final_report.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to reflect changes made in modules\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "#setup\n",
    "from zipfile import ZipFile\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import machine_learning\n",
    "import clean_data\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Impact of Gun Reform Laws in the US"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Introduction\n",
    "This project was created by **Team Purple**: Alicia Delgado, Haricharani, Jr-Shin Chen, Priyanka Patel, Teresa Pham, and Michelle Ramirez. <br> <br>\n",
    "The big idea of our project is to Explore the correlation between gun violence cases with gun reform laws in the area. One main question that we are investigating is has gun reform impacted the number of gun violence cases in the US? We have multiple datasets that show gun-related cases by state and year, firearm laws passed by states, and state populations. The scope of our project is to visualize the number of gun cases in the US using the Gun Violence dataset from the Gun Violence Archive.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Reading and Cleaning\n",
    "\n",
    "Because of GitHub storage limits, we could not include our `gun-violence-data_01-2013_03-2018.csv` in this repository. If you would like to run the code below, you will have to download the dataset yourself at https://github.com/jamesqo/gun-violence-data#how-did-you-get-the-data by going to the section \"How did you get the data\" and following the steps under \"stage 3\" to download and unpack the tar.gz file. Once you unpack the tar.gz file, you should have a file named stage3.csv. This is the same file as our `gun-violence-data_01-2013_03-2018.csv`, but we renamed the file. You will also have to rename the file from 'stage3.csv' to 'gun-violence-data_01-2013_03-2018.csv'. This file has 239,678 rows and 29 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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>incident_id</th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>n_killed</th>\n",
       "      <th>n_injured</th>\n",
       "      <th>incident_url</th>\n",
       "      <th>congressional_district</th>\n",
       "      <th>incident_characteristics</th>\n",
       "      <th>participant_age</th>\n",
       "      <th>participant_age_group</th>\n",
       "      <th>participant_gender</th>\n",
       "      <th>participant_status</th>\n",
       "      <th>participant_type</th>\n",
       "      <th>state_house_district</th>\n",
       "      <th>state_senate_district</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>92342</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Shot - Dead (murder, accidental, suicide)</td>\n",
       "      <td>0::21||1::24</td>\n",
       "      <td>0::Adult 18+||1::Adult 18+</td>\n",
       "      <td>0::Female||1::Male</td>\n",
       "      <td>0::Killed||1::Unharmed</td>\n",
       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>94194</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/94194</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Home Invasion||Home Invasion - Resident injure...</td>\n",
       "      <td>3::17||4::19||5::19||6::19</td>\n",
       "      <td>3::Teen 12-17||4::Adult 18+||5::Adult 18+||6::...</td>\n",
       "      <td>0::Male||1::Male||2::Female||3::Male||4::Male|...</td>\n",
       "      <td>0::Injured||1::Injured||2::Injured||3::Unharme...</td>\n",
       "      <td>0::Victim||1::Victim||2::Victim||3::Subject-Su...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>92337</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92337</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Shot - Dead (murder, accidental, suicide)</td>\n",
       "      <td>0::18||1::19</td>\n",
       "      <td>0::Adult 18+||1::Adult 18+</td>\n",
       "      <td>0::Female||1::Male</td>\n",
       "      <td>0::Killed||1::Unharmed</td>\n",
       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>90.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>95279</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/95279</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Shot - Wounded/Injured</td>\n",
       "      <td>0::73</td>\n",
       "      <td>0::Adult 18+</td>\n",
       "      <td>0::Female</td>\n",
       "      <td>0::Injured</td>\n",
       "      <td>0::Victim</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>92221</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.gunviolencearchive.org/incident/92221</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Shot - Dead (murder, accidental, suicide)</td>\n",
       "      <td>0::36</td>\n",
       "      <td>0::Adult 18+||1::Adult 18+</td>\n",
       "      <td>0::Male||1::Male</td>\n",
       "      <td>0::Killed||1::Unharmed</td>\n",
       "      <td>0::Victim||1::Subject-Suspect</td>\n",
       "      <td>52.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    incident_id       date    state  n_killed  n_injured  \\\n",
       "296       92342 2014-01-01  Alabama         1          0   \n",
       "315       94194 2014-01-01  Alabama         0          3   \n",
       "320       92337 2014-01-01  Alabama         1          0   \n",
       "383       95279 2014-01-01  Alabama         0          1   \n",
       "400       92221 2014-01-01  Alabama         1          0   \n",
       "\n",
       "                                         incident_url congressional_district  \\\n",
       "296  http://www.gunviolencearchive.org/incident/92342                    5.0   \n",
       "315  http://www.gunviolencearchive.org/incident/94194                    4.0   \n",
       "320  http://www.gunviolencearchive.org/incident/92337                    2.0   \n",
       "383  http://www.gunviolencearchive.org/incident/95279                    4.0   \n",
       "400  http://www.gunviolencearchive.org/incident/92221                    7.0   \n",
       "\n",
       "                              incident_characteristics  \\\n",
       "296          Shot - Dead (murder, accidental, suicide)   \n",
       "315  Home Invasion||Home Invasion - Resident injure...   \n",
       "320          Shot - Dead (murder, accidental, suicide)   \n",
       "383                             Shot - Wounded/Injured   \n",
       "400          Shot - Dead (murder, accidental, suicide)   \n",
       "\n",
       "                participant_age  \\\n",
       "296                0::21||1::24   \n",
       "315  3::17||4::19||5::19||6::19   \n",
       "320                0::18||1::19   \n",
       "383                       0::73   \n",
       "400                       0::36   \n",
       "\n",
       "                                 participant_age_group  \\\n",
       "296                         0::Adult 18+||1::Adult 18+   \n",
       "315  3::Teen 12-17||4::Adult 18+||5::Adult 18+||6::...   \n",
       "320                         0::Adult 18+||1::Adult 18+   \n",
       "383                                       0::Adult 18+   \n",
       "400                         0::Adult 18+||1::Adult 18+   \n",
       "\n",
       "                                    participant_gender  \\\n",
       "296                                 0::Female||1::Male   \n",
       "315  0::Male||1::Male||2::Female||3::Male||4::Male|...   \n",
       "320                                 0::Female||1::Male   \n",
       "383                                          0::Female   \n",
       "400                                   0::Male||1::Male   \n",
       "\n",
       "                                    participant_status  \\\n",
       "296                             0::Killed||1::Unharmed   \n",
       "315  0::Injured||1::Injured||2::Injured||3::Unharme...   \n",
       "320                             0::Killed||1::Unharmed   \n",
       "383                                         0::Injured   \n",
       "400                             0::Killed||1::Unharmed   \n",
       "\n",
       "                                      participant_type state_house_district  \\\n",
       "296                      0::Victim||1::Subject-Suspect                 19.0   \n",
       "315  0::Victim||1::Victim||2::Victim||3::Subject-Su...                 61.0   \n",
       "320                      0::Victim||1::Subject-Suspect                 90.0   \n",
       "383                                          0::Victim                 28.0   \n",
       "400                      0::Victim||1::Subject-Suspect                 52.0   \n",
       "\n",
       "    state_senate_district  year  \n",
       "296                   7.0  2014  \n",
       "315                  21.0  2014  \n",
       "320                  25.0  2014  \n",
       "383                  10.0  2014  \n",
       "400                  18.0  2014  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading in data for gun violence counts and firearm laws\n",
    "gun_data = pd.read_csv('gun-violence-data_01-2013_03-2018.csv')\n",
    "gunlaws = pd.read_csv('statefirearmlaws.csv')    # 1,501 rows, 137 cols\n",
    "gun_data, missing_row, gun_laws = clean_data.processAll(gun_data, gunlaws)\n",
    "\n",
    "gun_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We processed our data by filtering out the necessary rows and columns based on the year range that best applied to all of our datasets, missing values, and features that most directly impact our EDA. We applied this process to all of our datasets (gun violence, state firearm laws, gun reform). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "The datasets that we are using contain information about gun violence incidents from 2013-2018, gun reform laws from 1991-2020, and state populations from 2010-2019. The Gun Violence dataset indicates that there has been an increase in gun violence from 2014 to 2017. Additionally, there has been an overall increase in gun reform laws across the US, which led us to explore whether gun reform laws have an impact on gun violence at the state level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gun_data['date'] = pd.to_datetime(gun_data['date'])\n",
    "gun_data['year'] = gun_data['date'].dt.year\n",
    "gun_data_year = gun_data\n",
    "gun_data_year = gun_data_year.groupby(['year']).size().reset_index(name='counts');\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(4,4)});\n",
    "sns.barplot(x=\"year\", y=\"counts\", data=gun_data_year.loc[0:4, :], palette='viridis').set(title=\"Gun violence cases from 2014-2017\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gun_laws_year = gun_laws.groupby(['year']).sum().reset_index()\n",
    "sns.set(rc = {'figure.figsize':(15,5)});\n",
    "sns.barplot(x='year', y='lawtotal', data=gun_laws_year, palette='viridis').set(title='Number of Gun Laws in the US from 1991-2020');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis Testing\n",
    "The hypothesis that we will be testing is that States with a higher count of gun laws have a smaller amount of gun violence incidents compared to states with a lower gun law count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>state</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>12845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>8087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>7387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New York</td>\n",
       "      <td>6881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>5607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         state  counts\n",
       "13    Illinois   12845\n",
       "4   California    8087\n",
       "9      Florida    7387\n",
       "32    New York    6881\n",
       "35        Ohio    5607"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_state = gun_data.groupby(['state']).size().reset_index(name='counts')\n",
    "df_state = df_state.sort_values(by='counts', ascending=False)\n",
    "sns.set(rc = {'figure.figsize':(10,8)})\n",
    "sns.barplot(x='counts', y='state', data=df_state, palette='viridis').set(title=\"Gun Violence Count by State\");\n",
    "\n",
    "df_state.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqIAAAHwCAYAAAB9rw2SAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAADK+UlEQVR4nOzdeVxOef/H8dfV1UJCyb4OxhSyzqAUqdxjQqHsFMYwQ1kGkRGTfUhIBmMWhmEyRFIaM2PNkm24bVnukSVLIUWS6rr6/dHPNVIooq76PB+PHned5Xu+53zvx31/fM8576PIzMzMRAghhBBCiHdMp7A7IIQQQgghSiYpRIUQQgghRKGQQlQIIYQQQhQKKUSFEEIIIUShkEJUCCGEEEIUCilEhRBCCCFEoZBCVAghCsCmTZvo1asXnTt3pmPHjgwZMoT//ve/BXoMMzMzEhISCrTNvLK3t+f06dOvvX9ycjI+Pj44OTnh7OxM9+7d2bhxo2b9xo0bWbdu3Svbyet2QgjtoFvYHRBCCG23cOFCjh49yuLFi6lRowYAhw4d4vPPP2fz5s1Ur169kHtY+Pz9/TE0NCQ0NBSFQkFcXBx9+vShWrVq2NjYcPz4cRo0aPDKdvK6nRBCO8iMqBBCvIG7d+/y888/ExAQoClCAaysrPD29ubx48dAzhnFp3/HxsbSsWNHZs6cSc+ePfn444/5888/892HkSNH0qdPH+zt7XFzc+PevXusXr0aLy8vANLT02nZsiXBwcEAHDt2jF69evHo0SNGjx5Nt27d6NGjBz4+PqjV6lyPs379enr06EGXLl3YtGkTAD4+PixatEizzdatW/Hw8Mix7507d3jy5Anp6ekAVKlShcDAQOrUqcOff/7Jrl27WL16NevWrXvh+Ty/HcDy5cvp0aMH3bp1Y+TIkcTFxeXr2gkhCpcUokII8QZOnjxJ/fr1qVy5co513bt3p379+q9s4/r169jY2LBp0ybGjx/PnDlz8tWH8PBwmjdvzoYNG9i5cyelSpVi69atfPzxx+zfvx+1Ws3x48cxNDTk4MGDAOzatUtT9D569IitW7dqisvr16/nehwDAwO2bNnCTz/9xMKFC7l06RIDBgwgODiYjIwMAH777Tf69u2bY19PT0+ioqKwtLRk6NChfPvttxgZGVGrVi3+85//YG9vz+DBgxkwYMALz+f57UJCQrh48SIbN25k69at2Nra4uPjk69rJ4QoXHJrXggh3sDzX0lOTk5mwIABAKSkpODo6Mi4ceNe2oaenh62trYANGrUiMTExHz1YdCgQRw7doxVq1Zx5coVLl26RLNmzahevTrVqlXjzJkzREZGMnz4cFauXElmZia7du1i5cqVKBQKFi1ahJubG23btmXQoEHUqVMn1+M8LTCrVKmCtbU1hw4dwt3dnZo1a7Jnzx7q1q1LfHw8NjY2OfY1Nzfn999/5+zZsxw9epQDBw6wYsUKAgICsLe3z9P5PG/37t2cPn0aV1dXANRqtWYGWgihHaQQFUKIN9C0aVNiYmK4f/8+JiYmGBkZsXXrVgACAwO5f/++Zttni9a0tDTN73p6eujoZN2gUigU+e6Dn58fp06dwtXVlTZt2pCRkaE5VseOHdm3bx8HDhzgu+++IywsjO3bt1OqVClq164NwJ9//snhw4eJiopiyJAhzJgxI0dxCGj6CFlFn65u1v+FPJ0Vfe+99+jdu3eOc8jIyGDGjBmMGzcOCwsLLCwsGDJkCMuWLWPDhg05jvWy83mWWq3ms88+o3///pprmpSUlO/rJ4QoPHJrXggh3kCVKlVwd3dnzJgx3Lx5U7P8xo0b/P3335rirUKFCpw5cwaAw4cPc+fOnQLrw/79+xk0aBDdu3fH1NSUgwcPolKpAPj444/Ztm0barVaM5Pp5+fHxx9/DGQ99zl58mRsbGzw8vLCxsaGc+fO5XqcLVu2AHDz5k0OHTqElZUVAJ06dSI6OpodO3ZoZiefpaurS0xMDMuWLdM8I5qRkcE///xDo0aNAFAqlZrb+y87n2e3e/o4Q3JyMgABAQFMnDjxzS+oEOKdkRlRIYR4Q19++SWhoaGMHz+ex48f8/DhQ8qXL0/nzp01t+knTJiAr68vGzZsoHHjxjRu3Pi1juXg4JDt74ULF+Lh4cH8+fMJCAhAT0+Pli1bcu3aNQDef/99FAqFpmi0sbFh2bJldOrUCch6jvXIkSN07tyZ0qVLU61aNdzc3HI99pMnT+jRowfp6en4+PhQt25dAPT19enUqRN3796lQoUKue4bEBCAn58fnTp1onTp0qjVav7zn/9oXmxq374933zzDcBLz+fZ7YYNG0ZcXJxmFrZatWqadUII7aDIzO1+hxBCCJFHKSkpDBw4kGnTptG8efPC7o4QQovIrXkhhBCvLTIykg4dOtCuXTspQoUQ+SYzokIIIYQQolDIjKgQQgghhCgUUogKIYQQQohCIYWoEEIIIYQoFBLfJIQQQgjxEk8ePyE5Jb2wu1EkmJoace9ecp6319FRYGJS5oXri20hmpycjL+/P0ePHkWpVFKuXDm8vb1fmN0XGxuLu7s7u3btIiAgAAsLCxwcHFiyZAmbN29m0KBBDBky5I361K1bN80XV95Uv+afE3e94AKxhRBCCJG7Xfc28yA57dUblhBqdcG9514sC1G1Ws2wYcNo06YNISEh6OrqEhUVxbBhwwgPD8fExOSl+48ZM0bz+9atW1m1apUmuPlNFFQRKoQQQghRHBTLQvTw4cPcunWL0aNHaz6vZ2lpydy5c1Gr1fj4+HDp0iXu3r2LmZkZCxcuzLa/t7c3rVu35uTJk8TFxeHh4YG/vz+3b99m8eLFqNVqatWqxYwZM6hYsSL29vY0bdqU6Oho/Pz8mDZtGg0aNCA6OhpTU1MCAgIwNjbGzMyMCxcuEBcXx1dffcXDhw+Jj4+nR48e2YpfIYQQQjyjXDpUTS20N1vc3d1RqQon7bJ06VL06tWfli0/KpTjv23FshA9d+4c5ubmmiL0KVtbW44ePYqenh4bNmxArVYzaNAg9u7dm+st+xkzZrB//35WrlxJ6dKlGT58OL/++is1a9bkhx9+YMaMGSxZsgTI+uzc4sWLiY2N5fz588yZM4dGjRoxatQotm3blu2TeWFhYXTt2pUePXrw8OFDbG1tcXNze+Gn8YQQQogSrfITFIbqQjv8jRs3Cu3YAGFhIVKIahMdHR0MDAxyXdeqVSuMjY1Zt24dly9f5sqVK6SkpLyyzVOnTtG0aVNq1qwJQJ8+fVi5cqVmfbNmzTS/m5qa0qhRIwAaNGhAUlJStraGDh1KVFQUP/74I5cuXSI9PZ3Hjx/n+zyFEEKIEiHegEydwpsRrVm/WqHOiHbt2r1Qjv0uFMtC1MLCgvXr15OZmYlCodAsX7hwIU2bNiUwMBB3d3dcXFy4f/8+efm4lFqd/V9imZmZZGRkaP5+tvB99neFQpGj/W+++Ybr16/TtWtXOnbsyMGDB/PUByGEEKJEeqCX9VNI1hxYw507Dwvt+MVZsSxEP/roI0xNTVm6dCkjR45EqVQSGRnJ5s2bSUpKwtHREVdXV65fv87hw4exsrJ6ZZvNmjXj66+/JjY2lpo1a7JhwwbatGnzWv07cOAA06dPp2XLluzZs4e4uLgche6r/Hryu9c6thBCCCHyJzXlSWF3odgqloWoQqFg2bJlzJ07l65du6Krq4uJiQkrV65EqVQyYcIEwsPD0dPTo2XLlsTGxr6yzYoVKzJjxgw8PT1JT0+nevXqzJ49+7X69/nnnzNx4kRKlSpF1apVsbCwIDY2ltq1a+e5jXv3kgs0PkG8XZUqlZV/TWsZGTPtI2OmfWTMhCLzHd4Tjo2NxcHBgT59+jBjxgzN8ujoaLp3787cuXNxcXF5V90BIDAwEIBRo0a9lfZ/++03DA0N6dq1K7t37+bKlStvnEcqhBBCCO2XmvKEh4+0K580v/940NFRYGpq9ML173xG1NjYmMjISFQqFUqlEoDt27cX2zfG//77b1q3bg3AmTNnCqzdgW1GERd7t8DaE0IIIcS79eeNX7WuEC1o77wQLVOmDObm5hw9ehRLS0sg65nJtm3bAvDLL7+wdetWHj9+jJ6eHv7+/tSrV4958+Zx4MABdHR06NixI56enhw6dAg/Pz8Aypcvj7+/PxUqVGDRokUcOnSIpKQkKleuzKJFi6hYsSLbtm1j+fLlKBQKmjRpwsyZM4GsN+L79u1LXFwcLi4ujBo1is2bN3PkyBG++eYbANzc3PD09KROnTpMmDCBlJQUdHR08PHxoXnz5pw6dYq5c+eSmpqKiYkJ06dP5/r16+zatYuoqCgePnxIUFAQANWrV6d69eq59l0IIYQQb0dmmSdkVnoEOkXj0bbCzCeFopFRWijPiDo6OrJjxw4sLS05deoUZmZmZGZmkpyczK5du1i7di2lSpUiICCAdevW8emnn7Jv3z7Cw8N5/PgxkydP5smTJyxbtgxfX1+aNm3K999/z7lz56hVqxaXL18mKCgIHR0dJk6cSGhoKF26dGHu3Lls3ryZqlWr4uXlxd69ewG4d+8eQUFBJCcnY29v/9Jb55s2baJDhw589tln7Nu3j+PHj9OoUSN8fHxYsWIF1atXJzIykqlTp7J69Wrs7e1p3bo1Li4uPHjwAABXV1fc3Nxy9N3GxuadXH8hhBCiJMo0TYFSGa/e8B0p7HxSKPyM0kIpRO3t7TVfKIqIiMDR0ZHt27djZGSEv78/4eHhXLlyhcjISBo2bEiVKlUwMDCgb9++2NnZMWHCBAwMDHBwcMDT05OOHTvi4OCAtbU1AJMmTWLjxo3ExMRw8uRJateuzYkTJ2jZsiVVq1YF0MxGRkdH065dO/T19alQoQImJiY5cj+fZWVlxahRo4iOjsbW1paBAwdy5coVrl+/zogRIzTbJScnv/QavKjvQgghhHg7FPcMydQpOjOiNepWLfQZ0cLOKC2UQvTp7fnjx48TFRXF+PHj2b59O7du3aJPnz4MHDiQ9u3bU7FiRaKjo9HV1WXjxo0cOXKEffv20bdvX9auXcvgwYOxs7Nj9+7d+Pn5cerUKdq1a8f48eMZPHgwnTp1QkdHh8zMTHR1dbNliiYkJPx7EXT/vQxPcz+fz/9MT08H4MMPPyQ8PJw9e/awfft2tmzZwqRJk6hZs6bmW/IqlYq7d1/+/GZufX+2kBVCCCFEwVI8MkDxKPcP3hSGNZGST1pI3yjIuj3v7++PhYWFphA0NDSkTp06DB48mCZNmvDXX3+hUqk4d+4cAwcOpFWrVkyaNIn69esTExNDr169ePToEYMHD2bw4MGcO3eOo0eP0rp1a/r168d7773Hnj17UKlUNGnShJMnT3Lnzh0A5syZw86dO1/YPxMTE/755x8yMzO5fv06Fy5cAGD+/PmEhobSo0cPpk2bxrlz56hXrx5JSUkcO3YMgODgYCZMmACAUqlEpVJpfn8agp9b34UQQgghSpJCyxG1s7NjypQpjBkzRrNMT08PtVpN586dyczMpFWrVly6dIlGjRrRvHlzunbtSunSpWnZsiXt27endOnSeHt7o6uri6GhIbNmzaJ06dJ4enri5OQEoMnorFKlClOmTGHo0KGo1WqaN2+Oi4sLy5Yty7V/bdu2JTg4mE8++YS6devy4YcfAlkvLY0fP57NmzejVCqZN28e+vr6BAQEMHv2bJ48eYKRkRHz5s3TtLNw4ULKli2rKaQrVqzIuHHjcvQ9P345HPg6l10IIYQQRYQE5b/jHFFRcCTQXrtIaLP2kTHTPjJm2kfGTPtofY7om0hOTsbf35+jR4+iVCopV64c3t7eNG7cuFD68/DhQ7y9vfn222+Ji4vDx8eH77//Pt/tTJ48GU9PT2rUqJHnfV42qKJoqlSpbGF3QeSTjJn20YYxS338hIfJJTs7UointKYQVavVDBs2jDZt2hASEoKuri5RUVEMGzaM8PBwTExM3nmfkpKSiI6OBqBKlSqvVYQCHD58GA8Pj3zt495uAnE3JNBeCCG0zY7Lq6UQFeL/aU0hevjwYW7dusXo0aPR0cl6x8rS0pK5c+eiVqtZsWIFoaGhKJVKrK2t8fLy4tatW3h6etKgQQOio6MxNTUlICAAY2NjbGxs6NSpE8ePH0epVLJ48WJq1aqVazB9rVq1iI6OZtq0aaSmplK+fHkWLFjArFmziI+Px8PDg8mTJ+Pu7s6uXbu4ceMGkydPJiEhgVKlSjFr1iyMjIw06+HfT4saGBgQHx/P8OHDWbduXaEU1EIIURKoSz9GbZJU6NE9hR1i/lRRCDMXQmsK0XPnzmFubq4pQp+ytbVl79697Nq1i+DgYPT09Bg1ahRBQUHY2tpy/vx55syZQ6NGjRg1ahTbtm3Dzc2NO3fuYGVlxdSpU/nmm29Yt24d48aNe2Ew/YQJE5gwYQJ2dnasX7+en3/+GR8fH9zd3fn222+JjY3V9Gn69Ol06tSJAQMGsHfvXpYvX46Xl1eu5zV8+HCCgoJYuXKlFKFCCPEWqcs/BIP0wu5GkQgxf6qww8yF0JpCVEdHBwOD3LO/oqKi6NKlC6VLlwayvlwUEhKCra0tpqamNGrUCIAGDRpkC6tv166dZvmxY8deGEyfkJDAnTt3sLOzA6B///4A2YrPZx09epSFCxcCWYWyra3tC7cVQgjxbugklUWtoy70GdEa71UpMjOihR1mLoTWFKIWFhasX79eEzb/1MKFCzl06BA9evTItv3TvM5ni9fnQ+qfrnu6XK1W5xpMr6enl+2YT548IT4+PtuyZz0bkJ+Zmck///xD6dKlsx07IyMj23ZCCCHeLp3HpdF5XLqwu8GafavlTXEh/l+hBdrn10cffYSpqSlLly7VBMRHRkayefNmBg0aRHh4OKmpqWRkZBAcHIylpWW+j/GiYPqyZctSpUoV9u/fD8DWrVsJCAhAV1dXU/A+39fw8HAADh48yNSpUylXrhyJiYkkJCSQlpZGZGSkZvtnQ++FEEIIIUoKrZmSUygULFu2jLlz59K1a1d0dXUxMTFh5cqVNGrUiFu3buHq6kpGRgY2NjYMHDiQ27dv5+sYLwum9/Pzw9fXFz8/P0xMTJg/fz4mJiZUr14dNzc35s6dq2ln2rRp+Pj4sH79ekqXLs2sWbMoW7Ysn332GT179qRq1ao0adJEs32HDh0YPnw4P/zwA7Vq1cpTX9dELsjXuQkhhCgaUh9LiLkQT0mgvRBCCKElilsGqQTaa58SHWj/tsXGxmaLWHrKzMxM8635gvJsAH5AQAAWFhY4ODjkef9BDpOJv3mvQPskhBCiaIuIXlmsClEhpBAtJM8G4I8ZM6aQeyOEEEII8e5JIZpHycnJfPXVV8TFxREfH4+VlRWzZ8/G2dmZxYsXU79+fcaPH4+RkRHTp0/nxIkTLF++nGXLluHr68ulS5e4e/cuZmZmLFy4kLt372pmX729vWndujUuLi6FfZpCCCEKgNrgEaqyCWQWcFRUQYbhS6C9KAqkEH1OfHw83bp1y7F8z549NGzYkCVLlpCWlkaXLl04e/Ystra2HDp0iPr163Px4kXN9pGRkXTo0IETJ06gp6fHhg0bUKvVDBo0iL1799K4ceN3eVpCCCHeIZVRIpn6BX8LvaDD8CXQXhQ2KUSfU7lyZU2O6FNmZmZ07dqVU6dOsXr1ai5fvkxiYiIpKSnY2tqyevVqLC0tef/997l8+TL37t1j3759LFmyhOrVq2NsbMy6deu4fPkyV65cISUlpZDOTgghxLugTDZGpSj4GdEadSoX6IyoBNqLwiaFaB6tXbuWHTt20Lt3b9q2bcvFixfJzMykZcuWeHt7c/DgQVq3bo2pqSm///47GRkZVK9enZ07d7JkyRLc3d1xcXHh/v37SFCBEEIUbzpPyqDzpEyBt7tm70p5y1wUK1KI5tGBAwfo06cPTk5OnD59mvPnz6NWq9HV1aVp06asXbuWFStWUKlSJaZPn6553vPQoUM4Ojri6urK9evXOXz4MFZWVm/cn593zn31RkIIIYoVySAVxY0Uonk0aNAgfH19WblyJUZGRrRo0ULz/XhbW1uOHj1K/fr1qVSpEvfu3aNDhw4A9OrViwkTJhAeHo6enh4tW7YskO/O37uXjFotM6vaQrLytI+MmfaRMRNC+2hNoH1sbCwODg789NNPWFtba5bb29uzZs0aatasWaDHO3z4MEuXLmXt2rUF2q4QQojCkfo4jYfJMqNYlMg/HrRPiQ6019PTY+rUqYSGhmJk9OKTKgkGO04j/mZCYXdDCCG0xvb/LpVCVIgiRqewO5AflStXpm3btprvvz9v5cqV9OjRA2dnZ+bPn09mZiZffPEFe/fuBWDhwoV89tlnQFZMU9euXfN03Lt37zJy5EhcXFxwdXXl4MGDAAQGBjJ06FA6d+7M+vXrWbVqFc7OznTv3p1p06YBoFKpmDt3rqZfq1evBsDLy4vffvtNcww3Nzf++9//vtZ1EUIIIYTQRlo1Iwrg7e2Nk5MTBw4cyHaLft++fZw5c4ZNmzahUCjw8vIiNDQUW1tboqKisLW15dixY9y+fRuVSkVkZCS2trZ5Oubs2bNxdXXFwcGB+Ph4+vfvT0hICABpaWls374dlUqFtbU1kZGRKJVKpkyZQlxcnOZzoVu2bCEtLY2hQ4diYWGBq6srgYGB9O7dmxs3bpCQkECzZs0K/HoJIYQ2U+k9RFXmLijUb9yWhMELUfRoXSFqZGTEzJkzNbfonzp06BCnTp3SvK2emppK9erV6d+/PyNGjCA5ORnIygQ9e/Ys+/btw83NLU/HPHjwIJcvX2bJkiUAZGRkcP36dQCaNm0KgFKppEWLFvTs2RMHBweGDBlClSpVOHToENHR0URFRQGQkpLChQsX6N+/P1OnTiU2NpatW7fmGqIvhBAlncowgUzd1AJpS8LghSh6tK4QBbCxsclxi16lUjFo0CCGDBkCwIMHD1AqlZQpUwa1Ws0ff/xBy5YtqVixIlFRUZw9e5YWLVpka/fcuXOULl2aunXrkpmZiVKpBECtVvPzzz9jbGwMZN3WNzU15a+//qJUqVKa/ZctW8bJkyfZt28fn332GQsWLEClUuHl5cXHH38MQEJCAmXKlEGhUNC9e3fCw8OJiIjgxx9/fJuXTAghtJIypQKqMuoCmRGtXruShMELUcRoZSEK/96iv3PnDgCWlpYsWbKE3r17Y2BggIeHBz169MDFxYX27duzfPlypk6dSuXKlRk2bBitW7fWFJpP7dq1i5SUFCZOnMiFCxeoVauWpu3169czcuRI/ve//zFgwAB27tyZbd+EhAQGDBjApk2baNGiBbdv3+bChQtYWlry22+/YWdnR1paGv3792f69Om0adMGFxcX+vfvz/vvv0+VKlXezYUTQggtokwvizKxbIG0tWbPUnlDW4giRmsL0ae36IcOHQpkxTidP3+e3r17o1KpaNeuHT169ACgQ4cOrFq1ig8//BBDQ0PS09Oxs7PL0Wa/fv0YO3YsXbp0wdDQkEWLFgHg4+PDtGnTcHJyAmD+/Pk53tqvUKECffr0oWfPnppZVVdXV/T09Lh69So9evQgIyMDFxcX2rRpA0C1atWoVq2app/5sTpiRr73EUKIkiz1ccF/+10I8Wa0Jke0uMnMzCQ+Ph43NzfCwsLQ19fP1/4SaK9dJCtP+8iYaR8ZM+0jY6Z9SnSOaF69i/D7zz//nHr16jFp0iTNsqCgIDZv3syvv/6a47b/89avX8+sWbNYtGhRvotQ4KWDKoqmSpUK5vaieHdkzN49CZ0XomQploUovP3w+xkzZuDs7IyTkxONGjUiLi6OpUuXsmbNmlcWoZD1WdAff/yRTz755LWOP8R5BvG37r/WvkIIUVSFH10khagQJUixLUSfDb+fOXNmjvUrV64kIiIClUqFjY0NXl5ejBgxgn79+mFra8vChQs5d+4cP/zwA/Hx8Xz66aeEhYVp9q9SpQoTJkzAx8eHTZs2MWvWLIYNG0a9evXYvXs3ixcvRq1WU6tWLWbMmEHFihWxt7enadOmREdH4+fnp2lrx44dfPvtt6xevZoKFSq8k+sjhBAvotJ9QIZBHKB658d+k6xPpVLxWvtKJqgQhafYFqLw9sPve/XqRUREBF5eXiQmJuLu7s69e/eYNm0av/76KzVr1uSHH35gxowZmgzS9u3bs3jxYmJjYwHYv38/3377LT/99JMUoUKIIiFD/w6ZyseFcuyCzvrMK8kEFaJwFOtC9F2E38+cORN7e3t27tyJQqHg1KlTNG3aVPMcap8+fVi5cqVm+2e/nnT//n1GjRrFqFGjqFixYoGfvxBCvA7dtEpkKNQUxozom2R9vsmMqGSCClE4inUhCm8v/P6pGjVqAGgKT7U6e+hyZmYmGRkZmr8NDAw0vysUCr799lsmTJhAly5dJEtUCFEkKDPKocwoVyjHXrNm0Wu/RS1vYAuhfXQKuwPvgre3N/v37yc+Ph7ICqjfunUrjx49IiMjAw8PD3bs2AGgCb9v3bo1lpaWrF27lmbNmuXpBSTImvH873//q7n1vmHDBk1u6POMjY2xsrKiX79+zJo1qwDOVAghhBBCexT7GVF4O+H3L1KxYkVmzJiBp6cn6enpVK9endmzZ790n+HDh+Ps7Mxff/1Fx44d83ScVaHT8twnIYTQFhI6L0TJIoH2QgghipzXyROVW/PaR8ZM+0ig/TtiZmbGhQsXOHz4MEuXLmXt2rW4ubnh6emJoaEhQUFBr5zpzM2vv/4KZH1O9E186jqH+NuSIyqEKJ7CDvhJnqgQJYAUoq+hSZMmNGnS5LX2fdMCVAghhBCiuJBC9DU8P0vapEkTjh8/TkJCAj4+Ptja2nL37l2mTJnCzZs30dXV5csvv6R9+/YEBgYC8MUXX/DVV19x6dIlAPr370/v3r0L87SEEOKNqXSSSNe9Cahfue3LvE6w/bPxTRJSL4R2kEK0AKSnp7NhwwZ27dpFQEAAtra2zJw5E0tLS4YMGcL169fp168fISEhmn1OnDhBUlISISEhxMXF4e/vL4WoEELrZejGkanz5mH4BRFsLyH1QhR9UogWgHbt2gHQoEEDEhMTAYiKitJEMtWqVUsT6/RUgwYNiImJYejQobRv356JEye+834LIURB082oQrquijedEa1eq+Ibz4hKSL0QRZ8UogXgaUi9QqHQLHs+jCAzMxOV6t+vlJiYmBAeHs6BAwfYu3cvPXr0IDw8nHLlCidEWgghCoJSXR5lWvk3bmfNGr98v00tb2ALoX2kEH1LLC0t2bRpk+bW/N9//42vry8XLlwAYOfOnYSGhrJ48WLatWvHoUOHuHXrVp4L0Z+Cv3qb3RdCiEIleaJClAxSiL4lU6ZMYdq0aWzevBmAWbNmUblyZc369u3b88cff9ClSxcMDAxwdnbGzMwsz+3fu5eMWi0RsNpCZmq0j4yZEEK8fcU60D42NhYHBwf69OnDjBkzNMujo6Pp3r07c+fOxcXF5bXb9/b2pnXr1m/UBmR/C18IIQpLamoaDx9qb3an/ONB+8iYaR8JtM8nY2NjIiMjUalUmu/Fb9++nQoVKhRyz97Mp33nER+XWNjdEEIUI2G752p1ISqE0D7FvhAtU6YM5ubmHD16FEtLSwAOHDhA27ZtAfjll1/YunUrjx8/Rk9PD39/f+rVq4e9vT1NmzYlOjoaS0tLjI2N+fLLL4GsmdD27dtnO86iRYs4dOgQSUlJVK5cmUWLFlGxYkVsbGzo1KkTx48fR6lUsnjxYmrVqsX+/fuZO3cuBgYG1K1b991eFCGEEEKIIqDYF6IAjo6O7NixA0tLS06dOoWZmRmZmZkkJyeza9cu1q5dS6lSpQgICGDdunVMnToVyHqOc/HixVy/fp1BgwYxduxYUlNTiYqKYvr06ezbtw+Aq1evcvnyZYKCgtDR0WHixImEhoby6aefcufOHaysrJg6dSrffPMN69atY9y4cXh7e/Pzzz9Tv359pkyZUpiXRwhRhKhIJF0RC6heuW1Be50Q+fySoHkhxLNKRCFqb2/P4sWLUavVRERE4OjoyPbt2zEyMsLf35/w8HCuXLlCZGQkDRs21OzXrFkzICsHtEaNGhw9epSbN29ia2uriWwCqFOnDpMmTWLjxo3ExMRw8uRJateurVn/bM7osWPHuHDhApUrV6Z+/foA9OjRg4CAgHdxKYQQRVyG4haZipRCOXZBhMjnhQTNCyGeKhGF6NPb88ePHycqKorx48ezfft2bt26RZ8+fRg4cCDt27enYsWKREdHa/Z7tth0dXUlLCyMmzdvMmrUqGztnzlzhvHjxzN48GA6deqEjo5OthzRZ3NGMzMzNf/51NNnV4UQQjezGumoKIwZ0eo18x8in18SNC+EeFaJKEQh6/a8v78/FhYW6OpmnbahoSF16tRh8ODBpKamsmTJEqpWrZrr/p988gnffvstZcqU0cyUPnX06FFat25Nv379uH//Pnv27OHjjz9+YV/MzMy4e/cu58+fx9zcnPDw8II7USGEVlNijDLTuFCOvWbNXHmDWQjxTpWYQtTOzo4pU6YwZswYzTI9PT3UajWdO3cmMzOTVq1acenSpVz3L1WqFM2bN+eDDz7Isa5z5854enri5OQEgIWFBbGxsS/si56eHgsXLsTLywtdXV0aNWqU7/P5KWhSvvcRQoiXSU2VEHkhxLtVrHNEC0pmZiaPHj2iT58+rF69mkqVKhV2lyTQXstIVp72kTHTPjJm2kfGTPuU2BzRIUOGMGDAADp27AjAvHnzCAoK4vDhw+jr6wNgY2NDUFAQNWvWzFObbm5ueHp60qZNm2zLAwICsLCwwMHBAYDTp0/z2Wef4eHh8coidMmSJbRt25aPPnq7D+K/bFBF0VSpUtnC7kKeaXuwuRBCCO2gNYWopaUlx48f1xSiBw8epHnz5hw/fhwrKyuuXr2KoaFhnovQl3n29j1A06ZNOXLkSJ72PXr0aI7C9m341H2BBNqLtyZsxywpRIUQQrx1WlOIWllZMWfOHADi4uLQ19enU6dO7N+/HysrK44dO4a1tTU+Pj5cunSJu3fvYmZmxsKFC8nIyGDcuHHcvXsXAA8PD81s56ZNm/jmm2948OABU6ZMwd7eXvPpztatW+Pp6UmDBg2Ijo7G1NSUgIAAjI2N2b59O0uWLMHQ0JCGDRuiUqmwtLTkzJkz+Pj4sHTpUvT19Zk2bRqJiYkYGhoyZcoUmjZtire3N0ZGRpw9e5a4uDg8PDxwdXUttGsrtI9KfZ909XXe1pvVbztPUrIkhRBCgBYVoo0bN+batWs8efKE/fv3Y21tjbW1NZ6ennh5eXHs2DFq1KjBvXv32LBhA2q1mkGDBrF3715SUlKoUaMGK1euJDo6mtDQUE0hWrZsWbZs2cLu3btZunQp9vb22Y57/vx55syZQ6NGjRg1ahTbtm2jS5cuzJkzh+DgYCpVqsTo0aMxMjKie/fuBAcH4+npiZmZGT179mT48OF8/PHHnDx5kjFjxrBjxw4Abt++zfr167l48SLu7u5SiIp8yVDfJJNHb639d5EnKVmSQgghtKYQVSqVNGvWjNOnT7N//34GDBhArVq1SE1NJSkpiRMnTjBlyhRu3brFunXruHz5MleuXCElJYUWLVqwcOFC4uLi6NChAx4eHpp2n97qf//997l//36O45qammream/QoAFJSUkcO3aMFi1aUKVKFQC6d+/OX3/9lW2/R48ece3aNU2MU/PmzSlfvjyXL18GwNraGoVCwQcffEBiYmKBXy9RvOnqVCdd/fayJqvXMH3rM6KSJSmEEEJrClHIek7077//5tSpU/j5+QFZt+x37tyJiYkJhw8fZsmSJbi7u+Pi4sL9+/fJzMzkvffeIyIigsjISHbv3s1PP/3E9u3bgX/D5BUKRa7HfDbU/mkQvY6ODmq1+qV9zS2MIDMzE5VKla3dFx1XiJdR6pig1DF5a+2vWTNL3mQVQgjx1ukUdgfyw8rKiq1bt/LBBx9oQumtra1ZtWoV1tbWHDp0CEdHR1xdXSlXrhyHDx9GpVLxyy+/EBgYiKOjI19//TUJCQkkJye/dj9atmzJ6dOniY+PJzMzk+3bt2sKSqVSiUqlwsjIiJo1a/LHH38AcPLkSe7evUuDBg3e/EIIIYQQQhQDWjUj+vQ2dv/+/TXLLC0tGTt2LG3btqVMmTJMmDCB8PBw9PT0aNmyJbGxsQwbNoxx48bh5OSEUqnEy8uLcuXKvXY/KlSogI+PD59++in6+vrUrFlT0167du34+uuvmTdvHn5+fvj6+hIYGIienh6BgYGaqKk39dOaCQXSjhC5kWBzIYQQ74IE2r+G+/fvs3btWjw9PdHR0WHWrFnUqVMHNze3d9YHCbTXLhLarH1kzLSPjJn2kTHTPsU60D42NpZPPvmE+vXrA6BWq3n06BHdu3dn9OjRHD58mKVLl7J27drXan/z5s0cOXKEb775Jk/bBwYGAjBq1Khsy42NjXnw4AHt27fnyZMnODg40Lt379fq01NmZmZcuHAhz9tLoL320aZAe5GloMZMPhAghBC5K1KFKEDlypXZunWr5u+4uDg6depEly5dCrFX2SkUCnx8fGjUqFG+CtuCNOTTRcTHJ77z4woh8i88bLoUokIIkYsiV4g+786dO2RmZlKmTBnu3r1LQkICw4YN49q1a9StW5clS5agr69PcHAwq1atQqFQ0LhxY6ZOnUqZMmUICQlh+fLlGBkZUaNGDQwNDQE4deoUc+fOJTU1FRMTE6ZPn06tWrVe2I9Vq1axZcsWdHR0aNq0KTNmzMi2PiIiglWrVpGamkpaWhpz5syhZcuWuLm50aRJE44fP05CQgI+Pj7Y2toSGxuLl5cXKSkpNGvW7K1eQyHE26NSJZCRfpWXRWnl5QMBEvIvhCiJilwhGh8fT7du3Xjy5An379+nSZMmLF26lKpVq3L16lVu3rzJihUrqFGjBr179+bgwYNUq1aNFStW8Ntvv2mKyqVLlzJ48GAWLFhASEgIxsbGfP755xgaGpKWloaPjw8rVqygevXqREZGMnXqVFavXp1rn1QqFd999x2RkZEolUqmTJlCXFycZr1arSYoKIgVK1ZQoUIFNm3axMqVK1mxYgUA6enpbNiwgV27dhEQEICtrS0zZ87ExcWFXr16ERISwoYNG97F5RVCFLCMjFgyM1/+cYG8fiBAQv6FECVNkStEn96aV6vVfPPNN/zzzz9YW1tr1pubm2tmLuvXr8/9+/eJjY3Fzs4OE5OsXMU+ffowefJkmjVrRosWLahYsSIATk5OREVFceXKFa5fv86IESM07b4szkmpVNKiRQt69uyJg4MDQ4YM0YTZA+jo6PDtt9+ya9cuYmJiOHLkCDo6/yZjtWvXDsgKxH8aXn/kyBH8/f0BcHZ2xsfH500umxCikOjq1nzljGj16q/+QICE/AshSqIiV4g+paOjw8SJE+nevTs//vgjw4YNA9Dkh8K/AfPPh8tnZmaSkZGhWf/U033VajU1a9bUPIuqUqk036F/kWXLlnHy5En27dvHZ599xoIFCzTrHj16RM+ePXF2dqZVq1aYmZmxbt06zfoXhdc/7ZtCochWuAohtIdSWQGlssJLt1mzZrq8GSyEELko0tWPrq4uEydOZNmyZdy5c+eF27Vu3Zpdu3ZpZht/++032rRpw4cffsjJkyeJi4tDrVZrvqZUr149zac6AYKDg5kw4cW5nAkJCXTu3JkPPviAMWPGYG1tne0N9ytXrqBQKPjiiy9o06YNf/75p+YLSi/Stm1bQkNDAfjjjz948kReZBBCCCFEyVJkZ0Sfat++PS1atCAgIAAnJ6dctzE3N+fzzz/Hzc2N9PR0GjduzPTp0zEyMsLHx4fBgwdTunRp3n//fQD09fUJCAhg9uzZPHnyBCMjI+bNm/fCPlSoUIE+ffrQs2dPSpcuTd26dXF1deX333/XHL9hw4Y4OjqiUCiwsbHh+PHjLz2vadOm4eXlxYYNG7CwsKBMmTL5ui6rfvoyX9sLIQqPfCBACCFyJ4H2Qmg5yah8OyRoW/vImGkfGTPtU6wD7QvDo0ePWLBgAfv376d06dIYGRkxatQorKysCrtrLzVo+GLJERUARIT4SiEqhBBCK5XoQjQzM5MvvviChg0bEh4ejr6+PufOnWP48OH4+/vTpk2bwu6iEEIIIUSxVaIL0SNHjnDz5k3WrFmjeaO9UaNGjBgxgmXLlrF06VLKly/PpUuXWLx4MXfu3GHJkiVkZGRQs2ZNZs6ciYmJCYcPH2bWrFkolUqaN2/OP//8w9q1a4mJiWHatGkkJiZiaGjIlClTaNq0Kd7e3hgZGXH27Fni4uLw8PDA1dW1kK+GKEzqjARUT66Smfnyl9xyk5ew9JeRIHUhhBCFpUQXoqdPn8bCwiJHrFKrVq3w9/encePGmJmZsXTpUhISEvD29mbNmjWUL1+eoKAgFixYgK+vLxMnTuS7777D3NycWbNmadrx8vJi+PDhfPzxx5w8eZIxY8awY8cOAG7fvs369eu5ePEi7u7uUoiWcKq0WDLVL86yfZm8hqW/jASpCyGEKAwluhBVKBS5xiylp6dritOmTZsC8N///pdbt27h7u4OZGWRli9fnosXL2Jqaoq5uTkAPXv2ZPbs2Tx69Ihr167x8ccfA9C8eXPKly/P5cuXAbC2tkahUPDBBx9oYqdEyaXUr4nqieq1ZkRrVK/wxjOiEqQuhBCiMJToQrRZs2asXbuW9PR09PT0NMtPnjyJhYUFarWaUqVKAVmh9y1bttR8tvPJkyc8evSI+Pj4HIH6ALmFEWRmZmoK3xeF3IuSSUe3Ajq6Lw9Ff5E1a3zlrVMhhBBaqUQXoh999BHvv/8+c+bM4auvvkJPT48zZ86wfPly/P39Wbp0qWbbZs2a4ePjQ0xMDHXr1mXZsmXExcUxY8YMHjx4wIULFzAzM2Pbtm0AGBkZUbNmTf744w/Nrfm7d+/SoEGDAun7zyvHFkg7QvtJRqUQQghtVaILUYClS5eyaNEiunbtilKppHz58vj5+dGmTZtshWilSpWYM2cOY8eORa1WU6VKFfz8/NDX12f+/PlMmjQJHR0d6tatq5lF9fPzw9fXl8DAQPT09AgMDERfX79A+n3vXjJqtUTAagvJyhNCCCFyKjaB9rGxsTg4ONCnTx9mzJihWR4dHU337t2ZO3cuP//8s+b78gVFrVazYMECPD09MTQ0ZNWqVcTFxeHt7Z1j2927d3PlyhWGDBlSoH0QoqjTxtB9+ceD9pEx0z4yZtpHAu1fwtjYmMjISFQqFUqlEoDt27dToULWs3cFXYQC6OjoYGxsTM+ePdHT06NGjRrMnj07123PnDlTYMd1H7GYuDtJBdaeEG/Tjk1fa10hKoQQ4u0rVoVomTJlMDc35+jRo1haWgJw4MAB2rZtC4CZmRkXLlwgMDCQuLg4rl69yo0bN+jVqxcjRoxg8+bNREZGkpSUxPXr17G2tsbX1xeAlStXEhERgUqlwsbGBi8vLx49esS4ceO4e/cupUqVwsPDAwcHB2JiYhg9enS2/FBDQ0OCgoIAqF69OjY2Nnz11Vc8fPiQ+Ph4evTowZgxYwrlugmRV+r0e6gfX4V8vt3/Olmnkm8qhBDFX7EqRAEcHR3ZsWMHlpaWnDp1CjMzs1zfYL9w4QLr1q3j4cOHdOzYkQEDBgBw4sQJwsLCUCqVfPLJJ/Tr14+4uDjOnDnDpk2bUCgUeHl5ERoailqtpkaNGqxcuZLo6GhCQ0NxcHB4YX5o3759AXB1deXHH3+ka9eu9OjRg4cPH2Jra4ubm5tm9laIokidGguq/Oedvm7WqeSbCiFE8VbsClF7e3sWL16MWq0mIiICR0dHtm/fnmO7Nm3aoK+vj6mpKcbGxjx8mPW8Q4sWLTAyynqWoVatWiQlJXHo0CFOnTqFi4sLAKmpqVSvXh1XV1cWLlxIXFwcHTp0wMPD45X5oU8NHTqUqKgofvzxRy5dukR6ejqPHz9+m5dGiDemU6om6seqfM+I1qiW/6xTyTcVQojir9gVok9vzx8/fpyoqCjGjx+fayH6NMcTsrI8n86a5rZcpVIxaNAgzUtGDx48QKlUUqZMGSIiIoiMjGT37t389NNPbNq0Kcexns0Pfeqbb77h+vXrdO3alY4dO3Lw4MFcZ26FKEp09EzR0TPN935r1nwtLyQIIYTIQaewO/A2ODo64u/vj4WFBbq6b15rW1pasnXrVh49ekRGRgYeHh7s2LGDX375hcDAQBwdHfn6669JSEhArVZr8kOBbPmhSqWSjIwMIOvZ1aFDh+Lo6EhMTAxxcXG5BuMLIYQQQhRXxW5GFMDOzo4pU6YU2Ms/9vb2nD9/nt69e6NSqWjXrh09evTQvKzk5OSEUqnEy8uLcuXKvTA/tFWrVkyaNImKFSvy+eefM3HiREqVKkXVqlWxsLAgNjaW2rVr56lPa5aPLZBzE+JdkNB9IYQQuSk2OaIljQTaaxfJytM+MmbaR8ZM+8iYaR/JEc2j2NhYPvnkE+rXrw9kvWDUsmVLxo8fT8WKFV+4j7u7O7t27XqXXX0tLxtUUTRVqlS2UI+vjaHyQgghirdiW4gCVK5cWRNin5mZycKFCxk9ejTr168v5J69uYGjAiTQXuTLn0HTpBAVQghRpBTrQvRZCoWCUaNGYW1tzfnz59m3b1+OgPpnXbx4kZkzZ5KSkkJCQgLDhw+nU6dOdOvWjcjISADatWvH5MmT6dy5M9999x1KpRInJ6dcg+o3b97Mli1bSExMxM7ODnd3d6ZNm8bt27dRKBSMHz9eE7wvxPMyn9wjM+VKvmOTnvU6ofK5kaB5IYQQBaXEFKIA+vr61KlTh/Pnz+caUP/hhx9qtt24cSMjR47EysqK69ev4+zsTL9+/ahWrRoXL15EqVSiUqk4cuQInTt3JjIykunTpxMWFpZrUD1AXFwc27dvR1dXly+//BJXV1ccHByIj4+nf//+hISEaDJMhXhW5uPrkJH/IPlnvW6ofG4kaF4IIURBKFGFKGTNjK5Zs4aEhIQcAfXPFqLe3t5ERkby3XffcfHiRVJSUgBo3749hw4dQldXF3d3d8LDw3n48CF3796lfv361K9f/4VB9Y0aNdLESR08eJDLly+zZMkSADIyMrh+/ToNGzZ8l5dDaAlF6VpkZuY/SP5ZNarmP1Q+NxI0L4QQoqCUqEI0LS2NmJgY2rRpg5OTU46A+vv372u2HTt2LOXKlcPOzo7OnTsTFhYGQIcOHVi6dCn6+vqMGTOGiIgItm3bho2NDfDyoPpSpUpp2ler1fz8888YGxsDEB8fj6lp/oPCRcmgMDBFYfBm//1Ys2aavJ0qhBCiSCmWgfa5UavVBAYG0qxZM1xdXXMNqH/WgQMHGD16NB07dmTfvn0AqFQqGjduTExMDFeuXKF+/fq0adOG5cuXY2dnp9kvL0H1lpaWmpem/ve//+Hk5CSf+BRCCCFEiVKsZ0Tj4+Pp1q0bkFWINmzYkIULF1K+fPlcA+qffYZu1KhR9O/fHwMDA8zNzalRowaxsbHUqVOHDz/8UFM0WlpasnHjRlq1agXwwqD65/n4+DBt2jScnJwAmD9/fr6eD/0lsGDC+kXJIaHyQgghihoJtBdCFJqinG0qQdvaR8ZM+8iYaZ9iHWj/bAi9QqEgPT2dypUrM3fuXKpWrfrO++Pt7U3r1q01LzW9qZ07d3LmzJkC+fRo/7FLiLsrOaJCu+38ZWqRLUSFEEK8fUWqEIXsIfSQ9fLP/PnzWbhwYSH2qmA4ODjg4OBQ2N0QQgghhCgSilwh+rw2bdqwcOFCTp06xdy5c0lNTcXExITp06dTq1Yt3NzcaNKkCcePHychIQEfHx9sbW3x9vbGyMiIs2fPEhcXh4eHB66urhw6dAg/Pz8Aypcvj7+/P2vWrCEzM5Mvv/wSyJoJbd++vaYPc+fOpUqVKnz66adA1vOjzs7O1KlTJ0fofb9+/QgMDCQuLo6rV69y48YNevXqxYgRI9i8eTNHjhzhm2++ISIiglWrVpGamkpaWhpz5syhZcuW7/4CC/EmUu/Bw5jXjpXKb8i+hOkLIUTxUqTfmk9PT2fHjh1YWFjg4+ODv78/W7ZsYciQIUydOjXbdhs2bGDy5MkEBARolt++fZv169ezfPly5s+fD8CyZcvw9fVl8+bNtG3blnPnzuHq6sq2bdvIzMzk8ePHREVFZZu57Natmya+KTk5mRMnTmBra6sJvQ8ODmbNmjWaYwBcuHCBH3/8kY0bN7Jy5UoePHigWadWqwkKCmLFihWEhoby2WefsXLlyrd2HYV4ax5dywraVz1+rZ8bN25w+/bNPP/ExFwmLCyksM9aCCFEASlyM6LPvumelpZG06ZNcXV15ffff2fEiBGa7ZKT//3KTLt27QBo0KABiYmJmuXW1tYoFAo++OADzXIHBwc8PT3p2LEjDg4OWFtbA1CjRg2OHj3KzZs3sbW1xcDAQNNOo0aNSEtL4+rVq5w4cQJ7e3v09fVfGHoPWTO5+vr6mJqaYmxszMOH/z7Yq6Ojw7fffsuuXbuIiYnhyJEj6OgU6X8TCJG7MrVB/fozovkN2ZcwfSGEKF6KXCH6/DOiAOfPn6dmzZqa5SqVirt372rWPy0aFQpFtv1yWz548GDs7OzYvXs3fn5+nDp1ihEjRuDq6kpYWBg3b95k1KhROfrl7OzM9u3bOXHiBMOHDwdeHHr/7LGfHv/ZcIJHjx7Rs2dPnJ2dadWqFWZmZqxbty5/F0qIoqCUadbPa1qzZqq8MSuEECVYkStEc1OvXj2SkpI4duwYH330EcHBwWzbto21a9fmu61evXoxffp0Bg8ejLGxMTt37gTgk08+4dtvv6VMmTI0a9Ysx35OTk4MHz6ctLQ0zadADxw4QEREBFWqVNEUkirVq2eGrly5gkKh4IsvviAzM5OJEyfmab9nrV88Ol/bC1EUSbapEEKUbFpRiOrr6xMQEMDs2bN58uQJRkZGzJs377XaGjduHN7e3ujq6mJoaMisWbOArM9vNm/enA8++CDX/apVq4aJiQktWrTQzLC+KPT+VczNzWnYsCGOjo4oFApsbGw4fvx4vs7j3r1k1GqJgNUWkpUnhBBC5FTsA+2Tk5Px9/fn6NGjKJVKypUrh7e3N40bN9Zsk5mZyaNHj+jTpw9z585l7Nix7Nq1q8D7YmZmxoULFwq8XVG4Up+k8fDBy7MwpRDVPjJm2kfGTPvImGmfYh1oX9DUajXDhg2jTZs2hISEoKurS1RUFMOGDSM8PBwTExMATp8+zWeffYaHhwcVKlQo5F7nTd8JEmhfVOxePZWHSCi7EEIIkV/FuhA9fPgwt27dYvTo0Zq30i0tLZk7dy5qtVoTn6RUKnFxcWHgwIHcunVLs//du3eZMmUKN2/eRFdXly+//JL27dsTGBjIyZMnuXXrFgMHDuT9999n0aJFpKam8uDBAyZPnkzHjh2JjY3Fy8uLlJSUbM+dPn78GB8fHy5cuIBCoWDo0KF07979XV8eIYQQQohCVawL0XPnzmFubp4jGsnW1pa9e/eya9cugoOD0dPTY9SoUQQFBWFra6vZbubMmVhaWjJkyBCuX79Ov379CAkJAbKipbZv3w7A6NGjmTVrFvXr1+fQoUPMmTOHjh07MnPmTFxcXOjVqxchISFs2LABgMDAQExMTAgLCyMhIYFevXphbm6Oubn5u7kwIrvH91Akvd1QdqVSkes2EtAuhBCiJCvWhaiOjk62GKVnRUVF0aVLF0qXLg2Aq6srISEh2QrRqKgozctMtWrVolmzZvz3v/8FoGnTpprt/Pz82L17N7///jv//e9/efToEQBHjhzB398fyIp/8vHx0bQ7Z84cACpUqICDgwNHjhyRQrSQKB5eQ5Ge/OoNX+DGjRtvdPywsBApRIUQQpRIxboQtbCwYP369WRmZmbLEl24cCGHDh2iR48e2bbPyMjI9vfz73FlZmZqYpZKlSqlWd6/f3/atGlDmzZtsLKyYsKECTnaUCgUmpnZl7Ur3r3Msm8Wyl6zyqtD2V82IyoB7UIIIUqqYl2IfvTRR5iamrJ06VJGjhyJUqkkMjKSzZs3M378eH799Vf69OmDrq4uwcHBWFpaZtvf0tKSTZs2aW7N//333/j6+mZ78z0xMZErV66wfv169PX1WbBggaaobNu2LaGhoQwYMIA//viDJ0+eZGvXx8eHhIQEdu7cSWBg4Lu7MCK70qZkln6DUPbVrw5llzdDhRBCiJyKdSGqUChYtmwZc+fOpWvXrujq6mJiYsLKlStp1KgRt27dwtXVlYyMDGxsbBg4cCC3b9/W7D9lyhSmTZvG5s2bAZg1axaVK1fOdgxjY2N69uxJly5d0NXVxdLSktTUVFJSUpg2bRpeXl5s2LABCwsLypQpA4CHhwe+vr44OTmhUqn44osvssVJ5UXQAgm0LypSn0gouxBCCPE6in2OaHElgfbaRWZEtY+MmfaRMdM+MmbaR+tzRGNjY3FwcOCnn37C2tpas9ze3p41a9ZQs2bNPLVz/fp1li9fzpw5czh8+DBLly595Sc/09LS+Pbbb9m1a5fmRaaxY8fStm3bNzonyDovd3d3du3aRUBAABYWFjg4OLxxuy/yskEVRVOlSmULuws8fpJG8ivC94UQQoh3pVBuzevp6TF16lRCQ0MxMnq9gurmzZtcv349X/tMnjwZfX19Nm3ahIGBARcuXODTTz/l559/5v3333+tfuRmzJgxBdbWi/SZuITb9yTQXuTP3h+nkizh+0IIIYqIQilEK1euTNu2bZk3bx4zZ87Msf7ZoHlra2u8vLy4desWn332GSYmJpQqVYq7d+8SGxvL9OnT+eSTT0hISGDYsGFcu3aNunXrsmTJEvT19TVtXr16lT/++IPDhw9rIp3MzMxYuHCh5g34RYsWcejQIZKSkqhcuTKLFi2iYsWKWFpaYmFhwZ07d9i0aRM//vhjjv49y9vbm9atW9O6dWs8PT1p0KAB0dHRmJqaEhAQgLGxMb/88gtbt27l8ePH6Onp4e/vT7169d7iVRfFmSLlHjqJMaB++Zv/r8o8lVxTIYQQ75LOqzd5O7y9vdm/fz8HDhzItvzZoPktW7Zw9epVgoKCAIiJicHPz49Vq1bh4+ODhYUFX3/9NZA1Qzpt2jQiIiK4e/cuBw8ezNZudHQ07733HoaGhtmWt2nThpo1a3L16lUuX75MUFAQO3bsoFq1aoSGhgJw//59hg0bxtatWzl48OAL+5eb8+fPM2TIEMLCwihXrhzbtm0jOTmZv/76i7Vr1xIWFkaHDh1Yt27dG19TUXLpJF1DkZaMIuPxS39u3LjB7ds3X/gTE3OZsLCQwj4dIYQQJUShvTVvZGTEzJkzNbfon3pZ0LypqekLnyE1NzenVq1aANSvX5/79+9nW/+ycHuAOnXqMGnSJDZu3EhMTAwnT56kdu3amvVPP9GZlyD8Z5mamtKoUSMAGjRoQFJSEkZGRvj7+xMeHs6VK1eIjIykYcOGL71eQryMunztPM2IvirzVHJNhRBCvEuFGt9kY2OjuUX/lFqtzrHd06D5Z0Pkn6er+++pKBSKHKHxFhYW/PPPP6SmpmZrZ/Xq1VSqVIk6deowfvx4Bg8eTKdOndDR0cnWxtN9Xta/3Dxb/D7t161bt3Bzc2PgwIG0b9+eihUrEh0d/cI2hHiVTENTVIavzkJd8+OrM0+FEEKId6XQbs0/9fQWfXx8PJAV9h4eHk5qaioZGRm5Bs0DKJXKlxaAz6tevTodOnRg5syZmmD5c+fO8cMPP9CgQQOOHj1K69at6devH++99x579uzJ9WtHee3fy5w+fZo6deowePBgmjRpwl9//SVfVhJCCCFEiVPogfZPb9EPHToUADs7O6Kjo18aNA9Zt98fPnyIl5cXPXv2zNOx5syZw4IFC+jWrRv6+vqULl0aPz8/PvjgA8qXL4+npydOTk5A1gxqbGxsjjby2r+Xsba25tdff6Vz585kZmbSqlUrLl26lOf9ATbMl0B7kX+PJXxfCCFEESKB9kIraHv+pYQ2ax8ZM+0jY6Z9ZMy0j9YH2hcVBRWsnx8F2Xavr0pWjmjkd5J/KYQQQhQ3hf6MaGF6GqyfnJxc2F0RQgghhChxSuyMKLw8WH/lypVERESgUqmwsbHBy8uLb775hipVqvDpp58CMGrUKJydnalTpw4zZ84kJSWFhIQEhg8fTr9+/UhMTMTLy4vbt29Tv359zUtSycnJfPXVV8TFxREfH4+VlRWzZ89GoVC882tQ2BSP7qFMiEHxhkHsz5NgdiGEEKLoK9EzopB7sH5kZCRnzpxh06ZNhISEEBcXR2hoKN26dSMsLAzIKiZPnDiBra0tGzduZOTIkQQHB7NmzRrmz58PwJIlS2jUqBHbtm1jwIAB3L17F4A9e/bQsGFDNmzYwI4dOzh69Chnz5599ydfBCjvX0PnSTKK9Mcv/XlVELsEswshhBDap0TPiELuwfqHDh3i1KlTuLi4AJCamkr16tXp1q0baWlpXL16lRMnTmBvb4++vj7e3t5ERkby3XffcfHiRVJSUgA4cuQI/v7+ALRq1UoTuN+1a1dOnTrF6tWruXz5MomJiZp9ShqVSW3Iw4xozcovD2J/ngSzCyGEEEVfiS9EIWewvkqlYtCgQQwZMgSABw8eoFQqAXB2dmb79u2cOHGC4cOHAzB27FjKlSuHnZ0dnTt31syaPh+s/7SNtWvXsmPHDnr37k3btm25ePFijgD+kiKzjCkZZfIQxP6dBLELIYQQxY0Uov/P29sbJycn7ty5w5AhQwgODqZ3794YGBjg4eFBjx49cHFxwcnJieHDh5OWlsaHH34IwIEDB4iIiKBKlSqab8arVCqsrKzYunUr5ubmnDp1imvXrmm279OnD05OTpw+fZrz58/n+sWml9k4p2TliEr+pRBCCFH8SCH6/54N1rezs+Phw4f07t0blUpFu3bt6NGjBwDVqlXDxMSEFi1aaF4uGjVqFP3798fAwABzc3Nq1KhBbGwso0ePxtvbmy5dulCvXj3NrflBgwbh6+vLypUrMTIyokWLFrmG57/MvXvJqNUlcxZVCCGEEMWDBNo/IzY2lk8++YT69etnW25sbEy3bt00z4y+yunTpwkKCmL27Nk52nd3d2fXrl0F1mdRvGh7cH9xIkHb2kfGTPvImGkfCbR/yypXrszWrVuzLfP29s5XG02aNKFJkyYF2a0cXH2WcDuh5ATalxQHlklwvxBCiJJDCtF8Cg4OZtWqVSgUCho3bszUqVMpU6YMlpaWWFhYcOfOHSZOnMiKFStYu3Yt586dY8qUKQCYm5tr2rl48WKu2aNCCCGEECWFFKLPiY+Pp1u3bpq/nZycNL9fuHCBFStW8Ntvv2FiYsL06dNZunQpkyZN4v79+wwbNow2bdpw+PBhzT6TJk3C29sba2trvv32W826p9mjVlZWXL9+HWdnZylESxCd5Hvo3o2B52KrXhTcLwH9QgghiiMpRJ/zslvzR48exc7ODhMTEwD69OnD5MmTNds1a9Ys234JCQnEx8drvmXv4uJCcHCwps3cskdFyaCbkBXk/7wbN268cJ+wsBApRIUQQhQrUojmw/MRS5mZmWRkZGj+LlWqVLb1L8oRhRdnj4qSIaNC7VxnRGu9ILhfAvqFEEIUR1KI5kPr1q1Zs2YNI0eOxNjYmN9++402bdq8cHsTExOqV6/Onj176NChQ7Zi80XZo88Wq6L4UhuZkmaUM8h/zTIJ7hdCCFFySCGaD+bm5nz++ee4ubmRnp5O48aNmT59+kv38fPzY/LkySxevJjmzZtrlr8oe7ROnTp56kvwrJIVaF9SSHC/EEKIkkRyRLWUBNprF8nK0z4yZtpHxkz7yJhpH8kRzafY2FgcHBzo06cPM2bM0CyPjo6me/fuzJ07l59//jnHC0rPMjMz48KFCzmWDxs2jFmzZlGlSpW30veXedmgiqKpUqWyb7S/hN0LIYQobop9IQpZX0aKjIzM9gzm9u3bqVChAsBLi9CX+f777wusj/nl8rUE2pc0BwMl7F4IIUTxUiIK0TJlymBubs7Ro0extLQEsl4Watu2LfDvjGdiYiJTpkzh8uXL6Ovr4+3tjZWVFQDTpk3j5MmTAAQGBlKnTh3s7e1Zs2YNVapU4euvv+b48eNUqVIFhULByJEj+fDDD/H19eXSpUvcvXsXMzMzFi5cyN27d/H09KRBgwZER0djampKQEAAxsbGhXF5RBGl8/Aeenf+fbP++YxRyRYVQgih7XQKuwPviqOjIzt27ADg1KlTmJmZoaenl22bgIAAateuTUREBPPnz2fx4sWadW3btiU0NBRra2uCgoKy7RcUFMTjx4/5/fffmTt3LqdPnwbgxIkT6OnpsWHDBv78808ePnzI3r17ATh//jxDhgwhLCyMcuXKsW3btrd49kIb6d67hk5qMjppj9FJe8yNGze4ffum5icm5jJhYSGF3U0hhBDitZWIGVEAe3t7Fi9ejFqtJiIiAkdHR7Zv355tm6NHj7JgwQIga5Z0w4YNmnUdO3YE4P333+fYsWPZ9jtw4AC9e/dGoVBQo0YNzSxqq1atMDY2Zt26dVy+fJkrV65ogutNTU1p1KgRAA0aNCApSW6zi+wyTGujUP87I1qrUoUcM6KSLSqEEEKblZhC9Ont+ePHjxMVFcX48eNzFKK6urooFArN3//88w9169bVrIOcIfWQFVT/fNg9wM6dO1myZAnu7u64uLhw//59zb4GBgaa7XJrUwh1WVOelP03a3RNoGSMCiGEKF5KzK15yLo97+/vj4WFhaawfNZHH31EeHg4kFWEDhs2LFth+iJt27Zl+/btZGZmEhcXx5EjR1AoFBw6dAhHR0dcXV0pV64chw8fRqVSvbI9IYQQQoiSoMTMiALY2dkxZcoUxowZk+v60aNH4+Pjg7OzM7q6usyfPz9PhWjv3r05f/48Tk5OVKpUierVq1OqVCl69erFhAkTCA8PR09Pj5YtWxIbG1sg57J5ugTalzQSdi+EEKK4kUD7ArBnzx4yMzOxs7Pj4cOHdO/eneDg4Lf6FrwE2msXCW3WPjJm2kfGTPvImGkfCbR/gSFDhjBgwADNS0Xz5s0jKCiIw4cPo6+vD4CNjQ0NGzYs8BD6+vXrM3HiRM1b9qNHj861CC3IAHwJtNc++Qm0l/B6IYQQJUGxKUQtLS05fvy4phA9ePAgzZs35/jx41hZWXH16lUMDQ3fSgh9rVq1+PXXX1+5XUEeu8eMJdy6L2/aF1dRiyS8XgghRPFXbApRKysr5syZA0BcXBz6+vp06tSJ/fv3Y2VlxbFjx7C2ttaE0CcnJzNt2jQyMjIwMDBg7ty5vPfee2zbto3ly5ejUCho0qQJM2fOJCMjAx8fHy5cuIBCoWDo0KF0796dzZs3ExkZSVJSEtevX8fa2hpfX19u377NhAkTSElJQUdHBx8fH5o3b6459pEjR3LdT5Q8ygf30IuLQfHcS2wSXi+EEKIkKDaFaOPGjbl27RpPnjxh//79WFtbY21tjaenJ15eXhw7dgwHBwdNoPzPP//MkCFDcHR0ZMuWLZw8eZLSpUszd+5cNm/eTNWqVfHy8mLv3r38/fffmJiYEBYWRkJCAr169cLc3BzICq0PCwtDqVTyySef0K9fP/788086dOjAZ599xr59+zh+/DjNmzfP1t/c9jMzM3vXl00UMr0711A+Ts6x/MaNGzmWhYWFSCEqhBCiWCk2hahSqaRZs2acPn2a/fv3M2DAAGrVqkVqaipJSUmcOHGCKVOmaLa3tbVlxowZREZGYm9vj52dHX/++SctW7akatWqAPj5+QGwbNkyzWxrhQoVcHBw4MiRIxgZGdGiRQuMjLKe16xVqxZJSUlYWVkxatQooqOjsbW1ZeDAgTn6m9t+ouRJr1Qb1DlnRCW8XgghRElQbApRyHpO9O+//+bUqVOaItLKyoqdO3diYmKiKfwAPvnkE1q0aMHu3btZvXo1e/bsoUOHDtnimhISEgByhM1nZmZq8kBzC6b/8MMPCQ8PZ8+ePWzfvp0tW7awatWqbG1IoL0AUJUzRVXONMfyNYskvF4IIUTxV6wC7a2srNi6dSsffPCBJrDe2tqaVatWYW1tnW3bsWPHcvr0afr27cuYMWM4d+4cTZo04eTJk9y5cweAOXPmsHPnTiwtLdm0aROQVZzu3LmT1q1bv7Af8+fPJzQ0lB49ejBt2jTOnTv3ls5YCCGEEEJ7FasZ0Q8++IDExET69++vWWZpacnYsWNp27Zttm2/+OILpkyZwrfffouenh6+vr5UqVKFKVOmMHToUNRqNc2bN8fFxYXHjx/j6+uLk5MTKpWKL774gsaNG3PhwoVc++Hm5sb48ePZvHkzSqWSefPmFfi5bpkmgfbFmYTXCyGEKAkk0F6IIkByQ4seCdrWPjJm2kfGTPtIoH0RYGZm9sLZ0Hel22zJES1OjiyQ3FAhhBAlT7F6RlQIIYQQQmgPmRF9QytWrCA0NBSlUom1tTVeXl54eHjQr18/bG1tWbhwIefOneOHH34gPj6eTz/9lLCwMBYtWsShQ4dISkqicuXKLFq0iIoVKxb26Yh3QJl0D4NbMSjU/0Y2PR9gDxJiL4QQoviTGdE3sHfvXnbt2kVwcDBbtmzh6tWrBAUFYWtrS1RUFADHjh3j8uXLqFQqIiMjsbW15erVq1y+fJmgoCB27NhBtWrVCA0NLeSzEe+KfnxWiL3Ok8eanxs3bnD79s1sPzExlwkLCyns7gohhBBvjcyIvoGoqCi6dOlC6dKlAXB1dSUkJIQpU6YwYsQIkpOzvphjZmbG2bNn2bdvH25ubtSpU4dJkyaxceNGYmJiOHnyJLVr1y7MUxHvUFrl2ihU2WdEa1WskOuMqITYCyGEKM6kEH0DarU6x7KMjAyqVauGWq3mjz/+oGXLllSsWJGoqCjOnj1LixYtOHPmDOPHj2fw4MF06tQJHR0dCbQvQVTlTUkpnz3Efs0CCbAXQghR8kgh+gYsLS1Zvnw5ffr0QVdXl+DgYCwtLQFo3749y5cvZ+rUqVSuXJlhw4bRunVrlEolR48epXXr1vTr14/79++zZ88ePv7443wde+sUyREtTiQ3VAghREkkhegbsLOzIzo6GldXVzIyMrCxsdF8V75Dhw6sWrWKDz/8EENDQ9LT07GzswOgc+fOeHp64uTkBICFhQWxsbH5Ova9e8mo1TKLqi0kK08IIYTIqUQH2vfr14+BAwfSpUsXzbKUlBRatGhB+/bt+f777/Pc1pQpU+jbty9NmjTJdX1cXBw+Pj75alO8maIUEi+FqPaRMdM+MmbaR8ZM+xR0oH2JLkQ3bdrEX3/9xYoVKzTLQkJC2LVrF0uWLCnEnr2a8zcBEmj/CkfnTSsy/wMn/2OrfWTMtI+MmfaRMdM+8mWlAuTo6Mj8+fNJTEzE2NgYgNDQUDp27Ii9vT27du3C29ubxMRErl69ipeXF2XKlGHWrFkolUqaN2/OP//8w9q1a3Fzc8PT0xOA7777jlKlSvHPP/9gZmbGggULiI+Px93dnV27dnHx4kVmzpxJSkoKCQkJDB8+nH79+hXildB+uon3MLh5BVSSzSmEEEJoixKdI1qmTBkcHBz4/fffgazb5zExMdjY2GTbztjYmIiICNq1a8fEiRPx8/MjJCQEXd3c6/gTJ04wbdo0IiIiuHnzJvv378+2fuPGjYwcOZLg4GDWrFnD/Pnz384JliD6t6+jTElG+eSx5keyOYUQQoiirUQXogAuLi6EhYUBsG3bNpydndHRyX5ZmjZtCsDFixcxNTXF3NwcgJ49e+baZoMGDahatSo6OjrUr1+fpKTst9C9vb158uQJ3333HYsXLyYlJaWgT6vESataC5WhESqD0pqfGjVqULVq9Ww/devWk2xOIYQQoogo0bfmAVq1asWdO3e4desWoaGhLF26NMc2pUqVAkCpVOaaHfo8AwMDze8KhSJHRujYsWMpV64cdnZ2dO7cWVMIi9eXYWxKhvFz2ZxF6BlRIYQQQuRU4mdEAbp3787y5cspX778S79wVK9ePR48eMCFCxeArBnU13HgwAFGjx5Nx44d2bdvHwCqZ55tFEIIIYQoCUr8jChk3Z63t7dn9uzZL91OX1+f+fPnM2nSJHR0dKhbt65mtjQ/Ro0aRf/+/TEwMMDc3JwaNWoQGxtLnTp18txGqPeYfB+3pJGQeCGEEKJoK9HxTfmlVqtZsGABnp6eGBoasmrVKuLi4vD29n7nfZFAe+0iESXaR8ZM+8iYaR8ZM+0j8U3PmD59On///Tfp6elcu3aN+vXrA1mxPa6urgV+PB0dHYyNjenZsyd6enrUqFHjlbOob8vLBlUUrqIUpC+EEEIUZcViRjQ2NlaT0VlSdPUL4FaiBNoXRcdn53xJSv7Vr31kzLSPjJn2kTHTPjIj+gpXr17F19eXxMRESpUqxdSpU2nUqBHe3t4YGRlx9uxZ4uLi8PDwwNXVlcDAQOLi4rh69So3btygV69ejBgxApVKxfz58zly5AgqlQoXFxcGDx7M7du3mTBhAikpKejo6ODj40Pz5s2ZN28eBw4cQEdHh44dO+Lp6cmjR4+YMWMGly5dQqVSMWzYMLp27frCtoV20L1/j1KxV1C84AWz3IL0lUoFKlWmBOoLIYQQzyh2heikSZOYNm0ajRo14n//+x8eHh7s2LEDgNu3b7N+/XouXryY7fb9hQsXWLduHQ8fPqRjx44MGDCA8PBwALZs2UJaWhpDhw7FwsKCqKgoOnTowGeffca+ffs4fvw4lSpVYt++fYSHh/P48WMmT57MkydPWL58OY0bN2bevHkkJyfTt29fmjVrpgm4f77tjz6S4kQblLp5Hd1HyS9cf+PGjZfuHxYWIoWoEEIIQTErRB89esSZM2eYPHmyZllKSgr3798HwNraGoVCwQcffEBiYqJmmzZt2qCvr4+pqSnGxsY8fPiQQ4cOER0dTVRUlKadCxcuYGVlxahRo4iOjsbW1paBAweiVCoxMDCgb9++2NnZMWHCBAwMDDh48CCpqakEBwdr2rh06dIL25ZCVDukVq9FqVjVC2dEa5tWeOmMqATqCyGEEFmKVSGqVqvR19dn69atmmW3b9/WfEf+adC8QqHItl9uAfQqlQovLy8+/vhjABISEihTpgwGBgaEh4ezZ88etm/fzpYtW1i1ahUbN27kyJEj7Nu3j759+7J27VrUajV+fn40btwYgLt371K+fHmCg4NzbVtohwwTU5JNTF+4fo08IyqEEELkSbEKtC9btizvvfeephA9cOAAAwYMeK22LC0t+e2330hPT+fRo0f079+fkydPMn/+fEJDQ+nRowfTpk3j3LlznDt3joEDB9KqVSsmTZpE/fr1iYmJwdLSkl9//RWA+Ph4nJ2duXXr1gvbFkIIIYQoSYrVjCiAn58fvr6+/PDDD+jp6bFo0aIcM6B50bdvX65evUqPHj3IyMjAxcWFNm3aULt2bcaPH8/mzZtRKpXMmzePRo0a0bx5c7p27Urp0qVp2bIl7du3p3Xr1vj6+mpeUPLy8qJ27dovbDs/wrwk0L6okiB9IYQQIm+KRXyTEO/K62aEyq157SNjpn1kzLSPjJn2KdbxTUOGDGHAgAF07NgRgHnz5hEUFMThw4fR19cHwMbGhqCgIGrWrJnndnfv3s2VK1cYMmSIZllycjLt2rXj999/p0qVKprlR44cYe7cuXh6enLmzBnGjMn7zGO3bt2yPZ/6vJ07d+a7zRfpvHCx5IgWghMzviYZCasXQgghCkKRekbU0tKS48ePa/4+ePAgzZs31yy7evUqhoaG+SpCAc6cOUNycva4HSMjI/7zn/9oYpqeCgkJoWfPnjg4OOS7YHxZEQq8VptCCCGEEMVVkZoRtbKyYs6cOQDExcWhr69Pp06d2L9/P1ZWVhw7dgxra2sgq2D8+eefUavVNG7cmK+//hodHR2++uorLl26BED//v1p2bIlQUFBAFSvXj3bpz9dXFyYP38+n376KQBPnjxhz549TJo0ic2bN3PkyBG++eYb7O3tadq0KdHR0axfv57w8HB++eUXypYtS7169ahduzajRo3CzMyMCxcuvDAk/9k2IyIiWLVqFampqaSlpTFnzhxatmz5Li+3eIZewj1KX7v6wkimp3ILq3+WBNYLIYQQeVekZkQbN27MtWvXePLkCfv378fa2hpra2tNAPzTQvTSpUv89ttvBAUFsXXrVkxNTfnxxx85ceIESUlJhISE8N1333Hs2DHef/99+vbtS9++fXN8f75NmzY8ePCAy5cvA/DXX39hZWVF+fLlc/Stffv27Nixgzt37rBu3To2b97M+vXruXr1aq7ncuHCBX788Uc2btzIypUrefDggWadWq0mKCiIFStWEBoaymeffcbKlSsL6jKK11DqRiy6j5JRpj5+6c+NGze4ffvmC39iYi4TFhZS2KcjhBBCaIUiNSOqVCpp1qwZp0+fZv/+/QwYMIBatWqRmppKUlISJ06cYMqUKYSEhHD16lV69+4NQHp6Oo0aNaJfv37ExMQwdOhQ2rdvz8SJE196PIVCQffu3QkLC2P06NFs3br1hZ/abNasGQCHDh3Czs4OI6OsB2+7dOmSrch8KreQ/Kd0dHT49ttv2bVrFzExMRw5cgQdnSL1b4ISJ7VGTRSqF4fUP5VbWP2zJLBeCCGEyLsiVYhC1nOif//9N6dOncLPzw/IumW/c+dOTExMMDIyQqVS4ejoiI+PD5D1RSWVSkW5cuUIDw/nwIED7N27lx49euR4BvR5Li4ufPrpp/Tv358rV65gZWWV63ZPQ+91dHRQq9WvPI/cQvKfevToET179sTZ2ZlWrVphZmbGunXrXtmmeHvSK5iSXuHFIfVPrZnxtbzhKYQQQhSQPBeiv//+O9HR0XzxxRfs3LmTrl27vpUOWVlZ8eWXX/LBBx+gq5vVPWtra5YsWUKnTp2ArNnGn376iREjRlChQgV8fX2pXbs2jRo1IjQ0lMWLF9OuXTsOHTrErVu3UCqVPHmS+5vO1atXp1q1aixZsgRnZ+dXZo4+/cTn6NGj0dfX548//nhh8foiV65cQaFQ8MUXX5CZmcnEiRNRvWIm7nnbx43N1/aiYEhGqBBCCFFw8lSIrly5kgMHDnD79m0GDx7M0qVLuXr1Kh4eHgXeoaffge/fv79mmaWlJWPHjqVt27YAmJub4+npyaBBg1Cr1TRs2JDhw4ejo6PDH3/8QZcuXTAwMMDZ2RkzMzMePHjApEmTqFixIm5ubjmO6erqysSJE/nzzz/z1D93d3f69OmDoaEhJiYm2WY/88Lc3JyGDRvi6OiIQqHAxsYmW1pAXty7l4xaLRGwQgghhNBeeQq079atGxs3bqR3796EhITw4MED+vTpQ0RExLvoY77Fxsbi4ODATz/9pHnLHsDe3p41a9bkGv8UGxuLu7s7u3btemnbMTEx7N27V/Ms6YgRI+jVqxf29va5bu/t7U3r1q1xcXF5/RMSxd7jtDSSkySftCiRoG3tI2OmfWTMtE+hBNrr6upqAuUBypUrp7ltXlTp6ekxdepUQkNDNS8WFYQaNWpw+vRpunbtqpnNtLOzK7D286rz4sXcTJJA++Li5NcSlC+EEKLkydOr2tWqVWPPnj0oFArS0tJYvnw5NWrUeNt9eyOVK1embdu2zJs3L8e6lStX0qNHD5ydnZk/f77mRaInT54wZswYnJ2d8fT0JOn/Cz17e3vGjh1Lp06dePjwITVr1sTQ0JC0tDTOnDnDvXv3SE9Px8vLi+7du9O9e3d+++23bMd8/Pgx/fr107yUtGjRInr37k2nTp1wc3Pj7t27b/mKCCGEEEIULXkqRKdOncqqVau4cOECzZs3Z9++fUybNu1t9+2NeXt7s3//fg4cOKBZFhkZyZkzZ9i0aRMhISHExcURGhoKwL1793BzcyM0NJRatWrx7bffavZ7miOanJzM5cuXCQoKYseOHVSrVo3Q0NBcM0yfSk9Px9PTk06dOjFgwACuXr2aaxuiZNG7d4/yf/+N8dGjuLu7M3bsSMaOHcnkyeP4++9jr25ACCGE0HJ5vr/+888/8/jxY1QqFUZGRvzvf/97m/0qEEZGRsycOVNzix6yckBPnTqleWYzNTWV6tWr8+GHH1K3bl0++ijrizjdunXD29tb09bTHNE6deowadIkNm7cSExMDCdPnqR27do0aNDghRmmAQEB6OjosHTp0pe2IUqW0rGx6P7/p2dv3LiRbV1YWIh8nUkIIUSx99IZ0cTERBITExk2bBhJSUk8efIElUrF3bt38fT0fFd9fCM2NjbZbtGrVCoGDRrE1q1b2bp1Kxs3buSLL74AyPbca2ZmZra/n74Zf+bMGYYOHYparaZTp0507NiRzMxMTExMCA8PZ+DAgcTExNCjRw9N0H2XLl2wtbVlyZIlL21DlCyPa9Ykw8gIVenS1KhRg6pVq1O1anXq1q0nofhCCCFKhJfOiI4fP15zW7tNmzb/7qSrq8n01Abe3t44OTlx584dhgwZQnBwML1798bAwAAPDw969OhB69at+eeffzh37hyNGjUiODhYExf1rKNHj9K6dWv69evH/fv32bNnDx9//DE7d+7MNcMUoGHDhtjb29O1a1ecnZ1f2IYoWdJNTUkyzQrR3/O1BOULIYQoeV5aiP74448ATJ48mblz576TDr0NT2/RDx06FDs7Ox4+fEjv3r1RqVS0a9eOHj16cOPGDWrXrs23337LtWvX+OCDD/jyyy9ztNW5c2c8PT1xcnICwMLCgtjYWDw8PHLNMH3K2NiY8ePH4+Pjw7Jly3JtIz+2jx37+hdEFDmP0yQoXwghRMmTpxxRyLpN//jxYzIzM1GpVFy7di1bRqd4tyTQXrtIVp72kTHTPjJm2kfGTPsUSo7okiVL+O677wBQKpWkp6fz/vvvs23btjx35HW8TjD96zAzM8PGxkYzAwyQkJBAu3bt+OKLLxg1alS+2jt9+jRBQUHMnj27QPqXm5cNanEgAe9CCCFE8ZenQjQkJITdu3fzzTffMHHiRKKioti7d+/b7hvw9oLpnxcTE0NiYiLGxsYA/PHHH5QrV+612mrSpAlNmjQpwN7l5Lh0MTeTEt/qMQrTf6f4SsC7EEIIUczlqRCtUKEClStXpl69epw/f57u3bvz/fffv+2+AdmD6WfOnJlj/cqVK4mIiEClUmFjY4OXlxcjRoygX79+2NrasnDhQs6dO8cPP/xAfHw8n376KWFhYTnacXBwYOfOnbi6ugLw+++/85///Eez/tSpU8ydO5fU1FRMTEyYPn06JiYmODs7M3v2bKysrBg6dCj29va8//77LF26lLVr1xIdHc20adNITU2lfPnyLFiwgKpVq7JixQpCQ0NRKpVYW1vj5eWFUql8exeyCNK7m0CZmKsoMlQ51rm7u6NS5f7oQenSpejVq7/EGwkhhBBaLk+B9rq6uly7do169epx7NgxMjIyePLk3c1W5RZMD7Bv375cw+ltbW2JiooC4NixY1y+fBmVSkVkZCS2tra5HsPR0ZEdO3YAaL5yVKlSJQDS0tLw8fHB39+fLVu2MGTIEKZOnYqRkRGzZ8/G19eXdevWoVAoGDBgQLZ2J0yYwMiRI9m2bRudO3fm559/Zu/evezatYvg4GC2bNnC1atXCQoKKtBrpg0Mr8Wi+zAZ5ePHOX5u3LjB7ds3c/2JiblMWFhIYXdfCCGEEG8oTzOin3/+OVOnTmX58uUEBAQQEhJChw4d3nLX/pVbMD28OJy+f//+jBgxguT/Dws3MzPj7Nmz7Nu3Dzc3t1yP0aJFC2JiYnj48CG///47nTp10hSkV65c4fr164wYMUKz/dO2rayssLS0ZOHChURERGRrMyEhgTt37mi+Rd+/f38A5s2bR5cuXShdujQArq6uhISE5Chii7uU2jUpo1LlOiNau0KFl86ISs6mEEIIof3yVIg2atSIn3/+Gch6XvTq1avo6ORpMrXAPB9MD/+G0w8ZMgSABw8eoFQqKVOmDGq1mj/++IOWLVtSsWJFoqKiOHv2LC1atMi1fYVCgZ2dHTt37mTHjh0EBARovguvVqupWbMmW7du1Rz3aZGamZlJTEwMpUuXJiYmhsqVK2va1NPTQ6FQaP5+8uQJ8fHxqNXqHMfPyMh4wyukfdIrViCxYoVc1+2Z4itvUgohhBDFXL6+rJSYmMiTJ0+oWLEio0ePfld91Hh6iz4+Ph4AS0tLtm7dyqNHj8jIyMDDw0Nze719+/YsX76c1q1bY2lpydq1a2nWrNlLn8N0dHRk/fr16OvrU6HCvwVSvXr1SEpK0nw/Pjg4mAkTJgCwfv16DA0NWbZsGVOnTuXRo0ea/cqWLUuVKlXYv38/AFu3biUgIABLS0vCw8NJTU0lIyOD4OBgLC0tC/ZiCSGEEEIUcfn+spJCoUCpVBbKl5WeDaaHrBin8+fP5winB+jQoQOrVq3iww8/xNDQkPT0dM0t8hdp3rw5d+7coVevXtmW6+vrExAQwOzZs3ny5AlGRkbMmzeP69evs3z5cjZu3Ei1atWwsbHBz88PR0dHzb5+fn74+vri5+eHiYkJ8+fPp3LlykRHR+Pq6kpGRgY2NjYMHDgwX9ciwnNsvrbXNhLwLoQQQhR/eQq0nzx5MiNHjqRWrVrs3r2bs2fPMmjQIMqWLfsu+ijEW/UuMksltFn7yJhpHxkz7SNjpn0KJdBeV1eX77//nkGDBjFt2jRsbGz46quvCAwMzHNHICsSaeXKlWRkZJCZmUm3bt347LPP8tXGU7t37+bKlSsMGTJE049XBc/Hx8czf/58oqOjUSqVVKtWDR8fH2rVqvVafXjW5s2bOXLkCN988w3Dhg1j1qxZVKlS5Y3bfRHH5Yu5+SDxrbVfkvx3kmSWCiGEEIUhT4Xo2bNn2bRpEytXrqR79+6MHz9e86Z6XsXFxTFv3jw2b96MiYkJjx49ws3Njbp16+Lg4JDvjp85cyZf26ekpODm5sann36Kn58fCoWC0NBQhgwZQkREBHp6evnuw4u8q4xVIYQQQghtlqdCNDMzEx0dHQ4cOMAXX3wBZEUl5cf9+/dJT0/X7FemTBm++eYbDAwMADh58qTmGUwTExNmzJhBnTp1cHNzw9PTkzZt2hAbG4u7uzsrV67U5G5Wr14dyAqc79u3L3Fxcbi4uOSYHQ0PD6dChQr06dNHs8zZ2Rl9fX3S0tJ48uQJX331FXFxccTHx2NlZcXs2bM5cuQIfn5+qNVqGjRogK+vLz4+Ply4cAGFQsHQoUPp3r17tmM9/QTpkSNHiIyMJCkpievXr2NtbY2vry8ZGRn4+vpy6dIl7t69i5mZGQsXLqRUqVL5uqYib/TuJFDmf1dRqHLGRAG4n5XwfCGEEKIw5KkQrV27NsOGDSM2NpbWrVszfvx4zM3N83Ugc3NzHBwc6NixIw0bNqRNmzY4OTlRp04d0tLSGDduHIsXL6Zp06ZEREQwbtw4goODc23r/fffp2/fvkBWBmdgYCD37t0jKCiI5ORk7O3tGTJkSLZPgkZHR9O4ceMcbX3yyScAhIWF0bBhQ5YsWUJaWhpdunTh7NmzQFaO6O7duylbtizz58/HxMSEsLAwEhIS6NWr10uvxYkTJwgLC0OpVPLJJ5/Qr18/Hjx4gJ6eHhs2bECtVjNo0CD27t1bKC+AlQSGV7KC81/kRsqNl+4fFhYihagQQgjxFuSpEJ07dy5//vknH374IXp6enz00Uc5ZgHzYvr06YwcOZL9+/ezf/9+evfuzYIFC3jvvfcoV64cTZs2BbJilKZNm8bDh3l/GLZdu3aa2CUTExOSkpKyFaI6Ojro6+u/cP+uXbty6tQpVq9ezeXLl0lMTCQlJQWAunXral7MioqKYs6cOUDWp08dHBw4cuRItmM9q0WLFpp1tWrVIikpidatW2NsbMy6deu4fPkyV65c0RxLFLyU92pSJkP1whnR2iYSni+EEEIUhjwVooaGhnTr1k3zd79+/fJ9oD179pCSkkLnzp1xdXXF1dWV3377jU2bNjFu3Lgc22dmZqL6/8Lh6Yv9Lwt919X991QUCgXPhwFYWFiwefPmHPtNmTKFwYMHExUVxY4dO+jduzdt27bl4sWLmjaevWX+fLvP9jM3Tx89eLZfO3fuZMmSJbi7u+Pi4sL9+/dztCsKTnqlCiRWyj04H2DPJAnPF0IIIQpDngrRglCqVClmzpxJ06ZNqVmzJpmZmURHR9OwYUPq1atHYmIip06domnTpmzfvp3q1atjbGyMiYkJ//vf/7C0tOSvv/7StKdUKvP1vftPPvmEwMBANm7cqMkJDQ4O5siRI3z99df4+/vTp08fnJycOH36NOfPn0etVuf4gpSlpSWbNm3Cx8eHhIQEdu7cSWBgIBcuXMhzXw4dOoSjoyOurq5cv36dw4cPY2Vllef9ASJGjM3X9uLFJLNUCCGEKBzvrBC1tLTE09OTL774gvT0dCDrdrqHhwf6+vosWrSImTNn8vjxY8qXL8+iRYsA+Oyzz/D29iY4ODjb2/WtWrVi0qRJVKxYMU/HL1WqFKtXr2bOnDmsXr0ahUJBzZo1+emnn9DX12fQoEH4+vqycuVKjIyMaNGiBbGxsdSuXTtbOx4eHvj6+uLk5IRKpeKLL76gcePG+SpEe/XqxYQJEwgPD0dPT4+WLVsSGxub5/0B7t1LRq2WWVQhhBBCaK88BdqLnB49esSCBQvYv38/pUuXxsjIiFGjRmFlZZXtTf9nBQQEYGFh8VpxVaJoyW8IvoQ2ax8ZM+0jY6Z9ZMy0T6EE2ovsMjMz+eKLL2jYsCHh4eHo6+tz7tw5hg8fjr+//wv3GzNmTIH1wfH7RRJoX4j+O366hOALIYQQb0jn1ZuI5x05coSbN28yefJkzZv4jRo1YsSIESxbtgyATZs20aNHDxwcHNi1axcA3t7emhemgoOD6dq1K05OTnh7e/Po0aPCORkhhBBCiEIiM6Kv4fTp01hYWKBQKLItb9WqFf7+/jRu3JiyZcuyZcsWdu/ezdKlS7G3t9dsd+HCBVasWMFvv/2GiYkJ06dPZ+nSpUyaNOldn4rIA734BMpcvIoi4990BPf/Zg/Bl+B7IYQQIv+kEH0NCoUi18im9PR0TXHasWNHICt8//79+9m2O3r0KHZ2dpiYmADQp08fJk+e/JZ7LV6X4eVYdB9kn7HOLQRfgu+FEEKI/JFC9DU0a9aMtWvXkp6enu0b9SdPnsTCwgK1Wo1SqQTIMWsKoFars/2dmZn50oxUUbhS6tWkTEb2GdHaJqY5ZkQl+F4IIYTIHylEX8NHH33E+++/z5w5c/jqq6/Q09PjzJkzLF++HH9/f5YuXfrS/Vu3bs2aNWsYOXIkxsbG/PbbbznesBdFR3rlCiRWzh6Iv2f8dHnTUwghhHhDUoi+pqVLl7Jo0SK6du2KUqmkfPny+Pn50aZNm1cWoubm5nz++ee4ubmRnp5O48aNmT59er6OHzHsyzfpvnhDEoIvhBBCvDnJEdVSEmivXSQrT/vImGkfGTPtI2OmfSRH9DX169ePgQMH0qVLF82ylJQU7OzsiIiIoEKFF3+L/G1yc3Nj7dq1+d7vZYNakPIb3C6EEEIIkVclphB1dXVl27Zt2QrRP/74gzZt2hRaEQpZmaSvo/NPi7j5MLFgO5OLk2MkuF0IIYQQb0eJKUQdHR2ZP38+iYmJGBsbAxAaGsrAgQOZNGkSly5dQqVSMWzYMLp27crmzZvZsmULiYmJ2NnZER8fT+nSpTl37hwPHjxg3LhxbN26lfPnz9OxY0e8vb1Rq9XMmTOHQ4cOoVAocHZ2Zvjw4Rw+fJjvvvuOUqVK8c8//2BmZsaCBQuYP38+kPXt+Y0bN77za6IXl4Dh+exvgz/P/Xj2vMzcSIamEEIIIV5HiSlEy5Qpg4ODA7///jt9+/YlLi6OmJgYjh49SuPGjZk3bx7Jycn07duXZs2aARAXF8f27dvR1dXF29ub+Ph4NmzYwJYtW5g8eTI7duzAwMCA9u3b4+HhQWhoKLdu3SI0NJS0tDTc3Nz44IMPKF26NCdOnCAiIoLKlSvTu3dv9u/fj4+PD2vXri2UIhSg9P9i0U16+RedbjzKmZeZG8nQFEIIIUR+lZhCFMDFxYWAgAD69u3Ltm3bcHZ2JjIyktTUVIKDg4Gs50YvXboEZH22U1f330vUvn17AKpXr06DBg0wNTUFwNjYmKSkJA4fPkyPHj1QKpWULl0aJycnDh06hL29PQ0aNKBq1aoA1K9fn6SkpHd56rl6/H5NFBkvnxGtbWyapxlRydAUQgghRH6VqEK0VatW3LlzRzNruXTpUvbu3Yufnx+NGzcG4O7du5QvX55t27ZRqlSpbPs/G17/bIH6VG5B9U+/wGRgYKBZrlAoKAphBelVKpBU5eXPx+4ZI3mZQgghhHg7dAq7A+9a9+7dWb58OeXLl6d27dpYWlry66+/AhAfH4+zszO3bt16rbYtLS0JCQlBpVLx+PFjtm3b9sqgeqVSKV9VEkIIIUSJVKJmRCHr9ry9vT2zZ88GwNPTE19fX7p27YpKpcLLy4vatWtz7NixfLfdp08frly5Qrdu3UhPT8fJyYn//Oc/HD58+IX7ODg40K1bNzZv3pxt1vRVtn/6bgLtJbhdCCGEEG+LBNprKQm01y4S2qx9ZMy0j4yZ9pEx0z4SaP8OTZ8+nb///pv09HSuXbtG/fr1AXB3d8fV1bVQ+/auAu3FvyTcXwghhChYUoi+xNdffw1AbGws7u7ubN26tZB79K8uaxdy6x0E2ot//T1yhoT7CyGEEAVICtF8unr1Kr6+viQmJlKqVCmmTp2KmZkZffr0oU+fPvTq1YupU6dStmxZJk6cyKJFizh06BBJSUlUrlyZRYsWUb58eb766itNTFT//v3p3bt3IZ+ZeEr3VgKlz13LEWvlHpU93F+C/IUQQog3I4VoPk2aNIlp06bRqFEj/ve//+Hh4cGOHTuYN28egwcPpnTp0pw6dYqNGzdy9epVLl++TFBQEDo6OkycOJHQ0FAsLCxISkoiJCSEuLg4/P39pRAtQkpdvIFuYs6g/xvJOcP9JchfCCGEeH1SiObDo0ePOHPmDJMnT9YsS0lJ4f79+9SvXx83NzcmTpzIli1b0NfXp06dOkyaNImNGzcSExPDyZMnqV27Ng0aNCAmJoahQ4fSvn17Jk6cWIhnJZ6X+kENFBmqHDOitcub5pgRlSB/IYQQ4vVJIZoParUafX39bM+K3r59W/Pt+piYGIyNjYmOjsbMzIwzZ84wfvx4Bg8eTKdOndDR0SEzMxMTExPCw8M5cOAAe/fupUePHoSHh1OuXLlCOjPxrIxqFXhYLWfQ/5qRM+TtTiGEEKIASSGaD2XLluW9995j69atdOvWjQMHDjBt2jT++usv9uzZw/nz5wkKCmLgwIFYWVlx9OhRWrduTb9+/bh//z579uzh448/ZufOnYSGhrJ48WLatWvHoUOHuHXrVr4K0XC3cW/xTEVuJFNVCCGEKFhSiOaTn58fvr6+/PDDD+jp6bFo0SIePnyIr68vS5cupXbt2gwaNIipU6cyc+ZMPD09cXJyAsDCwoLY2Fg8PDz4448/6NKlCwYGBjg7O2NmZpavfkiOqBBCCCG0nQTaiyKrOOV2Smiz9pEx0z4yZtpHxkz7SKB9Hhw+fJilS5eydu3aN2qnW7duL8wOjYuLw8fHh++///6NjvG6uq3351ZyYqEc+105Mnym5HYKIYQQxZhOYXegKHtZgH2VKlUKrQgVQgghhCgOiuWM6IusWLGC0NBQlEol1tbWeHl5cevWLdzd3dm1axcAgYGBAIwaNQozMzMuXLjAoUOH8PPzA6B8+fL4+/uTkpKi2e/ixYvMnDmTlJQUEhISGD58OP369SMwMJC4uDiuXr3KjRs36NWrFyNGjCA5OZmvvvqKuLg44uPjsbKyYvbs2SgUikK7NoVFeTMBgzPXUaSrcqxz3589QP5ZEiYvhBBCaL8SU4ju3buXXbt2ERwcjJ6eHqNGjSIoKAhbW9tX7rts2TJ8fX1p2rQp33//PefOneO9997TrN+4cSMjR47EysqK69ev4+zsTL9+/QC4cOEC69at4+HDh3Ts2JEBAwawb98+GjZsyJIlS0hLS6NLly6cPXsWCwuLt3X6RZb++Zso7+cMj4fcA+SfJWHyQgghhHYrMYVoVFQUXbp0oXTp0gC4uroSEhKSp0LUwcEBT09POnbsiIODA9bW1sTGxmrWe3t7ExkZyXfffcfFixdJSUnRrGvTpg36+vqYmppibGzMw4cP6dq1K6dOnWL16tVcvnyZxMTEbPuUJGnm1bPC43OZEa31XID8syRMXgghhNB+JaYQVavVOZZlZGSgUCh4NjggIyMDXd3sl2Xw4MHY2dmxe/du/Pz8OHXqlCaSCWDs2LGUK1cOOzs7OnfuTFhYmGadgYGB5venx1q7di07duygd+/etG3blosXL1JSwwtU1SuQUj1neDzAmuEz5W1KIYQQohgrMYWopaUly5cvp0+fPujq6hIcHIylpSXlypUjMTGRhIQEjIyMiIyMxM7OLtu+vXr1Yvr06QwePBhjY2N27tyZrRA9cOAAERERVKlShXXr1gGgUuWc4Xt2+z59+uDk5MTp06c5f/58roXyy2ztPz5f22sjCZAXQgghirdiW4geO3aMFi1aaP52cnKiQ4cOuLq6kpGRgY2NDQMHDkRXV5fPPvuMnj17UrVqVZo0aZKjrXHjxuHt7Y2uri6GhobMmjUr2/pRo0bRv39/DAwMMDc3p0aNGtlu3T9v0KBB+Pr6snLlSoyMjGjRosVLt8+NBNoLIYQQQtuVqED72NhYHBwc6NOnDzNmzNAsj46Opnv37sydO5eff/75pbFNb8OUKVPo27dvrkWwKB7B9hLarH1kzLSPjJn2kTHTPhJo/4aMjY2JjIxEpVKhVCoB2L59OxUqZD2n+K6LUIDZs2fnex+XjQu4XcwD7Z86OGSWBNsLIYQQxVCJK0TLlCmDubk5R48exdLSEsh6ZrNt27YAL80O1dfXZ9y4cdy9excADw8PHBwciImJYdq0aSQmJmJoaMiUKVNo2rQp3t7etG7dGhcXl2xtBwYGcvLkSW7dusXAgQOJiIjA09OTNm3aFMIVKRw6sffRO3Udcnlb/nnuu1+cJ/osyRYVQgghtEuJK0QBHB0d2bFjB5aWlpw6dQozM7Mcb63nlh16584datSowcqVK4mOjiY0NBQHBwe8vLwYPnw4H3/8MSdPnmTMmDHs2LHjpX1IS0tj+/btAERERLy1cy2qdM/dRCch9/zQ5914+PI80WdJtqgQQgihPUpkIWpvb8/ixYtRq9VERETg6OioKQqfyi079MqVKyxcuJC4uDg6dOiAh4cHjx494tq1a3z88ccANG/enPLly3P58uWX9qFp06Zv7fy0QUajrPzQvMyI1ir34jzRZ0m2qBBCCKFdSmQh+vT2/PHjx4mKimL8+PE5CtHcskNHjBhBREQEkZGR7N69m59++olNmzblaD8zMxOVSpUtozQ9PT3bNqVKlXp7J6gF1DVNeFLTJE/brhkySx5mF0IIIYohncLuQGFxdHTE398fCwuLHAH2kJUd+ujRIwYPHszgwYM5d+4cv/zyC4GBgTg6OvL111+TkJCAWq2mZs2a/PHHHwCcPHmSu3fv0qBBA4yNjfnf//4HwF9//fVOz08IIYQQoqgrkTOiAHZ2dkyZMoUxY8bkuj637NCKFSsybtw4nJycUCqVeHl5Ua5cOfz8/PD19SUwMBA9PT0CAwPR19enX79+jB07FicnJywtLalUqVKB9X9zrwkF1lZRJ8H2QgghRPFUonJEixMJtNcukpWnfWTMtI+MmfaRMdM+kiP6Bi5evIiTkxNLliyhU6dOQNaLS2vWrKFmzZq57nP48GGWLl3K2rVr32VXX+llg1pSpKan8TBR8kWFEEIIbVWiCtHg4GA++eQTNmzYoClEtVXfzfO5/SixsLtRqPa4zeGhBN0LIYQQWqvEFKLp6els27aNdevW0bdvX65du0bt2rU165OTk/nqq6+Ii4sjPj4eKysrzReP7t+/z9ChQ4mPj6dp06Z8/fXX6Ovr88svv7B161YeP36Mnp4e/v7+1KtXD3t7e7p06cKBAwfQ1dVl5MiR/PTTT1y9epVJkybRuXNnLl68yMyZM0lJSSEhIYHhw4fTr1+/wro8Rd/1++icjIV0tWaR+5+5B91LsL0QQgihHUrMW/N79+6levXq1K1bl44dO7Jhw4Zs6/fs2UPDhg3ZsGEDO3bs4Oj/tXfnUVXU/x/Hn5cdJUNCMdcUDUxzKxBwQcCvCkLuiguoaWXhmhspGrlvqIFWmmZppKigKGq4IribabiiJaiggBub7Jf7+4OfNwk1MRUuvB/n3PP1zp35zGfmc+L7Pp+Zec3Jk5w/fx4ofEf9tGnT2LZtGw8ePGD9+vVkZGSwd+9e1q1bR1hYGB06dCAwMFDdnqmpKSEhIZibm7Ny5Up++OEHFi5cyMqVKwHYtGkTn332GcHBwaxdu5YFCxa8upOhgbTO3UJxNxNFWrb6k5CQQGLizWKf2NirhIVtLe0uCyGEEOJfVJgZ0eDgYFxdXQFwcXFhwoQJRZ6Yd3V1JTo6mh9//JGrV6+SkpJCZmYmAO+//z5vvfUWAG5uboSEhDB48GD8/PzYsWMHcXFxREVF0bhxY3V77du3B6BmzZpUr14dHR0datasSVpaGgDe3t5ERUWxYsUKLl++rN6XeLyCpm+ila8sMiNa+7XHB91LsL0QQgihGSpEIXr37l2ioqI4f/48a9euRaVSkZaWxp49e9TrrFu3jvDwcPr27YudnR2XL19Wh9E/mjOqUqnQ0dHh1q1beHh4MGjQINq3b4+pqSkXL15Ur6erq6v+9+NySseOHUuVKlVwcHDAxcWFsLCwl3Ho5UedqhTUKRqAv9ZjjjxtKYQQQmiwClGIhoaGYmNjw6pVq9TLAgIC2LBhg/r74cOH6devH25ubpw9e5ZLly5RUFCAlpYWp06d4ubNm9SoUYOtW7fSrl07zp49S7169RgyZAjZ2dn4+/tTo0aNZ+7T4cOH2bVrF2ZmZupL+kqlEm1t7Rd34EIIIYQQZViFKES3bNnCuHHjiiwbOHAgq1atwsioMAZp8ODB+Pr6snLlSoyMjGjZsiXx8fHUrVuXhg0bMmXKFG7fvo2NjQ29e/cmOzub9evX4+LigkqlwsrKiitXrjxzn0aNGsWAAQPQ19fH0tKSWrVqER8fT7169Z5p+w09Jz37CSinsvMk6F4IIYTQZBJoLzSapmSJSmiz5pEx0zwyZppHxkzzSKB9GRAfH4+npyf79+8vstzCwoK9e/fy7bffMmfOnBJvXxIe2+eTVMFzRAF2u8+VLFEhhBBCQ1WY+KZX5ebNm9y4caO0uyGEEEIIUebJjOgLNmvWLOLj4/nqq6+YOnUqvr6+XLlyhTt37mBhYcHixYsByM7OZty4cVy5coUqVaqwfPlyqlat+i+tC9X1FApOxUOuEgDPnX+H2kuQvRBCCKFZZEb0BfPx8aFp06Z8+eWXnD59Gl1dXYKCgtizZw/p6ekcPHgQgHv37jF06FDCwsIwNTVl586dpdxzzVAQfQvuZEJaDqTlFAm1lyB7IYQQQrPIjOhz0NIqXr+rVCoUCkWRZVZWVhgbGxMYGMjVq1eJi4tTB9dXr16dZs2aAdCwYUPu37//8jteDmg1e5OCPKV6RrTWa6ZFZkQlyF4IIYTQHFKIPocqVaqQnl70ibG7d+/y+uuvF1m2b98+/P398fT0pGfPnty/f/+xIfkKhQIJL3g2irrGaNc1Vn9f6z5XnrgUQgghNJQUos/ByMiIevXqER4eTufOnQEICgrC1tYWbW1t8vPzATh69CjOzs706tWLGzducPz4cWxtbV9IH9a5TX4h7Wg6yRIVQgghNJcUos9p4cKF+Pr6snz5cvLy8rCwsGD69OkoFArS09OZOHEiw4cPZ8KECezYsQNdXV1atWpFfHz8C9n/3bsZFBTILKoQQgghNFeFCLS/fPkybm5u+Pv7q2cwH/Uicj1F6ZBAe/GyyJhpHhkzzSNjpnkk0P45BAcH06VLF4KCgh5biGqi4bvmkZwpDzht6zVfAu2FEEIIDVXuC9G8vDy2b99OYGAg7u7uXL9+nbp16+Lo6EizZs24ePEiCxcuVK8fHh7O8uXL+fHHH7lz5w4zZ84kMzOTe/fu8fHHH9O/f38CAgJISkri2rVrJCQk0KdPHz799FMuXbrE9OnTyc/PR19fn7lz5/LWW2/x888/ExoaSlZWFrq6uvj5+dGgQQPmz5/P4cOH0dLSomPHjowcObIUz5TmyI9LIe/kTVS5SjxDJUdUCCGE0FTlPkf04MGD1KxZk/r169OxY0eCgoLUv7Vv357w8HBMTEwAOHToEMuXL+eHH37AxMSETZs28dlnnxEcHMzatWtZsGCBetuYmBhWr17Npk2bWLlyJWlpafz0008MHTqUkJAQ+vbty5kzZ8jIyGDv3r2sW7eOsLAwOnToQGBgIAkJCURGRrJt2zbWr1/Pn3/+SU6OzOw9i7wziRTczkSVKjmiQgghhCYr9zOiwcHBuLq6AuDi4sKECRMYM2YMAM2bN1evd//+fUaNGsWoUaMwNTUFwNvbm6ioKFasWMHly5fVGaAArVu3Rk9PjzfeeANjY2PS09Oxt7dnxowZREVF4ejoiIODA9ra2vj5+bFjxw7i4uKIioqicePGmJmZoa+vj7u7Ow4ODkyYMAF9ff1XeGY0l26LGuTlFc6I1jSSHFEhhBBCU5XrQvTu3btERUVx/vx51q5di0qlIi0tjT179gAUKfwUCgXLly9nwoQJdO3aFTMzM8aOHUuVKlVwcHDAxcWFsLAw9fr/3FalUtGlSxdatmzJgQMH+PHHH4mIiMDLywsPDw8GDRpE+/btMTU15eLFi+jo6LBp0yZOnDhBZGQk7u7urFu3jvr167+6E6ShdN4yRuctYwDW9povN7oLIYQQGqpcX5oPDQ3FxsaGyMhI9u/fz4EDBxgxYgQbNmwotq6xsTG2trb079+fWbNmAXD48GFGjx5Nx44diYyMBECpVD5xf2PHjuXs2bO4u7szZswYLly4wNmzZ6lXrx5Dhgzh3XffZe/evSiVSi5cuMCgQYOwsrJi8uTJmJubExsb+3JOhBBCCCFEGVSuZ0S3bNnCuHHjiiwbOHAgq1atwsjo8VECH3/8MR988AF79+5l1KhRDBgwAH19fSwtLalVq9ZTc0BHjBjB1KlTWb58Obq6uvj6+mJubs769etxcXFBpVJhZWXFlStXeOedd2jRogWurq4YGhrSqlUr2rdv/8zHtsrZ+5nXLc8k0F4IIYTQXBUiR7Q8kkB7zSJZeZpHxkzzyJhpHhkzzSM5oi9Ifn4+33//Pdu2bUOhUKBUKunRoweffPIJCoXipexz/fr1APTv3/8/t/W0QdVkmhJQL4QQQoj/rsIWol999RV37twhKCiIKlWqkJGRgZeXF6+99hoDBw58Kft8EQXoQ1575nA7q/wF2m/8YKEE1AshhBAVRIUsRBMTE9m2bRuRkZFUqVIFACMjI6ZPn86ff/7JnTt3mD59OomJiSgUCsaPH4+dnR1ZWVn4+PgQExODQqFg2LBhdO/enZCQELZs2UJKSgoODg4MGDCACRMmkJqayttvv83JkyeJjIwkICAAgFGjRj0x5L4iyY1NJfPYTVS5Beplnpv/Dqh/SILqhRBCiPKpQhai0dHRmJub8/rrrxdZbm5ujrm5OePGjaNXr144OTmRnJzMgAED2Lp1K9988w1Vq1YlLCyMe/fu0adPHywtLQFISkpi586d6OjoMGrUKJydnRk4cCB79uwpEvsEFAm5NzAw4OuvvyYwMJBp06a9snNQFmSdSkKZnFVkWUJKwmPXDQvbKoWoEEIIUc5UyEIUKHIf6K+//sq3335LQUEBenp6xMfHc/XqVfz9/YHC+0lv3LjBsWPHmDNnDgAmJiY4OTlx4sQJjIyMeOedd9DRKTydhw8fZu7cuQD873//U8+6PmRkZPTYkPuKxvA9MzLzlEVmRN+sbPrYGVEJqhdCCCHKnwpZiDZt2pS//vqLjIwMjIyM6NKlC126dCE+Ph5PT08KCgr46aefMDY2BiA5OZk33niDfwYMqFQqda6ogYGBerm2tnaxdR9169atx4bcVzR69V9Hr37RWem1HyyUJyiFEEKICqJcB9o/Sc2aNfnggw+YPHkyaWlpQOGsZ0REBFpaWtjY2PDLL78A8Oeff+Lm5kZWVhY2NjZs3rwZgHv37rFv3z6sra2LtW9ra8v27duBwnfdP9zHQ08KuRdCCCGEqEgq5IwogK+vL2vWrMHT0xOlUsmDBw9o3bo133//PZUqVWL69Om4ubkBsGDBAoyMjPDy8sLX1xc3NzeUSiUjRoygSZMmxMTEFGl76tSpTJ48mY0bN2JpaVns0nybNm0eG3JfEsv/N+W/nYAySgLqhRBCiIpDAu1fgrVr12JnZ0fDhg05f/4806ZNIyQkpLS7VaaV9/xQCW3WPDJmmkfGTPPImGkejQi0j4+Px8nJiR9++IE2bdqolzs6OrJ27Vpq1679QvZjYWFRbDbyRe/jWYSEhHDixAnmzZsHQL169fj888/JyckhLS2NVatWvfB9jj8wizvlKEf0Jxc/yQ8VQgghKpiXdmleV1eXadOmsW3btie+1728sre3x97evrS7IYQQQghRpr20QrR69erY2dkxf/58Zs6cWez3lStXsmvXLpRKJW3btmXixIl8+umn9O/fH3t7exYvXsyFCxdYtWoVycnJfPjhh8XyOJ8mPz8fX19frly5wp07d7CwsGDx4sXcuXMHLy8vGjRowJ9//sk777xDy5Yt2bJlC6mpqSxfvhxzc3McHR3p0qULR44cAWDOnDm88847rFmzhi1btqClpUWzZs2YMWMGANeuXcPDw4ObN29ia2vLrFmzOH78OMuWLWPdunV4eHjw+uuvc+XKFZYuXcrt27fx9/cnPz+f2rVrM3PmTKpWrfpiTn4Zl3U1lbTDiajyHgmy3yBB9kIIIURF81Kfmvf29ubQoUMcPny4yPLIyEjOnTvH5s2b2bp1K0lJSWzbtg17e3uOHTsGwG+//cbVq1dRKpVERUU9cYaxW7duRT7JyckAnD59Gl1dXYKCgtizZw/p6ekcPHgQgJiYGD766CNCQ0P5/fffSUhIICgoCFdXV4KCgtRtV6pUia1btzJ69GgmT56MUqlkxYoVBAcHExISQl5eHklJSUBhJFNAQAC7du0iMjLysQ8fWVhYEB4ejpmZGX5+fqxevZqtW7fStm1bFi1a9N9PuIZIP5lMXnIW+fdz1J+EhAQSE28W+cTGXiUsbGtpd1cIIYQQL8lLfWreyMiImTNnqi/RP3T06FGio6Pp2bMnANnZ2dSsWZMBAwbw6aefkpGRARQWbufPnycyMhIPD4/H7iM0NLTId0dHRwCsrKwwNjYmMDCQq1evEhcXR2ZmJgCmpqa88847ANSoUQNbW1ugMNYpPj5e3Vbfvn3VbXp7e5OamkrLli3p3bs3Tk5ODB06FDMzMwDef/99de5o3bp1uX+/+P2bzZo1A+CPP/7g1q1beHp6AlBQUFDsLU/l2WtW1UnLLTojalZJguyFEEKIiualxze1bdtWfYn+IaVSyeDBgxk6dCgAaWlpaGtrU7lyZQoKCti9ezetWrXC1NSUY8eOcf78eVq2bFmi/e7btw9/f388PT3p2bMn9+/fV4fM6+npFVlXW1v7sW08fFMSFBaL2trafPPNN5w5c4bIyEiGDx+unsl8dF2FQvHYQPuHofdKpZJWrVrx3XffAZCTk8ODBw9KdHyazLDB6xg2KFp4/+TiJ09OCiGEEBXMK8kR9fb2xs3Njdu3bwNgY2ODv78/ffv2RV9fHy8vL3r06EHPnj1p37493377LdOmTaN69ep89NFHWFtbP7FYfJKjR4/i7OxMr169uHHjBsePH1fPfD6rHTt24OHhwZ49ezA3N0epVOLi4sLmzZtp2bIliYmJxMTEULly5RK127x5c3x8fIiNjaV+/fp88803JCUlqZ+6fxZ+Dj4l2mdZJ/mhQgghRMXzSgrRh5fohw0bBhRe6r506RJ9+/ZFqVTSrl07evToAUCHDh1Ys2YN7733HpUqVSIvLw8HB4cS77NPnz5MmDCBHTt2oKurS6tWrYpcdn8Wv//+O5s3b8bQ0JB58+ZhYmJCv3796N27N4aGhtSvX59evXrx66+/lqjdatWqMWfOHMaOHUtBQQFmZmYsXLiwRG3cvZtBQYFEwAohhBBCc1X4QPtHn2wHyMjI4MMPP+Svv/4iNDT0leaRir/l5OWSVo4C7iW0WfPImGkeGTPNI2OmeTQi0F5TPXjwgOHDh2Ntbc2dO3dKuztPNTVqBvey75V2N16ab/+3FCTgXgghhCjXpBD9f5mZmXz88cfY2NgwduxYJkyYwM8//0xoaChZWVno6uri5+dHgwYNcHR05IMPPuDQoUNkZWUxf/58mjZt+tiM0YyMDKZMmUJSUhLJycnY2toye/ZskpKSmDBhApmZmWhpaeHj40OLFi1K+zQIIYQQQrwyUogCWVlZfPLJJ1y+fJnly5cDhZfo9+7dy7p16zAwMODrr78mMDCQadOmAWBsbMzmzZtZt24dK1asYOnSpaxYsYKoqCi0tbWZOnUqSUlJnDx5ksaNG+Pv709ubi5du3bl/PnzRERE0KFDB4YPH05kZCSnTp2qMIVoxp9p3I1KoiC34InreK4rHnD/kATdCyGEEOWDFKLA2bNnGTNmDA0aNMDHx4dly5ZhZGSEn58fO3bsIC4ujqioKBo3bqzepl27dgA0atSI3bt3o62t/diMUVdXV6Kjo/nxxx+5evUqKSkpZGZmYmtry6hRo7h48SL29vYMGjSotA7/lbt//A45SdlPXSfhfsJTfw8L2yqFqBBCCKHhpBAFWrZsyWeffUZWVhbdu3dnw4YN2Nvb4+HhwaBBg2jfvj2mpqZcvHhRvY2+vj5QmBn60OMyRmNiYggPD6dv377Y2dlx+fJlVCoV7733Hjt27CAiIoKdO3eyZcsW1qxZ88qPvTRUbW1KQa7yqTOi1StVe+qMqATdCyGEEJpPClFAV1cXAENDQxYsWMDQoUNJSUmhXr16DBkyhOzsbPz9/alRo8YT27h37x4DBw4sljF6+PBh+vXrh5ubG2fPnuXSpUsUFBSwYMECzMzMGDx4MK1bt1bHV1UERg2rYNSwylPX+fZ/S+VJSiGEEKKck0L0H5o3b86QIUPYtGkTdevWxcXFBZVKhZWV1WPfH//QkzJGGzZsiK+vLytXrsTIyIiWLVsSHx+Ph4cH48ePJyQkBG1t7SJvnnoWs9tN/6+HWqblSMC9EEIIUe5V+BxRTSWB9ppFsvI0j4yZ5pEx0zwyZpqn3OaIxsfH4+TkxA8//ECbNm3Uyx0dHVm7du0zB8vfuHGDb7/9ljlz5hQLq38SCwsLLC0tAcjNzcXc3JyJEydSr169f90uJiamxP16EZ42qBVNeQu/F0IIISqKMlOIQuG9mtOmTWPbtm0YGT1foXXz5k1u3LhR4u1CQ0PV/16/fj3Dhg1j586d6OnpPVc/XlS/nmTm0encL8eB9iWx2GEZEn4vhBBCaJ4yVYhWr14dOzs75s+fz8yZM4v9/t1337Ft2za0tbVp06YNEydO5NatWwwfPpyqVatiYGDAnTt3iI+P56uvvqJLly7cu3ePjz76iOvXr1O/fn38/f3/tbjs378/P//8M1FRUTg5ObFkyRKOHj1Kamoq1atXZ8mSJZiamqrX//333/H29ub777+nevXq+Pj4EBMTg0KhYNiwYXTv3p1Zs2ap+zV16lR8fX25cuUKd+7cwcLCgsWLF2NgYPDCz2l5lHYlncSDd1D+/1P3nmsKM0clX1QIIYTQLFql3YF/8vb25tChQxw+fLjI8oMHD7J//36Cg4PZsmUL165dY8OGDQDExsaycOFC1qxZg4+PD02bNuXLL78ECmcip0+fzq5du7hz5w5Hjhx5pn40bNiQq1evcu3aNa5evcqGDRsIDw/nzTffZNu2ber1Ll26xNSpU/nuu++oV68eAQEBVK1albCwMH766ScCAgK4dOlSkX6dPn0aXV1dgoKC2LNnD+np6Rw8ePAFncHyL/noPbISs8m9l0vuvVwSEhJITLxJbOxVwsK2lnb3hBBCCPGMytSMKICRkREzZ85UX6J/6NixY3Tt2hVDQ0MAevXqxdatW7G3t+eNN9544j2klpaW1KlTBwBzc3Pu37//TP1QKBQYGBhQr149Jk+ezKZNm4iNjeXMmTPUrVtXvd6wYcPo0qULDRo0UPfz4X2gJiYmODk5ceLECSwsLNTbWFlZYWxsTGBgIFevXiUuLo7MzMwSnKWKrbqtCYm5BeoZ0WqG1dQzopIvKoQQQmiOMleIArRt21Z9if6hgoLi4ef5+fkAT72kraPz9yEqFAqeNSQgJiaGfv36ce7cOcaPH8+QIUPo3LkzWlpaRdpYtGgRkyZNok+fPlhaWhZrX6VSoVQqiyzbt28f/v7+eHp60rNnT+7fv//M/RJQpdFrVGn0mvr7Yodl8tSlEEIIoYHK3KX5hx5eok9OTgbAxsaGHTt2kJ2dTX5+PsHBwdjY2BTbTltbW12gPq9ffvkFhUJB69atOXnyJNbW1vTv35+33nqLiIiIIoWlra0t48ePx8fHh4KCAmxsbNi8eTNQGHK/b98+rK2ti/Tr6NGjODs706tXL6pUqcLx48eLFatCCCGEEOVdmZwRhb8v0Q8bNgwABwcHLl68SK9evcjPz6dt27YMGjSIxMTEItuZm5uTnp7OxIkT6d279zPvr1u3bkDhzGudOnX4/vvv0dLSwsXFhZEjR+Lm5gZA06ZNiY+PL7Jt9+7dCQkJYd26dXh5eeHr64ubmxtKpZIRI0bQpEkT7t+/r+7X8OHDmTBhAjt27EBXV5dWrVoVa/PfTLOdUaL1yzMJvxdCCCE0kwTaizItJz+HtPuaX2hKaLPmkTHTPDJmmkfGTPOU20D70nT58mXc3Nzw9/enc+fOxX5PSkrCx8eH77//vhR693gLjk0hJeduaXfjpZtjvwLQ/EJUCCGEEMWV2XtEX6Xg4GC6dOlCUFDQY383MzMrU0WoEEIIIUR5UOFnRPPy8ti+fTuBgYG4u7tz/fp16tati6OjI82aNePixYssXLiQsWPHsn//foYMGaKOgEpKSqJ58+asWLGC4OBg1qxZg0KhoEmTJkybNo3KlSvTtm1bOnfuzKlTp9DW1mbp0qXUqVOHXbt2sWbNGrKzs8nNzWXOnDm0atWqlM9G6bp/+QHxB+9RkPN3QoLn6sKw+ocktF4IIYQoPyr8jOjBgwepWbMm9evXp2PHjkVmRdu3b094eDgmJibqZT/++COhoaEEBARgZGTEF198QUxMDN999x3r1q1j+/btGBoasmzZMgBu376Nra0tW7duxcrKisDAQAoKCtiwYYP6TVHDhw9n5cqVr/zYy5pbR++TeSuH7Ht56s/DsPqHHwmtF0IIIcqPCl+IBgcH4+rqCoCLiwshISHk5hbek9i8efPHbpORkYGXlxfTpk3jrbfe4uTJkzg4OFC1alUA+vXrx7Fjx9Trt2vXDoBGjRqRmpqKlpYWy5cv59ChQ3z99dds2bKFBw8evMzD1Ahv2lal0pv6GJjoqj+1atWiRo2a6k/9+g0ktF4IIYQoJyr0pfm7d+8SFRXF+fPnWbt2LSqVirS0NPbs2QOAvr5+sW1UKhUTJkzAxcUFe3t7oHjYvkqlKpJl+rCdh4H6Dx48oHfv3nzwwQdYWVlhYWFBYGDgyzpMjVH17cpUfbtykWVz7FfIE5VCCCFEOVWhC9HQ0FBsbGxYtWqVellAQID6HfaPs2TJEvT09BgxYoR6mbW1NWvXruWzzz7D2NiYjRs30rp16ye2ERcXh0KhYMSIEahUKiZNmlTiQPtJNnNKtL6mysnPKe0uCCGEEOIlqdCF6JYtWxg3blyRZQMHDmTVqlUYGRXPvEpKSmLFihVYWFjQo0cPVCoVVapUYd26dXzyySd4eHiQl5dHkyZN+Oqrr564X0tLSxo3boyzszMKhYK2bdty6tSpEvX97t0MCgokAlYIIYQQmqtcBdo/ePCARYsWcejQIQwNDTEyMmLUqFHY2tri4eHByJEji8xUHj9+nGXLlrFu3bontunt7Y21tTU9e/Z8FYdQYeXm55BaDoLrn0RCmzWPjJnmkTHTPDJmmkcC7Z9ApVIxYsQIGjduzI4dO9DT0+PChQt8/PHH+Pn5lXb3XrivT0wgtRwF2k9vtwYJrhdCCCEqlnJTiJ44cYKbN2+ydu1aFAoFAO+88w6ffvop33zzjXq9u3fvMnjwYMaOHctrr71WZPslS5aQnZ1NWloaX3zxBR07dgQgIiKCX375hbt37zJixAj69etHVlYWPj4+xMTEoFAoGDZsmPqd8xEREaSkpJCcnIy7uzsJCQkcO3YMY2NjVq1ahb6+PkuWLOHo0aOkpqZSvXp1lixZgqmp6as9aUIIIYQQpajcFKJnz56ladOm6iL0ISsrK/z8/GjSpAnp6el8/PHHjBw5ko4dO3L8+HH1ej///DOzZs3C3Nyco0ePMmfOHHUhmpuby6ZNm7hy5Qqenp7069ePgIAAqlatSlhYGPfu3aNPnz5YWlqq+7J9+3ZSU1NxdHRk1apVTJ06FQ8PD6KiomjUqBFXr15lw4YNaGlpMWnSJLZt28aHH3746k5YGXD3chZxB1JQ5qjw/P7v4HoJrRdCCCEqhnJTiCoUisc+eZ6Xl6cuTr/88ktMTU3p1KlTsfUWLlzIgQMH+PXXX/njjz+K5Ho6OTmhUCho1KiR+q1Kx44dY86cwifXTUxMcHJy4sSJExgZGdGqVSuMjIzUDzzZ2toCUKtWLdLS0qhXrx6TJ09m06ZNxMbGcubMGerWrftiT4gGuHE4jYxbeQAkkFDkt7CwrVKICiGEEOVcuQm0b968OefOnSMvL6/I8jNnztC0aVMAPvroI0xMTFi/fn2x7QcMGEB0dDRNmzYtEs0EoK2tDVBktvWfz3ipVCp1Iayrq1vkNx2dovX+uXPnGDZsGAUFBXTu3JmOHTsWa68iqNOmCkZv6mJoolMkuF5C64UQQoiKodzMiL7//vs0bNiQOXPmMGXKFHR1dTl37hzffvstfn5+LFu2jMaNG9OhQwf69++vvuwOkJKSQlxcHL/88gt6enosWrToX3M9bWxs2Lx5Mz4+Pty7d499+/YREBBATEzMv/b15MmTWFtb079/f+7fv09ERMRjZ2nLuzfeNuSNtw2BwoeV5MlJIYQQomIpN4UowLJly1iyZAmurq5oa2vz+uuvs3DhQlq3bq1+9/tbb73FwIEDmTFjBp6engAYGxvTu3dvunbtio6ODjY2NmRnZ5OZmfnEfXl5eeHr64ubmxtKpZIRI0bQpEmTZypEXVxcGDlyJG5ubgA0bdqU+Pj4Eh3rGOtFJVq/rMuV4HohhBCiwilXOaIViQTaaxbJytM8MmaaR8ZM88iYaR7JEX3BLCwsnmkW86GAgAAARo0aRbdu3QgNDX1ZXXuqpw1qeVDeA+6FEEIIIYXof1JaRSjAipNjSMu5U2r7f9kmtg1EAu6FEEKI8k0K0f93/PhxVqxYgYGBAX/99RcWFhYsWrQIPT09Vq1axcaNG6latSpVqlShWbNmwN+zqUlJSUyZMoX09HSSk5Pp0aMHY8aMISQkhKioKFJTU7lx4wZt2rTB19eX/Px8fH19uXLlCnfu3MHCwoLFixdjYGBQymehdCVfzubP/Rnk56o4u7IwV1QyRYUQQojySwrRR5w+fZpdu3ZRvXp1+vbty6FDh6hWrRrBwcFs2bIFhUJBv3791IXoQ2FhYbi6utKjRw/S09Oxt7fHw8ND3WZYWBja2tp06dKF/v37k5aWhq6uLkFBQRQUFDB48GAOHjxI586dS+Owy4zYww9Iu5UPQOYjuaKSKSqEEEKUT1KIPqJRo0bUqFEDAHNzc1JTU4mNjcXe3p7KlSsD0KVLFwoKCopsN2zYMI4dO8bq1au5cuUKeXl5ZGVlAdCyZUt1sH2dOnVITU3F2toaY2NjAgMDuXr1KnFxcU99Qr+iqN+mMsqcwhnRqgY11DOikikqhBBClE9SiD5CX19f/W+FQoFKpVL/70M6Ojrk5ha9d3HevHncuHEDV1dXOnbsyJEjR9TbPK7Nffv24e/vj6enJz179uT+/fsVMtD+n6q/bUD1twtvT5jYdq08SSmEEEKUc+XmzUovi62tLQcOHCA9PZ2cnBz27NlTbJ3Dhw8zbNgwnJ2diY2NJSkpqdis6aOOHj2Ks7MzvXr1okqVKhw/fvxfA/SFEEIIIcobmRH9F40bN2bw4MH07t2bKlWqULNmzWLrfPLJJ0yaNAkDAwNq1KjxrwH1ffr0YcKECezYsQNdXV1atWpV4kD7T6y+LvGxaBIJuBdCCCHKPwm011ASaK9ZJLRZ88iYaR4ZM80jY6Z5JND+GQ0dOpSBAweq3yk/f/58NmzYwPHjx9HT0wOgbdu2bNiwgdq1a7+w/SYlJeHj48P333//wtp8HE0MtJeQeiGEEEI8qtwWojY2Npw6dUpdiB45coQWLVpw6tQpbG1tuXbtGpUqVXqhRSiAmZnZSy9CAX469SnpObdf+n5epJF2m5GQeiGEEEI8VG4LUVtbW+bMmQMUzlLq6enRuXNnDh06hK2tLb/99huJiYmMHz8ePz8/oPD1nfr6+nh4eODj40NMTAwKhYJhw4bRvXt3QkJCiIiIICUlheTkZNzd3UlISODYsWMYGxuzatUqbt++jaenJ/v378fb2xsjIyPOnz9PUlISXl5e9OrVi/T0dCZNmsT169epU6cOiYmJLFu27IUXxWVBYkweF/dnkZej4sR3hSH1gATVCyGEEKL8PjXfpEkTrl+/Tk5ODocOHaJNmza0adOGQ4cOAfDbb78xc+ZMjh49SkZGBlAYTN+tWzcCAgKoWrUqYWFh/PTTTwQEBHDp0iUAzp49yzfffMPq1auZO3cu7du3Z/v27QBERUUV60diYiK//PIL3377LQsWLABg+fLl1K9fnx07duDl5cXly5dfxSkpFVcOZZNyU8mDuwUkJCSQmHiTxMSbxMZeJSxsa2l3TwghhBClqNwWotra2jRv3pyzZ89y6NAh2rZtS506dcjOziY1NZXTp0/j5OSEvb09e/bs4bfffqNOnTqYmZlx7NgxevfuDYCJiQlOTk6cOHECgFatWmFkZEStWrWAwplXgFq1apGWllasH23atEGhUPD222+TkpICFMY9devWDYB3332Xt99++2WfjlLTqK0BxjW1qfyGFrVq1aJGjZrUqFGT+vUbSFC9EEIIUcGV20vzUHif6O+//050dDQLFy4ECgvHffv2UbVqVYyMjOjVqxfffvsttWvXpmfPngDFwuVVKpU651NXV7fIbzo6Tz+FDwPtFQqFepm2tnaFCbCvYaFLDYvCczbSTkLqhRBCCPG3cjsjCoVFZ2hoKG+//ba6YGzTpg1r1qyhTZs2ALz//vskJiZy/Phx9YNNNjY2bN68GYB79+6xb98+rK2tX2i/Hl7Oj4mJ4cqVK0UKVSGEEEKIiqBcz4g+vBw+YMAA9TIbGxvGjh2LnZ2detn//vc/UlJS1LFOXl5e+Pr64ubmhlKpZMSIETRp0oSYmJgX0i8vLy+++OIL3NzcqFu3LqamphgYGJSojcHvfftC+vIqSUi9EEIIIR5VoQPtVSoVeXl5DB06lClTptCkSZNXst/Q0FBq167Ne++9x82bNxk0aBB79+5FS6tcT1D/q/KcMyqhzZpHxkzzyJhpHhkzzSOB9i/Q7du36dq1K3369ClWhMbHx+Pk5ES/fv2YMWOGevnFixfp3r07c+fOVd9T+k8fffQRs2bNwszM7LG/N2jQgC+//JKCggK0tLSYMWNGiYvQoN+HkZGTXKJtyrphttuRnFEhhBCi4qjQhWj16tU5efLkE383NjYmKioKpVKJtrY2ADt37sTExOSp7f5boP27775LSEhIyTsshBBCCFGOVOhC9N9UrlwZS0tLTp48iY2NDVAYvfTw/tKff/6Z0NBQsrKy0NXVxc/PjwYNGuDo6MjatWs5ceIEUVFRpKamcuPGDdq0aYOvry8AK1euZNeuXSiVStq2bcvEiRMr5ANLCTFKovflkp8DB78tDLyXsHshhBCiYqjYNyU+A2dnZ8LDwwGIjo7GwsICXV1dMjIy2Lt3L+vWrSMsLIwOHToQGBhYbPvTp0/j7+/Ptm3bOHDgADExMURGRnLu3Dk2b97M1q1bSUpKYtu2ba/60MqEi1F53L+pIv2uSh14L2H3QgghRMUgM6L/wtHRkaVLl1JQUMCuXbtwdnZm586dGBkZ4efnx44dO4iLiyMqKorGjRsX275ly5YYGRXepFunTh1SU1M5evQo0dHR6ntMs7OzqVmz5is9rrKicTtd8nILZ0SrGNRUz4hK2L0QQghR/kkh+i8eXp4/deoUx44dY/z48ezcuZNbt27Rr18/Bg0aRPv27TE1NeXixYvFtn8YaA+FofYPw/EHDx7M0KFDAUhLS1Pfg1rR1LLQppaFIQDDbCXwXgghhKhIpBB9Bs7Ozvj5+dG0aVN1MH6lSpWoV68eQ4YMITs7G39/f2rUqPFM7dnY2ODv70/fvn3R19fHy8uLHj16PPEp/Mfp12r1cx1LWSY5o0IIIUTFIoXoM3BwcGDq1KmMGTNGvUxXV5eCggJcXFxQqVRYWVlx5cqVZ2rP0dGRS5cu0bdvX5RKJe3ataNHjx4l6tPduxkUFFTYCFghhBBClAMVOtD+aS5fvoybmxv+/v507twZQP00fO3atUvU1vNuV9bk5WeTcj+vtLuhkSS0WfPImGkeGTPNI2OmeSTQ/hUJDg6mS5cuBAUFqQvRsiT09CAe5CS90n0OsNkDSCEqhBBCiBdDCtHHyMvLY/v27QQGBuLu7s7169epW7eu+veMjAymTJlCUlISycnJ2NraMnv2bJKSkpgwYQKZmZloaWnh4+NDixYt1NvFxsbyySefsGDBAho2bPjYNspCluj1Syp+36ci7x+3bP5qUJjz+SjJ/BRCCCHE85Ic0cc4ePAgNWvWpH79+nTs2JGgoKAiv0dERNC4cWOCgoIIDw/n5MmTnD9/ns2bN9OhQwdCQkIYPXo0p06dUm+TmJjIyJEjmTNnDi1atHhiG2XB2UMq7t6EtLtFPw9zPh/9SOanEEIIIZ6XzIg+RnBwMK6urgC4uLgwYcKEIg8qubq6Eh0dzY8//sjVq1dJSUkhMzMTW1tbRo0axcWLF7G3t2fQoEHqbcaMGcO7777L+++//9Q2yoJ32yrIyyk+I/qaQa3HzohK5qcQQgghnocUov9w9+5doqKiOH/+PGvXrkWlUpGWlsaePXvU66xbt47w8HD69u2LnZ0dly9fRqVS8d5777Fjxw4iIiLYuXMnW7ZsYc2aNQBMnTqV5cuXExERQYcOHZ7YRllQ11JBXcvitwgMsJGcTyGEEEK8OFKI/kNoaCg2NjasWrVKvSwgIIANGzaovx8+fJh+/frh5ubG2bNnuXTpEgUFBSxYsAAzMzMGDx5M69ati0QyNWvWDF9fXyZPnoy1tfUT2xBCCCGEqCikEP2HLVu2MG7cuCLLBg4cyKpVq9Sv6hw8eDC+vr6sXLkSIyMjWrZsSXx8PB4eHowfP56QkBC0tbWZP39+kXasrKxo3bo1S5cufWIbz6pby5//+8GWUF5+9ivfpxBCCCHKL8kR1VASaK9ZJCtP88iYaR4ZM80jY6Z5JEf0EfHx8Tg5OdGvXz9mzJihXn7x4kW6d+/O3Llz+emnnwgNDS1Ru1OnTsXd3Z133333mbdZv349AP379y/Rvp53f08b1NIkofdCCCGEeFYaXYgCGBsbExUVhVKpRFtbG4CdO3diYmICUOIiFGD27Nkl3uZ5CtD/sr/w033JzE187n2+LD1aRyKh90IIIYR4FhpfiFauXBlLS0tOnjyJjY0NUPgwkZ2dHQAWFhbExMRw9OhRFi5cCMDrr7+On58fenp6fP7559y5cwcALy8vnJyc8PDwYOTIkdSrV++xAfXz58/n8OHDaGlp0bFjR0aOHElAQAAAo0aNwtbWlv/973+cPn2aypUrs2jRImrXro2joyNdunThyJEjAMyZM4d33nlHvb/WrVu/6tP3XOIuKTi+V5vcnOK/bXlM6D1I8L0QQgghiisXgfbOzs6Eh4cDEB0djYWFBbq6ukXW+eabb/D19SUkJAQ7OzsuXLjAnj17qFWrFiEhIcyePZvffvutyDaPC6hPSEggMjKSbdu2sX79ev78809ycopWZPfu3aNly5Zs376drl27MmvWLPVvlSpVYuvWrYwePZrJkye/pDPycv0epcXtmwpS7xb/PC70XoLvhRBCCPE4Gj8jCuDo6MjSpUspKChg165dODs7s3PnziLrODk5MXLkSDp27IiTkxNt2rQhLi6OxYsXk5SURIcOHfDy8iqyzeMC6rW1tdHX18fd3R0HBwcmTJiAvr5+ke309fXp3r07AD169GDx4sXq3/r27avus7e3N/fu3XsJZ+TlatWugLwcxWNnRI0Maj9xRlSC74UQQgjxqHJRiD68PH/q1CmOHTvG+PHjixWiQ4YMwcHBgQMHDrBw4UKio6P59NNP2bVrF1FRURw4cIAffvihyHZPCqjftGkTJ06cIDIyEnd3d9atW1dkX1paWup3xhcUFKjvXQXQ0fn7lP/zN03xlqWKtyzzH/tbj9YSei+EEEKIZ1MuLs1D4eV5Pz8/mjZtWqTYe6hPnz48ePCAIUOGMGTIEC5cuMDPP/9MQEAAzs7OfPnll9y7d4+MjAz1NgsWLGDbtm306NGD6dOnc+HCBS5cuMCgQYOwsrJi8uTJmJubExsbW2RfWVlZ7N+/H4CQkBDat2+v/m3Hjh0A7NmzB3Nzc15//fWXcTqEEEIIIcq8cjEjCuDg4MDUqVOLvBP+UZ9//jne3t7o6OhQqVIlZs2ahampKZ9//jlubm5oa2szceJEqlSpot7mcQH177zzDi1atMDV1RVDQ0NatWpF+/btOX/+fJH9/frrryxZsoTq1asXCbb//fff2bx5M4aGhsybN++5j7dzy43Pve3LJKH3QgghhHhWEmj/Ejx8Uv+fHB0dWbt2LbVr1y6FXr1ckh/6dBLarHlkzDSPjJnmkTHTPBJoXwLHjx9n2bJl6ns4MzIy+PDDD2nVqhXe3t4lamvjxo1UqlQJV1fXEvfjSYXpf3HwdA+yc2+90Db/i86tjyH5oUIIIYQoiXJzj+i/efDgAcOHD8fa2rrERSgUXlLPzc19pnWfVHTu37+/XM6GCiGEEEI8jwpRiGZmZvLxxx9jY2PDhAkTAIiMjKR37950796dkSNHcv/+feDvKKjevXvTtWtXzp07x5EjR9i/fz/+/v5ERUXh7e1NSEiIun0LCwsAUlJS8PLywtnZmW7dunH06NEi/fj999/p1KkTsbGxODo6qh9yyszMxN7evlgeqSb465I2Py0zwNPTk7FjP+OLLz7n999/+/cNhRBCCFHhlftCNCsri08++YTLly8zZMgQoDBw3s/Pj9WrV7N161batm3LokWL1NsYGxuzefNm3N3dWbFiBXZ2djg6OjJ69GjatWv3xH19/fXX1K1bl127drFgwQKWLl2q/u3SpUtMnTqV7777jvr169O9e3e2bdsGwO7du+nQoUOxPFJNcCJKl6Sb2uogewmuF0IIIcSzKveF6NmzZ7G1tcXFxQUfHx8A/vjjD27duoWnpyfdunUjMDCQa9euqbd5WGw2atSIlJSUZ97XyZMn6datG1A4SxoUFKT+bdiwYdjZ2dGgQQMAevbsSVhYGABbtmyhZ8+e/+k4S4t1uzzMaiqpVasWNWrUpH79BhJcL4QQQohnUq4fVgJo2bIln332GVlZWXTv3p0NGzZgampKq1at+O677wDIycnhwYMH6m0ezkw+DKX/J4VCwcOwgby8vx/Q0dHRKbLNX3/9Rf369QFYtGgRkyZNok+fPlhaWlK7dm1q1qzJ7t27uXv3Ls2bN3+xB/6KmFsqMbdU0lmC7IUQQghRQuW+EH34znlDQ0MWLFjA0KFDWbVqFWfOnCE2Npb69evzzTffkJSU9NRcT21tbZRKJVB46f7PP/8EYO/evep13n//fXbs2IGFhQV//fUXH330Efv27QMKXxc6fvx4fHx82LhxI1paWvTq1YtZs2YxaNCgEh+XfcstJd7mZZL8UCGEEEKUVLkvRB/VvHlzhgwZwpdffsmcOXMYO3YsBQUFmJmZsXDhwqdua2dnx+LFi3nttdfo378/Y8eOxc3NDRsbG6pVqwbA6NGj8fHx4YMPPkBHR4cFCxYUmSHt3r07ISEhrFu3jsGDB9OpUyemTZumvpxfEnfvZlBQIBGwQgghhNBcEmj/DJ6UA+rt7Y21tfVT7+980rYqlYrIyEjWr1+vvkWgrMrPz+a+hNX/JxLarHlkzDSPjJnmkTHTPBJoX07MmTOHAwcO8P333z/X9sfPuJHzigLt21v/hoTVCyGEEOJFk0K0BFQqFfPmzSMiIoLq1aujVCqxtrYGYMmSJRw9epTU1FSqV6/OkiVLMDU1BWD69OmcOXMGgICAAOrVq0fXrl05c+YMY8aMoWrVqsyYMYN69eqV1qEJIYQQQrxyUoiWQHh4OBcuXCAsLIz09HQ++OADAK5du8bVq1fZsGEDWlpaTJo0iW3btvHhhx8ChfeXzpgxg/nz57NhwwbGjRvH559/ztKlS2nWrBm7du3i888/Jzg4uDQPT+3yJR0i9hjwMF9/lYEnSuXfd3AYGhrQp88AWrV6v5R6KIQQQojyQArREjhx4gSdOnVCV1cXExMT2rdvD0C9evWYPHkymzZtIjY2ljNnzlC3bl31dh07dgSgYcOG/Pbbb8TFxVGlShWaNWsGgLOzM9OnTyc9PZ3XXnvt1R/YPxyJ1OfWTe1HliQUWycsbKsUokIIIYT4T6QQLYFH80OhMDcU4Ny5c4wfP54hQ4bQuXNntLS0Hrvew+0LCgqKta1SqdTxUKXNrn0OuTkK9YyooUHdYjOiElovhBBCiP9KCtESsLW1ZfXq1bi7u5OVlUVUVBQtWrTg5MmTWFtb079/f+7fv09ERASdOnV6YjsNGjQgJSWF6OhomjVrxs6dO6lZsybGxsav7mCe4m3LfN62zFB/b28tYfVCCCGEePGkEC2Bjh07cvbsWVxdXTE1NcXc3BwAFxcXRo4ciZubGwBNmzYlPj7+ie3o6emxZMkSZs6cSVZWFq+//jpLliwpUV9at9j+/AdSQvkSVi+EEEKIl0ByRDWUBNprFsnK0zwyZppHxkzzyJhpnnKbI/rrr7+ycuVK8vPzUalUdOvWjeHDhz9XWwcOHCAuLo6hQ4cSEBAAwKhRo564/vHjxxkxYgR169ZFpVKRk5ODvb09Y8aMoXLlyk/dbtmyZaxbt67E/fqvnjaoL5OE2wshhBDiRSkThWhSUhLz588nJCSEqlWr8uDBAzw8PKhfvz5OTk4lbu/cuXMl3qZp06bqgjIvL48pU6bg6+v7r6/+fNn9epLoaFdyX1Gg/aPef/8UEm4vhBBCiBehTBSi9+/fJy8vj+zswnsRK1euzLx589DX1wfgzJkzzJ49m5ycnCLh7x4eHowcOZLWrVsTHx+Pp6cnK1euZMOGDQDUrFkTgOjoaNzd3UlKSqJnz55PnR0F0NXVZdKkSXTo0IFp06ahpaXFlClTSEpKIjk5GVtbW2bPnl1km59++om9e/eycuVKEhMTmT59OikpKVSqVImpU6dSqVKlIv1q27YtU6ZMIT09neTkZHr06MGYMWNe6Hl9kS5e1GHPHgPAEz09fckRFUIIIcR/ViYKUUtLS5ycnOjYsSONGzemdevWuLm5Ua9ePXJzc0sU/t6wYUPc3d0B6NWrFwEBAdy9e5cNGzaQkZGBo6MjQ4cOxcjo6Ze2q1WrRpUqVYiLi+P69es0btwYf39/cnNz6dq1K+fPn1evGxISwu7du1m5ciWGhoZMnDiRjz/+mE6dOqnfnhQeHl6kX6tXr8bV1ZUePXqQnp6Ovb09Hh4emJiYvKCz+mJFRhqQkKDDw0xRyREVQgghxH+lVdodeOirr75i//799O/fn5s3b9K3b19279792PD369evk57+7DfKtmvXDj09PUxMTKhatSqpqanPtJ1CoUBfXx9XV1fatGnDjz/+yKxZs0hJSSEzMxOAy5cvM23aNDw9PalcuTIPHjzg+vXr6vimFi1a8Prrr3P16tUibQ8bNow333yT1atXM3v2bPLy8sjKynrmY3rV2rfPplatfGrVqkX9+g0kR1QIIYQQ/1mZmBGNiIggMzMTFxcXevXqRa9evdi4cSObN2/m888/L7b+o+HvDx/6z8/Pf2L7DwPloXgo/ZPcuXOH9PR06taty7p16wgPD6dv377Y2dlx+fJldRuVK1dm7ty5zJ49m3bt2j227ceF1c+bN48bN27g6upKx44dOXLkyDP1q7Q0bpxP48YZvP++ZIoKIYQQ4sUoEzOiBgYG+Pn5qbM3VSoVFy9epHHjxkXC34Ei4e9Vq1blzz//BGDv3r3q9rS1tZ9amP6b3NxcFixYQI8ePTA0NOTw4cP069ePDz74gJycHC5duqR+O1KtWrVwdHTE2toaf39/jIyMqF27Nrt37wYK72+9c+cOjRo1KtKvw4cPM2zYMJydnYmNjSUpKemxb1wSQgghhCivysSMqI2NDSNHjmTEiBHk5RU+kd2uXTu8vLyeGv4+fPhwvL29CQ4OLvJ0vZWVFZMnT8bU1PSZ+3Du3Dm6desGgFKpxMbGhkmTJgEwePBgfH19WblyJUZGRrRs2ZL4+Pgi75OfNGkSrq6uuLm5sXDhQnx9fQkICEBXV5eAgAD09PSK9OuTTz5h0qRJGBgYUKNGDXUI/qNtPk2zZmHPfGwvkoTbCyGEEOJFkUB7UYTkhL4cEtqseWTMNI+MmeaRMdM85TbQ/mWKj4/HycmJfv36MWPGDPXyixcv0r17d+bOnUvPnj1L1ObGjRupVKkSrq6uL7q7z+Sv8z3Iewk5opYtjyE5oUIIIYR4FcrEPaKvgrGxMVFRUUUeGtq5c+dzxyX9/vvv5ObmvqjuCSGEEEJUOBViRhQKn263tLTk5MmT2NjYAIUPDNnZ2QGFr99cunQpBQUF1KlThxkzZmBqaoqjoyMffPABhw4dIisri/nz55OWlsb+/fs5duwY1apVw8zMjJkzZ5KZmcm9e/f4+OOP6d+/PwEBASQlJXHt2jUSEhLo06cPn376KRkZGY8NyFcoFKV2fs5d0CYsXJcCPFEqVRgaGkhovRBCCCFeqgozIwqFGaTh4eFA4duWLCws0NXV5e7du0yfPp3ly5ezfft2WrVqVeQSvrGxMZs3b8bd3Z0VK1ZgZ2eHo6Mjo0ePpl27dmzatInPPvuM4OBg1q5dy4IFC9TbxsTEsHr1ajZt2sTKlStJS0sjIiKCxo0bExQURHh4OCdPniwSkF8a9kbociNBm4SEBBITbxIbe5WwsK2l2ichhBBClG8VqhB1dHQkMjKSgoICdu3ahbOzMwCGhoY0a9aM2rVrA9CvXz+OHTum3q5du3YANGrUiJSUlGLtent7k5OTw4oVK1i6dKk67B6gdevW6Onp8cYbb2BsbEx6evpTA/JLS8cOedSppaRWrVrUqFFTQuuFEEII8dJVmEvz8Pfl+VOnTnHs2DHGjx/Pzp07i+V3qlSqIjmkD995/6RL52PHjqVKlSo4ODjg4uJCWFhYsW0fbq9SqZ4akF9amr6jpOk7SixbSmC9EEIIIV6NClWIQuHleT8/P5o2bap+41J2djZ//PEH8fHx1K5dm6CgIFq3bv3UdrS1tdUPPh0+fJhdu3ZhZmZGYGAgQLE3KT3qYUC+m5sbZ8+eLRKQ/6zMm2wp0frPKj8/By2t0rtXtTyT86p5ZMw0j4yZ5pEx0zwlGbN/W7fCFaIODg5MnTqVMWPGqJeZmpoyY8YMRo4cSV5eHjVr1mT27NlPbcfOzo7Fixfz2muvMWrUKAYMGIC+vj6WlpbUqlVL/Zaox3lSQH5ZoKOjzxtv6P/7iqLEnpajJsomGTPNI2OmeWTMNM+LHDMJtBdCCCGEEKWiQj2sJIQQQgghyg4pRIUQQgghRKmQQlQIIYQQQpQKKUSFEEIIIUSpkEJUCCGEEEKUCilEhRBCCCFEqZBCVAghhBBClAopRIUQQgghRKmQQlQIIYQQQpQKKUQ1zPbt23FxcaFTp07q99qLsmXZsmV07dqVrl27smDBAgCOHDmCm5sbnTp1YsmSJaXcQ/Ek8+fPx9vbG5AxK+v2799Pz549cXZ2ZtasWYCMWVkXGhqq/ts4f/58QMasrMrIyMDV1VX9+vEnjdPFixfp2bMnnTt3ZurUqeTn55d8ZyqhMRITE1UODg6q+/fvqx48eKByc3NTXblypbS7JR5x+PBhVb9+/VQ5OTmq3Nxclaenp2r79u0qe3t71fXr11V5eXmqDz/8UBUREVHaXRX/cOTIEVXr1q1VkydPVmVlZcmYlWHXr19XtW3bVnXr1i1Vbm6uqn///qqIiAgZszIsMzNTZWVlpbp7964qLy9P1bt3b9W+fftkzMqgM2fOqFxdXVVNmjRR3bhx46l/D7t27ao6ffq0SqVSqb744gtVYGBgifcnM6Ia5MiRI9jY2GBsbEylSpXo3Lkzv/76a2l3SzyiWrVqeHt7o6enh66uLubm5sTFxVGvXj3q1KmDjo4Obm5uMm5lTEpKCkuWLGHEiBEAREdHy5iVYXv27MHFxYUaNWqgq6vLkiVLMDQ0lDErw5RKJQUFBWRlZZGfn09+fj5GRkYyZmXQxo0b+fLLL6levTrw5L+HCQkJZGdn06JFCwB69uz5XOOn8yI7L16u5ORkqlWrpv5evXp1oqOjS7FH4p8aNWqk/ndcXBy7du1i0KBBxcYtKSmpNLonnmD69OmMGzeOW7duAY//b03GrOy4du0aurq6jBgxglu3btGhQwcaNWokY1aGGRkZMWbMGJydnTE0NMTKykr+OyujZs+eXeT7k8bpn8urVav2XOMnM6IapKCgAIVCof6uUqmKfBdlx5UrV/jwww+ZNGkSderUkXErwzZt2sSbb76Jra2tepn8t1a2KZVKjh49ypw5cwgKCiI6OpobN27ImJVhly5dIjg4mAMHDhAVFYWWlhZxcXEyZhrgSX8PX9TfSZkR1SA1atTgt99+U3+/ffu2eupclB2nTp1i9OjRTJkyha5du3LixAlu376t/l3GrWzZuXMnt2/fplu3bqSmppKZmUlCQgLa2trqdWTMyhZTU1NsbW0xMTEBoGPHjvz6668yZmXYoUOHsLW15Y033gAKL+OuXr1axkwD1KhR47H/H/bP5Xfu3Hmu8ZMZUQ1iZ2fH0aNHuXfvHllZWezevZv27duXdrfEI27duoWXlxeLFi2ia9euADRv3pzY2FiuXbuGUqkkLCxMxq0MWbNmDWFhYYSGhjJ69GgcHR1ZtWqVjFkZ5uDgwKFDh0hLS0OpVBIVFUWXLl1kzMowS0tLjhw5QmZmJiqViv3798vfRg3xpHGqVasW+vr6nDp1CihMRXie8ZMZUQ1iZmbGuHHj8PT0JC8vj969e9OsWbPS7pZ4xOrVq8nJyWHevHnqZe7u7sybN49Ro0aRk5ODvb09Xbp0KcVein+jr68vY1aGNW/enOHDhzNgwADy8vJo06YN/fv3p0GDBjJmZVTbtm25cOECPXv2RFdXl3fffZdRo0bRpk0bGbMy7ml/DxctWoSPjw8ZGRk0adIET0/PErevUKlUqhfdaSGEEEIIIf6NXJoXQgghhBClQgpRIYQQQghRKqQQFUIIIYQQpUIKUSGEEEIIUSqkEBVCCCGEEKVCClEhhNBgx48fx9XV9T+1kZ6e/kyxK8+6XkhICJ988sl/6pMQomKQQlQIISq41NRUzp49+8LWE0KIZyWFqBBClAOxsbEMHTqUvn374uDgwKeffkpOTg6zZ89m6dKlACQnJ2NhYcGxY8eAwjehjB07li+++ILs7Gy6deuGUqnkt99+o2/fvri5udGzZ08iIyMBiq23efNm+vTpQ/fu3XFwcOCXX34prcMXQmgoKUSFEKIc2LhxI927d2fjxo3s3r2b+Ph4IiIi6NSpk7qQjIqKolq1ahw5cgSA/fv306lTJ+bOnYuBgQGhoaGkpaUxevRopk6dyvbt25k/fz4TJ07kxo0bRdbLzs5m06ZNrFy5kq1bt7JkyRIWLlxYmqdACKGBpBAVQohyYOLEiZiYmPD999/j6+tLcnIymZmZvPfeeyQlJXHnzh2ioqL49NNPOXz4MLm5uZw8eRJ7e/si7URHR1O3bl2aN28OQKNGjWjVqhUnTpwosl7lypX57rvvOHjwIEuXLuW7774jMzPzlR2vEKJ8kEJUCCHKgc8//5yNGzdSq1YthgwZQpMmTVCpVGhpadGhQwcOHjxIdHQ0ffr04fbt2/z666+0bNmSypUrF2lHqVSiUCiKLFOpVOTn5xdZlpiYSPfu3UlISOC9995j7NixL/sQhRDlkBSiQghRDhw6dAgvLy9cXFwA+OOPP1AqlQB06tSJVatW8fbbb6Onp4eNjQ2LFy+mU6dOAOjo6KBUKlGpVLRo0YKrV68SHR0NwJUrVzh58iTW1tZF1jt37hwmJiZ89tlntG3blgMHDgCo9ymEEM9Cp7Q7IIQQ4r8bN24cXl5eVKpUCSMjI6ysrLh+/ToAtra2JCcn079/fwDatm3Lzp07cXR0BKBatWo0a9aMrl27EhgYyNdff83MmTPJzs5GoVAwd+5c6tevj1KpVK+3Zs0azMzM6NKlCwqFAmtra0xMTLh27VqpnQMhhOZRqFQqVWl3QgghhBBCVDxyaV4IIYQQQpQKKUSFEEIIIUSpkEJUCCGEEEKUCilEhRBCCCFEqZBCVAghhBBClAopRIUQQgghRKmQQlQIIYQQQpSK/wNYBGyNJ4wEdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_laws = gun_laws.sort_values(by='lawtotal', ascending=False)\n",
    "sns.set(rc = {'figure.figsize':(10,8)})\n",
    "sns.barplot(x='lawtotal', y='state', data=df_laws, palette='viridis').set(title=\"Gun Laws by State\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the visualizations, **Illinois** has the highest gun violence incidents and **Hawaii** has the least number of incidents. However, the population difference is very large. Because of this, we will calculate a proportion that represents (# of cases/ population). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>state</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>prop_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>626210.0</td>\n",
       "      <td>625214.0</td>\n",
       "      <td>625216.0</td>\n",
       "      <td>623657.0</td>\n",
       "      <td>624344.0</td>\n",
       "      <td>624358.0</td>\n",
       "      <td>0.004640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>1853914.0</td>\n",
       "      <td>1849489.0</td>\n",
       "      <td>1842050.0</td>\n",
       "      <td>1831023.0</td>\n",
       "      <td>1817004.0</td>\n",
       "      <td>1804291.0</td>\n",
       "      <td>0.001535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>923576.0</td>\n",
       "      <td>932487.0</td>\n",
       "      <td>941252.0</td>\n",
       "      <td>948921.0</td>\n",
       "      <td>956823.0</td>\n",
       "      <td>965479.0</td>\n",
       "      <td>0.001210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>4624527.0</td>\n",
       "      <td>4644013.0</td>\n",
       "      <td>4664628.0</td>\n",
       "      <td>4678135.0</td>\n",
       "      <td>4670560.0</td>\n",
       "      <td>4659690.0</td>\n",
       "      <td>0.001013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>12895129.0</td>\n",
       "      <td>12884493.0</td>\n",
       "      <td>12858913.0</td>\n",
       "      <td>12820527.0</td>\n",
       "      <td>12778828.0</td>\n",
       "      <td>12723071.0</td>\n",
       "      <td>0.001010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            state        2013        2014        2015        2016        2017  \\\n",
       "45        Vermont    626210.0    625214.0    625216.0    623657.0    624344.0   \n",
       "48  West Virginia   1853914.0   1849489.0   1842050.0   1831023.0   1817004.0   \n",
       "7        Delaware    923576.0    932487.0    941252.0    948921.0    956823.0   \n",
       "18      Louisiana   4624527.0   4644013.0   4664628.0   4678135.0   4670560.0   \n",
       "13       Illinois  12895129.0  12884493.0  12858913.0  12820527.0  12778828.0   \n",
       "\n",
       "          2018  prop_cases  \n",
       "45    624358.0    0.004640  \n",
       "48   1804291.0    0.001535  \n",
       "7     965479.0    0.001210  \n",
       "18   4659690.0    0.001013  \n",
       "13  12723071.0    0.001010  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uspopulation = pd.read_csv('us_states_census.csv') \n",
    "population = pd.DataFrame(data=uspopulation, columns=['state','2013','2014','2015','2016','2017', '2018'])\n",
    "\n",
    "state_cases = gun_data.groupby(['state']).size().reset_index(name='counts')\n",
    "state_cases = state_cases.sort_values(by='state', ascending=True)\n",
    "state_cases['population'] = population['2018']\n",
    "\n",
    "prop_cases = population\n",
    "prop_cases['prop_cases'] = state_cases['counts'] / population['2018']\n",
    "prop_cases = prop_cases.sort_values(by=['prop_cases'], ascending=False)\n",
    "\n",
    "prop_cases.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the new proportion calculations, we will compare the case proportions between **Alaska** and **Hawaii** and if the number of laws affects the proportion.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAAEXCAYAAAAXwHkbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAy+UlEQVR4nO3dfVxUZf7/8Rc3I2q4kTojYrXfyjJTCROM1EBtV+4VSbNEsNx0dSvRklY0QTQyic2bvlq5Wu66YrC1QSSgpUtkqD/wZrVV15XWW+LOIUFuZIa5fn/4cL6OiI7paKf9PB+PHo/Ouc51neucI++5zpkz5zgppRRCCKEBzre6A0IIYS8JLCGEZkhgCSE0QwJLCKEZElhCCM2QwBJCaIYE1lWcPHmS3r17M2rUKOt/I0eO5OOPP74l/Tlx4gQvvfQSABUVFTz99NM3Zb1JSUkMHz6cJUuW3JT1/RgxMTHk5+ff6m5c1cmTJ+nfv/9Vl/vrX//K+vXrAdiwYQOrVq26Ievfv38/06dPv6Y6TU1NJCQkEB4eTlhYGAkJCTQ1NQFw9OhRoqOjCQ0NZcyYMZSWltrUbW5u5rnnnrM5NvX19cTFxREeHk5oaChr1qyxryNKXNGJEyeUj4+Pzbzy8nLl6+urDh48eNP7s2PHDhUWFnbT19urVy/1/fff3/T1XosJEyaovLy8W92Nq7rcv6nL+f3vf69Wr159E3p0dW+//baKj49XLS0tymw2q5kzZ6qlS5cqpZR68skn1WeffaaUUqqgoECFhYUpi8WilFJq9+7dKjIyUnl7e9scm+XLl6tXX31VKaVUXV2dCggIUP/4xz+u2g/Xa4pZAUC3bt345S9/ydGjRzlw4AAff/wxjY2NuLu7s27dOlasWMHGjRtxcXHhnnvuYd68eej1emJiYnjooYfYtWsXNTU1jBo1yvpJ9+WXX/K///u/WCwWbrvtNhISEvD29uadd95h7969VFZWcv/997N//34qKir4zW9+Q3JyMhEREezZsweTycSbb77J9u3bcXFxwdvbm4SEBNzd3Rk+fDijR49m+/btfP/994waNYoZM2a02q5///vfLFiwgB9++AEnJycmTZpEZGQk48ePRynF5MmTSUpKwtfX11rHbDbz1ltvUVBQgIuLC/379ycpKYna2loSExM5ffo0VVVV9OjRg6VLl9KlSxfS09P56KOP0Ol0uLm5sWDBAnr27ElFRQULFizg+++/x2QyERYWxtSpUzGbzSxcuJDdu3ej0+m48847WbRoEbfddlurbfjiiy9YtWoVTU1NREREMG3aNN59912OHDnCH/7wBwBKSkp4/fXXycrKsqn7Y4/PsWPHKC8vp6qqigcffJCUlBTrfl+2bBn9+vUDsE7fcccd1nVWV1dfdj/t3r2brVu38s0339C+fXuMRiM1NTUkJia2eZx27tzJkiVLuOuuu/j3v/+N2WwmOTmZAQMG2Gznzp07WbhwIZ9//jmzZ8/G3d2df/3rX5SXl9OrVy8WL17cat/6+fnRo0cPnJ3Pn5T17t2bI0eOUFFRwXfffUdYWBgAgYGBJCcnc+DAAfr06cO6det45ZVXeP/9923aa2lpob6+HrPZzLlz57BYLLRr1+7yf3AXu8FB/LNzuU/D3bt3Kz8/P1VWVqY++eQT5efnp+rq6pRSSn388cdq3Lhxqr6+Xil1/pNk0qRJSqnzI4DJkyer5uZmdebMGRUUFKS2bt2qjhw5ogYNGqSOHz+ulFKqqKhIDR48WNXV1anly5eroKAgZTKZlFK2I6yL+7Zs2TL14osvqubmZtXS0qJmz56t5s2bp5RSatiwYerNN99USp0fHfbr18+6rgtMJpN64okn1KZNm6zLPf7442r37t1KKaUeeOABdfr06Vb7509/+pOKjo5WjY2NqqWlRcXFxalPP/1UrV27Vr3//vtKKaUsFot6/vnn1Zo1a5TZbFZ9+vRRFRUVSimlPv30U/XRRx8ppZSKiYlRW7ZsUUop1dTUpGJiYtTGjRtVcXGxCg4Otn5qp6amql27drXqy4QJE9Rvf/tbZTKZVF1dnQoODlYFBQWqurpaPfLII6qmpkYppVR8fLzasGHDZev/mOMTEBCgqqqqVEtLi3r55Zet+3rYsGFq37591vYvTF983NraT0rZjrCWL1+ukpOTr3icduzYoXr37q0OHDiglFJqzZo1Kjo6utV2Xvxv6Pe//70aN26cOnfunGpublaRkZHq448/blXnYidPnlSDBw9WW7duVXv27FFBQUE25U8//bT68ssvW+3bi0dYdXV1KjIyUvn7+6u+ffuqRYsWXXGdF8gIyw5NTU2MGjUKOP/JcMcdd/DWW2/RvXt3AHr16oW7uzsAhYWFREVF0bFjRwBiY2N57733aG5uBmDcuHHodDp0Oh3BwcFs27aNe++9F39/f+666y4AHnvsMTp37sy3334LgI+PD66uVz5UhYWFzJw5E51OB5wfLbzwwgvW8ieeeAI4Pzrs0qULZ86csa4Pzl+HOHfuHCNGjLAuN2LECL7++usrXm8pKipi1KhRtG/fHoClS5day0pKSvjwww85evQo//73v3n44YdxcXEhODiYp59+mqFDhzJkyBACAwNpaGiguLiYM2fOsGzZMgAaGho4dOgQQ4YMwcXFhbFjxzJkyBCCgoLw9va+bH/GjBmDq6sr7u7uBAUFUVRURGBgIEOHDiU7O5vIyEi2bdtGUlLSZev/mOMTHBxM165dret/4403+P3vf9/mPrvYxIkTL7uf2nKl4/Too4/i5eVF7969AXjooYf49NNPr9qHxx9/3Dq6eeCBBzhz5kyby3777be8+OKLTJgwgWHDhrF7926cnJxsllFK4eLicsV1LliwgMGDB/Pyyy9TXV3Nc889R//+/QkKCrpiPQksO7Rv357s7Ow2yy+EE4DFYrE5gBaLBbPZbJ2+OHiUUjg7O7eqc6HsQr2L22/L5dZrMpms025ubtb/d3JyQl3yE9KWlpYr9qEtlwZpdXU1FouFP/3pT+zbt48nn3ySRx99FLPZbF1nWloahw8fpqioiFWrVpGdnU1KSgpKKT766CM6dOgAgNFoxM3Njdtuu43s7Gx2797Njh07mDFjBr/5zW+Ijo5u1Z+L/1CUUtb+RUdHM3/+fFxdXRkxYsRlTycv3R57j8/F67RYLNbTpgvLXXDhQ+tib731Vpv76XKudpwufHDA5Y/z5dhbZ+PGjSQnJzNv3jwiIiIA8PLyoqqqCqWUtV+VlZV4enpecZ1ffPEFn332Gc7OzhgMBoKDg9m5c+dVA0u+JbzBHn/8cT755BMaGhoAWLduHX5+ftZPsM8++wyLxcKZM2fIy8tj+PDhPPbYY2zbto0TJ04AWK81Xe6T1sXFxSaILl7vhg0bMJlMWCwW1q9fz+DBg+3u97333ourqyubN28Gzn8DuWnTJgYNGnTFeo899hiff/45zc3NWCwW5s+fz8aNG9m2bRsTJ04kMjKSLl26UFRUREtLC0ajkcDAQDw8PHj22WeZMWMG+/fvx93dHR8fHz788EMAamtreeaZZ9iyZQt///vfefbZZ+nfvz8vvfQSkZGR1tHNpbKyslBKWffv448/DsAjjzyCs7Mza9asueI3qz/m+GzZsoW6ujosFguZmZkMGzYMwGYUtnPnTqqqqlqtr639BOeP9aUfGD/2OF2vrVu38vrrr7NmzRprWAF4enpy9913k5ubC8DXX3+Ns7MzDzzwwBXbe+ihh8jLywPOj6S//vrrK44sL5AR1g02ZswYvv/+e8aOHYvFYuGXv/wlaWlp1vKmpibGjBlDfX0948eP57HHHgPO3zbw4osv0tLSQvv27Xnvvffo1KlTq/Z79uyJm5sbY8aMsbnFYNq0aSxevJjIyEjMZjPe3t7MmzfP7n7rdDpWrlzJ66+/zjvvvENLSwsvvPAC/v7+V6z39NNPc+rUKaKiolBKMXDgQGJiYujRowepqaksW7YMnU7HI488wvHjx+ncuTPTpk3j2WefpX379ri4uPD6668D50deCxcuJCIigubmZsLDwxk5ciQtLS0UFhYSHh5Ox44duf3221m4cOFl+9OpUyeioqJoampiwoQJNv2PiooiNzeXBx98sM3t+THHp2vXrkyePJmamhr8/PyYOnUqALNmzWL+/PlkZGTQp08f+vTp02p9L7zwwmX3E0BAQABvvvmm3cdp586dVzxW12Px4sUopXjttdes8x555BGSkpJ4++23mTdvHu+++y7t2rVj2bJlNqPMttpbsGABWVlZODs7ExISYr3sciVOyp4xo7ghYmJiiI6OJjg4+FZ35b+O2WzmxRdfZOTIkYSGhl52mR9zfN555x3rt3fC8eSUUPzsHTlyhMcee4w77rhDPiw0TkZYQgjNkBGWEEIzJLCEEJohgSWE0AwJLCGEZsh9WFdRU1OPxSLfSwhxszg7O3HHHW38EuEm90VzLBYlgSXET4ScEgohNEMCSwihGRJYQgjNkMASQmiGBJYQQjMksIQQmiGBJYTQDLkPS/zXu+P2dri2c7v6guK6mJvPUXOm9WOir4UElviv59rOjV2pz9/qbvzsDXh1NXB9gSWnhEIIzZDAEkJohgSWEEIzJLCEEJohgSWE0AwJLCGEZkhgCSE0QwJLCKEZElhCCM2QwBJCaIYElhBCMySwhBCaIYElhNAMCSwhhGZIYAkhNMOhgZWTk0NoaCgjRoxg/fr1rcoPHjxIVFQUQUFBzJ07F7PZDEBZWRnR0dEEBwczbdo06uvrAaitrWXKlCmEhIQQHR1NVVWVTXvffPMNEydOtE7X19cTFxdHREQEERERbNy40YFbK4RwNIcFVkVFBUuWLCE9PZ2srCwyMjI4cuSIzTLx8fEkJiayadMmlFJkZmYCkJyczPjx48nPz6dv376sXLkSgKVLl+Lr60teXh5jx44lJSUFAIvFwgcffMDLL7+MxWKxtr9q1Sq8vLzIyclh7dq1LFq0iOrqakdtshDCwRwWWEVFRfj7++Ph4UHHjh0JCgoiPz/fWn7q1Cmamprw8fEBICoqivz8fEwmE8XFxQQFBdnMBygoKCAiIgKA8PBwCgsLMZlMlJaWUlpaysKFC236MHDgQGJiYgDo0qULHh4eElhCaJjDAquyshK9Xm+dNhgMVFRUtFmu1+upqKigpqYGd3d3XF1dbeZfWsfV1RV3d3eMRiP3338/KSkp3H777TZ9GDx4MF5eXgDk5ubS3NxMz549HbPBQgiHc9gz3S0WC05OTtZppZTNdFvlly4HtJq+uI6z89UzNy8vjzfeeIPVq1dbg9BeXbq4X9PyQoi26fWdrqu+wwLL09OTkpIS63RVVRUGg8Gm/OKL5tXV1RgMBjp37kxdXR0tLS24uLjY1DMYDFRXV+Pp6YnZbKa+vh4PD48r9mPdunWsWbOGNWvW0KtXr2vejtOnz2KxqGuuJ7Tjev+IhP2qququuoyzs1ObAwWHnRIOGjSI7du3YzQaaWxsZPPmzQQEBFjLe/TogZubG7t27QIgOzubgIAAdDodvr6+5ObmApCVlWWtFxgYSFZWFnD+FM/X1xedTtdmH7788kvWrl3Lhg0bflRYCSF+WhwWWN26dWPmzJnExsYSGRlJeHg43t7eTJ48mf379wOQlpbGokWLCA4OpqGhgdjYWACSkpLIzMwkNDSUkpISZsyYAUBcXBx79+4lLCyM9PR0EhMTr9iH5cuXc+7cOaZOncqoUaMYNWqUdd1CCO1xUkrJ+c4VyCnhz59e30neS3gTDHh19U/3lFAIIW40CSwhhGZIYAkhNEMCSwihGRJYQgjNkMASQmiGBJYQQjMksIQQmiGBJYTQDAksIYRmSGAJITRDAksIoRkSWEIIzZDAEkJohgSWEEIzJLCEEJohgSWE0AwJLCGEZkhgCSE0QwJLCKEZElhCCM2QwBJCaIZDAysnJ4fQ0FBGjBjB+vXrW5UfPHiQqKgogoKCmDt3LmazGYCysjKio6MJDg5m2rRp1NfXA1BbW8uUKVMICQkhOjra5s3RAN988w0TJ060TiulWLx4McHBwYSGhlpf2iqE0CaHBVZFRQVLliwhPT2drKwsMjIyOHLkiM0y8fHxJCYmsmnTJpRSZGZmApCcnMz48ePJz8+nb9++rFy5EoClS5fi6+tLXl4eY8eOJSUlBQCLxcIHH3zAyy+/jMVisba/adMmSktLyc3NZcWKFSQkJFhDUQihPQ4LrKKiIvz9/fHw8KBjx44EBQWRn59vLT916hRNTU34+PgAEBUVRX5+PiaTieLiYoKCgmzmAxQUFBAREQFAeHg4hYWFmEwmSktLKS0tZeHChTZ9+OqrrwgNDcXZ2Zl77rmH7t27s2fPHkdtshDCwRwWWJWVlej1euu0wWCgoqKizXK9Xk9FRQU1NTW4u7vj6upqM//SOq6urri7u2M0Grn//vtJSUnh9ttvb9UHg8Fgs47y8vIbv7FCiJvC1VENWywWnJycrNNKKZvptsovXQ5oNX1xHWfntjP3cuu40vKX09Yrs4UQ106v73Rd9R0WWJ6enpSUlFinq6qqbEY7np6eNhfNq6urMRgMdO7cmbq6OlpaWnBxcbGpZzAYqK6uxtPTE7PZTH19PR4eHlfsQ2VlZat1XIvTp89isairLtfpF+1p76a7prbFtWk6Z6KutumGt3u9f0TCflVVdVddxtnZqc2BgsMCa9CgQbzzzjsYjUY6dOjA5s2bba4x9ejRAzc3N3bt2sWAAQPIzs4mICAAnU6Hr68vubm5REREkJWVRUBAAACBgYFkZWUxdepUcnNz8fX1RadrOyQCAgL45JNPCA8P5+TJkxw9epR+/fo5ZHvbu+kY/2rrb0LFjZOeGk0dNz6whHY4LLC6devGzJkziY2NxWQyMWbMGLy9vZk8eTLTp0+nX79+pKWl8dprr3H27Fn69OlDbGwsAElJScyePZt3332X7t278/bbbwMQFxfH7NmzCQsLo1OnTqSlpV2xD8HBwezbt4+RI0cCkJKSQvv27R21yUIIB3NSSl39fOe/mL2nhHp9JxlhOVh6arRdpxTXSq/vxK7U5294u8LWgFdXX/cpodzpLoTQDAksIYRmSGAJITRDAksIoRkSWEIIzZDAEkJohgSWEEIzJLCEEJohgSWE0AwJLCGEZkhgCSE0QwJLCKEZElhCCM2wO7DOnj0LwIEDB8jKysJkMjmsU0IIcTl2PQ9r2bJlHD9+nFdeeYXnn3+enj17UlxcbH1rjRBC3Ax2jbC++uorXn/9dTZv3kxYWBh//vOfOXTokKP7JoQQNuw+JezQoYP11V0Azc3NDuuUEEJcjl2BdccddzB//ny+/fZbBg0aRFpa2jW/zEEIIa6XXYG1ePFiDAYD77//Ph06dMDJyYnFixc7um9CCGHDrsDq2rUrUVFRGI1GWlpaeOaZZ+jatauj+yaEEDbsCqyCggKefvppkpOTOX36NGFhYXz55ZeO7psQQtiwK7BWrFhBZmYmv/jFLzAYDKSnp7N8+XJH900IIWzYFVgtLS02F9l79+7d5uvjL5aTk0NoaCgjRoxg/frWr8A6ePAgUVFRBAUFMXfuXMxmMwBlZWVER0cTHBzMtGnTqK+vB6C2tpYpU6YQEhJCdHS09c3Rzc3NxMfHExISwujRoyktLbWu44033iAsLIzw8HA+//xzezZXCPETZVdgdejQgbKyMmtIlZSU4ObmdsU6FRUVLFmyhPT0dLKyssjIyODIkSM2y8THx5OYmMimTZtQSpGZmQlAcnIy48ePJz8/n759+7Jy5UoAli5diq+vL3l5eYwdO9Z64+q6devo0KEDeXl5zJkzh4SEBAC2b9/Ovn37+Oyzz1i7di3Jyck0NjZew+4RQvyU2BVYs2bNYtKkSRw/fpxx48bxwgsvMGvWrCvWuXDPloeHBx07diQoKIj8/Hxr+alTp2hqasLHxweAqKgo8vPzMZlMFBcXExQUZDMfzl9Li4iIACA8PJzCwkJMJhMFBQXWtzv7+flhNBopKyujpaWFc+fOYTabaWxspF27dte2d4QQPyl2/TSnf//+ZGZmsmfPHiwWCw8//DCdO3e+Yp3Kykr0er112mAwsG/fvjbL9Xo9FRUV1NTU4O7ujqurq838S+u4urri7u6O0Wi8bFvl5eUMGTKEzMxMAgICaGhoYNasWXTo0MGeTRZC/ATZFViNjY189913BAYGsmHDBr744gtefPFFvLy82qxjsVhsrnMppWym2yq/dDmgzetlSimcnZ1b1bkwPyMjAxcXF7Zt28YPP/xAbGwsDz/8sHVUZ4+2Xpktbg29vtOt7oK4Dtd7/OwKrISEBO666y5cXFxYvXo1kZGRzJs3jzVr1rRZx9PTk5KSEut0VVWVzYV7T09P60VzgOrqagwGA507d6auro6WlhZcXFxs6hkMBqqrq/H09MRsNlNfX4+HhwfdunWjsrKSu+++26atlStX8swzz6DT6dDr9QwdOpSSkpJrCqzTp89isairLid/SDdHVVXdDW9Tjt3NY8/xc3Z2anOgYNc1rBMnTvDKK6+wdetWRo8ezUsvvcQPP/xwxTqDBg1i+/btGI1GGhsb2bx5MwEBAdbyHj164Obmxq5duwDIzs4mICAAnU6Hr68vubm5AGRlZVnrBQYGkpWVBUBubi6+vr7odDoCAwPJzs4G/u8LAS8vLx588EHr/WINDQ3s2LGDvn372rPJQoifILsC68LtBtu2bcPf35+WlhYaGhquWKdbt27MnDmT2NhYIiMjCQ8Px9vbm8mTJ7N//34A0tLSWLRoEcHBwTQ0NBAbGwtAUlISmZmZhIaGUlJSwowZMwCIi4tj7969hIWFkZ6eTmJiIgAxMTE0NzcTFhZGSkoKqampAEydOhWz2UxISAhPPfUUo0aNsv54WwihPU5Kqaue78yfP5//9//+Hy4uLmRnZzNx4kTuv/9+a2D8nF3LKeH4V1vfayZunPTUaIedEu5Kff6GtytsDXh19XWfEtp1DWvevHns2bOHXr164ezszG9+8xsCAwOvrbdCCHGd7DoldHFxoWPHjvzrX/+iuLiY9u3b89e//tXRfRNCCBt2jbDmzp3L1q1bOXfuHAaDgePHjzNgwACeeuopR/dPCCGs7Bphbd++nS1btvDrX/+aVatW8eGHH9K+fXtH900IIWzYFVh6vZ6OHTty7733cvjwYR599FHKy8sd3TchhLBhV2DpdDqKi4u57777KCwspK6u7qq3NQghxI1m94+fP/roIwIDAzl06BD+/v7WHxsLIcTNYtdFdx8fH7p06YJOp+ODDz7g2LFj9OnTx9F9E0IIG3aNsNatW8fvfvc7AGpqapg+fbrc1iCEuOnsCqyMjAw2bNgAwF133UVWVhZ//vOfHdoxIYS4lN2PSHZ3/79b5Tt16mTXI5KFEOJGsiuw7r33XtLS0jhx4gQnTpxg2bJl/M///I+DuyaEELbsCqzk5GSOHTtGZGQkY8aM4ejRo8yfP9/BXRNCCFt2fUvYtWtX3nnnncuWvfnmm8yePfuGdkoIIS7HrhHWlezcufNG9EMIIa7qugPLjsdpCSHEDXHdgSXfFgohbpbrDiwhhLhZJLCEEJoh17CEEJph120NcP43hCUlJTg7OzNw4EA6dTr/LreEhASHdU4IIS5m1wjriy++YMSIEaxdu5bVq1fz61//mh07dgDw6KOPOrSDQghxgV2BtWTJEv7yl7+wfv16NmzYwJo1a1i8ePFV6+Xk5BAaGsqIESNYv771K7AOHjxIVFQUQUFBzJ071/r+w7KyMqKjowkODmbatGnU19cDUFtby5QpUwgJCSE6Otr65ujm5mbi4+MJCQlh9OjRlJaWAudPV1esWEFkZCRBQUHWl7AKIbTJrsBq3749vXr1sk736dPnqrczVFRUsGTJEtLT08nKyiIjI4MjR47YLBMfH09iYiKbNm1CKUVmZiZw/qdA48ePJz8/n759+7Jy5UoAli5diq+vL3l5eYwdO5aUlBTg/ONvOnToQF5eHnPmzLGepn722WcUFRWRmZnJX/7yF1JTU6mtrbVz1wghfmrsCqyAgABWrVpFQ0MD586dIyMjg/vvv58zZ860+cr6oqIi/P398fDwoGPHjgQFBZGfn28tP3XqFE1NTfj4+AAQFRVFfn4+JpOJ4uJigoKCbOYDFBQUEBERAUB4eDiFhYWYTCYKCgqsT0D18/PDaDRSVlZGXl4ekyZNol27duj1etLT0+XlGUJomF0X3f/4xz/S0tLC22+/bTM/OzsbJycnDh482KpOZWUler3eOm0wGNi3b1+b5Xq9noqKCmpqanB3d8fV1dVm/qV1XF1dcXd3x2g0Xrat8vJyjh07RmlpKX/605+oq6tj8uTJ8pQJITTMrsD65z//ec0NWywWm9NGpZTNdFvlly4Hbd9Nr5TC2dm5VZ0L81taWvjXv/7FmjVrqK6u5plnnuGhhx66ptBq65XZ4tbQ6zvd6i6I63C9x8+uwLJYLKxZs4bCwkLMZjODBw9m6tSp1lHQ5Xh6elJSUmKdrqqqwmAw2JRfuGgOUF1djcFgoHPnztTV1dHS0oKLi4tNPYPBQHV1NZ6enpjNZurr6/Hw8KBbt25UVlZy991327TVtWtXgoOD0el0dO/enYcffpgDBw5cU2CdPn0Wi+Xq95rJH9LNUVVVd8PblGN389hz/JydndocKNh1DesPf/gDO3bsYOLEiTz33HPs2bPnqt8SDho0iO3bt2M0GmlsbGTz5s0EBARYy3v06IGbmxu7du0Czp9eBgQEoNPp8PX1JTc3F4CsrCxrvcDAQOs3fbm5ufj6+qLT6QgMDCQ7OxuAkpIS3Nzc8PLyYtiwYeTl5aGUoqamhn379tG7d297NlkI8RNk1wjr66+/5pNPPkGn0wEwdOjQq77mq1u3bsycOZPY2FhMJhNjxozB29ubyZMnM336dPr160daWhqvvfYaZ8+epU+fPsTGxgKQlJTE7Nmzeffdd+nevbv12llcXByzZ88mLCyMTp06kZaWBkBMTAyJiYmEhYXRrl07UlNTAXj22Wd56623CA8Pp6Wlhd/97nfcc889P25PCSFuOSdlx29rIiIiyMnJueq8n6NrOSUc/2rre83EjZOeGu2wU8Jdqc/f8HaFrQGvrr45p4QPPvggb7zxBsePH+fEiRMsWrSIBx544Np6K4QQ18muwEpKSqK2tpann36ap556CqPRyLx58xzdNyGEsGHXNaz333+fN99809F9EUKIK7JrhFVQUODgbgghxNXZNcK68847mTRpEo888gi33Xabdf5zzz3nsI4JIcSl7AosDw8P4Pwd7y4uLtZnYQkhxM1k1ynh888/z+HDh/nqq6/YunUrJ06c4KWXXnJ034QQwoZdgTVnzhyeeuop/vGPf7B3717r86uEEOJmsiuwGhsbGTduHDqdjnbt2hETE0N1dbWj+yaEEDbsCqx7772X3bt3W6cPHz7MnXfe6bBOCSHE5dh10b2srIyYmBh69eqFq6srBw4cQK/XWx+m99/wEx0hxK1nV2DNmjXL0f0QQoirsiuwBg4c6Oh+CCHEVcmbn4UQmiGBJYTQDAksIYRmSGAJITRDAksIoRkSWEIIzZDAEkJohgSWEEIzJLCEEJrh0MDKyckhNDSUESNGsH5961dgHTx4kKioKOvjasxmM3D+t4vR0dEEBwczbdo06uvrAaitrWXKlCmEhIQQHR1tfXN0c3Mz8fHxhISEMHr0aEpLS23WYzabGTduHH/7298cublCCAdzWGBVVFSwZMkS0tPTycrKIiMjgyNHjtgsEx8fT2JiIps2bUIpRWZmJgDJycmMHz+e/Px8+vbty8qVKwFYunQpvr6+5OXlMXbsWFJSUgBYt24dHTp0IC8vjzlz5pCQkGCznhUrVnD06FFHbaoQ4iZxWGAVFRXh7++Ph4cHHTt2JCgoiPz8fGv5qVOnaGpqwsfHB4CoqCjy8/MxmUwUFxcTFBRkMx/OvwzjwhMiwsPDKSwsxGQyUVBQYH0TtZ+fH0ajkbKyMgB2797NoUOHGDZsmKM2VQhxkzgssCorK9Hr9dZpg8FARUVFm+V6vZ6Kigpqampwd3fH1dXVZv6ldVxdXXF3d8doNF62rfLycs6ePcuiRYtYuHChozZTCHET2fW0hh/DYrHg5ORknVZK2Uy3VX7pckCr6YvrODs7t6pzYX5ycjK//e1v6dq164/ejrZemS1uDb1eXoCiZdd7/BwWWJ6enpSUlFinq6qqMBgMNuUXLpoDVFdXYzAY6Ny5M3V1dbS0tODi4mJTz2AwUF1djaenJ2azmfr6ejw8POjWrRuVlZXcfffd1rb0ej3bt2/n8OHDvPPOO3z//ffs2LEDV1dX6+mjPU6fPovFoq66nPwh3RxVVXU3vE05djePPcfP2dmpzYGCw04JBw0axPbt2zEajTQ2NrJ582YCAgKs5T169MDNzY1du3YBkJ2dTUBAADqdDl9fX3JzcwHIysqy1gsMDCQrKwuA3NxcfH190el0BAYGkp2dDUBJSQlubm706NGDbdu2kZ2dTXZ2NsOHD2f69OnXFFZCiJ8WhwVWt27dmDlzJrGxsURGRhIeHo63tzeTJ09m//79AKSlpbFo0SKCg4NpaGggNjYWgKSkJDIzMwkNDaWkpIQZM2YAEBcXx969ewkLCyM9PZ3ExEQAYmJiaG5uJiwsjJSUFFJTUx21WUKIW8hJKXX1853/YtdySjj+1db3mokbJz012mGnhLtSn7/h7QpbA15d/dM9JRRCiBtNAksIoRkSWEIIzZDAEkJohgSWEEIzJLCEEJohgSWE0AwJLCGEZkhgCSE0QwJLCKEZElhCCM2QwBJCaIYElhBCMySwhBCaIYElhNAMCSwhhGZIYAkhNEMCSwihGRJYQgjNkMASQmiGBJYQQjMksIQQmiGBJYTQDIcGVk5ODqGhoYwYMYL161u/s+/gwYNERUURFBTE3LlzMZvNAJSVlREdHU1wcDDTpk2jvr4egNraWqZMmUJISAjR0dHWV903NzcTHx9PSEgIo0ePprS0FID6+nri4uKIiIggIiKCjRs3OnJzhRAO5rDAqqioYMmSJaSnp5OVlUVGRgZHjhyxWSY+Pp7ExEQ2bdqEUorMzEwAkpOTGT9+PPn5+fTt25eVK1cCsHTpUnx9fcnLy2Ps2LGkpKQAsG7dOjp06EBeXh5z5swhISEBgFWrVuHl5UVOTg5r165l0aJFVFdXO2qThRAO5rDAKioqwt/fHw8PDzp27EhQUBD5+fnW8lOnTtHU1ISPjw8AUVFR5OfnYzKZKC4uJigoyGY+QEFBAREREQCEh4dTWFiIyWSioKCAkSNHAuDn54fRaKSsrIyBAwcSExMDQJcuXfDw8JDAEkLDXB3VcGVlJXq93jptMBjYt29fm+V6vZ6Kigpqampwd3fH1dXVZv6ldVxdXXF3d8doNF62rfLycgYPHmydl5ubS3NzMz179rym7Wjrldni1tDrO93qLojrcL3Hz2GBZbFYcHJysk4rpWym2yq/dDmg1fTFdZydnVvVuTD/gry8PN544w1Wr15tDUJ7nT59FotFXXU5+UO6Oaqq6m54m3Lsbh57jp+zs1ObAwWHnRJ6enpaL4oDVFVVYTAY2iyvrq7GYDDQuXNn6urqaGlpaVXPYDBYT+nMZjP19fV4eHjQrVs3KisrW7UF569vLV68mDVr1vDggw86anOFEDeBwwJr0KBBbN++HaPRSGNjI5s3byYgIMBa3qNHD9zc3Ni1axcA2dnZBAQEoNPp8PX1JTc3F4CsrCxrvcDAQLKysoDzp3i+vr7odDoCAwPJzs4GoKSkBDc3N7y8vPjyyy9Zu3YtGzZsoFevXo7aVCHETeKwwOrWrRszZ84kNjaWyMhIwsPD8fb2ZvLkyezfvx+AtLQ0Fi1aRHBwMA0NDcTGxgKQlJREZmYmoaGhlJSUMGPGDADi4uLYu3cvYWFhpKenk5iYCEBMTAzNzc2EhYWRkpJCamoqAMuXL+fcuXNMnTqVUaNGMWrUKOu6hRDa46SUuvoFmv9i13INa/yrre81EzdOemq0w65h7Up9/oa3K2wNeHX1T/calhBC3GgSWEIIzZDAEkJohgSWEEIzJLCEEJohgSWE0AwJLCGEZkhgCSE0QwJLCKEZElhCCM2QwBJCaIYElhBCMySwhBCaIYElhNAMCSwhhGZIYAkhNEMCSwihGRJYQgjNkMASQmiGBJYQQjMksIQQmiGBJYTQDIcGVk5ODqGhoYwYMYL161u/AuvgwYNERUURFBTE3LlzMZvNAJSVlREdHU1wcDDTpk2jvr4egNraWqZMmUJISAjR0dHWN0c3NzcTHx9PSEgIo0ePprS0FDj/yvrFixcTHBxMaGio9aWtQghtclhgVVRUsGTJEtLT08nKyiIjI4MjR47YLBMfH09iYiKbNm1CKUVmZiYAycnJjB8/nvz8fPr27cvKlSsBWLp0Kb6+vuTl5TF27FhSUlKA86+j79ChA3l5ecyZM4eEhAQANm3aRGlpKbm5uaxYsYKEhARrKAohtMfVUQ0XFRXh7++Ph4cHAEFBQeTn5/Piiy8CcOrUKZqamvDx8QEgKiqK5cuXM3bsWIqLi1mxYoV1/oQJE4iPj6egoMA6UgsPD2fBggWYTCYKCgqIi4sDwM/PD6PRSFlZGV999RWhoaE4Oztzzz330L17d/bs2YOfn5/d2+Hs7GT3sl3vuM3uZcWPcy3H41q0+0UXh7QrbNlz/K60jMMCq7KyEr1eb502GAzs27evzXK9Xk9FRQU1NTW4u7vj6upqM//SOq6urri7u2M0Gi/bVnl5OZWVlRgMhlbzr8Ud1xBCyxMir6ltce3aeiPw9eo3dbFD2hW2rvf4OeyU0GKx4OT0f0mplLKZbqv80uWAVtMX13F2dm5V58L8y63D2Vm+ZxBCqxz21+vp6Wm9KA5QVVVlM9q5tLy6uhqDwUDnzp2pq6ujpaWlVT2DwUB1dTUAZrOZ+vp6PDw86NatG5WVla3a8vT0vOx8IYQ2OSywBg0axPbt2zEajTQ2NrJ582YCAgKs5T169MDNzc36zV12djYBAQHodDp8fX3Jzc0FICsry1ovMDCQrKwsAHJzc/H19UWn0xEYGEh2djYAJSUluLm54eXlRUBAADk5ObS0tHDs2DGOHj1Kv379HLXJQggHc1JKKUc1npOTw/vvv4/JZGLMmDFMnjyZyZMnM336dPr168ehQ4d47bXXOHv2LH369GHRokW0a9eOU6dOMXv2bE6fPk337t15++23uf322/nhhx+YPXs2J06coFOnTqSlpXHnnXdy7tw5EhMT+fbbb2nXrh2vv/46ffr0QSlFamoqhYWFACQkJDBkyBBHba4QwsEcGlhCCHEjyRVoIYRmSGAJITRDAksIoRkSWEIIzZDA+hk4fPgwvXr1YtOmTdZ5w4cP5+TJk9fc1o+tJ67NyZMnGT58eKv5vXr1uuHrqqioYPLkyQAsW7aMLVu23PB13CwSWD8Dn3zyCcHBwWRkZNzqroifoG7duvHHP/4RgLi4OJ544olb3KMfTwJL40wmEzk5OcyYMYN//vOfHD9+3Kb87NmzTJ8+nXHjxjFs2DDmzJmDUory8nImTJhAVFQUY8aMYe/evTb1/vOf/zBixAj27t3bZhvCMdra3xEREdZHJ73yyiskJSUBsGfPHqZMmYLZbOa1115j3LhxPPHEE/zud7+jqanJZjQ3e/Zs/va3v92ybbteDvvxs7g5vvrqK7y8vLjnnnv41a9+RUZGBvHx8dbygoICevfuzfLly2lubiYsLIx//vOfFBQUMHToUJ5//nkKCwvZtWuX9ckZ5eXlJCUl8cYbb+Dj48Pnn39+2Tb69u17i7b656GyspJRo0a1mt/WMQsMDGT79u3cd999HD582Lr8119/zdChQ9mzZw86nY6MjAwsFgsTJ07kq6++ok+fPjdzsxxKAkvjPvnkE8LDwwEIDQ1l1qxZ1kftwPnH8Ozbt4+1a9fy3Xff8cMPP9DQ0MBjjz3GSy+9xMGDBwkMDGTChAnWOnFxcfTr1w9fX98rtiGuj8FgsP6k7IJevXq1ub8DAwNZu3Yt/v7+9OzZk++++47Tp09TWFjI8uXL8fLywsPDg/Xr1/Pdd99x9OjRn91xklNCDTt9+jRff/01H3zwAcOHD+e1116jtraWL774wrrMunXrSE1NpXPnzkyYMIH77rsPpRQDBgxg48aNDBkyhNzcXKZOnWqtM3fuXE6cOEFBQcEV2xCO0db+7t+/P4cOHaKoqIiBAwfi5+dHfn4+ZrMZLy8vtmzZwqxZs2jfvj1RUVH4+fn97I6TBJaGZWdn4+/vT2FhIVu3buXvf/87U6dO5aOPPrIu88033zBu3DhGjhzJuXPnOHToEBaLhdTUVD777DNGjx5NYmIiBw4csNbx9vZm/vz5LFiwgIaGhjbbEI7R1v52dXXF29ubdevWMXDgQPz9/XnvvfcIDAwEYPv27YSEhPDkk0/yi1/8gp07d1qfevJzIaeEGvbpp58yc+ZMm3nR0dGsXr0ad/fzD0qbOHEi8+fPZ9WqVbi7u9O/f39OnjxJTEwMr7zyCn/7299wcXFh8WLbB9j5+fnx6KOPsnTp0jbbEI5xpf0dGBhIcXEx9913H3q9ntOnTzN06FAAxo4dy6xZs9i4cSM6nY5HHnnkZ3ec5MfPQgjNkFNCIYRmSGAJITRDAksIoRkSWEIIzZDAEkJohgSW0LRJkyZhNBpv2HLip00CS2jaN998c0OXEz9tcuOo0IT6+noSEhI4duwYzs7O9OnTx3oX98SJE1m1ahWHDh3i/fffp7m5GaPRSGRkJDNmzCAhIcFmOWdnZxYsWMD333+PyWQiLCzM5qdJ4idMCaEBn376qZo0aZJSSimz2azmzp2rjh49qh544AF1+vRpZbFY1IQJE9R//vMfpZRS5eXlqnfv3ur06dNKKWVdTimlYmJi1JYtW5RSSjU1NamYmBi1cePGm79R4prJCEtowoABA1iyZAkxMTEMGjSIiRMn8stf/tJa7uTkxHvvvUdBQQGff/45paWlKKVobGy0aaehoYHi4mLOnDnDsmXLrPMOHTpEaGjoTd0mce0ksIQm3HXXXXzxxRfs3LmTHTt28Nxzz7FgwQJreUNDA6NHj+ZXv/oVvr6+PPnkk3z55ZetnlZgsVhQSvHRRx/RoUMHAIxGI25ubjd1e8SPIxfdhSakp6db39wdHx/PkCFDOHDgAC4uLpjNZo4dO8bZs2eZMWMGw4cPZ+fOnTQ3N1ufKnFhOXd3d3x8fPjwww8BqK2t5ZlnntH0c87/m0hgCU2IjIykpaWF0NBQoqKiqKurIyYmhuDgYGJiYnBycmLo0KGEhIQQEhLC3//+d3r27MmxY8cArMsdPnyYtLQ0/vGPfxAREcHYsWMJDw9n5MiRt3gLhT3kaQ1CCM2QEZYQQjMksIQQmiGBJYTQDAksIYRmSGAJITRDAksIoRkSWEIIzZDAEkJoxv8H7TQkX7pThZ4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_state_inc = pd.DataFrame(population[population['state'] == 'Alaska'])\n",
    "low_state_inc = pd.DataFrame(population[population['state'] =='Hawaii'])\n",
    "\n",
    "list_states_inc = [top_state_inc, low_state_inc]\n",
    "stats_inc = pd.concat(list_states_inc)\n",
    "stats_inc\n",
    "sns.set(rc = {'figure.figsize':(4,4)});\n",
    "sns.barplot(data=stats_inc, x=\"state\", y=\"prop_cases\").set(title=\"Proportion of cases by population in 2018\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `Alaska`, roughly 3 laws were passed each year between 2013 and 2018, and the proportion of cases by population is **0.001835**. In `Hawaii`, roughly 79-80 laws were added by 2018. There is a lower proportion, **0.000203**. \n",
    "\n",
    "# Hypothesis Results\n",
    "\n",
    "We observed that the more strict gun laws could have lower gun violence incidents. Alaska has one of the  highest proportions of gun-related incidents by population, but a lower number of gun laws. Hawaii has a lower proportion of gun-related incidents by population, and a higher number of gun laws passes. The hypothesis test result shows that we do not have enough evidence to reject that these two factors are independent. So we would like to implement ML methods to see if we could have better classification results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=-0.183, p=0.204\n",
      "There is not enough evidence to reject H0. Probably independent\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "df_states = pd.read_csv('df_states.csv')\n",
    "stat, p = scipy.stats.pearsonr(df_states['per_counts'], df_states['lawtotal'])\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "\tprint('There is not enough evidence to reject H0. Probably independent')\n",
    "else:\n",
    "\tprint('There is enough evidence to reject H0. Probably dependent')\n",
    "# law_total vs. gun_violence_counts per 10000 people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "ax = sns.lmplot(x=\"per_counts\", y=\"lawtotal\", data=df_states, scatter_kws={'s': 6}, legend=True)\n",
    "ax.set(ylabel=\"Gun Laws\")\n",
    "ax.set(xlabel=\"Gun Violence per 10,000 people\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Analysis: Data Cleaning + Feature Engineering\n",
    "In this section, we merge the gun reform and gun violence data, add additional features and our target variable, and oversample to balance classes. \n",
    "- We calculate `proportion_harmed` by dividing the number of people injured in a gun violence incident by the state's population that year. \n",
    "- Our target variable is  `decrease_in_gun_violence` which is measured by comparing the proportion of people harmed (killed or injured) for a state's current and previous years from the time frame of 2014-2017. Thus, each state in our data set `data` has an entry for 2015, 2016, and 2017. \n",
    "\n",
    "- Using Alabama as an example of how we determine the target variable for each state+year, there are three entries in our dataset: Alabama 2015, Alabama 2016, and Alabama 2017. Alabama 2015's value for `decrease_in_gun_violence` is determined by looking at the proportion of people harmed in the previous year, Alabama 2014, and the proportion of people harmed in the current row's year, Alabama 2015. If there is a decrease in proportion of people harmed then `decrease_in_gun_violence=True`. Otherwise `decrease_in_gun_violence=False`. The same process is repeated to get target variable values for Alabama 2016 and Alabama 2017. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>proportion_harmed</th>\n",
       "      <th>decrease_in_gun_violence</th>\n",
       "      <th>felony</th>\n",
       "      <th>invcommitment</th>\n",
       "      <th>invoutpatient</th>\n",
       "      <th>danger</th>\n",
       "      <th>drugmisdemeanor</th>\n",
       "      <th>alctreatment</th>\n",
       "      <th>...</th>\n",
       "      <th>expartedating</th>\n",
       "      <th>dvrosurrender</th>\n",
       "      <th>dvrosurrendernoconditions</th>\n",
       "      <th>dvrosurrenderdating</th>\n",
       "      <th>expartesurrender</th>\n",
       "      <th>expartesurrendernoconditions</th>\n",
       "      <th>expartesurrenderdating</th>\n",
       "      <th>dvroremoval</th>\n",
       "      <th>stalking</th>\n",
       "      <th>lawtotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     state  year  proportion_harmed  decrease_in_gun_violence  felony  \\\n",
       "0  Alabama  2015           0.000195                     False       0   \n",
       "1  Alabama  2016           0.000257                     False       0   \n",
       "2  Alabama  2017           0.000287                     False       0   \n",
       "3   Alaska  2015           0.000209                     False       1   \n",
       "\n",
       "   invcommitment  invoutpatient  danger  drugmisdemeanor  alctreatment  ...  \\\n",
       "0              1              0       1                0             0  ...   \n",
       "1              1              0       1                0             0  ...   \n",
       "2              1              0       1                0             0  ...   \n",
       "3              0              0       0                0             0  ...   \n",
       "\n",
       "   expartedating  dvrosurrender  dvrosurrendernoconditions  \\\n",
       "0              0              0                          0   \n",
       "1              0              0                          0   \n",
       "2              0              0                          0   \n",
       "3              0              0                          0   \n",
       "\n",
       "   dvrosurrenderdating  expartesurrender  expartesurrendernoconditions  \\\n",
       "0                    0                 0                             0   \n",
       "1                    0                 0                             0   \n",
       "2                    0                 0                             0   \n",
       "3                    0                 0                             0   \n",
       "\n",
       "   expartesurrenderdating  dvroremoval  stalking  lawtotal  \n",
       "0                       0            0         0        10  \n",
       "1                       0            0         0        10  \n",
       "2                       0            0         0        10  \n",
       "3                       0            0         0         3  \n",
       "\n",
       "[4 rows x 139 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean and merge data set for ML \n",
    "gun_cases = 'gun-violence-data_01-2013_03-2018.csv'\n",
    "gun_laws = 'statefirearmlaws.csv'\n",
    "population = 'us_states_census.csv'\n",
    "data = machine_learning.combineDatasets(gun_cases, gun_laws, population)\n",
    "data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples in our dataset where decrease_in_gun_violence=0: 101\n",
      "Number of examples in our dataset where decrease_in_gun_violence=1: 98\n"
     ]
    }
   ],
   "source": [
    "#oversampling from decrease_in_gun_violence=1 to balance classes out\n",
    "oversampled_rows = data[data.decrease_in_gun_violence == True].copy(deep=True)\n",
    "balanced_data = pd.concat([data, oversampled_rows], ignore_index=True)\n",
    "\n",
    "#create features and labels for ML application. Label is (0,1) based on \n",
    "#whether a decrease in gun_violence was observed\n",
    "X, y = machine_learning.create_features_labels(balanced_data)\n",
    "\n",
    "#the proportion of classes w.r.t 'decrease_in_gun_violence' (0,1) are now balanced\n",
    "print(f'Number of examples in our dataset where decrease_in_gun_violence=0: {len(y[y == 0])}')\n",
    "print(f'Number of examples in our dataset where decrease_in_gun_violence=1: {len(y[y == 1])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML Baseline Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6838235294117647\n"
     ]
    }
   ],
   "source": [
    "#building our baseline classifier (predicts the majority label)\n",
    "baseline = machine_learning.MajorityLabelClassifier()\n",
    "baseline.fit(X, y)\n",
    "labels = baseline.predict(X)\n",
    "print(baseline.evaluate_accuracy(y, labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML Analysis: Linear SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold(n_splits=4, random_state=1, shuffle=True)\n",
      "{'linear': 0.5407051282051282, 'rbf': 0.452724358974359, 'poly': 0.49022435897435895, 'sigmoid': 0.515224358974359}\n",
      "Best kernel selected via cross-validation: linear\n"
     ]
    }
   ],
   "source": [
    "#cross validation to determine best kernel\n",
    "kf = sklearn.model_selection.KFold(n_splits=4, random_state=1, shuffle=True) #5-fold cross validation\n",
    "print(kf)\n",
    "\n",
    "#reset indices of X_train and y_train to avoid indexing errors during CV\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,random_state=1) #changed split to 80% train, 20% test\n",
    "X_train.reset_index(inplace=True, drop=True)\n",
    "y_train.reset_index(inplace=True, drop=True)\n",
    "\n",
    "#use CV to find the best_kernel for our SVC model\n",
    "svm = machine_learning.SVM()\n",
    "best_kernel = svm.best_model_selection(kf, X_train, y_train)\n",
    "print(f'Best kernel selected via cross-validation: {best_kernel}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(kernel='linear')\n",
      "Train Accuracy: 0.6633165829145728\n",
      "Test Accuracy: 0.4\n",
      "precision: [0.47058824 0.34782609]\n",
      "recall   : [0.34782609 0.47058824]\n",
      "f1 score : [0.4 0.4]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train and predict classifier\n",
    "classifier = svm.learn_classifier(X_train, y_train, kernel=best_kernel)\n",
    "y_predict = classifier.predict(X_test)\n",
    "\n",
    "print(classifier)\n",
    "svm.display_metrics(X, y, y_test, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML Analysis: Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.9346733668341709\n",
      "Test Accuracy: 0.7\n",
      "precision: [0.78947368 0.61904762]\n",
      "recall   : [0.65217391 0.76470588]\n",
      "f1 score : [0.71428571 0.68421053]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#evaluating our Decision Tree classifier by training, fitting, and predicting on our data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,random_state=1) #changed split to 80% train, 20% test\n",
    "decision_tree = machine_learning.DecisionTree()\n",
    "decision_tree.fit(X_train, y_train)\n",
    "y_pred = decision_tree.predict(X_test)\n",
    "\n",
    "decision_tree.display_metrics(X, y, y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results + Reflection \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our work of applying machine learning to predict if gun reform leads to a decrease in gun violence, the `decision tree classifier` yielded the best results, although only with an accuracy of **70%**. Since our decrease_in_gun_violence classes in our dataset were heavily imbalanced, we performed oversampling and ended up with 101 examples of class decrease_in_gun_violence=0 (no decrease) and 98 examples of class decrease_in_gun_violence=1 (yes decrease). The oversampling helped our decision tree improve its accuracy from the version we submitted in the progress report, and thus we ended up with the current and final 70% accuracy. The linear svm we trained on the oversampled dataset did not better than our baseline classifier and only provided a **40%** accuracy even after implementing cross validation to select the best kernel. \n",
    "\n",
    "To improve the predicition performance of our decision tree model's ability to determine if gun violence will decrease with gun reform laws, we suggest the following considerations:\n",
    "- reducing the dimension of attributes with regards to the columns providing the counts of each type of gun reform law. In our merged dataset used to train our models, columns 5-138 were counts of each type of individual law. We suggest perfoming additional feature transformation by either aggregating these counts, or taking the top X most significant columns. \n",
    "- engineer more creative and representative features to capture the scope of our problem. This entails conducting further research as to the underlying factors and causes of gun violence on the data scientist's end to gain a more informed view and understanding of the task at hand. \n",
    "- incorporating socioeconomic data by region. \n",
    "\n",
    "Overall, our group was able to takeaway a more informed understanding of the role of gun reform to prevent gun violence. Gun violence is influenced by several factors, which makes it hard to attribute what exactly the trend in gun violence corresponds to. The expectation of introducing additional gun reform is that gun violence will decrease, but several socioeconomic and additional factors may offset this expectation."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "87eb1506ace0c76867aaab3383ec0484b429857f88291368ffd3c4acf06de760"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}