1rVh62RmfrPao3G0HrPDgMCoiYqbpSxAIXnqfVus0PZBTxM3I/oCfbMAzlTAn4ib4ENOxo0Gp6E6mklOjEHp5nsrgGP5wsx5mHjAjBBRmER8eT54xY/oE9/5Gb34I98ZrJGKFo3IBt187yP0+x/+Nl3LQjRvNTa/uSC+0G00Imks0KcBygPARKWf/wjbicyDAMHia0nTf/5/b6ZPX3sPvfZz+QGFIhQ2IMHzhbsXGBNWXYn0Js55HrO0OnOKb74dAayqezkA0c99WNP0VMAHJ2Ke1KDMukdqcOq1M+/+6h30vPddLc6Ks96F1hRmWq5hNDjOexdh32wcmzl1DI2XVZ8FcIRzt+nHxdppzbHAMQ8mwOECoQGUOJhtNDgNzQrNtYnq3oe3lZEyXGLmYzGiHp0titiRRwIIJ+KDc98M8o1w4hqcqU6X/vDja+iOX26m/85CNHnCr5mEk/OIlr0XGWfkBPwwrNeonzmKoNHPn51EYAOcGudSTtfeXYRr//TB3NGWP7rI3Jg0TFSWsyUntNBrrQHn4RFeXEthvW4PcPHNBLat26M3/uut9H/W3JNdT/SwcQCkdVij3Ghj7eEOyBEn40Tf+fGDjAe3R0/zzEQ1jAbHee87DIdry8lYaz3QmOLlWFq5SI6bkInK0soGgJh1krxsA+O3nIyNzOFF3U2Y+G5P3W6Pbr73EXdyrtu4nX7ZT+k+1yaq3/n7q+iVn7yBrv/pw8L+KzQpsxTZRER0xy830zU/etD8nc8JywdC2+YRRc6ietz+e7F6h+8X98HZMdWBiwgHOJHwZot4+OqYYRrj7+4Rwy8mYou3+DLfCMYSOR7AK5tvpPyQyqquehMVJ+uohpB/DWif1hpYPhY/fajSUJjh4w4IrzNRXXXnA/TvN/2C/uaLt4vrvF9WJJZtorLbY2lLttXkwYmYAq1NvUVynKX2JWBfp8FBG7UHcHh5fKO2xjTn+daPHqRj3vmf9MVb7pf1iTPf6lMEzOQsKqtvk8JXKAKCIAv1iO0PlpMxB4nqvTYanN2cOt0evegj36b/9rFr6a2X/QDy7Jzu0LP+9pu08vxv0uR0d5c5GX/7xw+JTZ6vS0J6mKGJ6vc//G161SdvoFsMk00kJ8RIAODwW2+7fxOd9MFv0RU/XC94ljOTAooWitKIADjV8+HRVaPMjLV5BlmBZVp767wZJsUbAMfaePTGjxZELVlzHsukGvXB4VpEfnxBIn5LBPzzSBepwcH8dZm0cw0O/u1+ph20nrXn9IwEoB1THXrpx75L7/v6XeZmydv8i0e2wTFib5C2H4YlkPEjAeBZVKZ/TTWIrE1XT/P0HFOoPapPCBtgLHqbLI8C4xoNy/GXP8db7ttIj+6Yptd//hbBwzWXVrh96CDNATMZi3by0G1Ty2NrXqrr1f/DaXAagLNb08TmHXRnfwG/x5CuuPf+ozum3NN1Z5M275gynTAjDm4R4jbobxtanIhpKeSDw+69c+JRumv9o/Tqz9woeDhQ+vkMopusTKzL2AbNF3qeY2NQ4u9l5zTOnsvbgw651DyccqnLBxG9Xk8BnPqcIVUb8mvcpPYoeE5ag1O3aPJ6ewENDgcOSEp1NTj8XCfD70bcO6AG58o7HqCb7t1IF1z1ExpldkFLW9ntSUdmxCOuO6YMM4cL29Cgk7GxWfI5HD0XLbUpAZztk/k75FpNBMo80+R2Q/swaazBkSAIcX6WpcFh5UTOohpUgxNxIJZJHnH5EV8eARIVT3MW1W5OfHJZqkjuWNfp9XZZFNWm7VNSwjXy0cxEg8NzstwJImSI5CSyfAV0FA+iUMgkqwttBFHiCzFfJA/YZ1H5P1dzb94xTd1uj265b+PATuR60UCAgr87K3Q7elQDWpQ8PymUC6Xb7cFNHk0B7hiLQtz5HPr0tffQ0ed9na5XRyyMMW2ZmQfHGB78bCa0GehNk28GHJRYGlBOCJiktiOAs/d4ZebcakQv6fd194NbSJPlyDrtvFfUno56r5E8OCUJDQ5uD58bvV5V1z59gIP8gvg7hm12TGb83daZqLrdXsjkUtceIqXBCTgrDwpwZGRTwfPMJx5AT33MElYvN2PBYkKJMj1zWDSCba5olwCcCy+8kA4//HBavHgxnXDCCXTDDTe4/JdeeikdeeSRtHjxYjr66KPpa1/7Wvnb1NQU/fVf/zUdffTRtM8++9Bhhx1Gr3zlK2ndunVz3Y2hiA92+zh7CSx2lYlq47YpseBaYGcmTsb3MhBxE4vY4iRShhu7kJWHRZQTmEycZcNW20T1y03b6WNX/4Q2GtoQvmHw97psb3ZCNOPZvH2KLvr2T+mUC79L5/zrrbXt5KQ3y7pNxQI4kYymvR5exDmg0xsqAmzWu9AbW7fbo5+yDXn7VCfbXHhzvv+LTbRtskOvu/gmwbNIOH3jMW1tqlsMLQxvk+xD9b+l/Ymo/FN70iGg26fyzXuMaW3WsVQBfOPU/ULnWlkmjmmxOamoLrBe6bUAmqgs81OgPfJco+r6Pn2gh+qr89HylgUr0SFqnyV85WViDZ9V75Rxphnv/8AmKqDBOeXYx9LXXv8cOuKgfYrrNXlwijZwAcEAONMOwNndTVSXXHIJnXPOOfSOd7yDbrrpJjrmmGPopJNOogceeADyX3vttXTGGWfQ2WefTTfffDOdcsopdMopp9Btt91GRETbtm2jm266if7mb/6GbrrpJvrCF75Ad911F/3BH/zBXHdlKOLvNwJwdk534QY2F1RocKq6LXXkTExUPGopYjqx6uIKHMs5OOI0zOu6+4GtdOanbqCr7szH4h/+wxp67+V30flfw5mD+SLG+7X/3rYG56NX/piIiL5qnN9ktV9vGHVmgUGdjDsGyJU8dijoxTfcK4538OrSfXnbF2+jj3zzJ+KaNlMhsKAjvcaZQ7flW2DhdL4JQROVmrdCQmfaHwFeTICTa0z2W5y0Ez6g4A7EvE36mSJ/L8sEIZ2M5W9ovcrGYg3g4MSvRswdnKc0UdUAKpRYz3N65n2eqtHgRI+s6RgaPsnD1138PETYfqdLP1y3mf74098TPmuRKKo0ppOvUjLpeZmMy7bx9gS0RZFDO3clzTnA+cAHPkCvfvWr6ayzzqKnPvWp9PGPf5z23ntv+uQnPwn5P/zhD9PJJ59Mb3rTm+ioo46id73rXfT0pz+dLrjgAiIiWrp0KV1xxRX0spe9jH7913+dTjzxRLrgggto7dq1dO+99851dwYmocGxHERV/gnLlPW5639OF171E/jbMKRNVELFHkgTHiGexXfHFM6tIdWguC6RWC6wUVvE+3XJjffR1Xc9SGd9+nsZX0qsd+PPN8By+IbBDybdh507xBeQR3dMmVlNE62++GZ69t99M9vgdb9waG71/4YZ+OBYG6GnwfnCTffTM99zpUhGGAFTRMVJ25p0LhdUlDbj8nBt+ywqwweHHz4Z8MEReU6MPCHWniqcNjsS4NRpTLjpidernyk63NOMohI+OPL5bEPOymotQGtVxPHVmsNcG8if5z6GD0632xPPetvOabrs5vvpfV+/qxQYvFVBrHM17gTRddA62FTwqP4j8KRNVC/6yLfpm3c+QH/7/+6APJwmwbNOZv4U/OCdRVVe5+N1AHNY2b45TjFSR3MKcCYnJ2nt2rW0atWqqsJ2m1atWkVr1qyB96xZs0bwExGddNJJJj8R0aZNm6jVatGyZcvg7zt37qTNmzeLv11F/IWbaJ4Nbs8/463/cRv9/dfvop88gH1ZBqVN26eUYyTW4Mzk+AQt2a88/0q645fy+UdC0rmJys4JUd+eSC4UXv4RB+8Lefgidv/GCsTxxVZocIzjExL9/OGt9NUf/JJ+8ch2uunejeI3vYghHxzp9NyFvgp2JuP6kNJIriJuahr0XBxOm9UGHdHMjRtng9VFSBFV2iBrbGROxoaPRcQhE/HsNz6WlZWIb3x3P1BpcLZ5GhzgqG2aqJz3uh2MIW2mQZqOiK+G5RO00zB3WBocPaa3TnboLy+5hS646idVlnZn/AifqDoNTnAdHNQHh6geKPIujLPDPCPPOrVntAQ4xVwR4DagFbc1OPZY3K01OA899BB1Oh069NBDxfVDDz2UJiYm4D0TExMD8e/YsYP++q//ms444wxasmQJ5Dn//PNp6dKl5d+KFSuG6M1wJBzyprtGYih/YhHJwcU1BjOhjdsmTR8cPnGskMkI5SdV76S/ulT6oETCxDnAiTwjiyLzjSebOwTkZSGSE/eXm6r30VPauER1UVQ8Jbr2+xnGLID8cGIaHEvlbZuo0L1RDQ4ibWKJ+FbxRZ+TNaY5pU3VqicLEzf6afmwWfdOlz44MRMV39yFiarPk5ScSINjZrF2wC3WKA0GtjkhrYImAXDYM6yiqGTf8kg0GZFK5M956efC1mGkwQmug5EoKt1uBPisYc9TXYR8cDpSg7MIaHCs9yFzlNW/1yZMfBZpamqKXvayl1Gv16N/+Id/MPnOPfdc2rRpU/l333337bI26heOzFQ6DBgRn+ybthfOwe/88g/py7cO71zd7Wl1Pl64Z2KiQgu9VttGPPU5WQBn0Cgqi3hKefuQQm5+qvrDS+eLJAct6CTyH66rtFr6LCj9DCPp8R/Z6odbW/daTt4RENoJmLpiGhxlogrcMzaKndAjTpLpqjV+PIBj1RVR+ZcanORkDDZDy1yNNDgpgg/54Fi+bW4UFcxxE9DgDOGMmognjePPcK9+oj/9PHQ53Dcr+Zp4GkChPRZh4sObqHibInlwiAwTVQAkR8Bk4ik1OH3HdT4mZuJkLHxw9qREfwcddBCNjIzQ+vUy2dr69etp+fLl8J7ly5eH+BO4+fnPf05XXHGFqb0hIhofH6clS5aIv11FelNBUhofBCgPCJEcyJu2T9GXv7+OPvndn9Gf/9+bzbq/8+OH6NPf/dkAbWX1BfKHRAgnjVPnzQQitvgzGuTsI02RLKtc5WrVxReHLTuwHwZfZK68o3JkXrI4T2bH+71+s9TQ6UVjy84puvCqn9Bt91cHhurnjPxwzKMaIjkzAotqTINjj6W9+o7CWgNhvTKuybJ4BGivObfHBDiOiYpTJA9OD2yoS/bqa3CmOtlmbM2HbcAHZ1nfwR1pC80wcSfE98FHd2baRN0eaFoxnvOgJio+nlJW8MlpWXbuYF09l7SRezNeO/KW/0Mn48EBTiSTsVW2BTo6AdCBHIhHlA8Oevdj6ow9sSdYB5DuqVFUixYtouOOO46uvPLK8lq326Urr7ySVq5cCe9ZuXKl4CciuuKKKwR/Ajc//vGP6Rvf+AYdeOCBc9OBWSA9/uoyf24JIP5N26fonofqc7j80Seup/O+/ENaazjKapISaHU9qppFhG7VOW0ih20KM56V+TNioopoeYQ/S73f1KM7qw1F+OCwdvIDLNOGxomPgfWP+hqcC6+6m/7+63fRf/nod0yeN/7rLfTmf/++bPNMwEtAOxORLDVY4ybApIHIw8RxWTxjs61VYe2riWwyTVSOkzEny0kf1cXLSRqcXi8H1JbWAGpw+gBnECdjTzP3qe/eQ8e+8woBpvRaMIgGR6T0D5io0lxttao0ABoI6Pn8iNCUFuuMa6Iy+o8ARzyKimlwZuCDY46hwDxD5qeU5HQM+OCkuhYrU68QEIzuTxpmRV73fNGcm6jOOecc+qd/+if6l3/5F7rjjjvota99LW3dupXOOussIiJ65StfSeeee27J//rXv54uv/xyev/730933nknnXfeeXTjjTfS6tWriagAN3/4h39IN954I33uc5+jTqdDExMTNDExQZOTOHpkPimSrZQP5IjX/abtU6amJxEfmNEjCcxMxjPwhEeAgmcT7vV6mZ33ka2TQjtBJAGF6YMTkWYCXeHF2Bqc6npEg8MJanDYc8o0OOoZrk3Ok4z0OHtoyyR9/nv30QOsrMhRDbZpKaLlGcxZmYhoCcv8nM6h8vLgcNrADpa02hQBz2lMWJqHsImqm28YeXuq/1OblyweLTdwHVVmbaoyD04f4AxhohLnERlt5uHpmQbHSIiHKHI+EteepvaMtls01jfr6vr0e+e+ZyPtehOVfB9yjOdmpGAUFdfgBPLgWGVHUg3YTsZcWO0DnJEEcFp9nvx98HQLRDgKcVQJqOKdqfZEBMq5pDkHOKeffjq9733vo7e//e107LHH0i233EKXX3556Uh877330i9/WTlYPutZz6KLL76YLrroIjrmmGPo3/7t3+iyyy6jpz3taUREdP/999OXvvQl+sUvfkHHHnssPeYxjyn/rr322rnuzsAU8cHxnNLS5OQDZfP2KSilWfUsHsMOmJosX5iZABxoomITBDnbHf+eb9B/+eh3zKicQaOouBYmYqLiC4uVk4hLP8IHh2twDICzF3gfXFJ/QAGcmWimIsdgeCdKl9cjPjiOP4d1Pb2P/3rMYXTk8v2IKI8ktDMwBwBVoP8Da3AC4MWyxKH2LBpt0x8/+4lERPRJZVK2tJXbhVal4Nm/D3C2TnYyE5BlEpJaNwPMOwnwUPsi2hmTB+TBabda0DGWKJ/PVmJOi0QwhQ6B19q04DoYipzV4Gk6fx7mGGLXI46/WoNTRVHlPHstkpCAH7aZ2JNTfNl257iP+dbg5LryOaDVq1eXGhhNV199dXbttNNOo9NOOw3yH3744TM6BXpXk36/MHzXQfydbo9GR1pZYrm6zL78fB9+ho0vzeRgisje0CKENgN+ILZ+Pmt++nD5PH764NYyTHvQSDNOO6a6tN/iVF/OYx3wRxQ7SJLnbeELQmrnfuOj9OhOrOVJxPv3gNK4RRaJmTh2RoDJoOAl4ojL+c581uH0pf6pzPr9WmPWkr7lvbh9qHz7tGTt2ArZYg7NQDM60m7Rq5/zRPr4NXfT/Ru30/bJTulUa809pMHhWbS37JwufXKKuiwNTr7J7Ts+Kpx1ubY44mScnsPSvcbE3BCbLov84o9dmqiKz5F2qzSraEDlaXBSO9Ijb7VylwF5VlkujKb3gOq2SAusvV5PHDSK6kLJO2fizI5y3JSJ/vqf8gyz4n9tohKgvc+z3+IxeoTtLyh5ZdWH4YXj2aBf6SiqXwXSizNO0sYBjnZoTJ8Vz4atk7UaHLGwdHC2VU3pN704Ryc2LBNpcNhk1xveehZBxDVPfOIMGkVVp8HZW2lUeJtNE5XhmMvnc3puB6tQc7T38Q1o22RHtDkUHRaQ5EJ+KhGbfkAbEEksx7+PtltldNnOTEqHRYUciIXZqKb/dpSIfdim1R7bf6L6n0e3HLDPIlq29xj1er5JKBHywVk8OkJ79zdknXcp5DfVqYAJJy4spXGalLBeHhx+LpvmTW3WjxKZqEZa1dio98Hh/nCpjuKTH3tR3s9urzuGIuowq/PXoKjH9KxT+LvOzM3b7ZUfcTJO776dZTLOBZLxMfmM0PmE2odQ+J4tMA1OA3DmmCI+OJxHD/Q0wPhAeXjLZK0PjiU5eSaaUlU/pO0ZEZqAkU2X36uzle7sdOmnD27JJpPVNwkW8t/3WiQnrCVRcjK1AUyDM2UAHPRM9DPmm0pkYbX6buUVse6NbIQz0eDkKuxKi1ECnCkNcOqBmeVjIrU8fv8tUJaFJodMVJFn3d/A221qtVr0a31tJc9YHIqiSiBxpFWaD3QkldW3KfBelyiAw9eS5Hu2T3/OIICTyjlQAxzg8/Fbj18meFCivzbX4Azgg5N+ShyjI7nWO73LXq+Xabne/dUf0sQm7sOW3Q5Jj/Ev3Xp/JhSkehOY3AIE1ki0XkSwSSw60Z/U8hSfWoPDQ9BSvSkxJWpnrsFpAM5uTfr91p3vok1YJehgk+8XG7dn2V457ZjqiPOIrOygmqxwWWtx7PV60OTGCZ7tE/SJQeCOiOgfrrqbnv/+a+i9X79LXI+E+SJzhz6onJdjmajszbL6Pz33Q5YsFjyonfoZ84U6Eqaf1ioNprw06lWb6xdM76iG8t5QMkBt7ulL1yPtMlkf3wi1E/qg7Rbq/BpgYoJEDbhCdUEWqC1LG8+vHVwcgsgBjumDM8WfdVVO8u/S49YUUsAzXKokdAFw+uMpnXKutW1E1fPRmiD+HNN4evrj96cvvu636aq/+l0iKjZmrXkZabdYFJW/gfK2pnakLo6N5NtdD6wxKSv2134wQX/62bUZbx3pteGv//0H2VqlwSRyRo44syeeox6zRBxnM+lqcECYeDJRaW22GB/dfpttzxY9hxoNzm5GvV5PDEA9KWCiP8dElQawPq+Kq7E5bdo2RSvPv5Je+7mbBH/VHq/tlNVFRPT/bpugcy65JQMzf3v5nXT0ef9Jt9y30SwTSbu8j97eXS0+kumGezYQEdHHr7m7ti4iCSqRv4Ll+ErkORlbPh/yPRHl2ZDRRqrTwPNw15BjdL8Phy3bS1z3Qjj1vUSeD04EvNSDCcsJ0dLgeF1Hafb331tuqp6PheaxI3sG1+BEfHC49oqoenfcB8sCJkjrNtJulSAx9xuKt1kDEx6VlZ5hOhuKAxJdzoiSHKQGp4rIOWbFMjpw30rbU2aWThqcVgseEOn1i/etNFEhDU6fiYN27kTL17ao62fq26lPf1x5Ta/X5TrUf9aX3z6RHZKb2r9IATOUjuGtLzqK7njnyfSKEx5PRNJpOU1XL9FfaebUUVSi3uJzv8WOBqf/HJ/5xAPor08+kk76DZzvbldRA3BmkT573c/pied+jV77uQr1D6rB0Ui+9A8IOmv9v9t+KezQRGphcTUm/U8gFX3h5vuzgz7/8ZqfUqfbo3MuucUsE63PPALEbU//3qgUYBW1A6hrX/vcX6NvvvG5RORHZpgmKlOrVf1vARy0KKc2pE1eaHACTt6pzeNqMRRn0gwgEWqKRFqFfHAM7eBou1VKztZ4veqvfpe++hfPpqccWphykDZE+33wMW1qgpIQYQKcaB4cW7Ap7wUak2rjaYn2EOHoGl0+1+AkH4osn05Ee5d8LBZ7PjjSRFVcw1oVbRKCkT2q77xNnZKnmhd1mYw56XeANDjpdi5g6A28LM9NGVhR6tvrnvdr9MHTjyEinZSyCkFPYPLqux6kl190nWxbn2ffxdqEno+hJCBUztjcRys5dPc1OP38QFNAu7vY8cFJe5COopLzsOA54YkH0Gt/99fouU85mOaTGoAzi5QGkLDFZ07GyCmvupb54CQJI7jJbwQ5MGbqg5PonodxcsGfGtokIryp8oytofYMkWCLEweVqUwuFerNmK+htomqXrK2nIw9E1UCQ8NqcNptKh1NiWRytYgUHwoTt0BQACjFfHCwCfPAfRfRbxy2tJRAke3/xUc/RpRfFwJe8JDLo8FCyERlyCPiWatNvlWuHwzgmEktq//T/BgZaZfSvh63lsYRtUdrcISJqpM0OHZ0Ede8cEK+d6nvMvBA8bRapfZlEA0O968hwj446V3yzT45/mqKanCqqKV2aTJEWmQi+azvWi8PUU7t11pJpMFJz7FyxgZAOuXB6c8fNF/1mW7SKb74zLQ8HAQZ2rv5ogbgzCKldyokYjUr0IIlfXBwWvios5ZWc+o6vZwqdZvBTmVe42cqmZE1/bKe9tgl5TUeWeC3p/iMnuIbkcBLgNNulZMwS07F+r9jqku3r9tEf/a5taHTsvnV9NwP2U/64EATVX9BOrTvryN9cOrfPfdX+M83/E55XWpD8L2hXDkg54xW+Q+TB4f74CwCocD8UaVNMAXDIEn2jBMeTx8947fo5c9Y0efx+8XLKfPSKElfm3si/lcRExXvOxGx8VjxWz44HQDuxhwNjudHV7WnuMcDONNAg6Mdf7VmCvVFgyCOhbRZ3ncy7mb3J0rvw/PB0X5+I+2WEBBQeXVUljXSKgMYthtZnJftjbVFvG1aK9kB4yx1bRF4TpmmEIDFMkx8zIYE5RqjHjYf6hyULgRqAM4sUhtIYFpNCs9ucUxUnQDA4XX8YuP27Pdo1FIqxsITetE84qB9yv//6ds/g/ckAPNXL/x1etNJv15eTwkNvUWjzjeCSD6vWBRVWljZRFcd1qDrxR/5Dn3tBxP0J5+5sbxm5RXht6bnrjcMz0RVanB4NEhgZeUbxuP235ue8+SDRBsKHtnPtJAjvxBN6KiGZz7xALrhrS8o7f7DaXCqjWW8PG8IaxzTfpnmWfqp1+tVm/xIm/7rMYfR4/bfS/QtMu4TD9dOEIEwccsHh9Vh5+7h2hkp7Y6A9SM9dz2GEDARPjgGEPDao/1rEm3cnpuoFo22yw3TAjj6SBYuIKVa07vU2c2JqufJtXt63qX2o+zgZV6vBHBYmHga+2l9TX0YbbeEhoKDtFITVKOd4Jt8nQZHR6zx39Ir239vZXYFpuCUtRn5Kk2ztYHzTAMtj+dkrH3GEvE3UvYdaMvmgxqAM4uUJrQwUal1xcsbQZSbqNL48qR4/tN9G3IzEgoZxOUkaQYvhp7j4nu/fid9Zs09Zhba/RaP0uue96RswkdMB54PCnfItDfV6v9U3Ui7VS54vZ5aWIzqfvpgZYqzfKKQk/Gi0Ta99w9/swQdqPxpBXA2bMsdOz3S5o7kzyIj6IrPFx/9GHrPf3sa/ev/XNm/ni+YVvmcZ6TdpkP2W1xuPtIR2SpHahPT4xplkTI7jfFaSfsSCHCeUcPc448zCYL2VmkDMifjgInKjoDh5VSbKlG1fvB7NfCt6qr+L31wRlrwvRPZ5hXpP5FAotycuICQAMbYSLt87y+/aA29/z+rKCGev+b/vf45dPwT9ieiPDqOqNK8cBNVqcERJqr+xm1oprRfCK8j+c5wE1XiL9cYBpB5pnG+mSdeZOrixM005XpnaHA0wHl4C1vPLA2OWKukxqQ6SJMDJbk2IHNfKlNnWZdjmkQ5iEdnTZ5vagDOLFJpourlAzBRXWpzazFNg5SbhTQPEdH9j+QanJ1AFYkoFWNpcHS2zSllSnj7F2+nf1aaHK2KTpJTqcEJmA68DX594KwltPG0Wi2xUKHTdzXxOTuIk/Gi0Ta97PgV9JerniLawCk5gZbp9hnQDeXBUQtLGXINtCH7jo/SK054Ai1furh/nfUrYFpKICVlRE0btNTg1PuOcM3ZyEhlXrE0jqWJqiV/46BJ+3SUJioHJJfjnoFfTnGAg/8X96LNoC37hfy4DlliR+JxwIn8mIjyE7VH2hIA8vZozQsCXKMjlVblnoe30Ue/WQUglD4oIy066jFL6L1/+JtFm0A0Z6mVA0CCAwXTybhXbcxas5KGBTJRJT+b0sexX+7YSEuYaWSZFQiySIP2dPTBdsMXbpkCOEhg2z8zUeXrWeXHlVpa8aT/0pxAUVRVmLg6qkEA4EqD89tPqg64RpmVGx+c3ZCQiUpvuuh04EE2+aR6RzxEOJHgwE7GAVMPETbTXP+zDbJMNeBLVfN0kr4jkrXtg/NI6ETpfCKPtFriCAt+r2Ve4FKm5RckIrBUZJTl80OUR1FZkRIWcX8FXg4CC20FAoiq92RHUdkRMEk93hEqb9xO6/yosXa7jADjWkD+LrSJqtwIWTnpnWqwENHgpK2g1SJ610t+g0477nFl+dNCSKgrxx7XyAcnAW0EOmw/rup/7mOBItFQe0o/Iw7KlK+GLp+I+/u0M18lzZPeExqLqT38CAP9zuRRDa2yX+gZIt+Z6qiGBEy4BmdM8EyWwK0tjmdAGhyr37zvRMU8W+yYqFqtXPOUDkbmfdROxijq0QL2vI+JYB6c/g2Zk7EYZ1TW9Zk/PoE+eebx/bZWPHptmG9qAM4sUmmiYmuLXgzv37id/utHv0OXfK86Ndg1P3XTZ7WwZDx8UwVRP3EfHH+T0xFgyGF6XGmYyo23pSegXxfn8Z5PR2wqBg8AL9wHhwjbozXxOWtpBDyHO+RjkfjSpbTAIK0KOqQzkdbgoGgaHnbLeXmbppmj6X7jo3T0Y5eKNhBVzypJskiDM2hGZCGl15ioUrtRkja90Gv/mpF2K9tUSlNGr6rnf6w8nN75kqeVPJGTsPlrjWRf1v4Tqc1SY1L8r01UyG9qlPngXPStn4rcK7o5ScK3MivLNlc86d2MjbagNpkoF2oQcEv/cq1otjawtSONZ21O5iBR+w4ltsTNwVQaAzoPziKWcLIol5/jl67Zmzfvo0y82M2EiNF2K4vYeuDRHYKHKPfBERqcnnzWSAtYjmsnWs8KEydx2GZfg9MqAjQO2GdclK/7thCoATizSBET1bV3P0w/uH8T/fW//6C8NsgmP9Ju0T+98vjSn4PfP93pwk0+muivUtVjpiz0FFSmndS03TatneUxDJ5k3b/X88GRTnmYDy2I7XZLTEKukbFeh9Tg4ImMHMxTn1H0D5EEigkg9sDis5eSUPkGY4WLIqk5AYQWm/3amf2YFcvo1ne8kFY//0lEVKfBSQCnHgQIMxZ7r3xztjSO6fG3ynmGy5E8av60WrSfsRGmz/RG+fMVp1wHTKF2mDjYnNVz5HKDedyHscmn8bNx2xT93geugfUW5ab5l5c52m7RS449DNZVgam2CXD0pjsCtQrFZ4uq+aN9GDlQ4mYhFAI90so1OBq48nmWnJL1b6MjLTHPuSai55ioUDqPkXZLzNlk4ufOurqsh7YUGmk+xngSRCL1zpSjugb2/P/UEzTOUpmuBqcn70+PBmvUFga0WBit2E0Im6jq74vkcuAb2O899VD65JnPqOroD1QrKV1E+izK6bk8mQYH1KfVt11rsevmC2zWnogGB9iRrXJ4fSOtVuGHkzZn7pRnlMMXu7Th6yRcUoovPrX2Siu++MKLTFSp/3oBH2PtSc1Pm0TpbIpCc9W74O3mSeM4CJSOyNVmkHgzHgtssuecQGWrVbRpEXCQrUwZleSp51mqK5UjefrlCA0OzsTaUzsBN40MfORFRIOTmRdItoMqsKcBDo7GkqCDvwPdnNK3D2iCRtot+tDpx9LnX3OiWdei0XoTVQmkgWYqgQUuH5QColqH+NggwuvZSDvX4Gghis+zJIh1yt+qcc/7K7WcxScCOOU6zcb3SLslznYqAyvY89FtTusQf19L97KjqFK9lnM9EX/WtiY58XiHbXIfHF4eAsmOFW+X0gJpxu5B6IVHzi8ZxGyEE2MVv5kAJ+yD47cn88EBYqqVG6XKYSIXu1j4ru2DEwtNrv6v8uAU3wfRPvBnnhaifVTETVoo+HvXkpN+BxxcJWDSBW3WJiphnisXzeI7OvaA+x/p/mSRK8mBGJwBxEEQURUSGjHzofeVzK7jSOuUNIDcV0NpwpBaXD9rDibyzLDFZ2oZfy7I1BfJg2OGiYv+qyiqBASAD85B+9pRVNIHB5sxrfZY5oVWq1UelgmdjJm/jyZtouIgRmtVuIlKb7zTbEzzd2slDMx9cPp19r8//fH705MP2Zde+NRDyw1Ym2bHRtpQuOBt94586ChwVPjhSEfjUvBpt+hJh+xLb/79I5nJrCvKI8rTFiBBoq2eNdSWtUjwoshRDVqhyd3RWlfau4UBLRZGK3YTQiaqSASMr6GQPKNg0eiUAAdn3UUJnRBFzuQRaeSB6cjK+qo1OJH8PuXi40XAiElqbTw5j5cTwiqHP3NLq1IuqqyIXIMjy09Asd2qnGRRtI02UUn1sVxYUNRJlfFWmguLsuSmoo8PQKHko8oHJ6TBAeVosxrS4HDQoTU4aXzzhVdrQyI+OKW2iP02znwoiGztDS9H95MTjn7SJqr8OXrZY4UPjpGozWo12sxbhjDC2zw20jajibRZGjnq9pz3qrVLI33AVR24CUB7u5ULGwpMjY206T/f8Dt00SuPr8ZQV44hDXCkw3tVjqb07KbZXE7PT6fG0GP6T5/7a3TacStEOXz+P27/vcWYhVFLhnaTP4f0pD0/Hd03L0y8NIcx/kaDsxuTnjREMRNVLJJIIvVWq5WpdLm0/rTHLqFVRx1SXA/mwUk/pcmFJLQtLLwbLeL6QE5La5C6HPEJ8gCgp8F56mOWZNe1RqnMG6HysyDioayJf+9xf7PkdZVaBQ1wGGDw7NpZjgrQr7SwIH8WT4OTut9RYAFruKTmYQSkfreeIdKUjSqAwyNluIkqUamG7+py2rU8I8Cxs3xXStIlYqa+/tyK5G3S/1s8PPdKaptoD1Vm4EUjbbr2zc+nN/RTDUANzkjLNBtFwH/61/OdqYBAi+6akEcLVGBSjiHu3FuBl7wt2gdHz1Uvf8tIu5XNxdL0yMxhGryluqo52BIaXxHa7vjgJOEBaTl0LhydAyjVS1SNCT5/xkfb9L23rqJPnfUMUQfn98yc6b+WXofEmOiv+ZEw8dL0mNe10Hxw7HPPGxqYRtr5guCBl0QRJ1pta031dTu9sr40GQ/cZxF95c+fQ1/5/jr6xh0PhKOoqvDM4nOf8VHaOS2Pfkht5eap0XarnGg6TL0CZtT/VCYqNtn1o4r44HD/icT21hcdRa0W0T0Pb6Uf/nIzVMVWh/zlJhirugqc9Ur+fcexVkWU0X9lWnuVqMzB0ZdU9f2p/VpbBE1UnjbE0KYV9clnncbZmFp4JU+9BufFRz+GDj9obzrqMUto9cU3U7dXtKPdblU+H/06OKDeOd2lxWMjIrIpUbIQZG1mpgNt7vE0OJ6JqkqcJ/0nEA0bJq6BAK9jMmlMRlt02LK96KmH5aCdRz+ZGpwA4NKSPtIo8UR/j4KDgUda1TPXwL7gSaCDBE/xv2yH9t9bNNqmrZMdA+C0aR+t4ezKvqOQdB1FNdZuZz5QvV6PWq1WWR4yUaWxrLVyRESLF0kNDhzTyheQv5d2q0VjY23aW/kNcRCUnWfGQJo2ByJNsm2iYuNVrZ3IoblJ9Lcbk47cIKoGiBVxQOT7mGg/FD5xWmoRT4twWpTR2T7+Ai15tO2X/zbdyScXkX2WVqURINHm9DuSPLVEiAg55Z163OPoT55zBHb6LqXC4jvanC0pHdm39xoz1OJMcZvuKyPI1Dv45p0PEBHR2GgbSleJPzdTsGekNpUqioqFiSsevgal33SocKmdQT44yckYaMHSOxsfbdObTjqSnvOk6lRhXZfW4BBVY7ZS51dttULAOfg3TVStVnneV6KuGouc9NEHER82jw859Y4qwIk1JjK/jzBRiRPZBztHCUn6mTDCNUosX8wbf+8poqzKj4tEv/i7088ahYkjUEpUPYPJ6XwsjrSQCbcSfoik6VGnGuDt+a3HLxPlpHefah0F2gltVuM8WoOT3gV/LtU6lI+zxKc1wMgZWq95Mo+U1uBU9Se+8SyTMe+jdjJOPPl+15iodkPykPFiD+A4i2a5GQDJQEckpYmYBikKFfZNQnKzRIslikh49pOqkHVLg5PlglGqWC9Ds5U0DrWZ1+ElXtQmKpmV2QI4Oa8GgelOXkS+sFQ/3v3gFvrfX/5h0ZZ2G4IoS4PD+bR2Znwkf/faho7MnBosVDluWJh4B/N4qnN+No0GyWkz4CA3tVubZlO7i74noIR8cOQizn01/uQ5R9AzDz+glMS1uZRL+jq7ciQKseDDPB027rU2sXR8FRqTpN3rAxywOXHgajn+Wl44CJSl0O0RsJ5Vzrgt+vMXPJlufNuq7P60EcKEkuUEoew3nUcsN1HZAttIu02b2PEmRP571cJhCe5aLfrD41bQ//6D3yh5S4DT5x0Dz7jywcnX6QRwdkxJHxwOuSpTcC6wpbbqNiMtsV4/OE+qTc95zpdpcEQeHNm3lqqL/8+f9XxSA3BmkdKLl3bL4ouWvjlFTDCVZJBPijKKaqqSmomMbLYOwqkOuaOsrvL+bgI4VTl/94e/Scf1z5vZznxwuNmoXOwMOztKYleaDpw2l0557BnqCCm+2ZTh1NrJuJtPZE1IZW9lT0X5W9CGcS87O2y03SJuBtN91Gck8bK0dgadKo3SqGsgMM18EfgnzoWSNmbwDHU5rE4rYqvVykPFu+p9Ff+T+A374CQeuekmH5x//dOVdPazjxDPJd92chOVd7A9kmQ1lVon9nuVfTnXmPCTqTmPlW/JTL5nTCEv+R4a70l7kuYN92fSAokX8Yk1OJgny4IOAM5ou0X3q4OGNRAQEVtqHeLPcKTdoleufELJn959ej4oz581pokqzVLlgyP7S1Q9z3Q/T0iatTmZ3oCWWPvFIA0OGmeVpUF2Dgla2hwmcnapdWi+qQE4s0ieicqyjRORf06OkgzaQgrp319qcKSJCqVtDyXWKyMBPIBTOT8etO84vfMlhcSzlWlw+OTQWhXtbIgAYFctUIhQPg8/g6qcpGnjlZFmuC4UNYDMRvyT38fBXWrH3uz+ndOd7J0SVWPAe0ZaclyENDhg8dFmCO1AjHyUtBnL0+AgPwwdccJ9Z7TmCS30eoFGUnN+nAOJtvIyK2m32uTK9igTVezIh3oTFX9WKeEiClKoNB24zUTSp8PS4KTnmE4l//VD98vq0tK38J3pyvGBwMt0F48hYaJSGgou6esw8fTOMidjI0z8L17wZGgOS0CAr2Zai6E35hZ7lklw5PlktB+OFrSQBmebEUXF+XUCRvR8OuoZ6nbzOpCWBwU7pP+0c7DrM1byVPxlu2lhUANwZpEQMq5MVLYGJxKZwSMlEmUanGSi6tc1Bja5yLlXWnISbdXmhX57UogmOnOFqNpI9SKWPtHCXD0Wu80on44Xlp3+96RU6xkhAKtBh94sRV1AVc+f8dbJDsylZIWk83ZbTsYC3JY81f3aZBgBL7oc7SDJ+bUZq+CzHTK1EzrU4PTr1f41ArwonmllNuFlZlFUbHlO4zKZF5B274kH7SPayvn0RlhpDKprIwpQILORHtNIam4RNivzMv/5VcfTu095WnkAJm+HBpN8vGptiG4zEQcLxXcIlNT84E9HO1nrwAqY+oCN+99+0kH0g/NOot9/2nJRl9ZMEXFhI80fyij3v6rKSQfVVu2QJkz+7HQqCtQe7YMDEyEa/ou8LL1+cC1PyQN9cPo8JAlZI7KIWMGfCwnzSQukGbsHIRNVGoSeiSoNHLTJ56rPikc7JZYanDHbROVpQ7S0q08VJqom16Ryfkyb77bJaRgqnR3VoPo1OtLOAJWWQrwEW3yByhyagZSqHRcHyWTMf9bvrKfaTFRtmPx5pjbxxXpyusvqyNuTnxNjS7vo3aOzhrRZQANp7KPU71fyYwIgSOfl4f4+Wtrl56uN6o2nXDABMFFgG2tw5PjwHJFLSZ9vPGX/9TyszGkvOnq5KKf4v/hcYmRNhpsTApPKlIM0fOk/buIjkuaq1LYD91lEf3TiE2jZ3vKwSd7marxW7daa0mpDZTz6nZVt5gAnPet0fw5KSxCkQDsS2LRWYZ/xUQgCs7pKMEVme7R5ko/75cpR3dPg6PeqMwtz/jJMXM0xzoOywFcAWPYHapKB4IfyEhV1oDkty8FmzoWhw2nCxGeR9GZB5IOXRGlQH7zfOP3iEWlHzqNAqt+0xJP54AwYReXVlaiS9CtnQ6LKztztVSG+vK5cSs0Xw7GRFgQjlZZnhKY6Ks+OkvZEXcjJWJlO0AaesM5eYyOl3ZzfwxdO20TVY/eR6Dtvk04RgLREOiybU3kYa7n4yLog6ICSrKxLh9F7my7KlaOj1YgKEDs53S2fNTQtKW1QB5RjnSaOIgw9s6sOyU+8cFPpyXE20mrRVX/1uzSxaQc9tGWnKIfzLdlrjB5mJ94rZZFoExyvCnT4Y1oCOO7XpjcemHVdaQ1geHdPtjkB126PMl8m7TcleMr2VPVbh6i2NcABJndeTjmHNCirWExNMh9n2o+Nl7N86V5E9EjJW4V35+3RaQ26oO9aSEhtR20uHaMdDY5OcsjLcseZWvP5OM2DFGS/+P+ND85uSPDMlf6/rganUwGc7DcNOrgGR2kotIkqbU7C1gomciINKKCJqv/blHI25A6wydbMQUcZemosLCPt/OA5vahCDdeAErH2QxkDDrLpedUd3keUR8fpNvO6kH+APpEdn++S+6ro/ljqYy9XjuDTGrUEcFgenErTUVDadJEjMhpDYwZ4EWZXNT70hlr8L+tAvjyapyyHva4spLbsV0U6TJ4/w8cu24uOe8L+brTeEpVzB/mM6TZrR1P+WxrT0rTUb3erJcYxH78V6Ei8sp3F//1/1GYp2oS0XCr7tusgmwAe4Mn885SgtbfKJ8PLw5muU12pX974yDfm0kRV+uBUxTxu/72Ik6+ZUv1CIAisQ/Xl5Hz6vUpH5P7aADTbPcMNoNfjwSfyGaW6pMUi1QWL2+XUAJxZpEoCqa55PiaJ0sJw8L45wNGStYiiUvVpJ+M0kKfBBp+Oupd1aTCVj1Kd6C9tKlxlv41lO9Zt1aphblqxzT3FJwKJejFst5jt39l4tJQ6DTZnnVdjGmxOmQZHlUGUS1e83SbA4e/MeR9ZFFW7JXj5WEQh19okpkOuUfRTBTpSGQAkArQwWkasdUXfR0B79GaANrDUDgT+s4zZwCxQbXIknoEnfUMThCqHqNIGLdnLOtizupY7odeDZGEWYO3gWhsUvVSan8BapTd56V8jP0XSvLTOlCCQElPJY4Vle6eJ6zGdDqbcwhIMuod2qk1e+rNoQSvvV3YOGXs+//N3jqAj+r5XRDy8G4C7TPNCZTmJtJCANEraXw6dd1etH+kZVPeXGlfHtITcEtJv2gm/FRhD800NwJlFwpJc8RnxwUEaHL2wonwgZaK/ZKIaSw6ibbM96SA9VJenwSk1D9PSB4fIltB5WVayqkK9rrUhsm4EEsu6QJuhT4M2wZQHReZmPB26XjoJsmu5k3EuXeGolOJzUpmoPDMnyg6qpasRZyMs3yvY5HW+pdF23ma9iKdikA8OWsTHDH8W7oNjOVJKE4Qc9wj86/6XG3NVDNOGyPcqfSOkia4DNh7P8Tf3waGMV4NtrUnldaBM1xy8PPWwJfScJx+U3V9peWR5cJNTAgLvNwILmdbN43G0GJY/WLp3376WeCsHOKlfHCipjVeb+ThPR5l4hWYqAx1U8izbexF9869+twQ5nlO8XvMQCNDRnAhwleDfBcAaJDMQlN59Ox9D1XMk+sgZv0X/9ZjDqt/6n7nDe/o9H/cLBN80AGc2Kb1UpKpHYeLa1gwBjmE6IMqlXW2iQhocL7FeorTJeWHi03BzkhOQH8Ogbf863fhIu5UBKi3N8Cybjz9gb/GbTgrG/5ebgWwrdjIuPrWJKmke+MKin2OuMah+QxsGD3klwhuYq8ExALAOF0U8RfukxKcdkTmALQFOamvyC3G0YMIHR4EF5IOTb4SU8WQmKuBkrPvfq34g/a/eDPhTHlFzqIoSscvhfVyyl3VWWXUtmxvABKFDyRGYTO3465OPzHgs52CkCdIbIWoT0ry4PGkz7MnPFpyv+dpAROUp8Ft28gMw0/soL+XaIvV8eJna7Oz5elXAPl+DtQlTALfy+ZAoD7VHmydROQgA6/eqwR1Rrr1DJqpWi+gPjjmM3v2Sp5W/WeZid31dIAinATizSDDMMwEKkLu6HKj9f5YpdTbnQZucnqTaRMVNWBooIVVk5kjp8JRRVKN5e6osxSTaUfDIcviE0D60evHhGpwnHFgAnAQ6yrraoC5g7imdjJ3NWWtnpsowz2oxsMxqSIOBNgxtokK5exAQ0OWYEQ4gyeGIs6no8GahwVHHJ/ganAQWmJSqHLq91AfaHIgk4vSISrOa64eRAy7LVwNtPNo5FgJp9qxTmVYUlafBQX46LcULk6tpLSkAL2W7W/K6KMfZeHVeHlSfBxasSCvOU2+iqjIWV0POXmNSo/ns0cIoPt1cllMBt4rydyb7QpSbqJDT86gy83rloCgqne247DvjscZZ0X9ZX4sta5Yjdqu8FwBpWhjUAJxZJKyq7v+G/FnSAW2lP4sdJo42OT1JKw2ONFER+QtL2dbSzt6vC2pw+m1XafaL/+VE1gtUUa+cpNzpNw9RlJ9co/LkQ/YT7dEnZfO6UMbOxAdPE+/zZBqcjnQ2bLdamQZHS1f6EerNgAOcww/cO5PAOO9Iu0Uf/6Pj6H+c+IQSMFvvDNvZ0/uw25NHUTGAozZ5bXrjz7B6RlVdOmu054Ojj/JAJiqdrwhpebTGhL+O3FdDli/75pVjP2t9cjnUzmiVf0/2i9cBo59Um/AGJp8jN+PkaR3y/nsmQy0kQLAAQJcup60EEj2m08G2W4UGJ/WHlaNMVCUP0hSqNQYDVzk+cHi3Tkxpj2kEkmManOLTMxfmoIyBZLU2eMCEt01rg3IwRRkv2u/mgxqAM4uk/RmIfECR57jJmfTig5yMcx8caaLidSA/DN1WDkyO7x/BkEg7iHLNlB0FkS90KEGdbpOepItG23TJa06kS15zIh2476J+OfaZRV6odKV9yDP1Jn59NMKUeoYtss9uQSp4/iyqPDhVvf/6P1fCzZKbYE5+2nJ61ylPK4FV9c5I9B+ZTZA2RIPkPFKilS++/Xvb6hl2QC4h3n/tZ1CZXcEYAhq+iofEb9CfI9Omgk1XbYRI+rT8S9DJ1HzvTu3fe1ybqNInaLMh6fPfIJhSffNAkOaRbZL9IfJ8Z4AgoTVPaOP1NHNam6i0kvuOF9ow7mTs+WihQyl1mzMg4Iwh9M6y8O5y3ud15VFm+dzQZmD0fBIodbWJqc2sz4mrnPNAw1eCF3ZfNj9aeR/10RALBN/sGoBz4YUX0uGHH06LFy+mE044gW644QaX/9JLL6UjjzySFi9eTEcffTR97WtfE79/4QtfoBe+8IV04IEHUqvVoltuuWUOWx+ntkLqRLnGgBNKDnXu7x9JTzpkX/q1g/vOa0myTn4x0ERVfE9mI6TBQSYhTeVAZsDkY3/0dHrj7z2lSgzWF9KrU4WRpEJZv0qecrLLdrVaOeovHyNr8wlHHEgnHHEg23RT2/OJhVT12qdDZxDlz0FHUeVRRK389F3ltKgnug7RTFqhU5/+ODpkyWLox4X8rzSY7ioeV9JH0qV6H/xd5A6QEghgH5xUV1X/qPJ3gpGBhiSLTRk2mMw3sLw9mZ8O4NFOxlUUUV4OCsnfdxw7ocMNzPDV4PXpZHiobygVfy6hgzY54yMHt/a8Rzxm6DYjnWZDr1XpYNstO3gUFaor9UvWhQFwPqd1OZkvD2tzzKTab48DgnSwg2cuJCqeEYwwVBm8kZ+OHmeib2mccQBM+BlFtDzzTXMOcC655BI655xz6B3veAfddNNNdMwxx9BJJ51EDzzwAOS/9tpr6YwzzqCzzz6bbr75ZjrllFPolFNOodtuu63k2bp1Kz372c+mv/u7v5vr5g9EKPSy0hjkL7wDFvr/+dxfo2+c81w6tJ8ps7RHq/NdeJmViapQ3S5SYeK8jkrTQfTeU3+TxkfblbmjJ3lGRlp0yH6L6c9f8GR6zNK9RH+SxmMMaXC0JgiBDgAWNBjQk5T/rIEJjKJSz4fXq0NzxYGcfR4dRdXpFrlguLSHfKt4v1rKGq2l1FITNmoDE50VmLfb8q3SCx2vk7fIkmT5u8h8bNQihvLgoA1jTJkD+ThLlCeCzMuJ+EbYm5yzEZbX880pz0KL6soB3j5Kg5MBSQeQexocmTW5EhKIcs0u75s2L/A2YTBJoizPROfyGH0Lge0+SzL3bVUH+lrt8cBk2a8MmFTlZI7IQAuoHecrASHve55LyCmnXI84eGEAp1cdZMw7n9YbBCS1SRlGWpW81X3W+EAgGYGu+aQ5Bzgf+MAH6NWvfjWdddZZ9NSnPpU+/vGP0957702f/OQnIf+HP/xhOvnkk+lNb3oTHXXUUfSud72Lnv70p9MFF1xQ8vyP//E/6O1vfzutWrVqrps/EGETVfotf+Fuem+lZkUb+IiyyWq/GKHBARLGy56xgm7/3yfR7/76waKtyBlVSyFJ0hgDmxMyPyXSphOhwTFMVHpxTu3nbUVRVEhSqfxZZB+RKQed/TTV6QlJTkfHeUCBKF/sJhVQRBtYNT6qcvQmpn2Q0MnlrpRqSM1ElealOghQ9g1pcNBmWWmCJLhDGhwd/i/NaniBxn4YNkjOfCyACSLbnABwRTmHUn37mU7GeTn6XfTYuK0k6z4PWGP0EQtepBXenOy+ddWOify4Mq2bSKqo+taT1/n/mValNFGBPDgOcPXevX5neG5gsM0L0mMfjo+W5OmCvmfl1ERhdrt43Ov1NZQnqMd/SwC4umYJG7yPqQj0XueT5hTgTE5O0tq1awUQabfbtGrVKlqzZg28Z82aNRlwOemkk0z+CO3cuZM2b94s/uaCkH2cbwaHLpFh4J4PjpZ4UC4U7SymVZ98kGkVapUHpp21W58YXPwvJYypEuDwzMqqHATKtD069bfVyrRcno9FpTWwo6iQZJ3aXzp2gs3AiqIiko607VYr0+B4Km/ePq3BSc8xjwDh4wNpcDQPif5hE1V1LTf32MBEb06pIHggJ5DQtYkK+eCYKfShY7TsF/ItykEy77viAZtT7kAr7+V9RO/siIP2oRcceQg98/ADYL/cd4Gy0BqgjLfJk9BdHxyQpTh3Mu73GTwjZMIty1HabfSss5Owu7I9ZRTVjqAPTrnrksmTZRcmxCPbLMG/WocG0Mzh9cx5hkqD44My2eaQzxTxvSMfH5kPjhtptTAQzpwCnIceeog6nQ4deuih4vqhhx5KExMT8J6JiYmB+CN0/vnn09KlS8u/FStWDF2WR14CpZF2iy5//e/QV//i2aWPjFbVQx8LDYJG8gXB8kPhDqI6dBtlIq3Ookpt4AtUvz3lxtxvszBR4Tb7EQXVwqJRfw4Wqt/0JEWSPjJRle9Dbxhgc7I0OHyi52Hisu06XnJEvTOdMNHNgwM2DA2Aqw1MtYMMqVABJWRD1+Hd1plF08CPia9z2kQ1rUAZUW4yjIXv2mDBCwPOc+WADd4AXNjUlY+z0ZEWfeLMZ9BH//tv9Xnk7whM4MMUZb80UOC/ae2d8OVRvLJveZuy5wiekXUUAac8kzEYi2rsa41BaaLaWeeDo+vKeXLncdnOgocEj6cJqs6iysvJ1iEHvEw7QInzcx8c3h6tuURrZ7YGs/uxk7Hsm++Dk4+P+aQ9Iorq3HPPpU2bNpV/991335zUUy50wOej1WrR/vssot84bGl1YrJKruZJPCGHzP71FihnWgEBdGin1piMOGroKcdEleXBAZopLRW1WnkUWSTzp6cFQ07GGghhh8ziE2dO7grJyQ4Tr3g46c2gikZTgIu1x+tbFh3XluXwvaYqEkl8tgSW+xmkUlrid5QsEZWj8wmhcV85T/frAhtGpXkI8Dibd/nO4GGbsu9VXaAcYArVKn/fqVX1XUjWGJBD6ZsJWkW0TcXjmSA8rYF3VEPo/DAj/5UESqkOPRaL6ynR39bJjn+QZlZXDrgskOyZOfV1IqS9coQIBySX5XTkM0Q+SkTFM8JjiMT91fOJjrO8zNwHB4Egyat9D+eL5vQ08YMOOohGRkZo/fr14vr69etp+fLl8J7ly5cPxB+h8fFxGh/PswTPNiF/Ds/RVqP1UaSdMSR0Xp+2NWcLSyefgNDfR9XlOezCTMa6zc4GhiQn20SVeKrfct+I+oWF/68P/0Sh/QeBzNLTSnLKcxf1xGfmg6MW1kmlCfPMajAHkvHOsPYKLOLZ0QiU8eS5PmQbuP9Ar9ejVqsFnT/HVKSIt4jrDQwCcmeTy8xPwEF0RGkuy7HIytFOxp62qAfmvd4wen3QgeeqAhOgrgookWhz8VsftKvNqSuYZHtEWZ4mzAMCBthGmjAdTuxFvukNlecU2jo5Xfg3pfEK1qoc3IJnrXjQ+yij2rqgX8qEiYC9lQyQj7MS/LvjrPq/0+sZ4wOvr374O7u/LKe6lu8veR+zd7ZAVCdz2oxFixbRcccdR1deeWV5rdvt0pVXXkkrV66E96xcuVLwExFdccUVJv9CIjfxEUfrIyplPTgaQUshyIylNzm0OWnpuwMksPRfut81LfVUmx2tAgZKsl98c7JNVLbEky0sDigjqjYPrcFBjsj7jI/St970PFpz7vNLTc10tyek+H0WjdAzDt+fFo+lSDQSn1YeHMsHB0VReXb9nhofVXSYvNdqk7XxID+DSkMj2wMP5Ox/hxqcbDPI++WF3ZamUEdjkGklZcJoyDOIkzH296nKSXzaFJrKgJqHkCYEAw7eDm3KkL488pP3G70zlEtK8+gEn6icai5Sv939NoMy85wyxfXx0XbZnm39E8U93xltgnHrSvMHOEZ7flx6fcUmVd33fD5b517pMc2tBHjNk3XwQ4gT6dxGnCoNXw6A9fsQICh9gnbPJ82pBoeI6JxzzqFXvepVdPzxx9Mzn/lM+tCHPkRbt26ls846i4iIXvnKV9JjH/tYOv/884mI6PWvfz0997nPpfe///304he/mD7/+c/TjTfeSBdddFFZ5oYNG+jee++ldevWERHRXXfdRUSF9mcmmp6ZEh9E3W6P2u2Wuzn5i7gchJUPTu7U60+u4tOKtuE8Gql7JpG0aCIzlgYv2EQlF/FWK58U2cRiv5lqX8QjjiuQbYJmLAaCHt8/EmKs3aJJSiaqCnC1Wi361/+5kn78wBZ64Qe/5aq8Uf91NFq1WebABGVi1YumZ6KqNp7qmvbjgg6QBphMamgeSTbZ6dLoSBuXY+b6AEDaWehzCR0AE2Nzwg6ZNqCwTBBos9TmF/6bPocMbZZZploAAPVc7YG6+CatzTPa1MXb680hzxm3HNOZ6THn8d6HjmzSc6jVatH4aJu2TXbKpKZl9+C7J7M9WoBkMJGVg8dHKNO1VxcYZ5UvT1qs8jGd7un2en0nY/tdqNeuzg4rPj2QXNRV9CeN1vx9VLx169580ZwDnNNPP50efPBBevvb304TExN07LHH0uWXX146Et97773UZjPyWc96Fl188cX0tre9jd7ylrfQk5/8ZLrsssvoaU+rDv/60pe+VAIkIqKXv/zlRET0jne8g84777y57pJJwkba61GbGMCBm5MtFWk1YumQ6WhV8KaSUvqrxUdIKnJSwEMQDTWrJ6lUWoWqriyKim1yOhliRLKc7tiLhmd+0tIuMuXw9hTPsSPDxNnCG1GL83ZXGpziM2mIvJBjuKkoAKwdAKXjaz4+sjEEHMz1OTm6nMWjlTP2jqku7b0Ia4v0waZeeLd3NEBksxwMBPV5wJlFEYdVnXyPP/Mys7QCHejQTp2pFvlqaEdcPk4Sl04IhwQetDl5G3gukPA29evKAF5eb66VJZMHrTEJ4Ex2OiZPBJjYvmesPYa5lI8PnY/LE1Y9QdTKiAx9+Lq9vmYO9Z1q+2VlRNZltVotIva7XmOFVtIJUphPmnOAQ0S0evVqWr16Nfzt6quvzq6ddtppdNppp5nlnXnmmXTmmWfOUutmj5BaD0YkmRqcqixLhYoS/XkOgKmcLNGfHsjENzkApoxJyodxJA+O5cHfalFmt81t8dVvo3qTc7QBvM1pkpb5YkCYOAJmPAIITfDIwsLrTeNi0jRRcWBC4jdUX5m7qNTg9PsCyoERQI42Mde8yHLa7SJcfrLTpR1TnX5d9jub6uJyivb32+0Cadn3UtUO5k+2yVHOo7UhSHulxzSqS5sLeVu1icprTyoDjaFsrnIwBcYlf/9Ffa2ynP7+5QpIdhQZ49Fz2lnPymzH4L3mp4Dn5YyPjhDRFO1IGhwISkncDyO/9NrpAqV+eZTzmGs5CtDQABA8w2k1zjROGGHtRgA4N7tSxqO1d8gJu+Aj6pD97nnTci3PwgA4C8QVaPcgYaLyNucsd0L/B7GwyHKgVkVtYij0MiIZaEnWy8uTpS2H0mVP8Hq+Ebwcy0TlTdJBVMNcYVaaqACgQFo3HimEnVrTqqrarKIJrCiqKky84EMHhPpSqnz3XFUdSnaXSWkVj3VODi8nmakqgCPLJ8rz4LibpeoXdoon0R7sF6Pnhs2DAZcCyaCuXDuR90evDXAeCg0wHkPV5t0H7FWxmdM3USFZIx5ed9lep02eD1KudUt12eO1W3WupDxMPN/Ak6YzHSzsAaXI+uGBKb0Gw7Oo0lruaJJzoS6vSx9Ei+YYL6vT68FjQ7J1gQCP0vBZ46OlxoenwfHWj/mkBuDMIskXrhdxgPqdcFk9UN0waAe8RLQqlg8BPH3X8xtS0jdus+SRTsYa4NgbYVpY9KKBpBndHv6bXsBF/4EWY4qFicu6cJstDU7uZCwXDQlMZB1Etm9EeVSD0hjwT39zyp+jDgNHZs6UFDFJ1uidZVFUqg1Fv3Dqe7mIp/6o50OcR2+WBMpJY6hfHtIGBHxQsnFWo1XhUjN6p6ketFno94r8J7S/j+djwfuEfXCqcojwhpknqcvHvu1flK8fld9hzjNeAhypKUTvNRvToF8Z+HeFiLw9uXbGLscTMvl8tqLsiu9U1uNlKU5t9fyqynIYwhFAkfHIvpH4LOqz94X5pAbgzCLJgSM/+W+RzJ/aB6c0m3iSbGBhwT4N8n44KQwvf0/61o6vRHakSFKZc8qjIPK+u+nPs4WlmskRDQ53oOYSFt509buo+sUp0+BMF5+LlImKl+FlmLV8Z5A20dtUrVBQImai0mHi7H2kKLLtSoODgJIOhUVSs476g6aMwIaam5/qNzCxeWsA7IBEvanw37Twg9ojgEnXCCVX4AVtTpmEbm1gAU2YLdjkZXpaLiulBQal8r3yeZg0hUmD45k5PdNjPoY8HhLt4mTniKrI0kqicZbKQnuCKMvgifgfjWRjkQFgVpflV5iu40ir/BnNJzUAZxaJj8VMshabiqGOBEjcK8dSDfNhmoWJp0XDWeh0+Zw/+Xpg6UEuGmhzyg+eq/prmajgphuQ9C3AxctCGhwvTH66gyXrRNoWrUGbloiTD47OgyPbnepDm7zkTfejxScRMtNEpUveR9635Gi8c0qG70I/po5tXrA0JtCfw1h4EQ/eUOX9aGO2zS95OeWm0s1/E+/V9K+p/rfynOgIKWtz4mPf3sCq9vD2y/osIGCPD2R+0v6CVRvAs3a0xOPlOLN9cHRd5foKzofSgqgc9+k3B7hZPjhgHfIEWi4ETnd7EHCJsgwtT/rXc5HQ+xTHJPhML1kmMs9m2Y4XiI2qATizSFL67r9wsFnmfjH2pIhEEvnSFYm60mCN5KbBk9TenCJ5cPIokGqB0nPCk6yzM6RCmxxlv8E8OM4748cRyNDL+gWTKJeIMxMV0ALC6KdsIcP94jyeujrThiDtVfKdAc7spYlqWka3iDOLSjOfnBtIukRRdlWbcb9wqLA9f6xDO4Vkrc0vwmNBlllqW2s1OFgLpn1n0GZhOSvr37j20uLRYxPNM9PJmGweDDiLT1eTnG2oqT0c4BQvf1I5vEe0GEjD5wGBSoNhr0N2JmM0pmWdSLuZykI8vNyChzLKThNP1w0w1VU+WhII4XePeMoywLOeT2oAziySziDKP/3cCflgts6ZEhJYpvrsXwcTR+fTwar61GabR2f1xE6Csl8i5452uGNt1kc1VKpRBDj6GpxswbQBoIxuSRuP7DN/DshPqdfzgaSe6Xqi53lwik9koorY/i3pG2mCkPSdm1dyHj1ekdS8l/LBQf2vws21eaHiMf3KjHdR1WSAF6XmcTUvAHRE/Cf0PBSawv5vLfU+6k6LNvOcqPFh+tcwAcD2wQkAEyUARHg8M40HFqznyJ9R6YOTmUJ5v2Q7PD8dz1cysgZrf0pv/ugxjcpJZZk+fGVZWBix3A28cSbHB+9/v6z+d9Q3WzheGAinATizSHoR459yoU8TR/s0VOT5qiTKtTNowGMwJQ44VJIK2niysGx1L68ry4MD6sqSTLVyaUWbaCLhxHLRIFEXV9V7RxroNPu8D5b/RCU52W0mcpyMRz0TFQJU+Dl6EQ6e75BVDlGuvUKbnI6iQkBgTGVE9jWFZPJYkiWaPzlwy/uuN0IUvpulRwAbas95ztzHrAAmsi9ElPvOqHbqMrs9qZW0/IsYi+EY32+/c9ionkNYi9EVPBGTqndGEhpneRSVPTfyIzgQcCVRlwTbsq3l+2CVWWu51BbhMYT2BKJinpVtVjiBPyNfeyX7RUDbnPqG+s7v0GZOMRdJz7N8fMwnNQBnFkksYo5JKJOI4UDtlxNA4t6mqtOoI/NLS9XlS2n17dGbJYqiQg7Ef3TiE4iTZ6LKDkH0QADU4KRyZJt5WXUaHE7Z++riia5NWVYeHM6DfKsyx071zhDYhqA0G2fyOlEe3o3ehxVFRaCcqY58ftjM2S8HjNdcQkf90gJCeh98TJP4DW0YmQkCtNnShOjElTrBWtEewSLeh6dJTTxIE8TrkuYOycM3sOIzryOUWdr0McH9IpK+d7rNerOUGpy+D44KE/ciGj0NjvfuM540p6tiMn9KJCDkIBHU1a6OYUiRVJqnqK+V8XCOyL4h+IxyUptS/5F5nyh/1pZpfr6oATizTJFcDvm5I2ARz9S1lJWT+33Y5aQ8DTA3jbbbQsmaRJur/QtsTv1NGznr2lIa0XOfcjBd86bfpXe+5DeMfrH2tPQzlOXz9mSq4Vb1HDWP1W6uNagkuao96TGk39BGyPuQ58FxgImj6UjPrwSKCbjxjVC/M1BO5OT2UoMDNtUK4Dh5cFQ5rik0sPHoNkuNCYn7kYbPiiKSG4Yer15dcj6b0ncXS99EcgOrIuN4GdX/PJLGitbr9nrQP43zZAdggvZo86Roc/mMqKpTFZQdiQLXs+IzDxyoeLIwcSeJX0e/+0Bdnpa0mmP2Wo606Fqos/xreMZw5OdWlAXWIbBOlY8ZzHlejjw2RK9VVVnC7ApBoNSQNwBnNyUtzcHjCrK8IsV1OElVJABa7FwzTeJxNwwSbYb+LJnGACwIbbmwwISBJgAsvj/hwH3KRcxTMcdMB3qztAEX8sGxtAbVRoAnuugXSdILfRoDaXHjwKQX8nvACzTSBPlnBKl2Mx7rLCreu8X9d1Y5Ged1Zc7KDnDzzsYyfWccAQFtBraWh/Vd51tCdWmgZIAODoRsDU7VJqgNaMv3irQlnK/TxesCavcg/iOeIBF51h6g8LQBZZj4lPLj8o6FoMRj1+XnIpPlIQEhTzhaD9pN0MHMk/k7o5IHaYnReqb7JdrEQLIeQ+lrVwEcfuxIKrYAQXk755sagDPL1GIDkMhYEDLQYU8c75TnmPStwqlL0FHVVdpk1UIHN1RHo1RG9qhNF56f1ZUTkC9QeSZSbyO0pavMMdppD46iqq4JnwawIPBn1RM89sIi2tTO+T2NgD2GWhmv3jA8Uxc00yjfGbQ5axMVAu08WaJVVx7aL68TedrNqi7tFxM6iwoCW2MDq4rJ3r1tFqhAMAIBvJ+FE2ner5YaH8iXhZdbbJb5usDbp30sPCGhfEYBvzqsdSu+V/M1L8eb98lENZmlGqh4rLUTai5TXak9FYsw45h1GfPZB4B4fHBBot7J2PDjUuO+TtDi2hk9hqoIKRVKzngQIEdlzRc1AGeWKTMdwE0VJ4dCtk0vt4QlOUoJHZfDHc0iKvZRNUkxKJORTWUeHLA5uY7RxqIBc284EqHeLKsIMsYDNTg9cT8vl6fHl07GFXGJx5PSEi8vi/N7BzxmkrXKOaSdWotywDMy3j0CyTpvE+dZrI9qAJvqmHVoZ9Wc7N1HJGsvJ5PrV1YCDvl8eHvMMHoAlFKbqqlqAwrTR4uBhRAw6V/LxxmrC+3exAQJBqZ0fblpOt8MrcSlCEz6GX/7bXbWxSqKSjoZIxDonXsV0lprs1oqH2iSvYSjlnCYgVuWJ8rywRHjI6AFs7SJEgAnHlI8xac3PtJ/PZLPqbVAkMUCacbuQ5YqFoGF6XLl8TbePoezQEfS7FebvF2Oag6epM4mpxdDlFQwd0ROv9iLD1owK38fBRTIbk9a8OAZW9wHBywuXKvkqYZTu62FpS6vCAImyKk5pOVSC713GGsOBFg5abzqwzZZe7QPDqor0+AAAKw3SwRKOdiU7bFBO04Ip3gAEKgDpPp/S8NXfK/emYE5pAkCjHteTo+NxWyz5BuYqVFibWbXEcDznIMjUUIZmARjyAoT511blPngyPL5/9m7B+3xhUw5x+Bars4VdPPg6HBz9T64IIHWPCJsxvKdfi0gXXx2HLDNy7J8cDiPBYLmkxqAM8vUYgOn+Cy+iwFfHtAWT1YF/WsMrQpafLTfg8hNY0wKOEmV2hdqTDJzWA46XLWv0oJBs0nbaA/QBHnnZ+lQUFEf0OAIqZkYsS9c7ZttKlo17rz7nuZp2+8jMoZ0X1B70II4phwpCTzrDOAA7UwWjYU2HgNQeBsPoXHPwYShxbB8ULxxT1BbVP1vHbGg+2bxcPOKWY4AL/1umeOM8RhmLD5edX22NpW3mRRP+oWVM8C498a0jqJC4N8yPUoePcfQeib7hYSWPA9OPu5zV4K8HCIJ7hGQFmX1/Ci7zPRmACXuKmCND94vXRZ3VJcAhxYENQBnlsn0nWFPOnfatBdNzwcnoo7MwsRd6Yr6PHk5lknEd6ZLfUebk71gZhlmnUMQdag9Uh+X2iun71yDUwIzY+Hw1OupPrSAcz7fuVHz5HXUJfrj/+faorwu3xwow7uRxiTzwSnLz8eiTvTnAQpvvHpaFekX42+o3jjTTsao77quOqnZ89ES0S1WOTwnk2HGGgEAx9Yoyc3J1WB1wfgwniNaz7ygAMu8z9fO8cBp4pa2CJl4fbOaLAeC9oCGL+KjxNvk+eBwXybPH8zbN3hd3KSq60pfhZlTlZX+80DyfFIDcGaZcqkwH2DW5CKhYrckHl5X8TnYZhAHHaicPPtyxWNlDkZmk+pMq9TzwAYPTBnuOTqG1gnlJMImqrysXq+HTXhqQluOhJbWALU706oAidjNsGtogvCmYm8GY1oNX8b4VuVkPjjgGaZ3NuVGUfX73LXfme0/QRlP+h1tThXgkJ/oXWRA2nj3fDOw/B6En44xPrwDF6XmhbJ+EUmzCAKtvO7cBJGXo01UvKTMpxCYwfNDXXOe9K97FlUZRaU0haxBJrAP8eTrou8LZ+XBIcZjgWRJo8wHxwJBvN1Ya51+Lz57Rl38sFF7fPDxiscHEvx4O+ebGoAzy5RJxGCyxzQ4GHTA3BKO9JD5xUAfHBJ1oEUslMk4AxQ5T6YadfueWNDmhDceXFfqX79fnvTJJV4BAqnk83w+Sp4u+IH3LQDM8rBbzlPVldpt9j+gLctT8Vc8qZwp51mnwzZ3OKc8Vyey26eSW8ANaRNLYJLuBZsTL8PiCT1D732pAzCRGUvXZ20qUEI3QbKn5WFAqazfKge3U5ZTfMfzjMq6Ch57nFWmE1UpgTUPaImzKCp1L//fE6KsaD2kTczWIQDa/YM0qd8ePc5k/3V9ui5ebs+oKxdWcx5ZlzM+WCJMew71ecjmmU9qAM4skwVMEOr3JkWGxAnwaLu2c/6PXny83DSVZOBtPLakkqfZH2wD0+Y5xMNTm/PTmZF2wlMNc0mG15W1m208ntNiKqPeB8cDiiR4oNkooHXTEjFc6APjNQGT3KG7ojzRH9gMjKMavA0DaQEjRzXkJkP0zqrfizZTxpMnukv35mOjKMveVHh9qM28Ps5jS/GsrvbgdfF5FtbgBOa0CzocjWMsk3F9FJXOUO1pXHNNO+eR5Xjgf1pn3ga+gDrvmaedsf2vqjah91oCjl4S1sBkJRKaa2t88ASwlnZGRuLxMdQAnN2Scn8WBASKzyxcVkzSlvgNggW1gSFtkV58QlEpQHLS4AVJ37ZUlNel7fWcItELfEHnUQfuogoyw1YgSNZFZDtiVxshnsQ8lNze5Cpeq92Zmhn0Pzvg0C0HLb5Vv/gn2uSnnEiRsUCOGx1u7pnVvNPEdZvLDQyA9qKMmuejcrOQM6a9hJLpd8sHR+S4SfeqFbjNpP1aPwwO3AjzCFNXBoL4BlZdF3M6A6WU9c3ym+Kk0xHAMa0BORgfWRQV2L8tIRO9M7c9pp8OH9MY3Hnh5kgbr9tkZTKWZqOcJ/M9S/fJqmD0U65NLEsSz9A6bFOOoYVBDcCZZcpABxg8laRiL/StwIJgSVdIItagY0TUJRd6CMoCdZn+Po7mwQMmOU/er8TnOTYWfbMyw1a/87YTkcjlIP0eUF28PdisJsuxNwy5+GCpKPY+SPyGQGnElyeS5yRLnAaBPW4z1gLWg6DcR4kynvQ73lD1u7DnT0Tbmtpkbxg5MDFNBwyYmPl0ujaQhs7KJMkaZxHwAt+9+4ysd5+PRU8rqZ2MkXYzAlxzMFUvaEDgFtBsR7RX/LsHTJBmzlzzHE2QnIsYSCMwlWmdWAsXog/O6Hw3YHcja5Jyk1AkksiUvoGE4dlbc18VbyKrTRdshJ70need6dcFsibnAJD3ncT9nm9EalOFSzBPp9eDQEkvPnwNgyCwh23WYnHo2YtYDlzBu2faO2ky4zz9ckKbiqwz4u/k+YzBqDa2MRc8sr/8/1Lxktrczt+ZZzLLx0cqn7+vql7LTFO9C9Ue8Hy0vxwnK7twvmFUPIYQDzUvpiaoh82lgscBQUJjwLrlgpeSJ39nWXg3mPfa5O5pVRB4sQ/btMuJRGx5pnLPnJyfK5jz5LmE6sdHvfbOEoz5WKwanY2Pcpzhvov2dPHayb9rIcLSbu9qajQ4s0z5ppKu2zxo0chNB+mXfAPzzu3JwxhzHr3QI0QfymRsACWkeagcAAObbtZzBV46hjTDebrYsdMKJdd1cNMSyuZqptB3Fij+CZ8Ri4DR9WlTjtf/XH1e8UTMiiNl/pr4Cc5Yso7wWBtG1R7LkRJtcul3tDllG2EqH5moHCCp+2ZrXvLNyT4kE4M7fo9vXqieY91YZEJ8Vp9tMqz4rXPqvMhIbLpPzzrx5u0ZY5FGFk+luex/ev1yNC8RB3MdReUJY54WkH/na4yt5fH9ylJ7TW0Ra1Md2O7xvlvHOThtnk9qAM4sUzaRu/lLzyQDR7KuJIz6CQgncsBGXC4IZAOTkCOhqa6t3zC8gxIhMGH/S+0M5hFSET/p11joivoqPmleSNf479X/lkmE8/EMs0Ras5DXRSRNZpFDEK2xiNX59rMeC4BkbaJC/mD6pGOkVakAueRBSQ4rAIzmT/W/uRmU+WRKRrNfXnoE3s9ioc/bwL9751VxIQGNV36PpZni7fZAENIY6LKsxJyembNaG3ib8bwnISRU7Sl4bNCuhTFr/nBezpM5+4P5kyeUzPsVESBzX0BjDPH8Rsb6IbSAqp26/b0e9nHkfZA+Y2p8UFWXNe7lGJJlLwRqTFSzTO1s40ELa/3mlGgwXxUEKIpPTyK2wBTSzkQkOW+BshYxsfiUPCTahZ5hKstTQ6eyXOCmQADvT8GX2sKz4uaLIZGUeDwJTJqfMDDhG48nWXsLq5Zk0bPW/UdAeqrMQGy3JzNliLrwxoNBsoxKQcDE9S3SGhxn/ni+cPZRBei99tRmoHi4ackAHUiy9nw1UH+JjDw4JAn5fOiyZhI9Cdc8tcZAc7E3PkwNH54/ZQd1e/Q6BI/LIdWe9Bzz8ZEfxorGUL85/evmMS7M5O6nEbCfYeqTqcFhz9oWxhIKimmdrHE/n9RocGaZLBNEJCwbbwaKB0zSfEGoeLLTxMHGG8mdoCU5lOcli9hCmhdDXYukEG/j4f3kCwJaDFN9nvq4p96X5kNJryI+FpZqmJdjtZtrA4jw+FBreA0Aznny7LHxcnjXbOk733iqumyQ3FHvw5OsPUk/tQnnXVFtBjzWYZt6Ca/6X6/liZwPxaOoLGAiQZDiYdFY9lhMbVYaHPg+FHDlY8gcH/m776hdHgdfVO2y2qMz9aIjOHQeHKTd1RpHpEnuZX2v6rI0OJ4Jz/bRysGC5/OCwBRvmzCnq3LkmVZ165nX5vSfrZWcT2oAziyTbX6yJQyE1rkKkciYpNnmjECQWqAg6k/3yzqlNkSfKB2R5ECbDTAV2Sz1vKlLay80OEIq4n1XoIwhHBR2LFTDqj1C4jGcP9ECpQsT/hxs08VjqOLNeNQ5W14CtohZUQMT3rVIXp5BAJfWgOJjM+z5w+u2fBGsbOFoQ02bhal5YdI3mqu8PVx7Z5411HXqAsDEKke2WbDYQNp4H1ZZlhnP15bl4yOx++eQ9XkCWjddF4m66kGHji5F70PnwSnHNF87VKI/BEx43XKNweOsx8ALyrlTlmOAWz6ne0Z7ZGJKXI70G8I880kNwJllyhdfG3TwQVpcZzwB8JJpXoCPRXaWzkAnSvOJLNsRAy89cS/vV0cBJT4lcu2VvBfxeSCRSOcVyReoOumKAwrLmS596/bwc87bjN99C/SruM7KMRZxD1DMFHC6flxaDQ9QoAauninDP5latmcQvxg0D33NZfW/f8J31TdbO8M2AzDuNY89Fnk5Vnuq52idVyV9vfy6uCZIl2X61RltJiK4gdtpBBCPLof3i1Q5OQ9/rxxwormRm6gqKjXkKU8QMKlGsrfzurnPmPa/Qv55SPAhIuoZSUl5Oe6Brf3vPbLnWPra6+HnM9/UAJxZJj2Rq0ys+SDMnEihdGmjfstXBUlOvnSV7q8HZZ6q3kqMZZlf6ni8jVDwdbF6tNVqVc/I4LFBGQYmEYlYOIg6ixi6zuvmETC6vhwsVH3O21N8j5hpKsfWwd6HxYPfK4lPmDitfz8yc1q+RbZ0iXPBZCYzADp0OoLIGWOWdiYiEct37/fLSyrI52s3f+2iDxIoWe2RfjoIUKRn46WiiPiMRbSA2kSFtBie5kUIP90eERS00v0S2COttZs+I+t73h7R7h4WVnndls+LpcHRoIP7F9kAmJeTrwuaxxqv80kNwJllsvKTWHlwLCk+N3XJ8gseJfGAcqzTbj1VbETTAbU8bNIU5eUT0DqXpQX6rjcwrULVEp9uD/9u+SK0lRN2x1hY0ETW7Ulfq+XSXjQ0eLEApyU1VxuYrQUs26zfvVCf480ALeI+KO3X5WgBI2YKDaQ936o6E2b1ztj7cIAbyhkiHdXtd8+jciwAPCKiZCxgUs8j+lUDlOTmpMoR/bKAW/GZH+eQr2fpZ6Tl0hpgrFHr82gTdzsfZ5kPH3Aw99ZF6aNljLNMK5mvDXkeHDCmhTkoFpFUrXk2TzUW898zHmt8uACYA2l5Leex/X3mkxqAM8s0mC+CNkGgSVp89502dV35JI1l7PTAC+4X2gzKrMCBDQxJjhH7OK/bO0+F9x9JPLlaXPZXt1uYF6zNqVufQt81P/G6uAYH1eWAoOzwPrAgWpI1Gmf6EFVUjs4eCzcwByhZTr0x8G+8s26vAtvsdz5+eHmeiao0Lxih2zIE3BpD9RFS0hzmlePXJRK5mf4cdu4emeeE/5D/WwEKJLQEQLKai956VmkBAwJSORfzNS+VEdNay/KJcg0ONKu15BiyhB+0NpiAgvDcaLVaCgBba1W+xljmsF6vfg0WIEgzzSM1AGeWKT9HSV4nUhshTyQOJ6kCFKgupXnhpFX+3oKgNx4EXlzJOqLlUSAI9SvTzID2aD5TUmHPCGmv9CKWPnMTFYG6SPHwRdNoD/OdsdLjC0muBrxoyRoDJW8DJ8jjleM5dHvAXptdYWiubk/KpwO0TpXGAIugfKNDQNradPlg1JtTHVgQpgPVnpYYi3jjEZoX0xxG/XKwFF+U029zr0dklgPabPJ42mb9PuT1oly1NjggKBe0AI/W4IC6PG1RniMrb7NlvuUFRc6iEglHPW0ZS39Q5xPFQWmmJaaqnDqg1OnZY7oCrhTyKWwS/e0BZDpAch6gzi7uRZO0+O4t0JmTJJsVJTBRh+UNajqwnHHRohHx58i0ARDckWqPAV56eDEU7TYk69wk4i/0PWciVwuCHb7LwUvXePfWpiJBEN+c8uu6LiJLq6IBp9MetYi3QV35cwQbRuJBh8Ma72NQc5hodw+bjawcN8jxNbXJNhvldeXtoYzHNB0ENidPQkcmCM/sip6P5JHX0bvPTTm8PVVdnNfTOCJzcUxDLutCYIr/b71XS7uLNTjybCzEQ0T9U7fzvvPv3vgon3XXNnVJ7T8G21LQtMopPnsO2E4kc+5gnvmgXQJwLrzwQjr88MNp8eLFdMIJJ9ANN9zg8l966aV05JFH0uLFi+noo4+mr33ta+L3Xq9Hb3/72+kxj3kM7bXXXrRq1Sr68Y9/PJddCJMOUUwD1cqDUycVVZIsknioLIfXCTUUGeioePRGCDcnpQnS5Qsezx5tADfXZEaYuFRoT3YqeWDeFUPlrTU4lfSNAYfuW2TxscxPPGV9vQmzXrL21fm4PVDrpsqx+kXkm7E84KqBNDJztrIxna7LNpWgy5Ca7XEv6+JjyDYb9cvqYtDO+8nHRw6AU/9tIC3fvdWe6jlG2lMHynrkaQrTfzbgzDUv+cZrgWT0zrK8PCCzcqZFN4Cr1f8sTBzUlecZy8vRWde7VYMERTR80lQu78t5BjuqwQfSed81D9o35pvmHOBccskldM4559A73vEOuummm+iYY46hk046iR544AHIf+2119IZZ5xBZ599Nt188810yimn0CmnnEK33XZbyfPe976XPvKRj9DHP/5xuv7662mfffahk046iXbs2DHX3aml3ETlS6B8r3AjpMrr+aKhwRTniZyGbJnV0CT1spUmnmSP7gAJPQ8ptRc6T9oryqKyb9ZmwAEnUrPmSbhwXfx9WBql0sm4Z7cHmbo0n9AG1Jkp2OKjy7EkUDTOOll7WLfU+0BjyAL2LnAFG2/MmV1vPMYzUm0i0n3HoMzTAlbzUNUFAKelcfTMC8lR35XQubOy0XepCcrnGC+HS+gZDwOl5nglvOYhcOuZ7m2NCeKp2l7woLrUGgN4iOxnbWVmF+X0n2Hug5PzENX54FBZX/06NNihrvrFjoC69LtP5fScumSGd8wznzTnAOcDH/gAvfrVr6azzjqLnvrUp9LHP/5x2nvvvemTn/wk5P/whz9MJ598Mr3pTW+io446it71rnfR05/+dLrggguIqHiQH/rQh+htb3sbveQlL6Hf/M3fpM985jO0bt06uuyyy+a6O7XENwwiX9rt9eTvaALmGwZl/K7/hGlawoCLiEsqFWW+MwgsZAtUPuDzKCq77zlIJEE886klpWITFatLtQeF9cs2SVCKeTygVD1HywcHAWBLAvOiW4Svl6HlkaYM3p4cCEROgM8TSnLglvpV9S9rT6adsTcnPTdi0mX+Oy8LlSOyvlpgodwMbOmbax8sQBE5Q6qNxr1uM9DgRDRBlv9EtyfHEOpXnvHXBrf6Xt0vzhsR6qz5I9pDOU/iG8wUWt2bNDi9Xn8OOUIUkZxntn8NDQQ4rfcqxqJRDn+v+Xjtl0MYkBbl1o/F+aQ5BTiTk5O0du1aWrVqVVVhu02rVq2iNWvWwHvWrFkj+ImITjrppJL/Zz/7GU1MTAiepUuX0gknnGCWuXPnTtq8ebP4myuKSLIcmNhSfMXDPz0JFKliLSdjJPEkcnNLlIsGAi8YTEEfHBdwyWeI0rrz78JRzpjsVl6RLPKrm/eLf/ey0HJVPUohz+/xfGfgAmVsllE/nZ5RVyRlgQ4TRzZ7bcJDIMgG5Pb7SOXINAskfouFXOd9l5uc/awjYEFq3TAPAsDmeBWgQ7YnlKSNa51qNrAuTzVgbLp6vFoaYKmd4vzU51HAXrSHyn4lXt3utskD1kWd+qAt28wBJ1obLG28ZeqatjRB3I+Lj0UTkAfHWWTcq7JLnnKt9sBLVQ5ybcjqMsbrfNKcApyHHnqIOp0OHXrooeL6oYceShMTE/CeiYkJlz99DlLm+eefT0uXLi3/VqxYMVR/IpT5zpSTC/GQcDCBWowsWRXjAXk1ins5T38gO07GkY0n10zl5XDfEYtHS2m+BCbbFVHDmzyGT0N+iKi8L5FexFFdCLz4jqY+mIpI8d7Gg9TZuj6RyK0GbOtnhJ6jlr49CR0tvtnhp44UX2fCFBs4kIj5M3WfNQPu5XPMAAUvx3ivIkom1aV4GFC0TV1VXRYP1yjZgIuX4/P0eEGE52uPbCDN8y1Z4zWPxMt5stw0qS7R5jTOeuJT77tizIL3kZlLKecZZS/ZEqJE6Ha3ft5zPx0bUFBobTCd0Nm6F8mqbUZIQWFs4SCcPSKK6txzz6VNmzaVf/fdd9+c1cWdBImMTdUwC/iLeBrwGCxYi4bW4Lg26wRMHN+ZCpjkk4JLn6l/ui5r0bB8UHidepYKE0xiMaQiqeVhv7N+9Xo95tRqLRreWVRUllVnVuNOgraUZpdjScRY8yJzGAsJlG0G1lg0cyCJ9qhxBsqxgQlrczYWwXgtwUT6rJdkMdiueLk/S7YRtvN35prDwEG0kqcekPsHcubjwwJK+r1iHiKklSu+5xsYv5ffo8cZWmM6arxCoU6vVdZZSyag6P+uQZDxjDpdw3zNwR0rD2kciYpIKuudcUBRp70T4yw0hvT6UbXb0gQJS4MFboV/jcEj5hgGZfNJc9qUgw46iEZGRmj9+vXi+vr162n58uXwnuXLl7v86XOQMsfHx2nJkiXib66IS1f801LXWpI13yz5Jx9eKIqouDffnLT/BEFTl96ccF2iPWCyuyG+hoQuN0syyiFB7chmwByRUXvyPCcRycmQZkg+R90v3mYvo6nc5FI5eKHzwC0CSkRELRBBp52MLRBIhBdfrQX0MlR7PFbYrefPYQFF6KsCNtTEgwCwLKfef0KaBQweL0Kqzd9Zumr1yzZBIPPkTDQ4/lqVb7pFq/nakNfF2ynLIfFpzdcue7E+UMrrIiKhdYPjTGuLwLvnGhwe/WSbhOqdx8XaQB4PGTwcKFnAlVh7CPMw/xoUxcv72SNbkzqfNKcAZ9GiRXTcccfRlVdeWV7rdrt05ZVX0sqVK+E9K1euFPxERFdccUXJ/8QnPpGWL18ueDZv3kzXX3+9WeauJK0eRYsUstfz65qHiPu88AWBqnL4ouEcbulLKukzTa4cvHjAzcp1AfNYKPEKbXLZhmo4UkpJ1uLB4CVLwtVvV5bJmGtDzI2Q8roM6Yov9JYvj+XPwO/JfBrAe+VAiUguiCPt/BnqdmeAEyx2eSh5Xg53Wkxt59ej5ViSvrX4WmYjoQ1wpF1sFpB1le/VMS1xE0RP3ZeIC0j2u+ftsfpelWP7ceUgSG9NIpMxuK55LCCNHGh1myKCDS/T8p0xNXyqbxxweuOs0+2J+9C4JypAuTWnuWayFpQaminB08OAi99jRY7ydvPDWDWVmiBy2tP/9CJH55NG57qCc845h171qlfR8ccfT8985jPpQx/6EG3dupXOOussIiJ65StfSY997GPp/PPPJyKi17/+9fTc5z6X3v/+99OLX/xi+vznP0833ngjXXTRRURUvKi//Mu/pHe/+9305Cc/mZ74xCfS3/zN39Bhhx1Gp5xyylx3p5ZsE1XFE1MxU59Hg4W8HL2wQBOV0rx4JiFX69TV7bEXhFTOCC9HR1GBNpuqancDS+1RPEAbgkxvRT22FBIDJvx9+Dwyz4lsMzZR2ZsuHxuWpoMDHPisuz3qdTlPzq81JrxFvJziGfhAmi++2EdLliNOpFdj0ZLQ+TtDPJy9KApvhHp+6DYT6aST/obaY0Dad4q33j0a9xZP1AfHb48LlKgqxxpnyDFa82TCIXhGXPAQYMkbr3WggwNO9nu+BueCX6vVopF2izrdnnAyNk2PBtiW7a4aHfHPcwE56Jfuu629Y6As0GZLEzSfNOcA5/TTT6cHH3yQ3v72t9PExAQde+yxdPnll5dOwvfeey+1mUj9rGc9iy6++GJ629veRm95y1voyU9+Ml122WX0tKc9reT5X//rf9HWrVvpNa95DW3cuJGe/exn0+WXX06LFy+e6+7UknWoHJKI3XTbfLBTvcrfktDNs6jQhuFoXrT6uKfuJXJymBh+Q5Z/QMtoj6/2xTxcYwQ1Buz/TrdXRVG1jcnOFtV8IyTWN3mtKifxeOYn6tflOLUC7US2iHH/IktqZpq5Wn8w9fKx1CzfGTKFpjb55lv5aUWZ8Tqt/mvtFGqPC5L5+6hT1QfntAk60EaozaXA3JODKWLtibc51/KkuogAllDlkLkOIc1UxpOZhGT5+n/xzkB79Du3NGHWs9aO89YzGmm1qEO9mpD8fl1dW6uCTIZWWLYY01m/AqAUAXLz3Xum0OJTzueFg3DmHOAQEa1evZpWr14Nf7v66quza6eddhqddtppZnmtVove+c530jvf+c7ZauKsUW6iyl+62CwDC1Ti1eXYIb4VTx4mLu/V7ZFtzvvl5TnRzqheqnXdf+v58LryTb5qb33CM/4Mq9/5XPSlq6quWl8NsiU57CRo89RJ354DoBwfefm8HO3HxYuyMhm3wPiIjCH+u75uaxPR+LCFCP7d8lXhbeuJ7LFWObbzJwcC9vhIPM4ZUqyuige3xxtD0oSJ2yz9J/xxT56Zgm1yHEhLAFy/VpWAwzNR8flqCDa5tjkBYNlukQQUvPs8Y7YxPtpE1ImuQ8yZ3QSc9Zp9cjUvVLXXEvx4klTCPGh8eFpSS9CYT1pA/s67B+VmmuJ6C0xSz1eDDy4rbwTyQdF1WWYjJH3rvDPoTCsN3IRqOOLvw31eDIc7y48pn6TxRdw6i0qeE1MfRdXrUbmjWpKKtI9bm1P9xiy0M9YG5jk2sg3DStIGN9QWNuVoE5UpWTMVhdjA2GpjmWe1HwbKS8Ql71SWLke2G0vfcQ1OvjnrNx8x5YhoLAtQRJxR2buvH0McKEkqnw9Dv9Zp4kJzaWy63FdD88mQfcwTCRPPj1jw1g8Sn9Zz7HTJMJemejSQxuV0WTmWz4tnEuLzzAId6H3YPjgB7WbXWWP6n65DM9unLDA1n9QAnFkmvqgWn/ngGQksYmKwW9I38KjXPNoHB2sxWuI3NCn0xoMW1oiDqFyg6mzf/f4BHl5usbDUbCpdrJ2xTov2tCGVFK/ag0wHjnRlbmBt1q8ACKrbCDl40WVxKbUelClwyyg/ndneeCqefhuAM7venLA2IF3xF/puD7973jY5Fq2NxwEU4vgEg4cBdwuQy+NHrHJYXTWSvtV3Xq709ZI86as3pgmZTVSbkLO/5rHGmTmGjGeUafgMJ1px6LHqLxE4VqbkUe+sRogq+pCv+ZH34QFpa3zwd1atVXgsysSDkpDzuDaXpmJlAlRaMNQAnFkm64RiNAF7hmTJebiErssRwERsYHl7Mh8cZ0GAZiwTvORt1nWNiLoqfqFCbaN+qTYbodvyjCC98fCFxe5XKqeMosp8cKo21zmIuu+1zReNvD26LltqTDyeFF+VYyVpw5KlrMvyjRASeiS6hfGLRTOwyQltIgMKvF2eLxPanPJstj5Q9AEFf/fWJtevK6Kd6dVvhPy1WqaDQceZm1ASaNP4dw7c+L1WXUTyfeSmabucsiyYwDH9bpfDy7LOsrP8IH2tm2yDrkusDd54Beui5LGFOhgd54Ap+90T46kXfhaiD04DcGaZdN4ZPHGKTw+Fy4HDfjA2AykV5Ru4l/68QvwkeODG4/TLOgnaksDEQi/6Tv06fKdNlDBRTy0OgqwNg2c7to9qYAtC/5quK3338qXIaJL6Dax6rxaYGkwi5Nf5PZYTNufxkjPy/4uNt0b6NhZxPjf4pxyLiSfVVw86IlquSsWueap6LCAdcexEZiNr3keOhRCgTFYlxkedhst9Ptp8TTVmLMHHy8E8+L3qd++BUtkGWZcCwHreIw0F4zEPq3UBnrHGBECySMxpvg/GQ3nfCx4q66pf82JWBMv0FtHyzCctoKbsHpQnsutfrwEmlnTBN4u8HCrr6BqLhm2iyjennprIArwYkjVvdaqrStIGyhE+L1VdBNqD+sYp4s8io6h8HgGCDMnJz+rZf47kSTypX/UOxJ4GRyw+AZBsYGSVWK4OKElwKzRBCrygjYfzW4uvluJR33JH5JxH81lSqjhnKt3nZSk2x2LV3roomZ6heSAyIr+yzYmqNtf23dZwIbNrbu7wQYDoF7H5rMpCphXeBiI2FpUwZoGFJAAV5eTtqd6VtYHzeQ/ak8rJhEM1htq8HMyD3sdw76zqW72Gr35Mu9pvtp7VmzDtNXg+qQE4s0yjxiTFmhdHGxBZEIQU7/P4Z/tQv5z0mS8IXGrivLyuRaPFcJrqyI0QhV6mspCjHAcXPghkk9Sa7G3EY0g8TJrRif6Ek6Qh8aSXKI+OUO0Bm6W18ErQYfW9XvqO+D1ETp2uxkfOx2+xtFN8kbVU41pTCI9qYOPVywgtVOxlGxQPzGYreVr8vQ4AJq2x6GluZbSNX5fQ3gUOQXSfT0CKt0Biq3wf3jgrPvOs63m/NLj1cvOg5xgxdRHpIIV6sF03pz0fvsg4E2bwWmHDBkFS8MPjY4SteVaaCflejX6Bd5/7aM0fNQBnliktYtOlKad/HQKK2GCXUlH1v1x88IQYHZEbBtIIRCRiK1SYl7OoP2smp7uKh/Wd+7xwmzXlz6fqm+yvbpMAeI6EbjoiRxYopA2QxRjgxVkQaiT9TrDv5TtVL59nRrVAMsz0bEmoWpIFfS9+r/ef6Bn1WWYKBIJSXeg6/x7aMLoYcMlybHMHmtN2e+yNpxQkuk6bhbmnf597lEc9eKnVSgYAea8n3wfKUN1jYFPzIJ8Yfl23W2jdIDCxxysvl88PXzjMeXTfBtGqmOPDBdJU9cnSqqB+OakGrDVGrh9WXfm71zzzSQ3AmWUaAYOCCG/yYiPQJhEwabJygCbImjTTenMKbSp5OV722LFRBXBA6CWvzzqKIPPnMCZ7ZDOIhGdyLU+V6E+WI7UB1gbWb7OTM0RK+n6be4IH912CWwzuPAldSo0EeVpqfJSjkfHJjNCWbxUGQYInA9KyL3k59UJCj+2qpomKbTwWcO0574xnMLczGVOfh2tcjfYEtEU8zb4rIKn7YL9qQbsdKpy+e2MaRRGFBBbQ7jpAoTV8huxTa57N02f44CXyzgS4Ncrx52sOKHxAjl9a5ZvJNFPW+CCvLurX1eTB2SNI2mOr62gD55KKKV30pEpX8rQyHj2xch8cNJGrSVPwyPILnqo9RATV8KUGp1OcqmvllEFpyxGYSvXV+Su4yarYRB7E4S6yGWQZRJm/giUVIb8h0zeia4MOrnmwJKfQJseeYb3mQYJbSztjRazxYq0xK8EtA2/i/DBZjqV5wbmCBAtMQGcDPE/LU98eaFryNvlS46hBUFVXnfNn15hjkqdey1P8nN6F1S/7+QgfvhowxX1Z+PWMT2gmrTFEkEeXg8YrFxCIyAFKlLXbeo48k7FmQho1773a8zW11zGFgnJs4GrPn1KDQ40Pzh5B1WnITmRTuWhE8q5Ila7lO1OZaHB7qoMS7XK0U56pLRITueJJPjhEhR9OnaRiOXbKjdBRDYuF1QBBYOOxJjtfoHSYOFp8LK0Kb0++ofJy6iU5WzuBNmYbmFgSujAd1EjWXLLUZfG6O4xHm0Llc0Tgtvrfevd6A6szQXBTn+6ccCStlb7rI2Dk+JA8KHzX8yszwS0E9oonVBexfsk2ah5PK9kC5Vhgm69VdjlSa91y/IuwBqf64pnveboBVA7XOqWy+HW/HBtQWFouqZG3eHjfUztxOe7cCPiDCU07YZ4KBNma5PmkBuDMMiFtAJFlIyamYnYWVeMQRIHmgTMmL8c7YiFP9Je3WR9yhzancQZwJjvOybrlM7KiIGILlIiCSMCtbdXlAAGuxTD9YsA7k80JadSw7bt+47EXH3sRizkrF59W7pqinLw9iI+/V90GfY+10Qmg1GUmQwPg+M/I38B4m7nDuyWhSzON1y8LBPE2+8C1MB34G7MfJk799kQ0wDYghzlVjHJk3/UYqn/OyK+Ml19+B5uz0BSqjNnWPCu1KkY5WvCr638nkJjTA3hI8M20uyypYp05UGrBdJup3+ZYotC6PDi8XwsI3zQAZ7ap0piQOUmFFNuV1yr+4pMvGgUfnoDWIp+cjL2jIyynPEsq6hj1jbEjnyenu6Y2BJ1ki/pFRP0zgnKeolwq+2b7IvDNIC9ftKdbAcER4314zxppeTIzFqsrop2xk8bli3wOOFibyxu9umRfy3LYItYRY1GXVXxOM4Tjms1SfeDg18SDJWvOEzQHpnstZ0ueZl8WoxZ63K9IyLXUzGEeHt1i87RKHkvrlL7KNtsbqvV80jdPU1jWRTbgQhouM7uuEg51RGMdMMtN3LIveZtwOTqrdq0Pn3gfsi4pJNQLSLb2m/ptijgiB02Gqo3VharN9SbMJtHfHkGVk2BXARz+P1/ofM2L2JysctzziDTAQRJx8VlOZGD7t/we9IKQFoWpTtfUKgnJKV0DbS55ap6RJ/FUzygQJeQACu6nVOsASDGzQExVn/ql6+Ll1D0fqtXwScCFnyFRNY6KNvljDZXFoy6qhZ6Pxep/vjlbTsYc3PqbirymyxK+Xg6YtEFpqqte2u2yNmuS2gl/LHacdxZp86DJCa1nKLQKdePe9TvMAaDfJgzMNEiOvA/od6jGs2mehM9xcHALTZja34mtQ11jccDma8XDzZzmO6vWs4hmu1yHFhCqWEBN2T2IR+RYjnL64Er9O5HWBuANA050NZDzvDw5n9bgIAlUSEV8YVWzi4eKRyRQ5EQqJfT6KIhIFFXHqEvzdAxTl9wM5DXdbtckAjZ40wTBNwNn46lzSPRAiZSsfRCUlZX5RiRwa0uFdcBMn1WGNgyh4ePaskg+EHd81LU5Lzuvq2duPHJO43Kg+cl9hlabWd9r2+w4T6d37I6PxOI8w3ZelweCrLWT89Ud1pvqq/NlMv3B2O9F//rlBJLv5XVVPLGIRgyABWgH/eXleJo5mFBS10V5eywhwtMozSc1AGeWqYpastV/cEKochAyttSDXjnc6ZnI8sGpyil4UhvychKftZCN9W07O5mJKpN4hMakX45wMm7ByW6rWamKzLBAkAMEeKRZZaIyFl+n70g74ZqW1LWyX8JvKNWPN55osre6BcqV5Fi50wIs4fo4CIrZ7POx6LXbOlE623jZM7IW+kH8a/z5mtcVARS2Rs1uT8xhterXIJGa2fgA2hkr6q/X434h9jO05o9IvGf4HXK+ngBmeV1lWZa2jI991V/+eyX41YPkeh+caM4hn8fT/lM5huy9Q2rU6ttj8eBIvAbg7LaENicirIa3whM5D8/h4S+YaZPDm2XpZFzem7cttQVpZ3jV3qm5i0ZHiMjX4PD+1/nOiEWMNE/VXtP8xCayKYGWILBranmkRFy3YXCfD8zDD/a0QVksxNfMgwOkZhNMiQUTvy8iphYHZelzz3h/dVnWpmpJ3+gw1qqc/F6rLlvTUaHkmIbCW+jlfYkGOWuo44EgsKF6Ts+2doYDk8Qj65Iag5rnQ462hIGJunGmtQre+oFNSxVvj/lCZmsjXD9xe4iYT6UHzEwtV2qzAzpEOXlf+HfJo58P4jHaE9A6eRpp+O5p4VADcGaZ0sCZ5rsc4UW8Z0wsIr6w2ANHDkBZdiIzCy1fEFRCKzTgRRRV117IxsvjGiqwkJ/MzSSVWttuIEGfBxSFyRCXU5nxqufkqX1NG3r/c6a2eJEskjAPkoh9qbF+g7feKX9/0wHwwp2MbXU+lgo1mKpbWH2Vf/VeTYDHNp46zZynTY1oTDhYqAXkfLM0NuYOQy/2+mG/15KHm28H1LhZ/dJ9F6bZdJ/iKbWJrBzUbpmTK38fmQbHFJD8NUab7muBmRtFxdtczxN7H5aglQM3y0QltVeSePJK8933P0XOqgWEcBqAM8s0IlTeeDBDG3omgVU89WpfW10ptCVMa2ABrlQekVxYhR+GkHZlhSkXzmSnW4IFa8MUGhzJojZCazNgEzndp8pB/iwWCJzu2lonEUpfs6G6KmZhNsHvLGIf59qi2g2sZy9iWKOCNzCiOifj4lOYsRxAhd4HfxZem4SQoK7psiImOvmMvLlo1cU3FZ+n4wEuoQH2549MBOm0J5J3xQAB4vnUaBO5tsjyYfM0BiiTcas1uPDD2fn66QElrE0k1i9vDlFt3wYTNmwgjaInveNgqvchyxERYjWgjEfHeRpZa9zPJzUAZ5ZppD+SdKgjf+fYvCDLQWd8eFKjpQkZZQXbUnNVTlGpvE4kFyy5sMg2JR8cHiZubfJWuDn/Ls+JIczjbGCRDYP74CQAaGmdPI1S+uo5W8IFyopG8rRXYMH0N1SsDRB+CDWLGJHSzjjPGt3Ly9abWFUmTwZYafh8MwXenTVwr2tzXTlSIrbq8rR3qV+eVoX3KzVHz5/qvdb5BEWEqB7VAyUPtKdvUX8fe2PO3ynaLOvehx5DtfOsi9uU+3r17zPcAFyfOQ5eQqfN+zyeSRmZS/UbiRydAV0gjLqERqkBOLsvJRWzNJsokxCf7OU1Z0M1NCE8pb/pF8Imo7Vw5In+UlnG5HImxSJ2HpUFXuR5XakNqt3lZCdHAuPlGBt4O5/slqlr2p3sVJZjOS2KZGZ1Ek/YATD1S9eV2lMP7ryzhuS5PrKNJU9LjiHdhup7ccGLooqZe3i7rYWV+uV4eYkGA5N1YFtKxLg9et6b5dRq72boh8GdI4wxhEzl+fuq6kLmoKwcdU33q6imThtga6T5tchRJtLpW/KgM600UErkg7cIEGDl1CTok75FVl1e3+vrQmew6Z4JIVNdy/tFZpvnkxqAM8uEwpLdTcVwsOEbfHXNWnhttbjU4OBJYduacbu7rN0arfPzqFI5I3phZeXYDrtgIhuAS0ZjqTYLbQjuFw+lt6OoqgWzzpdHSkUGuHM2Jxi67IZA43IkCMI8Qmo2gXT1f8RE5UdRVW2yAUXiqZzjLc2L0HBKFijtulJq2S9JCExqGsRc7GkV5Mn2eLPEeVdUm4m3GdeFUh+Yaww5GpxyrbLHPdRMGe898SEeXr90wrfXBkszx9uExqJuTx2Y8tYYOT7yunIea10sPnsMcEbMRvn8KT5lFJVdjhkQkdpDvtZtvqgBOLNMpYkqqp2pHeyeCYIAj25PdYFrKKR/TfGZBrq1AMl067jdYywPTsdcfOoXu1IiDvTNPYYhPSNjEePlTDs8SGNgbbpR6crcdNv8OdcvYpFNt04L5h/VUIXtexFSwvnV4EGZnC3NS8REF3HI9MwiKCeTCYK6NpiM5LaKmFRlvhifxwOSLfB8vLFIhqAhgFIX8wgQZGRmj2mm2FrVsTU4MEzcaJMPJlnfwBjKUmMYiSBDPlHc964GvHSZKszMx8Uzb7uCrz/H3HWobI9nngR1LSBUsYCasnuQTLVuDa7iM5qF1ZZQc6nAmnxlfWBxQZsFahMyLWkebqKy/CdGIosv3zBMiS8gFfEN3JAKZaK/HACKurq21gkd0+HZx83Nkj3nenu9JxFS2a+Q3d+RwNI16UCM+zbd6Zo8EVCOTHT24ad29FMozBVoy3xBAtcl5qsxzmJ+KKgue/7Ykn792jDI/GHTMOt7Is8kIsdrWRKsi6gaZ2gsCs2L6kvWNweQ12k6RtlOzYWf7FkDza1Vl+Xvw3n4M/IdrPHaKYSxdE21uRJGHCf9dn1dbbHf4X7NJzUAZ5aJbwR1Kk1PuoCOdMbiLDYL7S3PbrJ9cKgshysDIhoj3e5xFkUVkUCtyTUiFt+aRcPTqnCwYIAy5BiemdXAYmiG73advkc2+Hbe93wRi7977vNhLnTOeC3aWPHpvlTtLj6nu7jNvBy+YXrOwRFfBEs7JcBkWZUDOmrKKdrsj0UJKBRPKenb73UEbJam1s3dLKnfZtsHRYJbo82Mx9Yks+dTs55ZQpb+npzZEcBBkapWu71jQ+rm2Rg7kG7K8SmEZldLQHLWMynU4vnKgX2tWdEBSoNpLuv93Hpkm9XmkxqAM8uUDrf07ZbVxhNxtKyzWfOFBaFwrjFC9aHJB9sk1OeYZxHLg2NNwBEWRWVpp2IZf4tPmceDFA+SriQTiqKyFjGuitVtxtEteFHl70w/ROQA6Gln6tTQrgMtWOShBkeBF15+1m5H+kagNOZjYmwqHJg5GYhRZGBWV5cgQc1LKK2DLEduYLKNus0u+BfjDPcLCkiqX8hcmL+yqhwrWzjSpkVMqha4I6pMVB5I9v24qnEme8J5ik9Le9dqtcq1YapjAwGokTbqirkl1GtlPUd1rClUPAgkOz6OEV8v613MJzUAZ5YJSyoBxG/yEMWcyWz0jKR06UyXgykiWz0qwIJq1Jg4i6pO4qtWREsCi566bW6E3J+lBgRNdysnY99mLa8lGuV1lfcZ/fI0OGzzrovG6vVsW3ySQKc79aH2RAyYgFUBaXAsnlKDA3g42EZt4DzdHjMZBjZwe6F3xhA3Kxq5nVBaB1sgsTcncbRITRRVz+nXiJivxsYTWmPY+kEWT1VOvcbAm4dVOfo+9D1iouJrQ87Tr08Acskj8+7g+tJ65glsUrCRbdR9c7NYw3em+9UvhxyhFoxFF5DXAE5vTCNtEfKbmi9qAM4sk3BYtRL0DWyCIFhORCLk9fEcJhLgpHL0AoT75oUdpygqfhaVFZ5abCq43SEprZ2Xo9uDTFS25sE7XoKy9pibpbcYBt5rJBW/cNpUbUw0BiLarE2XyN9Ucu1MxsLAi21eiJm64u8+YhbgZprM7yPyXsE8M49q6HpSPNtQa3yiOg5Pi/HosvV3MacdABgDbhhMlFoFckxvoD3W8yHiYyivT2R7Tvfqd8+EjUR+ZBce10lImOp0zfQQSONoARNuLrU0cz4IyuvSJLXfPkgOjWm3X8TajPs1n9QAnFkmHnJc74NTr8HhC4tdjqdirto03eELYvU7H8iofH2PjO6RPMLJ2JJkgdnIC5O3wBSapCZYcKS0kRHWnn5Btg+O7YfBHfdq31mXyIpcgYeRGgtL1+nXaL8T0x1raSZxInhyDkZOgulSAsmIZ0SNMzQW26ocVBYa13niRerzRPwVApF4ArTXAyVPW1S9MwxMPH8fqCX15o9lBgcHjVoCkrc5CZ6acjxN0CAZeIl4FJUNkn2TUL7meetnyaV4KpO7s56Lk8Jl2bpvnokOmwytcjwNTs5jCmPd+jHtmcHxHrRwAM7ofDdgdyNu+7VNNMVnz9kskenA2giIyPQdIao23ikjuqXF2hNRIXcd279U6dZLD/bCiqQHe9GwQhTlgXq4zSUAdDYVGebZb6NaDTm4tdOf14MyqXWqX1gsU0Zpoura2jSswaGM9JlmiEdrZzDA4QAvXdM8xafvRJpAB4/uwc/IOxpB+rz4czGiffDHKwfA/fY4mjlrQ0Xau1BETqDNmiJClDyqwXpfxWckcy4Rd1QHAAf4AnprQ9lOVY489wuXkyKpirP1rLrioLTbs03KMZNhBbbrtCpiXTTWmA5/Z7IYpXXDbS73DuLrGS0YagDOLNMg3uk8fFczQd8IY9EgqjEvlGpWrMFBdXntFgui4hkXYeIGMBNRELAYKBW5Uoi5YfTL4SaRtuapykkY0Mw/4S3iEChJHqRVsMpxfUfEQifbmKjyh6rXThTtrjctVf419TzYf6L49EwHGEh7QNHfVOVCj9vsSbv8ndkpC4pP6wgK3R7T/0qAF7/NAiwQppgQ4aU1qMqxQHv65tUlfIsI8+Aw8bxPg4AleTgsHkMez9ho8X2y0yUytG7o7Dhz7QxoTASgkCzQt8r390k3qrqQwGa8DxdMUd73xkS1GxPf5CKLhjlwuOnA2FT4gLQyvhJhDQ7ywSHyAQ5O1iWZ+GGbqSgrK7DI9hwAU1l7BpAuvcVwhC10tuMvb48sO2+PF/1Tv6mUx3242jQqy7HaXGmmvJD96n8vuRr3MSku5DzlGWMOwKnePR+LmKfbs6PaSqDkzDNkWjLHNDeXOikbYj4WmEckAzTffXo+zvgATq0eAIw4tdYCabL7XtYtJH3M4815XnYyl0KBDWqw7PrI4mnnPJZGerpja1w5eIn4S9afFG5rpDn4HyhlgRe2b6ZHoIqnRlPGtXdNor/dmGS6/uKaNQBdaYZ9RyeA6++d0u/BXhCsgxIFUHKkmfTVk3gWBaKo+AJllYNOCrckPs+Uw/1ZrMWnDO1n5eSgjFh7sNQsfHlMrVPx6aYIAP2ygJtn6hoDPjiemdMzCyRNmC9ZtwQPYKl4Ova7D2k6kOZF1YU0FKaJyt0si08pETvl1GlMIpGBXRsk8ygqGuT5mBuqPcdagb4jgU2/i4jjOJEM0kD9kn0LhDhzgGPwSD8dvJ5NsbxeWd/Eu68BJlwYU+Ug7YyruaxxQnedjME+ZR0b4pnB+fioHjWa+fNDcwZwNmzYQK94xStoyZIltGzZMjr77LNpy5Yt7j07duyg173udXTggQfSvvvuS6eeeiqtX79e8PzFX/wFHXfccTQ+Pk7HHnvsXDV/aBpMiq/Pm0BkbzzYvADa1L84ZWwqlgbHlhz5oiFpbDQHOLZ0FZjsEVOfULNKHuzPoniQaSlzaq02lTrfGU97F9HgIBOmfwQFfj4iisrwi4n64GT+NY6JqlM6IuflcKlQX6u+882gX7ZhVvQyMEcckaUZS/ZD98s3G7G6DC1XRAtYasE4j2pz+Qy9csDG42lwIgdJ1oHNHtX7xPSEViGnlh5nYBBVPHn5micSJm4JfkSV8MM10lY+LisNB2+faxIq1zMp/OFybJNQxOxageT63EV+Ljb07mHT54XmDOC84hWvoNtvv52uuOIK+spXvkLf+ta36DWveY17zxve8Ab68pe/TJdeeildc801tG7dOnrpS1+a8f3xH/8xnX766XPV9BlRxA8D+aBENDjWBp/qQ+Xwa1ZEASqHyF7sPOmb54Kp25x4hkx7M4g5Epo87WrRsE1C1aJa+uB4UpG1aAD/q5D/hFUO0yp4WkBroRst8+B0aw+STHyoHH4t4htR8gCmlioH1adNXYineh/1ZhFPiwF95ow2eyr/kI8JH0PqWlkOEJA86ds2L7C+m4kH2TgbBCgRJt8cVq0LERNVMqcjc0fpHCzACx5D0w7AiYxFoQWtM+OFNI71+4KXlwfn49J1VeVEkoDaWmIK8OTr2ULywZkTJ+M77riDLr/8cvre975Hxx9/PBERffSjH6UXvehF9L73vY8OO+yw7J5NmzbRJz7xCbr44ovp+c9/PhERfepTn6KjjjqKrrvuOjrxxBOJiOgjH/kIERE9+OCD9P3vf38umj8jGgWbd+QkaG9MWOCFf/WiW0ZZLgfdBv2/72QMeDxVbJ/PNvcMFi2gu4b8J/INgyqeVuqX3pzYAan9cvKw5KpfdUnaxIIQyrtiA65QBExdoj9n8Wm1ioM0u72a0NwSdNhh4uUm1sFtLniobHfZBqscxzcCaVUsoBg+iyq1xxj3EkhLHq4xiJjVImPaOnARRlo546zOD8MDiamFctxrMMHBNmGegPaKaLBs2NOB8SEzGdvzzCpnTJioUjmqLu4TZWieBjGX+tpEKnmsdQg+a9VmMc5SG82IPvu9jjGzvCXUzSfNiQZnzZo1tGzZshLcEBGtWrWK2u02XX/99fCetWvX0tTUFK1ataq8duSRR9LjH/94WrNmzYzas3PnTtq8ebP4myuSmXP714wBmPiIfO1MhKfUqjiLhgVwhBTPtEXWBJziKl1VFzed1C30EghIwhK6AxSNFiFbs27PKJukVohzBFCMhDZLvhn4m263WsNqNBi4HJ5V2kqzz8uOnCGVhhAEL5n/hLM5OVrA0tQXPrQztcl6RvYiPojJsFDnW+Mj1UWmYCPaY4AXLFnj9vjnOvExZM1D6vcraHozxlD62mPaIv9YGXsj1OMDg+3Uf74OGe81ILB5Y1H44Bg84mgVE1AUn25gSdlmJ38NBKUOmDLeBz8up95vyAYv3MznAdf5ojkBOBMTE3TIIYeIa6Ojo3TAAQfQxMSEec+iRYto2bJl4vqhhx5q3hOl888/n5YuXVr+rVixYkbleVQO9o63QFX/W1lfOU/d+UhEFQjyfHAsiQdpcDwHwI63OSHp0uJxpFTop2QsUB1n0Qgl+kvPp1Md1WBpnfgirkman+Q13C/Mgw8TVOVAM5bkSTk8pp328LLcyJWyb7Z/jdbwYS2P5EFlpe9TEQ0OjzYKOHaaGj62yVshtV0BXOvHq7UZxDQ4TpvbvO9pc9Ljo/+PMw/hZmmlEQhoHnguFN0vZAaHflxqrfLGmTA/qXffAjym+do6hIyYD85011yHhD+YVRcDXPYYKj59EERlXYP4lbkCW/mIjPY4GmCeRHYhmqgGAjhvfvObqdVquX933nnnXLV1aDr33HNp06ZN5d999903Z3UhM4VeePnAtiQVuCAYg5QoBkysRH8ScNlAKWKz1k6CqCx5FIExSQPZQaWJKu8XkXQyrjvbpzgF3O+X5zfFI+isxZBL8VY0FgJKFrjzJOIx4YODQRmRdI5G5fD63QRsAU2QHkOtlqehqMarNhlK/xFfAOBjyPL1Eg7dRps9YAJ9VUxNUH2ElBfdEtOAVm2uCwMW0VgGUIhsul0+FjXYZDd5KS0q8FK8DC1oEMWCHZD5yZpnU856VvrgOEJC+ur5oaAxZIF2puALOvVa46weuLraooCwOgqfDy0YGsgH541vfCOdeeaZLs8RRxxBy5cvpwceeEBcn56epg0bNtDy5cvhfcuXL6fJyUnauHGj0OKsX7/evCdK4+PjND4+PqMyohTLUVH9b5lEWoiHbJ5IFtopI8+JBFN9CR1IVzxaALWBl+WBIBQCbpXDJ7ttymELtDdJ60xLPXa4Y6ZRorKcugXBzRsB7Nozy65bn+hvysnhwcv2Ev1FMhlXPHZEX6XBCTg0u5rC4tOPkAIbhl7o2fuwpe+qrnoAbDt9843QHh+oLt2vnCczU1DFM5APjmfCI8zDgZK96Vb/eyktIiaqkKM6AjiqKKyRljzSRIWFBG6isk3cxae3Dsk57Y8hwZOByeKzAJu+FnDQDN6ZeR8IdSi4YL5oIIBz8MEH08EHH1zLt3LlStq4cSOtXbuWjjvuOCIi+uY3v0ndbpdOOOEEeM9xxx1HY2NjdOWVV9Kpp55KRER33XUX3XvvvbRy5cpBmjmvJJPh4YWXS6OWRFxoxIrF2dLOCB7nHCGtwbEmDZENuPh9Xlhl2niEv08ILOANQ2p5rEWs3rmx07M3Hpm7qGayd22NCV/ozAgpIMXbpoxY4jBz8eEHBXpSc2YWqN94vPDdkA9OQFsUAdKetBtx/JXHhlibU/7OLB4/umUwHjPNPtRcWuU4YcDQFCp5+DiztDMom21ozaOcdPI9bx2KmDmF/5UxhzygxE1U5Vxs4/fB5KyaIIV0TferanOdqU+YZj2wXYIpXI5MR2Cs0w4g53uL9e7nk+bEB+eoo46ik08+mV796lfTDTfcQN/97ndp9erV9PKXv7yMoLr//vvpyCOPpBtuuIGIiJYuXUpnn302nXPOOXTVVVfR2rVr6ayzzqKVK1eWEVRERD/5yU/olltuoYmJCdq+fTvdcsstdMstt9Dk5ORcdGVgqvw5bKc0/t1aeDlfDHTUl2P5WKBEf97m5IfvIh5VTsBEhc6JiYAFXRA/qsFSoY6UphyW6M9YoERWXFUZShhoSbLieAl3c6rbUL0Ih0p9bC1Q/Fook7GziGU+OJBHjWmoKZRjGrVbmAxrpWZiTtbqfTCtZM9c6NHmZGwGPSJLakbv1ZOsTTDFNI510nfkZGq+MVsgsUf83eO+p76hcqA5Hew+qawpT5uYgImbLFKOabR2ar8yxMe1oIaLlljzOjXz3gsBF+PM0s4ALY8mIUCaTsbV+LCd0BHYljzJRCXXs4UDcebsLKrPfe5ztHr1anrBC15A7XabTj311DLEm4hoamqK7rrrLtq2bVt57YMf/GDJu3PnTjrppJPoYx/7mCj3T/7kT+iaa64pv//Wb/0WERH97Gc/o8MPP3yuuhMmcXxAwPw0XRNS26E6ibjgsRYfokpDMVlzynNXaIswD28zalPIvMCkB2tSIPWo9Ry97LHC3GNJxFyaqc2DUw86prteOWiTw4uPt2gIkGyMjzGgXocLfWZ+QqCDyr6h9hDlvjxYy0OiLh+046i/4nvx6ZuW8s3AXcTNfDFU8tSBDi+R2wgzHVhaJ+R/ZY5X7j/hpSNQZVf9ijwf1uYaQJ7ajXjEmueZwVPfEtiGICgHJhkPixIq6rLL4T44lpl3umMnLuWmewu8SX8WA5iIPDi4PdL3Tvaj7IPQqKVyVF1sHtaBUs+cLg8qlmUvBJozgHPAAQfQxRdfbP5++OGHlwt8osWLF9OFF15IF154oXnf1VdfPVtNnBPCYckBaQZuBi0i4hKqzTNtTKziWgIdvt9Dl9mQPT8MLxlgtRHamxPKweBLoH45nmkJ5qaxeFwJjC9Q1C8Hb2DiyIdMAqv+t541DinFCx2R7dSbFp+pTtd0oOVlecnVItrE1gCbUx1oL9pTPz6k5gWXEz2LqlyOTBDkaTqoz2MDgYizMjd31I1Xz3FeminwA+IgqLyPNE/9Jse/mpGKcM2jjFLZU974QOuQ9c48s6vywUHtWcTNvKrsRMih2Tp/jz9H23HeHh8YlFogKJAAlfngaEImTGsN5gAQvbP5ouYsqlkm6WjqS/pEdX4PigfVl5C445Q3qhaEiDkMVVapYm2Vrt7AEA+eyNaGwTOx4sklVOxGMi/vIDz+fHoWMCknO3e2lMQTBtblMEn1oYKk/xGuS0biYf+ryFlUvE1+eHdayOwxlGl5YBgwibq8TY6DKS/Dbl22Vg+YSF8E3G5kXvCAQJ1GKXJ4oX/QKm+z1S/qtycQutyzgUkLPmdSPPXgBY/7/OVrR/VoqgHr3Q+UeRutneVxJ/WZrkMaR0eTLE1CNeWEgLTjBwrmRkSo08+RJxNdiIn+5kyDs6cSPKrAkb79vCJVWUQxidhzMp6ctjU4rcBkz00Qts2em6i0pDKC/B6MBdFzyOQboemLADQ4+jmOJDtyp2du8lyjVJdTxQspxf5XGJR52UGRBsdbfCImoSkHJFebim1e0KYD1x8soMGJOCv7ang+huLAxCunduPp9spKPHOPGQEjwK0lIFHeL2Pck6MNwOYnNZ+ZucM0ibCvJkhkDyNkBnd4Iprk3B/MXs+8iD5u5rU1t3L+EFHmiBwBC8LsaoEgLtRFfL3UNd3miMO7J6yXQp1j6ppPajQ4s0xiUBgOXin6iSioVQmcERQJ350eBEwhHiV9o2FcgSDHRIU2nmyBppLHnFwBSVbmwfHbw8vxjpewHFbR0RFaE8QTklnvXkbiGW2GG4bkGWWVWxF0vCwPmHBTH1Gd74wNTHhkU1051bi3eaQjpeahPo+tweFmRTtrdL7JR0BQ6IgWMz2+7cclTFSEeZCk7/WrzleDyPZnifjg8LIiTuiRMHH3YGDNA9czBZQAzyJwzE2+yec8IfOTI0TZ44z65QQS9DlrZ4u9CwsowcSUeo1pV++rMVHtAYTCId0F2t0MZDl4QajnqY5qSFIjAkrFp2fqGkb65tfK78L+S/12W5OdLb4R04ExST2zEQwTN5IzSnW+rotLM7gu4YtQk+SR12X5haT6irokT9LgEFWLLxof6T7P7yEUAs6eo1WZNsF4vhGu+RYcWJtHSPENw5d2oxqTkNRco1XxQBneVHA54uBKyaJAmdWvvD2eacnSbvJvg4R3R8aQr+WpN5VHolQ9s6s47kTdZ5WDyhLAxOgbAh3eezUjPoUfV94nfo+XqkOur7KviSrzPgujXzj4pjFRzTbxwTblmITaLRkh5S0I1mZJJBdNsy4lYUTAiyuhO+Ug6crM1sqlSwNQRMPNLem7fD6B1PfclBOxj+uHxE1vlvQN/a9kc0ISOv9qtXmMaXA886ROeOZK3x1jxWQ8EdNSZANLgBxls+UAmIyFNd3G/VAiif50bQhse1Jz+s3WqpCZ7E1scjUaR3lMBQYdou9OeyyNEr+lLgcQ5zHXoW6vZo0pPqe8vF7lGOLtxO12/cHYs7baPAoATj4+ZJtRWSMAdHhrTOQIGwtQlOPeWT+QGdwCU14QxyjwwVlACpwG4Mw2IdMBeuEp+ilybk8sx41dzqgCOGj8tdRk93x5PJVu2ea0OSFtEbcjWzb71J4STTjmJ0erglS6nsq/PjKh3vbtnY2FgIllNnHP2AJAKVt82IVJ55TMCtx6Wazlu49EWqHxmi65hymW5dgmKrz44s2g07U3Z5noT5ZdlUOMx3gffAMzTWaJxzELcI1JDSD3tU58Q7U2MKraU6O5TPWh9vC6I9rdyIn0kZxMIf9FZw3Wpnvsg1Nc2xkALzETVSTXVl52xUMlT72zsj3uIw74EUfkpLWe7izMPDiNiWqWiS9QUwOBF2ehd1ObF5+uY2dbT3YbdLg+OJnEk/PkWZNROUx66F+zNh5fg1N8SinVLqcus/R0twqntjOR2pk/+ank5SIW0OB4ZpM6f46Cz07gWOZAmvbeR/FpHeWh24Taw3kqIJ3z5JuTs8kFNkvfgbj4jPg0eGChhd49eNaJp07r5ua44SZuQ4shgXS/bNPfJxZtU6eZSu1GdQktjwco1HtF22A5hgLHhvjmUtUeUJc2uyKeRaO5BscyF/tHR/Tb45rTc54MTFIr47HGtNAEOePM8quTeXkwzxgy7zcAZ/clbKLK+fINI6cWW8isciJ5IyoNTmQDi5uxIiYIrAmikqcu9FSGmxsLvePzIqOocDn4qAa8iHsbhmyP1ebq/zrpW4SUOvl0PF+VZKbaOR2Jfgq8V0cNrQGwxzPIUQ3eZsnWcHN8uKHSyBcha3Picc5FE+PDB65e+C43SVjzTJxeXWemYOPezM0i0v7rcqoLpn8a60Uk1YDrYJ6lEbDBbShjtrPp6ozIqD3IB8cCFNJEZbTHOVaGa1UsH7VK60a1ZnDXN5ELWsa+wPcfW4NTAdLGB2cPIOlkHAELnhq++PSl5j6Pk1ytcjL2fYKIYqaD6YjDqqPBkVFU5Z2wPZ1A9ELXmezY/ITLcR2jhbo2bjpwTUvGs5aO0ekqXgxTfaguor5WaYppcND40Kp6J4V+xAl9kOM+IHjRgBx0TOb6SNdwXZ6jLR+L+r7qe/EZOfag2yNqpSFrApP6vDxE9gbON/jQUR5d/BwjWoUIkBZaHgMk8rKnHPM1SkWhSTuhh3wBnXI6xvMhqoSfndOd6j5rk2dAYZi8TUjrZicMjJ1cbo4zEM1p5xCzXQC41trq13xSo8GZZeIDwNeYFJ8dY2OWPJ5ErBYEmFxNLwh5OcipV1NENRzhQYmobH+W/Jr+zjMi55tBDhZ8ECSv6XLcs6iYNsDcVLhq2DDTtMEC5Y0h71nzPB6ozfy+UKI/T2qO+OmEJHQJpLGTcb+cADBxQ7cj/izl+PCASfHp+dcgQOH54FiZpVEUYq6ZqtpT56PEtWAWD5Ft7mkBHs987Ql+LfXuI6Z7NBh5Tqq6crz2IBNVtsa069vM1zw7GWLxyf2mrPca8TvsUX3+JyLblwkBLr2ejTIfHGv+zCc1AGeWqdVqifTVRD7oKLMCO+DFU7PyQVjUj8oh2R40AfufXlLBTKMUAFxoc6o0HfUShsiabNTlSTMjYnPCfeOgzArbrzan4FlUBpjiZVlSasTpmV/zUs2PsdOQUV1E1fNwAbkar+44c8C2BtLe86k2+HpQVtSH29xzN4McTNpZte0NA+WU8SRr04SJJGs9pkU5WCDh4MUyHfB+2ZE01f+WMCY1OBHgWi8gzTQZYK5NtHk80F6aqBwn4/R90tE4tiPvHq1nRiJI/7wqNqYp8chy4OnujoBgpr1goNVb8+aLGoAzBxRSsyqeyAaGymmpye5FP1kHwfG6vLw8eSQN2sD67enYGyEKg7Y2DB4Kai2s7jlTbKGz8+Dk+WuysFux+MhrqF+R/BvW4sujqLzQy2yBBkypb5OhsNt60OEfLSLHUKzNOU8ozX46FsI51BU5/uYLPZVtthMG5iDINy/4UnPHGffYNwKPs8iRD1JL6rW53x79DIV/DZ7TyFw69Hstwa2zDpXPsd6Z3QdTJHg8HzbPB6dc7x2No0goWV6zgXQdmPSisdI39ywq9M5Um3kKhYiJylvz5osagDMHlEvE9obhqXRLjUlIKkqTC5XTCrdnIMCVVyWkS6Ia6Tuw+HrnXqGDErPNiYdc1/jpdLqDqo9xezynPH7NetZoI8Rq737fHK2K1uB45slItmNLg8F5Ir5eITAVyIMjTKrGO+MmQy8iyfblKT49p02u4bMkWVFOF29OwkfL8M9Dx49YoINvThZw67k81f/eOtRS77UuuMAqJ2IuHQQoeXMj1ybmPKNq/qCyIi4Aes4jPuQzFvGvscYH11pnQJprcAwBWmqU+vVnZvl22S8LJM8nNQBnDqiyS9pOm3mIYmSy5+VkwMR13Isn6Bta6+Qs2GV7xKnbqX5dF4m6II+QMHB9gzgZR8O77TDPBDb9U9mzzUD9zjUhvukxbXR5GxNpCdQDJiHp202hX3xOG5s3vy9ybEgZKlwDkqtrmEeo6o1yhOnACbnW1zKebtDx19AUojObvANkS2dUQ4rv9nrm+Kh8Ncj0K5OSviwb8UUim7wkfrlfTF5OPl69chzAFVnPwDjLj9coPitH/vr1FbVJmp/SHMLt8cxGXBNkaS55E619gQvhlq8kP6rB8j2bT2oAzhxQ5kMQkWSdyJWIWcDbUPnGW5QD6spOgvbaU3/Sr/Wdt7HLHDItHpE8y9N0WD44QEK3QNB0t96uTeSYDsDiE0mIZ5q6nE23KIf65dibQcrEujMSQTeQZF3fL08r6Wpn1HjFQIlEXUXZxgbOvGg9AEyGiQpqQ4x+eQklkUTsbeCWT1TENwJpZ2LJ53C/Eh+qi6h6Hp0AuHX9YpTZaBgfNl6Oayou17z6iE+ewdsCrqkcb7z6oeSpzfbaENEkcyBt7QutVnUeogVcBxEgeQ6xRoOzmxMKO854AhJG7heTl6Ol5hjPDIFSYJOzvou6+MQxND/uqeQCKBFsUyRKRuTBsUwHbKZYkWZSEyT7IftWfJqSEzR15fXpZ4R9CPqbpedkHJGaA2Bbm6i8MV3mbXI2wik3ikqOV9TumJmGynJi/iyy7ETydGajLhYB44XUap8oK7eT3MB0v9jcMDfCvF+WSYSoRkDS2hD0XpUQFTLfwkg89XxylpDGMTJ/kCbZWuNCdTnloNQHbph4jZ+O1ATZ42za0JRibZEsI2mIm0R/exANEirtT1IS5SAe7YPjLZiDRR04bQ4kDLS+EympudRQ6HJItLnV8lWxdWG3XkRSJH9NJOpAOn86i53SUHhtriK/0PuQ7XFNVBGzwAA5btzxGkiPEAkBj+TBiWw8AgAbYeJ+Lpjigu+rUoGOSHt8c2DxaeWSEsc5GGYsMTcMyboSoMj0UeL1J00hemekeHxA4eVbkptuKNO1awqNCGxehKEc90S2P8vkAFpSxMfBtuXvxN9rxHncAziob6g93JXAWjunOvbaOZ/UAJw5oHLAe5mMB3C48yKbYpJTPQjKwBRo88gAElh1jz+xKrstnqQucCvL4dEJBugImqgs/xFhogo4f3bKhSVrNjuB15f0hRNpXkzm3OjVFfHB8U/vVgAHjQ+1Ofk+OJHNKZAHhzmhWzy+BgeBIKscJ+9M/2vMEdn29SJCUY8YlBHZQguOgMFjWp4WnTWnEmxcf7DiszogNeeJaK21Od037ztjGgAKsz0Dal6s91qZVPO6srQglAM8BKQtHmG+Vu0RGZFDwLUetFspNsZG0FhcOAinAThzQDo0N2YSysuJOMq11CAd1pRRtadecvIWlvywPtSe4lOGXONykinD2wh6YuPR5SSQ6IQ6ssUnPaMxtUrBqAMr0V+NRJwOqLN8tEbEQkdmOYOAwJBzsHt+WPEZStA3gObSNak6GhycfdrewK0IKXlWmWyjbrOXNVmYw2o1JnbiNM5Xd9p8weM7iFLPHveVNsAGQfy+jtGvoo36vXrjrL6umLZZfsc8kQzedl068qvoB36vocz1TloDaebEPOmrn8CRqnL6zwiunzUArw3GvSfAVqbprKp5o+aohjmg8X72yx1TRXpvvMnLSREBL345niqWRF2u6WCGgCuiwZEnOONFPDOHeccHeBOQSZ8WWOALXbs/STOAIzQ4htTM3qnVHiKgVTEWFqIY6JipGj50eGE2Xu32eIfDajDljY/SB8eJQvScP6sNPHIWVSycOnTOVM1p870eD7nO+2b5EiWS2Y7rNVN1fm6en05Vf891Zs/GYgCUutqikL/gICHgdnv0PehaJKorkoaj4wByPl7thKN8vPavOZogT5Pc1mtD9jtrj+FgPsoqrywWCwfhNABnDiil9945NTvgJRJ1MFuHZIbMYS7osBfjqs1Vv0qzgCGFeKcK8+dT59gZ8a/pdns0SQng4I0wlYXqEiYzZ8Ooc9ZGJghvEY+kiI8djeCNM1mOZ8aaCmyE/magAI63YfTsDYP7mNih28Vnl0VaZQt9ycN9bPB4FRuPIxG7GpMMiMjf+S12lEzVr1jaf1B4ulTWhftV3CZBh+cY7mk6Iiktcu1dPaCIPGcI/tX8GVaowz5juByhUTM04v6ZVtQvx09XkWeNxmu31PDJMkbZOjnpOKHPFzUmqjmgRUqDE9GG4AlI9Tw1akbMk7cnkjxLq2tR6LIXZlrylFoVMlX+us2+icrOMIscMl0fHMNExfmsKCF+FpWXFC0tCqYTKQBTEXU+ktIyXwR3LNaH/8cc3u13lvnXIBONHveOGcc9qkFoKOS1RMIxnDCPMHXVZDsWB3Kq9pRCjRMBQ2RHzug2E9kOu1DSd9ocy7ztJYvst6d8Z4hHjg8o1GVh4qg91G/PINrmnKfuOcNy8mJqAakshycMtAB5JNWAZ3rk4z6tDYG+GeV4UaEy8/bC0+A0AGcOaFF/p9kxnQCOvYhFEmNFVLEx05KjUep/xpJn1WfFTQRNEMjhzpjIVggj5/E0ONJGjMECP6phyjBREdW/M+7MbZkFeJsslS42QdhjyOUZwGnTNXVlkmzGkoOgANhGICgW4kuiPahNiUdEPxnt8aKohDbEACacx/Ll4eawSJSd1S80PrL2sP8t7S6X9C1wx/lcX8CyLk9jkMqZobZZjWkPKHnr62gEmJSHlsr2ccqAElyrSLQ5YuaDZQOBzTuLqjpbsH4uemdRmabZdqu85p13N1/UAJw5oEqDU69mjYTCdowFsyi7+LQiLoq6is+BEv1BHrnJeQuU9Z2XzR3uNFfiKaMyoKSfnk/9pkJk+4+gKCptoirKopIP1cVVup5Pw2j57n3bN5Hv0J2ZBVwprT6KqlrEvbqGN60U5VC/PbakXy30trkjkhkWnUWVH+fQ5+nah23yDcMoRpRTl7KgxzR8HlDUZZff4Zi2550171vB8cpBIGoPb1Moi3VEwxcIJY+ktHBBsgaSTjZ5xgTKkd99zWV9m736eZOt/nNg75kV075gmZT5umCZZomqZKJeYtv5ogbgzAFpJ2M0KbSZwlvo3KilTDsDeDLnPnsiddzFh/ptts0dEbWvPtcItVsfYOdJPNx0YG0qRVm+iYrT2KitwbHaFE30V0VR+UCJiCXEgws09dvjmRX1u895vDBkXU5E42h959c8dXbE1yvUrzR/XBMV47HMT8zZMpZ7hPrl4I2Hm408J+tEaE7nmjnVZiDpW6kPvFw5xUVZjsPCcmQ5oMMBt6GUBQok47Eo2+ytQ1X77PbocuW1wLjXwqEzx0SbMpM/B7f+uxdnnjmaJxso8fUsIrDZz3q+qAE4c0Clk7HjVZ6uVVK8AzoifhgDHEwY8sFxFl7/TCt1j1POFNfgGFJq1Gxi2oiFiQpLhRDggNVXt8nqqzy0E5Sd+eDYC6R3kvxw6nx7fCTywLYPkuV3N2V9QNKfdCKt9Abmmef4Cc6aCwMTzCPD9nF7pLlH8iDfiIgk7419ywmfb4z2oa7E+hUQfgYwg+M2kyhnpmkvvKMj9FrlzR/rO6of+x3itQuVE4lkFWU7GhxzLrKxWJmo8rJzLZdqDxNEfYGtuLbTyfs2X9REUc0BLRodISLfyXg023hynpjdth4955N9yAmo2uxFSOlyEY/nP5G+T3mbHPNpaCXpW/GgDMS5D44NQlBZVh4cHZJd1JUVA5yV5e+RRG68H5Es1n6CPlwuula+s8CG4YKyQTQ4Ho8joZZRXU7uEe7YWUZRZf1gwCRwXlVddEthOqjXullt5vVbWcX5PaYzKqENLG9PBNy2Mh5Ujhz3/piuf/eWwFK0R4OyvJzMBycQFRrR4MCQ7Ei/HLCPvkcS9EUSSlpBE3y8Won+iCp/Re+IlvmiRoMzB1Q6GQd8cHb2HZGRU2ueQdTbeAIRDgGg5B1wWOZvccxqMYc7uTkR2ZJ11N/HSpyGo6hUm4GqOuLPktXFABdqY6JcpZuxgPw19ZuKp+mInFqv7xHXZslEpaVGv802uNX+R95Y9IC0yHRtgGSunanwnfXuK3W+5e9DxM1vdv+tNvP6pgxtCC/DMoW2WL8iGiVf8yJ5IkcDRIQxb+0cJOmk9wz1Pd41Tzj0yyk+I+kRxDUjOo6TZUbj+XS8YI86TbLIqB4ASgtJg9MAnDmgKg+Onegv+eBsn+wDnJAaPi+nzvGVCPkr1G8qnoThalUCk70KN/eiqKhfl2OvDywSEalI92Os3XbBpAVMIhotXp8n8QyTRt7VugU0c7pceY1UXZF3D+pK7zU0zjyza789zvMpgaQXmluasWy/GC4R22kNis9ej+xDEAPaRM3Hy+ak/cFyUFZdMaOoGCC3Ish4/b7ZqPistCpDjiGWm8dqTy6w1a+dIfCSVxXiiWncEiir97l06w/wcJOqJxzXad24KTCSuLTR4OwhNK58cLDdtuDZ3gdB0Kk1k/SdTc6RiAeZgN6iEdl0PYfZrM1uiK/cmDF4yC65qljzu7oHmaeK+3Abq/bUb/pEVVi662Der6vyQwE85UJvbyqlFiNgCk3kAS5XY5Kp/O13X0n6eTmtQL8GAndOtmOpnbFU/sWndzozitjyAJ9roopoDVTfvI3QypMk+5WuOWuDZ6LKjmrIWLI5PchmzanSXtWD9kFMgfg5k+Kpb3PoDLagBicStKEvcc1ceVSDszZY2sSoSbUKmLF55osagDMHlDQ4k47KrkoKV3xHJiptfkLjJna2T0QKKD4958/s1NwZqte9c1kywBXQxBDlUQdE9YsEz+VAhMEmKkdXj3x53AU6BCZnljNEbwYhJ0lnDEUyK+v2obK98P/KgTbiXxM3ZRABHy02fywpFR1e6JkF6iKtiGLn1Fnfef1WRBL21cDlcg0OfPf9z67z7rV2xj1EdAAzjQeUPK1CuhRxQo/UVZU7XJt1klQPBMn6NE99G7nmpRN4H/Z5ZvkYwhocOfgQz3xRA3DmgBaN1L/wzCyC8q6EUD/1eerNHYlivjzexlx/Lov1nbeHbzx5e4rPyOYt70Obof7ul6UnrHWffq7R9sQSjNWDoMyfJSLpg8piwEQD6ZkBJes7LztiCo0cdZLKQfUhh3fLLOFtGOWmwvLgZGAK9SNwLaYNwW0mqtfyCBOEp3VzzR1qfDjjbMrV8uC6B+dJdcXXqmHH9EAa8oE0SkAjHlhjIHB1HJ8tzUvkeBqr3QuF5hTgbNiwgV7xilfQkiVLaNmyZXT22WfTli1b3Ht27NhBr3vd6+jAAw+kfffdl0499VRav359+futt95KZ5xxBq1YsYL22msvOuqoo+jDH/7wXHZjYFqkNABocOlNzjsaILKIeypmPXA9TYOnZtTOsV4CNt0HxCPTllvt8ZK95W308j24PKydi0wTFd4cdZslT6CcwCbvSYVTAUdbd2EdQAKN+D3oPnjXPP+ayKbrOVhr3wBUXypnkvFY/fA1OK1+m+2kkxGfNXTNOxLFDgGvvlv+V1wT4jmRajCJgWu/LlcrmXjq/a+qcuvHkKdR8g5+jeQb0tewJkh/d8ar46eib4usr+i+iEmVyNf66XI9QTMX2BYOwplTgPOKV7yCbr/9drriiivoK1/5Cn3rW9+i17zmNe49b3jDG+jLX/4yXXrppXTNNdfQunXr6KUvfWn5+9q1a+mQQw6hz372s3T77bfTW9/6Vjr33HPpggsumMuuDEQa4Hg29EQYdBSfg+Qw8U6Nte4pyk511YdlR/LyVOXaYMKTmrNEZo4mCPUDtbv6nvNwrU3URDUIcBR8BoASdSkThKcJSxvqsE7fkXcWOVXZyyJt1eUBYOsefi2iURJh4sZmJP108HjhGYit4xQ8c0/EKZ63SZeN7rOyT/N76hK5SZ68roh/Xku9j4gfyrDmnpAJLwDsc/+a+jVm2Pa02fiwymm1WuK9WUChbr4ik2rEv8cSMol8UKrXs4UEcOYsD84dd9xBl19+OX3ve9+j448/noiIPvrRj9KLXvQiet/73keHHXZYds+mTZvoE5/4BF188cX0/Oc/n4iIPvWpT9FRRx1F1113HZ144on0x3/8x+KeI444gtasWUNf+MIXaPXq1XPVnYFIm6jQ69aboQZFREw17EzS7NTrwKKBNuKIijmSNyJ34EU8xacXJl4CrjJUuL6u6LU6DQ56PkT10mXy5akLEx8J2Ky1BicCkiMSsWcO89tcLZomT2QzGEB7ZZVLhHyC6p8hLLsEkrYGhzsQW5lhud9D5NiQquzsUhAo+s8xbZa9Xn2iP6K6Yw+Kz8iBnJ4DcV1iOaLhzEZ+GgF7fOS+I3l78voD72KI98Wve8n5iIr+doX/oi6j+Kw7TbxuLqIcYqjZ+XqG2z0fNGcanDVr1tCyZctKcENEtGrVKmq323T99dfDe9auXUtTU1O0atWq8tqRRx5Jj3/842nNmjVmXZs2baIDDjjA/H3nzp20efNm8TeXlJmoApMU+X1EFo1RDaaGVqFSv65BHABnxtPxpOaQ1FhfP1HMV4ZfQ+ZCVA6qS9/rmfo8Hm0SiqT0j2i5hpea5fdIxElsc6qvyz8osN7HwjNRoazapk9Dl8zNh4c3J3MXElpCYNvYsLxyPC2PJSBF0v7zcgY5bBMLEZJnWKfaCI/2G4pocGIms7yciFDnaUfkva1anjqfOemDg9uo60LlYC1g3h69nu0RYeITExN0yCGHiGujo6N0wAEH0MTEhHnPokWLaNmyZeL6oYceat5z7bXX0iWXXOKavs4//3xaunRp+bdixYrBOjMg5QAn58l9cOzNwPN50flzIlKIbyNOdeXlDJJhteQJqKpRu9P3ugPcQpEQmeZpOIBT52RMJLV3rZYlOdYvCKlsLxIvlC8lIO0Ok/Bspqp6rz2R5xzZLFHiRc1WmajqczJx7Yy1OfR6vfJUZW9OW3Uhnljkm/2s68KAiXxNR9n/kEN3vYmqdPwdEpCHwrv7l7yIrYgPjrUuiWuRd5oJI3k5ug0REIT4uEnVy2RcN6f575GcXYkWEL4ZHOC8+c1v7qs/7b8777xzLtqa0W233UYveclL6B3veAe98IUvNPnOPfdc2rRpU/l33333zWm7xgM+OFqth/w+2mrxhZulWkQjXvaeBsddfCJAwZCQUTneSdARJ1Le7qKM2AREbeKgI5oHB01kDm6tBUq/M7iwKu1DSDMVUkPndenbvA2srGtINTwypdSW422WA0T0tcH4QH46lrQtNEHGc+30eiWfXgeIYlrAQXyQIuVY0jd2RHbKcbId5wdy2mPRO7V+OC0xKKc0Pcajf4YFmyGNY6AuXZ9l6sk1cfp79ZwrlwNQTo0PEs4Cj/qm27dwEM7APjhvfOMb6cwzz3R5jjjiCFq+fDk98MAD4vr09DRt2LCBli9fDu9bvnw5TU5O0saNG4UWZ/369dk9P/zhD+kFL3gBveY1r6G3ve1tbnvGx8dpfHzc5ZlNygFOzpNpcKCJqvj0c9NEwFR8UY3kTdDt88p2wxMj9nqnPVVZzsIL2l23Oc+WicpaoCLtSeVMOge25kCpHgTFfGcCi/iQG0/ERJNrefJyIj4WsZPti8+prq3BSV+FxtHQInS7ld+DFxlZlZOxDKXF8ECQFUqOnIzRkC37HznqpWtrgIfR8MVMVGg9Kz4HO018yDEdETQCbdbXEVDCbdIgrCCevDLSfw84DRLebq1780EDA5yDDz6YDj744Fq+lStX0saNG2nt2rV03HHHERHRN7/5Tep2u3TCCSfAe4477jgaGxujK6+8kk499VQiIrrrrrvo3nvvpZUrV5Z8t99+Oz3/+c+nV73qVfSe97xn0C7MOQ3jgwPV2WnRcKTU3Mk459HXIhFbcEMNbE6RxSf13TiyR7XHBkG8LKs9RDEJg4OFqIkKbU783Vu2aO1vhd5Hee7XtJcLJgLc5PfQZhlw6MZaHvU9oAUM9cvZMKxEd0REI3puOHX1evwa5uGRVtZc6PUqbRAOHMBly3b696D7vM259MMwgBuR/xxzXx4w7/ufna43Xv3vRf317ywGyBPgckwrQwhsIWd/B5BX33Me3QZ7PfPbzd/7IIkFPUf1gY68WEAanDnzwTnqqKPo5JNPple/+tV0ww030He/+11avXo1vfzlLy8jqO6//3468sgj6YYbbiAioqVLl9LZZ59N55xzDl111VW0du1aOuuss2jlypV04oknElFhlnre855HL3zhC+mcc86hiYkJmpiYoAcffHCuujIwLRoZEd/RxNELpHYWJpLhfkR4skeySA6l8g9MCCucmN/qSc1eOdniHJBmTBAUAALSBwcXFNPg1AOufJPP330aD9XGM+QCHTJR1fcrlAwwoCmMSPEZSHbGoqtxDEjfnnO//j7p+un029PrlXwIKOt5Puwz8hxCo+Xw790AeIlErLln4g0AyiqejCUEKBKLF5Y9jEZpmOcc5SGSfbOAQh1ILtfOrn8ER0TbrsPtUZMiQRPzRXMWJk5E9LnPfY5Wr15NL3jBC6jdbtOpp55KH/nIR8rfp6am6K677qJt27aV1z74wQ+WvDt37qSTTjqJPvaxj5W//9u//Rs9+OCD9NnPfpY++9nPltef8IQn0D333DOX3QnTMBocHVpe8MjvdRsqUcy05OVr8c4Iyv19cp5UlpdgKuRjEJD09b0mT2ACSh+coAYHPOtFoyPsd1hMDm6hiSrwjALAdRgT1WxJuxGeSPi/p8Fxz2nT0YMBHiI7Smfa8cFJ9ySNG5Gf+sGqK8oTMjvr70a/iGLgxT3OoX/N0xhEQPJwDu/D8eT+NTlFNJexjOb+d1SWt756bUpld5haMhJcYL97O8IQlfMr7YMzCB1wwAF08cUXm78ffvjh5bktiRYvXkwXXnghXXjhhfCe8847j84777zZbOasUyTRX2aiGo1M0ryukMNqYAImFs+pNSyFsEkRWRAijnuWNCOd8gwepeWpkxzjPjg5D8+CPBOfoIg5MAKAIyr/kOkg44mUE2jzsKa3dgLk9Vmc/fbk88dKnMZNVFaIPvfTiQgtQ288Ic2cusd5HpEsxX54t3xGsTEUaXOknOHWzgiwz4/AqAfJM1k7eX0xLY83Xu30CNE21Y2haDnzRc1ZVHNAWaI/BEwyKd6OokqEo38CKu8B/B78lNz6CAprAvptHiSHR1UOrCpmomL1WWYj6YMTAyZYo8acjI3nE9HgxPIbRUBQ/UIfy3QdAEpDaXmGBWWzM14HCbcWjsiBvkIT1TAmZbg5+/fANjpRgO5J4Uq7i+drKqf+LDu3zUOCUqs9jCvnaevMwXk5uj74LmapX0VZ9Tx1LgBau0lE+BDigK9XZP2IvI/5ogbgzAFF8uBovwso7QUkjMyUEUicFkn0h6VmfY81ARmgCGyEEZVuxPxk8YgFyuRhRzXMyEQViaKq33gz0yPcnAPjbAh1Pmr2UCnrA4AiFEXl8AwWvpqxBCNg+mDKyfGix8xIuxWcQ3mbYpqwYYSEfD3JtTN2e7yItXTJTSw3hLknFtk0HNjW9UUAxfDH5fjfUVkRHveA3e7MNTi5OWy49zpf1ACcOaDFY/VSmjYtofOJIpt8JO3/QFEHXl6RwCanyx82L03EXq/bEDEJmRocxoMcvot7dRtzHhkmbtSl3z00UUXAi/weOQoAg219z3DgdhgwFfMbylgykwjcLANAehCg4KUs0Gkeoo7qM03r4PFEzLxV37xDIItrPgiqb0/kUMbIeNU8w+ZkIpLjz9qY67TEs+pkHAJcde0pPoVJNSBoRjRhkXnWmKh2c9p3XLo2hXxwwKYaU4MPLs14Z1G5EVsBh2bdzpmm4re+ozYMq1HS5URPE0dlRcLEY3lw6oHAIGdIeW0axjl4kFONxbWQBifQd7UxDyt9DnIQbXVPxpL50CGNrK4vEhmI6ieKgcmBDi0NnBLvJgPU9wTMrhFzRyzzdl5O5Nw8zRcCFOD3SALUCNjU7YykvXD9oZzDjFH5MU3YcOXMFzUAZw5oHwVwIhMQSXyRhT4iFUXKyaSiITcMXf7wPkFUy5PXBVlqbeiaxzRRBSYy39isKIiI/1V+plX9whKKSBoSTEZAkOXsKOoPqfPrQVC6rwLkeTkRR+1IiGudqacoR5mcjRPpRwNjcZjzj4Y1dSWnWS+RW2pA3UnYon2RZx0Yi7FQ8nowZQobNdoQIr1+BNaqIeeP5otolLzx6kX9oWvDzvuoOXA+qAE4c0BjI22RzThylkwo62lgIkdMEF6iP7cutYhHwhhjG/NwoCxSl67PAkpcO2WaqAbUqJkmqgDo0G2IOWsPuUDpd+8ke7O+o/bgRVXXFRiLAdPsrGXeHrJfWkAxNThiA7PGtN9GIgSChgOu6ZKfyE3fY5dj3UMUHK9DvbOcR78Pa8+tE8aKduL/q2v1zsrZ2jkDjXQdT6p/Smhw8nIi7yw0P4KC73xQA3DmiLiZKjLZEcCJDMCIKcM6f0eWHZCu5srJOLBgWlFUwr/G1PJgfsHDKrRMVJFNJXIW1QhwSNUUOkQ1pOkYfAOPnEgf0xggnlkCSoFFNdt4AlJsRFti5XjhcxGdLafvjWxyRNamQoonLyef0zaPl8gt4oQe0jYHQHsI3AV4on4hnG9YDQ6RAh1onAVcCXQbrLWqDnBpDU6rFdOmRszOcH4EcnbNFzUAZ45o38UM4AQWVu+ohrKcgFZlWBtpTMUcDBOvkWYi5qfoAlU32XVZEa3KTPLg8Hut9sQ0OIENIwJegkCgrpxImHjM8bV+nEV4ItoZonofCyufDadIRFtRV/XuLQ1OxFyaAemacoqy6ncVDyxEjmHwyonwDGMOjAiHsXUxL0eXFRHYLBJCXaDvkbUqlPsLzsPi0zqiA5VDNHsanMYHZw+gfRZVAAe9bxG1024NHU2RbYSBBSFiosL+HP49qJ2xU8n9MqxyiGLmp5CTMX8fQRNVnZNxpC6iWB6cmG9VXtcwpgPIM4TmJXbuVaRfw4E7fd3SJkrzwnDvgkgCIRMkhzZU+d0yi9TxDJKAbpCzhiLzNZIJfVhAPpvjI+KDEzIrcsffyJgOjdf69gzrc4muD6sJa6Ko9kDiJirsg1M9ehQiThQzLUU0ONkmF5iAeHOK2ZGlBqe+rmFzoRRtqt8wQhoc9g50mL91LzRRRcLEA0BJm6giDsSzZe7BPh/qO5T2dLmAJzOr5TxZtt8QKKt/96bULDaM/PfI+yKSoMZyMh7UqTVqXhjeMbz4jGQy9urSS1xEg4OBW305EdCeZ3gffm2o00gT1fvFRKO6Ij6FrZrxqm+LrNNElunVvwe1cwHhmwbgzBVxExX/P9FsmUQipozhNDjDSVe6DSHzQgS4GSO1brLrNkS0PIvHRiBPRIMVMVFl7wPs4ENlMg68+4iDaERjMKzkGHEwjwD7aHIx8e5DICiwOZkanHoTVSRMPJKuP9L/HFAg4Fpc8/Lg5OOjfv3AYDtgTo9oEwPvQ6+ppnZGhFxjnghIlu8VlBH0weHtsUGZz5Nr04xyIlrAkOBLigdWNy/UAJw5Ih4qftjSxdnvfNDZERfyeyiTcWCQRnKqRA4mjICFyCaHAFfkwNKiDfU8kU2Ft8HS4EQWVmGiMjU4bfUdAZzA+wgA4NnyaRjuJOj6MRRK9Bcxc1rjY8CzwbAQUe8UXvCxOR0KE6/feEwz8BCbvBdcMNip2zmP1lLHcjtF6oqMs7ycuI+WH+2q64uYlmbipyIBOWRRdaHf5fcIKLPaFAOcsbV6PqgBOHNE+45XWoDHLN0r+11mzq3fmImGN1HpazhzbkQijDnu1al0I2povUFEpJCZ2Nn5JN3L0OBEFt9IJuOQBBqSdv32ResaZlOBC2vIjDUEkB5Se6Xvjfhx4Xcaq4sLKpHzzCJh4tamGzmLKs+l5NdltSkDFEMeOBkZixFtYmwexgCFWKsssxHXmGCWWlNXFHBFtHd1PBENKLo3Zg4cbh2aL2oAzhwRX1weAzQ4fLGNJ5bLeSKniWebQUB9jB0y/fZVfFwKwROQF1/nrOvV1YpsYDXtIZIAb9w0UdVLoDKTMSwmlOgvNz2i9gzhiAyByeDgJZJqYNiw9cjCG41KiSSCrMuFEtYGBDQ4ISfSAU0iVpuyXDCBzTAmkAAeqi8nYjKLjQ91T2A9i/jg2IBzMB4siMYAV12EFJEEt8P6OKJ2xt4Zak9sfswHNQBnjmjLjuny/2V7j2W/h3xwAoAiU0MHFoRI1EFIWzSkiUqXDwFOMHJl0LBbO1dOdX3x6PAanEURk4jWCABpf7hMxrOzYQxtfhpGExRpc8CkGlL5B8yTqBitTUOAlEi+s5lEUQ2qlSSyzNezo8EZxo8rpm1G7al/rxFBIxKSrsu3eGKRVn5dkWcYKae47rcnst7rcqw26XcWSc7YJPrbA2gzAzh1i4alzh5Gko1ESCETVSjEN4jU5SQNgKCQiaq+LttMUd+eOfHBMdqTb5jDAc6Ic1+22A35XmM5M+rbk5uWUDn1PNENLCSh14COdltqHK0Ng/vqmFFUAYAT0joF3lkEJM+WH1dos4ycmxfSKA0O7kxtKmuTZQ7k91pbd31uGjWGAu815OM4Iw2Ovi/nCaUhaTQ4ex4tXzru/s6RsSURRrzhQ1JRAARFzu3JNA8h8AJZaidypsEJTPZQVFdAo2RHUQ22qUQ2XatNkSM4Qmd6BaTdYU6mHta0NFx4cz0gj2j4zGzYgfHK55kFtrn2zkz0F9IGDAaCLL6I79Bw/ldoA5ffh9fg6HIjYzEvJzw+IhqcCCjl65kpRNX758XePefPf9e3xUHycPMsGiE2H5THLzc0K/SGVU+hzdun6WXHr4C/Cw1OQNojMhx/A3b2SNRSBl6Qz0dAciKKSSG1JiqtwZmBiSrkaBoAOBEpVYaJGwvdSP37iDhJxlL6z84iFvHViAC3yEaoLw0b/q75YmZO+51Ndvr8gQ0sosGxJXT2xdx09T31IBk1O98MA4AionUb0tQ1e2HisU1X+jvVr2cmKA1FcxJRJ+fnNKhP0LBmYMiHnuMQmfIXEL5pAM5c0YH7jtNHzvgt83eRWM50SNTfA1IaVLHXlxPLmSG/2/4sfl3FdVZOwEQ1E8kpIsVHTFQRc5g8iwrXNVQeoGGlq8h7VZegaSkA7vSlkIkq0OZhTwHXZUXArWnKCJTDBRUzcGCWAFf2PpDD+xAmqsg7g+9efwflRBxtZ0ujFAF3RDF/J97fyKnkpgmz3Saibk1dvEzIUjumI5p/dD327iM8CwfhNCaqeSI+KJbulTshE8VMELMVJh7JUhw+32VAtW8oisoEU4NtGNZk52Q5GUe0RXxTiTi1ou9EMfNCzC8mAoAH31SGdWiOOazWA8Bo7o3ZMkHwDdMC0jz7dCQPjrUPRJJXDmOimi3TI2pSKOt6QEucaZSGzJUzTNSSCYICYeIyctQqZ3bAbd34yDSggbqIDEFrlvym5osagDNPxCcgirIiig2cUJh4ZPEJgKlcVV0/ASPSAypHS5/2ycu8PZAltGhMdXrl/xETlSWlyDDx2MKCnlHESTJSzjAgCG4qQ+S4mS2gBLU8QSk1FEHHx5BpfvLHK1EsiiriYzFUFBXgCflxhd59/XvVlyLC2LARWzkoy1hyjXTOktVngslB31lAsAnVNeR4jQgIBZ9dbqKIpjQCguaLGoAzT8QHgaXBifhPDBUpEdkIAU+r1Qo63Ml7EAkQBFhy9TosJuaUFwBcKVU9EdF4KAIGtydiotLlR/LgRBxEY+anerAwe46mGUsoiioU/h4A9rp8G7zU5y4aC2hwIueZ8fZEolsijvNE9WtDqxUzKw6v5fG/E8U0fEM5vkY02xFBK7B+WONjUMfwyNoZ0ZAPCwB1OVabhsu11QCcPZ74Yrhs70WQJ7KIhTQv6i1HwpLn0vyky7Gif7iKPWKiGtahmYhoqlMBnJkshjwLssWzaERqiLDkNLgPTiiKCtQVWRAjYCp2nINfbnGf4gmNV+udBSJXBjQdmP5XDFDsN47dG0cCYKo14IZq8Yn5E9i8i+85TyQMOE/0hwBXZCwG6gr0PaLZ1vWFMktb5QTGhzRP1r+PYU1UkahI1M66MWTxRMqZL2oAzjwRH+xLLB8cNVCGDb0cxjdiJqd3S+3M7GwYEfPCTCJpuInKoohD896LKvBitXl8TPYrdsZYPaCIbU71IChyoN58m6iidn/+GG0AzMoxVkS+YZobGONBB+xmdYXGK27PoElAIxpQophPVOiMsYjmZUgArPmwk3Fsk4/4RMV8Z3Db7HIwz2yYqCL7Brq37p1ZWsDoOVvzQQ3AmSfiktwyy0QVQMaRMPHMyRieXq0G6YxUurw9kEUsiJGIpJmEiUupCPNMMw2ORdJ0gHn2XlQfmGiZwDhF/CeGAa74yIf6coY6TTzQ5mHBy1Cp782Np17Lw8NlrfHKAcW+4/Vm55n4BEXAbSRlQcRENQy4HdavLAfbOY8ufyYBEZGkipGooJCfzoDa5mHHdPwsKv0dPUcGcGApMS3xfFEDcOaJIk7GkU0lkmkyhtSDUSmzZBKKLAiRc50iWpWRAJia6g6mwbEA4F5Mg9Pt4TLHWZRWx6h3rF2/SM2WT0PERBU54Tt0Uvgw2qKA6W0mKn8+hWaiBeQb+D7jhqP6wIDc6Ffg3UsTFa4rkt8oouXJfAHncLzqNlhAmt9qH1oaGR+B98GuR47piADOyGniw5qBdXvQfURS0Ipowaw2zRc1AGeeKBImHnG21ImYItFPsaMBYJPmJOogpMEJAKVQjgrLRDUd0eDUl8NNVDumcJlWCDGnPI9H/SY/tyBodoB0RNIPhRwHpcbRgB9KZEyPCRNVPc9+hgZnUD8MiyLJ1SKO0ZHNMCKhhzbLWTRR1Tna6voiZiPboZsYT305FsAZtD02uGNtC81DXFdESBgNtKfR4DSUkdDg7GU4GauBYql061Taw4UT1y++kTDxkHOwpcEJ+OBIbRFkCTkATgc0OLGNsGrEjqlObTkWRaKoImaK0AYW2CwjpowYCJLfI1FUw/pz6Osz0SaOBsaiMFEZPjgRDY5MoYB59InjdVqVqJMxXGOydQjVpcYr1DYPY3bNefR1W7MwWMqGGWnU2OXISfIzWV/rQFBEiCAK+vCpcRYpp9HgNEQdZr4IJ/oLLL54c2op1B/ZLCNSkTVxeDmQJajBqY9IquuXrstaNKYCPjiRBYqTBXAilKdIz3m0T1BEyxPbVCKAIi8ny3Q9JDAZCpAHNgPrlY0GTDkRyZrPjX2tKKoI4IpIzYGxyEFHRPNglTXUYawBrduwp9br8mdLe2eZsYSWGFelTFTWOj0omIrw5EwRYQRdr3dUt54hLwOyzBs1AGeeaH8WGm5GXOgBaEmOAy6IyMk4P4uqfkGIHLZp8gS0KosiC1REKgq0+aB9/cNRdTsjAGdnwOxl0aAZRKPOqMPnQpmdzSliVtO3RRL9zQRIR8ycY4EoqkkWibefGUVVX1dEOxHZVMTmFJjPVn0hX8CAD98w/oIhrZvBM3BivQAIjABF86DVgEYp8u75ZaxxCz7DAYWNmWi45ouas6jmiRaPjdANb3kBjbRbzgBU3y1TzmibtvZPAvTUkR3qmeXMZmpzIV2ZCxTm5yR9cHBdI4G6Ik7P5/7+kbRh6yT90YlPwBWRBlMmW0mWD06EImHiInQ58L7Qd3Qtop0Z1kQ1lPnJyK7bbhEly2LMvybCA1nkaeJGOVxjN5NkkYOeOB451DXqaIrYIiAoYqIK+YMFIrb09YhZ0QYmvO76tSpi6jIPWmW3hqKoAqaliB9TJD0CUX2Urv2cG4DTEKBDlix2f486ixVROVNE5CUPq/7HZ1EFJeIAWIgku4togmRWYEuaGWzDsMo5ZMli+pc/fiYuIN0bkL457ZyBiUpLxNh5fDCwWdwDeIbQziDAqcuerUgabzPodhJohywDm5ZCPEZlXGMXcXgPaZQgx+D+YHENH2oP1fLoKEx0+Ocw5+bNyDE8MF8jiRcj74OXbzsZ1+clCp1DJrRX/u/o+yB8kTxBkXVovmhOTVQbNmygV7ziFbRkyRJatmwZnX322bRlyxb3nh07dtDrXvc6OvDAA2nfffelU089ldavX1/+/vDDD9PJJ59Mhx12GI2Pj9OKFSto9erVtHnz5rnsyrxQdKBGgEAdMIlsqPq6VRcPkTUdfwML/aIB7b+RFPpWeyIUSQjHacf0DHxwstPUUXvqn2Esu/DgIMgyZfDLEf+AOuBGFNNOzcRXI+ZkXF1HAgJRDNCG/GsG1DpZviNy/uD2hEyhAS1PzMk4AqbqeYrrgWcU0CwIP6WAD07E7zASJj6T9axufOhLkXGv21ddG8zMudA0OHMKcF7xilfQ7bffTldccQV95StfoW9961v0mte8xr3nDW94A335y1+mSy+9lK655hpat24dvfSlL60a3G7TS17yEvrSl75EP/rRj+jTn/40feMb36A//dM/ncuuzAtFzAtEsZTsdY62YafNAFrnye5mklckEiYeCRflh2dGIpgsivgWcYpkR7YoT5EO3llg4c20KgEJPeJoGgvJ938nimmCrHc2GkjQx8FIBLRbr3U04M8S8bmKhYnz/zFPRNKPmA4iwDXCE1mrhjrQNzDOrHc2FtCYRLI9D+yDY50kPxJ594ONDyvzNL8cGfe63EQR/87Iu5gvmjMT1R133EGXX345fe9736Pjjz+eiIg++tGP0ote9CJ63/veR4cddlh2z6ZNm+gTn/gEXXzxxfT85z+fiIg+9alP0VFHHUXXXXcdnXjiibT//vvTa1/72vKeJzzhCfRnf/Zn9Pd///dz1ZV5Iz1YzIkcyHdRZ6LKFp8hNUFERPvw4woCkxQ5PROpKCqjLnlyOWQRuWlmImHsSkllfAQniuMU0k4ETEIhR9Owb0SLyPH1GiaKyj6Zu749EQAc0ZjwhT7ig2NRyAcnsqEGwG1k847kExrm0F9sxpqdsajbORP/q4gZr04rWfAwgBM6SR6yhLRF0XJSpG4U3EIhKhL5tSdqcNasWUPLli0rwQ0R0apVq6jdbtP1118P71m7di1NTU3RqlWrymtHHnkkPf7xj6c1a9bAe9atW0df+MIX6LnPfe7sdmABUCTRH5HK+BvImzGThSWyaOzFNDgzyT2yKADcIu2ZLQ3OrpRUIskAB3VI1PeU9+qFbsjcNET1m0FEutR9sQBwRKsio/UgS1DLU69NfOWzDiciouc8+SBcEcW0gCGTSCD53FhEY5CZn9D48L8X7akXkCKpD6J+h6HDLQNOxovEM8I8ovwA4DQ1OBHzZEBgi/gv8qv2Goz/5yTfmfGcA4B8vmjOAM7ExAQdcsgh4tro6CgdcMABNDExYd6zaNEiWrZsmbh+6KGHZvecccYZtPfee9NjH/tYWrJkCf3zP/+z2ZadO3fS5s2bxd+vAkVBR2TiWPzltewsKnxvBK1zH5zIomFNZOlbZLSHrz0W4Ao4PUdoV0oqgwKciL2eyE4WKb6HzBT1bTLHUM0GHnUyjvlGDMoDWUJm4Oc+5WD69v96Hn3yzGfgQijoNxQA0tLRFDMJza5p5qt/r4NmvLXqa7fV8QkQAOvvkXcPWUIAL3JeV8zJGNfLKRbxieu16gppJQMmqpk4c+9WGpw3v/nNfSnM/rvzzjvnoq2CPvjBD9JNN91EX/ziF+nuu++mc845x+Q9//zzaenSpeXfihUr5rx9s0HRY+8jqmhRzpD2cc1n1cV9cEKmLlPzMjsS+myZqCJOz1GyHFUTRTRNEfNLLGrJ/06Ug5WY+jwAKEBlejxoyT9RKPV9xA8lADqijuorDtjbNKkV9wbaHNjkBje/4LpmC9xGzyOq84uJRnNGBJtQpFVIiKp/1vxMOR0gMEh7IsA1Au7GA4EnEWASSjUQaM980cA+OG984xvpzDPPdHmOOOIIWr58OT3wwAPi+vT0NG3YsIGWL18O71u+fDlNTk7Sxo0bhRZn/fr12T3/f3vnHh1Vee/970wmM7lOQshlEkkICJhwCcWEhAE5iEkFpGpr3lYp9oBy8GDBGx6XUI9iu1Ybz7KnrupBONoWy6mUt2q1ahHLAcVLASGFykUjFiwuNEHkDQkCIZfn/YMyefZkP3v/hr1nZmfy+6zFWmH2M89+9jP78t2/2xMIBBAIBFBWVoacnBxMnToVDz74IAoLC/v0u3z5co0Aamtr6xcih3qxU/zI8rqP+mni5imc4WOixOCQLlJFm7JAZu++LLyha11Uuk1IaC52gyt5eG46Dh3/ytBcm+R2kZaHMIJiBSPVnSG0oWb0mbmogH+IpW79fYf3AahdVKQ3UML5QRFKHkI2CQXKmEmlDygPb0oNE4LA6VsrR+cFiWAtCm3rVu+Lep7R4qbM74u05Sx6/1Zd0/LCuupCf5GJbdpvr9/I50lCO7rOt1f1Q7AUUlLbnZxFFbHAycvLQ15enmm7YDCI1tZWNDY2orKyEgCwZcsW9PT0oKamRvc7lZWVSE5OxubNm1FfXw8AaGpqwpEjRxAMBpX76uk5n73Q0dGhu93n88HnM69U6zT6vlmrLlLChSN9TMlwUF845jeEVILFRDMeRZuJpTmhv7t69DNUKG9XqQTBRYFqKfvl/Ilo2PABFk8foWxjJRaodwyUG2b4d/TahD/AjPel1+8FKA9eGb2CeOFzE17CIJJ9UUQg6U2WYHGkQKmDQ7HMuQhtkgkxKJRCf+GfmVlwjH53M3c6WeDYJAK9hCBaygNctuCoBA6tsrK0X8pLhOKcJlm/IxT/FEuZk9ahAqKYRVVeXo6ZM2di4cKFWL16NTo7O7FkyRLcdNNNoQyqo0ePora2FmvXrkV1dTWysrKwYMECLF26FDk5OfD7/bjjjjsQDAYxadIkAMCGDRvQ0tKCiRMnIiMjA/v378d9992HKVOmoLS0NFqHExeosQiUB2+BPwX/73Snbr96fav2pRE4KguOtA4P6cGjeMUYlpse+rvtbJduG8rDKY2Q1UWBIiSB8+N+6p+rlNsBewQO6Y2wjzC5uIdK+NdIsRGKYzwnpVPL4lO1f2WQMeHGalecDtVCYUb4Ipn6bWDahuIWoKTR22Xh8xDdtx6ToN6+55l+P7TfTPqcJAIJYlJ/OBprLMWdbi1N3LyNfJ/2efQzMuXzjCTubHqpiSVRrWT87LPPYsmSJaitrYXb7UZ9fT0ef/zx0PbOzk40NTXh9OnToc8ee+yxUNuOjg7MmDEDTz75ZGh7amoqnn76adxzzz3o6OhAcXExbrjhBixbtiyahxIX+qbx6bejmFkvyU7Fh83tAGhp4krfriZgV7+NRlAQ3h7UbyouNNwwDq/vb8assfpuzWTCxSUHGVtxCkUa62SEWQwOBUrhwYt5gOmN7EL2kzBZGkE+LFUb2TOnZ8EJnxrVXJHiUDzmgotUB4fgmqUQaSYNJYtKmYXoMR/zxawPpbc76vyYFY6j1uPSWjEu/sWPZuWS96vfqJvgbo70t9cT/0DkbnnZmhNpP5HWUnKYASe6AicnJwfr1q1Tbi8tLYUQ2hMjJSUFK1euxMqVK3W/M336dPz5z3+2dZxO5aIK/SnaXDIo1bANNWMrhbDCd7rXY9qGEssDAHOqSzCnukS5nZICLrc5Z2UBTMJieeS+rAQD/QNN6jLBpw/o3+j71rhRv+13CeOlESiuA82+dfYV/pmVIFKfx1yQU4QSZd0vCpT05oiDUS1YJ0jWO8K9gboekZmbpm9ihX4/kbon1QKYEoNj/gCnxNNpSw3ot5H3laYQONqXCP1+5BcH+QVPOx6KsI+sSr7TYnCiWsmYsUb4RUC5kam4JDvVcDt1qQaNb5fgErLylkrBl2x+E5cvdisCh/JGTMUWCw5xDs1ca5T1iMK/a6WGi11Q0m7l355WS0l/X5SVuSnYtZBmxNWXlQ8w898+/Lu6Aocag2NyLlL2Ff45LTZRfzyUGBzN76HfBD0kgaPfp6qNSphQ3PLyS52qH00wO8HCZ8VdGC9Y4DgYapo4xRowZFCa4fbwOBij6PzeNvp9pUkxOJ3diuBgm0z+skVJNQ3yPHZYWB+KklFAxZYgY42LykjgGJvh+7og9PuSPyYtMxBlhzzF5C8LHEtxOjadr5SHHMUKRonl8af0XoeqKsvh/ehWsyWIIPnaMKrhZFYUjrIvQDsvFNe9XXVwVL8ZxYJDsQDLn8uxjKrxqI5dY8FRuboolktCmySCCIoXLHAcTN/4Cf12qsJSMtMuy0NmigdVQwfpbu8Tg0OIzlfd6OU3hjOKG6s8ZCsPQh/BoiRjxYKTTHhYUqkrLwAAFGWpV5Q3mxZqcJ9ZfARl4VDq/ihprnZB2ZeP4MKkBCvblkVFEMmah7fCZkB5a86QHpDHT53TbUN5gPV1Ger0I13QqqBWQFsjK+pp4rJ4UYyH0oZSNZoSgyOffxk+c9GhEiaUODdtDA4llke3Cc0V6GALTlRjcBhrUC92iosqw+fBzgfqSH5/QH0Tly8WivlcZcGRb/RW3DU+QkyQDGVBRBWUgGYq988sw6iCTFxVlq9s43G7cU4xfwD9xmK22jz1pkTJkIqliypSC44VUWZXqYFUiuDSxNTp90OZZ/n6PHmmU7+fCB9gqv3J1gmfIqgVADJSjIuAXkyaOGUJG8q9kxaDY8WC0/vdrNRkxb56/04juaj090WJwdFm61FEokpsSn87S9+wwHEyfWqYH0ABIQAAHmFJREFUELIFjFApeaDvzYYSg2Nkqq+/fAgOfN6GmmGDdbfblbodUwuONM/CWo0+pHqT8N0adfA0AE1RND0oaaeAuYuKOv+U4EZKbIhdRGqqp6zXpTqHZCH6Rbt+vS0KAclip44ZI8SwEawKFDTWK9UDzBX+fx2BI4myFAMLTobPzIJj/P8LUKwPmsrjilukV5Nlp9+G4g7sVtTpUvWTlerVbxOhi4oUg6O0BEVmwaHE4DgNFjgOpu/bjH47L8FFZbqvPr5vys1X3d9/fme84f40tXJsisGhdGPFgiPfxHusKhzq/vRfvM9vlw7YKMjRLLCTen+iCCr5U7MbH2W9LSMomTuUwpQUi0lhVirurB2JJ9/4GFePKbiI0V7op1fgnO7Qr+0kP5CUVlKipSw5yYXObvW5QQp67pNF1beNfC4aWnCk615vUVc748HssuBQAqgjTRNXW3B621BcVJTECkoWlaUYHKlN9O+KkcECx8FQ08TtSDk22/cFKFlUFDJlU7WF4fsIFTtlrAQZyzdDyg3NKmauO80q8gZNzVI9qfFEJPeTxspjMn6L5y0lnkUW5CrB5SUEmgLA0q+PwuLplxrGmJghC/tjCkuQNgtRvx9q/FVehg+fnTyr3J5ESAOmiA75HmRowUkxseAQxBRAezhTfleK9VtjxVBYuSi3A3mcWWn6AkfelzpN3Py3p8TgUK5nD8EKJo85Fi9+kcBBxg6G6o9WVXq1AiUGx4rASbdpAUxKXR4Zu1xUsRA44euDhZPm9WDFtaMR8Kdg1ti+a7D19kN7GJpBSROXPzUVOBYtOJRAdR9BBFLSiXv7u3hxE05zm77wSEvuFQGq84y6gnNupvESNR7CsYdrAN0gY6IFJ5NQ5VxGWYWX4qKSf3tF/3ZZcFRLycjI8YgqC478e8vngQxFmFCyqLwmrmsgcgsOYRpiCgscB0PxfQPW34T1UMc0mAdJUpDfZK3049PEBKnb3VU7EgDw0LWjL3pf8ji7Y+GiIszLLVOGYfsPanFX3UhSP1bcgZTUXHlW9FwQMnZacJSmekLMGKXYWzRoUVhW5AeSKr2bsl4VAFxbcX5ZnGyFxYDysKS8aFFjcNJNYnDCUWWIugnnNMVF5aUEGRNcs90GbsALnJJckukK0XFWsjDTsqj09+Uj1MGhxC9q6+CYCxyK0Isl7KJyMBezmrhdqPrUXhQX37/si7eSRUW1KN3z9VG4/cpLDQOtI8Eguck27LLM2VVpNNJ+oh6DQ0jdpgTsUt7io4FKJMsP9bOdlEVm1fu49YphyEn3omZ4ju52s7o04ftS7U/uR7U0ABAWg0OYatVDNSXi+Br9/jVLeRCsGCooLzyywFEdl/x7q64PyuKWlBgcuY3KtUSxXvk8SZhdUYg/vv85KoZk6zeKEyxwHAy90F8UXFSqGByPeQAkBTNfPBW9tYxU2CVuAFrlUqvYUQwwvB+7XFQq79m0UXk49MVXffarB0XgGK9rRLn5msdoUUz1dvI/C6qx7IW9eKR+nO52+bpSF+iT/jYYdJLbhfrKIcrtFEsIyYIjp4lbiMGhkkqIU/IS6lbR1qIyF1MUl/UpxYLBMh2K31tGPhbVPU224KR49a8zyhI2lGMHgJXfvRzLZp5GnolLNNawwHEw1IXnxhZl2bpfozcWapq4GXa5qOSLlFKLwi5iYYq1S+DYVZuG8lB94JpyZKUmI82bZCooKZZHoymgWJQoWX+xtuBMHZmHd5ddRWp7VhEUb5dopcVYaP+v1yzpIiw40Y6980W42KbKKkuxclHuPacUWXMyKkErQykYKB+vyoIjC3tVvTLqEhwAUJxjXC0/HrDAcTDUG9f44mz84p+rMCTHeL0pKoMz9Gs0AOEuoYvfR4ZNaeLyG3qnhQDiSImFlkqmlhg2gXKDzsv04Yv2DgwdrL5JUUz+niQ37q4bRRoXxYJjZCWkHJeXsBZVJEHGsUblotKIQ0ui1fzYKZZkeTxGwjYjwiBjFdpUev02svtJmUoujVv10kIRk8tnleH+F/Zi/uRSxYhpLyyq31tG7iZDUStH9jgps6jk2BlFDJEmBkcZqu1cWOA4mEjecOpGX3xtjnAGp6vNjJS6IhTkC9NKoSj5IdfJFhzTflRz/R/14/DxsVP4TlWxsp9IM9bM8Fm04FAyieTzVXV2UIJR44XqjV4Ty2Khf8oyFZRKxvI5ZuQ2ll1UZi5uoyB0SoCsN4livevdoHIzUWJebpxYgn8alYeAX730yoprR2Ph2kYsnj5C2YZSxkIeQ0aKfvC4kBQOxVLaobDgyNf8P43KNe3HabDAcTB2PeAihWzBsclFZSUjSb7YVWbWaBCLZIFYBhlfVVaAq8qMRXKkVaPNoFhwjPajja9RtJHG3KU4P6h1cOJBh+KNXr5+rJQ+oFQEDp9bvWayUDB6oI4tysKw3HRSrIbRGnuUJS8owlWbAUQROMohoTDL2II+Ij8Tb/zblYZtVOnjmvHIAkfhoor0jqq6dw5K9+K+GZfB43Zh4dThEfYaf1jgOJh4vU3mZqhvPnYF6sppkpTAOgqxdFHFpg6O/QLHyilltwXHn2p++zHaDyXlWBYv5xRmeG2auOmQYopqLTJ5nSJK3IYKivjtW+jPuJ8kA2Hi9bjxv0unkaxOyQYCOJXgKqcUwpQFrdJNQyj0Zxd31Y3CkROn8W0DS6osODN8+oIo0iQIo3dMI4uT02GB42DidbMdU+RXbkshvBFTkCtkqlYcjxSjhSntJhZ1cOyKwaGY2CnIv70VffPjb43FM+9+ggdmm9ckMtpPGqFYpHyeqc7XeKWJW0G2np4+FzuB43Lpn0PyHJqlVVOFu5GLirLobzJhKQ8Z1XpSsVxfLSfdizW3VBu2cWtcVPqP8MkjzruTjKxgAwEWOA4m1ubyNfMn4q2DX2CeQaCcnJVi19vMmXP2CJNYuqj6kwXHSp0hGU1GkoU+59YMxdyaoaS2hhYcb2QZOarzw8lBxhROnzPPzlFBWkyRIII0NYlsEuZGrq5UQv0rShVrGYqLygnnh5vgohpVkImNd09FfqY6JmggwAKHCTG9LB/TpVWT9Uhyu3DnVSPQeqYTJQYZNxTKApn4sLkd08vyLPVzgXNdsQsyjoXAueOqEdh26Etc/7UiS/3YZZVIsanIYyQY7SfNF1lGn2rRSSfH4FCwYgGliBf5Y9U8y/3YJaiNYrQoBUe1QcYUC078XVQU5ENRuagAoCygtsQPFFjgOByfx21pBexosPTqy2zp55U7rsBXHV3ITlMHNUdCbIOMoy9wJo/IReO/1yEn3dr82BWsLLsFrGS+RYKRFUu24FCEiSrzrT+6qGSMVgs3g2KdoCwLIX9ul+XRyEVFseBo08TN96eaR8qirrFEjhVSuaiY8/BaVA4nXVHnIBFITnLbJm4AWBYCkRCLGBwAGJzhs2xVsMtl4CNUhrUbo/1QVt2WUQWROjHIOFbjoBT606zFROjTrrgPWaCEQ1mihbLOlIwqBkdjkXKAAJZjrtJsrM6eiLDAcTjpCh8r08t/f68SM8YU4G6DBSftJpZVk61iV5yhz6Yij5FgtJ9IF25UBaEnJ0X2ph8LLqwOf2leelT34ybE4ERaCTsmMTiyuFU0o9Y3qho6CABw3fhLdLdTiiHGEjnmykos3EAgcc0DCYJshmf0mTEmgBljAjHdZyxcVHZh100w0jdiOzDaTxrhISczZJB+zJh8XDEyzJnSUD8OE0qy8Y0Ka/FXZlAK/cmnD+VUsisGx2qQMSVNHAD+778GcaqjS1mDxmkxOFay5sLxuF396mUtUvjp6XAS2UXVn+lPN4VoPHCcIHDSfbQYnHULa7C16Qt8b5J+5layply/M35Xf0oy/sWksJodDydKob9IV5E3KjMRCUYVkSkB77JwNXohSXK7DAvsxTJNnIKdAic5yY2uHvv6cxr89HQ48lsqw1wMdrkMNG/EMXJuU+vgGAU9T740F5MvVZeZ1y646AyBQyHNm4Q2wgrVRlxMHRwVb/zblWhpO4uRBZmWxnQBq3Vw5PPVihCMZMHJWPCdqiFYvfVvuPIy69mnyUkunOm0YVAOhQWOw2EXFWMV+yw4UqaMEyw40rXRY8G3pFmPyCk+KgJpXo+tAkftopIe8Abn0rDcdAzLtS9myMhFpRE4ijaU1bIpuAlzFEuG52Xg/YevRoYNzwavJwmAtXPIyfDT0+Gwi4qxim1pu3FYlJIaZExZhVmF/NDqTxacVBusu5Q0cdkAGMvHO3WpBnV6tz2/K2XV+ljjVyyyGSneBK90zFlUDoezqBir2CVGtAXxbOnSFKOxy4LLynpMMqpUcify8HVjAAC3X3npRfchiwBVYb2kCLOo7MIo3Vy24KhW4NYuxGtPrSAnBBnbiZGITATYPOBwMrmQk6MYnpuOQ8e/si2QMhbYVegvLhYcovXJrvXMVLVQnMi0UXnY+/DVyLTwNi8/vFVWAZcrPi6asoA6lkcedwbBym3FReVxWKE/OzGKc0oE+OnpcP7liuF4afdn+EZFYbyHwgBYu6Aa/7Pt74brdTkNu1xU2iwqW7o0hbqfMzZlljgli4qKFXEDaM8NVSZRrNdiem5REFubvsD8ycMM2/3XdyfgWFsHhudlmPZJEUEqnJZFZSdGcU6JAAschzMo3Yt37p/uiOA25nwtleXXlMd7GBFhV0BwPCw4g0wqXU8dmYu3Dx637QWgP8Xg2IEcX+JP1X8caOvgRP93n1iag4mlOabtKDWCHrlhHPYePYlpoy4+4yiRXVQPfmM05jy9Hf86zbgkQX+FBU4/gMUNY4VorA0U7Qqq//29Sjz11iE8+n/GG7Zbe2s1znR2I82mbMP+ZsGxiixYVC4qbSXjqA/JVm6qLsFNFvtwYpCxXQQvHYz9P5yRsMksUbVPnThxAnPnzoXf70d2djYWLFiAU6dOGX7n7NmzWLx4MQYPHoyMjAzU19ejpaVFt+2XX36JIUOGwOVyobW1NQpHwDD9n2i4qKLNjDEBvHD7ZNMV610ul23iBhiIFpze39SvcFE5LU061lBS6fsziSpugCgLnLlz52L//v3YtGkTXn31Vbz11lu47bbbDL9zzz334JVXXsFzzz2HrVu34rPPPsMNN9yg23bBggWoqKiIxtAZJmGoGJJlSz+qLJtEIpYr0juBpCRzC452NfGoD8lxOK3QH0MnatLtgw8+wMaNG7Fz505UVVUBAJ544glcc801+OlPf4qior7+05MnT+KXv/wl1q1bh6uuugoAsGbNGpSXl2P79u2YNGlSqO2qVavQ2tqKhx56CK+99lq0DoNh+j3TRuXh5zd9DWUBa5lflxlktSQKA82CI4sXVZCx/FC3yxrYn4h0NXXGOUTtlWzbtm3Izs4OiRsAqKurg9vtxo4dO3S/09jYiM7OTtTV1YU+KysrQ0lJCbZt2xb67MCBA/jRj36EtWvXwm1TGXqGSVRcLheu/9ollgVKVmoydv17Hd5/+GqbRuY8BloMjixYVCUp5Af8QKysLltwBtjp0e+J2tna3NyM/Px87c48HuTk5KC5uVn5Ha/Xi+zsbM3nBQUFoe90dHRgzpw5ePTRR1FSUoJDhw6ZjqWjowMdHR2h/7e1tUV4NAzDAEBuhi/eQ4gqdlWI7S9o6smoBI7LvE0ik6QROKxw+hMRmz+WLVsGl8tl+O/DDz+MxlgBAMuXL0d5eTluvvlm8ncaGhqQlZUV+ldcXBy18TEM0/9Y+d3LMWXEYNw/67J4DyWmUArmyW6szAQOSFUhB2KzwOlfRHy23nvvvZg/f75hm+HDhyMQCODYsWOaz7u6unDixAkEAgHd7wUCAZw7dw6tra0aK05LS0voO1u2bMHevXvx/PPPAwDEP0643NxcPPDAA/jhD3/Yp9/ly5dj6dKlof+3tbWxyGEYJsTsikLMHuDFNFXZNPLK8YmccaNCjoJgF1X/IuKzNS8vD3l55kWTgsEgWltb0djYiMrKSgDnxUlPTw9qamp0v1NZWYnk5GRs3rwZ9fX1AICmpiYcOXIEwWAQAPDCCy/gzJkzoe/s3LkTt956K95++21ceqn+miw+nw8+X2Kb1hmGYSIl0+dBeaEfnd09CPhTdNvIFpyBKHBkC85AC0Lv70TtbC0vL8fMmTOxcOFCrF69Gp2dnViyZAluuummUAbV0aNHUVtbi7Vr16K6uhpZWVlYsGABli5dipycHPj9ftxxxx0IBoOhDKpwEXP8+PHQ/sJjdxiGYRg1brcLryyZApfLpcyQ0sTgDMDFf+VpEeyi6ldEVY4/++yzWLJkCWpra+F2u1FfX4/HH388tL2zsxNNTU04ffp06LPHHnss1LajowMzZszAk08+Gc1hMgzDDFg8JgUcZRdNhm9gBWED2uJ+bMDpX0RV4OTk5GDdunXK7aWlpX0UcUpKClauXImVK1eS9nHllVeyqmYYhokSbo2LauBZcGQ4yLh/wUVkGIZhGCVJLvNMq4FCD5tw+hUscBiGYRgl8vIMAzHIWIb1Tf+CBQ7DMAyjxDXAC/3JsIuqf8ECh2EYhiEx0Co9h9PNAqdfMbDlOMMwDGPKnOpifNZ6FhOKs+M9lLjC+qZ/wQKHYRiGMaThhop4D8ERcJBx/4JdVAzDMAxDgPVN/4IFDsMwDMMQ4CDj/gULHIZhGIYhwAKnf8ECh2EYhmEIsMDpX7DAYRiGYRgC3T3xHgETCSxwGIZhGIYAr3vYv2CBwzAMwzAExg/wOkD9Da6DwzAMwzAG/O/Sadj5yQl8p6o43kNhIoAFDsMwDMMYMCI/AyPyM+I9DCZC2EXFMAzDMEzCwQKHYRiGYZiEgwUOwzAMwzAJBwschmEYhmESDhY4DMMwDMMkHCxwGIZhGIZJOFjgMAzDMAyTcLDAYRiGYRgm4WCBwzAMwzBMwsECh2EYhmGYhIMFDsMwDMMwCQcLHIZhGIZhEg4WOAzDMAzDJBwDcjVxIQQAoK2tLc4jYRiGYRiGyoXn9oXnuBEDUuC0t7cDAIqLi+M8EoZhGIZhIqW9vR1ZWVmGbVyCIoMSjJ6eHnz22WfIzMyEy+Wypc+2tjYUFxfj008/hd/vt6XPRIbniw7PVWTwfNHhuYoMni860ZorIQTa29tRVFQEt9s4ymZAWnDcbjeGDBkSlb79fj+f+BHA80WH5yoyeL7o8FxFBs8XnWjMlZnl5gIcZMwwDMMwTMLBAodhGIZhmISDBY5N+Hw+rFixAj6fL95D6RfwfNHhuYoMni86PFeRwfNFxwlzNSCDjBmGYRiGSWzYgsMwDMMwTMLBAodhGIZhmISDBQ7DMAzDMAkHCxyGYRiGYRIOFjg2sXLlSpSWliIlJQU1NTV477334j2kmPPWW2/h2muvRVFREVwuF1566SXNdiEEHnroIRQWFiI1NRV1dXU4ePCgps2JEycwd+5c+P1+ZGdnY8GCBTh16lQMjyI2NDQ0YOLEicjMzER+fj6++c1voqmpSdPm7NmzWLx4MQYPHoyMjAzU19ejpaVF0+bIkSOYPXs20tLSkJ+fj/vuuw9dXV2xPJSYsGrVKlRUVISKhgWDQbz22muh7TxXah555BG4XC7cfffdoc94vnp5+OGH4XK5NP/KyspC23mutBw9ehQ333wzBg8ejNTUVIwbNw67du0KbXfUfV4wllm/fr3wer3iV7/6ldi/f79YuHChyM7OFi0tLfEeWkzZsGGDeOCBB8Tvf/97AUC8+OKLmu2PPPKIyMrKEi+99JL461//Kq677joxbNgwcebMmVCbmTNnivHjx4vt27eLt99+W4wYMULMmTMnxkcSfWbMmCHWrFkj9u3bJ/bs2SOuueYaUVJSIk6dOhVqs2jRIlFcXCw2b94sdu3aJSZNmiQmT54c2t7V1SXGjh0r6urqxO7du8WGDRtEbm6uWL58eTwOKaq8/PLL4o9//KP46KOPRFNTk/jBD34gkpOTxb59+4QQPFcq3nvvPVFaWioqKirEXXfdFfqc56uXFStWiDFjxojPP/889O+LL74Ibee56uXEiRNi6NChYv78+WLHjh3i0KFD4vXXXxcff/xxqI2T7vMscGygurpaLF68OPT/7u5uUVRUJBoaGuI4qvgSLnB6enpEIBAQjz76aOiz1tZW4fP5xG9/+1shhBAHDhwQAMTOnTtDbV577TXhcrnE0aNHYzb2eHDs2DEBQGzdulUIcX5ukpOTxXPPPRdq88EHHwgAYtu2bUKI84LS7XaL5ubmUJtVq1YJv98vOjo6YnsAcWDQoEHiF7/4Bc+Vgvb2djFy5EixadMmMW3atJDA4fnSsmLFCjF+/HjdbTxXWu6//35xxRVXKLc77T7PLiqLnDt3Do2Njairqwt95na7UVdXh23btsVxZM7i8OHDaG5u1sxTVlYWampqQvO0bds2ZGdno6qqKtSmrq4ObrcbO3bsiPmYY8nJkycBADk5OQCAxsZGdHZ2auarrKwMJSUlmvkaN24cCgoKQm1mzJiBtrY27N+/P4ajjy3d3d1Yv349vvrqKwSDQZ4rBYsXL8bs2bM18wLwuaXHwYMHUVRUhOHDh2Pu3Lk4cuQIAJ6rcF5++WVUVVXh29/+NvLz8zFhwgQ8/fTToe1Ou8+zwLHI8ePH0d3drTm5AaCgoADNzc1xGpXzuDAXRvPU3NyM/Px8zXaPx4OcnJyEnsuenh7cfffdmDJlCsaOHQvg/Fx4vV5kZ2dr2obPl958XtiWaOzduxcZGRnw+XxYtGgRXnzxRYwePZrnSof169fjL3/5CxoaGvps4/nSUlNTg2eeeQYbN27EqlWrcPjwYUydOhXt7e08V2EcOnQIq1atwsiRI/H666/j9ttvx5133olf//rXAJx3nx+Qq4kzjJNYvHgx9u3bh3feeSfeQ3E0l112Gfbs2YOTJ0/i+eefx7x587B169Z4D8txfPrpp7jrrruwadMmpKSkxHs4jmfWrFmhvysqKlBTU4OhQ4fid7/7HVJTU+M4MufR09ODqqoq/OQnPwEATJgwAfv27cPq1asxb968OI+uL2zBsUhubi6SkpL6RNW3tLQgEAjEaVTO48JcGM1TIBDAsWPHNNu7urpw4sSJhJ3LJUuW4NVXX8Ubb7yBIUOGhD4PBAI4d+4cWltbNe3D50tvPi9sSzS8Xi9GjBiByspKNDQ0YPz48fj5z3/OcxVGY2Mjjh07hssvvxwejwcejwdbt27F448/Do/Hg4KCAp4vA7KzszFq1Ch8/PHHfG6FUVhYiNGjR2s+Ky8vD7n0nHafZ4FjEa/Xi8rKSmzevDn0WU9PDzZv3oxgMBjHkTmLYcOGIRAIaOapra0NO3bsCM1TMBhEa2srGhsbQ222bNmCnp4e1NTUxHzM0UQIgSVLluDFF1/Eli1bMGzYMM32yspKJCcna+arqakJR44c0czX3r17NTeLTZs2we/397kJJSI9PT3o6OjguQqjtrYWe/fuxZ49e0L/qqqqMHfu3NDfPF9qTp06hb/97W8oLCzkcyuMKVOm9Cln8dFHH2Ho0KEAHHiftzVkeYCyfv164fP5xDPPPCMOHDggbrvtNpGdna2Jqh8ItLe3i927d4vdu3cLAOJnP/uZ2L17t/j73/8uhDifPpidnS3+8Ic/iPfff19cf/31uumDEyZMEDt27BDvvPOOGDlyZEKmid9+++0iKytLvPnmm5r01NOnT4faLFq0SJSUlIgtW7aIXbt2iWAwKILBYGj7hfTUq6++WuzZs0ds3LhR5OXlJWR66rJly8TWrVvF4cOHxfvvvy+WLVsmXC6X+NOf/iSE4LkyQ86iEoLnS+bee+8Vb775pjh8+LB49913RV1dncjNzRXHjh0TQvBcybz33nvC4/GIH//4x+LgwYPi2WefFWlpaeI3v/lNqI2T7vMscGziiSeeECUlJcLr9Yrq6mqxffv2eA8p5rzxxhsCQJ9/8+bNE0KcTyF88MEHRUFBgfD5fKK2tlY0NTVp+vjyyy/FnDlzREZGhvD7/eKWW24R7e3tcTia6KI3TwDEmjVrQm3OnDkjvv/974tBgwaJtLQ08a1vfUt8/vnnmn4++eQTMWvWLJGamipyc3PFvffeKzo7O2N8NNHn1ltvFUOHDhVer1fk5eWJ2trakLgRgufKjHCBw/PVy4033igKCwuF1+sVl1xyibjxxhs1dV14rrS88sorYuzYscLn84mysjLx1FNPabY76T7vEkIIe21CDMMwDMMw8YVjcBiGYRiGSThY4DAMwzAMk3CwwGEYhmEYJuFggcMwDMMwTMLBAodhGIZhmISDBQ7DMAzDMAkHCxyGYRiGYRIOFjgMwzAMwyQcLHAYhmEYhkk4WOAwDMMwDJNwsMBhGIZhGCbhYIHDMAzDMEzC8f8B+PVgmBS1qxcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ce1a6bee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(L,lags=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "058da2fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(L,lags=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e16cba8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python310\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "C:\\Python310\\lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Value</td>      <th>  No. Observations:  </th>    <td>600</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>ARIMA(12, 2, 14)</td> <th>  Log Likelihood     </th> <td>2389.411</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 15 Oct 2023</td> <th>  AIC                </th> <td>-4724.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>10:00:53</td>     <th>  BIC                </th> <td>-4606.196</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>-4678.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 600</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.3977</td> <td>    0.732</td> <td>   -0.543</td> <td> 0.587</td> <td>   -1.833</td> <td>    1.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>    0.1741</td> <td>    0.949</td> <td>    0.183</td> <td> 0.854</td> <td>   -1.686</td> <td>    2.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>  <td>    0.0136</td> <td>    0.770</td> <td>    0.018</td> <td> 0.986</td> <td>   -1.496</td> <td>    1.523</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4</th>  <td>   -0.0789</td> <td>    0.671</td> <td>   -0.118</td> <td> 0.906</td> <td>   -1.395</td> <td>    1.237</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5</th>  <td>   -0.0871</td> <td>    0.685</td> <td>   -0.127</td> <td> 0.899</td> <td>   -1.430</td> <td>    1.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L6</th>  <td>   -0.0412</td> <td>    0.700</td> <td>   -0.059</td> <td> 0.953</td> <td>   -1.413</td> <td>    1.331</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L7</th>  <td>    0.0425</td> <td>    0.667</td> <td>    0.064</td> <td> 0.949</td> <td>   -1.266</td> <td>    1.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L8</th>  <td>   -0.1752</td> <td>    0.586</td> <td>   -0.299</td> <td> 0.765</td> <td>   -1.325</td> <td>    0.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L9</th>  <td>   -0.1996</td> <td>    0.682</td> <td>   -0.293</td> <td> 0.770</td> <td>   -1.537</td> <td>    1.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L10</th> <td>   -0.1062</td> <td>    0.763</td> <td>   -0.139</td> <td> 0.889</td> <td>   -1.601</td> <td>    1.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L11</th> <td>    0.3156</td> <td>    0.787</td> <td>    0.401</td> <td> 0.688</td> <td>   -1.226</td> <td>    1.857</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L12</th> <td>    0.6422</td> <td>    0.498</td> <td>    1.290</td> <td> 0.197</td> <td>   -0.334</td> <td>    1.618</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.2349</td> <td>    0.743</td> <td>   -0.316</td> <td> 0.752</td> <td>   -1.691</td> <td>    1.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>   -0.5083</td> <td>    0.529</td> <td>   -0.960</td> <td> 0.337</td> <td>   -1.546</td> <td>    0.529</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>  <td>   -0.1587</td> <td>    0.183</td> <td>   -0.867</td> <td> 0.386</td> <td>   -0.517</td> <td>    0.200</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L4</th>  <td>   -0.1773</td> <td>    0.130</td> <td>   -1.365</td> <td> 0.172</td> <td>   -0.432</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L5</th>  <td>    0.2512</td> <td>    0.188</td> <td>    1.333</td> <td> 0.182</td> <td>   -0.118</td> <td>    0.620</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L6</th>  <td>    0.2334</td> <td>    0.088</td> <td>    2.665</td> <td> 0.008</td> <td>    0.062</td> <td>    0.405</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L7</th>  <td>   -0.3791</td> <td>    0.210</td> <td>   -1.807</td> <td> 0.071</td> <td>   -0.790</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L8</th>  <td>   -0.0098</td> <td>    0.159</td> <td>   -0.061</td> <td> 0.951</td> <td>   -0.322</td> <td>    0.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L9</th>  <td>    0.0288</td> <td>    0.161</td> <td>    0.179</td> <td> 0.858</td> <td>   -0.287</td> <td>    0.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L10</th> <td>    0.1150</td> <td>    0.149</td> <td>    0.773</td> <td> 0.439</td> <td>   -0.176</td> <td>    0.406</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L11</th> <td>    0.3632</td> <td>    0.120</td> <td>    3.039</td> <td> 0.002</td> <td>    0.129</td> <td>    0.597</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L12</th> <td>   -0.1016</td> <td>    0.296</td> <td>   -0.343</td> <td> 0.732</td> <td>   -0.682</td> <td>    0.479</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L13</th> <td>    0.0627</td> <td>    0.221</td> <td>    0.284</td> <td> 0.777</td> <td>   -0.370</td> <td>    0.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L14</th> <td>   -0.2573</td> <td>    0.247</td> <td>   -1.041</td> <td> 0.298</td> <td>   -0.742</td> <td>    0.227</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>  1.99e-05</td> <td> 1.72e-06</td> <td>   11.578</td> <td> 0.000</td> <td> 1.65e-05</td> <td> 2.33e-05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>13.66</td> <th>  Jarque-Bera (JB):  </th> <td>5.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.00</td>  <th>  Prob(JB):          </th> <td>0.07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.94</td>  <th>  Skew:              </th> <td>0.08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.65</td>  <th>  Kurtosis:          </th> <td>2.57</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &      Value       & \\textbf{  No. Observations:  } &    600      \\\\\n",
       "\\textbf{Model:}                  & ARIMA(12, 2, 14) & \\textbf{  Log Likelihood     } &  2389.411   \\\\\n",
       "\\textbf{Date:}                   & Sun, 15 Oct 2023 & \\textbf{  AIC                } & -4724.823   \\\\\n",
       "\\textbf{Time:}                   &     10:00:53     & \\textbf{  BIC                } & -4606.196   \\\\\n",
       "\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } & -4678.637   \\\\\n",
       "\\textbf{}                        &       - 600      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{ar.L1}  &      -0.3977  &        0.732     &    -0.543  &         0.587        &       -1.833    &        1.038     \\\\\n",
       "\\textbf{ar.L2}  &       0.1741  &        0.949     &     0.183  &         0.854        &       -1.686    &        2.034     \\\\\n",
       "\\textbf{ar.L3}  &       0.0136  &        0.770     &     0.018  &         0.986        &       -1.496    &        1.523     \\\\\n",
       "\\textbf{ar.L4}  &      -0.0789  &        0.671     &    -0.118  &         0.906        &       -1.395    &        1.237     \\\\\n",
       "\\textbf{ar.L5}  &      -0.0871  &        0.685     &    -0.127  &         0.899        &       -1.430    &        1.256     \\\\\n",
       "\\textbf{ar.L6}  &      -0.0412  &        0.700     &    -0.059  &         0.953        &       -1.413    &        1.331     \\\\\n",
       "\\textbf{ar.L7}  &       0.0425  &        0.667     &     0.064  &         0.949        &       -1.266    &        1.351     \\\\\n",
       "\\textbf{ar.L8}  &      -0.1752  &        0.586     &    -0.299  &         0.765        &       -1.325    &        0.974     \\\\\n",
       "\\textbf{ar.L9}  &      -0.1996  &        0.682     &    -0.293  &         0.770        &       -1.537    &        1.138     \\\\\n",
       "\\textbf{ar.L10} &      -0.1062  &        0.763     &    -0.139  &         0.889        &       -1.601    &        1.388     \\\\\n",
       "\\textbf{ar.L11} &       0.3156  &        0.787     &     0.401  &         0.688        &       -1.226    &        1.857     \\\\\n",
       "\\textbf{ar.L12} &       0.6422  &        0.498     &     1.290  &         0.197        &       -0.334    &        1.618     \\\\\n",
       "\\textbf{ma.L1}  &      -0.2349  &        0.743     &    -0.316  &         0.752        &       -1.691    &        1.221     \\\\\n",
       "\\textbf{ma.L2}  &      -0.5083  &        0.529     &    -0.960  &         0.337        &       -1.546    &        0.529     \\\\\n",
       "\\textbf{ma.L3}  &      -0.1587  &        0.183     &    -0.867  &         0.386        &       -0.517    &        0.200     \\\\\n",
       "\\textbf{ma.L4}  &      -0.1773  &        0.130     &    -1.365  &         0.172        &       -0.432    &        0.077     \\\\\n",
       "\\textbf{ma.L5}  &       0.2512  &        0.188     &     1.333  &         0.182        &       -0.118    &        0.620     \\\\\n",
       "\\textbf{ma.L6}  &       0.2334  &        0.088     &     2.665  &         0.008        &        0.062    &        0.405     \\\\\n",
       "\\textbf{ma.L7}  &      -0.3791  &        0.210     &    -1.807  &         0.071        &       -0.790    &        0.032     \\\\\n",
       "\\textbf{ma.L8}  &      -0.0098  &        0.159     &    -0.061  &         0.951        &       -0.322    &        0.303     \\\\\n",
       "\\textbf{ma.L9}  &       0.0288  &        0.161     &     0.179  &         0.858        &       -0.287    &        0.344     \\\\\n",
       "\\textbf{ma.L10} &       0.1150  &        0.149     &     0.773  &         0.439        &       -0.176    &        0.406     \\\\\n",
       "\\textbf{ma.L11} &       0.3632  &        0.120     &     3.039  &         0.002        &        0.129    &        0.597     \\\\\n",
       "\\textbf{ma.L12} &      -0.1016  &        0.296     &    -0.343  &         0.732        &       -0.682    &        0.479     \\\\\n",
       "\\textbf{ma.L13} &       0.0627  &        0.221     &     0.284  &         0.777        &       -0.370    &        0.496     \\\\\n",
       "\\textbf{ma.L14} &      -0.2573  &        0.247     &    -1.041  &         0.298        &       -0.742    &        0.227     \\\\\n",
       "\\textbf{sigma2} &     1.99e-05  &     1.72e-06     &    11.578  &         0.000        &     1.65e-05    &     2.33e-05     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 13.66 & \\textbf{  Jarque-Bera (JB):  } & 5.39  \\\\\n",
       "\\textbf{Prob(Q):}                &  0.00 & \\textbf{  Prob(JB):          } & 0.07  \\\\\n",
       "\\textbf{Heteroskedasticity (H):} &  0.94 & \\textbf{  Skew:              } & 0.08  \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    &  0.65 & \\textbf{  Kurtosis:          } & 2.57  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  Value   No. Observations:                  600\n",
       "Model:               ARIMA(12, 2, 14)   Log Likelihood                2389.411\n",
       "Date:                Sun, 15 Oct 2023   AIC                          -4724.823\n",
       "Time:                        10:00:53   BIC                          -4606.196\n",
       "Sample:                             0   HQIC                         -4678.637\n",
       "                                - 600                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.3977      0.732     -0.543      0.587      -1.833       1.038\n",
       "ar.L2          0.1741      0.949      0.183      0.854      -1.686       2.034\n",
       "ar.L3          0.0136      0.770      0.018      0.986      -1.496       1.523\n",
       "ar.L4         -0.0789      0.671     -0.118      0.906      -1.395       1.237\n",
       "ar.L5         -0.0871      0.685     -0.127      0.899      -1.430       1.256\n",
       "ar.L6         -0.0412      0.700     -0.059      0.953      -1.413       1.331\n",
       "ar.L7          0.0425      0.667      0.064      0.949      -1.266       1.351\n",
       "ar.L8         -0.1752      0.586     -0.299      0.765      -1.325       0.974\n",
       "ar.L9         -0.1996      0.682     -0.293      0.770      -1.537       1.138\n",
       "ar.L10        -0.1062      0.763     -0.139      0.889      -1.601       1.388\n",
       "ar.L11         0.3156      0.787      0.401      0.688      -1.226       1.857\n",
       "ar.L12         0.6422      0.498      1.290      0.197      -0.334       1.618\n",
       "ma.L1         -0.2349      0.743     -0.316      0.752      -1.691       1.221\n",
       "ma.L2         -0.5083      0.529     -0.960      0.337      -1.546       0.529\n",
       "ma.L3         -0.1587      0.183     -0.867      0.386      -0.517       0.200\n",
       "ma.L4         -0.1773      0.130     -1.365      0.172      -0.432       0.077\n",
       "ma.L5          0.2512      0.188      1.333      0.182      -0.118       0.620\n",
       "ma.L6          0.2334      0.088      2.665      0.008       0.062       0.405\n",
       "ma.L7         -0.3791      0.210     -1.807      0.071      -0.790       0.032\n",
       "ma.L8         -0.0098      0.159     -0.061      0.951      -0.322       0.303\n",
       "ma.L9          0.0288      0.161      0.179      0.858      -0.287       0.344\n",
       "ma.L10         0.1150      0.149      0.773      0.439      -0.176       0.406\n",
       "ma.L11         0.3632      0.120      3.039      0.002       0.129       0.597\n",
       "ma.L12        -0.1016      0.296     -0.343      0.732      -0.682       0.479\n",
       "ma.L13         0.0627      0.221      0.284      0.777      -0.370       0.496\n",
       "ma.L14        -0.2573      0.247     -1.041      0.298      -0.742       0.227\n",
       "sigma2       1.99e-05   1.72e-06     11.578      0.000    1.65e-05    2.33e-05\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                  13.66   Jarque-Bera (JB):                 5.39\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.07\n",
       "Heteroskedasticity (H):               0.94   Skew:                             0.08\n",
       "Prob(H) (two-sided):                  0.65   Kurtosis:                         2.57\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ARIMA(x_train,order=(12,2,14))\n",
    "model = model.fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cac3d236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600    0.716106\n",
       "601    0.727768\n",
       "602    0.731245\n",
       "603    0.728662\n",
       "604    0.714793\n",
       "         ...   \n",
       "749    1.095757\n",
       "750    1.096738\n",
       "751    1.096711\n",
       "752    1.097236\n",
       "753    1.099328\n",
       "Name: predicted_mean, Length: 154, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = len(x_train)\n",
    "end = len(x_train)+len(x_test)-1\n",
    "pred = model.predict(start=start,end=end)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0430ec74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28d5c69a9b0>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(x_train)),x_train,color='green',label='train')\n",
    "plt.plot(range(len(x_train),len(x_train)+len(x_test)),x_test,color='red')\n",
    "plt.plot(range(len(x_train),len(x_train)+len(pred)),pred,color='yellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "813ab658",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x_test.values*(MAX-MIN) + MIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "452404f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pred.values*(MAX-MIN) + MIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "750f3471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6118083157750966"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(sum((x-y)**2)/len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2498fd11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1d8d385e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([457.1048704 , 458.24340592, 459.29941844, 460.8187778 ,\n",
       "       461.76350285, 460.97542112, 459.03260045, 456.8980715 ,\n",
       "       455.45507936, 455.6684099 , 457.13468413, 458.57724058,\n",
       "       459.86061321, 461.04631072, 462.21382613, 463.6519667 ,\n",
       "       464.50679749, 463.77225395, 461.8769868 , 459.736384  ,\n",
       "       458.34069549, 458.56157839, 459.93124504, 461.38307196])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d313804",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6363c6db",
   "metadata": {},
   "source": [
    "# Predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2d9db503",
   "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>id</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2049</td>\n",
       "      <td>January</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2049</td>\n",
       "      <td>February</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2049</td>\n",
       "      <td>March</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2049</td>\n",
       "      <td>April</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2049</td>\n",
       "      <td>May</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2049</td>\n",
       "      <td>June</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2049</td>\n",
       "      <td>July</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2049</td>\n",
       "      <td>August</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2049</td>\n",
       "      <td>September</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2049</td>\n",
       "      <td>October</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2049</td>\n",
       "      <td>November</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>2049</td>\n",
       "      <td>December</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2050</td>\n",
       "      <td>January</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>2050</td>\n",
       "      <td>February</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2050</td>\n",
       "      <td>March</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>2050</td>\n",
       "      <td>April</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>2050</td>\n",
       "      <td>May</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>2050</td>\n",
       "      <td>June</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>2050</td>\n",
       "      <td>July</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>2050</td>\n",
       "      <td>August</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>2050</td>\n",
       "      <td>September</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>2050</td>\n",
       "      <td>October</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>2050</td>\n",
       "      <td>November</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>2050</td>\n",
       "      <td>December</td>\n",
       "      <td>CO2 levels</td>\n",
       "      <td>PPM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  Year      Month   Indicator Unit\n",
       "0    0  2049    January  CO2 levels  PPM\n",
       "1    1  2049   February  CO2 levels  PPM\n",
       "2    2  2049      March  CO2 levels  PPM\n",
       "3    3  2049      April  CO2 levels  PPM\n",
       "4    4  2049        May  CO2 levels  PPM\n",
       "5    5  2049       June  CO2 levels  PPM\n",
       "6    6  2049       July  CO2 levels  PPM\n",
       "7    7  2049     August  CO2 levels  PPM\n",
       "8    8  2049  September  CO2 levels  PPM\n",
       "9    9  2049    October  CO2 levels  PPM\n",
       "10  10  2049   November  CO2 levels  PPM\n",
       "11  11  2049   December  CO2 levels  PPM\n",
       "12  12  2050    January  CO2 levels  PPM\n",
       "13  13  2050   February  CO2 levels  PPM\n",
       "14  14  2050      March  CO2 levels  PPM\n",
       "15  15  2050      April  CO2 levels  PPM\n",
       "16  16  2050        May  CO2 levels  PPM\n",
       "17  17  2050       June  CO2 levels  PPM\n",
       "18  18  2050       July  CO2 levels  PPM\n",
       "19  19  2050     August  CO2 levels  PPM\n",
       "20  20  2050  September  CO2 levels  PPM\n",
       "21  21  2050    October  CO2 levels  PPM\n",
       "22  22  2050   November  CO2 levels  PPM\n",
       "23  23  2050   December  CO2 levels  PPM"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('test.csv')\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3c082c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID = df_test.id.values\n",
    "ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "53b767a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([457.1048704 , 458.24340592, 459.29941844, 460.8187778 ,\n",
       "       461.76350285, 460.97542112, 459.03260045, 456.8980715 ,\n",
       "       455.45507936, 455.6684099 , 457.13468413, 458.57724058,\n",
       "       459.86061321, 461.04631072, 462.21382613, 463.6519667 ,\n",
       "       464.50679749, 463.77225395, 461.8769868 , 459.736384  ,\n",
       "       458.34069549, 458.56157839, 459.93124504, 461.38307196])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = pred.values*(MAX-MIN) + MIN\n",
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a69e987c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([457.1 , 458.24, 459.3 , 460.82, 461.76, 460.98, 459.03, 456.9 ,\n",
       "       455.46, 455.67, 457.13, 458.58, 459.86, 461.05, 462.21, 463.65,\n",
       "       464.51, 463.77, 461.88, 459.74, 458.34, 458.56, 459.93, 461.38])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(val,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "49c60574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>457.10</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>458.24</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>459.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>460.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>461.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>460.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>459.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>456.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>455.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>455.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>457.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>458.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>459.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>461.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>462.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>463.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>464.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>463.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>461.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>459.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>458.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>458.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>459.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>461.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id   Label\n",
       "0    0  457.10\n",
       "1    1  458.24\n",
       "2    2  459.30\n",
       "3    3  460.82\n",
       "4    4  461.76\n",
       "5    5  460.98\n",
       "6    6  459.03\n",
       "7    7  456.90\n",
       "8    8  455.46\n",
       "9    9  455.67\n",
       "10  10  457.13\n",
       "11  11  458.58\n",
       "12  12  459.86\n",
       "13  13  461.05\n",
       "14  14  462.21\n",
       "15  15  463.65\n",
       "16  16  464.51\n",
       "17  17  463.77\n",
       "18  18  461.88\n",
       "19  19  459.74\n",
       "20  20  458.34\n",
       "21  21  458.56\n",
       "22  22  459.93\n",
       "23  23  461.38"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = pd.DataFrame()\n",
    "final['id'] = ID\n",
    "final['Label'] = np.round(val,2)\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "cdc9b273",
   "metadata": {},
   "outputs": [],
   "source": [
    "final.to_csv('final1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7c5e9318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>457.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>458.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>459.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>460.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>461.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>460.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>458.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>456.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>454.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>455.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>456.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>458.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>459.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>460.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>461.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>462.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>463.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>462.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>460.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>458.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>457.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>457.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>459.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>460.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id   Label\n",
       "0    0  457.08\n",
       "1    1  458.06\n",
       "2    2  459.00\n",
       "3    3  460.23\n",
       "4    4  461.05\n",
       "5    5  460.37\n",
       "6    6  458.50\n",
       "7    7  456.26\n",
       "8    8  454.83\n",
       "9    9  455.11\n",
       "10  10  456.64\n",
       "11  11  458.27\n",
       "12  12  459.53\n",
       "13  13  460.50\n",
       "14  14  461.49\n",
       "15  15  462.66\n",
       "16  16  463.40\n",
       "17  17  462.81\n",
       "18  18  460.95\n",
       "19  19  458.77\n",
       "20  20  457.43\n",
       "21  21  457.66\n",
       "22  22  459.13\n",
       "23  23  460.77"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = pd.read_csv('final0.csv')\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ee490c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6953386465121337"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(sum((l.Label.values - final.Label.values)**2)/len(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "73f66b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([457.1 , 458.24, 459.3 , 460.82, 461.76, 460.98, 459.03, 456.9 ,\n",
       "       455.46, 455.67, 457.13, 458.58, 459.86, 461.05, 462.21, 463.65,\n",
       "       464.51, 463.77, 461.88, 459.74, 458.34, 458.56, 459.93, 461.38])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.Label.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3046ea2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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