{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/davemlz/spyndex/blob/main/docs/tutorials/pandas.ipynb)\n",
    "![level3](https://raw.githubusercontent.com/davemlz/spyndex/main/docs/_static/level3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After passing levels 1 and 2, you are ready to start this: Level 3 - `spyndex + pandas`!\n",
    "\n",
    "Remember to install `spyndex`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -U spyndex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's start!\n",
    "\n",
    "First, import `spyndex` and `pandas`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spyndex\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `pandas.Series`\n",
    "\n",
    "We have all worked with `pandas`. Well, `spyndex` also works with `pandas` so you can continue using it! :)\n",
    "\n",
    "Let's use a `pandas.DataFrame` that is stored in the `spyndex` datasets: `spectral`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = spyndex.datasets.open(\"spectral\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each column of this dataset is the Surface Reflectance from Landsat 8 for 3 different classes. The samples were taken over Oporto:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>SR_B1</th>\n",
       "      <th>SR_B2</th>\n",
       "      <th>SR_B3</th>\n",
       "      <th>SR_B4</th>\n",
       "      <th>SR_B5</th>\n",
       "      <th>SR_B6</th>\n",
       "      <th>SR_B7</th>\n",
       "      <th>ST_B10</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.089850</td>\n",
       "      <td>0.100795</td>\n",
       "      <td>0.132227</td>\n",
       "      <td>0.165764</td>\n",
       "      <td>0.269054</td>\n",
       "      <td>0.306206</td>\n",
       "      <td>0.251949</td>\n",
       "      <td>297.328396</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.073859</td>\n",
       "      <td>0.086990</td>\n",
       "      <td>0.124404</td>\n",
       "      <td>0.160979</td>\n",
       "      <td>0.281264</td>\n",
       "      <td>0.267596</td>\n",
       "      <td>0.217917</td>\n",
       "      <td>297.107934</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.072938</td>\n",
       "      <td>0.086028</td>\n",
       "      <td>0.120994</td>\n",
       "      <td>0.140203</td>\n",
       "      <td>0.284220</td>\n",
       "      <td>0.258384</td>\n",
       "      <td>0.200098</td>\n",
       "      <td>297.436064</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.087733</td>\n",
       "      <td>0.103916</td>\n",
       "      <td>0.135981</td>\n",
       "      <td>0.163976</td>\n",
       "      <td>0.254479</td>\n",
       "      <td>0.259580</td>\n",
       "      <td>0.216735</td>\n",
       "      <td>297.203638</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.090593</td>\n",
       "      <td>0.109306</td>\n",
       "      <td>0.150350</td>\n",
       "      <td>0.181260</td>\n",
       "      <td>0.269535</td>\n",
       "      <td>0.273234</td>\n",
       "      <td>0.219554</td>\n",
       "      <td>297.097680</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SR_B1     SR_B2     SR_B3     SR_B4     SR_B5     SR_B6     SR_B7  \\\n",
       "0  0.089850  0.100795  0.132227  0.165764  0.269054  0.306206  0.251949   \n",
       "1  0.073859  0.086990  0.124404  0.160979  0.281264  0.267596  0.217917   \n",
       "2  0.072938  0.086028  0.120994  0.140203  0.284220  0.258384  0.200098   \n",
       "3  0.087733  0.103916  0.135981  0.163976  0.254479  0.259580  0.216735   \n",
       "4  0.090593  0.109306  0.150350  0.181260  0.269535  0.273234  0.219554   \n",
       "\n",
       "       ST_B10  class  \n",
       "0  297.328396  Urban  \n",
       "1  297.107934  Urban  \n",
       "2  297.436064  Urban  \n",
       "3  297.203638  Urban  \n",
       "4  297.097680  Urban  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can see the classes stored in the `class` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Urban', 'Water', 'Vegetation'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"class\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each column of the data frame is a `pandas.Series` data type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"SR_B2\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, we can use that to compute Spectral Indices with `spyndex`!\n",
    "\n",
    "Since we have `vegetation`, `water` and `urban` classes, let's compute 3 different indices, each one highlighting an specific class: `NDVI`, `NDWI` and `NDBI`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NDVI: Normalized Difference Vegetation Index (attributes = ['bands', 'contributor', 'date_of_addition', 'formula', 'long_name', 'reference', 'short_name', 'type'])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spyndex.indices.NDVI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NDWI: Normalized Difference Water Index (attributes = ['bands', 'contributor', 'date_of_addition', 'formula', 'long_name', 'reference', 'short_name', 'type'])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spyndex.indices.NDWI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NDBI: Normalized Difference Built-Up Index (attributes = ['bands', 'contributor', 'date_of_addition', 'formula', 'long_name', 'reference', 'short_name', 'type'])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spyndex.indices.NDBI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What bands do we need?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('N', 'R')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spyndex.indices.NDVI.bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('G', 'N')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spyndex.indices.NDWI.bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('S1', 'N')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spyndex.indices.NDBI.bands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Green, Red, NIR and SWIR1 bands... easy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    \"G\": df[\"SR_B3\"],\n",
    "    \"R\": df[\"SR_B4\"],\n",
    "    \"N\": df[\"SR_B5\"],\n",
    "    \"S1\": df[\"SR_B6\"],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our `dict` of parameters ready we can compute the indices!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = spyndex.computeIndex([\"NDVI\",\"NDWI\",\"NDBI\"],parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, what's the data type of the result?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "idx type: <class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(f\"idx type: {type(idx)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's right! A `pandas.DataFrame`! Why? Because each computed spectral index is now a column (`pandas.Series`) of a new dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NDVI</th>\n",
       "      <th>NDWI</th>\n",
       "      <th>NDBI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.237548</td>\n",
       "      <td>-0.340973</td>\n",
       "      <td>0.064584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.271989</td>\n",
       "      <td>-0.386671</td>\n",
       "      <td>-0.024902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.339326</td>\n",
       "      <td>-0.402815</td>\n",
       "      <td>-0.047615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.216278</td>\n",
       "      <td>-0.303482</td>\n",
       "      <td>0.009923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.195821</td>\n",
       "      <td>-0.283852</td>\n",
       "      <td>0.006815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       NDVI      NDWI      NDBI\n",
       "0  0.237548 -0.340973  0.064584\n",
       "1  0.271989 -0.386671 -0.024902\n",
       "2  0.339326 -0.402815 -0.047615\n",
       "3  0.216278 -0.303482  0.009923\n",
       "4  0.195821 -0.283852  0.006815"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want them diectly on the original dataframe as new columns, you just have to play with the code a little bit!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "indicesToCompute = [\"NDVI\",\"NDWI\",\"NDBI\"]\n",
    "df[indicesToCompute] = spyndex.computeIndex(indicesToCompute,parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, if you check you original dataframe, you should have the new indices there!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>SR_B1</th>\n",
       "      <th>SR_B2</th>\n",
       "      <th>SR_B3</th>\n",
       "      <th>SR_B4</th>\n",
       "      <th>SR_B5</th>\n",
       "      <th>SR_B6</th>\n",
       "      <th>SR_B7</th>\n",
       "      <th>ST_B10</th>\n",
       "      <th>class</th>\n",
       "      <th>NDVI</th>\n",
       "      <th>NDWI</th>\n",
       "      <th>NDBI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.089850</td>\n",
       "      <td>0.100795</td>\n",
       "      <td>0.132227</td>\n",
       "      <td>0.165764</td>\n",
       "      <td>0.269054</td>\n",
       "      <td>0.306206</td>\n",
       "      <td>0.251949</td>\n",
       "      <td>297.328396</td>\n",
       "      <td>Urban</td>\n",
       "      <td>0.237548</td>\n",
       "      <td>-0.340973</td>\n",
       "      <td>0.064584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.073859</td>\n",
       "      <td>0.086990</td>\n",
       "      <td>0.124404</td>\n",
       "      <td>0.160979</td>\n",
       "      <td>0.281264</td>\n",
       "      <td>0.267596</td>\n",
       "      <td>0.217917</td>\n",
       "      <td>297.107934</td>\n",
       "      <td>Urban</td>\n",
       "      <td>0.271989</td>\n",
       "      <td>-0.386671</td>\n",
       "      <td>-0.024902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.072938</td>\n",
       "      <td>0.086028</td>\n",
       "      <td>0.120994</td>\n",
       "      <td>0.140203</td>\n",
       "      <td>0.284220</td>\n",
       "      <td>0.258384</td>\n",
       "      <td>0.200098</td>\n",
       "      <td>297.436064</td>\n",
       "      <td>Urban</td>\n",
       "      <td>0.339326</td>\n",
       "      <td>-0.402815</td>\n",
       "      <td>-0.047615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.087733</td>\n",
       "      <td>0.103916</td>\n",
       "      <td>0.135981</td>\n",
       "      <td>0.163976</td>\n",
       "      <td>0.254479</td>\n",
       "      <td>0.259580</td>\n",
       "      <td>0.216735</td>\n",
       "      <td>297.203638</td>\n",
       "      <td>Urban</td>\n",
       "      <td>0.216278</td>\n",
       "      <td>-0.303482</td>\n",
       "      <td>0.009923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.090593</td>\n",
       "      <td>0.109306</td>\n",
       "      <td>0.150350</td>\n",
       "      <td>0.181260</td>\n",
       "      <td>0.269535</td>\n",
       "      <td>0.273234</td>\n",
       "      <td>0.219554</td>\n",
       "      <td>297.097680</td>\n",
       "      <td>Urban</td>\n",
       "      <td>0.195821</td>\n",
       "      <td>-0.283852</td>\n",
       "      <td>0.006815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SR_B1     SR_B2     SR_B3     SR_B4     SR_B5     SR_B6     SR_B7  \\\n",
       "0  0.089850  0.100795  0.132227  0.165764  0.269054  0.306206  0.251949   \n",
       "1  0.073859  0.086990  0.124404  0.160979  0.281264  0.267596  0.217917   \n",
       "2  0.072938  0.086028  0.120994  0.140203  0.284220  0.258384  0.200098   \n",
       "3  0.087733  0.103916  0.135981  0.163976  0.254479  0.259580  0.216735   \n",
       "4  0.090593  0.109306  0.150350  0.181260  0.269535  0.273234  0.219554   \n",
       "\n",
       "       ST_B10  class      NDVI      NDWI      NDBI  \n",
       "0  297.328396  Urban  0.237548 -0.340973  0.064584  \n",
       "1  297.107934  Urban  0.271989 -0.386671 -0.024902  \n",
       "2  297.436064  Urban  0.339326 -0.402815 -0.047615  \n",
       "3  297.203638  Urban  0.216278 -0.303482  0.009923  \n",
       "4  297.097680  Urban  0.195821 -0.283852  0.006815  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beautiful! Right?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, just for the sake of life, let's make some visualizations!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define some colors for each one of the classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [\"#E33F62\",\"#3FDDE3\",\"#4CBA4B\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create a gorgeous pair grid!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lp1HRSqUuEMBt4Zi6nJOtOUliikyWmF/kgl0YmwfRpRK5O3+GHk+gRyZhLEHhp/dLsBNrim/LRmBm96tz9Ow2h3oihfvgYfTDR2RWrFgzZParWJCXL6BdFxUoT0xQIX9LJ0pkK2Pq5jM1ri5MuImt6hz2ts14QyN4ZybJHj0JQCgWxbn/EFMr/MmMWLGWFCcmqR6Bm8nmpj/2CiVCyRzeAwehLyazYkXHk1AnFuTFUxjR8PTEBCMYwBmNt6w9OWfxUCe7SswvcsEuSieG0KfH8EbjWJsHycfTeKks9s8fk2An1pSpZU3sc7dR+MHdGP1deEeHiPeU/+hzSnn0iUn6RkKEYxHMS3bhgQQ70bEk1IkFTYW6KSroL+8F2yI5N1dzOZMptrIpuaUmtqjzTFXrAmcmOX34KOO+cmDvHXHYBJQ2D8qOE2JNmBorB2Bs3YhzegwvX8AZyTM6GIHBCMXhPMrQjI+MsP1BJNiJjiZj6sSC3MkkRiQ4fV8F/a2dKOHk8EmlbkUiF+zC/7T9qA099PiD6GyOEV3kgJvm9JMnKX31+0x++/utbqYQy1YriNnnbsPY1E84FkFXumB79+1h44uew+hghKGA4vjICO6Dh2UdO9GxJNSJBXnxFCpcFeoCfrxMboFnrK5ypc6e93HLsCh5UqlbTOSCXZhP3YN/oBtdKOGms4R37eBAPs7pJ07g/vwRmTghOt7UZImp7cKMrRtRA930+UPkjxyfPm8q3M0OdtWVPiE6gYQ6saDq5UygHOp0C0NdppSpY/arVOrqYW/bjP3S57C7ZwCdL5AfHsXs6WJcOeQeOCwzYkVHm6rWVQe76mrdVjPI8O0zK9JTwe7Y0Gm802PTy51ItU50Cgl1YkHuRBIVOrsunBH042XzLWtP1skuOFHCMmwKsqtEXabWrgu/+OlcFOzBGY8T2DjAsFtgspDDu/+QBDvR0aqDnW/LRooTk+i+GOYlu+iz/GzOa4Zv/z4TDzzCxAOPlM/d2M8pVSR98BhqPNHK5guxZDJRQizInUxiDlStyu6zoeSgHQdlNf/tk3UyDAYH533cUiYlCXV1m5rluhXglm/x8IFDWH3djOXy9IzGCZ4eK28lNut8ITqRb8tGioAKBolcsAMOn4R8Fhs/x4ZPMUE52BnhEOP5AqH7HseUZU5EB5FKnViQN5nEqB5Tp1R5skSmNdW6rJtbsPvVMiyKMqZuSaYmTmx51mVcFOjGGY+jQkEms2msnu4ZW4lJ1U50mtlBzLdlIzoWwbriIiK7ttLnD9E9meHSLecyMJKmODyGb2M/KhQkm86iHz4yPWlCiHYnlTqxIDeenN5NYooKBcqzx2KRprcnt0j3q61kR4nliFywC14OWwB+eC+qqFHBAKkf3Efo0vPxgBLlcXjpg4elYic6no5FMDb10+Wz8caSUCzRbwfQiRxqJI012Mf4kye599ytqFAQNRHHfORxXrbn/FY3XYh5SagTCypPlAjOOGYE/Hjp1kyWWGjvVwDTsChK9+uyVAe73AOHMYLlf3c9Mom1fSO66tx2D3a3DY9w3cb5u+nF+lU9o1Xt3YVKpOHkMIwniPR3ERqL8O0LdqAsC3XZRViuh87kIZ3FBb55ply1u3bDQIs+AyHmJ6FOzEtrjZdIz5j9CuW16nS2NaFu0YkSMqZuRaaCHYA+M0kxNf9C0+0a7G4bHpnxfwl3IrJ755wZrFO7TehYBJ2L8YOLz0cn0xg+G388jc4WMGwbbNAGkMtDJERxfBJfX4+EO9GWJNSJeelMrvzXqm/munDK72vZmLq8m194TJ2yKEj364pMBbvCT+/Hd/8hANzjwxjd0dY2rMpUYJsyFdxmH586JsFOzA52d0zEMQf7AXCD5eq/6oqgAbXRD8lysFN+HwrQgK5U64qAr688gUzCnWgnEurEvNzJJEY0NOd4K9eqW2zv1/LiwxLqVmqqAlcA9OkxALyhEdi2ecZ57VKtqxXmZj8uwW59++aZUYolBypBzqx6zIxGcFNp8FdtQVgJehowfHY52Nnz/8r85plRCXai5STUiXl5kylUV3jOceX34bWg+9XVLkWvuODer5ZhyTp1DTIV1konhvCGRjA2D1I6MYQ9K9h1Cgl269NUJQ3K1bXi+GTN88zozIlfLkAkhE5nFwxzs19Lgp1opQWXNFFKpZRSyRq3lFIq2axGitZwJxMYkVqhzm7JVmFTVTql1LznWMqS2a8NNLVAsbF5cM7xKZ20zMliFT2x9vn6eqa7Tmcfqz5uRiOY0QgqEipX8KZuFfOFw2+eGZ0RJIVopgX//NBat88gGtF0s7cIm6L8vpbMfs06WYJmcMFzLMMi68w/uF8sXeSCXdOLD88+3omkYrd+LBSuZge7+Y4v909EqdqJVlisUvc1pdRrlVJzf7OLNc+dSGCE5y4fUh5T1/zglHWyCy5nApWJEp7s/dpokQt2dWyIq0UqdqJetSp7U8cXIxU70WyL7SjxMeClwDGl1M1KqZcrpeYf0LRESqlrlVIHlVKHlVJ/WuNxpZT6YOXxB5VSlzXqtcXivPFEuethFhXw4aWbH+oyTmbBSRIwNVFCdpRYLe0S7qTSJpqtuou2nkA3RYKdaKYFQ53W+ita69cA24EvAq8DjiulPqGUumYlL6yUMoGPAC8G9gCvUUrtmXXai4HdldubgX9byWuKpSnPfq0xpi7YmtmvuToqdbYhO0qI+ki1TjSLBDvRLHXt/aq1zmmtb9Za/xLwC8ClwDdX+NpXAoe11ke11kXgc8D1s865Hvi0LvsJ0K2U2rTC1xV18ibmW9KkNevUZd36ul9l79f1oRHVOgl2a1s7jWmTYCeaoa5Qp5TaoJT6PaXUj4AvA98CnrrC194CnKi6f7JybKnniFXixlNz9n2FyjZhLajUZZwsvkW6X23DlnXqhBBtSYKdWG2LTZR4k1Lqu8C9wAXAn2itd2qt36m1vn+Fr11rXQq9jHPKJyr1ZqXUPUqpe0ZH5RunEead/dqixYezTobAArtJwFSlrv1Cnbw/25dU68rkPdocEuzEalqsUvcM4H3ANq3127TWP2rga58EtlXd3woMLeMcALTWN2qtL9daXz4w0D4l906lCyV0qYQKzg1RKuhvyeLDmVIGn7nwPB3LaM9QJ+/P1dGoCRMS7Nbue7SdumCFWG2LTZR4A/Bz4K1KqY9Ubm9TSvU14LXvBnYrpc6tzKi9Abh11jm3Ar9RmQV7FZDQWp9uwGuLRbjxJEZXpOZCvyrgR2fzaF2zaLpq0k5m8TF1hoXjOU1qkWgHMhNWLKbdgp1U68RqWaz79SnAw5THzz0OHAKuAB5SSl24khfWWjvA24DbgUeBz2utDyil3qKUekvltNuAo8Bhysur/O5KXlPUz5us3fUKoCwTDANdaG5FLF1Ky0QJsWqkWieaSYKdWA2LbWj318Dbtdafrz6olHoF8LfAK1by4lrr2ygHt+pjH636WANvXclriOVx47Vnvk4xQgF0OguBhce4NVLGSRM0dyx4jkyUWJ+u2zjYkFAmu02sXdduGJAgJda8xcbUXTw70AForf8buGh1miTagTeZRIXn35JLBQNNnwFb1+LDShYfFkLUthrdsA9MxqdvSyUhUzTaYqEus8zHRIcrz3xdINSF/E3fVSLjZOra+7XklZo+3k+0nkyaEPVoZLCbCnK7wuHp+0sNdxLsRCMt1v06qJT6gxrHFdBeI09FQ7kTSVRo/gBlBAPodHMrdVknS9BaONQZysBQBo52sJXdpJaJdtGoblixtk0Fu0YEqqlAN/X/w5kMD0zG2dfTveJrC7FU9ez9Gq1xiwAfX92miVZyJ5PzTpSA8gzYZlfqsnVsEwbSBStWToLh+tCIqt3hzMxOq6lwJ0QrLFip01r/VbMaItqLF09in7N53sdV0I+Xal4PvOu5FN0iPmPhdeqgsv+rVyTE/KFUrF1SrRNLsZKq3b6e7prdrbvC4SVV6755ZrTtll0RnWnBUKeUetcCD2ut9V83uD2iTXjxFGqhMXV+H16qeZW6tJMmaAUx1OI729mGTdGVGbDrWSOCncyEXV9WEu4OZzI1K3TSDSuarZ6JErNvAG8E3rmK7RIt5iXSGOEFul9DAbxk8yp1mVJ60UkSU8oLEEv363ongUwsx1IrZlOhTbphRTtYbEeJD0zdgBuBIPAG4HPAzia0T7SIl0wvWKkzQgG8ZLpp7Uk7mUUnSUyxlE1B1qoTrDzYSTfu+nTthoElhbv5gt1SyCxY0QiL9mUppXqVUn8DPEi5u/YyrfU7tdby026N8vIFtOuh/POPX1OhAF6imaGu/kqdbViyALGYJhU7sVzNDnZCrNRi24S9n/IerSnKCxH/X631ZFNaJlrGS2YwIqGa+75OMULBpk6USJdSdVfqyhMlpPtVnLWSYCfVuvVtqcFuX083hzMZCXeiJRZbp+4PgQLwF8CfV/2SV5QnSnStYttEi3iJ9ILLmUALKnWlxRcenmIqi6JbWOUWiU4js2LFci11EoVMjhCtstiYOkNrHdRaR7XWXVW3qAS6tctLpBfcIgzACDe3UpcqpfDXsUYdlLtfpVInapGuWLESq73siIyrEyu1+PoQYt3xkmmMRUKdCgfx0lm05zWlTclSglC9EyUMi6InlTpR23UbB5cc7qTCJ6bIenKinUmoE3N4qQwquHBVTFkmyu9DN2lXiWQxVf+SJsqi5Dmr3CLR6aRqJ5ZLgp1oVxLqxBxeIo0R8i96nhEN406mmtCiqUpdfTtEWIaMqRP1WUqwk2qdqCbBTrQjCXViDjeRhkUqdQBGNISXaE6oS5fS9Yc6JWPqRP2kYieWS4KdaDcS6sQcuo4xdQBGJIQ7kWxCi8oTJULmEip1sk6dWAIJdmK5Gh3sZLKEWAkJdWION5lBBRfvflWREN5EogktqoS6pVTqpPtVLFE9wU66YEUtS92BQojVIqFOzKGTGYxQvZW61Q91rnbJOtklLT6cl9mvYhmkYidWQoKdaDUJdWIOL51F1TNRoiuMO7r6G4ykS2mCVhBTmXWdbxkWJVe6X8XyLBbspFonFiLBTrSShDoxh5fKoOqp1MWiTQl1yVKSsBWu+3xb2RSkUidWQCp2YiUk2IlWkVAn5vAyOYw6xtQZsQjuWBNCXTGxpFBnGRYFqdSJFZJgJ1ZCxtmJVpBQJ2bQhXIYUj570XON7ijuaHyVWwSJ4tIqdbKjhGiU+YKddMGKekmwE80koU7M4KUyGKH69lhVoQB4Ht4q7yqRKCUI2UvofjVsqdSJhpGKnVgpqdqJZpFQJ2bwUllUHWvUASilMPpiOGfGV7VNyWKCUJ1bhEF5TJ1U6kQjSbATjSDhTqw2q9UNEO3FS2Ux6thNYorZ1417egzO27ZqbZosTC5tooRhU5RKnWgwCXaiUWYHu6kFhyXwiZWSUCdm8NL1LTw8xeiJ4pxe3RXQJ4uTbA1vrft8y7Bk9qsQomNImBONIt2vYgYvlUUtpVLXE8M5ubqDxicLk0StaN3n+wyfVOqEEEKsOxLqxAxeOosK+Oo+3+jvxjl5ZhVbVJ4oEbEjdZ8ve78KIYRYjyTUiRl0KlPXGnVTzIEenFOrG+rixThRe4mVOgl1Qggh1pmWjKlTSvUCNwPnAE8Cv6K1nrOKrVLqSSAFuICjtb68ea1cn9xkBgJLCHV93bhjcXTJQdmNfzsV3SIFt0DArL9L2DZsCXVCCCHWnVZV6v4U+I7Wejfwncr9+TxPa71fAl1z6CWsUwegLBOzL4YztDrj6iaLk3TZXRiq/reqpSwcz8HT3qq0SQghhGhHrQp11wOfqnz8KeDlLWqHmKU8UaL+Sh2AOdiHc3x4VdozWZigy+5a0nOUUlhSrRNCCLHOtGpJkw1a69MAWuvTSqn5FoDSwLeUUhr4d631jU1rYYfKeR4fm0jw3XQOpeAXIiHe0NNFwKgvv3vpZYS6gW5Kx09T//LA9RsvTBD11T+eborPsJfcbSuEEEJ0slULdUqpbwMbazz050u4zDO11kOV0HeHUuoxrfX353m9NwNvBti+ffuS27sWZD2PdwyNEjMNfq8vhgvcnsry+0OjfGDTAFFz8WDnpZe2+DCAOdBL6YmhZbZ6YROFiSVNkpjiM3wU3PZZq07en6LdyXtUiM63at2vWusXaq0vqnH7CnBGKbUJoPL/mgOytNZDlf+PAF8Crlzg9W7UWl+utb58YGD9LeSotea9IxP0WiaviUXotUwGLJNf7Y6wwTJ515kxXK0Xv046t4zu116c46eX2/QFjeXHlrRG3RSf4WurBYjX+/tTtD95jwrR+Vo1pu5W4HWVj18HfGX2CUqpsFIqOvUx8AvAw01rYYf5bibH0WKJX+4Ko5SaPq6U4uVdYbKe5r8mk4tex8tkUUuYKAFgbujDOXEGXUdoXKrR/ChdvqWNqYPyDNh2qtQJIYQQq61Voe59wDVKqUPANZX7KKU2K6Vuq5yzAfihUuoB4GfA17XW32xJa9tc1vP4yHicV8Yi2FWBboqhFDd0R/hCMs3RYmne62jXQ+eLKP/SKnVGOAiWiTceX2rTFzWaHyVmx5b8PJ/pI+/mG94eIYQQol21ZKKE1noceEGN40PAdZWPjwL7mty0jnRLPMW5PptzfPa853SbJtdGwvzD6CQf2Twwo5o3ZWqShDLmPrYYa2MfpWOnMft7lvzchYwXxoj5lh7qbMOWUCeEEGJdkR0lOlza8/hCMs2LIqFFz70q5CfjeXwnk6v5uE5lylW3ZSiPq2vssiaudpksTC4r1JUnSkioE0IIsX5IqOtwX06kudDvY8AyFz3XUIqXdYX59/EEBW/u+DcvnUWFlhnqBnopPXlqWc+dT7wQJ2SFsI35K5DzsQ0feRlTJ4QQYh2RUNfBCp7mC4k0Vy+hurbTZ7PFNrklkZrzmJfKoEJLG083xdzQS+mJxoa6kfwIPb7ldef6pPtVCCHEOiOhroN9J51li22xeYl7rr44GubmRIqE68447qWWvkbdFHND43eVOJM7Q49/uaFOul+FEEKsLxLqOpTWmpsTKZ4dXnoIG7RM9gX8fHrWEifL2SJsitEdRecLeKnssp5fy5ncmWWNp4PyRImchDohhBDriIS6DvXzXIGS1py/wIzXhVwTCXF7OsvpkjN9zEtnUIHlhTqlFOaGPkrHG7ezxFD21Aq6X31kncYFTCGEEKLdSajrULckUjwrHKy5NEk9ukyDZ4WC3DiRmD7mJdLLrtRBpQv2WON2lhjKnqbX37us5/pMHzmn9ixfIYQQYi2SUNeBTpZKPFIo8tQVBDCAq8NB7ssVeCxfBMBLZjCWuJtEtUbvAXsmN0yfv29Zz/UbfnKuVOqEEEKsHxLqOtAtiTRPDwbxLbNKN8VvKF4UDfGh8Thaa7xkGrXMdepgagbsyRW1aUrezZMqpZY9ps5v+qX7VQghxLoioa7DpFyPb6ezPCO8sirdlCuDflKex3czObxEZsn7vlazNvY3rPv1TG6YvkAfhlreW1S6X4UQQqw3Euo6zJeTafb6fcTMxRcbroehFC/vCvOR8ThOMo2xzMWHAYy+GG4ijZdd+azToewQ/f7+ZT8/YATIuRLqhBBCrB8S6jpIwdP89xIXG67HuT6b830+son0iip1yjAq1bqVj6s7lRla9hp1UO5+lUqdEEKI9URCXQf5eirNdp/FpiUuNlyPl0SDWJksJ3wru7a5sa8hO0uczJxY9iQJqIypk4kSQggh1hEJdR2i4Gn+K57iBQ2u0k2J5Isoy+Qz6SxFPXdf2HqZg70Uj6x8ssTJ7En6AyvofjUDUqkTQgixrkio6xBfTqbYaltsX+Ziw4tRyQxEwvRZJl9Oppd9HWvTAKUjJ1bcnqHsEAP+gWU/3zbKX6eiW1xxW4QQQohOIKGuAyRdj8/E01wbCa3aaxiJFDoS5NmhII/kizyQKyzrOubmAZwVdr+mS2mKbpGoHV3RdYJWkIyTWdE1hBBCiE4hoa4D/MdEgksCvlUZSzfFiKfxIiH8huKaSJDPJ9KccZzFnzj7OrEI2nFxq3aqWKpT2VMMBgeXvVvGlKAZJF1aftVRCCGE6CQS6trcI/kCd2ZyXBtdvSodVCp1lZmvG2yLp4cC3DiRIO15S7qOUgpr6wZKh5ffBXsyc3JFy5lMCVoh0o6EOiGEEOuDhLo2lvc83jMyyfVdYcLG6v5TGZNJdOTsJIwLAzbn2jYfm0gseeKEtamf4uHjy27Licxxelcw83VKyAySKqVWfB0hhBCiE0ioa2P/MhZno21y6Qr3eK2HmkjgzRqzd2XIT8Qw+NhEgtISgp25eYDSwSeX3Zbj6RMMrGDm65SgJaFOCCHE+iGhrk3dmkhzX77AK7rCTXk9cyyBjs58LUMpnhsOYCq4cSJBoc5gZ23dQPHx5VfqTmZOMBgcXPbzpwTNEKlScsXXEUIIITqBhLo29ONsjv+YTPKbPV0EVrnbdYoxEcerESANpXh+OIhPKT48FiflLj7Gzhzsw5tI4KWXvvhvySsxmh9tyJi6sBUiXoyv+DpCCCFEJ5BQ12buyeZ578gkb+iJMmA1Zn/XehjjCXQsUvsxpbg6HGCzbfGBsUlOlhaeFatMA2vbRorL6II9lTlFX6APy1j5TN+wHWayMLni6wghhBCdQEJdG7krk+PdIxO8rifKjlVaZLgm18OIp2pW6qYopbgi5OeqUIB/HY/zo0wOmL871tq2geKjTyy5KU+mn2RDYMOSn1dL1IpKqBNCCLFurN7CZ2JJbk2k+Y/JJG/sia7arhHzMSYS6HAQrMXfDrv8Nn2WwR3pHI8VitzQHa05M9favonCQ4eW3JYnUkcbMp4OIGpHmShONORaQgghRLuTSl2LuVrzkfE4n4mneGtfrOmBDsA8M47bG6v7/B7T5BVdYWyl+LvRSQ7k5+4+YZ2zmeIjR9BLXOfucPIwm4Obl/Sc+XT5upjIS6gTQgixPkioa6Gk6/Enp8d4MFfg9/pjTR1DV80YGsXrqz/UAZhK8YxwgBeGg9ySSPNf8RQ572x3rNkdxQj4cZ4cqvuanvY4kjrClvCWJbVlPmErTM7NUXCXt+WZEEII0Ukk1LXIo/kiv3XyDF2mwZt6u1Z9ceGFWMeH8ZZQqau22Wfx6liEoufxvtEJHi2cDVD2ru3k73tsznO01nz12K3cfPRzlLzS9PFTmVMEzMCK93ydYiiDXn8vZ3JnGnI9IYQQop1JqGsyR2s+NZHgncOj/GJXiJd1hTFXuMfpSllPnMLbuPwdHGxD8ZxIkKvDQW6Op7mpUrWzd28n/5MH55z/xWNf5LZT3+Bnoz/jxsf+ffr4g5MPck7knGW3o5b+QD+nsqcaek0hhBCiHUmoa6IH8wXedPIMP8kVeEd/N5cEVn+niEVpjXX0JO6mgRVfapvP4ldiEXKex3tHJzh0zmaKDx/Gy+amz5ksTHLLE5/nhnNfzQ07b+Anoz/hkfgjAPx45C52d+1ecTuq9QcGOJY61tBrCiGEEO1IQt0qc7Xmp9k8fzg0yl+dGedZ4SBv6onSY7Zm/NxsxvA4aI3X05guT1+laveCcJAvlUqc2raRoTt/Pv34zUc/x/7eS+n19xIwA1y75Vo+eOCDPJ54nCPJI1zYfWFD2jFlc3ATjycPNvSaQgghRDuSJU2WwdGaSdcj7XkUPI2LxgM8DQWtSXkew47DwXyJ+/MFekyDq0IBbuiOYLW4q3U23z0HKO3eDg1u1xafxa/YEYb2X0Dxlm9xyxV7+IVAgTuH7+T39/z+9HkX91zM8cxx/vjuP+bl21+Oz/A1tB3nRM7hqye+iuu5mEZ7BGkhhBBiNUioW0DB0zxZKnG4UOJQscjRYolTJYe46xExDMKGwlYKk3ImMlBYCkLKIGYabLFNnhuO0duiWa2L8jyCX/8B+RdcuSqXN5Vi26UXEP7+zznyo/v5/Yt2sHHrH/AkXVyoS9iqvKjxS7a9hBdvfTGmavzXqcvXRb+/n7vH7uaqwasafn0hhBCiXayrUOdoXb5VPi5oTdYrV9YSrse44zLsOJwoORwrOZxxHAZNi822ySbL5OmhAIOmScw0Wj65YcXyBcKf+AoYBs75O1btZTxg+CVX8IwbP0f6JZtJPvd6bssF+aQXYavpsN102Gh69BoeEeURVBq/0lgKfGjMBnyZn7PxOXzs4I30B/rZ1bVr5RcUQggh2pDSev6tnjqVUmoUmBod3w+MoRTPPXz8qS1sVlt54Xd/xuv/6+stee3X3fiXuHXsXmFmHyF0+oMNfe1vXn/7fU7Omb0icj/wmNb62oa+2DxmvT9XU/m93z7aqT3t1BZYvD1jzXp/woreo+32dV2MtLdxmvoeFbWtyVBXTSl1j9b68la3o53I12Smtfr1aLfPq53a005tgfZrz3J12uch7RVrjcx+FUIIIYRYAyTUCSGEEEKsAesh1N3Y6ga0IfmazLRWvx7t9nm1U3vaqS3Qfu1Zrk77PKS9Yk1Z82PqhBBCCCHWg/VQqRNCCCGEWPMk1AkhhBBCrAES6oQQQggh1gAJdUIIIYQQa4CEOiGEEEKINUBCnRBCCCHEGiChTgghhBBiDZBQJ4QQQgixBkioE0IIIYRYA9ZkqLv22ms1IDe5LeXWNPL+lNsybk0l71G5LeMm2sCaDHVjY2OtboIQ85L3p2h38h4VojOtyVAnhBBCCLHeSKgTQgghhFgDJNQJIYQQQqwBEuqEEEIIIdaAloY6pdQnlFIjSqmH53lcKaU+qJQ6rJR6UCl1WbPbKIQQQgjRCVpdqfskcO0Cj78Y2F25vRn4tya0SQghhBCi47Q01Gmtvw9MLHDK9cCnddlPgG6l1KbmtE6I9pFzcnzm8GfIOtlWN0UIIUSbanWlbjFbgBNV909Wjs2hlHqzUuoepdQ9o6OjTWmcEPVa6fvzu6e/w+eeuIk7T9/Z+MYJgfwMFWItaPdQp2ocq7lytdb6Rq315VrrywcGBla5WUIszUrfn/eN38+u6C4emnxwFVonhPwMFWItaPdQdxLYVnV/KzDUorYI0TJPpI5yWd9lHEsfa3VThBBCtKl2D3W3Ar9RmQV7FZDQWp9udaOEaKaSV2KiMMH5sfM5kzuD1rLNohBCiLmsVr64Uuom4GqgXyl1Evg/gA2gtf4ocBtwHXAYyAJvaE1LhWid4dwwPb4eQlYIy7BIlpLEfLFWN0sIIUSbaWmo01q/ZpHHNfDWJjVHiLY0khuhx98DQLevm4nCuIQ6IYQQc7R796sQ695ofnQ6xEXtKOOFhVYBEkIIsV61tFLXibTWHCqWeKxQxFKKPX4f5/jsVjdLrGFj+VGidhcAYStCvBhvbYOEEEK0JQl1S/RwocgfDI1SqtyPGop/2jTALr+vpe0Sa9dYYZzYdKgLkZBQJ4QQogbpfl0CR2u+EE9NBzqAlKf5STbfsjaJtW88P07UFwUgZIWIFxMtbpEQQoh2JKFuCTytGXHdOcdHnbnHhGiUyeIEUetsqEsVky1ukRBCiHYkoW4JfIbBy7sic44/KxxsQWvEehEvxInalVBnhkiUJNQJIYSYS0LdEl0VCvD2vm4GTJMtlsW7Bnu5KCDj6cTqcLVL2kkTtsMABK0g6VKqxa0SQgjRjmSixBLFTJNfikW4OhzEUOX7QqyWZDFJ0AxiqvL7LGgGSTvpFrdKCCFEO5JQt0w9loQ5sfoSxcR01yuUK3XZUraFLRJCCNGuJNS1QMp1OVwsMe64bLItzvPZBAzpCRdzxYtxIvbZcZxBM0jGybSwRUIIIdqVhLomy7oen55McUuy3IW2x+/jNbEI/ZbJFtumy5RwJ86KF+OErfD0fZ/ho+SVcD0X05BqsRBCiLMk1DXZsVJpOtA9LxzEUop3jUyggb0+m3cO9rJddqgQFYliglBVqFNKETADZN0sUSO6wDOFEJ3stuGRGfev2zjYopaITiJloSZLeh5Q/sLv8Nnckc6iK48dKJb4SjKDq/W8zxfrS7wYJ2TNXDInYAWkC1aINei24ZHpW63HhFiMhLom22xZBJWi2zQYdZw5j/8omyNdCX5CTBYnpxcenhIwA+ScXMNfyx2Pk7zpGw2/rhBifgsFOSGWSkJdk23z2bx3Yz8xw6C/xnIo+wN+QjJpQlTEC5PTa9RNCZgBsk7jZ8BmbvshyX//As4p+eUixGqTICdWg4ypa4H9QT//tGmAMcfhULHEXZW9YzeYJq+KRbCVanELRbuIF+NErJm7mPgNP7lVCHXFg0+W/3/4ONYWGb8jxGqQICdWk4S6Fum2TLotk/89YHGs5FDwNNt8FoOW/JOIsxLFxIwlTQB8po+c2/juV2doFHvXNqnUCbFKVhLoZKKEqIckiBaLmiYX1eiGLXqaotZEZImTdUtrTbKUnLGkCZQrdavR/eqOThC46hLcM+MNv7YQ69lKq3MS6ES9JNS1oQdyeW6Op7jQ76fHMggqxQV+H9tkqZN1Zaoa5zf9M47bRuMrdbpYQmcLWJsGKB052dBrC7GeSXeraCYJdW3mUL7InwyP8yuxCLelMww7LgBRQ/GBTQOc7/e1uIWiWeLF+Iwtwqb4TB85J9/Q13InkxhdYYyuCO5EvKHXFmI9alSYkyqdWArp22szh4pFfApyWk8HOoCUp/lKMo0na9itG5OFyZqhzm/4yLqNXafOm0xhRMOoSBAvnm7otYVYbxYKdPdOTE7fFiOBTiyVVOrajKUUPabJaFWgm3KoUKKkNf6q2bFFT3PGcTAU9BgGjxRK/E8mS5dh8NxwiAsDUtnrVPFi7VBXrtQ1tvvVS6QwIkGMcBAvKaFOiOWYL8xVB7jdkfIY2UNpWUBcNJ6Eujaz2+fD0ZrzfDbfy8z8xf0L0RD+qjXszpQc/jOe5LZUlqABb+3t5u/H4tOPfzGZ4cObB9gtXbYdaaIwOWfmK5QnSsQL8Ya+lptIo0JBVCiAzhXQjouyZG9ZIepVK9DVCnP1kiqdWA7pfm0z5/pt/mKwlwHT4Fe7IwSUwgJe2RXhueGZ20V9L5Pja6ksHnBJIMCXkzP/8itozc9zheY1XjTUeGFszsxXAJ/hI9vgiRJeMo0K+VGGgQr68dKNn10rxFq1UKDbHQlLoBNNI5W6NrQn4Ge330fWcbkuWv5hsMGysKq6XeOOw7erfvGawNwOWyjKGLyONZYfo9/fP+e4z/RRcBs7UcJLZjCCAQCMSBAvncXsntv1K4Q4a7nVOel6FatFQl2bspUiZlvEajyWcV2+lczw1ICPK0IBYoZBQXtc6PdxpFiaPs8EugyDE8WSLIfSgcby4+yM7pxz3Gf4yK1CqFOVUKdCAbyk/NIRYiGLVecWc1lvT83jUqUTKyGhrgM9WXIIWSZpBz6XSFHUcLHfx2u6o7yzv5tvpMsTJS4PBrgpkSLtefyahLqOM14YI+abG+t9xipU6lJpzA19ABjBAFq6X4WY10oCnVTpxGqSMXUdKOl6mMCn4+VAB/BQocgd6SynSw5bLYuE6zLiuJxxXO7JNTYAiNWntWaiMEG33T3nMZ/pI9/wUJc9W6kL+vFS8otHiFpWWqEDqdKJ1SOVug601bZ4tFCcc/zeXIGucJBuy+S2dJb9lV/Sz5o1wUK0v0QpgW3Y+My5M5fLlbrGToDRqez0mDoV8ONlGr+3rBCdbKHlSuoNc1KlE6utpZU6pdS1SqmDSqnDSqk/rfH41UqphFLq/srtXa1oZ7vZaltssefm8XN9NiOOg6s159gWW22L3+mNETMMjleNtRPtbyR3hl5/b83HfMYqVOoyWVSwvB2ZCvjwUtL9KsSU+apzywl0UqUTq6lllTqllAl8BLgGOAncrZS6VWv9yKxTf6C1fknTG9jGlFJcFvRzRdDP3ZUlSyKG4lnhAGcclyP5Ir/bF+NvRiZJeh5QnjDxng29XFSpxoj2diZ3hh5f7R/+tmFT8kp42sNQjfm7zEvnzoY6v3S/CjGlEd2tU+YLdEI0Siu7X68EDmutjwIopT4HXA/MDnWihgHL4s8HenmsUGTcdVEoPK3Zbln0hAzuyuanAx1A0vP4RjrLBssiZpqcKpUoaM1m26bLlKGV7WY4d4ZuX3fNxwxlYBkWRa9IwGxMSNeZqlAX9MlECSFoXKBbrNtVqnSiUVoZ6rYAJ6runwSeVuO8pyulHgCGgD/SWh+odTGl1JuBNwNs3769wU1tP6dKJe7K5LkvX+CpQR9pV/PpeAoX2OP3ETTUnOeccVzOOA5fTKa5OZHGAy7w2fzZYC87qmbHJishMSphr2GW+v4cyp6i29897+NTXbCNCHXacdHFEspXHr+nAn7c8cSKrys6y3r7GbqQxbb7Wk6gk25X0Qyt/K09N3XA7JVy7wV2aK33AR8CvjzfxbTWN2qtL9daXz4wMNC4VrahuOPy3pFJPjKR4K5sng+NJzlZcnhddxd7/D4OF4vsD/jnPO/yoJ9hx+WmSqADOFgs8flECkdrkq7L15Np3nJqhLcOjfDddJZsVbVPLN9S35+ns8P0+mqPqYPyVmGNGlens+Uqnar8IWAEAzJRYh1aTz9DFyKBTnSyVoa6k8C2qvtbKVfjpmmtk1rrdOXj2wBbKTV3if115njJ4eGq2a+vjEUIGga3p7NEDYM39cYIG4rXdUcJKUVIKV4Vi5B0PU6VnDnX+3E2z/FikR9mcrx/LM6Q43K85PDukQkeys+dZStW35nc8LwTJWBqV4nGzID1MjlU6GzFTwX86LSEOrH+SKATna6V3a93A7uVUucCp4AbgNdWn6CU2gic0VprpdSVlEPoeNNb2maqy5kX+X2cKTn8IFuu2pxyHA4UCvz9xn5sIK81HvD9TI5Rx+VNPV1zrrfH7+PryQyP1wh8301neVpIJlc0k+u5xIvxecfUQWMXIPbSOYyqyq4K+qRSJ9YdCXRiLWhZqNNaO0qptwG3U97R6hNa6wNKqbdUHv8o8Ergd5RSDpADbtBaNjPdbltc4LM5WCxxadDPZ+KpGY+nPc2442Ioxc2J9IzHjpUcnh0KTIfAXtPg0oCfb6azbLDMOa81UOOYWF3jhXEidgTLmP/b0zZs8g2q1FVPkoDKOnVZCXVifZgvzMHSA131hAgJdKIVWrr4cKVL9bZZxz5a9fGHgQ83u13trscy+YvBXr6XyeFXCp9S5GdlXQcIKfizgR5OlxxSnubWVJofZnP82+ZBXt0dZdxxcbTm78fiFLTmF6IhfpbLT+9SEVSK58jCxU03kh+ZdzmTKY1cq87L5FAzKnV+dEZ2IRFr32oEuoWWLZFAJ1ab7CjRobb5bH7NZ+N5Hi7w0YmzsxXPtS3irsu30gV+XKnI9ZsGb+yJ0WsabPPZbAPuSGV4tFAiZCgKruameIrXdXeR9Dw2WCb7An7O88/d0aCWlOthKwgYMmN2pcbyozX3fK1mN3BXCS+dnRnq/D50voD2PJT8e4o1aqFAN6WeQCfVOdFOJNR1OMMw+MVoiHNsiwfyBQYtk2HHJenp6UAHMOZ6PJQv8L/7z/7Q2WBZ/Mt4nNf3dPGZeIoJ1+Oz8RR/MtDDs8JBTFVrgvJME47L9zI5vphM02sYvK63i0sCfqw6nitqm+p+XYhtWBS8Bna/Bs6Gd2UYKL+NzuZRkVBDXkOIdrJYoKt3pwipzol2I6FuDYiaJleFg1wVDnJbMs1Xkxmujc79gXSgUCSPZuqRXT6b13ZH+fRkkmsiYWKmwVMDfvYG5y6HMp/vpLN8pFIlPAH88ekxPrR5gD01llQR9RnLjxO1owueYzdyokQmh5pVkVWVZU0MCXViDamnOldPoJPqnGhXEurWmE22RVZrumssHLw/4CfpeuS8EoeKJTyteXowwNODAVKeZqNtMmjV/5aYdFxumTURwwUeLRQl1K3ARGGcreGtC55jG1Zju19nh7pQAC0zYMUaUm+gW0g9YQ4k0InWkVC3xpzv8/E7vTEezRe4LhLim+ksHuVxdrt8Nm8/PcYrYxE+MZkEynvG/uOmAS5ZQnVuiqXKzx9xZx4PyjisFYkX41wYu3DBc2zDbmyom7UnsBHw48ladWKNqCfQTalVpZMwJzqFhLo1QmvN8ZLDUMnh0oCPK4J+HK0ZtEw8yluEfWIyySUBP1stk1/rjnJXNs/RYonbkhnOH6hvQkS1qGnyW70x/uzM2aUDuw2DvXVOrhC1JYqJOsbU2eTcxoQuL53F7p05MUMF/XgZ2f9VdL6lBLpq9QY5kDAn2oeEujXi57kCf3ZmbHo5kld2RXhZV5jPJdLTy528KhbhZMnh3aOTWMC10TDbbIsnSuWuWKOOyQ2O1piAqpx7eTDAv2wa4P5cnphpsj/on7GPrFi6VClF2Fp4TI9t2OScBm0Tlp65Th3IrhJibVhOoKtn8kM1CXSinUioWwMmHIf3j01OBzqALyTTPD8c4Ne6o3x8MsmAaVLUZ2fEOsDXUhne0NPFRiuwaKBLuy735gp8KZmmxzT5pViEvX4fPkOxL+hn3zK6b8VcWmsyToaQtfAEBZ/hI+vEG/Kas5c0AVABH15aKnVifak3yIGEOdGeJNStAWlPc8Zx5xwfdj1e2hVmi20xVHS4o0Z32pmSw8srM2UnHZfHi0XGHI8ttsn5Ph+hyoSLn2QL/M3oxPTzvp/J8aHNAzxFJkQ0VMbJYBs2plp4J49y92uDKnWzFh+Gyq4SMlFCdLDldrsuRsKcaGcS6taAHtOc3jas2mbLImaaPC8SwtGaU67Dk7P2d90T8BGzTFKuy0cnEtxeVZ353d4Yr4hFyHsen40nZzzPAe7LFyTUNVi6lF606xUavPdrplb3qw8vmZ7nGUK0Nwl0Yr2SaYprQNQ0+MOBHrbb5YweVIp3DvSws2psm6UUv9wVoadqqZPzfTaXVmY9PllyZgQ6gI9PJjhVclComosJW8gCw42WKqUW7XqFqdmvxYa8ppfJYcwKdUYwgJfKzPMMIdaX6zYOSqATHWHBSp1Sqnehx7XWEws9LprnfL+PD24a4IzjEjEVmy1rejLDlF1+H/+6eZAniyVspTjXZ9FXWZcu43pzrlnUcCBfZNRxuCEW5d1V3a8Bpdgv4+gaLuNkCJiBRc9rVKVOF0vgujBrcosKBfCGRld8fSE6mQS5tUUp9X+BtNb6H1rdltWyWPfrzwENNUsyGtjZ8BaJZeu2TLqts2Oxsq7HY8UijxdKDFome/0+NtkWm+y5/+xbbYuwUmT02dkW59oWP8/n+XY6xyV+m7/d0McPMzm6TYOrwyHO9/vIex4jjoutFBstc06QFEuTdtIEzeCi59mG3ZBtwrx0FhUKzvl3U0G/VOpER2pU16sEOtGJFgx1Wutzm9UQ0XjfSmf55/H49P29fh/v3tA7XZ2rttVn83eb+vnwWJxDxRKXBv1cHgzwscoWYA8WSjyjVOKdg2eLt6dKJT42keB7mTwBpXhTbxcvioQJ19jNQtQnU0rjNxevgDZq8WEvlcUIz60MGqGAzH4V65KEubVDKfUbwB9RLkI9CBypeuxNwJsBH3AY+HWtdVYp9Srg/1DeICmhtX6OUmov8P8q5xrAK7TWh5r6ydRpse7XLwM/Au4C7tZaN2YQj1h1p0sON1YC2ZQDhSJHik7NUAdwUcDPP2zqJ+F6fCae4qOznl/wzlbxPK35cjLDnZlyF2BOaz44nmC7bXN5aPHuQ1FbxsnUFep8ho+it/JvRy+dQdX491KhAFoqdWIdkTC3tlSC2J8Dz9Raj1WGk/1+1Slf1Fp/rHLu3wBvBD4EvAt4kdb6lFKqu3LuW4B/0Vp/RinlAxZenqCFFiupfBzoAf4WGFZK3aWUer9S6peUUhtWv3liuUpak6vqSp2S8+aOnasWMU22+Gwum7VtlAk8teqXf9x1+U6NSs7jRcn9K5FxsviNJlfqgrVDnVTqxHogkyDWrOcDX9Baj0HNOQAXKaV+oJR6CPhVYG/l+I+AT1YqeVPh7cfAnyml3gns0Fq37XpPC4Y6rfXXtNZ/prW+GhignHLPAO8Hhla/eWK5NlgmV4dnjs0KKFX3bg9PC/l512Ave/0+nh4M8A+b+rmwavuvkGFwXo1rbTDb9g+YjpAppeuaKGEbdkMqdTqVrV2p8/vRhRLacWo8S4j2VW9AkzC35inK3a7z+STwNq31xcBfAQEArfVbgL8AtgH3K6X6tNafBV4G5IDblVLPX82Gr8Si69QppfqBZ1RuV1H+xL9NObmKNuU3DH6rt4t+y+Q76Szn2DZv7O3inDpDXdQ0eX4kxLNC5d0mZi9pEjAM3tDTxcP5semK4F6/j4tk3boVyTpZev0LTjoHwFQmWmtKXgnbWP62bF4qM2fhYQBlKFQ4iJfMYM7aF1aIdidhTQDfAb6klPonrfV4jdU8osBppZRNuVJ3CkApdZ7W+qfAT5VSLwW2KaViwFGt9QeVUjuBS4DvNu9Tqd9iY+oOAQngv4Hbgb/RWsuKpB1ii23zlt4Yr4lFCBkGAWPpExh8CzxnT8DPR7cMcqxYImAYnFe1RIpYnqybZaO5cdHzlFL4TB8Ft7CiUOcmUhjzjIE0IkG8RFpCnRCi42itDyil/hb4nlLKBe4Dnqw65S+BnwLHgIcohzyA9yuldlOu9H0HeAD4U+DXlFIlYBh4d1M+iWVY7DfwJyhX514BXEy5D/rHwH1a67n7Uom2YypF7yoGrR0+u+4uXbG4bJ1j6mBqrboCETuy7Nfz4ilUjTF1AEYkJLtKCCE6ltb6U8Cn5nns34B/q3H8l2uc/t7Kre0ttqTJ9CehlDqfchfsm4BnK6VGtdbPXeX2CbGu5JxcXbNfoTELEHvxNOa22nOeVCiIG0+t6PpCCCGap67+uEof8pXA0yhX7gYA+WkvRIPl3CWEOtO34gWI3XgSI1x7sWMjXO5+FUII0RkWG1P3JcohLkF5YsSPgA9prR9pQtuEWHfybr7u7le70v26El4ijRGpvdesEQ7gSaVOCCE6xmKDrf4f8KapdV6EEKsr7+aX0P268rXqvEQaNU+oU+EQbtWOJEIIIdrbYmPqblVK9Smlfg+4sHL4UeAmrfX4qrdOiHUm7+brns3qM3zkVxDqtOfhJTPzV+qiIUrHZDlKIYToFAuOqVNKPQV4GHgq8DhwCLgCeEgpdeFCzxVCLE3JKwFgqfpmK9uGTcFb/kQJL5lBBXwoq/aC0SoawptILvv6Qgghmmux3x5/Dbxda/356oNKqVdQ3jrsFavVMCHWm6nxdGrWQs/zsQ17RZU6byKBEZt/ORQjEsadlFAnhOg8SqlzgK9prS+qOvZ/gbTW+h9mnfvJyrlfaGYbV8Nioe5irfUrZx/UWv+3Uuo9q9QmIdalpSxnAlOhbvmVOnc8vnCo6wrLRAkhxIqdvPqNw0Aj94s/s/XO/1h8lfY6KFVn10iHWGxJk8wyHxNCLFHBLeAzfIufWGGvcKKEOxbHiIbnfVwFA+h8EV0sLfs1hBCCxga6FV9PKXWnUuo9SqnvAW+vHH6hUuoHSqnHlVIvqZx3TuXYvZXbMyrHr65c4wtKqceUUp9R9XaxrLLFEuqgUuoPahxXlNeqWxGl1LXAvwAm8HGt9ftmPa4qj18HZIHXa63vXenrCtGOcm4On7m0ULeiSt3oJEbX/JU6ZSiMWBh3IoG1sX/ZryOEEG2oe2oDhUr36znAc4HzgP9RSu0CRoBrtNb5ytZhNwGXV55/KbAXGKK83NszgR828xOoZbFK3cco74c2+xYBPr6SF1ZKmcBHgBcDe4DXKKX2zDrtxcDuyu3N1NjSQ4i1ouDml1ypW0moc86MY3QvvMWY0RXBHU8s+zWEEKJF9CLHb551/PNaa09rfQg4SnnFDxv4mFLqIeAWylllys+01ie11h5wP+VQ2HKLLWnyV6v42lcCh7XWRwGUUp8DrgeqFza+Hvi01loDP1FKdSulNmmtT69iu4RoidwSQ53P8JFzc8t+Pff0GP7LnrLgOUY0jCeTJYQQnWcc6Jl1rBd4ovLx7CFks0OgBv4XcAbYR7kIVv1XdPXYF5fFez6bYrEdJd61wMNaa/3XK3jtLcCJqvsnKW9Dttg5WwAJdWLNKbh57CWGusnC5LJfzxkZJ9jbteA5RjQkM2CFEB1Ha51WSp1WSr1Aa/0dpVQvMDXk6w01nvIqpdSngHOBncBBIAac1Fp7SqnXUR4q1tbqmSgx+wbwRuCdK3ztWoMKZyfles4pn6jUm5VS9yil7hkdHV1h04RorHren0tZeBhW1v2qtcYdmcTsjS14nhEO4cmuEuuC/AwVa9BvAH+hlLof+C7wV1rrI/OcexD4HvAN4C1a6zzwr8DrlFI/Ac6nAyaILtb9+oGpj5VSUcqzRN4AfA74wHzPq9NJYFvV/a2UBxwu9Zyptt4I3Ahw+eWXz9eXLkRL1PP+zLsFfGb9oW4lO0p4EwmU30b5F64MqmhIxtStE/IzVKyiMzR4SZN6TqrsU/+8GsevnnX/9fM8/xBwSdWh/105fidwZ9V5b6unPc2waB9wpWT5B8CvAp8CLtNaL7/P56y7gd1KqXOBU8ANwGtnnXMr8LbKeLunAQkZTyfWqoJbwFZLq9QVllmpc06PYfbPHm4ylxENUzp8fFmvIYQQAI1aU04sbrExde8HfpnyX28Xa63TjXphrbWjlHobcDvlfupPaK0PKKXeUnn8o8BtlJczOUx5SZNa/eBCrAl5N7ek7le/6Se/zG3CnKFRzL6Fu16hPKZOtgoTQojOsFil7g8pz/D4C+DPq9bWU5QnSiw8ynoRWuvbKAe36mMfrfpYA29dyWsI0SmWM/t1ud2vztAoZs/i374qEsJNyK4SQgjRCRYbU7fYRAohRIPk3RwBI1D3+SvpfnWHRjAWmSQBYERCeImGFeiFEEKsIgltQrSJgltY0o4SPsO37G3CnKHRukJdeauwArrkLOt1hBBCNI+EOiHaxFKXNPGZ5VBXHqWwNO6ZccxF1qiDylZh4SCedMEKIUTbk1AnRJsouIUljakzlYmhDIpecUmvo10PdzKJ0R2t63wVDeNKF6wQooMopf5JKfWOqvu3K6U+XnX/A/PsbY9S6vVKqc1NaGbDtcW2FkKIpVfqoDID1s3jN/11P8ebSGCEgyirvm9/GVcnhFiJq4+eHKbB69TduXPrYsuk3AW8CvhnpZQB9APV3RPPAN4xz3NfDzzMPOvi1qKUsrTWLR+nIqFOTCudPEPp4JN4mRz2ru34zt+Bstp+V5Q1Y6mVOgC/4V/yuDpnZAKjjq7XKUYogJeUUCfWhvTBw3OORS7Y1YKWrCuNDHT1Xu9HwD9VPt5LOaRtUkr1UF4i7SnAi5RSHwaClEPgbwOvAC4HPqOUygFPB/YA/whEgDHg9Vrr00qpOyvPeybldXVXuinDikmoE0A50I398T/inh4rHzAU/X/3vwhcsbe1DVtHCl5hyZU6n+kj52aX9Bx3dBKjq76uVwAVCkilTqwJtQKdWJu01kNKKUcptZ1yVe7HlPeOfzqQAB4EPqy1fjeAUuo/gZdorb9QWUP3j7TW9yilbOBDwPVa61Gl1KuBvwV+s/JS3Vrr5zb3s5ufhDoBQPGRI2cDHYCnSXzsv7H37MQMB1vXsHUkv4xK3XLWqnPH4xhd4brPV0EJdaLzSaBbl35EOdA9g3KlbUvl4wTlCtvzlFJ/AoSAXuAA8NVZ17gAuAi4o7JWrwlU72x18yq2f8kk1AkAvOTcfYrdsTi6WAIJdU1RdJdeqfObfvJObknPccfiGNElhLpQQCZKiHVnvhAoXbUd5S7KIe5iyt2vJyhvqpAEPgF8HLhca31CKfV/gVoLhSrggNb66fO8xtxfni0koU4A4LvwHFAKtAal8F28m/BLn4NVx64DojEK3tLWqYNypS7rLi3UeWOTGH3ddZ9vhAJ44/ElvYYQ7WSxKt1SqnhT50q46wg/ohzijmqtXWBCKdVNeYzdmyrnjCmlIsArgS9UjqWAqTEqB4EBpdTTtdY/rnTHnq+1PtCsT2IpJNQJAHwXnEP/P/0RpSMnMQd7KDx4iNR/fR3n5BkCV+3D/5RzW93ENc3THo7nYKsljqkzfEveVcIdj2Ods6Xu81UogHtUKnVi7SudWHyyo72tI1e6WK8eojzr9bOzjkW01mNKqY9V7j8J3F11zieBj1ZNlHgl8EGlVIxybvpnyl21bUdCnQDAGZnEOT6MF0+h01nM3hi+PeeR+tRX8eIpCPjwn1t/EBBLMzXztWp/5br4TB+5pXa/TiSX1P1qhILo1NImYwjRTiIX7KpZjasV4lR8/oW2dZ1rO4o5ztDgJU3qOalSneuadez1VR//BeW97Wc/77+B/646dD/wnBrnXV1PO5pJQp0AoHDfY8T/6b/K3a+Af/8FhF70DHLf/RnZb/2EwNMuAQl1q6bgLX2SBJT3f80ttfs1kUZFQ3Wfr0IBvFRbDRsRYsmmukunwt1UoJsT4sYTtS/QF8PX30vxxJBU65aojjXlRIPIjhKC0plxkp/48nSgAyjcfxAvX8Dc2IcKBdAlB2d4bP6LiBXJu/klj6eDcvdrbgndr1prvGQGI7LEUJeWSp3ofLUCnXtsqBzkKjfn6Cn0eGrGbUpxbEICnWhrUqkTUCzhTdT469T1cEcnib3lVXiFAqN/9I/0v+f3sLdvan4b17jCMnaTgPLiw1mn/iqaTmVQfntJi0oblVCntV5y97AQ7aC663VGhW48gR6Jk33sxIzzzap6hy8WmXM9mSQh2pVU6gRmfw+Bqy6eddDA3DRA9zt+Da3BCPqJvvpFFB48ROGxJ1rT0DVsObtJQHlMXdapv4rmTqaWNJ4OQNkWKIUuLG2PWSHawXyBzrn7ETI/fIDRU2dI5LKkowESuSzBZz+VRKmAb285uLmJJG46g5td2jAHIVpBKnUCI+gn9ruvBtMkf9cDmAM9RH/jpXjZHPH3/Qfdb3sNk3/7cXS+/Evd6Omi731vx3/BOa1t+BqSd5e+nAlAwAwwmh+t+3wvkV5S1+sUI1yZLBGof4/ZdpP3PH6UzTPsOJzns7kiGMCUyuOaNl+gK/3oAUaOn2bMyTHsFTDCIbbqKAxEMe8/QGDfhYzef4BIKo/ZG8XcsRndHcXetlmqdKKtSagTANjbN9H7l7+NOx4n9717KPz0IbxCke4/ej35nzwwHegAvMkk+Z8+hAoFsDcNyP6wDZB389jLqNT5Df+SZr96iRRqGYtJT02WMAd6lvzcdnB/rsBfj4yz0bLYYJl8K5XlEyrJX2/sY4MlPwbXogUD3dETPJSfxOyJEdy4HYBRXf4Zd/L0EFt1ie7RFFqZqMFtuNkc/osvaP4nIcQSyU8zMc0I+DC2DJIDsC2Cl15I/kf34Y5NzjnXeXKIib/8CIGrLiHyqmuwlrCYrZir4ObxLWNMnc/0kV3C3q/lSt3SQ53RwTNgf5LN8Z6RSV7THeFCfzk4a625M5Pn906N8q9bBumXP0zWrNmTIopjCcYoYfbE2Hrt3C07R+8vLz/m5Qr4d22HgR6MzYOAjKXrJEqpO4H3aq1vrzr2DsoLB/9uA66/H9istb5tKecppV4G7NFav2+lbahFQp2YI/jsyygdP03pxGkKDxwkcv3zKB08NuMc+7yt5P7nbtJPDmHt2ETkxc9qUWvXhvwyJ0oEzMCSKnVuIo0KLr0LVQUDeB24Vt2hQpG/HZngN3u6OMd39uurlOJ5kSAlrXnXmXE+uHkAS7pi14zZa9L5+nspxVPokTjxbBYjHMQ/OP9uOc6ZMbpDIdRgN8bmQel2XaGX3vGSYRq8Tt1Xr/naYsuk3ATcANxedewG4I8b1Ib9wOXAgqFu9nla61uBWxvUhjlkooSYw962keivvxSdL6HTOZzhcSK//AJUJIjR20XXG3+Jws8fnV4CJf/jB1rc4s6XdwvL6341/UuaKOHFU6jQMrpfg/6OW9Yk43n85ZlxXt4VmRHoqr0wEsTVms8vsOCs6Hylw8fKS5dQHkNsbeinMDzG6P0Hpm9QrtLljhyntwC+/hh06HCDNtTIQFfv9b4AvEQp5QdQSp0DbAZCSqkfK6XuVUrdUtkiDKXUdUqpx5RSP1RKfVAp9bXK8bBS6hNKqbuVUvcppa5XSvmAdwOvVkrdr5R6tVLqSqXUXZVz7lJKXTDPea9XSn24cu0dSqnvKKUerPx/e+X4JyttuEspdVQp9cp6vzAS6kRNvi2DBK++HIDcd35K7kf3E772mfT84etIf/m7FO59dPpc+5wtODW6aEX9llup8xt+8ktYp86LJzGWMabOCPo7rvv1g2NxzvXZXLZAZdJQilfGInw2kWLEcZrYOtFsZiSMGStX5/qUzUbDx4Aq36YCXu7IcTa4VrlKt6FHqnQdTGs9DvwMuLZy6AbgO8CfAy/UWl8G3AP8gVIqAPw78GKt9bOAgapL/TnwXa31FcDzgPcDNvAu4Gat9X6t9c3AY8BztNaXVh57j9a6WOO8ah8GPq21vgT4DPDBqsc2Ac8CXgLU3VUroU7My7//Avre+3Z8F+/G6I5ixKK48dSMRYrNjf1Y2zeS/d49FB45QuHAEdy0TP1fqvwyx9QFzAA5N4eu+jdZiJtIo0KBJb8OAT9esnNC3U+zOX6ey/OyOnbO6LdMnh4M8ImJZBNaJpqpdGJozo4RvVs2MbB/Lxs2bqJP2dMBr2ckyUW+bnb3DRLYvwu1eUACXeeb6oKl8v8ngD3Aj5RS9wOvA3YAFwJHtdZPVD1vyi8Af1o5/04gAGyv8Vox4Bal1MPAPwF762jf0zm7L+1/Ug5xU76stfa01o+whEqnjKkT8zL8PgJX7KF45DiG7SPxH18EIPqqazA3D6BTWdyxOKn/+hpdr7ue/L2PYvh85H72ENFXXIPZtbT10NaznJNd1jp1lmGhUBS8AgFz8bDmJdPLrNQF8JLpJT+vFbKexz+MxnllLELAqO/v1udFgrx3dJKTJYettvxYXAuq93W1Nw5Sqmz/FX7qXjI/P8DA/r0UD5TH3kXC5W5W37YoarAbBnrwP21/s5ssGu/LwD8qpS4DgsB9wB1a69dUn6SUunSBayjgFVrrg7Oe87RZ5/018D9a61+qdPXeuYz2Vv91XpjVhrpIpU4sSFkW/gt3onM5rMFeKJZwxxOkb76dxEdvIf2FO3CePM3k3/8/jGAAoyuE1d9D4b5HcWrtUiFqWm73K0DQDNY9WcJLZlawpElnjKn7+ESCnT6LC/z1h+SgYfCMUJD/mpRqXaerniTh6+8FoHTgEPb2mXtXFw8cxheLlG+9UfwXbkPtPRdj3wXTgU6qdJ1Na52mHK4+Qbn69hPgmUqpXQBKqZBS6nzKXac7K2EM4NVVl7kd+D1V2U6nKgCmgGjVeTHgVOXj11cdn31etbs4W0n8VeCH9X92tUmoE4vyX7Ib/6VPIfpr19H9x68j8KxLcY4PzzhH5wp48RTuWJz4Bz7NxP/5Nyb+8l9lv9g65dwcfmN5C/sGrACZOrcK81IZjGV0vxqhzqjUPV4o8u10jpcso0r87HCAH2RzjDvuKrRMtELp8DHsjYNzjk+FOTeRxPMbWFfsmV66ZGpvVwl0a8ZNwD7gc1rrUcqB6yal1IOUQ96FWusc8LvAN5VSPwTOAFNVib+mPIbuwUrX6l9Xjv8PsGdqAgTw98B7lVI/AqrXSJp9XrXfB95QacuvA29f6Scr/QxiUcpn4993Pv595+PlixQPHEb5fXO2jVK2hS6Upu8XDxym8OAhrI39zW5yx8k5uWXtKAHlSl09+79qz0Nn8sub/doBlTpXa94/Osl10RCROrtdq4UNg0sDfr6UTPNbvbFVaKFoluqu1+ljx8tFlNLhY7iJJNbOLVjnb8XN5tDd0enFhSXMrYozNHhJk3pP1Fp/iaruS631d4Erapz6P1rrCysVuY9QnkRBJfD9do3rTtS4zvlVH//lAud9svLYk8Dza1z79bPuz92AeB4S6sSSGAEf/ksvpOu3X0nig5+dPh58/pUUHj6Mf8/OGec7J4dnX0LUkF/m3q9QniyRrifUpbMov40ylx54VCjQ9kuafCWZRgNXLmMdvinPCgf46HiS3+jpwifr1nWc2V2vpXiK0vAI9t7dlA4cQvWVe8GU38AN+soTwCrbf4EEutVSx5py7eBNSqnXAT7KY+/+vcXtWRYJdWLJlGEQvu6Z2Ds2UTp0DO24uBMJgs/YT/xfPzfjXN+e81rUys6Sd3P4zWV2v5pBMqXFu0a9ZGZZ+75CuftVt/Hs13HH5f9NJvnd3hhqBWFsg2Wx0Tb5fibHC5f5tRKtZ2/bTPHEEL5dOygdPkZpeAT6qqqvQZ90tYoZtNb/RHnWakdrSahTSvUCNwPnAE8Cv6K1nrPQmVLqScqDDF3A0Vpf3rxWioUYgQCBp+7Bv+983IlkuZKTTOO7aBf5798LPouu33gpvlmVO1Fbzs2toFLnr6tS5y5zkgRUKnXZ8tIpKwlNq+VD43GeFgywsQEzV68KBvhSIi2hrgNFLtg1Xa2bCnZ0zx2jXr2PqwQ6sZa0qlL3p8B3tNbvU0r9aeX+O+c593laaxlt36aUZZVnxQJmJETv//4tnDeMoWwLa9PAsrr61qMVdb9aQdJ1VeqWt5wJgDJNlG2jMzlUm4Wdn+fyPJQv8CcNWv3/ooCPLyUzHCuW2DHPThSiM0xV4mqRMCfWolaFuuuBqysff4rylOP5Qp3oIEbQj+/cLQueM1mYZLI4ScyO0Rfoa1LL2ttKul+DZoB0afFtrrxkZnkLD1eoSHBFXbiroaQ1/zga5/qucMPGwJlKcUXQz1eTGd7W392Qa4rVc8fIHQBcM3gNIGFNrG+tCnUbtNanAbTWp5VSc+ecl2ngW0opDfy71vrGprVQrIpHJh/hnx7+R4bzw/T6e3nHnnewv+/StuzSaxatNXknv+xKXdAMkiwtvr6al0xjBJcf6oxwCC+Rgs0Di5/cJLfEU3SbBhcHlj85opanhQJ8aDzBb/fFsNfxe7NdTQW52cemgp0Q69Wq9Y0ppb6tlHq4xu36JVzmmZX92V4MvFUp9ZwFXu/NSql7lFL3jI6Orrj9ovFG86O898H3MJwvz4idKEzwtw/8LUPZucsPrDULvT9LXglDGVjG8v7GClpBUvVU6hJp1ApmhhqRIG4bTZYYd1xuSqS4fhV2Lum3TDZZJj/KrJ8t7zrhZ+gdI3fUDHRCiLJVC3Va6xdqrS+qcfsKcEYptQmg8v+Rea4xVPn/CPAl4MoFXu9GrfXlWuvLBwbap5IgzhrLjxEvxmccK3gFRnI1//nXlIXen1k3u+yuV4CQFSJZrKNSF0+hIssbUwegQkG8+OLhsVk+PpHgimCAActc/ORluCLk59Y2CrGrrZ1/htYT5qRKJ0TrdpS4lfJGulT+/5XZJyilwkqp6NTHlDfVfbhpLRQN12VH53QxKhQxX1eLWtQeck6urn1b5xMyQ6ScxcOWm0hhLGPh4SlGOFDufm0DR4slfpTN88IVhNTFXBLw83ixyOmSs2qvIRZWb2VOAp0QZa0aU/c+4PNKqTcCx4FXASilNgMf11pfR3n16S9VxlpZwGe11t9sUXtFA2wKbeYtF/4OH3rkg+jKvsWv2/U6toa3TZ/jeA6ns6cpeSUGAgOczp3mZOYkYSvMrq7z6Ausvd0psk52RaEubIVJFuvofo2nUXuXP8lBhYK4E+2xN+q/j8d5fjhIcBk7R9TLVorLggG+nsrIDhNNNDvEPTz50Iz7F/VcPOO+BDohzmpJqNNajwMvqHF8CLiu8vFRyvu1iTXCUAZXb7yandGdjORG6Av0sj28Y3p7rFQpxVeP38otT9yCox32du9lX+8+Pnu0vHPF3u69/PHFf7zmgt1KQ13ICpFx0njaw1Dzh5zykiYrmCgRDeG1wYb3B/IFDhVLvCJW9845y3ZV0M/HJ5O8vqcLSyZMrJrqIDc7xO0Mn13A/GjmyIzHJNAJMZPsKCGayjZtzus6j/O65u40cTB+kJuO3jR9/0D8AFf2P40/vOiPKHklksUkjyceZ58VImS1z7IaK5V1VjamzjIs/IafdClN1wJd2V4ijQov/+tmRMOUjp5c9vMb5eMTSV4QCTVlVuom26LHNPlxNs+zl7nGn5hroWrcsfgxenxn1xy8P3sfAPsHLl3wOhLwhJBQJ9rIE+mjM+6/eOuLGS+OczB5kLtGfjR9/PW7Xs/Ld/wSprE6A+SbLeNkVhTqACJ2hEQxMW+o01qX15hbQTBR0TDueGLZz2+Eh/MFTpRKvLZ79at0U54e8vPfiZSEuhVYLMQB00EulUrh85e/Hy7bfBkAB5OPNaOZQnQ8CXWibWwInt3z2TZsuuwYlmFy6/GZ82j+68h/sTt2PlE7ykBggIjdvF/wqyHrZFbU/QoQtaNMFifZxraaj+t0DmVbqBVso2V0hfEmWhvqPjWZ5HnhUFO7QvcF/Hw1meWJYolzZYeJus3XpVod4o6PHwegz99PppgF4Lmbri4/Z+Jh7h26l/Csxa4fnnxozrg6IUSZhDrRNraHt3NRz8U8PPkQ3b5uxvKj9AfKSysMBgaJ2lFCVohLei9hsjDBwcRB8k6Opw1exfmx81vc+uXLOBl8xsoqdVE7ykRhfN7H3XgSI7qyLmsjGsZLZdGOg7Ka/6PjUKHIoUKJX2nCWLpqllI8PRTg5niKP61siSfmNxXmpoLcfCHu4NDjAAwGByk6xenn35u5FwBf+OxM+amu153h8+aMqwPpehViioQ60TYGA4PsiT2FF25+IWdyw8TsGIlSkjed/yZOZE4wWZxkf+9+7h67h88c+Qw9vh5++8K3cDDxGN2+bgaD821M0t5SpTTBFVbqInaE8QVCnTeZQq1wkV5lGhixCO5YHGtj8yerfCae4jnhYEsmLDwzFOB9Y5O8yXHpW6V18TpdrTDX4+shlUrNCHFu2iFNmg2hcmXey3gzrnPpOZfywOgDQLn7tbrrVQKdEAuT3dZF2wjZIZ6x4ZlMFia46ehNPJp4lJ3RnXz26Gf51qlvsSG4gVQpxb7efbxh9xsIWSFuPfYVksUUD048iNa61Z/CsqRLKYLmysZrxewYI7n5dwFwJxMYkZXvvGD2dOGOTKz4Oks1VHK4J5fn6aHGbgdWr4hp8NRggJvaaPHldjI70CWzSVKpFJl0llwqz7Enn8RNO5ROl9hQ+Y+snntbwFSgq+56lUAnxExSqRNt5byu8wiZIX42djdHU0c5L3oeGSfDy7a/jPvG7+NE5gQABgav3/16bj56M5f3X8Hh5CG2hrZwQfeFHbePbLKUZENww4qu0e3r5rHE/IPJvckkRmTlM4aN3hjO0Cj+S5rb3f25eIqrggECq7gu3WKeFw7wD2NxbuiO0i/Vumm1At0FXReSSWcpZoq4aadclctqiIKeZ5MOVfmb44HRB7hs22U8PPHwdJVudqCTMCdEbVKpE21nU3gTf7j3D3nj7t8i65QHT0ft6HSgA/DweCT+KO/Y+w6CZoC7x+7myye+zIMTD5IpddbWTqlSmqC1skpdr7+X4dzwvI+744kVj6kDMHu7cE41d1u3uOvynXS25bNPu02Tq4IB/qPFk0XayXyBrtp0oKMq0GVm3WZ5eOLh6QkSXaHyjG4JdEIsTkKdaEsbQhvYEt6CbfgImSFK3sytml6y7SXYhs17H3ovnz7yaV6w+YX4lA8Pj5OZ1q+lthSpUpKwtbKu0T5/HyP5EVzt1nzcHZvEiDag+3Wwl9ITp1Z8naX4YiLNvqCfLrP1P65eEAlyVzbPY4Xi4ievcbOXKdkZPo/9A5fOu/zIjEAHXLrrUi7dNXPtuTPZYUZyI4wXxgAJdEIsVet/Sgoxj8HgIJZhcsPOG9gY3IhRebv2+fvwtOYHZ76Ppz1ybo6bjn6Wp3Tv4WjqCFmn0yp1qRUvpuwzfXTZXQxlh2o+7o7FMRowa9TcNEDpSPNCc87z+HIyw3NXsBNGIwUNg1+Mhvj70QlKHTqGczVc1HPxjEkM9w7dSzFT5PTo0MyxclWBDuC+w/cxHB9mODnM4YnDmBGLYDTA9r7tMwLdNYPXSKATog4S6kTbMpTBCza9kL5AP6O5Ud6x93+xObSZfT37uH/ivjnnP5l+Er8R4JunvslQprnVpOXytEfGyay4UgewJbSFw8nDNR9zRyYaE+oGe/EmE3ip5gTnryUz7PRZDLZgCZX5XB70E1IGH1/n3bC1Qtb9o2e/L0+PDk3PcK0eRzc70BGFjD+NETUIRgNEo1G6Ql1c1HPxdKATQtRHQp1oazF/jOdsfA6v3fVanrXxWfz+U97OczY+l03BzXPODVkhPnno/7Ejcg5D2dMtaO3SpUopAmYAU6184P2OyA7uG7+35mPuWByjO7ri11CmgXXOFgoPHlrxtRZT8DQ3JVI8vwETPBpJKcWrYxG+nc5yZzrb6ua01FTgql5ceGqCxNRYuqlAd+k5Z8PcN+75BsPuMGlfmow/Tff2HjZs30A0GmVH9w7pbhVimSTUiY5hGza7unZxMnuCqwafNmMZkC2hLWg8Cl6Br5/4GqYyKLiFFra2PpOFSaL2ysMWwJ7uPfxs9GekSjOX3fAyOXAcVIMmGvjO307uxw805FoLuS2VZrNlsW0Fu2Cslohp8PqeLv5xLM69uXyrm9My1ePqktkkmXSWk2MnOT06xOkzpzl9ujx5ZyrQEWZGmDO3GPg3+xncPMBF2y6aDnTS3SrE8kioEx3Fb/m5cuBpdFkx/tdFf8DvXPi7vHbna7ms7zK+9OSXAEiX0liGxWPx9t8vcqIwTpdde7/WpYr5Yuzv3c8HHvoArnd2woQzPIbR192wpV58F59P/vs/RzvO4icvU97z+M94imsi7bvf6lbb4te7I/yfM+P8OJtrdXOarnrmazKbnD4+GBzES2k2RTeysavc/Xrfk/fxjQe/wbA3PCPMbdi+gR3bd0yHOeluFWJl2u9PYCEWsSm0icHgILeduI2Mk+aWJ2+h5JWmH3/OpueSc/LcN3Ev+/r2tbClixsrjBNtUKgDeNGWF/GfR/6Tm45+ll/b9esAuEOjmP3dDXsNs78bc0Mf+bseIPicpzbsutW+kEiz3bbY3uZ7re7y+/jNni7eNzLJq2MlbuiOYnTYOolLNXtP153h87g/e990t+vp0SE2RcthbjhZWWYnUhk3FzDo3tQzPXZOulqFaCyp1ImOZCqTPd17+PbJb/P63W9gd9f5dNldXLf1Oi7tvRRDGUSs5u4RuhyjuRG6fI0LdZZh8cs7fpmvnfga4/nytmGl48MNDXUA/isvIv2l7zb0mlPGHZebEymua8ASLM1wjs/m7f0x7khn+ePTY4ysYgWz1WYHunmFFKdT5aqcsUmRC2SnK3Ozu1pBAp0QjSKhTnSs87rO461738YTySfY13MJv7/n7ezu2s1NRz+LbdhcOXBlq5u4qFPZIfr8jd0kPuaLcUnvPm47+XUASkdPYg72NfQ1/PsuoHj4+KosRPzB8ThPCwYY6KBdG3pMk7f2xdhgmbzx5Bluiadw1tmSJ5dtvgxf2IcZsRgzxrA32fg3+4lsinDFpVewY/uOGWFOxs4J0XjS/So62q6uXTw6+SiHk4f4+smv42mP39vze4TNMOd1ndfq5i3qZOYEe7v3NPy6V/ZfwacPf5rX7vxVSoeO49/X2G29lG0RuGIv6VvvpPt3fqVh1/1uOstjhSJ/0ODKYjOYSnFNNMQlQR9fSWb4SjLD7/TFeEYo0HFb19Xjop6LeXjyIY5mjrB/4FLuH72PcCTEBZHye22yOAnAju4dADxjyzOnnytBTojVIaFOdLSoHeX6HddzNHWU5xauZjA4yM7ITvxWazZ+XwrXczmVPcVgcLDh194Q3ECXL8a9J+5i85lxzE0DDX+NwFX7SHz4JmK/+XKU37fi6z1RLPHPY3F+q7cLXweHoA2WxZt6unikUOIj43E+Ezf57d4Y+4Lt/55cqqnu04cnH5peLHjK1Fpz1STMCbG6JNSJjhe2w1zce/HiJ7aZJ9NP0uPrIWCuzm4Jl/c9lQfu/Arbz92CWoWuTHOgB2v7JjK330XkZVev6FonSyX++PQoL4uG23IJk6VSSrE34OMpfpuf5wr8zcgEW2yTN/TEuCTg69jKXXUoqx5fNzu81TpfCLH6Ov+npxAd6p6xe9i5il3El/RewvBPv0d2105iq/QawRdcSfLTXyX0wqswQssLpz/K5Hj/6CTXRkM8NbS2qlmGUlwRCnBZ0M/duQJ/OzpBj2nwqliUZ4eC+I3ODHcggU2IdiQTJYRogayT5Rsnb2N/7+otuRI4NMZTjvn5Yu/9ZJ3V2fnAPmcLvl3bmXzff6CLpcWfUJFyPf4nneXtQyP8y3icX+uJ8rRlhsJOYCrFVaEA7+zv5hmhAF9IpPjl40P89ZlxvpXKcLJUwltnEyuEEI2n9Br8QaKUGgWOVe72A2MtbE47kq/JTP3AY1rra5vxYkqp0Rd+7gVdgd6Ab/S+sfhqvMY779kWvmKky07ajn7iHEcBlMYLxfyRTMPTnU8ZxpXdG7sAbh05OvbWR+88Nvucvhf+QvSif/+PObM1vGJRJ+/7eVK7blN/EOmS41e21dItR6zuHiu6Z++C6+4UR0dLP3/ZtY8UR0aGm/X+hDk/Q5ei0362SHsbZ6yZ71FR25oMddWUUvdorS9vdTvaiXxNZlqrX492+7zaqT3t1BZov/YsV6d9HtJesdZI96sQQgghxBogoU4IIYQQYg1YD6HuxlY3oA3J12Smtfr1aLfPq53a005tgfZrz3J12uch7RVrypofUyeEEEIIsR6sh0qdEEIIIcSaJ6FOCCGEEGINkFAnhBBCCLEGSKgTQgghhFgDJNQJIYQQQqwBEuqEEEIIIdYACXVCCCGEEGuAhDohhBBCiDVAQp0QQgghxBqwJkPdtddeqwG5yW0pt6aR96fclnFrKnmPym0ZN9EG1mSoGxsba3UThJiXvD9Fu5P3qBCdaU2GOiGEEEKI9UZCnRBCCCHEGmC1ugGd6FixxNFiCVMpdvlsNtvyZRRCnJVwXQ4VSky4Lltsi/N8NgFD/oYWQqwuSSNLdDBf5A9Oj5LR5XGhg6bJ+zf1s8Nnt7hlQoh2kHJdPjqe4Bvp7PSxP+rv5hejYZRSLWyZEGKtkz8dl8DVmi8m09OBDmDEdflpNt/CVgkh2skTRWdGoAP4yHiCIcdpUYuEEOuFhLolKGnNE8XSnOPHSnOPCSHWp5TnzTmW05qcJ6s+CCFWl4S6JQgYBtdFw3OOPyMUbEFrhBDtaKtt4ZvVy7rbZzNoyWgXIcTqklC3RM8KB/jVWASfgrBSvK03xiUBX6ubJYRoE9tti7/b2M/2ygSqpwb8/O+BXrpM+XErhFhd8qfjEvVbFr/ZG+MlXRFMhfz1LYSYQSnFpcEAH9w0QEZ79BgmIQl0QogmaOlPGqXUtUqpg0qpw0qpP53nnKuVUvcrpQ4opb7X7DbWYirFJtuSQCeEmFe3ZbLFtiXQCSGapmWpRCllAh8BrgFOAncrpW7VWj9SdU438K/AtVrr40qpwZY0VgghhBCizbXyT8grgcNa66Na6yLwOeD6Wee8Fvii1vo4gNZ6pMltFEIIIYToCK0MdVuAE1X3T1aOVTsf6FFK3amU+rlS6jea1johhBBCiA7SykFhtZZWn72QkwU8FXgBEAR+rJT6idb68TkXU+rNwJsBtm/f3uCmCrEy8v4U7U7eo0J0vlZW6k4C26rubwWGapzzTa11Rms9Bnwf2FfrYlrrG7XWl2utLx8YGFiVBguxXPL+FO1O3qNCdL5Whrq7gd1KqXOVUj7gBuDWWed8BXi2UspSSoWApwGPNrmdQgghhBBtr2Xdr1prRyn1NuB2wAQ+obU+oJR6S+Xxj2qtH1VKfRN4EPCAj2utH25Vmxsp47qkPU3MNAgYsuSBEJ3G1Zpx18VG0WOZrW6OEEK0dvFhrfVtwG2zjn101v33A+9vZrtW2yP5Ah8Zi3OwWOKKYIA39cbY6bdb3SwhRJ1GSg5fSqb5UjJDl2HwO30xnhEK4Jc/0IQQLSQ/gZrsVKnEnwyPcaBYwgF+nMvz1yPjxB231U0TQtRBa8030hluSqTJa82I6/JXIxM8Vii2umlCiHVOQl2TnSo5pL2Zk3yfKDkMO06LWiSEWIq46/G1ZHbO8UcLpRa0RgghzpJQ12ThGt0zNhCUbhshOoJfKTZYc79fe2U7MCFEi8lPoSbbYVtcFwnNOPabvTG22LKPrBCdIGQavLE3NmNA8lbLZG/A17I2CSEEtHiixHoUMU3e1Bvj6kiQMcdjs22y2+fDUrXWYhZCtKN9AT//umWQJ4olAkqx2+9js/xhJoRoMfkp1AI9lsmVVrDVzRBCLJOhFOf7fZzvl+qcEKJ9SKhbYzytOVgo8bNsDg1cGQpwod+HIZVAIeZIuy4HCkXuyRXYbJk8NRhgu0+WFxJCdCYJdWvMo4Ui7xgaZWoe3n/GU/zL5gEuCvhb2i4h2tH/ZHJ8YCw+fX+jmeYDmwdkjKsQoiPJRIk15vZUluqFFVzgq8lMq5ojRNsac1w+NpGccWzYdTks680JITqUhLo1Ju15c44lPVnYWIjZXK3J67nfL47WNc4Wa5X2PLSsEyrWCAl1LZb3PA4VijySL5BwVx6+ro2G5xx7WTSy4usKsdYMWCav6pr5vRFQip11TH4YdRweyhU4VixJCOxg2nEYecvfcPrV78SdSLS6OUKsmAwcaaFxx+WTkwm+miqvTn++z+bPB3vZsYKB2pcEfLxnQx+fjafw0Lymu4t9QRlPJ8RshlK8PBahxzL5ejLDNtvihu4o5y7y/fdIvsBfnBlnwvWwgN/ti3FdNExAFhDvOLnv/RwA/97zSH3+W3S/5VUtbpEQKyOhroUO5AvTgQ7g8WKJLyXSvK2/e9nr1gUMg2eEg1xWCXLyi0aI+Q1YFq+MRXlxJITfMBb9vos7Ln83OsmEW+62dYAPjie40O9jj0xG6ji57/0c/6UXYm3fROqTXyH2269EyUoBooPJb/wWOlicu1fkT3J5MjXGxS1VwDAk0AlRp7Bp1vWHVNzzOFaaO/7qjCPjVjtR4aFD2Lu3Y27qRzsu7qmRVjdJiBVp6W99pdS1SqmDSqnDSqk/XeC8K5RSrlLqlc1s32rbVaOb59Kgv+b+sEKI1usyDDZb5pzjAzWOifbmjsfRJQejN4ZSCnvnVgoHjrS6WUKsSMvSg1LKBD4CvBjYA7xGKbVnnvP+Dri9uS1cfRcFfFwdPruzxJbKwG3ZMkyI9tRrmfzJQC8Ro/w9qoA39nSx05YFiztN6YlTWJsHprtbzS0DFB97osWtEmJlWjmm7krgsNb6KIBS6nPA9cAjs877PeC/gSua27zVN2BZ/FF/N78Si1DUmq22Tb/8xS9EW9sf9PPvWwYZLrl0mQbbbQu/VNc7jnN8GHOgZ/q+tWmA/F0PtLBFQqxcK0PdFuBE1f2TwNOqT1BKbQF+CXg+azDUAURMkz2mBDkhOskW22aLVOc6WunkGYy+2PR9c2M/zvHTLWyRECvXyj8va/Uxzl7w6Z+Bd2qtFx2FrJR6s1LqHqXUPaOjo41oX0dKuC6P5AscKhTJN2DChWgMeX92hqTrTX//5NbZ9896e4+6Q6OYvWdDnRGLoAtFvKoVCYToNK2s1J0EtlXd3woMzTrncuBzlTEP/cB1SilHa/3l2RfTWt8I3Ahw+eWXr8vVQI8XS7xnZILHKrNqXxoN8fqeLvosWbmm1eT92f5OFEu8b3SCA4Xy98+LoyHe2NNF/zr5/mnGe9TVLnecuoOA6ee5G69u6fIhzugE/qdfMn1fKYU50ItzagTfhee0rF1CrEQrf1rdDexWSp0LnAJuAF5bfYLW+typj5VSnwS+VivQifKWR19JZqYDHcBXU1kuDwZ4bmR9/FISYrm01tyWykwHOoBvpLJcEQjw/Kh8/zTKfxz8OA9NPkTGyWAqi2dvfHbL2uKNxTFj0RnHzP5unCEJdaJztaz7VWvtAG+jPKv1UeDzWusDSqm3KKXe0qp2tYMJx+VHmRyfjSe5K5Njso41sDKe5sfZ3Jzjj9W5OXnO8xguOaQbsFWZEO0i7boMl5xFu1Kznsdd2fyc4/fm8/wkk2NU9gZdscPJw3x/+Pv8+nm/znVbr+OWJ25pWVu04+Clc6hoaMZxoyeKe2a8Ra0SYuVa+ieo1vo24LZZxz46z7mvb0abWi3renw6niDvlZdP+HQ8yYU+H2/piy24mHDIUFwa9DM0azzI7jq2HDtSKPLRiQT35Aqc77P5/f5u9srq+KLDPZIv8MGxOAeLJZ4a9PM7vTHOm2df14BhcFnQP2dh4e22zU+zeb6VzvD2/h5iMqlp2T539CaevfE5BK0gu7t288Unv8iZ3Bk2BDc0vS3uRBIjGkLN+plqdHfhDK398YRi7ZJ5+G3mlOOwybJ5IF/g5niKAdMkahqcqLGKfTVLKV7ZFWFL1ZIozw4FuHiRfV/jjsu7Rya4O1dAU97l4p3DYwyV5u52IUSnGCqV+JPhMR4rltDAPbkC7x6ZmLfqbSrF9V0RtlWNn3tqwM9QyeF72Rzn+HycLEq1brnixTgPTjzIU/suA8BQBud17eKBidYsIeJNJDG6InOOGz1dOMNjLWiREI0hg0XaTMJ1+feJBFOdRT/I5tHAM0OBOee6WnOwUOS+XAGfodgf8PMvmwc5WSphK8V22yK6SGVh2HHmVCfSnuZkyWGzLNkgOtSpkkPamznW/1jJYdhx6JlnLUifUry1L8aE6xIyDI4WS3w6ngLgE5NJdg72rnq716qfjPyEC2IX4DfP/pG5PbyNRyYP8AtbfqHp7XEnExizul4BzO4o7uhk09sjRKNIqGszo47H7NE/P87m+a2erjnnPpwv8Aenx5iqPQSV4l82DbA/ODcAzidoGFiUNyavFpHFVEUHq7XVngWE5nlfjzou/2d4jENVf+C8OhZho2UyXKnuPVFyeNaqtHbt+9noTzk/dv6MY1vDW/naia+3pD1ePIWKzA11RiyCOx5vfoOEaBD5zd1mYubcf5I+05wzlsfRmlsSaao7k3Ja86NZkyVSrseRQpHT83TfbrEtfqtqrSYoL4Wyw5a8LzrXDtvmZdHwjGO/2xcjoODJQnFON+zRYnFGoAO4NZmZsY2f7O+6PJ72eCT+COdFz5txfENwA8O50zhe87u1vXgKIzz3j18VDqFzBXRBhp+IziS/udvMbr/NJX4fDxaKBJXiqlCA67vCc7qMPA1xd+6MvqGSww/SWfYF/Yy5Hn9fWbcuYih+v6+bq8MhfMbZtaEspXhZNMyFfpvTjku/aXK+3yYsA8JFBwubBr/Z28Wzw0HGXJcdlsEZV/N/RyYpac3zwkEuC/h5SmXMacGbuyzbbr/NeT6b3T6bnOdxcaD2JAuxsJOZkwTNEFF75vIhtmHT7etmKDvE9sj2prbJjadQweCc48pQ5WrdRBxr00BT2yREI0ioazMDlsVfbujjeKHEGdfl5kSKvxud5LXdUZ4TDk5X7HyG4pdjEQ6PTuJXikRlyYadfh9/OTLBO/u7+X4mP71uXdrTvGd0km22xVNmzWwNmQb7gwH2N/UzFWJ1dZsmV4TK3y8/yeT4q5GJ6ccOF0u8uaeLfstkwLY4x2cTUoqs1vgU/FZPjPtyef5tIsFTg/7yJCQZY7osh5OH2BreUvOxgcAgJzInmh7qvHgKozta87FyF2xCQp3oSBLq2tCAZXKsWOLvx84O2P3AWJyAUlxT1aW0w7Z4Q08XI47DVtvGUvDVZAaAW5IZNtfoLjpZcuaEOiHWulpr0I05Lo8Winw1lWGrbfH+Tf18Lp5ip8/m5kSK8Uol/I50jmNFh/dv6pclTZbhSOrIvMuW9Pl7GcrO3kho9XmJFObm2qHNiMq4OtG5JNS1qR9k5i4k/KVkhudWuk+PFUv88fAYE1VdsH/Y381QZVxQVClCNbbg6ZVfSmIdihgzvxe2WCZBU/GuqurdhX6bdw/28USxxKcqs16nPF4sMVRyJNQtw5OpJ7m079Kaj/X4eziVOdnkFoGXzGCE53a/AhjREN5kssktEqIxZKJEm+qz5v7T9JsGU7+bHikUZwQ6gP9OpPmjgR5M4Nd7unhRNEx1h9GLIiF2+aULSaw/zw4HCVT9kXNdNMwXEpkZ5zxWKHGs5BCuMVnJAPwt3Ke0k53MnGQgULsq1ufv43TudJNbBF4qi6qxTBSAEQ7ijiea3CIhGkMqdW3qGaEgn0+kp9fasoBXdUexKr9YcjUGdmc8zZPFEv+6aYCdAR9jjsOfDfZS0prNlsV2n0WXVBrEOvSUgJ9/3tTP8WKJErDBsijqWt9DHk/x+7k6HOTOqmr5q2IRtsqYuiXLu3lSTopuX3fNx3v9vZzJnWluowAvlUGFalfqVDSEOyGVOtGZJNS1qV1+Hx/cPMiBfIGi1uz1+7igaoujC/02JsxY0uTqSJCvpjLs9vvQhSJ/eWac0Uo1b8A0+ftN/XSZJlprhh2XktZssEz8siadWOM8rfEpRQ7Nt1I5XhQJ8bRggJ/kzo61ixiKPtMkYpq8rS/GCyIhjhdL7PTZPCXgmzFrXNRnODtMn78PQ9X+GRPzxUgUEzieg2U059eR1hovncWYZ7cdIxKidEq2ChOdSUJdG9vps9k5z96tXUrxZ4M9fDGRIe56PCscYNx18SvF/bk8DkwHOoBR1+V/0lkGY1FuT2f42ESSvNY8Nxzgzb0x2T1CrFnjJYfb01k+FU9R0ppnhgJETMU5Pos+K8zPc3m22xbPCAUpeB45z6Pfsni2ZcE8465EfYZzw/T659+Jw1QmUTvKWGGMjcGNTWmTLhRBKdQ8P1uNSBg3LpU60Zkk1HWowyWH76RyXBX080TJ4fZUlpzWvKOvG7+Cr6Wyc57zWKHIY4UiH6waL3JnJs8Gy+K3e2MYyxgzlHJdhh0Xn1Jssa3p7mEhGkFrzSnHIe2Wq8rzbfFVS9b1uCeX54zjcmPVwPcfZMvveUvBfbk8Fwf8jDoOBa35Wa7AgGWywy9r0jXCSP4MsXm6Xqf0+HsYzY02L9SlsxjzjKcDUJEgXjzdlLYI0WgS6jpQzvMoeh7n+W222Ca9lsmWyg4QH5tMYAKviEW4N1+Y8bxrIiEOFotzrvftdJZXx6L0LnHF/OPFEu8fm+ShfBET+LXuKK+IRWTcnmiIoqf5TjrDB8cT5LRmi2Xyrg19M4YhLOTefIEPjZfXmZvtp7k8zwkHeHUsSkZ7bLEtvpBI8/RQgEKNsXZiec7kztBtxxY8J+aLMZpvXnfnQpMkoNz96iUl1InO1NLBVEqpa5VSB5VSh5VSf1rj8V9VSj1Yud2llNrXina2mxPFEkmt+Vw8xRMllw+MxfnPeIr/jKeYcD1GXY+NpsmruyJYlJP7DbEIlwX9bLTm5vidPpvQEscLOVrzhUSKh/LlkOgCn4qneDQ/NzQKsRxHiyX+bixOrhKyTjku/zA6SbLGTiqzuVrzxUSapOfRV+OPla22RcbxSHoeN04k+eRkkgnXZU/AZtR1a1xRLMeZ3Agx38KhLmp3MVYYa1KLwEsvHOpUMIDOF9BO87cvE2KlWlapU0qZwEeAa4CTwN1KqVu11o9UnfYE8Fyt9aRS6sXAjcDTmt/a9pL0PD46nsABHA2WguKs4oJpGLypL8ZLu8IoYEOla3SPH/b6fRwolMNXUCne0NNFoMZkiZPF8hIPtlLs9Nn0V/1yTLouP6yxoOvhYomnyTgk0QCna/xSPVQsMVxy6DIXr9ZFDEVBa1yt2WFbHKus4RgxFK/qirDFNrk/X+SZwQABQ/HMUJCH8wVCpsEzw4tcXNRlPD/G/t6F/xaP2V2M5Eaa1KJyqDMWWIBdGQojHMRLpDH7upvWLiEaoZXdr1cCh7XWRwGUUp8DrgemQ53W+q6q838CbG1qC9tUTmumft19P5PjF6NhvpQ8u+bWDtviAp+NpRQbbIsTxRIP5QsMWiZbbJt3b+jlSNEh73ns8NnsqDFg+PFCkT88PUqqsnTKbp/FX23oZ3OlmzdsGOzx++YEu6229OiLxqi1UPagaXJ3Lk+3aTC4wHvNVOVt9H6YzXNzIs3LusI8PxKixzAYcV2Snsd+y8+xYoaM9tCewfvGJihqeM+GvtX8tNaV8cI4XXbXgufEfDEe/f/t3XecY1Xd+PHPufeml+l9e29sp+yuFKmyKFgAEayoiD8fe+/68xF5/KmPCiJiAxEERBSkI0VEqQsLbGX77szOTk/vuef3R7Kz03ZqMsnMnDeveU1yc5N8M5xNTk75fn3bxykikOEo4jg7X48Sbqfq1CkTUiE/gRuAQz2uNzL4KNyHgYeOd6MQ4irgKoAZM8a3juB4m2YYGEAKaEqlaEkZfLTMS1s6zWyrhTV2G9UWg7hp8mAwzPUdftJkRij+b00Fqx12KgaYhj0qlZ3aDfbIhbcrkWJzNN7dqbNpGu8v9fJ6LNFdd3ad08YSVfR8QFOpfebKXKvBu0vc3OnPrG+yCrik1M3vOwNMtxiDduoAphs6364uZ1M0jpRgEYKbfQE60ybX11WhCcEFXhf72lP8JxrDAnywzMOyKdqGc91G0zJNIBnAYxm4xupR3kJMvw5RKlFzO0n7Q6icAMpEU8hO3UCLuAZcoSyEeDOZTt2bjvdgUsqbyEzPsnbt2km90nm61cJXqsr4YXsXCQmvROO81ePiClfvb8T7Eylu7Azg1ARBUxIyJT9o7eKXDdW9plL7ipqSNxLJfscPJnsfW2C3cmNDNYeSSWxCY5bVUGWUjmMqtc9cces67/C68WoaMSkRwB2+IBEpaR3Guret8STfb+vkUxWl/NkX4r5gGB24qszLnGxllQaLhW/VlNOcTGEIQf0U3sGd6zYaSARw6s4h8895rV46452DnpNLZjiKGGKzjea0Y/rVZgll4ilkp64RmN7j+jSgX2VnIcRy4DfA+VLKjnGKragZQnCm28l8m5WudJpKPbP7tSmZ5MVInO3xBCc5bHh0jXd63cSkSZVh8FgwzL5kKnOfQTp1Hl3jLJejX/3L5QN8u62zGNSpKVclTyoNnZZUivv6pOiZc5y8ioF0mq2xTOoej6ZxgcfFDR1+NnpdnCYEDiFYarPws3Yfi2wWTnLaabBYmKNSmORcZ7wTr3XwqVcAt+EmkoqQNJNYtPyPjZnBMGKI0VjhcqgdsMqEVMhP4xeB+UKI2UATcBlwec8ThBAzgHuA90kp3xj/EIuXJkRmPVx2gqAzleJ7rZ3siGdG02oMnb93hunK7hQUwNXlJdwXCA24Vqmvt3hcNCVTPB6OYhHw/lLvlJ2WUgrHEIJLSzy0pUyejcZwCMFHy70sGqATlpaSewNhftsjJ91Cm4XTXA7uzo66nO1y8Hg4yp5EkkdCMD8Y5traykGXIyij05Xowm1xD3meJjS8Fi9d8S6qHdV5j8sMRoecfhUOG2YgPOg5ilKMCvZOJqVMCSH+C3gE0IHfSSm3CiGuzt5+I/AtoAK4QWSmRFJSyrWFirmY7U+mujt0BmBCd4cOMvPaj4QifL2qfMAUD33VWQy+WFXG+8q86ALqDWNUyYkVZaymWS18u6a8O8l1naEjBmiLh5Mpbu1TCWBnPMm6bI3PmRaD2VYL/+hR03VXIsX+ZEp16vLAF+/CbQzdqQPwWkvoiHeMS6dOhiNoZYOv8xMOO2afmQpFmQgK+k4mpXwQeLDPsRt7XP4I8JHxjmsiSvdImGoIQXKABKqhtDmiqVKbpjHTqurCKoVn1zRmDdEWU1L2S+0DMMNicF1dFU4NrhqgpmdaJRvOi66ED5cxvNwwXouHjvj4rK4xQxEsQ22UcDkwe1TeUZSJYtB3SSFE+WA/4xWkMrRZVgt12RG4U10OFtqs/f7nXlziHrTMUjoYJrGviVSLWrqoQKq5jcS+JsxIdOiTi0CtxWC9o3dSWa+msdBm4QSHjXqLhVNdvW+v1XVmHacGqDI2vkQXLsvwOnVui2fcNkvIcGzo6VennbRaU6dMQEMN22wiM3N3vJ2qc3IekTIqVYbBNbWVbIvFeSwU4bedAa4uL+HJcBR/Os27Styc4XIe9/6JvY10/egWktv2IjwuSj9zBc7TViOOsyBdmbzMWJzIEy/gv/4OZCSGbdUiSj/zXiwz6wod2qAcmpYtU6fxcjTOLKvBeW4nnmxibYemcXV5KQttEZ4MRVnhsLHR7aJaTb3mRWe8i7ph1nP1GG46YuM0UheOIhxD7X5VGyWUiWnQkTop5Wwp5Zzs774/qkNXZGZbLZTqmSz5TakUv+r049U0Tnc5OM/tPO5aOjMSxX/dn0hu2wuADIbp+u9fE89eV6aW5K6D+H54MzKbWDr+yg78v/srZp9awsWmLZni+22dbIknWOmwETEl32vrYm+P9Dy1FoP3lHq5rr6K/1Newmyb+tKSL/6RTL9avXSO1/RrZBgbJZx2ZJ8d14oyEQz5FVUIYQBpKaUUQkwnkyB4t5Ryc76DU0bO1yN/V5pM4fJXYvB2rxv3cWZe0x1+4q/sAE2QuuRcWpfPR5NgiSYwWjsxqtVM+1SSOnSk37HYM5tJ7m3EtmRuASIanrCUdKZNOtMmjdmSYDqQlrA5GsMQghkWA6+uYxugLJ6SW/6Ef1i7XwE8Fg87/TvzHFGGjMSGl6cuqHa/KhPPoJ06IcRHgf8BQkKI7wFfBF4GVgkhfiel/J9xiFEZgekDTJee7nRSPshaOuFyoFeX4//ghfxoyRx2ZytJrLIafDYUoV4IjKqyvMWsFI/U4TYY4APPmFGL6Q8hUylEkU5XVuoaS60WtvYYmftAmZefd/i6676e5LDxucoyalVuxbwLJIMj2CjhHZeNElJKZDQ+rJE6MxRFSjngTmtFKVZDfV39DDCXTCWHnwLrpZSXAauA9+c1MmVUFtgsfKmyFLeWeSM6yWHjfWUeLIO8MeleN96PvpNnFszq7tABvJJI8ZLDRsc3rid5sDnvsSuFldh1gNaP/zdpXxDbmiXHbrBacF1wGrEXt4JZvDtF3brO56rKWJLd+LDQYuBLp7s7dAAvROO8HI0d7yGUHJFSEkoGcRrHX8fb09E8dfkmo3GE1YLQB//oExYDYWhI1VaUCWaor6sJKWUX0CWE2C2lbAeQUkaEEIn8h6eMlF3T2Oh1s9phJyYlNYaOY8ipJkmsvYtNS+ZCvPf/1teB8z/xbpJHOuiqr+JgNI5N15hps+LNJjFuTCQ5mExh1wRzLBZKh5EHTykeZiJBujNAqrmN0s9cgVbqxezwYVs+H5lKI3SN4O0P4DxvA6IIdopKKTmUTHEklaJU15lhMbBrGsF0mrAp+WhFCRYhqNY1vtrSf0fl1niCjQWIeyqJpqPoQh92hQi7biclU8TSMey6feg7jJIMRxGOwUfpjhJOB2YwgpbNc6goE8FQnTqHEGIVmRE9a/ayyP7k71+eMmYjmV4ShoFr8RzWRaJs71NtYqUvhOwKcGh2A99sbKXVzCQ0Xm+38pmqcjrSab54pJ1QdgRnrd3Gl6rKhiy2rhSH+I59pJpakYkkMhwjub8Jy9zp6OUl+K+/49iJho5l3nTMWBxtiKmrfNsUjfONlo7uerBXlnk53+Pk5q4A92cXt1uAH9VVssFpZ0+fOsYrChz/VBBIBIa9ng5ACEGJpYTOeCf1zvq8xWVGht+p01yOTFWJmoq8xaMouTbUJ28z8JPs5SM9Lh+9rkwStiVzOeNIO68Ar6Qymy1O0zSWPv8awf2HuWfONFrNY5sw/hNLcGYowuOxeHeHDuClWKb2rOrUFb/kviYS2/cS+PU93TtdLYtmY1uxADMQoeTqS4hv2Y2wWbEumEngxj9jmV6LdcHMgsXcnkpzbVsnsWzCYAn8tivAAqulu0MHkASubevi+zUVbI7FeS2WGYE+x+Vg1TA/1JXRCyT9w15Pd1RmB2yeO3XDyFF3VGZdndoBq0wsg37ySinfPF6BKIVxOJliSyxOayrNIreTL7d0cMQfQkRilD/5IuJfLxN7x5m8OkDW/T3JFEdS6f6PmUr1O6YUl3QgTKq9i+jTL3d36ACSO/YhQ1HMQJDgH+7HMqcBGU8Sffx5gILn7vKn07T3KH93VFu6dzs8z+2kzmLwdDjGlaVeNJHJUzfNYgxjOYIyVoFEYNjr6Y7yWDx5z1UnwxHEMGtYC4cdqXbAKhPMcFKaVACXA4uyh7YDt0spxyf9t5I3rckU3zzSzp4eC8k/X1nKmq9fj9l5rESOSwjWS8k9fe6/WNOI2W3sS/buxM2zDu9NUykMmUgS/NODyECE1P7D/W5PtXZiW7UIvaqM5J7G7uPC7cSoz39tzsGU6zoNhk5Tjy8Tgkxt4qNOcdjxp00eCR2rA/sur5uryr0qlck4CYxgk8RRbsOd91x1Zjg67OUDmsOGqXLVKRPMUGXCFgNbgDXAG8Au4ERgixBi0WD3VYrf7kSyV4cO4KZwFHnNJ9HKvCAE9lOWY/G4OHf7PpZmd9QK4O3AvO17eGeJm5Oy01lWAVeXl7BYJXQtaomd+wnd+QjxLbuwr13S73ajrpLkvkZKP30FlgUzMsdm1VP5g09j1FeNd7i9lBk6X6supzK7e9EmBF+qLGWR1crXqspwa4JFdivP9dm1eE8g1J27Tsm/QDKAXR/ZBgOPxUN7vD1PEWXI8NA56o4STpvKVadMOEON1H0P+LSU8q6eB4UQ7wK+D7wrX4Ep+RcbYEo1YkqShk7J6WuwrVxEurUD/01/wQ1841OX0z5vOqKpldK/P42tppyKU9fwneoKjqRSWIWg3mKgqbxORS3d4QNTkjrQjOPU1dhOXEr8xa0Ihw3XO88m3RlAWA3i2/ZgzJ+J/eTlmKEIxrTCjtIdtdRu45f11bSm05RpGrXZNneux8UJdht7E/035ksgPkB7V/IjmAjgGOEuVq/Vy6Fw49AnjoE5kulXu03Vf1UmnKE6dSdIKS/ue1BK+RchxDV5ikkZpWA6TXsqjVPXqBkgQWzClGyLx3kmHMOpCVY77FTrOq091iOdp2u4H32OyKPPopd6ifzzJTyXb8Qyu57QXY9Stn1f97nO738y81vXmKOrKdcJw25Db6gm3dRK8I8PYFuzBO9H3oFeU0Hgd38j3dyOVu7FufE0NKuF4K33g6HjvvQ8iiVZTYmucyiZ4q5QCI+m8SannYV2G3UWAx2o0XVaerTreRaj1xStkl/+5MjX1HktXjrzvKbODMcGTK49EOGwIwNqpE6ZWIZ6lxusRY+5tQsh3gL8jEw1n99IKa/tc7vI3r4RiAAflFK+PNbnnYz2xJP8qK2T7YkkJZrGZytL2eBy9Eo6/GosxhePHHvTvNMf4pqaCm73BzmYSHGu3cqZm7YhDrdiP3EpkX++RGpfE8F9TdhPXY11xUJSRzpA1/C853ysKxYU4qUqY2RUluJ++5uJPfcaia17kOk0wm7D9+M/ILO7RM3saJ0MZUa3HKetIXXoCJa6ykKG3u2laIyvtRxry3f7Q/y/2kqWOWxUWwyuqa3gdl+QzbE4pzjtXFLiUfkTx1Eg6WeGa8aI7lNiKcl7VQkzGEYb5kid5rSTbM9/QmRFyaWhOnXVQojPDXBcAGNaXCOE0IFfAOcAjcCLQoj7pJTbepx2PjA/+3My8Mvsb6WHcDrNT9ozHToAv2ny3dZObmyoZqHNSiidxpRwuy/Y635xKdkWT/D9mgqipsSbTBGzWJCrFhLbtB3RI2dd7F8vo5WXUPKJd6OVeTOlxdwj+yauFAfrvBnIZAoznsR+8nKM2fV0fO066LPmTC/zEnn43zjPOQWtopTO795Iza+/XZB1deF0GovQsGqCmGnyxz5tOSolm6IxlmXXd861WflyVTlh08SjaxhqScC4CiaCOEtGOFJn9eJL+DCliSbys6FFhqNopZ5hnat2vyoT0VCdul8Dx/sX8JsxPvdJwG4p5V4AIcQdwEVAz07dRcAfpJQSeE4IUSqEqJNSqppVPXSkTbbGeydYlUAgbfJ4MMzuRJKIKYkMUOIpaprYNQ27Bhg61hULiL+2C+d56zE7/fj+94/HHjMSRQbDBB/6N97/ejeJA80kdx9AxpNgSmzL52OZUZfnV6uMVnzXQVIHmzEDYSwzanG/482kuoKk232UXH0JwVvuyyRbBfRpNejTqnGsX0ls0zZSjz0HQDoYRvjsCF1H8+S/U9+RSvF0OMZ9gRA1hsEVpR7mWI0B14P6TZO/+IJscDmotRhYNYFVU6NzhRBKhXDqI2sfFs2CTbfhT/gps+Wn1rQZiqDXDm+0WTjt3f8eFGWiGCpP3Xfz+NwNwKEe1xvpPwo30DkNZJIi9yKEuAq4CmDGjJEN+090Lk2jQtfo6JG/a4nNyuZojPa0ySOhCDYhuKLUw84e2fU1YF22BI5Mpoi/sgP/r+/B9AVwnLYGY+FMyr/1MaJPb0K4HFgaaoj+51UcJy0j8NPbEQLs61YQfvQ/OE5eTtfTmyj/zHsLvkOyGBW6fSb2NJJ4dSeB3/4VGY2DruH92CVYF80i+sDTpA4047r4HITVgukLIqwWZDBC6O7Huh/D8ZYNJLbspvNbNyAcNko+/HZsJ54w7Oms0XgkGOGmrkxqkn3JFJuiMW5oqOZCj4ufdvi6z9OBKsPguk4/OxNJPl9ZqtKXjFAu22goGcJhjLy8Vqm1lI54R147dcOuKKGSDysT0KCdOiHEtwa5WUopvzeG5x5oPqTv1+/hnHM0mJuAmwDWrl07pba5VRg6X6gs4xstHRxdGv42j4vGVIrHs29KcSnZHI3z4TIvz0ViuDWNd5e6WZz9QE68sZ/2L/8UsiMgob/8A/fFZ2Nbs4Tk3iZkNEak8z+UfPgd+H95bDN0fPNOSj5+Kf5f/RnvBy8i9tJWHGeciO4dWTb5ya7Q7TN1uIXgHx/IdOgA0iYyGKbja9chs20k+cYBnBvfRPLQEZKv76b8vz+BcNqRkRh6XSW2hTPx/ez27sfs+OYNVP7vF7CvWpyXmNtTae709959mAR2xZOsdFi5qszLM5EYDk1witPOX7PnPhaKcHmph1lW1akbiVy20VAqNOKNEgAl1hLaY+3M884by9MflwxHh19RwmHDDEXzEoei5MtoNkq4gA8DFWRSnoxWIzC9x/VpQN9MqMM5RwFOctq5qaGaxmSKEl2jTNc4FExiEYJUtqP2cizOlnicH9ZWssxu67XOKLFjf3eH7qjIky/hOH8DWmUpiZe3Yz1hPrGXtvZ77sT2vRjTapHJFOkOP8md+9FPXJrX16uMjIwnMfusQ0PTujt0R0UefRbPpechwzEsc6ZT/etvY/qCaBUldH79un6PG3vu9bx16gzAoQn8fQpIWDXBLKsVl9BYYLNyS1eAGzr83d/2DCiaXbpTUdJMkjST2LSRl2PzWLx05DFXnRmODXukTjgyX2ikaSLUqK8yQQzaUqWUPz76Q+YbnAP4EHAHMGeMz/0iMF8IMVsIYQUuA+7rc859wPtFximAX62nG5guBHNtVk53O1npsDPDYqHG0HlbnxGzCl2n1jD6LRzXXP2nSvQyD0ZtFWWfvhzXhWeAw4oYILGwsFoyb3wOGySTJPc1IvsUUVcKS68uR6so7XVsoL0DwtDRG6oo/8ZHsdRXYWmoxrZ0LkZ5KVpl/ykxvbwkTxFDqaFzVVnvxy/XNRZaM22wymIw02ohbJq9hu/fU+qhTtUeLphQMrOeToxic4rX4qE12paHqDJkOII23E6drmXe2yKxoU9WlCIxnDJh5cDngCuAW4DVUsox7/OWUqaEEP8FPELmi/XvpJRbhRBXZ2+/EXiQTDqT3WRSmnxorM87VQgheLPbya54kukWgx3xBDMtFk5x2qkd4APPunQu8oLTaD51NV1OGzWBMHNL3OguB7rLQemnLifV1kV8625iz78ORzddGDqWedOxzJ2OsNvw3/J3PO+9gOS+JqwLZ43vi1aOSysvpfSTl+H7ya2Zxd9WA2NWA3ptBemjaW50Hc+HLsJ+xkkYrt6JY4XVwHv5Rtpe2gbZ/G+a14UtzyOy6112fqxX8lI0RqWhs8Juw5c22RWKUGvozLFa+F5tBZuicfbEk6x12DjBYVO7XQsolAzhtIxuE02ptZTm6JEcR5QhpcSMxIY9/QogXPZMGhS101+ZIIZaU/f/gHeSGaU7QUqZ0/TaUsoHyXTceh67scdlCXwil885lXh1nTVOnTXA24Y4N1lfzd8/8DbuOjod53bypcpSzpcS0iaJHfuIvbAFBJR/5+PEX9kBAuwnLiMdjBB56F8kXtmJ44y1JHcfxKivUp26IqKVuEh3BHBffj5C0zEjUdLxBK53noXZ5kMr86C5HCT3HSb+75dhxUKMmopej2E9YR5Vv/gqyW17ETYL1qVzscxqyGvcdk1jjdPOGqedmGlymy/IrT2mkT9TUcrbvC4u9KrSdMUimAriGGGJsKNKrCW81vVajiPKSiRBCMQIRnE1lyNT/1Vt6lcmiKFa9+eBOPAN4Os9htMFmT6XN4+xKeNofzJ5rEOXdV2Hn+V2G5Vb99D++R91j84Ju5Wyb34MoQnS/iCa04HjTauxr1hEYs8hNLtt2GkDlPGhO2xg0dG8pZA2sVSVkurwY503g1RlKYmXthN58F8AhP8GtrVLKf/mVegl7u7HEJqGbdFsbItmF+Q1HEimenXoAG7o9LHKYWOmVXXqikUoGcRpjG6jVKm1lPZYftbUmaEImnNkpcuEw67qvyoTylApTdTq0CnCnzb7HYtKSSidxn7XI8emWwEZS5B4fReRR5/F7PQD4Dh9LcasOoyGaqL/fInkgcN4r3w79jWLERb1gVtIZixG7IWthP/6BCSSOM4+mdgbB4g9+xqa10X59/4L30PP9LpP/KWtpA4cRl9ePFVDBmqjCQkhs/9xpXCCydCI674e5bVkEhCnzTR6jnMMjiSdyVGa054ZqVOUCUJ12hQAGiwGtj7rkBoMnWpNG/Cbqtnh77VpIr55B5rTQeiOh0k3t5N84wAdX/05ie378x26MoTElr10fusGUvuaSDW1Erzl71gXzkbYrJiBMKnDrf12PkMmd2ExqTd0HH3aaFV2449SPILJIPZRduoMzcBj8eSlXJgZjCBGPFJnwwypkTpl4lCduinojViCO31BbvcF2BGLY0rJdIvBNTUV1GTrYy6wWvh2TQXlNivud53T7zEsc6eRbj42TWJbs4Tw/f/sfZKUxF/dkdfXogwt+vSmfsdiL2zBesJ8AFIHm7GuXNjrdr2uAmN67bjEN1zTrBauqa2kPttG51oMvl9TTlc6zd3+IH/sCrA1Fu9O4aMURjARGFXi4aPKbeW0RFtyGFGGGYqgOUbRqVNVJZQJRH3FnWJ2xBJ8urmNePaDzwB+Wl/FMruNNU47v6yvImhKynUdj57p89tOzKyvCt75CMJuxfOe8wk90Hu6TsaTaCUeONT7zVjzqCTEhaYNkHZE87ow/Zn1aca0GlwbTyX80DPEnnkF64qFuN9xJkZ1+XiHOqRVDhu/qK8mYJqU6TqtqRSfOtxGJNue9S74cV0VK0c4zabkTiAZGPVGCcisq2uLteYwogwZHPn0q7CrTp0ysahO3RTzRDjS3aEDSAH3BkIsy27zLzcM+n6U624nzrNOxr5uJWgCzW5Fry7HFwqT2LwTY0Yt7ovPBtOk/Ys/6V5/p5W4sfUZAVLGn2PDSkJ3PXIs35ahYztxKf5f3Y3n/W/FfspyjIpSSj76LjyXb0Sz2xBG8abvLTN0yrLphe8LxLo7dABp4E++IEvtViwqrUlBBJIBprumD33icXgtJRzJx0hdMIwY6Uid044ZyGnSB0XJK9Wpm2K60un+x1ImppRoQ3wIas5j33Ktc6dTec0nSXcF0FxO9FIPMpWi6udfIbFlN8LlwHbCfCyz6nP+GpSRsc6fQdV1XyHx+i5kMoV12Ty0Mi+O9SvRa8q7s+ULIdAnWD4u/wCbJHzpNGkpVaeuQELJ0JhG6spspRyJ5j7HvBkMo40gRx2A5rCTbM5fhQtFyTXVqZtiznG7eKxPPcOLvK4hO3QD0ZwONOexN29hGNiWzcO2LD91G5XRs86djnXu6EdPitV6p50/96kPe3GJB7sq61QwwWRwVHVfjyq3lrPdl/u1uGl/COEc4fSr066mX5UJRb3zTTEn2K18r6aCBVYLcywG36wuZ/UIpyQUpVgstdn4QU0Fi60WZloMvlJVxskj/OBWcmusnboyWxmteVhTZ/pDI55+1Zx2zJBKaaJMHGqkbopxaBqnuhystdswkbj04l07pShDsWqCdS4HK+w2pGrPRSGcCo9p+rXEWkIg4SdpJrFouctxaQZCGCNcDiIcdqRKPqxMIGqkbopy6Jr6AFQmDadqz0UhZaaIp+OjzlMHoAs9swM2mtvROtMfGnlFCZdKPqxMLKpTpyiKouREKBXCaTgRY9ykUm4rpzl6JEdRZZjBMMI1shFEYbMhE0lkqrgScSvK8ahOnaIoipITwUQQ1yjrvvZUZivL+Q5YMxBGG+HubqGJzGYJNVqnTBBqTZ0yruLpOAdDB2mNtVJhq2Cme+aYss8rxwQSAfaH9hNKhqh31jPDPQNNqO9tyvgJJMdWTeKoMmsZTeGmHESUIVMpZDQ+4uTDAJrLgRkIo5d5cxaPouRLQTp1Qohy4E5gFrAfuFRK2dXnnOnAH4BawARuklL+bHwjVXIpLdM82fwkv9h+ffex9819PxfNvAibPvCbbTAR5FDkEEkzyTTnNCrsFeMVbtHqinfRGG5EExrTXdPxWr0EEgF+88ZveLL5CSCzLumbK7/Fmso1BY5WmUoyO1/HPlJXbitnp39nDiLKMP2ZqVcxilQ3wqUSECsTR6FG6r4CPC6lvFYI8ZXs9S/3OScFfF5K+bIQwgNsEkI8JqXcNt7BKrlxOHKYm3b+qtexP+65lTWVa5jrndvv/LZoG7/ccQMvtr8IQK29jm+s/AYzPTPHJd5i1Bhu5NpXf8CB8AEAFpcs4bPLPktrtKW7QweZDvQvtl/Pj0/6CWW2skKFq0wxwTGWCDuqwlbBkRyuqTP9QTTv6DqbmtOhpl+VCaNQczMXAbdkL98CvL3vCVLKZinly9nLQWA70DBeASq5F0qGSJrJXsckkkAiMOD5W7q2dHfoAI7Emrn/0P2kzf5VMaaKfzb/s7tDB7Ddv42XO14mmOw/ktAWayOSUh9GyvgJJoM4jLHnvaywVdAaa8WU/SuGjEa6MzDi9XRHCYcaqVMmjkJ16mqklM2Q6bwB1YOdLISYBawCnh/knKuEEC8JIV5qa2vLZaxKjlTbqyiz9h41sut2qp0D/+/fG9zb79hrXa8STUcHOLu45aJ9ps00r3S+3O/4lq4tTHdNR9B7x+EJZcspt/Wt5KsoA8tFG/UnAjj1sZeas+pWnLqTjnjHmB8LwOwKoHlGF5fmtGOOIFddxDSJm3LoExUlD/LWqRNC/EMIsWWAn4tG+Dhu4C/AZ6SUAw/pAFLKm6SUa6WUa6uqqsYavpIHFfZKvrbia9Q7MgOulbZKvr7iGzQ4Bx6AXVS6qN+xkytPGVO2+kLJRfvUNZ111ev6HV9dsZp6Vz1fOOGLuA03AAu9C/nYoo+pTSjKsOWijfqT/pz9+6y0V3I4cjgnj5Xu8qO5RjtSZ8P0Dz1S90Y8wSeaWnnHgWYuPHCY77V04Bug1rai5FPe1tRJKc8+3m1CiBYhRJ2UslkIUQcMmGVSCGEh06G7TUp5T55CVcbRotLF/PDEH+JL+vAYHsrtxx9JWlKymPMbzufhpoeRSBaXLOa8aedN6R2dG6o38Frn67zcsQmA02tPZ2XFSiyahdNqT2NRySKi6SiVtkpclrEvWFeUkfAn/NTYB514GbZyWzlHIs2sKF8x5sdKt/sQoxypEy77kJ26/4SjXNvWxQUeJx8q85CUkkdCUT7R1MrP66upMFRibGV8FGqjxH3AB4Brs7/v7XuCyGSv/C2wXUr5k/ENT8mnElsJJbaSIc8rs5fz4QUf4fzpG0mZSeqc9bgt7nGIsHjVOuv40glfojlyGE3TqXPU9RqNq3bk5gNVUUYjmAjkJE8dZNKaNEZyk9Yk3e5DrxndznnN6SDZdvwRwy2xOD9o6+QjZV5mWDNlzXQhuMjr4pGg4Bst7VxXX40xxoTMijIchRryuBY4RwixCzgnex0hRL0Q4sHsORuA9wFnCiE2Z382FiZcZbykZZrGcCP7g/uJpCLYDBuzPbOZX7IAu27ncLiJI5EjSDn11qzE0jEaw42EU2Hmlcyn0lZJc7SZI5HcJmlVlNEKJAM5m36tsFfQFG7MyWOl27vQSkb3hVC4HMcdqfOl03y7pYN3l3i6O3Q9neN2IBDc1nXclUOKklMFGamTUnYAZw1w/DCwMXv5GUB9tZlCgskgDx16iD/tvZ2UTLGqfBUfW3Q1Da4GWqOt3L3vzzxy+BEswsJlc97DuQ3n4rVOjYSgTeEmfr/rdzzf9jxei5fPLfs8v9/1Ow6EDuA0nFy98GreVHMqFj13BdAVZaSCydxUlIDMmtt/Rf6Vk8dKt/tG3anTnAN36qSU/L+2LlbYbSy1Wwe+rxBcXOLiJ+0+zvW4qLOofP9Kfk3dxUlK0XnD/wa37vkDKZmps/hK5ys8cOh+0jLNMy3P8FDTQ5jSJG7GuWX3zWz3TY2UhSkzxd37/szzbZnN36srVvPH3bdyIJRJbRJJRfjJ1p+wP7S/gFEqU11apommo7kbqbNV0BJrGXNaEyklZocfrcQzqvsLt2PA3a//DEfZm0jyliHW6pXpOqc6HdzU6R/V8yvKSKhOnVI09gb39Dv279Z/sy+wjyeaH+8+VmmrZFnZCWzrmhqdumAyyKb2zMaI1RWrOanqZHYHd/c7rznHtTIVZSSCySAO3ZGzjUxW3YrTcNIeax/T48hQFARooygRBtkyYX2SDwfTJj/v8HFxiRvLMNbKne5ysCkaZ28iOeS5ijIWaixYKRq1jtp+x2a5ZxFMBVlRtpJD4UNcNvsyOuOdHAwfwmVx0RptnbSbAw6EDvBk8xNs9+3g3GnnIRBMd08nLdNU2atoi/XOJeaagKlelMnDn/DlfCNTla2KpkjTmP6Np9s60cqH3ph1PMJqASkxo/HujuFvOv0sslmZM8A6uoHYNMHpLge3dgX49ig3bCjKcKiROqVoTHfNYHHJ4u7rbsPN6oo1fOvlb1Jpr+Sy2ZfxZPOTPNz0MNt8W7l1z638YfctxNKxAkadHy3RFr798rf4y/6/sM23lTv33UF7vJ2/HriHG7b/ggtnXIQhjn0nO632dEIDVJVQlPHiS/hz3qmrsFeMOVddqrUTvWxsa281j7O7qsTOeIKnwlE2jjBFynqnjRejMQ4nU2OKRVEGozp1StHwWj3MdM/kM0s/w2VzLuMt087ntj1/BOC2PX+k3lnfb4rxn0f+yZFI7mpEFosDoQP9suk/0fw4qyvWEE1H+duBv3LJ7Ev5+KL/wyeXfAqk5KkjTxUmWEUhM1KXq00SR5Xbymkc4w7YdEvnqDdJHKW5nZi+IGkp+VFbFxs9TlzayD4+7ZrGKQ47d/mDY4pFUQajpl+VolFuq2BR6WKaI83cue8OABy6g4tmvB27bseuO5jjmdOrfJjI/jfZHH1NNs3GuQ3n4TSc6ELHnf3Q7Ih38Ke9t/OeOe/Bqtl4uuVpPrbw6kKGrExxvoQPZ447dZW2Sl7ven1Mj5E60j7qTRJHCZcT0x/i79ldsCeOcn3eBpedH7X7+Eh5Ce4RdgoVZThUp04pKhuqN7ArsIu/HfgraZnmvfPex11778Sf9KOh8a5Z72KGa0b3qNQZdW/Gro+9gHixmeWeRb2jngtmvJW/7L+bzngnAsFbpp3P++d+gKRMApLFpUu4buvPWVuxlhOr1hY6bGUK64p3dX/pyJUqe2ZN3Vikm9vQ68e27lZzOejs8HGzr4xPVJQgRplIuFTXWWi18lAgzCWlY+toKspAVKdOKSp2w86ysmV8a9W3ea71WR5pfBh/MpMKwMTkz/v/zBeXfREhBTO9s3AbLv7V8jTrqtfR4JpW4Ohzp8pRxeeXfYFf7byRzngnABLJQ40P8pEFH+EPb9wCgMtw8bUVX2euZ64qC6YUVGe8kxLr6DckDKTMVkZXvItEOoFVHzgX3FBSLR1Yl80fUxzCZefxplZOW7uEGmNsH5sbXHb+4g/xrhI3mqoyoeSYGv9Vio4QguXlyzm34VwOhg/2u/1g+BCzPLPRELzQ9gK37rmVa179Aa92vFqAaPOjMXyI/eF9vBF4o99tgeSxNTnhVJgHDt2PRVNJh5XC6ox34jFyO/qkC33MmyXSLZ1o5WPbKLHfMND8Qd7scgx98hBmWww0BC9H42N+LEXpS3XqlKJVYatkpmtmv+MWzcCqW/l3y795of0FbJoNl+Hkz/vuYqdvZwEiza2UmeLhxofpiHYwzzuv3+3WPh243YHdk3IHsDKxdMY78VhyP6VYba/mUPjQqO4rE0nMQAjNO/qNEvsTSbYYOsujcfQcjKwJIVjntHFPQO1WV3JPdeqUouWxerhq0ccosWSmdDQ0Lpj+VvYG9+KxuNkf2s/75r6fi2ddzHzvfNbXbKA5chhf3FfYwMdoV2AXboubUCrEFXOuoNxWDmRe/ztmvoOtXVt7nX9q7ak5TyWhKCPlS/jy0qmrtFdyMNR/xH440q2daKUehD66j7qudJrfdQWYW16CM4edsDUOO6/G4rSmVHoTJbfUmjqlqM10zeSTSz7JvtB+TGmy27+LMxvOwqk5+czSz3IofAhfoovDkcN0xDtYVnYCTx/5JxfOvKjQoY/KodAhHm16lN2B3ewP7eORpkf48vKvEEvHsOs2bMIOErb4tpA0k6ytWMs59efmLIu/ooxGykwRToXz8uWiyl7FvtC+Ud03daQDvWJ06/xCpskvO/2ssFupLHWjdeUuFYlNE6y22/h7IMyHx5AYWVH6Up8ESlHrSnTyv1v/l654FzNdM7lw5kU82/If/Ek/N+74JU2RRpaVnYChGRwIHaA11ooudDpjnYUOfcSawk082PgA23xbaXDW84F5H2Rx6WISZpxXOl7htj238bdDf2Np2TK+uvxrfHzRx7Folkm5+1eZWI5Ovebjy0Wto5aD2TrHI5U+0o5eNvJOUyBtcn2Hj1kWCyscNqTbgebP7XTpOqedvwfCJKXM6eMqU1tBRuqEEOXAncAsYD9wqZSy6zjn6sBLQJOU8q3jFaNSHNwWDxbNwoOND1DrqKVcluGyuHiy+QmumHsFTsPJz7b+lLiZWXTceKCRt05/26hTDhRKKBni+m3XscW3BYDDkcO8EXiDD8z9ALftvo2NMy5AYuJL+DgcOcwM9wx+ueOXrCxfpXa9KgXXEe+gxFqal8eutFXSHm8nno5j00eWHy51pB1thKlDWlIpftXpZ6HNyppsPjrT7cx5p67OYlBt6DwdjnKWW5X4U3KjUCN1XwEel1LOBx7PXj+eTwPbxyUqpehU2iv55JJPYQiDOZ45tERb8VpKWFi6iG2+bTh0J6sqVvUaIXi06ZEJVzKrKdzU3aGDTJ6602tPx244uGLee/HFu6h11NIV7+J3u35La7SFcms575v3PjVSpxRce6yNEsvYdpgej6EZVNurOTCK0bpUczvaCEqEbY3F+Xm7n9V2e3eHDgCbFdImxBIjjmEw652Z9CaKkiuFWlN3EXBG9vItwFPAl/ueJISYBlwAfB/43DjFphSZNZVr+PFJP+HW3bfyUseLANh1Ox9Z8FGuee37zPHM4cMLPsKtu/+QXXtm59nWZzE0gzpnXYGjH1owGeRwpAmBQCJ52/S3EUqFeKH1BTShcde+u7rPfefMd5E0kzzY+CBXL/o40yZRbj5l4mqLteO15qdTB1DnrGNPcA8LShaM6H7p5jYsi2YN40zJI8EIz4RjnO9xUGvp89EoBGaJC60rgFlXOaIYBrPMbuXvwTA74wkW2kaXh09ReirUSF2NlLIZIPv7eOm+fwp8CTDHKS6lCOlCpz3e1t2hA4ilYzzU+CAnVp7ErsAu7t73Z86pPxeAjdM3ct/Be9nUvqlQIY/IvuA+Hmh8gNNqT6fGUUPSTPJk85Osr13PPfvv6XXu3w7+lfU1G5BI2uPtNEeaj/OoijJ+jkSPdO9Sz4c6Rx1v+EeerijV0ok+xEaEpJTc3BVkcyzOxSWu/h26LOnJdOpySReCDU4Hd/pUPVglN/I2UieE+AdQO8BNXx/m/d8KtEopNwkhzhjG+VcBVwHMmDFj+IEqE0JXvP+Sy0PhQ7yp5k2sr16PEAKbZmOudy7/OPwY/qSf7f7tvJXiWIY5WPvsiney07+T02tP58y6MwkmgzS4phFPx0nJ3ikPTGliSpPTak4jlAyiC33cXoMyuY3lPbQleoQlpUvyERYA013Tue/gfSO6j0ymMH1BtJLj78iNmJJfdfpwaIKLvC6MQdbimh5nzjt1AKc4bXy/rYvWVIrqMVarUJS8jdRJKc+WUi4b4OdeoEUIUQeQ/d06wENsAC4UQuwH7gDOFEL8cZDnu0lKuVZKubaqqioPr0gppHpnQ79jqytWM801nbv3381v3/gNmzs3E0gEcOiZrO9rKlaPd5jHNVj7rHHUcOmsdyOR3LTzV/xu129pj7WzwLsAt9H7A8lluFhUsghf3E9jpIl6R/14vgxlEhvLe2hLtKU7n2I+1DnraIu1EUwOf0Qr3daFVuJG6AN/8QmZJtd1dFGu65zpcgzaoQOQ7vx06hyaxokOuxqtU3KiUNOv9wEfyF7+AHBv3xOklF+VUk6TUs4CLgOekFK+d/xCVIrJPO88rpx/JTYts3h5ZflKLpxxEXuCe3hTzQbWV6/nocYHaYm1sKp8FefUn8Py8hUFjnp4ZrlnM9szm1/v/DVNkSY6450EkgE64h18aMGVfGj+hyi1llJhq+CqhR/jycNPsqpyJeuqTsFqqHU4SmGZ0qQ11kqZtSxvz6ELnVmeWWzpen3Y90m1HD9HXdSU/KLDR4NhsN5pG1YNVtPtROvwD/v5R+J0l52HQxF86XReHl+ZOgo11nstcJcQ4sPAQeASACFEPfAbKeXGAsWlFCkhBJW2Kt42423Mcs/CbXHz/Vf/m3AqDGSSFH9+2RdoCjcy3T2Dc6edN+L0B4WSlml2+HegC51z6s9hcdkS/tH4GD9r/ikAVs3KF5Z9kUg6wmOHH6XOUUdbrB2XrlKZKIXXEe/AqTvz/u9trmcuL7a9yLrq9cM6P31k4J2vSSn5daefGsPgZKdt2OmPpNeF1j5g5q0xK9V1Vtlt/MkX5OMVpXl5DmVqKMhInZSyQ0p5lpRyfvZ3Z/b44YE6dFLKp1SOuqmtOdLMXfvvpM5Rz5aurTzc+HB3hw7gQPgAB0L7efTwo5jSxJ/wFS7YEdrm20aptYTL517Bq12v0Rpt5XXfsRGJhJngzn138nzbc2zp2sJjhx/jzn130BJrmXCpW5TJpzHcSJU9/0teFpcu5vm250mbwxvNSjW3o/fLUSe5wxdEF7B+BB06ANPrQm/3DT/gETrL7eCBYJiOlBqtU0ZPVZRQJgQpJftD+4mlY6ysWMnBcP9akJ2JzLfo377xWwKJibM+5Ui0maWlyzgYOkBL5AiJdLzfOU2RRqrtxzaJd8Y7J1yCZWVyOhQ+RKU9d2k+jqfCVkGZrZxNHS8N6/xUUytan52vj4eiNKVSnOl2DGvKtSfT60br8I3oPiNRquuc7LDz6878TPEqU4Pq1CkTQr2zng3VG/BavNx/8O+sGGC9XJ2jls54JwfDB4ikIgWIcuSklAg0Xu96jV2BXZiY2A1Hv/NOqjyJrb6tvY5V2ivzUmtTUUZif3DfuIzUAZxceRJ/3nc3chiltdLNbb06dW8kEjwZinKe24llFF+IZIk7b2vqjjrL7eDZSIyd8dwmOVamDtWpUyaElExxxdwrKLWVssW3Batm5ZSqdWho2DQbF864kB3+HUgks92zCaUmxrRkZ7yTA6EDvNz+MktLlwHwQtsLvHv2ZbiMzJq5VeWrOLP+LBZ4F7Cmci0aGtNd01lUsqiQoSsKAHuDe8ctyfcJ5SfQlejkubbnhjw3deTYRgl/2uTWziBnux149NF97EmnHZFKIyKxUd1/OByaxkaPkx+2dZFSNWGVUVBJcZSiljJTvNb5GjfvvpnOWGY36Cz3LO49eC+LSxbz3VX/l65EF3fuu4OmSBMVtgrObjiHlJksdOjDogudQ+FDVDmqmOOZw1zPXHb6d9Aea+P9896PRbMSS0fxxX08feRpZrhm8tXlX2WGe+aEqJahTG5pM82h8CFqHQOlJM09Xei8dfpb+dWOG1lRvgKnMXDNVBlPYgZCaKUe0khu6Qqw1G5lmnUMH3lCYJZ70do6Sc/MXyqhEx02NscS3NYV4ANDJE5WlL7USJ1S1PYE9vDdV77DvuBe/Ek/12+7jsvmXEaZtYzt/u3cuOOXSCE5tfZU3j37Mk6pXseLbS8wzTW90KEPS6mtlPMazqPWWcszLc9wyaxLec+cyzm34TyeaXmGP+y+BY/Fy1NHniSUCrHNv5WfbP0JUhVZUYrAgfABymxl41p/eK5nLnM8c7h5183HPSd1pA29ohShaTwUCJNGstox9vQ/ZpkXvaVzzI8zGCEEl5a4uCcQ5tVo//W1ijIYNVKnFLX9oX2YPTowKZnipp038f011xBMBnFbXJRYSnnD8NAUaaLeWc+59ecyxzungFGPzCnVp7DDv51ySzmGZrCsbBlHIs28ue7N1DsbuHnX79nh39F9fjQdpTHcSIOq+6oU2HbfNma4xr+Cz3kN5/GzrT/jgukbmeme1e/2VFMremUpO+IJno/GucTrHvHGiIGYJR60lo4xP85QSnWdy0rdfKe1gxvqq6k7TukyRelLtRSlqDmN/rnYHLqDUmsp093HRuNOrDqREzlxPEPLGYfhYFXFaqg4dmx5+XIgk8plX3Bfv/uM58iIohzP5o5XmemeOe7P6zScnFp7KrfuvpVvrPxmv9tTjS0ky0u4rSuzjs6p52anuFnmQT/clpPHGsoim5U3u5x8obmNn9dXU2GokoDK0NT0q1LU5nvnU+88tn5FILhywYfxWHvnn0qbaWKp/C1gHg/RVJS07J2jqsZRw3vnva/XsdUVq5npmTWOkSlKf2mZZkvX68z1zC3I859UeRLburbRGG7sd1t8fzPPOe0sc1hpGMs6uj7MylL0xpacPd5QTnXZWWG38anDrTQnU0PfQZny1EidUtRqnbV8Z9V32RV4g2AyyBzPXOZ55/U6Z3dgN/cdvI99wX2cXX8WG2o2UDlOKRZyoTXawtNH/sU/jzzFAu8CLpj+1u7pY01onNtwLrPdszkYPkClvZIF3gWUWksLG7Qy5e3w7aDEWkKJtTCL+a26lbVVJ3LvgXv5xJJP9LqtcfdBQicuZZU9t2X00pWl2MZppO6oczxOHJrGx5ta+XZNOascapReOT7VqVOKXp2z7rg7PRvDjXxj09e7q0v85o3f0Bpt5UMLrsTQir95J9IJbttzO080Pw7A/tB+nmt7jh+d9OPu1+w0nKyoWMGKiolRy1aZGp458i8Wly4uaAwnVZ7I9duv50MLPtS9E/ZvvgBLGltYfvE5OVlH15NZWYre2gXJFIzjOrc3uexUGRrfbenkXI+TK8u82DU10ab0p1qFMqEdCB3oVS4M4MHGB2mLje+36dFqibbwZPMTvY4FkgEOhvpXzFCUYpE0kzzd8jTLy5YXNI4SawlzPHN5svlJAJ6NRLln32FsuoalJA+1kQ0Ds7JkXKdgj1pos/K5ylL2JJK871AL/whFMFUuO6UP1alTJjRD9F88bNEs6GJiNG1d6OgDvAZDU4uileL1XOuzVNmrxqU82FBOqjyR+w/+nc2RGNe0dvKhti7Mhuqh7zhK6boqjN2H8vb4g/HoGu8t9XBpiZvbuwK8v7GFBwJhYqZKcaRkTIxPPkU5jlme2dQ6ek/NvmfO5VTZ8/emnks1zhounn1Jr2MzXTMHTNOgKMVASsnd++9mXfW6QocCwBzPHIKWGXytpZX3lnqo332QdD47dQ1VWLb335E+nubZLPxXRQlv9Th5KBjm4oPN/LCtk5ciMVWJYoor/kVHijKIGkcN31n1bV7peIVD4UZWV6xmSdmSCVPs/miG/Dnu2Wzu2MxMzyxWla8sihEQRRnI823PE0vHiqZM3fMJG20Vl1EX+jcLat+B9dU3iL9pZd6eLzWrHsffnsrb4w+XEIKFNisLbVa60mleiSb4RYePllSa5XYbaxw2TrDbmGezYEyQ90Nl7ArSqRNClAN3ArOA/cClUsquAc4rBX4DLAMkcKWU8tlxC1SZEBpc0yZ0It4SawnratazrmZ9oUNRlEHF0jF+vfMmNk7biFbgJQ5RKfhLxMmulME7HSGeaN/JgcPbqDjQTOq9G/P2vOmGajRfAK21E7O6PG/PMxJlus6Zbgdnuh0E0ya7EklejcW5NximLZVmrtXCMruVFXYby+xWSnS1vGOyKtRI3VeAx6WU1wohvpK9/uUBzvsZ8LCU8mIhhBUYuNCfoiiKkldSSm7Y/gumuaaxoGRBweJISXghYeP+mIOZeopLHWGsQnBS5Yk03n8nyxbMAKslfwFoGsml87A/8QKRy96Sv+cZJY+usdphY7XDBkDMNDmQTLE/keKPvgD7EilqDZ3VDhtrHHaW2214dLUSa7IoVKfuIuCM7OVbgKfo06kTQniB04APAkgpE0BivAJUFEVRMpJmkpt2/Irdgd1cOf/KcX9+U8LBtM6rSSvPJ2yUa2nOt0Wp1Y8l655rNDD/qQD/frOD5WYKSx5TGsXXLcf9+3uJnv8mZIk7b8+TC3ZN656mBUhLyaFkit09OnnTLAYr7FZOsNtYaLNSa+gTZgmL0luhOnU1UspmACllsxBioFWtc4A24PdCiBXAJuDTUsrwAOcqiqIoOdQUbuKxw4+yJ7CXzZ2vUGYt450z30l7rD2nzxOXGlvMUpJopNBIoBGXOmEMAtJCJzZS2T19tURYItoplQn8CfBnH2P6pv2c9ot/EKz20qEJHnz+LsrtFVTYKqh31uelg6LVVFB52Zc5/N4LOPKus2ACTWnqQrDQZmGhzUJKSg4mU+xOJHk8FMWX3UkrgFpDp9rQKdN1PJqGSxPYhMCqCSwIyg2dM1wOtWaviAiZp50yQoh/ALUD3PR14BYpZWmPc7uklGV97r8WeA7YIKV8XgjxMyAgpexf6C9z/lXAVdmrC4Gd2cuVQG7fhSY+9TfprRLYIaXM21zKIO0zn4rt/3MxxVNMscDQ8bTns31C/za65KrFgTkXz+mu0Rc6FIoO9RjSlIbQxIjqWcmK1RonfM426EmxDknq+N/nP/H7J8S6V/ZxqK4MUwh69jFk0szLx5wAZrb5BMCVn7446vPkf3WQTKcNoet5rxemu926rbpmWOU4Xjj79Nej+/YmGIc2qgwtb526QZ9UiJ3AGdlRujrgKSnlwj7n1ALPSSlnZa+fCnxFSnnBCJ/rJSnl2hyFPimov0lvk/XvUWyvq5jiKaZYoPjiGa2J9jpUvMpkU6jVkfcBH8he/gBwb98TpJRHgENCiKOdvbOAbeMTnqIoiqIoysRSqE7dtcA5QohdwDnZ6wgh6oUQD/Y475PAbUKI14CVwDXjHaiiKIqiKMpEUJCNElLKDjIjb32PHwY29ri+GRjrUPNNY7z/ZKT+Jr1N1r9Hsb2uYoqnmGKB4otntCba61DxKpNKQdbUKYqiKIqiKLmlMg4qiqIoiqJMAlOiUyeEuEQIsVUIYWZTpUxJQoi3CCF2CiF2Zyt5TGlCiN8JIVqFEFsKHUsuCCHKhRCPCSF2ZX+XHee8/UKI14UQm4UQL+U4hkHbmMj4efb214QQq3P5/KOI5wwhhD/7t9gshPhWHmMZtL2N998mF4qhzQ0zzqJql0MppnarTDBSykn/AywmkxvsKWBtoeMp0N9AB/aQSepsBV4FlhQ6rgL/TU4DVgNbCh1Ljl7PD8mk/YFM6b3/Oc55+4HKPDz/kG2MzJrZh8ik+ToFeD6Pf4/hxHMGcH8xtLfx/NtMljaXw3ZQNH/7Ymu36mdi/UyJkTop5XYp5Xgkey1mJwG7pZR7Zabk2h1kyrVNWVLKp4HOQseRQxeRKbtH9vfbx/n5h9PGLgL+IDOeA0qzuSoLFc+4GUZ7G8+/Ta4Uus0NR7G1y6EUVbtVJpYp0alTAGgADvW43pg9pkwevcrvAQOV3wOQwKNCiE3ZKgK5Mpw2Np7tcLjPtU4I8aoQ4iEhxNI8xTIcE/HfaKHb3HAUW7scykRrt0oRKVTt15wbrCyZlLJfcuMpaKDifGrr8wQzRPm94dogpTwsMjWXHxNC7MiOIo05vAGO9W1j49kOh/NcLwMzpZQhIcRG4G/A/DzFM5Si/Dda5G1uOIqtXQ5lorVbpYhMmk6dlPLsQsdQ5BqB6T2uTwMOFygWZZQGa+dCiBYhRJ08Vn6v9TiPcTj7u1UI8Vcy0z25+IAdThsbz3Y45HNJKQM9Lj8ohLhBCFEppSxEXdii/Dda5G1uOIqtXQ5lorVbpYio6dep40VgvhBithDCClxGplybMnkMWX5PCOESQniOXgbOBXK1+3c4bew+4P3Z3YanAP6j03d5MGQ8QohaITLl34UQJ5F5T+zIUzxDGc+/Ta4Uus0NR7G1y6FMtHarFJFJM1I3GCHEO4DrgCrgASHEZinleQUOa1xJKVNCiP8CHiGzu+p3UsqtBQ6roIQQfyKzi6xSCNEIfFtK+dvCRjUm1wJ3CSE+DBwELoFM+T3gN1LKjUAN8Nfs54EB3C6lfDgXT368NiaEuDp7+43Ag2R2Gu4GIsCHcvHcY4jnYuDjQogUEAUuk1LmZdptoPYGWHrEMm5/mxwqaJsbjmJrlzmKd9zarTKxqIoSiqIoiqIok4CaflUURVEURZkEVKdOURRFURRlElCdOkVRFEVRlElAdeoURVEURVEmAdWpUxRFURRFmQRUp26CE0JIIcSPe1z/ghDiO9nL3xFCNAkhNgshdgkh7hFCLMnedrMQ4mN9HuvtQogHs5dD4/gylEloDG3zIiHE33rc76tCiN09rr9NCHFf9vJ+IUTleL0mZfIZbTvN3v6UEGJn9vbtokcJNNU2lUJQnbqJLw68c5A3j/+VUq6UUs4H7gSeEEJUAX8ik9Syp8uyxxUlF0bbNv8DrOtx3jogIDIlpgDWA//OV9DKlDPadnrUFVLKlcAG4H+yCYMVpSBUp27iSwE3AZ8d6kQp5Z3Ao8DlwD+ARSJT2gchhBM4m0wNQUXJhVG1TSllG+AXQszL3twA/IVMZ47s7//kPlxlihrte2hfbiAMpHManaKMgOrUTQ6/AK4QQpQM49yXgUVSyjRwD3Bp9viFwJNSymCeYlSmphG3zezl/wDrhRALgV3Ac9nrBrCcTCklRcmV0bZTgNuEEK8BO4HvZd9bFaUgVKduEsgWd/4D8KlhnC56XO45BaumXpWcG0Pb/DeZEbn1wLPAC8DJwCpgp5QyluNQlSlsDO0UMtOvy4EZwBeEEDNzHZ+iDJfq1E0ePwU+DLiGOG8VsD17+d9AnRBiBZkPzwfzFp0ylf2UkbfN/9CjU5cdQbaTqZ2q1tMp+fBTRt5Ou2WXDbxM5suHohSE6tRNElLKTuAuMm9KAxJCvAs4l+yIXLYA9F3ALcCDavRDyYfRtE1gG1APnAq8kj22GbgatZ5OyYNRttOetznJdPj25CtGRRmK6tRNLj8G+u7g+uzR7fjAe4Ezs98oj/oTsAK4Y5xiVKamEbXN7BeO54F2KWUye/6zwBxUp07Jn9G8h94mhNgMbAJullJuGp9QFaU/kXnvVBRFURRFUSYyNVKnKIqiKIoyCahOnaIoiqIoyiSgOnWKoiiKoiiTgOrUKYqiKIqiTAKqU6coiqIoijIJqE6doiiKoijKJKA6dYqiKIqiKJOA6tQpiqIoiqJMAv8fjBDwYtuC1L4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 629.875x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,15))\n",
    "g = sns.PairGrid(df[['NDVI', 'NDWI', 'NDBI','class']],hue = \"class\",palette = sns.color_palette(colors))\n",
    "g.map_lower(sns.scatterplot)\n",
    "g.map_upper(sns.kdeplot,fill = True,alpha = .5)\n",
    "g.map_diag(sns.kdeplot,fill = True)\n",
    "g.add_legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ec6a25acaecf7f06cb08206f3f56e96ccaf6fbab432a979bcf67c9e0ca577c87"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('spyndex': conda)",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}