biodiversity.ipynb

{
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   "source": [
    "# Introduction\n",
    "\n",
    "The goal of this project is to analyze biodiversity data from the National Parks Service, particularly around various species observed in different national park locations.\n",
    "\n",
    "This project will scope, analyze, prepare, plot data, and seek to explain the findings from the analysis.\n",
    "\n",
    "Here are a few questions that this project has sought to answer:\n",
    "\n",
    "- What is the distribution of conservation status for species?\n",
    "- Are certain types of species more likely to be endangered?\n",
    "- Are the differences between species and their conservation status significant?\n",
    "- Which animal is most prevalent and what is their distribution amongst parks?\n",
    "\n",
    "**Data sources:**\n",
    "\n",
    "Both `Observations.csv` and `Species_info.csv` was provided by https://www.codecademy.com/.\n",
    "\n",
    "The data for this project is *inspired* by real data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Python Modules\n",
    "Here are the primary modules that will be used in this project:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting data to know"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### species\n",
    "\n",
    "The `species_info.csv` contains information on the different species in the National Parks. The columns in the data set include:\n",
    "- **category** - The category of taxonomy for each species\n",
    "- **scientific_name** - The scientific name of each species\n",
    "- **common_names** - The common names of each species\n",
    "- **conservation_status** - The species conservation status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['category', 'scientific_name', 'common_names', 'conservation_status'], dtype='object')\n"
     ]
    },
    {
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       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_6b8c3\">\n",
       "  <caption>Species DataFrame Head</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6b8c3_level0_col0\" class=\"col_heading level0 col0\" >category</th>\n",
       "      <th id=\"T_6b8c3_level0_col1\" class=\"col_heading level0 col1\" >scientific_name</th>\n",
       "      <th id=\"T_6b8c3_level0_col2\" class=\"col_heading level0 col2\" >common_names</th>\n",
       "      <th id=\"T_6b8c3_level0_col3\" class=\"col_heading level0 col3\" >conservation_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6b8c3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_6b8c3_row0_col0\" class=\"data row0 col0\" >Mammal</td>\n",
       "      <td id=\"T_6b8c3_row0_col1\" class=\"data row0 col1\" >Clethrionomys gapperi gapperi</td>\n",
       "      <td id=\"T_6b8c3_row0_col2\" class=\"data row0 col2\" >Gapper's Red-Backed Vole</td>\n",
       "      <td id=\"T_6b8c3_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b8c3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_6b8c3_row1_col0\" class=\"data row1 col0\" >Mammal</td>\n",
       "      <td id=\"T_6b8c3_row1_col1\" class=\"data row1 col1\" >Bos bison</td>\n",
       "      <td id=\"T_6b8c3_row1_col2\" class=\"data row1 col2\" >American Bison, Bison</td>\n",
       "      <td id=\"T_6b8c3_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b8c3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_6b8c3_row2_col0\" class=\"data row2 col0\" >Mammal</td>\n",
       "      <td id=\"T_6b8c3_row2_col1\" class=\"data row2 col1\" >Bos taurus</td>\n",
       "      <td id=\"T_6b8c3_row2_col2\" class=\"data row2 col2\" >Aurochs, Aurochs, Domestic Cattle (Feral), Domesticated Cattle</td>\n",
       "      <td id=\"T_6b8c3_row2_col3\" class=\"data row2 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b8c3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_6b8c3_row3_col0\" class=\"data row3 col0\" >Mammal</td>\n",
       "      <td id=\"T_6b8c3_row3_col1\" class=\"data row3 col1\" >Ovis aries</td>\n",
       "      <td id=\"T_6b8c3_row3_col2\" class=\"data row3 col2\" >Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)</td>\n",
       "      <td id=\"T_6b8c3_row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b8c3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_6b8c3_row4_col0\" class=\"data row4 col0\" >Mammal</td>\n",
       "      <td id=\"T_6b8c3_row4_col1\" class=\"data row4 col1\" >Cervus elaphus</td>\n",
       "      <td id=\"T_6b8c3_row4_col2\" class=\"data row4 col2\" >Wapiti Or Elk</td>\n",
       "      <td id=\"T_6b8c3_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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     "execution_count": 147,
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   "source": [
    "species = pd.read_csv('species_info.csv',encoding='utf-8')\n",
    "print(\"Columns:\",species.columns)\n",
    "species.head().style.set_caption(\"Species DataFrame Head\").background_gradient(cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### observations\n",
    "\n",
    "The `Observations.csv` contains information from recorded sightings of different species throughout the national parks in the past 7 days. The columns included are:\n",
    "\n",
    "- **scientific_name** - The scientific name of each species\n",
    "- **park_name** - The name of the national park\n",
    "- **observations** - The number of observations in the past 7 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns: Index(['scientific_name', 'park_name', 'observations'], dtype='object')\n"
     ]
    },
    {
     "data": {
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       "#T_a8a96_row0_col2 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a8a96_row1_col2 {\n",
       "  background-color: #472d7b;\n",
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       "</style>\n",
       "<table id=\"T_a8a96\">\n",
       "  <caption>Conservations DataFrame Head</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a8a96_level0_col0\" class=\"col_heading level0 col0\" >scientific_name</th>\n",
       "      <th id=\"T_a8a96_level0_col1\" class=\"col_heading level0 col1\" >park_name</th>\n",
       "      <th id=\"T_a8a96_level0_col2\" class=\"col_heading level0 col2\" >observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a8a96_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_a8a96_row0_col0\" class=\"data row0 col0\" >Vicia benghalensis</td>\n",
       "      <td id=\"T_a8a96_row0_col1\" class=\"data row0 col1\" >Great Smoky Mountains National Park</td>\n",
       "      <td id=\"T_a8a96_row0_col2\" class=\"data row0 col2\" >68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8a96_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_a8a96_row1_col0\" class=\"data row1 col0\" >Neovison vison</td>\n",
       "      <td id=\"T_a8a96_row1_col1\" class=\"data row1 col1\" >Great Smoky Mountains National Park</td>\n",
       "      <td id=\"T_a8a96_row1_col2\" class=\"data row1 col2\" >77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8a96_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_a8a96_row2_col0\" class=\"data row2 col0\" >Prunus subcordata</td>\n",
       "      <td id=\"T_a8a96_row2_col1\" class=\"data row2 col1\" >Yosemite National Park</td>\n",
       "      <td id=\"T_a8a96_row2_col2\" class=\"data row2 col2\" >138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8a96_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_a8a96_row3_col0\" class=\"data row3 col0\" >Abutilon theophrasti</td>\n",
       "      <td id=\"T_a8a96_row3_col1\" class=\"data row3 col1\" >Bryce National Park</td>\n",
       "      <td id=\"T_a8a96_row3_col2\" class=\"data row3 col2\" >84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8a96_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_a8a96_row4_col0\" class=\"data row4 col0\" >Githopsis specularioides</td>\n",
       "      <td id=\"T_a8a96_row4_col1\" class=\"data row4 col1\" >Great Smoky Mountains National Park</td>\n",
       "      <td id=\"T_a8a96_row4_col2\" class=\"data row4 col2\" >85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "observations=pd.read_csv('observations.csv',encoding='utf-8')\n",
    "print(\"Columns:\",observations.columns)\n",
    "observations.head().style.set_caption(\"Conservations DataFrame Head\").background_gradient(cmap='viridis')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Characteristics\n",
    "\n",
    "Dimensions of the data sets, for `species` there are 5,824 rows and 4 columns while `observations` has 23,296 rows and 3 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "species shape: (5824, 4)\n",
      "observations shape: (23296, 3)\n"
     ]
    }
   ],
   "source": [
    "print(f\"species shape: {species.shape}\")\n",
    "print(f\"observations shape: {observations.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring data\n",
    "Exploring the `species` data a little more in depth.Finding the number of distinct species in the data.Column `scientific_name` have 5,541 unique species. There seems to be a lot of species in the national parks!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of species:5541\n"
     ]
    }
   ],
   "source": [
    "print(f\"number of species:{species.scientific_name.nunique()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unique `categories`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nnumber of categories:7\n",
      "categories:['Mammal' 'Bird' 'Reptile' 'Amphibian' 'Fish' 'Vascular Plant'\n",
      " 'Nonvascular Plant']\n"
     ]
    }
   ],
   "source": [
    "print(f\"nnumber of categories:{species.category.nunique()}\")\n",
    "print(f\"categories:{species.category.unique()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amount of each `category`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "Amphibian              80\n",
       "Bird                  521\n",
       "Fish                  127\n",
       "Mammal                214\n",
       "Nonvascular Plant     333\n",
       "Reptile                79\n",
       "Vascular Plant       4470\n",
       "dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.groupby(\"category\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining both tables together and create one table depending on their scientific name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns: Index(['scientific_name', 'park_name', 'observations', 'category',\n",
      "       'common_names', 'conservation_status'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "combined = pd.merge(observations, species, on='scientific_name', how='left')\n",
    "combined.head()\n",
    "print(\"Columns:\",combined.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets find out Least observed and Most observed animal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Least Observed Animal:\n",
      "common_names           Golden Corydalis, Scrambled Eggs\n",
      "scientific_name                         Corydalis aurea\n",
      "observations                                          9\n",
      "park_name                           Bryce National Park\n",
      "category                                 Vascular Plant\n",
      "conservation_status                                 NaN\n",
      "Name: 10368, dtype: object\n",
      "\n",
      "Most Observed Animal:\n",
      "common_names           Deep-Root Clubmoss, Ground Cedar\n",
      "scientific_name                  Lycopodium tristachyum\n",
      "observations                                        321\n",
      "park_name                     Yellowstone National Park\n",
      "category                                 Vascular Plant\n",
      "conservation_status                                 NaN\n",
      "Name: 12447, dtype: object\n"
     ]
    }
   ],
   "source": [
    "least_obs_idx = combined['observations'].idxmin()\n",
    "most_obs_idx = combined['observations'].idxmax()\n",
    "\n",
    "least_observed = combined.loc[least_obs_idx]\n",
    "most_observed = combined.loc[most_obs_idx]\n",
    "\n",
    "print(\"Least Observed Animal:\")\n",
    "print(least_observed[['common_names', 'scientific_name', 'observations', 'park_name', 'category', 'conservation_status']])\n",
    "print(\"\\nMost Observed Animal:\")\n",
    "print(most_observed[['common_names', 'scientific_name', 'observations', 'park_name', 'category', 'conservation_status']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check conservation status to find out is there any endangered species out there,and what kind of conservation status is existing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with conservation_status values: 880\n",
      "Unique conservation statuses: ['Species of Concern' 'Threatened' 'Endangered' 'In Recovery']\n"
     ]
    }
   ],
   "source": [
    "not_nan_conservation = combined[combined['conservation_status'].notna()]\n",
    "\n",
    "print(\"Number of rows with conservation_status values:\", not_nan_conservation.shape[0])\n",
    "\n",
    "print(\"Unique conservation statuses:\", not_nan_conservation['conservation_status'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column has 4 categories, `Species of Concern`, `Endangered`, `Threatened`, `In Recovery`, and  `nan` values. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding out exact amount of each conservation status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of values is  `NaN` so i convert them to `No Intervention` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "conservation_status\n",
       "Endangered               80\n",
       "In Recovery              24\n",
       "No Intervention       24752\n",
       "Species of Concern      732\n",
       "Threatened               44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.fillna('No Intervention', inplace=True)\n",
    "combined.groupby(\"conservation_status\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Building a pie chart having this data including only info that we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xq0OHDrrwwgtPmWHBggUKCgrSZZddVmE/vXr1UmRk5En76dWrl1q0aFH+by8vL3Xo0KF8CPqZpKam6p///KeaN28uNzc3ubu7q2XLlpKkvXv3Sjo+THPTpk26/PLL5eHhUX5fPz+/k44gnu3zVB2GYZz29l69esnDw0N33XWXpk+fXu1h5FdddVWV1+3atat69uxZYdmNN96o3NxcbdmypVr7r6rly5drxIgRat68eYXl48aNU2Fh4UlDdv/xj39U+HePHj0k6bSvmYKCAv3xxx+6+uqr5efnV77c1dVVt9xyi+Lj46s8JLy69u/fr8TERN1yyy3lQ4Ol46/Dq666SuvXr1dhYWGF+1T2WIuLi8tneT8xJPjaa6/V999/X+ls/AsWLFC3bt3Uq1evCq/pUaNGVTps+oILLlBwcHCFZd27d1efPn0qDP/du3evNmzYcNLQ8R9++EGDBg2Sn59f+e/k//73v/Lfx7NVF8/bqfTr10+HDh3SkiVL9NRTT+m8887Tr7/+qltvvVX/+Mc/zvi7K0lWq1UvvfSSunTpIg8PD7m5ucnDw0MHDx6s9nNwKv3791d2drZuuOEGzZ07t9LTWE7l2muvlb+/f4WjyZ9//rksFkv5yAJJ2rp1q/7xj38oNDRUrq6ucnd316233iqbzaYDBw7U+DEUFxfr119/1RVXXCEfH58Kr9FLLrlExcXFWr9+ffnj3b59u+699179/PPPys3NrfH+ATRslGgAZ/Tll19q48aN2rp1qxITE7Vjxw4NGjRI0vFL8BiGoYiICLm7u1f4s379+pM+fFV2XmBaWtoZhxumpKQoOztbHh4eJ+0nOTn5pP2EhoaetA1PT08VFRWd8fHa7XZddNFFmj17tiZNmqRff/1VGzZsKP/AdWIbWVlZ5Y/97/6+7Gyfp7NVUFCgjIwMNW3a9JTrtG3bVsuWLVN4eLjuu+8+tW3bVm3btj3r8/5OdW5nZSIjI0+5LCMj46z2e7YyMjIqzXriOfr7/v/+mjkx3Pp0r5kTr4Gz2U9VtGjRQmlpaVWaAf/E9k+VwW63Kysrq8LyMz3WIUOGaM6cObJarbr11lvVrFkzdevWTTNnziy/T0pKinbs2HHS69nf31+GYVTpd1+Sbr/9dq1bt658/oNp06bJ09NTN9xwQ/k6s2fP1rXXXqvo6GjNmDFD69at08aNG3X77beruLj4jM9RZerieTsdd3d3jRo1Si+++KJ+/vlnxcXFadiwYVqwYIEWL158xvs/+uijevrpp3X55Zdr/vz5+uOPP7Rx40b17NmzSvs/G7fccos+//xzxcbG6qqrrlJ4eLgGDBigX3755Yz39fHx0fXXX68lS5YoOTlZVqtVM2bM0NChQ8tPlzl27JgGDx6shIQEvfPOO1q9erU2btxYfq5ybTyejIwMWa1Wvffeeye9Ri+55BJJKn+NPvnkk3r99de1fv16jR49WqGhoRoxYoQ2bdpU4xwAGibOiQZwRp07d64wI/RfhYWFyWKxaPXq1ZWe4/n3ZZVNPtWkSRPFx8efNkNYWJhCQ0O1ZMmSSm/39/c/7f3Pxq5du7R9+3Z98cUXuu2228qXnzgX/ITg4GBZLJZKz39OTk6u8O+zfZ7O1sKFC2Wz2SqdNOivBg8erMGDB8tms2nTpk1677339PDDDysiIkLXX399lfZ1Ntee/vvz8NdlJwqJl5eXpOPnvf5VTb9YCA0NVVJS0knLExMTJR3/mdRUcHCwXFxcan0/o0aN0tKlSzV//vwz/lxOPI+nyuDi4nLSEeCqGDt2rMaOHauSkhKtX79eL7/8sm688Ua1atVK5513nsLCwuTt7X3K81f//rhP9bq54YYb9Oijj+qLL77Qiy++qK+++kqXX355hcwzZsxQ69at9d1331XYzt9fM2ejrp63s9n/ww8/rJUrV2rXrl3lxe5UZsyYoVtvvVUvvfRSheXp6ekKCgqq0j69vLwqnQitst+18ePHa/z48SooKNCqVav07LPPasyYMTpw4ED5qJxTmTBhgj799FN9+eWX6tChg1JTU/XGG2+U3z5nzhwVFBRo9uzZFbZVlctyner94u9fVgUHB5ePCLnvvvsq3Vbr1q0lHZ9Z/NFHH9Wjjz6q7OxsLVu2TE899ZRGjRqluLg4riAB4CQciQZQI2PGjJFhGEpISFDfvn1P+tO9e/czbmP06NE6cOBAhUmLKttPRkaGbDZbpfvp2LHjWWc/1VGkEx/S/15s/zrztST5+vqqb9++mjNnjkpLS8uX5+fna8GCBSflr+nzdCrHjh3TxIkTFRgYqLvvvrtK93F1ddWAAQPKj/ycGFp9NkfWqmL37t3avn17hWXffPON/P39yydzOzEr8I4dOyqsN2/evJO2V9XRBJI0YsQILV++vLzMnvDll1/Kx8enVi535OvrqwEDBmj27NkVctntds2YMUPNmjVThw4dznq7EyZMUGRkpCZNmlTpUGrp+NFZSerYsaOio6P1zTffVBgWXFBQoFmzZpXPPF1dnp6eGjp0qF599VVJx4fhSsdf04cPH1ZoaGilr+nTzT7+V8HBwbr88sv15ZdfasGCBUpOTj5pKLfFYpGHh0eFAp2cnHzS7Nwn8lblNVLXz9sJZWVlpxyNcGIY9olRC6f7/bNYLCe9Jy1cuPCk18fpttGqVSsdOHCgQgHNyMjQ2rVrT5nf19dXo0eP1r///W+VlpZq9+7dp1z3hAEDBqhbt26aNm2apk2bpsDAwAqngVT2HmsYhj799NMzbjsiIkJeXl4nvV/8/bXg4+Oj4cOHa+vWrerRo0elr9HKRiwFBQXp6quv1n333afMzMyTrhoAABJHogHU0KBBg3TXXXdp/Pjx2rRpk4YMGSJfX18lJSVpzZo16t69u+65557TbuPhhx/Wd999p7Fjx+qJJ55Q//79VVRUpN9++01jxozR8OHDdf311+vrr7/WJZdcooceekj9+/eXu7u74uPjtWLFCo0dO1ZXXHHFWWU/cR3TTz75RP7+/vLy8lLr1q3VqVMntW3bVk888YQMw1BISIjmz59f6VDG559/XpdeeqlGjRqlhx56SDabTa+99pr8/PyUmZlZq8+TdPwo+Ynz+lJTU7V69WpNmzZNrq6u+umnn057/dWPPvpIy5cv16WXXqoWLVqouLi4/CjiifPR/f391bJlS82dO1cjRoxQSEiIwsLCqlyI/q5p06b6xz/+ocmTJysqKkozZszQL7/8oldffbW8oPTr108dO3bUxIkTZbVaFRwcrJ9++klr1qw5aXvdu3fX7Nmz9eGHH6pPnz5ycXE55SiJZ599VgsWLNDw4cP1zDPPKCQkRF9//bUWLlyoqVOnKjAwsFqP6e9efvlljRw5UsOHD9fEiRPl4eGhDz74QLt27dLMmTPP6sj9CYGBgZo7d67GjBmjc845R/fff7/OO++88nNgZ8yYoe3bt+vKK6+Ui4uLpk6dqptuukljxozR3XffrZKSEr322mvKzs7WK6+8ctb7f+aZZxQfH68RI0aoWbNmys7O1jvvvCN3d3cNHTpU0vHf21mzZmnIkCF65JFH1KNHD9ntdh07dkxLly7VY489pgEDBlRpf7fffru+++473X///WrWrNlJ8yOMGTNGs2fP1r333qurr75acXFxeuGFFxQVFaWDBw9WWLd79+5auXKl5s+fr6ioKPn7+1f6JVtdPG+VycnJUatWrXTNNdfowgsvVPPmzZWfn6+VK1fqnXfeUefOnXXllVdKOv3v35gxY/TFF1+oU6dO6tGjhzZv3qzXXnvtpFNh2rZtK29vb3399dfq3Lmz/Pz81LRpUzVt2lS33HKLPv74Y91888268847lZGRoalTp540g/mdd94pb29vDRo0SFFRUUpOTtbLL7+swMDA8vPlz+T222/Xo48+qv379+vuu++ucPWEkSNHysPDQzfccIMmTZqk4uJiffjhhycNn6/MibklPv/8c7Vt21Y9e/bUhg0b9M0335y07jvvvKPzzz9fgwcP1j333KNWrVopLy9Phw4d0vz588u/uL3sssvUrVs39e3bV02aNFFsbKzefvtttWzZUu3bt6/S4wXQyJgynRkAp1CVGaFP+Pzzz40BAwYYvr6+hre3t9G2bVvj1ltvNTZt2lS+ztChQ42uXbtWev+srCzjoYceMlq0aGG4u7sb4eHhxqWXXmrs27evfJ2ysjLj9ddfN3r27Gl4eXkZfn5+RqdOnYy7777bOHjwYPl6LVu2NC699NKT9lHZzLRvv/220bp1a8PV1bXCjK979uwxRo4cafj7+xvBwcHGNddcYxw7dqzSmXN/+ukno3v37oaHh4fRokUL45VXXjEefPBBIzg4uFrPU2VO/CxO/PHw8DDCw8ONoUOHGi+99JKRmpp60n3+PmP2unXrjCuuuMJo2bKl4enpaYSGhhpDhw415s2bV+F+y5YtM8455xzD09PTkFQ+0/GJ7aWlpZ1xX4bx/z+HH3/80ejatavh4eFhtGrVynjzzTdPuv+BAweMiy66yAgICDCaNGliPPDAA8bChQtPmp07MzPTuPrqq42goCDDYrFU2GdlP5udO3cal112mREYGGh4eHgYPXv2PGlW3xOzNv/www8Vlp9uFuC/W716tXHBBReU/1zPPfdcY/78+ZVuryqzc5+QnJxsPP7440bXrl0NHx8fw9PT02jXrp1x9913Gzt37qyw7pw5c4wBAwYYXl5ehq+vrzFixAjj999/r7DOqX6Gf58hfcGCBcbo0aON6Ojo8tfaJZdcYqxevbrC/fLz843//Oc/RseOHQ0PDw8jMDDQ6N69u/HII48YycnJ5etJMu67775TPk6bzWY0b978tDM4v/LKK0arVq0MT09Po3Pnzsann35a6etu27ZtxqBBgwwfHx9DUvnvfGWzvdf281aZkpIS4/XXXzdGjx5ttGjRwvD09DS8vLyMzp07G5MmTTIyMjIqrH+q37+srCxjwoQJRnh4uOHj42Ocf/75xurVqyt9X5s5c6bRqVMnw93d/aTfi+nTpxudO3c2vLy8jC5duhjffffdSTNgT58+3Rg+fLgRERFheHh4GE2bNjWuvfZaY8eOHad8nH+XlpZmeHh4GJKMDRs2nHT7/Pnzy9/Lo6OjjX/961/G4sWLT/oZ/T2bYRhGTk6OcccddxgRERGGr6+vcdlllxkxMTGVvgccPXrUuP32243o6GjD3d3daNKkiTFw4EBjypQp5eu88cYbxsCBA42wsLDy9/EJEyYYMTExVX68ABoXi2FUYUpIAECVlZWVqVevXoqOjtbSpUvNjgMAAIBaxHBuAKihCRMmaOTIkeXDHj/66CPt3bv3rGe9BgAAgOOjRANADeXl5WnixIlKS0uTu7u7evfurUWLFp32utcAAABwTgznBgAAAACgirjEFQAAAAAAVUSJBgAAAACgiijRAAAAAABUESUaAAAAAIAqokQDAAAAAFBFlGgAAAAAAKqIEg0AAAAAQBVRogEAAAAAqCJKNAAAAAAAVUSJBgAAAACgiijRAAAAAABUESUaAAAAAIAqokQDAAAAAFBFlGgAAAAAAKqIEg0AAAAAQBVRogEAAAAAqCJKNAAAAAAAVUSJBgAAAACgiijRAAAAAABUESUaAAAAAIAqokQDAAAAAFBFlGgAAAAAAKqIEg0AAAAAQBVRogEAAAAAqCJKNAAAAAAAVUSJBgAAAACgiijRAAAAAABUESUaAAAAAIAqokQDAAAAAFBFlGgAAAAAAKqIEg0AAAAAQBVRogEAAAAAqCJKNAAAAAAAVeRmdgAAAJxVqdWu7KJS5RZZlVdcpvwSq/KLrcortirvz/8uLLOq1GpXmc0uq81Q6Z9/l9nsKvvzb6vdrjKroTL7/69ntRtyc7HIzdUiNxcXuf/5t5urRe6uLnJz+fPvv97+523+nm7y93KXv5ebArz//PvPf/t7uSvA202ebq5mP30AADglSjQAAH+RU1im5NxipeQWKzm3WKl//p2eV6rsolJlF5Ypp+j4n8JSm9lxq83DzUUBXu4K8HIrL9tN/DwVHuCliABPRfz5d7i/l8IDPCndAAD8yWIYhmF2CAAA6pphGErJLVFcVqGSc46X5ONFuaT8v1NzS1RU5rzFuC4F+bgr4s9C/deCHRnopebBPmoV5iMfD76bBwA0fJRoAECDkpZXopiMAh1NK9DRjALFpBfoaHqBYjMKKch1LNzfU63CfNUq1OfPv//8Q8EGADQglGgAgNPJKy7TwdR8HU0rOF6Y04//HZNeqPwSq9nxUIlwf8/yQt0y1FdtwnzVMdJfrUJ95eJiMTseAABVRokGADi0xOwi7UnM1Z6k3PK/47IKxf+9GgYfD1d1iPBX56gAdWkaoC5R/uoUGSBfT45cAwAcEyUaAOAQrDa7DqbmVyjMe5NzlV1YZnY01DOLRWoR4qMuUQHqXP7HX82CfcyOBgAAJRoAUP8Mw9DB1HxtisnStrgs7U7M1cHUfJVa7WZHgwML9HZX5yh/ndMiWH1aBKtPy2AF+3qYHQsA0MhQogEAda6o1KZtcdnacixLm2IyteVYtnKKOMKMmrFYpNZhvuWFuk/LYLUL95PFwjnWAIC6Q4kGANS61NxibYrN0qaYLG2OzdSepFyV2fjfDepekI+7zmkepD4tg9W7ZbDOaR4sbw+ucQ0AqD2UaABAjcVmFGjNoXRtPJqpzceyFJdZZHYkQJLk5mJRpyh/9WsVokFtwzSgTYj8vdzNjgUAcGKUaADAWcstLtPaQ+ladTBdaw6m61hmodmRgCpxc7GoR7NADWoXpoFtw9SnZbA83FzMjgUAcCKUaADAGdnshrbFZWnVgXStPpim7fE5stn53wecn7e7q/q3DtGQDk00tEOY2oX7mx0JAODgKNEAgEodyyjUqoNpWn0wTWsPZyiv2Gp2JKDONQ300pAOTTSkQxMNahemQG+GfgMAKqJEAwAkHT/a/MfRDC3dnaLl+1IZoo1Gz9XFoj4tgzWqa6Qu6hKh5iFcpxoAQIkGgEatuMymNQfT9fPuZP26L1WZBaVmRwIcVpeogOOFumuEOkcFmB0HAGASSjQANDJ5xWVavi9VS3enaOX+VBWU2syOBDidlqE+Gtk5QqO6RapPi2C5uHBtagBoLCjRANAIpOeX6Jc9KVqyK1nrDmeo1GY3OxLQYIT5eerCzuEa1TVSA9uFytON61IDQENGiQaABio1r1jztydpya4kbY7NEpNpA3XPz9NNwzuF6/JeTTW0QxO5uXL5LABoaCjRANCAFJZa9fPuZM3ekqC1hzO4DBVgohBfD43pEaXLz4lW7xbBZscBANQSSjQAODm73dDvh9P105YE/bw7mXOcAQfUOsxXY3s11RXnRKtlqK/ZcQAANUCJBgAntTcpVz9tTdDcbQlKyS0xOw6AKurdIkhXnBOtMT2aKtjXw+w4AICzRIkGACeSklusudsSNHtLgvYl55kdB0ANuLtaNLRDuK44J1ojOofLy50JyQDAGVCiAcDBlVrt+nl3sr7fFKffD6UzQRjQAPl7uemKc6J187kt1SHC3+w4AIDToEQDgIOKSS/QzA3H9OPmeGUUlJodB0A96dcqWDcNaKnR3SO5XBYAOCBKNAA4kDKbXUt3p+ibDbFaezhDvEMDjVeIr4eu6dNMNw5owWRkAOBAKNEA4ACSc4r1zR+xmrkxTml5TBIG4P9ZLNL57cJ004CWurBzONeeBgCTUaIBwERrD6frq3Wx+mVPiqyc7AzgDCIDvHRtv+a6oX9zRQV6mx0HABolSjQA1LOCEqtmbYnXV+tidTA13+w4AJyQq4tFF3QK1+2DWuu8tqFmxwGARoUSDQD1JC2vRF+sPaoZ648pp6jM7DgAGoiezQJ1x+A2uqR7lFxdLGbHAYAGjxINAHXscFq+Plt9RLO2JKjUajc7DoAGqnmIt24f1FrX9WsuHw83s+MAQINFiQaAOrI5NlMf/XZEy/amMMs2gHoT5OOumwe01G0DW6mJv6fZcQCgwaFEA0AtMgxDS/ek6JNVR7Q5NsvsOAAaMQ83F115TrTuGNxG7cL9zI4DAA0GJRoAakGJ1abZWxL06eojOpJWYHYcAChnsUgjOoXrriFt1b91iNlxAMDpUaIBoAbyisv05bpYfbE2hus7A3B457QI0oMj2mt4x3CzowCA06JEA0A1FJRY9cXaGH26+oiyC5lpG4BzOadFkB65sIOGdGhidhQAcDqUaAA4C0WlNn25LkYfrzqizIJSs+MAQI30aRmsRy7soPPbh5kdBQCcBiUaAKqguMymr/84pg9XHlZ6PsO2ATQs/VuF6JGRHXRe21CzowCAw6NEA8BplFrtmrnhmD5YeUgpuZRnAA3buW1C9OjIjkxABgCnQYkGgEqU2ez6flOc3l9+SIk5xWbHAYB6NahdqB4d2UF9WlKmAeDvKNEA8BdWm12ztyTo3eUHFZ9VZHYcADDV4PZhmnhRR/VsHmR2FABwGJRoAPjT0t3JemXxPh1J5zrPAHCCxSL9o2dTTbq4k6KDvM2OAwCmo0QDaPR2JeRoysI9Wn8k0+woAOCwPN1cdPv5rXXvsLby93I3Ow4AmIYSDaDRSskt1tQl+/XT1njZeScEgCoJ9fXQwxe21w39W8jN1cXsOABQ7yjRABqdwlKrPv7tiD5ZdURFZTaz4wCAU2oX7qcnR3fSiM4RZkcBgHpFiQbQaNjthn7cEq83lu7nclUAUEsGtQvVvy/poi5NA8yOAgD1ghINoFFYezhdLy7cq92JuWZHAYAGx8UiXdm7mf41qqMiArzMjgMAdYoSDaBBO5KWr5cW7dWyvalmRwGABs/b3VV3D22jfw5tKy93V7PjAECdoEQDaJCKSm1659eD+t+aIyqz8TYHAPWpZaiPnvtHVw3rGG52FACodZRoAA3O8n0pembubsVnFZkdBQAatUu6R+qZMV0VGcgQbwANByUaQIORlFOkyfN26+fdKWZHAQD8yc/TTQ9f2F7jB7WWq4vF7DgAUGOUaABOz2qz64u1MXrrlwMqKOWSVQDgiDpHBWjK5d3Up2Ww2VEAoEYo0QCc2pZjWfr3T7u0N4lZtwHA0Vks0vX9muvxizspyMfD7DgAUC2UaABOKaewTK8s2advNx4T72IA4FxCfD30xOhOuqZPM1ksDPEG4Fwo0QCczuwt8Xpp0V6l55eaHQUAUAP9WgVryuXd1THS3+woAFBllGgATiMus1CPz9qhtYczzI4CAKgl7q4W3Te8ne4b3k7uri5mxwGAM6JEA3B4hmFoxh/H9PKivSpk4jAAaJA6RwXotat7qFt0oNlRAOC0KNEAHFpCdpEe/3GH1hxKNzsKAKCOublY9M+hbfXgiPbycOOoNADHRIkG4LC+23hMUxbsVV6J1ewoAIB61CHCT69d3VM9mweZHQUATkKJBuBwknOK9cTsHVq5P83sKAAAk7i6WHTP0LZ66ML2nCsNwKFQogE4lNlb4jV53m7lFnP0GQAgdYkK0JvX9VSnyACzowCAJEo0AAeRlleip37aqV/2pJgdBQDgYDxcXfTwyPa6e0hbubpwXWkA5qJEAzDdvO2JenbuLmUVlpkdBQDgwHq3CNIb1/ZS6zBfs6MAaMQo0QBMk1tcpqdm79SCHUlmRwEAOAlfD1c9P7abrurTzOwoABopSjQAU2yLy9YDM7coLrPI7CgAACd0xTnRmnJ5N/l6upkdBUAjQ4kGUK8Mw9DHq47ojaX7VWbj7QcAUH2tQn303g291b1ZoNlRADQilGgA9SY9v0SPfr9dqw5w6SoAQO3wcHXRpIs7asL5rWWxMOkYgLpHiQZQL34/lK5Hvtum1LwSs6MAABqg4R2b6PVreirUz9PsKAAaOEo0gDpltdn11rID+nDlYdl5twEA1KFwf0+9fV0vDWwXZnYUAA0YJRpAnUnILtJDM7dqU2yW2VEAAI2Ei0W6Z1hbPXJhB7m5upgdB0ADRIkGUCeW7ErW47N2KKeIaz8DAOpfn5bBeuf6XmoW7GN2FAANDCUaQK0qtdo1ZeEefbku1uwoAIBGLtDbXW9f30vDO4abHQVAA0KJBlBrUnOL9c8Zm7XlWLbZUQAAkHR8ePejIzvovuHtmL0bQK2gRAOoFZtjs3TPjM3Mvg0AcEijukbojWt7yc/TzewoAJwcJRpAjc3ccEzPzt2tUpvd7CgAAJxSu3A/fXJLH7Vp4md2FABOjBINoNpKrXY9O2+3Zm44ZnYUAACqxN/LTW9d20sXdokwOwoAJ0WJBlAtqbnFuufrLdrM5asAAE7GYpEevKC9Hr6wPedJAzhrlGgAZ43znwEADcGFncP11nW95O/lbnYUAE6EEg3grHD+MwCgIWkT5qtPbu2jduH+ZkcB4CQo0QCqhPOfAQANlZ+nm16/pqcu7hZpdhQAToASDeCM0vNL9M+vNmsT5z8DABooi0WaeFFH3Te8ndlRADg4SjSA0zqUmqfxX2xUXGaR2VEAAKhz1/drrimXd5Obq4vZUQA4KEo0gFP6/VC67pmxWbnFVrOjAABQbwa3D9MHN/VmwjEAlaJEA6jUdxuP6T9zdqnMxlsEAKDx6RTpr8/H9VPTIG+zowBwMJRoABUYhqGpP+/XhysPmx0FAABTRQR46n+39VO36ECzowBwIJRoAOVKrDY99v12LdiRZHYUAAAcgq+Hq9678Rxd0CnC7CgAHAQlGoAkKaewTHd+tUkbjmaaHQUAAIfi6mLR5Mu66JbzWpkdBYADoEQDUEJ2kcZ9vkEHU/PNjgIAgMO64/zWeuqSznJxsZgdBYCJKNFAI7c7MUfjp21Ual6J2VEAAHB4F3eN1NvX95KXu6vZUQCYhBINNGKrD6bpnhlblF/CJawAAKiq3i2CNG1cfwX6cAksoDGiRAON1OKdSXro220qtdnNjgIAgNPpFOmvryYMUBN/T7OjAKhnlGigEfpxc7wen7VDNju//gAAVFfrMF/NuGOAormWNNCoUKKBRmb62hhNnr9b/OYDAFBzTQO9NOOOAWrTxM/sKADqCSUaaETeX3FIr/283+wYAAA0KGF+Hpp+e391bRpodhQA9YASDTQSryzep49+O2x2DAAAGqQALzdNG99PfVqGmB0FQB2jRAMNnGEYenruLs1Yf8zsKAAANGje7q76+JY+GtKhidlRANQhSjTQgFltdv3rxx36aWuC2VEAAGgUPFxd9O4NvXRxtyizowCoI5RooIEqsdr0wDdbtXRPitlRAABoVFxdLHr1qh66uk8zs6MAqAOUaKABKiq16a6vNmn1wXSzowAA0ChZLNKzY7po3KDWZkcBUMso0UADk19i1bjPN2hTbJbZUQAAaPSeHtNFE86nSAMNiYvZAQDUnsJSq26ftpECDQCAg3hhwR59uS7G7BgAahElGmggistsmvDFJm2IyTQ7CgAA+Itn5+3WN39wlQygoaBEAw1AidWmO7/cpHVHMsyOAgAA/sYwpH/P2anvN8aZHQVALaBEA06u1GrXP7/azCRiAAA4MMOQnpi9Q7M2x5sdBUANUaIBJ1Zms+ver7doxf40s6MAAIAzsBvSv37crrnbEsyOAqAGKNGAk7La7Hpw5lYt28t1oAEAcBZ2Q3r0++1auCPJ7CgAqokSDTghu93Qo99v1+JdyWZHAQAAZ8lmN/TQt1u1hP+PA06JEg04Gbvd0MQft2ve9kSzowAAgGqy2g09MHOLlu1hRBngbCjRgBMxDENP/bRTs7dwLhUAAM6uzGYcn9tkX6rZUQCcBUo04ESenbdb33J5DAAAGoxSm133fL1ZG2MyzY4CoIoo0YCTeHvZAX25LtbsGAAAoJYVl9l1x/RNOpCSZ3YUAFVAiQacwMwNx/T2soNmxwAAAHUkp6hMt32+QYnZRWZHAXAGlGjAwS3dnaz/zNlldgwAAFDHknKKdevnG5RdWGp2FACnQYkGHNimmEw9+O1W2eyG2VEAAEA9OJSar9u/2KiiUpvZUQCcAiUacFAHU/I0YfomFZfZzY4CAADq0ZZj2br/my2y2vgMADgiSjTggJJyinTb5xuUU1RmdhQAAGCCX/el6onZO82OAaASlGjAwZRPLJJTbHYUAABgoh83x+vVJfvMjgHgbyjRgAMpLrPpzumbdCAl3+woAADAAXy48rCm/X7U7BgA/oISDTgIu93QQ99u1YaYTLOjAAAAB/L8gj2avz3R7BgA/kSJBhzE03N36efdKWbHAAAADsYwpMe+3661h9PNjgJAlGjAIXz822F9/ccxs2MAAAAHVWqz696vtygmvcDsKECjR4kGTPbr3hQmDQEAAGeUXVimCdM3KreYq3cAZqJEAyY6kJKnh77dJrthdhIAAOAMDqcV6P5vtsrGhwfANJRowCSZBaWaMH2j8kusZkcBAABOZNWBNE1ZuMfsGECjRYkGTFBms+ueGZsVl1lkdhQAAOCEpv0eo5kbmE8FMAMlGjDBM3N36Y+jXMoKAABU3zNzd2nd4QyzYwCNDiUaqGfTfj+qmRvizI4BAACcXJnN0L1fb1ZsBjN2A/WJEg3Uo9UH0zRl4V6zYwAAgAYiq7BMd0zfpDxm7AbqDSUaqCdH0vJ139dbmE0TAADUqoOp+XpgJjN2A/WFEg3Ug5yi498S5xYzEzcAAKh9K/en6UVGuwH1ghIN1DGb3dD932zRkXTOVwIAAHXn89+P6odNzLsC1DVKNFDHXvt5v1YfTDc7BgAAaASenrtLuxNzzI4BNGiUaKAOLduToo9XHTY7BgAAaCSKy+y6Z8YW5RQx0RhQVyjRQB2JyyzUYz9sl8EcHwAAoB4dyyzUI99tk8GHEKBOUKKBOlBqtev+b/gWGAAAmGP5vlT9d/khs2MADRIlGqgDUxbu0fZ4zkcCAADmeWvZAa0+mGZ2DKDBoUQDtWz+9kR9uS7W7BgAAKCRsxvSw99uU3JOsdlRgAaFEg3UosNp+Xpi1g6zYwAAAEiSMgpK9cDMLbLa7GZHARoMSjRQS4pKbbp3xhYVlNrMjgIAAFBuY0yWXl96wOwYQINBiQZqyX/m7NL+lDyzYwAAAJzk41WHtXxfitkxgAaBEg3Ugu82HtOsLfFmxwAAAKiUYUiPfb9didlFZkcBnB4lGqihPYm5embubrNjAAAAnFZWYZnu/4bzo4GaokQDNVBUatP932xRiZX/GQEAAMe35Vi23uP60UCNUKKBGpiycI+OpBeYHQMAAKDK3l9xSFuOZZkdA3BalGigmpbvS9HXfxwzOwYAAMBZsdoNPfrdNhWUWM2OAjglSjRQDRn5JZr0406zYwAAAFRLTEahnp+/x+wYgFOiRAPV8MTsnUrPLzE7BgAAQLV9tylOS3cnmx0DcDqNtkRbLBbNmTPH1AyTJ09WRESEQ2RB1X238Zh+2cN1FgEAgPN7YvZOpeYVmx0DcCr1XqJTU1N19913q0WLFvL09FRkZKRGjRqldevW1WuOpKQkjR49ul73+Vd79+7Vc889p48//viMWWbNmqVhw4YpMDBQfn5+6tGjh55//nllZmbWY2JIUmxGAUOfAABAg5FZUKpJP+4wOwbgVOq9RF911VXavn27pk+frgMHDmjevHkaNmxYvRfCyMhIeXp61us+/+rw4cOSpLFjx542y7///W9dd9116tevnxYvXqxdu3bpjTfe0Pbt2/XVV1/VZ+SzVlZWZnaEWmWzG3rku20qKLWZHQUAAKDWrNyfpi/XxZgdA3Aa9Vqis7OztWbNGr366qsaPny4WrZsqf79++vJJ5/UpZdeWr6exWLRhx9+qNGjR8vb21utW7fWDz/8UGFbCQkJuu666xQcHKzQ0FCNHTtWMTExFdb5/PPP1bVrV3l6eioqKkr3339/hX38dQj1mba3cuVK9e/fX76+vgoKCtKgQYMUGxt7yse6c+dOXXDBBfL29lZoaKjuuusu5efnSzo+jPuyyy6TJLm4uMhisVS6jQ0bNuill17SG2+8oddee00DBw5Uq1atNHLkSM2aNUu33XZb+boffvih2rZtKw8PD3Xs2PGkgm2xWPTZZ5/piiuukI+Pj9q3b6958+ZVWGf37t269NJLFRAQIH9/fw0ePLi87EvStGnT1LlzZ3l5ealTp0764IMPym+LiYmRxWLR999/r2HDhsnLy0szZszQuHHjdPnll+v1119XVFSUQkNDdd999zllwf5gxSFtOZZtdgwAAIBa99KivTqUmm92DMAp1GuJ9vPzk5+fn+bMmaOSktNPyvT000+XH7W++eabdcMNN2jv3r2SpMLCQg0fPlx+fn5atWqV1qxZIz8/P1188cUqLS2VdLxU3nfffbrrrru0c+dOzZs3T+3atat0X2fantVq1eWXX66hQ4dqx44dWrdune66665Tlt/CwkJdfPHFCg4O1saNG/XDDz9o2bJl5SV+4sSJmjZtmqTjw8qTkpIq3c7XX38tPz8/3XvvvZXeHhQUJEn66aef9NBDD+mxxx7Trl27dPfdd2v8+PFasWJFhfWfe+45XXvttdqxY4cuueQS3XTTTeUjABISEjRkyBB5eXlp+fLl2rx5s26//XZZrccvffDpp5/q3//+t1588UXt3btXL730kp5++mlNnz69wj4ef/xxPfjgg9q7d69GjRolSVqxYoUOHz6sFStWaPr06friiy/0xRdfVPqYHNWO+Gy98+tBs2MAAADUieIyux7+bqvKbHazowAOz2IYhlGfO5w1a5buvPNOFRUVqXfv3ho6dKiuv/569ejR4/9DWSz65z//qQ8//LB82bnnnqvevXvrgw8+0Oeff66pU6dq79695UW2tLRUQUFBmjNnji666CJFR0dr/PjxmjJlSqU5LBaLfvrpJ11++eVn3F7fvn0VGhqqlStXaujQoWd8jJ9++qkef/xxxcXFydfXV5K0aNEiXXbZZUpMTFRERITmzJmjK664Qqd7+i+55BIlJCRo+/btp93foEGD1LVrV33yySfly6699loVFBRo4cKF5Y/3P//5j1544QVJUkFBgfz9/bVo0SJdfPHFeuqpp/Ttt99q//79cnd3P2kfLVq00KuvvqobbrihfNmUKVO0aNEirV27VjExMWrdurXefvttPfTQQ+XrjBs3TitXrtThw4fl6upans3FxUXffvvtmZ5Kh1BUatOl763WkbQCs6MAAADUqXuHtdWkizuZHQNwaKacE52YmKh58+Zp1KhRWrlypXr37n3SkcnzzjvvpH+fOBK9efNmHTp0SP7+/uVHt0NCQlRcXKzDhw8rNTVViYmJGjFiRJUynWl7ISEhGjdunEaNGqXLLrtM77zzzimPHkvHJw3r2bNneYGWjhddu92u/fv3V/GZkgzDOOXR7r/vb9CgQRWWDRo0qPz5OuGvX1T4+vrK399fqampkqRt27Zp8ODBlRbotLQ0xcXFacKECeXPj5+fn6ZMmVJhuLck9e3b96T7d+3atbxAS1JUVFT5fp3By4v3UqABAECj8NFvh7UtLtvsGIBDczNjp15eXho5cqRGjhypZ555RnfccYeeffZZjRs37rT3O1Eo7Xa7+vTpo6+//vqkdZo0aSIXl7P7buBM25OOnw/84IMPasmSJfruu+/0n//8R7/88ovOPffck+5zuvJblVJ8QocOHbRmzRqVlZVVWm5Pt93KMvx9GxaLRXb78SE73t7ep9z2iXU+/fRTDRgwoMJtfy3Hkip8cVCV/Tq6P45k6Kv1pz73HQAAoCGxG9ITs3Zo/gPny9210V4NFzgth/jN6NKliwoKKh7pW79+/Un/7tTp+NCS3r176+DBgwoPD1e7du0q/AkMDJS/v79atWqlX3/9tUr7P9P2TjjnnHP05JNPau3aterWrZu++eabUz6ebdu2VXhMv//+u1xcXNShQ4cqZZKkG2+8Ufn5+RUm8Pqr7OxsSVLnzp21Zs2aCretXbtWnTt3rvK+evToodWrV1c64VdERISio6N15MiRk56f1q1bV3kfzqbEatOTP+1U/Z7wAAAAYK59yXn6cOXhM68INFL1WqIzMjJ0wQUXaMaMGdqxY4eOHj2qH374QVOnTtXYsWMrrPvDDz/o888/14EDB/Tss89qw4YN5RNz3XTTTQoLC9PYsWO1evVqHT16VL/99pseeughxcfHSzo+A/Ybb7yhd999VwcPHtSWLVv03nvvVZrrTNs7evSonnzySa1bt06xsbFaunSpDhw4cMqSetNNN8nLy0u33Xabdu3apRUrVuiBBx7QLbfcooiIiCo/XwMGDNCkSZP02GOPadKkSeX7//XXX3XNNdeUT+r1r3/9S1988YU++ugjHTx4UG+++aZmz56tiRMnVnlf999/v3Jzc3X99ddr06ZNOnjwoL766qvy4eeTJ0/Wyy+/rHfeeUcHDhzQzp07NW3aNL355ptV3oezee/XQwzjBgAAjdJ/lx/SodQ8s2MADqleh3P7+flpwIABeuutt3T48GGVlZWpefPmuvPOO/XUU09VWPe5557Tt99+q3vvvVeRkZH6+uuv1aVLF0mSj4+PVq1apccff1xXXnml8vLyFB0drREjRiggIECSdNttt6m4uFhvvfWWJk6cqLCwMF199dWV5jrT9oqKirRv3z5Nnz5dGRkZ5ZfLuvvuu0+5vZ9//lkPPfSQ+vXrJx8fH1111VXVKpyvvvqq+vTpo/fff18fffSR7Ha72rZtq6uvvrr8EleXX3653nnnHb322mt68MEH1bp1a02bNk3Dhg2r8n5CQ0O1fPly/etf/9LQoUPl6uqqXr16lZ9rfccdd8jHx0evvfaaJk2aJF9fX3Xv3l0PP/zwWT8mZ7AvOVcfr+IbWAAA0DiV2ux6YtZO/fDP887qdESgMaj32bmr4q8zZwP1zW43dMWHa7WdSTUAAEAj9/zYrrr1vFZmxwAcikOcEw04kmlrYyjQAAAAkqYu2a/E7CKzYwAOhRIN/EV8VqHeWFr1y5ABAAA0ZPklVv1nzi6zYwAOxZRLXJ2JA44wRyPx7592qbDUZnYMAAAAh7F8X6rmbkvQ2F7RZkcBHAJHooE/zdmaoN8OpJkdAwAAwOE8P3+PsgpKzY4BOARKNCAps6BUzy/YY3YMAAAAh5RRUKoX+KwESKJEA5KkFxbsUSbfrgIAAJzSbEbtAZIo0YBWHUjTT1sTzI4BAADg8J6Zu0slVuaPQeNGiUajVmq169l5u82OAQAA4BRiMwr12eqjZscATEWJRqP22ZojOppeYHYMAAAAp/H+ikNKyuHa0Wi8KNFotJJzivXf5YfMjgEAAOBUCkttemnRPrNjAKahRKPRmrJwD9eEBgAAqIb52xP1x5EMs2MApqBEo1FadzhDC3YkmR0DAADAaU2ev0c2u2F2DKDeUaLR6NjshiYzmRgAAECN7E3K1Td/xJodA6h3lGg0Ol//Eav9KXlmxwAAAHB6b/xyQNmFpWbHAOoVJRqNSnZhqd785YDZMQAAABqE7MIyvb50v9kxgHpFiUaj8vayg8ouLDM7BgAAQIPxzR/HtDsxx+wYQL2hRKPROJiSpxnrOW8HAACgNtkN6bl5e8yOAdQbSjQajRcW7pWVGSQBAABq3YaYTM3dlmB2DKBeUKLRKCzfl6JVB9LMjgEAANBgvbp4n4rLbGbHAOocJRoNnt1u6OVF+8yOAQAA0KAl5hTrq3WcOoeGjxKNBm/21gQdTM03OwYAAECD98HKQ8otZhJXNGyUaDRopVa73uKSVgAAAPUiq7BMn/x2xOwYQJ2iRKNBm7E+VgnZRWbHAAAAaDQ+//2oUvOKzY4B1BlKNBqsghKr3l9xyOwYAAAAjUphqU3v/cpnMDRclGg0WJ+tPqqMglKzYwAAADQ63248ptiMArNjAHWCEo0GKbOgVJ+t5nwcAAAAM5TZDL2xlHlp0DBRotEgfbDikPJKrGbHAAAAaLTm70jU7sQcs2MAtY4SjQYnMbtIX63nGoUAAABmMgxp6pL9ZscAah0lGg3O28sOqMRqNzsGAABAo/fbgTStP5JhdgygVlGi0aAcSs3XrC0JZscAAADAn15dss/sCECtokSjQXlj6X7Z7IbZMQAAAPCnrceytXR3stkxgFpDiUaDsSshR4t38QYNAADgaN5dftDsCECtoUSjwfjv8kNmRwAAAEAldiXkasX+VLNjALWCEo0G4VBqnn7ew1FoAAAAR/U+BzzQQFCi0SB8sOKwDE6FBgAAcFibYrOYqRsNAiUaTi8us1DztieaHQMAAABnwOl3aAgo0XB6H/12WFZm5AYAAHB4aw6la1tcttkxgBqhRMOppeYW64fN8WbHAAAAQBX9l5m64eQo0XBqn64+olKr3ewYAAAAqKJf96Vqb1Ku2TGAaqNEw2llF5bqmz+OmR0DAAAAZ8EwpP+u4NxoOC9KNJzW57/HqKDUZnYMAAAAnKXFO5N0OC3f7BhAtVCi4ZTyS6yavjbG7BgAAACoBrshfbjysNkxgGqhRMMpzVgfq5yiMrNjAAAAoJrmbE1QfFah2TGAs0aJhtMpLrPps9VHzY4BAACAGrDaDX266ojZMYCzRomG05m9JUHp+SVmxwAAAEAN/bg5XrnFjC6Ec6FEw+l8sZaj0AAAAA1BQalN32+MMzsGcFYo0XAqvx9K14EUZnIEAABoKKavi5HdbpgdA6gySjScyrTfY8yOAAAAgFoUl1mkZXtTzI4BVBklGk4jLrNQy/fxBgsAANDQcKAEzoQSDacxfW2MGOkDAADQ8Kw7kqF9yblmxwCqhBINp1BYatX3m5h0AgAAoKH6gqPRcBKUaDiFWVsSlFtsNTsGAAAA6sicbQnKKig1OwZwRpRoOIXpa2PMjgAAAIA6VFxm1zcbjpkdAzgjSjQc3uqDaTqUymWtAAAAGroZ62NltdnNjgGcFiUaDo/zYwAAABqHpJxiLdmdbHYM4LQo0XBosRkFWrE/1ewYAAAAqCdc7gqOjhINhzZ9bSyXtQIAAGhENsdmaVdCjtkxgFOiRMNhFZfZ9ONmLmsFAADQ2HBpUzgySjQc1s+7k7msFQAAQCM0d1uiistsZscAKkWJhsPiG0gAAIDGKaeoTD8zwRgcFCUaDik+q1BrD2eYHQMAAAAm+WFTvNkRgEpRouGQftwcL4MJxQAAABqt3w+nKy6z0OwYwEko0XA4hmHox8188wgAANCYGYb0A58J4YAo0XA4aw9nKD6ryOwYAAAAMNmszfGyc71TOBhKNBwOE4oBAABAkhKyi7TmULrZMYAKKNFwKLnFzMQIAACA//cdB1jgYCjRcCjztiWquMxudgwAAAA4iF/2pCi7sNTsGEA5SjQcyg980wgAAIC/KLXa9dPWBLNjAOUo0XAY+5PztD0+x+wYAAAAcDDfc81oOBBKNBwGE4oBAACgMnuTcrUrgYMtcAyUaDgEm93Q3G2JZscAAACAg5q3nc+KcAyUaDiE9UcylJ5fYnYMAAAAOKgF2xNlGFwzGuajRMMhLNiRZHYEAAAAOLDEnGJtjMkyOwZAiYb5bHaDa0MDAADgjOYzpBsOgBIN0609nK7MAq79BwAAgNNbtDNJVpvd7Bho5CjRMN1ChnIDAACgCjIKSvX74QyzY6CRo0TDVFabnaHcAAAAqLIFDOmGydzMDoDG7ffDGcoqLDM7Bkxi2G3KXvONCvaslL0gS66+wfLtfqECB14ni+X4d3zZa75Wwd7VsuWlyeLiJo/Idgoacqs8m3Y85Xbzti1Rwe7lKkuLlaRK75O/e4Wyf5suo6xYfj0uUvDw28tvs+akKOW7pxV129ty8fSpo0cPAACqY+meFL1ks8vdleOBMAclGqZauINvEhuz3PU/Kn/bYoVe+og8wlqoJOmgMha/IxdPHwX0HStJcg+JVsjIf8otKFJGWYnyNs1VyndPK/ruT+XqE1jpdovjdsq381B5XthZFjd35fwxSynfP6OmE96Xm3+YbIU5ylzynkIveVhuQZFK/fE5ebboLp+2/SRJGT9/oOCh4yjQAAA4oJyiMq09nKGhHZqYHQWNFF/fwDRlNrt+3p1idgyYqCRxn7zbDZBP235yC4yQb6fz5d3qHJUmHypfx7fLMHm36iX3oEh5NGmp4AvukFFaqNLUo6fcbpPL/iX/3pfKI6KN3EObK/TiByTDruLY7ZIka3ayLJ4+8u08RJ5RHeTVoofK0o9Jkgr2rJTF1U0+HQfW7YMHAADVtog5dWAiSjRMs+ZgunKKGMrdmHk266Li2O0qy0yQJJWmHlFx/B55t+lb6fqGrUx525bI4ukrj/DWVd6PUVYi2W1y8fKXJLmFRMsoK1FpymHZivJUmnRAHk1ayVaUp+zVXytk5D9r/uAAAECdWbonmVm6YRqGc8M0C/gGsdELGHC17CUFSvz0n5KLi2S3K2jILfLtMrTCeoWHNih93lQZZSVy9QtWxHUvnHIod2WyfpsuV79QebfqJUly9fJT2KWPKH3BmzKspfLtdoG82/RR+qK35d9njKw5KUqd9YJktypw0I3y7XR+bT5sAABQQ1mFZVp3JEOD2zOkG/WPEg1TlFrt+mUPs3I3doV7V6lg90qFXTZR7k1aqjTliLJ+/VSufqHy6z6ifD2vFj0UNf5d2Qtzlbf9Z6XNfVVRt7whV9+gM+4j548fVbj3N0Xc8LIsbh7ly306DJRPh/8fsl18bIfK0mIVMvKfSvzkLoVd9i+5+gYr6ctH5dW8W5X2BQAA6s+inUmUaJiC4dwwxZpDacottpodAybLWjlNgedeLd8uQ+XRpJX8ul0g/35jlbP+hwrruXh4yT24qTyjOynskodkcXFR/o6lZ9x+zh+zlbPuB4Vf+8Jph38b1jJlLv1QIaPukzUrSYbdJq8W3eUe2kzuIdEqSdpf48cKAABq17K9qTIMw+wYaIQo0TDFsr2pZkeAAzDKSiRLxbchi8VFMs5wjpNx/Pzo08n5Y5Zy1n6riGuek2dU+9Oum732W3m16SPPyHbH9223/f+u7FbJzjlXAAA4mrS8Eu1MyDE7BhohhnPDFCv3UaIhebfrr5y138k1oIk8wlqoNOWwcjfOkV+PkZIke2mxctZ9J592A+TqFyJbUa7yty6SNS9dPh3//zzl9AVvyNU/VMFDx0k6PoQ7e/UMhV32L7kFRsiWnyVJsnh4ycXDu0KG0rRYFe5bpahx70mS3EKaSRYX5W1fKle/YJVlxMvjDCUcAACYY/m+VPVoFmR2DDQylGjUu33JuUrMKTY7BhxAyIV3K3v1DGUu/UD2why5+oXIr9doBQ26XpJkcXFRWWa80ub8KltRrly9A+QR2V6RN70qjyYty7djzU2rcEQ7b8siyWZV+pyXK+wvcNANCjr/pvJ/G4ahzJ//q+AL7pSLh5ckycXdU6GXPKzMXz6UYSs7fo1q/7C6fBoAAEA1rdiXqocv7GB2DDQyFoMTCVDPPlh5SFOXcI4pAAAAasZikTY8daGa+HuaHQWNCOdEo96t3JdmdgQAAAA0AIYhrdjPaYKoX5Ro1KucojJtPpZldgwAAAA0ECuYawf1jBKNerXqQJpsds4gAAAAQO1YczBdZTaupIH6Q4lGveKbQgAAANSmvBKrNh7NNDsGGhFKNOqN3W7otwOcDw0AAIDatZwDNahHlGjUm+3x2cooKDU7BgAAABoYSjTqEyUa9Yah3AAAAKgLR9ILFJNeYHYMNBKUaNSbFfsZyg0AAIC6wdFo1BdKNOpFal6xdiXmmB0DAAAADRTXi0Z9oUSjXqw5mC6DK1sBAACgjmyOzeJSV6gXlGjUi/VHMsyOAAAAgAassNSmHfHZZsdAI0CJRr1Yf4Rr9wEAAKBu8ZkT9YESjTqXlFOkY5mFZscAAABAA8foR9QHSjTq3B98IwgAAIB6wHnRqA+UaNS5P47yjSAAAADqHudFoz5QolHnODcFAAAA9YXPnqhrlGjUqdTcYh1NLzA7BgAAABoJzotGXaNEo06tP8o3gQAAAKg/nBeNukaJRp3im0AAAADUJ86LRl2jRKNO/UGJBgAAQD3jvGjUJUo06kxaXokOp3E+NAAAAOoXoyFRlyjRqDNc2goAAABm4Lxo1CVKNOrMHwyjAQAAgAkKS23ak5hrdgw0UJRo1JnNsVlmRwAAAEAjtZ3JxVBHKNGoE8VlNh1IyTM7BgAAABqp7XE5ZkdAA0WJRp3YmZAjq90wOwYAAAAaKS5zhbpCiUad2B6XbXYEAAAANGKH0/JVUGI1OwYaIEo06sQ2SjQAAABMZDeOj44EahslGnWCiRwAAABgNoZ0oy5QolHrMgtKFZdZZHYMAAAANHLb4zkSjdpHiUat4xs/AAAAOAI+l6IuUKJR63Zx7gkAAAAcQFxmkTILSs2OgQaGEo1atysh1+wIAAAAgCSORqP2UaJR65gFEQAAAI5iB+dFo5ZRolGrsgtLlZDNpGIAAABwDByJRm2jRKNWcRQaAAAAjoQZulHbKNGoVbsTOR8aAAAAjiMtr0Tp+SVmx0ADQolGrdqXRIkGAACAYzmYkm92BDQglGjUqkNpvEEBAADAsRxMzTM7AhoQSjRqjWEYOpxaYHYMAAAAoAKORKM2UaJRa+KzilRUZjM7BgAAAFABR6JRmyjRqDWHUvmGDwAAAI6Hz6moTZRo1BrenAAAAOCI0vNLlVVQanYMNBCUaNQahskAAADAUR3kgA9qCSUatYYj0QAAAHBUB1I44IPaQYlGraFEAwAAwFHxWRW1hRKNWpGSW6zcYqvZMQAAAIBKceohagslGrWCb/YAAADgyLhWNGoLJRq14iDnmAAAAMCBpeaVKKeozOwYaAAo0agVh9L4Zg8AAACO7RBDulELKNGoFQznBgAAgKOLSS80OwIaAEo0akVcZpHZEQAAAIDTSsjmMytqjhKNGrPa7ErOLTY7BgAAAHBa8VkciUbNUaJRY8m5xbLZDbNjAAAAAKcVn8WRaNQcJRo1lsCbEQAAAJwAw7lRGyjRqLHEHN6MAAAA4PiSsotlZwQlaogSjRrjSDQAAACcQanNrtS8ErNjwMlRolFjDIsBAACAs2ByMdQUJRo1xgQNAAAAcBYcAEJNUaJRY7wRAQAAwFlwAAg1RYlGjSVlc41oAAAAOAdKNGqKEo0aycgvUVGZzewYAAAAQJVwTjRqihKNGmEoNwAAAJwJV5ZBTVGiUSO8CQEAAMCZJGQXyTC4VjSqjxKNGuFINAAAAJxJidWu3CKr2THgxCjRqJG0fC5WDwAAAOeSUcBnWFQfJRo1klVQanYEAAAA4Kxk8hkWNUCJRo3wBgQAAABnk8FnWNQAJRo1QokGAACAs+EzLGqCEo0aySosMzsCAAAAcFYo0agJSjRqJIOJxQAAAOBkMvIp0ag+SjSqzWqzK6+EywMAAADAuWQyOzdqgBKNassqLBPXqQcAAICzYWIx1AQlGtXGuSQAAABwRlmFfI5F9VGiUW2UaAAAADijTM6JRg1QolFtfIMHAAAAZ8RwbtQEJRrVxpsPAAAAnFGJ1a4CJshFNVGiUW1ZlGgAAAA4KU5NRHVRolFtDOcGAACAs8opKjM7ApwUJRrVllfMEBgAAAA4p+Iym9kR4KQo0ai2It54AAAA4KQKS/ksi+qhRKPainnjAQAAgJPigBCqixKNaiu28sYDAAAA51TEASFUEyUa1cYbDwAAAJwVR6JRXZRoVFtRmd3sCAAAAEC1cE40qosSjWpjRkMAAAA4Kz7Loroo0ag2hnMDAADAWRWWcrlWVA8lGtXGeSQAAABwVkWlnJqI6qFEo9oo0QAAAHBWRWUciUb1UKJRLXa7oVIr394BAADAOXFqIqqLEo1q4RrRAAAAcGbMzo3qokSjWvjmDgAAAM6MUxNRXZRoVEsxQ7kBAADgxDg1EdVFiUa1WG286QAAAMB5GYbZCeCsKNGoFossZkcAAAAAqs0QLRrVQ4kGAAAA0OjY6dCoJko0qsXCgWgAAAA4MTvjuVFNlGgAAAAAjQ5HolFdlGgAAAAAjQ9HolFNbmYHgHNiODcA1C0fN5ve6LBc8Xl2RadLTZIK5ZuYLktpqdnRAKBBcM1rL+l8s2PACVGiUS0WWjQA1KlCq6tUGqArir/VR+f01aw2eyWbXb1KI3VOXojaZnkoMrlEvvEZMuKSJJvN7MgA4FTcA/zNjgAnRYkGAMBBPXq0r7Y3ma//bFmo20Jb6oOWXbUoe482eSZKYZLaH1/Px+6hfiVR6pEXrDbpLgpLKpLXsVQZyamm5gcAR8ZBIVSXxTA4GQBnLzG7SANfWW52DABo8B5rcUgPpD5T/u+DER31btPWWpm954z3DbP7qn9hpLrm+KtlhkUhCXlyj02WkZVdh4kBwDl49+qlVt/ONDsGnBAlGtWSlFOk816mRANAfdjW6j0FJa+ruKx5L70bGqqNOQfPenutrEHqVxihzlk+apZmV0B8jlxjE2UUFtZWZABweN69e6vVN1+bHQNOiOHcAAA4uKcKbtD7lj9kMezly3rFbdPncdLaNufqHT8P7cmLqfL2YtyyFROQLQVIaimpr2QxpG5lTdW7oIk6ZHoqKqVMfvGZUmyCZLXW9kMCAPO5MJwb1UOJRrVYxJsOANSXRWlhOtLucrWNn33SbQOPrNd5smhpx8H6r3uZYgoSqrUPwyLt9EjVTo9UKVhS2+PLPQ1X9S5ppl55IWqb4aYmycXyiUuXkZDE5WEAODWLhav9onoo0QAAOIH7ki/RYo+fZSktOOk2iwyN2r9KF1pcNbfzcH2oLCUXpdXKfkssNq3zitc6r3ipiaROx5cHGr7qXxilbrkBapXhorDEAnnEpshIz6iV/QJAnXOhRKN6KNGoFndXjkQDQH3al++jNe1v0eC4j065jqth05V7lmmMq6e+7TJcn5UmKKs0p07y5FiK9YvvUf3iKylKUrfjy6NswRpQGKnO2b5qkS4FJeTJLSZRRl5eneQAgOpy8fIyOwKcFBOLoVqKSm3q/MwSs2MAQKPi72bV1uAn5ZZXtSHbBZ7++rLzEE0vOKwCq7mThnUoC1XfwnB1zPRW01SrAhKy5RKTIKOkxNRcABov/9EXq9lbb5kdA06II9GoFi93F1ksnA4HAPUpz+qmr/3G67a8KVVa37ckT/dsW6jrfUP1WYfz9F3efpXYzCmtB9wzdCAwQwqU1Pr4MldZ1Kukuc7JD1W7LA9FJpXINy5DRnySZLOZkhNA4+Hi7WN2BDgpjkSj2ro8s0SFpXzIAYD6ZLEY2tXsNfmmbTvr+yYHReujNr00J3uPbIbjvn/72N3VryRKPfKC1DrdTU2SCuV1LFVGcqrZ0QA0IME33aTIp/9jdgw4IY5Eo9p8PFwp0QBQzwzDohest+gVbTvr+0ZmJ2jylgSNa9JW7zXvpF+y9siQ432XXuhSpt+8j+k372NSuKQux5eH2QPVvzBSXXP81TLDopDEfLnHJMnIyjYzLmrZJxkZWpafpyMlpfJysaiXt7cea9JErT08T3mfp5ISNSc396TlbT08NL91G0nS2oICvZCSrAybTSP8/PRcZJQ8LMfneMmz2XRtbIz+17yFmrq7180Dg8Nx8fE2OwKcFEeiUW2Dpy5XXGaR2TEAoFH6ve1Xik5YXKNt7GnaVe9GROv37H21lMocLa1B6l8Yoc5ZPopOsyswIUeuMYkyCs09DxzVc1dcnEYH+Kubl7dshqF30tN0oKRE81u3kc8pZlPOs9lU/JePtDbD0BUxR3VTcLDuD2siu2FoyOFDuiMkVIN8ffVIYoJuDArWjcHBkqTJyclq5eGhcSEh9fIY4RjCHnxATe691+wYcEIciUa1+bjz8gEAszyQfrlmua2QxVpc7W10SdytjxJ3a2PLvnonyF/bcw/XYsL6E+uWrdiAbClAUktJfSWLIXUra6reBWHqkOWlqOQy+cVnSscSpbIykxPjdD5p3rzCv1+MjNL5hw9pT3Gx+vpUfg6rv6ur/P/y72V5ecq123VFYJAkKctmU6bNphuCguTp4qLhfn46XHp8foAthYXaXVyspyMi6uLhwIFxTjSqixaEavP2cDU7AgA0Wlty/LWl/fXqE/dFjbfVL3aTZsRKK9udr3e9pYP5x2oe0GSGRdrpkaqdHqlSsKTjI3rlabiqd0kr9cwLVttMd4UnFcsnLk1GYopkt5uaGZXL+/PnEuha9c8ds3OydZ6Pj6L/HJod4uqqJq5u+r2wQAN9fLW5sEhjAwNUahh6PiVFU6Ii5Wrh8p2NjYs3w7lRPZRoVJsPJRoATHXPseFa77tQLoVptbK9YYfWaIjFRYs6DtP7rvmKL0yule06khKLTeu84rXOK15qIqnj8eWBho/6FUWpe06AWmW4KCypUB4xyTLSM0zN29gZhqGpqanq7e2t9p6nPif6r9KsVq0uKNDUqKblyywWi95s2lSvpqXq5dRUDfH11ZWBQfo0I0Pn+vrI0+Kim2JjlWWz6abgYN305zBvNGycE43qokSj2ijRAGCu1BJ3zWk2TlcWvlZr23Qx7Bqzb7lGubhrdpfh+tiWqrTizFrbvqPKsRRrmc9RLfORFCWp2/HlUbZg9S+KVJcsX7VIl4IScuUWkyQjL8/MuI3GlNQU7S8p1owWLat8n59ycuTv6qoR/v4Vlvfx8dH3LVuV/zumtFTzcnM0q1Vr3XosVrcGh+h8X1+NjTmqvt7e6ujlVVsPAw7KwpFoVBMlGtXm7cHLBwDM9vjRXrokspO8Mmt3cjB3e5mu27VU//Dw0TedhurzkmPKLW18xTHJNU9z/fI0109Sc0nnHF/eoSxCfQvC1THbW01TrApIyJZLbKKM4uqfo46KpqQka0V+vr5s3kKRVZwx2zAMzc7J1j8CAspn3j7Ves8mJ2lSeLgMw9DekhJd5O8vbxcX9fX20caiQkp0I8A50aguWhCqzcedI9EAYLYyu0VvuYzTk3qiTrbvXVqoCTsW6xrvQH3R8XzNKDikIitXZjjgnqEDQRlSkKRWx5e5yqJeJc11Tn6o2mV5KDKpRL5xGTLikyQbl4SsKsMw9GJqipbl5+uL5i3UzMOjyvfdWFSoY2VluurPCcVOZVZOjoJcXXWBn79y/vzZWP+c3dsqQ3auXdMoMJwb1UWJRrX5eFKiAcARfBzfQne2HqawpJV1to+Aohw9uG2hbvQL1yft++vH3L0qszPL9V/ZZGizZ5I2eyZJoZLaHV/uY/dQv5Km6pEXqNbprmqSXCyv2BQZyamm5nVUL6SmaGFurv4b3Uy+Li5Ks1olSf4uLvL68xJXb6alKtVq1St/Oe9ZOl6Oe3h5nfb86QyrVR9lpOvrP4eIB7q6qo2Hh77MytIgX1+tLyjUXSGhdfTo4EiYWAzVRYlGtXFONAA4jok512iayxpZ7NY63U9Yfqqe2rpAt4a00Aetumth9m7ZDWa1Pp1ClzL95h2r37wlhUvqcnx5mD1Q/Yoi1C0nQC3TLQpJzJd7TJKMrGwT05rv2+xsSdJtcRVniX8xMrL8klXpVquS/napsjybTb/k5enJ8NNfqurl1BSNDwlVxF+GiL8UGaWnkpM0IytTt4eEqAflqlGgRKO6LIZhMGAF1fLhysN6dUntnoMHAKi+xe3nqXPct/W6z4MRHfVedGutyNpTr/ttyFpag9S/IEKdcnzVLNWmwIQcucYkyigsNDsa0KC0X71Kbk2amB0DTogSjWr7fmOcJs3aYXYMAMCfWnkXa4XHI7KU5NT7vrc376l3Q5toQ86Bet93Y2AxpG5l4epdEKb2mV6KSi2Tf1ymdCxRKmNYPVAdnXbukKWKk9YBf0WJRrX9ujdFE6ZvMjsGAOAvPmm3XhfFv2va/te2HqB3A7y0O/eoaRkaEw/DVX1KotQzL1htM90Vnlwsn2NpMhJTJDvD7IFTcfHzU8dNG82OASdFiUa1bYvL1uXv/252DADAX/i62rUt7Gm555hXYg1JyzoM0XseZTpakGBajsYs0PBSv6JIdc8NVKt0F4UlFcojNkVGWrrZ0QCH4N68udr9stTsGHBSlGhUW1xmoQZPXWF2DADA30xqeVD3pjxrdgzZLK6a13m4PlSWkorSzI4DSVE2f/UvjFCXHH+1SDMUlJArt5gkGXmN7xrgaNy8evZQ6+++MzsGnBQlGtVWWGpVl2d+NjsGAKAS21u+q8CU9WbHkCSVunrq+y7D9WlZojJLss2Og0q0t4aqX364OmZ5q2maVQHx2XKJTZRRXGx2NKBO+A0dquYff2R2jHLjxo1Tdna25syZU+1tTJ48Wc8995wkyWKxKDIyUsOHD9crr7yi5s2b11JSSFziCjXg4+Emb3dXFZXZzI4CAPib/xTdoHctG2RxgMtPedhKdPPOJbrS00/TOw/Vl4VHlF9WYHYs/MVBtwwdDMqQgiS1ltRfcpVFvUqb65y8ULXL9FBESon84jJlxCdJ1rq9lBpQ11yDg82OUCe6du2qZcuWyW636/Dhw7rvvvt07bXXat26dWZHq5GysjK5O9AkcC5mB4BzC/H1MDsCAKAS81ObKCb6H2bHqMCnJF/3bFuoxfEpui2ohzxdPc2OhNOwydBmjyR9FrpLT7TfovHn79Y1NyRp3GPu+u/DbbRqQm/Fj+2nkgHdZYk6/bWZAUfj6CV62LBhevDBBzVp0iSFhIQoMjJSkydPPuP93NzcFBkZqaZNm2rw4MG68847tX79euXm5pavM3/+fPXp00deXl5q06aNnnvuOVn/8sVYdna27rrrLkVERMjLy0vdunXTggULym+fNWuWunbtKk9PT7Vq1UpvvPFG+W1PPvmkzj333JNy9ejRQ88++/+nGU2bNk2dO3eWl5eXOnXqpA8++KD8tpiYGFksFn3//fcaNmyYvLy89MknnyggIEA//vhjhe3Onz9fvr6+yqvnU1I4Eo0aCfXzUEJ2kdkxAACVuDd5jBa5/yKLgx31DSrM1MStC3RzULQ+atNLc7P3ympwZNNZFLqUaZX3Ma3yPiaFS+pyfHmoPUD9iyLVLSdALdMtCknMl3tMkoysbDPjApVyCws1O8IZTZ8+XY8++qj++OMPrVu3TuPGjdOgQYM0cuTIKt0/OTlZs2fPlqurq1xdXSVJP//8s26++Wa9++67Gjx4sA4fPqy77rpLkvTss8/Kbrdr9OjRysvL04wZM9S2bVvt2bOn/P6bN2/Wtddeq8mTJ+u6667T2rVrde+99yo0NFTjxo3TTTfdpFdeeUWHDx9W27ZtJUm7d+/Wzp07ywvwp59+qmeffVb//e9/dc4552jr1q2688475evrq9tuu608/+OPP6433nhD06ZNk6enp7Zv365p06bp6quvLl/nxL/9/f1r/oSfBc6JRo2Mn7ZBK/YzWQwAOKpv2q/UwLhPzI5xWjFN2ur95h30c9YeGeJjSUPT0hqk/gUR6pTjo2ZpdgXG58g1JlFGYaHZ0dCINX31FQWOHWt2jHJ/Pyd62LBhstlsWr16dfk6/fv31wUXXKBXXnml0m1MnjxZL7zwgry9vWW321VUdPxA14MPPqh33nlHkjRkyBCNHj1aTz75ZPn9ZsyYoUmTJikxMVFLly7V6NGjtXfvXnXo0OGkfdx0001KS0vT0qX/P7P5pEmTtHDhQu3evVuS1LNnT1199dV6+umnJUlPPfWUli1bpg0bNkiSWrRooVdffVU33HBD+TamTJmiRYsWae3atYqJiVHr1q319ttv66GHHipfZ8OGDRo4cKCOHTumpk2bKj09XU2bNtUvv/yioUOHVv3JrgUciUaNhPoxFA8AHNl9MedrU+ACueYnmh3llFqlHdZraYd1e1QXvRvZTGuy95kdCbUo1i1bsYHZUqCkFpL6SBZD6mqNVp/8ULXP9FJUapn847Ok2ASprMzkxGgMXMPCzI5wRj169Kjw76ioKKWmpp72Ph07dtS8efNUUlKiuXPn6ocfftCLL75YfvvmzZu1cePGCstsNpuKi4tVWFiobdu2qVmzZpUWaEnau3evxv7ty4dBgwbp7bffls1mk6urq2666SZ9/vnnevrpp2UYhmbOnKmHH35YkpSWlqa4uDhNmDBBd955Z/k2rFarAgMDK2y3b9++Ff7dv39/de3aVV9++aWeeOIJffXVV2rRooWGDBly2uekLlCiUSOhnBMNAA4tq8xNMwPG6+b8F8+8ssk6J+3Rh0l7tLlFH70TEqitOYfMjoQ6YlikXe4p2hWcIgVLOj7qUx6Gq3qXtFSv/BC1zXBXeHKxfOLSZSQkS3bzJ8lDw+HmBCX67xNpWSwW2c/we+Dh4aF27dpJOj7J2MGDB3XPPffoq6++kiTZ7XY999xzuvLKK0+6r5eXl7y9vU+7fcMwZLFYTlr2VzfeeKOeeOIJbdmyRUVFRYqLi9P1119fvn/p+JDuAQMGVLjfiSHjJ/j6+p60/zvuuEP//e9/9cQTT2jatGkaP378SXnqAyUaNRLqR4kGAEf39NEuujK6p3zSt5sdpUr6HNusL49Jv7UdqHd9XHQg/5jZkVBPSi02rfdK0HqvBClMUsfjywMNH/UrilS33EC1znBVWGKBPGJTZKSlm5oXzssZSnRtePrpp9WhQwc98sgj6t27t3r37q39+/eXF+2/69Gjh+Lj43XgwIFKj0Z36dJFa9asqbBs7dq16tChQ3kJbtasmYYMGaKvv/5aRUVFuvDCCxURcXzywYiICEVHR+vIkSO66aabzvrx3HzzzZo0aZLeffdd7d69u8I51PWJEo0aCfFlODcAODrDsOhl+816Qc5Rok8YenithsiiRZ2G6X3XAsUVJpsdCSbJsRRrmU+MlvlIipTU9fjyKFuw+hdGqEuOv5qnGQpOyJVbTJKMep6pF07Gzc3hZ+euLW3atNHYsWP1zDPPaMGCBXrmmWc0ZswYNW/eXNdcc41cXFy0Y8cO7dy5U1OmTNHQoUM1ZMgQXXXVVXrzzTfVrl077du3TxaLRRdffLEee+wx9evXTy+88IKuu+46rVu3Tv/9738rzK4tHT93evLkySotLdVbb71V4bbJkyfrwQcfVEBAgEaPHq2SkhJt2rRJWVlZevTRR0/7eIKDg3XllVfqX//6ly666CI1a9as1p+zqqBEo0bC/SnRAOAMvkqM1r1tRykq4Wezo5wViwxdum+FLnJx109dhutjW5pSizPMjgUHkeSap7n+eZrrL6mZpHOOL29fFqG+heHqlOmtpmlWBcRnyyU2UUZxsZlx4SDcgoNlcWk8V/p97LHHNGjQIP3xxx8aNWqUFixYoOeff15Tp06Vu7u7OnXqpDvuuKN8/VmzZmnixIm64YYbVFBQoHbt2pVPZta7d299//33euaZZ/TCCy8oKipKzz//vMaNG1dhn9dcc40eeOABubq66vLLL69w2x133CEfHx+99tprmjRpknx9fdW9e/fy86bPZMKECfrmm290++231+RpqRFm50aNHErN14Vv/mZ2DABAFfQPytV3ZQ/JYisxO0q1Fbt765vOw/R5SZxySnPPfAfgT66yqGdJpM4pCFX7DA9FpJTINy5Tik+SrFxirTHx6t5drX/43uwYqKavv/5aDz30kBITE+XhYc6ppRyJRo1EB51+8gEAgOPYkB2g7e2vU6+4L82OUm1eZUW6fcdiXeMVqGmdzteMgkMqshaZHQtOwCZDWzyTtMUzSQqR1P74ch+7u/qWtFCPvCC1yXBTk6QieR1LlZGcKnGsqUFybxZtdgRUQ2FhoY4ePaqXX35Zd999t2kFWuJINGrBOc8vVVYhl6MAAGcQ6VmqtT4T5VLUMCZkSvcL16cd+uuHnL0qs/P/ItSeULuP+hdFqltOgFqkWxSamC/3mCQZWdlmR0MNhd55h8Ife8zsGDhLkydP1osvvqghQ4Zo7ty58vPzMy0LJRo1Nua91dqVwJA6AHAW77TdorEJr5sdo1YlhLTQB626aUH2HtkNLoWEutPSGqR+hRHqnO2jZml2BSbkyjUmUUZBgdnRUEWRkycr+PrrzI4BJ0aJRo3d9eUmLd2TYnYMAEAVubsY2hXxvDyz9psdpdYdiuio96Jba3nWHrOjoBGxGFLXsnD1LmiiDpmeikotk398lhSbIJUxQsLRNP/sM/mdP8jsGHBinBONGosO5rxoAHAmZXaL3nW7Tf/SU2ZHqXXtUvbrnZT92tGsp94Na6I/cg6YHQmNgGGRdnmkapdHqhQsqe3x5R6Gq3qXtFTPvBC1y3JXeFKxfOLSZSQkS3ZGTJjFg3OiUUMciUaN/W/NUb2wgG/8AcDZbG79kUKTVpkdo06ta91f7wZ4a1fuUbOjAOX87Z7qXxKl7jmBap3uotDkQnnGpMhIaxhzFTg0Fxd12rZVFhMnpYLzo0SjxpbuTtZdX202OwYA4CyNCM3UZ0UPy2Jv+Jf3WdZhsN7ztOlIfrzZUYBTirT5qX9hlLrm+Kl5mqHgxDy5xSTJyGXumdriFhmp9itXmB0DTo4SjRrbl5yri99ebXYMAEA1/Nx+jjrGNY7rpdosrprfebg+VLYSi1LNjgNUWfuyUPUtbKKOWT6KTrUqID5bLrGJMoqLzY7mdLz79lGrGTPMjgEnR4lGjRWWWtXlmZ/NjgEAqIY2PsX61f1hWUoaz5GuMlcPfd/lAn1SlqTMkiyz4wDV4iqLepZEqld+iNpneSoypVS+xzKk+CTJ2vBHl1RX4NixavrqK2bHgJOjRKNW9J3yi9LzS82OAQCohv+1X6cRce+ZHaPeFXr66ctOQzS96Kjyy7g8ERoGL8NN/UqaqmdukNpkuKlJcpG8YlNlJKdKfOxX2H33qckD95sdA06OEo1acfn7v2tbXLbZMQAA1eDrate20H/LPTfW7CimyPYJ0f86DtTMvP0qsZWYHQeoE6F2H/UvilK3HH+1SLcoNDFf7rHJMjIb12iMqJdfVtAVl5sdA06OEo1a8eDMrZq3PdHsGACAanqq1QHdlTzZ7BimSglsqo/anqM52XtlNRgOi8ahpTVIfQsj1CXHV81SbQpMyJVrTKKMgoY5OqPlV1/Kp18/s2PAyVGiUSve/OWA3v31oNkxAAA1sKPFWwpI3Wh2DNPFhrXR+y06aUnWbhniYxIaH4shdS0LV++CMLXP8lLT1DL5x2VJsQlSWZnZ8Wqk3Yrlco+KMjsGnBwlGrVi/vZEPTBzq9kxAAA1cGVEqt7IeUQWiqMkaV9UF70b2Vyrs/eaHQVwCB6Gq84pjVSv3BC1zXJXRHKJfI6lyUhIlux2s+OdkcXdXR23b5PFxcXsKHBylGjUiv3JeRr19iqzYwAAaui3dt+qZfw8s2M4lM0teuvdkCBtyTlkdhTAIfnbPdWvOEo9cgPVOt1FocmF8oxNkZGabna0CjzatVXbBQvMjoEGgBKNWlFqtavLM0tktfNyAgBn1t2/QPP0kCxlhWZHcTir2g7Uu76u2p/XOCdgA85WpM1P/Quj1DXXT81TDQUn5sktJklGrjmX1PO/+GI1e/stU/aNhoUSjVpz4Zu/6VBqvtkxAAA19G375To37jOzYzgkQxYt7jRU77sW6VhhktlxAKfUvixUfQubqGOWt6LT7AqIy5LLsUQZRcV1ul8ub4XaQolGrblnxmYt3pVsdgwAQA2FepRpg//jci3gPf1UrC5umt15uD62pyu1OMPsOIDTc5VFPUojdU5eiNpneSoypVR+cZky4hIla+3Mlh/91psKGD26VraFxo0SjVrDDN0A0HC83Ganbkh82ewYDq/Y3VszOw/T/0rilFNqzhBVoCHzMtzUtyRKPXOD1TbDTU2Si+R1LE1GUop0ljWmzfx58mzfvo6SojGhRKPWLNiRqPu/YYZuAGgILBZDu6NfkU/6TrOjOIU8r0B90el8fVVwSEXWIrPjAA1eqN1H/Yui1C3XXy3SLApNzJd7bLKMzKzK7+Dmpk5bt8ji7l6/QdEgUaJRaw6k5Omit5ihGwAaituaJui5zH+ZHcOpZPg10aftB+j73L0qszv39XQBZ9TSGqS+hRHqnO2jZmmGghJy5BqTKPfISLVdyMzcqB2UaNSaMtvxGbrLbLykAKChWN9mmiITfzE7htNJDG6hD1p314LsPbIZNrPjAI3eTU3/oSdGvmh2DDQQXGkctcbd1UWtQn3NjgEAqEWPZF0pw9XD7BhOp2nWMU3ZslCz8910YXBXs+MAjV5QeHOzI6ABcTM7ABqW9hF+OshlrgCgwViXFaid7a9Vj7gZZkdxSm1SD+qt1IPaFd1dbzeJ0B85B8yOZJqUn1KUNjetwjK3ADd1erfTKe9TsK9ASTOTVJJQIrdgNzUZ3UQhF4SU356/K1+JXyXKmmtVQO8ANR3fVC5ux48R2QptOvzcYbWa1EoeoXwR1Nh1CO5gdgQ0IJRo1Kr24f6SuCQKADQk98SN0GrvxXIp4lJO1dUtYac+S9ip9a37690AH+3MPWJ2JFN4Rnuq1b9alf/b4mI55bqlaaWKeTNGIUND1OzuZio8WKikL5Pk6u+qwH6BMuyG4j6OU5NLm8ivu5/i/hunrJVZCr0wVJKU/H2yQoaHUKAhiRKN2sVwbtSqDhH+ZkcAANSyhGJPLQq9zewYDcK5Rzfom+0r9bZbS7X1a2Z2nHpncbHIPci9/I9bwKmP52SuyJRHqIeiboqSV1MvhQwNUdDgIKUvSZck2fJtsuXZFHJBiLyiveR/jr9KEkskSQUHC1QUU6TQi0Lr5XHBsfm5+ynaL9rsGGhAKNGoVZ2iKNEA0BA9dqS3SoO5vmptGXFwtWbvWq8pXu3V1Dvc7Dj1piSlRPse3qf9E/cr7oM4laaWnnLdwkOF8uvmV2GZf3d/FcUUybAacvV3lVuQm/J358tealfBgQJ5NfeS3WpX4vRERd8Wfdoj3Wg82gW1k8XCawG1hxKNWtUmzFf+XpwlAAANTYndRe+7czS6NrkYdo3d+6sW7N+hJ/w6K8Qz2OxIdcqnrY+a3dlMrR5rpejx0SrLKdORKUdkzbdWur41xyrXANcKy9wC3CSbZM23ymKxqPm9zZU2L00Hnzoo7xbeCh4crPSF6fLr4ieLh0VHphzRgScOKGMZpyI0Zh1DOpodAQ0MJRq1ymKxqEezQLNjAADqwDvH2igz8nyzYzQ47rZS3bTzZy0+ckgPBHSTv7vfme/khPx7+CuwX6C8mnvJr6ufWj3aSpKUvSb7lPf5+9FDQxUvo+nbwVdtn22rjq93VNNbm6o0vVTZa7MVfmW44j+JV/DwYLV5qo1S56aqOK64th8SnESX0C5mR0ADQ4lGrevRLMjsCACAOvJE/vUyLK5nXhFnzae0QHdtX6TF8UkaH9RdXq6eZkeqUy6eLvJs7qnSlMqHdLsFusmaU/EotS3XJrlKbn4nj3ozDEMJ0xIUeX2kZEjFscUK7BsotwA3+Xb0VcG+gjp5HHB83cO6mx0BDQwlGrWuJyUaABqspekhOtTsCrNjNGiBhVl6dOtCLUzL1zXB3eVmaZinSdnL7CpJLJFbUOWPz6edj/J3V7xsZv6ufHm38pbF7eTzW7NWZcnNz00B5wRI9uPLDJtR/rdhN066Dxo+X3dftQ1qa3YMNDCUaNS6Xs2DzI4AAKhD9yaOluHJRJJ1LTwnSc9sWai5uYZGB3eTRc49MVLSt0kq2Feg0rRSFR4uVNx/42QvsitoUJAkKfmHZMV/El++fsjwEJWmlyppZpKKE4uVtSpLWauyFHZx2EnbtuZalTYvTVE3R0mSXH1d5dnUUxlLM1R4qFAFewvk096nXh4nHEuX0C5ysVB5ULt4RaHWRQZ6KSKgYQ9BA4DG7GCBt1aG32p2jEajRfpRTd2ySD8U+WpIUGez41SbNdOquI/idPCJgzr23jFZ3Cxq83QbeYQdv46zNduq0oz/H9rt0cRDrR5tpYJ9BTr8zGGlzktV1E1RCux38twrSV8nKWx0mNyD3cuXRd8RrZw/chT7VqzCRofJpw0lujFiKDfqgsUwDMa2oNbd+eUm/bInxewYAIA64utm0/aQp+SWG2d2lEZnS4veeickSFtyDpkdBXB4bw97WyNajjA7BhoYjkSjTjCkGwAatgKrq6b7jjc7RqPU+9gWTd+2XB+4RKuTf0uz4wAOrXsTjkSj9lGiUSe4zBUANHwvHO2k/PA+ZsdotAYfXqfvd6zRax5t1NK3qdlxAIcT7hOucJ9ws2OgAaJEo070aBYki3PPfwIAqILJpTfLcPIJr5yZRYYu3r9Sc/Zs0rM+HRXudfKkW0Bj1SOsh9kR0EBRolEnAr3d1TrU1+wYAIA69mNyhOKbXWJ2jEbPzW7V1bt/0aKDe/SYf1cFeTAiDGAoN+oKJRp1pifnRQNAo/BA6lgZbt5mx4AkT2uxxu1YrMWxsbo7sLt83JiRGo0XM3OjrlCiUWc4LxoAGodtuX7aGHWj2THwF37Fubp/20ItTkzXzcE95OHiYXYkoF65WFzUNbSr2THQQFGiUWf6tgwxOwIAoJ7cEztENt8Is2Pgb0IK0vX4lgVakFmiy4O7y9XianYkoF60DWorH3dGYqBuUKJRZ7o2DVCAl5vZMQAA9SCj1F2zgsaZHQOnEJUVpxe2LNTsfDeNDOboHBq+Xk16mR0BDRglGnXGxcWi/q05Gg0AjcWTR7qriOGTDq1N6kG9uWWxvi0N1HlBHc2OA9SZ/lH9zY6ABowSjTp1bptQsyMAAOqJzXDR67rV7Biogq4JO/XJ1l/0mSLUI6CN2XGAWtcvop/ZEdCAUaJRp85rS4kGgMbkfwnNldr0ArNjoIoGHN2or7ev1NtuLdXOr7nZcYBa0S6onUK9+QyKukOJRp3qHBmgQG93s2MAAOrRo9nXyHDhvd+ZjDi4WrN2rdOLXu0U7cMEcXBu/SMZyo26RYlGnXJxsWgA50UDQKOyJjNQu6OvNTsGzpKLYdc/9i7X/H3b9aRfZ4V6BpsdCagWSjTqGiUadY4h3QDQ+Pwz7kLZvShhzsjdVqobd/6sRUcO6cGAbvJ39zM7ElBlLhYX9Y3sa3YMNHCUaNQ5JhcDgMYnvthTP4fdZnYM1IBPaYHu3L5Ii+MSdXtQd3m5epodCTijjsEdFegZaHYMNHCUaNS5TpH+CvH1MDsGAKCePXykr0qD2podAzUUWJStR7Yu1KLUPF0b3F1uLm5mRwJOqV8ks3Kj7lGiUecsFs6LBoDGqMTuoo89x5kdA7WkSW6ynt6yUPOy7bo0uJtcLHyMhOPhfGjUB979UC8Y0g0AjdMbsW2VHTnQ7BioRc0zYvTKlkX6ocBLQ4M6mx0HKOdqcVWfiD5mx0AjQIlGvWByMQBovJ4quF4GRy0bnA4p+/TfrT/rK2uo+gS2NzsOoC6hXeTnwUR4qHv8Hw31okOEv8L8OC8aABqjRWlhOtLsCrNjoI70ituqL7b9qg9dotXZv6XZcdCIcT406gslGvVmSPsmZkcAAJjknsRLZHj4mh0Ddej8w+v03Y41es2jtVr5NjU7Dhqh85qeZ3YENBKUaNSbCzqHmx0BAGCSAwXeWh1xq9kxUMcsMnTx/t/0055NmuzTQRHeYWZHQiPh6+7L+dCoN5Ro1JshHZrI3dVidgwAgEnuO3qerP7RZsdAPXCzW3XV7mVaeGCPJvp3VbAH1+1F3RrYdKDcXdzNjoFGghKNehPg5a6+LbnUFQA0VnlWN83wu93sGKhHntZi3bZjsRbHxOiewO7ydfMxOxIaqCHNhpgdAY0IJRr1agRDugGgUZt8tLPym5xjdgzUM9+SPN27baEWJabr5qAe8nBhslHUHheLCyUa9YoSjXp1QSdKNAA0dlOsN5sdASYJKUjX41sXaEFmia4I7i5Xi6vZkdAAdAvrphAvRjui/lCiUa/aNPFT6zBmZwWAxuzbpCjFN7vE7BgwUVRWnJ7fslCz8101MrirLGLOFFTf0GZDzY6ARoYSjXrH0WgAwINpY2W4eZkdAyZrk3pIb25ZrJkl/hoY1NHsOHBSlGjUN0o06t0ISjQANHpbcvy1JeoGs2PAQXRN3KWPt/6iz40I9Qhoa3YcOJFI30h1DOELGNQvSjTqXb/WIfL3dDM7BgDAZPccGya7TxOzY8CB9IvZqK+3r9C7bi3Uzq+52XHgBDgKDTNQolHv3F1dNLhDmNkxAAAmSy1x15zgcWbHgAMafnCNZu1ap5e82inaJ8LsOHBgzMoNM1CiYYoLOvE/RACA9K8jPVUc0tnsGHBALoZdl+1drvl7t+kp384K82T2ZVTk7eatAVEDzI6BRogSDVMM79hELkzECQCNns1w0Vsut5kdAw7M3V6mG3b9rEVHDuihgG7yd/czOxIcxIDIAfJ09TQ7BhohSjRMEernqT4tg82OAQBwAB/Ht1B61DCzY8DBeZcW6o7ti7Q4LlETgrrL25XZ3Ru7Yc2HmR0BjRQlGqa5tHuU2REAAA5iYs41MlyYdBJnFliUrYe3LtSilBxdF9xdbrxuGiU3i5tGtBhhdgw0UpRomOaSHlEM6QYASJJWZgZrb/Q1ZseAEwnLS9F/tizUvGybxgR3k4uFj7WNSf+o/gryCjI7Bhop3m1gmnB/Lw1oHWp2DACAg/hn/EjZ+VCMs9Q8I1Yvb1mkHwq8NCyoi9lxUE8uanmR2RHQiFGiYarLejY1OwIAwEEcK/LSsrBbzY4BJ9UhZZ/e27pEX1lD1DewvdlxUIcYyg2zUaJhqtHdIuXGmG4AwJ8eOtpPZYFtzI4BJ9YrbpumbftVH1maqrN/K7PjoA4wlBtmo0TDVMG+HhrULszsGAAAB1Fkc9WnXlzyCjU36Mh6fbdjtV73aK1WvtFmx0EtYig3zEaJhunG9GCWbgDA/5sa2145EeeaHQP/1959x0ddH34cf99ILsll7wlhJCRA2BtBloIgMhyACMQt2p/QqljrQq0tbrTDapVq66j+fhasgCjWhVpRQWQjIyGMsFeADHJ3vz+i0SjjgCSfG6/n45FHMbkkLygk9873vt8LABZ5NHjdR5qz6gvdF56r1PAk00k4S3arXYOaDjKdgSDHiIZxg9umKtTOX0UAwA/uKh8nD1dbRj2xeVwavfo9zVu3UrdFtlZcaIzpJJyh3um9FePg/z+YxXcnGBcdFqK+OfxkGADwg7d2Jak48yLTGQgwoa5KTVyxQG8XF+vGmAI57RGmk3CaLmh2gekEgBEN3zC8PQ/pBgDUdWPphfKEOE1nIAA5K8s0edk8vb19tybEFijUGmo6CV4It4erf1Z/0xkAIxq+YVB+isJC+OsIAPjBmsMR+iz1CtMZCGBxR/Zq2tfzNG9vhUbHFchmsZlOwkn0y+yniBD/efRAcXGxLBaLli1bZjrFJ0yfPl0dOnQwnVEvWC3wCU6HXQPykk1nAAB8zI3FveWKTDedgQCXemCr7ls6T3PKrDo/ro0s4uk3fZEvPZTbYrGc9KWwsNB0ovr166epU6eazghIjGj4jIvacycJAFDXwWN2vRp9pekMBIns3Rv12NK39c/KSPWOzTOdgx+JdcTqnIxzTGfUKi0trX2ZOXOmoqOj67zuySefPKOP63K55Ha767kW9Y0RDZ8xIC9F8U7OSQIA1HV3UWsdTWxvOgNBpPX2VfrL1+9qljtZ7aNbmM6BpAubX6gQW4jpjFqpqam1LzExMbJYLD973fc2bdqk/v37KyIiQu3bt9d///vf2re98MILio2N1dy5c9W6dWs5HA5t3rxZVVVVmjZtmjIyMuR0OtW9e3d9+OGHte+3d+9ejRs3TpmZmYqIiFBBQYFeffXV2rcXFhbqo48+0pNPPll7dLy4uFiStHr1ag0dOlSRkZFKSUnRhAkTtGfPntr37devn26++WZNmzZN8fHxSk1N1fTp0+v8/g8ePKjrrrtOycnJio6O1oABA/TNN9/Uuc2MGTOUkpKiqKgoXX311aqoqKiHP3nfwIiGzwi1WzWqY4bpDACAj/F4LPq9m3Oj0fi6bv5KL33zgf5ga6KcyCamc4LaqJxRphPO2J133qlbb71Vy5YtU25ursaNG6fq6uratx89elS///3v9dxzz2nVqlVKTk7WlVdeqU8//VT//Oc/tXz5cl166aUaMmSI1q9fL0mqqKhQ586dNXfuXK1cuVLXXXedJkyYoMWLF0uSnnzySfXs2VPXXntt7dHxrKwslZaW6txzz1WHDh301VdfacGCBdq5c6cuu+yyOs0vvviinE6nFi9erIcfflj333+/Fi5cKEnyeDwaNmyYduzYofnz52vJkiXq1KmTBg4cqH379kmSXn/9dd1777168MEH9dVXXyktLU1//vOfG+OPu1FYPB6Px3QE8L1vd5bp/Cc+Np0BAPBBn7X4u9K3LTCdgSDltlg1v1U//cl2WFuP7jCdE1TaJrTVqxe+euobGvLCCy9o6tSpOnDgQJ3XFxcXq1mzZnruued09dVXS6o5CtymTRutWbNGeXl5euGFF3TllVdq2bJlat++5hE3GzduVE5OjrZu3ar09B9Odxw0aJC6deum3/3ud8ftGDZsmPLz8/Xoo49Kqjmi3KFDB82cObP2Nvfcc48WL16sd955p/Z1W7duVVZWltatW6fc3Fz169dPLpdLixYtqr1Nt27dNGDAAM2YMUPvv/++Ro0apV27dsnhcNTepmXLlpo2bZquu+469erVS+3bt9fTTz9d+/YePXqooqIiIC60xpFo+JTclCh1yIo1nQEA8EFT946Ux+Y49Q2BBmD1uHXh2vf17zVf6y5nnpLC4k0nBQ1/PgotSe3atav9dVpazdO67tq1q/Z1oaGhdW6zdOlSeTwe5ebmKjIysvblo48+0saNGyXVnDv94IMPql27dkpISFBkZKTeffddlZSUnLRlyZIl+uCDD+p83Ly8mvP/v//YP23+vvv75iVLlujw4cO1n/f7l6KiotqPsWbNGvXs2bPOx/jpf/szu+kA4KfGds3Ssi0HTGcAAHzMFweitSx3rDqWvGg6BUEsxH1MY1a+q4tCI/RK3rmaVVmiQ1VlprMCVpgtzKeuyn0mQkJ+OJfbYqm58vuPLx4WHh5e+/rv32az2bRkyRLZbHWfdi0yMlKS9Nhjj+mJJ57QzJkzVVBQIKfTqalTp6qqquqkLW63W8OHD9dDDz30s7d9P/B/2vx99/fNbrdbaWlpdc7R/l5sbOxJP3+gYETD51zYPl33z12to1Uu0ykAAB8zeXN/fRYxT9byPae+MdCAwquO6urlb+vS8Bj9Le8cvVy2XuWuwLlwkq84r+l5igqNMp3RqDp27CiXy6Vdu3apT58+x73NokWLNGLECF1xRc31Itxut9avX6/8/Pza24SGhsrlqnt/ulOnTnrjjTeUnZ0tu/3MpmCnTp20Y8cO2e12ZWdnH/c2+fn5+vzzzzVx4sTa133++edn9Pl8EQ/nhs+JdNg1rCDt1DcEAASdHZWheiu+0HQGUCu6/KCmfD1P83ce1Ni4drJbOUZVn/z9odxnIjc3V+PHj9fEiRP1r3/9S0VFRfryyy/10EMPaf78+ZJqzj9euHChPvvsM61Zs0bXX3+9duyoe65+dna2Fi9erOLiYu3Zs0dut1s33XST9u3bp3HjxumLL77Qpk2b9O677+qqq6762eA+kUGDBqlnz54aOXKk3nnnHRUXF+uzzz7TXXfdpa+++kqSNGXKFM2aNUuzZs3St99+q3vvvVerVq2q3z8ogxjR8EljumaZTgAA+KhbizqqMq6V6QygjsSynbpz6Vy9tb9aw+MKZLVwN/tsNYlqoq6pXU1nGPG3v/1NEydO1C233KJWrVrpoosu0uLFi5WVVXMf+e6771anTp00ePBg9evXT6mpqRo5cmSdj3HrrbfKZrOpdevWSkpKUklJidLT0/Xpp5/K5XJp8ODBatu2raZMmaKYmBhZrd79nbVYLJo/f7769u2rq666Srm5uRo7dqyKi4uVkpIiSRozZozuuece3X777ercubM2b96syZMn1+ufkUlcnRs+a+BjH2rj7iOmMwAAPuimrGLdtvs3pjOAE1qf0kp/yGimD/avNp3it27ueLOubXet6QzgZ/gRGXwWR6MBACfypy3Z2pt2rukM4IRydq7TU0sX6KXqOHWLyTWd43dsFptGtBxhOgM4LkY0fNboTpkKsVlOfUMAQFC67dBl8nD+KXxc+y3f6Pll7+kZpalNdDPTOX6jV3ovJUckm84AjosRDZ+VGOnQgDy+eAIAju/9vXH6NuNi0xmAV3oVLdar33ykx0Ky1cyZYTrH543OGW06ATghRjR82tiuTUwnAAB82ORtg+VxRJvOALxikXT+tx9r9qovdH94rlLDk0wn+aSUiBT1y+pnOgM4IUY0fNq5uUlqmhBhOgMA4KM2HQ3T+8mTTGcAp8XmcWnU6vc0b91KTYtsrXhHrOkknzKm1RieKgw+jRENn2a1WjSpZ7bpDACAD7t5U3cdi25qOgM4baGuSk1YsUDzi4p0Y0yBIkOcppOMc9gcuiT3EtMZwEkxouHzLu2SqUgHP40EABzfEZdVf4u40nQGcMaclWWavGye3t66U5Ni28lhc5hOMmZos6GKC4sznQGcFCMaPi8qLESXdsk0nQEA8GG/K87VoZRupjOAsxJ7dJ9u/Xqu5u45qovjCmS3BN9BhPH5400nAKfEiIZfKOyVLSvPdgUAOIl7Ki6XR3yzgP9LPbBN05fO0+wyi4bEtZElSP5ed07prFbxrUxnAKfEiIZfaJrg1IC8FNMZAAAfNmdnskoyh5vOAOpN9u6NemTp23qtwqnesXmmcxrcFflXmE4AvMKIht+4qne26QQAgI/7xc7h8oTwrA4ILPmlq/WXr9/V39xJ6hDdwnROg0h3pqt/Vn/TGYBXGNHwG71aJiovNcp0BgDAh60oc2pxauCeU1lW6dHUBRVqOrNM4Q8eUq/nj+jLba4T3r60zK3L3ziqVn88LOt9hzR1QcXPbrNwY7Vy/3BYMTMOadKcclW5PLVvO1jhUe4fDqvkoLtBfj84PV02L9E/vvlAf7RlKTeyiemcejUmb4xsVpvpDMArjGj4lSs5Gg0AOIXJxX3kcqaazmgQ17xVroWbqvWPUeFaMTlS57ewadA/jmjboeOP3EqXlBRh1Z19QtU+9ed3+9wej8b/q1w3dAnRZ1c59cU2l/665Fjt229/r0I3dAlRkxjuMvqSczd8qv9b8almOFooK8L//66H28N1cc7FpjMAr/EVEX5lRIcMxTtDTWcAAHzY/mN2vR4TeE95VX7MozdWV+vhQQ71bWpXy3irpvcLU7NYq57+quq475Mda9WTF4RpYvtQxTh+fnGqPUc92n3Uoxu7hqpNsk0X5dq1enfNke1PS6r11XaXpnTn+64vssijYWs/0JtrvtbdzjwlhcWbTjpjw5oPU4wjxnQG4DVGNPxKWIhNl3cLrIcvAQDq32+K2upoYoHpjHpV7ZZcHinMXncMh4dY9EnJiR/SfTJJERalRVr07sZqlR/zaFGJS+1SbKpyeTR5XoX+cmG4bDw9hk8LcR/TZSvf1fwN6/TLqDaKDvW/U9/G5wXuKRgITIxo+J0JPZsqxMY3dADAiXk8Fj3knmg6o15FOSzqmWnTAx9XanuZWy63Ry8tr9LirS6VHvac+gMch8Vi0euXhuuBjyvV+s+H1THVqqs6hmjGJ1Ua2MyucLvUe9YRtfrjYf3xi+Mf7YZvCDtWrquWv60Fm7fq2pgChdvDTSd5pWdaT7WMa2k6AzgtjGj4nZToMA0rSDOdAQDwcS9uz9COjPNMZ9Srf4wKl0dSxuOH5fhtmZ5aXKXLC0J0Nj9bPqeJXV9eG6miKVH607BwFR1w6x/Lj+mBAQ5NmF2u6zuHaNGVEbr/o0ot33lmR7zReKIqDurmZfM0v3S/xsW2U4g1xHTSSV3b7lrTCcBpY0TDL11/bmA+vQMAoH79ct9oeWyBc05vi3irPip06vAdUdryy0h9cW2kjrk9ahZXP3fpPB6PrnurQo+d75DbI329w61LWoco2WnVudk2fVTMiPYXiYd36Tdfz9Vb+4/porgCWS2+d7e/Q1IHdU3tajoDOG2+968J8EJ+WrQG5CWbzgAA+Lj/7o/RivQxpjPqnTPUorQoq/aXe/TOhmqNaGWvl4/7/NfHlBBh0UWtQuT67oLfx1w//K/Lc2YPG4c5GftK9ODSeXrjiEMD4lqbzqmDo9DwV4xo+K2b+nP+DADg1K7fMlDu8ATTGfXinQ3VWrChWkX73Vq4sVr9XzyiVolWXdmh5iG7d7xXoYmzy+u8z7IdLi3b4dLhKo92H3Vr2Q5X7RW4f2zXEbd++3GlnhoSJkmKC7coP9GqmZ9X6b9bqvWfomr1yqqfsY7G13LnOj25dIFePhan7jG5pnPUKq6V+mb2NZ0BnBG+EsJvdW4ap+7N4rW4aJ/pFACADyutCNX8zEm6cOvjplPO2sFKj+74T4W2HvIoPtyii/PtenBAWO0FN0sPe1RysO5zRnd85kjtr5eUuvXKimo1jbGoeGrdqzhPWVChW3s5lBH9wzGWF0aGa9Kccj31RZVu6+VQtwxbA/7u0Bjabf1Gz22V/tusm56MDteqQ0VGOq5pd42RzwvUB4vHw+Ny4L8+/na3Js76wnQGAMDHOaxurUi5T6H715tOAXzKe7l99AeHS5sOb220z5kdna03R77pk+dpA97gby78Wt/cJLXLjDGdAQDwcZVuq/4YMsl0BuBzBn27SP9auVj3h+coPbxxrjdzVdurGNDwa/zthd/7BedGAwC88FRJc+1LPcd0BuBzbB6XRq3+j+auW65fR7ZWvCOuwT5XmjNNF7a4sME+PtAYGNHwe+e1TlF+WrTpDACAH/j14bHyWDivFzieEFeVxq9YoLeLNuqm6LaKDHHW++cobFPo889dDZwKIxp+z2Kx6H8GcDQaAHBq7+6J14bM0aYzAJ8WUXlYN3wzX29v3anC2HZy2Bz18nETwhJ0ce7F9fKxAJMY0QgIF7RNVauUqFPfEAAQ9G7cPkQeB98zgFOJPbpPt3w9V/N2H9ElcQWyW87uiX0mtJ5Qb4McMIkRjYBgsVj0C45GAwC8sP5IuD5Mnmg6A/AbKQe3696l8zTnkHRBXFtZZDntjxEdGq2xeWMboA5ofIxoBIxhBWlqmRxpOgMA4Ad+UdRD1dFZpjMAv9J0zyY9vHS+Xq9wqk9s/mm975Vtr5SzAc6xBkxgRCNgWK2cGw0A8M6RaptedF5pOgPwS3mlq/Xnr9/RC65EdYo59X2vhLAEXZ53eSOUAY2DEY2AclH7dLXmSt0AAC88UJSnsuQupjMAv9W5ZKleXPa+/mTNVKuopie83TUF1ygiJKIRy4CGxYhGQLFYLJo2pJXpDACAn7ivarw8Z3B+J4Af9N34mf53+Sd6yNFCTSLS6rwt1Zmqy1pdZqgMaBiMaAScfq2S1bN5gukMAIAf+L8dKdqaOcx0BuD3LPJo6NoP9OaaJbo7opWSw2rui13f7nqF2kIN1wH1y+LxeDymI4D6tmzLAY3806emMwAAfqBD9GHNdk+RpbrcdAoQMCpCwjWn02hdMvgp2a1n99RYgK/hSDQCUoesWF3QNtV0BgDADyw7FKkv07joEVCfwo6Va2zWQAY0AhIjGgHrtsGtZLdynhsA4NQmb+4rlzPFdAYQONLaS21Gm64AGgQjGgGreVKkLu3Cc4ACAE5tb1WI3ogtNJ0BBI5B0yULBzMQmBjRCGi/HJSj8BCb6QwAgB+4Y1OByhPams4A/F+zc6UWA0xXAA2GEY2Alhwdpit7Z5vOAAD4AZfHqkc1wXQG4OcsNUehgQDGiEbAu6FfC8VGhJjOAAD4gee3ZWlX+kDTGYD/anuxlNHJdAXQoBjRCHjRYSG6qV9L0xkAAD/xqwOXyGPlh6/AaQuJkM67z3QF0OAY0QgKE3s1VUZsuOkMAIAf+GRfjFZlXGY6A/A/vW6WYjJNVwANjhGNoOCw2zRtSCvTGQAAP3HDlkFyh8WZzgD8R3SG1HuK6QqgUTCiETRGdMhQ92bxpjMAAH5ga4VD7yROMp0B+I9B90mhEaYrgEbBiEZQmX5RG9msPGchAODUpm7qoqrYFqYzAN+X2U1qd6npCqDRMKIRVPLTonVF9yamMwAAfqDSbdVfQgtNZwA+ziJdMMN0BNCoGNEIOr86r5XinaGmMwAAfuDxkhY6kNrLdAbgu9qPlTI6m64AGhUjGkEnJiJEtw3mImMAAO/ccWSsPBbuMgE/E+KUBt5rugJodHxHQFAa0yVLBRkxpjMAAH7g7d2J2pQ5ynQG4Hv6/FKKTjNdATQ6RjSCktVq0X0j2sjCNcYAAF6YvH2oPKFO0xmA74htIvX8H9MVgBGMaAStTk3idHGnTNMZAAA/8O2RcC1KmWg6A/AdFzwihYSZrgCMYEQjqN0+JE9RDrvpDACAH7ipqKeqo/jhK6D84VKrIaYrAGMY0QhqSVEOTRmUYzoDAOAHyqrteinyStMZgFmhUdIFD5uuAIxiRCPoFfbKVk5ypOkMAIAfmF6Ur8NJHU1nAOYMuEuKTjddARjFiEbQs9us+u3ItlxkDADglQeqJ5hOAMxI7yh1u850BWAcIxqQ1L15gsZ1a2I6AwDgB14rTdXWzKGmM4DGZbFJF86UrMwHgH8FwHfuuCBPqdFcZRIAcGo37x4hj53vGQgi3a+X0juYrgB8AiMa+E5UWIgeHNXWdAYAwA8sPRilpWnjTGcAjSM6Q+p/p+kKwGcwooEfGZifouHtuVgGAODUJpf0kzsiyXQG0PAueEhycBFW4HuMaOAnpg9vrbiIENMZAAAft6syRLPjCk1nAA2r1dCa54UGUIsRDfxEQqRD9wxvbToDAOAHpm1qr4r4fNMZQMNwREtDHzFdAfgcRjRwHKM6ZqpfKx6iBwA4OZfHqsetk0xnAA1j8INSTKbpCsDnMKKBE3hwVIGcoTbTGQAAH/fs1iband7fdAZQv3LOlzpNNF0B+CRGNHACGbHhmjYkz3QGAMAP3HrwEnmsdtMZQP0Ii5GGP2W6AvBZjGjgJCb2bKouTeNMZwAAfNxHe+O0JuNS0xlA/bjgYSk6zXQF4LMY0cBJWCwWzbi4nULt/FMBAJzcDVvPkzss1nQGcHZaDZPajzVdAfg0lgFwCi2TI3Xb+a1MZwAAfFxJeZgWJnIOKfxYeLw0fKbpCsDnMaIBL1zTp5l6t0wwnQEA8HFTi7rqWExz0xnAmRn6iBSZbLoC8HmMaMALFotFj13aQbERIaZTAAA+rNxl07NhPOUV/FDrEVLBJaYrAL/AiAa8lBoTpt+NKjCdAQDwcY9sztHBlB6mMwDvRSRKwx43XQH4DUY0cBqGFqTp4k6ZpjMAAD7urvJx8li4mwU/MXym5Ew0XQH4Db66A6fpvhFt1CQ+wnQGAMCHvbUrScWZF5nOAE6tc6GUP9x0BeBXGNHAaYp02PXEmPayWS2mUwAAPuzG0gvlCXWazgBOLClPGjLDdAXgdxjRwBno3DReN/VvaToDAODD1hyO0GcpV5jOAI7PHiZdMksKCTddAvgdRjRwhm4e0FIdsmJNZwAAfNiNxb1VHZVhOgP4ufN/K6W0MV0B+CVGNHCG7DarZo7pIGeozXQKAMBHHTxm16uRPOUVfEyrYVK3a01XAH6LEQ2chexEp+4Z3tp0BgDAh91T3EZHE9ubzgBqRGdII/5ougLwa4xo4CyN6dpEQwtSTWcAAHyUx2PRg64JpjMAyWKVRj8rRcSbLgH8GiMaqAcPXdxOzRK5AisA4PheLk3X9owhpjMQ7PrcImWfY7oC8HuMaKAeRIWF6M/jOykshH9SAIDjm7p3pDw2h+kMBKus7lK/O0xXAAGBe/xAPclPi9ZvRxaYzgAA+KgvDkRrWcZY0xkIRmGx0sXPSVYuhgrUB0Y0UI8u6Zypcd2yTGcAAHzU5M395Y5INJ2BYGKxShc/L8U2MV0CBAxGNFDPpl/URm0zok1nAAB80I7KUP07rtB0BoLJubdLOYNMVwABhREN1DOH3aanx3dWdJjddAoAwAfdVtRRlfGtTGcgGOQMrhnRAOoVIxpoAFnxEXr8sg6yWEyXAAB8zTG3RTNtk0xnINDFZUujnxF3RoD6x4gGGsig1im64dwWpjMAAD7o6S3Z2pt2rukMBCp7uDTmJSk8znQJEJAY0UADuvX8VurRPN50BgDAB9126DJ5rJz6gwYwfKaUyjOGAA2FEQ00IJvVoj+M66SUaJ4XFABQ1/t74/RtxsWmMxBoulwtteep1ICGxIgGGlhSlEN/vLyTQmyckwQAqGvytsHyOGJMZyBQZHaVhswwXQEEPEY00Ai6Zsfr/hFtTWcAAHzMpqNh+k/SBNMZCATOJOmyv0v2UNMlQMBjRAONZFy3JrqqdzPTGQAAHzOlqIeOxWSbzoA/s4VKl74oRaebLgGCAiMaaER3DstX/1ZJpjMAAD7kiMuqWWGFpjPgzy6cKWX3Nl0BBA1GNNCIbFaLnhrXUbkpkaZTAAA+5Pebc3UopZvpDPij3lOkjuNNVwBBhRENNLKosBA9P6mr4p2cswQA+ME9FZfLIy5CidOQd6E0cLrpCiDoMKIBA7LiI/TMhM4KtfFPEABQY87OZJVkDjedAX+RWiCNflaycl8CaGz8qwMM6Zodr9+NLjCdAQDwIb/YOVyekAjTGfB1kanSuNekUKfpEiAoMaIBgy7pnKnr+zY3nQEA8BErypz6PJXzW3ES9nBp3KtSTIbpEiBoMaIBw24fkqdB+SmmMwAAPuLG4j5yRaaZzoBPskijnpYyOpkOAYIaIxowzGq16MmxHZSXGmU6BQDgA/Yfs+u16ELTGfBF/e+U2owyXQEEPUY04AOcDrueL+yq5CiH6RQAgA+4s6itjiZy3Qz8SLsx0rm3ma4AIEY04DMyYsP14lXdFBVmN50CADDM47HoIfdE0xnwFc37SyP+ZLoCwHcY0YAPyU+L1nMTu8hh558mAAS7F7dnaEfGeaYzYFp6R2nMS5ItxHQJgO9wTx3wMd2bJ+jJsR1ltZguAQCY9st9o+WxhZrOgCnxLaTx/yc5Ik2XAPgRRjTgg4a0TdVvR3IuHAAEu//uj9Hy9DGmM2BCZIo04V+SM9F0CYCfYEQDPury7k30y0G5pjMAAIbdsGWg3OEJpjPQmBzRNUeg47JNlwA4DkY04MOmDMrRxJ5NTWcAAAwqrQjVvPhJpjPQWGwOaezLUlo70yUAToARDfi46cPbaFhBmukMAIBBtxZ1UlVcjukMNDSLVRr9rNSsr+kSACfBiAZ8nNVq0RNjOqhXCx7KBwDBqtJt1R9DOBod8C54WGoz0nQFgFNgRAN+INRu1bMTu6hNerTpFACAIU+VNNe+1HNMZ6Ch9L1N6nat6QoAXmBEA34i0mHXC1d2U9OECNMpAABDbj88Vh6LzXQG6lv3G6QBd5muAOAlRjTgR5KiHHrp6u5KjwkznQIAMGDhnnitzxxtOgP1qdMkacgM0xUATgMjGvAzWfERevW6HkqJdphOAQAYcNP2IfI4okxnoD60GyNdOFOyWEyXADgNjGjADzVNcOqVa3soMZIhDQDBZv2RcH2YPMF0Bs5W/kXSyKclK3fHAX/Dv1rAT7VIitQr13ZXvDPUdAoAoJH9oqinqqObmM7AmcodIl0yS7JyfjvgjxjRgB/LTYnSS1d3V2xEiOkUAEAjOlJt04sRhaYzcCZanidd9nfJxvduwF8xogE/1zo9Wi9d3V0x4XwzBoBg8kBxnsqSu5jOwOloMUAa85Jk53QswJ8xooEA0DYjhiENAEFoeuV4ecRFqfxCs3Olsa9IITzDBuDvGNFAgCjIZEgDQLB5Y2eKtmQOM52BU8nuI437pxQSbroEQD1gRAMB5PshHR1mN50CAGgkN++6SB4748xntRgojf9fKTTCdAmAesKIBgJMQWaMXr6mB0MaAILEskOR+jLtctMZOJ5WwzgCDQQgRjQQgAoyY/TKtT2UwNNfAUBQmLy5r1zOFNMZ+LG2F9dchdvO92Ig0DCigQDVNiNGr9/QU+kxXMAEAALd3qoQvRFTaDoD3+t4hTT6OcnGo8KAQGTxeDwe0xEAGs62A+Wa8NxibdpzxHQKAKAB2SxurUyfofC9K02nBLdu10kXPCxZuGo6EKg4Eg0EuIzYcL1+Q0+1Tos2nQIAaEAuj1WPaILpjODWe4o09BEGNBDgGNFAEEiMdOif1/dQ1+w40ykAgAY0a1uWdqYPNJ0RnPrdIZ13v+kKAI2AEQ0EieiwEP39qu46NzfJdAoAoAHdcuASeawhpjOCy3kPSP1+bboCQCNhRANBJDzUpucmddGwdmmmUwAADeSTfTFamXGZ6YzgYLFJF86Uet9sugRAI+LCYkAQcrs9+s3sFfrnl1tMpwAAGkBmWKU+DvuVrBX7TacELnu4dMksKW+o6RIAjYwj0UAQslotmnFxO13ft7npFABAA9ha4dA7iZNMZwSu8Hhp0lsMaCBIcSQaCHJ/+mCDHnlnnekMAEA9c1jdWpF8r0IPbDSdElhim0hXzJYSW5ouAWAIR6KBIHdT/5Z6Ykx7hdr4cgAAgaTSbdVfQgtNZwSW1HbS1e8F/YD+8MMPZbFYdODAAdMpDaqwsFAjR440nQEfxL1mABrVMVP/uLqbYiO4misABJLHS1roQGov0xmBoXl/6cr5UlSK6ZLTUlhYKIvF8rOXIUOGmE4D/BYjGoAkqXvzBP1rci81TYgwnQIAqEd3HBkrj4W7fGel3Rhp/P9KjijTJWdkyJAhKi0trfPy6quvms5qFFVVVaYTEID4igqgVvOkSM2+sbc6N40znQIAqCdv707UxsxRpjP8V+8p0qhnJJv/PlrL4XAoNTW1zktcXM33eovFoueee06jRo1SRESEcnJy9O9//7vO+8+fP1+5ubkKDw9X//79VVxcXOfte/fu1bhx45SZmamIiAgVFBT8bKT369dPN998s6ZNm6b4+HilpqZq+vTpdW6zdu1anXPOOQoLC1Pr1q313nvvyWKxaM6cObW32bZtm8aMGaO4uDglJCRoxIgRdXq+fwj273//e6Wnpys3N9er93O5XPrVr36l2NhYJSQkaNq0aeLSUTgRRjSAOuKdoXrl2u66kOeSBoCAceP2ofKERprO8C8Wm3TBI9J590sWi+maBnXffffpsssu0/LlyzV06FCNHz9e+/btkyRt2bJFo0eP1tChQ7Vs2TJdc801+vWvf13n/SsqKtS5c2fNnTtXK1eu1HXXXacJEyZo8eLFdW734osvyul0avHixXr44Yd1//33a+HChZIkt9utkSNHKiIiQosXL9azzz6rO++8s877Hz16VP3791dkZKQ+/vhjffLJJ4qMjNSQIUPqHHH+z3/+ozVr1mjhwoWaO3euV+/32GOPadasWXr++ef1ySefaN++fZo9e3a9/1kjMHB1bgDH5fF49Mg76/TnD7mqKwAEgr/nLFLfLU+bzvAPYTHSJX+TWg40XXLWCgsL9dJLLyksLKzO62+//Xbdfffdslgsuuuuu/TAAw9Iko4cOaKoqCjNnz9fQ4YM0W9+8xvNmTNHq1atkuW7Hyb8+te/1kMPPaT9+/crNjb2uJ932LBhys/P16OPPiqp5ki0y+XSokWLam/TrVs3DRgwQDNmzNCCBQs0fPhwbdmyRampqZKk9957T+edd55mz56tkSNHatasWXr44Ye1Zs2a2paqqirFxsZqzpw5Ov/881VYWKgFCxaopKREoaGhkuTV+6Wnp2vKlCm6/fbbJUnV1dVq1qyZOnfuXOdIOCBJdtMBAHyTxWLRtCF5yk5w6s45K3TMxc/bAMCf3VTUU1/HvSV72VbTKb4tvoV0+WtSYo7pknrTv39/Pf103R+gxMfH1/66Xbt2tb92Op2KiorSrl27JElr1qxRjx49asenJPXs2bPOx3K5XJoxY4Zee+01bdu2TZWVlaqsrJTT6axzux9/HklKS0ur/Tzr1q1TVlZW7YCWakb2jy1ZskQbNmxQVFTdc9MrKiq0ceMPP/QvKCioHdDevN/BgwdVWlpa5/dlt9vVpUsXHtKN42JEAzipy7pmKSMuXDe8tERlFdWmcwAAZ6is2q6XIq9UYdkDplN8V/N+0qUvSOGBdW0Qp9Opli1P/LRcISF1z/e2WCxyu92S5NWIfOyxx/TEE09o5syZKigokNPp1NSpU392Ua9TfR7LKR4273a71blzZ7388ss/e1tSUlLtr3863r19P8BbnBMN4JR6t0zUvyb3UmZcuOkUAMBZmF6Ur8NJHU1n+Kau10rj3wi4AX22Wrdurc8//7zO637634sWLdKIESN0xRVXqH379mrevLnWr19/Wp8nLy9PJSUl2rlzZ+3rvvzyyzq36dSpk9avX6/k5GS1bNmyzktMTMwJP/ap3i8mJkZpaWl1fl/V1dVasmTJaf0eEDwY0QC8kpMSpTdv6q2ezRNMpwAAzsID1RPkUWBfKOu0WO3SsMekYY9KtsB8kGZlZaV27NhR52XPnj1eve8NN9ygjRs36le/+pXWrVunV155RS+88EKd27Rs2VILFy7UZ599pjVr1uj666/Xjh07TqvxvPPOU4sWLTRp0iQtX75cn376ae2Fxb4/Qj1+/HglJiZqxIgRWrRokYqKivTRRx9pypQp2rr1xKcpePN+U6ZM0YwZMzR79mytXbtWN954ow4cOHBavwcED0Y0AK8lRDr00jXddW2fZqZTAABn6LXSVG3LuMB0hm8Ij5MmzJa6XmO6pEEtWLBAaWlpdV7OOeccr963SZMmeuONN/TWW2+pffv2+stf/qLf/e53dW5z9913q1OnTho8eLD69eun1NRUjRw58rQabTab5syZo8OHD6tr16665pprdNddd0lS7UXRIiIi9PHHH6tJkyYaPXq08vPzddVVV6m8vFzR0dEn/NjevN8tt9yiiRMnqrCwUD179lRUVJRGjeKp4XB8XJ0bwBl565vtuv2N5Tpa5TKdAgA4TZ1iDusN182yVFeYTjEnMVca908poYXpEpzAp59+qnPOOUcbNmxQixb8/wTfwYgGcMbW7jik6/+xRJv3HjWdAgA4TW/kLFTnLX8znWFG7gXS6GdqnsoKPmP27NmKjIxUTk6ONmzYoClTpiguLk6ffPKJ6TSgDh7ODeCM5aVG69+/OEf9W3FlSwDwN5NL+skdEWRfvy02aeC90rhXGdA+qKysTDfeeKPy8vJUWFiorl276s033zSdBfwMR6IBnDW326OZ732rP3ywQXxFAQD/8ViLr3XxtkdMZzQOZ5J0ySypWV/TJQD8HCMaQL15d9UO3fL6Nyqr5PmkAcAf2CxurUp7UGH71phOaVhZPWqe/zk6zXQJgADAw7kB1Jvz26TqzV/0Vk5ypOkUAIAXXB6rHrdOMp3RsHrcJBXOY0ADqDcciQZQ745UVuu2//tG81ec3nNEAgDM+LLZX5VU+oHpjPoVGiWN+KPUZqTpEgABhhENoMG89Plm/XbealUcc5tOAQCcxLkJ+/VC+VRZ3MdMp9SP5NbSZf+QEluaLgEQgBjRABrUuh1l+p9Xl+rbnYdNpwAATmJ+zltqveVV0xlnr90Y6cKZUmiE6RIAAYoRDaDBVRxz6YG5q/Xy4hLTKQCAE2gSXqEPHb+SteKA6ZQzExopDX1E6nC56RIAAY4RDaDRLFhZqtvfWKGD5QHycEEACDDPtPxcg7c+ZTrj9GV0lkb/VUpoYboEQBBgRANoVNsPlGvKP7/Wl8X7TacAAH4i3ObS8sR7FXJwk+kU71is0jm/lPr9RrLZTdcACBI8xRWARpUeG65/XtdTNw/Mkc1qMZ0DAPiRcpdNz4QVms7wTnSmNOktaeA9DGgAjYoj0QCMWbxpr6a+tkylBytMpwAAfuSbpk8pZufnpjNOrPVIafhMKTzOdAmAIMSRaADGdG+eoLen9NH5rVNMpwAAfuTOo5fLY/HBu4khTumiP0qXvciABmAMR6IB+ITXv9qiB+auVllFtekUAICkD1q+rmZb55jO+EF6R+ni57l4GADjGNEAfEbpwXL9+o0V+ujb3aZTACDo5Uce1XzrFFmqjpgNsYZIfW+V+twi2ULMtgCAGNEAfNDrX27RA/M4Kg0Apr2c85F6b3nGXEBqO2nk01JqW3MNAPATjGgAPomj0gBgXkxItZbE3iF72bbG/cS2UKnvtJqnr+LK2wB8DCMagE977csS/XbeGo5KA4AhDzRbpQmlDzbeJ0zrUHP0OaV1431OADgNjGgAPo+j0gBgjsXi0crMR+TcvaxhP5HNIfW7Xeo1haPPAHwaIxqA33jtyxL9du4alVVyVBoAGtP4tO16cP+tDfcJMjpLI/4sJec13OcAgHrCiAbgV7YfKNcd/+KoNAA0ts9a/F3p2xbU7we1OaT+d0i9bpastvr92ADQQBjRAPzSW99s12/nrdbOQ5WmUwAgKHSLPaTXjk2RxVVPX3dbDJCGPsrzPgPwO4xoAH7rcGW1Hn/3W73432K53HwpA4CGNjvnHXXc8uLZfZCoNGnI76U2o+onCgAaGSMagN9bvf2Q7pqzQktLDphOAYCAluqo0mfOW2U9uuf039lik7pfL/X/jeSIqv84AGgkjGgAAcHj8ei1L7fooQVrtf/oMdM5ABCwZrZcqpFbHz29d8rsKg17XEpr1zBRANCIGNEAAsr+I1Wa8fZavb5ki/jqBgD1L8Tq0crU++XYt+7UNw6LlQZNlzoXShZLA5cBQONgRAMISEs279ddc1ZqTekh0ykAEHAmZxXr9t2/OfmN2l8unf+A5ExsnCgAaCSMaAABy+X26IXPivXEwm91mOeWBoB6taTZM0oo/ejnb0gtkC54WGraq/GjAKARMKIBBLxdhyr06Lvr9H9LtoqLeANA/RiQsF/Pl0+Rxf3dDymdydLAu6UOV0hWq9k4AGhAjGgAQWNN6SH9bv4aLVp/BleVBQD8zIKcN5W3fbbUY7LU51YpLNp0EgA0OEY0gKDz4bpd+v38tVq3s8x0CgD4tas7xejuQelSfHPTKQDQaBjRAIKSy+3R/361RY8t/Fa7yypN5wCAX+mQFau7huWrS3a86RQAaHSMaABB7WhVtf7y0Sb99eNNKj/mMp0DAD4tKz5c0wbnaXj7dNMpAGAMIxoAJO08VKHHuPgYABxXdJhdvxjQUpN6Zctht5nOAQCjGNEA8CNcfAwAfhDpsOuq3tm6uk9zxYSHmM4BAJ/AiAaA4/hswx49tvBbLdm833QKADS68BCbJvXK1vV9myvOGWo6BwB8CiMaAE7iw3W79PjCb7V860HTKQDQ4Bx2q67o0VST+7VQYqTDdA4A+CRGNAB4YeHqnXp84bdaU3rIdAoA1LtQm1VjumbpFwNaKiU6zHQOAPg0RjQAeMnj8WjByh168j/rtXYHzzENwP/ZrRZd0jlT/zMwRxmx4aZzAMAvMKIB4DR5PB69s2qn/vD+eq3azpFpAP7HZrVoRId0TRmYo6YJTtM5AOBXGNEAcBYWrq4Z05wzDcAfhNqsuqhDum44t4VaJkeazgEAv8SIBoB68MHaXXr6o436omif6RQA+Jkoh13jujfRVb2bKTWGc54B4GwwogGgHn2z5YCeXbRJC1bukMvNl1cAZiVHOXRl72Ya36OJosN4nmcAqA+MaABoAFv2HdWsT4v0+pdbdKTKZToHQJBpnuTUdX2aa1SnDDnsNtM5ABBQGNEA0IAOlh/Ty4s368XPirXzUKXpHAABrnPTOF3Xt7nOb50ii8ViOgcAAhIjGgAaQVW1W28u26bnFhVp3U6eHgtA/bFYpIF5ybrh3Bbqkh1vOgcAAh4jGgAa2Yfrdumvizbp0w17TacA8GNRDrtGd8rQhJ5N1TI5ynQOAAQNRjQAGLJ6+yG9tHiz3vx6G+dNA/Baflq0rujRRKM6Zigi1G46BwCCDiMaAAw7XFmtN5dt08ufl2h16SHTOQB8UKjdqqFtUzWhZ1N1bspDtgHAJEY0APiQZVsO6OXPN2vu8lKVH+PoNBDssuLDdXm3phrTNUvxzlDTOQAAMaIBwCcdqjimfy3Zqle+KNG3Ow+bzgHQiKwWqV+rZF3Ro4n65SbLauUq2wDgSxjRAODjvizep1cWl2j+ilJVVrtN5wBoICnRDo3ulKnLuzVRVnyE6RwAwAkwogHAT+w/UqU3lm7VG0u3aQ3nTgMBISLUpsFtUjW6U4Z6t0jkqDMA+AFGNAD4oXU7yjRn2Tb9e9l2bTtQbjoHwGmwWqSeLRI0qmOmLmibKqeDK2wDgD9hRAOAH/N4PPqiaJ/mLNum+St26GD5MdNJAE4gJzlSozplaFTHDKXFhJvOAQCcIUY0AASIqmq33l+7S28u26b/rN2lKs6fBoxLcIZqePt0XdwpUwWZMaZzAAD1gBENAAHoYPkxLVhZqtlfb9Pion3iKz3QeKLC7BqYl6wL26WrX6sk2W1W00kAgHrEiAaAAFd6sFzzV+zQu6t26KvN++Vy82UfqG+JkaE6r3WKBrdJVe+WiQphOANAwGJEA0AQ2X+kSu+t2al3V+/UovW7VXGMh3wDZyojNlznt0nRkDap6podz5W1ASBIMKIBIEiVV7n08frdenfVTr2/dqf2H+WiZMCpNE9yakibVA1pm6p2mbGmcwAABjCiAQByuWuu8v3u6h1auHqntu7nabMASbJYpIKMGJ2Xn6IhbVOVkxJlOgkAYBgjGgDwM6u3H9J7a2oe8v11yQFVcx41gkhylEN9cpLUNzdRfXKSFO8MNZ0EAPAhjGgAwEkdrqzW5xv36pMNe/TJhj3asOuw6SSgXjnsVnVrFq8+OYnqm5ukvNRo00kAAB/GiAYAnJYdBytqBvX63fp0417tLqs0nQScttyUyO+ONiepe7N4hYXYTCcBAPwEIxoAcFbW7jikT9bXHKX+omifjla5TCcBP5MVH64uTePVs3mC+uQmKi0m3HQSAMBPMaIBAPWmqtqt5VsPaGnJfi3ZvF9LSw5wpBqNzma1qHVatDo3jVPX7Hh1yY5TSnSY6SwAQIBgRAMAGtSWfUd/NKr3a21pGRcqQ72KdNjVsUls7WjukBUrp8NuOgsAEKAY0QCARnW0qlrfbDmopSX7tfS7Yc1zVON0NImPULvMGHXNjlfnpnHKT4uWzWoxnQUACBKMaACAcZt2H9aKbQe1dkeZ1pQe0prSQ9p5iIeBBzuLRWqW4FTbjBi1zYhW2/QYtcmIUUx4iOk0AEAQY0QDAHzS/iNVNYP6R8N6/a7Dqqp2m05DA4gItalVapTy06JrXlKjlJcWrUgelg0A8DGMaACA36h2ubVpz5HvRnWZ1u44pA27Dmv7gXJxmrV/iI0IUbNEp5olOtU80anmSZHKT4tW0/gIWXlINgDADzCiAQB+r7LapZK9R7VpzxEV7Tmi4j1HtGnPEZXsPaqdZRXiO13jigi1KTvBqWZJTjVLqBnMzZJqRnNsRKjpPAAAzgojGgAQ0CqOubR1f7lK9tWM6pJ95dqy/6h2HKzQzkMV2nukSi4OY5+W6DC7UmPClBIdptToMKXGhCk9NrxmOCc6lRrD00kBAAIXIxoAENRcbo/2HqnUrkOV2lVWoV2HKrXz+1+XVWrXoZr/3V1WGfBPzRUWYlVcRGidcZwSHabUGIdSo8OVGlPz+vBQm+lUAACMYUQDAOAFj8ejfUeqtP/oMZVVHFNZRbUOV1bX/vqHl2Pfvf67t33364oql6rdHrncHh1zu+vlIeYWixRis8phsyrEblWIzaIQm1WhNqvCQmyKDrcrJjyk9iU6LEQxET/8Ovr71393O4edcQwAwKkwogEAMMDt9qja7VG1210zrl0/+m9XzdiudtdciTzEZq0Zx3Zr7UgOsVlkt1kN/y4AAAg+jGgAAAAAALzEj7ABAAAAAPASIxoAAAAAAC8xogEAAAAA8BIjGgAAAAAALzGiAQAAAADwEiMaAAAAAAAvMaIBAAAAAPASIxoAAAAAAC8xogEAAAAA8BIjGgAAAAAALzGiAQAAAADwEiMaAAAAAAAvMaIBAAAAAPASIxoAAAAAAC8xogEAAAAA8BIjGgAAAAAALzGiAQAAAADwEiMaAAAAAAAvMaIBAAAAAPASIxoAAAAAAC8xogEAAAAA8BIjGgAAAAAALzGiAQAAAADwEiMaAAAAAAAvMaIBAAAAAPASIxoAAAAAAC8xogEAAAAA8BIjGgAAAAAALzGiAQAAAADwEiMaAAAAAAAvMaIBAAAAAPASIxoAAAAAAC8xogEAAAAA8BIjGgAAAAAALzGiAQAAAADwEiMaAAAAAAAvMaIBAAAAAPDS/wPbnnYkZxJHoAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "status_counts = not_nan_conservation['conservation_status'].value_counts()\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.pie(status_counts.values,labels=status_counts.index,autopct='%1.1f%%')\n",
    "plt.title(\"Percentage Distribution of Conservation Status Values\")\n",
    "plt.axis('equal')  \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding out which category of animals in each conservation status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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>category</th>\n",
       "      <th>Amphibian</th>\n",
       "      <th>Bird</th>\n",
       "      <th>Fish</th>\n",
       "      <th>Mammal</th>\n",
       "      <th>Nonvascular Plant</th>\n",
       "      <th>Reptile</th>\n",
       "      <th>Vascular Plant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conservation_status</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>Endangered</th>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>In Recovery</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species of Concern</th>\n",
       "      <td>16.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Threatened</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "category             Amphibian   Bird  Fish  Mammal  Nonvascular Plant  \\\n",
       "conservation_status                                                      \n",
       "Endangered                 4.0   16.0  12.0    44.0                NaN   \n",
       "In Recovery                NaN   12.0   NaN    12.0                NaN   \n",
       "Species of Concern        16.0  320.0  16.0   168.0               20.0   \n",
       "Threatened                 8.0    NaN  20.0     8.0                NaN   \n",
       "\n",
       "category             Reptile  Vascular Plant  \n",
       "conservation_status                           \n",
       "Endangered               NaN             4.0  \n",
       "In Recovery              NaN             NaN  \n",
       "Species of Concern      20.0           172.0  \n",
       "Threatened               NaN             8.0  "
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conservationCategory = combined[combined.conservation_status != \"No Intervention\"]\\\n",
    "    .groupby([\"conservation_status\", \"category\"])['scientific_name']\\\n",
    "    .count()\\\n",
    "    .unstack()\n",
    "\n",
    "conservationCategory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building stacked bar chart depending on this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = conservationCategory.plot(kind = 'bar', figsize=(8,6),stacked=True)\n",
    "ax.set_xlabel(\"Conservation Status\")\n",
    "ax.set_ylabel(\"Number of Species\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding if certain types of species are more likely to be endangered.To do so I created new column called `is_protected` that will include any species that had value other than no `No Intevention`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['is_protected'] = combined.conservation_status != 'No Intervention'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the new column is created, group by `category` and `is_protected` to show the break down of each species type and protection status. \n",
    "\n",
    "It's easy to see that Birds, Vascular Plants, and Mammals have a higher absolute number of species protected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>category</th>\n",
       "      <th>not_protected</th>\n",
       "      <th>protected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amphibian</td>\n",
       "      <td>72</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bird</td>\n",
       "      <td>413</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fish</td>\n",
       "      <td>115</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>146</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nonvascular Plant</td>\n",
       "      <td>328</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Reptile</td>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>4216</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            category  not_protected  protected\n",
       "0          Amphibian             72          7\n",
       "1               Bird            413         75\n",
       "2               Fish            115         11\n",
       "3             Mammal            146         30\n",
       "4  Nonvascular Plant            328          5\n",
       "5            Reptile             73          5\n",
       "6     Vascular Plant           4216         46"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_counts = combined.groupby(['category', 'is_protected'])\\\n",
    "                        .scientific_name.nunique()\\\n",
    "                        .reset_index()\\\n",
    "                        .pivot(columns='is_protected',\n",
    "                                      index='category',\n",
    "                                      values='scientific_name')\\\n",
    "                        .reset_index()\n",
    "category_counts.columns = ['category', 'not_protected', 'protected']\n",
    "\n",
    "category_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating rate of protection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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>category</th>\n",
       "      <th>not_protected</th>\n",
       "      <th>protected</th>\n",
       "      <th>percent_protected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amphibian</td>\n",
       "      <td>72</td>\n",
       "      <td>7</td>\n",
       "      <td>8.860759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bird</td>\n",
       "      <td>413</td>\n",
       "      <td>75</td>\n",
       "      <td>15.368852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fish</td>\n",
       "      <td>115</td>\n",
       "      <td>11</td>\n",
       "      <td>8.730159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>146</td>\n",
       "      <td>30</td>\n",
       "      <td>17.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nonvascular Plant</td>\n",
       "      <td>328</td>\n",
       "      <td>5</td>\n",
       "      <td>1.501502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Reptile</td>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
       "      <td>6.410256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Vascular Plant</td>\n",
       "      <td>4216</td>\n",
       "      <td>46</td>\n",
       "      <td>1.079305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            category  not_protected  protected  percent_protected\n",
       "0          Amphibian             72          7           8.860759\n",
       "1               Bird            413         75          15.368852\n",
       "2               Fish            115         11           8.730159\n",
       "3             Mammal            146         30          17.045455\n",
       "4  Nonvascular Plant            328          5           1.501502\n",
       "5            Reptile             73          5           6.410256\n",
       "6     Vascular Plant           4216         46           1.079305"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_counts['percent_protected'] = category_counts.protected /\\\n",
    "(category_counts.protected + category_counts.not_protected) * 100\n",
    "\n",
    "category_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistical Significance\n",
    "\n",
    "This section will run some chi-squared tests to see if different species have statistically significant differences in conservation status rates.\n",
    "The first test will be called `contingency1` and will need to be filled with the correct numbers for mammals and birds.\n",
    "\n",
    "The results from the chi-squared test returns many values, the second value which is 0.69 is the p-value. The standard p-value to test statistical significance is 0.05. For the value retrieved from this test, the value of 0.69 is much larger than 0.05. In the case of mammals and birds there doesn't seem to be any significant relationship between them i.e. the variables independent. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chi2ContingencyResult(statistic=0.1617014831654557, pvalue=0.6875948096661336, dof=1, expected_freq=array([[ 27.8313253, 148.1686747],\n",
       "       [ 77.1686747, 410.8313253]]))"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "contingency1 = [[30, 146],\n",
    "              [75, 413]]\n",
    "chi2_contingency(contingency1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next pair, is going to test the difference between `Reptile` and `Mammal`.\n",
    "This time the p-value is 0.039 which is below the standard threshold of 0.05 which can be take that the difference between reptile and mammal is statistically significant. Mammals are shown to have a statistically significant higher rate of needed protection compared with Reptiles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chi2ContingencyResult(statistic=4.289183096203645, pvalue=0.038355590229699, dof=1, expected_freq=array([[ 24.2519685, 151.7480315],\n",
       "       [ 10.7480315,  67.2519685]]))"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency2 = [[30, 146],\n",
    "               [5, 73]]\n",
    "chi2_contingency(contingency2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## National Parks\n",
    "Lets see all unique national parks and everything related to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Parks:['Great Smoky Mountains National Park' 'Yosemite National Park'\n",
      " 'Bryce National Park' 'Yellowstone National Park']\n",
      "Total number of observations:3645247\n"
     ]
    }
   ],
   "source": [
    "unique_categories = combined.groupby('park_name')['category'].nunique()\n",
    "print(f\"All Parks:{combined.park_name.unique()}\")\n",
    "print(f\"Total number of observations:{combined.observations.sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets make bar chart from them:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar Chart: Unique Parks and Amount of different species per each park\n",
    "\n",
    "As we can see that each park have same amount of species per park which can happen beacause the data is artificial in other case that would be really strange on real data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species_count = combined.groupby('park_name')['scientific_name'].nunique()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.bar(species_count.index, species_count.values, color='skyblue')\n",
    "\n",
    "plt.xlabel(\"Park Name\")\n",
    "plt.ylabel(\"Number of Species\")\n",
    "plt.title(\"Number of Species per Park\")\n",
    "plt.xticks(rotation=45)  \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar Chart: Unique Parks vs. Most Popular Category Count\n",
    "Same thing here but for plants to be popular is explainable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_counts = combined.groupby(['park_name', 'category']).size().reset_index(name='count')\n",
    "\n",
    "most_popular = category_counts.loc[category_counts.groupby('park_name')['count'].idxmax()]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "bars = plt.bar(most_popular['park_name'], most_popular['count'], color='lightgreen')\n",
    "\n",
    "plt.xlabel(\"Park Name\")\n",
    "plt.ylabel(\"Count of Most Popular Category\")\n",
    "plt.title(\"Most Popular Category per Park\")\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "\n",
    "for index, row in most_popular.iterrows():\n",
    "    plt.text(row['park_name'],row['count'] + 5,row['category'],ha='center',va='bottom')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So making our observations we can say that most popular category per park is `Vascular plant` and each park has over `5000 species`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Species in Parks\n",
    "The next set of analysis will come from data from the conservationists as they have been recording sightings of different species at several national parks for the past 7 days.\n",
    "The first step is to look at the the common names from `species` to get an idea of the most prevalent animals in the dataset. The data will be need to be split up into individual names. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['American', 'Mink'],\n",
       " ['Northern', 'Short', 'Tailed', 'Shrew', 'Northern', 'ShortTailed', 'Shrew'],\n",
       " ['WhiteTailed', 'Deer'],\n",
       " ['WhiteTailed', 'Deer', 'WhiteTailed', 'Deer'],\n",
       " ['Panther', 'Mountain', 'Lion'],\n",
       " ['Cougar', 'Mountain', 'Lion', 'Puma']]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import chain\n",
    "import string\n",
    "\n",
    "def remove_punctuations(text):\n",
    "    for punctuation in string.punctuation:\n",
    "        text = text.replace(punctuation, '')\n",
    "    return text\n",
    "\n",
    "common_Names = combined[combined.category == \"Mammal\"]\\\n",
    "    .common_names\\\n",
    "    .apply(remove_punctuations)\\\n",
    "    .str.split().tolist()\n",
    "\n",
    "common_Names[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['American', 'Mink'],\n",
       " ['Northern', 'Short', 'Tailed', 'Shrew', 'ShortTailed'],\n",
       " ['WhiteTailed', 'Deer'],\n",
       " ['WhiteTailed', 'Deer'],\n",
       " ['Panther', 'Mountain', 'Lion'],\n",
       " ['Cougar', 'Mountain', 'Lion', 'Puma']]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanRows = []\n",
    "\n",
    "for item in common_Names:\n",
    "    item = list(dict.fromkeys(item))\n",
    "    cleanRows.append(item)\n",
    "    \n",
    "cleanRows[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting everything into one list,and the counting most popular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['American', 'Mink', 'Northern', 'Short', 'Tailed', 'Shrew']"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = list(chain.from_iterable(i if isinstance(i, list) else [i] for i in cleanRows))\n",
    "res\n",
    "res[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "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>Word</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bat</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Myotis</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Shrew</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>American</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Mouse</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Mountain</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Common</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Gray</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Chipmunk</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Brown</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Word  Count\n",
       "21        Bat    144\n",
       "32     Myotis    108\n",
       "98      Shrew    104\n",
       "54   American     96\n",
       "184     Mouse     72\n",
       "80   Mountain     68\n",
       "74     Common     64\n",
       "144      Gray     64\n",
       "119  Chipmunk     60\n",
       "33      Brown     56"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_counted = []\n",
    "\n",
    "for i in res:\n",
    "    x = res.count(i)\n",
    "    words_counted.append((i,x))\n",
    "\n",
    "pd.DataFrame(set(words_counted), columns =['Word', 'Count']).sort_values(\"Count\", ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this analysis, it seems that most popular is `Bats`,`Myotis` and `Shrews`, so `Bat` occurred 144 times, `Myotis` 108 times while `Shrew` came up 104 times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the data, there are several different scientific names for different types of bats.  The next task is to figure out which rows of `species` are referring to bats. A new column made up of boolean values will be created to check if `is_bat` is `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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>category</th>\n",
       "      <th>scientific_name</th>\n",
       "      <th>common_names</th>\n",
       "      <th>conservation_status</th>\n",
       "      <th>is_bat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Clethrionomys gapperi gapperi</td>\n",
       "      <td>Gapper's Red-Backed Vole</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Bos bison</td>\n",
       "      <td>American Bison, Bison</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Bos taurus</td>\n",
       "      <td>Aurochs, Aurochs, Domestic Cattle (Feral), Dom...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Ovis aries</td>\n",
       "      <td>Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Cervus elaphus</td>\n",
       "      <td>Wapiti Or Elk</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Odocoileus virginianus</td>\n",
       "      <td>White-Tailed Deer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Sus scrofa</td>\n",
       "      <td>Feral Hog, Wild Pig</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Canis latrans</td>\n",
       "      <td>Coyote</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Canis lupus</td>\n",
       "      <td>Gray Wolf</td>\n",
       "      <td>Endangered</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mammal</td>\n",
       "      <td>Canis rufus</td>\n",
       "      <td>Red Wolf</td>\n",
       "      <td>Endangered</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category                scientific_name  \\\n",
       "0   Mammal  Clethrionomys gapperi gapperi   \n",
       "1   Mammal                      Bos bison   \n",
       "2   Mammal                     Bos taurus   \n",
       "3   Mammal                     Ovis aries   \n",
       "4   Mammal                 Cervus elaphus   \n",
       "5   Mammal         Odocoileus virginianus   \n",
       "6   Mammal                     Sus scrofa   \n",
       "7   Mammal                  Canis latrans   \n",
       "8   Mammal                    Canis lupus   \n",
       "9   Mammal                    Canis rufus   \n",
       "\n",
       "                                        common_names conservation_status  \\\n",
       "0                           Gapper's Red-Backed Vole                 NaN   \n",
       "1                              American Bison, Bison                 NaN   \n",
       "2  Aurochs, Aurochs, Domestic Cattle (Feral), Dom...                 NaN   \n",
       "3  Domestic Sheep, Mouflon, Red Sheep, Sheep (Feral)                 NaN   \n",
       "4                                      Wapiti Or Elk                 NaN   \n",
       "5                                  White-Tailed Deer                 NaN   \n",
       "6                                Feral Hog, Wild Pig                 NaN   \n",
       "7                                             Coyote  Species of Concern   \n",
       "8                                          Gray Wolf          Endangered   \n",
       "9                                           Red Wolf          Endangered   \n",
       "\n",
       "   is_bat  \n",
       "0   False  \n",
       "1   False  \n",
       "2   False  \n",
       "3   False  \n",
       "4   False  \n",
       "5   False  \n",
       "6   False  \n",
       "7   False  \n",
       "8   False  \n",
       "9   False  "
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined['is_bat'] = combined.common_names.str.contains(r\"\\bBat\\b\", regex = True)\n",
    "\n",
    "species.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a subset of the data where is_bat is true, returning see the rows that matched. There seems to be a lot of species of bats and a mix of protected vs. non-protected species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "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>scientific_name</th>\n",
       "      <th>park_name</th>\n",
       "      <th>observations</th>\n",
       "      <th>category</th>\n",
       "      <th>common_names</th>\n",
       "      <th>conservation_status</th>\n",
       "      <th>is_protected</th>\n",
       "      <th>is_bat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Lasiurus blossevillii</td>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>113</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Western Red Bat</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>Corynorhinus rafinesquii</td>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>188</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Rafinesque's Big-Eared Bat</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Nycticeius humeralis</td>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>219</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Evening Bat</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>Lasiurus blossevillii</td>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>70</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Western Red Bat</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>Lasiurus borealis</td>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>134</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Eastern Red Bat, Red Bat</td>\n",
       "      <td>No Intervention</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25460</th>\n",
       "      <td>Eptesicus fuscus</td>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>72</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Big Brown Bat</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25461</th>\n",
       "      <td>Eptesicus fuscus</td>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>72</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Big Brown Bat, Big Brown Bat</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25476</th>\n",
       "      <td>Myotis leibii</td>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>233</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Eastern Small-Footed Bat, Eastern Small-Footed...</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25530</th>\n",
       "      <td>Lasionycteris noctivagans</td>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>128</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Silver-Haired Bat</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25531</th>\n",
       "      <td>Lasionycteris noctivagans</td>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>128</td>\n",
       "      <td>Mammal</td>\n",
       "      <td>Silver-Haired Bat, Silver-Haired Bat</td>\n",
       "      <td>Species of Concern</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 scientific_name                            park_name  \\\n",
       "286        Lasiurus blossevillii                  Bryce National Park   \n",
       "331     Corynorhinus rafinesquii               Yosemite National Park   \n",
       "450         Nycticeius humeralis            Yellowstone National Park   \n",
       "670        Lasiurus blossevillii  Great Smoky Mountains National Park   \n",
       "827            Lasiurus borealis               Yosemite National Park   \n",
       "...                          ...                                  ...   \n",
       "25460           Eptesicus fuscus                  Bryce National Park   \n",
       "25461           Eptesicus fuscus                  Bryce National Park   \n",
       "25476              Myotis leibii            Yellowstone National Park   \n",
       "25530  Lasionycteris noctivagans                  Bryce National Park   \n",
       "25531  Lasionycteris noctivagans                  Bryce National Park   \n",
       "\n",
       "       observations category  \\\n",
       "286             113   Mammal   \n",
       "331             188   Mammal   \n",
       "450             219   Mammal   \n",
       "670              70   Mammal   \n",
       "827             134   Mammal   \n",
       "...             ...      ...   \n",
       "25460            72   Mammal   \n",
       "25461            72   Mammal   \n",
       "25476           233   Mammal   \n",
       "25530           128   Mammal   \n",
       "25531           128   Mammal   \n",
       "\n",
       "                                            common_names conservation_status  \\\n",
       "286                                      Western Red Bat  Species of Concern   \n",
       "331                           Rafinesque's Big-Eared Bat     No Intervention   \n",
       "450                                          Evening Bat     No Intervention   \n",
       "670                                      Western Red Bat  Species of Concern   \n",
       "827                             Eastern Red Bat, Red Bat     No Intervention   \n",
       "...                                                  ...                 ...   \n",
       "25460                                      Big Brown Bat  Species of Concern   \n",
       "25461                       Big Brown Bat, Big Brown Bat  Species of Concern   \n",
       "25476  Eastern Small-Footed Bat, Eastern Small-Footed...  Species of Concern   \n",
       "25530                                  Silver-Haired Bat  Species of Concern   \n",
       "25531               Silver-Haired Bat, Silver-Haired Bat  Species of Concern   \n",
       "\n",
       "       is_protected  is_bat  \n",
       "286            True    True  \n",
       "331           False    True  \n",
       "450           False    True  \n",
       "670            True    True  \n",
       "827           False    True  \n",
       "...             ...     ...  \n",
       "25460          True    True  \n",
       "25461          True    True  \n",
       "25476          True    True  \n",
       "25530          True    True  \n",
       "25531          True    True  \n",
       "\n",
       "[144 rows x 8 columns]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[combined.is_bat]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many total bat observations(across all species) were made at each national park.  \n",
    "\n",
    "The total number of bats observed in each park over the past 7 days are in the table below. Yellowstone National Park seems to have the largest with 8,362 observations and the Great Smoky Mountains National Park having the lowest with 2,411. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "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>park_name</th>\n",
       "      <th>observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>3433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>2411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>8362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>4786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             park_name  observations\n",
       "0                  Bryce National Park          3433\n",
       "1  Great Smoky Mountains National Park          2411\n",
       "2            Yellowstone National Park          8362\n",
       "3               Yosemite National Park          4786"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[combined.is_bat].groupby('park_name').observations.sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see each park broken down by protected bats vs. non-protected bat sightings. It seems that every park except for the Great Smoky Mountains National Park has more sightings of protected bats than not. This could be considered a great sign for bats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "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>park_name</th>\n",
       "      <th>is_protected</th>\n",
       "      <th>observations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>False</td>\n",
       "      <td>1596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bryce National Park</td>\n",
       "      <td>True</td>\n",
       "      <td>1837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>False</td>\n",
       "      <td>1299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Great Smoky Mountains National Park</td>\n",
       "      <td>True</td>\n",
       "      <td>1112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>False</td>\n",
       "      <td>4044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yellowstone National Park</td>\n",
       "      <td>True</td>\n",
       "      <td>4318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>False</td>\n",
       "      <td>2345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Yosemite National Park</td>\n",
       "      <td>True</td>\n",
       "      <td>2441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             park_name  is_protected  observations\n",
       "0                  Bryce National Park         False          1596\n",
       "1                  Bryce National Park          True          1837\n",
       "2  Great Smoky Mountains National Park         False          1299\n",
       "3  Great Smoky Mountains National Park          True          1112\n",
       "4            Yellowstone National Park         False          4044\n",
       "5            Yellowstone National Park          True          4318\n",
       "6               Yosemite National Park         False          2345\n",
       "7               Yosemite National Park          True          2441"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_by_park = combined[combined.is_bat].groupby(['park_name', 'is_protected']).observations.sum().reset_index()\n",
    "obs_by_park"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a bar chart based on this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 4))\n",
    "sns.barplot(x=obs_by_park.park_name, y= obs_by_park.observations, hue=obs_by_park.is_protected)\n",
    "plt.xlabel('National Parks')\n",
    "plt.ylabel('Number of Observations')\n",
    "plt.title('Observations of Bats per Week')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "The project was able to make several data visualizations and inferences about the various species in four of the National Parks that comprised this data set.\n",
    "\n",
    "This project was also able to answer some of the questions first posed in the beginning:\n",
    "\n",
    "- What is the distribution of conservation status for species?\n",
    "    - The vast majority of species were not part of conservation.(24752 vs 880)\n",
    "- Are certain types of species more likely to be endangered?\n",
    "    - Mammals and Birds had the highest percentage of being in protection.\n",
    "- Are the differences between species and their conservation status significant?\n",
    "    - While mammals and Birds did not have significant difference in conservation percentage, mammals and reptiles exhibited a statistically significant difference.\n",
    "- Which animal is most prevalent and what is their distribution amongst parks?\n",
    "    - the study found that bats occurred the most number of times and they were most likely to be found in Yellowstone National Park.\n",
    "    - Found that number of species per park and most popular category is the same for all parks which proves that data is most likely to be artificial because its hard to imagine in real life"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Research \n",
    "\n",
    "This dataset only included observations from the last 7 days which prohibits analyze changes over time. It would be curious to see how the conservation status for various species changes over time. Another piece that is missing is the Area of each park, it can be assumed that Yellowstone National Park might be much larger than the other parks which would mean that it would exhibit more observations and greater biodiversity. Lastly, if precise locations were recorded, the spatial distribution of the species could also be observed and test if these observations are spatially clustered. "
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