P6 - Plotting with the Pandas .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "73a113c8",
   "metadata": {},
   "source": [
    "Visualizations are one of the most powerful tools at our disposal for exploring data and communicating data insights. Let's start by loading some packages, as well as a \"Diamonds\" data set which we'll use as a sample data set for plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da231dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "108f76df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data into the notebook\n",
    "df = pd.read_csv('diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4f23570",
   "metadata": {},
   "outputs": [
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       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
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       "      <td>60.8</td>\n",
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       "<p>53940 rows × 11 columns</p>\n",
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       "       Unnamed: 0  carat        cut color clarity  depth  table  price     x  \\\n",
       "0               1   0.23      Ideal     E     SI2   61.5   55.0    326  3.95   \n",
       "1               2   0.21    Premium     E     SI1   59.8   61.0    326  3.89   \n",
       "2               3   0.23       Good     E     VS1   56.9   65.0    327  4.05   \n",
       "3               4   0.29    Premium     I     VS2   62.4   58.0    334  4.20   \n",
       "4               5   0.31       Good     J     SI2   63.3   58.0    335  4.34   \n",
       "...           ...    ...        ...   ...     ...    ...    ...    ...   ...   \n",
       "53935       53936   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75   \n",
       "53936       53937   0.72       Good     D     SI1   63.1   55.0   2757  5.69   \n",
       "53937       53938   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66   \n",
       "53938       53939   0.86    Premium     H     SI2   61.0   58.0   2757  6.15   \n",
       "53939       53940   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83   \n",
       "\n",
       "          y     z  \n",
       "0      3.98  2.43  \n",
       "1      3.84  2.31  \n",
       "2      4.07  2.31  \n",
       "3      4.23  2.63  \n",
       "4      4.35  2.75  \n",
       "...     ...   ...  \n",
       "53935  5.76  3.50  \n",
       "53936  5.75  3.61  \n",
       "53937  5.68  3.56  \n",
       "53938  6.12  3.74  \n",
       "53939  5.87  3.64  \n",
       "\n",
       "[53940 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the dataset \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ad0e93d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the number of rows and columns into our dataset \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "033b2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "593340"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eedc3128",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  53940 non-null  int64  \n",
      " 1   carat       53940 non-null  float64\n",
      " 2   cut         53940 non-null  object \n",
      " 3   color       53940 non-null  object \n",
      " 4   clarity     53940 non-null  object \n",
      " 5   depth       53940 non-null  float64\n",
      " 6   table       53940 non-null  float64\n",
      " 7   price       53940 non-null  int64  \n",
      " 8   x           53940 non-null  float64\n",
      " 9   y           53940 non-null  float64\n",
      " 10  z           53940 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(3)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()# checking the complete information of the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89376360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "carat         0\n",
       "cut           0\n",
       "color         0\n",
       "clarity       0\n",
       "depth         0\n",
       "table         0\n",
       "price         0\n",
       "x             0\n",
       "y             0\n",
       "z             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # no missing values in the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "622369ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, carat, cut, color, clarity, depth, table, price, x, y, z]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()] # no duplicate entry in the dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0c83374",
   "metadata": {},
   "source": [
    "* Since our dataset is cleaned so we can start anlysing the data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3860452a",
   "metadata": {},
   "source": [
    "**Histograms**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8436ac1",
   "metadata": {},
   "source": [
    "A histogram is a univariate plot (a plot that displays one variable) that groups a numeric variable into bins and displays the number of observations that fall within each bin. A histogram is a useful tool for getting a sense of the distribution of a numeric variable. Let's create a histogram of diamond carat weight with the df.hist() function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72c961ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'carat'}>]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DwHBiDgDDHTHmVXVjVT1eVR/dMPZ1VXV7VT20fD17Ga+qeltV7auq+6rqRRuOuWrZ/6GqumrD+Iur6iPLMW+rqlr1kwSAk9nRnJn/SpLLDxq7Lskd3X1hkjuW75Pk5UkuXG5XJ3l7sh7/JNcneUmSi5Ncf+APgGWfH9pw3ME/CwD4Co4Y8+7+vSRPHDR8RZKblu2bkrxyw/g7e90HkpxVVecmeVmS27v7ie5+MsntSS5f7nted3+guzvJOzc8FgBwFDb7nvmO7n5s2f7TJDuW7fOSPLxhv0eWsa80/sghxgGAo3T6Vh+gu7uqehWTOZKqujrrL99nx44dWVtbS5Ls37//y9ubtWfPFid3Etm5c3/27Fl71sdt8X+Ck9oqfkd5mvVcPWu6Wsd7PTcb809V1bnd/djyUvnjy/ijSc7fsN/OZezRJLsPGl9bxnceYv9D6u4bktyQJLt27erdu9cfcm1tLQe2N+vSS7d0+Ellz561vOENu5/1cX1c/qSbaRW/ozzNeq6eNV2t472em32Z/ZYkB65IvyrJ+zaMv2a5qv2SJJ9bXo6/LcllVXX2cuHbZUluW+77fFVdslzF/poNjwUAHIUjnplX1a9l/az6nKp6JOtXpb8lybur6nVJPpnk+5fdb03yiiT7kvx5ktcmSXc/UVVvSnL3st/PdveBi+p+JOtXzH9Nkt9ebgDAUTpizLv7ysPc9V2H2LeTXHOYx7kxyY2HGL8nyQuONA8A4NB8AhwADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMNzp2z0BTh5V2z2DI+ve7hkArJ4zcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOG2FPOq+kRVfaSq7q2qe5axr6uq26vqoeXr2ct4VdXbqmpfVd1XVS/a8DhXLfs/VFVXbe0pAcCpZRVn5pd29wu7e9fy/XVJ7ujuC5PcsXyfJC9PcuFyuzrJ25P1+Ce5PslLklyc5PoDfwAAAEd2LF5mvyLJTcv2TUleuWH8nb3uA0nOqqpzk7wsye3d/UR3P5nk9iSXH4N5AcBJqbp78wdX/UmSJ5N0kv/a3TdU1We7+6zl/kryZHefVVXvT/KW7r5rue+OJG9MsjvJV3f3zy3j/zbJX3T3nkP8vKuzflafHTt2vPjmm29Okuzfvz9nnnnmpp9Hkuzdu6XDTyo7d+7PI49sbT1PVC9+8fb83FX8jvI067l61nS1jsV6XnrppXs3vAr+DKdv8bG/o7sfraq/k+T2qvqjjXd2d1fV5v9aOEh335DkhiTZtWtX7969O0mytraWA9ubdemlW5zcSWTPnrW84Q27t3sax8QW/nbdklX8jvI067l61nS1jvd6bull9u5+dPn6eJLfzPp73p9aXj7P8vXxZfdHk5y/4fCdy9jhxgGAo7DpmFfVGVX1tQe2k1yW5KNJbkly4Ir0q5K8b9m+JclrlqvaL0nyue5+LMltSS6rqrOXC98uW8YAgKOwlZfZdyT5zfW3xXN6kv/e3b9TVXcneXdVvS7JJ5N8/7L/rUlekWRfkj9P8tok6e4nqupNSe5e9vvZ7n5iC/MCgFPKpmPe3R9P8g8OMf6ZJN91iPFOcs1hHuvGJDdudi4AcCrzCXAAMJyYA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYs4ppWp7bnv3Ht1+AJsh5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADHf6dk8AeFrVds/gyLq3ewbAwZyZA8BwYg4Aw4k5AAwn5gAwnJgDwHAnTMyr6vKq+lhV7auq67Z7PgAwxQkR86o6LckvJXl5kouSXFlVF23vrABghhMi5kkuTrKvuz/e3X+V5OYkV2zznABghBMl5ucleXjD948sY8AJpmr7b3v3Hv4+OBVVnwAf51RV35fk8u7+V8v3r07yku6+9qD9rk5y9fLtNyf52LJ9TpJPH6fpngqs5+pZ09WynqtnTVfrWKznN3b3NxzqjhPl41wfTXL+hu93LmPP0N03JLnh4PGquqe7dx276Z1arOfqWdPVsp6rZ01X63iv54nyMvvdSS6squdX1XOTvCrJLds8JwAY4YQ4M+/up6rq2iS3JTktyY3dff82TwsARjghYp4k3X1rkls3efjfeOmdLbGeq2dNV8t6rp41Xa3jup4nxAVwAMDmnSjvmQMAmzQ+5j4GdnWq6saqeryqPrrdczkZVNX5VXVnVT1QVfdX1eu3e07TVdVXV9WHquoPlzX9me2e08mgqk6rqj+oqvdv91xOBlX1iar6SFXdW1X3HJefOfll9uVjYP93kn+a9Q+auTvJld39wLZObKiq+sdJ9id5Z3e/YLvnM11VnZvk3O7+cFV9bZK9SV7p93PzqqqSnNHd+6vqOUnuSvL67v7ANk9ttKr6sSS7kjyvu79nu+czXVV9Ismu7j5u/25/+pm5j4Fdoe7+vSRPbPc8Thbd/Vh3f3jZ/kKSB+OTDbek1+1fvn3Ocpt7RnICqKqdSb47yX/b7rmwedNj7mNgGaGqLkjy7Uk+uM1TGW95SfjeJI8nub27renW/Ick/ybJX2/zPE4mneR/VtXe5ZNLj7npMYcTXlWdmeQ9SX60uz+/3fOZrru/1N0vzPonRV5cVd4S2qSq+p4kj3f33u2ey0nmO7r7RVn/L4Fes7yFeUxNj/lRfQwsbJflfd33JHlXd793u+dzMunuzya5M8nl2zyVyV6a5J8v7/HenOQ7q+pXt3dK83X3o8vXx5P8ZtbfEj6mpsfcx8Bywlou1npHkge7+63bPZ+TQVV9Q1WdtWx/TdYvfv2jbZ3UYN39k929s7svyPr/f/5ud//ANk9rtKo6Y7ngNVV1RpLLkhzzfyE0Oubd/VSSAx8D+2CSd/sY2M2rql9L8vtJvrmqHqmq1233nIZ7aZJXZ/1s597l9ortntRw5ya5s6ruy/of87d3t39OxYlkR5K7quoPk3woyW919+8c6x86+p+mAQDDz8wBADEHgPHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAY7v8DQik9FsdZ+PgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column = 'carat',\n",
    "       figsize = (8,8),\n",
    "       color = 'blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53bd4e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30    2604\n",
       "0.31    2249\n",
       "1.01    2242\n",
       "0.70    1981\n",
       "0.32    1840\n",
       "        ... \n",
       "3.02       1\n",
       "3.65       1\n",
       "3.50       1\n",
       "3.22       1\n",
       "3.11       1\n",
       "Name: carat, Length: 273, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['carat'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a93ab2",
   "metadata": {},
   "source": [
    "We see immediately that the carat weights are positively skewed: most diamonds are around 1 carat or below but there are extreme cases of larger diamonds.\n",
    "\n",
    "The plot above has fairly wide bins and there doesn't appear to be any data beyond a carat size of 3.5. We can make try to get more out of hour histogram by adding some additional arguments to control the size of the bins and limits of the x-axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9f4a746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'carat'}>]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column = 'carat',\n",
    "       figsize = (8,8),\n",
    "       color = 'blue',\n",
    "       bins = 50,\n",
    "       range = (0,3.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ab0389",
   "metadata": {},
   "source": [
    "This histogram gives us a better sense of some subtleties within the distribution, but we can't be sure that it contains all the data. Limiting the X-axis to 3.5 might have cut out some outliers with counts so small that they didn't show up as bars on our original chart. Let's check to see if any diamonds are larger than 3.5 carats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "41744ff7",
   "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>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23644</th>\n",
       "      <td>23645</td>\n",
       "      <td>3.65</td>\n",
       "      <td>Fair</td>\n",
       "      <td>H</td>\n",
       "      <td>I1</td>\n",
       "      <td>67.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11668</td>\n",
       "      <td>9.53</td>\n",
       "      <td>9.48</td>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25998</th>\n",
       "      <td>25999</td>\n",
       "      <td>4.01</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>I1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>15223</td>\n",
       "      <td>10.14</td>\n",
       "      <td>10.10</td>\n",
       "      <td>6.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25999</th>\n",
       "      <td>26000</td>\n",
       "      <td>4.01</td>\n",
       "      <td>Premium</td>\n",
       "      <td>J</td>\n",
       "      <td>I1</td>\n",
       "      <td>62.5</td>\n",
       "      <td>62.0</td>\n",
       "      <td>15223</td>\n",
       "      <td>10.02</td>\n",
       "      <td>9.94</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26444</th>\n",
       "      <td>26445</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>I</td>\n",
       "      <td>I1</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>15984</td>\n",
       "      <td>10.01</td>\n",
       "      <td>9.94</td>\n",
       "      <td>6.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26534</th>\n",
       "      <td>26535</td>\n",
       "      <td>3.67</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>I1</td>\n",
       "      <td>62.4</td>\n",
       "      <td>56.0</td>\n",
       "      <td>16193</td>\n",
       "      <td>9.86</td>\n",
       "      <td>9.81</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27130</th>\n",
       "      <td>27131</td>\n",
       "      <td>4.13</td>\n",
       "      <td>Fair</td>\n",
       "      <td>H</td>\n",
       "      <td>I1</td>\n",
       "      <td>64.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>17329</td>\n",
       "      <td>10.00</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27415</th>\n",
       "      <td>27416</td>\n",
       "      <td>5.01</td>\n",
       "      <td>Fair</td>\n",
       "      <td>J</td>\n",
       "      <td>I1</td>\n",
       "      <td>65.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>18018</td>\n",
       "      <td>10.74</td>\n",
       "      <td>10.54</td>\n",
       "      <td>6.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27630</th>\n",
       "      <td>27631</td>\n",
       "      <td>4.50</td>\n",
       "      <td>Fair</td>\n",
       "      <td>J</td>\n",
       "      <td>I1</td>\n",
       "      <td>65.8</td>\n",
       "      <td>58.0</td>\n",
       "      <td>18531</td>\n",
       "      <td>10.23</td>\n",
       "      <td>10.16</td>\n",
       "      <td>6.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27679</th>\n",
       "      <td>27680</td>\n",
       "      <td>3.51</td>\n",
       "      <td>Premium</td>\n",
       "      <td>J</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>18701</td>\n",
       "      <td>9.66</td>\n",
       "      <td>9.63</td>\n",
       "      <td>6.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  carat        cut color clarity  depth  table  price      x  \\\n",
       "23644       23645   3.65       Fair     H      I1   67.1   53.0  11668   9.53   \n",
       "25998       25999   4.01    Premium     I      I1   61.0   61.0  15223  10.14   \n",
       "25999       26000   4.01    Premium     J      I1   62.5   62.0  15223  10.02   \n",
       "26444       26445   4.00  Very Good     I      I1   63.3   58.0  15984  10.01   \n",
       "26534       26535   3.67    Premium     I      I1   62.4   56.0  16193   9.86   \n",
       "27130       27131   4.13       Fair     H      I1   64.8   61.0  17329  10.00   \n",
       "27415       27416   5.01       Fair     J      I1   65.5   59.0  18018  10.74   \n",
       "27630       27631   4.50       Fair     J      I1   65.8   58.0  18531  10.23   \n",
       "27679       27680   3.51    Premium     J     VS2   62.5   59.0  18701   9.66   \n",
       "\n",
       "           y     z  \n",
       "23644   9.48  6.38  \n",
       "25998  10.10  6.17  \n",
       "25999   9.94  6.24  \n",
       "26444   9.94  6.31  \n",
       "26534   9.81  6.13  \n",
       "27130   9.85  6.43  \n",
       "27415  10.54  6.98  \n",
       "27630  10.16  6.72  \n",
       "27679   9.63  6.03  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['carat']>3.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c56c8c7d",
   "metadata": {},
   "source": [
    "* So we are having total 9 diamonds that are having the carat weigth >3.5\n",
    "* Should cutting these diamonds out concern us? On one hand, these outliers have very little bearing on the shape of the distribution. On the other hand, limiting the X-axis to 3.5 implies that no data lies beyond that point. For our own exploratory purposes this is not an issue but if we were to show this plot to someone else, it could be misleading. Including a note that 9 diamonds lie beyond the chart range could be helpful."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "732e3961",
   "metadata": {},
   "source": [
    "**Boxplots**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ce9c42",
   "metadata": {},
   "source": [
    "Boxplots are another type of univariate plot for summarizing distributions of numeric data graphically. Let's make a boxplot of carat using the pd.boxplot() function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10a953f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3TOnxf5X09dLI+V/M7Hzp51Oll3xW0u+a2UUz+2JifxhQwqwPpJqZ/YaklyV9yt2vmNkqSS7pZ+7uZvYnkjLu/iUze0bS5yVtcfdJM/tFSdfcfcrMfk3SoLu3mlmbpD9398cT+aOAj2AXcqTdI5K+4e5XJMnd/9fMHpL092a2RtIvSPrvW47/J3efLN2uldRvZpslzUjasHSxgeg49YFK9DeS+t39IUl/Kqn+lscmbrn9RUn/I+lhSa26XupAcChqpN0ZSU+a2S9JUunUx/36cIXHnXd47v2SfuLu1yR9QdfXspGkMUkr4okLLBxFjVQrLRDWJ+k7ZvampK9KekbSN8xsWHde0vSApJ2l5/26PhxtvyVppvTlJF8mInF8mQgAgWNEDQCBo6gBIHAUNQAEjqIGgMBR1AAQOIoaAAJHUQNA4P4fvyv1XeHC2aYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column = 'carat')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c51f670",
   "metadata": {},
   "source": [
    "the central box of the boxplot represents the middle 50% of the observations, the central bar is the median and the bars at the end of the dotted lines (whiskers) encapsulate the great majority of the observations. Circles that lie beyond the end of the whiskers are data points that may be outliers.\n",
    "\n",
    "In this case, our data set has over 50,000 observations and we see many data points beyond the top whisker. We probably wouldn't want to classify all of those points as outliers, but the handful of diamonds at 4 carats and above are definitely far outside the norm."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e8f540",
   "metadata": {},
   "source": [
    "One of the most useful features of a boxplot is the ability to make side-by-side boxplots. A side-by-side boxplot takes a numeric variable and splits it on based on some categorical variable, drawing a different boxplot for each level of the categorical variable. Let's make a side-by-side boxplot of diamond price split by diamond clarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "670f5ceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'price'}, xlabel='clarity'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column = 'price',\n",
    "          by = 'clarity',\n",
    "          figsize = (10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567d23ba",
   "metadata": {},
   "source": [
    "The boxplot above is curious: we'd expect diamonds with better clarity to fetch higher prices and yet diamonds on the highest end of the clarity spectrum (IF = internally flawless) actually have lower median prices than low clarity diamonds! What gives? Perhaps another boxplot can shed some light on this situation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8fb08bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'carat'}, xlabel='clarity'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column = 'carat',\n",
    "          by  = 'clarity',\n",
    "          figsize = (10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d00cc6",
   "metadata": {},
   "source": [
    "* The plot above shows that diamonds with low clarity ratings also tend to be larger. Since size is an important factor in determining a diamond's value, it isn't too surprising that low clarity diamonds have higher median prices."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f7d527",
   "metadata": {},
   "source": [
    "**Density Plots**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41f3b4e",
   "metadata": {},
   "source": [
    "A density plot shows the distribution of a numeric variable with a continuous curve. It is similar to a histogram but without discrete bins, a density plot gives a better picture of the underlying shape of a distribution. Create a density plot with series.plot(kind=\"density\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e3cff39d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['carat'].plot(kind = 'density',\n",
    "                figsize = (8,8),\n",
    "                xlim = (0,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad69f38",
   "metadata": {},
   "source": [
    "**Barplots**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "881940a7",
   "metadata": {},
   "source": [
    "Barplots are graphs that visually display counts of categorical variables. We can create a barplot by creating a table of counts for a certain variable using the pd.crosstab() function and then passing the counts to df.plot(kind=\"bar\"):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "793a6350",
   "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>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  carat      cut color clarity  depth  table  price     x     y  \\\n",
       "0           1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "1           2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "\n",
       "      z  \n",
       "0  2.43  \n",
       "1  2.31  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "826c0978",
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clarity</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SI1</th>\n",
       "      <td>13065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VS2</th>\n",
       "      <td>12258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI2</th>\n",
       "      <td>9194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VS1</th>\n",
       "      <td>8171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VVS2</th>\n",
       "      <td>5066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VVS1</th>\n",
       "      <td>3655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IF</th>\n",
       "      <td>1790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I1</th>\n",
       "      <td>741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    count\n",
       "clarity       \n",
       "SI1      13065\n",
       "VS2      12258\n",
       "SI2       9194\n",
       "VS1       8171\n",
       "VVS2      5066\n",
       "VVS1      3655\n",
       "IF        1790\n",
       "I1         741"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clarity_table = pd.crosstab(index = df['clarity'],\n",
    "                         columns = 'count')\n",
    "clarity_table = clarity_table.sort_values(by = 'count',ascending = False)\n",
    "clarity_table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "23c6d5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='clarity'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clarity_table.plot(kind = 'bar',\n",
    "                figsize = (8,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a8db30",
   "metadata": {},
   "source": [
    "We can use a two dimensional table to create a stacked barplot. Stacked barplots show the distribution of a second categorical variable within each bar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "18518efa",
   "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>color</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "      <th>J</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clarity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I1</th>\n",
       "      <td>42</td>\n",
       "      <td>102</td>\n",
       "      <td>143</td>\n",
       "      <td>150</td>\n",
       "      <td>162</td>\n",
       "      <td>92</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IF</th>\n",
       "      <td>73</td>\n",
       "      <td>158</td>\n",
       "      <td>385</td>\n",
       "      <td>681</td>\n",
       "      <td>299</td>\n",
       "      <td>143</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI1</th>\n",
       "      <td>2083</td>\n",
       "      <td>2426</td>\n",
       "      <td>2131</td>\n",
       "      <td>1976</td>\n",
       "      <td>2275</td>\n",
       "      <td>1424</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI2</th>\n",
       "      <td>1370</td>\n",
       "      <td>1713</td>\n",
       "      <td>1609</td>\n",
       "      <td>1548</td>\n",
       "      <td>1563</td>\n",
       "      <td>912</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VS1</th>\n",
       "      <td>705</td>\n",
       "      <td>1281</td>\n",
       "      <td>1364</td>\n",
       "      <td>2148</td>\n",
       "      <td>1169</td>\n",
       "      <td>962</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VS2</th>\n",
       "      <td>1697</td>\n",
       "      <td>2470</td>\n",
       "      <td>2201</td>\n",
       "      <td>2347</td>\n",
       "      <td>1643</td>\n",
       "      <td>1169</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VVS1</th>\n",
       "      <td>252</td>\n",
       "      <td>656</td>\n",
       "      <td>734</td>\n",
       "      <td>999</td>\n",
       "      <td>585</td>\n",
       "      <td>355</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VVS2</th>\n",
       "      <td>553</td>\n",
       "      <td>991</td>\n",
       "      <td>975</td>\n",
       "      <td>1443</td>\n",
       "      <td>608</td>\n",
       "      <td>365</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "color       D     E     F     G     H     I    J\n",
       "clarity                                         \n",
       "I1         42   102   143   150   162    92   50\n",
       "IF         73   158   385   681   299   143   51\n",
       "SI1      2083  2426  2131  1976  2275  1424  750\n",
       "SI2      1370  1713  1609  1548  1563   912  479\n",
       "VS1       705  1281  1364  2148  1169   962  542\n",
       "VS2      1697  2470  2201  2347  1643  1169  731\n",
       "VVS1      252   656   734   999   585   355   74\n",
       "VVS2      553   991   975  1443   608   365  131"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carat_table  = pd.crosstab(index= df['clarity'],\n",
    "                          columns = df['color'])\n",
    "carat_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c05f81e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='clarity'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "carat_table.plot(kind = 'bar',\n",
    "                figsize = (10,10),\n",
    "                stacked = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "499bb7ed",
   "metadata": {},
   "source": [
    "A grouped barplot is an alternative to a stacked barplot that gives each stacked section its own bar. To make a grouped barplot, do not include the stacked argument (or set stacked=False):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "148c650f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='clarity'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "carat_table.plot(kind = 'bar',\n",
    "                figsize  = (10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c253c5",
   "metadata": {},
   "source": [
    "**Scatterplots**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecacbcc",
   "metadata": {},
   "source": [
    "Scatterplots are bivariate (two variable) plots that take two numeric variables and plot data points on the x/y plane. To create a single scatterplot, use df.plot(kind=\"scatter\"):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "254f1e4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='carat', ylabel='price'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YuoO2HZp1xfJNTaow3Wh2199Qbx1p+tGsczH9vBFoONKJ+zc2merG4vG5czI7tr/4Agq27otsYOwNKFjxirlr1woGhsa2TsyYOML0WiQTZ8DaA2/+RaWOzpvJXHthGUXfCKIfpMreygkk4oisxKrYXgFHZUkhAGSkW35xgQdL51aYGhVkzvGNYHrwwInTWPG3fSbvscklg7FjyUyTiLIz+x1XXICqsiE42N4NgMMlqCOmcoL1ZrF2Udr9o+V0YkS66PHLuGVVLVbMr8K08mFobOsEwFBZUhixYaPucAesfIz/b3cL/u/9I2n9R5sgiPSRqDGMiYJEHJGVaMX2d6/dA1FgkBVuECaJMOdNRuTppqnjAA7ct6ERksCgcHXc1ZyV2+ESBPhkc3pUExZWnmOXnVsMBmNlIAtur/2kHXc9X2+Yb6ooHJt/eHlMXnqR/tFyOjEinXgDHHc+Xw+u88UTGXD/tZNtvyf1rZ2Wx+qVAcgK7l67B0Py3KgsKczK+jiCIOIj0+Yik4gjspZQYwMPlzH9N+dNZri8IEeCwNRUH+cynnunBX6Zh/5RkAS1ts9KnIZTXODBr26oxp1r6gDGAM7xyIJq/Oq1fZZzRT2SaIjyOSHSP1pOJ0akG39YWE3mwM9ebMDPr51sinC2d3kxOkrXrzeg4Ad/2g0FnKJyBHEWkWlzkamxgchKtOiQN8Bxxi9bdlzGa/0QqeA90ev2yWaBIYkCFEUJ6tLos0k5AEFgcAkCBIHhWGev7WB4bQZoLET6R8vJxIhoXFM1Ou73CgByJQEuETh3eF7M71+6oREd3b7Q90TrVv3F5r1R33vGL2dM0wxBEKkh00y+KRJHZCXJDGmn+tjhaIPZ/YoaMdNSl9r79ZHF9i4vFq9VRSGg7v/Q3+wFSPiILydEmh37VvPJmI8XzuaGo472ExlCdWoMqtWIzIGeYI704xNnYj53QOa46rFtePh6tWYuPG3sEoB/mjYeeS4Jv912AAJjOBMWyYznu5GKJhGCIJJDJpl8k4gjspJkhrRTfWxJAERBgCQy+AIKBABeXXTOJQhYvesw/ueNZlN6d/Wuw6bOU5ExBGwieLkuKaLgsBMXdv9o1beeiuNTMOJUV/7qhi+iMNcFNfbIcNvq93DaG4j2tqhoUz6e/M5FJoGd45IwZ8oYVJUNwcIvjUdjWydu/mOtocs41u9GJnW2EQQRH5kyF5nSqURWksyQdqqOne8R4ZYELLtmMu6dUwF/QIFLYAYBBwA+WcHjW/cb0rt3rd2D2k/a8fjW/aZzhL9fTyTBYWd8q197eHp6athoq3iwmJplSWePHzMmDseMiSNQWVJoOXYtGhYDPgAgOBuWRRTvxQUedJzxQ9GpTpfIYvpuJDNVTxDE2QdF4oisJZkh7WQf+1hnLx565SN4RIb7NzRC4WptnD7Ck+8RISsct32lHE++eQDeQF/UyRdQcMPvdkISBdjVzbkEYF51CTbUt8EjSRG7dONtm8+VUvd74PJNTZg9eVToN+DwFO+8qhK8VNcGl8gQkDlkRTEIRI/E8LuFNchzibjxqV2maFplSaFl2hgA6ltOId8tYskLewzHFBhCqW4nZFpnG0EQ2Q2JOCKrSWZI2+rYiahlevLvH+MXL6t1a1aGsgCQ7xaxbG4lrpikmtQ+/kazaZ+AAgQiRKNcooiFl03AT6+qiLrmeMXFH3Yesn0tGejXM696DCpGF2J78wkcPHkGz73bApegdo4u/vr5GFWYYxBk98ypwOBcN0qLcvHw9VWWNX7h4n1780lMe3ALXIIAb0CGEDZxwy2KMQmwTOtsIwgiuyERRxAOSUQt0+qdh0ICLhIy57hi0giD791da/fAF5Z79IgMAa4OZQ8noCjo7PGjtCg34pxRID5x0d7lxdHO1KUBe/2KYYbsurojJh88bTW/2LzXYB/ScKTTNPHCyjxZT0e3zxSdDHcAjlWARWoSIQiCiBUScURWka6uvkjpRsDcNWp3jPs2NNq+7pEEuEXrB7uWgjUJQMbAbKJxfpnjttXvORKc8YiL1o4euAXAF3tpWlx4RBbyuNO6csPtWfQs29CIqROGorPHHxo9pr93O5bMNIlbvVD3BmRTolpkgCgK8NjcJydkUmcbQRDZTdJEHGPsaQBzAHzKOZ8c3PYXAOcHdxkC4BTnvJoxNh7AhwA+Cr62k3P+g+B7LgLwBwC5ADYD+BHnnDPGhgL4C4DxAA4CWMA570jW9RDpJ51dfXbpRruuUQ296Gzt6IEkMEvh4RKA3y2sweBcF/LdqiFv8/HT6Pb1+bo9+pp5Xujl5cV4Y98Jy7I4DoS6N53Ut+nFhbaG9i6v7Xvy3SIU07yI/pMjMfQGLI7JWOizaO3ogSjYdCn07Y5vPLYNAmOmDl6rVLGVUA9H5sCiGedg5hdG9kuAZUpnG0EQ2U0yI3F/ALASwCptA+f8m9qfGWOPANDPtvmYc15tcZwnANwCYBdUETcbwMsAfgzgdc75A4yxHwd/XpLYSyAyhXgK7xMVtWvv8qKzxw+fbHywa12j3gC3XFO46Lzn6grTSC0NQRBQWVKI7c0nseSFPeAKh1fmoVmnt32lHJKFaNn60QnLGZ/hOC2eLy7whNagryWbXDLY8Dlq18btLqgfXPmFkdj4wTHT9kVXlIfOX1qUa5lC1uMNCUHzflZpUCcefgDwxN8/xsIvjScRRhBE2kmaiOOcvxmMsJlgjDEACwDMjHQMxthoAIWc853Bn1cBuBaqiLsGwFeCu/4RwBsgETdgibXwPlFRO/1xZEWBS2TIkUT4FcWya1RbEwCT6Fy+qQn3zq3AvesaDQJEFBhWzFe7IFXj3r5r1Ix/V27db+mnJjIG2YHRmtPaLSux/LMXG5DvFiFzdcSU3hQ30YgM+GujWcABwNB8d+jPxQUe3Du3Aj97sSGm4+e6BHAglAbVC32rukArXCJ1kxIEkRmkyyfucgDHOed6k6sJjLH3GWN/Z4xdHtw2BkCrbp/W4DYAGMk516zejwEYmdQVE2kllsL7RHlxhR8noKiWEo/f9EXsWDITN04da7smTXTqcQkCyopyTV5lkqDaVDy17YAp7afhFkV8f/oE03YeIZXpEhkGeSR4JAG3faXc0TVbrRsAun19I6Ya2zot93EKA0LrCkfm9r5xyzc1Ge7h5JLBhkYHJyyZPQk7lszEvOoxJl+8Hc0n8dB1U+CRBOS5RXgkAVYJ24DCqZuUIIiMIF0i7lsAntP9fBTAWM75FwHcAeBZxlih04NxdZaQ7dOMMXYrY6yWMVZ74sSJeNdMpJFYDHjtBJQWIXOK1XHcoojBuW6DV5nVmuxE51sftxv8ybRjPrXtAJ74+wHbtXhlBdddWIoFNcZo4pcnDrfc/58uG4edP5mFW2acA4DjyTcPWBr4hhMtGmVnihsLHMB//eMF+N3CGsRiMycKzHAPS4tyHUUhNSQBmFtVEorAWQn9070BdYVc9eKzOro+rWtHe5cX9S2nyMSXIIikkvLuVMaYBOAfAVykbeOcexF0B+Cc72aMfQxgIoAjAEp1by8NbgOA44yx0Zzzo8G066d25+ScPwngSQCoqalJfBEPkRKcdvU5jdpFq5lzchy7NVl1e94zpwL3W3Sn+mQFT22zF3AAIMsKrv71NlNN3Za91r+UzPyCGph+fGszvAEeSvnevXYPhuS5UVlSaHnN+nWLAkO31zgn1CsryHOpkb2VW5vBGOJKq/b6ZUwaNcjxtAZA7bbVf/baWu8OS0GHk+MSwDnHvXMrQyLQKj0vCgzLNjQGRbZseSy3yHDj1LER10ljtQiCSBXpsBi5EsBeznkoTcoYGw7gM865zBg7B8B5AA5wzj9jjH3OGLsUamPDQgC/Dr5tPYDvAngg+P/rUnkRRHpw0tXnxC7DyYPWqe2G3ZrCBV5rRw/comiooQOA6y4cg3V1bfD7rIUDoKYZZYuOTSvpIjCgsqTQcq6qN6DgB3/aDQXcVlzo191wpBPLNzUBUMWaLCuY/9udkASE0svxUF02BN0+GR7RPGbMjqVzK0x1bPOqx2BInhs/+NNunPEbPz+RAfdfMxmTxww2+cTdc3WFWaDLHC5RgE+2vw/zLyq1fQ2If/IFQfSXdNkvEeklmRYjz0FtPBjGGGsFsJRz/nsAN8CYSgWAGQDuZ4z5oT6XfsA5/yz42r+iz2Lk5eB/gCre1jDGvg/gENRGCYIAEDlqF8uDtr+eXuECzyoNOb44P6a0YDQUrhrVWs1VBRASO3evNV5z+ENAm5U6dcJQXPXYNgB9XreaNoynOXXhZWNRPnIQ2ru8YAIzGeha8dOrJuGmqeMsxfe08mEIT3y6BOA3374IHWf88AdkLN/UZGoyuWdOhVHYBX+OxLq6Nvzf+0dsBTCN1SLSAUV/z16S2Z36LZvt/2Sx7QUAL9jsXwtgssX2dgCz+rdKYiBjFyGL9UGbKE+v4gIP5lWVYE1tq2H7o6/twx1XTsTDr+6DyICeCOlJkQGiIJjsTsL5W+Mxy6ifHm9AwbO7DuP2WedFfAh0+2R4JBE+2f5YTrjl8gn4esVITBheEJpFqqVl7dKhksCwePb5uHXGuSGDXyvT3vCI6cXjivD9VbtDx3GFdZOIAoNHFLBx0fSQF19xgQeDPFLE9KxmNmwn+mmsFpFqKPp7dkMTG4izjmQ+aCOlNNq7vHjpfXNjgaJwrHjlI7hFdUqAWwTsMqvXXVSKWy8/B1c9ts3UIBGOk+aDlVv34xuTR0V8CDi13ojG6p2H8Ie3DoJzDpGxkA+erFhbBgtQp1g8+uo+jCrMwcH2M7amvdPKh+Hh+VU42dWLsqI8g4ADYDJY7vbKWLq+MWSbok1u0CKvz+46jJXBSKbXIo2tF/3tXV40tn0OgKOyZDCN1SJSCkV/z25IxBFnHcUFHiyoKcWqtw+Hti2oKe33P3jRUhqtHT1gFjVkmhjzR6jF0lhf34YlsyfhhkvKDOsP5/W9x0NF/xpXThqBTQ1GDza3KKKu5VTEh4C+NlAzIdZq4twiiyomNc7oIoyBoGSL1BShoC/yddfze8C5eV+frKDhSCeue2JHKMVrV6fnEhnckhBq1rCLqhUXeHD7rPPwjcmj8I1gGjkcr6zOcV1XdwR3rqkLndslMjxyfVXUuawazcdPo67lFKrLhqB85CDb/QjCDor+nt2QiCPOOtq7vKaU5praVvxo1sS4hZyTlEa+W7SM6sSCKDA0tnWa1h/Oe4c7MbygzRC5ys8R4ZGYYQ1+RUF12ZCYunC1kVz5bhHbm0/ivg2R68gSgV36+FuXlOH+jY2GLle7Or3nbp6Kg+1n8OP/22PYX1a4ZdRic8Mx29msjHNc/ettkBVuOJZf5rh77R689WPzXFYNLVq7etchw31ceNlY3H/NBdaLJwgb4pl7TAwcSMQRWUOiuq+SkX5wcsy2zth86qxQRQVzNB7qb01G1501tUfw029MwqOv7TP8Y18+clBMD4GifDfKR2bGA2L1zkNgFuFNgRnF3MLLxqJmQjEAs5mwX+bwB4xR0PYur21jCICIHbWan12kSSICjJFJAFj19mEsvHQ8ReSImOlvAxaRvZCII7ICu1RlPMIuGekHJ8c8EqPZsBVL51agsqQw7hq1ofluy1RftIeA3eefCGHaH1QdZFGzJgpY/f1LcLD9jCFVebD9jOVxDrafCYk8ALZ2ME6QbSY66KO1dtS1nCIRR8RFohqwiOwiXRMbCMIxdu76q3ceMoxNijaNQCOW6Q9OcXLMSIa0TtDGVWnn8kgCcl2xjZ2qLhsSsg6x8ruz2h5pjFkihKkVHknAtHOKo+8IdRJDeCzOF1DQ1tmL+TVlBlE0vjjP8hjVYanP0qLcqB3AVrhEdQ6u00ki0dZBEAQRCYrEERlPNHf9eNrqk5F+iHbM6eXD+nV8jj5vNx7cIscQkTt/ZH5cUZ5IqeL+ClM7FEXB+y2fRd8RanpUYoC+3FD9rOoN3wctmiiGWdNpvnV6tjefjOmzdQsMnHHcN7fS1p8rWpev1ToSCTVREMTAg0QckfFYpiot3PVjrWtLRvoh0jHLRw7CpROKsPOTjriPrzU2LHlhT8xNEh8d70bz8dMxP8AjpYpjHUDvFL8C+GNwErb6KETWNy+3se3zkMecnh/NLMe/f+18wzYt8hiLPvUF17p8UxNmTx4VdaSZlpa+48qJGJrvTrqwuvelD7BqZ183MzVREMTAgNKpRMZjlapcOrfCNOUg09vq27u82H34lKN93aIA0cIqQ1bUxgYpznlXdS3Ozq9Hs2TRo1mylI8chCG5yRFyekQG/Nc/TEaey/l1BxTVfmTag1vwgz/ttowaPvH3j01D6ls7ehBpgMbl5cXIcQnIsxCwosCwde+ntoPv51WPwY4lM/HMzVOxY8lM3Prlc00p30TTfPy0QcABahNF8/HTSTsnQRCpgUQckRWEP/xumjou4XVtyaaxrdPWskKPKACbfzgdv/xmNSTd31Ct3qqypNCxN1s48dRc2VmyaEJlQU3kgfCJIMclYkxRnq0JshXfnz4hNG4rfK6qhkvsi9Zp7DrQHjFN/O6hDmxcNB2/+faF8EhGUamZCEeq0bSrPUwWdsI9HkFPEERmQelUImsIT1Umuq4t+QOknUWROFdtPLTr008D0Na1dG4FfvZiQ8xnbzr6ecxRn2j2KR+0dsZ0vGgIgMk8ReYcJYNzwB3OmPVIDJedOwzP7Dwc0YolENZJ2t7lxYpXPop4bJcgoNsnY8bEEVgxvwqLX9gDUWBRTYTThZ1wpyYKgsh+KBJHZDWJimqsqzsSV6ernvYuL+pbTtmm0ipLCg2RNTsUrkbtAPX6ZkwcjsqSwWjt6Akd+6ap4/DzayebZoJGggOhrtJYiFQT197lxdufOGtAcEq45HKLAu64ciLqWk4hN0oNXq5LhEcSsGJ+laUViwDjRAdZUbCj+WTo59W7DkeNlurT9lqEeNncSlN9oCZ00035yEFYeJkxWprsJgqCIFIDReKIs55EDJCONnILUAXZZecUY1tzu4Mj9ikNu2PfdOk4AMC96xrgNLsaj6lxJEf4+pZTEAHEkOWMGVlW8IuX95qmTWjkuAQEZAWca58aD607fLxauK1cQOm71wAiGvwCqvVJeNq+uMCDKyaNwH+sM0ZGM6lG8/5rLsDCS8dTdypBDDBIxBFnPf2d4OBUBDYfP+1IwAlMjdpFOzagdkPGUh7nDcQnLOxS16VFuRAEIA5LNROiwIKNG0Y0gagXcPkeEbLCcc/VFSgbmotbVtXCG+Ch2rfFL+xBxehC/OXdyOPJAGPELJLBr0cS8Mj1UzCnymwhkg2jj8pHDiLxRhADDBJxxFlPfyc4OBWB8RSSRzq29udo47f0XHH+cABAfcupmGv/rOxTigs8uGjsEOw8eMrxceywEnB2XFs1Bnd8bWIoGhguvlyCgKd3HHTkY6e/170B+5iiN6DgzufroXBYesHR6COCIFIN1cQRZz39neDgVAQ6LSTX18RF82jzxhgCmzRqUL9r/8J5JwECLlbWvtcS+rPVZ+STFazdfTj8bSbC06PRGie8AR6xrjDVnacEQZzdkIgjCJgtTOxc961wKgLLRw7C5BKn6SwW6pa9Z06F6djbm09izsrtYBFER/hc+PLhefjttgOW47Pipfn46RjigImDMRaKRlp9/ouuKIdHipxoEABsun166F63dvQg1xU9OZEpDQsEQRCUTiWIIP2Z4OA0lfaNyaPR0BbZZFVkwFsfn8Qtq2rhFtX6qnuursDkMYND0b1pD26JOEh9XtVo/LXhmMFP7vBnPXCHtcfG0+ig58X3o9ecJYNev4J8t2gYJbVjyczQ5w8Aj7/RHPEYOW4R3T45JJbz3WLEsVgamdSwQBDE2Q2JOIJIEJoQ0keIwvFFqdGSBNUn7jd/PwAAoZqu+zc24a0fzwzVgEWrhdv0wVHkukTjWDJRMJkE91eQnOqxbgKIBQFArluET1YcmSFr3Pl8Pep1HnXho6Qeum4K7lq7x/YzlxWOXQfacf2r++AWGQIKx7yqErxU1waXyOD1y2CMQRQYev0KPCIDE1jGNSzESvL9EAmCSBUk4ggiQUSzGWnv8uKJv39s+36RqRYZVnNAvQEFj76yD3d8bWLUQeqA2i0aLl5kzrF0bgXu39AU6gTtryCZef5wrN4VvfYsEvfOrcAXxxahtCgXf204hmUbGgEg6lSK+jCT4VVvH8bCS8eHOjDnVY9BxehCXPXYNtOxXCLDNdUl+MXLe9VzBbXomtpW5LkE+AMKls2bjNmTR4WidN0+OeuFjxMrHIIgsgeqiSMynmgmupmA3grErt6staMHYnihmg4GwC3Zm9mufucwvvTA69jRfBIPXTfFlBoN5/vTJ5hq6Qo8EgAe9EqLb3SXnp4IKV2n7D36eUgczZ48Co8uqDbNxXVKeAdw+chBePj6KuS4BOS7RbgE4P99+Ry8/MPL8dL71k0dZ/wKfDLH8k1NAICqoK+aVcNCNnw3NZx8RwmCyC4oEkdkNE4jB+lOETmxGVEjaPbi5OLxRXg/yggrrTtyx5KZ2Hz7dMx+bBsCFhErkQE3X34Obr78HEOd2LQHtwT91hIzGupQe1dc79Pz59pWPL+7FTdNHYs1u1sBHr/vnFUHsFW9Yn3LqWB62d5SxCUIaGzrxOBct+X3KtuiWv31QyQIIvOgSByRsTiNHCRiZFZ/cWIzUlzgQcXoAttjvHOwI9SJmu+xj8hpD97ykYOwbF6l5T73Xzs51KihRZC0h7jVseIlUuQwFmQOrNp5WDU1duDtZjVujEGdOWtFuPVHaVFu1Ghfb0DGLatqLb9X2RjV6q8fIkEQmQeJOCJjcSI6MuVh6sRmpPn4aew5Yt+Z6pEETC4ZjB1LZuLZmy/Fz6+dDI9FylT/4NVmqIq63UQGDPKYg+zJeIgfPZVaq41ZXxiOVd+7GL//7sXIcRk/G4/L/N2wS3Xq71euy/qfQUXh8AZ46Ht199o9+MOOT9B8/HRSBHGy6a8fIkEQmQelU4mMxYnoyKQUUTSbkWgTGwIcyHeLoQjawfZucM7BYKxem3X+CHR0+0LnmT15FO7f2BiaeCBzNU1aMjgHB9vPhGZlFhd4cPG4IsPor4vHFfXrc/q81x/3e+Nh2/52/GT2FwDAZLHS61fgD05csEt16tPu2v1qbOsMje3ScIkMblFAt68v3eoNKLhvg1ont6BmTNTvZrpT/FbQVAmCGFiQiCMyFifzKOONLiXrARvJa258cV7E98qygjkrt+Oh66ZgWvkw3PV8vaXlxqaGY9jUcCxkeXHbV8pNY6dkhWP+b3eGfl542VgsvHS8aXbrtuZ2NB8/HfdMzYvGDcXa99riem88+AIKrnpsG26feR48IoM37PO58aldWDq3Ess3NRnmzd69dg+ajn6O/91xMNSZu2K+KuxmTByBFfOrDN+ze+ZUYPnGJtt1rKk9gp9+YxIefW2f5Xczk+vl+uOHSBBEZsGijZkZaNTU1PDa2tp0L4OIgfYub3AMFUNlSaHpAbS+7ohJ6EV6YIY/YO+ZU4HJJYOTHpmobzmFf3h8R9QJBzkuAQ/Pr8Ki5953dFyPxACwqHNC7/7aRKx4ZZ9p+8Pzp2B+TRmA2MVte5cXF/3na47WmUgkARCYAJ9FB4QkMnjComhWuESGnT+ZFbrO8Gu/d90HWPW2vX3Kw/On4IpJI0yfV3uX12TG7JGEkM8fQRBELDDGdnPOa6xeo0gckfFsbz4ZMaoRS4pIX0OnRWl+9mID8t0iZM6TGjEpLcqFSxKiii2XIOBkDDV9jDH4dcdkTDUMDqfucIfl+7WOzniiR5+c6H93KqAKKr/MkRP0aIvm+atergKBqbNmDa/JHIEIXacafpnjxfdacfOMcwEYI1TtXV6sqY08jWJ8cZ7ld84qxe8NKLhn3Qf4n5ss/x0m0kwmpr4Jwgkk4oiMxkp0WdliOE0RWT1gAYSiNv213IhEcYEHi64oxyOvmqNhevyKgsklhY6PG14bZhdc3/LRCSyoKTWIk4WXjUX5yEGOP+dw/tZ0zPE67bho7GD86MqJ+LwngMJcCSWDczH7V2/CQZOqScCFk+sSInrZ/efmvXjtw+P48798ybDd7nuicXl5Mb799DuWgre0KNcyQrj5g+P9Sl0TySGTU98EEQ0ScURGk+jGhWjTDpLdFNHyWXfUfeZVleCMP3okySUyMMbAODfVhlnhFgXcNHUcbr38nNC8UU1QxPM5r6s7gv/dfjDqeaOx+3An/vl/30WeW4JfUTD93GJLAafVsjkl3y3i7q+fj/96+UND00I4Oz/pwC9f+Qj//rXzQ9usviduEfjpVV/A5JLB+PbT79gK3uICD667cAyefafFdK66llMk4jKIeH95IYhMgSxGiIzGKqrRH1uM4gIPFtSU2r6eTN+s5uOnsWZ3dA+7NbWt2PLhpxH3kQTg99+twebbp0etsdOQOUdpUS7KRw7C/Joyg5iItUFEe/hF0EYxIXOErDxe23vCcp/vTxsf4zE55laV4JsXl0Xdd+XWZoMViZUdx8PXV+Ofpp0DlyRGtRf53rQJluexMiMm0kc2WsUQhB6KxBEZzfbmk6YZoAtqSuP+Ldmu1infIyZklmgktjdbixMrntl1CCKDbW3Y4q9PwoyJI9De5XUcnfrqF0baXpuTTmA90dKNyeDJbZ843tclqoPqAeAv70aubQMAt8hMUUe7Wksngrd85CAsvGysoTFCS12nG6r/6oMMkIlsh0QckbG0d3mxeG29acLnX95txY9mTYzrAWQlPvLdIpbNrcQVk0Yk9aE2rCDH8b4BBaguLURd6+em19wCMPWcYgBAY1tn1Lowjdf2Hkd7l9f2GmNpEImWlk43AgOmlQ/D6l2HTY0k4b57AMAZs3xwW9VaOhW8919zARZeOt6Uuk4nVP9lJNZfXggi0yARR2Qs6sB4AdqcTw1RMEdNnGIlPmTO+yXgnEY2Jo2K7SFuJeAAQBAFneAwj5+ywy2KUT83pw0i2sPvThsvOytyJAaZw/H+gBpRO294PpqOxdYFK4nq3NPHXjc3kbglAQsvG4ent38Ct8jAGbN8cOvvKwDDPXYqeMtHDsoI8QZkV/1XKqOFmWaATJFSIhZIxBEZizrf0hztkRXer5q4RP7mHUtkY+8x+5FbTnFLxlFJlSWFkASYGgGsmgASnSaaVz0Gb+771LHZb2+A49IJRahr7YTAGM5E9XFTR1/FKuAAVSh+3uO3bJCYM2U0fvDlc/GDL59r+7BcV3cEi9fugSgweP0yBIEhRxIN9zjbTHMzabpJJNIRLcyUe0mRUiJWSMQRGUtxgQffvLjMUFckCgwr5vcv3ZGo37xjjWx8eLQz7jUDat3W5tunGyI7xQUeDM134dPTxvFXVnVy98ypSPiDKs8V2z8hOz/pwNp/uRRn/LJp1FU4Dhp0bbnrqxNRmOu2fG3TnjZs+uCo7QOyvctrmpYhyxx+WZ2IkanRq2hkQ/1XNkULE83ZfO1E/FB3KpGxWDUhSIJa69Rfigs8qCobguICT8RB6ZFei7WzLc8dm+D50jlDw7ojq0ypuZ9vbDIJOCvy3SImlwyO6fxOONHdG/N7DrafwYyJI3Dv3ErL192isxSxJKi2Kab3B2sGtShlOL0Bjl6/gsUv7LG8r41tnRFTvtnavWjVcZtp9V9nc7fo2XztRPxQJI7IWKzSP/q6rkTUjkRKX0RLbcQa2fh65SjLsVd2LKgpxdcqRyFHElBSlIeSwTmobzkVut72Li9+v91Zx6ZPTk7E5fyRhXi5IbIdSjgfHTuNtbUt6PEGLF/3OaiZc4kMj1xfhYrRhbjqsW2G9wiigHy3+j1Zds1kLFvfaHlM+9rKyCIy06JXsZBp9V/hZEO0MFmczddOxA+JOCJjifSPWiJqRyKlLzq6fbh77R74An2v3bV2DypGF4aiYbHW150644tpff+2Zk/oz1pHZY5L/U39oeumYFxxPtwi0Osg7agoHB3dPsPaEiGCCzyx/xPyO4fCMxIMwJA8N4ry3Xj4euPw+gU1pZizcnvo5+9Pn4D/3XEQvWEFcl6/9QPSrs4wzy1C4cm1oUkFmVL/ZcXZ3C16Nl87ET+M283oGaDU1NTw2tradC+DcIjVcPtp5cNMA8ZzXAJ2LHE2YFwTL509Pty2+n2c1kWEBnkk3DLjHKzcst8yeuMWGR6+vsogGJ2KoUdf+QiPbWl2eukRyXEJ2LhoOq7+9faos1g19GtPVAH17atrseGD4zG/LxHkuUQo4KHvRGtHD/LdIuas3B42fJ4BYKbPSWTAOz+70jAvVTvGyw3H8Ost+wGokUG3AIAxLJ1biZsuHZeqSzxrOZs7NM/mayesYYzt5pxbDl6mSByR0Vilf+pbTsXdZacXLz5ZNnms+WQFj2+1FnDq69xUbOw0slFVmriaNJcgYHPDMZMRciS0tVeMLkxYAbU7xsaGRKKNJlv8wh7sWDITVWVDLL8bblHE3KrRpjFYeW4p9J3RvhdcUUeYqRFPHjJb9ikAwLF8UxNmTx5FD9ckk8nRwmRzNl87ETvU2EBkPPomBEAt0vfGMYpLnz497Q3AG+BQFA6P1FfoveiKcrhFMeJxohUb2zVDDBvk3Ow3Gr0BGY9v3W8yrY2GSxBQFxQ64dvjKaC+vLw45vckA23tdin4702bEIzIGbeXFuUavhfaDNpevwKfDITPsadCc4IgMgmKxBFZhRYxYcEyAH2NWLTfXq0aJWQO3DptPGZPHh0SgY+/ETnlGUkwRkpT5rsji8NYUBQOj1uENxCbD4dfUVBdNiRhBdSneqJ3xiabXr8S+mzt6orKRw7CivlVlvVGVtE7O6jQnCCITIJEHJE16CMmGorCsfmHlztyxS8tyoUvPLQC4Okdn+Dmy88JicBwEbCgphRralujFhtH83nq9snwiCwU7XGKWwAYEwzRxxyXGLWL8/995RyUDsnD8k1NwfSxgtu+Uo6ifHfUAmqndTkeKXHC1I7p5xZj9+EO9PitRZZHZOjWGQfbdWDGMgtVQxIAURDgFqnQnCCIzINEHJE1NLapTv96PJJoeIBHorjAg0VXlOORV402H+HjqKwe9j+aNTGqqInmiK+KyNgbiXwKIAnmUWFL51Zg2YYmy7q4XJeA2ZWjUVU2BLMnj8LqXYfx+Nb9ePLNA3j8jWY8dN0U7Fgy0/KaYml6GJST/H9C3j7QjkgfGxPMc0/t6oqizUI11sTB0DRBheYEQWQaJOKIrEAdg1RvcviPNb1149SxWLl1v+E4VsfQHvZafVtpUS6qyoZEPHY0n6e6wx0x17Dp8UjGiNC86jGYOn4ovv7fb5pEjsIRqvdqbPscj29thjfA4Q30TR3QmgH0xOoabzcVIZFE072JmEShF+75bvUXA71oI/FGEEQmQiKOyHg0YREu4DxS7I7zxQWeUG2UKDD4ZW4rAmK14Yjm8/RKU/xWHB6XiCduugiDc10GcVGU7wbTTOR03Du3AtubT2LJC3sgWNhr2HXz2kUTG9s6MTjXbYpGVZYUxn1NiSCRkyioK5AgiGyDRByR8VgJizyXiN985yLMmDjcsK+TWq551WNwujeAZRsa4RIFLN/YhEEeyeT9Fo8NRyRH/MvOGYq/hI0Rc4pf5iHBpHVHFhd40NrRg1yXZPC6y3eLKCvKxa1/2m2oHzQczyaCaRVN7A2oc07domgSs5+ciH04fSKROQ9FHCnlSRDE2QaJOCLjsRIWCrgpCuQ0ctbe5cXyTU3wyRw+uc9rTC/QotW3RcIuojO0H+Kiakwh1u5uxS9f22cyPg7/bGTOAbCIHZcLakpta8b00USfrEBWFHhlGFKx2mf1Ul1b3NfUX9wiwz1zKoL1fs2mVHM8pEMMkgAlCCJeyCeOyHg0YeGRBOS5Rcs0argHXKQB59EGTbd3edHZ4zd1soZHr+z84Oz4vB92HO8eOoX/enmv6foAWH42lSWFth2XALCmttV23fOqx2DHkpl45uap+N3CGuSGGfrqP6vPunvjvqZYsJhzD7/Mce9LDXj01X3wBqLf92isqzuCaQ9uwbef2oVpD27B+rojhtdjvd+JOCdBEEQkKBJHZAVc+19uUQCG2CJnTmeyyooCl8iQI4mm+rZ4xlYluglAuz6rz6a4wIMFNaVY9fbhiO+1i/zoGzt6w7zoegNySMyGT7xIFlZ6lMO66cF+sL2KVeTLLn1eMboQ3T4ZDUc6Q1Yt/Y326deRqMkZ/VkDRQEJInshEUdkPMbGBuv0Z7TOUD12DQgATA9VjwQ8ftMXUVkyOOoDP9rDN9FNAH5FgT8g4+61e+AL+2wqRhdiTYT6u1i6esPnK+t/nl05Cn9t/DT2xcdILFqx26uKLqtuYjvxbfVLAABc9dg2uEQhZGOTSLHVn5R9IkjU/FyCINIHpVOJjMduzJF+uybMclx9I7Qida7qU4Y7lswMPcglwehD5xZFDM51G44TLR2bbNwig1sSMK+qBDc+tcvkE2c3WgtQ/eOifTZ6tMYJ4zGk0LVKUUaUxYokAC6RRd8xCss3NZnSnpFS7pYNHX4FPplb+hAm4n7H8otHooml/IAgiMyFInFExpPvFk1dlvpRSxqROkOtCG9AaDjSiS6v8YFt9VAtLcqNmGK0o7GtM+LrTvHJHB6B20baegMyqsuGmNYIAAFFwX1zJzuOuFhNudB/Jh8eTcw1aSy7ZjK6ewP4xct7+3Ucq4hWpF8GqsqGGKKzPX4ZgQi5Yq9s/v7FSjRLmmSS7iggEZnm46dR13IK1WVDHE2jIc5eSMQRGc/mhmOmbZIAQ4REX9sTzZQ3fH+t9mv5pibTfnYecpFSjPb0P8Kk4Y0w5jMgc5w644NiIUL8shqlmj15lKOH9fbmk5B10SKXyAxCo8fhtAynDM5xYflG832wIlcS8E/TxuOp7Z/AL0c3gY72y4D2S0BjWydu/uO7luf0SAK8AQWMc8xZub3fKchYf/FIFOmMAhKRufelD7BqZ18t68LLxuL+ay5I44qITIbSqURG097lxcot+0zbA0rfQPlYO/ys9rdKkdoZydqlGBvbOiN2L+a5UvPXjQNY8NudtpMOtIhLtG5LLeWmz9YKDJhWPizxiw5S13IKInMmdhVw3Hz5Odj5k1m486sT4ZFYxFS6NrtWT/jc1eICDwbnuuGRzL/fzrlgFJSgWPfKPGEpyOICD6rKhqQ0AhZr+QGRGpqPnzYIOABY9fZhNB8/naYVEZkOReKIjEatUxNDfm4aruDDN9YmA7v9Ny6abum3lghDXEAVjnc/Xx/fhxAHEQJ18MkK/vjWJ9j0wbGI3mpWKbfwObOJfug/s/MgegPRo5qSAKyYXxU6/+2zzsONU8eaoqv6n0uLcsEEZmhptZq7anV/AeD1Dz81RfyyOQWZriggYU9dyynb7ZRWJaygSByR0ZQW5ULm5geqwNSHb6xNBnb7d/tkx5GJ8CiGRxLAOYc3wC2LxDXh6Is2BDTJuEUBoqCO4Pq/99uieqs5SblNnTA0oWuMJuByJAH3za3Arp9eaRKdWkQLAB57fT++9IAx2uo0+qTfL9/TV/fWEzB/D7M9BZmOKCBhT7VNKYjddoJIWiSOMfY0gDkAPuWcTw5uuw/ALQBOBHf7Ked8c/C1nwD4PlSfhB9yzv8W3D4bwK8AiACe4pw/ENw+AcCfARQD2A3gO5xzX7Kuh0gPxQUeXDJ+KLY1t4e2CQxYMb/v4RtLbY+dMMl3ixhXnI+Ni6abhp9r6CM7+ihGZ48ft61+zzD6Sh+hsbOvSCWSANw/rwI/frHB8nUrb7Vohffr6o5g8drURRcBAAyYW1ViKzq0NWlzdrWZsVp01kn0qb3LG/ou1LWcwtL1jaYO1Ty3CIXzpKUgyb/t7KR85CAsvGyswd9x4WVjKQpH2JLMdOofAKwEsCps+y855w/rNzDGKgDcAKASQAmA1xhjE4MvPw7gqwBaAbzLGFvPOW8C8GDwWH9mjP0GqgB8IlkXQ6SH5uOnDQIOUA1mK0arnmuxdvgVF3hwz5wKLNvQBJfIICscC2pKMWfldsP7w5sj7Dy1tLRdJCFpl56LlTy3CFnhkBUFFkGhiCy7ZjIkq7EHQey81exEj9G7L3XYjQuLtia9qI406D78Pt9zdUVwjFkfHonhN9++0OAdmEjIv+3s5v5rLsDCS8dTdyrhiKSlUznnbwL4zOHu1wD4M+fcyzn/BEAzgEuC/zVzzg8Eo2x/BnANY4wBmAlgbfD9fwRwbSLXT2QGkWpENKw83+xYV3cEyzc2wSUw+AMK7rhyItbUthr8su5au8dQSBzNUytamk573R1BRDnhG5Uj8cj1U0xNFdFYUDMGN00dFzUlc/9Gs7caYJ1ys0pLp4JI48IirckqOhve2NHe5cXitcb7vHxTE+6ZU2G4tyvmV2HGxBFJi8CRfxtRPnIQ5teUkYAjopKOxoZFjLGFAGoB3Mk57wAwBsBO3T6twW0A0BK2fSrUFOopznnAYn9iAOG0RiRSdEVD/4DUePiVj+CWjA9+X0DBVY9tw8PXV9m6+YcXtOvtKQBmms4wr3oMWj7rxopX9ke5YhWr4WIvvN+GDXvawKNYlYS/d339USyZ/QWUjxyE80fm46Pj3ZbvizauSk+ioosaAlP/EwUhlAK1Ql/vGB4dtFuT1axdq2jXwfYzpnO7BAGTSwZjx5KZKUlvJtO/jVK0BDHwSLWIewLAcqjPmOUAHgHwvWSflDF2K4BbAWDs2LHJPh2RQBJZI2L5gBQFy4YDn8xDdVROPbW2N5+MmAYbV1zgeK12SUqfDEgCh0dSmxTOhNVqiUEhpDfo1URAR7fPVsABgKxYd+NaoUUX73q+vt8NG3d/bSJuuET9e9nY1olbVtXapmn9ioKGI5345pNvW6a29al1nyxj0RVq16petFh1KN+9dg+4RQONT1ZCoicVwidZ/m2UoiWIgUlK8yGc8+Occ5mr/1r+Dmq6FACOACjT7Voa3Ga3vR3AEMaYFLbd7rxPcs5rOOc1w4cPT8zFECnj/msuwGv/PgMPz5+C1/59RtzGl9bTBzju+upEUzQOMEZA7plTAbekditadTU6SYONKkyMCMh1SXjk+ilY/PXz4Q7zPZM5bCcsPL3jYMTjLrqiPCahMq96DJbNq3S8vxWSANxwydiQSJoxcQTunVsJtyQg18UgQBWm+R4RbknAHVdOxPJNTbafsz61/taPZ+H2WecBgCFtapV2FQUGSTBPYIj1M+kvyfBvoxQtQQxcUhqJY4yN5pwfDf74DwC0Vrn1AJ5ljD0KtbHhPADvQM0MnRfsRD0CtfnhRs45Z4xtBTAfap3cdwGsS92VEKmmfOSgfteHhE8fAADGOR59bR/u+upEPPzKR4aokiZ+wuvols6ttPRUs0KfBjvYfqZf69foDci48/l6uEW1Q1JgarOHFVoqEQDW7j5svRPUYv0bp8Yepf6sO34hIAnAowuqAagiK98tYnPDMTy+tRkuAfDKwI+vmoR8t4RlGxrhEgU8/MpHEMLm24anWPXjsKwiUNPKh5k9ARWO8PhnvJ9Jf0m0fxuN2CKIgUsyLUaeA/AVAMMYY60AlgL4CmOsGuq/lgcB/AsAcM4bGWNrADQBCAC4jXMuB4+zCMDfoFqMPM05bwyeYgmAPzPG/hPA+wB+n6xrIbIfq+kDgOq8D1kVckvnVQbFWt8DH4Cpjs5qbJWT+a7ji/MirlEAIIksanpSVjj8HPAGAhH3kwTgJ9+YFBIEHkmCTza+hwHw9CPaE0uKOBxRYDjdG8C0B7eAK1y9F0E0afiLzXshCeqEjpDhs8WILS3FCqifu0dkQFDc+mVuMHbesWSmZUez9nqkLudU1ZUlMn1LI7YIYuCSNBHHOf+WxWZbocU5/zmAn1ts3wxgs8X2A+hLxxJERKJ5tYkCg0cUTD5x9S2nHEUxtJFOeiESPtLJJUUemP7fN1RjSJ4bP/jTbpzx288kjTCX3UBAAVb87SM88Ne9uOfqClOaFQAEAXjme5egZkKxs4OGcdm58b0PAAQm4L4NjaYpCOHY9TnkBMeY3TOnAvdvaDI0JXhtjqndu3nVY1AxutBk4xApAmayH5lTgcklgzO+USBWGx6CILIHGrtFnBVE66bs9spYur4RctDAVfNLcxrFcDLSyR+IPCxem0mq2LY1xI4mIpdvasL3po3HE38/YHhdVoAbn9oV6sSNlY7u+P21eyIIVScoCsfmH16OzQ3HIna06tGnyO18/+wMgBev3QNvoK8Z4mcvNiDfLYa+M5ncKEAjtghiYEJjt4gBhd1Q9/CCcZfIIAkwjFXq9skmn7h4RjXZ7dfQ1hlx7Y++sg8ALEc+9ReXIOCyc4fBI5ntSbRO3HgK3e18/FKBRxLR1tmDx7fa27YITK0L1Boj7plTAQCOCv3136Wnth2wFIradyYbGgVoxBZBDDwoEkdkHXZ1SdFsFMKjEQCwde+nprFK4T5xVu+rbzllOn+0aIcnSjp19TuHsfa9FqyYXxUa+dTy2Rn8aktz/B9WEL+ioLKkECvmV+GutXvgs/BDi6fQPZ0zHdUIKYNbFG3rAwUG3BlsWnGJApZvbMJnXb6oKXL9d+mMLxBehmeCGgUIgkgHJOKIrMJOqFl5f2k+b+HzQPU/XzFpBP5jnXmeqN4nTj+qafXOQ1i2sQlukSGgmNNokQrSB+VE/+vmDXDc+Xw9BAa4RRF+RcGCmjF48f0jkJgAmXN19FYUUSEJql+cWzTWQGm1YFc9ts2yE9cJehENAJNG5mNvBP+5aFgZG0dD67qtLCmMmCZ3iWKo61hrjFi5dX/wrH3or9/quxQNahQgCCIdkIgjsoZIQs2JxYcVIdNaB9Gp1TsP4WcvqYLPFwz8WAlFO073Ru4m1dAK/bXo0vr6o3j5hzPQ7VPnmy5d3xBSPZKgislwFn99Eq67qNQyelg+chAevr4Kd6/dA1FQ58c6LXQ3Rqhk01zReIj1CNd9sQQ/vboitN4FNaUGM2g9flmBRxL6OluhiuNbZ5yDx99otiz0j9YEA6h1KC7JLJIJgiBSCYk4IuPRIj+dPfZpMDuLj2jNBAAcRafau7xYtqHR9N5YRlW1dsTnE+cShFDH7DeffNvQranYiCi3xEJRQavopfouDnDncbB4IlTJ4JovloY+7/YuL/7ybqvtvouuOBe/edPYzOFXFNw4dSxunDrWMvXtZKTY8n+YjNmVo6hRgCCItEIijsho9ALEJ8smew1NaLV29JgsPgDnnZdadMrOhqG1oyc4ossoCv2y81FVscecVLyy6jdnFSFyCwy9FrlVjySGDHStRkwBPDjaSr0eJxHF1o4eSELkua3JRhJgmEu7etdh287UBTVj8G9fPR/nDC+wva9W11tc4MHXK0ZiXf1R02sukeG+uZW4aeo42/cTBEGkChJxRMZiFfkJ1xALavqiMuEWH4C5tk1/7PAoip13GKBGZ6xSh0vnVlg+yK2OX1qUH8enAMiygqt/vR3fmzbe5PXGmXVN2X0bGoMF/7JpwoEosGAETudh56Awv+FIJ7q8/bMF6Q9ukeHh66sMUTirzlRRAP5yy6Uh77tY7TXau7zY9IFZwAHAz676Am66dFw/r4QgCCIxkIgjMharyFN4JG5NbSt+NGtiTLVtxuiegkVXlOPGqWMjDrDXG6aKjMEvq+O3tIiMHrvmCyeNDVbIHJADisnjDQC+ebE6Wji8Jswb4H0dm2HC1mrEVLTC/PYuL5Zvaopj9fHBoE6vcIkCArKC6y8qwz9PG28Q1q0dPZadqf82ayJqJhSbhLTTqJl6XAEBi5TqsAIP2ru8FIEjCCIjIBFHZCxOapP0Am1e9Rgc6+zFL17ea9gnWufhI6/uw6+37AMHM41o0kfwnER0IjVfFOa6+/eBWPCXd1vBHTYXaBMOrEZM3TOnAo1tnwPgqCwZbLo2J8X+iYRDTVVzRUaAAy+834oX3m8NTUnId4vo7PGbIpPavNNodjORKC3KtbxKBuDH/7cHPplj6dwKSwFPEASRSkjEERlL+Lggn6xAVhRDYX+4QHv0tX2m49wzp8JY22YhRlSbOKMYskoxRovoRBo27qTJIla8AcVxnZpfVvC3H80wjZhqONKJpesaQp+rS2R4JKyO0ImgTgaB4C3RmlZ+9mID3ALgU9R16kd2uUSGFfOrAMCR3Ywd+u+dALUmkSuAAoTSyT97Ue0QjjW1mqrZqwRBnB2QiCMymvDo147mk5GbD8IEVL5bxOSSwaGfYxEj8Xh/RRrT9ctXP4rpWE6xshixQlaAts6ekIgLWXT89i2jMJY57l5rFD0GYcMYzvjSVxvnU/rWqUdgfcLUybzbSOi/d509fvzr6t2mesBlGxoxe/Iox8fsT3SQIAjCChJxRMajj35FSmlaCSiZG7tHiws8tr5iAjPW3OmbJqywiqpEGjZ+rNPay66/iOZ+jggYo3atHT0QmQB9kwOgRvie3XUYt886L7RN++wb2zpxy6raYHdr5uAWxdD9cDLvNhraPe3s8ZvqLAHAJToXhk7NqAmCIGKBZqcSWYfdDEgn80vbu7xYU2v2FfNIgqnzdU1tq+08zHV1RzDtwS349lO7MO3BLVhfdyQ0a3Na+TDsWDITz9w8FTuWzAxFW75eMbKfV26NUwEXbs8BqMI33DZFY+XW/ZYzaGdMHIEV86uQ4xLgFtNrOaJHs2JxOu82GuvqjuBLD2zBD57ZHWwGMRL+C0IktOigHi06mGrs5gsTBJF9UCSOGFBEaz6wSrXluUQsnn0+HnllH057+zodXYKAxrZODM51G45lFVUJH5VllSo7nWJ7DpcAgDG4BAEyV7BivtGeQzNJtkOLbFmJH+1zfrXxGH78onlsmRUekcGvcFOHcaKQZQVzVm4PffZ2djFOaO/y4q7n6w0pW61j1i2q489iEYaJig72F0rpEsTAgkQcMeCI1HxQWpSL3rAGA7+iYHr5MDzwV2NXa29Axi2rak3CzEoIho/KskqVbd9/Mu5r8kgs5vSlIAjYdPv00LSH4qA9xupdh/H41ma4RQHegAzJxk4jmsgoLvBg+KAYoluMQRI4klVOJ3NA9itY/MIenO4NYPmmJkgCi6ub9KltB0w1dxzALxdUoWxofsyNCZHS7KmCUroEMfAgEUecdYRbcnDOUZTvxkPXTTHME5UVBV7ZLMxitT7ROO1zNjs1nJ9eNQluUcB9G2Lzabt3boUhArWu7ggWr60PicHQpAPLiQ/OUpCvNB13vJ7rLhyDDfVH4ZPj+xycIgoMyzY0GkaoxdJN2t7lxVPbzJ58AFCY60ZV2ZC41hWr6XCiSUTDB0EQmQXVxBFnFa0dPch1GX93yXVJaO3o0c0TBRRFgSRa1zCF11x5JAFS2N8kqyjW9HOHxbzePLeAqROKMbeqxFSzF4lclxDqym3v8uLNfSeweO0ey2ieR2RwS9q1MNz51Yl468czHaXZLjtnqOM1tX7WY/J1s8Itqmt47d9n4J8uM4suBlVk2uGXOVwWtXrLNjQ6qgNr7eiBWzKnmUVmrimMFbt6zlSQKSldgiASB0XiiKwnFu8tuweZNmNUP080fL/egBx64MVifaJx/qjY6rIAwBfgobq1WFoIAopadL965yEs29gEkcF2xigTGDYtMqZdnTJhuPNrerP5JAQAksDgcQnwyxxKmO+fW2R46rsXY8bE4Wrq9x1zF3GeR8QTN12Etw+cxBNvmCNmd311Ih5+1ewX6LSb1G7E2v3XTs7qiFUmpHQJgkgsJOKIrEZNEfalQFfMj1yobWUxsqCmFN0+OepEgvA0rFPrE40dcdTEBRSO2b96E1+rHBmDjQgw6/wR+GvDMfzspchNB1raNNbCf41YDYwVAIrCAb+M+ReW4s9hncI+maNkcA7au7x45JV9pro0QI20VZYUYnCuC6veOoTusCK7utYOLJ1boaZQdTjtJo1lxFq2ke6ULkEQiYVEHJG1WHUQ3vl8PaaVq2lLqweVlcXIX95txcJLzcPlw9HSrnYPvmjTHD7ric/SIaAAmz9wXnsGAK9/dByvf/Sp5Wt5bhGyouB70yfgsnOG9StF2NDWGdf7AgpMAg5QU7svvNeKp7Z/YingAGDp3L4JHFZGx5s/OI47rjwfP792MpZtaIQrjm7SgSx2YpkjSxBEZkMijshaGts6TQ96v8zx1LYD+N+3DlraKFgVd3sDCh74614EdCLOJTJwzm1HfMXD1AnDUN96Ou73x4KsqB2t4Z+PSwAWf/18+AIKHn1tH555+zB8soJFV5TjxqljTYI3moixE1pxr5sDT/zduqkAAG64uBSzK0ehvuUUSoty8dUvjMDGD46Z9qtrOYWbLh2H2ZNHxS3ESOwQBJHpkIgjshjrKrGnth2AX4GljYJqbmuOuL32oTFqJTDg3nmTsXxjU1z1Q1YCaErp4CjvShwKV4Wc1fYVf/solILUPqNHXt2HlVv3Y8V8dWaqUz8xl5jY3igrU109a2pb8XxtKzwuEV6/Ylm7BgDVwQ5SEmIEQQxkSMQRWUtlSSEkAYZomcgAtyTCr6uT0neVFhd4sOiKcjxiUfiuR2ACyopysWPJzJgjOck0VHWLAgQBuHhcEbY1t0fcd27VKKyvP2qIlskcphoyDW+AY/ELe1AxutCxn9jkfnZr6pEE9frO+O3T2prGizS7dUFNadw1flbQ0HqCIDIVshghspbiAg8eXVANj8SQ5xLhkRjuv3ayKToTnga9cepYeKTIvZ49ftXod0fzyZgsIfSGqqe9AfQGzWcTNeKIQ8Ez37sE7x7qiLpvzbihMdmSAKrgrWs55XhE1Bl/4px7Awrg7Wd61sWA9fVtWF93JCFrshqvRhAEkSlQJI7IaqwK0Ad5pIg2CsUFHtw7t9LUvRiOFpmKxdE+kqHqx592xXeROtySiIPtZ6J20gLAoBwJomAebh8Jv6KgumxIDH5iiZ2dGi2dGg0/B/xB4dzfSQSxTDigaB1BEOmARByR9YTXPTnpLJxcMhj5btE2taghCgxb936KKyaNiPpwbu/yorPHb6q50wTQ3uOfx3BV1vhlbimywmEA7lhTZzniKs8t2qYjtVSkUz+xksE58VxG0knEJAKnEw5oHilBEOmCRBwxIIlW0G5n6BpOt1fG0vWN+I91DREfzvoHuawocIkMOZJoEEAXjh2KlxusbT+ccuMlZaERYYtf2IOAzC1tNjhgEnBuUcBdX5+IoXluLF3faClg19S24kezJjq22Nh7LDXdtrGSiEkETiYc0DxSgiDSCdXEEWclxQUe3DOnAm6b7spcV9/YpW6fHLG2LbwOLqCo3a2P3/RF7FjSN75KcTByKhp/eOsQpv7iNQDAjiUzccPFZY7fKzCOh1/Zh8/O+CyFH6BGHrXaNycjok4mqNYvkbhEhnvmVPRbRIWPV8txmefJatE6w/lt6gcJgiASDUXiiLOSdXVHcP+GJtMUBgBwC4BfluGRmGHWqF2Kzirt5hZFDM51G/a18jOLhsTUqJq+3j+gAHevrcdbP56F453OxUJvgAPg+MXmvbhy0nC8tveEaR+/rI750nzYogmhsjTM3XQJDDkuET1+2VqMco7lG5swyCP1O60ZLSJJ80gJgkgnJOKIrKX5+GnUtZxCddmQiJYS4UXnVpMe9PiCz+RwgWCVSmvt6EG+W3T0IB9R6AbaYrlCwGJePQBAZAIa2zqx9SOzENNgUH3crHzx3th3Ai6BwR92jddWl2DOyu2O67s6zvgdXUci+e6XxmPGxGE40tGDn1g0p/gV9fNPVBNCpNQ8zSMlCCKdkIgjspJ7X/oAq3b2zT9deNlY3H/NBab9rIrOPzz6uaNJAy5R7bx0SwJkxTi2Kfy4C2pKsaa2NeKDfNzQfACxz0+1QuYKAAa3JCBg46vGAcg2DRABBRBg/AzmVY3G+vq2mOq7NFPdWGDBtUV6fcZ5w/B3m1mzz+46hGd2HcI9V1dEPFaqmhAG8ogugiAyGxJxRNbRfPy0QcABwKq3D2PhpeMNETmrovO719Y7trHwyxx5LgH+gDoAXXvYWx13TW0rNi6ajm6fnJIH+TXVY1BZUmiKpIUjMGbbwBEu7/7acAxuybq+y+56YjHVlQTgtq+UY+XWZkTS0BzA9mZ7sauZAS/f1IT//IfJuPelBsvj+eTUNSHQZAiCINIBNTYQWUddy6mo29u7vNi691NIYW63IhMgMufeZmf8Cnwyx/JNTaGmBrti9m6fHLERoCeBxrgvva+azt79tfMj7heL7Zqaeo2cQjat470Wx8dXFGDlG5EFnIaTfVyCOlVDEq3vp6wo2KETg9SEQBDEQINEHJF12KXwtO2ay/7S9Y3o8hqFk8wVRwLBCu1hb1XMHh71sSJRUxsAVXC1dvRg5qQRuHjcENv9wqNwAgC3jegJKBzfnzYeHsm+GzOcWJo1FFjPc40X9R4wuEXR8vWAAkNHMTUhEAQx0CARR2Qd5SMHYeFlYw3bFl42FuUjB6H5+GncvVZNmel90PI9InJcAu6dW4nIFVnW9PoV+APq8bRidpdODIVHfawYlOuO+bx2yJxj9c6DuPKXb+LdQ6ccvy/XI+L2mechxyXAE1x/jkuAS2SQFQXP7DwMgOPWGecY7FHs+Mp5w/pxFfGh3cuHrpsSTCnbK0N9pM2JZQhBEEQ2QTVxRFZy/zUXYN6UEry5/yRmnDcMNROKsa7uCO5+vt6UEsx3i1g2txJXTBqB1o4e5LoknPYGDPuoaVeOXJcEr6xAls0Ruxuf2oWHr68KFbLrM7Va1CdSfdXl5cV44b34Zm+KTE0xugQGQWC448qJ+MXLe2M+jl/muHHqWNw4dWyos7atswe3rKqFV0boc3n8jWbcOFUVypG6gIfkp04AuUWGpXMrMXnMYEPdodYdKgoM3WGR1/BIGzUhEAQxkCARR2QF4bYQ+i7DJ7cdwD1XV2D5piaTgAPUqJV+bFZ45MYtMmz+4eUoynejtaMHWz48jl9taTYdxyf3zVJt7eiBWxThDfSJwWhNAEdO9cZ9/ddWl2DDnjZIggCZK/joWHwjvO766sTQ+rT/7/bJttfyq9f2RewC3rK3fxMonCIyYOncStx06TjTa3ph1nCkE8s3NUXsEqYmBIIgBgok4oiMJ9wWQhNs+i7DZRsaTZ2VgGoPon+QFxd4sKCmFKve7hMmN1xSFoow/bXhmKWA09DETTz1VWd88XuqratvQ0ABfLIaaXqpLkbDuSCTRpu7Se2uxR+QLbuA500pQc2EYrR3ebGhPr7IYqzIXO1GnT15lKUA04RZVdkQzJ48KiGRNqc+hARBEOmCauKIjCZ8pFWvX8GyDY2mrlOrzkq3yLD59umGuq7m46fx53dbDfutqW1Fe5cX7V1eLNvQGHE9PllBvltEa0cP7plTgRyXgHyPCLckRB31NCQvfkERCCv7irc5Q3Vh60OLcGrXoq8VO9h+xvIIN/xuJ9bXHUFrRw+EGDp9+0sqO0nvfekDXPnLN3HX2j248pdv4t51H6TkvARBELFAkTgio7EaaWUl2GTOsXRuBZZvNKbS9BEUu5o5vThQj21vBeKXFcz+1ZvIdUnwKwrmTRmNl+ra4BKFqKOefIHEWYwAaopRYOqEAidIAlBZUhj62SrCqa83az5uPdxeq//buGi6I9PkRGEX6dSn2rc3n+y3ma9TH8JsI9ZJFQRBZD4k4oiMxirVZyfY5lWPwexK61SaFtGzqpnzK0qwwL/XckSVgD5jXIWr/2kNAGt2q+lETfhFam5wS9ZWGPGS4xbxT5eNw+NvHIi6r1tkePj6qtC6rIxvl29qwo4lM0P7aF3A+tSzhuaLN696NF6qO5rAq7JauwBBgGV9m16I+mQZClebN/pj5hvJhzAZIi4V4ioZkyoIgkg/JOKIjMZuNqWdYLMrWreK6GlcPK4Ic1Zuh8iYZWQpFmuzSM0NLjGx1Qu+gII8t7O/wj+aVW54aFtGOC3WrnUB3/C7nYaUrhYVW3TFeUkVcZIAPPXdi1BZMthyBmq4EA0nWrOJFdF8CBNJKsRVMidVEASRXkjEERmPnS1EpC7D8OiGVURPY1tze8LWGqm5oSzBprLzqkrw9cpRWPHKvqj7uiUR9S2nkO8W0e2Tke8W0RuW3u0NyJZrnzC8AD+aNRErtzbDLQZTr3Mq0NrRg5bPrOvmEsWyayZjxsQRhm3ave3s8dkKc414zHytIpCaD2EiSZW4cirYCYLIPkjEEVmBJtjau7wGMWKVgrKLbjx03RTctXYPfOFdAjEiMiDPLRkG34uCGsWL1NwQzQw4Vr51cRnaOp3Zlvx8815IglrPluMSwDk3zZDlFjNW9Z+lZgI8NN8dSmWf8QVM70kU86pGY3LJYMO4M72FiJY+1SMJgCgIIbEZr5nv/ddcgIWXjk9qd2qqxBVNqiCIgQuJOCJr0AQFoE5Q8IgMTGCGFFSk6Ma86jEoGZyD+b/d6eh8HkmArCiGNKJHYth0++UGAXn+yEIs29AYtbnh4Gfd/fwE+rh0QhG+/fQ7EOC8O1S7jl6bTohcl2QQEFaf5cqt+wEweAP2KcxwBBbbDFeN9fVH8fqHn8IbkMEYg1sUQlM4tHNLgnqf9KItFjPfSPVo5SMHJbWRIVXiyq4kgaJwBJH9kIgjsgJtnJY+iuaVOaAz4C0u8FhGN0SBYeveT3HFpBFwSWIoIhUJt8jwu4U1aOk4g2UbmoJjqbip47W9yxsyGY7W3FA+fBC27O1/NO47U8fi+fdabcVYvIQLCMvPkgnhLiVRiUfAafSNTuPwW3QN57okPH7ThRic6zKl2qOR7mL/VIormlRBEAMTEnFExmNnDaKhT0H5A7Kp1qvbK2Pp+kb8x7oG3HHlxKgCDlCnM7z+4XH8+d0WiExtIrhvbqXpIR9LSiyQoOnvz717GFICmiQYVDNku9SjdWewAvDUecNFwycrIdsU/YzUaB2fmVLsn0pxRZMqCGLgQSKOyGgiWYNoaBGke1/6wODvJQkMgWAYSIvoPPzKR3CLLOLxNP749iHDz/dtaDRNDCgtyrVsEOjs8aO9y2vY9/Pe+Cc26AkoQCDC0HencACcK7h1RjlunDrWcjyVVaQIQGhbb0BOqVdcOAFZwT0vNeD1vZ+GxOiCi0qxZndrxAhbJhX7k7giCCJeSMQRGU0kaxB9TVxHt89k0BqwyOMxBngD8YkOv8zR2NZp6pYMbwjwyxz/75ndkDk3CAhRyLwBKT7ZOOw+HLtI0bTyYVi96zB+9Vr0ztj+wqAKTqufZQ5sbjgGAPAGQ6za9yBShC1SPRqZ4hIEkS2QiCMyGqtIlygAf7nlUrgkMfSgXVvb4uh40QScS4g2AcGYSmzt6EGuSwqZ/2pokT9NQADAS3WpmTMaK/FGoP7njeZ+jP9yhiio3cA+3Vcg1lNaXZ9dlDEREx8IgiBSBYk4IuMJj3QxqN5l+oeyUyNWiQGRdNy8qpLQsHkrwn3RIvnPAcaRXiITACR29FYisPOHa+/yYvWuw3hc5w+niZpIEdJ40U/G0FAUINcjRhyFFg27js/wKCMATHtwS9rr5AiCIJySefkdgtChRbr0aFYYespHDsKCmtKIx3KL0acvbNhzFBZ2aSGWb2oK+ZYBfREdjyQg12Ueq6UJiNKiXHgT1NiQaOz84b70wOt49NV98AYUnPYG0OtXsPiFPWjv8kYVr5HwiAxukSHPLUJkqijPkRisss0cfWlSJ7hEhoWXjUWOS8Agj4QclxCx47O4wIOqsiGGzmbD8XQinCAIItMgEUdkNE69tNbVHcH6+jbk2sSWPZKAu742KardhSQK8ESYcSoKzPBQb+/y4mD7GXDOwaCm/lwiMwmI4gIPzhueF/nkScItRu4mDRfFWjOJVepZn5p86LopyHHF/k+Iejc5fAEZMleFWm+A20Y/Y2mcEBjwo1kTsWPJTDxz81TsWDLTcTqUTHEJgsg2SMQRGY1eLNhFVvR2ET0WAwQ0z7eh+e6o5wvIPGLnZ7dXRsORTgDGaJVP5jjjl0M1Yv/1jxeYBUSanDl8MocU4W+6lT+cJFgvVp96nVc9Bs9875KY16MoHD45uldfPEhin8jUImxOifZd06aF6COxBEEQ6YRq4oiMx65DUj9D0050AKqIKRmcg5LBObb7eCQB3oACBg6Zc4gCM42l0li+qQlTJwy1jVb5ZY471tTh4eurDCLugpIh2HsscVMbYoHzvskGvQEZnHPkuiRLf7iGI53o8lrXoIWnXg+2xzY7VZuw0O2zV3BOzJi1+xWOX+b9ipzZfdfSbQxMEARhBYk4IisI99LSP1R9soxo5WYvNxzD7bPOMw02B9RwtBZ98wZDaRKLnMKrazkVVTiGF8WPKExfcbzHJeA3366BmrxkKBmcYzl7VptAYUf4aC6nDSUaAQUIRBBwYrifiAWSADxyfRXufL7OJKKXzrWfXeuU8O9aphgDEwRBhEPpVCIr0Key9A/V094AvAFuWZyvZ+XW/Wjv8uL+ay7A2n+51JDZVACTCIw0EaHXr6DlszO20SoNc1F8+kxxfQGOls/O4NY/7cZtq9/DnJXbcai92yRCrIr79SS7RkzmkbuHNS47txgr5lchxyUg3y3CLTL8/NrJuGnquISviRoeMhdKcRNnO0mLxDHGngYwB8CnnPPJwW0rAMwF4APwMYB/5pyfYoyNB/AhgI+Cb9/JOf9B8D0XAfgDgFwAmwH8iHPOGWNDAfwFwHgABwEs4Jx3JOt6iPQRnsq67SvlJnuLHLeIhZeOw++3fwLOAX9YKtQtiqEI0hm/HFVO9UbI57lFhif+/nHUdesFj/qQSd+4KkXhuH9jk2FwvVMTXADI94ih2bH6/bc3n0jYGt2iAAYeioYCgEcUIAhAj868T4sGpmpkFTU8OCPVJsmU4iaI5Ebi/gBgdti2VwFM5pxPAbAPwE90r33MOa8O/vcD3fYnANwC4Lzgf9oxfwzgdc75eQBeD/5MDDDCo269fgUrt+63nI9aWpSHt38yC1+eOMx0HP1D9/Oefo6/YgyuKLNL3VJfUfy6uiOY9uAW/O7NA/07r9VSHO6nRhuN0tUlCGhs6zREMqyK+39+7WQ8e/Ollp2en5xITI3ftdWjsfmH08HCU9SMmzqK9fdSa2AAkLSIjF3DQzLPmW1o3/FvP7UL0x7cgvVJNra2+ndBs78hiLOJpEXiOOdvBiNs+m2v6H7cCWB+pGMwxkYDKOSc7wz+vArAtQBeBnANgK8Ed/0jgDcALOn/yolMwspUVhIF9PjMqcz7Nzbh/JEFeG2vOTr0gxnnhKIDhbnRu1T1iExN82l2GvfMqcB96xtt93eLDJtvn47ykYMMD5tkMKN8GP7efNLRvuFjyHoDMm5ZVQu3KBoiGVqEq7GtEwBDZUmhbWRl36en+3sJANTaum6fjHvmVGD5xqZQdGVBTSme3dVXw+gSmSkaGEtEJt5oUXjUb3vzSUx7cAtFgZCemsFMmn1LEOkknY0N34OaDtWYwBh7H8DnAP6Dc74NwBgArbp9WoPbAGAk5/xo8M/HAIxM8nqJNGCZypI5PKKAnrCUpygwvLnfWtA89nozzhlegHnVY1BZUuioA1JDEAARwG1fKQ/NGF26rsF2/2u/OAblIwcBiDz7NRHsOHASVWMKUX/kc0f7u0SGHEmET1YgKwq8MuANqL4s+gev0/FTUycU4+0DzqoYIn3mv9j8ITyS2i17z9UVmDxmMPLdIuas3G54j8AQGmMGxCYg+pt+0xoeqNHBSDoEFaW4CUIlLY0NjLGfAQgAWB3cdBTAWM75FwHcAeBZxlih0+NxtardtsyJMXYrY6yWMVZ74kTianiI5GOVylo6twKKxe2WFY6q0sGWx1EA3L22Hu1dXhQXePDoguqoJrgafrlvUDygPrRyLKYzaKypbcXqnYcARB/L1V8CCtBw1JmAA4Df3HQhnrl5Kn63sMY0CUN78MaSqvrOZeNjWqsdPhmhc92/sQmdPX60dZobCrTaRg2rpoNwQ2YAaD5+GnevTUz6jRodjKRDUDnxjySIs4GUR+IYY/8EteFhVlB8gXPuBeAN/nk3Y+xjABMBHAGgn6VUGtwGAMcZY6M550eDaddP7c7JOX8SwJMAUFNTk74WQSIurArYB3kk3LGmLiQMXCLDNy8uxW3PvW85gxNQZ5dq0QEOgDFnnmQaAmP441sHMeO8YfBFmSKwbEMjZk8eZRi0rprcJv7rF8s0r3999j3cO7cSZUV5prpCzci3sa0TAjMK3FSmqrwBBT/4027IXIlYDwdYCwjNkFmrlVtXdwR3P19v+uzjvSaKAhnRf8f1Uc5kf1dS1dhCEJlMSkUcY2w2gMUAvsw5P6PbPhzAZ5xzmTF2DtQGhgOc888YY58zxi4FsAvAQgC/Dr5tPYDvAngg+P/rUngpRIoJ9+6aVj4MT//TJfi8x4fCXBdKBudizsrtEWvPAsEHbaSxUpE445Px2JZmPLalGZdOKMLOT+zTiC7d5ADtYfPie634z817YzpnovEGOH72YgNyJGYaZ8U5x18bjuH+jY2mz8ZOpKh1c4nnjN9c82hVD1dc4ME9cyrwsxeN6e3lm5owe/IoAMCSF/ZYiud4hVe6REsmoxdU+W4R3T45FPVOJuH/LhDE2UYyLUaeg9p4MIwx1gpgKdRuVA+AV5n6m75mJTIDwP2MMT/UIMoPOOefBQ/1r+izGHk5+B+girc1jLHvAzgEYEGyroXILNbVHcHitXtCUxVWzJ+CwbnuqLVnCufY0XwS44rzIxr1SgIgCgIUztX6O4vpADs/6bCN+AGAzLnBXqS1owejBmdOpKbXQsB6XCKWbWg0CR6PZJ+q6nenbwyE18NptHf5TNu0KFtnj88UVQSM3cPxQFEgM7HUUhIEkRiS2Z36LYvNv7fZ9wUAL9i8VgtgssX2dgCz+rNGIvto7/LirufrDVGkO5+vx8s/vDxq7VlAUQvQ77hyoqVRb75HREDmmDVpBF778DhcggAGGVdMHI6/Nh037e8SBXjDcpm5LgEcMNiLaA+1bq/FYNcMwi9zuER1AoZGnkvEQ/MvQNnQ/H5FVq6pGo119Uej7xgBvdefRnuXF49v3W/atzegplStoor67uH+QFEgI9TwQRCphyY2EFlFY1unKQ3olzn2HjuN275SHrVZQRQYHn7lI9P2eVWj8S8zzgXnCjY3HAsNtPfJwOt7zQIOABRuFHBukWHJ7EnYuGg65lWPMTUIJK+9oX/kusRQw4jMzWnUO5+vt/X/cmLX4hYZvloxst9Wx1rNnp7Wjh5Lzz45ZG5sjio+fH1VvwUcYYYaPggi9dDsVCLLsJYC//6X9yEwFrVxwB9Qguk1434vNxyFwI7Dwn4OjAlgYf2wDMBXK0Zhc8OxvmPLHI+8sg8P/HUvHrpuCsYV5yfVXiQRMAC//c6FqCwZHGoY0Wq9fLKCgKzAz61tSACgZHBO1HPUjBuKO5+v7/fQMavRaqVFuZb33CMKJuPgPJeI33znIsyYOLyfKyGsoIYPgkg9FIkjsgrN4y0cvwLDuCY7ZIVbjtSSmADRpk7OJ5sNTTiA1z48Ztqmt6/Id4umDtBMQxQQEnCAWuu1Y8lM3DLjHCiKgvCPNDyyUnvwM0TjnYPtjppI/ulLkeeeauO29BQXeLB0boVpXwXcNKFCAUdliWPnIiJGyPaDIFIPReKIrELzeLt7bT1EphZPC3Am4ACYRImGX1Eg2vxO4xIZwDn0ja8ukUESjPVjhvcIAvYeOw0l3CMjzbgEGK7DLYlobOvEjIkjDPv9zxvNsGr0DY+sNDjoTnVq4VIzbiie3XXYNppqF9W5aeo4gKu2Li5RgMx5aCxWfzpIw6c7pHo2aDZCDR8EkVqYVYpiIFNTU8Nra2vTvQwiDpqPn0ZdyylUlw1BUb4bjW2d2LznKP5c2xr9zQ6oHD0IjUfNY6RExky1Yh5JAMBtI0xWwi8TsOq09UgMK+ZXhboI61tO4dtP7cLpsEYMtyTg4fnGbsPndh3CT160n14RC3kuNXKp170CA/LdkqNORyuRFa/wCp/usOCiUqzZ3UpdlwRBpBzG2G7OeY3layTiiGzg3pc+wKqdfTM0p5cXY+eBdsdRHrcYvV4uFn7+D5MN9WO9ARmcc+S6pNBIK6drSyVXTx6F1/Z+ahJyOS4BO5bMDEWcpj24xeC55xYZNv/wclNDQHuXFxf952tJW69HYvjdwhpDyjfZWF1/OPrPiyAIIplEEnFUE0dkPM3HTxsEHABsb3Yu4K66YCSe+u7F8DgcsxWNXJeAsqJcTCsfhie/U4PHb/oidv5kFnb99ErbkVYAInrTpYpXPzyOR66fgrywsWH6Wjer2ia7js7iAg++dE5R0tbrFkUMznWHxGV9y6m4RmVpODmGVZdlONR1SRBEJkA1cUTGU9dyKq73iQz4y62XomZCMdq7vGq3YgKicT1+Bf/8v+9CEJhagxU0HJ5XPSYkNsK79NwiQ83YIrz1SfRGgGTikzlqD3XAF+ZvF27fEUtt084D9pMrNNyiAAbuuHZRQ6uD6+/wesCcIrU7hpN5t9R1SRBEJkCROCLj+azb7MjvhDy3BJfUF3G67Svl8EgC8txiVD+5aMhctRQ545PhDahealp0xy6SlePOjL9uf3jrEALhnZsWDRjFBR5UlQ2JKOCeeeuTqAYqcy4YhWdvvgTcYnKCHfkeMdTdCMDgtxfP8Ppwz75Ix7C6fwsvG0tdlwRBZBwUiSMymvYuLx59bV9c7/XJCjp7/Fi98xCWb2oCV9RIkNklrv/4ZR7q8mzv8mJccT42LpqObp8cmiV5QUkhtnx0MsFnTgwyB57ddRi3zzovpve9EGb+a8Vn3T7c9Pt3oMjO8t/5bhHL5lbiikkjUFzgQX3LKZPfXqzD67UUqdUxtNf1EUerSOSPZk2krkuCIDIKEnFERmP18HWCS2SQFQX/75nd6A5z8E1eKw/D6p2HsGxjE9wiQ0DhWFBTijW1rVkxdmvl1v24cerYmARK+bACvH84ss3IWwcip5AlwWhDInMeEnBAYkxk7Y7RcKQTC377tmEOr5ZiDR+rRWO2CILINDIjv0MQNjipT7KCc46AApOASya7D36Gn73UAF9AQZdXRq9fwaq3D2fE2C2XwJAb1swQjjabNBYqxwzuz7IAAIu/PiliqjIRJrJWx7hnTgXu29AIb0CxTIsTBEFkOhSJIzKa4gIPFtSUYtXbh6PvrMMlCAjEIf4ikSsJAFMbG8JhAB5/ozmh50skClfTyAJgKyatZpNGY3r5sH6tiwG47qJSXHdRaURj3USYyIYfw24Or5X5MUEQRCZCIo7IaNq7vPjLu7GZ+YoMltE7l8hMD+1YUMABbl2cz6Ea02YqMgfO+CNHJePxjDx1Jr6mE40CjzpKS99AYddFmoh0pvEYdjcsg28kQRCEDkqnEhlNa0eP7UzTcDyiAJfIsGT2JJs9OH76jUm486sTIcXwnM5zq52SK+ZXYcX8Keo0BgusMrfXVI2GWxKQ7xGRIJu6fuMRmeU1WM0mjcab+/vXqOGTjbVtsXSR9herObySAJqvehaRCO9BgkgnJOKIjKa0KBd+B12NF40dDBkcLoHhoVc+glU0xS8Dj762DzdOHYv/vqHa0fndIsPir5+PjYumh9Jx98+rjPo+l8iwoGYM/tp4HCIDfAEFE0fkOzpnorD7y83B8JubLjTZrMTjfTbjvNjSqf/4xRKDgJQVBTua+4SgldFurMa6Th/M2hxej8SQ5xLhkRgeXVBNzQtnCevqjmDag1vw7ad2YdqDW7DeQac1QWQalE4lMh67NJ8kAP9+5XnwBTh+tUWtRwtESZdqgqAw1x31vAIDZIXjkVf24b9e3otZk0bg9b2fBo1rI3e5yjLHC7vbDDNXPzzeHfWcicRO+nLOcdtz7+OGS8pCnbPxDIgHgAnDC2La/8ZLxmLTB0dDPwcUdUj9tPJhKC7woLQo12REHIu4jNUUOF0D2+Od6UokBn3EV+t8138PCSJbIBFHZDStHT3IdUmmYewAIIkCLigdgpv/6HwWriYISotyIUYZ4KD532rn3txwDABMc0ct3wsAGTqX2K9w+BWONbWtIS+7eMVEa0cPBAZYeAWbWHjZWJzxyxAFAUBf7lnv+ba9+SRkXT2jS2SOxWW8D+ZUW4ckYvpEPJBw7COSb+DZ/tkQ2QWJOCKjsYrMaPT6FXzQ2gmXw+H2HqnPmuK3f/84iX5x2YFLENDtk1FVNgRAfA/5fLdoK+CqSgfhkeu/iLqWU6guG4LGo5/jllW18AbCOkKDwloTYXqNzABMc9gBmw0P5nRFgNIlHDOVRHgPEkQmQDVxREZTXODB96aNt339l6/tQ7cvemRMALDpdrWubfHzdfivl/c6ih5lKgLUKFWuK/6/wvqHVrz1QW2d9rVqVWOKUD5yEK6YNAJtnb1YvHaPScDphXVjWyeEsNFcPpnj2V2R7WW0Grh8t5jxD+ZE1PzFSiqbRbKFRHgPEkQmQJE4IuPZd/y07WsOMpsAgHyPhG6fjNpP2rFmd/8LmD2SgGuqS7Cm1rn9ydiiHBzu6O33uQE1Xbvm5qnY/MFRPP3WIcfvEwW1iN8nK7jtK+UAokeHIkXotnz4qe25Vu06DDBgze5WCGCmNHSeS8RvvnMRZkwcjnV1R7B4bb1J5AGRJ0mER5j0EzLirfNLJumIAGVDhDIdpKsekiASCYk4IqNpPn4ar+890e/j+GR1xNK96xv6fazrvliCn15dgdaOHmzac9TRVIifXjUJG+qOAAkScR6R4YxfxtHPnR9PZMDffnQ5Njccw+Nb9+PJNw9g5dZmzL+wFFKYjYv2kN/efNI2Ddfe5cXqXZEF5Kqd9lE0BRyVJYUhEWkl4IC+SRLhD1kr8ZmIOr9kokWAFod9pslcJ6UO7aFRakS2QyKOyGi2N8cm4CRBnVkazvemjcd9GxrhcAZ7RF58vw1fPn8EppUPM3Sf2pHrEjB1QjE21MVmWhwJBcAtq2qhOKgFdIsMjAEr5lehKN+N/3mjGd4AhzegNmysfscstHoDMvLdYsQIXWtHD9ySCH+Mo83y3CIUzkPixWrAvR47wWEXYdLX+WUiqY4ApUM4EgSRGkjEERmNR4o87zMcxULAaQau/ZnWYDgHgLvX1uOtH88KPRxlhdseP6Bw9WGdnwOgKyFrkBUOf5TLkQSG//5mNcqG5oXEQn3LKVPUzQrOOdo6I6fhVA+/2D5Tj8Twm29fiMqSwREH3ANAvkeErHBbwZHNEaZUR4AodUgQAxNqbCAymkE5zn/PYLD2RkuG04fIVDEzrXwYls6piHiOu792PooLPCjKdyXs/E6GPwQUjkmjBhlGWjUc6USXN3rkLNclAWARRVJxgQcXjy9yvGZJYLh3biVmTBwRdcD9z6+djGdvvhQ7lsy07aKk4vTYKC7wGL4LBEFkPxSJIzIaJ6a8gJoylBVu6fuW4xJRmOuGJDhvhIiGzNUau/m/eStqNGrU4BwAQEe3PzEnR2R/Ow1JgKFer/n4ady3odHR8f2KgsqSwqhpuPNH5mPHx+2OjhlQOO7f0IhBHskkzOKNFFGEiSCIsxkScURGU1lSGNWU94aLS3HVBSX4wZ92Ww55DyhqAf2jC6px1/P1jjzlIiEy4N65lbh/Q5OjdKJWR/Z5b/+GxccKY8xgIXL38/W26xUZIAgMOZJoEGvRRNKF44rx9FuRLUD0eAPc1hfNaYoxvFvW6n3aPvluMWObHAiCIPoLiTgio9nefBIsyoyrtbtbsW3fSUsBB6gTFn71+j7cf80FEBhw5/N7HE1dsOMvt14KlyRCdFBbBvTVkQ1LsYi4ZPxQtHb04JMTXbh77R5b8eoSGV7+4eUoyndbirVI4mrSqEExr6s/9hZOTGu1fbjC4ZU5coJeeme7wS1BEAMPEnFExmLl4G9FQAGOdEa22lj19mGUDsnDI69+ZGtl4ZTn3jmMn15dEXVOq4ZWRzZhWAGA/tulOGXHx+1Y8Nu3Il6vS2R45PoqlI9UxViswsqJvUo4TpsPwiNuTqYd6PfR0P5MszEJghhokIgjMhYrC4n+8ODf9ibEYuT/3m9DaVGeYcanHdpEAgB4tel4/08eI3YCziUAv/zmF3HZucUGURPr6K2dMVrAuEQWMhmOhFXEbVxxPkRm7WenrTXSd4YMbgmCGGiQiCMylkhzU+MhUYfiAH61pTnqflddMBLLr7kA25tP4ksPbOlXCjfR3HfNZMypKjFsi2e+5l8/jE2YBmSOJ988gMffaLY9vl3E7WtfGGGK/IVH9UqLctEbsI4OZov9CEEQhFPIYoTIWIoLPFh0RfSoTaby+ocn8PbH7bjr+fqMEnB5LgGTSwYbtsU7X7N8WEFM5+ZA1ONbzRHlHFi/55hp+x1XTgylWutbTqGj2wdu4fein9FKEAQxUKBIHJHRXHbO0HQvwRGSALhFAWd0tVjegII71tQlzGQ4USiAKSJlN4A9WvqxcsxgPP9efLNo7dKb+W7RUNMGwFYED813GyKI3oAMSRQQ0KW69TNaCYIgBhIk4oiM5mD7mXQvwRGcA70WQqO/diaJxi4iZSWcev0K8t2RJ2aU9SM9aZfe7PbJ8IgMXt1n5xIAv4WOG1+ch28//Y4h9RruR6PNaCUIghhoUDqVyGg+OnY6IcfxSAJcIoNDV5CYkTlgMfEro/h6xQi89WPrCQiacNLjERm6fXIoVWmV+uw4E5uBsSSwqNMVSotywcJulCgKuKZqtGHbwsvGwiWJcAnGf8ZcIoNbjH4egiCIbIcicUTG0t7lxdNvfeJ4/0imwLKsQBQZbrp0HP7w9qEErTC7uOXyc2zFTEg46T9AxvByw1H8746DcIvWzQ7ji/Nszxd+PzwSw6bbL49qvms1sH1BTSnW1LYizyXAJytY/PVJuPXL56K9y2seDSZzuEXg1hnn4MapY0nAEQQxYKFIHJGxtHb0QIohcvady8Yi12X9lQ5w1W7j2XfSI+DcAlDoiZyaTCYMwLeffgfr66zr18LnkLpEhoCs4Dd/PwBvwL7Z4djnkRsfXLqI2Ir5qh+dk/md86rHYMeSmXjm5qnYuGg61tS2otev4IxfQUABHn1tH9q7vKF1eyTjfffJwONvRO8gPpuIFFElCCI7oUgckbGUFuVC5lHGNQS5dEIRnnunJaqRL4ez4yUaJqji6HMHw+eTAQdCIszO8HZa+TA8+Z0afN7jx53P18Fv8TGFNyOcjCAIZA5IDHj8pi+ismRwzBExbVJEfcspk/ebfh3zqsdgSJ7bNHaNfOH6iMc+hiCIzIdEHJGxFBd4cOHYIXjnYEfUfd892OFoKHwqOkVFAbj6glF4ueF46HyyoiAnSpNAKhAYQ2NbJ2ZMHGHYHt7hKQjWYje8GWFIbuR/QtyiiMG57n4JqdKiXHPKNGwdlSWFUMLWS75wKla+e3et3YOK0YWhSR0EQWQnlE4lMpphBS5H+4Wn09IJAzBmSB70meCAAhw8mf5O2zM+GTf/sdaQVg33iPPJ3NSpClh3th6Ick0+WUFnj79fKbzwVK9Vs4KTfc5WtCkWenwBBVc9ts02vU4QRHZAkTgio3n3k+hRuC+WFeLDY10pWI0zAgrwxN8PmLZLogA5gRMo4sUnc9z5fD0qRheirbMXB06chiSYO1M5Y/CIAnyyjEVXnGfZJDDjvGF4LML0ioCs4LbV7/U7hTevegymlQ+LOBIs0j6xjhMbSFhFMgH1e0DzZAkiuyERR2QszcdP40R3dAuL708/F2/uP4E1ta0pWJURBsAlCo7GgyVyhFh/8cscX/vlm7ZTaZnAsGnR9KidpBOGR57YIHN1QgMQ/wB6vQCrKhsScV+tjk7P2V4PpkUp71q7B74wL0OqGySI7CZzclAEEUZdyylH++0/fjotAg4Arq8pxeYfTodbjN5GO8JhajhVWAm4fI8YSkU66SRtbOu0fS38E9EEQyysqzuCaQ9uwbef2oVpD26JOf0X7zixeMnUDtB51WOw+Xbz95TqBgkiuyERR2Qs1VGiLgDgFllarSRefP8IivLduOGSsoj7eSSGfE9mB75zJIbvT5uAjYumxxCpshev4W0RsQqGRAgwq3qweMSkE/orOJNN+chBePj6KuS4BOR7RLglAffMqaAoHEFkMSTiiIylfOQgVIyK3D3Hoc4sTRd+mePtj9ujRgLvnVuJQIaN4AqnN8Dx++2fYM7K7Y4FSMngHNvXBABuKf5Gg0QIMCedrYkg1RG/eJlXPQb3XF0Bf0CBS2BYvrEp48QmQRDOyezQAHFW097lxf4TkRsWzhtegI9Pdjs+pkcS4A8otrVg8XCovcvUGKAn3y1icslgCFnwK1O3T/VZc1q/1u2T4RaZ5YxYBcCz378ELkmMq6EgEQLMavpDMrpWNcFp52WXKbR3ebF8UxN8ModPju1eEwSReWTBY4U4W2ls64zq6/bhsdP43rTxjo4nMuBbF5clVMABwNB8j6lgXI/MOUqLcjE8wx6SbgG4b24F7ptbYRp07zTiVVqUi4DN0Fi3yOCSRFNdndO6sUTZhuinP+xYYj07NpZ1WZGqiF9/SWV6mSCI5EOROCKDid4swAEcbncWiXNLIp7ZlfixW//xUgNsdIzBW23U4BwA9o0AiWbi8HzsO2H/2QiigLlVJQCAB/661/CaUwHyyYku22sHY6ZjxNop6sRaxAlWXav9WZfV8R+6bgruXrsHosAgKzxmwZkKGxQrsan38qNoHEFkFxSJIzKWSPVWejY1HHe0X49fRjJcPmRuPchLHePa90pRXmofkB+f7IZLADyiALcILLxsrGVUqz8Rr5fq2mxfu+urE00RuHjqxooLPI7mrcZLourZuPa/vO8np6SqKcJqRq6sqF5+mdiMQRBEZCgSR2Qs3T4ZIoOjcVpOSWVrgV8BoPQZqkay44iHaJ+NzLXXVeW6prYVG2283+KNeA2OMHbLG5ZiztS6sUSsSxOC6uze2GrNrMZiJbNOTbvXjW2duGVVLbxy/738CIJIDxSJIzKW0qLcNIyqTzyaIOgKPigThcyBilH5Ma2j2ycnNKp14dgi29cee32/IZplVzeW7xYNtWip9lpLRD1bf2rN0lGnVlzgweBcN9xifLWQBEFkBhSJIzKbAaDitJqjovzEm/02HbOveQuP1EUSJvHWhHWciTxRo7GtEzMmjgBg3Sm6oKYUc1Zu7/v5olKs2d2a0ukKiehg7Y8QTFdTRLY0YxAEYQ/jfAA8JWOgpqaG19bWpnsZhAPqW07hxt+9jW5f5oyrigeRAXluKZSyShUMfRrYJTI8cn2VpSBq7/Ji2oNbDEPvc1wCdiyZGVXINB8/jSt/+abt6x6JYcV843m1Av58t4g5K7cbzhuO03Ukgv42FqyvO2ISgk4FaH/e2x/SdV6CIJzDGNvNOa+xeo0icUTGUlqUG9ViJBOw80nT0M8PTSX6FQkMmFY+zHK//tSElY8chIWXjcWqtw9bvu4NmIesa80U9S2nTOcNJ5U1c9E6WKPRn07aRHXhxkq6zksQRGKgmjgiYyku8OD70yekexlRiW6Ekn7cohiqddLXnLV3edHZ44dPjj+tdv81F+C1f5+BKycNQ1GeaPpHRV9npT+3VTovHJ+cXem9/nTSJrsLN9POSxBE/6FIHJHRzJo0Ak/8/UC6lxERry4Kp9WheSQBnAOyoiS0uzZeNFGmr33r8QfAGEOOJEJWFLhEBrckwC/zmGdqfvv3O3Hsc5/jc2upO6sauefeaQlFYGVFwY7mk5TiIwiCsIBEHJHRPPeOdZouXhJtWaIn3y1i2bxKfNbtw8OvfASXKEDhgEtQhVKXN5CyPg2RAYwxiAKDwlVRBgCL1+6BN6DoUpgcfllN9UoChy8gwy2KWL6xCYM8kiPx9NJ7LbYCTjM7BmBpo7FjyUzsWDIzlM4DgL+82xJ6f0Ah2wuCIAg7KJ1KZCztXV783/v2ZrLxkMyomMw5qsuG4NHX9sEnc3T7ZAQUtR7t8Zu+iHFDU5cWlDkQUDi8AUWNrL3UgB/8qdbk3aYnoAB+WfXn6/UruGvtHjQfPx31XBs/OGa5nQH43cIazKseE9FGQ5/Oa+3oyTjbi1RbnhAEQTglqSKOMfY0Y+xTxliDbttQxtirjLH9wf8vCm5njLHHGGPNjLE9jLELde/5bnD//Yyx7+q2X8QY+yD4nscYY9lQnkQ4pLGtM+MdRlwiM0w56PbJJrGiiZKDn6VPiCgcePfQqZje4wsouOqxbVFd/OdcMMpyuwCgsqQQgHM7i0yzvUjVJAWCIIh4SHYk7g8AZodt+zGA1znn5wF4PfgzAHwDwHnB/24F8ASgij4ASwFMBXAJgKWa8Avuc4vufeHnIrKYfceiR4HSzd1fO98wWN1OhGRH+4MZn8yjjqC69sIyFHrM/5T88oZqQ0fqgppSw+sLakpNKdJEDb1PBIkax0UQBJEskiriOOdvAvgsbPM1AP4Y/PMfAVyr276Kq+wEMIQxNhrA1wG8yjn/jHPeAeBVALODrxVyzndy1exule5YxADg2OeZ/7B8+JWPDNYMdiKksqQw42WcW2SQBPMqo6UzV+88hM+9RuHqkRimlQ8LpSKbj5/GmtpWwz5ralstBdG86jHYsWSmQRyng3RMUiAIgoiFdDQ2jOScHw3++RiAkcE/jwHQotuvNbgt0vZWi+3EAGF25Ug8tf2TdC8jIi7R7GNm571VVuTB4Y7MFaYcALNIYEdKZ7Z3ebFsQ6NpuygIeOSVfXjhvVa4RQHegAwhTCBG8oDrr2dbIsi01G6i6K+pMUEQmYNjEccYGwfgPM75a4yxXAAS57xf+S7OOWeMJb3siTF2K9QULcaOHZvs0xEJomZCMdwCkMkDG/wKR75bNG23EiHhBfvJxiUAEYYhhMh1qV20sqIgvO9B6y61e9i3dvTAJQrwybJh+xmfjGeDncWhZoqwrpJMF0SJGMeVacQ7Xo0giMzEkYhjjN0CVQQNBXAugFIAvwEwK45zHmeMjeacHw2mRD8Nbj8CoEy3X2lw2xEAXwnb/kZwe6nF/iY4508CeBJQx27FsWYiDdR+0p7RAg4AGOeYs3K7o4fh8a7UpuGcCDiPxPDb71wEgOG21e8ZJkvkuUT85jsXYcbE4bbvLy3KhexwdJ9HZOCMwSNmjyAaSBMN9DV+epsXO/sWitgRRObjNBJ3G9Smgl0AwDnfzxgbEec51wP4LoAHgv+/Trd9EWPsz1CbGDqDQu9vAH6ha2b4GoCfcM4/Y4x9zhi7NLiuhQB+HeeaiAzkpbrE2oskA6/MAdk8WsoKngGCVGSAKAoGITVj4gi0d3lNUxtkzkPdpXboo1X+QGRjYyYwbFo0Hd0+OauEQSakdhNBLOPVKGJHENmBUxHn5Zz7NAcPxpgERHd/YIw9BzWKNowx1gq1y/QBAGsYY98HcAjAguDumwFcBaAZwBkA/wwAQbG2HMC7wf3u55xrzRL/CrUDNhfAy8H/iAHC4Nzs8aLWF7yHRy+0iMaIwhx0nUxvUbwoCth8u1lIbW8+CTms/ivStAR9lGZe9Rhs2tOGvzV9atpPz1fOG47ykYMSdzFETDit8Ys1YkcQRPpw+pT8O2PspwByGWNfhSqeNkR7E+f8WzYvmdKwwQ7T22yO8zSApy221wKYHG0dRHZyxfkj8PgbmT1yS8OvKGg40olvPvm2IXrBgVBEQ5+qTBdL51aYhJT20A6vh7OblhAepbnjyolRBRwAvPrhcbR3eTNGCJxt6UKnNX6xROwIgkgvTkXcjwF8H8AHAP4FatTsqWQtiiAAwCWlthEgHnJcqgXFPXMqsHxjkyF6cdfz9eAA/DI3PBBTRfiIMTs/IauHtkb4w9sqSvPQ3/Y6Wo9bZBkjBM7WdKGTGr+B2pVLEAMRpz5xuQCe5pxfzzmfDzUqRn+jiX4RbZzRydO9KV5R7MiKgo2LpmNyyWCTp5hP5qFB7skikvfcf147GZJuSQqAn73YgNU7Dxn2s3poa4Q/vC2900RnDnicsYwQAukw8c2k0V36MWd2r2eK4TJBEJFxGol7HcCVALqCP+cCeAXAl5KxKGLg4yQSUt/amabVqbhEYO6UkojzW0VBQF3LKVSXDbEVQunilcbjphQpANy7vgGzJ48yGRQvfmEPuMLhlXkowhj+8LYSfDJniFYi645iVZJKUp0uzMao30DqyiWIgYxTEZfDOdcEHDjnXYyxvCStiRjgOC2cPmdYYr5iUjCtGGtMjDEB//qVcqyra7Ptuuz1K1i6vhEy51hQU4o1ta1gAHqc+HskgEjXtHXfCcvtsqLOpZ0xsa/BXP/QzneLth2kVnVVl00otj0XoEbqNt8+PWOaGlKZLszmJoGB0pVLEAMZpyKumzF2Ief8PUAdPA+AZs8QceE0EmIVRYqHQJwZzRsvUW0Ii/Nd+LTLb7tft081uv3zu6149vuXYNcnn2HFK/viO2nKMKdAwx/aWgowXMyFC76v//ebEc/0w5nnZYyAA1Jr4ktNAgRBJBOnIu7fADzPGGuD+q//KADfTNaiiIGN00hIddmQFK7KzB/eOow/vHXY8f6+gIJvPrkTzKZErChXQkdP+jtURYao/m/RUoCa4KtvOQWBmYYxhHCLDDdOzbwpKalKF1KTAEEQycRRYwPn/F0AkwD8PwA/APAFzvnuZC6MGLg4LZwuHzkI5QlKqaYKmZsjiJIA/PSqSRkh4ADg1hnnRBQtsRT+57vFiJMhFM6xo/lkIpadcKIV+CfqHNQkQBBEsogYiWOMzeScb2GM/WPYSxMZY+Cc/18S10YMYJxEQpqPn0bzyTNpWF1iueXyczB1QnHCj+sWGVySgG6vHH1nHZedOyzi67GkALt9MiTBPvVt5zV3NkFNAgRBJIto6dQvA9gCYK7FaxwAiTgibqIVTte1nErdYvqBS2QRrUR+v/0TXHdhKQQGKAl0HOGcY+Gl4/DE32MzRN577HMMznUlxCcs3y1GrV2kGjBqEiAIIjlEFHGc86WMMQHAy5zzNSlaE0EASH9NnFOuv6gU//f+EQiM4YzPHBWTRAHdPhnjhubik/bE9QP5FeB322KfaPGLzXuR7xYhc25pdxGp8D98ykFbZ/Tr8clUA0YQBJEMojY2cM4VxthiACTiiJRSPnIQyoo8aOlIv0FqJL43bQK+N20CtjefxM83NZlqxGSFo7QoF+cMz0+oiAOcu3WHo3XU2qU6rVKAVs0OQ/LcUc+16IpyikIRBEEkAafPgNcYY3cxxsoYY0O1/5K6MuKsp73LiyOnMlvATRyej12ffIY5K7fjkVf2gQMQdN2pLpFhxXw1iiXHVrrmCDv7FAHAF8cOjvp+LdVphb7w367ZoWRwjuF6w/FImdmdShAEMRBwajHyTag1cP8atv2cxC6HIPp4++P2hNaQJYN9J7rxs5caAMDQCOAR1TFX982tDKUre/yJV3F2n48C4P3D0SdeOLW7sGt2aOvssaz1y/eIkBVOnZgEQRBJxKmIq4Aq4KZDFXPbAPwmWYsiBj7htVVWHGrvstyeDWgNo/dvbELZ0DxUlhQikCFjuSSBweMSYhJZds0OAEOuS8Jpb599Sr5bxLK5lbhi0ggScARBEEnEqYj7I4DPATwW/PnG4LYFyVgUMbBxOkvSLYlpWF1i8QYU/OBPu6GAQ3I2Jz7puEUGf0DBUl2UMBp2zQ6VJYXoDRgjjD5ZIQFHEASRApyKuMmc8wrdz1sZY03JWBAxsIlllqQvkIQisjRwJglp1P5wJth5sXxTE2ZPHuVYbFk1O7R3ecG5MZca/jNBEASRHJw2NrzHGLtU+4ExNhVAbXKWRAxktNoqPXbF9eOKC1K1rH4jQG1iSDVOz+gSGSSLv+12TQ12hE85aO3oQa7L+LugWxLR2Ba9Hs8KbV6r1XQIJ68TBEGcTTiNxF0E4C3GmDZIciyAjxhjHwDgnPMpSVkdMeCIxUg21xWvgUbqyfWI+PJ5w7G54VhoG4NaQJpMnB5fVrip+aDXryDfHT1lHal+0ep+nvHJuGVVLVbMr3KcrgWA1TsPYdnGJrhFhoBi9rCzSsPTJASCIM5mnIq42UldBXHWEMlINpz61viiOemg2yvjlaZjhm2iAIiCALcowCsr8EUbbZBErLpY3SIL+cXZEa1+Ubufd6/dA6/u+rwBHtO4rdU7D4W6fH3BHgn9+63S8Hc+Xw+BAW5RjFhbSRAEMVBxJOI454eSvRDi7GFe9RhUjC5EXcspVJcNQfnIQZb7nTMsL8Ur6x/hGs0tiXjougtQNjQf+W4RX/3lm0mPzMUEYxHtRZzWL86rHoMheW784E+7DfV/TsdttXd5sWxDo2m7KLDQ+60sTrRRZ95AwHZtBEEQA5nsyVcRA4Z1dUcwZ+V2LNvQhDkrt2N93RHL/U71+FO8Mmd4JCGiwa3GGZ+MO5+vx6H2bpSPHITy4fkJXYdbZMhxCbi8vDiu9y+dWxFR8MRSv1hZUgg5rKEhJg860fxPkV/mofdbpW3DiWRcTBAEMRBxmk4liIQQS3dqpKHy6cQbQ1pUn1asGV+E/Se6E7IGBuD6i8rwz9PGo3zkINR+0o7n3j0Mn19Bfo6EF99vgyhYz3J1iQz3za3ETVPHRTxHLPWL25tPQtbt6xJZTB504QIQMIrM8DS8T1YgK4oh+ulUNBIEQQwUSMQRKcXO+d8q7Xb01MCIqogCw9a9n6I3gVYjHMDqdw5j7Xst+GZNGVbvajEIIQZ1/JdbAHyKKqr8ModLULcPyon+V99p/WJ7lxeL1+4xCCqBAdPKhzm6Fv15RMbgl1UPu3CRGW5xsqP5pKPaSoIgiIEKiTgipcQS3WntOJOqZSWVbq+MpesbozYRxIM3wLFq52HTdg5A5up/QF9U068AULipacCuw9PKGy6c1bsOm6KTblF0VA8Xy3kAVfBprzl9D0EQxECFRByRUmLpTh2U607DCpNDMgRcf9Cin9ubT0adnqEXTuG0d3nx+Nb9pu0+OfbUZqTzJPI9mYST8XMEQRB2kIgjUo7TCMq3Li7DC+9ZNz0Q/cMnqx5xTusT7Wjt6IFbFEMdohpX0titqDgdP0cQBGEHdacSaaG4wIPSoly0dvTYuu9PGJ49ExuyjUVXlKPbJzvuPrWjtCjXNDsVAF7bezzmqQqZNo0hmevRN/ic9gbQ61ew+IU9GXPtBEFkBxSJI9KCkyjE2x+3p2l1AxuPxHDj1LEA4Lg+MRKKhZOwJDrziNPItKhUstcTS4MPQRCEHRSJI1KO0yjESYpKREV0YlgXRBJUX7kV86tCtWQPXTcFOS4BgzwSclxCzB2erR09yHGZR3f5A87FYKZFpVKxnlgafAiCIOygSByRcpxGIaY7tKg4m7n9inPx3683O9r3P66ehLlVY0zTFvrT4Wnn8SYrHDuaTzqKXmVaVCoV64mlwYcgCMIOEnFEynEahSjKHzjdqcnig7ZOMMDROK9hNp2cdh2eTjon7Wanytx5k0SmRaVStR6ySCEIor9QOpVIOU7TeFQTF53XPzwByeHf4sIYLFvW1R3BtAe34NtP7cK0B7fYjkYDVDHyu4U1yAtLqzptkkhEWjeRpHI9xQUeVJUNIQFHEERcUCSOSAtOohAfHu1Mw8qyj+tryvDsOy0R95EEdb6pE2IZjaZRWVIIBfHNTgUSH5Xqr/8aRckIgsgGSMQRaSOaUWuem76eTvjgyCnL7QID3KIADh5qZgCiC5x4asISUePlxLjXiTjLtE5XgiCIZEFPSSJj+XrlKKx4ZV+6l5HxfHDktGlbnlvAb75dg8G5LoPgcSJw4q0J00ev8t0iun0y2ru8CYtiOVl7PFHEeM8VCzSZgSCIZEA1cQSRRTg1FAnIHJ/3+NDy2Rk0tnWivctraZ1x91qzdUZ/asKKCzw42N6NOSu3O6qnc4pT2w8tiqgnVgPjRFuMxFJfSBAEEQsUiSMylu3NJ9K9hIwjR2LoCUTvRfXJHIueqwv97BIZfjjzPFOa1BtQ8Oyuw7h91nmG98dbE5aoSFg4TlO8iegsTaTFSLI+D4IgCIAicUQG03nGn+4lRMSdhr89VgLOSXTOL3P8est++GTF9NrKrfsto0zxdE4mIhJmhVNxlojO0kRajCTr8yAIggBIxBEZTEsGP+hyJAYw59MSkgWDGmVztC9jmDNllGm7WxQTJiqS5bEWizibVz0GO5bMxDM3T8WOJTNjrmVLpMVIpnngEQQxsKB0KpEWnBR6jyzM3HRTb4BDZKqAEgWGXr85wiUKDJLADCa4iSbXJWLCsDw0HjU3N4TjDSjYUN8GSQD0S0qkqCgu8GBBTSlWvX04tG1BTWlCUodOU7yJaCJIlMUITWYgCCKZkIgjUo7Tzr8rzh+Bx984kIYVOkPmgAgOiwyl+rrCcceV5+Hxrc04YyHyEkFAURwJOA2frHrGeSQBbjHxoqK9y4s1ta2GbWtqW/GjWRMTco5oNiSJ7Cp1YnniBPKcIwgiWZCII1JKLIXedS2n0rDC2JAEEQHFXqB92NaJZMXhJAGYf1FpVKPfcHJdEh6/6UKT/UgiSOcc1ExuIkiUICQIgtBDNXFESoml0PutLBi7JXMFcoRm0b82HscdV05M+Hnv/tp5+OuPZuBL5w6L+b1+RUFlSWHCxz01Hz+N9w93mJonUlUDRk0EBEGcbVAkjkgpsRR6M+ZkrHv6kASGFfOrcOBEF/779WbLfQSB4WD7mYSf+7RXxpyV2x39FiYwQNF9lImqUdOzeG29IY0qCgx5LjGlNWDUREAQxNkGReKIlBJL55/Xn9kiLqBwbG8+gTlTSmz38ckcf3n3sO3r8SAw4H93fIJev2JbaycyYJBHgkcSIIQ1r66pbY1oXNve5UV9yynH5rZP/v1jUx2crHD81z9eEFd3aLykcnB9NqLd1+bjp2O6vwRBZC4UiSNSjtNC7+GD3CleWeysqT2CtbWRHfgjpVvjRWACANn29f+8djIqSgajs8eP21a/h9PeQOi1SDVqsTYGtHd58cBf91q+1uuXUy6gqInAGu2+AkCvX4FHZGACo7myBJHlkIgj0oKTQu/yEYMAHE3NgvpB8gxEbM7HgR6/vYADgIvHD0X5yEFo7/I6TjHG0xjQ2NZpSNXqqS4bEv1ikgA1ERjR31cNr8wBmWdM4wdBEPFB6VQiY5HS76WblXhEFho+39rRg3vmVERMMWpptsa2zjgaA6xv0uTRg1A+clC/riPWtC5hjVXDhwY1fhBEdkOROCJp9Nd09dUPjydhVQMfJjA0HOnEN598O5QWvefqCkweM9h0L/TpU58sm6Jq0RoDKksKITJzynj/iS60d3njjvAk0u/tbMeq4UODGj8IIruhSByRFNbVHcG0B7fg20/twrQHt2B9XeS6MSvcopiElQ1c8twiPJKAf/7SeNy/sQm9fgWnvQH0+hUs39QUEnD6AnctzXbaG4A3wME5h0dy3hhQXODBv1lYqPRnlJc+/aetf/ELe1IekRsokUB9w0eOS/0n3yMyavwgiAEAReKIhJMo09Uh+fT1dIpLAC4vH4atH32KJ7cdsJwi0drRg+3NJ0MRLm9AhhDWuhqPEfCNU8di5db98Ab6wnHRIjyRorTpNAzWGGiRQH3DR75bRLdPpsYPghgA0FOSSDiJegi3d/mSsbwBiV8B/tZkn37u9StoauvEfRua4A30ievwPKhmBBzLfSou8GDF/CrH80GjCaR0+71l8uSH/kANHwQx8CARRyQcJw9hJ/VyviTNG001LpEhRxLR5QuAp9H67r71TfCGheg8IgNnDJ5+zlGNZTh9NIGU7qHxmRAJJAiCcAKJOCLhRHsIr6s7gsVr90AUGGSFY8V861SVL8JM0mxBYMDLP7wcbZ09WPj0uwk9NgNw4yVjsfa9FkMq045wAQeoTRCbFk1PSHrNSaTHqUBKp99buiOBBEEQTiERRyQFu4dwe5cXdz1fD78ujXfn8/WWqaqh+Zlv9hsNhQNtnT0YnJv4a+EALh4/BKvfiX0iRJ5bhMI5HrpuSr+tQGIhFoGUrvRfuiOBBEEQTiERRyQNq4dwY1unQcABgF/maGzrxIyJIwzbfYHsj8SpsKRFce54fk/E1wWYzYg9EsNvvn0hKksGp1yYZItAoskPBEFkAyTiiBRj5+Br3j4AsqkQGZDnEvD2x+0YPciNo6cT26xhNy0h9HrYzx5JwIr5U0yCOZVki0CiRgCCIDKdlIs4xtj5AP6i23QOgHsBDAFwC4ATwe0/5ZxvDr7nJwC+D3VY5A85538Lbp8N4FcARABPcc4fSMU1EPFTWVIISQD0QTaRASWDc0z7ynwAqDgOzP/tzqQcmqmHj4lHrp+COVXpt8oggUQQBNF/Um72yzn/iHNezTmvBnARgDMAXgy+/EvtNZ2AqwBwA4BKALMB/A9jTGSMiQAeB/ANABUAvhXcl8hgigs8eHRBNTwSg1tUo2+SwDBn5XaTIfBASKdGnnDaPyIJuJnnD7fcXpiE2rx4GShmugRBEOki3enUWQA+5pwfYsx2UOY1AP7MOfcC+IQx1gzgkuBrzZzzAwDAGPtzcN+mJK+Z6CfzqsegYnQhrnpsGwD7YdztXf50LjPj0exB/AHFJOje3H8CAjOmWyVBjYRmAgPNTJcgCCIdpHvs1g0AntP9vIgxtocx9jRjrCi4bQyAFt0+rcFtdtuJLKDbJ8MjGcdqhQ/j7g2QiIsEExg23z4dsyaZo24BRU23ukWGPJcIj8Tw6ILqjEhhZspYLYIgiGwnbZE4xpgbwDwAPwluegLAcqhZouUAHgHwvQSd61YAtwLA2LFjE3FIop/ku0WTb5lmNaEZAcvpdMbNUNyiAElU/fUeum4KAOC1vScs9/W4RDx03QXo9SuoLhuSMiuR9i4vGts6ATDL6Q9kpksQBJEY0plO/QaA9zjnxwFA+38AYIz9DsDG4I9HAJTp3lca3IYI2w1wzp8E8CQA1NTUkDJIM1oqjQVFmjaU+6Hrphhme5720q3SU106GB8e+xzgfS0NdS2nbPf3ywrufL4eblFMWcpyXd0Rgw+gJACPLqjOqLFaBEEQA4V0plO/BV0qlTE2WvfaPwBoCP55PYAbGGMextgEAOcBeAfAuwDOY4xNCEb1bgjuS2Qw+lSaN/igVxSOjYumY1r5MEOajejDJQAfHvsc3gDHGb8Mb0CtISzKc1nuLwoMnHN4AzxlKcv2Li8Wr91j8AEMKMDda+sN59W84nJcAgZ5JOS4hIz0iiMIgsh00hKJY4zlA/gqgH/RbX6IMVYNNcRwUHuNc97IGFsDtWEhAOA2zrkcPM4iAH+DajHyNOe8MVXXQMSHVSrNI4no9sno9plfI1S+VjEKf99/At5AX7+rSxDQccYPkRnn2DMA91z9BTzyyj6DGE52yrKxrdPSBVBkmTVWiyAIYqCQFhHHOe8GUBy27TsR9v85gJ9bbN8MYHPCF0gkjdKiXPQGjMYbvQE5lEoLT7MRKi83HjMZ+/oVtdZNEBhknYqTRIbJJYXo8dt/zolm9c5DuG9Do2kaB6D6/aVirJZWS0mikCCIs4V0W4wQZwH6hysA8LCGBe3n8JFMZ3wBWGiCsxKryQz3zKlAUb4bctiLAZnjxqd2ItxmL/xzTxSrdx7Cz15qsHxNEoAV86uSLqpW7zyEZRub4BYZAsGmD7IsIQhioEMijkgq4X5gt32lHJIoIKCLuEliX7pNn2b7+YZGvHP4VPoWn8F4JIbJJYPR2NZpEngcgM/CZTjXJSU8ndre5cW9680CziMx/OQbX8DcqpKUCDhNRPqC2eNwz0GCIIiBCIk4Imnomxi0Ordfb9kPX1h4rdevwK9LsWpptiOnelO63mzCG+DId4to+cy5l14yOkAb2zohW2TAOUdSBZwW3c13i1i2wVwKKwqMLEsIghjwkIgjkoZVE4MkCuBcgT8sfPTN3+3Ev82aiBunjg09eD/rJhEXiZcbjmHCsDxH+3qkZHWAWk9a+f70CUkTUProrjcgw2rYi1/mZFlCEMSAJ90TG4gBjJUfmKxwy4eurACPvLoPX3rg9dAMVasID9HHyq37cbrX3oolxyXAIzHc+dWJeOvHM5NSI1ZZUggp7F8RkQE3X35Ows8FAM3HT+PutX02ND5ZtVEJZ+ncCorCEQQx4CERRyQNKz+wFfOn4PaZ59m+R/M/a+/yYtTgnBSuNvuQRAHe8O4FHYrCsen2y/9/e3ceX1V57gv896y1h4SEIQYFQpg0oE2QRJsKFNSK2jogeK84VD108vTce6u1PSpoPU549RZRe+pw2no9nltaWyeqgNo6YasgWIMmSBA1VSEJyBAhJiHsYa33/rH3DntYaw/JTvb0+34+lGTtvdZ+90D34/u+z/PgmjOnDlpAU17qxv2X1MHtEBQ5NTh1wbILp6O81B23wX282+ysbmzHeQ+8CW/Uc3brApdDQ4lLh0sX3HXhdFwxc9KAnxsRUbbjcioNKqt6YB3dHjy47iPLzfdAWA9V/idGXD5DYXqchvah+nuDbUHdeHQd9uOOtc1w6RrufH4bPtz9JZ7a3GbZ4D462SWZTNLQDFz0fkog0EP2havnosdrsLwIERUUfk3SoCsvdaN2wqi+L9fyUjcuO8W+h63XCGzA7+5l14Z4brugGk6HDrduvS/NY5gocemDPo6Obg/ufGEbvIZCj9fAYZ+JlZt2Wja4D092SbaThN0MHAC4gnv9qsYMj/iMEREVAgZxNOQ6uj14qqHN9nbDNLGhZT/GjuQXsp3vzp6Ec2rGorKsGKJZB3GGYeL8B9f37TEcLKEElnhCs6tW9+2bebUQCvqsZuBcuuDFa+ayHhwRFSwGcTTk2g70QrfKbgjym8D1z2yB12LDei6LTgDoD00CiQOr3m3HnOXrsKFlP26ZX215X0MBHr+J655usp3p6s/etGhWCSzRQuVNrO4br/SJXYDocmi49+JaVI0Z3u9xZ1I6XnciIgZxlDbJfjE9vumzhHu1vH4TLfsPpXN4GWcftibPVIHgLHwpckJZMYqd9v+UfYZC867OmOOrGwOB4JWPvo05y9f1e8auvNSNW+ZXB5IL3DqKnBoWz55o2eDeKtklXukTq6Av3gxcLgRH6XrdiYiY2EBpkexm9ZY9XXhqc+yXVnQT93xS4tZhmArf+/pk/Opvn6T12oFZqkCrqfgiQ0irQsz97XKwurEdy9Zugy6B4Pv2C2pwxaxJuPbMaZa9TK2SXcLHFX48uhVb6LNlNQPXn4SJoZbO152IiEEcDVgqX0zrW/ZZXuPCkyqwunF3EsFI7rmwdjy+N2cyHn97R9qvfdhvYJhTi+mfGq31i8hZTatCzKG9aakEEx3dHlz/dFNE4/vb1zZjwlHDUFMxArUTRlmeFwrQwtkFYfGCvvBx5EJwlK7XnYgI4HIqpUEqm9XdDutsyRPGjoAg/wI4AHiyYSfOe+BN/PatgQVxTl1ilmQNU+Hbj26K6Z8a7c4XtkUsMaa6N81O867OiAAOCCzf/nBlQ0pLhYmyVqMznKOlmjCRKel63YmIAAZxlAapfDGNL7NuE+XUNTj0/Pw4+k3Aayj0twFFsUPD7RdU4z+/8zWUuiMnz00F+JIoBRcd0KS6N82e9U6/w34zqfIhIQMNwnIlOErf605ExOVUSoPyUjcuqa/Eyo07+45dUl9p+cVUUzECAkTMuQmAuVWjcTfbbFlSEmgmDyBhFqgdq4AmmWXKREJtt+waRyS7VJhsEBa9Zy7Ebu9cNgZH6XjdiYgAzsRR0ECy+qzqvj3V0GZ5rQM93phFUwVg3fa9uG2BdamMQnfL/GrLzE63Q4spW+LUgIe+fRLuunB6UrM9iZYpgfifjfJSN+5YON323GRnw5KZoUqU1bmgbjw2LJ2H3181ExuWDk6v2HRJ5nUnIkqEM3E04Ky+VDZrN7YetLzGPS9tx9s/OwtQwO1rt8IhGnrj9AUtFA4BpleM7Ps9ehbnl699FDED+u2ZEzE/OGt3zvSxA57tSeazMb1iJEpcsS2+Qt0Uoh+7ZU8XGlsPom7CqIgsU7sZqo5uD5p3fYklz2yBxx8/ccEqYYKIKF8xiCtw6cjqS2U/Up1NtqJLDwR9V8ya1Bd8XPnoW+jy5GeyQ7L8CjGts0KBit0M6LVnTuu7z0ACmmQ/G5VlxfAake+/rgEvXjM3phTIrc+9j5WbjgSdi2dPxLKFJ8Y8t5DHN+3AHc8Hypd4ooJ6ZnUSUaHjcmqBs9s4nkpWXyqbtavGDMcl9ZUxxw0FdPb60NHt6Vtq6s6zAM6lC6qOtk7ssOPUYFsYebAzMpO9/l+2fh6ToSoAykpcAI4sxzZ82hERwAHAyo070bKny/LxH9+0Azc/txVev4leX+ysbDYmLmSzXCiETESp4UxcgStx6Tgc9QV52Jd64/RUNmvfs6gW40YW4cHXWvoWYD1+E//823cgmvQt2Y0Z4cLnX3pTfUpZyakLrv/m8bj7z9tTOk/XNdtAxWoG1GukL7BJZoa1o9uDW9dsjTnX7dTRdqAX61v29y3H9vr8lo/T2HowZsauo9uDO9Y2W95/mEuHqVTWJi5ko1wohExEqeNMXIHr8Rpw65FlIty6JGyLZSXZzdqrG9vxq7/+I6bkhsdQEWUpqo7Ozb6YVpSp8OHnXyZ9f0FgT1koqcFKaAbUGfb+GaaJDS37BzrciOvHm2Ft3tUJw2Lros8f+A+B8NpvdlscrZbY2w70wmlRcsapC3595clZn7iQTRLV4COi3MWZuAJXWVYM0SJ7XokmaZvNiS4JYVXhP1poyW53Z/70TvUrYNV7u5K+vwLgEIU7n9+G4W5HTMASel0/7zwc8Vr6zfR2Kkg8w2pdJ27OcaOx/fOumISX6PZqi2dPtGyhVVlWDEPFfkZuX1CD06Yd06/nUqjYJYIofzGIK3CDUV8rFGBsbe/EnS9si7juqGHOuAEccGTJrvWL7Kq2n6yFtePwosU+sVQd8ikACktWbUH1uBHo8RqoLCvuW6LUxXrGVCmk9Qs6XoKEXZ241z/ah9c/2gddiwzynA4NT37/FHzWcSgiOzVez1RdBD7DxG0X1OCKmZPS8pwKSa4UQiai1DGIo7QWHw3tvQkPMMIzG+9dNMP2XF0CX/KhILKfdW0z7oX3d2PJt07Aipc/HHAgBwSCsvMeeBNuhw6vYQS7NNhf1+NPfk+jXfHcZJWXunH5zIkRZU7CGaaC26HBpR8J5OunlKN+SnnffUIZqC5d4DdVSj1TKbFcKoRMRKlhEEcA0lNfK3zvjRWnpmFEsSumY0OICHDvolrMPi7wBW+9DT77+U3gvlc+xOlTR+PV7fsGfL1QaQ2vkfwr8uetn+Mai2XKcOnY7G5V5iTaTeeegJMmllkGYqEMVADwBp9e+HJwpuq+DTS4zTYMiInyE4M4ShurvTfhfKaJipFFEAnMLkXzm8CSZ7bARGA2JpeJCP720cADuP566PWPcfnMibZf1umoDwgE3nOHZr0vLmRu1WjLfW92Gai6Jhndr5WvmZwshEyUf5idSmljtfcGAErcel9m467OXphxVhgP+Yy+7LlcZpgKLkdqZVqS5dAAt0PDsDhLpi5dj1svLl015gKFfu3f0IW14ywDuL4xWGSg+gyVsf1azOQkolzCII7SxqokxV0XTscfrprVVxKiPckgITrAyDU3fPN4y+zKdLh85kS8deM8/PrKk+F2WM+CJdq4nq7N7uWlbtx2gXXP22/VHINffvtkANaFZu0yUG+7wL6symAb7ALKRETpxOVUSqtEe2++6LGe0YguPWE1o5crXJpg5rHluOeiGbgh2O8znZ5qaMPiWZMxstiFWy+owZ3Pb4MyFTyGQpEzEIAk2rg+0M3u4XvGrpg5CVDAHWuboQvgMxWWfOsE/PD04wDYL09mYwYqMzmJKJeIGqTZgmxVX1+vGhoaMj2MgvV80y5c/cf3Yo5fWl+JZxt3wakLjGCG4o+faBz6AabJVyeOxLVnHY/32w7ggdda4ElDlmpIkVODaSq4HTp8polbzq/G9PFHmtCnsnG9Pxv47YIyq2u17OnCeQ+uhzcskC1yatiwdF5Eg/ts2nC/prE9JrjNhz1xRJSbRGSzUqre8jYGcTSUWvZ04axfvBFz3KUDTl0/MhszaxIm3/hCBkaYe6KDosHU0e3BnOXrIjKQ7R5/dWM7bni6KWbP3HC3A7+/aiZqLTo1ZItsCyyJqHDFC+K4nEppk8wXX6jNV/TMlNcAvEagrtydL2zDzClHDfp4s5kugf6jfkPBZ5iWJVlCrKrvD1YQkkz1/5Y9XVjfsh//58/bLZMecmF5kpmcRJQLGMRRWiRblqGyrNimAMkRAuCl5s8HZZy5QhPgkNeAQ6xr6oU77DdQ4tLR1HowoqNDoveiP4Feoj1jtz73PlZusi78CwT6wbLQLBFRejCIowFLteaYEa/GCIBen4l7X/5oUMaaK0Krlf4kdjsYpsL5D74Jlx7Z0SHee9HfWmjxEiJa9nTFD+B0wYvXzLUtOUJERKlhEEcDlkqD7eZdnXHrxIUU1k7N5Axz6fCbCqJUxHK0qQCPX8Hjt+7oEP1eDLTQr1UGcke3B2uadlne3+3QIBLImGUAR0SUPgziaMBSK8sQv7p/rnNogW4N0b1NS1w6vjJ2OBp2HuzXdd0Owa+vPBnbd3fh7j9vT+nc6PfCqsuCXdBtJ3zPWGhWz66y330XB1qpcQmViCi9cruiKmVEdOFWqyK/dvuehjnz+yPnN4FfX3EyXHpkkGQohXOmj035em5dUOTUsGJRLWoqRuL+VxMvM4c6Oti9F1vbO9HtMSLO6W+yQfis3iGLnrmLZ0/E/NoKBnBERIOAM3GUEru9VMk22H770y+GeMRDy60LRg8vwr0X18bsG9vS1pn0da47exrOnT42ou5bU+vBuL1pgUDwtmLRDNv3oqPbgztf2BZz3i3z+9clwWopfZhTw1WnHosFtRVcPiUiGkQM4ihpifZShf6EZuqsgrlDXl8mhj5kDBVYXq6dMComkDLjdKFwCPDTs6fhqBIXxpcNQ03FiJjXzq43bcgwp45f/9NXcdq0owHAMiizCrpKXDqmV4xM9anajskE8J2vT+bsGxHRIMvvtS1Kq2T6Sq5ubMec5etw5aNvY87ydVjT2B5x/8qykiEZa6b4TYUDPV4AgSAqVNC2qfUg2g8etj3vjguno/KoYbjj+W340ePvWr524cvWJW495ho+00RNxYi447MKugzV/4bzqSylp5NVL1YiokLDmThKWqIEhmSyHvd+aR/I5ItzH3gD911chwV14yOWn3u91tmjAKADSWWMhpatm3d14nv/9U5Ev9lkuq/YlQgBYDt7msiCuvGoHjciWNtPoXpc/EByoPpbHoWIKN8wiKOkJWqanqjUSEe3B5v7mZ2ZS3xGIACrHjciJjCzs3VXZ9JlWspL3RhZ7MIwlwNdniOBYbHTkVSGafT+xfUt+zFn+bp+B0WrG9vxkyca+8rCrHj5YyyePRHLFp6Y9DWSNdDyKERE+YRBHKUkXgJDvJm60OyJMhL1a8gPTk1DYxKJCCFP/L0VElX2wypjNNRlocSlx7zWHsNEiSt2mdVK+P7FgQRFHd0e3PB0Y0xdv5Ubd2LxrMlpSWwI7yyRSk1CIqJ8xyCOUmbXVzLeUl0oUCgUPtNE3YRRcRMRwvkV4FAKbocGlx47ywnELiNeUl+JpxraAACHfSZEKcx/aH1KM2n9DYpCgVVnrxcCDbAIVBtbDw44iIt+zrecX51CTUIiovzGII4Seu7dVjz//ueYf+JYXHjyhLj3tZqpa2o9GFNcNp+F+oNWjRmOey6agRue2QJdE/gMM6YIcDi3U8evrvgqRhY7LUuDRM+YPdXQht9//xRc/ujbABDo4mColGbSUivUHBAeWHkNA36bFhx1waSO/rJ6zne+sA23zK/Gnc9vs1zSJyIqJAziKK5Zd7+Cz78MZFu++sFeLP/Ldmz82dlxz4meqassK4Y3TvCST5xR/UEVAgkHhgmYCfqN+QxlWVoEsJ8x+6zjENwOHV7DH3E82eXFRPsco1kFVg4NEBXZKm3x7IkDnoWze87TK0Ziw9J5CWsSEhHlOwZxZOu5d1v7AriQ3V968dy7rQln5MKVl7px2wXVuPnZrekeYtb55lfG9AUvHd0eXP90U3D2LXEQe/3Z02wDErsZM6sl21SXF5Mt1AxYB1bFTgcevuJkfNnrw/7uw5hbdXRa9sLFmyW0W9InIiokrBNHtp5///OUjodY1fC6YuYk3HXh9DzvnAq8un1P3/Nu3tUZd/k0nEsDZh5bbnu7XT220JLtQOu0hWraJTrPLrCqqRiB+bUV+O6cY1MO4Dq6PXjjo31446O9EZ+ZTNWgIyLKFZyJI1vzTxyLVz/Ya3nczurGdiwJ7gEzTIUVi45ssr9i1iSsfOtTfLi3Z9DGnGkOPXwpM/mQVdO1hLNndjNmqcykDVSqy6+JrG5sx3VPNcIfjAuduuC+i2v7PjND+dyIiHINgziydeHJE7D8L9uxO2xJddwIl+1SauTyYcB1TzdFbLKvrhieMIgb5tJgKuRkNqvPONL9oGJkUVLn6JokHQjFywweqgAnXYFVR7cHS55p6gvggMDrd8MzkYkZXDolIrLG5VSK6+zqyFm3s2vsZ+Gslg99hkLzriON3yeXlyZ8zCXfOh4bls6DOwfXXsP3tfV4Dbj1xE+ifsKolDsOZLrtVLLLr/G0HeiFLrH/F6RrEtHKjYiIrHEmjmy17OnCyk07I46FiriWlbgsZmLsApYjx5t3fZnwcUcHZ148OZbQGr2vrbKsOFDAN8G+uIYdB9DR7Uk6IIqpnTa/GtMrRubccmNlWTEMFTvbapj97+VKRFRIGMSRrcbWg5bHH9vwGf70XluwTpiJq8+owuUzJ6KmYgQcGiKWxxwaIpqy7+5MPMNy3dNNSFCNIyspkYjgI3r/2CGfAcPiiRkA/vD2Tlxz5tSEj2FV4uPmZ7eixKXDUCqnArryUjdWLKrFv0btiVuxiMkLRETJkGSaZueT+vp61dDQkOlh5ISWPV046xdvxBx36YDXiD4muPfiWgDADc80QRcNhjKxYlFtxFLhpb/ZgLc/PZjwsd0OgcefW59NXYC/33xWTAAS6m6w+r02PPbWDstz3Q7BWzeemTB4aWo9iCsffTuiZ2q0UECXDY3hw1tmxas9F5ihVaipGMkAjogojIhsVkrVW93GmTiyVTVmOBbPnoiVG48sqZ42tRx///QAotsseQ2F655uwqabzsRbN57Z19+zx2tELBWWOJP7yAX2ShkJ75dN3M7IIrvhAUzthFFY//E+23Ndut53rlXgE69narSeYISd6cbw0cu+dkFleakbp007OgMjJCLKbQziKK5lC0/E4lmT0dh6EG98tBdrttjXiAslMZw27Risb9kf8wU+p2o0tu3uSupxrfZKZTuv/8heLqsA5oSx9vXTQkVsrc5TgGXPVE0Eh6KnRMNksjG81bJvpoNKIqJ8wyCOEgoVb73+mS1J3Fssv8Cve7oJmiDpJdJzasZiddPu/g45I0JbE+wCmGvOqLI8z6EJbplfjeZdX2LJM1vg8R8574ZntgBQ8PhVRM/U56+ei8bWg/jZn7bAaxPvJtO5IZnlzv6wa5mVqaCSiCgfZSyIE5HPAHQhsGbmV0rVi8hRAJ4EMBnAZwAuUUodEBEB8EsA5wE4BOC7Sql3g9f5DoB/C172fyulfjuUzyPf2C3lrXjpw4TnhpIYrL7Ak+1cELImxwI4AChy6XFLYxzy+iyP11aOwLK12wAAHn9kRKZrAihB+NKyU9PQ4zVwxgnHQNM1IGp5tcStwzBVwtpzyS53Rou33Bs6Fq9lFhERpUemZ+LOUErtD/v9RgCvKaV+LiI3Bn9fCuBcAFODf2YC+BWAmcGg7zYA9Qg0p9wsImuUUgeG8knkC7ulvCXPNCWcQXNowB0Lpye9byuR3EppCDjsM1Hi0vt+jr6tsqzE8rzNOzstjwMIZrNG1d4L6x8a3T3hlvOrMX184uzU/i53JrPcGwoG09nZgYiIYmU6iIu2EMA3gj//FsBfEQjiFgJYqQLrVZtEZJSIjAve9xWl1BcAICKvADgHwB+Hdti5z+pLPXwpLxnL1jbDpet9+7aefKcNQOzsUr4yTIXzH3wTV58xFW5d4AmbfXTrguFFyf9zK3ZqUADuuWgGANgGQ/3tntCf5U7rz0gTAIlYAg4Fg2yZRUQ0uDIZxCkAL4uIAvAbpdQjAMYopULraJ8DGBP8eTyA1rBz24LH7I5THFbLYVZf6romMAwgmXkxvwn4TQWPP1D64vebduZkrbeB8vgVHlz3MQI7AI68AKKl1n7ivOlj8bPzq/ven3jBUH/aUvVnudPyMyJaTI3n8GCQLbOIiAZPJttuzVVKnYzAUumPROS08BuDs25pCQNE5Ici0iAiDfv22Zd5KASrG9sxZ/k6XPno25izfB3WNLYDsP5SN0wFj9G/WbRCDOBCvIbCWSccgyKnhuFuB4qcGm6ZX40dHYeSvsbaLbsifk9Hm6vo691z0YyIMSZa7rT8jCgzpoAx974REQ2NjM3EKaXag3/vFZFnAZwCYI+IjFNK7Q4ul+4N3r0dQHjX9crgsXYcWX4NHf+rxWM9AuARIFDsN73PJHck2gcVvYfpX8+ahrv/vD3Do85Nr27fgxeuORU9XgNb2ztx5/PbYNoExBqiq+4BDk23XNpMZzZpqsudVp+RRMu9REQ0eDISxIlICQBNKdUV/PmbAJYBWAPgOwB+Hvx7dfCUNQCuFpEnEEhs6AwGei8BuFtEyoL3+yaAm4bwqeSURPugor/UX9++N87VCAh0qrhgxjisem9X1PFAoePKsmJc+sjGmESHcFErrwACM1zRs1n9zSaNJ9XlTrvAj3vfiIiGXqZm4sYAeDawbwgOAH9QSv1FRN4B8JSI/ADADgCXBO//IgLlRVoQKDHyPQBQSn0hIncCeCd4v2WhJAeKFW8fVHR3AQCoC/5N1pzBVmNdh/0xQVzodbUKnKMtPecErHj5w74yLA4NWLGoNiIYyqbiuVaBH/e+ERENvYwEcUqpTwDUWhzvAHCmxXEF4Ec213oMwGPpHmM+slsOs+qusKBuPKrGDMf508fiha32XRoK1Xe/PgnXzAs0rP/6z9fF3H7L/CNJCfHKrSyePRE/PP04XPTVSjTv6gQgqKkYERMQsXguERFFy7YSIzTIopfDAGDO8nW2Mzw/PXsag7goGoCTJ5ahvNSNB177OKaESolLx/SKkQBiA+fDfgN+Q0GTQFJn/aSj+u532rRjbB+TxXOJiChaJrNTKUPCMx1DMzzhQjM8QKCZultPrTxGNLdDg9uh4X+efuyArpMtTATaiLXs6cLDr38cc7vfVBHB1YK68diwdB4evuIk+A0FBcBQgF8BP3myER3dnoSP2Z9s0o5uD5paDyZ1fSIiyj2ciSsQdlmNiWZ4trZ3RhSt7Y+vH3sU3vqkA7/ftHNA18kmPkPh2ffa4NL1vtp4IVefUWW5Z2zXgd6YmjmmAh7f9Bl+fNbxCR8zlWzSwUiCICKi7MIgrgDE+0K32ydXXupGR7cHt69tHvDjv/5RoLNadLCT6x7+6yeB3qZh3A7B5TMnWt6/ZV+P5fH7X23B/h4vli08MeFjJpNAkE1JEERENHgYxOW5ZL7Q7WZ4mnd1pty4vtBEF7q99GsTYrJKQ6/r7GOPwqPrP7W8zsqNO7F41mRUjRk+4DExCYKIqDAwiMtzyX6hW8/wDGwvXCF6qqEN1545DeWl7pgZ0B99owoWJeH6NLYeTEsQxyQIIqLCwMSGPDeQL/SaihFw8BOSklCAHD4D2uXx47DPxEOvfwxnnCSRIqeeliSE/iRBEBFR7uFMXJ4LfaHf8MyWQEN7UyX9hV5e6sb9l9Thx080Dv5A80S8Ir8uXceVsybiV3/7JOY8AXDTn97v25M40A4IqbbUIiKi3MMgrgCo0P+qeIt51l6IasRO9lyOyBkvqxnQq049FpVlw3D72mZoUPArQJmBsiVdnkDix3VPN0GTQNA3kMxSdlEgIspvXCzLQ+H1wULLeh6/wiGfAY9fYcmqLUkt27Xs6cJL29g/NRkuXfDiNXMjsn5vmV8Nl0NDiVu3XNLUNA0aAJcz8p+hz1Dw+FXfMmyy7xcRERUWzsTlGavN9KlkKoZnU77UzE4NyXDpGu69eEZEUsLqxnbc+fw2ODXA4zex5FvHY0HdeDy+aQdufm4rAPRl/sZrywUws5SIiKwxiMsjVuVEHnr9Y0RnmVo1vS8vdePxTTtwx/Pb4NQEh30GWF0kOYtnT4pY7gx/H0LufnE7oIDlL223vIZDExQ7dXgNE4ZpIryTFzNLiYjICoO4PGK3mf7UqaPxYlj/00vqK7G+ZT+WhCU7LKwdh6c2twMAvEM+8tx2Ts2YiN/bDvTCocVmoS5/aTsMm0k3TRQevuIk1FSMxIaW/ZbFl4mIiMIxiMsjVuVEvIaJ17bviTj25Dtt+P2mnQivUxsK4Cg1dZUjUT+lPOJYZVkxvBbTmLoAhs113A4HRha7UF7qZmYpERElhYkNecSqPtjVZ1RBJHJWyOM3YXKpdMCcGvCf3/1azPHyUjduu6A65riKUzw5esm0vNSN2gmjGMAREZEtzsTlmehZnAM9Xtz3ykeZHlZe+OrEUXi/vRNuR6D0xy3zq9F2oBcA+oKt0D7Dc2rGAgq4Y20znLoGQyncMr8at63eGrHfDQCcunDJlIiIUsYgLg+F1wdrO9ALty7wMEthwDbvPAgAOKu6HHOOOzqYfXpk35oCIjKD77loBjbedGbEsuja99qx6bMDEdfVBJhTNXronxAREeU0BnF5rrKsGKIJEqWaXnTyeKxtbIc3bJYo9dLAheHF9/fglea98JmqL4nkhme2AAjUdws/9n8X16OmYgTKS91o2dMVE8ABgM4SIkRE1A/cE1cAfvSNKrgdGtw2jVA1AMePGR4RwAGAzk+HLV/UpkJdE+gS+YJ5/Cb+x+82Y87ydVjT2I71Lfssr+X1s4QIERGljl/TeWx1YzvmLF+Hh//aAo/fhCd6M1bQjeeegPtfjd03x8AieYapLIv2HvIZfV0X3A7d8twrZ03kLBwREaWMy6l5yqrgbDSnLrjhW8fjqGEu6BKbObmjo3cwh5g3HJrAMM2+19Cla/BGFYRzahp6gr1Ro53K/XBERNQPnInLQx3dHry+fa9lwdkQt0PDD+ZOwf2vfITb1jSjxxtbwYzLqZHOmDYarqjJNKcu0ETBb6IveUTBhEuP7ZLx+ZfW/U+b2joHZbxERJTf+DWdZ0JLqLetaUa3x660bMB/bfgUh32mZQAHIKYURiFz6YJ7L6nDvRfXRdTh+/G8qdC0yH9GuqbhmnlTI+53z0UzYjo7hJw2lTNxRESUOi6n5hG7JVS3Q4vYD+fUBd+fMxm/3bgD4T0EHAL4mY5qqbpiuGU3Bas6fId9Js6dPhaXz5wY03Xh1KpyvNnS0XffU6vKYzo+EBERJYNBXB6x7p0q+PG8KnyrZix2dR4GoND6RS+WPd8MT1TExgDOXmPrl2jZ04WqMcMT1uFz64Ier4GqMe6YhIVF9ROw6ZMOAAKlFE6ZUo6Obg8TG4iIKGVcTs0j1r1TFVa8/BFWbvoMp007GjUVI7Hs+W0xARwl1th6MOZYXx2+MKKJZWZvaKbUZwZKlPgVcN8rH+HrP38NaxrZu5aIiFLDIC6PhHqnWtWDW7lxJ1r2dOGB1z62LTUSbphLR5y8iIJUN2FUzDGrfrV2LbRCLbqiefwKS1ZtQUe3deIDERGRFS6n5pkFdePxRY8Xt6/dFnPbTX/agnd2HEx4DYcG3HPRDFz7x/cGYYS5KV48G71Pzm5ptMSl25Z8UQrs2kBERCnhTFwemmtTdyyZAC5kRLGDn44wCsB5D7xpu+xZXupG7YRRcYOwHq9hO7vp8Zsoia5fQkREFAe/pvNQ1ZjhWDx7YsSxrx97VNLnO3QNgMBgiZEIXmNgy54lLh2mzVZEXWBb6oWIiMgKl1Pz1LKFJ2LxrMlobD3Yt5frrF+8kdS5h30mKkYW4bjyYvyDXRsiOAfQrL7Ha8CpAVYrqg5dY5szIiJKCYO4PFY1Zjiqxgzv+33x7IlYuXFnwvOcmmBXZy+6vNZtogqZz+x/s/rKsmJABIHF2SN0AVYssk6GICIissMgLo91dHvQvKsTX/YGgrGzvjIWp089Gj/83WYYcSqM+EyF7/3XO9ALsApJdIjl0AIdGFy6Bp9p2maeJkup2BeVWcBERNQfDOLy1OrGdlz/dBN8UdGaBsDpEBgJ6sQZKryXQ2FwaYHnrWmCIofeF7Qlk3majLYDvSh2OtDliZzh9JnAdU83YU7VaM7GERFR0hjE5aGGTztw3VNN8FvsojcBFvq14Q3uVTMMhavmTsRVpx7bF1SlI7iqLCvGYb91aOwzFJp3deK0accM+HGIiKgwMDs1z9z63PtY9JtNlgEcxa/3Fu4/1386KI9vxn1fuK5KRETJYxCXR1r2dGHlpsSJC8mwaPqQ81y6wKknFyg5dMHr2/emtYtC24FeFDmta8HpAtRUjEjbYxERUf7Lw6/qwnXr6q1puc6sKWV47LunpOVa2eS2BTW49+LapAK5Q14Tt63ZijnL16Wtr2llWTEMi8QGhwb84tI67ocjIqKUMIjLEy17uvDWJ1+k5VqbPj2A7z/297RcK1sMc2mYXjESc6pG4+bzvgJ3Es0RerwmDvvMtPU1De+zWuLS4dSA/3n6sXj7Z2dhQd34AV+fiIgKCxMb8kRj68G0Xi/fKsT5TWBreycufWQjdBF4Uki91TVJW1/TZPusEhERJcIgLk+EujKQtf91+rG484Vttg3o4/EZKq3dFMpL3QzeiIhowLicmies+qVSgFMD/uOv/4ARlRnqdgjO+krikh63XVCd1qCrZU8XnmloRcuerrRdk4iICg9n4vLIghkVSbXVKjSBybfYhAKPX+G1D/banufUBbdfUIMrZk5K21hufe79iAzixbMnYtnCE9N2fSIiKhwM4vLIGx/vz/QQco5V1TaXLrhm3lScO30serwGOro9aZmJsyoBs3LjTpw+9WicWT12wNcnIqLCwuXUPHLa1NGZHkLOczs0PPqdr2Fi+TDMf2g9rnz07bSVGbFLPvnBys24dfX7A74+EREVFgZxeaR+SjlOGFOS6WHkvIqRRVi6agsO+0x0efxpKzMSL/lk5cad3CNHREQpYRCXR1Y3tuMf+3oyPYysFq/Qr64Jbr2gGj1eA04t8p+GU9PQdqB3QI+dKPkk3WViiIgovzGIyxMd3R4sXbUF/aigkRc0JG4V5tQFl32t0vZ2ly5YtnYbtrZ3wmdGvpA+00xLmZFlC0/Ev18yw/I2lokhIqJUMIjLE20HemNmjwqJaMBPz5oa9z5OTfCHt1ttb+/1mfD4Tdy+thm3zK9GkVPDcLcDRU4N91w0Iy3JDasb23Hjs1sRPSG4ePZEVI0ZPuDrExFR4WB2ap6oLCuOmT0qJIYJuBzxe2kdSnKa0mcoTCgrxoal89LaWSE0WxpecNihAU/88yzUTykf8PWJiKiwFO7UTZ4pL3WjbvzITA8jo5b/ZXvarvVJcG9h7YRRaSv0azVbWux0wJkg+CQiIrLCIC5PtOzpwqbPDmR6GBnlT+NE5IqXPkxbaZEQq9nSdO21IyKiwsMgLk8ws9GefT6qvR6vkbbSIiHlpW7cc9GMQdlrR0REhYd74vIEMxutOTRAE4HXsOrNEHVfXeDSJGLvXKi0SLoCrQV14zGnanRa99oREVFh4kxcnigrcWEil+Vi6JqWVADn1AR3LKhB9IrsYCx3lpe607rXjoiIChODuDywurEdc5avw84BFqPNJyUuHUVODVefUQV3nAK/IZomGO52cLmTiIhyhiiVeJYin9TX16uGhoZMDyNtOro9mLN8XUTZikJX4tZxxwU1OOOEYwAg5vURWDe+L3Jq2LB0HoBAJmmJS0eP1+CyJxERZYyIbFZK1Vvdxpm4HFfoRX6tGKbCGSccg/JSd0wygdsh0DTrmbnw/W+fdfRg/kPrccWjmzD75+vw+Ns7hvhZEBERxTfk3/4iMkFEXheRbSLSLCLXBo/fLiLtItIY/HNe2Dk3iUiLiHwoIt8KO35O8FiLiNw41M8lGxR6kV8gMLPmdtgvgS6oG48NS+fh91fNxH0X18EwrWefQ/vfwovydnsMeP0mbn52Kx7fxECOiIiyRyayU/0ArlNKvSsiwwFsFpFXgrf9Qil1b/idRaQawGUAagBUAHhVRKYFb34YwNkA2gC8IyJrlFLbhuRZZFBHtyciu/Gei2bguqeb4EtiA38+cWgCXQNWLKpNmPEZmpXr7PVZXsulHwn+mloPwmExW3fH2macM30sl1aJiCgrDHkQp5TaDWB38OcuEfkAwPg4pywE8IRSygPgUxFpAXBK8LYWpdQnACAiTwTvm9dB3OrGdixdtQVOTYPPNHHPRTNQPW4ETq0ajXUf7sv08IaUiMIL15zW13M0meCqpmIEHFpkYWBdgBd/PLfvOpVlxZYZrSJIa7kRIiKigcjoZioRmQzgJABvBw9dLSJbROQxESkLHhsPILxreVvwmN3xvBW+zNfl8eOwz8RPn2rCWb94o+ACOAAocjjQ4zVSOqe81I37L6mD2yEY5tThdgh+cWldRPP58lI3rj97Wsy5Hr9CiYstsoiIKDtkLIgTkVIAqwD8RCn1JYBfATgOQB0CM3X3pfGxfigiDSLSsG9f7gY7VkkMdvu7CkGvz8D+rsNoaj0Y0VWho9sTcyzcgrrx+I/LT8bZ1WNwwzePx5yq0TH3mXlsOVxR/zrcuqQcNBIREQ2WjHRsEBEnAgHc40qpPwGAUmpP2O3/F8DzwV/bAUwIO70yeAxxjkdQSj0C4BEgUGIkDU8hLaL3trXs6UJj60HUTRgVMTMUUohJDKNLnNjfY72PzW8q/GDlZugCOB2BPW0KiFlunlM1Gs27OgEIaipG4Non3sP6lg4AwOqmXbj7z9vx75fWYUHdkYncyrJiaLoGhL3eogn7nBIRUdYY8jpxIiIAfgvgC6XUT8KOjwvul4OI/BTATKXUZSJSA+APCOyDqwDwGoCpCCQlfgTgTASCt3cAXK6Uao73+NlSJy56b9vXJpXhzWBgAQCLZ0/EsoUnxpy3prEdS4LnefwmvEZhBXXxuB0CQOAJ2/AWSFBQfXvg7GrEuXQNG2+aF7HfLfy1DgWE4YEeERHRYItXJy4TM3FzAPwTgPdFpDF47GcAvi0idQh8x34G4F8AQCnVLCJPIZCw4AfwI6WUAQAicjWAlwDoAB5LFMBli/C9bYeDjZ7CAzgAWLlxJxbPmhwzI7egbjwqRhbhjY/3Y9zIIvzbc1tRYEmptnTRYrrd+6OWm+1eKgUVk7TAPqdERJTNMpGduh4xX7UAgBfjnHMXgLssjr8Y77xsFdrbdjimU2ekxtaDMUHcrc+9j5Wbdvb9rovd3FLh8RoG9AEUPrZaKg2VJiEiIso2LPWfAcnubSsb5oz4vWVPV0QABwBGgbVNS+TWC6rhcqT+sb59QQ2DNSIiyikM4jIguhWU06ZB+4FDkRv617fkbmbtUHA7dUyvGIkXr5kLl81r6tAC+w2dGuCQQI24uy6cjitmThri0RIREQ1MRrJTKXK/lc9vYNFvNsXcp27CqIjfR5cWDdHocpPPUH171+69uLYvKcFrGPj+nCmYfdxo1FSMQHmpG9eeOY173YiIKKcxiMug0H6rjm5PTNakACgrcUXcf/Zx5bbZlQTcdkF1X0CWKCmBe92IiCjXcTk1C7Qd6EWpOzKeLnU70HagN+JYeakbv7ysDi4dtsuF+SSQtJGcS+rHxyyJlpe6UTthFIM1IiLKSwzisoBVooPPNC2zJRfUjcfGm87C3f/txLwK5ASIaTqfStLGmqbdth0aiIiI8hGDuCwQnehQ5Ax0HwBg2T6qvNSNM044JieXVe2SOBQAGcAz0jWJmbkkIiLKZ9wTlyWi93Ctb9mPOcvXWXYLCLXrGj+qCJ915E7gcml9Jf70nmVnNDg1gdOhwdfP3qShpAYiIqJCwSAuw6L7p4YSHaI7Olz/zBZUjxuB5t1fYumqLRCl0OvPrbm4JxvabG9TUDDM/j+f8KQGIiKiQsAgLoOi+6eGZtuslgW9fhPnPvAGAIEvD/tsKQUsrKvAU3ECPSu6BixbwDpvRERUeBjEZYjVbNuSVVswp2o0Slw6DvtiOzr4DCBfC4yICJ6zWWqN9t9PqsCFJ1UCUKipGMkZOCIiKkgM4jKkeVcnrDpvtR3oRWevd+gHlAXMJLNR/9c3qmJ6yhIRERUaZqdmwOrGdlz123fgNSKjuMM+EyUuHYGCG4Xl6jOOgz9xO1lUHT2MARwREREYxA250DKqVRKmSxf0eA1s3/3l0A8sw3xGEhEcgJZ9h9Cyp2uQR0NERJT9uJw6xNoO9MKpaX374MJ5DYX/+OvHeKl5bwZGllkfpxCYNbYe5GwcEREVPM7EDTGr7gzhCjGAC0h+CbluwqjBGwYREVGOYBA3xMK7Mwxz6ZkezpCbNaUs5pgAmDqmNKnzBUBZiSu9gyIiIspBDOIyYEHdeGxYOg+/vvJkuB2FlcTQ2NaJS+orI45dXF+JM44/JqnzS90OttciIiICg7iMKS9147Rpx2DFoloUOTWUuAtjVk7XYuvBPdXQhte274VNW9UIPtNkey0iIiIwsSHjwnumbm3vxJ0vbIMuAq9hwm+onCrt69SAq047Fo+t/xQuXYfXMGGYZkTpEJ+h4NQF3qiuE4+++QkcugYjqs6IBsDp0ODSj3S1YHFfIiIiBnFZIdQztXbCKJwzfSzaDvSixKXj3557H5s+PZDp4SXFqQvuu7gWC+rG46q5x/b1g93Qsh9LwlqL3TK/Gnes3RZzvsuhY2FdBf7w99aI4yVuBx6+4mSMLHb29ZclIiIiBnFZ50CPF0++sxPPbG6PKQacjZwacNWpx+KqU4/tC7BCQSkQOdMYHoTd/OzWiOsYSuH7c6Zg1btt8PiPzNL5TBM1FSMYvBEREUVhEJcFOro92PiPDqzc+Cn+/tnBTA8nKceNHob7Lz0pqdmx8KAOQKBZvQLuWNsMp67BUAr3XDQDVWOGY8Wi2oiZOy6fEhERWWMQl2GPb9qBf3tua07tfQOAz744hB0dPagNq9nW0e2JmXEDgIZPO/DGx/tx2tTRqJ9Sjo5uD6aPH4kXf3wqerxGxP3tZu6IiIgoEoO4DHp80w7c/NzWxHfMQoYJLFm1BXOqRqO81I3Vje1YGjWDtqBuPK58dBPWt3QAAB5Y14Ljx5Rgxxe9EferjSreGz1zR0RERLEYxGVIR7cHt6zOzQAuXKhm29JVW3DYZ/a1E1uyagtKXHpfABfy4Z4eAIi4XygQTIbdbB8REVGhYRCXIRv/0QEz19ZQoxz2mShx6Zb9YJ2ahpe37Ul4Daemoe1Ab1IBmd1sHxERUSFisd8h0LKnC880tKIlrMn7B7s7Mzii9HBo6NvTdthvRNx22G/gxIoRCa+RbPHejm5P32xfl8ePwz4TS1ZtQUe3p9/jJyIiymWciRtktz73PlZu2tn3++LZE7Fs4YkxXQtykd8EWr84hBKXDqUipxWVUvAr66lGpy4ocugpZZ/azfYlO4tHRESUbxjEDaKWPV0RARwArNy4E6dPPRrtnfkxg3TdU41QABy6Br95JMAqdjowurTI8pw/XjUTToee0r62yrJi+MzIunlswUVERIWMy6mDqLH1oOXxZPaK5QqPoeA1FA77YgOs2ceVY/HsiRHHF8+eiPop5aidMCqlGbTyUjfuuWgGipwahrsdKHJqrCFHREQFjTNxg6guqnRGSNPOL4Z2IEPArQuUCNxRPU6XLTwRi2dNRmPrQdRNGIWqMcP7/RisIUdERHQEg7hBVDVmOBbPnoiVGyP3xP1u4844Z+Um0QQvXD03pngvEHgdBhK8hWMNOSIiogAupw6y48eMgEMLzFS5dKB+0lE5051hWJIhvlOXvrZZqS6TEhERUf8wiBtEoY4MfjO0dyxQ3DYX6AIcVZJcMKYJMKdq9CCPiIiIiMIxiBskHd0e3LG2Oea4JpKB0aTGoQG/uLQOZuK7AgBcut7XuYGIiIiGBvfEDZK2A71w6hq8RmQR3ENew+aMzHNowL9fehJmH1eO8lI37n15e1LnsdQHERHR0ONM3CCpLCuGYVPsNlu5HDomHDWsb0/bOTXj4t5/mEtnqQ8iIqIMYRA3SEJ1zZx69i+fhhimiphR+5fTj7O9r9sh+PWVJ2PD0nnsX0pERJQBDOIG0Zyq0THtqLKVrglWLIqcUSsvdeOBy+oiPiRuXVDk1LBiUS1Om3YMZ+CIiIgyhHviBtGDr30Mf7LZAUNg3vGjse7D/Za3/fLSOsyvrYg5Hl5gt8SlW9aBIyIioqHHIG6QPL5pB/7fxh2ZHkafxbMnYtnCE9HwaQcu/s2miFp1Dg2YfVy57bkssEtERJR9uJw6CDq6Pbj5ua2ZHgYA4NjRw/DqT0/DsoUnAgDqp5Tjl5fVwe0QDHPqcDsE919SxyCNiIgox3AmbhDcuSY7AjgAmH3c6JiWV+xBSkRElPsYxA2Cv7Xsy/QQ+kwcVWR5nEukREREuY3LqYPAMLInm+GDPV2ZHgIRERENAgZxg+BLz9CXFfnKmBLL4zUVI4d4JERERDQUuJya4/751Cm4tH4Cykpc+Or/fjXm9v9+cmUGRkVERESDjTNxadbR7RnSx7u0fgKqxgzvK8zrEEAXwCHAA5cx65SIiChfcSYuzaxmwwbTrs7evuxTZp0SEREVDgZxaTT5xhcy8KiRvVmZdUpERFQYuJyaJtc/+e6QP6ZDA2oqRgz54xIREVHmMYhLk5c/2Duo19cFOHVqOVw62GmBiIiIuJyaLl+dUIbXP7ZuLj8QZxw/Gj856/i+PW4d3R7ueSMiIiIGcely76V1aUtqOHnCSCyoq8DcqqNjWmZxzxsREREBDOLSJlTi48dPNA7oOnddOB1XzJqUnkERERFR3mIQl0ahEh/NuzrRfqAXW9oOovWLQ5g2ZjgunzkJZSWuvqVQANj4jw5s290JAfCVcSMx+7hyzrIRERFRUkSpoW8RlUn19fWqoaEh08MgIiIiSkhENiul6q1uy/nsVBE5R0Q+FJEWEbkx0+MhIiIiGgo5HcSJiA7gYQDnAqgG8G0Rqc7sqIiIiIgGX04HcQBOAdCilPpEKeUF8ASAhRkeExEREdGgy/UgbjyA1rDf24LHiIiIiPJargdxSRGRH4pIg4g07Nu3L9PDISIiIhqwXA/i2gFMCPu9MngsglLqEaVUvVKq/uijjx6ywRERERENllwP4t4BMFVEpoiIC8BlANZkeExEREREgy6ni/0qpfwicjWAlwDoAB5TSjVneFhEREREgy6ngzgAUEq9CODFTI+DiIiIaCjl+nIqERERUUFiEEdERESUgxjEEREREeUgBnFEREREOYhBHBEREVEOYhBHRERElIMYxBERERHlIAZxRERERDmIQRwRERFRDmIQR0RERJSDGMQRERER5SBRSmV6DENKRPYB2BF2aDSA/RkaDiWP71P243uU/fge5Qa+T9lvKN+jSUqpo61uKLggLpqINCil6jM9DoqP71P243uU/fge5Qa+T9kvW94jLqcSERER5SAGcUREREQ5iEEc8EimB0BJ4fuU/fgeZT++R7mB71P2y4r3qOD3xBERERHlIs7EEREREeWggg7iROQcEflQRFpE5MZMj4ciichjIrJXRLZmeixkT0QmiMjrIrJNRJpF5NpMj4kiiUiRiPxdRJqC79EdmR4TWRMRXUTeE5HnMz0WsiYin4nI+yLSKCINGR1LoS6niogO4CMAZwNoA/AOgG8rpbZldGDUR0ROA9ANYKVSanqmx0PWRGQcgHFKqXdFZDiAzQAu5L+l7CEiAqBEKdUtIk4A6wFcq5TalOGhURQR+VcA9QBGKKXmZ3o8FEtEPgNQr5TKeC2/Qp6JOwVAi1LqE6WUF8ATABZmeEwURin1BoAvMj0Oik8ptVsp9W7w5y4AHwAYn9lRUTgV0B381Rn8U5j/BZ/FRKQSwPkAHs30WCg3FHIQNx5Aa9jvbeAXD9GAiMhkACcBeDvDQ6EowWW6RgB7AbyilOJ7lH3+HcASAGaGx0HxKQAvi8hmEflhJgdSyEEcEaWRiJQCWAXgJ0qpLzM9HoqklDKUUnUAKgGcIiLcopBFRGQ+gL1Kqc2ZHgslNFcpdTKAcwH8KLj1JyMKOYhrBzAh7PfK4DEiSlFwn9UqAI8rpf6U6fGQPaXUQQCvAzgnw0OhSHMALAjut3oCwDwR+X1mh0RWlFLtwb/3AngWge1ZGVHIQdw7AKaKyBQRcQG4DMCaDI+JKOcEN83/J4APlFL3Z3o8FEtEjhaRUcGfixFI6Nqe0UFRBKXUTUqpSqXUZAS+j9Yppa7M8LAoioiUBBO4ICIlAL4JIGMVFAo2iFNK+QFcDeAlBDZiP6WUas7sqCiciPwRwEYAx4tIm4j8INNjIktzAPwTAjMHjcE/52V6UBRhHIDXRWQLAv8B+4pSiiUsiFI3BsB6EWkC8HcALyil/pKpwRRsiREiIiKiXFawM3FEREREuYxBHBEREVEOYhBHRERElIMYxBERERHlIAZxRERERDmIQRwR0RAQkckicnmmx0FE+YNBHBFRmoiII87NkwEwiCOitGEQR0RkQUQWi8gWEWkSkd+JyAUi8raIvCcir4rImOD9bg/evgHA74Izbm+KyLvBP18PXvLnAE4NFkP+acaeGBHlDRb7JSKKIiI1CPRE/LpSar+IHAVAATiolFIichWAryilrhOR2wFcgEBT7F4RGQbAVEodFpGpAP6olKoXkW8AuF4pNT8jT4qI8k68qX8iokI1D8DTSqn9AKCU+kJETgTwpIiMA+AC8GnY/dcopXqDPzsBPCQidQAMANOGbthEVEi4nEpElJwHATyklDoRwL8AKAq7rSfs558C2AOgFkA9AgEfEVHaMYgjIoq1DsDFIlIOAMHl1JEA2oO3fyfOuSMB7FZKmQD+CYAePN4FYPjgDJeIChGDOCKiKEqpZgB3AfibiDQBuB/A7QCeFpHNAPbHOf0/AHwneN4JODJLtwWAEUyUYGIDEQ0YExuIiIiIchBn4oiIiIhyEIM4IiIiohzEII6IiIgoBzGIIyIiIspBDOKIiIiIchCDOCIiIqIcxCCOiIiIKAcxiCMiIiLKQf8fiW7bv+f+UVcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'scatter',\n",
    "       x = 'carat',\n",
    "       y = 'price',\n",
    "       figsize = (10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fca1036",
   "metadata": {},
   "source": [
    "* Although the scatterplot above has many overlapping points, it still gives us some insight into the relationship between diamond carat weight and price: bigger diamonds are generally more expensive."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d6c4ef7",
   "metadata": {},
   "source": [
    "**Line Plots**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "713f9f49",
   "metadata": {},
   "source": [
    "Line plots are charts used to show the change in a numeric variable based on some other ordered variable. Line plots are often used to plot time series data to show the evolution of a variable over time. Line plots are the default plot type when using df.plot() so you don't have to specify the kind argument when making a line plot in pandas. Let's create some fake time series data and plot it with a line plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5d247657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='years'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating some random data\n",
    "\n",
    "years = [y for y in range(1900,2022)]\n",
    "\n",
    "rate = [(y+np.random.uniform(0,20)-1900) for y in years]\n",
    "df1 = pd.DataFrame({\n",
    "    'years':years,\n",
    "    'rate' : rate\n",
    "})\n",
    "df1.plot(x= 'years',\n",
    "        y = 'rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c2c73b",
   "metadata": {},
   "source": [
    "**Saving Plots**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e606ebc",
   "metadata": {},
   "source": [
    "If we want to save plots for later use, we can export the plot figure (plot information) to a file. First get the plot figure with plot.get_figure() and then save it to a file with figure.savefig(\"filename\"). we can save plots to a variety of common image file formats, such as png, jpeg and pdf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "579b32e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RcT9iyfxgt7AYh0En4LAYq7qudsRgRUREtEFoDePbetRgVWWf1ZhPueuvWI8VoPRZLYQT6HdYcHDIWfF8N252I5mWeGnMn3d8YTEBt90Ena50xavdMVgRERFtEFrFaluNFasJdThosaVAYKmB/Q37+qoKRYdGuwAAzy3b3mYhHF/T/VUAgxUREdGG4Y8kIQSwtdsOoPpZVuO+KIRQtrApRuuzekMVy4AA4LKZcGDIiR+dnMk7Pr+YWNP9VQCDFRER0YbhjyTgsBjR06lUhapdChz3RdHXaYHJUDw2vHF/P955/RBuGnVXfS13Xz+EYxMBnJwMZo8thONretQCwGBFRES0YfijSbhsRthMBthM+qpHLoz7IiWXAQHg2hEXPvnz18Kgrz5WvP26IZj0Onz9yFj22MJiYv0vBQohviCEmBVCHM859tdCiNNCiKNCiG8LIVw5j31MCHFeCHFGCPGGFl03ERER1cgXScJlUypCng4TFsLlK1ZSSvzDw+fxzCUv9lfRlF4Ll82E1+/rw7dfnEAsmUYkkUIkkV7TGzAD1VWsvgjgjcuOPQRgv5TyIICzAD4GAEKIvQDeA2Cf+pr/K4TQN+1qiYiINrBEKoNP/fgs/JHapqZrApEEXFZllIHHbsZ8mYpVMp3BH3zzKP76h2fwtmsG8dE37a7rPcv5hRtHEIgm8dDJmeyyZPca3oAZqCJYSSkfBeBdduxHUsqU+tunAQyrX98F4GtSyriU8hKA8wBuauL1EhERbViPnp3Dp358Dl9+5mpdr9eWAgFlCGepHqtANIkP/vOz+PqRcfzma7bj0++5FhZj8+skt27rxpDLiq8fGctWzzZCxaqSXwXwA/XrIQBjOY+Nq8eIiIioQU9eWAAA/MfRqbpe7wsn0KUtBZaoWGUyEu/7p6fx7CUvPvHua/C7r99VdpJ6I3Q6gXfdMIzHz8/j6Lhfua713mNVjhDiDwGkAHy5jtfeI4Q4IoQ4Mjc318hlEBERbQhPXVyAEMDJqSAuzYdrem06IxGMpeDUlgI7TPCGE8hkZN7zJvxRnJgM4mNv2oN33TBc7FRNpb3H5x67pFzXRr0rUAjxQQBvBfCLUmp7ZWMCwEjO04bVYwWklPdKKQ9JKQ/19PTUexlEREQbgi+cwKmpIN570yYAwPeP1Va1Cqj7BHbZtGBlRiojEYwl8553fm4RAHBguLnN6qWMuG24dVs3rnoj6nVtwGAlhHgjgN8H8DYpZSTnoQcAvEcIYRZCbAGwA8CzjV8mERHRxvbMJWUZ8J3XDeGGzV34Xo3LgVrDu3ZXoDaIc/mQ0ItzSiVM2/ZmJbz7kFK1spn0sJkMK/a+rVDNuIWvAngKwC4hxLgQ4kMA/h5AJ4CHhBAvCSH+HwBIKU8A+DqAkwAeBPARKWV1W2cTERFRSU9eWIDVqMfBYRfefGAAp6aCuKhWl6rhU/cJ1JrXPXZtSGh+n9WFuUW4bEa4V3BJ7g37+uG0Gtd8tQoAKsZCKeV7ixz+fJnn/zmAP2/kooiIiCjfUxcWcOMWN0wGHd58oB9/+r2T+P6xKfzGa3ZU9fpANL9ipYWY5bOsLs4tZre8WSkWox6/9/qd8FaYq7UWcPI6ERFRm5sLxXFudhG3bPUAUPbsu2FzF/7j2HTV5/CFl/dYqcFqWcXq4lx4RZcBNe+/ZRS//bqdK/6+zcZgRURE1Oaevqj0V92yzZM99pYalwP9avO6y6oEKretsMcqFEtiNhTH1lUIVusFgxUREVGbe/LCAjrMBuwfdGSPvfnAAIDq7w4MRBLQCaDTonQBGfQ6dNmMWAgvVay0xvWtPSu7FLieMFgRERG1uacvLuDmLe68TY77nRYcquHuQF8kCafVCJ1uadhnd4c5b/r6xXml+rUaS4HrBYMVERFRG5sOxHBpPpy3DKh5y8EBnJ4O4UIVy4HKdjb5d915lm1rc2E2DL1OYJPb1viFb1AMVkRERG3sqYvzAIDDWwuD1Zv2K8uBD52cqXgefySRHbWg8XSYMZ+7FDi/iE1uG0wGxoN68ZMjIiJqY0+eX4DTasTeAUfBY/1OCzx2E64sRIq8Mp8/koTLmh+suu2FFatt7K9qCIMVERFRG3vq4gIOb3Xn9Ubl6nNYMBuMVTyPL7K0AbPG02FGIJpEIpVBOiNxaSHMOwIbxGBFRETUpsa8EYz7otn5VcX0Oy2YriJYBSJJOAuWApWg5YskMOmPIpHKrPhw0PWGwYqIiKhN/eC4csffK7Z3l3xOn8OCmQrBKpnOIBRPFVas1G1t5hfj2c2Xt/WyYtUIBisiIqI25Asn8Pc/PY9X7ujGjjJhp89hxvxiAolUpuRzAtH8fQI13dnp64mlGVasWDWEwYqIiKgNffon57AYT+F/vmUvhCjeXwUA/Q4LAGBu2dY0ufzZDZgLe6wAYCEcx8W5RTitK7v58nrEYEVERNRmzs8u4l+evoL33rQJu/o7yz63z6kEq+lA6eVAf0TdgNlavMdqYTGBC3OL2NZjLxviqDIGKyIiohZIpTP4mx+dwVQgWvNr//f3T8Fm1ON37qy8KbFWsSrXZ7VUscoPVp1mA0x6HebVpUDeEdg4BisiIqIWOD0dwt/99Dw+9q1jkFJW/brHzs3hp6dn8ZHXbEe3ulRXjhasylWsfGrFannzuhACng4TriyE1c2X2V/VKAYrIiKiFpjwK5Wqn52Zww9PVJ6MDihVrj/73imMuK34lVtHq3qNy2aEyaDDTKh0sNKa15ePWwCU5cDnLnsBcI/AZmCwIiIiaoFJNVht9tjwv757ApFEquJrvvXCBM7MhPCxN+2B2aCv6n2EEOhzmDFToWKl1wl0mg0Fj3nsyl2FADh1vQkYrIiIiFpg0h+F2aDD37z7GkwGYvjMT85XfM1TFxfQ77DgTfv7a3qvfkf5IaHadjbFGtO1BnZl82UGq0YxWBEREbXApD+GIZcVh0bdePcNw/jcYxdxbiZU9jUTvig2uW0135mnDAktP25heeO6Ruvj4ubLzcFPkIiIqAUm/FEMuqwAgI++aTfsZgP+v/uPl21kn/BHMdRlrfm9+hwWTAdiJc/tjyYKZlhpPOrcKg4GbQ4GKyIiohaY9Ecx6FLu2PN0mPH7b9yFpy968b2jU0Wfn0pnMB2MZV9Ti36HBdFkGqF48T4uXziJrhIVK21IKO8IbA4GKyIioiaLp9KYDcWzFSsAeO+Nm9DdYcKjZ+eKvmYmFEc6IzHkstX8ftqQ0FIN7IFoEk5riYqV2mPFOwKbg8GKiIioyWYCSr9TbrDS6QRGPXZc9UaKvka7i7CepcDsLKsSDez+SKJkj9W+AQd293film2emt+XCjFYERHRhhKKJfGXD57G6z75SNlp5Y3QZlgNufJD0iaPrWSwmvBpr6l9KbDPoSznFRsSmkhlEE6kSy4F9josePC3b8dmD5cCm4HBioiINoR0RuKrz17Fqz/xM/zjzy7g/Owinrww35L30raxGVwWrDa77ZgOxhBLpgteo4Wx5a+pRp9asZoNFd4Z6I8qM6qcJZrXqbkYrIiIaN0b80bwls88ho996xhGPXZ868OvgMWow7HxYEveT1vWG3DmV582eayQEhj3FVatJvxRuO0m2EyFQzwrsRj1cNmMRStW2j6BpSpW1Fy1/9sjIiJaY7723FWcm13E37/vOrzlwACEENg36MSxCX9L3m/CH4PHboLFmD89XRvAedUbwfbezvzX+KJ13RGoKTUkNLsBc4nmdWouVqyIiGjdW1hMwG034a0HB7PDNw8MOXF8Ioh0pvoNkqs1mTPDKtdmj3LH35WF4hWr5T1Zteh1WIr2jGkbMJdqXqfmYrAiIqJ1byGcyA7C1BwcdiKaTOPC3GLT3y93hlUuj90Eu0lfEKyklJj0R+sataDpd5iLBquAVrFisFoRDFZERLTuecNKxSrXgSEnAODYeKCp76WFpGIVKyEERtw2jC27M9AfSSKSSNc1akHT77BgLhRHKp3JO65VrLrYvL4iGKyIiGjdKxastvZ0wGbS49hEc4NVMJpCOJEuuay32WPDlWXBamk8Q/09Vn1OCzISmF9M5B33R5Mw6gVsJn2JV1IzMVgREdG6t7AYL1gK1OsE9g86cXTc39T3qjQ2YbM6JDST09u1FKzqXwrs6yw+JHQ2GEeXzVTzxs5UHwYrIiJa15LpDIKxFNx2c8FjB4adODkVLFg+a8RkhWA14rYhkcpgJrQUgLLDQRtZCtS2tckJVlJKPHF+Hjds7qr7vFQbBisiIlrXfGFlaczdUdhjdGDIiVgyg/NNbGBfGg5afFlvs1upSl3NaWCf8EdhMeoamjWlDQnNDVYnp4KYDsbwmt29dZ+XasNgRURE69qCGqyWLwUCSsUKAI42sYF9wh+DSa9Dd5EKGZAzciGnz2pSHbXQyHKdx26CQSfyhoT+9NQsAOCOXQxWK4XBioiI1jWvVrEqEqy2eOzoMBuaemfgpD+KAZcFOl3xkDToskKvEwUVq6Gu+vurAGWT595Oc16P1U9Oz+KaERd6OouHPGo+BisiIlrXylWsdDqB/UMOHG3inYGT/igGnaV7pYx6HQZdlrzNmCd80YbuCNT0OZeGhM6F4nh53I/XchlwRTFYERHRuuZdVDYmLlaxAoCDwy6cmgoi2aQG9lIzrHJtdtuzS4HRRBoL4URDU9c1/Q4LZoLK9/uzM7OQEuyvWmEMVkREtK55wwkIAbhKDMjcP+REIpXB2ZlQw++VSmcwHYxVrD6NuG24uhAGAEwGGr8jUNPnsGBG7bF6+Mws+hxm7Bt0NHxeqh6DFRERrWsL4QS6bCboS/Q8HWziBPaZUBwZCQxUqlh5bPBFkgjGkkujFhqYYaXpc1gQiqfgjyTw6Nl5vGZ3L+dXrTAGKyIiWteKTV3PtdljQ6fF0JQ+q0ozrLLvmTNyYWmgaOM9Vv1OpUn9uy9PYjGewmt29zV8TqoNgxUREa1rCxWClRACB4edON7EYFXNUiAAXPVGMOmPQq8T6Hc0oXldPceXn7kKk0GHW7d7Gj4n1YbBioiI1jVvOFH0jsBc+4ecODUVRDyVbui9tOrTQJm7AoGcWVYLEUz4ouh3WGDQN/6fZC2cnZ4O4RXbPLCZDA2fk2rDYEVEROtapaVAADg45EIyLXF2urEJ7FP+GFw2I+zm8oGm02KE227CVW8E4/5oU5YBgaWKFQCOWVglDFZERLRupTMSvkjlitUBrYG9weXASjOsco24bbjqDWenrjeD3WxApxrqXs1gtSpYIyQionXLH0lAytIzrDQjbivsJj3OTAcber8JfxTDVU5Q3+y24chlL2ZD8aaMWtD0Oy0YFKLq66DmYrAiIqJ1K7udTUf5LV2EENjZ34nT043Nspr0R3HzFndVz93sseGBlycBVL6LsBYf/7l9sJq4ILVaGKyIiGjdKredzXK7+zvxg+PTkFLWNfspFEsiGEtVHZI2uZcqSs1aCgSA23Z0N+1cVDtGWiIiWrfKbcC83K6+TvgjScyG4nW915Q68byeYDXcxKVAWl0MVkREtG7VUrHa1a9s/VLPcqCUEo+fmwdQfbDa7LFnv27mUiCtLi4FEhHRuuVdVIJVV5VLgQBwZjqIV+3sqfo9xn0RfPz+E/jJ6VnsH3JUvTdfb6cZZoMONpOe86bWEf6bJCKiNe/eRy9gR29nwYiBhXAcDosBxiqGb3bZTejtNFddsUqmM/j845fw6R+fAwD84Zv34IO3jlb1XgCg0wmMuG2wGLl4tJ4wWBER0ZqWSGXwiR+dxSu2eYoEqwQ8Fe4IzLWrvxNnZ6oLVv/w8Hl86sfn8Pq9ffj42/bV1YD+a6/ahiYMXKc2wmBFRERr2tmZEBKpDM7NFE5N9y5Wnrqea3d/J7701BWkMxJ6Xfk7Ax86OYObRt249/2Har5mzbtuGK77tdSemJOJiGhN06alT/ijWIyn8h6rZjubXLv6HYinMri8EC77vPnFOE5MBnH7To42oHwMVkREtKYdHV/ahub8bH7VaqGKDZhzLTWwl18OfOK8cgfgbTuqb3KnjYHBioiI1rRjE/7sHKhzOf1RGXWfwFoqVtt7O6ATlUcuPH5uHk6rMbvHIJGGwYqIiNasWDKNM9MhvOXAAEwGHc7lVKyCsSTSGVlTsLIY9Rj12MvuGSilxGPn5nHrdk/FPizaeBisiIhozTozHUIyLXHtiAvbejryKlbZ4aAd1QcrQLkzsNxS4IW5RUwHY3gllwGpCAYrIiJas46qjesHhp3Y0duBszl3Bi5tZ1P9uAVACVZXvBFEEqmijz+mTli/bTsb16kQgxUREa1Zx8b9cNtNGHJZsbOvAxP+KMLqnYELi9VvZ5Nrd38npETR8Q2AEqy2dNsxkrPXH5GGwYqIiNaso+MBHBhyQgiB7b3KHX3anYG1bMCcS9szsNhyYCKVwdMXF1itopIYrIiIaE2KJdM4N7uIg8PKnXk7+zoAINvA7g3HAdQerDap28wUuzPwxas+RBJp3LaDwYqKY7AiIqI16eRUEOmMzI482OS2waTXZRvYF8IJ2E16WIz6ms6r1wns7OvEmZnCOwMfOzcPvU7glm2exr8BWpcYrIiIaE06pg4GPTjsAgAY9Dps7bHnVKwScNd4R6BmV1/xOwMfOz+Pa0dccFiM9V00rXsMVkREtCYdHQ+gp9OMPsfSXX87+pY2UVa2s6ntjkDNrv5OzC8mML8Yzx7zRxI4Ou7HK7kMSGVUDFZCiC8IIWaFEMdzjrmFEA8JIc6pv3apx4UQ4jNCiPNCiKNCiOtbefFERLRxHZvw46DauK7Z2duBcV8UkUQKC4u1bWeTa5e6tc3ZnKrVkxcWICUYrKisaipWXwTwxmXHPgrgJ1LKHQB+ov4eAN4EYIf6zz0A/rE5l0lERLQkHE/h/OwiDgznbymzQ21gPz+7WPMGzLm0YPX1I2P4vz87j//13ZP4zE/OodNswDXq0iNRMYZKT5BSPiqEGF12+C4Ad6hf3wfgZwD+QD3+JSmlBPC0EMIlhBiQUk417YqJiGjDOzkVREYie0egZkefWmmaUYJVvRWrng4zhlxWfOelSQCA3aRHd6cZH3rlFhj07KKh0ioGqxL6csLSNIA+9eshAGM5zxtXjzFYERFR0xxVG9f3L9sEebN6Z+BLYz4k0pm6K1ZCCHzvv96GxXgK3R1mWE213VlIG1e9wSpLSimFELLW1wkh7oGyXIhNmzY1ehlERLSBHBv3Y8BpQW+nJe+4dmfg0xe9AGqfYZWry25CVwOvp42p3nrmjBBiAADUX2fV4xMARnKeN6weKyClvFdKeUhKeainhxtZEhFR9Y5OBLLzq5bb0deZnb5e6wbMRI2qN1g9AOAD6tcfAHB/zvH3q3cHHgYQYH8VERHVIpORWMgZc7BcKJbExblw6WDV25H9ut5xC0T1qmbcwlcBPAVglxBiXAjxIQB/AeBOIcQ5AK9Tfw8A3wdwEcB5AP8E4MMtuWoiIlq37nvqMl75Vw9jUd1MeTltTtWeAUfRx7WtbYDaN2AmalQ1dwW+t8RDry3yXAngI41eFBERbVwPHp9GJJHG6akgDo26Cx7X9vDTRiIsp23GDHApkFYe7xklIqK2EYol8fwVHwDg1FThXn0AcGY6hA6zAcNd1qKPj3psMOoFLEYdbKaG79Eiqgl/4oiIqG08dWEBqYxyo/nJqcK9+gClYrWzryNv4noug16Hrd0dJZcSiVqJwYqIiNrGI2fnYDfpsau/E6enCytWUkqcmQ7hzQcGyp7njt09mPLHWnWZRCUxWBERUVuQUuKRs3N4xfZuDLms+PqRMWQyEjrdUmVqJhhHIJrE7hL9VZqPvWlPqy+XqCj2WBERUVu4OB/GuC+KV+3swZ6BTkQSaVzxRvKeo1WxSjWuE602BisiImoLj56dAwA1WCmjFE4va2A/o94RWKliRbRaGKyIiKgtPHJ2Dlu77Rhx27CzrxM6UXhn4JnpEPocZrhsHKNA7YnBioiIVl0smcbTFxdw+05lizOLUY8t3faCOwNPT4ewq7/4YFCidsBgRUREq+7ZS17Ekhm8atfS3rF7Bhx5dwam0hmcn1vkMiC1NQYrIiKq2vePTeGvf3i66ed95OwcTAYdDm/xZI/tGXBg3BdFMJYEAFxeCCORymBXH4MVtS8GKyIiqoqUEn/zozP4wuOXoexg1jyPnp3DzVvcsJr02WN7BpQAdVpdDqy0lQ1RO2CwIiKiqpyeDuHCXBjRZBrBWPOmmk/4ozg3u4hX7ezJO67dGag1sJ+ZDkGvE9je21FwDqJ2wWBFRERV+e7Lk9mvZ4LNm2qeO2YhV7/DApfNmO2zOj0dwqjHBotRX3AOonbBYEVERBVJKfHdo5PoshkBANOB5gWrn56exaDTUlCJEkJgd39n9s7AM9Mh7OYdgdTmGKyIiKiil8cDGPNG8cuHNwNoXrCKJFJ49Owc7tzbV3RT5T0DDpyZDiIUS+KqN8L+Kmp7DFZERFTRd1+ehEmvwy/fMgoAmG7SUuAjZ+YQT2Xwhv39RR/fM+BALJnBQydnALBxndofgxUREZWVyUh87+gkXrWrBz2dZrjtpqYFqwdPTMNtN+GmUXfRx/eoS3/feUnp7+IMK2p3DFZERFTWc5e9mAnG8XPXDAIA+hwWzDRhKTCeSuOnp2Zx554+GPTF/3O0o68Dep3A4+fmYDPpMdJla/h9iVqJwYqIiMr67tFJWIw6vHZ3LwCg32HGVBOC1ZMXFhCKp/DGEsuAgLK1zdZuOzIS2NHXCZ2usA+LqJ0wWBERUUmpdAbfPzaN1+7pg91sAAD0O61NGbfww+PT6DAb8IrtnrLP263Os9rNieu0BjBYERFRSU9eWIA3nMDPHRzMHut3WLAQTiCeStd93nRG4kcnZ/Ca3b0wG8rPpdImsLNxndYCBisiIirpuy9PosNswB05myP3O80AgNlgvO7zPnfZC284UXYZUHPtsAsAcHDYWff7Ea0UBisiog0qnkrjz//jJGZLLOt5wwl89+gk3nygP2/aeZ/DAqCxkQsPHp+G2aArmLZezC3bPPj+b74Sh0rcOUjUThisiIg2qOMTAfzTY5fw1z88U/Tx+568jFgyg3tu35p3fMBpBVD/kNBMRuKHJ6bxqp092b6tcoQQ2DvIieu0NjBYERFtUHMhZSnvmy+M48LcYt5jkUQK9z11Ga/b04ftvfm9Tf1qxareBvajEwFMBWJVLQMSrTUMVkREG5QWrPQ6gb996GzeY//23Bj8kSR+/Y6tBa9zWA2wGHV1V6wePD4Ng07gtbv76no9UTtjsCIi2qDmQnHoBPCfXrkV3zs6hZOTQQBAMp3B5x67hBtHu3DD5sK+JiEE+h0WTNVYsZoJxvAn3z2Bf37iEm7d3g2nuqEz0XrCYEVEtEHNLcbh6TDj1161DQ6LAZ9Uq1bfOzqJCX8Uv/aqbSVf2++sfvr6pD+KP7r/OF75Vw/jS09dwduuGcRf3n2wKd8DUbup3DVIRERrUiSRgi+SxJDLWvTx2WAcPR1mOK1G3HP7VnziR2fx4lUfPvvIRezs68Crd/WWPHe/w4IjV3wVryGWTOOtf/c4QrEk3nXDCD58xzaMuLktDa1frFgREa1Tf/LASbzjH54o+fjcYhw9ncpMql+5dQs8dhM+/OUXcHo6hP9y+7ay28f0OS2YDcaRyciy13BiMgBvOIFPv+c6/J93HmCoonWPwYqIaB2KJtL43tFJzIbiCMdTRZ8zF1oKVnazAb9+xzZMBWIYdFrwtmsHi75GM+CwIJHOwBtJlH3eS2MBAMChzV11fBdEaw+DFRHROvTQqRmEE8qWM8U2TM5kJOZzKlYA8EuHN+OGzV34vdfvglFf/j8P/U51SGiFPquj4370OyzoVUc0EK137LEiIlqHvv3COIQApFTCz/bejrzHA9EkkmmJno6lYGUx6vHNX39FVefvy5lltX+o9FYzR8cD3IqGNhRWrIiIVpmUEp/44Rnc/Y9PNuV884txPHpuHm8+MAAAmApEC54zt6jMsMqtWNVCq1gVq4ZpAtEkLs2Hcc2Iq673IFqLGKyIiFbZp358Dn//8Hk8f8WHQCTZ8Pm+9/Ik0hmJX7tdGZdQbLlOGw5ab7Dq6TBDJ8pPXz82rvRXsWJFGwmDFRHRKvrHn13Ap39yDjvUpboxX6Thc377pUnsHXDgwLATHrup6CBPLVj11hmsDHodejrNZXusXh73AwAODrnqeg+itYjBioholXzxiUv4ywdP465rB/GJd18DABhvMFhdmFvEy2N+vOO6IQDKkl0rKlaAMstqukzF6uUxP7Z02zlhnTYUBisiolXwnRcn8MffPYk37OvDJ959DTZ7lPlOY97Cfqha3P/iBHQC2XEJA04LJv2F55wNxWAx6tBhrv8eplKhTcPGddqIGKyIiFbBV5+9ih29HfjMe6+DUa+D02pEp9nQUMVKSolvvzSBW7d3Z+/a63cWryppM6yEKD0EtJJyFavZYAzTwRgODrvqPj/RWsRgRUS0CnyRBLb3dsBs0ANQNjYedtsw5qu/YvXCVR/GvFG8/dqh7LEBpxX+SBJRdaaVZm4xnjdqoR59TgtCsRQiicIBpC+rjevXsGJFGwyDFRHRKvCGk3DZTHnHhrusDVWsvv3iBCxGHd6wvz97bEAb5LmsspQ7db1e/Y7SQ0KPjvuh1wnsG2Swoo2FwYqIaIVJKeGLJOC25zd1j3TZMOaNQsry+++V8tSFBdy2vTuvb2pp3lR+JawpwarM9PWXxwPY0dsBq0nf0HsQrTUMVkREDYokUrj30QtIpTNVPT8YSyGdkegqUrGKJtNYCJfff6+YWDKNywsR7Blw5B0fcFoB5IefRCoDXySJno7GtpnJVqyWVcOklDg67sc17K+iDYjBioioQY+cmcP//v5pvHDVX9XzfWpwctvzg9WIW7kzcLyOPquLc2GkMxI7+zrzjmvhJ3dC+kK48VELQE7FalmwuuqNwB9JcuI6bUgMVkREDfJGlKBUbgp5sed3FQQrpbo05q29z+rsTAgAsKs/P1hZTXq4bMa8pcBmzLACAJvJAIfFgJllS4Evc+I6bWAMVkREDdIqUNUGK78arNwFS4H1V6zOzIRg1AuMeuwFj/U78udNNTp1Pe/cTkvBfoFHx/wwG3QFIY9oI2CwIiJqkE/d36/csMxc3rDy/OU9Vh1mA7psxrq2tTk7HcLW7g6YDIV/rQ+6rHnhp1kVKwDoc1gKAuXR8QD2Djpg1PM/MbTx8KeeiKhBPm0pUA0sFZ8f1pYCC7d6Ge6y1bUUeGYmhJ0lKkTLJ6TPqtfp6TAVfX4tlg8JTaUzODYRYOM6bVj172VAREQAAL9asVrea1SKN5KAUS+Kbicz4rbi9FSopvdfjKcw7oviPTeOFH18wGHBQjiBWDINi1GPuVAcLpsxO5y0EQNOC+ZCcfzR/ccx6Y9i3BdFNJlmfxVtWKxYERE1SKtYlduQOO/54QS6bKai28mMdNkw7osik6l+ltU5tXF9+R2BGu3uvdmgUqmaCzU+dV2zb8iJjFT2Phz3RTHosuKDrxjF6/b2NeX8RGsNK1ZERA3SKlbTwRiklBX33/OGEwWjFjTDXVYk0hnMLcaz+/1VUuqOQI02y2oyEMUmj03ZzqYJ/VUA8IZ9/Tj7Z28q2ttFtBHxTwIRUYN8kQR0Qhm8GYgmq3r+8sZ1zbA6y6qWPqsz04uwGHUYUe8qXG75hPRmTF3PxVBFtIR/GoiIGpDOSASiSWzt6QBQ3XKgL5IsWbEaqWPkwtmZEHb2dUKnK14pG3AuDQmVUjZ1KZCI8jFYERE1IBBNQkpgt7oMV83IBV84AZet8I5AQFkKBGqsWKnBqhS7WRnkOR2IIpxII5pMN7ViRURLGKyIiBqgNa5re/RpDeKlZDLaBszFK1YWox49neaqZ1l5wwnMheLYVSZYAUqf1VQg1tQZVkRUiMGKiKgB2hR1rWJUaSkwGEsiIwuHg+Ya7rJWvRSoNa6XmmGl6Xcq86aWpq43tgEzERXHYEVE1ACfOkW9t9MMt91UMVh5S2zAnGuky1Z1xSp7R2DFipWy9cxsSLk+VqyIWoPBioioAdpSYJfNhD6HBbMVgpWvxAbMuUbcVkz5Y0ilMxXf/8x0CA6LAX2O8kGp32nB/GIck36lEsZgRdQaDFZERA3QZli57Eb0O8xVVKyU5y/fgDnXcJcNqYys6g7DczOL2NXfWXF21oDTAimB4xNBGHQCLmvx5nkiagyDFRFRA3yRBAw6gU6zAX0OC6YD5ZvXtYpVqbsCgepHLkgpK94RqOlXh4Qemwigu8NccjQDETWGwYqIqAG+SBIudXuaPocFC+E4kmWW8HxV9FhVO3JhNhRHIJosOXE916A6y+rSfJjLgEQtxGBFRNQAZd8/pfrUry63aXfeFeONJGAy6GAzld4AedBlhRDAWIWK1Znp8nsE5tKmrwPsryJqJQYrIqIG5G5P06/u7VeuN8oXTsBdYgNmjcmgw4DDgvEKdwaerbD5cq5OixEdZmV7WE5dJ2odBisiogb4I8lsv1SvemfeTJnp695wsuwdgZrhLhvGvZUrVj3qmIdqaFUrVqyIWofBioioAcUqVjPlKlaRBNz2ynfkDbutVVWsKs2vyjXAYEXUcg0FKyHE7wghTgghjgshviqEsAghtgghnhFCnBdC/JsQorr/lSIiWmOklErFSg1KbrsJRr3AdJltbZSerMp/LY502TAVjCGRKt4In0xnqr4jUKMFv14GK6KWqTtYCSGGAPwmgENSyv0A9ADeA+AvAfytlHI7AB+ADzXjQomI2k0kkUYincnOpBJCoLfTUrFiVVWwctsgJUpOYD82EUAsmcGh0a6qr5cVK6LWa3Qp0ADAKoQwALABmALwGgDfUB+/D8DbG3wPIqK2lDt1XdPvLB2s0hkJf7S6HqtrR5wAgGcveYs+/sxF5fiNo+6qr3eTxw4AGHBZq34NEdWm7mAlpZwA8AkAV6EEqgCA5wH4pZQp9WnjAIYavUgionaUnbqeM+yz32EpeVdgIJqElIC7zHBQzbaeDvQ5zHji/HzRx5+5tIBtPfaaqk9vu2YQ//5rt2CIwYqoZRpZCuwCcBeALQAGAdgBvLGG198jhDgihDgyNzdX72UQEdVkNhjDL33umbLLddXSNlTOrUD1Oswl7wos9vxShBC4dVs3nrywgExG5j2WzkgcuezDzVs9NV2vyaCrqcJFRLVrZCnwdQAuSSnnpJRJAN8CcCsAl7o0CADDACaKvVhKea+U8pCU8lBPT08Dl0FEVL0Xx/x4/Pw8fnBsquFzLS0F5leswok0FuOpks+vdjzCrdu74Q0ncFodBKo5ORnEYjyFm7cwJBG1m0aC1VUAh4UQNqFMunstgJMAHgbwLvU5HwBwf2OXSETUPNqWMo+eK77EVoulpcD8HisAmC5StcpWrKpoXgeUYAWgYDnwmUsLAIDDNVasiKj1GumxegZKk/oLAI6p57oXwB8A+F0hxHkAHgCfb8J1EhE1hVetGj11YQHxVLqhc2U3VLYuVaz6ysyy8tWwFAgoIW1bjx1PXMgPVk9f9GLUY8u+FxG1j4buCpRSflxKuVtKuV9K+ctSyriU8qKU8iYp5XYp5bullOW3eiciWkFauIkm03j+iq+hc/kjSTgsBhj0S3+Vlg1WaoXLXWXFCgBu296NZy56s/OsMhmJ5y57cfMWVquI2hEnrxPRhrIQTsBtN8GgE3isweVAXyRRUH0qt1+gL5KAxaiDtcwGzMu9Yns3osk0XryqhMDT0yEEokncvJX9VUTtiMGKiDYUXziBIZcV12/uwmPnGrsj2RdJ5vVXAYDVpIfDYih6Z6BX3YC5Foe3eqATwBMXlL4qrb/qJjauE7UlBisi2lC8EWVA5+07unF8Ioj5xfq7FZTtaQpnUvWVmGXlCxdWuCpxWo04MOzKNrA/e8mLIZcVw122+i6aiFqKwYqINhRfOAGP3YRX7lDGvJQawFnVuUpsT6NMXy8MbN5IoupRC7lu2+7BS2N+hGJJPHvJy2VAojbGYEVEG4pX3QR5/5ATXTYjHj1bf7DyR5J5U9c1fY7i29r4womCpcNq3LqtG+mMxFeeuYqFcAKH2bhO1LYYrIhow4inlMGdbrsRep3Ardu78di5OUgpK794mUQqo5yrSFDqc5gxG4ojvWxiutJjVXk7m+Wu39wFs0GH//fIBQBgxYqojTFYEdGGoQ301Pqcbt/Rg9lQHGdmliabh+MpfOQrL+Bfnr5S/lxRdYZVkaW9focF6YzEQnhpOTCVziAYS9XcYwUAFqMeN4664Ysk0e+wYJOb/VVE7YrBiog2DG3yuVZleuVOZbL5Y+pyYDSRxq9+8Tn8x9Ep/MX3T8GvDgAtJhvSSiwFAsBMYClY+aPqDKs6ghWwNIX95q1uKJtdEFE7YrAiog0jG6zUcDPgtGJHbwcePTeHWDKN//Sl5/DcZS9+67U7EE6k8YXHL5U8l6/M9jR9RWZZlXt+NV65QwlWt3AbG6K2xmBFRBvG8mAFAK/c0YNnLnnxn790BE9eWMBfv+sa/M6dO/Gm/f345ycuI6BWppbzZfcJLKxYjbhtEAI4csVb9r1rsX/IiW/82i141w3Ddb2eiFYGgxURbRja3n65fU6v3NmNRCqDx87N4y/eeQB3q8HlN16zHaF4Cv/8ZPGqVfZcRSpQbrsJb94/gK88fRXBWLLi86t1aNSdt30OEbUf/gklog1Dqxrlbpp8eIsHr9jmwV+88wB+4cZN2eP7Bp24c28fvvD4pWw4ylUpKP36HdsQiqfwL09dUd9ba5yv/a5AIlo7GKyIaMPwhRNwWo15VR+rSY+v/OfDeM9Nmwqe/1uv3YFgLIX7nrhc8Jg/kiy779/+ISdu39mDf37iEmLJdFMqVkTU/gyrfQFERJoJfxSff+wSJCQMOgG9TgeTQYdtPXbsHXBgS7e9oaWwBXXqerX2Dznx2t29+Nzjl/Art21Bh3npr0xfuPjU9VwfvmMb3nPv0/j6kTH4wgnYTHpYjNVvwExEaw+DFRG1jS89eRlfeOISOi0GpDMSqbREIp3JPm4y6LCrrxN/+vb9uHbEVfP5fZHa9+r7zdfuwF3/8AS+9NRlfPiO7TnnKtyAebmbt7hxw+YufPaRi7hhcxerVUQbAIMVEbWNR87O4ZatHnz1nsPZY4lUBhfmFnFqKohTU0F87vFL+OmpmbqClTecxJDLWtNrrhlx4VU7e/CFxy/hQ7dtgdmgVJz8keIbMOcSQuDDd2zDh+47gvnFOHb2ddZ8zUS0trDHiojawkwwhtPTIdy+syfvuMmgw54BB955/TD+8C170WUzwVtmcGc5vnAC7jqaxz902xbMLybwg2PTS+cqsQHzcq/Z3Yvd/Z2IpzJ1TV0norWFwYqI2sKjZ+cAAK9aFqyW67IZs3f31UJKqezVZzfX/Nrbtndja7cdX3zycvZYqQ2YlxNC4Nfv2Aag+JR2IlpfGKyIqC08cnYOPZ1m7Bkov1zmsZvrClbhRBqJdKauipVOJ/D+WzbjpTE/Xh7zI5ORVVesAOAtBwZw/SYXrt/UVfN7E9HawmBFRKsunZF4/Pw8bt/RU3EfvC57fRWrRreUufuGYdhNetz31GWEYilkJKpe2jPodfjWh2/FB14xWtd7E9HawWBFRKvu6Lgf/kgSt6ubIpfjtpuywzZr0eiWMp0WI+6+YRjfe3kK5+cWAXBpj4gKMVgR0ap79Ow8hFD27avEbTfBF0lASlnTe3iLbGdTq/ffshmJdAb/75ELyrk4PoGIlmGwIqJV9+i5ORwcclZVTeqymZDOSASjqZrew7uoBKtaBoQut723E7dt78ZDJ2cAFN+AmYg2NgYrIlpVgUgSL171VbwbUOPpUIJRrSMXim3AXI/337I5+zUrVkS0HIMVEa2qJy7MIyNRML+qFC3M1NrA7g0nYNAJdJobm4v82j192SGjDFZEtByDFRGtqkfOzKHTYqh6krq2XFhrsNK2s6l012Elep3Ab7xmO7b3dqDTws0riCgf/1YgolUjpcSj5+Zw2/buqjdX1oKVr8ZgtbBY2wbM5bz3pk14702bmnIuIlpfWLEiolVzbnYRU4FY1cuAwFKwWqinYsWlOyJqMQYrIlo12jY2tQQrq1EPs0GXbUavlrKdDYMVEbUWgxURrZqXxwMY7rJmm8GrIYSAx26qo8cqia46trMhIqoFgxURrZpJfxQjXbaaX9dVY7BKZyT8kQTcXAokohZjsCKiVTPpj2Koq/pqlcZdY7AKRJPIyPq3syEiqhaDFRGtimQ6g5lgDIM1LANqag1W2nMbHQ5KRFQJgxURrYrpQAwZCQy5LDW/tstmqmncgtbozooVEbUagxURrYpJfxQAMOSqvcfKYzchFE8hkcpU9fxsxYo9VkTUYgxWRLQqJgNKsBqsp2KlDQmtcuSCFqy0fQaJiFqFwYqIVsWETwtWtfdYeWrc1oYVKyJaKQxWRLQqJvwxdHeYYDHqa35tV43ByhdOwGbS1/VeRES1YLAiolUx6Y/WVa0Cat+I2cvtbIhohTBYEdGqmPBHMehsLFjV0mPFOwKJaCUwWBHRipNSNlSxclmVrWkWFqtfCmSwIqKVwGBFRC0RjCXxjv/7BL53dLLgsUA0iUgiXdfUdQAw6HVwWo3VV6wiDFZEtDIYrIioJf7kgZN48aofPzoxU/DYuE+bYVX7qAWNx27CQtXN60n2WBHRimCwIiIAwGcfuYAXr/qacq4Hj0/jmy+Mw6TX4dRUsOBxbThovUuBgHJnYDXT1+OpNBbjKbjtxrrfi4ioWgxWRIRYMo2/ePA0PvfYpYbPNb8Yxx9++xj2DTrwK7eN4uJ8GLFkOu85S1PX6w9W1e4X6Asn1eeb634vIqJqMVgRESb8UUgJPHfZCyll3eeRUuJj3zqGUDyFv/2Fa3FwyIV0RuLczGLB+5kNuob6nty26oKV9hxWrIhoJTBYERHGvBEAwGwojjFvtO7zfPOFCTx0cgb//fW7sLOvE3sGOgGgYDlw0h/DkMsKIUTd79VlN8EXSVQMglqDO3usiGglMFgRUTZYAUrVqh6+cAJ/8sAJ3LTFjV+9bQsAYLPHDqtRj5PLgtWEP1r3HYEaj92EZFoiFE+Vfd5SxYrBiohaz7DaF0BEq++qNwKTQQeLQYcjV7y4+4bhms9xbnYRoXgKH75jG/Q6pRKl1wns6u8sqFhN+KN4za7ehq45uxFzOAGHZWmZb34xjsfPzWMqEMNUIIoXr/rznk9E1EoMVkSEMW8UI11WbHLb8Nzl+u4M1CpD3R35TeJ7Bhz4j6OTkFJCCIF4Ko25ULyhOwKB/I2YN3vs2eN/dP9xfP/YNADAYTFgwGnFu28Yzj6fiKiVGKyICFe9EWxy23Bo1I2Hz5yBL5youcKj9TItX3LbO+jAV5+9ismA0lc1HYgBAAYbmGEFFN+IWUqJZy568ZaDA/iruw/CbuZfcUS0sthjRbTBSSkx5o1gxG3DjaNuAMDzV2qvWpXqZdqrNbBPKsuBE9pw0AZ7rNy2wmB1eSGChXACt27rZqgiolXBYEW0wQWiSYTiKWxy23Bw2AmjXuC5K7U3sHvDCdhMeliM+rzju/odAJbuDJxowgwrAHB3FAYrLRDesLmroXMTEdWLwYpog9PGK4y4bbAY9Tgw5MSROvqsfOFE0ZEGHWYDNnts2TsDJ/3KUmC/s7GlQLtJD5NeB28kN1h54bAYsKO3o6FzExHVi8GKaIO7qo5aGOmyAQBuHHXj6Li/YFp6Jd5IAp6O4n1Ze/odORWrCHo7zTAb9EWfWy0hBNzLtrU5ctmH6zd3Qaerfz4WEVEjGKyINrhssHIrS3OHRt1IpiWOjgdqOo+3RMUKUO4MvOKNIBxPYdIfa/iOQE1XzrY2/kgC52YXcYjLgES0ihisiDa4MV8EXTYjOtVZUFp/Uq2DQr3hRMkhnHsGOiElcHo6hEl/tOH+Ko3bbswGqxeuav1V7qacm4ioHgxWRBvcmDpqQeO2m7C9t6PmOwN9ZYOV0sB+cirYlKnrGrfdDF9E2WT5yGUfDDqBa0dcTTk3EVE9GKyINjht1EKuQ5u7cOSyF5lMdRsyx5JphBPpksFquMuKTosBT5ybRzyVwWCDjesat82IhcU4AOWOwH2DDlhNjfVuERE1gsGKaANLZyTGfdHCYDXqRjCWwrnZxarOU2mjYyEE9vQ78Ni5OQBoao9VMJZCNJHGy+N+LgMS0apjsCLawKYCUaQyMm8pEABuHK2tz2ppOKix5HP2DjoQTih3GjYrWGnb1Dxxfh6xZAaHRtm4TkSri8GKaAPLzrDqyg9Wm9w29HSacaTKYOULK31Obru55HP2qBPYAWVpsBm0bW1+dFLZG5B3BBLRamOwItrAxtRRC8srVkIIXDfiqnrkwkJY6XMqV7HSGthtJj2c1tLPq4XW0/WTU7MYcVvR62hO7xYRUb0YrIg2sDFfBHqdwECRDZH3DTpxaSGMcDxV8TzakM5SPVYAsLOvEzqhLAMK0ZwBnlqwWggncMMmVquIaPUxWBFtYFe9EQw4LTDqC/8q2DfogJRLe/yV440kIQTgKhOsLEY9dvU7MOqxN3TNudw573fDKBvXiWj1cft3og1s+QyrXPuHnACA4xMBHKoQWrzhOFxWI/QVtpL57C/dAJOhef8/15Uz3oH9VUTUDhr6G04I4RJCfEMIcVoIcUoIcYsQwi2EeEgIcU79lX/bEbWpq95oQeO6ps9hhsduwonJyhUrXziZF3JK2eSxNbz5ci6jXodOiwGdZgN29nVWfgERUYs1+r+OnwbwoJRyN4BrAJwC8FEAP5FS7gDwE/X3RNRmIokU5hfj2OQpHqyEENg76KgqWHnDiezog5XW22nGDaNdFatlREQroe5gJYRwArgdwOcBQEqZkFL6AdwF4D71afcBeHtjl0hErTDuU0ctlFgKBJQG9nOzIcRT6bLn8kVKb8Dcap9+z3X407v2r8p7ExEt10jFaguAOQD/LIR4UQjxOSGEHUCflHJKfc40gL5GL5KImu/qgjJqYaTMTKn9Qw4k0xLnZspPYF8os09gq+0fcpYNh0REK6mRYGUAcD2Af5RSXgcgjGXLflJKCaDoZmNCiHuEEEeEEEfm5uYauAwiqseYr/gMq1z7BpUG9hOTpedZSSnhCyeq6rEiIlrvGglW4wDGpZTPqL//BpSgNSOEGAAA9dfZYi+WUt4rpTwkpTzU09PTwGUQUT2ueiOwmfRlK02b3TZ0mA1l+6xC8RRSGblqPVZERO2k7mAlpZwGMCaE2KUeei2AkwAeAPAB9dgHANzf0BUSUUuMeaPY5LaVHdap0wnsGegsG6y8i5WHgxIRbRSNzrH6rwC+LIQwAbgI4FeghLWvCyE+BOAKgJ9v8D2IqAXGvJGqepP2DTrx9SNjSGdk0TvvvBFtA2YGKyKihoKVlPIlAIeKPPTaRs5LRK0lpcRVbwS3bu+u+Nx9gw5EEmlcmg9je29HwePadjYMVkRE3NKGaENaCCcQTaaxyV36jkBNpQb2BQYrIqIsBiuiDWjSr8ywGnRVDlY7+jpg0utwskSfVXYDZgYrIiIGK6JW+42vvIDPP35ptS8jz1QgBqC6YGXU67Crv3QDuzeSgMmgg92kb+o1EhGtRQxWRC32yJk5/OxM0akjq2ZaDVZ9jur27ds36MDxyQCU0XT5fOEE3DZT2bsLiYg2CgYrohZKpDIIxVO4vBBe7UvJMx2MwagXVc+e2jfogD+SxKQayHJ5ORyUiCiLwYqohXzqKIIJXxSJVGaVr2bJdCCGPocFuio3Lt6rNbBPFDawe8MJuO3Gpl4fEdFaxWBF1EJetbE7I4FxdQuZdjAViKK/ymVAANgz0AmdQNE+K18kCbfd3MzLIyJasxisiFpIC1YAcMVbX7CaX4zjtr/8KZ48P9+sy8JMMI5+Z/XBymYyYGtPR9GRCwuLcbhtrFgREQEMVkQtlRes5uvrs/rOixMY90VxtMgyXCkvj/nxDw+fL/qYlLLmihWgNrBP5FeskukMgrEUe6yIiFQMVkQtpAUrnQAuL9RXsfrG8+MAgLlQvOrXfO25Mfz1D88gmkgXPBaMphBLZmqqWAHA9Zu6MB2MYSyn8uaPJAGAGzATEakYrIhaaCGcgBDAjt5OXKnjzsATkwGcng4BqC1Yaf1cV4ssP04FleGgA87KM6xy3bzVDQB45pI3e0xrzmfFiohIwWBF1EK+cAJOqxHbeu24UkfF6hvPj8Ok12FHb0dNwUoLVEWDlToyod9ZW8P5zt5OuGxGPHNxIXtsYVHdzsbGYEVEBDBYEbWUMorAhM0eO8Z8EaQzhQM2S0mmM3jgpUm8bm8vtvd2YG6xumCVzkhM+JSqVLEq2Uw2WNVWsdLpBG4adePpS0vBSqtYuTsYrIiIAAYropbyhhPw2E0Y9diQTMvsHn3V+NmZOSyEE7j7+mH0dJoxX2WwmgpEkVIDXKmKlRBAb2ftIxIOb/VgzBvNfh9aDxkrVkRECgYrohbyhhPospmwyW0HgKLLgU9fXMCHv/x8QXD65vPj6O4w4fadPejpMMMfSSKeKmxGX04LU0IUf7/pQAzdHWYY9bX/8V/qs1KqVlqwcjFYEREBYLAiailvJAFPhwmj3TYAKLq1zZefuYrvH5vGe+59GrNBZZnOF07gJ6dncNe1QzDqdehRq0taT1M52l17B4acRStW08EYBmq8I1Czu98Bh8WAZy4qDezecAKdZgNMBv5VQkQEMFgRtYyUEj61YtXXaYHZoCvoeZJS4rlLXuwdcGDSH8V77n0a04EYHnh5Esm0xLtuGAYAdHcowaqaBvYxbxR6ncDhrR6MF+nr0razqYdeJ3DTFjeeVhvYfZEE+6uIiHIwWBG1SDCWQioj4baboNMJbPbYCmZZTfijmA7G8J6bRvClX70Js6E4fv6zT+Ffn76CvQMO7BlwAEC2YlVNsLrqjWDIZcWWbjuSaWUYaK6pQLTuihUA3LzFg8sLEcwEY9mlTiIiUjBYEbVItrFbnfG02WMvqFgduewDABza7MahUTf+9T/dDH8kgXOzi7hbrVYBOcGqigb2q94INrlt2OxWlh+v5oS5SCKFYCxV83DQXIe3egAovWHaXY9ERKRgsCIq4/zsIp7NGYhZi4Jg5bbhykIEmZyluecue9FpNmBXfycA4NoRF77ynw/j3TcM413XLwUrj7rcNl9FxWrcF8GI24pNHiVY5e5ROK2NWqhzKRAA9g460Gk24JlLXvgYrIiI8jBYEZXxqR+fxe/820t1vbYgWHXbEU9lMJsTjp677MX1m7ug14nssf1DTvz1u6+BM2djY7NBD5fNWLFiFY6nML+YwIjbhgGnFUa9yLszcDqozbCqP1jpdQKHRruUilWEwYqIKBeDFVEZ3nBCmQuVztT8Wt+yYDXqyb8z0B9J4OzMIm7a4q7qfN0d5oo9VmPqVjYjXTbodQLDXTZc9S4tP2oVq1q3s1nu5q0eXJwLI5bMsMeKiCgHgxVRGb5IEhkJzNSwnYxmQQ1WHrvSHzXq0WZZKUHn+Staf1VXVefrqSZYeZVG9U1qf9UmdflRM9WEpUAAuDknDHIDZiKiJQxWRGUE1C1bpmqYmK7xhuOwGHWwmvQAgAGnBUa9yN4Z+NxlH4x6gWtGXFWdr6fTXHEpUJtbpQWrzR4bri5EIKXS1zUTjMFpNWavqV77h5ywq+fgBsxEREsYrIjK8EeTAJSxCLXyhpPZahUAGPQ6DHfZshWrI5e9ODDkhMVYXcjp6aymYhVBh9kAl9qftcltQyiegj+ifB9TgVjD1SoAMOp1uGFUqVq57cYKzyYi2jgYrIhKiKfSiCSULWQm/bGaX+8Nx9G1LHRs9thweT6CWDKNo+MB3FhlfxWgBKtIIo1wPFXyOWPeCEbcNggh1PdTlx/VStZ0INZQ43oubTnQba99z0EiovXKsNoXQNSuAmqVB0DBkM1qeCPJgtAx6rHjuUteHB0PIJHO4MbN1Qcrbfr6/GIcdnPxP7pXvRFs7bFnf79ZG7mwEMa1Iy5MB2PYN+io9Vsp6pcOb4Zb3WCaiIgUrFgRlaAtAwLAZJ09Vm5bYcUqnEjjwePTAIAbqmxcBypPX5dSYswXwUjXUtDRvr66EEEilcH8Yrzu7WyWc1qNeO9Nm7LVMSIiYrAiKkkbl2A16jFRx1KgL1y8YgUA33lpAjt6O2pq/O6psF/g3GIcsWQmOxgUAKwmPXo7zbjijWA2FIOUaGg7GyIiKo/BiqgErWK1e6Cz5opVPJXGYjxV0NithR5vOIFDo9UvAwKVt7UZU/uoRtz5S3ObPTZc9Sp7+wGNDQclIqLyGKyIStB6rPYNOhCIJss2jS/nCyuvXV6xGu6yQhuyftOW6pcBlXOZoBOlK1baqIXcpUAA2OS24+pCZGmGFYMVEVHLMFgRleBTZ1jtHXACqK2BfSGshJ/lFSuzQY9BlzL1/FANjeuAspWM227GfMmKlXJ9w135U9U3e2yYDsayg0IHHI1NXSciotIYrIhK8EeTMOgEdvR1AEBNfValKlYAsKXbjn6HpSAAVaPcLKur3gj6HOaCuVjanYHPXvLCYtTBYeXNwERErcK/YYlK8EeScNlM2QpTLdPXlypWhc3p/+PNe7AYT9V1N12lYLXJXTj6QDv2/BUfBpxW3sVHRNRCrFgRleCPJOCyGdHXaYZO1DZyYfkGzLn2DDhwY42N65py+wWOq8NBl9OC1WI81ZSp60REVBqDFVEJ/kgSLqsRBr0OfQ5LTUuB3nACOqHMemqmnk4z5hcT2b3/NPFUGlPBWEHjOqCEuw51oCgb14mIWovBiqgEf1RZCgSAQZe1xub1BFw2E/S65i67dXeYkEhnEIzm36E46VdmVBVbChRCZI8zWBERtRaDFVEJ2lIgoASrmpYCI4miy4CNWppllV8900YtbCqxvYzWwM7hoERErcVgRVSCthQIAINOCyYDsYIluFIWFhNw21oXrGaX9VmVmmGl0QJXs7azISKi4hisiIqIJdOIJtPZLWcGXVYkUhksqE3plbSqYtVbYr/AcW8EJoMu+/hym93KVjpsXiciai2OWyAqIqBuZ6M1n2sjFyb9UXR3FA8vubzhBG7Y3IKKVYcSjJYHqysLEYx0WaEr0dP15gP9mAvFsX/I2fRrIiKiJaxYERXhV7ez0XqstN6kavqsMhkJXyQJTwsqVg6rASa9Lm+/wHgqjScuzOOaEVfJ17lsJvzW63Y0vZmeiIjyMVgRFeFXt7PpUvukhrIVq8ojF4KxJNIZ2ZKlQCEEujtMmA8tLUk+fHoOoVgKd1071PT3IyKi2jBYERXhi+QvBbpsRliN+qoqVt4yw0GboafTnFexeuDlCXR3mHDrNk9L3o+IiKrHYEVURCCqhCNtKVAIgQGXBZNVzLJakWCl9lgFY0n8+NQs3npwEAY9/zgTEa02/k1MVITWY9WVMzJhyGWtailwJYPVg8enkUhl8LZrB1vyXkREVBsGK6IifJEkjHoBm0mfPTborG5IaKuDVXeHGd5wHOmMxAMvTWKT24bryjSuExHRymGwIioiEE3AaTVBiKW76AZcFswtxpFIZcq+dmEFKlYZCZyaCuLJC/O469rBvOskIqLVw2BFVIQ/kkSXLX8D5UGXFVICM8Hyy4G+cAI2kx4Wo77s8+rVo87R+uKTl5GRwF1cBiQiahsMVtS2UunylaFW8uXsE6jRRi5MVFgO9IYTeb1ZzaZta3P/SxPYN+jA9t7Olr0XERHVhsGK2tKkP4q9H/8hXhrzr8r7+yNJOK354ajaIaHeSAKejtYHq2Ra4u2cXUVE1FYYrKgtXZ4PI5HK4Pzs4qq8fyBafCkQAKYC5ZcCveHW7BOo0bbUEQL4uWu4DEhE1E4YrKgtaQ3gQXXPvpVWbCnQYtTDYzdVtRTobuFSoN1sgN2kx+EtHvQ7uakyEVE74SbM1JZ86pYywdjKB6tYMo1YMgNXkXA04LJUXgpsccUKAP7i7oPY3tvR0vcgIqLaMVhRW9JmQQXKVKw+8pUX0NNhxh+/bV9T31t7z+UVK0CZZXV5IVzytbFkGpFEGl0tDlZcAiQiak8MVtSWvNmlwFTJ5xwbD6DT0vwfYa1a5rIWhqNBlxVPXVgo+donL8wDAHb38049IqKNiD1W1JaywarMUmAgmqzY71QPbTubYhWrUY8NoXgKF+aKN9V/84UJuO0m3L6zp+nXRURE7Y/BitqSVjUqtRSYyUgEY0n4I0mE46WrWvUoF6zefHAABp3AV565WvBYMJbEQydn8HMHB2DkhshERBsS//antrSwWP6uwFAsBSmVr5tdtfJrS4FFmtd7Oy144/5+/PuRMUQT6bzHfnBsColUBu+4frip10NERGsHgxW1Ja1iFYoVr0blVrImfE0OVlrzurWwYgUA779lFMFYCt99eTLv+DdfmMDWHjuuGXY29XqIiGjtYLCitiOlhC+shJtSS4H+aCL79XjTK1ZJmPQ62EzF9/q7cbQLu/o68aWnL0OqZbMxbwTPXvLindcNcUNkIqINjMGK2s5iPIVEOgO7SY/FeKronoEtrVhFEnDajCUDkhACv3TLZhyfCOLl8QAAZd8+ALiLW8wQEW1oDFbUdrRq1Wi3HYAStJbTGsx1Ahj3RZr6/v5IsuQyoOYd1w3BbtLjS08pVatvvTCBm7a4MeK2NfVaiIhobWGworbjVfurRj1KsCq2HKgd29rT0fzm9WgCXRW2pOkwG/DO64fxvaNT+NnZOVycD+Pu61mtIiLa6BisqO341BlWo91K9afYkFAtWO0dcLRgKTAJZ5FRC8v90uHNSKQy+L2vvwyzQYc3HRho6nUQEdHa03CwEkLohRAvCiG+p/5+ixDiGSHEeSHEvwkhWru3B6072gbMWsWq2JDQQDQJs0GHrT12zIbiiKfSBc+pVzVLgQCwq78TN21xwxtO4HV7++CwVH4NERGtb82oWP0WgFM5v/9LAH8rpdwOwAfgQ014D9pAlipWZZYCI0k4rUYMuawAgCl/rGnv748mqt7r7wO3jAIA3nUDZ1cREVGDwUoIMQzgLQA+p/5eAHgNgG+oT7kPwNsbeQ/aeLyRBIx6kQ1NxYaE+qMJuGxGDHUpz2lWn1UsmUYsmYGziooVALz5QD8e/O1X4tW7epvy/kREtLY1WrH6FIDfB6DdD+8B4JdSak0x4wDY0Us18S4qzeNauCm1FOi0GjHsUvqwmtVnVW47m2KEENjd72jKexMR0dpXd7ASQrwVwKyU8vk6X3+PEOKIEOLI3NxcvZdB65A3koDbboLNpIdeJ0rcFZiC02pCv9OijFxoUsVKGzxa6a5AIiKiYhqpWN0K4G1CiMsAvgZlCfDTAFxCCIP6nGEAE8VeLKW8V0p5SEp5qKenp4HLoPXGF1aClRACDouh+F2BkQScViNMBh36HJamzbLKVqyqXAokIiLKVXewklJ+TEo5LKUcBfAeAD+VUv4igIcBvEt92gcA3N/wVdKG4g0vNY87rcayS4EAMOSyNnEpUKlYVTNugYiIaLlWzLH6AwC/K4Q4D6Xn6vMteA9ax7yRBNzqUpzDaixYCkymMwgn0tk+qKEua9Oa17WKFZcCiYioHobKT6lMSvkzAD9Tv74I4KZmnJc2nlQ6g0A0CbdasXJYjAV3BWpBK7di9R9Hp5DOSOh1jW2A7I/W1rxORESUi5PXqa34o0lIiaVgZTUgGMvvsQosCz9DXVakMhIzwcZnWfkiCZj0OliN+obPRUREGw+DFbUVbThobo/V8qVAbbnOoVashrvUkQsVlgPnQnG8596n8K0XxiGlLPqcQCQJl80IZSQbERFRbRisqMCEP4qLc4ur8t5eNVhle6yKLAVqv3flLAUClWdZPXVxAU9f9OJ3v/4y7vmX5zEXihc8x68GKyIionowWFGBP/z2MbzhU4/iq89eXfH39ql35S0tBRoRT2UQSy7tBajNmnIuD1YVKlZnp0PQ6wT+4I278cjZObz+bx/J9mZpFSxfJAGXlY3rRERUn6Y0r9P6MuGLIiOBj33rGE5MBvBHb90Hk2Epg4diSUz6Y9jV39n099Y2YM4NVoAyfd2i9j0FIvnN61aTHh67qeIsqzMzIWzttuPX79iG1+3pxe/9+8v4yFdeyD5u1AukMhJ37ulr7jdFREQbBoMVFZhfjOPnDw3DYTXis49cxNnpRXz8bXvxwhUffnRyBk9fXEAyLfHY778aI25bU997qcdKCU0Oi/IjGoym0KvmuIA6MDR3P7+hLivGKywFnp0JYf+QEwCwo68T3/z1V+DbL0xgKhBDOpNBMiORSmfw5gMDTf2eiIho42CwojzJdAa+SBK9nRb8zp07sXfAgd//xlG85TOPAwC2dNtxx65ePHRyBlOBWNODlTecRIfZALNBqU45iuwX6I8m0GE2wKBfqqINuaw4MxMqed5IIoWr3gjeed1w9phRr8PP3zjS1OsnIqKNjcGK8mjN492dZgDAXdcOYVd/J5656MWt2z3Y1tOB4xNBPHRyJtsP1dz3j2erVcBSVSr3zsDcqeuaIZcVD5+ZhZSy6B1952cXISWwq7+j6ddMRESkYbCiPNqdcj0dSw3cu/sd2N3vyP5eu2vO34pgFUlm7wgElLsCAeTdGRiIFAar4S4rYskMFsIJdHeYC857ZlqpZu3sa35fGBERkYZ3BVKe+UUlWBULJxptxpQvUriHX6O0DZg1DqvWY1WhYqXNsirRZ3V2JgSTQYfNHnuzL5mIiCiLwYryzC+qS4FlgpXdpIdRL1q0FLi0ATOQU7HKmb4eiBbOmqo0cuHMzCJ29HY0vOUNERFROQxWlCdbseosHayEEHDZTNmxB/W4MLeIB49PFxz3hhN5S4EWox5mgy6vYuUvWrEqPyT07HQIu7gMSERELcZgRXnmQ3FYjDrYTeX3yuuyGRuqWP3dT87hN77yAiKJpUpUNJFGNJmGuyN/QKcjZ1sbKaWyFLisYuW0GtFpNhSdZRWIJDEdjGFnC+ZuERER5WKwojzzi3F0d5gr7pXnspka6rE6PhlEKiPx4lV/9pg3kr+djcZpNWbHLcSSGSRSmYKKFaBUrYotBZ6dVRrXWbEiIqJWY7CiPPOLxe+qW85lNdZ9V2A0kc7uRfjMxYXs8eUbMGscFgOC6lDQQHafwMJtZ4ZcxYeEZu8IZMWKiIhajMGK8mgVq0q6GqhYnZ4OIiMBnQCeueTNHtdmaHmWB6ucpUDt12IVq90DnTg3u1iwufLZmRA6zAYMOi11XS8REVG1GKwoz/xiHD2dlTchdtmNCESS2c2La3FiMggAeP3efrw45kc8pWyw7C1RscpdCtSqZMWC1TuuG0I6I3H/SxN5x89Mh7Czr6Pi8iYREVGjGKwoK52R8JYYsLlcl82ERDqDSCJd8/ucmAzCaTXiHdcPIZHK4OWxAIClYLW8x8phMWbvCswuBdoKg9X23k5cO+LCvx8ZzwY+KSXOzoRasmE0ERHRcgxWlOUNJ5CR5WdYabrUYFPPnYEnJwPYN+jATaNuAMCzlxay59KJwmqUw2pAMJaClBL+MkuBAPCuG4ZxZiaE4xNKVWxuMQ5fJMmJ60REtCIYrCirmqnrGpdaVfLX2GeVSmdwejqEfYMOdNlN2NXXme2z8oYT6LKZoFs2xNNpNSKdkQgn0tnK1fJxC5qfu2YQZoMO//78GADg7LTSJM87AomIaCUwWFHWUrCqosfKWl/F6sJcGPFUBvsGnQCAm7a48fwVH1LpTMHUdU3ufoGBaBI6AXSYim9z6bQa8YZ9/bj/pUnEkmmcmeEdgUREtHIYrCirmqnrmnr3CzwxqfRT7RtUNnW+easbkUQaxyeDytT1YsFKDXGBaBJ+dQPm5VWtXO+6YRiBaBI/OTWLs9MheOymqqpwREREjSr+v/20Ic2HKu8TqNGaxwM1VqxOTAZhNuiwpVvZDDm3z8oXSWBrd0fBa7R+Kq1iVaq/SnPr9m4MOC349+fH4Gd/FRERrSBWrNaZi3OL+NJTl+t67fxiHCa9Dg5L5bytDeisp2K1e8ABg1750et1WLCl245nL3krLwXGUkX3CVxOrxO4+/phPHp2DqemgrwjkIiIVgyD1TrztefG8Ef3n6hrg2Rl6rqpqnlPJoMOHWZDTT1WUkqcnAxmlwE1N29x49lLXvgiyYLhoIByVyCgLAUq+wRW7gG7+4ZhZCQQT2VYsSIiohXDYLXOTAViAIAr3nDNr51fjFfVX6Vx2Yw13RU47osiGEsVBKubtrgRjKWQzsiiFavcpcBgFRUrANjSbceNo10AgF39hcuLRERErcBgtcY8e8mLKwulQ9OUugnxlYVIzeeudjsbjbKtTfUVq6XGdWfe8Zu3erJfu+2FoanDrFSsgrEk/JFE9o7ESj502xYMOC3Y1e+o/GQiIqImYLBaY377ay/ib350tuTj2YpVmfBVihKsKi+zaVw2Y009Vicmg9DrBHYv63kaclkx5LICANz2wmBn0CvLjv5Idc3rmjfuH8BTH3ttNpgRERG1GoPVGrMQTmDMV7walc5IzAS1YFVbxSqTkVhYrG47G43LZqrprsATk0Fs67HDYtQXPHbzFuXuwOXb2WicViMm/VFkZOmp60RERKuNwWoNiSXTiKcymPBFiz4+vxhHKqPskVdrsApEk0hlZI1LgbVWrAIFy4CaO3b3wqgXGHBZij7eaTFgTP2+S01dJyIiWm1cI1lDtA2IZ0NxxFNpmA35lR9tGdBjN9XcvK4NB/XUtBRoQjCWRDojoS8zsFM7/0wwXtC4rvm5gwO4ZaunZLBzWI04Oans/8eKFRERtStWrNaQ3DvwJv2xgse1xvWbt7oxE4wjmkhXfe45NVj11FixknIp8JVzQg1Fe0sEKyEEesrckei0GrEYTwFA1c3rREREK43Bag3x5/QzFVsOnFQrVreod9ld9Va/HDi/qE5dr2HcQpdNGxJauc8qe0fgQPGlwEq0IaEAlwKJiKh9MVitIbmVoQl/YWiaDkRhMepwcNgFALhc5M5AKSX+/chYQZVpPqRtwFzbHCsgP/CVcmIyiOEua92hSBsSCixNfSciImo3DFZriD83WJWoWA04rRj1KPvwXS3SwH56OoT//o2juO/Jy3nH5xfj0OtETctsLrViVWlIaCSRwqNn57L7AtYjt6+KPVZERNSuGKzWkKAarDotBowXCVZT/igGnBY4bUa4bMaiFauXx/wAgCfOz+cdn1+Mw2M3QVehCT1Xl1p9qnRn4HdenEQolsL7bt5U9bmX05YCTXodLEb+2BIRUXvif6HWEH8kCb1OYFdfJ8b9hcFqOhBDv1MZV7DZbSvaY/XyuB8A8MJVHyKJVPb4fI0zrIDcilXppUApJb701GXsGXDghs1dNZ0/l0OtUjltxqr2MiQiIloNDFZrSCCahMNiwIjbVrAUmM5IzITiGHQqE8w3e+wlKlYBdJoNSKYlnrvsyx6vdZ9AAHBYDNDrRNnm9SNXfDg9HcL7b9ncUCDSlv+4DEhERO2MwWoN8UeTcNlMGHJZMR2MIZXOZB+bDcWQzsjsgM3NHiV8JVJLz4kl0zgzE8K7D43ApNflLQfOh2rbzgZQRiS4rOWHhH7pqSvotBhw17WDNZ17OYdFaV7nqAUiImpnDFZriD+SgMNqxFCXFemMxHRwaZaVNhx0QFsK9NiRkcBEzpLhickg0hmJw1vduH6zC4+fU4KVlBLzi4maZlhpXDZjyaXA2VAMDx6fwrtvGIHN1NgsWgcrVkREtAYwWK0hwWgSLqsxu2Fx7nLglF8LVtpSoA1A/mbMWuP6NSMu3La9GyengvCGEwjGUkikMzX3WAHKLKtSdwV+7dkxJNMSv3zL5prPuxyXAomIaC1gsFpDlKVAI4a71GCVU42aCihfDxYEq6UG9qPjfvQ5zOhzWPCK7d0AgCcvzGe3s+nurH0+lKvEfoGpdAZfeeYqXrmjG1u67TWfd7nc5nUiIqJ2xWC1ggLRJGaChVvR1PJ6p9WIwWIVq0AMVqM+O0izp8MMm0m/LFgFcI06PPTgkBOdZgOeOD9f13BQjctmKroU+NDJGUwHY3j/LaM1n7MYu0mPTW4bdvd3NuV8RERErcBgtYI+9q2jeN8/PV3XazMZiYC6FGgx6tHdYS6oWA24LNk774QQ2OS2ZZcCA9EkLs6Hcc2ICwBg0OtweJsHj5+fX9rOpq6lQGPRuwK/9NQVDLmseM3u3prPWYwQAo/+/qvxCzfWPwuLiIio1RisVkgsmcZPT8/iwlwYvnDlLWCWC8VSkHJpSWyoy5o3JHQqEMsuA2o2e2y4os6yOjau7NV3cHhpr77btndjzBvFi1eVsQv1VqxiyQxiyaUNnwPRJJ6+tIC7bxiGvoaBo0RERGsdg9UKefzcPGJJZfTBsYlAza/X9vbThnIOd1nzK1b+peGgmlGPHVe9EWQyMjsY9OCQK/v4rWqf1XePTkInALe99h6rYhsxv3DVBymBw1vq38KGiIhoLWKwWiE/PjUDm0kPoL5g5Y8qwUWb4zTsUoJVJiORSmcwG4phcFmw2uSxIZHKYDoYw8tjfmzptuc1f2/rsaPPYcZMMA633VRXdakruxHzUgP785d90OtEdtmRiIhoo2CwWgGZjMSPT83iNbt7MeqxZZflaqFVrLRgNNRlRSKVwXw4jtlQHBkJ9C9bCtQ2Y768EMbR8UDeMiCg9C1pVat6lgFzrye3YvX8FR/2DHTCbm5sdhUREdFaw2C1Al4e92N+MY479/bhwLCrvoqVWhHSKlbaLKtxXzQ7akGbuq7Z5FZGLjx3yYfpYAwH1TsCc93WYLDqyu4XqFxfMp3BS2N+HNrMZUAiItp4GKxWwI9PzUCvE7hjZy8ODjkx4Y9mZ0dVy69VrLSlwC4lNE34ophUh4Mub14fdFlh1As88PIEAODakfyKFYCcilXt/VVAYY/Vqakgosl0QxsuExERrVUMVivgoZMzuGnUDafNiAPqclytVaugGqxy7woElCGh0+p2Nsub1/U6gZEuGy7MhaHXCewdKAxWfQ4LfvHmTXjDvv7avimVa1mP1RF1Y+dDowxWRES08TBYtdiVhTDOzizidXv7AAD7Bh0QAjX3WfkjCViNeliMSgN8h9kAp9WoVKwCUdhN+uxGxbm0Cew7+zphVZvnl/vzdxzAmw4M1HQ9GotRD6tRnx0S+vxVHwadluzWOkRERBsJu4tb7MenZgEAr9ujDMrstBixtdtec8VKm7qea8hlxbgvAotRjwGXNTscNNdmjx3AXNFlwGbpUre1kVLi+cs+3MgxC0REtEGxYtViPz45g519HWrAURwcdtVRsUpml900Q+osq8lADAPLlgE1WsWqWON6s2jb2kz4o5gOxnCI/VVERLRBMVi1UCCSxLOXvXjdnr684/uHnJgOxjBbw76B/mgy21+lGe6yYsIXxZQ/WjJYXTvigkmvw+Gtntq/gSppGzE/f0Xpr2LjOhERbVQMVi308JlZpDMSd+7ND1YH62hgD6r7BOYaclkRTqQxG4oXzLDSXLepC8f/5A3Y0m0v+ngzdNlM8EUSeP6KD3aTnhslExHRhsVg1UIPnZpBd4cZ1yxbhts74IBOAEdrWA4sthQ43LUUppZPXc9lMrT2X7PLZoQ/ksSRyz5cu8kFg54/VkREtDHxv4At9MzFBdy+oxu6ZVvF2M0GbO/twPEaKlbFm9dt2a8HXKt3F16X2mN1ejqIGzgYlIiINjAGqxaZDcUwv5jAvqHid+MdGHLh6EQAUsqK54ol04gm09kNmDW5FatSPVYrwWUzIiOBjAQb14mIaENjsGqRU1MhAMCegeL9RgeHnZgLxTETrDyBfflwUI3LZsxu7LyawUqbvi4EcN0m16pdBxER0WpjsGqRU1NBAEo/VTHaBPaj4/6K59I2YF7evC6EwJDLik6zAZ0WY7GXroguu/Leu/o6V/U6iIiIVhuDVYucmgpi0GkpWL7T7B1wQK8TVd0ZqO0TuLx5HQBGu+0YcdsKjq8kp1X5HrmNDRERbXScvN4ip6aC2FOiWgUoW8Hs6O2oKlgFIvkbMOf6k7ftQyyZrv9Cm2C4ywqDTuD2HT2reh1ERESrjcGqBWLJNC7MhfH6veU3Nj447MRPTs1CSll0OxpNtmJlLax+Da7i3YCaPocFz/yP18LTYV7tSyEiIlpVXApsgXMzi0hnZNmKFaBMYF8IJyo2sGsbHBerWLULhioiIqIGgpUQYkQI8bAQ4qQQ4oQQ4rfU424hxENCiHPqrxuu8UZrXC91R6BmR6/y+PnZxbLPC0aTEALotLDASERE1M4aqVilAPyelHIvgMMAPiKE2AvgowB+IqXcAeAn6u83lJNTQViN+ryNl4vZ0dcBADg3Gyr7PL86HHT5oFEiIiJqL3UHKynllJTyBfXrEIBTAIYA3AXgPvVp9wF4e4PXuOacmgpiV38n9BWCkMduQpfNiLMz5StWxaauExERUftpSo+VEGIUwHUAngHQJ6WcUh+aBtBX6nVrSSKVwYtXfZgNxcpOS5dSVrwjUCOEwI7eTpyvVLGKFG7ATERERO2n4aYdIUQHgG8C+G0pZTD37jYppRRCFE0hQoh7ANwDAJs2bWr0MlruOy9O4Pe/eRSA0kS+o7cD+4ec+G9v2IUO89LHOBmIIRhLYe9g5WAFANv7OvAfR6fK3hnojyYLpq4TERFR+2moYiWEMEIJVV+WUn5LPTwjhBhQHx8AMFvstVLKe6WUh6SUh3p62n/+0YQ/CiGAP3rrXrz14AB0QuCLT17GPz9+Ke95Jye1ievlG9c1O3o7EIgmMb+YKPmcYDRZctAoERERtY+6K1ZCKa98HsApKeUncx56AMAHAPyF+uv9DV1hm/BFEnBajfjV27Zkj/3y55/Bvz5zBb92xzYY9UpG1e4I3NVfXcVqZ58SwM7NhNDTWXxkgT+S4FIgERHRGtBIxepWAL8M4DVCiJfUf94MJVDdKYQ4B+B16u/XPG84Abc9v2r0wVeMYiYYxw9PTGePnZoKYrPHlrc8WM6OXu3OwOIN7JmMZPM6ERHRGlF3xUpK+TiAUre9vbbe87YrXyQB97LluDt29WKT24YvPnEZbz04CEDdyqbKahUA9HSa4bAYSo5cWEykkJHF9wkkIiKi9sLJ61XyhpPoWlax0usE3n/LZhy54sPxiQDC8RSueCNV3RGoEUJgR18nzpUYuaDtE8jmdSIiovbHYFUlX7iwYgUA7z40AqtRj/uevIzT0yFIWXni+nI7ejtKTl8PZPcJZLAiIiJqdwxWVZBSwhtJFFSsAGX0wjuvH8L9L0/iyfPzAFBTxQoAdvR1YiGcwMJi4Z6BfrVixbsCiYiI2h+DVRUiiTQSqQzc9uJVow++YhSJVAb/+MgFdFoMGO6y1nT+cg3sWsWKzetERETtj8GqCt6wMmOqq0TVaEdfJ27d7kEkkcaeAUfJQZ+lLO0ZWBis/FHlvdm8TkRE1P4YrKrgiyjhZvm4hVwfuGUUALC3xmVAAOh3WNBhNuD8TOGdgdpSICtWRERE7Y/BqgrZilWZYPXaPX34wC2bcff1wzWfXwiB7b0dRTdjDkaTMBt0sBj1NZ+XiIiIVlbDewVuBNmKVZkGcr1O4E/u2l/3e+zs68BPT88VHPdHklwGJCIiWiNYsaqCN6wsx5WrWDVqR28n5hfj8IXz9wzk1HUiIqK1g8GqCr5wAnqdgMPSugLfdrWB/fxc/nKgP5qAy8pRC0RERGsBg1UVFsIJdNlMNd/tV4vsyIVlfVb+SJJT14mIiNYIBqsq+MKJkjOsmmXQaYXNpMfZZXcGBqPssSIiIlorGKyq4I0kSs6wahadTrkzcPnWNv5oktvZEBERrREMVlVQKlat73Pa0duJc7NLFasxbwSRRJrN60RERGsExy1UwVdin8Bm29HXgW++MI5P/ugMHj4zh2MTAQDANrX/ioiIiNobg1UFmYyEL5IsO8OqWXb3dwIAPvPT87h+kwsffdNuvH5vH7b2MFgRERGtBQxWFYRiKaQzckUqVrfv6ME/f/BG7Bt0oNdhafn7ERERUXMxWFXgze4T2Po+J51O4NW7e1v+PkRERNQabF6vILtP4AosBRIREdHaxmBVgbbFzErcFUhERERrG4NVBdpSICtWREREVAmDVQWsWBEREVG1GKwq8EYSMBl0sJn0q30pRERE1OYYrCrwhRNwt3gDZiIiIlofGKwq8IaTKzLDioiIiNY+BqsKfJHEisywIiIiorWPwaoCXzjBOwKJiIioKgxWFXgjCd4RSERERFVhsCojlc4gEE2yYkVERERVYbAqIxBNQkrOsCIiIqLqMFiV4dOmrjNYERERURUYrMrwhpMAADeXAomIiKgKDFZleMNaxYrjFoiIiKgyBqsytKVA9lgRERFRNRisyshWrLgUSERERFVgsCrDF07AZtLDYuQGzERERFQZg1UZ3ginrhMREVH1DKt9Aaslk5E4P7eIF6/68OJVP14a8+P1+/rxu3fuzD7HF+bUdSIiIqrehgxWsWQaP/d3j+Pc7CIAwGUzwm4y4HOPXcR/uX0r7GblY/FGkpxhRURERFXbkMHqgZcncW52ER97027cubcPW7rteO6yDz//2afw4PFp3H3DMAClYrXFY1vlqyUiIqK1YsP1WEkp8cUnLmNXXyfuuX0rtvZ0QAiBG0e7sMltwzdfGM8+1xdOsGJFREREVdtwweq5yz6cnAriA68YhRAie1wIgbuvH8ZTFxcw7osgkcogFE9x6joRERFVbcMFq/uevAyn1Yi3XzdY8Ng7rx+ClMC3X5iAn/sEEhERUY02VLCa9Efx4IlpvOfGEdhMhe1lI24bDm9141svTmAhzKnrREREVJsNFaz+9ekrkFLilw5vLvmcu68fxqX5MH58cgYAp64TERFR9TZMsIol0/jqs1dx594+jLhL3+n35gMDsJn0uO+pywBYsSIiIqLqbZhg9cBLk/BFkvjAK0bLPs9uNuCN+/sxv6j1WBlX4OqIiIhoPdgQwUpKiX9+UhmxcMtWT8Xnv0udYwVwKZCIiIiqtyGC1ZErPpyaCuKDt+aPWCjl8BYPhlxWOCwGGPUb4iMiIiKiJtgQk9evG3HhH3/xetyxq7eq5+t0Ar9z506cmAy0+MqIiIhoPRFSytW+Bhw6dEgeOXJktS+DiIiIqCIhxPNSykPFHuM6FxEREVGTMFgRERERNQmDFREREVGTMFgRERERNQmDFREREVGTMFgRERERNQmDFREREVGTMFgRERERNQmDFREREVGTMFgRERERNQmDFREREVGTMFgRERERNQmDFREREVGTMFgRERERNQmDFREREVGTtCxYCSHeKIQ4I4Q4L4T4aKveh4iIiKhdtCRYCSH0AP4BwJsA7AXwXiHE3la8FxEREVG7aFXF6iYA56WUF6WUCQBfA3BXi96LiIiIqC20KlgNARjL+f24eoyIiIho3Vq15nUhxD1CiCNCiCNzc3OrdRlERERETdOqYDUBYCTn98PqsSwp5b1SykNSykM9PT0tugwiIiKildOqYPUcgB1CiC1CCBOA9wB4oEXvRURERNQWDK04qZQyJYT4DQA/BKAH8AUp5YlWvBcRERFRuxBSytW+Bggh5gBcWYG36gYwvwLvsxHws2wufp7Nxc+zufh5Nhc/z+Zajc9zs5SyaB9TWwSrlSKEOCKlPLTa17Ee8LNsLn6ezcXPs7n4eTYXP8/marfPk1vaEBERETUJgxURERFRk2y0YHXval/AOsLPsrn4eTYXP8/m4ufZXPw8m6utPs8N1WNFRERE1EobrWJFRERE1DJrOlgJIb4ghJgVQhzPOXaNEOIpIcQxIcR3hRCOnMc+JoQ4L4Q4I4R4Q87xN6rHzgshPrrS30e7qOXzFELcKYR4Xj3+vBDiNTmvuUE9fl4I8RkhhFiN72e11frzqT6+SQixKIT4bznH+POJuv68H1QfO6E+blGPb/ifzxr/rBuFEPepx08JIT6W8xr+bAIQQowIIR4WQpxUf95+Sz3uFkI8JIQ4p/7apR4X6s/eeSHEUSHE9Tnn+oD6/HNCiA+s1ve0mur4PH9R/RyPCSGeFEJck3Oulf8ZlVKu2X8A3A7gegDHc449B+BV6te/CuBP1a/3AngZgBnAFgAXoAwv1atfbwVgUp+zd7W/tzXweV4HYFD9ej+AiZzXPAvgMAAB4AcA3rTa31u7f545j38DwL8D+G/q7/nzWcfnCWX48VEA16i/9wDQq19v+J/PGj/L9wH4mvq1DcBlAKP82cz7PAcAXK9+3QngrPrfnL8C8FH1+EcB/KX69ZvVnz2h/iw+ox53A7io/tqlft212t/fGvg8X6F9TgDelPN5rsrP6JquWEkpHwXgXXZ4J4BH1a8fAnC3+vVdUP5yiEspLwE4D+Am9Z/zUsqLUsoEgK+pz91wavk8pZQvSikn1eMnAFiFEGYhxAAAh5Tyaan8ZH8JwNtbfvFtqMafTwgh3g7gEpTPU8OfT1WNn+frARyVUr6svnZBSpnmz6eixs9SArALIQwArAASAILgz2aWlHJKSvmC+nUIwCkAQ1A+j/vUp92HpZ+1uwB8SSqeBuBSfzbfAOAhKaVXSumD8u/hjSv3nbSHWj9PKeWT6ucFAE9D2Z8YWKWf0TUdrEo4gaUP7t1Y2gx6CMBYzvPG1WOljpOi1OeZ624AL0gp41A+u/Gcx/h55iv6eQohOgD8AYA/WfZ8/nyWV+rncycAKYT4oRDiBSHE76vH+fNZWqnP8hsAwgCmAFwF8AkppRf82SxKCDEKpaL/DIA+KeWU+tA0gD71a/73qEpVfp65PgSlGgis0ue5HoPVrwL4sBDieSglxMQqX89aV/bzFELsA/CXAP7LKlzbWlTq8/xjAH8rpVxcrQtbo0p9ngYAtwH4RfXXdwghXrs6l7hmlPosbwKQBjAIpY3i94QQW1fnEtub+j9I3wTw21LKYO5jaoWUt+HXoNbPUwjxaijB6g9W7CKLaMkmzKtJSnkayjIAhBA7AbxFfWgC+dWWYfUYyhzf8Mp8nhBCDAP4NoD3SykvqIcnsFSGBfh55inzed4M4F1CiL8C4AKQEULEADwP/nyWVObzHAfwqJRyXn3s+1B6iv4V/Pksqsxn+T4AD0opkwBmhRBPADgEpRLAn02VEMIIJQR8WUr5LfXwjBBiQEo5pS71zarHS/33aALAHcuO/6yV192uavw8IYQ4COBzUHomF9TD5f673zLrrmIlhOhVf9UB+J8A/p/60AMA3qP2AW0BsANKE+tzAHYIIbYIIUwA3qM+l1D68xRCuAD8B5RGwie056tl2qAQ4rB6t9X7Ady/0tfdrkp9nlLKV0opR6WUowA+BeB/Syn/Hvz5LKvMn/cfAjgghLCpvUGvAnCSP5+llfksrwJ4jfqYHUqz9WnwZzNL/Vn6PIBTUspP5jz0AADtzr4PYOln7QEA71fvDjwMIKD+bP4QwOuFEF3qHW+vV49tKLV+nkKITQC+BeCXpZRnc56/Oj+jre6Ob+U/AL4KZd0/CeX/UD8E4Leg3EFwFsBfQB2Cqj7/D6HcIXAGOXcCQblD46z62B+u9ve1Fj5PKH/xhgG8lPNPr/rYIQDH1c/z73P/HWykf2r9+cx53R9DvStQ/T1/Puv4PAH8EpS+oeMA/irn+Ib/+azxz3oHlDtVTwA4CeC/55yHP5vK53AblGWpozl/H74Zyt2oPwFwDsCPAbjV5wsA/6B+bscAHMo5169CubnqPIBfWe3vbY18np8D4Mt57pGcc634zygnrxMRERE1ybpbCiQiIiJaLQxWRERERE3CYEVERETUJAxWRERERE3CYEVERETUJAxWRERERE3CYEVEG5YQQr/a10BE6wvnWBHRmiCE+F8AvFLKT6m//3MoW1qYAPw8ADOAb0spP64+/h0o21lYAHxaSnmvenwRwGcBvA7ARwC8FcDbAKQA/EhK+d9W7rsiovWGwYqI1gR1l/tvSSmvV7ddOQfgfwB4LZRNwAWU7Sr+Skr5qBDCLaX0CiGsULa2eJWUckEIIQH8gpTy60IID4AnAeyWUkohhEtK6V+Fb4+I1ol1twkzEa1PUsrLQogFIcR1APoAvAjgRij7qb2oPq0Dyj6gjwL4TSHEO9TjI+rxBQBpKJu7AkAAQAzA54UQ3wPwvZX4Xoho/WKwIqK15HMAPgigH8AXoFSr/o+U8rO5TxJC3AFlqe8WKWVECPEzKEuCABCTUqYBQEqZEkLcpJ7nXQB+A+qGw0RE9WCwIqK15NsA/hcAI4D3QemL+lMhxJellItCiCEoGws7AfjUULUbwOFiJxNCdACwSSm/L4R4AsDFFfkuiGjdYrAiojVDSpkQQjwMwK9WnX4khNgD4CkhBAAsAvglAA8C+DUhxCkAZwA8XeKUnQDuF0JYoPRo/W6rvwciWt/YvE5Ea4batP4CgHdLKc+t9vUQES3HOVZEtCYIIfYCOA/gJwxVRNSuWLEiIiIiahJWrIiIiIiahMGKiIiIqEkYrIiIiIiahMGKiIiIqEkYrIiIiIiahMGKiIiIqEn+f3DCuHExdr35AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = df1.plot(x = 'years',\n",
    "        y = 'rate',\n",
    "       figsize = (10,10))\n",
    "\n",
    "my_fig = plot.get_figure()\n",
    "\n",
    "my_fig.savefig('Lineplot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "532b097c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}